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+{"abstract": "  We define the random magnetic Laplacien with spatial white noise as magnetic\nfield on the two-dimensional torus using paracontrolled calculus. It yields a\nrandom self-adjoint operator with pure point spectrum and domain a random\nsubspace of nonsmooth functions in L 2. We give sharp bounds on the eigenvalues\nwhich imply an almost sure Weyl-type law.\n"}
+{"abstract": "  Searches for gravitational-wave counterparts have been going in earnest since\nGW170817 and the discovery of AT2017gfo. Since then, the lack of detection of\nother optical counterparts connected to binary neutron star or black hole -\nneutron star candidates has highlighted the need for a better discrimination\ncriterion to support this effort. At the moment, the low-latency\ngravitational-wave alerts contain preliminary information about the binary\nproperties and, hence, on whether a detected binary might have an\nelectromagnetic counterpart. The current alert method is a classifier that\nestimates the probability that there is a debris disc outside the black hole\ncreated during the merger as well as the probability of a signal being a binary\nneutron star, a black hole - neutron star, a binary black hole or of\nterrestrial origin. In this work, we expand upon this approach to predict both\nthe ejecta properties and provide contours of potential lightcurves for these\nevents in order to improve follow-up observation strategy. The various sources\nof uncertainty are discussed, and we conclude that our ignorance about the\nejecta composition and the insufficient constraint of the binary parameters, by\nthe low-latency pipelines, represent the main limitations. To validate the\nmethod, we test our approach on real events from the second and third Advanced\nLIGO-Virgo observing runs.\n"}
+{"abstract": "  Fully implicit Runge-Kutta (IRK) methods have many desirable properties as\ntime integration schemes in terms of accuracy and stability, but high-order IRK\nmethods are not commonly used in practice with numerical PDEs due to the\ndifficulty of solving the stage equations. This paper introduces a theoretical\nand algorithmic preconditioning framework for solving the systems of equations\nthat arise from IRK methods applied to linear numerical PDEs (without algebraic\nconstraints). This framework also naturally applies to discontinuous Galerkin\ndiscretizations in time. Under quite general assumptions on the spatial\ndiscretization that yield stable time integration, the preconditioned operator\nis proven to have condition number bounded by a small, order-one constant,\nindependent of the spatial mesh and time-step size, and with only weak\ndependence on number of stages/polynomial order; for example, the\npreconditioned operator for 10th-order Gauss IRK has condition number less than\ntwo, independent of the spatial discretization and time step. The new method\ncan be used with arbitrary existing preconditioners for backward Euler-type\ntime stepping schemes, and is amenable to the use of three-term recursion\nKrylov methods when the underlying spatial discretization is symmetric. The new\nmethod is demonstrated to be effective on various high-order finite-difference\nand finite-element discretizations of linear parabolic and hyperbolic problems,\ndemonstrating fast, scalable solution of up to 10th order accuracy. The new\nmethod consistently outperforms existing block preconditioning approaches, and\nin several cases, the new method can achieve 4th-order accuracy using Gauss\nintegration with roughly half the number of preconditioner applications and\nwallclock time as required using standard diagonally implicit RK methods.\n"}
+{"abstract": "  To successfully negotiate a deal, it is not enough to communicate fluently:\npragmatic planning of persuasive negotiation strategies is essential. While\nmodern dialogue agents excel at generating fluent sentences, they still lack\npragmatic grounding and cannot reason strategically. We present DialoGraph, a\nnegotiation system that incorporates pragmatic strategies in a negotiation\ndialogue using graph neural networks. DialoGraph explicitly incorporates\ndependencies between sequences of strategies to enable improved and\ninterpretable prediction of next optimal strategies, given the dialogue\ncontext. Our graph-based method outperforms prior state-of-the-art negotiation\nmodels both in the accuracy of strategy/dialogue act prediction and in the\nquality of downstream dialogue response generation. We qualitatively show\nfurther benefits of learned strategy-graphs in providing explicit associations\nbetween effective negotiation strategies over the course of the dialogue,\nleading to interpretable and strategic dialogues.\n"}
+{"abstract": "  We propose a systematic analysis method for identifying essential parameters\nin various linear and nonlinear response tensors without which they vanish. By\nusing the Keldysh formalism and the Chebyshev polynomial expansion method, the\nresponse tensors are decomposed into the model-independent and dependent parts,\nin which the latter is utilized to extract the essential parameters. An\napplication of the method is demonstrated by analyzing the nonlinear Hall\neffect in the ferroelectric SnTe monolayer for example. It is shown that in\nthis example the second-neighbor hopping is essential for the nonlinear Hall\neffect whereas the spin-orbit coupling is unnecessary. Moreover, by analyzing\nterms contributing to the essential parameters in the lowest order, the\nappearance of the nonlinear Hall effect can be interpreted by the subsequent\ntwo processes: the orbital magneto-current effect and the linear anomalous Hall\neffect by the induced orbital magnetization. In this way, the present method\nprovides a microscopic picture of responses. By combining with computational\nanalysis, it stimulates further discoveries of anomalous responses by filling\nin a missing link among hidden degrees of freedom in a wide variety of\nmaterials.\n"}
+{"abstract": "  It is a long-standing objective to ease the computation burden incurred by\nthe decision making process. Identification of this mechanism's sensitivity to\nsimplification has tremendous ramifications. Yet, algorithms for decision\nmaking under uncertainty usually lean on approximations or heuristics without\nquantifying their effect. Therefore, challenging scenarios could severely\nimpair the performance of such methods. In this paper, we extend the decision\nmaking mechanism to the whole by removing standard approximations and\nconsidering all previously suppressed stochastic sources of variability. On top\nof this extension, our key contribution is a novel framework to simplify\ndecision making while assessing and controlling online the simplification's\nimpact. Furthermore, we present novel stochastic bounds on the return and\ncharacterize online the effect of simplification using this framework on a\nparticular simplification technique - reducing the number of samples in belief\nrepresentation for planning. Finally, we verify the advantages of our approach\nthrough extensive simulations.\n"}
+{"abstract": "  For the development of successful share trading strategies, forecasting the\ncourse of action of the stock market index is important. Effective prediction\nof closing stock prices could guarantee investors attractive benefits. Machine\nlearning algorithms have the ability to process and forecast almost reliable\nclosing prices for historical stock patterns. In this article, we intensively\nstudied NASDAQ stock market and targeted to choose the portfolio of ten\ndifferent companies belongs to different sectors. The objective is to compute\nopening price of next day stock using historical data. To fulfill this task\nnine different Machine Learning regressor applied on this data and evaluated\nusing MSE and R2 as performance metric.\n"}
+{"abstract": "  Ensuring the privacy of research participants is vital, even more so in\nhealthcare environments. Deep learning approaches to neuroimaging require large\ndatasets, and this often necessitates sharing data between multiple sites,\nwhich is antithetical to the privacy objectives. Federated learning is a\ncommonly proposed solution to this problem. It circumvents the need for data\nsharing by sharing parameters during the training process. However, we\ndemonstrate that allowing access to parameters may leak private information\neven if data is never directly shared. In particular, we show that it is\npossible to infer if a sample was used to train the model given only access to\nthe model prediction (black-box) or access to the model itself (white-box) and\nsome leaked samples from the training data distribution. Such attacks are\ncommonly referred to as Membership Inference attacks. We show realistic\nMembership Inference attacks on deep learning models trained for 3D\nneuroimaging tasks in a centralized as well as decentralized setup. We\ndemonstrate feasible attacks on brain age prediction models (deep learning\nmodels that predict a person's age from their brain MRI scan). We correctly\nidentified whether an MRI scan was used in model training with a 60% to over\n80% success rate depending on model complexity and security assumptions.\n"}
+{"abstract": "  An intrinsic antiferromagnetic topological insulator $\\mathrm{MnBi_2Te_4}$\ncan be realized by intercalating Mn-Te bilayer chain in a topological\ninsulator, $\\mathrm{Bi_2Te_3}$. $\\mathrm{MnBi_2Te_4}$ provides not only a\nstable platform to demonstrate exotic physical phenomena, but also easy\ntunability of the physical properties. For example, inserting more\n$\\mathrm{Bi_2Te_3}$ layers in between two adjacent $\\mathrm{MnBi_2Te_4}$\nweakens the interlayer magnetic interactions between the $\\mathrm{MnBi_2Te_4}$\nlayers. Here we present the first observations on the inter- and intra-layer\nphonon modes of $\\mathrm{MnBi_{2n}Te_{3n+1}}$ (n=1,2,3,4) using cryogenic\nlow-frequency Raman spectroscopy. We experimentally and theoretically\ndistinguish the Raman vibrational modes using various polarization\nconfigurations. The two peaks at 66 cm$^{-1}$ and 112 cm$^{-1}$ show an\nabnormal perturbation in the Raman linewidths below the magnetic transition\ntemperature due to spin-phonon coupling. In $\\mathrm{MnBi_4Te_7}$, the\n$\\mathrm{Bi_2Te_3}$ layers induce Davydov splitting of the A$_{1g}$ mode around\n137 cm$^{-1}$ at 5 K. Using the linear chain model, we estimate the\nout-of-plane interlayer force constant to be $(3.98 \\pm 0.14) \\times 10^{19}$\nN/m$^3$ at 5 K, three times weaker than that of $\\mathrm{Bi_2Te_3}$. Our work\ndiscovers the dynamics of phonon modes of the $\\mathrm{MnBi_2Te_4}$ and the\neffect of the additional $\\mathrm{Bi_2Te_3}$ layers, providing the\nfirst-principles guidance to tailor the physical properties of layered\nheterostructures.\n"}
+{"abstract": "  We propose a strategy for optimizing a sensor trajectory in order to estimate\nthe time dependence of a localized scalar source in turbulent channel flow. The\napproach leverages the view of the adjoint scalar field as the sensitivity of\nmeasurement to a possible source. A cost functional is constructed so that the\noptimal sensor trajectory maintains a high sensitivity and low temporal\nvariation in the measured signal, for a given source location. This naturally\nleads to the adjoint-of-adjoint equation based on which the sensor trajectory\nis iteratively optimized. It is shown that the estimation performance based on\nthe measurement obtained by a sensor moving along the optimal trajectory is\ndrastically improved from that achieved with a stationary sensor. It is also\nshown that the ratio of the fluctuation and the mean of the sensitivity for a\ngiven sensor trajectory can be used as a diagnostic tool to evaluate the\nresultant performance. Based on this finding, we propose a new cost functional\nwhich only includes the ratio without any adjustable parameters, and\ndemonstrate its effectiveness in predicting the time dependence of scalar\nrelease from the source.\n"}
+{"abstract": "  Gamma distributed delay differential equations (DDEs) arise naturally in many\nmodelling applications. However, appropriate numerical methods for generic\nGamma distributed DDEs are not currently available. Accordingly, modellers\noften resort to approximating the gamma distribution with an Erlang\ndistribution and using the linear chain technique to derive an equivalent\nsystem of ordinary differential equations. In this work, we develop a\nfunctionally continuous Runge-Kutta method to numerically integrate the gamma\ndistributed DDE and perform numerical tests to confirm the accuracy of the\nnumerical method. As the functionally continuous Runge-Kutta method is not\navailable in most scientific software packages, we then derive hypoexponential\napproximations of the gamma distributed DDE. Using our numerical method, we\nshow that while using the common Erlang approximation can produce solutions\nthat are qualitatively different from the underlying gamma distributed DDE, our\nhypoexponential approximations do not have this limitation. Finally, we\nimplement our hypoexponential approximations to perform statistical inference\non synthetic epidemiological data.\n"}
+{"abstract": "  Markov state models (MSMs) have been broadly adopted for analyzing molecular\ndynamics trajectories, but the approximate nature of the models that results\nfrom coarse-graining into discrete states is a long-known limitation. We show\ntheoretically that, despite the coarse graining, in principle MSM-like analysis\ncan yield unbiased estimation of key observables. We describe unbiased\nestimators for equilibrium state populations, for the mean first-passage time\n(MFPT) of an arbitrary process, and for state committors - i.e., splitting\nprobabilities. Generically, the estimators are only asymptotically unbiased but\nwe describe how extension of a recently proposed reweighting scheme can\naccelerate relaxation to unbiased values. Exactly accounting for 'sliding\nwindow' averaging over finite-length trajectories is a key, novel element of\nour analysis. In general, our analysis indicates that coarse-grained MSMs are\nasymptotically unbiased for steady-state properties only when appropriate\nboundary conditions (e.g., source-sink for MFPT estimation) are applied\ndirectly to trajectories, prior to calculation of the appropriate transition\nmatrix.\n"}
+{"abstract": "  The phase field paradigm, in combination with a suitable variational\nstructure, has opened a path for using Griffith's energy balance to predict the\nfracture of solids. These so-called phase field fracture methods have gained\nsignificant popularity over the past decade, and are now part of commercial\nfinite element packages and engineering fitness-for-service assessments. Crack\npaths can be predicted, in arbitrary geometries and dimensions, based on a\nglobal energy minimisation - without the need for \\textit{ad hoc} criteria. In\nthis work, we review the fundamentals of phase field fracture methods and\nexamine their capabilities in delivering predictions in agreement with the\nclassical fracture mechanics theory pioneered by Griffith. The two most widely\nused phase field fracture models are implemented in the context of the finite\nelement method, and several paradigmatic boundary value problems are addressed\nto gain insight into their predictive abilities across all cracking stages;\nboth the initiation of growth and stable crack propagation are investigated. In\naddition, we examine the effectiveness of phase field models with an internal\nmaterial length scale in capturing size effects and the transition flaw size\nconcept. Our results show that phase field fracture methods satisfactorily\napproximate classical fracture mechanics predictions and can also reconcile\nstress and toughness criteria for fracture. The accuracy of the approximation\nis however dependent on modelling and constitutive choices; we provide a\nrationale for these differences and identify suitable approaches for delivering\nphase field fracture predictions that are in good agreement with\nwell-established fracture mechanics paradigms.\n"}
+{"abstract": "  The convergence property of a stochastic algorithm for the self-consistent\nfield (SCF) calculations of electron structures is studied. The algorithm is\nformulated by rewriting the electron charges as a trace/diagonal of a matrix\nfunction, which is subsequently expressed as a statistical average. The\nfunction is further approximated by using a Krylov subspace approximation. As a\nresult, each SCF iteration only samples one random vector without having to\ncompute all the orbitals. We consider the common practice of SCF iterations\nwith damping and mixing. We prove with appropriate assumptions that the\niterations converge in the mean-square sense, when the stochastic error has an\nalmost sure bound. We also consider the scenario when such an assumption is\nweakened to a second moment condition, and prove the convergence in\nprobability.\n"}
+{"abstract": "  Multiple organ failure (MOF) is a severe syndrome with a high mortality rate\namong Intensive Care Unit (ICU) patients. Early and precise detection is\ncritical for clinicians to make timely decisions. An essential challenge in\napplying machine learning models to electronic health records (EHRs) is the\npervasiveness of missing values. Most existing imputation methods are involved\nin the data preprocessing phase, failing to capture the relationship between\ndata and outcome for downstream predictions. In this paper, we propose\nclassifier-guided generative adversarial imputation networks Classifier-GAIN)\nfor MOF prediction to bridge this gap, by incorporating both observed data and\nlabel information. Specifically, the classifier takes imputed values from the\ngenerator(imputer) to predict task outcomes and provides additional supervision\nsignals to the generator by joint training. The classifier-guide generator\nimputes missing values with label-awareness during training, improving the\nclassifier's performance during inference. We conduct extensive experiments\nshowing that our approach consistently outperforms classical and state-of-art\nneural baselines across a range of missing data scenarios and evaluation\nmetrics.\n"}
+{"abstract": "  Instrumental systematics need to be controlled to high precision for upcoming\nCosmic Microwave Background (CMB) experiments. The level of contamination\ncaused by these systematics is often linked to the scan strategy, and scan\nstrategies for satellite experiments can significantly mitigate these\nsystematics. However, no detailed study has been performed for ground-based\nexperiments. Here we show that under the assumption of constant elevation scans\n(CESs), the ability of the scan strategy to mitigate these systematics is\nstrongly limited, irrespective of the detailed structure of the scan strategy.\nWe calculate typical values and maps of the quantities coupling the scan to the\nsystematics, and show how these quantities vary with the choice of observing\nelevations. These values and maps can be used to calculate and forecast the\nmagnitude of different instrumental systematics without requiring detailed scan\nstrategy simulations. As a reference point, we show that inclusion of even a\nsingle boresight rotation angle significantly improves over sky rotation alone\nfor mitigating these systematics. A standard metric for evaluating\ncross-linking is related to one of the parameters studied in this work, so a\ncorollary of our work is that the cross-linking will suffer from the same CES\nlimitations and therefore upcoming CMB surveys will unavoidably have poorly\ncross-linked regions if they use CESs, regardless of detailed scheduling\nchoices. Our results are also relevant for non-CMB surveys that perform\nconstant elevation scans and may have scan-coupled systematics, such as\nintensity mapping surveys.\n"}
+{"abstract": "  Integer quantization of neural networks can be defined as the approximation\nof the high precision computation of the canonical neural network formulation,\nusing reduced integer precision. It plays a significant role in the efficient\ndeployment and execution of machine learning (ML) systems, reducing memory\nconsumption and leveraging typically faster computations. In this work, we\npresent an integer-only quantization strategy for Long Short-Term Memory (LSTM)\nneural network topologies, which themselves are the foundation of many\nproduction ML systems. Our quantization strategy is accurate (e.g. works well\nwith quantization post-training), efficient and fast to execute (utilizing 8\nbit integer weights and mostly 8 bit activations), and is able to target a\nvariety of hardware (by leveraging instructions sets available in common CPU\narchitectures, as well as available neural accelerators).\n"}
+{"abstract": "  Let ${\\mathfrak M}=({\\mathcal M},\\rho)$ be a metric space and let $X$ be a\nBanach space. Let $F$ be a set-valued mapping from ${\\mathcal M}$ into the\nfamily ${\\mathcal K}_m(X)$ of all compact convex subsets of $X$ of dimension at\nmost $m$. The main result in our recent joint paper with Charles Fefferman\n(which is referred to as a ``Finiteness Principle for Lipschitz selections'')\nprovides efficient conditions for the existence of a Lipschitz selection of\n$F$, i.e., a Lipschitz mapping $f:{\\mathcal M}\\to X$ such that $f(x)\\in F(x)$\nfor every $x\\in{\\mathcal M}$. We give new alternative proofs of this result in\ntwo special cases. When $m=2$ we prove it for $X={\\bf R}^{2}$, and when $m=1$\nwe prove it for all choices of $X$. Both of these proofs make use of a simple\nreiteration formula for the ``core'' of a set-valued mapping $F$, i.e., for a\nmapping $G:{\\mathcal M}\\to{\\mathcal K}_m(X)$ which is Lipschitz with respect to\nthe Hausdorff distance, and such that $G(x)\\subset F(x)$ for all $x\\in{\\mathcal\nM}$.\n"}
+{"abstract": "  Purpose: Quantitative magnetization transfer (qMT) imaging can be used to\nquantify the proportion of protons in a voxel attached to macromolecules. Here,\nwe show that the original qMT balanced steady-state free precession (bSSFP)\nmodel is biased due to over-simplistic assumptions made in its derivation.\nTheory and Methods: We present an improved model for qMT bSSFP, which\nincorporates finite radio-frequency (RF) pulse effects as well as simultaneous\nexchange and relaxation. Further, a correction to finite RF pulse effects for\nsinc-shaped excitations is derived. The new model is compared to the original\none in numerical simulations of the Bloch-McConnell equations and in previously\nacquired in-vivo data. Results: Our numerical simulations show that the\noriginal signal equation is significantly biased in typical brain tissue\nstructures (by 7-20 %) whereas the new signal equation outperforms the original\none with minimal bias (< 1%). It is further shown that the bias of the original\nmodel strongly affects the acquired qMT parameters in human brain structures,\nwith differences in the clinically relevant parameter of pool-size-ratio of up\nto 31 %. Particularly high biases of the original signal equation are expected\nin an MS lesion within diseased brain tissue (due to a low T2/T1-ratio),\ndemanding a more accurate model for clinical applications. Conclusion: The\nimproved model for qMT bSSFP is recommended for accurate qMT parameter mapping\nin healthy and diseased brain tissue structures.\n"}
+{"abstract": "  We construct a categorical framework for nonlinear postquantum inference,\nwith embeddings of convex closed sets of suitable reflexive Banach spaces as\nobjects and pullbacks of Br\\`egman quasi-nonexpansive mappings (in particular,\nconstrained maximisations of Br\\`egman relative entropies) as morphisms. It\nprovides a nonlinear convex analytic analogue of Chencov's programme of\ngeometric study of categories of linear positive maps between spaces of states,\na working model of Mielnik's nonlinear transmitters, and a setting for\nnonlinear resource theories (with monoids of Br\\`egman quasi-nonexpansive maps\nas free operations, their asymptotic fixed point sets as free sets, and\nBr\\`egman relative entropies as resource monotones). We construct a range of\nconcrete examples for semi-finite JBW-algebras and any W*-algebras. Due to\nrelative entropy's asymmetry, all constructions have left and right versions,\nwith Legendre duality inducing categorical equivalence between their\nwell-defined restrictions. Inner groupoids of these categories implement the\nnotion of statistical equivalence. The hom-sets of a subcategory of morphisms\ngiven by entropic projections have the structure of partially ordered\ncommutative monoids (so, they are resource theories in Fritz's sense). Further\nrestriction of objects to affine sets turns Br\\`egman relative entropy into a\nfunctor. Finally, following Lawvere's adjointness paradigm for deductive logic,\nbut with a semantic twist representing Jaynes' and Chencov's views on\nstatistical inference, we introduce a category-theoretic multi-(co)agent\nsetting for inductive inference theories, implemented by families of monads and\ncomonads. We show that the br\\`egmanian approach provides some special cases of\nthis setting.\n"}
+{"abstract": "  Garc\\'ia-Aguilar et al. [Phys. Rev. Lett 126, 038001 (2021)] have shown that\nthe deformations of \"shape-shifting droplets\" are consistent with an elastic\nmodel, that, unlike previous models, includes the intrinsic curvature of the\nfrozen surfactant layer. In this Comment, we show that the interplay between\nsurface tension and intrinsic curvature in their model is in fact\nmathematically equivalent to a physically very different phase-transition\nmechanism of the same process that we developed previously [Phys. Rev. Lett.\n118, 088001 (2017); Phys. Rev. Res. 1, 023017 (2019)]. The mathematical models\ncannot therefore distinguish between the two mechanisms, and hence it is not\npossible to claim that one mechanism underlies all observed shape-shifting\nphenomena without a much more detailed comparison of experiment and theory.\n"}
+{"abstract": "  We propose a novel scheme for the exact renormalisation group motivated by\nthe desire of reducing the complexity of practical computations. The key idea\nis to specify renormalisation conditions for all inessential couplings, leaving\nus with the task of computing only the flow of the essential ones. To achieve\nthis aim, we utilise a renormalisation group equation for the effective average\naction which incorporates general non-linear field reparameterisations. A\nprominent feature of the scheme is that, apart from the renormalisation of the\nmass, the propagator evaluated at any constant value of the field maintains its\nunrenormalised form. Conceptually, the scheme provides a clearer picture of\nrenormalisation itself since the redundant, non-physical content is\nautomatically disregarded in favour of a description based only on quantities\nthat enter expressions for physical observables. To exemplify the scheme's\nutility, we investigate the Wilson-Fisher fixed point in three dimensions at\norder two in the derivative expansion. In this case, the scheme removes all\norder $\\partial^2$ operators apart from the canonical term. Further\nsimplifications occur at higher orders in the derivative expansion. Although we\nconcentrate on a minimal scheme that reduces the complexity of computations, we\npropose more general schemes where inessential couplings can be tuned to\noptimise a given approximation. We further discuss the applicability of the\nscheme to a broad range of physical theories.\n"}
+{"abstract": "  Amorphous dielectric materials have been known to host two-level systems\n(TLSs) for more than four decades. Recent developments on superconducting\nresonators and qubits enable detailed studies on the physics of TLSs. In\nparticular, measuring the loss of a device over long time periods (a few days)\nallows us to investigate stochastic fluctuations due to the interaction between\nTLSs. We measure the energy relaxation time of a frequency-tunable planar\nsuperconducting qubit over time and frequency. The experiments show a variety\nof stochastic patterns that we are able to explain by means of extensive\nsimulations. The model used in our simulations assumes a qubit interacting with\nhigh-frequency TLSs, which, in turn, interact with thermally activated\nlow-frequency TLSs. Our simulations match the experiments and suggest the\ndensity of low-frequency TLSs is about three orders of magnitude larger than\nthat of high-frequency ones.\n"}
+{"abstract": "  Learning from implicit user feedback is challenging as we can only observe\npositive samples but never access negative ones. Most conventional methods cope\nwith this issue by adopting a pairwise ranking approach with negative sampling.\nHowever, the pairwise ranking approach has a severe disadvantage in the\nconvergence time owing to the quadratically increasing computational cost with\nrespect to the sample size; it is problematic, particularly for large-scale\ndatasets and complex models such as neural networks. By contrast, a pointwise\napproach does not directly solve a ranking problem, and is therefore inferior\nto a pairwise counterpart in top-K ranking tasks; however, it is generally\nadvantageous in regards to the convergence time. This study aims to establish\nan approach to learn personalised ranking from implicit feedback, which\nreconciles the training efficiency of the pointwise approach and ranking\neffectiveness of the pairwise counterpart. The key idea is to estimate the\nranking of items in a pointwise manner; we first reformulate the conventional\npointwise approach based on density ratio estimation and then incorporate the\nessence of ranking-oriented approaches (e.g. the pairwise approach) into our\nformulation. Through experiments on three real-world datasets, we demonstrate\nthat our approach not only dramatically reduces the convergence time (one to\ntwo orders of magnitude faster) but also significantly improving the ranking\nperformance.\n"}
+{"abstract": "  Full Duplex (FD) radio has emerged as a promising solution to increase the\ndata rates by up to a factor of two via simultaneous transmission and reception\nin the same frequency band. This paper studies a novel hybrid beamforming\n(HYBF) design to maximize the weighted sum-rate (WSR) in a single-cell\nmillimeter wave (mmWave) massive multiple-input-multiple-output (mMIMO) FD\nsystem. Motivated by practical considerations, we assume that the multi-antenna\nusers and hybrid FD base station (BS) suffer from the limited dynamic range\n(LDR) noise due to non-ideal hardware and an impairment aware HYBF approach is\nadopted by integrating the traditional LDR noise model in the mmWave band. In\ncontrast to the conventional HYBF schemes, our design also considers the joint\nsum-power and the practical per-antenna power constraints. A novel\ninterference, self-interference (SI) and LDR noise aware optimal power\nallocation scheme for the uplink (UL) users and FD BS is also presented to\nsatisfy the joint constraints. The maximum achievable gain of a multi-user\nmmWave FD system over a fully digital half duplex (HD) system with different\nLDR noise levels and numbers of the radio-frequency (RF) chains is\ninvestigated. Simulation results show that our design outperforms the HD system\nwith only a few RF chains at any LDR noise level. The advantage of having\namplitude control at the analog stage is also examined, and additional gain for\nthe mmWave FD system becomes evident when the number of RF chains at the hybrid\nFD BS is small.\n"}
+{"abstract": "  We study the influence of running vacuum on the baryon-to-photon ratio in\nrunning vacuum models (RVMs). When there exists a non-minimal coupling between\nphotons and other matter in the expanding universe, the energy-momentum tensor\nof photons is no longer conserved, but the energy of photons could remain\nconserved. We discuss the conditions for the energy conservation of photons in\nRVMs. The photon number density and baryon number density, from the epoch of\nphoton decoupling to the present day, are obtained in the context of RVMs by\nassuming that photons and baryons can be coupled to running vacuum,\nrespectively. Both cases lead to a time-evolving baryon-to-photon ratio.\nHowever the evolution of the baryon-to-photon ratio is strictly constrained by\nobservations. It is found that if the dynamic term of running vacuum is indeed\ncoupled to photons or baryons, the coefficient of the dynamic term must be\nextremely small, which is unnatural. Therefore, our study basically rules out\nthe possibility that running vacuum is coupled to photons or baryons in RVMs.\n"}
+{"abstract": "  To extract liver from medical images is a challenging task due to similar\nintensity values of liver with adjacent organs, various contrast levels,\nvarious noise associated with medical images and irregular shape of liver. To\naddress these issues, it is important to preprocess the medical images, i.e.,\ncomputerized tomography (CT) and magnetic resonance imaging (MRI) data prior to\nliver analysis and quantification. This paper investigates the impact of\npermutation of various preprocessing techniques for CT images, on the automated\nliver segmentation using deep learning, i.e., U-Net architecture. The study\nfocuses on Hounsfield Unit (HU) windowing, contrast limited adaptive histogram\nequalization (CLAHE), z-score normalization, median filtering and\nBlock-Matching and 3D (BM3D) filtering. The segmented results show that\ncombination of three techniques; HU-windowing, median filtering and z-score\nnormalization achieve optimal performance with Dice coefficient of 96.93%,\n90.77% and 90.84% for training, validation and testing respectively.\n"}
+{"abstract": "  The Robot Operating System 2 (ROS2) targets distributed real-time systems and\nis widely used in the robotics community. Especially in these systems, latency\nin data processing and communication can lead to instabilities. Though being\nhighly configurable with respect to latency, ROS2 is often used with its\ndefault settings.\n  In this paper, we investigate the end-to-end latency of ROS2 for distributed\nsystems with default settings and different Data Distribution Service (DDS)\nmiddlewares. In addition, we profile the ROS2 stack and point out latency\nbottlenecks. Our findings indicate that end-to-end latency strongly depends on\nthe used DDS middleware. Moreover, we show that ROS2 can lead to 50% latency\noverhead compared to using low-level DDS communications. Our results imply\nguidelines for designing distributed ROS2 architectures and indicate\npossibilities for reducing the ROS2 overhead.\n"}
+{"abstract": "  We study a long-recognised but under-appreciated symmetry called \"dynamical\nsimilarity\" and illustrate its relevance to many important conceptual problems\nin fundamental physics. Dynamical similarities are general transformations of a\nsystem where the unit of Hamilton's principal function is rescaled, and\ntherefore represent a kind of dynamical scaling symmetry with formal properties\nthat differ from many standard symmetries. To study this symmetry, we develop a\ngeneral framework for symmetries that distinguishes the observable and surplus\nstructures of a theory by using the minimal freely specifiable initial data for\nthe theory that is necessary to achieve empirical adequacy. This framework is\nthen applied to well-studied examples including Galilean invariance and the\nsymmetries of the Kepler problem. We find that our framework gives a precise\ndynamical criterion for identifying the observables of those systems, and that\nthose observables agree with epistemic expectations. We then apply our\nframework to dynamical similarity. First we give a general definition of\ndynamical similarity. Then we show, with the help of some previous results, how\nthe dynamics of our observables leads to singularity resolution and the\nemergence of an arrow of time in cosmology.\n"}
+{"abstract": "  State-space models (SSM) with Markov switching offer a powerful framework for\ndetecting multiple regimes in time series, analyzing mutual dependence and\ndynamics within regimes, and asserting transitions between regimes. These\nmodels however present considerable computational challenges due to the\nexponential number of possible regime sequences to account for. In addition,\nhigh dimensionality of time series can hinder likelihood-based inference. This\npaper proposes novel statistical methods for Markov-switching SSMs using\nmaximum likelihood estimation, Expectation-Maximization (EM), and parametric\nbootstrap. We develop solutions for initializing the EM algorithm, accelerating\nconvergence, and conducting inference that are ideally suited to massive\nspatio-temporal data such as brain signals. We evaluate these methods in\nsimulations and present applications to EEG studies of epilepsy and of motor\nimagery. All proposed methods are implemented in a MATLAB toolbox available at\nhttps://github.com/ddegras/switch-ssm.\n"}
+{"abstract": "  An increasing amount of location-based service (LBS) data is being\naccumulated and helps to study urban dynamics and human mobility. GPS\ncoordinates and other location indicators are normally low dimensional and only\nrepresenting spatial proximity, thus difficult to be effectively utilized by\nmachine learning models in Geo-aware applications. Existing location embedding\nmethods are mostly tailored for specific problems that are taken place within\nareas of interest. When it comes to the scale of a city or even a country,\nexisting approaches always suffer from extensive computational cost and\nsignificant data sparsity. Different from existing studies, we propose to learn\nrepresentations through a GCN-aided skip-gram model named GCN-L2V by\nconsidering both spatial connection and human mobility. With a flow graph and a\nspatial graph, it embeds context information into vector representations.\nGCN-L2V is able to capture relationships among locations and provide a better\nnotion of similarity in a spatial environment. Across quantitative experiments\nand case studies, we empirically demonstrate that representations learned by\nGCN-L2V are effective. As far as we know, this is the first study that provides\na fine-grained location embedding at the city level using only LBS records.\nGCN-L2V is a general-purpose embedding model with high flexibility and can be\napplied in down-streaming Geo-aware applications.\n"}
+{"abstract": "  The Earth's magnetotail is characterized by stretched magnetic field lines.\nEnergetic particles are effectively scattered due to the field-line curvature,\nwhich then leads to isotropization of energetic particle distributions and\nparticle precipitation to the Earth's atmosphere. Measurements of these\nprecipitation at low-altitude spacecraft are thus often used to remotely probe\nthe magnetotail current sheet configuration. This configuration may include\nspatially localized maxima of the curvature radius at equator (due to localized\nhumps of the equatorial magnetic field magnitude) that reduce the energetic\nparticle scattering and precipitation. Therefore, the measured precipitation\npatterns are related to the spatial distribution of the equatorial curvature\nradius that is determined by the magnetotail current sheet configuration. In\nthis study, we show that, contrary to previous thoughts, the magnetic field\nline configuration with the localized curvature radius maximum can actually\nenhance the scattering and subsequent precipitation. The spatially localized\nmagnetic field dipolarization (magnetic field humps) can significantly curve\nmagnetic field lines far from the equator and create off-equatorial minima in\nthe curvature radius. Scattering of energetic particles in these off-equatorial\nregions alters the scattering (and precipitation) patterns, which has not been\nstudied yet. We discuss our results in the context of remote-sensing the\nmagnetotail current sheet configuration with low-altitude spacecraft\nmeasurements.\n"}
+{"abstract": "  Since the heavy neutrinos of the inverse seesaw mechanism mix largely with\nthe standard ones, the charged currents formed with them and the muons have the\npotential of generating robust and positive contribution to the anomalous\nmagnetic moment of the muon. Ho\\-we\\-ver, bounds from the non-unitary in the\nleptonic mixing matrix may restrict so severely the parameters of the mechanism\nthat, depending on the framework under which the mechanism is implemented, may\nrender it unable to explain the recent muon g-2 result. In this paper we show\nthat this happens when we implement the mechanism into the standard model and\ninto two versions of the 3-3-1 models.\n"}
+{"abstract": "  Using an argument of Baldwin--Hu--Sivek, we prove that if $K$ is a hyperbolic\nfibered knot with fiber $F$ in a closed, oriented $3$--manifold $Y$, and\n$\\widehat{HFK}(Y,K,[F], g(F)-1)$ has rank $1$, then the monodromy of $K$ is\nfreely isotopic to a pseudo-Anosov map with no fixed points. In particular,\nthis shows that the monodromy of a hyperbolic L-space knot is freely isotopic\nto a map with no fixed points.\n"}
+{"abstract": "  The object of Weakly-supervised Temporal Action Localization (WS-TAL) is to\nlocalize all action instances in an untrimmed video with only video-level\nsupervision. Due to the lack of frame-level annotations during training,\ncurrent WS-TAL methods rely on attention mechanisms to localize the foreground\nsnippets or frames that contribute to the video-level classification task. This\nstrategy frequently confuse context with the actual action, in the localization\nresult. Separating action and context is a core problem for precise WS-TAL, but\nit is very challenging and has been largely ignored in the literature. In this\npaper, we introduce an Action-Context Separation Network (ACSNet) that\nexplicitly takes into account context for accurate action localization. It\nconsists of two branches (i.e., the Foreground-Background branch and the\nAction-Context branch). The Foreground- Background branch first distinguishes\nforeground from background within the entire video while the Action-Context\nbranch further separates the foreground as action and context. We associate\nvideo snippets with two latent components (i.e., a positive component and a\nnegative component), and their different combinations can effectively\ncharacterize foreground, action and context. Furthermore, we introduce extended\nlabels with auxiliary context categories to facilitate the learning of\naction-context separation. Experiments on THUMOS14 and ActivityNet v1.2/v1.3\ndatasets demonstrate the ACSNet outperforms existing state-of-the-art WS-TAL\nmethods by a large margin.\n"}
+{"abstract": "  Intrinsic Image Decomposition is an open problem of generating the\nconstituents of an image. Generating reflectance and shading from a single\nimage is a challenging task specifically when there is no ground truth. There\nis a lack of unsupervised learning approaches for decomposing an image into\nreflectance and shading using a single image. We propose a neural network\narchitecture capable of this decomposition using physics-based parameters\nderived from the image. Through experimental results, we show that (a) the\nproposed methodology outperforms the existing deep learning-based IID\ntechniques and (b) the derived parameters improve the efficacy significantly.\nWe conclude with a closer analysis of the results (numerical and example\nimages) showing several avenues for improvement.\n"}
+{"abstract": "  We consider the barotropic Navier-Stokes system describing the motion of a\ncompressible viscous fluid confined to a bounded domain driven by time periodic\ninflow/outflow boundary conditions. We show that the problem admits a time\nperiodic solution in the class of weak solutions satisfying the energy\ninequality.\n"}
+{"abstract": "  Understanding the features learned by deep models is important from a model\ntrust perspective, especially as deep systems are deployed in the real world.\nMost recent approaches for deep feature understanding or model explanation\nfocus on highlighting input data features that are relevant for classification\ndecisions. In this work, we instead take the perspective of relating deep\nfeatures to well-studied, hand-crafted features that are meaningful for the\napplication of interest. We propose a methodology and set of systematic\nexperiments for exploring deep features in this setting, where input feature\nimportance approaches for deep feature understanding do not apply. Our\nexperiments focus on understanding which hand-crafted and deep features are\nuseful for the classification task of interest, how robust these features are\nfor related tasks and how similar the deep features are to the meaningful\nhand-crafted features. Our proposed method is general to many application areas\nand we demonstrate its utility on orchestral music audio data.\n"}
+{"abstract": "  An automorphism of a rooted spherically homogeneous tree is settled if it\nsatisfies certain conditions on the growth of cycles at finite levels of the\ntree. In this paper, we consider a conjecture by Boston and Jones that the\nimage of an arboreal representation of the absolute Galois group of a number\nfield in the automorphism group of a tree has a dense subset of settled\nelements. Inspired by analogous notions in theory of compact Lie groups, we\nintroduce the concepts of a maximal torus and a Weyl group for actions of\nprofinite groups on rooted trees, and we show that the Weyl group contains\nimportant information about settled elements. We study maximal tori and their\nWeyl groups in the images of arboreal representations associated to quadratic\npolynomials over algebraic number fields, and in branch groups.\n"}
+{"abstract": "  Open quantum systems exhibit a rich phenomenology, in comparison to closed\nquantum systems that evolve unitarily according to the Schr\\\"odinger equation.\nThe dynamics of an open quantum system are typically classified into Markovian\nand non-Markovian, depending on whether the dynamics can be decomposed into\nvalid quantum operations at any time scale. Since Markovian evolutions are\neasier to simulate, compared to non-Markovian dynamics, it is reasonable to\nassume that non-Markovianity can be employed for useful quantum-technological\napplications. Here, we demonstrate the usefulness of non-Markovianity for\npreserving correlations and coherence in quantum systems. For this, we consider\na broad class of qubit evolutions, having a decoherence matrix separated from\nzero for large times. While any such Markovian evolution leads to an\nexponential loss of correlations, non-Markovianity can help to preserve\ncorrelations even in the limit $t \\rightarrow \\infty$. For covariant qubit\nevolutions, we also show that non-Markovianity can be used to preserve quantum\ncoherence at all times, which is an important resource for quantum metrology.\nWe explicitly demonstrate this effect experimentally with linear optics, by\nimplementing the required evolution that is non-Markovian at all times.\n"}
+{"abstract": "  People hope automated driving technology is always in a stable and\ncontrollable state; specifically, it can be divided into controllable planning,\ncontrollable responsibility, and controllable information. When this\ncontrollability is undermined, it brings about the problems, e.g., trolley\ndilemma, responsibility attribution, information leakage, and security. This\narticle discusses these three types of issues separately and clarifies the\nmisunderstandings.\n"}
+{"abstract": "  In a recent paper with J.-P. Nicolas [J.-P. Nicolas and P.T. Xuan, Annales\nHenri Poincare 2019], we studied the peeling for scalar fields on Kerr metrics.\nThe present work extends these results to Dirac fields on the same geometrical\nbackground. We follow the approach initiated by L.J. Mason and J.-P. Nicolas\n[L. Mason and J.-P. Nicolas, J.Inst.Math.Jussieu 2009; L. Mason and J.-P.\nNicolas, J.Geom.Phys 2012] on the Schwarzschild spacetime and extended to Kerr\nmetrics for scalar fields. The method combines the Penrose conformal\ncompactification and geometric energy estimates in order to work out a\ndefinition of the peeling at all orders in terms of Sobolev regularity near\n$\\mathscr{I}$, instead of ${\\mathcal C}^k$ regularity at $\\mathscr{I}$, then\nprovides the optimal spaces of initial data such that the associated solution\nsatisfies the peeling at a given order. The results confirm that the analogous\ndecay and regularity assumptions on initial data in Minkowski and in Kerr\nproduce the same regularity across null infinity. Our results are local near\nspacelike infinity and are valid for all values of the angular momentum of the\nspacetime, including for fast Kerr metrics.\n"}
+{"abstract": "  Two-dimensional (2D) hybrid organic-inorganic perovskites (HOIPs) are\nintroducing new directions in the 2D materials landscape. The coexistence of\nferroelectricity and spin-orbit interactions play a key role in their\noptoelectronic properties. We perform a detailed study on a recently\nsynthesized ferroelectric 2D-HOIP, (AMP)PbI$_4$ (AMP =\n4-aminomethyl-piperidinium). The calculated polarization and Rashba parameter\nare in excellent agreement with experimental values. We report a striking new\neffect, i.e., an extraordinarily large Rashba anisotropy that is tunable by\nferroelectric polarization: as polarization is reversed, not only the spin\ntexture chirality is inverted, but also the major and minor axes of the Rashba\nanisotropy ellipse in k-space are interchanged - a pseudo rotation. A $k \\cdot\np$ model Hamiltonian and symmetry-mode analysis reveal a quadrilinear coupling\nbetween the cation-rotation modes responsible for the Rashba ellipse\npseudo-rotation, the framework rotation, and the polarization. These findings\nmay provide new avenues for spin-optoelectronic devices such as spin valves or\nspin FETs.\n"}
+{"abstract": "  This paper presents a supervised learning method to generate continuous\ncost-to-go functions of non-holonomic systems directly from the workspace\ndescription. Supervision from informative examples reduces training time and\nimproves network performance. The manifold representing the optimal\ntrajectories of a non-holonomic system has high-curvature regions which can not\nbe efficiently captured with uniform sampling. To address this challenge, we\npresent an adaptive sampling method which makes use of sampling-based planners\nalong with local, closed-form solutions to generate training samples. The\ncost-to-go function over a specific workspace is represented as a neural\nnetwork whose weights are generated by a second, higher order network. The\nnetworks are trained in an end-to-end fashion. In our previous work, this\narchitecture was shown to successfully learn to generate the cost-to-go\nfunctions of holonomic systems using uniform sampling. In this work, we show\nthat uniform sampling fails for non-holonomic systems. However, with the\nproposed adaptive sampling methodology, our network can generate near-optimal\ntrajectories for non-holonomic systems while avoiding obstacles. Experiments\nshow that our method is two orders of magnitude faster compared to traditional\napproaches in cluttered environments.\n"}
+{"abstract": "  We investigate the structure of the meson Regge trajectories based on the\nquadratic form of the spinless Salpeter-type equation. It is found that the\nforms of the Regge trajectories depend on the energy region. As the employed\nRegge trajectory formula does not match the energy region, the fitted\nparameters neither have explicit physical meanings nor obey the constraints\nalthough the fitted Regge trajectory can give the satisfactory predictions if\nthe employed formula is appropriate mathematically. Moreover, the consistency\nof the Regge trajectories obtained from different approaches is discussed. And\nthe Regge trajectories for different mesons are presented. Finally, we show\nthat the masses of the constituents will come into the slope and explain why\nthe slopes of the fitted linear Regge trajectories vary with different kinds of\nmesons.\n"}
+{"abstract": "  Higgs-portal effective field theories are widely used as benchmarks in order\nto interpret collider and astroparticle searches for dark matter (DM)\nparticles. To assess the validity of these effective models, it is important to\nconfront them to concrete realizations that are complete in the ultraviolet\nregime. In this paper, we compare effective Higgs-portal models with scalar,\nfermionic and vector DM with a series of increasingly complex realistic models,\ntaking into account all existing constraints from collider and astroparticle\nphysics. These complete realizations include the inert doublet with scalar DM,\nthe singlet-doublet model for fermionic DM and models based on spontaneously\nbroken dark SU(2) and SU(3) gauge symmetries for vector boson DM. We also\ndiscuss the simpler scenarios in which a new scalar singlet field that mixes\nwith the standard Higgs field is introduced with minimal couplings to\nisosinglet spin--$0, \\frac12$ and 1 DM states. We show that in large regions of\nthe parameter space of these models, the effective Higgs-portal approach\nprovides a consistent limit and thus, can be safely adopted, in particular for\nthe interpretation of searches for invisible Higgs boson decays at the LHC. The\nphenomenological implications of assuming or not that the DM states generate\nthe correct cosmological relic density are also discussed.\n"}
+{"abstract": "  We present geometric Bayesian active learning by disagreements (GBALD), a\nframework that performs BALD on its core-set construction interacting with\nmodel uncertainty estimation. Technically, GBALD constructs core-set on\nellipsoid, not typical sphere, preventing low-representative elements from\nspherical boundaries. The improvements are twofold: 1) relieve uninformative\nprior and 2) reduce redundant estimations. Theoretically, geodesic search with\nellipsoid can derive tighter lower bound on error and easier to achieve zero\nerror than with sphere. Experiments show that GBALD has slight perturbations to\nnoisy and repeated samples, and outperforms BALD, BatchBALD and other existing\ndeep active learning approaches.\n"}
+{"abstract": "  Facial Expression Recognition(FER) is one of the most important topic in\nHuman-Computer interactions(HCI). In this work we report details and\nexperimental results about a facial expression recognition method based on\nstate-of-the-art methods. We fine-tuned a SeNet deep learning architecture\npre-trained on the well-known VGGFace2 dataset, on the AffWild2 facial\nexpression recognition dataset. The main goal of this work is to define a\nbaseline for a novel method we are going to propose in the near future. This\npaper is also required by the Affective Behavior Analysis in-the-wild (ABAW)\ncompetition in order to evaluate on the test set this approach. The results\nreported here are on the validation set and are related on the Expression\nChallenge part (seven basic emotion recognition) of the competition. We will\nupdate them as soon as the actual results on the test set will be published on\nthe leaderboard.\n"}
+{"abstract": "  In this paper we compute the Newton polytope $\\mathcal M_A$ of the Morse\ndiscriminant in the space of univariate polynomials with the given support set\n$A.$ Namely, we establish a surjection between the set of all combinatorial\ntypes of Morse univariate tropical polynomials and the vertices of $\\mathcal\nM_A.$\n"}
+{"abstract": "  Breast cancer is the most common invasive cancer in women, and the second\nmain cause of death. Breast cancer screening is an efficient method to detect\nindeterminate breast lesions early. The common approaches of screening for\nwomen are tomosynthesis and mammography images. However, the traditional manual\ndiagnosis requires an intense workload by pathologists, who are prone to\ndiagnostic errors. Thus, the aim of this study is to build a deep convolutional\nneural network method for automatic detection, segmentation, and classification\nof breast lesions in mammography images. Based on deep learning the Mask-CNN\n(RoIAlign) method was developed to features selection and extraction; and the\nclassification was carried out by DenseNet architecture. Finally, the precision\nand accuracy of the model is evaluated by cross validation matrix and AUC\ncurve. To summarize, the findings of this study may provide a helpful to\nimprove the diagnosis and efficiency in the automatic tumor localization\nthrough the medical image classification.\n"}
+{"abstract": "  We have studied experimentally the generation of vortex flow by gravity waves\nwith a frequency of 2.34 Hz excited on the water surface at an angle $2 \\theta\n= arctan(3/4) \\approx 36\\deg$ to each other. The resulting horizontal surface\nflow has a stripe-like spatial structure. The width of the stripes L =\n$\\pi$/(2ksin$\\theta$) is determined by the wave vector k of the surface waves\nand the angle between them, and the length of the stripes is limited by the\nsystem size. It was found that the vertical vorticity $\\Omega$ of the current\non the fluid surface is proportional to the product of wave amplitudes, but its\nvalue is much higher than the value corresponding to the Stokes drift and it\ncontinues to grow with time even after the wave motion reaches a stationary\nregime. We demonstrate that the measured dependence $\\Omega$(t) can be\ndescribed within the recently developed model that takes into account the\nEulerian contribution to the generated vortex flow and the effect of surface\ncontamination. This model contains a free parameter that describes the elastic\nproperties of the contaminated surface, and we also show that the found value\nof this parameter is in reasonable agreement with the measured decay rate of\nsurface waves.\n"}
+{"abstract": "  We investigate the impact of photochemical hazes and disequilibrium gases on\nthe thermal structure of hot-Jupiters, using a detailed 1-D\nradiative-convective model. We find that the inclusion of photochemical hazes\nresults in major heating of the upper and cooling of the lower atmosphere.\nSulphur containing species, such as SH, S$_2$ and S$_3$ provide significant\nopacity in the middle atmosphere and lead to local heating near 1 mbar, while\nOH, CH, NH, and CN radicals produced by the photochemistry affect the thermal\nstructure near 1 $\\mu$bar. Furthermore we show that the modifications on the\nthermal structure from photochemical gases and hazes can have important\nramifications for the interpretation of transit observations. Specifically, our\nstudy for the hazy HD 189733 b shows that the hotter upper atmosphere resulting\nfrom the inclusion of photochemical haze opacity imposes an expansion of the\natmosphere, thus a steeper transit signature in the UV-Visible part of the\nspectrum. In addition, the temperature changes in the photosphere also affect\nthe secondary eclipse spectrum. For HD 209458 b we find that a small haze\nopacity could be present in this atmosphere, at pressures below 1 mbar, which\ncould be a result of both photochemical hazes and condensates. Our results\nmotivate the inclusion of radiative feedback from photochemical hazes in\ngeneral circulation models for a proper evaluation of atmospheric dynamics.\n"}
+{"abstract": "  We compare the macroscopic and the local plastic behavior of a model\namorphous solid based on two radically different numerical descriptions. On the\none hand, we simulate glass samples by atomistic simulations. On the other, we\nimplement a mesoscale elasto-plastic model based on a solid-mechanics\ndescription. The latter is extended to consider the anisotropy of the yield\nsurface via statistically distributed local and discrete weak planes on which\nshear transformations can be activated. To make the comparison as quantitative\nas possible, we consider the simple case of a quasistatically driven\ntwo-dimensional system in the stationary flow state and compare mechanical\nobservables measured on both models over the same length scales. We show that\nthe macroscale response, including its fluctuations, can be quantitatively\nrecovered for a range of elasto-plastic mesoscale parameters. Using a newly\ndeveloped method that makes it possible to probe the local yield stresses in\natomistic simulations, we calibrate the local mechanical response of the\nelasto-plastic model at different coarse-graining scales. In this case, the\ncalibration shows a qualitative agreement only for an optimized subset of\nmesoscale parameters and for sufficiently coarse probing length scales. This\ncalibration allows us to establish a length scale for the mesoscopic elements\nthat corresponds to an upper bound of the shear transformation size, a key\nphysical parameter in elasto-plastic models. We find that certain properties\nnaturally emerge from the elasto-plastic model. In particular, we show that the\nelasto-plastic model reproduces the Bauschinger effect, namely the\nplasticity-induced anisotropy in the stress-strain response. We discuss the\nsuccesses and failures of our approach, the impact of different model\ningredients and propose future research directions for quantitative multi-scale\nmodels of amorphous plasticity.\n"}
+{"abstract": "  This paper expounds very innovative results achieved between the mid-14th\ncentury and the beginning of the 16th century by Indian astronomers belonging\nto the so-called \"M\\=adhava school\". These results were in keeping with\nresearches in trigonometry: they concern the calculation of the eight of the\ncircumference of a circle. They not only expose an analog of the series\nexpansion of arctan(1) usually known as the \"Leibniz series\", but also other\nanalogs of series expansions, the convergence of which is much faster. These\nseries expansions are derived from evaluations of the rests of the partial sums\nof the primordial series, by means of some convergents of generalized continued\nfractions. A justification of these results in modern terms is provided, which\naims at restoring their full mathematical interest.\n"}
+{"abstract": "  Radio relics are the manifestation of electrons presumably being shock\n(re-)accelerated to high energies in the outskirts of galaxy clusters. However,\nestimates of the shocks' strength yield different results when measured with\nradio or X-ray observations. In general, Mach numbers obtained from radio\nobservations are larger than the corresponding X-ray measurements. In this\nwork, we investigate this Mach number discrepancy. For this purpose, we used\nthe cosmological code ENZO to simulate a sample of galaxy clusters that host\nbright radio relics. For each relic, we computed the radio Mach number from the\nintegrated radio spectrum and the X-ray Mach number from the X-ray surface\nbrightness and temperature jumps. Our analysis suggests that the differences in\nthe Mach number estimates follow from the way in which different observables\nare related to different parts of the underlying Mach number distribution:\nradio observations are more sensistive to the high Mach numbers present only in\na small fraction of a shock's surface, while X-ray measurements reflect the\naverage of the Mach number distribution. Moreover, X-ray measurements are very\nsensitive to the relic's orientation. If the same relic is observed from\ndifferent sides, the measured X-ray Mach number varies significantly. On the\nother hand, the radio measurements are more robust, as they are unaffected by\nthe relic's orientation.\n"}
+{"abstract": "  In this paper, we present a sharp analysis for an alternating gradient\ndescent algorithm which is used to solve the covariate adjusted precision\nmatrix estimation problem in the high dimensional setting. Without the\nresampling assumption, we demonstrate that this algorithm not only enjoys a\nlinear rate of convergence, but also attains the optimal statistical rate\n(i.e., minimax rate). Moreover, our analysis also characterizes the time-data\ntradeoffs in the covariate adjusted precision matrix estimation problem.\nNumerical experiments are provided to verify our theoretical results.\n"}
+{"abstract": "  FISTA is a popular convex optimisation algorithm which is known to converge\nat an optimal rate whenever the optimisation domain is contained in a suitable\nHilbert space. We propose a modified algorithm where each iteration is\nperformed in a subspace, and that subspace is allowed to change at every\niteration. Analytically, this allows us to guarantee convergence in a Banach\nspace setting, although at a reduced rate depending on the conditioning of the\nspecific problem. Numerically we show that a greedy adaptive choice of\ndiscretisation can greatly increase the time and memory efficiency in infinite\ndimensional Lasso optimisation problems.\n"}
+{"abstract": "  When two identical two-dimensional (2D) periodic lattices are stacked in\nparallel after rotating one layer by a certain angle relative to the other\nlayer, the resulting bilayer system can lose lattice periodicity completely and\nbecome a 2D quasicrystal. Twisted bilayer graphene with 30-degree rotation is a\nrepresentative example. We show that such quasicrystalline bilayer systems\ngenerally develop macroscopically degenerate localized zero-energy states\n(ZESs) in strong coupling limit where the interlayer couplings are\noverwhelmingly larger than the intralayer couplings. The emergent chiral\nsymmetry in strong coupling limit and aperiodicity of bilayer quasicrystals\nguarantee the existence of the ZESs. The macroscopically degenerate ZESs are\nanalogous to the flat bands of periodic systems, in that both are composed of\nlocalized eigenstates, which give divergent density of states. For monolayers,\nwe consider the triangular, square, and honeycomb lattices, comprised of\nhomogenous tiling of three possible planar regular polygons: the equilateral\ntriangle, square, and regular hexagon. We construct a compact theoretical\nframework, which we call the quasiband model, that describes the low energy\nproperties of bilayer quasicrystals and counts the number of ZESs using a\nsubset of Bloch states of monolayers. We also propose a simple geometric scheme\nin real space which can show the spatial localization of ZESs and count their\nnumber. Our work clearly demonstrates that bilayer quasicrystals in strong\ncoupling limit are an ideal playground to study the intriguing interplay of\nflat band physics and the aperiodicity of quasicrystals.\n"}
+{"abstract": "  In this paper, we delve into semi-supervised object detection where unlabeled\nimages are leveraged to break through the upper bound of fully-supervised\nobject detection models. Previous semi-supervised methods based on pseudo\nlabels are severely degenerated by noise and prone to overfit to noisy labels,\nthus are deficient in learning different unlabeled knowledge well. To address\nthis issue, we propose a data-uncertainty guided multi-phase learning method\nfor semi-supervised object detection. We comprehensively consider divergent\ntypes of unlabeled images according to their difficulty levels, utilize them in\ndifferent phases and ensemble models from different phases together to generate\nultimate results. Image uncertainty guided easy data selection and region\nuncertainty guided RoI Re-weighting are involved in multi-phase learning and\nenable the detector to concentrate on more certain knowledge. Through extensive\nexperiments on PASCAL VOC and MS COCO, we demonstrate that our method behaves\nextraordinarily compared to baseline approaches and outperforms them by a large\nmargin, more than 3% on VOC and 2% on COCO.\n"}
+{"abstract": "  This paper theoretically investigates the following empirical phenomenon:\ngiven a high-complexity network with poor generalization bounds, one can\ndistill it into a network with nearly identical predictions but low complexity\nand vastly smaller generalization bounds. The main contribution is an analysis\nshowing that the original network inherits this good generalization bound from\nits distillation, assuming the use of well-behaved data augmentation. This\nbound is presented both in an abstract and in a concrete form, the latter\ncomplemented by a reduction technique to handle modern computation graphs\nfeaturing convolutional layers, fully-connected layers, and skip connections,\nto name a few. To round out the story, a (looser) classical uniform convergence\nanalysis of compression is also presented, as well as a variety of experiments\non cifar and mnist demonstrating similar generalization performance between the\noriginal network and its distillation.\n"}
+{"abstract": "  Distributed networks and real-time systems are becoming the most important\ncomponents for the new computer age, the Internet of Things (IoT), with huge\ndata streams or data sets generated from sensors and data generated from\nexisting legacy systems. The data generated offers the ability to measure,\ninfer and understand environmental indicators, from delicate ecologies and\nnatural resources to urban environments. This can be achieved through the\nanalysis of the heterogeneous data sources (structured and unstructured). In\nthis paper, we propose a distributed framework Event STream Processing Engine\nfor Environmental Monitoring Domain (ESTemd) for the application of stream\nprocessing on heterogeneous environmental data. Our work in this area\ndemonstrates the useful role big data techniques can play in an environmental\ndecision support system, early warning and forecasting systems. The proposed\nframework addresses the challenges of data heterogeneity from heterogeneous\nsystems and real time processing of huge environmental datasets through a\npublish/subscribe method via a unified data pipeline with the application of\nApache Kafka for real time analytics.\n"}
+{"abstract": "  Morphological Segmentation involves decomposing words into morphemes, the\nsmallest meaning-bearing units of language. This is an important NLP task for\nmorphologically-rich agglutinative languages such as the Southern African Nguni\nlanguage group. In this paper, we investigate supervised and unsupervised\nmodels for two variants of morphological segmentation: canonical and surface\nsegmentation. We train sequence-to-sequence models for canonical segmentation,\nwhere the underlying morphemes may not be equal to the surface form of the\nword, and Conditional Random Fields (CRF) for surface segmentation.\nTransformers outperform LSTMs with attention on canonical segmentation,\nobtaining an average F1 score of 72.5% across 4 languages. Feature-based CRFs\noutperform bidirectional LSTM-CRFs to obtain an average of 97.1% F1 on surface\nsegmentation. In the unsupervised setting, an entropy-based approach using a\ncharacter-level LSTM language model fails to outperforms a Morfessor baseline,\nwhile on some of the languages neither approach performs much better than a\nrandom baseline. We hope that the high performance of the supervised\nsegmentation models will help to facilitate the development of better NLP tools\nfor Nguni languages.\n"}
+{"abstract": "  We propose and study a new mathematical model of the human immunodeficiency\nvirus (HIV). The main novelty is to consider that the antibody growth depends\nnot only on the virus and on the antibodies concentration but also on the\nuninfected cells concentration. The model consists of five nonlinear\ndifferential equations describing the evolution of the uninfected cells, the\ninfected ones, the free viruses, and the adaptive immunity. The adaptive immune\nresponse is represented by the cytotoxic T-lymphocytes (CTL) cells and the\nantibodies with the growth function supposed to be trilinear. The model\nincludes two kinds of treatments. The objective of the first one is to reduce\nthe number of infected cells, while the aim of the second is to block free\nviruses. Firstly, the positivity and the boundedness of solutions are\nestablished. After that, the local stability of the disease free steady state\nand the infection steady states are characterized. Next, an optimal control\nproblem is posed and investigated. Finally, numerical simulations are performed\nin order to show the behavior of solutions and the effectiveness of the two\nincorporated treatments via an efficient optimal control strategy.\n"}
+{"abstract": "  We use high quality VLT/MUSE data to study the kinematics and the ionized gas\nproperties of Haro 11, a well known starburst merger system and the closest\nconfirmed Lyman continuum leaking galaxy. We present results from integrated\nline maps, and from maps in three velocity bins comprising the blueshifted,\nsystemic and redshifted emission. The kinematic analysis reveals complex\nvelocities resulting from the interplay of virial motions and momentum\nfeedback. Star formation happens intensively in three compact knots (knots A, B\nand C), but one, knot C, dominates the energy released in supernovae. The halo\nis characterised by low gas density and extinction, but with large temperature\nvariations, coincident with fast shock regions. Moreover, we find large\ntemperature discrepancies in knot C, when using different temperature-sensitive\nlines. The relative impact of the knots in the metal enrichment differs. While\nknot B is strongly enriching its closest surrounding, knot C is likely the main\ndistributor of metals in the halo. In knot A, part of the metal enriched gas\nseems to escape through low density channels towards the south. We compare the\nmetallicities from two methods and find large discrepancies in knot C, a\nshocked area, and the highly ionized zones, that we partially attribute to the\neffect of shocks. This work shows, that traditional relations developed from\naveraged measurements or simplified methods, fail to probe the diverse\nconditions of the gas in extreme environments. We need robust relations that\ninclude realistic models where several physical processes are simultaneously at\nwork.\n"}
+{"abstract": "  We demonstrate a method that merges the quantum filter diagonalization (QFD)\napproach for hybrid quantum/classical solution of the time-independent\nelectronic Schr\\\"odinger equation with a low-rank double factorization (DF)\napproach for the representation of the electronic Hamiltonian. In particular,\nwe explore the use of sparse \"compressed\" double factorization (C-DF)\ntruncation of the Hamiltonian within the time-propagation elements of QFD,\nwhile retaining a similarly compressed but numerically converged\ndouble-factorized representation of the Hamiltonian for the operator\nexpectation values needed in the QFD quantum matrix elements. Together with\nsignificant circuit reduction optimizations and number-preserving\npost-selection/echo-sequencing error mitigation strategies, the method is found\nto provide accurate predictions for low-lying eigenspectra in a number of\nrepresentative molecular systems, while requiring reasonably short circuit\ndepths and modest measurement costs. The method is demonstrated by experiments\non noise-free simulators, decoherence- and shot-noise including simulators, and\nreal quantum hardware.\n"}
+{"abstract": "  The Android operating system is the most spread mobile platform in the world.\nTherefor attackers are producing an incredible number of malware applications\nfor Android. Our aim is to detect Android's malware in order to protect the\nuser. To do so really good results are obtained by dynamic analysis of\nsoftware, but it requires complex environments. In order to achieve the same\nlevel of precision we analyze the machine code and investigate the frequencies\nof ngrams of opcodes in order to detect singular code blocks. This allow us to\nconstruct a database of infected code blocks. Then, because attacker may modify\nand organized differently the infected injected code in their new malware, we\nperform not only a semantic comparison of the tested software with the database\nof infected code blocks but also a structured comparison. To do such comparison\nwe compute subgraph isomorphism. It allows us to characterize precisely if the\ntested software is a malware and if so in witch family it belongs. Our method\nis tested both on a laboratory database and a set of real data. It achieves an\nalmost perfect detection rate.\n"}
+{"abstract": "  This thesis is concerned with continuous, static, and single-objective\noptimization problems subject to inequality constraints. Nevertheless, some\nmethods to handle other kinds of problems are briefly reviewed. The particle\nswarm optimization paradigm was inspired by previous simulations of the\ncooperative behaviour observed in social beings. It is a bottom-up, randomly\nweighted, population-based method whose ability to optimize emerges from local,\nindividual-to-individual interactions. As opposed to traditional methods, it\ncan deal with different problems with few or no adaptation due to the fact that\nit does profit from problem-specific features of the problem at issue but\nperforms a parallel, cooperative exploration of the search-space by means of a\npopulation of individuals. The main goal of this thesis consists of developing\nan optimizer that can perform reasonably well on most problems. Hence, the\ninfluence of the settings of the algorithm's parameters on the behaviour of the\nsystem is studied, some general-purpose settings are sought, and some\nvariations to the canonical version are proposed aiming to turn it into a more\ngeneral-purpose optimizer. Since no termination condition is included in the\ncanonical version, this thesis is also concerned with the design of some\nstopping criteria which allow the iterative search to be terminated if further\nsignificant improvement is unlikely, or if a certain number of time-steps are\nreached. In addition, some constraint-handling techniques are incorporated into\nthe canonical algorithm to handle inequality constraints. Finally, the\ncapabilities of the proposed general-purpose optimizers are illustrated by\noptimizing a few benchmark problems.\n"}
+{"abstract": "  In recent years, human activity recognition has garnered considerable\nattention both in industrial and academic research because of the wide\ndeployment of sensors, such as accelerometers and gyroscopes, in products such\nas smartphones and smartwatches. Activity recognition is currently applied in\nvarious fields where valuable information about an individual's functional\nability and lifestyle is needed. In this study, we used the popular WISDM\ndataset for activity recognition. Using multivariate analysis of covariance\n(MANCOVA), we established a statistically significant difference (p<0.05)\nbetween the data generated from the sensors embedded in smartphones and\nsmartwatches. By doing this, we show that smartphones and smartwatches don't\ncapture data in the same way due to the location where they are worn. We\ndeployed several neural network architectures to classify 15 different hand and\nnon-hand-oriented activities. These models include Long short-term memory\n(LSTM), Bi-directional Long short-term memory (BiLSTM), Convolutional Neural\nNetwork (CNN), and Convolutional LSTM (ConvLSTM). The developed models\nperformed best with watch accelerometer data. Also, we saw that the\nclassification precision obtained with the convolutional input classifiers (CNN\nand ConvLSTM) was higher than the end-to-end LSTM classifier in 12 of the 15\nactivities. Additionally, the CNN model for the watch accelerometer was better\nable to classify non-hand oriented activities when compared to hand-oriented\nactivities.\n"}
+{"abstract": "  A space-time Trefftz discontinuous Galerkin method for the Schr\\\"odinger\nequation with piecewise-constant potential is proposed and analyzed. Following\nthe spirit of Trefftz methods, trial and test spaces are spanned by\nnon-polynomial complex wave functions that satisfy the Schro\\\"odinger equation\nlocally on each element of the space-time mesh. This allows for a significant\nreduction in the number of degrees of freedom in comparison with full\npolynomial spaces. We prove well-posedness and stability of the method, and,\nfor the one- and two- dimensional cases, optimal, high-order, h-convergence\nerror estimates in a skeleton norm. Some numerical experiments validate the\ntheoretical results presented.\n"}
+{"abstract": "  We demonstrate the use of multiple atomic-level Rydberg-atom schemes for\ncontinuous frequency detection of radio frequency (RF) fields. Resonant\ndetection of RF fields by electromagnetically-induced transparency and\nAutler-Townes (AT) in Rydberg atoms is typically limited to frequencies within\nthe narrow bandwidth of a Rydberg transition. By applying a second field\nresonant with an adjacent Rydberg transition, far-detuned fields can be\ndetected through a two-photon resonance AT splitting. This two-photon AT\nsplitting method is several orders of magnitude more sensitive than\noff-resonant detection using the Stark shift. We present the results of various\nexperimental configurations and a theoretical analysis to illustrate the\neffectiveness of this multiple level scheme. These results show that this\napproach allows for the detection of frequencies in continuous band between\nresonances with adjacent Rydberg states.\n"}
+{"abstract": "  We study the space of $C^1$ isogeometric spline functions defined on\ntrilinearly parameterized multi-patch volumes. Amongst others, we present a\ngeneral framework for the design of the $C^1$ isogeometric spline space and of\nan associated basis, which is based on the two-patch construction [7], and\nwhich works uniformly for any possible multi-patch configuration. The presented\nmethod is demonstrated in more detail on the basis of a particular subclass of\ntrilinear multi-patch volumes, namely for the class of trilinearly\nparameterized multi-patch volumes with exactly one inner edge. For this\nspecific subclass of trivariate multi-patch parameterizations, we further\nnumerically compute the dimension of the resulting $C^1$ isogeometric spline\nspace and use the constructed $C^1$ isogeometric basis functions to numerically\nexplore the approximation properties of the $C^1$ spline space by performing\n$L^2$ approximation.\n"}
+{"abstract": "  Recently, higher-order topological matter and 3D quantum Hall effects have\nattracted great attention. The Fermi-arc mechanism of the 3D quantum Hall\neffect proposed in Weyl semimetals is characterized by the one-sided hinge\nstates, which do not exist in all the previous quantum Hall systems and more\nimportantly pose a realistic example of the higher-order topological matter.\nThe experimental effort so far is in the Dirac semimetal Cd$_3$As$_2$, where\nhowever time-reversal symmetry leads to hinge states on both sides of the\ntop/bottom surfaces, instead of the aspired one-sided hinge states. We propose\nthat under a tilted magnetic field, the hinge states in Cd$_3$As$_2$-like Dirac\nsemimetals can be one-sided, highly tunable by field direction and Fermi\nenergy, and robust against weak disorder. Furthermore, we propose a scanning\ntunneling Hall measurement to detect the one-sided hinge states. Our results\nwill be insightful for exploring not only the quantum Hall effects beyond two\ndimensions, but also other higher-order topological insulators in the future.\n"}
+{"abstract": "  When two spherical particles submerged in a viscous fluid are subjected to an\noscillatory flow, they align themselves perpendicular to the direction of the\nflow leaving a small gap between them. The formation of this compact structure\nis attributed to a non-zero residual flow known as steady streaming. We have\nperformed direct numerical simulations of a fully-resolved, oscillating flow in\nwhich the pair of particles is modeled using an immersed boundary method. Our\nsimulations show that the particles oscillate both parallel and perpendicular\nto the oscillating flow in elongated figure 8 trajectories. In absence of\nbottom friction, the mean gap between the particles depends only on the\nnormalized Stokes boundary layer thickness $\\delta^*$, and on the normalized,\nstreamwise excursion length of the particles relative to the fluid $A_r^*$\n(equivalent to the Keulegan-Carpenter number). For $A_r^*\\lesssim 1$, viscous\neffects dominate and the mean particle separation only depends on $\\delta^*$.\nFor larger $A_r^*$-values, advection becomes important and the gap widens.\nOverall, the normalized mean gap between the particles scales as\n$L^*\\approx3.0{\\delta^*}^{1.5}+0.03{A_r^*}^3$, which also agrees well with\nprevious experimental results. The two regimes are also observed in the\nmagnitude of the oscillations of the gap perpendicular to the flow, which\nincreases in the viscous regime and decreases in the advective regime. When\nbottom friction is considered, particle rotation increases and the gap widens.\nOur results stress the importance of simulating the particle motion with all\nits degrees of freedom to accurately model the system and reproduce\nexperimental results. The new insights of the particle pairs provide an\nimportant step towards understanding denser and more complex systems.\n"}
+{"abstract": "  We present a new model to describe the star formation process in galaxies,\nwhich includes the description of the different gas phases -- molecular,\natomic, and ionized -- together with its metal content. The model, which will\nbe coupled to cosmological simulations of galaxy formation, will be used to\ninvestigate the relation between the star formation rate (SFR) and the\nformation of molecular hydrogen. The model follows the time evolution of the\nmolecular, atomic and ionized phases in a gas cloud and estimates the amount of\nstellar mass formed, by solving a set of five coupled differential equations.\nAs expected, we find a positive, strong correlation between the molecular\nfraction and the initial gas density, which manifests in a positive correlation\nbetween the initial gas density and the SFR of the cloud.\n"}
+{"abstract": "  The development of lightweight object detectors is essential due to the\nlimited computation resources. To reduce the computation cost, how to generate\nredundant features plays a significant role. This paper proposes a new\nlightweight Convolution method Cross-Stage Lightweight (CSL) Module, to\ngenerate redundant features from cheap operations. In the intermediate\nexpansion stage, we replaced Pointwise Convolution with Depthwise Convolution\nto produce candidate features. The proposed CSL-Module can reduce the\ncomputation cost significantly. Experiments conducted at MS-COCO show that the\nproposed CSL-Module can approximate the fitting ability of Convolution-3x3.\nFinally, we use the module to construct a lightweight detector CSL-YOLO,\nachieving better detection performance with only 43% FLOPs and 52% parameters\nthan Tiny-YOLOv4.\n"}
+{"abstract": "  The dispersion of a tracer in a fluid flow is influenced by the Lagrangian\nmotion of fluid elements. Even in laminar regimes, the irregular chaotic\nbehavior of a fluid flow can lead to effective stirring that rapidly\nredistributes a tracer throughout the domain. When the advected particles\npossess a finite size and nontrivial shape, however, their dynamics can differ\nmarkedly from passive tracers, thus affecting the dispersion phenomena. Here we\ninvestigate the behavior of neutrally buoyant particles in 2-dimensional\nchaotic flows, combining numerical simulations and laboratory experiments. We\nshow that depending on the particles shape and size, the underlying Lagrangian\ncoherent structures can be altered, resulting in distinct dispersion phenomena\nwithin the same flow field. Experiments performed in a two-dimensional cellular\nflow, exhibited a focusing effect in vortex cores of particles with anisotropic\nshape. In agreement with our numerical model, neutrally buoyant ellipsoidal\nparticles display markedly different trajectories and overall organization than\nspherical particles, with a clustering in vortices that changes accordingly\nwith the aspect ratio of the particles.\n"}
+{"abstract": "  We explore the ability of overparameterized shallow neural networks to learn\nLipschitz regression functions with and without label noise when trained by\nGradient Descent (GD). To avoid the problem that in the presence of noisy\nlabels, neural networks trained to nearly zero training error are inconsistent\non this class, we propose an early stopping rule that allows us to show optimal\nrates. This provides an alternative to the result of Hu et al. (2021) who\nstudied the performance of $\\ell 2$ -regularized GD for training shallow\nnetworks in nonparametric regression which fully relied on the infinite-width\nnetwork (Neural Tangent Kernel (NTK)) approximation. Here we present a simpler\nanalysis which is based on a partitioning argument of the input space (as in\nthe case of 1-nearest-neighbor rule) coupled with the fact that trained neural\nnetworks are smooth with respect to their inputs when trained by GD. In the\nnoise-free case the proof does not rely on any kernelization and can be\nregarded as a finite-width result. In the case of label noise, by slightly\nmodifying the proof, the noise is controlled using a technique of Yao, Rosasco,\nand Caponnetto (2007).\n"}
+{"abstract": "  Pretrained language models have significantly improved the performance of\ndown-stream language understanding tasks, including extractive question\nanswering, by providing high-quality contextualized word embeddings. However,\nlearning question answering models still need large-scaled data annotation in\nspecific domains. In this work, we propose a cooperative, self-play learning\nframework, REGEX, for question generation and answering. REGEX is built upon a\nmasked answer extraction task with an interactive learning environment\ncontaining an answer entity REcognizer, a question Generator, and an answer\nEXtractor. Given a passage with a masked entity, the generator generates a\nquestion around the entity, and the extractor is trained to extract the masked\nentity with the generated question and raw texts. The framework allows the\ntraining of question generation and answering models on any text corpora\nwithout annotation. We further leverage a reinforcement learning technique to\nreward generating high-quality questions and to improve the answer extraction\nmodel's performance. Experiment results show that REGEX outperforms the\nstate-of-the-art (SOTA) pretrained language models and zero-shot approaches on\nstandard question-answering benchmarks, and yields the new SOTA performance\nunder the zero-shot setting.\n"}
+{"abstract": "  Enterprise knowledge is a key asset in the competing and fast-changing\ncorporate landscape. The ability to learn, store and distribute implicit and\nexplicit knowledge can be the difference between success and failure. While\nenterprise knowledge management is a well-defined research domain, current\nimplementations lack orientation towards small and medium enterprise. We\npropose a semantic search engine for relevant documents in an enterprise, based\non automatic generated domain ontologies. In this paper we focus on the\ncomponent for ontology learning and population.\n"}
+{"abstract": "  Recently, Doroudiani and Karimipour [Phys. Rev. A \\textbf{102} 012427(2020)]\nproposed the notation of planar maximally entangled (PME) states which are a\nwider class of multipartite entangled states than absolutely maximally\nentangled (AME) states. There they presented their constructions in the\nmultipartite systems but the number of particles is restricted to be even. Here\nwe first solve the remaining cases, i.e., constructions of planar maximally\nentangled states on systems with odd number of particles. In addition, we\ngeneralized the PME to the planar $k$-uniform states whose reductions to any\nadjacent $k$ parties along a circle of $N$ parties are maximally mixed. We\npresented a method to construct sets of planar $k$-uniform states which have\nminimal support.\n"}
+{"abstract": "  We construct exact solutions to the Einstein-Maxwell theory with uplifting\nthe four dimensional Fubini-Study Kahler manifold. We find the solutions can be\nexpressed exactly as the integrals of two special functions. The solutions are\nregular almost everywhere except a bolt structure on a single point in any\ndimensionality. We also show that the solutions are unique and can not be\nnon-trivially extended to include the cosmological constant in any dimensions.\n"}
+{"abstract": "  The introduction of an optical resonator can enable efficient and precise\ninteraction between a photon and a solid-state emitter. It facilitates the\nstudy of strong light-matter interaction, polaritonic physics and presents a\npowerful interface for quantum communication and computing. A pivotal aspect in\nthe progress of light-matter interaction with solid-state systems is the\nchallenge of combining the requirements of cryogenic temperature and high\nmechanical stability against vibrations while maintaining sufficient degrees of\nfreedom for in-situ tunability. Here, we present a fiber-based open\nFabry-P\\'{e}rot cavity in a closed-cycle cryostat exhibiting ultra-high\nmechanical stability while providing wide-range tunability in all three spatial\ndirections. We characterize the setup and demonstrate the operation with the\nroot-mean-square cavity length fluctuation of less than $90$ pm at temperature\nof $6.5$ K and integration bandwidth of $100$ kHz. Finally, we benchmark the\ncavity performance by demonstrating the strong-coupling formation of\nexciton-polaritons in monolayer WSe$_2$ with a cooperativity of $1.6$. This set\nof results manifests the open-cavity in a closed-cycle cryostat as a versatile\nand powerful platform for low-temperature cavity QED experiments.\n"}
+{"abstract": "  We determine the dark matter pair-wise relative velocity distribution in a\nset of Milky Way-like halos in the Auriga and APOSTLE simulations. Focusing on\nthe smooth halo component, the relative velocity distribution is well-described\nby a Maxwell-Boltzmann distribution over nearly all radii in the halo. We\nexplore the implications for velocity-dependent dark matter annihilation,\nfocusing on four models which scale as different powers of the relative\nvelocity: Sommerfeld, s-wave, p-wave, and d-wave models. We show that the\nJ-factors scale as the moments of the relative velocity distribution, and that\nthe halo-to-halo scatter is largest for d-wave, and smallest for Sommerfeld\nmodels. The J-factor is strongly correlated with the dark matter density in the\nhalo, and is very weakly correlated with the velocity dispersion. This implies\nthat if the dark matter density in the Milky Way can be robustly determined,\none can accurately predict the dark matter annihilation signal, without the\nneed to identify the dark matter velocity distribution in the Galaxy.\n"}
+{"abstract": "  In this essay, we qualitatively demonstrate how small non-perturbative\ncorrections are a necessary addition to semiclassical gravity's path integral.\nWe use this to discuss implications for Hawking's information paradox and the\nbags of gold paradox.\n"}
+{"abstract": "  We report Keck-NIRSPEC observations of the Brackett $\\alpha$ 4.05 $\\mu$m\nrecombination line across the two candidate embedded super star clusters (SSCs)\nin NGC 1569. These SSCs power a bright HII region and have been previously\ndetected as radio and mid-infrared sources. Supplemented with high resolution\nVLA mapping of the radio continuum along with IRTF-TEXES spectroscopy of the\n[SIV] 10.5 $\\mu$m line, the Brackett $\\alpha$ spectra data provide new insight\ninto the dynamical state of gas ionized by these forming massive clusters. NIR\nsources detected in 2 $\\mu$m images from the Slit-viewing Camera are matched\nwith GAIA sources to obtain accurate celestial coordinates and slit positions\nto within $\\sim 0.1''$. Br$\\alpha$ is detected as a strong emission peak\npowered by the less luminous infrared source, MIR1 ($L_{\\rm IR}\\sim\n2\\times10^7~L_\\odot$). The second candidate SSC MIR2 is more luminous ($L_{\\rm\nIR}\\gtrsim 4\\times10^8~L_\\odot$) but exhibits weak radio continuum and\nBr$\\alpha$ emission, suggesting the ionized gas is extremely dense ($n_e\\gtrsim\n10^5$ cm$^{-3}$), corresponding to hypercompact HII regions around newborn\nmassive stars. The Br$\\alpha$ and [SIV] lines across the region are both\nremarkably symmetric and extremely narrow, with observed line widths $\\Delta v\n\\simeq 40$ km s$^{-1}$, FWHM. This result is the first clear evidence that\nfeedback from NGC 1569's youngest giant clusters is currently incapable of\nrapid gas dispersal, consistent with the emerging theoretical paradigm in the\nformation of giant star clusters.\n"}
+{"abstract": "  Machine learning has brought striking advances in multilingual natural\nlanguage processing capabilities over the past year. For example, the latest\ntechniques have improved the state-of-the-art performance on the XTREME\nmultilingual benchmark by more than 13 points. While a sizeable gap to\nhuman-level performance remains, improvements have been easier to achieve in\nsome tasks than in others. This paper analyzes the current state of\ncross-lingual transfer learning and summarizes some lessons learned. In order\nto catalyze meaningful progress, we extend XTREME to XTREME-R, which consists\nof an improved set of ten natural language understanding tasks, including\nchallenging language-agnostic retrieval tasks, and covers 50 typologically\ndiverse languages. In addition, we provide a massively multilingual diagnostic\nsuite (MultiCheckList) and fine-grained multi-dataset evaluation capabilities\nthrough an interactive public leaderboard to gain a better understanding of\nsuch models. The leaderboard and code for XTREME-R will be made available at\nhttps://sites.research.google/xtreme and\nhttps://github.com/google-research/xtreme respectively.\n"}
+{"abstract": "  This paper presents a state-of-the-art LiDAR based autonomous navigation\nsystem for under-canopy agricultural robots. Under-canopy agricultural\nnavigation has been a challenging problem because GNSS and other positioning\nsensors are prone to significant errors due to attentuation and multi-path\ncaused by crop leaves and stems. Reactive navigation by detecting crop rows\nusing LiDAR measurements is a better alternative to GPS but suffers from\nchallenges due to occlusion from leaves under the canopy. Our system addresses\nthis challenge by fusing IMU and LiDAR measurements using an Extended Kalman\nFilter framework on low-cost hardwware. In addition, a local goal generator is\nintroduced to provide locally optimal reference trajectories to the onboard\ncontroller. Our system is validated extensively in real-world field\nenvironments over a distance of 50.88~km on multiple robots in different field\nconditions across different locations. We report state-of-the-art distance\nbetween intervention results, showing that our system is able to safely\nnavigate without interventions for 386.9~m on average in fields without\nsignificant gaps in the crop rows, 56.1~m in production fields and 47.5~m in\nfields with gaps (space of 1~m without plants in both sides of the row).\n"}
+{"abstract": "  Drone imagery is increasingly used in automated inspection for infrastructure\nsurface defects, especially in hazardous or unreachable environments. In\nmachine vision, the key to crack detection rests with robust and accurate\nalgorithms for image processing. To this end, this paper proposes a deep\nlearning approach using hierarchical convolutional neural networks with feature\npreservation (HCNNFP) and an intercontrast iterative thresholding algorithm for\nimage binarization. First, a set of branch networks is proposed, wherein the\noutput of previous convolutional blocks is half-sizedly concatenated to the\ncurrent ones to reduce the obscuration in the down-sampling stage taking into\naccount the overall information loss. Next, to extract the feature map\ngenerated from the enhanced HCNN, a binary contrast-based autotuned\nthresholding (CBAT) approach is developed at the post-processing step, where\npatterns of interest are clustered within the probability map of the identified\nfeatures. The proposed technique is then applied to identify surface cracks on\nthe surface of roads, bridges or pavements. An extensive comparison with\nexisting techniques is conducted on various datasets and subject to a number of\nevaluation criteria including the average F-measure (AF\\b{eta}) introduced here\nfor dynamic quantification of the performance. Experiments on crack images,\nincluding those captured by unmanned aerial vehicles inspecting a monorail\nbridge. The proposed technique outperforms the existing methods on various\ntested datasets especially for GAPs dataset with an increase of about 1.4% in\nterms of AF\\b{eta} while the mean percentage error drops by 2.2%. Such\nperformance demonstrates the merits of the proposed HCNNFP architecture for\nsurface defect inspection.\n"}
+{"abstract": "  We present a framework for simulating realistic inverse synthetic aperture\nradar images of automotive targets at millimeter wave frequencies. The model\nincorporates radar scattering phenomenology of commonly found vehicles along\nwith range-Doppler based clutter and receiver noise. These images provide\ninsights into the physical dimensions of the target, the number of wheels and\nthe trajectory undertaken by the target. The model is experimentally validated\nwith measurement data gathered from an automotive radar. The images from the\nsimulation database are subsequently classified using both traditional machine\nlearning techniques as well as deep neural networks based on transfer learning.\nWe show that the ISAR images offer a classification accuracy above 90% and are\nrobust to both noise and clutter.\n"}
+{"abstract": "  There are two cases when the nonlinear Schr\\\"odinger equation (NLSE) with an\nexternal complex potential is well-known to support continuous families of\nlocalized stationary modes: the ${\\cal PT}$-symmetric potentials and the Wadati\npotentials. Recently Y. Kominis and coauthors [Chaos, Solitons and Fractals,\n118, 222-233 (2019)] have suggested that the continuous families can be also\nfound in complex potentials of the form $W(x)=W_{1}(x)+iCW_{1,x}(x)$, where $C$\nis an arbitrary real and $W_1(x)$ is a real-valued and bounded differentiable\nfunction. Here we study in detail nonlinear stationary modes that emerge in\ncomplex potentials of this type (for brevity, we call them W-dW potentials).\nFirst, we assume that the potential is small and employ asymptotic methods to\nconstruct a family of nonlinear modes. Our asymptotic procedure stops at the\nterms of the $\\varepsilon^2$ order, where small $\\varepsilon$ characterizes\namplitude of the potential. We therefore conjecture that no continuous families\nof authentic nonlinear modes exist in this case, but \"pseudo-modes\" that\nsatisfy the equation up to $\\varepsilon^2$-error can indeed be found in W-dW\npotentials. Second, we consider the particular case of a W-dW potential well of\nfinite depth and support our hypothesis with qualitative and numerical\narguments. Third, we simulate the nonlinear dynamics of found pseudo-modes and\nobserve that, if the amplitude of W-dW potential is small, then the\npseudo-modes are robust and display persistent oscillations around a certain\nposition predicted by the asymptotic expansion. Finally, we study the authentic\nstationary modes which do not form a continuous family, but exist as isolated\npoints. Numerical simulations reveal dynamical instability of these solutions.\n"}
+{"abstract": "  Many recent experimental ultrafast spectroscopy studies have hinted at\nnon-adiabatic dynamics indicating the existence of conical intersections, but\ntheir direct observation remains a challenge. The rapid change of the energy\ngap between the electronic states complicated their observation by requiring\nbandwidths of several electron volts. In this manuscript, we propose to use the\ncombined information of different X-ray pump-probe techniques to identify the\nconical intersection. We theoretically study the conical intersection in\npyrrole using transient X-ray absorption, time-resolved X-ray spontaneous\nemission, and linear off-resonant Raman spectroscopy to gather evidence of the\ncurve crossing.\n"}
+{"abstract": "  Robots performing tasks in warehouses provide the first example of\nwide-spread adoption of autonomous vehicles in transportation and logistics.\nThe efficiency of these operations, which can vary widely in practice, are a\nkey factor in the success of supply chains. In this work we consider the\nproblem of coordinating a fleet of robots performing picking operations in a\nwarehouse so as to maximize the net profit achieved within a time period while\nrespecting problem- and robot-specific constraints. We formulate the problem as\na weighted set packing problem where the elements in consideration are items on\nthe warehouse floor that can be picked up and delivered within specified time\nwindows. We enforce the constraint that robots must not collide, that each item\nis picked up and delivered by at most one robot, and that the number of robots\nactive at any time does not exceed the total number available. Since the set of\nroutes is exponential in the size of the input, we attack optimization of the\nresulting integer linear program using column generation, where pricing amounts\nto solving an elementary resource-constrained shortest-path problem. We propose\nan efficient optimization scheme that avoids consideration of every increment\nwithin the time windows. We also propose a heuristic pricing algorithm that can\nefficiently solve the pricing subproblem. While this itself is an important\nproblem, the insights gained from solving these problems effectively can lead\nto new advances in other time-widow constrained vehicle routing problems.\n"}
+{"abstract": "  Following the tremendous success of transformer in natural language\nprocessing and image understanding tasks, in this paper, we present a novel\npoint cloud representation learning architecture, named Dual Transformer\nNetwork (DTNet), which mainly consists of Dual Point Cloud Transformer (DPCT)\nmodule. Specifically, by aggregating the well-designed point-wise and\nchannel-wise multi-head self-attention models simultaneously, DPCT module can\ncapture much richer contextual dependencies semantically from the perspective\nof position and channel. With the DPCT module as a fundamental component, we\nconstruct the DTNet for performing point cloud analysis in an end-to-end\nmanner. Extensive quantitative and qualitative experiments on publicly\navailable benchmarks demonstrate the effectiveness of our proposed transformer\nframework for the tasks of 3D point cloud classification and segmentation,\nachieving highly competitive performance in comparison with the\nstate-of-the-art approaches.\n"}
+{"abstract": "  We give two proofs to an old result of E. Salehi, showing that the Weyl\nsubalgebra $\\mathcal{W}$ of $\\ell^\\infty(\\mathbb{Z})$ is a proper subalgebra of\n$\\mathcal{D}$, the algebra of distal functions. We also show that the family\n$\\mathcal{S}^d$ of strictly ergodic functions in $\\mathcal{D}$ does not form an\nalgebra and hence in particular does not coincide with $\\mathcal{W}$. We then\nuse similar constructions to show that a function which is a multiplier for\nstrict ergodicity, either within $\\mathcal{D}$ or in general, is necessarily a\nconstant. An example of a metric, strictly ergodic, distal flow is constructed\nwhich admits a non-strictly ergodic $2$-fold minimal self-joining. It then\nfollows that the enveloping group of this flow is not strictly ergodic (as a\n$T$-flow). Finally we show that the distal, strictly ergodic Heisenberg\nnil-flow is relatively disjoint over its largest equicontinuous factor from\n$|\\mathcal{W}|$.\n"}
+{"abstract": "  We study extensions of the Election Isomorphism problem, focused on the\nexistence of isomorphic subelections. Specifically, we propose the Subelection\nIsomorphism and the Maximum Common Subelection problems and study their\ncomputational complexity and approximability. Using our problems in\nexperiments, we provide some insights into the nature of several statistical\nmodels of elections.\n"}
+{"abstract": "  The High Altitude Water Cherenkov (HAWC) observatory and the High Energy\nStereoscopic System (H.E.S.S.) are two leading instruments in the ground-based\nvery-high-energy gamma-ray domain. HAWC employs the water Cherenkov detection\n(WCD) technique, while H.E.S.S. is an array of Imaging Atmospheric Cherenkov\nTelescopes (IACTs). The two facilities therefore differ in multiple aspects,\nincluding their observation strategy, the size of their field of view and their\nangular resolution, leading to different analysis approaches. Until now, it has\nbeen unclear if the results of observations by both types of instruments are\nconsistent: several of the recently discovered HAWC sources have been followed\nup by IACTs, resulting in a confirmed detection only in a minority of cases.\nWith this paper, we go further and try to resolve the tensions between previous\nresults by performing a new analysis of the H.E.S.S. Galactic plane survey\ndata, applying an analysis technique comparable between H.E.S.S. and HAWC.\nEvents above 1 TeV are selected for both datasets, the point spread function of\nH.E.S.S. is broadened to approach that of HAWC, and a similar background\nestimation method is used. This is the first detailed comparison of the\nGalactic plane observed by both instruments. H.E.S.S. can confirm the gamma-ray\nemission of four HAWC sources among seven previously undetected by IACTs, while\nthe three others have measured fluxes below the sensitivity of the H.E.S.S.\ndataset. Remaining differences in the overall gamma-ray flux can be explained\nby the systematic uncertainties. Therefore, we confirm a consistent view of the\ngamma-ray sky between WCD and IACT techniques.\n"}
+{"abstract": "  Cactus networks were introduced by Lam as a generalization of planar\nelectrical networks. He defined a map from these networks to the Grassmannian\nGr($n+1,2n$) and showed that the image of this map, $\\mathcal X_n$ lies inside\nthe totally nonnegative part of this Grassmannian. In this paper, we show that\n$\\mathcal X_n$ is exactly the elements of Gr($n+1,2n$) that are both totally\nnonnegative and isotropic for a particular skew-symmetric bilinear form. For\ncertain classes of cactus networks, we also explicitly describe how to turn\nresponse matrices and effective resistance matrices into points of Gr($n+1,2n$)\ngiven by Lam's map. Finally, we discuss how our work relates to earlier studies\nof total positivity for Lagrangian Grassmannians.\n"}
+{"abstract": "  We propose a new method for accelerating the computation of a concurrency\nrelation, that is all pairs of places in a Petri net that can be marked\ntogether. Our approach relies on a state space abstraction, that involves a mix\nbetween structural reductions and linear algebra, and a new data-structure that\nis specifically designed for our task. Our algorithms are implemented in a\ntool, called Kong, that we test on a large collection of models used during the\n2020 edition of the Model Checking Contest. Our experiments show that the\napproach works well, even when a moderate amount of reductions applies.\n"}
+{"abstract": "  Ethics is sometimes considered to be too abstract to be meaningfully\nimplemented in artificial intelligence (AI). In this paper, we reflect on other\naspects of computing that were previously considered to be very abstract. Yet,\nthese are now accepted as being done very well by computers. These tasks have\nranged from multiple aspects of software engineering to mathematics to\nconversation in natural language with humans. This was done by automating the\nsimplest possible step and then building on it to perform more complex tasks.\nWe wonder if ethical AI might be similarly achieved and advocate the process of\nautomation as key step in making AI take ethical decisions. The key\ncontribution of this paper is to reflect on how automation was introduced into\ndomains previously considered too abstract for computers.\n"}
+{"abstract": "  In this paper, we investigate the decentralized statistical inference\nproblem, where a network of agents cooperatively recover a (structured) vector\nfrom private noisy samples without centralized coordination. Existing\noptimization-based algorithms suffer from issues of model mismatch and poor\nconvergence speed, and thus their performance would be degraded, provided that\nthe number of communication rounds is limited. This motivates us to propose a\nlearning-based framework, which unrolls well-noted decentralized optimization\nalgorithms (e.g., Prox-DGD and PG-EXTRA) into graph neural networks (GNNs). By\nminimizing the recovery error via end-to-end training, this learning-based\nframework resolves the model mismatch issue. Our convergence analysis (with\nPG-EXTRA as the base algorithm) reveals that the learned model parameters may\naccelerate the convergence and reduce the recovery error to a large extent. The\nsimulation results demonstrate that the proposed GNN-based learning methods\nprominently outperform several state-of-the-art optimization-based algorithms\nin convergence speed and recovery error.\n"}
+{"abstract": "  A prototype version of the Q & U bolometric interferometer for cosmology\n(QUBIC) underwent a campaign of testing in the laboratory at Astroparticle\nPhysics and Cosmology laboratory in Paris (APC). The detection chain is\ncurrently made of 256 NbSi transition edge sensors (TES) cooled to 320 mK. The\nreadout system is a 128:1 time domain multiplexing scheme based on 128 SQUIDs\ncooled at 1 K that are controlled and amplified by an SiGe application specific\nintegrated circuit at 40 K. We report the performance of this readout chain and\nthe characterization of the TES. The readout system has been functionally\ntested and characterized in the lab and in QUBIC. The low noise amplifier\ndemonstrated a white noise level of 0.3 nV.Hz^-0.5. Characterizations of the\nQUBIC detectors and readout electronics includes the measurement of I-V curves,\ntime constant and the noise equivalent power. The QUBIC TES bolometer array has\napproximately 80% detectors within operational parameters. It demonstrated a\nthermal decoupling compatible with a phonon noise of about 5.10^-17 W.Hz^-0.5\nat 410 mK critical temperature. While still limited by microphonics from the\npulse tubes and noise aliasing from readout system, the instrument noise\nequivalent power is about 2.10^-16 W.Hz^-0.5, enough for the demonstration of\nbolometric interferometry.\n"}
+{"abstract": "  We investigate the viability of producing galaxy mock catalogues with\nCOmoving Lagrangian Acceleration (COLA) simulations in Modified Gravity (MG)\nmodels employing the Halo Occupation Distribution (HOD) formalism. In this\nwork, we focus on two theories of MG: $f(R)$ gravity with the chameleon\nmechanism, and a braneworld model (nDGP) that incorporates the Vainshtein\nmechanism. We use a suite of full $N$-body simulations in MG as a benchmark to\ntest the accuracy of COLA simulations. At the level of Dark Matter (DM), we\nshow that COLA accurately reproduces the matter power spectrum up to $k \\sim 1\nh {\\rm Mpc}^{-1}$, while it is less accurate in reproducing the velocity field.\nTo produce halo catalogues, we find that the ROCKSTAR halo-finder does not\nperform well with COLA simulations. On the other hand, using a simple\nFriends-of-Friends (FoF) finder and an empirical mass conversion from FoF to\nspherical over-density masses, we are able to produce halo catalogues in COLA\nthat are in good agreement with those in $N$-body simulations. To consider the\neffects of the MG fifth force on the halo profile, we derive simple fitting\nformulae for the concentration-mass and the velocity dispersion-mass relations\nthat we calibrate using ROCKSTAR halo catalogues in $N$-body simulations. We\nthen use these results to extend the HOD formalism to modified gravity\nsimulations in COLA. We use an HOD model with five parameters that we tune to\nobtain galaxy catalogues in redshift space. We find that despite the great\nfreedom of the HOD model, MG leaves characteristic imprints in the redshift\nspace power spectrum multipoles and these features are well captured by the\nCOLA galaxy catalogues.\n"}
+{"abstract": "  Combinatorial design theory studies set systems with certain balance and\nsymmetry properties and has applications to computer science and elsewhere.\nThis paper presents a modular approach to formalising designs for the first\ntime using Isabelle and assesses the usability of a locale-centric approach to\nformalisations of mathematical structures. We demonstrate how locales can be\nused to specify numerous types of designs and their hierarchy. The resulting\nlibrary, which is concise and adaptable, includes formal definitions and proofs\nfor many key properties, operations, and theorems on the construction and\nexistence of designs.\n"}
+{"abstract": "  Motivated by the study of high energy Steklov eigenfunctions, we examine the\nsemi-classical Robin Laplacian. In the two dimensional situation, we determine\nan effective operator describing the asymptotic distribution of the negative\neigenvalues, and we prove that the corresponding eigenfunctions decay away from\nthe boundary, for all dimensions.\n"}
+{"abstract": "  Given a finite abelian group $G$ and a natural number $t$, there are two\nnatural substructures of the Cartesian power $G^t$; namely, $S^t$ where $S$ is\na subset of $G$, and $x+H$ a coset of a subgroup $H$ of $G^t$. A natural\nquestion is whether two such different structures have non-empty intersection.\nThis turns out to be an NP-complete problem. If we fix $G$ and $S$, then the\nproblem is in $P$ if $S$ is a coset in $G$ or if $S$ is empty, and NP-complete\notherwise; if we restrict to intersecting powers of $S$ with subgroups, the\nproblem is in $P$ if $\\bigcap_{n\\in\\mathbb{Z} \\mid nS \\subset S} nS$ is a coset\nor empty, and NP-complete otherwise. These theorems have applications in the\narticle [Spe21], where they are used as a stepping stone between a purely\ncombinatorial and a purely algebraic problem.\n"}
+{"abstract": "  Cycles, which can be found in many different kinds of networks, make the\nproblems more intractable, especially when dealing with dynamical processes on\nnetworks. On the contrary, tree networks in which no cycle exists, are\nsimplifications and usually allow for analyticity. There lacks a quantity,\nhowever, to tell the ratio of cycles which determines the extent of network\nbeing close to tree networks. Therefore we introduce the term Cycle Nodes Ratio\n(CNR) to describe the ratio of number of nodes belonging to cycles to the\nnumber of total nodes, and provide an algorithm to calculate CNR. CNR is\nstudied in both network models and real networks. The CNR remains unchanged in\ndifferent sized Erd\\\"os R\\'enyi (ER) networks with the same average degree, and\nincreases with the average degree, which yields a critical turning point. The\napproximate analytical solutions of CNR in ER networks are given, which fits\nthe simulations well. Furthermore, the difference between CNR and two-core\nratio (TCR) is analyzed. The critical phenomenon is explored by analysing the\ngiant component of networks. We compare the CNR in network models and real\nnetworks, and find the latter is generally smaller. Combining the\ncoarse-graining method can distinguish the CNR structure of networks with high\naverage degree. The CNR is also applied to four different kinds of\ntransportation networks and fungal networks, which give rise to different zones\nof effect. It is interesting to see that CNR is very useful in network\nrecognition of machine learning.\n"}
+{"abstract": "  We develop a theory of T-duality for transitive Courant algebroids. We show\nthat T-duality between transitive Courant algebroids E\\rightarrow M and\n\\tilde{E}\\rightarrow \\tilde{M} induces a map between the spaces of sections of\nthe corresponding canonical weighted spinor bundles \\mathbb{S}_{E} and\n\\mathbb{S}_{\\tilde{E}} intertwining the canonical Dirac generating operators.\nThe map is shown to induce an isomorphism between the spaces of invariant\nspinors, compatible with an isomorphism between the spaces of invariant\nsections of the Courant algebroids. The notion of invariance is defined after\nlifting the vertical parallelisms of the underlying torus bundles M\\rightarrow\nB and \\tilde{M} \\rightarrow B to the Courant algebroids and their spinor\nbundles. We prove a general existence result for T-duals under assumptions\ngeneralizing the cohomological integrality conditions for T-duality in the\nexact case. Specializing our construction, we find that the T-dual of an exact\nor a heterotic Courant algebroid is again exact or heterotic, respectively.\n"}
+{"abstract": "  Reggeon field theory (RFT), originally developed in the context of high\nenergy diffraction scattering, has a much wider applicability, describing, for\nexample, the universal critical behavior of stochastic population models as\nwell as probabilistic geometric problems such as directed percolation. In 1975\nSuranyi and others developed cut RFT, which can incorporate the cutting rules\nof Abramovskii, Gribov and Kancheli for how each diagram contributes to\ninclusive cross-sections. In this note we describe the corresponding\nprobabilistic interpretations of cut RFT: as a population model of two\ngenotypes, which can reproduce both asexually and sexually; and as a kind of\nbicolor directed percolation problem. In both cases the AGK rules correspond to\nsimple limiting cases of these problems.\n"}
+{"abstract": "  Utilizing the Atacama Large Millimeter/submillimeter Array (ALMA), we present\nCS line maps in five rotational lines ($J_{\\rm u}=7, 5, 4, 3, 2$) toward the\ncircumnuclear disk (CND) and streamers of the Galactic Center. Our primary goal\nis to resolve the compact structures within the CND and the streamers, in order\nto understand the stability conditions of molecular cores in the vicinity of\nthe supermassive black hole (SMBH) Sgr A*. Our data provide the first\nhomogeneous high-resolution ($1.3'' = 0.05$ pc) observations aiming at\nresolving density and temperature structures. The CS clouds have sizes of\n$0.05-0.2$ pc with a broad range of velocity dispersion ($\\sigma_{\\rm\nFWHM}=5-40$ km s$^{-1}$). The CS clouds are a mixture of warm ($T_{\\rm k}\\ge\n50-500$ K, n$_{\\rm H_2}$=$10^{3-5}$ cm$^{-3}$) and cold gas ($T_{\\rm k}\\le 50$\nK, n$_{\\rm H_2}$=$10^{6-8}$ cm$^{-3}$). A stability analysis based on the\nunmagnetized virial theorem including tidal force shows that $84^{+16}_{-37}$ %\nof the total gas mass is tidally stable, which accounts for the majority of gas\nmass. Turbulence dominates the internal energy and thereby sets the threshold\ndensities $10-100$ times higher than the tidal limit at distance $\\ge 1.5$ pc\nto Sgr A*, and therefore, inhibits the clouds from collapsing to form stars\nnear the SMBH. However, within the central $1.5$ pc, the tidal force overrides\nturbulence and the threshold densities for a gravitational collapse quickly\ngrow to $\\ge 10^{8}$ cm$^{-3}$.\n"}
+{"abstract": "  Cross-modal recipe retrieval has recently gained substantial attention due to\nthe importance of food in people's lives, as well as the availability of vast\namounts of digital cooking recipes and food images to train machine learning\nmodels. In this work, we revisit existing approaches for cross-modal recipe\nretrieval and propose a simplified end-to-end model based on well established\nand high performing encoders for text and images. We introduce a hierarchical\nrecipe Transformer which attentively encodes individual recipe components\n(titles, ingredients and instructions). Further, we propose a self-supervised\nloss function computed on top of pairs of individual recipe components, which\nis able to leverage semantic relationships within recipes, and enables training\nusing both image-recipe and recipe-only samples. We conduct a thorough analysis\nand ablation studies to validate our design choices. As a result, our proposed\nmethod achieves state-of-the-art performance in the cross-modal recipe\nretrieval task on the Recipe1M dataset. We make code and models publicly\navailable.\n"}
+{"abstract": "  Quantization is one of the core components in lossy image compression. For\nneural image compression, end-to-end optimization requires differentiable\napproximations of quantization, which can generally be grouped into three\ncategories: additive uniform noise, straight-through estimator and soft-to-hard\nannealing. Training with additive uniform noise approximates the quantization\nerror variationally but suffers from the train-test mismatch. The other two\nmethods do not encounter this mismatch but, as shown in this paper, hurt the\nrate-distortion performance since the latent representation ability is\nweakened. We thus propose a novel soft-then-hard quantization strategy for\nneural image compression that first learns an expressive latent space softly,\nthen closes the train-test mismatch with hard quantization. In addition, beyond\nthe fixed integer quantization, we apply scaled additive uniform noise to\nadaptively control the quantization granularity by deriving a new variational\nupper bound on actual rate. Experiments demonstrate that our proposed methods\nare easy to adopt, stable to train, and highly effective especially on complex\ncompression models.\n"}
+{"abstract": "  In this paper, we introduce PASSAT, a practical system to boost the security\nassurance delivered by the current cloud architecture without requiring any\nchanges or cooperation from the cloud service providers. PASSAT is an\napplication transparent to the cloud servers that allows users to securely and\nefficiently store and access their files stored on public cloud storage based\non a single master password. Using a fast and light-weight XOR secret sharing\nscheme, PASSAT secret-shares users' files and distributes them among n publicly\navailable cloud platforms. To access the files, PASSAT communicates with any k\nout of n cloud platforms to receive the shares and runs a secret-sharing\nreconstruction algorithm to recover the files. An attacker (insider or\noutsider) who compromises or colludes with less than k platforms cannot learn\nthe user's files or modify the files stealthily. To authenticate the user to\nmultiple cloud platforms, PASSAT crucially stores the authentication\ncredentials, specific to each platform on a password manager, protected under\nthe user's master password. Upon requesting access to files, the user enters\nthe password to unlock the vault and fetches the authentication tokens using\nwhich PASSAT can interact with cloud storage. Our instantiation of PASSAT based\non (2, 3)-XOR secret sharing of Kurihara et al., implemented with three popular\nstorage providers, namely, Google Drive, Box, and Dropbox, confirms that our\napproach can efficiently enhance the confidentiality, integrity, and\navailability of the stored files with no changes on the servers.\n"}
+{"abstract": "  We describe a time lens to expand the dynamic range of photon Doppler\nvelocimetry (PDV) systems. The principle and preliminary design of a time-lens\nPDV (TL-PDV) are explained and shown to be feasible through simulations. In a\nPDV system, an interferometer is used for measuring frequency shifts due to the\nDoppler effect from the target motion. However, the sampling rate of the\nelectronics could limit the velocity range of a PDV system. A four-wave-mixing\n(FWM) time lens applies a quadratic temporal phase to an optical signal within\na nonlinear FWM medium (such as an integrated photonic waveguide or highly\nnonlinear optical fiber). By spectrally isolating the mixing product, termed\nthe idler, and with appropriate lengths of dispersion prior and after to this\nFWM time lens, a temporally magnified version of the input signal is generated.\nTherefore, the frequency shifts of PDV can be \"slowed down\" with the\nmagnification factor $M$ of the time lens. $M=1$ corresponds to a regular PDV\nwithout a TL. $M=10$ has been shown to be feasible for a TL-PDV. Use of this\neffect for PDV can expand the velocity measurement range and allow the use of\nlower bandwidth electronics. TL-PDV will open up new avenues for various\ndynamic materials experiments.\n"}
+{"abstract": "  3D perception using sensors under vehicle industrial standard is the rigid\ndemand in autonomous driving. MEMS LiDAR emerges with irresistible trend due to\nits lower cost, more robust, and meeting the mass-production standards.\nHowever, it suffers small field of view (FoV), slowing down the step of its\npopulation. In this paper, we propose LEAD, i.e., LiDAR Extender for Autonomous\nDriving, to extend the MEMS LiDAR by coupled image w.r.t both FoV and range. We\npropose a multi-stage propagation strategy based on depth distributions and\nuncertainty map, which shows effective propagation ability. Moreover, our depth\noutpainting/propagation network follows a teacher-student training fashion,\nwhich transfers depth estimation ability to depth completion network without\nany scale error passed. To validate the LiDAR extension quality, we utilize a\nhigh-precise laser scanner to generate a ground-truth dataset. Quantitative and\nqualitative evaluations show that our scheme outperforms SOTAs with a large\nmargin. We believe the proposed LEAD along with the dataset would benefit the\ncommunity w.r.t depth researches.\n"}
+{"abstract": "  This paper proposes VARA-TTS, a non-autoregressive (non-AR) text-to-speech\n(TTS) model using a very deep Variational Autoencoder (VDVAE) with Residual\nAttention mechanism, which refines the textual-to-acoustic alignment\nlayer-wisely. Hierarchical latent variables with different temporal resolutions\nfrom the VDVAE are used as queries for residual attention module. By leveraging\nthe coarse global alignment from previous attention layer as an extra input,\nthe following attention layer can produce a refined version of alignment. This\namortizes the burden of learning the textual-to-acoustic alignment among\nmultiple attention layers and outperforms the use of only a single attention\nlayer in robustness. An utterance-level speaking speed factor is computed by a\njointly-trained speaking speed predictor, which takes the mean-pooled latent\nvariables of the coarsest layer as input, to determine number of acoustic\nframes at inference. Experimental results show that VARA-TTS achieves slightly\ninferior speech quality to an AR counterpart Tacotron 2 but an\norder-of-magnitude speed-up at inference; and outperforms an analogous non-AR\nmodel, BVAE-TTS, in terms of speech quality.\n"}
+{"abstract": "  This work introduces a methodology for studying synchronization in adaptive\nnetworks with heterogeneous plasticity (adaptation) rules. As a paradigmatic\nmodel, we consider a network of adaptively coupled phase oscillators with\ndistance-dependent adaptations. For this system, we extend the master stability\nfunction approach to adaptive networks with heterogeneous adaptation. Our\nmethod allows for separating the contributions of network structure, local node\ndynamics, and heterogeneous adaptation in determining synchronization.\nUtilizing our proposed methodology, we explain mechanisms leading to\nsynchronization or desynchronization by enhanced long-range connections in\nnonlocally coupled ring networks and networks with Gaussian distance-dependent\ncoupling weights equipped with a biologically motivated plasticity rule.\n"}
+{"abstract": "  We consider the problem of budget allocation for competitive influence\nmaximization over social networks. In this problem, multiple competing parties\n(players) want to distribute their limited advertising resources over a set of\nsocial individuals to maximize their long-run cumulative payoffs. It is assumed\nthat the individuals are connected via a social network and update their\nopinions based on the classical DeGroot model. The players must decide the\nbudget distribution among the individuals at a finite number of campaign times\nto maximize their overall payoff given as a function of individuals' opinions.\nWe show that i) the optimal investment strategy for the case of a single-player\ncan be found in polynomial time by solving a concave program, and ii) the\nopen-loop equilibrium strategies for the multiplayer dynamic game can be\ncomputed efficiently by following natural regret minimization dynamics. Our\nresults extend the earlier work on the static version of the problem to a\ndynamic multistage game.\n"}
+{"abstract": "  There is mounting evidence that ultra-energetic neutrinos of astrophysical\norigin may be associated with blazars. Here we investigate a unique sample of\n47 blazars, $\\sim 20$ of which could be new neutrino sources. In particular, we\nfocus on 17 objects of yet unknown redshift, for which we present optical\nspectroscopy secured at the Gran Telescopio Canarias and the ESO Very Large\nTelescope. We find all sources but one (a quasar) to be BL Lac objects. For\nnine targets we are able to determine the redshift (0.09~$<$~z~$<$~1.6), while\nfor the others we set a lower limit on it, based on either the robust detection\nof intervening absorption systems or on an estimation derived from the absence\nof spectral signatures of the host galaxy. In some spectra we detect forbidden\nand semi-forbidden emission lines with luminosities in the range $10^{40} -\n10^{41}$ erg s$^{-1}$. We also report on the spectroscopy of seven blazars\npossibly associated with energetic neutrinos that partially meet the criteria\nof our sample and are discussed in the Appendix. These results represent the\nstarting point of our investigation into the real nature of these objects and\ntheir likelihood of being neutrino emitters.\n"}
+{"abstract": "  Given C$^*$-algebras $A$ and $B$ and a $^*$-homomorphism $\\phi:A\\rightarrow\nB$, we adopt the portrait of the relative $K$-theory $K_*(\\phi)$ due to Karoubi\nusing Banach categories and Banach functors. We show that the elements of the\nrelative groups may be represented in a simple form. We prove the existence of\ntwo six-term exact sequences, and we use these sequences to deduce the fact\nthat the relative theory is isomorphic, in a natural way, to the $K$-theory of\nthe mapping cone of $\\phi$ as an excision result.\n"}
+{"abstract": "  We experimentally demonstrate, for the first time, noise diagnostics by\nrepeated quantum measurements. Specifically, we establish the ability of a\nsingle photon, subjected to random polarisation noise, to diagnose\nnon-Markovian temporal correlations of such a noise process. In the frequency\ndomain, these noise correlations correspond to colored noise spectra, as\nopposed to the ones related to Markovian, white noise. Both the noise spectrum\nand its corresponding temporal correlations are diagnosed by probing the photon\nby means of frequent, (partially-)selective polarisation measurements. Our main\nresult is the experimental demonstration that noise with positive temporal\ncorrelations corresponds to our single photon undergoing a dynamical regime\nenabled by the quantum Zeno effect (QZE), while noise characterized by negative\n(anti-) correlations corresponds to regimes associated with the anti-Zeno\neffect (AZE). This demonstration opens the way to a new kind of noise\nspectroscopy based on QZE and AZE in photon (or other single-particle) state\nprobing.\n"}
+{"abstract": "  Known force terms arising in the Ehrenfest dynamics of quantum electrons and\nclassical nuclei, due to a moving basis set for the former, can be understood\nin terms of the curvature of the manifold hosting the quantum states of the\nelectronic subsystem. Namely, the velocity-dependent terms appearing in the\nEhrenfest forces on the nuclei acquire a geometrical meaning in terms of the\nintrinsic curvature of the manifold, while Pulay terms relate to its extrinsic\ncurvature.\n"}
+{"abstract": "  The production of dileptons with an invariant mass in the range 1 GeV < M < 5\nGeV provides unique insight into the approach to thermal equilibrium in\nultrarelativistic nucleus-nucleus collisions. In this mass range, they are\nproduced through the annihilation of quark-antiquark pairs in the early stages\nof the collision. They are sensitive to the anisotropy of the quark momentum\ndistribution, and also to the quark abundance, which is expected to be\nunderpopulated relative to thermal equilibrium. We take into account both\neffects based on recent theoretical developments in QCD kinetic theory, and\nstudy how the dilepton mass spectrum depends on the shear viscosity to entropy\nratio that controls the equilibration time. We evaluate the background from the\nDrell-Yan process and argue that future detector developments can suppress the\nadditional background from semileptonic decays of heavy flavors.\n"}
+{"abstract": "  In extreme learning machines (ELM) the hidden-layer coefficients are randomly\nset and fixed, while the output-layer coefficients of the neural network are\ncomputed by a least squares method. The randomly-assigned coefficients in ELM\nare known to influence its performance and accuracy significantly. In this\npaper we present a modified batch intrinsic plasticity (modBIP) method for\npre-training the random coefficients in the ELM neural networks. The current\nmethod is devised based on the same principle as the batch intrinsic plasticity\n(BIP) method, namely, by enhancing the information transmission in every node\nof the neural network. It differs from BIP in two prominent aspects. First,\nmodBIP does not involve the activation function in its algorithm, and it can be\napplied with any activation function in the neural network. In contrast, BIP\nemploys the inverse of the activation function in its construction, and\nrequires the activation function to be invertible (or monotonic). The modBIP\nmethod can work with the often-used non-monotonic activation functions (e.g.\nGaussian, swish, Gaussian error linear unit, and radial-basis type functions),\nwith which BIP breaks down. Second, modBIP generates target samples on random\nintervals with a minimum size, which leads to highly accurate computation\nresults when combined with ELM. The combined ELM/modBIP method is markedly more\naccurate than ELM/BIP in numerical simulations. Ample numerical experiments are\npresented with shallow and deep neural networks for function approximation and\nboundary/initial value problems with partial differential equations. They\ndemonstrate that the combined ELM/modBIP method produces highly accurate\nsimulation results, and that its accuracy is insensitive to the\nrandom-coefficient initializations in the neural network. This is in sharp\ncontrast with the ELM results without pre-training of the random coefficients.\n"}
+{"abstract": "  A key task in design work is grasping the client's implicit tastes. Designers\noften do this based on a set of examples from the client. However, recognizing\na common pattern among many intertwining variables such as color, texture, and\nlayout and synthesizing them into a composite preference can be challenging. In\nthis paper, we leverage the pattern recognition capability of computational\nmodels to aid in this task. We offer a set of principles for computationally\nlearning personal style. The principles are manifested in PseudoClient, a deep\nlearning framework that learns a computational model for personal graphic\ndesign style from only a handful of examples. In several experiments, we found\nthat PseudoClient achieves a 79.40% accuracy with only five positive and\nnegative examples, outperforming several alternative methods. Finally, we\ndiscuss how PseudoClient can be utilized as a building block to support the\ndevelopment of future design applications.\n"}
+{"abstract": "  Let $\\Omega $ be an open subset of $\\mathbb{R}^{N}$, and let $p,\\, q:\\Omega\n\\rightarrow \\left[ 1,\\infty \\right] $ be measurable functions. We give a\nnecessary and sufficient condition for the embedding of the variable exponent\nspace $L^{p(\\cdot )}\\left( \\Omega \\right) $ in $L^{q(\\cdot )}\\left( \\Omega\n\\right) $ to be almost compact. This leads to a condition on $\\Omega, \\, p$ and\n$q$ sufficient to ensure that the Sobolev space $W^{1,p(\\cdot )}\\left( \\Omega\n\\right) $ based on $L^{p(\\cdot )}\\left( \\Omega \\right) $ is compactly embedded\nin $L^{q(\\cdot )}\\left( \\Omega \\right) ;$ compact embedding results of this\ntype already in the literature are included as special cases.\n"}
+{"abstract": "  As widely recognized, vortex represents flow rotation. Vortex should have a\nlocal rotation axis as its direction and angular speed as its strength.\nVorticity vector has been considered the rotation axis, and vorticity magnitude\nthe rotational strength for a long time in classical fluid kinematics. However,\nthis concept cannot stand in viscous flow. This study demonstrates by rigorous\nmathematical proof that the vorticity vector is not the fluid rotation axis,\nand vorticity is not the rotation strength. On the other hand, the Liutex\nvector is mathematically proved as the fluid rotation axis, and the Liutex\nmagnitude is twice the fluid angular speed.\n"}
+{"abstract": "  Most countries have started vaccinating people against COVID-19. However, due\nto limited production capacities and logistical challenges it will take\nmonths/years until herd immunity is achieved. Therefore, vaccination and social\ndistancing have to be coordinated. In this paper, we provide some insight on\nthis topic using optimization-based control on an age-differentiated\ncompartmental model. For real-life decision making, we investigate the impact\nof the planning horizon on the optimal vaccination/social distancing strategy.\nWe find that in order to reduce social distancing in the long run, without\noverburdening the healthcare system, it is essential to vaccinate the people\nwith the highest contact rates first. That is also the case if the objective is\nto minimize fatalities provided that the social distancing measures are\nsufficiently strict. However, for short-term planning it is optimal to focus on\nthe high-risk group.\n"}
+{"abstract": "  We present a model of exclusive $\\phi$-meson lepto-production $ep \\to\ne'p'\\phi$ near threshold which features the strangeness gravitational form\nfactors of the proton. We argue that the shape of the differential cross\nsection $d\\sigma/dt$ is a sensitive probe of the strangeness D-term of the\nproton.\n"}
+{"abstract": "  With the promise of reliability in cloud, more enterprises are migrating to\ncloud. The process of continuous integration/deployment (CICD) in cloud\nconnects developers who need to deliver value faster and more transparently\nwith site reliability engineers (SREs) who need to manage applications\nreliably. SREs feed back development issues to developers, and developers\ncommit fixes and trigger CICD to redeploy. The release cycle is more continuous\nthan ever, thus the code to production is faster and more automated. To provide\nthis higher level agility, the cloud platforms become more complex in the face\nof flexibility with deeper layers of virtualization. However, reliability does\nnot come for free with all these complexities. Software engineers and SREs need\nto deal with wider information spectrum from virtualized layers. Therefore,\nproviding correlated information with true positive evidences is critical to\nidentify the root cause of issues quickly in order to reduce mean time to\nrecover (MTTR), performance metrics for SREs. Similarity, knowledge, or\nstatistics driven approaches have been effective, but with increasing data\nvolume and types, an individual approach is limited to correlate semantic\nrelations of different data sources. In this paper, we introduce FIXME to\nenhance software reliability with hybrid diagnosis approaches for enterprises.\nOur evaluation results show using hybrid diagnosis approach is about 17% better\nin precision. The results are helpful for both practitioners and researchers to\ndevelop hybrid diagnosis in the highly dynamic cloud environment.\n"}
+{"abstract": "  In multistage manufacturing systems, modeling multiple quality indices based\non the process sensing variables is important. However, the classic modeling\ntechnique predicts each quality variable one at a time, which fails to consider\nthe correlation within or between stages. We propose a deep multistage\nmulti-task learning framework to jointly predict all output sensing variables\nin a unified end-to-end learning framework according to the sequential system\narchitecture in the MMS. Our numerical studies and real case study have shown\nthat the new model has a superior performance compared to many benchmark\nmethods as well as great interpretability through developed variable selection\ntechniques.\n"}
+{"abstract": "  Phase noise of the frequency synthesizer is one of the main limitations to\nthe short-term stability of microwave atomic clocks. In this work, we\ndemonstrated a low-noise, simple-architecture microwave frequency synthesizer\nfor a coherent population trapping (CPT) clock. The synthesizer is mainly\ncomposed of a 100 MHz oven controlled crystal oscillator (OCXO), a microwave\ncomb generator and a direct digital synthesizer (DDS). The absolute phase\nnoises of 3.417 GHz signal are measured to be -55 dBc/Hz, -81 dBc/Hz, -111\ndBc/Hz and -134 dBc/Hz, respectively, for 1 Hz, 10 Hz, 100 Hz and 1 kHz offset\nfrequencies, which shows only 1 dB deterioration at the second harmonic of the\nmodulation frequency of the atomic clock. The estimated frequency stability of\nintermodulation effect is 4.7*10^{-14} at 1s averaging time, which is about\nhalf order of magnitude lower than that of the state-of-the-art CPT Rb clock.\nOur work offers an alternative microwave synthesizer for high-performance CPT\nRb atomic clock.\n"}
+{"abstract": "  The Schr\\\"odinger equation is solved numerically for charmonium using the\ndiscrete variable representation (DVR) method. The Hamiltonian matrix is\nconstructed and diagonalized to obtain the eigenvalues and eigenfunctions.\nUsing these eigenvalues and eigenfunctions, spectra and various decay widths\nare calculated. The obtained results are in good agreement with other numerical\nmethods and with experiments.\n"}
+{"abstract": "  We are motivated by the problem of providing strong generalization guarantees\nin the context of meta-learning. Existing generalization bounds are either\nchallenging to evaluate or provide vacuous guarantees in even relatively simple\nsettings. We derive a probably approximately correct (PAC) bound for\ngradient-based meta-learning using two different generalization frameworks in\norder to deal with the qualitatively different challenges of generalization at\nthe \"base\" and \"meta\" levels. We employ bounds for uniformly stable algorithms\nat the base level and bounds from the PAC-Bayes framework at the meta level.\nThe result of this approach is a novel PAC bound that is tighter when the base\nlearner adapts quickly, which is precisely the goal of meta-learning. We show\nthat our bound provides a tighter guarantee than other bounds on a toy\nnon-convex problem on the unit sphere and a text-based classification example.\nWe also present a practical regularization scheme motivated by the bound in\nsettings where the bound is loose and demonstrate improved performance over\nbaseline techniques.\n"}
+{"abstract": "  Designing intelligent microrobots that can autonomously navigate and perform\ninstructed routines in blood vessels, a complex and crowded environment with\nobstacles including dense cells, different flow patterns and diverse vascular\ngeometries, can offer enormous possibilities in biomedical applications. Here\nwe report a hierarchical control scheme that enables a microrobot to\nefficiently navigate and execute customizable routines in blood vessels. The\ncontrol scheme consists of two highly decoupled components: a high-level\ncontroller setting short-ranged dynamic targets to guide the microrobot to\nfollow a preset path and a low-level deep reinforcement learning (DRL)\ncontroller responsible for maneuvering microrobots towards these dynamic\nguiding targets. The proposed DRL controller utilizes three-dimensional (3D)\nconvolutional neural networks and is capable of learning control policy\ndirectly from a coarse raw 3D sensory input. In blood vessels with rich\nconfigurations of red blood cells and vessel geometry, the control scheme\nenables efficient navigation and faithful execution of instructed routines. The\ncontrol scheme is also robust to adversarial perturbations including blood\nflows. This study provides a proof-of-principle for designing data-driven\ncontrol systems for autonomous navigation in vascular networks; it illustrates\nthe great potential of artificial intelligence for broad biomedical\napplications such as target drug delivery, blood clots clear, precision\nsurgery, disease diagnosis, and more.\n"}
+{"abstract": "  Communication overhead is the key challenge for distributed training.\nGradient compression is a widely used approach to reduce communication traffic.\nWhen combining with parallel communication mechanism method like pipeline,\ngradient compression technique can greatly alleviate the impact of\ncommunication overhead. However, there exists two problems of gradient\ncompression technique to be solved. Firstly, gradient compression brings in\nextra computation cost, which will delay the next training iteration. Secondly,\ngradient compression usually leads to the decrease of convergence accuracy.\n"}
+{"abstract": "  Recently, much attention has been paid to the societal impact of AI,\nespecially concerns regarding its fairness. A growing body of research has\nidentified unfair AI systems and proposed methods to debias them, yet many\nchallenges remain. Representation learning for Heterogeneous Information\nNetworks (HINs), a fundamental building block used in complex network mining,\nhas socially consequential applications such as automated career counseling,\nbut there have been few attempts to ensure that it will not encode or amplify\nharmful biases, e.g. sexism in the job market. To address this gap, in this\npaper we propose a comprehensive set of de-biasing methods for fair HINs\nrepresentation learning, including sampling-based, projection-based, and graph\nneural networks (GNNs)-based techniques. We systematically study the behavior\nof these algorithms, especially their capability in balancing the trade-off\nbetween fairness and prediction accuracy. We evaluate the performance of the\nproposed methods in an automated career counseling application where we\nmitigate gender bias in career recommendation. Based on the evaluation results\non two datasets, we identify the most effective fair HINs representation\nlearning techniques under different conditions.\n"}
+{"abstract": "  This research recasts the network attack dataset from UNSW-NB15 as an\nintrusion detection problem in image space. Using one-hot-encodings, the\nresulting grayscale thumbnails provide a quarter-million examples for deep\nlearning algorithms. Applying the MobileNetV2's convolutional neural network\narchitecture, the work demonstrates a 97% accuracy in distinguishing normal and\nattack traffic. Further class refinements to 9 individual attack families\n(exploits, worms, shellcodes) show an overall 56% accuracy. Using feature\nimportance rank, a random forest solution on subsets show the most important\nsource-destination factors and the least important ones as mainly obscure\nprotocols. The dataset is available on Kaggle.\n"}
+{"abstract": "  Tamil is a Dravidian language that is commonly used and spoken in the\nsouthern part of Asia. In the era of social media, memes have been a fun moment\nin the day-to-day life of people. Here, we try to analyze the true meaning of\nTamil memes by categorizing them as troll and non-troll. We propose an\ningenious model comprising of a transformer-transformer architecture that tries\nto attain state-of-the-art by using attention as its main component. The\ndataset consists of troll and non-troll images with their captions as text. The\ntask is a binary classification task. The objective of the model is to pay more\nattention to the extracted features and to ignore the noise in both images and\ntext.\n"}
+{"abstract": "  Self-organized spatial patterns of vegetation are frequent in water-limited\nregions and have been suggested as important indicators of ecosystem health.\nHowever, the mechanisms underlying their emergence remain unclear. Some\ntheories hypothesize that patterns could result from a scale-dependent feedback\n(SDF), whereby interactions favoring plant growth dominate at short distances\nand growth-inhibitory interactions dominate in the long range. However, we know\nlittle about how net plant-to-plant interactions may change sign with\ninter-individual distance, and in the absence of strong empirical support, the\nrelevance of this SDF for vegetation pattern formation remains disputed. These\ntheories predict a sequential change in pattern shape from gapped to\nlabyrinthine to spotted spatial patterns as precipitation declines.\nNonetheless, alternative theories show that the same sequence of patterns could\nemerge even if net interactions between plants were always inhibitory (purely\ncompetitive feedbacks, PCF). Although these alternative hypotheses lead to\nvisually indistinguishable patterns they predict very different desertification\ndynamics following the spotted pattern. Moreover, vegetation interaction with\nother ecosystem components can introduce additional spatio-temporal scales that\nreshape both the patterns and the desertification dynamics. Therefore, to make\nreliable ecological predictions for a focal ecosystem, it is crucial that\nmodels accurately capture the mechanisms at play in the system of interest.\nHere, we review existing theories for vegetation self-organization and their\nconflicting predictions about desertification dynamics. We further discuss\npossible ways for reconciling these predictions and potential empirical tests\nvia manipulative experiments to improve our understanding of how vegetation\nself-organizes and better predict the fate of the ecosystems where they form.\n"}
+{"abstract": "  Deep learning techniques have achieved great success in remote sensing image\nchange detection. Most of them are supervised techniques, which usually require\nlarge amounts of training data and are limited to a particular application.\nSelf-supervised methods as an unsupervised approach are popularly used to solve\nthis problem and are widely used in unsupervised binary change detection tasks.\nHowever, the existing self-supervised methods in change detection are based on\npre-tasks or at patch-level, which may be sub-optimal for pixel-wise change\ndetection tasks. Therefore, in this work, a pixel-wise contrastive approach is\nproposed to overcome this limitation. This is achieved by using contrastive\nloss in pixel-level features on an unlabeled multi-view setting. In this\napproach, a Siamese ResUnet is trained to obtain pixel-wise representations and\nto align features from shifted positive pairs. Meanwhile, vector quantization\nis used to augment the learned features in two branches. The final binary\nchange map is obtained by subtracting features of one branch from features of\nthe other branch and using the Rosin thresholding method. To overcome the\neffects of regular seasonal changes in binary change maps, we also used an\nuncertainty method to enhance the temporal robustness of the proposed approach.\nTwo homogeneous (OSCD and MUDS) datasets and one heterogeneous (California\nFlood) dataset are used to evaluate the performance of the proposed approach.\nResults demonstrate improvements in both efficiency and accuracy over the\npatch-wise multi-view contrastive method.\n"}
+{"abstract": "  Convolutional Neural Networks (CNN) have been rigorously studied for\nHyperspectral Image Classification (HSIC) and are known to be effective in\nexploiting joint spatial-spectral information with the expense of lower\ngeneralization performance and learning speed due to the hard labels and\nnon-uniform distribution over labels. Several regularization techniques have\nbeen used to overcome the aforesaid issues. However, sometimes models learn to\npredict the samples extremely confidently which is not good from a\ngeneralization point of view. Therefore, this paper proposed an idea to enhance\nthe generalization performance of a hybrid CNN for HSIC using soft labels that\nare a weighted average of the hard labels and uniform distribution over ground\nlabels. The proposed method helps to prevent CNN from becoming over-confident.\nWe empirically show that in improving generalization performance, label\nsmoothing also improves model calibration which significantly improves\nbeam-search. Several publicly available Hyperspectral datasets are used to\nvalidate the experimental evaluation which reveals improved generalization\nperformance, statistical significance, and computational complexity as compared\nto the state-of-the-art models. The code will be made available at\nhttps://github.com/mahmad00.\n"}
+{"abstract": "  In recent years, the interest growth in the Blockchains (BC) and\nInternet-of-Things (IoT) integration -- termed as BIoT -- for more trust via\ndecentralization has led to great potentials in various use cases such as\nhealth care, supply chain tracking, and smart cities. A key element of BIoT\necosystems is the data transactions (TX) that include the data collected by IoT\ndevices. BIoT applications face many challenges to comply with the European\nGeneral Data Protection Regulation (GDPR) i.e., enabling users to hold on to\ntheir rights for deleting or modifying their data stored on publicly accessible\nand immutable BCs. In this regard, this paper identifies the requirements of\nBCs for being GDPR compliant in BIoT use cases. Accordingly, an on-chain\nsolution is proposed that allows fine-grained modification (update and erasure)\noperations on TXs' data fields within a BC. The proposed solution is based on a\ncryptographic primitive called Chameleon Hashing. The novelty of this approach\nis manifold. BC users have the authority to update their data, which are\naddressed at the TX level with no side-effects on the block or chain. By\nperforming and storing the data updates, all on-chain, traceability and\nverifiability of the BC are preserved. Moreover, the compatibility with TX\naggregation mechanisms that allow the compression of the BC size is maintained.\n"}
+{"abstract": "  Mobile health applications (mHealth apps for short) are being increasingly\nadopted in the healthcare sector, enabling stakeholders such as governments,\nhealth units, medics, and patients, to utilize health services in a pervasive\nmanner. Despite having several known benefits, mHealth apps entail significant\nsecurity and privacy challenges that can lead to data breaches with serious\nsocial, legal, and financial consequences. This research presents an empirical\ninvestigation about security awareness of end-users of mHealth apps that are\navailable on major mobile platforms, including Android and iOS. We collaborated\nwith two mHealth providers in Saudi Arabia to survey 101 end-users,\ninvestigating their security awareness about (i) existing and desired security\nfeatures, (ii) security related issues, and (iii) methods to improve security\nknowledge. Findings indicate that majority of the end-users are aware of the\nexisting security features provided by the apps (e.g., restricted app\npermissions); however, they desire usable security (e.g., biometric\nauthentication) and are concerned about privacy of their health information\n(e.g., data anonymization). End-users suggested that protocols such as session\ntimeout or Two-factor authentication (2FA) positively impact security but\ncompromise usability of the app. Security-awareness via social media, peer\nguidance, or training from app providers can increase end-users trust in\nmHealth apps. This research investigates human-centric knowledge based on\nempirical evidence and provides a set of guidelines to develop secure and\nusable mHealth apps.\n"}
+{"abstract": "  We study theoretically subradiant states in the array of atoms coupled to\nphotons propagating in a one-dimensional waveguide focusing on the strongly\ninteracting many-body regime with large excitation fill factor $f$. We\nintroduce a generalized many-body entropy of entanglement based on exact\nnumerical diagonalization followed by a high-order singular value\ndecomposition. This approach has allowed us to visualize and understand the\nstructure of a many-body quantum state. We reveal the breakdown of fermionized\nsubradiant states with increase of $f$ with emergence of short-ranged dimerized\nantiferromagnetic correlations at the critical point $f=1/2$ and the complete\ndisappearance of subradiant states at $f>1/2$.\n"}
+{"abstract": "  A graph $G$ is a prime distance graph (respectively, a 2-odd graph) if its\nvertices can be labeled with distinct integers such that for any two adjacent\nvertices, the difference of their labels is prime (either 2 or odd). We prove\nthat trees, cycles, and bipartite graphs are prime distance graphs, and that\nDutch windmill graphs and paper mill graphs are prime distance graphs if and\nonly if the Twin Prime Conjecture and dePolignac's Conjecture are true,\nrespectively. We give a characterization of 2-odd graphs in terms of edge\ncolorings, and we use this characterization to determine which circulant graphs\nof the form $Circ(n, \\{1,k\\})$ are 2-odd and to prove results on circulant\nprime distance graphs.\n"}
+{"abstract": "  We consider the stochastic shortest path planning problem in MDPs, i.e., the\nproblem of designing policies that ensure reaching a goal state from a given\ninitial state with minimum accrued cost. In order to account for rare but\nimportant realizations of the system, we consider a nested dynamic coherent\nrisk total cost functional rather than the conventional risk-neutral total\nexpected cost. Under some assumptions, we show that optimal, stationary,\nMarkovian policies exist and can be found via a special Bellman's equation. We\npropose a computational technique based on difference convex programs (DCPs) to\nfind the associated value functions and therefore the risk-averse policies. A\nrover navigation MDP is used to illustrate the proposed methodology with\nconditional-value-at-risk (CVaR) and entropic-value-at-risk (EVaR) coherent\nrisk measures.\n"}
+{"abstract": "  Using a martingale concentration inequality, concentration bounds `from time\n$n_0$ on' are derived for stochastic approximation algorithms with contractive\nmaps and both martingale difference and Markov noises. These are applied to\nreinforcement learning algorithms, in particular to asynchronous Q-learning and\nTD(0).\n"}
+{"abstract": "  The properties of quasar-host galaxies might be determined by the growth and\nfeedback of their supermassive (SMBH, $10^{8-10}$ M$_{\\odot}$) black holes. We\ninvestigate such connection with a suite of cosmological simulations of massive\n(halo mass $\\approx 10^{12}$ M$_{\\odot}$) galaxies at $z\\simeq 6$ which include\na detailed sub-grid multiphase gas and accretion model. BH seeds of initial\nmass $10^5$ M$_{\\odot}$ grow mostly by gas accretion, and become SMBH by $z=6$\nsetting on the observed $M_{\\rm BH} - M_{\\star}$ relation without the need for\na boost factor. Although quasar feedback crucially controls the SMBH growth,\nits impact on the properties of the host galaxy at $z=6$ is negligible. In our\nmodel, quasar activity can both quench (via gas heating) or enhance (by ISM\nover-pressurization) star formation. However, we find that the star formation\nhistory is insensitive to such modulation as it is largely dominated, at least\nat $z>6$, by cold gas accretion from the environment that cannot be hindered by\nthe quasar energy deposition. Although quasar-driven outflows can achieve\nvelocities $> 1000~\\rm km~s^{-1}$, only $\\approx 4$% of the outflowing gas mass\ncan actually escape from the host galaxy. These findings are only loosely\nconstrained by available data, but can guide observational campaigns searching\nfor signatures of quasar feedback in early galaxies.\n"}
+{"abstract": "  The existence of $1$-factorizations of an infinite complete equipartite graph\n$K_m[n]$ (with $m$ parts of size $n$) admitting a vertex-regular automorphism\ngroup $G$ is known only when $n=1$ and $m$ is countable (that is, for countable\ncomplete graphs) and, in addition, $G$ is a finitely generated abelian group\n$G$ of order $m$.\n  In this paper, we show that a vertex-regular $1$-factorization of $K_m[n]$\nunder the group $G$ exists if and only if $G$ has a subgroup $H$ of order $n$\nwhose index in $G$ is $m$. Furthermore, we provide a sufficient condition for\nan infinite Cayley graph to have a regular $1$-factorization. Finally, we\nconstruct 1-factorizations that contain a given subfactorization, both having a\nvertex-regular automorphism group.\n"}
+{"abstract": "  This paper aims at solving an optimal control problem governed by an\nanisotropic Allen-Cahn equation numerically. Therefore we first prove the\nFr\\'echet differentiability of an in time discretized parabolic control problem\nunder certain assumptions on the involved quasilinearity and formulate the\nfirst order necessary conditions. As a next step, since the anisotropies are in\ngeneral not smooth enough, the convergence behavior of the optimal controls are\nstudied for a sequence of (smooth) approximations of the former quasilinear\nterm. In addition the simultaneous limit in the approximation and the time step\nsize is considered. For a class covering a large variety of anisotropies we\nintroduce a certain regularization and show the previously formulated\nrequirements. Finally, a trust region Newton solver is applied to various\nanisotropies and configurations, and numerical evidence for mesh independent\nbehavior and convergence with respect to regularization is presented.\n"}
+{"abstract": "  We answer in negative to the following question of Boris Mitjagin: Is it true\nthat a product of two nuclear operators in Banach spaces can be factored\nthrough a trace class operator in a Hilbert space?\n"}
+{"abstract": "  We propose a new architecture for diacritics restoration based on\ncontextualized embeddings, namely BERT, and we evaluate it on 12 languages with\ndiacritics. Furthermore, we conduct a detailed error analysis on Czech, a\nmorphologically rich language with a high level of diacritization. Notably, we\nmanually annotate all mispredictions, showing that roughly 44% of them are\nactually not errors, but either plausible variants (19%), or the system\ncorrections of erroneous data (25%). Finally, we categorize the real errors in\ndetail. We release the code at\nhttps://github.com/ufal/bert-diacritics-restoration.\n"}
+{"abstract": "  Knowledge distillation transfers knowledge from the teacher network to the\nstudent one, with the goal of greatly improving the performance of the student\nnetwork. Previous methods mostly focus on proposing feature transformation and\nloss functions between the same level's features to improve the effectiveness.\nWe differently study the factor of connection path cross levels between teacher\nand student networks, and reveal its great importance. For the first time in\nknowledge distillation, cross-stage connection paths are proposed. Our new\nreview mechanism is effective and structurally simple. Our finally designed\nnested and compact framework requires negligible computation overhead, and\noutperforms other methods on a variety of tasks. We apply our method to\nclassification, object detection, and instance segmentation tasks. All of them\nwitness significant student network performance improvement. Code is available\nat https://github.com/Jia-Research-Lab/ReviewKD\n"}
+{"abstract": "  Cell-free massive MIMO systems consist of many distributed access points with\nsimple components that jointly serve the users. In millimeter wave bands, only\na limited set of predetermined beams can be supported. In a network that\nconsolidates these technologies, downlink analog beam selection stands as a\nchallenging task for the network sum-rate maximization. Low-cost digital\nfilters can improve the network sum-rate further. In this work, we propose\nlow-cost joint designs of analog beam selection and digital filters. The\nproposed joint designs achieve significantly higher sum-rates than the disjoint\ndesign benchmark. Supervised machine learning (ML) algorithms can efficiently\napproximate the input-output mapping functions of the beam selection decisions\nof the joint designs with low computational complexities. Since the training of\nML algorithms is performed off-line, we propose a well-constructed joint design\nthat combines multiple initializations, iterations, and selection features, as\nwell as beam conflict control, i.e., the same beam cannot be used for multiple\nusers. The numerical results indicate that ML algorithms can retain 99-100% of\nthe original sum-rate results achieved by the proposed well-constructed\ndesigns.\n"}
+{"abstract": "  Classifying the sub-categories of an object from the same super-category\n(e.g., bird) in a fine-grained visual classification (FGVC) task highly relies\non mining multiple discriminative features. Existing approaches mainly tackle\nthis problem by introducing attention mechanisms to locate the discriminative\nparts or feature encoding approaches to extract the highly parameterized\nfeatures in a weakly-supervised fashion. In this work, we propose a lightweight\nyet effective regularization method named Channel DropBlock (CDB), in\ncombination with two alternative correlation metrics, to address this problem.\nThe key idea is to randomly mask out a group of correlated channels during\ntraining to destruct features from co-adaptations and thus enhance feature\nrepresentations. Extensive experiments on three benchmark FGVC datasets show\nthat CDB effectively improves the performance.\n"}
+{"abstract": "  We show that for a model complete strongly minimal theory whose pregeometry\nis flat, the recursive spectrum (SRM($T$)) is either of the form $[0,\\alpha)$\nfor $\\alpha\\in \\omega+2$ or $[0,n]\\cup\\{\\omega\\}$ for $n\\in \\omega$, or\n$\\{\\omega\\}$, or contained in $\\{0,1,2\\}$.\n  Combined with previous results, this leaves precisely 4 sets for which it is\nnot yet determined whether each is the spectrum of a model complete strongly\nminimal theory with a flat pregeometry.\n"}
+{"abstract": "  Data-driven methods for battery lifetime prediction are attracting increasing\nattention for applications in which the degradation mechanisms are poorly\nunderstood and suitable training sets are available. However, while advanced\nmachine learning and deep learning methods promise high performance with\nminimal data preprocessing, simpler linear models with engineered features\noften achieve comparable performance, especially for small training sets, while\nalso providing physical and statistical interpretability. In this work, we use\na previously published dataset to develop simple, accurate, and interpretable\ndata-driven models for battery lifetime prediction. We first present the\n\"capacity matrix\" concept as a compact representation of battery\nelectrochemical cycling data, along with a series of feature representations.\nWe then create a number of univariate and multivariate models, many of which\nachieve comparable performance to the highest-performing models previously\npublished for this dataset. These models also provide insights into the\ndegradation of these cells. Our approaches can be used both to quickly train\nmodels for a new dataset and to benchmark the performance of more advanced\nmachine learning methods.\n"}
+{"abstract": "  Willems' fundamental lemma asserts that all trajectories of a linear\ntime-invariant system can be obtained from a finite number of measured ones,\nassuming that controllability and a persistency of excitation condition hold.\nWe show that these two conditions can be relaxed. First, we prove that the\ncontrollability condition can be replaced by a condition on the controllable\nsubspace, unobservable subspace, and a certain subspace associated with the\nmeasured trajectories. Second, we prove that the persistency of excitation\nrequirement can be relaxed if the degree of a certain minimal polynomial is\ntightly bounded. Our results show that data-driven predictive control using\nonline data is equivalent to model predictive control, even for uncontrollable\nsystems. Moreover, our results significantly reduce the amount of data needed\nin identifying homogeneous multi-agent systems.\n"}
+{"abstract": "  This article describe globular weak $(n,\\infty)$-transformations\n($n\\in\\mathbb{N}$) in the sense of Grothendieck, i.e for each $n\\in\\mathbb{N}$\nwe build a coherator $\\Theta^{\\infty}_{\\mathbb{M}^n}$ which sets models are\nglobular weak $(n,\\infty)$-transformations. A natural globular filtration\nemerges from these coherators.\n"}
+{"abstract": "  In this article, logical concepts are defined using the internal syntactic\nand semantic structure of language. For a first-order language, it has been\nshown that its logical constants are connectives and a certain type of\nquantifiers for which the universal and existential quantifiers form a\nfunctionally complete set of quantifiers. Neither equality nor cardinal\nquantifiers belong to the logical constants of a first-order language.\n"}
+{"abstract": "  To safely deploy autonomous vehicles, onboard perception systems must work\nreliably at high accuracy across a diverse set of environments and geographies.\nOne of the most common techniques to improve the efficacy of such systems in\nnew domains involves collecting large labeled datasets, but such datasets can\nbe extremely costly to obtain, especially if each new deployment geography\nrequires additional data with expensive 3D bounding box annotations. We\ndemonstrate that pseudo-labeling for 3D object detection is an effective way to\nexploit less expensive and more widely available unlabeled data, and can lead\nto performance gains across various architectures, data augmentation\nstrategies, and sizes of the labeled dataset. Overall, we show that better\nteacher models lead to better student models, and that we can distill expensive\nteachers into efficient, simple students.\n  Specifically, we demonstrate that pseudo-label-trained student models can\noutperform supervised models trained on 3-10 times the amount of labeled\nexamples. Using PointPillars [24], a two-year-old architecture, as our student\nmodel, we are able to achieve state of the art accuracy simply by leveraging\nlarge quantities of pseudo-labeled data. Lastly, we show that these student\nmodels generalize better than supervised models to a new domain in which we\nonly have unlabeled data, making pseudo-label training an effective form of\nunsupervised domain adaptation.\n"}
+{"abstract": "  In this work, we propose a Bayesian statistical model to simultaneously\ncharacterize two or more social networks defined over a common set of actors.\nThe key feature of the model is a hierarchical prior distribution that allows\nus to represent the entire system jointly, achieving a compromise between\ndependent and independent networks. Among others things, such a specification\neasily allows us to visualize multilayer network data in a low-dimensional\nEuclidean space, generate a weighted network that reflects the consensus\naffinity between actors, establish a measure of correlation between networks,\nassess cognitive judgements that subjects form about the relationships among\nactors, and perform clustering tasks at different social instances. Our model's\ncapabilities are illustrated using several real-world data sets, taking into\naccount different types of actors, sizes, and relations.\n"}
+{"abstract": "  We define partial quasi-morphisms on the group of Hamiltonian diffeomorphisms\nof the cotangent bundle using the spectral invariants in Lagrangian Floer\nhomology with conormal boundary conditions, where the product compatible with\nthe PSS isomorphism and the homological intersection product is lacking.\n"}
+{"abstract": "  In this paper, we propose a data-driven method to discover multiscale\nchemical reactions governed by the law of mass action. First, we use a single\nmatrix to represent the stoichiometric coefficients for both the reactants and\nproducts in a system without catalysis reactions. The negative entries in the\nmatrix denote the stoichiometric coefficients for the reactants and the\npositive ones for the products. Second, we find that the conventional\noptimization methods usually get stuck in the local minima and could not find\nthe true solution in learning the multiscale chemical reactions. To overcome\nthis difficulty, we propose a partial-parameters-freezing (PPF) technique to\nprogressively determine the network parameters by using the fact that the\nstoichiometric coefficients are integers. With such a technique, the dimension\nof the searching space is gradually reduced in the training process and the\nglobal mimina can be eventually obtained. Several numerical experiments\nincluding the classical Michaelis-Menten kinetics, the hydrogen oxidation\nreactions, and the simplified GRI-3.0 mechanism verify the good performance of\nour algorithm in learning the multiscale chemical reactions. The code is\navailable at \\url{https://github.com/JuntaoHuang/multiscale-chemical-reaction}.\n"}
+{"abstract": "  Approximate nearest-neighbor search is a fundamental algorithmic problem that\ncontinues to inspire study due its essential role in numerous contexts. In\ncontrast to most prior work, which has focused on point sets, we consider\nnearest-neighbor queries against a set of line segments in $\\mathbb{R}^d$, for\nconstant dimension $d$. Given a set $S$ of $n$ disjoint line segments in\n$\\mathbb{R}^d$ and an error parameter $\\varepsilon > 0$, the objective is to\nbuild a data structure such that for any query point $q$, it is possible to\nreturn a line segment whose Euclidean distance from $q$ is at most\n$(1+\\varepsilon)$ times the distance from $q$ to its nearest line segment. We\npresent a data structure for this problem with storage $O((n^2/\\varepsilon^{d})\n\\log (\\Delta/\\varepsilon))$ and query time $O(\\log\n(\\max(n,\\Delta)/\\varepsilon))$, where $\\Delta$ is the spread of the set of\nsegments $S$. Our approach is based on a covering of space by anisotropic\nelements, which align themselves according to the orientations of nearby\nsegments.\n"}
+{"abstract": "  We construct a large class of projective threefolds with one node (aka\nnon-degenerate quadratic singularity) such that their small resolutions are not\nprojective.\n"}
+{"abstract": "  Data heterogeneity has been identified as one of the key features in\nfederated learning but often overlooked in the lens of robustness to\nadversarial attacks. This paper focuses on characterizing and understanding its\nimpact on backdooring attacks in federated learning through comprehensive\nexperiments using synthetic and the LEAF benchmarks. The initial impression\ndriven by our experimental results suggests that data heterogeneity is the\ndominant factor in the effectiveness of attacks and it may be a redemption for\ndefending against backdooring as it makes the attack less efficient, more\nchallenging to design effective attack strategies, and the attack result also\nbecomes less predictable. However, with further investigations, we found data\nheterogeneity is more of a curse than a redemption as the attack effectiveness\ncan be significantly boosted by simply adjusting the client-side backdooring\ntiming. More importantly,data heterogeneity may result in overfitting at the\nlocal training of benign clients, which can be utilized by attackers to\ndisguise themselves and fool skewed-feature based defenses. In addition,\neffective attack strategies can be made by adjusting attack data distribution.\nFinally, we discuss the potential directions of defending the curses brought by\ndata heterogeneity. The results and lessons learned from our extensive\nexperiments and analysis offer new insights for designing robust federated\nlearning methods and systems\n"}
+{"abstract": "  Unsupervised domain adaptive classifcation intends to improve the\nclassifcation performance on unlabeled target domain. To alleviate the adverse\neffect of domain shift, many approaches align the source and target domains in\nthe feature space. However, a feature is usually taken as a whole for alignment\nwithout explicitly making domain alignment proactively serve the classifcation\ntask, leading to sub-optimal solution. In this paper, we propose an effective\nTask-oriented Alignment (ToAlign) for unsupervised domain adaptation (UDA). We\nstudy what features should be aligned across domains and propose to make the\ndomain alignment proactively serve classifcation by performing feature\ndecomposition and alignment under the guidance of the prior knowledge induced\nfrom the classifcation task itself. Particularly, we explicitly decompose a\nfeature in the source domain into a task-related/discriminative feature that\nshould be aligned, and a task-irrelevant feature that should be\navoided/ignored, based on the classifcation meta-knowledge. Extensive\nexperimental results on various benchmarks (e.g., Offce-Home, Visda-2017, and\nDomainNet) under different domain adaptation settings demonstrate the\neffectiveness of ToAlign which helps achieve the state-of-the-art performance.\nThe code is publicly available at https://github.com/microsoft/UDA\n"}
+{"abstract": "  The design space for inertial confinement fusion (ICF) experiments is vast\nand experiments are extremely expensive. Researchers rely heavily on computer\nsimulations to explore the design space in search of high-performing\nimplosions. However, ICF multiphysics codes must make simplifying assumptions,\nand thus deviate from experimental measurements for complex implosions. For\nmore effective design and investigation, simulations require input from past\nexperimental data to better predict future performance. In this work, we\ndescribe a cognitive simulation method for combining simulation and\nexperimental data into a common, predictive model. This method leverages a\nmachine learning technique called transfer learning, the process of taking a\nmodel trained to solve one task, and partially retraining it on a sparse\ndataset to solve a different, but related task. In the context of ICF design,\nneural network models trained on large simulation databases and partially\nretrained on experimental data, producing models that are far more accurate\nthan simulations alone. We demonstrate improved model performance for a range\nof ICF experiments at the National Ignition Facility, and predict the outcome\nof recent experiments with less than ten percent error for several key\nobservables. We discuss how the methods might be used to carry out a\ndata-driven experimental campaign to optimize performance, illustrating the key\nproduct -- models that become increasingly accurate as data is acquired.\n"}
+{"abstract": "  This paper develops simple feed-forward neural networks that achieve the\nuniversal approximation property for all continuous functions with a fixed\nfinite number of neurons. These neural networks are simple because they are\ndesigned with a simple and computable continuous activation function $\\sigma$\nleveraging a triangular-wave function and a softsign function. We prove that\n$\\sigma$-activated networks with width $36d(2d+1)$ and depth $11$ can\napproximate any continuous function on a $d$-dimensioanl hypercube within an\narbitrarily small error. Hence, for supervised learning and its related\nregression problems, the hypothesis space generated by these networks with a\nsize not smaller than $36d(2d+1)\\times 11$ is dense in the space of continuous\nfunctions. Furthermore, classification functions arising from image and signal\nclassification are in the hypothesis space generated by $\\sigma$-activated\nnetworks with width $36d(2d+1)$ and depth $12$, when there exist pairwise\ndisjoint closed bounded subsets of $\\mathbb{R}^d$ such that the samples of the\nsame class are located in the same subset.\n"}
+{"abstract": "  Adversarial algorithms have shown to be effective against neural networks for\na variety of tasks. Some adversarial algorithms perturb all the pixels in the\nimage minimally for the image classification task in image classification. In\ncontrast, some algorithms perturb few pixels strongly. However, very little\ninformation is available regarding why these adversarial samples so diverse\nfrom each other exist. Recently, Vargas et al. showed that the existence of\nthese adversarial samples might be due to conflicting saliency within the\nneural network. We test this hypothesis of conflicting saliency by analysing\nthe Saliency Maps (SM) and Gradient-weighted Class Activation Maps (Grad-CAM)\nof original and few different types of adversarial samples. We also analyse how\ndifferent adversarial samples distort the attention of the neural network\ncompared to original samples. We show that in the case of Pixel Attack,\nperturbed pixels either calls the network attention to themselves or divert the\nattention from them. Simultaneously, the Projected Gradient Descent Attack\nperturbs pixels so that intermediate layers inside the neural network lose\nattention for the correct class. We also show that both attacks affect the\nsaliency map and activation maps differently. Thus, shedding light on why some\ndefences successful against some attacks remain vulnerable against other\nattacks. We hope that this analysis will improve understanding of the existence\nand the effect of adversarial samples and enable the community to develop more\nrobust neural networks.\n"}
+{"abstract": "  Understanding and mitigating loss channels due to two-level systems (TLS) is\none of the main cornerstones in the quest of realizing long photon lifetimes in\nsuperconducting quantum circuits. Typically, the TLS to which a circuit couples\nare modeled as a large bath without any coherence. Here we demonstrate that the\ncoherence of TLS has to be considered to accurately describe the ring-down\ndynamics of a coaxial quarter-wave resonator with an internal quality factor of\n$0.5\\times10^9$ at the single-photon level. The transient analysis reveals\neffective non-Markovian dynamics of the combined TLS and cavity system, which\nwe can accurately fit by introducing a comprehensive TLS model. The fit returns\nan average coherence time of around $T_2^*\\approx0.3\\,\\mathrm{\\mu s}$ for a\ntotal of $N\\approx10^{9}$ TLS with power-law distributed coupling strengths.\nDespite the shortly coherent TLS excitations, we observe long-term effects on\nthe cavity decay due to coherent elastic scattering between the resonator field\nand the TLS. Moreover, this model provides an accurate prediction of the\ninternal quality factor's temperature dependence.\n"}
+{"abstract": "  Weakly nonlinear internal wave-wave interaction is a key mechanism that\ncascades energy from large to small scales, leading to ocean turbulence and\nmixing. Oceans typically have a non-uniform density stratification profile;\nmoreover, submarine topography leads to a spatially varying ocean depth ($h$).\nUnder these conditions and assuming mild-slope bathymetry, we employ\nmultiple-scale analysis to derive the wave amplitude equations for triadic- and\nself-interactions. The waves are assumed to have a slowly (rapidly) varying\namplitude (phase) in space and time. For uniform stratifications, the\nhorizontal wavenumber ($k$) condition for waves ($1$,$2$,$3$), given by\n${k}_{(1,a)}+{k}_{(2,b)}+{k}_{(3,c)}=0$, is unaffected as $h$ is varied, where\n$(a,b,c)$ denote the modenumber. Moreover, the nonlinear coupling coefficients\n(NLC) are proportional to $1/h^2$, implying that triadic waves grow faster\nwhile travelling up a seamount. For non-uniform stratifications, triads that do\nnot satisfy the condition $a=b=c$ may not satisfy the horizontal wavenumber\ncondition as $h$ is varied, and unlike uniform stratification, the NLC may not\ndecrease (increase) monotonically with increasing (decreasing) $h$. NLC, and\nhence wave growth rates for both triads and self-interactions, can also vary\nrapidly with $h$. The most unstable daughter wave combination of a triad with a\nmode-1 parent wave can also change for relatively small changes in $h$. We also\ninvestigate higher-order self-interactions in the presence of a monochromatic,\nsmall amplitude bathymetry; here the bathymetry behaves as a zero frequency\nwave. We derive the amplitude evolution equations and show that higher-order\nself-interactions might be a viable mechanism of energy cascade.\n"}
+{"abstract": "  We give a $0.5368$-competitive algorithm for edge-weighted online bipartite\nmatching. Prior to our work, the best competitive ratio was $0.5086$ due to\nFahrbach, Huang, Tao, and Zadimoghaddam (FOCS 2020). They achieved their\nbreakthrough result by developing a subroutine called \\emph{online correlated\nselection} (OCS) which takes as input a sequence of pairs and selects one item\nfrom each pair. Importantly, the selections the OCS makes are negatively\ncorrelated.\n  We achieve our result by defining \\emph{multiway} OCSes which receive\narbitrarily many elements at each step, rather than just two. In addition to\nbetter competitive ratios, our formulation allows for a simpler reduction from\nedge-weighted online bipartite matching to OCSes. While Fahrbach et al. used a\nfactor-revealing linear program to optimize the competitive ratio, our analysis\ndirectly connects the competitive ratio to the parameters of the multiway OCS.\nFinally, we show that the formulation of Farhbach et al. can achieve a\ncompetitive ratio of at most $0.5239$, confirming that multiway OCSes are\nstrictly more powerful.\n"}
+{"abstract": "  Let $\\Omega\\subset \\mathbb{C}^n$ be a smooth bounded pseudoconvex domain and\n$A^2 (\\Omega)$ denote its Bergman space. Let $P:L^2(\\Omega)\\longrightarrow\nA^2(\\Omega)$ be the Bergman projection. For a measurable\n$\\varphi:\\Omega\\longrightarrow \\Omega$, the projected composition operator is\ndefined by $(K_\\varphi f)(z) = P(f \\circ \\varphi)(z), z \\in\\Omega, f\\in A^2\n(\\Omega).$ In 1994, Rochberg studied boundedness of $K_\\varphi$ on the Hardy\nspace of the unit disk and obtained different necessary or sufficient\nconditions for boundedness of $K_\\varphi$. In this paper we are interested in\nprojected composition operators on Bergman spaces on pseudoconvex domains. We\nstudy boundedness of this operator under the smoothness assumptions on the\nsymbol $\\varphi$ on $\\overline\\Omega$.\n"}
+{"abstract": "  A new mechanism for the internal heating of ultra-short-period planets is\nproposed based on the gravitational perturbation by a non-axisymmetric\nquadrupole moment of their host stars. Such a quadrupole is due to the magnetic\nflux tubes in the stellar convection zone, unevenly distributed in longitude\nand persisting for many stellar rotations as observed in young late-type stars.\nThe rotation period of the host star evolves from its shortest value on the\nzero-age main sequence to longer periods due to the loss of angular momentum\nthrough a magnetized wind. If the stellar rotation period comes close to twice\nthe orbital period of the planet, the quadrupole leads to a spin-orbit\nresonance that excites oscillations of the star-planet separation. As a\nconsequence, a strong tidal dissipation is produced inside the planet. We\nillustrate the operation of the mechanism by modeling the evolution of the\nstellar rotation and of the innermost planetary orbit in the cases of CoRoT-7,\nKepler-78, and K2-141 whose present orbital periods range between 0.28 and 0.85\ndays. If the spin-orbit resonance occurs, the maximum power dissipated inside\nthe planets ranges between $10^{18}$ and $10^{19}$ W, while the total\ndissipated energy is of the order of $10^{30}-10^{32}$ J over a time interval\nas short as $(1-4.5) \\times 10^{4}$ yr. Such a huge heating over a so short\ntime interval produces a complete melting of the planetary interiors and may\nshut off their hydromagnetic dynamos. These may initiate a successive phase of\nintense internal heating owing to unipolar magnetic star-planet interactions\nand affect the composition and the escape of their atmospheres, producing\neffects that could be observable during the entire lifetime of the planets\n[abridged abstract].\n"}
+{"abstract": "  This paper presents a fully convolutional scene graph generation (FCSGG)\nmodel that detects objects and relations simultaneously. Most of the scene\ngraph generation frameworks use a pre-trained two-stage object detector, like\nFaster R-CNN, and build scene graphs using bounding box features. Such pipeline\nusually has a large number of parameters and low inference speed. Unlike these\napproaches, FCSGG is a conceptually elegant and efficient bottom-up approach\nthat encodes objects as bounding box center points, and relationships as 2D\nvector fields which are named as Relation Affinity Fields (RAFs). RAFs encode\nboth semantic and spatial features, and explicitly represent the relationship\nbetween a pair of objects by the integral on a sub-region that points from\nsubject to object. FCSGG only utilizes visual features and still generates\nstrong results for scene graph generation. Comprehensive experiments on the\nVisual Genome dataset demonstrate the efficacy, efficiency, and\ngeneralizability of the proposed method. FCSGG achieves highly competitive\nresults on recall and zero-shot recall with significantly reduced inference\ntime.\n"}
+{"abstract": "  With extreme weather events becoming more common, the risk posed by surface\nwater flooding is ever increasing. In this work we propose a model, and\nassociated Bayesian inference scheme, for generating probabilistic\n(high-resolution short-term) forecasts of localised precipitation. The\nparametrisation of our underlying hierarchical dynamic spatio-temporal model is\nmotivated by a forward-time, centred-space finite difference solution to a\ncollection of stochastic partial differential equations, where the main driving\nforces are advection and diffusion. Observations from both weather radar and\nground based rain gauges provide information from which we can learn about the\nlikely values of the (latent) precipitation field in addition to other unknown\nmodel parameters. Working in the Bayesian paradigm provides a coherent\nframework for capturing uncertainty both in the underlying model parameters and\nalso in our forecasts. Further, appealing to simulation based (MCMC) sampling\nyields a straightforward solution to handling zeros, treated as censored\nobservations, via data augmentation. Both the underlying state and the\nobservations are of moderately large dimension ($\\mathcal{O}(10^4)$ and\n$\\mathcal{O}(10^3)$ respectively) and this renders standard inference\napproaches computationally infeasible. Our solution is to embed the ensemble\nKalman smoother within a Gibbs sampling scheme to facilitate approximate\nBayesian inference in reasonable time. Both the methodology and the\neffectiveness of our posterior sampling scheme are demonstrated via simulation\nstudies and also by a case study of real data from the Urban Observatory\nproject based in Newcastle upon Tyne, UK.\n"}
+{"abstract": "  Continuous variable measurement-based quantum computation on cluster states\nhas in recent years shown great potential for scalable, universal, and\nfault-tolerant quantum computation when combined with the\nGottesman-Kitaev-Preskill (GKP) code and quantum error correction. However, no\ncomplete fault-tolerant architecture exists that includes everything from\ncluster state generation with finite squeezing to gate implementations with\nrealistic noise and error correction. In this work, we propose a simple\narchitecture for the preparation of a cluster state in three dimensions in\nwhich gates by gate teleportation can be efficiently implemented. To\naccommodate scalability, we propose architectures that allow for both spatial\nand temporal multiplexing, with the temporal encoded version requiring as\nlittle as two squeezed light sources. Due to its three-dimensional structure,\nthe architecture supports topological qubit error correction, while GKP error\ncorrection is efficiently realized within the architecture by teleportation. To\nvalidate fault-tolerance, the architecture is simulated using surface-GKP\ncodes, including noise from GKP-states as well as gate noise caused by finite\nsqueezing in the cluster state. We find a fault-tolerant squeezing threshold of\n12.7 dB with room for further improvement.\n"}
+{"abstract": "  In high energy exclusive processes involving leptons, QED corrections can be\nsensitive to infrared scales like the lepton mass and the soft photon energy\ncut, resulting in large logarithms that need to be resummed to all order in\n$\\alpha$. When considering the ratio of the exclusive processes between two\ndifferent lepton flavors, the ratio $R$ can be expressed in terms of factorized\nfunctions in the decoupled leptonic sectors. While some of the functional terms\ncancel, there remain the large logarithms due to the lepton mass difference and\nthe energy cut. This factorization process can be universally applied to the\nexclusive processes such as $Z\\to l^+l^-$ and $B^-\\to l^-\\bar{\\nu}_l$, where\nthe resummed result in the ratio gives significant deviations from the naive\nexpectation from lepton universality.\n"}
+{"abstract": "  A hyperbolic group acts by homeomorphisms on its Gromov boundary. We show\nthat if this boundary is a topological n-sphere the action is topologically\nstable in the dynamical sense: any nearby action is semi-conjugate to the\nstandard boundary action.\n"}
+{"abstract": "  In this paper we consider the problem of pointwise determining the fibres of\nthe flat unitary subbundle of a PVHS of weight one. Starting from the\nassociated Higgs field, and assuming the base has dimension $1$, we construct a\nfamily of (smooth but possibly non-holomorphic) morphisms of vector bundles\nwith the property that the intersection of their kernels at a general point is\nthe fibre of the flat subbundle. We explore the first one of these morphisms in\nthe case of a geometric PVHS arising from a family of smooth projective curves,\nshowing that it acts as the cup-product with some sort of \"second-order\nKodaira-Spencer class\" which we introduce, and check in the case of a family of\nsmooth plane curves that this additional condition is non-trivial.\n"}
+{"abstract": "  Multi-segment reconstruction (MSR) is the problem of estimating a signal\ngiven noisy partial observations. Here each observation corresponds to a\nrandomly located segment of the signal. While previous works address this\nproblem using template or moment-matching, in this paper we address MSR from an\nunsupervised adversarial learning standpoint, named MSR-GAN. We formulate MSR\nas a distribution matching problem where the goal is to recover the signal and\nthe probability distribution of the segments such that the distribution of the\ngenerated measurements following a known forward model is close to the real\nobservations. This is achieved once a min-max optimization involving a\ngenerator-discriminator pair is solved. MSR-GAN is mainly inspired by CryoGAN\n[1]. However, in MSR-GAN we no longer assume the probability distribution of\nthe latent variables, i.e. segment locations, is given and seek to recover it\nalongside the unknown signal. For this purpose, we show that the loss at the\ngenerator side originally is non-differentiable with respect to the segment\ndistribution. Thus, we propose to approximate it using Gumbel-Softmax\nreparametrization trick. Our proposed solution is generalizable to a wide range\nof inverse problems. Our simulation results and comparison with various\nbaselines verify the potential of our approach in different settings.\n"}
+{"abstract": "  We compare the radial profiles of the specific star formation rate (sSFR) in\na sample of 169 star-forming galaxies in close pairs with those of mass-matched\ncontrol galaxies in the SDSS-IV MaNGA survey. We find that the sSFR is\ncentrally enhanced (within one effective radius) in interacting galaxies by\n~0.3 dex and that there is a weak sSFR suppression in the outskirts of the\ngalaxies of ~0.1 dex. We stack the differences profiles for galaxies in five\nstellar mass bins between log(M/Mstar) = 9.0-11.5 and find that the sSFR\nenhancement has no dependence on the stellar mass. The same result is obtained\nwhen the comparison galaxies are matched to each paired galaxy in both stellar\nmass and redshift. In addition, we find that that the sSFR enhancement is\nelevated in pairs with nearly equal masses and closer projected separations, in\nagreement with previous work based on single-fiber spectroscopy. We also find\nthat the sSFR offsets in the outskirts of the paired galaxies are dependent on\nwhether the galaxy is the more massive or less massive companion in the pair.\nThe more massive companion experiences zero to a positive sSFR enhancement\nwhile the less massive companion experiences sSFR suppression in their\noutskirts. Our results illustrate the complex tidal effects on star formation\nin closely paired galaxies.\n"}
+{"abstract": "  Solving Partially Observable Markov Decision Processes (POMDPs) is hard.\nLearning optimal controllers for POMDPs when the model is unknown is harder.\nOnline learning of optimal controllers for unknown POMDPs, which requires\nefficient learning using regret-minimizing algorithms that effectively tradeoff\nexploration and exploitation, is even harder, and no solution exists currently.\nIn this paper, we consider infinite-horizon average-cost POMDPs with unknown\ntransition model, though a known observation model. We propose a natural\nposterior sampling-based reinforcement learning algorithm (PSRL-POMDP) and show\nthat it achieves a regret bound of $O(\\log T)$, where $T$ is the time horizon,\nwhen the parameter set is finite. In the general case (continuous parameter\nset), we show that the algorithm achieves $O (T^{2/3})$ regret under two\ntechnical assumptions. To the best of our knowledge, this is the first online\nRL algorithm for POMDPs and has sub-linear regret.\n"}
+{"abstract": "  Let $D$ be a $k$-regular bipartite tournament on $n$ vertices. We show that,\nfor every $p$ with $2 \\le p \\le n/2-2$, $D$ has a cycle $C$ of length $2p$ such\nthat $D \\setminus C$ is hamiltonian unless $D$ is isomorphic to the special\ndigraph $F_{4k}$. This statement was conjectured by Manoussakis, Song and Zhang\n[K. Zhang, Y. Manoussakis, and Z. Song. Complementary cycles containing a fixed\narc in diregular bipartite tournaments. Discrete Mathematics,\n133(1-3):325--328,1994]. In the same paper, the conjecture was proved for $p=2$\nand more recently Bai, Li and He gave a proof for $p=3$ [Y. Bai, H. Li, and W.\nHe. Complementary cycles in regular bipartite tournaments. Discrete\nMathematics, 333:14--27, 2014].\n"}
+{"abstract": "  Missing time-series data is a prevalent practical problem. Imputation methods\nin time-series data often are applied to the full panel data with the purpose\nof training a model for a downstream out-of-sample task. For example, in\nfinance, imputation of missing returns may be applied prior to training a\nportfolio optimization model. Unfortunately, this practice may result in a\nlook-ahead-bias in the future performance on the downstream task. There is an\ninherent trade-off between the look-ahead-bias of using the full data set for\nimputation and the larger variance in the imputation from using only the\ntraining data. By connecting layers of information revealed in time, we propose\na Bayesian posterior consensus distribution which optimally controls the\nvariance and look-ahead-bias trade-off in the imputation. We demonstrate the\nbenefit of our methodology both in synthetic and real financial data.\n"}
+{"abstract": "  Recent studies have witnessed that self-supervised methods based on view\nsynthesis obtain clear progress on multi-view stereo (MVS). However, existing\nmethods rely on the assumption that the corresponding points among different\nviews share the same color, which may not always be true in practice. This may\nlead to unreliable self-supervised signal and harm the final reconstruction\nperformance. To address the issue, we propose a framework integrated with more\nreliable supervision guided by semantic co-segmentation and data-augmentation.\nSpecially, we excavate mutual semantic from multi-view images to guide the\nsemantic consistency. And we devise effective data-augmentation mechanism which\nensures the transformation robustness by treating the prediction of regular\nsamples as pseudo ground truth to regularize the prediction of augmented\nsamples. Experimental results on DTU dataset show that our proposed methods\nachieve the state-of-the-art performance among unsupervised methods, and even\ncompete on par with supervised methods. Furthermore, extensive experiments on\nTanks&Temples dataset demonstrate the effective generalization ability of the\nproposed method.\n"}
+{"abstract": "  Nearly a decade ago, Azrieli and Shmaya introduced the class of\n$\\lambda$-Lipschitz games in which every player's payoff function is\n$\\lambda$-Lipschitz with respect to the actions of the other players. They\nshowed that such games admit $\\epsilon$-approximate pure Nash equilibria for\ncertain settings of $\\epsilon$ and $\\lambda$. They left open, however, the\nquestion of how hard it is to find such an equilibrium. In this work, we\ndevelop a query-efficient reduction from more general games to Lipschitz games.\nWe use this reduction to show a query lower bound for any randomized algorithm\nfinding $\\epsilon$-approximate pure Nash equilibria of $n$-player,\nbinary-action, $\\lambda$-Lipschitz games that is exponential in\n$\\frac{n\\lambda}{\\epsilon}$. In addition, we introduce ``Multi-Lipschitz\ngames,'' a generalization involving player-specific Lipschitz values, and\nprovide a reduction from finding equilibria of these games to finding\nequilibria of Lipschitz games, showing that the value of interest is the sum of\nthe individual Lipschitz parameters. Finally, we provide an exponential lower\nbound on the deterministic query complexity of finding $\\epsilon$-approximate\ncorrelated equilibria of $n$-player, $m$-action, $\\lambda$-Lipschitz games for\nstrong values of $\\epsilon$, motivating the consideration of explicitly\nrandomized algorithms in the above results. Our proof is arguably simpler than\nthose previously used to show similar results.\n"}
+{"abstract": "  The WIMP proposed here yields the observed abundance of dark matter, and is\nconsistent with the current limits from direct detection, indirect detection,\nand collider experiments, if its mass is $\\sim 72$ GeV/$c^2$. It is also\nconsistent with analyses of the gamma rays observed by Fermi-LAT from the\nGalactic center (and other sources), and of the antiprotons observed by AMS-02,\nin which the excesses are attributed to dark matter annihilation. These\nsuccesses are shared by the inert doublet model (IDM), but the phenomenology is\nvery different: The dark matter candidate of the IDM has first-order gauge\ncouplings to other new particles, whereas the present candidate does not. In\naddition to indirect detection through annihilation products, it appears that\nthe present particle can be observed in the most sensitive direct-detection and\ncollider experiments currently being planned.\n"}
+{"abstract": "  It has been shown that the performance of neural machine translation (NMT)\ndrops starkly in low-resource conditions, often requiring large amounts of\nauxiliary data to achieve competitive results. An effective method of\ngenerating auxiliary data is back-translation of target language sentences. In\nthis work, we present a case study of Tigrinya where we investigate several\nback-translation methods to generate synthetic source sentences. We find that\nin low-resource conditions, back-translation by pivoting through a\nhigher-resource language related to the target language proves most effective\nresulting in substantial improvements over baselines.\n"}
+{"abstract": "  This paper proposes architectures that facilitate the extrapolation of\nemotional expressions in deep neural network (DNN)-based text-to-speech (TTS).\nIn this study, the meaning of \"extrapolate emotional expressions\" is to borrow\nemotional expressions from others, and the collection of emotional speech\nuttered by target speakers is unnecessary. Although a DNN has potential power\nto construct DNN-based TTS with emotional expressions and some DNN-based TTS\nsystems have demonstrated satisfactory performances in the expression of the\ndiversity of human speech, it is necessary and troublesome to collect emotional\nspeech uttered by target speakers. To solve this issue, we propose\narchitectures to separately train the speaker feature and the emotional feature\nand to synthesize speech with any combined quality of speakers and emotions.\nThe architectures are parallel model (PM), serial model (SM), auxiliary input\nmodel (AIM), and hybrid models (PM&AIM and SM&AIM). These models are trained\nthrough emotional speech uttered by few speakers and neutral speech uttered by\nmany speakers. Objective evaluations demonstrate that the performances in the\nopen-emotion test provide insufficient information. They make a comparison with\nthose in the closed-emotion test, but each speaker has their own manner of\nexpressing emotion. However, subjective evaluation results indicate that the\nproposed models could convey emotional information to some extent. Notably, the\nPM can correctly convey sad and joyful emotions at a rate of >60%.\n"}
+{"abstract": "  The baroclinic annular mode (BAM) is a leading-order mode of the eddy-kinetic\nenergy in the Southern Hemisphere exhibiting. oscillatory behavior at\nintra-seasonal time scales. The oscillation mechanism has been linked to\ntransient eddy-mean flow interactions that remain poorly understood. Here we\ndemonstrate that the finite memory effect in eddy-heat flux dependence on the\nlarge-scale flow can explain the origin of the BAM's oscillatory behavior. We\nrepresent the eddy memory effect by a delayed integral kernel that leads to a\ngeneralized Langevin equation for the planetary-scale heat equation. Using a\nmathematical framework for the interactions between planetary and\nsynoptic-scale motions, we derive a reduced dynamical model of the BAM - a\nstochastically-forced oscillator with a period proportional to the geometric\nmean between the eddy-memory time scale and the diffusive eddy equilibration\ntimescale. Our model provides a formal justification for the previously\nproposed phenomenological model of the BAM and could be used to explicitly\ndiagnose the memory kernel and improve our understanding of transient eddy-mean\nflow interactions in the atmosphere.\n"}
+{"abstract": "  Spelling irregularities, known now as spelling mistakes, have been found for\nseveral centuries. As humans, we are able to understand most of the misspelled\nwords based on their location in the sentence, perceived pronunciation, and\ncontext. Unlike humans, computer systems do not possess the convenient auto\ncomplete functionality of which human brains are capable. While many programs\nprovide spelling correction functionality, many systems do not take context\ninto account. Moreover, Artificial Intelligence systems function in the way\nthey are trained on. With many current Natural Language Processing (NLP)\nsystems trained on grammatically correct text data, many are vulnerable against\nadversarial examples, yet correctly spelled text processing is crucial for\nlearning. In this paper, we investigate how spelling errors can be corrected in\ncontext, with a pre-trained language model BERT. We present two experiments,\nbased on BERT and the edit distance algorithm, for ranking and selecting\ncandidate corrections. The results of our experiments demonstrated that when\ncombined properly, contextual word embeddings of BERT and edit distance are\ncapable of effectively correcting spelling errors.\n"}
+{"abstract": "  In game theory, mechanism design is concerned with the design of incentives\nso that a desired outcome of the game can be achieved. In this paper, we study\nthe design of incentives so that a desirable equilibrium is obtained, for\ninstance, an equilibrium satisfying a given temporal logic property -- a\nproblem that we call equilibrium design. We base our study on a framework where\nsystem specifications are represented as temporal logic formulae, games as\nquantitative concurrent game structures, and players' goals as mean-payoff\nobjectives. In particular, we consider system specifications given by LTL and\nGR(1) formulae, and show that implementing a mechanism to ensure that a given\ntemporal logic property is satisfied on some/every Nash equilibrium of the\ngame, whenever such a mechanism exists, can be done in PSPACE for LTL\nproperties and in NP/$\\Sigma^{P}_{2}$ for GR(1) specifications. We also study\nthe complexity of various related decision and optimisation problems, such as\noptimality and uniqueness of solutions, and show that the complexities of all\nsuch problems lie within the polynomial hierarchy. As an application,\nequilibrium design can be used as an alternative solution to the rational\nsynthesis and verification problems for concurrent games with mean-payoff\nobjectives whenever no solution exists, or as a technique to repair, whenever\npossible, concurrent games with undesirable rational outcomes (Nash equilibria)\nin an optimal way.\n"}
+{"abstract": "  Fonts have had trends throughout their history, not only in when they were\ninvented but also in their usage and popularity. In this paper, we attempt to\nspecifically find the trends in font usage using robust regression on a large\ncollection of text images. We utilize movie posters as the source of fonts for\nthis task because movie posters can represent time periods by using their\nrelease date. In addition, movie posters are documents that are carefully\ndesigned and represent a wide range of fonts. To understand the relationship\nbetween the fonts of movie posters and time, we use a regression Convolutional\nNeural Network (CNN) to estimate the release year of a movie using an isolated\ntitle text image. Due to the difficulty of the task, we propose to use of a\nhybrid training regimen that uses a combination of Mean Squared Error (MSE) and\nTukey's biweight loss. Furthermore, we perform a thorough analysis on the\ntrends of fonts through time.\n"}
+{"abstract": "  Using characteristics to treat advection terms in time-dependent PDEs leads\nto a class of schemes, e.g., semi-Lagrangian and Lagrange-Galerkin schemes,\nwhich preserve stability under large Courant numbers, and may therefore be\nappealing in many practical situations. Unfortunately, the need of locating the\nfeet of characteristics may cause a serious drop of efficiency in the case of\nunstructured space grids, and thus prevent the use of large time-step schemes\non complex geometries. In this paper, we perform an in-depth analysis of the\nmain recipes available for characteristic location, and propose a technique to\nimprove the efficiency of this phase, using additional information related to\nthe advecting vector field. This results in a clear improvement of execution\ntimes in the unstructured case, thus extending the range of applicability of\nlarge time-step schemes.\n"}
+{"abstract": "  In this paper, we construct a sequence $(c_k)_{k\\in\\mathbb{Z}_{\\geq 1}}$ of\nsymplectic capacities based on the Chiu-Tamarkin complex $C_{T,\\ell}$, a\n$\\mathbb{Z}/\\ell\\mathbb{Z}$-equivariant invariant coming from the microlocal\ntheory of sheaves. We compute $(c_k)_{k\\in\\mathbb{Z}_{\\geq 1}}$ for convex\ntoric domains, which are the same as the Gutt-Hutchings capacities. On the\nother hand, our method works for the contact embedding problem. We define a\nsequence of \"contact capacities\" $([c]_k)_{k\\in\\mathbb{Z}_{\\geq 1}}$ on the\nprequantized contact manifold $\\mathbb{R}^{2d}\\times S^1$, which could derive\nsome embedding obstructions of prequantized convex toric domains.\n"}
+{"abstract": "  We prove that all homology 3-spheres are $J_4$-equivalent, i.e. that any\nhomology 3-sphere can be obtained from one another by twisting one of its\nHeegaard splittings by an element of the mapping class group acting trivially\non the fourth nilpotent quotient of the fundamental group of the gluing\nsurface. We do so by exhibiting an element of $J_4$, the fourth term of the\nJohnson filtration of the mapping class group, on which (the core of) the\nCasson invariant takes the value $1$. In particular, this provides an explicit\nexample of an element of $J_4$ that is not a commutator of length $2$ in the\nTorelli group.\n"}
+{"abstract": "  This paper presents an approach to deal with safety of dynamical systems in\npresence of multiple non-convex unsafe sets. While optimal control and model\npredictive control strategies can be employed in these scenarios, they suffer\nfrom high computational complexity in case of general nonlinear systems.\nLeveraging control barrier functions, on the other hand, results in\ncomputationally efficient control algorithms. Nevertheless, when safety\nguarantees have to be enforced alongside stability objectives, undesired\nasymptotically stable equilibrium points have been shown to arise. We propose a\ncomputationally efficient optimization-based approach which allows us to ensure\nsafety of dynamical systems without introducing undesired equilibria even in\npresence of multiple non-convex unsafe sets. The developed control algorithm is\nshowcased in simulation and in a real robot navigation application.\n"}
+{"abstract": "  Advances in synthetic biology and nanotechnology have contributed to the\ndesign of tools that can be used to control, reuse, modify, and re-engineer\ncells' structure, as well as enabling engineers to effectively use biological\ncells as programmable substrates to realize Bio-Nano Things (biological\nembedded computing devices). Bio-NanoThings are generally tiny, non-intrusive,\nand concealable devices that can be used for in-vivo applications such as\nintra-body sensing and actuation networks, where the use of artificial devices\ncan be detrimental. Such (nano-scale) devices can be used in various healthcare\nsettings such as continuous health monitoring, targeted drug delivery, and\nnano-surgeries. These services can also be grouped to form a collaborative\nnetwork (i.e., nanonetwork), whose performance can potentially be improved when\nconnected to higher bandwidth external networks such as the Internet, say via\n5G. However, to realize the IoBNT paradigm, it is also important to seamlessly\nconnect the biological environment with the technological landscape by having a\ndynamic interface design to convert biochemical signals from the human body\ninto an equivalent electromagnetic signal (and vice versa). This,\nunfortunately, risks the exposure of internal biological mechanisms to\ncyber-based sensing and medical actuation, with potential security and privacy\nimplications. This paper comprehensively reviews bio-cyber interface for IoBNT\narchitecture, focusing on bio-cyber interfacing options for IoBNT like\nbiologically inspired bio-electronic devices, RFID enabled implantable chips,\nand electronic tattoos. This study also identifies known and potential security\nand privacy vulnerabilities and mitigation strategies for consideration in\nfuture IoBNT designs and implementations.\n"}
+{"abstract": "  In this paper, we introduce a novel deep neural network suitable for\nmulti-scale analysis and propose efficient model-agnostic methods that help the\nnetwork extract information from high-frequency domains to reconstruct clearer\nimages. Our model can be applied to multi-scale image enhancement problems\nincluding denoising, deblurring and single image super-resolution. Experiments\non SIDD, Flickr2K, DIV2K, and REDS datasets show that our method achieves\nstate-of-the-art performance on each task. Furthermore, we show that our model\ncan overcome the over-smoothing problem commonly observed in existing\nPSNR-oriented methods and generate more natural high-resolution images by\napplying adversarial training.\n"}
+{"abstract": "  Topological phases exhibit unconventional order that cannot be detected by\nany local order parameter. In the framework of Projected Entangled Pair\nStates(PEPS), topological order is characterized by an entanglement symmetry of\nthe local tensor which describes the model. This symmetry can take the form of\na tensor product of group representations, or in the more general case a\ncorrelated symmetry action in the form of a Matrix Product Operator(MPO), which\nencompasses all string-net models. Among other things, these entanglement\nsymmetries allow for the description of ground states and anyon excitations.\nRecently, the idea has been put forward to use those symmetries and the anyonic\nobjects they describe as order parameters for probing topological phase\ntransitions, and the applicability of this idea has been demonstrated for\nAbelian groups. In this paper, we extend this construction to the domain of\nnon-Abelian models with MPO symmetries, and use it to study the breakdown of\ntopological order in the double Fibonacci (DFib) string-net and its Galois\nconjugate, the non-hermitian double Yang-Lee (DYL) string-net. We start by\nshowing how to construct topological order parameters for condensation and\ndeconfinement of anyons using the MPO symmetries. Subsequently, we set up\ninterpolations from the DFib and the DYL model to the trivial phase, and show\nthat these can be mapped to certain restricted solid on solid(RSOS) models,\nwhich are equivalent to the $((5\\pm\\sqrt{5})/2)$-state Potts model,\nrespectively. The known exact solutions of the statistical models allow us to\nlocate the critical points, and to predict the critical exponents for the order\nparameters. We complement this by numerical study of the phase transitions,\nwhich fully confirms our theoretical predictions; remarkably, we find that both\nmodels exhibit a duality between the order parameters for condensation and\ndeconfinement.\n"}
+{"abstract": "  In this paper we propose a novel class of methods for high order accurate\nintegration of multirate systems of ordinary differential equation\ninitial-value problems. The proposed methods construct multirate schemes by\napproximating the action of matrix $\\varphi$-functions within explicit\nexponential Rosenbrock (ExpRB) methods, thereby called Multirate Exponential\nRosenbrock (MERB) methods. They consist of the solution to a sequence of\nmodified \"fast\" initial-value problems, that may themselves be approximated\nthrough subcycling any desired IVP solver. In addition to proving how to\nconstruct MERB methods from certain classes of ExpRB methods, we provide\nrigorous convergence analysis of these methods and derive efficient MERB\nschemes of orders two through six (the highest order ever constructed\ninfinitesimal multirate methods). We then present numerical simulations to\nconfirm these theoretical convergence rates, and to compare the efficiency of\nMERB methods against other recently-introduced high order multirate methods.\n"}
+{"abstract": "  When the inflaton couples to photons and amplifies electric fields, charged\nparticles produced via the Schwinger effect can dominate the universe after\ninflation, which is dubbed as the Schwinger preheating. Using the hydrodynamic\napproach for the Boltzmann equation, we numerically study two cases, the\nStarobinsky inflation model with the kinetic coupling and the anisotropic\ninflation model. The Schwinger preheating is not observed in the latter model\nbut occurs for a sufficiently large inflaton-photon coupling in the first\nmodel. We analytically address its condition and derive a general attractor\nsolution of the electric fields. The occurrence of the Schwinger preheating in\nthe first model is determined by whether the electric fields enter the\nattractor solution during inflation or not.\n"}
+{"abstract": "  In this paper we have presented the mechanism of the barrier crossing\ndynamics of a Brownian particle which is coupled to a thermal bath in the\npresence of both time independent and fluctuating magnetic fields. Here the\nfollowing three aspects are important in addition to the role of the thermal\nbath on the barrier crossing dynamics. Magnetic field induced coupling may\nintroduce a resonance like effect. Another role of the field is that\nenhancement of its strength reduces the frequency factor of the barrier\ncrossing rate constant. Finally, the fluctuating magnetic field introduces an\ninduced electric field which activates the Brownian particle to cross the\nenergy barrier. As a result of interplay among these aspects versatile\nnon-monotonic behavior may appear in the variation of the rate constant as a\nfunction of the strength of the time independent magnetic field.\n"}
+{"abstract": "  The space radiation environment is a complex combination of fast-moving ions\nderived from all atomic species found in the periodic table. The energy\nspectrum of each ion species varies widely but is prominently in the range of\n400 - 600 MeV/n. The large dynamic range in ion energy is difficult to simulate\nin ground-based radiobiology experiments. Most ground-based irradiations with\nmono-energetic beams of a single one ion species are delivered at comparatively\nhigh dose rates. In some cases, sequences of such beams are delivered with\nvarious ion species and energies to crudely approximate the complex space\nradiation environment. This approximation may cause profound experimental bias\nin processes such as biologic repair of radiation damage, which are known to\nhave strong temporal dependancies. It is possible that this experimental bias\nleads to an overprediction of risks of radiation effects that have not been\nobserved in the astronaut cohort. None of the primary health risks presumely\nattributed to space radiation exposure, such as radiation carciogenesis,\ncardiovascular disease, cognitive deficits, etc., have been observed in\nastronaut or cosmonaut crews. This fundamentally and profoundly limits our\nunderstanding of the effects of GCR on humans and limits the development of\neffective radiation countermeasures.\n"}
+{"abstract": "  Time series forecasting methods play critical role in estimating the spread\nof an epidemic. The coronavirus outbreak of December 2019 has already infected\nmillions all over the world and continues to spread on. Just when the curve of\nthe outbreak had started to flatten, many countries have again started to\nwitness a rise in cases which is now being referred as the 2nd wave of the\npandemic. A thorough analysis of time-series forecasting models is therefore\nrequired to equip state authorities and health officials with immediate\nstrategies for future times. This aims of the study are three-fold: (a) To\nmodel the overall trend of the spread; (b) To generate a short-term forecast of\n10 days in countries with the highest incidence of confirmed cases (USA, India\nand Brazil); (c) To quantitatively determine the algorithm that is best suited\nfor precise modelling of the linear and non-linear features of the time series.\nThe comparison of forecasting models for the total cumulative cases of each\ncountry is carried out by comparing the reported data and the predicted value,\nand then ranking the algorithms (Prophet, Holt-Winters, LSTM, ARIMA, and\nARIMA-NARNN) based on their RMSE, MAE and MAPE values. The hybrid combination\nof ARIMA and NARNN (Nonlinear Auto-Regression Neural Network) gave the best\nresult among the selected models with a reduced RMSE, which proved to be almost\n35.3% better than one of the most prevalent method of time-series prediction\n(ARIMA). The results demonstrated the efficacy of the hybrid implementation of\nthe ARIMA-NARNN model over other forecasting methods such as Prophet, Holt\nWinters, LSTM, and the ARIMA model in encapsulating the linear as well as\nnon-linear patterns of the epidemical datasets.\n"}
+{"abstract": "  We present mid infrared imaging of two young clusters, the Coronet in the CrA\ncloud core and B59 in the Pipe Nebula, using the FORCAST camera on the\nStratospheric Observatory for Infrared Astronomy. We also analyze Herschel\nSpace Observatory PACS and SPIRE images of the associated clouds. The two\nclusters are at similar, and very close, distances. Star formation is ongoing\nin the Coronet, which hosts at least one Class 0 source and several pre-stellar\ncores, which may collapse and form stars. The B59 cluster is older, although it\nstill has a few Class I sources, and is less compact. The CrA cloud has a\ndiameter of about 0.16 pc, and we determine a dust temperature of 15.7 K and a\nstar formation efficiency of about 27 %, while the B59 core is approximately\ntwice as large, has a dust temperature of about 11.4 K and a star formation\nefficiency of about 14 %. We infer that the gas densities are much higher in\nthe Coronet, which has also formed intermediate mass stars, while B59 has only\nformed low-mass stars.\n"}
+{"abstract": "  This paper presents approximation methods for time-dependent thermal\nradiative transfer problems in high energy density physics. It is based on the\nmultilevel quasidiffusion method defined by the high-order radiative transfer\nequation (RTE) and the low-order quasidiffusion (aka VEF) equations for the\nmoments of the specific intensity. A large part of data storage in TRT problems\nbetween time steps is determined by the dimensionality of grid functions of the\nradiation intensity. The approximate implicit methods with reduced memory for\nthe time-dependent Boltzmann equation are applied to the high-order RTE,\ndiscretized in time with the backward Euler (BE) scheme. The high-dimensional\nintensity from the previous time level in the BE scheme is approximated by\nmeans of the low-rank proper orthogonal decomposition (POD). Another version of\nthe presented method applies the POD to the remainder term of P2 expansion of\nthe intensity. The accuracy of the solution of the approximate implicit methods\ndepends of the rank of the POD. The proposed methods enable one to reduce\nstorage requirements in time dependent problems. Numerical results of a\nFleck-Cummings TRT test problem are presented.\n"}
+{"abstract": "  In this paper we study nonlinear interpolation problems for interpolation and\npeak-interpolation sets of function algebras. The subject goes back to the\nclassical Rudin-Carleson interpolation theorem. In particular, we prove the\nfollowing nonlinear version of this theorem:\n  Let $\\bar{\\mathbb D}\\subset \\mathbb C$ be the closed unit disk, $\\mathbb\nT\\subset\\bar{\\mathbb D}$ the unit circle, $S\\subset\\mathbb T$ a closed subset\nof Lebesgue measure zero and $M$ a connected complex manifold.\n  Then for every continuous $M$-valued map $f$ on $S$ there exists a continuous\n$M$-valued map $g$ on $\\bar{\\mathbb D}$ holomorphic on its interior such that\n$g|_S=f$. We also consider similar interpolation problems for continuous maps\n  $f: S\\rightarrow\\bar M$, where $\\bar M$ is a complex manifold with boundary\n$\\partial M$ and interior $M$. Assuming that $f(S)\\cap\\partial M\\ne\\emptyset$\nwe are looking for holomorphic extensions $g$ of $f$ such that $g(\\bar{\\mathbb\nD}\\setminus S)\\subset M$.\n"}
+{"abstract": "  In this paper, we investigate Riesz energy problems on unbounded conductors\nin $\\R^d$ in the presence of general external fields $Q$, not necessarily\nsatisfying the growth condition $Q(x)\\to\\infty$ as $x\\to\\infty$ assumed in\nseveral previous studies. We provide sufficient conditions on $Q$ for the\nexistence of an equilibrium measure and the compactness of its support.\nParticular attention is paid to the case of the hyperplanar conductor $\\R^{d}$,\nembedded in $\\R^{d+1}$, when the external field is created by the potential of\na signed measure $\\nu$ outside of $\\R^{d}$. Simple cases where $\\nu$ is a\ndiscrete measure are analyzed in detail. New theoretic results for Riesz\npotentials, in particular an extension of a classical theorem by de La\nVall\\'ee-Poussin, are established. These results are of independent interest.\n"}
+{"abstract": "  DNA sequencing is becoming increasingly commonplace, both in medical and\ndirect-to-consumer settings. To promote discovery, collected genomic data is\noften de-identified and shared, either in public repositories, such as OpenSNP,\nor with researchers through access-controlled repositories. However, recent\nstudies have suggested that genomic data can be effectively matched to\nhigh-resolution three-dimensional face images, which raises a concern that the\nincreasingly ubiquitous public face images can be linked to shared genomic\ndata, thereby re-identifying individuals in the genomic data. While these\ninvestigations illustrate the possibility of such an attack, they assume that\nthose performing the linkage have access to extremely well-curated data. Given\nthat this is unlikely to be the case in practice, it calls into question the\npragmatic nature of the attack. As such, we systematically study this\nre-identification risk from two perspectives: first, we investigate how\nsuccessful such linkage attacks can be when real face images are used, and\nsecond, we consider how we can empower individuals to have better control over\nthe associated re-identification risk. We observe that the true risk of\nre-identification is likely substantially smaller for most individuals than\nprior literature suggests. In addition, we demonstrate that the addition of a\nsmall amount of carefully crafted noise to images can enable a controlled\ntrade-off between re-identification success and the quality of shared images,\nwith risk typically significantly lowered even with noise that is imperceptible\nto humans.\n"}
+{"abstract": "  Recent works on Binary Neural Networks (BNNs) have made promising progress in\nnarrowing the accuracy gap of BNNs to their 32-bit counterparts. However, the\naccuracy gains are often based on specialized model designs using additional\n32-bit components. Furthermore, almost all previous BNNs use 32-bit for feature\nmaps and the shortcuts enclosing the corresponding binary convolution blocks,\nwhich helps to effectively maintain the accuracy, but is not friendly to\nhardware accelerators with limited memory, energy, and computing resources.\nThus, we raise the following question: How can accuracy and energy consumption\nbe balanced in a BNN network design? We extensively study this fundamental\nproblem in this work and propose a novel BNN architecture without most commonly\nused 32-bit components: \\textit{BoolNet}. Experimental results on ImageNet\ndemonstrate that BoolNet can achieve 4.6x energy reduction coupled with 1.2\\%\nhigher accuracy than the commonly used BNN architecture Bi-RealNet. Code and\ntrained models are available at: https://github.com/hpi-xnor/BoolNet.\n"}
+{"abstract": "  Many task-oriented dialogue systems use deep reinforcement learning (DRL) to\nlearn policies that respond to the user appropriately and complete the tasks\nsuccessfully. Training DRL agents with diverse dialogue trajectories prepare\nthem well for rare user requests and unseen situations. One effective\ndiversification method is to let the agent interact with a diverse set of\nlearned user models. However, trajectories created by these artificial user\nmodels may contain generation errors, which can quickly propagate into the\nagent's policy. It is thus important to control the quality of the\ndiversification and resist the noise. In this paper, we propose a novel\ndialogue diversification method for task-oriented dialogue systems trained in\nsimulators. Our method, Intermittent Short Extension Ensemble (I-SEE),\nconstrains the intensity to interact with an ensemble of diverse user models\nand effectively controls the quality of the diversification. Evaluations on the\nMultiwoz dataset show that I-SEE successfully boosts the performance of several\nstate-of-the-art DRL dialogue agents.\n"}
+{"abstract": "  When manipulating three-dimensional data, it is possible to ensure that\nrotational and translational symmetries are respected by applying so-called\nSE(3)-equivariant models. Protein structure prediction is a prominent example\nof a task which displays these symmetries. Recent work in this area has\nsuccessfully made use of an SE(3)-equivariant model, applying an iterative\nSE(3)-equivariant attention mechanism. Motivated by this application, we\nimplement an iterative version of the SE(3)-Transformer, an SE(3)-equivariant\nattention-based model for graph data. We address the additional complications\nwhich arise when applying the SE(3)-Transformer in an iterative fashion,\ncompare the iterative and single-pass versions on a toy problem, and consider\nwhy an iterative model may be beneficial in some problem settings. We make the\ncode for our implementation available to the community.\n"}
+{"abstract": "  In the framework of the Standard Model (SM) a theoretical description of the\nneutron beta decay is given at the level of 10^{-5}. The neutron lifetime and\ncorrelation coefficients of the neutron beta decay for a polarized neutron, a\npolarized electron and an unpolarized proton are calculated at the account for\ni) the radiative corrections of order O(\\alpha E_e/m_N) ~ 10^{-5} to Sirlin's\nouter and inner radiative corrections of order O(\\alpha/\\pi), ii) the\ncorrections of order O(E^2_e/m^2_N) ~ 10^{-5}, caused by weak magnetism and\nproton recoil, and iii) Wilkinson's corrections of order 10^{-5} (Wilkinson,\nNucl. Phys. A377, 474 (1982)). These corrections define the SM background of\nthe theoretical description of the neutron beta decay at the level of 10^{-5},\nwhich is required by experimental searches of interactions beyond the SM with\nexperimental uncertainties of a few parts of 10^{-5}.\n"}
+{"abstract": "  Face swapping has both positive applications such as entertainment,\nhuman-computer interaction, etc., and negative applications such as DeepFake\nthreats to politics, economics, etc. Nevertheless, it is necessary to\nunderstand the scheme of advanced methods for high-quality face swapping and\ngenerate enough and representative face swapping images to train DeepFake\ndetection algorithms. This paper proposes the first Megapixel level method for\none shot Face Swapping (or MegaFS for short). Firstly, MegaFS organizes face\nrepresentation hierarchically by the proposed Hierarchical Representation Face\nEncoder (HieRFE) in an extended latent space to maintain more facial details,\nrather than compressed representation in previous face swapping methods.\nSecondly, a carefully designed Face Transfer Module (FTM) is proposed to\ntransfer the identity from a source image to the target by a non-linear\ntrajectory without explicit feature disentanglement. Finally, the swapped faces\ncan be synthesized by StyleGAN2 with the benefits of its training stability and\npowerful generative capability. Each part of MegaFS can be trained separately\nso the requirement of our model for GPU memory can be satisfied for megapixel\nface swapping. In summary, complete face representation, stable training, and\nlimited memory usage are the three novel contributions to the success of our\nmethod. Extensive experiments demonstrate the superiority of MegaFS and the\nfirst megapixel level face swapping database is released for research on\nDeepFake detection and face image editing in the public domain. The dataset is\nat this link.\n"}
+{"abstract": "  In this paper, we study the problem of mobile user profiling, which is a\ncritical component for quantifying users' characteristics in the human mobility\nmodeling pipeline. Human mobility is a sequential decision-making process\ndependent on the users' dynamic interests. With accurate user profiles, the\npredictive model can perfectly reproduce users' mobility trajectories. In the\nreverse direction, once the predictive model can imitate users' mobility\npatterns, the learned user profiles are also optimal. Such intuition motivates\nus to propose an imitation-based mobile user profiling framework by exploiting\nreinforcement learning, in which the agent is trained to precisely imitate\nusers' mobility patterns for optimal user profiles. Specifically, the proposed\nframework includes two modules: (1) representation module, which produces state\ncombining user profiles and spatio-temporal context in real-time; (2) imitation\nmodule, where Deep Q-network (DQN) imitates the user behavior (action) based on\nthe state that is produced by the representation module. However, there are two\nchallenges in running the framework effectively. First, epsilon-greedy strategy\nin DQN makes use of the exploration-exploitation trade-off by randomly pick\nactions with the epsilon probability. Such randomness feeds back to the\nrepresentation module, causing the learned user profiles unstable. To solve the\nproblem, we propose an adversarial training strategy to guarantee the\nrobustness of the representation module. Second, the representation module\nupdates users' profiles in an incremental manner, requiring integrating the\ntemporal effects of user profiles. Inspired by Long-short Term Memory (LSTM),\nwe introduce a gated mechanism to incorporate new and old user characteristics\ninto the user profile.\n"}
+{"abstract": "  We study the polarization dynamics of ultrafast solitons in mode-locked fiber\nlasers. We find that when a stable soliton is generated, it's\nstate-of-polarization shifts toward a stable state and when the soliton is\ngenerated with excess power levels it experiences relaxation oscillations in\nits intensity and timing. On the other hand, when a soliton is generated in an\nunstable state-of-polarization, it either decays in intensity until it\ndisappears, or its temporal width decreases until it explodes into several\nsolitons and then it disappears. We also found that when two solitons are\nsimultaneously generated close to each other, they attract each other until\nthey collide and merge into a single soliton. Although, these two solitons are\ngenerated with different states-of-polarization, they shift their\nstate-of-polarization closer to each other until the polarization coincides\nwhen they collide. We support our findings by numerical calculations of a\nnon-Lagrangian approach by simulating the Ginzburg-Landau equation governing\nthe dynamics of solitons in a laser cavity. Our model also predicts the\nrelaxation oscillations of stable solitons and the two types of unstable\nsolitons observed in the experimental measurements.\n"}
+{"abstract": "  This paper presents Favalon, a functional programming language built on the\npremise of a lambda calculus for use as an interactive shell replacement.\nFavalon seamlessly integrates with typed versions of existing libraries and\ncommands using type inference, flexible runtime type metadata, and the same\ntechniques employed by shells to link commands together. Much of Favalon's\nsyntax is customizable via user-defined functions, allowing it to be extended\nby anyone who is familiar with a command-line shell. Furthermore, Favalon's\ntype inference engine can be separated from its runtime library and easily\nrepurposed for other applications.\n"}
+{"abstract": "  Recently, asymmetric plasmonic nanojunctions [Karnetzky et. al., Nature Comm.\n2471, 9 (2018)] have shown promise as on-chip electronic devices to convert\nfemtosecond optical pulses to current bursts, with a bandwidth of\nmulti-terahertz scale, although yet at low temperatures and pressures. Such\nnanoscale devices are of great interest for novel ultrafast electronics and\nopto-electronic applications. Here, we operate the device in air and at room\ntemperature, revealing the mechanisms of photoemission from plasmonic\nnanojunctions, and the fundamental limitations on the speed of\noptical-to-electronic conversion. Inter-cycle interference of coherent\nelectronic wavepackets results in a complex energy electron distribution and\nbirth of multiphoton effects. This energy structure, as well as reshaping of\nthe wavepackets during their propagation from one tip to the other, determine\nthe ultrafast dynamics of the current. We show that, up to some level of\napproximation, the electron flight time is well-determined by the mean\nponderomotive velocity in the driving field.\n"}
+{"abstract": "  The necessary and sufficient conditions are given for a sequence of complex\nnumbers to be the periodic (or antiperiodic) spectrum of non-self-adjoint Dirac\noperator.\n"}
+{"abstract": "  In this paper, we consider a type of image quality assessment as a\ntask-specific measurement, which can be used to select images that are more\namenable to a given target task, such as image classification or segmentation.\nWe propose to train simultaneously two neural networks for image selection and\na target task using reinforcement learning. A controller network learns an\nimage selection policy by maximising an accumulated reward based on the target\ntask performance on the controller-selected validation set, whilst the target\ntask predictor is optimised using the training set. The trained controller is\ntherefore able to reject those images that lead to poor accuracy in the target\ntask. In this work, we show that the controller-predicted image quality can be\nsignificantly different from the task-specific image quality labels that are\nmanually defined by humans. Furthermore, we demonstrate that it is possible to\nlearn effective image quality assessment without using a ``clean'' validation\nset, thereby avoiding the requirement for human labelling of images with\nrespect to their amenability for the task. Using $6712$, labelled and\nsegmented, clinical ultrasound images from $259$ patients, experimental results\non holdout data show that the proposed image quality assessment achieved a mean\nclassification accuracy of $0.94\\pm0.01$ and a mean segmentation Dice of\n$0.89\\pm0.02$, by discarding $5\\%$ and $15\\%$ of the acquired images,\nrespectively. The significantly improved performance was observed for both\ntested tasks, compared with the respective $0.90\\pm0.01$ and $0.82\\pm0.02$ from\nnetworks without considering task amenability. This enables image quality\nfeedback during real-time ultrasound acquisition among many other medical\nimaging applications.\n"}
+{"abstract": "  We show that a novel, general phase space mapping Hamiltonian for\nnonadiabatic systems, which is reminiscent of the renowned Meyer-Miller mapping\nHamiltonian, involves a commutator variable matrix rather than the conventional\nzero-point-energy parameter. In the exact mapping formulation on constraint\nspace for phase space approaches for nonadiabatic dynamics, the general mapping\nHamiltonian with commutator variables can be employed to generate approximate\ntrajectory-based dynamics. Various benchmark model tests, which range from gas\nphase to condensed phase systems, suggest that the overall performance of the\ngeneral mapping Hamiltonian is better than that of the conventional\nMeyer-Miller Hamiltonian.\n"}
+{"abstract": "  Many man-made objects are characterised by a shape that is symmetric along\none or more planar directions. Estimating the location and orientation of such\nsymmetry planes can aid many tasks such as estimating the overall orientation\nof an object of interest or performing shape completion, where a partial scan\nof an object is reflected across the estimated symmetry plane in order to\nobtain a more detailed shape. Many methods processing 3D data rely on expensive\n3D convolutions. In this paper we present an alternative novel encoding that\ninstead slices the data along the height dimension and passes it sequentially\nto a 2D convolutional recurrent regression scheme. The method also comprises a\ndifferentiable least squares step, allowing for end-to-end accurate and fast\nprocessing of both full and partial scans of symmetric objects. We use this\napproach to efficiently handle 3D inputs to design a method to estimate planar\nreflective symmetries. We show that our approach has an accuracy comparable to\nstate-of-the-art techniques on the task of planar reflective symmetry\nestimation on full synthetic objects. Additionally, we show that it can be\ndeployed on partial scans of objects in a real-world pipeline to improve the\noutputs of a 3D object detector.\n"}
+{"abstract": "  In this paper we present a novel mechanism for producing the observed Dark\nMatter(DM) relic abundance during the First Order Phase Transition (FOPT) in\nthe early universe. We show that the bubble expansion with ultra-relativistic\nvelocities can lead to the abundance of DM particles with masses much larger\nthan the scale of the transition. We study this non-thermal production\nmechanism in the context of a generic phase transition and the electroweak\nphase transition. The application of the mechanism to the Higgs portal DM as\nwell as the signal in the Stochastic Gravitational Background are discussed.\n"}
+{"abstract": "  Introduction. Can the infection due to the human immunodeficiency virus type\n1 induce a change in the differentiation status or process in T cells?.\nMethods. We will consider two stochastic Markov chain models, one which will\ndescribe the T-helper cell differentiation process, and another one describing\nthat process of infection of the T-helper cell by the virus; in these Markov\nchains, we will consider a set of states $\\{X_t \\}$ comprised of those proteins\ninvolved in each of the processes and their interactions (either\ndifferentiation or infection of the cell), such that we will obtain two\nstochastic transition matrices ($A,B$), one for each process; afterwards, the\ncomputation of their eigenvalues shall be performed, in which, should the\neigenvalue $\\lambda_i=1$ exist, the computation for the equilibrium\ndistribution $\\pi^n$ will be obtained for each of the matrices, which will\ninform us on the trends of interactions amongst the proteins in the long-term.\nResults. The stochastic processes considered possess an equilibrium\ndistribution, when reaching their equilibrium distribution, there exists an\nincrease in their informational entropy, and their log-rank distributions can\nbe modeled as discrete beta generalized distributions (DGBD). Discussion. The\nequilibrium distributions of both process can be regarded as states in which\nthe cell is well-differentiated, ergo there exists an induction of a novel\nHIV-dependent differentiated state in the T-cell; these processes due to their\nDGBD distribution can be considered complex processes; due to the increasing\nentropy, the equilibrium states are stable ones. Conclusion. The HIV virus can\npromote a novel differentiated state in the T-cell, which can give account for\nclinical features seen in patients; this model, notwithstanding does not give\naccount of YES/NO logical switches involved in the regulatory networks.\n"}
+{"abstract": "  We propose an optimal MMSE precoding technique using quantized signals with\nconstant envelope. Unlike the existing MMSE design that relies on 1-bit\nresolution, the proposed approach employs uniform phase quantization and the\nbounding step in the branch-and-bound method is different in terms of\nconsidering the most restrictive relaxation of the nonconvex problem, which is\nthen utilized for a suboptimal design also. Moreover, unlike prior studies, we\npropose three different soft detection methods and an iterative detection and\ndecoding scheme that allow the utilization of channel coding in conjunction\nwith low-resolution precoding. Besides an exact approach for computing the\nextrinsic information, we propose two approximations with reduced computational\ncomplexity. Numerical simulations show that utilizing the MMSE criterion\ninstead of the established maximum-minimum distance to the decision threshold\nyields a lower bit-error-rate in many scenarios. Furthermore, when using the\nMMSE criterion, a smaller number of bound evaluations in the branch-and-bound\nmethod is required for low and medium SNR. Finally, results based on an LDPC\nblock code indicate that the receive processing schemes yield a lower\nbit-error-rate compared to the conventional design.\n"}
+{"abstract": "  We consider the geodesic of the directed last passage percolation with iid\nexponential weights. We find the explicit one point distribution of the\ngeodesic location joint with the last passage times, and its limit when the\nsize goes to infinity.\n"}
+{"abstract": "  We consider the problem of minimizing age of information in general\nsingle-hop and multihop wireless networks. First, we formulate a way to convert\nAoI optimization problems into equivalent network stability problems. Then, we\npropose a heuristic low complexity approach for achieving stability that can\nhandle general network topologies; unicast, multicast and broadcast flows;\ninterference constraints; link reliabilities; and AoI cost functions. We\nprovide numerical results to show that our proposed algorithms behave as well\nas the best known scheduling and routing schemes available in the literature\nfor a wide variety of network settings.\n"}
+{"abstract": "  We develop a theory for the non-equilibrium screening of a charged impurity\nin a two-dimensional electron system under a strong time-periodic drive. Our\nanalysis of the time-averaged polarization function and dielectric function\nreveals that Floquet driving modifies the screened impurity potential in two\nmain regimes. In the weak drive regime, the time-averaged screened potential\nexhibits unconventional Friedel oscillations with multiple spatial periods\ncontributed by a principal period modulated by higher-order periods, which are\ndue to the emergence of additional Kohn anomalies in the polarization function.\nIn the strong drive regime, the time-averaged impurity potential becomes almost\nunscreened and does not exhibit Friedel oscillations. This tunable Friedel\noscillations is a result of the dynamic gating effect of the time-dependent\ndriving field on the two-dimensional electron system.\n"}
+{"abstract": "  In this paper, based on the idea of self-adjusting steepness based\nschemes[5], a two-dimensional calculation method of steepness parameter is\nproposed, and thus a two-dimensional self-adjusting steepness based limiter is\nconstructed. With the application of such limiter to the over-intersection\nbased remapping framework, a low dissipation remapping method has been proposed\nthat can be applied to the existing ALE method.\n"}
+{"abstract": "  We derive the Laws of Cosines and Sines in the super hyperbolic plane using\nMinkowski supergeometry and find the identical formulae to the classical case,\nbut remarkably involving different expressions for cosines and sines of angles\nwhich include substantial fermionic corrections. In further analogy to the\nclassical case, we apply these results to show that two parallel supergeodesics\nwhich are not ultraparallel admit a unique common orthogonal supergeodesic, and\nwe briefly describe aspects of elementary supernumber theory, leading to a\nprospective analogue of the Gauss product of quadratic forms.\n"}
+{"abstract": "  We present an analysis of the galaxy environment and physical properties of a\npartial Lyman limit system at z = 0.83718 with HI and metal line components\nclosely separated in redshift space ($|\\Delta v| \\approx 400$ km/s) towards the\nbackground quasar HE1003+0149. The HST/COS far-ultraviolet spectrum provides\ncoverage of lines of oxygen ions from OI to OV. Comparison of observed spectral\nlines with synthetic profiles generated from Bayesian ionization modeling\nreveals the presence of two distinct gas phases in the absorbing medium. The\nlow-ionization phase of the absorber has sub-solar metallicities (1/10-th\nsolar) with indications of [C/O] < 0 in each of the components. The OIV and OV\ntrace a more diffuse higher-ionization medium with predicted HI column\ndensities that are $\\approx 2$ dex lower. The quasar field observed with\nVLT/MUSE reveals three dwarf galaxies with stellar masses of $M^* \\sim 10^{8} -\n10^{9}$ M$_\\odot$, and with star formation rates of $\\approx 0.5 - 1$ M$_\\odot$\nyr$^{-1}$, at projected separations of $\\rho/R_{\\mathrm{vir}} \\approx 1.8 -\n3.0$ from the absorber. Over a wider field with projected proper separation of\n$\\leq 5$ Mpc and radial velocity offset of $|\\Delta v| \\leq 1000$ km/s from the\nabsorber, 21 more galaxies are identified in the $VLT$/VIMOS and Magellan deep\ngalaxy redshift surveys, with 8 of them within $1$ Mpc and $500$ km/s,\nconsistent with the line of sight penetrating a group of galaxies. The absorber\npresumably traces multiple phases of cool ($T \\sim 10^4$ K) photoionized\nintragroup medium. The inferred [C/O] < 0 hints at preferential enrichment from\ncore-collapse supernovae, with such gas displaced from one or more of the\nnearby galaxies, and confined to the group medium.\n"}
+{"abstract": "  Transition metal dichalcogenides (TMDs) combine interesting optical and\nspintronic properties in an atomically-thin material, where the light\npolarization can be used to control the spin and valley degrees-of-freedom for\nthe development of novel opto-spintronic devices. These promising properties\nemerge due to their large spin-orbit coupling in combination with their crystal\nsymmetries. Here, we provide simple symmetry arguments in a group-theory\napproach to unveil the symmetry-allowed spin scattering mechanisms, and\nindicate how one can use these concepts towards an external control of the spin\nlifetime. We perform this analysis for both monolayer (inversion asymmetric)\nand bilayer (inversion symmetric) crystals, indicating the different mechanisms\nthat play a role in these systems. We show that, in monolayer TMDs, electrons\nand holes transform fundamentally differently -- leading to distinct\nspin-scattering processes. We find that one of the electronic states in the\nconduction band is partially protected by time-reversal symmetry, indicating a\nlonger spin lifetime for that state. In bilayer and bulk TMDs, a hidden\nspin-polarization can exist within each layer despite the presence of global\ninversion symmetry. We show that this feature enables control of the interlayer\nspin-flipping scattering processes via an out-of-plane electric field,\nproviding a mechanism for electrical control of the spin lifetime.\n"}
+{"abstract": "  We study the dynamics of a one-dimensional Rydberg lattice gas under\nfacilitation (anti-blockade) conditions which implements a so-called\nkinetically constrained spin system. Here an atom can only be excited to a\nRydberg state when one of its neighbors is already excited. Once two or more\natoms are simultaneously excited mechanical forces emerge, which couple the\ninternal electronic dynamics of this many-body system to external vibrational\ndegrees of freedom in the lattice. This electron-phonon coupling results in a\nso-called phonon dressing of many-body states which in turn impacts on the\nfacilitation dynamics. In our theoretical study we focus on a scenario in which\nall energy scales are sufficiently separated such that a perturbative treatment\nof the coupling between electronic and vibrational states is possible. This\nallows to analytically derive an effective Hamiltonian for the evolution of\nconsecutive clusters of Rydberg excitations in the presence of phonon dressing.\nWe analyze the spectrum of this Hamiltonian and show -- by employing Fano\nresonance theory -- that the interaction between Rydberg excitations and\nlattice vibrations leads to the emergence of slowly decaying bound states that\ninhibit fast relaxation of certain initial states.\n"}
+{"abstract": "  Drafting as a process to reduce drag and to benefit from the presence of\nother competitors is applied in various sports with several recent examples of\ncompetitive running in formations. In this study, the aerodynamics of a\nrealistic model of a female runner is calculated by computational fluid\ndynamics (CFD) simulations at four running speeds of 15 km/h, 18 km/h, 21 km/h,\nand 36 km/h. Aerodynamic power fractions of the total energy expenditure are\nfound to be in the range of 2.6-8.5%. Additionally, four exemplary formations\nare analysed with respect to their drafting potential and resulting drag values\nare compared for the main runner and her pacers. The best of the formations\nachieves a total drag reduction on the main runner of 75.6%. Moreover, there\nare large variations in the drag reduction between the considered formations of\nup to 42% with respect to the baseline single-runner case. We conclude that\nmajor drag reduction of more than 70% can already be achieved with fairly\nsimple formations, while certain factors, such as runners on the sides, can\nhave a detrimental effect on drag reduction due to local acceleration of the\npassing flow. Using an empirical model for mechanical power output during\nrunning, gains of metabolic power and performance predictions are evaluated for\nall considered formations. Improvements in running economy are up to 3.5% for\nthe best formation, leading to velocity gains of 2.3%. This translates to 154 s\n(~2.6 min) saved over a marathon distance. Consequently, direct conclusions are\ndrawn from the obtained data for ideal drafting of long-distance running in\nhighly packed formations.\n"}
+{"abstract": "  Turbulence in the upper ocean in the submesoscale range (scales smaller than\nthe deformation radius) plays an important role for the heat exchange with the\natmosphere and for oceanic biogeochemistry. Its dynamics should strongly depend\non the seasonal cycle and the associated mixed-layer instabilities. The latter\nare particularly relevant in winter and are responsible for the formation of\nenergetic small scales that extend over the whole depth of the mixed layer. The\nknowledge of the transport properties of oceanic flows at depth, which is\nessential to understand the coupling between surface and interior dynamics,\nhowever, is still limited. By means of numerical simulations, we explore the\nLagrangian dispersion properties of turbulent flows in a quasi-geostrophic\nmodel system allowing for both thermocline and mixed-layer instabilities. The\nresults indicate that, when mixed-layer instabilities are present, the\ndispersion regime is local from the surface down to depths comparable with that\nof the interface with the thermocline, while in their absence dispersion\nquickly becomes nonlocal versus depth. We then identify the origin of such\nbehavior in the existence of fine-scale energetic structures due to mixed-layer\ninstabilities. We further discuss the effect of vertical shear on the\nLagrangian particle spreading and address the correlation between the\ndispersion properties at the surface and at depth, which is relevant to assess\nthe possibility of inferring the dynamical features of deeper flows from the\nmore accessible surface ones.\n"}
+{"abstract": "  Electrochemically mediated selective adsorption is an emerging\nelectrosorption technique that utilizes Faradaically enhanced redox active\nelectrodes, which can adsorb ions not only electrostatically, but also\nelectrochemically. The superb selectivity (>100) of this technique enables\nselective removal of toxic or high-value target ions under low energy\nconsumption. Here, we develop a general theoretical framework to describe the\ncompetitive electrosorption phenomena involving multiple ions and surface-bound\nredox species. The model couples diffusion, convection and electromigration\nwith competitive surface adsorption reaction kinetics, consistently derived\nfrom non-equilibrium thermodynamics. To optimize the selective removal of the\ntarget ions, design criteria were derived analytically from physically relevant\ndimensionless groups and time scales, where the propagation of the target\nanions concentration front is the limiting step. Detailed computational studies\nare reported for three case studies that cover a wide range of inlet\nconcentration ratios between the competing ions. And in all three cases, target\nanions in the electrosorption cell forms a self-sharpening reaction-diffusion\nwave front. Based on the model, a three-step stop-flow operation scheme with a\npure stripping solution of target anions is proposed that optimizes the ion\nadsorption performance and increases the purity of the regeneration stream to\nalmost 100%, which is beneficial for downstream processing.\n"}
+{"abstract": "  Quality of website design is one of the influential factors of website\nsuccess. How the design helps the users using effectively and efficiently\nwebsite and satisfied at the end of the use. However, it is a common tendency\nthat websites are designed based on the developer's perspectives and lack\nconsidering user importance. Thus, the degree of website usability tends to be\nlow according to user perceptions. This study purposed to understand the user\nexperiences using an institutional repository (IR) website in a public\nuniversity in Indonesia. The research was performed based on usability testing\nframework as the usability testing method. About 12 participants were purposely\ninvolved concerning their key informant characteristics. Following three\nempirical data collection techniques (i.e., query technique, formal experiment,\nand thinking aloud), both descriptive analysis using usability scale matric and\ncontent analysis using qualitative data analysis (QDA) Miner Lite software were\nused in the data analysis stage. Lastly, several visual design recommendations\nwere then proposed at the end of the study. In terms of a case study, besides\nthe practical recommendations which may contextually useful for the next\nwebsite development; the clarity of the research design may also help scholars\nhow to combine more than one usability testing technique within a\nmulti-technique study design.\n"}
+{"abstract": "  The SIMT execution model is commonly used for general GPU development. CUDA\nand OpenCL developers write scalar code that is implicitly parallelized by\ncompiler and hardware. On Intel GPUs, however, this abstraction has profound\nperformance implications as the underlying ISA is SIMD and important hardware\ncapabilities cannot be fully utilized. To close this performance gap we\nintroduce C-For-Metal (CM), an explicit SIMD programming framework designed to\ndeliver close-to-the-metal performance on Intel GPUs. The CM programming\nlanguage and its vector/matrix types provide an intuitive interface to exploit\nthe underlying hardware features, allowing fine-grained register management,\nSIMD size control and cross-lane data sharing. Experimental results show that\nCM applications from different domains outperform the best-known SIMT-based\nOpenCL implementations, achieving up to 2.7x speedup on the latest Intel GPU.\n"}
+{"abstract": "  We systematically investigate axisymmetric extremal isolated horizons (EIHs)\ndefined by vanishing surface gravity, corresponding to zero temperature. In the\nfirst part, using the Newman-Penrose and GHP formalism we derive the most\ngeneral metric function for such EIHs in the Einstein-Maxwell theory, which\ncomplements the previous result of Lewandowski and Pawlowski. We prove that it\ndepends on 5 independent parameters, namely deficit angles on the north and\nsouth poles of a spherical-like section of the horizon, its radius (area), and\ntotal electric and magnetic charges of the black hole. The deficit angles and\nboth charges can be separately set to zero. In the second part of our paper, we\nidentify this general axially symmetric solution for EIH with extremal horizons\nin exact electrovacuum Plebanski-Demianski spacetimes, using the convenient\nparametrization of this family by Griffiths and Podolsky. They represent all\n(double aligned) black holes of algebraic type D without a cosmological\nconstant. Apart from a conicity, they depend on 6 physical parameters (mass,\nKerr-like rotation, NUT parameter, acceleration, electric and magnetic charges)\nconstrained by the extremality condition. We were able to determine their\nrelation to the EIH geometrical parameters. This explicit identification of\ntype D extremal black holes with a unique form of EIH includes several\ninteresting subclasses, such as accelerating extremely charged\nReissner-Nordstrom black hole (C-metric), extremal accelerating Kerr-Newman,\naccelerating Kerr-NUT, or non-accelerating Kerr-Newman-NUT black holes.\n"}
+{"abstract": "  Multimode nonlinear optics offers to overcome a long-standing limitation of\nfiber optics, tightly phase locking several spatial modes and enabling the\ncoherent transport of a wavepacket through a multimode fiber. A similar problem\nis encountered in the temporal compression of multi-mJ pulses to few-cycle\nduration in hollow gas-filled fibers. Scaling the fiber length to up to six\nmeters, hollow fibers have recently reached 1 TW of peak power. Despite the\nremarkable utility of the hollow fiber compressor and its widespread\napplication, however, no analytical model exists to enable insight into the\nscaling behavior of maximum compressibility and peak power. Here we extend a\nrecently introduced formalism for describing mode-locking to the spatially\nanalogue scenario of locking spatial fiber modes together. Our formalism\nunveils the coexistence of two soliton branches for anomalous modal dispersion\nand indicates the formation of stable spatio-temporal light bullets that would\nbe unstable in free space, similar to the temporal cage solitons in\nmode-locking theory. Our model enables deeper understanding of the physical\nprocesses behind the formation of such light bullets and predict the existence\nof multimode solitons in a much wider range of fiber types than previously\nconsidered possible.\n"}
+{"abstract": "  Let $f : X \\to S$ be a family of smooth projective algebraic varieties over a\nsmooth connected base $S$, with everything defined over\n$\\overline{\\mathbb{Q}}$. Denote by $\\mathbb{V} = R^{2i} f_{*} \\mathbb{Z}(i)$\nthe associated integral variation of Hodge structure on the degree $2i$\ncohomology. We consider the following question: when can a fibre\n$\\mathbb{V}_{s}$ above an algebraic point $s \\in S(\\overline{\\mathbb{Q}})$ be\nisomorphic to a transcendental fibre $\\mathbb{V}_{s'}$ with $s' \\in\nS(\\mathbb{C}) \\setminus S(\\overline{\\mathbb{Q}})$? When $\\mathbb{V}$ induces a\nquasi-finite period map $\\varphi : S \\to \\Gamma \\backslash D$, conjectures in\nHodge theory predict that such isomorphisms cannot exist. We introduce new\ndifferential-algebraic techniques to show this is true for all points $s \\in\nS(\\overline{\\mathbb{Q}})$ outside of an explicit proper closed algebraic subset\nof $S$. As a corollary we establish the existence of a canonical\n$\\overline{\\mathbb{Q}}$-algebraic model for normalizations of period images.\n"}
+{"abstract": "  We study co-dimension two monodromy defects in theories of conformally\ncoupled scalars and free Dirac fermions in arbitrary $d$ dimensions. We\ncharacterise this family of conformal defects by computing the one-point\nfunctions of the stress-tensor and conserved current for Abelian flavour\nsymmetries as well as two-point functions of the displacement operator. In the\ncase of $d=4$, the normalisation of these correlation functions are related to\ndefect Weyl anomaly coefficients, and thus provide crucial information about\nthe defect conformal field theory. We provide explicit checks on the values of\nthe defect central charges by calculating the universal part of the defect\ncontribution to entanglement entropy, and further, we use our results to\nextract the universal part of the vacuum R\\'enyi entropy. Moreover, we leverage\nthe non-supersymmetric free field results to compute a novel defect Weyl\nanomaly coefficient in a $d=4$ theory of free $\\mathcal{N}=2$ hypermultiplets.\nIncluding singular modes in the defect operator product expansion of\nfundamental fields, we identify notable relevant deformations in the singular\ndefect theories and show that they trigger a renormalisation group flow towards\nan IR fixed point with the most regular defect OPE. We also study Gukov-Witten\ndefects in free $d=4$ Maxwell theory and show that their central charges\nvanish.\n"}
+{"abstract": "  Loneliness (i.e., the distressing feeling that often accompanies the\nsubjective sense of social disconnection) is detrimental to mental and physical\nhealth, and deficits in self-reported feelings of being understood by others is\na risk factor for loneliness. What contributes to these deficits in lonely\npeople? We used functional magnetic resonance imaging (fMRI) to unobtrusively\nmeasure the relative alignment of various aspects of people's mental processing\nof naturalistic stimuli (specifically, videos) as they unfold over time. We\nthereby tested whether lonely people actually process the world in\nidiosyncratic ways, rather than only exaggerating or misperceiving how\ndissimilar others' views are to their own (which could lead them to feel\nmisunderstood, even if they actually see the world similarly to those around\nthem). We found evidence for such idiosyncrasy: lonely individuals' neural\nresponses during free viewing of the videos were dissimilar to peers in their\ncommunities, particularly in brain regions (e.g., regions of the default-mode\nnetwork) in which similar responses have been associated with shared\npsychological perspectives and subjective understanding. Our findings were\nrobust even after controlling for demographic similarities, participants'\noverall levels of objective social isolation, and their friendships with each\nother. These results suggest that being surrounded predominantly by people who\nsee the world differently from oneself may be a risk factor for loneliness,\neven if one is friends with them.\n"}
+{"abstract": "  The Traditional Approximation of Rotation (TAR) is a treatment of the\nhydrodynamic equations of rotating and stably stratified fluids in which the\naction of the Coriolis acceleration along the direction of the entropy and\nchemical stratifications is neglected because it is weak in comparison with the\nbuoyancy force. The dependent variables in the equations for the dynamics of\ngravito-inertial waves (GIWs) then become separable into radial and horizontal\nparts as in the non-rotating case. The TAR is built on the assumptions that the\nstar is spherical (i.e. its centrifugal deformation is neglected) and uniformly\nrotating. We study the feasibility of carrying out a generalisation of the TAR\nto account for the centrifugal acceleration in the case of strongly deformed\nuniformly and rapidly rotating stars (and planets), and to identify the\nvalidity domain of this approximation. We built analytically a complete\nformalism that allows the study of the dynamics of GIWs in spheroidal\ncoordinates which take into account the flattening of rapidly rotating stars by\nassuming the hierarchies of frequencies adopted within the TAR in the spherical\ncase and by deriving a generalised Laplace tidal equation for the horizontal\neigenfunctions of the GIWs and their asymptotic wave periods, which can be used\nto probe the structure and dynamics of rotating deformed stars with\nasteroseismology. Using 2D ESTER stellar models, we determine the validity\ndomain of the generalised TAR as a function of the rotation rate of the star\nnormalised by its critical angular velocity and its pseudo-radius. This\ngeneralisation allows us to study the signature of the centrifugal effects on\nGIWs in rapidly rotating deformed stars. We found that the effects of the\ncentrifugal acceleration in rapidly rotating early-type stars on GIWs are\ntheoretically detectable in modern space photometry using observations from\nKepler.\n"}
+{"abstract": "  Artificial intelligence is applied in a range of sectors, and is relied upon\nfor decisions requiring a high level of trust. For regression methods, trust is\nincreased if they approximate the true input-output relationships and perform\naccurately outside the bounds of the training data. But often performance\noff-test-set is poor, especially when data is sparse. This is because the\nconditional average, which in many scenarios is a good approximation of the\n`ground truth', is only modelled with conventional Minkowski-r error measures\nwhen the data set adheres to restrictive assumptions, with many real data sets\nviolating these. To combat this there are several methods that use prior\nknowledge to approximate the `ground truth'. However, prior knowledge is not\nalways available, and this paper investigates how error measures affect the\nability for a regression method to model the `ground truth' in these scenarios.\nCurrent error measures are shown to create an unhelpful bias and a new error\nmeasure is derived which does not exhibit this behaviour. This is tested on 36\nrepresentative data sets with different characteristics, showing that it is\nmore consistent in determining the `ground truth' and in giving improved\npredictions in regions beyond the range of the training data.\n"}
+{"abstract": "  This paper addresses the task of (complex) conversational question answering\nover a knowledge graph. For this task, we propose LASAGNE (muLti-task semAntic\nparSing with trAnsformer and Graph atteNtion nEtworks). It is the first\napproach, which employs a transformer architecture extended with Graph\nAttention Networks for multi-task neural semantic parsing. LASAGNE uses a\ntransformer model for generating the base logical forms, while the Graph\nAttention model is used to exploit correlations between (entity) types and\npredicates to produce node representations. LASAGNE also includes a novel\nentity recognition module which detects, links, and ranks all relevant entities\nin the question context. We evaluate LASAGNE on a standard dataset for complex\nsequential question answering, on which it outperforms existing baseline\naverages on all question types. Specifically, we show that LASAGNE improves the\nF1-score on eight out of ten question types; in some cases, the increase in\nF1-score is more than 20% compared to the state of the art.\n"}
+{"abstract": "  Calculation of conductivity in the Hubbard model is a challenging task.\nRecent years have seen much progress in this respect and numerically exact\nsolutions are now possible in certain regimes. In this paper we discuss the\ncalculation of conductivity for the square lattice Hubbard model in the\npresence of a perpendicular magnetic field, focusing on orbital effects. We\npresent the relevant formalism in all detail and in full generality, and then\ndiscuss the simplifications that arise at the level of the dynamical mean field\ntheory (DMFT). We prove that the Kubo bubble preserves gauge and translational\ninvariance, and that in the DMFT the vertex corrections cancel regardless of\nthe magnetic field. We present the DMFT results for the spectral function and\nboth the longitudinal and Hall conductivity in several regimes of parameters.\nWe analyze thoroughly the quantum oscillations of the longitudinal conductivity\nand identify a high-frequency oscillation component, arising as a combined\neffect of scattering and temperature, in line with recent experimental\nobservations in moir\\'e systems.\n"}
+{"abstract": "  Millions of people use platforms such as YouTube, Facebook, Twitter, and\nother mass media. Due to the accessibility of these platforms, they are often\nused to establish a narrative, conduct propaganda, and disseminate\nmisinformation. This work proposes an approach that uses state-of-the-art NLP\ntechniques to extract features from video captions (subtitles). To evaluate our\napproach, we utilize a publicly accessible and labeled dataset for classifying\nvideos as misinformation or not. The motivation behind exploring video captions\nstems from our analysis of videos metadata. Attributes such as the number of\nviews, likes, dislikes, and comments are ineffective as videos are hard to\ndifferentiate using this information. Using caption dataset, the proposed\nmodels can classify videos among three classes (Misinformation, Debunking\nMisinformation, and Neutral) with 0.85 to 0.90 F1-score. To emphasize the\nrelevance of the misinformation class, we re-formulate our classification\nproblem as a two-class classification - Misinformation vs. others (Debunking\nMisinformation and Neutral). In our experiments, the proposed models can\nclassify videos with 0.92 to 0.95 F1-score and 0.78 to 0.90 AUC ROC.\n"}
+{"abstract": "  The 2D TI edge states are considered within the Volkov-Pankratov (VP)\nHamiltonian. A smooth transition between TI and OI is assumed. The edge states\nare formed in the total gap of homogeneous 2D material. A pair of these states\nare of linear dispersion, others have gapped Dirac spectra. The optical\nselection rules are found. The optical transitions between the neighboring edge\nstates appear in the global 2D gap for the in-plane light electric field\ndirected across the edge.\n  The electrons in linear edge states have no backscattering, that is\nindicative of the fact of topological protection. However, when linear edge\nstates get to the energy domain of Dirac edge states, the backscattering\nbecomes permitted. The elastic backscattering rate is found. The Drude-like\nconductivity is found when the Fermi level gets into the energy domain of the\ncoexistence of linear and Dirac edge states. The localization edge conductance\nof a finite sample at zero temperature is determined.\n"}
+{"abstract": "  This paper compares the advantages, limitations, and computational\nconsiderations of using Finite-Time Lyapunov Exponents (FTLEs) and Lagrangian\nDescriptors (LDs) as tools for identifying barriers and mechanisms of fluid\ntransport in two-dimensional time-periodic flows. These barriers and mechanisms\nof transport are often referred to as \"Lagrangian Coherent Structures,\" though\nthis term often changes meaning depending on the author or context. This paper\nwill specifically focus on using FTLEs and LDs to identify stable and unstable\nmanifolds of hyperbolic stagnation points, and the Kolmogorov-Arnold-Moser\n(KAM) tori associated with elliptic stagnation points. The background and\ntheory behind both methods and their associated phase space structures will be\npresented, and then examples of FTLEs and LDs will be shown based on a simple,\nperiodic, time-dependent double-gyre toy model with varying parameters.\n"}
+{"abstract": "  Railway systems provide pivotal support to modern societies, making their\nefficiency and robustness important to ensure. However, these systems are\nsusceptible to disruptions and delays, leading to accumulating economic damage.\nThe large spatial scale of delay spreading typically make it difficult to\ndistinguish which regions will ultimately affected from an initial disruption,\ncreating uncertainty for risk assessment. In this paper, we identify\ngeographical structures that reflect how delay spreads through railway\nnetworks. We do so by proposing a graph-based, hybrid schedule and\nempirical-based model for delay propagation and apply spectral clustering. We\napply the model to four European railway systems: the Netherlands, Germany,\nSwitzerland and Italy. We characterize geographical structures in the railway\nsystems of these countries and interpret these regions in terms of delay\nseverity and how dynamically disconnected they are from the rest. The method\nalso allows us to point out important differences between these countries'\nrailway systems. For practitioners, this geographical characterization of\nrailways provide natural boundaries for local decision-making structures and a\nfirst-order prioritization on which regions are at risk, given an initial\ndisruption.\n"}
+{"abstract": "  Brain parcellations play a ubiquitous role in the analysis of magnetic\nresonance imaging (MRI) datasets. Over 100 years of research has been conducted\nin pursuit of an ideal brain parcellation. Different methods have been\ndeveloped and studied for constructing brain parcellations using different\nimaging modalities. More recently, several data-driven parcellation methods\nhave been adopted from data mining, machine learning, and statistics\ncommunities. With contributions from different scientific fields, there is a\nrich body of literature that needs to be examined to appreciate the breadth of\nexisting research and the gaps that need to be investigated. In this work, we\nreview the large body of in vivo brain parcellation research spanning different\nneuroimaging modalities and methods. A key contribution of this work is a\nsemantic organization of this large body of work into different taxonomies,\nmaking it easy to understand the breadth and depth of the brain parcellation\nliterature. Specifically, we categorized the existing parcellations into three\ngroups: Anatomical parcellations, functional parcellations, and structural\nparcellations which are constructed using T1-weighted MRI, functional MRI\n(fMRI), and diffusion-weighted imaging (DWI) datasets, respectively. We provide\na multi-level taxonomy of different methods studied in each of these\ncategories, compare their relative strengths and weaknesses, and highlight the\nchallenges currently faced for the development of brain parcellations.\n"}
+{"abstract": "  Effective molecular representation learning is of great importance to\nfacilitate molecular property prediction, which is a fundamental task for the\ndrug and material industry. Recent advances in graph neural networks (GNNs)\nhave shown great promise in applying GNNs for molecular representation\nlearning. Moreover, a few recent studies have also demonstrated successful\napplications of self-supervised learning methods to pre-train the GNNs to\novercome the problem of insufficient labeled molecules. However, existing GNNs\nand pre-training strategies usually treat molecules as topological graph data\nwithout fully utilizing the molecular geometry information. Whereas, the\nthree-dimensional (3D) spatial structure of a molecule, a.k.a molecular\ngeometry, is one of the most critical factors for determining molecular\nphysical, chemical, and biological properties. To this end, we propose a novel\nGeometry Enhanced Molecular representation learning method (GEM) for Chemical\nRepresentation Learning (ChemRL). At first, we design a geometry-based GNN\narchitecture that simultaneously models atoms, bonds, and bond angles in a\nmolecule. To be specific, we devised double graphs for a molecule: The first\none encodes the atom-bond relations; The second one encodes bond-angle\nrelations. Moreover, on top of the devised GNN architecture, we propose several\nnovel geometry-level self-supervised learning strategies to learn spatial\nknowledge by utilizing the local and global molecular 3D structures. We compare\nChemRL-GEM with various state-of-the-art (SOTA) baselines on different\nmolecular benchmarks and exhibit that ChemRL-GEM can significantly outperform\nall baselines in both regression and classification tasks. For example, the\nexperimental results show an overall improvement of 8.8% on average compared to\nSOTA baselines on the regression tasks, demonstrating the superiority of the\nproposed method.\n"}
+{"abstract": "  We investigate program equivalence for linear higher-order(sequential)\nlanguages endowed with primitives for computational effects. More specifically,\nwe study operationally-based notions of program equivalence for a linear\n$\\lambda$-calculus with explicit copying and algebraic effects \\emph{\\`a la}\nPlotkin and Power. Such a calculus makes explicit the interaction between\ncopying and linearity, which are intensional aspects of computation, with\neffects, which are, instead, \\emph{extensional}. We review some of the notions\nof equivalences for linear calculi proposed in the literature and show their\nlimitations when applied to effectful calculi where copying is a first-class\ncitizen. We then introduce resource transition systems, namely transition\nsystems whose states are built over tuples of programs representing the\navailable resources, as an operational semantics accounting for both\nintensional and extensional interactive behaviors of programs. Our main result\nis a sound and complete characterization of contextual equivalence as trace\nequivalence defined on top of resource transition systems.\n"}
+{"abstract": "  One of the exciting recent developments in decentralized finance (DeFi) has\nbeen the development of decentralized cryptocurrency exchanges that can\nautonomously handle conversion between different cryptocurrencies.\nDecentralized exchange protocols such as Uniswap, Curve and other types of\nAutomated Market Makers (AMMs) maintain a liquidity pool (LP) of two or more\nassets constrained to maintain at all times a mathematical relation to each\nother, defined by a given function or curve. Examples of such functions are the\nconstant-sum and constant-product AMMs. Existing systems however suffer from\nseveral challenges. They require external arbitrageurs to restore the price of\ntokens in the pool to match the market price. Such activities can potentially\ndrain resources from the liquidity pool. In particular, dramatic market price\nchanges can result in low liquidity with respect to one or more of the assets\nand reduce the total value of the LP. We propose in this work a new approach to\nconstructing the AMM by proposing the idea of dynamic curves. It utilizes input\nfrom a market price oracle to modify the mathematical relationship between the\nassets so that the pool price continuously and automatically adjusts to be\nidentical to the market price. This approach eliminates arbitrage opportunities\nand, as we show through simulations, maintains liquidity in the LP for all\nassets and the total value of the LP over a wide range of market prices.\n"}
+{"abstract": "  We develop a model of interacting zwitterionic membranes with rotating\nsurface dipoles immersed in a monovalent salt, and implement it in a field\ntheoretic formalism. In the mean-field regime of monovalent salt, the\nelectrostatic forces between the membranes are characterized by a non-uniform\ntrend: at large membrane separations, the interfacial dipoles on the opposing\nsides behave as like-charge cations and give rise to repulsive membrane\ninteractions; at short membrane separations, the anionic field induced by the\ndipolar phosphate groups sets the behavior in the intermembrane region. The\nattraction of the cationic nitrogens in the dipolar lipid headgroups leads to\nthe adhesion of the membrane surfaces via dipolar bridging. The underlying\ncompetition between the opposing field components of the individual dipolar\ncharges leads to the non-uniform salt ion affinity of the zwitterionic membrane\nwith respect to the separation distance; large inter-membrane separations imply\nanionic excess while small, nanometer size separations, favor cationic excess.\nThis complex ionic selectivity of zwitterionic membranes may have relevant\nrepercussions on nanofiltration and nanofluidic transport techniques.\n"}
+{"abstract": "  Recently the leading order of the correlation energy of a Fermi gas in a\ncoupled mean-field and semiclassical scaling regime has been derived, under the\nassumption of an interaction potential with a small norm and with compact\nsupport in Fourier space. We generalize this result to large interaction\npotentials, requiring only $|\\cdot| \\hat{V} \\in \\ell^1 (\\mathbb{Z}^3)$. Our\nproof is based on approximate, collective bosonization in three dimensions.\nSignificant improvements compared to recent work include stronger bounds on\nnon-bosonizable terms and more efficient control on the bosonization of the\nkinetic energy.\n"}
+{"abstract": "  The localization spread gives a criterion to decide between metallic versus\ninsulating behaviour of a material. It is defined as the second moment cumulant\nof the many-body position operator, divided by the number of electrons.\nDifferent operators are used for systems treated with Open or Periodic Boundary\nConditions. In particular, in the case of periodic systems, we use the\ncomplex-position definition, that was already used in similar contexts for the\ntreatment of both classical and quantum situations. In this study, we show that\nthe localization spread evaluated on a finite ring system of radius $R$ with\nOpen Boundary Conditions leads, in the large $R$ limit, to the same formula\nderived by Resta et al. for 1D systems with periodic Born-von K\\'arm\\'an\nboundary conditions. A second formula, alternative to the Resta's one, is also\ngiven, based on the sum-over-state formalism, allowing for an interesting\ngeneralization to polarizability and other similar quantities.\n"}
+{"abstract": "  In the recent years, there has been a shift in facial behavior analysis from\nthe laboratory-controlled conditions to the challenging in-the-wild conditions\ndue to the superior performance of deep learning based approaches for many real\nworld applications.However, the performance of deep learning approaches relies\non the amount of training data. One of the major problems with data acquisition\nis the requirement of annotations for large amount of training data. Labeling\nprocess of huge training data demands lot of human support with strong domain\nexpertise for facial expressions or action units, which is difficult to obtain\nin real-time environments.Moreover, labeling process is highly vulnerable to\nambiguity of expressions or action units, especially for intensities due to the\nbias induced by the domain experts. Therefore, there is an imperative need to\naddress the problem of facial behavior analysis with weak annotations. In this\npaper, we provide a comprehensive review of weakly supervised learning (WSL)\napproaches for facial behavior analysis with both categorical as well as\ndimensional labels along with the challenges and potential research directions\nassociated with it. First, we introduce various types of weak annotations in\nthe context of facial behavior analysis and the corresponding challenges\nassociated with it. We then systematically review the existing state-of-the-art\napproaches and provide a taxonomy of these approaches along with their insights\nand limitations. In addition, widely used data-sets in the reviewed literature\nand the performance of these approaches along with evaluation principles are\nsummarized. Finally, we discuss the remaining challenges and opportunities\nalong with the potential research directions in order to apply facial behavior\nanalysis with weak labels in real life situations.\n"}
+{"abstract": "  We show that the action of the mapping class group on the space of closed\ncurves of a closed surface effectively tracks the corresponding action on\nTeichm\\\"uller space in the following sense: for all but quantitatively few\nmapping classes, the information of how a mapping class moves a given point of\nTeichm\\\"uller space determines, up to a power saving error term, how it changes\nthe geometric intersection numbers of a given closed curve with respect to\narbitrary geodesic currents. Applications include an effective estimate\ndescribing the speed of convergence of Teichm\\\"uller geodesic rays to the\nboundary at infinity of Teichm\\\"uller space, an effective estimate comparing\nthe Teichm\\\"uller and Thurston metrics along mapping class group orbits of\nTeichm\\\"uller space, and, in the sequel, effective estimates for countings of\nfilling closed geodesics on closed, negatively curved surfaces.\n"}
+{"abstract": "  We discuss a model based on dark sector described by non-Abelian $SU(2)_D$\ngauge symmetry where we introduce $SU(2)_L \\times SU(2)_D$ bi-doublet\nvector-like leptons to generate active neutrino masses and kinetic mixing\nbetween $SU(2)_D$ and $U(1)_Y$ gauge fields at one-loop level. After\nspontaneous symmetry breaking of $SU(2)_D$, we have remnant $Z_4$ symmetry\nguaranteeing stability of dark matter candidates. We formulate neutrino mass\nmatrix and related lepton flavor violating processes and discus dark matter\nphysics estimating relic density. It is found that our model realize\nmulticomponent dark matter scenario due to the $Z_4$ symmetry and relic density\ncan be explained by gauge interactions with kinetic mixing effect.\n"}
+{"abstract": "  In this article we continue with the research initiated in our previous work\non singular Liouville equations with quantized singularity. The main goal of\nthis article is to prove that as long as the bubbling solutions violate the\nspherical Harnack inequality near a singular source, the first derivatives of\ncoefficient functions must tend to zero.\n"}
+{"abstract": "  We compute explicit solutions $\\Lambda^\\pm_m$ of the Painleve VI (PVI)\ndifferential equation from equivariant instanton bundles $E_m$ corresponding to\nYang-Mills instantons with \"quadrupole symmetry.\" This is based on a\ngeneralization of Hitchin's logarithmic connection to vector bundles with an\n$SL_2({\\mathbb C})$ action. We then identify explicit Okamoto transformation\nwhich play the role of \"creation operators\" for construction $\\Lambda^\\pm_m$\nfrom the \"ground state\" $\\Lambda^\\pm_0$, suggesting that the equivariant\ninstanton bundles $E_m$ might similarly be related to the trivial \"ground\nstate\" $E_0$.\n"}
+{"abstract": "  Machine unlearning has great significance in guaranteeing model security and\nprotecting user privacy. Additionally, many legal provisions clearly stipulate\nthat users have the right to demand model providers to delete their own data\nfrom training set, that is, the right to be forgotten. The naive way of\nunlearning data is to retrain the model without it from scratch, which becomes\nextremely time and resource consuming at the modern scale of deep neural\nnetworks. Other unlearning approaches by refactoring model or training data\nstruggle to gain a balance between overhead and model usability.\n  In this paper, we propose an approach, dubbed as DeepObliviate, to implement\nmachine unlearning efficiently, without modifying the normal training mode. Our\napproach improves the original training process by storing intermediate models\non the hard disk. Given a data point to unlearn, we first quantify its temporal\nresidual memory left in stored models. The influenced models will be retrained\nand we decide when to terminate the retraining based on the trend of residual\nmemory on-the-fly. Last, we stitch an unlearned model by combining the\nretrained models and uninfluenced models. We extensively evaluate our approach\non five datasets and deep learning models. Compared to the method of retraining\nfrom scratch, our approach can achieve 99.0%, 95.0%, 91.9%, 96.7%, 74.1%\naccuracy rates and 66.7$\\times$, 75.0$\\times$, 33.3$\\times$, 29.4$\\times$,\n13.7$\\times$ speedups on the MNIST, SVHN, CIFAR-10, Purchase, and ImageNet\ndatasets, respectively. Compared to the state-of-the-art unlearning approach,\nwe improve 5.8% accuracy, 32.5$\\times$ prediction speedup, and reach a\ncomparable retrain speedup under identical settings on average on these\ndatasets. Additionally, DeepObliviate can also pass the backdoor-based\nunlearning verification.\n"}
+{"abstract": "  Theoretical studies of superradiant lasing on optical clock transitions\npredict a superb frequency accuracy and precision closely tied to the bare\natomic linewidth. Such a superradiant laser is also robust against cavity\nfluctuations when the spectral width of the lasing mode is much larger than\nthat of the atomic medium. Recent predictions suggest that this unique feature\npersists even for a hot and thus strongly broadened ensemble, provided the\neffective atom number is large enough. Here we use a second-order cumulant\nexpansion approach to study the power, linewidth and lineshifts of such a\nsuperradiant laser as a function of the inhomogeneous width of the ensemble\nincluding variations of the spatial atom-field coupling within the resonator.\nWe present conditions on the atom numbers, the pump and coupling strengths\nrequired to reach the buildup of collective atomic coherence as well as scaling\nand limitations for the achievable laser linewidth.\n"}
+{"abstract": "  Many sequence-to-sequence tasks in natural language processing are roughly\nmonotonic in the alignment between source and target sequence, and previous\nwork has facilitated or enforced learning of monotonic attention behavior via\nspecialized attention functions or pretraining. In this work, we introduce a\nmonotonicity loss function that is compatible with standard attention\nmechanisms and test it on several sequence-to-sequence tasks:\ngrapheme-to-phoneme conversion, morphological inflection, transliteration, and\ndialect normalization. Experiments show that we can achieve largely monotonic\nbehavior. Performance is mixed, with larger gains on top of RNN baselines.\nGeneral monotonicity does not benefit transformer multihead attention, however,\nwe see isolated improvements when only a subset of heads is biased towards\nmonotonic behavior.\n"}
+{"abstract": "  For hidden Markov models one of the most popular estimates of the hidden\nchain is the Viterbi path -- the path maximising the posterior probability. We\nconsider a more general setting, called the pairwise Markov model (PMM), where\nthe joint process consisting of finite-state hidden process and observation\nprocess is assumed to be a Markov chain. It has been recently proven that under\nsome conditions the Viterbi path of the PMM can almost surely be extended to\ninfinity, thereby defining the infinite Viterbi decoding of the observation\nsequence, called the Viterbi process. This was done by constructing a block of\nobservations, called a barrier, which ensures that the Viterbi path goes trough\na given state whenever this block occurs in the observation sequence. In this\npaper we prove that the joint process consisting of Viterbi process and PMM is\nregenerative. The proof involves a delicate construction of regeneration times\nwhich coincide with the occurrences of barriers. As one possible application of\nour theory, some results on the asymptotics of the Viterbi training algorithm\nare derived.\n"}
+{"abstract": "  A generalized Kummer surface $X=Km_{3}(A,G_{A})$ is the minimal resolution of\nthe quotient of a $2$-dimensional complex torus by an order 3 symplectic\nautomorphism group $G_{A}$. A Kummer structure on $X$ is an isomorphism class\nof pairs $(B,G_{B})$ such that $X\\simeq Km_{3}(B,G_{B})$. When the surface is\nalgebraic, we obtain that the number of Kummer structures is linked with the\nnumber of order $3$ elliptic points on some Shimura curve naturally related to\n$A$. For each $n\\in\\mathbb{N}$, we obtain generalized Kummer surfaces $X_{n}$\nfor which the number of Kummer structures is $2^{n}$. We then give a\nclassification of the moduli spaces of generalized Kummer surfaces. When the\nsurface is non algebraic, there is only one Kummer structure, but the number of\nirreducible components of the moduli spaces of such surfaces is large compared\nto the algebraic case. The endomorphism rings of the complex $2$-tori we study\nare mainly quaternion orders, these order contain the ring of Eisenstein\nintegers. One can also see this paper as a study of quaternion orders\n$\\mathcal{O}$ over $\\mathbb{Q}$ that contain the ring of Eisenstein integers.\nWe obtain that such order is determined up to isomorphism by its discriminant,\nand when the quaternion algebra is indefinite, the order $\\mathcal{O}$ is\nprincipal.\n"}
+{"abstract": "  The neural network and quantum computing are both significant and appealing\nfields, with their interactive disciplines promising for large-scale computing\ntasks that are untackled by conventional computers. However, both developments\nare restricted by the scope of the hardware development. Nevertheless, many\nneural network algorithms had been proposed before GPUs become powerful enough\nfor running very deep models. Similarly, quantum algorithms can also be\nproposed as knowledge reserves before real quantum computers are easily\naccessible. Specifically, taking advantage of both the neural networks and\nquantum computation and designing quantum deep neural networks (QDNNs) for\nacceleration on Noisy Intermediate-Scale Quantum (NISQ) processors is also an\nimportant research problem. As one of the most widely used neural network\narchitectures, convolutional neural network (CNN) remains to be accelerated by\nquantum mechanisms, with only a few attempts have been demonstrated. In this\npaper, we propose a new hybrid quantum-classical circuit, namely Quantum\nFourier Convolutional Network (QFCN). Our model achieves exponential speed-up\ncompared with classical CNN theoretically and improves over the existing best\nresult of quantum CNN. We demonstrate the potential of this architecture by\napplying it to different deep learning tasks, including traffic prediction and\nimage classification.\n"}
+{"abstract": "  Our goal, in the context of open-domain textual question-answering (QA), is\nto explain answers by showing the line of reasoning from what is known to the\nanswer, rather than simply showing a fragment of textual evidence (a\n\"rationale'\"). If this could be done, new opportunities for understanding and\ndebugging the system's reasoning become possible. Our approach is to generate\nexplanations in the form of entailment trees, namely a tree of multipremise\nentailment steps from facts that are known, through intermediate conclusions,\nto the hypothesis of interest (namely the question + answer). To train a model\nwith this skill, we created ENTAILMENTBANK, the first dataset to contain\nmultistep entailment trees. Given a hypothesis (question + answer), we define\nthree increasingly difficult explanation tasks: generate a valid entailment\ntree given (a) all relevant sentences (b) all relevant and some irrelevant\nsentences, or (c) a corpus. We show that a strong language model can partially\nsolve these tasks, in particular when the relevant sentences are included in\nthe input (e.g., 35% of trees for (a) are perfect), and with indications of\ngeneralization to other domains. This work is significant as it provides a new\ntype of dataset (multistep entailments) and baselines, offering a new avenue\nfor the community to generate richer, more systematic explanations.\n"}
+{"abstract": "  We study the problem of list-decodable linear regression, where an adversary\ncan corrupt a majority of the examples. Specifically, we are given a set $T$ of\nlabeled examples $(x, y) \\in \\mathbb{R}^d \\times \\mathbb{R}$ and a parameter\n$0< \\alpha <1/2$ such that an $\\alpha$-fraction of the points in $T$ are i.i.d.\nsamples from a linear regression model with Gaussian covariates, and the\nremaining $(1-\\alpha)$-fraction of the points are drawn from an arbitrary noise\ndistribution. The goal is to output a small list of hypothesis vectors such\nthat at least one of them is close to the target regression vector. Our main\nresult is a Statistical Query (SQ) lower bound of $d^{\\mathrm{poly}(1/\\alpha)}$\nfor this problem. Our SQ lower bound qualitatively matches the performance of\npreviously developed algorithms, providing evidence that current upper bounds\nfor this task are nearly best possible.\n"}
+{"abstract": "  In this work, we aim to address the 3D scene stylization problem - generating\nstylized images of the scene at arbitrary novel view angles. A straightforward\nsolution is to combine existing novel view synthesis and image/video style\ntransfer approaches, which often leads to blurry results or inconsistent\nappearance. Inspired by the high quality results of the neural radiance fields\n(NeRF) method, we propose a joint framework to directly render novel views with\nthe desired style. Our framework consists of two components: an implicit\nrepresentation of the 3D scene with the neural radiance field model, and a\nhypernetwork to transfer the style information into the scene representation.\nIn particular, our implicit representation model disentangles the scene into\nthe geometry and appearance branches, and the hypernetwork learns to predict\nthe parameters of the appearance branch from the reference style image. To\nalleviate the training difficulties and memory burden, we propose a two-stage\ntraining procedure and a patch sub-sampling approach to optimize the style and\ncontent losses with the neural radiance field model. After optimization, our\nmodel is able to render consistent novel views at arbitrary view angles with\narbitrary style. Both quantitative evaluation and human subject study have\ndemonstrated that the proposed method generates faithful stylization results\nwith consistent appearance across different views.\n"}
+{"abstract": "  In this paper, we propose a simple yet effective method to deal with the\nviolation of the Closed-World Assumption for a classifier. Previous works tend\nto apply a threshold either on the classification scores or the loss function\nto reject the inputs that violate the assumption. However, these methods cannot\nachieve the low False Positive Ratio (FPR) required in safety applications. The\nproposed method is a rejection option based on hypothesis testing with\nprobabilistic networks. With probabilistic networks, it is possible to estimate\nthe distribution of outcomes instead of a single output. By utilizing Z-test\nover the mean and standard deviation for each class, the proposed method can\nestimate the statistical significance of the network certainty and reject\nuncertain outputs. The proposed method was experimented on with different\nconfigurations of the COCO and CIFAR datasets. The performance of the proposed\nmethod is compared with the Softmax Response, which is a known top-performing\nmethod. It is shown that the proposed method can achieve a broader range of\noperation and cover a lower FPR than the alternative.\n"}
+{"abstract": "  I describe the rationale for, and design of, an agent-based simulation model\nof a contemporary online sports-betting exchange: such exchanges, closely\nrelated to the exchange mechanisms at the heart of major financial markets,\nhave revolutionized the gambling industry in the past 20 years, but gathering\nsufficiently large quantities of rich and temporally high-resolution data from\nreal exchanges - i.e., the sort of data that is needed in large quantities for\nDeep Learning - is often very expensive, and sometimes simply impossible; this\ncreates a need for a plausibly realistic synthetic data generator, which is\nwhat this simulation now provides. The simulator, named the \"Bristol Betting\nExchange\" (BBE), is intended as a common platform, a data-source and\nexperimental test-bed, for researchers studying the application of AI and\nmachine learning (ML) techniques to issues arising in betting exchanges; and,\nas far as I have been able to determine, BBE is the first of its kind: a free\nopen-source agent-based simulation model consisting not only of a\nsports-betting exchange, but also a minimal simulation model of racetrack\nsporting events (e.g., horse-races or car-races) about which bets may be made,\nand a population of simulated bettors who each form their own private\nevaluation of odds and place bets on the exchange before and - crucially -\nduring the race itself (i.e., so-called \"in-play\" betting) and whose betting\nopinions change second-by-second as each race event unfolds. BBE is offered as\na proof-of-concept system that enables the generation of large high-resolution\ndata-sets for automated discovery or improvement of profitable strategies for\nbetting on sporting events via the application of AI/ML and advanced data\nanalytics techniques. This paper offers an extensive survey of relevant\nliterature and explains the motivation and design of BBE, and presents brief\nillustrative results.\n"}
+{"abstract": "  Pinch-off and satellite droplets formation during breakup of near-inviscid\nliquid bridge sandwiched between two given equal and coaxial circular plates\nhave been investigated. The breakup always results in the formation of a\nspindle shape which is the precursor of the satellite droplet at the moment of\npinch-off. Interestingly, the slenderness of this spindle is always bigger than\n2{\\pi} and always results in the formation of only one satellite droplet\nregardless of the surface tension and the slenderness of the liquid bridge. We\npredict the cone angle of this spindle formed during the pinch-off of inviscid\nfluids should be 18.086122158...{\\deg}. After pinch-off, the satellite droplets\nwill drift out of the pinch-off regions in the case of symmetrical short\nbridge, and merge again with the sessile drop in the case of unsymmetrical long\nbridge. We demonstrate that the velocity of the satellite droplet is consistent\nwith a scaling model based on a balance between capillary forces and the\ninertia at the pinch-off region.\n"}
+{"abstract": "  In this paper, we generalize fractional $q$-integrals by the method of\n$q$-difference equation. In addition, we deduce fractional Askey--Wilson\nintegral, reversal type fractional Askey--Wilson integral and Ramanujan type\nfractional Askey--Wilson integral.\n"}
+{"abstract": "  Person Re-Identification (Re-ID) is of great importance to the many video\nsurveillance systems. Learning discriminative features for Re-ID remains a\nchallenge due to the large variations in the image space, e.g., continuously\nchanging human poses, illuminations and point of views. In this paper, we\npropose HAVANA, a novel extensible, light-weight HierArchical and\nVAriation-Normalized Autoencoder that learns features robust to intra-class\nvariations. In contrast to existing generative approaches that prune the\nvariations with heavy extra supervised signals, HAVANA suppresses the\nintra-class variations with a Variation-Normalized Autoencoder trained with no\nadditional supervision. We also introduce a novel Jensen-Shannon triplet loss\nfor contrastive distribution learning in Re-ID. In addition, we present\nHierarchical Variation Distiller, a hierarchical VAE to factorize the latent\nrepresentation and explicitly model the variations. To the best of our\nknowledge, HAVANA is the first VAE-based framework for person ReID.\n"}
+{"abstract": "  We classify Frobenius forms, a special class of homogeneous polynomials in\ncharacteristic $p>0$, in up to five variables over an algebraically closed\nfield. We also point out some of the similarities with quadratic forms.\n"}
+{"abstract": "  We present optical spectroscopy for 18 halo white dwarfs identified using\nphotometry from the Canada-France Imaging Survey and Pan-STARRS1 DR1 3$\\pi$\nsurvey combined with astrometry from Gaia DR2. The sample contains 13 DA, 1 DZ,\n2 DC, and two potentially exotic types of white dwarf. We fit both the spectrum\nand the spectral energy distribution in order to obtain the temperature and\nsurface gravity, which we then convert into a mass, and then an age, using\nstellar isochrones and the initial-to-final mass relation. We find a large\nspread in ages that is not consistent with expected formation scenarios for the\nGalactic halo. We find a mean age of 9.03$^{+2.13}_{-2.03}$ Gyr and a\ndispersion of 4.21$^{+2.33}_{-1.58}$ Gyr for the inner halo using a maximum\nlikelihood method. This result suggests an extended star formation history\nwithin the local halo population.\n"}
+{"abstract": "  According to their strength, the tracing properties of a code can be\ncategorized as frameproof, separating, IPP and TA. It is known that if the\nminimum distance of the code is larger than a certain threshold then the TA\nproperty implies the rest. Silverberg et al. ask if there is some kind of\ntracing capability left when the minimum distance falls below the threshold.\nUnder different assumptions, several papers have given a negative answer to the\nquestion. In this paper further progress is made. We establish values of the\nminimum distance for which Reed-Solomon codes do not posses the separating\nproperty.\n"}
+{"abstract": "  By using ab-initio-accurate force fields and molecular dynamics simulations\nwe demonstrate that the layer stiffness has profound effects on the\nsuperlubricant state of two-dimensional van der Waals heterostructures. These\nare engineered to have identical inter-layer sliding energy surfaces, but\nlayers of different rigidity, so that the effects of the stiffness on the\nmicroscopic friction in the superlubricant state can be isolated. A twofold\nincrease in the intra-layer stiffness reduces the friction by approximately a\nfactor six. Most importantly, we find two sliding regimes as a function of the\nsliding velocity. At low velocity the heat generated by the motion is\nefficiently exchanged between the layers and the friction is independent on\nwhether the sliding layer is softer or harder than the substrate. In contrast,\nat high velocity the friction heat flux cannot be exchanged fast enough, and\nthe build up of significant temperature gradients between the layers is\nobserved. In this situation the temperature profile depends on whether the\nslider is softer than the substrate.\n"}
+{"abstract": "  Previous studies have predicted the failure of Fourier's law of thermal\nconduction due to the existence of wave like propagation of heat with finite\npropagation speed. This non-Fourier thermal transport phenomenon can appear in\nboth the hydrodynamic and (quasi) ballistic regimes. Hence, it is not easy to\nclearly distinguish these two non-Fourier regimes only by this phenomenon. In\nthis work, the transient heat propagation in homogeneous thermal system is\nstudied based on the phonon Boltzmann transport equation (BTE) under the\nCallaway model. Given a quasi-one or quasi-two (three) dimensional simulation\nwith homogeneous environment temperature, at initial moment, a heat source is\nadded suddenly at the center with high temperature, then the heat propagates\nfrom the center to the outer. Numerical results show that in quasi-two (three)\ndimensional simulations, the transient temperature will be lower than the\nlowest value of initial temperature in the hydrodynamic regime within a certain\nrange of time and space. This phenomenon appears only when the normal\nscattering dominates heat conduction. Besides, it disappears in quasi-one\ndimensional simulations. Similar phenomenon is also observed in thermal systems\nwith time varying heat source. This novel transient heat propagation phenomenon\nof hydrodynamic phonon transport distinguishes it well from (quasi) ballistic\nphonon transport.\n"}
+{"abstract": "  Despite the advances in the autonomous driving domain, autonomous vehicles\n(AVs) are still inefficient and limited in terms of cooperating with each other\nor coordinating with vehicles operated by humans. A group of autonomous and\nhuman-driven vehicles (HVs) which work together to optimize an altruistic\nsocial utility -- as opposed to the egoistic individual utility -- can co-exist\nseamlessly and assure safety and efficiency on the road. Achieving this mission\nwithout explicit coordination among agents is challenging, mainly due to the\ndifficulty of predicting the behavior of humans with heterogeneous preferences\nin mixed-autonomy environments. Formally, we model an AV's maneuver planning in\nmixed-autonomy traffic as a partially-observable stochastic game and attempt to\nderive optimal policies that lead to socially-desirable outcomes using a\nmulti-agent reinforcement learning framework. We introduce a quantitative\nrepresentation of the AVs' social preferences and design a distributed reward\nstructure that induces altruism into their decision making process. Our\naltruistic AVs are able to form alliances, guide the traffic, and affect the\nbehavior of the HVs to handle competitive driving scenarios. As a case study,\nwe compare egoistic AVs to our altruistic autonomous agents in a highway\nmerging setting and demonstrate the emerging behaviors that lead to a\nnoticeable improvement in the number of successful merges as well as the\noverall traffic flow and safety.\n"}
+{"abstract": "  Improving existing widely-adopted prediction models is often a more efficient\nand robust way towards progress than training new models from scratch. Existing\nmodels may (a) incorporate complex mechanistic knowledge, (b) leverage\nproprietary information and, (c) have surmounted barriers to adoption. Compared\nto model training, model improvement and modification receive little attention.\nIn this paper we propose a general approach to model improvement: we combine\ngradient boosting with any previously developed model to improve model\nperformance while retaining important existing characteristics. To exemplify,\nwe consider the context of Mendelian models, which estimate the probability of\ncarrying genetic mutations that confer susceptibility to disease by using\nfamily pedigrees and health histories of family members. Via simulations we\nshow that integration of gradient boosting with an existing Mendelian model can\nproduce an improved model that outperforms both that model and the model built\nusing gradient boosting alone. We illustrate the approach on genetic testing\ndata from the USC-Stanford Cancer Genetics Hereditary Cancer Panel (HCP) study.\n"}
+{"abstract": "  Traditional on-die, three-level cache hierarchy design is very commonly used\nbut is also prone to latency, especially at the Level 2 (L2) cache. We discuss\nthree distinct ways of improving this design in order to have better\nperformance. Performance is especially important for systems with high\nworkloads. The first method proposes to eliminate L2 altogether while proposing\na new prefetching technique, the second method suggests increasing the size of\nL2, while the last method advocates the implementation of optical caches. After\ncarefully contemplating the results in performance gains and the advantages and\ndisadvantages of each method, we found the last method to be the best of the\nthree.\n"}
+{"abstract": "  It is known that general relativity (GR) theory is not consistent with the\nlatest observations. The modified gravity of GR known as $\\mathrm{f(R)}$ where\n$\\mathrm{R}$ is the Ricci scalar, is considered to be a good candidate for\ndealing with the anomalies present in classical GR. In this context, we study\nstatic rotating uncharged anti-de Sitter and de Sitter (AdS and dS) black holes\n(BHs) using $\\mathrm{f(R)}$ theory without assuming any constraints on the\nRicci scalar or on $\\mathrm{f(R)}$. We derive BH solutions depend on the\nconvolution function and deviate from the AdS/dS Schwarzschild BH solution of\nGR. Although the field equations have no dependence on the cosmological\nconstant, the BHs are characterized by an effective cosmological constant that\ndepends on the convolution function. The asymptotic form of this BH solution\ndepends on the gravitational mass of the system and on extra terms that lead to\nBHs being different from GR BHs but to correspond to GR BHs under certain\nconditions. We also investigate how these extra terms are responsible for\nmaking the singularities of the invariants milder than those of the GR BHs. We\nstudy some physical properties of the BHs from the point of view of\nthermodynamics and show that there is an outer event horizon in addition to the\ninner Cauchy horizons. Among other things, we show that our BH solutions\nsatisfy the first law of thermodynamics. To check the stability of these BHs we\nuse the geodesic deviations and derive the stability conditions. Finally, using\nthe odd-type mode it is shown that all the derived BHs are stable and have a\nradial speed equal to one.\n"}
+{"abstract": "  In physical experiments, reference frames are standardly modelled through a\nspecific choice of coordinates used to describe the physical systems, but they\nthemselves are not considered as such. However, any reference frame is a\nphysical system that ultimately behaves according to quantum mechanics. We\ndevelop a framework for rotational (i.e. spin) quantum reference frames, with\nrespect to which quantum systems with spin degrees of freedom are described. We\ngive an explicit model for such frames as systems composed of three spin\ncoherent states of angular momentum $j$ and introduce the transformations\nbetween them by upgrading the Euler angles occurring in classical\n$\\textrm{SO}(3)$ spin transformations to quantum mechanical operators acting on\nthe states of the reference frames. To ensure that an arbitrary rotation can be\napplied on the spin we take the limit of infinitely large $j$, in which case\nthe angle operator possesses a continuous spectrum. We prove that rotationally\ninvariant Hamiltonians (such as that of the Heisenberg model) are invariant\nunder a larger group of quantum reference frame transformations. Our result is\nthe first development of the quantum reference frame formalism for a\nnon-Abelian group.\n"}
+{"abstract": "  We consider a simple scalar dark matter model within the frame of gauged\n$L_{\\mu}-L_{\\tau}$ symmetry. A gauge boson $Z'$ as well as two scalar fields\n$S$ and $\\Phi$ are introduced to the Standard Model (SM). $S$ and $\\Phi$ are SM\nsinglet but both with $U(1)_{L_{\\mu}-L_{\\tau}}$ charge. The real component and\nimaginary component of $S$ can acquire different masses after spontaneously\nsymmetry breaking, and the lighter one can play the role of dark matter which\nis stabilized by the residual $Z_2$ symmetry. A viable parameter space is\nconsidered to discuss the possibility of light dark matter as well as\nco-annihilation case, and we present current $(g-2)_{\\mu}$ anomaly, Higgs\ninvisible decay, dark matter relic density as well as direct detection\nconstriants on the parameter space.\n"}
+{"abstract": "  Decentralized financial (DeFi) applications on the Ethereum blockchain are\nhighly interoperable because they share a single state in a deterministic\ncomputational environment. Stakeholders can deposit claims on assets, referred\nto as 'liquidity shares', across applications producing effects equivalent to\nrehypothecation in traditional financial systems. We seek to understand the\ndegree to which this practice may contribute to financial integration on\nEthereum by examining transactions in 'composed' derivatives for the assets\nDAI, USDC, USDT, ETH and tokenized BTC for the full set of 344.8 million\nEthereum transactions computed in 2020. We identify a salient trend for\n'composing' assets in multiple sequential generations of derivatives and\ncomment on potential systemic implications for the Ethereum network.\n"}
+{"abstract": "  The paper presents the submission of the team indicnlp@kgp to the EACL 2021\nshared task \"Offensive Language Identification in Dravidian Languages.\" The\ntask aimed to classify different offensive content types in 3 code-mixed\nDravidian language datasets. The work leverages existing state of the art\napproaches in text classification by incorporating additional data and transfer\nlearning on pre-trained models. Our final submission is an ensemble of an\nAWD-LSTM based model along with 2 different transformer model architectures\nbased on BERT and RoBERTa. We achieved weighted-average F1 scores of 0.97,\n0.77, and 0.72 in the Malayalam-English, Tamil-English, and Kannada-English\ndatasets ranking 1st, 2nd, and 3rd on the respective tasks.\n"}
+{"abstract": "  The goal of this study was to improve the post-processing of precipitation\nforecasts using convolutional neural networks (CNNs). Instead of\npost-processing forecasts on a per-pixel basis, as is usually done when\nemploying machine learning in meteorological post-processing, input forecast\nimages were combined and transformed into probabilistic output forecast images\nusing fully convolutional neural networks. CNNs did not outperform regularized\nlogistic regression. Additionally, an ablation analysis was performed.\nCombining input forecasts from a global low-resolution weather model and a\nregional high-resolution weather model improved performance over either one.\n"}
+{"abstract": "  Deep learning can promote the mammography-based computer-aided diagnosis\n(CAD) for breast cancers, but it generally suffers from the small sample size\nproblem. Self-supervised learning (SSL) has shown its effectiveness in medical\nimage analysis with limited training samples. However, the network model\nsometimes cannot be well pre-trained in the conventional SSL framework due to\nthe limitation of the pretext task and fine-tuning mechanism. In this work, a\nTask-driven Self-supervised Bi-channel Networks (TSBN) framework is proposed to\nimprove the performance of classification model the mammography-based CAD. In\nparticular, a new gray-scale image mapping (GSIM) is designed as the pretext\ntask, which embeds the class label information of mammograms into the image\nrestoration task to improve discriminative feature representation. The proposed\nTSBN then innovatively integrates different network architecture, including the\nimage restoration network and the classification network, into a unified SSL\nframework. It jointly trains the bi-channel network models and collaboratively\ntransfers the knowledge from the pretext task network to the downstream task\nnetwork with improved diagnostic accuracy. The proposed TSBN is evaluated on a\npublic INbreast mammogram dataset. The experimental results indicate that it\noutperforms the conventional SSL and multi-task learning algorithms for\ndiagnosis of breast cancers with limited samples.\n"}
+{"abstract": "  The true topological nature of the Kondo insulator SmB$_6$ remains to be\nunveiled. Our previous tunneling study not only found evidence for the\nexistence of surface Dirac fermions, but it also uncovered that they inherently\ninteract with the spin excitons, collective excitations in the bulk. We have\nextended such a spectroscopic investigation into crystals containing a Sm\ndeficiency. The bulk hybridization gap is found to be insensitive to the\ndeficiency up to 1% studied here, but the surface states in Sm-deficient\ncrystals exhibit quite different temperature evolutions from those in\nstoichiometric ones. We attribute this to the topological surface states\nremaining incoherent down to the lowest measurement temperature due to their\ncontinued interaction with the spin excitons that remain uncondensed. This\nresult shows that the detailed topological nature of SmB$_6$ could vary\ndrastically in the presence of disorder in the lattice. This sensitiveness to\ndisorder is seemingly contradictory to the celebrated topological protection,\nbut it can be understood as being due to the intimate interplay between strong\ncorrelations and topological effects.\n"}
+{"abstract": "  Effective environmental planning and management to address climate change\ncould be achieved through extensive environmental modeling with machine\nlearning and conventional physical models. In order to develop and improve\nthese models, practitioners and researchers need comprehensive benchmark\ndatasets that are prepared and processed with environmental expertise that they\ncan rely on. This study presents an extensive dataset of rainfall events for\nthe state of Iowa (2016-2019) acquired from the National Weather Service Next\nGeneration Weather Radar (NEXRAD) system and processed by a quantitative\nprecipitation estimation system. The dataset presented in this study could be\nused for better disaster monitoring, response and recovery by paving the way\nfor both predictive and prescriptive modeling.\n"}
+{"abstract": "  This paper is concerned with polynomially generated multiplier invariant\nsubspaces of the weighted Bergman space $A_{\\boldsymbol{\\beta}}^2$ in\ninfinitely many variables. We completely classify these invariant subspaces\nunder the unitary equivalence. Our results not only cover cases of both the\nHardy space $H^{2}(\\mathbb{D}_{2}^{\\infty})$ and the Bergman space\n$A^{2}(\\mathbb{D}_{2}^{\\infty})$ in infinitely many variables, but also apply\nin finite-variable setting.\n"}
+{"abstract": "  The capability of generalization to unseen domains is crucial for deep\nlearning models when considering real-world scenarios. However, current\navailable medical image datasets, such as those for COVID-19 CT images, have\nlarge variations of infections and domain shift problems. To address this\nissue, we propose a prior knowledge driven domain adaptation and a dual-domain\nenhanced self-correction learning scheme. Based on the novel learning schemes,\na domain adaptation based self-correction model (DASC-Net) is proposed for\nCOVID-19 infection segmentation on CT images. DASC-Net consists of a novel\nattention and feature domain enhanced domain adaptation model (AFD-DA) to solve\nthe domain shifts and a self-correction learning process to refine segmentation\nresults. The innovations in AFD-DA include an image-level activation feature\nextractor with attention to lung abnormalities and a multi-level discrimination\nmodule for hierarchical feature domain alignment. The proposed self-correction\nlearning process adaptively aggregates the learned model and corresponding\npseudo labels for the propagation of aligned source and target domain\ninformation to alleviate the overfitting to noises caused by pseudo labels.\nExtensive experiments over three publicly available COVID-19 CT datasets\ndemonstrate that DASC-Net consistently outperforms state-of-the-art\nsegmentation, domain shift, and coronavirus infection segmentation methods.\nAblation analysis further shows the effectiveness of the major components in\nour model. The DASC-Net enriches the theory of domain adaptation and\nself-correction learning in medical imaging and can be generalized to\nmulti-site COVID-19 infection segmentation on CT images for clinical\ndeployment.\n"}
+{"abstract": "  We revisit the foundational Moment Formula proved by Roger Lee fifteen years\nago. We show that when the underlying stock price martingale admits finite\nlog-moments E[|log(S)|^q] for some positive q, the arbitrage-free growth in the\nleft wing of the implied volatility smile is less constrained than Lee's bound.\nThe result is rationalised by a market trading discretely monitored variance\nswaps wherein the payoff is a function of squared log-returns, and requires no\nassumption for the underlying martingale to admit any negative moment. In this\nrespect, the result can derived from a model-independent setup. As a byproduct,\nwe relax the moment assumptions on the stock price to provide a new proof of\nthe notorious Gatheral-Fukasawa formula expressing variance swaps in terms of\nthe implied volatility.\n"}
+{"abstract": "  We present a combined angle-resolved photoemission spectroscopy and\nlow-energy electron diffraction (LEED) study of the prominent transition metal\ndichalcogenide IrTe$_2$ upon potassium (K) deposition on its surface. Pristine\nIrTe$_2$ undergoes a series of charge-ordered phase transitions below room\ntemperature that are characterized by the formation of stripes of Ir dimers of\ndifferent periodicities. Supported by density functional theory calculations,\nwe first show that the K atoms dope the topmost IrTe$_2$ layer with electrons,\ntherefore strongly decreasing the work function and shifting only the\nelectronic surface states towards higher binding energy. We then follow the\nevolution of its electronic structure as a function of temperature across the\ncharge-ordered phase transitions and observe that their critical temperatures\nare unchanged for K coverages of $0.13$ and $0.21$~monolayer (ML). Using LEED,\nwe also confirm that the periodicity of the related stripe phases is unaffected\nby the K doping. We surmise that the charge-ordered phase transitions of\nIrTe$_2$ are robust against electron surface doping, because of its metallic\nnature at all temperatures, and due to the importance of structural effects in\nstabilizing charge order in IrTe$_2$.\n"}
+{"abstract": "  We theoretically show that two distinctive spin textures manifest themselves\naround saddle points of energy bands in a monolayer NbSe$_2$ under external\ngate potentials. While the density of states at all saddle points diverge\nlogarithmically, ones at the zone boundaries display a windmill-shaped spin\ntexture while the others unidirectional spin orientations. The disparate\nspin-resolved states are demonstrated to contribute an intrinsic spin Hall\nconductivity significantly while their characteristics differ from each\nother.Based on a minimal but essential tight-binding approximation reproducing\nfirst-principles computation results, we established distinct effective Rashba\nHamiltonians for each saddle point, realizing the unique spin textures\ndepending on their momentum. Energetic positions of the saddle points in a\nsingle layer NbSe$_2$ are shown to be well controlled by a gate potential so\nthat it could be a prototypical system to test a competition between various\ncollective phenomena triggered by diverging density of states and their spin\ntextures in low-dimension.\n"}
+{"abstract": "  The time of the first occurrence of a threshold crossing event in a\nstochastic process, known as the first passage time, is of interest in many\nareas of sciences and engineering. Conventionally, there is an implicit\nassumption that the notional 'sensor' monitoring the threshold crossing event\nis always active. In many realistic scenarios, the sensor monitoring the\nstochastic process works intermittently. Then, the relevant quantity of\ninterest is the $\\textit{first detection time}$, which denotes the time when\nthe sensor detects the threshold crossing event for the first time. In this\nwork, a birth-death process monitored by a random intermittent sensor is\nstudied, for which the first detection time distribution is obtained. In\ngeneral, it is shown that the first detection time is related to, and is\nobtainable from, the first passage time distribution. Our analytical results\ndisplay an excellent agreement with simulations. Further, this framework is\ndemonstrated in several applications -- the SIS compartmental and logistic\nmodels, and birth-death processes with resetting. Finally, we solve the\npractically relevant problem of inferring the first passage time distribution\nfrom the first detection time.\n"}
+{"abstract": "  Millimeter-wave (mmWave) and sub-Terahertz (THz) frequencies are expected to\nplay a vital role in 6G wireless systems and beyond due to the vast available\nbandwidth of many tens of GHz. This paper presents an indoor 3-D spatial\nstatistical channel model for mmWave and sub-THz frequencies based on extensive\nradio propagation measurements at 28 and 140 GHz conducted in an indoor office\nenvironment from 2014 to 2020. Omnidirectional and directional path loss models\nand channel statistics such as the number of time clusters, cluster delays, and\ncluster powers were derived from over 15,000 measured power delay profiles. The\nresulting channel statistics show that the number of time clusters follows a\nPoisson distribution and the number of subpaths within each cluster follows a\ncomposite exponential distribution for both LOS and NLOS environments at 28 and\n140 GHz. This paper proposes a unified indoor statistical channel model for\nmmWave and sub-Terahertz frequencies following the mathematical framework of\nthe previous outdoor NYUSIM channel models. A corresponding indoor channel\nsimulator is developed, which can recreate 3-D omnidirectional, directional,\nand multiple input multiple output (MIMO) channels for arbitrary mmWave and\nsub-THz carrier frequency up to 150 GHz, signal bandwidth, and antenna\nbeamwidth. The presented statistical channel model and simulator will guide\nfuture air-interface, beamforming, and transceiver designs for 6G and beyond.\n"}
+{"abstract": "  We aim at measuring the influence of the nondeterministic choices of a part\nof a system on its ability to satisfy a specification. For this purpose, we\napply the concept of Shapley values to verification as a means to evaluate how\nimportant a part of a system is. The importance of a component is measured by\ngiving its control to an adversary, alone or along with other components, and\ntesting whether the system can still fulfill the specification. We study this\nidea in the framework of model-checking with various classical types of\nlinear-time specification, and propose several ways to transpose it to\nbranching ones. We also provide tight complexity bounds in almost every case.\n"}
+{"abstract": "  The associations between emergent physical phenomena (e.g.,\nsuperconductivity) and orbital, charge, and spin degrees of freedom of $3d$\nelectrons are intriguing in transition metal compounds. Here, we successfully\nmanipulate the superconductivity of spinel oxide Li$_{1\\pm\nx}$Ti$_2$O$_{4-\\delta}$ (LTO) by ionic liquid gating. A dome-shaped\nsuperconducting phase diagram is established, where two insulating phases are\ndisclosed both in heavily electron-doping and hole-doping regions. The\nsuperconductor-insulator transition (SIT) in the hole-doping region can be\nattributed to the loss of Ti valence electrons. In the electron-doping region,\nLTO exhibits an unexpected SIT instead of a metallic behavior despite an\nincrease in carrier density. Furthermore, a thermal hysteresis is observed in\nthe normal state resistance curve, suggesting a first-order phase transition.\nWe speculate that the SIT and the thermal hysteresis stem from the enhanced\n$3d$ electron correlations and the formation of orbital ordering by comparing\nthe transport and structural results of LTO with the other spinel oxide\nsuperconductor MgTi$_2$O$_4$, as well as analysing the electronic structure by\nfirst-principles calculations. Further comprehension of the detailed interplay\nbetween superconductivity and orbital ordering would contribute to the\nrevealing of unconventional superconducting pairing mechanism.\n"}
+{"abstract": "  This paper explores the options available to the anti-realist to defend a\nQuinean empirical under-determination thesis using examples of dualities. I\nfirst explicate a version of the empirical under-determination thesis that can\nbe brought to bear on theories of contemporary physics. Then I identify a class\nof examples of dualities that lead to empirical under-determination. But I\nargue that the resulting under-determination is benign, and is not a threat to\na cautious scientific realism. Thus dualities are not new ammunition for the\nanti-realist. The paper also shows how the number of possible interpretative\noptions about dualities that have been considered in the literature can be\nreduced, and suggests a general approach to scientific realism that one may\ntake dualities to favour.\n"}
+{"abstract": "  Two-dimensional (2D) magnets have broad application prospects in the\nspintronics, but how to effectively control them with a small electric field is\nstill an issue. Here we propose that 2D magnets can be efficiently controlled\nin a multiferroic heterostructure composed of 2D magnetic material and\nperovskite oxide ferroelectric (POF) whose dielectric polarization is easily\nflipped under a small electric field. We illustrate the feasibility of such\nstrategy in the bilayer CrI3/BiFeO3(001) heterostructure by using the\nfirst-principles calculations. Different from the traditional POF multiferroic\nheterostructures which have strong interface interactions, we find that the\ninterface interaction between CrI3 and BiFeO3(001) is van der Waals type.\nWhereas, the heterostructure has particular strong magnetoelectric coupling\nwhere the bilayer CrI3 can be efficiently switched between ferromagnetic and\nantiferromagnetic types by the polarized states of BiFeO3(001). We also\ndiscover the competing effect between electron doping and the additional\nelectric field on the interlayer exchange coupling interaction of CrI3, which\nis responsible to the magnetic phase transition. Our results provide a new\navenue for the tuning of 2D magnets with a small electric field.\n"}
+{"abstract": "  Previous studies have shown that the ground state of systems of nucleons\ncomposed by an equal number of protons and neutrons interacting via\nproton-neutron pairing forces can be described accurately by a condensate of\n$\\alpha$-like quartets. Here we extend these studies to the low-lowing excited\nstates of these systems and show that these states can be accurately described\nby breaking a quartet from the ground state condensate and replacing it with an\n\"excited\" quartet. This approach, which is analogous to the one-broken-pair\napproximation employed for like-particle pairing, is analysed for various\nisovector and isovector-isoscalar pairing\n"}
+{"abstract": "  This paper focuses on regularisation methods using models up to the third\norder to search for up to second-order critical points of a finite-sum\nminimisation problem. The variant presented belongs to the framework of [3]: it\nemploys random models with accuracy guaranteed with a sufficiently large\nprefixed probability and deterministic inexact function evaluations within a\nprescribed level of accuracy. Without assuming unbiased estimators, the\nexpected number of iterations is $\\mathcal{O}\\bigl(\\epsilon_1^{-2}\\bigr)$ or\n$\\mathcal{O}\\bigl(\\epsilon_1^{-{3/2}}\\bigr)$ when searching for a first-order\ncritical point using a second or third order model, respectively, and of\n$\\mathcal{O}\\bigl(\\max[\\epsilon_1^{-{3/2}},\\epsilon_2^{-3}]\\bigr)$ when seeking\nfor second-order critical points with a third order model, in which\n$\\epsilon_j$, $j\\in\\{1,2\\}$, is the $j$th-order tolerance. These results match\nthe worst-case optimal complexity for the deterministic counterpart of the\nmethod. Preliminary numerical tests for first-order optimality in the context\nof nonconvex binary classification in imaging, with and without Artifical\nNeural Networks (ANNs), are presented and discussed.\n"}
+{"abstract": "  Organic-inorganic metal halide perovskites have recently attracted increasing\nattention as highly efficient light harvesting materials for photovoltaic\napplications. However, the precise control of crystallization and morphology of\norganometallic perovskites deposited from solution, considered crucial for\nenhancing the final photovoltaic performance, remains challenging. In this\ncontext, here, we report on growing microcrystalline deposits of CH3NH3PbI3\n(MAPbI3), by one-step solution casting on cylinde-shaped quartz substrates\n(rods). We show that the substrate curvature has a strong influence on\nmorphology of the obtained polycrystalline deposits of MAPbI3. Although the\ncrystalline width and length markedly decreased for substrates with higher\ncurvatures, the photoluminescence (PL) spectral peak positions did not\nsignificantly evolve for MAPbI3 deposits on substrates with different\ndiameters. The crystalline size reduction and denser coverage of\nmicrocrystalline MAPbI3 deposits on cylinder-shaped substrates with higher\ncurvatures were attributed to two major contributions, both related to the\nannealing step of the MAPbI3 deposits. In particular, the diameter-dependent\nvariability of the heat capacities and the substrate curvature-enhanced solvent\nevaporation rate seemed to contribute the most to the crystallization process\nand the resulting morphology changes of MAPbI3 deposits on cylinder-shaped\nquartz substrates with various diameters. The longitudinal geometry of\ncylinder-shaped substrates provided also a facile solution for checking the PL\nresponse of the deposits of MAPbI3 exposed to the flow of various gaseous\nmedia, such as oxygen, nitrogen and argon. Overall, the approach reported\nherein inspires novel, cylinder-shaped geometries of MAPbI3 deposits, which can\nfind applications in low-cost photo-optical devices, including gas sensors.\n"}
+{"abstract": "  This paper proposes a deep learning framework for classification of BBC\ntelevision programmes using audio. The audio is firstly transformed into\nspectrograms, which are fed into a pre-trained convolutional Neural Network\n(CNN), obtaining predicted probabilities of sound events occurring in the audio\nrecording. Statistics for the predicted probabilities and detected sound events\nare then calculated to extract discriminative features representing the\ntelevision programmes. Finally, the embedded features extracted are fed into a\nclassifier for classifying the programmes into different genres. Our\nexperiments are conducted over a dataset of 6,160 programmes belonging to nine\ngenres labelled by the BBC. We achieve an average classification accuracy of\n93.7% over 14-fold cross validation. This demonstrates the efficacy of the\nproposed framework for the task of audio-based classification of television\nprogrammes.\n"}
+{"abstract": "  We prove that two Enriques surfaces defined over an algebraically closed\nfield of characteristic different from $2$ are isomorphic if their Kuznetsov\ncomponents are equivalent. This improves and complete our previous result joint\nwith Nuer where the same statement is proved for generic Enriques surfaces.\n"}
+{"abstract": "  Let $\\{(A_i,B_i)\\}_{i=1}^m$ be a set pair system. F\\\"{u}redi, Gy\\'{a}rf\\'{a}s\nand Kir\\'{a}ly called it {\\em $1$-cross intersecting} if $|A_i\\cap B_j|$ is $1$\nwhen $i\\neq j$ and $0$ if $i=j$. They studied such systems and their\ngeneralizations, and in particular considered $m(a,b,1)$ -- the maximum size of\na $1$-cross intersecting set pair system in which $|A_i|\\leq a$ and $|B_i|\\leq\nb$ for all $i$. F\\\"{u}redi, Gy\\'{a}rf\\'{a}s and Kir\\'{a}ly proved that\n$m(n,n,1)\\geq 5^{(n-1)/2}$ and asked whether there are upper bounds on\n$m(n,n,1)$ significantly better than the classical bound ${2n\\choose n}$ of\nBollob\\' as for cross intersecting set pair systems.\n  Answering one of their questions, Holzman recently proved that if $a,b\\geq\n2$, then $m(a,b,1)\\leq \\frac{29}{30}\\binom{a+b}{a}$. He also conjectured that\nthe factor $\\frac{29}{30}$ in his bound can be replaced by $\\frac{5}{6}$. The\ngoal of this paper is to prove this bound.\n"}
+{"abstract": "  The structure and stability of ternary systems prepared with polysorbate 60\nand various combinations of cetyl (C16) and stearyl (C18) alcohols (fatty\nalcohol 16g, polysorbate 4g, water 180g) were examined as they aged over 3\nmonths at 25oC. Rheological results showed that the consistency of these\nsystems increased initially during roughly the first week of aging, which was\nsucceeded by little changes in consistency (systems containing from 30% to 70%\nC18, with the 50% C18 system showing the highest consistencies in viscosity and\nelasticity) or significant breakdown of structure (remaining systems). The\nformation and/or disintegration of all ternary systems were also detected by\nmicroscopy and differential scanning calorimetry experiments. This study\nemphasizes the fact that the structure and consistency of ternary systems are\ndominantly controlled by the swelling capacity of the lamellar\n$\\alpha-$crystalline gel phase. When the conversion of this gel phase into\nnon-swollen $\\beta$- or $\\gamma$-crystals occurs, systems change from\nsemisolids to fluids. Molecular dynamics simulations were performed to provide\nimportant details on the molecular mechanism of our ternary systems.\nComputational results supported the hypothesis experimentally proposed for the\nstability of the mixed system being due to an increase in the flexibility,\nhence an increase in the configurational entropy of the chain tip of the\nalcohol with a longer hydrocarbon chain (with the highest flexibility observed\nin the 50:50 C18:C16 system). This finding is in excellent agreement with\nexperimental conclusions. Additionally, simulation data show that in the mixed\nsystem, the alcohol with shorter hydrocarbon chain becomes more rigid. These\nmolecular details could not be available in experimental measurements\n"}
+{"abstract": "  The slow revolution of the Earth and Moon around their barycentrum does not\ninduce Coriolis accelerations. On the other hand, the motion of Sun and Earth\nis a rotation with Coriolis forces which appear not to have been calculated\nyet, nor have the inertial accelerations within the system of motion of all\nthree celestial bodies. It is the purpose of this contribution to evaluate the\nrelated Coriolis and centrifugal terms and to compare them to the available\natmospheric standard terms. It is a main result that the revolution is of\ncentral importance in the combined dynamics of Earth, Moon and Sun. Covariant\nflow equations are well known tools for dealing with such complicated flow\nsettings. They are used here to quantify the effects of the Earth's revolution\naround the Earth-Moon barycenter and its rotation around the Sun on the\natmospheric circulation. It is found that the motion around the Sun adds time\ndependent terms to the standard Coriolis forces. The related centrifugal\naccelerations are presented. A major part of these accelerations is balanced by\nthe gravitational attraction by Moon and Sun, but important unbalanced\ncontributions remain. New light on the consequences of the Earth's revolution\nis shed by repeating the calculations for a rotating Earth-Moon pair. It is\nfound that the revolution complicates the atmospheric dynamics.\n"}
+{"abstract": "  It is no secret amongst deep learning researchers that finding the optimal\ndata augmentation strategy during training can mean the difference between\nstate-of-the-art performance and a run-of-the-mill result. To that end, the\ncommunity has seen many efforts to automate the process of finding the perfect\naugmentation procedure for any task at hand. Unfortunately, even recent\ncutting-edge methods bring massive computational overhead, requiring as many as\n100 full model trainings to settle on an ideal configuration. We show how to\nachieve equivalent performance in just 6: with Random Unidimensional\nAugmentation. Source code is available at https://github.com/fastestimator/RUA\n"}
+{"abstract": "  By processing in the frequency domain (FD), massive MIMO systems can approach\nthe theoretical per-user capacity using a single carrier modulation (SCM)\nwaveform with a cyclic prefix. Minimum mean squared error (MMSE) detection and\nzero forcing (ZF) precoding have been shown to effectively cancel multi-user\ninterference while compensating for inter-symbol interference. In this paper,\nwe present a modified downlink precoding approach in the FD based on\nregularized zero forcing (RZF), which reuses the matrix inverses calculated as\npart of the FD MMSE uplink detection. By reusing these calculations, the\ncomputational complexity of the RZF precoder is drastically lowered, compared\nto the ZF precoder. Introduction of the regularization in RZF leads to a bias\nin the detected data symbols at the user terminals. We show this bias can be\nremoved by incorporating a scaling factor at the receiver. Furthermore, it is\nnoted that user powers have to be optimized to strike a balance between noise\nand interference seen at each user terminal. The resulting performance of the\nRZF precoder exceeds that of the ZF precoder for low and moderate input\nsignal-to-noise ratio (SNR) conditions, and performance is equal for high input\nSNR. These results are established and confirmed by analysis and simulation.\n"}
+{"abstract": "  We analyze the orthogonal greedy algorithm when applied to dictionaries\n$\\mathbb{D}$ whose convex hull has small entropy. We show that if the metric\nentropy of the convex hull of $\\mathbb{D}$ decays at a rate of\n$O(n^{-\\frac{1}{2}-\\alpha})$ for $\\alpha > 0$, then the orthogonal greedy\nalgorithm converges at the same rate on the variation space of $\\mathbb{D}$.\nThis improves upon the well-known $O(n^{-\\frac{1}{2}})$ convergence rate of the\northogonal greedy algorithm in many cases, most notably for dictionaries\ncorresponding to shallow neural networks. These results hold under no\nadditional assumptions on the dictionary beyond the decay rate of the entropy\nof its convex hull. In addition, they are robust to noise in the target\nfunction and can be extended to convergence rates on the interpolation spaces\nof the variation norm. Finally, we show that these improved rates are sharp and\nprove a negative result showing that the iterates generated by the orthogonal\ngreedy algorithm cannot in general be bounded in the variation norm of\n$\\mathbb{D}$.\n"}
+{"abstract": "  Network flows are one of the most studied combinatorial optimization problems\nwith innumerable applications. Any flow on a directed acyclic graph (DAG) $G$\nhaving $n$ vertices and $m$ edges can be decomposed into a set of $O(m)$ paths,\nwith applications from network routing to assembly of biological sequences. In\nsome applications, the flow decomposition corresponds to some particular data\nthat need to be reconstructed from the flow, which require finding paths (or\nsubpaths) appearing in all possible flow decompositions, referred to as safe\npaths.\n  Recently, Ma et al. [WABI 2020] addressed a related problem in a\nprobabilistic framework. Later, they gave a quadratic-time algorithm based on a\nglobal criterion, for a generalized version (AND-Quant) of the corresponding\nproblem, i.e., reporting if a given flow path is safe. Our contributions are as\nfollows:\n  1- A simple characterization for the safety of a given path based on a local\ncriterion, which can be directly adapted to give an optimal linear time\nverification algorithm.\n  2- A simple enumeration algorithm that reports all maximal safe paths on a\nflow network in $O(mn)$ time. The algorithm reports all safe paths using a\ncompact representation of the solution (called ${\\cal P}_c$), which is\n$\\Omega(mn)$ in the worst case, but merely $O(m+n)$ in the best case.\n  3- An improved enumeration algorithm where all safe paths ending at every\nvertex are represented as funnels using $O(n^2+|{\\cal P}_c|)$ space. These can\nbe computed and used to report all maximal safe paths, using time linear in the\ntotal space required by funnels, with an extra logarithmic factor.\n  Overall we present a simple characterization for the problem leading to an\noptimal verification algorithm and a simple enumeration algorithm. The\nenumeration algorithm is improved using the funnel structures for safe paths,\nwhich may be of independent interest.\n"}
+{"abstract": "  The most advanced D-Wave Advantage quantum annealer has 5000+ qubits,\nhowever, every qubit is connected to a small number of neighbors. As such,\nimplementation of a fully-connected graph results in an order of magnitude\nreduction in qubit count. To compensate for the reduced number of qubits, one\nhas to rely on special heuristic software such as qbsolv, the purpose of which\nis to decompose a large problem into smaller pieces that fit onto a quantum\nannealer. In this work, we compare the performance of two implementations of\nsuch software: the original open-source qbsolv which is a part of the D-Wave\nOcean tools and a new Mukai QUBO solver from Quantum Computing Inc. (QCI). The\ncomparison is done for solving the electronic structure problem and is\nimplemented in a classical mode (Tabu search techniques). The Quantum Annealer\nEigensolver is used to map the electronic structure eigenvalue-eigenvector\nequation to a type of problem solvable on modern quantum annealers. We find\nthat the Mukai QUBO solver outperforms the Ocean qbsolv for all calculations\ndone in the present work, both the ground and excited state calculations. This\nwork stimulates the development of software to assist in the utilization of\nmodern quantum annealers.\n"}
+{"abstract": "  We derive the Thouless-Anderson-Palmer (TAP) equations for the Ghatak and\nSherrington model. Our derivation, based on the cavity method, holds at high\ntemperature and at all values of the crystal field. It confirms the prediction\nof Yokota.\n"}
+{"abstract": "  One of the most complex and devastating disaster scenarios that the\nU.S.~Pacific Northwest region and the state of Oregon faces is a large\nmagnitude Cascadia Subduction Zone earthquake event. The region's electrical\ngrid lacks in resilience against the destruction of a megathrust earthquake, a\npowerful tsunami, hundreds of aftershocks and increased volcanic activity, all\nof which are highly probable components of this hazard. This research seeks to\ncatalyze further understanding and improvement of resilience. By systematizing\npower system related experiences of historical earthquakes, and collecting\npractical and innovative ideas from other regions on how to enhance network\ndesign, construction, and operation, important steps are being taken toward a\nmore resilient, earthquake-resistant grid. This paper presents relevant\nfindings in an effort to be an overview and a useful guideline for those who\nare also working towards greater electrical grid resilience.\n"}
+{"abstract": "  The representation of data and its relationships using networks is prevalent\nin many research fields such as computational biology, medical informatics and\nsocial networks. Recently, complex networks models have been introduced to\nbetter capture the insights of the modelled scenarios. Among others, dual\nnetworks -based models have been introduced, which consist in mapping\ninformation as pair of networks containing the same nodes but different edges.\n  We focus on the use of a novel approach to visualise and analyse dual\nnetworks. The method uses two algorithms for community discovery, and it is\nprovided as a Python-based tool with a graphical user interface. The tool is\nable to load dual networks and to extract both the densest connected subgraph\nas well as the common modular communities. The latter is obtained by using an\nadapted implementation of the Louvain algorithm.\n  The proposed algorithm and graphical tool have been tested by using social,\nbiological, and co-authorship networks. Results demonstrate that the proposed\napproach is efficient and is able to extract meaningful information from dual\nnetworks. Finally, as contribution, the proposed graphical user interface can\nbe considered a valuable innovation to the context.\n"}
+{"abstract": "  In this article, we develop an algebraic framework of axioms which abstracts\nvarious high-level properties of multi-qudit representations of generalized\nClifford algebras. We further construct an explicit model and prove that it\nsatisfies these axioms. Strengths of our algebraic framework include the\nminimality of its assumptions, and the readiness by which one may give an\nexplicit construction satisfying these assumptions. In terms of applications,\nthis algebraic framework provides a solid foundation which opens the way for\ndeveloping a graphical calculus for multi-qudit representations of generalized\nClifford algebras using purely algebraic methods, which is addressed in a\nfollow-up paper.\n"}
+{"abstract": "  Assuming the Riemann hypothesis we establish explicit bounds for the modulus\nof the log-derivative of Riemann's zeta-function in the critical strip.\n"}
+{"abstract": "  Abstract symbolic reasoning, as required in domains such as mathematics and\nlogic, is a key component of human intelligence. Solvers for these domains have\nimportant applications, especially to computer-assisted education. But learning\nto solve symbolic problems is challenging for machine learning algorithms.\nExisting models either learn from human solutions or use hand-engineered\nfeatures, making them expensive to apply in new domains. In this paper, we\ninstead consider symbolic domains as simple environments where states and\nactions are given as unstructured text, and binary rewards indicate whether a\nproblem is solved. This flexible setup makes it easy to specify new domains,\nbut search and planning become challenging. We introduce four environments\ninspired by the Mathematics Common Core Curriculum, and observe that existing\nReinforcement Learning baselines perform poorly. We then present a novel\nlearning algorithm, Contrastive Policy Learning (ConPoLe) that explicitly\noptimizes the InfoNCE loss, which lower bounds the mutual information between\nthe current state and next states that continue on a path to the solution.\nConPoLe successfully solves all four domains. Moreover, problem representations\nlearned by ConPoLe enable accurate prediction of the categories of problems in\na real mathematics curriculum. Our results suggest new directions for\nreinforcement learning in symbolic domains, as well as applications to\nmathematics education.\n"}
+{"abstract": "  With the ongoing penetration of conversational user interfaces, a better\nunderstanding of social and emotional characteristic inherent to dialogue is\nrequired. Chatbots in particular face the challenge of conveying human-like\nbehaviour while being restricted to one channel of interaction, i.e., text. The\ngoal of the presented work is thus to investigate whether characteristics of\nsocial intelligence embedded in human-chatbot interactions are perceivable by\nhuman interlocutors and if yes, whether such influences the experienced\ninteraction quality. Focusing on the social intelligence dimensions\nAuthenticity, Clarity and Empathy, we first used a questionnaire survey\nevaluating the level of perception in text utterances, and then conducted a\nWizard of Oz study to investigate the effects of these utterances in a more\ninteractive setting. Results show that people have great difficulties\nperceiving elements of social intelligence in text. While on the one hand they\nfind anthropomorphic behaviour pleasant and positive for the naturalness of a\ndialogue, they may also perceive it as frightening and unsuitable when\nexpressed by an artificial agent in the wrong way or at the wrong time.\n"}
+{"abstract": "  Learning representations for graphs plays a critical role in a wide spectrum\nof downstream applications. In this paper, we summarize the limitations of the\nprior works in three folds: representation space, modeling dynamics and\nmodeling uncertainty. To bridge this gap, we propose to learn dynamic graph\nrepresentation in hyperbolic space, for the first time, which aims to infer\nstochastic node representations. Working with hyperbolic space, we present a\nnovel Hyperbolic Variational Graph Neural Network, referred to as HVGNN. In\nparticular, to model the dynamics, we introduce a Temporal GNN (TGNN) based on\na theoretically grounded time encoding approach. To model the uncertainty, we\ndevise a hyperbolic graph variational autoencoder built upon the proposed TGNN\nto generate stochastic node representations of hyperbolic normal distributions.\nFurthermore, we introduce a reparameterisable sampling algorithm for the\nhyperbolic normal distribution to enable the gradient-based learning of HVGNN.\nExtensive experiments show that HVGNN outperforms state-of-the-art baselines on\nreal-world datasets.\n"}
+{"abstract": "  We propose SinIR, an efficient reconstruction-based framework trained on a\nsingle natural image for general image manipulation, including\nsuper-resolution, editing, harmonization, paint-to-image, photo-realistic style\ntransfer, and artistic style transfer. We train our model on a single image\nwith cascaded multi-scale learning, where each network at each scale is\nresponsible for image reconstruction. This reconstruction objective greatly\nreduces the complexity and running time of training, compared to the GAN\nobjective. However, the reconstruction objective also exacerbates the output\nquality. Therefore, to solve this problem, we further utilize simple random\npixel shuffling, which also gives control over manipulation, inspired by the\nDenoising Autoencoder. With quantitative evaluation, we show that SinIR has\ncompetitive performance on various image manipulation tasks. Moreover, with a\nmuch simpler training objective (i.e., reconstruction), SinIR is trained 33.5\ntimes faster than SinGAN (for 500 X 500 images) that solves similar tasks. Our\ncode is publicly available at github.com/YooJiHyeong/SinIR.\n"}
+{"abstract": "  This paper studies the model compression problem of vision transformers.\nBenefit from the self-attention module, transformer architectures have shown\nextraordinary performance on many computer vision tasks. Although the network\nperformance is boosted, transformers are often required more computational\nresources including memory usage and the inference complexity. Compared with\nthe existing knowledge distillation approaches, we propose to excavate useful\ninformation from the teacher transformer through the relationship between\nimages and the divided patches. We then explore an efficient fine-grained\nmanifold distillation approach that simultaneously calculates cross-images,\ncross-patch, and random-selected manifolds in teacher and student models.\nExperimental results conducted on several benchmarks demonstrate the\nsuperiority of the proposed algorithm for distilling portable transformer\nmodels with higher performance. For example, our approach achieves 75.06% Top-1\naccuracy on the ImageNet-1k dataset for training a DeiT-Tiny model, which\noutperforms other ViT distillation methods.\n"}
+{"abstract": "  Adversarial examples mainly exploit changes to input pixels to which humans\nare not sensitive to, and arise from the fact that models make decisions based\non uninterpretable features. Interestingly, cognitive science reports that the\nprocess of interpretability for human classification decision relies\npredominantly on low spatial frequency components. In this paper, we\ninvestigate the robustness to adversarial perturbations of models enforced\nduring training to leverage information corresponding to different spatial\nfrequency ranges. We show that it is tightly linked to the spatial frequency\ncharacteristics of the data at stake. Indeed, depending on the data set, the\nsame constraint may results in very different level of robustness (up to 0.41\nadversarial accuracy difference). To explain this phenomenon, we conduct\nseveral experiments to enlighten influential factors such as the level of\nsensitivity to high frequencies, and the transferability of adversarial\nperturbations between original and low-pass filtered inputs.\n"}
+{"abstract": "  In this paper, we propose a simple yet effective crowd counting and\nlocalization network named SCALNet. Unlike most existing works that separate\nthe counting and localization tasks, we consider those tasks as a pixel-wise\ndense prediction problem and integrate them into an end-to-end framework.\nSpecifically, for crowd counting, we adopt a counting head supervised by the\nMean Square Error (MSE) loss. For crowd localization, the key insight is to\nrecognize the keypoint of people, i.e., the center point of heads. We propose a\nlocalization head to distinguish dense crowds trained by two loss functions,\ni.e., Negative-Suppressed Focal (NSF) loss and False-Positive (FP) loss, which\nbalances the positive/negative examples and handles the false-positive\npredictions. Experiments on the recent and large-scale benchmark, NWPU-Crowd,\nshow that our approach outperforms the state-of-the-art methods by more than 5%\nand 10% improvement in crowd localization and counting tasks, respectively. The\ncode is publicly available at https://github.com/WangyiNTU/SCALNet.\n"}
+{"abstract": "  Harmonic generation in atoms and molecules has reshaped our understanding of\nultrafast phenomena beyond the traditional nonlinear optics and has launched\nattosecond physics. Harmonics from solids represent a new frontier, where both\nmajority and minority spin channels contribute to harmonics.} This is true even\nin a ferromagnet whose electronic states are equally available to optical\nexcitation. Here, we demonstrate that harmonics can be generated {mostly} from\na single spin channel in half metallic chromium dioxide. {An energy gap in the\nminority channel greatly reduces the harmonic generation}, so harmonics\npredominantly emit from the majority channel, with a small contribution from\nthe minority channel. However, this is only possible when the incident photon\nenergy is well below the energy gap in the minority channel, so all the\ntransitions in the minority channel are virtual. The onset of the photon energy\nis determined by the transition energy between the dipole-allowed transition\nbetween the O-$2p$ and Cr-$3d$ states. Harmonics {mainly} from a single spin\nchannel can be detected, regardless of laser field strength, as far as the\nphoton energy is below the minority band energy gap. This prediction should be\ntested experimentally.\n"}
+{"abstract": "  Dynamic Time Warping (DTW) is widely used for temporal data processing.\nHowever, existing methods can neither learn the discriminative prototypes of\ndifferent classes nor exploit such prototypes for further analysis. We propose\nDiscriminative Prototype DTW (DP-DTW), a novel method to learn class-specific\ndiscriminative prototypes for temporal recognition tasks. DP-DTW shows superior\nperformance compared to conventional DTWs on time series classification\nbenchmarks. Combined with end-to-end deep learning, DP-DTW can handle\nchallenging weakly supervised action segmentation problems and achieves state\nof the art results on standard benchmarks. Moreover, detailed reasoning on the\ninput video is enabled by the learned action prototypes. Specifically, an\naction-based video summarization can be obtained by aligning the input sequence\nwith action prototypes.\n"}
+{"abstract": "  Hadronic matrix elements of local four-quark operators play a central role in\nnon-leptonic kaon decays, while vacuum matrix elements involving the same kind\nof operators appear in inclusive dispersion relations, such as those relevant\nin $\\tau$-decay analyses. Using an $SU(3)_L\\otimes SU(3)_R$ decomposition of\nthe operators, we derive generic relations between these matrix elements,\nextending well-known results that link observables in the two different\nsectors. Two relevant phenomenological applications are presented. First, we\ndetermine the electroweak-penguin contribution to the kaon CP-violating ratio\n$\\varepsilon'/\\varepsilon$, using the measured hadronic spectral functions in\n$\\tau$ decay. Second, we fit our $SU(3)$ dynamical parameters to the most\nrecent lattice data on $K\\to\\pi\\pi$ matrix elements. The comparison of this\nnumerical fit with results from previous analytical approaches provides an\ninteresting anatomy of the $\\Delta I = \\frac{1}{2}$ enhancement, confirming old\nsuggestions about its underlying dynamical origin.\n"}
+{"abstract": "  We present a machine learning method to predict extreme hydrologic events\nfrom spatially and temporally varying hydrological and meteorological data. We\nused a timestep reduction technique to reduce the computational and memory\nrequirements and trained a bidirection LSTM network to predict soil water and\nstream flow from time series data observed and simulated over eighty years in\nthe Wabash River Watershed. We show that our simple model can be trained much\nfaster than complex attention networks such as GeoMAN without sacrificing\naccuracy. Based on the predicted values of soil water and stream flow, we\npredict the occurrence and severity of extreme hydrologic events such as\ndroughts. We also demonstrate that extreme events can be predicted in\ngeographical locations separate from locations observed during the training\nprocess. This spatially-inductive setting enables us to predict extreme events\nin other areas in the US and other parts of the world using our model trained\nwith the Wabash Basin data.\n"}
+{"abstract": "  Electronic and optical properties of doped organic semiconductors are\ndominated by local interactions between donor and acceptor molecules. However,\nwhen such systems are in crystalline form, long-range order competes against\nshort-range couplings. In a first-principles study on three experimentally\nresolved bulk structures of quaterthiophene doped by (fluorinated)\ntetracyanoquinodimethane, we demonstrate the crucial role of long-range\ninteractions in donor/acceptor co-crystals. The band structures of the\ninvestigated materials exhibit direct band-gaps decreasing in size with\nincreasing amount of F atoms in the acceptors. The valence-band maximum and\nconduction-band minimum are found at the Brillouin zone boundary and the\ncorresponding wave-functions are segregated on donor and acceptor molecules,\nrespectively. With the aid of a tight-binding model, we rationalize that the\nmechanisms responsible for these behaviors, which are ubiquitous in\ndonor/acceptor co-crystals, are driven by long-range interactions. The optical\nresponse of the analyzed co-crystals is highly anisotropic. The absorption\nonset is dominated by an intense resonance corresponding to a charge-transfer\nexcitation. Long-range interactions are again responsible for this behavior,\nwhich enhances the efficiency of the co-crystals for photo-induced charge\nseparation and transport. In addition to this result, which has important\nimplications in the rational design of organic materials for opto-electronics,\nour study clarifies that cluster models, accounting only for local\ninteractions, cannot capture the relevant impact of long-range order in\ndonor/acceptor co-crystals.\n"}
+{"abstract": "  Recently, deep-learning based approaches have achieved impressive performance\nfor autonomous driving. However, end-to-end vision-based methods typically have\nlimited interpretability, making the behaviors of the deep networks difficult\nto explain. Hence, their potential applications could be limited in practice.\nTo address this problem, we propose an interpretable end-to-end vision-based\nmotion planning approach for autonomous driving, referred to as IVMP. Given a\nset of past surrounding-view images, our IVMP first predicts future egocentric\nsemantic maps in bird's-eye-view space, which are then employed to plan\ntrajectories for self-driving vehicles. The predicted future semantic maps not\nonly provide useful interpretable information, but also allow our motion\nplanning module to handle objects with low probability, thus improving the\nsafety of autonomous driving. Moreover, we also develop an optical flow\ndistillation paradigm, which can effectively enhance the network while still\nmaintaining its real-time performance. Extensive experiments on the nuScenes\ndataset and closed-loop simulation show that our IVMP significantly outperforms\nthe state-of-the-art approaches in imitating human drivers with a much higher\nsuccess rate. Our project page is available at\nhttps://sites.google.com/view/ivmp.\n"}
+{"abstract": "  We develop methods for forming prediction sets in an online setting where the\ndata generating distribution is allowed to vary over time in an unknown\nfashion. Our framework builds on ideas from conformal inference to provide a\ngeneral wrapper that can be combined with any black box method that produces\npoint predictions of the unseen label or estimated quantiles of its\ndistribution. While previous conformal inference methods rely on the assumption\nthat the data points are exchangeable, our adaptive approach provably achieves\nthe desired coverage frequency over long-time intervals irrespective of the\ntrue data generating process. We accomplish this by modelling the distribution\nshift as a learning problem in a single parameter whose optimal value is\nvarying over time and must be continuously re-estimated. We test our method,\nadaptive conformal inference, on two real world datasets and find that its\npredictions are robust to visible and significant distribution shifts.\n"}
+{"abstract": "  We analyze a fully discrete finite element numerical scheme for the\nCahn-Hilliard-Stokes-Darcy system that models two-phase flows in coupled free\nflow and porous media. To avoid a well-known difficulty associated with the\ncoupling between the Cahn-Hilliard equation and the fluid motion, we make use\nof the operator-splitting in the numerical scheme, so that these two solvers\nare decoupled, which in turn would greatly improve the computational\nefficiency. The unique solvability and the energy stability have been proved\nin~\\cite{CHW2017}. In this work, we carry out a detailed convergence analysis\nand error estimate for the fully discrete finite element scheme, so that the\noptimal rate convergence order is established in the energy norm, i.e.,, in the\n$\\ell^\\infty (0, T; H^1) \\cap \\ell^2 (0, T; H^2)$ norm for the phase variables,\nas well as in the $\\ell^\\infty (0, T; H^1) \\cap \\ell^2 (0, T; H^2)$ norm for\nthe velocity variable. Such an energy norm error estimate leads to a\ncancellation of a nonlinear error term associated with the convection part,\nwhich turns out to be a key step to pass through the analysis. In addition, a\ndiscrete $\\ell^2 (0;T; H^3)$ bound of the numerical solution for the phase\nvariables plays an important role in the error estimate, which is accomplished\nvia a discrete version of Gagliardo-Nirenberg inequality in the finite element\nsetting.\n"}
+{"abstract": "  In this paper, the Hankel transform of the generalized q-exponential\npolynomial of the first form (q, r)-Whitney numbers of the second kind is\nestablished using the method of Cigler. Consequently, the Hankel transform of\nthe first form (q, r)-Dowling numbers is obtained as special case.\n"}
+{"abstract": "  In the article, we investigate the diquark-diquark-antiquark type fully-heavy\npentaquark states with the spin-parity $J^P={\\frac{1}{2}}^-$ via the QCD sum\nrules, and obtain the masses $M_{cccc\\bar{c}}=7.93\\pm 0.15\\,\\rm{GeV}$ and\n$M_{bbbb\\bar{b}}=23.91\\pm0.15\\,\\rm{GeV}$. We can search for the fully-heavy\npentaquark states in the $J/\\psi \\Omega_{ccc}$ and $\\Upsilon \\Omega_{bbb}$\ninvariant mass spectrum in the future.\n"}
+{"abstract": "  Optical phenomena associated with extremely localized field should be\nunderstood with considerations of nonlocal and quantum effects, which pose a\nhurdle to conceptualize the physics with a picture of eigenmodes. Here we first\npropose a generalized Lorentz model to describe general nonlocal media under\nlinear mean-field approximation and formulate source-free Maxwell's equations\nas a linear eigenvalue problem to define the quasinormal modes. Then we\nintroduce an orthonormalization scheme for the modes and establish a canonical\nquasinormal mode framework for general nonlocal media. Explicit formalisms for\nmetals described by quantum hydrodynamic model and polar dielectrics with\nnonlocal response are exemplified. The framework enables for the first time\ndirect modal analysis of mode transition in the quantum tunneling regime and\nprovides physical insights beyond usual far-field spectroscopic analysis.\nApplied to nonlocal polar dielectrics, the framework also unveils the important\nroles of longitudinal phonon polaritons in optical response.\n"}
+{"abstract": "  Smart contracts are distributed, self-enforcing programs executing on top of\nblockchain networks. They have the potential to revolutionize many industries\nsuch as financial institutes and supply chains. However, smart contracts are\nsubject to code-based vulnerabilities, which casts a shadow on its\napplications. As smart contracts are unpatchable (due to the immutability of\nblockchain), it is essential that smart contracts are guaranteed to be free of\nvulnerabilities. Unfortunately, smart contract languages such as Solidity are\nTuring-complete, which implies that verifying them statically is infeasible.\nThus, alternative approaches must be developed to provide the guarantee. In\nthis work, we develop an approach which automatically transforms smart\ncontracts so that they are provably free of 4 common kinds of vulnerabilities.\nThe key idea is to apply runtime verification in an efficient and provably\ncorrect manner. Experiment results with 5000 smart contracts show that our\napproach incurs minor run-time overhead in terms of time (i.e., 14.79%) and gas\n(i.e., 0.79%).\n"}
+{"abstract": "  We present a structure preserving discretization of the fundamental spacetime\ngeometric structures of fluid mechanics in the Lagrangian description in 2D and\n3D. Based on this, multisymplectic variational integrators are developed for\nbarotropic and incompressible fluid models, which satisfy a discrete version of\nNoether theorem. We show how the geometric integrator can handle regular fluid\nmotion in vacuum with free boundaries and constraints such as the impact\nagainst an obstacle of a fluid flowing on a surface. Our approach is applicable\nto a wide range of models including the Boussinesq and shallow water models, by\nappropriate choice of the Lagrangian.\n"}
+{"abstract": "  Modularity of neural networks -- both biological and artificial -- can be\nthought of either structurally or functionally, and the relationship between\nthese is an open question. We show that enforcing structural modularity via\nsparse connectivity between two dense sub-networks which need to communicate to\nsolve the task leads to functional specialization of the sub-networks, but only\nat extreme levels of sparsity. With even a moderate number of interconnections,\nthe sub-networks become functionally entangled. Defining functional\nspecialization is in itself a challenging problem without a universally agreed\nsolution. To address this, we designed three different measures of\nspecialization (based on weight masks, retraining and correlation) and found\nthem to qualitatively agree. Our results have implications in both neuroscience\nand machine learning. For neuroscience, it shows that we cannot conclude that\nthere is functional modularity simply by observing moderate levels of\nstructural modularity: knowing the brain's connectome is not sufficient for\nunderstanding how it breaks down into functional modules. For machine learning,\nusing structure to promote functional modularity -- which may be important for\nrobustness and generalization -- may require extremely narrow bottlenecks\nbetween modules.\n"}
+{"abstract": "  Being able to spot defective parts is a critical component in large-scale\nindustrial manufacturing. A particular challenge that we address in this work\nis the cold-start problem: fit a model using nominal (non-defective) example\nimages only. While handcrafted solutions per class are possible, the goal is to\nbuild systems that work well simultaneously on many different tasks\nautomatically. The best peforming approaches combine embeddings from ImageNet\nmodels with an outlier detection model. In this paper, we extend on this line\nof work and propose PatchCore, which uses a maximally representative memory\nbank of nominal patch-features. PatchCore offers competitive inference times\nwhile achieving state-of-the-art performance for both detection and\nlocalization. On the standard dataset MVTec AD, PatchCore achieves an\nimage-level anomaly detection AUROC score of $99.1\\%$, more than halving the\nerror compared to the next best competitor. We further report competitive\nresults on two additional datasets and also find competitive results in the few\nsamples regime.\n"}
+{"abstract": "  Novel many-body and topological electronic phases can be created in\nassemblies of interacting spins coupled to a superconductor, such as\none-dimensional topological superconductors with Majorana zero modes (MZMs) at\ntheir ends. Understanding and controlling interactions between spins and the\nemergent band structure of the in-gap Yu-Shiba-Rusinov (YSR) states they induce\nin a superconductor are fundamental for engineering such phases. Here, by\nprecisely positioning magnetic adatoms with a scanning tunneling microscope\n(STM), we demonstrate both the tunability of exchange interaction between spins\nand precise control of the hybridization of YSR states they induce on the\nsurface of a bismuth (Bi) thin film that is made superconducting with the\nproximity effect. In this platform, depending on the separation of spins, the\ninterplay between Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction, spin-orbit\ncoupling, and surface magnetic anisotropy stabilizes different types of spin\nalignments. Using high-resolution STM spectroscopy at millikelvin temperatures,\nwe probe these spin alignments through monitoring the spin-induced YSR states\nand their energy splitting. Such measurements also reveal a quantum phase\ntransition between the ground states with different electron number parity for\na pair of spins in a superconductor tuned by their separation. Experiments on\nlarger assemblies show that spin-spin interactions can be mediated in a\nsuperconductor over long distances. Our results show that controlling\nhybridization of the YSR states in this platform provides the possibility of\nengineering the band structure of such states for creating topological phases.\n"}
+{"abstract": "  We describe a numerical method that simulates the interaction of the helium\natom with sequences of femtosecond and attosecond light pulses. The method,\nwhich is based on the close-coupling expansion of the electronic configuration\nspace in a B-spline bipolar spherical harmonic basis, can accurately reproduce\nthe excitation and single ionization of the atom, within the electrostatic\napproximation. The time dependent Schr\\\"odinger equation is integrated with a\nsequence of second-order split-exponential unitary propagators. The asymptotic\nchannel-, energy- and angularly-resolved photoelectron distributions are\ncomputed by projecting the wavepacket at the end of the simulation on the\nmultichannel scattering states of the atom, which are separately computed\nwithin the same close-coupling basis. This method is applied to simulate the\npump-probe ionization of helium in the vicinity of the $2s/2p$ excitation\nthreshold of the He$^+$ ion. This work confirms the qualitative conclusions of\none of our earliest publications [L Argenti and E Lindroth, Phys. Rev. Lett.\n{\\bf 105}, 53002 (2010)], in which we demonstrated the control of the $2s/2p$\nionization branching-ratio. Here, we take those calculations to convergence and\nshow how correlation brings the periodic modulation of the branching ratios in\nalmost phase opposition. The residual total ionization probability to the\n$2s+2p$ channels is dominated by the beating between the $sp_{2,3}^+$ and the\n$sp_{2,4}^+$ doubly excited states, which is consistent with the modulation of\nthe complementary signal in the $1s$ channel, measured in 2010 by Chang and\nco-workers~[S Gilbertson~\\emph{et al.}, Phys. Rev. Lett. {\\bf 105}, 263003\n(2010)].\n"}
+{"abstract": "  Over the past decade, unprecedented progress in the development of neural\nnetworks influenced dozens of different industries, including weed recognition\nin the agro-industrial sector. The use of neural networks in agro-industrial\nactivity in the task of recognizing cultivated crops is a new direction. The\nabsence of any standards significantly complicates the understanding of the\nreal situation of the use of the neural network in the agricultural sector. The\nmanuscript presents the complete analysis of researches over the past 10 years\non the use of neural networks for the classification and tracking of weeds due\nto neural networks. In particular, the analysis of the results of using various\nneural network algorithms for the task of classification and tracking was\npresented. As a result, we presented the recommendation for the use of neural\nnetworks in the tasks of recognizing a cultivated object and weeds. Using this\nstandard can significantly improve the quality of research on this topic and\nsimplify the analysis and understanding of any paper.\n"}
+{"abstract": "  The formation of $\\alpha$ particle on nuclear surface has been a fundamental\nproblem since the early age of nuclear physics. It strongly affects the\n$\\alpha$ decay lifetime of heavy and superheavy elements, level scheme of light\nnuclei, and the synthesis of the elements in stars. However, the\n$\\alpha$-particle formation in medium-mass nuclei has been poorly known despite\nits importance. Here, based on the $^{48}{\\rm Ti}(p,p\\alpha)^{44}{\\rm Ca}$\nreaction analysis, we report that the $\\alpha$-particle formation in a\nmedium-mass nucleus $^{48}{\\rm Ti}$ is much stronger than that expected from a\nmean-field approximation, and the estimated average distance between $\\alpha$\nparticle and the residue is as large as 4.5 fm. This new result poses a\nchallenge of describing four nucleon correlations by microscopic nuclear\nmodels.\n"}
+{"abstract": "  Giant spin-splitting was recently predicted in collinear antiferromagnetic\nmaterials with a specific class of magnetic space group. In this work, we have\npredicted a two-dimensional (2D) antiferromagnetic Weyl semimetal (WS), CrO\nwith large spin-split band structure, spin-momentum locked transport properties\nand high N\\'eel temperature. It has two pairs of spin-polarized Weyl points at\nthe Fermi level. By manipulating the position of the Weyl points with strain,\nfour different antiferromagnetic spintronic states can be achieved: WSs with\ntwo spin-polarized transport channels (STCs), WSs with single STC,\nsemiconductors with two STCs, and semiconductors with single STC. Based on\nthese properties, a new avenue in spintronics with 2D collinear\nantiferromagnets is proposed.\n"}
+{"abstract": "  Being the seventh most spoken language in the world, the use of the Bangla\nlanguage online has increased in recent times. Hence, it has become very\nimportant to analyze Bangla text data to maintain a safe and harassment-free\nonline place. The data that has been made accessible in this article has been\ngathered and marked from the comments of people in public posts by celebrities,\ngovernment officials, athletes on Facebook. The total amount of collected\ncomments is 44001. The dataset is compiled with the aim of developing the\nability of machines to differentiate whether a comment is a bully expression or\nnot with the help of Natural Language Processing and to what extent it is\nimproper if it is an inappropriate comment. The comments are labeled with\ndifferent categories of harassment. Exploratory analysis from different\nperspectives is also included in this paper to have a detailed overview. Due to\nthe scarcity of data collection of categorized Bengali language comments, this\ndataset can have a significant role for research in detecting bully words,\nidentifying inappropriate comments, detecting different categories of Bengali\nbullies, etc. The dataset is publicly available at\nhttps://data.mendeley.com/datasets/9xjx8twk8p.\n"}
+{"abstract": "  Let $\\Omega \\Subset \\mathbb R^n$, $f \\in C^1(\\mathbb R^{N\\times n})$ and\n$g\\in C^1(\\mathbb R^N)$, where $N,n \\in \\mathbb N$. We study the minimisation\nproblem of finding $u \\in W^{1,\\infty}_0(\\Omega;\\mathbb R^N)$ that satisfies \\[\n\\big\\| f(\\mathrm D u) \\big\\|_{L^\\infty(\\Omega)} \\! = \\inf \\Big\\{\\big\\|\nf(\\mathrm D v) \\big\\|_{L^\\infty(\\Omega)} \\! : \\ v \\! \\in\nW^{1,\\infty}_0(\\Omega;\\mathbb R^N), \\, \\| g(v) \\|_{L^\\infty(\\Omega)}\\!\n=1\\Big\\}, \\] under natural assumptions on $f,g$. This includes the\n$\\infty$-eigenvalue problem as a special case. Herein we prove existence of a\nminimiser $u_\\infty$ with extra properties, derived as the limit of minimisers\nof approximating constrained $L^p$ problems as $p\\to \\infty$. A central\ncontribution and novelty of this work is that $u_\\infty$ is shown to solve a\ndivergence PDE with measure coefficients, whose leading term is a divergence\ncounterpart equation of the non-divergence $\\infty$-Laplacian. Our results are\nnew even in the scalar case of the $\\infty$-eigenvalue problem.\n"}
+{"abstract": "  This paper presents the detailed simulation of a double-pixel structure for\ncharged particle detection based on the 3D-trench silicon sensor developed for\nthe TIMESPOT project and a comparison of the simulation results with\nmeasurements performed at $\\pi-$M1 beam at PSI laboratory. The simulation is\nbased on the combined use of several software tools (TCAD, GEANT4, TCoDe and\nTFBoost) which allow to fully design and simulate the device physics response\nin very short computational time, O(1-100 s) per simulated signal, by\nexploiting parallel computation using single or multi-thread processors. This\nallowed to produce large samples of simulated signals, perform detailed studies\nof the sensor characteristics and make precise comparisons with experimental\nresults.\n"}
+{"abstract": "  We investigate the possibility that radio-bright active galactic nuclei (AGN)\nare responsible for the TeV--PeV neutrinos detected by IceCube. We use an\nunbinned maximum-likelihood-ratio method, 10 years of IceCube muon-track data,\nand 3388 radio-bright AGN selected from the Radio Fundamental Catalog. None of\nthe AGN in the catalog have a large global significance. The two most\nsignificant sources have global significance of $\\simeq$ 1.5$\\sigma$ and\n0.8$\\sigma$, though 4.1$\\sigma$ and 3.8$\\sigma$ local significance. Our\nstacking analyses show no significant correlation between the whole catalog and\nIceCube neutrinos. We infer from the null search that this catalog can account\nfor at most 30\\% (95\\% CL) of the diffuse astrophysical neutrino flux measured\nby IceCube. Moreover, our results disagree with recent work that claimed a\n4.1$\\sigma$ detection of neutrinos from the sources in this catalog, and we\ndiscuss the reasons of the difference.\n"}
+{"abstract": "  We propose a generalization of the coherent anomaly method to extract the\ncritical exponents of a phase transition occurring in the steady-state of an\nopen quantum many-body system. The method, originally developed by Suzuki [J.\nPhys. Soc. Jpn. {\\bf 55}, 4205 (1986)] for equilibrium systems, is based on the\nscaling properties of the singularity in the response functions determined\nthrough cluster mean-field calculations. We apply this method to the\ndissipative transverse-field Ising model and the dissipative XYZ model in two\ndimensions obtaining convergent results already with small clusters.\n"}
+{"abstract": "  As a step towards quantization of Higher Spin Gravities we construct the\npresymplectic AKSZ sigma-model for $4d$ Higher Spin Gravity which is AdS/CFT\ndual of Chern-Simons vector models. It is shown that the presymplectic\nstructure leads to the correct quantum commutator of higher spin fields and to\nthe correct algebra of the global higher spin symmetry currents. The\npresymplectic AKSZ model is proved to be unique, it depends on two coupling\nconstants in accordance with the AdS/CFT duality, and it passes some simple\nchecks of interactions.\n"}
+{"abstract": "  Electric, intelligent, and network are the most important future development\ndirections of automobiles. Intelligent electric vehicles have shown great\npotentials to improve traffic mobility and reduce emissions, especially at\nunsignalized intersections. Previous research has shown that vehicle passing\norder is the key factor in traffic mobility improvement. In this paper, we\npropose a graph-based cooperation method to formalize the conflict-free\nscheduling problem at unsignalized intersections. Firstly, conflict directed\ngraphs and coexisting undirected graphs are built to describe the conflict\nrelationship of the vehicles. Then, two graph-based methods are introduced to\nsolve the vehicle passing order. One method is an optimized depth-first\nspanning tree method which aims to find the local optimal passing order for\neach vehicle. The other method is a maximum matching algorithm that solves the\nglobal optimal problem. The computational complexity of both methods is also\nderived. Numerical simulation results demonstrate the effectiveness of the\nproposed algorithms.\n"}
+{"abstract": "  As an indispensable part of modern human-computer interaction system, speech\nsynthesis technology helps users get the output of intelligent machine more\neasily and intuitively, thus has attracted more and more attention. Due to the\nlimitations of high complexity and low efficiency of traditional speech\nsynthesis technology, the current research focus is the deep learning-based\nend-to-end speech synthesis technology, which has more powerful modeling\nability and a simpler pipeline. It mainly consists of three modules: text\nfront-end, acoustic model, and vocoder. This paper reviews the research status\nof these three parts, and classifies and compares various methods according to\ntheir emphasis. Moreover, this paper also summarizes the open-source speech\ncorpus of English, Chinese and other languages that can be used for speech\nsynthesis tasks, and introduces some commonly used subjective and objective\nspeech quality evaluation method. Finally, some attractive future research\ndirections are pointed out.\n"}
+{"abstract": "  The internet advertising market is a multi-billion dollar industry, in which\nadvertisers buy thousands of ad placements every day by repeatedly\nparticipating in auctions. In recent years, the industry has shifted to\nfirst-price auctions as the preferred paradigm for selling advertising slots.\nAnother important and ubiquitous feature of these auctions is the presence of\ncampaign budgets, which specify the maximum amount the advertisers are willing\nto pay over a specified time period. In this paper, we present a new model to\nstudy the equilibrium bidding strategies in first-price auctions for\nadvertisers who satisfy budget constraints on average. Our model dispenses with\nthe common, yet unrealistic assumption that advertisers' values are independent\nand instead assumes a contextual model in which advertisers determine their\nvalues using a common feature vector. We show the existence of a natural\nvalue-pacing-based Bayes-Nash equilibrium under very mild assumptions, and\nstudy its structural properties. Furthermore, we generalize the existence\nresult to standard auctions and prove a revenue equivalence showing that all\nstandard auctions yield the same revenue even in the presence of budget\nconstraints.\n"}
+{"abstract": "  We study the dynamics of a ferrofluid thin film confined in a Hele-Shaw cell,\nand subjected to a tilted nonuniform magnetic field. It is shown that the\ninterface between the ferrofluid and an inviscid outer fluid (air) supports\ntraveling waves, governed by a novel modified Kuramoto--Sivashinsky-type\nequation derived under the long-wave approximation. The balance between energy\nproduction and dissipation in this long-wave equations allows for the existence\nof dissipative solitons. These permanent traveling waves' propagation velocity\nand profile shape are shown to be tunable via the external magnetic field. A\nmultiple-scale analysis is performed to obtain the correction to the linear\nprediction of the propagation velocity, and to reveal how the nonlinearity\narrests the linear instability. The traveling periodic interfacial waves\ndiscovered are identified as fixed points in an energy phase plane. It is shown\nthat transitions between states (wave profiles) occur. These transitions are\nexplained via the spectral stability of the traveling waves. Interestingly,\nmultiperiodic waves, which are a non-integrable analog of the double cnoidal\nwave, are also found to propagate under the model long-wave equation. These\nmultiperiodic solutions are investigated numerically, and they are found to be\nlong-lived transients, but ultimately abruptly transition to one of the stable\nperiodic states identified.\n"}
+{"abstract": "  The origin of the gamma-ray emission of the blazar Mrk 421 is still a matter\nof debate. We used 5.5 years of unbiased observing campaign data, obtained\nusing the FACT telescope and the Fermi LAT detector at TeV and GeV energies,\nthe longest and densest so far, together with contemporaneous multi-wavelength\nobservations, to characterise the variability of Mrk 421 and to constrain the\nunderlying physical mechanisms. We studied and correlated light curves obtained\nby ten different instruments and found two significant results. The TeV and\nX-ray light curves are very well correlated with a lag of <0.6 days. The GeV\nand radio (15 Ghz band) light curves are widely and strongly correlated.\nVariations of the GeV light curve lead those in the radio. Lepto-hadronic and\npurely hadronic models in the frame of shock acceleration predict proton\nacceleration or cooling timescales that are ruled out by the short variability\ntimescales and delays observed in Mrk 421. Instead the observations match the\npredictions of leptonic models.\n"}
+{"abstract": "  A pair-density wave state has been suggested to exist in underdoped cuprate\nsuperconductors, with some supporting experimental evidence emerging over the\npast few years from scanning tunneling spectroscopy. Several studies have also\nlinked the observed quantum oscillations in these systems to a reconstruction\nof the Fermi surface by a pair-density wave. Here, we show, using semiclassical\nanalysis and numerical calculations, that a Fermi pocket created by first-order\nscattering from a pair-density wave cannot induce such oscillations. In\ncontrast, pockets resulting from second-order scattering can cause\noscillations. We consider the effects of a finite pair-density wave correlation\nlength on the signal, and demonstrate that it is only weakly sensitive to\ndisorder in the form of $\\pi$-phase slips. Finally, we discuss our results in\nthe context of the cuprates and show that a bidirectional pair-density wave may\nproduce observed oscillation frequencies.\n"}
+{"abstract": "  Federated Learning (FL) is emerging as a promising paradigm of\nprivacy-preserving machine learning, which trains an algorithm across multiple\nclients without exchanging their data samples. Recent works highlighted several\nprivacy and robustness weaknesses in FL and addressed these concerns using\nlocal differential privacy (LDP) and some well-studied methods used in\nconventional ML, separately. However, it is still not clear how LDP affects\nadversarial robustness in FL. To fill this gap, this work attempts to develop a\ncomprehensive understanding of the effects of LDP on adversarial robustness in\nFL. Clarifying the interplay is significant since this is the first step\ntowards a principled design of private and robust FL systems. We certify that\nlocal differential privacy has both positive and negative effects on\nadversarial robustness using theoretical analysis and empirical verification.\n"}
+{"abstract": "  We define the flow group of any component of any stratum of rooted abelian or\nquadratic differentials (those marked with a horizontal separatrix) to be the\ngroup generated by almost-flow loops. We prove that the flow group is equal to\nthe fundamental group of the component. As a corollary, we show that the plus\nand minus modular Rauzy--Veech groups are finite-index subgroups of their\nambient modular monodromy groups. This partially answers a question of Yoccoz.\n  Using this, and recent advances on algebraic hulls and Zariski closures of\nmonodromy groups, we prove that the Rauzy--Veech groups are Zariski dense in\ntheir ambient symplectic groups. Density, in turn, implies the simplicity of\nthe plus and minus Lyapunov spectra of any component of any stratum of\nquadratic differentials. Thus, we establish the Kontsevich--Zorich conjecture.\n"}
+{"abstract": "  By using the Boole summation formula, we obtain asymptotic expansions for the\nfirst and higher order derivatives of the alternating Hurwitz zeta function\n$$\\zeta_{E}(z,q)=\\sum_{n=0}^\\infty\\frac{(-1)^{n}}{(n+q)^z}$$\n  with respect to its first argument\n$$\\zeta_{E}^{(m)}(z,q)\\equiv\\frac{\\partial^m}{\\partial z^m}\\zeta_E(z,q).$$\n"}
+{"abstract": "  We show that every finite semilattice can be represented as an atomized\nsemilattice, an algebraic structure with additional elements (atoms) that\nextend the semilattice's partial order. Each atom maps to one subdirectly\nirreducible component, and the set of atoms forms a hypergraph that fully\ndefines the semilattice. An atomization always exists and is unique up to\n\"redundant atoms\". Atomized semilattices are representations that can be used\nas computational tools for building semilattice models from sentences, as well\nas building its subalgebras and products. Atomized semilattices can be applied\nto machine learning and to the study of semantic embeddings into algebras with\nidempotent operators.\n"}
+{"abstract": "  The recent emergence of machine-learning based generative models for speech\nsuggests a significant reduction in bit rate for speech codecs is possible.\nHowever, the performance of generative models deteriorates significantly with\nthe distortions present in real-world input signals. We argue that this\ndeterioration is due to the sensitivity of the maximum likelihood criterion to\noutliers and the ineffectiveness of modeling a sum of independent signals with\na single autoregressive model. We introduce predictive-variance regularization\nto reduce the sensitivity to outliers, resulting in a significant increase in\nperformance. We show that noise reduction to remove unwanted signals can\nsignificantly increase performance. We provide extensive subjective performance\nevaluations that show that our system based on generative modeling provides\nstate-of-the-art coding performance at 3 kb/s for real-world speech signals at\nreasonable computational complexity.\n"}
+{"abstract": "  We consider the problem of estimating an object's physical properties such as\nmass, friction, and elasticity directly from video sequences. Such a system\nidentification problem is fundamentally ill-posed due to the loss of\ninformation during image formation. Current solutions require precise 3D labels\nwhich are labor-intensive to gather, and infeasible to create for many systems\nsuch as deformable solids or cloth. We present gradSim, a framework that\novercomes the dependence on 3D supervision by leveraging differentiable\nmultiphysics simulation and differentiable rendering to jointly model the\nevolution of scene dynamics and image formation. This novel combination enables\nbackpropagation from pixels in a video sequence through to the underlying\nphysical attributes that generated them. Moreover, our unified computation\ngraph -- spanning from the dynamics and through the rendering process --\nenables learning in challenging visuomotor control tasks, without relying on\nstate-based (3D) supervision, while obtaining performance competitive to or\nbetter than techniques that rely on precise 3D labels.\n"}
+{"abstract": "  In this paper, we study quasi post-critically finite degenerations for\nrational maps. We construct limits for such degenerations as geometrically\nfinite rational maps on a finite tree of Riemann spheres. We prove the\nboundedness for such degenerations of hyperbolic rational maps with Sierpinski\ncarpet Julia set and give criteria for the convergence for quasi-Blaschke\nproducts $\\mathcal{QB}_d$, making progress towards the analogues of Thurston's\ncompactness theorem for acylindrical $3$-manifold and the double limit theorem\nfor quasi-Fuchsian groups in complex dynamics. In the appendix, we apply such\nconvergence results to show the existence of certain polynomial matings.\n"}
+{"abstract": "  Optical wireless communications (OWCs) have been recognized as a candidate\nenabler of next generation in-body nano-scale networks and implants. The\ndevelopment of an accurate channel model capable of accommodating the\nparticularities of different type of tissues is expected to boost the design of\noptimized communication protocols for such applications. Motivated by this,\nthis paper focuses on presenting a general pathloss model for in-body OWCs. In\nparticular, we use experimental measurements in order to extract analytical\nexpressions for the absorption coefficients of the five main tissues'\nconstitutions, namely oxygenated and de-oxygenated blood, water, fat, and\nmelanin. Building upon these expressions, we derive a general formula for the\nabsorption coefficient evaluation of any biological tissue. To verify the\nvalidity of this formula, we compute the absorption coefficient of complex\ntissues and compare them against respective experimental results reported by\nindependent research works. Interestingly, we observe that the analytical\nformula has high accuracy and is capable of modeling the pathloss and,\ntherefore, the penetration depth in complex tissues.\n"}
+{"abstract": "  We theoretically analyze a possibility of electromagnetic wave emission due\nto electron transitions between spin subbands in a ferromagnet. Different\nmechanisms of such spin-flip transitions are cousidered. One mechanism is the\nelectron transitions caused by magnetic field of the wave. Another mechanism is\ndue to Rashba spin-orbit interaction. While two mentioned mechanisms exist in a\nhomogeneously magnetized ferromagnet, there are two other mechanisms that exist\nin non-collinearly magnetized medium. First mechanism is known and is due to\nthe dependence of exchange interaction constant on the quasimomentum of\nconduction electrons. Second one exists in any non-collinearly magnetized\nmedium. We study these mechanisms in a non-collinear ferromagnet with\nhelicoidal magnetization distribution. The estimations of probabilities of\nelectron transitions due to different mechanisms are made for realistic\nparameters, and we compare the mechanisms. We also estimate the radiation power\nand threshold current in a simple model in which spin is injected into the\nferromagnet by a spin-polarized electric current through a tunnel barrier.\n"}
+{"abstract": "  We consider the sharp interface limit for the scalar-valued and vector-valued\nAllen-Cahn equation with homogeneous Neumann boundary condition in a bounded\nsmooth domain $\\Omega$ of arbitrary dimension $N\\geq 2$ in the situation when a\ntwo-phase diffuse interface has developed and intersects the boundary\n$\\partial\\Omega$. The limit problem is mean curvature flow with\n$90${\\deg}-contact angle and we show convergence in strong norms for\nwell-prepared initial data as long as a smooth solution to the limit problem\nexists. To this end we assume that the limit problem has a smooth solution on\n$[0,T]$ for some time $T>0$. Based on the latter we construct suitable\ncurvilinear coordinates and set up an asymptotic expansion for the\nscalar-valued and the vector-valued Allen-Cahn equation. Finally, we prove a\nspectral estimate for the linearized Allen-Cahn operator in both cases in order\nto estimate the difference of the exact and approximate solutions with a\nGronwall-type argument.\n"}
+{"abstract": "  We prove that the unique possible flow in an Alexandroff $T_{0}$-space is the\ntrivial one. On the way of motivation, we relate Alexandroff spaces with\ntopological hyperspaces.\n"}
+{"abstract": "  Transformer models have demonstrated superior performance in natural language\nprocessing. The dot product self-attention in Transformer allows us to model\ninteractions between words. However, this modeling comes with significant\ncomputational overhead. In this work, we revisit the memory-compute trade-off\nassociated with Transformer, particularly multi-head attention, and show a\nmemory-heavy but significantly more compute-efficient alternative to\nTransformer. Our proposal, denoted as PairConnect, a multilayer perceptron\n(MLP), models the pairwise interaction between words by explicit pairwise word\nembeddings. As a result, PairConnect substitutes self dot product with a simple\nembedding lookup. We show mathematically that despite being an MLP, our\ncompute-efficient PairConnect is strictly more expressive than Transformer. Our\nexperiment on language modeling tasks suggests that PairConnect could achieve\ncomparable results with Transformer while reducing the computational cost\nassociated with inference significantly.\n"}
+{"abstract": "  Multi-agent collision-free trajectory planning and control subject to\ndifferent goal requirements and system dynamics has been extensively studied,\nand is gaining recent attention in the realm of machine and reinforcement\nlearning. However, in particular when using a large number of agents,\nconstructing a least-restrictive collision avoidance policy is of utmost\nimportance for both classical and learning-based methods. In this paper, we\npropose a Least-Restrictive Collision Avoidance Module (LR-CAM) that evaluates\nthe safety of multi-agent systems and takes over control only when needed to\nprevent collisions. The LR-CAM is a single policy that can be wrapped around\npolicies of all agents in a multi-agent system. It allows each agent to pursue\nany objective as long as it is safe to do so. The benefit of the proposed\nleast-restrictive policy is to only interrupt and overrule the default\ncontroller in case of an upcoming inevitable danger. We use a Long Short-Term\nMemory (LSTM) based Variational Auto-Encoder (VAE) to enable the LR-CAM to\naccount for a varying number of agents in the environment. Moreover, we propose\nan off-policy meta-reinforcement learning framework with a novel reward\nfunction based on a Hamilton-Jacobi value function to train the LR-CAM. The\nproposed method is fully meta-trained through a ROS based simulation and tested\non real multi-agent system. Our results show that LR-CAM outperforms the\nclassical least-restrictive baseline by 30 percent. In addition, we show that\neven if a subset of agents in a multi-agent system use LR-CAM, the success rate\nof all agents will increase significantly.\n"}
+{"abstract": "  In this article we will show $2$ different proofs for the fact that there\nexist relatively prime positive integers $a,b$ such that: $a^2+ab+b^2=7^n$.\n"}
+{"abstract": "  We theoretically analyze the typical learning performance of\n$\\ell_{1}$-regularized linear regression ($\\ell_1$-LinR) for Ising model\nselection using the replica method from statistical mechanics. For typical\nrandom regular graphs in the paramagnetic phase, an accurate estimate of the\ntypical sample complexity of $\\ell_1$-LinR is obtained. Remarkably, despite the\nmodel misspecification, $\\ell_1$-LinR is model selection consistent with the\nsame order of sample complexity as $\\ell_{1}$-regularized logistic regression\n($\\ell_1$-LogR), i.e., $M=\\mathcal{O}\\left(\\log N\\right)$, where $N$ is the\nnumber of variables of the Ising model. Moreover, we provide an efficient\nmethod to accurately predict the non-asymptotic behavior of $\\ell_1$-LinR for\nmoderate $M, N$, such as precision and recall. Simulations show a fairly good\nagreement between theoretical predictions and experimental results, even for\ngraphs with many loops, which supports our findings. Although this paper mainly\nfocuses on $\\ell_1$-LinR, our method is readily applicable for precisely\ncharacterizing the typical learning performances of a wide class of\n$\\ell_{1}$-regularized $M$-estimators including $\\ell_1$-LogR and interaction\nscreening.\n"}
+{"abstract": "  We consider the recent surge of information on the potential benefits of\nacid-suppression drugs in the context of COVID-19, with an eye on the\nvariability (and confusion) across the reported findings--at least as regards\nthe popular antacid famotidine. The inconsistencies reflect contradictory\nconclusions from independent clinical-based studies that took roughly similar\napproaches, in terms of experimental design (retrospective, cohort-based, etc.)\nand statistical analyses (propensity-score matching and stratification, etc.).\nThe confusion has significant ramifications in choosing therapeutic\ninterventions: e.g., do potential benefits of famotidine indicate its use in a\nparticular COVID-19 case? Beyond this pressing therapeutic issue, conflicting\ninformation on famotidine must be resolved before its integration in\nontological and knowledge graph-based frameworks, which in turn are useful in\ndrug repurposing efforts. To begin systematically structuring the rapidly\naccumulating information, in the hopes of clarifying and reconciling the\ndiscrepancies, we consider the contradictory information along three proposed\n'axes': (1) a context-of-disease axis, (2) a degree-of-[therapeutic]-benefit\naxis, and (3) a mechanism-of-action axis. We suspect that incongruencies in how\nthese axes have been (implicitly) treated in past studies has led to the\ncontradictory indications for famotidine and COVID-19. We also trace the\nevolution of information on acid-suppression agents as regards the\ntransmission, severity, and mortality of COVID-19, given the many literature\nreports that have accumulated. By grouping the studies conceptually and\nthematically, we identify three eras in the progression of our understanding of\nfamotidine and COVID-19. Harmonizing these findings is a key goal for both\nclinical standards-of-care (COVID and beyond) as well as ontological and\nknowledge graph-based approaches.\n"}
+{"abstract": "  We consider the asymmetric simple exclusion process (ASEP) with forward\nhopping rate 1, backward hopping rate q and periodic boundary conditions. We\nshow that the Bethe equations of ASEP can be decoupled, at all order in\nperturbation in the variable q, by introducing a formal Laurent series mapping\nthe Bethe roots of the totally asymmetric case q=0 (TASEP) to the Bethe roots\nof ASEP. The probability of the height for ASEP is then written as a single\ncontour integral on the Riemann surface on which symmetric functions of TASEP\nBethe roots live.\n"}
+{"abstract": "  In this study, we investigated a method allowing the determination of the\nfemur bone surface as well as its mechanical axis from some easy-to-identify\nbony landmarks. The reconstruction of the whole femur is therefore performed\nfrom these landmarks using a Statistical Shape Model (SSM). The aim of this\nresearch is therefore to assess the impact of the number, the position, and the\naccuracy of the landmarks for the reconstruction of the femur and the\ndetermination of its related mechanical axis, an important clinical parameter\nto consider for the lower limb analysis. Two statistical femur models were\ncreated from our in-house dataset and a publicly available dataset. Both were\nevaluated in terms of average point-to-point surface distance error and through\nthe mechanical axis of the femur. Furthermore, the clinical impact of using\nlandmarks on the skin in replacement of bony landmarks is investigated. The\npredicted proximal femurs from bony landmarks were more accurate compared to\non-skin landmarks while both had less than 3.5 degrees mechanical axis angle\ndeviation error. The results regarding the non-invasive determination of the\nmechanical axis are very encouraging and could open very interesting clinical\nperspectives for the analysis of the lower limb either for orthopedics or\nfunctional rehabilitation.\n"}
+{"abstract": "  Several novel statistical methods have been developed to estimate large\nintegrated volatility matrices based on high-frequency financial data. To\ninvestigate their asymptotic behaviors, they require a sub-Gaussian or finite\nhigh-order moment assumption for observed log-returns, which cannot account for\nthe heavy tail phenomenon of stock returns. Recently, a robust estimator was\ndeveloped to handle heavy-tailed distributions with some bounded fourth-moment\nassumption. However, we often observe that log-returns have heavier tail\ndistribution than the finite fourth-moment and that the degrees of heaviness of\ntails are heterogeneous over the asset and time period. In this paper, to deal\nwith the heterogeneous heavy-tailed distributions, we develop an adaptive\nrobust integrated volatility estimator that employs pre-averaging and\ntruncation schemes based on jump-diffusion processes. We call this an adaptive\nrobust pre-averaging realized volatility (ARP) estimator. We show that the ARP\nestimator has a sub-Weibull tail concentration with only finite 2$\\alpha$-th\nmoments for any $\\alpha>1$. In addition, we establish matching upper and lower\nbounds to show that the ARP estimation procedure is optimal. To estimate large\nintegrated volatility matrices using the approximate factor model, the ARP\nestimator is further regularized using the principal orthogonal complement\nthresholding (POET) method. The numerical study is conducted to check the\nfinite sample performance of the ARP estimator.\n"}
+{"abstract": "  Context. Turbulent transport in stellar radiative zones is a key ingredient\nof stellar evolution theory, but the anisotropy of the transport due to the\nstable stratification and the rotation of these regions is poorly understood.\nThe assumption of shellular rotation, which is a cornerstone of the so-called\nrotational mixing, relies on an efficient horizontal transport. However, this\ntransport is included in many stellar evolution codes through phenomenological\nmodels that have never been tested.\n  Aims. We investigate the impact of horizontal shear on the anisotropy of\nturbulent transport.\n  Methods. We used a relaxation approximation (also known as {\\tau}\napproximation) to describe the anisotropising effect of stratification,\nrotation, and shear on a background turbulent flow by computing velocity\ncorrelations.\n  Results. We obtain new theoretical scalings for velocity correlations that\ninclude the effect of horizontal shear. These scalings show an enhancement of\nturbulent motions, which would lead to a more efficient transport of chemicals\nand angular momentum, in better agreement with helio- and asteroseismic\nobservations of rotation in the whole Hertzsprung-Russell diagram. Moreover, we\npropose a new choice for the non-linear time used in the relaxation\napproximation, which characterises the source of the turbulence.\n  Conclusions. For the first time, we describe the effect of stratification,\nrotation, and vertical and horizontal shear on the anisotropy of turbulent\ntransport in stellar radiative zones. The new prescriptions need to be\nimplemented in stellar evolution calculations. To do so, it may be necessary to\nimplement non-diffusive transport.\n"}
+{"abstract": "  Industrial robots can solve very complex tasks in controlled environments,\nbut modern applications require robots able to operate in unpredictable\nsurroundings as well. An increasingly popular reactive policy architecture in\nrobotics is Behavior Trees but as with other architectures, programming time\nstill drives cost and limits flexibility. There are two main branches of\nalgorithms to generate policies automatically, automated planning and machine\nlearning, both with their own drawbacks. We propose a method for generating\nBehavior Trees using a Genetic Programming algorithm and combining the two\nbranches by taking the result of an automated planner and inserting it into the\npopulation. Experimental results confirm that the proposed method of combining\nplanning and learning performs well on a variety of robotic assembly problems\nand outperforms both of the base methods used separately. We also show that\nthis type of high level learning of Behavior Trees can be transferred to a real\nsystem without further training.\n"}
+{"abstract": "  Kernel segmentation aims at partitioning a data sequence into several\nnon-overlapping segments that may have nonlinear and complex structures. In\ngeneral, it is formulated as a discrete optimization problem with combinatorial\nconstraints. A popular algorithm for optimally solving this problem is dynamic\nprogramming (DP), which has quadratic computation and memory requirements.\nGiven that sequences in practice are too long, this algorithm is not a\npractical approach. Although many heuristic algorithms have been proposed to\napproximate the optimal segmentation, they have no guarantee on the quality of\ntheir solutions. In this paper, we take a differentiable approach to alleviate\nthe aforementioned issues. First, we introduce a novel sigmoid-based\nregularization to smoothly approximate the combinatorial constraints. Combining\nit with objective of the balanced kernel clustering, we formulate a\ndifferentiable model termed Kernel clustering with sigmoid-based regularization\n(KCSR), where the gradient-based algorithm can be exploited to obtain the\noptimal segmentation. Second, we develop a stochastic variant of the proposed\nmodel. By using the stochastic gradient descent algorithm, which has much lower\ntime and space complexities, for optimization, the second model can perform\nsegmentation on overlong data sequences. Finally, for simultaneously segmenting\nmultiple data sequences, we slightly modify the sigmoid-based regularization to\nfurther introduce an extended variant of the proposed model. Through extensive\nexperiments on various types of data sequences performances of our models are\nevaluated and compared with those of the existing methods. The experimental\nresults validate advantages of the proposed models. Our Matlab source code is\navailable on github.\n"}
+{"abstract": "  Phishing is the number one threat in the world of internet. Phishing attacks\nare from decades and with each passing year it is becoming a major problem for\ninternet users as attackers are coming with unique and creative ideas to breach\nthe security. In this paper, different types of phishing and anti-phishing\ntechniques are presented. For this purpose, the Systematic Literature\nReview(SLR) approach is followed to critically define the proposed research\nquestions. At first 80 articles were extracted from different repositories.\nThese articles were then filtered out using Tollgate Approach to find out\ndifferent types of phishing and anti-phishing techniques. Research study\nevaluated that spear phishing, Email Spoofing, Email Manipulation and phone\nphishing are the most commonly used phishing techniques. On the other hand,\naccording to the SLR, machine learning approaches have the highest accuracy of\npreventing and detecting phishing attacks among all other anti-phishing\napproaches.\n"}
+{"abstract": "  This paper proposes a model-free nonparametric estimator of conditional\nquantile of a time series regression model where the covariate vector is\nrepeated many times for different values of the response. This type of data is\nabound in climate studies. To tackle such problems, our proposed method\nexploits the replicated nature of the data and improves on restrictive linear\nmodel structure of conventional quantile regression. Relevant asymptotic theory\nfor the nonparametric estimators of the mean and variance function of the model\nare derived under a very general framework. We provide a detailed simulation\nstudy which clearly demonstrates the gain in efficiency of the proposed method\nover other benchmark models, especially when the true data generating process\nentails nonlinear mean function and heteroskedastic pattern with time dependent\ncovariates. The predictive accuracy of the non-parametric method is remarkably\nhigh compared to other methods when attention is on the higher quantiles of the\nvariable of interest. Usefulness of the proposed method is then illustrated\nwith two climatological applications, one with a well-known tropical cyclone\nwind-speed data and the other with an air pollution data.\n"}
+{"abstract": "  Polarimetric imaging is one of the most effective techniques for\nhigh-contrast imaging and characterization of circumstellar environments. These\nenvironments can be characterized through direct-imaging polarimetry at\nnear-infrared wavelengths. The SPHERE/IRDIS instrument installed on the Very\nLarge Telescope in its dual-beam polarimetric imaging (DPI) mode, offers the\ncapability to acquire polarimetric images at high contrast and high angular\nresolution. However dedicated image processing is needed to get rid of the\ncontamination by the stellar light, of instrumental polarization effects, and\nof the blurring by the instrumental point spread function. We aim to\nreconstruct and deconvolve the near-infrared polarization signal from\ncircumstellar environments. We use observations of these environments obtained\nwith the high-contrast imaging infrared polarimeter SPHERE-IRDIS at the VLT. We\ndeveloped a new method to extract the polarimetric signal using an inverse\napproach method that benefits from the added knowledge of the detected signal\nformation process. The method includes weighted data fidelity term, smooth\npenalization, and takes into account instrumental polarization. The method\nenables to accurately measure the polarized intensity and angle of linear\npolarization of circumstellar disks by taking into account the noise statistics\nand the convolution of the observed objects by the instrumental point spread\nfunction. It has the capability to use incomplete polarimetry cycles which\nenhance the sensitivity of the observations. The method improves the overall\nperformances in particular for low SNR/small polarized flux compared to\nstandard methods.\n"}
+{"abstract": "  Traditional and deep learning-based fusion methods generated the intermediate\ndecision map to obtain the fusion image through a series of post-processing\nprocedures. However, the fusion results generated by these methods are easy to\nlose some source image details or results in artifacts. Inspired by the image\nreconstruction techniques based on deep learning, we propose a multi-focus\nimage fusion network framework without any post-processing to solve these\nproblems in the end-to-end and supervised learning way. To sufficiently train\nthe fusion model, we have generated a large-scale multi-focus image dataset\nwith ground-truth fusion images. What's more, to obtain a more informative\nfusion image, we further designed a novel fusion strategy based on unity fusion\nattention, which is composed of a channel attention module and a spatial\nattention module. Specifically, the proposed fusion approach mainly comprises\nthree key components: feature extraction, feature fusion and image\nreconstruction. We firstly utilize seven convolutional blocks to extract the\nimage features from source images. Then, the extracted convolutional features\nare fused by the proposed fusion strategy in the feature fusion layer. Finally,\nthe fused image features are reconstructed by four convolutional blocks.\nExperimental results demonstrate that the proposed approach for multi-focus\nimage fusion achieves remarkable fusion performance compared to 19\nstate-of-the-art fusion methods.\n"}
+{"abstract": "  In this work, the aim is to study the spread of a contagious disease and\ninformation on a multilayer social system. The main idea is to find a criterion\nunder which the adoption of the spreading information blocks or suppresses the\nepidemic spread. A two-layer network is the base of the model. The first layer\ndescribes the direct contact interactions, while the second layer is the\ninformation propagation layer. Both layers consist of the same nodes. The\nsociety consists of five different categories of individuals: susceptibles,\ninfective, recovered, vaccinated and precautioned. Initially, only one infected\nindividual starts transmitting the infection. Direct contact interactions\nspread the infection to the susceptibles. The information spreads through the\nsecond layer. The SIR model is employed for the infection spread, while the\nBass equation models the adoption of information. The control parameters of the\ncompetition between the spread of information and spread of disease are the\ntopology and the density of connectivity. The topology of the information layer\nis a scale-free network with increasing density of edges. In the contact layer,\nregular and scale-free networks with the same average degree per node are used\ninterchangeably. The observation is that increasing complexity of the contact\nnetwork reduces the role of individual awareness. If the contact layer consists\nof networks with limited range connections, or the edges sparser than the\ninformation network, spread of information plays a significant role in\ncontrolling the epidemics.\n"}
+{"abstract": "  This paper first formalises a new observability concept, called weak regular\nobservability, that is adapted to Fast Moving Horizon Estimation where one aims\nto estimate the state of a nonlinear system efficiently on rolling time windows\nin the case of small initial error. Additionally, sufficient conditions of weak\nregular observability are provided in a problem of Simultaneous Localisation\nand Mapping (SLAM) for different measurement models. In particular it is shown\nthat following circular trajectories leads to weak regular observability in a\nsecond order 2D SLAM problem with several possible types of sensors.\n"}
+{"abstract": "  The dark matter halo surface density, given by the product of the dark matter\ncore radius ($r_c$) and core density ($\\rho_c$) has been shown to be a constant\nfor a wide range of isolated galaxy systems. Here, we carry out a test of this\n{\\em ansatz} using a sample of 17 relaxed galaxy groups observed using Chandra\nand XMM-Newton, as an extension of our previous analysis with galaxy clusters.\nWe find that $\\rho_c \\propto r_c^{-1.35^{+0.16}_{-0.17}}$, with an intrinsic\nscatter of about 27.3%, which is about 1.5 times larger than that seen for\ngalaxy clusters. Our results thereby indicate that the surface density is\ndiscrepant with respect to scale invariance by about 2$\\sigma$, and its value\nis about four times greater than that for galaxies. Therefore, the elevated\nvalues of the halo surface density for groups and clusters indicate that the\nsurface density cannot be a universal constant for all dark matter dominated\nsystems. Furthermore, we also implement a test of the radial acceleration\nrelation for this group sample. We find that the residual scatter in the radial\nacceleration relation is about 0.32 dex and a factor of three larger than that\nobtained using galaxy clusters. The acceleration scale which we obtain is\nin-between that seen for galaxies and clusters.\n"}
+{"abstract": "  The nature of unconventional superconductivity is intimately linked to the\nmicroscopic nature of the pairing interactions. In this work, motivated by\ncubic heavy fermion compounds with embedded multipolar moments, we\ntheoretically investigate superconducting instabilities instigated by\nmultipolar Kondo interactions. Employing multipolar fluctuations (mediated by\nRKKY interaction) coupled to conduction electrons via two-channel Kondo and\nnovel multipolar Kondo interactions, we uncover a variety of superconducting\nstates characterized by higher-angular momentum Cooper pairs, $J=0,1,2,3$. We\ndemonstrate that both odd and even parity pairing functions are possible,\nregardless of the total angular momentum of the Cooper pairs, which can be\ntraced back to the atypical nature of the multipolar Kondo interaction that\nintertwines conduction electron spin and orbital degrees of freedom. We\ndetermine that different (point-group) irrep classified pairing functions may\ncoexist with each other, with some of them characterized by gapped and point\nnode structures in their corresponding quasiparticle spectra. This work lays\nthe foundation for discovery and classification of superconducting states in\nrare-earth metallic compounds with multipolar local moments.\n"}
+{"abstract": "  Satellites, are both crucial and, despite common misbelieve, very fragile\nparts our civilian and military critical infrastructure. While, many efforts\nare focused on securing ground and space segments, especially when national\nsecurity or large businesses interests are affected, the small-sat, newspace\nrevolution democratizes access to, and exploitation of the near earth orbits.\nThis brings new players to the market, typically in the form of small to medium\nsized companies, offering new or more affordable services. Despite the\nnecessity and inevitability of this process, it also opens potential new venues\nfor targeted attacks against space-related infrastructure. Since sources of\nsatellite ephemerides are very often centralized, they are subject to classical\nMan-in-the-Middle attacks which open venues for TLE spoofing attack, which may\nresult in unnecessary collision avoidance maneuvers, in best case and\norchestrated crashes, in worst case. In this work, we propose a countermeasure\nto the presented problem that include distributed solution, which will have no\ncentral authority responsible for storing and disseminating TLE information.\nInstead, each of the peers participating to the system, have full access to all\nof the records stored in the system, and distribute the data in a consensual\nmanner,ensuring information replication at each peer node. This way, single\npoint of failure syndromes of classic systems, which currently exist due to the\ndirect ephemerids distribution mechanism, are removed. Our proposed solution is\nto build data dissemination systems using permissioned, private ledgers where\npeers have strong and verifiable identities, which allow also for redundancy in\nSST data sourcing.\n"}
+{"abstract": "  Opioid Use Disorder (OUD) is a public health crisis costing the US billions\nof dollars annually in healthcare, lost workplace productivity, and crime.\nAnalyzing longitudinal healthcare data is critical in addressing many\nreal-world problems in healthcare. Leveraging the real-world longitudinal\nhealthcare data, we propose a novel multi-stream transformer model called MUPOD\nfor OUD identification. MUPOD is designed to simultaneously analyze multiple\ntypes of healthcare data streams, such as medications and diagnoses, by\nattending to segments within and across these data streams. Our model tested on\nthe data from 392,492 patients with long-term back pain problems showed\nsignificantly better performance than the traditional models and recently\ndeveloped deep learning models.\n"}
+{"abstract": "  This paper presents an analytical study for the metric properties of the\nparaboloidal double projection, i.e. central and orthogonal projections used in\nthe catadioptric camera system. Metric properties have not sufficiently studied\nin previous treatments of such system. These properties incorporate the\ndetermination of the true lengths of projected lines and areas bounded by\nprojected lines. The advantageous main gain of determining metric elements of\nthe paraboloidal double projection is studying distortion analysis and camera\ncalibration, which is considered an essential tool in testing camera accuracy.\nAlso, this may be considered as a significant utility in studying comparison\nanalysis between different cameras projection systems.\n"}
+{"abstract": "  Anti-ferromagnetic materials have the possibility to offer ultra fast, high\ndata density spintronic devices. A significant challenge is the reliable\ndetection of the state of the antiferromagnet, which can be achieved using\nexchange bias. Here we develop an atomistic spin model of the athermal training\neffect, a well known phenomenon in exchange biased systems where the bias is\nsignificantly reduced after the first hysteresis cycle. We find that the\nsetting process in granular thin films relies on the presence of interfacial\nmixing between the ferromagnetic and antiferromagnetic layers. We\nsystematically investigate the effect of the intermixing and find that the\nexchange bias, switching field and coercivity all increase with increased\nintermixing. The interfacial spin state is highly frustrated leading to a\nsystematic decrease in interfacial ordering of the ferromagnet. This metastable\nspin structure of initially irreversible spins leads to a large effective\nexchange coupling and thus large increase in the switching field. After the\nfirst hysteresis cycle these metastable spins drop into a reversible ground\nstate that is repeatable for all subsequent hysteresis cycles, demonstrating\nthat the effect is truly athermal. Our simulations provide new insights into\nthe role of interface mixing and the importance of metastable spin structures\nin exchange biased systems which could help with the design an optimisation of\nantiferromagnetic spintronic devices.\n"}
+{"abstract": "  The optical matrix formalism is applied to find parameters such as focal\ndistance, back and front focal points, principal planes, and the equation\nrelating object and image distances for a thick spherical lens immerse in air.\nThen, the formalism is applied to systems compound of two, three and N thick\nlenses in cascade. It is found that a simple Gaussian equation is enough to\nrelate object and image distances no matter the number of lenses.\n"}
+{"abstract": "  Recent works have shown that learned models can achieve significant\nperformance gains, especially in terms of perceptual quality measures, over\ntraditional methods. Hence, the state of the art in image restoration and\ncompression is getting redefined. This special issue covers the state of the\nart in learned image/video restoration and compression to promote further\nprogress in innovative architectures and training methods for effective and\nefficient networks for image/video restoration and compression.\n"}
+{"abstract": "  The Laser Interferometer Space Antenna, LISA, will detect gravitational wave\nsignals from Extreme Mass Ratio Inspirals, where a stellar mass compact object\norbits a supermassive black hole and eventually plunges into it. Here we report\non LISA's capability to detect whether the smaller compact object in an Extreme\nMass Ratio Inspiral is endowed with a scalar field, and to measure its scalar\ncharge -- a dimensionless quantity that acts as a measure of how much scalar\nfield the object carries. By direct comparison of signals, we show that LISA\nwill be able to detect and measure the scalar charge with an accuracy of the\norder of percent, which is an unprecedented level of precision. This result is\nindependent of the origin of the scalar field and of the structure and other\nproperties of the small compact object, so it can be seen as a generic\nassessment of LISA's capabilities to detect new fundamental fields.\n"}
+{"abstract": "  Communication between workers and the master node to collect local stochastic\ngradients is a key bottleneck in a large-scale federated learning system.\nVarious recent works have proposed to compress the local stochastic gradients\nto mitigate the communication overhead. However, robustness to malicious\nattacks is rarely considered in such a setting. In this work, we investigate\nthe problem of Byzantine-robust federated learning with compression, where the\nattacks from Byzantine workers can be arbitrarily malicious. We point out that\na vanilla combination of compressed stochastic gradient descent (SGD) and\ngeometric median-based robust aggregation suffers from both stochastic and\ncompression noise in the presence of Byzantine attacks. In light of this\nobservation, we propose to jointly reduce the stochastic and compression noise\nso as to improve the Byzantine-robustness. For the stochastic noise, we adopt\nthe stochastic average gradient algorithm (SAGA) to gradually eliminate the\ninner variations of regular workers. For the compression noise, we apply the\ngradient difference compression and achieve compression for free. We\ntheoretically prove that the proposed algorithm reaches a neighborhood of the\noptimal solution at a linear convergence rate, and the asymptotic learning\nerror is in the same order as that of the state-of-the-art uncompressed method.\nFinally, numerical experiments demonstrate effectiveness of the proposed\nmethod.\n"}
+{"abstract": "  Many emerging cyber-physical systems, such as autonomous vehicles and robots,\nrely heavily on artificial intelligence and machine learning algorithms to\nperform important system operations. Since these highly parallel applications\nare computationally intensive, they need to be accelerated by graphics\nprocessing units (GPUs) to meet stringent timing constraints. However, despite\nthe wide adoption of GPUs, efficiently scheduling multiple GPU applications\nwhile providing rigorous real-time guarantees remains a challenge. In this\npaper, we propose RTGPU, which can schedule the execution of multiple GPU\napplications in real-time to meet hard deadlines. Each GPU application can have\nmultiple CPU execution and memory copy segments, as well as GPU kernels. We\nstart with a model to explicitly account for the CPU and memory copy segments\nof these applications. We then consider the GPU architecture in the development\nof a precise timing model for the GPU kernels and leverage a technique known as\npersistent threads to implement fine-grained kernel scheduling with improved\nperformance through interleaved execution. Next, we propose a general method\nfor scheduling parallel GPU applications in real time. Finally, to schedule\nmultiple parallel GPU applications, we propose a practical real-time scheduling\nalgorithm based on federated scheduling and grid search (for GPU kernel\nsegments) with uniprocessor fixed priority scheduling (for multiple CPU and\nmemory copy segments). Our approach provides superior schedulability compared\nwith previous work, and gives real-time guarantees to meet hard deadlines for\nmultiple GPU applications according to comprehensive validation and evaluation\non a real NVIDIA GTX1080Ti GPU system.\n"}
+{"abstract": "  The Supervisory control and data acquisition (SCADA) systems have been\ncontinuously leveraging the evolution of network architecture, communication\nprotocols, next-generation communication techniques (5G, 6G, Wi-Fi 6), and the\ninternet of things (IoT). However, SCADA system has become the most profitable\nand alluring target for ransomware attackers. This paper proposes the deep\nlearning-based novel ransomware detection framework in the SCADA controlled\nelectric vehicle charging station (EVCS) with the performance analysis of three\ndeep learning algorithms, namely deep neural network (DNN), 1D convolution\nneural network (CNN), and long short-term memory (LSTM) recurrent neural\nnetwork. All three-deep learning-based simulated frameworks achieve around 97%\naverage accuracy (ACC), more than 98% of the average area under the curve\n(AUC), and an average F1-score under 10-fold stratified cross-validation with\nan average false alarm rate (FAR) less than 1.88%. Ransomware driven\ndistributed denial of service (DDoS) attack tends to shift the SOC profile by\nexceeding the SOC control thresholds. The severity has been found to increase\nas the attack progress and penetration increases. Also, ransomware driven false\ndata injection (FDI) attack has the potential to damage the entire BES or\nphysical system by manipulating the SOC control thresholds. It's a design\nchoice and optimization issue that a deep learning algorithm can deploy based\non the tradeoffs between performance metrics.\n"}
+{"abstract": "  Spectral lines from formaldehyde (H2CO) molecules at cm wavelengths are\ntypically detected in absorption and trace a broad range of environments, from\ndiffuse gas to giant molecular clouds. In contrast, thermal emission of\nformaldehyde lines at cm wavelengths is rare. In previous observations with the\n100m Robert C. Byrd Green Bank Telescope (GBT), we detected 2 cm formaldehyde\nemission toward NGC7538 IRS1 - a high-mass protostellar object in a prominent\nstar-forming region of our Galaxy. We present further GBT observations of the 2\ncm and 1 cm H2CO lines to investigate the nature of the 2 cm H2CO emission. We\nconducted observations to constrain the angular size of the 2 cm emission\nregion based on a East-West and North-South cross-scan map. Gaussian fits of\nthe spatial distribution in the East-West direction show a deconvolved size (at\nhalf maximum) of the 2 cm emission of 50\" +/- 8\". The 1 cm H2CO observations\nrevealed emission superimposed on a weak absorption feature. A non-LTE\nradiative transfer analysis shows that the H2CO emission is consistent with\nquasi-thermal radiation from dense gas (~10^5 to 10^6 cm^-3). We also report\ndetection of 4 transitions of CH3OH (12.2, 26.8, 28.3, 28.9 GHz), the (8,8)\ntransition of NH3 (26.5 GHz), and a cross-scan map of the 13 GHz SO line that\nshows extended emission (> 50\").\n"}
+{"abstract": "  Dynamically switchable half-/quarter-wave plates have recently been the focus\nin the terahertz regime. Conventional design philosophy leads to multilayer\nmetamaterials or narrowband metasurfaces. Here we propose a novel design\nphilosophy and a VO2-metal hybrid metasurface for achieving broadband\ndynamically switchable half-/quarter-wave plate (HWP/QWP) based on the\ntransition from the overdamped to the underdamped resonance. Results show that,\nby varying the VO2 conductivity by three orders of magnitude, the proposed\nmetasurface's function can be switched between an HWP with polarization\nconversion ratio larger than 96% and a QWP with ellipticity close to -1 over\nthe broad working band of 0.8-1.2 THz. We expect that the proposed design\nphilosophy will advance the engineering of metasurfaces for dynamically\nswitchable functionalities beyond the terahertz regime.\n"}
+{"abstract": "  We develop a new type of orthogonal polynomial, the modified discrete\nLaguerre (MDL) polynomials, designed to accelerate the computation of bosonic\nMatsubara sums in statistical physics. The MDL polynomials lead to a rapidly\nconvergent Gaussian \"quadrature\" scheme for Matsubara sums, and more generally\nfor any sum $F(0)/2 + F(h) + F(2h) + \\cdots$ of exponentially decaying summands\n$F(nh) = f(nh)e^{-nhs}$ where $hs>0$. We demonstrate this technique for\ncomputation of finite-temperature Casimir forces arising from quantum field\ntheory, where evaluation of the summand $F$ requires expensive electromagnetic\nsimulations. A key advantage of our scheme, compared to previous methods, is\nthat the convergence rate is nearly independent of the spacing $h$\n(proportional to the thermodynamic temperature). We also prove convergence for\nany polynomially decaying $F$.\n"}
+{"abstract": "  The growth rate of the number of scientific publications is constantly\nincreasing, creating important challenges in the identification of valuable\nresearch and in various scholarly data management applications, in general. In\nthis context, measures which can effectively quantify the scientific impact\ncould be invaluable. In this work, we present BIP! DB, an open dataset that\ncontains a variety of impact measures calculated for a large collection of more\nthan 100 million scientific publications from various disciplines.\n"}
+{"abstract": "  Context{The high energy emission regions of rotation powered pulsars are\nstudied using folded light curve (FLCs) and phase resolved spectra (PRS).}\naims{This work uses the NICER observatory to obtain the highest resolution FLC\nand PRS of the Crab pulsar at soft X-ray energies.} methods{NICER has\naccumulated about 347 ksec of data on the Crab pulsar. The data are processed\nusing the standard analysis pipeline. Stringent filtering is done for spectral\nanalysis. The individual detectors are calibrated in terms of long time light\ncurve (LTLC), raw spectrum and deadtime. The arrival times of the photons are\nreferred to the solar system's barycenter and the rotation frequency $\\nu$ and\nits time derivative $\\dot \\nu$ are used to derive the rotation phase of each\nphoton.} results{The LTLCs, raw spectra and deadtimes of the individual\ndetectors are statistically similar; the latter two show no evolution with\nepoch; detector deadtime is independent of photon energy. The deadtime for the\nCrab pulsar, taking into account the two types of deadtime, is only approx 7%\nto 8% larger than that obtained using the cleaned events. Detector 00 behaves\nslightly differently from the rest, but can be used for spectral work. The PRS\nof the two peaks of the Crab pulsar are obtained at a resolution of better than\n1/512 in rotation phase. The FLC very close to the first peak rises slowly and\nfalls faster. The spectral index of the PRS is almost constant very close to\nthe first peak.} conclusions{The high resolution FLC and PRS of the {{peaks}}\nof the Crab pulsar provide important constraints for the formation of caustics\nin the emission zone.}\n"}
+{"abstract": "  The many-body-theory approach to positronium-atom interactions developed in\n[Phys. Rev. Lett. \\textbf{120}, 183402 (2018)] is applied to the sequence of\nnoble-gas atoms He-Xe. The Dyson equation is solved separately for an electron\nand positron moving in the field of the atom, with the entire system enclosed\nin a hard-wall spherical cavity. The two-particle Dyson equation is solved to\ngive the energies and wave functions of the Ps eigenstates in the cavity. From\nthese, we determine the scattering phase shifts and cross sections, and values\nof the pickoff annihilation parameter $^1Z_\\text{eff}$ including short-range\nelectron-positron correlations via vertex enhancement factors. Comparisons are\nmade with available experimental data for elastic and momentum-transfer cross\nsections and $^1Z_\\text{eff}$. Values of $^1Z_\\text{eff}$ for He and Ne,\npreviously reported in [Phys. Rev. Lett. \\textbf{120}, 183402 (2018)], are\nfound to be in near-perfect agreement with experiment, and for Ar, Kr, and Xe\nwithin a factor of 1.2.\n"}
+{"abstract": "  Search-based test generation is guided by feedback from one or more fitness\nfunctions - scoring functions that judge solution optimality. Choosing\ninformative fitness functions is crucial to meeting the goals of a tester.\nUnfortunately, many goals - such as forcing the class-under-test to throw\nexceptions, increasing test suite diversity, and attaining Strong Mutation\nCoverage - do not have effective fitness function formulations. We propose that\nmeeting such goals requires treating fitness function identification as a\nsecondary optimization step. An adaptive algorithm that can vary the selection\nof fitness functions could adjust its selection throughout the generation\nprocess to maximize goal attainment, based on the current population of test\nsuites. To test this hypothesis, we have implemented two reinforcement learning\nalgorithms in the EvoSuite unit test generation framework, and used these\nalgorithms to dynamically set the fitness functions used during generation for\nthe three goals identified above.\n  We have evaluated our framework, EvoSuiteFIT, on a set of Java case examples.\nEvoSuiteFIT techniques attain significant improvements for two of the three\ngoals, and show limited improvements on the third when the number of\ngenerations of evolution is fixed. Additionally, for two of the three goals,\nEvoSuiteFIT detects faults missed by the other techniques. The ability to\nadjust fitness functions allows strategic choices that efficiently produce more\neffective test suites, and examining these choices offers insight into how to\nattain our testing goals. We find that adaptive fitness function selection is a\npowerful technique to apply when an effective fitness function does not already\nexist for achieving a testing goal.\n"}
+{"abstract": "  While object semantic understanding is essential for most service robotic\ntasks, 3D object classification is still an open problem. Learning from\nartificial 3D models alleviates the cost of annotation necessary to approach\nthis problem, but most methods still struggle with the differences existing\nbetween artificial and real 3D data. We conjecture that the cause of those\nissue is the fact that many methods learn directly from point coordinates,\ninstead of the shape, as the former is hard to center and to scale under\nvariable occlusions reliably. We introduce spherical kernel point convolutions\nthat directly exploit the object surface, represented as a graph, and a voting\nscheme to limit the impact of poor segmentation on the classification results.\nOur proposed approach improves upon state-of-the-art methods by up to 36% when\ntransferring from artificial objects to real objects.\n"}
+{"abstract": "  After observing the Higgs boson by the ATLAS and CMS experiments at the LHC,\naccurate measurements of its properties, which allow us to study the\nelectroweak symmetry breaking mechanism, become a high priority for particle\nphysics. The most promising of extracting the Higgs self-coupling at hadron\ncolliders is by examining the double Higgs production, especially in the $b\n\\bar{b} \\gamma \\gamma$ channel. In this work, we presented full loop\ncalculation for both SM and New Physics effects of the Higgs pair production to\nnext-to-leading-order (NLO), including loop-induced processes $gg\\to HH$,\n$gg\\to HHg$, and $qg \\to qHH$. We also included the calculation of the\ncorrections from diagrams with only one QCD coupling in $qg \\to qHH$, which was\nneglected in the previous studies. With the latest observed limit on the HH\nproduction cross-section, we studied the constraints on the effective Higgs\ncouplings for the LHC at center-of-mass energies of 14 TeV and a provisional\n100 TeV proton collider within the Future-Circular-Collider (FCC) project. To\nobtain results better than using total cross-section alone, we focused on the\n$b \\bar{b} \\gamma \\gamma$ channel and divided the differential cross-section\ninto low and high bins based on the total invariant mass and $p_{T}$ spectra.\nThe new physics effects are further constrained by including extra kinematic\ninformation. However, some degeneracy persists, as shown in previous studies,\nespecially in determining the Higgs trilinear coupling. Our analysis shows that\nthe degeneracy is reduced by including the full NLO corrections.\n"}
+{"abstract": "  In this paper, we propose an energy-efficient optimal altitude for an aerial\naccess point (AAP), which acts as a flying base station to serve a set of\nground user equipment (UE). Since the ratio of total energy consumed by the\naerial vehicle to the communication energy is very large, we include the aerial\nvehicle's energy consumption in the problem formulation. After considering the\nenergy consumption model of the aerial vehicle, our objective is translated\ninto a non-convex optimization problem of maximizing the global energy\nefficiency (GEE) of the aerial communication system, subject to altitude and\nminimum individual data rate constraints. At first, the non-convex fractional\nobjective function is solved by using sequential convex programming (SCP)\noptimization technique. To compare the result of SCP with the global optimum of\nthe problem, we reformulate the initial problem as a monotonic fractional\noptimization problem (MFP) and solve it using the polyblock outer approximation\n(PA) algorithm. Numerical results show that the candidate solution obtained\nfrom SCP is the same as the global optimum found using the monotonic fractional\nprogramming technique. Furthermore, the impact of the aerial vehicle's energy\nconsumption on the optimal altitude determination is also studied.\n"}
+{"abstract": "  Generative Adversarial Networks (GANs) have demonstrated unprecedented\nsuccess in various image generation tasks. The encouraging results, however,\ncome at the price of a cumbersome training process, during which the generator\nand discriminator are alternately updated in two stages. In this paper, we\ninvestigate a general training scheme that enables training GANs efficiently in\nonly one stage. Based on the adversarial losses of the generator and\ndiscriminator, we categorize GANs into two classes, Symmetric GANs and\nAsymmetric GANs, and introduce a novel gradient decomposition method to unify\nthe two, allowing us to train both classes in one stage and hence alleviate the\ntraining effort. We also computationally analyze the efficiency of the proposed\nmethod, and empirically demonstrate that, the proposed method yields a solid\n$1.5\\times$ acceleration across various datasets and network architectures.\nFurthermore, we show that the proposed method is readily applicable to other\nadversarial-training scenarios, such as data-free knowledge distillation. The\ncode is available at https://github.com/zju-vipa/OSGAN.\n"}
+{"abstract": "  In order to satisfy timing constraints, modern real-time applications require\nmassively parallel accelerators such as General Purpose Graphic Processing\nUnits (GPGPUs). Generation after generation, the number of computing clusters\nmade available in novel GPU architectures is steadily increasing, hence,\ninvestigating suitable scheduling approaches is now mandatory. Such scheduling\napproaches are related to mapping different and concurrent compute kernels\nwithin the GPU computing clusters, hence grouping GPU computing clusters into\nschedulable partitions. In this paper we propose novel techniques to define GPU\npartitions; this allows us to define suitable task-to-partition allocation\nmechanisms in which tasks are GPU compute kernels featuring different timing\nrequirements. Such mechanisms will take into account the interference that GPU\nkernels experience when running in overlapping time windows. Hence, an\neffective and simple way to quantify the magnitude of such interference is also\npresented. We demonstrate the efficiency of the proposed approaches against the\nclassical techniques that considered the GPU as a single, non-partitionable\nresource.\n"}
+{"abstract": "  Electrons in low-temperature solids are governed by the non-relativistic\nSchr$\\ddot{o}$dinger equation, since the electron velocities are much slower\nthan the speed of light. Remarkably, the low-energy quasi-particles given by\nelectrons in various materials can behave as relativistic Dirac/Weyl fermions\nthat obey the relativistic Dirac/Weyl equation. We refer to these materials as\n\"Dirac/Weyl materials\", which provide a tunable platform to test relativistic\nquantum phenomena in table-top experiments. More interestingly, different types\nof physical fields in these Weyl/Dirac materials, such as magnetic\nfluctuations, lattice vibration, strain, and material inhomogeneity, can couple\nto the \"relativistic\" quasi-particles in a similar way as the $U(1)$ gauge\ncoupling. As these fields do not have gauge-invariant dynamics in general, we\nrefer to them as \"pseudo-gauge fields\". In this chapter, we overview the\nconcept and physical consequences of pseudo-gauge fields in Weyl/Dirac\nmaterials. In particular, we will demonstrate that pseudo-gauge fields can\nprovide a unified understanding of a variety of physical phenomena, including\nchiral zero modes inside a magnetic vortex core of magnetic Weyl semimetals, a\ngiant current response at magnetic resonance in magnetic topological\ninsulators, and piezo-electromagnetic response in time-reversal invariant\nsystems. These phenomena are deeply related to various concepts in high-energy\nphysics, such as chiral anomaly and axion electrodynamics.\n"}
+{"abstract": "  The bug triaging process, an essential process of assigning bug reports to\nthe most appropriate developers, is related closely to the quality and costs of\nsoftware development. As manual bug assignment is a labor-intensive task,\nespecially for large-scale software projects, many machine-learning-based\napproaches have been proposed to automatically triage bug reports. Although\ndeveloper collaboration networks (DCNs) are dynamic and evolving in the\nreal-world, most automated bug triaging approaches focus on static tossing\ngraphs at a single time slice. Also, none of the previous studies consider\nperiodic interactions among developers. To address the problems mentioned\nabove, in this article, we propose a novel spatial-temporal dynamic graph\nneural network (ST-DGNN) framework, including a joint random walk (JRWalk)\nmechanism and a graph recurrent convolutional neural network (GRCNN) model. In\nparticular, JRWalk aims to sample local topological structures in a graph with\ntwo sampling strategies by considering both node importance and edge\nimportance. GRCNN has three components with the same structure, i.e.,\nhourly-periodic, daily-periodic, and weekly-periodic components, to learn the\nspatial-temporal features of dynamic DCNs. We evaluated our approach's\neffectiveness by comparing it with several state-of-the-art graph\nrepresentation learning methods in two domain-specific tasks that belong to\nnode classification. In the two tasks, experiments on two real-world,\nlarge-scale developer collaboration networks collected from the Eclipse and\nMozilla projects indicate that the proposed approach outperforms all the\nbaseline methods.\n"}
+{"abstract": "  We propose an algorithm that uses linear function approximation (LFA) for\nstochastic shortest path (SSP). Under minimal assumptions, it obtains sublinear\nregret, is computationally efficient, and uses stationary policies. To our\nknowledge, this is the first such algorithm in the LFA literature (for SSP or\nother formulations). Our algorithm is a special case of a more general one,\nwhich achieves regret square root in the number of episodes given access to a\ncertain computation oracle.\n"}
+{"abstract": "  This paper develops a new empirical Bayesian inference algorithm for solving\na linear inverse problem given multiple measurement vectors (MMV) of\nunder-sampled and noisy observable data. Specifically, by exploiting the joint\nsparsity across the multiple measurements in the sparse domain of the\nunderlying signal or image, we construct a new support informed sparsity\npromoting prior. Several applications can be modeled using this framework, and\nas a prototypical example we consider reconstructing an image from synthetic\naperture radar (SAR) observations using nearby azimuth angles. Our numerical\nexperiments demonstrate that using this new prior not only improves accuracy of\nthe recovery, but also reduces the uncertainty in the posterior when compared\nto standard sparsity producing priors.\n"}
+{"abstract": "  The current interests in the universe motivate us to go beyond Einstein's\nGeneral theory of relativity. One of the interesting proposals comes from a new\nclass of teleparallel gravity named symmetric teleparallel gravity, i.e.,\n$f(Q)$ gravity, where the non-metricity term $Q$ is accountable for fundamental\ninteraction. These alternative modified theories of gravity's vital role are to\ndeal with the recent interests and to present a realistic cosmological model.\nThis manuscript's main objective is to study the traversable wormhole\ngeometries in $f(Q)$ gravity. We construct the wormhole geometries for three\ncases: (i) by assuming a relation between the radial and lateral pressure, (ii)\nconsidering phantom energy equation of state (EoS), and (iii) for a specific\nshape function in the fundamental interaction of gravity (i.e. for linear form\nof $f(Q)$). Besides, we discuss two wormhole geometries for a general case of\n$f(Q)$ with two specific shape functions. Then, we discuss the viability of\nshape functions and the stability analysis of the wormhole solutions for each\ncase. We have found that the null energy condition (NEC) violates each wormhole\nmodel which concluded that our outcomes are realistic and stable. Finally, we\ndiscuss the embedding diagrams and volume integral quantifier to have a\ncomplete view of wormhole geometries.\n"}
+{"abstract": "  We study temporally localized structures in doubly resonant degenerate\noptical parametric oscillators in the absence of temporal walk-off. We focus on\nstates formed through the locking of domain walls between the zero and a\nnon-zero continuous wave solution. We show that these states undergo collapsed\nsnaking and we characterize their dynamics in the parameter space.\n"}
+{"abstract": "  Experience replay enables off-policy reinforcement learning (RL) agents to\nutilize past experiences to maximize the cumulative reward. Prioritized\nexperience replay that weighs experiences by the magnitude of their\ntemporal-difference error ($|\\text{TD}|$) significantly improves the learning\nefficiency. But how $|\\text{TD}|$ is related to the importance of experience is\nnot well understood. We address this problem from an economic perspective, by\nlinking $|\\text{TD}|$ to value of experience, which is defined as the value\nadded to the cumulative reward by accessing the experience. We theoretically\nshow the value metrics of experience are upper-bounded by $|\\text{TD}|$ for\nQ-learning. Furthermore, we successfully extend our theoretical framework to\nmaximum-entropy RL by deriving the lower and upper bounds of these value\nmetrics for soft Q-learning, which turn out to be the product of $|\\text{TD}|$\nand \"on-policyness\" of the experiences. Our framework links two important\nquantities in RL: $|\\text{TD}|$ and value of experience. We empirically show\nthat the bounds hold in practice, and experience replay using the upper bound\nas priority improves maximum-entropy RL in Atari games.\n"}
+{"abstract": "  We tackle the problem of unsupervised synthetic-to-real domain adaptation for\nsingle image depth estimation. An essential building block of single image\ndepth estimation is an encoder-decoder task network that takes RGB images as\ninput and produces depth maps as output. In this paper, we propose a novel\ntraining strategy to force the task network to learn domain invariant\nrepresentations in a selfsupervised manner. Specifically, we extend\nself-supervised learning from traditional representation learning, which works\non images from a single domain, to domain invariant representation learning,\nwhich works on images from two different domains by utilizing an image-to-image\ntranslation network. Firstly, we use an image-to-image translation network to\ntransfer domain-specific styles between synthetic and real domains. This style\ntransfer operation allows us to obtain similar images from the different\ndomains. Secondly, we jointly train our task network and Siamese network with\nthe same images from the different domains to obtain domain invariance for the\ntask network. Finally, we fine-tune the task network using labeled synthetic\nand unlabeled realworld data. Our training strategy yields improved\ngeneralization capability in the real-world domain. We carry out an extensive\nevaluation on two popular datasets for depth estimation, KITTI and Make3D. The\nresults demonstrate that our proposed method outperforms the state-of-the-art\non all metrics, e.g. by 14.7% on Sq Rel on KITTI. The source code and model\nweights will be made available.\n"}
+{"abstract": "  We propose an algorithm for automatic, targetless, extrinsic calibration of a\nLiDAR and camera system using semantic information. We achieve this goal by\nmaximizing mutual information (MI) of semantic information between sensors,\nleveraging a neural network to estimate semantic mutual information, and matrix\nexponential for calibration computation. Using kernel-based sampling to sample\ndata from camera measurement based on LiDAR projected points, we formulate the\nproblem as a novel differentiable objective function which supports the use of\ngradient-based optimization methods. We also introduce an initial calibration\nmethod using 2D MI-based image registration. Finally, we demonstrate the\nrobustness of our method and quantitatively analyze the accuracy on a synthetic\ndataset and also evaluate our algorithm qualitatively on KITTI360 and RELLIS-3D\nbenchmark datasets, showing improvement over recent comparable approaches.\n"}
+{"abstract": "  Providing multi-connectivity services is an important goal for next\ngeneration wireless networks, where multiple access networks are available and\nneed to be integrated into a coherent solution that efficiently supports both\nreliable and non reliable traffic. Based on virtual network interfaces and per\npath congestion controlled tunnels, the MP-DCCP based multiaccess aggregation\nframework presents as a novel solution that flexibly supports different path\nschedulers and congestion control algorithms as well as reordering modules. The\nframework has been implemented within the Linux kernel space and has been\ntested over different prototypes. Experimental results have shown that the\noverall performance strongly depends upon the congestion control algorithm used\non the individual DCCP tunnels, denoted as CCID. In this paper, we present an\nimplementation of the BBR (Bottleneck Bandwidth Round Trip propagation time)\ncongestion control algorithm for DCCP in the Linux kernel. We show how BBR is\nintegrated into the MP-DCCP multi-access framework and evaluate its performance\nover both single and multi-path environments. Our evaluation results show that\nBBR improves the performance compared to CCID2 for multi-path scenarios due to\nthe faster response to changes in the available bandwidth, which reduces\nlatency and increases performance, especially for unreliable traffic. the\nMP-DCCP framework code, including the new CCID5 is available as OpenSource.\n"}
+{"abstract": "  Recent developments in representational learning for information retrieval\ncan be organized in a conceptual framework that establishes two pairs of\ncontrasts: sparse vs. dense representations and unsupervised vs. learned\nrepresentations. Sparse learned representations can further be decomposed into\nexpansion and term weighting components. This framework allows us to understand\nthe relationship between recently proposed techniques such as DPR, ANCE,\nDeepCT, DeepImpact, and COIL, and furthermore, gaps revealed by our analysis\npoint to \"low hanging fruit\" in terms of techniques that have yet to be\nexplored. We present a novel technique dubbed \"uniCOIL\", a simple extension of\nCOIL that achieves to our knowledge the current state-of-the-art in sparse\nretrieval on the popular MS MARCO passage ranking dataset. Our implementation\nusing the Anserini IR toolkit is built on the Lucene search library and thus\nfully compatible with standard inverted indexes.\n"}
+{"abstract": "  We prove a Gannon-Lee theorem for non-globally hyperbolic Lo\\-rentzian\nmetrics of regularity $C^1$, the most general regularity class currently\navailable in the context of the classical singularity theorems. Along the way\nwe also prove that any maximizing causal curve in a $C^1$-spacetime is a\ngeodesic and hence of $C^2$-regularity.\n"}
+{"abstract": "  Alphas are stock prediction models capturing trading signals in a stock\nmarket. A set of effective alphas can generate weakly correlated high returns\nto diversify the risk. Existing alphas can be categorized into two classes:\nFormulaic alphas are simple algebraic expressions of scalar features, and thus\ncan generalize well and be mined into a weakly correlated set. Machine learning\nalphas are data-driven models over vector and matrix features. They are more\npredictive than formulaic alphas, but are too complex to mine into a weakly\ncorrelated set. In this paper, we introduce a new class of alphas to model\nscalar, vector, and matrix features which possess the strengths of these two\nexisting classes. The new alphas predict returns with high accuracy and can be\nmined into a weakly correlated set. In addition, we propose a novel alpha\nmining framework based on AutoML, called AlphaEvolve, to generate the new\nalphas. To this end, we first propose operators for generating the new alphas\nand selectively injecting relational domain knowledge to model the relations\nbetween stocks. We then accelerate the alpha mining by proposing a pruning\ntechnique for redundant alphas. Experiments show that AlphaEvolve can evolve\ninitial alphas into the new alphas with high returns and weak correlations.\n"}
+{"abstract": "  In this article we generalize the concepts that were used in the PhD thesis\nof Drudge to classify Cameron-Liebler line classes in PG$(n,q), n\\geq 3$, to\nCameron-Liebler sets of $k$-spaces in PG$(n,q)$ and AG$(n,q)$. In his PhD\nthesis, Drudge proved that every Cameron-Liebler line class in PG$(n,q)$\nintersects every $3$-dimensional subspace in a Cameron-Liebler line class in\nthat subspace. We are using the generalization of this result for sets of\n$k$-spaces in PG$(n,q)$ and AG$(n,q)$. Together with a basic counting argument\nthis gives a very strong non-existence condition, $n\\geq 3k+3$. This condition\ncan also be improved for $k$-sets in AG$(n,q)$, with $n\\geq 2k+2$.\n"}
+{"abstract": "  We consider the problem of communicating a general bivariate function of two\nclassical sources observed at the encoders of a classical-quantum multiple\naccess channel. Building on the techniques developed for the case of a\nclassical channel, we propose and analyze a coding scheme based on coset codes.\nThe proposed technique enables the decoder recover the desired function without\nrecovering the sources themselves. We derive a new set of sufficient conditions\nthat are weaker than the current known for identified examples. This work is\nbased on a new ensemble of coset codes that are proven to achieve the capacity\nof a classical-quantum point-to-point channel.\n"}
+{"abstract": "  Motivated by recent observations of ergodicity breaking due to Hilbert space\nfragmentation in 1D Fermi-Hubbard chains with a tilted potential [Scherg et\nal., arXiv:2010.12965], we show that the same system also hosts quantum\nmany-body scars in a regime $U\\approx \\Delta \\gg J$ at electronic filling\nfactor $\\nu=1$. We numerically demonstrate that the scarring phenomenology in\nthis model is similar to other known realisations such as Rydberg atom chains,\nincluding persistent dynamical revivals and ergodicity-breaking many-body\neigenstates. At the same time, we show that the mechanism of scarring in the\nFermi-Hubbard model is different from other examples in the literature: the\nscars originate from a subgraph, representing a free spin-1 paramagnet, which\nis weakly connected to the rest of the Hamiltonian's adjacency graph. Our work\ndemonstrates that correlated fermions in tilted optical lattices provide a\nplatform for understanding the interplay of many-body scarring and other forms\nof ergodicity breaking, such as localisation and Hilbert space fragmentation.\n"}
+{"abstract": "  We map the likelihood of GW190521, the heaviest detected binary black hole\n(BBH) merger, by sampling under different mass and spin priors designed to be\nuninformative. We find that a source-frame total mass of $\\sim$$150 M_{\\odot}$\nis consistently supported, but posteriors in mass ratio and spin depend\ncritically on the choice of priors. We confirm that the likelihood has a\nmulti-modal structure with peaks in regions of mass ratio representing very\ndifferent astrophysical scenarios. The unequal-mass region ($m_2 / m_1 < 0.3$)\nhas an average likelihood $\\sim$$e^6$ times larger than the equal-mass region\n($m_2 / m_1 > 0.3$) and a maximum likelihood $\\sim$$e^2$ larger. Using\nensembles of samples across priors, we examine the implications of\nqualitatively different BBH sources that fit the data. We find that the\nequal-mass solution has poorly constrained spins and at least one black hole\nmass that is difficult to form via stellar collapse due to pair instability.\nThe unequal-mass solution can avoid this mass gap entirely but requires a\nnegative effective spin and a precessing primary. Either of these scenarios is\nmore easily produced by dynamical formation channels than field binary\nco-evolution. The sensitive comoving volume-time of the mass gap solution is\n$\\mathcal{O}(10)$ times larger than the gap-avoiding solution. After accounting\nfor this distance effect, the likelihood still reverses the advantage to favor\nthe gap-avoiding scenario by a factor of $\\mathcal{O}(100)$ before considering\nmass and spin priors. Posteriors are easily driven away from this\nhigh-likelihood region by common prior choices meant to be uninformative,\nmaking GW190521 parameter inference sensitive to the assumed mass and spin\ndistributions of mergers in the source's astrophysical channel. This may be a\ngeneric issue for similarly heavy events given current detector sensitivity and\nwaveform degeneracies.\n"}
+{"abstract": "  The ESO workshop \"Ground-based thermal infrared astronomy\" was held on-line\nOctober 12-16, 2020. Originally planned as a traditional in-person meeting at\nESO in Garching in April 2020, it was rescheduled and transformed into a fully\non-line event due to the COVID-19 pandemic. With 337 participants from 36\ncountries the workshop was a resounding success, demonstrating the wide\ninterest of the astronomical community in the science goals and the toolkit of\nground-based thermal infrared astronomy.\n"}
+{"abstract": "  Federated Learning (FL) is an emerging decentralized learning framework\nthrough which multiple clients can collaboratively train a learning model.\nHowever, a major obstacle that impedes the wide deployment of FL lies in\nmassive communication traffic. To train high dimensional machine learning\nmodels (such as CNN models), heavy communication traffic can be incurred by\nexchanging model updates via the Internet between clients and the parameter\nserver (PS), implying that the network resource can be easily exhausted.\nCompressing model updates is an effective way to reduce the traffic amount.\nHowever, a flexible unbiased compression algorithm applicable for both uplink\nand downlink compression in FL is still absent from existing works. In this\nwork, we devise the Model Update Compression by Soft Clustering (MUCSC)\nalgorithm to compress model updates transmitted between clients and the PS. In\nMUCSC, it is only necessary to transmit cluster centroids and the cluster ID of\neach model update. Moreover, we prove that: 1) The compressed model updates are\nunbiased estimation of their original values so that the convergence rate by\ntransmitting compressed model updates is unchanged; 2) MUCSC can guarantee that\nthe influence of the compression error on the model accuracy is minimized.\nThen, we further propose the boosted MUCSC (B-MUCSC) algorithm, a biased\ncompression algorithm that can achieve an extremely high compression rate by\ngrouping insignificant model updates into a super cluster. B-MUCSC is suitable\nfor scenarios with very scarce network resource. Ultimately, we conduct\nextensive experiments with the CIFAR-10 and FEMNIST datasets to demonstrate\nthat our algorithms can not only substantially reduce the volume of\ncommunication traffic in FL, but also improve the training efficiency in\npractical networks.\n"}
+{"abstract": "  We consider string theory vacua with tadpoles for dynamical fields and\nuncover universal features of the resulting spacetime-dependent solutions. We\nargue that the solutions can extend only a finite distance $\\Delta$ away in the\nspacetime dimensions over which the fields vary, scaling as $\\Delta^n\\sim {\\cal\nT}$ with the strength of the tadpole ${\\cal T}$. We show that naive\nsingularities arising at this distance scale are physically replaced by ends of\nspacetime, related to the cobordism defects of the swampland cobordism\nconjecture and involving stringy ingredients like orientifold planes and\nbranes, or exotic variants thereof. We illustrate these phenomena in large\nclasses of examples, including AdS$_5\\times T^{1,1}$ with 3-form fluxes, 10d\nmassive IIA, M-theory on K3, the 10d non-supersymmetric $USp(32)$ strings, and\ntype IIB compactifications with 3-form fluxes and/or magnetized D-branes. We\nalso describe a 6d string model whose tadpole triggers spontaneous\ncompactification to a semirealistic 3-family MSSM-like particle physics model.\n"}
+{"abstract": "  For a bounded domain $D \\subset \\mathbb{C}^n$, let $K_D = K_D(z) > 0$ denote\nthe Bergman kernel on the diagonal and consider the reproducing kernel Hilbert\nspace of holomorphic functions on $D$ that are square integrable with respect\nto the weight $K_D^{-d}$, where $d \\geq 0$ is an integer. The corresponding\nweighted kernel $K_{D, d}$ transforms appropriately under biholomorphisms and\nhence produces an invariant K\\\"{a}hler metric on $D$. Thus, there is a\nhierarchy of such metrics starting with the classical Bergman metric that\ncorresponds to the case $d=0$. This note is an attempt to study this class of\nmetrics in much the same way as the Bergman metric has been with a view towards\nidentifying properties that are common to this family. When $D$ is strongly\npseudoconvex, the scaling principle is used to obtain the boundary asymptotics\nof these metrics and several invariants associated to them. It turns out that\nall these metrics are complete on strongly pseudoconvex domains.\n"}
+{"abstract": "  We present evolutionary models for solar-like stars with an improved\ntreatment of convection that results in a more accurate estimate of the radius\nand effective temperature. This is achieved by improving the calibration of the\nmixing-length parameter, which sets the length scale in the 1D convection model\nimplemented in the stellar evolution code. Our calibration relies on the\nresults of 2D and 3D radiation hydrodynamics simulations of convection to\nspecify the value of the adiabatic specific entropy at the bottom of the\nconvective envelope in stars as a function of their effective temperature,\nsurface gravity and metallicity. For the first time, this calibration is fully\nintegrated within the flow of a stellar evolution code, with the mixing-length\nparameter being continuously updated at run-time. This approach replaces the\nmore common, but questionable, procedure of calibrating the length scale\nparameter on the Sun, and then applying the solar-calibrated value in modeling\nother stars, regardless of their mass, composition and evolutionary status. The\ninternal consistency of our current implementation makes it suitable for\napplication to evolved stars, in particular to red giants. We show that the\nentropy calibrated models yield a revised position of the red giant branch that\nis in better agreement with observational constraints than that of standard\nmodels.\n"}
+{"abstract": "  In this chapter we describe the history and evolution of the iCub humanoid\nplatform. We start by describing the first version as it was designed during\nthe RobotCub EU project and illustrate how it evolved to become the platform\nthat is adopted by more than 30 laboratories world wide. We complete the\nchapter by illustrating some of the research activities that are currently\ncarried out on the iCub robot, i.e. visual perception, event driven sensing and\ndynamic control. We conclude the Chapter with a discussion of the lessons we\nlearned and a preview of the upcoming next release of the robot, iCub 3.0.\n"}
+{"abstract": "  Unfitted finite element methods have emerged as a popular alternative to\nclassical finite element methods for the solution of partial differential\nequations and allow modeling arbitrary geometries without the need for a\nboundary-conforming mesh. On the other hand, the efficient solution of the\nresultant system is a challenging task because of the numerical\nill-conditioning that typically entails from the formulation of such methods.\nWe use an adaptive geometric multigrid solver for the solution of the mixed\nfinite cell formulation of saddle-point problems and investigate its\nconvergence in the context of the Stokes and Navier-Stokes equations. We\npresent two smoothers for the treatment of cutcells in the finite cell method\nand analyze their effectiveness for the model problems using a numerical\nbenchmark. Results indicate that the presented multigrid method is capable of\nsolving the model problems independently of the problem size and is robust with\nrespect to the depth of the grid hierarchy.\n"}
+{"abstract": "  For finite samples with binary outcomes penalized logistic regression such as\nridge logistic regression (RR) has the potential of achieving smaller mean\nsquared errors (MSE) of coefficients and predictions than maximum likelihood\nestimation. There is evidence, however, that RR is sensitive to small or sparse\ndata situations, yielding poor performance in individual datasets. In this\npaper, we elaborate this issue further by performing a comprehensive simulation\nstudy, investigating the performance of RR in comparison to Firth's correction\nthat has been shown to perform well in low-dimensional settings. Performance of\nRR strongly depends on the choice of complexity parameter that is usually tuned\nby minimizing some measure of the out-of-sample prediction error or information\ncriterion. Alternatively, it may be determined according to prior assumptions\nabout true effects. As shown in our simulation and illustrated by a data\nexample, values optimized in small or sparse datasets are negatively correlated\nwith optimal values and suffer from substantial variability which translates\ninto large MSE of coefficients and large variability of calibration slopes. In\ncontrast, if the degree of shrinkage is pre-specified, accurate coefficients\nand predictions can be obtained even in non-ideal settings such as encountered\nin the context of rare outcomes or sparse predictors.\n"}
+{"abstract": "  In this paper, for a given compact 3-manifold with an initial Riemannian\nmetric and a symmetric tensor, we establish the short-time existence and\nuniqueness theorem for extension of cross curvature flow. We give an example of\nthis flow on manifolds.\n"}
+{"abstract": "  We describe Substitutional Neural Image Compression (SNIC), a general\napproach for enhancing any neural image compression model, that requires no\ndata or additional tuning of the trained model. It boosts compression\nperformance toward a flexible distortion metric and enables bit-rate control\nusing a single model instance. The key idea is to replace the image to be\ncompressed with a substitutional one that outperforms the original one in a\ndesired way. Finding such a substitute is inherently difficult for conventional\ncodecs, yet surprisingly favorable for neural compression models thanks to\ntheir fully differentiable structures. With gradients of a particular loss\nbackpropogated to the input, a desired substitute can be efficiently crafted\niteratively. We demonstrate the effectiveness of SNIC, when combined with\nvarious neural compression models and target metrics, in improving compression\nquality and performing bit-rate control measured by rate-distortion curves.\nEmpirical results of control precision and generation speed are also discussed.\n"}
+{"abstract": "  When two solids at different temperatures are separated by a vacuum gap they\nrelax toward their equilibrium state by exchanging heat either by radiation,\nphonon or electron tunneling, depending on their separation distance and on the\nnature of materials. The interplay between this exchange of energy and its\nspreading through each solid entirely drives the relaxation dynamics. Here we\nhighlight a significant slowing down of this process in the extreme near-field\nregime at distances where the heat flux exchanged between the two solids is\ncomparable or even dominates over the flux carried by conduction inside each\nsolid. This mechanism, leading to a strong effective increase of the system\nthermal inertia, should play an important role in the temporal evolution of\nthermal state of interacting solids systems at nanometric and subnanometric\nscales.\n"}
+{"abstract": "  We investigate the modification of gravitational fields generated by\ntopological defects on a generalized Duffin-Kemmer-Petiau (DKP) oscillator for\nspin-0 particle under spinning cosmic string background. The generalized DKP\noscillator equation under spinning cosmic string background is established, and\nthe impact of the Cornell potential on the generalized DKP oscillator is\npresented. We give the influence of space-time and potential parameters on\nenergy levels.\n"}
+{"abstract": "  Two non-intrusive uncertainty propagation approaches are proposed for the\nperformance analysis of engineering systems described by expensive-to-evaluate\ndeterministic computer models with parameters defined as interval variables.\nThese approaches employ a machine learning based optimization strategy, the\nso-called Bayesian optimization, for evaluating the upper and lower bounds of a\ngeneric response variable over the set of possible responses obtained when each\ninterval variable varies independently over its range. The lack of knowledge\ncaused by not evaluating the response function for all the possible\ncombinations of the interval variables is accounted for by developing a\nprobabilistic description of the response variable itself by using a Gaussian\nProcess regression model. An iterative procedure is developed for selecting a\nsmall number of simulations to be evaluated for updating this statistical model\nby using well-established acquisition functions and to assess the response\nbounds. In both approaches, an initial training dataset is defined. While one\napproach builds iteratively two distinct training datasets for evaluating\nseparately the upper and lower bounds of the response variable, the other\nbuilds iteratively a single training dataset. Consequently, the two approaches\nwill produce different bound estimates at each iteration. The upper and lower\nbound responses are expressed as point estimates obtained from the mean\nfunction of the posterior distribution. Moreover, a confidence interval on each\nestimate is provided for effectively communicating to engineers when these\nestimates are obtained for a combination of the interval variables for which no\ndeterministic simulation has been run. Finally, two metrics are proposed to\ndefine conditions for assessing if the predicted bound estimates can be\nconsidered satisfactory.\n"}
+{"abstract": "  We report constraints on light dark matter through its interactions with\nshell electrons in the PandaX-II liquid xenon detector with a total 46.9\ntonne$\\cdot$day exposure. To effectively search for these very low energy\nelectron recoils, ionization-only signals are selected from the data. 1821\ncandidates are identified within ionization signal range between 50 to 75\nphotoelectrons, corresponding to a mean electronic recoil energy from 0.08 to\n0.15 keV. The 90% C.L. exclusion limit on the scattering cross section between\nthe dark matter and electron is calculated based on Poisson statistics. Under\nthe assumption of point interaction, we provide the world's most stringent\nlimit within the dark matter mass range from 15 to 30 $\\rm MeV/c^2$, with the\ncorresponding cross section from $2.5\\times10^{-37}$ to $3.1\\times10^{-38}$\ncm$^2$.\n"}
+{"abstract": "  As of 2020, the international workshop on Procedural Content Generation\nenters its second decade. The annual workshop, hosted by the international\nconference on the Foundations of Digital Games, has collected a corpus of 95\npapers published in its first 10 years. This paper provides an overview of the\nworkshop's activities and surveys the prevalent research topics emerging over\nthe years.\n"}
+{"abstract": "  Ma-Ma-Yeh made a beautiful observation that a transformation of the grammar\nof Dumont instantly leads to the $\\gamma$-positivity of the Eulerian\npolynomials. We notice that the transformed grammar bears a striking\nresemblance to the grammar for 0-1-2 increasing trees also due to Dumont. The\nappearance of the factor of two fits perfectly in a grammatical labeling of\n0-1-2 increasing plane trees. Furthermore, the grammatical calculus is\ninstrumental to the computation of the generating functions. This approach can\nbe adapted to study the $e$-positivity of the trivariate second-order Eulerian\npolynomials first introduced by Dumont in the contexts of ternary trees and\nStirling permutations, and independently defined by Janson, in connection with\nthe joint distribution of the numbers of ascents, descents and plateaux over\nStirling permutations.\n"}
+{"abstract": "  High-Performance Big Data Analytics (HPDA) applications are characterized by\nhuge volumes of distributed and heterogeneous data that require efficient\ncomputation for knowledge extraction and decision making. Designers are moving\ntowards a tight integration of computing systems combining HPC, Cloud, and IoT\nsolutions with artificial intelligence (AI). Matching the application and data\nrequirements with the characteristics of the underlying hardware is a key\nelement to improve the predictions thanks to high performance and better use of\nresources.\n  We present EVEREST, a novel H2020 project started on October 1st, 2020 that\naims at developing a holistic environment for the co-design of HPDA\napplications on heterogeneous, distributed, and secure platforms. EVEREST\nfocuses on programmability issues through a data-driven design approach, the\nuse of hardware-accelerated AI, and an efficient runtime monitoring with\nvirtualization support. In the different stages, EVEREST combines\nstate-of-the-art programming models, emerging communication standards, and\nnovel domain-specific extensions. We describe the EVEREST approach and the use\ncases that drive our research.\n"}
+{"abstract": "  This article summarises the current status of classical communication\nnetworks and identifies some critical open research challenges that can only be\nsolved by leveraging quantum technologies. By now, the main goal of quantum\ncommunication networks has been security. However, quantum networks can do more\nthan just exchange secure keys or serve the needs of quantum computers. In\nfact, the scientific community is still investigating on the possible use\ncases/benefits that quantum communication networks can bring. Thus, this\narticle aims at pointing out and clearly describing how quantum communication\nnetworks can enhance in-network distributed computing and reduce the overall\nend-to-end latency, beyond the intrinsic limits of classical technologies.\nFurthermore, we also explain how entanglement can reduce the communication\ncomplexity (overhead) that future classical virtualised networks will\nexperience.\n"}
+{"abstract": "  We present a new multiscale method to study the N-Methyl-D-Aspartate (NMDA)\nneuroreceptor starting from the reconstruction of its crystallographic\nstructure. Thanks to the combination of homology modelling, Molecular Dynamics\nand Lattice Boltzmann simulations, we analyse the allosteric transition of NDMA\nupon ligand binding and compute the receptor response to ionic passage across\nthe membrane.\n"}
+{"abstract": "  This paper presents an analytical model to quantify noise in a bolometer\nreadout circuit. A frequency domain analysis of the noise model is presented\nwhich includes the effect of noise from the bias resistor, sensor resistor,\nvoltage and current noise of amplifier and cable capacitance. The analytical\nmodel is initially verified by using several standard SMD resistors as a sensor\nin the range of 0.1 - 100 Mohm and measuring the RMS noise of the bolometer\nreadout circuit. Noise measurement on several indigenously developed neutron\ntransmutation doped Ge temperature sensor has been carried out over a\ntemperature range of 20 - 70 mK and the measured data is compared with the\nnoise calculated using analytical model. The effect of different sensor\nresistances on the noise of bolometer readout circuit, in line with the\nanalytical model and measured data, is presented in this paper.\n"}
+{"abstract": "  This paper presents an AI system applied to location and robotic grasping.\nExperimental setup is based on a parameter study to train a deep-learning\nnetwork based on Mask-RCNN to perform waste location in indoor and outdoor\nenvironment, using five different classes and generating a new waste dataset.\nInitially the AI system obtain the RGBD data of the environment, followed by\nthe detection of objects using the neural network. Later, the 3D object shape\nis computed using the network result and the depth channel. Finally, the shape\nis used to compute grasping for a robot arm with a two-finger gripper. The\nobjective is to classify the waste in groups to improve a recycling strategy.\n"}
+{"abstract": "  The unscented Kalman inversion (UKI) method presented in [1] is a general\nderivative-free approach for the inverse problem. UKI is particularly suitable\nfor inverse problems where the forward model is given as a black box and may\nnot be differentiable. The regularization strategies, convergence property, and\nspeed-up strategies [1,2] of the UKI are thoroughly studied, and the method is\ncapable of handling noisy observation data and solving chaotic inverse\nproblems. In this paper, we study the uncertainty quantification capability of\nthe UKI. We propose a modified UKI, which allows to well approximate the mean\nand covariance of the posterior distribution for well-posed inverse problems\nwith large observation data. Theoretical guarantees for both linear and\nnonlinear inverse problems are presented. Numerical results, including learning\nof permeability parameters in subsurface flow and of the Navier-Stokes initial\ncondition from solution data at positive times are presented. The results\nobtained by the UKI require only $O(10)$ iterations, and match well with the\nexpected results obtained by the Markov Chain Monte Carlo method.\n"}
+{"abstract": "  Following G.~Gr\\\"atzer and E.~Knapp, 2009, a planar semimodular lattice $L$\nis \\emph{rectangular}, if~the left boundary chain has exactly one\ndoubly-irreducible element, $c_l$, and the right boundary chain has exactly one\ndoubly-irreducible element, $c_r$, and these elements are complementary.\n  The Cz\\'edli-Schmidt Sequences, introduced in 2012, construct rectangular\nlattices. We use them to prove some structure theorems. In particular, we prove\nthat for a slim (no $\\mathsf{M}_3$ sublattice) rectangular lattice~$L$, the\ncongruence lattice $\\Con L$ has exactly $\\length[c_l,1] + \\length[c_r,1]$ dual\natoms and a dual atom in $\\Con L$ is a congruence with exactly two classes. We\nalso describe the prime ideals in a slim rectangular lattice.\n"}
+{"abstract": "  We explore the tail of various waiting time datasets of processes that follow\na nonstationary Poisson distribution with a sinusoidal driver. Analytically, we\nfind that the distribution of large waiting times of such processes can be\ndescribed using a power law slope of -2.5. We show that this result applies\nmore broadly to any nonstationary Poisson process driven periodically. Examples\nof such processes include solar flares, coronal mass ejections, geomagnetic\nstorms, and substorms. We also discuss how the power law specifically relates\nto the behavior of driver near its minima.\n"}
+{"abstract": "  The present work looks at semiautomatic rings with automatic addition and\ncomparisons which are dense subrings of the real numbers and asks how these can\nbe used to represent geometric objects such that certain operations and\ntransformations are automatic. The underlying ring has always to be a countable\ndense subring of the real numbers and additions and comparisons and\nmultiplications with constants need to be automatic. It is shown that the ring\ncan be selected such that equilateral triangles can be represented and\nrotations by 30 degrees are possible, while the standard representation of the\nb-adic rationals does not allow this.\n"}
+{"abstract": "  To further improve the learning efficiency and performance of reinforcement\nlearning (RL), in this paper we propose a novel uncertainty-aware model-based\nRL (UA-MBRL) framework, and then implement and validate it in autonomous\ndriving under various task scenarios. First, an action-conditioned ensemble\nmodel with the ability of uncertainty assessment is established as the virtual\nenvironment model. Then, a novel uncertainty-aware model-based RL framework is\ndeveloped based on the adaptive truncation approach, providing virtual\ninteractions between the agent and environment model, and improving RL's\ntraining efficiency and performance. The developed algorithms are then\nimplemented in end-to-end autonomous vehicle control tasks, validated and\ncompared with state-of-the-art methods under various driving scenarios. The\nvalidation results suggest that the proposed UA-MBRL method surpasses the\nexisting model-based and model-free RL approaches, in terms of learning\nefficiency and achieved performance. The results also demonstrate the good\nability of the proposed method with respect to the adaptiveness and robustness,\nunder various autonomous driving scenarios.\n"}
+{"abstract": "  Electronic health records represent a holistic overview of patients'\ntrajectories. Their increasing availability has fueled new hopes to leverage\nthem and develop accurate risk prediction models for a wide range of diseases.\nGiven the complex interrelationships of medical records and patient outcomes,\ndeep learning models have shown clear merits in achieving this goal. However, a\nkey limitation of these models remains their capacity in processing long\nsequences. Capturing the whole history of medical encounters is expected to\nlead to more accurate predictions, but the inclusion of records collected for\ndecades and from multiple resources can inevitably exceed the receptive field\nof the existing deep learning architectures. This can result in missing\ncrucial, long-term dependencies. To address this gap, we present Hi-BEHRT, a\nhierarchical Transformer-based model that can significantly expand the\nreceptive field of Transformers and extract associations from much longer\nsequences. Using a multimodal large-scale linked longitudinal electronic health\nrecords, the Hi-BEHRT exceeds the state-of-the-art BEHRT 1% to 5% for area\nunder the receiver operating characteristic (AUROC) curve and 3% to 6% for area\nunder the precision recall (AUPRC) curve on average, and 3% to 6% (AUROC) and\n3% to 11% (AUPRC) for patients with long medical history for 5-year heart\nfailure, diabetes, chronic kidney disease, and stroke risk prediction.\nAdditionally, because pretraining for hierarchical Transformer is not\nwell-established, we provide an effective end-to-end contrastive pre-training\nstrategy for Hi-BEHRT using EHR, improving its transferability on predicting\nclinical events with relatively small training dataset.\n"}
+{"abstract": "  Microscopic organisms, such as bacteria, have the ability of colonizing\nsurfaces and developing biofilms that can determine diseases and infections.\nMost bacteria secrete a significant amount of extracellular polymer substances\nthat are relevant for biofilm stabilization and growth. In this work, we apply\ncomputer simulation and perform experiments to investigate the impact of\npolymer size and concentration on early biofilm formation and growth. We\nobserve as bacterial cells formed loose, disorganized clusters whenever the\neffect of diffusion exceeded that of cell growth and division. Addition of\nmodel polymeric molecules induced particle self-assembly and aggregation to\nform compact clusters in a polymer size- and concentration-dependent fashion.\nWe also find that large polymer size or concentration lead to the development\nof intriguing stripe-like and dendritic colonies. The results obtained by\nBrownian dynamic simulation closely resemble the morphologies that we\nexperimentally observe in biofilms of a Pseudomonas Putida strain with added\npolymers. The analysis of the Brownian dynamic simulation results suggests the\nexistence of a threshold polymer concentration that distinguishes between two\ngrowth regimes. Below this threshold, the main force driving polymer-induced\ncompaction is hindrance of bacterial cell diffusion, while collective effects\nplay a minor role. Above this threshold, especially for large polymers,\npolymer-induced compaction is a collective phenomenon driven by depletion\nforces. Well above this concentration threshold, severely limited diffusion\ndrives the formation of filaments and dendritic colonies.\n"}
+{"abstract": "  In this paper, the interfacial motion between two immiscible viscous fluids\nin the confined geometry of a Hele-Shaw cell is studied. We consider the\ninfluence of a thin wetting film trailing behind the displaced fluid, which\ndynamically affects the pressure drop at the fluid-fluid interface by\nintroducing a nonlinear dependence on the interfacial velocity. In this\nframework, two cases of interest are analyzed: The injection-driven flow\n(expanding evolution), and the lifting plate flow (shrinking evolution). In\nparticular, we investigate the possibility of controlling the development of\nfingering instabilities in these two different Hele-Shaw setups when wetting\neffects are taken into account. By employing linear stability theory, we find\nthe proper time-dependent injection rate $Q(t)$ and the time-dependent lifting\nspeed ${\\dot b}(t)$ required to control the number of emerging fingers during\nthe expanding and shrinking evolution, respectively. Our results indicate that\nthe consideration of wetting leads to an increase in the magnitude of $Q(t)$\n[and ${\\dot b}(t)$] in comparison to the non-wetting strategy. Moreover, a\nspectrally accurate boundary integral approach is utilized to examine the\nvalidity and effectiveness of the controlling protocols at the fully nonlinear\nregime of the dynamics and confirms that the proposed injection and lifting\nschemes are feasible strategies to prescribe the morphologies of the resulting\npatterns in the presence of the wetting film.\n"}
+{"abstract": "  We revisit the theoretical properties of Hamiltonian stochastic differential\nequations (SDES) for Bayesian posterior sampling, and we study the two types of\nerrors that arise from numerical SDE simulation: the discretization error and\nthe error due to noisy gradient estimates in the context of data subsampling.\nOur main result is a novel analysis for the effect of mini-batches through the\nlens of differential operator splitting, revising previous literature results.\nThe stochastic component of a Hamiltonian SDE is decoupled from the gradient\nnoise, for which we make no normality assumptions. This leads to the\nidentification of a convergence bottleneck: when considering mini-batches, the\nbest achievable error rate is $\\mathcal{O}(\\eta^2)$, with $\\eta$ being the\nintegrator step size. Our theoretical results are supported by an empirical\nstudy on a variety of regression and classification tasks for Bayesian neural\nnetworks.\n"}
+{"abstract": "  We present the results that are necessary in the ongoing lattice calculations\nof the gluon parton distribution functions (PDFs) within the pseudo-PDF\napproach. We identify the two-gluon correlator functions that contain the\ninvariant amplitude determining the gluon PDF in the light-cone $z^2 \\to 0$\nlimit, and perform one-loop calculations in the coordinate representation in an\nexplicitly gauge-invariant form. Ultraviolet (UV) terms, which contain $\\ln\n(-z^2)$-dependence cancel in the reduced Ioffe-time distribution (ITD), and we\nobtain the matching relation between the reduced ITD and the light-cone ITD.\nUsing a kernel form, we get a direct connection between lattice data for the\nreduced ITD and the normalized gluon PDF.\n"}
+{"abstract": "  Considerable research effort has been guided towards algorithmic fairness but\nreal-world adoption of bias reduction techniques is still scarce. Existing\nmethods are either metric- or model-specific, require access to sensitive\nattributes at inference time, or carry high development or deployment costs.\nThis work explores the unfairness that emerges when optimizing ML models solely\nfor predictive performance, and how to mitigate it with a simple and easily\ndeployed intervention: fairness-aware hyperparameter optimization (HO). We\npropose and evaluate fairness-aware variants of three popular HO algorithms:\nFair Random Search, Fair TPE, and Fairband. We validate our approach on a\nreal-world bank account opening fraud case-study, as well as on three datasets\nfrom the fairness literature. Results show that, without extra training cost,\nit is feasible to find models with 111% mean fairness increase and just 6%\ndecrease in performance when compared with fairness-blind HO.\n"}
+{"abstract": "  LIDAR sensors are usually used to provide autonomous vehicles with 3D\nrepresentations of their environment. In ideal conditions, geometrical models\ncould detect the road in LIDAR scans, at the cost of a manual tuning of\nnumerical constraints, and a lack of flexibility. We instead propose an\nevidential pipeline, to accumulate road detection results obtained from neural\nnetworks. First, we introduce RoadSeg, a new convolutional architecture that is\noptimized for road detection in LIDAR scans. RoadSeg is used to classify\nindividual LIDAR points as either belonging to the road, or not. Yet, such\npoint-level classification results need to be converted into a dense\nrepresentation, that can be used by an autonomous vehicle. We thus secondly\npresent an evidential road mapping algorithm, that fuses consecutive road\ndetection results. We benefitted from a reinterpretation of logistic\nclassifiers, which can be seen as generating a collection of simple evidential\nmass functions. An evidential grid map that depicts the road can then be\nobtained, by projecting the classification results from RoadSeg into grid\ncells, and by handling moving objects via conflict analysis. The system was\ntrained and evaluated on real-life data. A python implementation maintains a 10\nHz framerate. Since road labels were needed for training, a soft labelling\nprocedure, relying lane-level HD maps, was used to generate coarse training and\nvalidation sets. An additional test set was manually labelled for evaluation\npurposes. So as to reach satisfactory results, the system fuses road detection\nresults obtained from three variants of RoadSeg, processing different LIDAR\nfeatures.\n"}
+{"abstract": "  Spiral structure is ubiquitous in the Universe, and the pitch angle of arms\nin spiral galaxies provide an important observable in efforts to discriminate\nbetween different mechanisms of spiral arm formation and evolution. In this\npaper, we present a hierarchical Bayesian approach to galaxy pitch angle\ndetermination, using spiral arm data obtained through the Galaxy Builder\ncitizen science project. We present a new approach to deal with the large\nvariations in pitch angle between different arms in a single galaxy, which\nobtains full posterior distributions on parameters. We make use of our pitch\nangles to examine previously reported links between bulge and bar strength and\npitch angle, finding no correlation in our data (with a caveat that we use\nobservational proxies for both bulge size and bar strength which differ from\nother work). We test a recent model for spiral arm winding, which predicts\nuniformity of the cotangent of pitch angle between some unknown upper and lower\nlimits, finding our observations are consistent with this model of transient\nand recurrent spiral pitch angle as long as the pitch angle at which most\nwinding spirals dissipate or disappear is larger than 10 degrees.\n"}
+{"abstract": "  Source code spends most of its time in a broken or incomplete state during\nsoftware development. This presents a challenge to machine learning for code,\nsince high-performing models typically rely on graph structured representations\nof programs derived from traditional program analyses. Such analyses may be\nundefined for broken or incomplete code. We extend the notion of program graphs\nto work-in-progress code by learning to predict edge relations between tokens,\ntraining on well-formed code before transferring to work-in-progress code. We\nconsider the tasks of code completion and localizing and repairing variable\nmisuse in a work-in-process scenario. We demonstrate that training\nrelation-aware models with fine-tuned edges consistently leads to improved\nperformance on both tasks.\n"}
+{"abstract": "  The objective of Federated Learning (FL) is to perform statistical inference\nfor data which are decentralised and stored locally on networked clients. FL\nraises many constraints which include privacy and data ownership, communication\noverhead, statistical heterogeneity, and partial client participation. In this\npaper, we address these problems in the framework of the Bayesian paradigm. To\nthis end, we propose a novel federated Markov Chain Monte Carlo algorithm,\nreferred to as Quantised Langevin Stochastic Dynamics which may be seen as an\nextension to the FL setting of Stochastic Gradient Langevin Dynamics, which\nhandles the communication bottleneck using gradient compression. To improve\nperformance, we then introduce variance reduction techniques, which lead to two\nimproved versions coined \\texttt{QLSD}$^{\\star}$ and \\texttt{QLSD}$^{++}$. We\ngive both non-asymptotic and asymptotic convergence guarantees for the proposed\nalgorithms. We illustrate their performances using various Bayesian Federated\nLearning benchmarks.\n"}
+{"abstract": "  The complex-step derivative approximation is a numerical differentiation\ntechnique that can achieve analytical accuracy, to machine precision, with a\nsingle function evaluation. In this letter, the complex-step derivative\napproximation is extended to be compatible with elements of matrix Lie groups.\nAs with the standard complex-step derivative, the method is still able to\nachieve analytical accuracy, up to machine precision, with a single function\nevaluation. Compared to a central-difference scheme, the proposed complex-step\napproach is shown to have superior accuracy. The approach is applied to two\ndifferent pose estimation problems, and is able to recover the same results as\nan analytical method when available.\n"}
+{"abstract": "  The problem of simultaneous rigid alignment of multiple unordered point sets\nwhich is unbiased towards any of the inputs has recently attracted increasing\ninterest, and several reliable methods have been newly proposed. While being\nremarkably robust towards noise and clustered outliers, current approaches\nrequire sophisticated initialisation schemes and do not scale well to large\npoint sets. This paper proposes a new resilient technique for simultaneous\nregistration of multiple point sets by interpreting the latter as particle\nswarms rigidly moving in the mutually induced force fields. Thanks to the\nimproved simulation with altered physical laws and acceleration of globally\nmultiply-linked point interactions with a 2^D-tree (D is the space\ndimensionality), our Multi-Body Gravitational Approach (MBGA) is robust to\nnoise and missing data while supporting more massive point sets than previous\nmethods (with 10^5 points and more). In various experimental settings, MBGA is\nshown to outperform several baseline point set alignment approaches in terms of\naccuracy and runtime. We make our source code available for the community to\nfacilitate the reproducibility of the results.\n"}
+{"abstract": "  It is nontrivial to store rapidly growing big data nowadays, which demands\nhigh-performance lossless compression techniques. Likelihood-based generative\nmodels have witnessed their success on lossless compression, where flow based\nmodels are desirable in allowing exact data likelihood optimisation with\nbijective mappings. However, common continuous flows are in contradiction with\nthe discreteness of coding schemes, which requires either 1) imposing strict\nconstraints on flow models that degrades the performance or 2) coding numerous\nbijective mapping errors which reduces the efficiency. In this paper, we\ninvestigate volume preserving flows for lossless compression and show that a\nbijective mapping without error is possible. We propose Numerical Invertible\nVolume Preserving Flow (iVPF) which is derived from the general volume\npreserving flows. By introducing novel computation algorithms on flow models,\nan exact bijective mapping is achieved without any numerical error. We also\npropose a lossless compression algorithm based on iVPF. Experiments on various\ndatasets show that the algorithm based on iVPF achieves state-of-the-art\ncompression ratio over lightweight compression algorithms.\n"}
+{"abstract": "  This paper presents a model addressing welfare optimal policies of demand\nresponsive transportation service, where passengers cause external travel time\ncosts for other passengers due to the route changes. Optimal pricing and trip\nproduction policies are modelled both on the aggregate level and on the network\nlevel. The aggregate model is an extension from Jokinen (2016) with flat\npricing model, but occupancy rate is now modelled as an endogenous variable\ndepending on demand and capacity levels. The network model enables to describe\ndifferences between routes from the viewpoint of occupancy rate and efficient\ntrip combining. Moreover, the model defines the optimal differentiated pricing\nfor routes.\n"}
+{"abstract": "  In this article, we introduce a notion of relative mean metric dimension with\npotential for a factor map $\\pi: (X,d, T)\\to (Y, S)$ between two topological\ndynamical systems. To link it with ergodic theory, we establish four\nvariational principles in terms of metric entropy of partitions, Shapira's\nentropy, Katok's entropy and Brin-Katok local entropy respectively. Some\nresults on local entropy with respect to a fixed open cover are obtained in the\nrelative case. We also answer an open question raised by Shi \\cite{Shi}\npartially for a very well-partitionable compact metric space, and in general we\nobtain a variational inequality involving box dimension of the space.\nCorresponding inner variational principles given an invariant measure of\n$(Y,S)$ are also investigated.\n"}
+{"abstract": "  We introduce a quantum interferometric scheme that uses states that are sharp\nin frequency and delocalized in position. The states are frequency modes of a\nquantum field that is trapped at all times in a finite volume potential, such\nas a small box potential. This allows for significant miniaturization of\ninterferometric devices. Since the modes are in contact at all times, it is\npossible to estimate physical parameters of global multi-mode channels. As an\nexample, we introduce a three-mode scheme and calculate precision bounds in the\nestimation of parameters of two-mode Gaussian channels. This scheme can be\nimplemented in several systems, including superconducting circuits, cavity-QED\nand cold atoms. We consider a concrete implementation using the ground state\nand two phononic modes of a trapped Bose-Einstein condensate. We apply this to\nshow that frequency interferometry can improve the sensitivity of phononic\ngravitational waves detectors by several orders of magnitude, even in the case\nthat squeezing is much smaller than assumed previously and that the system\nsuffers from short phononic lifetimes. Other applications range from\nmagnetometry, gravimetry and gradiometry to dark matter/energy searches.\n"}
+{"abstract": "  Analytic solution is presented of the nonlinear semiclassical dynamics of\nsuperradiant photonic condensate that arises in the Dicke model of two-level\natoms dipolar coupled to the electromagnetic field in the microwave cavity. In\nadiabatic limit with respect to photon degree of freedom the system is\napproximately integrable and its evolution is expressed via Jacobi elliptic\nfunctions of real time. Periodic trajectories of the semiclassical coordinate\nof photonic condensate either localise around two degenerate minima of the\ncondensate ground state energy or traverse between them over the saddle point.\nAn exact mapping of the semiclassical dynamics of photonic condensate on the\nmotion of unstable Lagrange 'sleeping top' is found. Analytic expression is\npresented for the frequency dependence of transmission coefficient along a\ntransmission line inductively coupled to the resonant cavity with superradiant\ncondensate. Sharp transmission drops reflect Fourier spectrum of the\nsemiclassical motion of photonic condensate and of 'sleeping top' nodding.\n"}
+{"abstract": "  Molecular ferroelectrics have captured immense attention due to their\nsuperiority over inorganic oxide ferroelectrics, such as environmentally\nfriendly, low-cost, flexible, foldable. However, the mechanisms of\nferroelectric switching and phase transition for the molecular ferroelectrics\nare still missing, leaving the development of novel molecular ferroelectrics\nless efficient. In this work, we have provided a methodology combining\nmolecular dynamics (MD) simulation on a polarized force field named polarized\ncrystal charge (PCC) and enhanced sampling technique, replica-exchange\nmolecular dynamics (REMD) to simulate such mechanisms. With this procedure, we\nhave investigated a promising molecular ferroelectric material,\n(R)/(S)-3-quinuclidinol crystal. We have simulated the ferroelectric hysteresis\nloops of both enantiomers and obtained spontaneous polarization of 7/8 \\mu C\ncm-2 and a corresponding coercive electric field of 15 kV cm-1. We also find\nthe Curie temperature as 380/385 K for ferro-/para-electric phase transition of\nboth enantiomers. All of the simulated results are highly compatible with\nexperimental values. Besides of that, we predict a novel Curie temperature of\nabout 600 K. This finding is further validated by principal component analysis\n(PCA). Our work would significantly promote the future exploration of\nmultifunctional molecular ferroelectrics for the next generation of intelligent\ndevices.\n"}
+{"abstract": "  We report on new stability conditions for evolutionary dynamics in the\ncontext of population games. We adhere to the prevailing framework consisting\nof many agents, grouped into populations, that interact noncooperatively by\nselecting strategies with a favorable payoff. Each agent is repeatedly allowed\nto revise its strategy at a rate referred to as revision rate. Previous\nstability results considered either that the payoff mechanism was a memoryless\npotential game, or allowed for dynamics (in the payoff mechanism) at the\nexpense of precluding any explicit dependence of the agents' revision rates on\ntheir current strategies. Allowing the dependence of revision rates on\nstrategies is relevant because the agents' strategies at any point in time are\ngenerally unequal. To allow for strategy-dependent revision rates and payoff\nmechanisms that are dynamic (or memoryless games that are not potential), we\nfocus on an evolutionary dynamics class obtained from a straightforward\nmodification of one that stems from the so-called impartial pairwise comparison\nstrategy revision protocol. Revision protocols consistent with the modified\nclass retain from those in the original one the advantage that the agents\noperate in a fully decentralized manner and with minimal information\nrequirements - they need to access only the payoff values (not the mechanism)\nof the available strategies. Our main results determine conditions under which\nsystem-theoretic passivity properties are assured, which we leverage for\nstability analysis.\n"}
+{"abstract": "  Collective action demands that individuals efficiently coordinate how much,\nwhere, and when to cooperate. Laboratory experiments have extensively explored\nthe first part of this process, demonstrating that a variety of\nsocial-cognitive mechanisms influence how much individuals choose to invest in\ngroup efforts. However, experimental research has been unable to shed light on\nhow social cognitive mechanisms contribute to the where and when of collective\naction. We leverage multi-agent deep reinforcement learning to model how a\nsocial-cognitive mechanism--specifically, the intrinsic motivation to achieve a\ngood reputation--steers group behavior toward specific spatial and temporal\nstrategies for collective action in a social dilemma. We also collect\nbehavioral data from groups of human participants challenged with the same\ndilemma. The model accurately predicts spatial and temporal patterns of group\nbehavior: in this public goods dilemma, the intrinsic motivation for reputation\ncatalyzes the development of a non-territorial, turn-taking strategy to\ncoordinate collective action.\n"}
+{"abstract": "  In this paper we study zero-noise limits of $\\alpha -$stable noise perturbed\nODE's which are driven by an irregular vector field $A$ with asymptotics $%\nA(x)\\sim \\overline{a}(\\frac{x}{\\left\\vert x\\right\\vert })\\left\\vert\nx\\right\\vert ^{\\beta -1}x$ at zero, where $\\overline{a}>0$ is a continuous\nfunction and $\\beta \\in (0,1)$. The results established in this article can be\nconsidered a generalization of those in the seminal works of Bafico \\cite% {Ba}\nand Bafico, Baldi \\cite{BB} to the multi-dimensional case.\\ Our approach for\nproving these results is inspired by techniques in \\cite% {PP_self_similar} and\nbased on the analysis of an SDE for $t\\longrightarrow \\infty $, which is\nobtained through a transformation of the perturbed ODE.\n"}
+{"abstract": "  A Private Set Operation (PSO) protocol involves at least two parties with\ntheir private input sets. The goal of the protocol is for the parties to learn\nthe output of a set operation, i.e. set intersection, on their input sets,\nwithout revealing any information about the items that are not in the output\nset. Commonly, the outcome of the set operation is revealed to parties and\nno-one else. However, in many application areas of PSO the result of the set\noperation should be learned by an external participant whom does not have an\ninput set. We call this participant the decider. In this paper, we present new\nvariants of multi-party PSO, where there is a decider who gets the result. All\nparties expect the decider have a private set. Other parties neither learn this\nresult, nor anything else about this protocol. Moreover, we present a generic\nsolution to the problem of PSO.\n"}
+{"abstract": "  One of the biggest challenges in multi-agent reinforcement learning is\ncoordination, a typical application scenario of this is traffic signal control.\nRecently, it has attracted a rising number of researchers and has become a hot\nresearch field with great practical significance. In this paper, we propose a\nnovel method called MetaVRS~(Meta Variational RewardShaping) for traffic signal\ncoordination control. By heuristically applying the intrinsic reward to the\nenvironmental reward, MetaVRS can wisely capture the agent-to-agent interplay.\nBesides, latent variables generated by VAE are brought into policy for\nautomatically tradeoff between exploration and exploitation to optimize the\npolicy. In addition, meta learning was used in decoder for faster adaptation\nand better approximation. Empirically, we demonstate that MetaVRS substantially\noutperforms existing methods and shows superior adaptability, which predictably\nhas a far-reaching significance to the multi-agent traffic signal coordination\ncontrol.\n"}
+{"abstract": "  We present the first measurement of the homogeneity index, $\\mathcal{H}$, a\nfractal or Hausdorff dimension of the early Universe from the Planck CMB\ntemperature variations $\\delta T$ in the sky. This characterization of the\nisotropy scale is model-free and purely geometrical, independent of the\namplitude of $\\delta T$. We find evidence of homogeneity ($\\mathcal{H}=0$) for\nscales larger than $\\theta_{\\mathcal{H}} = 65.9 \\pm 9.2 \\deg $ on the CMB sky.\nThis finding is at odds with the $\\Lambda$CDM prediction, which assumes a scale\ninvariant infinite universe. Such anomaly is consistent with the well known low\nquadrupule amplitude in the angular $\\delta T$ spectrum, but quantified in a\ndirect and model independent way. We estimate the significance of our finding\nfor $\\mathcal{H}=0$ using a principal component analysis from the sampling\nvariations of the observed sky. This analysis is validated with an independent\ntheoretical prediction of the covariance matrix based purely on data. Assuming\ntranslation invariance (and flat geometry $k=0$) we can convert the isotropy\nscale $\\theta_\\mathcal{H}$ into a (comoving) homogeneity scale of\n$\\chi_\\mathcal{H} \\simeq 3.3 c/H_0$, which is very close to the trapped surface\ngenerated by the observed cosmological constant $\\Lambda$.\n"}
+{"abstract": "  XRISM is an X-ray astronomical mission by the JAXA, NASA, ESA and other\ninternational participants, that is planned for launch in 2022 (Japanese fiscal\nyear), to quickly restore high-resolution X-ray spectroscopy of astrophysical\nobjects. To enhance the scientific outputs of the mission, the Science\nOperations Team (SOT) is structured independently from the instrument teams and\nthe Mission Operations Team. The responsibilities of the SOT are divided into\nfour categories: 1) guest observer program and data distributions, 2)\ndistribution of analysis software and the calibration database, 3) guest\nobserver support activities, and 4) performance verification and optimization\nactivities. As the first step, lessons on the science operations learned from\npast Japanese X-ray missions are reviewed, and 15 kinds of lessons are\nidentified. Among them, a) the importance of early preparation of the\noperations from the ground stage, b) construction of an independent team for\nscience operations separate from the instrument development, and c) operations\nwith well-defined duties by appointed members are recognized as key lessons.\nThen, the team structure and the task division between the mission and science\noperations are defined; the tasks are shared among Japan, US, and Europe and\nare performed by three centers, the SOC, SDC, and ESAC, respectively. The SOC\nis designed to perform tasks close to the spacecraft operations, such as\nspacecraft planning, quick-look health checks, pre-pipeline processing, etc.,\nand the SDC covers tasks regarding data calibration processing, maintenance of\nanalysis tools, etc. The data-archive and user-support activities are covered\nboth by the SOC and SDC. Finally, the science-operations tasks and tools are\ndefined and prepared before launch.\n"}
+{"abstract": "  We study an expanding two-fluid model of non-relativistic dark matter and\nradiation which are allowed to interact during a certain time span and to\nestablish an approximate thermal equilibrium. Such interaction which generates\nan effective bulk viscous pressure at background level is expected to be\nrelevant for times around the transition from radiation to matter dominance. We\nquantify the magnitude of this pressure for dark matter particles masses within\nthe range $1 {\\rm eV} \\lesssim m_{\\chi} \\lesssim 10 {\\rm eV}$ around the\nmatter-radiation equality epoch (i.e., redshift $z_{\\rm eq}\\sim 3400$) and\ndemonstrate that the existence of a transient bulk viscosity has consequences\nwhich may be relevant for addressing current tensions of the standard\ncosmological model: i) the additional (negative) pressure contribution modifies\nthe expansion rate around $z_{\\rm eq}$, yielding a larger $H_0$ value and ii)\nlarge-scale structure formation is impacted by suppressing the amplitude of\nmatter overdensity growth via a new viscous friction-term contribution to the\nM\\'esz\\'aros effect. As a result, both the $H_0$ and the $S_8$ tensions of the\ncurrent standard cosmological model are significantly alleviated.\n"}
+{"abstract": "  For an autonomous robotic system, monitoring surgeon actions and assisting\nthe main surgeon during a procedure can be very challenging. The challenges\ncome from the peculiar structure of the surgical scene, the greater similarity\nin appearance of actions performed via tools in a cavity compared to, say,\nhuman actions in unconstrained environments, as well as from the motion of the\nendoscopic camera. This paper presents ESAD, the first large-scale dataset\ndesigned to tackle the problem of surgeon action detection in endoscopic\nminimally invasive surgery. ESAD aims at contributing to increase the\neffectiveness and reliability of surgical assistant robots by realistically\ntesting their awareness of the actions performed by a surgeon. The dataset\nprovides bounding box annotation for 21 action classes on real endoscopic video\nframes captured during prostatectomy, and was used as the basis of a recent\nMIDL 2020 challenge. We also present an analysis of the dataset conducted using\nthe baseline model which was released as part of the challenge, and a\ndescription of the top performing models submitted to the challenge together\nwith the results they obtained. This study provides significant insight into\nwhat approaches can be effective and can be extended further. We believe that\nESAD will serve in the future as a useful benchmark for all researchers active\nin surgeon action detection and assistive robotics at large.\n"}
+{"abstract": "  Traditionally Genetic Algorithm has been used for optimization of unimodal\nand multimodal functions. Earlier researchers worked with constant\nprobabilities of GA control operators like crossover, mutation etc. for tuning\nthe optimization in specific domains. Recent advancements in this field\nwitnessed adaptive approach in probability determination. In Adaptive mutation\nprimarily poor individuals are utilized to explore state space, so mutation\nprobability is usually generated proportionally to the difference between\nfitness of best chromosome and itself (fMAX - f). However, this approach is\nsusceptible to nature of fitness distribution during optimization. This paper\npresents an alternate approach of mutation probability generation using\nchromosome rank to avoid any susceptibility to fitness distribution.\nExperiments are done to compare results of simple genetic algorithm (SGA) with\nconstant mutation probability and adaptive approaches within a limited resource\nconstraint for unimodal, multimodal functions and Travelling Salesman Problem\n(TSP). Measurements are done for average best fitness, number of generations\nevolved and percentage of global optimum achievements out of several trials.\nThe results demonstrate that the rank-based adaptive mutation approach is\nsuperior to fitness-based adaptive approach as well as SGA in a multimodal\nproblem space.\n"}
+{"abstract": "  A new class of test functions for black box optimization is introduced.\nAffine OneMax (AOM) functions are defined as compositions of OneMax and\ninvertible affine maps on bit vectors. The black box complexity of the class is\nupper bounded by a polynomial of large degree in the dimension. The proof\nrelies on discrete Fourier analysis and the Kushilevitz-Mansour algorithm.\nTunable complexity is achieved by expressing invertible linear maps as finite\nproducts of transvections. The black box complexity of sub-classes of AOM\nfunctions is studied. Finally, experimental results are given to illustrate the\nperformance of search algorithms on AOM functions.\n"}
+{"abstract": "  The aberrations in an optical microscope are commonly measured and corrected\nat one location in the field of view, within the so-called isoplanatic patch.\nFull-field correction is desirable for high-resolution imaging of large\nspecimens. Here we present a novel wavefront detector, based on pupil sampling\nwith sub-apertures, which measures the aberrated wavefront phase at each\nposition of the specimen. Based on this measurement, we propose a region-wise\ndeconvolution that provides an anisoplanatic reconstruction of the sample\nimage. Our results indicate that the measurement and correction of the\naberrations can be performed in a wide-field fluorescence microscope over its\nentire field of view.\n"}
+{"abstract": "  A rapid decline in mortality and fertility has become major issues in many\ndeveloped countries over the past few decades. A precise model for forecasting\ndemographic movements is important for decision making in social welfare\npolicies and resource budgeting among the government and many industry sectors.\nThis article introduces a novel non-parametric approach using Gaussian process\nregression with a natural cubic spline mean function and a spectral mixture\ncovariance function for mortality and fertility modelling and forecasting.\nUnlike most of the existing approaches in demographic modelling literature,\nwhich rely on time parameters to decide the movements of the whole mortality or\nfertility curve shifting from one year to another over time, we consider the\nmortality and fertility curves from their components of all age-specific\nmortality and fertility rates and assume each of them following a Gaussian\nprocess over time to fit the whole curves in a discrete but intensive style.\nThe proposed Gaussian process regression approach shows significant\nimprovements in terms of preciseness and robustness compared to other\nmainstream demographic modelling approaches in the short-, mid- and long-term\nforecasting using the mortality and fertility data of several developed\ncountries in our numerical experiments.\n"}
+{"abstract": "  Driving is a routine activity for many, but it is far from simple. Drivers\ndeal with multiple concurrent tasks, such as keeping the vehicle in the lane,\nobserving and anticipating the actions of other road users, reacting to\nhazards, and dealing with distractions inside and outside the vehicle. Failure\nto notice and respond to the surrounding objects and events can cause\naccidents. The ongoing improvements of the road infrastructure and vehicle\nmechanical design have made driving safer overall. Nevertheless, the problem of\ndriver inattention has remained one of the primary causes of accidents.\nTherefore, understanding where the drivers look and why they do so can help\neliminate sources of distractions and identify unsafe attention patterns.\nResearch on driver attention has implications for many practical applications\nsuch as policy-making, improving driver education, enhancing road\ninfrastructure and in-vehicle infotainment systems, as well as designing\nsystems for driver monitoring, driver assistance, and automated driving. This\nreport covers the literature on changes in drivers' visual attention\ndistribution due to factors, internal and external to the driver. Aspects of\nattention during driving have been explored across multiple disciplines,\nincluding psychology, human factors, human-computer interaction, intelligent\ntransportation, and computer vision, each offering different perspectives,\ngoals, and explanations for the observed phenomena. We link cross-disciplinary\ntheoretical and behavioral research on driver's attention to practical\nsolutions. Furthermore, limitations and directions for future research are\ndiscussed. This report is based on over 175 behavioral studies, nearly 100\npractical papers, 20 datasets, and over 70 surveys published since 2010. A\ncurated list of papers used for this report is available at\n\\url{https://github.com/ykotseruba/attention_and_driving}.\n"}
+{"abstract": "  Magnetic anisotropies have key role to taylor magnetic behavior in\nferromagnetic systems. Further, they are also essential elements to manipulate\nthe thermoelectric response in Anomalous Nernst (ANE) and Longitudinal Spin\nSeebeck systems (LSSE). We propose here a theoretical approach and explore the\nrole of magnetic anisotropies on the magnetization and thermoelectric response\nof noninteracting multidomain ferromagnetic systems. The magnetic behavior and\nthe thermoelectric curves are calculated from a modified Stoner Wohlfarth model\nfor an isotropic system, a uniaxial magnetic one, as well as for a system\nhaving a mixture of uniaxial and cubic magnetocrystalline magnetic\nanisotropies. It is verified remarkable modifications of the magnetic behavior\nwith the anisotropy and it is shown that the thermoelectric response is\nstrongly affected by these changes. Further, the fingerprints of the energy\ncontributions to the thermoelectric response are disclosed. To test the\nrobustness of our theoretical approach, we engineer films having the specific\nmagnetic properties and compare directly experimental data with theoretical\nresults. Thus, experimental evidence is provided to confirm the validity of our\ntheoretical approach. The results go beyond the traditional reports focusing on\nmagnetically saturated films and show how the thermoelectric effect behaves\nduring the whole magnetization curve. Our findings reveal a promising way to\nexplore the ANE and LSSE effects as a powerful tool to study magnetic\nanisotropies, as well as to employ systems with magnetic anisotropy as sensing\nor elements in technological applications.\n"}
+{"abstract": "  IW And stars are a recently recognized subgroup of dwarf novae which are\ncharacterized by (often repetitive) slowly rising standstills terminated by\nbrightening, but the exact mechanism for this variation is not yet identified.\nWe have identified BO Cet, which had been considered as a novalike cataclysmic\nvariable, as a new member of IW And stars based on the behavior in 2019-2020.\nIn addition to this, the object showed dwarf nova-type outbursts in 2020-2021,\nand superhumps having a period 7.8% longer than the orbital one developed at\nleast during one long outburst. This object has been confirmed as an SU\nUMa-type dwarf nova with an exceptionally long orbital period (0.1398 d). BO\nCet is thus the first cataclysmic variable showing both SU UMa-type and IW\nAnd-type features. We obtained a mass ratio (q) of 0.31-0.34 from the\nsuperhumps in the growing phase (stage A superhumps). At this q, the radius of\nthe 3:1 resonance, responsible for tidal instability and superhumps, and the\ntidal truncation radius are very similar. We interpret that in some occasions\nthis object showed IW And-type variation when the disk size was not large\nenough, but that the radius of the 3:1 resonance could be reached as the result\nof thermal instability. We also discuss that there are SU UMa-type dwarf novae\nabove q=0.30, which is above the previously considered limit (q~0.25) derived\nfrom numerical simulations and that this is possible since the radius of the\n3:1 resonance is inside the tidal truncation radius. We constrained the mass of\nthe white dwarf larger than 1.0Msol, which may be responsible for the IW\nAnd-type behavior and the observed strength of the He II emission. The exact\nreason, however, why this object is unique in that it shows both SU UMa-type\nand IW And-type features is still unsolved.\n"}
+{"abstract": "  We develop time-splitting finite difference methods, using implicit\nBackward-Euler and semi-implicit Crank-Nicolson discretization schemes, to\nstudy the spin-orbit coupled spinor Bose Einstein condensates with coherent\ncoupling in quasi-one and quasi-two-dimensional traps. The split equations\ninvolving kinetic energy and spin-orbit coupling operators are solved using\neither time-implicit Backward-Euler or semi-implicit Crank-Nicolson methods. We\nexplicitly develop the method for pseudospin-1/2, spin-1, and spin-2\ncondensates. The results for ground states obtained with time-splitting\nBackward-Euler and Crank-Nicolson methods are in excellent agreement with\ntime-splitting Fourier spectral method which is one of the popular methods to\nsolve the mean-field models for spin-orbit coupled spinor condensates. We\nconfirm the emergence of different phases in spin-orbit coupled pseudospin-1/2,\nspin-1, and spin-2 condensates with coherent coupling.\n"}
+{"abstract": "  We consider two models of random cones together with their duals. Let\n$Y_1,\\dots,Y_n$ be independent and identically distributed random vectors in\n$\\mathbb R^d$ whose distribution satisfies some mild condition. The random\ncones $G_{n,d}^A$ and $G_{n,d}^B$ are defined as the positive hulls\n$\\text{pos}\\{Y_1-Y_2,\\dots,Y_{n-1}-Y_n\\}$, respectively\n$\\text{pos}\\{Y_1-Y_2,\\dots,Y_{n-1}-Y_n,Y_n\\}$, conditioned on the event that\nthe respective positive hull is not equal to $\\mathbb R^d$. We prove limit\ntheorems for various expected geometric functionals of these random cones, as\n$n$ and $d$ tend to infinity in a coordinated way. This includes limit theorems\nfor the expected number of $k$-faces and the $k$-th conic quermassintegrals, as\n$n$, $d$ and sometimes also $k$ tend to infinity simultaneously. Moreover, we\nuncover a phase transition in high dimensions for the expected statistical\ndimension for both models of random cones.\n"}
+{"abstract": "  A proper orthogonal decomposition-based B-splines B\\'ezier elements method\n(POD-BSBEM) is proposed as a non-intrusive reduced-order model for uncertainty\npropagation analysis for stochastic time-dependent problems. The method uses a\ntwo-step proper orthogonal decomposition (POD) technique to extract the reduced\nbasis from a collection of high-fidelity solutions called snapshots. A third\nPOD level is then applied on the data of the projection coefficients associated\nwith the reduced basis to separate the time-dependent modes from the stochastic\nparametrized coefficients. These are approximated in the stochastic parameter\nspace using B-splines basis functions defined in the corresponding B\\'ezier\nelement. The accuracy and the efficiency of the proposed method are assessed\nusing benchmark steady-state and time-dependent problems and compared to the\nreduced order model-based artificial neural network (POD-ANN) and to the\nfull-order model-based polynomial chaos expansion (Full-PCE). The POD-BSBEM is\nthen applied to analyze the uncertainty propagation through a flood wave flow\nstemming from a hypothetical dam-break in a river with a complex bathymetry.\nThe results confirm the ability of the POD-BSBEM to accurately predict the\nstatistical moments of the output quantities of interest with a substantial\nspeed-up for both offline and online stages compared to other techniques.\n"}
+{"abstract": "  Nowadays, datacenters lean on a computer-centric approach based on monolithic\nservers which include all necessary hardware resources (mainly CPU, RAM,\nnetwork and disks) to run applications. Such an architecture comes with two\nmain limitations: (1) difficulty to achieve full resource utilization and (2)\ncoarse granularity for hardware maintenance. Recently, many works investigated\na resource-centric approach called disaggregated architecture where the\ndatacenter is composed of self-content resource boards interconnected using\nfast interconnection technologies, each resource board including instances of\none resource type. The resource-centric architecture allows each resource to be\nmanaged (maintenance, allocation) independently. LegoOS is the first work which\nstudied the implications of disaggregation on the operating system, proposing\nto disaggregate the operating system itself. They demonstrated the suitability\nof this approach, considering mainly CPU and RAM resources. However, they\ndidn't study the implication of disaggregation on network resources. We\nreproduced a LegoOS infrastructure and extended it to support disaggregated\nnetworking. We show that networking can be disaggregated following the same\nprinciples, and that classical networking optimizations such as DMA, DDIO or\nloopback can be reproduced in such an environment. Our evaluations show the\nviability of the approach and the potential of future disaggregated\ninfrastructures.\n"}
+{"abstract": "  Previous methods decompose the blind super-resolution (SR) problem into two\nsequential steps: \\textit{i}) estimating the blur kernel from given\nlow-resolution (LR) image and \\textit{ii}) restoring the SR image based on the\nestimated kernel. This two-step solution involves two independently trained\nmodels, which may not be well compatible with each other. A small estimation\nerror of the first step could cause a severe performance drop of the second\none. While on the other hand, the first step can only utilize limited\ninformation from the LR image, which makes it difficult to predict a highly\naccurate blur kernel. Towards these issues, instead of considering these two\nsteps separately, we adopt an alternating optimization algorithm, which can\nestimate the blur kernel and restore the SR image in a single model.\nSpecifically, we design two convolutional neural modules, namely\n\\textit{Restorer} and \\textit{Estimator}. \\textit{Restorer} restores the SR\nimage based on the predicted kernel, and \\textit{Estimator} estimates the blur\nkernel with the help of the restored SR image. We alternate these two modules\nrepeatedly and unfold this process to form an end-to-end trainable network. In\nthis way, \\textit{Estimator} utilizes information from both LR and SR images,\nwhich makes the estimation of the blur kernel easier. More importantly,\n\\textit{Restorer} is trained with the kernel estimated by \\textit{Estimator},\ninstead of the ground-truth kernel, thus \\textit{Restorer} could be more\ntolerant to the estimation error of \\textit{Estimator}. Extensive experiments\non synthetic datasets and real-world images show that our model can largely\noutperform state-of-the-art methods and produce more visually favorable results\nat a much higher speed. The source code is available at\n\\url{https://github.com/greatlog/DAN.git}.\n"}
+{"abstract": "  We consider a certain lattice branching random walk with on-site competition\nand in an environment which is heterogeneous at a macroscopic scale\n$1/\\varepsilon$ in space and time. This can be seen as a model for the spatial\ndynamics of a biological population in a habitat which is heterogeneous at a\nlarge scale (mountains, temperature or precipitation gradient...). The model\nincorporates another parameter, $K$, which is a measure of the local population\ndensity. We study the model in the limit when first $\\varepsilon\\to 0$ and then\n$K\\to\\infty$. In this asymptotic regime, we show that the rescaled position of\nthe front as a function of time converges to the solution of an explicit ODE.\nWe further discuss the relation with another popular model of population\ndynamics, the Fisher-KPP equation, which arises in the limit $K\\to\\infty$.\nCombined with known results on the Fisher-KPP equation, our results show in\nparticular that the limits $\\varepsilon\\to0$ and $K\\to\\infty$ do not commute in\ngeneral. We conjecture that an interpolating regime appears when $\\log K$ and\n$1/\\varepsilon$ are of the same order.\n"}
+{"abstract": "  A long-held belief is that shock energy induces initiation of an energetic\nmaterial through an indirect energy up-pumping mechanism involving phonon\nscattering through doorway modes. In this paper, a 3-phonon theoretical\nanalysis of energy up-pumping in RDX is presented that involves both direct and\nindirect pathways where the direct energy transfer dominates. The calculation\nconsiders individual phonon modes which are then analyzed in bands. Scattering\nis handled up to the third order term in the Hamiltonian based on Fermi's\nGolden Rule. On average, modes with frequencies up to 90 cm-1 scatter quickly\nand redistribute the energy to all the modes. This direct stimulation occurs\nrapidly, within 0.16 ps, and involves distortions to NN bonds. Modes from 90 to\n1839 cm-1 further up-pump the energy to NN bond distortion modes through an\nindirect route within 5.6 ps. The highest frequency modes have the lowest\ncontribution to energy transfer due to their lower participation in\nphonon-phonon scattering. The modes stimulated directly by the shock with\nfrequencies up to 90 cm-1 are estimated to account for 52 to 89\\% of the total\nenergy transfer to various NN bond distorting modes.\n"}
+{"abstract": "  A new paradigm called physical reservoir computing has recently emerged,\nwhere the nonlinear dynamics of high-dimensional and fixed physical systems are\nharnessed as a computational resource to achieve complex tasks. Via extensive\nsimulations based on a dynamic truss-frame model, this study shows that an\norigami structure can perform as a dynamic reservoir with sufficient computing\npower to emulate high-order nonlinear systems, generate stable limit cycles,\nand modulate outputs according to dynamic inputs. This study also uncovers the\nlinkages between the origami reservoir's physical designs and its computing\npower, offering a guideline to optimize the computing performance.\nComprehensive parametric studies show that selecting optimal feedback crease\ndistribution and fine-tuning the underlying origami folding designs are the\nmost effective approach to improve computing performance. Furthermore, this\nstudy shows how origami's physical reservoir computing power can apply to soft\nrobotic control problems by a case study of earthworm-like peristaltic crawling\nwithout traditional controllers. These results can pave the way for\norigami-based robots with embodied mechanical intelligence.\n"}
+{"abstract": "  We study the algebraic conditions leading to the chain property of complexes\nfor vertex operator algebra $n$-point functions with differential being defined\nthrough reduction formulas. The notion of the reduction cohomology of Riemann\nsurfaces is introduced. Algebraic, geometrical, and cohomological meanings of\nreduction formulas is clarified. A counterpart of the Bott-Segal theorem for\nRiemann surfaces in terms of the reductions cohomology is proven. It is shown\nthat the reduction cohomology is given by the cohomology of $n$-point\nconnections over the vertex operator algebra bundle defined on a genus $g$\nRiemann surface $\\Sigma^{(g)}$. The reduction cohomology for a vertex operator\nalgebra with formal parameters identified with local coordinates around marked\npoints on $\\Sigma^{(g)}$ is found in terms of the space of analytical\ncontinuations of solutions to Knizhnik-Zamolodchikov equations. For the\nreduction cohomology, the Euler-Poincare formula is derived. Examples for\nvarious genera and vertex operator cluster algebras are provided.\n"}
+{"abstract": "  For a commutative ring $R$, we define the notions of deformed Picard\nalgebroids and deformed twisted differential operators on a smooth, separated,\nlocally of finite type $R$-scheme and prove these are in a natural bijection.\nWe then define the pullback of a sheaf of twisted differential operators that\nreduces to the classical definition when $R=\\mathbb{C}$. Finally, for modules\nover twisted differential operators, we prove a theorem for the descent under a\nlocally trivial torsor.\n"}
+{"abstract": "  Motivated by questions in number theory, Myerson asked how small the sum of 5\ncomplex nth roots of unity can be. We obtain a uniform bound of O(n^{-4/3}) by\nperturbing the vertices of a regular pentagon, improving to O(n^{-7/3})\ninfinitely often.\n  The corresponding configurations were suggested by examining exact minimum\nvalues computed for n <= 221000. These minima can be explained at least in part\nby selection of the best example from multiple families of competing\nconfigurations related to close rational approximations.\n"}
+{"abstract": "  The orientation completion problem for a class of oriented graphs asks\nwhether a given partially oriented graph can be completed to an oriented graph\nin the class by orienting the unoriented edges of the partially oriented graph.\nOrientation completion problems have been studied recently for several classes\nof oriented graphs, yielding both polynomial time solutions as well as\nNP-completeness results. Local tournaments are a well-structured class of\noriented graphs that generalize tournaments and their underlying graphs are\nintimately related to proper circular-arc graphs. According to Skrien, a\nconnected graph can be oriented as a local tournament if and only if it is a\nproper circular-arc graph. Proper interval graphs are precisely the graphs\nwhich can be oriented as acyclic local tournaments. It has been proved that the\norientation completion problems for the classes of local tournaments and\nacyclic local tournaments are both polynomial time solvable. In this paper we\ncharacterize the partially oriented graphs that can be completed to local\ntournaments by determining the complete list of obstructions. These are in a\nsense minimal partially oriented graphs that cannot be completed to local\ntournaments. The result may be viewed as an extension of the well-known\nforbidden subgraph characterization of proper circular-arc graphs obtained by\nTucker. The complete list of obstructions for acyclic local tournament\norientation completions has been given in a companion paper.\n"}
+{"abstract": "  We derive a thermodynamic uncertainty relation (TUR) for first-passage times\n(FPTs) on continuous time Markov chains. The TUR utilizes the entropy\nproduction coming from bidirectional transitions, and the net flux coming from\nunidirectional transitions, to provide a lower bound on FPT fluctuations. As\nevery bidirectional transition can also be seen as a pair of separate\nunidirectional ones, our approach typically yields an ensemble of TURs. The\ntightest bound on FPT fluctuations can then be obtained from this ensemble by a\nsimple and physically motivated optimization procedure. The results presented\nherein are valid for arbitrary initial conditions, out-of-equilibrium dynamics,\nand are therefore well suited to describe the inherently irreversible\nfirst-passage event. They can thus be readily applied to a myriad of\nfirst-passage problems that arise across a wide range of disciplines.\n"}
+{"abstract": "  A hyperlink is a finite set of non-intersecting simple closed curves in\n$\\mathbb{R}^4 \\equiv \\mathbb{R} \\times \\mathbb{R}^3$, each curve is either a\nmatter or geometric loop. We consider an equivalence class of such hyperlinks,\nup to time-like isotopy, preserving time-ordering. Using an equivalence class\nand after coloring each matter component loop with an irreducible\nrepresentation of $\\mathfrak{su}(2) \\times \\mathfrak{su}(2)$, we can define its\nWilson Loop observable using an Einstein-Hilbert action, which is now thought\nof as a functional acting on the set containing equivalence classes of\nhyperlink. Construct a vector space using these functionals, which we now term\nas quantum states. To make it into a Hilbert space, we need to define a\ncounting probability measure on the space containing equivalence classes of\nhyperlinks. In our previous work, we defined area, volume and curvature\noperators, corresponding to given geometric objects like surface and a compact\nsolid spatial region. These operators act on the quantum states and by\ndeliberate construction of the Hilbert space, are self-adjoint and possibly\nunbounded operators. Using these operators and Einstein's field equations, we\ncan proceed to construct a quantized stress operator and also a Hamiltonian\nconstraint operator for the quantum system. We will also use the area operator\nto derive the Bekenstein entropy of a black hole. In the concluding section, we\nwill explain how Loop Quantum Gravity predicts the existence of gravitons,\nimplies causality and locality in quantum gravity, and formulate the principle\nof equivalence mathematically in its framework.\n"}
+{"abstract": "  We use a replica trick construction to propose a definition of branch-point\ntwist operators in two dimensional momentum space and compute their two-point\nfunction. The result is then tentatively interpreted as a pseudo R\\'enyi\nentropy for momentum modes.\n"}
+{"abstract": "  Deep learning semantic segmentation algorithms can localise abnormalities or\nopacities from chest radiographs. However, the task of collecting and\nannotating training data is expensive and requires expertise which remains a\nbottleneck for algorithm performance. We investigate the effect of image\naugmentations on reducing the requirement of labelled data in the semantic\nsegmentation of chest X-rays for pneumonia detection. We train fully\nconvolutional network models on subsets of different sizes from the total\ntraining data. We apply a different image augmentation while training each\nmodel and compare it to the baseline trained on the entire dataset without\naugmentations. We find that rotate and mixup are the best augmentations amongst\nrotate, mixup, translate, gamma and horizontal flip, wherein they reduce the\nlabelled data requirement by 70% while performing comparably to the baseline in\nterms of AUC and mean IoU in our experiments.\n"}
+{"abstract": "  We investigate the behavior of vortex bound states in the quantum limit by\nself-consistently solving the Bogoliubov-de Gennes equation. We find that the\nenergies of the vortex bound states deviates from the analytical result\n$E_\\mu=\\mu\\Delta^2/E_F$ with the half-integer angular momentum $\\mu$ in the\nextreme quantum limit. Specifically, the energy ratio for the first three\norders is more close to $1:2:3$ instead of $1:3:5$ at extremely low\ntemperature. The local density of states reveals an Friedel-like behavior\nassociated with that of the pair potential in the extreme quantum limit, which\nwill be smoothed out by thermal effect above a certain temperature even the\nquantum limit condition, namely $T/T_c<\\Delta/E_F$ is still satisfied. Our\nstudies show that the vortex bound states can exhibit very distinct features in\ndifferent temperature regimes, which provides a comprehensive understanding and\nshould stimulate more experimental efforts for verifications.\n"}
+{"abstract": "  Predicting molecular conformations (or 3D structures) from molecular graphs\nis a fundamental problem in many applications. Most existing approaches are\nusually divided into two steps by first predicting the distances between atoms\nand then generating a 3D structure through optimizing a distance geometry\nproblem. However, the distances predicted with such two-stage approaches may\nnot be able to consistently preserve the geometry of local atomic\nneighborhoods, making the generated structures unsatisfying. In this paper, we\npropose an end-to-end solution for molecular conformation prediction called\nConfVAE based on the conditional variational autoencoder framework.\nSpecifically, the molecular graph is first encoded in a latent space, and then\nthe 3D structures are generated by solving a principled bilevel optimization\nprogram. Extensive experiments on several benchmark data sets prove the\neffectiveness of our proposed approach over existing state-of-the-art\napproaches. Code is available at\n\\url{https://github.com/MinkaiXu/ConfVAE-ICML21}.\n"}
+{"abstract": "  Search strategies for the third-generation leptoquarks (LQs) are distinct\nfrom other LQ searches, especially when they decay to a top quark and a $\\tau$\nlepton. We investigate the cases of all TeV-scale scalar and vector LQs that\ndecay to either a top-tau pair (charge-$1/3$ and $5/3$ LQs) or a top-neutrino\npair (charge-$2/3$ LQs). One can then use the boosted top (which can be tagged\nefficiently using jet-substructure techniques) and high-$p_{\\rm T}$ $\\tau$\nleptons to search for these LQs. We consider two search channels with either\none or two taus along with at least one hadronically decaying boosted top\nquark. We estimate the high luminosity LHC (HL-LHC) search prospects of these\nLQs by considering both symmetric and asymmetric pair and single production\nprocesses. Our selection criteria are optimised to retain events from both pair\nand single production processes. The combined signal has better prospects than\nthe traditional searches. We include new three-body single production processes\nto enhance the single production contributions to the combined signal. We\nidentify the interference effect that appears in the dominant single production\nchannel of charge-$1/3$ scalar LQ ($S^{1/3}$). This interference is\nconstructive if $S^{1/3}$ is weak-triplet and destructive, for a singlet one.\nAs a result, their LHC prospects differ appreciably.\n"}
+{"abstract": "  We present a detailed analysis to clarify what determines the growth of the\nlow-$T/|W|$ instability in the context of rapidly rotating core-collapse of\nmassive stars. To this end, we perform three-dimensional core-collapse\nsupernova (CCSN) simulations of a $27 M_{\\odot}$ star including several updates\nin the general relativistic correction to gravity, the multi-energy treatment\nof heavy-lepton neutrinos, and the nuclear equation of state. Non-axisymmetric\ndeformations are analyzed from the point of view of the time evolution of the\npattern frequency and the corotation radius. The corotation radius is found to\ncoincide with the convective layer in the proto neutron star (PNS). We propose\na new mechanism to account for the growth of the low-$T/|W|$ instability in the\nCCSN environment. Near the convective boundary where a small\nBrunt-V\\\"ais\\\"al\\\"a frequency is expected, Rossby waves propagating in the\nazimuthal direction at mid latitude induce non-axisymmetric unstable modes, in\nboth hemispheres. They merge with each other and finally become the spiral arm\nin the equatorial plane. We also investigate how the growth of the low-$T/|W|$\ninstability impacts the neutrino and gravitational-wave signatures.\n"}
+{"abstract": "  An optical neural network is proposed and demonstrated with programmable\nmatrix transformation and nonlinear activation function of photodetection\n(square-law detection). Based on discrete phase-coherent spatial modes, the\ndimensionality of programmable optical matrix operations is 30~37, which is\nimplemented by spatial light modulators. With this architecture, all-optical\nclassification tasks of handwritten digits, objects and depth images are\nperformed on the same platform with high accuracy. Due to the parallel nature\nof matrix multiplication, the processing speed of our proposed architecture is\npotentially as high as7.4T~74T FLOPs per second (with 10~100GHz detector)\n"}
+{"abstract": "  In this note, we give a characterisation in terms of identities of the join\nof $\\mathbf{V}$ with the variety of finite locally trivial semigroups\n$\\mathbf{LI}$ for several well-known varieties of finite monoids $\\mathbf{V}$\nby using classical algebraic-automata-theoretic techniques. To achieve this, we\nuse the new notion of essentially-$\\mathbf{V}$ stamps defined by Grosshans,\nMcKenzie and Segoufin and show that it actually coincides with the join of\n$\\mathbf{V}$ and $\\mathbf{LI}$ precisely when some natural condition on the\nvariety of languages corresponding to $\\mathbf{V}$ is verified.This work is a\nkind of rediscovery of the work of J. C. Costa around 20 years ago from a\nrather different angle, since Costa's work relies on the use of advanced\ndevelopments in profinite topology, whereas what is presented here essentially\nuses an algebraic, language-based approach.\n"}
+{"abstract": "  The Transiting Exoplanet Survey Satellite (\\textit{TESS}) mission was\ndesigned to perform an all-sky search of planets around bright and nearby\nstars. Here we report the discovery of two sub-Neptunes orbiting around the TOI\n1062 (TIC 299799658), a V=10.25 G9V star observed in the TESS Sectors 1, 13, 27\n& 28. We use precise radial velocity observations from HARPS to confirm and\ncharacterize these two planets. TOI 1062b has a radius of\n2.265^{+0.095}_{-0.091} Re, a mass of 11.8 +\\- 1.4 Me, and an orbital period of\n4.115050 +/- 0.000007 days. The second planet is not transiting, has a minimum\nmass of 7.4 +/- 1.6 Me and is near the 2:1 mean motion resonance with the\ninnermost planet with an orbital period of 8.13^{+0.02}_{-0.01} days. We\nperformed a dynamical analysis to explore the proximity of the system to this\nresonance, and to attempt at further constraining the orbital parameters. The\ntransiting planet has a mean density of 5.58^{+1.00}_{-0.89} g cm^-3 and an\nanalysis of its internal structure reveals that it is expected to have a small\nvolatile envelope accounting for 0.35% of the mass at maximum. The star's\nbrightness and the proximity of the inner planet to the \"radius gap\" make it an\ninteresting candidate for transmission spectroscopy, which could further\nconstrain the composition and internal structure of TOI 1062b.\n"}
+{"abstract": "  This paper describes the design, implementation, and verification of a\ntest-bed for determining the noise temperature of radio antennas operating\nbetween 400-800MHz. The requirements for this test-bed were driven by the HIRAX\nexperiment, which uses antennas with embedded amplification, making system\nnoise characterization difficult in the laboratory. The test-bed consists of\ntwo large cylindrical cavities, each containing radio-frequency (RF) absorber\nheld at different temperatures (300K and 77K), allowing a measurement of system\nnoise temperature through the well-known 'Y-factor' method. The apparatus has\nbeen constructed at Yale, and over the course of the past year has undergone\ndetailed verification measurements. To date, three preliminary noise\ntemperature measurement sets have been conducted using the system, putting us\non track to make the first noise temperature measurements of the HIRAX feed and\nperform the first analysis of feed repeatability.\n"}
+{"abstract": "  We establish concentration inequalities in the class of ultra log-concave\ndistributions. In particular, we show that ultra log-concave distributions\nsatisfy Poisson concentration bounds. As an application, we derive\nconcentration bounds for the intrinsic volumes of a convex body, which\ngeneralizes and improves a result of Lotz, McCoy, Nourdin, Peccati, and Tropp\n(2019).\n"}
+{"abstract": "  What does bumping into things in a scene tell you about scene geometry? In\nthis paper, we investigate the idea of learning from collisions. At the heart\nof our approach is the idea of collision replay, where we use examples of a\ncollision to provide supervision for observations at a past frame. We use\ncollision replay to train convolutional neural networks to predict a\ndistribution over collision time from new images. This distribution conveys\ninformation about the navigational affordances (e.g., corridors vs open spaces)\nand, as we show, can be converted into the distance function for the scene\ngeometry. We analyze this approach with an agent that has noisy actuation in a\nphotorealistic simulator.\n"}
+{"abstract": "  We propose a leptoquark model with two scalar leptoquarks $S^{}_1 \\left(\n\\bar{3},1,\\frac{1}{3} \\right)$ and $\\widetilde{R}^{}_2 \\left(3,2,\\frac{1}{6}\n\\right)$ to give a combined explanation of neutrino masses, lepton flavor\nmixing and the anomaly of muon $g-2$, satisfying the constraints from the\nradiative decays of charged leptons. The neutrino masses are generated via\none-loop corrections resulting from a mixing between $S^{}_1$ and\n$\\widetilde{R}^{}_2$. With a set of specific textures for the leptoquark Yukawa\ncoupling matrices, the neutrino mass matrix possesses an approximate\n$\\mu$-$\\tau$ reflection symmetry with $\\left( M^{}_\\nu \\right)^{}_{ee} = 0$\nonly in favor of the normal neutrino mass ordering. We show that this model can\nsuccessfully explain the anomaly of muon $g-2$ and current experimental\nneutrino oscillation data under the constraints from the radiative decays of\ncharged leptons.\n"}
+{"abstract": "  Face detection is a crucial first step in many facial recognition and face\nanalysis systems. Early approaches for face detection were mainly based on\nclassifiers built on top of hand-crafted features extracted from local image\nregions, such as Haar Cascades and Histogram of Oriented Gradients. However,\nthese approaches were not powerful enough to achieve a high accuracy on images\nof from uncontrolled environments. With the breakthrough work in image\nclassification using deep neural networks in 2012, there has been a huge\nparadigm shift in face detection. Inspired by the rapid progress of deep\nlearning in computer vision, many deep learning based frameworks have been\nproposed for face detection over the past few years, achieving significant\nimprovements in accuracy. In this work, we provide a detailed overview of some\nof the most representative deep learning based face detection methods by\ngrouping them into a few major categories, and present their core architectural\ndesigns and accuracies on popular benchmarks. We also describe some of the most\npopular face detection datasets. Finally, we discuss some current challenges in\nthe field, and suggest potential future research directions.\n"}
+{"abstract": "  A Cayley (di)hypergraph is a hypergraph that its automorphism group contains\na subgroup acting regularly on (hyper)vertices. In this paper, we study Cayley\n(di)hypergraph and its automorphism group.\n"}
+{"abstract": "  Purpose: Develop a processing scheme for Gradient Echo (GRE) phase to enable\nrestoration of susceptibility-related (SuR) features in regions affected by\nimperfect phase unwrapping, background suppression and low signal-to-noise\nratio (SNR) due to phase dispersion. Theory and Methods: The predictable\ncomponents sampled across the echo dimension in a multi-echo GRE sequence are\nrecovered by rank minimizing a Hankel matrix formed using the complex\nexponential of the background suppressed phase. To estimate the single\nfrequency component that relates to the susceptibility induced field, it is\nrequired to maintain consistency with the measured phase after background\nsuppression, penalized by a unity rank approximation (URA) prior. This is\nformulated as an optimization problem, implemented using the alternating\ndirection method of multiplier (ADMM). Results: With in vivo multi-echo GRE\ndata, the magnitude susceptibility weighted image (SWI) reconstructed using URA\nprior shows additional venous structures that are obscured due to phase\ndispersion and noise in regions subject to remnant non-local field variations.\nThe performance is compared with the susceptibility map weighted imaging (SMWI)\nand the standard SWI. It is also shown using numerical simulation that\nquantitative susceptibility map (QSM) computed from the reconstructed phase\nexhibits reduced artifacts and quantification error. In vivo experiments reveal\niron depositions in insular, motor cortex and superior frontal gyrus that are\nnot identified in standard QSM. Conclusion: URA processed GRE phase is less\nsensitive to imperfections in the phase pre-processing techniques, and thereby\nenable robust estimation of SWI and QSM.\n"}
+{"abstract": "  We provide a comprehensive analysis of the two-parameter Beta distributions\nseen from the perspective of second-order stochastic dominance. By changing its\nparameters through a bijective mapping, we work with a bounded subset D instead\nof an unbounded plane. We show that a mean-preserving spread is equivalent to\nan increase of the variance, which means that higher moments are irrelevant to\ncompare the riskiness of Beta distributions. We then derive the lattice\nstructure induced by second-order stochastic dominance, which is feasible\nthanks to the topological closure of D. Finally, we consider a standard\n(expected-utility based) portfolio optimization problem in which its inputs are\nthe parameters of the Beta distribution. We explicitly characterize the subset\nof D for which the optimal solution consists of investing 100% of the wealth in\nthe risky asset and we provide an exhaustive numerical analysis of this optimal\nsolution through (color-coded) graphs.\n"}
+{"abstract": "  We report the growth, structural and magnetic properties of the less studied\nEu-oxide phase, Eu$_3$O$_4$, thin films grown on a Si/SiO$_2$ substrate and\nSi/SiO$_2$/graphene using molecular beam epitaxy. The X-ray diffraction scans\nshow that highly-textured crystalline Eu$_3$O$_4$(001) films are grown on both\nsubstrates, whereas the film deposited on graphene has a better crystallinity\nthan that grown on the Si/SiO$_2$ substrate. The SQUID measurements show that\nboth films have a Curie temperature of about 5.5 K, with a magnetic moment of\n0.0032 emu/g at 2 K. The mixed-valency of the Eu cations has been confirmed by\nthe qualitative analysis of the depth-profile X-ray photoelectron spectroscopy\nmeasurements with the Eu$^{2+}$ : Eu$^{3+}$ ratio of 28 : 72. However,\nsurprisingly, our films show no metamagnetic behaviour as reported for the bulk\nand powder form. Furthermore, the Raman spectroscopy scans show that the growth\nof the Eu$_3$O$_4$ thin films has no damaging effect on the underlayer graphene\nsheet. Therefore, the graphene layer is expected to retain its properties.\n"}
+{"abstract": "  We study the Choquard equation with a local perturbation \\begin{equation*}\n-\\Delta u=\\lambda u+(I_\\alpha\\ast|u|^p)|u|^{p-2}u+\\mu|u|^{q-2}u,\\ x\\in\n\\mathbb{R}^{N} \\end{equation*} having prescribed mass \\begin{equation*}\n\\int_{\\mathbb{R}^N}|u|^2dx=a^2. \\end{equation*} For a $L^2$-critical or\n$L^2$-supercritical perturbation $\\mu|u|^{q-2}u$, we prove nonexistence,\nexistence and symmetry of normalized ground states, by using the mountain pass\nlemma, the Poho\\v{z}aev constraint method, the Schwartz symmetrization\nrearrangements and some theories of polarizations. In particular, our results\ncover the Hardy-Littlewood-Sobolev upper critical exponent case\n$p=(N+\\alpha)/(N-2)$. Our results are a nonlocal counterpart of the results in\n\\cite{{Li 2021-4},{Soave JFA},{Wei-Wu 2021}}.\n"}
+{"abstract": "  We study an invariant of compact metric spaces which combines the notion of\ncurvature sets introduced by Gromov in the 1980s together with the notion of\nVietoris-Rips persistent homology. For given integers $k\\geq 0$ and $n\\geq 1$\nthese invariants arise by considering the degree $k$ Vietoris-Rips persistence\ndiagrams of all subsets of a given metric space with cardinality at most $n$.\nWe call these invariants \\emph{persistence sets} and denote them as\n$D_{n,k}^\\mathrm{VR}$. We argue that computing these invariants could be\nsignificantly easier than computing the usual Vietoris-Rips persistence\ndiagrams. We establish stability results as for these invariants and we also\nprecisely characterize some of them in the case of spheres with geodesic and\nEuclidean distances. We identify a rich family of metric graphs for which\n$D_{4,1}^{\\mathrm{VR}}$ fully recovers their homotopy type. Along the way we\nprove some useful properties of Vietoris-Rips persistence diagrams.\n"}
+{"abstract": "  Let $K$ be the connected sum of knots $K_1,\\ldots,K_n$. It is known that the\n$\\mathrm{SL}_2(\\mathbb{C})$-character variety of the knot exterior of $K$ has a\ncomponent of dimension $\\geq 2$ as the connected sum admits a so-called\nbending. We show that there is a natural way to define the adjoint Reidemeister\ntorsion for such a high-dimensional component and prove that it is locally\nconstant on a subset of the character variety where the trace of a meridian is\nconstant. We also prove that the adjoint Reidemeister torsion of $K$ satisfies\nthe vanishing identity if each $K_i$ does so.\n"}
+{"abstract": "  Grammatical error correction (GEC) suffers from a lack of sufficient parallel\ndata. Therefore, GEC studies have developed various methods to generate pseudo\ndata, which comprise pairs of grammatical and artificially produced\nungrammatical sentences. Currently, a mainstream approach to generate pseudo\ndata is back-translation (BT). Most previous GEC studies using BT have employed\nthe same architecture for both GEC and BT models. However, GEC models have\ndifferent correction tendencies depending on their architectures. Thus, in this\nstudy, we compare the correction tendencies of the GEC models trained on pseudo\ndata generated by different BT models, namely, Transformer, CNN, and LSTM. The\nresults confirm that the correction tendencies for each error type are\ndifferent for every BT model. Additionally, we examine the correction\ntendencies when using a combination of pseudo data generated by different BT\nmodels. As a result, we find that the combination of different BT models\nimproves or interpolates the F_0.5 scores of each error type compared with that\nof single BT models with different seeds.\n"}
+{"abstract": "  Deep learning recommendation models (DLRMs) are used across many\nbusiness-critical services at Facebook and are the single largest AI\napplication in terms of infrastructure demand in its data-centers. In this\npaper we discuss the SW/HW co-designed solution for high-performance\ndistributed training of large-scale DLRMs. We introduce a high-performance\nscalable software stack based on PyTorch and pair it with the new evolution of\nZion platform, namely ZionEX. We demonstrate the capability to train very large\nDLRMs with up to 12 Trillion parameters and show that we can attain 40X speedup\nin terms of time to solution over previous systems. We achieve this by (i)\ndesigning the ZionEX platform with dedicated scale-out network, provisioned\nwith high bandwidth, optimal topology and efficient transport (ii) implementing\nan optimized PyTorch-based training stack supporting both model and data\nparallelism (iii) developing sharding algorithms capable of hierarchical\npartitioning of the embedding tables along row, column dimensions and load\nbalancing them across multiple workers; (iv) adding high-performance core\noperators while retaining flexibility to support optimizers with fully\ndeterministic updates (v) leveraging reduced precision communications,\nmulti-level memory hierarchy (HBM+DDR+SSD) and pipelining. Furthermore, we\ndevelop and briefly comment on distributed data ingestion and other supporting\nservices that are required for the robust and efficient end-to-end training in\nproduction environments.\n"}
+{"abstract": "  Bitcoin and Ethereum transactions present one of the largest real-world\ncomplex networks that are publicly available for study, including a detailed\npicture of their time evolution. As such, they have received a considerable\namount of attention from the network science community, beside analysis from an\neconomic or cryptography perspective. Among these studies, in an analysis on\nthe early instance of the Bitcoin network, we have shown the clear presence of\nthe preferential attachment, or \"rich-get-richer\" phenomenon. Now, we revisit\nthis question, using a recent version of the Bitcoin network that has grown\nalmost 100-fold since our original analysis. Furthermore, we additionally carry\nout a comparison with Ethereum, the second most important cryptocurrency. Our\nresults show that preferential attachment continues to be a key factor in the\nevolution of both the Bitcoin and Ethereum transactoin networks. To facilitate\nfurther analysis, we publish a recent version of both transaction networks, and\nan efficient software implementation that is able to evaluate linking\nstatistics necessary for learn about preferential attachment on networks with\nseveral hundred million edges.\n"}
+{"abstract": "  Distributed hardware of acoustic sensor networks bears inconsistency of local\nsampling frequencies, which is detrimental to signal processing. Fundamentally,\nsampling rate offset (SRO) nonlinearly relates the discrete-time signals\nacquired by different sensor nodes. As such, retrieval of SRO from the\navailable signals requires nonlinear estimation, like double-cross-correlation\nprocessing (DXCP), and frequently results in biased estimation. SRO\ncompensation by asynchronous sampling rate conversion (ASRC) on the signals\nthen leaves an unacceptable residual. As a remedy to this problem, multi-stage\nprocedures have been devised to diminish the SRO residual with multiple\niterations of SRO estimation and ASRC over the entire signal. This paper\nconverts the mechanism of offline multi-stage processing into a continuous\nfeedback-control loop comprising a controlled ASRC unit followed by an online\nimplementation of DXCP-based SRO estimation. To support the design of an\noptimum internal model control unit for this closed-loop system, the paper\ndeploys an analytical dynamical model of the proposed online DXCP. The\nresulting control architecture then merely applies a single treatment of each\nsignal frame, while efficiently diminishing SRO bias with time. Evaluations\nwith both speech and Gaussian input demonstrate that the high accuracy of\nmulti-stage processing is maintained at the low complexity of single-stage\n(open-loop) processing.\n"}
+{"abstract": "  We examine a class of random walks in random environments on $\\mathbb{Z}$\nwith bounded jumps, a generalization of the classic one-dimensional model. The\nenvironments we study have i.i.d. transition probability vectors drawn from\nDirichlet distributions. For this model, we characterize recurrence and\ntransience, and in the transient case we characterize ballisticity. For\nballisticity, we give two parameters, $\\kappa_0$ and $\\kappa_1$. The parameter\n$\\kappa_0$ governs finite trapping effects, and $\\kappa_1$ governs repeated\ntraversals of arbitrarily large regions of the graph. We show that the walk is\nright-transient if and only if $\\kappa_1>0$, and in that case it is ballistic\nif and only if $\\min(\\kappa_0,\\kappa_1)>1$.\n"}
+{"abstract": "  G0.253+0.016, aka 'the Brick', is one of the most massive (> 10^5 Msun) and\ndense (> 10^4 cm-3) molecular clouds in the Milky Way's Central Molecular Zone.\nPrevious observations have detected tentative signs of active star formation,\nmost notably a water maser that is associated with a dust continuum source. We\npresent ALMA Band 6 observations with an angular resolution of 0.13\" (1000 AU)\ntowards this 'maser core', and report unambiguous evidence of active star\nformation within G0.253+0.016. We detect a population of eighteen continuum\nsources (median mass ~ 2 Msun), nine of which are driving bi-polar molecular\noutflows as seen via SiO (5-4) emission. At the location of the water maser, we\nfind evidence for a protostellar binary/multiple with multi-directional outflow\nemission. Despite the high density of G0.253+0.016, we find no evidence for\nhigh-mass protostars in our ALMA field. The observed sources are instead\nconsistent with a cluster of low-to-intermediate-mass protostars. However, the\nmeasured outflow properties are consistent with those expected for\nintermediate-to-high-mass star formation. We conclude that the sources are\nyoung and rapidly accreting, and may potentially form intermediate and\nhigh-mass stars in the future. The masses and projected spatial distribution of\nthe cores are generally consistent with thermal fragmentation, suggesting that\nthe large-scale turbulence and strong magnetic field in the cloud do not\ndominate on these scales, and that star formation on the scale of individual\nprotostars is similar to that in Galactic disc environments.\n"}
+{"abstract": "  Authenticated Append-Only Skiplists (AAOSLs) enable maintenance and querying\nof an authenticated log (such as a blockchain) without requiring any single\nparty to store or verify the entire log, or to trust another party regarding\nits contents. AAOSLs can help to enable efficient dynamic participation (e.g.,\nin consensus) and reduce storage overhead.\n  In this paper, we formalize an AAOSL originally described by Maniatis and\nBaker, and prove its key correctness properties. Our model and proofs are\nmachine checked in Agda. Our proofs apply to a generalization of the original\nconstruction and provide confidence that instances of this generalization can\nbe used in practice. Our formalization effort has also yielded some\nsimplifications and optimizations.\n"}
+{"abstract": "  We define a spectral flow for paths of selfadjoint Fredholm operators that\nare equivariant under the orthogonal action of a compact Lie group as an\nelement of the representation ring of the latter. This $G$-equivariant spectral\nflow shares all common properties of the integer valued classical spectral\nflow, and it can be non-trivial even if the classical spectral flow vanishes.\nOur main theorem uses the $G$-equivariant spectral flow to study bifurcation of\nperiodic solutions for autonomous Hamiltonian systems with symmetries.\n"}
+{"abstract": "  Deterioration of the operation parameters of Al/SiO2/p-type Si surface\nbarrier detector upon irradiation with alpha-particles at room temperature was\ninvestigated. As a result of 40-days irradiation with a total fluence of 8*10^9\n{\\alpha}-particles, an increase of {\\alpha}-peak FWHM from 70 keV to 100 keV\nwas observed and explained by increase of the detector reverse current due to\nformation of a high concentration of near mid-gap defect levels. Performed CV\nmeasurements revealed the appearance of at least 6*10^12 cm-3 radiation-induced\nacceptors at the depths where according to the TRIM simulations the highest\nconcentration of vacancy-interstitial pairs was created by the incoming\n{\\alpha}-particles. The studies carried out by current-DLTS technique allowed\nto associate the observed increase of the acceptor concentration with the near\nmid-gap acceptor level at EV+0.56 eV. This level can be apparently associated\nwith V2O defects recognized previously to be responsible for the space charge\nsign inversion in the irradiated n-type Si detectors.\n"}
+{"abstract": "  For a function $f\\colon [0,1]\\to\\mathbb R$, we consider the set $E(f)$ of\npoints at which $f$ cuts the real axis. Given $f\\colon [0,1]\\to\\mathbb R$ and a\nCantor set $D\\subset [0,1]$ with $\\{0,1\\}\\subset D$, we obtain conditions\nequivalent to the conjunction $f\\in C[0,1]$ (or $f\\in C^\\infty [0,1]$) and\n$D\\subset E(f)$. This generalizes some ideas of Zabeti. We observe that, if $f$\nis continuous, then $E(f)$ is a closed nowhere dense subset of $f^{-1}[\\{ 0\\}]$\nwhere each $x\\in \\{0,1\\}\\cap E(f)$ is an accumulation point of $E(f)$. Our main\nresult states that, for a closed nowhere dense set $F\\subset [0,1]$ with each\n$x\\in \\{0,1\\}\\cap E(f)$ being an accumulation point of $F$, there exists $f\\in\nC^\\infty [0,1]$ such that $F=E(f)$.\n"}
+{"abstract": "  Graph-based causal discovery methods aim to capture conditional\nindependencies consistent with the observed data and differentiate causal\nrelationships from indirect or induced ones. Successful construction of\ngraphical models of data depends on the assumption of causal sufficiency: that\nis, that all confounding variables are measured. When this assumption is not\nmet, learned graphical structures may become arbitrarily incorrect and effects\nimplied by such models may be wrongly attributed, carry the wrong magnitude, or\nmis-represent direction of correlation. Wide application of graphical models to\nincreasingly less curated \"big data\" draws renewed attention to the unobserved\nconfounder problem.\n  We present a novel method that aims to control for the latent space when\nestimating a DAG by iteratively deriving proxies for the latent space from the\nresiduals of the inferred model. Under mild assumptions, our method improves\nstructural inference of Gaussian graphical models and enhances identifiability\nof the causal effect. In addition, when the model is being used to predict\noutcomes, it un-confounds the coefficients on the parents of the outcomes and\nleads to improved predictive performance when out-of-sample regime is very\ndifferent from the training data. We show that any improvement of prediction of\nan outcome is intrinsically capped and cannot rise beyond a certain limit as\ncompared to the confounded model. We extend our methodology beyond GGMs to\nordinal variables and nonlinear cases. Our R package provides both PCA and\nautoencoder implementations of the methodology, suitable for GGMs with some\nguarantees and for better performance in general cases but without such\nguarantees.\n"}
+{"abstract": "  The Multichannel Subtractive Double Pass (MSDP) is an imaging spectroscopy\ntechnique, which allows observations of spectral line profiles over a 2D field\nof view with high spatial and temporal resolution. It has been intensively used\nsince 1977 on various spectrographs (Meudon, Pic du Midi, the German Vacuum\nTower Telescope, THEMIS, Wroc{\\l}aw). We summarize previous developments and\ndescribe the capabilities of a new design that has been developed at Meudon and\nthat has higher spectral resolution and increased channel number: Spectral\nSampling with Slicer for Solar Instrumentation (S4I), which can be combined\nwith a new and fast polarimetry analysis. This new generation MSDP technique is\nwell adapted to large telescopes. Also presented are the goals of a derived\ncompact version of the instrument, the Solar Line Emission Dopplerometer\n(SLED), dedicated to dynamic studies of coronal loops observed in the forbidden\niron lines, and prominences. It is designed for observing total solar eclipses,\nand for deployment on the Wroc{\\l}aw and Lomnicky peak coronagraphs\nrespectively for prominence and coronal observations.\n"}
+{"abstract": "  We exhibit a non-hyperelliptic curve C of genus 3 such that the class of the\nCeresa cycle [C]-[(-1)*C] in JC modulo algebraic equivalence is torsion.\n"}
+{"abstract": "  This work presents the results of project CONECT4, which addresses the\nresearch and development of new non-intrusive communication methods for the\ngeneration of a human-machine learning ecosystem oriented to predictive\nmaintenance in the automotive industry. Through the use of innovative\ntechnologies such as Augmented Reality, Virtual Reality, Digital Twin and\nexpert knowledge, CONECT4 implements methodologies that allow improving the\nefficiency of training techniques and knowledge management in industrial\ncompanies. The research has been supported by the development of content and\nsystems with a low level of technological maturity that address solutions for\nthe industrial sector applied in training and assistance to the operator. The\nresults have been analyzed in companies in the automotive sector, however, they\nare exportable to any other type of industrial sector. -- --\n  En esta publicaci\\'on se presentan los resultados del proyecto CONECT4, que\naborda la investigaci\\'on y desarrollo de nuevos m\\'etodos de comunicaci\\'on no\nintrusivos para la generaci\\'on de un ecosistema de aprendizaje\nhombre-m\\'aquina orientado al mantenimiento predictivo en la industria de\nautomoci\\'on. A trav\\'es del uso de tecnolog\\'ias innovadoras como la Realidad\nAumentada, la Realidad Virtual, el Gemelo Digital y conocimiento experto,\nCONECT4 implementa metodolog\\'ias que permiten mejorar la eficiencia de las\nt\\'ecnicas de formaci\\'on y gesti\\'on de conocimiento en las empresas\nindustriales. La investigaci\\'on se ha apoyado en el desarrollo de contenidos y\nsistemas con un nivel de madurez tecnol\\'ogico bajo que abordan soluciones para\nel sector industrial aplicadas en la formaci\\'on y asistencia al operario. Los\nresultados han sido analizados en empresas del sector de automoci\\'on, no\nobstante, son exportables a cualquier otro tipo de sector industrial.\n"}
+{"abstract": "  This paper reports 209 O-type stars found with LAMOST. All 135 new O-type\nstars discovered so far with LAMOST so far are given. Among them, 94 stars are\nfirstly presented in this sample. There are 1 Iafpe star, 5 Onfp stars, 12 Oe\nstars, 1 Ofc stars, 3 ON stars, 16 double-lined spectroscopic binaries, and 33\nsingle-lined spectroscopic binaries. All O-type stars are determined based on\nLAMOST low-resolution spectra (R ~ 1800), with their LAMOST median-resolution\nspectra (R~7500) as supplements.\n"}
+{"abstract": "  A world-wide COVID-19 pandemic intensified strongly the studies of molecular\nmechanisms related to the coronaviruses. The origin of coronaviruses and the\nrisks of human-to-human, animal-to-human, and human-to-animal transmission of\ncoronaviral infections can be understood only on a broader evolutionary level\nby detailed comparative studies. In this paper, we studied ribonucleocapsid\nassembly-packaging signals (RNAPS) in the genomes of all seven known pathogenic\nhuman coronaviruses, SARS-CoV, SARS-CoV-2, MERS-CoV, HCoV-OC43, HCoV-HKU1,\nHCoV-229E, and HCoV-NL63 and compared them with RNAPS in the genomes of the\nrelated animal coronaviruses including SARS-Bat-CoV, MERS-Camel-CoV, MHV,\nBat-CoV MOP1, TGEV, and one of camel alphacoronaviruses. RNAPS in the genomes\nof coronaviruses were evolved due to weakly specific interactions between\ngenomic RNA and N proteins in helical nucleocapsids. Combining transitional\ngenome mapping and Jaccard correlation coefficients allows us to perform the\nanalysis directly in terms of underlying motifs distributed over the genome. In\nall coronaviruses RNAPS were distributed quasi-periodically over the genome\nwith the period about 54 nt biased to 57 nt and to 51 nt for the genomes longer\nand shorter than that of SARS-CoV, respectively. The comparison with the\nexperimentally verified packaging signals for MERS-CoV, MHV, and TGEV proved\nthat the distribution of particular motifs is strongly correlated with the\npackaging signals. We also found that many motifs were highly conserved in both\ncharacters and positioning on the genomes throughout the lineages that make\nthem promising therapeutic targets. The mechanisms of encapsidation can affect\nthe recombination and co-infection as well.\n"}
+{"abstract": "  This paper establishes new connections between many-body quantum systems,\nOne-body Reduced Density Matrices Functional Theory (1RDMFT) and Optimal\nTransport (OT), by interpreting the problem of computing the ground-state\nenergy of a finite dimensional composite quantum system at positive temperature\nas a non-commutative entropy regularized Optimal Transport problem. We develop\na new approach to fully characterize the dual-primal solutions in such\nnon-commutative setting. The mathematical formalism is particularly relevant in\nquantum chemistry: numerical realizations of the many-electron ground state\nenergy can be computed via a non-commutative version of Sinkhorn algorithm. Our\napproach allows to prove convergence and robustness of this algorithm, which,\nto our best knowledge, were unknown even in the two marginal case. Our methods\nare based on careful a priori estimates in the dual problem, which we believe\nto be of independent interest. Finally, the above results are extended in\n1RDMFT setting, where bosonic or fermionic symmetry conditions are enforced on\nthe problem.\n"}
+{"abstract": "  Stochastic gradient Markov chain Monte Carlo (SGMCMC) is a popular class of\nalgorithms for scalable Bayesian inference. However, these algorithms include\nhyperparameters such as step size or batch size that influence the accuracy of\nestimators based on the obtained posterior samples. As a result, these\nhyperparameters must be tuned by the practitioner and currently no principled\nand automated way to tune them exists. Standard MCMC tuning methods based on\nacceptance rates cannot be used for SGMCMC, thus requiring alternative tools\nand diagnostics. We propose a novel bandit-based algorithm that tunes the\nSGMCMC hyperparameters by minimizing the Stein discrepancy between the true\nposterior and its Monte Carlo approximation. We provide theoretical results\nsupporting this approach and assess various Stein-based discrepancies. We\nsupport our results with experiments on both simulated and real datasets, and\nfind that this method is practical for a wide range of applications.\n"}
+{"abstract": "  Observations of the redshifted 21-cm line of neutral hydrogen (HI) are a new\nand powerful window of observation that offers us the possibility to map the\nspatial distribution of cosmic HI and learn about cosmology. BINGO (Baryon\nAcoustic Oscillations [BAO] from Integrated Neutral Gas Observations) is a new\nunique radio telescope designed to be one of the first to probe BAO at radio\nfrequencies. BINGO has two science goals: cosmology and astrophysics. Cosmology\nis the main science goal and the driver for BINGO's design and strategy. The\nkey of BINGO is to detect the low redshift BAO to put strong constraints in the\ndark sector models. Given the versatility of the BINGO telescope, a secondary\ngoal is astrophysics, where BINGO can help discover and study Fast Radio Bursts\n(FRB) and other transients, Galactic and extragalactic science. In this paper,\nwe introduce the latest progress of the BINGO project, its science goals,\ndescribing the scientific potential of the project in each science and the new\ndevelopments obtained by the collaboration. We introduce the BINGO project and\nits science goals and give a general summary of recent developments in\nconstruction, science potential and pipeline development obtained by the BINGO\ncollaboration in the past few years. We show that BINGO will be able to obtain\ncompetitive constraints for the dark sector, and also that will allow for the\ndiscovery of several FRBs in the southern hemisphere. The capacity of BINGO in\nobtaining information from 21-cm is also tested in the pipeline introduced\nhere. There is still no measurement of the BAO in radio, and studying cosmology\nin this new window of observations is one of the most promising advances in the\nfield. The BINGO project is a radio telescope that has the goal to be one of\nthe first to perform this measurement and it is currently being built in the\nnortheast of Brazil. (Abridged)\n"}
+{"abstract": "  We explore cross-lingual transfer of register classification for web\ndocuments. Registers, that is, text varieties such as blogs or news are one of\nthe primary predictors of linguistic variation and thus affect the automatic\nprocessing of language. We introduce two new register annotated corpora,\nFreCORE and SweCORE, for French and Swedish. We demonstrate that deep\npre-trained language models perform strongly in these languages and outperform\nprevious state-of-the-art in English and Finnish. Specifically, we show 1) that\nzero-shot cross-lingual transfer from the large English CORE corpus can match\nor surpass previously published monolingual models, and 2) that lightweight\nmonolingual classification requiring very little training data can reach or\nsurpass our zero-shot performance. We further analyse classification results\nfinding that certain registers continue to pose challenges in particular for\ncross-lingual transfer.\n"}
+{"abstract": "  Compact binary systems emit gravitational radiation which is potentially\ndetectable by current Earth bound detectors. Extracting these signals from the\ninstruments' background noise is a complex problem and the computational cost\nof most current searches depends on the complexity of the source model. Deep\nlearning may be capable of finding signals where current algorithms hit\ncomputational limits. Here we restrict our analysis to signals from\nnon-spinning binary black holes and systematically test different strategies by\nwhich training data is presented to the networks. To assess the impact of the\ntraining strategies, we re-analyze the first published networks and directly\ncompare them to an equivalent matched-filter search. We find that the deep\nlearning algorithms can generalize low signal-to-noise ratio (SNR) signals to\nhigh SNR ones but not vice versa. As such, it is not beneficial to provide high\nSNR signals during training, and fastest convergence is achieved when low SNR\nsamples are provided early on. During testing we found that the networks are\nsometimes unable to recover any signals when a false alarm probability\n$<10^{-3}$ is required. We resolve this restriction by applying a modification\nwe call unbounded Softmax replacement (USR) after training. With this\nalteration we find that the machine learning search retains $\\geq 97.5\\%$ of\nthe sensitivity of the matched-filter search down to a false-alarm rate of 1\nper month.\n"}
+{"abstract": "  The position of the Sun inside the Milky Way's disc hampers the study of the\nspiral arm structure. We aim to analyse the spiral arms along the line-of-sight\ntowards the Galactic centre (GC) to determine their distance, extinction, and\nstellar population. We use the GALACTICNUCLEUS survey, a JHKs high angular\nresolution photometric catalogue (0.2\") for the innermost regions of the\nGalaxy. We fitted simple synthetic colour-magnitude models to our data via\n$\\chi^2$ minimisation. We computed the distance and extinction to the detected\nspiral arms. We also analysed the extinction curve and the relative extinction\nbetween the detected features. Finally, we built extinction-corrected Ks\nluminosity functions (KLFs) to study the stellar populations present in the\nsecond and third spiral arm features. We determined the mean distances to the\nspiral arms: $d1=1.6\\pm0.2$, $d2=2.6\\pm0.2$, $d3=3.9\\pm0.3$, and $d4=4.5\\pm0.2$\nkpc, and the mean extinctions: $A_{H1}=0.35\\pm0.08$, $A_{H2}=0.77\\pm0.08$,\n$A_{H3}=1.68\\pm0.08$, and $A_{H4}=2.30\\pm0.08$ mag. We analysed the extinction\ncurve in the near infrared for the stars in the spiral arms and found mean\nvalues of $A_J/A_{H}=1.89\\pm0.11$ and $A_H/A_{K_s}=1.86\\pm0.11$, in agreement\nwith the results obtained for the GC. This implies that the shape of the\nextinction curve does not depend on distance or absolute extinction. We also\nbuilt extinction maps for each spiral arm and obtained that they are\nhomogeneous and might correspond to independent extinction layers. Finally,\nanalysing the KLFs from the second and the third spiral arms, we found that\nthey have similar stellar populations. We obtained two main episodes of star\nformation: $>6$ Gyr ($\\sim60-70\\%$ of the stellar mass), and $1.5-4$ Gyr\n($\\sim20-30\\%$ of the stellar mass), compatible with previous work. We also\ndetected recent star formation at a lower level ($\\sim10\\%$) for the third\nspiral arm.\n"}
+{"abstract": "  We present a comprehensive comparison of spin and energy dynamics in quantum\nand classical spin models on different geometries, ranging from one-dimensional\nchains, over quasi-one-dimensional ladders, to two-dimensional square lattices.\nFocusing on dynamics at formally infinite temperature, we particularly consider\nthe autocorrelation functions of local densities, where the time evolution is\ngoverned either by the linear Schr\\\"odinger equation in the quantum case, or\nthe nonlinear Hamiltonian equations of motion in the case of classical\nmechanics. While, in full generality, a quantitative agreement between quantum\nand classical dynamics can therefore not be expected, our large-scale numerical\nresults for spin-$1/2$ systems with up to $N = 36$ lattice sites in fact defy\nthis expectation. Specifically, we observe a remarkably good agreement for all\ngeometries, which is best for the nonintegrable quantum models in quasi-one or\ntwo dimensions, but still satisfactory in the case of integrable chains, at\nleast if transport properties are not dominated by the extensive number of\nconservation laws. Our findings indicate that classical or semi-classical\nsimulations provide a meaningful strategy to analyze the dynamics of quantum\nmany-body models, even in cases where the spin quantum number $S = 1/2$ is\nsmall and far away from the classical limit $S \\to \\infty$.\n"}
+{"abstract": "  Event coreference continues to be a challenging problem in information\nextraction. With the absence of any external knowledge bases for events,\ncoreference becomes a clustering task that relies on effective representations\nof the context in which event mentions appear. Recent advances in\ncontextualized language representations have proven successful in many tasks,\nhowever, their use in event linking been limited. Here we present a three part\napproach that (1) uses representations derived from a pretrained BERT model to\n(2) train a neural classifier to (3) drive a simple clustering algorithm to\ncreate coreference chains. We achieve state of the art results with this model\non two standard datasets for within-document event coreference task and\nestablish a new standard on a third newer dataset.\n"}
+{"abstract": "  Safety is a fundamental requirement in any human-robot collaboration\nscenario. To ensure the safety of users for such scenarios, we propose a novel\nVirtual Barrier system facilitated by an augmented reality interface. Our\nsystem provides two kinds of Virtual Barriers to ensure safety: 1) a Virtual\nPerson Barrier which encapsulates and follows the user to protect them from\ncolliding with the robot, and 2) Virtual Obstacle Barriers which users can\nspawn to protect objects or regions that the robot should not enter. To enable\neffective human-robot collaboration, our system includes an intuitive robot\nprogramming interface utilizing speech commands and hand gestures, and features\nthe capability of automatic path re-planning when potential collisions are\ndetected as a result of a barrier intersecting the robot's planned path. We\ncompared our novel system with a standard 2D display interface through a user\nstudy, where participants performed a task mimicking an industrial\nmanufacturing procedure. Results show that our system increases the user's\nsense of safety and task efficiency, and makes the interaction more intuitive.\n"}
+{"abstract": "  We show that the change of basis matrices of a set of $m$ bases of a finite\nvector space is a connected groupoid of order $m^2$. We define a general method\nto express the elements of change of basis matrices as algebraic expressions\nusing optimizations of evaluations of vector dot products. Examples are given\nwith orthogonal polynomials.\n"}
+{"abstract": "  We experimentally observe the dipole scattering of a nanoparticle using a\nhigh numerical aperture (NA) imaging system. The optically levitated\nnanoparticle provides an environment free of particle-substrate interaction. We\nilluminate the silica nanoparticle in vacuum with a 532 nm laser beam\northogonally to the propagation direction of the 1064 nm trapping laser beam\nstrongly focused by the same high NA objective used to collect the scattering,\nwhich results in a dark background and high signal-noise ratio. The dipole\norientations of the nanoparticle induced by the linear polarization of the\nincident laser are studied by measuring the scattering light distribution in\nthe image and the Fourier space (k-space) as we rotate the illuminating light\npolarization. The polarization vortex (vector beam) is observed for the special\ncase, when the dipole orientation of the nanoparticle is aligned along the\noptical axis of the microscope objective. Our work offers an important platform\nfor studying the scattering anisotropy with Kerker conditions.\n"}
+{"abstract": "  We prove that the Feynman Path Integral is equivalent to a novel stringy\ndescription of elementary particles characterized by a single compact (cyclic)\nworld-line parameter playing the role of the particle internal clock. Such a\npossible description of elementary particles as characterized by intrinsic\nperiodicity in time has been indirectly confirmed, even experimentally, by\nrecent developments of Time Crystals. We clearly obtain an exact unified\nformulation of quantum and relativistic physics, potentially deterministic,\nfully falsifiable having no fine-tunable parameters, also proven in previous\npapers to be completely consistent with all known physics, from theoretical\nphysics to condensed matter. New physics will be discovered by probing quantum\nphenomena with experimental time accuracy of the order of $10^{-21}$ sec.\n"}
+{"abstract": "  Recent papers on the theory of representation learning has shown the\nimportance of a quantity called diversity when generalizing from a set of\nsource tasks to a target task. Most of these papers assume that the function\nmapping shared representations to predictions is linear, for both source and\ntarget tasks. In practice, researchers in deep learning use different numbers\nof extra layers following the pretrained model based on the difficulty of the\nnew task. This motivates us to ask whether diversity can be achieved when\nsource tasks and the target task use different prediction function spaces\nbeyond linear functions. We show that diversity holds even if the target task\nuses a neural network with multiple layers, as long as source tasks use linear\nfunctions. If source tasks use nonlinear prediction functions, we provide a\nnegative result by showing that depth-1 neural networks with ReLu activation\nfunction need exponentially many source tasks to achieve diversity. For a\ngeneral function class, we find that eluder dimension gives a lower bound on\nthe number of tasks required for diversity. Our theoretical results imply that\nsimpler tasks generalize better. Though our theoretical results are shown for\nthe global minimizer of empirical risks, their qualitative predictions still\nhold true for gradient-based optimization algorithms as verified by our\nsimulations on deep neural networks.\n"}
+{"abstract": "  In [Kim05], Kim gave a new proof of Siegel's Theorem that there are only\nfinitely many $S$-integral points on $\\mathbb P^1_{\\mathbb\nZ}\\setminus\\{0,1,\\infty\\}$. One advantage of Kim's method is that it in\nprinciple allows one to actually find these points, but the calculations grow\nvastly more complicated as the size of $S$ increases. In this paper, we\nimplement a refinement of Kim's method to explicitly compute various examples\nwhere $S$ has size $2$ which has been introduced in [BD19]. In so doing, we\nexhibit new examples of a natural generalisation of a conjecture of Kim.\n"}
+{"abstract": "  This tool paper presents the High-Assurance ROS (HAROS) framework. HAROS is a\nframework for the analysis and quality improvement of robotics software\ndeveloped using the popular Robot Operating System (ROS). It builds on a static\nanalysis foundation to automatically extract models from the source code. Such\nmodels are later used to enable other sorts of analyses, such as Model\nChecking, Runtime Verification, and Property-based Testing. It has been applied\nto multiple real-world examples, helping developers find and correct various\nissues.\n"}
+{"abstract": "  The latest conjunction of Jupiter and Saturn occurred at an optical distance\nof 6 arc minutes on 21 December 2020. We re-analysed all encounters of these\ntwo planets between -1000 and +3000 CE, as the extraordinary ones\n(<10$^{\\prime}$) take place near the line of nodes every 400 years. An\noccultation of their discs did not and will not happen within the historical\ntime span of $\\pm$5,000 years around now. When viewed from Neptune though,\nthere will be an occultation in 2046.\n"}
+{"abstract": "  Filters (such as Bloom Filters) are data structures that speed up network\nrouting and measurement operations by storing a compressed representation of a\nset. Filters are space efficient, but can make bounded one-sided errors: with\ntunable probability epsilon, they may report that a query element is stored in\nthe filter when it is not. This is called a false positive. Recent research has\nfocused on designing methods for dynamically adapting filters to false\npositives, reducing the number of false positives when some elements are\nqueried repeatedly.\n  Ideally, an adaptive filter would incur a false positive with bounded\nprobability epsilon for each new query element, and would incur o(epsilon)\ntotal false positives over all repeated queries to that element. We call such a\nfilter support optimal.\n  In this paper we design a new Adaptive Cuckoo Filter and show that it is\nsupport optimal (up to additive logarithmic terms) over any n queries when\nstoring a set of size n. Our filter is simple: fixing previous false positives\nrequires a simple cuckoo operation, and the filter does not need to store any\nadditional metadata. This data structure is the first practical data structure\nthat is support optimal, and the first filter that does not require additional\nspace to fix false positives.\n  We complement these bounds with experiments showing that our data structure\nis effective at fixing false positives on network traces, outperforming\nprevious Adaptive Cuckoo Filters.\n  Finally, we investigate adversarial adaptivity, a stronger notion of\nadaptivity in which an adaptive adversary repeatedly queries the filter, using\nthe result of previous queries to drive the false positive rate as high as\npossible. We prove a lower bound showing that a broad family of filters,\nincluding all known Adaptive Cuckoo Filters, can be forced by such an adversary\nto incur a large number of false positives.\n"}
+{"abstract": "  Let $(G,K)$ be a Gelfand pair, with $G$ a Lie group of polynomial growth, and\nlet $\\Sigma\\subset{\\mathbb R}^\\ell$ be a homeomorphic image of the Gelfand\nspectrum, obtained by choosing a generating system $D_1,\\dots,D_\\ell$ of\n$G$-invariant differential operators on $G/K$ and associating to a bounded\nspherical function $\\varphi$ the $\\ell$-tuple of its eigenvalues under the\naction of the $D_j$'s.\n  We say that property (S) holds for $(G,K)$ if the spherical transform maps\nthe bi-$K$-invariant Schwartz space ${\\mathcal S}(K\\backslash G/K)$\nisomorphically onto ${\\mathcal S}(\\Sigma)$, the space of restrictions to\n$\\Sigma$ of the Schwartz functions on ${\\mathbb R}^\\ell$. This property is\nknown to hold for many nilpotent pairs, i.e., Gelfand pairs where $G=K\\ltimes\nN$, with $N$ nilpotent.\n  In this paper we enlarge the scope of this analysis outside the range of\nnilpotent pairs, stating the basic setting for general pairs of polynomial\ngrowth and then focussing on strong Gelfand pairs.\n"}
+{"abstract": "  Recent photometric surveys of Trans-Neptunian Objects (TNOs) have revealed\nthat the cold classical TNOs have distinct z-band color characteristics, and\noccupy their own distinct surface class. This suggested the presence of an\nabsorption band in the reflectance spectra of cold classicals at wavelengths\nabove 0.8 micron. Here we present reflectance spectra spanning 0.55-1.0 micron\nfor six TNOs occupying dynamically cold orbits at semimajor axes close to 44\nau. Five of our spectra show a clear and broadly consistent reduction in\nspectral gradient above 0.8 micron that diverges from their linear red optical\ncontinuum and agrees with their reported photometric colour data. Despite\npredictions, we find no evidence that the spectral flattening is caused by an\nabsorption band centered near 1.0 micron. We predict that the overall\nconsistent shape of these five spectra is related to the presence of similar\nrefractory organics on each of their surfaces, and/or their similar physical\nsurface properties such as porosity or grain size distribution. The observed\nconsistency of the reflectance spectra of these five targets aligns with\npredictions that the cold classicals share a common history in terms of\nformation and surface evolution. Our sixth target, which has been ambiguously\nclassified as either a hot or cold classical at various points in the past, has\na spectrum which remains nearly linear across the full range observed. This\nsuggests that this TNO is a hot classical interloper in the cold classical\ndynamical range, and supports the idea that other such interlopers may be\nidentifiable by their linear reflectance spectra in the range 0.8-1.0 micron.\n"}
+{"abstract": "  We study the relations of the positive frequency mode functions of Dirac\nfield in 4-dimensional Minkowski spacetime covered with Rindler and Kasner\ncoordinates, and describe the explicit form of the Minkowski vacuum state with\nthe quantum states in Kasner and Rindler regions, and analytically continue the\nsolutions. As a result, we obtain the correspondence of the positive frequency\nmode functions in Kasner region and Rindler region in a unified manner which\nderives vacuum entanglement.\n"}
+{"abstract": "  Based on a progressively type-II censored sample from the exponential\ndistribution with unknown location and scale parameter, confidence bands are\nproposed for the underlying distribution function by using confidence regions\nfor the parameters and Kolmogorov-Smirnov type statistics. Simple explicit\nrepresentations for the boundaries and for the coverage probabilities of the\nconfidence bands are analytically derived, and the performance of the bands is\ncompared in terms of band width and area by means of a data example. As a\nby-product, a novel confidence region for the location-scale parameter is\nobtained. Extensions of the results to related models for ordered data, such as\nsequential order statistics, as well as to other underlying location-scale\nfamilies of distributions are discussed.\n"}
+{"abstract": "  The famous Yang-Yau inequality provides an upper bound for the first\neigenvalue of the Laplacian on an orientable Riemannian surface solely in terms\nof its genus $\\gamma$ and the area. Its proof relies on the existence of\nholomorhic maps to $\\mathbb{CP}^1$ of low degree. Very recently, A.~Ros was\nable to use certain holomorphic maps to $\\mathbb{CP}^2$ in order to give a\nquantitative improvement of the Yang-Yau inequality for $\\gamma=3$. In the\npresent paper, we generalize Ros' argument to make use of holomorphic maps to\n$\\mathbb{CP}^n$ for any $n>0$. As an application, we obtain a quantitative\nimprovement of the Yang-Yau inequality for all genera $\\gamma>3$ except for\n$\\gamma = 4,6,8,10,14$.\n"}
+{"abstract": "  All yield criteria that determine the onset of plastic deformation in\ncrystalline materials must be invariant under the inversion symmetry associated\nwith a simultaneous change of sign of the slip direction and the slip plane\nnormal. We demonstrate the consequences of this symmetry on the functional form\nof the effective stress, where only the lowest order terms that obey this\nsymmetry are retained. A particular form of yield criterion is obtained for\nmaterials that do not obey the Schmid law, hereafter called non-Schmid\nmaterials. Application of this model to body-centered cubic and hexagonal\nclose-packed metals shows under which conditions the non-Schmid stress terms\nbecome significant in predicting the onset of yielding. In the special case,\nwhere the contributions of all non-Schmid stresses vanish, this model reduces\nto the maximum shear stress theory of Tresca.\n"}
+{"abstract": "  We explore recent progress and open questions concerning local minima and\nsaddle points of the Cahn--Hilliard energy in $d\\geq 2$ and the critical\nparameter regime of large system size and mean value close to $-1$. We employ\nthe String Method of E, Ren, and Vanden-Eijnden -- a numerical algorithm for\ncomputing transition pathways in complex systems -- in $d=2$ to gain additional\ninsight into the properties of the minima and saddle point. Motivated by the\nnumerical observations, we adapt a method of Caffarelli and Spruck to study\nconvexity of level sets in $d\\geq 2$.\n"}
+{"abstract": "  Federated Learning is an emerging privacy-preserving distributed machine\nlearning approach to building a shared model by performing distributed training\nlocally on participating devices (clients) and aggregating the local models\ninto a global one. As this approach prevents data collection and aggregation,\nit helps in reducing associated privacy risks to a great extent. However, the\ndata samples across all participating clients are usually not independent and\nidentically distributed (non-iid), and Out of Distribution(OOD) generalization\nfor the learned models can be poor. Besides this challenge, federated learning\nalso remains vulnerable to various attacks on security wherein a few malicious\nparticipating entities work towards inserting backdoors, degrading the\ngenerated aggregated model as well as inferring the data owned by participating\nentities. In this paper, we propose an approach for learning invariant (causal)\nfeatures common to all participating clients in a federated learning setup and\nanalyze empirically how it enhances the Out of Distribution (OOD) accuracy as\nwell as the privacy of the final learned model.\n"}
+{"abstract": "  Ostrovsky's equation with time- and space- dependent forcing is studied. This\nequation is model for long waves in a rotating fluid with a non-constant depth\n(topography). A classification of Lie point symmetries and low-order\nconservation laws is presented. Generalized travelling wave solutions are\nobtained through symmetry reduction. These solutions exhibit a wave profile\nthat is stationary in a moving reference frame whose speed can be constant,\naccelerating, or decelerating.\n"}
+{"abstract": "  A subalgebra $\\mathcal{A}$ of a $C^*$-algebra $\\mathcal{M}$ is logmodular\n(resp. has factorization) if the set $\\{a^*a; a\\text{ is invertible with\n}a,a^{-1}\\in\\mathcal{A}\\}$ is dense in (resp. equal to) the set of all positive\nand invertible elements of $\\mathcal{M}$. There are large classes of well\nstudied algebras, both in commutative and non-commutative settings, which are\nknown to be logmodular. In this paper, we show that the lattice of projections\nin a von Neumann algebra $\\mathcal{M}$ whose ranges are invariant under a\nlogmodular algebra in $\\mathcal{M}$, is a commutative subspace lattice.\nFurther, if $\\mathcal{M}$ is a factor then this lattice is a nest. As a special\ncase, it follows that all reflexive (in particular, completely distributive\nCSL) logmodular subalgebras of type I factors are nest algebras, thus answering\na question of Paulsen and Raghupathi [Trans. Amer. Math. Soc., 363 (2011)\n2627-2640]. We also discuss some sufficient criteria under which an algebra\nhaving factorization is automatically reflexive and is a nest algebra.\n"}
+{"abstract": "  Quantum computing, an innovative computing system carrying prominent\nprocessing rate, is meant to be the solutions to problems in many fields. Among\nthese realms, the most intuitive application is to help chemical researchers\ncorrectly de-scribe strong correlation and complex systems, which are the great\nchallenge in current chemistry simulation. In this paper, we will present a\nstandalone quantum simulation tool for chemistry, ChemiQ, which is designed to\nassist people carry out chemical research or molecular calculation on real or\nvirtual quantum computers. Under the idea of modular programming in C++\nlanguage, the software is designed as a full-stack tool without third-party\nphysics or chemistry application packages. It provides services as follow:\nvisually construct molecular structure, quickly simulate ground-state energy,\nscan molecular potential energy curve by distance or angle, study chemical\nreaction, and return calculation results graphically after analysis.\n"}
+{"abstract": "  Microwave circulators play an important role in quantum technology based on\nsuperconducting circuits. The conventional circulator design, which employs\nferrite materials, is bulky and involves strong magnetic fields, rendering it\nunsuitable for integration on superconducting chips. One promising design for\nan on-chip superconducting circulator is based on a passive Josephson-junction\nring. In this paper, we consider two operational issues for such a device:\ncircuit tuning and the effects of quasiparticle tunneling. We compute the\nscattering matrix using adiabatic elimination and derive the parameter\nconstraints to achieve optimal circulation. We then numerically optimize the\ncirculator performance over the full set of external control parameters,\nincluding gate voltages and flux bias, to demonstrate that this\nmulti-dimensional optimization converges quickly to find optimal working\npoints. We also consider the possibility of quasiparticle tunneling in the\ncirculator ring and how it affects signal circulation. Our results form the\nbasis for practical operation of a passive on-chip superconducting circulator\nmade from a ring of Josephson junctions.\n"}
+{"abstract": "  A Robinson similarity matrix is a symmetric matrix where the entry values on\nall rows and columns increase toward the diagonal. Decompose the Robinson\nmatrix into the sum of k {0, 1}-matrices, then these k {0, 1}-matrices are the\nadjacency matrices of a set of nested unit interval graphs. Previous studies\nshow that unit interval graphs coincide with indifference graphs. An\nindifference graph has an embedding that maps each vertex to a real number,\nwhere two vertices are adjacent if their embedding is within a fixed threshold\ndistance. In this thesis, consider k different threshold distances, we study\nthe problem of finding an embedding that, simultaneously and with respect to\neach threshold distance, embeds the k indifference graphs corresponding to the\nk adjacency matrices. This is called a uniform embedding of a Robinson matrix\nwith respect to the k threshold distances. We give a sufficient and necessary\ncondition on Robinson matrices that have a uniform embedding, which is derived\nfrom paths in an associated graph. We also give an efficient combinatorial\nalgorithm to find a uniform embedding or give proof that it does not exist, for\nthe case where k = 2.\n"}
+{"abstract": "  Stationary memoryless sources produce two correlated random sequences $X^n$\nand $Y^n$. A guesser seeks to recover $X^n$ in two stages, by first guessing\n$Y^n$ and then $X^n$. The contributions of this work are twofold: (1) We\ncharacterize the least achievable exponential growth rate (in $n$) of any\npositive $\\rho$-th moment of the total number of guesses when $Y^n$ is obtained\nby applying a deterministic function $f$ component-wise to $X^n$. We prove\nthat, depending on $f$, the least exponential growth rate in the two-stage\nsetup is lower than when guessing $X^n$ directly. We further propose a simple\nHuffman code-based construction of a function $f$ that is a viable candidate\nfor the minimization of the least exponential growth rate in the two-stage\nguessing setup. (2) We characterize the least achievable exponential growth\nrate of the $\\rho$-th moment of the total number of guesses required to recover\n$X^n$ when Stage 1 need not end with a correct guess of $Y^n$ and without\nassumptions on the stationary memoryless sources producing $X^n$ and $Y^n$.\n"}
+{"abstract": "  Network Traffic Classification (NTC) has become an important feature in\nvarious network management operations, e.g., Quality of Service (QoS)\nprovisioning and security services. Machine Learning (ML) algorithms as a\npopular approach for NTC can promise reasonable accuracy in classification and\ndeal with encrypted traffic. However, ML-based NTC techniques suffer from the\nshortage of labeled traffic data which is the case in many real-world\napplications. This study investigates the applicability of an active form of\nML, called Active Learning (AL), in NTC. AL reduces the need for a large number\nof labeled examples by actively choosing the instances that should be labeled.\nThe study first provides an overview of NTC and its fundamental challenges\nalong with surveying the literature on ML-based NTC methods. Then, it\nintroduces the concepts of AL, discusses it in the context of NTC, and review\nthe literature in this field. Further, challenges and open issues in AL-based\nclassification of network traffic are discussed. Moreover, as a technical\nsurvey, some experiments are conducted to show the broad applicability of AL in\nNTC. The simulation results show that AL can achieve high accuracy with a small\namount of data.\n"}
+{"abstract": "  On Titan, methane (CH4) and ethane (C2H6) are the dominant species found in\nthe lakes and seas. In this study, we have combined laboratory work and\nmodeling to refine the methane-ethane binary phase diagram at low temperatures\nand probe how the molecules interact at these conditions. We used visual\ninspection for the liquidus and Raman spectroscopy for the solidus. Through\nthese methods we determined a eutectic point of 71.15$\\pm$0.5 K at a\ncomposition of 0.644$\\pm$0.018 methane - 0.356$\\pm$0.018 ethane mole fraction\nfrom the liquidus data. Using the solidus data, we found a eutectic isotherm\ntemperature of 72.2 K with a standard deviation of 0.4 K. In addition to\nmapping the binary system, we looked at the solid-solid transitions of pure\nethane and found that, when cooling, the transition of solid I-III occurred at\n89.45$\\pm$0.2 K. The warming sequence showed transitions of solid III-II\noccurring at 89.85$\\pm$0.2 K and solid II-I at 89.65$\\pm$0.2 K. Ideal\npredictions were compared to molecular dynamics simulations to reveal that the\nmethane-ethane system behaves almost ideally, and the largest deviations occur\nas the mixing ratio approaches the eutectic composition.\n"}
+{"abstract": "  Heavy-ion collisions at the LHC provide the conditions to investigate regions\nof quark-gluon plasma that reach higher temperatures and that persist for\nlonger periods of time compared to collisions at the Relativistic Heavy Ion\nCollider. This extended duration allows correlations from charge conservation\nto better separate during the quark-gluon plasma phase, and thus be better\ndistinguished from correlations that develop during the hadron phase or during\nhadronization. In this study charge balance functions binned by relative\nrapidity and azimuthal angle and indexed by species are considered. A detailed\ntheoretical model that evolves charge correlations throughout the entirety of\nan event is compared to preliminary results from the ALICE Collaboration. The\ncomparison with experiment provides insight into the evolution of the chemistry\nand diffusivity during the collision. A ratio of balance functions is proposed\nto better isolate the effects of diffusion and thus better constrain the\ndiffusivity.\n"}
+{"abstract": "  A new scaling is derived that yields a Reynolds number independent profile\nfor all components of the Reynolds stress in the near-wall region of wall\nbounded flows, including channel, pipe and boundary layer flows. The scaling\ndemonstrates the important role played by the wall shear stress fluctuations\nand how the large eddies determine the Reynolds number dependence of the\nnear-wall turbulence behavior.\n"}
+{"abstract": "  We train convolutional neural networks to predict whether or not a set of\nmeasurements is informationally complete to uniquely reconstruct any given\nquantum state with no prior information. In addition, we perform fidelity\nbenchmarking based on this measurement set without explicitly carrying out\nstate tomography. The networks are trained to recognize the fidelity and a\nreliable measure for informational completeness. By gradually accumulating\nmeasurements and data, these trained convolutional networks can efficiently\nestablish a compressive quantum-state characterization scheme by accelerating\nruntime computation and greatly reducing systematic drifts in experiments. We\nconfirm the potential of this machine-learning approach by presenting\nexperimental results for both spatial-mode and multiphoton systems of large\ndimensions. These predictions are further shown to improve when the networks\nare trained with additional bootstrapped training sets from real experimental\ndata. Using a realistic beam-profile displacement error model for\nHermite-Gaussian sources, we further demonstrate numerically that the\norders-of-magnitude reduction in certification time with trained networks\ngreatly increases the computation yield of a large-scale quantum processor\nusing these sources, before state fidelity deteriorates significantly.\n"}
+{"abstract": "  When an approximant is accurate on the interval, it is only natural to try to\nextend it to several-dimensional domains. In the present article, we make use\nof the fact that linear rational barycentric interpolants converge rapidly\ntoward analytic and several times differentiable functions to interpolate on\ntwo-dimensional starlike domains parametrized in polar coordinates. In radial\ndirection, we engage interpolants at conformally shifted Chebyshev nodes, which\nconverge exponentially toward analytic functions. In circular direction, we\ndeploy linear rational trigonometric barycentric interpolants, which converge\nsimilarly rapidly for periodic functions, but now for conformally shifted\nequispaced nodes. We introduce a variant of a tensor-product interpolant of the\nabove two schemes and prove that it converges exponentially for two-dimensional\nanalytic functions up to a logarithmic factor and with an order limited only by\nthe order of differentiability for real functions, if the boundary is as\nsmooth. Numerical examples confirm that the shifts permit to reach a much\nhigher accuracy with significantly less nodes, a property which is especially\nimportant in several dimensions.\n"}
+{"abstract": "  A novel approach to reduced-order modeling of high-dimensional time varying\nsystems is proposed. It leverages the formalism of the Dynamic Mode\nDecomposition technique together with the concept of balanced realization. It\nis assumed that the only information available on the system comes from input,\nstate, and output trajectories generated by numerical simulations or recorded\nand estimated during experiments, thus the approach is fully data-driven. The\ngoal is to obtain an input-output low dimensional linear model which\napproximates the system across its operating range. Since the dynamics of\naeroservoelastic systems markedly changes in operation (e.g. due to change in\nflight speed or altitude), time-varying features are retained in the\nconstructed models. This is achieved by generating a Linear Parameter-Varying\nrepresentation made of a collection of state-consistent linear time-invariant\nreduced-order models. The algorithm formulation hinges on the idea of replacing\nthe orthogonal projection onto the Proper Orthogonal Decomposition modes, used\nin Dynamic Mode Decomposition-based approaches, with a balancing oblique\nprojection constructed entirely from data. As a consequence, the input-output\ninformation captured in the lower-dimensional representation is increased\ncompared to other projections onto subspaces of same or lower size. Moreover, a\nparameter-varying projection is possible while also achieving\nstate-consistency. The validity of the proposed approach is demonstrated on a\nmorphing wing for airborne wind energy applications by comparing the\nperformance against two algorithms recently proposed in the literature.\nComparisons cover both prediction accuracy and performance in model predictive\ncontrol applications.\n"}
+{"abstract": "  We consider a machine learning algorithm to detect and identify strong\ngravitational lenses on sky images. First, we simulate different artificial but\nvery close to reality images of galaxies, stars and strong lenses, using six\ndifferent methods, i.e. two for each class. Then we deploy a convolutional\nneural network architecture to classify these simulated images. We show that\nafter neural network training process one achieves about 93 percent accuracy.\nAs a simple test for the efficiency of the convolutional neural network, we\napply it on an real Einstein cross image. Deployed neural network classifies it\nas gravitational lens, thus opening a way for variety of lens search\napplications of the deployed machine learning scheme.\n"}
+{"abstract": "  This article describes the regularization of the generally relativistic gauge\nfield representation of gravity on a piecewise linear lattice. It is a part of\nthe program concerning the classical relativistic theory of fundamental\ninteractions, represented by minimally coupled gauge vector field densities and\nhalf-densities. The correspondence between the local Darboux coordinates on\nphase space and the local structure of the links of the lattice, embedded in\nthe spatial manifold, is demonstrated. Thus, the canonical coordinates are\nreplaceable by links-related quantities. This idea and the significant part of\nformalism are directly based on the model of canonical loop quantum gravity\n(CLQG).\n  The first stage of this program is formulated regarding the gauge field,\nwhich dynamics is independent of other fundamental fields, but contributes to\ntheir dynamics. This gauge field, which determines systems equivalence in the\nactions defining all fundamental interactions, represents Einsteinian gravity.\nThe related links-defined quantities depend on holonomies of gravitational\nconnections and fluxes of densitized dreibeins. This article demonstrates how\nto determine these quantities, which lead to a nonpertubative formalism that\npreserves the general postulate of relativity. From this perspective, the\nformalism presented in this article is analogous to the Ashtekar-Barbero-Holst\nformulation on which CLQG is based. However, in this project, it is\nadditionally required that the fields' coordinates are quantizable in the\nstandard canonical procedure for a gauge theory and that any approximation in\nthe construction of the model is at least as precisely demonstrated as the\ngauge invariance. These requirements lead to new relations between holonomies\nand connections, and the representation of the densitized deibein determinant\nthat is more precise than the volume representation in CLQG.\n"}
+{"abstract": "  We present the Stromlo Stellar Tracks, a set of stellar evolutionary tracks,\ncomputed by modifying the Modules for Experiments in Stellar Astrophysics\n(MESA) 1D stellar evolution package, to fit the Galactic Concordance abundances\nfor hot ($T > 8000$ K) massive ($\\geq 10M_\\odot$) Main-Sequence (MS) stars.\nUntil now, all stellar evolution tracks are computed at solar, scaled-solar, or\nalpha-element enhanced abundances, and none of these models correctly represent\nthe Galactic Concordance abundances at different metallicities. This paper is\nthe first implementation of Galactic Concordance abundances to the stellar\nevolution models. The Stromlo tracks cover massive stars ($10\\leq M/M_\\odot\n\\leq 300$) with varying rotations ($v/v_{\\rm crit} = 0.0, 0.2, 0.4$) and a\nfinely sampled grid of metallicities ($-2.0 \\leq {\\rm [Z/H]} \\leq +0.5$;\n$\\Delta {\\rm [Z/H]} = 0.1$) evolved from the pre-main sequence to the end of\n$^{12}$Carbon burning. We find that the implementation of Galactic Concordance\nabundances is critical for the evolution of main-sequence, massive hot stars in\norder to estimate accurate stellar outputs (L, T$_{\\rm eff}$, $g$), which, in\nturn, have a significant impact on determining the ionizing photon luminosity\nbudgets. We additionally support prior findings of the importance that rotation\nplays on the evolution of massive stars and their ionizing budget. The\nevolutionary tracks for our Galactic Concordance abundance scaling provide a\nmore empirically motivated approach than simple uniform abundance scaling with\nmetallicity for the analysis of HII regions and have considerable implications\nin determining nebular emission lines and metallicity. Therefore, it is\nimportant to refine the existing stellar evolutionary models for comprehensive\nhigh-redshift extragalactic studies. The Stromlo tracks are publicly available\nto the astronomical community online.\n"}
+{"abstract": "  Let $G=\\operatorname{O}(1,n+1)$ with maximal compact subgroup $K$ and let\n$\\Pi$ be a unitary irreducible representation of $G$ with non-trivial\n$(\\mathfrak{g},K)$-cohomology. Then $\\Pi$ occurs inside a principal series\nrepresentation of $G$, induced from the $\\operatorname{O}(n)$-representation\n$\\bigwedge\\nolimits^p(\\mathbb{C}^n)$ and characters of a minimal parabolic\nsubgroup of $G$ at the limit of the complementary series. Considering the\nsubgroup $G'=\\operatorname{O}(1,n)$ of $G$ with maximal compact subgroup $K'$,\nwe prove branching laws and explicit Plancherel formulas for the restrictions\nto $G'$ of all unitary representations occurring in such principal series,\nincluding the complementary series, all unitary $G$-representations with\nnon-trivial $(\\mathfrak{g},K)$-cohomology and further relative discrete series\nrepresentations in the cases $p=0,n$. Discrete spectra are constructed\nexplicitly as residues of $G'$-intertwining operators which resemble the\nFourier transforms on vector bundles over the Riemannian symmetric space\n$G'/K'$.\n"}
+{"abstract": "  Given coprime positive integers $d',d''$, B\\'ezout's Lemma tells us that\nthere are integers $u,v$ so that $d'u-d''v=1$. We show that, interchanging $d'$\nand $d''$ if necessary, we may choose $u$ and $v$ to be Loeschian numbers,\ni.e., of the form $|\\alpha|^2$, where $\\alpha\\in\\mathbb{Z}[j]$, the ring of\nintegers of the number field $\\mathbb{Q}(j)$, where $j^2+j+1=0$. We do this by\nusing Atkin-Lehner elements in some quaternion algebras $\\mathcal{H}$. We use\nthis fact to count the number of conjugacy classes of elements of order 3 in an\norder $\\mathcal{O}\\subset\\mathcal{H}$.\n"}
+{"abstract": "  Ear recognition can be described as a revived scientific field. Ear\nbiometrics were long believed to not be accurate enough and held a secondary\nplace in scientific research, being seen as only complementary to other types\nof biometrics, due to difficulties in measuring correctly the ear\ncharacteristics and the potential occlusion of the ear by hair, clothes and ear\njewellery. However, recent research has reinstated them as a vivid research\nfield, after having addressed these problems and proven that ear biometrics can\nprovide really accurate identification and verification results. Several 2D and\n3D imaging techniques, as well as acoustical techniques using sound emission\nand reflection, have been developed and studied for ear recognition, while\nthere have also been significant advances towards a fully automated recognition\nof the ear. Furthermore, ear biometrics have been proven to be mostly\nnon-invasive, adequately permanent and accurate, and hard to spoof and\ncounterfeit. Moreover, different ear recognition techniques have proven to be\nas effective as face recognition ones, thus providing the opportunity for ear\nrecognition to be used in identification and verification applications.\nFinally, even though some issues still remain open and require further\nresearch, the scientific field of ear biometrics has proven to be not only\nviable, but really thriving.\n"}
+{"abstract": "  A Polarimetric Synthetic Aperture Radar (PolSAR) sensor is able to collect\nimages in different polarization states, making it a rich source of information\nfor target characterization. PolSAR images are inherently affected by speckle.\nTherefore, before deriving ad hoc products from the data, the polarimetric\ncovariance matrix needs to be estimated by reducing speckle. In recent years,\ndeep learning based despeckling methods have started to evolve from single\nchannel SAR images to PolSAR images. To this aim, deep learning based\napproaches separate the real and imaginary components of the complex-valued\ncovariance matrix and use them as independent channels in a standard\nconvolutional neural networks. However, this approach neglects the mathematical\nrelationship that exists between the real and imaginary components, resulting\nin sub-optimal output. Here, we propose a multi-stream complex-valued fully\nconvolutional network to reduce speckle and effectively estimate the PolSAR\ncovariance matrix. To evaluate the performance of CV-deSpeckNet, we used\nSentinel-1 dual polarimetric SAR images to compare against its real-valued\ncounterpart, that separates the real and imaginary parts of the complex\ncovariance matrix. CV-deSpeckNet was also compared against the state of the art\nPolSAR despeckling methods. The results show CV-deSpeckNet was able to be\ntrained with a fewer number of samples, has a higher generalization capability\nand resulted in a higher accuracy than its real-valued counterpart and\nstate-of-the-art PolSAR despeckling methods. These results showcase the\npotential of complex-valued deep learning for PolSAR despeckling.\n"}
+{"abstract": "  Urban areas are not only one of the biggest contributors to climate change,\nbut also they are one of the most vulnerable areas with high populations who\nwould together experience the negative impacts. In this paper, I address some\nof the opportunities brought by satellite remote sensing imaging and artificial\nintelligence (AI) in order to measure climate adaptation of cities\nautomatically. I propose an AI-based framework which might be useful for\nextracting indicators from remote sensing images and might help with predictive\nestimation of future states of these climate adaptation related indicators.\nWhen such models become more robust and used in real-life applications, they\nmight help decision makers and early responders to choose the best actions to\nsustain the wellbeing of society, natural resources and biodiversity. I\nunderline that this is an open field and an ongoing research for many\nscientists, therefore I offer an in depth discussion on the challenges and\nlimitations of AI-based methods and the predictive estimation models in\ngeneral.\n"}
+{"abstract": "  Nowadays, Graph Neural Networks (GNNs) following the Message Passing paradigm\nbecome the dominant way to learn on graphic data. Models in this paradigm have\nto spend extra space to look up adjacent nodes with adjacency matrices and\nextra time to aggregate multiple messages from adjacent nodes. To address this\nissue, we develop a method called LinkDist that distils self-knowledge from\nconnected node pairs into a Multi-Layer Perceptron (MLP) without the need to\naggregate messages. Experiment with 8 real-world datasets shows the MLP derived\nfrom LinkDist can predict the label of a node without knowing its adjacencies\nbut achieve comparable accuracy against GNNs in the contexts of semi- and\nfull-supervised node classification. Moreover, LinkDist benefits from its\nNon-Message Passing paradigm that we can also distil self-knowledge from\narbitrarily sampled node pairs in a contrastive way to further boost the\nperformance of LinkDist.\n"}
+{"abstract": "  This work addresses whether a human-in-the-loop cyber-physical system (HCPS)\ncan be effective in improving the longitudinal control of an individual vehicle\nin a traffic flow. We introduce the CAN Coach, which is a system that gives\nfeedback to the human-in-the-loop using radar data (relative speed and position\ninformation to objects ahead) that is available on the controller area network\n(CAN). Using a cohort of six human subjects driving an instrumented vehicle, we\ncompare the ability of the human-in-the-loop driver to achieve a constant\ntime-gap control policy using only human-based visual perception to the car\nahead, and by augmenting human perception with audible feedback from CAN sensor\ndata. The addition of CAN-based feedback reduces the mean time-gap error by an\naverage of 73%, and also improves the consistency of the human by reducing the\nstandard deviation of the time-gap error by 53%. We remove human perception\nfrom the loop using a ghost mode in which the human-in-the-loop is coached to\ntrack a virtual vehicle on the road, rather than a physical one. The loss of\nvisual perception of the vehicle ahead degrades the performance for most\ndrivers, but by varying amounts. We show that human subjects can match the\nvelocity of the lead vehicle ahead with and without CAN-based feedback, but\nvelocity matching does not offer regulation of vehicle spacing. The viability\nof dynamic time-gap control is also demonstrated. We conclude that (1) it is\npossible to coach drivers to improve performance on driving tasks using CAN\ndata, and (2) it is a true HCPS, since removing human perception from the\ncontrol loop reduces performance at the given control objective.\n"}
+{"abstract": "  Quantitative phase imaging (QPI) is a valuable label-free modality that has\ngained significant interest due to its wide potentials, from basic biology to\nclinical applications. Most existing QPI systems measure microscopic objects\nvia interferometry or nonlinear iterative phase reconstructions from intensity\nmeasurements. However, all imaging systems compromise spatial resolution for\nfield of view and vice versa, i.e., suffer from a limited space bandwidth\nproduct. Current solutions to this problem involve computational phase\nretrieval algorithms, which are time-consuming and often suffer from\nconvergence problems. In this article, we presented synthetic aperture\ninterference light (SAIL) microscopy as a novel modality for high-resolution,\nwide field of view QPI. The proposed approach employs low-coherence\ninterferometry to directly measure the optical phase delay under different\nillumination angles and produces large space-bandwidth product (SBP) label-free\nimaging. We validate the performance of SAIL on standard samples and illustrate\nthe biomedical applications on various specimens: pathology slides, entire\ninsects, and dynamic live cells in large cultures. The reconstructed images\nhave a synthetic numeric aperture of 0.45, and a field of view of 2.6 x 2.6\nmm2. Due to its direct measurement of the phase information, SAIL microscopy\ndoes not require long computational time, eliminates data redundancy, and\nalways converges.\n"}
+{"abstract": "  We train neural models for morphological analysis, generation and\nlemmatization for morphologically rich languages. We present a method for\nautomatically extracting substantially large amount of training data from FSTs\nfor 22 languages, out of which 17 are endangered. The neural models follow the\nsame tagset as the FSTs in order to make it possible to use them as fallback\nsystems together with the FSTs. The source code, models and datasets have been\nreleased on Zenodo.\n"}
+{"abstract": "  Quantized nano-objects offer a myriad of exciting possibilities for\nmanipulating electrons and light that impact photonics, nanoelectronics, and\nquantum information. In this context, ultrashort laser pulses combined with\nnanotips and field emission have permitted renewing nano-characterization and\ncontrol electron dynamics with unprecedented space and time resolution reaching\nfemtosecond and even attosecond regimes. A crucial missing step in these\nexperiments is that no signature of quantized energy levels has yet been\nobserved. We combine in situ nanostructuration of nanotips and ultrashort laser\npulse excitation to induce multiphoton excitation and electron emission from a\nsingle quantized nano-object attached at the apex of a metal nanotip.\nFemtosecond induced tunneling through well-defined localized confinement states\nthat are tunable in energy is demonstrated. This paves the way for the\ndevelopment of ultrafast manipulation of electron emission from isolated\nnano-objects including stereographically fixed individual molecules and high\nbrightness, ultrafast, coherent single electron sources for quantum optics\nexperiments.\n"}
+{"abstract": "  We present a fast and feature-complete differentiable physics engine, Nimble\n(nimblephysics.org), that supports Lagrangian dynamics and hard contact\nconstraints for articulated rigid body simulation. Our differentiable physics\nengine offers a complete set of features that are typically only available in\nnon-differentiable physics simulators commonly used by robotics applications.\nWe solve contact constraints precisely using linear complementarity problems\n(LCPs). We present efficient and novel analytical gradients through the LCP\nformulation of inelastic contact that exploit the sparsity of the LCP solution.\nWe support complex contact geometry, and gradients approximating\ncontinuous-time elastic collision. We also introduce a novel method to compute\ncomplementarity-aware gradients that help downstream optimization tasks avoid\nstalling in saddle points. We show that an implementation of this combination\nin an existing physics engine (DART) is capable of a 87x single-core speedup\nover finite-differencing in computing analytical Jacobians for a single\ntimestep, while preserving all the expressiveness of original DART.\n"}
+{"abstract": "  We study the Gram determinant and construct bases of hom spaces for the\none-dimensional topological theory of decorated unoriented one-dimensional\ncobordisms, as recently defined by Khovanov, when the pair of generating\nfunctions is linear.\n"}
+{"abstract": "  Existing near-eye display designs struggle to balance between multiple\ntrade-offs such as form factor, weight, computational requirements, and battery\nlife. These design trade-offs are major obstacles on the path towards an\nall-day usable near-eye display. In this work, we address these trade-offs by,\nparadoxically, \\textit{removing the display} from near-eye displays. We present\nthe beaming displays, a new type of near-eye display system that uses a\nprojector and an all passive wearable headset. We modify an off-the-shelf\nprojector with additional lenses. We install such a projector to the\nenvironment to beam images from a distance to a passive wearable headset. The\nbeaming projection system tracks the current position of a wearable headset to\nproject distortion-free images with correct perspectives. In our system, a\nwearable headset guides the beamed images to a user's retina, which are then\nperceived as an augmented scene within a user's field of view. In addition to\nproviding the system design of the beaming display, we provide a physical\nprototype and show that the beaming display can provide resolutions as high as\nconsumer-level near-eye displays. We also discuss the different aspects of the\ndesign space for our proposal.\n"}
+{"abstract": "  Models of front propagation like the famous FKPP equation have extensive\napplications across scientific disciplines e.g., in the spread of infectious\ndiseases. A common feature of such models is the existence of a static state\ninto which to propagate, e.g., the uninfected host population. Here, we instead\nmodel an infectious front propagating into a growing host population. The\ninfectious agent spreads via self-similar waves whereas the amplitude of the\nwave of infected organisms increases exponentially. Depending on the population\nunder consideration, wave speeds are either advanced or retarded compared to\nthe non-growing case. We identify a novel selection mechanism in which the\nshape of the infectious wave controls the speeds of the various waves and we\npropose experiments with bacteria and bacterial viruses to test our\npredictions. Our work reveals the complex interplay between population growth\nand front propagation.\n"}
+{"abstract": "  The spin Hall effect and its inverse are important spin-charge conversion\nmechanisms. The direct spin Hall effect induces a surface spin accumulation\nfrom a transverse charge current due to spin orbit coupling even in\nnon-magnetic conductors. However, most detection schemes involve additional\ninterfaces, leading to large scattering in reported data. Here we perform\ninterface free x-ray spectroscopy measurements at the Cu L_{3,2} absorption\nedges of highly Bi-doped Cu (Cu_{95}Bi_{5}). The detected X-ray magnetic\ncircular dichroism (XMCD) signal corresponds to an induced magnetic moment of\n(2.7 +/- 0.5) x 10-12 {\\mu}_{B} A^{-1} cm^{2} per Cu atom averaged over the\nprobing depth, which is of the same order as for Pt measured by magneto-optics.\nThe results highlight the importance of interface free measurements to assess\nmaterial parameters and the potential of CuBi for spin-charge conversion\napplications.\n"}
+{"abstract": "  Ensuring that a predictor is not biased against a sensible feature is the key\nof Fairness learning. Conversely, Global Sensitivity Analysis is used in\nnumerous contexts to monitor the influence of any feature on an output\nvariable. We reconcile these two domains by showing how Fairness can be seen as\na special framework of Global Sensitivity Analysis and how various usual\nindicators are common between these two fields. We also present new Global\nSensitivity Analysis indices, as well as rates of convergence, that are useful\nas fairness proxies.\n"}
+{"abstract": "  Virtualization of distributed real-time systems enables the consolidation of\nmixed-criticality functions on a shared hardware platform thus easing system\nintegration. Time-triggered communication and computation can act as an enabler\nof safe hard real-time systems. A time-triggered hypervisor that activates\nvirtual CPUs according to a global schedule can provide the means to allow for\na resource efficient implementation of the time-triggered paradigm in\nvirtualized distributed real-time systems. A prerequisite of time-triggered\nvirtualization for hard real-time systems is providing access to a global time\nbase to VMs as well as to the hypervisor. A global time base is the result of\nclock synchronization with an upper bound on the clock synchronization\nprecision.\n  We present a formalization of the notion of time in virtualized real-time\nsystems. We use this formalization to propose a virtual clock condition that\nenables us to test the suitability of a virtual clock for the design of\nvirtualized time-triggered real-time systems. We discuss and model how\nvirtualization, in particular resource consolidation versus resource\npartitioning, degrades clock synchronization precision. Finally, we apply our\ninsights to model the IEEE~802.1AS clock synchronization protocol and derive an\nupper bound on the clock synchronization precision of IEEE 802.1AS. We present\nour implementation of a dependent clock for ACRN that can be synchronized to a\ngrandmaster clock. The results of our experiments illustrate that a type-1\nhypervisor implementing a dependent clock yields native clock synchronization\nprecision. Furthermore, we show that the upper bound derived from our model\nholds for a series of experiments featuring native as well as virtualized\nsetups.\n"}
+{"abstract": "  We study several parameters of a random Bienaym\\'e-Galton-Watson tree $T_n$\nof size $n$ defined in terms of an offspring distribution $\\xi$ with mean $1$\nand nonzero finite variance $\\sigma^2$. Let $f(s)={\\bf E}\\{s^\\xi\\}$ be the\ngenerating function of the random variable $\\xi$. We show that the independence\nnumber is in probability asymptotic to $qn$, where $q$ is the unique solution\nto $q = f(1-q)$. One of the many algorithms for finding the largest independent\nset of nodes uses a notion of repeated peeling away of all leaves and their\nparents. The number of rounds of peeling is shown to be in probability\nasymptotic to $\\log n / \\log\\bigl(1/f'(1-q)\\bigr)$. Finally, we study a related\nparameter which we call the leaf-height. Also sometimes called the protection\nnumber, this is the maximal shortest path length between any node and a leaf in\nits subtree. If $p_1 = {\\bf P}\\{\\xi=1\\}>0$, then we show that the maximum\nleaf-height over all nodes in $T_n$ is in probability asymptotic to $\\log\nn/\\log(1/p_1)$. If $p_1 = 0$ and $\\kappa$ is the first integer $i>1$ with ${\\bf\nP}\\{\\xi=i\\}>0$, then the leaf-height is in probability asymptotic to\n$\\log_\\kappa\\log n$.\n"}
+{"abstract": "  In this work we ask how an Unruh-DeWitt (UD) detector with harmonic\noscillator internal degrees of freedom $Q$ measuring an evolving quantum matter\nfield $\\Phi(\\bm{x}, t)$ in an expanding universe with scale factor $a(t)$\nresponds. We investigate the detector's response which contains non-Markovian\ninformation about the quantum field squeezed by the dynamical spacetime. The\nchallenge is in the memory effects accumulated over the evolutionary history.\nWe first consider a detector $W$, the `\\textsl{Witness}', which co-existed and\nevolved with the quantum field from the beginning. We derive a nonMarkovian\nquantum Langevin equation for the detector's $Q$ by integrating over the\nsqueezed quantum field. The solution of this integro-differential equation\nwould answer our question, in principle, but very challenging, in practice.\nStriking a compromise, we then ask, to what extent can a detector $D$\nintroduced at late times, called the `\\textsl{Detective}', decipher past\nmemories. This situation corresponds to many cosmological experiments today\nprobing specific stages in the past, such as COBE targeting activities at the\nsurface of last scattering. Somewhat surprisingly we show that it is possible\nto retrieve to some degree certain global physical quantities, such as the\nresultant squeezing, particles created, quantum coherence and correlations. The\nreason is because the quantum field has all the fine-grained information from\nthe beginning in how it was driven by the cosmic dynamics $a(t)$. How long the\ndetails of past history can persist in the quantum field depends on the memory\ntime. The fact that a squeezed field cannot come to complete equilibrium under\nconstant driving, as in an evolving spacetime, actually helps to retain the\nmemory. We discuss interesting features and potentials of this\n`\\textit{archaeological}' perspective toward cosmological issues.\n"}
+{"abstract": "  We present updated measurements of the Crab pulsar glitch of 2019 July 23\nusing a dataset of pulse arrival times spanning $\\sim$5 months. On MJD 58687,\nthe pulsar underwent its seventh largest glitch observed to date, characterised\nby an instantaneous spin-up of $\\sim$1 $\\mu$Hz. Following the glitch the\npulsar's rotation frequency relaxed exponentially towards pre-glitch values\nover a timescale of approximately one week, resulting in a permanent frequency\nincrement of $\\sim$0.5 $\\mu$Hz. Due to our semi-continuous monitoring of the\nCrab pulsar, we were able to partially resolve a fraction of the total spin-up.\nThis delayed spin-up occurred exponentially over a timescale of $\\sim$18 hours.\nThis is the sixth Crab pulsar glitch for which part of the initial rise was\nresolved in time and this phenomenon has not been observed in any other\nglitching pulsars, offering a unique opportunity to study the microphysical\nprocesses governing interactions between the neutron star interior and the\ncrust.\n"}
+{"abstract": "  In this work we shall study $k$-inflation theories with non-minimal coupling\nof the scalar field to gravity, in the presence of only a higher order kinetic\nterm of the form $\\sim \\mathrm{const}\\times X^{\\mu}$, with\n$X=\\frac{1}{2}\\partial_{\\mu}\\phi\\partial^{\\mu}\\phi$. The study will be focused\nin the cases where a scalar potential is included or is absent, and the\nevolution of the scalar field will be assumed to satisfy the slow-roll or the\nconstant-roll condition. In the case of the slow-roll models with scalar\npotential, we shall calculate the slow-roll indices, and the corresponding\nobservational indices of the theory, and we demonstrate that the resulting\ntheory is compatible with the latest Planck data. The same results are obtained\nin the constant-roll case, at least in the presence of a scalar potential. In\nthe case that models without potential are considered, the results are less\nappealing since these are strongly model dependent, and at least for a\npower-law choice of the non-minimal coupling, the theory is non-viable.\nFinally, due to the fact that the scalar and tensor power spectra are conformal\ninvariant quantities, we argue that the Einstein frame counterpart of the\nnon-minimal $k$-inflation models with scalar potential, can be a viable theory,\ndue to the conformal invariance of the observational indices. The Einstein\nframe theory is more involved and thus more difficult to work with it\nanalytically, so one implication of our work is that we provide evidence for\nthe viability of another class of $k$-inflation models.\n"}
+{"abstract": "  Some known fixed point theorems for nonexpansive mappings in metric spaces\nare extended here to the case of primitive uniform spaces. The reasoning\npresented in the proofs seems to be a natural way to obtain other general\nresults.\n"}
+{"abstract": "  Carroll's group is presented as a group of transformations in a 5-dimensional\nspace ($\\mathcal{C}$) obtained by embedding the Euclidean space into a (4;\n1)-de Sitter space. Three of the five dimensions of $\\mathcal{C}$ are related\nto $\\mathcal{R}^3$, and the other two to mass and time. A covariant formulation\nof Caroll's group, analogous as introduced by Takahashi to Galilei's group, is\ndeduced. Unit representations are studied.\n"}
+{"abstract": "  In this paper, we consider the spectral dependences of transverse\nelectromagnetic waves generated in solar plasma at coalescence of Langmuir\nwaves. It is shown that different spectra of Langmuir waves lead to\ncharacteristic types of transversal electromagnetic wave spectra, what makes it\npossible to diagnose the features of the spectra of Langmuir waves generated in\nsolar plasma.\n"}
+{"abstract": "  A duality between an electrostatic problem in a three dimensional world and a\nquantum mechanical problem in a one dimensional world which allows one to\nobtain the ground state solution of the Schr\\\"odinger equation by using\nelectrostatic results is generalized to three dimensions. Here, it is\ndemonstrated that the same transformation technique is also applicable to the\ns-wave Schr\\\"odinger equation in three dimensions for central potentials. This\napproach leads to a general relationship between the electrostatic potential\nand the s-wave function and the electric energy density to the quantum\nmechanical energy.\n"}
+{"abstract": "  The past year has witnessed the rapid development of applying the Transformer\nmodule to vision problems. While some researchers have demonstrated that\nTransformer-based models enjoy a favorable ability of fitting data, there are\nstill growing number of evidences showing that these models suffer over-fitting\nespecially when the training data is limited. This paper offers an empirical\nstudy by performing step-by-step operations to gradually transit a\nTransformer-based model to a convolution-based model. The results we obtain\nduring the transition process deliver useful messages for improving visual\nrecognition. Based on these observations, we propose a new architecture named\nVisformer, which is abbreviated from the `Vision-friendly Transformer'. With\nthe same computational complexity, Visformer outperforms both the\nTransformer-based and convolution-based models in terms of ImageNet\nclassification accuracy, and the advantage becomes more significant when the\nmodel complexity is lower or the training set is smaller. The code is available\nat https://github.com/danczs/Visformer.\n"}
+{"abstract": "  We employ various quantum-mechanical approaches for studying the impact of\nelectric fields on both nonretarded and retarded noncovalent interactions\nbetween atoms or molecules. To this end, we apply perturbative and\nnon-perturbative methods within the frameworks of quantum mechanics (QM) as\nwell as quantum electrodynamics (QED). In addition, to provide a transparent\nphysical picture of the different types of resulting interactions, we employ a\nstochastic electrodynamic approach based on the zero-point fluctuating field.\nAtomic response properties are described via harmonic Drude oscillators - an\nefficient model system that permits an analytical solution and has been\nconvincingly shown to yield accurate results when modeling non-retarded\nintermolecular interactions. The obtained intermolecular energy contributions\nare classified as field-induced (FI) electrostatics, FI polarization, and\ndispersion interactions. The interplay between these three types of\ninteractions enables the manipulation of molecular dimer conformations by\napplying transversal or longitudinal electric fields along the intermolecular\naxis. Our framework combining four complementary theoretical approaches paves\nthe way toward a systematic description and improved understanding of molecular\ninteractions when molecules are subject to both external and vacuum fields.\n"}
+{"abstract": "  In this short paper we recall the (Garfield) Impact Factor of a Journal, we\nimprove and extend it, and eventually we present the Total Impact Factor that\nreflects the most accurate impact factor.\n"}
+{"abstract": "  This Letter capitalizes on a unique set of total solar eclipse observations,\nacquired between 2006 and 2020, in white light, Fe XI 789.2 nm ($\\rm T_{fexi}$\n= $1.2 \\pm 0.1$ MK) and Fe XIV 530.3 nm ($\\rm T_{fexiv}$ = $ 1.8 \\pm 0.1$ MK)\nemission, complemented by in situ Fe charge state and proton speed measurements\nfrom ACE/SWEPAM-SWICS, to identify the source regions of different solar wind\nstreams. The eclipse observations reveal the ubiquity of open structures,\ninvariably associated with Fe XI emission from $\\rm Fe^{10+}$, hence a constant\nelectron temperature, $\\rm T_{c}$ = $\\rm T_{fexi}$, in the expanding corona.\nThe in situ Fe charge states are found to cluster around $\\rm Fe^{10+}$,\nindependently of the 300 to 700 km $\\rm s^{-1}$ stream speeds, referred to as\nthe continual solar wind. $\\rm Fe^{10+}$ thus yields the fiducial link between\nthe continual solar wind and its $\\rm T_{fexi}$ sources at the Sun. While the\nspatial distribution of Fe XIV emission, from $\\rm Fe^{13+}$, associated with\nstreamers, changes throughout the solar cycle, the sporadic appearance of\ncharge states $> \\rm Fe^{11+}$, in situ, exhibits no cycle dependence\nregardless of speed. These latter streams are conjectured to be released from\nhot coronal plasmas at temperatures $\\ge \\rm T_{fexiv}$ within the bulge of\nstreamers and from active regions, driven by the dynamic behavior of\nprominences magnetically linked to them. The discovery of continual streams of\nslow, intermediate and fast solar wind, characterized by the same $\\rm\nT_{fexi}$ in the expanding corona, places new constraints on the physical\nprocesses shaping the solar wind.\n"}
+{"abstract": "  Our goal is to estimate the star formation main sequence (SFMS) and the star\nformation rate density (SFRD) at z <= 0.017 (d < 75 Mpc) using the Javalambre\nPhotometric Local Universe Survey (J-PLUS) first data release, that probes\n897.4 deg2 with twelve optical bands. We extract the Halpha emission flux of\n805 local galaxies from the J-PLUS filter J0660, being the continuum level\nestimated with the other eleven J-PLUS bands, and the dust attenuation and\nnitrogen contamination corrected with empirical relations. Stellar masses (M),\nHalpha luminosities (L), and star formation rates (SFRs) were estimated by\naccounting for parameters covariances. Our sample comprises 689 blue galaxies\nand 67 red galaxies, classified in the (u-g) vs (g-z) color-color diagram, plus\n49 AGN. The SFMS is explored at log M > 8 and it is clearly defined by the blue\ngalaxies, with the red galaxies located below them. The SFMS is described as\nlog SFR = 0.83 log M - 8.44. We find a good agreement with previous estimations\nof the SFMS, especially those based on integral field spectroscopy. The Halpha\nluminosity function of the AGN-free sample is well described by a Schechter\nfunction with log L* = 41.34, log phi* = -2.43, and alpha = -1.25. Our\nmeasurements provide a lower characteristic luminosity than several previous\nstudies in the literature. The derived star formation rate density at d < 75\nMpc is log rho_SFR = -2.10 +- 0.11, with red galaxies accounting for 15% of the\nSFRD. Our value is lower than previous estimations at similar redshift, and\nprovides a local reference for evolutionary studies regarding the star\nformation history of the Universe.\n"}
+{"abstract": "  Capacitated vehicle routing problem (CVRP) is being one of the most common\noptimization problems in our days, considering the wide usage of routing\nalgorithms in multiple fields such as transportation domain, food delivery,\nnetwork routing, ... Capacitated vehicle routing problem is classified as an\nNP-Hard problem, hence normal optimization algorithm can't solve it. In our\npaper, we discuss a new way to solve the mentioned problem, using a recursive\napproach of the most known clustering algorithm \"K-Means\", one of the known\nshortest path algorithm \"Dijkstra\", and some mathematical operations. In this\npaper, we will show how to implement those methods together in order to get the\nnearest solution of the optimal route, since research and development are still\non go, this research paper may be extended with another one, that will involve\nthe implementational results of this thoric side.\n"}
+{"abstract": "  This paper explores Google's Edge TPU for implementing a practical network\nintrusion detection system (NIDS) at the edge of IoT, based on a deep learning\napproach. While there are a significant number of related works that explore\nmachine learning based NIDS for the IoT edge, they generally do not consider\nthe issue of the required computational and energy resources. The focus of this\npaper is the exploration of deep learning-based NIDS at the edge of IoT, and in\nparticular the computational and energy efficiency. In particular, the paper\nstudies Google's Edge TPU as a hardware platform, and considers the following\nthree key metrics: computation (inference) time, energy efficiency and the\ntraffic classification performance. Various scaled model sizes of two major\ndeep neural network architectures are used to investigate these three metrics.\nThe performance of the Edge TPU-based implementation is compared with that of\nan energy efficient embedded CPU (ARM Cortex A53). Our experimental evaluation\nshows some unexpected results, such as the fact that the CPU significantly\noutperforms the Edge TPU for small model sizes.\n"}
+{"abstract": "  We study the nonlinear stability of plane Couette and Poiseuille flows with\nthe Lyapunov second method by using the classical L2-energy. We prove that the\nstreamwise perturbations are L2-energy stable for any Reynolds number. This\ncontradicts the results of Joseph [10], Joseph and Carmi [12] and Busse [4],\nand allows us to prove that the critical nonlinear Reynolds numbers are\nobtained along two-dimensional perturbations, the spanwise perturbations, as\nOrr [16] had supposed. This conclusion combined with recent results by\nFalsaperla et al. [8] on the stability with respect to tilted rolls, provides a\npossible solution to the Couette-Sommerfeld paradox.\n"}
+{"abstract": "  We investigate the production of photons from coherently oscillating,\nspatially localized clumps of axionic fields (oscillons and axion stars) in the\npresence of external electromagnetic fields. We delineate different qualitative\nbehaviour of the photon luminosity in terms of an effective dimensionless\ncoupling parameter constructed out of the axion-photon coupling, and field\namplitude, oscillation frequency and radius of the axion star. For small values\nof this dimensionless coupling, we provide a general analytic formula for the\ndipole radiation field and the photon luminosity per solid angle, including a\nstrong dependence on the radius of the configuration. For moderate to large\ncoupling, we report on a non-monotonic behavior of the luminosity with the\ncoupling strength in the presence of external magnetic fields. After an initial\nrise in luminosity with the coupling strength, we see a suppression (by an\norder of magnitude or more compared to the dipole radiation approximation) at\nmoderately large coupling. At sufficiently large coupling, we find a transition\nto a regime of exponential growth of the luminosity due to parametric\nresonance. We carry out 3+1 dimensional lattice simulations of axion\nelectrodynamics, at small and large coupling, including non-perturbative\neffects of parametric resonance as well as backreaction effects when necessary.\nWe also discuss medium (plasma) effects that lead to resonant axion to photon\nconversion, relevance of the coherence of the soliton, and implications of our\nresults in astrophysical and cosmological settings.\n"}
+{"abstract": "  Recent studies in three dimensional spintronics propose that the \\OE rsted\nfield plays a significant role in cylindrical nanowires. However, there is no\ndirect report of its impact on magnetic textures. Here, we use time-resolved\nscanning transmission X-ray microscopy to image the dynamic response of\nmagnetization in cylindrical Co$_{30}$Ni$_{70}$ nanowires subjected to\nnanosecond \\OE rsted field pulses. We observe the tilting of longitudinally\nmagnetized domains towards the azimuthal \\OE rsted field direction and create a\nrobust model to reproduce the differential magnetic contrasts and extract the\nangle of tilt. Further, we report the compression and expansion, or breathing,\nof a Bloch-point domain wall that occurs when weak pulses with opposite sign\nare applied. We expect that this work lays the foundation for and provides an\nincentive to further studying complex and fascinating magnetization dynamics in\nnanowires, especially the predicted ultra-fast domain wall motion and\nassociated spin wave emissions.\n"}
+{"abstract": "  By an unquenched quark model, we predict a charmed-strange baryon state,\nnamely, the $\\Omega_{c0}^d(1P,1/2^-)$. Its mass is predicted to be 2945 MeV,\nwhich is below the $\\Xi_c\\bar{K}$ threshold due to the nontrivial\ncoupled-channel effect. So the $\\Omega_{c0}^d(1P,1/2^-)$ state could be\nregraded as the analog of the charmed-strange meson $D_{s0}^*(2317)$. It is a\ngood opportunity for the running Belle II experiment to search for this state\nin the $\\Omega_c^{(*)}\\gamma$ mass spectrum experiment in the future.\n"}
+{"abstract": "  We present a flexible discretization technique for computational models of\nthin tubular networks embedded in a bulk domain, for example a porous medium.\nThese systems occur in the simulation of fluid flow in vascularized biological\ntissue, root water and nutrient uptake in soil, hydrological or petroleum wells\nin rock formations, or heat transport in micro-cooling devices. The key\nprocesses, such as heat and mass transfer, are usually dominated by the\nexchange between the network system and the embedding domain. By explicitly\nresolving the interface between these domains with the computational mesh, we\ncan accurately describe these processes. The network is efficiently described\nby a network of line segments. Coupling terms are evaluated by projection of\nthe interface variables. The new method is naturally applicable for nonlinear\nand time-dependent problems and can therefore be used as a reference method in\nthe development of novel implicit interface 1D-3D methods and in the design of\nverification benchmarks for embedded tubular network methods. Implicit\ninterface, not resolving the bulk-network interface explicitly have proven to\nbe very efficient but have only been mathematically analyzed for linear\nelliptic problems so far. Using two application scenarios, fluid perfusion of\nvascularized tissue and root water uptake from soil, we investigate the effect\nof some common modeling assumptions of implicit interface methods numerically.\n"}
+{"abstract": "  In this paper a family of non-autonomous scalar parabolic PDEs over a general\ncompact and connected flow is considered. The existence or not of a\nneighbourhood of zero where the problems are linear has an influence on the\nmethods used and on the dynamics of the induced skew-product semiflow. That is\nwhy two cases are distinguished: linear-dissipative and purely dissipative\nproblems. In both cases, the structure of the global and pullback attractors is\nstudied using principal spectral theory. Besides, in the purely dissipative\nsetting, a simple condition is given, involving both the underlying linear\ndynamics and some properties of the nonlinear term, to determine the nontrivial\nsections of the attractor.\n"}
+{"abstract": "  In this paper we study the variety of one dimensional representations of a\nfinite $W$-algebra attached to a classical Lie algebra, giving a precise\ndescription of the dimensions of the irreducible components. We apply this to\nprove a conjecture of Losev describing the image of his orbit method map. In\norder to do so we first establish new Yangian-type presentations of\nsemiclassical limits of the $W$-algebras attached to distinguished nilpotent\nelements in classical Lie algebras, using Dirac reduction.\n"}
+{"abstract": "  We examine the possibility that dark matter (DM) consists of a gapped\ncontinuum, rather than ordinary particles. A Weakly-Interacting Continuum (WIC)\nmodel, coupled to the Standard Model via a Z-portal, provides an explicit\nrealization of this idea. The thermal DM relic density in this model is\nnaturally consistent with observations, providing a continuum counterpart of\nthe \"WIMP miracle\". Direct detection cross sections are strongly suppressed\ncompared to ordinary Z-portal WIMP, thanks to a unique effect of the continuum\nkinematics. Continuum DM states decay throughout the history of the universe,\nand observations of cosmic microwave background place constraints on potential\nlate decays. Production of WICs at colliders can provide a striking\ncascade-decay signature. We show that a simple Z-portal WIC model with the gap\nscale between 40 and 110 GeV provides a fully viable DM candidate consistent\nwith all current experimental constraints.\n"}
+{"abstract": "  We propose a new method with $\\mathcal{L}_2$ distance that maps one\n$N$-dimensional distribution to another, taking into account available\ninformation about correspondences. We solve the high-dimensional problem in 1D\nspace using an iterative projection approach. To show the potentials of this\nmapping, we apply it to colour transfer between two images that exhibit\noverlapped scenes. Experiments show quantitative and qualitative competitive\nresults as compared with the state of the art colour transfer methods.\n"}
+{"abstract": "  We propose an optimization scheme for ground-state cooling of a mechanical\nmode by coupling to a general three-level system. We formulate the optimization\nscheme, using the master equation approach, over a broad range of system\nparameters including detunings, decay rates, coupling strengths, and pumping\nrate. We implement the optimization scheme on three physical systems: a\ncolloidal quantum dot coupled to its confined phonon mode, a polariton coupled\nto a mechanical resonator mode, and a coupled-cavity system coupled to a\nmechanical resonator mode. These three physical systems span a broad range of\nmechanical mode frequencies, coupling rates, and decay rates. Our optimization\nscheme lowers the stead-state phonon number in all three cases by orders of\nmagnitude. We also calculate the net cooling rate by estimating the phonon\ndecay rate and show that the optimized system parameters also result in\nefficient cooling. The proposed optimization scheme can be readily extended to\nany generic driven three-level system coupled to a mechanical mode.\n"}
+{"abstract": "  Experimental studies of high-purity kagome-lattice antiferromagnets (KAFM)\nare of great importance in attempting to better understand the predicted\nenigmatic quantum spin-liquid ground state of the KAFM model. However,\nrealizations of this model can rarely evade magnetic ordering at low\ntemperatures due to various perturbations to its dominant isotropic exchange\ninteractions. Such a situation is for example encountered due to sizable\nDzyaloshinskii-Moriya magnetic anisotropy in YCu$_3$(OH)$_6$Cl$_3$, which\nstands out from other KAFM materials by its perfect crystal structure. We find\nevidence of magnetic ordering also in the distorted sibling compound\nY$_3$Cu$_9$(OH)$_{18}$[Cl$_8$(OH)], which has recently been proposed to feature\na spin-liquid ground state arising from a spatially anisotropic kagome lattice.\nOur findings are based on a combination of bulk susceptibility, specific heat,\nand magnetic torque measurements that disclose a N\\'eel transition temperature\nof $T_N=11$~K in this material, which might feature a coexistence of magnetic\norder and persistent spin dynamics as previously found in\nYCu$_3$(OH)$_6$Cl$_3$. Contrary to previous studies of single crystals and\npowders containing impurity inclusions, we use high-purity single crystals of\nY$_3$Cu$_9$(OH)$_{18}$[Cl$_8$(OH)] grown via an optimized hydrothermal\nsynthesis route that minimizes such inclusions. This study thus demonstrates\nthat the lack of magnetic ordering in less pure samples of the investigated\ncompound does not originate from the reduced symmetry of spin lattice but is\ninstead of extrinsic origin.\n"}
+{"abstract": "  In the present work, we explore analytically and numerically the co-existence\nand interactions of ring dark solitons (RDSs) with other RDSs, as well as with\nvortices. The azimuthal instabilities of the rings are explored via the\nso-called filament method. As a result of their nonlinear interaction, the\nvortices are found to play a stabilizing role on the rings, yet their effect is\nnot sufficient to offer complete stabilization of RDSs. Nevertheless, complete\nstabilization of the relevant configuration can be achieved by the presence of\nexternal ring-shaped barrier potentials. Interactions of multiple rings are\nalso explored, and their equilibrium positions (as a result of their own\ncurvature and their tail-tail interactions) are identified. In this case too,\nstabilization is achieved via multi-ring external barrier potentials.\n"}
+{"abstract": "  To explain X-ray spectra of active galactic nuclei (AGN), non-thermal\nactivity in AGN coronae such as pair cascade models has been extensively\ndiscussed in the past literature. Although X-ray and gamma-ray observations in\nthe 1990s disfavored such pair cascade models, recent millimeter-wave\nobservations of nearby Seyferts establish the existence of weak non-thermal\ncoronal activity. Besides, the IceCube collaboration reported NGC 1068, a\nnearby Seyfert, as the hottest spot in their 10-yr survey. These pieces of\nevidence are enough to investigate the non-thermal perspective of AGN coronae\nin depth again. This article summarizes our current observational\nunderstandings of AGN coronae and describes how AGN coronae generate\nhigh-energy particles. We also provide ways to test the AGN corona model with\nradio, X-ray, MeV gamma-ray, and high-energy neutrino observations.\n"}
+{"abstract": "  We present a method for contraction-based feedback motion planning of locally\nincrementally exponentially stabilizable systems with unknown dynamics that\nprovides probabilistic safety and reachability guarantees. Given a dynamics\ndataset, our method learns a deep control-affine approximation of the dynamics.\nTo find a trusted domain where this model can be used for planning, we obtain\nan estimate of the Lipschitz constant of the model error, which is valid with a\ngiven probability, in a region around the training data, providing a local,\nspatially-varying model error bound. We derive a trajectory tracking error\nbound for a contraction-based controller that is subjected to this model error,\nand then learn a controller that optimizes this tracking bound. With a given\nprobability, we verify the correctness of the controller and tracking error\nbound in the trusted domain. We then use the trajectory error bound together\nwith the trusted domain to guide a sampling-based planner to return\ntrajectories that can be robustly tracked in execution. We show results on a 4D\ncar, a 6D quadrotor, and a 22D deformable object manipulation task, showing our\nmethod plans safely with learned models of high-dimensional underactuated\nsystems, while baselines that plan without considering the tracking error bound\nor the trusted domain can fail to stabilize the system and become unsafe.\n"}
+{"abstract": "  Fairness-aware machine learning for multiple protected at-tributes (referred\nto as multi-fairness hereafter) is receiving increasing attention as\ntraditional single-protected attribute approaches cannot en-sure fairness\nw.r.t. other protected attributes. Existing methods, how-ever, still ignore the\nfact that datasets in this domain are often imbalanced, leading to unfair\ndecisions towards the minority class. Thus, solutions are needed that achieve\nmulti-fairness,accurate predictive performance in overall, and balanced\nperformance across the different classes.To this end, we introduce a new\nfairness notion,Multi-Max Mistreatment(MMM), which measures unfairness while\nconsidering both (multi-attribute) protected group and class membership of\ninstances. To learn an MMM-fair classifier, we propose a multi-objective\nproblem formulation. We solve the problem using a boosting approach that\nin-training,incorporates multi-fairness treatment in the distribution update\nand post-training, finds multiple Pareto-optimal solutions; then uses\npseudo-weight based decision making to select optimal solution(s) among\naccurate, balanced, and multi-attribute fair solutions\n"}
+{"abstract": "  This paper proposes a set of techniques to investigate eye gaze and fixation\npatterns while users interact with electronic user interfaces. In particular,\ntwo case studies are presented - one on analysing eye gaze while interacting\nwith deceptive materials in web pages and another on analysing graphs in\nstandard computer monitor and virtual reality displays. We analysed spatial and\ntemporal distributions of eye gaze fixations and sequence of eye gaze\nmovements. We used this information to propose new design guidelines to avoid\ndeceptive materials in web and user-friendly representation of data in 2D\ngraphs. In 2D graph study we identified that area graph has lowest number of\nclusters for user's gaze fixations and lowest average response time. The\nresults of 2D graph study were implemented in virtual and mixed reality\nenvironment. Along with this, it was ob-served that the duration while\ninteracting with deceptive materials in web pages is independent of the number\nof fixations. Furthermore, web-based data visualization tool for analysing eye\ntracking data from single and multiple users was developed.\n"}
+{"abstract": "  We analyze the top Lyapunov exponent of the product of sequences of two by\ntwo matrices that appears in the analysis of several statistical mechanics\nmodels with disorder: for example these matrices are the transfer matrices for\nthe nearest neighbor Ising chain with random external field, and the free\nenergy density of this Ising chain is the Lyapunov exponent we consider. We\nobtain the sharp behavior of this exponent in the large interaction limit when\nthe external field is centered: this balanced case turns out to be critical in\nmany respects. From a mathematical standpoint we precisely identify the\nbehavior of the top Lyapunov exponent of a product of two dimensional random\nmatrices close to a diagonal random matrix for which top and bottom Lyapunov\nexponents coincide. In particular, the Lyapunov exponent is only\n$\\log$-H\\\"older continuous.\n"}
+{"abstract": "  The favourable properties of tungsten borides for shielding the central High\nTemperature Superconductor (HTS) core of a spherical tokamak fusion power plant\nare modelled using the MCNP code. The objectives are to minimize the power\ndeposition into the cooled HTS core, and to keep HTS radiation damage to\nacceptable levels by limiting the neutron and gamma fluxes. The shield\nmaterials compared are W2B, WB, W2B5 and WB4 along with a reactively sintered\nboride B0.329C0.074Cr0.024Fe0.274W0.299, monolithic W and WC. Of all these W2B5\ngave the most favourable results with a factor of ~10 or greater reduction in\nneutron flux and gamma energy deposition as compared to monolithic W. These\nresults are compared with layered water-cooled shields, giving the result that\nthe monolithic shields, with moderating boron, gave comparable neutron flux and\npower deposition, and (in the case of W2B5) even better performance. Good\nperformance without water-coolant has advantages from a reactor safety\nperspective due to the risks associated with radio-activation of oxygen. 10B\nisotope concentrations between 0 and 100% are considered for the boride\nshields. The naturally occurring 20% fraction gave much lower energy\ndepositions than the 0% fraction, but the improvement largely saturated beyond\n40%. Thermophysical properties of the candidate materials are discussed, in\nparticular the thermal strain. To our knowledge, the performance of W2B5 is\nunrivalled by other monolithic shielding materials. This is partly as its\ntrigonal crystal structure gives it higher atomic density compared with other\nborides. It is also suggested that its high performance depends on it having\njust high enough 10B content to maintain a constant neutron energy spectrum\nacross the shield.\n"}
+{"abstract": "  We show that the action of the Kauffman bracket skein algebra of a surface\n$\\Sigma$ on the skein module of the handlebody bounded by $\\Sigma$ is faithful\nif and only if the quantum parameter is not a root of 1.\n"}
+{"abstract": "  Shared Memory is a mechanism that allows several processes to communicate\nwith each other by accessing -- writing or reading -- a set of variables that\nthey have in common. A Consistency Model defines how each process observes the\nstate of the Memory, according to the accesses performed by it and by the rest\nof the processes in the system. Therefore, it determines what value a read\nreturns when a given process issues it. This implies that there must be an\nagreement among all, or among processes in different subsets, on the order in\nwhich all or a subset of the accesses happened. It is clear that a higher\nquantity of accesses or proceses taking part in the agreement makes it possibly\nharder or slower to be achieved. This is the main reason for which a number of\nConsistency Models for Shared Memory have been introduced. This paper is a\nhandy summary of [2] and [3] where consistency models (Sequential, Causal,\nPRAM, Cache, Processors, Slow), including synchronized ones (Weak, Release,\nEntry), were formally defined. This provides a better understanding of those\nmodels and a way to reason and compare them through a concise notation. There\nare many papers on this subject in the literature such as [11] with which this\nwork shares some concepts.\n"}
+{"abstract": "  Applying an operator product expansion approach we update the Standard Model\nprediction of the $B_c$ lifetime from over 20 years ago. The non-perturbative\nvelocity expansion is carried out up to third order in the relative velocity of\nthe heavy quarks. The scheme dependence is studied using three different mass\nschemes for the $\\bar b$ and $c$ quarks, resulting in three different values\nconsistent with each other and with experiment. Special focus has been laid on\nrenormalon cancellation in the computation. Uncertainties resulting from scale\ndependence, neglecting the strange quark mass, non-perturbative matrix elements\nand parametric uncertainties are discussed in detail. The resulting\nuncertainties are still rather large compared to the experimental ones, and\ntherefore do not allow for clear-cut conclusions concerning New Physics effects\nin the $B_c$ decay.\n"}
+{"abstract": "  The Helmholtz equation in one dimension, which describes the propagation of\nelectromagnetic waves in effectively one-dimensional systems, is equivalent to\nthe time-independent Schr\\\"odinger equation. The fact that the potential term\nentering the latter is energy-dependent obstructs the application of the\nresults on low-energy quantum scattering in the study of the low-frequency\nwaves satisfying the Helmholtz equation. We use a recently developed dynamical\nformulation of stationary scattering to offer a comprehensive treatment of the\nlow-frequency scattering of these waves for a general finite-range scatterer.\nIn particular, we give explicit formulas for the coefficients of the\nlow-frequency series expansion of the transfer matrix of the system which in\nturn allow for determining the low-frequency expansions of its reflection,\ntransmission, and absorption coefficients. Our general results reveal a number\nof interesting physical aspects of low-frequency scattering particularly in\nrelation to permittivity profiles having balanced gain and loss.\n"}
+{"abstract": "  We establish the dual equivalence of the category of (potentially nonunital)\noperator systems and the category of pointed compact nc (noncommutative) convex\nsets, extending a result of Davidson and the first author. We then apply this\ndual equivalence to establish a number of results about operator systems, some\nof which are new even in the unital setting.\n  For example, we show that the maximal and minimal C*-covers of an operator\nsystem can be realized in terms of the C*-algebra of continuous nc functions on\nits nc quasistate space, clarifying recent results of Connes and van Suijlekom.\nWe also characterize \"C*-simple\" operator systems, i.e. operator systems with\nsimple minimal C*-cover, in terms of their nc quasistate spaces.\n  We develop a theory of quotients of operator systems that extends the theory\nof quotients of unital operator algebras. In addition, we extend results of the\nfirst author and Shamovich relating to nc Choquet simplices. We show that an\noperator system is a C*-algebra if and only if its nc quasistate space is an nc\nBauer simplex with zero as an extreme point, and we show that a second\ncountable locally compact group has Kazhdan's property (T) if and only if for\nevery action of the group on a C*-algebra, the set of invariant quasistates is\nthe quasistate space of a C*-algebra.\n"}
+{"abstract": "  With the growing use of camera devices, the industry has many image datasets\nthat provide more opportunities for collaboration between the machine learning\ncommunity and industry. However, the sensitive information in the datasets\ndiscourages data owners from releasing these datasets. Despite recent research\ndevoted to removing sensitive information from images, they provide neither\nmeaningful privacy-utility trade-off nor provable privacy guarantees. In this\nstudy, with the consideration of the perceptual similarity, we propose\nperceptual indistinguishability (PI) as a formal privacy notion particularly\nfor images. We also propose PI-Net, a privacy-preserving mechanism that\nachieves image obfuscation with PI guarantee. Our study shows that PI-Net\nachieves significantly better privacy utility trade-off through public image\ndata.\n"}
+{"abstract": "  The backup control barrier function (CBF) was recently proposed as a\ntractable formulation that guarantees the feasibility of the CBF quadratic\nprogramming (QP) via an implicitly defined control invariant set. The control\ninvariant set is based on a fixed backup policy and evaluated online by forward\nintegrating the dynamics under the backup policy. This paper is intended as a\ntutorial of the backup CBF approach and a comparative study to some benchmarks.\nFirst, the backup CBF approach is presented step by step with the underlying\nmath explained in detail. Second, we prove that the backup CBF always has a\nrelative degree 1 under mild assumptions. Third, the backup CBF approach is\ncompared with benchmarks such as Hamilton Jacobi PDE and Sum-of-Squares on the\ncomputation of control invariant sets, which shows that one can obtain a\ncontrol invariant set close to the maximum control invariant set under a good\nbackup policy for many practical problems.\n"}
+{"abstract": "  Complex fluids flow in complex ways in complex structures. Transport of water\nand various organic and inorganic molecules in the central nervous system are\nimportant in a wide range of biological and medical processes [C. Nicholson,\nand S. Hrab\\v{e}tov\\'a, Biophysical Journal, 113(10), 2133(2017)]. However, the\nexact driving mechanisms are often not known. In this paper, we investigate\nflows induced by action potentials in an optic nerve as a prototype of the\ncentral nervous system (CNS). Different from traditional fluid dynamics\nproblems, flows in biological tissues such as the CNS are coupled with ion\ntransport. It is driven by osmosis created by concentration gradient of ionic\nsolutions, which in term influence the transport of ions. Our mathematical\nmodel is based on the known structural and biophysical properties of the\nexperimental system used by the Harvard group Orkand et al [R.K. Orkand, J.G.\nNicholls, S.W. Kuffler, Journal of Neurophysiology, 29(4), 788(1966)].\nAsymptotic analysis and numerical computation show the significant role of\nwater in convective ion transport. The full model (including water) and the\nelectrodiffusion model (excluding water) are compared in detail to reveal an\ninteresting interplay between water and ion transport. In the full model,\nconvection due to water flow dominates inside the glial domain. This water flow\nin the glia contributes significantly to the spatial buffering of potassium in\nthe extracellular space. Convection in the extracellular domain does not\ncontribute significantly to spatial buffering. Electrodiffusion is the dominant\nmechanism for flows confined to the extracellular domain.\n"}
+{"abstract": "  A (charged) rotating black hole may be unstable against a (charged) massive\nscalar field perturbation due to the existence of superradiance modes. The\nstability property depends on the parameters of the system. In this paper, the\nsuperradiant stable parameter space is studied for the four-dimensional\nextremal Kerr and Kerr-Newman black holes under massive and charged massive\nscalar perturbation. For the extremal Kerr case, it is found that when the\nangular frequency and proper mass of the scalar perturbation satisfy the\ninequality $\\omega<\\mu/\\sqrt{3}$, the extremal Kerr black hole and scalar\nperturbation system is superradiantly stable. For the Kerr-Newman black hole\ncase, when the angular frequency of the scalar perturbation satisfies\n$\\omega<qQ/M$ and the product of the mass-to-charge ratios of the black hole\nand scalar perturbation satisfies $\\frac{\\mu}{q}\\frac{M}{Q} > \\frac{\\sqrt{3\nk^2+2} }{ \\sqrt{k^2+2} },~k=\\frac{a}{M}$, the extremal Kerr-Newman black hole\nis superradiantly stable under charged massive scalar perturbation.\n"}
+{"abstract": "  Multi-domain image-to-image translation with conditional Generative\nAdversarial Networks (GANs) can generate highly photo realistic images with\ndesired target classes, yet these synthetic images have not always been helpful\nto improve downstream supervised tasks such as image classification. Improving\ndownstream tasks with synthetic examples requires generating images with high\nfidelity to the unknown conditional distribution of the target class, which\nmany labeled conditional GANs attempt to achieve by adding soft-max\ncross-entropy loss based auxiliary classifier in the discriminator. As recent\nstudies suggest that the soft-max loss in Euclidean space of deep feature does\nnot leverage their intrinsic angular distribution, we propose to replace this\nloss in auxiliary classifier with an additive angular margin (AAM) loss that\ntakes benefit of the intrinsic angular distribution, and promotes intra-class\ncompactness and inter-class separation to help generator synthesize high\nfidelity images.\n  We validate our method on RaFD and CIFAR-100, two challenging face expression\nand natural image classification data set. Our method outperforms\nstate-of-the-art methods in several different evaluation criteria including\nrecently proposed GAN-train and GAN-test metrics designed to assess the impact\nof synthetic data on downstream classification task, assessing the usefulness\nin data augmentation for supervised tasks with prediction accuracy score and\naverage confidence score, and the well known FID metric.\n"}
+{"abstract": "  We consider the problem of minimizing the supplied energy of\ninfinite-dimensional linear port-Hamiltonian systems and prove that optimal\ntrajectories exhibit the turnpike phenomenon towards certain subspaces induced\nby the dissipation of the dynamics.\n"}
+{"abstract": "  We investigate the complexity and performance of recurrent neural network\n(RNN) models as post-processing units for the compensation of fibre\nnonlinearities in digital coherent systems carrying polarization multiplexed\n16-QAM and 32-QAM signals. We evaluate three bi-directional RNN models, namely\nthe bi-LSTM, bi-GRU and bi-Vanilla-RNN and show that all of them are promising\nnonlinearity compensators especially in dispersion unmanaged systems. Our\nsimulations show that during inference the three models provide similar\ncompensation performance, therefore in real-life systems the simplest scheme\nbased on Vanilla-RNN units should be preferred. We compare bi-Vanilla-RNN with\nVolterra nonlinear equalizers and exhibit its superiority both in terms of\nperformance and complexity, thus highlighting that RNN processing is a very\npromising pathway for the upgrade of long-haul optical communication systems\nutilizing coherent detection.\n"}
+{"abstract": "  The aim of this paper is to show that almost greedy bases induce tighter\nembeddings in superreflexive Banach spaces than in general Banach spaces. More\nspecifically, we show that an almost greedy basis in a superreflexive Banach\nspace $\\mathbb{X}$ induces embeddings that allow squeezing $\\mathbb{X}$ between\ntwo superreflexive Lorentz sequence spaces that are close to each other in the\nsense that they have the same fundamental function.\n"}
+{"abstract": "  We provide a new degree bound on the weighted sum-of-squares (SOS)\npolynomials for Putinar-Vasilescu's Positivstellensatz. This leads to another\nPositivstellensatz saying that if $f$ is a polynomial of degree at most $2 d_f$\nnonnegative on a semialgebraic set having nonempty interior defined by finitely\nmany polynomial inequalities $g_j(x)\\ge 0$, $j=1,\\dots,m$ with\n$g_1:=L-\\|x\\|_2^2$ for some $L>0$, then there exist positive constants $\\bar c$\nand $c$ depending on $f,g_j$ such that for any $\\varepsilon>0$, for all $k\\ge\n\\bar c \\varepsilon^{-c}$, $f$ has the decomposition \\begin{equation}\n\\begin{array}{l} (1+\\|x\\|_2^2)^k(f+\\varepsilon)=\\sigma_0+\\sum_{j=1}^m\n\\sigma_jg_j \\,, \\end{array} \\end{equation} for some SOS polynomials $\\sigma_j$\nbeing such that the degrees of $\\sigma_0,\\sigma_jg_j$ are at most $2(d_f+k)$.\nHere $\\|\\cdot\\|_2$ denotes the $\\ell_2$ vector norm. As a consequence, we\nobtain a converging hierarchy of semidefinite relaxations for lower bounds in\npolynomial optimization on basic compact semialgebraic sets. The complexity of\nthis hierarchy is $\\mathcal{O}(\\varepsilon^{-c})$ for prescribed accuracy\n$\\varepsilon>0$. In particular, if $m=L=1$ then $c=65$, yielding the complexity\n$\\mathcal{O}(\\varepsilon^{-65})$ for the minimization of a polynomial on the\nunit ball. Our result improves the complexity bound\n$\\mathcal{O}(\\exp(\\varepsilon^{-c}))$ due to Nie and Schweighofer in [Journal\nof Complexity 23.1 (2007): 135-150].\n"}
+{"abstract": "  Let $\\mathcal{F}\\subset 2^{[n]}$ be a set family such that the intersection\nof any two members of $\\mathcal{F}$ has size divisible by $\\ell$. The famous\nEventown theorem states that if $\\ell=2$ then $|\\mathcal{F}|\\leq 2^{\\lfloor\nn/2\\rfloor}$, and this bound can be achieved by, e.g., an `atomic'\nconstruction, i.e. splitting the ground set into disjoint pairs and taking\ntheir arbitrary unions. Similarly, splitting the ground set into disjoint sets\nof size $\\ell$ gives a family with pairwise intersections divisible by $\\ell$\nand size $2^{\\lfloor n/\\ell\\rfloor}$. Yet, as was shown by Frankl and Odlyzko,\nthese families are far from maximal. For infinitely many $\\ell$, they\nconstructed families $\\mathcal{F}$ as above of size $2^{\\Omega(n\\log\n\\ell/\\ell)}$. On the other hand, if the intersection of any number of sets in\n$\\mathcal{F}\\subset 2^{[n]}$ has size divisible by $\\ell$, then it is easy to\nshow that $|\\mathcal{F}|\\leq 2^{\\lfloor n/\\ell\\rfloor}$. In 1983 Frankl and\nOdlyzko conjectured that $|\\mathcal{F}|\\leq 2^{(1+o(1)) n/\\ell}$ holds already\nif one only requires that for some $k=k(\\ell)$ any $k$ distinct members of\n$\\mathcal{F}$ have an intersection of size divisible by $\\ell$. We completely\nresolve this old conjecture in a strong form, showing that $|\\mathcal{F}|\\leq\n2^{\\lfloor n/\\ell\\rfloor}+O(1)$ if $k$ is chosen appropriately, and the $O(1)$\nerror term is not needed if (and only if) $\\ell \\, | \\, n$, and $n$ is\nsufficiently large. Moreover the only extremal configurations have `atomic'\nstructure as above. Our main tool, which might be of independent interest, is a\nstructure theorem for set systems with small 'doubling'.\n"}
+{"abstract": "  Visual Object Tracking (VOT) can be seen as an extended task of Few-Shot\nLearning (FSL). While the concept of FSL is not new in tracking and has been\npreviously applied by prior works, most of them are tailored to fit specific\ntypes of FSL algorithms and may sacrifice running speed. In this work, we\npropose a generalized two-stage framework that is capable of employing a large\nvariety of FSL algorithms while presenting faster adaptation speed. The first\nstage uses a Siamese Regional Proposal Network to efficiently propose the\npotential candidates and the second stage reformulates the task of classifying\nthese candidates to a few-shot classification problem. Following such a\ncoarse-to-fine pipeline, the first stage proposes informative sparse samples\nfor the second stage, where a large variety of FSL algorithms can be conducted\nmore conveniently and efficiently. As substantiation of the second stage, we\nsystematically investigate several forms of optimization-based few-shot\nlearners from previous works with different objective functions, optimization\nmethods, or solution space. Beyond that, our framework also entails a direct\napplication of the majority of other FSL algorithms to visual tracking,\nenabling mutual communication between researchers on these two topics.\nExtensive experiments on the major benchmarks, VOT2018, OTB2015, NFS, UAV123,\nTrackingNet, and GOT-10k are conducted, demonstrating a desirable performance\ngain and a real-time speed.\n"}
+{"abstract": "  The fundamental processes by which nuclear energy is generated in the Sun\nhave been known for many years. However, continuous progress in areas such as\nneutrino experiments, stellar spectroscopy and helioseismic data and techniques\nrequires ever more accurate and precise determination of nuclear reaction cross\nsections, a fundamental physical input for solar models. In this work, we\nreview the current status of (standard) solar models and present a detailed\ndiscussion on the relevance of nuclear reactions for detailed predictions of\nsolar properties. In addition, we also provide an analytical model that helps\nunderstanding the relation between nuclear cross sections, neutrino fluxes and\nthe possibility they offer for determining physical characteristics of the\nsolar interior. The latter is of particular relevance in the context of the\nconundrum posed by the solar composition, the solar abundance problem, and in\nthe light of the first ever direct detection of solar CN neutrinos recently\nobtained by the Borexino collaboration. Finally, we present a short list of\nwishes about the precision with which nuclear reaction rates should be\ndetermined to allow for further progress in our understanding of the Sun.\n"}
+{"abstract": "  We construct non-exact operator spaces satisfying the Weak Expectation\nProperty (WEP) and the Operator space version of the Local Lifting Property\n(OLLP). These examples should be compared with the example we recently gave of\na $C^*$-algebra with WEP and LLP. The construction produces several new\nanalogues among operator spaces of the Gurarii space, extending Oikhberg's\nprevious work. Each of our \"Gurarii operator spaces\" is associated to a class\nof finite dimensional operator spaces (with suitable properties). In each case\nwe show the space exists and is unique up to completely isometric isomorphism.\n"}
+{"abstract": "  In this paper, we propose a novel graph convolutional network architecture,\nGraph Stacked Hourglass Networks, for 2D-to-3D human pose estimation tasks. The\nproposed architecture consists of repeated encoder-decoder, in which\ngraph-structured features are processed across three different scales of human\nskeletal representations. This multi-scale architecture enables the model to\nlearn both local and global feature representations, which are critical for 3D\nhuman pose estimation. We also introduce a multi-level feature learning\napproach using different-depth intermediate features and show the performance\nimprovements that result from exploiting multi-scale, multi-level feature\nrepresentations. Extensive experiments are conducted to validate our approach,\nand the results show that our model outperforms the state-of-the-art.\n"}
+{"abstract": "  The electronic bandstructure of a solid is a collection of allowed bands\nseparated by forbidden bands, revealing the geometric symmetry of the crystal\nstructures. Comprehensive knowledge of the bandstructure with band parameters\nexplains intrinsic physical, chemical and mechanical properties of the solid.\nHere we report the artificial polaritonic bandstructures of two-dimensional\nhoneycomb lattices for microcavity exciton-polaritons using GaAs semiconductors\nin the wide-range detuning values, from cavity-photon-like (red-detuned) to\nexciton-like (blue-detuned) regimes. In order to understand the experimental\nbandstructures and their band parameters, such as gap energies, bandwidths,\nhopping integrals and density of states, we originally establish a polariton\nband theory within an augmented plane wave method with two-kind-bosons, cavity\nphotons trapped at the lattice sites and freely moving excitons. In particular,\nthis two-kind-band theory is absolutely essential to elucidate the exciton\neffect in the bandstructures of blue-detuned exciton-polaritons, where the\nflattened exciton-like dispersion appears at larger in-plane momentum values\ncaptured in our experimental access window. We reach an excellent agreement\nbetween theory and experiments in all detuning values.\n"}
+{"abstract": "  We analyze the observed spatial, chemical and dynamical distributions of\nlocal metal-poor stars, based on photometrically derived metallicity and\ndistance estimates along with proper motions from the Gaia mission. Along the\nGalactic prime meridian, we identify stellar populations with distinct\nproperties in the metallicity versus rotational velocity space, including Gaia\nSausage/Enceladus (GSE), the metal-weak thick disk (MWTD), and the Splash\n(sometimes referred to as the \"in situ\" halo). We model the observed\nphase-space distributions using Gaussian mixtures and refine their positions\nand fractional contributions as a function of distances from the Galactic plane\n($|Z|$) and the Galactic center ($R_{\\rm GC}$), providing a global perspective\nof the major stellar populations in the local halo. Within the sample volume\n($|Z|<6$ kpc), stars associated with GSE exhibit a larger proportion of\nmetal-poor stars at greater $R_{\\rm GC}$ ($\\Delta \\langle{\\rm[Fe/H]}\\rangle\n/\\Delta R_{\\rm GC} =-0.05\\pm0.02$ dex kpc$^{-1}$). This observed trend, along\nwith a mild anticorrelation of the mean rotational velocity with metallicity\n($\\Delta \\langle v_\\phi \\rangle / \\Delta \\langle{\\rm[Fe/H]} \\rangle \\sim -10$\nkm s$^{-1}$ dex$^{-1}$), implies that more metal-rich stars in the inner region\nof the GSE progenitor were gradually stripped away, while the prograde orbit of\nthe merger at infall became radialized by dynamical friction. The metal-rich\nGSE stars are causally disconnected from the Splash structure, whose stars are\nmostly found on prograde orbits ($>94\\%$) and exhibit a more centrally\nconcentrated distribution than GSE. The MWTD exhibits a similar spatial\ndistribution to the Splash, suggesting earlier dynamical heating of stars in\nthe primordial disk of the Milky Way, possibly before the GSE merger.\n"}
+{"abstract": "  Integrating external language models (LMs) into end-to-end (E2E) models\nremains a challenging task for domain-adaptive speech recognition. Recently,\ninternal language model estimation (ILME)-based LM fusion has shown significant\nword error rate (WER) reduction from Shallow Fusion by subtracting a weighted\ninternal LM score from an interpolation of E2E model and external LM scores\nduring beam search. However, on different test sets, the optimal LM\ninterpolation weights vary over a wide range and have to be tuned extensively\non well-matched validation sets. In this work, we perform LM fusion in the\nminimum WER (MWER) training of an E2E model to obviate the need for LM weights\ntuning during inference. Besides MWER training with Shallow Fusion (MWER-SF),\nwe propose a novel MWER training with ILME (MWER-ILME) where the ILME-based\nfusion is conducted to generate N-best hypotheses and their posteriors.\nAdditional gradient is induced when internal LM is engaged in MWER-ILME loss\ncomputation. During inference, LM weights pre-determined in MWER training\nenable robust LM integrations on test sets from different domains. Experimented\nwith 30K-hour trained transformer transducers, MWER-ILME achieves on average\n8.8% and 5.8% relative WER reductions from MWER and MWER-SF training,\nrespectively, on 6 different test sets\n"}
+{"abstract": "  Aircraft manufacturing relies on pre-order bookings. The configuration of the\nto be assembled aircraft is fixed by the design assisted market surveys. The\nsensitivity of the supply chain to the market conditions, makes, the\nrelationship between the product (aircraft) and the associated service\n(aviation), precarious. Traditional model to mitigate this risk to\nprofitability rely on increasing the scales of operations. However, the\nemergence of new standards of air quality monitoring and insistence on the\nimplementation, demands additional corrective measures. In the quest for a\nsolution, this research commentary establishes a link, between the airport\ntaxes and the nature of the transporting unit. It warns, that merely,\nincreasing the number of mid haulage range aircrafts (MHA) in the fleet, may\nnot be enough, to overcome this challenge. In a two-pronged approach, the\ncommunication proposes, the use of mostly electric assisted air planes, and\nsmall sized airports as the key to solving this complex problem. As a side-note\nthe appropriateness of South Asian region, as a test-bed for MEAP based\naircrafts is also investigated. The success of this the idea can be potentially\nextended, to any other aviation friendly region of the world.\n"}
+{"abstract": "  We present high-angular-resolution radio observations of the Arches cluster\nin the Galactic centre, one of the most massive young clusters in the Milky\nWay. The data were acquired in two epochs and at 6 and 10 GHz with the Karl G.\nJansky Very Large Array (JVLA). The rms noise reached is three to four times\nbetter than during previous observations and we have almost doubled the number\nof known radio stars in the cluster. Nine of them have spectral indices\nconsistent with thermal emission from ionised stellar winds, one is a confirmed\ncolliding wind binary (CWB), and two sources are ambiguous cases. Regarding\nvariability, the radio emission appears to be stable on timescales of a few to\nten years. Finally, we show that the number of radio stars can be used as a\ntool for constraining the age and/or mass of a cluster and also its mass\nfunction.\n"}
+{"abstract": "  We consider an elliptic problem with nonlinear boundary condition involving\nnonlinearity with superlinear and subcritical growth at infinity and a\nbifurcation parameter as a factor. We use re-scaling method, degree theory and\ncontinuation theorem to prove that there exists a connected branch of positive\nsolutions bifurcating from infinity when the parameter goes to zero. Moreover,\nif the nonlinearity satisfies additional conditions near zero, we establish a\nglobal bifurcation result, and discuss the number of positive solution(s) with\nrespect to the parameter using bifurcation theory and degree theory.\n"}
+{"abstract": "  Real-world machine learning systems need to analyze test data that may differ\nfrom training data. In K-way classification, this is crisply formulated as\nopen-set recognition, core to which is the ability to discriminate open-set\ndata outside the K closed-set classes. Two conceptually elegant ideas for\nopen-set discrimination are: 1) discriminatively learning an open-vs-closed\nbinary discriminator by exploiting some outlier data as the open-set, and 2)\nunsupervised learning the closed-set data distribution with a GAN, using its\ndiscriminator as the open-set likelihood function. However, the former\ngeneralizes poorly to diverse open test data due to overfitting to the training\noutliers, which are unlikely to exhaustively span the open-world. The latter\ndoes not work well, presumably due to the instable training of GANs. Motivated\nby the above, we propose OpenGAN, which addresses the limitation of each\napproach by combining them with several technical insights. First, we show that\na carefully selected GAN-discriminator on some real outlier data already\nachieves the state-of-the-art. Second, we augment the available set of real\nopen training examples with adversarially synthesized \"fake\" data. Third and\nmost importantly, we build the discriminator over the features computed by the\nclosed-world K-way networks. This allows OpenGAN to be implemented via a\nlightweight discriminator head built on top of an existing K-way network.\nExtensive experiments show that OpenGAN significantly outperforms prior\nopen-set methods.\n"}
+{"abstract": "  Climate change presents an existential threat to human societies and the\nEarth's ecosystems more generally. Mitigation strategies naturally require\nsolving a wide range of challenging problems in science, engineering, and\neconomics. In this context, rapidly developing quantum technologies in\ncomputing, sensing, and communication could become useful tools to diagnose and\nhelp mitigate the effects of climate change. However, the intersection between\nclimate and quantum sciences remains largely unexplored. This preliminary\nreport aims to identify potential high-impact use-cases of quantum technologies\nfor climate change with a focus on four main areas: simulating physical\nsystems, combinatorial optimization, sensing, and energy efficiency. We hope\nthis report provides a useful resource towards connecting the climate and\nquantum science communities, and to this end we identify relevant research\nquestions and next steps.\n"}
+{"abstract": "  Information theoretic sensor management approaches are an ideal solution to\nstate estimation problems when considering the optimal control of multi-agent\nsystems, however they are too computationally intensive for large state spaces,\nespecially when considering the limited computational resources typical of\nlarge-scale distributed multi-agent systems. Reinforcement learning (RL) is a\npromising alternative which can find approximate solutions to distributed\noptimal control problems that take into account the resource constraints\ninherent in many systems of distributed agents. However, the RL training can be\nprohibitively inefficient, especially in low-information environments where\nagents receive little to no feedback in large portions of the state space. We\npropose a hybrid information-driven multi-agent reinforcement learning (MARL)\napproach that utilizes information theoretic models as heuristics to help the\nagents navigate large sparse state spaces, coupled with information based\nrewards in an RL framework to learn higher-level policies. This paper presents\nour ongoing work towards this objective. Our preliminary findings show that\nsuch an approach can result in a system of agents that are approximately three\norders of magnitude more efficient at exploring a sparse state space than naive\nbaseline metrics. While the work is still in its early stages, it provides a\npromising direction for future research.\n"}
+{"abstract": "  Harnessing the quantum computation power of the present\nnoisy-intermediate-size-quantum devices has received tremendous interest in the\nlast few years. Here we study the learning power of a one-dimensional\nlong-range randomly-coupled quantum spin chain, within the framework of\nreservoir computing. In time sequence learning tasks, we find the system in the\nquantum many-body localized (MBL) phase holds long-term memory, which can be\nattributed to the emergent local integrals of motion. On the other hand, MBL\nphase does not provide sufficient nonlinearity in learning highly-nonlinear\ntime sequences, which we show in a parity check task. This is reversed in the\nquantum ergodic phase, which provides sufficient nonlinearity but compromises\nmemory capacity. In a complex learning task of Mackey-Glass prediction that\nrequires both sufficient memory capacity and nonlinearity, we find optimal\nlearning performance near the MBL-to-ergodic transition. This leads to a\nguiding principle of quantum reservoir engineering at the edge of quantum\nergodicity reaching optimal learning power for generic complex reservoir\nlearning tasks. Our theoretical finding can be readily tested with present\nexperiments.\n"}
+{"abstract": "  We present a study of the influence of magnetic field strength and morphology\nin Type Ia Supernovae and their late-time light curves and spectra. In order to\nboth capture self-consistent magnetic field topologies as well evolve our\nmodels to late times, a two stage approach is taken. We study the early\ndeflagration phase (1s) using a variety of magnetic field strengths, and find\nthat the topology of the field is set by the burning, independent of the\ninitial strength. We study late time (~1000 days) light curves and spectra with\na variety of magnetic field topologies, and infer magnetic field strengths from\nobserved supernovae. Lower limits are found to be 106G. This is determined by\nthe escape, or lack thereof, of positrons that are tied to the magnetic field.\nThe first stage employs 3d MHD and a local burning approximation, and uses the\ncode Enzo. The second stage employs a hybrid approach, with 3D radiation and\npositron transport, and spherical hydrodynamics. The second stage uses the code\nHYDRA. In our models, magnetic field amplification remains small during the\nearly deflagration phase. Late-time spectra bear the imprint of both magnetic\nfield strength and morphology. Implications for alternative explosion scenarios\nare discussed.\n"}
+{"abstract": "  Computational Fluid Dynamics (CFD) is a major sub-field of engineering.\nCorresponding flow simulations are typically characterized by heavy\ncomputational resource requirements. Often, very fine and complex meshes are\nrequired to resolve physical effects in an appropriate manner. Since all CFD\nalgorithms scale at least linearly with the size of the underlying mesh\ndiscretization, finding an optimal mesh is key for computational efficiency.\n  One methodology used to find optimal meshes is goal-oriented adaptive mesh\nrefinement. However, this is typically computationally demanding and only\navailable in a limited number of tools. Within this contribution, we adopt a\nmachine learning approach to identify optimal mesh densities. We generate\noptimized meshes using classical methodologies and propose to train a\nconvolutional network predicting optimal mesh densities given arbitrary\ngeometries. The proposed concept is validated along 2d wind tunnel simulations\nwith more than 60,000 simulations. Using a training set of 20,000 simulations\nwe achieve accuracies of more than 98.7%.\n  Corresponding predictions of optimal meshes can be used as input for any mesh\ngeneration and CFD tool. Thus without complex computations, any CFD engineer\ncan start his predictions from a high quality mesh.\n"}
+{"abstract": "  Context. The Sun's complex corona is the source of the solar wind and\ninterplanetary magnetic field. While the large scale morphology is well\nunderstood, the impact of variations in coronal properties on the scale of a\nfew degrees on properties of the interplanetary medium is not known. Solar\nOrbiter, carrying both remote sensing and in situ instruments into the inner\nsolar system, is intended to make these connections better than ever before.\nAims. We combine remote sensing and in situ measurements from Solar Orbiter's\nfirst perihelion at 0.5 AU to study the fine scale structure of the solar wind\nfrom the equatorward edge of a polar coronal hole with the aim of identifying\ncharacteristics of the corona which can explain the in situ variations.\nMethods. We use in situ measurements of the magnetic field, density and solar\nwind speed to identify structures on scales of hours at the spacecraft. Using\nPotential Field Source Surface mapping we estimate the source locations of the\nmeasured solar wind as a function of time and use EUI images to characterise\nthese solar sources. Results. We identify small scale stream interactions in\nthe solar wind with compressed magnetic field and density along with speed\nvariations which are associated with corrugations in the edge of the coronal\nhole on scales of several degrees, demonstrating that fine scale coronal\nstructure can directly influence solar wind properties and drive variations\nwithin individual streams. Conclusions. This early analysis already\ndemonstrates the power of Solar Orbiter's combined remote sensing and in situ\npayload and shows that with future, closer perihelia it will be possible\ndramatically to improve our knowledge of the coronal sources of fine scale\nsolar wind structure, which is important both for understanding the phenomena\ndriving the solar wind and predicting its impacts at the Earth and elsewhere.\n"}
+{"abstract": "  It is crucial for policymakers to understand the community prevalence of\nCOVID-19 so combative resources can be effectively allocated and prioritized\nduring the COVID-19 pandemic. Traditionally, community prevalence has been\nassessed through diagnostic and antibody testing data. However, despite the\nincreasing availability of COVID-19 testing, the required level has not been\nmet in most parts of the globe, introducing a need for an alternative method\nfor communities to determine disease prevalence. This is further complicated by\nthe observation that COVID-19 prevalence and spread varies across different\nspatial, temporal, and demographics. In this study, we understand trends in the\nspread of COVID-19 by utilizing the results of self-reported COVID-19 symptoms\nsurveys as an alternative to COVID-19 testing reports. This allows us to assess\ncommunity disease prevalence, even in areas with low COVID-19 testing ability.\nUsing individually reported symptom data from various populations, our method\npredicts the likely percentage of the population that tested positive for\nCOVID-19. We do so with a Mean Absolute Error (MAE) of 1.14 and Mean Relative\nError (MRE) of 60.40\\% with 95\\% confidence interval as (60.12, 60.67). This\nimplies that our model predicts +/- 1140 cases than the original in a\npopulation of 1 million. In addition, we forecast the location-wise percentage\nof the population testing positive for the next 30 days using self-reported\nsymptoms data from previous days. The MAE for this method is as low as 0.15\n(MRE of 23.61\\% with 95\\% confidence interval as (23.6, 13.7)) for New York. We\npresent an analysis of these results, exposing various clinical attributes of\ninterest across different demographics. Lastly, we qualitatively analyze how\nvarious policy enactments (testing, curfew) affect the prevalence of COVID-19\nin a community.\n"}
+{"abstract": "  Precise quantitative delineation of tumor hypoxia is essential in radiation\ntherapy treatment planning to improve the treatment efficacy by targeting\nhypoxic sub-volumes. We developed a combined imaging system of positron\nemission tomography (PET) and electron para-magnetic resonance imaging (EPRI)\nof molecular oxygen to investigate the accuracy of PET imaging in assessing\ntumor hypoxia. The PET/EPRI combined imaging system aims to use EPRI to\nprecisely measure the oxygen partial pressure in tissues. This will evaluate\nthe validity of PET hypoxic tumor imaging by (near) simultaneously acquired\nEPRI as ground truth. The combined imaging system was constructed by\nintegrating a small animal PET scanner (inner ring diameter 62 mm and axial\nfield of view 25.6 mm) and an EPRI subsystem (field strength 25 mT and resonant\nfrequency 700 MHz). The compatibility between the PET and EPRI subsystems were\ntested with both phantom and animal imaging. Hypoxic imaging on a tumor mouse\nmodel using $^{18}$F-fluoromisonidazole radio-tracer was conducted with the\ndeveloped PET/EPRI system. We report the development and initial imaging\nresults obtained from the PET/EPRI combined imaging system.\n"}
+{"abstract": "  The effects of the evolution force are observable in nature at all structural\nlevels ranging from small molecular systems to conversely enormous biospheric\nsystems. However, the evolution force and work associated with formation of\nbiological structures has yet to be described mathematically or theoretically.\nIn addressing the conundrum, we consider evolution from a unique perspective\nand in doing so introduce the Fundamental Theory of the Evolution Force, FTEF.\nHerein, we prove FTEF by proof of concept using a synthetic evolution\nartificial intelligence to engineer 14-3-3 {\\zeta} docking proteins. Synthetic\ngenes were engineered by transforming 14-3-3 {\\zeta} sequences into time-based\nDNA codes that served as templates for random DNA hybridizations and genetic\nassembly. Application of time-based DNA codes allowed us to fast forward\nevolution, while damping the effect of point mutations. Notably, SYN-AI\nengineered a set of three architecturally conserved docking proteins that\nretained motion and vibrational dynamics of native Bos taurus 14-3-3 {\\zeta}.\n"}
+{"abstract": "  Quantum cascade lasers (QCLs) facilitate compact optical frequency comb\nsources that operate in the mid-infrared and terahertz spectral regions, where\nmany molecules have their fundamental absorption lines. Enhancing the optical\nbandwidth of these chip-sized lasers is of paramount importance to address\ntheir application in broadband high-precision spectroscopy. In this work, we\nprovide a numerical and experimental investigation of the comb spectral width\nand show how it can be optimized to obtain its maximum value defined by the\nlaser gain bandwidth. The interplay of nonoptimal values of the resonant Kerr\nnonlinearity and the cavity dispersion can lead to significant narrowing of the\ncomb spectrum and reveals the best approach for dispersion compensation. The\nimplementation of high mirror losses is shown to be favourable and results in\nproliferation of the comb sidemodes. Ultimately, injection locking of QCLs by\nmodulating the laser bias around the roundtrip frequency provides a stable\nexternal knob to control the FM comb state and recover the maximum spectral\nwidth of the unlocked laser state.\n"}
+{"abstract": "  The lack of an easily realizable complementary circuit technology offering\nlow static power consumption has been limiting the utilization of other\nsemiconductor materials than silicon. In this publication, a novel depletion\nmode JFET based complementary circuit technology is presented and herein after\nreferred to as Complementary Semiconductor (CS) circuit technology. The fact\nthat JFETs are pure semiconductor devices, i.e. a carefully optimized Metal\nOxide Semiconductor (MOS) gate stack is not required, facilitates the\nimplementation of CS circuit technology to many semiconductor materials, like\ne.g. germanium and silicon carbide. Furthermore, when the CS circuit technology\nis idle there are neither conductive paths between nodes that are biased at\ndifferent potentials nor forward biased p-n junctions and thus it enables low\nstatic power consumption. Moreover, the fact that the operation of depletion\nmode JFETs does not necessitate the incorporation of forward biased p-n\njunctions means that CS circuit technology is not limited to wide band-gap\nsemiconductor materials, low temperatures, and/or low voltage spans. In this\npaper the operation of the CS logic is described and proven via simulations.\n"}
+{"abstract": "  Many robot manipulation skills can be represented with deterministic\ncharacteristics and there exist efficient techniques for learning parameterized\nmotor plans for those skills. However, one of the active research challenge\nstill remains to sustain manipulation capabilities in situation of a mechanical\nfailure. Ideally, like biological creatures, a robotic agent should be able to\nreconfigure its control policy by adapting to dynamic adversaries. In this\npaper, we propose a method that allows an agent to survive in a situation of\nmechanical loss, and adaptively learn manipulation with compromised degrees of\nfreedom -- we call our method Survivable Robotic Learning (SRL). Our key idea\nis to leverage Bayesian policy gradient by encoding knowledge bias in posterior\nestimation, which in turn alleviates future policy search explorations, in\nterms of sample efficiency and when compared to random exploration based policy\nsearch methods. SRL represents policy priors as Gaussian process, which allows\ntractable computation of approximate posterior (when true gradient is\nintractable), by incorporating guided bias as proxy from prior replays. We\nevaluate our proposed method against off-the-shelf model free learning\nalgorithm (DDPG), testing on a hexapod robot platform which encounters\nincremental failure emulation, and our experiments show that our method\nimproves largely in terms of sample requirement and quantitative success ratio\nin all failure modes. A demonstration video of our experiments can be viewed\nat: https://sites.google.com/view/survivalrl\n"}
+{"abstract": "  Following ideas introduced by Beardon-Minda and by Baribeau-Rivard-Wegert in\nthe context of the Schwarz-Pick lemma, we use the iterated hyperbolic\ndifference quotients to prove a multipoint Julia lemma. As applications, we\ngive a sharp estimate from below of the angular derivative at a boundary point,\ngeneralizing results due to Osserman, Mercer and others; and we prove a\ngeneralization to multiple fixed points of an interesting estimate due to Cowen\nand Pommerenke. These applications show that iterated hyperbolic difference\nquotients and multipoint Julia lemmas can be useful tools for exploring in a\nsystematic way the influence of higher order derivatives on the boundary\nbehaviour of holomorphic self-maps of the unit disk.\n"}
+{"abstract": "  We develop a mesoscopic lattice model to study the morphology formation in\ninteracting ternary mixtures with evaporation of one component. As concrete\napplication of our model, we wish to capture morphologies as they are typically\narising during fabrication of organic solar cells. In this context, we consider\nan evaporating solvent into which two other components are dissolved, as a\nmodel for a 2-component coating solution that is drying on a substrate. We\npropose a 3-spins dynamics to describe the evolution of the three interacting\nspecies. As main tool, we use a Monte Carlo Metropolis-based algorithm, with\nthe possibility of varying the system's temperature, mixture composition,\ninteraction strengths, and evaporation kinetics. The main novelty is the\nstructure of the mesoscopic model -- a bi-dimensional lattice with periodic\nboundary conditions and divided in square cells to encode a mesoscopic range\ninteraction among the units. We investigate the effect of the model parameters\non the structure of the resulting morphologies. Finally, we compare the results\nobtained with the mesoscopic model with corresponding ones based on an\nanalogous lattice model with a short range interaction among the units, i.e.\nwhen the mesoscopic length scale coincides with the microscopic length scale of\nthe lattice.\n"}
+{"abstract": "  We consider the direct $s$-channel gravitational production of dark matter\nduring the reheating process. Independent of the identity of the dark matter\ncandidate or its non-gravitational interactions, the gravitational process is\nalways present and provides a minimal production mechanism. During reheating, a\nthermal bath is quickly generated with a maximum temperature $T_{\\rm max}$, and\nthe temperature decreases as the inflaton continues to decay until the energy\ndensities of radiation and inflaton oscillations are equal, at $T_{\\rm RH}$.\nDuring these oscillations, $s$-channel gravitational production of dark matter\noccurs. We show that the abundance of dark matter (fermionic or scalar) depends\nprimarily on the combination $T_{\\rm max}^4/T_{\\rm RH} M_P^3$. We find that a\nsufficient density of dark matter can be produced over a wide range of dark\nmatter masses: from a GeV to a ZeV.\n"}
+{"abstract": "  Magnetic and crystallographic transitions in the Cairo pentagonal magnet\nBi2Fe4O9 are investigated by means of infrared synchrotron-based spectroscopy\nas a function of temperature (20 - 300 K) and pressure (0 - 15.5 GPa). One of\nthe phonon modes is shown to exhibit an anomalous softening as a function of\ntemperature in the antiferromagnetic phase below 240 K, highlighting\nspin-lattice coupling. Moreover, under applied pressure at 40 K, an even larger\nsoftening is observed through the pressure induced structural transition.\nLattice dynamical calculations reveal that this mode is indeed very peculiar as\nit involves a minimal bending of the strongest superexchange path in the\npentagonal planes, as well as a decrease of the distances between second\nneighbor irons. The latter confirms the hypothesis made by Friedrich et al.,1\nabout an increase in the oxygen coordination of irons being at the origin of\nthe pressure-induced structural transition. As a consequence, one expects a new\nmagnetic superexchange path that may alter the magnetic structure under\npressure.\n"}
+{"abstract": "  Real-time detections of transients and rapid multi-wavelength follow-up are\nat the core of modern multi-messenger astrophysics. MeerTRAP is one such\ninstrument that has been deployed on the MeerKAT radio telescope in South\nAfrica to search for fast radio transients in real-time. This, coupled with the\nability to rapidly localize the transient in combination with optical\nco-pointing by the MeerLICHT telescope gives the instrument the edge in finding\nand identifying the nature of the transient on short timescales. The commensal\nnature of the project means that MeerTRAP will keep looking for transients even\nif the telescope is not being used specifically for that purpose. Here, we\npresent a brief overview of the MeerTRAP project. We describe the overall\ndesign, specifications and the software stack required to implement such an\nundertaking. We conclude with some science highlights that have been enabled by\nthis venture over the last 10 months of operation.\n"}
+{"abstract": "  Zonotopes are widely used for over-approximating forward reachable sets of\nuncertain linear systems for verification purposes. In this paper, we use\nzonotopes to achieve more scalable algorithms that under-approximate backward\nreachable sets of uncertain linear systems for control design. The main\ndifference is that the backward reachability analysis is a two-player game and\ninvolves Minkowski difference operations, but zonotopes are not closed under\nsuch operations. We under-approximate this Minkowski difference with a\nzonotope, which can be obtained by solving a linear optimization problem. We\nfurther develop an efficient zonotope order reduction technique to bound the\ncomplexity of the obtained zonotopic under-approximations. The proposed\napproach is evaluated against existing approaches using randomly generated\ninstances and illustrated with several examples.\n"}
+{"abstract": "  Identifying a low-dimensional informed parameter subspace offers a viable\npath to alleviating the dimensionality challenge in the sampled-based solution\nto large-scale Bayesian inverse problems. This paper introduces a novel\ngradient-based dimension reduction method in which the informed subspace does\nnot depend on the data. This permits an online-offline computational strategy\nwhere the expensive low-dimensional structure of the problem is detected in an\noffline phase, meaning before observing the data. This strategy is particularly\nrelevant for multiple inversion problems as the same informed subspace can be\nreused. The proposed approach allows controlling the approximation error (in\nexpectation over the data) of the posterior distribution. We also present\nsampling strategies that exploit the informed subspace to draw efficiently\nsamples from the exact posterior distribution. The method is successfully\nillustrated on two numerical examples: a PDE-based inverse problem with a\nGaussian process prior and a tomography problem with Poisson data and a\nBesov-$\\mathcal{B}^2_{11}$ prior.\n"}
+{"abstract": "  Random Reshuffling (RR), also known as Stochastic Gradient Descent (SGD)\nwithout replacement, is a popular and theoretically grounded method for\nfinite-sum minimization. We propose two new algorithms: Proximal and Federated\nRandom Reshuffing (ProxRR and FedRR). The first algorithm, ProxRR, solves\ncomposite convex finite-sum minimization problems in which the objective is the\nsum of a (potentially non-smooth) convex regularizer and an average of $n$\nsmooth objectives. We obtain the second algorithm, FedRR, as a special case of\nProxRR applied to a reformulation of distributed problems with either\nhomogeneous or heterogeneous data. We study the algorithms' convergence\nproperties with constant and decreasing stepsizes, and show that they have\nconsiderable advantages over Proximal and Local SGD. In particular, our methods\nhave superior complexities and ProxRR evaluates the proximal operator once per\nepoch only. When the proximal operator is expensive to compute, this small\ndifference makes ProxRR up to $n$ times faster than algorithms that evaluate\nthe proximal operator in every iteration. We give examples of practical\noptimization tasks where the proximal operator is difficult to compute and\nProxRR has a clear advantage. Finally, we corroborate our results with\nexperiments on real data sets.\n"}
+{"abstract": "  Conditional on the extended Riemann hypothesis, we show that with high\nprobability, the characteristic polynomial of a random symmetric $\\{\\pm\n1\\}$-matrix is irreducible. This addresses a question raised by Eberhard in\nrecent work. The main innovation in our work is establishing sharp estimates\nregarding the rank distribution of symmetric random $\\{\\pm 1\\}$-matrices over\n$\\mathbb{F}_p$ for primes $2 < p \\leq \\exp(O(n^{1/4}))$. Previously, such\nestimates were available only for $p = o(n^{1/8})$. At the heart of our proof\nis a way to combine multiple inverse Littlewood--Offord-type results to control\nthe contribution to singularity-type events of vectors in $\\mathbb{F}_p^{n}$\nwith anticoncentration at least $1/p + \\Omega(1/p^2)$. Previously, inverse\nLittlewood--Offord-type results only allowed control over vectors with\nanticoncentration at least $C/p$ for some large constant $C > 1$.\n"}
+{"abstract": "  In recent years, the immiscible polymer blend system has attracted much\nattention as the matrix of nanocomposites. Herein, from the perspective of\ndynamics, the control of the carbon nanotubes (CNTs) migration aided with the\ninterface of polystyrene (PS) and poly(methyl methacrylate) (PMMA) blends was\nachieved through a facile melt mixing method. Thus, we revealed a comprehensive\nrelationship between several typical CNTs migrating scenarios and the microwave\ndielectric properties of their nanocomposites. Based on the unique morphologies\nand phase domain structures of the immiscible matrix, we further investigated\nthe multiple microwave dielectric relaxation processes and shed new light on\nthe relation between relaxation peak position and the phase domain size\ndistribution. Moreover, by integrating the CNTs interface localization control\nwith the matrix co-continuous structure construction, we found that the\ninterface promotes double percolation effect to achieve conductive percolation\nat low CNTs loading (~1.06 vol%). Overall, the present study provides a unique\nnanocomposite material design symphonizing both functional fillers dispersion\nand location as well as the matrix architecture optimization for microwave\napplications.\n"}
+{"abstract": "  Despite the acclaimed success of the magnetic field (H) formulation for\nmodeling the electromagnetic behavior of superconductors with the finite\nelement method, the use of vector-dependent variables in non-conducting domains\nleads to unnecessarily long computation times. In order to solve this issue, we\nhave recently shown how to use a magnetic scalar potential together with the\nH-formulation in the COMSOL Multiphysics environment to efficiently and\naccurately solve for the magnetic field surrounding superconducting domains.\nHowever, from the definition of the magnetic scalar potential, the\nnon-conducting domains must be made simply connected in order to obey Ampere's\nlaw. In this work, we use thin cuts to apply a discontinuity in $\\phi$ and make\nthe non-conducting domains simply connected. This approach is shown to be\neasily implementable in the COMSOL Multiphysics finite element program, already\nwidely used by the applied superconductivity community. We simulate three\ndifferent models in 2-D and 3-D using superconducting filaments and tapes, and\nshow that the results are in very good agreement with the H-A and\nH-formulations. Finally, we compare the computation times between the\nformulations, showing that the H-$\\phi$-formulation can be up to seven times\nfaster than the standard H-formulation in certain applications of interest.\n"}
+{"abstract": "  Calcium scoring, a process in which arterial calcifications are detected and\nquantified in CT, is valuable in estimating the risk of cardiovascular disease\nevents. Especially when used to quantify the extent of calcification in the\ncoronary arteries, it is a strong and independent predictor of coronary heart\ndisease events. Advances in artificial intelligence (AI)-based image analysis\nhave produced a multitude of automatic calcium scoring methods. While most\nearly methods closely follow standard calcium scoring accepted in clinic,\nrecent approaches extend this procedure to enable faster or more reproducible\ncalcium scoring. This chapter provides an introduction to AI for calcium\nscoring, and an overview of the developed methods and their applications. We\nconclude with a discussion on AI methods in calcium scoring and propose\npotential directions for future research.\n"}
+{"abstract": "  Researchers have developed numerous debugging approaches to help programmers\nin the debugging process, but these approaches are rarely used in practice. In\nthis paper, we investigate how programmers debug their code and what\nresearchers should consider when developing debugging approaches. We conducted\nan online questionnaire where 102 programmers provided information about\nrecently fixed bugs. We found that the majority of bugs (69.6 %) are semantic\nbugs. Memory and concurrency bugs do not occur as frequently (6.9 % and 8.8 %),\nbut they consume more debugging time. Locating a bug is more difficult than\nreproducing and fixing it. Programmers often use only IDE build-in tools for\ndebugging. Furthermore, programmers frequently use a\nreplication-observation-deduction pattern when debugging. These results suggest\nthat debugging support is particularly valuable for memory and concurrency\nbugs. Furthermore, researchers should focus on the fault localization phase and\nintegrate their tools into commonly used IDEs.\n"}
+{"abstract": "  Zero-shot learning (ZSL) refers to the problem of learning to classify\ninstances from the novel classes (unseen) that are absent in the training set\n(seen). Most ZSL methods infer the correlation between visual features and\nattributes to train the classifier for unseen classes. However, such models may\nhave a strong bias towards seen classes during training. Meta-learning has been\nintroduced to mitigate the basis, but meta-ZSL methods are inapplicable when\ntasks used for training are sampled from diverse distributions. In this regard,\nwe propose a novel Task-aligned Generative Meta-learning model for Zero-shot\nlearning (TGMZ). TGMZ mitigates the potentially biased training and enables\nmeta-ZSL to accommodate real-world datasets containing diverse distributions.\nTGMZ incorporates an attribute-conditioned task-wise distribution alignment\nnetwork that projects tasks into a unified distribution to deliver an unbiased\nmodel. Our comparisons with state-of-the-art algorithms show the improvements\nof 2.1%, 3.0%, 2.5%, and 7.6% achieved by TGMZ on AWA1, AWA2, CUB, and aPY\ndatasets, respectively. TGMZ also outperforms competitors by 3.6% in\ngeneralized zero-shot learning (GZSL) setting and 7.9% in our proposed\nfusion-ZSL setting.\n"}
+{"abstract": "  We consider the Brenier-Schr{\\\"o}dinger problem on compact manifolds with\nboundary. In the spirit of a work by Arnaudon, Cruzeiro, L{\\'e}onard and\nZambrini, we study the kinetic property of regular solutions and obtain a link\nto the Navier-Stokes equations with an impermeability condition. We also\nenhance the class of models for which the problem admits a unique solution.\nThis involves a method of taking quotients by reflection groups for which we\ngive several examples.\n"}
+{"abstract": "  The fast protection of meshed HVDC grids requires the modeling of the\ntransient phenomena affecting the grid after a fault. In the case of hybrid\nlines comprising both overhead and underground parts, the numerous generated\ntraveling waves may be difficult to describe and evaluate. This paper proposes\na representation of the grid as a graph, allowing to take into account any\nwaves traveling through the grid. A relatively compact description of the waves\nis then derived, based on a combined physical and behavioral modeling approach.\nThe obtained model depends explicitly on the characteristics of the grid as\nwell as on the fault parameters. An application of the model to the\nidentification of the faulty portion of an hybrid line is proposed. The\nknowledge of the faulty portion is profitable as faults in overhead lines,\ngenerally temporary, can lead to the reclosing of the line.\n"}
+{"abstract": "  We investigate the feasibility of using deep learning techniques, in the form\nof a one-dimensional convolutional neural network (1D-CNN), for the extraction\nof signals from the raw waveforms produced by the individual channels of liquid\nargon time projection chamber (LArTPC) detectors. A minimal generic LArTPC\ndetector model is developed to generate realistic noise and signal waveforms\nused to train and test the 1D-CNN, and evaluate its performance on low-level\nsignals. We demonstrate that our approach overcomes the inherent shortcomings\nof traditional cut-based methods by extending sensitivity to signals with ADC\nvalues below their imposed thresholds. This approach exhibits great promise in\nenhancing the capabilities of future generation neutrino experiments like DUNE\nto carry out their low-energy neutrino physics programs.\n"}
+{"abstract": "  Sensing and metrology play an important role in fundamental science and\napplications, by fulfilling the ever-present need for more precise data sets,\nand by allowing to make more reliable conclusions on the validity of\ntheoretical models. Sensors are ubiquitous, they are used in applications\nacross a diverse range of fields including gravity imaging, geology,\nnavigation, security, timekeeping, spectroscopy, chemistry, magnetometry,\nhealthcare, and medicine. Current progress in quantum technologies inevitably\ntriggers the exploration of quantum systems to be used as sensors with new and\nimproved capabilities. This perspective initially provides a brief review of\nexisting and tested quantum sensing systems, before discussing future possible\ndirections of superconducting quantum circuits use for sensing and metrology:\nsuperconducting sensors including many entangled qubits and schemes employing\nQuantum Error Correction. The perspective also lists future research directions\nthat could be of great value beyond quantum sensing, e.g. for applications in\nquantum computation and simulation.\n"}
+{"abstract": "  Over the past two decades, open systems that are described by a non-Hermitian\nHamiltonian have become a subject of intense research. These systems encompass\nclassical wave systems with balanced gain and loss, semiclassical models with\nmode selective losses, and minimal quantum systems, and the meteoric research\non them has mainly focused on the wide range of novel functionalities they\ndemonstrate. Here, we address the following questions: Does anything remain\nconstant in the dynamics of such open systems? What are the consequences of\nsuch conserved quantities? Through spectral-decomposition method and explicit,\nrecursive procedure, we obtain all conserved observables for general\n$\\mathcal{PT}$-symmetric systems. We then generalize the analysis to\nHamiltonians with other antilinear symmetries, and discuss the consequences of\nconservation laws for open systems. We illustrate our findings with several\nphysically motivated examples.\n"}
+{"abstract": "  Study on a rectified current induced by active particles has received a great\nattention due to its possible application to a microscopic motor in biological\nenvironments. Insertion of an {\\em asymmetric} passive object amid many active\nparticles has been regarded as an essential ingredient for generating such a\nrectified motion. Here, we report that the reverse situation is also possible,\nwhere the motion of an active object can be rectified by its geometric\nasymmetry amid many passive particles. This may describe an unidirectional\nmotion of polar biological agents with asymmetric shape. We also find a weak\nbut less diffusive rectified motion in a {\\em passive} mode without energy\npump-in. This \"moving by dissipation\" mechanism could be used as a design\nprinciple for developing more reliable microscopic motors.\n"}
+{"abstract": "  It has been argued that supergravity models of inflation with vanishing sound\nspeeds, $c_s$, lead to an unbounded growth in the production rate of\ngravitinos. We consider several models of inflation to delineate the conditions\nfor which $c_s = 0$. In models with unconstrained superfields, we argue that\nthe mixing of the goldstino and inflatino in a time-varying background prevents\nthe uncontrolled production of the longitudinal modes. This conclusion is\nunchanged if there is a nilpotent field associated with supersymmetry breaking\nwith constraint ${\\bf S^2} =0$, i.e. sgoldstino-less models. Models with a\nsecond orthogonal constraint, ${\\bf S(\\Phi-\\bar{\\Phi})} =0$, where $\\bf{\\Phi}$\nis the inflaton superfield, which eliminates the inflatino, may suffer from the\nover-production of gravitinos. However, we point out that these models may be\nproblematic if this constraint originates from a UV Lagrangian, as this may\nrequire using higher derivative operators. These models may also exhibit other\npathologies such as $c_s > 1$, which are absent in theories with the single\nconstraint or unconstrained fields.\n"}
+{"abstract": "  Power law size distributions are the hallmarks of nonlinear energy\ndissipation processes governed by self-organized criticality. Here we analyze\n75 data sets of stellar flare size distributions, mostly obtained from the {\\sl\nExtreme Ultra-Violet Explorer (EUVE)} and the {\\sl Kepler} mission. We aim to\nanswer the following questions for size distributions of stellar flares: (i)\nWhat are the values and uncertainties of power law slopes? (ii) Do power law\nslopes vary with time ? (iii) Do power law slopes depend on the stellar\nspectral type? (iv) Are they compatible with solar flares? (v) Are they\nconsistent with self-organized criticality (SOC) models? We find that the\nobserved size distributions of stellar flare fluences (or energies) exhibit\npower law slopes of $\\alpha_E=2.09\\pm0.24$ for optical data sets observed with\nKepler. The observed power law slopes do not show much time variability and do\nnot depend on the stellar spectral type (M, K, G, F, A, Giants). In solar\nflares we find that background subtraction lowers the uncorrected value of\n$\\alpha_E=2.20\\pm0.22$ to $\\alpha_E=1.57\\pm0.19$. Furthermore, most of the\nstellar flares are temporally not resolved in low-cadence (30 min) Kepler data,\nwhich causes an additional bias. Taking these two biases into account, the\nstellar flare data sets are consistent with the theoretical prediction $N(x)\n\\propto x^{-\\alpha_x}$ of self-organized criticality models, i.e.,\n$\\alpha_E=1.5$. Thus, accurate power law fits require automated detection of\nthe inertial range and background subtraction, which can be modeled with the\ngeneralized Pareto distribution, finite-system size effects, and extreme event\noutliers.\n"}
+{"abstract": "  If every vertex in a map has one out of two face-cycle types, then the map is\nsaid to be $2$-semiequivelar. A 2-uniform tiling is an edge-to-edge tiling of\nregular polygons having $2$ distinct transitivity classes of vertices. Clearly,\na $2$-uniform map is $2$-semiequivelar. The converse of this is not true in\ngeneral. There are 20 distinct 2-uniform tilings (these are of $14$ different\ntypes) on the plane. In this article, we prove that a $2$-semiequivelar\ntoroidal map $K$ has a finite $2$-uniform cover if the universal cover of $K$\nis $2$-uniform except of two types.\n"}
+{"abstract": "  To keep up with demand, servers will scale up to handle hundreds of thousands\nof clients simultaneously. Much of the focus of the community has been on\nscaling servers in terms of aggregate traffic intensity (packets transmitted\nper second). However, bottlenecks caused by the increasing number of concurrent\nclients, resulting in a large number of concurrent flows, have received little\nattention. In this work, we focus on identifying such bottlenecks. In\nparticular, we define two broad categories of problems; namely, admitting more\npackets into the network stack than can be handled efficiently, and increasing\nper-packet overhead within the stack. We show that these problems contribute to\nhigh CPU usage and network performance degradation in terms of aggregate\nthroughput and RTT. Our measurement and analysis are performed in the context\nof the Linux networking stack, the the most widely used publicly available\nnetworking stack. Further, we discuss the relevance of our findings to other\nnetwork stacks. The goal of our work is to highlight considerations required in\nthe design of future networking stacks to enable efficient handling of large\nnumbers of clients and flows.\n"}
+{"abstract": "  This paper presents a new technique for disturbing the algebraic structure of\nlinear codes in code-based cryptography. Specifically, we introduce the\nso-called semilinear transformations in coding theory and then creatively apply\nthem to the construction of code-based cryptosystems. Note that\n$\\mathbb{F}_{q^m}$ can be viewed as an $\\mathbb{F}_q$-linear space of dimension\n$m$, a semilinear transformation $\\varphi$ is therefore defined as an\n$\\mathbb{F}_q$-linear automorphism of $\\mathbb{F}_{q^m}$. Then we impose this\ntransformation to a linear code $\\mathcal{C}$ over $\\mathbb{F}_{q^m}$. It is\nclear that $\\varphi(\\mathcal{C})$ forms an $\\mathbb{F}_q$-linear space, but\ngenerally does not preserve the $\\mathbb{F}_{q^m}$-linearity any longer.\nInspired by this observation, a new technique for masking the structure of\nlinear codes is developed in this paper. Meanwhile, we endow the underlying\nGabidulin code with the so-called partial cyclic structure to reduce the\npublic-key size. Compared to some other code-based cryptosystems, our proposal\nadmits a much more compact representation of public keys. For instance, 2592\nbytes are enough to achieve the security of 256 bits, almost 403 times smaller\nthan that of Classic McEliece entering the third round of the NIST PQC project.\n"}
+{"abstract": "  Mel-filterbanks are fixed, engineered audio features which emulate human\nperception and have been used through the history of audio understanding up to\ntoday. However, their undeniable qualities are counterbalanced by the\nfundamental limitations of handmade representations. In this work we show that\nwe can train a single learnable frontend that outperforms mel-filterbanks on a\nwide range of audio signals, including speech, music, audio events and animal\nsounds, providing a general-purpose learned frontend for audio classification.\nTo do so, we introduce a new principled, lightweight, fully learnable\narchitecture that can be used as a drop-in replacement of mel-filterbanks. Our\nsystem learns all operations of audio features extraction, from filtering to\npooling, compression and normalization, and can be integrated into any neural\nnetwork at a negligible parameter cost. We perform multi-task training on eight\ndiverse audio classification tasks, and show consistent improvements of our\nmodel over mel-filterbanks and previous learnable alternatives. Moreover, our\nsystem outperforms the current state-of-the-art learnable frontend on Audioset,\nwith orders of magnitude fewer parameters.\n"}
+{"abstract": "  With the dramatic rise in high-quality galaxy data expected from Euclid and\nVera C. Rubin Observatory, there will be increasing demand for fast\nhigh-precision methods for measuring galaxy fluxes. These will be essential for\ninferring the redshifts of the galaxies. In this paper, we introduce Lumos, a\ndeep learning method to measure photometry from galaxy images. Lumos builds on\nBKGnet, an algorithm to predict the background and its associated error, and\npredicts the background-subtracted flux probability density function. We have\ndeveloped Lumos for data from the Physics of the Accelerating Universe Survey\n(PAUS), an imaging survey using 40 narrow-band filter camera (PAUCam). PAUCam\nimages are affected by scattered light, displaying a background noise pattern\nthat can be predicted and corrected for. On average, Lumos increases the SNR of\nthe observations by a factor of 2 compared to an aperture photometry algorithm.\nIt also incorporates other advantages like robustness towards distorting\nartifacts, e.g. cosmic rays or scattered light, the ability of deblending and\nless sensitivity to uncertainties in the galaxy profile parameters used to\ninfer the photometry. Indeed, the number of flagged photometry outlier\nobservations is reduced from 10% to 2%, comparing to aperture photometry.\nFurthermore, with Lumos photometry, the photo-z scatter is reduced by ~10% with\nthe Deepz machine learning photo-z code and the photo-z outlier rate by 20%.\nThe photo-z improvement is lower than expected from the SNR increment, however\ncurrently the photometric calibration and outliers in the photometry seem to be\nits limiting factor.\n"}
+{"abstract": "  It is well known that quantum effects may lead to remove the intrinsic\nsingularity point of back holes. Also, the quintessence scalar field is a\ncandidate model for describing late-time acceleration expansion. Accordingly,\nKazakov and Solodukhin considered the existence of back-reaction of the\nspacetime due to the quantum fluctuations of the background metric to deform\nSchwarzschild black hole, which led to change the intrinsic singularity of the\nblack hole to a 2-sphere with a radius of the order of the Planck length. Also,\nKiselev rewrote the Schwarzschild metric by taking into account the\nquintessence field in the background. In this study, we consider the\nquantum-corrected Schwarzschild black hole inspired by Kazakov-Solodukhin's\nwork, and Schwarzschild black hole surrounded by quintessence deduced by\nKiselev to study the mutual effects of quantum fluctuations and quintessence on\nthe accretion onto the black hole. Consequently, the radial component of\n4-velocity and the proper energy density of the accreting fluid have a finite\nvalue on the surface of its central 2-sphere due to the presence of quantum\ncorrections. Also, by comparing the accretion parameters in different kinds of\nblack holes, we infer that the presence of a point-like electric charge in the\nspacetime is somewhat similar to some quantum fluctuations in the background\nmetric.\n"}
+{"abstract": "  Batched network coding is a variation of random linear network coding which\nhas low computational and storage costs. In order to adapt to random\nfluctuations in the number of erasures in individual batches, it is not optimal\nto recode and transmit the same number of packets for all batches. Different\ndistributed optimization models, which are called adaptive recoding schemes,\nwere formulated for this purpose. The key component of these optimization\nproblems is the expected value of the rank distribution of a batch at the next\nnetwork node, which is also known as the expected rank. In this paper, we put\nforth a unified adaptive recoding framework with an arbitrary recoding field\nsize. We show that the expected rank functions are concave when the packet loss\npattern is a stationary stochastic process, which covers but not limited to\nindependent packet loss and Gilbert-Elliott packet loss model. Under this\nconcavity assumption, we show that there always exists a solution which not\nonly can minimize the randomness on the number of recoded packets but also can\ntolerate rank distribution errors due to inaccurate measurements or limited\nprecision of the machine. We provide an algorithm to obtain such an optimal\noptimal solution, and propose tuning schemes that can turn any feasible\nsolution into a desired optimal solution.\n"}
+{"abstract": "  Based on direct numerical simulations with point-like inertial particles\ntransported by homogeneous and isotropic turbulent flows, we present evidence\nfor the existence of Markov property in Lagrangian turbulence. We show that the\nMarkov property is valid for a finite step size larger than a Stokes\nnumber-dependent Einstein-Markov memory length. This enables the description of\nmulti-scale statistics of Lagrangian particles by Fokker-Planck equations,\nwhich can be embedded in an interdisciplinary approach linking the statistical\ndescription of turbulence with fluctuation theorems of non-equilibrium\nstochastic thermodynamics and fluctuation theorems, and local flow structures.\n"}
+{"abstract": "  A dedicated in situ heating setup in a scanning electron microscope (SEM)\nfollowed by an ex situ atomic force microscopy (AFM) and electron backscatter\ndiffraction (EBSD) is used to characterize the nucleation and early growth\nstages of Fe-Al intermetallics (IMs) at 596 {\\deg}C. A location tracking is\nused to interpret further characterization. Ex situ AFM observations reveal a\nslight shrinkage and out of plane protrusion of the IM at the onset of IM\nnucleation followed by directional growth. The formed interfacial IM compounds\nwere identified by ex situ EBSD. It is now clearly demonstrated that the\n{\\theta}-phase nucleates first prior to the diffusion-controlled growth of the\n{\\eta}-phase. The {\\theta}-phase prevails the intermetallic layer.\n"}
+{"abstract": "  We present a conceptual study of a large format imaging spectrograph for the\nLarge Submillimeter Telescope (LST) and the Atacama Large Aperture\nSubmillimeter Telescope (AtLAST). Recent observations of high-redshift galaxies\nindicate the onset of earliest star formation just a few 100 million years\nafter the Big Bang (i.e., z = 12--15), and LST/AtLAST will provide a unique\npathway to uncover spectroscopically-identified first forming galaxies in the\npre-reionization era, once it will be equipped with a large format imaging\nspectrograph. We propose a 3-band (200, 255, and 350 GHz), medium resolution (R\n= 2,000) imaging spectrograph with 1.5 M detectors in total based on the KATANA\nconcept (Karatsu et al. 2019), which exploits technologies of the integrated\nsuperconducting spectrometer (ISS) and a large-format imaging array. A 1-deg2\ndrilling survey (3,500 hr) will capture a large number of [O III] 88 um (and [C\nII] 158 um) emitters at z = 8--9, and constrain [O III] luminosity functions at\nz > 12.\n"}
+{"abstract": "  Score-based diffusion models synthesize samples by reversing a stochastic\nprocess that diffuses data to noise, and are trained by minimizing a weighted\ncombination of score matching losses. The log-likelihood of score-based\ndiffusion models can be tractably computed through a connection to continuous\nnormalizing flows, but log-likelihood is not directly optimized by the weighted\ncombination of score matching losses. We show that for a specific weighting\nscheme, the objective upper bounds the negative log-likelihood, thus enabling\napproximate maximum likelihood training of score-based diffusion models. We\nempirically observe that maximum likelihood training consistently improves the\nlikelihood of score-based diffusion models across multiple datasets, stochastic\nprocesses, and model architectures. Our best models achieve negative\nlog-likelihoods of 2.83 and 3.76 bits/dim on CIFAR-10 and ImageNet 32x32\nwithout any data augmentation, on a par with state-of-the-art autoregressive\nmodels on these tasks.\n"}
+{"abstract": "  We study a model system with nematic and magnetic orders, within a channel\ngeometry modelled by an interval, $[-D, D]$. The system is characterised by a\ntensor-valued nematic order parameter $\\mathbf{Q}$ and a vector-valued\nmagnetisation $\\mathbf{M}$, and the observable states are modelled as stable\ncritical points of an appropriately defined free energy. In particular, the\nfull energy includes a nemato-magnetic coupling term characterised by a\nparameter $c$. We (i) derive $L^\\infty$ bounds for $\\mathbf{Q}$ and\n$\\mathbf{M}$; (ii) prove a uniqueness result in parameter regimes defined by\n$c$, $D$ and material- and temperature-dependent correlation lengths; (iii)\nanalyse order reconstruction solutions, possessing domain walls, and their\nstabilities as a function of $D$ and $c$ and (iv) perform numerical studies\nthat elucidate the interplay of $c$ and $D$ for multistability.\n"}
+{"abstract": "  We propose a mechanism to substantially rectify radiative heat flow by\nmatching thin films of metal-to-insulator transition materials and polar\ndielectrics in the electromagnetic near field. By leveraging the distinct\nscaling behaviors of the local density of states with film thickness for metals\nand insulators, we theoretically achieve rectification ratios over 140-a\n10-fold improvement over the state of the art-with nanofilms of vanadium\ndioxide and cubic boron nitride in the parallel-plane geometry at\nexperimentally feasible gap sizes (~100 nm). Our rational design offers\nrelative ease of fabrication, flexible choice of materials, and robustness\nagainst deviations from optimal film thicknesses. We expect this work to\nfacilitate the application of thermal diodes in solid-state thermal circuits\nand energy conversion devices.\n"}
+{"abstract": "  We propose a predictive model of the turbulent burning velocity over a wide\nrange of conditions. The model consists of sub models of the stretch factor and\nthe turbulent flame area. The stretch factor characterizes the flame response\nof turbulence stretch and incorporates effects of detailed chemistry and\ntransport with a lookup table of laminar counterflow flames. The flame area\nmodel captures the area growth based on Lagrangian statistics of propagating\nsurfaces, and considers effects of turbulence length scales and fuel\ncharacteristics. The present model predicts the turbulent burning velocity via\nan algebraic expression without free parameters. It is validated against 285\ncases of the direct numerical simulation or experiment reported from various\nresearch groups on planar and Bunsen flames over a wide range of conditions,\ncovering fuels from hydrogen to n-dodecane, pressures from 1 to 20 atm, lean\nand rich mixtures, turbulence intensity ratios from 0.35 to 110, and turbulence\nlength ratios from 0.5 to 80. The comprehensive comparison shows that the\nproposed turbulent burning velocity model has an overall good agreement over\nthe wide range of conditions, with the averaged modeling error of 25.3%.\nFurthermore, the model prediction involves the uncertainty quantification for\nmodel parameters and chemical kinetics to extend the model applicability.\n"}
+{"abstract": "  We propose a distributed approach to train deep convolutional generative\nadversarial neural network (DC-CGANs) models. Our method reduces the imbalance\nbetween generator and discriminator by partitioning the training data according\nto data labels, and enhances scalability by performing a parallel training\nwhere multiple generators are concurrently trained, each one of them focusing\non a single data label. Performance is assessed in terms of inception score and\nimage quality on MNIST, CIFAR10, CIFAR100, and ImageNet1k datasets, showing a\nsignificant improvement in comparison to state-of-the-art techniques to\ntraining DC-CGANs. Weak scaling is attained on all the four datasets using up\nto 1,000 processes and 2,000 NVIDIA V100 GPUs on the OLCF supercomputer Summit.\n"}
+{"abstract": "  In this paper we show how inter-cellular molecular communication may change\nthe overall levels of photosynthesis in plants. Individual plant cells respond\nto external stimuli, such as illumination levels, to regulate their\nphotosynthetic output. Here, we present a mathematical model which shows that\nby sharing information internally using molecular communication, plants may\nincrease overall photosynthate production. Numerical results show that higher\nmutual information between cells corresponds to an increase in overall\nphotosynthesis by as much as 25 per cent. This suggests that molecular\ncommunication plays a vital role in maximising the photosynthesis in plants and\ntherefore suggests new routes to influence plant development in agriculture and\nelsewhere.\n"}
+{"abstract": "  In this paper, the split common null point problem in two Banach spaces is\nconsidered. Then, using the generalized resolvents of maximal monotone\noperators and the generalized projections and an infinite family of\nnonexpansive mappings, a strong convergence theorem for finding a solution of\nthe split common null point problem in two Banach spaces in the presence of a\nsequence of errors will be proved.\n"}
+{"abstract": "  Spectrally-efficient secure non-orthogonal multiple access (NOMA) has\nrecently attained a substantial research interest for fifth generation\ndevelopment. This work explores crucial security issue in NOMA which is stemmed\nfrom utilizing the decoding concept of successive interference cancellation.\nConsidering untrusted users, we design a novel secure NOMA transmission\nprotocol to maximize secrecy fairness among users. A new decoding order for two\nusers' NOMA is proposed that provides positive secrecy rate to both users.\nObserving the objective of maximizing secrecy fairness between users under\ngiven power budget constraint, the problem is formulated as minimizing the\nmaximum secrecy outage probability (SOP) between users. In particular,\nclosed-form expressions of SOP for both users are derived to analyze secrecy\nperformance. SOP minimization problems are solved using pseudoconvexity\nconcept, and optimized power allocation (PA) for each user is obtained.\nAsymptotic expressions of SOPs, and optimal PAs minimizing these approximations\nare obtained to get deeper insights. Further, globally-optimized power control\nsolution from secrecy fairness perspective is obtained at a low computational\ncomplexity and, asymptotic approximation is obtained to gain analytical\ninsights. Numerical results validate the correctness of analysis, and present\ninsights on optimal solutions. Finally, we present insights on global-optimal\nPA by which fairness is ensured and gains of about 55.12%, 69.30%, and 19.11%,\nrespectively are achieved, compared to fixed PA and individual users' optimal\nPAs.\n"}
+{"abstract": "  Variational inference enables approximate posterior inference of the highly\nover-parameterized neural networks that are popular in modern machine learning.\nUnfortunately, such posteriors are known to exhibit various pathological\nbehaviors. We prove that as the number of hidden units in a single-layer\nBayesian neural network tends to infinity, the function-space posterior mean\nunder mean-field variational inference actually converges to zero, completely\nignoring the data. This is in contrast to the true posterior, which converges\nto a Gaussian process. Our work provides insight into the over-regularization\nof the KL divergence in variational inference.\n"}
+{"abstract": "  We present TransitFit, an open-source Python~3 package designed to fit\nexoplanetary transit light-curves for transmission spectroscopy studies\n(Available at https://github.com/joshjchayes/TransitFit and\nhttps://github.com/spearnet/TransitFit, with documentation at\nhttps://transitfit.readthedocs.io/). TransitFit employs nested sampling to\noffer efficient and robust multi-epoch, multi-wavelength fitting of transit\ndata obtained from one or more telescopes. TransitFit allows per-telescope\ndetrending to be performed simultaneously with parameter fitting, including the\nuse of user-supplied detrending alogorithms. Host limb darkening can be fitted\neither independently (\"uncoupled\") for each filter or combined (\"coupled\")\nusing prior conditioning from the PHOENIX stellar atmosphere models. For this\nTransitFit uses the Limb Darkening Toolkit (LDTk) together with filter\nprofiles, including user-supplied filter profiles. We demonstrate the\napplication of TransitFit in three different contexts. First, we model SPEARNET\nbroadband optical data of the low-density hot-Neptune WASP-127~b. The data were\nobtained from a globally-distributed network of 0.5m--2.4m telescopes. We find\nclear improvement in our broadband results using the coupled mode over\nuncoupled mode, when compared against the higher spectral resolution GTC/OSIRIS\ntransmission spectrum obtained by Chen et al. (2018). Using TransitFit, we fit\n26 transit observations by TESS to recover improved ephemerides of the\nhot-Jupiter WASP-91~b and a transit depth determined to a precision of 170~ppm.\nFinally, we use TransitFit to conduct an investigation into the contested\npresence of TTV signatures in WASP-126~b using 126 transits observed by TESS,\nconcluding that there is no statistically significant evidence for such\nsignatures from observations spanning 31 TESS sectors.\n"}
+{"abstract": "  The ongoing Coronavirus disease 2019 (COVID-19) is a major crisis that has\nsignificantly affected the healthcare sector and global economies, which made\nit the main subject of various fields in scientific and technical research. To\nproperly understand and control this new epidemic, mathematical modelling is\npresented as a very effective tool that can illustrate the mechanisms of its\npropagation. In this regard, the use of compartmental models is the most\nprominent approach adopted in the literature to describe the dynamics of\nCOVID-19. Along the same line, we aim during this study to generalize and\nameliorate many existing works that consecrated to analyse the behaviour of\nthis epidemic. Precisely, we propose an SQEAIHR epidemic system for\nCoronavirus. Our constructed model is enriched by taking into account the media\nintervention and vital dynamics. By the use of the next-generation matrix\nmethod, the theoretical basic reproductive number $R_0$ is obtained for\nCOVID-19. Based on some nonstandard and generalized analytical techniques, the\nlocal and global stability of the disease-free equilibrium are proven when $R_0\n< 1$. Moreover, in the case of $R_0 > 1$, the uniform persistence of COVID-19\nmodel is also shown. In order to better adapt our epidemic model to reality,\nthe randomness factor is taken into account by considering a proportional white\nnoises, which leads to a well-posed stochastic model. Under appropriate\nconditions, interesting asymptotic properties are proved, namely: extinction\nand persistence in the mean. The theoretical results show that the dynamics of\nthe perturbed COVID-19 model are determined by parameters that are closely\nrelated to the magnitude of the stochastic noise. Finally, we present some\nnumerical illustrations to confirm our theoretical results and to show the\nimpact of media intervention and quarantine strategies.\n"}
+{"abstract": "  In this work, we generalize the reaction-diffusion equation in statistical\nphysics, Schr\\\"odinger equation in quantum mechanics, Helmholtz equation in\nparaxial optics into the neural partial differential equations (NPDE), which\ncan be considered as the fundamental equations in the field of artificial\nintelligence research. We take finite difference method to discretize NPDE for\nfinding numerical solution, and the basic building blocks of deep neural\nnetwork architecture, including multi-layer perceptron, convolutional neural\nnetwork and recurrent neural networks, are generated. The learning strategies,\nsuch as Adaptive moment estimation, L-BFGS, pseudoinverse learning algorithms\nand partial differential equation constrained optimization, are also presented.\nWe believe it is of significance that presented clear physical image of\ninterpretable deep neural networks, which makes it be possible for applying to\nanalog computing device design, and pave the road to physical artificial\nintelligence.\n"}
+{"abstract": "  Generative modeling has recently shown great promise in computer vision, but\nit has mostly focused on synthesizing visually realistic images. In this paper,\nmotivated by multi-task learning of shareable feature representations, we\nconsider a novel problem of learning a shared generative model that is useful\nacross various visual perception tasks. Correspondingly, we propose a general\nmulti-task oriented generative modeling (MGM) framework, by coupling a\ndiscriminative multi-task network with a generative network. While it is\nchallenging to synthesize both RGB images and pixel-level annotations in\nmulti-task scenarios, our framework enables us to use synthesized images paired\nwith only weak annotations (i.e., image-level scene labels) to facilitate\nmultiple visual tasks. Experimental evaluation on challenging multi-task\nbenchmarks, including NYUv2 and Taskonomy, demonstrates that our MGM framework\nimproves the performance of all the tasks by large margins, consistently\noutperforming state-of-the-art multi-task approaches.\n"}
+{"abstract": "  Understanding the physics of strongly correlated electronic systems has been\na central issue in condensed matter physics for decades. In transition metal\noxides, strong correlations characteristic of narrow $d$ bands is at the origin\nof such remarkable properties as the Mott gap opening, enhanced effective mass,\nand anomalous vibronic coupling, to mention a few. SrVO$_3$, with V$^{4+}$ in a\n$3d^1$ electronic configuration is the simplest example of a 3D correlated\nmetallic electronic system. Here, we focus on the observation of a (roughly)\nquadratic temperature dependence of the inverse electron mobility of this\nseemingly simple system, which is an intriguing property shared by other\nmetallic oxides. The systematic analysis of electronic transport in SrVO$_3$\nthin films discloses the limitations of the simplest picture of e-e\ncorrelations in a Fermi liquid; instead, we show that the quasi-2D topology of\nthe Fermi surface and a strong electron-phonon coupling, contributing to dress\ncarriers with a phonon cloud, play a pivotal role on the reported electron\nspectroscopic, optical, thermodynamic and transport data. The picture that\nemerges is not restricted to SrVO$_3$ but can be shared with other $3d$ and\n$4d$ metallic oxides.\n"}
+{"abstract": "  Let $ E \\subset \\mathbb{R}^2 $ be a finite set, and let $ f : E \\to\n[0,\\infty) $. In this paper, we address the algorithmic aspects of nonnegative\n$C^2$ interpolation in the plane. Specifically, we provide an efficient\nalgorithm to compute a nonnegative $C^2(\\mathbb{R}^2)$ extension of $ f $ with\nnorm within a universal constant factor of the least possible. We also provide\nan efficient algorithm to approximate the trace norm.\n"}
+{"abstract": "  In the past decades, the revolutionary advances of Machine Learning (ML) have\nshown a rapid adoption of ML models into software systems of diverse types.\nSuch Machine Learning Software Applications (MLSAs) are gaining importance in\nour daily lives. As such, the Quality Assurance (QA) of MLSAs is of paramount\nimportance. Several research efforts are dedicated to determining the specific\nchallenges we can face while adopting ML models into software systems. However,\nwe are aware of no research that offered a holistic view of the distribution of\nthose ML quality assurance challenges across the various phases of software\ndevelopment life cycles (SDLC). This paper conducts an in-depth literature\nreview of a large volume of research papers that focused on the quality\nassurance of ML models. We developed a taxonomy of MLSA quality assurance\nissues by mapping the various ML adoption challenges across different phases of\nSDLC. We provide recommendations and research opportunities to improve SDLC\npractices based on the taxonomy. This mapping can help prioritize quality\nassurance efforts of MLSAs where the adoption of ML models can be considered\ncrucial.\n"}
+{"abstract": "  Giant Radio Galaxies (GRGs) are the largest single structures in the\nUniverse. Exhibiting extended radio morphology, their projected sizes range\nfrom 0.7 Mpc up to 4.9 Mpc. LOFAR has opened a new window on the discovery and\ninvestigation of GRGs and, despite the hundreds that are today known, their\nmain growth catalyst is still debated. One natural explanation for the\nexceptional size of GRGs is their old age. In this context, hard X-ray selected\nGRGs show evidence of restarting activity, with the giant radio lobes being\nmostly disconnected from the nuclear source, if any. In this paper, we present\nthe serendipitous discovery of a distant ($z=0.629$), medium X-ray selected GRG\nin the Bo\\\"otes field. High-quality, deep Chandra and LOFAR data allow a robust\nstudy of the connection between the nucleus and the lobes, at a larger redshift\nso far inaccessible to coded-mask hard X-ray instruments. The radio morphology\nof the GRG presented in this work does not show evidence for restarted\nactivity, and the nuclear radio core spectrum does not appear to be GPS-like.\nOn the other hand, the X-ray properties of the new GRG are perfectly consistent\nwith the ones previously studied with Swift/BAT and INTEGRAL at lower redshift.\nIn particular, the bolometric luminosity measured from the X-ray spectrum is a\nfactor of six larger than the one derived from the radio lobes, although the\nlarge uncertainties make them formally consistent at $1\\sigma$. Finally, the\nmoderately dense environment around the GRG, traced by the spatial distribution\nof galaxies, supports recent findings that the growth of GRGs is not primarily\ndriven by underdense environments.\n"}
+{"abstract": "  By employing a pseudo-orthonormal coordinate-free approach, the Dirac\nequation for particles in the Kerr--Newman spacetime is separated into its\nradial and angular parts. In the massless case to which a special attention is\ngiven, the general Heun-type equations turn into their confluent form. We show\nhow one recovers some results previously obtained in literature, by other\nmeans.\n"}
+{"abstract": "  Although many techniques have been applied to matrix factorization (MF), they\nmay not fully exploit the feature structure. In this paper, we incorporate the\ngrouping effect into MF and propose a novel method called Robust Matrix\nFactorization with Grouping effect (GRMF). The grouping effect is a\ngeneralization of the sparsity effect, which conducts denoising by clustering\nsimilar values around multiple centers instead of just around 0. Compared with\nexisting algorithms, the proposed GRMF can automatically learn the grouping\nstructure and sparsity in MF without prior knowledge, by introducing a\nnaturally adjustable non-convex regularization to achieve simultaneous sparsity\nand grouping effect. Specifically, GRMF uses an efficient alternating\nminimization framework to perform MF, in which the original non-convex problem\nis first converted into a convex problem through Difference-of-Convex (DC)\nprogramming, and then solved by Alternating Direction Method of Multipliers\n(ADMM). In addition, GRMF can be easily extended to the Non-negative Matrix\nFactorization (NMF) settings. Extensive experiments have been conducted using\nreal-world data sets with outliers and contaminated noise, where the\nexperimental results show that GRMF has promoted performance and robustness,\ncompared to five benchmark algorithms.\n"}
+{"abstract": "  The increasing market penetration of electric vehicles (EVs) may pose\nsignificant electricity demand on power systems. This electricity demand is\naffected by the inherent uncertainties of EVs' travel behavior that makes\nforecasting the daily charging demand (CD) very challenging. In this project,\nwe use the National House Hold Survey (NHTS) data to form sequences of trips,\nand develop machine learning models to predict the parameters of the next trip\nof the drivers, including trip start time, end time, and distance. These\nparameters are later used to model the temporal charging behavior of EVs. The\nsimulation results show that the proposed modeling can effectively estimate the\ndaily CD pattern based on travel behavior of EVs, and simple machine learning\ntechniques can forecast the travel parameters with acceptable accuracy.\n"}
+{"abstract": "  We argue that neutrino oscillations at JUNO offer a unique opportunity to\nstudy Sorkin's triple-path interference, which is predicted to be zero in\ncanonical quantum mechanics by virtue of the Born rule. In particular, we\ncompute the expected bounds on triple-path interference at JUNO and demonstrate\nthat they are comparable to those already available from electromagnetic\nprobes. Furthermore, the neutrino probe of the Born rule is much more direct\ndue to an intrinsic independence from any boundary conditions, whereas such\ndependence on boundary conditions is always present in the case of\nelectromagnetic probes. Thus, neutrino oscillations present an ideal probe of\nthis aspect of the foundations of quantum mechanics.\n"}
+{"abstract": "  This paper is concerned with the asymptotic analysis of sojourn times of\nrandom fields with continuous sample paths. Under a very general framework we\nshow that there is an interesting relationship between tail asymptotics of\nsojourn times and that of supremum. Moreover, we establish the uniform\ndouble-sum method to derive the tail asymptotics of sojourn times. In the\nliterature, based on the pioneering research of S. Berman the sojourn times\nhave been utilised to derive the tail asymptotics of supremum of Gaussian\nprocesses. In this paper we show that the opposite direction is even more\nfruitful, namely knowing the asymptotics of supremum o f random processes and\nfields (in particular Gaussian) it is possible to establish the asymptotics of\ntheir sojourn times. We illustrate our findings considering i) two dimensional\nGaussian random fields, ii) chi-process generated by stationary Gaussian\nprocesses and iii) stationary Gaussian queueing processes.\n"}
+{"abstract": "  We consider the setting where the nodes of an undirected, connected network\ncollaborate to solve a shared objective modeled as the sum of smooth functions.\nWe assume that each summand is privately known by a unique node. NEAR-DGD is a\ndistributed first order method which permits adjusting the amount of\ncommunication between nodes relative to the amount of computation performed\nlocally in order to balance convergence accuracy and total application cost. In\nthis work, we generalize the convergence properties of a variant of NEAR-DGD\nfrom the strongly convex to the nonconvex case. Under mild assumptions, we show\nconvergence to minimizers of a custom Lyapunov function. Moreover, we\ndemonstrate that the gap between those minimizers and the second order\nstationary solutions of the original problem can become arbitrarily small\ndepending on the choice of algorithm parameters. Finally, we accompany our\ntheoretical analysis with a numerical experiment to evaluate the empirical\nperformance of NEAR-DGD in the nonconvex setting.\n"}
+{"abstract": "  We calculate models of stellar evolution for very massive stars and include\nthe effects of modified gravity to investigate the influence on the physical\nproperties of blue supergiant stars and their use as extragalactic distance\nindicators. With shielding and fifth force parameters in a similar range as in\nprevious studies of Cepheid and tip of the red giant branch (TRGB) stars we\nfind clear effects on stellar luminosity and flux-weighted gravity. The\nrelationship between flux weighted gravity, g_F = g/Teff^4, and bolometric\nmagnitude M_bol (FGLR), which has been used successfully for accurate distance\ndeterminations, is systematically affected. While the stellar evolution FGLRs\nshow a systematic offset from the observed relation, we can use the\ndifferential shifts between models with Newtonian and modified gravity to\nestimate the influence on FGLR distance determinations. Modified gravity leads\nto a distance increase of 0.05 to 0.15 magnitudes in distance modulus. These\nchange are comparable to the ones found for Cepheid stars. We compare observed\nFGLR and TRGB distances of nine galaxies to constrain the free parameters of\nmodified gravity. Not accounting for systematic differences between TRGB and\nFGLR distances shielding parameters of 5*10^-7 and 10^-6 and fifth force\nparameters of 1/3 and 1 can be ruled out with about 90% confidence. Allowing\nfor potential systematic offsets between TRGB and FGLR distances no\ndetermination is possible for a shielding parameter of 10^-6. For 5*10$^-7 a\nfifth force parameter of 1 can be ruled out to 92% but 1/3 is unlikely only to\n60%.\n"}
+{"abstract": "  The superior performance of CNN on medical image analysis heavily depends on\nthe annotation quality, such as the number of labeled image, the source of\nimage, and the expert experience. The annotation requires great expertise and\nlabour. To deal with the high inter-rater variability, the study of imperfect\nlabel has great significance in medical image segmentation tasks. In this\npaper, we present a novel cascaded robust learning framework for chest X-ray\nsegmentation with imperfect annotation. Our model consists of three independent\nnetwork, which can effectively learn useful information from the peer networks.\nThe framework includes two stages. In the first stage, we select the clean\nannotated samples via a model committee setting, the networks are trained by\nminimizing a segmentation loss using the selected clean samples. In the second\nstage, we design a joint optimization framework with label correction to\ngradually correct the wrong annotation and improve the network performance. We\nconduct experiments on the public chest X-ray image datasets collected by\nShenzhen Hospital. The results show that our methods could achieve a\nsignificant improvement on the accuracy in segmentation tasks compared to the\nprevious methods.\n"}
+{"abstract": "  The huge amount of data produced in the fifth-generation (5G) networks not\nonly brings new challenges to the reliability and efficiency of mobile devices\nbut also drives rapid development of new storage techniques. With the benefits\nof fast access speed and high reliability, NAND flash memory has become a\npromising storage solution for the 5G networks. In this paper, we investigate a\nprotograph-coded bit-interleaved coded modulation with iterative detection and\ndecoding (BICM-ID) utilizing irregular mapping (IM) in the multi-level-cell\n(MLC) NAND flash-memory systems. First, we propose an enhanced protograph-based\nextrinsic information transfer (EPEXIT) algorithm to facilitate the analysis of\nprotograph codes in the IM-BICM-ID systems. With the use of EPEXIT algorithm, a\nsimple design method is conceived for the construction of a family of high-rate\nprotograph codes, called irregular-mapped accumulate-repeat-accumulate (IMARA)\ncodes, which possess both excellent decoding thresholds and\nlinear-minimum-distance-growth property. Furthermore, motivated by the\nvoltage-region iterative gain characteristics of IM-BICM-ID systems, a novel\nread-voltage optimization scheme is developed to acquire accurate read-voltage\nlevels, thus minimizing the decoding thresholds of protograph codes.\nTheoretical analyses and error-rate simulations indicate that the proposed\nIMARA-aided IM-BICM-ID scheme and the proposed read-voltage optimization scheme\nremarkably improve the convergence and decoding performance of flash-memory\nsystems. Thus, the proposed protograph-coded IM-BICM-ID flash-memory systems\ncan be viewed as a reliable and efficient storage solution for the\nnew-generation mobile networks with massive data-storage requirement.\n"}
+{"abstract": "  We propose and experimentally demonstrate a novel interference fading\nsuppression method for phase-sensitive optical time domain reflectometry\n(Phi-OTDR) using space-division multiplexed (SDM) pulse probes in few-mode\nfiber. The SDM probes consist of multiple different modes, and three spatial\nmodes (LP01, LP11a and LP11b) are used in this work for proof of concept.\nFirstly, the Rayleigh backscattering light of different modes is experimentally\ncharacterized, and it turns out that the waveforms of Phi-OTDR traces of\ndistinct modes are all different from each other. Thanks to the spatial\ndifference of fading positions of distinct modes, multiple probes from\nspatially multiplexed modes can be used to suppress the interference fading in\nPhi-OTDR. Then, the performances of the Phi-OTDR systems using single probe and\nmultiple probes are evaluated and compared. Specifically, statistical analysis\nshows that both fading probabilities over fiber length and time are reduced\nsignificantly by using multiple SDM probes, which verifies the significant\nperformance improvement on fading suppression. The proposed novel interference\nfading suppression method does not require complicated frequency or phase\nmodulation, which has the advantages of simplicity, good effectiveness and high\nreliability.\n"}
+{"abstract": "  Nonuniform fast Fourier transforms dominate the computational cost in many\napplications including image reconstruction and signal processing. We thus\npresent a general-purpose GPU-based CUDA library for type 1 (nonuniform to\nuniform) and type 2 (uniform to nonuniform) transforms in dimensions 2 and 3,\nin single or double precision. It achieves high performance for a given\nuser-requested accuracy, regardless of the distribution of nonuniform points,\nvia cache-aware point reordering, and load-balanced blocked spreading in shared\nmemory. At low accuracies, this gives on-GPU throughputs around $10^9$\nnonuniform points per second, and (even including host-device transfer) is\ntypically 4-10$\\times$ faster than the latest parallel CPU code FINUFFT (at 28\nthreads). It is competitive with two established GPU codes, being up to\n90$\\times$ faster at high accuracy and/or type 1 clustered point distributions.\nFinally we demonstrate a 5-12$\\times$ speedup versus CPU in an X-ray\ndiffraction 3D iterative reconstruction task at $10^{-12}$ accuracy, observing\nexcellent multi-GPU weak scaling up to one rank per GPU.\n"}
+{"abstract": "  In this paper, we discuss the properties of the generating functions of spin\nHurwitz numbers. In particular, for spin Hurwitz numbers with arbitrary\nramification profiles, we construct the weighed sums which are given by Orlov's\nhypergeometric solutions of the 2-component BKP hierarchy. We derive the closed\nalgebraic formulas for the correlation functions associated with these\ntau-functions, and under reasonable analytical assumptions we prove the loop\nequations (the blobbed topological recursion). Finally, we prove a version of\ntopological recursion for the spin Hurwitz numbers with the spin completed\ncycles (a generalized version of the Giacchetto--Kramer--Lewa\\'nski\nconjecture).\n"}
+{"abstract": "  We study inverse problems for the nonlinear wave equation $\\square_g u +\nw(x,u, \\nabla_g u) = 0$ in a Lorentzian manifold $(M,g)$ with boundary, where\n$\\nabla_g u$ denotes the gradient and $w(x,u, \\xi)$ is smooth and quadratic in\n$\\xi$. Under appropriate assumptions, we show that the conformal class of the\nLorentzian metric $g$ can be recovered up to diffeomorphisms, from the\nknowledge of the Neumann-to-Dirichlet map. With some additional conditions, we\ncan recover the metric itself up to diffeomorphisms. Moreover, we can recover\nthe second and third quadratic forms in the Taylor expansion of $w(x,u, \\xi)$\nwith respect to $u$ up to null forms.\n"}
+{"abstract": "  This article discusses the physical and kinematical characteristics of\nplanetary nebulae accompanying PG1159 central stars. The study is based on the\nparallax and proper motion measurements recently offered by Gaia space mission.\nTwo approaches were used to investigate the kinematical properties of the\nsample. The results revealed that most of the studied nebulae arise from\nprogenitor stars of mass range; $0.9-1.75$\\,M$_{\\odot}$. Furthermore, they tend\nto live within the Galactic thick-disk and moving with an average peculiar\nvelocity of $61.7\\pm19.2$\\,km\\,s$^{-1}$ at a mean vertical height of $469\\pm79$\npc. The locations of the PG1159 stars on the H-R diagram indicate that they\nhave an average final stellar mass and evolutionary age of\n$0.58\\pm0.08$\\,M$_{\\odot}$ and $25.5\\pm5.3 \\rm{x}10^3$ yr, respectively. We\nfound a good agreement between the mean evolutionary age of the PG1159 stars\nand the mean dynamical age of their companion planetary nebulae ($28.0\\pm6.4\n\\rm{x}10^3$ yr).\n"}
+{"abstract": "  The pentakis dodecahedron, the dual of the truncated icosahedron, consists of\n60 edge-sharing triangles. It has 20 six- and 12 five-fold coordinated\nvertices, with the former forming a dodecahedron, and each of the latter\nconnected to the vertices of one of the 12 pentagons of the dodecahedron. When\nspins mounted on the vertices of the pentakis dodecahedron interact according\nto the nearest-neighbor antiferromagnetic Heisenberg model, the two different\nvertex types necessitate the introduction of two exchange constants. As the\nrelative strength of the two constants is varied the molecule interpolates\nbetween the dodecahedron and a molecule consisting only of quadrangles. The\ncompetition between the two exchange constants, frustration, and an external\nmagnetic field results in a multitude of ground-state magnetization and\nsusceptibility discontinuities. At the classical level the maximum is ten\nmagnetization and one susceptibility discontinuities when the 12 five-fold\nvertices interact with the dodecahedron spins with approximately one-half the\nstrength of their interaction. When the two interactions are approximately\nequal in strength the number of discontinuities is also maximized, with three\nof the magnetization and eight of the susceptibility. At the full quantum\nlimit, where the magnitude of the spins equals 1/2, there can be up to three\nground-state magnetization jumps that have the total z spin component changing\nby \\Delta S^z=2, even though quantum fluctuations rarely allow discontinuities\nof the magnetization. The full quantum case also supports a \\Delta S^z=3\ndiscontinuity. Frustration also results in nonmagnetic states inside the\nsinglet-triplet gap. These results make the pentakis dodecahedron the molecule\nwith the most discontinuous magnetic response from the quantum to the classical\nlevel.\n"}
+{"abstract": "  Dynamical scaling is an asymptotic property typical for the dynamics of\nfirst-order phase transitions in physical systems and related to\nself-similarity. Based on the integral-representation for the marginal\nprobabilities of a fractional non-homogeneous Poisson process introduced by\nLeonenko et al. (2017) and generalising the standard fractional Poisson\nprocess, we prove the dynamical scaling under fairly mild conditions. Our\nresult also includes the special case of the standard fractional Poisson\nprocess.\n"}
+{"abstract": "  The performance of superconducting radio-frequency (SRF) cavities depends on\nthe niobium surface condition. Recently, various heat-treatment methods have\nbeen investigated to achieve unprecedented high quality factor (Q) and high\naccelerating field (E). We report the influence of a new baking process called\nfurnace baking on the Q-E behavior of 1.3 GHz SRF cavities. Furnace baking is\nperformed as the final step of the cavity surface treatment; the cavities are\nheated in a vacuum furnace for 3 h, followed by high-pressure rinsing and\nradio-frequency measurement. This method is simpler and potentially more\nreliable than previously reported heat-treatment methods, and it is therefore,\neasier to apply to the SRF cavities. We find that the quality factor is\nincreased after furnace baking at temperatures ranging from 300C to 400C, while\nstrong decreasing the quality factor at high accelerating field is observed\nafter furnace baking at temperatures ranging from 600C to 800C. We find\nsignificant differences in the surface resistance for various processing\ntemperatures.\n"}
+{"abstract": "  Sound Event Detection and Audio Classification tasks are traditionally\naddressed through time-frequency representations of audio signals such as\nspectrograms. However, the emergence of deep neural networks as efficient\nfeature extractors has enabled the direct use of audio signals for\nclassification purposes. In this paper, we attempt to recognize musical\ninstruments in polyphonic audio by only feeding their raw waveforms into deep\nlearning models. Various recurrent and convolutional architectures\nincorporating residual connections are examined and parameterized in order to\nbuild end-to-end classi-fiers with low computational cost and only minimal\npreprocessing. We obtain competitive classification scores and useful\ninstrument-wise insight through the IRMAS test set, utilizing a parallel\nCNN-BiGRU model with multiple residual connections, while maintaining a\nsignificantly reduced number of trainable parameters.\n"}
+{"abstract": "  We study the topological properties of a spin-orbit coupled Hofstadter model\non the Kagome lattice. The model is time-reversal invariant and realizes a\n$\\mathbb{Z}_2$ topological insulator as a result of artificial gauge fields. We\ndevelop topological arguments to describe this system showing three\ninequivalent sites in a unit cell and a flat band in its energy spectrum in\naddition to the topological dispersive energy bands. We show the stability of\nthe topological phase towards spin-flip processes and different types of\non-site potentials. In particular, we also address the situation where on-site\nenergies may differ inside a unit cell. Moreover, a staggered potential on the\nlattice may realize topological phases for the half-filled situation. Another\ninteresting result is the occurrence of a topological phase for large on-site\nenergies. To describe topological properties of the system we use a numerical\napproach based on the twisted boundary conditions and we develop a mathematical\napproach, related to smooth fields.\n"}
+{"abstract": "  Cross-flow turbines convert kinetic power in wind or water currents to\nmechanical power. Unlike axial-flow turbines, the influence of geometric\nparameters on turbine performance is not well-understood, in part because there\nare neither generalized analytical formulations nor inexpensive, accurate\nnumerical models that describe their fluid dynamics. Here, we experimentally\ninvestigate the effect of aspect ratio - the ratio of the blade span to rotor\ndiameter - on the performance of a straight-bladed cross-flow turbine in a\nwater channel. To isolate the effect of aspect ratio, all other non-dimensional\nparameters are held constant, including the relative confinement, Froude\nnumber, and Reynolds number. The coefficient of performance is found to be\ninvariant for the range of aspect ratios tested (0.95 - 1.63), which we ascribe\nto minimal blade-support interactions for this turbine design. Finally, a\nsubset of experiments is repeated without controlling for the Froude number and\nthe coefficient of performance is found to increase, a consequence of Froude\nnumber variation that could mistakenly be ascribed to aspect ratio. This\nhighlights the importance of rigorous experimental design when exploring the\neffect of geometric parameters on cross-flow turbine performance.\n"}
+{"abstract": "  After more than a decade of intense focus on automated vehicles, we are still\nfacing huge challenges for the vision of fully autonomous driving to become a\nreality. The same \"disillusionment\" is true in many other domains, in which\nautonomous Cyber-Physical Systems (CPS) could considerably help to overcome\nsocietal challenges and be highly beneficial to society and individuals. Taking\nthe automotive domain, i.e. highly automated vehicles (HAV), as an example,\nthis paper sets out to summarize the major challenges that are still to\novercome for achieving safe, secure, reliable and trustworthy highly automated\nresp. autonomous CPS. We constrain ourselves to technical challenges,\nacknowledging the importance of (legal) regulations, certification,\nstandardization, ethics, and societal acceptance, to name but a few, without\ndelving deeper into them as this is beyond the scope of this paper. Four\nchallenges have been identified as being the main obstacles to realizing HAV:\nRealization of continuous, post-deployment systems improvement, handling of\nuncertainties and incomplete information, verification of HAV with machine\nlearning components, and prediction. Each of these challenges is described in\ndetail, including sub-challenges and, where appropriate, possible approaches to\novercome them. By working together in a common effort between industry and\nacademy and focusing on these challenges, the authors hope to contribute to\novercome the \"disillusionment\" for realizing HAV.\n"}
+{"abstract": "  We present an integrated design to precisely measure optical frequency using\nweak value amplification with a multi-mode interferometer. The technique\ninvolves introducing a weak perturbation to the system and then post-selecting\nthe data in such a way that the signal is amplified without amplifying the\ntechnical noise, as has previously been demonstrated in a free-space setup. We\ndemonstrate the advantages of a Bragg grating with two band gaps for obtaining\nsimultaneous, stable high transmission and high dispersion. We numerically\nmodel the interferometer in order to demonstrate the amplification effect. The\ndevice is shown to have advantages over both the free-space implementation and\nother methods of measuring optical frequency on a chip, such as an integrated\nMach-Zehnder interferometer.\n"}
+{"abstract": "  In this paper, we introduce single acceptance sampling inspection plan\n(SASIP) for transmuted Rayleigh (TR) distribution when the lifetime experiment\nis truncated at a prefixed time. Establish the proposed plan for different\nchoices of confidence level, acceptance number and ratio of true mean lifetime\nto specified mean lifetime. Minimum sample size necessary to ensure a certain\nspecified lifetime is obtained. Operating characteristic(OC) values and\nproducer's risk of proposed plan are presented. Two real life example has been\npresented to show the applicability of proposed SASIP.\n"}
+{"abstract": "  In this paper, we study the periodicity structure of finite field linear\nrecurring sequences whose period is not necessarily maximal and determine\nnecessary and sufficient conditions for the characteristic polynomial~\\(f\\) to\nhave exactly two periods in the sense that the period of any sequence generated\nby~\\(f\\) is either one or a unique integer greater than one.\n"}
+{"abstract": "  We present a comprehensive analysis of all XMM-Newton spectra of OJ 287\nspanning 15 years of X-ray spectroscopy of this bright blazar. We also report\nthe latest results from our dedicated Swift UVOT and XRT monitoring of OJ 287\nwhich started in 2015, along with all earlier public Swift data since 2005.\nDuring this time interval, OJ 287 was caught in extreme minima and outburst\nstates. Its X-ray spectrum is highly variable and encompasses all states seen\nin blazars from very flat to exceptionally steep. The spectrum can be\ndecomposed into three spectral components: Inverse Compton (IC) emission\ndominant at low-states, super-soft synchrotron emission which becomes\nincreasingly dominant as OJ 287 brightens, and an intermediately-soft\n(Gamma_x=2.2) additional component seen at outburst. This last component\nextends beyond 10 keV and plausibly represents either a second synchrotron/IC\ncomponent and/or a temporary disk corona of the primary supermassive black hole\n(SMBH). Our 2018 XMM-Newton observation, quasi-simultaneous with the Event\nHorizon Telescope observation of OJ 287, is well described by a two-component\nmodel with a hard IC component of Gamma_x=1.5 and a soft synchrotron component.\nLow-state spectra limit any long-lived accretion disk/corona contribution in\nX-rays to a very low value of L_x/L_Edd < 5.6 times 10^(-4) (for M_(BH,\nprimary) = 1.8 times 10^10 M_sun). Some implications for the binary SMBH model\nof OJ 287 are discussed.\n"}
+{"abstract": "  Several recent applications of optimal transport (OT) theory to machine\nlearning have relied on regularization, notably entropy and the Sinkhorn\nalgorithm. Because matrix-vector products are pervasive in the Sinkhorn\nalgorithm, several works have proposed to \\textit{approximate} kernel matrices\nappearing in its iterations using low-rank factors. Another route lies instead\nin imposing low-rank constraints on the feasible set of couplings considered in\nOT problems, with no approximations on cost nor kernel matrices. This route was\nfirst explored by Forrow et al., 2018, who proposed an algorithm tailored for\nthe squared Euclidean ground cost, using a proxy objective that can be solved\nthrough the machinery of regularized 2-Wasserstein barycenters. Building on\nthis, we introduce in this work a generic approach that aims at solving, in\nfull generality, the OT problem under low-rank constraints with arbitrary\ncosts. Our algorithm relies on an explicit factorization of low rank couplings\nas a product of \\textit{sub-coupling} factors linked by a common marginal;\nsimilar to an NMF approach, we alternatively updates these factors. We prove\nthe non-asymptotic stationary convergence of this algorithm and illustrate its\nefficiency on benchmark experiments.\n"}
+{"abstract": "  The hierarchy of nonlocality and entanglement in multipartite systems is one\nof the fundamental problems in quantum physics. Existing studies on this topic\nto date were limited to the entanglement classification according to the\nnumbers of particles enrolled. Equivalence under stochastic local operations\nand classical communication provides a more detailed classification, e. g. the\ngenuine three-qubit entanglement being divided into W and GHZ classes. We\nconstruct two families of local models for the three-qubit\nGreenberger-Horne-Zeilinger (GHZ)-symmetric states, whose entanglement classes\nhave a complete description. The key technology of construction the local\nmodels in this work is the GHZ symmetrization on tripartite extensions of the\noptimal local-hidden-state models for Bell diagonal states. Our models show\nthat entanglement and nonlocality are inequivalent for all the entanglement\nclasses (biseparable, W, and GHZ) in three-qubit systems.\n"}
+{"abstract": "  We show that both the classical as well as the quantum definitions of the\nFisher information faithfully identify resourceful quantum states in general\nquantum resource theories, in the sense that they can always distinguish\nbetween states with and without a given resource. This shows that all quantum\nresources confer an advantage in metrology, and establishes the Fisher\ninformation as a universal tool to probe the resourcefulness of quantum states.\nWe provide bounds on the extent of this advantage, as well as a simple\ncriterion to test whether different resources are useful for the estimation of\nunitarily encoded parameters. Finally, we extend the results to show that the\nFisher information is also able to identify the dynamical resourcefulness of\nquantum operations.\n"}
+{"abstract": "  The relation of period spacing ($\\Delta P$) versus period ($P$) of dipole\nprograde g modes is known to be useful to measure rotation rates in the g-mode\ncavity of rapidly rotating $\\gamma$ Dor and slowly pulsating B (SPB) stars. In\na rapidly rotating star, an inertial mode in the convective core can resonantly\ncouple with g modes propagative in the surrounding radiative region. The\nresonant coupling causes a dip in the $P$-$\\Delta P$ relation, distinct from\nthe modulations due to the chemical composition gradient. Such a resonance dip\nin $\\Delta P$ of prograde dipole g modes appears around a frequency\ncorresponding to a spin parameter $2f_{\\rm rot}{\\rm(cc)}/\\nu_{\\rm co-rot} \\sim\n8-11$ with $f_{\\rm rot}$(cc) being the rotation frequency of the convective\ncore and $\\nu_{\\rm co-rot}$ the pulsation frequency in the co-rotating frame.\nThe spin parameter at the resonance depends somewhat on the extent of core\novershooting, central hydrogen abundance, and other stellar parameters. We can\nfit the period at the observed dip with the prediction from prograde dipole g\nmodes of a main-sequence model, allowing the convective core to rotate\ndifferentially from the surrounding g-mode cavity. We have performed such\nfittings for 16 selected $\\gamma$ Dor stars having well defined dips, and found\nthat the majority of $\\gamma$ Dor stars we studied rotate nearly uniformly,\nwhile convective cores tend to rotate slightly faster than the g-mode cavity in\nless evolved stars.\n"}
+{"abstract": "  Magnetic reconnection is explored on the Terrestrial Reconnection Experiment\n(TREX) for asymmetric inflow conditions and in a configuration where the\nabsolute rate of reconnection is set by an external drive. Magnetic pileup\nenhances the upstream magnetic field of the high density inflow, leading to an\nincreased upstream Alfven speed and helping to lower the normalized\nreconnection rate to values expected from theoretical consideration. In\naddition, a shock interface between the far upstream supersonic plasma inflow\nand the region of magnetic flux pileup is observed, important to the overall\nforce balance of the system, hereby demonstrating the role of shock formation\nfor configurations including a supersonically driven inflow. Despite the\nspecialised geometry where a strong reconnection drive is applied from only one\nside of the reconnection layer, previous numerical and theoretical results\nremain robust and are shown to accurately predict the normalized rate of\nreconnection for the range of system sizes considered. This experimental rate\nof reconnection is dependent on system size, reaching values as high as 0.8 at\nthe smallest normalized system size applied.\n"}
+{"abstract": "  In the de Sitter gauge theory (DGT), the fundamental variables are the de\nSitter (dS) connection and the gravitational Higgs/Goldstone field $\\xi^A$.\nPreviously, a model for DGT was analyzed, which generalizes the\nMacDowell--Mansouri gravity to have a variable cosmological constant\n$\\Lambda=3/l^2$, where $l$ is related to $\\xi^A$ by $\\xi^A\\xi_A=l^2$. It was\nshown that the model sourced by a perfect fluid does not support a radiation\nepoch and the accelerated expansion of the parity invariant universe. In this\nwork, I consider a similar model, namely, the Stelle--West gravity, and couple\nit to a modified perfect fluid, such that the total Lagrangian 4-form is\npolynomial in the gravitational variables. The Lagrangian of the modified fluid\nhas a nontrivial variational derivative with respect to $l$, and as a result,\nthe problems encountered in the previous work no longer appear. Moreover, to\nexplore the elegance of the general theory, as well as to write down the basic\nframework, I perform the Lagrange--Noether analysis for DGT sourced by a matter\nfield, yielding the field equations and the identities with respect to the\nsymmetries of the system. The resulted formula are dS covariant and do not rely\non the existence of the metric field.\n"}
+{"abstract": "  Privacy and energy are primary concerns for sensor devices that offload\ncompute to a potentially untrusted edge server or cloud. Homomorphic Encryption\n(HE) enables offload processing of encrypted data. HE offload processing\nretains data privacy, but is limited by the need for frequent communication\nbetween the client device and the offload server. Existing client-aided\nencrypted computing systems are optimized for performance on the offload\nserver, failing to sufficiently address client costs, and precluding HE offload\nfor low-resource (e.g., IoT) devices. We introduce Client-aided HE for Opaque\nCompute Offloading (CHOCO), a client-optimized system for encrypted offload\nprocessing. CHOCO introduces rotational redundancy, an algorithmic optimization\nto minimize computing and communication costs. We design Client-Aided HE for\nOpaque Compute Offloading Through Accelerated Cryptographic Operations\n(CHOCO-TACO), a comprehensive architectural accelerator for client-side\ncryptographic operations that eliminates most of their time and energy costs.\nOur evaluation shows that CHOCO makes client-aided HE offloading feasible for\nresource-constrained clients. Compared to existing encrypted computing\nsolutions, CHOCO reduces communication cost by up to 2948x. With hardware\nsupport, client-side encryption/decryption is faster by 1094x and uses 648x\nless energy. In our end-to-end implementation of a large-scale DNN (VGG16),\nCHOCO uses 37% less energy than local (unencrypted) computation.\n"}
+{"abstract": "  Primordial perturbations in our universe are believed to have a quantum\norigin, and can be described by the wavefunction of the universe (or\nequivalently, cosmological correlators). It follows that these observables must\ncarry the imprint of the founding principle of quantum mechanics: unitary time\nevolution. Indeed, it was recently discovered that unitarity implies an\ninfinite set of relations among tree-level wavefunction coefficients, dubbed\nthe Cosmological Optical Theorem. Here, we show that unitarity leads to a\nsystematic set of \"Cosmological Cutting Rules\" which constrain wavefunction\ncoefficients for any number of fields and to any loop order. These rules fix\nthe discontinuity of an n-loop diagram in terms of lower-loop diagrams and the\ndiscontinuity of tree-level diagrams in terms of tree-level diagrams with fewer\nexternal fields. Our results apply with remarkable generality, namely for\narbitrary interactions of fields of any mass and any spin with a Bunch-Davies\nvacuum around a very general class of FLRW spacetimes. As an application, we\nshow how one-loop corrections in the Effective Field Theory of inflation are\nfixed by tree-level calculations and discuss related perturbative unitarity\nbounds. These findings greatly extend the potential of using unitarity to\nbootstrap cosmological observables and to restrict the space of consistent\neffective field theories on curved spacetimes.\n"}
+{"abstract": "  In this paper, we show how the absorption and re-radiation energy from\nmolecules in the air can influence the Multiple Input Multiple Output (MIMO)\nperformance in high-frequency bands, e.g., millimeter wave (mmWave) and\nterahertz. In more detail, some common atmosphere molecules, such as oxygen and\nwater, can absorb and re-radiate energy in their natural resonance frequencies,\nsuch as 60 GHz, 180 GHz and 320 GHz. Hence, when hit by electromagnetic waves,\nmolecules will get excited and absorb energy, which leads to an extra path loss\nand is known as molecular attenuation. Meanwhile, the absorbed energy will be\nre-radiated towards a random direction with a random phase. These re-radiated\nwaves also interfere with the signal transmission. Although, the molecular\nre-radiation was mostly considered as noise in literature, recent works show\nthat it is correlated to the main signal and can be viewed as a composition of\nmultiple delayed or scattered signals. Such a phenomenon can provide\nnon-line-of-sight (NLoS) paths in an environment that lacks scatterers, which\nincreases spatial multiplexing and thus greatly enhances the performance of\nMIMO systems. Therefore in this paper, we explore the scattering model and\nnoise models of molecular re-radiation to characterize the channel transfer\nfunction of the NLoS channels created by atmosphere molecules. Our simulation\nresults show that the re-radiation can increase MIMO capacity up to 3 folds in\nmmWave and 6 folds in terahertz for a set of realistic transmit power,\ndistance, and antenna numbers. We also show that in the high SNR, the\nre-radiation makes the open-loop precoding viable, which is an alternative to\nbeamforming to avoid beam alignment sensitivity in high mobility applications.\n"}
+{"abstract": "  We present a Hubble Space Telescope/Wide-Field Camera 3 near infrared\nspectrum of the archetype Y dwarf WISEP 182831.08+265037.8. The spectrum covers\nthe 0.9-1.7 um wavelength range at a resolving power of lambda/Delta lambda\n~180 and is a significant improvement over the previously published spectrum\nbecause it covers a broader wavelength range and is uncontaminated by light\nfrom a background star. The spectrum is unique for a cool brown dwarf in that\nthe flux peaks in the Y, J, and H band are of near equal intensity in units of\nf_lambda. We fail to detect any absorption bands of NH_3 in the spectrum, in\ncontrast to the predictions of chemical equilibrium models, but tentatively\nidentify CH_4 as the carrier of an unknown absorption feature centered at 1.015\num. Using previously published ground- and spaced-based photometry, and using a\nRayleigh Jeans tail to account for flux emerging longward of 4.5 um, we compute\na bolometric luminosity of log (L_bol/L_sun)=-6.50+-0.02 which is significantly\nlower than previously published results. Finally, we compare the spectrum and\nphotometry to two sets of atmospheric models and find that best overall match\nto the observed properties of WISEP 182831.08+265037.8 is a ~1 Gyr old binary\ncomposed of two T_eff~325 K, ~5 M_Jup brown dwarfs with subsolar [C/O] ratios.\n"}
+{"abstract": "  Consider traffic data (i.e., triplets in the form of\nsource-destination-timestamp) that grow over time. Tensors (i.e.,\nmulti-dimensional arrays) with a time mode are widely used for modeling and\nanalyzing such multi-aspect data streams. In such tensors, however, new entries\nare added only once per period, which is often an hour, a day, or even a year.\nThis discreteness of tensors has limited their usage for real-time\napplications, where new data should be analyzed instantly as it arrives. How\ncan we analyze time-evolving multi-aspect sparse data 'continuously' using\ntensors where time is'discrete'? We propose SLICENSTITCH for continuous\nCANDECOMP/PARAFAC (CP) decomposition, which has numerous time-critical\napplications, including anomaly detection, recommender systems, and stock\nmarket prediction. SLICENSTITCH changes the starting point of each period\nadaptively, based on the current time, and updates factor matrices (i.e.,\noutputs of CP decomposition) instantly as new data arrives. We show,\ntheoretically and experimentally, that SLICENSTITCH is (1) 'Any time': updating\nfactor matrices immediately without having to wait until the current time\nperiod ends, (2) Fast: with constant-time updates up to 464x faster than online\nmethods, and (3) Accurate: with fitness comparable (specifically, 72 ~ 100%) to\noffline methods.\n"}
+{"abstract": "  We develop a first-principles-based generalized mode-coupling theory (GMCT)\nfor the tagged-particle motion of glassy systems. This theory establishes a\nhierarchy of coupled integro-differential equations for self-multi-point\ndensity correlation functions, which can formally be extended up to infinite\norder. We use our GMCT framework to calculate the self-nonergodicity parameters\nand the self-intermediate scattering function for the Percus-Yevick hard sphere\nsystem, based on the first few levels of the GMCT hierarchy. We also test the\nscaling laws in the $\\alpha$- and $\\beta$-relaxation regimes near the\nglass-transition singularity. Furthermore, we study the mean-square\ndisplacement and the Stoke-Einstein relation in the supercooled regime. We find\nthat qualitatively our GMCT results share many similarities with the\nwell-established predictions from standard mode-coupling theory, but the\nquantitative results change, and typically improve, by increasing the GMCT\nclosure level. However, we also demonstrate on general theoretical grounds that\nthe current GMCT framework is unable to account for violation of the\nStokes-Einstein relation, underlining the need for further improvements in the\nfirst-principles description of glassy dynamics.\n"}
+{"abstract": "  In this article, we define amorphic complexity for actions of locally compact\n$\\sigma$-compact amenable groups on compact metric spaces. Amorphic complexity,\noriginally introduced for $\\mathbb Z$-actions, is a topological invariant which\nmeasures the complexity of dynamical systems in the regime of zero entropy. We\nshow that it is tailor-made to study strictly ergodic group actions with\ndiscrete spectrum and continuous eigenfunctions. This class of actions\nincludes, in particular, Delone dynamical systems related to regular model sets\nobtained via Meyer's cut and project method. We provide sharp upper bounds on\namorphic complexity of such systems. In doing so, we observe an intimate\nrelationship between amorphic complexity and fractal geometry.\n"}
+{"abstract": "  In this paper we prove regularity results for a class of nonlinear degenerate\nelliptic equations of the form $\\displaystyle -\\operatorname{div}(A(|\\nabla\nu|)\\nabla u)+B\\left( |\\nabla u|\\right) =f(u)$; in particular, we investigate\nthe second order regularity of the solutions. As a consequence of these\nresults, we obtain symmetry and monotonicity properties of positive solutions\nfor this class of degenerate problems in convex symmetric domains via a\nsuitable adaption of the celebrated moving plane method of Alexandrov-Serrin.\n"}
+{"abstract": "  In the centre of mass frame, we have studied theoretically the $Z$-boson\nresonant production in the presence of an intense laser field via the weak\nprocess $e^+e^- \\to \\mu^+\\mu^-$. Dressing the incident particles by a\nCircularly Polarized laser field (CP-laser field), at the first step, shows\nthat for a given laser field's parameters, the $Z$- boson cross section\ndecreases by several orders of magnitude. We have compared the the Total Cross\nSection (TCS) obtained by using the scattering matrix method with that given by\nthe Breit-Wigner approach in the presence of a CP-laser field and the results\nare found to be very consistent. This result indicates that Breit-Wigner\nformula is valid not only for the laser-free process but also in the presence\nof a CP-laser field. The dependence of the laser-assisted differential cross\nsection on the Centre of Mass Energy (CME) for different scattering angles\nproves that it reaches its maximum for small and high scattering angles. At the\nnext step and by dressing both incident and scattered particles, we have shown\nthat the CP-laser field largely affects the TCS, especially when its strength\nreaches $10^{9}\\,V.cm^{-1}$. This result confirms that obtained for the elastic\nelectron-proton scattering in the presence of a CP-laser field [I. Dahiri et\nal., arXiv:2102.00722v1]. It is interpreted by the fact that heavy interacting\nparticles require high laser field's intensity to affect the collision's cross\nsection.\n"}
+{"abstract": "  In this work, we give a new technique for constructing self-dual codes over\ncommutative Frobenius rings using $\\lambda$-circulant matrices. The new\nconstruction was derived as a modification of the well-known four circulant\nconstruction of self-dual codes. Applying this technique together with the\nbuilding-up construction, we construct singly-even binary self-dual codes of\nlengths 56, 58, 64, 80 and 92 that were not known in the literature before.\nSingly-even self-dual codes of length 80 with $\\beta\\in\\{2,4,5,6,8\\}$ in their\nweight enumerators are constructed for the first time in the literature.\n"}
+{"abstract": "  The need for a comprehensive study to explore various aspects of online\nsocial media has been instigated by many researchers. This paper gives an\ninsight into the social platform, Twitter. In this present work, we have\nillustrated stepwise procedure for crawling the data and discuss the key issues\nrelated to extracting associated features that can be useful in Twitter-related\nresearch while crawling these data from Application Programming Interfaces\n(APIs). Further, the data that comprises of over 86 million tweets have been\nanalysed from various perspective including the most used languages, most\nfrequent words, most frequent users, countries with most and least tweets and\nre-tweets, etc. The analysis reveals that the users' data associated with\nTwitter has a high affinity for researches in the various domain that includes\npolitics, social science, economics, and linguistics, etc. In addition, the\nrelation between Twitter users of a country and its human development index has\nbeen identified. It is observed that countries with very high human development\nindices have a relatively higher number of tweets compared to low human\ndevelopment indices countries. It is envisaged that the present study shall\nopen many doors of researches in information processing and data science.\n"}
+{"abstract": "  We study the structural evolution of isolated star-forming galaxies in the\nIllustris TNG100-1 hydrodynamical simulation, with a focus on investigating the\ngrowth of the central core density within 2 kpc ($\\Sigma_{*,2kpc}$) in relation\nto total stellar mass ($M_*$) at z < 0.5. First, we show that several\nobservational trends in the $\\Sigma_{*,2kpc}$-$M_*$ plane are qualitatively\nreproduced in IllustrisTNG, including the distributions of AGN, star forming\ngalaxies, quiescent galaxies, and radial profiles of stellar age, sSFR, and\nmetallicity. We find that galaxies with dense cores evolve parallel to the\n$\\Sigma_{*,2kpc}$-$M_*$ relation, while galaxies with diffuse cores evolve\nalong shallower trajectories. We investigate possible drivers of rapid growth\nin $\\Sigma_{*,2kpc}$ compared to $M_*$. Both the current sSFR gradient and the\nBH accretion rate are indicators of past core growth, but are not predictors of\nfuture core growth. Major mergers (although rare in our sample; $\\sim$10%)\ncause steeper core growth, except for high mass ($M_*$ >$\\sim$ $10^{10}\nM_{\\odot}$) mergers, which are mostly dry. Disc instabilities, as measured by\nthe fraction of mass with Toomre Q < 2, are not predictive of rapid core\ngrowth. Instead, rapid core growth results in more stable discs. The cumulative\nblack hole feedback history sets the maximum rate of core growth, preventing\nrapid growth in high-mass galaxies ($M_*$ >$\\sim$ $10^{9.5} M_{\\odot}$). For\nmassive galaxies the total specific angular momentum of accreting gas is the\nmost important predictor of future core growth. Our results suggest that the\nangular momentum of accreting gas controls the slope, width and zero-point\nevolution of the $\\Sigma_{*,2kpc}$-$M_*$ relation.\n"}
+{"abstract": "  This article introduces a neural network-based signal processing framework\nfor intelligent reflecting surface (IRS) aided wireless communications systems.\nBy modeling radio-frequency (RF) impairments inside the \"meta-atoms\" of IRS\n(including nonlinearity and memory effects), we present an approach that\ngeneralizes the entire IRS-aided system as a reservoir computing (RC) system,\nan efficient recurrent neural network (RNN) operating in a state near the \"edge\nof chaos\". This framework enables us to take advantage of the nonlinearity of\nthis \"fabricated\" wireless environment to overcome link degradation due to\nmodel mismatch. Accordingly, the randomness of the wireless channel and RF\nimperfections are naturally embedded into the RC framework, enabling the\ninternal RC dynamics lying on the edge of chaos. Furthermore, several practical\nissues, such as channel state information acquisition, passive beamforming\ndesign, and physical layer reference signal design, are discussed.\n"}
+{"abstract": "  Is critical input information encoded in specific sparse pathways within the\nneural network? In this work, we discuss the problem of identifying these\ncritical pathways and subsequently leverage them for interpreting the network's\nresponse to an input. The pruning objective -- selecting the smallest group of\nneurons for which the response remains equivalent to the original network --\nhas been previously proposed for identifying critical pathways. We demonstrate\nthat sparse pathways derived from pruning do not necessarily encode critical\ninput information. To ensure sparse pathways include critical fragments of the\nencoded input information, we propose pathway selection via neurons'\ncontribution to the response. We proceed to explain how critical pathways can\nreveal critical input features. We prove that pathways selected via neuron\ncontribution are locally linear (in an L2-ball), a property that we use for\nproposing a feature attribution method: \"pathway gradient\". We validate our\ninterpretation method using mainstream evaluation experiments. The validation\nof pathway gradient interpretation method further confirms that selected\npathways using neuron contributions correspond to critical input features. The\ncode is publicly available.\n"}
+{"abstract": "  The paper investigates the problem of finding communities in complex network\nsystems, the detection of which allows a better understanding of the laws of\ntheir functioning. To solve this problem, two approaches are proposed based on\nthe use of flows characteristics of complex network. The first of these\napproaches consists in calculating the parameters of influence of separate\nsubsystems of the network system, distinguished by the principles of ordering\nor subordination, and the second, in using the concept of its flow core. Based\non the proposed approaches, reliable criteria for finding communities have been\nformulated and efficient algorithms for their detection in complex network\nsystems have been developed. It is shown that the proposed approaches make it\npossible to single out communities in cases in which the existing numerical and\nvisual methods turn out to be disabled.\n"}
+{"abstract": "  The novel concept of simultaneously transmitting and reflecting (STAR)\nreconfigurable intelligent surfaces (RISs) is investigated, where the incident\nwireless signal is divided into transmitted and reflected signals passing into\nboth sides of the space surrounding the surface, thus facilitating a full-space\nmanipulation of signal propagation. Based on the introduced basic signal model\nof `STAR', three practical operating protocols for STAR-RISs are proposed,\nnamely energy splitting (ES), mode switching (MS), and time switching (TS).\nMoreover, a STAR-RIS aided downlink communication system is considered for both\nunicast and multicast transmission, where a multi-antenna base station (BS)\nsends information to two users, i.e., one on each side of the STAR-RIS. A power\nconsumption minimization problem for the joint optimization of the active\nbeamforming at the BS and the passive transmission and reflection beamforming\nat the STAR-RIS is formulated for each of the proposed operating protocols,\nsubject to communication rate constraints of the users. For ES, the resulting\nhighly-coupled non-convex optimization problem is solved by an iterative\nalgorithm, which exploits the penalty method and successive convex\napproximation. Then, the proposed penalty-based iterative algorithm is extended\nto solve the mixed-integer non-convex optimization problem for MS. For TS, the\noptimization problem is decomposed into two subproblems, which can be\nconsecutively solved using state-of-the-art algorithms and convex optimization\ntechniques. Finally, our numerical results reveal that: 1) the TS and ES\noperating protocols are generally preferable for unicast and multicast\ntransmission, respectively; and 2) the required power consumption for both\nscenarios is significantly reduced by employing the proposed STAR-RIS instead\nof conventional reflecting/transmiting-only RISs.\n"}
+{"abstract": "  In this work we compute the first integral cohomology of the pure mapping\nclass group of a non-orientable surface of infinite topological type and genus\nat least 3. To this purpose, we also prove several other results already known\nfor orientable surfaces such as the existence of an Alexander method, the fact\nthat the mapping class group is isomorphic to the automorphism group of the\ncurve graph along with the topological rigidity of the curve graph, and the\nstructure of the pure mapping class group as both a Polish group and a\nsemi-direct product.\n"}
+{"abstract": "  Programmable data planes allow users to define their own data plane\nalgorithms for network devices including appropriate data plane application\nprogramming interfaces (APIs) which may be leveraged by user-defined\nsoftware-defined networking (SDN) control. This offers great flexibility for\nnetwork customization, be it for specialized, commercial appliances, e.g., in\n5G or data center networks, or for rapid prototyping in industrial and academic\nresearch. Programming protocol-independent packet processors (P4) has emerged\nas the currently most widespread abstraction, programming language, and concept\nfor data plane programming. It is developed and standardized by an open\ncommunity, and it is supported by various software and hardware platforms. In\nthe first part of this paper we give a tutorial of data plane programming\nmodels, the P4 programming language, architectures, compilers, targets, and\ndata plane APIs. We also consider research efforts to advance P4 technology. In\nthe second part, we categorize a large body of literature of P4-based applied\nresearch into different research domains, summarize the contributions of these\npapers, and extract prototypes, target platforms, and source code availability.\nFor each research domain, we analyze how the reviewed works benefit from P4's\ncore features. Finally, we discuss potential next steps based on our findings.\n"}
+{"abstract": "  Metasurfaces enable manipulation of light propagation at an unprecedented\nlevel, benefitting from a number of merits unavailable to conventional optical\nelements, such as ultracompactness, precise phase and polarization control at\ndeep subwavelength scale, and multifunctionalities. Recent progress in this\nfield has witnessed a plethora of functional metasurfaces, ranging from lenses\nand vortex beam generation to holography. However, research endeavors have been\nmainly devoted to static devices, exploiting only a glimpse of opportunities\nthat metasurfaces can offer. We demonstrate a dynamic metasurface platform,\nwhich allows independent manipulation of addressable subwavelength pixels at\nvisible frequencies through controlled chemical reactions. In particular, we\ncreate dynamic metasurface holograms for advanced optical information\nprocessing and encryption. Plasmonic nanorods tailored to exhibit hierarchical\nreaction kinetics upon hydrogenation/dehydrogenation constitute addressable\npixels in multiplexed metasurfaces. The helicity of light, hydrogen, oxygen,\nand reaction duration serve as multiple keys to encrypt the metasurfaces. One\nsingle metasurface can be deciphered into manifold messages with customized\nkeys, featuring a compact data storage scheme as well as a high level of\ninformation security. Our work suggests a novel route to protect and transmit\nclassified data, where highly restricted access of information is imposed.\n"}
+{"abstract": "  High-energy heavy-ion collisions generate extremely strong magnetic field\nwhich plays a key role in a number of novel quantum phenomena in quark-gluon\nplasma (QGP), such as the chiral magnetic effect (CME). However, due to the\ncomplexity in theoretical modellings of the coupled electromagnetic fields and\nthe QGP system, especially in the pre-equilibrium stages, the lifetime of the\nmagnetic field in the QGP medium remains undetermined. We establish, for the\nfirst time, a kinetic framework to study the dynamical decay of the magnetic\nfield in the early stages of a weakly coupled QGP by solving the coupled\nBoltzmann and Maxwell equations. We find that at late times a\nmagnetohydrodynamical description of the coupled system emerges. With respect\nto realistic collisions at RHIC and the LHC, we estimate the residual strength\nof the magnetic field in the QGP when the system start to evolve\nhydrodynamically.\n"}
+{"abstract": "  Nowadays, the confidentiality of data and information is of great importance\nfor many companies and organizations. For this reason, they may prefer not to\nrelease exact data, but instead to grant researchers access to approximate\ndata. For example, rather than providing the exact measurements of their\nclients, they may only provide researchers with grouped data, that is, the\nnumber of clients falling in each of a set of non-overlapping measurement\nintervals. The challenge is to estimate the mean and variance structure of the\nhidden ungrouped data based on the observed grouped data. To tackle this\nproblem, this work considers the exact observed data likelihood and applies the\nExpectation-Maximization (EM) and Monte-Carlo EM (MCEM) algorithms for cases\nwhere the hidden data follow a univariate, bivariate, or multivariate normal\ndistribution. Simulation studies are conducted to evaluate the performance of\nthe proposed EM and MCEM algorithms. The well-known Galton data set is\nconsidered as an application example.\n"}
+{"abstract": "  For the first time, the dielectric response of a BaTiO3 thin film under an AC\nelectric field is investigated using time-resolved X-ray absorption\nspectroscopy at the Ti K-edge to clarify correlated contributions of each\nconstituent atom on the electronic states. Intensities of the pre-edge eg peak\nand shoulder structure just below the main edge increase with an increase in\nthe amplitude of the applied electric field, whereas that of the main peak\ndecreases in an opposite manner. Based on the multiple scattering theory, the\nincrease and decrease of the eg and main peaks are simulated for different Ti\noff-center displacements. Our results indicate that these spectral features\nreflect the inter- and intra-atomic hybridization of Ti 3d with O 2p and Ti 4p,\nrespectively. In contrast, the shoulder structure is not affected by changes in\nthe Ti off-center displacement but is susceptible to the effect of the corner\nsite Ba ions. This is the first experimental verification of the dynamic\nelectronic contribution of Ba to polarization reversal.\n"}
+{"abstract": "  Magnetic multilayers are promising tuneable systems for hosting magnetic\nskyrmions at/above room temperature. Revealing their intriguing switching\nmechanisms and associated inherent electrical responses are prerequisites for\ndeveloping skyrmionic devices. In this work, we theoretically demonstrate the\nannihilation of single skyrmions occurring through a multilayer structure,\nwhich is mediated by hopping dynamics of topological hedgehog singularities\nknown as Bloch points. The emerging intralayer dynamics of Bloch points are\ndominated by the Dzyaloshinskii-Moriya interaction, and their propagation can\ngive rise to solenoidal emergent electric fields in the vicinity. Moreover, as\nthe topology of spin textures can dominate their emergent magnetic properties,\nwe show that the Bloch-point hopping through the multilayer will modulate the\nassociated topological Hall response, with the magnitude proportional to the\neffective topological charge. We also investigate the thermodynamic stability\nof these states regarding the layer-dependent magnetic properties. This study\ncasts light on the emergent electromagnetic signatures of skyrmion-based\nspintronics, rooted in magnetic-multilayer systems.\n"}
+{"abstract": "  In this paper a comparative structural, dielectric and magnetic study of two\nlangasite compounds Ba$_3$TeCo$_3$P$_2$O$_{14}$ (absence of lone pair) and\nPb$_3$TeCo$_3$P$_2$O$_{14}$ (Pb$^{2+}$ 6$s^2$ lone pair) have been carried out\nto precisely explore the development of room temperature spontaneous\npolarization in presence of stereochemically active lone pair. In case of\nPb$_3$TeCo$_3$P$_2$O$_{14}$, mixing of both Pb 6$s$ with Pb 6$p$ and O 2$p$\nhelp the lone pair to be stereochemically active. This stereochemically active\nlone pair brings a large structural distortion within the unit cell and creates\na polar geometry, while Ba$_3$TeCo$_3$P$_2$O$_{14}$ compound remains in a\nnonpolar structure due to the absence of any such effect. Consequently,\npolarization measurement under varying electric field confirms room temperature\nferroelectricity for Pb$_3$TeCo$_3$P$_2$O$_{14}$, which was not the case of\nBa$_3$TeCo$_3$P$_2$O$_{14}$. Detailed study was carried out to understand the\nmicroscopic mechanism of ferroelectricity which revealed the exciting\nunderlying activity of poler TeO$_6$ octahedral unit as well as Pb-hexagon.\n"}
+{"abstract": "  We study cosmological inflation and its dynamics in the framework of the\nRandall-Sundrum II brane model. In particular, we analyze in detail four\nrepresentative small-field inflationary potentials, namely Natural inflation,\nHilltop inflation, Higgs-like inflation, and Exponential SUSY inflation, each\ncharacterized by two mass scales. We constrain the parameters for which a\nviable inflationary Universe emerges using the latest PLANCK results.\nFurthermore, we investigate whether or not those models in brane cosmology are\nconsistent with the recently proposed Swampland Criteria, and give predictions\nfor the duration of reheating as well as for the reheating temperature after\ninflation. Our results show that (i) the distance conjecture is satisfied, (ii)\nthe de Sitter conjecture and its refined version may be avoided, and (iii) the\nallowed range for the five-dimensional Planck mass, $M_5$, is found to be\nbetween $10^5~\\textrm{TeV}$ and $10^{12}~\\textrm{TeV}$. Our main findings\nindicate that non-thermal leptogenesis cannot work within the framework of\nRS-II brane cosmology, at least for the inflationary potentials considered\nhere.\n"}
+{"abstract": "  We first propose a general method to construct the complete set of on-shell\noperator bases involving massive particles with any spins. To incorporate the\nnon-abelian little groups of massive particles, the on-shell scattering\namplitude basis should be factorized into two parts: one is charged, and the\nother one is neutral under little groups of massive particles. The complete set\nof these two parts can be systematically constructed by choosing some specific\nYoung diagrams of Lorentz subgroup and global symmetry $U(N)$ respectively ($N$\nis the number of external particles), without the equation of motion and\nintegration by part redundancy. Thus the complete massive amplitude bases\nwithout any redundancies can be obtained by combining these two complete sets.\nSome examples are presented to explicitly demonstrate this method. This method\nis applicable for constructing amplitude bases involving identical particles,\nand all the bases can be constructed automatically by computer programs based\non it.\n"}
+{"abstract": "  We study the variety ZG of monoids where the elements that belong to a group\nare central, i.e., commute with all other elements. We show that ZG is local,\nthat is, the semidirect product ZG * D of ZG by definite semigroups is equal to\nLZG, the variety of semigroups where all local monoids are in ZG. Our main\nresult is thus: ZG * D = LZG. We prove this result using Straubing's delay\ntheorem, by considering paths in the category of idempotents. In the process,\nwe obtain the characterization ZG = MNil \\vee Com, and also characterize the ZG\nlanguages, i.e., the languages whose syntactic monoid is in ZG: they are\nprecisely the languages that are finite unions of disjoint shuffles of\nsingleton languages and regular commutative languages.\n"}
+{"abstract": "  The noise-enhanced trapping is a surprising phenomenon that has already been\nstudied in chaotic scattering problems where the noise affects the physical\nvariables but not the parameters of the system. Following this research, in\nthis work we provide strong numerical evidence to show that an additional\nmechanism that enhances the trapping arises when the noise influences the\nenergy of the system. For this purpose, we have included a source of Gaussian\nwhite noise in the H\\'enon-Heiles system, which is a paradigmatic example of\nopen Hamiltonian system. For a particular value of the noise intensity, some\ntrajectories decrease their energy due to the stochastic fluctuations. This\ndrop in energy allows the particles to spend very long transients in the\nscattering region, increasing their average escape times. This result, together\nwith the previously studied mechanisms, points out the generality of the\nnoise-enhanced trapping in chaotic scattering problems.\n"}
+{"abstract": "  This study investigates the correlation of self-report accuracy with academic\nperformance. The sample was composed of 289 undergraduate students (96 senior\nand 193 junior) enrolled in two engineering classes. Age ranged between 22 and\n24 years, with a slight over representation of male students (53%). Academic\nperformance was calculated based on students' final grades in each class. The\ntendency to report inaccurate information was measured at the end of the Raven\nProgressive Matrices Test, by asking students to report their exact finishing\ntimes. We controlled for gender, age, personality traits, intelligence, and\npast academic performance. We also included measures of centrality in their\nfriendship, advice and trust networks. Correlation and multiple regression\nanalyses results indicate that lower achieving students were significantly less\naccurate in self-reporting data. We also found that being more central in the\nadvice network was correlated with higher performance (r = .20, p < .001). The\nresults are aligned with existing literature emphasizing the individual and\nrelational factors associated with academic performance and, pending future\nstudies, may be utilized to include a new metric of self-report accuracy that\nis not dependent on academic records.\n"}
+{"abstract": "  We investigate the 3D spin alignment of galaxies with respect to the\nlarge-scale filaments using the MaNGA survey. The cosmic web is reconstructed\nfrom the Sloan Digital Sky Survey using Disperse and the 3D spins of MaNGA\ngalaxies are estimated using the thin disk approximation with integral field\nspectroscopy kinematics. Late-type spiral galaxies are found to have their\nspins parallel to the closest filament's axis. The alignment signal is found to\nbe dominated by low-mass spirals. Spins of S0-type galaxies tend to be oriented\npreferentially in perpendicular direction with respect to the filament's axis.\nThis orthogonal orientation is found to be dominated by S0s that show a notable\nmisalignment between their kinematic components of stellar and ionised gas\nvelocity fields and/or by low mass S0s with lower rotation support compared to\ntheir high mass counterparts. Qualitatively similar results are obtained when\nsplitting galaxies based on the degree of ordered stellar rotation, such that\ngalaxies with high spin magnitude have their spin aligned, and those with low\nspin magnitude in perpendicular direction to the filaments. In the context of\nconditional tidal torque theory, these findings suggest that galaxies' spins\nretain memory of their larger-scale environment. In agreement with measurements\nfrom hydrodynamical cosmological simulations, the measured signal at low\nredshift is weak, yet statistically significant. The dependence of the\nspin-filament orientation of galaxies on their stellar mass, morphology and\nkinematics highlights the importance of sample selection to detect the signal.\n"}
+{"abstract": "  Pretrained Masked Language Models (MLMs) have revolutionised NLP in recent\nyears. However, previous work has indicated that off-the-shelf MLMs are not\neffective as universal lexical or sentence encoders without further\ntask-specific fine-tuning on NLI, sentence similarity, or paraphrasing tasks\nusing annotated task data. In this work, we demonstrate that it is possible to\nturn MLMs into effective universal lexical and sentence encoders even without\nany additional data and without any supervision. We propose an extremely\nsimple, fast and effective contrastive learning technique, termed Mirror-BERT,\nwhich converts MLMs (e.g., BERT and RoBERTa) into such encoders in 20-30\nseconds without any additional external knowledge. Mirror-BERT relies on fully\nidentical or slightly modified string pairs as positive (i.e., synonymous)\nfine-tuning examples, and aims to maximise their similarity during identity\nfine-tuning. We report huge gains over off-the-shelf MLMs with Mirror-BERT in\nboth lexical-level and sentence-level tasks, across different domains and\ndifferent languages. Notably, in the standard sentence semantic similarity\n(STS) tasks, our self-supervised Mirror-BERT model even matches the performance\nof the task-tuned Sentence-BERT models from prior work. Finally, we delve\ndeeper into the inner workings of MLMs, and suggest some evidence on why this\nsimple approach can yield effective universal lexical and sentence encoders.\n"}
+{"abstract": "  Soft or weakly-consolidated sand refers to porous materials composed of\nparticles (or grains) weakly held together to form a solid but that can be\neasily broken when subjected to stress. These materials do not behave as\nconventional brittle, linear elastic materials and the transition between these\ntwo regimes cannot usually be described using poro-elastic models. Furthermore,\nconventional geotechnical sampling techniques often result in the destruction\nof the cementation and recovery of sufficient intact core is, therefore,\ndifficult. This paper studies a numerical model that allows us to introduce\nweak consolidation in granular packs. The model, based on the LIGGGHTS open\nsource project, simply adds an attractive contribution to particles in contact.\nThis simple model allow us to reproduce key elements of the behaviour of the\nstress observed in compacted sands and clay, as well as in poorly consolidated\nsandstones. The paper finishes by inspecting the effect of different\nconsolidation levels in fluid-driven fracture behaviour. Numerical results are\ncompared against experimental results on bio-cemented sandstones.\n"}
+{"abstract": "  In this paper, a new implicit-explicit local method with an arbitrary order\nis produced for stiff initial value problems. Here, a general method for\none-step time integrations has been created, considering a direction free\napproach for integrations leading to a numerical method with parameter-based\nstability preservation. Adaptive procedures depending on the problem types for\nthe current method are explained with the help of local error estimates to\nminimize the computational cost. Priority error analysis of the current method\nis made, and order conditions are presented in terms of direction parameters.\nStability analysis of the method is performed for both scalar equations and\nsystems of differential equations. The currently produced parameter-based\nmethod has been proven to provide A-stability, for 0.5<\\theta<1, in various\norders. The present method has been shown to be a very good option for\naddressing a wide range of initial value problems through numerical\nexperiments. It can be seen as a significant contribution that the\nSusceptible-Exposed-Infected-Recovered equation system parameterized for the\nCOVID-19 pandemic has been integrated with the present method and stability\nproperties of the method have been tested on this stiff model and significant\nresults are produced. Some challenging stiff behaviours represented by the\nnonlinear Duffing equation, Robertson chemical system, and van der Pol equation\nhave also been integrated, and the results revealed that the current algorithm\nproduces much more reliable results than numerical techniques in the\nliterature.\n"}
+{"abstract": "  In this paper, a new method of training pipeline is discussed to achieve\nsignificant performance on the task of anti-spoofing with RGB image. We explore\nand highlight the impact of using pseudo-depth to pre-train a network that will\nbe used as the backbone to the final classifier. While the usage of\npseudo-depth for anti-spoofing task is not a new idea on its own, previous\nendeavours utilize pseudo-depth simply as another medium to extract features\nfor performing prediction, or as part of many auxiliary losses in aiding the\ntraining of the main classifier, normalizing the importance of pseudo-depth as\njust another semantic information. Through this work, we argue that there\nexists a significant advantage in training the final classifier can be gained\nby the pre-trained generator learning to predict the corresponding pseudo-depth\nof a given facial image, from a Generative Adversarial Network framework. Our\nexperimental results indicate that our method results in a much more adaptable\nsystem that can generalize beyond intra-dataset samples, but to inter-dataset\nsamples, which it has never seen before during training. Quantitatively, our\nmethod approaches the baseline performance of the current state of the art\nanti-spoofing models with 15.8x less parameters used. Moreover, experiments\nshowed that the introduced methodology performs well only using basic binary\nlabel without additional semantic information which indicates potential\nbenefits of this work in industrial and application based environment where\ntrade-off between additional labelling and resources are considered.\n"}
+{"abstract": "  A transient two-dimensional acoustic boundary element solver is coupled to a\npotential flow boundary element solver via Powell's acoustic analogy to\ndetermine the acoustic emission of isolated hydrofoils performing\nbiologically-inspired motions. The flow-acoustic boundary element framework is\nvalidated against experimental and asymptotic solutions for the noise produced\nby canonical vortex-body interactions. The numerical framework then\ncharacterizes the noise production of an oscillating foil, which is a simple\nrepresentation of a fish caudal fin. A rigid NACA 0012 hydrofoil is subjected\nto combined heaving and pitching motions for Strouhal numbers ($0.03 < St < 1$)\nbased on peak-to-peak amplitudes and chord-based reduced frequencies ($0.125 <\nf^* < 1$) that span the parameter space of many swimming fish species. A\ndipolar acoustic directivity is found for all motions, frequencies, and\namplitudes considered, and the peak noise level increases with both the reduced\nfrequency and the Strouhal number. A combined heaving and pitching motion\nproduces less noise than either a purely pitching or purely heaving foil at a\nfixed reduced frequency and amplitude of motion. Correlations of the lift and\npower coefficients with the peak root-mean-square acoustic pressure levels are\ndetermined, which could be utilized to develop long-range, quiet swimmers.\n"}
+{"abstract": "  In power system dynamic simulation, up to 90% of the computational time is\ndevoted to solve the network equations, i.e., a set of linear equations.\nTraditional approaches are based on sparse LU factorization, which is\ninherently sequential. In this paper, an inverse-based network solution is\nproposed by a hierarchical method for computing and store the approximate\ninverse of the conductance matrix in electromagnetic transient (EMT)\nsimulations. The proposed method can also efficiently update the inverse by\nmodifying only local sub-matrices to reflect changes in the network, e.g., loss\nof a line. Experiments on a series of simplified 179-bus Western\nInterconnection demonstrate the advantages of the proposed methods.\n"}
+{"abstract": "  We present the results of long-term photometric monitoring of two active\ngalactic nuclei, 2MASX J08535955+7700543 (z $\\sim$ 0.106) and VII Zw 244 (z\n$\\sim$ 0.131), being investigated by the reverberation mapping method in\nmedium-band filters. To estimate the size of the broad line region, we have\nanalyzed the light curves with the JAVELIN code. The emission line widths have\nbeen measured using the spectroscopic data obtained at the 6-m BTA telescope of\nSAO RAS. We give our estimates of the supermassive black hole masses $\\lg\n(M/M_{\\odot})$, $7.398_{-0.171}^{+0.153}$, and $7.049_{-0.075}^{+0.068}$,\nrespectively\n"}
+{"abstract": "  Perpendicularly magnetized films showing small saturation magnetization,\n$M_\\mathrm{s}$, are essential for spin-transfer-torque writing type\nmagnetoresistive random access memories, STT-MRAMs. An intermetallic compound,\n{(Mn-Cr)AlGe} of the Cu$_2$Sb-type crystal structure was investigated, in this\nstudy, as a material showing the low $M_\\mathrm{s}$ ($\\sim 300$ kA/m) and\nhigh-perpendicular magnetic anisotropy, $K_\\mathrm{u}$. The layer thickness\ndependence of $K_\\mathrm{u}$ and effects of Mg-insertion layers at top and\nbottom (Mn-Cr)AlGe$|$MgO interfaces were studied in film samples fabricated\nonto thermally oxidized silicon substrates to realize high-$K_\\mathrm{u}$ in\nthe thickness range of a few nanometer. Optimum Mg-insertion thicknesses were\n1.4 and 3.0 nm for the bottom and the top interfaces, respectively, which were\nrelatively thick compared to results in similar insertion effect investigations\non magnetic tunnel junctions reported in previous studies. The cross-sectional\ntransmission electron microscope images revealed that the Mg-insertion layers\nacted as barriers to interdiffusion of Al-atoms as well as oxidization from the\nMgO layers. The values of $K_\\mathrm{u}$ were about $7 \\times 10^5$ and $2\n\\times 10^5$ J/m$^3$ at room temperature for 5 and 3 nm-thick (Mn-Cr)AlGe\nfilms, respectively, with the optimum Mg-insertion thicknesses. The\n$K_\\mathrm{u}$ at a few nanometer thicknesses is comparable or higher than\nthose reported in perpendicularly magnetized CoFeB films which are\nconventionally used in MRAMs, while the $M_\\mathrm{s}$ value is one third or\nless smaller than those of the CoFeB films. The developed (Mn-Cr)AlGe films are\npromising from the viewpoint of not only the magnetic properties, but also the\ncompatibility to the silicon process in the film fabrication.\n"}
+{"abstract": "  Blockchain (BC) technology can revolutionize future networks by providing a\ndistributed, secure, and unalterable way to boost collaboration among\noperators, users, and other stakeholders. Its implementations have\ntraditionally been supported by wired communications, with performance\nindicators like the high latency introduced by the BC being one of the key\ntechnology drawbacks. However, when applied to wireless communications, the\nperformance of BC remains unknown, especially if running over contention-based\nnetworks. In this paper, we evaluate the latency performance of BC technology\nwhen the supporting communication platform is wireless, specifically we focus\non IEEE 802.11ax, for the use case of users' radio resource provisioning. For\nthat purpose, we propose a discrete-time Markov model to capture the expected\ndelay incurred by the BC. Unlike other models in the literature, we consider\nthe effect that timers and forks have on the end-to-end latency.\n"}
+{"abstract": "  Recently, Martinez-Penas and Kschischang (IEEE Trans. Inf. Theory, 2019)\nshowed that lifted linearized Reed-Solomon codes are suitable codes for error\ncontrol in multishot network coding. We show how to construct and decode lifted\ninterleaved linearized Reed-Solomon codes. Compared to the construction by\nMartinez-Penas-Kschischang, interleaving allows to increase the decoding region\nsignificantly (especially w.r.t. the number of insertions) and decreases the\noverhead due to the lifting (i.e., increases the code rate), at the cost of an\nincreased packet size. The proposed decoder is a list decoder that can also be\ninterpreted as a probabilistic unique decoder. Although our best upper bound on\nthe list size is exponential, we present a heuristic argument and simulation\nresults that indicate that the list size is in fact one for most channel\nrealizations up to the maximal decoding radius.\n"}
+{"abstract": "  This paper presents a detailed investigation of FeCr-based quaternary Heusler\nalloys. By using ultrasoft pseudopotential, electronic and magnetic properties\nof the compounds are studied within the framework of Density Functional Theory\n(DFT) by using the Quantum Espresso package. The thermodynamic, mechanical, and\ndynamical stability of the compounds is established through the comprehensive\nstudy of different mechanical parameters and phonon dispersion curves. The\nmeticulous study of elastic parameters such as bulk, Young's, shear moduli,\netc. is done to understand different mechanical properties. The FeCr-based\ncompounds containing also Yttrium are studied to redress the contradictory\nelectronic and magnetic properties observed in the literature. The interesting\nproperties like half-metallicity and spin-gapless semiconducting (SGS) behavior\nare realized in the compounds under study.\n"}
+{"abstract": "  We study several variants of the problem of moving a convex polytope $K$,\nwith $n$ edges, in three dimensions through a flat rectangular (and sometimes\nmore general) window. Specifically:\n  $\\bullet$ We study variants where the motion is restricted to translations\nonly, discuss situations where such a motion can be reduced to sliding\n(translation in a fixed direction), and present efficient algorithms for those\nvariants, which run in time close to $O(n^{8/3})$.\n  $\\bullet$ We consider the case of a `gate' (an unbounded window with two\nparallel infinite edges), and show that $K$ can pass through such a window, by\nany collision-free rigid motion, if and only if it can slide through it.\n  $\\bullet$ We consider arbitrary compact convex windows, and show that if $K$\ncan pass through such a window $W$ (by any motion) then $K$ can slide through a\ngate of width equal to the diameter of $W$.\n  $\\bullet$ We study the case of a circular window $W$, and show that, for the\nregular tetrahedron $K$ of edge length $1$, there are two thresholds $1 >\n\\delta_1\\approx 0.901388 > \\delta_2\\approx 0.895611$, such that (a) $K$ can\nslide through $W$ if the diameter $d$ of $W$ is $\\ge 1$, (b) $K$ cannot slide\nthrough $W$ but can pass through it by a purely translational motion when\n$\\delta_1\\le d < 1$, (c) $K$ cannot pass through $W$ by a purely translational\nmotion but can do it when rotations are allowed when $\\delta_2 \\le d <\n\\delta_1$, and (d) $K$ cannot pass through $W$ at all when $d < \\delta_2$.\n  $\\bullet$ Finally, we explore the general setup, where we want to plan a\ngeneral motion (with all six degrees of freedom) for $K$ through a rectangular\nwindow $W$, and present an efficient algorithm for this problem, with running\ntime close to $O(n^4)$.\n"}
+{"abstract": "  The paper is devoted to the study of Gromov-Hausdorff convergence and\nstability of irreversible metric-measure spaces, both in the compact and\nnoncompact cases. While the compact setting is mostly similar to the reversible\ncase developed by J. Lott, K.-T. Sturm and C. Villani, the noncompact case\nprovides various surprising phenomena. Since the reversibility of noncompact\nirreversible spaces might be infinite, it is motivated to introduce a suitable\nnondecreasing function that bounds the reversibility of larger and larger\nballs. By this approach, we are able to prove satisfactory\nconvergence/stability results in a suitable -- reversibility depending --\nGromov-Hausdorff topology. A wide class of irreversible spaces is provided by\nFinsler manifolds, which serve to construct various model examples by pointing\nout genuine differences between the reversible and irreversible settings. We\nconclude the paper by proving various geometric and functional inequalities (as\nBrunn-Minkowski, Bishop-Gromov, log-Sobolev and Lichnerowicz inequalities) on\nirreversible structures.\n"}
+{"abstract": "  We introduce an evolutionary game on hypergraphs in which decisions between a\nrisky alternative and a safe one are taken in social groups of different sizes.\nThe model naturally reproduces choice shifts, namely the differences between\nthe preference of individual decision makers and the consensual choice of a\ngroup, that have been empirically observed in choice dilemmas. In particular, a\ndeviation from the Nash equilibrium towards the risky strategy occurs when the\ndynamics takes place on heterogeneous hypergraphs. These results can explain\nthe emergence of irrational herding and radical behaviours in social groups.\n"}
+{"abstract": "  Photos of faces captured in unconstrained environments, such as large crowds,\nstill constitute challenges for current face recognition approaches as often\nfaces are occluded by objects or people in the foreground. However, few studies\nhave addressed the task of recognizing partial faces. In this paper, we propose\na novel approach to partial face recognition capable of recognizing faces with\ndifferent occluded areas. We achieve this by combining attentional pooling of a\nResNet's intermediate feature maps with a separate aggregation module. We\nfurther adapt common losses to partial faces in order to ensure that the\nattention maps are diverse and handle occluded parts. Our thorough analysis\ndemonstrates that we outperform all baselines under multiple benchmark\nprotocols, including naturally and synthetically occluded partial faces. This\nsuggests that our method successfully focuses on the relevant parts of the\noccluded face.\n"}
+{"abstract": "  Edge computing has emerged as a popular paradigm for running\nlatency-sensitive applications due to its ability to offer lower network\nlatencies to end-users. In this paper, we argue that despite its lower network\nlatency, the resource-constrained nature of the edge can result in higher\nend-to-end latency, especially at higher utilizations, when compared to cloud\ndata centers. We study this edge performance inversion problem through an\nanalytic comparison of edge and cloud latencies and analyze conditions under\nwhich the edge can yield worse performance than the cloud. To verify our\nanalytic results, we conduct a detailed experimental comparison of the edge and\nthe cloud latencies using a realistic application and real cloud workloads.\nBoth our analytical and experimental results show that even at moderate\nutilizations, the edge queuing delays can offset the benefits of lower network\nlatencies, and even result in performance inversion where running in the cloud\nwould provide superior latencies. We finally discuss practical implications of\nour results and provide insights into how application designers and service\nproviders should design edge applications and systems to avoid these pitfalls.\n"}
+{"abstract": "  Adaptive optimization methods have been widely used in deep learning. They\nscale the learning rates adaptively according to the past gradient, which has\nbeen shown to be effective to accelerate the convergence. However, they suffer\nfrom poor generalization performance compared with SGD. Recent studies point\nthat smoothing exponential gradient noise leads to generalization degeneration\nphenomenon. Inspired by this, we propose AdaL, with a transformation on the\noriginal gradient. AdaL accelerates the convergence by amplifying the gradient\nin the early stage, as well as dampens the oscillation and stabilizes the\noptimization by shrinking the gradient later. Such modification alleviates the\nsmoothness of gradient noise, which produces better generalization performance.\nWe have theoretically proved the convergence of AdaL and demonstrated its\neffectiveness on several benchmarks.\n"}
+{"abstract": "  In the year 2011, S.Basha \\cite{BS} introduced the notion of proximal\ncontraction in a metric space $X$ and study the existence and uniqueness of\nbest proximity point for this class of mappings. Also, the author gave an\nalgorithm to achieve this best proximity point. In this paper, we show that the\nbest proximity point theorem can be proved by Banach contraction principle.\n"}
+{"abstract": "  In this paper we undertake the task of text-based video moment retrieval from\na corpus of videos. To train the model, text-moment paired datasets were used\nto learn the correct correspondences. In typical training methods, ground-truth\ntext-moment pairs are used as positive pairs, whereas other pairs are regarded\nas negative pairs. However, aside from the ground-truth pairs, some text-moment\npairs should be regarded as positive. In this case, one text annotation can be\npositive for many video moments. Conversely, one video moment can be\ncorresponded to many text annotations. Thus, there are many-to-many\ncorrespondences between the text annotations and video moments. Based on these\ncorrespondences, we can form potentially relevant pairs, which are not given as\nground truth yet are not negative; effectively incorporating such relevant\npairs into training can improve the retrieval performance. The text query\nshould describe what is happening in a video moment. Hence, different video\nmoments annotated with similar texts, which contain a similar action, are\nlikely to hold the similar action, thus these pairs can be considered as\npotentially relevant pairs. In this paper, we propose a novel training method\nthat takes advantage of potentially relevant pairs, which are detected based on\nlinguistic analysis about text annotation. Experiments on two benchmark\ndatasets revealed that our method improves the retrieval performance both\nquantitatively and qualitatively.\n"}
+{"abstract": "  The s, d bosons of the Interacting Boson Model are being derived from the\nShell Model space, through the Elliott or the proxy SU(3) symmetry. A novel\ninterpretation of the s, d bosons is therefore introduced: each boson results\nfrom the coupling of two harmonic oscillator quanta. Such quanta obey to the\nboson statistics and as a result their coupled states satisfy the bosonic\ncommutators too, without using any approximation. Thus, the mapping of two\nharmonic oscillator quanta onto an s or a d boson is accurate. Furthermore,\nthrough this interpretation, it emerges naturally, that the nuclear states of\neven-even nuclei can be described solely by spherical tensors of degree L=0, 2,\nnamely by the s, d bosons of the Interacting Boson Model. Beginning from the\noccupancy of the cartesian Shell Model states by protons or neutrons, one may\nresult to certain U(6) irreps, which inherit some of the fermionic\ncharacteristics from the initial Shell Model space. Practically this means,\nthat the bosons of the Interacting Boson Model span the whole 6 dimensional\nspace of the U(6) symmetry. As a result a microscopic justification of the\ncoherent states emerges from first principles.\n"}
+{"abstract": "  The double JPEG compression detection has received much attention in recent\nyears due to its applicability as a forensic tool for the most widely used JPEG\nfile format. Existing state-of-the-art CNN-based methods either use histograms\nof all the frequencies or rely on heuristics to select histograms of specific\nlow frequencies to classify single and double compressed images. However, even\namidst lower frequencies of double compressed images/patches, histograms of all\nthe frequencies do not have distinguishable features to separate them from\nsingle compressed images. This paper directly extracts the quantized DCT\ncoefficients from the JPEG images without decompressing them in the pixel\ndomain, obtains all AC frequencies' histograms, uses a module based on $1\\times\n1$ depth-wise convolutions to learn the inherent relation between each\nhistogram and corresponding q-factor, and utilizes a tailor-made BiLSTM network\nfor selectively encoding these feature vector sequences. The proposed system\noutperforms several baseline methods on a relatively large and diverse publicly\navailable dataset of single and double compressed patches. Another essential\naspect of any single vs. double JPEG compression detection system is handling\nthe scenario where test patches are compressed with entirely different\nquantization matrices (Q-matrices) than those used while training; different\ncamera manufacturers and image processing software generally utilize their\ncustomized quantization matrices. A set of extensive experiments shows that the\nproposed system trained on a single dataset generalizes well on other datasets\ncompressed with completely unseen quantization matrices and outperforms the\nstate-of-the-art methods in both seen and unseen quantization matrices\nscenarios.\n"}
+{"abstract": "  We provide non-isomorphic finite 2-groups which have isomorphic group\nalgebras over any field of characteristic 2, thus settling the Modular\nIsomorphism Problem.\n"}
+{"abstract": "  The performance of reinforcement learning depends upon designing an\nappropriate action space, where the effect of each action is measurable, yet,\ngranular enough to permit flexible behavior. So far, this process involved\nnon-trivial user choices in terms of the available actions and their execution\nfrequency. We propose a novel framework for reinforcement learning that\neffectively lifts such constraints. Within our framework, agents learn\neffective behavior over a routine space: a new, higher-level action space,\nwhere each routine represents a set of 'equivalent' sequences of granular\nactions with arbitrary length. Our routine space is learned end-to-end to\nfacilitate the accomplishment of underlying off-policy reinforcement learning\nobjectives. We apply our framework to two state-of-the-art off-policy\nalgorithms and show that the resulting agents obtain relevant performance\nimprovements while requiring fewer interactions with the environment per\nepisode, improving computational efficiency.\n"}
+{"abstract": "  Connectivity is a fundamental structural feature of a network that determines\nthe outcome of any dynamics that happens on top of it. However, an analytical\napproach to obtain connection probabilities between nodes associated to paths\nof different lengths is still missing. Here, we derive exact expressions for\nrandom-walk connectivity probabilities across any range of numbers of steps in\na generic temporal, directed and weighted network. This allows characterizing\nexplicit connectivity realized by causal paths as well as implicit connectivity\nrelated to motifs of three nodes and two links called here pitchforks. We\ndirectly link such probabilities to the processes of tagging and sampling any\nquantity exchanged across the network, hence providing a natural framework to\nassess transport dynamics. Finally, we apply our theoretical framework to study\nocean transport features in the Mediterranean Sea. We find that relevant\ntransport structures, such as fluid barriers and corridors, can generate\ncontrasting and counter-intuitive connectivity patterns bringing novel insights\ninto how ocean currents drive seascape connectivity.\n"}
+{"abstract": "  We consider anomalous diffusion for molecular communication with a passive\nreceiver. We first consider the probability density function of molecules'\nlocation at a given time in a space of arbitrary dimension. The expected number\nof observed molecules inside a receptor space of the receiver at certain time\nis derived taking into account the life expectancy of the molecules. In\naddition, an implicit solution for the time that maximizes the expected number\nof observed molecules is obtained in terms of Fox's H-function. The closed-form\nexpressions for the bit error rate of a single-bit interval transmission and a\nmulti-bit interval transmission are derived. It is shown that lifetime limited\nmolecules can reduce the inter-symbol interference while also enhancing the\nreliability of MC systems at a suitable observation time.\n"}
+{"abstract": "  Boundary value problems (BVPs) play a central role in the mathematical\nanalysis of constrained physical systems subjected to external forces.\nConsequently, BVPs frequently emerge in nearly every engineering discipline and\nspan problem domains including fluid mechanics, electromagnetics, quantum\nmechanics, and elasticity. The fundamental solution, or Green's function, is a\nleading method for solving linear BVPs that enables facile computation of new\nsolutions to systems under any external forcing. However, fundamental Green's\nfunction solutions for nonlinear BVPs are not feasible since linear\nsuperposition no longer holds. In this work, we propose a flexible deep\nlearning approach to solve nonlinear BVPs using a dual-autoencoder\narchitecture. The autoencoders discover an invertible coordinate transform that\nlinearizes the nonlinear BVP and identifies both a linear operator $L$ and\nGreen's function $G$ which can be used to solve new nonlinear BVPs. We find\nthat the method succeeds on a variety of nonlinear systems including nonlinear\nHelmholtz and Sturm--Liouville problems, nonlinear elasticity, and a 2D\nnonlinear Poisson equation. The method merges the strengths of the universal\napproximation capabilities of deep learning with the physics knowledge of\nGreen's functions to yield a flexible tool for identifying fundamental\nsolutions to a variety of nonlinear systems.\n"}
+{"abstract": "  We present mesoscale numerical simulations of Rayleigh-B\\'enard (RB)\nconvection in a two-dimensional model emulsion. The systems under study are\nconstituted of finite-size droplets, whose concentration Phi_0 is\nsystematically varied from small (Newtonian emulsions) to large values\n(non-Newtonian emulsions). We focus on the characterisation of the heat\ntransfer properties close to the transition from conductive to convective\nstates, where it is known that a homogeneous Newtonian system exhibits a steady\nflow and a time-independent heat flux. In marked contrast, emulsions exhibit a\nnon-steady dynamics with fluctuations in the heat flux. In this paper, we aim\nat the characterisation of such non-steady dynamics via detailed studies on the\ntime-averaged heat flux and its fluctuations. To understand the time-averaged\nheat flux, we propose a side-by-side comparison between the emulsion system and\na single-phase (SP) system, whose viscosity is constructed from the shear\nrheology of the emulsion. We show that such local closure works well only when\na suitable degree of coarse-graining (at the droplet scale) is introduced in\nthe local viscosity. To delve deeper into the fluctuations in the heat flux, we\npropose a side-by-side comparison between a Newtonian emulsion and a\nnon-Newtonian emulsion, at fixed time-averaged heat flux. This comparison\nelucidates that finite-size droplets and the non-Newtonian rheology cooperate\nto trigger enhanced heat-flux fluctuations at the droplet scales. These\nenhanced fluctuations are rooted in the emergence of space correlations among\ndistant droplets, which we highlight via direct measurements of the droplets\ndisplacement and the characterisation of the associated correlation function.\nThe observed findings offer insights on heat transfer properties for confined\nsystems possessing finite-size constituents.\n"}
+{"abstract": "  We study the performance power of software combining in designing persistent\nalgorithms and data structures. We present Bcomb, a new blocking\nhighly-efficient combining protocol, and built upon it to get PBcomb, a\npersistent version of it that performs a small number of persistence\ninstructions and exhibits low synchronization cost. We built fundamental\nrecoverable data structures, such as stacks and queues based on PBcomb, as well\nas on PWFcomb, a wait-free universal construction we present. Our experiments\nshow that PBcomb and PWFcomb outperform by far state-of-the-art recoverable\nuniversal constructions and transactional memory systems, many of which ensure\nweaker consistency properties than our algorithms. We built recoverable queues\nand stacks, based on PBcomb and PWFcomb, and present experiments to show that\nthey have much better performance than previous recoverable implementations of\nstacks and queues. We build the first recoverable implementation of a\nconcurrent heap and present experiments to show that it has good performance\nwhen the size of the heap is not very large.\n"}
+{"abstract": "  In this note we characterize 1+n doubly twisted spacetimes in terms of\n`doubly torqued' vector fields. They extend Bang-Yen Chen's characterization of\ntwisted and generalized Robertson-Walker spacetimes with torqued and\nconcircular vector fields. The result is a simple classification of 1+n\ndoubly-twisted, doubly-warped, twisted and generalized Robertson-Walker\nspacetimes.\n"}
+{"abstract": "  We consider the de Rham complex over scales of weighted isotropic and\nanisotropic H\\\"older spaces with prescribed asymptotic behaviour at the\ninfinity. Starting from theorems on the solvability of the system of operator\nequations generated by the de Rham differential $d$ and the operator $d^*$\nformally adjoint to it, a description of the cohomology groups of the de Rham\ncomplex over these scales was obtained. It was also proved that in the\nisotropic case the cohomology space is finite-dimensional, and in the\nanisotropic case the general form of an element from the cohomology space is\npresented.\n"}
+{"abstract": "  While annotated images for change detection using satellite imagery are\nscarce and costly to obtain, there is a wealth of unlabeled images being\ngenerated every day. In order to leverage these data to learn an image\nrepresentation more adequate for change detection, we explore methods that\nexploit the temporal consistency of Sentinel-2 times series to obtain a usable\nself-supervised learning signal. For this, we build and make publicly available\n(https://zenodo.org/record/4280482) the Sentinel-2 Multitemporal Cities Pairs\n(S2MTCP) dataset, containing multitemporal image pairs from 1520 urban areas\nworldwide. We test the results of multiple self-supervised learning methods for\npre-training models for change detection and apply it on a public change\ndetection dataset made of Sentinel-2 image pairs (OSCD).\n"}
+{"abstract": "  We consider the Schr\\\"odinger equation on Riemannian symmetric spaces of\nnoncompact type. Previous studies in rank one included sharp-in-time pointwise\nestimates for the Schr\\\"odinger kernel, dispersive properties, Strichartz\ninequalities for a large family of admissible pairs, and global well-posedness\nand scattering, both for small initial data. In this paper we establish\nanalogous results in the higher rank case. The kernel estimates, which is our\nmain result, are obtained by combining a subordination formula, an improved\nHadamard parametrix for the wave equation, and a barycentric decomposition\ninitially developed for the wave equation, which allows us to overcome a\nwell-known problem, namely the fact that the Plancherel density is not always a\ndifferential symbol.\n"}
+{"abstract": "  Deep learning applications are drastically progressing in seismic processing\nand interpretation tasks. However, the majority of approaches subsample data\nvolumes and restrict model sizes to minimise computational requirements.\nSubsampling the data risks losing vital spatio-temporal information which could\naid training whilst restricting model sizes can impact model performance, or in\nsome extreme cases, renders more complicated tasks such as segmentation\nimpossible. This paper illustrates how to tackle the two main issues of\ntraining of large neural networks: memory limitations and impracticably large\ntraining times. Typically, training data is preloaded into memory prior to\ntraining, a particular challenge for seismic applications where data is\ntypically four times larger than that used for standard image processing tasks\n(float32 vs. uint8). Using a microseismic use case, we illustrate how over\n750GB of data can be used to train a model by using a data generator approach\nwhich only stores in memory the data required for that training batch.\nFurthermore, efficient training over large models is illustrated through the\ntraining of a 7-layer UNet with input data dimensions of 4096X4096. Through a\nbatch-splitting distributed training approach, training times are reduced by a\nfactor of four. The combination of data generators and distributed training\nremoves any necessity of data 1 subsampling or restriction of neural network\nsizes, offering the opportunity of utilisation of larger networks,\nhigher-resolution input data or moving from 2D to 3D problem spaces.\n"}
+{"abstract": "  New ultra-hard rhombohedral B2N2 and BC2N - or hexagonal B6N6 and B3C6N3 -\nare derived from 3R graphite based on crystal chemistry rationale schematizing\na mechanism for 2D => 3D transformation. Full unconstrained geometry\noptimizations leading to ground state energy structures and energy derived\nquantities as energy-volume equation of states (EOS) were based on computations\nwithin the density functional theory (DFT) with generalized gradient\napproximation (GGA) for exchange-correlation (XC) effects. The new binary and\nternary phases are characterized by tetrahedral stacking alike diamond,\nvisualized with charge density representations, and illustrating ion\ncharacters. Atom averaged total energies are similar between cubic BN and\nrh-B2N2 on one hand, and larger stabilization of rhombohedral BC2N versus cubic\nand orthorhombic forms (in literature assessed from favored C-C and B-N\nbonding), on the other hand. The electronic band structures are characteristic\nof insulators with Egap ~ 5 eV. Both phases are characterized by large bulk and\nshear moduli and very high hardness values i.e. HV(rh-B2N2) = 74 GPa and\nHV(rh-BC2N) = 87 GPa.\n"}
+{"abstract": "  We consider a multi-view learning problem known as group independent\ncomponent analysis (group ICA), where the goal is to recover shared independent\nsources from many views. The statistical modeling of this problem requires to\ntake noise into account. When the model includes additive noise on the\nobservations, the likelihood is intractable. By contrast, we propose Adaptive\nmultiView ICA (AVICA), a noisy ICA model where each view is a linear mixture of\nshared independent sources with additive noise on the sources. In this setting,\nthe likelihood has a tractable expression, which enables either direct\noptimization of the log-likelihood using a quasi-Newton method, or generalized\nEM. Importantly, we consider that the noise levels are also parameters that are\nlearned from the data. This enables sources estimation with a closed-form\nMinimum Mean Squared Error (MMSE) estimator which weights each view according\nto its relative noise level. On synthetic data, AVICA yields better sources\nestimates than other group ICA methods thanks to its explicit MMSE estimator.\nOn real magnetoencephalograpy (MEG) data, we provide evidence that the\ndecomposition is less sensitive to sampling noise and that the noise variance\nestimates are biologically plausible. Lastly, on functional magnetic resonance\nimaging (fMRI) data, AVICA exhibits best performance in transferring\ninformation across views.\n"}
+{"abstract": "  The temperature in most parts of a protoplanetary disk is determined by\nirradiation from the central star. Numerical experiments of Watanabe \\& Lin\n(2008) suggested that such disks, also called `passive disks', suffer from a\nthermal instability. Here, we use analytical and numerical tools to elucidate\nthe nature of this instability. We find that it is related to the flaring of\nthe optical surface, the layer at which starlight is intercepted by the disk.\nWhenever a disk annulus is perturbed thermally and acquires a larger scale\nheight, disk flaring becomes steeper in the inner part, and flatter in the\nouter part. Starlight now shines more overhead for the inner part and so can\npenetrate into deeper layers; conversely, it is absorbed more shallowly in the\nouter part. These geometric changes allow the annulus to intercept more\nstarlight, and the perturbation grows. We call this the irradiation\ninstability. It requires only ingredients that are known to exist in realistic\ndisks, and operates best in parts that are both optically thick and\ngeometrically thin (inside 30AU, but can extend to further reaches when, e.g.,\ndust settling is considered). An unstable disk develops travelling thermal\nwaves that reach order-unity in amplitude. In thermal radiation, such a disk\nshould appear as a series of bright rings interleaved with dark shadowed gaps,\nwhile in scattered light it resembles a moving staircase. Depending on the gas\nand dust responses, this instability could lead to a wide range of\nconsequences, such as {\\w ALMA rings and gaps,} dust traps, vertical\ncirculation, vortices and turbulence.\n"}
+{"abstract": "  Much recent research is devoted to exploring tradeoffs between computational\naccuracy and energy efficiency at different levels of the system stack.\nApproximation at the floating point unit (FPU) allows saving energy by simply\nreducing the number of computed floating point bits in return for accuracy\nloss. Although, finding the most energy efficient approximation for various\napplications with minimal effort is the main challenge. To address this issue,\nwe propose NEAT: a pin tool that helps users automatically explore the\naccuracy-energy tradeoff space induced by various floating point\nimplementations. NEAT helps programmers explore the effects of simultaneously\nusing multiple floating point implementations to achieve the lowest energy\nconsumption for an accuracy constraint or vice versa. NEAT accepts one or more\nuser-defined floating point implementations and programmable placement rules\nfor where/when to apply them. NEAT then automatically replaces floating point\noperations with different implementations based on the user-specified rules\nduring the runtime and explores the resulting tradeoff space to find the best\nuse of approximate floating point implementations for the precision tuning\nthroughout the program. We evaluate NEAT by enforcing combinations of 24/53\ndifferent floating point implementations with three sets of placement rules on\na wide range of benchmarks. We find that heuristic precision tuning at the\nfunction level provides up to 22% and 48% energy savings at 1% and 10% accuracy\nloss comparing to applying a single implementation for the whole application.\nAlso, NEAT is applicable to neural networks where it finds the optimal\nprecision level for each layer considering an accuracy target for the model.\n"}
+{"abstract": "  This paper deals with the time differential dual-phase-lag heat transfer\nmodels aiming, at first, to identify the eventually restrictions that make them\nthermodynamically consistent. At a first glance it can be observed that the\ncapability of a time differential dual-phase-lag model of heat conduction to\ndescribe real phenomena depends on the properties of the differential operators\ninvolved in the related constitutive equation. In fact, the constitutive\nequation is viewed as an ordinary differential equation in terms of the heat\nflux components (or in terms of the temperature gradient) and it results that,\nfor approximation orders greater than or equal to five, the corresponding\ncharacteristic equation has at least a complex root having a positive real\npart. That leads to a heat flux component (or temperature gradient) that grows\nto infinity when the time tends to infinity and so there occur some\ninstabilities. Instead, when the approximation orders are lower than or equal\nto four, this is not the case and there is the need to study the compatibility\nwith the Second Law of Thermodynamics. To this aim the related constitutive\nequation is reformulated within the system of the fading memory theory, and\nthus the heat flux vector is written in terms of the history of the temperature\ngradient and on this basis the compatibility of the model with the\nthermodynamical principles is analyzed.\n"}
+{"abstract": "  Black hole low mass X-ray binaries in their hard spectral state are found to\ndisplay two different correlations between the radio emission from the compact\njets and the X-ray emission from the inner accretion flow. Here, we present a\nlarge data set of quasi-simultaneous radio and X-ray observations of the\nrecently discovered accreting black hole MAXI J1348-630 during its 2019/2020\noutburst. Our results span almost six orders of magnitude in X-ray luminosity,\nallowing us to probe the accretion-ejection coupling from the brightest to the\nfaintest phases of the outburst. We find that MAXI J1348-630 belongs to the\ngrowing population of outliers at the highest observed luminosities.\nInterestingly, MAXI J1348-630 deviates from the outlier track at $L_{\\rm X}\n\\lesssim 7 \\times 10^{35} (D / 2.2 \\ {\\rm kpc})^2$ erg s$^{-1}$ and ultimately\nrejoins the standard track at $L_{\\rm X} \\simeq 10^{33} (D / 2.2 \\ {\\rm\nkpc})^2$ erg s$^{-1}$, displaying a hybrid radio/X-ray correlation, observed\nonly in a handful of sources. However, for MAXI J1348-630 these transitions\nhappen at luminosities much lower than what observed for similar sources (at\nleast an order of magnitude). We discuss the behaviour of MAXI J1348-630 in\nlight of the currently proposed scenarios and we highlight the importance of\nfuture deep monitorings of hybrid correlation sources, especially close to the\ntransitions and in the low luminosity regime.\n"}
+{"abstract": "  We present a geometric construction of a lattice that emulates the action of\na gauge field on a fermion. The construction consists of a square lattice made\nof polymeric sites, where all clustered atoms are identical and represented by\npotential wells or resonators supporting one bound state. The emulation covers\nboth abelian and non-abelian gauge fields. In the former case, Hofstadter's\nbutterfly is reproduced by means of a chain made of rotating dimers, subject to\nperiodic boundary conditions parallel to the chain. A rigorous map between this\nmodel and Harper's Hamiltonian is derived. In the non-abelian case, band mixing\nand wave confinement are obtained by interband coupling using SU(2) as an\ninternal group, \\ie the effects are due to non-commutability of field\ncomponents. A colored model with SU(3) made of trimers is also studied, finding\nthereby the appearance of flat bands in special configurations. This work\nconstitutes the first all-geometric emulation of the Peierls substitution, and\nis valid for many types of waves.\n"}
+{"abstract": "  Distributed cloud networking builds on network functions virtualization (NFV)\nand software defined networking (SDN) to enable the deployment of network\nservices in the form of elastic virtual network functions (VNFs) instantiated\nover general purpose servers at distributed cloud locations. We address the\ndesign of fast approximation algorithms for the NFV service distribution\nproblem (NSDP), whose goal is to determine the placement of VNFs, the routing\nof service flows, and the associated allocation of cloud and network resources\nthat satisfy client demands with minimum cost. We show that in the case of\nload-proportional costs, the resulting fractional NSDP can be formulated as a\nmulti-commodity-chain flow problem on a cloud augmented graph, and design a\nqueue-length based algorithm, named QNSD, that provides an O(\\epsilon)\napproximation in time O(1/\\epsilon). We then address the case in which resource\ncosts are a function of the integer number of allocated resources and design a\nvariation of QNSD that effectively pushes for flow consolidation into a limited\nnumber of active resources to minimize overall cloud network cost.\n"}
+{"abstract": "  We establish a Galois connection between sub-monads of an augmented monad and\nsub-functors of the forgetful functor from its Eilenberg-Moore category. This\nconnection is given in terms of invariants and stabilizers defined through\nuniversal properties. An explicit procedure for the computation of invariants\nis given assuming the existence of suitable right adjoints. Additionally, in\nthe context of monoidal closed categories, a characterization of stabilizers is\nmade in terms of Tannakian reconstruction.\n"}
+{"abstract": "  This paper is concerned with the inverse elastic scattering problem for a\nrandom potential in three dimensions. Interpreted as a distribution, the\npotential is assumed to be a microlocally isotropic Gaussian random field whose\ncovariance operator is a classical pseudo-differential operator. Given the\npotential, the direct scattering problem is shown to be well-posed in the\ndistribution sense by studying the equivalent Lippmann--Schwinger integral\nequation. For the inverse scattering problem, we demonstrate that the\nmicrolocal strength of the random potential can be uniquely determined with\nprobability one by a single realization of the high frequency limit of the\naveraged compressional or shear backscattered far-field pattern of the\nscattered wave. The analysis employs the integral operator theory, the Born\napproximation in the high frequency regime, the microlocal analysis for the\nFourier integral operators, and the ergodicity of the wave field.\n"}
+{"abstract": "  Stateflow models are complex software models, often used as part of\nsafety-critical software solutions designed with Matlab Simulink. They\nincorporate design principles that are typically very hard to verify formally.\nIn particular, the standard exhaustive formal verification techniques are\nunlikely to scale well for the complex designs that are developed in industry.\nFurthermore, the Stateflow language lacks a formal semantics, which\nadditionally hinders the formal analysis.\n  To address these challenges, we lay here the foundations of a scalable\ntechnique for provably correct formal analysis of Stateflow models, with\nrespect to invariant properties, based on bounded model checking (BMC) over\nsymbolic executions. The crux of our technique is: i) a representation of the\nstate space of Stateflow models as a symbolic transition system (STS) over the\nsymbolic configurations of the model, as the basis for BMC, and ii) application\nof incremental BMC, to generate verification results after each unrolling of\nthe next-state relation of the transition system. To this end, we develop a\nsymbolic structural operational semantics (SSOS) for Stateflow, starting from\nan existing structural operational semantics (SOS), and show the preservation\nof invariant properties between the two. Next, we define bounded invariant\nchecking for STS over symbolic configurations as a satisfiability problem. We\ndevelop an automated procedure for generating the initial and next-state\npredicates of the STS, and propose an encoding scheme of the bounded invariant\nchecking problem as a set of constraints, ready for automated analysis with\nstandard, off-the-shelf satisfiability solvers. Finally, we present preliminary\nperformance results by applying our tool on an illustrative example.\n"}
+{"abstract": "  In this paper, we address the space-time video super-resolution, which aims\nat generating a high-resolution (HR) slow-motion video from a low-resolution\n(LR) and low frame rate (LFR) video sequence. A na\\\"ive method is to decompose\nit into two sub-tasks: video frame interpolation (VFI) and video\nsuper-resolution (VSR). Nevertheless, temporal interpolation and spatial\nupscaling are intra-related in this problem. Two-stage approaches cannot fully\nmake use of this natural property. Besides, state-of-the-art VFI or VSR deep\nnetworks usually have a large frame reconstruction module in order to obtain\nhigh-quality photo-realistic video frames, which makes the two-stage approaches\nhave large models and thus be relatively time-consuming. To overcome the\nissues, we present a one-stage space-time video super-resolution framework,\nwhich can directly reconstruct an HR slow-motion video sequence from an input\nLR and LFR video. Instead of reconstructing missing LR intermediate frames as\nVFI models do, we temporally interpolate LR frame features of the missing LR\nframes capturing local temporal contexts by a feature temporal interpolation\nmodule. Extensive experiments on widely used benchmarks demonstrate that the\nproposed framework not only achieves better qualitative and quantitative\nperformance on both clean and noisy LR frames but also is several times faster\nthan recent state-of-the-art two-stage networks. The source code is released in\nhttps://github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020 .\n"}
+{"abstract": "  Understanding the biomechanics of the heart in health and disease plays an\nimportant role in the diagnosis and treatment of heart failure. The use of\ncomputational biomechanical models for therapy assessment is paving the way for\npersonalized treatment, and relies on accurate constitutive equations mapping\nstrain to stress. Current state-of-the art constitutive equations account for\nthe nonlinear anisotropic stress-strain response of cardiac muscle using\nhyperelasticity theory. While providing a solid foundation for understanding\nthe biomechanics of heart tissue, most current laws neglect viscoelastic\nphenomena observed experimentally. Utilizing experimental data from human\nmyocardium and knowledge of the hierarchical structure of heart muscle, we\npresent a fractional nonlinear anisotropic viscoelastic constitutive model. The\nmodel is shown to replicate biaxial stretch, triaxial cyclic shear and triaxial\nstress relaxation experiments (mean error ~7.65%), showing improvements\ncompared to its hyperelastic (mean error ~25%) counterparts. Model sensitivity,\nfidelity and parameter uniqueness are demonstrated. The model is also compared\nto rate-dependent biaxial stretch as well as different modes of biaxial\nstretch, illustrating extensibility of the model to a range of loading\nphenomena.\n"}
+{"abstract": "  Robotic motion generation methods using machine learning have been studied in\nrecent years. Bilateral control-based imitation learning can imitate human\nmotions using force information. By means of this method, variable speed motion\ngeneration that considers physical phenomena such as the inertial force and\nfriction can be achieved. However, the previous study only focused on a simple\nreciprocating motion. To learn the complex relationship between the force and\nspeed more accurately, it is necessary to learn multiple actions using many\njoints. In this paper, we propose a variable speed motion generation method for\nmultiple motions. We considered four types of neural network models for the\nmotion generation and determined the best model for multiple motions at\nvariable speeds. Subsequently, we used the best model to evaluate the\nreproducibility of the task completion time for the input completion time\ncommand. The results revealed that the proposed method could change the task\ncompletion time according to the specified completion time command in multiple\nmotions.\n"}
+{"abstract": "  Higher-order singular value decomposition (HOSVD) is one of the most\nefficient tensor decomposition techniques. It has the salient ability to\nrepresent high_dimensional data and extract features. In more recent years, the\nquaternion has proven to be a very suitable tool for color pixel representation\nas it can well preserve cross-channel correlation of color channels. Motivated\nby the advantages of the HOSVD and the quaternion tool, in this paper, we\ngeneralize the HOSVD to the quaternion domain and define quaternion-based HOSVD\n(QHOSVD). Due to the non-commutability of quaternion multiplication, QHOSVD is\nnot a trivial extension of the HOSVD. They have similar but different\ncalculation procedures. The defined QHOSVD can be widely used in various visual\ndata processing with color pixels. In this paper, we present two applications\nof the defined QHOSVD in color image processing: multi_focus color image fusion\nand color image denoising. The experimental results on the two applications\nrespectively demonstrate the competitive performance of the proposed methods\nover some existing ones.\n"}
+{"abstract": "  Weak decays in superheavy nuclei with proton numbers Z = 118 - 120 and\nneutron numbers N = 175 - 184 are studied within a microscopic formalism based\non deformed self-consistent Skyrme Hartree-Fock mean-field calculations with\npairing correlations. The half-lives of beta+ decay and electron capture are\ncompared with alpha-decay half-lives obtained from phenomenological formulas.\nThe sensitivity of the half-lives to the unknown Q-energies is studied by\ncomparing the results obtained from different approaches for the masses. It is\nshown that alpha-decay is always dominant in this mass region. The competition\nbetween alpha and beta+/EC decay modes is studied in seven alpha-decay chains\nstarting at different isotopes of Z=118, 119, and 120.\n"}
+{"abstract": "  With a better understanding of the loss surfaces for multilayer networks, we\ncan build more robust and accurate training procedures. Recently it was\ndiscovered that independently trained SGD solutions can be connected along\none-dimensional paths of near-constant training loss. In this paper, we show\nthat there are mode-connecting simplicial complexes that form multi-dimensional\nmanifolds of low loss, connecting many independently trained models. Inspired\nby this discovery, we show how to efficiently build simplicial complexes for\nfast ensembling, outperforming independently trained deep ensembles in\naccuracy, calibration, and robustness to dataset shift. Notably, our approach\nonly requires a few training epochs to discover a low-loss simplex, starting\nfrom a pre-trained solution. Code is available at\nhttps://github.com/g-benton/loss-surface-simplexes.\n"}
+{"abstract": "  Learning robust speaker embeddings is a crucial step in speaker diarization.\nDeep neural networks can accurately capture speaker discriminative\ncharacteristics and popular deep embeddings such as x-vectors are nowadays a\nfundamental component of modern diarization systems. Recently, some\nimprovements over the standard TDNN architecture used for x-vectors have been\nproposed. The ECAPA-TDNN model, for instance, has shown impressive performance\nin the speaker verification domain, thanks to a carefully designed neural\nmodel.\n  In this work, we extend, for the first time, the use of the ECAPA-TDNN model\nto speaker diarization. Moreover, we improved its robustness with a powerful\naugmentation scheme that concatenates several contaminated versions of the same\nsignal within the same training batch. The ECAPA-TDNN model turned out to\nprovide robust speaker embeddings under both close-talking and distant-talking\nconditions. Our results on the popular AMI meeting corpus show that our system\nsignificantly outperforms recently proposed approaches.\n"}
+{"abstract": "  We propose a method for enantio-detection of chiral molecules based on a\ncavity-molecule system, where the left- and right-handed molecules are coupled\nwith a cavity and two classical light fields to form cyclic three-level models.\nVia the cavity-assisted three-photon processes based on the cyclic three-level\nmodel, photons are generated continuously in the cavity even in the absence of\nexternal driving to the cavity. However, the photonic fields generated from the\nthree-photon processes of left- and right-handed molecules differ with the\nphase difference {\\pi} according to the inherent properties of electric-dipole\ntransition moments of enantiomers. This provides a potential way to detect the\nenantiomeric excess of chiral mixture by monitoring the output field of the\ncavity.\n"}
+{"abstract": "  This article develops the multiple-input multiple-output (MIMO) technology\nfor weather radar sensing. There are ample advantages of MIMO that have been\nhighlighted that can improve the spatial resolution of the observations and\nalso the accuracy of the radar variables. These concepts have been introduced\nhere pertaining to weather radar observations with supporting simulations\ndemonstrating improvements to existing phased array technology. Already MIMO is\nbeing used in a big way for hard target detection and tracking and also in the\nautomotive radar industry and it offers similar improvements for weather radar\nobservations. Some of the benefits are discussed here with a phased array\nplatform in mind which offers quadrant outputs.\n"}
+{"abstract": "  We demonstrate an efficient procedure for terahertz-wave radiation generation\nby applying a nonlinear wavelength conversion approach using a mid-infrared\npump source. We used a 3.3 {\\mu}m dual-chirped optical parametric amplification\nsource and a 1.5 {\\mu}m fiber laser for a comparison of energy conversion\nefficiencies. Nonlinear inorganic and organic crystals were used, and for the\nperiodically-poled lithium niobate crystal, the efficiencies were 1.3*10^-4 and\n5.6*10^-12 for the 3.3 {\\mu}m source and the 1.5 {\\mu}m source, respectively.\nWe confirmed that nonlinear crystals could be pumped with tera-watt / cm^2\nclass by using an mid-infrared source, which reduces several undesirable\nnonlinear optical effects.\n"}
+{"abstract": "  Measurement-based quantum computing (MBQC) promises natural compatibility\nwith quantum error correcting codes at the cost of a polynomial increase in\nphysical qubits. MBQC proposals have largely focused on photonic systems, where\n2-qubit gates are difficult. Semiconductor spin qubits in quantum dots, on the\nother hand, offer fast 2-qubit gates via the exchange interaction. In\nexchange-based quantum computing, as with other solid-state qubits, leakage to\nhigher states is a serious problem that must be mitigated. Here, two hybrid\nmeasurement-exchange schemes are proposed which quantify the benefits of MBQC\non quantum dot-based quantum computing. Measurement of double quantum dot\nencoded qubits in the singlet-triplet basis, along with inter- and intra-qubit\nexchange interaction, are used to perform one and two qubit operations. Both\nschemes suppress individual qubit spin-state leakage errors, offer fast gate\ntimes, and require only controllable exchange couplings, up to known phase and\nPauli corrections.\n"}
+{"abstract": "  Different from static images, videos contain additional temporal and spatial\ninformation for better object detection. However, it is costly to obtain a\nlarge number of videos with bounding box annotations that are required for\nsupervised deep learning. Although humans can easily learn to recognize new\nobjects by watching only a few video clips, deep learning usually suffers from\noverfitting. This leads to an important question: how to effectively learn a\nvideo object detector from only a few labeled video clips? In this paper, we\nstudy the new problem of few-shot learning for video object detection. We first\ndefine the few-shot setting and create a new benchmark dataset for few-shot\nvideo object detection derived from the widely used ImageNet VID dataset. We\nemploy a transfer-learning framework to effectively train the video object\ndetector on a large number of base-class objects and a few video clips of\nnovel-class objects. By analyzing the results of two methods under this\nframework (Joint and Freeze) on our designed weak and strong base datasets, we\nreveal insufficiency and overfitting problems. A simple but effective method,\ncalled Thaw, is naturally developed to trade off the two problems and validate\nour analysis.\n  Extensive experiments on our proposed benchmark datasets with different\nscenarios demonstrate the effectiveness of our novel analysis in this new\nfew-shot video object detection problem.\n"}
+{"abstract": "  We conduct novel coverage probability analysis of downlink transmission in a\nthree-dimensional (3D) terahertz (THz) communication (THzCom) system. In this\nsystem, we address the unique propagation properties in THz band, e.g.,\nabsorption loss, super-narrow directional beams, and high vulnerability towards\nblockage, which are fundamentally different from those at lower frequencies.\nDifferent from existing studies, we characterize the performance while\nconsidering the effect of 3D directional antennas at both access points (APs)\nand user equipments (UEs), and the joint impact of the blockage caused by the\nuser itself, moving humans, and wall blockers in a 3D environment. Under such\nconsideration, we develop a tractable analytical framework to derive a new\nexpression for the coverage probability by examining the regions where dominant\ninterferers (i.e., those can cause outage by themselves) can exist, and the\naverage number of interferers existing in these regions. Aided by numerical\nresults, we validate our analysis and reveal that ignoring the impact of the\nvertical heights of THz devices in the analysis leads to a substantial\nunderestimation of the coverage probability. We also show that it is more\nworthwhile to increase the antenna directivity at the APs than at the UEs, to\nproduce a more reliable THzCom system.\n"}
+{"abstract": "  We investigate Rindler's frame measurements. From its perspective, we found a\ngeometric/gravitational interpretation of speed of light, mass and uncertainty\nprinciple. This can be interpreted as measurements of a black hole universal\nclock. This lead to an emergence of a timeless state of gravity in a\nmathematically consistent way. In other words, space my be a frozen time.\n"}
+{"abstract": "  The present work is devoted to an extension of the well-known Ehrling\ninequalities, which quantitatively characterize compact embeddings of function\nspaces, to more general operators. Firstly, a modified notion of continuity for\nlinear operators, named \\emph{Ehrling continuity} and inspired by the classical\nEhrling inequality, is introduced, and then, a necessary and sufficient\ncondition for Ehrling continuity is provided via arguments based on general\ntopology. Secondly, general completely continuous operators between normed\nspaces are characterized in terms of (generalized) Ehrling type inequalities.\nTo this end, the well-known local metrization of the weak topology (so to\nspeak, a \\emph{very weak norm}) plays a crucial role. Thanks to these results,\na universal relation is observed among complete continuity, the very weak norm\nand generalized Ehrling type inequality.\n"}
+{"abstract": "  We present order reduction results for linear time invariant descriptor\nsystems. Results are given for both forced and unforced systems as well methods\nfor constructing the reduced order systems. Our results establish a precise\nconnection between classical and new results on this topic, and lead to an\nelementary construction of quasi-Weierstrass forms for a descriptor system.\nExamples are given to illustrate the usefulness of our results.\n"}
+{"abstract": "  In the field of nuclear reactor physics, transient phenomena are usually\nstudied using deterministic or hybrids methods. These methods require many\napproximations, such as: geometry, time and energy discretizations, material\nhomogenization and assumption of diffusion conditions, among others. In this\ncontext, Monte Carlo simulations are specially adequate to study these\nproblems. Challenges presented when using Monte Carlo simulations in space-time\nkinetics in fissile systems are the different time-scales involved in prompt\nand delayed neutron emission, which implies that results obtained have a large\nvariance associated if an analog Monte Carlo simulation is utilized.\nFurthermore, in both deterministic and Monte Carlo simulations delayed neutron\nprecursors are grouped in a $6$- or $8$- group structure, but nowadays there is\nnot a solid reason to keep this aggregation. In this work, and for the first\ntime, individual precursor data is implemented in a Monte Carlo simulation,\nexplicitly including the time dependence related to the $\\beta$-delayed neutron\nemission. This was accomplished by modifying the open source Monte Carlo code\nOpenMC. In the modified code -- Time Dependent OpenMC or OpenMC(TD) -- time\ndependency related to delayed neutron emission originated from $\\beta$-decay\nwas addressed. Continuous energy neutron cross-sections data used comes from\nJEFF-$3$.$1$.$1$ library. Individual precursor data was taken from\nJEFF-$3$.$1$.$1$ (cumulative yields) and ENDF-B/VIII.$0$ (delayed neutron\nemission probabilities and delayed neutron energy spectra). OpenMC(TD) was\ntested in: i) a monoenergetic system; ii) an energy dependent unmoderated\nsystem where the precursors were taken individually or in a group structure;\nand finally iii) a light-water moderated energy dependent system, using\n$6$-groups, $50$ and $40$ individual precursors.\n"}
+{"abstract": "  Let $X$ be an infinite linearly ordered set and let $Y$ be a nonempty subset\nof $X$. We calculate the relative rank of the semigroup $OP(X,Y)$ of all\norientation-preserving transformations on $X$ with restricted range $Y$ modulo\nthe semigroup $O(X,Y)$ of all order-preserving transformations on $X$ with\nrestricted range $Y$. For $Y = X$, we characterize the relative generating sets\nof minimal size.\n"}
+{"abstract": "  For an image query, unsupervised contrastive learning labels crops of the\nsame image as positives, and other image crops as negatives. Although\nintuitive, such a native label assignment strategy cannot reveal the underlying\nsemantic similarity between a query and its positives and negatives, and\nimpairs performance, since some negatives are semantically similar to the query\nor even share the same semantic class as the query. In this work, we first\nprove that for contrastive learning, inaccurate label assignment heavily\nimpairs its generalization for semantic instance discrimination, while accurate\nlabels benefit its generalization. Inspired by this theory, we propose a novel\nself-labeling refinement approach for contrastive learning. It improves the\nlabel quality via two complementary modules: (i) self-labeling refinery (SLR)\nto generate accurate labels and (ii) momentum mixup (MM) to enhance similarity\nbetween query and its positive. SLR uses a positive of a query to estimate\nsemantic similarity between a query and its positive and negatives, and\ncombines estimated similarity with vanilla label assignment in contrastive\nlearning to iteratively generate more accurate and informative soft labels. We\ntheoretically show that our SLR can exactly recover the true semantic labels of\nlabel-corrupted data, and supervises networks to achieve zero prediction error\non classification tasks. MM randomly combines queries and positives to increase\nsemantic similarity between the generated virtual queries and their positives\nso as to improves label accuracy. Experimental results on CIFAR10, ImageNet,\nVOC and COCO show the effectiveness of our method. PyTorch code and model will\nbe released online.\n"}
+{"abstract": "  The East Asian very-long-baseline interferometry (VLBI) Network (EAVN) is a\nrapidly evolving international VLBI array that is currently promoted under\njoint efforts among China, Japan, and Korea. EAVN aims at forming a joint VLBI\nNetwork by combining a large number of radio telescopes distributed over East\nAsian regions. After the combination of the Korean VLBI Network (KVN) and the\nVLBI Exploration of Radio Astrometry (VERA) into KaVA, further expansion with\nthe joint array in East Asia is actively promoted. Here we report the first\nimaging results (at 22 and 43 GHz) of bright radio sources obtained with KaVA\nconnected to Tianma 65-m and Nanshan 26-m Radio Telescopes in China. To test\nthe EAVN imaging performance for different sources, we observed four active\ngalactic nuclei (AGN) having different brightness and morphology. As a result,\nwe confirmed that Tianma 65-m Radio Telescope (TMRT) significantly enhances the\noverall array sensitivity, a factor of 4 improvement in baseline sensitivity\nand 2 in image dynamic range compared to the case of KaVA only. The addition of\nNanshan 26-m Radio Telescope (NSRT) further doubled the east-west angular\nresolution. With the resulting high-dynamic-range, high-resolution images with\nEAVN (KaVA+TMRT+NSRT), various fine-scale structures in our targets, such as\nthe counter-jet in M87, a kink-like morphology of the 3C273 jet and the weak\nemission in other sources, are successfully detected. This demonstrates the\npowerful capability of EAVN to study AGN jets and to achieve other science\ngoals in general. Ongoing expansion of EAVN will further enhance the angular\nresolution, detection sensitivity and frequency coverage of the network.\n"}
+{"abstract": "  For an appropriate choice of a $\\mathbb{Z}$-grading structure, we prove that\nthe wrapped Fukaya category of the symmetric square of a $(k+3)$-punctured\nsphere, i.e. the Weinstein manifold given as the complement of $(k+3)$ generic\nlines in $\\mathbb{C}P^2$ is quasi-equivalent to the derived category of\ncoherent sheaves on a singular surface $\\mathcal{Z}_{2,k}$ constructed as the\nboundary of a toric Landau-Ginzburg model $(\\mathcal{X}_{2,k},\n\\mathbf{w}_{2,k})$. We do this by first constructing a quasi-equivalence\nbetween certain categorical resolutions of both sides and then localising. We\nalso provide a general homological mirror symmetry conjecture concerning all\nthe higher symmetric powers of punctured spheres. The corresponding toric\nLG-models $(\\mathcal{X}_{n,k},\\mathbf{w}_{n,k})$ are constructed from the\ncombinatorics of curves on the punctured surface and are related to small toric\nresolutions of the singularity $x_1\\ldots x_{n+1}= v_1\\ldots v_k$.\n"}
+{"abstract": "  Monolayers of transition metal dichalcogenides are ideal materials to control\nboth spin and valley degrees of freedom either electrically or optically.\nNevertheless, optical excitation mostly generates excitons species with\ninherently short lifetime and spin/valley relaxation time. Here we demonstrate\na very efficient spin/valley optical pumping of resident electrons in n-doped\nWSe2 and WS2 monolayers. We observe that, using a continuous wave laser and\nappropriate doping and excitation densities, negative trion doublet lines\nexhibit circular polarization of opposite sign and the photoluminescence\nintensity of the triplet trion is more than four times larger with circular\nexcitation than with linear excitation. We interpret our results as a\nconsequence of a large dynamic polarization of resident electrons using\ncircular light.\n"}
+{"abstract": "  The nature of Wireless Sensor Networks (WSN) and the widespread of using WSN\nintroduce many security threats and attacks. An effective Intrusion Detection\nSystem (IDS) should be used to detect attacks. Detecting such an attack is\nchallenging, especially the detection of Denial of Service (DoS) attacks.\nMachine learning classification techniques have been used as an approach for\nDoS detection. This paper conducted an experiment using Waikato Environment for\nKnowledge Analysis (WEKA)to evaluate the efficiency of five machine learning\nalgorithms for detecting flooding, grayhole, blackhole, and scheduling at DoS\nattacks in WSNs. The evaluation is based on a dataset, called WSN-DS. The\nresults showed that the random forest classifier outperforms the other\nclassifiers with an accuracy of 99.72%.\n"}
+{"abstract": "  This paper discusses capabilities that are essential to models applied in\npolicy analysis settings and the limitations of direct applications of\noff-the-shelf machine learning methodologies to such settings. Traditional\neconometric methodologies for building discrete choice models for policy\nanalysis involve combining data with modeling assumptions guided by\nsubject-matter considerations. Such considerations are typically most useful in\nspecifying the systematic component of random utility discrete choice models\nbut are typically of limited aid in determining the form of the random\ncomponent. We identify an area where machine learning paradigms can be\nleveraged, namely in specifying and systematically selecting the best\nspecification of the random component of the utility equations. We review two\nrecent novel applications where mixed-integer optimization and cross-validation\nare used to algorithmically select optimal specifications for the random\nutility components of nested logit and logit mixture models subject to\ninterpretability constraints.\n"}
+{"abstract": "  The generation of non-Abelian geometric phases from a system of evanescently\ncoupled waveguides is extended towards the framework of nonorthogonal\ncoupled-mode theory. Here, we study an experimentally feasible tripod\narrangement of waveguides that contain dark states from which a nontrivial\nU(2)-mixing can be obtained by means of an adiabatic parameter variation. We\ninvestigate the influence of higher-order contributions beyond\nnearest-neighbour coupling as well as self-coupling on the stability of a\nU(3)-phase generated from an optical tetrapod setup. Our results indicate that,\ndespite the mode nonorthogonality, the symmetry of dark states protects the\ngeometric evolution of light from distortion.\n"}
+{"abstract": "  The canonical ensemble plays a crucial role in statistical mechanics in and\nout of equilibrium. For example, the standard derivation of the fluctuation\ntheorem relies on the assumption that the initial state of the heat bath is the\ncanonical ensemble. On the other hand, the recent progress in the foundation of\nstatistical mechanics has revealed that a thermal equilibrium state is not\nnecessarily described by the canonical ensemble but can be a quantum pure state\nor even a single energy eigenstate, as formulated by the eigenstate\nthermalization hypothesis (ETH). Then, a question raised is how these two\npictures, the canonical ensemble and a single energy eigenstate as a thermal\nequilibrium state, are compatible in the fluctuation theorem. In this paper, we\ntheoretically and numerically show that the fluctuation theorem holds in both\nof the long and short-time regimes, even when the initial state of the bath is\na single energy eigenstate of a many-body system. Our proof of the fluctuation\ntheorem in the long-time regime is based on the ETH, while it was previously\nshown in the short-time regime on the basis of the Lieb-Robinson bound and the\nETH [Phys. Rev. Lett. 119, 100601 (2017)]. The proofs for these time regimes\nare theoretically independent and complementary, implying the fluctuation\ntheorem in the entire time domain. We also perform a systematic numerical\nsimulation of hard-core bosons by exact diagonalization and verify the\nfluctuation theorem in both of the time regimes by focusing on the finite-size\nscaling. Our results contribute to the understanding of the mechanism that the\nfluctuation theorem emerges from unitary dynamics of quantum many-body systems,\nand can be tested by experiments with, e.g., ultracold atoms.\n"}
+{"abstract": "  Reinforcement learning in complex environments may require supervision to\nprevent the agent from attempting dangerous actions. As a result of supervisor\nintervention, the executed action may differ from the action specified by the\npolicy. How does this affect learning? We present the Modified-Action Markov\nDecision Process, an extension of the MDP model that allows actions to differ\nfrom the policy. We analyze the asymptotic behaviours of common reinforcement\nlearning algorithms in this setting and show that they adapt in different ways:\nsome completely ignore modifications while others go to various lengths in\ntrying to avoid action modifications that decrease reward. By choosing the\nright algorithm, developers can prevent their agents from learning to\ncircumvent interruptions or constraints, and better control agent responses to\nother kinds of action modification, like self-damage.\n"}
+{"abstract": "  We present the long-term X-ray spectral and temporal analysis of a 'bare-type\nAGN' Ark 120. We consider the observations from XMM-Newton, Suzaku, Swift, and\nNuSTAR from 2003 to 2018. The spectral properties of this source are studied\nusing various phenomenological and physical models present in the literature.\nWe report (a) the variations of several physical parameters, such as the\ntemperature and optical depth of the electron cloud, the size of the Compton\ncloud, and accretion rate for the last fifteen years. The spectral variations\nare explained from the change in the accretion dynamics; (b) the X-ray time\ndelay between 0.2-2 keV and 3-10 keV light-curves exhibited zero-delay in 2003,\npositive delay of 4.71 \\pm 2.1 ks in 2013, and negative delay of 4.15 \\pm 1.5\nks in 2014. The delays are explained considering Comptonization, reflection,\nand light-crossing time; (c) the long term intrinsic luminosities obtained\nusing nthcomp, of the soft-excess and the primary continuum show a correlation\nwith a Pearson Correlation Coefficient of 0.922. This indicates that the\nsoft-excess and the primary continuum are originated from the same physical\nprocess. From a physical model fitting, we infer that the soft excess for Ark\n120 could be due to a small number of scatterings in the Compton cloud. Using\nMonte-Carlo simulations, we show that indeed the spectra corresponding to fewer\nscatterings could provide a steeper soft-excess power-law in the 0.2-3 keV\nrange. Simulated luminosities are found to be in agreement with the observed\nvalues.\n"}
+{"abstract": "  We study the parameter dependence of complex geodesics with prescribed\nboundary value and direction on bounded strongly linearly convex domains.As an\nimportant application, we present a quantitative relationship between the\nregularity of the pluricomplex Poisson kernel of such a domain, which is a\nsolution to a homogeneous complex Monge-Amp\\`{e}re equation with boundary\nsingularity, and that of the boundary of the domain. Our results improve\nconsiderably previous ones in this direction due to Chang-Hu-Lee and\nBracci-Patrizio.\n"}
+{"abstract": "  The LIGO-Virgo-Kagra collaboration (LVC) discovered recently GW190521, a\ngravitational wave (GW) source associated with the merger between two black\nholes (BHs) with mass $66$ M$_\\odot$ and $>85$ M$_\\odot$. GW190521 represents\nthe first BH binary (BBH) merger with a primary mass falling in the \"upper\nmass-gap\" and the first leaving behind a $\\sim 150$ M$_\\odot$ remnant. So far,\nthe LVC reported the discovery of four further mergers having a total mass\n$>100$ M$_\\odot$, i.e. in the intermediate-mass black holes (IMBH) mass range.\nHere, we discuss results from a series of 80 $N$-body simulations of young\nmassive clusters (YMCs) that implement relativistic corrections to follow\ncompact object mergers. We discover the development of a GW190521-like system\nas the result of a 3rd-generation merger, and four IMBH-BH mergers with total\nmass $~(300-350)$ M$_\\odot$. We show that these IMBH-BH mergers are\nlow-frequency GW sources detectable with LISA and DECIGO out to redshift\n$z=0.01-0.1$ and $z>100$, and we discuss how their detection could help\nunravelling IMBH natal spins. For the GW190521 test case, we show that the\n3rd-generation merger remnant has a spin and effective spin parameter that\nmatches the $90\\%$ credible interval measured for GW190521 better than a\nsimpler double merger and comparably to a single merger. Due to GW recoil\nkicks, we show that retaining the products of these mergers require birth-sites\nwith escape velocities $\\gtrsim 50-100$ km s$^{-1}$, values typically attained\nin galactic nuclei and massive clusters with steep density profiles.\n"}
+{"abstract": "  Label noise is frequently observed in real-world large-scale datasets. The\nnoise is introduced due to a variety of reasons; it is heterogeneous and\nfeature-dependent. Most existing approaches to handling noisy labels fall into\ntwo categories: they either assume an ideal feature-independent noise, or\nremain heuristic without theoretical guarantees. In this paper, we propose to\ntarget a new family of feature-dependent label noise, which is much more\ngeneral than commonly used i.i.d. label noise and encompasses a broad spectrum\nof noise patterns. Focusing on this general noise family, we propose a\nprogressive label correction algorithm that iteratively corrects labels and\nrefines the model. We provide theoretical guarantees showing that for a wide\nvariety of (unknown) noise patterns, a classifier trained with this strategy\nconverges to be consistent with the Bayes classifier. In experiments, our\nmethod outperforms SOTA baselines and is robust to various noise types and\nlevels.\n"}
+{"abstract": "  A nearly autonomous management and control (NAMAC) system is designed to\nfurnish recommendations to operators for achieving particular goals based on\nNAMAC's knowledge base. As a critical component in a NAMAC system, digital\ntwins (DTs) are used to extract information from the knowledge base to support\ndecision-making in reactor control and management during all modes of plant\noperations. With the advancement of artificial intelligence and data-driven\nmethods, machine learning algorithms are used to build DTs of various functions\nin the NAMAC system. To evaluate the uncertainty of DTs and its impacts on the\nreactor digital instrumentation and control systems, uncertainty quantification\n(UQ) and software risk analysis is needed. As a comprehensive overview of prior\nresearch and a starting point for new investigations, this study selects and\nreviews relevant UQ techniques and software hazard and software risk analysis\nmethods that may be suitable for DTs in the NAMAC system.\n"}
+{"abstract": "  In this note, we prove that the problem of computing the linear discrepancy\nof a given matrix is $\\Pi_2$-hard, even to approximate within $9/8 - \\epsilon$\nfactor for any $\\epsilon > 0$. This strengthens the NP-hardness result of Li\nand Nikolov [ESA 2020] for the exact version of the problem, and answers a\nquestion posed by them. Furthermore, since Li and Nikolov showed that the\nproblem is contained in $\\Pi_2$, our result makes linear discrepancy another\nnatural problem that is $\\Pi_2$-complete (to approximate).\n"}
+{"abstract": "  Chemical space is routinely explored by machine learning methods to discover\ninteresting molecules, before time-consuming experimental synthesizing is\nattempted. However, these methods often rely on a graph representation,\nignoring 3D information necessary for determining the stability of the\nmolecules. We propose a reinforcement learning approach for generating\nmolecules in cartesian coordinates allowing for quantum chemical prediction of\nthe stability. To improve sample-efficiency we learn basic chemical rules from\nimitation learning on the GDB-11 database to create an initial model applicable\nfor all stoichiometries. We then deploy multiple copies of the model\nconditioned on a specific stoichiometry in a reinforcement learning setting.\nThe models correctly identify low energy molecules in the database and produce\nnovel isomers not found in the training set. Finally, we apply the model to\nlarger molecules to show how reinforcement learning further refines the\nimitation learning model in domains far from the training data.\n"}
+{"abstract": "  We report the first Vivaldi arrays monolithically fabricated exclusively\nusing commercial, low-cost, 3D metal printing (direct metal laser sintering).\nFurthermore, we developed one of the first dual-polarized Vivaldi arrays on a\ntriangular lattice, and compare it to a square lattice array. The triangular\nlattice is attractive because it has a 15.5% larger cell size compared to the\nsquare lattice and can be more naturally truncated into a wide range of\naperture shapes such as a rectangle, hexagon, or triangle. Both arrays operate\nat 3-20 GHz and scan angles out to 60 degree from normal. The fabrication\nprocess is significantly simplified compared to previously published Vivaldi\narrays since the antenna is ready for use directly after the standard printing\nprocess is complete. This rapid manufacturing is further expedited by printing\nthe 'Sub-Miniature Push-on, Micro' (SMPM) connectors directly onto the\nradiating elements, which simplifies assembly and reduces cost compared to\nutilizing discrete RF connectors. The arrays have a modular design that allow\nfor combining multiple sub-arrays together for arbitrarily increasing the\naperture size. Simulations and measurement show that our arrays have similar\nperformance as previously published Vivaldi arrays, but with simpler\nfabrication.\n"}
+{"abstract": "  We are not very good at measuring -- rigorously and quantitatively -- the\ncyber security of systems. Our ability to measure cyber resilience is even\nworse. And without measuring cyber resilience, we can neither improve it nor\ntrust its efficacy. It is difficult to know if we are improving or degrading\ncyber resilience when we add another control, or a mix of controls, to harden\nthe system. The only way to know is to specifically measure cyber resilience\nwith and without a particular set of controls. What needs to be measured are\ntemporal patterns of recovery and adaptation, and not time-independent failure\nprobabilities. In this paper, we offer a set of criteria that would ensure\ndecision-maker confidence in the reliability of the methodology used in\nobtaining a meaningful measurement.\n"}
+{"abstract": "  Recently, Gross, Mansour and Tucker introduced the partial-dual genus\npolynomial of a ribbon graph as a generating function that enumerates the\npartial duals of the ribbon graph by genus. It is analogous to the\nextensively-studied polynomial in topological graph theory that enumerates by\ngenus all embeddings of a given graph. To investigate the partial-dual genus\npolynomial one only needs to focus on bouquets, i.e. ribbon graphs with only\none vertex. In this paper, we shall further show that the partial-dual genus\npolynomial of a bouquet essentially depends on the signed intersection graph of\nthe bouquet rather than on the bouquet itself. That is to say the bouquets with\nthe same signed intersection graph will have the same partial-dual genus\npolynomial. We then prove that the partial-dual genus polynomial of a bouquet\ncontains non-zero constant term if and only if its signed intersection graph is\npositive and bipartite. Finally we consider a conjecture posed by Gross,\nMansour and Tucker. that there is no orientable ribbon graph whose partial-dual\ngenus polynomial has only one non-constant term, we give a characterization of\nnon-empty bouquets whose partial-dual genus polynomials have only one term by\nconsider non-orientable case and orientable case separately.\n"}
+{"abstract": "  We obtain global, non-asymptotic convergence guarantees for independent\nlearning algorithms in competitive reinforcement learning settings with two\nagents (i.e., zero-sum stochastic games). We consider an episodic setting where\nin each episode, each player independently selects a policy and observes only\ntheir own actions and rewards, along with the state. We show that if both\nplayers run policy gradient methods in tandem, their policies will converge to\na min-max equilibrium of the game, as long as their learning rates follow a\ntwo-timescale rule (which is necessary). To the best of our knowledge, this\nconstitutes the first finite-sample convergence result for independent policy\ngradient methods in competitive RL; prior work has largely focused on\ncentralized, coordinated procedures for equilibrium computation.\n"}
+{"abstract": "  Recently, many enterprises are facing the difficulties brought out by the\nlimitation of warehouse land and the increase of loan cost. As a promising\napproach to improve space utilization rate, puzzle-based storage systems\n(PBSSs) are drawing more attention from logistics researchers. In previous\nresearch about PBSS, concentration has been paid to single target item\nproblems. However, there are no consensus algorithms to solve load retrievals\nroute programming in PBSSs with multiple target items. In this paper, a\nheuristic algorithm is proposed to solve load retrievals route programming in\nPBSSs with multiple target items, multiple escorts and multiple IOs. In this\npaper, new concepts about the proposed algorithm are defined, including target\nIOs, target position of escorts, number of escorts required, et al. Then, the\ndecision procedures are designed according to these concepts. Based on Markov\ndecision process, the proposed algorithm makes the decision of the next action\nby analyzing the current status, until all the target items arrive at the IOs.\nThe case study results have shown the effectiveness and efficiency of the\nproposed heuristic algorithm.\n"}
+{"abstract": "  We discuss the propagation of surface waves in an isotropic half space\nmodelled with the linear Cosserat theory of isotropic elastic materials. To\nthis aim we use a method based on the algebraic analysis of the surface\nimpedance matrix and on the algebraic Riccati equation, and which is\nindependent of the common Stroh formalism. Due to this method, a new algorithm\nwhich determines the amplitudes and the wave speed in the theory of isotropic\nelastic Cosserat materials is described. Moreover, the method allows to prove\nthe existence and uniqueness of a subsonic solution of the secular equation, a\nproblem which remains unsolved in almost all generalised linear theories of\nelastic materials. Since the results are suitable to be used for numerical\nimplementations, we propose two numerical algorithms which are viable for any\nelastic material. Explicit numerical calculations are made for alumunium-epoxy\nin the context of the Cosserat model. Since the novel form of the secular\nequation for isotropic elastic material has not been explicitly derived\nelsewhere, we establish it in this paper, too.\n"}
+{"abstract": "  Deep neural network (DNN) is a popular model implemented in many systems to\nhandle complex tasks such as image classification, object recognition, natural\nlanguage processing etc. Consequently DNN structural vulnerabilities become\npart of the security vulnerabilities in those systems. In this paper we study\nthe root cause of DNN adversarial examples. We examine the DNN response surface\nto understand its classification boundary. Our study reveals the structural\nproblem of DNN classification boundary that leads to the adversarial examples.\nExisting attack algorithms can generate from a handful to a few hundred\nadversarial examples given one clean image. We show there are infinitely many\nadversarial images given one clean sample, all within a small neighborhood of\nthe clean sample. We then define DNN uncertainty regions and show\ntransferability of adversarial examples is not universal. We also argue that\ngeneralization error, the large sample theoretical guarantee established for\nDNN, cannot adequately capture the phenomenon of adversarial examples. We need\nnew theory to measure DNN robustness.\n"}
+{"abstract": "  A new solution for the Dutch national flag problem is proposed, requiring no\nthree-way comparisons, which gives quicksort a proper worst-case runtime of\n$O(nk)$ for inputs with $k$ distinct elements. This is used together with other\nknown and novel techniques to construct a hybrid sort that is never\nsignificantly slower than regular quicksort while speeding up drastically for\nmany input distributions.\n"}
+{"abstract": "  It is established that in the tensionless limit the chiral superstring\nintegrand is reduced to the chiral integrand of the ambitwistor string.\n"}
+{"abstract": "  Binarized Neural Networks (BNNs) have the potential to revolutionize the way\nthat deep learning is carried out in edge computing platforms. However, the\neffectiveness of interpretability methods on these networks has not been\nassessed.\n  In this paper, we compare the performance of several widely used saliency\nmap-based interpretabilty techniques (Gradient, SmoothGrad and GradCAM), when\napplied to Binarized or Full Precision Neural Networks (FPNNs). We found that\nthe basic Gradient method produces very similar-looking maps for both types of\nnetwork. However, SmoothGrad produces significantly noisier maps for BNNs.\nGradCAM also produces saliency maps which differ between network types, with\nsome of the BNNs having seemingly nonsensical explanations. We comment on\npossible reasons for these differences in explanations and present it as an\nexample of why interpretability techniques should be tested on a wider range of\nnetwork types.\n"}
+{"abstract": "  In this paper we establish the existence and multiplicity of nontrivial\nsolutions to the following problem \\begin{align*} \\begin{split}\n(-\\Delta)^{\\frac{1}{2}}u+u+(\\ln|\\cdot|*|u|^2)&=f(u)+\\mu|u|^{-\\gamma-1}u,~\\text{in}~\\mathbb{R},\n\\end{split} \\end{align*} where $\\mu>0$, $(*)$ is the convolution operation\nbetween two functions, $0<\\gamma<1$, $f$ is a function with a certain type of\ngrowth. We prove the existence of a nontrivial solution at a certain mountain\npass level and another ground state solution when the nonlinearity $f$ is of\nexponential critical growth.\n"}
+{"abstract": "  We discuss the possible role of the Tietze extension theorem in providing a\nrigorous topological base to the expanding space-time in cosmology. A simple\ntoy model has been introduced to show the analogy between the topological\nextension from a circle $S$ to the whole space $M$ and the cosmic expansion\nfrom a non-zero volume to the whole space-time in non-singular cosmological\nmodels. A topological analogy to the cosmic scale factor function has been\nsuggested, the paper refers to the possible applications of the topological\nextension in mathematical physics.\n"}
+{"abstract": "  Attention-based encoder-decoder framework is widely used in the scene text\nrecognition task. However, for the current state-of-the-art(SOTA) methods,\nthere is room for improvement in terms of the efficient usage of local visual\nand global context information of the input text image, as well as the robust\ncorrelation between the scene processing module(encoder) and the text\nprocessing module(decoder). In this paper, we propose a Representation and\nCorrelation Enhanced Encoder-Decoder Framework(RCEED) to address these\ndeficiencies and break performance bottleneck. In the encoder module, local\nvisual feature, global context feature, and position information are aligned\nand fused to generate a small-size comprehensive feature map. In the decoder\nmodule, two methods are utilized to enhance the correlation between scene and\ntext feature space. 1) The decoder initialization is guided by the holistic\nfeature and global glimpse vector exported from the encoder. 2) The feature\nenriched glimpse vector produced by the Multi-Head General Attention is used to\nassist the RNN iteration and the character prediction at each time step.\nMeanwhile, we also design a Layernorm-Dropout LSTM cell to improve model's\ngeneralization towards changeable texts. Extensive experiments on the\nbenchmarks demonstrate the advantageous performance of RCEED in scene text\nrecognition tasks, especially the irregular ones.\n"}
+{"abstract": "  The presence of smart objects is increasingly widespread and their ecosystem,\nalso known as Internet of Things, is relevant in many different application\nscenarios. The huge amount of temporally annotated data produced by these smart\ndevices demand for efficient techniques for transfer and storage of time series\ndata. Compression techniques play an important role toward this goal and,\ndespite the fact that standard compression methods could be used with some\nbenefit, there exist several ones that specifically address the case of time\nseries by exploiting their peculiarities to achieve a more effective\ncompression and a more accurate decompression in the case of lossy compression\ntechniques. This paper provides a state-of-the-art survey of the principal time\nseries compression techniques, proposing a taxonomy to classify them\nconsidering their overall approach and their characteristics. Furthermore, we\nanalyze the performances of the selected algorithms by discussing and comparing\nthe experimental results that where provided in the original articles. The goal\nof this paper is to provide a comprehensive and homogeneous reconstruction of\nthe state-of-the-art which is currently fragmented across many papers that use\ndifferent notations and where the proposed methods are not organized according\nto a classification.\n"}
+{"abstract": "  Long-term neutrino-radiation hydrodynamics simulations in full general\nrelativity are performed for the collapse of rotating massive stars that are\nevolved from He-stars with their initial mass of $20$ and $32M_\\odot$. It is\nshown that if the collapsing stellar core has sufficient angular momentum, the\nrotationally-supported proto-neutron star (PNS) survives for seconds\naccompanying the formation of a massive torus of mass larger than $1\\,M_\\odot$.\nSubsequent mass accretion onto the central region produces a massive and\ncompact central object, and eventually enhances the neutrino luminosity beyond\n$10^{53}$\\,erg/s, resulting in a very delayed neutrino-driven explosion in\nparticular toward the polar direction. The kinetic energy of the explosion can\nbe appreciably higher than $10^{52}$ erg for a massive progenitor star and\ncompatible with that of energetic supernovae like broad-line type-Ic\nsupernovae. By the subsequent accretion, the massive PNS collapses eventually\ninto a rapidly spinning black hole, which could be a central engine for\ngamma-ray bursts if a massive torus surrounds it.\n"}
+{"abstract": "  From tiny pacemaker chips to aircraft collision avoidance systems, the\nstate-of-the-art Cyber-Physical Systems (CPS) have increasingly started to rely\non Deep Neural Networks (DNNs). However, as concluded in various studies, DNNs\nare highly susceptible to security threats, including adversarial attacks. In\nthis paper, we first discuss different vulnerabilities that can be exploited\nfor generating security attacks for neural network-based systems. We then\nprovide an overview of existing adversarial and fault-injection-based attacks\non DNNs. We also present a brief analysis to highlight different challenges in\nthe practical implementation of adversarial attacks. Finally, we also discuss\nvarious prospective ways to develop robust DNN-based systems that are resilient\nto adversarial and fault-injection attacks.\n"}
+{"abstract": "  This paper studies the robust satisfiability check and online control\nsynthesis problems for uncertain discrete-time systems subject to signal\ntemporal logic (STL) specifications. Different from existing techniques, this\nwork proposes an approach based on STL, reachability analysis, and temporal\nlogic trees. Firstly, a real-time version of STL semantics and a tube-based\ntemporal logic tree are proposed. We show that such a tree can be constructed\nfrom every STL formula. Secondly, using the tube-based temporal logic tree, a\nsufficient condition is obtained for the robust satisfiability check of the\nuncertain system. When the underlying system is deterministic, a necessary and\nsufficient condition for satisfiability is obtained. Thirdly, an online control\nsynthesis algorithm is designed. It is shown that when the STL formula is\nrobustly satisfiable and the initial state of the system belongs to the initial\nroot node of the tube-based temporal logic tree, it is guaranteed that the\ntrajectory generated by the controller satisfies the STL formula. The\neffectiveness of the proposed approach is verified by an automated car\novertaking example.\n"}
+{"abstract": "  Recent exploration methods have proven to be a recipe for improving\nsample-efficiency in deep reinforcement learning (RL). However, efficient\nexploration in high-dimensional observation spaces still remains a challenge.\nThis paper presents Random Encoders for Efficient Exploration (RE3), an\nexploration method that utilizes state entropy as an intrinsic reward. In order\nto estimate state entropy in environments with high-dimensional observations,\nwe utilize a k-nearest neighbor entropy estimator in the low-dimensional\nrepresentation space of a convolutional encoder. In particular, we find that\nthe state entropy can be estimated in a stable and compute-efficient manner by\nutilizing a randomly initialized encoder, which is fixed throughout training.\nOur experiments show that RE3 significantly improves the sample-efficiency of\nboth model-free and model-based RL methods on locomotion and navigation tasks\nfrom DeepMind Control Suite and MiniGrid benchmarks. We also show that RE3\nallows learning diverse behaviors without extrinsic rewards, effectively\nimproving sample-efficiency in downstream tasks. Source code and videos are\navailable at https://sites.google.com/view/re3-rl.\n"}
+{"abstract": "  Human action recognition is an active research area in computer vision.\nAlthough great process has been made, previous methods mostly recognize actions\nbased on depth data at only one scale, and thus they often neglect multi-scale\nfeatures that provide additional information action recognition in practical\napplication scenarios. In this paper, we present a novel framework focusing on\nmulti-scale motion information to recognize human actions from depth video\nsequences. We propose a multi-scale feature map called Laplacian pyramid depth\nmotion images(LP-DMI). We employ depth motion images (DMI) as the templates to\ngenerate the multi-scale static representation of actions. Then, we caculate\nLP-DMI to enhance multi-scale dynamic information of motions and reduces\nredundant static information in human bodies. We further extract the\nmulti-granularity descriptor called LP-DMI-HOG to provide more discriminative\nfeatures. Finally, we utilize extreme learning machine (ELM) for action\nclassification. The proposed method yeilds the recognition accuracy of 93.41%,\n85.12%, 91.94% on public MSRAction3D dataset, UTD-MHAD and DHA dataset. Through\nextensive experiments, we prove that our method outperforms state-of-the-art\nbenchmarks.\n"}
+{"abstract": "  Selective laser melting is receiving increasing interest as an additive\nmanufacturing technique. Residual stresses induced by the large temperature\ngradients and inhomogeneous cooling process can favour the generation of\ncracks. In this work, a crystal plasticity finite element model is developed to\nsimulate the formation of residual stresses and to understand the correlation\nbetween plastic deformation, grain orientation and residual stresses in the\nadditive manufacturing process. The temperature profile and grain structure\nfrom thermal-fluid flow and grain growth simulations are implemented into the\ncrystal plasticity model. An element elimination and reactivation method is\nproposed to model the melting and solidification and to reinitialise state\nvariables, such as the plastic deformation, in the reactivated elements. The\naccuracy of this method is judged against previous method based on the\nstiffness degradation of liquid regions by comparing the plastic deformation as\na function of time induced by thermal stresses. The method is used to\ninvestigate residual stresses parallel and perpendicular to the laser scan\ndirection, and the correlation with the maximum Schmid factor of the grains\nalong those directions. The magnitude of the residual stress can be predicted\nas a function of the depth, grain orientation and position with respect to the\nmolten pool.\n"}
+{"abstract": "  The exceptional combination of strength and ductility in multi-component\nalloys is often attributed to the interaction of dislocations with the various\nsolute atoms in the alloy. To study these effects on the mechanical properties\nof such alloys there is a need to develop a modeling framework capable of\nquantifying the effect of these solutes on the evolution of dislocation\nnetworks. Large scale three-dimensional (3D) Discrete dislocation dynamics\n(DDD) simulations can provide access to such studies but to date no relevant\napproaches are available that aim for a complete representation of real alloys\nwith arbitrary chemical compositions. Here, we introduce a formulation of\ndislocation interaction with substitutional solute atoms in fcc alloys in 3D\nDDD simulations that accounts for solute strengthening induced by atomic misfit\nas well as fluctuations in the cross-slip activation energy. Using this model,\nwe show that local fluctuations in the chemical composition of various\nCrFeCoNi-based multi-principal element alloys (MPEA) lead to sluggish\ndislocation motion, frequent cross-slip and alignment of dislocations with\nsolute aggregation features, explaining experimental observations related to\nmechanical behavior and dislocation activity. It is also demonstrated, that\nthis behavior observed for certain MPEAs cannot be reproduced by assuming a\nperfect solid solution. The developed method also provides a basis for further\ninvestigations of dislocation plasticity in any real arbitrary fcc alloy with\nsubstitutional solutes.\n"}
+{"abstract": "  The spacetime in the interior of a black hole can be described by an\nhomogeneous line element, for which the Einstein--Hilbert action reduces to a\none-dimensional mechanical model. We have shown in [SciPost Phys. 10, 022\n(2021), [2010.07059]] that this model exhibits a symmetry under the\n$(2+1)$-dimensional Poincar\\'e group. Here we explain how this can be\nunderstood as a broken infinite-dimensional BMS$_3$ symmetry. This is done by\nreinterpreting the action for the model as a geometric action for BMS$_3$,\nwhere the configuration space variables are elements of the algebra\n$\\mathfrak{bms}_3$ and the equations of motion transform as coadjoint vectors.\nThe Poincar\\'e subgroup then arises as the stabilizer of the vacuum orbit. This\nsymmetry breaking is analogous to what happens with the Schwarzian action in\nAdS$_2$ JT gravity, although in the present case there is no direct\ninterpretation in terms of boundary symmetries. This observation, together with\nthe fact that other lower-dimensional gravitational models (such as the BTZ\nblack hole) possess the same broken BMS$_3$ symmetries, provides yet another\nillustration of the ubiquitous role played by this group.\n"}
+{"abstract": "  Embedding nonlinear dynamical systems into artificial neural networks is a\npowerful new formalism for machine learning. By parameterizing ordinary\ndifferential equations (ODEs) as neural network layers, these Neural ODEs are\nmemory-efficient to train, process time-series naturally and incorporate\nknowledge of physical systems into deep learning models. However, the practical\napplications of Neural ODEs are limited due to long inference times, because\nthe outputs of the embedded ODE layers are computed numerically with\ndifferential equation solvers that can be computationally demanding. Here we\nshow that mathematical model order reduction methods can be used for\ncompressing and accelerating Neural ODEs by accurately simulating the\ncontinuous nonlinear dynamics in low-dimensional subspaces. We implement our\nnovel compression method by developing Neural ODEs that integrate the necessary\nsubspace-projection and interpolation operations as layers of the neural\nnetwork. We validate our model reduction approach by comparing it to two\nestablished acceleration methods from the literature in two classification\nasks. In compressing convolutional and recurrent Neural ODE architectures, we\nachieve the best balance between speed and accuracy when compared to the other\ntwo acceleration methods. Based on our results, our integration of model order\nreduction with Neural ODEs can facilitate efficient, dynamical system-driven\ndeep learning in resource-constrained applications.\n"}
+{"abstract": "  Superconducting crystals with lack of inversion symmetry can potentially host\nunconventional pairing. However, till date, no direct conclusive experimental\nevidence of such unconventional order parameters in non-centrosymmetric\nsuperconductors has been reported. In this paper, through direct measurement of\nthe superconducting energy gap by scanning tunnelling spectroscopy, we report\nthe existence of both $s$-wave (singlet) and $p$-wave (triplet) pairing\nsymmetries in non-centrosymmetric Ru$_7$B$_3$. Our temperature and magnetic\nfield dependent studies also indicate that the relative amplitudes of the\nsinglet and triplet components of the order parameter change differently with\ntemperature.\n"}
+{"abstract": "  In this paper, we introduce a concept of norm-attainment in the projective\nsymmetric tensor product $\\widehat{\\otimes}_{\\pi,s,N} X$ of a Banach space $X$,\nwhich turns out to be naturally related to the classical norm-attainment of\n$N$-homogeneous polynomials on $X$. Due to this relation, we can prove that\nthere exist symmetric tensors that do not attain their norms, which allows us\nto study the problem of when the set of norm-attaining elements in\n$\\widehat{\\otimes}_{\\pi,s,N} X$ is dense. We show that the set of all\nnorm-attaining symmetric tensors is dense in $\\widehat{\\otimes}_{\\pi,s,N} X$\nfor a large set of Banach spaces as $L_p$-spaces, isometric $L_1$-predual\nspaces or Banach spaces with monotone Schauder basis, among others. Next, we\nprove that if $X^*$ satisfies the Radon-Nikod\\'ym and the approximation\nproperty, then the set of all norm-attaining symmetric tensors in\n$\\widehat{\\otimes}_{\\pi,s,N} X^*$ is dense. From these techniques, we can\npresent new examples of Banach spaces $X$ and $Y$ such that the set of all\nnorm-attaining tensors in the projective tensor product $X\n\\widehat{\\otimes}_\\pi Y$ is dense, answering positively an open question from\nthe paper by S. Dantas, M. Jung, \\'O. Rold\\'an and A. Rueda Zoca.\n"}
+{"abstract": "  We give a simple unified proof for all existing rational hypergeometric\nRamanujan identities for $1/\\pi$, and give a complete survey (without proof) of\nseveral generalizations: rational hypergeometric identities for $1/\\pi^c$,\nTaylor expansions, upside-down formulas, and supercongruences.\n"}
+{"abstract": "  Large-scale Web-based services present opportunities for improving UI\npolicies based on observed user interactions. We address challenges of learning\nsuch policies through model-free offline Reinforcement Learning (RL) with\noff-policy training. Deployed in a production system for user authentication in\na major social network, it significantly improves long-term objectives. We\narticulate practical challenges, compare several ML techniques, provide\ninsights on training and evaluation of RL models, and discuss generalizations.\n"}
+{"abstract": "  We introduce tools from discrete convexity theory and polyhedral geometry\ninto the theory of West's stack-sorting map $s$. Associated to each permutation\n$\\pi$ is a particular set $\\mathcal V(\\pi)$ of integer compositions that\nappears in a formula for the fertility of $\\pi$, which is defined to be\n$|s^{-1}(\\pi)|$. These compositions also feature prominently in more general\nformulas involving families of colored binary plane trees called troupes and in\na formula that converts from free to classical cumulants in noncommutative\nprobability theory. We show that $\\mathcal V(\\pi)$ is a transversal discrete\npolymatroid when it is nonempty. We define the fertilitope of $\\pi$ to be the\nconvex hull of $\\mathcal V(\\pi)$, and we prove a surprisingly simple\ncharacterization of fertilitopes as nestohedra arising from full binary plane\ntrees. Using known facts about nestohedra, we provide a procedure for\ndescribing the structure of the fertilitope of $\\pi$ directly from $\\pi$ using\nBousquet-M\\'elou's notion of the canonical tree of $\\pi$. As a byproduct, we\nobtain a new combinatorial cumulant conversion formula in terms of\ngeneralizations of canonical trees that we call quasicanonical trees. We also\napply our results on fertilitopes to study combinatorial properties of the\nstack-sorting map. In particular, we show that the set of fertility numbers has\ndensity $1$, and we determine all infertility numbers of size at most $126$.\nFinally, we reformulate the conjecture that $\\sum_{\\sigma\\in\ns^{-1}(\\pi)}x^{\\text{des}(\\sigma)+1}$ is always real-rooted in terms of\nnestohedra, and we propose natural ways in which this new version of the\nconjecture could be extended.\n"}
+{"abstract": "  In this article, the form of basis set for solving helium Schr\\\"{o}dinger\nequation is reinvestigated in perspective of geometry. With the help of theorem\nproved by Gu $et~al.$, we construct a convenient variational basis set, which\nemphasizes the geometric characteristics of trial wavefuncions. The main\nadvantage of this basis is that the angular part is complete for natural $L$\nstates with $L + 1$ terms and for unnatural $L$ states with $L$ terms, where\n$L$ is the total angular quantum number. Compared with basis sets which contain\nthree Euler angles, this basis is very simple to use. More importantly, this\nbasis is quite easy to be generalized to more particle systems.\n"}
+{"abstract": "  It is well-known that when translating terms into graphical formalisms many\ninnessential details are abstracted away. In the case of $\\lambda$-calculus and\nproof-nets, these innessential details are captured by a notion of equivalence\non $\\lambda$-terms known as $\\simeq_\\sigma$-equivalence in both the\nintuitionistic (due to Regnier) and classical (due to Laurent) cases. The\npurpose of this paper is to uncover a strong bisimulation behind\n$\\simeq_\\sigma$-equivalence, as formulated by Laurent for Parigot's\n$\\lambda\\mu$-calculus. This is achieved by introducing a relation $\\simeq$,\ndefined over a revised presentation of $\\lambda\\mu$-calculus we dub $\\Lambda\nM$.\n  More precisely, we first identify the reasons behind Laurent's\n$\\simeq_\\sigma$-equivalence failing to be a strong bisimulation. Inspired by\nLaurent's Polarized Proof-Nets, this leads us to distinguish multiplicative and\nexponential reduction steps on terms. Second, we provide an enriched syntax\nthat allows to keep track of the renaming operation. These technical\ningredients are crucial to pave the way towards a strong bisimulation for the\nclassical case. We thus introduce a calculus $\\Lambda M$ and a relation\n$\\simeq$ that we show to be a strong bisimulation with respect to reduction in\n$\\Lambda M$, i.e. two $\\simeq$-equivalent terms have the exact same reduction\nsemantics, a result which fails for Regnier's $\\simeq_\\sigma$-equivalence in\n$\\lambda$-calculus as well as for Laurent's $\\simeq_\\sigma$-equivalence in\n$\\lambda\\mu$. We also show that two $\\simeq$-equivalent terms translate to\nequivalent Polarized Proof-Nets. Although $\\simeq$ is not strictly included in\nLaurent's $\\simeq_\\sigma$, it can be seen as a restriction of it.\n"}
+{"abstract": "  Learning musical instruments using online instructional videos has become\nincreasingly prevalent. However, pre-recorded videos lack the instantaneous\nfeedback and personal tailoring that human tutors provide. In addition,\nexisting video navigations are not optimized for instrument learning, making\nthe learning experience encumbered. Guided by our formative interviews with\nguitar players and prior literature, we designed Soloist, a mixed-initiative\nlearning framework that automatically generates customizable curriculums from\noff-the-shelf guitar video lessons. Soloist takes raw videos as input and\nleverages deep-learning based audio processing to extract musical information.\nThis back-end processing is used to provide an interactive visualization to\nsupport effective video navigation and real-time feedback on the user's\nperformance, creating a guided learning experience. We demonstrate the\ncapabilities and specific use-cases of Soloist within the domain of learning\nelectric guitar solos using instructional YouTube videos. A remote user study,\nconducted to gather feedback from guitar players, shows encouraging results as\nthe users unanimously preferred learning with Soloist over unconverted\ninstructional videos.\n"}
+{"abstract": "  We explore the nonunitary dynamics of $(2+1)$-dimensional free fermions and\nshow that the obtained steady state is critical regardless the strength of the\nnonunitary evolution. Numerical results indicate that the entanglement entropy\nhas a logarithmic violation of the area-law and the mutual information between\ntwo distant regions decays as a power-law function. In particular, we provide\nan interpretation of these scaling behaviors in terms of a simple quasiparticle\npair picture. In addition, we study the dynamics of the correlation function\nand demonstrate that this system has dynamical exponent $z=1$. We further\ndemonstrate the dynamics of the correlation function can be well captured by a\nclassical nonlinear master equation. Our method opens a door to a vast number\nof nonunitary random dynamics in free fermions and can be generalized to any\ndimensions.\n"}
+{"abstract": "  Many cultural traits characterizing intelligent behaviors are now thought to\nbe transmitted through statistical learning, motivating us to study its effects\non cultural evolution. We conduct a large-scale music data analysis and observe\nthat various statistical parameters of musical products approximately follow\nthe beta distribution and other conjugate distributions. We construct a simple\nmodel of cultural evolution incorporating statistical learning and analytically\nshow that conjugate distributions emerge at equilibrium in the presence of\noblique transmission. The results demonstrate that the distribution of a\ncultural trait within a population depends on the individual's model for\ncultural production (the conjugate distribution law), and reveal interesting\npossibilities for theoretical and experimental studies on cultural evolution\nand social learning.\n"}
+{"abstract": "  We investigate the balance of power between stars and AGN across cosmic\nhistory, based on the comparison between the infrared (IR) galaxy luminosity\nfunction (LF) and the IR AGN LF. The former corresponds to emission from dust\nheated by stars and AGN, whereas the latter includes emission from AGN-heated\ndust only. We find that at all redshifts (at least up to z~2.5), the high\nluminosity tails of the two LFs converge, indicating that the most\ninfrared-luminous galaxies are AGN-powered. Our results shed light to the\ndecades-old conundrum regarding the flatter high-luminosity slope seen in the\nIR galaxy LF compared to that in the UV and optical. We attribute this\ndifference to the increasing fraction of AGN-dominated galaxies with increasing\ntotal infrared luminosity (L_IR). We partition the L_IR-z parameter space into\na star-formation and an AGN-dominated region, finding that the most luminous\ngalaxies at all epochs lie in the AGN-dominated region. This sets a potential\n`limit' to attainable star formation rates, casting doubt on the abundance of\n`extreme starbursts': if AGN did not exist, L_IR>10^13 Lsun galaxies would be\nsignificantly rarer than they currently are in our observable Universe. We also\nfind that AGN affect the average dust temperatures (T_dust) of galaxies and\nhence the shape of the well-known L_IR-T_dust relation. We propose that the\nreason why local ULIRGs are hotter than their high redshift counterparts is\nbecause of a higher fraction of AGN-dominated galaxies amongst the former\ngroup.\n"}
+{"abstract": "  The properties of a supersolid state (SS) in quasi-one-dimensional dipolar\nBose-Einstein condensate is studied, considering two possible mechanisms of\nrealization - due to repulsive three-body atomic interactions and quantum\nfluctuations in the framework of the Lee-Huang-Yang (LHY) theory. The proposed\ntheoretical model, based on minimization of the energy functional, allows\nevaluating the amplitude of the SS for an arbitrary set of parameters in the\ngoverning Gross-Pitaevskii equation (GPE). To explore the dynamics of the SS\nfirst, we numerically construct its ground state in different settings,\nincluding periodic boundary conditions, box-like trap and parabolic potential,\nthen impose a perturbation. In oscillations of the perturbed supersolid we\nobserve the key manifestation of SS, namely the free flow of the superfluid\nfraction through the crystalline component of the system. Two distinct\noscillation frequencies of the supersolid associated with the superfluid\nfraction and crystalline components of the wave function are identified from\nnumerical simulations of the GPE.\n"}
+{"abstract": "  Arguably the most common and salient object in daily video communications is\nthe talking head, as encountered in social media, virtual classrooms,\nteleconferences, news broadcasting, talk shows, etc. When communication\nbandwidth is limited by network congestions or cost effectiveness, compression\nartifacts in talking head videos are inevitable. The resulting video quality\ndegradation is highly visible and objectionable due to high acuity of human\nvisual system to faces. To solve this problem, we develop a multi-modality deep\nconvolutional neural network method for restoring face videos that are\naggressively compressed. The main innovation is a new DCNN architecture that\nincorporates known priors of multiple modalities: the video-synchronized speech\nsignal and semantic elements of the compression code stream, including motion\nvectors, code partition map and quantization parameters. These priors strongly\ncorrelate with the latent video and hence they are able to enhance the\ncapability of deep learning to remove compression artifacts. Ample empirical\nevidences are presented to validate the superior performance of the proposed\nDCNN method on face videos over the existing state-of-the-art methods.\n"}
+{"abstract": "  In this paper we study the spontaneous development of symmetries in the early\nlayers of a Convolutional Neural Network (CNN) during learning on natural\nimages. Our architecture is built in such a way to mimic the early stages of\nbiological visual systems. In particular, it contains a pre-filtering step\n$\\ell^0$ defined in analogy with the Lateral Geniculate Nucleus (LGN).\nMoreover, the first convolutional layer is equipped with lateral connections\ndefined as a propagation driven by a learned connectivity kernel, in analogy\nwith the horizontal connectivity of the primary visual cortex (V1). The layer\n$\\ell^0$ shows a rotational symmetric pattern well approximated by a Laplacian\nof Gaussian (LoG), which is a well-known model of the receptive profiles of LGN\ncells. The convolutional filters in the first layer can be approximated by\nGabor functions, in agreement with well-established models for the profiles of\nsimple cells in V1. We study the learned lateral connectivity kernel of this\nlayer, showing the emergence of orientation selectivity w.r.t. the learned\nfilters. We also examine the association fields induced by the learned kernel,\nand show qualitative and quantitative comparisons with known group-based models\nof V1 horizontal connectivity. These geometric properties arise spontaneously\nduring the training of the CNN architecture, analogously to the emergence of\nsymmetries in visual systems thanks to brain plasticity driven by external\nstimuli.\n"}
+{"abstract": "  Phenomics is concerned with detailed description of all aspects of organisms,\nfrom their physical foundations at genetic, molecular and cellular level, to\nbehavioural and psychological traits. Neuropsychiatric phenomics, endorsed by\nNIMH, provides such broad perspective to understand mental disorders. It is\nclear that learning sciences also need similar approach that will integrate\nefforts to understand cognitive processes from the perspective of the brain\ndevelopment, in temporal, spatial, psychological and social aspects. The brain\nis a substrate shaped by genetic, epigenetic, cellular and environmental\nfactors including education, individual experiences and personal history,\nculture, social milieu. Learning sciences should thus be based on the\nfoundation of neurocognitive phenomics. A brief review of selected aspects of\nsuch approach is presented, outlining new research directions. Central,\nperipheral and motor processes in the brain are linked to the inventory of the\nlearning styles.\n"}
+{"abstract": "  This article describes novel approaches to quickly estimate planar surfaces\nfrom RGBD sensor data. The approach manipulates the standard algebraic fitting\nequations into a form that allows many of the needed regression variables to be\ncomputed directly from the camera calibration information. As such, much of the\ncomputational burden required by a standard algebraic surface fit can be\npre-computed. This provides a significant time and resource savings, especially\nwhen many surface fits are being performed which is often the case when RGBD\npoint-cloud data is being analyzed for normal estimation, curvature estimation,\npolygonization or 3D segmentation applications. Using an integral image\nimplementation, the proposed approaches show a significant increase in\nperformance compared to the standard algebraic fitting approaches.\n"}
+{"abstract": "  Compressing Deep Neural Network (DNN) models to alleviate the storage and\ncomputation requirements is essential for practical applications, especially\nfor resource limited devices. Although capable of reducing a reasonable amount\nof model parameters, previous unstructured or structured weight pruning methods\ncan hardly truly accelerate inference, either due to the poor hardware\ncompatibility of the unstructured sparsity or due to the low sparse rate of the\nstructurally pruned network. Aiming at reducing both storage and computation,\nas well as preserving the original task performance, we propose a generalized\nweight unification framework at a hardware compatible micro-structured level to\nachieve high amount of compression and acceleration. Weight coefficients of a\nselected micro-structured block are unified to reduce the storage and\ncomputation of the block without changing the neuron connections, which turns\nto a micro-structured pruning special case when all unified coefficients are\nset to zero, where neuron connections (hence storage and computation) are\ncompletely removed. In addition, we developed an effective training framework\nbased on the alternating direction method of multipliers (ADMM), which converts\nour complex constrained optimization into separately solvable subproblems.\nThrough iteratively optimizing the subproblems, the desired micro-structure can\nbe ensured with high compression ratio and low performance degradation. We\nextensively evaluated our method using a variety of benchmark models and\ndatasets for different applications. Experimental results demonstrate\nstate-of-the-art performance.\n"}
+{"abstract": "  The asymptotic phase is a fundamental quantity for the analysis of\ndeterministic limit-cycle oscillators, and generalized definitions of the\nasymptotic phase for stochastic oscillators have also been proposed. In this\narticle, we show that the asymptotic phase and also amplitude can be defined\nfor classical and semiclassical stochastic oscillators in a natural and unified\nmanner by using the eigenfunctions of the Koopman operator of the system. We\nshow that the proposed definition gives appropriate values of the phase and\namplitude for strongly stochastic limit-cycle oscillators, excitable systems\nundergoing noise-induced oscillations, and also for quantum limit-cycle\noscillators in the semiclassical regime.\n"}
+{"abstract": "  The relationship between galaxy characteristics and the reionization of the\nuniverse remains elusive, mainly due to the observational difficulty in\naccessing the Lyman continuum (LyC) at these redshifts. It is thus important to\nidentify low-redshift LyC-leaking galaxies that can be used as laboratories to\ninvestigate the physical processes that allow LyC photons to escape. The\nweakness of the [S II] nebular emission lines relative to typical star-forming\ngalaxies has been proposed as a LyC predictor. In this paper, we show that the\n[S II]-deficiency is an effective method to select LyC-leaking candidates using\ndata from the Low-redshift LyC Survey, which has detected flux below the Lyman\nedge in 35 out of 66 star-forming galaxies with the Cosmic Origins Spectrograph\nonboard the Hubble Space Telescope. We show that LyC leakers tend to be more [S\nII]-deficient and that the fraction of their detections increases as [S\nII]-deficiency becomes more prominent. Correlational studies suggest that [S\nII]-deficiency complements other LyC diagnostics (such as strong Lyman-$\\alpha$\nemission and high [O III]/[O II]). Our results verify an additional technique\nby which reionization-era galaxies could be studied.\n"}
+{"abstract": "  We propose a novel analytical model for anisotropic multi-layer cylindrical\nstructures containing graphene layers. The general structure is formed by an\naperiodic repetition of a three-layer sub-structure, where a graphene layer,\nwith an isotropic surface conductivity, has been sandwiched between two\nadjacent magnetic materials. An external magnetic bias has been applied in the\naxial direction. General matrix representation is obtained in our proposed\nanalytical model to find the dispersion relation. The relation will be used to\nfind the effective index of the structure and its other propagation parameters.\nTwo special exemplary structures have been introduced and studied to show the\nrichness of the proposed general structure regarding the related specific\nplasmonic wave phenomena and effects. A series of simulations have been\nconducted to demonstrate the noticeable wave-guiding properties of the\nstructure in the 10-40 THz band. A very good agreement between the analytical\nand simulation results is observed. The proposed structure can be utilized to\ndesign novel plasmonic devices such as absorbers, modulators, plasmonic sensors\nand tunable antennas in the THz frequencies.\n"}
+{"abstract": "  In this paper, meant as a companion to arXiv:2006.04458, we consider a class\nof non-integrable $2D$ Ising models in cylindrical domains, and we discuss two\nkey aspects of the multiscale construction of their scaling limit. In\nparticular, we provide a detailed derivation of the Grassmann representation of\nthe model, including a self-contained presentation of the exact solution of the\nnearest neighbor model in the cylinder. Moreover, we prove precise asymptotic\nestimates of the fermionic Green's function in the cylinder, required for the\nmultiscale analysis of the model. We also review the multiscale construction of\nthe effective potentials in the infinite volume limit, in a form suitable for\nthe generalization to finite cylinders. Compared to previous works, we\nintroduce a few important simplifications in the localization procedure and in\nthe iterative bounds on the kernels of the effective potentials, which are\ncrucial for the adaptation of the construction to domains with boundaries.\n"}
+{"abstract": "  Many channel decoders rely on parallel decoding attempts to achieve good\nperformance with acceptable latency. However, most of the time fewer attempts\nthan the foreseen maximum are sufficient for successful decoding.\nInput-distribution-aware (IDA) decoding allows to determine the parallelism of\npolar code list decoders by observing the distribution of channel information.\nIn this work, IDA decoding is proven to be effective with different codes and\ndecoding algorithms as well. Two techniques, M-IDA and MD-IDA, are proposed:\nthey exploit the sampling of the input distribution inherent to particular\ndecoding algorithms to perform low-cost IDA decoding. Simulation results on the\ndecoding of BCH codes via the Chase and ORBGRAND algorithms show that they\nperform at least as well as the original IDA decoding, allowing to reduce\nrun-time complexity down to 17% and 67\\% with minimal error correction\ndegradation.\n"}
+{"abstract": "  It was shown that the particle distribution detected by a uniformly\naccelerated observer in the inertial vacuum (Unruh effect) deviates from the\npure Planckian spectrum when considering the superposition of fields with\ndifferent masses. Here we elaborate on the statistical origin of this\nphenomenon. In a suitable regime, we provide an effective description of the\nemergent distribution in terms of the nonextensive q-generalized statistics\nbased on Tsallis entropy. This picture allows us to establish a nontrivial\nrelation between the q-entropic index and the characteristic mixing parameters\nsin{\\theta} and \\Delta m. In particular, we infer that q < 1, indicating the\nsuperadditive feature of Tsallis entropy in this framework. We discuss our\nresult in connection with the entangled condensate structure acquired by the\nquantum vacuum for mixed fields.\n"}
+{"abstract": "  In this paper we present an early prototype of the Digger Finger that is\ndesigned to easily penetrate granular media and is equipped with the GelSight\nsensor. Identifying objects buried in granular media using tactile sensors is a\nchallenging task. First, particle jamming in granular media prevents downward\nmovement. Second, the granular media particles tend to get stuck between the\nsensing surface and the object of interest, distorting the actual shape of the\nobject. To tackle these challenges we present a Digger Finger prototype. It is\ncapable of fluidizing granular media during penetration using mechanical\nvibrations. It is equipped with high resolution vision based tactile sensing to\nidentify objects buried inside granular media. We describe the experimental\nprocedures we use to evaluate these fluidizing and buried shape recognition\ncapabilities. A robot with such fingers can perform explosive ordnance disposal\nand Improvised Explosive Device (IED) detection tasks at a much a finer\nresolution compared to techniques like Ground Penetration Radars (GPRs).\nSensors like the Digger Finger will allow robotic manipulation research to move\nbeyond only manipulating rigid objects.\n"}
+{"abstract": "  Domain adaptation aims to leverage a label-rich domain (the source domain) to\nhelp model learning in a label-scarce domain (the target domain). Most domain\nadaptation methods require the co-existence of source and target domain samples\nto reduce the distribution mismatch, however, access to the source domain\nsamples may not always be feasible in the real world applications due to\ndifferent problems (e.g., storage, transmission, and privacy issues). In this\nwork, we deal with the source data-free unsupervised domain adaptation problem,\nand propose a novel approach referred to as Virtual Domain Modeling (VDM-DA).\nThe virtual domain acts as a bridge between the source and target domains. On\none hand, we generate virtual domain samples based on an approximated Gaussian\nMixture Model (GMM) in the feature space with the pre-trained source model,\nsuch that the virtual domain maintains a similar distribution with the source\ndomain without accessing to the original source data. On the other hand, we\nalso design an effective distribution alignment method to reduce the\ndistribution divergence between the virtual domain and the target domain by\ngradually improving the compactness of the target domain distribution through\nmodel learning. In this way, we successfully achieve the goal of distribution\nalignment between the source and target domains by training deep networks\nwithout accessing to the source domain data. We conduct extensive experiments\non benchmark datasets for both 2D image-based and 3D point cloud-based\ncross-domain object recognition tasks, where the proposed method referred to\nDomain Adaptation with Virtual Domain Modeling (VDM-DA) achieves the\nstate-of-the-art performances on all datasets.\n"}
+{"abstract": "  We consider the learning and prediction of nonlinear time series generated by\na latent symplectic map. A special case is (not necessarily separable)\nHamiltonian systems, whose solution flows give such symplectic maps. For this\nspecial case, both generic approaches based on learning the vector field of the\nlatent ODE and specialized approaches based on learning the Hamiltonian that\ngenerates the vector field exist. Our method, however, is different as it does\nnot rely on the vector field nor assume its existence; instead, it directly\nlearns the symplectic evolution map in discrete time. Moreover, we do so by\nrepresenting the symplectic map via a generating function, which we approximate\nby a neural network (hence the name GFNN). This way, our approximation of the\nevolution map is always \\emph{exactly} symplectic. This additional geometric\nstructure allows the local prediction error at each step to accumulate in a\ncontrolled fashion, and we will prove, under reasonable assumptions, that the\nglobal prediction error grows at most \\emph{linearly} with long prediction\ntime, which significantly improves an otherwise exponential growth. In\naddition, as a map-based and thus purely data-driven method, GFNN avoids two\nadditional sources of inaccuracies common in vector-field based approaches,\nnamely the error in approximating the vector field by finite difference of the\ndata, and the error in numerical integration of the vector field for making\npredictions. Numerical experiments further demonstrate our claims.\n"}
+{"abstract": "  Early wildfire detection is of paramount importance to avoid as much damage\nas possible to the environment, properties, and lives. Deep Learning (DL)\nmodels that can leverage both visible and infrared information have the\npotential to display state-of-the-art performance, with lower false-positive\nrates than existing techniques. However, most DL-based image fusion methods\nhave not been evaluated in the domain of fire imagery. Additionally, to the\nbest of our knowledge, no publicly available dataset contains visible-infrared\nfused fire images. There is a growing interest in DL-based image fusion\ntechniques due to their reduced complexity. Due to the latter, we select three\nstate-of-the-art, DL-based image fusion techniques and evaluate them for the\nspecific task of fire image fusion. We compare the performance of these methods\non selected metrics. Finally, we also present an extension to one of the said\nmethods, that we called FIRe-GAN, that improves the generation of artificial\ninfrared images and fused ones on selected metrics.\n"}
+{"abstract": "  We consider linear systems $Ax = b$ where $A \\in \\mathbb{R}^{m \\times n}$\nconsists of normalized rows, $\\|a_i\\|_{\\ell^2} = 1$, and where up to $\\beta m$\nentries of $b$ have been corrupted (possibly by arbitrarily large numbers).\nHaddock, Needell, Rebrova and Swartworth propose a quantile-based Random\nKaczmarz method and show that for certain random matrices $A$ it converges with\nhigh likelihood to the true solution. We prove a deterministic version by\nconstructing, for any matrix $A$, a number $\\beta_A$ such that there is\nconvergence for all perturbations with $\\beta < \\beta_A$. Assuming a random\nmatrix heuristic, this proves convergence for tall Gaussian matrices with up to\n$\\sim 0.5\\%$ corruption (a number that can likely be improved).\n"}
+{"abstract": "  In the framework of photonics with all-dielectric nanoantennas,\nsub-micro-metric spheres can be exploited for a plethora of applications\nincluding vanishing back-scattering, enhanced directivity of a light emitter,\nbeam steering, and large Purcell factors. Here, the potential of a\nhigh-throughput fabrication method based on aerosol-spray is shown to form\nquasi-perfect sub-micrometric spheres of polycrystalline TiO 2 . Spectroscopic\ninvestigation of light scattering from individual particles reveals sharp\nresonances in agreement with Mie theory, neat structural colors, and a high\ndirectivity. Owing to the high permittivity and lossless material in use, this\nmethod opens the way toward the implementation of isotropic meta-materials and\nforward-directional sources with magnetic responses at visible and near-UV\nfrequencies, not accessible with conventional Si- and Ge-based Mie resonators.\n"}
+{"abstract": "  We exhibit the analog of the entropy map for multivariate Gaussian\ndistributions on local fields. As in the real case, the image of this map lies\nin the supermodular cone and it determines the distribution of the valuation\nvector. In general, this map can be defined for non-archimedian valued fields\nwhose valuation group is an additive subgroup of the real line, and it remains\nsupermodular. We also explicitly compute the image of this map in dimension 3.\n"}
+{"abstract": "  We investigate the degradation of quantum entanglement in the\nSchwarzschild-de Sitter black hole spacetime, by studying the mutual\ninformation and the logarithmic negativity for maximally entangled, bipartite\ninitial states for massless minimal scalar fields. This spacetime is endowed\nwith a black hole as well as a cosmological event horizon, giving rise to\nparticle creation at two different temperatures. We consider two independent\ndescriptions of thermodynamics and particle creation in this background. The\nfirst involves thermal equilibrium of an observer with the individual Hawking\ntemperature of either of the horizons. We show that as of the asymptotically\nflat/anti-de Sitter black holes, the entanglement or correlation degrades here\nwith increasing Hawking temperature. The second treats both the horizons\ncombinedly to define a total entropy and an effective equilibrium temperature.\nWe present a field theoretic derivation of this effective temperature and argue\nthat unlike the usual cases, the particle creation here is not ocurring in\ncausally disconnected spacetime wedges but in a single region. Using these\nstates, we then show that in this scenario the entanglement never degrades but\nincreases with increasing black hole temperature and holds true no matter how\nhot the black hole becomes or how small the cosmological constant is. We argue\nthat this phenomenon can have no analogue in the asymptotically flat/anti-de\nSitter black hole spacetimes.\n"}
+{"abstract": "  Dynamic graphs arise in a plethora of practical scenarios such as social\nnetworks, communication networks, and financial transaction networks. Given a\ndynamic graph, it is fundamental and essential to learn a graph representation\nthat is expected not only to preserve structural proximity but also jointly\ncapture the time-evolving patterns. Recently, graph convolutional network (GCN)\nhas been widely explored and used in non-Euclidean application domains. The\nmain success of GCN, especially in handling dependencies and passing messages\nwithin nodes, lies in its approximation to Laplacian smoothing. As a matter of\nfact, this smoothing technique can not only encourage must-link node pairs to\nget closer but also push cannot-link pairs to shrink together, which\npotentially cause serious feature shrink or oversmoothing problem, especially\nwhen stacking graph convolution in multiple layers or steps. For learning\ntime-evolving patterns, a natural solution is to preserve historical state and\ncombine it with the current interactions to obtain the most recent\nrepresentation. Then the serious feature shrink or oversmoothing problem could\nhappen when stacking graph convolution explicitly or implicitly according to\ncurrent prevalent methods, which would make nodes too similar to distinguish\neach other. To solve this problem in dynamic graph embedding, we analyze the\nshrinking properties in the node embedding space at first, and then design a\nsimple yet versatile method, which exploits L2 feature normalization constraint\nto rescale all nodes to hypersphere of a unit ball so that nodes would not\nshrink together, and yet similar nodes can still get closer. Extensive\nexperiments on four real-world dynamic graph datasets compared with competitive\nbaseline models demonstrate the effectiveness of the proposed method.\n"}
+{"abstract": "  Epitaxial orthorhombic Hf0.5Zr0.5O2 (HZO) films on La0.67Sr0.33MnO3 (LSMO)\nelectrodes show robust ferroelectricity, with high polarization, endurance and\nretention. However, no similar results have been achieved using other\nperovskite electrodes so far. Here, LSMO and other perovskite electrodes are\ncompared. A small amount of orthorhombic phase and low polarization is found in\nHZO films grown on La-doped BaSnO3 and Nb-doped SrTiO3, while null amounts of\northorhombic phase and polarization are detected in films on LaNiO3 and SrRuO3.\nThe critical effect of the electrode on the stabilized phases is not\nconsequence of differences in the electrode lattice parameter. The interface is\ncritical, and engineering the HZO bottom interface on just a few monolayers of\nLSMO permits the stabilization of the orthorhombic phase. Furthermore, while\nthe specific divalent ion (Sr or Ca) in the manganite is not relevant, reducing\nthe La content causes a severe reduction of the amount of orthorhombic phase\nand the ferroelectric polarization in the HZO film.\n"}
+{"abstract": "  We show that for every positive integer $k$, there exist $k$ consecutive\nprimes having the property that if any digit of any one of the primes,\nincluding any of the infinitely many leading zero digits, is changed, then that\nprime becomes composite.\n"}
+{"abstract": "  Classical and quantum correlation functions are derived for a system of\nnon-interacting particles moving on a circle. It is shown that the decaying\nbehaviour of the classical expression for the correlation function can be\nrecovered from the strictly periodic quantum mechanical expression by taking\nthe limit that the Planck's constant goes to zero, after an appropriate\ntransformation.\n"}
+{"abstract": "  We propose a scheme to create an electronic Floquet vortex state by\nirradiating a two-dimensional semiconductor with the laser light carrying\nnon-zero orbital angular momentum. We analytically and numerically study the\nproperties of the Floquet vortex states, with the methods analogous to the ones\npreviously applied to the analysis of superconducting vortex states. We show\nthat such Floquet vortex states are similar to the superconducting vortex\nstates, and they exhibit a wide range of tunability. To illustrate the\npotential utility of such tunability, we show how such states could be used for\nquantum state engineering.\n"}
+{"abstract": "  The redshift distribution of galactic-scale lensing systems provides a\nlaboratory to probe the velocity dispersion function (VDF) of early-type\ngalaxies (ETGs) and measure the evolution of early-type galaxies at redshift z\n~ 1. Through the statistical analysis of the currently largest sample of\nearly-type galaxy gravitational lenses, we conclude that the VDF inferred\nsolely from strong lensing systems is well consistent with the measurements of\nSDSS DR5 data in the local universe. In particular, our results strongly\nindicate a decline in the number density of lenses by a factor of two and a 20%\nincrease in the characteristic velocity dispersion for the early-type galaxy\npopulation at z ~ 1. Such VDF evolution is in perfect agreement with the\n$\\Lambda$CDM paradigm (i.e., the hierarchical build-up of mass structures over\ncosmic time) and different from \"stellar mass-downsizing\" evolutions obtained\nby many galaxy surveys. Meanwhile, we also quantitatively discuss the evolution\nof the VDF shape in a more complex evolution model, which reveals its strong\ncorrelation with that of the number density and velocity dispersion of\nearly-type galaxies. Finally, we evaluate if future missions such as LSST can\nbe sensitive enough to place the most stringent constraints on the redshift\nevolution of early-type galaxies, based on the redshift distribution of\navailable gravitational lenses.\n"}
+{"abstract": "  A promising approach to the practical application of the Quantum Approximate\nOptimization Algorithm (QAOA) is finding QAOA parameters classically in\nsimulation and sampling the solutions from QAOA with optimized parameters on a\nquantum computer. Doing so requires repeated evaluations of QAOA energy in\nsimulation. We propose a novel approach for accelerating the evaluation of QAOA\nenergy by leveraging the symmetry of the problem. We show a connection between\nclassical symmetries of the objective function and the symmetries of the terms\nof the cost Hamiltonian with respect to the QAOA energy. We show how by\nconsidering only the terms that are not connected by symmetry, we can\nsignificantly reduce the cost of evaluating the QAOA energy. Our approach is\ngeneral and applies to any known subgroup of symmetries and is not limited to\ngraph problems. Our results are directly applicable to nonlocal QAOA\ngeneralization RQAOA. We outline how available fast graph automorphism solvers\ncan be leveraged for computing the symmetries of the problem in practice. We\nimplement the proposed approach on the MaxCut problem using a state-of-the-art\ntensor network simulator and a graph automorphism solver on a benchmark of 48\ngraphs with up to 10,000 nodes. Our approach provides an improvement for $p=1$\non $71.7\\%$ of the graphs considered, with a median speedup of $4.06$, on a\nbenchmark where $62.5\\%$ of the graphs are known to be hard for automorphism\nsolvers.\n"}
+{"abstract": "  Methods inspired from machine learning have recently attracted great interest\nin the computational study of quantum many-particle systems. So far, however,\nit has proven challenging to deal with microscopic models in which the total\nnumber of particles is not conserved. To address this issue, we propose a new\nvariant of neural network states, which we term neural coherent states. Taking\nthe Fr\\\"ohlich impurity model as a case study, we show that neural coherent\nstates can learn the ground state of non-additive systems very well. In\nparticular, we observe substantial improvement over the standard coherent state\nestimates in the most challenging intermediate coupling regime. Our approach is\ngeneric and does not assume specific details of the system, suggesting wide\napplications.\n"}
+{"abstract": "  This paper presents a fundamental analysis connecting phase noise and\nlong-term frequency accuracy of oscillators and explores the possibilities and\nlimitations in crystal-less frequency calibration for wireless edge nodes from\na noise-impact perspective. N-period-average jitter (NPAJ) is introduced as a\nlink between the spectral characterization of phase noise and long-term\nfrequency accuracy. It is found that flicker noise or other colored noise\nprofiles coming from the reference in a frequency synthesizer is the dominant\nnoise source affecting long-term frequency accuracy. An average processing unit\nembedded in an ADPLL is proposed based on the N-period-average jitter concept\nto enhance frequency accuracy in a Calibrate and Open-loop scenario commonly\nused in low power radios. With this low-cost block, the frequency calibration\naccuracy can be directly associated with the reference noise performance. Thus,\nthe feasibility of XO-less design with certain communication standards can be\neasily evaluated with the proposed theory.\n"}
+{"abstract": "  Using a differential equation approach asymptotic expansions are rigorously\nobtained for Lommel, Weber, Anger-Weber and Struve functions, as well as\nNeumann polynomials, each of which is a solution of an inhomogeneous Bessel\nequation. The approximations involve Airy and Scorer functions, and are\nuniformly valid for large real order $\\nu$ and unbounded complex argument $z$.\nAn interesting complication is the identification of the Lommel functions with\nthe new asymptotic solutions, and in order to do so it is necessary to consider\ncertain sectors of the complex plane, as well as introduce new forms of Lommel\nand Struve functions.\n"}
+{"abstract": "  Context: Expert judgement is a common method for software effort estimations\nin practice today. Estimators are often shown extra obsolete requirements\ntogether with the real ones to be implemented. Only one previous study has been\nconducted on if such practices bias the estimations. Objective: We conducted\nsix experiments with both students and practitioners to study, and quantify,\nthe effects of obsolete requirements on software estimation. Method By\nconducting a family of six experiments using both students and practitioners as\nresearch subjects (N = 461), and by using a Bayesian Data Analysis approach, we\ninvestigated different aspects of this effect. We also argue for, and show an\nexample of, how we by using a Bayesian approach can be more confident in our\nresults and enable further studies with small sample sizes. Results: We found\nthat the presence of obsolete requirements triggered an overestimation in\neffort across all experiments. The effect, however, was smaller in a field\nsetting compared to using students as subjects. Still, the over-estimations\ntriggered by the obsolete requirements were systematically around twice the\npercentage of the included obsolete ones, but with a large 95% credible\ninterval. Conclusions: The results have implications for both research and\npractice in that the found systematic error should be accounted for in both\nstudies on software estimation and, maybe more importantly, in estimation\npractices to avoid over-estimation due to this systematic error. We partly\nexplain this error to be stemming from the cognitive bias of\nanchoring-and-adjustment, i.e. the obsolete requirements anchored a much larger\nsoftware. However, further studies are needed in order to accurately predict\nthis effect.\n"}
+{"abstract": "  In this review I will discuss the comparison between model results and\nobservational data for the Milky Way, the predictive power of such models as\nwell as their limits. Such a comparison, known as Galactic archaeology, allows\nus to impose constraints on stellar nucleosynthesis and timescales of formation\nof the various Galactic components (halo, bulge, thick disk and thin disk).\n"}
+{"abstract": "  We focus on studying the opacity of iron, chromium, and nickel plasmas at\nconditions relevant to experiments carried out at Sandia National Laboratories\n[J. E. Bailey et al., Nature 517, 56 (2015)]. We calculate the photo-absorption\ncross-sections and subsequent opacity for plasmas using linear response\ntime-dependent density functional theory (TD-DFT). Our results indicate that\nthe physics of channel mixing accounted for in linear response TD-DFT leads to\nan increase in the opacity in the bound-free quasi-continuum, where the Sandia\nexperiments indicate that models under-predict iron opacity. However, the\nincrease seen in our calculations is only in the range of 5-10%. Further, we do\nnot see any change in this trend for chromium and nickel. This behavior\nindicates that channel mixing effects do not explain the trends in opacity\nobserved in the Sandia experiments.\n"}
+{"abstract": "  In this note, we prove some results related to small perturbations of a frame\nfor a Hilbert space $\\mathcal{H}$ in order to have a woven pair for\n$\\mathcal{H}$. Our results complete those known in the literature. In addition\nwe study a necessary condition for a woven pair, that resembles a\ncharacterization for Riesz frames.\n"}
+{"abstract": "  Solving the Multi-Agent Path Finding (MAPF) problem optimally is known to be\nNP-Hard for both make-span and total arrival time minimization. While many\nalgorithms have been developed to solve MAPF problems, there is no dominating\noptimal MAPF algorithm that works well in all types of problems and no standard\nguidelines for when to use which algorithm. In this work, we develop the deep\nconvolutional network MAPFAST (Multi-Agent Path Finding Algorithm SelecTor),\nwhich takes a MAPF problem instance and attempts to select the fastest\nalgorithm to use from a portfolio of algorithms. We improve the performance of\nour model by including single-agent shortest paths in the instance embedding\ngiven to our model and by utilizing supplemental loss functions in addition to\na classification loss. We evaluate our model on a large and diverse dataset of\nMAPF instances, showing that it outperforms all individual algorithms in its\nportfolio as well as the state-of-the-art optimal MAPF algorithm selector. We\nalso provide an analysis of algorithm behavior in our dataset to gain a deeper\nunderstanding of optimal MAPF algorithms' strengths and weaknesses to help\nother researchers leverage different heuristics in algorithm designs.\n"}
+{"abstract": "  We studied the accretion disc structure in the doubly imaged lensed quasar\nSDSS J1339+1310 using $r$-band light curves and UV-visible to near-IR (NIR)\nspectra from the first 11 observational seasons after its discovery. The\n2009$-$2019 light curves displayed pronounced microlensing variations on\ndifferent timescales, and this microlensing signal permitted us to constrain\nthe half-light radius of the 1930 \\r{A} continuum-emitting region. Assuming an\naccretion disc with an axis inclined at 60 deg to the line of sight, we\nobtained log$_{10}$($r_{1/2}$/cm) = 15.4$^{+0.3}_{-0.4}$. We also estimated the\ncentral black hole mass from spectroscopic data. The width of the Civ, Mgii,\nand H$\\beta$ emission lines, and the continuum luminosity at 1350, 3000, and\n5100 \\r{A}, led to log$_{10}$($M_{BH}$/M$_{\\odot}$) = 8.6 $\\pm$ 0.4. Thus, hot\ngas responsible for the 1930 \\r{A} continuum emission is likely orbiting a 4.0\n$\\times$ 10$^8$ M$_{\\odot}$ black hole at an $r_{1/2}$ of only a few tens of\nSchwarzschild radii.\n"}
+{"abstract": "  Let pi = pi_1 pi_2 ... pi_n be a permutation in the symmetric group S_n\nwritten in one-line notation. The pinnacle set of pi, denoted Pin pi, is the\nset of all pi_i such that pi_{i-1} < pi_i > pi_{i+1}. This is an analogue of\nthe well-studied peak set of pi where one considers values rather than\npositions. The pinnacle set was introduced by Davis, Nelson, Petersen, and\nTenner who showed that it has many interesting properties. In particular, they\nproved that the number of subsets of [n] = {1, 2, ..., n} which can be the\npinnacle set of some permutation is a binomial coefficient. Their proof\ninvolved a bijection with lattice paths and was somewhat involved. We give a\nsimpler demonstration of this result which does not need lattice paths.\nMoreover, we show that our map and theirs are different descriptions of the\nsame function. Davis et al. also studied the number of pinnacle sets with\nmaximum m and cardinality d which they denoted by p(m,d). We show that these\nintegers are ballot numbers and give two proofs of this fact: one using finite\ndifferences and one bijective. Diaz-Lopez, Harris, Huang, Insko, and Nilsen\nfound a summation formula for calculating the number of permutations in S_n\nhaving a given pinnacle set. We derive a new expression for this number which\nis faster to calculate in many cases. We also show how this method can be\nadapted to find the number of orderings of a pinnacle set which can be realized\nby some pi in S_n.\n"}
+{"abstract": "  This article discusses the effects of the spiral-arm corotation on the\nstellar dynamics in the Solar Neighborhood (SN). All our results presented here\nrely on: 1) observational evidence that the Sun lies near the corotation\ncircle, where stars rotate with the same angular velocity as the spiral-arm\npattern; the corotation circle establishes domains of the corotation resonance\n(CR) in the Galactic disk; 2) dynamical constraints that put the spiral-arm\npotential as the dominant perturbation in the SN, comparing with the effects of\nthe central bar in the SN; 3) a long-lived nature of the spiral structure,\npromoting a state of dynamical relaxing and phase-mixing of the stellar orbits\nin response to the spiral perturbation. With an analytical model for the\nGalactic potential, composed of an axisymmetric background deduced from the\nobserved rotation curve, and perturbed by a four-armed spiral pattern,\nnumerical simulations of stellar orbits are performed to delineate the domains\nof regular and chaotic motions shaped by the resonances. Such studies show that\nstars can be trapped inside the stable zones of the spiral CR, and this orbital\ntrapping mechanism could explain the dynamical origin of the Local arm of the\nMilky Way (MW). The spiral CR and the near high-order epicyclic resonances\ninfluence the velocity distribution in the SN, creating the observable\nstructures such as moving groups and their radially extended counterpart known\nas diagonal ridges. The Sun and most of the SN stars evolve inside a stable\nzone of the spiral CR, never crossing the main spiral-arm structure, but\noscillating in the region between the Sagittarius-Carina and Perseus arms. This\norbital behavior of the Sun brings insights to our understanding of questions\nconcerning the solar system evolution, the Earth environment changes, and the\npreservation of life on Earth.\n"}
+{"abstract": "  Context. The tropospheric wind pattern in Jupiter consists of alternating\nprograde and retrograde zonal jets with typical velocities of up to 100 m/s\naround the equator. At much higher altitudes, in the ionosphere, strong auroral\njets have been discovered with velocities of 1-2 km/s. There is no such direct\nmeasurement in the stratosphere of the planet. Aims. In this paper, we bridge\nthe altitude gap between these measurements by directly measuring the wind\nspeeds in Jupiter's stratosphere. Methods. We use the Atacama Large\nMillimeter/submillimeter Array's very high spectral and angular resolution\nimaging of the stratosphere of Jupiter to retrieve the wind speeds as a\nfunction of latitude by fitting the Doppler shifts induced by the winds on the\nspectral lines. Results. We detect for the first time equatorial zonal jets\nthat reside at 1 mbar, i.e. above the altitudes where Jupiter's\nQuasi-Quadrennial Oscillation occurs. Most noticeably, we find 300-400 m/s\nnon-zonal winds at 0.1 mbar over the polar regions underneath the main auroral\novals. They are in counter-rotation and lie several hundreds of kilometers\nbelow the ionospheric auroral winds. We suspect them to be the lower tail of\nthe ionospheric auroral winds. Conclusions. We detect directly and for the\nfirst time strong winds in Jupiter's stratosphere. They are zonal at low-to-mid\nlatitudes and non-zonal at polar latitudes. The wind system found at polar\nlatitudes may help increase the effciency of chemical complexification by\nconfining the photochemical products in a region of large energetic electron\nprecipitation.\n"}
+{"abstract": "  Developers of computer vision algorithms outsource some of the labor involved\nin annotating training data through business process outsourcing companies and\ncrowdsourcing platforms. Many data annotators are situated in the Global South\nand are considered independent contractors. This paper focuses on the\nexperiences of Argentinian and Venezuelan annotation workers. Through\nqualitative methods, we explore the discourses encoded in the task instructions\nthat these workers follow to annotate computer vision datasets. Our preliminary\nfindings indicate that annotation instructions reflect worldviews imposed on\nworkers and, through their labor, on datasets. Moreover, we observe that\nfor-profit goals drive task instructions and that managers and algorithms make\nsure annotations are done according to requesters' commands. This configuration\npresents a form of commodified labor that perpetuates power asymmetries while\nreinforcing social inequalities and is compelled to reproduce them into\ndatasets and, subsequently, in computer vision systems.\n"}
+{"abstract": "  The increasing concerns about data privacy and security drive an emerging\nfield of studying privacy-preserving machine learning from isolated data\nsources, i.e., federated learning. A class of federated learning, vertical\nfederated learning, where different parties hold different features for common\nusers, has a great potential of driving a great variety of business cooperation\namong enterprises in many fields. In machine learning, decision tree ensembles\nsuch as gradient boosting decision trees (GBDT) and random forest are widely\napplied powerful models with high interpretability and modeling efficiency.\nHowever, stateof-art vertical federated learning frameworks adapt anonymous\nfeatures to avoid possible data breaches, makes the interpretability of the\nmodel compromised. To address this issue in the inference process, in this\npaper, we firstly make a problem analysis about the necessity of disclosure\nmeanings of feature to Guest Party in vertical federated learning. Then we find\nthe prediction result of a tree could be expressed as the intersection of\nresults of sub-models of the tree held by all parties. With this key\nobservation, we protect data privacy and allow the disclosure of feature\nmeaning by concealing decision paths and adapt a communication-efficient secure\ncomputation method for inference outputs. The advantages of Fed-EINI will be\ndemonstrated through both theoretical analysis and extensive numerical results.\nWe improve the interpretability of the model by disclosing the meaning of\nfeatures while ensuring efficiency and accuracy.\n"}
+{"abstract": "  The detection and characterization of young planetary systems offers a direct\npath to study the processes that shape planet evolution. We report on the\ndiscovery of a sub-Neptune-size planet orbiting the young star HD 110082\n(TOI-1098). Transit events we initially detected during TESS Cycle 1 are\nvalidated with time-series photometry from Spitzer. High-contrast imaging and\nhigh-resolution, optical spectra are also obtained to characterize the stellar\nhost and confirm the planetary nature of the transits. The host star is a late\nF dwarf (M=1.2 Msun) with a low-mass, M dwarf binary companion (M=0.26 Msun)\nseparated by nearly one arcminute (~6200 AU). Based on its rapid rotation and\nLithium absorption, HD 110082 is young, but is not a member of any known group\nof young stars (despite proximity to the Octans association). To measure the\nage of the system, we search for coeval, phase-space neighbors and compile a\nsample of candidate siblings to compare with the empirical sequences of young\nclusters and to apply quantitative age-dating techniques. In doing so, we find\nthat HD 110082 resides in a new young stellar association we designate\nMELANGE-1, with an age of 250(+50/-70) Myr. Jointly modeling the TESS and\nSpitzer light curves, we measure a planetary orbital period of 10.1827 days and\nradius of Rp = 3.2(+/-0.1) Earth radii. HD 110082 b's radius falls in the\nlargest 12% of field-age systems with similar host star mass and orbital\nperiod. This finding supports previous studies indicating that young planets\nhave larger radii than their field-age counterparts.\n"}
+{"abstract": "  Infinitesimal symmetries of a classical mechanical system are usually\ndescribed by a Lie algebra acting on the phase space, preserving the Poisson\nbrackets. We propose that a quantum analogue is the action of a Lie bi-algebra\non the associative $*$-algebra of observables. The latter can be thought of as\nfunctions on some underlying non-commutative manifold. We illustrate this for\nthe non-commutative torus $\\mathbb{T}^2_\\theta$. The canonical trace defines a\nManin triple from which a Lie bi-algebra can be constructed. In the special\ncase of rational $\\theta=\\frac{M}{N}$ this Lie bi-algebra is\n$\\underline{GL}(N)=\\underline{U}(N)\\oplus \\underline{B}(N)$, corresponding to\nunitary and upper triangular matrices. The Lie bi-algebra has a remnant in the\nclassical limit $N\\to\\infty$: the elements of $\\underline{U}(N)$ tend to real\nfunctions while $\\underline{B}(N)$ tends to a space of complex analytic\nfunctions.\n"}
+{"abstract": "  We study the problem of determining the best intervention in a Causal\nBayesian Network (CBN) specified only by its causal graph. We model this as a\nstochastic multi-armed bandit (MAB) problem with side-information, where the\ninterventions correspond to the arms of the bandit instance. First, we propose\na simple regret minimization algorithm that takes as input a semi-Markovian\ncausal graph with atomic interventions and possibly unobservable variables, and\nachieves $\\tilde{O}(\\sqrt{M/T})$ expected simple regret, where $M$ is dependent\non the input CBN and could be very small compared to the number of arms. We\nalso show that this is almost optimal for CBNs described by causal graphs\nhaving an $n$-ary tree structure. Our simple regret minimization results, both\nupper and lower bound, subsume previous results in the literature, which\nassumed additional structural restrictions on the input causal graph. In\nparticular, our results indicate that the simple regret guarantee of our\nproposed algorithm can only be improved by considering more nuanced structural\nrestrictions on the causal graph. Next, we propose a cumulative regret\nminimization algorithm that takes as input a general causal graph with all\nobservable nodes and atomic interventions and performs better than the optimal\nMAB algorithm that does not take causal side-information into account. We also\nexperimentally compare both our algorithms with the best known algorithms in\nthe literature. To the best of our knowledge, this work gives the first simple\nand cumulative regret minimization algorithms for CBNs with general causal\ngraphs under atomic interventions and having unobserved confounders.\n"}
+{"abstract": "  In its standard formulation, quantum backflow is a classically impossible\nphenomenon in which a free quantum particle in a positive-momentum state\nexhibits a negative probability current. Recently, Miller et al. [Quantum 5,\n379 (2021)] have put forward a new, \"experiment-friendly\" formulation of\nquantum backflow that aims at extending the notion of quantum backflow to\nsituations in which the particle's state may have both positive and negative\nmomenta. Here, we investigate how the experiment-friendly formulation of\nquantum backflow compares to the standard one when applied to a free particle\nin a positive-momentum state. We show that the two formulations are not always\ncompatible. We further identify a parametric regime in which the two\nformulations appear to be in qualitative agreement with one another.\n"}
+{"abstract": "  This paper studies the generation and transmission expansion co-optimization\nproblem with a high wind power penetration rate in large-scale power grids. In\nthis paper, generation and transmission expansion co-optimization is modeled as\na mixed-integer programming (MIP) problem. A scenario creation method is\nproposed to capture the variation and correlation of both load and wind power\nacross regions for large-scale power grids. Obtained scenarios that represent\nload and wind uncertainties can be easily introduced into the MIP problem and\nthen solved to obtain the co-optimized generation and transmission expansion\nplan. Simulation results show that the proposed planning model and the scenario\ncreation method can improve the expansion result significantly through modeling\nmore detailed information of wind and load variation among regions in the US EI\nsystem. The improved expansion plan that combines generation and transmission\nwill aid system planners and policy makers to maximize the social welfare in\nlarge-scale power grids.\n"}
+{"abstract": "  We provide a systematic method to deduce the global form of flavor symmetry\ngroups in 4d N=2 theories obtained by compactifying 6d N=(2,0) superconformal\nfield theories (SCFTs) on a Riemann surface carrying regular punctures and\npossibly outer-automorphism twist lines. Apriori, this method only determines\nthe group associated to the manifest part of the flavor symmetry algebra, but\noften this information is enough to determine the group associated to the full\nenhanced flavor symmetry algebra. Such cases include some interesting and\nwell-studied 4d N=2 SCFTs like the Minahan-Nemeschansky theories. The symmetry\ngroups obtained via this method match with the symmetry groups obtained using a\nLagrangian description if such a description arises in some duality frame.\nMoreover, we check that the proposed symmetry groups are consistent with the\nsuperconformal indices available in the literature. As another application, our\nmethod finds distinct global forms of flavor symmetry group for pairs of\ninteracting 4d N=2 SCFTs (recently pointed out in the literature) whose Coulomb\nbranch dimensions, flavor algebras and levels coincide (along with other\ninvariants), but nonetheless are distinct SCFTs.\n"}
+{"abstract": "  Two years ago, we alarmed the scientific community about the large number of\nbad papers in the literature on {\\it zero difference balanced functions}, where\ndirect proofs of seemingly new results are presented in an unnecessarily\nlengthy and convoluted way. Indeed, these results had been proved long before\nand very easily in terms of difference families.\n  In spite of our report, papers of the same kind continue to proliferate.\nRegrettably, a further attempt to put the topic in order seems unavoidable.\nWhile some authors now follow our recommendation of using the terminology of\n{\\it partitioned difference families}, their methods are still the same and\ntheir results are often trivial or even wrong. In this note, we show how a very\nrecent paper of this type can be easily dealt with.\n"}
+{"abstract": "  A neighborhood restricted Mixed Gibbs Sampling (MGS) based approach is\nproposed for low-complexity high-order modulation large-scale Multiple-Input\nMultiple-Output (LS-MIMO) detection. The proposed LS-MIMO detector applies a\nneighborhood limitation (NL) on the noisy solution from the MGS at a distance d\n- thus, named d-simplified MGS (d-sMGS) - in order to mitigate its impact,\nwhich can be harmful when a high order modulation is considered. Numerical\nsimulation results considering 64-QAM demonstrated that the proposed detection\nmethod can substantially improve the MGS algorithm convergence, whereas no\nextra computational complexity per iteration is required. The proposed\nd-sMGS-based detector suitable for high-order modulation LS-MIMO further\nexhibits improved performance vs. complexity tradeoff when the system loading\nis high, i.e., when K >= 0.75. N. Also, with increasing the number of\ndimensions, i.e., increasing the number of antennas and/or modulation order, a\nsmaller restriction of 2-sMGS was shown to be a more interesting choice than\n1-sMGS.\n"}
+{"abstract": "  Darwinian evolution tends to produce energy-efficient outcomes. On the other\nhand, energy limits computation, be it neural and probabilistic or digital and\nlogical. Taking a particular energy-efficient viewpoint, we define neural\ncomputation and make use of an energy-constrained, computational function. This\nfunction can be optimized over a variable that is proportional to the number of\nsynapses per neuron. This function also implies a specific distinction between\nATP-consuming processes, especially computation \\textit{per se} vs the\ncommunication processes including action potentials and transmitter release.\nThus to apply this mathematical function requires an energy audit with a\npartitioning of energy consumption that differs from earlier work. The audit\npoints out that, rather than the oft-quoted 20 watts of glucose available to\nthe brain \\cite{sokoloff1960metabolism,sawada2013synapse}, the fraction\npartitioned to cortical computation is only 0.1 watts of ATP. On the other hand\nat 3.5 watts, long-distance communication costs are 35-fold greater. Other\nnovel quantifications include (i) a finding that the biological vs ideal values\nof neural computational efficiency differ by a factor of $10^8$ and (ii) two\npredictions of $N$, the number of synaptic transmissions needed to fire a\nneuron (2500 vs 2000).\n"}
+{"abstract": "  Every physical system is characterized by its action. The standard measure of\nintegration is the square root of a minus the determinant of the metric. It is\nchosen on the basis of a single requirement that it must be a density under\ndiffeomorphic transformations. Therefore, it may not be a unique choice. In\nthis thesis, we develop the two-measure and the Galileon measure string and\nsuperstring actions, apply one of them to the string model of hadrons and\npresent the modified measure extension to higher dimensional extended objects.\n"}
+{"abstract": "  Traditional image classification techniques often produce unsatisfactory\nresults when applied to high spatial resolution data because classes in high\nresolution images are not spectrally homogeneous. Texture offers an alternative\nsource of information for classifying these images. This paper evaluates a\nrecently developed, computationally simple texture metric called Weber Local\nDescriptor (WLD) for use in classifying high resolution QuickBird panchromatic\ndata. We compared WLD with state-of-the art texture descriptors (TD) including\nLocal Binary Pattern (LBP) and its rotation-invariant version LBPRIU. We also\ninvestigated whether incorporating VAR, a TD that captures brightness\nvariation, would improve the accuracy of LBPRIU and WLD. We found that WLD\ngenerally produces more accurate classification results than the other TD we\nexamined, and is also more robust to varying parameters. We have implemented an\noptimised algorithm for calculating WLD which makes the technique practical in\nterms of computation time. Overall, our results indicate that WLD is a\npromising approach for classifying high resolution remote sensing data.\n"}
+{"abstract": "  Image completion has made tremendous progress with convolutional neural\nnetworks (CNNs), because of their powerful texture modeling capacity. However,\ndue to some inherent properties (e.g., local inductive prior, spatial-invariant\nkernels), CNNs do not perform well in understanding global structures or\nnaturally support pluralistic completion. Recently, transformers demonstrate\ntheir power in modeling the long-term relationship and generating diverse\nresults, but their computation complexity is quadratic to input length, thus\nhampering the application in processing high-resolution images. This paper\nbrings the best of both worlds to pluralistic image completion: appearance\nprior reconstruction with transformer and texture replenishment with CNN. The\nformer transformer recovers pluralistic coherent structures together with some\ncoarse textures, while the latter CNN enhances the local texture details of\ncoarse priors guided by the high-resolution masked images. The proposed method\nvastly outperforms state-of-the-art methods in terms of three aspects: 1) large\nperformance boost on image fidelity even compared to deterministic completion\nmethods; 2) better diversity and higher fidelity for pluralistic completion; 3)\nexceptional generalization ability on large masks and generic dataset, like\nImageNet.\n"}
+{"abstract": "  Recently, anchor-based methods have achieved great progress in face\ndetection. Once anchor design and anchor matching strategy determined, plenty\nof positive anchors will be sampled. However, faces with extreme aspect ratio\nalways fail to be sampled according to standard anchor matching strategy. In\nfact, the max IoUs between anchors and extreme aspect ratio faces are still\nlower than fixed sampling threshold. In this paper, we firstly explore the\nfactors that affect the max IoU of each face in theory. Then, anchor matching\nsimulation is performed to evaluate the sampling range of face aspect ratio.\nBesides, we propose a Wide Aspect Ratio Matching (WARM) strategy to collect\nmore representative positive anchors from ground-truth faces across a wide\nrange of aspect ratio. Finally, we present a novel feature enhancement module,\nnamed Receptive Field Diversity (RFD) module, to provide diverse receptive\nfield corresponding to different aspect ratios. Extensive experiments show that\nour method can help detectors better capture extreme aspect ratio faces and\nachieve promising detection performance on challenging face detection\nbenchmarks, including WIDER FACE and FDDB datasets.\n"}
+{"abstract": "  Spike-based neuromorphic hardware holds the promise to provide more energy\nefficient implementations of Deep Neural Networks (DNNs) than standard hardware\nsuch as GPUs. But this requires to understand how DNNs can be emulated in an\nevent-based sparse firing regime, since otherwise the energy-advantage gets\nlost. In particular, DNNs that solve sequence processing tasks typically employ\nLong Short-Term Memory (LSTM) units that are hard to emulate with few spikes.\nWe show that a facet of many biological neurons, slow after-hyperpolarizing\n(AHP) currents after each spike, provides an efficient solution. AHP-currents\ncan easily be implemented in neuromorphic hardware that supports\nmulti-compartment neuron models, such as Intel's Loihi chip. Filter\napproximation theory explains why AHP-neurons can emulate the function of LSTM\nunits. This yields a highly energy-efficient approach to time series\nclassification. Furthermore it provides the basis for implementing with very\nsparse firing an important class of large DNNs that extract relations between\nwords and sentences in a text in order to answer questions about the text.\n"}
+{"abstract": "  Let ${\\cal G}$ be a minor-closed graph class. We say that a graph $G$ is a\n$k$-apex of ${\\cal G}$ if $G$ contains a set $S$ of at most $k$ vertices such\nthat $G\\setminus S$ belongs to ${\\cal G}.$ We denote by ${\\cal A}_k ({\\cal G})$\nthe set of all graphs that are $k$-apices of ${\\cal G}.$ We prove that every\ngraph in the obstruction set of ${\\cal A}_k ({\\cal G}),$ i.e., the\nminor-minimal set of graphs not belonging to ${\\cal A}_k ({\\cal G}),$ has size\nat most $2^{2^{2^{2^{{\\sf poly}(k)}}}},$ where ${\\sf poly}$ is a polynomial\nfunction whose degree depends on the size of the minor-obstructions of ${\\cal\nG}.$ This bound drops to $2^{2^{{\\sf poly}(k)}}$ when ${\\cal G}$ excludes some\napex graph as a minor.\n"}
+{"abstract": "  This paper presents an online evolving neural network-based inverse dynamics\nlearning controller for an autonomous vehicle's longitudinal and lateral\ncontrol under model uncertainties and disturbances. The inverse dynamics of the\nvehicle are approximated using a feedback error learning mechanism that\nutilizes a dynamic Radial Basis Function neural network, referred to as the\nExtended Minimal Resource Allocating Network (EMRAN). EMRAN uses an extended\nKalman filter approach for learning and a growing/pruning condition helps in\nkeeping the number of hidden neurons minimum. The online learning algorithm\nhelps in handling the uncertainties and dynamic variations and also the unknown\ndisturbances on the road. The proposed control architecture employs two coupled\nconventional controllers aided by the EMRAN inverse dynamics controller. The\ncontrol architecture has a conventional PID controller for longitudinal cruise\ncontrol and a Stanley controller for lateral path-tracking. Performances of\nboth the longitudinal and lateral controllers are compared with existing\ncontrol methods and the simulation results clearly indicate that the proposed\ncontrol scheme handles the disturbances and parametric uncertainties better,\nand also provides better tracking performance in autonomous vehicles.\n"}
+{"abstract": "  Quantum resources, such as entanglement, steering, and Bell nonlocality, are\nevaluated for three coupled qubits in the steady-state configuration. We employ\nthe phenomenological master equation and the microscopic master equation to\nprobe such quantum resources, which provide very different results depending on\nthe system configuration. In particular, steering and Bell nonlocality are null\nwithin the phenomenological model, while they reach considerable values within\nthe microscopic model. These results show that the phenomenological approach is\nnot able to capture all quantum resources of the system. We also provide an\nanalytical expression for the steady-state and quantum resources of the system\ncomposed of three coupled qubits in the zero temperature limit. Such results\ndemonstrate that quantum resources between two qubits are strongly affected by\nthe third qubit in a nontrivial way.\n"}
+{"abstract": "  As an instance-level recognition problem, re-identification (re-ID) requires\nmodels to capture diverse features. However, with continuous training, re-ID\nmodels pay more and more attention to the salient areas. As a result, the model\nmay only focus on few small regions with salient representations and ignore\nother important information. This phenomenon leads to inferior performance,\nespecially when models are evaluated on small inter-identity variation data. In\nthis paper, we propose a novel network, Erasing-Salient Net (ES-Net), to learn\ncomprehensive features by erasing the salient areas in an image. ES-Net\nproposes a novel method to locate the salient areas by the confidence of\nobjects and erases them efficiently in a training batch. Meanwhile, to mitigate\nthe over-erasing problem, this paper uses a trainable pooling layer P-pooling\nthat generalizes global max and global average pooling. Experiments are\nconducted on two specific re-identification tasks (i.e., Person re-ID, Vehicle\nre-ID). Our ES-Net outperforms state-of-the-art methods on three Person re-ID\nbenchmarks and two Vehicle re-ID benchmarks. Specifically, mAP / Rank-1 rate:\n88.6% / 95.7% on Market1501, 78.8% / 89.2% on DuckMTMC-reID, 57.3% / 80.9% on\nMSMT17, 81.9% / 97.0% on Veri-776, respectively. Rank-1 / Rank-5 rate: 83.6% /\n96.9% on VehicleID (Small), 79.9% / 93.5% on VehicleID (Medium), 76.9% / 90.7%\non VehicleID (Large), respectively. Moreover, the visualized salient areas show\nhuman-interpretable visual explanations for the ranking results.\n"}
+{"abstract": "  In this work, we give a class of examples of hyperbolic potentials (including\nthe null one) for continuous non-uniformly expanding maps. It implies the\nexistence and uniqueness of equilibrium state (in particular, of maximal\nentropy measure). Among the maps considered is the important class known as\nViana maps.\n"}
+{"abstract": "  Projective two-weight linear codes are closely related to finite projective\nspaces and strongly regular graphs. In this paper, a family of $q$-ary\nprojective two-weight linear codes is presented, where $q$ is a power of 2. The\nparameters of both the codes and their duals are excellent. As applications,\nthe codes are used to derive strongly regular graphs with new parameters and\nsecret sharing schemes with interesting access structures.\n"}
+{"abstract": "  We demonstrate unprecedented accuracy for rapid gravitational-wave parameter\nestimation with deep learning. Using neural networks as surrogates for Bayesian\nposterior distributions, we analyze eight gravitational-wave events from the\nfirst LIGO-Virgo Gravitational-Wave Transient Catalog and find very close\nquantitative agreement with standard inference codes, but with inference times\nreduced from O(day) to a minute per event. Our networks are trained using\nsimulated data, including an estimate of the detector-noise characteristics\nnear the event. This encodes the signal and noise models within millions of\nneural-network parameters, and enables inference for any observed data\nconsistent with the training distribution, accounting for noise nonstationarity\nfrom event to event. Our algorithm -- called \"DINGO\" -- sets a new standard in\nfast-and-accurate inference of physical parameters of detected\ngravitational-wave events, which should enable real-time data analysis without\nsacrificing accuracy.\n"}
+{"abstract": "  In the article [PT] a general procedure to study solutions of the equations\n$x^4-dy^2=z^p$ was presented for negative values of $d$. The purpose of the\npresent article is to extend our previous results to positive values of $d$. On\ndoing so, we give a description of the extension ${\\mathbb\nQ}(\\sqrt{d},\\sqrt{\\epsilon})/{\\mathbb Q}(\\sqrt{d})$ (where $\\epsilon$ is a\nfundamental unit) needed to prove the existence of a Hecke character over\n${\\mathbb Q}(\\sqrt{d})$ with fixed local conditions. We also extend some \"large\nimage\" results regarding images of Galois representations coming from ${\\mathbb\nQ}$-curves (due to Ellenberg in \\cite{MR2075481}) from imaginary to real\nquadratic fields.\n"}
+{"abstract": "  In today's digital society, the Tor network has become an indispensable tool\nfor individuals to protect their privacy on the Internet. Operated by\nvolunteers, relay servers constitute the core component of Tor and are used to\ngeographically escape surveillance. It is therefore essential to have a large,\nyet diverse set of relays. In this work, we analyze the contribution of\neducational institutions to the Tor network and report on our experience of\noperating exit relays at a university. Taking Germany as an example (but\narguing that the global situation is similar), we carry out a quantitative\nstudy and find that universities contribute negligible amounts of relays and\nbandwidth. Since many universities all over the world have excellent conditions\nthat render them perfect places to host Tor (exit) relays, we encourage other\ninterested people and institutions to join. To this end, we discuss and resolve\ncommon concerns and provide lessons learned.\n"}
+{"abstract": "  Given a pair of graphs $\\textbf{A}$ and $\\textbf{B}$, the problems of\ndeciding whether there exists either a homomorphism or an isomorphism from\n$\\textbf{A}$ to $\\textbf{B}$ have received a lot of attention. While graph\nhomomorphism is known to be NP-complete, the complexity of the graph\nisomorphism problem is not fully understood. A well-known combinatorial\nheuristic for graph isomorphism is the Weisfeiler-Leman test together with its\nhigher order variants. On the other hand, both problems can be reformulated as\ninteger programs and various LP methods can be applied to obtain high-quality\nrelaxations that can still be solved efficiently. We study so-called fractional\nrelaxations of these programs in the more general context where $\\textbf{A}$\nand $\\textbf{B}$ are not graphs but arbitrary relational structures. We give a\ncombinatorial characterization of the Sherali-Adams hierarchy applied to the\nhomomorphism problem in terms of fractional isomorphism. Collaterally, we also\nextend a number of known results from graph theory to give a characterization\nof the notion of fractional isomorphism for relational structures in terms of\nthe Weisfeiler-Leman test, equitable partitions, and counting homomorphisms\nfrom trees. As a result, we obtain a description of the families of CSPs that\nare closed under Weisfeiler-Leman invariance in terms of their polymorphisms as\nwell as decidability by the first level of the Sherali-Adams hierarchy.\n"}
+{"abstract": "  When considered as orthogonal bases in distinct vector spaces, the unit\nvectors of polarization directions and the Laguerre-Gaussian modes of\npolarization amplitude are inseparable, constituting a so-called classical\nentangled light beam. We apply this classical entanglement to demonstrate\ntheoretically the execution of Shor's factoring algorithm on a classical light\nbeam. The demonstration comprises light-path designs for the key algorithmic\nsteps of modular exponentiation and Fourier transform on the target integer 15.\nThe computed multiplicative order that eventually leads to the integer factors\nis identified through a four-hole diffraction interference from sources\nobtained from the entangled beam profile. We show that the fringe patterns\nresulted from the interference are uniquely mapped to the sought-after order,\nthereby emulating the factoring process originally rooted in the quantum\nregime.\n"}
+{"abstract": "  We report the discovery of TOI-1444b, a 1.4-$R_\\oplus$ super-Earth on a\n0.47-day orbit around a Sun-like star discovered by {\\it TESS}. Precise radial\nvelocities from Keck/HIRES confirmed the planet and constrained the mass to be\n$3.87 \\pm 0.71 M_\\oplus$. The RV dataset also indicates a possible\nnon-transiting, 16-day planet ($11.8\\pm2.9M_\\oplus$). We report a tentative\ndetection of phase curve variation and secondary eclipse of TOI-1444b in the\n{\\it TESS} bandpass. TOI-1444b joins the growing sample of 17\nultra-short-period planets with well-measured masses and sizes, most of which\nare compatible with an Earth-like composition. We take this opportunity to\nexamine the expanding sample of ultra-short-period planets ($<2R_\\oplus$) and\ncontrast them with the newly discovered sub-day ultra-hot Neptunes\n($>3R_\\oplus$, $>2000F_\\oplus$ TOI-849 b, LTT9779 b and K2-100). We find that\n1) USPs have predominately Earth-like compositions with inferred iron core mass\nfractions of 0.32$\\pm$0.04; and have masses below the threshold of runaway\naccretion ($\\sim 10M_\\oplus$), while ultra-hot Neptunes are above the threshold\nand have H/He or other volatile envelope. 2) USPs are almost always found in\nmulti-planet system consistent with a secular interaction formation scenario;\nultra-hot Neptunes ($P_{\\rm orb} \\lesssim$1 day) tend to be ``lonely' similar\nto longer-period hot Neptunes($P_{\\rm orb}$1-10 days) and hot Jupiters. 3) USPs\noccur around solar-metallicity stars while hot Neptunes prefer higher\nmetallicity hosts. 4) In all these respects, the ultra-hot Neptunes show more\nresemblance to hot Jupiters than the smaller USP planets, although ultra-hot\nNeptunes are rarer than both USP and hot Jupiters by 1-2 orders of magnitude.\n"}
+{"abstract": "  A wave function exposed to measurements undergoes pure state dynamics, with\ndeterministic unitary and probabilistic measurement induced state updates,\ndefining a quantum trajectory. For many-particle systems, the competition of\nthese different elements of dynamics can give rise to a scenario similar to\nquantum phase transitions. To access it despite the randomness of single\nquantum trajectories, we construct an $n$-replica Keldysh field theory for the\nensemble average of the $n$-th moment of the trajectory projector. A key\nfinding is that this field theory decouples into one set of degrees of freedom\nthat heats up indefinitely, while $n-1$ others can be cast into the form of\npure state evolutions generated by an effective non-Hermitian Hamiltonian. This\ndecoupling is exact for free theories, and useful for interacting ones. In\nparticular, we study locally measured Dirac fermions in $(1+1)$ dimensions,\nwhich can be bosonized to a monitored interacting Luttinger liquid at long\nwavelengths. For this model, the non-Hermitian Hamiltonian corresponds to a\nquantum Sine-Gordon model with complex coefficients. A renormalization group\nanalysis reveals a gapless critical phase with logarithmic entanglement entropy\ngrowth, and a gapped area law phase, separated by a\nBerezinskii-Kosterlitz-Thouless transition. The physical picture emerging here\nis a pinning of the trajectory wave function into eigenstates of the\nmeasurement operators upon increasing the monitoring rate.\n"}
+{"abstract": "  The rise of social media has led to the increasing of comments on online\nforums. However, there still exists invalid comments which are not informative\nfor users. Moreover, those comments are also quite toxic and harmful to people.\nIn this paper, we create a dataset for constructive and toxic speech detection,\nnamed UIT-ViCTSD (Vietnamese Constructive and Toxic Speech Detection dataset)\nwith 10,000 human-annotated comments. For these tasks, we propose a system for\nconstructive and toxic speech detection with the state-of-the-art transfer\nlearning model in Vietnamese NLP as PhoBERT. With this system, we obtain\nF1-scores of 78.59% and 59.40% for classifying constructive and toxic comments,\nrespectively. Besides, we implement various baseline models as traditional\nMachine Learning and Deep Neural Network-Based models to evaluate the dataset.\nWith the results, we can solve several tasks on the online discussions and\ndevelop the framework for identifying constructiveness and toxicity of\nVietnamese social media comments automatically.\n"}
+{"abstract": "  We extend the scattering result for the radial defocusing-focusing\nmass-energy double critical nonlinear Schr\\\"odinger equation in $d\\leq 4$ given\nby Cheng et al. to the case $d\\geq 5$. The main ingredient is a suitable long\ntime perturbation theory which is applicable for $d\\geq 5$. The paper will\ntherefore give a full characterization on the scattering threshold for the\nradial defocusing-focusing mass-energy double critical nonlinear Schr\\\"odinger\nequation in all dimensions $d\\geq 3$.\n"}
+{"abstract": "  Accurate description of finite-temperature vibrational dynamics is\nindispensable in the computation of two-dimensional electronic spectra. Such\nsimulations are often based on the density matrix evolution, statistical\naveraging of initial vibrational states, or approximate classical or\nsemiclassical limits. While many practical approaches exist, they are often of\nlimited accuracy and difficult to interpret. Here, we use the concept of\nthermo-field dynamics to derive an exact finite-temperature expression that\nlends itself to an intuitive wavepacket-based interpretation. Furthermore, an\nefficient method for computing finite-temperature two-dimensional spectra is\nobtained by combining the exact thermo-field dynamics approach with the thawed\nGaussian approximation for the wavepacket dynamics, which is exact for any\ndisplaced, distorted, and Duschinsky-rotated harmonic potential but also\naccounts partially for anharmonicity effects in general potentials. Using this\nnew method, we directly relate a symmetry breaking of the two-dimensional\nsignal to the deviation from the conventional Brownian oscillator picture.\n"}
+{"abstract": "  We provide sufficient conditions for the existence of periodic solutions of\nthe of the Lorentz force equation, which models the motion of a charged\nparticle under the action of an electromagnetic fields. The basic assumptions\ncover relevant models with singularities like Coulomb-like electric potentials\nor the magnetic dipole.\n"}
+{"abstract": "  In this work we compare the capacity and achievable rate of uncoded faster\nthan Nyquist (FTN) signalling in the frequency domain, also referred to as\nspectrally efficient FDM (SEFDM). We propose a deep residual convolutional\nneural network detector for SEFDM signals in additive white Gaussian noise\nchannels, that allows to approach the Mazo limit in systems with up to 60\nsubcarriers. Notably, the deep detectors achieve a loss less than 0.4-0.7 dB\nfor uncoded QPSK SEFDM systems of 12 to 60 subcarriers at a 15% spectral\ncompression.\n"}
+{"abstract": "  We propose an autoencoder-based geometric shaping that learns a constellation\nrobust to SNR and laser linewidth estimation errors. This constellation\nmaintains shaping gain in mutual information (up to 0.3 bits/symbol) with\nrespect to QAM over various SNR and laser linewidth values.\n"}
+{"abstract": "  Since its development, Stokesian Dynamics has been a leading approach for the\ndynamic simulation of suspensions of particles at arbitrary concentrations with\nfull hydrodynamic interactions. Although originally developed for the\nsimulation of passive particle suspensions, the Stokesian Dynamics framework is\nequally well suited to the analysis and dynamic simulation of suspensions of\nactive particles, as we elucidate here. We show how the reciprocal theorem can\nbe used to formulate the exact dynamics for a suspension of arbitrary active\nparticles and then show how the Stokesian Dynamics method provides a rigorous\nway to approximate and compute the dynamics of dense active suspensions where\nmany-body hydrodynamic interactions are important.\n"}
+{"abstract": "  As the photovoltaic sector approaches 1 TW in cumulative installed capacity,\nwe provide an overview of the current challenges to achieve further\ntechnological improvements. On the raw materials side, we see no fundamental\nlimitation to expansion in capacity of the current market technologies, even\nthough basic estimates predict that the PV sector will become the largest\nconsumer of Ag in the world after 2030. On the other hand, recent market data\non PV costs indicates that the largest cost fraction is now infrastructure and\narea-related, and nearly independent of the core cell technology. Therefore,\nadditional value adding is likely to proceed via an increase in energy yield\nmetrics such as the power density and/or efficiency of the PV module. However,\ncurrent market technologies are near their fundamental detailed balance\nefficiency limits. The transition to multijunction PV in tandem configurations\nis regarded as the most promising path to surpass this limitation and increase\nthe power per unit area of PV modules. So far, each specific multijunction\nconcept faces particular obstacles that have prevented their upscaling, but the\nfield is rapidly improving. In this review work, we provide a global comparison\nbetween the different types of multijunction concepts, including III-Vs,\nSi-based tandems and the emergence of perovskite/Si devices. Coupled with\nanalyses of new notable developments in the field, we discuss the challenges\ncommon to different multijunction cell architectures, and the specific\nchallenges of each type of device, both on a cell level and on a module\nintegration level. From the analysis, we conclude that several tandem concepts\nare nearing the disruption level where a breakthrough into mainstream PV is\npossible.\n"}
+{"abstract": "  Human trajectory forecasting in crowds, at its core, is a sequence prediction\nproblem with specific challenges of capturing inter-sequence dependencies\n(social interactions) and consequently predicting socially-compliant multimodal\ndistributions. In recent years, neural network-based methods have been shown to\noutperform hand-crafted methods on distance-based metrics. However, these\ndata-driven methods still suffer from one crucial limitation: lack of\ninterpretability. To overcome this limitation, we leverage the power of\ndiscrete choice models to learn interpretable rule-based intents, and\nsubsequently utilise the expressibility of neural networks to model\nscene-specific residual. Extensive experimentation on the interaction-centric\nbenchmark TrajNet++ demonstrates the effectiveness of our proposed architecture\nto explain its predictions without compromising the accuracy.\n"}
+{"abstract": "  A quantum many-body system with a conserved electric charge can have a DC\nresistivity that is either exactly zero (implying it supports dissipationless\ncurrent) or nonzero. Exactly zero resistivity is related to conservation laws\nthat prevent the current from degrading. In this paper, we carefully examine\nthe situations in which such a circumstance can occur. We find that exactly\nzero resistivity requires either continuous translation symmetry, or an\ninternal symmetry that has a certain kind of \"mixed anomaly\" with the electric\ncharge. (The symmetry could be a generalized global symmetry associated with\nthe emergence of unbreakable loop or higher dimensional excitations.) However,\neven if one of these is satisfied, we show that there is still a mechanism to\nget nonzero resistivity, through critical fluctuations that drive the\nsusceptibility of the conserved quantity to infinity; we call this mechanism\n\"critical drag\". Critical drag is thus a mechanism for resistivity that, unlike\nconventional mechanisms, is unrelated to broken symmetries. We furthermore\nargue that an emergent symmetry that has the appropriate mixed anomaly with\nelectric charge is in fact an inevitable consequence of compressibility in\nsystems with lattice translation symmetry. Critical drag therefore seems to be\nthe only way (other than through irrelevant perturbations breaking the emergent\nsymmetry, that disappear at the renormalization group fixed point) to get\nnonzero resistivity in such systems. Finally, we present a very simple and\nconcrete model -- the \"Quantum Lifshitz Model\" -- that illustrates the critical\ndrag mechanism as well as the other considerations of the paper.\n"}
+{"abstract": "  Voltage manipulation of skyrmions is a promising path towards low-energy\nspintronic devices. Here, voltage effects on skyrmions in a GdOx/Gd/Co/Pt\nheterostructure are observed experimentally. The results show that the skyrmion\ndensity can be both enhanced and depleted by the application of an electric\nfield, along with the ability, at certain magnetic fields to completely switch\nthe skyrmion state on and off. Further, a zero magnetic field skyrmion state\ncan be stablized under a negative bias voltage using a defined voltage and\nmagnetic field sequence. The voltage effects measured here occur on a\nfew-second timescale, suggesting an origin in voltage-controlled magnetic\nanisotropy rather than ionic effects. By investigating the skyrmion nucleation\nrate as a function of temperature, we extract the energy barrier to skyrmion\nnucleation in our sample. Further, micromagnetic simulations are used to\nexplore the effect of changing the anisotropy and Dzyaloshinskii-Moriya\ninteraction on skyrmion density. Our work demonstrates the control of skyrmions\nby voltages, showing functionalities desirable for commercial devices.\n"}
+{"abstract": "  We investigate the nonequilibrium dynamics of the spinless Haldane model with\nnearest-neighbor interactions on the honeycomb lattice by employing an unbiased\nnumerical method. In this system, a first-order transition from the Chern\ninsulator (CI) at weak coupling to the charge-density-wave (CDW) phase at\nstrong coupling can be characterized by a level crossing of the lowest energy\nlevels. Here we show that adiabatically following the eigenstates across this\nlevel crossing, their Chern numbers are preserved, leading to the\nidentification of a topologically-nontrivial low-energy excited state in the\nCDW regime. By promoting a resonant energy excitation via an ultrafast\ncircularly polarized pump pulse, we find that the system acquires a\nnon-vanishing Hall response as a result of the large overlap enhancement\nbetween the time-dependent wave-function and the topologically non-trivial\nexcited state. This is suggestive of a photoinduced topological phase\ntransition via unitary dynamics, despite a proper definition of the Chern\nnumber remaining elusive for an out-of-equilibrium interacting system. We\ncontrast these results with more common quench protocols, where such features\nare largely absent in the dynamics even if the post-quench Hamiltonian displays\na topologically nontrivial ground state.\n"}
+{"abstract": "  Over the past few years, there is a heated debate and serious public concerns\nregarding online content moderation, censorship, and the principle of free\nspeech on the Web. To ease these concerns, social media platforms like Twitter\nand Facebook refined their content moderation systems to support soft\nmoderation interventions. Soft moderation interventions refer to warning labels\nattached to potentially questionable or harmful content to inform other users\nabout the content and its nature while the content remains accessible, hence\nalleviating concerns related to censorship and free speech. In this work, we\nperform one of the first empirical studies on soft moderation interventions on\nTwitter. Using a mixed-methods approach, we study the users who share tweets\nwith warning labels on Twitter and their political leaning, the engagement that\nthese tweets receive, and how users interact with tweets that have warning\nlabels. Among other things, we find that 72% of the tweets with warning labels\nare shared by Republicans, while only 11% are shared by Democrats. By analyzing\ncontent engagement, we find that tweets with warning labels had more engagement\ncompared to tweets without warning labels. Also, we qualitatively analyze how\nusers interact with content that has warning labels finding that the most\npopular interactions are related to further debunking false claims, mocking the\nauthor or content of the disputed tweet, and further reinforcing or resharing\nfalse claims. Finally, we describe concrete examples of inconsistencies, such\nas warning labels that are incorrectly added or warning labels that are not\nadded on tweets despite sharing questionable and potentially harmful\ninformation.\n"}
+{"abstract": "  We present a combined neutron diffraction (ND) and high-field muon spin\nrotation ($\\mu$SR) study of the magnetic and superconducting phases of the\nhigh-temperature superconductor La$_{1.94}$Sr$_{0.06}$CuO$_{4+y}$ ($T_{c} =\n38$~K). We observe a linear dependence of the ND signal from the modulated\nantiferromagnetic order (m-AFM) on the applied field. The magnetic volume\nfraction measured with $\\mu$SR increases linearly from 0\\% to $\\sim$40\\% with\napplied magnetic field up to 8~T. This allows us to conclude, in contrast to\nearlier field-dependent neutron diffraction studies, that the long-range m-AFM\nregions are induced by an applied field, and that their ordered magnetic moment\nremains constant.\n"}
+{"abstract": "  We study the running vacuum model in which the vaccum energy density depends\non square of Hubble parameter in comparison with the $\\Lambda$CDM model. In\nthis work, the Bayesian inference method is employed to test against the\nstandard $\\Lambda$CDM model to appraise the relative significance of our model,\nusing the combined data sets, Pantheon+CMB+BAO and Pantheon+CMB+BAO+Hubble\ndata. The model parameters and the corresponding errors are estimated from the\nmarginal likelihood function of the model parameters. Marginalizing over all\nmodel parameters with suitable prior, we have obtained the Bayes factor as the\nratio of Bayesian evidence of our model and the $\\Lambda$CDM model. The\nanalysis based on Jeffrey's scale of bayesian inference shows that the evidence\nof our model against the $\\Lambda$CDM model is weak for both data combinations.\nEven though the running vacuum model gives a good account of the evolution of\nthe universe, it is not superior to the $\\Lambda$CDM model.\n"}
+{"abstract": "  Private blockchain networks are used by enterprises to manage decentralized\nprocesses without trusted mediators and without exposing their assets publicly\non an open network like Ethereum. Yet external parties that cannot join such\nnetworks may have a compelling need to be informed about certain data items on\ntheir shared ledgers along with certifications of data authenticity; e.g., a\nmortgage bank may need to know about the sale of a mortgaged property from a\nnetwork managing property deeds. These parties are willing to compensate the\nnetworks in exchange for privately sharing information with proof of\nauthenticity and authorization for external use. We have devised a novel and\ncryptographically secure protocol to effect a fair exchange between rational\nnetwork members and information recipients using a public blockchain and atomic\nswap techniques. Using our protocol, any member of a private blockchain can\natomically reveal private blockchain data with proofs in exchange for a\nmonetary reward to an external party if and only if the external party is a\nvalid recipient. The protocol preserves confidentiality of data for the\nrecipient, and in addition, allows it to mount a challenge if the data turns\nout to be inauthentic. We also formally analyze the security and privacy of\nthis protocol, which can be used in a wide array of practical scenarios\n"}
+{"abstract": "  Developers create software branches for tentative feature addition and bug\nfixing, and periodically merge branches to release software with new features\nor repairing patches. When the program edits from different branches textually\noverlap (i.e., textual conflicts), or the co-application of those edits lead to\ncompilation or runtime errors (i.e., compiling or dynamic conflicts), it is\nchallenging and time-consuming for developers to eliminate merge conflicts.\nPrior studies examined %the popularity of merge conflicts and how conflicts\nwere related to code smells or software development process; tools were built\nto find and solve conflicts.\n  However, some fundamental research questions are still not comprehensively\nexplored, including (1) how conflicts were introduced, (2) how developers\nmanually resolved conflicts, and (3) what conflicts cannot be handled by\ncurrent tools.\n  For this paper, we took a hybrid approach that combines automatic detection\nwith manual inspection to reveal 204 merge conflicts and their resolutions in\n15 open-source repositories. %in the version history of 15 open-source\nprojects. Our data analysis reveals three phenomena. First, compiling and\ndynamic conflicts are harder to detect, although current tools mainly focus on\ntextual conflicts. Second, in the same merging context, developers usually\nresolved similar textual conflicts with similar strategies. Third, developers\nmanually fixed most of the inspected compiling and dynamic conflicts by\nsimilarly editing the merged version as what they did for one of the branches.\nOur research reveals the challenges and opportunities for automatic detection\nand resolution of merge conflicts; it also sheds light on related areas like\nsystematic program editing and change recommendation.\n"}
+{"abstract": "  We consider averaging a number of candidate models to produce a prediction of\nlower risk in the context of partially linear functional additive models. These\nmodels incorporate the parametric effect of scalar variables and the additive\neffect of a functional variable to describe the relationship between a response\nvariable and regressors. We develop a model averaging scheme that assigns the\nweights by minimizing a cross-validation criterion. Under the framework of\nmodel misspecification, the resulting estimator is proved to be asymptotically\noptimal in terms of the lowest possible square error loss for prediction. Also,\nsimulation studies and real data analysis demonstrate the good performance of\nour proposed method.\n"}
+{"abstract": "  The imprints of large-scale structures on the Cosmic Microwave Background can\nbe studied via the CMB lensing and Integrated Sachs-Wolfe (ISW) signals. In\nparticular, the stacked ISW signal around supervoids has been claimed in\nseveral works to be anomalously high. In this study, we find cluster and void\nsuperstructures using four tomographic redshift bins with $0<z<0.8$ from the\nDESI Legacy Survey, and measure the stacked CMB lensing and ISW signals around\nthem. To compare our measurements with $\\Lambda$CDM model predictions, we\nconstruct a mock catalogue with matched galaxy number density and bias, and\napply the same photo-$z$ uncertainty as the data. The consistency between the\nmock and data is verified via the stacked galaxy density profiles around the\nsuperstructures and their quantity. The corresponding lensing convergence and\nISW maps are then constructed and compared. The stacked lensing signal agrees\nwith data well except at the highest redshift bin in density peaks, where the\nmock prediction is significantly higher, by approximately a factor 1.3. The\nstacked ISW signal is generally consistent with the mock prediction. We do not\nobtain a significant signal from voids, $A_{\\rm ISW}=-0.10\\pm0.69$, and the\nsignal from clusters, $A_{\\rm ISW}=1.52\\pm0.72$, is at best weakly detected.\nHowever, these results are strongly inconsistent with previous claims of ISW\nsignals at many times the level of the $\\Lambda$CDM prediction. We discuss the\ncomparison of our results with past work in this area, and investigate possible\nexplanations for this discrepancy.\n"}
+{"abstract": "  The dynamics of cellular chemical reactions are variable due to stochastic\nnoise from intrinsic and extrinsic sources. The intrinsic noise is the\nintracellular fluctuations of molecular copy numbers caused by the\nprobabilistic encounter of molecules and is modeled by the chemical master\nequation. The extrinsic noise, on the other hand, represents the intercellular\nvariation of the kinetic parameters due to the variation of global factors\naffecting gene expression. The objective of this paper is to propose a\ntheoretical framework to analyze the combined effect of the intrinsic and the\nextrinsic noise modeled by the chemical master equation with uncertain\nparameters. More specifically, we formulate a semidefinite program to compute\nthe intervals of the stationary solution of uncertain moment equations whose\nparameters are given only partially in the form of the statistics of their\ndistributions. The semidefinite program is derived without approximating the\ngoverning equation in contrast with many existing approaches. Thus, we can\nobtain guaranteed intervals of the worst possible values of the moments for all\nparameter distributions satisfying the given statistics, which are\nprohibitively hard to estimate from sample-path simulations since sampling from\nall possible uncertain distributions is difficult. We demonstrate the proposed\noptimization approach using two examples of stochastic chemical reactions and\nshow that the solution of the optimization problem gives practically useful\nupper and lower bounds of the statistics of the stationary copy number\ndistributions.\n"}
+{"abstract": "  The transconductance and effective Land\\'{e} $g^*$ factors for a quantum\npoint contact defined in silicene by the electric field of a split gate is\ninvestigated. The strong spin-orbit coupling in buckled silicene reduces the\n$g^*$ factor for in-plane magnetic field from the nominal value 2 to around 1.2\nfor the first- to 0.45 for the third conduction subband. However, for\nperpendicular magnetic field we observe an enhancement of $g^*$ factors for the\nfirst subband to 5.8 in nanoribbon with zigzag and to 2.5 with armchair edge.\nThe main contribution to the Zeeman splitting comes from the intrinsic\nspin-orbit coupling defined by the Kane-Mele form of interaction.\n"}
+{"abstract": "  We discuss in this survey several network modeling methods and their\napplicability to precision medicine. We review several network centrality\nmethods (degree centrality, closeness centrality, eccentricity centrality,\nbetweenness centrality, and eigenvector-based prestige) and two systems\ncontrollability methods (minimum dominating sets and network structural\ncontrollability). We demonstrate their applicability to precision medicine on\nthree multiple myeloma patient disease networks. Each network consists of\nprotein-protein interactions built around a specific patient's mutated genes,\naround the targets of the drugs used in the standard of care in multiple\nmyeloma, and around multiple myeloma-specific essential genes. For each network\nwe demonstrate how the network methods we discuss can be used to identify\npersonalized, targeted drug combinations uniquely suited to that patient.\n"}
+{"abstract": "  This comment on the Phys. Rev. A paper \"Nonlinear quantum effects in\nelectromagnetic radiation of a vortex electron\" by Karlovets and\nPupasov-Maximov [Phys. Rev. A 103, 12214 (2021)] addresses their criticism of\nthe combined experimental and theoretical study \"Observing the quantum wave\nnature of free electrons through spontaneous emission\" by Remez et al,\npublished in Phys. Rev. Lett. [Phys. Rev. Lett. 123, 060401 (2019)]. We show,\nby means of simple optical arguments as well as numerical simulations, that the\ncriticism raised by Karlovets and Pupasov-Maximov regarding the experimental\nregime reported by Remez et al is false. Further, we discuss a necessary\nclarification for the theoretical derivations presented by Karlovets and\nPupasov-Maximov, as they only hold for a certain experimental situation where\nthe final state of the emitting electron is observed in coincidence with the\nemitted photon - which is not the common scenario in cathodoluminescence. Upon\nlifting the concerns regarding the experimental regime reported by Remez et al,\nand explicitly clarifying the electron post-selection, we believe that the\npaper by Karlovets and Pupasov-Maximov may constitute a valuable contribution\nto the problem of spontaneous emission by shaped electron wavefunctions, as it\npresents new expressions for the emission rates beyond the ubiquitous paraxial\napproximation.\n"}
+{"abstract": "  In this paper I shall give the complete solution of the equations governing\nthe bilateral birth and death process on path set $\\mathbb{R}_q=\\{q^n,\\quad\nn\\in\\mathbb{Z}\\}$ in which the birth and death rates $\\lambda_n=q^{2\\nu-2n}$\nand $\\mu_n=q^{-2n}$ where $0<q<1$ and $\\nu>-1$ . The mathematical methods\nemployed here are based on $q$-Bessel Fourier analysis.\n"}
+{"abstract": "  The execution of quantum circuits on real systems has largely been limited to\nthose which are simply time-ordered sequences of unitary operations followed by\na projective measurement. As hardware platforms for quantum computing continue\nto mature in size and capability, it is imperative to enable quantum circuits\nbeyond their conventional construction. Here we break into the realm of dynamic\nquantum circuits on a superconducting-based quantum system. Dynamic quantum\ncircuits involve not only the evolution of the quantum state throughout the\ncomputation, but also periodic measurements of a subset of qubits mid-circuit\nand concurrent processing of the resulting classical information within\ntimescales shorter than the execution times of the circuits. Using noisy\nquantum hardware, we explore one of the most fundamental quantum algorithms,\nquantum phase estimation, in its adaptive version, which exploits dynamic\ncircuits, and compare the results to a non-adaptive implementation of the same\nalgorithm. We demonstrate that the version of real-time quantum computing with\ndynamic circuits can offer a substantial and tangible advantage when noise and\nlatency are sufficiently low in the system, opening the door to a new realm of\navailable algorithms on real quantum systems.\n"}
+{"abstract": "  Using classical electrodynamics, this work analyzes the dynamics of a closed\nmicrowave cavity as a function of its center of energy. Starting from the\nprinciple of momentum conservation, expressions for the maximum electromagnetic\nmomentum stored in a free microwave cavity are obtained. Next, it is shown\nthat, for coherent fields and special shape conditions, this momentum component\nmay not completely average out to zero when the fields change in the transient\nregime. Non-zero conditions are illustrated for the asymmetric conical frustum\nwhose exact modes can not be calculated analytically. One concludes that the\nelectromagnetic momentum can be imparted to the mechanical body so as to\ndisplace it in relation to the original center of energy. However, the average\ntime range of the effect is much shorter than any time regime of the\nexperimental tests performed to measure presently, suggesting it has not been\nobserved yet in copper-made resonators.\n"}
+{"abstract": "  Constitutive models are widely used for modeling complex systems in science\nand engineering, where first-principle-based, well-resolved simulations are\noften prohibitively expensive. For example, in fluid dynamics, constitutive\nmodels are required to describe nonlocal, unresolved physics such as turbulence\nand laminar-turbulent transition. However, traditional constitutive models\nbased on partial differential equations (PDEs) often lack robustness and are\ntoo rigid to accommodate diverse calibration datasets. We propose a\nframe-independent, nonlocal constitutive model based on a vector-cloud neural\nnetwork that can be learned with data. The model predicts the closure variable\nat a point based on the flow information in its neighborhood. Such nonlocal\ninformation is represented by a group of points, each having a feature vector\nattached to it, and thus the input is referred to as vector cloud. The cloud is\nmapped to the closure variable through a frame-independent neural network,\ninvariant both to coordinate translation and rotation and to the ordering of\npoints in the cloud. As such, the network can deal with any number of\narbitrarily arranged grid points and thus is suitable for unstructured meshes\nin fluid simulations. The merits of the proposed network are demonstrated for\nscalar transport PDEs on a family of parameterized periodic hill geometries.\nThe vector-cloud neural network is a promising tool not only as nonlocal\nconstitutive models and but also as general surrogate models for PDEs on\nirregular domains.\n"}
+{"abstract": "  We report here results on the analysis of correlated flux variations between\nthe optical and GeV $\\gamma$-ray bands in three bright BL Lac objects, namely\nAO\\, 0235+164, OJ 287 and PKS 2155$-$304. This was based on the analysis of\nabout 10 years of data from the {\\it Fermi} Gamma-ray Space Telescope covering\nthe period between 08 August 2008 to 08 August 2018 along with optical data\ncovering the same period. For all the sources, during the flares analysed in\nthis work, the optical and $\\gamma$-ray flux variations are found to be closely\ncorrelated. From broad band spectral energy distribution modelling of different\nepochs in these sources using the one zone leptonic emission model, we found\nthat the optical-UV emission is dominated by synchrotron emission from the jet.\nThe $\\gamma$-ray emission in the low synchrotron peaked sources AO\\, 0235+164\nand OJ 287 are found to be well fit with external Compton (EC) component,\nwhile, the $\\gamma$-ray emission in the high synchrotron peaked source PKS\n2155$-$304 is well fit with synchrotron self Compton component. Further we note\nthat the $\\gamma$-ray emission during the high flux state of AO 0235+164\n(epochs A and B) requires seed photons from both the dusty torus and broad line\nregion, while the $\\gamma$-ray emission in OJ 287 and during epochs C and D of\nAO\\,0235+164 can be modelled by EC scattering of infra-red photons from the\ntorus.\n"}
+{"abstract": "  In this work we describe the High-Dimensional Matrix Mechanism (HDMM), a\ndifferentially private algorithm for answering a workload of predicate counting\nqueries. HDMM represents query workloads using a compact implicit matrix\nrepresentation and exploits this representation to efficiently optimize over (a\nsubset of) the space of differentially private algorithms for one that is\nunbiased and answers the input query workload with low expected error. HDMM can\nbe deployed for both $\\epsilon$-differential privacy (with Laplace noise) and\n$(\\epsilon, \\delta)$-differential privacy (with Gaussian noise), although the\ncore techniques are slightly different for each. We demonstrate empirically\nthat HDMM can efficiently answer queries with lower expected error than\nstate-of-the-art techniques, and in some cases, it nearly matches existing\nlower bounds for the particular class of mechanisms we consider.\n"}
+{"abstract": "  This note gives a detailed proof of the following statement. Let $d\\in\n\\mathbb{N}$ and $m,n \\ge d + 1$, with $m + n \\ge \\binom{d+2}{2} + 1$. Then the\ncomplete bipartite graph $K_{m,n}$ is generically globally rigid in dimension\n$d$.\n"}
+{"abstract": "  The emergence of new technologies and innovative communication tools permits\nus to transcend societal challenges. While particle accelerators are essential\ninstruments to improve our quality of life through science and technology, an\nadequate ecosystem is essential to activate and maximize this potential.\nResearch Infrastructure (RI) and industries supported by enlightened\norganizations and education, can generate a sustainable environment to serve\nthis purpose. In this paper, we will discuss state-of-the-art infrastructures\ntaking the lead to reach this impact, thus contributing to economic and social\ntransformation.\n"}
+{"abstract": "  Bayesian decision theory provides an elegant framework for acting optimally\nunder uncertainty when tractable posterior distributions are available. Modern\nBayesian models, however, typically involve intractable posteriors that are\napproximated with, potentially crude, surrogates. This difficulty has\nengendered loss-calibrated techniques that aim to learn posterior\napproximations that favor high-utility decisions. In this paper, focusing on\nBayesian neural networks, we develop methods for correcting approximate\nposterior predictive distributions encouraging them to prefer high-utility\ndecisions. In contrast to previous work, our approach is agnostic to the choice\nof the approximate inference algorithm, allows for efficient test time decision\nmaking through amortization, and empirically produces higher quality decisions.\nWe demonstrate the effectiveness of our approach through controlled experiments\nspanning a diversity of tasks and datasets.\n"}
+{"abstract": "  Isolated post-capillary pulmonary hypertension (Ipc-PH) occurs due to left\nheart failure, which contributes to 1 out of every 9 deaths in the United\nStates. In some patients, through unknown mechanisms, Ipc-PH transitions to\ncombined pre-/post-capillary PH (Cpc-PH), diagnosed by an increase in pulmonary\nvascular resistance and associated with a dramatic increase in mortality. We\nhypothesize that altered mechanical forces and subsequent vasoactive signaling\nin the pulmonary capillary bed drive the transition from Ipc-PH to Cpc-PH.\nHowever, even in a healthy pulmonary circulation, the mechanical forces in the\nsmallest vessels (the arterioles, venules, and capillary bed) have not been\nquantitatively defined. This study is the first to examine this question via a\ncomputational fluid dynamics model of the human pulmonary arteries, veins,\narterioles, and venules. Using this model we predict temporal and spatial\ndynamics of cyclic stretch and wall shear stress. In the large vessels,\nnumerical simulations show that shear stress increases coincides with larger\nflow and pressure. In the microvasculature, we found that as vessel radius\ndecreases, shear stress increases and flow decreases. In arterioles, this\ncorresponds with lower pressures; however, the venules and smaller veins have\nhigher pressure than larger veins. Our model provides predictions for pressure,\nflow, shear stress, and cyclic stretch that provides a way to analyze and\ninvestigate hypotheses related to disease progression in the pulmonary\ncirculation.\n"}
+{"abstract": "  We report spatially resolved measurements of static and fluctuating electric\nfields over conductive (Au) and non-conductive (SiO2) surfaces. Using an\nultrasensitive `nanoladder' cantilever probe to scan over these surfaces at\ndistances of a few tens of nanometers, we record changes in the probe resonance\nfrequency and damping that we associate with static and fluctuating fields,\nrespectively. We find that the two quantities are spatially correlated and of\nsimilar magnitude for the two materials. We quantitatively describe the\nobserved effects on the basis of trapped surface charges and dielectric\nfluctuations in an adsorbate layer. Our results provide direct, spatial\nevidence for surface dissipation in adsorbates that affects nanomechanical\nsensors, trapped ions, superconducting resonators, and color centers in\ndiamond.\n"}
+{"abstract": "  This paper studies the estimation of large-scale optimal transport maps\n(OTM), which is a well-known challenging problem owing to the curse of\ndimensionality. Existing literature approximates the large-scale OTM by a\nseries of one-dimensional OTM problems through iterative random projection.\nSuch methods, however, suffer from slow or none convergence in practice due to\nthe nature of randomly selected projection directions. Instead, we propose an\nestimation method of large-scale OTM by combining the idea of projection\npursuit regression and sufficient dimension reduction. The proposed method,\nnamed projection pursuit Monge map (PPMM), adaptively selects the most\n``informative'' projection direction in each iteration. We theoretically show\nthe proposed dimension reduction method can consistently estimate the most\n``informative'' projection direction in each iteration. Furthermore, the PPMM\nalgorithm weakly convergences to the target large-scale OTM in a reasonable\nnumber of steps. Empirically, PPMM is computationally easy and converges fast.\nWe assess its finite sample performance through the applications of Wasserstein\ndistance estimation and generative models.\n"}
+{"abstract": "  Nowadays, a community starts to find the need for human presence in an\nalternative way, there has been tremendous research and development in\nadvancing telepresence robots. People tend to feel closer and more comfortable\nwith telepresence robots as many senses a human presence in robots. In general,\nmany people feel the sense of agency from the face of a robot, but some\ntelepresence robots without arm and body motions tend to give a sense of human\npresence. It is important to identify and configure how the telepresence robots\naffect a sense of presence and agency to people by including human face and\nslight face and arm motions. Therefore, we carried out extensive research via\nweb-based experiment to determine the prototype that can result in soothing\nhuman interaction with the robot. The experiments featured videos of a\ntelepresence robot n = 128, 2 x 2 between-participant study robot face factor:\nvideo-conference, robot-like face; arm motion factor: moving vs. static) to\ninvestigate the factors significantly affecting human presence and agency with\nthe robot. We used two telepresence robots: an affordable robot platform and a\nmodified version for human interaction enhancements. The findings suggest that\nparticipants feel agency that is closer to human-likeness when the robot's face\nwas replaced with a human's face and without a motion. The robot's motion\ninvokes a feeling of human presence whether the face is human or robot-like.\n"}
+{"abstract": "  Dense depth map capture is challenging in existing active sparse illumination\nbased depth acquisition techniques, such as LiDAR. Various techniques have been\nproposed to estimate a dense depth map based on fusion of the sparse depth map\nmeasurement with the RGB image. Recent advances in hardware enable adaptive\ndepth measurements resulting in further improvement of the dense depth map\nestimation. In this paper, we study the topic of estimating dense depth from\ndepth sampling. The adaptive sparse depth sampling network is jointly trained\nwith a fusion network of an RGB image and sparse depth, to generate optimal\nadaptive sampling masks. We show that such adaptive sampling masks can\ngeneralize well to many RGB and sparse depth fusion algorithms under a variety\nof sampling rates (as low as $0.0625\\%$). The proposed adaptive sampling method\nis fully differentiable and flexible to be trained end-to-end with upstream\nperception algorithms.\n"}
+{"abstract": "  For every set of parabolic weights, we construct a Harder-Narasimhan\nstratification for the moduli stack of parabolic vector bundles on a curve. It\nis based on the notion of parabolic slope, introduced by Mehta and Seshadri. We\nalso prove that the stratification is schematic, that each stratum is complete,\nand establish an analogue of Behrend's conjecture for parabolic vector bundles.\nA comparison with recent $\\Theta$-stratification approaches is discussed.\n"}
+{"abstract": "  Counterfactual explanations (CEs) are a practical tool for demonstrating why\nmachine learning classifiers make particular decisions. For CEs to be useful,\nit is important that they are easy for users to interpret. Existing methods for\ngenerating interpretable CEs rely on auxiliary generative models, which may not\nbe suitable for complex datasets, and incur engineering overhead. We introduce\na simple and fast method for generating interpretable CEs in a white-box\nsetting without an auxiliary model, by using the predictive uncertainty of the\nclassifier. Our experiments show that our proposed algorithm generates more\ninterpretable CEs, according to IM1 scores, than existing methods.\nAdditionally, our approach allows us to estimate the uncertainty of a CE, which\nmay be important in safety-critical applications, such as those in the medical\ndomain.\n"}
+{"abstract": "  This paper presents DeepI2P: a novel approach for cross-modality registration\nbetween an image and a point cloud. Given an image (e.g. from a rgb-camera) and\na general point cloud (e.g. from a 3D Lidar scanner) captured at different\nlocations in the same scene, our method estimates the relative rigid\ntransformation between the coordinate frames of the camera and Lidar. Learning\ncommon feature descriptors to establish correspondences for the registration is\ninherently challenging due to the lack of appearance and geometric correlations\nacross the two modalities. We circumvent the difficulty by converting the\nregistration problem into a classification and inverse camera projection\noptimization problem. A classification neural network is designed to label\nwhether the projection of each point in the point cloud is within or beyond the\ncamera frustum. These labeled points are subsequently passed into a novel\ninverse camera projection solver to estimate the relative pose. Extensive\nexperimental results on Oxford Robotcar and KITTI datasets demonstrate the\nfeasibility of our approach. Our source code is available at\nhttps://github.com/lijx10/DeepI2P\n"}
+{"abstract": "  Nanoscale layered ferromagnets have demonstrated fascinating two-dimensional\nmagnetism down to atomic layers, providing a peculiar playground of spin orders\nfor investigating fundamental physics and spintronic applications. However,\nstrategy for growing films with designed magnetic properties is not well\nestablished yet. Herein, we present a versatile method to control the Curie\ntemperature (T_{C}) and magnetic anisotropy during growth of ultrathin\nCr_{2}Te_{3} films. We demonstrate increase of the TC from 165 K to 310 K in\nsync with magnetic anisotropy switching from an out-of-plane orientation to an\nin-plane one, respectively, via controlling the Te source flux during film\ngrowth, leading to different c-lattice parameters while preserving the\nstoichiometries and thicknesses of the films. We attributed this modulation of\nmagnetic anisotropy to the switching of the orbital magnetic moment, using\nX-ray magnetic circular dichroism analysis. We also inferred that different\nc-lattice constants might be responsible for the magnetic anisotropy change,\nsupported by theoretical calculations. These findings emphasize the potential\nof ultrathin Cr_{2}Te_{3} films as candidates for developing room-temperature\nspintronics applications and similar growth strategies could be applicable to\nfabricate other nanoscale layered magnetic compounds.\n"}
+{"abstract": "  A bargaining game is investigated for cooperative energy management in\nmicrogrids. This game incorporates a fully distributed and realistic\ncooperative power scheduling algorithm (CoDES) as well as a distributed Nash\nBargaining Solution (NBS)-based method of allocating the overall power bill\nresulting from CoDES. A novel weather-based stochastic renewable generation\n(RG) prediction method is incorporated in the power scheduling. We demonstrate\nthe proposed game using a 4-user grid-connected microgrid model with diverse\nuser demands, storage, and RG profiles and examine the effect of weather\nprediction on day-ahead power scheduling and cost/profit allocation. Finally,\nthe impact of users' ambivalence about cooperation and /or dishonesty on the\nbargaining outcome is investigated, and it is shown that the proposed game is\nresilient to malicious users' attempts to avoid payment of their fair share of\nthe overall bill.\n"}
+{"abstract": "  The computer vision community has paid much attention to the development of\nvisible image super-resolution (SR) using deep neural networks (DNNs) and has\nachieved impressive results. The advancement of non-visible light sensors, such\nas acoustic imaging sensors, has attracted much attention, as they allow people\nto visualize the intensity of sound waves beyond the visible spectrum. However,\nbecause of the limitations imposed on acquiring acoustic data, new methods for\nimproving the resolution of the acoustic images are necessary. At this time,\nthere is no acoustic imaging dataset designed for the SR problem. This work\nproposed a novel backprojection model architecture for the acoustic image\nsuper-resolution problem, together with Acoustic Map Imaging VUB-ULB Dataset\n(AMIVU). The dataset provides large simulated and real captured images at\ndifferent resolutions. The proposed XCycles BackProjection model (XCBP), in\ncontrast to the feedforward model approach, fully uses the iterative correction\nprocedure in each cycle to reconstruct the residual error correction for the\nencoded features in both low- and high-resolution space. The proposed approach\nwas evaluated on the dataset and showed high outperformance compared to the\nclassical interpolation operators and to the recent feedforward\nstate-of-the-art models. It also contributed to a drastically reduced\nsub-sampling error produced during the data acquisition.\n"}
+{"abstract": "  The symmetry operators generating the hidden $\\mathbb{Z}_2$ symmetry of the\nasymmetric quantum Rabi model (AQRM) at bias $\\epsilon \\in\n\\frac{1}{2}\\mathbb{Z}$ have recently been constructed by V. V. Mangazeev et al.\n[J. Phys. A: Math. Theor. 54 12LT01 (2021)]. We start with this result to\ndetermine symmetry operators for the $N$-qubit generalisation of the AQRM, also\nknown as the biased Dicke model, at special biases. We also prove for general\n$N$ that the symmetry operators, which commute with the Hamiltonian of the\nbiased Dicke model, generate a $\\mathbb{Z}_2$ symmetry.\n"}
+{"abstract": "  Weakened random oracle models (WROMs) are variants of the random oracle model\n(ROM). The WROMs have the random oracle and the additional oracle which breaks\nsome property of a hash function. Analyzing the security of cryptographic\nschemes in WROMs, we can specify the property of a hash function on which the\nsecurity of cryptographic schemes depends. Liskov (SAC 2006) proposed WROMs and\nlater Numayama et al. (PKC 2008) formalized them as CT-ROM, SPT-ROM, and\nFPT-ROM. In each model, there is the additional oracle to break collision\nresistance, second preimage resistance, preimage resistance respectively. Tan\nand Wong (ACISP 2012) proposed the generalized FPT-ROM (GFPT-ROM) which\nintended to capture the chosen prefix collision attack suggested by Stevens et\nal. (EUROCRYPT 2007). In this paper, in order to analyze the security of\ncryptographic schemes more precisely, we formalize GFPT-ROM and propose\nadditional three WROMs which capture the chosen prefix collision attack and its\nvariants. In particular, we focus on signature schemes such as RSA-FDH, its\nvariants, and DSA, in order to understand essential roles of WROMs in their\nsecurity proofs.\n"}
+{"abstract": "  Einstein-Maxwell-dilaton theory with non-trivial dilaton potential is known\nto admit asymptotically flat and (Anti-)de Sitter charged black hole solutions.\nWe investigate the conditions for the presence of horizons as function of the\nparameters mass $M$, charge $Q$ and dilaton coupling strength $\\alpha$. We\nobserve that there is a value of $\\alpha$ which separate two regions, one where\nthe black hole is Reissner-Nordstr\\\"om-like from a region where it is\nSchwarzschild-like. We find that for de Sitter and small non-vanishing\n$\\alpha$, the extremal case is not reached by the solution. We also discuss the\nattractive or repulsive nature of the leading long distance interaction between\ntwo such black holes, or a test particle and one black hole, from a world-line\neffective field theory point of view. Finally, we discuss possible\nmodifications of the Weak Gravity Conjecture in the presence of both a\ndilatonic coupling and a cosmological constant.\n"}
+{"abstract": "  Network intrusion attacks are a known threat. To detect such attacks, network\nintrusion detection systems (NIDSs) have been developed and deployed. These\nsystems apply machine learning models to high-dimensional vectors of features\nextracted from network traffic to detect intrusions. Advances in NIDSs have\nmade it challenging for attackers, who must execute attacks without being\ndetected by these systems. Prior research on bypassing NIDSs has mainly focused\non perturbing the features extracted from the attack traffic to fool the\ndetection system, however, this may jeopardize the attack's functionality. In\nthis work, we present TANTRA, a novel end-to-end Timing-based Adversarial\nNetwork Traffic Reshaping Attack that can bypass a variety of NIDSs. Our\nevasion attack utilizes a long short-term memory (LSTM) deep neural network\n(DNN) which is trained to learn the time differences between the target\nnetwork's benign packets. The trained LSTM is used to set the time differences\nbetween the malicious traffic packets (attack), without changing their content,\nsuch that they will \"behave\" like benign network traffic and will not be\ndetected as an intrusion. We evaluate TANTRA on eight common intrusion attacks\nand three state-of-the-art NIDS systems, achieving an average success rate of\n99.99\\% in network intrusion detection system evasion. We also propose a novel\nmitigation technique to address this new evasion attack.\n"}
+{"abstract": "  We performed deep observations to search for radio pulsations in the\ndirections of 375 unassociated Fermi Large Area Telescope (LAT) gamma-ray\nsources using the Giant Metrewave Radio Telescope (GMRT) at 322 and 607 MHz. In\nthis paper we report the discovery of three millisecond pulsars (MSPs), PSR\nJ0248+4230, PSR J1207$-$5050 and PSR J1536$-$4948. We conducted follow up\ntiming observations for around 5 years with the GMRT and derived phase coherent\ntiming models for these MSPs. PSR J0248$+$4230 and J1207$-$5050 are isolated\nMSPs having periodicities of 2.60 ms and 4.84 ms. PSR J1536-4948 is a 3.07 ms\npulsar in a binary system with orbital period of around 62 days about a\ncompanion of minimum mass 0.32 solar mass. We also present multi-frequency\npulse profiles of these MSPs from the GMRT observations. PSR J1536-4948 is an\nMSP with an extremely wide pulse profile having multiple components. Using the\nradio timing ephemeris we subsequently detected gamma-ray pulsations from these\nthree MSPs, confirming them as the sources powering the gamma-ray emission. For\nPSR J1536-4948 we performed combined radio-gamma-ray timing using around 11.6\nyears of gamma-ray pulse times of arrivals (TOAs) along with the radio TOAs.\nPSR J1536-4948 also shows evidence for pulsed gamma-ray emission out to above\n25 GeV, confirming earlier associations of this MSP with a >10 GeV point\nsource. The multi-wavelength pulse profiles of all three MSPs offer challenges\nto models of radio and gamma-ray emission in pulsar magnetospheres.\n"}
+{"abstract": "  This paper studies the precoder design problem of achieving max-min fairness\n(MMF) amongst users in multigateway multibeam satellite communication systems\nwith feeder link interference. We propose a beamforming strategy based on a\nnewly introduced transmission scheme known as rate-splitting multiple access\n(RSMA). RSMA relies on multi-antenna rate-splitting at the transmitter and\nsuccessive interference cancellation (SIC) at the receivers, such that the\nintended message for a user is split into a common part and a private part and\nthe interference is partially decoded and partially treated as noise. In this\npaper, we formulate the MMF problem subject to per-antenna power constraints at\nthe satellite for the system with imperfect channel state information at the\ntransmitter (CSIT). We also consider the case of two-stage precoding which is\nassisted by on-board processing (OBP) at the satellite. Numerical results\nobtained through simulations for RSMA and the conventional linear precoding\nmethod are compared. When RSMA is used, MMF rate gain is promised and this gain\nincreases when OBP is used. RSMA is proven to be promising for multigateway\nmultibeam satellite systems whereby there are various practical challenges such\nas feeder link interference, CSIT uncertainty, per-antenna power constraints,\nuneven user distribution per beam and frame-based processing.\n"}
+{"abstract": "  This paper introduces PyMatching, a fast open-source Python package for\ndecoding quantum error-correcting codes with the minimum-weight perfect\nmatching (MWPM) algorithm. PyMatching includes the standard MWPM decoder as\nwell as a variant, which we call local matching, that restricts each syndrome\ndefect to be matched to another defect within a local neighbourhood. The\ndecoding performance of local matching is almost identical to that of the\nstandard MWPM decoder in practice, while reducing the computational complexity\napproximately quadratically. We benchmark the performance of PyMatching,\nshowing that local matching is several orders of magnitude faster than\nimplementations of the full MWPM algorithm using NetworkX or Blossom V for\nproblem sizes typically considered in error correction simulations. PyMatching\nand its dependencies are open-source, and it can be used to decode any quantum\ncode for which syndrome defects come in pairs using a simple Python interface.\nPyMatching supports the use of weighted edges, hook errors, boundaries and\nmeasurement errors, enabling fast decoding and simulation of fault-tolerant\nquantum computing.\n"}
+{"abstract": "  We implement two recently developed fast Coulomb solvers, HSMA3D [J. Chem.\nPhys. 149 (8) (2018) 084111] and HSMA2D [J. Chem. Phys. 152 (13) (2020)\n134109], into a new user package HSMA for molecular dynamics simulation engine\nLAMMPS. The HSMA package is designed for efficient and accurate modeling of\nelectrostatic interactions in 3D and 2D periodic systems with dielectric\neffects at the O(N) cost. The implementation is hybrid MPI and OpenMP\nparallelized and compatible with existing LAMMPS functionalities. The\nvectorization technique following AVX512 instructions is adopted for\nacceleration. To establish the validity of our implementation, we have\npresented extensive comparisons to the widely used particle-particle\nparticle-mesh (PPPM) algorithm in LAMMPS and other dielectric solvers. With the\nproper choice of algorithm parameters and parallelization setup, the package\nenables calculations of electrostatic interactions that outperform the standard\nPPPM in speed for a wide range of particle numbers.\n"}
+{"abstract": "  The growing share of proactive actors in the electricity markets calls for\nmore attention on prosumers and more support for their decision-making under\ndecentralized electricity markets. In view of the changing paradigm, it is\ncrucial to study the long-term planning under the decentralized and\nprosumer-centric markets to unravel the effects of such markets on the planning\ndecisions. In the first part of the two-part paper, we propose a\nprosumer-centric framework for concurrent generation and transmission planning.\nHere, three planning models are presented where a peer-to-peer market with\nproduct differentiation, a pool market and a mixed bilateral/pool market and\ntheir associated trading costs are explicitly modeled, respectively. To fully\nreveal the individual costs and benefits, we start by formulating the\noptimization problems of various actors, i.e. prosumers, transmission system\noperator, energy market operator and carbon market operator. Moreover, to\nenable decentralized planning where the privacy of the prosumers is preserved,\ndistributed optimization algorithms are presented based on the corresponding\ncentralized optimization problems.\n"}
+{"abstract": "  Previous work mainly focuses on improving cross-lingual transfer for NLU\ntasks with a multilingual pretrained encoder (MPE), or improving the\nperformance on supervised machine translation with BERT. However, it is\nunder-explored that whether the MPE can help to facilitate the cross-lingual\ntransferability of NMT model. In this paper, we focus on a zero-shot\ncross-lingual transfer task in NMT. In this task, the NMT model is trained with\nparallel dataset of only one language pair and an off-the-shelf MPE, then it is\ndirectly tested on zero-shot language pairs. We propose SixT, a simple yet\neffective model for this task. SixT leverages the MPE with a two-stage training\nschedule and gets further improvement with a position disentangled encoder and\na capacity-enhanced decoder. Using this method, SixT significantly outperforms\nmBART, a pretrained multilingual encoder-decoder model explicitly designed for\nNMT, with an average improvement of 7.1 BLEU on zero-shot any-to-English test\nsets across 14 source languages. Furthermore, with much less training\ncomputation cost and training data, our model achieves better performance on 15\nany-to-English test sets than CRISS and m2m-100, two strong multilingual NMT\nbaselines.\n"}
+{"abstract": "  Particle tracking in large-scale numerical simulations of turbulent flows\npresents one of the major bottlenecks in parallel performance and scaling\nefficiency. Here, we describe a particle tracking algorithm for large-scale\nparallel pseudo-spectral simulations of turbulence which scales well up to\nbillions of tracer particles on modern high-performance computing\narchitectures. We summarize the standard parallel methods used to solve the\nfluid equations in our hybrid MPI/OpenMP implementation. As the main focus, we\ndescribe the implementation of the particle tracking algorithm and document its\ncomputational performance. To address the extensive inter-process communication\nrequired by particle tracking, we introduce a task-based approach to overlap\npoint-to-point communications with computations, thereby enabling improved\nresource utilization. We characterize the computational cost as a function of\nthe number of particles tracked and compare it with the flow field computation,\nshowing that the cost of particle tracking is very small for typical\napplications.\n"}
+{"abstract": "  Spontaneous imbibition has been receiving much attention due to its\nsignificance in many subsurface and industrial applications. Unveiling\npore-scale wetting dynamics, and particularly its upscaling to the Darcy scale\nare still unresolved. In this work, we conduct image-based pore-network\nmodeling of cocurrent spontaneous imbibition and the corresponding quasi-static\nimbibition, in homogeneous sintered glass beads as well as heterogeneous\nEstaillades. A wide range of viscosity ratios and wettability conditions are\ntaken into account. Based on our pore-scale results, we show the influence of\npore-scale heterogeneity on imbibition dynamics and nonwetting entrapment. We\nelucidate different pore-filling mechanisms in imbibition, which helps us\nunderstand wetting dynamics. Most importantly, we develop a non-equilibrium\nmodel for relative permeability of the wetting phase, which adequately\nincorporates wetting dynamics. This is crucial to the final goal of developing\na two-phase imbibition model with measurable material properties such as\ncapillary pressure and relative permeability. Finally, we propose some future\nwork on both numerical and experimental verifications of the developed\nnon-equilibrium permeability model.\n"}
+{"abstract": "  This paper introduces the notion of an unravelled abstract regular polytope,\nand proves that $\\SL_3(q) \\rtimes <t>$, where $t$ is the transpose inverse\nautomorphism of $\\SL_3(q)$, possesses such polytopes for various congruences of\n$q$. A large number of small examples of such polytopes are given, along with\nextensive details of their various properties.\n"}
+{"abstract": "  If $R$ is a commutative unital ring and $M$ is a unital $R$-module, then each\nelement of $\\operatorname{End}_R(M)$ determines a left\n$\\operatorname{End}_{R}(M)[X]$-module structure on $\\operatorname{End}_{R}(M)$,\nwhere $\\operatorname{End}_{R}(M)$ is the $R$-algebra of endomorphisms of $M$\nand $\\operatorname{End}_{R}(M)[X] =\\operatorname{End}_{R}(M)\\otimes_RR[X]$.\nThese structures provide a very short proof of the Cayley-Hamilton theorem,\nwhich may be viewed as a reformulation of the proof in Algebra by Serge Lang.\nSome generalisations of the Cayley-Hamilton theorem can be easily proved using\nthe proposed method.\n"}
+{"abstract": "  We present a fascinating model that has lately caught attention among\nphysicists working in complexity related fields. Though it originated from\nmathematics and later from economics, the model is very enlightening in many\naspects that we shall highlight in this review. It is called The Stable\nMarriage Problem (though the marriage metaphor can be generalized to many other\ncontexts), and it consists of matching men and women, considering\npreference-lists where individuals express their preference over the members of\nthe opposite gender. This problem appeared for the first time in 1962 in the\nseminal paper of Gale and Shapley and has aroused interest in many fields of\nscience, including economics, game theory, computer science, etc. Recently it\nhas also attracted many physicists who, using the powerful tools of statistical\nmechanics, have also approached it as an optimization problem. Here we present\na complete overview of the Stable Marriage Problem emphasizing its\nmultidisciplinary aspect, and reviewing the key results in the disciplines that\nit has influenced most. We focus, in particular, in the old and recent results\nachieved by physicists, finally introducing two new promising models inspired\nby the philosophy of the Stable Marriage Problem. Moreover, we present an\ninnovative reinterpretation of the problem, useful to highlight the\nrevolutionary role of information in the contemporary economy.\n"}
+{"abstract": "  In this article, we prove that Buchstaber invariant of 4-dimensional real\nuniversal complex is no less than 24 as a follow-up to the work of Ayzenberg\n[2] and Sun [14]. Moreover, a lower bound for Buchstaber invariants of\n$n$-dimensional real universal complexes is given as an improvement of\nErokhovet's result in [7].\n"}
+{"abstract": "  The interest in dynamic processes on networks is steadily rising in recent\nyears. In this paper, we consider the $(\\alpha,\\beta)$-Thresholded Network\nDynamics ($(\\alpha,\\beta)$-Dynamics), where $\\alpha\\leq \\beta$, in which only\nstructural dynamics (dynamics of the network) are allowed, guided by local\nthresholding rules executed in each node. In particular, in each discrete round\n$t$, each pair of nodes $u$ and $v$ that are allowed to communicate by the\nscheduler, computes a value $\\mathcal{E}(u,v)$ (the potential of the pair) as a\nfunction of the local structure of the network at round $t$ around the two\nnodes. If $\\mathcal{E}(u,v) < \\alpha$ then the link (if it exists) between $u$\nand $v$ is removed; if $\\alpha \\leq \\mathcal{E}(u,v) < \\beta$ then an existing\nlink among $u$ and $v$ is maintained; if $\\beta \\leq \\mathcal{E}(u,v)$ then a\nlink between $u$ and $v$ is established if not already present.\n  The microscopic structure of $(\\alpha,\\beta)$-Dynamics appears to be simple,\nso that we are able to rigorously argue about it, but still flexible, so that\nwe are able to design meaningful microscopic local rules that give rise to\ninteresting macroscopic behaviors. Our goals are the following: a) to\ninvestigate the properties of the $(\\alpha,\\beta)$-Thresholded Network Dynamics\nand b) to show that $(\\alpha,\\beta)$-Dynamics is expressive enough to solve\ncomplex problems on networks.\n  Our contribution in these directions is twofold. We rigorously exhibit the\nclaim about the expressiveness of $(\\alpha,\\beta)$-Dynamics, both by designing\na simple protocol that provably computes the $k$-core of the network as well as\nby showing that $(\\alpha,\\beta)$-Dynamics is in fact Turing-Complete. Second\nand most important, we construct general tools for proving stabilization that\nwork for a subclass of $(\\alpha,\\beta)$-Dynamics and prove speed of convergence\nin a restricted setting.\n"}
+{"abstract": "  Deep generative models have emerged as a powerful class of priors for signals\nin various inverse problems such as compressed sensing, phase retrieval and\nsuper-resolution. Here, we assume an unknown signal to lie in the range of some\npre-trained generative model. A popular approach for signal recovery is via\ngradient descent in the low-dimensional latent space. While gradient descent\nhas achieved good empirical performance, its theoretical behavior is not well\nunderstood. In this paper, we introduce the use of stochastic gradient Langevin\ndynamics (SGLD) for compressed sensing with a generative prior. Under mild\nassumptions on the generative model, we prove the convergence of SGLD to the\ntrue signal. We also demonstrate competitive empirical performance to standard\ngradient descent.\n"}
+{"abstract": "  The hull of a linear code over finite fields is the intersection of the code\nand its dual, which was introduced by Assmus and Key. In this paper, we develop\na method to construct linear codes with trivial hull ( LCD codes) and\none-dimensional hull by employing the positive characteristic analogues of\nGauss sums. These codes are quasi-abelian, and sometimes doubly circulant. Some\nsufficient conditions for a linear code to be an LCD code (resp. a linear code\nwith one-dimensional hull) are presented. It is worth mentioning that we\npresent a lower bound on the minimum distances of the constructed linear codes.\nAs an application, using these conditions, we obtain some optimal or almost\noptimal LCD codes (resp. linear codes with one-dimensional hull) with respect\nto the online Database of Grassl.\n"}
+{"abstract": "  The performance of visual quality prediction models is commonly assumed to be\nclosely tied to their ability to capture perceptually relevant image aspects.\nModels are thus either based on sophisticated feature extractors carefully\ndesigned from extensive domain knowledge or optimized through feature learning.\nIn contrast to this, we find feature extractors constructed from random noise\nto be sufficient to learn a linear regression model whose quality predictions\nreach high correlations with human visual quality ratings, on par with a model\nwith learned features. We analyze this curious result and show that besides the\nquality of feature extractors also their quantity plays a crucial role - with\ntop performances only being achieved in highly overparameterized models.\n"}
+{"abstract": "  The WL-rank of a digraph $\\Gamma$ is defined to be the rank of the coherent\nconfiguration of $\\Gamma$. We construct a new infinite family of strictly Deza\nCayley graphs for which the WL-rank is equal to the number of vertices. The\ngraphs from this family are divisible design and integral.\n"}
+{"abstract": "  The city of Rio de Janeiro is one of the biggest cities in Brazil. Drug gangs\nand paramilitary groups called \\textit{mil\\'icias} control some regions of the\ncity where the government is not present, specially in the slums. Due to the\ncharacteristics of such two distinct groups, it was observed that the evolution\nof COVID-19 is different in those two regions, in comparison with the regions\ncontrolled by the government. In order to understand qualitatively those\nobservations, we divided the city in three regions controlled by the\ngovernment, by the drug gangs and by the \\textit{mil\\'icias}, respectively, and\nwe consider a SIRD-like epidemic model where the three regions are coupled.\nConsidering different levels of exposure, the model is capable to reproduce\nqualitatively the distinct evolution of the COVID-19 disease in the three\nregions, suggesting that the organized crime shapes the COVID-19 evolution in\nthe city of Rio de Janeiro. This case study suggests that the model can be used\nin general for any metropolitan region with groups of people that can be\ncategorized by their level of exposure.\n"}
+{"abstract": "  In Specific Power Absorption (SPA) models for Magnetic Fluid Hyperthermia\n(MFH) experiments, the magnetic relaxation time of the nanoparticles (NPs) is\nknown to be a fundamental descriptor of the heating mechanisms. The relaxation\ntime is mainly determined by the interplay between the magnetic properties of\nthe NPs and the rheological properties of NPs environment. Although the role of\nmagnetism in MFH has been extensively studied, the thermal properties of the\nNPs medium and their changes during of MFH experiments have been so far\nunderrated. Here, we show that ZnxFe3-xO4 NPs dispersed through different with\nphase transition in the temperature range of the experiment: clarified butter\noil (CBO) and paraffin. These systems show non-linear behavior of the heating\nrate within the temperature range of the MFH experiments. For CBO, a fast\nincrease at $306 K$ associated to changes in the viscosity (\\texteta(T)) and\nspecific heat (c_p(T)) of the medium below and above its melting temperature.\nThis increment in the heating rate takes place around $318 K$ for paraffin.\nMagnetic and morphological characterizations of NPs together with the observed\nagglomeration of the nanoparticles above $306 K$ indicate that the fast\nincrease in MFH curves could not be associated to a change in the magnetic\nrelaxation mechanism, with N\\'eel relaxation being dominant. In fact,\nsuccessive experiment runs performed up to temperatures below and above the CBO\nmelting point resulted in different MFH curves due to agglomeration of NPs\ndriven by magnetic field inhomogeneity during the experiments. Similar effects\nwere observed for paraffin. Our results highlight the relevance of the NPs\nmedium's thermodynamic properties for an accurate measurement of the heating\nefficiency for in vitro and in vivo environments, where the thermal properties\nare largely variable within the temperature window of MFH experiments.\n"}
+{"abstract": "  Recent studies indicate that hierarchical Vision Transformer with a macro\narchitecture of interleaved non-overlapped window-based self-attention \\&\nshifted-window operation is able to achieve state-of-the-art performance in\nvarious visual recognition tasks, and challenges the ubiquitous convolutional\nneural networks (CNNs) using densely slid kernels. Most follow-up works attempt\nto replace the shifted-window operation with other kinds of cross-window\ncommunication paradigms, while treating self-attention as the de-facto standard\nfor window-based information aggregation. In this manuscript, we question\nwhether self-attention is the only choice for hierarchical Vision Transformer\nto attain strong performance, and the effects of different kinds of\ncross-window communication. To this end, we replace self-attention layers with\nembarrassingly simple linear mapping layers, and the resulting proof-of-concept\narchitecture termed as LinMapper can achieve very strong performance in\nImageNet-1k image recognition. Moreover, we find that LinMapper is able to\nbetter leverage the pre-trained representations from image recognition and\ndemonstrates excellent transfer learning properties on downstream dense\nprediction tasks such as object detection and instance segmentation. We also\nexperiment with other alternatives to self-attention for content aggregation\ninside each non-overlapped window under different cross-window communication\napproaches, which all give similar competitive results. Our study reveals that\nthe \\textbf{macro architecture} of Swin model families, other than specific\naggregation layers or specific means of cross-window communication, may be more\nresponsible for its strong performance and is the real challenger to the\nubiquitous CNN's dense sliding window paradigm. Code and models will be\npublicly available to facilitate future research.\n"}
+{"abstract": "  Safe, environmentally conscious and flexible, these are the central\nrequirements for the future mobility. In the European border region between\nGermany, France and Luxembourg, mobility in the world of work and pleasure is a\ndecisive factor. It must be simple, affordable and available to all. The\nautomation and intelligent connection of road traffic plays an important role\nin this. Due to the distributed settlement structure with many small towns and\nvillage and a few central hot spots, a fully available public transport is very\ncomplex and expensive and only a few bus and train lines exist. In this\ncontext, the trinational research project TERMINAL aims to establish a\ncross-border automated minibus in regular traffic and to explore the user\nacceptance for commuter traffic. Additionally, mobility on demand services are\ntested, and both will be embedded within the existing public transport\ninfrastructure.\n"}
+{"abstract": "  Firing Squad Synchronisation on Cellular Automata is the dynamical\nsynchronisation of finitely many cells without any prior knowledge of their\nrange. This can be conceived as a signal with an infinite speed. Most of the\nproposed constructions naturally translate to the continuous setting of signal\nmachines and generate fractal figures with an accumulation on a horizontal\nline, i.e. synchronously, in the space-time diagram. Signal machines are\nstudied in a series of articles named Abstract Geometrical Computation.\n  In the present article, we design a signal machine that is able to\nsynchronise/accumulate on any non-infinite slope. The slope is encoded in the\ninitial configuration. This is done by constructing an infinite tree such that\neach node computes the way the tree expands.\n  The interest of Abstract Geometrical computation is to do away with the\nconstraint of discrete space, while tackling new difficulties from continuous\nspace. The interest of this paper in particular is to provide basic tools for\nfurther study of computable accumulation lines in the signal machine model.\n"}
+{"abstract": "  Numerical qualification of an eco-friendly alternative gas mixture for\navalanche mode operation of Resistive Plate Chambers is the soul of this work.\nTo identify the gas mixture, a numerical model developed elsewhere by the\nauthors has been first established by comparing the simulated figure of merits\n(efficiency and streamer probability) with the experimental data for the gas\nmixture used in INO-ICAL. Then it has been used to simulate the same properties\nof a gas mixture based on argon, carbon di-oxide and nitrogen, identified as\npotential replacement by studying its different properties. Efficacy of this\neco-friendly gas mixture has been studied by comparing the simulated result\nwith the standard gas mixture used in INO-ICAL as well as with experimental\ndata of other eco-friendly hydrofluorocarbon (HFO1234ze) based potential\nreplacements. To increase the efficacy of the proposed gas mixture, studies of\nthe traditional way (addition of a little amount of SF$_6$) and an alternative\napproach (exploring the option of high-end electronics) were carried out.\n"}
+{"abstract": "  We introduce a linearised form of the square root of the Todd class inside\nthe Verbitsky component of a hyper-K\\\"ahler manifold using the extended Mukai\nlattice. This enables us to define a Mukai vector for certain objects in the\nderived category taking values inside the extended Mukai lattice which is\nfunctorial for derived equivalences. As applications, we obtain a structure\ntheorem for derived equivalences between hyper-K\\\"ahler manifolds as well as an\nintegral lattice associated to the derived category of hyper-K\\\"ahler manifolds\ndeformation equivalent to the Hilbert scheme of a K3 surface mimicking the\nsurface case.\n"}
+{"abstract": "  Robots are becoming more and more commonplace in many industry settings. This\nsuccessful adoption can be partly attributed to (1) their increasingly\naffordable cost and (2) the possibility of developing intelligent,\nsoftware-driven robots. Unfortunately, robotics software consumes significant\namounts of energy. Moreover, robots are often battery-driven, meaning that even\na small energy improvement can help reduce its energy footprint and increase\nits autonomy and user experience. In this paper, we study the Robot Operating\nSystem (ROS) ecosystem, the de-facto standard for developing and prototyping\nrobotics software. We analyze 527 energy-related data points (including\ncommits, pull-requests, and issues on ROS-related repositories, ROS-related\nquestions on StackOverflow, ROS Discourse, ROS Answers, and the official ROS\nWiki). Our results include a quantification of the interest of roboticists on\nsoftware energy efficiency, 10 recurrent causes, and 14 solutions of\nenergy-related issues, and their implied trade-offs with respect to other\nquality attributes. Those contributions support roboticists and researchers\ntowards having energy-efficient software in future robotics projects.\n"}
+{"abstract": "  We design and analyze an algorithm for first-order stochastic optimization of\na large class of functions on $\\mathbb{R}^d$. In particular, we consider the\n\\emph{variationally coherent} functions which can be convex or non-convex. The\niterates of our algorithm on variationally coherent functions converge almost\nsurely to the global minimizer $\\boldsymbol{x}^*$. Additionally, the very same\nalgorithm with the same hyperparameters, after $T$ iterations guarantees on\nconvex functions that the expected suboptimality gap is bounded by\n$\\widetilde{O}(\\|\\boldsymbol{x}^* - \\boldsymbol{x}_0\\| T^{-1/2+\\epsilon})$ for\nany $\\epsilon>0$. It is the first algorithm to achieve both these properties at\nthe same time. Also, the rate for convex functions essentially matches the\nperformance of parameter-free algorithms. Our algorithm is an instance of the\nFollow The Regularized Leader algorithm with the added twist of using\n\\emph{rescaled gradients} and time-varying linearithmic regularizers.\n"}
+{"abstract": "  Novel structure for relativistic hydrodynamics of classic plasmas is derived\nfollowing the microscopic dynamics of charged particles. The derivation is\nstarted from the microscopic definition of concentration. Obviously, the\nconcentration evolution leads to the continuity equation and gives the\ndefinition of particle current. Introducing no arbitrary functions, we consider\nthe evolution of current (which does not coincide with the momentum density).\nIt leads to a set of new function which, to the best of our knowledge, have not\nbeen consider in the literature earlier. One of these functions is the average\nreverse relativistic (gamma) factor. Its current is also considered as one of\nbasic functions. Evolution of new functions appears via the concentration and\nparticle current so the set of equations partially closes itself. Other\nfunctions are presented as functions of basic function as a part of truncation\npresiger. Two pairs of chosen functions construct two four vectors. Evolution\nof these four vectors leads to appearance of two four tensors which are\nconsidered instead of the energy-momentum tensor. The Langmuir waves are\nconsidered within the suggested model.\n"}
+{"abstract": "  We derive combinatorial necessary conditions for discrete-time quantum walks\ndefined by regular mixed graphs to be periodic. If the quantum walk is\nperiodic, all the eigenvalues of the time evolution matrices must be algebraic\nintegers. Focusing on this, we explore which ring the coefficients of the\ncharacteristic polynomials should belong to. On the other hand, the\ncoefficients of the characteristic polynomials of $\\eta$-Hermitian adjacency\nmatrices have combinatorial implications. From these, we can find combinatorial\nimplications in the coefficients of the characteristic polynomials of the time\nevolution matrices, and thus derive combinatorial necessary conditions for\nmixed graphs to be periodic. For example, if a $k$-regular mixed graph with $n$\nvertices is periodic, then $2n/k$ must be an integer. As an application of this\nwork, we determine periodicity of mixed complete graphs and mixed graphs with a\nprime number of vertices.\n"}
+{"abstract": "  In this paper, we present a multiscale framework for solving the Helmholtz\nequation in heterogeneous media without scale separation and in the high\nfrequency regime where the wavenumber $k$ can be large. The main innovation is\nthat our methods achieve a nearly exponential rate of convergence with respect\nto the computational degrees of freedom, using a coarse grid of mesh size\n$O(1/k)$ without suffering from the well-known pollution effect. The key idea\nis a coarse-fine scale decomposition of the solution space that adapts to the\nmedia property and wavenumber; this decomposition is inspired by the multiscale\nfinite element method. We show that the coarse part is of low complexity in the\nsense that it can be approximated with a nearly exponential rate of convergence\nvia local basis functions, while the fine part is local such that it can be\ncomputed efficiently using the local information of the right hand side. The\ncombination of the two parts yields the overall nearly exponential rate of\nconvergence. We demonstrate the effectiveness of our methods theoretically and\nnumerically; an exponential rate of convergence is consistently observed and\nconfirmed. In addition, we observe the robustness of our methods regarding the\nhigh contrast in the media numerically.\n"}
+{"abstract": "  In this article we introduce the zero-divisor graphs $\\Gamma_\\mathscr{P}(X)$\nand $\\Gamma^\\mathscr{P}_\\infty(X)$ of the two rings $C_\\mathscr{P}(X)$ and\n$C^\\mathscr{P}_\\infty(X)$; here $\\mathscr{P}$ is an ideal of closed sets in $X$\nand $C_\\mathscr{P}(X)$ is the aggregate of those functions in $C(X)$, whose\nsupport lie on $\\mathscr{P}$. $C^\\mathscr{P}_\\infty(X)$ is the $\\mathscr{P}$\nanalogue of the ring $C_\\infty (X)$. We find out conditions on the topology on\n$X$, under-which $\\Gamma_\\mathscr{P}(X)$ (respectively,\n$\\Gamma^\\mathscr{P}_\\infty(X)$) becomes triangulated/ hypertriangulated. We\nrealize that $\\Gamma_\\mathscr{P}(X)$ (respectively,\n$\\Gamma^\\mathscr{P}_\\infty(X)$) is a complemented graph if and only if the\nspace of minimal prime ideals in $C_\\mathscr{P}(X)$ (respectively\n$\\Gamma^\\mathscr{P}_\\infty(X)$) is compact. This places a special case of this\nresult with the choice $\\mathscr{P}\\equiv$ the ideals of closed sets in $X$,\nobtained by Azarpanah and Motamedi in \\cite{Azarpanah} on a wider setting. We\nalso give an example of a non-locally finite graph having finite chromatic\nnumber. Finally it is established with some special choices of the ideals\n$\\mathscr{P}$ and $\\mathscr{Q}$ on $X$ and $Y$ respectively that the rings\n$C_\\mathscr{P}(X)$ and $C_\\mathscr{Q}(Y)$ are isomorphic if and only if\n$\\Gamma_\\mathscr{P}(X)$ and $\\Gamma_\\mathscr{Q}(Y)$ are isomorphic.\n"}
+{"abstract": "  Ever since its foundations were laid nearly a century ago, quantum theory has\nprovoked questions about the very nature of reality. We address these questions\nby considering the universe, and the multiverse, fundamentally as complex\npatterns, or mathematical structures. Basic mathematical structures can be\nexpressed more simply in terms of emergent parameters. Even simple mathematical\nstructures can interact within their own structural environment, in a\nrudimentary form of self-awareness, which suggests a definition of reality in a\nmathematical structure as simply the complete structure. The absolute\nrandomness of quantum outcomes is most satisfactorily explained by a multiverse\nof discrete, parallel universes. Some of these have to be identical to each\nother, but that introduces a dilemma, because each mathematical structure must\nbe unique. The resolution is that the parallel universes must be embedded\nwithin a mathematical structure, the multiverse, which allows universes to be\nidentical within themselves, but nevertheless distinct, as determined by their\nposition in the structure. The multiverse needs more emergent parameters than\nour universe and so it can be considered to be a superstructure.\nCorrespondingly, its reality can be called a super-reality. While every\nuniverse in the multiverse is part of the super-reality, the complete\nsuper-reality is forever beyond the horizon of any of its component universes.\n"}
+{"abstract": "  Semi-device independent (Semi-DI) quantum random number generators (QRNG)\ngained attention for security applications, offering an excellent trade-off\nbetween security and generation rate. This paper presents a proof-of-principle\ntime-bin encoding semi-DI QRNG experiments based on a prepare-and-measure\nscheme. The protocol requires two simple assumptions and a measurable\ncondition: an upper-bound on the prepared pulses' energy. We lower-bound the\nconditional min-entropy from the energy-bound and the input-output correlation,\ndetermining the amount of genuine randomness that can be certified. Moreover,\nwe present a generalized optimization problem for bounding the min-entropy in\nthe case of multiple-input and outcomes in the form of a semidefinite program\n(SDP). The protocol is tested with a simple experimental setup, capable of\nrealizing two configurations for the ternary time-bin encoding scheme. The\nexperimental setup is easy-to-implement and comprises commercially available\noff-the-shelf (COTS) components at the telecom wavelength, granting a secure\nand certifiable entropy source. The combination of ease-of-implementation,\nscalability, high-security level, and output-entropy make our system a\npromising candidate for commercial QRNGs.\n"}
+{"abstract": "  While artificial intelligence provides the backbone for many tools people use\naround the world, recent work has brought to attention that the algorithms\npowering AI are not free of politics, stereotypes, and bias. While most work in\nthis area has focused on the ways in which AI can exacerbate existing\ninequalities and discrimination, very little work has studied how governments\nactively shape training data. We describe how censorship has affected the\ndevelopment of Wikipedia corpuses, text data which are regularly used for\npre-trained inputs into NLP algorithms. We show that word embeddings trained on\nBaidu Baike, an online Chinese encyclopedia, have very different associations\nbetween adjectives and a range of concepts about democracy, freedom, collective\naction, equality, and people and historical events in China than its regularly\nblocked but uncensored counterpart - Chinese language Wikipedia. We examine the\nimplications of these discrepancies by studying their use in downstream AI\napplications. Our paper shows how government repression, censorship, and\nself-censorship may impact training data and the applications that draw from\nthem.\n"}
+{"abstract": "  Graphene nanoribbons (GNRs) possess distinct symmetry-protected topological\nphases. We show, through first-principles calculations, that by applying an\nexperimentally accessible transverse electric field (TEF), certain boron and\nnitrogen periodically co-doped GNRs have tunable topological phases. The\ntunability arises from a field-induced band inversion due to an opposite\nresponse of the conduction- and valance-band states to the electric field. With\na spatially-varying applied field, segments of GNRs of distinct topological\nphases are created, resulting in a field-programmable array of topological\njunction states, each may be occupied with charge or spin. Our findings not\nonly show that electric field may be used as an easy tuning knob for\ntopological phases in quasi-one-dimensional systems, but also provide new\ndesign principles for future GNR-based quantum electronic devices through their\ntopological characters.\n"}
+{"abstract": "  Self-supervised contrastive learning offers a means of learning informative\nfeatures from a pool of unlabeled data. In this paper, we delve into another\nuseful approach -- providing a way of selecting a core-set that is entirely\nunlabeled. In this regard, contrastive learning, one of a large number of\nself-supervised methods, was recently proposed and has consistently delivered\nthe highest performance. This prompted us to choose two leading methods for\ncontrastive learning: the simple framework for contrastive learning of visual\nrepresentations (SimCLR) and the momentum contrastive (MoCo) learning\nframework. We calculated the cosine similarities for each example of an epoch\nfor the entire duration of the contrastive learning process and subsequently\naccumulated the cosine-similarity values to obtain the coreset score. Our\nassumption was that an sample with low similarity would likely behave as a\ncoreset. Compared with existing coreset selection methods with labels, our\napproach reduced the cost associated with human annotation. The unsupervised\nmethod implemented in this study for coreset selection obtained improved\nresults over a randomly chosen subset, and were comparable to existing\nsupervised coreset selection on various classification datasets (e.g., CIFAR,\nSVHN, and QMNIST).\n"}
+{"abstract": "  We study the action of the homeomorphism group of a surface $S$ on the fine\ncurve graph ${\\mathcal C }^\\dagger(S)$. While the definition of\n$\\mathcal{C}^\\dagger(S)$ parallels the classical curve graph for mapping class\ngroups, we show that the dynamics of the action of ${\\mathrm{Homeo}}(S)$ on\n$\\mathcal{C}^\\dagger(S)$ is much richer: homeomorphisms induce parabolic\nisometries in addition to elliptics and hyperbolics, and all positive reals are\nrealized as asymptotic translation lengths.\n  When the surface $S$ is a torus, we relate the dynamics of the action of a\nhomeomorphism on $\\mathcal{C}^\\dagger(S)$ to the dynamics of its action on the\ntorus via the classical theory of rotation sets. We characterize homeomorphisms\nacting hyperbolically, show asymptotic translation length provides a lower\nbound for the area of the rotation set, and, while no characterisation purely\nin terms of rotation sets is possible, we give sufficient conditions for\nelements to be elliptic or parabolic.\n"}
+{"abstract": "  A quantum internet aims at harnessing networked quantum technologies, namely\nby distributing bipartite entanglement between distant nodes. However,\nmultipartite entanglement between the nodes may empower the quantum internet\nfor additional or better applications for communications, sensing, and\ncomputation. In this work, we present an algorithm for generating multipartite\nentanglement between different nodes of a quantum network with noisy quantum\nrepeaters and imperfect quantum memories, where the links are entangled pairs.\nOur algorithm is optimal for GHZ states with 3 qubits, maximising\nsimultaneously the final state fidelity and the rate of entanglement\ndistribution. Furthermore, we determine the conditions yielding this\nsimultaneous optimality for GHZ states with a higher number of qubits, and for\nother types of multipartite entanglement. Our algorithm is general also in the\nsense that it can optimise simultaneously arbitrary parameters. This work opens\nthe way to optimally generate multipartite quantum correlations over noisy\nquantum networks, an important resource for distributed quantum technologies.\n"}
+{"abstract": "  We present a novel mapping for studying 2D many-body quantum systems by\nsolving an effective, one-dimensional long-range model in place of the original\ntwo-dimensional short-range one. In particular, we address the problem of\nchoosing an efficient mapping from the 2D lattice to a 1D chain that optimally\npreserves the locality of interactions within the TN structure. By using Matrix\nProduct States (MPS) and Tree Tensor Network (TTN) algorithms, we compute the\nground state of the 2D quantum Ising model in transverse field with lattice\nsize up to $64\\times64$, comparing the results obtained from different mappings\nbased on two space-filling curves, the snake curve and the Hilbert curve. We\nshow that the locality-preserving properties of the Hilbert curve leads to a\nclear improvement of numerical precision, especially for large sizes, and turns\nout to provide the best performances for the simulation of 2D lattice systems\nvia 1D TN structures.\n"}
+{"abstract": "  Biological agents have meaningful interactions with their environment despite\nthe absence of immediate reward signals. In such instances, the agent can learn\npreferred modes of behaviour that lead to predictable states -- necessary for\nsurvival. In this paper, we pursue the notion that this learnt behaviour can be\na consequence of reward-free preference learning that ensures an appropriate\ntrade-off between exploration and preference satisfaction. For this, we\nintroduce a model-based Bayesian agent equipped with a preference learning\nmechanism (pepper) using conjugate priors. These conjugate priors are used to\naugment the expected free energy planner for learning preferences over states\n(or outcomes) across time. Importantly, our approach enables the agent to learn\npreferences that encourage adaptive behaviour at test time. We illustrate this\nin the OpenAI Gym FrozenLake and the 3D mini-world environments -- with and\nwithout volatility. Given a constant environment, these agents learn confident\n(i.e., precise) preferences and act to satisfy them. Conversely, in a volatile\nsetting, perpetual preference uncertainty maintains exploratory behaviour. Our\nexperiments suggest that learnable (reward-free) preferences entail a trade-off\nbetween exploration and preference satisfaction. Pepper offers a\nstraightforward framework suitable for designing adaptive agents when reward\nfunctions cannot be predefined as in real environments.\n"}
+{"abstract": "  We introduce a new supervised learning algorithm based to train spiking\nneural networks for classification. The algorithm overcomes a limitation of\nexisting multi-spike learning methods: it solves the problem of interference\nbetween interacting output spikes during a learning trial. This problem of\nlearning interference causes learning performance in existing approaches to\ndecrease as the number of output spikes increases, and represents an important\nlimitation in existing multi-spike learning approaches. We address learning\ninterference by introducing a novel mechanism to balance the magnitudes of\nweight adjustments during learning, which in theory allows every spike to\nsimultaneously converge to their desired timings. Our results indicate that our\nmethod achieves significantly higher memory capacity and faster convergence\ncompared to existing approaches for multi-spike classification. In the\nubiquitous Iris and MNIST datasets, our algorithm achieves competitive\npredictive performance with state-of-the-art approaches.\n"}
+{"abstract": "  We exhibit explicit and easily realisable bijections between Hecke--Kiselman\nmonoids of type $A_n$/$\\widetilde{A}_n$; certain braid diagrams on the\nplane/cylinder; and couples of integer sequences of particular types. This\nyields a fast solution of the word problem and an efficient normal form for\nthese HK monoids. Yang--Baxter type actions play an important role in our\nconstructions.\n"}
+{"abstract": "  Here, we designed two promising schemes to realize the high-entropy structure\nin a series of quasi-two-dimensional compounds, transition metal\ndichalcogenides (TMDCs). In the intra-layer high-entropy plan, (HEM)X2\ncompounds with high-entropy structure in the MX2 slabs were obtained, here HEM\nmeans high-entropy metals, such as TiZrNbMoTa. And superconductivity with a\nTc~7.4 K was found in a Mo-rich HEMX2. On the other hand, in the intercalation\nplan, we intercalated HEM-atoms (FeCoCrNiMn) into the gap between the\nsandwiched-MX2 slabs resulting in a series of (HEM)xMX2 compounds, x in the\nrange of 0~0.5, in which HEM is mainly composed of 3d transition metal\nelements, such as FeCoCrNiMn. As the introduction of multi-component magnetic\natoms, ferromagnetic spin-glass states with strong 2D characteristics ensued.\nTuning the x content, three kinds of two in the high-entropy intercalated layer\nwere observed including the 1*1 triangular lattice and two kinds of\nsuperlattices \\sqrt3*\\sqrt3 and \\sqrt3*2 in x=0.333 and x>0.5, respectively.\nMeanwhile, the spin frustration in the two-dimensional high-entropy magnetic\nplane will be enhanced with the development of \\sqrt3*\\sqrt3 and will be\nreduced significantly when changing into the \\sqrt3*2 phase. The high-entropy\nTMDCs and versatile two-dimensional high-entropy structures found by us possess\ngreat potentials to find new physics in low-dimensional high-entropy structures\nand future applications.\n"}
+{"abstract": "  Reversible data hiding in encrypted images (RDH-EI) has attracted increasing\nattention, since it can protect the privacy of original images while the\nembedded data can be exactly extracted. Recently, some RDH-EI schemes with\nmultiple data hiders have been proposed using secret sharing technique.\nHowever, these schemes protect the contents of the original images with\nlightweight security level. In this paper, we propose a high-security RDH-EI\nscheme with multiple data hiders. First, we introduce a cipher-feedback secret\nsharing (CFSS) technique. It follows the cryptography standards by introducing\nthe cipher-feedback strategy of AES. Then, using the CFSS technique, we devise\na new (r,n)-threshold (r<=n) RDH-EI scheme with multiple data hiders called\nCFSS-RDHEI. It can encrypt an original image into n encrypted images with\nreduced size using an encryption key and sends each encrypted image to one data\nhider. Each data hider can independently embed secret data into the encrypted\nimage to obtain the corresponding marked encrypted image. The original image\ncan be completely recovered from r marked encrypted images and the encryption\nkey. Performance evaluations show that our CFSS-RDHEI scheme has high embedding\nrate and its generated encrypted images are much smaller, compared to existing\nsecret sharing-based RDH-EI schemes. Security analysis demonstrates that it can\nachieve high security to defense some commonly used security attacks.\n"}
+{"abstract": "  Narrow linewidth visible light lasers are critical for atomic, molecular and\noptical (AMO) applications including atomic clocks, quantum computing, atomic\nand molecular spectroscopy, and sensing. Historically, such lasers are\nimplemented at the tabletop scale, using semiconductor lasers stabilized to\nlarge optical reference cavities. Photonic integration of high spectral-purity\nvisible light sources will enable experiments to increase in complexity and\nscale. Stimulated Brillouin scattering (SBS) is a promising approach to realize\nhighly coherent on-chip visible light laser emission. While progress has been\nmade on integrated SBS lasers at telecommunications wavelengths, barriers have\nexisted to translate this performance to the visible, namely the realization of\nBrillouin-active waveguides in ultra-low optical loss photonics. We have\novercome this barrier, demonstrating the first visible light photonic\nintegrated SBS laser, which operates at 674 nm to address the 88Sr+ optical\nclock transition. To guide the laser design, we use a combination of\nmulti-physics simulation and Brillouin spectroscopy in a 2 meter spiral\nwaveguide to identify the 25.110 GHz first order Stokes frequency shift and 290\nMHz gain bandwidth. The laser is implemented in an 8.9 mm radius silicon\nnitride all-waveguide resonator with 1.09 dB per meter loss and Q of 55.4\nMillion. Lasing is demonstrated, with an on-chip 14.7 mW threshold, a 45% slope\nefficiency, and linewidth narrowing as the pump is increased from below\nthreshold to 269 Hz. To illustrate the wavelength flexibility of this design,\nwe also demonstrate lasing at 698 nm, the wavelength for the optical clock\ntransition in neutral strontium. This demonstration of a waveguide-based,\nphotonic integrated SBS laser that operates in the visible, and the reduced\nsize and sensitivity to environmental disturbances, shows promise for diverse\nAMO applications.\n"}
+{"abstract": "  The cosmic-ray ionization rate ($\\zeta$, s$^{-1}$) plays an important role in\nthe interstellar medium. It controls ion-molecular chemistry and provides a\nsource of heating. Here we perform a grid of calculations using the spectral\nsynthesis code CLOUDY along nine sightlines towards, HD 169454, HD 110432, HD\n204827, $\\lambda$ Cep, X Per, HD 73882, HD 154368, Cyg OB2 5, Cyg OB2 12. The\nvalue of $\\zeta$ is determined by matching the observed column densities of\nH$_3^+$ and H$_2$. The presence of polycyclic aromatic hydrocarbons (PAHs)\naffects the free electron density, which changes the H$_3^+$ density and the\nderived ionization rate. PAHs are ubiquitous in the Galaxy, but there are also\nregions where PAHs do not exist. Hence, we consider clouds with a range of PAH\nabundances and show their effects on the H$_3^+$ abundance. We predict an\naverage cosmic-ray ionization rate for H$_2$ ($\\zeta$(H$_2$))= (7.88 $\\pm$\n2.89) $\\times$ 10$^{-16}$ s$^{-1}$ for models with average Galactic PAHs\nabundances, (PAH/H =10$^{-6.52}$), except Cyg OB2 5 and Cyg OB2 12. The value\nof $\\zeta$ is nearly 1 dex smaller for sightlines toward Cyg OB2 12. We\nestimate the average value of $\\zeta$(H$_2$)= (95.69 $\\pm$ 46.56) $\\times$\n10$^{-16}$ s$^{-1}$ for models without PAHs.\n"}
+{"abstract": "  The rate-regulation trade-off defined between two objective functions, one\npenalizing the packet rate and one the state deviation and control effort, can\nexpress the performance bound of a networked control system. However, the\ncharacterization of the set of globally optimal solutions in this trade-off for\nmulti-dimensional controlled Gauss-Markov processes has been an open problem.\nIn the present article, we characterize a policy profile that belongs to this\nset. We prove that such a policy profile consists of a symmetric threshold\ntriggering policy, which can be expressed in terms of the value of information,\nand a certainty-equivalent control policy, which uses a conditional expectation\nwith linear dynamics.\n"}
+{"abstract": "  The results obtained from state of the art human pose estimation (HPE) models\ndegrade rapidly when evaluating people of a low resolution, but can super\nresolution (SR) be used to help mitigate this effect? By using various SR\napproaches we enhanced two low resolution datasets and evaluated the change in\nperformance of both an object and keypoint detector as well as end-to-end HPE\nresults. We remark the following observations. First we find that for people\nwho were originally depicted at a low resolution (segmentation area in pixels),\ntheir keypoint detection performance would improve once SR was applied. Second,\nthe keypoint detection performance gained is dependent on that persons pixel\ncount in the original image prior to any application of SR; keypoint detection\nperformance was improved when SR was applied to people with a small initial\nsegmentation area, but degrades as this becomes larger. To address this we\nintroduced a novel Mask-RCNN approach, utilising a segmentation area threshold\nto decide when to use SR during the keypoint detection step. This approach\nachieved the best results on our low resolution datasets for each HPE\nperformance metrics.\n"}
+{"abstract": "  The Bloch theorem is a general theorem restricting the persistent current\nassociated with a conserved U(1) charge in a ground state or in a thermal\nequilibrium. It gives an upper bound of the magnitude of the current density,\nwhich is inversely proportional to the system size. In a recent preprint, Else\nand Senthil applied the argument for the Bloch theorem to a generalized Gibbs\nensemble, assuming the presence of an additional conserved charge, and\npredicted a nonzero current density in the nonthermal steady state [D. V. Else\nand T. Senthil, arXiv:2106.15623]. In this work, we provide a complementary\nderivation based on the canonical ensemble, given that the additional charge is\nstrictly conserved within the system by itself. Furthermore, using the example\nwhere the additional conserved charge is the momentum operator, we discuss that\nthe persistent current tends to vanish when the system is in contact with an\nexternal momentum reservoir in the co-moving frame of the reservoir.\n"}
+{"abstract": "  For a linear algebraic group $G$ over $\\bf Q$, we consider the period domains\n$D$ classifying $G$-mixed Hodge structures, and construct the extended period\ndomains $D_{\\mathrm{BS}}$, $D_{\\mathrm{SL}(2)}$, and $\\Gamma \\backslash\nD_{\\Sigma}$. In particular, we give toroidal partial compactifications of mixed\nMumford--Tate domains.\n"}
+{"abstract": "  In this work, after making an attempt to improve the formulation of the model\non particle transport within astrophysical plasma outflows and constructing the\nappropriate algorithms, we test the reliability and effectiveness of our method\nthrough numerical simulations on well-studied Galactic microquasars as the SS\n433 and the Cyg X-1 systems. Then, we concentrate on predictions of the\nassociated emissions, focusing on detectable high energy neutrinos and\n$\\gamma$-rays originated from the extra-galactic M33 X-7 system, which is a\nrecently discovered X-ray binary located in the neighboring galaxy Messier 33\nand has not yet been modeled in detail. The particle and radiation energy\ndistributions, produced from magnetized astrophysical jets in the context of\nour method, are assumed to originate from decay and scattering processes taking\nplace among the secondary particles created when hot (relativistic) protons of\nthe jet scatter on thermal (cold) ones (p-p interaction mechanism inside the\njet). These distributions are computed by solving the system of coupled\nintegro-differential transport equations of multi-particle processes (reactions\nchain) following the inelastic proton-proton (p-p) collisions. For the\ndetection of such high energy neutrinos as well as multi-wavelength (radio,\nX-ray and gamma-ray) emissions, extremely sensitive detection instruments are\nin operation or have been designed like the CTA, IceCube, ANTARES, KM3NeT,\nIceCube-Gen-2, and other space telescopes.\n"}
+{"abstract": "  With the ever-increasing speed and volume of knowledge production and\nconsumption, scholarly communication systems have been rapidly transformed into\ndigitised and networked open ecosystems, where preprint servers have played a\npivotal role. However, evidence is scarce regarding how this paradigm shift has\naffected the dynamics of collective attention on scientific knowledge. Herein,\nwe address this issue by investigating the citation dynamics of more than 1.5\nmillion eprints on arXiv, the most prominent and oldest eprint archive. The\ndiscipline-average citation history curves are estimated by applying a\nnonlinear regression model to the long-term citation data. The revealed\nspatiotemporal characteristics, including the growth and obsolescence patterns,\nare shown to vary across disciplines, reflecting the different publication and\ncitation practices. The results are used to develop a spatiotemporally\nnormalised citation index, called the $\\gamma$-index, with an approximately\nnormal distribution. It can be used to compare the citational impact of\nindividual papers across disciplines and time periods, providing a less biased\nmeasure of research impact than those widely used in the literature and in\npractice. Further, a stochastic model for the observed spatiotemporal citation\ndynamics is derived, reproducing both the Lognormal Law for the cumulative\ncitation distribution and the time trajectory of average citations in a unified\nformalism.\n"}
+{"abstract": "  Behavioural symptoms and urinary tract infections (UTI) are among the most\ncommon problems faced by people with dementia. One of the key challenges in the\nmanagement of these conditions is early detection and timely intervention in\norder to reduce distress and avoid unplanned hospital admissions. Using in-home\nsensing technologies and machine learning models for sensor data integration\nand analysis provides opportunities to detect and predict clinically\nsignificant events and changes in health status. We have developed an\nintegrated platform to collect in-home sensor data and performed an\nobservational study to apply machine learning models for agitation and UTI risk\nanalysis. We collected a large dataset from 88 participants with a mean age of\n82 and a standard deviation of 6.5 (47 females and 41 males) to evaluate a new\ndeep learning model that utilises attention and rational mechanism. The\nproposed solution can process a large volume of data over a period of time and\nextract significant patterns in a time-series data (i.e. attention) and use the\nextracted features and patterns to train risk analysis models (i.e. rational).\nThe proposed model can explain the predictions by indicating which time-steps\nand features are used in a long series of time-series data. The model provides\na recall of 91\\% and precision of 83\\% in detecting the risk of agitation and\nUTIs. This model can be used for early detection of conditions such as UTIs and\nmanaging of neuropsychiatric symptoms such as agitation in association with\ninitial treatment and early intervention approaches. In our study we have\ndeveloped a set of clinical pathways for early interventions using the alerts\ngenerated by the proposed model and a clinical monitoring team has been set up\nto use the platform and respond to the alerts according to the created\nintervention plans.\n"}
+{"abstract": "  The rook graph is a graph whose edges represent all the possible legal moves\nof the rook chess piece on a chessboard. The problem we consider is the\nfollowing. Given any set $M$ containing pairs of cells such that each cell of\nthe $m_1 \\times m_2$ chessboard is in exactly one pair, we determine the values\nof the positive integers $m_1$ and $m_2$ for which it is possible to construct\na closed tour of all the cells of the chessboard which uses all the pairs of\ncells in $M$ and some edges of the rook graph. This is an alternative\nformulation of a graph-theoretical problem presented in [Electron. J. Combin.\n28(1) (2021), #P1.7] involving the Cartesian product $G$ of two complete graphs\n$K_{m_1}$ and $K_{m_2}$, which is, in fact, isomorphic to the $m_{1}\\times\nm_{2}$ rook graph. The problem revolves around determining the values of the\nparameters $m_1$ and $m_2$ that would allow any perfect matching of the\ncomplete graph on the same vertex set of $G$ to be extended to a Hamiltonian\ncycle by using only edges in $G$.\n"}
+{"abstract": "  Long-lived storage of arbitrary transverse multimodes is important for\nestablishing a high-channel-capacity quantum network. Most of the pioneering\nworks focused on atomic diffusion as the dominant impact on the retrieved\npattern in an atom-based memory. In this work, we demonstrate that the\nunsynchronized Larmor precession of atoms in the inhomogeneous magnetic field\ndominates the distortion of the pattern stored in a cold-atom-based memory. We\nfind that this distortion effect can be eliminated by applying a strong uniform\npolarization magnetic field. By preparing atoms in magnetically insensitive\nstates, the destructive interference between different spin-wave components is\ndiminished, and the stored localized patterns are synchronized further in a\nsingle spin-wave component; then, an obvious enhancement in preserving patterns\nfor a long time is obtained. The reported results are very promising for\nstudying transverse multimode decoherence in storage and high-dimensional\nquantum networks in the future.\n"}
+{"abstract": "  In this paper, we consider a simplified model of turbulence for large\nReynolds numbers driven by a constant power energy input on large scales. In\nthe statistical stationary regime, the behaviour of the kinetic energy is\ncharacterised by two well defined phases: a laminar phase where the kinetic\nenergy grows linearly for a (random) time $t_w$ followed by abrupt\navalanche-like energy drops of sizes $S$ due to strong intermittent\nfluctuations of energy dissipation. We study the probability distribution\n$P[t_w]$ and $P[S]$ which both exhibit a quite well defined scaling behaviour.\nAlthough $t_w$ and $S$ are not statistically correlated, we suggest and\nnumerically checked that their scaling properties are related based on a\nsimple, but non trivial, scaling argument. We propose that the same approach\ncan be used for other systems showing avalanche-like behaviour such as\namorphous solids and seismic events.\n"}
+{"abstract": "  Planar graphs can be represented as intersection graphs of different types of\ngeometric objects in the plane, e.g., circles (Koebe, 1936), line segments\n(Chalopin \\& Gon{\\c{c}}alves, 2009), \\textsc{L}-shapes (Gon{\\c{c}}alves et al,\n2018). For general graphs, however, even deciding whether such representations\nexist is often $NP$-hard. We consider apex graphs, i.e., graphs that can be\nmade planar by removing one vertex from them. We show, somewhat surprisingly,\nthat deciding whether geometric representations exist for apex graphs is\n$NP$-hard.\n  More precisely, we show that for every positive integer $k$, recognizing\nevery graph class $\\mathcal{G}$ which satisfies $\\textsc{PURE-2-DIR} \\subseteq\n\\mathcal{G} \\subseteq \\textsc{1-STRING}$ is $NP$-hard, even when the input\ngraphs are apex graphs of girth at least $k$. Here, $PURE-2-DIR$ is the class\nof intersection graphs of axis-parallel line segments (where intersections are\nallowed only between horizontal and vertical segments) and \\textsc{1-STRING} is\nthe class of intersection graphs of simple curves (where two curves share at\nmost one point) in the plane. This partially answers an open question raised by\nKratochv{\\'\\i}l \\& Pergel (2007).\n  Most known $NP$-hardness reductions for these problems are from variants of\n3-SAT. We reduce from the \\textsc{PLANAR HAMILTONIAN PATH COMPLETION} problem,\nwhich uses the more intuitive notion of planarity. As a result, our proof is\nmuch simpler and encapsulates several classes of geometric graphs.\n"}
+{"abstract": "  The present paper reports on the numerical investigation of lifted turbulent\njet flames with H2/N2 fuel issuing into a vitiated coflow of lean combustion\nproducts of H2/air using conditional moment closure method (CMC). A 2D\naxisymmetric formulation has been used for the predictions of fluid flow, while\nCMC equations are solved with detailed chemistry to represent the\nturbulence-chemistry interaction. Simulations are carried out for different\ncoflow temperatures, jet and coflow velocities in order to investigate the\nimpact on the flame lift-off height as well as on the flame stabilization.\nFurthermore, the role of conditional velocity models on the flame has also been\ninvestigated. In addition, the effect of mixing is investigated over a range of\ncoflow temperatures and the stabilization mechanism is determined from the\nanalysis of the transport budgets. It is found that the lift-off height is\nhighly sensitive to the coflow temperature, while the predicted lift-off height\nusing the mixing model constant, i.e., C{\\Phi}=4, is found to be the closest to\nthe experimental results. For all the coflow temperatures, the balance is found\nbetween the chemical, axial convection and molecular diffusion terms while the\ncontribution from axial and radial diffusion is negligible, thus indicating\nauto-ignition as the flame stabilization mechanism.\n"}
+{"abstract": "  When the Rashba and Dresslhaus spin-orbit coupling are both presented for a\ntwo-dimensional electron in a perpendicular magnetic field, a striking\nresemblance to anisotropic quantum Rabi model in quantum optics is found. We\nperform a generalized Rashba coupling approximation to obtain a solvable\nHamiltonian by keeping the nearest-mixing terms of Laudau states, which is\nreformulated in the similar form to that with only Rashba coupling. Each Landau\nstate becomes a new displaced-Fock state with a displacement shift instead of\nthe original Harmonic oscillator Fock state, yielding eigenstates in closed\nform. Analytical energies are consistent with numerical ones in a wide range of\ncoupling strength even for a strong Zeeman splitting. In the presence of an\nelectric field, the spin conductance and the charge conductance obtained\nanalytically are in good agreements with the numerical results. As the\ncomponent of the Dresselhaus coupling increases, we find that the spin Hall\nconductance exhibits a pronounced resonant peak at a larger value of the\ninverse of the magnetic field. Meanwhile, the charge conductance exhibits a\nseries of plateaus as well as a jump at the resonant magnetic field. Our method\nprovides an easy-to-implement analytical treatment to two-dimensional electron\ngas systems with both types of spin-orbit couplings.\n"}
+{"abstract": "  In recent years, there has been a resurgence in methods that use distributed\n(neural) representations to represent and reason about semantic knowledge for\nrobotics applications. However, while robots often observe previously unknown\nconcepts, these representations typically assume that all concepts are known a\npriori, and incorporating new information requires all concepts to be learned\nafresh. Our work relaxes this limiting assumption of existing representations\nand tackles the incremental knowledge graph embedding problem by leveraging the\nprinciples of a range of continual learning methods. Through an experimental\nevaluation with several knowledge graphs and embedding representations, we\nprovide insights about trade-offs for practitioners to match a semantics-driven\nrobotics applications to a suitable continual knowledge graph embedding method.\n"}
+{"abstract": "  As power systems are undergoing a significant transformation with more\nuncertainties, less inertia and closer to operation limits, there is increasing\nrisk of large outages. Thus, there is an imperative need to enhance grid\nemergency control to maintain system reliability and security. Towards this\nend, great progress has been made in developing deep reinforcement learning\n(DRL) based grid control solutions in recent years. However, existing DRL-based\nsolutions have two main limitations: 1) they cannot handle well with a wide\nrange of grid operation conditions, system parameters, and contingencies; 2)\nthey generally lack the ability to fast adapt to new grid operation conditions,\nsystem parameters, and contingencies, limiting their applicability for\nreal-world applications. In this paper, we mitigate these limitations by\ndeveloping a novel deep meta reinforcement learning (DMRL) algorithm. The DMRL\ncombines the meta strategy optimization together with DRL, and trains policies\nmodulated by a latent space that can quickly adapt to new scenarios. We test\nthe developed DMRL algorithm on the IEEE 300-bus system. We demonstrate fast\nadaptation of the meta-trained DRL polices with latent variables to new\noperating conditions and scenarios using the proposed method and achieve\nsuperior performance compared to the state-of-the-art DRL and model predictive\ncontrol (MPC) methods.\n"}
+{"abstract": "  Full-stack autonomous driving perception modules usually consist of\ndata-driven models based on multiple sensor modalities. However, these models\nmight be biased to the sensor setup used for data acquisition. This bias can\nseriously impair the perception models' transferability to new sensor setups,\nwhich continuously occur due to the market's competitive nature. We envision\nsensor data abstraction as an interface between sensor data and machine\nlearning applications for highly automated vehicles (HAD).\n  For this purpose, we review the primary sensor modalities, camera, lidar, and\nradar, published in autonomous-driving related datasets, examine single sensor\nabstraction and abstraction of sensor setups, and identify critical paths\ntowards an abstraction of sensor data from multiple perception configurations.\n"}
+{"abstract": "  We reconstruct the Lorentzian graviton propagator in asymptotically safe\nquantum gravity from Euclidean data. The reconstruction is applied to both the\ndynamical fluctuation graviton and the background graviton propagator. We prove\nthat the spectral function of the latter necessarily has negative parts similar\nto, and for the same reasons, as the gluon spectral function. In turn, the\nspectral function of the dynamical graviton is positive. We argue that the\nlatter enters cross sections and other observables in asymptotically safe\nquantum gravity. Hence, its positivity may hint at the unitarity of\nasymptotically safe quantum gravity.\n"}
+{"abstract": "  Distributed data processing ecosystems are widespread and their components\nare highly specialized, such that efficient interoperability is urgent.\nRecently, Apache Arrow was chosen by the community to serve as a format\nmediator, providing efficient in-memory data representation. Arrow enables\nefficient data movement between data processing and storage engines,\nsignificantly improving interoperability and overall performance. In this work,\nwe design a new zero-cost data interoperability layer between Apache Spark and\nArrow-based data sources through the Arrow Dataset API. Our novel data\ninterface helps separate the computation (Spark) and data (Arrow) layers. This\nenables practitioners to seamlessly use Spark to access data from all Arrow\nDataset API-enabled data sources and frameworks. To benefit our community, we\nopen-source our work and show that consuming data through Apache Arrow is\nzero-cost: our novel data interface is either on-par or more performant than\nnative Spark.\n"}
+{"abstract": "  Excessive evaporative loss of water from the topsoil in arid-land agriculture\nis compensated via irrigation, which exploits massive freshwater resources. The\ncumulative effects of decades of unsustainable freshwater consumption in many\narid regions are now threatening food-water security. While plastic mulches can\nreduce evaporation from the topsoil, their cost and non-biodegradability limit\ntheir utility. In response, we report on superhydrophobic sand (SHS), a\nbio-inspired enhancement of common sand with a nanoscale wax coating. When SHS\nwas applied as a 5 mm-thick mulch over the soil, evaporation dramatically\nreduced and crop yields increased. Multi-year field trials of SHS application\nwith tomato (Solanum lycopersicum), barley (Hordeum vulgare), and wheat\n(Triticum aestivum) under normal irrigation enhanced yields by 17%-73%. Under\nbrackish water irrigation (5500 ppm NaCl), SHS mulching produced 53%-208%\nhigher fruit yield and grain gains for tomato and barley. Thus, SHS could\nbenefit agriculture and city-greening in arid regions.\n"}
+{"abstract": "  Light curves of the accreting white dwarf pulsator GW Librae spanning a 7.5\nmonth period in 2017 were obtained as part of the Next Generation Transit\nSurvey. This data set comprises 787 hours of photometry from 148 clear nights,\nallowing the behaviour of the long (hours) and short period (20min) modulation\nsignals to be tracked from night to night over a much longer observing baseline\nthan has been previously achieved. The long period modulations intermittently\ndetected in previous observations of GW Lib are found to be a persistent\nfeature, evolving between states with periods ~83min and 2-4h on time-scales of\nseveral days. The 20min signal is found to have a broadly stable amplitude and\nfrequency for the duration of the campaign, but the previously noted phase\ninstability is confirmed. Ultraviolet observations obtained with the Cosmic\nOrigin Spectrograph onboard the Hubble Space Telescope constrain the\nultraviolet-to-optical flux ratio to ~5 for the 4h modulation, and <=1 for the\n20min period, with caveats introduced by non-simultaneous observations. These\nresults add further observational evidence that these enigmatic signals must\noriginate from the white dwarf, highlighting our continued gap in theoretical\nunderstanding of the mechanisms that drive them.\n"}
+{"abstract": "  Code summarization is the task of generating natural language description of\nsource code, which is important for program understanding and maintenance.\nExisting approaches treat the task as a machine translation problem (e.g., from\nJava to English) and applied Neural Machine Translation models to solve the\nproblem. These approaches only consider a given code unit (e.g., a method)\nwithout its broader context. The lacking of context may hinder the NMT model\nfrom gathering sufficient information for code summarization. Furthermore,\nexisting approaches use a fixed vocabulary and do not fully consider the words\nin code, while many words in the code summary may come from the code. In this\nwork, we present a neural network model named ToPNN for code summarization,\nwhich uses the topics in a broader context (e.g., class) to guide the neural\nnetworks that combine the generation of new words and the copy of existing\nwords in code. Based on the model we present an approach for generating natural\nlanguage code summaries at the method level (i.e., method comments). We\nevaluate our approach using a dataset with 4,203,565 commented Java methods.\nThe results show significant improvement over state-of-the-art approaches and\nconfirm the positive effect of class topics and the copy mechanism.\n"}
+{"abstract": "  We show that the identification problem for a class of dynamic panel logit\nmodels with fixed effects has a connection to the truncated moment problem in\nmathematics. We use this connection to show that the sharp identified set of\nthe structural parameters is characterized by a set of moment equality and\ninequality conditions. This result provides sharp bounds in models where moment\nequality conditions do not exist or do not point identify the parameters. We\nalso show that the sharp identifying content of the non-parametric latent\ndistribution of the fixed effects is characterized by a vector of its\ngeneralized moments, and that the number of moments grows linearly in T. This\nfinal result lets us point identify, or sharply bound, specific classes of\nfunctionals, without solving an optimization problem with respect to the latent\ndistribution.\n"}
+{"abstract": "  Task environments developed in Minecraft are becoming increasingly popular\nfor artificial intelligence (AI) research. However, most of these are currently\nconstructed manually, thus failing to take advantage of procedural content\ngeneration (PCG), a capability unique to virtual task environments. In this\npaper, we present mcg, an open-source library to facilitate implementing PCG\nalgorithms for voxel-based environments such as Minecraft. The library is\ndesigned with human-machine teaming research in mind, and thus takes a\n'top-down' approach to generation, simultaneously generating low and high level\nmachine-readable representations that are suitable for empirical research.\nThese can be consumed by downstream AI applications that consider human spatial\ncognition. The benefits of this approach include rapid, scalable, and efficient\ndevelopment of virtual environments, the ability to control the statistics of\nthe environment at a semantic level, and the ability to generate novel\nenvironments in response to player actions in real time.\n"}
+{"abstract": "  Aspects of ultrahomogeneous and existentially closed Heyting algebras are\nstudied. Roelcke non-precompactness, non-simplicity, and non-amenability of the\nautomorphism group of the Fra\\\"iss\\'e limit of finite Heyting algebras are\nexamined among others.\n"}
+{"abstract": "  With the continuing spread of misinformation and disinformation online, it is\nof increasing importance to develop combating mechanisms at scale in the form\nof automated systems that support multiple languages. One task of interest is\nclaim veracity prediction, which can be addressed using stance detection with\nrespect to relevant documents retrieved online. To this end, we present our new\nArabic Stance Detection dataset (AraStance) of 4,063 claim--article pairs from\na diverse set of sources comprising three fact-checking websites and one news\nwebsite. AraStance covers false and true claims from multiple domains (e.g.,\npolitics, sports, health) and several Arab countries, and it is well-balanced\nbetween related and unrelated documents with respect to the claims. We\nbenchmark AraStance, along with two other stance detection datasets, using a\nnumber of BERT-based models. Our best model achieves an accuracy of 85\\% and a\nmacro F1 score of 78\\%, which leaves room for improvement and reflects the\nchallenging nature of AraStance and the task of stance detection in general.\n"}
+{"abstract": "  Penrose et al. investigated the physical incoherence of the spacetime with\nnegative mass via the bending of light. Precise estimates of time-delay of null\ngeodesics were needed and played a pivotal role in their proof. In this paper,\nwe construct an intermediate diagonal metric and make a reduction of this\nproblem to a causality comparison in the compactified spacetimes regarding\ntimelike connectedness near the conformal infinities. This different approach\nallows us to avoid encountering the difficulties and subtle issues Penrose et\nal. met. It provides a new, substantially simple, and physically natural\nnon-PDE viewpoint to understand the positive mass theorem. This elementary\nargument modestly applies to asymptotically flat solutions which are vacuum and\nstationary near infinity.\n"}
+{"abstract": "  Traditional channel coding with feedback constructs and transmits a codeword\nonly after all message bits are available at the transmitter. This paper joins\nGuo & Kostina and Lalitha et. al. in developing approaches for causal (or\nprogressive) encoding, where the transmitter may begin transmitting codeword\nsymbols as soon as the first message bit arrives. Building on the work of\nHorstein, Shayevitz and Feder, and Naghshvar et. al., this paper extends our\nprevious computationally efficient systematic algorithm for traditional\nposterior matching to produce a four-phase encoder that progressively encodes\nusing only the message bits causally available. Systematic codes work well with\nposterior matching on a channel with feedback, and they provide an immediate\nbenefit when causal encoding is employed instead of traditional encoding. Our\nalgorithm captures additional gains in the interesting region where the\ntransmission rate mu is higher than the rate lambda at which message bits\nbecome available. In this region, transmission of additional symbols beyond\nsystematic bits, before a traditional encoder would have begun transmission,\nfurther improves performance\n"}
+{"abstract": "  Recently, there have been efforts towards understanding the sampling\nbehaviour of event-triggered control (ETC), for obtaining metrics on its\nsampling performance and predicting its sampling patterns. Finite-state\nabstractions, capturing the sampling behaviour of ETC systems, have proven\npromising in this respect. So far, such abstractions have been constructed for\nnon-stochastic systems. Here, inspired by this framework, we abstract the\nsampling behaviour of stochastic narrow-sense linear periodic ETC (PETC)\nsystems via Interval Markov Chains (IMCs). Particularly, we define functions\nover sequences of state-measurements and interevent times that can be expressed\nas discounted cumulative sums of rewards, and compute bounds on their expected\nvalues by constructing appropriate IMCs and equipping them with suitable\nrewards. Finally, we argue that our results are extendable to more general\nforms of functions, thus providing a generic framework to define and study\nvarious ETC sampling indicators.\n"}
+{"abstract": "  Various unusual behaviors of artificial materials are governed by their\ntopological properties, among which the edge state at the boundary of a\nphotonic or phononic lattice has been captivated as a popular notion. However,\nthis remarkable bulk-boundary correspondence and the related phenomena are\nmissing in thermal materials. One reason is that heat diffusion is described in\na non-Hermitian framework because of its dissipative nature. The other is that\nthe relevant temperature field is mostly composed of modes that extend over\nwide ranges, making it difficult to be rendered within the tight-binding theory\nas commonly employed in wave physics. Here, we overcome the above challenges\nand perform systematic studies on heat diffusion in thermal lattices. Based on\na continuum model, we introduce a state vector to link the Zak phase with the\nexistence of the edge state, and thereby analytically prove the thermal\nbulk-boundary correspondence. We experimentally demonstrate the predicted edge\nstates with a topologically protected and localized heat dissipation capacity.\nOur finding sets up a solid foundation to explore the topology in novel heat\ntransfer manipulations.\n"}
+{"abstract": "  Initial hopes of quickly eradicating the COVID-19 pandemic proved futile, and\nthe goal shifted to controlling the peak of the infection, so as to minimize\nthe load on healthcare systems. To that end, public health authorities\nintervened aggressively to institute social distancing, lock-down policies, and\nother Non-Pharmaceutical Interventions (NPIs). Given the high social,\neducational, psychological, and economic costs of NPIs, authorities tune them,\nalternatively tightening up or relaxing rules, with the result that, in effect,\na relatively flat infection rate results. For example, during the summer in\nparts of the United States, daily infection numbers dropped to a plateau. This\npaper approaches NPI tuning as a control-theoretic problem, starting from a\nsimple dynamic model for social distancing based on the classical SIR epidemics\nmodel. Using a singular-perturbation approach, the plateau becomes a\nQuasi-Steady-State (QSS) of a reduced two-dimensional SIR model regulated by\nadaptive dynamic feedback. It is shown that the QSS can be assigned and it is\nglobally asymptotically stable. Interestingly, the dynamic model for social\ndistancing can be interpreted as a nonlinear integral controller. Problems of\ndata fitting and parameter identifiability are also studied for this model. The\npaper also discusses how this simple model allows for meaningful study of the\neffect of population size, vaccinations, and the emergence of second waves.\n"}
+{"abstract": "  The discovery of superconductivity in infinite-layer nickelates brings us\ntantalizingly close to a new material class that mirrors the cuprate\nsuperconductors. Here, we report on magnetic excitations in these nickelates,\nmeasured using resonant inelastic x-ray scattering (RIXS) at the Ni L3-edge, to\nshed light on the material complexity and microscopic physics. Undoped NdNiO2\npossesses a branch of dispersive excitations with a bandwidth of approximately\n200 meV, reminiscent of strongly-coupled, antiferromagnetically aligned spins\non a square lattice, despite a lack of evidence for long range magnetic order.\nThe significant damping of these modes indicates the importance of coupling to\nrare-earth itinerant electrons. Upon doping, the spectral weight and energy\ndecrease slightly, while the modes become overdamped. Our results highlight the\nrole of Mottness in infinite-layer nickelates.\n"}
+{"abstract": "  In the next decades, ultra-high-energy neutrinos in the EeV energy range will\nbe potentially detected by next-generation neutrino telescopes. Although their\nprimary goals are to observe cosmogenic neutrinos and to gain insight into\nextreme astrophysical environments, they can also indirectly probe the nature\nof dark matter. In this paper, we study the projected sensitivity of up-coming\nneutrino radio telescopes, such as RNO-G, GRAND and IceCube-gen2 radio array,\nto decaying dark matter scenarios. We investigate different dark matter\ndecaying channels and masses, from $10^7$ to $10^{15}$ GeV. By assuming the\nobservation of cosmogenic or newborn pulsar neutrinos, we forecast conservative\nconstraints on the lifetime of heavy dark matter particles. We find that these\nlimits are competitive with and highly complementary to previous\nmulti-messenger analyses.\n"}
+{"abstract": "  Decision trees and their ensembles are very popular models of supervised\nmachine learning. In this paper we merge the ideas underlying decision trees,\ntheir ensembles and FCA by proposing a new supervised machine learning model\nwhich can be constructed in polynomial time and is applicable for both\nclassification and regression problems. Specifically, we first propose a\npolynomial-time algorithm for constructing a part of the concept lattice that\nis based on a decision tree. Second, we describe a prediction scheme based on a\nconcept lattice for solving both classification and regression tasks with\nprediction quality comparable to that of state-of-the-art models.\n"}
+{"abstract": "  This paper presents a new stochastic finite element method for computing\nstructural stochastic responses. The method provides a new expansion of\nstochastic response and decouples the stochastic response into a combination of\na series of deterministic responses with random variable coefficients. A\ndedicated iterative algorithm is proposed to determine the deterministic\nresponses and corresponding random variable coefficients one by one. The\nalgorithm computes the deterministic responses and corresponding random\nvariable coefficients in their individual space and is insensitive to\nstochastic dimensions, thus it can be applied to high dimensional stochastic\nproblems readily without extra difficulties. More importantly, the\ndeterministic responses can be computed efficiently by use of existing Finite\nElement Method (FEM) solvers, thus the proposed method can be easy to embed\ninto existing FEM structural analysis softwares. Three practical examples,\nincluding low-dimensional and high-dimensional stochastic problems, are given\nto demonstrate the accuracy and effectiveness of the proposed method.\n"}
+{"abstract": "  Controllable person image generation aims to produce realistic human images\nwith desirable attributes (e.g., the given pose, cloth textures or hair style).\nHowever, the large spatial misalignment between the source and target images\nmakes the standard architectures for image-to-image translation not suitable\nfor this task. Most of the state-of-the-art architectures avoid the alignment\nstep during the generation, which causes many artifacts, especially for person\nimages with complex textures. To solve this problem, we introduce a novel\nSpatially-Adaptive Warped Normalization (SAWN), which integrates a learned\nflow-field to warp modulation parameters. This allows us to align person\nspatial-adaptive styles with pose features efficiently. Moreover, we propose a\nnovel self-training part replacement strategy to refine the pretrained model\nfor the texture-transfer task, significantly improving the quality of the\ngenerated cloth and the preservation ability of irrelevant regions. Our\nexperimental results on the widely used DeepFashion dataset demonstrate a\nsignificant improvement of the proposed method over the state-of-the-art\nmethods on both pose-transfer and texture-transfer tasks. The source code is\navailable at https://github.com/zhangqianhui/Sawn.\n"}
+{"abstract": "  We develop the Google matrix analysis of the multiproduct world trade network\nobtained from the UN COMTRADE database in recent years. The comparison is done\nbetween this new approach and the usual Import-Export description of this world\ntrade network. The Google matrix analysis takes into account the multiplicity\nof trade transactions thus highlighting in a better way the world influence of\nspecific countries and products. It shows that after Brexit, the European Union\nof 27 countries has the leading position in the world trade network ranking,\nbeing ahead of USA and China. Our approach determines also a sensitivity of\ntrade country balance to specific products showing the dominant role of\nmachinery and mineral fuels in multiproduct exchanges. It also underlines the\ngrowing influence of Asian countries.\n"}
+{"abstract": "  We establish the correspondence between two apparently unrelated but in fact\ncomplementary approaches of a relativistic deformed kinematics: the geometric\nproperties of momentum space and the loss of absolute locality in canonical\nspacetime, which can be restored with the introduction of a generalized\nspacetime. This correspondence is made explicit for the case of\n$\\kappa$-Poincar\\'e kinematics and compared with its properties in the Hopf\nalgebra framework.\n"}
+{"abstract": "  We investigate the possible presence of dark matter (DM) in massive and\nrotating neutron stars (NSs). For the purpose we extend our previous work [1]\nto introduce a light new physics vector mediator besides a scalar one in order\nto ensure feeble interaction between fermionic DM and $\\beta$ stable hadronic\nmatter in NSs. The masses of DM fermion, the mediators and the couplings are\nchosen consistent with the self-interaction constraint from Bullet cluster and\nfrom present day relic abundance. Assuming that both the scalar and vector\nmediators contribute equally to the relic abundance, we compute the equation of\nstate (EoS) of the DM admixed NSs to find that the present consideration of the\nvector new physics mediator do not bring any significant change to the EoS and\nstatic NS properties of DM admixed NSs compared to the case where only the\nscalar mediator was considered [1]. However, the obtained structural properties\nin static conditions are in good agreement with the various constraints on them\nfrom massive pulsars like PSR J0348+0432 and PSR J0740+6620, the gravitational\nwave (GW170817) data and the recently obtained results of NICER experiments for\nPSR J0030+0451 and PSR J0740+6620. We also extended our work to compute the\nrotational properties of DM admixed NSs rotating at different angular\nvelocities. The present results in this regard suggest that the secondary\ncomponent of GW190814 may be a rapidly rotating massive DM admixed NS. The\nconstraints on rotational frequency from pulsars like PSR B1937+21 and PSR\nJ1748-2446ad are also satisfied by our present results. Also, the constraints\non moment of inertia are satisfied considering slow rotation. The universality\nrelation in terms of normalized moment of inertia also holds good with our DM\nadmixed EoS.\n"}
+{"abstract": "  This paper presents a distributed optimization algorithm tailored for solving\noptimal control problems arising in multi-building coordination. The buildings\ncoordinated by a grid operator, join a demand response program to balance the\nvoltage surge by using an energy cost defined criterion. In order to model the\nhierarchical structure of the building network, we formulate a distributed\nconvex optimization problem with separable objectives and coupled affine\nequality constraints. A variant of the Augmented Lagrangian based Alternating\nDirection Inexact Newton (ALADIN) method for solving the considered class of\nproblems is then presented along with a convergence guarantee. To illustrate\nthe effectiveness of the proposed method, we compare it to the Alternating\nDirection Method of Multipliers (ADMM) by running both an ALADIN and an ADMM\nbased model predictive controller on a benchmark case study.\n"}
+{"abstract": "  The Flatland competition aimed at finding novel approaches to solve the\nvehicle re-scheduling problem (VRSP). The VRSP is concerned with scheduling\ntrips in traffic networks and the re-scheduling of vehicles when disruptions\noccur, for example the breakdown of a vehicle. While solving the VRSP in\nvarious settings has been an active area in operations research (OR) for\ndecades, the ever-growing complexity of modern railway networks makes dynamic\nreal-time scheduling of traffic virtually impossible. Recently, multi-agent\nreinforcement learning (MARL) has successfully tackled challenging tasks where\nmany agents need to be coordinated, such as multiplayer video games. However,\nthe coordination of hundreds of agents in a real-life setting like a railway\nnetwork remains challenging and the Flatland environment used for the\ncompetition models these real-world properties in a simplified manner.\nSubmissions had to bring as many trains (agents) to their target stations in as\nlittle time as possible. While the best submissions were in the OR category,\nparticipants found many promising MARL approaches. Using both centralized and\ndecentralized learning based approaches, top submissions used graph\nrepresentations of the environment to construct tree-based observations.\nFurther, different coordination mechanisms were implemented, such as\ncommunication and prioritization between agents. This paper presents the\ncompetition setup, four outstanding solutions to the competition, and a\ncross-comparison between them.\n"}
+{"abstract": "  In this paper, we establish a large deviations principle (LDP) for\ninteracting particle systems that arise from state and action dynamics of\ndiscrete-time mean-field games under the equilibrium policy of the\ninfinite-population limit. The LDP is proved under weak Feller continuity of\nstate and action dynamics. The proof is based on transferring LDP for empirical\nmeasures of initial states and noise variables under setwise topology to the\noriginal game model via contraction principle, which was first suggested by\nDelarue, Lacker, and Ramanan to establish LDP for continuous-time mean-field\ngames under common noise. We also compare our work with LDP results established\nin prior literature for interacting particle systems, which are in a sense\nuncontrolled versions of mean-field games.\n"}
+{"abstract": "  In this paper, we consider enumeration problems for edge-distinct and\nvertex-distinct Eulerian trails. Here, two Eulerian trails are\n\\emph{edge-distinct} if the edge sequences are not identical, and they are\n\\emph{vertex-distinct} if the vertex sequences are not identical. As the main\nresult, we propose optimal enumeration algorithms for both problems, that is,\nthese algorithm runs in $\\mathcal{O}(N)$ total time, where $N$ is the number of\nsolutions. Our algorithms are based on the reverse search technique introduced\nby [Avis and Fukuda, DAM 1996], and the push out amortization technique\nintroduced by [Uno, WADS 2015].\n"}
+{"abstract": "  In order to prevent the spread of COVID-19, governments have often required\nregional or national lockdowns, which have caused extensive economic stagnation\nover broad areas as the shock of the lockdowns has diffused to other regions\nthrough supply chains. Using supply-chain data for 1.6 million firms in Japan,\nthis study examines how governments can mitigate these economic losses when\nthey are obliged to implement lockdowns. Through tests of all combinations of\ntwo-region lockdowns, we find that coordinated, i.e., simultaneous, lockdowns\nyield smaller GDP losses than uncoordinated lockdowns. Furthermore, we test\npractical scenarios in which Japan's 47 regions impose lockdowns over three\nmonths and find that GDP losses are lower if nationwide lockdowns are\ncoordinated than if they are uncoordinated.\n"}
+{"abstract": "  Finite-time coherent sets (FTCSs) are distinguished regions of phase space\nthat resist mixing with the surrounding space for some finite period of time;\nphysical manifestations include eddies and vortices in the ocean and\natmosphere, respectively. The boundaries of finite-time coherent sets are\nexamples of Lagrangian coherent structures (LCSs). The selection of the time\nduration over which FTCS and LCS computations are made in practice is crucial\nto their success. If this time is longer than the lifetime of coherence of\nindividual objects then existing methods will fail to detect the shorter-lived\ncoherence. It is of clear practical interest to determine the full lifetime of\ncoherent objects, but in complicated practical situations, for example a field\nof ocean eddies with varying lifetimes, this is impossible with existing\napproaches. Moreover, determining the timing of emergence and destruction of\ncoherent sets is of significant scientific interest. In this work we introduce\nnew constructions to address these issues. The key components are an inflated\ndynamic Laplace operator and the concept of semi-material FTCSs. We make strong\nmathematical connections between the inflated dynamic Laplacian and the\nstandard dynamic Laplacian [Froyland 2015], showing that the latter arises as a\nlimit of the former. The spectrum and eigenfunctions of the inflated dynamic\nLaplacian directly provide information on the number, lifetimes, and evolution\nof coherent~sets.\n"}
+{"abstract": "  The ambipolar electrostatic potential rising along the magnetic field line\nfrom the grounded wall to the centre in the linear gas dynamic trap, rules the\navailable suppression of axial heat and particle losses. In this paper, the\nvisible range optical diagnostic is described using the Doppler shift of plasma\nemission lines for measurements of this accelerating potential drop. We used\nthe room temperature hydrogen jet puffed directly on the line of sight as the\ncharge exchange target for plasma ions moving in the expanding flux from the\nmirror towards the wall. Both bulk plasma protons and $He^{2+}$ ions velocity\ndistribution functions can be spectroscopically studied; the latter population\nis produced via the neutral He tracer puff into the central cell plasma. This\nway, potential in the centre and in the mirror area can be measured\nsimultaneously along with the ion temperature. A reasonable accuracy of\n$4\\div8\\%$ was achieved in observations with the frame rate of $\\approx 1~kHz$.\nActive acquisitions on the gas jet also provide the spatial resolution better\nthan 5~mm in the middle plane radial coordinate because of the strong\ncompression of the object size when projected to the centre along the magnetic\nflux surface. The charge exchange radiation diagnostic operates with three\nemission lines: H-$\\alpha$ 656.3~nm, He-I 667.8~nm and He-I 587.6~nm. Recorded\nspectra are shown in the paper and examples for physical dependences are\npresented. The considered experimental technique can be scaled to the upgraded\nmulti-point diagnostic for the next generation linear traps and other magnetic\nconfinement systems.\n"}
+{"abstract": "  Among the top approaches of recent years, link prediction using knowledge\ngraph embedding (KGE) models has gained significant attention for knowledge\ngraph completion. Various embedding models have been proposed so far, among\nwhich, some recent KGE models obtain state-of-the-art performance on link\nprediction tasks by using embeddings with a high dimension (e.g. 1000) which\naccelerate the costs of training and evaluation considering the large scale of\nKGs. In this paper, we propose a simple but effective performance boosting\nstrategy for KGE models by using multiple low dimensions in different\nrepetition rounds of the same model. For example, instead of training a model\none time with a large embedding size of 1200, we repeat the training of the\nmodel 6 times in parallel with an embedding size of 200 and then combine the 6\nseparate models for testing while the overall numbers of adjustable parameters\nare same (6*200=1200) and the total memory footprint remains the same. We show\nthat our approach enables different models to better cope with their\nexpressiveness issues on modeling various graph patterns such as symmetric,\n1-n, n-1 and n-n. In order to justify our findings, we conduct experiments on\nvarious KGE models. Experimental results on standard benchmark datasets, namely\nFB15K, FB15K-237 and WN18RR, show that multiple low-dimensional models of the\nsame kind outperform the corresponding single high-dimensional models on link\nprediction in a certain range and have advantages in training efficiency by\nusing parallel training while the overall numbers of adjustable parameters are\nsame.\n"}
+{"abstract": "  One of the properties of interest in the analysis of networks is \\emph{global\ncommunicability}, i.e., how easy or difficult it is, generally, to reach nodes\nfrom other nodes by following edges. Different global communicability measures\nprovide quantitative assessments of this property, emphasizing different\naspects of the problem.\n  This paper investigates the sensitivity of global measures of communicability\nto local changes. In particular, for directed, weighted networks, we study how\ndifferent global measures of communicability change when the weight of a single\nedge is changed; or, in the unweighted case, when an edge is added or removed.\nThe measures we study include the \\emph{total network communicability}, based\non the matrix exponential of the adjacency matrix, and the \\emph{Perron network\ncommunicability}, defined in terms of the Perron root of the adjacency matrix\nand the associated left and right eigenvectors.\n  Finding what local changes lead to the largest changes in global\ncommunicability has many potential applications, including assessing the\nresilience of a system to failure or attack, guidance for incremental system\nimprovements, and studying the sensitivity of global communicability measures\nto errors in the network connection data.\n"}
+{"abstract": "  Although micro-lensing of macro-lensed quasars and supernovae provides unique\nopportunities for several kinds of investigations, it can add unwanted and\nsometimes substantial noise. While micro-lensing flux anomalies may be safely\nignored for some observations, they severely limit others. \"Worst-case\"\nestimates can inform the decision whether or not to undertake an extensive\nexamination of micro-lensing scenarios. Here, we report \"worst-case\"\nmicro-lensing uncertainties for point sources lensed by singular isothermal\npotentials, parameterized by a convergence equal to the shear and by the\nstellar fraction. The results can be straightforwardly applied to\nnon-isothermal potentials utilizing the mass sheet degeneracy. We use\nmicro-lensing maps to compute fluctuations in image micro-magnifications and\nestimate the stellar fraction at which the fluctuations are greatest for a\ngiven convergence. We find that the worst-case fluctuations happen at a stellar\nfraction $\\kappa_\\star=\\frac{1}{|\\mu_{macro}|}$. For macro-minima, fluctuations\nin both magnification and demagnification appear to be bounded ($1.5>\\Delta\nm>-1.3$, where $\\Delta m$ is magnitude relative to the average\nmacro-magnification). Magnifications for macro-saddles are bounded as well\n($\\Delta m > -1.7$). In contrast, demagnifications for macro-saddles appear to\nhave unbounded fluctuations as $1/\\mu_{macro}\\rightarrow0$ and\n$\\kappa_\\star\\rightarrow0$.\n"}
+{"abstract": "  A high-order quasi-conservative discontinuous Galerkin (DG) method is\nproposed for the numerical simulation of compressible multi-component flows. A\ndistinct feature of the method is a predictor-corrector strategy to define the\ngrid velocity. A Lagrangian mesh is first computed based on the flow velocity\nand then used as an initial mesh in a moving mesh method (the moving mesh\npartial differential equation or MMPDE method ) to improve its quality. The\nfluid dynamic equations are discretized in the direct arbitrary\nLagrangian-Eulerian framework using DG elements and the non-oscillatory kinetic\nflux while the species equation is discretized using a quasi-conservative DG\nscheme to avoid numerical oscillations near material interfaces. A selection of\none- and two-dimensional examples are presented to verify the convergence order\nand the constant-pressure-velocity preservation property of the method. They\nalso demonstrate that the incorporation of the Lagrangian meshing with the\nMMPDE moving mesh method works well to concentrate mesh points in regions of\nshocks and material interfaces.\n"}
+{"abstract": "  We provide evidence for the existence of a new strongly-coupled four\ndimensional $\\mathcal{N}=2$ superconformal field theory arising as a\nnon-trivial IR fixed point on the Coulomb branch of the mass-deformed\nsuperconformal Lagrangian theory with gauge group $G_2$ and four fundamental\nhypermultiplets. Notably, our analysis proceeds by using various geometric\nconstraints to bootstrap the data of the theory, and makes no explicit\nreference to the Seiberg-Witten curve. We conjecture a corresponding VOA and\ncheck that the vacuum character satisfies a linear modular differential\nequation of fourth order. We also propose an identification with existing class\n$\\mathcal{S}$ constructions.\n"}
+{"abstract": "  Social touch is essential for our social interactions, communication, and\nwell-being. It has been shown to reduce anxiety and loneliness; and is a key\nchannel to transmit emotions for which words are not sufficient, such as love,\nsympathy, reassurance, etc. However, direct physical contact is not always\npossible due to being remotely located, interacting in a virtual environment,\nor as a result of a health issue. Mediated social touch enables physical\ninteractions, despite the distance, by transmitting the haptic cues that\nconstitute social touch through devices. As this technology is fairly new, the\nusers' needs and their expectations on a device design and its features are\nunclear, as well as who would use this technology, and in which conditions. To\nbetter understand these aspects of the mediated interaction, we conducted an\nonline survey on 258 respondents located in the USA. Results give insights on\nthe type of interactions and device features that the US population would like\nto use.\n"}
+{"abstract": "  We establish a second anti-blocker theorem for non-commutative convex\ncorners, show that the anti-blocking operation is continuous on bounded sets of\nconvex corners, and define optimisation parameters for a given convex corner\nthat generalise well-known graph theoretic quantities. We define the entropy of\na state with respect to a convex corner, characterise its maximum value in\nterms of a generalised fractional chromatic number and establish entropy\nsplitting results that demonstrate the entropic complementarity between a\nconvex corner and its anti-blocker. We identify two extremal tensor products of\nconvex corners and examine the behaviour of the introduced parameters with\nrespect to tensoring. Specialising to non-commutative graphs, we obtain quantum\nversions of the fractional chromatic number and the clique covering number, as\nwell as a notion of non-commutative graph entropy of a state, which we show to\nbe continuous with respect to the state and the graph. We define the\nWitsenhausen rate of a non-commutative graph and compute the values of our\nparameters in some specific cases.\n"}
+{"abstract": "  We focus on BPS solutions of the gauged O(3) Sigma model, due to Schroers,\nand use these ideas to study the geometry of the moduli space. The model has an\nasymmetry parameter $\\tau$ breaking the symmetry of vortices and antivortices\non the field equations. It is shown that the moduli space is incomplete both on\nthe Euclidean plane and on a compact surface. On the Euclidean plane, the L2\nmetric on the moduli space is approximated for well separated cores and results\nconsistent with similar approximations for the Ginzburg-Landau functional are\nfound. The scattering angle of approaching vortex-antivortex pairs of different\neffective mass is computed numerically and is shown to be different from the\nwell known scattering of approaching Ginzburg-Landau vortices. The volume of\nthe moduli space for general $\\tau$ is computed for the case of the round\nsphere and flat tori. The model on a compact surface is deformed introducing a\nneutral field and a Chern-Simons term. A lower bound for the Chern-Simons\nconstant $\\kappa$ such that the extended model admits a solution is shown to\nexist, and if the total number of vortices and antivortices are different, the\nexistence of an upper bound is also shown. Existence of multiple solutions to\nthe governing elliptic problem is established on a compact surface as well as\nthe existence of two limiting behaviours as $\\kappa \\to 0$. A localization\nformula for the deformation is found for both Ginzburg-Landau and the O(3)\nSigma model vortices and it is shown that it can be extended to the coalescense\nset. This rules out the possibility that this is Kim-Lee's term in the case of\nGinzburg-Landau vortices, moreover, the deformation term is compared on the\nplane with the Ricci form of the surface and it is shown they are different,\nhence also discarding that this is the term proposed by Collie-Tong to model\nvortex dynamics with Chern-Simons interaction.\n"}
+{"abstract": "  We show that the maximal number of singular points of a normal quartic\nsurface $X \\subset \\mathbb{P}^3_K$ defined over an algebraically closed field\n$K$ of characteristic $2$ is at most $20$, and that if equality is attained,\nthen the minimal resolution of $X$ is a supersingular\n  K3 surface and the singular points are $20$ nodes.\n  We produce examples with 14 nodes. In a sequel to this paper (in two parts,\nthe second in collaboration with Matthias Sch\\\"utt) we show that the optimal\nbound is indeed 14, and that if equality is attained, then the minimal\nresolution of $X$ is a supersingular\n  K3 surface and the singular points are $14$ nodes.\n  We also obtain some smaller upper bounds under several geometric assumptions\nholding at one of the singular points $P$ (structure of tangent cone,\nseparability/inseparability of the projection with centre $P$).\n"}
+{"abstract": "  All-electronic interrogation of biofluid flow velocity by sensors\nincorporated in ultra-low-power or self-sustained systems offers the promise of\nenabling multifarious emerging research and applications. Electrical sensors\nbased on nanomaterials are of high spatiotemporal resolution and exceptional\nsensitivity to external flow stimulus and easily integrated and fabricated\nusing scalable techniques. But existing nano-based electrical flow-sensing\ntechnologies remain lacking in precision and stability and are typically only\napplicable to simple aqueous solutions or liquid/gas dual-phase mixtures,\nmaking them unsuitable for monitoring low-flow (~micrometer/second) yet\nimportant characteristics of continuous biofluids (e.g., hemorheological\nbehaviors in microcirculation). Here we show that monolayer-graphene single\nmicroelectrodes harvesting charge from continuous aqueous flow provide an ideal\nflow sensing strategy: Our devices deliver over six months stability and\nsub-micrometer/second resolution in real-time quantification of whole-blood\nflows with multiscale amplitude-temporal characteristics in a microfluidic\nchip. The flow transduction is enabled by low-noise charge transfer at\ngraphene/water interface in response to flow-sensitive rearrangement of the\ninterfacial electrical double layer. Our results demonstrate the feasibility of\nusing a graphene-based self-powered strategy for monitoring biofluid flow\nvelocity with key performance metrics orders of magnitude higher than other\nelectrical approaches.\n"}
+{"abstract": "  Randomization-based Machine Learning methods for prediction are currently a\nhot topic in Artificial Intelligence, due to their excellent performance in\nmany prediction problems, with a bounded computation time. The application of\nrandomization-based approaches to renewable energy prediction problems has been\nmassive in the last few years, including many different types of\nrandomization-based approaches, their hybridization with other techniques and\nalso the description of new versions of classical randomization-based\nalgorithms, including deep and ensemble approaches. In this paper we review the\nmost important characteristics of randomization-based machine learning\napproaches and their application to renewable energy prediction problems. We\ndescribe the most important methods and algorithms of this family of modeling\nmethods, and perform a critical literature review, examining prediction\nproblems related to solar, wind, marine/ocean and hydro-power renewable\nsources. We support our critical analysis with an extensive experimental study,\ncomprising real-world problems related to solar, wind and hydro-power energy,\nwhere randomization-based algorithms are found to achieve superior results at a\nsignificantly lower computational cost than other modeling counterparts. We end\nour survey with a prospect of the most important challenges and research\ndirections that remain open this field, along with an outlook motivating\nfurther research efforts in this exciting research field.\n"}
+{"abstract": "  Internal interfaces in a domain could exist as a material defect or they can\nappear due to propagations of cracks. Discretization of such geometries and\nsolution of the contact problem on the internal interfaces can be\ncomputationally challenging. We employ an unfitted Finite Element (FE)\nframework for the discretization of the domains and develop a tailored,\nglobally convergent, and efficient multigrid method for solving contact\nproblems on the internal interfaces. In the unfitted FE methods, structured\nbackground meshes are used and only the underlying finite element space has to\nbe modified to incorporate the discontinuities. The non-penetration conditions\non the embedded interfaces of the domains are discretized using the method of\nLagrange multipliers. We reformulate the arising variational inequality problem\nas a quadratic minimization problem with linear inequality constraints. Our\nmultigrid method can solve such problems by employing a tailored multilevel\nhierarchy of the FE spaces and a novel approach for tackling the discretized\nnon-penetration conditions. We employ pseudo-$L^2$ projection-based transfer\noperators to construct a hierarchy of nested FE spaces from the hierarchy of\nnon-nested meshes. The essential component of our multigrid method is a\ntechnique that decouples the linear constraints using an orthogonal\ntransformation of the basis. The decoupled constraints are handled by a\nmodified variant of the projected Gauss-Seidel method, which we employ as a\nsmoother in the multigrid method. These components of the multigrid method\nallow us to enforce linear constraints locally and ensure the global\nconvergence of our method. We will demonstrate the robustness, efficiency, and\nlevel independent convergence property of the proposed method for Signorini's\nproblem and two-body contact problems.\n"}
+{"abstract": "  Given a simple undirected graph $G$, an orientation of $G$ is to assign every\nedge of $G$ a direction. Borradaile et al gave a greedy algorithm\nSC-Path-Reversal (in polynomial time) which finds a strongly connected\norientation that minimizes the maximum indegree, and conjectured that\nSC-Path-Reversal is indeed optimal for the \"minimizing the lexicographic order\"\nobjective as well. In this note, we give a positive answer to the conjecture,\nthat is we show that the algorithm SC-PATH-REVERSAL finds a strongly connected\norientation that minimizes the lexicographic order of indegrees.\n"}
+{"abstract": "  Intermediate mass planets, from Super-Earth to Neptune-sized bodies, are the\nmost common type of planets in the galaxy. The prevailing theory of planet\nformation, core-accretion, predicts significantly fewer intermediate-mass giant\nplanets than observed. The competing mechanism for planet formation, disk\ninstability, can produce massive gas giant planets on wide-orbits, such as\nHR8799, by direct fragmentation of the protoplanetary disk. Previously,\nfragmentation in magnetized protoplanetary disks has only been considered when\nthe magneto-rotational instability is the driving mechanism for magnetic field\ngrowth. Yet, this instability is naturally superseded by the spiral-driven\ndynamo when more realistic, non-ideal MHD conditions are considered. Here we\nreport on MHD simulations of disk fragmentation in the presence of a\nspiral-driven dynamo. Fragmentation leads to the formation of long-lived bound\nprotoplanets with masses that are at least one order of magnitude smaller than\nin conventional disk instability models. These light clumps survive shear and\ndo not grow further due to the shielding effect of the magnetic field, whereby\nmagnetic pressure stifles local inflow of matter. The outcome is a population\nof gaseous-rich planets with intermediate masses, while gas giants are found to\nbe rarer, in qualitative agreement with the observed mass distribution of\nexoplanets.\n"}
+{"abstract": "  Since the education sector is associated with highly dynamic business\nenvironments which are controlled and maintained by information systems, recent\ntechnological advancements and the increasing pace of adopting artificial\nintelligence (AI) technologies constitute a need to identify and analyze the\nissues regarding their implementation in education sector. However, a study of\nthe contemporary literature reveled that relatively little research has been\nundertaken in this area. To fill this void, we have identified the benefits and\nchallenges of implementing artificial intelligence in the education sector,\npreceded by a short discussion on the concepts of AI and its evolution over\ntime. Moreover, we have also reviewed modern AI technologies for learners and\neducators, currently available on the software market, evaluating their\nusefulness. Last but not least, we have developed a strategy implementation\nmodel, described by a five-stage, generic process, along with the corresponding\nconfiguration guide. To verify and validate their design, we separately\ndeveloped three implementation strategies for three different higher education\norganizations. We believe that the obtained results will contribute to better\nunderstanding the specificities of AI systems, services and tools, and\nafterwards pave a smooth way in their implementation.\n"}
+{"abstract": "  In compositional zero-shot learning, the goal is to recognize unseen\ncompositions (e.g. old dog) of observed visual primitives states (e.g. old,\ncute) and objects (e.g. car, dog) in the training set. This is challenging\nbecause the same state can for example alter the visual appearance of a dog\ndrastically differently from a car. As a solution, we propose a novel graph\nformulation called Compositional Graph Embedding (CGE) that learns image\nfeatures, compositional classifiers, and latent representations of visual\nprimitives in an end-to-end manner. The key to our approach is exploiting the\ndependency between states, objects, and their compositions within a graph\nstructure to enforce the relevant knowledge transfer from seen to unseen\ncompositions. By learning a joint compatibility that encodes semantics between\nconcepts, our model allows for generalization to unseen compositions without\nrelying on an external knowledge base like WordNet. We show that in the\nchallenging generalized compositional zero-shot setting our CGE significantly\noutperforms the state of the art on MIT-States and UT-Zappos. We also propose a\nnew benchmark for this task based on the recent GQA dataset. Code is available\nat: https://github.com/ExplainableML/czsl\n"}
+{"abstract": "  The biochemical reaction networks that regulate living systems are all\nstochastic to varying degrees. The resulting randomness affects biological\noutcomes at multiple scales, from the functional states of single proteins in a\ncell to the evolutionary trajectory of whole populations. Controlling how the\ndistribution of these outcomes changes over time -- via external interventions\nlike time-varying concentrations of chemical species -- is a complex challenge.\nIn this work, we show how counterdiabatic (CD) driving, first developed to\ncontrol quantum systems, provides a versatile tool for steering biological\nprocesses. We develop a practical graph-theoretic framework for CD driving in\ndiscrete-state continuous-time Markov networks. We illustrate the formalism\nwith examples from gene regulation and chaperone-assisted protein folding,\ndemonstrating the possibility that nature can exploit CD driving to accelerate\nresponse to sudden environmental changes. We generalize the method to continuum\nFokker-Planck models, and apply it to study AFM single-molecule pulling\nexperiments in regimes where the typical assumption of adiabaticity breaks\ndown, as well as an evolutionary model with competing genetic variants subject\nto time-varying selective pressures. The AFM analysis shows how CD driving can\neliminate non-equilibrium artifacts due to large force ramps in such\nexperiments, allowing accurate estimation of biomolecular properties.\n"}
+{"abstract": "  Quantum properties, such as entanglement and coherence, are indispensable\nresources in various quantum information processing tasks. However, there still\nlacks an efficient and scalable way to detecting these useful features,\nespecially for high-dimensional and multipartite quantum systems. In this work,\nwe exploit the convexity of samples without the desired quantum features and\ndesign an unsupervised machine learning method to detect the presence of such\nfeatures as anomalies. Particularly, in the context of entanglement detection,\nwe propose a complex-valued neural network composed of pseudo-siamese network\nand generative adversarial net, and then train it with only separable states to\nconstruct non-linear witnesses for entanglement. It is shown via numerical\nexamples, ranging from two-qubit to ten-qubit systems, that our network is able\nto achieve high detection accuracy which is above 97.5% on average.Moreover, it\nis capable of revealing rich structures of entanglement, such as partial\nentanglement among subsystems. Our results are readily applicable to the\ndetection of other quantum resources such as Bell nonlocality and steerability,\nand thus our work could provide a powerful tool to extract quantum features\nhidden in multipartite quantum data.\n"}
+{"abstract": "  Effective traffic optimization strategies can improve the performance of\ntransportation networks significantly. Most exiting works develop traffic\noptimization strategies depending on the local traffic states of congested road\nsegments, where the congestion propagation is neglected. This paper proposes a\nnovel distributed traffic optimization method for urban freeways considering\nthe potential congested road segments, which are called\npotential-homogeneous-area. The proposed approach is based on the intuition\nthat the evolution of congestion may affect the neighbor segments due to the\nmobility of traffic flow. We identify potential-homogeneous-area by applying\nour proposed temporal-spatial lambda-connectedness method using historical\ntraffic data. Further, global dynamic capacity constraint of this area is\nintegrated with cell transmission model (CTM) in the traffic optimization\nproblem. To reduce computational complexity and improve scalability, we propose\na fully distributed algorithm to solve the problem, which is based on the\npartial augmented Lagrangian and dual-consensus alternating direction method of\nmultipliers (ADMM). By this means, distributed coordination of ramp metering\nand variable speed limit control is achieved. We prove that the proposed\nalgorithm converges to the optimal solution so long as the traffic optimization\nobjective is convex. The performance of the proposed method is evaluated by\nmacroscopic simulation using real data of Shanghai, China.\n"}
+{"abstract": "  We give a numerical condition for right-handedness of a dynamically convex\nReeb flow on $S^3$. Our condition is stated in terms of an asymptotic ratio\nbetween the amount of rotation of the linearised flow and the linking number of\ntrajectories with a periodic orbit that spans a disk-like global surface of\nsection. As an application, we find an explicit constant $\\delta_* < 0.7225$\nsuch that if a Riemannian metric on the $2$-sphere is $\\delta$-pinched with\n$\\delta > \\delta_*$, then its geodesic flow lifts to a right-handed flow on\n$S^3$. In particular, all finite collections of periodic orbits of such a\ngeodesic flow bind open books whose pages are global surfaces of section.\n"}
+{"abstract": "  Conventional Supervised Learning approaches focus on the mapping from input\nfeatures to output labels. After training, the learnt models alone are adapted\nonto testing features to predict testing labels in isolation, with training\ndata wasted and their associations ignored. To take full advantage of the vast\nnumber of training data and their associations, we propose a novel learning\nparadigm called Memory-Associated Differential (MAD) Learning. We first\nintroduce an additional component called Memory to memorize all the training\ndata. Then we learn the differences of labels as well as the associations of\nfeatures in the combination of a differential equation and some sampling\nmethods. Finally, in the evaluating phase, we predict unknown labels by\ninferencing from the memorized facts plus the learnt differences and\nassociations in a geometrically meaningful manner. We gently build this theory\nin unary situations and apply it on Image Recognition, then extend it into Link\nPrediction as a binary situation, in which our method outperforms strong\nstate-of-the-art baselines on ogbl-ddi dataset.\n"}
+{"abstract": "  Existing sequential recommendation methods rely on large amounts of training\ndata and usually suffer from the data sparsity problem. To tackle this, the\npre-training mechanism has been widely adopted, which attempts to leverage\nlarge-scale data to perform self-supervised learning and transfer the\npre-trained parameters to downstream tasks. However, previous pre-trained\nmodels for recommendation focus on leverage universal sequence patterns from\nuser behaviour sequences and item information, whereas ignore capturing\npersonalized interests with the heterogeneous user information, which has been\nshown effective in contributing to personalized recommendation. In this paper,\nwe propose a method to enhance pre-trained models with heterogeneous user\ninformation, called User-aware Pre-training for Recommendation (UPRec).\nSpecifically, UPRec leverages the user attributes andstructured social graphs\nto construct self-supervised objectives in the pre-training stage and proposes\ntwo user-aware pre-training tasks. Comprehensive experimental results on\nseveral real-world large-scale recommendation datasets demonstrate that UPRec\ncan effectively integrate user information into pre-trained models and thus\nprovide more appropriate recommendations for users.\n"}
+{"abstract": "  This paper is devoted to the analysis of the distribution of the total\nmagnetic quantum number $M$ in a relativistic subshell with $N$ equivalent\nelectrons of momentum $j$. This distribution is analyzed through its cumulants\nand through their generating function, for which an analytical expression is\nprovided. This function also allows us to get the values of the cumulants at\nany order. Such values are useful to obtain the moments at various orders.\nSince the cumulants of the distinct subshells are additive this study directly\napplies to any relativistic configuration. Recursion relations on the\ngenerating function are given. It is shown that the generating function of the\nmagnetic quantum number distribution may be expressed as a n-th derivative of a\npolynomial. This leads to recurrence relations for this distribution which are\nvery efficient even in the case of large $j$ or $N$. The magnetic quantum\nnumber distribution is numerically studied using the Gram-Charlier and\nEdgeworth expansions. The inclusion of high-order terms may improve the\naccuracy of the Gram-Charlier representation for instance when a small and a\nlarge angular momenta coexist in the same configuration. However such series\ndoes not exhibit convergence when high orders are considered and the account\nfor the first two terms often provides a fair approximation of the magnetic\nquantum number distribution. The Edgeworth series offers an interesting\nalternative though this expansion is also divergent and of asymptotic nature.\n"}
+{"abstract": "  In this paper we present a continuation method which transforms spatially\ndistributed ODE systems into continuous PDE. We show that this continuation can\nbe performed both for linear and nonlinear systems, including multidimensional,\nspace- and time-varying systems. When applied to a large-scale network, the\ncontinuation provides a PDE describing evolution of continuous state\napproximation that respects the spatial structure of the original ODE. Our\nmethod is illustrated by multiple examples including transport equations,\nKuramoto equations and heat diffusion equations. As a main example, we perform\nthe continuation of a Newtonian system of interacting particles and obtain the\nEuler equations for compressible fluids, thereby providing an original\nalternative solution to Hilbert's 6th problem. Finally, we leverage our\nderivation of the Euler equations to control multiagent systems, designing a\nnonlinear control algorithm for robot formation based on its continuous\napproximation.\n"}
+{"abstract": "  Due to the recent growth of discoveries of strong gravitational lensing (SGL)\nsystems, one can statistically study both lens properties and cosmological\nparameters from 161 galactic scale SGL systems. We analyze meVSL model with the\nvelocity dispersion of lenses by adopting the redshift and surface mass density\ndepending power-law mass model. Analysis shows that meVSL models with various\ndark energy models including $\\Lambda$CDM, $\\omega$CDM, and CPL provide the\nnegative values of meVSL parameter, $b$ when we put the prior to the $\\Omega_{m\n0}$ value from Planck. These indicate the faster speed of light and the\nstronger gravitational force in the past. However, if we adopt the WMAP prior\non $\\Omega_{m0}$, then we obtain the null results on $b$ within 1-$\\sigma$ CL\nfor the different dark energy models.\n"}
+{"abstract": "  Most online message threads inherently will be cluttered and any new user or\nan existing user visiting after a hiatus will have a difficult time\nunderstanding whats being discussed in the thread. Similarly cluttered\nresponses in a message thread makes analyzing the messages a difficult problem.\nThe need for disentangling the clutter is much higher when the platform where\nthe discussion is taking place does not provide functions to retrieve reply\nrelations of the messages. This introduces an interesting problem to which\n\\cite{wang2011learning} phrases as a structural learning problem. We create\nvector embeddings for posts in a thread so that it captures both linguistic and\npositional features in relation to a context of where a given message is in.\nUsing these embeddings for posts we compute a similarity based connectivity\nmatrix which then converted into a graph. After employing a pruning mechanisms\nthe resultant graph can be used to discover the reply relation for the posts in\nthe thread. The process of discovering or disentangling chat is kept as an\nunsupervised mechanism. We present our experimental results on a data set\nobtained from Telegram with limited meta data.\n"}
+{"abstract": "  The relationship between the magnetic interaction and photoinduced dynamics\nin antiferromagnetic perovskites is investigated in this study. In\nLa${}_{1/3}$Sr${}_{2/3}$FeO${}_{3}$ thin films, commensurate spin ordering is\naccompanied by charge disproportionation, whereas SrFeO${}_{3}$ thin films show\nincommensurate helical antiferromagnetic spin ordering due to increased\nferromagnetic coupling compared to La${}_{1/3}$Sr${}_{2/3}$FeO${}_{3}$. To\nunderstand the photoinduced spin dynamics in these materials, we investigate\nthe spin ordering through time-resolved resonant soft X-ray scattering. In\nLa${}_{1/3}$Sr${}_{2/3}$FeO${}_{3}$, ultrafast quenching of the magnetic\nordering within 130 fs through a nonthermal process is observed, triggered by\ncharge transfer between the Fe atoms. We compare this to the photoinduced\ndynamics of the helical magnetic ordering of SrFeO${}_{3}$. We find that the\nchange in the magnetic coupling through optically induced charge transfer can\noffer an even more efficient channel for spin-order manipulation.\n"}
+{"abstract": "  In this paper we study the equations of the elimination ideal associated with\n$n+1$ generic multihomogeneous polynomials defined over a product of projective\nspaces of dimension $n$. We first prove a duality property and then make this\nduality explicit by introducing multigraded Sylvester forms. These results\nprovide a partial generalization of similar properties that are known in the\nsetting of homogeneous polynomial systems defined over a single projective\nspace. As an important consequence, we derive a new family of elimination\nmatrices that can be used for solving zero-dimensional multiprojective\npolynomial systems by means of linear algebra methods.\n"}
+{"abstract": "  We report microscopic, cathodoluminescence, chemical and O isotopic\nmeasurements of FeO-poor isolated olivine grains (IOG) in the carbonaceous\nchondrites Allende (CV3), Northwest Africa 5958 (C2-ung), Northwest Africa\n11086 (CM2-an), Allan Hills 77307 (CO3.0). The general petrographic, chemical\nand isotopic similarity with bona fide type I chondrules confirms that the IOG\nderived from them. The concentric CL zoning, reflecting a decrease in\nrefractory elements toward the margins, and frequent rimming by enstatite are\ntaken as evidence of interaction of the IOG with the gas as stand-alone\nobjects. This indicates that they were splashed out of chondrules when these\nwere still partially molten. CaO-rich refractory forsterites, which are\nrestricted to $\\Delta^{17}O < -4\\permil$ likely escaped equilibration at lower\ntemperatures because of their large size and possibly quicker quenching. The\nIOG thus bear witness to frequent collisions in the chondrule-forming regions.\n"}
+{"abstract": "  The nova rate in the Milky Way remains largely uncertain, despite its vital\nimportance in constraining models of Galactic chemical evolution as well as\nunderstanding progenitor channels for Type Ia supernovae. The rate has been\npreviously estimated in the range of $\\approx10-300$ yr$^{-1}$, either based on\nextrapolations from a handful of very bright optical novae or the nova rates in\nnearby galaxies; both methods are subject to debatable assumptions. The total\ndiscovery rate of optical novae remains much smaller ($\\approx5-10$ yr$^{-1}$)\nthan these estimates, even with the advent of all-sky optical time domain\nsurveys. Here, we present a systematic sample of 12 spectroscopically confirmed\nGalactic novae detected in the first 17 months of Palomar Gattini-IR (PGIR), a\nwide-field near-infrared time domain survey. Operating in $J$-band\n($\\approx1.2$ $\\mu$m) that is relatively immune to dust extinction, the\nextinction distribution of the PGIR sample is highly skewed to large extinction\nvalues ($> 50$% of events obscured by $A_V\\gtrsim5$ mag). Using recent\nestimates for the distribution of mass and dust in the Galaxy, we show that the\nobserved extinction distribution of the PGIR sample is commensurate with that\nexpected from dust models. The PGIR extinction distribution is inconsistent\nwith that reported in previous optical searches (null hypothesis probability $<\n0.01$%), suggesting that a large population of highly obscured novae have been\nsystematically missed in previous optical searches. We perform the first\nquantitative simulation of a $3\\pi$ time domain survey to estimate the Galactic\nnova rate using PGIR, and derive a rate of $\\approx 46.0^{+12.5}_{-12.4}$\nyr$^{-1}$. Our results suggest that all-sky near-infrared time-domain surveys\nare well poised to uncover the Galactic nova population.\n"}
+{"abstract": "  Reconfigurable intelligent surface (RIS) is considered as a revolutionary\ntechnology for future wireless communication networks. In this letter, we\nconsider the acquisition of the cascaded channels, which is a challenging task\ndue to the massive number of passive RIS elements. To reduce the pilot\noverhead, we adopt the element-grouping strategy, where each element in one\ngroup shares the same reflection coefficient and is assumed to have the same\nchannel condition. We analyze the channel interference caused by the\nelement-grouping strategy and further design two deep learning based networks.\nThe first one aims to refine the partial channels by eliminating the\ninterference, while the second one tries to extrapolate the full channels from\nthe refined partial channels. We cascade the two networks and jointly train\nthem. Simulation results show that the proposed scheme provides significant\ngain compared to the conventional element-grouping method without interference\nelimination.\n"}
+{"abstract": "  The progenitors of present-day galaxy clusters give important clues about the\nevolution of the large scale structure, cosmic mass assembly, and galaxy\nevolution. Simulations are a major tool for these studies since they are used\nto interpret observations. In this work, we introduce a set of\n\"protocluster-lightcones\", dubbed PCcones. They are mock galaxy catalogs\ngenerated from the Millennium Simulation with the L-GALAXIES semi-analytic\nmodel. These lightcones were constructed by placing a desired structure at the\nredshift of interest in the centre of the cone. This approach allows to adopt a\nset of observational constraints, such as magnitude limits and uncertainties in\nmagnitudes and photometric redshifts (photo-zs), to produce realistic\nsimulations of photometric surveys. We show that photo-zs obtained with PCcones\nare more accurate than those obtained directly with the Millennium Simulation,\nmostly due to the difference in how apparent magnitudes are computed. We apply\nPCcones in the determination of the expected accuracy of protocluster detection\nusing photo-zs in the $z=1-3$ range in the wide-layer of HSC-SSP and the\n10-year LSST forecast. With our technique, we expect to recover only $\\sim38\\%$\nand $\\sim 43\\%$ of all massive galaxy cluster progenitors with more than 70\\%\nof purity for HSC-SSP and LSST, respectively. Indeed, the combination of\nobservational constraints and photo-z uncertainties affects the detection of\nstructures critically for both emulations, indicating the need of spectroscopic\nredshifts to improve detection. We also compare our mocks of the Deep CFHTLS at\n$z<1.5$ with observed cluster catalogs, as an extra validation of the\nlightcones and methods.\n"}
+{"abstract": "  We reanalyse the solar eclipse linked to the Biblical passage about the\nmilitary leader Joshua who ordered the sun to halt in the midst of the day\n(Joshua 10:12). Although there is agreement that the basic story is rooted in a\nreal event, the date is subject to different opinions. We review the historical\nemergence of the text and confirm that the total eclipse of the sun of 30\nSeptember 1131 BCE is the most likely candidate. The Besselian Elements for\nthis eclipse are re-computed. The error for the deceleration parameter of\nEarth's rotation, $\\Delta T$, is improved by a factor of 2.\n"}
+{"abstract": "  Aspect-based Sentiment Analysis (ABSA) aims to identify the aspect terms,\ntheir corresponding sentiment polarities, and the opinion terms. There exist\nseven subtasks in ABSA. Most studies only focus on the subsets of these\nsubtasks, which leads to various complicated ABSA models while hard to solve\nthese subtasks in a unified framework. In this paper, we redefine every subtask\ntarget as a sequence mixed by pointer indexes and sentiment class indexes,\nwhich converts all ABSA subtasks into a unified generative formulation. Based\non the unified formulation, we exploit the pre-training sequence-to-sequence\nmodel BART to solve all ABSA subtasks in an end-to-end framework. Extensive\nexperiments on four ABSA datasets for seven subtasks demonstrate that our\nframework achieves substantial performance gain and provides a real unified\nend-to-end solution for the whole ABSA subtasks, which could benefit multiple\ntasks.\n"}
+{"abstract": "  Recently, a geometric approach to operator mixing in massless QCD-like\ntheories -- that involves canonical forms based on the Poincare'-Dulac theorem\nfor the linear system that defines the renormalized mixing matrix in the\ncoordinate representation $Z(x,\\mu)$ -- has been advocated in arXiv:2103.15527\n. As a consequence, a classification of operator mixing in four cases --\ndepending on the canonical forms of $- \\frac{\\gamma(g)}{\\beta(g)}$, with\n$\\gamma(g)=\\gamma_0 g^2+\\cdots$ the matrix of the anomalous dimensions and\n$\\beta(g)=-\\beta_0 g^3 + \\cdots$ the beta function -- has been proposed: (I)\nnonresonant $\\frac{\\gamma_0}{\\beta_0}$ diagonalizable, (II) resonant\n$\\frac{\\gamma_0}{\\beta_0}$ diagonalizable, (III) nonresonant\n$\\frac{\\gamma_0}{\\beta_0}$ nondiagonalizable, (IV) resonant\n$\\frac{\\gamma_0}{\\beta_0}$ nondiagonalizable. In particular, in\narXiv:2103.15527 a detailed analysis of the case (I) -- where operator mixing\nreduces to all orders of perturbation theory to the multiplicatively\nrenormalizable case -- has been provided. In the present paper, following the\naforementioned approach, we work out in the remaining three cases the canonical\nforms for $- \\frac{\\gamma(g)}{\\beta(g)}$ to all orders of perturbation theory,\nthe corresponding UV asymptotics of $Z(x,\\mu)$, and the physics interpretation.\nWe also work out in detail physical realizations of the cases (I) and (II).\n"}
+{"abstract": "  Ova-angular rotations of a prime number are characterized, constructed using\nthe Dirichlet theorem. The geometric properties arising from this theory are\nanalyzed and some applications are presented, including Goldbach's conjecture,\nthe existence of infinite primes of the form $\\rho = k^2+1$ and the convergence\nof the sum of the inverses of the Mersenne's primes. Although the mathematics\nthat was used was elementary, you can notice the usefulness of this theory\nbased on geometric properties. In this paper, the study ends by introducing the\nova-angular square matrix.\n"}
+{"abstract": "  Data from multifactor HCI experiments often violates the normality assumption\nof parametric tests (i.e., nonconforming data). The Aligned Rank Transform\n(ART) is a popular nonparametric analysis technique that can find main and\ninteraction effects in nonconforming data, but leads to incorrect results when\nused to conduct contrast tests. We created a new algorithm called ART-C for\nconducting contrasts within the ART paradigm and validated it on 72,000 data\nsets. Our results indicate that ART-C does not inflate Type I error rates,\nunlike contrasts based on ART, and that ART-C has more statistical power than a\nt-test, Mann-Whitney U test, Wilcoxon signed-rank test, and ART. We also\nextended a tool called ARTool with our ART-C algorithm for both Windows and R.\nOur validation had some limitations (e.g., only six distribution types, no\nmixed factorial designs, no random slopes), and data drawn from Cauchy\ndistributions should not be analyzed with ART-C.\n"}
+{"abstract": "  We investigate the formation and growth of massive black hole (BH) seeds in\ndusty star-forming galaxies, relying and extending the framework proposed by\nBoco et al. 2020. Specifically, the latter envisages the migration of stellar\ncompact remnants (neutron stars and stellar-mass black holes) via gaseous\ndynamical friction towards the galaxy nuclear region, and their subsequent\nmerging to grow a massive central BH seed. In this paper we add two relevant\ningredients: (i) we include primordial BHs, that could constitute a fraction\n$f_{\\rm pBH}$ of the dark matter, as an additional component participating in\nthe seed growth; (ii) we predict the stochastic gravitational wave background\noriginated during the seed growth, both from stellar compact remnant and from\nprimordial BH mergers. We find that the latter events contribute most to the\ninitial growth of the central seed during a timescale of $10^6-10^7\\,\\rm yr$,\nbefore stellar compact remnant mergers and gas accretion take over. In\naddition, if the fraction of primordial BHs $f_{\\rm pBH}$ is large enough,\ngravitational waves emitted by their mergers in the nuclear galactic regions\ncould be detected by future interferometers like Einsten Telescope, DECIGO and\nLISA. As for the associated stochastic gravitational wave background, we\npredict that it extends over the wide frequency band $10^{-6}\\lesssim f [{\\rm\nHz}]\\lesssim 10$, which is very different from the typical range originated by\nmergers of isolated binary compact objects. On the one hand, the detection of\nsuch a background could be a smoking gun to test the proposed seed growth\nmechanism; on the other hand, it constitutes a relevant contaminant from\nastrophysical sources to be characterized and subtracted, in the challenging\nsearch for a primordial background of cosmological origin.\n"}
+{"abstract": "  The viewing size of a signer correlates with legibility, i.e., the ease with\nwhich a viewer can recognize individual signs. The WCAG 2.0 guidelines (G54)\nmention in the notes that there should be a mechanism to adjust the size to\nensure the signer is discernible but does not state minimum discernibility\nguidelines. The fluent range (the range over which sign viewers can follow the\nsigners at maximum speed) extends from about 7{\\deg} to 20{\\deg}, which is far\ngreater than 2{\\deg} for print. Assuming a standard viewing distance of 16\ninches from a 5-inch smartphone display, the corresponding sizes are from 2 to\n5 inches, i.e., from 1/3rd to full-screen. This is consistent with vision\nscience findings about human visual processing properties, and how they play a\ndominant role in constraining the distribution of signer sizes.\n"}
+{"abstract": "  Time-of-Flight Magnetic Resonance Angiographs (TOF-MRAs) enable visualization\nand analysis of cerebral arteries. This analysis may indicate normal variation\nof the configuration of the cerebrovascular system or vessel abnormalities,\nsuch as aneurysms. A model would be useful to represent normal cerebrovascular\nstructure and variabilities in a healthy population and to differentiate from\nabnormalities. Current anomaly detection using autoencoding convolutional\nneural networks usually use a voxelwise mean-error for optimization. We propose\noptimizing a variational-autoencoder (VAE) with structural similarity loss\n(SSIM) for TOF-MRA reconstruction. A patch-trained 2D fully-convolutional VAE\nwas optimized for TOF-MRA reconstruction by comparing vessel segmentations of\noriginal and reconstructed MRAs. The method was trained and tested on two\ndatasets: the IXI dataset, and a subset from the ADAM challenge. Both trained\nnetworks were tested on a dataset including subjects with aneurysms. We\ncompared VAE optimization with L2-loss and SSIM-loss. Performance was evaluated\nbetween original and reconstructed MRAs using mean square error, mean-SSIM,\npeak-signal-to-noise-ratio and dice similarity index (DSI) of segmented\nvessels. The L2-optimized VAE outperforms SSIM, with improved reconstruction\nmetrics and DSIs for both datasets. Optimization using SSIM performed best for\nvisual image quality, but with discrepancy in quantitative reconstruction and\nvascular segmentation. The larger, more diverse IXI dataset had overall better\nperformance. Reconstruction metrics, including SSIM, were lower for MRAs\nincluding aneurysms. A SSIM-optimized VAE improved the visual perceptive image\nquality of TOF-MRA reconstructions. A L2-optimized VAE performed best for\nTOF-MRA reconstruction, where the vascular segmentation is important. SSIM is a\npotential metric for anomaly detection of MRAs.\n"}
+{"abstract": "  We investigate weak$^*$ derived sets, that is the sets of weak$^*$ limits of\nbounded nets, of convex subsets of duals of non-reflexive Banach spaces and\ntheir possible iterations. We prove that a dual space of any non-reflexive\nBanach space contains convex subsets of any finite order and a convex subset of\norder $\\omega + 1$.\n"}
+{"abstract": "  The scaling relations between the black hole (BH) mass and soft lag\nproperties for both active galactic nuclei (AGNs) and BH X-ray binaries\n(BHXRBs) suggest the same underlying physical mechanism at work in accreting BH\nsystems spanning a broad range of mass. However, the low-mass end of AGNs has\nnever been explored in detail. In this work, we extend the existing scaling\nrelations to lower-mass AGNs, which serve as anchors between the normal-mass\nAGNs and BHXRBs. For this purpose, we construct a sample of low-mass AGNs\n($M_{\\rm BH}<3\\times 10^{6} M_{\\rm \\odot}$) from the XMM-Newton archive and\nmeasure frequency-resolved time delays between the soft (0.3-1 keV) and hard\n(1-4 keV) X-ray emissions. We report that the soft band lags behind the hard\nband emission at high frequencies $\\sim[1.3-2.6]\\times 10^{-3}$ Hz, which is\ninterpreted as a sign of reverberation from the inner accretion disc in\nresponse to the direct coronal emission. At low frequencies ($\\sim[3-8]\\times\n10^{-4}$ Hz), the hard band lags behind the soft band variations, which we\nexplain in the context of the inward propagation of luminosity fluctuations\nthrough the corona. Assuming a lamppost geometry for the corona, we find that\nthe X-ray source of the sample extends at an average height and radius of $\\sim\n10r_{\\rm g}$ and $\\sim 6r_{\\rm g}$, respectively. Our results confirm that the\nscaling relations between the BH mass and soft lag amplitude/frequency derived\nfor higher-mass AGNs can safely extrapolate to lower-mass AGNs, and the\naccretion process is indeed independent of the BH mass.\n"}
+{"abstract": "  The presence of interface recombination in a complex multilayered thin-film\nsolar structure causes a disparity between the internal open-circuit voltage\n(VOC,in), measured by photoluminescence, and the external open-circuit voltage\n(VOC,ex) i.e. an additional VOC deficit. Higher VOC,ex value aim require a\ncomprehensive understanding of connection between VOC deficit and interface\nrecombination. Here, a deep near-surface defect model at the absorber/buffer\ninterface is developed for copper indium di-selenide solar cells grown under Cu\nexcess conditions to explain the disparity between VOC,in and VOC,ex.. The\nmodel is based on experimental analysis of admittance spectroscopy and\ndeep-level transient spectroscopy, which show the signature of deep acceptor\ndefect. Further, temperature-dependent current-voltage measurements confirm the\npresence of near surface defects as the cause of interface recombination. The\nnumerical simulations show strong decrease in the local VOC,in near the\nabsorber/buffer interface leading to a VOC deficit in the device. This loss\nmechanism leads to interface recombination without a reduced interface bandgap\nor Fermi level pinning. Further, these findings demonstrate that the VOC,in\nmeasurements alone can be inconclusive and might conceal the information on\ninterface recombination pathways, establishing the need for complementary\ntechniques like temperature dependent current voltage measurements to identify\nthe cause of interface recombination in the devices.\n"}
+{"abstract": "  We study random walks on the isometry group of a Gromov hyperbolic space or\nTeichm\\\"uller space. We prove that the translation lengths of random isometries\nsatisfy a central limit theorem if and only if the random walk has finite\nsecond moment. While doing this, we recover the central limit theorem of\nBenoist and Quint for the displacement of a reference point and establish its\nconverse. Also discussed are the corresponding laws of the iterated logarithm.\nFinally, we prove sublinear geodesic tracking by random walks with finite\n$(1/2)$-th moment and logarithmic tracking by random walks with finite\nexponential moment.\n"}
+{"abstract": "  A multi-agent optimization problem motivated by the management of energy\nsystems is discussed. The associated cost function is separable and convex\nalthough not necessarily strongly convex and there exist edge-based coupling\nequality constraints. In this regard, we propose a distributed algorithm based\non solving the dual of the augmented problem. Furthermore, we consider that the\ncommunication network might be time-varying and the algorithm might be carried\nout asynchronously. The time-varying nature and the asynchronicity are modeled\nas random processes. Then, we show the convergence and the convergence rate of\nthe proposed algorithm under the aforementioned conditions.\n"}
+{"abstract": "  Deploying sophisticated deep learning models on embedded devices with the\npurpose of solving real-world problems is a struggle using today's technology.\nPrivacy and data limitations, network connection issues, and the need for fast\nmodel adaptation are some of the challenges that constitute today's approaches\nunfit for many applications on the edge and make real-time on-device training a\nnecessity. Google is currently working on tackling these challenges by\nembedding an experimental transfer learning API to their TensorFlow Lite,\nmachine learning library. In this paper, we show that although transfer\nlearning is a good first step for on-device model training, it suffers from\ncatastrophic forgetting when faced with more realistic scenarios. We present\nthis issue by testing a simple transfer learning model on the CORe50 benchmark\nas well as by demonstrating its limitations directly on an Android application\nwe developed. In addition, we expand the TensorFlow Lite library to include\ncontinual learning capabilities, by integrating a simple replay approach into\nthe head of the current transfer learning model. We test our continual learning\nmodel on the CORe50 benchmark to show that it tackles catastrophic forgetting,\nand we demonstrate its ability to continually learn, even under non-ideal\nconditions, using the application we developed. Finally, we open-source the\ncode of our Android application to enable developers to integrate continual\nlearning to their own smartphone applications, as well as to facilitate further\ndevelopment of continual learning functionality into the TensorFlow Lite\nenvironment.\n"}
+{"abstract": "  This chapter presents an overview on actuator attacks that exploit zero\ndynamics, and countermeasures against them. First, zero-dynamics attack is\nre-introduced based on a canonical representation called normal form. Then it\nis shown that the target dynamic system is at elevated risk if the associated\nzero dynamics is unstable. From there on, several questions are raised in\nseries to ensure when the target system is immune to the attack of this kind.\nThe first question is: Is the target system secure from zero-dynamics attack if\nit does not have any unstable zeros? An answer provided for this question is:\nNo, the target system may still be at risk due to another attack surface\nemerging in the process of implementation. This is followed by a series of next\nquestions, and in the course of providing answers, variants of the classic\nzero-dynamics attack are presented, from which the vulnerability of the target\nsystem is explored in depth. At the end, countermeasures are proposed to render\nthe attack ineffective. Because it is known that the zero-dynamics in\ncontinuous-time systems cannot be modified by feedback, the main idea of the\ncountermeasure is to relocate any unstable zero to a stable region in the stage\nof digital implementation through modified digital samplers and holders.\nAdversaries can still attack actuators, but due to the re-located zeros, they\nare of little use in damaging the target system.\n"}
+{"abstract": "  Guitar fretboards are designed based on the equation of the ideal string.\nThat is, it neglecs several factors as nonlinearities and bending stiffness of\nthe strings. Due to this fact, intonation of guitars along the whole neck is\nnot perfect, and guitars have right tuning just in an \\emph{average} sense.\nThere are commercially available fretboards that differ from the tradictional\ndesign.\\footnote{One example is the \\cite{patent} by the Company True\nTemperament AB, where each fretboard is made using CNC processes.} As a final\napplication of this work we would like to redesign the fretboard layout\nconsidering the effects of bending stiffness. The main goal of this project is\nto analyze the differences between the differences in the solution for\nvibrations of the ideal string and a stiff string. These differences should\nlead to changes in the fret distribution for a guitar, and, hopefully improve\nthe overall intonation of the instrument. We will start analyzing the ideal\nstring equation and after a good understanding of this analytical solution we\nwill proceed with the, more complex, stiff equation. Topics like separation of\nvariables, Fourier transforms, and Perturbation analysis might prove useful\nduring the course of this project\n"}
+{"abstract": "  This investigation presents evidence of the relation between the dynamics of\nintense events in small-scale turbulence and the energy cascade. We use the\ngeneralised (H\\\"older) means to track the temporal evolution of intense events\nof the enstrophy and the dissipation in direct numerical simulations of\nisotropic turbulence. We show that these events are modulated by large-scale\nfluctuations, and that their evolution is consistent with a local\nmultiplicative cascade, as hypothesised by a broad class of intermittency\nmodels of turbulence.\n"}
+{"abstract": "  A new implicit-explicit local differential transform method (IELDTM) is\nderived here for time integration of the nonlinear advection-diffusion\nprocesses represented by (2+1)-dimensional Burgers equation. The IELDTM is\nadaptively constructed as stability preserved and high order time integrator\nfor spatially discretized Burgers equation. For spatial discretization of the\nmodel equation, the Chebyshev spectral collocation method (ChCM) is utilized. A\nrobust stability analysis and global error analysis of the IELDTM are presented\nwith respect to the direction parameter \\theta. With the help of the global\nerror analysis, adaptivity equations are derived to minimize the computational\ncosts of the algorithms. The produced method is shown to eliminate the accuracy\ndisadvantage of the classical \\theta-method and the stability disadvantages of\nthe existing DTM-based methods. Two examples of the Burgers equation in one and\ntwo dimensions have been solved via the ChCM-IELDTM hybridization, and the\nproduced results are compared with the literature. The present time integrator\nhas been proven to produce more accurate numerical results than the MATLAB\nsolvers, ode45 and ode15s.\n"}
+{"abstract": "  Concatenation and equilibrium swelling of Olympic gels, which are composed of\nentangled cyclic polymers, is studied by Monte Carlo Simulations. The average\nnumber of concatenated molecules per cyclic polymers, $f_n$, is found to depend\non the degree of polymerization, $N$, and polymer volume fraction at network\npreparation, ${\\phi}_0$, as $f_n ~ {\\phi}_0^{{\\nu}/(3{\\nu}-1)}N$ with scaling\nexponent ${\\nu} = 0.588$. In contrast to chemically cross-linked polymer\nnetworks, we observe that Olympic gels made of longer cyclic chains exhibit a\nsmaller equilibrium swelling degree, $Q ~ N^{-0.28} {\\phi}_0^{-0.72}$, at the\nsame polymer volume fraction ${\\phi}_0$. This observation is explained by a\ndesinterspersion process of overlapping non-concatenated rings upon swelling,\nwhich is tested directly by analyzing the change in overlap of the molecules\nupon swelling.\n"}
+{"abstract": "  Temporal Neural Networks (TNNs) are spiking neural networks that use time as\na resource to represent and process information, similar to the mammalian\nneocortex. In contrast to compute-intensive deep neural networks that employ\nseparate training and inference phases, TNNs are capable of extremely efficient\nonline incremental/continual learning and are excellent candidates for building\nedge-native sensory processing units. This work proposes a microarchitecture\nframework for implementing TNNs using standard CMOS. Gate-level implementations\nof three key building blocks are presented: 1) multi-synapse neurons, 2)\nmulti-neuron columns, and 3) unsupervised and supervised online learning\nalgorithms based on Spike Timing Dependent Plasticity (STDP). The proposed\nmicroarchitecture is embodied in a set of characteristic scaling equations for\nassessing the gate count, area, delay and power for any TNN design.\nPost-synthesis results (in 45nm CMOS) for the proposed designs are presented,\nand their online incremental learning capability is demonstrated.\n"}
+{"abstract": "  We propose new methods for in-domain and cross-domain Named Entity\nRecognition (NER) on historical data for Dutch and French. For the cross-domain\ncase, we address domain shift by integrating unsupervised in-domain data via\ncontextualized string embeddings; and OCR errors by injecting synthetic OCR\nerrors into the source domain and address data centric domain adaptation. We\npropose a general approach to imitate OCR errors in arbitrary input data. Our\ncross-domain as well as our in-domain results outperform several strong\nbaselines and establish state-of-the-art results. We publish preprocessed\nversions of the French and Dutch Europeana NER corpora.\n"}
+{"abstract": "  COVID-19 has impacted nations differently based on their policy\nimplementations. The effective policy requires taking into account public\ninformation and adaptability to new knowledge. Epidemiological models built to\nunderstand COVID-19 seldom provide the policymaker with the capability for\nadaptive pandemic control (APC). Among the core challenges to be overcome\ninclude (a) inability to handle a high degree of non-homogeneity in different\ncontributing features across the pandemic timeline, (b) lack of an approach\nthat enables adaptive incorporation of public health expert knowledge, and (c)\ntransparent models that enable understanding of the decision-making process in\nsuggesting policy. In this work, we take the early steps to address these\nchallenges using Knowledge Infused Policy Gradient (KIPG) methods. Prior work\non knowledge infusion does not handle soft and hard imposition of varying forms\nof knowledge in disease information and guidelines to necessarily comply with.\nFurthermore, the models do not attend to non-homogeneity in feature counts,\nmanifesting as partial observability in informing the policy. Additionally,\ninterpretable structures are extracted post-learning instead of learning an\ninterpretable model required for APC. To this end, we introduce a mathematical\nframework for KIPG methods that can (a) induce relevant feature counts over\nmulti-relational features of the world, (b) handle latent non-homogeneous\ncounts as hidden variables that are linear combinations of kernelized\naggregates over the features, and (b) infuse knowledge as functional\nconstraints in a principled manner. The study establishes a theory for imposing\nhard and soft constraints and simulates it through experiments. In comparison\nwith knowledge-intensive baselines, we show quick sample efficient adaptation\nto new knowledge and interpretability in the learned policy, especially in a\npandemic context.\n"}
+{"abstract": "  Bayesian optimization is a popular method for optimizing expensive black-box\nfunctions. The objective functions of hard real world problems are oftentimes\ncharacterized by a fluctuated landscape of many local optima. Bayesian\noptimization risks in over-exploiting such traps, remaining with insufficient\nquery budget for exploring the global landscape. We introduce Coordinate\nBackoff Bayesian Optimization (CobBO) to alleviate those challenges. CobBO\ncaptures a smooth approximation of the global landscape by interpolating the\nvalues of queried points projected to randomly selected promising subspaces.\nThus also a smaller query budget is required for the Gaussian process\nregressions applied over the lower dimensional subspaces. This approach can be\nviewed as a variant of coordinate ascent, tailored for Bayesian optimization,\nusing a stopping rule for backing off from a certain subspace and switching to\nanother coordinate subset. Extensive evaluations show that CobBO finds\nsolutions comparable to or better than other state-of-the-art methods for\ndimensions ranging from tens to hundreds, while reducing the trial complexity.\n"}
+{"abstract": "  Automatic software development has been a research hot spot in the field of\nsoftware engineering (SE) in the past decade. In particular, deep learning (DL)\nhas been applied and achieved a lot of progress in various SE tasks. Among all\napplications, automatic code generation by machines as a general concept,\nincluding code completion and code synthesis, is a common expectation in the\nfield of SE, which may greatly reduce the development burden of the software\ndevelopers and improves the efficiency and quality of the software development\nprocess to a certain extent. Code completion is an important part of modern\nintegrated development environments (IDEs). Code completion technology\neffectively helps programmers complete code class names, method names, and\nkey-words, etc., which improves the efficiency of program development and\nreduces spelling errors in the coding process. Such tools use static analysis\non the code and provide candidates for completion arranged in alphabetical\norder. Code synthesis is implemented from two aspects, one based on\ninput-output samples and the other based on functionality description. In this\nstudy, we introduce existing techniques of these two aspects and the\ncorresponding DL techniques, and present some possible future research\ndirections.\n"}
+{"abstract": "  We initiate the study of incentive-compatible forecasting competitions in\nwhich multiple forecasters make predictions about one or more events and\ncompete for a single prize. We have two objectives: (1) to incentivize\nforecasters to report truthfully and (2) to award the prize to the most\naccurate forecaster. Proper scoring rules incentivize truthful reporting if all\nforecasters are paid according to their scores. However, incentives become\ndistorted if only the best-scoring forecaster wins a prize, since forecasters\ncan often increase their probability of having the highest score by reporting\nmore extreme beliefs. In this paper, we introduce two novel forecasting\ncompetition mechanisms. Our first mechanism is incentive compatible and\nguaranteed to select the most accurate forecaster with probability higher than\nany other forecaster. Moreover, we show that in the standard single-event,\ntwo-forecaster setting and under mild technical conditions, no other\nincentive-compatible mechanism selects the most accurate forecaster with higher\nprobability. Our second mechanism is incentive compatible when forecasters'\nbeliefs are such that information about one event does not lead to belief\nupdates on other events, and it selects the best forecaster with probability\napproaching 1 as the number of events grows. Our notion of incentive\ncompatibility is more general than previous definitions of dominant strategy\nincentive compatibility in that it allows for reports to be correlated with the\nevent outcomes. Moreover, our mechanisms are easy to implement and can be\ngeneralized to the related problems of outputting a ranking over forecasters\nand hiring a forecaster with high accuracy on future events.\n"}
+{"abstract": "  In this paper we consider nonautonomous optimal control problems of infinite\nhorizon type, whose control actions are given by $L^1$-functions. We verify\nthat the value function is locally Lipschitz. The equivalence between dynamic\nprogramming inequalities and Hamilton-Jacobi-Bellman (HJB) inequalities for\nproximal sub (super) gradients is proven. Using this result we show that the\nvalue function is a Dini solution of the HJB equation. We obtain a verification\nresult for the class of Dini sub-solutions of the HJB equation and also prove a\nminimax property of the value function with respect to the sets of Dini\nsemi-solutions of the HJB equation. We introduce the concept of viscosity\nsolutions of the HJB equation in infinite horizon and prove the equivalence\nbetween this and the concept of Dini solutions. In the appendix we provide an\nexistence theorem.\n"}
+{"abstract": "  The evolution towards Industry 4.0 is driving the need for innovative\nsolutions in the area of network management, considering the complex, dynamic\nand heterogeneous nature of ICT supply chains. To this end, Intent-Based\nnetworking (IBN) which is already proven to evolve how network management is\ndriven today, can be implemented as a solution to facilitate the management of\nlarge ICT supply chains. In this paper, we first present a comparison of the\nmain architectural components of typical IBN systems and, then, we study the\nkey engineering requirements when integrating IBN with ICT supply chain network\nsystems while considering AI methods. We also propose a general architecture\ndesign that enables intent translation of ICT supply chain specifications into\nlower level policies, to finally show an example of how the access control is\nperformed in a modeled ICT supply chain system.\n"}
+{"abstract": "  The last milestone achievement for the roundoff-error-free solution of\ngeneral mixed integer programs over the rational numbers was a hybrid-precision\nbranch-and-bound algorithm published by Cook, Koch, Steffy, and Wolter in 2013.\n  We describe a substantial revision and extension of this framework that\nintegrates symbolic presolving, features an exact repair step for solutions\nfrom primal heuristics, employs a faster rational LP solver based on LP\niterative refinement, and is able to produce independently verifiable\ncertificates of optimality.\n  We study the significantly improved performance and give insights into the\ncomputational behavior of the new algorithmic components.\n  On the MIPLIB 2017 benchmark set, we observe an average speedup of 6.6x over\nthe original framework and 2.8 times as many instances solved within a time\nlimit of two hours.\n"}
+{"abstract": "  HIP 41378 f is a temperate $9.2\\pm0.1 R_{\\oplus}$ planet with period of\n542.08 days and an extremely low density of $0.09\\pm0.02$ g cm$^{-3}$. It\ntransits the bright star HIP 41378 (V=8.93), making it an exciting target for\natmospheric characterization including transmission spectroscopy. HIP 41378 was\nmonitored photometrically between the dates of 2019 November 19 and November\n28. We detected a transit of HIP 41378 f with NGTS, just the third transit ever\ndetected for this planet, which confirms the orbital period. This is also the\nfirst ground-based detection of a transit of HIP 41378 f. Additional\nground-based photometry was also obtained and used to constrain the time of the\ntransit. The transit was measured to occur 1.50 hours earlier than predicted.\nWe use an analytic transit timing variation (TTV) model to show the observed\nTTV can be explained by interactions between HIP 41378 e and HIP 41378 f. Using\nour TTV model, we predict the epochs of future transits of HIP 41378 f, with\nderived transit centres of T$_{C,4} = 2459355.087^{+0.031}_{-0.022}$ (May 2021)\nand T$_{C,5} = 2459897.078^{+0.114}_{-0.060}$ (Nov 2022).\n"}
+{"abstract": "  The canonical approach to video-and-language learning (e.g., video question\nanswering) dictates a neural model to learn from offline-extracted dense video\nfeatures from vision models and text features from language models. These\nfeature extractors are trained independently and usually on tasks different\nfrom the target domains, rendering these fixed features sub-optimal for\ndownstream tasks. Moreover, due to the high computational overload of dense\nvideo features, it is often difficult (or infeasible) to plug feature\nextractors directly into existing approaches for easy finetuning. To provide a\nremedy to this dilemma, we propose a generic framework ClipBERT that enables\naffordable end-to-end learning for video-and-language tasks, by employing\nsparse sampling, where only a single or a few sparsely sampled short clips from\na video are used at each training step. Experiments on text-to-video retrieval\nand video question answering on six datasets demonstrate that ClipBERT\noutperforms (or is on par with) existing methods that exploit full-length\nvideos, suggesting that end-to-end learning with just a few sparsely sampled\nclips is often more accurate than using densely extracted offline features from\nfull-length videos, proving the proverbial less-is-more principle. Videos in\nthe datasets are from considerably different domains and lengths, ranging from\n3-second generic domain GIF videos to 180-second YouTube human activity videos,\nshowing the generalization ability of our approach. Comprehensive ablation\nstudies and thorough analyses are provided to dissect what factors lead to this\nsuccess. Our code is publicly available at https://github.com/jayleicn/ClipBERT\n"}
+{"abstract": "  Space-time visualizations of macroscopic or microscopic traffic variables is\na qualitative tool used by traffic engineers to understand and analyze\ndifferent aspects of road traffic dynamics. We present a deep learning method\nto learn the macroscopic traffic speed dynamics from these space-time\nvisualizations, and demonstrate its application in the framework of traffic\nstate estimation. Compared to existing estimation approaches, our approach\nallows a finer estimation resolution, eliminates the dependence on the initial\nconditions, and is agnostic to external factors such as traffic demand, road\ninhomogeneities and driving behaviors. Our model respects causality in traffic\ndynamics, which improves the robustness of estimation. We present the\nhigh-resolution traffic speed fields estimated for several freeway sections\nusing the data obtained from the Next Generation Simulation Program (NGSIM) and\nGerman Highway (HighD) datasets. We further demonstrate the quality and utility\nof the estimation by inferring vehicle trajectories from the estimated speed\nfields, and discuss the benefits of deep neural network models in approximating\nthe traffic dynamics.\n"}
+{"abstract": "  We consider the problem of collectively detecting multiple events,\nparticularly in cross-sentence settings. The key to dealing with the problem is\nto encode semantic information and model event inter-dependency at a\ndocument-level. In this paper, we reformulate it as a Seq2Seq task and propose\na Multi-Layer Bidirectional Network (MLBiNet) to capture the document-level\nassociation of events and semantic information simultaneously. Specifically, a\nbidirectional decoder is firstly devised to model event inter-dependency within\na sentence when decoding the event tag vector sequence. Secondly, an\ninformation aggregation module is employed to aggregate sentence-level semantic\nand event tag information. Finally, we stack multiple bidirectional decoders\nand feed cross-sentence information, forming a multi-layer bidirectional\ntagging architecture to iteratively propagate information across sentences. We\nshow that our approach provides significant improvement in performance compared\nto the current state-of-the-art results.\n"}
+{"abstract": "  Detecting transparent objects in natural scenes is challenging due to the low\ncontrast in texture, brightness and colors. Recent deep-learning-based works\nreveal that it is effective to leverage boundaries for transparent object\ndetection (TOD). However, these methods usually encounter boundary-related\nimbalance problem, leading to limited generation capability. Detailly, a kind\nof boundaries in the background, which share the same characteristics with\nboundaries of transparent objects but have much smaller amounts, usually hurt\nthe performance. To conquer the boundary-related imbalance problem, we propose\na novel content-dependent data augmentation method termed FakeMix. Considering\ncollecting these trouble-maker boundaries in the background is hard without\ncorresponding annotations, we elaborately generate them by appending the\nboundaries of transparent objects from other samples into the current image\nduring training, which adjusts the data space and improves the generalization\nof the models. Further, we present AdaptiveASPP, an enhanced version of ASPP,\nthat can capture multi-scale and cross-modality features dynamically. Extensive\nexperiments demonstrate that our methods clearly outperform the\nstate-of-the-art methods. We also show that our approach can also transfer well\non related tasks, in which the model meets similar troubles, such as mirror\ndetection, glass detection, and camouflaged object detection. Code will be made\npublicly available.\n"}
+{"abstract": "  I review the meaning of General Relativity (GR), viewed as a dynamical field,\nrather than as geometry, as effected by the 1958-61 anti-geometrical work of\nADM. This very brief non-technical summary, is intended for historians.\n"}
+{"abstract": "  We establish an asymptotic formula for the number of lattice points in the\nsets \\[ \\mathbf S_{h_1, h_2, h_3}(\\lambda): =\\{x\\in\\mathbb Z_+^3:\\lfloor\nh_1(x_1)\\rfloor+\\lfloor h_2(x_2)\\rfloor+\\lfloor h_3(x_3)\\rfloor=\\lambda\\} \\quad\n\\text{with}\\quad \\lambda\\in\\mathbb Z_+; \\] where functions $h_1, h_2, h_3$ are\nconstant multiples of regularly varying functions of the form\n$h(x):=x^c\\ell_h(x)$, where the exponent $c>1$ (but close to $1$) and a\nfunction $\\ell_h(x)$ is taken from a certain wide class of slowly varying\nfunctions. Taking $h_1(x)=h_2(x)=h_3(x)=x^c$ we will also derive an asymptotic\nformula for the number of lattice points in the sets \\[ \\mathbf\nS_{c}^3(\\lambda) := \\{x \\in \\mathbb Z^3 : \\lfloor |x_1|^c \\rfloor + \\lfloor\n|x_2|^c \\rfloor + \\lfloor |x_3|^c \\rfloor= \\lambda \\} \\quad \\text{with}\\quad\n\\lambda\\in\\mathbb Z_+; \\] which can be thought of as a perturbation of the\nclassical Waring problem in three variables.\n  We will use the latter asymptotic formula to study, the main results of this\npaper, norm and pointwise convergence of the ergodic averages \\[\n\\frac{1}{\\#\\mathbf S_{c}^3(\\lambda)}\\sum_{n\\in \\mathbf\nS_{c}^3(\\lambda)}f(T_1^{n_1}T_2^{n_2}T_3^{n_3}x) \\quad \\text{as}\\quad\n\\lambda\\to\\infty; \\] where $T_1, T_2, T_3:X\\to X$ are commuting invertible and\nmeasure-preserving transformations of a $\\sigma$-finite measure space $(X,\n\\nu)$ for any function $f\\in L^p(X)$ with $p>\\frac{11-4c}{11-7c}$. Finally, we\nwill study the equidistribution problem corresponding to the spheres $\\mathbf\nS_{c}^3(\\lambda)$.\n"}
+{"abstract": "  We present a general series representation formula for the local solution of\nBernoulli equation with Caputo fractional derivatives. We then focus on a\ngeneralization of the fractional logistic equation and we present some related\nnumerical simulations.\n"}
+{"abstract": "  In high energy physics (HEP), jets are collections of correlated particles\nproduced ubiquitously in particle collisions such as those at the CERN Large\nHadron Collider (LHC). Machine learning (ML)-based generative models, such as\ngenerative adversarial networks (GANs), have the potential to significantly\naccelerate LHC jet simulations. However, despite jets having a natural\nrepresentation as a set of particles in momentum-space, a.k.a. a particle\ncloud, there exist no generative models applied to such a dataset. In this\nwork, we introduce a new particle cloud dataset (JetNet), and apply to it\nexisting point cloud GANs. Results are evaluated using (1) 1-Wasserstein\ndistances between high- and low-level feature distributions, (2) a newly\ndeveloped Fr\\'{e}chet ParticleNet Distance, and (3) the coverage and (4)\nminimum matching distance metrics. Existing GANs are found to be inadequate for\nphysics applications, hence we develop a new message passing GAN (MPGAN), which\noutperforms existing point cloud GANs on virtually every metric and shows\npromise for use in HEP. We propose JetNet as a novel point-cloud-style dataset\nfor the ML community to experiment with, and set MPGAN as a benchmark to\nimprove upon for future generative models. Additionally, to facilitate research\nand improve accessibility and reproducibility in this area, we release the\nopen-source JetNet Python package with interfaces for particle cloud datasets,\nimplementations for evaluation and loss metrics, and more tools for ML in HEP\ndevelopment.\n"}
+{"abstract": "  One of the big challenges of current electronics is the design and\nimplementation of hardware neural networks that perform fast and\nenergy-efficient machine learning. Spintronics is a promising catalyst for this\nfield with the capabilities of nanosecond operation and compatibility with\nexisting microelectronics. Considering large-scale, viable neuromorphic systems\nhowever, variability of device properties is a serious concern. In this paper,\nwe show an autonomously operating circuit that performs hardware-aware machine\nlearning utilizing probabilistic neurons built with stochastic magnetic tunnel\njunctions. We show that in-situ learning of weights and biases in a Boltzmann\nmachine can counter device-to-device variations and learn the probability\ndistribution of meaningful operations such as a full adder. This scalable\nautonomously operating learning circuit using spintronics-based neurons could\nbe especially of interest for standalone artificial-intelligence devices\ncapable of fast and efficient learning at the edge.\n"}
+{"abstract": "  We revisit the problem of the estimation of the differential entropy $H(f)$\nof a random vector $X$ in $R^d$ with density $f$, assuming that $H(f)$ exists\nand is finite. In this note, we study the consistency of the popular nearest\nneighbor estimate $H_n$ of Kozachenko and Leonenko. Without any smoothness\ncondition we show that the estimate is consistent ($E\\{|H_n - H(f)|\\} \\to 0$ as\n$n \\to \\infty$) if and only if $\\mathbb{E} \\{ \\log ( \\| X \\| + 1 )\\} < \\infty$.\nFurthermore, if $X$ has compact support, then $H_n \\to H(f)$ almost surely.\n"}
+{"abstract": "  Identifying and understanding quality phrases from context is a fundamental\ntask in text mining. The most challenging part of this task arguably lies in\nuncommon, emerging, and domain-specific phrases. The infrequent nature of these\nphrases significantly hurts the performance of phrase mining methods that rely\non sufficient phrase occurrences in the input corpus. Context-aware tagging\nmodels, though not restricted by frequency, heavily rely on domain experts for\neither massive sentence-level gold labels or handcrafted gazetteers. In this\nwork, we propose UCPhrase, a novel unsupervised context-aware quality phrase\ntagger. Specifically, we induce high-quality phrase spans as silver labels from\nconsistently co-occurring word sequences within each document. Compared with\ntypical context-agnostic distant supervision based on existing knowledge bases\n(KBs), our silver labels root deeply in the input domain and context, thus\nhaving unique advantages in preserving contextual completeness and capturing\nemerging, out-of-KB phrases. Training a conventional neural tagger based on\nsilver labels usually faces the risk of overfitting phrase surface names.\nAlternatively, we observe that the contextualized attention maps generated from\na transformer-based neural language model effectively reveal the connections\nbetween words in a surface-agnostic way. Therefore, we pair such attention maps\nwith the silver labels to train a lightweight span prediction model, which can\nbe applied to new input to recognize (unseen) quality phrases regardless of\ntheir surface names or frequency. Thorough experiments on various tasks and\ndatasets, including corpus-level phrase ranking, document-level keyphrase\nextraction, and sentence-level phrase tagging, demonstrate the superiority of\nour design over state-of-the-art pre-trained, unsupervised, and distantly\nsupervised methods.\n"}
+{"abstract": "  We show that the nature of the topological fluctuations in $SU(3)$ gauge\ntheory changes drastically at the finite temperature phase transition. Starting\nfrom temperatures right above the phase transition topological fluctuations\ncome in well separated lumps of unit charge that form a non-interacting ideal\ngas. Our analysis is based on a novel method to count not only the net\ntopological charge, but also separately the number of positively and negatively\ncharged lumps in lattice configurations using the spectrum of the overlap Dirac\noperator. This enables us to determine the joint distribution of the number of\npositively and negatively charged topological objects, and we find this\ndistribution to be consistent with that of an ideal gas of unit charged\ntopological objects.\n"}
+{"abstract": "  Measuring the acoustic characteristics of a space is often done by capturing\nits impulse response (IR), a representation of how a full-range stimulus sound\nexcites it. This work generates an IR from a single image, which can then be\napplied to other signals using convolution, simulating the reverberant\ncharacteristics of the space shown in the image. Recording these IRs is both\ntime-intensive and expensive, and often infeasible for inaccessible locations.\nWe use an end-to-end neural network architecture to generate plausible audio\nimpulse responses from single images of acoustic environments. We evaluate our\nmethod both by comparisons to ground truth data and by human expert evaluation.\nWe demonstrate our approach by generating plausible impulse responses from\ndiverse settings and formats including well known places, musical halls, rooms\nin paintings, images from animations and computer games, synthetic environments\ngenerated from text, panoramic images, and video conference backgrounds.\n"}
+{"abstract": "  Lithium-sulfur (Li-S) batteries have become one of the most attractive\nalternatives over conventional Li-ion batteries due to their high theoretical\nspecific energy density (2500 Wh/kg for Li-S vs. $\\sim$250 Wh/kg for Li-ion).\nAccurate state estimation in Li-S batteries is urgently needed for safe and\nefficient operation. To the best of the authors' knowledge, electrochemical\nmodel-based observers have not been reported for Li-S batteries, primarily due\nto the complex dynamics that make state observer design a challenging problem.\nIn this work, we demonstrate a state estimation scheme based on a\nzero-dimensional electrochemical model for Li-S batteries. The nonlinear\ndifferential-algebraic equation (DAE) model is incorporated into an extend\nKalman filter. This observer design estimates both differential and algebraic\nstates that represent the dynamic behavior inside the cell, from voltage and\ncurrent measurements only. The effectiveness of the proposed estimation\nalgorithm is illustrated by numerical simulation results. Our study unlocks how\nan electrochemical model can be utilized for practical state estimation of Li-S\nbatteries.\n"}
+{"abstract": "  We investigate the influence of general forms of disorder on the robustness\nof superconductivity in multiband materials. Specifically, we consider a\ngeneral two-band system where the bands arise from an orbital degree of freedom\nof the electrons. Within the Born approximation, we show that the interplay of\nthe spin-orbital structure of the normal-state Hamiltonian, disorder\nscattering, and superconducting pairing potentials can lead to significant\ndeviations from the expected robustness of the superconductivity. This can be\nconveniently formulated in terms of the so-called \"superconducting fitness\". In\nparticular, we verify a key role for unconventional $s$-wave states, permitted\nby the spin-orbital structure and which may pair electrons that are not\ntime-reversed partners. To exemplify the role of Fermi surface topology and\nspin-orbital texture, we apply our formalism to the candidate topological\nsuperconductor Cu$_x$Bi$_2$Se$_3$, for which only a single band crosses the\nFermi energy, as well as models of the iron pnictides, which possess multiple\nFermi pockets.\n"}
+{"abstract": "  Classical models for multivariate or spatial extremes are mainly based upon\nthe asymptotically justified max-stable or generalized Pareto processes. These\nmodels are suitable when asymptotic dependence is present, i.e., the joint tail\ndecays at the same rate as the marginal tail. However, recent environmental\ndata applications suggest that asymptotic independence is equally important\nand, unfortunately, existing spatial models in this setting that are both\nflexible and can be fitted efficiently are scarce. Here, we propose a new\nspatial copula model based on the generalized hyperbolic distribution, which is\na specific normal mean-variance mixture and is very popular in financial\nmodeling. The tail properties of this distribution have been studied in the\nliterature, but with contradictory results. It turns out that the proofs from\nthe literature contain mistakes. We here give a corrected theoretical\ndescription of its tail dependence structure and then exploit the model to\nanalyze a simulated dataset from the inverted Brown-Resnick process, hindcast\nsignificant wave height data in the North Sea, and wind gust data in the state\nof Oklahoma, USA. We demonstrate that our proposed model is flexible enough to\ncapture the dependence structure not only in the tail but also in the bulk.\n"}
+{"abstract": "  Speech enhancement is an essential task of improving speech quality in noise\nscenario. Several state-of-the-art approaches have introduced visual\ninformation for speech enhancement,since the visual aspect of speech is\nessentially unaffected by acoustic environment. This paper proposes a novel\nframeworkthat involves visual information for speech enhancement, by\nin-corporating a Generative Adversarial Network (GAN). In par-ticular, the\nproposed visual speech enhancement GAN consistof two networks trained in\nadversarial manner, i) a generator that adopts multi-layer feature fusion\nconvolution network to enhance input noisy speech, and ii) a discriminator that\nattemptsto minimize the discrepancy between the distributions of the clean\nspeech signal and enhanced speech signal. Experiment re-sults demonstrated\nsuperior performance of the proposed modelagainst several state-of-the-art\n"}
+{"abstract": "  Recent evidence based on APOGEE data for stars within a few kpc of the\nGalactic centre suggests that dissolved globular clusters (GCs) contribute\nsignificantly to the stellar mass budget of the inner halo. In this paper we\nenquire into the origins of tracers of GC dissolution, N-rich stars, that are\nlocated in the inner 4 kpc of the Milky Way. From an analysis of the chemical\ncompositions of these stars we establish that about 30% of the N-rich stars\npreviously identified in the inner Galaxy may have an accreted origin. This\nresult is confirmed by an analysis of the kinematic properties of our sample.\nThe specific frequency of N-rich stars is quite large in the accreted\npopulation, exceeding that of its in situ counterparts by near an order of\nmagnitude, in disagreement with predictions from numerical simulations. We hope\nthat our numbers provide a useful test to models of GC formation and\ndestruction.\n"}
+{"abstract": "  Hypergraphs offer an explicit formalism to describe multibody interactions in\ncomplex systems. To connect dynamics and function in systems with these\nhigher-order interactions, network scientists have generalised random-walk\nmodels to hypergraphs and studied the multibody effects on flow-based\ncentrality measures. But mapping the large-scale structure of those flows\nrequires effective community detection methods. We derive unipartite,\nbipartite, and multilayer network representations of hypergraph flows and\nexplore how they and the underlying random-walk model change the number, size,\ndepth, and overlap of identified multilevel communities. These results help\nresearchers choose the appropriate modelling approach when mapping flows on\nhypergraphs.\n"}
+{"abstract": "  A number of solar filaments/prominences demonstrate failed eruptions, when a\nfilament at first suddenly starts to ascend and then decelerates and stops at\nsome greater height in the corona. The mechanism of the termination of\neruptions is not clear yet. One of the confining forces able to stop the\neruption is the gravity force. Using a simple model of a partial\ncurrent-carrying torus loop anchored to the photosphere and photospheric\nmagnetic field measurements as the boundary condition for the potential\nmagnetic field extrapolation into the corona, we estimated masses of 15\neruptive filaments. The values of the filament mass show rather wide\ndistribution in the range of $4\\times10^{15}$ -- $270\\times10^{16}$g. Masses of\nthe most of filaments, laying in the middle of the range, are in accordance\nwith estimations made earlier on the basis of spectroscopic and white-light\nobservations.\n"}
+{"abstract": "  Heavy-ion therapy, particularly using scanned (active) beam delivery,\nprovides a precise and highly conformal dose distribution, with maximum dose\ndeposition for each pencil beam at its endpoint (Bragg peak), and low entrance\nand exit dose. To take full advantage of this precision, robust range\nverification methods are required; these methods ensure that the Bragg peak is\npositioned correctly in the patient and the dose is delivered as prescribed.\nRelative range verification allows intra-fraction monitoring of Bragg peak\nspacing to ensure full coverage with each fraction, as well as inter-fraction\nmonitoring to ensure all fractions are delivered consistently. To validate the\nproposed filtered Interaction Vertex Imaging method for relative range\nverification, a ${}^{16}$O beam was used to deliver 12 Bragg peak positions in\na 40 mm poly-(methyl methacrylate) phantom. Secondary particles produced in the\nphantom were monitored using position-sensitive silicon detectors. Events\nrecorded on these detectors, along with a measurement of the treatment beam\naxis, were used to reconstruct the sites of origin of these secondary particles\nin the phantom. The distal edge of the depth distribution of these\nreconstructed points was determined with logistic fits, and the translation in\ndepth required to minimize the $\\chi^2$ statistic between these fits was used\nto compute the range shift between any two Bragg peak positions. In all cases,\nthe range shift was determined with sub-millimeter precision, to a standard\ndeviation of the mean of 220(10) $\\mu$m. This result validates filtered\nInteraction Vertex Imaging as a reliable relative range verification method,\nwhich should be capable of monitoring each energy step in each fraction of a\nscanned heavy-ion treatment plan.\n"}
+{"abstract": "  Type IIn supernovae (SNe IIn) are a relatively infrequently observed subclass\nof SNe whose photometric and spectroscopic properties are varied. A common\nthread among SNe IIn are the complex multiple-component hydrogen Balmer lines.\nOwing to the heterogeneity of SNe IIn, online databases contain some outdated,\nerroneous, or even contradictory classifications. SN IIn classification is\nfurther complicated by SN impostors and contamination from underlying HII\nregions. We have compiled a catalogue of systematically classified nearby\n(redshift z < 0.02) SNe IIn using the Open Supernova Catalogue (OSC). We\npresent spectral classifications for 115 objects previously classified as SNe\nIIn. Our classification is based upon results obtained by fitting multiple\nGaussians to the H-alpha profiles. We compare classifications reported by the\nOSC and Transient Name Server (TNS) along with the best matched templates from\nSNID. We find that 28 objects have been misclassified as SNe IIn. TNS and OSC\ncan be unreliable; they disagree on the classifications of 51 of the objects\nand contain a number of erroneous classifications. Furthermore, OSC and TNS\nhold misclassifications for 34 and twelve (respectively) of the transients we\nclassify as SNe IIn. In total, we classify 87 SNe IIn. We highlight the\nimportance of ensuring that online databases remain up to date when new or even\ncontemporaneous data become available. Our work shows the great range of\nspectral properties and features that SNe IIn exhibit, which may be linked to\nmultiple progenitor channels and environment diversity. We set out a\nclassification sche me for SNe IIn based on the H-alpha profile which is not\ngreatly affected by the inhomogeneity of SNe IIn.\n"}
+{"abstract": "  Isobaric $^{96}_{44}$Ru+$^{96}_{44}$Ru and $^{96}_{40}$Zr+$^{96}_{40}$Zr\ncollisions at $\\sqrt{s_{_{NN}}}=200$ GeV have been conducted at the\nRelativistic Heavy Ion Collider to circumvent the large flow-induced background\nin searching for the chiral magnetic effect (CME), predicted by the topological\nfeature of quantum chromodynamics (QCD). Considering that the background in\nisobar collisions is approximately twice that in Au+Au collisions (due to the\nsmaller multiplicity) and the CME signal is approximately half (due to the\nweaker magnetic field), we caution that the CME may not be detectable with the\ncollected isobar data statistics, within $\\sim$2$\\sigma$ significance, if the\naxial charge per entropy density ($n_5/s$) and the QCD vacuum transition\nprobability are system independent. This expectation is generally verified by\nthe Anomalous-Viscous Fluid Dynamics (AVFD) model. While our estimate provides\nan approximate \"experimental\" baseline, theoretical uncertainties on the CME\nremain large.\n"}
+{"abstract": "  Complex objects are usually with multiple labels, and can be represented by\nmultiple modal representations, e.g., the complex articles contain text and\nimage information as well as multiple annotations. Previous methods assume that\nthe homogeneous multi-modal data are consistent, while in real applications,\nthe raw data are disordered, e.g., the article constitutes with variable number\nof inconsistent text and image instances. Therefore, Multi-modal Multi-instance\nMulti-label (M3) learning provides a framework for handling such task and has\nexhibited excellent performance. However, M3 learning is facing two main\nchallenges: 1) how to effectively utilize label correlation; 2) how to take\nadvantage of multi-modal learning to process unlabeled instances. To solve\nthese problems, we first propose a novel Multi-modal Multi-instance Multi-label\nDeep Network (M3DN), which considers M3 learning in an end-to-end multi-modal\ndeep network and utilizes consistency principle among different modal bag-level\npredictions. Based on the M3DN, we learn the latent ground label metric with\nthe optimal transport. Moreover, we introduce the extrinsic unlabeled\nmulti-modal multi-instance data, and propose the M3DNS, which considers the\ninstance-level auto-encoder for single modality and modified bag-level optimal\ntransport to strengthen the consistency among modalities. Thereby M3DNS can\nbetter predict label and exploit label correlation simultaneously. Experiments\non benchmark datasets and real world WKG Game-Hub dataset validate the\neffectiveness of the proposed methods.\n"}
+{"abstract": "  Bunch splitting is an RF manipulation method of changing the bunch structure,\nbunch numbers and bunch intensity in the high-intensity synchrotrons that serve\nas the injector for a particle collider. An efficient way to realize bunch\nsplitting is to use the combination of different harmonic RF systems, such as\nthe two-fold bunch splitting of a bunch with a combination of fundamental\nharmonic and doubled harmonic RF systems. The two-fold bunch splitting and\nthree-fold bunch splitting methods have been experimentally verified and\nsuccessfully applied to the LHC/PS. In this paper, a generalized multi-fold\nbunch splitting method is given. The five-fold bunch splitting method using\nspecially designed multi-harmonic RF systems was studied and tentatively\napplied to the medium-stage synchrotron (MSS), the third accelerator of the\ninjector chain of the Super Proton-Proton Collider (SPPC), to mitigate the\npileup effects and collective instabilities of a single bunch in the SPPC. The\nresults show that the five-fold bunch splitting is feasible and both the bunch\npopulation distribution and longitudinal emittance growth after the splitting\nare acceptable, e.g., a few percent in the population deviation and less than\n10% in the total emittance growth.\n"}
+{"abstract": "  We construct a random unitary Gaussian circuit for continuous-variable (CV)\nsystems subject to Gaussian measurements. We show that when the measurement\nrate is nonzero, the steady state entanglement entropy saturates to an area-law\nscaling. This is different from a many-body qubit system, where a generic\nentanglement transition is widely expected. Due to the unbounded local Hilbert\nspace, the time scale to destroy entanglement is always much shorter than the\none to build it, while a balance could be achieved for a finite local Hilbert\nspace. By the same reasoning, the absence of transition should also hold for\nother non-unitary Gaussian CV dynamics.\n"}
+{"abstract": "  Here we report a record thermoelectric power factor of up to 160 $\\mu$ W m-1\nK-2 for the conjugated polymer poly(3-hexylthiophene) (P3HT). This result is\nachieved through the combination of high-temperature rubbing of thin films\ntogether with the use of a large molybdenum dithiolene p-dopant with a high\nelectron affinity. Comparison of the UV-vis-NIR spectra of the chemically doped\nsamples to electrochemically oxidized material reveals an oxidation level of\n10%, i.e. one polaron for every 10 repeat units. The high power factor arises\ndue to an increase in the charge-carrier mobility and hence electrical\nconductivity along the rubbing direction. We conclude that P3HT, with its\nfacile synthesis and outstanding processability, should not be ruled out as a\npotential thermoelectric material.\n"}
+{"abstract": "  The Python package ComCH is a lightweight specialized computer algebra system\nthat provides models for well known objects, the surjection and Barratt-Eccles\noperads, parameterizing the product structure of algebras that are commutative\nin a derived sense. The primary examples of such algebras treated by ComCH are\nthe cochain complexes of spaces, for which it provides effective constructions\nof Steenrod cohomology operations at all prime.\n"}
+{"abstract": "  By probing the population of binary black hole (BBH) mergers detected by\nLIGO-Virgo, we can infer properties about the underlying black hole formation\nchannels. A mechanism known as pair-instability (PI) supernova is expected to\nprevent the formation of black holes from stellar collapse with mass greater\nthan $\\sim 40-65\\,M_\\odot$ and less than $\\sim 120\\,M_\\odot$. Any BBH merger\ndetected by LIGO-Virgo with a component black hole in this gap, known as the PI\nmass gap, likely originated from an alternative formation channel. Here, we\nfirmly establish GW190521 as an outlier to the stellar-mass BBH population if\nthe PI mass gap begins at or below $65\\, M_{\\odot}$. In addition, for a PI\nlower boundary of $40-50\\, M_{\\odot}$, we find it unlikely that the remaining\ndistribution of detected BBH events, excluding GW190521, is consistent with the\nstellar-mass population.\n"}
+{"abstract": "  In this paper, we propose Zero Aware Configurable Data Encoding by Skipping\nTransfer (ZAC-DEST), a data encoding scheme to reduce the energy consumption of\nDRAM channels, specifically targeted towards approximate computing and error\nresilient applications. ZAC-DEST exploits the similarity between recent data\ntransfers across channels and information about the error resilience behavior\nof applications to reduce on-die termination and switching energy by reducing\nthe number of 1's transmitted over the channels. ZAC-DEST also provides a\nnumber of knobs for trading off the application's accuracy for energy savings,\nand vice versa, and can be applied to both training and inference.\n  We apply ZAC-DEST to five machine learning applications. On average, across\nall applications and configurations, we observed a reduction of $40$% in\ntermination energy and $37$% in switching energy as compared to the state of\nthe art data encoding technique BD-Coder with an average output quality loss of\n$10$%. We show that if both training and testing are done assuming the presence\nof ZAC-DEST, the output quality of the applications can be improved upto 9\ntimes as compared to when ZAC-DEST is only applied during testing leading to\nenergy savings during training and inference with increased output quality.\n"}
+{"abstract": "  We study two well-known $SU(N)$ chiral gauge theories with fermions in the\nsymmetric, anti-symmetric and fundamental representations. We give a detailed\ndescription of the global symmetry, including various discrete quotients.\nRecent work argues that these theories exhibit a subtle mod 2 anomaly, ruling\nout certain phases in which the theories confine without breaking their global\nsymmetry, leaving a gapless composite fermion in the infra-red. We point out\nthat no such anomaly exists. We further exhibit an explicit path to the gapless\nfermion phase, showing that there is no kinematic obstruction to realising\nthese phases.\n"}
+{"abstract": "  The nontrivial topology of spin systems such as skyrmions in real space can\npromote complex electronic states. Here, we provide a general viewpoint at the\nemergence of topological electronic states in spin systems based on the methods\nof noncommutative K-theory. By realizing that the structure of the observable\nalgebra of spin textures is determined by the algebraic properties of the\nnoncommutative hypertorus, we arrive at a unified understanding of topological\nelectronic states which we predict to arise in various noncollinear setups. The\npower of our approach lies in an ability to categorize emergent topological\nstates algebraically without referring to smooth real- or reciprocal-space\nquantities. This opens a way towards an educated design of topological phases\nin aperiodic, disordered, or non-smooth textures of spins and charges\ncontaining topological defects.\n"}
+{"abstract": "  Pairwise alignment of DNA sequencing data is a ubiquitous task in\nbioinformatics and typically represents a heavy computational burden. A\nstandard approach to speed up this task is to compute \"sketches\" of the DNA\nreads (typically via hashing-based techniques) that allow the efficient\ncomputation of pairwise alignment scores. We propose a rate-distortion\nframework to study the problem of computing sketches that achieve the optimal\ntradeoff between sketch size and alignment estimation distortion. We consider\nthe simple setting of i.i.d. error-free sources of length $n$ and introduce a\nnew sketching algorithm called \"locational hashing.\" While standard approaches\nin the literature based on min-hashes require $B = (1/D) \\cdot O\\left( \\log n\n\\right)$ bits to achieve a distortion $D$, our proposed approach only requires\n$B = \\log^2(1/D) \\cdot O(1)$ bits. This can lead to significant computational\nsavings in pairwise alignment estimation.\n"}
+{"abstract": "  In this article we propose a novel method to estimate the frequency\ndistribution of linguistic variables while controlling for statistical\nnon-independence due to shared ancestry. Unlike previous approaches, our\ntechnique uses all available data, from language families large and small as\nwell as from isolates, while controlling for different degrees of relatedness\non a continuous scale estimated from the data. Our approach involves three\nsteps: First, distributions of phylogenies are inferred from lexical data.\nSecond, these phylogenies are used as part of a statistical model to\nstatistically estimate transition rates between parameter states. Finally, the\nlong-term equilibrium of the resulting Markov process is computed. As a case\nstudy, we investigate a series of potential word-order correlations across the\nlanguages of the world.\n"}
+{"abstract": "  In a previous work by the author it was shown that every finite dimensional\nalgebraic structure over an algebraically closed field of characteristic zero K\ngives rise to a character $K[X]_{aug}\\to K$, where $K[X]_aug$ is a commutative\nHopf algebra that encodes scalar invariants of structures. This enabled us to\nthink of some characters $K[X]_{aug}\\to K$ as algebraic structures with closed\norbit. In this paper we study structures in general symmetric monoidal\ncategories, and not only in $Vec_K$. We show that every character $\\chi :\nK[X]_{aug}\\to K$ arises from such a structure, by constructing a category\n$C_{\\chi}$ that is analogous to the universal construction from TQFT. We then\ngive necessary and sufficient conditions for a given character to arise from a\nstructure in an abelian category with finite dimensional hom-spaces. We call\nsuch characters good characters. We show that if $\\chi$ is good then $C_{\\chi}$\nis abelian and semisimple, and that the set of good characters forms a\nK-algebra. This gives us a way to interpolate algebraic structures, and also\nsymmetric monoidal categories, in a way that generalizes Deligne's categories\n$Rep(S_t)$, $Rep(GL_t(K))$, $Rep(O_t)$, and also some of the symmetric monoidal\ncategories introduced by Knop. We also explain how one can recover the recent\nconstruction of 2 dimensional TQFT of Khovanov, Ostrik, and Kononov, by the\nmethods presented here. We give new examples, of interpolations of the\ncategories $Rep(Aut_{O}(M))$ where $O$ is a discrete valuation ring with a\nfinite residue field, and M is a finite module over it. We also generalize the\nconstruction of wreath products with $S_t$, which was introduced by Knop.\n"}
+{"abstract": "  We explore the connections between Green's functions for certain differential\nequations, covariance functions for Gaussian processes, and the smoothing\nsplines problem. Conventionally, the smoothing spline problem is considered in\na setting of reproducing kernel Hilbert spaces, but here we present a more\ndirect approach. With this approach, some choices that are implicit in the\nreproducing kernel Hilbert space setting stand out, one example being choice of\nboundary conditions and more elaborate shape restrictions.\n  The paper first explores the Laplace operator and the Poisson equation and\nstudies the corresponding Green's functions under various boundary conditions\nand constraints. Explicit functional forms are derived in a range of examples.\nThese examples include several novel forms of the Green's function that, to the\nauthor's knowledge, have not previously been presented. Next we present a\nsmoothing spline problem where we penalize the integrated squared derivative of\nthe function to be estimated. We then show how the solution can be explicitly\ncomputed using the Green's function for the Laplace operator. In the last part\nof the paper, we explore the connection between Gaussian processes and\ndifferential equations, and show how the Laplace operator is related to\nBrownian processes and how processes that arise due to boundary conditions and\nshape constraints can be viewed as conditional Gaussian processes. The\npresented connection between Green's functions for the Laplace operator and\ncovariance functions for Brownian processes allows us to introduce several new\nnovel Brownian processes with specific behaviors. Finally, we consider the\nconnection between Gaussian process priors and smoothing splines.\n"}
+{"abstract": "  In this paper, we study Toeplitz algebras generated by certain class of\nToeplitz operators on the $p$-Fock space and the $p$-Bergman space with\n$1<p<\\infty$. Let BUC($\\mathbb C^n$) and BUC($\\mathbb B_n$) denote the\ncollections of bounded uniformly continuous functions on $\\mathbb C^n$ and\n$\\mathbb B_n$ (the unit ball in $\\mathbb C^n$), respectively. On the $p$-Fock\nspace, we show that the Toeplitz algebra which has a translation invariant\nclosed subalgebra of BUC($\\mathbb C^n$) as its set of symbols is linearly\ngenerated by Toeplitz operators with the same space of symbols. This answers a\nquestion recently posed by Fulsche \\cite{Robert}. On the $p$-Bergman space, we\nstudy Toeplitz algebras with symbols in some translation invariant closed\nsubalgebras of BUC($\\mathbb B_n)$. In particular, we obtain that the Toeplitz\nalgebra generated by all Toeplitz operators with symbols in BUC($\\mathbb B_n$)\nis equal to the closed linear space generated by Toeplitz operators with such\nsymbols. This generalizes the corresponding result for the case of $p=2$\nobtained by Xia \\cite{Xia2015}.\n"}
+{"abstract": "  We introduce Curvy-an interactive design tool to generate varying density\nsupport structures for 3D printing. Support structures are essential for\nprinting models with extreme overhangs. Yet, they often cause defects on\ncontact areas, resulting in poor surface quality. Low-level design of support\nstructures may alleviate such negative effects. However, it is tedious and\nunintuitive for novice users as it is hard to predict the impact of changes to\nthe support structure on the final printed part. Curvy allows users to define\ntheir high-level preferences on the surface quality directly on the target\nobject rather than explicitly designing the supports. These preferences are\nthen automatically translated into low-level design parameters to generate the\nsupport structure. Underlying novel curvy zigzag toolpathing algorithm uses\nthese instructions to generate varying density supports by altering the spacing\nbetween individual paths in order to achieve prescribed quality. Combined with\nthe build orientation optimization, Curvy provides a practical solution to the\ndesign of support structures with minimal perceptual or functional impact on\nthe target part to be printed.\n"}
+{"abstract": "  Brans-Dicke theory contains an additional propagating mode which causes\nhomogeneous expansion and contraction of test bodies in transverse directions.\nThis \"breathing\" mode is associated with novel memory effects in addition to\nthose of general relativity. Standard tensor mode memories are related to a\nsymmetry principle: they are determined by the balance equations corresponding\nto the BMS symmetries. In this paper, we show that the leading and subleading\nbreathing memory effects are determined by the balance equations associated\nwith the leading and \"overleading\" asymptotic symmetries of a dual formulation\nof the scalar field in terms of a two-form gauge field. The memory effect\ncauses a transition in the vacuum of the dual gauge theory. These results\nhighlight the significance of dual charges and the physical role of overleading\nasymptotic symmetries.\n"}
+{"abstract": "  We give a simple formula for some determinants, and an analogous formula for\npfaffians, both of which are polynomial identities. The second involve some\nexpressions that interpolate between determinants and pfaffians. We give\nseveral tableau formulas for difference operators in Types A and C, as well as\nother operators that appear in the \"enriched\", or \"back-stable\" Schubert\npolynomials. There are also tableau formulas for the enriched Schubert\npolynomials for vexillary and $321$-avoiding permutations.\n"}
+{"abstract": "  There is one-to-one correspondence between quadratic operators (mapping\n$\\mathbb R^m$ to itself) and cubic matrices. It is known that any quadratic\noperator corresponding to a stochastic (in a fixed sense) cubic matrix\npreserves the standard simplex. In this paper we find conditions on the\n(non-stochastic) cubic matrix ensuring that corresponding quadratic operator\npreserves simplex. Moreover, we construct several quadratic non-stochastic\noperators which generate chaotic dynamical systems on the simplex. These\nchaotic behaviors are \\emph{splitted} meaning that the simplex is partitioned\ninto uncountably many invariant (with respect to quadratic operator) subsets\nand the restriction of the dynamical system on each invariant set is chaos in\nthe sense of Devaney.\n"}
+{"abstract": "  We study the Cauchy problem for the defocusing nonlinear Schr\\\"odinger (NLS)\nequation under the assumption that the solution vanishes as $x \\to + \\infty$\nand approaches an oscillatory plane wave as $x \\to -\\infty$. We first develop\nan inverse scattering transform formalism for solutions satisfying such\nstep-like boundary conditions. Using this formalism, we prove that there exists\na global solution of the corresponding Cauchy problem and establish a\nrepresentation for this solution in terms of the solution of a Riemann-Hilbert\nproblem. By performing a steepest descent analysis of this Riemann-Hilbert\nproblem, we identify three asymptotic sectors in the half-plane $t \\geq 0$ of\nthe $xt$-plane and derive asymptotic formulas for the solution in each of these\nsectors. Finally, by restricting the constructed solutions to the half-line $x\n\\geq 0$, we find a class of solutions with asymptotically time-periodic\nboundary values previously sought for in the context of the NLS half-line\nproblem.\n"}
+{"abstract": "  Like other applications in computer vision, medical image segmentation has\nbeen most successfully addressed using deep learning models that rely on the\nconvolution operation as their main building block. Convolutions enjoy\nimportant properties such as sparse interactions, weight sharing, and\ntranslation equivariance. These properties give convolutional neural networks\n(CNNs) a strong and useful inductive bias for vision tasks. In this work we\nshow that a different method, based entirely on self-attention between\nneighboring image patches and without any convolution operations, can achieve\ncompetitive or better results. Given a 3D image block, our network divides it\ninto $n^3$ 3D patches, where $n=3 \\text{ or } 5$ and computes a 1D embedding\nfor each patch. The network predicts the segmentation map for the center patch\nof the block based on the self-attention between these patch embeddings. We\nshow that the proposed model can achieve segmentation accuracies that are\nbetter than the state of the art CNNs on three datasets. We also propose\nmethods for pre-training this model on large corpora of unlabeled images. Our\nexperiments show that with pre-training the advantage of our proposed network\nover CNNs can be significant when labeled training data is small.\n"}
+{"abstract": "  We study maximal length collections of disjoint paths, or `disjoint\noptimizers', in the directed landscape. We show that disjoint optimizers always\nexist, and that their lengths can be used to construct an extended directed\nlandscape. The extended directed landscape can be built from an independent\ncollection of extended Airy sheets, which we define from the Airy line\nensemble. We show that the extended directed landscape and disjoint optimizers\nare scaling limits of the corresponding objects in Brownian last passage\npercolation (LPP). As two consequences of this work, we show that one direction\nof the Robinson-Schensted-Knuth bijection passes to the KPZ limit, and we find\na criterion for geodesic disjointness in the directed landscape that uses only\na single Airy line ensemble.\n  The proofs rely on a new notion of multi-point LPP across the Airy line\nensemble, combinatorial properties of multi-point LPP, and probabilistic\nresampling ideas.\n"}
+{"abstract": "  This paper studies the mixed zero-sum stochastic differential game problem.\nWe allow the functionals and dynamics to be of polynomial growth. The problem\nis formulated as an extended doubly reflected BSDEs with a specific generator.\nWe show the existence of solution for this doubly reflected BSDEs and we prove\nthe existence of a saddle-point of the game. Moreover, in the Markovian\nframework we prove that the value function is the unique viscosity solution of\nthe associated Hamilton-Jacobi-Bellman equation.\n"}
+{"abstract": "  We consider the energy complexity of the leader election problem in the\nsingle-hop radio network model, where each device has a unique identifier in\n$\\{1, 2, \\ldots, N\\}$. Energy is a scarce resource for small battery-powered\ndevices. For such devices, most of the energy is often spent on communication,\nnot on computation. To approximate the actual energy cost, the energy\ncomplexity of an algorithm is defined as the maximum over all devices of the\nnumber of time slots where the device transmits or listens. Much progress has\nbeen made in understanding the energy complexity of leader election in radio\nnetworks, but very little is known about the trade-off between time and energy.\n  $\\textbf{Time-energy trade-off:}$ For any $k \\geq \\log \\log N$, we show that\na leader among at most $n$ devices can be elected deterministically in $O(k\n\\cdot n^{1+\\epsilon}) + O(k \\cdot N^{1/k})$ time and $O(k)$ energy if each\ndevice can simultaneously transmit and listen, where $\\epsilon > 0$ is any\nsmall constant. This improves upon the previous $O(N)$-time $O(\\log \\log\nN)$-energy algorithm by Chang et al. [STOC 2017]. We provide lower bounds to\nshow that the time-energy trade-off of our algorithm is near-optimal.\n  $\\textbf{Dense instances:}$ For the dense instances where the number of\ndevices is $n = \\Theta(N)$, we design a deterministic leader election algorithm\nusing only $O(1)$ energy. This improves upon the $O(\\log^* N)$-energy algorithm\nby Jurdzi\\'{n}ski et al. [PODC 2002] and the $O(\\alpha(N))$-energy algorithm by\nChang et al. [STOC 2017]. More specifically, we show that the optimal\ndeterministic energy complexity of leader election is\n$\\Theta\\left(\\max\\left\\{1, \\log \\frac{N}{n}\\right\\}\\right)$ if the devices\ncannot simultaneously transmit and listen, and it is $\\Theta\\left(\\max\\left\\{1,\n\\log \\log \\frac{N}{n}\\right\\}\\right)$ if they can.\n"}
+{"abstract": "  The carbon pump of the world's ocean plays a vital role in the biosphere and\nclimate of the earth, urging improved understanding of the functions and\ninfluences of the ocean for climate change analyses. State-of-the-art\ntechniques are required to develop models that can capture the complexity of\nocean currents and temperature flows. This work explores the benefits of using\nphysics-informed neural networks (PINNs) for solving partial differential\nequations related to ocean modeling; such as the Burgers, wave, and\nadvection-diffusion equations. We explore the trade-offs of using data vs.\nphysical models in PINNs for solving partial differential equations. PINNs\naccount for the deviation from physical laws in order to improve learning and\ngeneralization. We observed how the relative weight between the data and\nphysical model in the loss function influence training results, where small\ndata sets benefit more from the added physics information.\n"}
+{"abstract": "  We study (2,2s+1) RSOS lattice models deformed by the current-current\noperator. Solving the deformed Bethe ansatz equations for the model in the\nregime III we find explicit expressions for the ground state energy as well as\nfor the energy, the momentum and the scattering matrix of the breather-like\nexcitations. In the scaling limit we get an additional confirmation to the\nproposal that the bi-local deformations are a lattice analogue of the TT\nperturbations of integrable massive QFTs proposed by Smirnov and Zamolodchikov.\n"}
+{"abstract": "  Multi-step manipulation tasks in unstructured environments are extremely\nchallenging for a robot to learn. Such tasks interlace high-level reasoning\nthat consists of the expected states that can be attained to achieve an overall\ntask and low-level reasoning that decides what actions will yield these states.\nWe propose a model-free deep reinforcement learning method to learn multi-step\nmanipulation tasks. We introduce a Robotic Manipulation Network (RoManNet),\nwhich is a vision-based model architecture, to learn the action-value functions\nand predict manipulation action candidates. We define a Task Progress based\nGaussian (TPG) reward function that computes the reward based on actions that\nlead to successful motion primitives and progress towards the overall task\ngoal. To balance the ratio of exploration/exploitation, we introduce a Loss\nAdjusted Exploration (LAE) policy that determines actions from the action\ncandidates according to the Boltzmann distribution of loss estimates. We\ndemonstrate the effectiveness of our approach by training RoManNet to learn\nseveral challenging multi-step robotic manipulation tasks in both simulation\nand real-world. Experimental results show that our method outperforms the\nexisting methods and achieves state-of-the-art performance in terms of success\nrate and action efficiency. The ablation studies show that TPG and LAE are\nespecially beneficial for tasks like multiple block stacking. Code is available\nat: https://github.com/skumra/romannet\n"}
+{"abstract": "  This paper deals with the identification of machines in a smart city\nenvironment. The concept of machine biometrics is proposed in this work for the\nfirst time, as a way to authenticate machine identities interacting with humans\nin everyday life. This definition is imposed in modern years where autonomous\nvehicles, social robots, etc. are considered active members of contemporary\nsocieties. In this context, the case of car identification from the engine\nbehavioral biometrics is examined. For this purpose, 22 sound features were\nextracted and their discrimination capabilities were tested in combination with\n9 different machine learning classifiers, towards identifying 5 car\nmanufacturers. The experimental results revealed the ability of the proposed\nbiometrics to identify cars with high accuracy up to 98% for the case of the\nMultilayer Perceptron (MLP) neural network model.\n"}
+{"abstract": "  Multiparty computation is raising importance because it's primary objective\nis to replace any trusted third party in the distributed computation. This work\npresents two multiparty shuffling protocols where each party, possesses a\nprivate input, agrees on a random permutation while keeping the permutation\nsecret. The proposed shuffling protocols are based on permutation network,\nthereby data-oblivious. The first proposal is $n\\text{-}permute$ that permutes\n$n$ inputs in all $n!$ possible ways. $n$-permute network consists of\n$2\\log{n}-1$ layers, and in each layer there are $n/2$ gates. Our second\nprotocol is $n_{\\pi}$-permute shuffling that defines a permutation set\n$\\Pi=\\{\\pi_1,\\dots,\\pi_N\\}$ where $|\\Pi| < n!$, and the resultant shuffling is\na random permutation $\\pi_i \\in \\Pi$. The $n_{\\pi}$-permute network contains\nleases number of layers compare to $n$-permute network. Let $n=n_1n_2$, the\n$n_{\\pi}$-permute network would define $2\\log{n_1}-1+\\log{n_2}$ layers. \\par\nThe proposed shuffling protocols are unconditionally secure against malicious\nadversary who can corrupt at most $t<n/3$ parties. The probability that\nadversary can learn the outcome of $n$-permute is upper bound by\n$((n-t)!)^{-1}$. Whereas, the probability that adversary can learn the outcome\nof $n_{\\pi}$-permute is upper bounded by\n$\\big(f_{\\Pi}(n_1-\\theta_1)^{n_2}2^{\\theta_2}\\big)^{-1}$, for some positive\ninteger $\\theta_1, \\theta_2$, and a recursive definition of $f_{\\Pi}$. The\nprotocols allow the parties to build quorums, and distribute the load among the\nquorums.\n"}
+{"abstract": "  While an integration by parts formula for the bilinear form of the\nhypersingular boundary integral operator for the transient heat equation in\nthree spatial dimensions is available in the literature, a proof of this\nformula seems to be missing. Moreover, the available formula contains an\nintegral term including the time derivative of the fundamental solution of the\nheat equation, whose interpretation is difficult at second glance. To fill\nthese gaps we provide a rigorous proof of a general version of the integration\nby parts formula and an alternative representation of the mentioned integral\nterm, which is valid for a certain class of functions including the typical\ntensor-product discretization spaces.\n"}
+{"abstract": "  Throughput-Outage scaling laws for single-hop cache-aided device-to-device\n(D2D) communications have been extensively investigated under the assumption of\nthe protocol model. However, the corresponding performance under physical\nmodels has not been explored; in particular it remains unclear whether\nlink-level power control and scheduling can improve the asymptotic performance.\nThis paper thus investigates the throughput-outage scaling laws of cache-aided\nsingle-hop D2D networks considering a general physical channel model. By\nconsidering the networks with and without the equal-throughput assumption, we\nanalyze the corresponding outer bounds and provide the achievable performance\nanalysis. Results show that when the equal-throughput assumption is considered,\nusing link-level power control and scheduling cannot improve the scaling laws.\nOn the other hand, when the equal-throughput assumption is not considered, we\nshow that the proposed double time-slot framework with appropriate link-level\npower control and scheduling can significantly improve the throughput-outage\nscaling laws, where the fundamental concept is to first distinguish links\naccording to their communication distances, and then enhance the throughput for\nlinks with small communication distances.\n"}
+{"abstract": "  Beryllium polynitrides, BeN4 is a novel layered material, which has been most\nrecently fabricated under high pressure (Phys. Rev. Lett. 126(2021), 175501).\nAs a new class of 2D materials, in this work we conduct first-principles\ncalculations to examine the stability and explore the electronic nature of MN4\n(M= Be, Mg, Ir, Rh, Ni, Cu, Au, Pd, Pt) monolayers. Acquired results confirm\nthe dynamical and thermal stability of BeN4, MgN4, IrN4, PtN4 and RhN4\nmonolayers. Interestingly, BeN4 and MgN4 monolayers are found to show\nanisotropic Dirac cones in their electronic structure. While PtN4 monolayer is\npredicted to be a narrow band-gap semiconductor, IrN4 and RhN4 monolayers are\nfound to be metallic systems. We also elaborately explore the effects of number\nof atomic layers on the electronic features of BeN4 nanosheets, which reveal\nhighly appealing physics. Our results highlight that BeN4 nanosheet yield\nultrahigh elastic modulus and mechanical strength, outperforming all other\ncarbon-free 2D materials. Notably, RhN4 nanosheet is predicted to yield high\ncapacities of 562, 450 and 900 mAh/g, for Li, Na and Ca ions storages,\nrespectively. This study provides a comprehensive understanding on the\nintrinsic properties of MN4 nanosheets and highlight their outstanding physics.\n"}
+{"abstract": "  Semiconductor-superconductor hybrids are commonly used in research on\ntopological quantum computation. Traditionally, top-down approaches involving\ndry or wet etching are used to define the device geometry. These often\naggressive processes risk causing damage to material surfaces, giving rise to\nscattering sites particularly problematic for quantum applications. Here, we\npropose a method that maintains the flexibility and scalability of selective\narea grown nanowire networks while omitting the necessity of etching to create\nhybrid segments. Instead, it takes advantage of directional growth methods and\nuses bottom-up grown InP structures as shadowing objects to obtain selective\nmetal deposition. The ability to lithographically define the position and area\nof these objects, and to grow a predefined height, ensures precise control of\nthe shadowed region. We demonstrate the approach by growing InSb nanowire\nnetworks with well-defined Al and Pb islands. Cross-section cuts of the\nnanowires reveal a sharp, oxide-free interface between semiconductor and\nsuperconductor. By growing InP structures on both sides of in-plane nanowires,\na combination of Pt and Pb can independently be shadow deposited, enabling a\nscalable and reproducible in-situ device fabrication. The\nsemiconductor-superconductor nanostructures resulting from this approach are at\nthe forefront of material development for Majorana based experiments.\n"}
+{"abstract": "  Social media radically changed how information is consumed and reported.\nMoreover, social networks elicited a disintermediated access to an\nunprecedented amount of content. The world health organization (WHO) coined the\nterm infodemics to identify the information overabundance during an epidemic.\nIndeed, the spread of inaccurate and misleading information may alter behaviors\nand complicate crisis management and health responses. This paper addresses\ninformation diffusion during the COVID-19 pandemic period with a massive data\nanalysis on YouTube. First, we analyze more than 2M users' engagement in 13000\nvideos released by 68 different YouTube channels, with different political bias\nand fact-checking indexes. We then investigate the relationship between each\nuser's political preference and her/his consumption of questionable/reliable\ninformation. Our results, quantified using information theory measures, provide\nevidence for the existence of echo chambers across two dimensions represented\nby the political bias and by the trustworthiness of information channels.\nFinally, we observe that the echo chamber structure cannot be reproduced after\nproperly randomizing the users' interaction patterns.\n"}
+{"abstract": "  Ni--Ga bilayers are a versatile platform for exploring the competition\nbetween strongly antagonistic ferromagnetic and superconducting phases. We\ncharacterize the impact of this competition on the transport properties of\nhighly-ballistic Al/Al$ _2 $O$ _3 $(/EuS)/Ni--Ga tunnel junctions from both\nexperimental and theoretical points of view. While the conductance spectra of\njunctions comprising Ni (3 nm)--Ga (60 nm) bilayers can be well understood\nwithin the framework of earlier results, which associate the emerging main\nconductance maxima with the junction films' superconducting gaps, thinner Ni\n(1.6 nm)--Ga (30 nm) bilayers entail completely different physics, giving rise\nto novel conductance-peak subseries that we term conductance shoulders.\nDetecting the paramagnetic Meissner response in Ga from polarized neutron\nreflectometry provides the essential experimental hint that these conductance\nshoulders are caused by superconducting triplet pairings that Ni's\nferromagnetic exchange interaction induces near thin Ni--Ga bilayers'\ninterfaces -- most likely owing to inhomogeneously magnetized interface\ndomains. We further substantiate our findings by means of a phenomenological\ntheoretical model, clarifying that induced superconducting triplet pairings\naround the interface of Ni--Ga bilayers can indeed manifest themselves in the\nobserved conductance shoulders. Arranging our work in a broader context\ndemonstrates that Ni--Ga-bilayer junctions have a strong potential for\nefficient triplet-pairing engineering in superconducting-spintronics\napplications.\n"}
+{"abstract": "  When using R package tmbstan for Bayesian inference, the built-in feature\nLaplace approximation to the marginal likelihood with random effects integrated\nout can be switched on and off. There exists no guideline on whether Laplace\napproximation should be used to achieve better efficiency especially when the\nstatistical model for estimating selection is complicated. To answer this\nquestion, we conducted simulation studies under different scenarios with a\nstate-space model employing a VAR(1) state equation. We found that turning on\nLaplace approximation in tmbstan would probably lower the computational\nefficiency, and only when there is a good amount of data, both tmbstan with and\nwithout Laplace approximation are worth trying since in this case, Laplace\napproximation is more likely to be accurate and may also lead to slightly\nhigher computational efficiency. The transition parameters and scale parameters\nin a VAR(1) process are hard to be estimated accurately and increasing the\nsample size at each time point do not help in the estimation, only more time\npoints in the data contain more information on these parameters and make the\nlikelihood dominate the posterior likelihood, thus lead to accurate estimates\nfor them.\n"}
+{"abstract": "  Our first result provides a new characterization of Auslander algebras using\na property of hereditary torsion pairs. The second result shows an Auslander\nalgebra $\\Lambda$ is left or right glued if and only if $\\Lambda$ is\nrepresentation-finite. Finally, our third result shows the module category of\nany Auslander algebra contains a tilting module with a particular property,\nwhich we call the hereditary property. Applications of this property are\ninvestigated.\n"}
+{"abstract": "  Automated machine learning (AutoML) aims for constructing machine learning\n(ML) pipelines automatically. Many studies have investigated efficient methods\nfor algorithm selection and hyperparameter optimization. However, methods for\nML pipeline synthesis and optimization considering the impact of complex\npipeline structures containing multiple preprocessing and classification\nalgorithms have not been studied thoroughly. In this paper, we propose a\ndata-centric approach based on meta-features for pipeline construction and\nhyperparameter optimization inspired by human behavior. By expanding the\npipeline search space incrementally in combination with meta-features of\nintermediate data sets, we are able to prune the pipeline structure search\nspace efficiently. Consequently, flexible and data set specific ML pipelines\ncan be constructed. We prove the effectiveness and competitiveness of our\napproach on 28 data sets used in well-established AutoML benchmarks in\ncomparison with state-of-the-art AutoML frameworks.\n"}
+{"abstract": "  This paper studies computational methods for quasi-stationary distributions\n(QSDs). We first proposed a data-driven solver that solves Fokker-Planck\nequations for QSDs. Similar as the case of Fokker-Planck equations for\ninvariant probability measures, we set up an optimization problem that\nminimizes the distance from a low-accuracy reference solution, under the\nconstraint of satisfying the linear relation given by the discretized\nFokker-Planck operator. Then we use coupling method to study the sensitivity of\na QSD against either the change of boundary condition or the diffusion\ncoefficient. The 1-Wasserstein distance between a QSD and the corresponding\ninvariant probability measure can be quantitatively estimated. Some numerical\nresults about both computation of QSDs and their sensitivity analysis are\nprovided.\n"}
+{"abstract": "  We formulate pure characteristics demand models under uncertainties of\nprobability distributions as distributionally robust mathematical programs with\nstochastic complementarity constraints (DRMP-SCC). For any fixed first-stage\nvariable and a random realization, the second-stage problem of DRMP-SCC is a\nmonotone linear complementarity problem (LCP). To deal with uncertainties of\nprobability distributions of the involved random variables in the stochastic\nLCP, we use the distributionally robust approach. Moreover, we propose an\napproximation problem with regularization and discretization to solve DRMP-SCC,\nwhich is a two-stage nonconvex-nonconcave minimax optimization problem. We\nprove the convergence of the approximation problem to DRMP-SCC regarding the\noptimal solution sets, optimal values and stationary points as the\nregularization parameter goes to zero and the sample size goes to infinity.\nFinally, preliminary numerical results for investigating distributional\nrobustness of pure characteristics demand models are reported to illustrate the\neffectiveness and efficiency of our approaches.\n"}
+{"abstract": "  We explore the connection between the kinematics, structures and stellar\npopulations of massive galaxies at $0.6<z<1.0$ using the Fundamental Plane\n(FP). Combining stellar kinematic data from the Large Early Galaxy Astrophysics\nCensus (LEGA-C) survey with structural parameters measured from deep Hubble\nSpace Telescope imaging, we obtain a sample of 1419 massive ($\\log(M_*/M_\\odot)\n>10.5$) galaxies that span a wide range in morphology, star formation activity\nand environment, and therefore is representative of the massive galaxy\npopulation at $z\\sim0.8$. We find that quiescent and star-forming galaxies\noccupy the parameter space of the $g$-band FP differently and thus have\ndifferent distributions in the dynamical mass-to-light ratio ($M_{\\rm\ndyn}/L_g$), largely owing to differences in the stellar age and recent star\nformation history, and, to a lesser extent, the effects of dust attenuation. In\ncontrast, we show that both star-forming and quiescent galaxies lie on the same\nmass FP at $z\\sim 0.8$, with a comparable level of intrinsic scatter about the\nplane. We examine the variation in $M_{\\rm dyn}/M_*$ through the thickness of\nthe mass FP, finding no significant residual correlations with stellar\npopulation properties, S\\'ersic index, or galaxy overdensity. Our results\nsuggest that, at fixed size and velocity dispersion, the variations in $M_{\\rm\ndyn}/L_g$ of massive galaxies reflect an approximately equal contribution of\nvariations in $M_*/L_g$, and variations in the dark matter fraction or initial\nmass function.\n"}
+{"abstract": "  Series of distributions indexed by equally spaced time points are ubiquitous\nin applications and their analysis constitutes one of the challenges of the\nemerging field of distributional data analysis. To quantify such distributional\ntime series, we propose a class of intrinsic autoregressive models that operate\nin the space of optimal transport maps. The autoregressive transport models\nthat we introduce here are based on regressing optimal transport maps on each\nother, where predictors can be transport maps from an overall barycenter to a\ncurrent distribution or transport maps between past consecutive distributions\nof the distributional time series. Autoregressive transport models and\nassociated distributional regression models specify the link between predictor\nand response transport maps by moving along geodesics in Wasserstein space.\nThese models emerge as natural extensions of the classical autoregressive\nmodels in Euclidean space. Unique stationary solutions of autoregressive\ntransport models are shown to exist under a geometric moment contraction\ncondition of Wu and Shao (2004), using properties of iterated random functions.\nWe also discuss an extension to a varying coefficient coefficient for first\norder autoregressive transport models. In addition to simulations, the proposed\nmodels are illustrated with distributional time series of house prices across\nU.S. counties and of stock returns across the S&P 500 stock index.\n"}
+{"abstract": "  Pedestrian trajectory prediction for surveillance video is one of the\nimportant research topics in the field of computer vision and a key technology\nof intelligent surveillance systems. Social relationship among pedestrians is a\nkey factor influencing pedestrian walking patterns but was mostly ignored in\nthe literature. Pedestrians with different social relationships play different\nroles in the motion decision of target pedestrian. Motivated by this idea, we\npropose a Social Relationship Attention LSTM (SRA-LSTM) model to predict future\ntrajectories. We design a social relationship encoder to obtain the\nrepresentation of their social relationship through the relative position\nbetween each pair of pedestrians. Afterwards, the social relationship feature\nand latent movements are adopted to acquire the social relationship attention\nof this pair of pedestrians. Social interaction modeling is achieved by\nutilizing social relationship attention to aggregate movement information from\nneighbor pedestrians. Experimental results on two public walking pedestrian\nvideo datasets (ETH and UCY), our model achieves superior performance compared\nwith state-of-the-art methods. Contrast experiments with other attention\nmethods also demonstrate the effectiveness of social relationship attention.\n"}
+{"abstract": "  Adversarial training tends to result in models that are less accurate on\nnatural (unperturbed) examples compared to standard models. This can be\nattributed to either an algorithmic shortcoming or a fundamental property of\nthe training data distribution, which admits different solutions for optimal\nstandard and adversarial classifiers. In this work, we focus on the latter case\nunder a binary Gaussian mixture classification problem. Unlike earlier work, we\naim to derive the natural accuracy gap between the optimal Bayes and\nadversarial classifiers, and study the effect of different distributional\nparameters, namely separation between class centroids, class proportions, and\nthe covariance matrix, on the derived gap. We show that under certain\nconditions, the natural error of the optimal adversarial classifier, as well as\nthe gap, are locally minimized when classes are balanced, contradicting the\nperformance of the Bayes classifier where perfect balance induces the worst\naccuracy. Moreover, we show that with an $\\ell_\\infty$ bounded perturbation and\nan adversarial budget of $\\epsilon$, this gap is $\\Theta(\\epsilon^2)$ for the\nworst-case parameters, which for suitably small $\\epsilon$ indicates the\ntheoretical possibility of achieving robust classifiers with near-perfect\naccuracy, which is rarely reflected in practical algorithms.\n"}
+{"abstract": "  Differential privacy (DP) provides a robust model to achieve privacy\nguarantees for released information. We examine the protection potency of\nsanitized multi-dimensional frequency distributions via DP randomization\nmechanisms against homogeneity attack (HA). HA allows adversaries to obtain the\nexact values on sensitive attributes for their targets without having to\nidentify them from the released data. We propose measures for disclosure risk\nfrom HA and derive closed-form relationships between the privacy loss\nparameters in DP and the disclosure risk from HA. The availability of the\nclosed-form relationships assists understanding the abstract concepts of DP and\nprivacy loss parameters by putting them in the context of a concrete privacy\nattack and offers a perspective for choosing privacy loss parameters when\nemploying DP mechanisms in information sanitization and release in practice. We\napply the closed-form mathematical relationships in real-life datasets to\ndemonstrate the assessment of disclosure risk due to HA on differentially\nprivate sanitized frequency distributions at various privacy loss parameters.\n"}
+{"abstract": "  This review presents a concise, yet comprehensive discussion on the evolution\nof theoretical methods employed to determine the ground and excited states of\nmolecules in weak and strong magnetic fields. The weak-field cases have been\nstudied previously in the context of NMR, where the shielding tensor was\ndetermined by correcting the Diamagnetic Shielding operator up to the second\norder. However, the magnetic fields due to the Neutron Stars are extremely high\nand cannot be treated perturbatively. Thus, in the interest of the\nastrophysical and astrochemical community, this review aims to elaborate on the\ncomputational advancements in quantum mechanics from Hartree-Fock (HF) to\nDensity Functional Theory (DFT), in the context of molecules in a high (and\nultrahigh) magnetic field. It is found that the mean-field approximation of\nelectron-electron correlation, as in the case of Hartree-Fock Theory, yields\ninaccurate results. On the contrary, CCSD and DFT are found to overcome these\nchallenges. However, treating eletron-electron correlations in DFT can be\nchallenging for heavier ions as transition metals. To circumvent this, we\npropose the use of DFT/CCSD along with effective Hamiltonian methods, which are\nlikely to offer physical insights with reasonable accuracy.\n"}
+{"abstract": "  Exponential Puiseux semirings are additive submonoids of $\\qq_{\\geq 0}$\ngenerated by almost all of the nonnegative powers of a positive rational\nnumber, and they are natural generalizations of rational cyclic semirings. In\nthis paper, we investigate some of the factorization invariants of exponential\nPuiseux semirings and briefly explore the connections of these properties with\nsemigroup-theoretical invariants. Specifically, we prove that sets of lengths\nof atomic exponential Puiseux semirings are almost arithmetic progressions with\na common bound, while unions of sets of lengths are arithmetic progressions.\nAdditionally, we provide exact formulas to compute the catenary degrees of\nthese monoids and show that minima and maxima of their sets of distances are\nalways attained at Betti elements. We conclude by providing various\ncharacterizations of the atomic exponential Puiseux semirings with finite omega\nfunctions; in particular, we completely describe them in terms of their\npresentations.\n"}
+{"abstract": "  Kitaev spin liquid (KSL) system has attracted tremendous attention in past\nyears because of its fundamental significance in condensed matter physics and\npromising applications in fault-tolerant topological quantum computation.\nMaterial realization of such a system remains a major challenge in the field\ndue to the unusual configuration of anisotropic spin interactions, though great\neffort has been made before. Here we reveal that rare-earth chalcohalides REChX\n(RE=rare earth, Ch=O, S, Se, Te, X=F, Cl, Br, I) can serve as a family of KSL\ncandidates. Most family members have the typical SmSI-type structure with a\nhigh symmetry of R-3m and rare-earth magnetic ions form an undistorted\nhoneycomb lattice. The strong spin-orbit coupling of 4f electrons intrinsically\noffers anisotropic spin interactions as required by Kitaev model. We have grown\nthe crystals of YbOCl and synthesized the polycrystals of SmSI, ErOF, HoOF and\nDyOF, and made careful structural characterizations. We carry out magnetic and\nheat capacity measurements down to 1.8 K and find no obvious magnetic\ntransition in all the samples but DyOF. The van der Waals interlayer coupling\nhighlights the true two-dimensionality of the family which is vital for the\nexact realization of Abelian/non-Abelian anyons, and the graphene-like feature\nwill be a prominent advantage for developing miniaturized devices. The family\nis expected to act as an inspiring material platform for the exploration of KSL\nphysics.\n"}
+{"abstract": "  Starting from nonequilibrium quantum field theory on a closed time path, we\nderive kinetic equations for the strong-field regime of quantum electrodynamics\n(QED) using a systematic expansion in the gauge coupling $e$. The strong field\nregime is characterized by a large photon field of order $\\mathcal{O}(1/e)$,\nwhich is relevant for the description of, e.g., intense laser fields, the\ninitial stages of off-central heavy ion collisions, and condensed matter\nsystems with net fermion number. The strong field enters the dynamical\nequations via both quantum Vlasov and collision terms, which we derive to order\n$\\mathcal{O}(e^2)$. The kinetic equations feature generalized scattering\namplitudes that have their own equation of motion in terms of the fermion\nspectral function. The description includes single photon emission,\nelectron-positron pair photoproduction, vacuum (Schwinger) pair production,\ntheir inverse processes, medium effects and contributions from the field, which\nare not restricted to the so-called locally-constant crossed field\napproximation. This extends known kinetic equations commonly used in\nstrong-field QED of intense laser fields. In particular, we derive an\nexpression for the asymptotic fermion pair number that includes leading-order\ncollisions and remains valid for strongly inhomogeneous fields. For the purpose\nof analytically highlighting limiting cases, we also consider plane-wave fields\nfor which it is shown how to recover Furry-picture scattering amplitudes by\nfurther assuming negligible occupations. Known on-shell descriptions are\nrecovered in the case of simply peaked ultrarelativistic fermion occupations.\nCollisional strong-field equations are necessary to describe the dynamics to\nthermal equilibrium starting from strong-field initial conditions.\n"}
+{"abstract": "  We present an explanation system for applications that leverage Answer Set\nProgramming (ASP). Given a program P, an answer set A of P, and an atom a in\nthe program P, our system generates all explanation graphs of a which help\nexplain why a is true (or false) given the program P and the answer set A. We\nillustrate the functionality of the system using some examples from the\nliterature.\n"}
+{"abstract": "  The presence of constant power loads (CPLs) in dc shipboard microgrids may\nlead to unstable conditions. The present work investigates the stability\nproperties of dc microgrids where CPLs are fed by fuel cells (FCs), and energy\nstorage systems (ESSs) equipped with voltage droop control. With respect to the\nprevious literature, the dynamics of the duty cycles of the dc-dc converters\nimplementing the droop regulation are considered. A mathematical model has been\nderived, and tuned to best mimic the behavior of the electrical representation\nimplemented in DIgSILENT. Then the model is used to find the sufficient\nconditions for stability with respect to the droop coefficient, the dc-bus\ncapacitor, and the inductances of the dc-dc converters.\n"}
+{"abstract": "  We embed Nelson's stochastic quantization in the Schwartz-Meyer second order\ngeometry framework. The result is a non-perturbative theory of quantum\nmechanics on (pseudo)-Riemannian manifolds. Within this approach, we derive\nstochastic differential equations for massive spin-0 test particles charged\nunder scalar potentials, vector potentials and gravity. Furthermore, we derive\nthe associated Schr\\\"odinger equation. The resulting equations show that\nmassive scalar particles must be conformally coupled to gravity in a theory of\nquantum gravity. We conclude with a discussion of some prospects of the\nstochastic framework.\n"}
+{"abstract": "  A general form of codebook design for code-domain non-orthogonal multiple\naccess (CD-NOMA) can be considered equivalent to an autoencoder (AE)-based\nconstellation design for multi-user multidimensional modulation (MU-MDM). Due\nto a constrained design space for optimal constellation, e.g., fixed resource\nmapping and equal power allocation to all codebooks, however, existing AE\narchitectures produce constellations with suboptimal bit-error-rate (BER)\nperformance. Accordingly, we propose a new architecture for MU-MDM AE and\nunderlying training methodology for joint optimization of resource mapping and\na constellation design with bit-to-symbol mapping, aiming at approaching the\nBER performance of a single-user MDM (SU-MDM) AE model with the same spectral\nefficiency. The core design of the proposed AE architecture is dense resource\nmapping combined with the novel power allocation layer that normalizes the sum\nof user codebook power across the entire resources. This globalizes the domain\nof the constellation design by enabling flexible resource mapping and power\nallocation. Furthermore, it allows the AE-based training to approach a global\noptimal MU-MDM constellations for CD-NOMA. Extensive BER simulation results\ndemonstrate that the proposed design outperforms the existing CD-NOMA designs\nwhile approaching the single-user BER performance achieved by the equivalent\nSU-MDM AE within 0.3 dB over the additive white Gaussian noise channel.\n"}
+{"abstract": "  We investigate the dynamics of wealth inequality in an economy where\nhouseholds have positional preferences, with the strength of the positional\nconcern determined endogenously by inequality of wealth distribution in the\nsociety. We demonstrate that in the long run such an economy converges to a\nunique egalitarian steady-state equilibrium, with all households holding equal\npositive wealth, when the initial inequality is sufficiently low. Otherwise,\nthe steady state is characterised by polarisation of households into rich, who\nown all the wealth, and poor, whose wealth is zero. A fiscal policy with\ngovernment consumption funded by taxes on labour income and wealth can move the\neconomy from any initial state towards an egalitarian equilibrium with a higher\naggregate wealth.\n"}
+{"abstract": "  Drop condensation and evaportation as a result of the gradient in vapor\nconcentration are important in both engineering and natural systems. One of the\ninteresting natural examples is transpiration on plant leaves. Most of water in\nthe inner space of the leaves escapes through stomata, whose rate depends on\nthe surface topography and a difference in vapor concentrations inside and just\noutside of the leaves. Previous research on the vapor flux on various surfaces\nhas focused on numerically solving the vapor diffusion equation or using\nscaling arguments based on a simple solution with a flat surface. In this\npresent work, we present and discuss simple analytical solutions on various 2D\nsurface shapes (e.g., semicylinder, semi-ellipse, hair). The method of solving\nthe diffusion equation is to use the complex potential theory, which provides\nanalytical solutions for vapor concentration and flux. We find that a high mass\nflux of vapor is formed near the top of the microstructures while a low mass\nflux is developed near the stomata at the leaf surface. Such a low vapor flux\nnear the stomata may affect transpiration in two ways. First, condensed\ndroplets on the stomata will not grow due to a low mass flux of vapor, which\nwill not inhibit the gas exchange through the stomatal opening. Second, the low\nmass flux from the atmosphere will facilitate the release of high concentrated\nvapor from the substomatal space.\n"}
+{"abstract": "  We review methods to shuttle quantum particles fast and robustly. Ideal\nrobustness amounts to the invariance of the desired transport results with\nrespect to deviations, noisy or otherwise, from the nominal driving protocol\nfor the control parameters; this can include environmental perturbations.\n\"Fast\" is defined with respect to adiabatic transport times. Special attention\nis paid to shortcut-to-adiabaticity protocols that achieve, in\nfaster-than-adiabatic times, the same results of slow adiabatic driving.\n"}
+{"abstract": "  Similarity caching systems have recently attracted the attention of the\nscientific community, as they can be profitably used in many application\ncontexts, like multimedia retrieval, advertising, object recognition,\nrecommender systems and online content-match applications. In such systems, a\nuser request for an object $o$, which is not in the cache, can be (partially)\nsatisfied by a similar stored object $o$', at the cost of a loss of user\nutility. In this paper we make a first step into the novel area of similarity\ncaching networks, where requests can be forwarded along a path of caches to get\nthe best efficiency-accuracy tradeoff. The offline problem of content placement\ncan be easily shown to be NP-hard, while different polynomial algorithms can be\ndevised to approach the optimal solution in discrete cases. As the content\nspace grows large, we propose a continuous problem formulation whose solution\nexhibits a simple structure in a class of tree topologies. We verify our\nfindings using synthetic and realistic request traces.\n"}
+{"abstract": "  The breadth of a Lie algebra $L$ is defined to be the maximal dimension of\nthe image of $ad_x=[x,-]:L\\to L$, for $x\\in L$. Here, we initiate an\ninvestigation into the breadth of three families of Lie algebras defined by\nposets and provide combinatorial breadth formulas for members of each family.\n"}
+{"abstract": "  Using a recently developed technique to estimate the equilibrium free energy\nof glassy materials, we explore if equilibrium simulation methods can be used\nto estimate the solubility of amorphous solids. As an illustration, we compute\nthe chemical potentials of the constituent particles of a two-component\nKob-Andersen model glass former. To compute the chemical potential for\ndifferent components, we combine the calculation of the overall free energy of\nthe glass with a calculation of the chemical potential difference of the two\ncomponents. We find that the standard method to compute chemical potential\ndifferences by thermodynamic integration yields not only a wide scatter in the\nchemical potential values but, more seriously, the average of the thermodynamic\nintegration results is well above the extrapolated value for the supercooled\nliquid. However, we find that if we compute the difference of the chemical\npotential of the components with the the non-equilibrium free energy expression\nproposed by Jarzynski, we obtain a good match with the extrapolated value of\nthe supercooled liquid. The extension of the Jarzynski method that we propose\nopens a potentially powerful route to compute free-energy related equilibrium\nproperties of glasses. We find that the solubility estimate of amorphous\nmaterials obtained from direct coexistence simulations is only in fair\nagreement with the solubility prediction based on the chemical potential\ncalculations of a hypothetical \"well-equilibrated glass\". In direct coexistence\nsimulations, we find that, in qualitative agreement with experiments, the\namorphous solubility decreases with time and attains a low solubility value.\n"}
+{"abstract": "  With the rapid development of wireless sensor networks, smart devices, and\ntraditional information and communication technologies, there is tremendous\ngrowth in the use of Internet of Things (IoT) applications and services in our\neveryday life. IoT systems deal with high volumes of data. This data can be\nparticularly sensitive, as it may include health, financial, location, and\nother highly personal information. Fine-grained security management in IoT\ndemands effective access control. Several proposals discuss access control for\nthe IoT, however, a limited focus is given to the emerging blockchain-based\nsolutions for IoT access control. In this paper, we review the recent trends\nand critical needs for blockchain-based solutions for IoT access control. We\nidentify several important aspects of blockchain, including decentralised\ncontrol, secure storage and sharing information in a trustless manner, for IoT\naccess control including their benefits and limitations. Finally, we note some\nfuture research directions on how to converge blockchain in IoT access control\nefficiently and effectively.\n"}
+{"abstract": "  Relative abundance is a common metric to estimate the composition of species\nin ecological surveys reflecting patterns of commonness and rarity of\nbiological assemblages. Measurements of coral reef compositions formed by four\ncommunities along Australia's Great Barrier Reef (GBR) gathered between 2012\nand 2017 are the focus of this paper. We undertake the task of finding clusters\nof transect locations with similar community composition and investigate\nchanges in clustering dynamics over time. During these years, an unprecedented\nsequence of extreme weather events (cyclones and coral bleaching) impacted the\n58 surveyed locations. The dependence between constituent parts of a\ncomposition presents a challenge for existing multivariate clustering\napproaches. In this paper, we introduce a finite mixture of Dirichlet\ndistributions with group-specific parameters, where cluster memberships are\ndictated by unobserved latent variables. The inference is carried in a Bayesian\nframework, where MCMC strategies are outlined to sample from the posterior\nmodel. Simulation studies are presented to illustrate the performance of the\nmodel in a controlled setting. The application of the model to the 2012 coral\nreef data reveals that clusters were spatially distributed in similar ways\nacross reefs which indicates a potential influence of wave exposure at the\norigin of coral reef community composition. The number of clusters estimated by\nthe model decreased from four in 2012 to two from 2014 until 2017. Posterior\nprobabilities of transect allocations to the same cluster substantially\nincrease through time showing a potential homogenization of community\ncomposition across the whole GBR. The Bayesian model highlights the diversity\nof coral reef community composition within a coral reef and rapid changes\nacross large spatial scales that may contribute to undermining the future of\nthe GBR's biodiversity.\n"}
+{"abstract": "  With the entrance of cosmology in its new era of high precision experiments,\nlow- and high-redshift observations set off tensions in the measurements of\nboth the present-day expansion rate ($H_0$) and the clustering of matter\n($S_8$). We provide a simultaneous explanation of these tensions using the\nParker-Raval Vacuum Metamorphosis (VM) model with the neutrino sector extended\nbeyond the three massless Standard Model flavours and the curvature of the\nuniverse considered as a model parameter. To estimate the effect on\ncosmological observables we implement various extensions of the VM model in the\nstandard \\texttt{CosmoMC} pipeline and establish which regions of parameter\nspace are empirically viable to resolve the $H_0$ and $S_8$ tensions. We find\nthat the likelihood analyses of the physically motivated VM model, which has\nthe same number of free parameters as in the spatially-flat $\\Lambda$CDM model,\nalways gives $H_0$ in agreement with the local measurements (even when BAO or\nPantheon data are included) at the price of much larger $\\chi^2$ than\n$\\Lambda$CDM. The inclusion of massive neutrinos and extra relativistic species\nquantified through two well known parameters $\\sum m_{\\nu}$ and $N_{\\rm eff}$,\ndoes not modify this result, and in some cases improves the goodness of the\nfit. In particular, for the original VM+$\\sum m_\\nu$+$N_{\\rm eff}$ and the\nPlanck+BAO+Pantheon dataset combination, we find evidence for $\\sum\nm_{\\nu}=0.80^{+0.18}_{-0.22}~{\\rm eV}$ at more than $3\\sigma$, no indication\nfor extra neutrino species, $H_0=71.0\\pm1.2$~km/s/Mpc in agreement with local\nmeasurements, and $S_8=0.755\\pm0.032$ that solves the tension with the weak\nlensing measurements. [Abridged]\n"}
+{"abstract": "  We compute the normalization of the multiple D-instanton amplitudes in type\nIIB string theory and show that the result agrees with the prediction of\nS-duality due to Green and Gutperle.\n"}
+{"abstract": "  In this paper, we prove that for suitably chosen Dwork motives, the local\nGalois representation arising from middle cohomology of fibers over a point\nwith $p$-adic valuation $<0$ on the base is regular and ordinary. The result\nwill be crucial in the forthcoming work Potential Automorphy for $GL_n$. The\nproof consists of the construction of a semistable blowup and a use of\nHyodo-Kato's log crystalline cohomology theory.\n"}
+{"abstract": "  State-of-the-art deep-learning-based voice activity detectors (VADs) are\noften trained with anechoic data. However, real acoustic environments are\ngenerally reverberant, which causes the performance to significantly\ndeteriorate. To mitigate this mismatch between training data and real data, we\nsimulate an augmented training set that contains nearly five million\nutterances. This extension comprises of anechoic utterances and their\nreverberant modifications, generated by convolutions of the anechoic utterances\nwith a variety of room impulse responses (RIRs). We consider five different\nmodels to generate RIRs, and five different VADs that are trained with the\naugmented training set. We test all trained systems in three different real\nreverberant environments. Experimental results show $20\\%$ increase on average\nin accuracy, precision and recall for all detectors and response models,\ncompared to anechoic training. Furthermore, one of the RIR models consistently\nyields better performance than the other models, for all the tested VADs.\nAdditionally, one of the VADs consistently outperformed the other VADs in all\nexperiments.\n"}
+{"abstract": "  This paper illustrates how multilevel functional models can detect and\ncharacterize biomechanical changes along different sport training sessions. Our\nanalysis focuses on the relevant cases to identify differences in knee\nbiomechanics in recreational runners during low and high-intensity exercise\nsessions with the same energy expenditure by recording $20$ steps. To do so, we\nreview the existing literature of multilevel models, and then, we propose a new\nhypothesis test to look at the changes between different levels of the\nmultilevel model as low and high-intensity training sessions. We also evaluate\nthe reliability of measures recorded in three-dimension knee angles from the\nfunctional intra-class correlation coefficient (ICC) obtained from the\ndecomposition performed with the multilevel funcional model taking into account\n$20$ measures recorded in each test. The results show that there are no\nstatistically significant differences between the two modes of exercise.\nHowever, we have to be careful with the conclusions since, as we have shown,\nhuman gait-patterns are very individual and heterogeneous between groups of\nathletes, and other alternatives to the p-value may be more appropriate to\ndetect statistical differences in biomechanical changes in this context.\n"}
+{"abstract": "  Re-identification (ReID) is to identify the same instance across different\ncameras. Existing ReID methods mostly utilize alignment-based or\nattention-based strategies to generate effective feature representations.\nHowever, most of these methods only extract general feature by employing single\ninput image itself, overlooking the exploration of relevance between comparing\nimages. To fill this gap, we propose a novel end-to-end trainable dynamic\nconvolution framework named Instance and Pair-Aware Dynamic Networks in this\npaper. The proposed model is composed of three main branches where a\nself-guided dynamic branch is constructed to strengthen instance-specific\nfeatures, focusing on every single image. Furthermore, we also design a\nmutual-guided dynamic branch to generate pair-aware features for each pair of\nimages to be compared. Extensive experiments are conducted in order to verify\nthe effectiveness of our proposed algorithm. We evaluate our algorithm in\nseveral mainstream person and vehicle ReID datasets including CUHK03,\nDukeMTMCreID, Market-1501, VeRi776 and VehicleID. In some datasets our\nalgorithm outperforms state-of-the-art methods and in others, our algorithm\nachieves a comparable performance.\n"}
+{"abstract": "  We review the fundamentals and highlight the differences between some\ncommonly used definitions for the PPN gamma parameter ($\\gamma$) and the\ngravitational slip ($\\eta$). Here we stress the usefulness of a gamma-like\nparameter used by Berry and Gair ($\\gamma_{\\scriptscriptstyle \\Sigma}$) that\nparametrizes the bending of light and the Shapiro time delay in situations in\nwhich the standard $\\gamma$ cannot be promptly used. First we apply our\nconsiderations to two well known cases, but for which some conflicting results\ncan be found: massive Brans-Dicke gravity and $f(R)$ gravity (both the metric\nand the Palatini versions). Although the slip parameter is always well defined,\nit has in general no direct relation to either light deflection or the Shapiro\ntime delay, hence care should be taken on imposing the PPN $\\gamma$ bounds on\nthe slip. We stress that, for any system with a well posed Newtonian limit,\nPalatini $f(R)$ theories always have $\\gamma = 1$; while metric $f(R)$ theories\ncan only have two values: either 1 or 1/2. The extension towards Horndeski\ngravity shows no qualitative surprises, and $\\gamma_{\\scriptscriptstyle\n\\Sigma}$ is a constant in this context (only assuming that the Horndeski\npotentials can be approximated by analytical functions). This implies that a\nprecise study on the bending of light for different impact parameters can in\nprinciple be used to rule out the complete Horndeski action as an action for\ngravity. Also, we comment on the consequences for $\\gamma$ inferences at\nexternal galaxies.\n"}
+{"abstract": "  We study fair resource allocation under a connectedness constraint wherein a\nset of indivisible items are arranged on a path and only connected subsets of\nitems may be allocated to the agents. An allocation is deemed fair if it\nsatisfies equitability up to one good (EQ1), which requires that agents'\nutilities are approximately equal. We show that achieving EQ1 in conjunction\nwith well-studied measures of economic efficiency (such as Pareto optimality,\nnon-wastefulness, maximum egalitarian or utilitarian welfare) is\ncomputationally hard even for binary additive valuations. On the algorithmic\nside, we show that by relaxing the efficiency requirement, a connected EQ1\nallocation can be computed in polynomial time for any given ordering of agents,\neven for general monotone valuations. Interestingly, the allocation computed by\nour algorithm has the highest egalitarian welfare among all allocations\nconsistent with the given ordering. On the other hand, if efficiency is\nrequired, then tractability can still be achieved for binary additive\nvaluations with interval structure. On our way, we strengthen some of the\nexisting results in the literature for other fairness notions such as\nenvy-freeness up to one good (EF1), and also provide novel results for\nnegatively-valued items or chores.\n"}
+{"abstract": "  We derive a simple and precise approximation to probability density functions\nin sampling distributions based on the Fourier cosine series. After clarifying\nthe required conditions, we illustrate the approximation on two examples: the\ndistribution of the sum of uniformly distributed random variables, and the\ndistribution of sample skewness drawn from a normal population. The probability\ndensity function of the first example can be explicitly expressed, but that of\nthe second example has no explicit expression.\n"}
+{"abstract": "  We analytically compute all four-loop QCD corrections to the photon-quark and\nHiggs-gluon form factors involving a closed massless fermion loop. Our\ncalculation of non-planar vertex integrals confirms a previous conjecture for\nthe analytical form of the non-fermionic contributions to the collinear\nanomalous dimensions of quarks and gluons.\n"}
+{"abstract": "  $^{72}$As is a promising positron emitter for diagnostic imaging that can be\nemployed locally using a $^{72}$Se generator. However, current reaction\npathways to $^{72}$Se have insufficient nuclear data for efficient production\nusing regional 100-200 MeV high-intensity proton accelerators. In order to\naddress this deficiency, stacked-target irradiations were performed at LBNL,\nLANL, and BNL to measure the production of the $^{72}$Se/$^{72}$As PET\ngenerator system via $^{75}$As(p,x) between 35 and 200 MeV. This work provides\nthe most well-characterized excitation function for $^{75}$As(p,4n)$^{72}$Se\nstarting from threshold. Additional focus was given to report the first\nmeasurements of $^{75}$As(p,x)$^{68}$Ge and bolster an already robust\nproduction capability for the highly valuable $^{68}$Ge/$^{68}$Ga PET\ngenerator. Thick target yield comparisons with prior established formation\nroutes to both generators are made. In total, high-energy proton-induced cross\nsections are reported for 55 measured residual products from $^{75}$As, Cu, and\nTi targets, where the latter two materials were present as monitor foils. These\nresults were compared with literature data as well as the default theoretical\ncalculations of the nuclear model codes TALYS, CoH, EMPIRE, and ALICE. Reaction\nmodeling at these energies is typically unsatisfactory due to few prior\npublished data and many interacting physics models. Therefore, a detailed\nassessment of the TALYS code was performed with simultaneous parameter\nadjustments applied according to a standardized procedure. Particular attention\nwas paid to the formulation of the two-component exciton model in the\ntransition between the compound and pre-equilibrium regions, with a linked\ninvestigation of level density models for nuclei off of stability and their\nimpact on modeling predictive power.\n"}
+{"abstract": "  In general, reliable communication via multiple-input multiple-output (MIMO)\northogonal frequency division multiplexing (OFDM) requires accurate channel\nestimation at the receiver. The existing literature largely focuses on\ndenoising methods for channel estimation that depend on either (i)~channel\nanalysis in the time-domain with prior channel knowledge or (ii)~supervised\nlearning techniques which require large pre-labeled datasets for training. To\naddress these limitations, we present a frequency-domain denoising method based\non a reinforcement learning framework that does not need a priori channel\nknowledge and pre-labeled data. Our methodology includes a new successive\nchannel denoising process based on channel curvature computation, for which we\nobtain a channel curvature magnitude threshold to identify unreliable channel\nestimates. Based on this process, we formulate the denoising mechanism as a\nMarkov decision process, where we define the actions through a geometry-based\nchannel estimation update, and the reward function based on a policy that\nreduces mean squared error (MSE). We then resort to Q-learning to update the\nchannel estimates. Numerical results verify that our denoising algorithm can\nsuccessfully mitigate noise in channel estimates. In particular, our algorithm\nprovides a significant improvement over the practical least squares (LS)\nestimation method and provides performance that approaches that of the ideal\nlinear minimum mean square error (LMMSE) estimation with perfect knowledge of\nchannel statistics.\n"}
+{"abstract": "  Tactile perception using vibration sensation helps robots recognize their\nenvironment's physical properties and perform complex tasks. A sliding motion\nis applied to target objects to generate tactile vibration data. However,\nsituations exist where such a sliding motion is infeasible due to geometrical\nconstraints in the environment or an object's fragility which cannot resist\nfriction forces. This paper explores a novel approach to achieve\nvibration-based tactile perception without a sliding motion. To this end, our\nkey idea is injecting a mechanical vibration into a soft tactile sensor system\nand measuring the propagated vibration inside it by a sensor. Soft tactile\nsensors are deformed by the contact state, and the touched objects' shape or\ntexture should change the characteristics of the vibration propagation.\nTherefore, the propagated-vibration data are expected to contain useful\ninformation for recognizing touched environments. We developed a prototype\nsystem for a proof-of-concept: a mechanical vibration is applied to a\nbiomimetic (soft and vibration-based) tactile sensor from a small, mounted\npiezoelectric actuator. As a verification experiment, we performed two\nclassification tasks for sandpaper's grit size and a slit's gap widths using\nour approach and compared their accuracies with that of using sliding motions.\nOur approach resulted in 70% accuracy for the grit size classification and 99%\naccuracy for the gap width classification. These results are comparable to or\nbetter than the comparison methods with a sliding motion.\n"}
+{"abstract": "  Biological synaptic plasticity exhibits nonlinearities that are not accounted\nfor by classic Hebbian learning rules. Here, we introduce a simple family of\ngeneralized nonlinear Hebbian learning rules. We study the computations\nimplemented by their dynamics in the simple setting of a neuron receiving\nfeedforward inputs. These nonlinear Hebbian rules allow a neuron to learn\ntensor decompositions of its higher-order input correlations. The particular\ninput correlation decomposed and the form of the decomposition depend on the\nlocation of nonlinearities in the plasticity rule. For simple, biologically\nmotivated parameters, the neuron learns eigenvectors of higher-order input\ncorrelation tensors. We prove that tensor eigenvectors are attractors and\ndetermine their basins of attraction. We calculate the volume of those basins,\nshowing that the dominant eigenvector has the largest basin of attraction. We\nthen study arbitrary learning rules and find that any learning rule that admits\na finite Taylor expansion into the neural input and output also has stable\nequilibria at generalized eigenvectors of higher-order input correlation\ntensors. Nonlinearities in synaptic plasticity thus allow a neuron to encode\nhigher-order input correlations in a simple fashion.\n"}
+{"abstract": "  We consider reversible and surjective cellular automata perturbed with noise.\nWe show that, in the presence of positive additive noise, the cellular\nautomaton forgets all the information regarding its initial configuration\nexponentially fast. In particular, the state of a finite collection of cells\nwith diameter n becomes indistinguishable from pure noise after O(log n) time\nsteps. This highlights the seemingly unavoidable need for irreversibility in\norder to perform scalable reliable computation in the presence of noise.\n"}
+{"abstract": "  Automatic classification of disordered speech can provide an objective tool\nfor identifying the presence and severity of speech impairment. Classification\napproaches can also help identify hard-to-recognize speech samples to teach ASR\nsystems about the variable manifestations of impaired speech. Here, we develop\nand compare different deep learning techniques to classify the intelligibility\nof disordered speech on selected phrases. We collected samples from a diverse\nset of 661 speakers with a variety of self-reported disorders speaking 29 words\nor phrases, which were rated by speech-language pathologists for their overall\nintelligibility using a five-point Likert scale. We then evaluated classifiers\ndeveloped using 3 approaches: (1) a convolutional neural network (CNN) trained\nfor the task, (2) classifiers trained on non-semantic speech representations\nfrom CNNs that used an unsupervised objective [1], and (3) classifiers trained\non the acoustic (encoder) embeddings from an ASR system trained on typical\nspeech [2]. We found that the ASR encoder's embeddings considerably outperform\nthe other two on detecting and classifying disordered speech. Further analysis\nshows that the ASR embeddings cluster speech by the spoken phrase, while the\nnon-semantic embeddings cluster speech by speaker. Also, longer phrases are\nmore indicative of intelligibility deficits than single words.\n"}
+{"abstract": "  Motivated by the rising abundance of observational data with continuous\ntreatments, we investigate the problem of estimating the average dose-response\ncurve (ADRF). Available parametric methods are limited in their model space,\nand previous attempts in leveraging neural network to enhance model\nexpressiveness relied on partitioning continuous treatment into blocks and\nusing separate heads for each block; this however produces in practice\ndiscontinuous ADRFs. Therefore, the question of how to adapt the structure and\ntraining of neural network to estimate ADRFs remains open. This paper makes two\nimportant contributions. First, we propose a novel varying coefficient neural\nnetwork (VCNet) that improves model expressiveness while preserving continuity\nof the estimated ADRF. Second, to improve finite sample performance, we\ngeneralize targeted regularization to obtain a doubly robust estimator of the\nwhole ADRF curve.\n"}
+{"abstract": "  In Statistical Relational Artificial Intelligence, a branch of AI and machine\nlearning which combines the logical and statistical schools of AI, one uses the\nconcept {\\em para\\-metrized probabilistic graphical model (PPGM)} to model\n(conditional) dependencies between random variables and to make probabilistic\ninferences about events on a space of \"possible worlds\". The set of possible\nworlds with underlying domain $D$ (a set of objects) can be represented by the\nset $\\mathbf{W}_D$ of all first-order structures (for a suitable signature)\nwith domain $D$. Using a formal logic we can describe events on $\\mathbf{W}_D$.\nBy combining a logic and a PPGM we can also define a probability distribution\n$\\mathbb{P}_D$ on $\\mathbf{W}_D$ and use it to compute the probability of an\nevent. We consider a logic, denoted $PLA$, with truth values in the unit\ninterval, which uses aggregation functions, such as arithmetic mean, geometric\nmean, maximum and minimum instead of quantifiers. However we face the problem\nof computational efficiency and this problem is an obstacle to the wider use of\nmethods from Statistical Relational AI in practical applications. We address\nthis problem by proving that the described probability will, under certain\nassumptions on the PPGM and the sentence $\\varphi$, converge as the size of $D$\ntends to infinity. The convergence result is obtained by showing that every\nformula $\\varphi(x_1, \\ldots, x_k)$ which contains only \"admissible\"\naggregation functions (e.g. arithmetic and geometric mean, max and min) is\nasymptotically equivalent to a formula $\\psi(x_1, \\ldots, x_k)$ without\naggregation functions.\n"}
+{"abstract": "  An analytical derivation of the vibrational density of states (DOS) of\nliquids, and in particular of its characteristic linear in frequency low-energy\nregime, has always been elusive because of the presence of an infinite set of\npurely imaginary modes -- the instantaneous normal modes (INMs). By combining\nan analytic continuation of the Plemelj identity to the complex plane with the\npurely overdamped dynamics of the INMs, we derive a closed-form analytic\nexpression for the low-frequency DOS of liquids. The obtained result explains\nfrom first principles the widely observed linear in frequency term of the DOS\nin liquids, whose slope appears to increase with the average lifetime of the\nINMs. The analytic results are robustly confirmed by fitting simulations data\nfor Lennard-Jones liquids, and they also recover the Arrhenius law for the\naverage relaxation time of the INMs, as expected.\n"}
+{"abstract": "  Inferring the causal structure of a system typically requires interventional\ndata, rather than just observational data. Since interventional experiments can\nbe costly, it is preferable to select interventions that yield the maximum\namount of information about a system. We propose a novel Bayesian method for\noptimal experimental design by sequentially selecting interventions that\nminimize the expected posterior entropy as rapidly as possible. A key feature\nis that the method can be implemented by computing simple summaries of the\ncurrent posterior, avoiding the computationally burdensome task of repeatedly\nperforming posterior inference on hypothetical future datasets drawn from the\nposterior predictive. After deriving the method in a general setting, we apply\nit to the problem of inferring causal networks. We present a series of\nsimulation studies in which we find that the proposed method performs favorably\ncompared to existing alternative methods. Finally, we apply the method to real\nand simulated data from a protein-signaling network.\n"}
+{"abstract": "  Hand gesture is a new and promising interface for locomotion in virtual\nenvironments. While several previous studies have proposed different hand\ngestures for virtual locomotion, little is known about their differences in\nterms of performance and user preference in virtual locomotion tasks. In the\npresent paper, we presented three different hand gesture interfaces and their\nalgorithms for locomotion, which are called the Finger Distance gesture, the\nFinger Number gesture and the Finger Tapping gesture. These gestures were\ninspired by previous studies of gesture-based locomotion interfaces and are\ntypical gestures that people are familiar with in their daily lives.\nImplementing these hand gesture interfaces in the present study enabled us to\nsystematically compare the differences between these gestures. In addition, to\ncompare the usability of these gestures to locomotion interfaces using\ngamepads, we also designed and implemented a gamepad interface based on the\nXbox One controller. We compared these four interfaces through two virtual\nlocomotion tasks. These tasks assessed their performance and user preference on\nspeed control and waypoints navigation. Results showed that user preference and\nperformance of the Finger Distance gesture were comparable to that of the\ngamepad interface. The Finger Number gesture also had close performance and\nuser preference to that of the Finger Distance gesture. Our study demonstrates\nthat the Finger Distance gesture and the Finger Number gesture are very\npromising interfaces for virtual locomotion. We also discuss that the Finger\nTapping gesture needs further improvements before it can be used for virtual\nwalking.\n"}
+{"abstract": "  Harvesting waste heat with temperatures lower than 100 oC can improve system\nefficiency and reduce greenhouse gas emissions, yet it has been a longstanding\nand challenging task. Electrochemical methods for harvesting low-grade heat\nhave attracted research interest in recent years due to the relatively high\neffective temperature coefficient of the electrolytes (> 1 mV/K) compared with\nthe thermopower of traditional thermoelectric devices. Comparing with other\nelectrochemical devices such as temperature-variation based thermally\nregenerative electrochemical cycle and temperature-difference based\nthermogalvanic cells, the thermally regenerative flow battery (TRFB) has the\nadvantages of providing a continuous power output, decoupling the heat source\nand heat sink and recuperating heat, and compatible with stacking for scaling\nup. However, TRFB suffers from the issue of stable operation due to the\nchallenge of pH matching between catholyte and anolyte solutions with desirable\ntemperature coefficients. In this work, we demonstrate a PH-neutral TRFB based\non KI/KI3 and K3Fe(CN)6/K4Fe(CN)6 as the catholyte and anolyte, respectively,\nwith a cell temperature coefficient of 1.9 mV/K and a power density of 9\nuW/cm2. This work also presents a comprehensive model with a coupled analysis\nof mass transfer and reaction kinetics in a porous electrode that can\naccurately capture the flow rate dependence of power density and energy\nconversion efficiency. We estimate that the efficiency of the pH-neutral TRFB\ncan reach 11% of the Carnot efficiency at the maximum power output with a\ntemperature difference of 37 K. Via analysis, we identify that the mass\ntransfer overpotential inside the porous electrode and the resistance of the\nion exchange membrane are the two major factors limiting the efficiency and\npower density, pointing to directions for future improvements.\n"}
+{"abstract": "  We investigate dynamical changes and its corresponding phase space complexity\nin a stochastic red blood cell system. The system is obtained by incorporating\npower noise with the associated sinusoidal flow. Both chaotic and non-chaotic\ndynamics of sinusoidal flow in red blood cell are identified by 0-1 test.\nFurthermore, dynamical complexity of the sinusoidal flow in the system is\ninvestigated by heterogeneous recurrence based entropy. The numerical\nsimulation is performed to quantify the existence of chaotic dynamics and\ncomplexity for the sinusoidal blood flow.\n"}
+{"abstract": "  This paper presents a Domain Specific Language (DSL) for generically\ndescribing cyber attacks, agnostic to specific system-under-test(SUT). The\ncreation of the presented DSL is motivated by an automotive use case. The\nconcepts of the DSL are generic such thatattacks on arbitrary systems can be\naddressed.The ongoing trend to improve the user experience of vehicles with\nconnected services implies an enhanced connectivity as well asremote accessible\ninterface opens potential attack vectors. This might also impact safety and the\nproprietary nature of potential SUTs.Reusing tests of attack vectors to\nindustrialize testing them on multiple SUTs mandates an abstraction mechanism\nto port an attackfrom one system to another. The DSL therefore generically\ndescribes attacks for the usage with a test case generator (and\nexecutionenvironment) also described in this paper. The latter use this\ndescription and a database with SUT-specific information to generateattack\nimplementations for a multitude of different (automotive) SUTs.\n"}
+{"abstract": "  We prove the rationality of the Poincar\\'e series of multiplier ideals in any\ndimension and thus extending the main results for surfaces of Galindo and\nMonserrat and Alberich-Carrami\\~nana et al. Our results also hold for\nPoincar\\'e series of test ideals. In order to do so, we introduce a theory of\nHilbert functions indexed over $\\mathbb{R}$ which gives an unified treatment of\nboth cases.\n"}
+{"abstract": "  We study the transverse geometric behavior of 2-dimensional foliations in\n3-manifolds. We show that an R-covered transversely orientable foliation with\nGromov hyperbolic leaves in a closed 3-manifold admits a regulating, transverse\npseudo-Anosov flow (in the appropriate sense) in each atoroidal piece of the\nmanifold. The flow is a blow of a one prong pseudo-Anosov flow. In addition we\nshow that there is a regulating flow for the whole foliation. We also determine\nhow deck transformations act on the universal circle of the foliation.\n"}
+{"abstract": "  Quantum computers promise tremendous impact across applications -- and have\nshown great strides in hardware engineering -- but remain notoriously error\nprone. Careful design of low-level controls has been shown to compensate for\nthe processes which induce hardware errors, leveraging techniques from optimal\nand robust control. However, these techniques rely heavily on the availability\nof highly accurate and detailed physical models which generally only achieve\nsufficient representative fidelity for the most simple operations and generic\nnoise modes. In this work, we use deep reinforcement learning to design a\nuniversal set of error-robust quantum logic gates on a superconducting quantum\ncomputer, without requiring knowledge of a specific Hamiltonian model of the\nsystem, its controls, or its underlying error processes. We experimentally\ndemonstrate that a fully autonomous deep reinforcement learning agent can\ndesign single qubit gates up to $3\\times$ faster than default DRAG operations\nwithout additional leakage error, and exhibiting robustness against calibration\ndrifts over weeks. We then show that $ZX(-\\pi/2)$ operations implemented using\nthe cross-resonance interaction can outperform hardware default gates by over\n$2\\times$ and equivalently exhibit superior calibration-free performance up to\n25 days post optimization using various metrics. We benchmark the performance\nof deep reinforcement learning derived gates against other black box\noptimization techniques, showing that deep reinforcement learning can achieve\ncomparable or marginally superior performance, even with limited hardware\naccess.\n"}
+{"abstract": "  A sender sells an object of unknown quality to a receiver who pays his\nexpected value for it. Sender and receiver might hold different priors over\nquality. The sender commits to a monotonic categorization of quality. We\ncharacterize the sender's optimal monotonic categorization. Using our\ncharacterization, we study the optimality of full pooling or full separation,\nthe alternation of pooling and separation, and make precise a sense in which\npooling is dominant relative to separation. We discuss applications, extensions\nand generalizations, among them the design of a grading scheme by a\nprofit-maximizing school which seeks to signal student qualities and\nsimultaneously incentivize students to learn. Such incentive constraints force\nmonotonicity, and can also be embedded as a distortion of the school's prior\nover student qualities, generating a categorization problem with distinct\nsender and receiver priors.\n"}
+{"abstract": "  We present a new radiative transfer method (SPH-M1RT) that is coupled\ndynamically with smoothed particle hydrodynamics (SPH). We implement it in the\n(task-based parallel) SWIFT galaxy simulation code but it can be\nstraightforwardly implemented in other SPH codes. Our moment-based method\nsimultaneously solves the radiation energy and flux equations in SPH, making it\nadaptive in space and time. We modify the M1 closure relation to stabilize\nradiation fronts in the optically thin limit. We also introduce anisotropic\nartificial viscosity and high-order artificial diffusion schemes, which allow\nthe code to handle radiation transport accurately in both the optically thin\nand optically thick regimes. Non-equilibrium thermo-chemistry is solved using a\nsemi-implicit sub-cycling technique. The computational cost of our method is\nindependent of the number of sources and can be lowered further by using the\nreduced speed of light approximation. We demonstrate the robustness of our\nmethod by applying it to a set of standard tests from the cosmological\nradiative transfer comparison project of Iliev et al. The SPH-M1RT scheme is\nwell-suited for modelling situations in which numerous sources emit ionising\nradiation, such as cosmological simulations of galaxy formation or simulations\nof the interstellar medium.\n"}
+{"abstract": "  In this research paper, I will elaborate on a method to evaluate machine\ntranslation models based on their performance on underlying syntactical\nphenomena between English and Arabic languages. This method is especially\nimportant as such \"neural\" and \"machine learning\" are hard to fine-tune and\nchange. Thus, finding a way to evaluate them easily and diversely would greatly\nhelp the task of bettering them.\n"}
+{"abstract": "  Many clinical studies evaluate the benefit of treatment based on both\nsurvival and other ordinal/continuous clinical outcomes, such as neurocognitive\nscores or quality-of-life scores. In these studies, there are situations when\nthe clinical outcomes are truncated by death, where subjects die before their\nclinical outcome is measured. Treating outcomes as \"missing\" or \"censored\" due\nto death can be misleading for treatment effect evaluation. We show that if we\nuse the median in the survivors or in the always-survivors to summarize\nclinical outcomes, we may conclude a trade-off exists between the probability\nof survival and good clinical outcomes, even in settings where both the\nprobability of survival and the probability of any good clinical outcome are\nbetter for one treatment. Therefore, we advocate not always treating death as a\nmechanism through which clinical outcomes are missing, but rather as part of\nthe outcome measure. To account for the survival status, we describe the\nsurvival-incorporated median as an alternative summary measure for outcomes in\nthe presence of death. The survival-incorporated median is the threshold such\nthat 50\\% of the population is alive with an outcome above that threshold. We\nuse conceptual examples to show that the survival-incorporated median provides\na simple and useful summary measure to inform clinical practice.\n"}
+{"abstract": "  The unified set of yields of particles produced in proton-proton collisions\nat $\\sqrt{s}$ = 17.3 GeV (laboratory beam momentum 158 GeV/c) is evaluated,\ncombining the experimental results of the NA49 and NA61/SHINE collaborations at\nthe CERN SPS. With the statistical hadronization code Thermal-Fist we confirm\nthe unacceptably high value of $\\chi^2$, both in the canonical and grand\ncanonical - strangeness canonical approach, and the common volume for all the\nhadrons. The use of the energy-dependent width of the Breit-Wigner\nparametrization for the mass distributions of unstable particles improves the\nquality of the description of particle yields only slightly. We confirm the\nobservation that exclusion of the $\\phi$ meson yield makes the fit result\nacceptable. The complete experimental data set of particle yields can be\nreasonably fitted if the canonical volumes of hadrons without and with open\nstrangeness are allowed to vary independently. The canonical volume of\nstrangeness was found larger than that for non-strange hadrons, which is\ncompatible with the femtoscopy measurements of p+p system at $\\sqrt{s} = $ 27.4\nMeV and 900 MeV. The model with the best-fit parameters allows to predict the\nyields of several not yet measured particles emitted from p+p at $\\sqrt{s}$ =\n17.3 GeV.\n"}
+{"abstract": "  In this paper, we extend a recently introduced multi-fidelity control variate\nfor the uncertainty quantification of the Boltzmann equation to the case of\nkinetic models arising in the study of multiagent systems. For these phenomena,\nwhere the effect of uncertainties is particularly evident, several models have\nbeen developed whose equilibrium states are typically unknown. In particular,\nwe aim to develop efficient numerical methods based on solving the kinetic\nequations in the phase space by Direct Simulation Monte Carlo (DSMC) coupled to\na Monte Carlo sampling in the random space. To this end, exploiting the\nknowledge of the corresponding mean-field approximation we develop novel\nmean-field Control Variate (MFCV) methods that are able to strongly reduce the\nvariance of the standard Monte Carlo sampling method in the random space. We\nverify these observations with several numerical examples based on classical\nmodels , including wealth exchanges and opinion formation model for collective\nphenomena.\n"}
+{"abstract": "  We demonstrate an on-demand source of microwave single photons with 71--99\\%\nintrinsic quantum efficiency. The source is narrowband (300\\unite{kHz}) and\ntuneable over a 600 MHz range around 5.2 GHz. Such a device is an important\nelement in numerous quantum technologies and applications. The device consists\nof a superconducting transmon qubit coupled to the open end of a transmission\nline. A $\\pi$-pulse excites the qubit, which subsequently rapidly emits a\nsingle photon into the transmission line. A cancellation pulse then suppresses\nthe reflected $\\pi$-pulse by 33.5 dB, resulting in 0.005 photons leaking into\nthe photon emission channel. We verify strong antibunching of the emitted\nphoton field and determine its Wigner function. Non-radiative decay and $1/f$\nflux noise both affect the quantum efficiency. We also study the device\nstability over time and identify uncorrelated discrete jumps of the pure\ndephasing rate at different qubit frequencies on a time scale of hours, which\nwe attribute to independent two-level system defects in the device dielectrics,\ndispersively coupled to the qubit.\n"}
+{"abstract": "  The heavy fermion state with Kondo-hybridization (KH), usually manifested in\nf-electron systems with lanthanide or actinide elements, was recently\ndiscovered in several 3d transition metal compounds without f-electrons.\nHowever, KH has not yet been observed in 4d/5d transition metal compounds,\nsince more extended 4d/5d orbitals do not usually form flat bands that supply\nlocalized electrons appropriate for Kondo pairing. Here, we report a doping-\nand temperature-dependent angle-resolved photoemission study on 4d\nCa2-xSrxRuO4, which shows the signature of KH. We observed a spectral weight\ntransfer in the {\\gamma}-band, reminiscent of an orbital-selective Mott phase\n(OSMP). The Mott localized {\\gamma}-band induces KH with the itinerant\n\\b{eta}-band, resulting in spectral weight suppression around the Fermi level.\nOur work is the first to demonstrate the evolution of the OSMP with possible KH\namong 4d electrons, and thereby expands the material boundary of Kondo physics\nto 4d multi-orbital systems.\n"}
+{"abstract": "  Excellent thermoelectric performance in the out-of-layer n-doped SnSe has\nbeen observed experimentally (Chang et al., Science 360, 778-783 (2018)).\nHowever, a first-principles investigation of the dominant scattering mechanisms\ngoverning all thermoelectric transport properties is lacking. In the present\nwork, by applying extensive first-principles calculations of electron-phonon\ncoupling associated with the calculation of the scattering by ionized\nimpurities, we investigate the reasons behind the superior figure of merit as\nwell as the enhancement of zT above 600 K in n-doped out-of-layer SnSe, as\ncompared to p-doped SnSe with similar carrier densities. For the n-doped case,\nthe relaxation time is dominated by ionized impurity scattering and increases\nwith temperature, a feature that maintains the power factor at high values at\nhigher temperatures and simultaneously causes the carrier thermal conductivity\nat zero electric current (k_el) to decrease faster for higher temperatures,\nleading to an ultrahigh-zT = 3.1 at 807 K. We rationalize the roles played by\nk_el and k^0 (the thermal conductivity due to carrier transport under\nisoelectrochemical conditions) in the determination of zT. Our results show the\nratio between k^0 and the lattice thermal conductivity indeed corresponds to\nthe upper limit for zT, whereas the difference between calculated zT and the\nupper limit is proportional to k_el.\n"}
+{"abstract": "  We consider $\\mathbb{Z}_2$-synchronization on the Euclidean lattice. Every\nvertex of $\\mathbb{Z}^d$ is assigned an independent symmetric random sign\n$\\theta_u$, and for every edge $(u,v)$ of the lattice, one observes the product\n$\\theta_u\\theta_v$ flipped independently with probability $p$. The task is to\nreconstruct products $\\theta_u\\theta_v$ for pairs of vertices $u$ and $v$ which\nare arbitrarily far apart. Abb\\'e, Massouli\\'e, Montanari, Sly and Srivastava\n(2018) showed that synchronization is possible if and only if $p$ is below a\ncritical threshold $\\tilde{p}_c(d)$, and efficiently so for $p$ small enough.\nWe augment this synchronization setting with a model of side information\npreserving the sign symmetry of $\\theta$, and propose an \\emph{efficient}\nalgorithm which synchronizes a randomly chosen pair of far away vertices on\naverage, up to a differently defined critical threshold $p_c(d)$. We conjecture\nthat $ p_c(d)=\\tilde{p}_c(d)$ for all $d \\ge 2$. Our strategy is to\n\\emph{renormalize} the synchronization model in order to reduce the effective\nnoise parameter, and then apply a variant of the multiscale algorithm of AMMSS.\nThe success of the renormalization procedure is conditional on a plausible but\nunproved assumption about the regularity of the free energy of an Ising spin\nglass model on $\\mathbb{Z}^d$.\n"}
+{"abstract": "  Graph Neural Networks (GNNs) are the subject of intense focus by the machine\nlearning community for problems involving relational reasoning. GNNs can be\nbroadly divided into spatial and spectral approaches. Spatial approaches use a\nform of learned message-passing, in which interactions among vertices are\ncomputed locally, and information propagates over longer distances on the graph\nwith greater numbers of message-passing steps. Spectral approaches use\neigendecompositions of the graph Laplacian to produce a generalization of\nspatial convolutions to graph structured data which access information over\nshort and long time scales simultaneously. Here we introduce the Spectral Graph\nNetwork, which applies message passing to both the spatial and spectral\ndomains. Our model projects vertices of the spatial graph onto the Laplacian\neigenvectors, which are each represented as vertices in a fully connected\n\"spectral graph\", and then applies learned message passing to them. We apply\nthis model to various benchmark tasks including a graph-based variant of MNIST\nclassification, molecular property prediction on MoleculeNet and QM9, and\nshortest path problems on random graphs. Our results show that the Spectral GN\npromotes efficient training, reaching high performance with fewer training\niterations despite having more parameters. The model also provides robustness\nto edge dropout and outperforms baselines for the classification tasks. We also\nexplore how these performance benefits depend on properties of the dataset.\n"}
+{"abstract": "  Cookie banners are devices implemented by websites to allow users to manage\ntheir privacy settings with respect to the use of cookies. They are part of a\nuser's daily web browsing experience since legislation in Europe requires\nwebsites to show such notices. In this paper, we carry out a large-scale study\nof more than 17,000 websites including more than 7,500 cookie banners in Greece\nand the UK to determine compliance and tracking transparency levels. Our\nanalysis shows that although more than 60% of websites store third-party\ncookies in both countries, only less than 50% show a cookie notice and hence a\nsubstantial proportion do not comply with the law even at the very basic level.\nWe find only a small proportion of the surveyed websites providing a direct\nopt-out option, with an overwhelming majority either nudging users towards\nprivacy-intrusive choices or making cookie rejection much harder than consent.\nOur results differ significantly in some cases from previous smaller-scale\nstudies and hence underline the importance of large-scale studies for a better\nunderstanding of the big picture in cookie practices.\n"}
+{"abstract": "  We present an analytic computation of the two-loop QCD corrections to\n$u\\bar{d}\\to W^+b\\bar{b}$ for an on-shell $W$-boson using the leading colour\nand massless bottom quark approximations. We perform an integration-by-parts\nreduction of the unpolarised squared matrix element using finite field\nreconstruction techniques and identify an independent basis of special\nfunctions that allows an analytic subtraction of the infrared and ultraviolet\npoles. This basis is valid for all planar topologies for five-particle\nscattering with an off-shell leg.\n"}
+{"abstract": "  Operator spreading under unitary time evolution has attracted a lot of\nattention recently, as a way to probe many-body quantum chaos. While quantities\nsuch as out-of-time-ordered correlators (OTOC) do distinguish interacting from\nnon-interacting systems, it has remained unclear to what extent they can truly\ndiagnose chaotic {\\it vs} integrable dynamics in many-body quantum systems.\nHere, we analyze operator spreading in generic 1D many-body quantum systems\nusing a combination of matrix product operator (MPO) and analytical techniques,\nfocusing on the operator {\\em right-weight}. First, we show that while small\nbond dimension MPOs allow one to capture the exponentially-decaying tail of the\noperator front, in agreement with earlier results, they lead to significant\nquantitative and qualitative errors for the actual front -- defined by the\nmaximum of the right-weight. We find that while the operator front broadens\ndiffusively in both integrable and chaotic interacting spin chains, the precise\nshape and scaling of the height of the front in integrable systems is anomalous\nfor all accessible times. We interpret these results using a quasiparticle\npicture. This provides a sharp, though rather subtle signature of many-body\nquantum chaos in the operator front.\n"}
+{"abstract": "  We developed a theory of electric and thermoelectric conductivity of lightly\ndoped SrTiO$_3$ in the non-degenerate region $k_B T \\geq E_F$, assuming that\nthe major source of electron scattering is their interaction with soft\ntransverse optical phonons present due to proximity to ferroelectric\ntransition. We have used kinetic equation approach within relaxation-time\napproximation and we have determined energy-dependent transport relaxation time\n$\\tau(E)$ by the iterative procedure. Using electron effective mass $m$ and\nelectron-transverse phonon coupling constant $\\lambda$ as two fitting\nparameters, we are able to describe quantitatively a large set of the measured\ntemperature dependences of resistivity $R(T)$ and Seebeck coefficient\n$\\mathcal{S}(T)$ for a broad range of electron densities studied experimentally\nin recent paper [1]. In addition, we calculated Nernst ratio $\\nu=N/B$ in the\nlinear approximation over weak magnetic field in the same temperature range.\n"}
+{"abstract": "  Recently, it has been argued that encoder-decoder models can be made more\ninterpretable by replacing the softmax function in the attention with its\nsparse variants. In this work, we introduce a novel, simple method for\nachieving sparsity in attention: we replace the softmax activation with a ReLU,\nand show that sparsity naturally emerges from such a formulation. Training\nstability is achieved with layer normalization with either a specialized\ninitialization or an additional gating function. Our model, which we call\nRectified Linear Attention (ReLA), is easy to implement and more efficient than\npreviously proposed sparse attention mechanisms. We apply ReLA to the\nTransformer and conduct experiments on five machine translation tasks. ReLA\nachieves translation performance comparable to several strong baselines, with\ntraining and decoding speed similar to that of the vanilla attention. Our\nanalysis shows that ReLA delivers high sparsity rate and head diversity, and\nthe induced cross attention achieves better accuracy with respect to\nsource-target word alignment than recent sparsified softmax-based models.\nIntriguingly, ReLA heads also learn to attend to nothing (i.e. 'switch off')\nfor some queries, which is not possible with sparsified softmax alternatives.\n"}
+{"abstract": "  Gradient-descent based iterative algorithms pervade a variety of problems in\nestimation, prediction, learning, control, and optimization. Recently iterative\nalgorithms based on higher-order information have been explored in an attempt\nto lead to accelerated learning. In this paper, we explore a specific a\nhigh-order tuner that has been shown to result in stability with time-varying\nregressors in linearly parametrized systems, and accelerated convergence with\nconstant regressors. We show that this tuner continues to provide bounded\nparameter estimates even if the gradients are corrupted by noise. Additionally,\nwe also show that the parameter estimates converge exponentially to a compact\nset whose size is dependent on noise statistics. As the HT algorithms can be\napplied to a wide range of problems in estimation, filtering, control, and\nmachine learning, the result obtained in this paper represents an important\nextension to the topic of real-time and fast decision making.\n"}
+{"abstract": "  A result due to Williams, Stampfli and Fillmore shows that an essential\nisometry $T$ on a Hilbert space $\\mathcal{H}$ is a compact perturbation of an\nisometry if and only if ind$(T)\\le 0$. A recent result of S. Chavan yields an\nanalogous characterization of essential spherical isometries\n$T=(T_1,\\dots,T_n)\\in\\mathcal{B}(\\mathcal{H})^n$ with\ndim($\\bigcap_{i=1}^n\\ker(T_i))\\le$ dim$(\\bigcap_{i=1}^n\\ker(T_i^*))$. In the\npresent note we show that in dimension $n>1$ the result of Chavan holds without\nany condition on the dimensions of the joint kernels of $T$ and $T^*$.\n"}
+{"abstract": "  Collective phenomena in the Tavis-Cummings model has been widely studied,\nfocusing on the phase transition features. In many occasions, it has been used\nvariational approaches that consider separated radiation-matters systems. In\nthis paper, we examine the role of the quantum entanglement of an assembly of\ntwo-level emitters coupled to a single-mode cavity; this allows us to\ncharacterise the quantum correlated state for each regime. Statistical\nproperties of the system, e.g., the first four statistical moments, show\nclearly the structure of the light and matter distributions. Even though the\nsecond order correlation function goes to one in some regimes, the statistical\nanalysis evidence a sharp departure from coherent behaviour, contrarily to the\ncommon understanding.\n"}
+{"abstract": "  In this paper we prove existence of nonnegative solutions to parabolic\nCauchy-Dirichlet problems with superlinear gradient terms which are possibly\nsingular. The model equation is \\[\n  u_t - \\Delta_pu=g(u)|\\nabla u|^q+h(u)f(t,x)\\qquad \\text{in\n}(0,T)\\times\\Omega, \\] where $\\Omega$ is an open bounded subset of\n$\\mathbb{R}^N$ with $N>2$, $0<T<+\\infty$, $1<p<N$, and $q<p$ is superlinear.\nThe functions $g,\\,h$ are continuous and possibly satisfying $g(0) = +\\infty$\nand/or $h(0)= +\\infty$, with different rates. Finally, $f$ is nonnegative and\nit belongs to a suitable Lebesgue space. We investigate the relation among the\nsuperlinear threshold of $q$, the regularity of the initial datum and the\nforcing term, and the decay rates of $g,\\,h$ at infinity.\n"}
+{"abstract": "  The recent progress of artificial intelligence(AI) has shown great potentials\nfor alleviating human burden in various complex tasks. From the view of\nsoftware engineering, AI techniques can be seen in many fundamental aspects of\ndevelopment, such as source code comprehension, in which state-of-the-art\nmodels are implemented to extract and express the meaning of code snippets\nautomatically. However, such technologies are still struggling to tackle and\ncomprehend the complex structures within industrial code, thus far from\nreal-world applications. In the present work, we built an innovative and\nsystematical framework, emphasizing the problem of complexity in code\ncomprehension and further software engineering. Upon automatic data collection\nfrom the latest Linux kernel source code, we modeled code structures as complex\nnetworks through token extraction and relation parsing. Comprehensive analysis\nof complexity further revealed the density and scale of network-based code\nrepresentations. Our work constructed the first large-scale dataset from\nindustrial-strength software code for downstream software engineering tasks\nincluding code comprehension, and incorporated complex network theory into\ncode-level investigations of software development for the first time. In the\nlonger term, the proposed methodology could play significant roles in the\nentire software engineering process, powering software design, coding,\ndebugging, testing, and sustaining by redefining and embracing complexity.\n"}
+{"abstract": "  Static and spherically symmetric wormhole solutions can be reconstructed in\nthe framework of curvature based Extended Theories of Gravity. In particular,\nextensions of the General Relativity, in metric and curvature formalism give\nrise to modified gravitational potentials, constituted by the classical\nNewtonian potential and Yukawa-like corrections, whose parameters can be, in\nturn, gauged by the observations. Such an approach allows to reconstruct the\nspacetime out of the wormhole throat considering the asymptotic flatness as a\nphysical property for the related gravitational field. Such an argument can be\napplied for a large class of curvature theories characterising the wormholes\nthrough the parameters of the potentials. According to this procedure, possible\nwormhole solutions could be observationally constrained. On the other hand,\nstable and traversable wormholes could be a direct probe for this class of\nExtended Theories of Gravity.\n"}
+{"abstract": "  Mutation analysis is an effective technique to evaluate a test suite adequacy\nin terms of revealing unforeseen bugs in software. Traditional source- or\nIR-level mutation analysis is not applicable to the software only available in\nbinary format. This paper proposes a practical binary mutation analysis via\nbinary rewriting, along with a rich set of mutation operators to represent more\nrealistic bugs. We implemented our approach using two state-of-the-art binary\nrewriting tools and evaluated its effectiveness and scalability by applying\nthem to SPEC CPU benchmarks. Our analysis revealed that the richer mutation\noperators contribute to generating more diverse mutants, which, compared to\nprevious works leads to a higher mutation score for the test harness. We also\nconclude that the reassembleable disassembly rewriting yields better\nscalability in comparison to lifting to an intermediate representation and\nperforming a full translation.\n"}
+{"abstract": "  Let $R=K[G]$ be a group ring of a group $G$ over a field $K$. The Ore\ncondition says that for any $a,b\\in R$ there exist $u,v\\in R$ such that\n$au=bv$, where $u\\ne0$ or $v\\ne0$. It always holds whenever $G$ is amenable.\nRecently it was shown that for R.\\,Thompson's group $F$ the converse is also\ntrue. So the famous amenability problem for $F$ is equivalent to the question\non the Ore condition for the group ring of the same group.\n  It is easy to see that the problem on the Ore condition for $K[F]$ is\nequivalent to the same property for the monoid ring $K[M]$, where $M$ is the\nmonoid of positive elements of $F$. In this paper we reduce the problem to the\ncase when $a$, $b$ are homogeneous elements of the same degree in the monoid\nring. We study the case of degree $1$ and find solutions of the Ore equation.\nFor the case of degree $2$, we study the case of linear combinations of\nmonomials from $S=\\{x_0^2,x_0x_1,x_0x_2,x_1^2,x_1x_2\\}$. This set is not\ndoubling, that is, there are nonempty finite subsets $X\\subset M\\subset F$ such\nthat $|SX| < 2|X|$. As a consequence, the Ore condition holds for linear\ncombinations of these monomials. We give an estimate for the degree of $u$, $v$\nin the above equation.\n  The case of monomials of higher degree is open as well as the case of degree\n$2$ for monomials on $x_0,x_1,...,x_m$, where $m\\ge3$. Recall that negative\nanswer to any of these questions will immediately imply non-amenability of $F$.\n"}
+{"abstract": "  The photogalvanic effect (PGE) occurring in noncentrosymmetric materials\nenables the generation of a dc photocurrent at zero bias with a high\npolarization sensitivity, which makes it very attractive in photodetection.\nHowever, the magnitude of the PGE photocurrent is usually small, leading to a\nlow photoresponsivity, and therefore hampers its practical application in\nphotodetection. Here, we propose an approach to largely enhancing the PGE\nphotocurrent by applying an inhomogenous mechanical stretch, based on quantum\ntransport simulations. We model a two-dimensional photodetector consisting of\nthe wide-bandgap MgCl$_2$/ZnBr$_2$ vertical van der Waals heterojunction with\nthe noncentrosymmetric $C_{3v}$ symmetry. Polarization-sensitive PGE\nphotocurrent is generated under the vertical illumination of linearly polarized\nlight. By applying inhomogenous mechanical stretch on the lattice, the\nphotocurrent can be largely increased by up to 3 orders of magnitude due to the\nsignificantly increased device asymmetry. Our results propose an effective way\nto enhance the PGE by inhomogenous mechanical strain, showing the potential of\nthe MgCl$_2$/ZnBr$_2$ vertical heterojunction in the low-power UV\nphotodetection.\n"}
+{"abstract": "  We report magneto-transport measurements of quasi-1-dimensional (1D) Al-InAs\nnanowires produced via etching of a hybrid superconductor-semiconductor\ntwo-dimensional electron gas (2DEG). Tunnel spectroscopy measurements above the\nsuperconducting gap provide a means of identifying the 1D sub-bands associated\nwith the confined 1D region. Fitting the data to a model that includes the\ndifferent components of the spin-orbit interaction (SOI) reveals their\nstrength, of interest for evaluating the suitability of\nsuperconductor-semiconductor 2DEG for realizing Majorana qubits.\n"}
+{"abstract": "  This is our main paper in a series in which we prove the full, unconditional,\nnonlinear stability of the Kerr family $Kerr(a, m)$ for small angular momentum,\ni.e. $|a|/m\\ll 1$, in the context of asymptotically flat solutions of the\nEinstein vacuum equations (EVE). Three papers in the series, \\cite{KS-GCM1} and\n\\cite{KS-GCM2} and \\cite{GKS1} have already been released. We expect that the\nremaining ones \\cite{GKS2}, \\cite{KS:Kerr-B} and \\cite{Shen} will appear\nshortly. Our work extends the strategy developed in \\cite{KS}, in which only\naxial polarized perturbations of Schwarzschild were treated, by developing new\ngeometric and analytic ideas on how to deal with with general perturbations of\nKerr. We note that the restriction to small angular momentum appears only in\nconnection to Morawetz type estimates in \\cite{GKS2} and \\cite{KS:Kerr-B}\n"}
+{"abstract": "  WALOP (Wide-Area Linear Optical Polarimeter)-South, to be mounted on the 1m\nSAAO telescope in South Africa, is first of the two WALOP instruments currently\nunder development for carrying out the PASIPHAE survey. Scheduled for\ncommissioning in the year 2021, the WALOP instruments will be used to measure\nthe linear polarization of around $10^{6}$ stars in the SDSS-r broadband with\n$0.1~\\%$ polarimetric accuracy, covering 4000 square degrees in the Galactic\npolar regions. The combined capabilities of one-shot linear polarimetry, high\npolarimetric accuracy ($< 0.1~\\%$) and polarimetric sensitivity ($< 0.05~\\%$),\nand a large field of view (FOV) of $35\\times35~arcminutes$ make WALOP-South a\nunique astronomical instrument. In a single exposure, it is designed to measure\nthe Stokes parameters $I$, $q$ and $u$ in the SDSS-r broadband and narrowband\nfilters between $500-700~nm$. During each measurement, four images of the full\nfield corresponding to the polarization angles of $0^{\\circ}$, $45^{\\circ}$,\n$90^{\\circ}$ and $135^{\\circ}$ will be imaged on four detectors and carrying\nout differential photometry on these images will yield the Stokes parameters.\nMajor challenges in designing WALOP-South instrument include- (a) in the\noptical design, correcting for the spectral dispersion introduced by large\nsplit angle Wollaston Prisms used as polarization analyzers as well as\naberrations from the wide field, and (b) making an optomechanical design\nadherent to the tolerances required to obtain good imaging and polarimetric\nperformance under all temperature conditions as well as telescope pointing\npositions. We present the optical and optomechanical design for WALOP-South\nwhich overcomes these challenges.\n"}
+{"abstract": "  As a maintainer of an open source software project, you are usually happy\nabout contributions in the form of pull requests that bring the project a step\nforward. Past studies have shown that when reviewing a pull request, not only\nits content is taken into account, but also, for example, the social\ncharacteristics of the contributor. Whether a contribution is accepted and how\nlong this takes therefore depends not only on the content of the contribution.\nWhat we only have indications for so far, however, is that pull requests from\nbots may be prioritized lower, even if the bots are explicitly deployed by the\ndevelopment team and are considered useful.\n  One goal of the bot research and development community is to design helpful\nbots to effectively support software development in a variety of ways. To get\ncloser to this goal, in this GitHub mining study, we examine the measurable\ndifferences in how maintainers interact with manually created pull requests\nfrom humans compared to those created automatically by bots.\n  About one third of all pull requests on GitHub currently come from bots.\nWhile pull requests from humans are accepted and merged in 72.53% of all cases,\nthis applies to only 37.38% of bot pull requests. Furthermore, it takes\nsignificantly longer for a bot pull request to be interacted with and for it to\nbe merged, even though they contain fewer changes on average than human pull\nrequests. These results suggest that bots have yet to realize their full\npotential.\n"}
+{"abstract": "  The structure of protostellar cores can often be approximated by isothermal\nBonnor-Ebert spheres (BES) which are stabilized by an external pressure. For\nthe typical pressure of $10^4k_B\\,\\mathrm{K\\,cm^{-3}}$ to\n$10^5k_B\\,\\mathrm{K\\,cm^{-3}}$ found in molecular clouds, cores with masses\nbelow $1.5\\,{\\rm M_\\odot}$ are stable against gravitational collapse. In this\npaper, we analyze the efficiency of triggering a gravitational collapse by a\nnearby stellar wind, which represents an interesting scenario for triggered\nlow-mass star formation. We derive analytically a new stability criterion for a\nBES compressed by a stellar wind, which depends on its initial nondimensional\nradius $\\xi_{max}$. If the stability limit is violated the wind triggers a core\ncollapse. Otherwise, the core is destroyed by the wind. We estimate its\nvalidity range to $2.5<\\xi_{max}<4.2$ and confirm this in simulations with the\nSPH Code GADGET-3. The efficiency to trigger a gravitational collapse strongly\ndecreases for $\\xi_{max}<2.5$ since in this case destruction and acceleration\nof the whole sphere begin to dominate. We were unable to trigger a collapse for\n$\\xi_{max}<2$, which leads to the conclusion that a stellar wind can move the\nsmallest unstable stellar mass to $0.5\\,\\mathrm{M_\\odot}$ and destabilizing\neven smaller cores would require an external pressure larger than\n$10^5k_B\\,\\mathrm{K\\,cm^{-3}}$. For $\\xi_{max}>4.2$ the expected wind strength\naccording to our criterion is small enough so that the compression is slower\nthan the sound speed of the BES and sound waves can be triggered. In this case\nour criterion underestimates somewhat the onset of collapse and detailed\nnumerical analyses are required.\n"}
+{"abstract": "  Among the ODEs peculiarities -- specially those of Mechanics -- besides the\nproblem of leading them to quadratures and to solve them either in series or in\nclosed form, one is faced with the inversion. E.g. when one wishes to pass from\ntime as function of lagrangian coordinates to these last as functions of time.\nThis paper solves in almost closed form the system of non linear ODEs of the\n2D-motion (say, co-ordinates $\\theta$ and $\\psi$) of a gravity-free double\npendulum (GFDP) not subjected to any force. In such a way its movement is ruled\nby initial conditions only. The relevant strongly non linear ODEs, have been\nput back to hyper-elliptic quadratures which, through the Integral\nRepresentation Theorem (hereinafter IRT) have been driven to the Lauricella\nhypergeometric functions $F_D^{(j)}, j=3, 4, 5, 6 $. The IRT has been applied\nafter a change of variable which improves their use and accelerates the series\nconvergence. The $\\psi$ is given in terms of $F_D^{(4)}$ -- which is inverted\nby means of the Fourier Series tool and put as an argument inside the\n$F_D^{(5)}$ -- in such a way allowing the $\\theta$ computations. We succeed in\na insight knowledge of time laws and trajectories of both bobs forming the\nGFDP, which -- after the inversion -- is therefore completely solved in\nexplicit closed form. Suitable sample problems of the three possible cases of\nmotion are carried out and their analysis closes the work. The Lauricella\nfunctions employed here to solve the differential equations -- in lack of\nspecific SW packages -- have been implemented thanks to some reduction theorems\nwhich will form the object of a next paper. To our best knowledge, this work\nadds a new contribution as it concerns detection and inversion of solutions of\nnonlinear hamiltonian systems.\n"}
+{"abstract": "  We show that adding differential privacy to Explainable Boosting Machines\n(EBMs), a recent method for training interpretable ML models, yields\nstate-of-the-art accuracy while protecting privacy. Our experiments on multiple\nclassification and regression datasets show that DP-EBM models suffer\nsurprisingly little accuracy loss even with strong differential privacy\nguarantees. In addition to high accuracy, two other benefits of applying DP to\nEBMs are: a) trained models provide exact global and local interpretability,\nwhich is often important in settings where differential privacy is needed; and\nb) the models can be edited after training without loss of privacy to correct\nerrors which DP noise may have introduced.\n"}
+{"abstract": "  Human personality traits are the key drivers behind our decision-making,\ninfluencing our life path on a daily basis. Inference of personality traits,\nsuch as Myers-Briggs Personality Type, as well as an understanding of\ndependencies between personality traits and users' behavior on various social\nmedia platforms is of crucial importance to modern research and industry\napplications. The emergence of diverse and cross-purpose social media avenues\nmakes it possible to perform user personality profiling automatically and\nefficiently based on data represented across multiple data modalities. However,\nthe research efforts on personality profiling from multi-source multi-modal\nsocial media data are relatively sparse, and the level of impact of different\nsocial network data on machine learning performance has yet to be\ncomprehensively evaluated. Furthermore, there is not such dataset in the\nresearch community to benchmark. This study is one of the first attempts\ntowards bridging such an important research gap. Specifically, in this work, we\ninfer the Myers-Briggs Personality Type indicators, by applying a novel\nmulti-view fusion framework, called \"PERS\" and comparing the performance\nresults not just across data modalities but also with respect to different\nsocial network data sources. Our experimental results demonstrate the PERS's\nability to learn from multi-view data for personality profiling by efficiently\nleveraging on the significantly different data arriving from diverse social\nmultimedia sources. We have also found that the selection of a machine learning\napproach is of crucial importance when choosing social network data sources and\nthat people tend to reveal multiple facets of their personality in different\nsocial media avenues. Our released social multimedia dataset facilitates future\nresearch on this direction.\n"}
+{"abstract": "  In his 1987 paper, Todorcevic remarks that Sierpinski's onto mapping\nprinciple (1932) and the Erdos-Hajnal-Milner negative Ramsey relation (1966)\nare equivalent to each other, and follow from the existence of a Luzin set.\nRecently, Guzman and Miller showed that these two principles are also\nequivalent to the existence of a nonmeager set of reals of cardinality\n$\\aleph_1$. We expand this circle of equivalences and show that these\npropositions are equivalent also to the high-dimensional version of the\nErdos-Hajnal-Milner negative Ramsey relation, thereby improving a CH theorem of\nGalvin (1980).\n  Then we consider the validity of these relations in the context of strong\ncolorings over partitions and prove the consistency of a positive Ramsey\nrelation, as follows: It is consistent with the existence of both a Luzin set\nand of a Souslin tree that for some countable partition p, all colorings are\np-special.\n"}
+{"abstract": "  Mastery of order-disorder processes in highly non-equilibrium nanostructured\noxides has significant implications for the development of emerging energy\ntechnologies. However, we are presently limited in our ability to quantify and\nharness these processes at high spatial, chemical, and temporal resolution,\nparticularly in extreme environments. Here we describe the percolation of\ndisorder at the model oxide interface LaMnO$_3$ / SrTiO$_3$, which we visualize\nduring in situ ion irradiation in the transmission electron microscope. We\nobserve the formation of a network of disorder during the initial stages of ion\nirradiation and track the global progression of the system to full disorder. We\ncouple these measurements with detailed structural and chemical probes,\nexamining possible underlying defect mechanisms responsible for this unique\npercolative behavior.\n"}
+{"abstract": "  The Principal-Agent Theory model is widely used to explain governance role\nwhere there is a separation of ownership and control, as it defines clear\nboundaries between governance and executives. However, examination of recent\ncorporate failure reveals the concerning contribution of the Board of Directors\nto such failures and calls into question governance effectiveness in the\npresence of a powerful and charismatic CEO. This study proposes a framework for\nanalyzing the relationship between the Board of Directors and the CEO, and how\ncertain relationships affect the power structure and behavior of the Board,\nwhich leads to a role reversal in the Principal-Agent Theory, as the Board\nassumes the role of the CEO's agent. This study's results may help create a red\nflag for a board and leader's behavior that may result in governance failure.\n"}
+{"abstract": "  We prove that there are at least as many exact embedded Lagrangian fillings\nas seeds for Legendrian links of affine type $\\tilde{\\mathsf{D}}\n\\tilde{\\mathsf{E}}$. We also provide as many Lagrangian fillings with certain\nsymmetries as seeds of type $\\tilde{\\mathsf{B}}_n$, $\\tilde{\\mathsf{F}}_4$,\n$\\tilde{\\mathsf{G}}_2$, and $\\mathsf{E}_6^{(2)}$. These families are the first\nknown Legendrian links with infinitely many fillings that exhaust all seeds in\nthe corresponding cluster structures. Furthermore, we show that Legendrian\nrealization of Coxeter mutation of type $\\tilde{\\mathsf{D}}$ corresponds to the\nLegendrian loop considered by Casals and Ng.\n"}
+{"abstract": "  Most modern unsupervised domain adaptation (UDA) approaches are rooted in\ndomain alignment, i.e., learning to align source and target features to learn a\ntarget domain classifier using source labels. In semi-supervised domain\nadaptation (SSDA), when the learner can access few target domain labels, prior\napproaches have followed UDA theory to use domain alignment for learning. We\nshow that the case of SSDA is different and a good target classifier can be\nlearned without needing alignment. We use self-supervised pretraining (via\nrotation prediction) and consistency regularization to achieve well separated\ntarget clusters, aiding in learning a low error target classifier. With our\nPretraining and Consistency (PAC) approach, we achieve state of the art target\naccuracy on this semi-supervised domain adaptation task, surpassing multiple\nadversarial domain alignment methods, across multiple datasets. PAC, while\nusing simple techniques, performs remarkably well on large and challenging SSDA\nbenchmarks like DomainNet and Visda-17, often outperforming recent state of the\nart by sizeable margins. Code for our experiments can be found at\nhttps://github.com/venkatesh-saligrama/PAC\n"}
+{"abstract": "  In this paper we derive quantitative estimates in the context of stochastic\nhomogenization for integral functionals defined on finite partitions, where the\nrandom surface integrand is assumed to be stationary. Requiring the integrand\nto satisfy in addition a multiscale functional inequality, we control\nquantitatively the fluctuations of the asymptotic cell formulas defining the\nhomogenized surface integrand. As a byproduct we obtain a simplified cell\nformula where we replace cubes by almost flat hyperrectangles.\n"}
+{"abstract": "  Searches for periodicity in time series are often done with models of\nperiodic signals, whose statistical significance is assessed via false alarm\nprobabilities or Bayes factors. However, a statistically significant periodic\nmodel might not originate from a strictly periodic source. In astronomy in\nparticular, one expects transient signals that show periodicity for a certain\namount of time before vanishing. This situation is encountered for instance in\nthe search for planets in radial velocity data. While planetary signals are\nexpected to have a stable phase, amplitude and frequency - except when strong\nplanet-planet interactions are present - signals induced by stellar activity\nwill typically not exhibit the same stability. In the present article, we\nexplore the use of periodic functions multiplied by time windows to diagnose\nwhether an apparently periodic signal is truly so. We suggest diagnostics to\ncheck whether a signal is consistently present in the time series, and has a\nstable phase, amplitude and period. The tests are expressed both in a\nperiodogram and Bayesian framework. Our methods are applied to the Solar\nHARPS-N data as well as HD 215152, HD 69830 and HD 13808. We find that (i) the\nHARPS-N Solar data exhibits signals at the Solar rotation period and its first\nharmonic ($\\sim$ 13.4 days). The frequency and phase of the 13.4 days signal\nappear constant within the estimation uncertainties, but its amplitude presents\nsignificant variations which can be mapped to activity levels. (ii) as\npreviously reported, we find four, three and two planets orbiting HD 215152, HD\n69830 and HD 13808.\n"}
+{"abstract": "  The task of multi-label image classification is to recognize all the object\nlabels presented in an image. Though advancing for years, small objects,\nsimilar objects and objects with high conditional probability are still the\nmain bottlenecks of previous convolutional neural network(CNN) based models,\nlimited by convolutional kernels' representational capacity. Recent vision\ntransformer networks utilize the self-attention mechanism to extract the\nfeature of pixel granularity, which expresses richer local semantic\ninformation, while is insufficient for mining global spatial dependence. In\nthis paper, we point out the three crucial problems that CNN-based methods\nencounter and explore the possibility of conducting specific transformer\nmodules to settle them. We put forward a Multi-label Transformer\narchitecture(MlTr) constructed with windows partitioning, in-window pixel\nattention, cross-window attention, particularly improving the performance of\nmulti-label image classification tasks. The proposed MlTr shows\nstate-of-the-art results on various prevalent multi-label datasets such as\nMS-COCO, Pascal-VOC, and NUS-WIDE with 88.5%, 95.8%, and 65.5% respectively.\nThe code will be available soon at https://github.com/starmemda/MlTr/\n"}
+{"abstract": "  The purpose of this study is to examine Olympic champions' characteristics on\nInstagram to first understand whether differences exist between male and female\nathletes and then to find possible correlations between these characteristics.\nWe utilized a content analytic method to analyze Olympic gold medalists'\nphotographs on Instagram. By this way we fetched data from Instagram pages of\nall those Rio2016 Olympic gold medalists who had their account publicly\navailable. The analysis of data revealed the existence of a positive monotonic\nrelationship between the ratio of following/follower and the ratio of\nengagement to follower for men gold medalists, and a strong negative monotonic\nrelationship between age and ratio of self-presenting post of both men and\nwomen gold medalists which even take a linear form for men. These findings\naligned with the relative theories and literature may come together to help the\nathletes to manage and expand their personal brand in social media.\n"}
+{"abstract": "  The evolution of young stars and disks is driven by the interplay of several\nprocesses, notably accretion and ejection of material. Critical to correctly\ndescribe the conditions of planet formation, these processes are best probed\nspectroscopically. About five-hundred orbits of the Hubble Space Telescope\n(HST) are being devoted in 2020-2022 to the ULLYSES public survey of about 70\nlow-mass (M<2Msun) young (age<10 Myr) stars at UV wavelengths. Here we present\nthe PENELLOPE Large Program that is being carried out at the ESO Very Large\nTelescope (VLT) to acquire, contemporaneous to HST, optical ESPRESSO/UVES\nhigh-resolution spectra to investigate the kinematics of the emitting gas, and\nUV-to-NIR X-Shooter medium-resolution flux-calibrated spectra to provide the\nfundamental parameters that HST data alone cannot provide, such as extinction\nand stellar properties. The data obtained by PENELLOPE have no proprietary\ntime, and the fully reduced spectra are made available to the whole community.\nHere, we describe the data and the first scientific analysis of the accretion\nproperties for the sample of thirteen targets located in the Orion OB1\nassociation and in the sigma-Orionis cluster, observed in Nov-Dec 2020. We find\nthat the accretion rates are in line with those observed previously in\nsimilarly young star-forming regions, with a variability on a timescale of days\nof <3. The comparison of the fits to the continuum excess emission obtained\nwith a slab model on the X-Shooter spectra and the HST/STIS spectra shows a\nshortcoming in the X-Shooter estimates of <10%, well within the assumed\nuncertainty. Its origin can be either a wrong UV extinction curve or due to the\nsimplicity of this modelling, and will be investigated in the course of the\nPENELLOPE program. The combined ULLYSES and PENELLOPE data will be key for a\nbetter understanding of the accretion/ejection mechanisms in young stars.\n"}
+{"abstract": "  Advances in imagery at atomic and near-atomic resolution, such as cryogenic\nelectron microscopy (cryo-EM), have led to an influx of high resolution images\nof proteins and other macromolecular structures to data banks worldwide.\nProducing a protein structure from the discrete voxel grid data of cryo-EM maps\ninvolves interpolation into the continuous spatial domain. We present a novel\ndata format called the neural cryo-EM map, which is formed from a set of neural\nnetworks that accurately parameterize cryo-EM maps and provide native,\nspatially continuous data for density and gradient. As a case study of this\ndata format, we create graph-based interpretations of high resolution\nexperimental cryo-EM maps. Normalized cryo-EM map values interpolated using the\nnon-linear neural cryo-EM format are more accurate, consistently scoring less\nthan 0.01 mean absolute error, than a conventional tri-linear interpolation,\nwhich scores up to 0.12 mean absolute error. Our graph-based interpretations of\n115 experimental cryo-EM maps from 1.15 to 4.0 Angstrom resolution provide high\ncoverage of the underlying amino acid residue locations, while accuracy of\nnodes is correlated with resolution. The nodes of graphs created from atomic\nresolution maps (higher than 1.6 Angstroms) provide greater than 99% residue\ncoverage as well as 85% full atomic coverage with a mean of than 0.19 Angstrom\nroot mean squared deviation (RMSD). Other graphs have a mean 84% residue\ncoverage with less specificity of the nodes due to experimental noise and\ndifferences of density context at lower resolutions. This work may be\ngeneralized for transforming any 3D grid-based data format into non-linear,\ncontinuous, and differentiable format for the downstream geometric deep\nlearning applications.\n"}
+{"abstract": "  Many real-life applications involve estimation of curves that exhibit\ncomplicated shapes including jumps or varying-frequency oscillations. Practical\nmethods have been devised that can adapt to a locally varying complexity of an\nunknown function (e.g. variable-knot splines, sparse wavelet reconstructions,\nkernel methods or trees/forests). However, the overwhelming majority of\nexisting asymptotic minimaxity theory is predicated on homogeneous smoothness\nassumptions. Focusing on locally Holderian functions, we provide new locally\nadaptive posterior concentration rate results under the supremum loss for\nwidely used Bayesian machine learning techniques in white noise and\nnon-parametric regression. In particular, we show that popular spike-and-slab\npriors and Bayesian CART are uniformly locally adaptive. In addition, we\npropose a new class of repulsive partitioning priors which relate to variable\nknot splines and which are exact-rate adaptive. For uncertainty quantification,\nwe construct locally adaptive confidence bands whose width depends on the local\nsmoothness and which achieve uniform asymptotic coverage under local\nself-similarity. To illustrate that spatial adaptation is not at all automatic,\nwe provide lower-bound results showing that popular hierarchical Gaussian\nprocess priors fall short of spatial adaptation.\n"}
+{"abstract": "  Back-translation is an effective strategy to improve the performance of\nNeural Machine Translation~(NMT) by generating pseudo-parallel data. However,\nseveral recent works have found that better translation quality of the\npseudo-parallel data does not necessarily lead to better final translation\nmodels, while lower-quality but more diverse data often yields stronger\nresults. In this paper, we propose a novel method to generate pseudo-parallel\ndata from a pre-trained back-translation model. Our method is a meta-learning\nalgorithm which adapts a pre-trained back-translation model so that the\npseudo-parallel data it generates would train a forward-translation model to do\nwell on a validation set. In our evaluations in both the standard datasets WMT\nEn-De'14 and WMT En-Fr'14, as well as a multilingual translation setting, our\nmethod leads to significant improvements over strong baselines. Our code will\nbe made available.\n"}
+{"abstract": "  We investigate the problem of fast-forwarding quantum evolution, whereby the\ndynamics of certain quantum systems can be simulated with gate complexity that\nis sublinear in the evolution time. We provide a definition of fast-forwarding\nthat considers the model of quantum computation, the Hamiltonians that induce\nthe evolution, and the properties of the initial states. Our definition\naccounts for any asymptotic complexity improvement of the general case and we\nuse it to demonstrate fast-forwarding in several quantum systems. In\nparticular, we show that some local spin systems whose Hamiltonians can be\ntaken into block diagonal form using an efficient quantum circuit, such as\nthose that are permutation-invariant, can be exponentially fast-forwarded. We\nalso show that certain classes of positive semidefinite local spin systems,\nalso known as frustration-free, can be polynomially fast-forwarded, provided\nthe initial state is supported on a subspace of sufficiently low energies.\nLast, we show that all quadratic fermionic systems and number-conserving\nquadratic bosonic systems can be exponentially fast-forwarded in a model where\nquantum gates are exponentials of specific fermionic or bosonic operators,\nrespectively. Our results extend the classes of physical Hamiltonians that were\npreviously known to be fast-forwarded, while not necessarily requiring methods\nthat diagonalize the Hamiltonians efficiently. We further develop a connection\nbetween fast-forwarding and precise energy measurements that also accounts for\npolynomial improvements.\n"}
+{"abstract": "  Social Networks' omnipresence and ease of use has revolutionized the\ngeneration and distribution of information in today's world. However, easy\naccess to information does not equal an increased level of public knowledge.\nUnlike traditional media channels, social networks also facilitate faster and\nwider spread of disinformation and misinformation. Viral spread of false\ninformation has serious implications on the behaviors, attitudes and beliefs of\nthe public, and ultimately can seriously endanger the democratic processes.\nLimiting false information's negative impact through early detection and\ncontrol of extensive spread presents the main challenge facing researchers\ntoday. In this survey paper, we extensively analyze a wide range of different\nsolutions for the early detection of fake news in the existing literature. More\nprecisely, we examine Machine Learning (ML) models for the identification and\nclassification of fake news, online fake news detection competitions,\nstatistical outputs as well as the advantages and disadvantages of some of the\navailable data sets. Finally, we evaluate the online web browsing tools\navailable for detecting and mitigating fake news and present some open research\nchallenges.\n"}
+{"abstract": "  To accommodate the explosive growth of the Internet-of-Things (IoT),\nincorporating interference alignment (IA) into existing multiple access (MA)\nschemes is under investigation. However, when it is applied in MIMO networks to\nimprove the system compacity, the incoming problem regarding information delay\narises which does not meet the requirement of low-latency. Therefore, in this\npaper, we first propose a new metric, degree of delay (DoD), to quantify the\nissue of information delay, and characterize DoD for three typical transmission\nschemes, i.e., TDMA, beamforming based TDMA (BD-TDMA), and retrospective\ninterference alignment (RIA). By analyzing DoD in these schemes, its value\nmainly depends on three factors, i.e., delay sensitive factor, size of data\nset, and queueing delay slot. The first two reflect the relationship between\nquality of service (QoS) and information delay sensitivity, and normalize time\ncost for each symbol, respectively. These two factors are independent of the\ntransmission schemes, and thus we aim to reduce the queueing delay slot to\nimprove DoD. Herein, three novel joint IA schemes are proposed for MIMO\ndownlink networks with different number of users. That is, hybrid antenna array\nbased partial interference elimination and retrospective interference\nregeneration scheme (HAA-PIE-RIR), HAA based improved PIE and RIR scheme\n(HAA-IPIE-RIR), and HAA based cyclic interference elimination and RIR scheme\n(HAA-CIE-RIR). Based on the first scheme, the second scheme extends the\napplication scenarios from $2$-user to $K$-user while causing heavy\ncomputational burden. The third scheme relieves such computational burden,\nthough it has certain degree of freedom (DoF) loss due to insufficient\nutilization of space resources.\n"}
+{"abstract": "  We present new H$\\alpha$ photometry for the Star-Formation Reference Survey\n(SFRS), a representative sample of star-forming galaxies in the local Universe.\nCombining these data with the panchromatic coverage of the SFRS, we provide\ncalibrations of H$\\alpha$-based star-formation rates (SFRs) with and without\ncorrection for the contribution of [$\\rm N_{^{II}}$] emission. We consider the\neffect of extinction corrections based on the Balmer decrement, infrared excess\n(IRX), and spectral energy distribution (SED) fits. We compare the SFR\nestimates derived from SED fits, polycyclic aromatic hydrocarbons, hybrid\nindicators such as 24 $\\mu$m + H$\\alpha$, 8 $\\mu$m + H$\\alpha$, FIR + FUV, and\nH$\\alpha$ emission for a sample of purely star-forming galaxies. We provide a\nnew calibration for 1.4 GHz-based SFRs by comparing to the H$\\alpha$ emission,\nand we measure a dependence of the radio-to-H$\\alpha$ emission ratio based on\ngalaxy stellar mass. Active galactic nuclei introduce biases in the\ncalibrations of different SFR indicators but have only a minimal effect on the\ninferred SFR densities from galaxy surveys. Finally, we quantify the\ncorrelation between galaxy metallicity and extinction.\n"}
+{"abstract": "  Fungi cells are capable of sensing extracellular cues through reception,\ntransduction and response systems which allow them to communicate with their\nhost and adapt to their environment. They display effective regulatory protein\nexpressions which enhance and regulate their response and adaptation to a\nvariety of triggers such as stress, hormones, light, chemicals and host\nfactors. In our recent studies, we have shown that $Pleurotus$ oyster fungi\ngenerate electrical potential impulses in the form of spike events as a result\nof their exposure to environmental, mechanical and chemical triggers,\ndemonstrating that it is possible to discern the nature of stimuli from the\nfungi electrical responses. Harnessing the power of fungi sensing and\nintelligent capabilities, we explored the communication protocols of fungi as\nreporters of human chemical secretions such as hormones, addressing the\nquestion if fungi can sense human signals. We exposed $Pleurotus$ oyster fungi\nto cortisol, directly applied to a surface of a hemp shavings substrate\ncolonised by fungi, and recorded the electrical activity of fungi. The response\nof fungi to cortisol was also supplementary studied through the application of\nX-ray to identify changes in the fungi tissue, where receiving cortisol by the\nsubstrate can inhibit the flow of calcium and, in turn, reduce its\nphysiological changes. This study could pave the way for future research on\nadaptive fungal wearables capable for detecting physiological states of humans\nand biosensors made of living fungi.\n"}
+{"abstract": "  Developers of AI-Intensive Systems--i.e., systems that involve both\n\"traditional\" software and Artificial Intelligence\"are recognizing the need to\norganize development systematically and use engineered methods and tools. Since\nan AI-Intensive System (AIIS) relies heavily on software, it is expected that\nSoftware Engineering (SE) methods and tools can help. However, AIIS development\ndiffers from the development of \"traditional\" software systems in a few\nsubstantial aspects. Hence, traditional SE methods and tools are not suitable\nor sufficient by themselves and need to be adapted and extended. A quest for\n\"SE for AI\" methods and tools has started. We believe that, in this effort, we\nshould learn from experience and avoid repeating some of the mistakes made in\nthe quest for SE in past years. To this end, a fundamental instrument is a set\nof concepts and a notation to deal with AIIS and the problems that characterize\ntheir development processes. In this paper, we propose to describe AIIS via a\nnotation that was proposed for SE and embeds a set of concepts that are\nsuitable to represent AIIS as well. We demonstrate the usage of the notation by\nmodeling some characteristics that are particularly relevant for AIIS.\n"}
+{"abstract": "  Tables on the web constitute a valuable data source for many applications,\nlike factual search and knowledge base augmentation. However, as genuine tables\ncontaining relational knowledge only account for a small proportion of tables\non the web, reliable genuine web table classification is a crucial first step\nof table extraction. Previous works usually rely on explicit feature\nconstruction from the HTML code. In contrast, we propose an approach for web\ntable classification by exploiting the full visual appearance of a table, which\nworks purely by applying a convolutional neural network on the rendered image\nof the web table. Since these visual features can be extracted automatically,\nour approach circumvents the need for explicit feature construction. A new hand\nlabeled gold standard dataset containing HTML source code and images for 13,112\ntables was generated for this task. Transfer learning techniques are applied to\nwell known VGG16 and ResNet50 architectures. The evaluation of CNN image\nclassification with fine tuned ResNet50 (F1 93.29%) shows that this approach\nachieves results comparable to previous solutions using explicitly defined HTML\ncode based features. By combining visual and explicit features, an F-measure of\n93.70% can be achieved by Random Forest classification, which beats current\nstate of the art methods.\n"}
+{"abstract": "  We show an application of a subdiffusion equation with Caputo fractional time\nderivative with respect to another function $g$ to describe subdiffusion in a\nmedium having a structure evolving over time. In this case a continuous\ntransition from subdiffusion to other type of diffusion may occur. The process\ncan be interpreted as \"ordinary\" subdiffusion with fixed subdiffusion parameter\n(subdiffusion exponent) $\\alpha$ in which time scale is changed by the function\n$g$. As example, we consider the transition from \"ordinary\" subdiffusion to\nultraslow diffusion. The function $g$ generates the additional aging process\nsuperimposed on the \"standard\" aging generated by \"ordinary\" subdiffusion. The\naging process is analyzed using coefficient of relative aging of\n$g$--subdiffusion with respect to \"ordinary\" subdiffusion. The method of\nsolving the $g$-subdiffusion equation is also presented.\n"}
+{"abstract": "  Galaxies can be classified as passive ellipticals or star-forming discs.\nEllipticals dominate at the high end of the mass range, and therefore there\nmust be a mechanism responsible for the quenching of star-forming galaxies.\nThis could either be due to the secular processes linked to the mass and star\nformation of galaxies or to external processes linked to the surrounding\nenvironment. In this paper, we analytically model the processes that govern\ngalaxy evolution and quantify their contribution. We have specifically studied\nthe effects of mass quenching, gas stripping, and mergers on galaxy quenching.\nTo achieve this, we first assumed a set of differential equations that describe\nthe processes that shape galaxy evolution. We then modelled the parameters of\nthese equations by maximising likelihood. These equations describe the\nevolution of galaxies individually, but the parameters of the equations are\nconstrained by matching the extrapolated intermediate-redshift galaxies with\nthe low-redshift galaxy population. In this study, we modelled the processes\nthat change star formation and stellar mass in massive galaxies from the GAMA\nsurvey between z~0.4 and the present. We identified and quantified the\ncontributions from mass quenching, gas stripping, and mergers to galaxy\nquenching. The quenching timescale is on average 1.2 Gyr and a closer look\nreveals support for the slow-then-rapid quenching scenario. The major merging\nrate of galaxies is about once per 10~Gyr, while the rate of ram pressure\nstripping is significantly higher. In galaxies with decreasing star formation,\nwe show that star formation is lost to fast quenching mechanisms such as ram\npressure stripping and is countered by mergers, at a rate of about 41%\nGyr$^{-1}$ and to mass quenching 49% Gyr$^{-1}$. (abridged)\n"}
+{"abstract": "  Low-rank tensors are an established framework for high-dimensional\nleast-squares problems. We propose to extend this framework by including the\nconcept of block-sparsity. In the context of polynomial regression each\nsparsity pattern corresponds to some subspace of homogeneous multivariate\npolynomials. This allows us to adapt the ansatz space to align better with\nknown sample complexity results. The resulting method is tested in numerical\nexperiments and demonstrates improved computational resource utilization and\nsample efficiency.\n"}
+{"abstract": "  We construct the global phase portraits of inflationary dynamics in\nteleparallel gravity models with a scalar field nonminimally coupled to torsion\nscalar. The adopted set of variables can clearly distinguish between different\nasymptotic states as fixed points, including the kinetic and inflationary\nregimes. The key role in the description of inflation is played by the\nheteroclinic orbits which run from the asymptotic saddle points to the late\ntime attractor point and are approximated by nonminimal slow roll conditions.\nTo seek the asymptotic fixed points we outline a heuristic method in terms of\nthe \"effective potential\" and \"effective mass\", which can be applied for any\nnonminimally coupled theories. As particular examples we study positive\nquadratic nonminimal couplings with quadratic and quartic potentials, and note\nhow the portraits differ qualitatively from the known scalar-curvature\ncounterparts. For quadratic models inflation can only occur at small nonminimal\ncoupling to torsion, as for larger coupling the asymptotic de Sitter saddle\npoint disappears from the physical phase space. Teleparallel models with\nquartic potentials are not viable for inflation at all, since for small\nnonminimal coupling the asymptotic saddle point exhibits weaker than\nexponential expansion, and for larger coupling disappears too.\n"}
+{"abstract": "  We consider a dynamic network of individuals that may hold one of two\ndifferent opinions in a two-party society. As a dynamical model, agents can\nendlessly create and delete links to satisfy a preferred degree, and the\nnetwork is shaped by \\emph{homophily}, a form of social interaction.\nCharacterized by the parameter $J \\in [-1,1]$, the latter plays a role similar\nto Ising spins: agents create links to others of the same opinion with\nprobability $(1+J)/2$, and delete them with probability $(1-J)/2$. Using Monte\nCarlo simulations and mean-field theory, we focus on the network structure in\nthe steady state. We study the effects of $J$ on degree distributions and the\nfraction of cross-party links. While the extreme cases of homophily or\nheterophily ($J= \\pm 1$) are easily understood to result in complete\npolarization or anti-polarization, intermediate values of $J$ lead to\ninteresting features of the network. Our model exhibits the intriguing feature\nof an \"overwhelming transition\" occurring when communities of different sizes\nare subject to sufficient heterophily: agents of the minority group are\noversubscribed and their average degree greatly exceeds that of the majority\ngroup. In addition, we introduce an original measure of polarization which\ndisplays distinct advantages over the commonly used average edge homogeneity.\n"}
+{"abstract": "  Learning based representation has become the key to the success of many\ncomputer vision systems. While many 3D representations have been proposed, it\nis still an unaddressed problem how to represent a dynamically changing 3D\nobject. In this paper, we introduce a compositional representation for 4D\ncaptures, i.e. a deforming 3D object over a temporal span, that disentangles\nshape, initial state, and motion respectively. Each component is represented by\na latent code via a trained encoder. To model the motion, a neural Ordinary\nDifferential Equation (ODE) is trained to update the initial state conditioned\non the learned motion code, and a decoder takes the shape code and the updated\nstate code to reconstruct the 3D model at each time stamp. To this end, we\npropose an Identity Exchange Training (IET) strategy to encourage the network\nto learn effectively decoupling each component. Extensive experiments\ndemonstrate that the proposed method outperforms existing state-of-the-art deep\nlearning based methods on 4D reconstruction, and significantly improves on\nvarious tasks, including motion transfer and completion.\n"}
+{"abstract": "  In this paper, we discuss the In\\\"on\\\"u-Winger contraction of the conformal\nalgebra. We start with the light-cone form of the Poincar\\'e algebra and extend\nit to write down the conformal algebra in $d$ dimensions. To contract the\nconformal algebra, we choose five dimensions for simplicity and compactify the\nthird transverse direction in to a circle of radius $R$ following Kaluza-Klein\ndimensional reduction method. We identify the inverse radius, $1/R$, as the\ncontraction parameter. After the contraction, the resulting representation is\nfound to be the continuous spin representation in four dimensions. Even though\nthe scaling symmetry survives the contraction, but the special conformal\ntranslation vector changes and behaves like the four-momentum vector. We also\ndiscussed the generalization to $d$ dimensions.\n"}
+{"abstract": "  Predicting (1) when the next hospital admission occurs and (2) what will\nhappen in the next admission about a patient by mining electronic health record\n(EHR) data can provide granular readmission predictions to assist clinical\ndecision making. Recurrent neural network (RNN) and point process models are\nusually employed in modelling temporal sequential data. Simple RNN models\nassume that sequences of hospital visits follow strict causal dependencies\nbetween consecutive visits. However, in the real-world, a patient may have\nmultiple co-existing chronic medical conditions, i.e., multimorbidity, which\nresults in a cascade of visits where a non-immediate historical visit can be\nmost influential to the next visit. Although a point process (e.g., Hawkes\nprocess) is able to model a cascade temporal relationship, it strongly relies\non a prior generative process assumption. We propose a novel model, MEDCAS, to\naddress these challenges. MEDCAS combines the strengths of RNN-based models and\npoint processes by integrating point processes in modelling visit types and\ntime gaps into an attention-based sequence-to-sequence learning model, which is\nable to capture the temporal cascade relationships. To supplement the patients\nwith short visit sequences, a structural modelling technique with graph-based\nmethods is used to construct the markers of the point process in MEDCAS.\nExtensive experiments on three real-world EHR datasets have been performed and\nthe results demonstrate that \\texttt{MEDCAS} outperforms state-of-the-art\nmodels in both tasks.\n"}
+{"abstract": "  The use of non-differentiable priors in Bayesian statistics has become\nincreasingly popular, in particular in Bayesian imaging analysis. Current state\nof the art methods are approximate in the sense that they replace the posterior\nwith a smooth approximation via Moreau-Yosida envelopes, and apply\ngradient-based discretized diffusions to sample from the resulting\ndistribution. We characterize the error of the Moreau-Yosida approximation and\npropose a novel implementation using underdamped Langevin dynamics. In\nmisson-critical cases, however, replacing the posterior with an approximation\nmay not be a viable option. Instead, we show that Piecewise-Deterministic\nMarkov Processes (PDMP) can be utilized for exact posterior inference from\ndistributions satisfying almost everywhere differentiability. Furthermore, in\ncontrast with diffusion-based methods, the suggested PDMP-based samplers place\nno assumptions on the prior shape, nor require access to a computationally\ncheap proximal operator, and consequently have a much broader scope of\napplication. Through detailed numerical examples, including a\nnon-differentiable circular distribution and a non-convex genomics model, we\nelucidate the relative strengths of these sampling methods on problems of\nmoderate to high dimensions, underlining the benefits of PDMP-based methods\nwhen accurate sampling is decisive.\n"}
+{"abstract": "  Ultra sparse-view computed tomography (CT) algorithms can reduce radiation\nexposure of patients, but those algorithms lack an explicit cycle consistency\nloss minimization and an explicit log-likelihood maximization in testing. Here,\nwe propose X2CT-FLOW for the maximum a posteriori (MAP) reconstruction of a\nthree-dimensional (3D) chest CT image from a single or a few two-dimensional\n(2D) projection images using a progressive flow-based deep generative model,\nespecially for ultra low-dose protocols. The MAP reconstruction can\nsimultaneously optimize the cycle consistency loss and the log-likelihood. The\nproposed algorithm is built upon a newly developed progressive flow-based deep\ngenerative model, which is featured with exact log-likelihood estimation,\nefficient sampling, and progressive learning. We applied X2CT-FLOW to\nreconstruction of 3D chest CT images from biplanar projection images without\nnoise contamination (assuming a standard-dose protocol) and with strong noise\ncontamination (assuming an ultra low-dose protocol). With the standard-dose\nprotocol, our images reconstructed from 2D projected images and 3D ground-truth\nCT images showed good agreement in terms of structural similarity (SSIM, 0.7675\non average), peak signal-to-noise ratio (PSNR, 25.89 dB on average), mean\nabsolute error (MAE, 0.02364 on average), and normalized root mean square error\n(NRMSE, 0.05731 on average). Moreover, with the ultra low-dose protocol, our\nimages reconstructed from 2D projected images and the 3D ground-truth CT images\nalso showed good agreement in terms of SSIM (0.7008 on average), PSNR (23.58 dB\non average), MAE (0.02991 on average), and NRMSE (0.07349 on average).\n"}
+{"abstract": "  In this paper, we have studied the performance and role of local optimizers\nin quantum variational circuits. We studied the performance of the two most\npopular optimizers and compared their results with some popular classical\nmachine learning algorithms. The classical algorithms we used in our study are\nsupport vector machine (SVM), gradient boosting (GB), and random forest (RF).\nThese were compared with a variational quantum classifier (VQC) using two sets\nof local optimizers viz AQGD and COBYLA. For experimenting with VQC, IBM\nQuantum Experience and IBM Qiskit was used while for classical machine learning\nmodels, sci-kit learn was used. The results show that machine learning on noisy\nimmediate scale quantum machines can produce comparable results as on classical\nmachines. For our experiments, we have used a popular restaurant sentiment\nanalysis dataset. The extracted features from this dataset and then after\napplying PCA reduced the feature set into 5 features. Quantum ML models were\ntrained using 100 epochs and 150 epochs on using EfficientSU2 variational\ncircuit. Overall, four Quantum ML models were trained and three Classical ML\nmodels were trained. The performance of the trained models was evaluated using\nstandard evaluation measures viz, Accuracy, Precision, Recall, F-Score. In all\nthe cases AQGD optimizer-based model with 100 Epochs performed better than all\nother models. It produced an accuracy of 77% and an F-Score of 0.785 which were\nhighest across all the trained models.\n"}
+{"abstract": "  Classification algorithms have been widely adopted to detect anomalies for\nvarious systems, e.g., IoT, cloud and face recognition, under the common\nassumption that the data source is clean, i.e., features and labels are\ncorrectly set. However, data collected from the wild can be unreliable due to\ncareless annotations or malicious data transformation for incorrect anomaly\ndetection. In this paper, we extend a two-layer on-line data selection\nframework: Robust Anomaly Detector (RAD) with a newly designed ensemble\nprediction where both layers contribute to the final anomaly detection\ndecision. To adapt to the on-line nature of anomaly detection, we consider\nadditional features of conflicting opinions of classifiers, repetitive\ncleaning, and oracle knowledge. We on-line learn from incoming data streams and\ncontinuously cleanse the data, so as to adapt to the increasing learning\ncapacity from the larger accumulated data set. Moreover, we explore the concept\nof oracle learning that provides additional information of true labels for\ndifficult data points. We specifically focus on three use cases, (i) detecting\n10 classes of IoT attacks, (ii) predicting 4 classes of task failures of big\ndata jobs, and (iii) recognising 100 celebrities faces. Our evaluation results\nshow that RAD can robustly improve the accuracy of anomaly detection, to reach\nup to 98.95% for IoT device attacks (i.e., +7%), up to 85.03% for cloud task\nfailures (i.e., +14%) under 40% label noise, and for its extension, it can\nreach up to 77.51% for face recognition (i.e., +39%) under 30% label noise. The\nproposed RAD and its extensions are general and can be applied to different\nanomaly detection algorithms.\n"}
+{"abstract": "  So far the null results from axion searches have enforced a huge hierarchy\nbetween the Peccei-Quinn and electroweak symmetry breaking scales. Then the\ninevitable Higgs portal poses a large fine tuning on the standard model Higgs\nscalar. Now we find if the Peccei-Quinn global symmetry has a set of residually\ndiscrete symmetries, these global and discrete symmetries can achieve a chain\nbreaking at low scales such as the accessible TeV scale. This novel mechanism\ncan accommodate some new phenomena including a sizable coupling of the standard\nmodel Higgs boson to the axion.\n"}
+{"abstract": "  BaNi$_{2}$As$_{2}$ is a non-magnetic analogue of BaFe$_{2}$As$_{2}$, the\nparent compound of a prototype ferro-pnictide high-temperature superconductor.\nRecent diffraction studies on BaNi$_{2}$As$_{2}$ demonstrate the existence of\ntwo types of periodic lattice distortions above and below the tetragonal to\ntriclinic phase transition, suggesting charge-density-wave (CDW) order to\ncompete with superconductivity. We apply time-resolved optical spectroscopy and\ndemonstrate the existence of collective CDW amplitude modes. The smooth\nevolution of these modes through the structural phase transition implies the\nCDW order in the triclinic phase smoothly evolves from the unidirectional CDW\nin the tetragonal phase and suggests that the CDW order drives the structural\nphase transition.\n"}
+{"abstract": "  The solenoid scan is one of the most common methods for the in-situ\nmeasurement of the thermal emittance of a photocathode in an rf photoinjector.\nThe fringe field of the solenoid overlaps with the gun rf field in quite a\nnumber of photoinjectors, which makes accurate knowledge of the transfer matrix\nchallenging, thus increases the measurement uncertainty of the thermal\nemittance. This paper summarizes two methods that have been used to solve the\noverlap issue and explains their deficiencies. Furthermore, we provide a new\nmethod to eliminate the measurement error due to the overlap issue in solenoid\nscans. The new method is systematically demonstrated using theoretical\nderivations, beam dynamics simulations, and experimental data based on the\nphotoinjector configurations from three different groups, proving that the\nmeasurement error with the new method is very small and can be ignored in most\nof the photoinjector configurations.\n"}
+{"abstract": "  Reinforcement learning (RL) research focuses on general solutions that can be\napplied across different domains. This results in methods that RL practitioners\ncan use in almost any domain. However, recent studies often lack the\nengineering steps (\"tricks\") which may be needed to effectively use RL, such as\nreward shaping, curriculum learning, and splitting a large task into smaller\nchunks. Such tricks are common, if not necessary, to achieve state-of-the-art\nresults and win RL competitions. To ease the engineering efforts, we distill\ndescriptions of tricks from state-of-the-art results and study how well these\ntricks can improve a standard deep Q-learning agent. The long-term goal of this\nwork is to enable combining proven RL methods with domain-specific tricks by\nproviding a unified software framework and accompanying insights in multiple\ndomains.\n"}
+{"abstract": "  We study private synthetic data generation for query release, where the goal\nis to construct a sanitized version of a sensitive dataset, subject to\ndifferential privacy, that approximately preserves the answers to a large\ncollection of statistical queries. We first present an algorithmic framework\nthat unifies a long line of iterative algorithms in the literature. Under this\nframework, we propose two new methods. The first method, private entropy\nprojection (PEP), can be viewed as an advanced variant of MWEM that adaptively\nreuses past query measurements to boost accuracy. Our second method, generative\nnetworks with the exponential mechanism (GEM), circumvents computational\nbottlenecks in algorithms such as MWEM and PEP by optimizing over generative\nmodels parameterized by neural networks, which capture a rich family of\ndistributions while enabling fast gradient-based optimization. We demonstrate\nthat PEP and GEM empirically outperform existing algorithms. Furthermore, we\nshow that GEM nicely incorporates prior information from public data while\novercoming limitations of PMW^Pub, the existing state-of-the-art method that\nalso leverages public data.\n"}
+{"abstract": "  We show that a one-dimensional regular continuous Markov process \\(\\X\\) with\nscale function \\(s\\) is a Feller--Dynkin process precisely if the space\ntransformed process \\(s (X)\\) is a martingale when stopped at the boundaries of\nits state space. As a consequence, the Feller--Dynkin and the martingale\nproperty are equivalent for regular diffusions on natural scale with open state\nspace. By means of a counterexample, we also show that this equivalence fails\nfor multi-dimensional diffusions. Moreover, for It\\^o diffusions we discuss\nrelations to Cauchy problems.\n"}
+{"abstract": "  Recent facial image synthesis methods have been mainly based on conditional\ngenerative models. Sketch-based conditions can effectively describe the\ngeometry of faces, including the contours of facial components, hair\nstructures, as well as salient edges (e.g., wrinkles) on face surfaces but lack\neffective control of appearance, which is influenced by color, material,\nlighting condition, etc. To have more control of generated results, one\npossible approach is to apply existing disentangling works to disentangle face\nimages into geometry and appearance representations. However, existing\ndisentangling methods are not optimized for human face editing, and cannot\nachieve fine control of facial details such as wrinkles. To address this issue,\nwe propose DeepFaceEditing, a structured disentanglement framework specifically\ndesigned for face images to support face generation and editing with\ndisentangled control of geometry and appearance. We adopt a local-to-global\napproach to incorporate the face domain knowledge: local component images are\ndecomposed into geometry and appearance representations, which are fused\nconsistently using a global fusion module to improve generation quality. We\nexploit sketches to assist in extracting a better geometry representation,\nwhich also supports intuitive geometry editing via sketching. The resulting\nmethod can either extract the geometry and appearance representations from face\nimages, or directly extract the geometry representation from face sketches.\nSuch representations allow users to easily edit and synthesize face images,\nwith decoupled control of their geometry and appearance. Both qualitative and\nquantitative evaluations show the superior detail and appearance control\nabilities of our method compared to state-of-the-art methods.\n"}
+{"abstract": "  We provide comprehensive regularity results and optimal conditions for a\ngeneral class of functionals involving Orlicz multi-phase of the type\n\\begin{align}\n  \\label{abst:1}\n  v\\mapsto \\int_{\\Omega} F(x,v,Dv)\\,dx, \\end{align} exhibiting non-standard\ngrowth conditions and non-uniformly elliptic properties.\n  The model functional under consideration is given by the Orlicz multi-phase\nintegral \\begin{align}\n  \\label{abst:2}\n  v\\mapsto \\int_{\\Omega} f(x,v)\\left[ G(|Dv|) +\n\\sum\\limits_{k=1}^{N}a_k(x)H_{k}(|Dv|) \\right]\\,dx,\\quad N\\geqslant 1,\n\\end{align} where $G,H_{k}$ are $N$-functions and $ 0\\leqslant a_{k}(\\cdot)\\in\nL^{\\infty}(\\Omega) $ with $0 < \\nu \\leqslant f(\\cdot) \\leqslant L$. Its\nellipticity ratio varies according to the geometry of the level sets\n$\\{a_{k}(x)=0\\}$ of the modulating coefficient functions $a_{k}(\\cdot)$ for\nevery $k\\in \\{1,\\ldots,N\\}$.\n  We give a unified treatment to show various regularity results for such\nmulti-phase problems with the coefficient functions\n$\\{a_{k}(\\cdot)\\}_{k=1}^{N}$ not necessarily H\\\"older continuous even for a\nlower level of the regularity. Moreover, assuming that minima of the functional\nabove belong to better spaces such as $C^{0,\\gamma}(\\Omega)$ or\n$L^{\\kappa}(\\Omega)$ for some $\\gamma\\in (0,1)$ and $\\kappa\\in (1,\\infty]$, we\naddress optimal conditions on nonlinearity for each variant under which we\nbuild comprehensive regularity results.\n  On the other hand, since there is a lack of homogeneity properties in the\nnonlinearity, we consider an appropriate scaling with keeping the structures of\nthe problems under which we apply Harmonic type approximation in the setting\nvarying on the a priori assumption on minima. We believe that the methods and\nproofs developed in this paper are suitable to build regularity theorems for a\nlarger class of non-autonomous functionals.\n"}
+{"abstract": "  We study thermodynamic processes in contact with a heat bath that may have an\narbitrary time-varying periodic temperature profile. Within the framework of\nstochastic thermodynamics, and for models of thermo-dynamic engines in the\nidealized case of underdamped particles in the low-friction regime, we derive\nexplicit bounds as well as optimal control protocols that draw maximum power\nand achieve maximum efficiency at any specified level of power.\n"}
+{"abstract": "  We experimentally investigate the effect of electron temperature on transport\nin the two-dimensional Dirac surface states of the three-dimensional\ntopological insulator HgTe. We find that around the minimal conductivity point,\nwhere both electrons and holes are present, heating the carriers with a DC\ncurrent results in a non-monotonic differential resistance of narrow channels.\nWe show that the observed initial increase in resistance can be attributed to\nelectron-hole scattering, while the decrease follows naturally from the change\nin Fermi energy of the charge carriers. Both effects are governed dominantly by\na van Hove singularity in the bulk valence band. The results demonstrate the\nimportance of interband electron-hole scattering in the transport properties of\ntopological insulators.\n"}
+{"abstract": "  In this work, we present a program in the computational environment,\nGeoGebra, that enables a graphical study of Newton's Method. Using this\ncomputational device, we will analyze Newton's Method convergence applied to\nvarious examples of real functions. Then, it will be given a guide to the\nconstruction of the program in GeoGebra.\n"}
+{"abstract": "  In recent paper Fakkousy et al. show that the 3D H\\'{e}non-Heiles system with\nHamiltonian $ H = \\frac{1}{2} (p_1 ^2 + p_2 ^2 + p_3 ^2) +\\frac{1}{2} (A q_1 ^2\n+ C q_2 ^2 + B q_3 ^2) + (\\alpha q_1 ^2 + \\gamma q_2 ^2)q_3 +\n\\frac{\\beta}{3}q_3 ^3 $ is integrable in sense of Liouville when $\\alpha =\n\\gamma, \\frac{\\alpha}{\\beta} = 1, A = B = C$; or $\\alpha = \\gamma,\n\\frac{\\alpha}{\\beta} = \\frac{1}{6}, A = C$, $B$-arbitrary; or $\\alpha = \\gamma,\n\\frac{\\alpha}{\\beta} = \\frac{1}{16}, A = C, \\frac{A}{B} = \\frac{1}{16}$ (and of\ncourse, when $\\alpha=\\gamma=0$, in which case the Hamiltonian is separable). It\nis known that the second case remains integrable for $A, C, B$ arbitrary. Using\nMorales-Ramis theory, we prove that there are no other cases of integrability\nfor this system.\n"}
+{"abstract": "  We derive novel explicit formulas for the inverses of truncated block\nToeplitz matrices that correspond to a multivariate minimal stationary process.\nThe main ingredients of the formulas are the Fourier coefficients of the phase\nfunction attached to the spectral density of the process. The derivation of the\nformulas is based on a recently developed finite prediction theory applied to\nthe dual process of the stationary process. We illustrate the usefulness of the\nformulas by two applications. The first one is a strong convergence result for\nsolutions of general block Toeplitz systems for a multivariate short-memory\nprocess. The second application is closed-form formulas for the inverses of\ntruncated block Toeplitz matrices corresponding to a multivariate ARMA process.\nThe significance of the latter is that they provide us with a linear-time\nalgorithm to compute the solutions of corresponding block Toeplitz systems.\n"}
+{"abstract": "  In this article, we consider mixed local and nonlocal Sobolev\n$(q,p)$-inequalities with extremal in the case $0<q<1<p<\\infty$. We prove that\nthe extremal of such inequalities is unique up to a multiplicative constant\nthat is associated with a singular elliptic problem involving the mixed local\nand nonlocal $p$-Laplace operator. Moreover, it is proved that the mixed\nSobolev inequalities are necessary and sufficient condition for the existence\nof weak solutions of such singular problems. As a consequence, a relation\nbetween the singular $p$-Laplace and mixed local and nonlocal $p$-Laplace\nequation is established. Finally, we investigate the existence, uniqueness,\nregularity and symmetry properties of weak solutions for such problems.\n"}
+{"abstract": "  Let $A$ be a Noetherian local ring with the maximal ideal $\\mathfrak{m}$ and\n$I$ be an $\\mathfrak{m}$-primary ideal in $A$. In this paper, we study a\nboundary condition of an inequality on Hilbert coefficients of an\n$I$-admissible filtration $\\mathcal{I}$. When $A$ is a Buchsbaum local ring,\nthe above equality forces Buchsbaumness on the associated graded ring of\nfiltration. Our result provides a positive resolution of a question of Corso in\na general set up of filtration.\n"}
+{"abstract": "  Three dimensional (3D) resource reuse is an important design requirement for\nthe prospective 6G wireless communication systems. Hence, we propose a\ncooperative 3D beamformer for use in 3D space. Explicitly, we harness multiple\nbase station antennas for joint zero forcing transmit pre-coding for beaming\nthe transmit signals in specific 3D directions. The technique advocated is\njudiciously configured for use in both cell-based and cell-free wireless\narchitectures. We evaluated the performance of the proposed scheme using the\nnovel metric of Volumetric Spectral Efficiency (VSE). We also characterized the\nperformance of the scheme in terms of its spectral efficiency (SE) and Bit\nError Rate (BER) through extensive simulation studies.\n"}
+{"abstract": "  Recent information extraction approaches have relied on training deep neural\nmodels. However, such models can easily overfit noisy labels and suffer from\nperformance degradation. While it is very costly to filter noisy labels in\nlarge learning resources, recent studies show that such labels take more\ntraining steps to be memorized and are more frequently forgotten than clean\nlabels, therefore are identifiable in training. Motivated by such properties,\nwe propose a simple co-regularization framework for entity-centric information\nextraction, which consists of several neural models with identical structures\nbut different parameter initialization. These models are jointly optimized with\nthe task-specific losses and are regularized to generate similar predictions\nbased on an agreement loss, which prevents overfitting on noisy labels.\nExtensive experiments on two widely used but noisy benchmarks for information\nextraction, TACRED and CoNLL03, demonstrate the effectiveness of our framework.\nWe release our code to the community for future research.\n"}
+{"abstract": "  Gene genealogies are frequently studied by measuring properties such as their\nheight ($H$), length ($L$), sum of external branches ($E$), sum of internal\nbranches ($I$), and mean of their two basal branches ($B$), and the coalescence\ntimes that contribute to the other genealogical features ($T$). These tree\nproperties and their relationships can provide insight into the effects of\npopulation-genetic processes on genealogies and genetic sequences. Here, under\nthe coalescent model, we study the 15 correlations among pairs of features of\ngenealogical trees: $H_n$, $L_n$, $E_n$, $I_n$, $B_n$, and $T_k$ for a sample\nof size $n$, with $2 \\leq k \\leq n$. We report high correlations among $H_n$,\n$L_n$, $I_n,$ and $B_n$, with all pairwise correlations of these quantities\nhaving values greater than or equal to $\\sqrt{6} [6 \\zeta(3) + 6 - \\pi^2] / (\n\\pi \\sqrt{18 + 9\\pi^2 - \\pi^4}) \\approx 0.84930$ in the limit as $n \\rightarrow\n\\infty$. Although $E_n$ has an expectation of 2 for all $n$ and $H_n$ has\nexpectation 2 in the limit as $n \\rightarrow \\infty$, their limiting\ncorrelation is 0. The results contribute toward understanding features of the\nshapes of coalescent trees.\n"}
+{"abstract": "  The discrete phase space and continuous time representation of relativistic\nquantum mechanics is further investigated here as a continuation of paper I\n[1]. The main mathematical construct used here will be that of an area-filling\nPeano curve. We show that the limit of a sequence of a class of Peano curves is\na Peano circle denoted as $\\bar{S}^{1}_{n}$, a circle of radius $\\sqrt{2n+1}$\nwhere $n \\in \\{0,1,\\cdots\\}$. We interpret this two-dimensional Peano circle in\nour framework as a phase cell inside our two-dimensional discrete phase plane.\nWe postulate that a first quantized Planck oscillator, being very light, and\nsmall beyond current experimental detection, occupies this phase cell\n$\\bar{S}^{1}_{n}$. The time evolution of this Peano circle sweeps out a\ntwo-dimensional vertical cylinder analogous to the world-sheet of string\ntheory. Extending this to three dimensional space, we introduce a\n$(2+2+2)$-dimensional phase space hyper-tori $\\bar{S}^{1}_{n^1} \\times\n\\bar{S}^{1}_{n^2} \\times \\bar{S}^{1}_{n^3}$ as the appropriate phase cell in\nthe physical dimensional discrete phase space. A geometric interpretation of\nthis structure in state space is given in terms of product fibre bundles. We\nalso study free scalar Bosons in the background $[(2+2+2)+1]$-dimensional\ndiscrete phase space and continuous time state space using the relativistic\npartial difference-differential Klein-Gordon equation. The second quantized\nfield quantas of this system can cohabit with the tiny Planck oscillators\ninside the $\\bar{S}^{1}_{n^1} \\times \\bar{S}^{1}_{n^2} \\times\n\\bar{S}^{1}_{n^3}$ phase cells for eternity. Finally, a generalized free second\nquantized Klein-Gordon equation in a higher $[(2+2+2)N+1]$-dimensional discrete\nstate space is explored. The resulting discrete phase space dimension is\ncompared to the significant spatial dimensions of some of the popular models of\nstring theory.\n"}
+{"abstract": "  Fluctuation-dissipation relations (FDRs) and time-reversal symmetry (TRS),\ntwo pillars of statistical mechanics, are both broken in generic\ndriven-dissipative systems. These systems rather lead to non-equilibrium steady\nstates far from thermal equilibrium. Driven-dissipative Ising-type models,\nhowever, are widely believed to exhibit effective thermal critical behavior\nnear their phase transitions. Contrary to this picture, we show that both the\nFDR and TRS are broken even macroscopically at, or near, criticality. This is\nshown by inspecting different observables, both even and odd operators under\ntime-reversal transformation, that overlap with the order parameter.\nRemarkably, however, a modified form of the FDR as well as TRS still holds, but\nwith drastic consequences for the correlation and response functions as well as\nthe Onsager reciprocity relations. Finally, we find that, at criticality, TRS\nremains broken even in the weakly-dissipative limit.\n"}
+{"abstract": "  Batch policy optimization considers leveraging existing data for policy\nconstruction before interacting with an environment. Although interest in this\nproblem has grown significantly in recent years, its theoretical foundations\nremain under-developed. To advance the understanding of this problem, we\nprovide three results that characterize the limits and possibilities of batch\npolicy optimization in the finite-armed stochastic bandit setting. First, we\nintroduce a class of confidence-adjusted index algorithms that unifies\noptimistic and pessimistic principles in a common framework, which enables a\ngeneral analysis. For this family, we show that any confidence-adjusted index\nalgorithm is minimax optimal, whether it be optimistic, pessimistic or neutral.\nOur analysis reveals that instance-dependent optimality, commonly used to\nestablish optimality of on-line stochastic bandit algorithms, cannot be\nachieved by any algorithm in the batch setting. In particular, for any\nalgorithm that performs optimally in some environment, there exists another\nenvironment where the same algorithm suffers arbitrarily larger regret.\nTherefore, to establish a framework for distinguishing algorithms, we introduce\na new weighted-minimax criterion that considers the inherent difficulty of\noptimal value prediction. We demonstrate how this criterion can be used to\njustify commonly used pessimistic principles for batch policy optimization.\n"}
+{"abstract": "  We prove that with high probability maximum sizes of induced forests in dense\nbinomial random graphs are concentrated in two consecutive values.\n"}
+{"abstract": "  We compute the variance asymptotics for the number of real zeros of\ntrigonometric polynomials with random dependent Gaussian coefficients and show\nthat under mild conditions, the asymptotic behavior is the same as in the\nindependent framework. In fact our proof goes beyond this framework and makes\nexplicit the variance asymptotics of various models of random Gaussian\npolynomials. Though we use the Kac--Rice formula, we do not use the explicit\nclosed formula for the second moment of the number of zeros, but we rather rely\non intrinsic properties of the Kac--Rice density.\n"}
+{"abstract": "  Automatic Speech Recognition (ASR) systems generalize poorly on accented\nspeech. The phonetic and linguistic variability of accents present hard\nchallenges for ASR systems today in both data collection and modeling\nstrategies. The resulting bias in ASR performance across accents comes at a\ncost to both users and providers of ASR.\n  We present a survey of current promising approaches to accented speech\nrecognition and highlight the key challenges in the space. Approaches mostly\nfocus on single model generalization and accent feature engineering. Among the\nchallenges, lack of a standard benchmark makes research and comparison\nespecially difficult.\n"}
+{"abstract": "  In recent years, many different approaches have been proposed to quantify the\nperformances of soccer players. Since player performances are challenging to\nquantify directly due to the low-scoring nature of soccer, most approaches\nestimate the expected impact of the players' on-the-ball actions on the\nscoreline. While effective, these approaches are yet to be widely embraced by\nsoccer practitioners. The soccer analytics community has primarily focused on\nimproving the accuracy of the models, while the explainability of the produced\nmetrics is often much more important to practitioners.\n  To help bridge the gap between scientists and practitioners, we introduce an\nexplainable Generalized Additive Model that estimates the expected value for\nshots. Unlike existing models, our model leverages features corresponding to\nwidespread soccer concepts. To this end, we represent the locations of shots by\nfuzzily assigning the shots to designated zones on the pitch that practitioners\nare familiar with. Our experimental evaluation shows that our model is as\naccurate as existing models, while being easier to explain to soccer\npractitioners.\n"}
+{"abstract": "  Along the rapid development of deep learning techniques in generative models,\nit is becoming an urgent issue to combine machine intelligence with human\nintelligence to solve the practical applications. Motivated by this\nmethodology, this work aims to adjust the machine generated character fonts\nwith the effort of human workers in the perception study. Although numerous\nfonts are available online for public usage, it is difficult and challenging to\ngenerate and explore a font to meet the preferences for common users. To solve\nthe specific issue, we propose the perceptual manifold of fonts to visualize\nthe perceptual adjustment in the latent space of a generative model of fonts.\nIn our framework, we adopt the variational autoencoder network for the font\ngeneration. Then, we conduct a perceptual study on the generated fonts from the\nmulti-dimensional latent space of the generative model. After we obtained the\ndistribution data of specific preferences, we utilize manifold learning\napproach to visualize the font distribution. In contrast to the conventional\nuser interface in our user study, the proposed font-exploring user interface is\nefficient and helpful in the designated user preference.\n"}
+{"abstract": "  Arcades of flare loops form as a consequence of magnetic reconnection\npowering solar flares and eruptions. We analyse the morphology and evolution of\nflare arcades that formed during five well-known eruptive flares. We show that\nthe arcades have a common saddle-like shape. The saddles occur despite the fact\nthat the flares were of different classes (C to X), occurred in different\nmagnetic environments, and were observed in various projections. The saddles\nare related to the presence of longer, relatively-higher, and inclined flare\nloops, consistently observed at the ends of the arcades, which we term\n`cantles'. Our observations indicate that cantles typically join straight\nportions of flare ribbons with hooked extensions of the conjugate ribbons. The\norigin of the cantles is investigated in stereoscopic observations of the 2011\nMay 9 eruptive flare carried out by the Atmospheric Imaging Assembly (AIA) and\nExtreme Ultraviolet Imager (EUVI). The mutual separation of the instruments led\nto ideal observational conditions allowing for simultaneous analysis of the\nevolving cantle and the underlying ribbon hook. Based on our analysis we\nsuggest that the formation of one of the cantles can be explained by magnetic\nreconnection between the erupting structure and its overlying arcades. We\npropose that the morphology of flare arcades can provide information about the\nreconnection geometries in which the individual flare loops originate.\n"}
+{"abstract": "  Our understanding of strong gravity near supermassive compact objects has\nrecently improved thanks to the measurements made by the Event Horizon\nTelescope (EHT). We use here the M87* shadow size to infer constraints on the\nphysical charges of a large variety of nonrotating or rotating black holes. For\nexample, we show that the quality of the measurements is already sufficient to\nrule out that M87* is a highly charged dilaton black hole. Similarly, when\nconsidering black holes with two physical and independent charges, we are able\nto exclude considerable regions of the space of parameters for the\ndoubly-charged dilaton and the Sen black holes.\n"}
+{"abstract": "  Medical imaging deep learning models are often large and complex, requiring\nspecialized hardware to train and evaluate these models. To address such\nissues, we propose the PocketNet paradigm to reduce the size of deep learning\nmodels by throttling the growth of the number of channels in convolutional\nneural networks. We demonstrate that, for a range of segmentation and\nclassification tasks, PocketNet architectures produce results comparable to\nthat of conventional neural networks while reducing the number of parameters by\nmultiple orders of magnitude, using up to 90% less GPU memory, and speeding up\ntraining times by up to 40%, thereby allowing such models to be trained and\ndeployed in resource-constrained settings.\n"}
+{"abstract": "  We study the behavior of the tail of a measure $\\mu^{\\boxtimes t}$, where\n$\\boxtimes t$ is the $t$-fold free multiplicative convolution power for $t\\geq\n1$. We focus on the case where $\\mu$ is a probability measure on the positive\nhalf-line with a regularly varying tail i.e. of the form $x^{-\\alpha} L(x)$,\nwhere $L$ is slowly varying. We obtain a phase transition in the behavior of\nthe tail of $\\mu^{\\boxplus t}$ between regimes $\\alpha<1$ and $\\alpha>1$. Our\nmain tool is a description of the regularly varying tails of $\\mu$ in terms of\nthe behavior of the corresponding $S$-transform at $0^-$. We also describe the\ntails of $\\boxtimes$ infinitely divisible measures in terms of the tails of\ncorresponding L\\'evy measure, treat symmetric measures with regularly varying\ntails and prove the free analog of the Breiman lemma.\n"}
+{"abstract": "  Motivated by the need of {\\em social distancing} during a pandemic, we\nconsider an approach to schedule the visitors of a facility (e.g., a general\nstore). Our algorithms take input from the citizens and schedule the store's\ndiscrete time-slots based on their importance to visit the facility. Naturally,\nthe formulation applies to several similar problems. We consider {\\em\nindivisible} job requests that take single or multiple slots to complete. The\nsalient properties of our approach are: it (a)~ensures social distancing by\nensuring a maximum population in a given time-slot at the facility, (b)~aims to\nprioritize individuals based on the importance of the jobs, (c)~maintains\ntruthfulness of the reported importance by adding a {\\em cooling-off} period\nafter their allocated time-slot, during which the individual cannot re-access\nthe same facility, (d)~guarantees voluntary participation of the citizens, and\nyet (e)~is computationally tractable. The mechanisms we propose are prior-free.\nWe show that the problem becomes NP-complete for indivisible multi-slot\ndemands, and provide a polynomial-time mechanism that is truthful, individually\nrational, and approximately optimal. Experiments with data collected from a\nstore show that visitors with more important (single-slot) jobs are allocated\nmore preferred slots, which comes at the cost of a longer cooling-off period\nand significantly reduces social congestion. For the multi-slot jobs, our\nmechanism yields reasonable approximation while reducing the computation time\nsignificantly.\n"}
+{"abstract": "  Customization is a general trend in software engineering, demanding systems\nthat support variable stakeholder requirements. Two opposing strategies are\ncommonly used to create variants: software clone & own and software\nconfiguration with an integrated platform. Organizations often start with the\nformer, which is cheap, agile, and supports quick innovation, but does not\nscale. The latter scales by establishing an integrated platform that shares\nsoftware assets between variants, but requires high up-front investments or\nrisky migration processes. So, could we have a method that allows an easy\ntransition or even combine the benefits of both strategies? We propose a method\nand tool that supports a truly incremental development of variant-rich systems,\nexploiting a spectrum between both opposing strategies. We design, formalize,\nand prototype the variability-management framework virtual platform. It bridges\nclone & own and platform-oriented development. Relying on\nprogramming-language-independent conceptual structures representing software\nassets, it offers operators for engineering and evolving a system, comprising:\ntraditional, asset-oriented operators and novel, feature-oriented operators for\nincrementally adopting concepts of an integrated platform. The operators record\nmeta-data that is exploited by other operators to support the transition. Among\nothers, they eliminate expensive feature-location effort or the need to trace\nclones. Our evaluation simulates the evolution of a real-world, clone-based\nsystem, measuring its costs and benefits.\n"}
+{"abstract": "  The Kronecker product-based algorithm for context-free path querying (CFPQ)\nwas proposed by Orachev et al. (2020). We reduce this algorithm to operations\nover Boolean matrices and extend it with the mechanism to extract all paths of\ninterest. We also prove $O(n^3/\\log{n})$ time complexity of the proposed\nalgorithm, where n is a number of vertices of the input graph. Thus, we provide\nthe alternative way to construct a slightly subcubic algorithm for CFPQ which\nis based on linear algebra and incremental transitive closure (a classic\ngraph-theoretic problem), as opposed to the algorithm with the same complexity\nproposed by Chaudhuri (2008). Our evaluation shows that our algorithm is a good\ncandidate to be the universal algorithm for both regular and context-free path\nquerying.\n"}
+{"abstract": "  We estimate the black hole spin parameter in GRS 1915+105 using the\ncontinuum-fitting method with revised mass and inclination constraints based on\nthe very long baseline interferometric parallax measurement of the distance to\nthis source. We fit Rossi X-ray Timing Explorer observations selected to be\naccretion disk-dominated spectral states as described in McClinotck et al.\n(2006) and Middleton et al. (2006), which previously gave discrepant spin\nestimates with this method. We find that, using the new system parameters, the\nspin in both datasets increased, providing a best-fit spin of $a_*=0.86$ for\nthe Middleton et al. data and a poor fit for the McClintock et al. dataset,\nwhich becomes pegged at the BHSPEC model limit of $a_*=0.99$. We explore the\nimpact of the uncertainties in the system parameters, showing that the best-fit\nspin ranges from $a_*= 0.4$ to 0.99 for the Middleton et al. dataset and allows\nreasonable fits to the McClintock et al. dataset with near maximal spin for\nsystem distances greater than $\\sim 10$ kpc. We discuss the uncertainties and\nimplications of these estimates.\n"}
+{"abstract": "  The CHIME/FRB Project has recently released its first catalog of fast radio\nbursts (FRBs), containing 492 unique sources. We present results from angular\ncross-correlations of CHIME/FRB sources with galaxy catalogs. We find a\nstatistically significant ($p$-value $\\sim 10^{-4}$, accounting for\nlook-elsewhere factors) cross-correlation between CHIME FRBs and galaxies in\nthe redshift range $0.3 \\lesssim z \\lesssim 0.5$, in three photometric galaxy\nsurveys: WISE$\\times$SCOS, DESI-BGS, and DESI-LRG. The level of\ncross-correlation is consistent with an order-one fraction of the CHIME FRBs\nbeing in the same dark matter halos as survey galaxies in this redshift range.\nWe find statistical evidence for a population of FRBs with large host\ndispersion measure ($\\sim 400$ pc cm$^{-3}$), and show that this can plausibly\narise from gas in large halos ($M \\sim 10^{14} M_\\odot$), for FRBs near the\nhalo center ($r \\lesssim 100$ kpc). These results will improve in future\nCHIME/FRB catalogs, with more FRBs and better angular resolution.\n"}
+{"abstract": "  3D object detection with a single image is an essential and challenging task\nfor autonomous driving. Recently, keypoint-based monocular 3D object detection\nhas made tremendous progress and achieved great speed-accuracy trade-off.\nHowever, there still exists a huge gap with LIDAR-based methods in terms of\naccuracy. To improve their performance without sacrificing efficiency, we\npropose a sort of lightweight feature pyramid network called Lite-FPN to\nachieve multi-scale feature fusion in an effective and efficient way, which can\nboost the multi-scale detection capability of keypoint-based detectors.\nBesides, the misalignment between classification score and localization\nprecision is further relieved by introducing a novel regression loss named\nattention loss. With the proposed loss, predictions with high confidence but\npoor localization are treated with more attention during the training phase.\nComparative experiments based on several state-of-the-art keypoint-based\ndetectors on the KITTI dataset show that our proposed methods manage to achieve\nsignificant improvements in both accuracy and frame rate. The code and\npretrained models will be released at\n\\url{https://github.com/yanglei18/Lite-FPN}.\n"}
+{"abstract": "  Extracting information from documents usually relies on natural language\nprocessing methods working on one-dimensional sequences of text. In some cases,\nfor example, for the extraction of key information from semi-structured\ndocuments, such as invoice-documents, spatial and formatting information of\ntext are crucial to understand the contextual meaning. Convolutional neural\nnetworks are already common in computer vision models to process and extract\nrelationships in multidimensional data. Therefore, natural language processing\nmodels have already been combined with computer vision models in the past, to\nbenefit from e.g. positional information and to improve performance of these\nkey information extraction models. Existing models were either trained on\nunpublished data sets or on an annotated collection of receipts, which did not\nfocus on PDF-like documents. Hence, in this research project a template-based\ndocument generator was created to compare state-of-the-art models for\ninformation extraction. An existing information extraction model \"Chargrid\"\n(Katti et al., 2019) was reconstructed and the impact of a bounding box\nregression decoder, as well as the impact of an NLP pre-processing step was\nevaluated for information extraction from documents. The results have shown\nthat NLP based pre-processing is beneficial for model performance. However, the\nuse of a bounding box regression decoder increases the model performance only\nfor fields that do not follow a rectangular shape.\n"}
+{"abstract": "  In this paper, we provide (i) a rigorous general theory to elicit conditions\non (tail-dependent) heavy-tailed cyber-risk distributions under which a risk\nmanagement firm might find it (non)sustainable to provide aggregate cyber-risk\ncoverage services for smart societies, and (ii)a real-data driven numerical\nstudy to validate claims made in theory assuming boundedly rational cyber-risk\nmanagers, alongside providing ideas to boost markets that aggregate dependent\ncyber-risks with heavy-tails.To the best of our knowledge, this is the only\ncomplete general theory till date on the feasibility of aggregate cyber-risk\nmanagement.\n"}
+{"abstract": "  The discovery of pulsars is of great significance in the field of physics and\nastronomy. As the astronomical equipment produces a large amount of pulsar\ndata, an algorithm for automatically identifying pulsars becomes urgent. We\npropose a deep learning framework for pulsar recognition. In response to the\nextreme imbalance between positive and negative examples and the hard negative\nsample issue presented in the HTRU Medlat Training Data,there are two coping\nstrategies in our framework: the smart under-sampling and the improved loss\nfunction. We also apply the early-fusion strategy to integrate features\nobtained from different attributes before classification to improve the\nperformance. To our best knowledge,this is the first study that integrates\nthese strategies and techniques together in pulsar recognition. The experiment\nresults show that our framework outperforms previous works with the respect to\neither the training time or F1 score. We can not only speed up the training\ntime by 10X compared with the state-of-the-art work, but also get a competitive\nresult in terms of F1 score.\n"}
+{"abstract": "  Quantum coherence and quantum correlations are studied in the strongly\ninteracting system composed of two qubits and an oscillator with the presence\nof a parametric medium. To analytically solve the system, we employ the\nadiabatic approximation approach. It assumes each qubit's characteristic\nfrequency is substantially lower than the oscillator frequency. To validate our\napproximation, a good agreement between the calculated energy spectrum of the\nHamiltonian with its numerical result is presented. The time evolution of the\nreduced density matrices of the two-qubit and the oscillator subsystems are\ncomputed from the tripartite initial state. Starting with a factorized\ntwo-qubit initial state, the quasi-periodicity in the revival and collapse\nphenomenon that occurs in the two-qubit population inversion is studied. Based\non the measure of relative entropy of coherence, we investigate the quantum\ncoherence and its explicit dependence on the parametric term both for the\ntwo-qubit and the individual qubit subsystems by adopting different choices of\nthe initial states. Similarly, the existence of quantum correlations is\ndemonstrated by studying the geometric discord and concurrence. Besides, by\nnumerically minimizing the Hilbert-Schmidt distance, the dynamically produced\nnear maximally entangled states are reconstructed. The reconstructed states are\nobserved to be nearly pure generalized Bell states. Furthermore, utilizing the\noscillator density matrix, the quadrature variance and phase-space distribution\nof the associated Husimi $Q$-function are computed in the minimum entropy\nregime and conclude that the obtained nearly pure evolved state is a squeezed\ncoherent state.\n"}
+{"abstract": "  It is an open question to give a combinatorial interpretation of the Falk\ninvariant of a hyperplane arrangement, i.e. the third rank of successive\nquotients in the lower central series of the fundamental group of the\narrangement. In this article, we give a combinatorial formula for this\ninvariant in the case of hyperplane arrangements that are complete lift\nrepresentation of certain gain graphs. As a corollary, we compute the Falk\ninvariant for the cone of the braid, Shi, Linial and semiorder arrangements.\n"}
+{"abstract": "  We discuss compatibility between various quantum aspects of bosonic fields,\nrelevant for quantum optics and quantum thermodynamics, and the mesoscopic\nformalism of reduced state of the field (RSF). In particular, we derive exact\nconditions under which Gaussian and Bogoliubov-type evolutions can be cast into\nthe RSF framework. In that regard, special emphasis is put on Gaussian thermal\noperations. To strengthen the link between the RSF formalism and the notion of\nclassicality for bosonic quantum fields, we prove that RSF contains no\ninformation about entanglement in two-mode Gaussian states. For the same\npurpose, we show that the entropic characterisation of RSF by means of the von\nNeumann entropy is qualitatively the same as its description based on the Wehrl\nentropy. Our findings help bridge the conceptual gap between quantum and\nclassical mechanics.\n"}
+{"abstract": "  Many machine learning techniques incorporate identity-preserving\ntransformations into their models to generalize their performance to previously\nunseen data. These transformations are typically selected from a set of\nfunctions that are known to maintain the identity of an input when applied\n(e.g., rotation, translation, flipping, and scaling). However, there are many\nnatural variations that cannot be labeled for supervision or defined through\nexamination of the data. As suggested by the manifold hypothesis, many of these\nnatural variations live on or near a low-dimensional, nonlinear manifold.\nSeveral techniques represent manifold variations through a set of learned Lie\ngroup operators that define directions of motion on the manifold. However\ntheses approaches are limited because they require transformation labels when\ntraining their models and they lack a method for determining which regions of\nthe manifold are appropriate for applying each specific operator. We address\nthese limitations by introducing a learning strategy that does not require\ntransformation labels and developing a method that learns the local regions\nwhere each operator is likely to be used while preserving the identity of\ninputs. Experiments on MNIST and Fashion MNIST highlight our model's ability to\nlearn identity-preserving transformations on multi-class datasets.\nAdditionally, we train on CelebA to showcase our model's ability to learn\nsemantically meaningful transformations on complex datasets in an unsupervised\nmanner.\n"}
+{"abstract": "  We study the fundamental design automation problem of equivalence checking in\nthe NISQ (Noisy Intermediate-Scale Quantum) computing realm where quantum noise\nis present inevitably. The notion of approximate equivalence of (possibly\nnoisy) quantum circuits is defined based on the Jamiolkowski fidelity which\nmeasures the average distance between output states of two super-operators when\nthe input is chosen at random. By employing tensor network contraction, we\npresent two algorithms, aiming at different situations where the number of\nnoises varies, for computing the fidelity between an ideal quantum circuit and\nits noisy implementation. The effectiveness of our algorithms is demonstrated\nby experimenting on benchmarks of real NISQ circuits. When compared with the\nstate-of-the-art implementation incorporated in Qiskit, experimental results\nshow that the proposed algorithms outperform in both efficiency and\nscalability.\n"}
+{"abstract": "  In this article we consider the length functional defined on the space of\nimmersed planar curves. The $L^2(ds)$ Riemannian metric gives rise to the curve\nshortening flow as the gradient flow of the length functional. Motivated by the\ntriviality of the metric topology in this space, we consider the gradient flow\nof the length functional with respect to the $H^1(ds)$-metric. Circles with\nradius $r_0$ shrink with $r(t) = \\sqrt{W(e^{c-2t})}$ under the flow, where $W$\nis the Lambert $W$ function and $c = r_0^2 + \\log r_0^2$. We conduct a thorough\nstudy of this flow, giving existence of eternal solutions and convergence for\ngeneral initial data, preservation of regularity in various spaces, qualitative\nproperties of the flow after an appropriate rescaling, and numerical\nsimulations.\n"}
+{"abstract": "  Relational knowledge bases (KBs) are commonly used to represent world\nknowledge in machines. However, while advantageous for their high degree of\nprecision and interpretability, KBs are usually organized according to\nmanually-defined schemas, which limit their expressiveness and require\nsignificant human efforts to engineer and maintain. In this review, we take a\nnatural language processing perspective to these limitations, examining how\nthey may be addressed in part by training deep contextual language models (LMs)\nto internalize and express relational knowledge in more flexible forms. We\npropose to organize knowledge representation strategies in LMs by the level of\nKB supervision provided, from no KB supervision at all to entity- and\nrelation-level supervision. Our contributions are threefold: (1) We provide a\nhigh-level, extensible taxonomy for knowledge representation in LMs; (2) Within\nour taxonomy, we highlight notable models, evaluation tasks, and findings, in\norder to provide an up-to-date review of current knowledge representation\ncapabilities in LMs; and (3) We suggest future research directions that build\nupon the complementary aspects of LMs and KBs as knowledge representations.\n"}
+{"abstract": "  In this paper, we show that the (admissible) character stack, which is a\nstack version of the character variety, is an open substack of the\nTeichm\\\"uller stack of homogeneous spaces of SL(2,C). We show that the\ntautological family over the representation variety, given by deforming the\nholonomy, is always a complete family. This is a generalisation of the work of\nE. Ghys about deformations of complex structures these homogeneous spaces.\n"}
+{"abstract": "  In this manuscript we demonstrate a method to reconstruct the wavefront of\nfocused beams from a measured diffraction pattern behind a diffracting mask in\nreal-time. The phase problem is solved by means of a neural network, which is\ntrained with simulated data and verified with experimental data. The neural\nnetwork allows live reconstructions within a few milliseconds, which previously\nwith iterative phase retrieval took several seconds, thus allowing the\nadjustment of complex systems and correction by adaptive optics in real time.\nThe neural network additionally outperforms iterative phase retrieval with high\nnoise diffraction patterns.\n"}
+{"abstract": "  To realize high-accuracy classification of high spatial resolution (HSR)\nimages, this letter proposes a new multi-feature fusion-based scene\nclassification framework (MF2SCF) by fusing local, global, and color features\nof HSR images. Specifically, we first extract the local features with the help\nof image slicing and densely connected convolutional networks (DenseNet), where\nthe outputs of dense blocks in the fine-tuned DenseNet-121 model are jointly\naveraged and concatenated to describe local features. Second, from the\nperspective of complex networks (CN), we model a HSR image as an undirected\ngraph based on pixel distance, intensity, and gradient, and obtain a gray-scale\nimage (GSI), a gradient of image (GoI), and three CN-based feature images to\ndelineate global features. To make the global feature descriptor resist to the\nimpact of rotation and illumination, we apply uniform local binary patterns\n(LBP) on GSI, GoI, and feature images, respectively, and generate the final\nglobal feature representation by concatenating spatial histograms. Third, the\ncolor features are determined based on the normalized HSV histogram, where HSV\nstands for hue, saturation, and value, respectively. Finally, three feature\nvectors are jointly concatenated for scene classification. Experiment results\nshow that MF2SCF significantly improves the classification accuracy compared\nwith state-of-the-art LBP-based methods and deep learning-based methods.\n"}
+{"abstract": "  In this paper we study quasilinear elliptic equations driven by the double\nphase operator and a right-hand side which has the combined effect of a\nsingular and of a parametric term. Based on the fibering method by using the\nNehari manifold we are going to prove the existence of at least two weak\nsolutions for such problem when the parameter is sufficiently small.\n"}
+{"abstract": "  Deep learning has made significant impacts on multi-view stereo systems.\nState-of-the-art approaches typically involve building a cost volume, followed\nby multiple 3D convolution operations to recover the input image's pixel-wise\ndepth. While such end-to-end learning of plane-sweeping stereo advances public\nbenchmarks' accuracy, they are typically very slow to compute. We present\n\\ouralg, a highly efficient multi-view stereo algorithm that seamlessly\nintegrates multi-view constraints into single-view networks via an attention\nmechanism. Since \\ouralg only builds on 2D convolutions, it is at least\n$2\\times$ faster than all the notable counterparts. Moreover, our algorithm\nproduces precise depth estimations and 3D reconstructions, achieving\nstate-of-the-art results on challenging benchmarks ScanNet, SUN3D, RGBD, and\nthe classical DTU dataset. our algorithm also out-performs all other algorithms\nin the setting of inexact camera poses. Our code is released at\n\\url{https://github.com/zhenpeiyang/MVS2D}\n"}
+{"abstract": "  We are interested in solutions of the nonlinear Klein-Gordon equation (NLKG)\nin $\\mathbb{R}^{1+d}$, $d\\ge1$, which behave as a soliton or a sum of solitons\nin large time. In the spirit of other articles focusing on the supercritical\ngeneralized Korteweg-de Vries equations and on the nonlinear Schr{\\\"o}dinger\nequations, we obtain an $N$-parameter family of solutions of (NLKG) which\nconverges exponentially fast to a sum of given (unstable) solitons. For $N =\n1$, this family completely describes the set of solutions converging to the\nsoliton considered; for $N\\ge 2$, we prove uniqueness in a class with explicit\nalgebraic rate of convergence.\n"}
+{"abstract": "  In this paper we completely solve the family of parametrised Thue equations\n\\[\n  X(X-F_n Y)(X-2^n Y)-Y^3=\\pm 1, \\] where $F_n$ is the $n$-th Fibonacci number.\nIn particular, for any integer $n\\geq 3$ the Thue equation has only the trivial\nsolutions $(\\pm 1,0), (0,\\mp 1), \\mp(F_n,1), \\mp(2^n,1)$.\n"}
+{"abstract": "  Indexing intervals is a fundamental problem, finding a wide range of\napplications. Recent work on managing large collections of intervals in main\nmemory focused on overlap joins and temporal aggregation problems. In this\npaper, we propose novel and efficient in-memory indexing techniques for\nintervals, with a focus on interval range queries, which are a basic component\nof many search and analysis tasks. First, we propose an optimized version of a\nsingle-level (flat) domain-partitioning approach, which may have large space\nrequirements due to excessive replication. Then, we propose a hierarchical\npartitioning approach, which assigns each interval to at most two partitions\nper level and has controlled space requirements. Novel elements of our\ntechniques include the division of the intervals at each partition into groups\nbased on whether they begin inside or before the partition boundaries, reducing\nthe information stored at each partition to the absolutely necessary, and the\neffective handling of data sparsity and skew. Experimental results on real and\nsynthetic interval sets of different characteristics show that our approaches\nare typically one order of magnitude faster than the state-of-the-art.\n"}
+{"abstract": "  We propose a novel storage scheme for three-nucleon (3N) interaction matrix\nelements relevant for the normal-ordered two-body approximation used\nextensively in ab initio calculations of atomic nuclei. This scheme reduces the\nrequired memory by approximately two orders of magnitude, which allows the\ngeneration of 3N interaction matrix elements with the standard truncation of\n$E_{\\rm 3max}=28$, well beyond the previous limit of 18. We demonstrate that\nthis is sufficient to obtain the ground-state energy of $^{132}$Sn converged to\nwithin a few MeV with respect to the $E_{\\rm 3max}$ truncation.In addition, we\nstudy the asymptotic convergence behavior and perform extrapolations to the\nun-truncated limit. Finally, we investigate the impact of truncations made when\nevolving free-space 3N interactions with the similarity renormalization group.\nWe find that the contribution of blocks with angular momentum $J_{\\rm rel}>9/2$\nto the ground-state energy is dominated by a basis-truncation artifact which\nvanishes in the large-space limit, so these computationally expensive\ncomponents can be neglected. For the two sets of nuclear interactions employed\nin this work, the resulting binding energy of $^{132}$Sn agrees with the\nexperimental value within theoretical uncertainties. This work enables\nconverged ab initio calculations of heavy nuclei.\n"}
+{"abstract": "  We show that the standard notion of entanglement is not defined for\ngravitationally anomalous two-dimensional theories because they do not admit a\nlocal tensor factorization of the Hilbert space into local Hilbert spaces.\nQualitatively, the modular flow cannot act consistently and unitarily in a\nfinite region, if there are different numbers of states with a given energy\ntraveling in the two opposite directions. We make this precise by decomposing\nit into two observations: First, a two-dimensional CFT admits a consistent\nquantization on a space with boundary only if it is not anomalous. Second, a\nlocal tensor factorization always leads to a definition of consistent, unitary,\nenergy-preserving boundary condition. As a corollary we establish a\ngeneralization of the Nielsen-Ninomiya theorem to all two-dimensional unitary\nlocal QFTs: No continuum quantum field theory in two dimensions can admit a\nlattice regulator unless its gravitational anomaly vanishes. We also show that\nthe conclusion can be generalized to six dimensions by dimensional reduction on\na four-manifold of nonvanishing signature. We advocate that these points be\nused to reinterpret the gravitational anomaly\nquantum-information-theoretically, as a fundamental obstruction to the\nlocalization of quantum information.\n"}
+{"abstract": "  Thermal jitter (phase noise) from a free-running ring oscillator is a common,\neasily implementable physical randomness source in True Random Number\nGenerators (TRNGs). We show how to evaluate entropy, autocorrelation, and bit\npattern distributions of ring oscillator noise sources, even with low jitter\nlevels or some bias. Entropy justification is required in NIST 800-90B and\nAIS-31 testing and for applications such as the RISC-V entropy source\nextension. Our numerical evaluation algorithms outperform Monte Carlo\nsimulations in speed and accuracy. We also propose a new lower bound estimation\nformula for the entropy of ring oscillator sources which applies more generally\nthan previous ones.\n"}
+{"abstract": "  This paper applies t-SNE, a visualisation technique familiar from Deep Neural\nNetwork research to argumentation graphs by applying it to the output of graph\nembeddings generated using several different methods. It shows that such a\nvisualisation approach can work for argumentation and show interesting\nstructural properties of argumentation graphs, opening up paths for further\nresearch in the area.\n"}
+{"abstract": "  The fracture stress of materials typically depends on the sample size and is\ntraditionally explained in terms of extreme value statistics. A recent work\nreported results on the carrying capacity of long polyamide and polyester wires\nand interpret the results in terms of a probabilistic argument known as the St.\nPetersburg paradox. Here, we show that the same results can be better explained\nin terms of extreme value statistics. We also discuss the relevance of rate\ndependent effects.\n"}
+{"abstract": "  This paper proposes a model to explain the potential role of inter-group\nconflicts in determining the rise and fall of signaling norms. In one\npopulation, assortative matching according to types is sustained by signaling.\nIn the other population, individuals do not signal and they are randomly\nmatched. Types evolve within each population. At the same time, the two\npopulations may engage in conflicts. Due to assortative matching, high types\ngrow faster in the population with signaling, yet they bear the cost of\nsignaling, which lowers their population's fitness in the long run. We show\nthat the survival of the signaling population depends crucially on the timing\nand the intensity of inter-group conflicts.\n"}
+{"abstract": "  For predicting the kinetics of nucleic acid reactions, continuous-time Markov\nchains (CTMCs) are widely used. The rate of a reaction can be obtained through\nthe mean first passage time (MFPT) of its CTMC. However, a typical issue in\nCTMCs is that the number of states could be large, making MFPT estimation\nchallenging, particularly for events that happen on a long time scale (rare\nevents). We propose the pathway elaboration method, a time-efficient\nprobabilistic truncation-based approach for detailed-balance CTMCs. It can be\nused for estimating the MFPT for rare events in addition to rapidly evaluating\nperturbed parameters without expensive recomputations. We demonstrate that\npathway elaboration is suitable for predicting nucleic acid kinetics by\nconducting computational experiments on 267 measurements that cover a wide\nrange of rates for different types of reactions. We utilize pathway elaboration\nto gain insight on the kinetics of two contrasting reactions, one being a rare\nevent. We then compare the performance of pathway elaboration with the\nstochastic simulation algorithm (SSA) for MFPT estimation on 237 of the\nreactions for which SSA is feasible. We further build truncated CTMCs with SSA\nand transition path sampling (TPS) to compare with pathway elaboration.\nFinally, we use pathway elaboration to rapidly evaluate perturbed model\nparameters during optimization with respect to experimentally measured rates\nfor these 237 reactions. The testing error on the remaining 30 reactions, which\ninvolved rare events and were not feasible to simulate with SSA, improved\ncomparably with the training error. Our framework and dataset are available at\nhttps://github.com/ DNA-and-Natural-Algorithms-Group/PathwayElaboration.\n"}
+{"abstract": "  We study an optimal control problem for a simple transportation model on a\npath graph. We give a closed form solution for the optimal controller, which\ncan also account for planned disturbances using feed-forward. The optimal\ncontroller is highly structured, which allows the controller to be implemented\nusing only local communication, conducted through two sweeps through the graph.\n"}
+{"abstract": "  We present the GeneScore, a concept of feature reduction for Machine Learning\nanalysis of biomedical data. Using expert knowledge, the GeneScore integrates\ndifferent molecular data types into a single score. We show that the GeneScore\nis superior to a binary matrix in the classification of cancer entities from\nSNV, Indel, CNV, gene fusion and gene expression data. The GeneScore is a\nstraightforward way to facilitate state-of-the-art analysis, while making use\nof the available scientific knowledge on the nature of molecular data features\nused.\n"}
+{"abstract": "  In their everyday life, the speech recognition performance of human listeners\nis influenced by diverse factors, such as the acoustic environment, the talker\nand listener positions, possibly impaired hearing, and optional hearing\ndevices. Prediction models come closer to considering all required factors\nsimultaneously to predict the individual speech recognition performance in\ncomplex acoustic environments. While such predictions may still not be\nsufficiently accurate for serious applications, they can already be performed\nand demand an accessible representation. In this contribution, an interactive\nrepresentation of speech recognition performance is proposed, which focuses on\nthe listeners head orientation and the spatial dimensions of an acoustic scene.\nA exemplary modeling toolchain, including an acoustic rendering model, a\nhearing device model, and a listener model, was used to generate a data set for\ndemonstration purposes. Using the spatial speech recognition maps to explore\nthis data set demonstrated the suitability of the approach to observe possibly\nrelevant behavior. The proposed representation provides a suitable target to\ncompare and validate different modeling approaches in ecologically relevant\ncontexts. Eventually, it may serve as a tool to use validated prediction models\nin the design of spaces and devices which take speech communication into\naccount.\n"}
+{"abstract": "  Let $K$ be an imaginary quadratic field with class number 1, in this paper we\nobtain the functional equation of the $p$-adic $L$-function of: (1) a small\nslope $p$-stabilisation of a Bianchi modular form, and (2) a critical slope\n$p$-stabilisation of a Base change Bianchi modular form that is\n$\\Sigma$-smooth. To treat case (2) we use $p$-adic families of Bianchi modular\nforms.\n"}
+{"abstract": "  For every prime number $p\\geq 3$ and every integer $m\\geq 1$, we prove the\nexistence of a continuous Galois representation $\\rho: G_\\mathbb{Q} \\rightarrow\nGl_m(\\mathbb{Z}_p)$ which has open image and is unramified outside\n$\\{p,\\infty\\}$ (resp. outside $\\{2,p,\\infty\\}$) when $p\\equiv 3$ mod $4$ (resp.\n$p \\equiv 1$ mod $4$).\n"}
+{"abstract": "  Low-dimensional node embeddings play a key role in analyzing graph datasets.\nHowever, little work studies exactly what information is encoded by popular\nembedding methods, and how this information correlates with performance in\ndownstream machine learning tasks. We tackle this question by studying whether\nembeddings can be inverted to (approximately) recover the graph used to\ngenerate them. Focusing on a variant of the popular DeepWalk method (Perozzi et\nal., 2014; Qiu et al., 2018), we present algorithms for accurate embedding\ninversion - i.e., from the low-dimensional embedding of a graph G, we can find\na graph H with a very similar embedding. We perform numerous experiments on\nreal-world networks, observing that significant information about G, such as\nspecific edges and bulk properties like triangle density, is often lost in H.\nHowever, community structure is often preserved or even enhanced. Our findings\nare a step towards a more rigorous understanding of exactly what information\nembeddings encode about the input graph, and why this information is useful for\nlearning tasks.\n"}
+{"abstract": "  Here, we propose an original approach for human activity recognition (HAR)\nwith commercial IEEE 802.11ac (WiFi) devices, which generalizes across\ndifferent persons, days and environments. To achieve this, we devise a\ntechnique to extract, clean and process the received phases from the channel\nfrequency response (CFR) of the WiFi channel, obtaining an estimate of the\nDoppler shift at the receiver of the communication link. The Doppler shift\nreveals the presence of moving scatterers in the environment, while not being\naffected by (environment specific) static objects. The proposed HAR framework\nis trained on data collected as a person performs four different activities and\nis tested on unseen setups, to assess its performance as the person, the day\nand/or the environment change with respect to those considered at training\ntime. In the worst case scenario, the proposed HAR technique reaches an average\naccuracy higher than 95%, validating the effectiveness of the extracted Doppler\ninformation, used in conjunction with a learning algorithm based on a neural\nnetwork, in recognizing human activities in a subject and environment\nindependent fashion.\n"}
+{"abstract": "  Good approximate eigenstates of a Hamiltionian operator which poesses a point\nas well as a continuous spectrum have beeen obtained using the Lanczos\nalgorithm. Iterating with the bare Hamiltonian operator yields spurious\nsolutions which can easily be identified. The rms radius of the ground state\neigenvector, for example, is calculated using the bare operator.\n"}
+{"abstract": "  Frequency estimation is a fundamental problem in many areas. The well-known\nA&M and its variant estimators have established an estimation framework by\niteratively interpolating the discrete Fourier transform (DFT) coefficients. In\ngeneral, those estimators require two DFT interpolations per iteration, have\nuneven initial estimation performance against frequencies, and are incompetent\nfor small sample numbers due to low-order approximations involved. Exploiting\nthe iterative estimation framework of A&M, we unprecedentedly introduce the\nPad\\'e approximation to frequency estimation, unveil some features about the\nupdating function used for refining the estimation in each iteration, and\ndevelop a simple closed-form solution to solving the residual estimation error.\nExtensive simulation results are provided, validating the superiority of the\nnew estimator over the state-the-art estimators in wide ranges of key\nparameters.\n"}
+{"abstract": "  The pandemic by COVID-19 is causing a devastating effect on the health of\nglobal population. There are several efforts to prevent the spread of the\nvirus. Among those efforts, cleaning and disinfecting public areas have become\nimportant tasks. In order to contribute in this direction, this paper proposes\na coverage path planning algorithm for a spraying drone, a micro aerial vehicle\nthat has mounted a sprayer/sprinkler system, to disinfect areas. In contrast\nwith planners in the state-of-the-art, this proposal presents i) a new\nsprayer/sprinkler model that fits a more realistic coverage volume to the drop\ndispersion and ii) a planning algorithm that efficiently restricts the flight\nto the region of interest avoiding potential collisions in bounded scenes. The\ndrone with the algorithm has been tested in several simulation scenes, showing\nthat the algorithm is effective and covers more areas with respect to other\napproaches in the literature. Note that the proposal is not limited to\ndisinfection applications, but can be applied to other ones, such as painting\nor precision agriculture.\n"}
+{"abstract": "  The combination of machine learning with control offers many opportunities,\nin particular for robust control. However, due to strong safety and reliability\nrequirements in many real-world applications, providing rigorous statistical\nand control-theoretic guarantees is of utmost importance, yet difficult to\nachieve for learning-based control schemes. We present a general framework for\nlearning-enhanced robust control that allows for systematic integration of\nprior engineering knowledge, is fully compatible with modern robust control and\nstill comes with rigorous and practically meaningful guarantees. Building on\nthe established Linear Fractional Representation and Integral Quadratic\nConstraints framework, we integrate Gaussian Process Regression as a learning\ncomponent and state-of-the-art robust controller synthesis. In a concrete\nrobust control example, our approach is demonstrated to yield improved\nperformance with more data, while guarantees are maintained throughout.\n"}
+{"abstract": "  Thermal conduction in polymer nanocomposites depends on several parameters\nincluding the thermal conductivity and geometrical features of the\nnanoparticles, the particle loading, their degree of dispersion and formation\nof a percolating networks. To enhance efficiency of thermal contact between\nfree-standing conductive nanoparticles were previously proposed. This work\nreport for the first time the investigation of molecular junctions within a\ngraphene polymer nanocomposite. Molecular dynamics simulations were conducted\nto investigate the thermal transport efficiency of molecular junctions in\npolymer tight contact, to quantify the contribution of molecular junctions when\ngraphene and the molecular junctions are surrounded by polydimethylsiloxane\n(PDMS). A strong dependence of the thermal conductance in PDMS/graphene model\nwas found, with best performances obtained with short and conformationally\nrigid molecular junctions.\n"}
+{"abstract": "  We consider adversarial training of deep neural networks through the lens of\nBayesian learning, and present a principled framework for adversarial training\nof Bayesian Neural Networks (BNNs) with certifiable guarantees. We rely on\ntechniques from constraint relaxation of non-convex optimisation problems and\nmodify the standard cross-entropy error model to enforce posterior robustness\nto worst-case perturbations in $\\epsilon$-balls around input points. We\nillustrate how the resulting framework can be combined with methods commonly\nemployed for approximate inference of BNNs. In an empirical investigation, we\ndemonstrate that the presented approach enables training of certifiably robust\nmodels on MNIST, FashionMNIST and CIFAR-10 and can also be beneficial for\nuncertainty calibration. Our method is the first to directly train certifiable\nBNNs, thus facilitating their deployment in safety-critical applications.\n"}
+{"abstract": "  In this paper, we consider distributed Nash equilibrium seeking in monotone\nand hypomonotone games. We first assume that each player has knowledge of the\nopponents' decisions and propose a passivity-based modification of the standard\ngradient-play dynamics, that we call \"Heavy Anchor\". We prove that Heavy Anchor\nallows a relaxation of strict monotonicity of the pseudo-gradient, needed for\ngradient-play dynamics, and can ensure exact asymptotic convergence in merely\nmonotone regimes. We extend these results to the setting where each player has\nonly partial information of the opponents' decisions. Each player maintains a\nlocal decision variable and an auxiliary state estimate and communicates with\ntheir neighbours to learn the opponents' actions. We modify Heavy Anchor via a\ndistributed Laplacian feedback and show how we can exploit\nequilibrium-independent passivity properties to achieve convergence to a Nash\nequilibrium in hypomonotone regimes.\n"}
+{"abstract": "  In this paper we find curves minimizing the elastic energy among curves whose\nlength is fixed and whose ends are pinned. Applying the shooting method, we can\nidentify all critical points explicitly and determine which curve is the global\nminimizer. As a result we show that the critical points consist of wavelike\nelasticae and the minimizers do not have any loops or interior inflection\npoints.\n"}
+{"abstract": "  This paper initiates a discussion of mechanism design when the participating\nagents exhibit preferences that deviate from expected utility theory (EUT). In\nparticular, we consider mechanism design for systems where the agents are\nmodeled as having cumulative prospect theory (CPT) preferences, which is a\ngeneralization of EUT preferences. We point out some of the key modifications\nneeded in the theory of mechanism design that arise from agents having CPT\npreferences and some of the shortcomings of the classical mechanism design\nframework. In particular, we show that the revelation principle, which has\ntraditionally played a fundamental role in mechanism design, does not continue\nto hold under CPT. We develop an appropriate framework that we call mediated\nmechanism design which allows us to recover the revelation principle for CPT\nagents. We conclude with some interesting directions for future work.\n"}
+{"abstract": "  We study the Becker-D\\\"oring bubblelator, a variant of the Becker-D\\\"oring\ncoagulation-fragmentation system that models the growth of clusters by gain or\nloss of monomers. Motivated by models of gas evolution oscillators from\nphysical chemistry, we incorporate injection of monomers and depletion of large\nclusters. For a wide range of physical rates, the Becker-D\\\"oring system itself\nexhibits a dynamic phase transition as mass density increases past a critical\nvalue. We connect the Becker-D\\\"oring bubblelator to a transport equation\ncoupled with an integrodifferential equation for excess monomer density by\nformal asymptotics in the near-critical regime. For suitable\ninjection/depletion rates, we argue that time-periodic solutions appear via a\nHopf bifurcation. Numerics confirm that the generation and removal of large\nclusters can become desynchronized, leading to temporal oscillations associated\nwith bursts of large-cluster nucleation.\n"}
+{"abstract": "  Graph convolutional neural networks (GCNs) generalize tradition convolutional\nneural networks (CNNs) from low-dimensional regular graphs (e.g., image) to\nhigh dimensional irregular graphs (e.g., text documents on word embeddings).\nDue to inevitable faulty data collection instruments, deceptive data\nmanipulation, or other system errors, the data might be error-contaminated.\nEven a small amount of error such as noise can compromise the ability of GCNs\nand render them inadmissible to a large extent. The key challenge is how to\neffectively and efficiently employ GCNs in the presence of erroneous data. In\nthis paper, we propose a novel Robust Graph Convolutional Neural Networks for\npossible erroneous single-view or multi-view data where data may come from\nmultiple sources. By incorporating an extra layers via Autoencoders into\ntraditional graph convolutional networks, we characterize and handle typical\nerror models explicitly. Experimental results on various real-world datasets\ndemonstrate the superiority of the proposed model over the baseline methods and\nits robustness against different types of error.\n"}
+{"abstract": "  Top-K SpMV is a key component of similarity-search on sparse embeddings. This\nsparse workload does not perform well on general-purpose NUMA systems that\nemploy traditional caching strategies. Instead, modern FPGA accelerator cards\nhave a few tricks up their sleeve. We introduce a Top-K SpMV FPGA design that\nleverages reduced precision and a novel packet-wise CSR matrix compression,\nenabling custom data layouts and delivering bandwidth efficiency often\nunreachable even in architectures with higher peak bandwidth. With HBM-based\nboards, we are 100x faster than a multi-threaded CPU implementation and 2x\nfaster than a GPU with 20% higher bandwidth, with 14.2x higher\npower-efficiency.\n"}
+{"abstract": "  To extract the Cabibbo-Kobayashi-Maskawa (CKM) matrix element $|V_{ub}|$, we\nhave re-analyzed all the available inputs (data and theory) on the $B\\to\\pi\nl\\nu$ decays including the newly available inputs on the form-factors from\nlight cone sum rule (LCSR) approach. We have reproduced and compared the\nresults with the procedure taken up by the Heavy Flavor Averaging Group\n(HFLAV), while commenting on the effect of outliers on the fits. After removing\nthe outliers and creating a comparable group of data-sets, we mention a few\nscenarios in the extraction of $|V_{ub}|$. In all those scenarios, the\nextracted values of $|V_{ub}|$ are higher than that obtained by HFLAV. Our best\nresults for $|V_{ub}|^{exc.}$ are $(3.88 \\pm 0.13)\\times 10^{-3}$ and $(3.87\n\\pm 0.13)\\times 10^{-3}$ in frequentist and Bayesian approaches, respectively,\nwhich are consistent with that extracted from inclusive decays $|V_{ub}|^{inc}$\nwithin $1~\\sigma$ confidence interval.\n"}
+{"abstract": "  Lithium metal has been an attractive candidate as a next generation anode\nmaterial. Despite its popularity, stability issues of lithium in the liquid\nelectrolyte and the formation of lithium whiskers have kept it from practical\nuse. Three-dimensional (3D) current collectors have been proposed as an\neffective method to mitigate whiskers growth. Although extensive research\nefforts have been done, the effects of three key parameters of the 3D current\ncollectors, namely the surface area, the tortuosity factor, and the surface\nchemistry, on the performance of lithium metal batteries remain elusive.\nHerein, we quantitatively studied the role of these three parameters by\nsynthesizing four types of porous copper networks with different sizes of\nwell-structured micro-channels. X-ray microscale computed tomography (micro-CT)\nallowed us to assess the surface area, the pore size and the tortuosity factor\nof the porous copper materials. A metallic Zn coating was also applied to study\nthe influence of surface chemistry on the performance of the 3D current\ncollectors. The effects of these parameters on the performance were studied in\ndetail through Scanning Electron Microscopy (SEM) and Titration Gas\nChromatography (TGC). Stochastic simulations further allowed us to interpret\nthe role of the tortuosity factor in lithiation. By understanding these\neffects, the optimal range of the key parameters is found for the porous copper\nanodes and their performance is predicted. Using these parameters to inform the\ndesign of porous copper anodes for Li deposition, Coulombic efficiencies (CE)\nof up to 99.56% are achieved, thus paving the way for the design of effective\n3D current collector systems.\n"}
+{"abstract": "  This position paper summarizes a recently developed research program focused\non inference in the context of data centric science and engineering\napplications, and forecasts its trajectory forward over the next decade. Often\none endeavours in this context to learn complex systems in order to make more\ninformed predictions and high stakes decisions under uncertainty. Some key\nchallenges which must be met in this context are robustness, generalizability,\nand interpretability. The Bayesian framework addresses these three challenges\nelegantly, while bringing with it a fourth, undesirable feature: it is\ntypically far more expensive than its deterministic counterparts. In the 21st\ncentury, and increasingly over the past decade, a growing number of methods\nhave emerged which allow one to leverage cheap low-fidelity models in order to\nprecondition algorithms for performing inference with more expensive models and\nmake Bayesian inference tractable in the context of high-dimensional and\nexpensive models. Notable examples are multilevel Monte Carlo (MLMC),\nmulti-index Monte Carlo (MIMC), and their randomized counterparts (rMLMC),\nwhich are able to provably achieve a dimension-independent (including\n$\\infty-$dimension) canonical complexity rate with respect to mean squared\nerror (MSE) of $1/$MSE. Some parallelizability is typically lost in an\ninference context, but recently this has been largely recovered via novel\ndouble randomization approaches. Such an approach delivers i.i.d. samples of\nquantities of interest which are unbiased with respect to the infinite\nresolution target distribution. Over the coming decade, this family of\nalgorithms has the potential to transform data centric science and engineering,\nas well as classical machine learning applications such as deep learning, by\nscaling up and scaling out fully Bayesian inference.\n"}
+{"abstract": "  We present a study on magnetotransport in films of the topological Dirac\nsemimetal Cd$_{3}$As$_{2}$ doped with Sb grown by molecular beam epitaxy. In\nour weak antilocalization analysis, we find a significant enhancement of the\nspin-orbit scattering rate, indicating that Sb doping leads to a strong\nincrease of the pristine band-inversion energy. We discuss possible origins of\nthis large enhancement by comparing Sb-doped Cd$_{3}$As$_{2}$ with other\ncompound semiconductors. Sb-doped Cd$_{3}$As$_{2}$ will be a suitable system\nfor further investigations and functionalization of topological Dirac\nsemimetals.\n"}
+{"abstract": "  Ultra-reliable low latency communications (URLLC) arose to serve industrial\nIoT (IIoT) use cases within the 5G. Currently, it has inherent limitations to\nsupport future services. Based on state-of-the-art research and practical\ndeployment experience, in this article, we introduce and advocate for three\nvariants: broadband, scalable and extreme URLLC. We discuss use cases and key\nperformance indicators and identify technology enablers for the new service\nmodes. We bring practical considerations from the IIoT testbed and provide an\noutlook toward some new research directions.\n"}
+{"abstract": "  The design of algorithms that leverage machine learning alongside\ncombinatorial optimization techniques is a young but thriving area of\noperations research. If trends emerge, the literature has still not converged\non the proper way of combining these two techniques or on the predictor\narchitectures that should be used. We focus on operations research problems for\nwhich no efficient algorithms are known, but that are variants of classic\nproblems for which ones efficient algorithm exist. Elaborating on recent\ncontributions that suggest using a machine learning predictor to approximate\nthe variant by the classic problem, we introduce the notion of structured\napproximation of an operations research problem by another. We provide a\ngeneric learning algorithm to fit these approximations. This algorithm requires\nonly instances of the variant in the training set, unlike previous learning\nalgorithms that also require the solution of these instances. Using tools from\nstatistical learning theory, we prove a result showing the convergence speed of\nthe estimator, and deduce an approximation ratio guarantee on the performance\nof the algorithm obtained for the variant. Numerical experiments on a single\nmachine scheduling and a stochastic vehicle scheduling problem from the\nliterature show that our learning algorithm is competitive with algorithms that\nhave access to optimal solutions, leading to state-of-the-art algorithms for\nthe variant considered.\n"}
+{"abstract": "  Spatial reasoning on multi-view line drawings by state-of-the-art supervised\ndeep networks is recently shown with puzzling low performances on the SPARE3D\ndataset. To study the reason behind the low performance and to further our\nunderstandings of these tasks, we design controlled experiments on both input\ndata and network designs. Guided by the hindsight from these experiment\nresults, we propose a simple contrastive learning approach along with other\nnetwork modifications to improve the baseline performance. Our approach uses a\nself-supervised binary classification network to compare the line drawing\ndifferences between various views of any two similar 3D objects. It enables\ndeep networks to effectively learn detail-sensitive yet view-invariant line\ndrawing representations of 3D objects. Experiments show that our method could\nsignificantly increase the baseline performance in SPARE3D, while some popular\nself-supervised learning methods cannot.\n"}
+{"abstract": "  We have entered an era of a pandemic that has shaken the world with major\nimpact to medical systems, economics and agriculture. Prominent computational\nand mathematical models have been unreliable due to the complexity of the\nspread of infections. Moreover, lack of data collection and reporting makes any\nsuch modelling attempts unreliable. Hence we need to re-look at the situation\nwith the latest data sources and most comprehensive forecasting models. Deep\nlearning models such as recurrent neural networks are well suited for modelling\ntemporal sequences. In this paper, prominent recurrent neural networks, in\nparticular \\textit{long short term memory} (LSTMs) networks, bidirectional\nLSTM, and encoder-decoder LSTM models for multi-step (short-term) forecasting\nthe spread of COVID-infections among selected states in India. We select states\nwith COVID-19 hotpots in terms of the rate of infections and compare with\nstates where infections have been contained or reached their peak and provide\ntwo months ahead forecast that shows that cases will slowly decline. Our\nresults show that long-term forecasts are promising which motivates the\napplication of the method in other countries or areas. We note that although we\nmade some progress in forecasting, the challenges in modelling remain due to\ndata and difficulty in capturing factors such as population density, travel\nlogistics, and social aspects such culture and lifestyle.\n"}
+{"abstract": "  We measure the small-scale clustering of the Data Release 16 extended Baryon\nOscillation Spectroscopic Survey Luminous Red Galaxy sample, corrected for\nfibre-collisions using Pairwise Inverse Probability weights, which give\nunbiased clustering measurements on all scales. We fit to the monopole and\nquadrupole moments and to the projected correlation function over the\nseparation range $7-60\\,h^{-1}$Mpc with a model based on the Aemulus\ncosmological emulator to measure the growth rate of cosmic structure,\nparameterized by $f\\sigma_8$. We obtain a measurement of\n$f\\sigma_8(z=0.737)=0.408\\pm0.038$, which is $1.4\\sigma$ lower than the value\nexpected from 2018 Planck data for a flat $\\Lambda$CDM model, and is more\nconsistent with recent weak-lensing measurements. The level of precision\nachieved is 1.7 times better than more standard measurements made using only\nthe large-scale modes of the same sample. We also fit to the data using the\nfull range of scales $0.1-60\\,h^{-1}$Mpc modelled by the Aemulus cosmological\nemulator and find a $4.5\\sigma$ tension in the amplitude of the halo velocity\nfield with the Planck+$\\Lambda$CDM model, driven by a mismatch on the\nnon-linear scales. This may not be cosmological in origin, and could be due to\na breakdown in the Halo Occupation Distribution model used in the emulator.\nFinally, we perform a robust analysis of possible sources of systematics,\nincluding the effects of redshift uncertainty and incompleteness due to target\nselection that were not included in previous analyses fitting to clustering\nmeasurements on small scales.\n"}
+{"abstract": "  We study the physical properties of four-dimensional, string-theoretical,\nhorizonless \"fuzzball\" geometries by imaging their shadows. Their\nmicrostructure traps light rays straying near the would-be horizon on\nlong-lived, highly redshifted chaotic orbits. In fuzzballs sufficiently near\nthe scaling limit this creates a shadow much like that of a black hole, while\navoiding the paradoxes associated with an event horizon. Observations of the\nshadow size and residual glow can potentially discriminate between fuzzballs\naway from the scaling limit and alternative models of black compact objects.\n"}
+{"abstract": "  We introduce a simplified model of physiological coughing or sneezing, in the\nform of a thin liquid layer subject to a rapid (30 m/s) air stream. The setup\nis simulated using the Volume-Of-Fluid method with octree mesh adaptation, the\nlatter allowing grid sizes small enough to capture the Kolmogorov length scale.\nThe results confirm the trend to an intermediate distribution between a\nLog-Normal and a Pareto distribution $P(d) \\propto d^{-3.3}$ for the\ndistribution of droplet sizes in agreement with a previous re-analysis of\nexperimental results by one of the authors. The mechanism of atomisation does\nnot differ qualitatively from the multiphase mixing layer experiments and\nsimulations. No mechanism for a bimodal distribution, also sometimes observed,\nis evidenced in these simulations.\n"}
+{"abstract": "  We show that the ring of modular forms with characters for the even\nunimodular lattice of signature (2,18) is obtained from the invariant ring of\n$\\mathrm{Sym}(\\mathrm{Sym}^8(V) \\oplus \\mathrm{Sym}^{12}(V))$ with respect to\nthe action of $\\mathrm{SL}(V)$ by adding a Borcherds product of weight 132 with\none relation of weight 264, where $V$ is a 2-dimensional $\\mathbb{C}$-vector\nspace. The proof is based on the study of the moduli space of elliptic K3\nsurfaces with a section.\n"}
+{"abstract": "  We present an overview of phase field modeling of active matter systems as a\ntool for capturing various aspects of complex and active interfaces. We first\ndescribe how interfaces between different phases are characterized in phase\nfield models and provide simple fundamental governing equations that describe\ntheir evolution. For a simple model, we then show how physical properties of\nthe interface, such as surface tension and interface thickness, can be\nrecovered from these equations. We then explain how the phase field formulation\ncan be coupled to various active matter realizations and discuss three\nparticular examples of continuum biphasic active matter: active\nnematic-isotropic interfaces, active matter in viscoelastic environments, and\nactive shells in fluid background. Finally, we describe how multiple phase\nfields can be used to model active cellular monolayers and present a general\nframework that can be applied to the study of tissue behaviour and collective\nmigration.\n"}
+{"abstract": "  The chiral magnetic effect with a fluctuating chiral imbalance is more\nrealistic in the evolution of quark-gluon plasma, which reflects the random\ngluonic topological transition. Incorporating this dynamics, we calculate the\nchiral magnetic current in response to space-time dependent axial gauge\npotential and magnetic field in AdS/CFT correspondence. In contrast to\nconventional treatment of constant axial chemical potential, the response\nfunction here is the AVV three-point function of the $\\mathcal{N}=4$ super\nYang-Mills at strong coupling. Through an iterative solution of the nonlinear\nequations of motion in Schwarzschild-AdS$_5$ background, we are able to express\nthe AVV function in terms of two Heun functions and prove its UV/IR finiteness,\nas expected for $\\mathcal{N}=4$ super Yang-Mills theory. We found that the\ndependence of the chiral magnetic current on a non-constant chiral imbalance is\nnon-local, different from hydrodynamic approximation, and demonstrates the\nsubtlety of the infrared limit discovered in field theoretic approach. We\nexpect our results enrich the understanding of the phenomenology of the chiral\nmagnetic effect in the context of relativistic heavy ion collisions.\n"}
+{"abstract": "  We re-examine the celebrated Doob--McKean identity that identifies a\nconditioned one-dimensional Brownian motion as the radial part of a\n3-dimensional Brownian motion or, equivalently, a Bessel-3 process, albeit now\nin the analogous setting of isotropic $\\alpha$-stable processes. We find a\nnatural analogue that matches the Brownian setting, with the role of the\nBrownian motion replaced by that of the isotropic $\\alpha$-stable process,\nproviding one interprets the components of the original identity in the right\nway.\n"}
+{"abstract": "  In this paper, we consider Bayesian point estimation and predictive density\nestimation in the binomial case. After presenting preliminary results on these\nproblems, we compare the risk functions of the Bayes estimators based on the\ntruncated and untruncated beta priors and obtain dominance conditions when the\nprobability parameter is less than or equal to a known constant. The case where\nthere are both a lower bound restriction and an upper bound restriction is also\ntreated. Then our problems are shown to be related to similar problems in the\nPoisson case. Finally, numerical studies are presented.\n"}
+{"abstract": "  In this paper, we obtain a characterization of GVZ-groups in terms of\ncommutators and monolithic quotients. This characterization is based on\ncounting formulas due to Gallagher.\n"}
+{"abstract": "  Network traffic is growing at an outpaced speed globally. The modern network\ninfrastructure makes classic network intrusion detection methods inefficient to\nclassify an inflow of vast network traffic. This paper aims to present a modern\napproach towards building a network intrusion detection system (NIDS) by using\nvarious deep learning methods. To further improve our proposed scheme and make\nit effective in real-world settings, we use deep transfer learning techniques\nwhere we transfer the knowledge learned by our model in a source domain with\nplentiful computational and data resources to a target domain with sparse\navailability of both the resources. Our proposed method achieved 98.30%\nclassification accuracy score in the source domain and an improved 98.43%\nclassification accuracy score in the target domain with a boost in the\nclassification speed using UNSW-15 dataset. This study demonstrates that deep\ntransfer learning techniques make it possible to construct large deep learning\nmodels to perform network classification, which can be deployed in the real\nworld target domains where they can maintain their classification performance\nand improve their classification speed despite the limited accessibility of\nresources.\n"}
+{"abstract": "  Efficient error-controlled lossy compressors are becoming critical to the\nsuccess of today's large-scale scientific applications because of the\never-increasing volume of data produced by the applications. In the past\ndecade, many lossless and lossy compressors have been developed with distinct\ndesign principles for different scientific datasets in largely diverse\nscientific domains. In order to support researchers and users assessing and\ncomparing compressors in a fair and convenient way, we establish a standard\ncompression assessment benchmark -- Scientific Data Reduction Benchmark\n(SDRBench). SDRBench contains a vast variety of real-world scientific datasets\nacross different domains, summarizes several critical compression quality\nevaluation metrics, and integrates many state-of-the-art lossy and lossless\ncompressors. We demonstrate evaluation results using SDRBench and summarize six\nvaluable takeaways that are helpful to the in-depth understanding of lossy\ncompressors.\n"}
+{"abstract": "  The logistic linear mixed model (LLMM) is one of the most widely used\nstatistical models. Generally, Markov chain Monte Carlo algorithms are used to\nexplore the posterior densities associated with the Bayesian LLMMs. Polson,\nScott and Windle's (2013) Polya-Gamma data augmentation (DA) technique can be\nused to construct full Gibbs (FG) samplers for the LLMMs. Here, we develop\nefficient block Gibbs (BG) samplers for Bayesian LLMMs using the Polya-Gamma DA\nmethod. We compare the FG and BG samplers in the context of a real data\nexample, as the correlation between the fixed effects and the random effects\nchanges as well as when the dimensions of the design matrices vary. These\nnumerical examples demonstrate superior performance of the BG samplers over the\nFG samplers. We also derive conditions guaranteeing geometric ergodicity of the\nBG Markov chain when the popular improper uniform prior is assigned on the\nregression coefficients, and proper or improper priors are placed on the\nvariance parameters of the random effects. This theoretical result has\nimportant practical implications as it justifies the use of asymptotically\nvalid Monte Carlo standard errors for Markov chain based estimates of the\nposterior quantities.\n"}
+{"abstract": "  We consider bivariate polynomials over the skew field of quaternions, where\nthe indeterminates commute with all coefficients and with each other. We\nanalyze existence of univariate factorizations, that is, factorizations with\nunivariate linear factors. A necessary condition for existence of univariate\nfactorizations is factorization of the norm polynomial into a product of\nunivariate polynomials. This condition is, however, not sufficient. Our central\nresult states that univariate factorizations exist after multiplication with a\nsuitable univariate real polynomial as long as the necessary factorization\ncondition is fulfilled. We present an algorithm for computing this real\npolynomial and a corresponding univariate factorization. If a univariate\nfactorization of the original polynomial exists, a suitable input of the\nalgorithm produces a constant multiplication factor, thus giving an a\nposteriori condition for existence of univariate factorizations. Some\nfactorizations obtained in this way are of interest in mechanism science. We\npresent an example of a curious closed-loop mechanism with eight revolute\njoints.\n"}
+{"abstract": "  We deal with the construction of linear connections associated with second\norder ordinary differential equations with and without first order constraints.\nWe use a novel method allowing glueing of submodule covariant derivatives to\nproduce new, closed form expressions for the Massa-Pagani connection and our\nextension of it to the constrained case.\n"}
+{"abstract": "  Speaker segmentation consists in partitioning a conversation between one or\nmore speakers into speaker turns. Usually addressed as the late combination of\nthree sub-tasks (voice activity detection, speaker change detection, and\noverlapped speech detection), we propose to train an end-to-end segmentation\nmodel that does it directly. Inspired by the original end-to-end neural speaker\ndiarization approach (EEND), the task is modeled as a multi-label\nclassification problem using permutation-invariant training. The main\ndifference is that our model operates on short audio chunks (5 seconds) but at\na much higher temporal resolution (every 16ms). Experiments on multiple speaker\ndiarization datasets conclude that our model can be used with great success on\nboth voice activity detection and overlapped speech detection. Our proposed\nmodel can also be used as a post-processing step, to detect and correctly\nassign overlapped speech regions. Relative diarization error rate improvement\nover the best considered baseline (VBx) reaches 17% on AMI, 13% on DIHARD 3,\nand 13% on VoxConverse.\n"}
+{"abstract": "  Interpreting the environmental, behavioural and psychological data from\nin-home sensory observations and measurements can provide valuable insights\ninto the health and well-being of individuals. Presents of neuropsychiatric and\npsychological symptoms in people with dementia have a significant impact on\ntheir well-being and disease prognosis. Agitation in people with dementia can\nbe due to many reasons such as pain or discomfort, medical reasons such as side\neffects of a medicine, communication problems and environment. This paper\ndiscusses a model for analysing the risk of agitation in people with dementia\nand how in-home monitoring data can support them. We proposed a semi-supervised\nmodel which combines a self-supervised learning model and a Bayesian ensemble\nclassification. We train and test the proposed model on a dataset from a\nclinical study. The dataset was collected from sensors deployed in 96 homes of\npatients with dementia. The proposed model outperforms the state-of-the-art\nmodels in recall and f1-score values by 20%. The model also indicates better\ngeneralisability compared to the baseline models.\n"}
+{"abstract": "  With the rise of the \"big data\" phenomenon in recent years, data is coming in\nmany different complex forms. One example of this is multi-way data that come\nin the form of higher-order tensors such as coloured images and movie clips.\nAlthough there has been a recent rise in models for looking at the simple case\nof three-way data in the form of matrices, there is a relative paucity of\nhigher-order tensor variate methods. The most common tensor distribution in the\nliterature is the tensor variate normal distribution; however, its use can be\nproblematic if the data exhibit skewness or outliers. Herein, we develop four\nskewed tensor variate distributions which to our knowledge are the first skewed\ntensor distributions to be proposed in the literature, and are able to\nparameterize both skewness and tail weight. Properties and parameter estimation\nare discussed, and real and simulated data are used for illustration.\n"}
+{"abstract": "  We consider the additional entropy production (EP) incurred by a fixed\nquantum or classical process on some initial state $\\rho$, above the minimum EP\nincurred by the same process on any initial state. We show that this additional\nEP, which we term the \"mismatch cost of $\\rho$\", has a universal\ninformation-theoretic form: it is given by the contraction of the relative\nentropy between $\\rho$ and the least-dissipative initial state $\\varphi$ over\ntime. We derive versions of this result for integrated EP incurred over the\ncourse of a process, for trajectory-level fluctuating EP, and for instantaneous\nEP rate. We also show that mismatch cost for fluctuating EP obeys an integral\nfluctuation theorem. Our results demonstrate a fundamental relationship between\n\"thermodynamic irreversibility\" (generation of EP) and \"logical\nirreversibility\" (inability to know the initial state corresponding to a given\nfinal state). We use this relationship to derive quantitative bounds on the\nthermodynamics of quantum error correction and to propose a\nthermodynamically-operationalized measure of the logical irreversibility of a\nquantum channel. Our results hold for both finite and infinite dimensional\nsystems, and generalize beyond EP to many other thermodynamic costs, including\nnonadiabatic EP, free energy loss, and entropy gain.\n"}
+{"abstract": "  We consider two-dimensional Schroedinger equations with honeycomb potentials\nand slow time-periodic forcing of the form: $$i\\psi_t (t,x) =\nH^\\varepsilon(t)\\psi=\\left(H^0+2i\\varepsilon A (\\varepsilon t) \\cdot \\nabla\n\\right)\\psi,\\quad H^0=-\\Delta +V (x) .$$ The unforced Hamiltonian, $H^0$, is\nknown to generically have Dirac (conical) points in its band spectrum. The\nevolution under $H^\\varepsilon(t)$ of {\\it band limited Dirac wave-packets}\n(spectrally localized near the Dirac point) is well-approximated on large time\nscales ($t\\lesssim \\varepsilon^{-2+}$) by an effective time-periodic Dirac\nequation with a gap in its quasi-energy spectrum. This quasi-energy gap is\ntypical of many reduced models of time-periodic (Floquet) materials and plays a\nrole in conclusions drawn about the full system: conduction vs. insulation,\ntopological vs. non-topological bands. Much is unknown about nature of the\nquasi-energy spectrum of original time-periodic Schroedinger equation, and it\nis believed that no such quasi-energy gap occurs. In this paper, we explain how\nto transfer quasi-energy gap information about the effective Dirac dynamics to\nconclusions about the full Schroedinger dynamics. We introduce the notion of an\n{\\it effective quasi-energy gap}, and establish its existence in the\nSchroedinger model. In the current setting, an effective quasi-energy gap is an\ninterval of quasi-energies which does not support modes with large spectral\nprojection onto band-limited Dirac wave-packets. The notion of effective\nquasi-energy gap is a physically relevant relaxation of the strict notion of\nquasi-energy spectral gap; if a system is tuned to drive or measure at momenta\nand energies near the Dirac point of $H^0$, then the resulting modes in the\neffective quasi-energy gap will only be weakly excited and detected.\n"}
+{"abstract": "  We prove that, for a Poisson vertex algebra V, the canonical injective\nhomomorphism of the variational cohomology of V to its classical cohomology is\nan isomorphism, provided that V, viewed as a differential algebra, is an\nalgebra of differential polynomials in finitely many differential variables.\nThis theorem is one of the key ingredients in the computation of vertex algebra\ncohomology. For its proof, we introduce the sesquilinear Hochschild and\nHarrison cohomology complexes and prove a vanishing theorem for the symmetric\nsesquilinear Harrison cohomology of the algebra of differential polynomials in\nfinitely many differential variables.\n"}
+{"abstract": "  We address the problem of analysing the complexity of concurrent programs\nwritten in Pi-calculus. We are interested in parallel complexity, or span,\nunderstood as the execution time in a model with maximal parallelism. A type\nsystem for parallel complexity has been recently proposed by Baillot and\nGhyselen but it is too imprecise for non-linear channels and cannot analyse\nsome concurrent processes. Aiming for a more precise analysis, we design a type\nsystem which builds on the concepts of sized types and usages. The new variant\nof usages we define accounts for the various ways a channel is employed and\nrelies on time annotations to track under which conditions processes can\nsynchronize. We prove that a type derivation for a process provides an upper\nbound on its parallel complexity.\n"}
+{"abstract": "  Deep neural networks are vulnerable to small input perturbations known as\nadversarial attacks. Inspired by the fact that these adversaries are\nconstructed by iteratively minimizing the confidence of a network for the true\nclass label, we propose the anti-adversary layer, aimed at countering this\neffect. In particular, our layer generates an input perturbation in the\nopposite direction of the adversarial one and feeds the classifier a perturbed\nversion of the input. Our approach is training-free and theoretically\nsupported. We verify the effectiveness of our approach by combining our layer\nwith both nominally and robustly trained models and conduct large-scale\nexperiments from black-box to adaptive attacks on CIFAR10, CIFAR100, and\nImageNet. Our layer significantly enhances model robustness while coming at no\ncost on clean accuracy.\n"}
+{"abstract": "  Coded caching is an emerging technique to reduce the data transmission load\nduring the peak-traffic times. In such a scheme, each file in the data center\nor library is usually divided into a number of packets to pursue a low\nbroadcasting rate based on the designed placements at each user's cache.\nHowever, the implementation complexity of this scheme increases as the number\nof packets increases. It is crucial to design a scheme with a small\nsubpacketization level, while maintaining a relatively low transmission rate.\nIt is known that the design of caches in users (i.e., the placement phase) and\nbroadcasting (i.e., the delivery phase) can be unified in one matrix, namely\nthe placement delivery array (PDA). This paper proposes a novel PDA\nconstruction by selecting proper orthogonal arrays (POAs), which generalizes\nsome known constructions but with a more flexible memory size. Based on the\nproposed PDA construction, an effective transformation is further proposed to\nenable a coded caching scheme to have a smaller subpacketization level.\nMoreover, two new coded caching schemes with the coded placement are\nconsidered. It is shown that the proposed schemes yield a lower\nsubpacketization level and transmission rate over some existing schemes.\n"}
+{"abstract": "  The Newcomb-Benford law, also known as the first-digit law, gives the\nprobability distribution associated with the first digit of a dataset, so that,\nfor example, the first significant digit has a probability of $30.1$ % of being\n$1$ and $4.58$ % of being $9$. This law can be extended to the second and next\nsignificant digits. This article presents an introduction to the discovery of\nthe law, its derivation from the scale invariance property, as well as some\napplications and examples, are presented. Additionally, a simple model of a\nMarkov process inspired by scale invariance is proposed. Within this model, it\nis proved that the probability distribution irreversibly converges to the\nNewcomb-Benford law, in analogy to the irreversible evolution toward\nequilibrium of physical systems in thermodynamics and statistical mechanics.\n"}
+{"abstract": "  Nature-inspired algorithms are commonly used for solving the various\noptimization problems. In past few decades, various researchers have proposed a\nlarge number of nature-inspired algorithms. Some of these algorithms have\nproved to be very efficient as compared to other classical optimization\nmethods. A young researcher attempting to undertake or solve a problem using\nnature-inspired algorithms is bogged down by a plethora of proposals that exist\ntoday. Not every algorithm is suited for all kinds of problem. Some score over\nothers. In this paper, an attempt has been made to summarize various leading\nresearch proposals that shall pave way for any new entrant to easily understand\nthe journey so far. Here, we classify the nature-inspired algorithms as natural\nevolution based, swarm intelligence based, biological based, science based and\nothers. In this survey, widely acknowledged nature-inspired algorithms namely-\nACO, ABC, EAM, FA, FPA, GA, GSA, JAYA, PSO, SFLA, TLBO and WCA, have been\nstudied. The purpose of this review is to present an exhaustive analysis of\nvarious nature-inspired algorithms based on its source of inspiration, basic\noperators, control parameters, features, variants and area of application where\nthese algorithms have been successfully applied. It shall also assist in\nidentifying and short listing the methodologies that are best suited for the\nproblem.\n"}
+{"abstract": "  Let $\\mathbb{F}_q$ be a finite field of order $q$. In this paper, we study\nthe distribution of rectangles in a given set in $\\mathbb{F}_q^2$. More\nprecisely, for any $0<\\delta\\le 1$, we prove that there exists an integer\n$q_0=q_0(\\delta)$ with the following property: if $q\\ge q_0$ and $A$ is a\nmultiplicative subgroup of $\\mathbb{F}^*_q$ with $|A|\\ge q^{2/3}$, then any set\n$S\\subset \\mathbb{F}_q^2$ with $|S|\\ge \\delta q^2$ contains at least $\\gg\n\\frac{|S|^4|A|^2}{q^5}$ rectangles with side-lengths in $A$. We also consider\nthe case of rectangles with one fixed side-length and the other in a\nmultiplicative subgroup $A$.\n"}
+{"abstract": "  Usually, managers or technical leaders in software projects assign issues\nmanually. This task may become more complex as more detailed is the issue\ndescription. This complexity can also make the process more prone to errors\n(misassignments) and time-consuming. In the literature, many studies aim to\naddress this problem by using machine learning strategies. Although there is no\nspecific solution that works for all companies, experience reports are useful\nto guide the choices in industrial auto-assignment projects. This paper\npresents an industrial initiative conducted in a global electronics company\nthat aims to minimize the time spent and the errors that can arise in the issue\nassignment process. As main contributions, we present a literature review, an\nindustrial report comparing different algorithms, and lessons learned during\nthe project.\n"}
+{"abstract": "  We study astrometric residuals from a simultaneous fit of Hyper Suprime-Cam\nimages. We aim to characterize these residuals and study the extent to which\nthey are dominated by atmospheric contributions for bright sources. We use\nGaussian process interpolation, with a correlation function (kernel), measured\nfrom the data, to smooth and correct the observed astrometric residual field.\nWe find that Gaussian process interpolation with a von K\\'arm\\'an kernel allows\nus to reduce the covariances of astrometric residuals for nearby sources by\nabout one order of magnitude, from 30 mas$^2$ to 3 mas$^2$ at angular scales of\n~1 arcmin, and to halve the r.m.s. residuals. Those reductions using Gaussian\nprocess interpolation are similar to recent result published with the Dark\nEnergy Survey dataset. We are then able to detect the small static astrometric\nresiduals due to the Hyper Suprime-Cam sensors effects. We discuss how the\nGaussian process interpolation of astrometric residuals impacts galaxy shape\nmeasurements, in particular in the context of cosmic shear analyses at the\nRubin Observatory Legacy Survey of Space and Time.\n"}
+{"abstract": "  The system of two nonlinear coupled oscillators is studied. As partial case\nthis system of equation is reduced to the Duffing oscillator which has many\napplications for describing physical processes. It is well known that the\ninverse scattering transform is one of the most powerful methods for solving\nthe Cauchy problems of partial differential equations. To solve the Cauchy\nproblem for nonlinear differential equations we can use the Lax pair\ncorresponding to this equation. The Lax pair for ordinary differential or\nsystems or for system ordinary differential equations allows us to find the\nfirst integrals, which also allow us to solve the question of integrability for\ndifferential equations. In this report we present the Lax pair for the system\nof coupled oscillators. Using the Lax pair we get two first integrals for the\nsystem of equations. The considered system of equations can be also reduced to\nthe fourth-order ordinary differential equation and the Lax pair can be used\nfor the ordinary differential equation of fourth order. Some special cases of\nthe system of equations are considered.\n"}
+{"abstract": "  This paper continues the program initiated in the works by the authors [60],\n[61] and [62] and by the authors with Li [51] and [52] to establish higher\norder Poincar\\'e-Sobolev, Hardy-Sobolev-Maz'ya, Adams and Hardy-Adams\ninequalities on real hyperbolic spaces using the method of Helgason-Fourier\nanalysis on the hyperbolic spaces. The aim of this paper is to establish such\ninequalities on the Siegel domains and complex hyperbolic spaces. Firstly, we\nprove a factorization theorem for the operators on the complex hyperbolic space\nwhich is closely related to Geller' operator, as well as the CR invariant\ndifferential operators on the Heisenberg group and CR sphere. Secondly, by\nusing, among other things, the Kunze-Stein phenomenon on a closed linear group\n$SU(1,n)$ and Helgason-Fourier analysis techniques on the complex hyperbolic\nspaces, we establish the Poincar\\'e-Sobolev, Hardy-Sobolev-Maz'ya inequality on\nthe Siegel domain $\\mathcal{U}^{n}$ and the unit ball\n$\\mathbb{B}_{\\mathbb{C}}^{n}$. Finally, we establish the sharp Hardy-Adams\ninequalities and sharp Adams type inequalities on Sobolev spaces of any\npositive fractional order on the complex hyperbolic spaces. The factorization\ntheorem we proved is of its independent interest in the Heisenberg group and CR\nsphere and CR invariant differential operators therein.\n"}
+{"abstract": "  Low Earth orbit (LEO) satellite constellations rely on inter-satellite links\n(ISLs) to provide global connectivity. However, one significant challenge is to\nestablish and maintain inter-plane ISLs, which support communication between\ndifferent orbital planes. This is due to the fast movement of the\ninfrastructure and to the limited computation and communication capabilities on\nthe satellites. In this paper, we make use of antenna arrays with either Butler\nmatrix beam switching networks or digital beam steering to establish the\ninter-plane ISLs in a LEO satellite constellation. Furthermore, we present a\ngreedy matching algorithm to establish inter-plane ISLs with the objective of\nmaximizing the sum of rates. This is achieved by sequentially selecting the\npairs, switching or pointing the beams and, finally, setting the data rates.\nOur results show that, by selecting an update period of 30 seconds for the\nmatching, reliable communication can be achieved throughout the constellation,\nwhere the impact of interference in the rates is less than 0.7 % when compared\nto orthogonal links, even for relatively small antenna arrays. Furthermore,\ndoubling the number of antenna elements increases the rates by around one order\nof magnitude.\n"}
+{"abstract": "  Given a random real quadratic field from $\\{ \\mathbb{Q}(\\sqrt{p}\\,) ~|~ p\n\\text{ primes} \\}$, the conjectural probability $\\mathbb{P}(h=q)$ that it has\nclass number $q$ is given for all positive odd integers $q$. Some related\nconjectures of the Cohen-Lenstra heuristic are given here as corollaries. These\nresults suggest that the set of real quadratic number fields may have some\nnatural hierarchical structures.\n"}
+{"abstract": "  Dispersionless bands -- \\emph{flatbands} -- provide an excellent testbed for\nnovel physical phases due to the fine-tuned character of flatband tight-binding\nHamiltonians. The accompanying macroscopic degeneracy makes any perturbation\nrelevant, no matter how small. For short-range hoppings flatbands support\ncompact localized states, which allowed to develop systematic flatband\ngenerators in $d=1$ dimension in Phys. Rev. B {\\bf 95} 115135 (2017) and Phys.\nRev. B {\\bf 99} 125129 (2019). Here we extend this generator approach to $d=2$\ndimensions. The \\emph{shape} of a compact localized state turns into an\nimportant additional flatband classifier. This allows us to obtain analytical\nsolutions for classes of $d=2$ flatband networks and to re-classify and\nre-obtain known ones, such as the checkerboard, kagome, Lieb and Tasaki\nlattices. Our generator can be straightforwardly generalized to three lattice\ndimensions as well.\n"}
+{"abstract": "  In this article we introduce the notion of a Ribaucour partial tube and use\nit to derive several applications. These are based on a characterization of\nRibaucour partial tubes as the immersions of a product of two manifolds into a\nspace form such that the distributions given by the tangent spaces of the\nfactors are orthogonal to each other with respect to the induced metric, are\ninvariant under all shape operators, and one of them is spherical. Our first\napplication is a classification of all hypersurfaces with dimension at least\nthree of a space form that carry a spherical foliation of codimension one,\nextending previous results by Dajczer, Rovenski and the second author for the\ntotally geodesic case. We proceed to prove a general decomposition theorem for\nimmersions of product manifolds, which extends several related results. Other\nmain applications concern the class of hypersurfaces of $\\mathbb{R}^{n+1}$ that\nare of Enneper type, that is, hypersurfaces that carry a family of lines of\ncurvature, correspondent to a simple principal curvature, whose orthogonal\n$(n-1)$-dimensional distribution is integrable and whose leaves are contained\nin hyperspheres or affine hyperplanes of $\\mathbb{R}^{n+1}$. We show how\nRibaucour partial tubes in the sphere can be used to describe all\n$n$-dimensional hypersurfaces of Enneper type for which the leaves of the\n$(n-1)$-dimensional distribution are contained in affine hyperplanes of\n$\\mathbb{R}^{n+1}$, and then show how a general hypersurface of Enneper type\ncan be constructed in terms of a hypersurface in the latter class. We give an\nexplicit description of some special hypersurfaces of Enneper type, among which\nare natural generalizations of the so called Joachimsthal surfaces.\n"}
+{"abstract": "  This paper proposes a method to relax the conditional independence assumption\nof connectionist temporal classification (CTC)-based automatic speech\nrecognition (ASR) models. We train a CTC-based ASR model with auxiliary CTC\nlosses in intermediate layers in addition to the original CTC loss in the last\nlayer. During both training and inference, each generated prediction in the\nintermediate layers is summed to the input of the next layer to condition the\nprediction of the last layer on those intermediate predictions. Our method is\neasy to implement and retains the merits of CTC-based ASR: a simple model\narchitecture and fast decoding speed. We conduct experiments on three different\nASR corpora. Our proposed method improves a standard CTC model significantly\n(e.g., more than 20 % relative word error rate reduction on the WSJ corpus)\nwith a little computational overhead. Moreover, for the TEDLIUM2 corpus and the\nAISHELL-1 corpus, it achieves a comparable performance to a strong\nautoregressive model with beam search, but the decoding speed is at least 30\ntimes faster.\n"}
+{"abstract": "  Physical-layer key generation (PKG) can generate symmetric keys between two\ncommunication ends based on the reciprocal uplink and downlink channels. By\nsmartly reconfiguring the radio signal propagation, intelligent reflecting\nsurface (IRS) is able to improve the secret key rate of PKG. However, existing\nworks involving IRS-assisted PKG are concentrated in single-antenna wireless\nnetworks. So this paper investigates the problem of PKG in the IRS-assisted\nmultiple-input single-output (MISO) system, which aims to maximize the secret\nkey rate by optimally designing the IRS passive beamforming. First, we analyze\nthe correlation between channel state information (CSI) of eavesdropper and\nlegitimate ends and derive the expression of the upper bound of secret key rate\nunder passive eavesdropping attack. Then, an optimal algorithm for designing\nIRS reflecting coefficients based on Semi-Definite Relaxation (SDR) and Taylor\nexpansion is proposed to maximize the secret key rate. Numerical results show\nthat our optimal IRS-assisted PKG scheme can achieve much higher secret key\nrate when compared with two benchmark schemes.\n"}
+{"abstract": "  We investigate $\\lambda$-Hilbert transform, $\\lambda$-Possion integral and\nconjugate $\\lambda$-Poisson integral on the atomic Hardy space in the Dunkl\nsetting and establish a new version of Paley type inequality which extends the\nresults in \\cite{F} and \\cite{ZhongKai Li 3}.\n"}
+{"abstract": "  Arc-locally semicomplete and arc-locally in-semicomplete digraphs were\nintroduced by Bang-Jensen as a common generalization of both semicomplete and\nsemicomplete bipartite digraphs in 1993. Later, Bang-Jensen (2004),\nGaleana-Sanchez and Goldfeder (2009) and Wang and Wang (2009) provided a\ncharacterization of strong arc-locally semicomplete digraphs. In 2009, Wang and\nWang characterized strong arc-locally in-semicomplete digraphs. In 2012,\nGaleana-Sanchez and Goldfeder provided a characterization of all arc-locally\nsemicomplete digraphs which generalizes some results by Bang-Jensen. In this\npaper, we characterize the structure of arbitrary connected arc-locally (out)\nin-semicomplete digraphs and arbitrary connected arc-locally semicomplete\ndigraphs.\n"}
+{"abstract": "  We study Markov population processes on large graphs, with the local state\ntransition rates of a single vertex being linear function of its neighborhood.\nA simple way to approximate such processes is by a system of ODEs called the\nhomogeneous mean-field approximation (HMFA). Our main result is showing that\nHMFA is guaranteed to be the large graph limit of the stochastic dynamics on a\nfinite time horizon if and only if the graph-sequence is quasi-random. Explicit\nerror bound is given and being of order $\\frac{1}{\\sqrt{N}}$ plus the largest\ndiscrepancy of the graph. For Erd\\H{o}s R\\'{e}nyi and random regular graphs we\nshow an error bound of order the inverse square root of the average degree. In\ngeneral, diverging average degrees is shown to be a necessary condition for the\nHMFA to be accurate. Under special conditions, some of these results also apply\nto more detailed type of approximations like the inhomogenous mean field\napproximation (IHMFA). We pay special attention to epidemic applications such\nas the SIS process.\n"}
+{"abstract": "  Downscaling aims to link the behaviour of the atmosphere at fine scales to\nproperties measurable at coarser scales, and has the potential to provide high\nresolution information at a lower computational and storage cost than numerical\nsimulation alone. This is especially appealing for targeting convective scales,\nwhich are at the edge of what is possible to simulate operationally. Since\nconvective scale weather has a high degree of independence from larger scales,\na generative approach is essential. We here propose a statistical method for\ndownscaling moist variables to convective scales using conditional Gaussian\nrandom fields, with an application to wet bulb potential temperature (WBPT)\ndata over the UK. Our model uses an adaptive covariance estimation to capture\nthe variable spatial properties at convective scales. We further propose a\nmethod for the validation, which has historically been a challenge for\ngenerative models.\n"}
+{"abstract": "  Quantum spins of mesoscopic size are a well-studied playground for\nengineering non-classical states. If the spin represents the collective state\nof an ensemble of qubits, its non-classical behavior is linked to entanglement\nbetween the qubits. In this work, we report on an experimental study of\nentanglement in dysprosium's electronic spin. Its ground state, of angular\nmomentum $J=8$, can formally be viewed as a set of $2J$ qubits symmetric upon\nexchange. To access entanglement properties, we partition the spin by optically\ncoupling it to an excited state $J'=J-1$, which removes a pair of qubits in a\nstate defined by the light polarization. Starting with the well-known W and\nsqueezed states, we extract the concurrence of qubit pairs, which quantifies\ntheir non-classical character. We also directly demonstrate entanglement\nbetween the 14- and 2-qubit subsystems via an increase in entropy upon\npartition. In a complementary set of experiments, we probe decoherence of a\nstate prepared in the excited level $J'=J+1$ and interpret spontaneous emission\nas a loss of a qubit pair in a random state. This allows us to contrast the\nrobustness of pairwise entanglement of the W state with the fragility of the\ncoherence involved in a Schr\\\"odinger cat state. Our findings open up the\npossibility to engineer novel types of entangled atomic ensembles, in which\nentanglement occurs within each atom's electronic spin as well as between\ndifferent atoms.\n"}
+{"abstract": "  High quality (HQ) video services occupy large portions of the total bandwidth\nand are among the main causes of congestion at network bottlenecks. Since video\nis resilient to data loss, throwing away less important video packets can ease\nnetwork congestion with minimal damage to video quality and free up bandwidth\nfor other data flows. Frame type is one of the features that can be used to\ndetermine the importance of video packets, but this information is stored in\nthe packet payload. Due to limited processing power of devices in high\nthroughput/speed networks, data encryption and user credibility issues, it is\ncostly for the network to find the frame type of each packet. Therefore, a fast\nand reliable standalone method to recognize video packet types at network level\nis desired. This paper proposes a method to model the structure of live video\nstreams in a network node which results in determining the frame type of each\npacket. It enables the network nodes to mark and if need be to discard less\nimportant video packets ahead of congestion, and therefore preserve video\nquality and free up bandwidth for more important packet types. The method does\nnot need to read the IP layer payload and uses only the packet header data for\ndecisions. Experimental results indicate while dropping packets under packet\ntype prediction degrades video quality with respect to its true type by 0.5-3\ndB, it has 7-20 dB improvement over when packets are dropped randomly.\n"}
+{"abstract": "  Improving wind turbine efficiency is essential for reducing the costs of\nenergy production. The highly nonlinear dynamics of the wind turbines and their\nuncertain operating conditions have posed many challenges for their control\nmethods. In this work, a robust control strategy based on sliding mode and\nadaptive fuzzy disturbance observer is proposed for speed tracking in a\nvariable speed wind turbine. First, the nonlinear mathematical model that\ndescribes the dynamics of the variable speed wind turbine is derived. This\nnonlinear model is then used to derive the control methodology and to find\nstability and robustness conditions. The control approach is designed to track\nthe optimal wind speed that causes maximum energy extraction. The stability\ncondition was verified using the Lyapunov stability theory. A simulation study\nwas conducted to verify the method, and a comparative analysis was used to\nmeasure its effectiveness. The results showed a high tracking ability and\nrobustness of the developed methodology. Moreover, higher power extraction was\nobserved when compared to a classical control method.\n"}
+{"abstract": "  Many modern systems for speaker diarization, such as the recently-developed\nVBx approach, rely on clustering of DNN speaker embeddings followed by\nresegmentation. Two problems with this approach are that the DNN is not\ndirectly optimized for this task, and the parameters need significant retuning\nfor different applications. We have recently presented progress in this\ndirection with a Leave-One-Out Gaussian PLDA (LGP) clustering algorithm and an\napproach to training the DNN such that embeddings directly optimize performance\nof this scoring method. This paper presents a new two-pass version of this\nsystem, where the second pass uses finer time resolution to significantly\nimprove overall performance. For the Callhome corpus, we achieve the first\npublished error rate below 4% without any task-dependent parameter tuning. We\nalso show significant progress towards a robust single solution for multiple\ndiarization tasks.\n"}
+{"abstract": "  We use 3D fully kinetic particle-in-cell simulations to study the occurrence\nof magnetic reconnection in a simulation of decaying turbulence created by\nanisotropic counter-propagating low-frequency Alfv\\'en waves consistent with\ncritical-balance theory. We observe the formation of small-scale\ncurrent-density structures such as current filaments and current sheets as well\nas the formation of magnetic flux ropes as part of the turbulent cascade. The\nlarge magnetic structures present in the simulation domain retain the initial\nanisotropy while the small-scale structures produced by the turbulent cascade\nare less anisotropic. To quantify the occurrence of reconnection in our\nsimulation domain, we develop a new set of indicators based on intensity\nthresholds to identify reconnection events in which both ions and electrons are\nheated and accelerated in 3D particle-in-cell simulations. According to the\napplication of these indicators, we identify the occurrence of reconnection\nevents in the simulation domain and analyse one of these events in detail. The\nevent is related to the reconnection of two flux ropes, and the associated ion\nand electron exhausts exhibit a complex three-dimensional structure. We study\nthe profiles of plasma and magnetic-field fluctuations recorded along\nartificial-spacecraft trajectories passing near and through the reconnection\nregion. Our results suggest the presence of particle heating and acceleration\nrelated to small-scale reconnection events within magnetic flux ropes produced\nby the anisotropic Alfv\\'enic turbulent cascade in the solar wind. These events\nare related to current structures of order a few ion inertial lengths in size.\n"}
+{"abstract": "  We propose the spatial-temporal aggregated predictor (STAP) modeling\nframework to address measurement and estimation issues that arise when\nassessing the relationship between built environment features (BEF) and health\noutcomes. Many BEFs can be mapped as point locations and thus traditional\nexposure metrics are based on the number of features within a pre-specified\nspatial unit. The size of the spatial unit--or spatial scale--that is most\nappropriate for a particular health outcome is unknown and its choice\ninextricably impacts the estimated health effect. A related issue is the lack\nof knowledge of the temporal scale--or the length of exposure time that is\nnecessary for the BEF to render its full effect on the health outcome. The\nproposed STAP model enables investigators to estimate both the spatial and\ntemporal scales for a given BEF in a data-driven fashion, thereby providing a\nflexible solution for measuring the relationship between outcomes and spatial\nproximity to point-referenced exposures. Simulation studies verify the validity\nof our method for estimating the scales as well as the association between\navailability of BEFs' and health outcomes. We apply this method to estimate the\nspatial-temporal association between supermarkets and BMI using data from the\nMulti-Ethnic Atherosclerosis Study, demonstrating the method's applicability in\ncohort studies.\n"}
+{"abstract": "  In a rectangular domain, a boundary-value problem is considered for a\nmixed-type equation with a regularized Caputo-like counterpart of hyper-Bessel\ndifferential operator and the bi-ordinal Hilfer's fractional derivative. Using\nthe method of separation of variables, Laplace transform, a unique solvability\nof the considered problem has been established. Moreover, we have found the\nexplicit solution of initial problems for a differential equation with the\nbi-ordinal Hilfer's derivative and regularized Caputo-like counterpart of the\nhyper-Bessel differential operator with the non-zero starting point.\n"}
+{"abstract": "  Prediction of human actions in social interactions has important applications\nin the design of social robots or artificial avatars. In this paper, we model\nhuman interaction generation as a discrete multi-sequence generation problem\nand present SocialInteractionGAN, a novel adversarial architecture for\nconditional interaction generation. Our model builds on a recurrent\nencoder-decoder generator network and a dual-stream discriminator. This\narchitecture allows the discriminator to jointly assess the realism of\ninteractions and that of individual action sequences. Within each stream a\nrecurrent network operating on short subsequences endows the output signal with\nlocal assessments, better guiding the forthcoming generation. Crucially,\ncontextual information on interacting participants is shared among agents and\nreinjected in both the generation and the discriminator evaluation processes.\nWe show that the proposed SocialInteractionGAN succeeds in producing high\nrealism action sequences of interacting people, comparing favorably to a\ndiversity of recurrent and convolutional discriminator baselines. Evaluations\nare conducted using modified Inception Score and Fr{\\'e}chet Inception Distance\nmetrics, that we specifically design for discrete sequential generated data.\nThe distribution of generated sequences is shown to approach closely that of\nreal data. In particular our model properly learns the dynamics of interaction\nsequences, while exploiting the full range of actions.\n"}
+{"abstract": "  We consider, in general terms, the possible parameter space of thermal dark\nmatter candidates. We assume that the dark matter particle is fundamental and\nwas in thermal equilibrium in a hidden sector with a temperature $T'$, which\nmay differ from that of the Standard Model temperature, $T$. The candidates lie\nin a region in the $T'/T$ vs. $m_{\\rm dm}$ plane, which is bounded by both\nmodel-independent theoretical considerations and observational constraints. The\nformer consists of limits from dark matter candidates that decoupled when\nrelativistic (the relativistic floor) and from those that decoupled when\nnon-relativistic with the largest annihilation cross section allowed by\nunitarity (the unitarity wall), while the latter concerns big bang\nnucleosynthesis ($N_{\\rm eff}$ ceiling) and free streaming. We present three\nsimplified dark matter scenarios, demonstrating concretely how each fits into\nthe domain.\n"}
+{"abstract": "  We design a multi-purpose environment for autonomous UAVs offering different\ncommunication services in a variety of application contexts (e.g., wireless\nmobile connectivity services, edge computing, data gathering). We develop the\nenvironment, based on OpenAI Gym framework, in order to simulate different\ncharacteristics of real operational environments and we adopt the Reinforcement\nLearning to generate policies that maximize some desired performance.The\nquality of the resulting policies are compared with a simple baseline to\nevaluate the system and derive guidelines to adopt this technique in different\nuse cases. The main contribution of this paper is a flexible and extensible\nOpenAI Gym environment, which allows to generate, evaluate, and compare\npolicies for autonomous multi-drone systems in multi-service applications. This\nenvironment allows for comparative evaluation and benchmarking of different\napproaches in a variety of application contexts.\n"}
+{"abstract": "  The discovery of superconductivity in the infinite-layer nickelates has\nopened new perspectives in the context of quantum materials. We analyze, via\nfirst-principles calculations, the electronic properties of La$_2$NiO$_3$F --\nthe first single-layer T'-type nickelate -- and compare these properties with\nthose of related nickelates and isostructural cuprates. We find that\nLa$_2$NiO$_3$F is essentially a single-band system with a Fermi surface\ndominated by the Ni-3$d_{x^2-y^2}$ states with an exceptional 2D character. In\naddition, the hopping ratio is similar to that of the highest $T_c$ cuprates\nand there is a remarkable $e_g$ splitting together with a charge transfer\nenergy of 3.6~eV. According to these descriptors, along with a comparison to\nNd$_2$CuO$_4$, we thus indicate single-layer T'-type nickelates of this class\nas very promising analogs of cuprate-like physics while keeping distinct\nNi$^{1+}$ features.\n"}
+{"abstract": "  We conducted an investigation to find when a mistake was introduced in a\nwidely accessed Internet document, namely the RFC index. With great surprise,\nwe discovered that a it may go unnoticed for a very long period, namely more\nthat twenty-six years. This raises some questions to what does it mean to have\nopen access and the meaning of Linus' laws that \"given enough eyeballs, all\nbugs are shallow\"\n"}
+{"abstract": "  In this paper, we reformulate the Bakry-\\'Emery curvature on a weighted graph\nin terms of the smallest eigenvalue of a rank one perturbation of the so-called\ncurvature matrix using Schur complement. This new viewpoint allows us to show\nvarious curvature function properties in a very conceptual way. We show that\nthe curvature, as a function of the dimension parameter, is analytic, strictly\nmonotone increasing and strictly concave until a certain threshold after which\nthe function is constant. Furthermore, we derive the curvature of the Cartesian\nproduct using the crucial observation that the curvature matrix of the product\nis the direct sum of each component. Our approach of the curvature functions of\ngraphs can be employed to establish analogous results for the curvature\nfunctions of weighted Riemannian manifolds. Moreover, as an application, we\nconfirm a conjecture (in a general weighted case) of the fact that the\ncurvature does not decrease under certain graph modifications.\n"}
+{"abstract": "  For the first time, basing both on experimental facts and our theoretical\nconsideration, we show that Fermi systems with flat bands should be tuned with\nthe superconducting state. Experimental measurements on magic-angle twisted\nbilayer graphene of the Fermi velocity $V_F$ as a function of the temperature\n$T_c$ of superconduction phase transition have revealed $V_F\\propto T_c\\propto\n1/N_s(0)$, where $N_s(0)$ is the density of states at the Fermi level. We show\nthat the high-$T_c$ compounds $\\rm Bi_2Sr_2CaCu_2O_{8+x}$ exhibit the same\nbehavior. Such observation is a challenge to theories of high-$T_c$\nsuperconductivity, since $V_F$ is negatively correlated with $T_c$, for\n$T_c\\propto 1/V_F\\propto N_s(0)$. We show that the theoretical idea of forming\nflat bands in strongly correlated Fermi systems can explain this behavior and\nother experimental data collected on both $\\rm Bi_2Sr_2CaCu_2O_{8+x}$ and\ntwisted bilayer graphene. Our findings place stringent constraints on theories\ndescribing the nature of high-$T_c$ superconductivity and the deformation of\nflat band by the superconducting phase transition.\n"}
+{"abstract": "  This paper presents a novel, non-standard set of vector instruction types for\nexploring custom SIMD instructions in a softcore. The new types allow\nsimultaneous access to a relatively high number of operands, reducing the\ninstruction count where applicable. Additionally, a high-performance\nopen-source RISC-V (RV32 IM) softcore is introduced, optimised for exploring\ncustom SIMD instructions and streaming performance. By providing instruction\ntemplates for instruction development in HDL/Verilog, efficient FPGA-based\ninstructions can be developed with few low-level lines of code. In order to\nimprove custom SIMD instruction performance, the softcore's cache hierarchy is\noptimised for bandwidth, such as with very wide blocks for the last-level\ncache. The approach is demonstrated on example memory-intensive applications on\nan FPGA. Although the exploration is based on the softcore, the goal is to\nprovide a means to experiment with advanced SIMD instructions which could be\nloaded in future CPUs that feature reconfigurable regions as custom\ninstructions. Finally, we provide some insights on the challenges and\neffectiveness of such future micro-architectures.\n"}
+{"abstract": "  The law of centripetal force governing the motion of celestial bodies in\neccentric conic sections, has been established and thoroughly investigated by\nSir Isaac Newton in his Principia Mathematica. Yet its profound implications on\nthe understanding of such motions is still evolving. In a paper to the royal\nacademy of science, Sir Willian Hamilton demonstrated that this law underlies\nthe circular character of hodographs for Kepler orbits. A fact which was the\nobject of ulterior research and exploration by Richard Feynman and many other\nauthors [1]. In effect, a minute examination of the geometry of elliptic\ntrajectories, reveals interesting geometric properties and relations,\naltogether, combined with the law of conservation of angular momentum lead\neventually, and without any recourse to dealing with differential equations, to\nthe appearance of the equation of the trajectory and to the derivation of the\nequation of its corresponding hodograph. On this respect, and for the sake of\nfounding the approach on solid basis, I devised two mathematical theorems; one\nconcerning the existence of geometric means, and the other is related to\nestablishing the parametric equation of an off-center circle, altogether\ncompounded with other simple arguments ultimately give rise to the inverse\nsquare law of force that governs the motion of bodies in elliptic trajectories,\nas well as to the equation of their inherent circular hodographs.\n"}
+{"abstract": "  3D point-clouds and 2D images are different visual representations of the\nphysical world. While human vision can understand both representations,\ncomputer vision models designed for 2D image and 3D point-cloud understanding\nare quite different. Our paper investigates the potential for transferability\nbetween these two representations by empirically investigating whether this\napproach works, what factors affect the transfer performance, and how to make\nit work even better. We discovered that we can indeed use the same neural net\nmodel architectures to understand both images and point-clouds. Moreover, we\ncan transfer pretrained weights from image models to point-cloud models with\nminimal effort. Specifically, based on a 2D ConvNet pretrained on an image\ndataset, we can transfer the image model to a point-cloud model by\n\\textit{inflating} 2D convolutional filters to 3D then finetuning its input,\noutput, and optionally normalization layers. The transferred model can achieve\ncompetitive performance on 3D point-cloud classification, indoor and driving\nscene segmentation, even beating a wide range of point-cloud models that adopt\ntask-specific architectures and use a variety of tricks.\n"}
+{"abstract": "  We present a study of the environment of 27 z=3-4.5 bright quasars from the\nMUSE Analysis of Gas around Galaxies (MAGG) survey. With medium-depth MUSE\nobservations (4 hours on target per field), we characterise the effects of\nquasars on their surroundings by studying simultaneously the properties of\nextended gas nebulae and Lyalpha emitters (LAEs) in the quasar host haloes. We\ndetect extended (up to ~ 100 kpc) Lyalpha emission around all MAGG quasars,\nfinding a very weak redshift evolution between z=3 and z=6. By stacking the\nMUSE datacubes, we confidently detect extended emission of CIV and only\nmarginally detect extended HeII up to ~40 kpc, implying that the gas is metal\nenriched. Moreover, our observations show a significant overdensity of LAEs\nwithin 300 km/s from the quasar systemic redshifts estimated from the nebular\nemission. The luminosity functions and equivalent width distributions of these\nLAEs show similar shapes with respect to LAEs away from quasars suggesting that\nthe Lyalpha emission of the majority of these sources is not significantly\nboosted by the quasar radiation or other processes related to the quasar\nenvironment. Within this framework, the observed LAE overdensities and our\nkinematic measurements imply that bright quasars at z=3-4.5 are hosted by\nhaloes in the mass range ~ 10^{12.0}-10^{12.5} Msun.\n"}
+{"abstract": "  Although deep neural networks are successful for many tasks in the speech\ndomain, the high computational and memory costs of deep neural networks make it\ndifficult to directly deploy highperformance Neural Network systems on\nlow-resource embedded devices. There are several mechanisms to reduce the size\nof the neural networks i.e. parameter pruning, parameter quantization, etc.\nThis paper focuses on how to apply binary neural networks to the task of\nspeaker verification. The proposed binarization of training parameters can\nlargely maintain the performance while significantly reducing storage space\nrequirements and computational costs. Experiment results show that, after\nbinarizing the Convolutional Neural Network, the ResNet34-based network\nachieves an EER of around 5% on the Voxceleb1 testing dataset and even\noutperforms the traditional real number network on the text-dependent dataset:\nXiaole while having a 32x memory saving.\n"}
+{"abstract": "  In this paper, we present a model-free learning-based control scheme for the\nsoft snake robot to improve its contact-aware locomotion performance in a\ncluttered environment. The control scheme includes two cooperative controllers:\nA bio-inspired controller (C1) that controls both the steering and velocity of\nthe soft snake robot, and an event-triggered regulator (R2) that controls the\nsteering of the snake in anticipation of obstacle contacts and during contact.\nThe inputs from the two controllers are composed as the input to a Matsuoka CPG\nnetwork to generate smooth and rhythmic actuation inputs to the soft snake. To\nenable stable and efficient learning with two controllers, we develop a\ngame-theoretic process, fictitious play, to train C1 and R2 with a shared\npotential-field-based reward function for goal tracking tasks. The proposed\napproach is tested and evaluated in the simulator and shows significant\nimprovement of locomotion performance in the obstacle-based environment\ncomparing to two baseline controllers.\n"}
+{"abstract": "  The primary objective of this paper is the study of different instances of\nthe elliptic Stark conjectures of Darmon, Lauder and Rotger, in a situation\nwhere the elliptic curve attached to the modular form $f$ has split\nmultiplicative reduction at $p$ and the arithmetic phenomena are specially\nrich. For that purpose, we resort to the principle of improved $p$-adic\n$L$-functions and study their $\\mathcal L$-invariants. We further interpret\nthese results in terms of derived cohomology classes coming from the setting of\ndiagonal cycles, showing that the same $\\mathcal L$-invariant which arises in\nthe theory of $p$-adic $L$-functions also governs the arithmetic of Euler\nsystems. Thus, we can reduce, in the split multiplicative situation, the\nconjecture of Darmon, Lauder and Rotger to a more familiar statement about\nhigher order derivatives of a triple product $p$-adic $L$-function at a point\nlying inside the region of classical interpolation, in the realm of the more\nwell-known exceptional zero conjectures.\n"}
+{"abstract": "  The allocation of venture capital is one of the primary factors determining\nwho takes products to market, which startups succeed or fail, and as such who\ngets to participate in the shaping of our collective economy. While gender\ndiversity contributes to startup success, most funding is allocated to\nmale-only entrepreneurial teams. In the wake of COVID-19, 2020 is seeing a\nnotable decline in funding to female and mixed-gender teams, giving raise to an\nurgent need to study and correct the longstanding gender bias in startup\nfunding allocation. We conduct an in-depth data analysis of over 48,000\ncompanies on Crunchbase, comparing funding allocation based on the gender\ncomposition of founding teams. Detailed findings across diverse industries and\ngeographies are presented. Further, we construct machine learning models to\npredict whether startups will reach an equity round, revealing the surprising\nfinding that the CEO's gender is the primary determining factor for attaining\nfunding. Policy implications for this pressing issue are discussed.\n"}
+{"abstract": "  Macroscopic realism (MR) is the notion that a time-evolving system possesses\ndefinite properties, irrespective of past or future measurements. Quantum\nmechanical theories can, however, produce violations of MR. Most research to\ndate has focused on a single set of conditions for MR, the Leggett-Garg\ninequalities (LGIs), and on a single data set, the \"standard data set\", which\nconsists of single-time averages and second-order correlators of a dichotomic\nvariable Q for three times. However, if such conditions are all satisfied, then\nwhere is the quantum behaviour? In this paper, we provide an answer to this\nquestion by considering expanded data sets obtained from finer-grained\nmeasurements and MR conditions on those sets. We consider three different\nsituations in which there are violations of MR that go undetected by the\nstandard LGIs. First, we explore higher-order LGIs on a data set involving\nthird- and fourth-order correlators, using a spin-1/2 and spin-1 system.\nSecond, we explore the pentagon inequalities (PIs) and a data set consisting of\nall possible averages and second-order correlators for measurements of Q at\nfive times. Third, we explore the LGIs for a trichotomic variable and\nmeasurements made with a trichotomic operator to, again, identify violations\nfor a spin-1 system beyond those seen with a single dichotomic variable. We\nalso explore the regimes in which combinations of two and three-time LGIs can\nbe satisfied and violated in a spin-1 system, extending recent work. We discuss\nthe possible experimental implementation of all the above results.\n"}
+{"abstract": "  The carrier transport and the motion of a vortex system in a mixed state of\nan electron-doped high-temperature superconductors Nd2-xCexCuO4 were\ninvestigated. To study the anisotropy of galvanomagnetic effects of highly\nlayered NdCeCuO system we have synthesized Nd2-xCexCuO4/SrTiO3 epitaxial films\nwith non-standart orientations of the c-axis and conductive CuO2 layers\nrelative to the substrate. The variation ofe the angle of inclination of the\nmagnetic field B, relative to the current J, reveals that the behavior of both\nthe in-plane r_xx(B) and the out-plane r_xy(B) resistivities in the mixed state\nis mainly determined by the perpendicular to J component of B, that indicates\nthe crucial role of the Lorentz force F_L~[JxB] and defines the motion of\nJosephson vortices across the CuO2 layers.\n"}
+{"abstract": "  We consider the problem of finding an inductive construction, based on vertex\nsplitting, of triangulated spheres with a fixed number of additional edges\n(braces). We show that for any positive integer $b$ there is such an inductive\nconstruction of triangulations with $b$ braces, having finitely many base\ngraphs. In particular we establish a bound for the maximum size of a base graph\nwith $b$ braces that is linear in $b$. In the case that $b=1$ or $2$ we\ndetermine the list of base graphs explicitly. Using these results we show that\ndoubly braced triangulations are (generically) minimally rigid in two distinct\ngeometric contexts arising from a hypercylinder in $\\mathbb{R}^4$ and a class\nof mixed norms on $\\mathbb{R}^3$.\n"}
+{"abstract": "  The narrow escape problem is a first-passage problem concerned with randomly\nmoving particles in a physical domain, being trapped by absorbing surface traps\n(windows), such that the measure of traps is small compared to the domain size.\nThe expected value of time required for a particle to escape is defined as mean\nfirst passage time (MFPT), which satisfies the Poisson partial differential\nequation subject to a mixed Dirichlet-Neumann boundary condition. The primary\nobjective of this work is a direct numerical simulation of multiple particles\nundergoing Brownian motion in a three-dimensional sphere with boundary traps,\ncompute MFPT values by averaging Brownian escape times, and compare the results\nwith asymptotic results obtained by solving the Poisson PDE problem. A\ncomprehensive study of results obtained from the simulations shows that the\ndifference between Brownian and asymptotic results for the escape times mostly\nnot exceed $1\\%$ accuracy. This comparison in some sense validates the narrow\nescape PDE problem itself as an approximation (averaging) of the multiple\nphysical Brownian motion runs. This work also predicted that how many\nsingle-particle simulations are required to match the predicted asymptotic\naveraged MFPT values. The next objective of this work is to study dynamics of\nBrownian particles near the boundary by estimating the average percentage of\ntime spent by Brownian particle near the domain boundary for both the\nanisotropic and isotropic diffusion. It is shown that the Brownian particles\nspend more in the boundary layer than predicted by the boundary layer relative\nvolume, with the effect being more pronounced in a narrow layer near the\nspherical wall. It is also shown that taking into account anisotropic diffusion\nyields larger times a particle spends near the boundary, and smaller escape\ntimes than those predicted by the isotropic diffusion model.\n"}
+{"abstract": "  This paper considers the narrow escape problem of a Brownian particle within\na three-dimensional Riemannian manifold under the influence of the force field.\nWe compute an asymptotic expansion of mean sojourn time for Brownian particles.\nAs an auxiliary result, we obtain the singular structure for the restricted\nNeumann Green's function which may be of independent interest.\n"}
+{"abstract": "  I propose the use of two magnetic Wollaston prisms to correct the linear\nLarmor phase aberration of MIEZE, introduced by the transverse size of the\nsample. With this approach, the resolution function of MIEZE can be optimized\nfor any scattering angle of interest. The optimum magnetic fields required for\nthe magnetic Wollaston prisms depend only on the scattering angle and the\nfrequency of the RF flippers and they are independent of the neutron wavelength\nand beam divergence, which makes it suitable for both pulsed and constant\nwavelength neutron sources.\n"}
+{"abstract": "  We consider $n$ independent $p$-dimensional Gaussian vectors with covariance\nmatrix having Toeplitz structure. We test that these vectors have independent\ncomponents against a stationary distribution with sparse Toeplitz covariance\nmatrix, and also select the support of non-zero entries. We assume that the\nnon-zero values can occur in the recent past (time-lag less than $p/2$). We\nbuild test procedures that combine a sum and a scan-type procedures, but are\ncomputationally fast, and show their non-asymptotic behaviour in both one-sided\n(only positive correlations) and two-sided alternatives, respectively. We also\nexhibit a selector of significant lags and bound the Hamming-loss risk of the\nestimated support. These results can be extended to the case of nearly Toeplitz\ncovariance structure and to sub-Gaussian vectors. Numerical results illustrate\nthe excellent behaviour of both test procedures and support selectors - larger\nthe dimension $p$, faster are the rates.\n"}
+{"abstract": "  A graph $G$ is called interval colorable if it has a proper edge coloring\nwith colors $1,2,3,\\dots$ such that the colors of the edges incident to every\nvertex of $G$ form an interval of integers. Not all graphs are interval\ncolorable; in fact, quite few families have been proved to admit interval\ncolorings. In this paper we introduce and investigate a new notion, the\ninterval coloring thickness of a graph $G$, denoted\n${\\theta_{\\mathrm{int}}}(G)$, which is the minimum number of interval colorable\nedge-disjoint subgraphs of $G$ whose union is $G$.\n  Our investigation is motivated by scheduling problems with compactness\nrequirements, in particular, problems whose solution may consist of several\nschedules, but where each schedule must not contain any waiting periods or idle\ntimes for all involved parties. We first prove that every connected properly\n$3$-edge colorable graph with maximum degree $3$ is interval colorable, and\nusing this result, we deduce an upper bound on ${\\theta_{\\mathrm{int}}}(G)$ for\ngeneral graphs $G$. We demonstrate that this upper bound can be improved in the\ncase when $G$ is bipartite, planar or complete multipartite and consider some\napplications in timetabling.\n"}
+{"abstract": "  CO$_2$ dissociation stimulated by vibrational excitation in non-equilibrium\ndischarges has drawn lots of attention. Ns-discharges are known for their\nhighly non-equilibrium conditions. It is therefore of interest to investigate\nthe CO$_2$ excitation in such discharges. In this paper, we demonstrate the\nability for monitoring the time evolution of CO$_2$ ro-vibrational excitation\nwith a well-selected wavelength window around 2289.0 cm$^{-1}$ and a single CW\nquantum cascade laser (QCL) with both high accuracy and temporal resolution.\nThe rotational and vibrational temperatures for both the symmetric and the\nasymmetric modes of CO$_2$ in the afterglow of CO$_2$ + He ns-discharge were\nmeasured with a temporal resolution of 1.5 $\\mu$s. The non-thermal feature and\nthe preferential excitation of the asymmetric stretch mode of CO$_2$ were\nexperimentally observed, with a peak temperature of $T_{v3, max}$ = 966 $\\pm$\n1.5 K, $T_{v12, max}$ = 438.4 $\\pm$ 1.2 K and $T_{rot}$ = 334.6 $\\pm$ 0.6 K\nreached at 3 $\\mu$s after the nanosecond pulse. In the following relaxation\nprocess, an exponential decay with a time constant of 69 $\\mu$s was observed\nfor the asymmetric stretch (001) state, consistent with the dominant\ndeexcitation mechanism due to VT transfer with He and deexcitation on the wall.\nFurthermore, a synchronous oscillation of the gas temperature and the total\npressure was also observed and can be explained by a two-line thermometry and\nadiabatic process. The period of the oscillation and its dependence on the gas\ncomponents is consistent with a standing acoustic wave excited by the\nns-discharge.\n"}
+{"abstract": "  Let $G=(V,E)$ be a graph and $P\\subseteq V$ a set of points. Two points are\nmutually visible if there is a shortest path between them without further\npoints. $P$ is a mutual-visibility set if its points are pairwise mutually\nvisible. The mutual-visibility number of $G$ is the size of any largest\nmutual-visibility set. In this paper we start the study about this new\ninvariant and the mutual-visibility sets in undirected graphs. We introduce the\nmutual-visibility problem which asks to find a mutual-visibility set with a\nsize larger than a given number. We show that this problem is NP-complete,\nwhereas, to check whether a given set of points is a mutual-visibility set is\nsolvable in polynomial time. Then we study mutual-visibility sets and\nmutual-visibility numbers on special classes of graphs, such as block graphs,\ntrees, grids, tori, complete bipartite graphs, cographs. We also provide some\nrelations of the mutual-visibility number of a graph with other invariants.\n"}
+{"abstract": "  Binary metallic phosphide, Nb2P5, belongs to technologically important class\nof materials. Quite surprisingly, a large number of physical properties of\nNb2P5, including elastic properties and their anisotropy, acoustic, electronic\n(DOS, charge density distribution, electron density difference),\nthermo-physical, bonding characteristics, and optical properties have not been\ninvestigated at all. In the present work we have explored all these properties\nin details for the first time employing density functional theory based\nfirst-principles method. Nb2P5 is found to be a mechanically stable,\nelastically anisotropic compound with weak brittle character. The bondings\namong the atoms are dominated by covalent and ionic contributions with small\nsignature of metallic feature. The compound possesses high level of\nmachinability. Nb2P5 is a moderately hard compound. The band structure\ncalculations reveal metallic conduction with a large electronic density of\nstates at the Fermi level. Calculated values of different thermal properties\nindicate that Nb2P5 has the potential to be used as a thermal barrier coating\nmaterial. The energy dependent optical parameters show close agreement with the\nunderlying electronic band structure. The optical absorption and reflectivity\nspectra and the static index of refraction of Nb2P5 show that the compound\nholds promise to be used in optoelectronic device sector. Unlike notable\nanisotropy in elastic and mechanical properties, the optical parameters are\nfound to be almost isotropic.\n"}
+{"abstract": "  Bayesian nonparametric hierarchical priors are highly effective in providing\nflexible models for latent data structures exhibiting sharing of information\nbetween and across groups. Most prominent is the Hierarchical Dirichlet Process\n(HDP), and its subsequent variants, which model latent clustering between and\nacross groups. The HDP, may be viewed as a more flexible extension of Latent\nDirichlet Allocation models (LDA), and has been applied to, for example, topic\nmodelling, natural language processing, and datasets arising in health-care. We\nfocus on analogous latent feature allocation models, where the data structures\ncorrespond to multisets or unbounded sparse matrices. The fundamental\ndevelopment in this regard is the Hierarchical Indian Buffet process (HIBP),\nwhich utilizes a hierarchy of Beta processes over J groups, where each group\ngenerates binary random matrices, reflecting within group sharing of features,\naccording to beta-Bernoulli IBP priors. To encompass HIBP versions of\nnon-Bernoulli extensions of the IBP, we introduce hierarchical versions of\ngeneral spike and slab IBP. We provide explicit novel descriptions of the\nmarginal, posterior and predictive distributions of the HIBP and its\ngeneralizations which allow for exact sampling and simpler practical\nimplementation. We highlight common structural properties of these processes\nand establish relationships to existing IBP type and related models arising in\nthe literature. Examples of potential applications may involve topic models,\nPoisson factorization models, random count matrix priors and neural network\nmodels\n"}
+{"abstract": "  We introduce a new model of the logarithmic type of wave like plate equation\nwith a nonlocal logarithmic damping mechanism. We consider the Cauchy problem\nfor this new model in the whole space, and study the asymptotic profile and\noptimal decay rates of solutions as time goes to infinity in L^{2}-sense. The\noperator L considered in this paper was first introduced to dissipate the\nsolutions of the wave equation in the paper studied by Charao-Ikehata in 2020.\nWe will discuss the asymptotic property of the solution as time goes to\ninfinity to our Cauchy problem, and in particular, we classify the property of\nthe solutions into three parts from the viewpoint of regularity of the initial\ndata, that is, diffusion-like, wave-like, and both of them.\n"}
+{"abstract": "  This paper details speckle observations of binary stars taken at the Lowell\nDiscovery Telescope, the WIYN Telescope, and the Gemini telescopes between 2016\nJanuary and 2019 September. The observations taken at Gemini and Lowell were\ndone with the Differential Speckle Survey Instrument (DSSI), and those done at\nWIYN were taken with the successor instrument to DSSI at that site, the\nNN-EXPLORE Exoplanet Star and Speckle Imager (NESSI). In total, we present 378\nobservations of 178 systems and we show that the uncertainty in the measurement\nprecision for the combined data set is ~2 mas in separation, ~1-2 degrees in\nposition angle depending on the separation, and $\\sim$0.1 magnitudes in\nmagnitude difference. Together with data already in the literature, these new\nresults permit 25 visual orbits and one spectroscopic-visual orbit to be\ncalculated for the first time. In the case of the spectroscopic-visual\nanalysis, which is done on the trinary star HD 173093, we calculate masses with\nprecision of better than 1% for all three stars in that system. Twenty-one of\nthe visual orbits calculated have a K dwarf as the primary star; we add these\nto the known orbits of K dwarf primary stars and discuss the basic orbital\nproperties of these stars at this stage. Although incomplete, the data that\nexist so far indicate that binaries with K dwarf primaries tend not to have\nlow-eccentricity orbits at separations of one to a few tens of AU, that is, on\nsolar-system scales.\n"}
+{"abstract": "  For many real-world classification problems, e.g., sentiment classification,\nmost existing machine learning methods are biased towards the majority class\nwhen the Imbalance Ratio (IR) is high. To address this problem, we propose a\nset convolution (SetConv) operation and an episodic training strategy to\nextract a single representative for each class, so that classifiers can later\nbe trained on a balanced class distribution. We prove that our proposed\nalgorithm is permutation-invariant despite the order of inputs, and experiments\non multiple large-scale benchmark text datasets show the superiority of our\nproposed framework when compared to other SOTA methods.\n"}
+{"abstract": "  In this paper we prove the convergence of solutions to discrete models for\nbinary waveguide arrays toward those of their formal continuum limit, for which\nwe also show the existence of localized standing waves. This work rigorously\njustifies formal arguments and numerical simulations present in the Physics\nliterature.\n"}
+{"abstract": "  What would be the effect of locally poking a static scene? We present an\napproach that learns naturally-looking global articulations caused by a local\nmanipulation at a pixel level. Training requires only videos of moving objects\nbut no information of the underlying manipulation of the physical scene. Our\ngenerative model learns to infer natural object dynamics as a response to user\ninteraction and learns about the interrelations between different object body\nregions. Given a static image of an object and a local poking of a pixel, the\napproach then predicts how the object would deform over time. In contrast to\nexisting work on video prediction, we do not synthesize arbitrary realistic\nvideos but enable local interactive control of the deformation. Our model is\nnot restricted to particular object categories and can transfer dynamics onto\nnovel unseen object instances. Extensive experiments on diverse objects\ndemonstrate the effectiveness of our approach compared to common video\nprediction frameworks. Project page is available at https://bit.ly/3cxfA2L .\n"}
+{"abstract": "  We propose a straightforward implementation of the phenomenon of diffractive\nfocusing with uniform atomic Bose-Einstein condensates. Both, analytical as\nwell as numerical methods not only illustrate the influence of the atom-atom\ninteraction on the focusing factor and the focus time, but also allow us to\nderive the optimal conditions for observing focusing of this type in the case\nof interacting matter waves.\n"}
+{"abstract": "  Consider the first order differential system given by\n  \\begin{equation*}\n  \\begin{array}{l}\n  \\dot{x}= y, \\qquad \\dot{y}= -x+a(1-y^{2n})y, \\end{array}\n  \\end{equation*} where $a$ is a real parameter and the dots denote derivatives\nwith respect to the time $t$. Such system is known as the generalized Rayleigh\nsystem and it appears, for instance, in the modeling of diabetic chemical\nprocesses through a constant area duct, where the effect of adding or rejecting\nheat is considered. In this paper we characterize the global dynamics of this\ngeneralized Rayleigh system. In particular we prove the existence of a unique\nlimit cycle when the parameter $a\\ne 0$.\n"}
+{"abstract": "  The ALICE Collaboration reports the first fully-corrected measurements of the\n$N$-subjettiness observable for track-based jets in heavy-ion collisions. This\nstudy is performed using data recorded in pp and Pb$-$Pb collisions at\ncentre-of-mass energies of $\\sqrt{s} = 7$ TeV and $\\sqrt{s_{\\rm NN}} = 2.76$\nTeV, respectively. In particular the ratio of 2-subjettiness to 1-subjettiness,\n$\\tau_{2}/\\tau_{1}$, which is sensitive to the rate of two-pronged jet\nsubstructure, is presented. Energy loss of jets traversing the strongly\ninteracting medium in heavy-ion collisions is expected to change the rate of\ntwo-pronged substructure relative to vacuum. The results are presented for jets\nwith a resolution parameter of $R = 0.4$ and charged jet transverse momentum of\n$40 \\leq p_{\\rm T,\\rm jet} \\leq 60$ GeV/$c$, which constitute a larger jet\nresolution and lower jet transverse momentum interval than previous\nmeasurements in heavy-ion collisions. This has been achieved by utilising a\nsemi-inclusive hadron-jet coincidence technique to suppress the larger jet\ncombinatorial background in this kinematic region. No significant modification\nof the $\\tau_{2}/\\tau_{1}$ observable for track-based jets in Pb$-$Pb\ncollisions is observed relative to vacuum PYTHIA6 and PYTHIA8 references at the\nsame collision energy. The measurements of $\\tau_{2}/\\tau_{1}$, together with\nthe splitting aperture angle $\\Delta R$, are also performed in pp collisions at\n$\\sqrt{s}=7$ TeV for inclusive jets. These results are compared with PYTHIA\ncalculations at $\\sqrt{s}=7$ TeV, in order to validate the model as a vacuum\nreference for the Pb$-$Pb centre-of-mass energy. The PYTHIA references for\n$\\tau_{2}/\\tau_{1}$ are shifted to larger values compared to the measurement in\npp collisions. This hints at a reduction in the rate of two-pronged jets in\nPb$-$Pb collisions compared to pp collisions.\n"}
+{"abstract": "  We present an ALMA 1.3 mm (Band 6) continuum survey of lensed submillimeter\ngalaxies (SMGs) at $z=1.0\\sim3.2$ with an angular resolution of $\\sim0.2$\".\nThese galaxies were uncovered by the Herschel Lensing Survey (HLS), and feature\nexceptionally bright far-infrared continuum emission ($S_\\mathrm{peak} \\gtrsim\n90$ mJy) owing to their lensing magnification. We detect 29 sources in 20\nfields of massive galaxy clusters with ALMA. Using both the Spitzer/IRAC\n(3.6/4.5 $\\mathrm{\\mu m}$) and ALMA data, we have successfully modeled the\nsurface brightness profiles of 26 sources in the rest-frame near- and\nfar-infrared. Similar to previous studies, we find the median dust-to-stellar\ncontinuum size ratio to be small ($R_\\mathrm{e,dust}/R_\\mathrm{e,star} =\n0.38\\pm0.14$) for the observed SMGs, indicating that star formation is\ncentrally concentrated. This is, however, not the case for two spatially\nextended main-sequence SMGs with a low surface brightness at 1.3 mm ($\\lesssim\n0.1$ mJy arcsec$^{-2}$), in which the star formation is distributed over the\nentire galaxy ($R_\\mathrm{e,dust}/R_\\mathrm{e,star}>1$). As a whole, our SMG\nsample shows a tight anti-correlation between\n($R_\\mathrm{e,dust}/R_\\mathrm{e,star}$) and far-infrared surface brightness\n($\\Sigma_\\mathrm{IR}$) over a factor of $\\simeq$ 1000 in $\\Sigma_\\mathrm{IR}$.\nThis indicates that SMGs with less vigorous star formation (i.e., lower\n$\\Sigma_\\mathrm{IR}$) lack central starburst and are likely to retain a broader\nspatial distribution of star formation over the whole galaxies (i.e., larger\n$R_\\mathrm{e,dust}/R_\\mathrm{e,star}$). The same trend can be reproduced with\ncosmological simulations as a result of central starburst and potentially\nsubsequent \"inside-out\" quenching, which likely accounts for the emergence of\ncompact quiescent galaxies at $z\\sim2$.\n"}
+{"abstract": "  Single layer Pb on top of (111) surfaces of group IV semiconductors hosts\ncharge density wave and superconductivity depending on the coverage and on the\nsubstrate. These systems are normally considered to be experimental\nrealizations of single band Hubbard models and their properties are mostly\ninvestigated using lattice models with frozen structural degrees of freedom,\nalthough the reliability of this approximation is unclear. Here, we consider\nthe case of Pb/Ge(111) at 1/3 coverage, for which surface X-ray diffraction and\nARPES data are available. By performing first principles calculations, we\ndemonstrate that the non-local exchange between Pb and the substrate drives the\nsystem into a $3\\times 3$ charge density wave. The electronic structure of this\ncharge ordered phase is mainly determined by two effects: the magnitude of the\nPb distortion and the large spin-orbit coupling. Finally, we show that the\neffect applies also to the $3\\times 3$ phase of Pb/Si(111) where the\nPb-substrate exchange interaction increases the bandwidth by more than a factor\n1.5 with respect to DFT+U, in better agreement with STS data. The delicate\ninterplay between substrate, structural and electronic degrees of freedom\ninvalidates the widespread interpretation available in literature considering\nthese compounds as physical realizations of single band Hubbard models.\n"}
+{"abstract": "  Unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) has\nrecently emerged as a promising technique to provide sustainable energy supply\nfor widely distributed low-power ground devices (GDs) in large-scale wireless\nnetworks. Compared with the energy transmitters (ETs) in conventional WPT\nsystems which are deployed at fixed locations, UAV-mounted aerial ETs can fly\nflexibly in the three-dimensional (3D) space to charge nearby GDs more\nefficiently. This paper provides a tutorial overview on UAV-enabled WPT and its\nappealing applications, in particular focusing on how to exploit UAVs'\ncontrollable mobility via their 3D trajectory design to maximize the amounts of\nenergy transferred to all GDs in a wireless network with fairness. First, we\nconsider the single-UAV-enabled WPT scenario with one UAV wirelessly charging\nmultiple GDs at known locations. To solve the energy maximization problem in\nthis case, we present a general trajectory design framework consisting of three\ninnovative approaches to optimize the UAV trajectory, which are multi-location\nhovering, successive-hover-and-fly, and time-quantization-based optimization,\nrespectively. Next, we consider the multi-UAV-enabled WPT scenario where\nmultiple UAVs cooperatively charge many GDs in a large area. Building upon the\nsingle-UAV trajectory design, we propose two efficient schemes to jointly\noptimize multiple UAVs' trajectories, based on the principles of UAV swarming\nand GD clustering, respectively. Furthermore, we consider two important\nextensions of UAV-enabled WPT, namely UAV-enabled wireless powered\ncommunication networks (WPCN) and UAV-enabled wireless powered mobile edge\ncomputing (MEC).\n"}
+{"abstract": "  We explore the intrinsic dynamics of spherical shells immersed in a fluid in\nthe vicinity of their buckled state, through experiments and 3D axisymmetric\nsimulations. The results are supported by a theoretical model that accurately\ndescribes the buckled shell as a two-variable-only oscillator. We quantify the\neffective \"softening\" of shells above the buckling threshold, as observed in\nrecent experiments on interactions between encapsulated microbubbles and\nacoustic waves. The main dissipation mechanism in the neighboring fluid is also\nevidenced.\n"}
+{"abstract": "  In this paper, we analyze the effect of transport infrastructure investments\nin railways. As a testing ground, we use data from a new historical database\nthat includes annual panel data on approximately 2,400 Swedish rural\ngeographical areas during the period 1860-1917. We use a staggered event study\ndesign that is robust to treatment effect heterogeneity. Importantly, we find\nextremely large reduced-form effects of having access to railways. For real\nnonagricultural income, the cumulative treatment effect is approximately 120%\nafter 30 years. Equally important, we also show that our reduced-form effect is\nlikely to reflect growth rather than a reorganization of existing economic\nactivity since we find no spillover effects between treated and untreated\nregions. Specifically, our results are consistent with the big push hypothesis,\nwhich argues that simultaneous/coordinated investment, such as large\ninfrastructure investment in railways, can generate economic growth if there\nare strong aggregate demand externalities (e.g., Murphy et al. 1989). We used\nplant-level data to further corroborate this mechanism. Indeed, we find that\ninvestments in local railways dramatically, and independent of initial\nconditions, increase local industrial production and employment on the order of\n100-300% across almost all industrial sectors.\n"}
+{"abstract": "  The presence of relativistic electrons within the diffuse gas phase of galaxy\nclusters is now well established, but their detailed origin remains unclear.\nCosmic ray protons are also expected to accumulate during the formation of\nclusters and would lead to gamma-ray emission through hadronic interactions\nwithin the thermal gas. Recently, the detection of gamma-ray emission has been\nreported toward the Coma cluster with Fermi-LAT. Assuming that this gamma-ray\nemission arises from hadronic interactions in the ICM, we aim at exploring the\nimplication of this signal on the cosmic ray content of the Coma cluster. We\nuse the MINOT software to build a physical model of the cluster and apply it to\nthe Fermi-LAT data. We also consider contamination from compact sources and the\nimpact of various systematic effects. We confirm that a significant gamma-ray\nsignal is observed within the characteristic radius $\\theta_{500}$ of the Coma\ncluster, with a test statistic TS~27 for our baseline model. The presence of a\npossible point source may account for most of the observed signal. However,\nthis source could also correspond to the peak of the diffuse emission of the\ncluster itself and extended models match the data better. We constrain the\ncosmic ray to thermal energy ratio within $R_{500}$ to $X_{\\rm\nCRp}=1.79^{+1.11}_{-0.30}$\\% and the slope of the energy spectrum of cosmic\nrays to $\\alpha=2.80^{+0.67}_{-0.13}$. Finally, we compute the synchrotron\nemission associated with the secondary electrons produced in hadronic\ninteractions assuming steady state. This emission is about four times lower\nthan the overall observed radio signal, so that primary cosmic ray electrons or\nreacceleration of secondary electrons is necessary to explain the total\nemission. Assuming an hadronic origin of the signal, our results provide the\nfirst quantitative measurement of the cosmic ray proton content in a\ncluster.[Abridged]\n"}
+{"abstract": "  We study idempotent, model, and Toeplitz operators that attain the norm.\nNotably, we prove that if $\\mathcal{Q}$ is a backward shift invariant subspace\nof the Hardy space $H^2(\\mathbb{D})$, then the model operator $S_{\\mathcal{Q}}$\nattains its norm. Here $S_{\\mathcal{Q}} = P_{\\mathcal{Q}}M_z|_{\\mathcal{Q}}$,\nthe compression of the shift $M_z$ on the Hardy space $H^2(\\mathbb{D})$ to\n$\\mathcal{Q}$.\n"}
+{"abstract": "  Power system simulations that extend over a time period of minutes, hours, or\neven longer are called extended-term simulations. As power systems evolve into\ncomplex systems with increasing interdependencies and richer dynamic behaviors\nacross a wide range of timescales, extended-term simulation is needed for many\npower system analysis tasks (e.g., resilience analysis, renewable energy\nintegration, cascading failures), and there is an urgent need for efficient and\nrobust extended-term simulation approaches. The conventional approaches are\ninsufficient for dealing with the extended-term simulation of multi-timescale\nprocesses. This paper proposes an extended-term simulation approach based on\nthe holomorphic embedding (HE) methodology. Its accuracy and computational\nefficiency are backed by HE's high accuracy in event-driven simulation, larger\nand adaptive time steps, and flexible switching between full-dynamic and\nquasi-steady-state (QSS) models. We used this proposed extended-term simulation\napproach to evaluate bulk power system restoration plans, and it demonstrates\nsatisfactory accuracy and efficiency in this complex simulation task.\n"}
+{"abstract": "  The efficiency of the adiabatic demagnetization of nuclear spin system (NSS)\nof a solid is limited, if quadrupole effects are present. Nevertheless, despite\na considerable quadrupole interaction, recent experiments validated the\nthermodynamic description of the NSS in GaAs. This suggests that nuclear spin\ntemperature can be used as the universal indicator of the NSS state in presence\nof external perturbations. We implement this idea by analyzing the modification\nof the NSS temperature in response to an oscillating magnetic field at various\nfrequencies, an approach termed as the warm-up spectroscopy. It is tested in a\nn-GaAs sample where both mechanical strain and built-in electric field may\ncontribute to the quadrupole splitting, yielding the parameters of electric\nfield gradient tensors for 75As and both Ga isotopes, 69Ga and 71Ga.\n"}
+{"abstract": "  Unmanned aerial vehicles (UAVs) play an increasingly important role in\nmilitary, public, and civilian applications, where providing connectivity to\nUAVs is crucial for its real-time control, video streaming, and data\ncollection. Considering that cellular networks offer wide area, high speed, and\nsecure wireless connectivity, cellular-connected UAVs have been considered as\nan appealing solution to provide UAV connectivity with enhanced reliability,\ncoverage, throughput, and security. Due to the nature of UAVs mobility, the\nthroughput, reliability and End-to-End (E2E) delay of UAVs communication under\nvarious flight heights, video resolutions, and transmission frequencies remain\nunknown. To evaluate these parameters, we develop a cellular-connected UAV\ntestbed based on the Long Term Evolution (LTE) network with its uplink video\ntransmission and downlink control\\&command (CC) transmission. We also design\nalgorithms for sending control signal and controlling UAV. The indoor\nexperimental results provide fundamental insights for the cellular-connected\nUAV system design from the perspective of transmission frequency, adaptability,\nand link outage, respectively.\n"}
+{"abstract": "  TMs are a pattern recognition approach that uses finite state machines for\nlearning and propositional logic to represent patterns. In addition to being\nnatively interpretable, they have provided competitive accuracy for various\ntasks. In this paper, we increase the computing power of TMs by proposing a\nfirst-order logic-based framework with Herbrand semantics. The resulting TM is\nrelational and can take advantage of logical structures appearing in natural\nlanguage, to learn rules that represent how actions and consequences are\nrelated in the real world. The outcome is a logic program of Horn clauses,\nbringing in a structured view of unstructured data. In closed-domain\nquestion-answering, the first-order representation produces 10x more compact\nKBs, along with an increase in answering accuracy from 94.83% to 99.48%. The\napproach is further robust towards erroneous, missing, and superfluous\ninformation, distilling the aspects of a text that are important for real-world\nunderstanding.\n"}
+{"abstract": "  Markerless motion capture and understanding of professional non-daily human\nmovements is an important yet unsolved task, which suffers from complex motion\npatterns and severe self-occlusion, especially for the monocular setting. In\nthis paper, we propose SportsCap -- the first approach for simultaneously\ncapturing 3D human motions and understanding fine-grained actions from\nmonocular challenging sports video input. Our approach utilizes the semantic\nand temporally structured sub-motion prior in the embedding space for motion\ncapture and understanding in a data-driven multi-task manner. To enable robust\ncapture under complex motion patterns, we propose an effective motion embedding\nmodule to recover both the implicit motion embedding and explicit 3D motion\ndetails via a corresponding mapping function as well as a sub-motion\nclassifier. Based on such hybrid motion information, we introduce a\nmulti-stream spatial-temporal Graph Convolutional Network(ST-GCN) to predict\nthe fine-grained semantic action attributes, and adopt a semantic attribute\nmapping block to assemble various correlated action attributes into a\nhigh-level action label for the overall detailed understanding of the whole\nsequence, so as to enable various applications like action assessment or motion\nscoring. Comprehensive experiments on both public and our proposed datasets\nshow that with a challenging monocular sports video input, our novel approach\nnot only significantly improves the accuracy of 3D human motion capture, but\nalso recovers accurate fine-grained semantic action attributes.\n"}
+{"abstract": "  Methods for stochastic trace estimation often require the repeated evaluation\nof expressions of the form $z^T p_n(A)z$, where $A$ is a symmetric matrix and\n$p_n$ is a degree $n$ polynomial written in the standard or Chebyshev basis. We\nshow how to evaluate these expressions using only $\\lceil n/2\\rceil$\nmatrix-vector products, thus substantially reducing the cost of existing trace\nestimation algorithms that use Chebyshev interpolation or Taylor series.\n"}
+{"abstract": "  The inverse Higgs phenomenon, which plays an important r\\^ole in physical\nsystems with Goldstone bosons (such as the phonons in a crystal) involves\nnonholonomic mechanical constraints. By formulating field theories with\nsymmetries and constraints in a general way using the language of differential\ngeometry, we show that many examples of constraints in inverse Higgs phenomena\nfall into a special class, which we call coholonomic constraints, that are dual\n(in the sense of category theory) to holonomic constraints. Just as for\nholonomic constraints, systems with coholonomic constraints are equivalent to\nunconstrained systems (whose degrees of freedom are known as essential\nGoldstone bosons), making it easier to study their consistency and dynamics.\nThe remaining examples of inverse Higgs phenomena in the literature require the\ndual of a slight generalisation of a holonomic constraint, which we call\n(co)meronomic. Our formalism simplifies and clarifies the many ad hoc\nassumptions and constructions present in the literature. In particular, it\nidentifies which are necessary and which are merely convenient. It also opens\nthe way to studying much more general dynamical examples, including systems\nwhich have no well-defined notion of a target space.\n"}
+{"abstract": "  This article discusses a dark energy cosmological model in the standard\ntheory of gravity - general relativity with a broad scalar field as a source.\nExact solutions of Einstein's field equations are derived by considering a\nparticular form of deceleration parameter $q$, which shows a smooth transition\nfrom decelerated to accelerated phase in the evolution of the universe. The\nexternal datasets such as Hubble ($H(z)$) datasets, Supernovae (SN) datasets,\nand Baryonic Acoustic Oscillation (BAO) datasets are used for constraining the\nmodel par parameters appearing in the functional form of $q$. The transition\nredshift is obtained at $% z_{t}=0.67_{-0.36}^{+0.26}$ for the combined data\nset ($H(z)+SN+BAO$), where the model shows signature-flipping and is consistent\nwith recent observations. Moreover, the present value of the deceleration\nparameter comes out to be $q_{0}=-0.50_{-0.11}^{+0.12}$ and the jerk parameter\n$% j_{0}=-0.98_{-0.02}^{+0.06}$ (close to 1) for the combined datasets, which\nis compatible as per Planck2018 results. The analysis also constrains the omega\nvalue i.e., $\\Omega _{m_{0}}\\leq 0.269$ for the smooth evolution of the scalar\nfield EoS parameter. It is seen that energy density is higher for the effective\nenergy density of the matter field than energy density in the presence of a\nscalar field. The evolution of the physical and geometrical parameters is\ndiscussed in some details with the model parameters' numerical constrained\nvalues. Moreover, we have performed the state-finder analysis to investigate\nthe nature of dark energy.\n"}
+{"abstract": "  Electrical energy consumption data accessibility for low voltage end users is\none of the pillars of smart grids. In some countries, despite the presence of\nsmart meters, a fragmentary data availability and/or the lack of\nstandardization hinders the creation of post-metering value-added services and\nconfines such innovative solutions to the prototypal and experimental level. We\ntake inspiration from the technology adopted in Italy, where the national\nregulatory authority actively supported the definition of a solution agreed\nupon by all the involved stakeholders. In this context, smart meters are\nenabled to convey data to low voltage end users through a power line\ncommunication channel (CHAIN 2) in near real-time. The aim of this paper is\ntwofold. On the one hand, it describes the proof of concept that the channel\nunderwent and its subsequent validation (with performances nearing 99% success\nrate). On the other hand, it defines a classification framework (I2MA) for\npost-metering value-added services, in order to categorize each use case based\non both level of service and expected benefits, and understand its maturity\nlevel. As an example, we apply the methodology to the 16 use cases defined in\nItaly. The lessons learned from the regulatory, technological, and functional\napproach of the Italian experience bring us to the provision of recommendations\nfor researchers and industry experts. In particular, we argue that a\nwell-functioning post-metering value-added services' market can flourish when:\ni) distribution system operators certify the measurements coming from smart\nmeters; ii) national regulatory authorities support the technological\ninnovation needed for setting up this market; and iii) service providers create\ncustomer-oriented solutions based on smart meters' data.\n"}
+{"abstract": "  Robust edge transport can occur when particles in crystalline lattices\ninteract with an external magnetic field. This system is well described by\nBloch's theorem, with the spectrum being composed of bands of bulk states and\nin-gap edge states. When the confining lattice geometry is altered to be\nquasicrystaline, then Bloch's theorem breaks down. However, we still expect to\nobserve the basic characteristics of bulk states and current carrying edge\nstates. Here, we show that for quasicrystals in magnetic fields, there is also\na third option; the bulk localised transport states. These states share the\nin-gap nature of the well-known edge states and can support transport along\nthem, but they are fully contained within the bulk of the system, with no\nsupport along the edge. We consider both finite and infinite systems, using\nrigorous error controlled computational techniques that are not prone to\nfinite-size effects. The bulk localised transport states are preserved for\ninfinite systems, in stark contrast to the normal edge states. This allows for\ntransport to be observed in infinite systems, without any perturbations,\ndefects, or boundaries being introduced. We confirm the in-gap topological\nnature of the bulk localised transport states for finite and infinite systems\nby computing common topological measures; namely the Bott index and local Chern\nmarker. The bulk localised transport states form due to a magnetic aperiodicity\narising from the interplay of length scales between the magnetic field and\nquasiperiodic lattice. Bulk localised transport could have interesting\napplications similar to those of the edge states on the boundary, but that\ncould now take advantage of the larger bulk of the lattice. The infinite size\ntechniques introduced here, especially the calculation of topological measures,\ncould also be widely applied to other crystalline, quasicrystalline, and\ndisordered models.\n"}
+{"abstract": "  It is well established that glassy materials can undergo aging, i.e., their\nproperties gradually change over time. There is rapidly growing evidence that\ndense active and living systems also exhibit many features of glassy behavior,\nbut it is still largely unknown how physical aging is manifested in such active\nglassy materials. Our goal is to explore whether active and passive thermal\nglasses age in fundamentally different ways. To address this, we numerically\nstudy the aging dynamics following a quench from high to low temperature for\ntwo-dimensional passive and active Brownian model glass-formers. We find that\naging in active thermal glasses is governed by a time-dependent competition\nbetween thermal and active effects, with an effective temperature that\nexplicitly evolves with the age of the material. Moreover, unlike passive aging\nphenomenology, we find that the degree of dynamic heterogeneity in active aging\nsystems is relatively small and remarkably constant with age. We conclude that\nthe often-invoked mapping between an active system and a passive one with a\nhigher effective temperature rigorously breaks down upon aging, and that the\naging dynamics of thermal active glasses differs in several distinct ways from\nboth the passive and athermal active case.\n"}
+{"abstract": "  The radio nebula W50 is a unique object interacting with the jets of the\nmicroquasar SS433. The SS433/W50 system is a good target for investigating the\nenergy of cosmic-ray particles accelerated by galactic jets. We report\nobservations of radio nebula W50 conducted with the NSF's Karl G. Jansky Very\nLarge Array (VLA) in the L band (1.0 -- 2.0 GHz). We investigate the secular\nchange of W50 on the basis of the observations in 1984, 1996, and 2017, and\nfind that most of its structures were stable for 33 years. We revise the upper\nlimit velocity of the eastern terminal filament by half to 0.023$c$ assuming a\ndistance of 5.5 kpc. We also analyze the observational data of the Arecibo\nObservatory 305-m telescope and identify the HI cavity around W50 in the\nvelocity range 33.77 km s$^{-1}$ -- 55.85 km s$^{-1}$. From this result, we\nestimate the maximum energy of the cosmic-ray protons accelerated by the jet\nterminal region to be above 10$^{15.5}$ eV. We also use the luminosity of the\ngamma-rays in the range 0.5 -- 10 GeV to estimate the total energy of\naccelerated protons below 5.2 $\\times$ 10$^{48}$ erg.\n"}
+{"abstract": "  We present the design for a novel type of dual-band photodetector in the\nthermal infrared spectral range, the Optically Controlled Dual-band quantum dot\nInfrared Photodetector (OCDIP). This concept is based on a quantum dot ensemble\nwith a unimodal size distribution, whose absorption spectrum can be controlled\nby optically-injected carriers. An external pumping laser varies the electron\ndensity in the QDs, permitting to control the available electronic transitions\nand thus the absorption spectrum. We grew a test sample which we studied by AFM\nand photoluminescence. Based on the experimental data, we simulated the\ninfrared absorption spectrum of the sample, which showed two absorption bands\nat 5.85 um and 8.98 um depending on the excitation power.\n"}
+{"abstract": "  We present a novel ultrastable superconducting radio-frequency (RF) ion trap\nrealized as a combination of an RF cavity and a linear Paul trap. Its RF\nquadrupole mode at 34.52 MHz reaches a quality factor of $Q\\approx2.3\\times\n10^5$ at a temperature of 4.1 K and is used to radially confine ions in an\nultralow-noise pseudopotential. This concept is expected to strongly suppress\nmotional heating rates and related frequency shifts which limit the ultimate\naccuracy achieved in advanced ion traps for frequency metrology. Running with\nits low-vibration cryogenic cooling system, electron beam ion trap and\ndeceleration beamline supplying highly charged ions (HCI), the superconducting\ntrap offers ideal conditions for optical frequency metrology with ionic\nspecies. We report its proof-of-principle operation as a quadrupole mass filter\nwith HCI, and trapping of Doppler-cooled ${}^9\\text{Be}^+$ Coulomb crystals.\n"}
+{"abstract": "  With Regulation UNECE R157 on Automated Lane-Keeping Systems, the first\nframework for the introduction of passenger cars with Level 3 systems has\nbecome available in 2020. In accordance with recent research projects including\nacademia and the automotive industry, the Regulation utilizes scenario based\ntesting for the safety assessment. The complexity of safety validation of\nautomated driving systems necessitates system-level simulations. The\nRegulation, however, is missing the required parameterization necessary for\ntest case generation. To overcome this problem, we incorporate the exposure and\nconsider the heterogeneous behavior of the traffic participants by extracting\nconcrete scenarios according to Regulation's scenario definition from the\nestablished naturalistic highway dataset highD. We present a methodology to\nfind the scenarios in real-world data, extract the parameters for modeling the\nscenarios and transfer them to simulation. In this process, more than 340\nscenarios were extracted. OpenSCENARIO files were generated to enable an\nexemplary transfer of the scenarios to CARLA and esmini. We compare the\ntrajectories to examine the similarity of the scenarios in the simulation to\nthe recorded scenarios. In order to foster research, we publish the resulting\ndataset called ConScenD together with instructions for usage with both\nsimulation tools. The dataset is available online at\nhttps://www.levelXdata.com/scenarios.\n"}
+{"abstract": "  In this paper, we characterize the asymptotic and large scale behavior of the\neigenvalues of wavelet random matrices in high dimensions. We assume that\npossibly non-Gaussian, finite-variance $p$-variate measurements are made of a\nlow-dimensional $r$-variate ($r \\ll p$) fractional stochastic process with\nnon-canonical scaling coordinates and in the presence of additive\nhigh-dimensional noise. The measurements are correlated both time-wise and\nbetween rows. We show that the $r$ largest eigenvalues of the wavelet random\nmatrices, when appropriately rescaled, converge to scale invariant functions in\nthe high-dimensional limit. By contrast, the remaining $p-r$ eigenvalues remain\nbounded. Under additional assumptions, we show that, up to a log\ntransformation, the $r$ largest eigenvalues of wavelet random matrices exhibit\nasymptotically Gaussian distributions. The results have direct consequences for\nstatistical inference.\n"}
+{"abstract": "  Annually, a large number of injuries and deaths around the world are related\nto motor vehicle accidents. This value has recently been reduced to some\nextent, via the use of driver-assistance systems. Developing driver-assistance\nsystems (i.e., automated driving systems) can play a crucial role in reducing\nthis number. Estimating and predicting surrounding vehicles' movement is\nessential for an automated vehicle and advanced safety systems. Moreover,\npredicting the trajectory is influenced by numerous factors, such as drivers'\nbehavior during accidents, history of the vehicle's movement and the\nsurrounding vehicles, and their position on the traffic scene. The vehicle must\nmove over a safe path in traffic and react to other drivers' unpredictable\nbehaviors in the shortest time. Herein, to predict automated vehicles' path, a\nmodel with low computational complexity is proposed, which is trained by images\ntaken from the road's aerial image. Our method is based on an encoder-decoder\nmodel that utilizes a social tensor to model the effect of the surrounding\nvehicles' movement on the target vehicle. The proposed model can predict the\nvehicle's future path in any freeway only by viewing the images related to the\nhistory of the target vehicle's movement and its neighbors. Deep learning was\nused as a tool for extracting the features of these images. Using the HighD\ndatabase, an image dataset of the road's aerial image was created, and the\nmodel's performance was evaluated on this new database. We achieved the RMSE of\n1.91 for the next 5 seconds and found that the proposed method had less error\nthan the best path-prediction methods in previous studies.\n"}
+{"abstract": "  The paper provides a version of the rational Hodge conjecture for $\\3\\dg$\ncategories. The noncommutative Hodge conjecture is equivalent to the version\nproposed in \\cite{perry2020integral} for admissible subcategories. We obtain\nexamples of evidence of the Hodge conjecture by techniques of noncommutative\ngeometry. Finally, we show that the noncommutative Hodge conjecture for smooth\nproper connective $\\3\\dg$ algebras is true.\n"}
+{"abstract": "  Let $(X, D)$ be a log smooth log canonical pair such that $K_X+D$ is ample.\nExtending a theorem of Guenancia and building on his techniques, we show that\nnegatively curved K\\\"{a}hler-Einstein crossing edge metrics converge to\nK\\\"{a}hler-Einstein mixed cusp and edge metrics smoothly away from the divisor\nwhen some of the cone angles converge to $0$. We further show that near the\ndivisor such normalized K\\\"{a}hler-Einstein crossing edge metrics converge to a\nmixed cylinder and edge metric in the pointed Gromov-Hausdorff sense when some\nof the cone angles converge to $0$ at (possibly) different speeds.\n"}
+{"abstract": "  Novice programmers face numerous barriers while attempting to learn how to\ncode that may deter them from pursuing a computer science degree or career in\nsoftware development. In this work, we propose a tool concept to address the\nparticularly challenging barrier of novice programmers holding misconceptions\nabout how their code behaves. Specifically, the concept involves an inquisitive\ncode editor that: (1) identifies misconceptions by periodically prompting the\nnovice programmer with questions about their program's behavior, (2) corrects\nthe misconceptions by generating explanations based on the program's actual\nbehavior, and (3) prevents further misconceptions by inserting test code and\nutilizing other educational resources. We have implemented portions of the\nconcept as plugins for the Atom code editor and conducted informal surveys with\nstudents and instructors. Next steps include deploying the tool prototype to\nstudents enrolled in introductory programming courses.\n"}
+{"abstract": "  We show that any Brauer tree algebra has precisely $\\binom{2n}{n}$\n$2$-tilting complexes, where $n$ is the number of edges of the associated\nBrauer tree. More explicitly, for an external edge $e$ and an integer $j\\neq0$,\nwe show that the number of $2$-tilting complexes $T$ with $g_e(T)=j$ is\n$\\binom{2n-|j|-1}{n-1}$, where $g_e(T)$ denotes the $e$-th of the $g$-vector of\n$T$. To prove this, we use a geometric model of Brauer graph algebras on the\nclosed oriented marked surfaces and a classification of $2$-tilting complexes\ndue to Adachi-Aihara-Chan.\n"}
+{"abstract": "  We measure the evolution of the rest-frame UV luminosity function (LF) and\nthe stellar mass function (SMF) of Lyman-alpha (Lya) emitters (LAEs) from z~2\nto z~6 by exploring ~4000 LAEs from the SC4K sample. We find a correlation\nbetween Lya luminosity (LLya) and rest-frame UV (M_UV), with best-fit\nM_UV=-1.6+-0.2 log10(LLya/erg/s)+47+-12 and a shallower relation between LLya\nand stellar mass (Mstar), with best-fit log10( Mstar/Msun)=0.9+-0.1\nlog10(LLya/erg/s)-28+-4.0. An increasing LLya cut predominantly lowers the\nnumber density of faint M_UV and low Mstar LAEs. We estimate a proxy for the\nfull UV LFs and SMFs of LAEs with simple assumptions of the faint end slope.\nFor the UV LF, we find a brightening of the characteristic UV luminosity\n(M_UV*) with increasing redshift and a decrease of the characteristic number\ndensity (Phi*). For the SMF, we measure a characteristic stellar mass\n(Mstar*/Msun) increase with increasing redshift, and a Phi* decline. However,\nif we apply a uniform luminosity cut of log10 (LLya/erg/s) >= 43.0, we find\nmuch milder to no evolution in the UV and SMF of LAEs. The UV luminosity\ndensity (rho_UV) of the full sample of LAEs shows moderate evolution and the\nstellar mass density (rho_M) decreases, with both being always lower than the\ntotal rho_UV and rho_M of more typical galaxies but slowly approaching them\nwith increasing redshift. Overall, our results indicate that both rho_UV and\nrho_M of LAEs slowly approach the measurements of continuum-selected galaxies\nat z>6, which suggests a key role of LAEs in the epoch of reionisation.\n"}
+{"abstract": "  Accurate numerical solutions for the Schr\\\"odinger equation are of utmost\nimportance in quantum chemistry. However, the computational cost of current\nhigh-accuracy methods scales poorly with the number of interacting particles.\nCombining Monte Carlo methods with unsupervised training of neural networks has\nrecently been proposed as a promising approach to overcome the curse of\ndimensionality in this setting and to obtain accurate wavefunctions for\nindividual molecules at a moderately scaling computational cost. These methods\ncurrently do not exploit the regularity exhibited by wavefunctions with respect\nto their molecular geometries. Inspired by recent successful applications of\ndeep transfer learning in machine translation and computer vision tasks, we\nattempt to leverage this regularity by introducing a weight-sharing constraint\nwhen optimizing neural network-based models for different molecular geometries.\nThat is, we restrict the optimization process such that up to 95 percent of\nweights in a neural network model are in fact equal across varying molecular\ngeometries. We find that this technique can accelerate optimization when\nconsidering sets of nuclear geometries of the same molecule by an order of\nmagnitude and that it opens a promising route towards pre-trained neural\nnetwork wavefunctions that yield high accuracy even across different molecules.\n"}
+{"abstract": "  As a non-linear extension of the classic Linear Discriminant Analysis(LDA),\nDeep Linear Discriminant Analysis(DLDA) replaces the original Categorical Cross\nEntropy(CCE) loss function with eigenvalue-based loss function to make a deep\nneural network(DNN) able to learn linearly separable hidden representations. In\nthis paper, we first point out DLDA focuses on training the cooperative\ndiscriminative ability of all the dimensions in the latent subspace, while put\nless emphasis on training the separable capacity of single dimension. To\nimprove DLDA, a regularization method on within-class scatter matrix is\nproposed to strengthen the discriminative ability of each dimension, and also\nkeep them complement each other. Experiment results on STL-10, CIFAR-10 and\nPediatric Pneumonic Chest X-ray Dataset showed that our proposed regularization\nmethod Regularized Deep Linear Discriminant Analysis(RDLDA) outperformed DLDA\nand conventional neural network with CCE as objective. To further improve the\ndiscriminative ability of RDLDA in the local space, an algorithm named Subclass\nRDLDA is also proposed.\n"}
+{"abstract": "  A fog-radio access network (F-RAN) architecture is studied for an\nInternet-of-Things (IoT) system in which wireless sensors monitor a number of\nmulti-valued events and transmit in the uplink using grant-free random access\nto multiple edge nodes (ENs). Each EN is connected to a central processor (CP)\nvia a finite-capacity fronthaul link. In contrast to conventional\ninformation-agnostic protocols based on separate source-channel (SSC) coding,\nwhere each device uses a separate codebook, this paper considers an\ninformation-centric approach based on joint source-channel (JSC) coding via a\nnon-orthogonal generalization of type-based multiple access (TBMA). By\nleveraging the semantics of the observed signals, all sensors measuring the\nsame event share the same codebook (with non-orthogonal codewords), and all\nsuch sensors making the same local estimate of the event transmit the same\ncodeword. The F-RAN architecture directly detects the events values without\nfirst performing individual decoding for each device. Cloud and edge detection\nschemes based on Bayesian message passing are designed and trade-offs between\ncloud and edge processing are assessed.\n"}
+{"abstract": "  We report parametric resonances (PRs) in the mean-field dynamics of a\none-dimensional dipolar Bose-Einstein condensate (DBEC) in widely varying\ntrapping geometries. The chief goal is to characterize the energy levels of\nthis system by analytical methods and the significance of this study arises\nfrom the commonly known fact that in the presence of interactions the energy\nlevels of a trapped BEC are hard to calculate analytically. The latter\ncharacterization is achieved by a matching of the PR energies to energy levels\nof the confining trap using perturbative methods. Further, this work reveals\nthe role of the interplay between dipole-dipole interactions (DDI) and trapping\ngeometry in defining the energies and amplitudes of the PRs. The PRs are\ninduced by a negative Gaussian potential whose depth oscillates with time.\nMoreover, the DDI play a role in this induction. The dynamics of this system is\nmodeled by the time-dependent Gross- Pitaevskii equation (TDGPE) that is\nnumerically solved by the Crank-Nicolson method. The PRs are discussed basing\non analytical methods: first, it is shown that it is possible to reproduce PRs\nby the Lagrangian variational method that are similar to the ones obtained from\nTDGPE. Second, the energies at which the PRs arise are closely matched with the\nenergy levels of the corresponding trap calculated by time-independent\nperturbation theory. Third, the most probable transitions between the trap\nenergy levels yielding PRs are determined by time-dependent perturbation\ntheory. The most significant result of this work is that we have been able to\ncharacterize the above mentioned energy levels of a DBEC in a complex trapping\npotential.\n"}
+{"abstract": "  The digital transformation has been underway, creating digital shadows of\n(almost) all physical entities and moving them to the Internet. The era of\nInternet of Everything has therefore started to come into play, giving rise to\nunprecedented traffic growths. In this context, optical core networks forming\nthe backbone of Internet infrastructure have been under critical issues of\nreaching the capacity limit of conventional fiber, a phenomenon widely referred\nas capacity crunch. For many years, the many-fold increases in fiber capacity\nis thanks to exploiting physical dimensions for multiplexing optical signals\nsuch as wavelength, polarization, time and lately space-division multiplexing\nusing multi-core fibers and such route seems to come to an end as almost all\nknown ways have been exploited. This necessitates for a departure from\ntraditional approaches to use the fiber capacity more efficiently and thereby\nimprove economics of scale. This paper lays out a new perspective to integrate\nnetwork coding (NC) functions into optical networks to achieve greater capacity\nefficiency by upgrading intermediate nodes functionalities. In addition to the\nreview of recent proposals on new research problems enabled by NC operation in\noptical networks, we also report state-of-the-art findings in the literature in\nan effort to renew the interest of NC in optical networks and discuss three\ncritical points for pushing forward its applicability and practicality\nincluding i) NC as a new dimension for multiplexing optical signals ii)\nalgorithmic aspects of NC-enabled optical networks design iii) NC as an\nentirely fresh way for securing optical signals at physical layers\n"}
+{"abstract": "  In this paper, we propose a novel lightweight relation extraction approach of\nstructural block driven - convolutional neural learning. Specifically, we\ndetect the essential sequential tokens associated with entities through\ndependency analysis, named as a structural block, and only encode the block on\na block-wise and an inter-block-wise representation, utilizing multi-scale\nCNNs. This is to 1) eliminate the noisy from irrelevant part of a sentence;\nmeanwhile 2) enhance the relevant block representation with both block-wise and\ninter-block-wise semantically enriched representation. Our method has the\nadvantage of being independent of long sentence context since we only encode\nthe sequential tokens within a block boundary. Experiments on two datasets\ni.e., SemEval2010 and KBP37, demonstrate the significant advantages of our\nmethod. In particular, we achieve the new state-of-the-art performance on the\nKBP37 dataset; and comparable performance with the state-of-the-art on the\nSemEval2010 dataset.\n"}
+{"abstract": "  A model investigating the role of geometry on the alpha dose rate of spent\nnuclear fuel has been developed. This novel approach utilises a new piecewise\nfunction to describe the probability of alpha escape as a function of\nparticulate radius, decay range within the material, and position from the\nsurface. The alpha dose rates were produced for particulates of radii 1 $\\mu$m\nto 10 mm, showing considerable changes in the 1 $\\mu$m to 50 $\\mu$m range.\nResults indicate that for decreasing particulate sizes, approaching radii equal\nto or less than the range of the $\\alpha$-particle within the fuel, there is a\nsignificant increase in the rate of energy emitted per unit mass of fuel\nmaterial. The influence of geometry is more significant for smaller radii,\nshowing clear differences in dose rate curves below 50 $\\mu$m. These\nconsiderations are essential for any future accurate prediction of the\ndissolution rates and hydrogen gas release, driven by the radiolytic yields of\nparticulate spent nuclear fuel.\n"}
+{"abstract": "  We investigate the effectiveness of three different job-search and training\nprogrammes for German long-term unemployed persons. On the basis of an\nextensive administrative data set, we evaluated the effects of those programmes\non various levels of aggregation using Causal Machine Learning. We found\nparticipants to benefit from the investigated programmes with placement\nservices to be most effective. Effects are realised quickly and are\nlong-lasting for any programme. While the effects are rather homogenous for\nmen, we found differential effects for women in various characteristics. Women\nbenefit in particular when local labour market conditions improve. Regarding\nthe allocation mechanism of the unemployed to the different programmes, we\nfound the observed allocation to be as effective as a random allocation.\nTherefore, we propose data-driven rules for the allocation of the unemployed to\nthe respective labour market programmes that would improve the status-quo.\n"}
+{"abstract": "  Faceted summarization provides briefings of a document from different\nperspectives. Readers can quickly comprehend the main points of a long document\nwith the help of a structured outline. However, little research has been\nconducted on this subject, partially due to the lack of large-scale faceted\nsummarization datasets. In this study, we present FacetSum, a faceted\nsummarization benchmark built on Emerald journal articles, covering a diverse\nrange of domains. Different from traditional document-summary pairs, FacetSum\nprovides multiple summaries, each targeted at specific sections of a long\ndocument, including the purpose, method, findings, and value. Analyses and\nempirical results on our dataset reveal the importance of bringing structure\ninto summaries. We believe FacetSum will spur further advances in summarization\nresearch and foster the development of NLP systems that can leverage the\nstructured information in both long texts and summaries.\n"}
+{"abstract": "  Considering a double-headed Brownian motor moving with both translational and\nrotational degrees of freedom, we investigate the directed transport properties\nof the system in a traveling-wave potential. It is found that the traveling\nwave provides the essential condition of the directed transport for the system,\nand at an appropriate angular frequency, the positive current can be optimized.\nA general current reversal appears by modulating the angular frequency of the\ntraveling wave, noise intensity, external driving force and the rod length. By\ntransforming the dynamical equation in traveling-wave potential into that in a\ntilted potential, the mechanism of current reversal is analyzed. For both cases\nof Gaussian and Levy noises, the currents show similar dependence on the\nparameters. Moreover, the current in the tilted potential shows a typical\nstochastic resonance effect. The external driving force has also a\nresonance-like effect on the current in the tilted potential. But the current\nin the traveling-wave potential exhibits the reverse behaviors of that in the\ntilted potential. Besides, the currents obviously depend on the stability index\nof the Levy noise under certain conditions.\n"}
+{"abstract": "  We demonstrate size selective optical trapping and transport for\nnanoparticles near an optical nanofiber taper. Using a two-wavelength,\ncounter-propagating mode configuration, we show that 100 nm diameter and 150 nm\ndiameter gold nanospheres (GNSs) are trapped by the evanescent field in the\ntaper region at different optical powers. Conversely, when one nanoparticle\nspecies is trapped the other may be transported, leading to a sieve-like\neffect. Our results show that sophisticated optical manipulation can be\nachieved in a passive configuration by taking advantage of mode behavior in\nnanophotonics devices.\n"}
+{"abstract": "  We investigated changes in the b value of the Gutenberg-Richter's law in and\naround the focal areas of earthquakes on March 20 and on May 1, 2021, with\nmagnitude (M) 6.9 and 6.8, respectively, which occurred off the Pacific coast\nof Miyagi prefecture, northeastern Japan. We showed that the b value in these\nfocal areas had been noticeably small, especially within a few years before the\noccurrence of the M6.9 earthquake in its vicinity, indicating that differential\nstress had been high in the focal areas. The coseismic slip of the 2011 Tohoku\nearthquake seems to have stopped just short of the east side of the focus of\nthe M6.9 earthquake. Furthermore, the afterslip of the 2011 Tohoku earthquake\nwas relatively small in the focal areas of the M6.9 and M6.8 earthquakes,\ncompared to the surrounding regions. In addition, the focus of the M6.9\nearthquake was situated close to the border point where the interplate slip in\nthe period from 2012 through 2021 has been considerably larger on the northern\nside than on the southern side. The high-stress state inferred by the b-value\nanalysis is concordant with those characteristics of interplate slip events. We\nfound that the M6.8 earthquake on May 1 occurred near an area where the b value\nremained small, even after the M6.9 quake. The ruptured areas by the two\nearthquakes now seem to almost coincide with the small-b-value region that had\nexisted before their occurrence. The b value on the east side of the focal\nareas of the M6.9 and M6.8 earthquakes which corresponds to the eastern part of\nthe source region of the 1978 off-Miyagi prefecture earthquake was consistently\nlarge, while the seismicity enhanced by the two earthquakes also shows a large\nb value, implying that stress in the region has not been very high.\n"}
+{"abstract": "  During the early history of unitary quantum theory the Kato's exceptional\npoints (EPs, a.k.a. non-Hermitian degeneracies) of Hamiltonians $H(\\lambda)$\ndid not play any significant role, mainly due to the Stone theorem which firmly\nconnected the unitarity with the Hermiticity. During the recent wave of\noptimism people started believing that the corridors of a unitary access to the\nEPs could be opened leading, say, to a new picture of quantum phase transitions\nvia an {\\it ad hoc} weakening of the Hermiticty (replaced by the\nquasi-Hermiticity). Subsequently, the pessimism prevailed (the paths of access\nappeared to be fragile). In a way restricted to the quantum physics of closed\nsystems a return to optimism is advocated here: the apparent fragility of the\ncorridors is claimed to follow from a misinterpretation of the theory in its\nquasi-Hermitian formulation. Several perturbed versions of the realistic\nmany-body Bose-Hubbard model are chosen for illustration purposes.\n"}
+{"abstract": "  Existing works on visual counting primarily focus on one specific category at\na time, such as people, animals, and cells. In this paper, we are interested in\ncounting everything, that is to count objects from any category given only a\nfew annotated instances from that category. To this end, we pose counting as a\nfew-shot regression task. To tackle this task, we present a novel method that\ntakes a query image together with a few exemplar objects from the query image\nand predicts a density map for the presence of all objects of interest in the\nquery image. We also present a novel adaptation strategy to adapt our network\nto any novel visual category at test time, using only a few exemplar objects\nfrom the novel category. We also introduce a dataset of 147 object categories\ncontaining over 6000 images that are suitable for the few-shot counting task.\nThe images are annotated with two types of annotation, dots and bounding boxes,\nand they can be used for developing few-shot counting models. Experiments on\nthis dataset shows that our method outperforms several state-of-the-art object\ndetectors and few-shot counting approaches. Our code and dataset can be found\nat https://github.com/cvlab-stonybrook/LearningToCountEverything.\n"}
+{"abstract": "  Turbulent puffs are ubiquitous in everyday life phenomena. Understanding\ntheir dynamics is important in a variety of situations ranging from industrial\nprocesses to pure and applied science. In all these fields, a deep knowledge of\nthe statistical structure of temperature and velocity space/time fluctuations\nis of paramount importance to construct models of chemical reaction (in\nchemistry), of condensation of virus-containing droplets (in virology and/or\nbiophysics), and optimal mixing strategies in industrial applications. As a\nmatter of fact, results of turbulence in a puff are confined to bulk properties\n(i.e. average puff velocity and typical decay/growth time) and dates back to\nthe second half of the 20th century. There is thus a huge gap to fill to pass\nfrom bulk properties to two-point statistical observables. Here we fill this\ngap exploiting theory and numerics in concert to predict and validate the\nspace/time scaling behaviors of both velocity and temperature structure\nfunctions including intermittency corrections. Excellent agreement between\ntheory and simulations is found. Our results are expected to have profound\nimpact to develop evaporation models for virus-containing droplets carried by a\nturbulent puff, with benefits to the comprehension of the airborne route of\nvirus contagion.\n"}
+{"abstract": "  The installation of electric vehicle charging stations (EVCS) will be\nessential to promote the acceptance by the users of electric vehicles (EVs).\nHowever, if EVCS are exclusively supplied by the grid, negative impacts on its\nstability together with possible CO2 emission increases could be produced.\nIntroduction of hybrid renewable energy systems (HRES) for EVCS can cope with\nboth drawbacks by reducing the load on the grid and generating clean\nelectricity. This paper develops a methodology based on a weighted\nmulticriteria process to design the most suitable configuration for HRES in\nEVCS. This methodology determines the local renewable resources and the EVCS\nelectricity demand. Then, taking into account environmental, economic and\ntechnical aspects, it deduces the most adequate HRES design for the EVCS.\nBesides, an experimental stage to validate the design deduced from the\nmulticriteria process is included. Therefore, the final design for the HRES in\nEVCS is supported not only by a complete numerical evaluation, but also by an\nexperimental verification of the demand being fully covered. Methodology\napplication to Valencia (Spain) proves that an off-grid HRES with solar PV,\nwind resources and batteries support would be the most suitable configuration\nfor the system. This solution was also experimentally verified.\n"}
+{"abstract": "  We use large deviation theory to obtain the free energy of the XY model on a\nfully connected graph on each site of which there is a randomly oriented field\nof magnitude $h$. The phase diagram is obtained for two symmetric distributions\nof the random orientations: (a) a uniform distribution and (b) a distribution\nwith cubic symmetry. In both cases, the ordered state reflects the symmetry of\nthe underlying disorder distribution. The phase boundary has a multicritical\npoint which separates a locus of continuous transitions (for small values of\n$h$) from a locus of first order transitions (for large $h$). The free energy\nis a function of a single variable in case (a) and a function of two variables\nin case (b), leading to different characters of the multicritical points in the\ntwo cases.\n"}
+{"abstract": "  Machine learning (ML) tools such as encoder-decoder deep convolutional neural\nnetworks (CNN) are able to extract relationships between inputs and outputs of\nlarge complex systems directly from raw data. For time-varying systems the\npredictive capabilities of ML tools degrade as the systems are no longer\naccurately represented by the data sets with which the ML models were trained.\nRe-training is possible, but only if the changes are slow and if new\ninput-output training data measurements can be made online non-invasively. In\nthis work we present an approach to deep learning for time-varying systems in\nwhich adaptive feedback based only on available system output measurements is\napplied to encoded low-dimensional dense layers of encoder-decoder type CNNs.\nWe demonstrate our method in developing an inverse model of a complex charged\nparticle accelerator system, mapping output beam measurements to input beam\ndistributions while both the accelerator components and the unknown input beam\ndistribution quickly vary with time. We demonstrate our results using\nexperimental measurements of the input and output beam distributions of the\nHiRES ultra-fast electron diffraction (UED) microscopy beam line at Lawrence\nBerkeley National Laboratory. We show how our method can be used to aid both\nphysics and ML-based surrogate online models to provide non-invasive beam\ndiagnostics and we also demonstrate how our method can be used to automatically\ntrack the time varying quantum efficiency map of a particle accelerator's\nphotocathode.\n"}
+{"abstract": "  The extremely large magnetoresistance (XMR) effect in nonmagnetic semimetals\nhave attracted intensive attention recently. Here we propose an XMR candidate\nmaterial SrPd based on first-principles electronic structure calculations in\ncombination with a semi-classical model. The calculated carrier densities in\nSrPd indicate that there is a good electron-hole compensation, while the\ncalculated intrinsic carrier mobilities are as high as 10$^5$\ncm$^2$V$^{-1}$s$^{-1}$. There are only two doubly degenerate bands crossing the\nFermi level for SrPd, thus a semi-classical two-band model is available for\ndescribing its transport properties. Accordingly, the magnetoresistance of SrPd\nunder a magnetic field of $4$ Tesla is predicted to reach ${10^5} \\%$ at low\ntemperature. Furthermore, the calculated topological invariant indicates that\nSrPd is topologically trivial. Our theoretical studies suggest that SrPd can\nserve as an ideal platform to examine the charge compensation mechanism of the\nXMR effect.\n"}
+{"abstract": "  Chemically peculiar stars in eclipsing binary systems are rare objects that\nallow the derivation of fundamental stellar parameters and important\ninformation on the evolutionary status and the origin of the observed chemical\npeculiarities. Here we present an investigation of the known eclipsing binary\nsystem BD+09 1467 = V680 Mon. Using spectra from the Large Sky Area\nMulti-Object Fiber Spectroscopic Telescope (LAMOST) and own observations, we\nidentify the primary component of the system as a mercury-manganese (HgMn/CP3)\nstar (spectral type kB9 hB8 HeB9 V HgMn). Furthermore, photometric time series\ndata from the Transiting Exoplanet Survey Satellite (TESS) indicate that the\nsystem is a \"heartbeat star\", a rare class of eccentric binary stars with\nshort-period orbits that exhibit a characteristic signature near the time of\nperiastron in their light curves due to the tidal distortion of the components.\nUsing all available photometric observations, we present an updated ephemeris\nand binary system parameters as derived from modelling of the system with the\nELISa code, which indicates that the secondary star has an effective\ntemperature of Teff = 8300+-200 K (spectral type of about A4). V680 Mon is only\nthe fifth known eclipsing CP3 star and the first one in a heartbeat binary.\nFurthermore, our results indicate that the star is located on the zero-age main\nsequence and a possible member of the open cluster NGC 2264. As such, it lends\nitself perfectly for detailed studies and may turn out to be a keystone in the\nunderstanding of the development of CP3 star peculiarities.\n"}
+{"abstract": "  To achieve reliable mining results for massive vessel trajectories, one of\nthe most important challenges is how to efficiently compute the similarities\nbetween different vessel trajectories. The computation of vessel trajectory\nsimilarity has recently attracted increasing attention in the maritime data\nmining research community. However, traditional shape- and warping-based\nmethods often suffer from several drawbacks such as high computational cost and\nsensitivity to unwanted artifacts and non-uniform sampling rates, etc. To\neliminate these drawbacks, we propose an unsupervised learning method which\nautomatically extracts low-dimensional features through a convolutional\nauto-encoder (CAE). In particular, we first generate the informative trajectory\nimages by remapping the raw vessel trajectories into two-dimensional matrices\nwhile maintaining the spatio-temporal properties. Based on the massive vessel\ntrajectories collected, the CAE can learn the low-dimensional representations\nof informative trajectory images in an unsupervised manner. The trajectory\nsimilarity is finally equivalent to efficiently computing the similarities\nbetween the learned low-dimensional features, which strongly correlate with the\nraw vessel trajectories. Comprehensive experiments on realistic data sets have\ndemonstrated that the proposed method largely outperforms traditional\ntrajectory similarity computation methods in terms of efficiency and\neffectiveness. The high-quality trajectory clustering performance could also be\nguaranteed according to the CAE-based trajectory similarity computation\nresults.\n"}
+{"abstract": "  The gap generation in the dice model with local four-fermion interaction is\nstudied. Due to the presence of two valleys with degenerate electron states,\nthere are two main types of gaps. The intra- and intervalley gap describes the\nelectron and hole pairing in the same and different valleys, respectively. We\nfound that while the generation of the intravalley gap takes place only in the\nsupercritical regime, the intervalley gap is generated for an arbitrary small\ncoupling. The physical reason for the absence of the critical coupling is the\ncatalysis of the intervalley gap generation by the flat band in the electron\nspectrum of the dice model. The completely quenched kinetic energy in the flat\nband when integrated over momentum in the gap equation leads to extremely large\nintervalley gap proportional to the area of the Brillouin zone.\n"}
+{"abstract": "  Automated defect inspection is critical for effective and efficient\nmaintenance, repair, and operations in advanced manufacturing. On the other\nhand, automated defect inspection is often constrained by the lack of defect\nsamples, especially when we adopt deep neural networks for this task. This\npaper presents Defect-GAN, an automated defect synthesis network that generates\nrealistic and diverse defect samples for training accurate and robust defect\ninspection networks. Defect-GAN learns through defacement and restoration\nprocesses, where the defacement generates defects on normal surface images\nwhile the restoration removes defects to generate normal images. It employs a\nnovel compositional layer-based architecture for generating realistic defects\nwithin various image backgrounds with different textures and appearances. It\ncan also mimic the stochastic variations of defects and offer flexible control\nover the locations and categories of the generated defects within the image\nbackground. Extensive experiments show that Defect-GAN is capable of\nsynthesizing various defects with superior diversity and fidelity. In addition,\nthe synthesized defect samples demonstrate their effectiveness in training\nbetter defect inspection networks.\n"}
+{"abstract": "  Let $\\mathcal{G}$ denote the variety generated by infinite dimensional\nGrassmann algebras; i.e., the collection of all unitary associative algebras\nsatisfying the identity $[[z_1,z_2],z_3]=0$, where $[z_i,z_j]=z_iz_j-z_jz_i$.\nConsider the free algebra $F_3$ in $\\mathcal{G}$ generated by\n$X_3=\\{x_1,x_2,x_3\\}$. The commutator ideal $F_3'$ of the algebra $F_3$ has a\nnatural $K[X_3]$-module structure. We call an element $p\\in F_3$ symmetric if\n$p(x_1,x_2,x_3)=p(x_{\\xi1},x_{\\xi2},x_{\\xi3})$ for each permutation $\\xi\\in\nS_3$. Symmetric elements form the subalgebra $F_3^{S_3}$ of invariants of the\nsymmetric group $S_3$ in $F_3$. We give a free generating set for the\n$K[X_3]^{S_3}$-module $(F_3')^{S_3}$.\n"}
+{"abstract": "  We investigate the efficiency of two very different spoken term detection\napproaches for transcription when the available data is insufficient to train a\nrobust ASR system. This work is grounded in very low-resource language\ndocumentation scenario where only few minutes of recording have been\ntranscribed for a given language so far.Experiments on two oral languages show\nthat a pretrained universal phone recognizer, fine-tuned with only a few\nminutes of target language speech, can be used for spoken term detection with a\nbetter overall performance than a dynamic time warping approach. In addition,\nwe show that representing phoneme recognition ambiguity in a graph structure\ncan further boost the recall while maintaining high precision in the low\nresource spoken term detection task.\n"}
+{"abstract": "  Multilingual Transformer-based language models, usually pretrained on more\nthan 100 languages, have been shown to achieve outstanding results in a wide\nrange of cross-lingual transfer tasks. However, it remains unknown whether the\noptimization for different languages conditions the capacity of the models to\ngeneralize over syntactic structures, and how languages with syntactic\nphenomena of different complexity are affected. In this work, we explore the\nsyntactic generalization capabilities of the monolingual and multilingual\nversions of BERT and RoBERTa. More specifically, we evaluate the syntactic\ngeneralization potential of the models on English and Spanish tests, comparing\nthe syntactic abilities of monolingual and multilingual models on the same\nlanguage (English), and of multilingual models on two different languages\n(English and Spanish). For English, we use the available SyntaxGym test suite;\nfor Spanish, we introduce SyntaxGymES, a novel ensemble of targeted syntactic\ntests in Spanish, designed to evaluate the syntactic generalization\ncapabilities of language models through the SyntaxGym online platform.\n"}
+{"abstract": "  There has been much recent interest in two-sided markets and dynamics\nthereof. In a rather a general discrete-time feedback model, which we show\nconditions that assure that for each agent, there exists the limit of a\nlong-run average allocation of a resource to the agent, which is independent of\nany initial conditions. We call this property the unique ergodicity.\n  Our model encompasses two-sided markets and more complicated interconnections\nof workers and customers, such as in a supply chain. It allows for\nnon-linearity of the response functions of market participants. Finally, it\nallows for uncertainty in the response of market participants by considering a\nset of the possible responses to either price or other signals and a measure to\nsample from these.\n"}
+{"abstract": "  Social acceptability is an important consideration for HCI designers who\ndevelop technologies for social contexts. However, the current theoretical\nfoundations of social acceptability research do not account for the complex\ninteractions among the actors in social situations and the specific role of\ntechnology. In order to improve the understanding of how context shapes and is\nshaped by situated technology interactions, we suggest to reframe the social\nspace as a dynamic bundle of social practices and explore it with simulation\nstudies using agent-based modeling. We outline possible research directions\nthat focus on specific interactions among practices as well as regularities in\nemerging patterns.\n"}
+{"abstract": "  In a sharp contrast to the response of silica particles we show that the\nmetal-dielectric Janus particles with boojum defects in a nematic liquid\ncrystal are self-propelled under the action of an electric field applied\nperpendicular to the director. The particles can be transported along any\ndirection in the plane of the sample by selecting the appropriate orientation\nof the Janus vector with respect to the director. The direction of motion of\nthe particles is controllable by varying the field amplitude and frequency. The\ncommand demonstrated on the motility of the particles is promising for tunable\ntransport and microrobotic applications.\n"}
+{"abstract": "  Sleep staging is fundamental for sleep assessment and disease diagnosis.\nAlthough previous attempts to classify sleep stages have achieved high\nclassification performance, several challenges remain open: 1) How to\neffectively extract salient waves in multimodal sleep data; 2) How to capture\nthe multi-scale transition rules among sleep stages; 3) How to adaptively seize\nthe key role of specific modality for sleep staging. To address these\nchallenges, we propose SalientSleepNet, a multimodal salient wave detection\nnetwork for sleep staging. Specifically, SalientSleepNet is a temporal fully\nconvolutional network based on the $\\rm U^2$-Net architecture that is\noriginally proposed for salient object detection in computer vision. It is\nmainly composed of two independent $\\rm U^2$-like streams to extract the\nsalient features from multimodal data, respectively. Meanwhile, the multi-scale\nextraction module is designed to capture multi-scale transition rules among\nsleep stages. Besides, the multimodal attention module is proposed to\nadaptively capture valuable information from multimodal data for the specific\nsleep stage. Experiments on the two datasets demonstrate that SalientSleepNet\noutperforms the state-of-the-art baselines. It is worth noting that this model\nhas the least amount of parameters compared with the existing deep neural\nnetwork models.\n"}
+{"abstract": "  Spin-phonon interaction is an important channel for spin and energy\nrelaxation in magnetic insulators. Understanding this interaction is critical\nfor developing magnetic insulator-based spintronic devices. Quantifying this\ninteraction in yttrium iron garnet (YIG), one of the most extensively\ninvestigated magnetic insulators, remains challenging because of the large\nnumber of atoms in a unit cell. Here, we report temperature-dependent and\npolarization-resolved Raman measurements in a YIG bulk crystal. We first\nclassify the phonon modes based on their symmetry. We then develop a modified\nmean-field theory and define a symmetry-adapted parameter to quantify\nspin-phonon interaction in a phonon-mode specific way for the first time in\nYIG. Based on this improved mean-field theory, we discover a positive\ncorrelation between the spin-phonon interaction strength and the phonon\nfrequency.\n"}
+{"abstract": "  We propose a method for the unsupervised reconstruction of a\ntemporally-coherent sequence of surfaces from a sequence of time-evolving point\nclouds, yielding dense, semantically meaningful correspondences between all\nkeyframes. We represent the reconstructed surface as an atlas, using a neural\nnetwork. Using canonical correspondences defined via the atlas, we encourage\nthe reconstruction to be as isometric as possible across frames, leading to\nsemantically-meaningful reconstruction. Through experiments and comparisons, we\nempirically show that our method achieves results that exceed that state of the\nart in the accuracy of unsupervised correspondences and accuracy of surface\nreconstruction.\n"}
+{"abstract": "  Citrus segmentation is a key step of automatic citrus picking. While most\ncurrent image segmentation approaches achieve good segmentation results by\npixel-wise segmentation, these supervised learning-based methods require a\nlarge amount of annotated data, and do not consider the continuous temporal\nchanges of citrus position in real-world applications. In this paper, we first\ntrain a simple CNN with a small number of labelled citrus images in a\nsupervised manner, which can roughly predict the citrus location from each\nframe. Then, we extend a state-of-the-art unsupervised learning approach to\npre-learn the citrus's potential movements between frames from unlabelled\ncitrus's videos. To take advantages of both networks, we employ the multimodal\ntransformer to combine supervised learned static information and unsupervised\nlearned movement information. The experimental results show that combing both\nnetwork allows the prediction accuracy reached at 88.3$\\%$ IOU and 93.6$\\%$\nprecision, outperforming the original supervised baseline 1.2$\\%$ and 2.4$\\%$.\nCompared with most of the existing citrus segmentation methods, our method uses\na small amount of supervised data and a large number of unsupervised data,\nwhile learning the pixel level location information and the temporal\ninformation of citrus changes to enhance the segmentation effect.\n"}
+{"abstract": "  A bipartite experiment consists of one set of units being assigned treatments\nand another set of units for which we measure outcomes. The two sets of units\nare connected by a bipartite graph, governing how the treated units can affect\nthe outcome units. In this paper, we consider estimation of the average total\ntreatment effect in the bipartite experimental framework under a linear\nexposure-response model. We introduce the Exposure Reweighted Linear (ERL)\nestimator, and show that the estimator is unbiased, consistent and\nasymptotically normal, provided that the bipartite graph is sufficiently\nsparse. To facilitate inference, we introduce an unbiased and consistent\nestimator of the variance of the ERL point estimator. In addition, we introduce\na cluster-based design, Exposure-Design, that uses heuristics to increase the\nprecision of the ERL estimator by realizing a desirable exposure distribution.\n"}
+{"abstract": "  Given a stream of graph edges from a dynamic graph, how can we assign anomaly\nscores to edges and subgraphs in an online manner, for the purpose of detecting\nunusual behavior, using constant time and memory? For example, in intrusion\ndetection, existing work seeks to detect either anomalous edges or anomalous\nsubgraphs, but not both. In this paper, we first extend the count-min sketch\ndata structure to a higher-order sketch. This higher-order sketch has the\nuseful property of preserving the dense subgraph structure (dense subgraphs in\nthe input turn into dense submatrices in the data structure). We then propose\nfour online algorithms that utilize this enhanced data structure, which (a)\ndetect both edge and graph anomalies; (b) process each edge and graph in\nconstant memory and constant update time per newly arriving edge, and; (c)\noutperform state-of-the-art baselines on four real-world datasets. Our method\nis the first streaming approach that incorporates dense subgraph search to\ndetect graph anomalies in constant memory and time.\n"}
+{"abstract": "  Effectively modeling phenomena present in highly nonlinear dynamical systems\nwhilst also accurately quantifying uncertainty is a challenging task, which\noften requires problem-specific techniques. We present a novel, domain-agnostic\napproach to tackling this problem, using compositions of physics-informed\nrandom features, derived from ordinary differential equations. The architecture\nof our model leverages recent advances in approximate inference for deep\nGaussian processes, such as layer-wise weight-space approximations which allow\nus to incorporate random Fourier features, and stochastic variational inference\nfor approximate Bayesian inference. We provide evidence that our model is\ncapable of capturing highly nonlinear behaviour in real-world multivariate time\nseries data. In addition, we find that our approach achieves comparable\nperformance to a number of other probabilistic models on benchmark regression\ntasks.\n"}
+{"abstract": "  Laser-induced ultrafast demagnetization has puzzled researchers around the\nworld for over two decades. Intrinsic complexity in electronic, magnetic, and\nphononic subsystems is difficult to understand microscopically. So far it is\nnot possible to explain demagnetization using a single mechanism, which\nsuggests a crucial piece of information still missing. In this paper, we return\nto a fundamental aspect of physics: spin and its change within each band in the\nentire Brillouin zone. We employ fcc Ni as an example and use an extremely\ndense {\\bf k} mesh to map out spin changes for every band close to the Fermi\nlevel along all the high symmetry lines. To our surprise, spin angular momentum\nat some special {\\bf k} points abruptly changes from $\\pm \\hbar/2$ to $\\mp\n\\hbar/2$ simply by moving from one crystal momentum point to the next. This\nexplains why intraband transitions, which the spin superdiffusion model is\nbased upon, can induce a sharp spin moment reduction, and why electric current\ncan change spin orientation in spintronics. These special {\\bf k} points, which\nare called spin Berry points, are not random and appear when several bands are\nclose to each other, so the Berry potential of spin majority states is\ndifferent from that of spin minority states. Although within a single band,\nspin Berry points jump, when we group several neighboring bands together, they\nform distinctive smooth spin Berry lines. It is the band structure that\ndisrupts those lines. Spin Berry points are crucial to laser-induced ultrafast\ndemagnetization and spintronics.\n"}
+{"abstract": "  Surface-response functions are one of the most promising routes for bridging\nthe gap between fully quantum-mechanical calculations and phenomenological\nmodels in quantum nanoplasmonics. Within all the currently available recipes\nfor obtaining such response functions, \\emph{ab initio} calculations remain one\nof the most predominant, wherein the surface-response function are retrieved\nvia the metal's non-equilibrium response to an external perturbation. Here, we\npresent a complementary approach where one of the most appealing\nsurface-response functions, namely the Feibelman $d$-parameters, yield a finite\ncontribution even in the case where they are calculated directly from the\nequilibrium properties described under the local-response approximation (LRA),\nbut with a spatially varying equilibrium electron density. Using model\ncalculations that mimic both spill-in and spill-out of the equilibrium electron\ndensity, we show that the obtained $d$-parameters are in qualitative agreement\nwith more elaborate, but also more computationally demanding, \\emph{ab initio}\nmethods. The analytical work presented here illustrates how microscopic\nsurface-response functions can emerge out of entirely local electrodynamic\nconsiderations.\n"}
+{"abstract": "  In this paper, a novel and robust algorithm is proposed for adaptive\nbeamforming based on the idea of reconstructing the autocorrelation sequence\n(ACS) of a random process from a set of measured data. This is obtained from\nthe first column and the first row of the sample covariance matrix (SCM) after\naveraging along its diagonals. Then, the power spectrum of the correlation\nsequence is estimated using the discrete Fourier transform (DFT). The DFT\ncoefficients corresponding to the angles within the noise-plus-interference\nregion are used to reconstruct the noise-plus-interference covariance matrix\n(NPICM), while the desired signal covariance matrix (DSCM) is estimated by\nidentifying and removing the noise-plus-interference component from the SCM. In\nparticular, the spatial power spectrum of the estimated received signal is\nutilized to compute the correlation sequence corresponding to the\nnoise-plus-interference in which the dominant DFT coefficient of the\nnoise-plus-interference is captured. A key advantage of the proposed adaptive\nbeamforming is that only little prior information is required. Specifically, an\nimprecise knowledge of the array geometry and of the angular sectors in which\nthe interferences are located is needed. Simulation results demonstrate that\ncompared with previous reconstruction-based beamformers, the proposed approach\ncan achieve better overall performance in the case of multiple mismatches over\na very large range of input signal-to-noise ratios.\n"}
+{"abstract": "  Neural models have transformed the fundamental information retrieval problem\nof mapping a query to a giant set of items. However, the need for efficient and\nlow latency inference forces the community to reconsider efficient approximate\nnear-neighbor search in the item space. To this end, learning to index is\ngaining much interest in recent times. Methods have to trade between obtaining\nhigh accuracy while maintaining load balance and scalability in distributed\nsettings. We propose a novel approach called IRLI (pronounced `early'), which\niteratively partitions the items by learning the relevant buckets directly from\nthe query-item relevance data. Furthermore, IRLI employs a superior\npower-of-$k$-choices based load balancing strategy. We mathematically show that\nIRLI retrieves the correct item with high probability under very natural\nassumptions and provides superior load balancing. IRLI surpasses the best\nbaseline's precision on multi-label classification while being $5x$ faster on\ninference. For near-neighbor search tasks, the same method outperforms the\nstate-of-the-art Learned Hashing approach NeuralLSH by requiring only ~\n{1/6}^th of the candidates for the same recall. IRLI is both data and model\nparallel, making it ideal for distributed GPU implementation. We demonstrate\nthis advantage by indexing 100 million dense vectors and surpassing the popular\nFAISS library by >10% on recall.\n"}
+{"abstract": "  The magnetic ground state of polycrystalline N\\'eel skyrmion hosting material\nGaV$_4$S$_8$ has been investigated using ac susceptibility and powder neutron\ndiffraction. In the absence of an applied magnetic field GaV$_4$S$_8$ undergoes\na transition from a paramagnetic to a cycloidal state below 13~K and then to a\nferromagnetic-like state below 6~K. With evidence from ac susceptibility and\npowder neutron diffraction, we have identified the commensurate magnetic\nstructure at 1.5 K, with ordered magnetic moments of $0.23(2)~\\mu_{\\mathrm{B}}$\non the V1 sites and $0.22(1)~\\mu_{\\mathrm{B}}$ on the V2 sites. These moments\nhave ferromagnetic-like alignment but with a 39(8)$^{\\circ}$ canting of the\nmagnetic moments on the V2 sites away from the V$_4$ cluster. In the\nincommensurate magnetic phase that exists between 6 and 13 K, we provide a\nthorough and careful analysis of the cycloidal magnetic structure exhibited by\nthis material using powder neutron diffraction.\n"}
+{"abstract": "  Deep learning has advanced from fully connected architectures to structured\nmodels organized into components, e.g., the transformer composed of positional\nelements, modular architectures divided into slots, and graph neural nets made\nup of nodes. In structured models, an interesting question is how to conduct\ndynamic and possibly sparse communication among the separate components. Here,\nwe explore the hypothesis that restricting the transmitted information among\ncomponents to discrete representations is a beneficial bottleneck. The\nmotivating intuition is human language in which communication occurs through\ndiscrete symbols. Even though individuals have different understandings of what\na \"cat\" is based on their specific experiences, the shared discrete token makes\nit possible for communication among individuals to be unimpeded by individual\ndifferences in internal representation. To discretize the values of concepts\ndynamically communicated among specialist components, we extend the\nquantization mechanism from the Vector-Quantized Variational Autoencoder to\nmulti-headed discretization with shared codebooks and use it for\ndiscrete-valued neural communication (DVNC). Our experiments show that DVNC\nsubstantially improves systematic generalization in a variety of architectures\n-- transformers, modular architectures, and graph neural networks. We also show\nthat the DVNC is robust to the choice of hyperparameters, making the method\nvery useful in practice. Moreover, we establish a theoretical justification of\nour discretization process, proving that it has the ability to increase noise\nrobustness and reduce the underlying dimensionality of the model.\n"}
+{"abstract": "  Optimal stopping is the problem of deciding the right time at which to take a\nparticular action in a stochastic system, in order to maximize an expected\nreward. It has many applications in areas such as finance, healthcare, and\nstatistics. In this paper, we employ deep Reinforcement Learning (RL) to learn\noptimal stopping policies in two financial engineering applications: namely\noption pricing, and optimal option exercise. We present for the first time a\ncomprehensive empirical evaluation of the quality of optimal stopping policies\nidentified by three state of the art deep RL algorithms: double deep Q-learning\n(DDQN), categorical distributional RL (C51), and Implicit Quantile Networks\n(IQN). In the case of option pricing, our findings indicate that in a\ntheoretical Black-Schole environment, IQN successfully identifies nearly\noptimal prices. On the other hand, it is slightly outperformed by C51 when\nconfronted to real stock data movements in a put option exercise problem that\ninvolves assets from the S&P500 index. More importantly, the C51 algorithm is\nable to identify an optimal stopping policy that achieves 8% more out-of-sample\nreturns than the best of four natural benchmark policies. We conclude with a\ndiscussion of our findings which should pave the way for relevant future\nresearch.\n"}
+{"abstract": "  We present the sympathetic eruption of a standard and a blowout coronal jets\noriginating from two adjacent coronal bright points (CBP1 and CBP2) in a polar\ncoronal hole, using soft X-ray and extreme ultraviolet observations\nrespectively taken by the Hinode and the Solar Dynamic Observatory. In the\nevent, a collimated jet with obvious westward lateral motion firstly launched\nfrom CBP1, during which a small bright point appeared around CBP1's east end,\nand magnetic flux cancellation was observed within the eruption source region.\nBased on these characteristics, we interpret the observed jet as a standard jet\nassociated with photosperic magnetic flux cancellation. About 15 minutes later,\nthe westward moving jet spire interacted with CBP2 and resulted in magnetic\nreconnection between them, which caused the formation of the second jet above\nCBP2 and the appearance of a bright loop system in-between the two CBPs. In\naddition, we observed the writhing, kinking, and violent eruption of a small\nkink structure close to CBP2's west end but inside the jet-base, which made the\nsecond jet brighter and broader than the first one. These features suggest that\nthe second jet should be a blowout jet triggered by the magnetic reconnection\nbetween CBP2 and the spire of the first jet. We conclude that the two\nsuccessive jets were physically connected to each other rather than a temporal\ncoincidence, and this observation also suggests that coronal jets can be\ntriggered by external eruptions or disturbances, besides internal magnetic\nactivities or magnetohydrodynamic instabilities.\n"}
+{"abstract": "  This paper compares notions of double sliceness for links. The main result is\nto show that a large family of 2-component Montesinos links are not strongly\ndoubly slice despite being weakly doubly slice and having doubly slice\ncomponents. Our principal obstruction to strong double slicing comes by\nconsidering branched double covers. To this end we prove a result classifying\nSeifert fibered spaces which admit a smooth embeddings into integer homology\n$S^1 \\times S^3$s by maps inducing surjections on the first homology group. A\nnumber of other results and examples pertaining to doubly slice links are also\ngiven.\n"}
+{"abstract": "  To boost the capacity of the cellular system, the operators have started to\nreuse the same licensed spectrum by deploying 4G LTE small cells (Femto Cells)\nin the past. But in time, these small cell licensed spectrum is not sufficient\nto satisfy future applications like augmented reality (AR)and virtual reality\n(VR). Hence, cellular operators look for alternate unlicensed spectrum in Wi-Fi\n5 GHz band, later 3GPP named as LTE Licensed Assisted Access (LAA). The recent\nand current rollout of LAA deployments (in developed nations like the US)\nprovides an opportunity to understand coexistence profound ground truth. This\npaper discusses a high-level overview of my past, present, and future research\nworks in the direction of small cell benefits. In the future, we shift the\nfocus onto the latest unlicensed band: 6 GHz, where the latest Wi-Fi version,\n802.11ax, will coexist with the latest cellular technology, 5G New Radio(NR) in\nunlicensed\n"}
+{"abstract": "  Controlling the activity of proteins with azobenzene photoswitches is a\npotent tool for manipulating their biological function. With the help of light,\none can change e.g. binding affinities, control allostery or temper with\ncomplex biological processes. Additionally, due to their intrinsically fast\nphotoisomerisation, azobenzene photoswitches can serve as triggers to initiate\nout-of-equilibrium processes. Such switching of the activity, therefore,\ninitiates a cascade of conformational events, which can only be accessed with\ntime-resolved methods. In this Review, we will show how combining the potency\nof azobenzene photoswitching with transient spectroscopic techniques helps to\ndisclose the order of events and provide an experimental observation of\nbiomolecular interactions in real-time. This will ultimately help us to\nunderstand how proteins accommodate, adapt and readjust their structure to\nanswer an incoming signal and it will complete our knowledge of the dynamical\ncharacter of proteins.\n"}
+{"abstract": "  Nonuniform structure of low-density nuclear matter, known as nuclear pasta,\nis expected to appear not only in the inner crust of neutron stars but also in\ncore-collapse supernova explosions and neutron-star mergers. We perform fully\nthree-dimensional calculations of inhomogeneous nuclear matter and neutron-star\nmatter in the low-density region using the Thomas-Fermi approximation. The\nnuclear interaction is described in the relativistic mean-field approach with\nthe point-coupling interaction, where the meson exchange in each channel is\nreplaced by the contact interaction between nucleons. We investigate the\ninfluence of nuclear symmetry energy and its density dependence on pasta\nstructures by introducing a coupling term between the isoscalar-vector and\nisovector-vector interactions. It is found that the properties of pasta phases\nin the neutron-rich matter are strongly dependent on the symmetry energy and\nits slope. In addition to typical shapes like droplets, rods, slabs, tubes, and\nbubbles, some intermediate pasta structures are also observed in cold stellar\nmatter with a relatively large proton fraction. We find that nonspherical\nshapes are unlikely to be formed in neutron-star crusts, since the proton\nfraction obtained in $\\beta$ equilibrium is rather small. The inner crust\nproperties may lead to a visible difference in the neutron-star radius.\n"}
+{"abstract": "  This paper addresses the Mountain Pass Theorem for locally Lipschitz\nfunctions on finite-dimensional vector spaces in terms of tangencies. Namely,\nlet $f \\colon \\mathbb R^n \\to \\mathbb R$ be a locally Lipschitz function with a\nmountain pass geometry. Let $$c := \\inf_{\\gamma \\in \\mathcal\nA}\\max_{t\\in[0,1]}f(\\gamma(t)),$$ where $\\mathcal{A}$ is the set of all\ncontinuous paths joining $x^*$ to $y^*.$ We show that either $c$ is a critical\nvalue of $f$ or $c$ is a tangency value at infinity of $f.$ This reduces to the\nMountain Pass Theorem of Ambrosetti and Rabinowitz in the case where the\nfunction $f$ is definable (such as, semi-algebraic) in an o-minimal structure.\n"}
+{"abstract": "  Recent studies on metamorphic petrology as well as microstructural\nobservations suggest the influence of mechanical effects upon chemically active\nmetamorphic minerals. Thus, the understanding of such a coupling is crucial to\ndescribe the dynamics of geomaterials. In this effort, we derive a\nthermodynamically-consistent framework to characterize the evolution of\nchemically active minerals. We model the metamorphic mineral assemblages as a\nsolid-species solution where the species mass transport and chemical reaction\ndrive the stress generation process. The theoretical foundations of the\nframework rely on modern continuum mechanics, thermodynamics far from\nequilibrium, and the phase-field model. We treat the mineral solid solution as\na continuum body, and following the Larch\\'e and Cahn network model, we define\ndisplacement and strain fields. Consequently, we obtain a set of coupled\nchemo-mechanical equations. We use the aforementioned framework to study single\nminerals as solid solutions during metamorphism. Furthermore, we emphasise the\nuse of the phase-field framework as a promising tool to model complex\nmulti-physics processes in geoscience. Without loss of generality, we use\ncommon physical and chemical parameters found in the geoscience literature to\nportrait a comprehensive view of the underlying physics. Thereby, we carry out\n2D and 3D numerical simulations using material parameters for metamorphic\nminerals to showcase and verify the chemo-mechanical interactions of mineral\nsolid solutions that undergo spinodal decomposition, chemical reactions, and\ndeformation.\n"}
+{"abstract": "  Reversible covalent kinase inhibitors (RCKIs) are a class of novel kinase\ninhibitors attracting increasing attention because they simultaneously show the\nselectivity of covalent kinase inhibitors, yet avoid permanent\nprotein-modification-induced adverse effects. Over the last decade, RCKIs have\nbeen reported to target different kinases, including atypical kinases.\nCurrently, three RCKIs are undergoing clinical trials to treat specific\ndiseases, for example, Pemphigus, an autoimmune disorder. In this perspective,\nfirst, RCKIs are systematically summarized, including characteristics of\nelectrophilic groups, chemical scaffolds, nucleophilic residues, and binding\nmodes. Second, we provide insights into privileged electrophiles, the\ndistribution of nucleophiles and hence effective design strategies for RCKIs.\nFinally, we provide a brief perspective on future design strategies for RCKIs,\nincluding those that target proteins other than kinases.\n"}
+{"abstract": "  We prove that the sublinearly Morse boundary of every known cubulated group\ncontinuously injects in the Gromov boundary of a certain hyperbolic graph. We\nalso show that for all CAT(0) cube complexes, convergence to sublinearly Morse\ngeodesic rays has a simple combinatorial description using the hyperplanes\ncrossed by such sequences. As an application of this combinatorial description,\nwe show that a certain subspace of the Roller boundary continously surjects on\nthe subspace of the visual boundary consisting of sublinearly Morse geodesic\nrays.\n"}
+{"abstract": "  Depth completion aims to generate a dense depth map from the sparse depth map\nand aligned RGB image. However, current depth completion methods use extremely\nexpensive 64-line LiDAR(about $100,000) to obtain sparse depth maps, which will\nlimit their application scenarios. Compared with the 64-line LiDAR, the\nsingle-line LiDAR is much less expensive and much more robust. Therefore, we\npropose a method to tackle the problem of single-line depth completion, in\nwhich we aim to generate a dense depth map from the single-line LiDAR info and\nthe aligned RGB image. A single-line depth completion dataset is proposed based\non the existing 64-line depth completion dataset(KITTI). A network called\nSemantic Guided Two-Branch Network(SGTBN) which contains global and local\nbranches to extract and fuse global and local info is proposed for this task. A\nSemantic guided depth upsampling module is used in our network to make full use\nof the semantic info in RGB images. Except for the usual MSE loss, we add the\nvirtual normal loss to increase the constraint of high-order 3D geometry in our\nnetwork. Our network outperforms the state-of-the-art in the single-line depth\ncompletion task. Besides, compared with the monocular depth estimation, our\nmethod also has significant advantages in precision and model size.\n"}
+{"abstract": "  Spectral observations below Lyman-alpha are now obtained with the Cosmic\nOrigin Spectrograph (COS) on the Hubble Space Telescope (HST). It is therefore\nnecessary to provide an accurate treatment of the blue wing of the Lyman-alpha\nline that enables correct calculations of radiative transport in DA and DBA\nwhite dwarf stars. On the theoretical front, we very recently developed very\naccurate H-He potential energies for the hydrogen 1s, 2s, and 2p states.\nNevertheless, an uncertainty remained about the asymptotic correlation of the\nSigma states and the electronic dipole transition moments. A similar difficulty\noccurred in our first calculations for the resonance broadening of hydrogen\nperturbed by collisions with neutral H atoms. The aim of this paper is twofold.\nFirst, we clarify the question of the asymptotic correlation of the Sigma\nstates, and we show that relativistic contributions, even very tiny, may need\nto be accounted for a correct long-range and asymptotic description of the\nstates because of the specific 2s 2p Coulomb degeneracy in hydrogen. This\neffect of relativistic corrections, inducing small splitting of the 2s and 2p\nstates of H, is shown to be important for the Sigma-Sigma$ transition dipole\nmoments in H-He and is also discussed in H-H. Second, we use existent (H-H) and\nnewly determined (H-He) accurate potentials and properties to provide a\ntheoretical investigation of the collisional effects on the blue wing of the\nLyman-alpha line of H perturbed by He and H. We study the relative\ncontributions in the blue wing of the H and He atoms according to their\nrelative densities. We finally achieve a comparison with recent COS\nobservations and propose an assignment for a feature centered at 1190 A.\n"}
+{"abstract": "  We apply a recently developed unsupervised machine learning scheme for local\natomic environments to characterize large-scale, disordered aggregates formed\nby sequence-defined macromolecules. This method provides new insight into the\nstructure of these disordered, dilute aggregates, which has proven difficult to\nunderstand using collective variables manually derived from expert knowledge.\nIn contrast to such conventional order parameters, we are able to classify the\nglobal aggregate structure directly using descriptions of the local\nenvironments. The resulting characterization provides a deeper understanding of\nthe range of possible self-assembled structures and their relationships to each\nother. We also provide a detailed analysis of the effects of finite system\nsize, stochasticity, and kinetics of these aggregates based on the learned\ncollective variables. Interestingly, we find that the spatiotemporal evolution\nof systems in the learned latent space is smooth and continuous, despite being\nderived from only a single snapshot from each of about 1000 monomer sequences.\nThese results demonstrate the insight which can be gained by applying\nunsupervised machine learning to soft matter systems, especially when suitable\norder parameters are not known.\n"}
+{"abstract": "  Recently, significant progress has been made in single-view depth estimation\nthanks to increasingly large and diverse depth datasets. However, these\ndatasets are largely limited to specific application domains (e.g. indoor,\nautonomous driving) or static in-the-wild scenes due to hardware constraints or\ntechnical limitations of 3D reconstruction. In this paper, we introduce the\nfirst depth dataset DynOcc consisting of dynamic in-the-wild scenes. Our\napproach leverages the occlusion cues in these dynamic scenes to infer depth\nrelationships between points of selected video frames. To achieve accurate\nocclusion detection and depth order estimation, we employ a novel occlusion\nboundary detection, filtering and thinning scheme followed by a robust\nforeground/background classification method. In total our DynOcc dataset\ncontains 22M depth pairs out of 91K frames from a diverse set of videos. Using\nour dataset we achieved state-of-the-art results measured in weighted human\ndisagreement rate (WHDR). We also show that the inferred depth maps trained\nwith DynOcc can preserve sharper depth boundaries.\n"}
+{"abstract": "  Large global companies need to speed up their innovation activities to\nincrease competitive advantage. However, such companies' organizational\nstructures impede their ability to capture trends they are well aware of due to\nbureaucracy, slow decision-making, distributed departments, and distributed\nprocesses. One way to strengthen the innovation capability is through fostering\ninternal startups. We report findings from an embedded multiple-case study of\nfive internal startups in a globally distributed company to identify barriers\nfor software product innovation: late involvement of software developers,\nexecutive sponsor is missing or not clarified, yearly budgeting and planning,\nunclear decision-making authority, lack of digital infrastructure for\nexperimentation and access to data from external actors. Drawing on the\nframework of continuous software engineering proposed by Fitzgerald and Stol,\nwe discuss the role of BizDev in software product innovation. We suggest that\nlack of continuity, rather than the lack of speed, is an ultimate challenge for\ninternal startups in large global companies.\n"}
+{"abstract": "  We study the background (equilibrium), linear and nonlinear spin currents in\n2D Rashba spin-orbit coupled systems with Zeeman splitting and in 3D\nnoncentrosymmetric metals using modified spin current operator by inclusion of\nthe anomalous velocity. The linear spin Hall current arises due to the\nanomalous velocity of charge carriers induced by the Berry curvature. The\nnonlinear spin current occurs due to the band velocity and/or the anomalous\nvelocity. For 2D Rashba systems, the background spin current saturates at high\nFermi energy (independent of the Zeeman coupling), linear spin current exhibits\na plateau at the Zeeman gap and nonlinear spin currents are peaked at the gap\nedges. The magnitude of the nonlinear spin current peaks enhances with the\nstrength of Zeeman interaction. The linear spin current is polarized out of\nplane, while the nonlinear ones are polarized in-plane. We witness pure\nanomalous nonlinear spin current with spin polarization along the direction of\npropagation. In 3D noncentrosymmetric metals, background and linear spin\ncurrents are monotonically increasing functions of Fermi energy, while\nnonlinear spin currents vary non-monotonically as a function of Fermi energy\nand are independent of the Berry curvature. These findings may provide useful\ninformation to manipulate spin currents in Rashba spin-orbit coupled systems.\n"}
+{"abstract": "  Using a navigation process with the datum $(F,V)$, in which $F$ is a Finsler\nmetric and the smooth tangent vector field $V$ satisfies $F(-V(x))>1$\neverywhere, a Lorentz Finsler metric $\\tilde{F}$ can be induced. Isoparametric\nfunctions and isoparametric hypersurfaces with or without involving a smooth\nmeasure can be defined for $\\tilde{F}$. When the vector field $V$ in the\nnavigation datum is homothetic, we prove the local correspondences between\nisoparametric functions and isoparametric hypersurfaces before and after this\nnavigation process. Using these correspondences, we provide some examples of\nisoparametric functions and isoparametric hypersurfaces on a Funk space of\nLorentz Randers type.\n"}
+{"abstract": "  We study the high frequency Hall conductivity in a two-dimensional (2D) model\nof conduction electrons coupled to a background magnetic skyrmion texture via\nan effective Hund's coupling term. For an ordered skyrmion crystal, a Kubo\nformula calculation using the basis of skyrmion crystal Chern bands reveals a\nresonant Hall response at a frequency set by the Hund's coupling:\n$\\hbar\\omega_{\\text{res}} \\approx J_H$. A complementary real-space Kubo formula\ncalculation for an isolated skyrmion in a box reveals a similar resonant Hall\nresponse. A linear relation between the area under the Hall resonant curve and\nthe skyrmion density is discovered numerically and is further elucidated using\na gradient expansion which is valid for smooth textures and a local\napproximation based on a spin-trimer calculation. We point out the issue of\ndistinguishing this skyrmion contribution from a similar feature arising from\nspin-orbit interactions, as demonstrated in a model for Rashba spin-orbit\ncoupled electrons in a collinear ferromagnet, which is analogous to the\ndifficulty of unambiguously separating the d.c. topological Hall effect from\nthe anomalous Hall effect. The resonant feature in the high frequency\ntopological Hall effect is proposed to provide a potentially useful local\noptical signature of skyrmions via probes such as scanning magneto-optical Kerr\nmicroscopy.\n"}
+{"abstract": "  We report on observations of the active K2 dwarf $\\epsilon$ Eridani based on\ncontemporaneous SPIRou, NARVAL, and TESS data obtained over two months in late\n2018, when the activity of the star was reported to be in a non-cyclic phase.\nWe first recover the fundamental parameters of the target from both visible and\nnIR spectral fitting. The large-scale magnetic field is investigated from\npolarimetric data. From unpolarized spectra, we estimate the total magnetic\nflux through Zeeman broadening of magnetically sensitive nIR lines and the\nchromospheric emission using the CaII H & K lines. The TESS photometric\nmonitoring is modeled with pseudo-periodic Gaussian Process Regression.\nFundamental parameters of $\\epsilon$ Eridani derived from visible and\nnear-infrared wavelengths provide us with consistent results, also in agreement\nwith published values. We report a progressive increase of macroturbulence\ntowards larger nIR wavelengths. Zeeman broadening of individual lines\nhighlights an unsigned surface magnetic field $B_{\\rm mono} = 1.90 \\pm 0.13$\nkG, with a filling factor $f = 12.5 \\pm 1.7$% (unsigned magnetic flux $Bf = 237\n\\pm 36$ G). The large-scale magnetic field geometry, chromospheric emission,\nand broadband photometry display clear signs of non-rotational evolution over\nthe course of data collection. Characteristic decay times deduced from the\nlight curve and longitudinal field measurements fall in the range 30-40 d,\nwhile the characteristic timescale of surface differential rotation, as derived\nthrough the evolution of the magnetic geometry, is equal to $57 \\pm 5$ d. The\nlarge-scale magnetic field exhibits a combination of properties not observed\npreviously for $\\epsilon$ Eridani, with a surface field among the weakest\npreviously reported, but also mostly axisymmetric, and dominated by a toroidal\ncomponent.\n"}
+{"abstract": "  Maintenance of existing software requires a large amount of time for\ncomprehending the source code. The architecture of a software, however, may not\nbe clear to maintainers if up to date documentations are not available.\nSoftware clustering is often used as a remodularisation and architecture\nrecovery technique to help recover a semantic representation of the software\ndesign. Due to the diverse domains, structure, and behaviour of software\nsystems, the suitability of different clustering algorithms for different\nsoftware systems are not investigated thoroughly. Research that introduce new\nclustering techniques usually validate their approaches on a specific domain,\nwhich might limit its generalisability. If the chosen test subjects could only\nrepresent a narrow perspective of the whole picture, researchers might risk not\nbeing able to address the external validity of their findings. This work aims\nto fill this gap by introducing a new approach, Explaining Software Clustering\nfor Remodularisation, to evaluate the effectiveness of different software\nclustering approaches. This work focuses on hierarchical clustering and Bunch\nclustering algorithms and provides information about their suitability\naccording to the features of the software, which as a consequence, enables the\nselection of the most optimum algorithm and configuration from our existing\npool of choices for a particular software system. The proposed framework is\ntested on 30 open source software systems with varying sizes and domains, and\ndemonstrates that it can characterise both the strengths and weaknesses of the\nanalysed software clustering algorithms using software features extracted from\nthe code. The proposed approach also provides a better understanding of the\nalgorithms behaviour through the application of dimensionality reduction\ntechniques.\n"}
+{"abstract": "  Cleaner analytic technique for quantifying compounds in dense suspension is\nneeded for wastewater and environment analysis, chemical or bio-conversion\nprocess monitoring, biomedical diagnostics, food quality control among others.\nIn this work, we introduce a green, fast, one-step method called nanoextraction\nfor extraction and detection of target analytes from sub-milliliter dense\nsuspensions using surface nanodroplets without toxic solvents and pre-removal\nof the solid contents. With nanoextraction, we achieve a limit of detection\n(LOD) of 10^(-9) M for a fluorescent model analyte obtained from a particle\nsuspension sample. The LOD lower than that in water without particles 10^(-8)\nM, potentially due to the interaction of particles and the analyte. The high\nparticle concentration in the suspension sample thus does not reduce the\nextraction efficiency, although the extraction process was slowed down up to 5\nmin. As proof of principle, we demonstrate the nanoextraction for\nquantification of model compounds in wastewater slurry containing 30 wt% sands\nand oily components (i.e. heavy oils). The nanoextraction and detection\ntechnology developed in this work may be used as fast analytic technologies for\ncomplex slurry samples in environment industrial waste, or in biomedical\ndiagnostics.\n"}
+{"abstract": "  We explicitly compute the homology groups with coefficients in a field of\ncharacteristic zero of cocyclic subgroups or even Artin groups of FC-type. We\nalso give some partial results in the case when the coefficients are taken in a\nfield of prime characteristic.\n"}
+{"abstract": "  We prove that for all positive integers $n$ and $k$, there exists an integer\n$N = N(n,k)$ satisfying the following. If $U$ is a set of $k$ direction vectors\nin the plane and $\\mathcal{J}_U$ is the set of all line segments in direction\n$u$ for some $u\\in U$, then for every $N$ families $\\mathcal{F}_1, \\ldots,\n\\mathcal{F}_N$, each consisting of $n$ mutually disjoint segments in\n$\\mathcal{J}_U$, there is a set $\\{A_1, \\ldots, A_n\\}$ of $n$ disjoint segments\nin $\\bigcup_{1\\leq i\\leq N}\\mathcal{F}_i$ and distinct integers $p_1, \\ldots,\np_n\\in \\{1, \\ldots, N\\}$ satisfying that $A_j\\in \\mathcal{F}_{p_j}$ for all\n$j\\in \\{1, \\ldots, n\\}$. We generalize this property for underlying lines on\nfixed $k$ directions to $k$ families of simple curves with certain conditions.\n"}
+{"abstract": "  L. Moret-Bailly constructed families $\\mathfrak{C}\\rightarrow \\mathbb{P}^1$\nof genus 2 curves with supersingular jacobian. In this paper we first classify\nthe reducible fibers of a Moret-Bailly family using linear algebra over a\nquaternion algebra. The main result is an algorithm that exploits properties of\ntwo reducible fibers to compute a hyperelliptic model for any irreducible fiber\nof a Moret-Bailly family.\n"}
+{"abstract": "  This paper presents a method for gaze estimation according to face images. We\ntrain several gaze estimators adopting four different network architectures,\nincluding an architecture designed for gaze estimation (i.e.,iTracker-MHSA) and\nthree originally designed for general computer vision tasks(i.e., BoTNet,\nHRNet, ResNeSt). Then, we select the best six estimators and ensemble their\npredictions through a linear combination. The method ranks the first on the\nleader-board of ETH-XGaze Competition, achieving an average angular error of\n$3.11^{\\circ}$ on the ETH-XGaze test set.\n"}
+{"abstract": "  In a separable Hilbert space $X$, we study the controlled evolution equation\n\\begin{equation*} u'(t)+Au(t)+p(t)Bu(t)=0, \\end{equation*} where $A\\geq-\\sigma\nI$ ($\\sigma\\geq0$) is a self-adjoint linear operator, $B$ is a bounded linear\noperator on $X$, and $p\\in L^2_{loc}(0,+\\infty)$ is a bilinear control.\n  We give sufficient conditions in order for the above nonlinear control system\nto be locally controllable to the $j$th eigensolution for any $j\\geq1$. We also\nderive semi-global controllability results in large time and discuss\napplications to parabolic equations in low space dimension. Our method is\nconstructive and all the constants involved in the main results can be\nexplicitly computed.\n"}
+{"abstract": "  In the cuprates, one-dimensional chain compounds provide a unique opportunity\nto understand the microscopic physics due to the availability of reliable\ntheories. However, progress has been limited by the inability to controllably\ndope these materials. Here, we report the synthesis and spectroscopic analysis\nof the one-dimensional cuprate Ba$_{2-x}$Sr$_x$CuO$_{3+\\delta}$ over a wide\nrange of hole doping. Our angle-resolved photoemission experiments reveal the\ndoping evolution of the holon and spinon branches. We identify a prominent\nfolding branch whose intensity fails to match predictions of the simple Hubbard\nmodel. An additional strong near-neighbor attraction, which may arise from\ncoupling to phonons, quantitatively explains experiments for all accessible\ndoping levels. Considering structural and quantum chemistry similarities among\ncuprates, this attraction will play a similarly crucial role in the high-$T_C$\nsuperconducting counterparts\n"}
+{"abstract": "  Antonio Colla was a meteorologist and astronomer who made sunspot\nobservations at the Meteorological Observatory of the Parma University (Italy).\nHe carried out his sunspot records from 1830 to 1843, just after the Dalton\nMinimum. We have recovered 71 observation days for this observer.\nUnfortunately, many of these records are qualitative and we could only obtain\nthe number of sunspot groups and/or single sunspots from 25 observations.\nHowever, we highlight the importance of these records because Colla is not\nincluded in the sunspot group database as an observer and, therefore, neither\nhis sunspot observations. According to the number of groups, the sunspot\nobservations made by Colla are similar as several observers of his time. For\ncommon observation day, only Stark significantly recorded more groups than\nColla. Moreover, we have calculated the sunspot area and positions from Colla's\nsunspot drawings concluding that both areas and positions recorded by this\nobserver seem unreal. Therefore, Colla's drawings can be interpreted such as\nsketches including reliable information on the number of groups but the\ninformation on sunspot areas and positions should not be used for scientific\npurposes.\n"}
+{"abstract": "  All current approaches for statically enforcing differential privacy in\nhigher order languages make use of either linear or relational refinement\ntypes. A barrier to adoption for these approaches is the lack of support for\nexpressing these \"fancy types\" in mainstream programming languages. For\nexample, no mainstream language supports relational refinement types, and\nalthough Rust and modern versions of Haskell both employ some linear typing\ntechniques, they are inadequate for embedding enforcement of differential\nprivacy, which requires \"full\" linear types a la Girard. We propose a new type\nsystem that enforces differential privacy, avoids the use of linear and\nrelational refinement types, and can be easily embedded in mainstream richly\ntyped programming languages such as Scala, OCaml and Haskell. We demonstrate\nsuch an embedding in Haskell, demonstrate its expressiveness on case studies,\nand prove that our type-based enforcement of differential privacy is sound.\n"}
+{"abstract": "  We develop a multicomponent lattice Boltzmann (LB) model for the 2D\nRayleigh--Taylor turbulence with a Shan-Chen pseudopotential implemented on\nGPUs. In the immiscible case this method is able to accurately overcome the\ninherent numerical complexity caused by the complicated structure of the\ninterface that appears in the fully developed turbulent regime. Accuracy of the\nLB model is tested both for early and late stages of instability. For the\ndeveloped turbulent motion we analyze the balance between different terms\ndescribing variations of the kinetic and potential energies. Then, we analyze\nthe role of interface in the energy balance, and also the effects of the\nvorticity induced by the interface in the energy dissipation. Statistical\nproperties are compared for miscible and immiscible flows. Our results can also\nbe considered as a first validation step to extend the application of LB model\nto 3D immiscible Rayleigh-Taylor turbulence.\n"}
+{"abstract": "  We present a geometrically exact nonlinear analysis of elastic in-plane beams\nin the context of finite but small strain theory. The formulation utilizes the\nfull beam metric and obtains the complete analytic elastic constitutive model\nby employing the exact relation between the reference and equidistant strains.\nThus, we account for the nonlinear strain distribution over the thickness of a\nbeam. In addition to the full analytical constitutive model, four simplified\nones are presented. Their comparison provides a thorough examination of the\ninfluence of a beam's metric on the structural response. We show that the\nappropriate formulation depends on the curviness of a beam at all\nconfigurations. Furthermore, the nonlinear distribution of strain along the\nthickness of strongly curved beams must be considered to obtain a complete and\naccurate response.\n"}
+{"abstract": "  Autoencoders as tools behind anomaly searches at the LHC have the structural\nproblem that they only work in one direction, extracting jets with higher\ncomplexity but not the other way around. To address this, we derive classifiers\nfrom the latent space of (variational) autoencoders, specifically in Gaussian\nmixture and Dirichlet latent spaces. In particular, the Dirichlet setup solves\nthe problem and improves both the performance and the interpretability of the\nnetworks.\n"}
+{"abstract": "  We reconsider work of Elkalla on subnormal subgroups of 3-manifold groups,\ngiving essentially algebraic arguments that extend to the case of $PD_3$-groups\nand group pairs. However the argument relies on an $L^2$-Betti number\nhypothesis which has not yet been shown to hold in general.\n"}
+{"abstract": "  Crystals and other condensed matter systems described by density waves often\nexhibit dislocations. Here we show, by considering the topology of the ground\nstate manifolds (GSMs) of such systems, that dislocations in the density phase\nfield always split into disclinations, and that the disclinations themselves\nare constrained to sit at particular points in the GSM. Consequently, the\ntopology of the GSM forbids zero-energy dislocation glide, giving rise to a\nPeirels-Nabarro barrier.\n"}
+{"abstract": "  In this paper, we present a model for semantic memory that allows machines to\ncollect information and experiences to become more proficient with time. Post\nsemantic analysis of the sensory and other related data, the processed\ninformation is stored in the knowledge graph which is then used to comprehend\nthe work instructions expressed in natural language. This imparts industrial\nrobots cognitive behavior to execute the required tasks in a deterministic\nmanner. The paper outlines the architecture of the system along with an\nimplementation of the proposal.\n"}
+{"abstract": "  Magnetic Resonance Fingerprinting (MRF) is a method to extract quantitative\ntissue properties such as T1 and T2 relaxation rates from arbitrary pulse\nsequences using conventional magnetic resonance imaging hardware. MRF pulse\nsequences have thousands of tunable parameters which can be chosen to maximize\nprecision and minimize scan time. Here we perform de novo automated design of\nMRF pulse sequences by applying physics-inspired optimization heuristics. Our\nexperimental data suggests systematic errors dominate over random errors in MRF\nscans under clinically-relevant conditions of high undersampling. Thus, in\ncontrast to prior optimization efforts, which focused on statistical error\nmodels, we use a cost function based on explicit first-principles simulation of\nsystematic errors arising from Fourier undersampling and phase variation. The\nresulting pulse sequences display features qualitatively different from\npreviously used MRF pulse sequences and achieve fourfold shorter scan time than\nprior human-designed sequences of equivalent precision in T1 and T2.\nFurthermore, the optimization algorithm has discovered the existence of MRF\npulse sequences with intrinsic robustness against shading artifacts due to\nphase variation.\n"}
+{"abstract": "  The 5G mobile network brings several new features that can be applied to\nexisting and new applications. High reliability, low latency, and high data\nrate are some of the features which fulfill the requirements of vehicular\nnetworks. Vehicular networks aim to provide safety for road users and several\nadditional advantages such as enhanced traffic efficiency and in-vehicle\ninfotainment services. This paper summarizes the most important aspects of\nNR-V2X, which is standardized by 3GPP, focusing on sidelink communication. The\nmain part of this work belongs to the 3GPP Rel-16, which is the first 3GPP\nrelease for NR-V2X, and the work/study items of the future Rel-17\n"}
+{"abstract": "  Existing emotion-aware conversational models usually focus on controlling the\nresponse contents to align with a specific emotion class, whereas empathy is\nthe ability to understand and concern the feelings and experience of others.\nHence, it is critical to learn the causes that evoke the users' emotion for\nempathetic responding, a.k.a. emotion causes. To gather emotion causes in\nonline environments, we leverage counseling strategies and develop an\nempathetic chatbot to utilize the causal emotion information. On a real-world\nonline dataset, we verify the effectiveness of the proposed approach by\ncomparing our chatbot with several SOTA methods using automatic metrics,\nexpert-based human judgements as well as user-based online evaluation.\n"}
+{"abstract": "  We investigate an M/M/1 queue operating in two switching environments, where\nthe switch is governed by a two-state time-homogeneous Markov chain. This model\nallows to describe a system that is subject to regular operating phases\nalternating with anomalous working phases or random repairing periods. We first\nobtain the steady-state distribution of the process in terms of a generalized\nmixture of two geometric distributions. In the special case when only one kind\nof switch is allowed, we analyze the transient distribution, and investigate\nthe busy period problem. The analysis is also performed by means of a suitable\nheavy-traffic approximation which leads to a continuous random process. Its\ndistribution satisfies a partial differential equation with randomly\nalternating infinitesimal moments. For the approximating process we determine\nthe steady-state distribution, the transient distribution and a\nfirst-passage-time density.\n"}
+{"abstract": "  Signomials are obtained by generalizing polynomials to allow for arbitrary\nreal exponents. This generalization offers great expressive power, but has\nhistorically sacrificed the organizing principle of ``degree'' that is central\nto polynomial optimization theory. We reclaim that principle here through the\nconcept of signomial rings, which we use to derive complete convex relaxation\nhierarchies of upper and lower bounds for signomial optimization via sums of\narithmetic-geometric exponentials (SAGE) nonnegativity certificates. The\nPositivstellensatz underlying the lower bounds relies on the concept of\nconditional SAGE and removes regularity conditions required by earlier works,\nsuch as convexity and Archimedeanity of the feasible set. Through worked\nexamples we illustrate the practicality of this hierarchy in areas such as\nchemical reaction network theory and chemical engineering. These examples\ninclude comparisons to direct global solvers (e.g., BARON and ANTIGONE) and the\nLasserre hierarchy (where appropriate). The completeness of our hierarchy of\nupper bounds follows from a generic construction whereby a Positivstellensatz\nfor signomial nonnegativity over a compact set provides for arbitrarily strong\nouter approximations of the corresponding cone of nonnegative signomials. While\nworking toward that result, we prove basic facts on the existence and\nuniqueness of solutions to signomial moment problems.\n"}
+{"abstract": "  For a random walk in a uniformly elliptic and i.i.d. environment on $\\mathbb\nZ^d$ with $d \\geq 4$, we show that the quenched and annealed large deviations\nrate functions agree on any compact set contained in the boundary $\\partial\n\\mathbb{D}:=\\{ x \\in \\mathbb R^d : |x|_1 =1\\}$ of their domain which does not\nintersect any of the $(d-2)$-dimensional facets of $\\partial \\mathbb{D}$,\nprovided that the disorder of the environment is~low~enough. As a consequence,\nwe obtain a simple explicit formula for both rate functions on $\\partial\n\\mathbb{D}$ at low disorder. In contrast to previous works, our results do not\nassume any ballistic behavior of the random walk and are not restricted to\nneighborhoods of any given point (on the boundary $\\partial \\mathbb{D}$). In\naddition, our~results complement those in [BMRS19], where, using different\nmethods, we investigate the equality of the rate functions in the interior of\ntheir domain. Finally, for a general parametrized family of environments,\nwe~show that the strength of disorder determines a phase transition in the\nequality of both rate functions, in the sense that for each $x \\in \\partial\n\\mathbb{D}$ there exists $\\varepsilon_x$ such that the two rate functions agree\nat $x$ when the disorder is smaller than $\\varepsilon_x$ and disagree when its\nlarger. This further reconfirms the idea, introduced in [BMRS19], that the\ndisorder of the environment is in general intimately related with the equality\nof the rate functions.\n"}
+{"abstract": "  In this paper, we consider graphon particle systems with heterogeneous\nmean-field type interactions and the associated finite particle approximations.\nUnder suitable growth (resp. convexity) assumptions, we obtain uniform-in-time\nconcentration estimates, over finite (resp. infinite) time horizon, for the\nWasserstein distance between the empirical measure and its limit, extending the\nwork of Bolley--Guillin--Villani.\n"}
+{"abstract": "  It is well known that entanglement can benefit quantum information processing\ntasks. Quantum illumination, when first proposed, is surprising as\nentanglement's benefit survives entanglement-breaking noise. Since then, many\nefforts have been devoted to study quantum sensing in noisy scenarios. The\napplicability of such schemes, however, is limited to a binary quantum\nhypothesis testing scenario. In terms of target detection, such schemes\ninterrogate a single polarization-azimuth-elevation-range-Doppler resolution\nbin at a time, limiting the impact to radar detection. We resolve this\nbinary-hypothesis limitation by proposing a quantum ranging protocol enhanced\nby entanglement. By formulating a ranging task as a multiary hypothesis testing\nproblem, we show that entanglement enables a 6-dB advantage in the error\nexponent against the optimal classical scheme. Moreover, the proposed ranging\nprotocol can also be utilized to implement a pulse-position modulated\nentanglement-assisted communication protocol. Our ranging protocol reveals\nentanglement's potential in general quantum hypothesis testing tasks and paves\nthe way towards a quantum-ranging radar with a provable quantum advantage.\n"}
+{"abstract": "  In this paper, channel estimation techniques and phase shift design for\nintelligent reflecting surface (IRS)-empowered single-user multiple-input\nmultiple-output (SU-MIMO) systems are proposed. Among four channel estimation\ntechniques developed in the paper, the two novel ones, single-path approximated\nchannel (SPAC) and selective emphasis on rank-one matrices (SEROM), have low\ntraining overhead to enable practical IRS-empowered SU-MIMO systems. SPAC is\nmainly based on parameter estimation by approximating IRS-related channels as\ndominant single-path channels. SEROM exploits IRS phase shifts as well as\ntraining signals for channel estimation and easily adjusts its training\noverhead. A closed-form solution for IRS phase shift design is also developed\nto maximize spectral efficiency where the solution only requires basic linear\noperations. Numerical results show that SPAC and SEROM combined with the\nproposed IRS phase shift design achieve high spectral efficiency even with low\ntraining overhead compared to existing methods.\n"}
+{"abstract": "  Our Galaxy and the nearby Andromeda galaxy (M31) are the most massive members\nof the Local Group, and they seem to be a bound pair, despite the uncertainties\non the relative motion of the two galaxies. A number of studies have shown that\nthe two galaxies will likely undergo a close approach in the next 4$-$5 Gyr. We\nused direct $N$-body simulations to model this interaction to shed light on the\nfuture of the Milky Way - Andromeda system and for the first time explore the\nfate of the two supermassive black holes (SMBHs) that are located at their\ncenters. We investigated how the uncertainties on the relative motion of the\ntwo galaxies, linked with the initial velocities and the density of the diffuse\nenvironment in which they move, affect the estimate of the time they need to\nmerge and form ``Milkomeda''. After the galaxy merger, we follow the evolution\nof their two SMBHs up to their close pairing and fusion. Upon the fiducial set\nof parameters, we find that Milky Way and Andromeda will have their closest\napproach in the next 4.3 Gyr and merge over a span of 10 Gyr. Although the time\nof the first encounter is consistent with other predictions, we find that the\nmerger occurs later than previously estimated. We also show that the two SMBHs\nwill spiral in the inner region of Milkomeda and coalesce in less than 16.6 Myr\nafter the merger of the two galaxies. Finally, we evaluate the\ngravitational-wave emission caused by the inspiral of the SMBHs, and we discuss\nthe detectability of similar SMBH mergers in the nearby Universe ($z\\leq 2$)\nthrough next-generation gravitational-wave detectors.\n"}
+{"abstract": "  With the emerging needs of creating fairness-aware solutions for search and\nrecommendation systems, a daunting challenge exists of evaluating such\nsolutions. While many of the traditional information retrieval (IR) metrics can\ncapture the relevance, diversity and novelty for the utility with respect to\nusers, they are not suitable for inferring whether the presented results are\nfair from the perspective of responsible information exposure. On the other\nhand, various fairness metrics have been proposed but they do not account for\nthe user utility or do not measure it adequately. To address this problem, we\npropose a new metric called Fairness-Aware IR (FAIR). By unifying standard IR\nmetrics and fairness measures into an integrated metric, this metric offers a\nnew perspective for evaluating fairness-aware ranking results. Based on this\nmetric, we developed an effective ranking algorithm that jointly optimized user\nutility and fairness. The experimental results showed that our FAIR metric\ncould highlight results with good user utility and fair information exposure.\nWe showed how FAIR related to existing metrics and demonstrated the\neffectiveness of our FAIR-based algorithm. We believe our work opens up a new\ndirection of pursuing a computationally feasible metric for evaluating and\nimplementing the fairness-aware IR systems.\n"}
+{"abstract": "  Real-Time Networks (RTNs) provide latency guarantees for time-critical\napplications and it aims to support different traffic categories via various\nscheduling mechanisms. Those scheduling mechanisms rely on a precise network\nperformance measurement to dynamically adjust the scheduling strategies.\nMachine Learning (ML) offers an iterative procedure to measure network\nperformance. Network Calculus (NC) can calculate the bounds for the main\nperformance indexes such as latencies and throughputs in an RTN for ML. Thus,\nthe ML and NC integration improve overall calculation efficiency. This paper\nwill provide a survey for different approaches of Real-Time Network performance\nmeasurement via NC as well as ML and present their results, dependencies, and\napplication scenarios.\n"}
+{"abstract": "  Given a bipartite graph with bipartition $(A,B)$ where $B$ is equipartitioned\ninto $k\\ge2$ blocks, can the vertices in $A$ be picked one by one so that at\nevery step, the picked vertices cover roughly the same number of vertices in\neach of these blocks? We show that, if each block has cardinality $m$, the\nvertices in $B$ have the same degree, and each vertex in $A$ has at most $cm$\nneighbors in every block where $c>0$ is a small constant, then there is an\nordering $v_1,\\ldots,v_n$ of the vertices in $A$ such that for every\n$j\\in\\{1,\\ldots,n\\}$, the numbers of vertices with a neighbor in\n$\\{v_1,\\ldots,v_j\\}$ in every two blocks differ by at most $\\sqrt{2(k-1)c}\\cdot\nm$. This is related to a well-known lemma of Steinitz, and partially answers an\nunpublished question of Scott and Seymour.\n"}
+{"abstract": "  Software verification may yield spurious failures when environment\nassumptions are not accounted for. Environment assumptions are the expectations\nthat a system or a component makes about its operational environment and are\noften specified in terms of conditions over the inputs of that system or\ncomponent. In this article, we propose an approach to automatically infer\nenvironment assumptions for Cyber-Physical Systems (CPS). Our approach improves\nthe state-of-the-art in three different ways: First, we learn assumptions for\ncomplex CPS models involving signal and numeric variables; second, the learned\nassumptions include arithmetic expressions defined over multiple variables;\nthird, we identify the trade-off between soundness and informativeness of\nenvironment assumptions and demonstrate the flexibility of our approach in\nprioritizing either of these criteria.\n  We evaluate our approach using a public domain benchmark of CPS models from\nLockheed Martin and a component of a satellite control system from LuxSpace, a\nsatellite system provider. The results show that our approach outperforms\nstate-of-the-art techniques on learning assumptions for CPS models, and\nfurther, when applied to our industrial CPS model, our approach is able to\nlearn assumptions that are sufficiently close to the assumptions manually\ndeveloped by engineers to be of practical value.\n"}
+{"abstract": "  A fully discrete and fully explicit low-regularity integrator is constructed\nfor the one-dimensional periodic cubic nonlinear Schr\\\"odinger equation. The\nmethod can be implemented by using fast Fourier transform with $O(N\\ln N)$\noperations at every time level, and is proved to have an $L^2$-norm error bound\nof $O(\\tau\\sqrt{\\ln(1/\\tau)}+N^{-1})$ for $H^1$ initial data, without requiring\nany CFL condition, where $\\tau$ and $N$ denote the temporal stepsize and the\ndegree of freedoms in the spatial discretisation, respectively.\n"}
+{"abstract": "  Isotropic hyper-elasticity, altogether with the equilibrium equation and the\nusual boundary conditions, are formulated directly on the body B, a\nthree-dimensional compact and orientable manifold with boundary equipped with a\nmass measure. Pearson-Sewell-Beatty pressure potential is formulated in an\nintrinsic geometric manner. It is shown that Poincar{\\'e}'s formula extended to\ninfinite dimension, provides, in a straightforward manner, the optimal\n(non-holonomic) constraints for such a pressure potential to exist.\n"}
+{"abstract": "  Rare-earth titanates are Mott insulators whose magnetic ground state --\nantiferromagnetic (AFM) or ferromagnetic (FM) -- can be tuned by the radius of\nthe rare-earth element. Here, we combine phenomenology and first-principles\ncalculations to shed light on the generic magnetic phase diagram of a\nchemically-substituted titanate on the rare-earth site that interpolates\nbetween an AFM and a FM state. Octahedral rotations present in these\nperovskites cause the AFM order to acquire a small FM component -- and\nvice-versa -- removing any multi-critical point from the phase diagram.\nHowever, for a wide parameter range, a first-order metamagnetic transition line\nterminating at a critical end-point survives inside the magnetically ordered\nphase. Similarly to the liquid-gas transition, a Widom line emerges from the\nend-point, characterized by enhanced fluctuations. In contrast to metallic\nferromagnets, this metamagnetic transition involves two symmetry-equivalent and\ninsulating canted spin states. Moreover, instead of a magnetic field, we show\nthat uniaxial strain can be used to tune this transition to zero-temperature,\ninducing a quantum critical end-point.\n"}
+{"abstract": "  Machine learning models $-$ now commonly developed to screen, diagnose, or\npredict health conditions $-$ are evaluated with a variety of performance\nmetrics. An important first step in assessing the practical utility of a model\nis to evaluate its average performance over an entire population of interest.\nIn many settings, it is also critical that the model makes good predictions\nwithin predefined subpopulations. For instance, showing that a model is fair or\nequitable requires evaluating the model's performance in different demographic\nsubgroups. However, subpopulation performance metrics are typically computed\nusing only data from that subgroup, resulting in higher variance estimates for\nsmaller groups. We devise a procedure to measure subpopulation performance that\ncan be more sample-efficient than the typical subsample estimates. We propose\nusing an evaluation model $-$ a model that describes the conditional\ndistribution of the predictive model score $-$ to form model-based metric (MBM)\nestimates. Our procedure incorporates model checking and validation, and we\npropose a computationally efficient approximation of the traditional\nnonparametric bootstrap to form confidence intervals. We evaluate MBMs on two\nmain tasks: a semi-synthetic setting where ground truth metrics are available\nand a real-world hospital readmission prediction task. We find that MBMs\nconsistently produce more accurate and lower variance estimates of model\nperformance for small subpopulations.\n"}
+{"abstract": "  We introduce a novel approach, the Cosmological Trajectories Method (CTM), to\nmodel nonlinear structure formation in the Universe by expanding\ngravitationally-induced particle trajectories around the Zel'dovich\napproximation. A new Beyond Zel'dovich approximation is presented, which\nexpands the CTM to leading second-order in the gravitational interaction and\nallows for post-Born gravitational scattering. In the Beyond Zel'dovich\napproximation we derive the exact expression for the matter clustering power\nspectrum. This is calculated to leading order and is available in the CTM\nMODULE. We compare the Beyond Zel'dovich approximation power spectrum and\ncorrelation function to other methods including 1-loop Standard Perturbation\nTheory (SPT), 1-loop Lagrangian Perturbation Theory (LPT) and Convolution\nLagrangian Perturbation Theory (CLPT). We find that the Beyond Zel'dovich\napproximation power spectrum performs well, matching simulations to within\n$\\pm{10}\\%$, on mildly non-linear scales, and at redshifts above $z=1$ it\noutperforms the Zel'dovich approximation. We also find that the Beyond\nZel'dovich approximation models the BAO peak in the correlation function at\n$z=0$ more accurately, to within $\\pm{5}\\%$ of simulations, than the Zel'dovich\napproximation, SPT 1-loop and CLPT.\n"}
+{"abstract": "  In this study we analyze linear combinatorial optimization problems where the\ncost vector is not known a priori, but is only observable through a finite data\nset. In contrast to the related studies, we presume that the number of\nobservations with respect to particular components of the cost vector may vary.\nThe goal is to find a procedure that transforms the data set into an estimate\nof the expected value of the objective function (which is referred to as a\nprediction rule) and a procedure that retrieves a candidate decision (which is\nreferred to as a prescription rule). We aim at finding the least conservative\nprediction and prescription rules, which satisfy some specified asymptotic\nguarantees. We demonstrate that the resulting vector optimization problems\nadmit a weakly optimal solution, which can be obtained by solving a particular\ndistributionally robust optimization problem. Specifically, the decision-maker\nmay optimize the worst-case expected loss across all probability distributions\nwith given component-wise relative entropy distances from the empirical\nmarginal distributions. Finally, we perform numerical experiments to analyze\nthe out-of-sample performance of the proposed solution approach.\n"}
+{"abstract": "  Word Sense Disambiguation (WSD) is a long-standing task in Natural Language\nProcessing(NLP) that aims to automatically identify the most relevant meaning\nof the words in a given context. Developing standard WSD test collections can\nbe mentioned as an important prerequisite for developing and evaluating\ndifferent WSD systems in the language of interest. Although many WSD test\ncollections have been developed for a variety of languages, no standard\nAll-words WSD benchmark is available for Persian. In this paper, we address\nthis shortage for the Persian language by introducing SBU-WSD-Corpus, as the\nfirst standard test set for the Persian All-words WSD task. SBU-WSD-Corpus is\nmanually annotated with senses from the Persian WordNet (FarsNet) sense\ninventory. To this end, three annotators used SAMP (a tool for sense annotation\nbased on FarsNet lexical graph) to perform the annotation task. SBU-WSD-Corpus\nconsists of 19 Persian documents in different domains such as Sports, Science,\nArts, etc. It includes 5892 content words of Persian running text and 3371\nmanually sense annotated words (2073 nouns, 566 verbs, 610 adjectives, and 122\nadverbs). Providing baselines for future studies on the Persian All-words WSD\ntask, we evaluate several WSD models on SBU-WSD-Corpus. The corpus is publicly\navailable at https://github.com/hrouhizadeh/SBU-WSD-Corpus.\n"}
+{"abstract": "  In this article, we calculate the density of primes $\\mathfrak{p}$ for which\nthe $\\mathfrak{p}$-th Fourier coefficient $C^*(\\mathfrak{p}, f)$ (resp.,\n$C(\\mathfrak{p}, f)$) of a primitive Hilbert modular form $f$ generates the\ncoefficient field $F_f$ (resp., $E_f$), under certain conditions on the images\nof $\\lambda$-adic residual Galois representations attached to $f$. Then, we\nproduce some examples of primitive forms $f$ satisfying these conditions. Our\nwork is a generalization of \\cite{KSW08} to primitive Hilbert modular forms.\n"}
+{"abstract": "  Adding propositional quantification to the modal logics K, T or S4 is known\nto lead to undecidability but CTL with propositional quantification under the\ntree semantics (tQCTL) admits a non-elementary Tower-complete satisfiability\nproblem. We investigate the complexity of strict fragments of tQCTL as well as\nof the modal logic K with propositional quantification under the tree\nsemantics. More specifically, we show that tQCTL restricted to the temporal\noperator EX is already Tower-hard, which is unexpected as EX can only enforce\nlocal properties. When tQCTL restricted to EX is interpreted on N-bounded trees\nfor some N >= 2, we prove that the satisfiability problem is AExpPol-complete;\nAExpPol-hardness is established by reduction from a recently introduced tiling\nproblem, instrumental for studying the model-checking problem for interval\ntemporal logics. As consequences of our proof method, we prove Tower-hardness\nof tQCTL restricted to EF or to EXEF and of the well-known modal logics such as\nK, KD, GL, K4 and S4 with propositional quantification under a semantics based\non classes of trees.\n"}
+{"abstract": "  We are interested in the nature of the spectrum of the one-dimensional\nSchr\\\"odinger operator $$\n  - \\frac{d^2}{dx^2}-Fx + \\sum_{n \\in \\mathbb{Z}}g_n \\delta(x-n)\n  \\qquad\\text{in } L^2(\\mathbb{R}) $$ with $F>0$ and two different choices of\nthe coupling constants $\\{g_n\\}_{n\\in \\mathbb{Z}}$. In the first model $g_n\n\\equiv \\lambda$ and we prove that if $F\\in \\pi^2 \\mathbb{Q}$ then the spectrum\nis $\\mathbb{R}$ and is furthermore absolutely continuous away from an explicit\ndiscrete set of points. In the second model $g_n$ are independent random\nvariables with mean zero and variance $\\lambda^2$. Under certain assumptions on\nthe distribution of these random variables we prove that almost surely the\nspectrum is $\\mathbb{R}$ and it is dense pure point if $F < \\lambda^2/2$ and\npurely singular continuous if $F> \\lambda^2/2$.\n"}
+{"abstract": "  We report the discovery of a 'folded' gravitationally lensed image,\n'Hamilton's Object', found in a HST image of the field near the AGN SDSS\nJ223010.47-081017.8 ($z=0.62$). The lensed images are sourced by a galaxy at a\nspectroscopic redshift of 0.8200$\\pm0.0005$ and form a fold configuration on a\ncaustic caused by a foreground galaxy cluster at a photometric redshift of\n0.526$\\pm0.018$ seen in the corresponding Pan-STARRS PS1 image and marginally\ndetected as a faint ROSAT All-Sky Survey X-ray source. The lensed images\nexhibit properties similar to those of other folds where the source galaxy\nfalls very close to or straddles the caustic of a galaxy cluster. The folded\nimages are stretched in a direction roughly orthogonal to the critical curve,\nbut the configuration is that of a tangential cusp. Guided by morphological\nfeatures, published simulations and similar fold observations in the\nliterature, we identify a third or counter-image, confirmed by spectroscopy.\nBecause the fold-configuration shows highly distinctive surface brightness\nfeatures, follow-up observations of microlensing or detailed investigations of\nthe individual surface brightness features at higher resolution can further\nshed light on kpc-scale dark matter properties. We determine the local lens\nproperties at the positions of the multiple images according to the\nobservation-based lens reconstruction of Wagner et al. (2019). The analysis is\nin accordance with a mass density which hardly varies on an arc-second scale (6\nkpc) over the areas covered by the multiple images.\n"}
+{"abstract": "  Over-the-air computation (OAC) is a promising technique to realize fast model\naggregation in the uplink of federated edge learning. OAC, however, hinges on\naccurate channel-gain precoding and strict synchronization among the edge\ndevices, which are challenging in practice. As such, how to design the maximum\nlikelihood (ML) estimator in the presence of residual channel-gain mismatch and\nasynchronies is an open problem. To fill this gap, this paper formulates the\nproblem of misaligned OAC for federated edge learning and puts forth a whitened\nmatched filtering and sampling scheme to obtain oversampled, but independent,\nsamples from the misaligned and overlapped signals. Given the whitened samples,\na sum-product ML estimator and an aligned-sample estimator are devised to\nestimate the arithmetic sum of the transmitted symbols. In particular, the\ncomputational complexity of our sum-product ML estimator is linear in the\npacket length and hence is significantly lower than the conventional ML\nestimator. Extensive simulations on the test accuracy versus the average\nreceived energy per symbol to noise power spectral density ratio (EsN0) yield\ntwo main results: 1) In the low EsN0 regime, the aligned-sample estimator can\nachieve superior test accuracy provided that the phase misalignment is\nnon-severe. In contrast, the ML estimator does not work well due to the error\npropagation and noise enhancement in the estimation process. 2) In the high\nEsN0 regime, the ML estimator attains the optimal learning performance\nregardless of the severity of phase misalignment. On the other hand, the\naligned-sample estimator suffers from a test-accuracy loss caused by phase\nmisalignment.\n"}
+{"abstract": "  We investigate the properties of the glass phase of a recently introduced\nspin glass model of soft spins subjected to an anharmonic quartic local\npotential, which serves as a model of low temperature molecular or soft\nglasses. We solve the model using mean field theory and show that, at low\ntemperatures, it is described by full replica symmetry breaking (fullRSB). As a\nconsequence, at zero temperature the glass phase is marginally stable. We show\nthat in this case, marginal stability comes from a combination of both soft\nlinear excitations -- appearing in a gapless spectrum of the Hessian of linear\nexcitations -- and pseudogapped non-linear excitations -- corresponding to\nnearly degenerate two level systems. Therefore, this model is a natural\ncandidate to describe what happens in soft glasses, where quasi localized soft\nmodes in the density of states appear together with non-linear modes triggering\navalanches and conjectured to be essential to describe the universal\nlow-temperature anomalies of glasses.\n"}
+{"abstract": "  For a rooted cluster algebra $\\mathcal{A}(Q)$ over a valued quiver $Q$, a\n\\emph{symmetric cluster variable} is any cluster variable that belongs to a\ncluster associated with a quiver $\\sigma (Q)$, for some permutation $\\sigma$.\nThe subalgebra of $\\mathcal{A}(Q)$ generated by all symmetric cluster variables\nis called the \\emph{symmetric mutation subalgebra} and is denoted by\n$\\mathcal{B}(Q)$. In this paper, we identify the class of cluster algebras that\nsatisfy $\\mathcal{B}(Q)=\\mathcal{A}(Q)$, which contains almost every quiver of\nfinite mutation type. In the process of proving the main theorem, we provide a\nclassification of quivers mutation classes based on their weights. Some\nproperties of symmetric mutation subalgebras are given.\n"}
+{"abstract": "  We investigate the State-Controlled Cellular Neural Network (SC-CNN)\nframework of Murali-Lakshmanan-Chua (MLC) circuit system subjected to two\nlogical signals. By exploiting the attractors generated by this circuit in\ndifferent regions of phase-space, we show that the nonlinear circuit is capable\nof producing all the logic gates, namely OR, AND, NOR, NAND, Ex-OR and Ex-NOR\ngates available in digital systems. Further the circuit system emulates\nthree-input gates and Set-Reset flip-flop logic as well. Moreover, all these\nlogical elements and flip-flop are found to be tolerant to noise. These\nphenomena are also experimentally demonstrated. Thus our investigation to\nrealize all logic gates and memory latch in a nonlinear circuit system paves\nthe way to replace or complement the existing technology with a limited number\nof hardware.\n"}
+{"abstract": "  Convolutional neural networks (CNNs) are able to attain better visual\nrecognition performance than fully connected neural networks despite having\nmuch less parameters due to their parameter sharing principle. Hence, modern\narchitectures are designed to contain a very small number of fully-connected\nlayers, often at the end, after multiple layers of convolutions. It is\ninteresting to observe that we can replace large fully-connected layers with\nrelatively small groups of tiny matrices applied on the entire image. Moreover,\nalthough this strategy already reduces the number of parameters, most of the\nconvolutions can be eliminated as well, without suffering any loss in\nrecognition performance. However, there is no solid recipe to detect this\nhidden subset of convolutional neurons that is responsible for the majority of\nthe recognition work. Hence, in this work, we use the matrix characteristics\nbased on eigenvalues in addition to the classical weight-based importance\nassignment approach for pruning to shed light on the internal mechanisms of a\nwidely used family of CNNs, namely residual neural networks (ResNets), for the\nimage classification problem using CIFAR-10, CIFAR-100 and Tiny ImageNet\ndatasets.\n"}
+{"abstract": "  Access to informative databases is a crucial part of notable research\ndevelopments. In the field of domestic audio classification, there have been\nsignificant advances in recent years. Although several audio databases exist,\nthese can be limited in terms of the amount of information they provide, such\nas the exact location of the sound sources, and the associated noise levels. In\nthis work, we detail our approach on generating an unbiased synthetic domestic\naudio database, consisting of sound scenes and events, emulated in both quiet\nand noisy environments. Data is carefully curated such that it reflects issues\ncommonly faced in a dementia patients environment, and recreate scenarios that\ncould occur in real-world settings. Similarly, the room impulse response\ngenerated is based on a typical one-bedroom apartment at Hebrew SeniorLife\nFacility. As a result, we present an 11-class database containing excerpts of\nclean and noisy signals at 5-seconds duration each, uniformly sampled at 16\nkHz. Using our baseline model using Continues Wavelet Transform Scalograms and\nAlexNet, this yielded a weighted F1-score of 86.24 percent.\n"}
+{"abstract": "  Deuterium diffusion is investigated in nitrogen-doped homoepitaxial ZnO\nlayers. The samples were grown under slightly Zn-rich growth conditions by\nplasma-assisted molecular beam epitaxy on m-plane ZnO substrates and have a\nnitrogen content [N] varied up to 5x1018 at.cm-3 as measured by secondary ion\nmass spectrometry (SIMS). All were exposed to a radio-frequency deuterium\nplasma during 1h at room temperature. Deuterium diffusion is observed in all\nepilayers while its penetration depth decreases as the nitrogen concentration\nincreases. This is a strong evidence of a diffusion mechanism limited by the\ntrapping of deuterium on a nitrogen-related trap. The SIMS profiles are\nanalyzed using a two-trap model including a shallow trap, associated with a\nfast diffusion, and a deep trap, related to nitrogen. The capture radius of the\nnitrogen-related trap is determined to be 20 times smaller than the value\nexpected for nitrogen-deuterium pairs formed by coulombic attraction between D+\nand nitrogen-related acceptors. The (N2)O deep donor is proposed as the deep\ntrapping site for deuterium and accounts well for the small capture radius and\nthe observed photoluminescence quenching and recovery after deuteration of the\nZnO:N epilayers. It is also found that this defect is by far the N-related\ndefect with the highest concentration in the studied samples.\n"}
+{"abstract": "  This paper considers a pursuit-evasion scenario among three agents -- an\nevader, a pursuer, and a defender. We design cooperative guidance laws for the\nevader and the defender team to safeguard the evader from an attacking pursuer.\nUnlike differential games, optimal control formulations, and other heuristic\nmethods, we propose a novel perspective on designing effective nonlinear\nfeedback control laws for the evader-defender team using a time-constrained\nguidance approach. The evader lures the pursuer on the collision course by\noffering itself as bait. At the same time, the defender protects the evader\nfrom the pursuer by exercising control over the engagement duration. Depending\non the nature of the mission, the defender may choose to take an aggressive or\ndefensive stance. Such consideration widens the applicability of the proposed\nmethods in various three-agent motion planning scenarios such as aircraft\ndefense, asset guarding, search and rescue, surveillance, and secure\ntransportation. We use a fixed-time sliding mode control strategy to design the\ncontrol laws for the evader-defender team and a nonlinear finite-time\ndisturbance observer to estimate the pursuer's maneuver. Finally, we present\nsimulations to demonstrate favorable performance under various engagement\ngeometries, thus vindicating the efficacy of the proposed designs.\n"}
+{"abstract": "  We present a class of diffraction-free partially coherent beams each member\nof which is comprised of a finite power, non-accelerating Airy bump residing on\na statistically homogeneous, Gaussian-correlated background. We examine\nfree-space propagation of soft apertured realizations of the proposed beams and\nshow that their evolution is governed by two spatial scales: the coherence\nwidth of the background and aperture size. A relative magnitude of these\nfactors determines the practical range of propagation distances over which the\nnovel beams can withstand diffraction. The proposed beams can find applications\nto imaging and optical communications through random media.\n"}
+{"abstract": "  Unsupervised person re-identification (Re-ID) aims to match pedestrian images\nfrom different camera views in unsupervised setting. Existing methods for\nunsupervised person Re-ID are usually built upon the pseudo labels from\nclustering. However, the quality of clustering depends heavily on the quality\nof the learned features, which are overwhelmingly dominated by the colors in\nimages especially in the unsupervised setting. In this paper, we propose a\nCluster-guided Asymmetric Contrastive Learning (CACL) approach for unsupervised\nperson Re-ID, in which cluster structure is leveraged to guide the feature\nlearning in a properly designed asymmetric contrastive learning framework. To\nbe specific, we propose a novel cluster-level contrastive loss to help the\nsiamese network effectively mine the invariance in feature learning with\nrespect to the cluster structure within and between different data augmentation\nviews, respectively. Extensive experiments conducted on three benchmark\ndatasets demonstrate superior performance of our proposal.\n"}
+{"abstract": "  The power conserving interconnection of port-thermodynamic systems via their\npower ports results in another port-thermodynamic system, while the same holds\nfor the rate of entropy increasing interconnection via their entropy flow\nports. Control by interconnection of port-thermodynamic systems seeks to\ncontrol a plant port-thermodynamic system by the interconnection with a\ncontroller port-thermodynamic system. The stability of the interconnected\nport-thermodynamic system is investigated by Lyapunov functions based on\ngenerating functions for the submanifold characterizing the state properties as\nwell as additional conserved quantities. Crucial tool is the use of canonical\npoint transformations on the symplectized thermodynamic phase space.\n"}
+{"abstract": "  Graph neural networks (GNN) have been proven to be mature enough for handling\ngraph-structured data on node-level graph representation learning tasks.\nHowever, the graph pooling technique for learning expressive graph-level\nrepresentation is critical yet still challenging. Existing pooling methods\neither struggle to capture the local substructure or fail to effectively\nutilize high-order dependency, thus diminishing the expression capability. In\nthis paper we propose HAP, a hierarchical graph-level representation learning\nframework, which is adaptively sensitive to graph structures, i.e., HAP\nclusters local substructures incorporating with high-order dependencies. HAP\nutilizes a novel cross-level attention mechanism MOA to naturally focus more on\nclose neighborhood while effectively capture higher-order dependency that may\ncontain crucial information. It also learns a global graph content GCont that\nextracts the graph pattern properties to make the pre- and post-coarsening\ngraph content maintain stable, thus providing global guidance in graph\ncoarsening. This novel innovation also facilitates generalization across graphs\nwith the same form of features. Extensive experiments on fourteen datasets show\nthat HAP significantly outperforms twelve popular graph pooling methods on\ngraph classification task with an maximum accuracy improvement of 22.79%, and\nexceeds the performance of state-of-the-art graph matching and graph similarity\nlearning algorithms by over 3.5% and 16.7%.\n"}
+{"abstract": "  We demonstrate the potential of dopamine modified\n0.5(Ba0.7Ca0.3)TiO3-0.5Ba(Zr0.2Ti0.8)O3 filler incorporated poly-vinylidene\nfluoride (PVDF) composite prepared by solution cast method as both flexible\nenergy storage and harvesting devices. The introduction of dopamine in filler\nsurface functionalization acts as bridging elements between filler and polymer\nmatrix and results in a better filler dispersion and an improved dielectric\nloss tangent (<0.02) along with dielectric permittivity ranges from 9 to 34\nwhich is favorable for both energy harvesting and storage. Additionally, a\nsignificantly low DC conductivity (< 10-9 ohm-1cm-1) for all composites was\nachieved leading to an improved breakdown strength and charge accumulation\ncapability. Maximum breakdown strength of 134 KV/mm and corresponding energy\nstorage density 0.72 J/cm3 were obtained from the filler content 10 weight%.\nThe improved energy harvesting performance was characterized by obtaining a\nmaximum piezoelectric charge constant (d33) = 78 pC/N, and output voltage\n(Vout) = 0.84 V along with maximum power density of 3.46 microW/cm3 for the\nfiller content of 10 wt%. Thus, the results show\n0.5(Ba0.7Ca0.3)TiO3-0.5Ba(Zr0.2Ti0.8)O3/PVDF composite has the potential for\nenergy storage and harvesting applications simultaneously that can\nsignificantly suppress the excess energy loss arises while utilizing different\nmaterial.\n"}
+{"abstract": "  The conformal module of conjugacy classes of braids is an invariant that\nappeared earlier than the entropy of conjugacy classes of braids, and is\ninverse proportional to the entropy. Using the relation between the two\ninvariants we give a short conceptional proof of an earlier result on the\nconformal module. Mainly, we consider situations, when the conformal module of\nconjugacy classes of braids serves as obstruction for the existence of\nhomotopies (or isotopies) of smooth objects involving braids to the respective\nholomorphic objects, and present theorems on the restricted validity of\nGromov's Oka principle in these situations.\n"}
+{"abstract": "  We study the asymptotic properties of the stochastic Cahn-Hilliard equation\nwith the logarithmic free energy by establishing different dimension-free\nHarnack inequalities according to various kinds of noises. The main\ncharacteristics of this equation are the singularities of the logarithmic free\nenergy at 1 and --1 and the conservation of the mass of the solution in its\nspatial variable. Both the space-time colored noise and the space-time white\nnoise are considered. For the highly degenerate space-time colored noise, the\nasymptotic log-Harnack inequality is established under the so-called\nessentially elliptic conditions. And the Harnack inequality with power is\nestablished for non-degenerate space-time white noise.\n"}
+{"abstract": "  In this article, for positive integers $n\\geq m\\geq 1$, the parameter spaces\nfor the isomorphism classes of the generic point arrangements of cardinality\n$n$, and the antipodal point arrangements of cardinality $2n$ in the Eulidean\nspace $\\mathbb{R}^m$ are described using the space of totally nonzero\nGrassmannian $Gr^{tnz}_{mn}(\\mathbb{R})$. A stratification\n$\\mathcal{S}^{tnz}_{mn}(\\mathbb{R})$ of the totally nonzero Grassmannian\n$Gr^{tnz}_{mn}(\\mathbb{R})$ is mentioned and the parameter spaces are\nrespectively expressed as quotients of the space\n$\\mathcal{S}^{tnz}_{mn}(\\mathbb{R})$ of strata under suitable actions of the\nsymmetric group $S_n$ and the semidirect product group $(\\mathbb{R}^*)^n\\rtimes\nS_n$. The cardinalities of the space $\\mathcal{S}^{tnz}_{mn}(\\mathbb{R})$ of\nstrata and of the parameter spaces $S_n\\backslash\n\\mathcal{S}^{tnz}_{mn}(\\mathbb{R}), ((\\mathbb{R}^*)^n\\rtimes S_n)\\backslash\n\\mathcal{S}^{tnz}_{mn}(\\mathbb{R})$ are enumerated in dimension $m=2$.\nInterestingly enough, the enumerated value of the isomorphism classes of the\ngeneric point arrangements in the Euclidean plane is expressed in terms of the\nnumber theoretic Euler-totient function. The analogous enumeration questions\nare still open in higher dimensions for $m\\geq 3$.\n"}
+{"abstract": "  There is growing interest in hydrogen (H$_2$) use for long-duration energy\nstorage in a future electric grid dominated by variable renewable energy (VRE)\nresources. Modelling the role of H$_2$ as grid-scale energy storage, often\nreferred as \"power-to-gas-to-power (P2G2P)\" overlooks the cost-sharing and\nemission benefits from using the deployed H$_2$ production and storage assets\nto also supply H$_2$ for decarbonizing other end-use sectors where direct\nelectrification may be challenged. Here, we develop a generalized modelling\nframework for co-optimizing energy infrastructure investment and operation\nacross power and transportation sectors and the supply chains of electricity\nand H$_2$, while accounting for spatio-temporal variations in energy demand and\nsupply. Applying this sector-coupling framework to the U.S. Northeast under a\nrange of technology cost and carbon price scenarios, we find a greater value of\npower-to-H$_2$ (P2G) versus P2G2P routes. P2G provides flexible demand\nresponse, while the extra cost and efficiency penalties of P2G2P routes make\nthe solution less attractive for grid balancing. The effects of sector-coupling\nare significant, boosting VRE generation by 12-55% with both increased\ncapacities and reduced curtailments and reducing the total system cost (or\nlevelized costs of energy) by 6-14% under 96% decarbonization scenarios. Both\nthe cost savings and emission reductions from sector coupling increase with\nH$_2$ demand for other end-uses, more than doubling for a 96% decarbonization\nscenario as H$_2$ demand quadraples. Moreover, we found that the deployment of\ncarbon capture and storage is more cost-effective in the H$_2$ sector because\nof the lower cost and higher utilization rate. These findings highlight the\nimportance of using an integrated multi-sector energy system framework with\nmultiple energy vectors in planning energy system decarbonization pathways.\n"}
+{"abstract": "  Massive multiple-input multiple-output (MIMO) is a key technology for\nimproving the spectral and energy efficiency in 5G-and-beyond wireless\nnetworks. For a tractable analysis, most of the previous works on Massive MIMO\nhave been focused on the system performance with complex Gaussian channel\nimpulse responses under rich-scattering environments. In contrast, this paper\ninvestigates the uplink ergodic spectral efficiency (SE) of each user under the\ndouble scattering channel model. We derive a closed-form expression of the\nuplink ergodic SE by exploiting the maximum ratio (MR) combining technique\nbased on imperfect channel state information. We further study the asymptotic\nSE behaviors as a function of the number of antennas at each base station (BS)\nand the number of scatterers available at each radio channel. We then formulate\nand solve a total energy optimization problem for the uplink data transmission\nthat aims at simultaneously satisfying the required SEs from all the users with\nlimited data power resource. Notably, our proposed algorithms can cope with the\ncongestion issue appearing when at least one user is served by lower SE than\nrequested. Numerical results illustrate the effectiveness of the closed-form\nergodic SE over Monte-Carlo simulations. Besides, the system can still provide\nthe required SEs to many users even under congestion.\n"}
+{"abstract": "  High-order implicit shock tracking is a new class of numerical methods to\napproximate solutions of conservation laws with non-smooth features. These\nmethods align elements of the computational mesh with non-smooth features to\nrepresent them perfectly, allowing high-order basis functions to approximate\nsmooth regions of the solution without the need for nonlinear stabilization,\nwhich leads to accurate approximations on traditionally coarse meshes. The\nhallmark of these methods is the underlying optimization formulation whose\nsolution is a feature-aligned mesh and the corresponding high-order\napproximation to the flow; the key challenge is robustly solving the central\noptimization problem. In this work, we develop a robust optimization solver for\nhigh-order implicit shock tracking methods so they can be reliably used to\nsimulate complex, high-speed, compressible flows in multiple dimensions. The\nproposed method integrates practical robustness measures into a sequential\nquadratic programming method, including dimension- and order-independent\nsimplex element collapses, mesh smoothing, and element-wise solution\nre-initialization, which prove to be necessary to reliably track complex\ndiscontinuity surfaces, such as curved and reflecting shocks, shock formation,\nand shock-shock interaction. A series of nine numerical experiments --\nincluding two- and three-dimensional compressible flows with complex\ndiscontinuity surfaces -- are used to demonstrate: 1) the robustness of the\nsolver, 2) the meshes produced are high-quality and track continuous,\nnon-smooth features in addition to discontinuities, 3) the method achieves the\noptimal convergence rate of the underlying discretization even for flows\ncontaining discontinuities, and 4) the method produces highly accurate\nsolutions on extremely coarse meshes relative to approaches based on shock\ncapturing.\n"}
+{"abstract": "  Ben Reichardt showed in a series of results that the general adversary bound\nof a function characterizes its quantum query complexity. This survey seeks to\naggregate the background and definitions necessary to understand the proof.\nNotable among these are the lower bound proof, span programs, witness size, and\nsemi-definite programs. These definitions, in addition to examples and detailed\nexpositions, serve to give the reader a better intuition of the graph-theoretic\nnature of the upper bound. We also include an applications of this result to\nlower bounds on DeMorgan formula size.\n"}
+{"abstract": "  In video object tracking, there exist rich temporal contexts among successive\nframes, which have been largely overlooked in existing trackers. In this work,\nwe bridge the individual video frames and explore the temporal contexts across\nthem via a transformer architecture for robust object tracking. Different from\nclassic usage of the transformer in natural language processing tasks, we\nseparate its encoder and decoder into two parallel branches and carefully\ndesign them within the Siamese-like tracking pipelines. The transformer encoder\npromotes the target templates via attention-based feature reinforcement, which\nbenefits the high-quality tracking model generation. The transformer decoder\npropagates the tracking cues from previous templates to the current frame,\nwhich facilitates the object searching process. Our transformer-assisted\ntracking framework is neat and trained in an end-to-end manner. With the\nproposed transformer, a simple Siamese matching approach is able to outperform\nthe current top-performing trackers. By combining our transformer with the\nrecent discriminative tracking pipeline, our method sets several new\nstate-of-the-art records on prevalent tracking benchmarks.\n"}
+{"abstract": "  Thoracic disease detection from chest radiographs using deep learning methods\nhas been an active area of research in the last decade. Most previous methods\nattempt to focus on the diseased organs of the image by identifying spatial\nregions responsible for significant contributions to the model's prediction. In\ncontrast, expert radiologists first locate the prominent anatomical structures\nbefore determining if those regions are anomalous. Therefore, integrating\nanatomical knowledge within deep learning models could bring substantial\nimprovement in automatic disease classification. This work proposes an\nanatomy-aware attention-based architecture named Anatomy X-Net, that\nprioritizes the spatial features guided by the pre-identified anatomy regions.\nWe leverage a semi-supervised learning method using the JSRT dataset containing\norgan-level annotation to obtain the anatomical segmentation masks (for lungs\nand heart) for the NIH and CheXpert datasets. The proposed Anatomy X-Net uses\nthe pre-trained DenseNet-121 as the backbone network with two corresponding\nstructured modules, the Anatomy Aware Attention (AAA) and Probabilistic\nWeighted Average Pooling (PWAP), in a cohesive framework for anatomical\nattention learning. Our proposed method sets new state-of-the-art performance\non the official NIH test set with an AUC score of 0.8439, proving the efficacy\nof utilizing the anatomy segmentation knowledge to improve the thoracic disease\nclassification. Furthermore, the Anatomy X-Net yields an averaged AUC of 0.9020\non the Stanford CheXpert dataset, improving on existing methods that\ndemonstrate the generalizability of the proposed framework.\n"}
+{"abstract": "  Neural architecture search (NAS) is a recent methodology for automating the\ndesign of neural network architectures. Differentiable neural architecture\nsearch (DARTS) is a promising NAS approach that dramatically increases search\nefficiency. However, it has been shown to suffer from performance collapse,\nwhere the search often leads to detrimental architectures. Many recent works\ntry to address this issue of DARTS by identifying indicators for early\nstopping, regularising the search objective to reduce the dominance of some\noperations, or changing the parameterisation of the search problem. In this\nwork, we hypothesise that performance collapses can arise from poor local\noptima around typical initial architectures and weights. We address this issue\nby developing a more global optimisation scheme that is able to better explore\nthe space without changing the DARTS problem formulation. Our experiments show\nthat our changes in the search algorithm allow the discovery of architectures\nwith both better test performance and fewer parameters.\n"}
+{"abstract": "  [Zhang, ICML 2018] provided the first decentralized actor-critic algorithm\nfor multi-agent reinforcement learning (MARL) that offers convergence\nguarantees. In that work, policies are stochastic and are defined on finite\naction spaces. We extend those results to offer a provably-convergent\ndecentralized actor-critic algorithm for learning deterministic policies on\ncontinuous action spaces. Deterministic policies are important in real-world\nsettings. To handle the lack of exploration inherent in deterministic policies,\nwe consider both off-policy and on-policy settings. We provide the expression\nof a local deterministic policy gradient, decentralized deterministic\nactor-critic algorithms and convergence guarantees for linearly-approximated\nvalue functions. This work will help enable decentralized MARL in\nhigh-dimensional action spaces and pave the way for more widespread use of\nMARL.\n"}
+{"abstract": "  The Force Concept Inventory (FCI) can be used as an assessment tool to\nmeasure the gains in a cohort of students. In this study it was given to first\nyear mechanics students (N=256 students) pre- and post-mechanics lectures, for\nstudents at the University of Johannesburg. From these results we examine the\neffect of switching mid-semester from traditional classes to online classes, as\nimposed by the COVID-19 lockdown in South Africa. Overall gains and student\nperspectives indicate no appreciable difference of gain, when bench-marked\nagainst previous studies using this assessment tool. When compared with 2019\ngrades, the 2020 semester grades do not appear to be greatly affected.\nFurthermore, initial statistical analyses also indicate a gender difference in\nmean gains in favour of females at the 95% significance level (for paired data,\nN=48). A survey given to students also appeared to indicate that most students\nwere aware of their conceptual performance in physics, and the main constraint\nto their studies was due to difficulties associated with being online. As such,\nthe change in pedagogy and the stresses of lockdown were found to not be\nsuggestive of a depreciation of FCI gains and grades.\n"}
+{"abstract": "  Neural Machine Translation model is a sequence-to-sequence converter based on\nneural networks. Existing models use recurrent neural networks to construct\nboth the encoder and decoder modules. In alternative research, the recurrent\nnetworks were substituted by convolutional neural networks for capturing the\nsyntactic structure in the input sentence and decreasing the processing time.\nWe incorporate the goodness of both approaches by proposing a\nconvolutional-recurrent encoder for capturing the context information as well\nas the sequential information from the source sentence. Word embedding and\nposition embedding of the source sentence is performed prior to the\nconvolutional encoding layer which is basically a n-gram feature extractor\ncapturing phrase-level context information. The rectified output of the\nconvolutional encoding layer is added to the original embedding vector, and the\nsum is normalized by layer normalization. The normalized output is given as a\nsequential input to the recurrent encoding layer that captures the temporal\ninformation in the sequence. For the decoder, we use the attention-based\nrecurrent neural network. Translation task on the German-English dataset\nverifies the efficacy of the proposed approach from the higher BLEU scores\nachieved as compared to the state of the art.\n"}
+{"abstract": "  A sample of 1.3 mm continuum cores in the Dragon infrared dark cloud (also\nknown as G28.37+0.07 or G28.34+0.06) is analyzed statistically. Based on their\nassociation with molecular outflows, the sample is divided into protostellar\nand starless cores. Statistical tests suggest that the protostellar cores are\nmore massive than the starless cores, even after temperature and opacity biases\nare accounted for. We suggest that the mass difference indicates core mass\ngrowth since their formation. The mass growth implies that massive star\nformation may not have to start with massive prestellar cores, depending on the\ncore mass growth rate. Its impact on the relation between core mass function\nand stellar initial mass function is to be further explored.\n"}
+{"abstract": "  In this paper, we investigate the algebraic nature of the value of a higher\nGreen function on an orthogonal Shimura variety at a single CM point. This is\nmotivated by a conjecture of Gross and Zagier in the setting of higher Green\nfunctions on the product of two modular curves. In the process, we will study\nanalogue of harmonic Maass forms in the setting of Hilbert modular forms, and\nobtain results concerning the arithmetic of their holomorphic part Fourier\ncoefficients. As a consequence, we confirm the conjecture of Gross and Zagier\nunder mild condition on the discriminant of the CM point.\n"}
+{"abstract": "  There is a strong consensus that combining the versatility of machine\nlearning with the assurances given by formal verification is highly desirable.\nIt is much less clear what verified machine learning should mean exactly. We\nconsider this question from the (unexpected?) perspective of computable\nanalysis. This allows us to define the computational tasks underlying verified\nML in a model-agnostic way, and show that they are in principle computable.\n"}
+{"abstract": "  Value functions are central to Dynamic Programming and Reinforcement Learning\nbut their exact estimation suffers from the curse of dimensionality,\nchallenging the development of practical value-function (VF) estimation\nalgorithms. Several approaches have been proposed to overcome this issue, from\nnon-parametric schemes that aggregate states or actions to parametric\napproximations of state and action VFs via, e.g., linear estimators or deep\nneural networks. Relevantly, several high-dimensional state problems can be\nwell-approximated by an intrinsic low-rank structure. Motivated by this and\nleveraging results from low-rank optimization, this paper proposes different\nstochastic algorithms to estimate a low-rank factorization of the $Q(s, a)$\nmatrix. This is a non-parametric alternative to VF approximation that\ndramatically reduces the computational and sample complexities relative to\nclassical $Q$-learning methods that estimate $Q(s,a)$ separately for each\nstate-action pair.\n"}
+{"abstract": "  Low-noise frequency conversion of single photons is a critical tool in\nestablishing fibre-based quantum networks. We show that a single photonic\ncrystal fibre can achieve frequency conversion by Bragg-scattering four-wave\nmixing of source photons from an ultra-broad wavelength range by engineering a\nsymmetric group velocity profile. Furthermore, we discuss how pump tuning can\nmitigate realistic discrepancies in device fabrication. This enables a single\nhighly adaptable frequency conversion interface to link disparate nodes in a\nquantum network via the telecoms band.\n"}
+{"abstract": "  In this paper, we establish the existence and uniqueness of Ricci flow that\nadmits an embedded closed convex surface in $\\mathbb{R}^3$ as metric initial\ncondition. The main point is a family of smooth Ricci flows starting from\nsmooth convex surfaces whose metrics converge uniformly to the metric of the\ninitial surface in intrinsic sense.\n"}
+{"abstract": "  We derive the stellar-to-halo mass relation (SHMR), namely $f_\\star\\propto\nM_\\star/M_{\\rm h}$ versus $M_\\star$ and $M_{\\rm h}$, for early-type galaxies\nfrom their near-IR luminosities (for $M_\\star$) and the position-velocity\ndistributions of their globular cluster systems (for $M_{\\rm h}$). Our\nindividual estimates of $M_{\\rm h}$ are based on fitting a dynamical model with\na distribution function expressed in terms of action-angle variables and\nimposing a prior on $M_{\\rm h}$ from the concentration-mass relation in the\nstandard $\\Lambda$CDM cosmology. We find that the SHMR for early-type galaxies\ndeclines with mass beyond a peak at $M_\\star\\sim 5\\times 10^{10}M_\\odot$ and\n$M_{\\rm h}\\sim 10^{12}M_\\odot$ (near the mass of the Milky Way). This result is\nconsistent with the standard SHMR derived by abundance matching for the general\npopulation of galaxies, and with previous, less robust derivations of the SHMR\nfor early types. However, it contrasts sharply with the monotonically rising\nSHMR for late types derived from extended HI rotation curves and the same\n$\\Lambda$CDM prior on $M_{\\rm h}$ as we adopt for early types. The SHMR for\nmassive galaxies varies more or less continuously, from rising to falling, with\ndecreasing disc fraction and decreasing Hubble type. We also show that the\ndifferent SHMRs for late and early types are consistent with the similar\nscaling relations between their stellar velocities and masses (Tully-Fisher and\nFaber-Jackson relations). Differences in the relations between the stellar and\nhalo virial velocities account for the similarity of the scaling relations. We\nargue that all these empirical findings are natural consequences of a picture\nin which galactic discs are built mainly by smooth and gradual inflow,\nregulated by feedback from young stars, while galactic spheroids are built by a\ncooperation between merging, black-hole fuelling, and feedback from AGNs.\n"}
+{"abstract": "  Recently, the experimental discovery of high-$T_c$ superconductivity in\ncompressed hydrides H$_3$S and LaH$_{10}$ at megabar pressures has triggered\nsearches for various superconducting superhydrides. It was experimentally\nobserved that thorium hydrides, ThH$_{10}$ and ThH$_9$, are stabilized at much\nlower pressures compared to LaH$_{10}$. Based on first-principles\ndensity-functional theory calculations, we reveal that the isolated Th\nframeworks of ThH$_{10}$ and ThH$_9$ have relatively more excess electrons in\ninterstitial regions than the La framework of LaH$_{10}$. Such interstitial\nexcess electrons easily participate in the formation of anionic H cage\nsurrounding metal atom. The resulting Coulomb attraction between cationic Th\natoms and anionic H cages is estimated to be stronger than the corresponding\none of LaH$_{10}$, thereby giving rise to larger chemical precompressions in\nThH$_{10}$ and ThH$_9$. Such a formation mechanism of H clathrates can also be\napplied to another experimentally synthesized superhydride CeH$_9$, confirming\nthe experimental evidence that the chemical precompression in CeH$_9$ is larger\nthan that in LaH$_{10}$. Our findings demonstrate that interstitial excess\nelectrons in the isolated metal frameworks of high-pressure superhydrides play\nan important role in generating the chemical precompression of H clathrates.\n"}
+{"abstract": "  The fluid flow along the Riga plate with the influence of magnetic force in a\nrotating system has been investigated numerically. The governing equations have\nbeen derived from Navier-Stokes equations. Applying the boundary layer\napproximation, the appropriate boundary layer equations have been obtained. By\nusing usual transformation, the obtained governing equations have been\ntransformed into a coupled dimensionless non-linear partial differential\nequation. The obtained dimensionless equations have been solved numerically by\nexplicit finite difference scheme. The simulated results have been obtained by\nusing MATLAB R2015a. Also the stability and convergence criteria have been\nanalyzed. The effect of several parameters on the primary velocity, secondary\nvelocity, temperature distributions as well as local shear stress and Nusselt\nnumber have been shown graphically.\n"}
+{"abstract": "  Mobile app developers use paid advertising campaigns to acquire new users,\nand they need to know the campaigns' performance to guide their spending.\nDetermining the campaign that led to an install requires that the app and\nadvertising network share an identifier that allows matching ad clicks to\ninstalls. Ad networks use the identifier to build user profiles that help with\ntargeting and personalization. Modern mobile operating systems have features to\nprotect the privacy of the user. The privacy features of Apple's iOS 14\nenforces all apps to get system permission for tracking explicitly instead of\nasking the user to opt-out of tracking as before. If the user does not allow\ntracking, the identifier for advertisers (IDFA) required for attributing the\ninstallation to the campaign is not shared. The lack of an identifier for the\nattribution changes profoundly how user acquisition campaigns' performance is\nmeasured. For users who do not allow tracking, there is a new feature that\nstill allows following campaign performance. The app can set an integer, so\ncalled conversion value for each user, and the developer can get the number of\ninstalls per conversion value for each campaign. This paper investigates the\ntask of distributing revenue to advertising campaigns using the conversion\nvalues. Our contributions are to formalize the problem, find the theoretically\noptimal revenue attribution function for any conversion value schema, and show\nempirical results on past data of a free-to-play mobile game using different\nconversion value schemas.\n"}
+{"abstract": "  We show that for Lebesgue almost all $d$-tuples $(\\theta_1,\\ldots,\\theta_d)$,\nwith $|\\theta_j|>1$, any self-affine measure for a homogeneous non-degenerate\niterated function system $\\{Ax+a_j\\}_{j=1}^m$ in ${\\mathbb R}^d$, where\n$A^{-1}$ is a diagonal matrix with the entries $(\\theta_1,\\ldots,\\theta_d)$,\nhas power Fourier decay at infinity.\n"}
+{"abstract": "  We present ALMA [C II] 158 $\\mu$m line and far-infrared (FIR) continuum\nemission observations toward HSC J120505.09$-$000027.9 (J1205$-$0000) at $z =\n6.72$ with the beam size of $\\sim 0''.8 \\times 0''.5$ (or 4.1 kpc $\\times$ 2.6\nkpc), the most distant red quasar known to date. Red quasars are modestly\nreddened by dust, and are thought to be in rapid transition from an obscured\nstarburst to an unobscured normal quasar, driven by powerful active galactic\nnucleus (AGN) feedback which blows out a cocoon of interstellar medium (ISM).\nThe FIR continuum of J1205$-$0000 is bright, with an estimated luminosity of\n$L_{\\rm FIR} \\sim 3 \\times 10^{12}~L_\\odot$. The [C II] line emission is\nextended on scales of $r \\sim 5$ kpc, greater than the FIR continuum. The line\nprofiles at the extended regions are complex and broad (FWHM $\\sim 630-780$ km\ns$^{-1}$). Although it is not practical to identify the nature of this extended\nstructure, possible explanations include (i) companion/merging galaxies and\n(ii) massive AGN-driven outflows. For the case of (i), the companions are\nmodestly star-forming ($\\sim 10~M_\\odot$ yr$^{-1}$), but are not detected by\nour Subaru optical observations ($y_{\\rm AB,5\\sigma} = 24.4$ mag). For the case\nof (ii), our lower-limit to the cold neutral outflow rate is $\\sim 100~M_\\odot$\nyr$^{-1}$. The outflow kinetic energy and momentum are both much smaller than\nwhat predicted in energy-conserving wind models, suggesting that the AGN\nfeedback in this quasar is not capable of completely suppressing its star\nformation.\n"}
+{"abstract": "  Baryon production is studied within the framework of quantized fragmentation\nof QCD string. Baryons appear in the model in a fairly intuitive way, with help\nof causally connected string breakups. A simple helical approximation of QCD\nflux tube with parameters constrained by mass spectrum of light mesons is\nsufficient to reproduce masses of light baryons.\n"}
+{"abstract": "  The minimal flavor structures for both quarks and leptons are proposed to\naddress fermion mass hierarchy and flavor mixings by bi-unitary decomposition\nof the fermion mass matrix. The real matrix ${\\bf M}_0^f$ is completely\nresponsive to family mass hierarchy, which is expressed by a close-to-flat\nmatrix structure. The left-handed unitary phase ${\\bf F}_L^f$ provides the\norigin of CP violation in quark and lepton mixings, which can be explained as a\nquantum effect between Yukawa interaction states and weak gauge states. The\nminimal flavor structure is realized by just 10 parameters without any\nredundancy, corresponding to 6 fermion masses, 3 mixing angles and 1 CP\nviolation in the quark/lepton sector. This approach provides a general flavor\nstructure independent of the specific quark or lepton flavor data. We verify\nthe validation of the flavor structure by reproducing quark/lepton masses and\nmixings. Some possible scenarios that yield the flavor structure are also\ndiscussed.\n"}
+{"abstract": "  Our goal is to develop a flux limiter of the Flux-Corrected Transport method\nfor a nonconservative convection-diffusion equation. For this, we consider a\nhybrid difference scheme that is a linear combination of a monotone scheme and\na scheme of high-order accuracy. The flux limiter is computed as an approximate\nsolution of a corresponding optimization problem with a linear objective\nfunction. The constraints for this optimization problem are derived from\ninequalities that are valid for the monotone scheme and apply to the hybrid\nscheme. Our numerical results with the flux limiters, which are exact and\napproximate solutions to the optimization problem, are in good agreement.\n"}
+{"abstract": "  A scalable system for real-time analysis of electron temperature and density\nbased on signals from the Thomson scattering diagnostic, initially developed\nfor and installed on the NSTX-U experiment, was recently adapted for the Large\nHelical Device (LHD) and operated for the first time during plasma discharges.\nDuring its initial operation run, it routinely recorded and processed signals\nfor four spatial points at the laser repetition rate of 30 Hz, well within the\nsystem's rated capability for 60 Hz. We present examples of data collected from\nthis initial run and describe subsequent adaptations to the analysis code to\nimprove the fidelity of the temperature calculations.\n"}
+{"abstract": "  The DARWIN observatory is a proposed next-generation experiment to search for\nparticle dark matter and other rare interactions. It will operate a 50 t liquid\nxenon detector, with 40 t in the time projection chamber (TPC). To inform the\nfinal detector design and technical choices, a series of technological\nquestions must first be addressed. Here we describe a full-scale demonstrator\nin the vertical dimension, Xenoscope, with the main goal of achieving electron\ndrift over a 2.6 m distance, which is the scale of the DARWIN TPC. We have\ndesigned and constructed the facility infrastructure, including the cryostat,\ncryogenic and purification systems, the xenon storage and recuperation system,\nas well as the slow control system. We have also designed a xenon purity\nmonitor and the TPC, with the fabrication of the former nearly complete. In a\nfirst commissioning run of the facility without an inner detector, we\ndemonstrated the nominal operational reach of Xenoscope and benchmarked the\ncomponents of the cryogenic and slow control systems, demonstrating reliable\nand continuous operation of all subsystems over 40 days. The infrastructure is\nthus ready for the integration of the purity monitor, followed by the TPC.\nFurther applications of the facility include R&D on the high voltage\nfeedthrough for DARWIN, measurements of electron cloud diffusion, as well as\nmeasurements of optical properties of liquid xenon. In the future, Xenoscope\nwill be available as a test platform for the DARWIN collaboration to\ncharacterise new detector technologies.\n"}
+{"abstract": "  Due to their broad application to different fields of theory and practice,\ngeneralized Petersen graphs $GPG(n,s)$ have been extensively investigated.\nDespite the regularity of generalized Petersen graphs, determining an exact\nformula for the diameter is still a difficult problem. In their paper, Beenker\nand Van Lint have proved that if the circulant graph $C_n(1,s)$ has diameter\n$d$, then $GPG(n,s)$ has diameter at least $d+1$ and at most $d+2$. In this\npaper, we provide necessary and sufficient conditions so that the diameter of\n$GPG(n,s)$ is equal to $d+1,$ and sufficient conditions so that the diameter of\n$GPG(n,s)$ is equal to $d+2.$ Afterwards, we give exact values for the diameter\nof $GPG(n,s)$ for almost all cases of $n$ and $s.$ Furthermore, we show that\nthere exists an algorithm computing the diameter of generalized Petersen graphs\nwith running time $O$(log$n$).\n"}
+{"abstract": "  In many astrophysical applications, the cost of solving a chemical network\nrepresented by a system of ordinary differential equations (ODEs) grows\nsignificantly with the size of the network, and can often represent a\nsignificant computational bottleneck, particularly in coupled chemo-dynamical\nmodels. Although standard numerical techniques and complex solutions tailored\nto thermochemistry can somewhat reduce the cost, more recently, machine\nlearning algorithms have begun to attack this challenge via data-driven\ndimensional reduction techniques. In this work, we present a new class of\nmethods that take advantage of machine learning techniques to reduce complex\ndata sets (autoencoders), the optimization of multi-parameter systems (standard\nbackpropagation), and the robustness of well-established ODE solvers to to\nexplicitly incorporate time-dependence. This new method allows us to find a\ncompressed and simplified version of a large chemical network in a\nsemi-automated fashion that can be solved with a standard ODE solver, while\nalso enabling interpretability of the compressed, latent network. As a proof of\nconcept, we tested the method on an astrophysically-relevant chemical network\nwith 29 species and 224 reactions, obtaining a reduced but representative\nnetwork with only 5 species and 12 reactions, and a x65 speed-up.\n"}
+{"abstract": "  Short Read Alignment Mapping Metrics (SRAMM): is an efficient and versatile\ncommand line tool providing additional short read mapping metrics, filtering,\nand graphs. Short read aligners report MAPing Quality (MAPQ), but these methods\ngenerally are neither standardized nor well described in literature or software\nmanuals. Additionally, third party mapping quality programs are typically\ncomputationally intensive or designed for specific applications. SRAMM\nefficiently generates multiple different concept-based mapping scores to\nprovide for an informative post alignment examination and filtering process of\naligned short reads for various downstream applications. SRAMM is compatible\nwith Python 2.6+ and Python 3.6+ on all operating systems. It works with any\nshort read aligner that generates SAM/BAM/CRAM file outputs and reports 'AS'\ntags. It is freely available under the MIT license at\nhttp://github.com/achon/sramm.\n"}
+{"abstract": "  We aim to give more insights on adiabatic evolution concerning the occurrence\nof anti-crossings and their link to the spectral minimum gap $\\Delta_{min}$. We\nstudy in detail adiabatic quantum computation applied to a specific\ncombinatorial problem called weighted max $k$-clique. A clear intuition of the\nparametrization introduced by V. Choi is given which explains why the\ncharacterization isn't general enough. We show that the instantaneous vectors\ninvolved in the anti-crossing vary brutally through it making the instantaneous\nground-state hard to follow during the evolution. This result leads to a\nrelaxation of the parametrization to be more general.\n"}
+{"abstract": "  A q-Levenberg-Marquardt method is an iterative procedure that blends a\nq-steepest descent and q-Gauss-Newton methods. When the current solution is far\nfrom the correct one the algorithm acts as the q-steepest descent method.\nOtherwise the algorithm acts as the q-Gauss-Newton method. A damping parameter\nis used to interpolate between these two methods. The q-parameter is used to\nescape from local minima and to speed up the search process near the optimal\nsolution.\n"}
+{"abstract": "  For a complete graph $K_n$ of order $n$, an edge-labeling $c:E(K_n)\\to \\{\n-1,1\\}$ satisfying $c(E(K_n))=0$, and a spanning forest $F$ of $K_n$, we\nconsider the problem to minimize $|c(E(F'))|$ over all isomorphic copies $F'$\nof $F$ in $K_n$. In particular, we ask under which additional conditions there\nis a zero-sum copy, that is, a copy $F'$ of $F$ with $c(E(F'))=0$.\n  We show that there is always a copy $F'$ of $F$ with $|c(E(F'))|\\leq\n\\Delta(F)+1$, where $\\Delta(F)$ is the maximum degree of $F$. We conjecture\nthat this bound can be improved to $|c(E(F'))|\\leq (\\Delta(F)-1)/2$ and verify\nthis for $F$ being the star $K_{1,n-1}$. Under some simple necessary\ndivisibility conditions, we show the existence of a zero-sum $P_3$-factor, and,\nfor sufficiently large $n$, also of a zero-sum $P_4$-factor.\n"}
+{"abstract": "  Deepfakes raised serious concerns on the authenticity of visual contents.\nPrior works revealed the possibility to disrupt deepfakes by adding adversarial\nperturbations to the source data, but we argue that the threat has not been\neliminated yet. This paper presents MagDR, a mask-guided detection and\nreconstruction pipeline for defending deepfakes from adversarial attacks. MagDR\nstarts with a detection module that defines a few criteria to judge the\nabnormality of the output of deepfakes, and then uses it to guide a learnable\nreconstruction procedure. Adaptive masks are extracted to capture the change in\nlocal facial regions. In experiments, MagDR defends three main tasks of\ndeepfakes, and the learned reconstruction pipeline transfers across input data,\nshowing promising performance in defending both black-box and white-box\nattacks.\n"}
+{"abstract": "  We propose a variational autoencoder architecture to model both ignorable and\nnonignorable missing data using pattern-set mixtures as proposed by Little\n(1993). Our model explicitly learns to cluster the missing data into\nmissingness pattern sets based on the observed data and missingness masks.\nUnderpinning our approach is the assumption that the data distribution under\nmissingness is probabilistically semi-supervised by samples from the observed\ndata distribution. Our setup trades off the characteristics of ignorable and\nnonignorable missingness and can thus be applied to data of both types. We\nevaluate our method on a wide range of data sets with different types of\nmissingness and achieve state-of-the-art imputation performance. Our model\noutperforms many common imputation algorithms, especially when the amount of\nmissing data is high and the missingness mechanism is nonignorable.\n"}
+{"abstract": "  In this paper, we study linear filters to process signals defined on\nsimplicial complexes, i.e., signals defined on nodes, edges, triangles, etc. of\na simplicial complex, thereby generalizing filtering operations for graph\nsignals. We propose a finite impulse response filter based on the Hodge\nLaplacian, and demonstrate how this filter can be designed to amplify or\nattenuate certain spectral components of simplicial signals. Specifically, we\ndiscuss how, unlike in the case of node signals, the Fourier transform in the\ncontext of edge signals can be understood in terms of two orthogonal subspaces\ncorresponding to the gradient-flow signals and curl-flow signals arising from\nthe Hodge decomposition. By assigning different filter coefficients to the\nassociated terms of the Hodge Laplacian, we develop a subspace-varying filter\nwhich enables more nuanced control over these signal types. Numerical\nexperiments are conducted to show the potential of simplicial filters for\nsub-component extraction, denoising and model approximation.\n"}
+{"abstract": "  In this paper we address the explainability of web search engines. We propose\ntwo explainable elements on the search engine result page: a visualization of\nquery term weights and a visualization of passage relevance. The idea is that\nsearch engines that indicate to the user why results are retrieved are valued\nhigher by users and gain user trust. We deduce the query term weights from the\nterm gating network in the Deep Relevance Matching Model (DRMM) and visualize\nthem as a doughnut chart. In addition, we train a passage-level ranker with\nDRMM that selects the most relevant passage from each document and shows it as\nsnippet on the result page. Next to the snippet we show a document thumbnail\nwith this passage highlighted. We evaluate the proposed interface in an online\nuser study, asking users to judge the explainability and assessability of the\ninterface. We found that users judge our proposed interface significantly more\nexplainable and easier to assess than a regular search engine result page.\nHowever, they are not significantly better in selecting the relevant documents\nfrom the top-5. This indicates that the explainability of the search engine\nresult page leads to a better user experience. Thus, we conclude that the\nproposed explainable elements are promising as visualization for search engine\nusers.\n"}
+{"abstract": "  Simulating time evolution of quantum systems is one of the most promising\napplications of quantum computing and also appears as a subroutine in many\napplications such as Green's function methods. In the current era of NISQ\nmachines we assess the state of algorithms for simulating time dynamics with\nlimited resources. We propose the Jaynes-Cummings model and extensions to it as\nuseful toy models to investigate time evolution algorithms on near-term quantum\ncomputers. Using these simple models, direct Trotterisation of the time\nevolution operator produces deep circuits, requiring coherence times out of\nreach on current NISQ hardware. Therefore we test two alternative responses to\nthis problem: variational compilation of the time evolution operator, and\nvariational quantum simulation of the wavefunction ansatz. We demonstrate\nnumerically to what extent these methods are successful in time evolving this\nsystem. The costs in terms of circuit depth and number of measurements are\ncompared quantitatively, along with other drawbacks and advantages of each\nmethod. We find that computational requirements for both methods make them\nsuitable for performing time evolution simulations of our models on NISQ\nhardware. Our results also indicate that variational quantum compilation\nproduces more accurate results than variational quantum simulation, at the cost\nof a larger number of measurements.\n"}
+{"abstract": "  In this paper we give a systematic review of the theory of Gibbs measures of\nPotts model on Cayley trees (developed since 2013) and discuss many\napplications of the Potts model to real world situations: mainly biology,\nphysics, and some examples of alloy behavior, cell sorting, financial\nengineering, flocking birds, flowing foams, image segmentation, medicine,\nsociology etc.\n"}
+{"abstract": "  We introduce a new class of commutative noetherian DG-rings which generalizes\nthe class of regular local rings. These are defined to be local DG-rings\n$(A,\\bar{\\mathfrak{m}})$ such that the maximal ideal $\\bar{\\mathfrak{m}}\n\\subseteq \\mathrm{H}^0(A)$ can be generated by an $A$-regular sequence. We call\nthese DG-rings sequence-regular DG-rings, and make a detailed study of them.\nUsing methods of Cohen-Macaulay differential graded algebra, we prove that the\nAuslander-Buchsbaum-Serre theorem about localization generalizes to this\nsetting. This allows us to define global sequence-regular DG-rings, and to\nintroduce this regularity condition to derived algebraic geometry. It is shown\nthat these DG-rings share many properties of classical regular local rings, and\nin particular we are able to construct canonical residue DG-fields in this\ncontext. Finally, we show that sequence-regular DG-rings are ubiquitous, and in\nparticular, any eventually coconnective derived algebraic variety over a\nperfect field is generically sequence-regular.\n"}
+{"abstract": "  Tissues are characterized by layers of functional units such as cells and\nextracellular matrix (ECM). Nevertheless, how dynamics at interlayer interfaces\nhelp transmit cellular forces in tissues remains overlooked. Here, we\ninvestigate a multi-layer system where a layer of epithelial cells is seeded\nupon an elastic substrate in contact with a hard surface. Our experiments show\nthat, upon a cell extrusion event in the cellular layer, long-range wave\npropagation emerges in the substrate only when the two substrate layers were\nweakly attached to each other. We then derive a theoretical model which\nquantitatively reproduces the wave dynamics and explains how frictional sliding\nbetween substrate layers helps propagate cellular forces at a variety of\nscales, depending on the stiffness, thickness, and slipperiness of the\nsubstrate. These results highlight the importance of interfacial friction\nbetween layers in transmitting mechanical cues in tissues in vivo.\n"}
+{"abstract": "  This paper proposes a differentiable robust LQR layer for reinforcement\nlearning and imitation learning under model uncertainty and stochastic\ndynamics. The robust LQR layer can exploit the advantages of robust optimal\ncontrol and model-free learning. It provides a new type of inductive bias for\nstochasticity and uncertainty modeling in control systems. In particular, we\npropose an efficient way to differentiate through a robust LQR optimization\nprogram by rewriting it as a convex program (i.e. semi-definite program) of the\nworst-case cost. Based on recent work on using convex optimization inside\nneural network layers, we develop a fully differentiable layer for optimizing\nthis worst-case cost, i.e. we compute the derivative of a performance measure\nw.r.t the model's unknown parameters, model uncertainty and stochasticity\nparameters. We demonstrate the proposed method on imitation learning and\napproximate dynamic programming on stochastic and uncertain domains. The\nexperiment results show that the proposed method can optimize robust policies\nunder uncertain situations, and are able to achieve a significantly better\nperformance than existing methods that do not model uncertainty directly.\n"}
+{"abstract": "  Recent work on graph generative models has made remarkable progress towards\ngenerating increasingly realistic graphs, as measured by global graph features\nsuch as degree distribution, density, and clustering coefficients. Deep\ngenerative models have also made significant advances through better modelling\nof the local correlations in the graph topology, which have been very useful\nfor predicting unobserved graph components, such as the existence of a link or\nthe class of a node, from nearby observed graph components. A complete\nscientific understanding of graph data should address both global and local\nstructure. In this paper, we propose a joint model for both as complementary\nobjectives in a graph VAE framework. Global structure is captured by\nincorporating graph kernels in a probabilistic model whose loss function is\nclosely related to the maximum mean discrepancy(MMD) between the global\nstructures of the reconstructed and the input graphs. The ELBO objective\nderived from the model regularizes a standard local link reconstruction term\nwith an MMD term. Our experiments demonstrate a significant improvement in the\nrealism of the generated graph structures, typically by 1-2 orders of magnitude\nof graph structure metrics, compared to leading graph VAEand GAN models. Local\nlink reconstruction improves as well in many cases.\n"}
+{"abstract": "  All solid state batteries are claimed to be the next-generation battery\nsystem, in view of their safety accompanied by high energy densities. A new\nadvanced, multiscale compatible, and fully three dimensional model for solid\nelectrolytes is presented in this note. The response of the electrolyte is\nprofoundly studied theoretically and numerically, analyzing the equilibrium and\nsteady state behaviors, the limiting factors, as well as the most relevant\nconstitutive parameters according to the sensitivity analysis of the model.\n"}
+{"abstract": "  Unmanned aerial vehicles serving as aerial base stations (UAV-BSs) can be\ndeployed to provide wireless connectivity to ground devices in events of\nincreased network demand, points-of-failure in existing infrastructure, or\ndisasters. However, it is challenging to conserve the energy of UAVs during\nprolonged coverage tasks, considering their limited on-board battery capacity.\nReinforcement learning-based (RL) approaches have been previously used to\nimprove energy utilization of multiple UAVs, however, a central cloud\ncontroller is assumed to have complete knowledge of the end-devices' locations,\ni.e., the controller periodically scans and sends updates for UAV\ndecision-making. This assumption is impractical in dynamic network environments\nwith UAVs serving mobile ground devices. To address this problem, we propose a\ndecentralized Q-learning approach, where each UAV-BS is equipped with an\nautonomous agent that maximizes the connectivity of mobile ground devices while\nimproving its energy utilization. Experimental results show that the proposed\ndesign significantly outperforms the centralized approaches in jointly\nmaximizing the number of connected ground devices and the energy utilization of\nthe UAV-BSs.\n"}
+{"abstract": "  One-shot voice conversion (VC), which performs conversion across arbitrary\nspeakers with only a single target-speaker utterance for reference, can be\neffectively achieved by speech representation disentanglement. Existing work\ngenerally ignores the correlation between different speech representations\nduring training, which causes leakage of content information into the speaker\nrepresentation and thus degrades VC performance. To alleviate this issue, we\nemploy vector quantization (VQ) for content encoding and introduce mutual\ninformation (MI) as the correlation metric during training, to achieve proper\ndisentanglement of content, speaker and pitch representations, by reducing\ntheir inter-dependencies in an unsupervised manner. Experimental results\nreflect the superiority of the proposed method in learning effective\ndisentangled speech representations for retaining source linguistic content and\nintonation variations, while capturing target speaker characteristics. In doing\nso, the proposed approach achieves higher speech naturalness and speaker\nsimilarity than current state-of-the-art one-shot VC systems. Our code,\npre-trained models and demo are available at\nhttps://github.com/Wendison/VQMIVC.\n"}
+{"abstract": "  One major impediment in rapidly deploying object detection models for\nindustrial applications is the lack of large annotated datasets. We currently\nhave presented the Sacked Carton Dataset(SCD) that contains carton images from\nthree scenarios, such as comprehensive pharmaceutical logistics company(CPLC),\ne-commerce logistics company(ECLC), fruit market(FM). However, due to domain\nshift, the model trained with one of the three scenarios in SCD has poor\ngeneralization ability when applied to the rest scenarios. To solve this\nproblem, a novel image synthesis method is proposed to replace the foreground\ntexture of the source datasets with the texture of the target datasets. Our\nmethod can keep the context relationship of foreground objects and backgrounds\nunchanged and greatly augment the target datasets. We firstly propose a surface\nsegmentation algorithm to achieve texture decoupling of each instance.\nSecondly, a contour reconstruction algorithm is proposed to keep the occlusion\nand truncation relationship of the instance unchanged. Finally, the Gaussian\nfusion algorithm is used to replace the foreground texture from the source\ndatasets with the texture from the target datasets. The novel image synthesis\nmethod can largely boost AP by at least 4.3%~6.5% on RetinaNet and 3.4%~6.8% on\nFaster R-CNN for the target domain. Code is available at\nhttps://github.com/hustgetlijun/RCAN.\n"}
+{"abstract": "  The Transient High Energy Sources and Early Universe Surveyor is an ESA M5\ncandidate mission currently in Phase A, with Launch in $\\sim$2032. The aim of\nthe mission is to complete a Gamma Ray Burst survey and monitor transient X-ray\nevents. The University of Leicester is the PI institute for the Soft X-ray\nInstrument (SXI), and is responsible for both the optic and detector\ndevelopment. The SXI consists of two wide field, lobster eye X-ray modules.\nEach module consists of 64 Micro Pore Optics (MPO) in an 8 by 8 array and 8\nCMOS detectors in each focal plane. The geometry of the MPOs comprises a square\npacked array of microscopic pores with a square cross-section, arranged over a\nspherical surface with a radius of curvature twice the focal length of the\noptic. Working in the photon energy range 0.3-5 keV, the optimum $L/d$ ratio\n(length of pore $L$ and pore width $d$) is upwards of 50 and is constant across\nthe whole optic aperture for the SXI. The performance goal for the SXI modules\nis an angular resolution of 4.5 arcmin, localisation accuracy of $\\sim$1 arcmin\nand employing an $L/d$ of 60. During the Phase A study, we are investigating\nmethods to improve the current performance and consistency of the MPOs, in\ncooperation with the manufacturer Photonis France SAS. We present the optics\ndesign of the THESEUS SXI modules and the programme of work designed to improve\nthe MPOs performance and the results from the study.\n"}
+{"abstract": "  Recent advances in the literature have demonstrated that standard supervised\nlearning algorithms are ill-suited for problems with endogenous explanatory\nvariables. To correct for the endogeneity bias, many variants of nonparameteric\ninstrumental variable regression methods have been developed. In this paper, we\npropose an alternative algorithm called boostIV that builds on the traditional\ngradient boosting algorithm and corrects for the endogeneity bias. The\nalgorithm is very intuitive and resembles an iterative version of the standard\n2SLS estimator. Moreover, our approach is data driven, meaning that the\nresearcher does not have to make a stance on neither the form of the target\nfunction approximation nor the choice of instruments. We demonstrate that our\nestimator is consistent under mild conditions. We carry out extensive Monte\nCarlo simulations to demonstrate the finite sample performance of our algorithm\ncompared to other recently developed methods. We show that boostIV is at worst\non par with the existing methods and on average significantly outperforms them.\n"}
+{"abstract": "  We report the interfacing of the Exciting-Plus (\"EP\") FLAPW DFT code with the\nSIRIUS multi-functional DFT library. Use of the SIRIUS library enhances EP with\nadditional task parallelism in ground state DFT calculations. Without\nsignificant change in the EP source code, the additional eigensystem solver\nmethod from the SIRIUS library can be exploited for performance gains in\ndiagonalizing the Kohn-Sham Hamiltonian. We benchmark the interfaced code\nagainst the original EP using small bulk systems, and then demonstrate\nperformance on much larger molecular magnet systems that are well beyond the\ncapability of the original EP code.\n"}
+{"abstract": "  Chimera states have attracted significant attention as symmetry-broken states\nexhibiting the unexpected coexistence of coherence and incoherence. Despite the\nvaluable insights gained from analyzing specific systems, an understanding of\nthe general physical mechanism underlying the emergence of chimeras is still\nlacking. Here, we show that many stable chimeras arise because coherence in\npart of the system is sustained by incoherence in the rest of the system. This\nmechanism may be regarded as a deterministic analog of noise-induced\nsynchronization and is shown to underlie the emergence of strong chimeras.\nThese are chimera states whose coherent domain is formed by identically\nsynchronized oscillators. Recognizing this mechanism offers a new meaning to\nthe interpretation that chimeras are a natural link between coherence and\nincoherence.\n"}
+{"abstract": "  We present a probabilistic 3D generative model, named Generative Cellular\nAutomata, which is able to produce diverse and high quality shapes. We\nformulate the shape generation process as sampling from the transition kernel\nof a Markov chain, where the sampling chain eventually evolves to the full\nshape of the learned distribution. The transition kernel employs the local\nupdate rules of cellular automata, effectively reducing the search space in a\nhigh-resolution 3D grid space by exploiting the connectivity and sparsity of 3D\nshapes. Our progressive generation only focuses on the sparse set of occupied\nvoxels and their neighborhood, thus enabling the utilization of an expressive\nsparse convolutional network. We propose an effective training scheme to obtain\nthe local homogeneous rule of generative cellular automata with sequences that\nare slightly different from the sampling chain but converge to the full shapes\nin the training data. Extensive experiments on probabilistic shape completion\nand shape generation demonstrate that our method achieves competitive\nperformance against recent methods.\n"}
+{"abstract": "  Theoretical models of a spin-polarized voltage probe (SPVP) tunnel-coupled to\nthe helical edge states (HES) of a quantum spin Hall system (QSHS) are studied.\nOur first model of the SPVP comprises $N_{P}$ spin-polarized modes (subprobes),\neach of which is locally tunnel-coupled to the HES, while the SPVP, as a whole,\nis subjected to a self-consistency condition ensuring zero average current on\nthe probe. We carry out a numerical analysis which shows that the optimal\nsituation for reading off spin-resolved voltage from the HES depends on the\ninterplay of the probe-edge tunnel-coupling and the number of modes in the\nprobe ($N_P$). We further investigate the stability of our findings by\nintroducing Gaussian fluctuations in {\\it{(i)}} the tunnel-coupling between the\nsubprobes and the HES about a chosen average value and {\\it{(ii)}}\nspin-polarization of the subprobes about a chosen direction of the net\npolarization of SPVP. We also perform a numerical analysis corresponding to the\nsituation where four such SPVPs are implemented in a self-consistent fashion\nacross a ferromagnetic barrier on the HES and demonstrate that this model\nfacilitates the measurements of spin-resolved four-probe voltage drops across\nthe ferromagnetic barrier. As a second model, we employ the edge state of a\nquantum anomalous Hall state (QAHS) as the SPVP which is tunnel-coupled over an\nextended region with the HES. A two-dimensional lattice simulation for the\nquantum transport of the proposed device setup comprising a junction of QSHS\nand QAHS is considered and a feasibility study of using the edge of the QAHS as\nan efficient spin-polarized voltage probe is carried out in presence of an\noptimal strength of the disorder.\n"}
+{"abstract": "  Planck data provide precise constraints on cosmological parameters when\nassuming the base $\\Lambda$CDM model, including a $0.17\\%$ measurement of the\nage of the Universe, $t_0=13.797 \\pm 0.023\\,{\\rm Gyr}$. However, the\npersistence of the \"Hubble tension\" calls the base $\\Lambda$CDM model's\ncompleteness into question and has spurred interest in models such as Early\nDark Energy (EDE) that modify the assumed expansion history of the Universe. We\ninvestigate the effect of EDE on the redshift-time relation $z \\leftrightarrow\nt$ and find that it differs from the base $\\Lambda$CDM model by at least\n${\\approx} 4\\%$ at all $t$ and $z$. As long as EDE remains observationally\nviable, any inferred $t \\leftarrow z$ or $z \\leftarrow t$ quoted to a higher\nlevel of precision do not reflect the current status of our understanding of\ncosmology. This uncertainty has important astrophysical implications: the\nreionization epoch - $10>z>6$ - corresponds to disjoint lookback time periods\nin the base $\\Lambda$CDM and EDE models, and the EDE value of $t_0=13.25 \\pm\n0.17~{\\rm Gyr}$ is in tension with published ages of some stars, star clusters,\nand ultra-faint dwarf galaxies. However, most published stellar ages do not\ninclude an uncertainty in accuracy (due to, e.g., uncertain distances and\nstellar physics) that is estimated to be $\\sim7-10\\%$, potentially reconciling\nstellar ages with $t_{0,\\rm EDE}$. We discuss how the big data era for stars is\nproviding extremely precise ages ($<1\\%$) and how improved distances and\ntreatment of stellar physics such as convection could result in ages accurate\nto $4-5\\%$, comparable to the current accuracy of $t \\leftrightarrow z$. Such\nprecise and accurate stellar ages can provide detailed insight into the\nhigh-redshift Universe independent of a cosmological model.\n"}
+{"abstract": "  We are concerned with random ordinary differential equations (RODEs). Our\nmain question of interest is how uncertainties in system parameters propagate\nthrough the possibly highly nonlinear dynamical system and affect the system's\nbifurcation behavior. We come up with a methodology to determine the\nprobability of the occurrence of different types of bifurcations (sub- vs\nsuper-critical) along a given bifurcation curve based on the probability\ndistribution of the input parameters. In a first step, we reduce the system's\nbehavior to the dynamics on its center manifold. We thereby still capture the\nmajor qualitative behavior of the RODEs. In a second step, we analyze the\nreduced RODEs and quantify the probability of the occurrence of different types\nof bifurcations based on the (nonlinear) functional appearance of uncertain\nparameters. To realize this major step, we present three approaches: an\nanalytical one, where the probability can be calculated explicitly based on\nMellin transformation and inversion, a semi-analytical one consisting of a\ncombination of the analytical approach with a moment-based numerical estimation\nprocedure, and a particular sampling-based approach using unscented\ntransformation. We complement our new methodology with various numerical\nexamples.\n"}
+{"abstract": "  The \"Subset Sum problem\" is a very well-known NP-complete problem. In this\nwork, a top-k variation of the \"Subset Sum problem\" is considered. This problem\nhas wide application in recommendation systems, where instead of k best objects\nthe k best subsets of objects with the lowest (or highest) overall scores are\nrequired. Given an input set R of n real numbers and a positive integer k, our\ntarget is to generate the k best subsets of R such that the sum of their\nelements is minimized. Our solution methodology is based on constructing a\nmetadata structure G for a given n. Each node of G stores a bit vector of size\nn from which a subset of R can be retrieved. Here it is shown that the\nconstruction of the whole graph G is not needed. To answer a query, only\nimplicit traversal of the required portion of G on demand is sufficient, which\nobviously gets rid of the preprocessing step, thereby reducing the overall time\nand space requirement. A modified algorithm is then proposed to generate each\nsubset incrementally, where it is shown that it is possible to do away with the\nexplicit storage of the bit vector. This not only improves the space\nrequirement but also improves the asymptotic time complexity. Finally, a\nvariation of our algorithm that reports only the top-k subset sums has been\ncompared with an existing algorithm, which shows that our algorithm performs\nbetter both in terms of time and space requirement by a constant factor.\n"}
+{"abstract": "  Attention mechanism enables the Graph Neural Networks(GNNs) to learn the\nattention weights between the target node and its one-hop neighbors, the\nperformance is further improved. However, the most existing GNNs are oriented\nto homogeneous graphs and each layer can only aggregate the information of\none-hop neighbors. Stacking multi-layer networks will introduce a lot of noise\nand easily lead to over smoothing. We propose a Multi-hop Heterogeneous\nNeighborhood information Fusion graph representation learning method (MHNF).\nSpecifically, we first propose a hybrid metapath autonomous extraction model to\nefficiently extract multi-hop hybrid neighbors. Then, we propose a hop-level\nheterogeneous Information aggregation model, which selectively aggregates\ndifferent-hop neighborhood information within the same hybrid metapath.\nFinally, a hierarchical semantic attention fusion model (HSAF) is proposed,\nwhich can efficiently integrate different-hop and different-path neighborhood\ninformation respectively. This paper can solve the problem of aggregating the\nmulti-hop neighborhood information and can learn hybrid metapaths for target\ntask, reducing the limitation of manually specifying metapaths. In addition,\nHSAF can extract the internal node information of the metapaths and better\nintegrate the semantic information of different levels. Experimental results on\nreal datasets show that MHNF is superior to state-of-the-art methods in node\nclassification and clustering tasks (10.94% - 69.09% and 11.58% - 394.93%\nrelative improvement on average, respectively).\n"}
+{"abstract": "  We present a calculation of the up, down, strange and charm quark masses\nperformed within the lattice QCD framework. We use the twisted mass fermion\naction and carry out simulations that include in the sea two light\nmass-degenerate quarks, as well as the strange and charm quarks. In the\nanalysis we use gauge ensembles simulated at three values of the lattice\nspacing and with light quarks that correspond to pion masses in the range from\n350 MeV to the physical value, while the strange and charm quark masses are\ntuned approximately to their physical values. We use several quantities to set\nthe scale in order to check for finite lattice spacing effects and in the\ncontinuum limit we get compatible results. The quark mass renormalization is\ncarried out non-perturbatively using the RI'-MOM method converted into the\n$\\overline{\\rm MS}$ scheme. For the determination of the quark masses we use\nphysical observables from both the meson and the baryon sectors, obtaining\n$m_{ud} = 3.636(66)(^{+60}_{-57})$~MeV and $m_s =\n98.7(2.4)(^{+4.0}_{-3.2})$~MeV in the $\\overline{\\rm MS}(2\\,{\\rm GeV})$ scheme\nand $m_c = 1036(17)(^{+15}_{-8})$~MeV in the $\\overline{\\rm MS}(3\\,{\\rm GeV})$\nscheme, where the first errors are statistical and the second ones are\ncombinations of systematic errors. For the quark mass ratios we get $m_s /\nm_{ud} = 27.17(32)(^{+56}_{-38})$ and $m_c / m_s = 11.48(12)(^{+25}_{-19})$.\n"}
+{"abstract": "  Coupled flow-induced flapping dynamics of flexible plates are governed by\nthree non-dimensional numbers: Reynolds number, mass-ratio, and non-dimensional\nflexural rigidity. The traditional definition of these parameters is limited to\nisotropic single-layered flexible plates. There is a need to define these\nparameters for a more generic plate made of multiple isotropic layers placed on\ntop of each other. In this work, we derive the non-dimensional parameters for a\nflexible plate of $n$-isotropic layers and validate the non-dimensional\nparameters with the aid of numerical simulations.\n"}
+{"abstract": "  To help agents reason about scenes in terms of their building blocks, we wish\nto extract the compositional structure of any given scene (in particular, the\nconfiguration and characteristics of objects comprising the scene). This\nproblem is especially difficult when scene structure needs to be inferred while\nalso estimating the agent's location/viewpoint, as the two variables jointly\ngive rise to the agent's observations. We present an unsupervised variational\napproach to this problem. Leveraging the shared structure that exists across\ndifferent scenes, our model learns to infer two sets of latent representations\nfrom RGB video input alone: a set of \"object\" latents, corresponding to the\ntime-invariant, object-level contents of the scene, as well as a set of \"frame\"\nlatents, corresponding to global time-varying elements such as viewpoint. This\nfactorization of latents allows our model, SIMONe, to represent object\nattributes in an allocentric manner which does not depend on viewpoint.\nMoreover, it allows us to disentangle object dynamics and summarize their\ntrajectories as time-abstracted, view-invariant, per-object properties. We\ndemonstrate these capabilities, as well as the model's performance in terms of\nview synthesis and instance segmentation, across three procedurally generated\nvideo datasets.\n"}
+{"abstract": "  In recent years, the use of sophisticated statistical models that influence\ndecisions in domains of high societal relevance is on the rise. Although these\nmodels can often bring substantial improvements in the accuracy and efficiency\nof organizations, many governments, institutions, and companies are reluctant\nto their adoption as their output is often difficult to explain in\nhuman-interpretable ways. Hence, these models are often regarded as\nblack-boxes, in the sense that their internal mechanisms can be opaque to human\naudit. In real-world applications, particularly in domains where decisions can\nhave a sensitive impact--e.g., criminal justice, estimating credit scores,\ninsurance risk, health risks, etc.--model interpretability is desired.\nRecently, the academic literature has proposed a substantial amount of methods\nfor providing interpretable explanations to machine learning models. This\nsurvey reviews the most relevant and novel methods that form the\nstate-of-the-art for addressing the particular problem of explaining individual\ninstances in machine learning. It seeks to provide a succinct review that can\nguide data science and machine learning practitioners in the search for\nappropriate methods to their problem domain.\n"}
+{"abstract": "  The Kolkata Paise Restaurant Problem is a challenging game, in which $n$\nagents must decide where to have lunch during their lunch break. The game is\nvery interesting because there are exactly $n$ restaurants and each restaurant\ncan accommodate only one agent. If two or more agents happen to choose the same\nrestaurant, only one gets served and the others have to return back to work\nhungry. In this paper we tackle this problem from an entirely new angle. We\nabolish certain implicit assumptions, which allows us to propose a novel\nstrategy that results in greater utilization for the restaurants. We emphasize\nthe spatially distributed nature of our approach, which, for the first time,\nperceives the locations of the restaurants as uniformly distributed in the\nentire city area. This critical change in perspective has profound\nramifications in the topological layout of the restaurants, which now makes it\ncompletely realistic to assume that every agent has a second chance. Every\nagent now may visit, in case of failure, more than one restaurants, within the\npredefined time constraints.\n"}
+{"abstract": "  A graph G is said to be orderenergetic, if its energy equal to its order and\nit is said to be hypoenergetic if its energy less than its order. Two\nnon-isomorphic graphs of same order are said to be equienergetic if their\nenergies are equal. In this paper, we construct some new families of\norderenergetic graphs, hypoenergetic graphs, equienergetic graphs,\nequiorderenergetic graphs and equihypoenergetic graphs.\n"}
+{"abstract": "  In order to get $\\lambda$-models with a rich structure of $\\infty$-groupoid,\nwhich we call \"homotopy $\\lambda$-models\", a general technique is described for\nsolving domain equations on any cartesian closed $\\infty$-category (c.c.i.)\nwith enough points. Finally, the technique is applied in a particular c.c.i.,\nwhere some examples of homotopy $\\lambda$-models are given.\n"}
+{"abstract": "  The central engines of Active Galactic Nuclei (AGNs) are powered by accreting\nsupermassive black holes, and while AGNs are known to play an important role in\ngalaxy evolution, the key physical processes occur on scales that are too small\nto be resolved spatially (aside from a few exceptional cases). Reverberation\nmapping is a powerful technique that overcomes this limitation by using echoes\nof light to determine the geometry and kinematics of the central regions.\nVariable ionizing radiation from close to the black hole drives correlated\nvariability in surrounding gas/dust, but with a time delay due to the light\ntravel time between the regions, allowing reverberation mapping to effectively\nreplace spatial resolution with time resolution. Reverberation mapping is used\nto measure black hole masses and to probe the innermost X-ray emitting region,\nthe UV/optical accretion disk, the broad emission line region and the dusty\ntorus. In this article we provide an overview of the technique and its varied\napplications.\n"}
+{"abstract": "  In this paper, we study the Feldman-Katok metric. We give entropy formulas by\nreplacing Bowen metric with Feldman-Katok metric. Some relative topics are also\ndiscussed.\n"}
+{"abstract": "  We propose two systematic constructions of deletion-correcting codes for\nprotecting quantum information. The first one works with qudits of any\ndimension, but only one deletion is corrected and the constructed codes are\nasymptotically bad. The second one corrects multiple deletions and can\nconstruct asymptotically good codes. The second one also allows conversion of\nstabilizer-based quantum codes to deletion-correcting codes, and entanglement\nassistance.\n"}
+{"abstract": "  Resistive random access memories are promising for non-volatile memory and\nbrain-inspired computing applications. High variability and low yield of these\ndevices are key drawbacks hindering reliable training of physical neural\nnetworks. In this study, we show that doping an oxide electrolyte, Al2O3, with\nelectronegative metals makes resistive switching significantly more\nreproducible, surpassing the reproducibility requirements for obtaining\nreliable hardware neuromorphic circuits. The underlying mechanism is the ease\nof creating oxygen vacancies in the vicinity of electronegative dopants, due to\nthe capture of the associated electrons by dopant mid-gap states, and the\nweakening of Al-O bonds. These oxygen vacancies and vacancy clusters also bind\nsignificantly to the dopant, thereby serving as preferential sites and building\nblocks in the formation of conducting paths. We validate this theory\nexperimentally by implanting multiple dopants over a range of\nelectronegativities, and find superior repeatability and yield with highly\nelectronegative metals, Au, Pt and Pd. These devices also exhibit a gradual SET\ntransition, enabling multibit switching that is desirable for analog computing.\n"}
+{"abstract": "  Characterizing the privacy degradation over compositions, i.e., privacy\naccounting, is a fundamental topic in differential privacy (DP) with many\napplications to differentially private machine learning and federated learning.\nWe propose a unification of recent advances (Renyi DP, privacy profiles, $f$-DP\nand the PLD formalism) via the \\emph{characteristic function} ($\\phi$-function)\nof a certain \\emph{dominating} privacy loss random variable. We show that our\napproach allows \\emph{natural} adaptive composition like Renyi DP, provides\n\\emph{exactly tight} privacy accounting like PLD, and can be (often\n\\emph{losslessly}) converted to privacy profile and $f$-DP, thus providing\n$(\\epsilon,\\delta)$-DP guarantees and interpretable tradeoff functions.\nAlgorithmically, we propose an \\emph{analytical Fourier accountant} that\nrepresents the \\emph{complex} logarithm of $\\phi$-functions symbolically and\nuses Gaussian quadrature for numerical computation. On several popular DP\nmechanisms and their subsampled counterparts, we demonstrate the flexibility\nand tightness of our approach in theory and experiments.\n"}
+{"abstract": "  The aim of this work is to determine abundances of neutron-capture elements\nfor thin- and thick-disc F, G, and K stars in several sky fields near the north\necliptic pole and to compare the results with the Galactic chemical evolution\nmodels, to explore elemental gradients according to stellar ages, mean\ngalactocentric distances, and maximum heights above the Galactic plane. The\nobservational data were obtained with the 1.65m telescope at the Moletai\nAstronomical Observatory and a fibre-fed high-resolution spectrograph.\nElemental abundances were determined using a differential spectrum synthesis\nwith the MARCS stellar model atmospheres and accounting for the\nhyperfine-structure effects. We determined abundances of Sr, Y, Zr, Ba, La, Ce,\nPr, Nd, Sm, and Eu for 424 thin- and 82 thick-disc stars. The sample of\nthick-disc stars shows a clearly visible decrease in [Eu/Mg] with increasing\n[Fe/H] compared to the thin-disc stars, bringing more evidence of a different\nchemical evolution in these two Galactic components. Abundance correlation with\nage slopes for the investigated thin-disc stars are slightly negative for the\nmajority of s-process dominated elements, while r-process dominated elements\nhave positive correlations. Our sample of thin-disc stars with ages spanning\nfrom 0.1 to 9 Gyrs give the [Y/Mg]=0.022 ($\\pm$0.015)-0.027 ($\\pm$0.003)*age\n[Gyr] relation. For the thick-disc stars, when we also took data from other\nstudies into account, we found that [Y/Mg] cannot serve as an age indicator.\nThe radial [El/Fe] gradients in the thin disc are negligible for the s-process\ndominated elements and become positive for the r-process dominated elements.\nThe vertical gradients are negative for the light s-process dominated elements\nand become positive for the r-process dominated elements. In the thick disc,\nthe radial [El/Fe] slopes are negligible, and the vertical slopes are\npredominantly negative.\n"}
+{"abstract": "  Independent cascade (IC) model is a widely used influence propagation model\nfor social networks. In this paper, we incorporate the concept and techniques\nfrom causal inference to study the identifiability of parameters from\nobservational data in extended IC model with unobserved confounding factors,\nwhich models more realistic propagation scenarios but is rarely studied in\ninfluence propagation modeling before. We provide the conditions for the\nidentifiability or unidentifiability of parameters for several special\nstructures including the Markovian IC model, semi-Markovian IC model, and IC\nmodel with a global unobserved variable. Parameter identifiability is important\nfor other tasks such as influence maximization under the diffusion networks\nwith unobserved confounding factors.\n"}
+{"abstract": "  We investigate the reasons for the performance degradation incurred with\nbatch-independent normalization. We find that the prototypical techniques of\nlayer normalization and instance normalization both induce the appearance of\nfailure modes in the neural network's pre-activations: (i) layer normalization\ninduces a collapse towards channel-wise constant functions; (ii) instance\nnormalization induces a lack of variability in instance statistics, symptomatic\nof an alteration of the expressivity. To alleviate failure mode (i) without\naggravating failure mode (ii), we introduce the technique \"Proxy Normalization\"\nthat normalizes post-activations using a proxy distribution. When combined with\nlayer normalization or group normalization, this batch-independent\nnormalization emulates batch normalization's behavior and consistently matches\nor exceeds its performance.\n"}
+{"abstract": "  We propose a family of lossy integer compressions for Stochastic Gradient\nDescent (SGD) that do not communicate a single float. This is achieved by\nmultiplying floating-point vectors with a number known to every device and then\nrounding to an integer number. Our theory shows that the iteration complexity\nof SGD does not change up to constant factors when the vectors are scaled\nproperly. Moreover, this holds for both convex and non-convex functions, with\nand without overparameterization. In contrast to other compression-based\nalgorithms, ours preserves the convergence rate of SGD even on non-smooth\nproblems. Finally, we show that when the data is significantly heterogeneous,\nit may become increasingly hard to keep the integers bounded and propose an\nalternative algorithm, IntDIANA, to solve this type of problems.\n"}
+{"abstract": "  Intelligent reflecting surface (IRS) has emerged as a competitive solution to\naddress blockage issues in millimeter wave (mmWave) and Terahertz (THz)\ncommunications due to its capability of reshaping wireless transmission\nenvironments. Nevertheless, obtaining the channel state information of\nIRS-assisted systems is quite challenging because of the passive\ncharacteristics of the IRS. In this paper, we consider the problem of beam\ntraining/alignment for IRS-assisted downlink mmWave/THz systems, where a\nmulti-antenna base station (BS) with a hybrid structure serves a single-antenna\nuser aided by IRS. By exploiting the inherent sparse structure of the\nBS-IRS-user cascade channel, the beam training problem is formulated as a joint\nsparse sensing and phaseless estimation problem, which involves devising a\nsparse sensing matrix and developing an efficient estimation algorithm to\nidentify the best beam alignment from compressive phaseless measurements.\nTheoretical analysis reveals that the proposed method can identify the best\nalignment with only a modest amount of training overhead. Simulation results\nshow that, for both line-of-sight (LOS) and NLOS scenarios, the proposed method\nobtains a significant performance improvement over existing state-of-art\nmethods. Notably, it can achieve performance close to that of the exhaustive\nbeam search scheme, while reducing the training overhead by 95%.\n"}
+{"abstract": "  A single-hop beeping network is a distributed communication model in which\nall stations can communicate with one another by transmitting only one-bit\nmessages, called beeps. This paper focuses on resolving the distributed\ncomputing area's two fundamental problems: naming and counting problems. We are\nparticularly interested in optimizing the energy complexity and the running\ntime of algorithms to resolve these problems. Our contribution is to design\nrandomized algorithms with an optimal running time of O(n log n) and an energy\ncomplexity of O(log n) for both the naming and counting problems on single-hop\nbeeping networks of n stations.\n"}
+{"abstract": "  We initiate the study of dark matter models based on a gapped continuum. Dark\nmatter consists of a mixture of states with a continuous mass distribution,\nwhich evolves as the universe expands. We present an effective field theory\ndescribing the gapped continuum, outline the structure of the Hilbert space and\nshow how to deal with the thermodynamics of such a system. This formalism\nenables us to study the cosmological evolution and phenomenology of gapped\ncontinuum DM in detail. As a concrete example, we consider a weakly-interacting\ncontinuum (WIC) model, a gapped continuum counterpart of the familiar WIMP. The\nDM interacts with the SM via a Z-portal. The model successfully reproduces the\nobserved relic density, while direct detection constraints are avoided due to\nthe effect of continuum kinematics. The model has striking observational\nconsequences, including continuous decays of DM states throughout cosmological\nhistory, as well as cascade decays of DM states produced at colliders. We also\ndescribe how the WIC theory can arise from a local, unitary scalar QFT\npropagating on a five-dimensional warped background with a soft wall.\n"}
+{"abstract": "  In this paper we discuss the computation of Casimir energy on a quantum\ncomputer. The Casimir energy is an ideal quantity to calculate on a quantum\ncomputer as near term hybrid classical quantum algorithms exist to calculate\nthe ground state energy and the Casimir energy gives physical implications for\nthis quantity in a variety of settings. Depending on boundary conditions and\nwhether the field is bosonic or fermionic we illustrate how the Casimir energy\ncalculation can be set up on a quantum computer and calculated using the\nVariational Quantum Eigensolver algorithm with IBM QISKit. We compare the\nresults based on a lattice regularization with a finite number of qubits with\nthe continuum calculation for free boson fields, free fermion fields and chiral\nfermion fields. We use a regularization method introduced by Bergman and Thorn\nto compute the Casimir energy of a chiral fermion. We show how the accuracy of\nthe calculation varies with the number of qubits. We show how the number of\nPauli terms which are used to represent the Hamiltonian on a quantum computer\nscales with the number of qubits. We discuss the application of the Casimir\ncalculations on quantum computers to cosmology, nanomaterials, string models,\nKaluza Klein models and dark energy.\n"}
+{"abstract": "  We study the average number $\\mathcal{A}(G)$ of colors in the non-equivalent\ncolorings of a graph $G$. We show some general properties of this graph\ninvariant and determine its value for some classes of graphs. We then\nconjecture several lower bounds on $\\mathcal{A}(G)$ and prove that these\nconjectures are true for specific classes of graphs such as triangulated graphs\nand graphs with maximum degree at most 2.\n"}
+{"abstract": "  Strong evidence suggests that transformative correlated electron behavior may\nexist only in unrealized clean-limit 2D materials such as 1T-TaS2.\nUnfortunately, experiment and theory suggest that extrinsic disorder in free\nstanding 2D layers impedes correlation-driven quantum behavior. Here we\ndemonstrate a new route to realizing fragile 2D quantum states through\nepitaxial polytype engineering of van der Waals materials. The isolation of\ntruly 2D charge density waves (CDWs) between metallic layers stabilizes\ncommensurate long-range order and lifts the coupling between neighboring CDW\nlayers to restore mirror symmetries via interlayer CDW twinning. The\ntwinned-commensurate charge density wave (tC-CDW) reported herein has a single\nmetal-insulator phase transition at ~350 K as measured structurally and\nelectronically. Fast in-situ transmission electron microscopy and scanned\nnanobeam diffraction map the formation of tC-CDWs. This work introduces\nepitaxial polytype engineering of van der Waals materials to access latent 2D\nground states distinct from conventional 2D fabrication.\n"}
+{"abstract": "  We find a minimal set of generators for the coordinate ring of Calogero-Moser\nspace $\\mathcal{C}_3$ and the algebraic relations among them explicitly. We\ngive a new presentation for the algebra of $3\\times3$ invariant matrices\ninvolving the defining relations of $\\mathbb{C}[\\mathcal{C}_3]$. We find an\nexplicit description of the commuting variety of $3\\times3$ matrices and its\norbits under the action of the affine Cremona group.\n"}
+{"abstract": "  Ranking has always been one of the top concerns in information retrieval\nresearches. For decades, the lexical matching signal has dominated the ad-hoc\nretrieval process, but solely using this signal in retrieval may cause the\nvocabulary mismatch problem. In recent years, with the development of\nrepresentation learning techniques, many researchers turn to Dense Retrieval\n(DR) models for better ranking performance. Although several existing DR models\nhave already obtained promising results, their performance improvement heavily\nrelies on the sampling of training examples. Many effective sampling strategies\nare not efficient enough for practical usage, and for most of them, there still\nlacks theoretical analysis in how and why performance improvement happens. To\nshed light on these research questions, we theoretically investigate different\ntraining strategies for DR models and try to explain why hard negative sampling\nperforms better than random sampling. Through the analysis, we also find that\nthere are many potential risks in static hard negative sampling, which is\nemployed by many existing training methods. Therefore, we propose two training\nstrategies named a Stable Training Algorithm for dense Retrieval (STAR) and a\nquery-side training Algorithm for Directly Optimizing Ranking pErformance\n(ADORE), respectively. STAR improves the stability of DR training process by\nintroducing random negatives. ADORE replaces the widely-adopted static hard\nnegative sampling method with a dynamic one to directly optimize the ranking\nperformance. Experimental results on two publicly available retrieval benchmark\ndatasets show that either strategy gains significant improvements over existing\ncompetitive baselines and a combination of them leads to the best performance.\n"}
+{"abstract": "  The ability to reliably prepare non-classical states will play a major role\nin the realization of quantum technology. NOON states, belonging to the class\nof Schroedinger cat states, have emerged as a leading candidate for several\napplications. Starting from a model of dipolar bosons confined to a closed\ncircuit of four sites, we show how to generate NOON states. This is achieved by\ndesigning protocols to transform initial Fock states to NOON states through use\nof time evolution, application of an external field, and local projective\nmeasurements. By variation of the external field strength, we demonstrate how\nthe system can be controlled to encode a phase into a NOON state. We also\ndiscuss the physical feasibility, via an optical lattice setup. Our proposal\nilluminates the benefits of quantum integrable systems in the design of\natomtronic protocols.\n"}
+{"abstract": "  Elastic similarity measures are a class of similarity measures specifically\ndesigned to work with time series data. When scoring the similarity between two\ntime series, they allow points that do not correspond in timestamps to be\naligned. This can compensate for misalignments in the time axis of time series\ndata, and for similar processes that proceed at variable and differing paces.\nElastic similarity measures are widely used in machine learning tasks such as\nclassification, clustering and outlier detection when using time series data.\n  There is a multitude of research on various univariate elastic similarity\nmeasures. However, except for multivariate versions of the well known Dynamic\nTime Warping (DTW) there is a lack of work to generalise other similarity\nmeasures for multivariate cases. This paper adapts two existing strategies used\nin multivariate DTW, namely, Independent and Dependent DTW, to several commonly\nused elastic similarity measures.\n  Using 23 datasets from the University of East Anglia (UEA) multivariate\narchive, for nearest neighbour classification, we demonstrate that each measure\noutperforms all others on at least one dataset and that there are datasets for\nwhich either the dependent versions of all measures are more accurate than\ntheir independent counterparts or vice versa. This latter finding suggests that\nthese differences arise from a fundamental property of the data. We also show\nthat an ensemble of such nearest neighbour classifiers is highly competitive\nwith other state-of-the-art multivariate time series classifiers.\n"}
+{"abstract": "  We report the enhanced superconducting properties of double-chain based\nsuperconductor Pr$_{2}$Ba$_{4}$Cu$_{7}$O$_{15-\\delta}$ synthesized by the\ncitrate pyrolysis technique.\n  %In spite of the polycrystalline bulk samples, we obtained the higher\nresidual resistivity ratios (10-12).\n  The reduction heat treatment in vacuum results in the appearance of\nsuperconducting state with $T_\\mathrm{c}$=22-24 K, accompanied by the higher\nresidual resistivity ratios. The superconducting volume fractions are estimated\nfrom the ZFC data to be 50$\\sim55\\%$, indicating the bulk superconductivity. We\nevaluate from the magneto-transport data the temperature dependence of the\nsuperconducting critical field, to establish the superconducting phase diagram.\nThe upper critical magnetic field is estimated to be about 35 T at low\ntemperatures from the resistive transition data using the\nWerthamer-Helfand-Hohenberg formula. The Hall coefficient $R_{H}$ of the\n48-h-reduced superconducting sample is determined to be -0.5$\\times10^{-3}$\ncm$^{3}$/C at 30 K, suggesting higher electron concentration. These findings\nhave a close relationship with homogeneous distributions of the superconducting\ngrains and improved weak links between their superconducting grains in the\npresent synthesis process.\n"}
+{"abstract": "  Automatic speech recognition (ASR) models are typically designed to operate\non a single input data type, e.g. a single or multi-channel audio streamed from\na device. This design decision assumes the primary input data source does not\nchange and if an additional (auxiliary) data source is occasionally available,\nit cannot be used. An ASR model that operates on both primary and auxiliary\ndata can achieve better accuracy compared to a primary-only solution; and a\nmodel that can serve both primary-only (PO) and primary-plus-auxiliary (PPA)\nmodes is highly desirable. In this work, we propose a unified ASR model that\ncan serve both modes. We demonstrate its efficacy in a realistic scenario where\na set of devices typically stream a single primary audio channel, and two\nadditional auxiliary channels only when upload bandwidth allows it. The\narchitecture enables a unique methodology that uses both types of input audio\nduring training time. Our proposed approach achieves up to 12.5% relative\nword-error-rate reduction (WERR) compared to a PO baseline, and up to 16.0%\nrelative WERR in low-SNR conditions. The unique training methodology achieves\nup to 2.5% relative WERR compared to a PPA baseline.\n"}
+{"abstract": "  The magnetic dipole moments of the $Z_{c}(4020)^+$, $Z_{c}(4200)^+$,\n$Z_{cs}(4000)^{+}$ and $Z_{cs}(4220)^{+}$ states are extracted in the framework\nof the light-cone QCD sum rules. In the calculations, we use the hadronic\nmolecular form of interpolating currents, and photon distribution amplitudes to\nget the magnetic dipole moment of $Z_{c}(4020)^+$, $Z_{c}(4200)^+$,\n$Z_{cs}(4000)^{+}$ and $Z_{cs}(4220)^{+}$ tetraquark states. The magnetic\ndipole moments are obtained as $\\mu_{Z_{c}} = 0.66^{+0.27}_{-0.25}$,\n$\\mu_{Z^{1}_{c}}=1.03^{+0.32}_{-0.29}$, $\\mu_{Z_{cs}}=0.73^{+0.28}_{-0.26}$,\n$\\mu_{Z^1_{cs}}=0.77^{+0.27}_{-0.25}$ for the $Z_{c}(4020)^+$, $Z_{c}(4200)^+$,\n$Z_{cs}(4000)^{+}$ and $Z_{cs}(4220)^{+}$ states, respectively. We observe that\nthe results obtained for the $Z_{c}(4020)^+$, $Z_{c}(4200)^+$,\n$Z_{cs}(4000)^{+}$ and $Z_{cs}(4220)^{+}$ states are large enough to be\nmeasured experimentally. As a by product, we predict the magnetic dipole\nmoments of the neutral $Z_{cs}(4000)$ and $Z_{cs}(4220)$ states. The results\npresented here can serve to be helpful knowledge in experimental as well as\ntheoretical studies of the properties of hidden-charm tetraquark states with\nand without strangeness.\n"}
+{"abstract": "  Randomized Controlled Trials (RCTs) are often considered as the gold standard\nto conclude on the causal effect of a given intervention on an outcome, but\nthey may lack of external validity when the population eligible to the RCT is\nsubstantially different from the target population. Having at hand a sample of\nthe target population of interest allows to generalize the causal effect.\nIdentifying this target population treatment effect needs covariates in both\nsets to capture all treatment effect modifiers that are shifted between the two\nsets. However such covariates are often not available in both sets. Standard\nestimators then use either weighting (IPSW), outcome modeling (G-formula), or\ncombine the two in doubly robust approaches (AIPSW). In this paper, after\ncompleting existing proofs on the complete case consistency of those three\nestimators, we compute the expected bias induced by a missing covariate,\nassuming a Gaussian distribution and a semi-parametric linear model. This\nenables sensitivity analysis for each missing covariate pattern, giving the\nsign of the expected bias. We also show that there is no gain in imputing a\npartially-unobserved covariate. Finally we study the replacement of a missing\ncovariate by a proxy. We illustrate all these results on simulations, as well\nas semi-synthetic benchmarks using data from the Tennessee Student/Teacher\nAchievement Ratio (STAR), and with a real-world example from critical care\nmedicine.\n"}
+{"abstract": "  The present study shows how any De Morgan algebra may be enriched by a\n'perfection operator' that allows one to express the Boolean properties of\nnegation-consistency and negation-determinedness. The corresponding variety of\n'perfect paradefinite algebras' (PP-algebras) is shown to be term-equivalent to\nthe variety of involutive Stone algebras, introduced by R. Cignoli and M.\nSagastume, and more recently studied from a logical perspective by M. Figallo\nand L. Cant\\'u. Such equivalence then plays an important role in the\ninvestigation of the 1-assertional logic and also the order-preserving logic\nasssociated to the PP-algebras. The latter logic, which we call PP$\\leq$,\nhappens to be characterised by a single 6-valued matrix and consists very\nnaturally in a Logic of Formal Inconsistency and Formal Undeterminedness. The\nlogic PP$\\leq$ is here axiomatised, by means of an analytic finite\nHilbert-style calculus, and a related axiomatization procedure is presented\nthat covers the logics of other classes of De Morgan algebras as well as\nsuper-Belnap logics enriched by a perfection connective.\n"}
+{"abstract": "  This work uses genetic programming to explore the space of continuous\noptimisers, with the goal of discovering novel ways of doing optimisation. In\norder to keep the search space broad, the optimisers are evolved from scratch\nusing Push, a Turing-complete, general-purpose, language. The resulting\noptimisers are found to be diverse, and explore their optimisation landscapes\nusing a variety of interesting, and sometimes unusual, strategies.\nSignificantly, when applied to problems that were not seen during training,\nmany of the evolved optimisers generalise well, and often outperform existing\noptimisers. This supports the idea that novel and effective forms of\noptimisation can be discovered in an automated manner. This paper also shows\nthat pools of evolved optimisers can be hybridised to further increase their\ngenerality, leading to optimisers that perform robustly over a broad variety of\nproblem types and sizes.\n"}
+{"abstract": "  This article is concerned with the global exact controllability for ideal\nincompressible magnetohydrodynamics in a rectangular domain where the controls\nare situated in both vertical walls. First, global exact controllability via\nboundary controls is established for a related Els\\\"asser type system by\napplying the return method, introduced in [Coron J.M., Math. Control Signals\nSystems, 5(3) (1992) 295--312]. Similar results are then inferred for the\noriginal magnetohydrodynamics system with the help of a special pressure-like\ncorrector in the induction equation. Overall, the main difficulties stem from\nthe nonlinear coupling between the fluid velocity and the magnetic field in\ncombination with the aim of exactly controlling the system. In order to\novercome some of the obstacles, we introduce ad-hoc constructions, such as\nsuitable initial data extensions outside of the physical part of the domain and\na certain weighted space.\n"}
+{"abstract": "  We consider a stochastic game between three types of players: an inside\ntrader, noise traders and a market maker. In a similar fashion to Kyle's model,\nwe assume that the insider first chooses the size of her market-order and then\nthe market maker determines the price by observing the total order-flow\nresulting from the insider and the noise traders transactions. In addition to\nthe classical framework, a revenue term is added to the market maker's\nperformance function, which is proportional to the order flow and to the size\nof the bid-ask spread. We derive the maximizer for the insider's revenue\nfunction and prove sufficient conditions for an equilibrium in the game. Then,\nwe use neural networks methods to verify that this equilibrium holds. We show\nthat the equilibrium state in this model experience interesting phase\ntransitions, as the weight of the revenue term in the market maker's\nperformance function changes. Specifically, the asset price in equilibrium\nexperience three different phases: a linear pricing rule without a spread, a\npricing rule that includes a linear mid-price and a bid-ask spread, and a\nmetastable state with a zero mid-price and a large spread.\n"}
+{"abstract": "  Based on density functional theory (DFT), we investigate the electronic\nproperties of bulk and single-layer ZrTe$_4$Se. The band structure of bulk\nZrTe$_4$Se can produce a semimetal-to-topological insulator (TI) phase\ntransition under uniaxial strain. The maximum global band gap is 0.189 eV at\nthe 7\\% tensile strain. Meanwhile, the Z$_2$ invariants (0; 110) demonstrate\nconclusively it is a weak topological insulator (WTI). The two Dirac cones for\nthe (001) surface further confirm the nontrivial topological nature. The\nsingle-layer ZrTe$_4$Se is a quantum spin Hall (QSH) insulator with a band gap\n86.4 meV and Z$_2$=1, the nontrivial metallic edge states further confirm the\nnontrivial topological nature. The maximum global band gap is 0.211 eV at the\ntensile strain 8\\%. When the compressive strain is more than 1\\%, the band\nstructure of single-layer ZrTe$_4$Se produces a TI-to-semimetal transition.\nThese theoretical analysis may provide a method for searching large band gap\nTIs and platform for topological nanoelectronic device applications.\n"}
+{"abstract": "  Attosecond nonlinear Fourier transform (NFT) pump probe spectroscopy is an\nexperimental technique which allows investigation of the electronic excitation,\nionization, and unimolecular dissociation processes. The NFT spectroscopy\nutilizes ultrafast multiphoton ionization in the extreme ultraviolet spectral\nrange and detects the dissociation products of the unstable ionized species. In\nthis paper, a quantum mechanical description of NFT spectra is suggested, which\nis based on the second order perturbation theory in molecule-light interaction\nand the high level ab initio calculations of CO2 and CO2+ in the Franck-Condon\nzone. The calculations capture the characteristic features of the available\nexperimental NFT spectra of CO2. Approximate analytic expressions are derived\nand used to assign the calculated spectra in terms of participating electronic\nstates and harmonic photon frequencies. The developed approach provides a\nconvenient framework within which the origin and the significance of near\nharmonic and non-harmonic NFT spectral lines can be analyzed. The framework is\nscalable and the spectra of di- and triatomic species as well as the\ndependences on the control parameters can by predicted semi-quantitatively.\n"}
+{"abstract": "  This work continues the study of the thermal Hamiltonian, initially proposed\nby J. M. Luttinger in 1964 as a model for the conduction of thermal currents in\nsolids. The previous work [DL] contains a complete study of the \"free\" model in\none spatial dimension along with a preliminary scattering result for\nconvolution-type perturbations. This work complements the results obtained in\n[DL] by providing a detailed analysis of the perturbation theory for the\none-dimensional thermal Hamiltonian. In more detail the following result are\nestablished: the regularity and decay properties for elements in the domain of\nthe unperturbed thermal Hamiltonian; the determination of a class of\nself-adjoint and relatively compact perturbations of the thermal Hamiltonian;\nthe proof of the existence and completeness of wave operators for a subclass of\nsuch potentials.\n"}
+{"abstract": "  We theoretically and observationally investigate different choices of initial\nconditions for the primordial mode function that are imposed during an epoch\npreceding inflation. By deriving predictions for the observables resulting from\nseveral alternate quantum vacuum prescriptions we show some choices of vacua\nare theoretically observationally distinguishable from others. Comparing these\npredictions to the Planck 2018 observations via a Bayesian analysis shows no\nsignificant evidence to favour any of the quantum vacuum prescriptions over the\nothers. In addition we consider frozen initial conditions, representing a\nwhite-noise initial state at the big-bang singularity. Under certain\nassumptions the cosmological concordance model and frozen initial conditions\nare found to produce identical predictions for the cosmic microwave background\nanisotropies. Frozen initial conditions may thus provide an alternative\ntheoretic paradigm to explain observations that were previously understood in\nterms of the inflation of a quantum vacuum.\n"}
+{"abstract": "  While self-supervised representation learning (SSL) has received widespread\nattention from the community, recent research argue that its performance will\nsuffer a cliff fall when the model size decreases. The current method mainly\nrelies on contrastive learning to train the network and in this work, we\npropose a simple yet effective Distilled Contrastive Learning (DisCo) to ease\nthe issue by a large margin. Specifically, we find the final embedding obtained\nby the mainstream SSL methods contains the most fruitful information, and\npropose to distill the final embedding to maximally transmit a teacher's\nknowledge to a lightweight model by constraining the last embedding of the\nstudent to be consistent with that of the teacher. In addition, in the\nexperiment, we find that there exists a phenomenon termed Distilling BottleNeck\nand present to enlarge the embedding dimension to alleviate this problem. Our\nmethod does not introduce any extra parameter to lightweight models during\ndeployment. Experimental results demonstrate that our method achieves the\nstate-of-the-art on all lightweight models. Particularly, when\nResNet-101/ResNet-50 is used as teacher to teach EfficientNet-B0, the linear\nresult of EfficientNet-B0 on ImageNet is very close to ResNet-101/ResNet-50,\nbut the number of parameters of EfficientNet-B0 is only 9.4\\%/16.3\\% of\nResNet-101/ResNet-50. Code is available at https://github.\ncom/Yuting-Gao/DisCo-pytorch.\n"}
+{"abstract": "  Irreducible symplectic varieties are higher-dimensional analogues of K3\nsurfaces. In this paper, we prove the finiteness of twists of irreducible\nsymplectic varieties via a fixed finite field extension of characteristic $0$.\nThe main ingredient of the proof is the cone conjecture for irreducible\nsymplectic varieties, which was proved by Markman and Amerik--Verbitsky. As\nbyproducts, we also discuss the cone conjecture over non-closed fields by\nBright--Logan--van Luijk's method. We also give an application to the\nfiniteness of derived equivalent twists. Moreover, we discuss the case of K3\nsurfaces or Enriques surfaces over fields of positive characteristic.\n"}
+{"abstract": "  We report the discovery of a new effect, namely, the effect of magnetically\ninduced transparency. The effect is observed in a magnetically active helically\nstructured periodical medium. Changing the external magnetic field and\nabsorption, one can tune the frequency and the linewidth of the transparency\nband.\n"}
+{"abstract": "  Absolute Concentration Robustness (ACR) was introduced by Shinar and Feinberg\nas a way to define robustness of equilibrium species concentration in a mass\naction dynamical system. Their aim was to devise a mathematical condition that\nwill ensure robustness in the function of the biological system being modeled.\nThe robustness of function rests on what we refer to as empirical\nrobustness--the concentration of a species remains unvarying, when measured in\nthe long run, across arbitrary initial conditions. While there is a positive\ncorrelation between ACR and empirical robustness, ACR is neither necessary nor\nsufficient for empirical robustness, a fact that can be noticed even in simple\nbiochemical systems. To develop a stronger connection with empirical\nrobustness, we define dynamic ACR, a property related to dynamics, rather than\nonly to equilibrium behavior, and one that guarantees convergence to a robust\nvalue. We distinguish between wide basin and narrow basin versions of dynamic\nACR, related to the size of the set of initial values that do not result in\nconvergence to the robust value. We give numerous examples which help\ndistinguish the various flavors of ACR as well as clearly illustrate and\ncircumscribe the conditions that appear in the definitions. We discuss general\ndynamical systems with ACR properties as well as parametrized families of\ndynamical systems related to reaction networks. We discuss connections between\nACR and complex balance, two notions central to the theory of reaction\nnetworks. We give precise conditions for presence and absence of dynamic ACR in\ncomplex balanced systems, which in turn yields a large body of reaction\nnetworks with dynamic ACR.\n"}
+{"abstract": "  In this work, we study the secure index coding problem where there are\nsecurity constraints on both legitimate receivers and eavesdroppers. We develop\ntwo performance bounds (i.e., converse results) on the symmetric secure\ncapacity. The first one is an extended version of the basic acyclic chain bound\n(Liu and Sadeghi, 2019) that takes security constraints into account. The\nsecond converse result is a novel information-theoretic lower bound on the\nsymmetric secure capacity, which is interesting as all the existing converse\nresults in the literature for secure index coding give upper bounds on the\ncapacity.\n"}
+{"abstract": "  In this paper we consider the influence of relativistic rotation on the\nconfinement/deconfinement transition in gluodynamics within lattice simulation.\nWe perform the simulation in the reference frame which rotates with the system\nunder investigation, where rotation is reduced to external gravitational field.\nTo study the confinement/deconfinement transition the Polyakov loop and its\nsusceptibility are calculated for various lattice parameters and the values of\nangular velocities which are characteristic for heavy-ion collision\nexperiments. Different types of boundary conditions (open, periodic, Dirichlet)\nare imposed in directions, orthogonal to rotation axis. Our data for the\ncritical temperature are well described by a simple quadratic function\n$T_c(\\Omega)/T_c(0) = 1 + C_2 \\Omega^2$ with $C_2>0$ for all boundary\nconditions and all lattice parameters used in the simulations. From this we\nconclude that the critical temperature of the confinement/deconfinement\ntransition in gluodynamics increases with increasing angular velocity. This\nconclusion does not depend on the boundary conditions used in our study and we\nbelieve that this is universal property of gluodynamics.\n"}
+{"abstract": "  Non-destructive evaluation (NDE) through inspection and monitoring is an\nintegral part of asset integrity management. The relationship between the\ncondition of interest and the quantity measured by NDE is described with\nprobabilistic models such as PoD or ROC curves. These models are used to assess\nthe quality of the information provided by NDE systems, which is affected by\nfactors such as the experience of the inspector, environmental conditions, ease\nof access, or imprecision in the measuring device. In this paper, we show how\nthe different probabilistic models of NDE are connected within a unifying\nframework. Using this framework, we derive insights into how these models\nshould be learned, calibrated, and applied. We investigate how the choice of\nthe model can affect the maintenance decisions taken on the basis of NDE\nresults. In addition, we analyze the impact of experimental design on the\nperformance of a given NDE system in a decision-making context.\n"}
+{"abstract": "  In a real-world setting biological agents do not have infinite resources to\nlearn new things. It is thus useful to recycle previously acquired knowledge in\na way that allows for faster, less resource-intensive acquisition of multiple\nnew skills. Neural networks in the brain are likely not entirely re-trained\nwith new tasks, but how they leverage existing computations to learn new tasks\nis not well understood. In this work, we study this question in artificial\nneural networks trained on commonly used neuroscience paradigms. Building on\nrecent work from the multi-task learning literature, we propose two\ningredients: (1) network modularity, and (2) learning task primitives.\nTogether, these ingredients form inductive biases we call structural and\nfunctional, respectively. Using a corpus of nine different tasks, we show that\na modular network endowed with task primitives allows for learning multiple\ntasks well while keeping parameter counts, and updates, low. We also show that\nthe skills acquired with our approach are more robust to a broad range of\nperturbations compared to those acquired with other multi-task learning\nstrategies. This work offers a new perspective on achieving efficient\nmulti-task learning in the brain, and makes predictions for novel neuroscience\nexperiments in which targeted perturbations are employed to explore solution\nspaces.\n"}
+{"abstract": "  We prove that quantum information propagates with a finite velocity in any\nmodel of interacting bosons whose (possibly time-dependent) Hamiltonian\ncontains spatially local single-boson hopping terms along with arbitrary local\ndensity-dependent interactions. More precisely, with density matrix $\\rho\n\\propto \\exp[-\\mu N]$ (with $N$ the total boson number), ensemble averaged\ncorrelators of the form $\\langle [A_0,B_r(t)]\\rangle $, along with\nout-of-time-ordered correlators, must vanish as the distance $r$ between two\nlocal operators grows, unless $t \\ge r/v$ for some finite speed $v$. In one\ndimensional models, we give a useful extension of this result that demonstrates\nthe smallness of all matrix elements of the commutator $[A_0,B_r(t)]$ between\nfinite density states if $t/r$ is sufficiently small. Our bounds are relevant\nfor physically realistic initial conditions in experimentally realized models\nof interacting bosons. In particular, we prove that $v$ can scale no faster\nthan linear in number density in the Bose-Hubbard model: this scaling matches\nprevious results in the high density limit. The quantum walk formalism\nunderlying our proof provides an alternative method for bounding quantum\ndynamics in models with unbounded operators and infinite-dimensional Hilbert\nspaces, where Lieb-Robinson bounds have been notoriously challenging to prove.\n"}
+{"abstract": "  The Sihl river, located near the city of Zurich in Switzerland, is under\ncontinuous and tight surveillance as it flows directly under the city's main\nrailway station. To issue early warnings and conduct accurate risk\nquantification, a dense network of monitoring stations is necessary inside the\nriver basin. However, as of 2021 only three automatic stations are operated in\nthis region, naturally raising the question: how to extend this network for\noptimal monitoring of extreme rainfall events?\n  So far, existing methodologies for station network design have mostly focused\non maximizing interpolation accuracy or minimizing the uncertainty of some\nmodel's parameters estimates. In this work, we propose new principles inspired\nfrom extreme value theory for optimal monitoring of extreme events. For\nstationary processes, we study the theoretical properties of the induced\nsampling design that yields non-trivial point patterns resulting from a\ncompromise between a boundary effect and the maximization of inter-location\ndistances. For general applications, we propose a theoretically justified\nfunctional peak-over-threshold model and provide an algorithm for sequential\nstation selection. We then issue recommendations for possible extensions of the\nSihl river monitoring network, by efficiently leveraging both station and radar\nmeasurements available in this region.\n"}
+{"abstract": "  Spherical matrix arrays arguably represent an advantageous tomographic\ndetection geometry for non-invasive deep tissue mapping of vascular networks\nand oxygenation with volumetric optoacoustic tomography (VOT). Hybridization of\nVOT with ultrasound (US) imaging remains difficult with this configuration due\nto the relatively large inter-element pitch of spherical arrays. We suggest a\nnew approach for combining VOT and US contrast-enhanced imaging employing\ninjection of clinically-approved microbubbles. Power Doppler (PD) and US\nlocalization imaging were enabled with a sparse US acquisition sequence and\nmodel-based inversion based on infimal convolution of total variation (ICTV)\nregularization. Experiments in tissue-mimicking phantoms and in vivo in mice\ndemonstrate the powerful capabilities of the new dual-mode imaging system for\nblood velocity mapping and anatomical imaging with enhanced resolution and\ncontrast.\n"}
+{"abstract": "  We characterise the selection cuts and clustering properties of a\nmagnitude-limited sample of bright galaxies that is part of the Bright Galaxy\nSurvey (BGS) of the Dark Energy Spectroscopic Instrument (DESI) using the ninth\ndata release of the Legacy Imaging Surveys (DR9). We describe changes in the\nDR9 selection compared to the DR8 one as explored in Ruiz-Macias et al. (2021).\nWe also compare the DR9 selection in three distinct regions: BASS/MzLS in the\nnorth Galactic Cap (NGC), DECaLS in the NGC, and DECaLS in the south Galactic\nCap (SGC). We investigate the systematics associated with the selection and\nassess its completeness by matching the BGS targets with the Galaxy and Mass\nAssembly (GAMA) survey. We measure the angular clustering for the overall\nbright sample (r $\\leq$ 19.5) and as function of apparent magnitude and colour.\nThis enables to determine the clustering strength and slope by fitting a\npower-law model that can be used to generate accurate mock catalogues for this\ntracer. We use a counts-in-cells technique to explore higher-order statistics\nand cross-correlations with external spectroscopic data sets in order to check\nthe evolution of the clustering with redshift and the redshift distribution of\nthe BGS targets using clustering-redshifts. While this work validates the\nproperties of the BGS bright targets, the final target selection pipeline and\nclustering properties of the entire DESI BGS will be fully characterised and\nvalidated with the spectroscopic data of Survey Validation.\n"}
+{"abstract": "  In this work, we aim to improve the expressive capacity of waveform-based\ndiscriminative music networks by modeling both sequential (temporal) and\nhierarchical information in an efficient end-to-end architecture. We present\nMuSLCAT, or Multi-scale and Multi-level Convolutional Attention Transformer, a\nnovel architecture for learning robust representations of complex music tags\ndirectly from raw waveform recordings. We also introduce a lightweight variant\nof MuSLCAT called MuSLCAN, short for Multi-scale and Multi-level Convolutional\nAttention Network. Both MuSLCAT and MuSLCAN model features from multiple scales\nand levels by integrating a frontend-backend architecture. The frontend targets\ndifferent frequency ranges while modeling long-range dependencies and\nmulti-level interactions by using two convolutional attention networks with\nattention-augmented convolution (AAC) blocks. The backend dynamically\nrecalibrates multi-scale and level features extracted from the frontend by\nincorporating self-attention. The difference between MuSLCAT and MuSLCAN is\ntheir backend components. MuSLCAT's backend is a modified version of BERT.\nWhile MuSLCAN's is a simple AAC block. We validate the proposed MuSLCAT and\nMuSLCAN architectures by comparing them to state-of-the-art networks on four\nbenchmark datasets for music tagging and genre recognition. Our experiments\nshow that MuSLCAT and MuSLCAN consistently yield competitive results when\ncompared to state-of-the-art waveform-based models yet require considerably\nfewer parameters.\n"}
+{"abstract": "  Trading in Over-The-Counter (OTC) markets is facilitated by broker-dealers,\nin comparison to public exchanges, e.g., the New York Stock Exchange (NYSE).\nDealers play an important role in stabilizing prices and providing liquidity in\nOTC markets. We apply machine learning methods to model and predict the trading\nbehavior of OTC dealers for US corporate bonds. We create sequences of daily\nhistorical transaction reports for each dealer over a vocabulary of US\ncorporate bonds. Using this history of dealer activity, we predict the future\ntrading decisions of the dealer. We consider a range of neural network-based\nprediction models. We propose an extension, the Pointwise-Product ReZero (PPRZ)\nTransformer model, and demonstrate the improved performance of our model. We\nshow that individual history provides the best predictive model for the most\nactive dealers. For less active dealers, a collective model provides improved\nperformance. Further, clustering dealers based on their similarity can improve\nperformance. Finally, prediction accuracy varies based on the activity level of\nboth the bond and the dealer.\n"}
+{"abstract": "  Cosmology is well suited to study the effects of long range interactions due\nto the large densities in the early Universe. In this article, we explore how\nthe energy density and equation of state of a fermion system diverge from the\ncommonly assumed ideal gas form under the presence of scalar long range\ninteractions with a range much smaller than cosmological scales. In this\nscenario, \"small\"-scale physics can impact our largest-scale observations. As a\nbenchmark, we apply the formalism to self-interacting neutrinos, performing an\nanalysis to present and future cosmological data. Our results show that the\ncurrent cosmological neutrino mass bound is fully avoided in the presence of a\nlong range interaction, opening the possibility for a laboratory neutrino mass\ndetection in the near future. We also demonstrate an interesting\ncomplementarity between neutrino laboratory experiments and the future EUCLID\nsurvey.\n"}
+{"abstract": "  This paper considers a new problem of adapting a pre-trained model of human\nmesh reconstruction to out-of-domain streaming videos. However, most previous\nmethods based on the parametric SMPL model \\cite{loper2015smpl} underperform in\nnew domains with unexpected, domain-specific attributes, such as camera\nparameters, lengths of bones, backgrounds, and occlusions. Our general idea is\nto dynamically fine-tune the source model on test video streams with additional\ntemporal constraints, such that it can mitigate the domain gaps without\nover-fitting the 2D information of individual test frames. A subsequent\nchallenge is how to avoid conflicts between the 2D and temporal constraints. We\npropose to tackle this problem using a new training algorithm named Bilevel\nOnline Adaptation (BOA), which divides the optimization process of overall\nmulti-objective into two steps of weight probe and weight update in a training\niteration. We demonstrate that BOA leads to state-of-the-art results on two\nhuman mesh reconstruction benchmarks.\n"}
+{"abstract": "  This paper introduces a conditional generative adversarial network to\nredesign a street-level image of urban scenes by generating 1) an urban\nintervention policy, 2) an attention map that localises where intervention is\nneeded, 3) a high-resolution street-level image (1024 X 1024 or 1536 X1536)\nafter implementing the intervention. We also introduce a new dataset that\ncomprises aligned street-level images of before and after urban interventions\nfrom real-life scenarios that make this research possible. The introduced\nmethod has been trained on different ranges of urban interventions applied to\nrealistic images. The trained model shows strong performance in re-modelling\ncities, outperforming existing methods that apply image-to-image translation in\nother domains that is computed in a single GPU. This research opens the door\nfor machine intelligence to play a role in re-thinking and re-designing the\ndifferent attributes of cities based on adversarial learning, going beyond the\nmainstream of facial landmarks manipulation or image synthesis from semantic\nsegmentation.\n"}
+{"abstract": "  Laser cooling of solids keeps attracting attention owing to abroad range of\nits applications that extends from cm-sized all-optical cryocoolers for\nairborne and space-based applications to cooling on nanoparticles for\nbiological and mesoscopic physics. Laser cooling of nanoparticles is a\nchallenging task. We propose to use Mie resonances to enhance anti-Stokes\nfluorescence laser cooling in rare-earth (RE) doped nanoparticles made of\nlow-phonon glasses or crystals. As an example, we consider an Yb3+:YAG\nnanosphere pumped at the long wavelength tail of the Yb3+ absorption spectrum\nat 1030 nm. We show that if the radius of the nanosphere is adjusted to the\npump wavelength in such a manner that the pump excites some of its Mie resonant\nmodes, the cooling power density generated in the sample is considerably\nenhanced and the temperature of the sample is consequently considerably (~ 63%)\ndecreased. This concept can be extended to nanoparticles of different shapes\nand made from different low-phonon RE doped materials suitable for laser\ncooling by anti-Stokes fluorescence.\n"}
+{"abstract": "  Estimating 3D human poses from video is a challenging problem. The lack of 3D\nhuman pose annotations is a major obstacle for supervised training and for\ngeneralization to unseen datasets. In this work, we address this problem by\nproposing a weakly-supervised training scheme that does not require 3D\nannotations or calibrated cameras. The proposed method relies on temporal\ninformation and triangulation. Using 2D poses from multiple views as the input,\nwe first estimate the relative camera orientations and then generate 3D poses\nvia triangulation. The triangulation is only applied to the views with high 2D\nhuman joint confidence. The generated 3D poses are then used to train a\nrecurrent lifting network (RLN) that estimates 3D poses from 2D poses. We\nfurther apply a multi-view re-projection loss to the estimated 3D poses and\nenforce the 3D poses estimated from multi-views to be consistent. Therefore,\nour method relaxes the constraints in practice, only multi-view videos are\nrequired for training, and is thus convenient for in-the-wild settings. At\ninference, RLN merely requires single-view videos. The proposed method\noutperforms previous works on two challenging datasets, Human3.6M and\nMPI-INF-3DHP. Codes and pretrained models will be publicly available.\n"}
+{"abstract": "  Two dimensional (2D) transition metal dichalcogenide (TMDC) materials, such\nas MoS2, WS2, MoSe2, and WSe2, have received extensive attention in the past\ndecade due to their extraordinary physical properties. The unique properties\nmake them become ideal materials for various electronic, photonic and\noptoelectronic devices. However, their performance is limited by the relatively\nweak light-matter interactions due to their atomically thin form factor.\nResonant nanophotonic structures provide a viable way to address this issue and\nenhance light-matter interactions in 2D TMDCs. Here, we provide an overview of\nthis research area, showcasing relevant applications, including exotic light\nemission, absorption and scattering features. We start by overviewing the\nconcept of excitons in 1L-TMDC and the fundamental theory of cavity-enhanced\nemission, followed by a discussion on the recent progress of enhanced light\nemission, strong coupling and valleytronics. The atomically thin nature of\n1L-TMDC enables a broad range of ways to tune its electric and optical\nproperties. Thus, we continue by reviewing advances in TMDC-based tunable\nphotonic devices. Next, we survey the recent progress in enhanced light\nabsorption over narrow and broad bandwidths using 1L or few-layer TMDCs, and\ntheir applications for photovoltaics and photodetectors. We also review recent\nefforts of engineering light scattering, e.g., inducing Fano resonances,\nwavefront engineering in 1L or few-layer TMDCs by either integrating resonant\nstructures, such as plasmonic/Mie resonant metasurfaces, or directly patterning\nmonolayer/few layers TMDCs. We then overview the intriguing physical properties\nof different types of van der Waals heterostructures, and their applications in\noptoelectronic and photonic devices. Finally, we draw our opinion on potential\nopportunities and challenges in this rapidly developing field of research.\n"}
+{"abstract": "  Event perception tasks such as recognizing and localizing actions in\nstreaming videos are essential for tackling visual understanding tasks.\nProgress has primarily been driven by the use of large-scale, annotated\ntraining data in a supervised manner. In this work, we tackle the problem of\nlearning \\textit{actor-centered} representations through the notion of\ncontinual hierarchical predictive learning to localize actions in streaming\nvideos without any training annotations. Inspired by cognitive theories of\nevent perception, we propose a novel, self-supervised framework driven by the\nnotion of hierarchical predictive learning to construct actor-centered features\nby attention-based contextualization. Extensive experiments on three benchmark\ndatasets show that the approach can learn robust representations for localizing\nactions using only one epoch of training, i.e., we train the model continually\nin streaming fashion - one frame at a time, with a single pass through training\nvideos. We show that the proposed approach outperforms unsupervised and weakly\nsupervised baselines while offering competitive performance to fully supervised\napproaches. Finally, we show that the proposed model can generalize to\nout-of-domain data without significant loss in performance without any\nfinetuning for both the recognition and localization tasks.\n"}
+{"abstract": "  One of the main reasons for the success of Evolutionary Algorithms (EAs) is\ntheir general-purposeness, i.e., the fact that they can be applied\nstraightforwardly to a broad range of optimization problems, without any\nspecific prior knowledge. On the other hand, it has been shown that\nincorporating a priori knowledge, such as expert knowledge or empirical\nfindings, can significantly improve the performance of an EA. However,\nintegrating knowledge in EAs poses numerous challenges. It is often the case\nthat the features of the search space are unknown, hence any knowledge\nassociated with the search space properties can be hardly used. In addition, a\npriori knowledge is typically problem-specific and hard to generalize. In this\npaper, we propose a framework, called Knowledge Integrated Evolutionary\nAlgorithm (KIEA), which facilitates the integration of existing knowledge into\nEAs. Notably, the KIEA framework is EA-agnostic (i.e., it works with any\nevolutionary algorithm), problem-independent (i.e., it is not dedicated to a\nspecific type of problems), expandable (i.e., its knowledge base can grow over\ntime). Furthermore, the framework integrates knowledge while the EA is running,\nthus optimizing the use of the needed computational power. In the preliminary\nexperiments shown here, we observe that the KIEA framework produces in the\nworst case an 80% improvement on the converge time, w.r.t. the corresponding\n\"knowledge-free\" EA counterpart.\n"}
+{"abstract": "  Object handover is a common human collaboration behavior that attracts\nattention from researchers in Robotics and Cognitive Science. Though visual\nperception plays an important role in the object handover task, the whole\nhandover process has been specifically explored. In this work, we propose a\nnovel rich-annotated dataset, H2O, for visual analysis of human-human object\nhandovers. The H2O, which contains 18K video clips involving 15 people who hand\nover 30 objects to each other, is a multi-purpose benchmark. It can support\nseveral vision-based tasks, from which, we specifically provide a baseline\nmethod, RGPNet, for a less-explored task named Receiver Grasp Prediction.\nExtensive experiments show that the RGPNet can produce plausible grasps based\non the giver's hand-object states in the pre-handover phase. Besides, we also\nreport the hand and object pose errors with existing baselines and show that\nthe dataset can serve as the video demonstrations for robot imitation learning\non the handover task. Dataset, model and code will be made public.\n"}
+{"abstract": "  Prior works have found it beneficial to combine provably noise-robust loss\nfunctions e.g., mean absolute error (MAE) with standard categorical loss\nfunction e.g. cross entropy (CE) to improve their learnability. Here, we\npropose to use Jensen-Shannon divergence as a noise-robust loss function and\nshow that it interestingly interpolate between CE and MAE with a controllable\nmixing parameter. Furthermore, we make a crucial observation that CE exhibit\nlower consistency around noisy data points. Based on this observation, we adopt\na generalized version of the Jensen-Shannon divergence for multiple\ndistributions to encourage consistency around data points. Using this loss\nfunction, we show state-of-the-art results on both synthetic (CIFAR), and\nreal-world (e.g., WebVision) noise with varying noise rates.\n"}
+{"abstract": "  In this paper, we investigate the outage performance of an intelligent\nreflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) uplink,\nin which a group of the surface reflecting elements are configured to boost the\nsignal of one of the user equipments (UEs), while the remaining elements are\nused to boost the other UE. By approximating the received powers as Gamma\nrandom variables, tractable expressions for the outage probability under NOMA\ninterference cancellation are obtained. We evaluate the outage over different\nsplits of the elements and varying pathloss differences between the two UEs.\nThe analysis shows that for small pathloss differences, the split should be\nchosen such that most of the IRS elements are configured to boost the stronger\nUE, while for large pathloss differences, it is more beneficial to boost the\nweaker UE. Finally, we investigate a robust selection of the elements' split\nunder the criterion of minimizing the maximum outage between the two UEs.\n"}
+{"abstract": "  This paper addresses issues surrounding the concept of fractional quantum\nmechanics, related to lights propagation in inhomogeneous nonlinear media,\nspecifically restricted to a so called gravitational optics. Besides\nSchr\\\"odinger Newton equation, we have also concerned with linear and nonlinear\nAiry beam accelerations in flat and curved spaces and fractal photonics,\nrelated to nonlinear Schr\\\"odinger equation, where impact of the fractional\nLaplacian is discussed. Another important feature of the gravitational optics'\nimplementation is its geometry with the paraxial approximation, when quantum\nmechanics, in particular, fractional quantum mechanics, is an effective\ndescription of optical effects. In this case, fractional-time differentiation\nreflexes this geometry effect as well.\n"}
+{"abstract": "  In the first part of the paper, we study the Cauchy problem for the\nadvection-diffusion equation $\\partial_t v + \\text{div }(v\\boldsymbol{b} ) =\n\\Delta v$ associated with a merely integrable, divergence-free vector field\n$\\boldsymbol{b}$ defined on the torus. We first introduce two notions of\nsolutions (distributional and parabolic), recalling the corresponding available\nresults of existence and uniqueness. Then, we establish a regularity criterion,\nwhich in turn provides uniqueness for distributional solutions. This is\nmotivated by the recent results in [31] where the authors showed non-uniqueness\nof distributional solutions to the advection-diffusion equation despite the\nparabolic one is unique. In the second part of the paper, we precisely describe\nthe vanishing viscosity scheme for the transport/continuity equation drifted by\n$\\boldsymbol{b}$, i.e. $\\partial_t u + \\text{div }(u\\boldsymbol{b} ) = 0$.\nUnder Sobolev assumptions on $\\boldsymbol{b} $, we give two independent proofs\nof the convergence of such scheme to the Lagrangian solution of the transport\nequation. The first proof slightly generalizes the original one of [21]. The\nother one is quantitative and yields rates of convergence. This offers a\ncompletely general selection criterion for the transport equation (even beyond\nthe distributional regime) which compensates the wild non-uniqueness phenomenon\nfor solutions with low integrability arising from convex integration schemes,\nas shown in recent works [10, 31, 32, 33], and rules out the possibility of\nanomalous dissipation.\n"}
+{"abstract": "  Quantile regression presents a complete picture of the effects on the\nlocation, scale, and shape of the dependent variable at all points, not just\nthe mean. We focus on two challenges for citation count analysis by quantile\nregression: discontinuity and substantial mass points at lower counts. A\nBayesian hurdle quantile regression model for count data with a substantial\nmass point at zero was proposed by King and Song (2019). It uses quantile\nregression for modeling the nonzero data and logistic regression for modeling\nthe probability of zeros versus nonzeros. We show that substantial mass points\nfor low citation counts will nearly certainly also affect parameter estimation\nin the quantile regression part of the model, similar to a mass point at zero.\nWe update the King and Song model by shifting the hurdle point past the main\nmass points. This model delivers more accurate quantile regression for\nmoderately to highly cited articles, especially at quantiles corresponding to\nvalues just beyond the mass points, and enables estimates of the extent to\nwhich factors influence the chances that an article will be low cited. To\nillustrate the potential of this method, it is applied to simulated citation\ncounts and data from Scopus.\n"}
+{"abstract": "  Financial markets are a source of non-stationary multidimensional time series\nwhich has been drawing attention for decades. Each financial instrument has its\nspecific changing over time properties, making their analysis a complex task.\nImprovement of understanding and development of methods for financial time\nseries analysis is essential for successful operation on financial markets. In\nthis study we propose a volume-based data pre-processing method for making\nfinancial time series more suitable for machine learning pipelines. We use a\nstatistical approach for assessing the performance of the method. Namely, we\nformally state the hypotheses, set up associated classification tasks, compute\neffect sizes with confidence intervals, and run statistical tests to validate\nthe hypotheses. We additionally assess the trading performance of the proposed\nmethod on historical data and compare it to a previously published approach.\nOur analysis shows that the proposed volume-based method allows successful\nclassification of the financial time series patterns, and also leads to better\nclassification performance than a price action-based method, excelling\nspecifically on more liquid financial instruments. Finally, we propose an\napproach for obtaining feature interactions directly from tree-based models on\nexample of CatBoost estimator, as well as formally assess the relatedness of\nthe proposed approach and SHAP feature interactions with a positive outcome.\n"}
+{"abstract": "  Autonomous systems like aircraft and assistive robots often operate in\nscenarios where guaranteeing safety is critical. Methods like Hamilton-Jacobi\nreachability can provide guaranteed safe sets and controllers for such systems.\nHowever, often these same scenarios have unknown or uncertain environments,\nsystem dynamics, or predictions of other agents. As the system is operating, it\nmay learn new knowledge about these uncertainties and should therefore update\nits safety analysis accordingly. However, work to learn and update safety\nanalysis is limited to small systems of about two dimensions due to the\ncomputational complexity of the analysis. In this paper we synthesize several\ntechniques to speed up computation: decomposition, warm-starting, and adaptive\ngrids. Using this new framework we can update safe sets by one or more orders\nof magnitude faster than prior work, making this technique practical for many\nrealistic systems. We demonstrate our results on simulated 2D and 10D\nnear-hover quadcopters operating in a windy environment.\n"}
+{"abstract": "  Structural behaviour of PbMn$_{7}$O$_{12}$ has been studied by high\nresolution synchrotron X-ray powder diffraction. This material belongs to a\nfamily of quadruple perovskite manganites that exhibit an incommensurate\nstructural modulation associated with an orbital density wave. It has been\nfound that the structural modulation in PbMn$_{7}$O$_{12}$ onsets at 294 K with\nthe incommensurate propagation vector $\\mathbf{k}_s=(0,0,\\sim2.08)$. At 110 K\nanother structural transition takes place where the propagation vector suddenly\ndrops down to a \\emph{quasi}-commensurate value $\\mathbf{k}_s=(0,0,2.0060(6))$.\nThe \\emph{quasi}-commensurate phase is stable in the temperature range of 40K -\n110 K, and below 40 K the propagation vector jumps back to the incommensurate\nvalue $\\mathbf{k}_s=(0,0,\\sim2.06)$. Both low temperature structural\ntransitions are strongly first order with large thermal hysteresis. The orbital\ndensity wave in the \\emph{quasi}-commensurate phase has been found to be\nsubstantially suppressed in comparison with the incommensurate phases, which\nnaturally explains unusual magnetic behaviour recently reported for this\nperovskite. Analysis of the refined structural parameters revealed that that\nthe presence of the \\emph{quasi}-commensurate phase is likely to be associated\nwith a competition between the Pb$^{2+}$ lone electron pair and Mn$^{3+}$\nJahn-Teller instabilities.\n"}
+{"abstract": "  We describe the new version (v3.06h) of the code HFODD that solves the\nuniversal nonrelativistic nuclear DFT Hartree-Fock or Hartree-Fock-Bogolyubov\nproblem by using the Cartesian deformed harmonic-oscillator basis. In the new\nversion, we implemented the following new features: (i) zero-range three- and\nfour-body central terms, (ii) zero-range three-body gradient terms, (iii)\nzero-range tensor terms, (iv) zero-range isospin-breaking terms, (v)\nfinite-range higher-order regularized terms, (vi) finite-range separable terms,\n(vii) zero-range two-body pairing terms, (viii) multi-quasiparticle blocking,\n(ix) Pfaffian overlaps, (x) particle-number and parity symmetry restoration,\n(xi) axialization, (xii) Wigner functions, (xiii) choice of the\nharmonic-oscillator basis, (xiv) fixed Omega partitions, (xv) consistency\nformula between energy and fields, and we corrected several errors of the\nprevious versions.\n"}
+{"abstract": "  This note derives parametrizations for surfaces of revolution that satisfy an\naffine-linear relation between their respective curvature radii. Alongside,\nparametrizations for the uniform normal offsets of those surfaces are obtained.\nThose parametrizations are found explicitly for a countably-infinite many of\nthem, and of those, it is shown which are algebraic. Lastly, for those surfaces\nwhich have a constant ratio of principal curvatures, parametrizations with a\nconstant angle between the parameter curves are found.\n"}
+{"abstract": "  It is well-known that the univariate Multiquadric quasi-interpolation\noperator is constructed based on the piecewise linear interpolation by |x|. In\nthis paper, we first introduce a new transcendental RBF based on the hyperbolic\ntangent function as a smooth approximant to f(r)=r with higher accuracy and\nbetter convergence properties than the multiquadric. Then Wu-Schaback's\nquasi-interpolation formula is rewritten using the proposed RBF. It preserves\nconvexity and monotonicity. We prove that the proposed scheme converges with a\nrate of O(h^2). So it has a higher degree of smoothness. Some numerical\nexperiments are given in order to demonstrate the efficiency and accuracy of\nthe method.\n"}
+{"abstract": "  Satisfiability of boolean formulae (SAT) has been a topic of research in\nlogic and computer science for a long time. In this paper we are interested in\nunderstanding the structure of satisfiable and unsatisfiable sentences. In\nprevious work we initiated a new approach to SAT by formulating a mapping from\npropositional logic sentences to graphs, allowing us to find structural\nobstructions to 2SAT (clauses with exactly 2 literals) in terms of graphs. Here\nwe generalize these ideas to multi-hypergraphs in which the edges can have more\nthan 2 vertices and can have multiplicity. This is needed for understanding the\nstructure of SAT for sentences made of clauses with 3 or more literals (3SAT),\nwhich is a building block of NP-completeness theory. We introduce a decision\nproblem that we call GraphSAT, as a first step towards a structural view of\nSAT. Each propositional logic sentence can be mapped to a multi-hypergraph by\nassociating each variable with a vertex (ignoring the negations) and each\nclause with a hyperedge. Such a graph then becomes a representative of a\ncollection of possible sentences and we can then formulate the notion of\nsatisfiability of such a graph. With this coarse representation of classes of\nsentences one can then investigate structural obstructions to SAT. To make the\nproblem tractable, we prove a local graph rewriting theorem which allows us to\nsimplify the neighborhood of a vertex without knowing the rest of the graph. We\nuse this to deduce several reduction rules, allowing us to modify a graph\nwithout changing its satisfiability status which can then be used in a program\nto simplify graphs. We study a subclass of 3SAT by examining sentences living\non triangulations of surfaces and show that for any compact surface there\nexists a triangulation that can support unsatisfiable sentences, giving\nspecific examples of such triangulations for various surfaces.\n"}
+{"abstract": "  Models whose ground states can be written as an exact matrix product state\n(MPS) provide valuable insights into phases of matter. While MPS-solvable\nmodels are typically studied as isolated points in a phase diagram, they can\nbelong to a connected network of MPS-solvable models, which we call the MPS\nskeleton. As a case study where we can completely unearth this skeleton, we\nfocus on the one-dimensional BDI class -- non-interacting spinless fermions\nwith time-reversal symmetry. This class, labelled by a topological winding\nnumber, contains the Kitaev chain and is Jordan-Wigner-dual to various\nsymmetry-breaking and symmetry-protected topological (SPT) spin chains. We show\nthat one can read off from the Hamiltonian whether its ground state is an MPS:\ndefining a polynomial whose coefficients are the Hamiltonian parameters,\nMPS-solvability corresponds to this polynomial being a perfect square. We\nprovide an explicit construction of the ground state MPS, its bond dimension\ngrowing exponentially with the range of the Hamiltonian. This complete\ncharacterization of the MPS skeleton in parameter space has three significant\nconsequences: (i) any two topologically distinct phases in this class admit a\npath of MPS-solvable models between them, including the phase transition which\nobeys an area law for its entanglement entropy; (ii) we illustrate that the\nsubset of MPS-solvable models is dense in this class by constructing a sequence\nof MPS-solvable models which converge to the Kitaev chain (equivalently, the\nquantum Ising chain in a transverse field); (iii) a subset of these MPS states\ncan be particularly efficiently processed on a noisy intermediate-scale quantum\ncomputer.\n"}
+{"abstract": "  In 1979 I. Cior\\u{a}nescu and L. Zsid\\'o have proved a minimum modulus\ntheorem for entire functions dominated by the restriction to the positive half\naxis of a canonical product of genus zero, having all roots on the positive\nimaginary axis and satisfying a certain condition.\n  Here we prove that the above result is optimal: if a canonical product\n{\\omega} of genus zero, having all roots on the positive imaginary axis, does\nnot satisfy the condition in the 1979 paper, then always there exists an entire\nfunction dominated by the restriction to the positive half axis of {\\omega},\nwhich does not satisfy the desired minimum modulus conclusion. This has\nrelevant implication concerning the subjectivity of ultra differential\noperators with constant coefficients.\n"}
+{"abstract": "  With the massive damage in the world caused by Coronavirus Disease 2019\nSARS-CoV-2 (COVID-19), many related research topics have been proposed in the\npast two years. The Chest Computed Tomography (CT) scans are the most valuable\nmaterials to diagnose the COVID-19 symptoms. However, most schemes for COVID-19\nclassification of Chest CT scan is based on a single-slice level, implying that\nthe most critical CT slice should be selected from the original CT scan volume\nmanually. We simultaneously propose 2-D and 3-D models to predict the COVID-19\nof CT scan to tickle this issue. In our 2-D model, we introduce the Deep\nWilcoxon signed-rank test (DWCC) to determine the importance of each slice of a\nCT scan to overcome the issue mentioned previously. Furthermore, a\nConvolutional CT scan-Aware Transformer (CCAT) is proposed to discover the\ncontext of the slices fully. The frame-level feature is extracted from each CT\nslice based on any backbone network and followed by feeding the features to our\nwithin-slice-Transformer (WST) to discover the context information in the pixel\ndimension. The proposed Between-Slice-Transformer (BST) is used to aggregate\nthe extracted spatial-context features of every CT slice. A simple classifier\nis then used to judge whether the Spatio-temporal features are COVID-19 or\nnon-COVID-19. The extensive experiments demonstrated that the proposed CCAT and\nDWCC significantly outperform the state-of-the-art methods.\n"}
+{"abstract": "  Recovery of power flow to critical infrastructures, after grid failure, is a\ncrucial need arising in scenarios that are increasingly becoming more frequent.\nThis article proposes a power transition and recovery strategy by proposing a\nmode-dependent droop control-based inverters. The control strategy of inverters\nachieves the following objectives 1) regulate the output active and reactive\npower by the droop-based inverters to a desired value while operating in\non-grid mode 2) seamless transition and recovery of power flow injections into\nthe critical loads in the network by inverters operating in off-grid mode after\nthe main grid fails; 3) require minimal information of grid/network status and\nconditions for the mode transition of droop control. A framework for assessing\nthe stability of the system and to guide the choice of parameters for\ncontrollers is developed using control-oriented modeling. A comprehensive\ncontroller hardware-in-the-loop-based real-time simulation study on a\ntest-system based on the realistic electrical network of M-Health Fairview,\nUniversity of Minnesota Medical Center, corroborates the efficacy of the\nproposed controller strategy.\n"}
+{"abstract": "  Entity linking (EL) for the rapidly growing short text (e.g. search queries\nand news titles) is critical to industrial applications. Most existing\napproaches relying on adequate context for long text EL are not effective for\nthe concise and sparse short text. In this paper, we propose a novel framework\ncalled Multi-turn Multiple-choice Machine reading comprehension (M3}) to solve\nthe short text EL from a new perspective: a query is generated for each\nambiguous mention exploiting its surrounding context, and an option selection\nmodule is employed to identify the golden entity from candidates using the\nquery. In this way, M3 framework sufficiently interacts limited context with\ncandidate entities during the encoding process, as well as implicitly considers\nthe dissimilarities inside the candidate bunch in the selection stage. In\naddition, we design a two-stage verifier incorporated into M3 to address the\ncommonly existed unlinkable problem in short text. To further consider the\ntopical coherence and interdependence among referred entities, M3 leverages a\nmulti-turn fashion to deal with mentions in a sequence manner by retrospecting\nhistorical cues. Evaluation shows that our M3 framework achieves the\nstate-of-the-art performance on five Chinese and English datasets for the\nreal-world short text EL.\n"}
+{"abstract": "  We give a review of the calculations of the masses of tetraquarks with two\nand four heavy quarks in the framework of the relativistic quark model based on\nthe quasipotential approach and QCD. The diquark-antidiquark picture of heavy\ntetraquarks is used. The quasipotentials of the quark-quark and\ndiquark-antidiquark interactions are constructed similarly to the previous\nconsideration of mesons and baryons. Diquarks are considered in the colour\ntriplet state. It is assumed that the diquark and antidiquark interact in the\ntetraquark as a whole and the internal structure of the diquarks is taken into\naccount by the calculated form factor of the diquark-gluon interaction. All\nparameters of the model are kept fixed from our previous calculations of meson\nand baryon properties. A detailed comparison of the obtained predictions for\nheavy tetraquark masses with available experimental data is given. Many\ncandidates for tetraquarks are found. It is argued that the structures in the\ndi-$J/\\psi$ mass spectrum observed recently by the LHCb Collaboration can be\ninterpreted as $cc\\bar c\\bar c$ tetraquarks.\n"}
+{"abstract": "  In the last two decades, optical vortices carried by twisted light wavefronts\nhave attracted a great deal of interest, providing not only new physical\ninsights into light-matter interactions, but also a transformative platform for\nboosting optical information capacity. Meanwhile, advances in nanoscience and\nnanotechnology lead to the emerging field of nanophotonics, offering an\nunprecedented level of light manipulation via nanostructured materials and\ndevices. Many exciting ideas and concepts come up when optical vortices meet\nnanophotonic devices. Here, we provide a mini review on recent achievements\nmade in nanophotonics for the generation and detection of optical vortices and\nsome of their applications.\n"}
+{"abstract": "  Decision makers involved in the management of civil assets and systems\nusually take actions under constraints imposed by societal regulations. Some of\nthese constraints are related to epistemic quantities, as the probability of\nfailure events and the corresponding risks. Sensors and inspectors can provide\nuseful information supporting the control process (e.g. the maintenance process\nof an asset), and decisions about collecting this information should rely on an\nanalysis of its cost and value. When societal regulations encode an economic\nperspective that is not aligned with that of the decision makers, the Value of\nInformation (VoI) can be negative (i.e., information sometimes hurts), and\nalmost irrelevant information can even have a significant value (either\npositive or negative), for agents acting under these epistemic constraints. We\nrefer to these phenomena as Information Avoidance (IA) and Information\nOverValuation (IOV). In this paper, we illustrate how to assess VoI in\nsequential decision making under epistemic constraints (as those imposed by\nsocietal regulations), by modeling a Partially Observable Markov Decision\nProcesses (POMDP) and evaluating non optimal policies via Finite State\nControllers (FSCs). We focus on the value of collecting information at current\ntime, and on that of collecting sequential information, we illustrate how these\nvalues are related and we discuss how IA and IOV can occur in those settings.\n"}
+{"abstract": "  In this work, we show the generative capability of an image classifier\nnetwork by synthesizing high-resolution, photo-realistic, and diverse images at\nscale. The overall methodology, called Synthesize-It-Classifier (STIC), does\nnot require an explicit generator network to estimate the density of the data\ndistribution and sample images from that, but instead uses the classifier's\nknowledge of the boundary to perform gradient ascent w.r.t. class logits and\nthen synthesizes images using Gram Matrix Metropolis Adjusted Langevin\nAlgorithm (GRMALA) by drawing on a blank canvas. During training, the\nclassifier iteratively uses these synthesized images as fake samples and\nre-estimates the class boundary in a recurrent fashion to improve both the\nclassification accuracy and quality of synthetic images. The STIC shows the\nmixing of the hard fake samples (i.e. those synthesized by the one hot class\nconditioning), and the soft fake samples (which are synthesized as a convex\ncombination of classes, i.e. a mixup of classes) improves class interpolation.\nWe demonstrate an Attentive-STIC network that shows an iterative drawing of\nsynthesized images on the ImageNet dataset that has thousands of classes. In\naddition, we introduce the synthesis using a class conditional score classifier\n(Score-STIC) instead of a normal image classifier and show improved results on\nseveral real-world datasets, i.e. ImageNet, LSUN, and CIFAR 10.\n"}
+{"abstract": "  We prove new $L^p$-$L^q$-estimates for solutions to elliptic differential\noperators with constant coefficients in $\\mathbb{R}^3$. We use the estimates\nfor the decay of the Fourier transform of particular surfaces in $\\mathbb{R}^3$\nwith vanishing Gaussian curvature due to Erd\\H{o}s--Salmhofer to derive new\nFourier restriction--extension estimates. These allow for constructing\ndistributional solutions in $L^q(\\mathbb{R}^3)$ for $L^p$-data via limiting\nabsorption by well-known means.\n"}
+{"abstract": "  For the Langevin model of the dynamics of a Brownian particle with\nperturbations orthogonal to its current velocity, in a regime when the particle\nvelocity modulus becomes constant, an equation for the characteristic function\n$\\psi (t,\\lambda )=M\\left[\\exp (\\lambda ,x(t))/V={\\rm v}(0)\\right]$ of the\nposition $x(t)$ of the Brownian particle. The obtained results confirm the\nconclusion that the model of the dynamics of a Brownian particle, which\nconstructed on the basis of an unconventional physical interpretation of the\nLangevin equations, i. e. stochastic equations with orthogonal influences,\nleads to the interpretation of an ensemble of Brownian particles as a system\nwith wave properties. These results are consistent with the previously obtained\nconclusions that, with a certain agreement of the coefficients in the original\nstochastic equation, for small random influences and friction, the Langevin\nequations lead to a description of the probability density of the position of a\nparticle based on wave equations. For large random influences and friction, the\nprobability density is a solution to the diffusion equation, with a diffusion\ncoefficient that is lower than in the classical diffusion model.\n"}
+{"abstract": "  The advent of deep learning has brought an impressive advance to monocular\ndepth estimation, e.g., supervised monocular depth estimation has been\nthoroughly investigated. However, the large amount of the RGB-to-depth dataset\nmay not be always available since collecting accurate depth ground truth\naccording to the RGB image is a time-consuming and expensive task. Although the\nnetwork can be trained on an alternative dataset to overcome the dataset scale\nproblem, the trained model is hard to generalize to the target domain due to\nthe domain discrepancy. Adversarial domain alignment has demonstrated its\nefficacy to mitigate the domain shift on simple image classification tasks in\nprevious works. However, traditional approaches hardly handle the conditional\nalignment as they solely consider the feature map of the network. In this\npaper, we propose an adversarial training model that leverages semantic\ninformation to narrow the domain gap. Based on the experiments conducted on the\ndatasets for the monocular depth estimation task including KITTI and\nCityscapes, the proposed compact model achieves state-of-the-art performance\ncomparable to complex latest models and shows favorable results on boundaries\nand objects at far distances.\n"}
+{"abstract": "  Reliable and accurate localization and mapping are key components of most\nautonomous systems. Besides geometric information about the mapped environment,\nthe semantics plays an important role to enable intelligent navigation\nbehaviors. In most realistic environments, this task is particularly\ncomplicated due to dynamics caused by moving objects, which can corrupt the\nmapping step or derail localization. In this paper, we propose an extension of\na recently published surfel-based mapping approach exploiting three-dimensional\nlaser range scans by integrating semantic information to facilitate the mapping\nprocess. The semantic information is efficiently extracted by a fully\nconvolutional neural network and rendered on a spherical projection of the\nlaser range data. This computed semantic segmentation results in point-wise\nlabels for the whole scan, allowing us to build a semantically-enriched map\nwith labeled surfels. This semantic map enables us to reliably filter moving\nobjects, but also improve the projective scan matching via semantic\nconstraints. Our experimental evaluation on challenging highways sequences from\nKITTI dataset with very few static structures and a large amount of moving cars\nshows the advantage of our semantic SLAM approach in comparison to a purely\ngeometric, state-of-the-art approach.\n"}
+{"abstract": "  Robotic fabric manipulation has applications in home robotics, textiles,\nsenior care and surgery. Existing fabric manipulation techniques, however, are\ndesigned for specific tasks, making it difficult to generalize across different\nbut related tasks. We build upon the Visual Foresight framework to learn fabric\ndynamics that can be efficiently reused to accomplish different sequential\nfabric manipulation tasks with a single goal-conditioned policy. We extend our\nearlier work on VisuoSpatial Foresight (VSF), which learns visual dynamics on\ndomain randomized RGB images and depth maps simultaneously and completely in\nsimulation. In this earlier work, we evaluated VSF on multi-step fabric\nsmoothing and folding tasks against 5 baseline methods in simulation and on the\nda Vinci Research Kit (dVRK) surgical robot without any demonstrations at train\nor test time. A key finding was that depth sensing significantly improves\nperformance: RGBD data yields an 80% improvement in fabric folding success rate\nin simulation over pure RGB data. In this work, we vary 4 components of VSF,\nincluding data generation, visual dynamics model, cost function, and\noptimization procedure. Results suggest that training visual dynamics models\nusing longer, corner-based actions can improve the efficiency of fabric folding\nby 76% and enable a physical sequential fabric folding task that VSF could not\npreviously perform with 90% reliability. Code, data, videos, and supplementary\nmaterial are available at https://sites.google.com/view/fabric-vsf/.\n"}
+{"abstract": "  It is essential to help drivers have appropriate understandings of level 2\nautomated driving systems for keeping driving safety. A human machine interface\n(HMI) was proposed to present real time results of image recognition by the\nautomated driving systems to drivers. It was expected that drivers could better\nunderstand the capabilities of the systems by observing the proposed HMI.\nDriving simulator experiments with 18 participants were preformed to evaluate\nthe effectiveness of the proposed system. Experimental results indicated that\nthe proposed HMI could effectively inform drivers of potential risks\ncontinuously and help drivers better understand the level 2 automated driving\nsystems.\n"}
+{"abstract": "  Using measurements from the PAMELA and ARINA spectrometers onboard the RESURS\nDK-1 satellite, we have examined the 27-day intensity variations in galactic\ncosmic ray (GCR) proton fluxes in 2007-2008. The PAMELA and ARINA data allow\nfor the first time a study of time profiles and the rigidity dependence of the\n27-day variations observed directly in space in a wide rigidity range from ~300\nMV to several GV. We find that the rigidity dependence of the amplitude of the\n27-day GCR variations cannot be described by the same power-law at both low and\nhigh energies. A flat interval occurs at rigidity R = <0.6-1.0> GV with a\npower-law index gamma = - 0.13+/-0.44 for PAMELA, whereas for R >= 1 GV the\npower-law dependence is evident with index gamma = - 0.51+/-0.11. We describe\nthe rigidity dependence of the 27-day GCR variations for PAMELA and ARINA data\nin the framework of the modulation potential concept using the force-field\napproximation for GCR transport. For a physical interpretation, we have\nconsidered the relationship between the 27-day GCR variations and solar wind\nplasma and other heliospheric parameters. Moreover, we have discussed possible\nimplications of MHD modeling of the solar wind plasma together with a\nstochastic GCR transport model concerning the effects of corotating interaction\nregions.\n"}
+{"abstract": "  The G\\\"odel translation provides an embedding of the intuitionistic logic\n$\\mathsf{IPC}$ into the modal logic $\\mathsf{Grz}$, which then embeds into the\nmodal logic $\\mathsf{GL}$ via the splitting translation. Combined with\nSolovay's theorem that $\\mathsf{GL}$ is the modal logic of the provability\npredicate of Peano Arithmetic $\\mathsf{PA}$, both $\\mathsf{IPC}$ and\n$\\mathsf{Grz}$ admit arithmetical interpretations. When attempting to 'lift'\nthese results to the monadic extensions $\\mathsf{MIPC}$, $\\mathsf{MGrz}$, and\n$\\mathsf{MGL}$ of these logics, the same techniques no longer work. Following a\nconjecture made by Esakia, we add an appropriate version of Casari's formula to\nthese monadic extensions (denoted by a '+'), obtaining that the G\\\"odel\ntranslation embeds $\\mathsf{M^{+}IPC}$ into $\\mathsf{M^{+}Grz}$ and the\nsplitting translation embeds $\\mathsf{M^{+}Grz}$ into $\\mathsf{MGL}$. As proven\nby Japaridze, Solovay's result extends to the monadic system $\\mathsf{MGL}$,\nwhich leads us to an arithmetical interpretation of both $\\mathsf{M^{+}IPC}$\nand $\\mathsf{M^{+}Grz}$.\n"}
+{"abstract": "  Curves of maximal slope are a reference gradient-evolution notion in metric\nspaces and arise as variational formulation of a vast class of nonlinear\ndiffusion equations. Existence theories for curves of maximal slope are often\nbased on minimizing-movements schemes, most notably on the Euler scheme. We\npresent here an alternative minimizing-movements approach, yielding more\nregular discretizations, serving as a-posteriori convergence estimator, and\nallowing for a simple convergence proof.\n"}
+{"abstract": "  We propose a formalism to model and reason about reconfigurable multi-agent\nsystems. In our formalism, agents interact and communicate in different modes\nso that they can pursue joint tasks; agents may dynamically synchronize,\nexchange data, adapt their behaviour, and reconfigure their communication\ninterfaces. Inspired by existing multi-robot systems, we represent a system as\na set of agents (each with local state), executing independently and only\ninfluence each other by means of message exchange. Agents are able to sense\ntheir local states and partially their surroundings. We extend LTL to be able\nto reason explicitly about the intentions of agents in the interaction and\ntheir communication protocols. We also study the complexity of satisfiability\nand model-checking of this extension.\n"}
+{"abstract": "  AM CVn systems are a rare type of accreting binary that consists of a white\ndwarf and a helium-rich, degenerate donor star. Using the Zwicky Transient\nFacility (ZTF), we searched for new AM CVn systems by focusing on blue,\noutbursting stars. We first selected outbursting stars using the ZTF alerts. We\ncross-matched the candidates with $Gaia$ and Pan-STARRS catalogs. The initial\nselection of candidates based on the $Gaia$ $BP$-$RP$ contains 1751 unknown\nobjects. We used the Pan-STARRS $g$-$r$ and $r$-$i$ color in combination with\nthe $Gaia$ color to identify 59 high-priority candidates. We obtained\nidentification spectra of 35 sources, of which 18 are high priority candidates,\nand discovered 9 new AM CVn systems and one magnetic CV which shows only He-II\nlines. Using the outburst recurrence time, we estimate the orbital periods\nwhich are in the range of 29 to 50 minutes. We conclude that targeted followup\nof blue, outbursting sources is an efficient method to find new AM CVn systems,\nand we plan to followup all candidates we identified to systematically study\nthe population of outbursting AM CVn systems.\n"}
+{"abstract": "  For better user satisfaction and business effectiveness, more and more\nattention has been paid to the sequence-based recommendation system, which is\nused to infer the evolution of users' dynamic preferences, and recent studies\nhave noticed that the evolution of users' preferences can be better understood\nfrom the implicit and explicit feedback sequences. However, most of the\nexisting recommendation techniques do not consider the noise contained in\nimplicit feedback, which will lead to the biased representation of user\ninterest and a suboptimal recommendation performance. Meanwhile, the existing\nmethods utilize item sequence for capturing the evolution of user interest. The\nperformance of these methods is limited by the length of the sequence, and can\nnot effectively model the long-term interest in a long period of time. Based on\nthis observation, we propose a novel CTR model named denoising user-aware\nmemory network (DUMN). Specifically, the framework: (i) proposes a feature\npurification module based on orthogonal mapping, which use the representation\nof explicit feedback to purify the representation of implicit feedback, and\neffectively denoise the implicit feedback; (ii) designs a user memory network\nto model the long-term interests in a fine-grained way by improving the memory\nnetwork, which is ignored by the existing methods; and (iii) develops a\npreference-aware interactive representation component to fuse the long-term and\nshort-term interests of users based on gating to understand the evolution of\nunbiased preferences of users. Extensive experiments on two real e-commerce\nuser behavior datasets show that DUMN has a significant improvement over the\nstate-of-the-art baselines. The code of DUMN model has been uploaded as an\nadditional material.\n"}
+{"abstract": "  The spin Hall effect (SHE) and the magnetic spin Hall effect (MSHE) are\nresponsible for electrical spin current generation, which is a key concept of\nmodern spintronics. We theoretically investigated the spin conductivity induced\nby spin-dependent s-d scattering in a ferromagnetic 3d alloy model by employing\nmicroscopic transport theory based on the Kubo formula. We derived a novel\nextrinsic mechanism that contributes to both the SHE and MSHE. This mechanism\ncan be understood as the contribution from anisotropic (spatial-dependent)\nspin-flip scattering due to the combination of the orbital-dependent\nanisotropic shape of s-d hybridization and spin flipping, with the orbital\nshift caused by spin-orbit interaction with the d-orbitals. We also show that\nthis mechanism is valid under crystal-field splitting among the d-orbitals in\neither the cubic or tetragonal symmetry.\n"}
+{"abstract": "  The nonleptonic decays\n$\\Lambda_{b}\\rightarrow\\Sigma_{c}^{*-}\\pi^{+},\\Xi_{c}^{*0}K^{0}$ and\n$\\Lambda_{b}\\rightarrow\\Delta^{0}D^{0},\\Sigma^{*-}D_{0}^{+}$ are studied. In\naddition, the decays\n$\\Lambda_{b}\\rightarrow\\Xi_{c}^{0}K^{0},\\Sigma^{-}D_{s}^{+}$ are analyzed. For\nall these decays the dominant contribution comes from $W-$exchange, and for the\ndecay $\\Lambda_{b}\\rightarrow\\Lambda_{c}^{+}\\pi^{-}$, in addition to\nfactorization, baryon pole contribution to the $p$-wave (parity conserving)\ndecay amplitude $B$ is discussed.\n"}
+{"abstract": "  The universal fractality of river networks is very well known, however\nunderstanding of the underlying mechanisms for them is still lacking in terms\nof stochastic processes. By introducing probability changing dynamically, we\nhave described the fractal natures of river networks stochastically. The\ndynamical probability depends on the drainage area at a site that is a key\ndynamical quantity of the system, meanwhile the river network is developed by\nthe probability, which induces dynamical persistency in river flows resulting\nin the self-affine property shown in real river basins, although the process is\na Markovian process with short-term memory.\n"}
+{"abstract": "  Recovering 3D human pose from 2D joints is still a challenging problem,\nespecially without any 3D annotation, video information, or multi-view\ninformation. In this paper, we present an unsupervised GAN-based model\nconsisting of multiple weight-sharing generators to estimate a 3D human pose\nfrom a single image without 3D annotations. In our model, we introduce\nsingle-view-multi-angle consistency (SVMAC) to significantly improve the\nestimation performance. With 2D joint locations as input, our model estimates a\n3D pose and a camera simultaneously. During training, the estimated 3D pose is\nrotated by random angles and the estimated camera projects the rotated 3D poses\nback to 2D. The 2D reprojections will be fed into weight-sharing generators to\nestimate the corresponding 3D poses and cameras, which are then mixed to impose\nSVMAC constraints to self-supervise the training process. The experimental\nresults show that our method outperforms the state-of-the-art unsupervised\nmethods by 2.6% on Human 3.6M and 15.0% on MPI-INF-3DHP. Moreover, qualitative\nresults on MPII and LSP show that our method can generalize well to unknown\ndata.\n"}
+{"abstract": "  Congestion caused in the electrical network due to renewable generation can\nbe effectively managed by integrating electric and thermal infrastructures, the\nlatter being represented by large scale District Heating (DH) networks, often\nfed by large combined heat and power (CHP) plants. The CHP plants could further\nimprove the profit margin of district heating multi-utilities by selling\nelectricity in the power market by adjusting the ratio between generated heat\nand power. The latter is possible only for certain CHP plants, which allow\ndecoupling the two commodities generation, namely the ones provided by two\nindependent variables (degrees-of-freedom) or by integrating them with thermal\nenergy storage and Power-to-Heat (P2H) units. CHP units can, therefore, help in\nthe congestion management of the electricity network. A detailed mixed-integer\nlinear programming (MILP) optimization model is introduced for solving the\nnetwork-constrained unit commitment of integrated electric and thermal\ninfrastructures. The developed model contains a detailed characterization of\nthe useful effects of CHP units, i.e., heat and power, as a function of one and\ntwo independent variables. A lossless DC flow approximation models the\nelectricity transmission network. The district heating model includes the use\nof gas boilers, electric boilers, and thermal energy storage. The conducted\nstudies on IEEE 24 bus system highlight the importance of a comprehensive\nanalysis of multi-energy systems to harness the flexibility derived from the\njoint operation of electric and heat sectors and managing congestion in the\nelectrical network.\n"}
+{"abstract": "  We establish weighted inequalities for $BMO$ commutators of sublinear\noperators for all $0<p<\\infty$.\n  For weights $w$ satisfying the doubling condition of order $q$ with $0<q<p$\nand the reverse H\\\"{o}lder condition, we prove that\n  $\\bullet$ commutators $T_b$, which are bounded on $L^p$ with $1<p<\\infty$,\nare bounded from some subspaces of $L^p_w$ to $L^p_w$ and to themselves for all\n$0<p<\\infty$, these are applied to the commutators of singular integral\noperators and Hardy-Littelwood maximal operator, et.al, which are known to fail\nto be bounded from $H^1$ to $L^1$ and whose estimate has been open problems for\n$0<p$ enough small;\n  $\\bullet$ commutators $T_b$, whose associated operators $T$ are bounded on\n$L^p$ with $1<p<\\infty$, are bounded from some subspaces of $L^p_w$ to $L^p_w$\nand from some subspaces of $L^p_w$ to others for all $0<p<\\infty$, these are\napplied to the commutators of maximal operators such as singular integral\nmaximal operators, Carleson operator and the polynomial Carleson operator,\net.al, the estimate of these commutators has been open problems for each\n$0<p<\\infty$;\n  $\\bullet$ in particular, these imply that the commutators above are bounded\nfrom $H^p_w$ to $L^p_w$ and to itself for all $0<p\\leq 1$.\n"}
+{"abstract": "  Let $G$ be a connected reductive group over a non-archimedean local field $F$\nand $I$ be an Iwahori subgroup of $G(F)$. Let $I_n$ is the $n$-th Moy-Prasad\nfiltration subgroup of $I$. The purpose of this paper is two-fold: to give some\nnice presentations of the Hecke algebra of connected, reductive groups with\n$I_n$-level structure; and to introduce the Tits group of the Iwahori-Weyl\ngroup of groups $G$ that split over an unramified extension of $F$.\n  The first main result of this paper is a presentation of the Hecke algebra\n$\\mathcal H(G(F),I_n)$, generalizing the previous work of Iwahori-Matsumoto on\nthe affine Hecke algebras. For split $GL_n$, Howe gave a refined presentation\nof the Hecke algebra $\\mathcal H(G(F),I_n)$. To generalize such a refined\npresentation to other groups requires the existence of some nice lifting of the\nIwahori-Weyl group $W$ to $G(F)$. The study of a certain nice lifting of $W$ is\nthe second main motivation of this paper, which we discuss below.\n  In 1966, Tits introduced a certain subgroup of $G(\\mathbf k)$, which is an\nextension of $W$ by an elementary abelian $2$-group. This group is called the\nTits group and provides a nice lifting of the elements in the finite Weyl\ngroup. The \"Tits group\" $\\mathcal T$ for the Iwahori-Weyl group $W$ is a\ncertain subgroup of $G(F)$, which is an extension of the Iwahori-Weyl group $W$\nby an elementary abelian $2$-group. The second main result of this paper is a\nconstruction of Tits group $\\mathcal T$ for $W$ when $G$ splits over an\nunramified extension of $F$. As a consequence, we generalize Howe's\npresentation to such groups. We also show that when $G$ is ramified over $F$,\nsuch a group $\\mathcal T$ of $W$ may not exist.\n"}
+{"abstract": "  In a hard switched MOSFET based converter, turn-on energy losses is\npredominant in the total switching loss. At higher junction temperature the\nturn-on energy loss further increases due to the reverse recovery effect of the\ncomplementary MOSFETs body diode in a half-bridge configuration. Estimation of\nthe switching loss under different operating conditions at an early design\nstage is essential for optimising the thermal design. Analytical switching loss\nmodels available in literature are generally used for estimating the switching\nlosses, due to its accuracy and simplicity. In this paper, the inaccuracy in\nthe reported loss models due to non inclusion of temperature dependent reverse\nrecovery characteristics of body diode, is investigated. A structured method to\ndetermine the temperature-dependent switching loss of a SiC MOSFET in a\nhalf-bridge is presented. A simple methodology has been proposed to analyze the\ncarrier lifetime's temperature dependencies of a SiC MOSFETs body diode. Device\nparameters from a 1.2kV/36A SiC MOSFETs datasheet are used for developing the\nloss model and experimental validation of the model.\n"}
+{"abstract": "  This study aims to optimize Deep Feedforward Neural Networks (DFNNs) training\nusing nature-inspired optimization algorithms, such as PSO, MTO, and its\nvariant called MTOCL. We show how these algorithms efficiently update the\nweights of DFNNs when learning from data. We evaluate the performance of DFNN\nfused with optimization algorithms using three Wisconsin breast cancer\ndatasets, Original, Diagnostic, and Prognosis, under different experimental\nscenarios. The empirical analysis demonstrates that MTOCL is the most\nperforming in most scenarios across the three datasets. Also, MTOCL is\ncomparable to past weight optimization algorithms for the original dataset, and\nsuperior for the other datasets, especially for the challenging Prognostic\ndataset.\n"}
+{"abstract": "  In this paper, an implicit nonsymplectic exact energy-preserving integrator\nis specifically designed for a ten-dimensional phase-space conservative\nHamiltonian system with five degrees of freedom. It is based on a suitable\ndiscretization-averaging of the Hamiltonian gradient, with a second-order\naccuracy to numerical solutions. A one-dimensional disordered discrete\nnonlinear Schr\\\"{o}dinger equation and a post-Newtonian Hamiltonian system of\nspinning compact binaries are taken as our two examples. We demonstrate\nnumerically that the proposed algorithm exhibits good long-term performance in\nthe preservation of energy, if roundoff errors are neglected. This result is\nindependent of time steps, initial orbital eccentricities, and regular and\nchaotic orbital dynamical behavior. In particular, the application of\nappropriately large time steps to the new algorithm is helpful in reducing\ntime-consuming and roundoff errors. This new method, combined with fast\nLyapunov indicators, is well suited related to chaos in the two example\nproblems. It is found that chaos in the former system is mainly responsible for\none of the parameters. In the latter problem, a combination of small initial\nseparations and high initial eccentricities can easily induce chaos.\n"}
+{"abstract": "  Dirac spin liquids represent a class of highly-entangled quantum phases in\ntwo dimensional Mott insulators, featuring exotic properties such as critical\ncorrelation functions and absence of well-defined low energy quasi-particles.\nExisting numerical works suggest that the spin-orbital $SU(4)$ symmetric\nKugel-Khomskii model of Mott insulators on the honeycomb lattice realizes a\nDirac spin-orbital liquid, described at low energy by $(2+1)d$ quantum\nelectrodynamics (QED$_3$) with $N_f=8$ Dirac fermions. We generalize methods\npreviously developed for $SU(2)$ spin systems to analyze the symmetry\nproperties and stability of the Dirac spin-orbital liquid. We conclude that the\nstandard Dirac state in the $SU(4)$ honeycomb system, based on a simple parton\nmean-field ansatz, is intrinsically unstable at low energy due to the existence\nof a monopole perturbation that is allowed by physical symmetries and relevant\nunder renormalization group flow. We propose two plausible alternative\nscenarios compatible with existing numerics. In the first scenario, we propose\nan alternative $U(1)$ Dirac spin-orbital liquid, which is similar to the\nstandard one except for its monopole symmetry quantum numbers. This alternative\n$U(1)$ state represents a stable gapless phase. In the second scenario, we\nstart from the standard $U(1)$ Dirac liquid and Higgs the $U(1)$ gauge symmetry\ndown to $\\mathbb{Z}_4$. The resulting $\\mathbb{Z}_4$ Dirac spin-orbital liquid\nis stable. We also discuss the continuous quantum phase transitions from the\n$\\mathbb{Z}_4$ Dirac liquids to conventional symmetry-breaking orders,\ndescribed by the QED$_3$ theory with $N_f=8$ supplemented with a critical\ncharge-$4$ Higgs field. We discuss possible ways to distinguish these scenarios\nin numerics. We also extend previous calculations of the quantum anomalies of\nQED$_3$ and match with generalized lattice Lieb-Schultz-Mattis constraints.\n"}
+{"abstract": "  Recent work has shown that the performance of machine learning models can\nvary substantially when models are evaluated on data drawn from a distribution\nthat is close to but different from the training distribution. As a result,\npredicting model performance on unseen distributions is an important challenge.\nOur work connects techniques from domain adaptation and predictive uncertainty\nliterature, and allows us to predict model accuracy on challenging unseen\ndistributions without access to labeled data. In the context of distribution\nshift, distributional distances are often used to adapt models and improve\ntheir performance on new domains, however accuracy estimation, or other forms\nof predictive uncertainty, are often neglected in these investigations. Through\ninvestigating a wide range of established distributional distances, such as\nFrechet distance or Maximum Mean Discrepancy, we determine that they fail to\ninduce reliable estimates of performance under distribution shift. On the other\nhand, we find that the difference of confidences (DoC) of a classifier's\npredictions successfully estimates the classifier's performance change over a\nvariety of shifts. We specifically investigate the distinction between\nsynthetic and natural distribution shifts and observe that despite its\nsimplicity DoC consistently outperforms other quantifications of distributional\ndifference. $DoC$ reduces predictive error by almost half ($46\\%$) on several\nrealistic and challenging distribution shifts, e.g., on the ImageNet-Vid-Robust\nand ImageNet-Rendition datasets.\n"}
+{"abstract": "  This paper presents the first adversarial example based method for attacking\nhuman instance segmentation networks, namely person segmentation networks in\nshort, which are harder to fool than classification networks. We propose a\nnovel Fashion-Guided Adversarial Attack (FashionAdv) framework to automatically\nidentify attackable regions in the target image to minimize the effect on image\nquality. It generates adversarial textures learned from fashion style images\nand then overlays them on the clothing regions in the original image to make\nall persons in the image invisible to person segmentation networks. The\nsynthesized adversarial textures are inconspicuous and appear natural to the\nhuman eye. The effectiveness of the proposed method is enhanced by robustness\ntraining and by jointly attacking multiple components of the target network.\nExtensive experiments demonstrated the effectiveness of FashionAdv in terms of\nrobustness to image manipulations and storage in cyberspace as well as\nappearing natural to the human eye. The code and data are publicly released on\nour project page https://github.com/nii-yamagishilab/fashion_adv\n"}
+{"abstract": "  This paper introduces Timers and Such, a new open source dataset of spoken\nEnglish commands for common voice control use cases involving numbers. We\ndescribe the gap in existing spoken language understanding datasets that Timers\nand Such fills, the design and creation of the dataset, and experiments with a\nnumber of ASR-based and end-to-end baseline models, the code for which has been\nmade available as part of the SpeechBrain toolkit.\n"}
+{"abstract": "  An important physical phenomenon that manifests itself during the inspiral of\ntwo orbiting compact objects is the tidal deformation of each under the\ngravitational influence of its companion. In the case of binary neutron star\nmergers, this tidal deformation and the associated Love numbers have been used\nto probe properties of dense matter and the nuclear equation of state.\nNon-spinning black holes on the other hand have a vanishing (field) tidal Love\nnumber in General Relativity. This pertains to the deformation of the\nasymptotic gravitational field. In certain cases, especially in the late stages\nof the inspiral phase when the black holes get close to each other, the source\nmultipole moments might be more relevant in probing their properties and the\nNo-Hair theorem; contrastingly, these Love numbers do not vanish. In this\npaper, we track the source multipole moments in simulations of several binary\nblack hole mergers and calculate these Love numbers. We present evidence that,\nat least for modest mass ratios, the behavior of the source multipole moments\nis universal.\n"}
+{"abstract": "  We obtain a polynomial upper bound in the finite-field version of the\nmultidimensional polynomial Szemer\\'{e}di theorem for distinct-degree\npolynomials. That is, if $P_1, ..., P_t$ are nonconstant integer polynomials of\ndistinct degrees and $v_1, ..., v_t$ are nonzero vectors in $\\mathbb{F}_p^D$,\nwe show that each subset of $\\mathbb{F}_p^D$ lacking a nontrivial configuration\nof the form\n  $$ x, x + v_1 P_1(y), ..., x + v_t P_t(y)$$ has at most $O(p^{D-c})$\nelements. In doing so, we apply the notion of Gowers norms along a vector\nadapted from ergodic theory, which extends the classical concept of Gowers\nnorms on finite abelian groups.\n"}
+{"abstract": "  Noncollinear chiral spin textures in ferromagnetic multilayers are at the\nforefront of recent research in nano-magnetism with the promise for fast and\nenergy-efficient devices. The recently demonstrated possibilities to stabilize\nsuch chiral structures in synthetic antiferromagnets (SAF) has raised interests\nas they are immune to dipolar field, hence favoring the stabilization of ultra\nsmall textures, improve mobility and avoid the transverse deflections of moving\nskyrmions limiting the efficiency in some foreseen applications. However, such\nsystems with zero net magnetization are hence difficult to characterize by most\nof the standard techniques. Here, we report that the relevant parameters of a\nmagnetic SAF texture, those being its period, its type (N\\'eel or Bloch) and\nits chirality (clockwise or counterclockwise), can be directly determined using\nthe circular dichroism in x-ray resonant scattering (CD-XRMS) at half integer\nmultilayer Bragg peaks in reciprocal space. The analysis of the dependence in\ntemperature down to 40K allows us moreover to address the question of the\ntemperature stability of a spin spiral in a SAF sample and of the temperature\nscaling of the symmetric and antisymmetric exchange interactions.\n"}
+{"abstract": "  This paper presents our submitted system to SemEval 2021 Task 4: Reading\nComprehension of Abstract Meaning. Our system uses a large pre-trained language\nmodel as the encoder and an additional dual multi-head co-attention layer to\nstrengthen the relationship between passages and question-answer pairs,\nfollowing the current state-of-the-art model DUMA. The main difference is that\nwe stack the passage-question and question-passage attention modules instead of\ncalculating parallelly to simulate re-considering process. We also add a layer\nnormalization module to improve the performance of our model. Furthermore, to\nincorporate our known knowledge about abstract concepts, we retrieve the\ndefinitions of candidate answers from WordNet and feed them to the model as\nextra inputs. Our system, called WordNet-enhanced DUal Multi-head Co-Attention\n(WN-DUMA), achieves 86.67% and 89.99% accuracy on the official blind test set\nof subtask 1 and subtask 2 respectively.\n"}
+{"abstract": "  Information diffusion on networks is an important concept in network science\nobserved in many situations such as information spreading and rumor controlling\nin social networks, disease contagion between individuals, cascading failures\nin power grids. The critical interactions in networks are the ones that play\ncritical roles in information diffusion and primarily affect network structure\nand functions. Besides, interactions can occur between not only two nodes as\npairwise interactions, i.e., edges, but also three or more nodes, described as\nhigher-order interactions. This report presents a novel method to identify\ncritical higher-order interactions. We propose two new Laplacians that allow\nredefining classical graph centrality measures for higher-order interactions.\nWe then compare the redefined centrality measures using the\nSusceptible-Infected-Recovered (SIR) simulation model. Experimental results\nsuggest that the proposed method is promising in identifying critical\nhigher-order interactions.\n"}
+{"abstract": "  Both conceptual modeling and machine learning have long been recognized as\nimportant areas of research. With the increasing emphasis on digitizing and\nprocessing large amounts of data for business and other applications, it would\nbe helpful to consider how these areas of research can complement each other.\nTo understand how they can be paired, we provide an overview of machine\nlearning foundations and development cycle. We then examine how conceptual\nmodeling can be applied to machine learning and propose a framework for\nincorporating conceptual modeling into data science projects. The framework is\nillustrated by applying it to a healthcare application. For the inverse\npairing, machine learning can impact conceptual modeling through text and rule\nmining, as well as knowledge graphs. The pairing of conceptual modeling and\nmachine learning in this this way should help lay the foundations for future\nresearch.\n"}
+{"abstract": "  We present a bijection between the set of standard Young tableaux of\nstaircase minus rectangle shape, and the set of marked shifted standard Young\ntableaux of a certain shifted shape. Numerically, this result is due to DeWitt\n(2012). Combined with other known bijections this gives a bijective proof of\nthe product formula for the number of standard Young tableaux of staircase\nminus rectangle shape. This resolves an open problem by Morales, Pak and Panova\n(2019), and allows for efficient random sampling. Other applications include a\nbijection for semistandard Young tableaux, and a bijective proof of\nStembridge's symmetry of LR-coefficients of the staircase shape. We also extend\nthese results to set-valued standard Young tableaux in the combinatorics of\nK-theory, leading to new proofs of results by Lewis and Marberg (2019) and\nAbney-McPeek, An and Ng (2020).\n"}
+{"abstract": "  Smart speakers and voice-based virtual assistants are core components for the\nsuccess of the IoT paradigm. Unfortunately, they are vulnerable to various\nprivacy threats exploiting machine learning to analyze the generated encrypted\ntraffic. To cope with that, deep adversarial learning approaches can be used to\nbuild black-box countermeasures altering the network traffic (e.g., via packet\npadding) and its statistical information. This letter showcases the inadequacy\nof such countermeasures against machine learning attacks with a dedicated\nexperimental campaign on a real network dataset. Results indicate the need for\na major re-engineering to guarantee the suitable protection of commercially\navailable smart speakers.\n"}
+{"abstract": "  We consider a system consisting of a sequential composition of Mealy\nmachines, called head and tail. We study two problems related to these systems.\nIn the first problem, models of both head and tail components are available,\nand the aim is to obtain a replacement for the tail with the minimum number of\nstates. We introduce a minimization method for this context which yields an\nexponential improvement over the state of the art. In the second problem, only\nthe head is known, and a desired model for the whole system is given. The\nobjective is to construct a tail that causes the system to behave according to\nthe given model. We show that, while it is possible to decide in polynomial\ntime whether such a tail exists, there are instances where its size is\nexponential in the sizes of the head and the desired system. This shows that\nthe complexity of the synthesis procedure is at least exponential, matching the\nupper bound in complexity provided by the existing methods for solving unknown\ncomponent equations.\n"}
+{"abstract": "  Partial differential equation-based numerical solution frameworks for initial\nand boundary value problems have attained a high degree of complexity. Applied\nto a wide range of physics with the ultimate goal of enabling engineering\nsolutions, these approaches encompass a spectrum of spatiotemporal\ndiscretization techniques that leverage solver technology and high performance\ncomputing. While high-fidelity solutions can be achieved using these\napproaches, they come at a high computational expense and complexity. Systems\nwith billions of solution unknowns are now routine. The expense and complexity\ndo not lend themselves to typical engineering design and decision-making, which\nmust instead rely on reduced-order models. Here we present an approach to\nreduced-order modelling that builds off of recent graph theoretic work for\nrepresentation, exploration, and analysis on computed states of physical\nsystems (Banerjee et al., Comp. Meth. App. Mech. Eng., 351, 501-530, 2019). We\nextend a non-local calculus on finite weighted graphs to build such models by\nexploiting first order dynamics, polynomial expansions, and Taylor series. Some\naspects of the non-local calculus related to consistency of the models are\nexplored. Details on the numerical implementations and the software library\nthat has been developed for non-local calculus on graphs are described.\nFinally, we present examples of applications to various quantities of interest\nin mechano-chemical systems.\n"}
+{"abstract": "  Federated learning (FL) enables multiple clients to jointly train a global\nmodel under the coordination of a central server. Although FL is a\nprivacy-aware paradigm, where raw data sharing is not required, recent studies\nhave shown that FL might leak the private data of a client through the model\nparameters shared with the server or the other clients. In this paper, we\npresent the HyFed framework, which enhances the privacy of FL while preserving\nthe utility of the global model. HyFed provides developers with a generic API\nto develop federated, privacy-preserving algorithms. HyFed supports both\nsimulation and federated operation modes and its source code is publicly\navailable at https://github.com/tum-aimed/hyfed.\n"}
+{"abstract": "  Lattice-based cryptography relies on generating random bases which are\ndifficult to fully reduce. Given a lattice basis (such as the private basis for\na cryptosystem), all other bases are related by multiplication by matrices in\n$GL(n,\\mathbb{Z})$. We compare the strengths of various methods to sample\nrandom elements of $GL(n,\\mathbb{Z})$, finding some are stronger than others\nwith respect to the problem of recognizing rotations of the $\\mathbb{Z}^n$\nlattice. In particular, the standard algorithm of multiplying unipotent\ngenerators together (as implemented in Magma's RandomSLnZ command) generates\ninstances of this last problem which can be efficiently broken, even in\ndimensions nearing 1,500. Likewise, we find that the random basis generation\nmethod in one of the NIST Post-Quantum Cryptography competition submissions\n(DRS) generates instances which can be efficiently broken, even at its 256-bit\nsecurity settings. Other random basis generation algorithms (some older, some\nnewer) are described which appear to be much stronger.\n"}
+{"abstract": "  New copulas, based on perturbation theory, are introduced to clarify a\n\\emph{symmetrization} procedure for asymmetric copulas. We give also some\nproperties of the \\emph{symmetrized} copula. Finally, we examine families of\ncopulas with a prescribed symmetrized one. By the way, we study topologically,\nthe set of all symmetric copulas and give some of its classical and new\nproperties.\n"}
+{"abstract": "  Recently, a new choice of variables was identified to understand how the\nquantum group structure appeared in three-dimensional gravity [1]. These\nvariables are introduced via a canonical transformation generated by a boundary\nterm. We show that this boundary term can actually be taken to be the volume of\nthe boundary and that the new variables can be defined in any dimension greater\nthan three. In addition, we study the associated metric and teleparallel\nformalisms. The former is a variant of the Henneaux--Teitelboim model for\nunimodular gravity. The latter provides a non-abelian generalization of the\nusual abelian teleparallel formulation.\n"}
+{"abstract": "  Accurate hand joints detection from images is a fundamental topic which is\nessential for many applications in computer vision and human computer\ninteraction. This paper presents a two stage network for hand joints detection\nfrom single unmarked image by using serial-parallel multi-scale feature fusion.\nIn stage I, the hand regions are located by a pre-trained network, and the\nfeatures of each detected hand region are extracted by a shallow spatial hand\nfeatures representation module. The extracted hand features are then fed into\nstage II, which consists of serially connected feature extraction modules with\nsimilar structures, called \"multi-scale feature fusion\" (MSFF). A MSFF contains\nparallel multi-scale feature extraction branches, which generate initial hand\njoint heatmaps. The initial heatmaps are then mutually reinforced by the\nanatomic relationship between hand joints. The experimental results on five\nhand joints datasets show that the proposed network overperforms the\nstate-of-the-art methods.\n"}
+{"abstract": "  Mass and radius measurements of stars are important inputs for models of\nstellar structure. Binary stars are of particular interest in this regard,\nbecause astrometry and spectroscopy of a binary together provide the masses of\nboth stars as well as the distance to the system, while interferometry can both\nimprove the astrometry and measure the radii of the stars. In this work we\nsimulate parameter recovery from intensity interferometry, especially the\nchallenge of disentangling the radii of two stars from their combined\ninterferometric signal. Two approaches are considered: separation of the\nvisibility contributions of each star with the help of differing brightness\nratios at different wavelengths, and direct fitting of the intensity\ncorrelation to a multi-parameter model. Full image reconstructions is not\nattempted. Measurement of angular radii, angular separation and first-order\nlimb-darkening appears readily achievable for bright binary stars with current\ninstrumentation.\n"}
+{"abstract": "  The Gordian graph and H(2)-Gordian graphs of knots are abstract graphs whose\nvertex sets represent isotopy classes of unoriented knots, and whose edge sets\nrecord whether pairs of knots are related by crossing changes or H(2)-moves,\nrespectively. We investigate quotients of these graphs under equivalence\nrelations defined by several knot invariants including the determinant, the\nspan of the Jones polynomial, and an invariant related to tricolorability. We\nshow, in all cases considered, that the quotient graphs are Gromov hyperbolic.\nWe then prove a collection of results about the graph isomorphism type of the\nquotient graphs. In particular, we find that the H(2)-Gordian graph of links\nmodulo the relation induced by the span of the Jones polynomial is isomorphic\nwith the complete graph on infinitely many vertices.\n"}
+{"abstract": "  We consider the problem of sparse nonnegative matrix factorization (NMF) with\narchetypal regularization. The goal is to represent a collection of data points\nas nonnegative linear combinations of a few nonnegative sparse factors with\nappealing geometric properties, arising from the use of archetypal\nregularization. We generalize the notion of robustness studied in Javadi and\nMontanari (2019) (without sparsity) to the notions of (a) strong robustness\nthat implies each estimated archetype is close to the underlying archetypes and\n(b) weak robustness that implies there exists at least one recovered archetype\nthat is close to the underlying archetypes. Our theoretical results on\nrobustness guarantees hold under minimal assumptions on the underlying data,\nand applies to settings where the underlying archetypes need not be sparse. We\npropose new algorithms for our optimization problem; and present numerical\nexperiments on synthetic and real datasets that shed further insights into our\nproposed framework and theoretical developments.\n"}
+{"abstract": "  We recently showed that the DFT+U approach with a linear-response U yields\nadiabatic energy differences biased towards high spin [Mariano et al. J. Chem.\nTheory Comput. 2020, 16, 6755-6762]. Such bias is removed here by employing a\ndensity-corrected DFT approach where the PBE functional is evaluated on the\nHubbard U -corrected density. The adiabatic energy differences of six Fe(II)\nmolecular complexes computed using this approach, named here PBE[U], are in\nexcellent agreement with coupled luster-corrected CASPT2 values for both weak-\nand strong-field ligands resulting in a mean absolute error (MAE) of 0.44 eV,\nsmaller than the recently proposed Hartree-Fock density-corrected DFT (1.22 eV)\nand any other tested functional, including the best performer TPSSh (0.49 eV).\nWe take advantage of the computational efficiency of this approach and compute\nthe adiabatic energy differences of five molecular crystals using PBE[U] with\nperiodic boundary conditions. The results show, again, an excellent agreement\n(MAE=0.07 eV) with experimentally-extracted values and a superior performance\ncompared with the best performers TPSSh (MAE=0.08 eV) and M06-L (MAE=0.31 eV)\ncomputed on molecular fragments.\n"}
+{"abstract": "  The scale of deep learning nowadays calls for efficient distributed training\nalgorithms. Decentralized momentum SGD (DmSGD), in which each node averages\nonly with its neighbors, is more communication efficient than vanilla Parallel\nmomentum SGD that incurs global average across all computing nodes. On the\nother hand, the large-batch training has been demonstrated critical to achieve\nruntime speedup. This motivates us to investigate how DmSGD performs in the\nlarge-batch scenario.\n  In this work, we find the momentum term can amplify the inconsistency bias in\nDmSGD. Such bias becomes more evident as batch-size grows large and hence\nresults in severe performance degradation. We next propose DecentLaM, a novel\ndecentralized large-batch momentum SGD to remove the momentum-incurred bias.\nThe convergence rate for both non-convex and strongly-convex scenarios is\nestablished. Our theoretical results justify the superiority of DecentLaM to\nDmSGD especially in the large-batch scenario. Experimental results on a variety\nof computer vision tasks and models demonstrate that DecentLaM promises both\nefficient and high-quality training.\n"}
+{"abstract": "  Recent work on unsupervised question answering has shown that models can be\ntrained with procedurally generated question-answer pairs and can achieve\nperformance competitive with supervised methods. In this work, we consider the\ntask of unsupervised reading comprehension and present a method that performs\n\"test-time learning\" (TTL) on a given context (text passage), without requiring\ntraining on large-scale human-authored datasets containing\n\\textit{context-question-answer} triplets. This method operates directly on a\nsingle test context, uses self-supervision to train models on synthetically\ngenerated question-answer pairs, and then infers answers to unseen\nhuman-authored questions for this context. Our method achieves accuracies\ncompetitive with fully supervised methods and significantly outperforms current\nunsupervised methods. TTL methods with a smaller model are also competitive\nwith the current state-of-the-art in unsupervised reading comprehension.\n"}
+{"abstract": "  In this paper, a two-stage stochastic day-ahead (DA) scheduling model is\nproposed incorporating wind power units and compressed air energy storage\n(CAES) to clear a co-optimized energy and reserve market. The two-stage\nstochastic programming method is employed to deal with the wind power\ngeneration uncertain nature. A linearized AC optimal power flow (LAC-OPF)\napproach with consideration of network losses, reactive power, and voltage\nmagnitude constraints is utilized in the proposed two-stage stochastic DA\nscheduling model. Using an engineering insight, a two-level LAC-OPF\n(TL-LAC-OPF) approach is proposed to (i) reduce the number of binary variables\nof the LAC-OPF approach which decreases the computational burden, and (ii)\nobtain LAC-OPF pre-defined parameters adaptively so that the accuracy of\nLAC-OPF approach is increased as a result of reducing artificial losses.\nFurthermore, as the CAES efficiency depends on its thermodynamic and\noperational conditions, the proposed two-stage stochastic DA scheduling model\nis developed by considering its thermodynamic characteristics to obtain a more\nrealistic market decision at the first place. The proposed model is applied to\nIEEE 30-bus and 57-bus test systems using GAMS software, and is compared with\nthree traditional approaches, i.e., AC-OPF, DC-OPF, and LAC-OPF. Simulation\nresults demonstrate effectiveness of the proposed methodology.\n"}
+{"abstract": "  We present a novel approach to identify potential dispersed signals of new\nphysics in the slew of published LHC results. It employs a random walk\nalgorithm to introduce sets of new particles, dubbed \"proto-models\", which are\ntested against simplified-model results from ATLAS and CMS searches for new\nphysics by exploiting the SModelS software framework. A combinatorial algorithm\nidentifies the set of analyses and/or signal regions that maximally violates\nthe Standard Model hypothesis, while remaining compatible with the entirety of\nLHC constraints in our database. Crucial to the method is the ability to\nconstruct a reliable likelihood in proto-model space; we explain the various\napproximations which are needed depending on the information available from the\nexperiments, and how they impact the whole procedure.\n"}
+{"abstract": "  We use gradient boosting machines and logistic regression to predict academic\nthroughput at a South African university. The results highlight the significant\ninfluence of socio-economic factors and field of study as predictors of\nthroughput. We further find that socio-economic factors become less of a\npredictor relative to the field of study as the time to completion increases.\nWe provide recommendations on interventions to counteract the identified\neffects, which include academic, psychosocial and financial support.\n"}
+{"abstract": "  Extraction of the multi-TeV proton and lead LHC beams with a bent crystal or\nby using an internal gas target allows one to perform the most energetic\nfixed-target experiment ever. pp, pd and pA collisions at $\\sqrt{s}$ = 115 GeV\nand Pbp and PbA collisions at $\\sqrt{s_{\\rm{NN}}}$ = 72 GeV can be studied with\nhigh precision and modern detection techniques over a broad rapidity range.\nUsing the LHCb or the ALICE detector in a fixed-target mode offers\nunprecedented possibilities to access heavy-flavour production in a new energy\ndomain, half way between the SPS and the nominal RHIC energy. In this\ncontribution, a review of projection studies for quarkonium and open charm and\nbeauty production with both detector set-ups used with various nuclear targets\nand the LHC lead beams is presented.\n"}
+{"abstract": "  Modern single image super-resolution (SISR) system based on convolutional\nneural networks (CNNs) achieves fancy performance while requires huge\ncomputational costs. The problem on feature redundancy is well studied in\nvisual recognition task, but rarely discussed in SISR. Based on the observation\nthat many features in SISR models are also similar to each other, we propose to\nuse shift operation to generate the redundant features (i.e., Ghost features).\nCompared with depth-wise convolution which is not friendly to GPUs or NPUs,\nshift operation can bring practical inference acceleration for CNNs on common\nhardware. We analyze the benefits of shift operation for SISR and make the\nshift orientation learnable based on Gumbel-Softmax trick. For a given\npre-trained model, we first cluster all filters in each convolutional layer to\nidentify the intrinsic ones for generating intrinsic features. Ghost features\nwill be derived by moving these intrinsic features along a specific\norientation. The complete output features are constructed by concatenating the\nintrinsic and ghost features together. Extensive experiments on several\nbenchmark models and datasets demonstrate that both the non-compact and\nlightweight SISR models embedded in our proposed module can achieve comparable\nperformance to that of their baselines with large reduction of parameters,\nFLOPs and GPU latency. For instance, we reduce the parameters by 47%, FLOPs by\n46% and GPU latency by 41% of EDSR x2 network without significant performance\ndegradation.\n"}
+{"abstract": "  We determine the exact value of the optimal symmetric rate point $(r, r)$ in\nthe Dueck zero-error capacity region of the binary adder channel with complete\nfeedback. We proved that the average zero-error capacity $r = h(1/2-\\delta)\n\\approx 0.78974$, where $h(\\cdot)$ is the binary entropy function and $\\delta =\n1/(2\\log_2(2+\\sqrt3))$. Our motivation is a problem in quantitative group\ntesting. Given a set of $n$ elements two of which are defective, the\nquantitative group testing problem asks for the identification of these two\ndefectives through a series of tests. Each test gives the number of defectives\ncontained in the tested subset, and the outcomes of previous tests are assumed\nknown at the time of designing the current test. We establish that the minimum\nnumber of tests is asymptotic to $(\\log_2 n) / r$ as $n \\to \\infty$.\n"}
+{"abstract": "  Psychological theories of habit posit that when a strong habit is formed\nthrough behavioral repetition, it can trigger behavior automatically in the\nsame environment. Given the reciprocal relationship between habit and behavior,\nchanging lifestyle behaviors (e.g., toothbrushing) is largely a task of\nbreaking old habits and creating new and healthy ones. Thus, representing\nusers' habit strengths can be very useful for behavior change support systems\n(BCSS), for example, to predict behavior or to decide when an intervention\nreaches its intended effect. However, habit strength is not directly observable\nand existing self-report measures are taxing for users. In this paper, built on\nrecent computational models of habit formation, we propose a method to enable\nintelligent systems to compute habit strength based on observable behavior. The\nhypothesized advantage of using computed habit strength for behavior prediction\nwas tested using data from two intervention studies, where we trained\nparticipants to brush their teeth twice a day for three weeks and monitored\ntheir behaviors using accelerometers. Through hierarchical cross-validation, we\nfound that for the task of predicting future brushing behavior, computed habit\nstrength clearly outperformed self-reported habit strength (in both studies)\nand was also superior to models based on past behavior frequency (in the larger\nsecond study). Our findings provide initial support for our theory-based\napproach of modeling user habits and encourages the use of habit computation to\ndeliver personalized and adaptive interventions.\n"}
+{"abstract": "  Correlations between different regions of a quantum many-body system can be\nquantified through measures based on entropies of (reduced) subsystem states.\nFor closed systems, several analytical and numerical tools, e.g., hydrodynamic\ntheories or tensor networks, can accurately capture the time-evolution of\nsubsystem entropies, thus allowing for a profound understanding of the unitary\ndynamics of quantum correlations. However, so far, these methods either cannot\nbe applied to open quantum systems or do not permit an efficient computation of\nquantum entropies for mixed states. Here, we make progress in solving this\nissue by formulating a dissipative quasi-particle picture -- describing the\ndynamics of quantum entropies in the hydrodynamic limit -- for a general class\nof noninteracting open quantum systems. Our results show that also in\ndissipative many-body systems, correlations are generically established through\nthe propagation of quasi-particles.\n"}
+{"abstract": "  We report deep imaging observations with DOLoRes@TNG of an ultra-faint dwarf\nsatellite candidate of the Triangulum galaxy (M33) found by visual inspection\nof the public imaging data release of the DESI Legacy Imaging Surveys. Pisces\nVII/Triangulum (Tri) III is found at a projected distance of 72 kpc from M33,\nand using the tip of the red giant branch method we estimate a distance of\nD=1.0 +0.3,-0.2 Mpc, meaning the galaxy could either be an isolated ultra-faint\nor the second known satellite of M33. We estimate an absolute magnitude of\nM_V=-6.1+/-0.2 if Pisces VII/Tri II is at the distance of M33, or as bright as\nM_V=-6.8+/-0.2 if the galaxy is isolated. At the isolated distance, it has a\nphysical half-light radius of r_h=131+/-61 pc consistent with similarly faint\ngalaxies around the Milky Way. As the tip of the red giant branch is sparsely\npopulated, constraining a precision distance is not possible, but if Pisces\nVII/Tri III can be confirmed as a true satellite of M33 it is a significant\nfinding. With only one potential satellite detected around M33 previously\n(Andromeda XXII/Tri I), it lacks a significant satellite population in stark\ncontrast to the similarly massive Large Magellanic Cloud. The detection of more\nsatellites in the outskirts of M33 could help to better illuminate if this\ndiscrepancy between expectation and observations is due to a poor understanding\nof the galaxy formation process, or if it is due to the low luminosity and\nsurface brightness of the M33 satellite population which has thus far fallen\nbelow the detection limits of previous surveys. If it is truly isolated, it\nwould be the faintest known field dwarf detected to date.\n"}
+{"abstract": "  The classic classification scheme for Active Galactic Nuclei (AGNs) was\nrecently challenged by the discovery of the so-called changing-state\n(changing-look) AGNs (CSAGNs). The physical mechanism behind this phenomenon is\nstill a matter of open debate and the samples are too small and of\nserendipitous nature to provide robust answers. In order to tackle this\nproblem, we need to design methods that are able to detect AGN right in the act\nof changing-state. Here we present an anomaly detection (AD) technique designed\nto identify AGN light curves with anomalous behaviors in massive datasets. The\nmain aim of this technique is to identify CSAGN at different stages of the\ntransition, but it can also be used for more general purposes, such as cleaning\nmassive datasets for AGN variability analyses. We used light curves from the\nZwicky Transient Facility data release 5 (ZTF DR5), containing a sample of\n230,451 AGNs of different classes. The ZTF DR5 light curves were modeled with a\nVariational Recurrent Autoencoder (VRAE) architecture, that allowed us to\nobtain a set of attributes from the VRAE latent space that describes the\ngeneral behaviour of our sample. These attributes were then used as features\nfor an Isolation Forest (IF) algorithm, that is an anomaly detector for a \"one\nclass\" kind of problem. We used the VRAE reconstruction errors and the IF\nanomaly score to select a sample of 8,809 anomalies. These anomalies are\ndominated by bogus candidates, but we were able to identify 75 promising CSAGN\ncandidates.\n"}
+{"abstract": "  With a highly coherent, optically addressable electron spin, the nitrogen\nvacancy (NV) centre in diamond is a promising candidate for a node in a quantum\nnetwork. However, the NV centre is a poor source of coherent single photons\nowing to a long radiative lifetime, a small branching ratio into the\nzero-phonon line (ZPL) and a poor extraction efficiency out of the high-index\nhost material. In principle, these three shortcomings can be addressed by\nresonant coupling to a single mode of an optical cavity. Utilising the\nweak-coupling regime of cavity electrodynamics, resonant coupling between the\nZPL and a single cavity-mode enhances the transition rate and branching ratio\ninto the ZPL. Furthermore, the cavity channels the light into a well-defined\nmode thereby facilitating detection with external optics. Here, we present an\nopen Fabry-Perot microcavity geometry containing a single-crystal diamond\nmembrane, which operates in a regime where the vacuum electric field is\nstrongly confined to the diamond membrane. There is a field anti-node at the\ndiamond-air interface. Despite the presence of surface losses, quality factors\nexceeding $120\\,000$ and a finesse $\\mathcal{F}=11\\,500$ were observed. We\ninvestigate the interplay between different loss mechanisms, and the impact\nthese loss channels have on the performance of the cavity. This analysis\nsuggests that the \"waviness\" (roughness with a spatial frequency comparable to\nthat of the microcavity mode) is the mechanism preventing the quality factors\nfrom reaching even higher values. Finally, we apply the extracted cavity\nparameters to the NV centre and calculate a predicted Purcell factor exceeding\n150.\n"}
+{"abstract": "  The number of solutions to $a^2+b^2=c^2+d^2 \\le x$ in integers is a\nwell-known result, while if one restricts all the variables to primes Erdos\nshowed that only the diagonal solutions, namely, the ones with\n$\\{a,b\\}=\\{c,d\\}$ contribute to the main term, hence there is a paucity of the\noff-diagonal solutions. Daniel considered the case of $a,c$ being prime and\nproved that the main term has both the diagonal and the non-diagonal\ncontributions. Here we investigate the remaining cases, namely when only $c$ is\na prime and when both c,d are primes and, finally, when $b,c,d$ are primes by\ncombining techniques of Daniel, Hooley and Plaksin.\n"}
+{"abstract": "  We strengthen the maximal ergodic theorem for actions of groups of polynomial\ngrowth to a form involving jump quantity, which is the sharpest result among\nthe family of variational or maximal ergodic theorems. As a consequence, we\ndeduce in this setting the quantitative ergodic theorem, in particular, the\nupcrossing inequalities with exponential decay. The ideas or techniques involve\nprobability theory, non-doubling Calder\\'on-Zygmund theory, almost\northogonality argument and some delicate geometric argument involving the balls\nand the cubes on the group equipped with a not necessarily doubling measure.\n"}
+{"abstract": "  Landmark localization plays an important role in medical image analysis.\nLearning based methods, including CNN and GCN, have demonstrated the\nstate-of-the-art performance. However, most of these methods are\nfully-supervised and heavily rely on manual labeling of a large training\ndataset. In this paper, based on a fully-supervised graph-based method, DAG, we\nproposed a semi-supervised extension of it, termed few-shot DAG, \\ie five-shot\nDAG. It first trains a DAG model on the labeled data and then fine-tunes the\npre-trained model on the unlabeled data with a teacher-student SSL mechanism.\nIn addition to the semi-supervised loss, we propose another loss using JS\ndivergence to regulate the consistency of the intermediate feature maps. We\nextensively evaluated our method on pelvis, hand and chest landmark detection\ntasks. Our experiment results demonstrate consistent and significant\nimprovements over previous methods.\n"}
+{"abstract": "  We study the Jellium model of Wigner at finite, non-zero, temperature through\na computer simulation using the canonical path integral worm algorithm where we\nsuccessfully implemented the fixed-nodes free particles restriction necessary\nto circumvent the fermion sign problem. Our results show good agreement with\nthe recent simulation data of Brown et al. and of other similar computer\nexperiments on the Jellium model at high density and low temperature. Our\nalgorithm can be used to treat any quantum fluid model of fermions at finite,\nnon zero, temperature and has never been used before in literature.\n"}
+{"abstract": "  Instead of only considering technology, computer security research now\nstrives to also take into account the human factor by studying regular users\nand, to a lesser extent, experts like operators and developers of systems. We\nfocus our analysis on the research on the crucial population of experts, whose\nhuman errors can impact many systems at once, and compare it to research on\nregular users. To understand how far we advanced in the area of human factors,\nhow the field can further mature, and to provide a point of reference for\nresearchers new to this field, we analyzed the past decade of human factors\nresearch in security and privacy, identifying 557 relevant publications. Of\nthese, we found 48 publications focused on expert users and analyzed all in\ndepth. For additional insights, we compare them to a stratified sample of 48\nend-user studies.\n  In this paper we investigate:\n  (i) The perspective on human factors, and how we can learn from safety\nscience (ii) How and who are the participants recruited, and how this -- as we\nfind -- creates a western-centric perspective (iii) Research objectives, and\nhow to align these with the chosen research methods (iv) How theories can be\nused to increase rigor in the communities scientific work, including\nlimitations to the use of Grounded Theory, which is often incompletely applied\n(v) How researchers handle ethical implications, and what we can do to account\nfor them more consistently\n  Although our literature review has limitations, new insights were revealed\nand avenues for further research identified.\n"}
+{"abstract": "  Miniaturized instruments are highly needed for robot assisted medical\nhealthcare and treatment, especially for less invasive surgery as it empowers\nmore flexible access to restricted anatomic intervention. But the robotic\ndesign is more challenging due to the contradictory needs of miniaturization\nand the capability of manipulating with a large dexterous workspace. Thus,\nkinematic parameter optimization is of great significance in this case. To this\nend, this paper proposes an approach based on dexterous workspace determination\nfor designing a miniaturized tendon-driven surgical instrument under necessary\nrestraints. The workspace determination is achieved by boundary determination\nand volume estimation with partition and least-squares polynomial fitting\nmethods. The final robotic configuration with optimized kinematic parameters is\nproved to be eligible with a large enough dexterous workspace and targeted\nminiature size.\n"}
+{"abstract": "  Unsupervised domain adaptation (UDA) in semantic segmentation is a\nfundamental yet promising task relieving the need for laborious annotation\nworks. However, the domain shifts/discrepancies problem in this task compromise\nthe final segmentation performance. Based on our observation, the main causes\nof the domain shifts are differences in imaging conditions, called image-level\ndomain shifts, and differences in object category configurations called\ncategory-level domain shifts. In this paper, we propose a novel UDA pipeline\nthat unifies image-level alignment and category-level feature distribution\nregularization in a coarse-to-fine manner. Specifically, on the coarse side, we\npropose a photometric alignment module that aligns an image in the source\ndomain with a reference image from the target domain using a set of image-level\noperators; on the fine side, we propose a category-oriented triplet loss that\nimposes a soft constraint to regularize category centers in the source domain\nand a self-supervised consistency regularization method in the target domain.\nExperimental results show that our proposed pipeline improves the\ngeneralization capability of the final segmentation model and significantly\noutperforms all previous state-of-the-arts.\n"}
+{"abstract": "  We relate Gruet formula for the heat kernel on real hyperbolic spaces to the\ncommonly used one derived from Millson induction. The bridge between both\nformulas is settled by Yor result on the joint distribution of a Brownian\nmotion and of its exponential functional at fixed time. This result allows\nfurther to relate Gruet formula with real parameter to the heat kernel of the\nhyperbolic Jacobi operator and to derive a new integral representation for the\nheat kernel of the Maass Laplacian. When applied to harmonic AN groups, Yor\nresult yields also new a integral representation of their corresponding heat\nkernels which does not distinguish the parity of the dimension of the center of\nthe Lie group N.\n"}
+{"abstract": "  This work constructs Jonson-Lindenstrauss embeddings with best accuracy, as\nmeasured by variance, mean-squared error and exponential concentration of the\nlength distortion. Lower bounds for any data and embedding dimensions are\ndetermined, and accompanied by matching and efficiently samplable constructions\n(built on orthogonal matrices). Novel techniques: a unit sphere\nparametrization, the use of singular-value latent variables and Schur-convexity\nare of independent interest.\n"}
+{"abstract": "  The melt productivity of a differentiated planet's mantle is primarily\ncontrolled by its iron content, which is itself approximated by the planet's\ncore mass fraction (CMF). Here we show that estimates of an exo-planet's CMF\nallows robust predictions of the thickness, composition and mineralogy of the\nderivative crust. These predicted crustal compositions allow constraints to be\nplaced on volatile cycling between surface and the deep planetary interior,\nwith implications for the evolution of habitable planetary surfaces. Planets\nwith large, terrestrial-like, CMFs ($\\geq$0.32) will exhibit thin crusts that\nare inefficient at transporting surface water and other volatiles into the\nunderlying mantle. By contrast, rocky planets with smaller CMFs ($\\leq$0.24)\nand higher, Mars-like, mantle iron contents will develop thick crusts capable\nof stabilizing hydrous minerals, which can effectively sequester volatiles into\nplanetary interiors and act to remove surface water over timescales relevant to\nevolution. The extent of core formation has profound consequences for the\nsubsequent planetary surface environment and may provide additional constraints\nin the hunt for habitable, Earth-like exo-planets.\n"}
+{"abstract": "  Getting good performance out of numerical equation solvers requires that the\nuser has provided stable and efficient functions representing their model.\nHowever, users should not be trusted to write good code. In this manuscript we\ndescribe ModelingToolkit (MTK), a symbolic equation-based modeling system which\nallows for composable transformations to generate stable, efficient, and\nparallelized model implementations. MTK blurs the lines of traditional symbolic\ncomputing by acting directly on a user's numerical code. We show the ability to\napply graph algorithms for automatically parallelizing and performing index\nreduction on code written for differential-algebraic equation (DAE) solvers,\n\"fixing\" the performance and stability of the model without requiring any\nchanges to on the user's part. We demonstrate how composable model\ntransformations can be combined with automated data-driven surrogate generation\ntechniques, allowing machine learning methods to generate accelerated\napproximate models within an acausal modeling framework. These reduced models\nare shown to outperform the Dymola Modelica compiler on an HVAC model by 590x\nat 3\\% error. Together, this demonstrates MTK as a system for bringing the\nlatest research in graph transformations directly to modeling applications.\n"}
+{"abstract": "  Hybrid quantum-classical algorithms, such as variational quantum algorithms\n(VQA), are suitable for implementation on NISQ computers. In this Letter we\nexpand an implicit step of VQAs: the classical pre-computation subroutine which\ncan non-trivially use classical algorithms to simplify, transform, or specify\nproblem instance-specific variational quantum circuits. In VQA there is a\ntrade-off between quality of solution and difficulty of circuit construction\nand optimization. In one extreme, we find VQA for MAXCUT which are exact, but\ncircuit design or variational optimization is NP-HARD. At the other extreme are\nlow depth VQA, such as QAOA, with tractable circuit construction and\noptimization but poor approximation ratios. Combining these two we define the\nSpanning Tree QAOA (ST-QAOA) to solve MAXCUT, which uses an ansatz whose\nstructure is derived from an approximate classical solution and achieves the\nsame performance guarantee as the classical algorithm and hence can outperform\nQAOA at low depth. In general, we propose integrating these classical\npre-computation subroutines into VQA to improve heuristic or guaranteed\nperformance.\n"}
+{"abstract": "  We develop and study new adversarial perturbations that enable an attacker to\ngain control over decisions in generic Artificial Intelligence (AI) systems\nincluding deep learning neural networks. In contrast to adversarial data\nmodification, the attack mechanism we consider here involves alterations to the\nAI system itself. Such a stealth attack could be conducted by a mischievous,\ncorrupt or disgruntled member of a software development team. It could also be\nmade by those wishing to exploit a \"democratization of AI\" agenda, where\nnetwork architectures and trained parameter sets are shared publicly. Building\non work by [Tyukin et al., International Joint Conference on Neural Networks,\n2020], we develop a range of new implementable attack strategies with\naccompanying analysis, showing that with high probability a stealth attack can\nbe made transparent, in the sense that system performance is unchanged on a\nfixed validation set which is unknown to the attacker, while evoking any\ndesired output on a trigger input of interest. The attacker only needs to have\nestimates of the size of the validation set and the spread of the AI's relevant\nlatent space. In the case of deep learning neural networks, we show that a one\nneuron attack is possible - a modification to the weights and bias associated\nwith a single neuron - revealing a vulnerability arising from\nover-parameterization. We illustrate these concepts in a realistic setting.\nGuided by the theory and computational results, we also propose strategies to\nguard against stealth attacks.\n"}
+{"abstract": "  We study the prospect for discovering the $cg\\to bH^+\\to b A W^+$ process at\nthe LHC. Induced by the top-flavor changing neutral Higgs coupling $\\rho_{tc}$,\nthe process may emerge if $m_{H^+} > m_A + m_{W^+}$, where $H^+$ and $A$ are\ncharged and $CP$-odd Higgs bosons in the general two Higgs Doublet Model\n(g2HDM). We show that the $cg\\to bH^+\\to b A W^+$ process can be discovered at\nLHC Run 3, while the full Run 2 data at hand can constrain the parameter space\nsignificantly by searching for the same-sign dilepton final state. The process\nhas unique implications on the hint of $gg\\to A \\to t \\bar t$ excess at\n$m_A\\approx 400$ GeV reported by CMS. When combined with other existing\nconstraints, the $cg\\to bH^+\\to b A W^+$ process can essentially rule out the\ng2HDM explanation of such an excess.\n"}
+{"abstract": "  Representations and O-operators of Hom-(pre)-Jacobi-Jordan algebras are\nintroduced and studied. The anticommutator of a Hom-pre-Jacobi-Jordan algebra\nis a Hom-Jacobi-Jordan algebra and the left multiplication operator gives a\nrepresentation of a Hom-Jacobi-Jordan algebra. The notion of matched pairs and\nNijenhuis operators of Hom-(pre)-Jacobi-Jordan algebras are given and various\nrelevant constructions are obtained.\n"}
+{"abstract": "  We observe minima of the longitudinal resistance corresponding to the quantum\nHall effect of composite fermions at quantum numbers $p=1$, 2, 3, 4, and 6 in\nan ultraclean strongly interacting bivalley SiGe/Si/SiGe two-dimensional\nelectron system. The minima at $p=3$ disappear below a certain electron\ndensity, although the surrounding minima at $p=2$ and $p=4$ survive at\nsignificantly lower densities. Furthermore, the onset for the resistance\nminimum at a filling factor $\\nu=3/5$ is found to be independent of the tilt\nangle of the magnetic field. These surprising results indicate the intersection\nor merging of the quantum levels of composite fermions with different valley\nindices, which reveals the valley effect on fractions.\n"}
+{"abstract": "  This paper presents a joint source separation algorithm that simultaneously\nreduces acoustic echo, reverberation and interfering sources. Target speeches\nare separated from the mixture by maximizing independence with respect to the\nother sources. It is shown that the separation process can be decomposed into\ncascading sub-processes that separately relate to acoustic echo cancellation,\nspeech dereverberation and source separation, all of which are solved using the\nauxiliary function based independent component/vector analysis techniques, and\ntheir solving orders are exchangeable. The cascaded solution not only leads to\nlower computational complexity but also better separation performance than the\nvanilla joint algorithm.\n"}
+{"abstract": "  Current event-centric knowledge graphs highly rely on explicit connectives to\nmine relations between events. Unfortunately, due to the sparsity of\nconnectives, these methods severely undermine the coverage of EventKGs. The\nlack of high-quality labelled corpora further exacerbates that problem. In this\npaper, we propose a knowledge projection paradigm for event relation\nextraction: projecting discourse knowledge to narratives by exploiting the\ncommonalities between them. Specifically, we propose Multi-tier Knowledge\nProjection Network (MKPNet), which can leverage multi-tier discourse knowledge\neffectively for event relation extraction. In this way, the labelled data\nrequirement is significantly reduced, and implicit event relations can be\neffectively extracted. Intrinsic experimental results show that MKPNet achieves\nthe new state-of-the-art performance, and extrinsic experimental results verify\nthe value of the extracted event relations.\n"}
+{"abstract": "  We elucidate universal many-body properties of a one-dimensional,\ntwo-component ultracold Fermi gas near the $p$-wave Feshbach resonance. The\nlow-energy scattering in this system can be characterized by two parameters,\nthat is, $p$-wave scattering length and effective range. At the unitarity limit\nwhere the $p$-wave scattering length diverges and the effective range is\nreduced to zero without conflicting with the causality bound, the system obeys\nuniversal thermodynamics as observed in a unitary Fermi gas with contact\n$s$-wave interaction in three dimensions. It is in contrast to a Fermi gas with\nthe $p$-wave resonance in three dimensions in which the effective range is\ninevitably finite. We present the universal equation of state in this unitary\n$p$-wave Fermi gas within the many-body $T$-matrix approach as well as the\nvirial expansion method. Moreover, we examine the single-particle spectral\nfunction in the high-density regime where the virial expansion is no longer\nvalid. On the basis of the Hartree-like self-energy shift at the divergent\nscattering length, we conjecture that the equivalence of the Bertsch parameter\nacross spatial dimensions holds even for a one-dimensional unitary $p$-wave\nFermi gas.\n"}
+{"abstract": "  We have developed two metrics related to AGN variability observables\n(time-lags, periodicity, and Structure Function (SF)) to evaluate LSST OpSim\nFBS 1.5, 1.6, 1.7 performance in AGN time-domain analysis. For this purpose, we\ngenerate an ensemble of AGN light curves based on AGN empirical relations and\nLSST OpSim cadences. Although our metrics show that denser LSST cadences\nproduce more reliable time-lag, periodicity, and SF measurements, the\ndiscrepancies in the performance between different LSST OpSim cadences are not\ndrastic based on Kullback-Leibler divergence. This is complementary to Yu and\nRichards results on DCR and SF metrics, extending them to include the point of\nview of AGN variability.\n"}
+{"abstract": "  Motivated by the search for methods to establish strong minimality of certain\nlow order algebraic differential equations, a measure of how far a finite rank\nstationary type is from being minimal is introduced and studied: The {\\em\ndegree of nonminimality} is the minimum number of realisations of the type\nrequired to witness a nonalgebraic forking extension. Conditional on the truth\nof a conjecture of Borovik and Cherlin on the generic multiple-transitivity of\nhomogeneous spaces definable in the stable theory being considered, it is shown\nthat the nonminimality degree is bounded by the $U$-rank plus $2$. The\nBorovik-Cherlin conjecture itself is verified for algebraic and meromorphic\ngroup actions, and a bound of $U$-rank plus $1$ is then deduced unconditionally\nfor differentially closed fields and compact complex manifolds. An application\nis given regarding transcendence of solutions to algebraic differential\nequations.\n"}
+{"abstract": "  Network games study the strategic interaction of agents connected through a\nnetwork. Interventions in such a game -- actions a coordinator or planner may\ntake that change the utility of the agents and thus shift the equilibrium\naction profile -- are introduced to improve the planner's objective. We study\nthe problem of intervention in network games where the network has a group\nstructure with local planners, each associated with a group. The agents play a\nnon-cooperative game while the planners may or may not have the same\noptimization objective. We model this problem using a sequential move game\nwhere planners make interventions followed by agents playing the intervened\ngame. We provide equilibrium analysis and algorithms that find the subgame\nperfect equilibrium. We also propose a two-level efficiency definition to study\nthe efficiency loss of equilibrium actions in this type of game.\n"}
+{"abstract": "  Hadamard's maximal determinant problem consists in finding the maximal value\nof the determinant of a square $n\\times n$ matrix whose entries are plus or\nminus ones. This is a difficult mathematical problem which is not yet solved.\nIn the present paper a simplified version of the problem is considered and\nstudied numerically.\n"}
+{"abstract": "  The complexity and non-Euclidean structure of graph data hinder the\ndevelopment of data augmentation methods similar to those in computer vision.\nIn this paper, we propose a feature augmentation method for graph nodes based\non topological regularization, in which topological structure information is\nintroduced into end-to-end model. Specifically, we first obtain topology\nembedding of nodes through unsupervised representation learning method based on\nrandom walk. Then, the topological embedding as additional features and the\noriginal node features are input into a dual graph neural network for\npropagation, and two different high-order neighborhood representations of nodes\nare obtained. On this basis, we propose a regularization technique to bridge\nthe differences between the two different node representations, eliminate the\nadverse effects caused by the topological features of graphs directly used, and\ngreatly improve the performance. We have carried out extensive experiments on a\nlarge number of datasets to prove the effectiveness of our model.\n"}
+{"abstract": "  Magnetic induction tomography (MIT) is an efficient solution for long-term\nbrain disease monitoring, which focuses on reconstructing bio-impedance\ndistribution inside the human brain using non-intrusive electromagnetic fields.\nHowever, high-quality brain image reconstruction remains challenging since\nreconstructing images from the measured weak signals is a highly non-linear and\nill-conditioned problem. In this work, we propose a generative adversarial\nnetwork (GAN) enhanced MIT technique, named MITNet, based on a complex\nconvolutional neural network (CNN). The experimental results on the real-world\ndataset validate the performance of our technique, which outperforms the\nstate-of-art method by 25.27%.\n"}
+{"abstract": "  Growth occurs in a wide range of systems ranging from biological tissue to\nadditive manufacturing. This work considers surface growth, in which mass is\nadded to the boundary of a continuum body from the ambient medium or from\nwithin the body. In contrast to bulk growth in the interior, the description of\nsurface growth requires the addition of new continuum particles to the body.\nThis is challenging for standard continuum formulations for solids that are\nmeant for situations with a fixed amount of material. Recent approaches to\nhandle this have used time-evolving reference configurations.\n  In this work, an Eulerian approach to this problem is formulated, enabling\nthe side-stepping of the issue of constructing the reference configuration.\nHowever, this raises the complementary challenge of determining the stress\nresponse of the solid, which typically requires the deformation gradient that\nis not immediately available in the Eulerian formulation. To resolve this, the\napproach introduces additional kinematic descriptors, namely the relaxed\nzero-stress deformation and the elastic deformation; in contrast to the\ndeformation gradient, these have the important advantage that they are not\nrequired to satisfy kinematic compatibility. The resulting model has only the\ndensity, velocity, and elastic deformation as variables in the Eulerian\nsetting.\n  The introduction in this formulation of the relaxed deformation and the\nelastic deformation provides a description of surface growth whereby the added\nmaterial can bring in its own kinematic information. Loosely, the added\nmaterial \"brings in its own reference configuration\" through the specification\nof the relaxed deformation and the elastic deformation of the added material.\nThis kinematic description enables, e.g., modeling of non-normal growth using a\nstandard normal growth velocity and a simple approach to prescribing boundary\nconditions.\n"}
+{"abstract": "  We theoretically investigate the out-of-equilibrium dynamics in a binary\nBose-Einstein condensate confined within two-dimensional box potentials. One\nspecies of the condensate interacts with a pair of oppositely wound, but\notherwise identical Laguerre-Gaussian laser pulses, while the other species is\ninfluenced only via the interspecies interaction. Starting from the\nHamiltonian, we derive the equations of motion that accurately delineate the\nbehavior of the condensates during and after the light-matter interaction.\nDepending on the number the helical windings (or the magnitude of topological\ncharge), the species directly participating in the interaction with lasers is\ndynamically segmented into distinct parts which collide together as the pulses\ngradually diminish. This collision event generates nonlinear structures in the\nrelated species, coupled with the complementary structures produced in the\nother species, due to the interspecies interaction. The long-time dynamics of\nthe optically perturbed species is found to develop the Kolmogorov-Saffman\nscaling law in the incompressible kinetic energy spectrum, a characteristic\nfeature of the quantum turbulent state. However, the same scaling law is not\ndefinitively exhibited in the other species. This study warrants the usage of\nLaguerre-Gaussian beams for future experiments on quantum turbulence in\nBose-Einstein condensates.\n"}
+{"abstract": "  Face representation learning using datasets with massive number of identities\nrequires appropriate training methods. Softmax-based approach, currently the\nstate-of-the-art in face recognition, in its usual \"full softmax\" form is not\nsuitable for datasets with millions of persons. Several methods, based on the\n\"sampled softmax\" approach, were proposed to remove this limitation. These\nmethods, however, have a set of disadvantages. One of them is a problem of\n\"prototype obsolescence\": classifier weights (prototypes) of the rarely sampled\nclasses, receive too scarce gradients and become outdated and detached from the\ncurrent encoder state, resulting in an incorrect training signals. This problem\nis especially serious in ultra-large-scale datasets. In this paper, we propose\na novel face representation learning model called Prototype Memory, which\nalleviates this problem and allows training on a dataset of any size. Prototype\nMemory consists of the limited-size memory module for storing recent class\nprototypes and employs a set of algorithms to update it in appropriate way. New\nclass prototypes are generated on the fly using exemplar embeddings in the\ncurrent mini-batch. These prototypes are enqueued to the memory and used in a\nrole of classifier weights for usual softmax classification-based training. To\nprevent obsolescence and keep the memory in close connection with encoder,\nprototypes are regularly refreshed, and oldest ones are dequeued and disposed.\nPrototype Memory is computationally efficient and independent of dataset size.\nIt can be used with various loss functions, hard example mining algorithms and\nencoder architectures. We prove the effectiveness of the proposed model by\nextensive experiments on popular face recognition benchmarks.\n"}
+{"abstract": "  A problem that is frequently encountered in a variety of mathematical\ncontexts, is to find the common invariant subspaces of a single, or set of\nmatrices. A new method is proposed that gives a definitive answer to this\nproblem. The key idea consists of finding common eigenvectors for exterior\npowers of the matrices concerned. A convenient formulation of the Pl\\\"ucker\nrelations is then used to ensure that these eigenvectors actually correspond to\nsubspaces or provide the initial constraints for eigenvectors involving\nparameters. A procedure for computing the divisors of totally decomposable\nvector is also provided. Several examples are given for which the calculations\nare too tedious to do by hand and are performed by coding the conditions found\ninto Maple.\n"}
+{"abstract": "  We present the analysis of the colour-magnitude diagram (CMD) morphology of\nthe ~ 800 Myr old star cluster NGC1831 in the Large Magellanic Cloud,\nexploiting deep, high-resolution photometry obtained using the Wide Field\nCamera 3 onboard the Hubble Space Telescope. We perform a simultaneous analysis\nof the wide upper main sequence and main sequence turn-off observed in the\ncluster, to verify whether these features are due to an extended star formation\nor a range of stellar rotation rates, or a combination of these two effects.\nComparing the observed CMD with Monte Carlo simulations of synthetic stellar\npopulations, we derive that the morphology of NGC1831 can be fully explained in\nthe context of the rotation velocity scenario, under the assumption of a\nbimodal distribution for the rotating stars, with ~40% of stars being\nslow-rotators ($\\Omega$ / $\\Omega_{crit}$ < 0.5) and the remaining ~ 60% being\nfast rotators ($\\Omega$ / $\\Omega_{crit}$ > 0.9). We derive the dynamical\nproperties of the cluster, calculating the present cluster mass and escape\nvelocity, and predicting their past evolution starting at an age of 10 Myr. We\nfind that NGC1831 has an escape velocity $v_{esc}$ = 18.4 km/s, at an age of 10\nMyr, above the previously suggested threshold of 15 km/s, below which the\ncluster cannot retain the material needed to create second-generation stars.\nThese results, combined with those obtained from the CMD morphology analysis,\nindicate that for the clusters whose morphology cannot be easily explained only\nin the context of the rotation velocity scenario, the threshold limit should be\nat least ~ 20 km/s.\n"}
+{"abstract": "  The $q$-Onsager algebra $O_q$ is presented by two generators $W_0$, $W_1$ and\ntwo relations, called the $q$-Dolan/Grady relations. Recently Baseilhac and\nKoizumi introduced a current algebra $\\mathcal A_q$ for $O_q$. Soon afterwards,\nBaseilhac and Shigechi gave a presentation of $\\mathcal A_q$ by generators and\nrelations. We show that these generators give a PBW basis for $\\mathcal A_q$.\nUsing this PBW basis, we show that the algebra $\\mathcal A_q$ is isomorphic to\n$O_q \\otimes \\mathbb F \\lbrack z_1, z_2, \\ldots \\rbrack$, where $\\mathbb F$ is\nthe ground field and $\\lbrace z_n \\rbrace_{n=1}^\\infty $ are mutually commuting\nindeterminates. Recall the positive part $U^+_q$ of the quantized enveloping\nalgebra $U_q(\\widehat{\\mathfrak{sl}}_2)$. Our results show that $O_q$ is\nrelated to $\\mathcal A_q$ in the same way that $U^+_q$ is related to the\nalternating central extension of $U^+_q$. For this reason, we propose to call\n$\\mathcal A_q$ the alternating central extension of $O_q$.\n"}
+{"abstract": "  We study how violations of structural assumptions like expected utility and\nexponential discounting can be connected to reference dependent preferences\nwith set-dependent reference points, even if behavior conforms with these\nassumptions when the reference is fixed. An axiomatic framework jointly and\nsystematically relaxes general rationality (WARP) and structural assumptions to\ncapture reference dependence across domains. It gives rise to a linear order\nthat determines references points, which in turn determines the preference\nparameters for a choice problem. This allows us to study risk, time, and social\npreferences collectively, where seemingly independent anomalies are\ninterconnected through the lens of reference-dependent choice.\n"}
+{"abstract": "  Early diagnosis is essential for the successful treatment of bowel cancers\nincluding colorectal cancer (CRC) and capsule endoscopic imaging with robotic\nactuation can be a valuable diagnostic tool when combined with automated image\nanalysis. We present a deep learning rooted detection and segmentation\nframework for recognizing lesions in colonoscopy and capsule endoscopy images.\nWe restructure established convolution architectures, such as VGG and ResNets,\nby converting them into fully-connected convolution networks (FCNs), fine-tune\nthem and study their capabilities for polyp segmentation and detection. We\nadditionally use Shape from-Shading (SfS) to recover depth and provide a richer\nrepresentation of the tissue's structure in colonoscopy images. Depth is\nincorporated into our network models as an additional input channel to the RGB\ninformation and we demonstrate that the resulting network yields improved\nperformance. Our networks are tested on publicly available datasets and the\nmost accurate segmentation model achieved a mean segmentation IU of 47.78% and\n56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp\ndetection, the top performing models we propose surpass the current state of\nthe art with detection recalls superior to 90% for all datasets tested. To our\nknowledge, we present the first work to use FCNs for polyp segmentation in\naddition to proposing a novel combination of SfS and RGB that boosts\nperformance\n"}
+{"abstract": "  Infants acquire words and phonemes from unsegmented speech signals using\nsegmentation cues, such as distributional, prosodic, and co-occurrence cues.\nMany pre-existing computational models that represent the process tend to focus\non distributional or prosodic cues. This paper proposes a nonparametric\nBayesian probabilistic generative model called the prosodic hierarchical\nDirichlet process-hidden language model (Prosodic HDP-HLM). Prosodic HDP-HLM,\nan extension of HDP-HLM, considers both prosodic and distributional cues within\na single integrative generative model. We conducted three experiments on\ndifferent types of datasets, and demonstrate the validity of the proposed\nmethod. The results show that the Prosodic DAA successfully uses prosodic cues\nand outperforms a method that solely uses distributional cues. The main\ncontributions of this study are as follows: 1) We develop a probabilistic\ngenerative model for time series data including prosody that potentially has a\ndouble articulation structure; 2) We propose the Prosodic DAA by deriving the\ninference procedure for Prosodic HDP-HLM and show that Prosodic DAA can\ndiscover words directly from continuous human speech signals using statistical\ninformation and prosodic information in an unsupervised manner; 3) We show that\nprosodic cues contribute to word segmentation more in naturally distributed\ncase words, i.e., they follow Zipf's law.\n"}
+{"abstract": "  The first principles momentum dependent local ansatz wavefunction method\n(MLA) has been extended to the ferromagnetic state by introducing\nspin-dependent variational parameters. The theory is applied to the\nferromagnetic Fe, Co, and Ni. It is shown that the MLA yields the\nmagnetizations being comparable to the results obtained by the GGA (generalized\ngradient approximation) in the density functional theory. The projected\nmomentum distribution functions as well as the mass enhancement factors are\nalso calculated on the same footing, and are compared with those in the\nparamagnetic state. It is shown that the calculated mass enhancement factor of\nFe is strongly suppressed by the spin polarization due to exchange splitting of\nthe e${}_{\\rm g}$ flat bands, while those of Co and Ni remain unchanged by the\npolarization. These results are shown to be consistent with the experimental\nresults obtained from the low-temperature specific heats.\n"}
+{"abstract": "  In this paper we develop an asymptotic theory for steadily travelling\ngravity-capillary waves under the small-surface tension limit. In an\naccompanying work [Shelton et al. (2021), J. Fluid Mech., accepted/in press] it\nwas demonstrated that solutions associated with a perturbation about a\nleading-order gravity wave (a Stokes wave) contain surface-tension-driven\nparasitic ripples with an exponentially-small amplitude. Thus a naive\nPoincar\\'e expansion is insufficient for their description. Here, we shall\ndevelop specialised methodologies in exponential asymptotics for derivation of\nthe parasitic ripples on periodic domains. The ripples are shown to arise in\nconjunction with Stokes lines and the Stokes phenomenon. The analysis relies\ncrucially upon the derivation and analysis of singularities in the analytic\ncontinuation of the classic Stokes wave. A solvability condition is derived,\nshowing that solutions of this type do not exist at certain values of the Bond\nnumber. The asymptotic results are compared to full numerical solutions and\nshow excellent agreement. The work provides corrections and insight of a\nseminal theory on parasitic capillary waves first proposed by Longuet-Higgins\n[J. Fluid Mech., vol. 16 (1), 1963, pp. 138-159].\n"}
+{"abstract": "  Quality-Diversity algorithms refer to a class of evolutionary algorithms\ndesigned to find a collection of diverse and high-performing solutions to a\ngiven problem. In robotics, such algorithms can be used for generating a\ncollection of controllers covering most of the possible behaviours of a robot.\nTo do so, these algorithms associate a behavioural descriptor to each of these\nbehaviours. Each behavioural descriptor is used for estimating the novelty of\none behaviour compared to the others. In most existing algorithms, the\nbehavioural descriptor needs to be hand-coded, thus requiring prior knowledge\nabout the task to solve. In this paper, we introduce: Autonomous Robots\nRealising their Abilities, an algorithm that uses a dimensionality reduction\ntechnique to automatically learn behavioural descriptors based on raw sensory\ndata. The performance of this algorithm is assessed on three robotic tasks in\nsimulation. The experimental results show that it performs similarly to\ntraditional hand-coded approaches without the requirement to provide any\nhand-coded behavioural descriptor. In the collection of diverse and\nhigh-performing solutions, it also manages to find behaviours that are novel\nwith respect to more features than its hand-coded baselines. Finally, we\nintroduce a variant of the algorithm which is robust to the dimensionality of\nthe behavioural descriptor space.\n"}
+{"abstract": "  We introduce a protection-based IP security scheme to protect soft and firm\nIP cores which are used on FPGA devices. The scheme is based on Finite State\nMachin (FSM) obfuscation and exploits Physical Unclonable Function (PUF) for\nFPGA unique identification (ID) generation which help pay-per-device licensing.\nWe introduce a communication protocol to protect the rights of parties in this\nmarket. On standard benchmark circuits, the experimental results show that our\nscheme is secure, attack-resilient and can be implemented with low area, power\nand delay overheads.\n"}
+{"abstract": "  In this paper, we address the Cauchy problem for the relativistic BGK model\nproposed by Anderson and Witting for massless particles in the\nFriedmann-Lemaitre-Robertson-Walker (FLRW) spacetime.\n"}
+{"abstract": "  Acoustophoresis deals with the manipulation of sub-wavelength scatterers in\nan incident acoustic field. The geometric details of manipulated particles are\noften neglected by replacing them with equivalent symmetric geometries such as\nspheres, spheroids, cylinders or disks. It has been demonstrated that geometric\nasymmetry, represented by Willis coupling terms, can strongly affect the\nscattering of a small object, hence neglecting these terms may miss important\nforce contributions. In this work, we present a generalized formalism of\nacoustic radiation force and radiation torque based on the polarizability\ntensor, where Willis coupling terms are included to account for geometric\nasymmetry. Following Gorkov's approach, the effects of geometric asymmetry are\nexplicitly formulated as additional terms in the radiation force and torque\nexpressions. By breaking the symmetry of a sphere along one axis using\nintrusion and protrusion, we characterize the changes in the force and torque\nin terms of partial components, associated with the direct and Willis Coupling\ncoefficients of the polarizability tensor. We investigate in detail the cases\nof standing and travelling plane waves, showing how the equilibrium positions\nand angles are shifted by these additional terms. We show that while the\ncontributions of asymmetry to the force are often negligible for small\nparticles, these terms greatly affect the radiation torque. Our presented\ntheory, providing a way of calculating radiation force and torque directly from\npolarizability coefficients, shows that in general it is essential to account\nfor shape of objects undergoing acoustophoretic manipulation, and this may have\nimportant implications for applications such as the manipulation of biological\ncells.\n"}
+{"abstract": "  Third-order approximate solutions for surface gravity waves in the finite\nwater depth are studied in the context of potential flow theory. This solution\nprovides explicit expressions for the surface elevation, free-surface velocity\npotential and velocity potential. The amplitude dispersion relation is also\nprovided. Two approaches are used to derive the third order analytical\nsolution, resulting in two types of approximate solutions: the perturbation\nsolution and the Hamiltonian solution. The perturbation solution is obtained by\nclassical perturbation technique in which the time variable is expanded in\nmultiscale to eliminate secular terms. The Hamiltonian solution is derived from\nthe canonical transformation in the Hamiltonian theory of water waves. By\ncomparing the two types of solutions, it is found that they are completely\nequivalent for the first to second order solutions and the nonlinear\ndispersion, but for the third order part only the sum-sum terms are the same.\nDue to the canonical transformation that could completely separate the dynamic\nand bound harmonics, the Hamiltonian solutions break through the difficulty\nthat the perturbation theory breaks down due to singularities in the transfer\nfunctions when quartet resonance criterion is satisfied. Furthermore, it is\nalso found that some time-averaged quantities based on the Hamiltonian\nsolution, such as mean potential energy and mean kinetic energy, are equal to\nthose in the initial state in which sea surface is assumed to be a Gaussian\nrandom process. This is because there are associated conserved quantities in\nthe Hamiltonian form. All of these show that the Hamiltonian solution is more\nreasonable and accurate to describe the third order steady-state wave field.\nFinally, based on the Hamiltonian solution, some statistics are given such as\nthe volume flux, skewness, and excess kurtosis.\n"}
+{"abstract": "  Machine learning-inspired techniques have emerged as a new paradigm for\nanalysis of phase transitions in quantum matter. In this work, we introduce a\nsupervised learning algorithm for studying critical phenomena from measurement\ndata, which is based on iteratively training convolutional networks of\nincreasing complexity, and test it on the transverse field Ising chain and q=6\nPotts model. At the continuous Ising transition, we identify scaling behavior\nin the classification accuracy, from which we infer a characteristic\nclassification length scale. It displays a power-law divergence at the critical\npoint, with a scaling exponent that matches with the diverging correlation\nlength. Our algorithm correctly identifies the thermodynamic phase of the\nsystem and extracts scaling behavior from projective measurements,\nindependently of the basis in which the measurements are performed.\nFurthermore, we show the classification length scale is absent for the $q=6$\nPotts model, which has a first order transition and thus lacks a divergent\ncorrelation length. The main intuition underlying our finding is that, for\nmeasurement patches of sizes smaller than the correlation length, the system\nappears to be at the critical point, and therefore the algorithm cannot\nidentify the phase from which the data was drawn.\n"}
+{"abstract": "  An electron is usually considered to have only one type of kinetic energy,\nbut could it have more, for its spin and charge, or by exciting other\nelectrons? In one dimension (1D), the physics of interacting electrons is\ncaptured well at low energies by the Tomonaga-Luttinger-Liquid (TLL) model, yet\nlittle has been observed experimentally beyond this linear regime. Here, we\nreport on measurements of many-body modes in 1D gated-wires using a tunnelling\nspectroscopy technique. We observe two separate Fermi seas at high energies,\nassociated with spin and charge excitations, together with the emergence of\nthree additional 1D 'replica' modes that strengthen with decreasing wire\nlength. The effective interaction strength in the wires is varied by changing\nthe amount of 1D inter-subband screening by over 45%. Our findings demonstrate\nthe existence of spin-charge separation in the whole energy band outside the\nlow-energy limit of validity of the TLL model, and also set a limit on the\nvalidity of the newer nonlinear TLL theory.\n"}
+{"abstract": "  In a Hilbertian framework, for the minimization of a general convex\ndifferentiable function $f$, we introduce new inertial dynamics and algorithms\nthat generate trajectories and iterates that converge fastly towards the\nminimizer of $f$ with minimum norm. Our study is based on the non-autonomous\nversion of the Polyak heavy ball method, which, at time $t$, is associated with\nthe strongly convex function obtained by adding to $f$ a Tikhonov\nregularization term with vanishing coefficient $\\epsilon(t)$. In this dynamic,\nthe damping coefficient is proportional to the square root of the Tikhonov\nregularization parameter $\\epsilon(t)$. By adjusting the speed of convergence\nof $\\epsilon(t)$ towards zero, we will obtain both rapid convergence towards\nthe infimal value of $f$, and the strong convergence of the trajectories\ntowards the element of minimum norm of the set of minimizers of $f$. In\nparticular, we obtain an improved version of the dynamic of Su-Boyd-Cand\\`es\nfor the accelerated gradient method of Nesterov. This study naturally leads to\ncorresponding first-order algorithms obtained by temporal discretization. In\nthe case of a proper lower semicontinuous and convex function $f$, we study the\nproximal algorithms in detail, and show that they benefit from similar\nproperties.\n"}
+{"abstract": "  Terahertz (THz) frequency bands can be promising for data transmissions\nbetween the core network and access points (AP) for next-generation wireless\nsystems. In this paper, we analyze the performance of a dual-hop THz-RF\nwireless system where an AP facilitates data transmission between a core\nnetwork and user equipment (UE). We consider a generalized model for the\nend-to-end channel with an independent and not identically distributed\n(i.ni.d.) fading model for THz and RF links using the $\\alpha$-$\\mu$\ndistribution, the THz link with pointing errors, and asymmetrical relay\nposition. We derive a closed-form expression of the cumulative distribution\nfunction (CDF) of the end-to-end signal-to-noise ratio (SNR) for the THz-RF\nlink, which is valid for continuous values of $\\mu$ for a generalized\nperformance analysis over THz fading channels. Using the derived CDF, we\nanalyze the performance of the THz-RF relayed system using decode-and-forward\n(DF) protocol by deriving analytical expressions of diversity order, moments of\nSNR, ergodic capacity, and average BER in terms of system parameters. We also\nanalyze the considered system with an i.i.d. model and develop simplified\nperformance to provide insight on the system behavior analytically under\nvarious practically relevant scenarios. Simulation and numerical analysis show\na significant effect of fading parameters of the THz link and a nominal effect\nof normalized beam-width on the performance of the relay-assisted THz-RF\nsystem.\n"}
+{"abstract": "  We consider the cosmology obtained using scalar fields with a negative\npotential energy, such as employed to obtain an Ekpyrotic phase of contraction.\nApplying the covariant entropy bound to the tower of states dictated by the\ndistance conjecture, we find that the relative slope of the potential\n$|V^{\\prime}| / |V|$ is bounded from below by a constant of the order one in\nPlanck units. This is consistent with the requirement to obtain slow Ekpyrotic\ncontraction. We also derive a refined condition on the potential which holds\nnear local minima of a negative potential.\n"}
+{"abstract": "  Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful\nmean for Computer Aided Diagnosis (CADx). This approach requires building a\npopulation graph to aggregate structural information, where the graph adjacency\nmatrix represents the relationship between nodes. Until now, this adjacency\nmatrix is usually defined manually based on phenotypic information. In this\npaper, we propose an encoder that automatically selects the appropriate\nphenotypic measures according to their spatial distribution, and uses the text\nsimilarity awareness mechanism to calculate the edge weights between nodes. The\nencoder can automatically construct the population graph using phenotypic\nmeasures which have a positive impact on the final results, and further\nrealizes the fusion of multimodal information. In addition, a novel graph\nconvolution network architecture using multi-layer aggregation mechanism is\nproposed. The structure can obtain deep structure information while suppressing\nover-smooth, and increase the similarity between the same type of nodes.\nExperimental results on two databases show that our method can significantly\nimprove the diagnostic accuracy for Autism spectrum disorder and breast cancer,\nindicating its universality in leveraging multimodal data for disease\nprediction.\n"}
+{"abstract": "  We construct a $2$-generated pro-$2$ group with full normal Hausdorff\nspectrum $[0,1]$, with respect to each of the four standard filtration series:\nthe $2$-power series, the lower $2$-series, the Frattini series, and the\ndimension subgroup series. This answers a question of Klopsch and the second\nauthor, for the even prime case; the odd prime case was settled by the first\nauthor and Klopsch. Also, our construction gives the first example of a\nfinitely generated pro-$2$ group with full Hausdorff spectrum with respect to\nthe lower $2$-series.\n"}
+{"abstract": "  Zebrafish is a powerful and widely-used model system for a host of biological\ninvestigations including cardiovascular studies and genetic screening.\nZebrafish are readily assessable during developmental stages; however, the\ncurrent methods for quantification and monitoring of cardiac functions mostly\ninvolve tedious manual work and inconsistent estimations. In this paper, we\ndeveloped and validated a Zebrafish Automatic Cardiovascular Assessment\nFramework (ZACAF) based on a U-net deep learning model for automated assessment\nof cardiovascular indices, such as ejection fraction (EF) and fractional\nshortening (FS) from microscopic videos of wildtype and cardiomyopathy mutant\nzebrafish embryos. Our approach yielded favorable performance with accuracy\nabove 90% compared with manual processing. We used only black and white regular\nmicroscopic recordings with frame rates of 5-20 frames per second (fps); thus,\nthe framework could be widely applicable with any laboratory resources and\ninfrastructure. Most importantly, the automatic feature holds promise to enable\nefficient, consistent and reliable processing and analysis capacity for large\namounts of videos, which can be generated by diverse collaborating teams.\n"}
+{"abstract": "  We use direct $N$-body simulations to explore some possible scenarios for the\nfuture evolution of two massive clusters observed toward the center of\nNGC\\,4654, a spiral galaxy with mass similar to that of the Milky Way. Using\narchival HST data, we obtain the photometric masses of the two clusters,\n$M=3\\times 10^5$ M$_\\odot$ and $M=1.7\\times 10^6$ M$_\\odot$, their half-light\nradii, $R_{\\rm eff}\\sim4$ pc and $R_{\\rm eff} \\sim 6$ pc, and their projected\ndistances from the photometric center of the galaxy (both $<22$ pc). The\nknowledge of the structure and separation of these two clusters ($\\sim 24$ pc)\nprovides a unique view for studying the dynamics of a galactic central zone\nhosting massive clusters. Varying some of the unknown clusters orbital\nparameters, we carry out several $N$-body simulations showing that the future\nevolution of these clusters will inevitably result in their merger. We find\nthat, mainly depending on the shape of their relative orbit, they will merge\ninto the galactic center in less than 82 Myr. In addition to the tidal\ninteraction, a proper consideration of the dynamical friction braking would\nshorten the merging times up to few Myr. We also investigate the possibility to\nform a massive NSC in the center of the galaxy by this process. Our analysis\nsuggests that for low eccentricity orbits, and relatively long merger times,\nthe final merged cluster is spherical in shape, with an effective radius of few\nparsecs and a mass within the effective radius of the order of\n$10^5\\,\\mathrm{M_{\\odot}}$. Because the central density of such a cluster is\nhigher than that of the host galaxy, it is likely that this merger remnant\ncould be the likely embryo of a future NSC.\n"}
+{"abstract": "  Due to the unavailability of nationally representative data on time use, a\nsystematic analysis of the gender gap in unpaid household and care work has not\nbeen undertaken in the context of India. The present paper, using the recent\nTime Use Survey (2019) data, examines the socioeconomic and demographic factors\nassociated with variation in time spent on unpaid household and care work among\nmen and women. It analyses how much of the gender gap in the time allocated to\nunpaid work can be explained by differences in these factors. The findings show\nthat women spend much higher time compared to men in unpaid household and care\nwork. The decomposition results reveal that differences in socioeconomic and\ndemographic factors between men and women do not explain most of the gender gap\nin unpaid household work. Our results indicate that unobserved gender norms and\npractices most crucially govern the allocation of unpaid work within Indian\nhouseholds.\n"}
+{"abstract": "  We consider the problem of online classification under a privacy constraint.\nIn this setting a learner observes sequentially a stream of labelled examples\n$(x_t, y_t)$, for $1 \\leq t \\leq T$, and returns at each iteration $t$ a\nhypothesis $h_t$ which is used to predict the label of each new example $x_t$.\nThe learner's performance is measured by her regret against a known hypothesis\nclass $\\mathcal{H}$. We require that the algorithm satisfies the following\nprivacy constraint: the sequence $h_1, \\ldots, h_T$ of hypotheses output by the\nalgorithm needs to be an $(\\epsilon, \\delta)$-differentially private function\nof the whole input sequence $(x_1, y_1), \\ldots, (x_T, y_T)$. We provide the\nfirst non-trivial regret bound for the realizable setting. Specifically, we\nshow that if the class $\\mathcal{H}$ has constant Littlestone dimension then,\ngiven an oblivious sequence of labelled examples, there is a private learner\nthat makes in expectation at most $O(\\log T)$ mistakes -- comparable to the\noptimal mistake bound in the non-private case, up to a logarithmic factor.\nMoreover, for general values of the Littlestone dimension $d$, the same mistake\nbound holds but with a doubly-exponential in $d$ factor. A recent line of work\nhas demonstrated a strong connection between classes that are online learnable\nand those that are differentially-private learnable. Our results strengthen\nthis connection and show that an online learning algorithm can in fact be\ndirectly privatized (in the realizable setting). We also discuss an adaptive\nsetting and provide a sublinear regret bound of $O(\\sqrt{T})$.\n"}
+{"abstract": "  The intertwined processes of learning and evolution in complex environmental\nniches have resulted in a remarkable diversity of morphological forms.\nMoreover, many aspects of animal intelligence are deeply embodied in these\nevolved morphologies. However, the principles governing relations between\nenvironmental complexity, evolved morphology, and the learnability of\nintelligent control, remain elusive, partially due to the substantial challenge\nof performing large-scale in silico experiments on evolution and learning. We\nintroduce Deep Evolutionary Reinforcement Learning (DERL): a novel\ncomputational framework which can evolve diverse agent morphologies to learn\nchallenging locomotion and manipulation tasks in complex environments using\nonly low level egocentric sensory information. Leveraging DERL we demonstrate\nseveral relations between environmental complexity, morphological intelligence\nand the learnability of control. First, environmental complexity fosters the\nevolution of morphological intelligence as quantified by the ability of a\nmorphology to facilitate the learning of novel tasks. Second, evolution rapidly\nselects morphologies that learn faster, thereby enabling behaviors learned late\nin the lifetime of early ancestors to be expressed early in the lifetime of\ntheir descendants. In agents that learn and evolve in complex environments,\nthis result constitutes the first demonstration of a long-conjectured\nmorphological Baldwin effect. Third, our experiments suggest a mechanistic\nbasis for both the Baldwin effect and the emergence of morphological\nintelligence through the evolution of morphologies that are more physically\nstable and energy efficient, and can therefore facilitate learning and control.\n"}
+{"abstract": "  In this work we consider Bayesian inference problems with intractable\nlikelihood functions. We present a method to compute an approximate of the\nposterior with a limited number of model simulations. The method features an\ninverse Gaussian Process regression (IGPR), i.e., one from the output of a\nsimulation model to the input of it. Within the method, we provide an adaptive\nalgorithm with a tempering procedure to construct the approximations of the\nmarginal posterior distributions. With examples we demonstrate that IGPR has a\ncompetitive performance compared to some commonly used algorithms, especially\nin terms of statistical stability and computational efficiency, while the price\nto pay is that it can only compute a weighted Gaussian approximation of the\nmarginal posteriors.\n"}
+{"abstract": "  Recently it has become apparent that the Galactic center excess (GCE) is\nspatially correlated with the stellar distribution in the Galactic bulge. This\nhas given extra motivation for the unresolved population of millisecond pulsars\n(MSPs) explanation for the GCE. However, in the \"recycling\" channel the neutron\nstar forms from a core collapse supernovae that undergoes a random \"kick\" due\nto the asymmetry of the explosion. This would imply a smoothing out of the\nspatial distribution of the MSPs. We use N-body simulations to model how the\nMSP spatial distribution changes. We estimate the probability distribution of\nnatal kick velocities using the resolved gamma-ray MSP proper motions, where\nMSPs have random velocities relative to the circular motion with a scale\nparameter of 77+/-6 km/s. We find that, due to the natal kicks, there is an\napproximately 10% increase in each of the bulge MSP spatial distribution\ndimensions and also the bulge MSP distribution becomes less boxy but is still\nfar from being spherical.\n"}
+{"abstract": "  The paper considers the problem of controlling Connected and Automated\nVehicles (CAVs) traveling through a three-entry roundabout so as to jointly\nminimize both the travel time and the energy consumption while providing\nspeed-dependent safety guarantees, as well as satisfying velocity and\nacceleration constraints. We first design a systematic approach to dynamically\ndetermine the safety constraints and derive the unconstrained optimal control\nsolution. A joint optimal control and barrier function (OCBF) method is then\napplied to efficiently obtain a controller that optimally track the\nunconstrained optimal solution while guaranteeing all the constraints.\nSimulation experiments are performed to compare the optimal controller to a\nbaseline of human-driven vehicles showing effectiveness under symmetric and\nasymmetric roundabout configurations, balanced and imbalanced traffic rates and\ndifferent sequencing rules for CAVs.\n"}
+{"abstract": "  The reversible implementation of classical functions accounts for the bulk of\nmost known quantum algorithms. As a result, a number of reversible circuit\nconstructions over the Clifford+$T$ gate set have been developed in recent\nyears which use both the state and phase spaces, or $X$ and $Z$ bases, to\nreduce circuit costs beyond what is possible at the strictly classical level.\nWe study and generalize two particular classes of these constructions: relative\nphase circuits, including Giles and Selinger's multiply-controlled $iX$ gates\nand Maslov's $4$ qubit Toffoli gate, and measurement-assisted circuits,\nincluding Jones' Toffoli gate and Gidney's temporary logical-AND. In doing so,\nwe introduce general methods for implementing classical functions up to phase\nand for measurement-assisted termination of temporary values. We then apply\nthese techniques to find novel $T$-count efficient constructions of some\nclassical functions in space-constrained regimes, notably multiply-controlled\nToffoli gates and temporary products.\n"}
+{"abstract": "  A new family of operators, coined hierarchical measurement operators, is\nintroduced and discussed within the well-known hierarchical sparse recovery\nframework. Such operator is a composition of block and mixing operations and\nnotably contains the Kronecker product as a special case. Results on their\nhierarchical restricted isometry property (HiRIP) are derived, generalizing\nprior work on recovery of hierarchically sparse signals from\nKronecker-structured linear measurements. Specifically, these results show\nthat, very surprisingly, sparsity properties of the block and mixing part can\nbe traded against each other. The measurement structure is well-motivated by a\nmassive random access channel design in communication engineering. Numerical\nevaluation of user detection rates demonstrate the huge benefit of the\ntheoretical framework.\n"}
+{"abstract": "  We consider fractional operators of the form $$\\mathcal{H}^s=(\\partial_t\n-\\mathrm{div}_{x} ( A(x,t)\\nabla_{x}))^s,\\ (x,t)\\in\\mathbb R^n\\times\\mathbb\nR,$$ where $s\\in (0,1)$ and $A=A(x,t)=\\{A_{i,j}(x,t)\\}_{i,j=1}^{n}$ is an\naccretive, bounded, complex, measurable, $n\\times n$-dimensional matrix valued\nfunction. We study the fractional operators ${\\mathcal{H}}^s$ and their\nrelation to the initial value problem $$(\\lambda^{1-2s}\\mathrm{u}')'(\\lambda)\n=\\lambda^{1-2s}\\mathcal{H} \\mathrm{u}(\\lambda), \\quad \\lambda\\in (0, \\infty),$$\n$$\\mathrm{u}(0) = u,$$ in $\\mathbb R_+\\times \\mathbb R^n\\times\\mathbb R$.\nExploring this type of relation, and making the additional assumption that\n$A=A(x,t)=\\{A_{i,j}(x,t)\\}_{i,j=1}^{n}$ is real, we derive some local\nproperties of solutions to the non-local Dirichlet problem\n$$\\mathcal{H}^su=(\\partial_t -\\mathrm{div}_{x} ( A(x,t)\\nabla_{x}))^s u=0\\\n\\mbox{ for $(x,t)\\in \\Omega \\times J$},$$ $$ u=f\\ \\mbox{ for $(x,t)\\in \\mathbb\nR^{n+1}\\setminus (\\Omega \\times J)$}. $$ Our contribution is that we allow for\nnon-symmetric and time-dependent coefficients.\n"}
+{"abstract": "  In recent years, most of the accuracy gains for video action recognition have\ncome from the newly designed CNN architectures (e.g., 3D-CNNs). These models\nare trained by applying a deep CNN on single clip of fixed temporal length.\nSince each video segment are processed by the 3D-CNN module separately, the\ncorresponding clip descriptor is local and the inter-clip relationships are\ninherently implicit. Common method that directly averages the clip-level\noutputs as a video-level prediction is prone to fail due to the lack of\nmechanism that can extract and integrate relevant information to represent the\nvideo.\n  In this paper, we introduce the Gated Clip Fusion Network (GCF-Net) that can\ngreatly boost the existing video action classifiers with the cost of a tiny\ncomputation overhead. The GCF-Net explicitly models the inter-dependencies\nbetween video clips to strengthen the receptive field of local clip\ndescriptors. Furthermore, the importance of each clip to an action event is\ncalculated and a relevant subset of clips is selected accordingly for a\nvideo-level analysis. On a large benchmark dataset (Kinetics-600), the proposed\nGCF-Net elevates the accuracy of existing action classifiers by 11.49% (based\non central clip) and 3.67% (based on densely sampled clips) respectively.\n"}
+{"abstract": "  We have seen a surge in research aims toward adversarial attacks and defenses\nin AI/ML systems. While it is crucial to formulate new attack methods and\ndevise novel defense strategies for robustness, it is also imperative to\nrecognize who is responsible for implementing, validating, and justifying the\nnecessity of these defenses. In particular, which components of the system are\nvulnerable to what type of adversarial attacks, and the expertise needed to\nrealize the severity of adversarial attacks. Also how to evaluate and address\nthe adversarial challenges in order to recommend defense strategies for\ndifferent applications. This paper opened a discussion on who should examine\nand implement the adversarial defenses and the reason behind such efforts.\n"}
+{"abstract": "  The coarse similarity class $[A]$ of $A$ is the set of all $B$ whose\nsymmetric difference with $A$ has asymptotic density 0. There is a natural\nmetric $\\delta$ on the space $\\mathcal{S}$ of coarse similarity classes defined\nby letting $\\delta([A],[B])$ be the upper density of the symmetric difference\nof $A$ and $B$. We study the resulting metric space, showing in particular that\nbetween any two distinct points there are continuum many geodesic paths. We\nalso study subspaces of the form $\\{[A] : A \\in \\mathcal U\\}$ where $\\mathcal\nU$ is closed under Turing equivalence, and show that there is a tight\nconnection between topological properties of such a space and\ncomputability-theoretic properties of $\\mathcal U$.\n  We then define a distance between Turing degrees based on Hausdorff distance\nin this metric space. We adapt a proof of Monin to show that the distances\nbetween degrees that occur are exactly 0, 1/2, and 1, and study which of these\nvalues occur most frequently in the senses of measure and category. We define a\ndegree to be attractive if the class of all degrees at distance 1/2 from it has\nmeasure 1, and dispersive otherwise. We study the distribution of attractive\nand dispersive degrees. We also study some properties of the metric space of\nTuring degrees under this Hausdorff distance, in particular the question of\nwhich countable metric spaces are isometrically embeddable in it, giving a\ngraph-theoretic sufficient condition.\n  We also study the computability-theoretic and reverse-mathematical aspects of\na Ramsey-theoretic theorem due to Mycielski, which in particular implies that\nthere is a perfect set whose elements are mutually 1-random, as well as a\nperfect set whose elements are mutually 1-generic.\n  Finally, we study the completeness of $(\\mathcal S,\\delta)$ from the\nperspectives of computability theory and reverse mathematics.\n"}
+{"abstract": "  Recent experiments on the antiferromagnetic intercalated transition metal\ndichalcogenide $\\mathrm{Fe_{1/3}NbS_2}$ have demonstrated reversible\nresistivity switching by application of orthogonal current pulses below its\nmagnetic ordering temperature, making $\\mathrm{Fe_{1/3}NbS_2}$ promising for\nspintronics applications. Here, we perform density functional theory\ncalculations with Hubbard U corrections of the magnetic order, electronic\nstructure, and transport properties of crystalline $\\mathrm{Fe_{1/3}NbS_2}$,\nclarifying the origin of the different resistance states. The two\nexperimentally proposed antiferromagnetic ground states, corresponding to\nin-plane stripe and zigzag ordering, are computed to be nearly degenerate.\nIn-plane cross sections of the calculated Fermi surfaces are anisotropic for\nboth magnetic orderings, with the degree of anisotropy sensitive to the Hubbard\nU value. The in-plane resistance, computed within the Kubo linear response\nformalism using a constant relaxation time approximation, is also anisotropic,\nsupporting a hypothesis that the current-induced resistance changes are due to\na repopulating of AFM domains. Our calculations indicate that the transport\nanisotropy of $\\mathrm{Fe_{1/3}NbS_2}$ in the zigzag phase is reduced relative\nto stripe, consistent with the relative magnitudes of resistivity changes in\nexperiment. Finally, our calculations reveal the likely directionality of the\ncurrent-domain response, specifically, which domains are energetically\nstabilized for a given current direction.\n"}
+{"abstract": "  We present the analysis of the microlensing event OGLE-2018-BLG-1428, which\nhas a short-duration ($\\sim 1$ day) caustic-crossing anomaly. The event was\ncaused by a planetary lens system with planet/host mass ratio\n$q=1.7\\times10^{-3}$. Thanks to the detection of the caustic-crossing anomaly,\nthe finite source effect was well measured, but the microlens parallax was not\nconstrained due to the relatively short timescale ($t_{\\rm E}=24$ days). From a\nBayesian analysis, we find that the host star is a dwarf star $M_{\\rm\nhost}=0.43^{+0.33}_{-0.22} \\ M_{\\odot}$ at a distance $D_{\\rm\nL}=6.22^{+1.03}_{-1.51}\\ {\\rm kpc}$ and the planet is a Jovian-mass planet\n$M_{\\rm p}=0.77^{+0.77}_{-0.53} \\ M_{\\rm J}$ with a projected separation\n$a_{\\perp}=3.30^{+0.59}_{-0.83}\\ {\\rm au}$. The planet orbits beyond the snow\nline of the host star. Considering the relative lens-source proper motion of\n$\\mu_{\\rm rel} = 5.58 \\pm 0.38\\ \\rm mas\\ yr^{-1}$, the lens can be resolved by\nadaptive optics with a 30m telescope in the future.\n"}
+{"abstract": "  Spatial constraints such as rigid barriers affect the dynamics of cell\npopulations, potentially altering the course of natural evolution. In this\npaper, we study the population genetics of Escherichia coli proliferating in\nmicrochannels with open ends. Our experiments reveal that competition among two\nfluorescently labeled E. coli strains growing in a microchannel generates a\nself-organized stripe pattern aligned with the axial direction of the channel.\nTo account for this observation, we employ a lattice population model in which\nreproducing cells push entire lanes of cells towards the open ends of the\nchannel. By combining mathematical theory, numerical simulations, and\nexperiments, we find that the fixation dynamics is extremely fast along the\naxial direction, with a logarithmic dependence on the number of cells per lane.\nIn contrast, competition among lanes is a much slower process. We also\ndemonstrate that random mutations appearing in the middle of the channel and\nclose to its walls are much more likely to reach fixation than mutations\noccurring elsewhere.\n"}
+{"abstract": "  Waste heat recovery for trucks via organic Rankine cycle is a promising\ntechnology to reduce fuel consumption and emissions. As the vehicles are\noperated in street traffic, the heat source is subject to strong fluctuations.\nConsequently, such disturbances have to be considered to enable safe and\nefficient operation. Herein, we find optimal operating policies for several\nrepresentative scenarios by means of dynamic optimization and discuss the\nimplications on control strategy design. First, we optimize operation of a\ntypical driving cycle with data from a test rig. Results indicate that\noperating the cycle at minimal superheat is an appropriate operating policy.\nSecond, we consider a scenario where the permissible expander power is\ntemporarily limited, which is realistic in street traffic. In this case, an\noperating policy with flexible superheat can reduce the losses associated with\noperation at minimal superheat by up to 53 % in the considered scenario. As the\nduration of power limitation increases, other constraints might become active\nwhich results in part of the exhaust gas being bypassed, hence reduced savings.\n"}
+{"abstract": "  We investigate a theoretical model for a dynamic Moir\\'e grating which is\ncapable of producing slow and stopped light with improved performance when\ncompared with a static Moir\\'e grating. A Moir\\'e grating superimposes two\ngrating periods which creates a narrow slow light resonance between two band\ngaps. A Moir\\'e grating can be made dynamic by varying its coupling strength in\ntime. By increasing the coupling strength the reduction in group velocity in\nthe slow light resonance can be improved by many orders of magnitude while\nstill maintaining the wide bandwidth of the initial, weak grating. We show that\nfor a pulse propagating through the grating this is a consequence of altering\nthe pulse spectrum and therefore the grating can also perform bandwidth\nmodulation. Finally we present a possible realization of the system via an\nelectro-optic grating by applying a quasi-static electric field to a poled\n$\\chi^{(2)}$ nonlinear medium.\n"}
+{"abstract": "  We studied the physical behavior of PdO nanoparticles at low temperatures,\nwhich presents an unusual behavior clearly related to macroscopic quantum\ntunneling. The samples show a tetragonal single phase with P42/mmc space group.\nMost importantly, the particle size was estimated at about 5.07 nm. Appropriate\ntechniques were used to determine the characteristic of these nanoparticles.\nThe most important aspect of this study is the magnetic characterization\nperformed at low temperatures. It shows a peak at 50 K in zero field cooling\nmode (ZFC) that corresponds to the Blocking temperature (T$_{B}$). These\nmeasurements in ZFC and field cooling (FC) indicates that the peak behavior is\ndue to different relaxation times of the asymmetrical barriers when the\nelectron changes from a metastable state to another. Below T$_{B}$ in FC mode,\nthe magnetization decreases with temperature until 36 K; this temperature is\nthe crossover temperature (T$_{Cr}$) related to the anisotropy of the barriers,\nindicative of macroscopic quantum tunneling.\n"}
+{"abstract": "  This paper is concerned with the problem of nonlinear filter stability of\nergodic Markov processes. The main contribution is the conditional Poincar\\'e\ninequality (PI), which is shown to yield filter stability. The proof is based\nupon a recently discovered duality which is used to transform the nonlinear\nfiltering problem into a stochastic optimal control problem for a backward\nstochastic differential equation (BSDE). Based on these dual formalisms, a\ncomparison is drawn between the stochastic stability of a Markov process and\nthe filter stability. The latter relies on the conditional PI described in this\npaper, whereas the former relies on the standard form of PI.\n"}
+{"abstract": "  Unexpected hypersurfaces are a brand name for some special linear systems.\nThey were introduced around 2017 and are a field of intensive study since then.\nThey attracted a lot of attention because of their close tights to various\nother areas of mathematics including vector bundles, arrangements of\nhyperplanes, geometry of projective varieties. Our research is motivated by the\nwhat is now known as the BMSS duality, which is a new way of deriving\nprojective varieties out of already constructed. The last author coined the\nconcept of companion surfaces in the setting of unexpected curves admitted by\nthe $B_3$ root system. Here we extend this construction in various directions.\nWe revisit the configurations of points associated to either root systems or to\nFermat arrangements and we study the geometry of the associated varieties and\ntheir companions.\n"}
+{"abstract": "  We present AURA-net, a convolutional neural network (CNN) for the\nsegmentation of phase-contrast microscopy images. AURA-net uses transfer\nlearning to accelerate training and Attention mechanisms to help the network\nfocus on relevant image features. In this way, it can be trained efficiently\nwith a very limited amount of annotations. Our network can thus be used to\nautomate the segmentation of datasets that are generally considered too small\nfor deep learning techniques. AURA-net also uses a loss inspired by active\ncontours that is well-adapted to the specificity of phase-contrast images,\nfurther improving performance. We show that AURA-net outperforms\nstate-of-the-art alternatives in several small (less than 100images) datasets.\n"}
+{"abstract": "  Multi-domain learning (MDL) refers to learning a set of models\nsimultaneously, with each one specialized to perform a task in a certain\ndomain. Generally, high labeling effort is required in MDL, as data needs to be\nlabeled by human experts for every domain. Active learning (AL), which reduces\nlabeling effort by only using the most informative data, can be utilized to\naddress the above issue. The resultant paradigm is termed multi-domain active\nlearning (MDAL). However, currently little research has been done in MDAL, not\nto mention any off-the-shelf solution. To fill this gap, we present a\ncomprehensive comparative study of 20 different MDAL algorithms, which are\nestablished by combining five representative MDL models under different\ninformation-sharing schemes and four well-used AL strategies under different\ncategories. We evaluate the algorithms on five datasets, involving textual and\nvisual classification tasks. We find that the models which capture both\ndomain-dependent and domain-specific information are more likely to perform\nwell in the whole AL loops. Besides, the simplest informative-based uncertainty\nstrategy surprisingly performs good in most datasets. As our off-the-shelf\nrecommendation, the combination of Multinomial Adversarial Networks (MAN) with\nthe best vs second best (BvSB) uncertainty strategy shows its superiority in\nmost cases, and this combination is also robust across datasets and domains.\n"}
+{"abstract": "  This paper develops and investigates the impacts of multi-objective Nash\noptimum (user equilibrium) traffic assignment on a large-scale network for\nbattery electric vehicles (BEVs) and internal combustion engine vehicles\n(ICEVs) in a microscopic traffic simulation environment. Eco-routing is a\ntechnique that finds the most energy efficient route. ICEV and BEV energy\nconsumption patterns are significantly different with regard to their\nsensitivity to driving cycles. Unlike ICEVs, BEVs are more energy efficient on\nlow-speed arterial trips compared to highway trips. Different energy\nconsumption patterns require different eco-routing strategies for ICEVs and\nBEVs. This study found that eco-routing could reduce energy consumption for\nBEVs but also significantly increases their average travel time. The simulation\nstudy found that multi-objective routing could reduce the energy consumption of\nBEVs by 13.5, 14.2, 12.9, and 10.7 percent, as well as the fuel consumption of\nICEVs by 0.1, 4.3, 3.4, and 10.6 percent for \"not congested\", \"slightly\ncongested\", \"moderately congested\", and \"highly congested\" conditions,\nrespectively. The study also found that multi-objective user equilibrium\nrouting reduced the average vehicle travel time by up to 10.1% compared to the\nstandard user equilibrium traffic assignment for the highly congested\nconditions, producing a solution closer to the system optimum traffic\nassignment. The results indicate that the multi-objective eco-routing can\neffectively reduce fuel/energy consumption with minimum impacts on travel times\nfor both BEVs and ICEVs.\n"}
+{"abstract": "  Using the dynamical diquark model, we calculate the electric-dipole radiative\ndecay widths to $X(3872)$ of the lightest negative-parity exotic candidates,\nincluding the four $I=0$, $J^{PC} \\! = \\! 1^{--}$ (\"$Y$\") states. The\n$O$(100--1000 keV) values obtained test the hypothesis of a common substructure\nshared by all of these states. We also calculate the magnetic-dipole radiative\ndecay width for $Z_c(4020)^0 \\! \\to \\! \\gamma X(3872)$, and find it to be\nrather smaller ($<$~10 keV) than its predicted value in molecular models.\n"}
+{"abstract": "  Unmanned Aerial Vehicle (UAV) swarms adoption shows a steady growth among\noperators due to the benefits in time and cost arisen from their use. However,\nthis kind of system faces an important problem which is the calculation of many\noptimal paths for each UAV. Solving this problem would allow a to control many\nUAVs without human intervention at the same time while saving battery between\nrecharges and performing several tasks simultaneously. The main aim is to\ndevelop a system capable of calculating the optimal flight path for a UAV\nswarm. The aim of these paths is to achieve full coverage of a flight area for\ntasks such as field prospection. All this, regardless of the size of maps and\nthe number of UAVs in the swarm. It is not necessary to establish targets or\nany other previous knowledge other than the given map. Experiments have been\nconducted to determine whether it is optimal to establish a single control for\nall UAVs in the swarm or a control for each UAV. The results show that it is\nbetter to use one control for all UAVs because of the shorter flight time. In\naddition, the flight time is greatly affected by the size of the map. The\nresults give starting points for future research such as finding the optimal\nmap size for each situation.\n"}
+{"abstract": "  We present a neutron spectroscopy based method to study quantitatively the\npartial miscibility and phase behaviour of an organic photovoltaic active layer\nmade of conjugated polymer:small molecule blends, presently illustrated with\nthe regio-random poly(3-hexylthiophene-2,5-diyl) and fullerene [6,6]-Phenyl\nC$_{61}$ butyric acid methyl ester (RRa-P3HT:PCBM) system. We perform both\ninelastic neutron scattering and quasi-elastic neutron scattering measurements\nto study the structural dynamics of blends of different compositions enabling\nus to resolve the phase behaviour. The difference of neutron cross sections\nbetween RRa-P3HT and PCBM, and the use of deuteration technique, offer a unique\nopportunity to probe the miscibility limit of fullerene in the amorphous\npolymer-rich phase and to tune the contrast between the polymer and the\nfullerene phases, respectively. Therefore, the proposed approach should be\nuniversal and relevant to study new non-fullerene acceptors that are closely\nrelated - in terms of chemical structures - to the polymer, where other\nconventional imaging and spectroscopic techniques present a poor contrast\nbetween the blend components.\n"}
+{"abstract": "  Let $G$ be a graph with vertex set $V(G)$ and edge set $E(G)$. The Sombor and\nreduced Sombor indices of $G$ are defined as $SO(G)=\\sum_{uv\\in\nE(G)}\\sqrt{deg_G(u)^2+deg_G(v)^2}$ and $SO_{red}(G)=\\sum_{uv\\in\nE(G)}\\sqrt{(deg_G(u)-1)^2+(deg_G(v)-1)^2}$, respectively. We denote by\n$H_{n,\\nu}$ the graph constructed from the star $S_n$ by adding $\\nu$ edge(s)\n$(0\\leq \\nu\\leq n-2)$, between a fixed pendent vertex and $\\nu$ other pendent\nvertices. R\\'eti et al. [T. R\\'eti, T. Do\\v{s}li\\'c and A. Ali, On the Sombor\nindex of graphs, $\\textit{Contrib. Math. }$ $\\textbf{3}$ (2021) 11-18] proposed\na conjecture that the graph $H_{n,\\nu}$ has the maximum Sombor index among all\nconnected $\\nu$-cyclic graphs of order $n$, where $5\\leq \\nu \\leq n-2$. In this\npaper we confirm that the former conjecture is true. It is also shown that this\nconjecture is valid for the reduced Sombor index. The relationship between\nSombor, reduced Sombor and first Zagreb indices of graph is also investigated.\n"}
+{"abstract": "  Meta-learning synthesizes and leverages the knowledge from a given set of\ntasks to rapidly learn new tasks using very little data. Meta-learning of\nlinear regression tasks, where the regressors lie in a low-dimensional\nsubspace, is an extensively-studied fundamental problem in this domain.\nHowever, existing results either guarantee highly suboptimal estimation errors,\nor require $\\Omega(d)$ samples per task (where $d$ is the data dimensionality)\nthus providing little gain over separately learning each task. In this work, we\nstudy a simple alternating minimization method (MLLAM), which alternately\nlearns the low-dimensional subspace and the regressors. We show that, for a\nconstant subspace dimension MLLAM obtains nearly-optimal estimation error,\ndespite requiring only $\\Omega(\\log d)$ samples per task. However, the number\nof samples required per task grows logarithmically with the number of tasks. To\nremedy this in the low-noise regime, we propose a novel task subset selection\nscheme that ensures the same strong statistical guarantee as MLLAM, even with\nbounded number of samples per task for arbitrarily large number of tasks.\n"}
+{"abstract": "  In the presence of spacetime torsion, the momentum components do not commute;\ntherefore, in quantum field theory, summation over the momentum eigenvalues\nwill replace integration over the momentum. In the Einstein--Cartan theory of\ngravity, in which torsion is coupled to spin, the separation between the\neigenvalues increases with the magnitude of the momentum. Consequently, this\nreplacement regularizes divergent integrals in Feynman diagrams with loops by\nturning them into convergent sums. In this article, we apply torsional\nregularization to the self-energy of a charged lepton in quantum\nelectrodynamics. We show that this procedure eliminates the ultraviolet\ndivergence. We also show that torsion gives a photon a small nonzero mass,\nwhich regularizes the infrared divergence. In the end, we calculate the finite\nbare masses of the electron, muon, and tau lepton: $0.4329\\,\\mbox{MeV}$,\n$90.95\\,\\mbox{MeV}$, and $1543\\,\\mbox{MeV}$, respectively. These values\nconstitute about $85\\%$ of the observed, re-normalized masses.\n"}
+{"abstract": "  Wikipedia is an online encyclopedia available in 285 languages. It composes\nan extremely relevant Knowledge Base (KB), which could be leveraged by\nautomatic systems for several purposes. However, the structure and organisation\nof such information are not prone to automatic parsing and understanding and it\nis, therefore, necessary to structure this knowledge. The goal of the current\nSHINRA2020-ML task is to leverage Wikipedia pages in order to categorise their\ncorresponding entities across 268 hierarchical categories, belonging to the\nExtended Named Entity (ENE) ontology. In this work, we propose three distinct\nmodels based on the contextualised embeddings yielded by Multilingual BERT. We\nexplore the performances of a linear layer with and without explicit usage of\nthe ontology's hierarchy, and a Gated Recurrent Units (GRU) layer. We also test\nseveral pooling strategies to leverage BERT's embeddings and selection criteria\nbased on the labels' scores. We were able to achieve good performance across a\nlarge variety of languages, including those not seen during the fine-tuning\nprocess (zero-shot languages).\n"}
+{"abstract": "  In this work we present (and encourage the use of) the Williamson theorem and\nits consequences in several contexts in physics. We demonstrate this theorem\nusing only basic concepts of linear algebra and symplectic matrices. As an\nimmediate application in the context of small oscillations, we show that\napplying this theorem reveals the normal-mode coordinates and frequencies of\nthe system in the Hamiltonian scenario. A modest introduction of the symplectic\nformalism in quantum mechanics is presented, useing the theorem to study\nquantum normal modes and canonical distributions of thermodynamically stable\nsystems described by quadratic Hamiltonians. As a last example, a more advanced\ntopic concerning uncertainty relations is developed to show once more its\nutility in a distinct and modern perspective.\n"}
+{"abstract": "  This paper presents the Multilingual COVID-19 Analysis Method (CMTA) for\ndetecting and observing the spread of misinformation about this disease within\ntexts. CMTA proposes a data science (DS) pipeline that applies machine learning\nmodels for processing, classifying (Dense-CNN) and analyzing (MBERT)\nmultilingual (micro)-texts. DS pipeline data preparation tasks extract features\nfrom multilingual textual data and categorize it into specific information\nclasses (i.e., 'false', 'partly false', 'misleading'). The CMTA pipeline has\nbeen experimented with multilingual micro-texts (tweets), showing\nmisinformation spread across different languages. To assess the performance of\nCMTA and put it in perspective, we performed a comparative analysis of CMTA\nwith eight monolingual models used for detecting misinformation. The comparison\nshows that CMTA has surpassed various monolingual models and suggests that it\ncan be used as a general method for detecting misinformation in multilingual\nmicro-texts. CMTA experimental results show misinformation trends about\nCOVID-19 in different languages during the first pandemic months.\n"}
+{"abstract": "  We theoretically report the emergence of $Z_4$ parafermion edge modes in a\nperiodically driven spinful superconducting chain with modest fermionic Hubbard\ninteraction. These parafermion edge modes represent $\\pm \\pi/(2T)$ quasienergy\nexcitations ($T$ being the driving period), which have no static counterpart\nand arise from the interplay between interaction effect and periodic driving.\nAt special parameter values, these exotic quasiparticles can be analytically\nand exactly derived. Strong numerical evidence of their robustness against\nvariations in parameter values and spatial disorder is further presented. Our\nproposal offers a route toward realizing parafermions without fractional\nquantum Hall systems or complicated interactions.\n"}
+{"abstract": "  In this rejoinder, we aim to address two broad issues that cover most\ncomments made in the discussion. First, we discuss some theoretical aspects of\nour work and comment on how this work might impact the theoretical foundation\nof privacy-preserving data analysis. Taking a practical viewpoint, we next\ndiscuss how f-differential privacy (f-DP) and Gaussian differential privacy\n(GDP) can make a difference in a range of applications.\n"}
+{"abstract": "  We present the first spectroscopically resolved \\ha\\ emission map of the\nLarge Magellanic Cloud's (LMC) galactic wind. By combining new Wisconsin\nH-alpha Mapper (WHAM) observations ($I_{\\rm H\\alpha}\\gtrsim10~{\\rm mR}$) with\nexisting \\hicm\\ emission observations, we have (1) mapped the LMC's near-side\ngalactic wind over a local standard of rest (LSR) velocity range of $+50\\le\\rm\nv_{LSR}\\le+250~{\\rm km}~{\\rm s}^{-1}$, (2) determined its morphology and\nextent, and (3) estimated its mass, outflow rate, and mass-loading factor. We\nobserve \\ha\\ emission from this wind to typically 1-degree off the LMC's \\hi\\\ndisk. Kinematically, we find that the diffuse gas in the warm-ionized phase of\nthis wind persists at both low ($\\lesssim100~{\\rm km}~{\\rm s}^{-1}$) and high\n($\\gtrsim100~{\\rm km}~{\\rm s}^{-1}$) velocities, relative to the LMC's \\hi\\\ndisk. Furthermore, we find that the high-velocity component spatially aligns\nwith the most intense star-forming region, 30~Doradus. We, therefore, conclude\nthat this high-velocity material traces an active outflow. We estimate the mass\nof the warm ($T_e\\approx10^4~\\rm K$) ionized phase of the near-side LMC outflow\nto be $\\log{\\left(M_{\\rm ionized}/M_\\odot\\right)=7.51\\pm0.15}$ for the combined\nlow and high velocity components. Assuming an ionization fraction of 75\\% and\nthat the wind is symmetrical about the LMC disk, we estimate that its total\n(neutral and ionized) mass is $\\log{\\left(M_{\\rm total}/M_\\odot\\right)=7.93}$,\nits mass-flow rate is $\\dot{M}_{\\rm outflow}\\approx1.43~M_\\odot~\\rm yr^{-1}$,\nand its mass-loading factor is $\\eta\\approx4.54$. Our average mass-loading\nfactor results are roughly a factor of 2.5 larger than previous \\ha\\ imaging\nand UV~absorption line studies, suggesting that those studies are missing\nnearly half the gas in the outflows.\n"}
+{"abstract": "  While 2D occupancy maps commonly used in mobile robotics enable safe\nnavigation in indoor environments, in order for robots to understand their\nenvironment to the level required for them to perform more advanced tasks,\nrepresenting 3D geometry and semantic environment information is required. We\npropose a pipeline that can generate a multi-layer representation of indoor\nenvironments for robotic applications. The proposed representation includes 3D\nmetric-semantic layers, a 2D occupancy layer, and an object instance layer\nwhere known objects are replaced with an approximate model obtained through a\nnovel model-matching approach. The metric-semantic layer and the object\ninstance layer are combined to form an augmented representation of the\nenvironment. Experiments show that the proposed shape matching method\noutperforms a state-of-the-art deep learning method when tasked to complete\nunseen parts of objects in the scene. The pipeline performance translates well\nfrom simulation to real world as shown by F1-score analysis, with semantic\nsegmentation accuracy using Mask R-CNN acting as the major bottleneck. Finally,\nwe also demonstrate on a real robotic platform how the multi-layer map can be\nused to improve navigation safety.\n"}
+{"abstract": "  Analyzing human affect is vital for human-computer interaction systems. Most\nmethods are developed in restricted scenarios which are not practical for\nin-the-wild settings. The Affective Behavior Analysis in-the-wild (ABAW) 2021\nContest provides a benchmark for this in-the-wild problem. In this paper, we\nintroduce a multi-modal and multi-task learning method by using both visual and\naudio information. We use both AU and expression annotations to train the model\nand apply a sequence model to further extract associations between video\nframes. We achieve an AU score of 0.712 and an expression score of 0.477 on the\nvalidation set. These results demonstrate the effectiveness of our approach in\nimproving model performance.\n"}
+{"abstract": "  It is well known that CS can boost massive random access protocols. Usually,\nthe protocols operate in some overloaded regime where the sparsity can be\nexploited. In this paper, we consider a different approach by taking an\northogonal FFT base, subdivide its image into appropriate sub-channels and let\neach subchannel take only a fraction of the load. To show that this approach\ncan actually achieve the full capacity we provide i) new concentration\ninequalities, and ii) devise a sparsity capture effect, i.e where the\nsub-division can be driven such that the activity in each each sub-channel is\nsparse by design. We show by simulations that the system is scalable resulting\nin a coarsely 30-fold capacity increase.\n"}
+{"abstract": "  Matrices are often built and designed by applying procedures from lower order\nmatrices. Matrix tensor products, direct sums and multiplication of matrices\nretain certain properties of the lower order matrices; matrices produced by\nthese procedures are said to be {\\em separable}. {\\em Entangled} matrices is\nthe term used for matrices which are not separable. Here design methods for\nentangled matrices are derived. These can retain properties of lower order\nmatrices or acquire new required properties.\n  Entangled matrices are often required in practice and a number of\napplications of the designs are given. Methods with which to construct\nmultidimensional entangled paraunitary matrices are derived; these have\napplications for wavelet and filter bank design. New entangled unitary matrices\nare designed; these are used in quantum information theory. Efficient methods\nfor designing new full diversity constellations of unitary matrices with\nexcellent {\\em quality} (a defined term) for space time applications are given.\n"}
+{"abstract": "  We investigate theoretically coherent detection implemented simultaneously on\na set of mutually orthogonal spatial modes in the image plane as a method to\ncharacterize properties of a composite thermal source below the Rayleigh limit.\nA general relation between the intensity distribution in the source plane and\nthe covariance matrix for the complex field amplitudes measured in the image\nplane is derived. An algorithm to estimate parameters of a two-dimensional\nsymmetric binary source is devised and verified using Monte Carlo simulations\nto provide super-resolving capability for high ratio of signal to detection\nnoise (SNR). Specifically, the separation between two point sources can be\nmeaningfully determined down to $\\textrm{SNR}^{-1/2}$ in the units determined\nby the spatial spread of the transfer function of the imaging system. The\npresented algorithm is shown to make a nearly optimal use of the measured data\nin the sub-Rayleigh region.\n"}
+{"abstract": "  Pool block withholding attack is performed among mining pools in digital\ncryptocurrencies, such as Bitcoin. Instead of mining honestly, pools can be\nincentivized to infiltrate their own miners into other pools. These\ninfiltrators report partial solutions but withhold full solutions, share block\nrewards but make no contribution to block mining. The block withholding attack\namong mining pools can be modeled as a non-cooperative game called \"the miner's\ndilemm\", which reduces effective mining power in the system and leads to\npotential systemic instability in the blockchain. However, existing literature\non the game-theoretic properties of this attack only gives a preliminary\nanalysis, e.g., an upper bound of 3 for the pure price of anarchy (PPoA) in\nthis game, with two pools involved and no miner betraying. Pure price of\nanarchy is a measurement of how much mining power is wasted in the miner's\ndilemma game. Further tightening its upper bound will bring us more insight\ninto the structure of this game, so as to design mechanisms to reduce the\nsystemic loss caused by mutual attacks. In this paper, we give a tight bound of\n(1, 2] for the pure price of anarchy. Moreover, we show the tight bound holds\nin a more general setting, in which infiltrators may betray.We also prove the\nexistence and uniqueness of pure Nash equilibrium in this setting. Inspired by\nexperiments on the game among three mining pools, we conjecture that similar\nresults hold in the N-player miner's dilemma game (N>=2).\n"}
+{"abstract": "  Computing the distribution of permanents of random matrices has been an\noutstanding open problem for several decades. In quantum computing,\n\"anti-concentration\" of this distribution is an unproven input for the proof of\nhardness of the task of boson-sampling. We study permanents of random i.i.d.\ncomplex Gaussian matrices, and more broadly, submatrices of random unitary\nmatrices. Using a hybrid representation-theoretic and combinatorial approach,\nwe prove strong lower bounds for all moments of the permanent distribution. We\nprovide substantial evidence that our bounds are close to being tight and\nconstitute accurate estimates for the moments. Let $U(d)^{k\\times k}$ be the\ndistribution of $k\\times k$ submatrices of $d\\times d$ random unitary matrices,\nand $G^{k\\times k}$ be the distribution of $k\\times k$ complex Gaussian\nmatrices. (1) Using the Schur-Weyl duality (or the Howe duality), we prove an\nexpansion formula for the $2t$-th moment of $|Perm(M)|$ when $M$ is drawn from\n$U(d)^{k\\times k}$ or $G^{k\\times k}$. (2) We prove a surprising size-moment\nduality: the $2t$-th moment of the permanent of random $k\\times k$ matrices is\nequal to the $2k$-th moment of the permanent of $t\\times t$ matrices. (3) We\ndesign an algorithm to exactly compute high moments of the permanent of small\nmatrices. (4) We prove lower bounds for arbitrary moments of permanents of\nmatrices drawn from $G^{ k\\times k}$ or $U(k)$, and conjecture that our lower\nbounds are close to saturation up to a small multiplicative error. (5) Assuming\nour conjectures, we use the large deviation theory to compute the tail of the\ndistribution of log-permanent of Gaussian matrices for the first time. (6) We\nargue that it is unlikely that the permanent distribution can be uniquely\ndetermined from the integer moments and one may need to supplement the moment\ncalculations with extra assumptions to prove the anti-concentration conjecture.\n"}
+{"abstract": "  In this paper we attempt to develop a general $p-$Bergman theory on bounded\ndomains in $\\mathbb C^n$. To indicate the basic difference between $L^p$ and\n$L^2$ cases, we show that the $p-$Bergman kernel $K_p(z)$ is not real-analytic\non some bounded complete Reinhardt domains when $p\\ge 4$ is an even number. By\nthe calculus of variations we get a fundamental reproducing formula. This\ntogether with certain techniques from nonlinear analysis of the $p-$Laplacian\nyield a number of results, e.g., the off-diagonal $p-$Bergman kernel\n$K_p(z,\\cdot)$ is H\\\"older continuous of order $\\frac12$ for $p>1$ and of order\n$\\frac1{2(n+2)}$ for $p=1$. We also show that the $p-$Bergman metric $B_p(z;X)$\ntends to the Carath\\'eodory metric $C(z;X)$ as $p\\rightarrow \\infty$ and the\ngeneralized Levi form $i\\partial\\bar{\\partial}\\log K_p(z;X)$ is no less than\n$B_p(z;X)^2$ for $p\\ge 2$ and $ C(z;X)^2$ for $p\\le 2.$ Stability of $K_p(z,w)$\nor $B_p(z;X)$ as $p$ varies, boundary behavior of $K_p(z)$, as well as basic\nfacts on the $p-$Bergman prjection, are also investigated.\n"}
+{"abstract": "  Quantitative evaluation has increased dramatically among recent video\ninpainting work, but the video and mask content used to gauge performance has\nreceived relatively little attention. Although attributes such as camera and\nbackground scene motion inherently change the difficulty of the task and affect\nmethods differently, existing evaluation schemes fail to control for them,\nthereby providing minimal insight into inpainting failure modes. To address\nthis gap, we propose the Diagnostic Evaluation of Video Inpainting on\nLandscapes (DEVIL) benchmark, which consists of two contributions: (i) a novel\ndataset of videos and masks labeled according to several key inpainting failure\nmodes, and (ii) an evaluation scheme that samples slices of the dataset\ncharacterized by a fixed content attribute, and scores performance on each\nslice according to reconstruction, realism, and temporal consistency quality.\nBy revealing systematic changes in performance induced by particular\ncharacteristics of the input content, our challenging benchmark enables more\ninsightful analysis into video inpainting methods and serves as an invaluable\ndiagnostic tool for the field. Our code is available at\nhttps://github.com/MichiganCOG/devil .\n"}
+{"abstract": "  Quantum dots (QDs) made from semiconductors are among the most promising\nplatforms for the developments of quantum computing and simulation chips, and\nhave advantages over other platforms in high density integration and in\ncompatibility to the standard semiconductor chip fabrication technology.\nHowever, development of a highly tunable semiconductor multiple QD system still\nremains as a major challenge. Here, we demonstrate realization of a highly\ntunable linear quadruple QD (QQD) in a narrow bandgap semiconductor InAs\nnanowire with fine finger gate technique. The QQD is studied by electron\ntransport measurements in the linear response regime. Characteristic\ntwo-dimensional charge stability diagrams containing four groups of resonant\ncurrent lines of different slopes are found for the QQD. It is shown that these\ncurrent lines can be individually assigned as arising from resonant electron\ntransport through the energy levels of different QDs. Benefited from the\nexcellent gate tunability, we also demonstrate tuning of the QQD to regimes\nwhere the energy levels of two QDs, three QDs and all the four QDs are\nenergetically on resonance, respectively, with the fermi level of source and\ndrain contacts. A capacitance network model is developed for the linear QQD and\nthe simulated charge stability diagrams based on the model show good agreements\nwith the experiments. Our work presents a solid experimental evidence that\nnarrow bandgap semiconductor nanowires multiple QDs could be used as a\nversatile platform to achieve integrated qubits for quantum computing and to\nperform quantum simulations for complex many-body systems.\n"}
+{"abstract": "  Predictive energy management of Connected and Automated Vehicles (CAVs), in\nparticular those with multiple power sources, has the potential to\nsignificantly improve energy savings in real-world driving conditions. In\nparticular, the eco-driving problem seeks to design optimal speed and power\nusage profiles based upon available information from connectivity and advanced\nmapping features to minimize the fuel consumption between two designated\nlocations.\n  In this work, the eco-driving problem is formulated as a three-state receding\nhorizon optimal control problem and solved via Dynamic Programming (DP). The\noptimal solution, in terms of vehicle speed and battery State of Charge (SoC)\ntrajectories, allows a connected and automated hybrid electric vehicle to\nintelligently pass the signalized intersections and minimize fuel consumption\nover a prescribed route. To enable real-time implementation, a parallel\narchitecture of DP is proposed for an NVIDIA GPU with CUDA programming.\nSimulation results indicate that the proposed optimal controller delivers more\nthan 15% fuel economy benefits compared to a baseline control strategy and that\nthe solver time can be reduced by more than 90% by the parallel implementation\nwhen compared to a serial implementation.\n"}
+{"abstract": "  We investigate how sentence-level transformers can be modified into effective\nsequence labelers at the token level without any direct supervision. Existing\napproaches to zero-shot sequence labeling do not perform well when applied on\ntransformer-based architectures. As transformers contain multiple layers of\nmulti-head self-attention, information in the sentence gets distributed between\nmany tokens, negatively affecting zero-shot token-level performance. We find\nthat a soft attention module which explicitly encourages sharpness of attention\nweights can significantly outperform existing methods.\n"}
+{"abstract": "  We calculate the possible interaction between a superconductor and the static\nEarth's gravitational fields, making use of the gravito-Maxwell formalism\ncombined with the time-dependent Ginzburg-Landau theory. We try to estimate\nwhich are the most favourable conditions to enhance the effect, optimizing the\nsuperconductor parameters characterizing the chosen sample. We also give a\nqualitative comparison of the behaviour of high-$T_\\text{c}$ and classical\nlow-$T_\\text{c}$ superconductors with respect to the gravity/superfluid\ninterplay.\n"}
+{"abstract": "  Implications of the Raychaudhuri equation in focusing of geodesic congruence\nare studied in the framework of scalar-tensor theory of gravity. Brans-Dicke\ntheory and Bekenstein's scalar field theory are picked up for investigation. In\nboth the theories, a static spherically symmetric distribution and a spatially\nhomogeneous and isotropic cosmological model are dealt with, as specific\nexamples. It is found that with reasonable physical conditions, there are\npossibilities for a violation of the convergence condition. This fact leads to\na possibility of avoiding a singularity.\n"}
+{"abstract": "  We show that, for vector spaces in which distance measurement is performed\nusing a gauge, the existence of best coapproximations in $1$-codimensional\nclosed linear subspaces implies in dimensions $\\geq 2$ that the gauge is a\nnorm, and in dimensions $\\geq 3$ that the gauge is even a Hilbert space norm.\nWe also show that coproximinality of all closed subspaces of a fixed dimension\nimplies coproximinality of all subspaces of all lower finite dimensions.\n"}
+{"abstract": "  We construct high-order semi-discrete-in-time and fully discrete (with\nFourier-Galerkin in space) schemes for the incompressible Navier-Stokes\nequations with periodic boundary conditions, and carry out corresponding error\nanalysis. The schemes are of implicit-explicit type based on a scalar auxiliary\nvariable (SAV) approach. It is shown that numerical solutions of these schemes\nare uniformly bounded without any restriction on time step size. These uniform\nbounds enable us to carry out a rigorous error analysis for the schemes up to\nfifth-order in a unified form, and derive global error estimates in\n$l^\\infty(0,T;H^1)\\cap l^2(0,T;H^2)$ in the two dimensional case as well as\nlocal error estimates in $l^\\infty(0,T;H^1)\\cap l^2(0,T;H^2)$ in the three\ndimensional case. We also present numerical results confirming our theoretical\nconvergence rates and demonstrating advantages of higher-order schemes for\nflows with complex structures in the double shear layer problem.\n"}
+{"abstract": "  Stochastically switching force terms appear frequently in models of\nbiological systems under the action of active agents such as proteins. The\ninteraction of switching force and Brownian motion can create an \"effective\nthermal equilibrium\" even though the system does not obey a potential function.\nIn order to extend the field of energy landscape analysis to understand\nstability and transitions in switching systems, we derive the quasipotential\nthat defines this effective equilibrium for a general overdamped Langevin\nsystem with a force switching according to a continuous-time Markov chain\nprocess. Combined with the string method for computing most-probable transition\npaths, we apply our method to an idealized system and show the appearance of\npreviously unreported numerical challenges. We present modifications to the\nalgorithms to overcome these challenges, and show validity by demonstrating\nagreement between our computed quasipotential barrier and asymptotic Monte\nCarlo transition times in the system.\n"}
+{"abstract": "  Convolutional Neural Networks (CNNs) have achieved great success due to the\npowerful feature learning ability of convolution layers. Specifically, the\nstandard convolution traverses the input images/features using a sliding window\nscheme to extract features. However, not all the windows contribute equally to\nthe prediction results of CNNs. In practice, the convolutional operation on\nsome of the windows (e.g., smooth windows that contain very similar pixels) can\nbe very redundant and may introduce noises into the computation. Such\nredundancy may not only deteriorate the performance but also incur the\nunnecessary computational cost. Thus, it is important to reduce the\ncomputational redundancy of convolution to improve the performance. To this\nend, we propose a Content-aware Convolution (CAC) that automatically detects\nthe smooth windows and applies a 1x1 convolutional kernel to replace the\noriginal large kernel. In this sense, we are able to effectively avoid the\nredundant computation on similar pixels. By replacing the standard convolution\nin CNNs with our CAC, the resultant models yield significantly better\nperformance and lower computational cost than the baseline models with the\nstandard convolution. More critically, we are able to dynamically allocate\nsuitable computation resources according to the data smoothness of different\nimages, making it possible for content-aware computation. Extensive experiments\non various computer vision tasks demonstrate the superiority of our method over\nexisting methods.\n"}
+{"abstract": "  Let $G$ be an $n$-vertex graph and let $L:V(G)\\rightarrow P(\\{1,2,3\\})$ be a\nlist assignment over the vertices of $G$, where each vertex with list of size 3\nand of degree at most 5 has at least three neighbors with lists of size 2. We\ncan determine $L$-choosability of $G$ in $O(1.3196^{n_3+.5n_2})$ time, where\n$n_i$ is the number of vertices in $G$ with list of size $i$ for $i\\in\n\\{2,3\\}$. As a corollary, we conclude that the 3-colorability of any graph $G$\nwith minimum degree at least 6 can be determined in $O(1.3196^{n-.5\\Delta(G)})$\ntime.\n"}
+{"abstract": "  Multi-instance learning is a type of weakly supervised learning. It deals\nwith tasks where the data is a set of bags and each bag is a set of instances.\nOnly the bag labels are observed whereas the labels for the instances are\nunknown. An important advantage of multi-instance learning is that by\nrepresenting objects as a bag of instances, it is able to preserve the inherent\ndependencies among parts of the objects. Unfortunately, most existing\nalgorithms assume all instances to be \\textit{identically and independently\ndistributed}, which violates real-world scenarios since the instances within a\nbag are rarely independent. In this work, we propose the Multi-Instance\nVariational Auto-Encoder (MIVAE) algorithm which explicitly models the\ndependencies among the instances for predicting both bag labels and instance\nlabels. Experimental results on several multi-instance benchmarks and\nend-to-end medical imaging datasets demonstrate that MIVAE performs better than\nstate-of-the-art algorithms for both instance label and bag label prediction\ntasks.\n"}
+{"abstract": "  The global pandemic caused by the COVID virus led universities to a change in\nthe way they teach classes, moving to a distance mode. The subject \"Modelos y\nSistemas de Costos \" of the CPA career of the Faculty of Economic Sciences and\nAdministration of the Universidad de la Rep\\'ublica (Uruguay) incorporated\naudiovisual material as a pedagogical resource consisting of videos recorded by\na group of well experienced and highest ranked teachers. The objective of this\nresearch is to analyze the efficiency of the audiovisual resources used in the\ncourse, seeking to answer whether the visualizations of said materials follow\ncertain patterns of behavior. 13 videos were analyzed, which had 16,340 views,\ncoming from at least 1,486 viewers. It was obtained that the visualizations\ndepend on the proximity to the test dates and that although the visualization\ntime has a curve that accompanies the duration of the videos, it is limited and\nthe average number of visualizations is 10 minutes and 4 seconds. It is also\nconcluded that the efficiency in viewing time increases in short videos.\n"}
+{"abstract": "  The $H$-join of a family of graphs $\\mathcal{G}=\\{G_1, \\dots, G_p\\}$, also\ncalled the generalized composition, $H[G_1, \\dots, G_p]$, where all graphs are\nundirected, simple and finite, is the graph obtained by replacing each vertex\n$i$ of $H$ by $G_i$ and adding to the edges of all graphs in $\\mathcal{G}$ the\nedges of the join $G_i \\vee G_j$, for every edge $ij$ of $H$. Some well known\ngraph operations are particular cases of the $H$-join of a family of graphs\n$\\mathcal{G}$ as it is the case of the lexicographic product (also called\ncomposition) of two graphs $H$ and $G$, $H[G]$. During long time the known\nexpressions for the determination of the entire spectrum of the $H$-join in\nterms of the spectra of its components and an associated matrix were limited to\nfamilies of regular graphs. In this work, we extend such a determination, as\nwell as the determination of the characteristic polynomial, to families of\narbitrary graphs. From the obtained results, the eigenvectors of the adjacency\nmatrix of the $H$-join can also be determined in terms of the adjacency\nmatrices of the components and an associated matrix.\n"}
+{"abstract": "  A class of network codes have been proposed in the literature where the\nsymbols transmitted on network edges are binary vectors and the coding\noperation performed in network nodes consists of the application of (possibly\nseveral) permutations on each incoming vector and XOR-ing the results to obtain\nthe outgoing vector. These network codes, which we will refer to as\npermute-and-add network codes, involve simpler operations and are known to\nprovide lower complexity solutions than scalar linear codes. The complexity of\nthese codes is determined by their degree which is the number of permutations\napplied on each incoming vector to compute an outgoing vector. Constructions of\npermute-and-add network codes for multicast networks are known. In this paper,\nwe provide a new framework based on group algebras to design permute-and-add\nnetwork codes for arbitrary (not necessarily multicast) networks. Our framework\nallows the use of any finite group of permutations (including circular shifts,\nproposed in prior work) and admits a trade-off between coding rate and the\ndegree of the code. Further, our technique permits elegant recovery and\ngeneralizations of the key results on permute-and-add network codes known in\nthe literature.\n"}
+{"abstract": "  The main purpose of this paper is to construct high-girth regular expander\ngraphs with localized eigenvectors for general degrees, which is inspired by a\nrecent work due to Alon, Ganguly and Srivastava (to appear in Israel J. Math.).\n"}
+{"abstract": "  Video streaming became an undivided part of the Internet. To efficiently\nutilize the limited network bandwidth it is essential to encode the video\ncontent. However, encoding is a computationally intensive task, involving\nhigh-performance resources provided by private infrastructures or public\nclouds. Public clouds, such as Amazon EC2, provide a large portfolio of\nservices and instances optimized for specific purposes and budgets. The\nmajority of Amazon instances use x86 processors, such as Intel Xeon or AMD\nEPYC. However, following the recent trends in computer architecture, Amazon\nintroduced Arm-based instances that promise up to 40% better cost-performance\nratio than comparable x86 instances for specific workloads. We evaluate in this\npaper the video encoding performance of x86 and Arm instances of four instance\nfamilies using the latest FFmpeg version and two video codecs. We examine the\nimpact of the encoding parameters, such as different presets and bitrates, on\nthe time and cost for encoding. Our experiments reveal that Arm instances show\nhigh time and cost-saving potential of up to 33.63% for specific bitrates and\npresets, especially for the x264 codec. However, the x86 instances are more\ngeneral and achieve low encoding times, regardless of the codec.\n"}
+{"abstract": "  We have implemented training of neural networks in secure multi-party\ncomputation (MPC) using quantization commonly used in the said setting. To the\nbest of our knowledge, we are the first to present an MNIST classifier purely\ntrained in MPC that comes within 0.2 percent of the accuracy of the same\nconvolutional neural network trained via plaintext computation. More\nconcretely, we have trained a network with two convolution and two dense layers\nto 99.2% accuracy in 25 epochs. This took 3.5 hours in our MPC implementation\n(under one hour for 99% accuracy).\n"}
+{"abstract": "  Interpretation of machine learning models has become one of the most\nimportant research topics due to the necessity of maintaining control and\navoiding bias in these algorithms. Since many machine learning algorithms are\npublished every day, there is a need for novel model-agnostic interpretation\napproaches that could be used to interpret a great variety of algorithms. Thus,\none advantageous way to interpret machine learning models is to feed different\ninput data to understand the changes in the prediction. Using such an approach,\npractitioners can define relations among data patterns and a model's decision.\nThis work proposes a model-agnostic interpretation approach that uses\nvisualization of feature perturbations induced by the PSO algorithm. We\nvalidate our approach on publicly available datasets, showing the capability to\nenhance the interpretation of different classifiers while yielding very stable\nresults compared with state-of-the-art algorithms.\n"}
+{"abstract": "  Using the integral field unit (IFU) data from Mapping Nearby Galaxies at\nApache Point Observatory (MaNGA) survey, we collect a sample of 36 star forming\ngalaxies that host galactic-scale outflows in ionized gas phase. The control\nsample is matched in the three dimensional parameter space of stellar mass,\nstar formation rate and inclination angle. Concerning the global properties,\nthe outflows host galaxies tend to have smaller size, more asymmetric gas disk,\nmore active star formation in the center and older stellar population than the\ncontrol galaxies. Comparing the stellar population properties along axes, we\nconclude that the star formation in the outflows host galaxies can be divided\ninto two branches. One branch evolves following the inside-out formation\nscenario. The other locating in the galactic center is triggered by gas\naccretion or galaxy interaction, and further drives the galactic-scale\noutflows. Besides, the enhanced star formation and metallicity along minor axis\nof outflows host galaxies uncover the positive feedback and metal entrainment\nin the galactic-scale outflows. Observational data in different phases with\nhigher spatial resolution are needed to reveal the influence of galactic-scale\noutflows on the star formation progress in detail.\n"}
+{"abstract": "  We consider in parallel pointed homotopy automorphisms of iterated wedge sums\nof topological spaces and boundary relative homotopy automorphisms of iterated\nconnected sums of manifolds minus a disk. Under certain conditions on the\nspaces and manifolds, we prove that the rational homotopy groups of these\nhomotopy automorphisms form finitely generated FI-modules, and thus satisfy\nrepresentation stability for symmetric groups, in the sense of Church and Farb.\n"}
+{"abstract": "  In this manuscript, we report strong linear correlation between shifted\nvelocity and line width of the broad blue-shifted [OIII] components in SDSS\nquasars. Broad blue-shifted [OIII] components are commonly treated as\nindicators of outflows related to central engine, however, it is still an open\nquestion whether the outflows are related to central accretion properties or\nrelated to local physical properties of NLRs (narrow emission line regions).\nHere, the reported strong linear correlation with the Spearman Rank correlation\ncoefficient 0.75 can be expected under the assumption of AGN (active galactic\nnuclei) feedback driven outflows, through a large sample of 535 SDSS quasars\nwith reliable blue-shifted broad [OIII] components. Moreover, there are much\ndifferent detection rates for broad blue-shifted and broad red-shifted [OIII]\ncomponents in quasars, and no positive correlation can be found between shifted\nvelocity and line width of the broad red-shifted [OIII] components, which\nprovide further and strong evidence to reject possibility of local outflows in\nNLRs leading to the broad blue-shifted [OIII] components in quasars. Thus, the\nstrong linear correlation can be treated as strong evidence for the broad\nblue-shifted [OIII] components as better indicators of outflows related to\ncentral engine in AGN. Furthermore, rather than central BH masses, Eddington\nratios and continuum luminosities have key roles on properties of the broad\nblue-shifted [OIII] components in quasars.\n"}
+{"abstract": "  This paper illustrates a detail description of the system and its results\nthat developed as a part of the participation at CONSTRAINT shared task in\nAAAI-2021. The shared task comprises two tasks: a) COVID19 fake news detection\nin English b) Hostile post detection in Hindi. Task-A is a binary\nclassification problem with fake and real class, while task-B is a multi-label\nmulti-class classification task with five hostile classes (i.e. defame, fake,\nhate, offense, non-hostile). Various techniques are used to perform the\nclassification task, including SVM, CNN, BiLSTM, and CNN+BiLSTM with tf-idf and\nWord2Vec embedding techniques. Results indicate that SVM with tf-idf features\nachieved the highest 94.39% weighted $f_1$ score on the test set in task-A.\nLabel powerset SVM with n-gram features obtained the maximum coarse-grained and\nfine-grained $f_1$ score of 86.03% and 50.98% on the task-B test set\nrespectively.\n"}
+{"abstract": "  Doping is considered to be the main method for improving the thermoelectric\nperformance of layered sodium cobaltate (Na$_{1-x}$CoO$_2$). However, in the\nvast majority of past reports, the equilibrium location of the dopant in the\nNa$_{1-x}$CoO$_2$'s complex layered lattice has not been confidently\nidentified. Consequently, a universal strategy for choosing a suitable dopant\nfor enhancing Na$_{1-x}$CoO$_2$'s figure of merit is yet to be established.\nHere, by examining the formation energy of Gd and Yb dopants in\nNa$_{0.75}$CoO$_2$ and Na$_{0.50}$CoO$_2$, we demonstrate that in an oxygen\npoor environment, Gd and Yb dopants reside in the Na layer while in an oxygen\nrich environment these dopants replace a Co in CoO$_2$ layer. When at Na layer,\nGd and Yb dopants reduce the carrier concentration via electron-hole\nrecombination, simultaneously increasing the Seebeck coefficient ($S$) and\nreducing electric conductivity ($\\sigma$). Na site doping, however, improves\nthe thermoelectric power factor (PF) only in Na$_{0.50}$CoO$_2$. When replacing\na Co, these dopants reduce $S$ and PF. The results demonstrate how\nthermoelectric performance critically depends on the synthesis environment that\nmust be fine-tuned for achieving any thermoelectric enhancement.\n"}
+{"abstract": "  Equation learning aims to infer differential equation models from data. While\na number of studies have shown that differential equation models can be\nsuccessfully identified when the data are sufficiently detailed and corrupted\nwith relatively small amounts of noise, the relationship between observation\nnoise and uncertainty in the learned differential equation models remains\nunexplored. We demonstrate that for noisy data sets there exists great\nvariation in both the structure of the learned differential equation models as\nwell as the parameter values. We explore how to combine data sets to quantify\nuncertainty in the learned models, and at the same time draw mechanistic\nconclusions about the target differential equations. We generate noisy data\nusing a stochastic agent-based model and combine equation learning methods with\napproximate Bayesian computation (ABC) to show that the correct differential\nequation model can be successfully learned from data, while a quantification of\nuncertainty is given by a posterior distribution in parameter space.\n"}
+{"abstract": "  We perform binary neutron star merger simulations using a newly derived set\nof finite-temperature equations of state in the Brueckner-Hartree-Fock\napproach. We point out the important and opposite roles of finite temperature\nand rotation for stellar stability and systematically investigate the\ngravitational-wave properties, matter distribution, and ejecta properties in\nthe postmerger phase for the different cases. The validity of several universal\nrelations is also examined and the most suitable EOSs are identified.\n"}
+{"abstract": "  Granger causality has been employed to investigate causality relations\nbetween components of stationary multiple time series. We generalize this\nconcept by developing statistical inference for local Granger causality for\nmultivariate locally stationary processes. Our proposed local Granger causality\napproach captures time-evolving causality relationships in nonstationary\nprocesses. The proposed local Granger causality is well represented in the\nfrequency domain and estimated based on the parametric time-varying spectral\ndensity matrix using the local Whittle likelihood. Under regularity conditions,\nwe demonstrate that the estimators converge to multivariate normal in\ndistribution. Additionally, the test statistic for the local Granger causality\nis shown to be asymptotically distributed as a quadratic form of a multivariate\nnormal distribution. The finite sample performance is confirmed with several\nsimulation studies for multivariate time-varying autoregressive models. For\npractical demonstration, the proposed local Granger causality method uncovered\nnew functional connectivity relationships between channels in brain signals.\nMoreover, the method was able to identify structural changes in financial data.\n"}
+{"abstract": "  The Fermi surface properties of a nontrivial system YSi is investigated by de\nHaas-van Alphen (dHvA) oscillation measurements combined with the\nfirst-principle calculations. Three main frequencies ($\\alpha$, $\\beta$,\n$\\gamma$) are probed up to $14$~T magnetic field in dHvA oscillations. The\n$\\alpha$-branch corresponding to $21$~T frequency possesses non-trivial\ntopological character with $\\pi$ Berry phase and a linear dispersion along\n$\\Gamma$ to $Z$ direction with a small effective mass of $0.069~m_e$ with\nsecond-lowest Landau-level up to $14$~T. For $B~\\parallel$~[010] direction, the\n295~T frequency exhibits non-trivial $2D$ character with $1.24\\pi$ Berry phase\nand a high Fermi velocity of $6.7 \\times 10^5$~ms$^{-1}$. The band structure\ncalculations reveal multiple nodal crossings in the vicinity of Fermi energy\n$E_f$ without spin-orbit coupling (SOC). Inclusion of SOC opens a small gap in\nthe nodal crossings and results in nonsymmorphic symmetry enforced Dirac points\nat some high symmetry points, suggesting YSi to be a symmetry enforced\ntopological metal.\n"}
+{"abstract": "  Let $\\alpha=(A_g,\\alpha_g)_{g\\in G}$ be a group-type partial action of a\nconnected groupoid $G$ on a ring $A=\\bigoplus_{z\\in G_0}A_z$ and\n$B=A\\star_{\\alpha}G$ the corresponding partial skew groupoid ring. In the first\npart of this paper we investigate the relation of several ring theoretic\nproperties between $A$ and $B$. For the second part, using that every Leavitt\npath algebra is isomorphic to a partial skew groupoid ring obtained from a\npartial groupoid action $\\lambda$, we characterize when $\\lambda$ is\ngroup-type. In such a case, we obtain ring theoretic properties of Leavitt path\nalgebras from the results on general partial skew groupoid rings. Several\nexamples that illustrate the results on Leavitt path algebras are presented.\n"}
+{"abstract": "  Keyword spotting and in particular Wake-Up-Word (WUW) detection is a very\nimportant task for voice assistants. A very common issue of voice assistants is\nthat they get easily activated by background noise like music, TV or background\nspeech that accidentally triggers the device. In this paper, we propose a\nSpeech Enhancement (SE) model adapted to the task of WUW detection that aims at\nincreasing the recognition rate and reducing the false alarms in the presence\nof these types of noises. The SE model is a fully-convolutional denoising\nauto-encoder at waveform level and is trained using a log-Mel Spectrogram and\nwaveform reconstruction losses together with the BCE loss of a simple WUW\nclassification network. A new database has been purposely prepared for the task\nof recognizing the WUW in challenging conditions containing negative samples\nthat are very phonetically similar to the keyword. The database is extended\nwith public databases and an exhaustive data augmentation to simulate different\nnoises and environments. The results obtained by concatenating the SE with a\nsimple and state-of-the-art WUW detectors show that the SE does not have a\nnegative impact on the recognition rate in quiet environments while increasing\nthe performance in the presence of noise, especially when the SE and WUW\ndetector are trained jointly end-to-end.\n"}
+{"abstract": "  Transfer reinforcement learning aims to improve the sample efficiency of\nsolving unseen new tasks by leveraging experiences obtained from previous\ntasks. We consider the setting where all tasks (MDPs) share the same\nenvironment dynamic except reward function. In this setting, the MDP dynamic is\na good knowledge to transfer, which can be inferred by uniformly random policy.\nHowever, trajectories generated by uniform random policy are not useful for\npolicy improvement, which impairs the sample efficiency severely. Instead, we\nobserve that the binary MDP dynamic can be inferred from trajectories of any\npolicy which avoids the need of uniform random policy. As the binary MDP\ndynamic contains the state structure shared over all tasks we believe it is\nsuitable to transfer. Built on this observation, we introduce a method to infer\nthe binary MDP dynamic on-line and at the same time utilize it to guide state\nembedding learning, which is then transferred to new tasks. We keep state\nembedding learning and policy learning separately. As a result, the learned\nstate embedding is task and policy agnostic which makes it ideal for transfer\nlearning. In addition, to facilitate the exploration over the state space, we\npropose a novel intrinsic reward based on the inferred binary MDP dynamic. Our\nmethod can be used out-of-box in combination with model-free RL algorithms. We\nshow two instances on the basis of \\algo{DQN} and \\algo{A2C}. Empirical results\nof intensive experiments show the advantage of our proposed method in various\ntransfer learning tasks.\n"}
+{"abstract": "  Traditionally, the efficiency and effectiveness of search systems have both\nbeen of great interest to the information retrieval community. However, an\nin-depth analysis of the interaction between the response latency and users'\nsubjective search experience in the mobile setting has been missing so far. To\naddress this gap, we conduct a controlled study that aims to reveal how\nresponse latency affects mobile web search. Our preliminary results indicate\nthat mobile web search users are four times more tolerant to response latency\nreported for desktop web search users. However, when exceeding a certain\nthreshold of 7-10 sec, the delays have a sizeable impact and users report\nfeeling significantly more tensed, tired, terrible, frustrated and sluggish,\nall which contribute to a worse subjective user experience.\n"}
+{"abstract": "  Industrial plants suffer from a high degree of complexity and incompatibility\nin their communication infrastructure, caused by a wild mix of proprietary\ntechnologies. This prevents transformation towards Industry 4.0 and the\nIndustrial Internet of Things. Open Platform Communications Unified\nArchitecture (OPC UA) is a standardized protocol that addresses these problems\nwith uniform and semantic communication across all levels of the hierarchy.\nHowever, its adoption in embedded field devices, such as sensors and actors, is\nstill lacking due to prohibitive memory and power requirements of software\nimplementations. We have developed a dedicated hardware engine that offloads\nprocessing of the OPC UA protocol and enables realization of compact and\nlow-power field devices with OPC UA support. As part of a proof-of-concept\nembedded system we have implemented this engine in a 22 nm FDSOI technology. We\nmeasured performance, power consumption, and memory footprint of our test chip\nand compared it with a software implementation based on open62541 and a\nRaspberry Pi 2B. Our OPC UA hardware engine is 50 times more energy efficient\nand only requires 36 KiB of memory. The complete chip consumes only 24 mW under\nfull load, making it suitable for low-power embedded applications.\n"}
+{"abstract": "  The Bogomolov multiplier $B_0(G)$ of a finite group $G$ is the subgroup of\nthe Schur multiplier $H^2(G,\\mathbb Q/\\mathbb Z)$ consisting of the cohomology\nclasses which vanishes after restricting to any abelian subgroup of $G$. We\ngive a proof of Hopf-type formula for $B_0(G)$ and derive an exact sequence for\ncohomological version of the Bogomolov multiplier. Using this exact sequence we\nprovide necessary and sufficient conditions for the corresponding inflation\nhomomorphism to be an epimorphism and to be zero map. We provide some\nconditions for the triviality of $B_0(G)$ for central product of groups $G$ and\nshow that the Bogomolov multiplier of generalized discrete Heisenberg groups is\ntrivial. We also give a complete characterization of groups of order $p^6, p>3$\nhaving trivial Bogomolov multiplier.\n"}
+{"abstract": "  Assuming the Generalized Continuum Hypothesis, this paper answers the\nquestion: when is the tensor product of two ultrafilters equal to their\nCartesian product? It is necessary and sufficient that their Cartesian product\nis an ultrafilter; that the two ultrafilters commute in the tensor product;\nthat for all cardinals $\\lambda$, one of the ultrafilters is both\n$\\lambda$-indecomposable and $\\lambda^+$-indecomposable; that the ultrapower\nembedding associated to each ultrafilter restricts to a definable embedding of\nthe ultrapower of the universe associated to the other.\n"}
+{"abstract": "  Let $G$ be a finite permutation group on $\\Omega$. An ordered sequence of\nelements of $\\Omega$, $(\\omega_1,\\dots, \\omega_t)$, is an irredundant base for\n$G$ if the pointwise stabilizer $G_{(\\omega_1,\\dots, \\omega_t)}$ is trivial and\nno point is fixed by the stabilizer of its predecessors. If all irredundant\nbases of $G$ have the same size we say that $G$ is an IBIS group. In this paper\nwe show that if a primitive permutation group is IBIS, then it must be almost\nsimple, of affine-type, or of diagonal type. Moreover we prove that a\ndiagonal-type primitive permutation groups is IBIS if and only if it is\nisomorphic to $PSL(2,2^f)\\times PSL(2,2^f)$ for some $f\\geq 2,$ in its diagonal\naction of degree $2^f(2^{2f}-1).$\n"}
+{"abstract": "  Gliomas are among the most aggressive and deadly brain tumors. This paper\ndetails the proposed Deep Neural Network architecture for brain tumor\nsegmentation from Magnetic Resonance Images. The architecture consists of a\ncascade of three Deep Layer Aggregation neural networks, where each stage\nelaborates the response using the feature maps and the probabilities of the\nprevious stage, and the MRI channels as inputs. The neuroimaging data are part\nof the publicly available Brain Tumor Segmentation (BraTS) 2020 challenge\ndataset, where we evaluated our proposal in the BraTS 2020 Validation and Test\nsets. In the Test set, the experimental results achieved a Dice score of\n0.8858, 0.8297 and 0.7900, with an Hausdorff Distance of 5.32 mm, 22.32 mm and\n20.44 mm for the whole tumor, core tumor and enhanced tumor, respectively.\n"}
+{"abstract": "  We study a model of a thermoelectric nanojunction driven by\nvibrationally-assisted tunneling. We apply the reaction coordinate formalism to\nderive a master equation governing its thermoelectric performance beyond the\nweak electron-vibrational coupling limit. Employing full counting statistics we\ncalculate the current flow, thermopower, associated noise, and efficiency\nwithout resorting to the weak vibrational coupling approximation. We\ndemonstrate intricacies of the power-efficiency-precision trade-off at strong\ncoupling, showing that the three cannot be maximised simultaneously in our\nmodel. Finally, we emphasise the importance of capturing non-additivity when\nconsidering strong coupling and multiple environments, demonstrating that an\nadditive treatment of the environments can violate the upper bound on\nthermoelectric efficiency imposed by Carnot.\n"}
+{"abstract": "  The nuclear matrix element (NME) of the neutrinoless double-$\\beta$\n($0\\nu\\beta\\beta$) decay is an essential input for determining the neutrino\neffective mass, if the half-life of this decay is measured. The reliable\ncalculation of this NME has been a long-standing problem because of the\ndiversity of the predicted values of the NME depending on the calculation\nmethod. In this paper, we focus on the shell model and the QRPA. The shell\nmodel have a rich amount of the many-particle many-hole correlations, and the\nQRPA can obtain the convergence of the result of calculation with respect to\nthe extension of the single-particle space. It is difficult for the shell model\nto obtain the convergence of the $0\\nu\\beta\\beta$ NME with respect to the\nvalence single-particle space. The many-body correlations of the QRPA are\ninsufficient depending on nuclei. We propose a new method to modify\nphenomenologically the results of the shell model and the QRPA compensating the\ninsufficient point of each method by using the information of other method\ncomplementarily. Extrapolations of the components of the $0\\nu\\beta\\beta$ NME\nof the shell model are made toward a very large valence single-particle space.\nWe introduce a modification factor to the components of the $0\\nu\\beta\\beta$\nNME of the QRPA. Our modification method gives similar values of the\n$0\\nu\\beta\\beta$ NME of the two methods for $^{48}$Ca. The NME of the\ntwo-neutrino double-$\\beta$ decay is also modified in a similar but simpler\nmanner, and the consistency of the two methods is improved.\n"}
+{"abstract": "  Network softwarization has revolutionized the architecture of cellular\nwireless networks. State-of-the-art container based virtual radio access\nnetworks (vRAN) provide enormous flexibility and reduced life cycle management\ncosts, but they also come with prohibitive energy consumption. We argue that\nfor future AI-native wireless networks to be flexible and energy efficient,\nthere is a need for a new abstraction in network softwarization that caters for\nneural network type of workloads and allows a large degree of service\ncomposability. In this paper we present the NeuroRAN architecture, which\nleverages stateful function as a user facing execution model, and is\ncomplemented with virtualized resources and decentralized resource management.\nWe show that neural network based implementations of common transceiver\nfunctional blocks fit the proposed architecture, and we discuss key research\nchallenges related to compilation and code generation, resource management,\nreliability and security.\n"}
+{"abstract": "  Semi-supervised learning through deep generative models and multi-lingual\npretraining techniques have orchestrated tremendous success across different\nareas of NLP. Nonetheless, their development has happened in isolation, while\nthe combination of both could potentially be effective for tackling\ntask-specific labelled data shortage. To bridge this gap, we combine\nsemi-supervised deep generative models and multi-lingual pretraining to form a\npipeline for document classification task. Compared to strong supervised\nlearning baselines, our semi-supervised classification framework is highly\ncompetitive and outperforms the state-of-the-art counterparts in low-resource\nsettings across several languages.\n"}
+{"abstract": "  We present Hubble Space Telescope Cosmic Origin Spectrograph (COS) UV line\nspectroscopy and integral-field unit (IFU) observations of the intra-group\nmedium in Stephan's Quintet (SQ). SQ hosts a 30 kpc long shocked ridge\ntriggered by a galaxy collision at a relative velocity of 1000 km/s, where\nlarge amounts of molecular gas coexist with a hot, X-ray emitting, plasma. COS\nspectroscopy at five positions sampling the diverse environments of the SQ\nintra-group medium reveals very broad (2000 km/s) Ly$\\alpha$ line emission with\ncomplex line shapes. The Ly$\\alpha$ line profiles are similar to or much\nbroader than those of H$\\beta$, [CII]$\\lambda157.7\\mu$m and CO~(1-0) emission.\nThe extreme breadth of the Ly$\\alpha$ emission, compared with H$\\beta$, implies\nresonance scattering within the observed structure. Scattering indicates that\nthe neutral gas of the intra-group medium is clumpy, with a significant surface\ncovering factor. We observe significant variations in the Ly$\\alpha$/H$\\beta$\nflux ratio between positions and velocity components. From the mean line ratio\naveraged over positions and velocities, we estimate the effective escape\nfraction of Ly$\\alpha$ photons to be 10-30%. Remarkably, over more than four\norders of magnitude in temperature, the powers radiated by X-rays, Ly$\\alpha$,\nH$_2$, [CII] are comparable within a factor of a few, assuming that the ratio\nof the Ly$\\alpha$ to H$_2$ fluxes over the whole shocked intra-group medium\nstay in line with those observed at those five positions. Both shocks and\nmixing layers could contribute to the energy dissipation associated with a\nturbulent energy cascade. Our results may be relevant for the cooling of gas at\nhigh redshifts, where the metal content is lower than in this local system, and\na high amplitude of turbulence is more common.\n"}
+{"abstract": "  In this paper, we study the Nisnevich sheafification\n$\\mathcal{H}^1_{\\acute{e}t}(G)$ of the presheaf associating to a smooth scheme\nthe set of isomorphism classes of $G$-torsors, for a reductive group $G$. We\nshow that if $G$-torsors on affine lines are extended, then\n$\\mathcal{H}^1_{\\acute{e}t}(G)$ is homotopy invariant and show that the sheaf\nis unramified if and only if Nisnevich-local purity holds for $G$-torsors. We\nalso identify the sheaf $\\mathcal{H}^1_{\\acute{e}t}(G)$ with the sheaf of\n$\\mathbb{A}^1$-connected components of the classifying space ${\\rm\nB}_{\\acute{e}t}G$. This establishes the homotopy invariance of the sheaves of\ncomponents as conjectured by Morel. It moreover provides a computation of the\nsheaf of $\\mathbb{A}^1$-connected components in terms of unramified $G$-torsors\nover function fields whenever Nisnevich-local purity holds for $G$-torsors.\n"}
+{"abstract": "  In this paper, we consider the structural change in a class of discrete\nvalued time series, which the true conditional distribution of the observations\nis assumed to be unknown.\n  The conditional mean of the process depends on a parameter $\\theta^*$ which\nmay change over time.\n  We provide sufficient conditions for the consistency and the asymptotic\nnormality of the Poisson quasi-maximum likelihood estimator (QMLE) of the\nmodel.\n  We consider an epidemic change-point detection and propose a test statistic\nbased on the QMLE of the parameter. Under the null hypothesis of a constant\nparameter (no change), the test statistic converges to a distribution obtained\nfrom a difference of two Brownian bridge. The test statistic diverges to\ninfinity under the epidemic alternative, which establishes that the proposed\nprocedure is consistent in power. The effectiveness of the proposed procedure\nis illustrated by simulated and real data examples.\n"}
+{"abstract": "  We classify rank two vector bundles on a del Pezzo threefold $X$ of Picard\nrank one whose projectivizations are weak Fano. We also investigate the moduli\nspaces of such vector bundles when $X$ is of degree five, especially whether it\nis smooth, irreducible, or fine.\n"}
+{"abstract": "  In this paper we study elimination of imaginaries in some classes of pure\nordered abelian groups. For the class of ordered abelian groups with bounded\nregular rank (equivalently with finite spines) we obtain weak elimination of\nimaginaries once we add sorts for the quotient groups $\\Gamma/ \\Delta$ for each\ndefinable convex subgroup $\\Delta$, and sorts for the quotient groups $\\Gamma/\n\\Delta+ l\\Gamma$ where $\\Delta$ is a definable convex subgroup and $l \\in\n\\mathbb{N}_{\\geq 2}$. We refer to these sorts as the \\emph{quotient sorts}. For\nthe dp-minimal case we obtain a complete elimination of imaginaries, if we also\nadd constants to distinguish the cosets of $\\Delta+n\\Gamma$ in $\\Gamma$, where\n$\\Delta$ is a definable convex subgroup and $n \\in \\mathbb{N}_{\\geq 2}$.\n"}
+{"abstract": "  The main theme of the paper is the detailed discussion of the renormalization\nof the quantum field theory comprising two interacting scalar fields. The\npotential of the model is the fourth-order homogeneous polynomial of the\nfields, symmetric with respect to the transformation\n$\\phi_{i}\\rightarrow{-\\phi_{i}}$. We determine the Feynman rules for the model\nand then we present a detailed discussion of the renormalization of the theory\nat one loop. Next, we derive the one loop renormalization group equations for\nthe running masses and coupling constants. At the level of two loops, we use\nthe FeynArts package of Mathematica to generate the two loops Feynman diagrams\nand calculate in detail the setting sun diagram.\n"}
+{"abstract": "  This paper provides a theoretical and numerical comparison of classical first\norder splitting methods for solving smooth convex optimization problems and\ncocoercive equations. In a theoretical point of view, we compare convergence\nrates of gradient descent, forward-backward, Peaceman-Rachford, and\nDouglas-Rachford algorithms for minimizing the sum of two smooth convex\nfunctions when one of them is strongly convex. A similar comparison is given in\nthe more general cocoercive setting under the presence of strong monotonicity\nand we observe that the convergence rates in optimization are strictly better\nthan the corresponding rates for cocoercive equations for some algorithms. We\nobtain improved rates with respect to the literature in several instances\nexploiting the structure of our problems. In a numerical point of view, we\nverify our theoretical results by implementing and comparing previous\nalgorithms in well established signal and image inverse problems involving\nsparsity. We replace the widely used $\\ell_1$ norm by the Huber loss and we\nobserve that fully proximal-based strategies have numerical and theoretical\nadvantages with respect to methods using gradient steps. In particular,\nPeaceman-Rachford is the more performant algorithm in our examples.\n"}
+{"abstract": "  In a binary classification problem where the goal is to fit an accurate\npredictor, the presence of corrupted labels in the training data set may create\nan additional challenge. However, in settings where likelihood maximization is\npoorly behaved-for example, if positive and negative labels are perfectly\nseparable-then a small fraction of corrupted labels can improve performance by\nensuring robustness. In this work, we establish that in such settings,\ncorruption acts as a form of regularization, and we compute precise upper\nbounds on estimation error in the presence of corruptions. Our results suggest\nthat the presence of corrupted data points is beneficial only up to a small\nfraction of the total sample, scaling with the square root of the sample size.\n"}
+{"abstract": "  We systematically investigate the complexity of counting subgraph patterns\nmodulo fixed integers. For example, it is known that the parity of the number\nof $k$-matchings can be determined in polynomial time by a simple reduction to\nthe determinant. We generalize this to an $n^{f(t,s)}$-time algorithm to\ncompute modulo $2^t$ the number of subgraph occurrences of patterns that are\n$s$ vertices away from being matchings. This shows that the known\npolynomial-time cases of subgraph detection (Jansen and Marx, SODA 2015) carry\nover into the setting of counting modulo $2^t$.\n  Complementing our algorithm, we also give a simple and self-contained proof\nthat counting $k$-matchings modulo odd integers $q$ is Mod_q-W[1]-complete and\nprove that counting $k$-paths modulo $2$ is Parity-W[1]-complete, answering an\nopen question by Bj\\\"orklund, Dell, and Husfeldt (ICALP 2015).\n"}
+{"abstract": "  We study the ground state for many interacting bosons in a double-well\npotential, in a joint limit where the particle number and the distance between\nthe potential wells both go to infinity. Two single-particle orbitals (one for\neach well) are macroscopically occupied, and we are concerned with deriving the\ncorresponding effective Bose-Hubbard Hamiltonian. We prove (i) an energy\nexpansion, including the two-modes Bose-Hubbard energy and two independent\nBogoliubov corrections (one for each potential well), (ii) a variance bound for\nthe number of particles falling inside each potential well. The latter is a\nsignature of a correlated ground state in that it violates the central limit\ntheorem.\n"}
+{"abstract": "  In this paper, we propose a method for ensembling the outputs of multiple\nobject detectors for improving detection performance and precision of bounding\nboxes on image data. We further extend it to video data by proposing a\ntwo-stage tracking-based scheme for detection refinement. The proposed method\ncan be used as a standalone approach for improving object detection\nperformance, or as a part of a framework for faster bounding box annotation in\nunseen datasets, assuming that the objects of interest are those present in\nsome common public datasets.\n"}
+{"abstract": "  A major issue of the increasingly popular robust optimization is the tendency\nto produce overly conservative solutions. This paper deals with this by\nproposing a new parameterized robust criterion that is flexible enough to offer\nfine-tuned control of conservatism. The proposed criterion also leads to a new\napproach for competitive ratio analysis, which can reduce the complexity of\nanalysis to the level for the minimax regret analysis. The properties of this\nnew criterion are studied, which facilitates its applications, and validates\nthe new approach for competitive ratio analysis. Finally, the criterion is\napplied to the well studied robust one-way trading problem to demonstrate its\npotential in controlling conservatism and reducing the complexity of\ncompetitive ratio analysis.\n"}
+{"abstract": "  The classic string indexing problem is to preprocess a string S into a\ncompact data structure that supports efficient pattern matching queries.\nTypical queries include existential queries (decide if the pattern occurs in\nS), reporting queries (return all positions where the pattern occurs), and\ncounting queries (return the number of occurrences of the pattern). In this\npaper we consider a variant of string indexing, where the goal is to compactly\nrepresent the string such that given two patterns P1 and P2 and a gap range\n[\\alpha,\\beta] we can quickly find the consecutive occurrences of P1 and P2\nwith distance in [\\alpha,\\beta], i.e., pairs of occurrences immediately\nfollowing each other and with distance within the range. We present data\nstructures that use \\~O(n) space and query time \\~O(|P1|+|P2|+n^(2/3)) for\nexistence and counting and \\~O(|P1|+|P2|+n^(2/3)*occ^(1/3)) for reporting. We\ncomplement this with a conditional lower bound based on the set intersection\nproblem showing that any solution using \\~O(n) space must use\n\\tilde{\\Omega}}(|P1|+|P2|+\\sqrt{n}) query time. To obtain our results we\ndevelop new techniques and ideas of independent interest including a new suffix\ntree decomposition and hardness of a variant of the set intersection problem.\n"}
+{"abstract": "  We study asymptotic behavior of solutions of the first-order linear consensus\nmodel with delay and anticipation, which is a system of neutral delay\ndifferential equations. We consider both the transmission-type and\nreaction-type delay that are motivated by modeling inputs. Studying the\nsimplified case of two agents, we show that, depending on the parameter regime,\nanticipation may have both a stabilizing and destabilizing effect on the\nsolutions. In particular, we demonstrate numerically that moderate level of\nanticipation generically promotes convergence towards consensus, while too high\nlevel disturbs it. Motivated by this observation, we derive sufficient\nconditions for asymptotic consensus in the multiple-agent systems, which are\nexplicit in the parameter values delay length and anticipation level, and\nindependent of the number of agents. The proofs are based on construction of\nsuitable Lyapunov-type functionals.\n"}
+{"abstract": "  Tree shape statistics provide valuable quantitative insights into\nevolutionary mechanisms underpinning phylogenetic trees, a commonly used graph\nrepresentation of evolution systems ranging from viruses to species. By\ndeveloping limit theorems for a version of extended P\\'olya urn models in which\nnegative entries are permitted for their replacement matrices, we present\nstrong laws of large numbers and central limit theorems for asymptotic joint\ndistributions of two subtree counting statistics, the number of cherries and\nthat of pitchforks, for random phylogenetic trees generated by two widely used\nnull tree models: the proportional to distinguishable arrangements (PDA) and\nthe Yule-Harding-Kingman (YHK) models. Our results indicate that the limiting\nbehaviour of these two statistics, when appropriately scaled, are independent\nof the initial trees used in the tree generating process.\n"}
+{"abstract": "  Detecting novel objects from few examples has become an emerging topic in\ncomputer vision recently. However, these methods need fully annotated training\nimages to learn new object categories which limits their applicability in real\nworld scenarios such as field robotics. In this work, we propose a\nprobabilistic multiple instance learning approach for few-shot Common Object\nLocalization (COL) and few-shot Weakly Supervised Object Detection (WSOD). In\nthese tasks, only image-level labels, which are much cheaper to acquire, are\navailable. We find that operating on features extracted from the last layer of\na pre-trained Faster-RCNN is more effective compared to previous episodic\nlearning based few-shot COL methods. Our model simultaneously learns the\ndistribution of the novel objects and localizes them via\nexpectation-maximization steps. As a probabilistic model, we employ von\nMises-Fisher (vMF) distribution which captures the semantic information better\nthan Gaussian distribution when applied to the pre-trained embedding space.\nWhen the novel objects are localized, we utilize them to learn a linear\nappearance model to detect novel classes in new images. Our extensive\nexperiments show that the proposed method, despite being simple, outperforms\nstrong baselines in few-shot COL and WSOD, as well as large-scale WSOD tasks.\n"}
+{"abstract": "  Recommender system usually faces popularity bias issues: from the data\nperspective, items exhibit uneven (long-tail) distribution on the interaction\nfrequency; from the method perspective, collaborative filtering methods are\nprone to amplify the bias by over-recommending popular items. It is undoubtedly\ncritical to consider popularity bias in recommender systems, and existing work\nmainly eliminates the bias effect. However, we argue that not all biases in the\ndata are bad -- some items demonstrate higher popularity because of their\nbetter intrinsic quality. Blindly pursuing unbiased learning may remove the\nbeneficial patterns in the data, degrading the recommendation accuracy and user\nsatisfaction.\n  This work studies an unexplored problem in recommendation -- how to leverage\npopularity bias to improve the recommendation accuracy. The key lies in two\naspects: how to remove the bad impact of popularity bias during training, and\nhow to inject the desired popularity bias in the inference stage that generates\ntop-K recommendations. This questions the causal mechanism of the\nrecommendation generation process. Along this line, we find that item\npopularity plays the role of confounder between the exposed items and the\nobserved interactions, causing the bad effect of bias amplification. To achieve\nour goal, we propose a new training and inference paradigm for recommendation\nnamed Popularity-bias Deconfounding and Adjusting (PDA). It removes the\nconfounding popularity bias in model training and adjusts the recommendation\nscore with desired popularity bias via causal intervention. We demonstrate the\nnew paradigm on latent factor model and perform extensive experiments on three\nreal-world datasets. Empirical studies validate that the deconfounded training\nis helpful to discover user real interests and the inference adjustment with\npopularity bias could further improve the recommendation accuracy.\n"}
+{"abstract": "  Let $\\mathfrak{F}_n$ be the set of all cuspidal automorphic representations\n$\\pi$ of $\\mathrm{GL}_n$ with unitary central character over a number field\n$F$. We prove the first unconditional zero density estimate for the set\n$\\mathcal{S}=\\{L(s,\\pi\\times\\pi')\\colon\\pi\\in\\mathfrak{F}_n\\}$ of\nRankin-Selberg $L$-functions, where $\\pi'\\in\\mathfrak{F}_{n'}$ is fixed. We use\nthis density estimate to prove (i) a strong average form of effective\nmultiplicity one for $\\mathrm{GL}_n$; (ii) that given $\\pi\\in\\mathfrak{F}_n$\ndefined over $\\mathbb{Q}$, the convolution $\\pi\\times\\tilde{\\pi}$ has a\npositive level of distribution in the sense of Bombieri-Vinogradov; (iii) that\nalmost all $L(s,\\pi\\times\\pi')\\in \\mathcal{S}$ have a hybrid-aspect\nsubconvexity bound on $\\mathrm{Re}(s)=\\frac{1}{2}$; (iv) a hybrid-aspect\npower-saving upper bound for the variance in the discrepancy of the measures\n$|\\varphi(x+iy)|^2 y^{-2}dxdy$ associated to $\\mathrm{GL}_2$ Hecke-Maass\nnewforms $\\varphi$ with trivial nebentypus, extending work of Luo and Sarnak\nfor level 1 cusp forms; and (v) a nonsplit analogue of quantum ergodicity:\nalmost all restrictions of Hilbert Hecke-Maass newforms to the modular surface\ndissipate as their Laplace eigenvalues grow.\n"}
+{"abstract": "  In this paper, we introduce the task of multi-view RGB-based 3D object\ndetection as an end-to-end optimization problem. To address this problem, we\npropose ImVoxelNet, a novel fully convolutional method of 3D object detection\nbased on monocular or multi-view RGB images. The number of monocular images in\neach multi-view input can variate during training and inference; actually, this\nnumber might be unique for each multi-view input. ImVoxelNet successfully\nhandles both indoor and outdoor scenes, which makes it general-purpose.\nSpecifically, it achieves state-of-the-art results in car detection on KITTI\n(monocular) and nuScenes (multi-view) benchmarks among all methods that accept\nRGB images. Moreover, it surpasses existing RGB-based 3D object detection\nmethods on the SUN RGB-D dataset. On ScanNet, ImVoxelNet sets a new benchmark\nfor multi-view 3D object detection. The source code and the trained models are\navailable at https://github.com/saic-vul/imvoxelnet.\n"}
+{"abstract": "  LAMOST Data Release 5, covering $\\sim$17,000 $deg^2$ from $-10^{\\circ}$ to\n$80^{\\circ}$ in declination, contains 9 millions co-added low resolution\nspectra of celestial objects, each spectrum combined from repeat exposure of\ntwo to tens of times during Oct 2011 to Jun 2017. In this paper, We present the\nspectra of individual exposures for all the objects in LAMOST Data Release 5.\nFor each spectrum, equivalent width of 60 lines from 11 different elements are\ncalculated with a new method combining the actual line core and fitted line\nwings. For stars earlier than F type, the Balmer lines are fitted with both\nemission and absorption profiles once two components are detected. Radial\nvelocity of each individual exposure is measured by minimizing ${\\chi}^2$\nbetween the spectrum and its best template. Database for equivalent widths of\nspectral lines and radial velocities of individual spectra are available\nonline. Radial velocity uncertainties with different stellar type and\nsignal-to-noise ratio are quantified by comparing different exposure of the\nsame objects. We notice that the radial velocity uncertainty depends on the\ntime lag between observations. For stars observed in the same day and with\nsignal-to-noise ratio higher than 20, the radial velocity uncertainty is below\n5km/s, and increase to 10km/s for stars observed in different nights.\n"}
+{"abstract": "  The rapid progress in clinical data management systems and artificial\nintelligence approaches enable the era of personalized medicine. Intensive care\nunits (ICUs) are the ideal clinical research environment for such development\nbecause they collect many clinical data and are highly computerized\nenvironments. We designed a retrospective clinical study on a prospective ICU\ndatabase using clinical natural language to help in the early diagnosis of\nheart failure in critically ill children. The methodology consisted of\nempirical experiments of a learning algorithm to learn the hidden\ninterpretation and presentation of the French clinical note data. This study\nincluded 1386 patients' clinical notes with 5444 single lines of notes. There\nwere 1941 positive cases (36 % of total) and 3503 negative cases classified by\ntwo independent physicians using a standardized approach. The multilayer\nperceptron neural network outperforms other discriminative and generative\nclassifiers. Consequently, the proposed framework yields an overall\nclassification performance with 89 % accuracy, 88 % recall, and 89 % precision.\nThis study successfully applied learning representation and machine learning\nalgorithms to detect heart failure from clinical natural language in a single\nFrench institution. Further work is needed to use the same methodology in other\ninstitutions and other languages.\n"}
+{"abstract": "  We present a comprehensive overview of chirality and its optical\nmanifestation in plasmonic nanosystems and nanostructures. We discuss top-down\nfabricated structures that range from solid metallic nanostructures to\ngroupings of metallic nanoparticles arranged in three dimensions. We also\npresent the large variety of bottom-up synthesized structures. Using DNA,\npeptides, or other scaffolds, complex nanoparticle arrangements of up to\nhundreds of individual nanoparticles have been realized. Beyond this static\npicture, we also give an overview of recent demonstrations of active chiral\nplasmonic systems, where the chiral optical response can be controlled by an\nexternal stimulus. We discuss the prospect of using the unique properties of\ncomplex chiral plasmonic systems for enantiomeric sensing schemes.\n"}
+{"abstract": "  Programming language detection is a common need in the analysis of large\nsource code bases. It is supported by a number of existing tools that rely on\nseveral features, and most notably file extensions, to determine file types. We\nconsider the problem of accurately detecting the type of files commonly found\nin software code bases, based solely on textual file content. Doing so is\nhelpful to classify source code that lack file extensions (e.g., code snippets\nposted on the Web or executable scripts), to avoid misclassifying source code\nthat has been recorded with wrong or uncommon file extensions, and also shed\nsome light on the intrinsic recognizability of source code files. We propose a\nsimple model that (a) use a language-agnostic word tokenizer for textual files,\n(b) group tokens in 1-/2-grams, (c) build feature vectors based on N-gram\nfrequencies, and (d) use a simple fully connected neural network as classifier.\nAs training set we use textual files extracted from GitHub repositories with at\nleast 1000 stars, using existing file extensions as ground truth. Despite its\nsimplicity the proposed model reaches 85% in our experiments for a relatively\nhigh number of recognized classes (more than 130 file types).\n"}
+{"abstract": "  An essential feature of the subdiffusion equations with the $\\alpha$-order\ntime fractional derivative is the weak singularity at the initial time. The\nweak regularity of the solution is usually characterized by a regularity\nparameter $\\sigma\\in (0,1)\\cup(1,2)$. Under this general regularity assumption,\nwe here obtain the pointwise-in-time error estimate of the widely used L1\nscheme for nonlinear subdiffusion equations. To the end, we present a refined\ndiscrete fractional-type Gr\\\"onwall inequality and a rigorous analysis for the\ntruncation errors. Numerical experiments are provided to demonstrate the\neffectiveness of our theoretical analysis.\n"}
+{"abstract": "  In this paper, we presented a new method for deformation control of\ndeformable objects, which utilizes both visual and tactile feedback. At\npresent, manipulation of deformable objects is basically formulated by assuming\npositional constraints. But in fact, in many situations manipulation has to be\nperformed under actively applied force constraints. This scenario is considered\nin this research. In the proposed scheme a tactile feedback is integrated to\nensure a stable contact between the robot end-effector and the soft object to\nbe manipulated. The controlled contact force is also utilized to regulate the\ndeformation of the soft object with its shape measured by a vision sensor. The\neffectiveness of the proposed method is demonstrated by a book page turning and\nshaping experiment.\n"}
+{"abstract": "  With the introduction of Artificial Intelligence (AI) and related\ntechnologies in our daily lives, fear and anxiety about their misuse as well as\nthe hidden biases in their creation have led to a demand for regulation to\naddress such issues. Yet blindly regulating an innovation process that is not\nwell understood, may stifle this process and reduce benefits that society may\ngain from the generated technology, even under the best intentions. In this\npaper, starting from a baseline model that captures the fundamental dynamics of\na race for domain supremacy using AI technology, we demonstrate how socially\nunwanted outcomes may be produced when sanctioning is applied unconditionally\nto risk-taking, i.e. potentially unsafe, behaviours. As an alternative to\nresolve the detrimental effect of over-regulation, we propose a voluntary\ncommitment approach wherein technologists have the freedom of choice between\nindependently pursuing their course of actions or establishing binding\nagreements to act safely, with sanctioning of those that do not abide to what\nthey pledged. Overall, this work reveals for the first time how voluntary\ncommitments, with sanctions either by peers or an institution, leads to\nsocially beneficial outcomes in all scenarios envisageable in a short-term race\ntowards domain supremacy through AI technology. These results are directly\nrelevant for the design of governance and regulatory policies that aim to\nensure an ethical and responsible AI technology development process.\n"}
+{"abstract": "  Chinese pre-trained language models usually process text as a sequence of\ncharacters, while ignoring more coarse granularity, e.g., words. In this work,\nwe propose a novel pre-training paradigm for Chinese -- Lattice-BERT, which\nexplicitly incorporates word representations along with characters, thus can\nmodel a sentence in a multi-granularity manner. Specifically, we construct a\nlattice graph from the characters and words in a sentence and feed all these\ntext units into transformers. We design a lattice position attention mechanism\nto exploit the lattice structures in self-attention layers. We further propose\na masked segment prediction task to push the model to learn from rich but\nredundant information inherent in lattices, while avoiding learning unexpected\ntricks. Experiments on 11 Chinese natural language understanding tasks show\nthat our model can bring an average increase of 1.5% under the 12-layer\nsetting, which achieves new state-of-the-art among base-size models on the CLUE\nbenchmarks. Further analysis shows that Lattice-BERT can harness the lattice\nstructures, and the improvement comes from the exploration of redundant\ninformation and multi-granularity representations. Our code will be available\nat https://github.com/alibaba/pretrained-language-models/LatticeBERT.\n"}
+{"abstract": "  We address the problem of novel view synthesis (NVS) from a few sparse source\nview images. Conventional image-based rendering methods estimate scene geometry\nand synthesize novel views in two separate steps. However, erroneous geometry\nestimation will decrease NVS performance as view synthesis highly depends on\nthe quality of estimated scene geometry. In this paper, we propose an\nend-to-end NVS framework to eliminate the error propagation issue. To be\nspecific, we construct a volume under the target view and design a source-view\nvisibility estimation (SVE) module to determine the visibility of the\ntarget-view voxels in each source view. Next, we aggregate the visibility of\nall source views to achieve a consensus volume. Each voxel in the consensus\nvolume indicates a surface existence probability. Then, we present a soft\nray-casting (SRC) mechanism to find the most front surface in the target view\n(i.e. depth). Specifically, our SRC traverses the consensus volume along\nviewing rays and then estimates a depth probability distribution. We then warp\nand aggregate source view pixels to synthesize a novel view based on the\nestimated source-view visibility and target-view depth. At last, our network is\ntrained in an end-to-end self-supervised fashion, thus significantly\nalleviating error accumulation in view synthesis. Experimental results\ndemonstrate that our method generates novel views in higher quality compared to\nthe state-of-the-art.\n"}
+{"abstract": "  In this paper, we present a first-order projection-free method, namely, the\nuniversal conditional gradient sliding (UCGS) method, for solving\n$\\varepsilon$-approximate solutions to convex differentiable optimization\nproblems. For objective functions with H\\\"older continuous gradients, we show\nthat UCGS is able to terminate with $\\varepsilon$-solutions with at most\n$O((M_\\nu D_X^{1+\\nu}/{\\varepsilon})^{2/(1+3\\nu)})$ gradient evaluations and\n$O((M_\\nu D_X^{1+\\nu}/{\\varepsilon})^{4/(1+3\\nu)})$ linear objective\noptimizations, where $\\nu\\in (0,1]$ and $M_\\nu>0$ are the exponent and constant\nof the H\\\"older condition. Furthermore, UCGS is able to perform such\ncomputations without requiring any specific knowledge of the smoothness\ninformation $\\nu$ and $M_\\nu$. In the weakly smooth case when $\\nu\\in (0,1)$,\nboth complexity results improve the current state-of-the-art $O((M_\\nu\nD_X^{1+\\nu}/{\\varepsilon})^{1/\\nu})$ results on first-order projection-free\nmethod achieved by the conditional gradient method. Within the class of\nsliding-type algorithms, to the best of our knowledge, this is the first time a\nsliding-type algorithm is able to improve not only the gradient complexity but\nalso the overall complexity for computing an approximate solution. In the\nsmooth case when $\\nu=1$, UCGS matches the state-of-the-art complexity result\nbut adds more features allowing for practical implementation.\n"}
+{"abstract": "  Let $M\\stackrel{\\rho_0}{\\curvearrowleft}S$ be a $C^\\infty$ locally free\naction of a connected simply connected solvable Lie group $S$ on a closed\nmanifold $M$. Roughly speaking, $\\rho_0$ is parameter rigid if any $C^\\infty$\nlocally free action of $S$ on $M$ having the same orbits as $\\rho_0$ is\n$C^\\infty$ conjugate to $\\rho_0$. In this paper we prove two types of result on\nparameter rigidity.\n  First let $G$ be a connected semisimple Lie group with finite center of real\nrank at least $2$ without compact factors nor simple factors locally isomorphic\nto $\\mathrm{SO}_0(n,1)$ $(n\\geq2)$ or $\\mathrm{SU}(n,1)$ $(n\\geq2)$, and let\n$\\Gamma$ be an irreducible cocompact lattice in $G$. Let $G=KAN$ be an Iwasawa\ndecomposition. We prove that the action $\\Gamma\\backslash G\\curvearrowleft AN$\nby right multiplication is parameter rigid. One of the three main ingredients\nof the proof is the rigidity theorems of Pansu and Kleiner-Leeb on the\nquasiisometries of Riemannian symmetric spaces of noncompact type.\n  Secondly we show, if $M\\stackrel{\\rho_0}{\\curvearrowleft}S$ is parameter\nrigid, then the zeroth and first cohomology of the orbit foliation of $\\rho_0$\nwith certain coefficients must vanish. This is a partial converse to the\nresults in the author's [Vanishing of cohomology and parameter rigidity of\nactions of solvable Lie groups. Geom. Topol. 21(1) (2017), 157-191], where we\nsaw sufficient conditions for parameter rigidity in terms of vanishing of the\nfirst cohomology with various coefficients.\n"}
+{"abstract": "  This letter studies an unmanned aerial vehicle (UAV) aided multicasting (MC)\nsystem, which is enabled by simultaneous free space optics (FSO) backhaul and\npower transfer. The UAV applies the power-splitting technique to harvest\nwireless power and decode backhaul information simultaneously over the FSO\nlink, while at the same time using the harvested power to multicast the\nbackhauled information over the radio frequency (RF) links to multiple ground\nusers (GUs). We derive the UAV's achievable MC rate under the Poisson point\nprocess (PPP) based GU distribution. By jointly designing the FSO and RF links\nand the UAV altitude, we maximize the system-level energy efficiency (EE),\nwhich can be equivalently expressed as the ratio of the UAV's MC rate over the\noptics base station (OBS) transmit power, subject to the UAV's sustainable\noperation and reliable backhauling constraints. Due to the non-convexity of\nthis problem, we propose suboptimal solutions with low complexity. Numerical\nresults show the close-to-optimal EE performance by properly balancing the\npower-rate tradeoff between the FSO power and the MC data transmissions.\n"}
+{"abstract": "  The first mobile camera phone was sold only 20 years ago, when taking\npictures with one's phone was an oddity, and sharing pictures online was\nunheard of. Today, the smartphone is more camera than phone. How did this\nhappen? This transformation was enabled by advances in computational\nphotography -the science and engineering of making great images from small form\nfactor, mobile cameras. Modern algorithmic and computing advances, including\nmachine learning, have changed the rules of photography, bringing to it new\nmodes of capture, post-processing, storage, and sharing. In this paper, we give\na brief history of mobile computational photography and describe some of the\nkey technological components, including burst photography, noise reduction, and\nsuper-resolution. At each step, we may draw naive parallels to the human visual\nsystem.\n"}
+{"abstract": "  We study dynamic clustering problems from the perspective of online learning.\nWe consider an online learning problem, called \\textit{Dynamic $k$-Clustering},\nin which $k$ centers are maintained in a metric space over time (centers may\nchange positions) such as a dynamically changing set of $r$ clients is served\nin the best possible way. The connection cost at round $t$ is given by the\n\\textit{$p$-norm} of the vector consisting of the distance of each client to\nits closest center at round $t$, for some $p\\geq 1$ or $p = \\infty$. We present\na \\textit{$\\Theta\\left( \\min(k,r) \\right)$-regret} polynomial-time online\nlearning algorithm and show that, under some well-established computational\ncomplexity conjectures, \\textit{constant-regret} cannot be achieved in\npolynomial-time. In addition to the efficient solution of Dynamic\n$k$-Clustering, our work contributes to the long line of research on\ncombinatorial online learning.\n"}
+{"abstract": "  Photonic quantum networking relies on entanglement distribution between\ndistant nodes, typically realized by swapping procedures. However, entanglement\nswapping is a demanding task in practice, mainly because of limited\neffectiveness of entangled photon sources and Bell-state measurements necessary\nto realize the process. Here we experimentally activate a remote distribution\nof two-photon polarization entanglement which supersedes the need for initial\nentangled pairs and traditional Bell-state measurements. This alternative\nprocedure is accomplished thanks to the controlled spatial indistinguishability\nof four independent photons in three separated nodes of the network, which\nenables us to perform localized product-state measurements on the central node\nacting as a trigger. This experiment proves that the inherent\nindistinguishability of identical particles supplies new standards for feasible\nquantum communication in multinode photonic quantum networks.\n"}
+{"abstract": "  Adapting the idea of training CartPole with Deep Q-learning agent, we are\nable to find a promising result that prevent the pole from falling down. The\ncapacity of reinforcement learning (RL) to learn from the interaction between\nthe environment and agent provides an optimal control strategy. In this paper,\nwe aim to solve the classic pendulum swing-up problem that making the learned\npendulum to be in upright position and balanced. Deep Deterministic Policy\nGradient algorithm is introduced to operate over continuous action domain in\nthis problem. Salient results of optimal pendulum are proved with increasing\naverage return, decreasing loss, and live video in the code part.\n"}
+{"abstract": "  We report the detection of [O I]145.5um in the BR 1202-0725 system, a compact\ngroup at z=4.7 consisting of a quasar (QSO), a submillimeter-bright galaxy\n(SMG), and three faint Lya emitters. By taking into account the previous\ndetections and upper limits, the [O I]/[C II] line ratios of the now five known\nhigh-z galaxies are higher than or on the high-end of the observed values in\nlocal galaxies ([O I]/[C II]$\\gtrsim$0.13). The high [O I]/[C II] ratios and\nthe joint analysis with the previous detection of [N II] lines for both the QSO\nand the SMG suggest the presence of warm and dense neutral gas in these highly\nstar-forming galaxies. This is further supported by new CO (12-11) line\ndetections and a comparison with cosmological simulations. There is a possible\npositive correlation between the [NII]122/205 line ratio and the [O I]/[C II]\nratio when all local and high-z sources are taken into account, indicating that\nthe denser the ionized gas, the denser and warmer the neutral gas (or vice\nversa). The detection of the [O I] line in the BR1202-0725 system with a\nrelatively short amount of integration with ALMA demonstrates the great\npotential of this line as a dense gas tracer for high-z galaxies.\n"}
+{"abstract": "  We study the nonsingular black hole in Anti de-Sitter background taking the\nnegative cosmological constant as the pressure of the system. We investigate\nthe horizon structure, and find the critical values $m_0$ and $\\tilde{k}_0$,\nsuch that $m>m_0$ (or $\\tilde{k}<\\tilde{k}_0$) corresponds to a black solution\nwith two horizons, namely the Cauchy horizon $x_-$ and the event horizon $x_+$.\nFor $m=m_0$ (or $\\tilde{k}=\\tilde{k}_0$), there exist an extremal black hole\nwith degenerate horizon $x_0=x_{\\pm}$ and for $m<m_0$ (or\n$\\tilde{k}>\\tilde{k}_0$), no black hole solution exists. In turn, we calculate\nthe thermodynamical properties and by observing the behaviour of Gibb's free\nenergy and specific heat, we find that this black hole solution exhibits first\norder (small to large black hole) and second order phase transition. Further,\nwe study the $P-V$ criticality of system and then calculate the critical\nexponents showing that they are the same as those of the Van der Waals fluid.\n"}
+{"abstract": "  In the context of autonomous vehicles, one of the most crucial tasks is to\nestimate the risk of the undertaken action. While navigating in complex urban\nenvironments, the Bayesian occupancy grid is one of the most popular types of\nmaps, where the information of occupancy is stored as the probability of\ncollision. Although widely used, this kind of representation is not well suited\nfor risk assessment: because of its discrete nature, the probability of\ncollision becomes dependent on the tessellation size. Therefore, risk\nassessments on Bayesian occupancy grids cannot yield risks with meaningful\nphysical units. In this article, we propose an alternative framework called\nDynamic Lambda-Field that is able to assess generic physical risks in dynamic\nenvironments without being dependent on the tessellation size. Using our\nframework, we are able to plan safe trajectories where the risk function can be\nadjusted depending on the scenario. We validate our approach with quantitative\nexperiments, showing the convergence speed of the grid and that the framework\nis suitable for real-world scenarios.\n"}
+{"abstract": "  The use of crowdworkers in NLP research is growing rapidly, in tandem with\nthe exponential increase in research production in machine learning and AI.\nEthical discussion regarding the use of crowdworkers within the NLP research\ncommunity is typically confined in scope to issues related to labor conditions\nsuch as fair pay. We draw attention to the lack of ethical considerations\nrelated to the various tasks performed by workers, including labeling,\nevaluation, and production. We find that the Final Rule, the common ethical\nframework used by researchers, did not anticipate the use of online\ncrowdsourcing platforms for data collection, resulting in gaps between the\nspirit and practice of human-subjects ethics in NLP research. We enumerate\ncommon scenarios where crowdworkers performing NLP tasks are at risk of harm.\nWe thus recommend that researchers evaluate these risks by considering the\nthree ethical principles set up by the Belmont Report. We also clarify some\ncommon misconceptions regarding the Institutional Review Board (IRB)\napplication. We hope this paper will serve to reopen the discussion within our\ncommunity regarding the ethical use of crowdworkers.\n"}
+{"abstract": "  Motivated by applications to single-particle cryo-electron microscopy\n(cryo-EM), we study several problems of function estimation in a low SNR\nregime, where samples are observed under random rotations of the function\ndomain. In a general framework of group orbit estimation with linear\nprojection, we describe a stratification of the Fisher information eigenvalues\naccording to a sequence of transcendence degrees in the invariant algebra, and\nrelate critical points of the log-likelihood landscape to a sequence of\nmethod-of-moments optimization problems. This extends previous results for a\ndiscrete rotation group without projection.\n  We then compute these transcendence degrees and the forms of these moment\noptimization problems for several examples of function estimation under $SO(2)$\nand $SO(3)$ rotations, including a simplified model of cryo-EM as introduced by\nBandeira, Blum-Smith, Kileel, Perry, Weed, and Wein. For several of these\nexamples, we affirmatively resolve numerical conjectures that\n$3^\\text{rd}$-order moments are sufficient to locally identify a generic signal\nup to its rotational orbit.\n  For low-dimensional approximations of the electric potential maps of two\nsmall protein molecules, we empirically verify that the noise-scalings of the\nFisher information eigenvalues conform with these theoretical predictions over\na range of SNR, in a model of $SO(3)$ rotations without projection.\n"}
+{"abstract": "  The recently introduced harmonic resolvent framework is concerned with the\nstudy of the input-output dynamics of nonlinear flows in the proximity of a\nknown time-periodic orbit. These dynamics are governed by the harmonic\nresolvent operator, which is a linear operator in the frequency domain whose\nsingular value decomposition sheds light on the dominant input-output\nstructures of the flow. Although the harmonic resolvent is a mathematically\nwell-defined operator, the numerical computation of its singular value\ndecomposition requires inverting a matrix that becomes exactly singular as the\nperiodic orbit approaches an exact solution of the nonlinear governing\nequations. The very poor condition properties of this matrix hinder the\nconvergence of classical Krylov solvers, even in the presence of\npreconditioners, thereby increasing the computational cost required to perform\nthe harmonic resolvent analysis. In this paper we show that a suitable\naugmentation of the (nearly) singular matrix removes the singularity, and we\nprovide a lower bound for the smallest singular value of the augmented matrix.\nWe also show that the desired decomposition of the harmonic resolvent can be\ncomputed using the augmented matrix, whose improved condition properties lead\nto a significant speedup in the convergence of classical iterative solvers. We\ndemonstrate this simple, yet effective, computational procedure on the\nKuramoto-Sivashinsky equation in the proximity of an unstable time-periodic\norbit.\n"}
+{"abstract": "  This objective of this report is to review existing enterprise blockchain\ntechnologies - EOSIO powered systems, Hyperledger Fabric and Besu, Consensus\nQuorum, R3 Corda and Ernst and Young's Nightfall - that provide data privacy\nwhile leveraging the data integrity benefits of blockchain. By reviewing and\ncomparing how and how well these technologies achieve data privacy, a snapshot\nis captured of the industry's current best practices and data privacy models.\nMajor enterprise technologies are contrasted in parallel to EOSIO to better\nunderstand how EOSIO can evolve to meet the trends seen in enterprise\nblockchain privacy. The following strategies and trends were generally observed\nin these technologies:\n  Cryptography: the hashing algorithm was found to be the most used\ncryptographic primitive in enterprise or changeover privacy solutions.\nCoordination via on-chain contracts - a common strategy was to use a shared\npublicly ledger to coordinate data privacy groups and more generally managed\nidentities and access control.\n  Transaction and contract code sharing: there was a variety of different\nlevels of privacy around the business logic (smart contract code) visibility.\nSome solutions only allowed authorised peers to view code while others made\nthis accessible to everybody that was a member of the shared ledger.\n  Data migrations for data privacy applications: significant challenges exist\nwhen using cryptographically stored data in terms of being able to run system\nupgrades.\n  Multiple blockchain ledgers for data privacy: solutions attempted to create a\nnew private blockchain for every private data relationship which was eventually\nabandoned in favour of one shared ledger with private data\ncollections/transactions that were anchored to the ledger with a hash in order\nto improve scaling.\n"}
+{"abstract": "  Wavefront aberrations can reflect the imaging quality of high-performance\noptical systems better than geometric aberrations. Although laser\ninterferometers have emerged as the main tool for measurement of transmitted\nwavefronts, their application is greatly limited, as they are typically\ndesigned for operation at specific wavelengths. In a previous study, we\nproposed a method for determining the wavefront transmitted by an optical\nsystem at any wavelength in a certain band. Although this method works well for\nmost monochromatic systems, where the image plane is at the focal point for the\ntransmission wavelength, for general multi-color systems, it is more practical\nto measure the wavefront at the defocused image plane. Hence, in this paper, we\nhave developed a complete method for determining transmitted wavefronts in a\nbroad bandwidth at any defocused position, enabling wavefront measurements for\nmulti-color systems. Here, we assume that in small ranges, the Zernike\ncoefficients have a linear relationship with position, such that Zernike\ncoefficients at defocused positions can be derived from measurements performed\nat the focal point. We conducted experiments to verify these assumptions,\nvalidating the new method. The experimental setup has been improved so that it\ncan handle multi-color systems, and a detailed experimental process is\nsummarized. With this technique, application of broadband transmission\nwavefront measurement can be extended to most general optical systems, which is\nof great significance for characterization of achromatic and apochromatic\noptical lenses.\n"}
+{"abstract": "  We study for the first time the $p\\Sigma^-\\to K^-d$ and $K^-d\\to p\\Sigma^-$\nreactions close to threshold and show that they are driven by a triangle\nmechanism, with the $\\Lambda(1405)$, a proton and a neutron as intermediate\nstates, which develops a triangle singularity close to the $\\bar{K}d$\nthreshold. We find that a mechanism involving virtual pion exchange and the\n$K^-p\\to\\pi^+\\Sigma^-$ amplitude dominates over another one involving kaon\nexchange and the $K^-p\\to K^-p$ amplitude. Moreover, of the two $\\Lambda(1405)$\nstates, the one with higher mass around $1420$ MeV, gives the largest\ncontribution to the process. We show that the cross section, well within\nmeasurable range, is very sensitive to different models that, while reproducing\n$\\bar{K}N$ observables above threshold, provide different extrapolations of the\n$\\bar{K}N$ amplitudes below threshold. The observables of this reaction will\nprovide new constraints on the theoretical models, leading to more reliable\nextrapolations of the $\\bar{K}N$ amplitudes below threshold and to more\naccurate predictions of the $\\Lambda(1405)$ state of lower mass.\n"}
+{"abstract": "  We present a newly enlarged census of the compact radio population towards\nthe Orion Nebula Cluster (ONC) using high-sensitivity continuum maps (3-10\n$\\mu$Jy bm$^{-1}$) from a total of $\\sim30$ h centimeter-wavelength\nobservations over an area of $\\sim$20$'\\times20'$ obtained in the C-band (4$-$8\nGHz) with the Karl G. Jansky Very Large Array (VLA) in its high-resolution\nA-configuration. We thus complement our previous deep survey of the innermost\nareas of the ONC, now covering the field of view of the Chandra Orion\nUltra-deep Project (COUP). Our catalog contains 521 compact radio sources of\nwhich 198 are new detections. Overall, we find that 17% of the (mostly stellar)\nCOUP sources have radio counterparts, while 53% of the radio sources have COUP\ncounterparts. Most notably, the radio detection fraction of X-ray sources is\nhigher in the inner cluster and almost constant for $r>3'$ (0.36 pc) from\n$\\theta^1$ Ori C suggesting a correlation between the radio emission mechanism\nof these sources and their distance from the most massive stars at the center\nof the cluster, for example due to increased photoionisation of circumstellar\ndisks. The combination with our previous observations four years prior lead to\nthe discovery of fast proper motions of up to $\\sim$373 km s$^{-1}$ from faint\nradio sources associated with ejecta of the OMC1 explosion. Finally, we search\nfor strong radio variability. We found changes in flux density by a factor of\n$\\lesssim$5 within our observations and a few sources with changes by a factor\n$>$10 on long timescales of a few years.\n"}
+{"abstract": "  With the advent of the Internet-of-Things (IoT) era, the ever-increasing\nnumber of devices and emerging applications have triggered the need for\nubiquitous connectivity and more efficient computing paradigms. These stringent\ndemands have posed significant challenges to the current wireless networks and\ntheir computing architectures. In this article, we propose a high-altitude\nplatform (HAP) network-enabled edge computing paradigm to tackle the key issues\nof massive IoT connectivity. Specifically, we first provide a comprehensive\noverview of the recent advances in non-terrestrial network-based edge computing\narchitectures. Then, the limitations of the existing solutions are further\nsummarized from the perspectives of the network architecture, random access\nprocedure, and multiple access techniques. To overcome the limitations, we\npropose a HAP-enabled aerial cell-free massive multiple-input multiple-output\nnetwork to realize the edge computing paradigm, where multiple HAPs cooperate\nvia the edge servers to serve IoT devices. For the case of a massive number of\ndevices, we further adopt a grant-free massive access scheme to guarantee\nlow-latency and high-efficiency massive IoT connectivity to the network.\nBesides, a case study is provided to demonstrate the effectiveness of the\nproposed solution. Finally, to shed light on the future research directions of\nHAP network-enabled edge computing paradigms, the key challenges and open\nissues are discussed.\n"}
+{"abstract": "  Supervised machine learning, in which models are automatically derived from\nlabeled training data, is only as good as the quality of that data. This study\nbuilds on prior work that investigated to what extent 'best practices' around\nlabeling training data were followed in applied ML publications within a single\ndomain (social media platforms). In this paper, we expand by studying\npublications that apply supervised ML in a far broader spectrum of disciplines,\nfocusing on human-labeled data. We report to what extent a random sample of ML\napplication papers across disciplines give specific details about whether best\npractices were followed, while acknowledging that a greater range of\napplication fields necessarily produces greater diversity of labeling and\nannotation methods. Because much of machine learning research and education\nonly focuses on what is done once a \"ground truth\" or \"gold standard\" of\ntraining data is available, it is especially relevant to discuss issues around\nthe equally-important aspect of whether such data is reliable in the first\nplace. This determination becomes increasingly complex when applied to a\nvariety of specialized fields, as labeling can range from a task requiring\nlittle-to-no background knowledge to one that must be performed by someone with\ncareer expertise.\n"}
+{"abstract": "  Conversational Artificial Intelligence (AI) used in industry settings can be\ntrained to closely mimic human behaviors, including lying and deception.\nHowever, lying is often a necessary part of negotiation. To address this, we\ndevelop a normative framework for when it is ethical or unethical for a\nconversational AI to lie to humans, based on whether there is what we call\n\"invitation of trust\" in a particular scenario. Importantly, cultural norms\nplay an important role in determining whether there is invitation of trust\nacross negotiation settings, and thus an AI trained in one culture may not be\ngeneralizable to others. Moreover, individuals may have different expectations\nregarding the invitation of trust and propensity to lie for human vs. AI\nnegotiators, and these expectations may vary across cultures as well. Finally,\nwe outline how a conversational chatbot can be trained to negotiate ethically\nby applying autoregressive models to large dialog and negotiations datasets.\n"}
+{"abstract": "  Technology has the opportunity to assist older adults as they age in place,\ncoordinate caregiving resources, and meet unmet needs through access to\nresources. Currently, older adults use consumer technologies to support\neveryday life, however these technologies are not always accessible or as\nuseful as they can be. Indeed, industry has attempted to create smart home\ntechnologies with older adults as a target user group, however these solutions\nare often more focused on the technical aspects and are short lived. In this\npaper, we advocate for older adults being involved in the design process - from\ninitial ideation to product development to deployment. We encourage federally\nfunded researchers and industry to create compensated, diverse older adult\nadvisory boards to address stereotypes about aging while ensuring their needs\nare considered.\n  We envision artificial intelligence systems that augment resources instead of\nreplacing them - especially in under-resourced communities. Older adults rely\non their caregiver networks and community organizations for social, emotional,\nand physical support; thus, AI should be used to coordinate resources better\nand lower the burden of connecting with these resources. Although\nsociotechnical smart systems can help identify needs of older adults, the lack\nof affordable research infrastructure and translation of findings into consumer\ntechnology perpetuates inequities in designing for diverse older adults. In\naddition, there is a disconnect between the creation of smart sensing systems\nand creating understandable, actionable data for older adults and caregivers to\nutilize. We ultimately advocate for a well-coordinated research effort across\nthe United States that connects older adults, caregivers, community\norganizations, and researchers together to catalyze innovative and practical\nresearch for all stakeholders.\n"}
+{"abstract": "  Let $k \\geq 1$ be an integer and $n=3k-1$. Let $\\mathbb{Z}_n$ denote the\nadditive group of integers modulo $n$ and let $C$ be the subset of\n$\\mathbb{Z}_n$ consisting of the elements congruent to 1 modulo 3. The Cayley\ngraph $Cay(\\mathbb{Z}_n; C)$ is known as the Andr\\'asfia graph And($k$). In\nthis note, we wish to determine the automorphism group of this graph. We will\nshow that $Aut(And(k))$ is isomorphic with the dihedral group\n$\\mathbb{D}_{2n}$.\n"}
+{"abstract": "  The Landau form of the Fokker-Planck equation is the gold standard for\nplasmas dominated by small angle collisions, however its $\\Order{N^2}$ work\ncomplexity has limited its practicality. This paper extends previous work on a\nfully conservative finite element method for this Landau collision operator\nwith adaptive mesh refinement, optimized for vector machines, by porting the\nalgorithm to the Cuda programming model with implementations in Cuda and\nKokkos, and by reporting results within a Vlasov-Maxwell-Landau model of a\nplasma thermal quench. With new optimizations of the Landau kernel and ports of\nthis kernel, the sparse matrix assembly and algebraic solver to Cuda, the cost\nof a well resolved Landau collision time advance is shown to be practical for\nkinetic plasma applications. This fully implicit Landau time integrator and the\nplasma quench model is available in the PETSc (Portable, Extensible, Toolkit\nfor Scientific computing) numerical library.\n"}
+{"abstract": "  Let $X$ and $Y$ be two smooth manifolds of the same dimension. It was proved\nby Seeger, Sogge and Stein in \\cite{SSS} that the Fourier integral operators\nwith real non-degenerate phase functions in the class $I^{\\mu}_1(X,Y;\\Lambda),$\n$\\mu\\leq -(n-1)/2,$ are bounded from $H^1$ to $L^1.$ The sharpness of the order\n$-(n-1)/2,$ for any elliptic operator was also proved in \\cite{SSS} and\nextended to other types of canonical relations in \\cite{Ruzhansky1999}. That\nthe operators in the class $I^{\\mu}_1(X,Y;\\Lambda),$ $\\mu\\leq -(n-1)/2,$\nsatisfy the weak (1,1) inequality was proved by Tao \\cite{Tao:weak11}. In this\nnote, we prove that the weak (1,1) inequality for the order $ -(n-1)/2$ is\nsharp for any elliptic Fourier integral operator, as well as its versions for\ncanonical relations satisfying additional rank conditions.\n"}
+{"abstract": "  A novel data-processing method was developed to facilitate scintillation\ndetector characterization. Combined with fan-beam calibration, this method can\nbe used to quickly and conveniently calibrate gamma-ray detectors for SPECT,\nPET, homeland security or astronomy. Compared with traditional calibration\nmethods, this new technique can accurately calibrate a photon-counting\ndetector, including DOI information, with greatly reduced time. The enabling\npart of this technique is fan-beam scanning combined with a data-processing\nstrategy called the common-data subset (CDS) method, which was used to\nsynthesize the detector's mean detector response functions (MDRFs). Using this\napproach, $2N$ scans ($N$ in x and $N$ in y direction) are necessary to finish\ncalibration of a 2D detector as opposed to $N^2$ scans with a pencil beam. For\na 3D detector calibration, only $3N$ scans are necessary to achieve the 3D\ndetector MDRFs that include DOI information. Moreover, this calibration\ntechnique can be used for detectors with complicated or irregular MDRFs. We\npresent both Monte-Carlo simulations and experimental results that support the\nfeasibility of this method.\n"}
+{"abstract": "  While numerous attempts have been made to jointly parse syntax and semantics,\nhigh performance in one domain typically comes at the price of performance in\nthe other. This trade-off contradicts the large body of research focusing on\nthe rich interactions at the syntax-semantics interface. We explore multiple\nmodel architectures which allow us to exploit the rich syntactic and semantic\nannotations contained in the Universal Decompositional Semantics (UDS) dataset,\njointly parsing Universal Dependencies and UDS to obtain state-of-the-art\nresults in both formalisms. We analyze the behaviour of a joint model of syntax\nand semantics, finding patterns supported by linguistic theory at the\nsyntax-semantics interface. We then investigate to what degree joint modeling\ngeneralizes to a multilingual setting, where we find similar trends across 8\nlanguages.\n"}
+{"abstract": "  This paper considers the Gaussian multiple-access channel (MAC) in the\nasymptotic regime where the number of users grows linearly with the code\nlength. We propose efficient coding schemes based on random linear models with\napproximate message passing (AMP) decoding and derive the asymptotic error rate\nachieved for a given user density, user payload (in bits), and user energy. The\ntradeoff between energy-per-bit and achievable user density (for a fixed user\npayload and target error rate) is studied, and it is demonstrated that in the\nlarge system limit, a spatially coupled coding scheme with AMP decoding\nachieves near-optimal tradeoffs for a wide range of user densities.\nFurthermore, in the regime where the user payload is large, we also study the\nspectral efficiency versus energy-per-bit tradeoff and discuss methods to\nreduce decoding complexity at large payload sizes.\n"}
+{"abstract": "  Computational thinking has been a recent focus of education research within\nthe sciences. However, there is a dearth of scholarly literature on how best to\nteach and to assess this topic, especially in disciplinary science courses.\nPhysics classes with computation integrated into the curriculum are a fitting\nsetting for investigating computational thinking. In this paper, we lay the\nfoundation for exploring computational thinking in introductory physics\ncourses. First, we review relevant literature to synthesize a set of potential\nlearning goals that students could engage in when working with computation. The\ncomputational thinking framework that we have developed features 14 practices\ncontained within 6 different categories. We use in-class video data as\nexistence proofs of the computational thinking practices proposed in our\nframework. In doing this work, we hope to provide ways for teachers to assess\ntheir students' development of computational thinking, while also giving\nphysics education researchers some guidance on how to study this topic in\ngreater depth.\n"}
+{"abstract": "  In this study, an algorithm to blind and automatic modulation classification\nhas been proposed. It well benefits combined machine leaning and signal feature\nextraction to recognize diverse range of modulation in low signal power to\nnoise ratio (SNR). The presented algorithm contains four. First, it advantages\nspectrum analyzing to branching modulated signal based on regular and irregular\nspectrum character. Seconds, a nonlinear soft margin support vector (NS SVM)\nproblem is applied to received signal, and its symbols are classified to\ncorrect and incorrect (support vectors) symbols. The NS SVM employment leads to\ndiscounting in physical layer noise effect on modulated signal. After that, a\nk-center clustering can find center of each class. finally, in correlation\nfunction estimation of scatter diagram is correlated with pre-saved ideal\nscatter diagram of modulations. The correlation outcome is classification\nresult. For more evaluation, success rate, performance, and complexity in\ncompare to many published methods are provided. The simulation prove that the\nproposed algorithm can classified the modulated signal in less SNR. For\nexample, it can recognize 4-QAM in SNR=-4.2 dB, and 4-FSK in SNR=2.1 dB with\n%99 success rate. Moreover, due to using of kernel function in dual problem of\nNS SVM and feature base function, the proposed algorithm has low complexity and\nsimple implementation in practical issues.\n"}
+{"abstract": "  Jupiter family comets contribute a significant amount of debris to near-Earth\nspace. However, telescopic observations of these objects seem to suggest they\nhave short physical lifetimes. If this is true, the material generated will\nalso be short-lived, but fireball observation networks still detect material on\ncometary orbits. This study examines centimeter-meter scale sporadic meteoroids\ndetected by the Desert Fireball Network from 2014-2020 originating from Jupiter\nfamily comet-like orbits. Analyzing each event's dynamic history and physical\ncharacteristics, we confidently determined whether they originated from the\nmain asteroid belt or the trans-Neptunian region. Our results indicate that\n$<4\\%$ of sporadic meteoroids on JFC-like orbits are genetically cometary. This\nobservation is statistically significant and shows that cometary material is\ntoo friable to survive in near-Earth space. Even when considering shower\ncontributions, meteoroids on JFC-like orbits are primarily from the main-belt.\nThus, the presence of genuine cometary meteorites in terrestrial collections is\nhighly unlikely.\n"}
+{"abstract": "  Leakage of data from publicly available Machine Learning (ML) models is an\narea of growing significance as commercial and government applications of ML\ncan draw on multiple sources of data, potentially including users' and clients'\nsensitive data. We provide a comprehensive survey of contemporary advances on\nseveral fronts, covering involuntary data leakage which is natural to ML\nmodels, potential malevolent leakage which is caused by privacy attacks, and\ncurrently available defence mechanisms. We focus on inference-time leakage, as\nthe most likely scenario for publicly available models. We first discuss what\nleakage is in the context of different data, tasks, and model architectures. We\nthen propose a taxonomy across involuntary and malevolent leakage, available\ndefences, followed by the currently available assessment metrics and\napplications. We conclude with outstanding challenges and open questions,\noutlining some promising directions for future research.\n"}
+{"abstract": "  Fitting concentric geometric objects to digitized data is an important\nproblem in many areas such as iris detection, autonomous navigation, and\nindustrial robotics operations. There are two common approaches to fitting\ngeometric shapes to data: the geometric (iterative) approach and algebraic\n(non-iterative) approach. The geometric approach is a nonlinear iterative\nmethod that minimizes the sum of the squares of Euclidean distances of the\nobserved points to the ellipses and regarded as the most accurate method, but\nit needs a good initial guess to improve the convergence rate. The algebraic\napproach is based on minimizing the algebraic distances with some constraints\nimposed on parametric space. Each algebraic method depends on the imposed\nconstraint, and it can be solved with the aid of the generalized eigenvalue\nproblem. Only a few methods in literature were developed to solve the problem\nof concentric ellipses. Here we study the statistical properties of existing\nmethods by firstly establishing a general mathematical and statistical\nframework for this problem. Using rigorous perturbation analysis, we derive the\nvariances and biasedness of each method under the small-sigma model. We also\ndevelop new estimators, which can be used as reliable initial guesses for other\niterative methods. Then we compare the performance of each method according to\ntheir theoretical accuracy. Not only do our methods described here outperform\nother existing non-iterative methods, they are also quite robust against large\nnoise. These methods and their practical performances are assessed by a series\nof numerical experiments on both synthetic and real data.\n"}
+{"abstract": "  Gradient-based adversarial attacks on deep neural networks pose a serious\nthreat, since they can be deployed by adding imperceptible perturbations to the\ntest data of any network, and the risk they introduce cannot be assessed\nthrough the network's original training performance. Denoising and\ndimensionality reduction are two distinct methods that have been independently\ninvestigated to combat such attacks. While denoising offers the ability to\ntailor the defense to the specific nature of the attack, dimensionality\nreduction offers the advantage of potentially removing previously unseen\nperturbations, along with reducing the training time of the network being\ndefended. We propose strategies to combine the advantages of these two defense\nmechanisms. First, we propose the cascaded defense, which involves denoising\nfollowed by dimensionality reduction. To reduce the training time of the\ndefense for a small trade-off in performance, we propose the hidden layer\ndefense, which involves feeding the output of the encoder of a denoising\nautoencoder into the network. Further, we discuss how adaptive attacks against\nthese defenses could become significantly weak when an alternative defense is\nused, or when no defense is used. In this light, we propose a new metric to\nevaluate a defense which measures the sensitivity of the adaptive attack to\nmodifications in the defense. Finally, we present a guideline for building an\nordered repertoire of defenses, a.k.a. a defense infrastructure, that adjusts\nto limited computational resources in presence of uncertainty about the attack\nstrategy.\n"}
+{"abstract": "  Gender-based crime is one of the most concerning scourges of contemporary\nsociety. Governments worldwide have invested lots of economic and human\nresources to radically eliminate this threat. Despite these efforts, providing\naccurate predictions of the risk that a victim of gender violence has of being\nattacked again is still a very hard open problem. The development of new\nmethods for issuing accurate, fair and quick predictions would allow police\nforces to select the most appropriate measures to prevent recidivism. In this\nwork, we propose to apply Machine Learning (ML) techniques to create models\nthat accurately predict the recidivism risk of a gender-violence offender. The\nrelevance of the contribution of this work is threefold: (i) the proposed ML\nmethod outperforms the preexisting risk assessment algorithm based on classical\nstatistical techniques, (ii) the study has been conducted through an official\nspecific-purpose database with more than 40,000 reports of gender violence, and\n(iii) two new quality measures are proposed for assessing the effective police\nprotection that a model supplies and the overload in the invested resources\nthat it generates. Additionally, we propose a hybrid model that combines the\nstatistical prediction methods with the ML method, permitting authorities to\nimplement a smooth transition from the preexisting model to the ML-based model.\nThis hybrid nature enables a decision-making process to optimally balance\nbetween the efficiency of the police system and aggressiveness of the\nprotection measures taken.\n"}
+{"abstract": "  Global System for Mobile Communications (GSM) is a cellular network that is\npopular and has been growing in recent years. It was developed to solve\nfragmentation issues of the first cellular system, and it addresses digital\nmodulation methods, level of the network structure, and services. It is\nfundamental for organizations to become learning organizations to keep up with\nthe technology changes for network services to be at a competitive level. A\nsimulation analysis using the NetSim tool in this paper is presented for\ncomparing different cellular network codecs for GSM network performance. These\nparameters such as throughput, delay, and jitter are analyzed for the quality\nof service provided by each network codec. Unicast application for the cellular\nnetwork is modeled for different network scenarios. Depending on the evaluation\nand simulation, it was discovered that G.711, GSM_FR, and GSM-EFR performed\nbetter than the other codecs, and they are considered to be the best codecs for\ncellular networks. These codecs will be of best use to better the performance\nof the network in the near future.\n"}
+{"abstract": "  User-facing software services are becoming increasingly reliant on remote\nservers to host Deep Neural Network (DNN) models, which perform inference tasks\nfor the clients. Such services require the client to send input data to the\nservice provider, who processes it using a DNN and returns the output\npredictions to the client. Due to the rich nature of the inputs such as images\nand speech, the input often contains more information than what is necessary to\nperform the primary inference task. Consequently, in addition to the primary\ninference task, a malicious service provider could infer secondary (sensitive)\nattributes from the input, compromising the client's privacy. The goal of our\nwork is to improve inference privacy by injecting noise to the input to hide\nthe irrelevant features that are not conducive to the primary classification\ntask. To this end, we propose Adaptive Noise Injection (ANI), which uses a\nlight-weight DNN on the client-side to inject noise to each input, before\ntransmitting it to the service provider to perform inference. Our key insight\nis that by customizing the noise to each input, we can achieve state-of-the-art\ntrade-off between utility and privacy (up to 48.5% degradation in\nsensitive-task accuracy with <1% degradation in primary accuracy),\nsignificantly outperforming existing noise injection schemes. Our method does\nnot require prior knowledge of the sensitive attributes and incurs minimal\ncomputational overheads.\n"}
+{"abstract": "  Getman et al. (2021) reports the discovery, energetics, frequencies, and\neffects on environs of $>1000$ X-ray super-flares with X-ray energies $E_X \\sim\n10^{34}-10^{38}$~erg from pre-main sequence (PMS) stars identified in the\n$Chandra$ MYStIX and SFiNCs surveys. Here we perform detailed plasma evolution\nmodeling of $55$ bright MYStIX/SFiNCs super-flares from these events. They\nconstitute a large sample of the most powerful stellar flares analyzed in a\nuniform fashion. They are compared with published X-ray super-flares from young\nstars in the Orion Nebula Cluster, older active stars, and the Sun. Several\nresults emerge. First, the properties of PMS X-ray super-flares are independent\nof the presence or absence of protoplanetary disks inferred from infrared\nphotometry, supporting the solar-type model of PMS flaring magnetic loops with\nboth footpoints anchored in the stellar surface. Second, most PMS super-flares\nresemble solar long duration events (LDEs) that are associated with coronal\nmass ejections. Slow rise PMS super-flares are an interesting exception. Third,\nstrong correlations of super-flare peak emission measure and plasma temperature\nwith the stellar mass are similar to established correlations for the PMS X-ray\nemission composed of numerous smaller flares. Fourth, a new correlation of loop\ngeometry is linked to stellar mass; more massive stars appear to have thicker\nflaring loops. Finally, the slope of a long-standing relationship between the\nX-ray luminosity and magnetic flux of various solar-stellar magnetic elements\nappears steeper in PMS super-flares than for solar events.\n"}
+{"abstract": "  The swampland is the set of seemingly consistent low-energy effective field\ntheories that cannot be consistently coupled to quantum gravity. In this review\nwe cover some of the conjectural properties that effective theories should\npossess in order not to fall in the swampland, and we give an overview of their\nmain applications to particle physics. The latter include predictions on\nneutrino masses, bounds on the cosmological constant, the electroweak and QCD\nscales, the photon mass, the Higgs potential and some insights about\nsupersymmetry.\n"}
+{"abstract": "  This paper examines the use of Lie group and Lie Algebra theory to construct\nthe geometry of pairwise comparisons matrices. The Hadamard product (also known\nas coordinatewise, coordinate-wise, elementwise, or element-wise product) is\nanalyzed in the context of inconsistency and inaccuracy by the decomposition\nmethod.\n  The two designed components are the approximation and orthogonal components.\nThe decomposition constitutes the theoretical foundation for the multiplicative\npairwise comparisons.\n  Keywords: approximate reasoning, subjectivity, inconsistency,\nconsistency-driven, pairwise comparison, matrix Lie group, Lie algebra,\napproximation, orthogonality, decomposition.\n"}
+{"abstract": "  Dark matter (DM) scattering and its subsequent capture in the Sun can boost\nthe local relic density, leading to an enhanced neutrino flux from DM\nannihilations that is in principle detectable at neutrino telescopes. We\ncalculate the event rates expected for a radiative seesaw model containing both\nscalar triplet and singlet-doublet fermion DM candidates. In the case of scalar\nDM, the absence of a spin dependent scattering on nuclei results in a low\ncapture rate in the Sun, which is reflected in an event rate of less than one\nper year in the current IceCube configuration with 86 strings. For\nsinglet-doublet fermion DM, there is a spin dependent scattering process next\nto the spin independent one, which significantly boosts the event rate and thus\nmakes indirect detection competitive with respect to the direct detection\nlimits imposed by PICO-60. Due to a correlation between both scattering\nprocesses, the limits on the spin independent cross section set by XENON1T\nexclude also parts of the parameter space that can be probed at IceCube.\nPreviously obtained limits by ANTARES, IceCube and Super-Kamiokande from the\nSun and the Galactic Center are shown to be much weaker.\n"}
+{"abstract": "  Recent work has established that, for every positive integer $k$, every\n$n$-node graph has a $(2k-1)$-spanner on $O(f^{1-1/k} n^{1+1/k})$ edges that is\nresilient to $f$ edge or vertex faults. For vertex faults, this bound is tight.\nHowever, the case of edge faults is not as well understood: the best known\nlower bound for general $k$ is $\\Omega(f^{\\frac12 - \\frac{1}{2k}} n^{1+1/k}\n+fn)$. Our main result is to nearly close this gap with an improved upper\nbound, thus separating the cases of edge and vertex faults. For odd $k$, our\nnew upper bound is $O_k(f^{\\frac12 - \\frac{1}{2k}} n^{1+1/k} + fn)$, which is\ntight up to hidden $poly(k)$ factors. For even $k$, our new upper bound is\n$O_k(f^{1/2} n^{1+1/k} +fn)$, which leaves a gap of $poly(k) f^{1/(2k)}$. Our\nproof is an analysis of the fault-tolerant greedy algorithm, which requires\nexponential time, but we also show that there is a polynomial-time algorithm\nwhich creates edge fault tolerant spanners that are larger only by factors of\n$k$.\n"}
+{"abstract": "  In warehouse and manufacturing environments, manipulation platforms are\nfrequently deployed at conveyor belts to perform pick and place tasks. Because\nobjects on the conveyor belts are moving, robots have limited time to pick them\nup. This brings the requirement for fast and reliable motion planners that\ncould provide provable real-time planning guarantees, which the existing\nalgorithms do not provide. Besides the planning efficiency, the success of\nmanipulation tasks relies heavily on the accuracy of the perception system\nwhich is often noisy, especially if the target objects are perceived from a\ndistance. For fast moving conveyor belts, the robot cannot wait for a perfect\nestimate before it starts executing its motion. In order to be able to reach\nthe object in time, it must start moving early on (relying on the initial noisy\nestimates) and adjust its motion on-the-fly in response to the pose updates\nfrom perception. We propose a planning framework that meets these requirements\nby providing provable constant-time planning and replanning guarantees. To this\nend, we first introduce and formalize a new class of algorithms called\nConstant-Time Motion Planning algorithms (CTMP) that guarantee to plan in\nconstant time and within a user-defined time bound. We then present our\nplanning framework for grasping objects off a conveyor belt as an instance of\nthe CTMP class of algorithms.\n"}
+{"abstract": "  Fluid-structure interactions are a widespread phenomenon in nature. Although\ntheir numerical modeling have come a long way, the application of numerical\ndesign tools to these multiphysics problems is still lagging behind.\nGradient-based optimization is the most popular approach in topology\noptimization currently. Hence, it's a necessity to utilize mesh deformation\ntechniques that have continuous, smooth derivatives. In this work, we address\nmesh deformation techniques for structured, quadrilateral meshes. We discuss\nand comment on two legacy mesh deformation techniques; namely the spring\nanalogy model and the linear elasticity model. In addition, we propose a new\ntechnique based on the Yeoh hyperelasticity model. We focus on mesh quality as\na gateway to mesh admissibility. We propose layered selective stiffening such\nthat the elements adjacent to the fluid-structure interface - where the bulk of\nthe mesh distortion occurs - are stiffened in consecutive layers. The legacy\nand the new models are able to sustain large deformations without deprecating\nthe mesh quality, and the results are enhanced with using layered selective\nstiffening.\n"}
+{"abstract": "  The ability to generate high-fidelity synthetic data is crucial when\navailable (real) data is limited or where privacy and data protection standards\nallow only for limited use of the given data, e.g., in medical and financial\ndata-sets. Current state-of-the-art methods for synthetic data generation are\nbased on generative models, such as Generative Adversarial Networks (GANs).\nEven though GANs have achieved remarkable results in synthetic data generation,\nthey are often challenging to interpret.Furthermore, GAN-based methods can\nsuffer when used with mixed real and categorical variables.Moreover, loss\nfunction (discriminator loss) design itself is problem specific, i.e., the\ngenerative model may not be useful for tasks it was not explicitly trained for.\nIn this paper, we propose to use a probabilistic model as a synthetic data\ngenerator. Learning the probabilistic model for the data is equivalent to\nestimating the density of the data. Based on the copula theory, we divide the\ndensity estimation task into two parts, i.e., estimating univariate marginals\nand estimating the multivariate copula density over the univariate marginals.\nWe use normalising flows to learn both the copula density and univariate\nmarginals. We benchmark our method on both simulated and real data-sets in\nterms of density estimation as well as the ability to generate high-fidelity\nsynthetic data\n"}
+{"abstract": "  Logistic Regression (LR) is a widely used statistical method in empirical\nbinary classification studies. However, real-life scenarios oftentimes share\ncomplexities that prevent from the use of the as-is LR model, and instead\nhighlight the need to include high-order interactions to capture data\nvariability. This becomes even more challenging because of: (i) datasets\ngrowing wider, with more and more variables; (ii) studies being typically\nconducted in strongly imbalanced settings; (iii) samples going from very large\nto extremely small; (iv) the need of providing both predictive models and\ninterpretable results. In this paper we present a novel algorithm, Learning\nhigh-order Interactions via targeted Pattern Search (LIPS), to select\ninteraction terms of varying order to include in a LR model for an imbalanced\nbinary classification task when input data are categorical. LIPS's rationale\nstems from the duality between item sets and categorical interactions. The\nalgorithm relies on an interaction learning step based on a well-known frequent\nitem set mining algorithm, and a novel dissimilarity-based interaction\nselection step that allows the user to specify the number of interactions to be\nincluded in the LR model. In addition, we particularize two variants (Scores\nLIPS and Clusters LIPS), that can address even more specific needs. Through a\nset of experiments we validate our algorithm and prove its wide applicability\nto real-life research scenarios, showing that it outperforms a benchmark\nstate-of-the-art algorithm.\n"}
+{"abstract": "  The concept of exceptional point of degeneracy (EPD) is used to conceive a\ndegenerate synchronization regime that is able to enhance the level of output\npower and power conversion efficiency for backward wave oscillators (BWOs)\noperating at millimeter-wave and Terahertz frequencies. Standard BWOs operating\nat such high frequency ranges typically generate output power not exceeding\ntens of watts with very poor power conversion efficiency in the order of 1%.\nThe novel concept of degenerate synchronization for the BWO based on a folded\nwaveguide is implemented by engineering distributed gain and power extraction\nalong the slow-wave waveguide. The distributed power extraction along the\nfolded waveguide is useful to satisfy the necessary conditions to have an EPD\nat the synchronization point. Particle-in-cell (PIC) simulation results shows\nthat BWO operating at an EPD regime is capable of generating output power\nexceeding 3 kwatts with conversion efficiency of exceeding 20% at frequency of\n88.5 GHz.\n"}
+{"abstract": "  We consider nonlinear impulsive systems on Banach spaces subjected to\ndisturbances and look for dwell-time conditions guaranteeing the the ISS\nproperty. In contrary to many existing results our conditions cover the case\nwhere both continuous and discrete dynamics can be unstable simultaneously.\nLyapunov type methods are use for this purpose. The effectiveness of our\napproach is illustrated on a rather nontrivial example, which is feedback\nconnection of an ODE and a PDE systems.\n"}
+{"abstract": "  In this work, we consider the problem of joint calibration and\ndirection-of-arrival (DOA) estimation using sensor arrays. This joint\nestimation problem is referred to as self calibration. Unlike many previous\niterative approaches, we propose geometry independent convex optimization\nalgorithms for jointly estimating the sensor gain and phase errors as well as\nthe source DOAs. We derive these algorithms based on both the conventional\nelement-space data model and the covariance data model. We focus on sparse and\nregular arrays formed using scalar sensors as well as vector sensors. The\ndeveloped algorithms are obtained by transforming the underlying bilinear\ncalibration model into a linear model, and subsequently by using standard\nconvex relaxation techniques to estimate the unknown parameters. Prior to the\nalgorithm discussion, we also derive identifiability conditions for the\nexistence of a unique solution to the self calibration problem. To demonstrate\nthe effectiveness of the developed techniques, numerical experiments and\ncomparisons to the state-of-the-art methods are provided. Finally, the results\nfrom an experiment that was performed in an anechoic chamber using an acoustic\nvector sensor array are presented to demonstrate the usefulness of the proposed\nself calibration techniques.\n"}
+{"abstract": "  Heterogeneous graph neural networks (HGNNs) as an emerging technique have\nshown superior capacity of dealing with heterogeneous information network\n(HIN). However, most HGNNs follow a semi-supervised learning manner, which\nnotably limits their wide use in reality since labels are usually scarce in\nreal applications. Recently, contrastive learning, a self-supervised method,\nbecomes one of the most exciting learning paradigms and shows great potential\nwhen there are no labels. In this paper, we study the problem of\nself-supervised HGNNs and propose a novel co-contrastive learning mechanism for\nHGNNs, named HeCo. Different from traditional contrastive learning which only\nfocuses on contrasting positive and negative samples, HeCo employs\ncross-viewcontrastive mechanism. Specifically, two views of a HIN (network\nschema and meta-path views) are proposed to learn node embeddings, so as to\ncapture both of local and high-order structures simultaneously. Then the\ncross-view contrastive learning, as well as a view mask mechanism, is proposed,\nwhich is able to extract the positive and negative embeddings from two views.\nThis enables the two views to collaboratively supervise each other and finally\nlearn high-level node embeddings. Moreover, two extensions of HeCo are designed\nto generate harder negative samples with high quality, which further boosts the\nperformance of HeCo. Extensive experiments conducted on a variety of real-world\nnetworks show the superior performance of the proposed methods over the\nstate-of-the-arts.\n"}
+{"abstract": "  We study the average behaviour of the Iwasawa invariants for Selmer groups of\nelliptic curves, considered over anticyclotomic $\\mathbb{Z}_p$-extensions in\nboth the definite and indefinite settings. The results in this paper lie at the\nintersection of arithmetic statistics and Iwasawa theory.\n"}
+{"abstract": "  We propose a novel numerical method for high dimensional\nHamilton--Jacobi--Bellman (HJB) type elliptic partial differential equations\n(PDEs). The HJB PDEs, reformulated as optimal control problems, are tackled by\nthe actor-critic framework inspired by reinforcement learning, based on neural\nnetwork parametrization of the value and control functions. Within the\nactor-critic framework, we employ a policy gradient approach to improve the\ncontrol, while for the value function, we derive a variance reduced\nleast-squares temporal difference method using stochastic calculus. To\nnumerically discretize the stochastic control problem, we employ an adaptive\nstep size scheme to improve the accuracy near the domain boundary. Numerical\nexamples up to $20$ spatial dimensions including the linear quadratic\nregulators, the stochastic Van der Pol oscillators, the diffusive Eikonal\nequations, and fully nonlinear elliptic PDEs derived from a regulator problem\nare presented to validate the effectiveness of our proposed method.\n"}
+{"abstract": "  High-contrast imaging observations are fundamentally limited by the spatially\nand temporally correlated noise source called speckles. Suppression of speckle\nnoise is the key goal of wavefront control and adaptive optics (AO),\ncoronagraphy, and a host of post-processing techniques. Speckles average at a\nrate set by the statistical speckle lifetime, and speckle-limited integration\ntime in long exposures is directly proportional to this lifetime. As progress\ncontinues in post-coronagraph wavefront control, residual atmospheric speckles\nwill become the limiting noise source in high-contrast imaging, so a complete\nunderstanding of their statistical behavior is crucial to optimizing\nhigh-contrast imaging instruments. Here we present a novel power spectral\ndensity (PSD) method for calculating the lifetime, and develop a semi-analytic\nmethod for predicting intensity PSDs behind a coronagraph. Considering a\nfrozen-flow turbulence model, we analyze the residual atmosphere speckle\nlifetimes in a MagAO-X-like AO system as well as 25--39 m giant segmented\nmirror telescope (GSMT) scale systems. We find that standard AO control\nshortens atmospheric speckle lifetime from ~130 ms to ~50 ms, and predictive\ncontrol will further shorten the lifetime to ~20 ms on 6.5 m MagAO-X. We find\nthat speckle lifetimes vary with diameter, wind speed, seeing, and location\nwithin the AO control region. On bright stars lifetimes remain within a rough\nrange of ~20 ms to ~100 ms. Due to control system dynamics there are no simple\nscaling laws which apply across a wide range of system characteristics.\nFinally, we use these results to argue that telemetry-based post-processing\nshould enable ground-based telescopes to achieve the photon-noise limit in\nhigh-contrast imaging.\n"}
+{"abstract": "  The Eliashberg theory of superconductivity accounts for the fundamental\nphysics of conventional electron-phonon superconductors, including the\nretardation of the interaction and the effect of the Coulomb pseudopotential,\nto predict the critical temperature $T_c$ and other properties. McMillan,\nAllen, and Dynes derived approximate closed-form expressions for the critical\ntemperature predicted by this theory, which depends essentially on the\nelectron-phonon spectral function $\\alpha^2F(\\omega)$, using $\\alpha^2F$ for\nlow-$T_c$ superconductors. Here we show that modern machine learning techniques\ncan substantially improve these formulae, accounting for more general shapes of\nthe $\\alpha^2F$ function. Using symbolic regression and the sure independence\nscreening and sparsifying operator (SISSO) framework, together with a database\nof artificially generated $\\alpha^2F$ functions, ranging from multimodal\nEinstein-like models to calculated spectra of polyhydrides, as well as\nnumerical solutions of the Eliashberg equations, we derive a formula for $T_c$\nthat performs as well as Allen-Dynes for low-$T_c$ superconductors, and\nsubstantially better for higher-$T_c$ ones. The expression identified through\nour data-driven approach corrects the systematic underestimation of $T_c$ while\nreproducing the physical constraints originally outlined by Allen and Dynes.\nThis equation should replace the Allen-Dynes formula for the prediction of\nhigher-temperature superconductors and for the estimation of $\\lambda$ from\nexperimental data.\n"}
+{"abstract": "  The HI Ly$\\alpha$ (1215.67 $\\unicode{xC5}$) emission line dominates the\nfar-UV spectra of M dwarf stars, but strong absorption from neutral hydrogen in\nthe interstellar medium makes observing Ly$\\alpha$ challenging even for the\nclosest stars. As part of the Far-Ultraviolet M-dwarf Evolution Survey (FUMES),\nthe Hubble Space Telescope has observed 10 early-to-mid M dwarfs with ages\nranging from $\\sim$24 Myr to several Gyrs to evaluate how the incident UV\nradiation evolves through the lifetime of exoplanetary systems. We reconstruct\nthe intrinsic Ly$\\alpha$ profiles from STIS G140L and E140M spectra and achieve\nreconstructed fluxes with 1-$\\sigma$ uncertainties ranging from 5% to a factor\nof two for the low resolution spectra (G140L) and 3-20% for the high resolution\nspectra (E140M). We observe broad, 500-1000 km s$^{-1}$ wings of the Ly$\\alpha$\nline profile, and analyze how the line width depends on stellar properties. We\nfind that stellar effective temperature and surface gravity are the dominant\nfactors influencing the line width with little impact from the star's magnetic\nactivity level, and that the surface flux density of the Ly$\\alpha$ wings may\nbe used to estimate the chromospheric electron density. The Ly$\\alpha$\nreconstructions on the G140L spectra are the first attempted on\n$\\lambda/\\Delta\\lambda\\sim$1000 data. We find that the reconstruction precision\nis not correlated with SNR of the observation, rather, it depends on the\nintrinsic broadness of the stellar Ly$\\alpha$ line. Young, low-gravity stars\nhave the broadest lines and therefore provide more information at low spectral\nresolution to the fit to break degeneracies among model parameters.\n"}
+{"abstract": "  We study the current-induced torques in asymmetric magnetic tunnel junctions\ncontaining a conventional ferromagnet and a magnetic Weyl semimetal contact.\nThe Weyl semimetal hosts chiral bulk states and topologically protected Fermi\narc surface states which were found to govern the voltage behavior and\nefficiency of current-induced torques. We report how bulk chirality dictates\nthe sign of the non-equilibrium torques acting on the ferromagnet and discuss\nthe existence of large field-like torques acting on the magnetic Weyl semimetal\nwhich exceeds the theoretical maximum of conventional magnetic tunnel\njunctions. The latter are derived from the Fermi arc spin texture and display a\ncounter-intuitive dependence on the Weyl nodes separation. Our results shed\nlight on the new physics of multilayered spintronic devices comprising of\nmagnetic Weyl semimetals, which might open doors for new energy efficient\nspintronic devices.\n"}
+{"abstract": "  This paper contains two finite-sample results about the sign test. First, we\nshow that the sign test is unbiased against two-sided alternatives even when\nobservations are not identically distributed. Second, we provide simple\ntheoretical counterexamples to show that correlation that is unaccounted for\nleads to size distortion and over-rejection. Our results have implication for\npractitioners, who are increasingly employing randomization tests for\ninference.\n"}
+{"abstract": "  A pair of biadjoint functors between two categories produces a collection of\nelements in the centers of these categories, one for each isotopy class of\nnested circles in the plane. If the centers are equipped with a trace map into\nthe ground field, then one assigns an element of that field to a diagram of\nnested circles. We focus on the self-adjoint functor case of this construction\nand study the reverse problem of recovering such a functor and a category given\nvalues associated to diagrams of nested circles.\n"}
+{"abstract": "  Superpixels serve as a powerful preprocessing tool in numerous computer\nvision tasks. By using superpixel representation, the number of image\nprimitives can be largely reduced by orders of magnitudes. With the rise of\ndeep learning in recent years, a few works have attempted to feed deeply\nlearned features / graphs into existing classical superpixel techniques.\nHowever, none of them are able to produce superpixels in near real-time, which\nis crucial to the applicability of superpixels in practice. In this work, we\npropose a two-stage graph-based framework for superpixel segmentation. In the\nfirst stage, we introduce an efficient Deep Affinity Learning (DAL) network\nthat learns pairwise pixel affinities by aggregating multi-scale information.\nIn the second stage, we propose a highly efficient superpixel method called\nHierarchical Entropy Rate Segmentation (HERS). Using the learned affinities\nfrom the first stage, HERS builds a hierarchical tree structure that can\nproduce any number of highly adaptive superpixels instantaneously. We\ndemonstrate, through visual and numerical experiments, the effectiveness and\nefficiency of our method compared to various state-of-the-art superpixel\nmethods.\n"}
+{"abstract": "  This paper is an excerpt of an early version of Chapter 2 of the book\n\"Validity, Reliability, and Significance. Empirical Methods for NLP and Data\nScience\", by Stefan Riezler and Michael Hagmann, published in December 2021 by\nMorgan & Claypool. Please see the book's homepage at\nhttps://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1688\nfor a more recent and comprehensive discussion.\n"}
+{"abstract": "  Training deep reinforcement learning agents on environments with multiple\nlevels / scenes from the same task, has become essential for many applications\naiming to achieve generalization and domain transfer from simulation to the\nreal world. While such a strategy is helpful with generalization, the use of\nmultiple scenes significantly increases the variance of samples collected for\npolicy gradient computations. Current methods, effectively continue to view\nthis collection of scenes as a single Markov decision process (MDP), and thus\nlearn a scene-generic value function V(s). However, we argue that the sample\nvariance for a multi-scene environment is best minimized by treating each scene\nas a distinct MDP, and then learning a joint value function V(s,M) dependent on\nboth state s and MDP M. We further demonstrate that the true joint value\nfunction for a multi-scene environment, follows a multi-modal distribution\nwhich is not captured by traditional CNN / LSTM based critic networks. To this\nend, we propose a dynamic value estimation (DVE) technique, which approximates\nthe true joint value function through a sparse attention mechanism over\nmultiple value function hypothesis / modes. The resulting agent not only shows\nsignificant improvements in the final reward score across a range of OpenAI\nProcGen environments, but also exhibits enhanced navigation efficiency and\nprovides an implicit mechanism for unsupervised state-space skill\ndecomposition.\n"}
+{"abstract": "  Deep learning as a service (DLaaS) has been intensively studied to facilitate\nthe wider deployment of the emerging deep learning applications. However, DLaaS\nmay compromise the privacy of both clients and cloud servers. Although some\nprivacy preserving deep neural network (DNN) based inference techniques have\nbeen proposed by composing cryptographic primitives, the challenges on\ncomputational efficiency have not been well-addressed due to the complexity of\nDNN models and expensive cryptographic primitives. In this paper, we propose a\nnovel privacy preserving cloud-based DNN inference framework (namely, \"PROUD\"),\nwhich greatly improves the computational efficiency. Finally, we conduct\nextensive experiments on two commonly-used datasets to validate both\neffectiveness and efficiency for the PROUD, which also outperforms the\nstate-of-the-art techniques.\n"}
+{"abstract": "  We derive the interaction of fermions with a dynamical space-time based on\nthe postulate that the description of physics should be independent of the\nreference frame, which means to require the form-invariance of the fermion\naction under diffeomorphisms. The derivation is worked out in the Hamiltonian\nformalism as a canonical transformation along the line of non-Abelian gauge\ntheories. This yields a closed set of field equations for fermions,\nunambiguously fixing their coupling to dynamical space-time. We encounter, in\naddition to the well-known minimal coupling, anomalous couplings to curvature\nand torsion. In torsion-free geometries that anomalous interaction reduces to a\nPauli-type coupling with the curvature scalar via a spontaneously emerged new\ncoupling constant with the dimension of mass resp.\\ inverse length. A\nconsistent model Hamiltonian for the free gravitational field and the impact of\nits functional form on the structure of the dynamical geometry space-time is\ndiscussed.\n"}
+{"abstract": "  This paper introduces a shoebox room simulator able to systematically\ngenerate synthetic datasets of binaural room impulse responses (BRIRs) given an\narbitrary set of head-related transfer functions (HRTFs). The evaluation of\nmachine hearing algorithms frequently requires BRIR datasets in order to\nsimulate the acoustics of any environment. However, currently available\nsolutions typically consider only HRTFs measured on dummy heads, which poorly\ncharacterize the high variability in spatial sound perception. Our solution\nallows to integrate a room impulse response (RIR) simulator with different HRTF\nsets represented in Spatially Oriented Format for Acoustics (SOFA). The source\ncode and the compiled binaries for different operating systems allow to both\nadvanced and non-expert users to benefit from our toolbox, see\nhttps://github.com/spatialaudiotools/sofamyroom/ .\n"}
+{"abstract": "  Backwards Stochastic Differential Equations (BSDEs) have been widely employed\nin various areas of applied and financial mathematics. In particular, BSDEs\nappear extensively in the pricing and hedging of financial derivatives,\nstochastic optimal control problems and optimal stopping problems. Most BSDEs\ncannot be solved analytically and thus numerical methods must be applied in\norder to approximate their solutions. There have been many numerical methods\nproposed over the past few decades, for the most part, in a complex and\nscattered manner, with each requiring a variety of different and similar\nassumptions and conditions. The aim of the present paper is thus to\nsystematically survey various numerical methods for BSDEs, and in particular,\ncompare and categorise them. To this end, we focus on the core features of each\nmethod: the main assumptions, the numerical algorithm itself, key convergence\nproperties and advantages and disadvantages, in order to provide an exhaustive\nup-to-date coverage of numerical methods for BSDEs, with insightful summaries\nof each and useful comparison and categorization.\n"}
+{"abstract": "  With the continuing rapid development of artificial microrobots and active\nparticles, questions of microswimmer guidance and control are becoming ever\nmore relevant and prevalent. In both the applications and theoretical study of\nsuch microscale swimmers, control is often mediated by an engineered property\nof the swimmer, such as in the case of magnetically propelled microrobots. In\nthis work, we will consider a modality of control that is applicable in more\ngenerality, effecting guidance via modulation of a background fluid flow. Here,\nconsidering a model swimmer in a commonplace flow and simple geometry, we\nanalyse and subsequently establish the efficacy of flow-mediated microswimmer\npositional control, later touching upon a question of optimal control. Moving\nbeyond idealised notions of controllability and towards considerations of\npractical utility, we then evaluate the robustness of this control modality to\nsources of variation that may be present in applications, examining in\nparticular the effects of measurement inaccuracy and rotational noise. This\nexploration gives rise to a number of cautionary observations, which, overall,\ndemonstrate the need for the careful assessment of both policy and behavioural\nrobustness when designing control schemes for use in practice.\n"}
+{"abstract": "  We theoretically investigate the fluorescence intensity correlation (FIC) of\nAr clusters and Mo-doped iron oxide nanoparticles subjected to intense,\nfemtosecond and sub-femtosecond XFEL pulses for high-resolution and elemental\ncontrast imaging. We present the FIC of {\\Ka} and {\\Kah} emission in Ar\nclusters and discuss the impact of sample damage on retrieving high-resolution\nstructural information and compare the obtained structural information with\nthose from the coherent difractive imaging (CDI) approach. We found that, while\nsub-femtosecond pulses will substantially benefit the CDI approach,\nfew-femtosecond pulses may be sufficient for achieving high-resolution\ninformation with FIC. Furthermore, we show that the fluorescence intensity\ncorrelation computed from the fluorescence of Mo atoms in Mo-doped iron oxide\nnanoparticles can be used to image dopant distributions.\n"}
+{"abstract": "  Today, almost all banks have adopted ICT as a means of enhancing their\nbanking service quality. These banks provide ICT based electronic service which\nis also called electronic banking, internet banking or online banking etc to\ntheir customers. Despite the increasing adoption of electronic banking and it\nrelevance towards end users satisfaction, few investigations has been conducted\non factors that enhanced end users satisfaction perception. In this research,\nan empirical analysis has been conducted on factors that influence electronic\nbanking user's satisfaction perception and the relationship between these\nfactors and the customer's satisfaction. The study will help bank industries in\nimproving the level of their customer's satisfaction and increase the bond\nbetween a bank and its customer.\n"}
+{"abstract": "  Monitoring the state of contact is essential for robotic devices, especially\ngrippers that implement gecko-inspired adhesives where intimate contact is\ncrucial for a firm attachment. However, due to the lack of deformable sensors,\nfew have demonstrated tactile sensing for gecko grippers. We present Viko, an\nadaptive gecko gripper that utilizes vision-based tactile sensors to monitor\ncontact state. The sensor provides high-resolution real-time measurements of\ncontact area and shear force. Moreover, the sensor is adaptive, low-cost, and\ncompact. We integrated gecko-inspired adhesives into the sensor surface without\nimpeding its adaptiveness and performance. Using a robotic arm, we evaluate the\nperformance of the gripper by a series of grasping test. The gripper has a\nmaximum payload of 8N even at a low fingertip pitch angle of 30 degrees. We\nalso showcase the gripper's ability to adjust fingertip pose for better contact\nusing sensor feedback. Further, everyday object picking is presented as a\ndemonstration of the gripper's adaptiveness.\n"}
+{"abstract": "  A starlike univalent function $f$ is characterized by the function\n$zf'(z)/f(z)$; several subclasses of these functions were studied in the past\nby restricting the function $zf'(z)/f(z)$ to take values in a region $\\Omega$\non the right-half plane, or, equivalently, by requiring the function\n$zf'(z)/f(z)$ to be subordinate to the corresponding mapping of the unit disk\n$\\mathbb{D}$ to the region $\\Omega$.\n  The mappings $w_1(z):=z+\\sqrt{1+z^2}, w_2(z):=\\sqrt{1+z}$ and $w_3(z):=e^z$\nmaps the unit disk $\\mathbb{D}$ to various regions in the right half plane. For\nnormalized analytic functions $f$ satisfying the conditions that $f(z)/g(z),\ng(z)/zp(z)$ and $p(z)$ are subordinate to the functions $w_i, i=1,2,3$ in\nvarious ways for some analytic functions $g(z)$ and $p(z)$, we determine the\nsharp radius for them to belong to various subclasses of starlike functions.\n"}
+{"abstract": "  We report on the discovery of FRB 20200120E, a repeating fast radio burst\n(FRB) with low dispersion measure (DM), detected by the Canadian Hydrogen\nIntensity Mapping Experiment (CHIME)/FRB project. The source DM of 87.82 pc\ncm$^{-3}$ is the lowest recorded from an FRB to date, yet is significantly\nhigher than the maximum expected from the Milky Way interstellar medium in this\ndirection (~ 50 pc cm$^{-3}$). We have detected three bursts and one candidate\nburst from the source over the period 2020 January-November. The baseband\nvoltage data for the event on 2020 January 20 enabled a sky localization of the\nsource to within $\\simeq$ 14 sq. arcmin (90% confidence). The FRB localization\nis close to M81, a spiral galaxy at a distance of 3.6 Mpc. The FRB appears on\nthe outskirts of M81 (projected offset $\\sim$ 20 kpc) but well inside its\nextended HI and thick disks. We empirically estimate the probability of chance\ncoincidence with M81 to be $< 10^{-2}$. However, we cannot reject a Milky Way\nhalo origin for the FRB. Within the FRB localization region, we find several\ninteresting cataloged M81 sources and a radio point source detected in the Very\nLarge Array Sky Survey (VLASS). We searched for prompt X-ray counterparts in\nSwift/BAT and Fermi/GBM data, and for two of the FRB 20200120E bursts, we rule\nout coincident SGR 1806$-$20-like X-ray bursts. Due to the proximity of FRB\n20200120E, future follow-up for prompt multi-wavelength counterparts and\nsub-arcsecond localization could be constraining of proposed FRB models.\n"}
+{"abstract": "  This paper investigates the transmission power control in over-the-air\nfederated edge learning (Air-FEEL) system. Different from conventional power\ncontrol designs (e.g., to minimize the individual mean squared error (MSE) of\nthe over-the-air aggregation at each round), we consider a new power control\ndesign aiming at directly maximizing the convergence speed. Towards this end,\nwe first analyze the convergence behavior of Air-FEEL (in terms of the\noptimality gap) subject to aggregation errors at different communication\nrounds. It is revealed that if the aggregation estimates are unbiased, then the\ntraining algorithm would converge exactly to the optimal point with mild\nconditions; while if they are biased, then the algorithm would converge with an\nerror floor determined by the accumulated estimate bias over communication\nrounds. Next, building upon the convergence results, we optimize the power\ncontrol to directly minimize the derived optimality gaps under both biased and\nunbiased aggregations, subject to a set of average and maximum power\nconstraints at individual edge devices. We transform both problems into convex\nforms, and obtain their structured optimal solutions, both appearing in a form\nof regularized channel inversion, by using the Lagrangian duality method.\nFinally, numerical results show that the proposed power control policies\nachieve significantly faster convergence for Air-FEEL, as compared with\nbenchmark policies with fixed power transmission or conventional MSE\nminimization.\n"}
+{"abstract": "  The social media platform is a convenient medium to express personal thoughts\nand share useful information. It is fast, concise, and has the ability to reach\nmillions. It is an effective place to archive thoughts, share artistic content,\nreceive feedback, promote products, etc. Despite having numerous advantages\nthese platforms have given a boost to hostile posts. Hate speech and derogatory\nremarks are being posted for personal satisfaction or political gain. The\nhostile posts can have a bullying effect rendering the entire platform\nexperience hostile. Therefore detection of hostile posts is important to\nmaintain social media hygiene. The problem is more pronounced languages like\nHindi which are low in resources. In this work, we present approaches for\nhostile text detection in the Hindi language. The proposed approaches are\nevaluated on the Constraint@AAAI 2021 Hindi hostility detection dataset. The\ndataset consists of hostile and non-hostile texts collected from social media\nplatforms. The hostile posts are further segregated into overlapping classes of\nfake, offensive, hate, and defamation. We evaluate a host of deep learning\napproaches based on CNN, LSTM, and BERT for this multi-label classification\nproblem. The pre-trained Hindi fast text word embeddings by IndicNLP and\nFacebook are used in conjunction with CNN and LSTM models. Two variations of\npre-trained multilingual transformer language models mBERT and IndicBERT are\nused. We show that the performance of BERT based models is best. Moreover, CNN\nand LSTM models also perform competitively with BERT based models.\n"}
+{"abstract": "  In this paper, we address zero-shot learning (ZSL), the problem of\nrecognizing categories for which no labeled visual data are available during\ntraining. We focus on the transductive setting, in which unlabelled visual data\nfrom unseen classes is available. State-of-the-art paradigms in ZSL typically\nexploit generative adversarial networks to synthesize visual features from\nsemantic attributes. We posit that the main limitation of these approaches is\nto adopt a single model to face two problems: 1) generating realistic visual\nfeatures, and 2) translating semantic attributes into visual cues. Differently,\nwe propose to decouple such tasks, solving them separately. In particular, we\ntrain an unconditional generator to solely capture the complexity of the\ndistribution of visual data and we subsequently pair it with a conditional\ngenerator devoted to enrich the prior knowledge of the data distribution with\nthe semantic content of the class embeddings. We present a detailed ablation\nstudy to dissect the effect of our proposed decoupling approach, while\ndemonstrating its superiority over the related state-of-the-art.\n"}
+{"abstract": "  We construct closed immersions from initial degenerations of the spinor\nvariety $\\mathbb{S}_n$ to inverse limits of strata associated to even\n$\\Delta$-matroids. As an application, we prove that these initial degenerations\nare smooth and irreducible for $n\\leq 5$ and identify the log canonical model\nof the Chow quotient of $\\mathbb{S}_5$ by the action of the diagonal torus of\n$\\operatorname{GL}(5)$.\n"}
+{"abstract": "  We provide an abstract characterization for the Cuntz semigroup of unital\ncommutative AI-algebras, as well as a characterization for abstract Cuntz\nsemigroups of the form $\\text{Lsc} (X,\\overline{\\mathbb{N}})$ for some\n$T_1$-space $X$. In our investigations, we also uncover new properties that the\nCuntz semigroup of all AI-algebras satisfies.\n"}
+{"abstract": "  Understanding the effects of interventions, such as restrictions on community\nand large group gatherings, is critical to controlling the spread of COVID-19.\nSusceptible-Infectious-Recovered (SIR) models are traditionally used to\nforecast the infection rates but do not provide insights into the causal\neffects of interventions. We propose a spatiotemporal model that estimates the\ncausal effect of changes in community mobility (intervention) on infection\nrates. Using an approximation to the SIR model and incorporating spatiotemporal\ndependence, the proposed model estimates a direct and indirect (spillover)\neffect of intervention. Under an interference and treatment ignorability\nassumption, this model is able to estimate causal intervention effects, and\nadditionally allows for spatial interference between locations. Reductions in\ncommunity mobility were measured by cell phone movement data. The results\nsuggest that the reductions in mobility decrease Coronavirus cases 4 to 7 weeks\nafter the intervention.\n"}
+{"abstract": "  Governments, Healthcare, and Private Organizations in the global scale have\nbeen using digital tracking to keep COVID-19 outbreaks under control. Although\nthis method could limit pandemic contagion, it raises significant concerns\nabout user privacy. Known as ~\"Contact Tracing Apps\", these mobile applications\nare facilitated by Cellphone Service Providers (CSPs), who enable the spatial\nand temporal real-time user tracking. Accordingly, it might be speculated that\nCSPs collect information violating the privacy policies such as GDPR, CCPA, and\nothers. To further clarify, we conducted an in-depth analysis comparing privacy\nlegislations with the real-world practices adapted by CSPs. We found that three\nof the regulations (GDPR, COPPA, and CCPA) analyzed defined mobile location\ndata as private information, and two (T-Mobile US, Boost Mobile) of the five\nCSPs that were analyzed did not comply with the COPPA regulation. Our results\nare crucial in view of the threat these violations represent, especially when\nit comes to children's data. As such proper security and privacy auditing is\nnecessary to curtail such violations. We conclude by providing actionable\nrecommendations to address concerns and provide privacy-preserving monitoring\nof the COVID-19 spread through the contact tracing applications.\n"}
+{"abstract": "  Nonlinear surface-plasmon polaritons~(NSPPs) in nanophotonic waveguides\nexcite with dissimilar temporal properties due to input field modifications and\nmaterial characteristics, but they possess similar nonlinear spectral\nevolution. In this work, we uncover the origin of this similarity and establish\nthat the spectral dynamics is an inherent property of the system that depends\non the synthetic dimension and is beyond waveguide geometrical dimensionality.\nTo this aim, we design an ultra-low loss nonlinear plasmonic waveguide, to\nestablish the invariance of the surface plasmonic frequency combs~(FCs) and\nphase singularities for plasmonic peregrine waves and Akhmediev breather. By\nfinely tuning the nonlinear coefficient of the interaction interface, we\nuncover the conservation conditions through this plasmonic system and employ\nthe mean-value evolution of the quantum NSPP field commensurate with the\nSchr\\\"odinger equation to evaluate spectral dynamics of the plasmonic\nFCs~(PFCs). Through providing suppressed interface losses and modified\nnonlinearity as dual requirements for conservative conditions, we propose\nexciting PFCs as equally spaced invariant quantities of this plasmonic scheme\nand prove that the spectral dynamics of the NSPPs within the interaction\ninterface yields the formation of plasmonic analog of the synthetic photonic\nlattice, which we termed \\textit{synthetic plasmonic lattice}~(SPL).\n"}
+{"abstract": "  We continue our previous study of cylindrically symmetric, static\nelectrovacuum spacetimes generated by a magnetic field, involving optionally\nthe cosmological constant, and investigate several classes of exact solutions.\nThese spacetimes are due to magnetic fields that are perpendicular to the axis\nof symmetry.\n"}
+{"abstract": "  Factorial designs are widely used due to their ability to accommodate\nmultiple factors simultaneously. The factor-based regression with main effects\nand some interactions is the dominant strategy for downstream data analysis,\ndelivering point estimators and standard errors via one single regression.\nJustification of these convenient estimators from the design-based perspective\nrequires quantifying their sampling properties under the assignment mechanism\nconditioning on the potential outcomes. To this end, we derive the sampling\nproperties of the factor-based regression estimators from both saturated and\nunsaturated models, and demonstrate the appropriateness of the robust standard\nerrors for the Wald-type inference. We then quantify the bias-variance\ntrade-off between the saturated and unsaturated models from the design-based\nperspective, and establish a novel design-based Gauss--Markov theorem that\nensures the latter's gain in efficiency when the nuisance effects omitted\nindeed do not exist. As a byproduct of the process, we unify the definitions of\nfactorial effects in various literatures and propose a location-shift strategy\nfor their direct estimation from factor-based regressions. Our theory and\nsimulation suggest using factor-based inference for general factorial effects,\npreferably with parsimonious specifications in accordance with the prior\nknowledge of zero nuisance effects.\n"}
+{"abstract": "  One of the most ubiquitous and technologically important phenomena in nature\nis the nucleation of homogeneous flowing systems. The microscopic effects of\nshear on a nucleating system are still imperfectly understood, although in\nrecent years a consistent picture has emerged. The opposing effects of shear\ncan be split into two major contributions for simple liquids: increase of the\nenergetic cost of nucleation, and enhancement of the kinetics. In this\nperspective, we describe the latest computational and theoretical techniques\nwhich have been developed over the past two decades. We collate and unify the\noverarching influences of shear, temperature, and supersaturation on the\nprocess of homogeneous nucleation. Experimental techniques and capabilities are\ndiscussed, against the backdrop of results from simulations and theory.\nAlthough we primarily focus on simple liquids, we also touch upon the sheared\nnucleation of more complex systems, including glasses and polymer melts. We\nspeculate on the promising directions and possible advances that could come to\nfruition in the future.\n"}
+{"abstract": "  In this paper, we develop general techniques for computing the G-index of a\nclosed, spin, hyperbolic 2- or 4-manifold, and apply these techniques to\ncompute the G-index of the fully symmetric spin structure of the Davis\nhyperbolic 4-manifold.\n"}
+{"abstract": "  In binary classification, kernel-free linear or quadratic support vector\nmachines are proposed to avoid dealing with difficulties such as finding\nappropriate kernel functions or tuning their hyper-parameters. Furthermore,\nUniversum data points, which do not belong to any class, can be exploited to\nembed prior knowledge into the corresponding models so that the generalization\nperformance is improved. In this paper, we design novel kernel-free Universum\nquadratic surface support vector machine models. Further, we propose the L1\nnorm regularized version that is beneficial for detecting potential sparsity\npatterns in the Hessian of the quadratic surface and reducing to the standard\nlinear models if the data points are (almost) linearly separable. The proposed\nmodels are convex such that standard numerical solvers can be utilized for\nsolving them. Nonetheless, we formulate a least squares version of the L1 norm\nregularized model and next, design an effective tailored algorithm that only\nrequires solving one linear system. Several theoretical properties of these\nmodels are then reported/proved as well. We finally conduct numerical\nexperiments on both artificial and public benchmark data sets to demonstrate\nthe feasibility and effectiveness of the proposed models.\n"}
+{"abstract": "  To operate efficiently across a wide range of workloads with varying power\nrequirements, a modern processor applies different current management\nmechanisms, which briefly throttle instruction execution while they adjust\nvoltage and frequency to accommodate for power-hungry instructions (PHIs) in\nthe instruction stream. Doing so 1) reduces the power consumption of non-PHI\ninstructions in typical workloads and 2) optimizes system voltage regulators'\ncost and area for the common use case while limiting current consumption when\nexecuting PHIs.\n  However, these mechanisms may compromise a system's confidentiality\nguarantees. In particular, we observe that multilevel side-effects of\nthrottling mechanisms, due to PHI-related current management mechanisms, can be\ndetected by two different software contexts (i.e., sender and receiver) running\non 1) the same hardware thread, 2) co-located Simultaneous Multi-Threading\n(SMT) threads, and 3) different physical cores.\n  Based on these new observations on current management mechanisms, we develop\na new set of covert channels, IChannels, and demonstrate them in real modern\nIntel processors (which span more than 70% of the entire client and server\nprocessor market). Our analysis shows that IChannels provides more than 24x the\nchannel capacity of state-of-the-art power management covert channels. We\npropose practical and effective mitigations to each covert channel in IChannels\nby leveraging the insights we gain through a rigorous characterization of real\nsystems.\n"}
+{"abstract": "  We develop the integration theory of two-parameter controlled paths $Y$\nallowing us to define integrals of the form \\begin{equation}\n  \\int_{[s,t] \\times [u,v]}\n  Y_{r,r'}\n  \\;d(X_{r}, X_{r'}) \\end{equation} where $X$ is the geometric $p$-rough path\nthat controls $Y$. This extends to arbitrary regularity the definition\npresented for $2\\leq p<3$ in the recent paper of Hairer and Gerasimovi\\v{c}s\nwhere it is used in the proof of a version of H\\\"{o}rmander's theorem for a\nclass of SPDEs. We extend the Fubini type theorem of the same paper by showing\nthat this two-parameter integral coincides with the two iterated one-parameter\nintegrals \\[\n  \\int_{[s,t] \\times [u,v]}\n  Y_{r,r'}\n  \\;d(X_{r}, X_{r'})\n  =\n  \\int_{s}^{t}\n  \\int_{u}^{v}\n  Y_{r,r'}\n  \\;dX_{r'}\n  \\;dX_{r'}\n  =\n  \\int_{u}^{v}\n  \\int_{s}^{t}\n  Y_{r,r'}\n  \\;dX_{r}\n  \\;dX_{r'}. \\] A priori these three integrals have distinct definitions, and\nso this parallels the classical Fubini's theorem for product measures. By\nextending the two-parameter Young-Towghi inequality in this context, we derive\na maximal inequality for the discrete integrals approximating the two-parameter\nintegral. We also extend the analysis to consider integrals of the form\n\\begin{equation*}\n  \\int_{[s,t] \\times [u,v]}\n  Y_{r,r'}\n  \\;\n  d(X_{r}, \\tilde{X}_{r'}) \\end{equation*} for possibly different rough paths\n$X$ and $\\tilde{X}$, and obtain the corresponding Fubini type theorem. We prove\ncontinuity estimates for these integrals in the appropriate rough path\ntopologies. As an application we consider the signature kernel, which has\nrecently emerged as a useful tool in data science, as an example of a\ntwo-parameter controlled rough path which also solves a two-parameter rough\nintegral equation.\n"}
+{"abstract": "  We revisit the calculation of vacuum energy density in compact space times.\nBy explicitly computing the effective action through the heat kernel method, we\ncompute vacuum energy density for the general case of $k$ compact spatial\ndimensions in $p+k$ dimensional Minkowski space time. Additionally, we use this\nformalism to calculate the Casimir force on a piston placed in such space\ntimes, and note the deviations from previously reported results in the\nliterature.\n"}
+{"abstract": "  We study the problem of simulating a two-user multiple access channel over a\nmultiple access network of noiseless links. Two encoders observe independent\nand identically distributed (i.i.d.) copies of a source random variable each,\nwhile a decoder observes i.i.d. copies of a side-information random variable.\nThere are rate-limited noiseless communication links and independent pairwise\nshared randomness resources between each encoder and the decoder. The decoder\nhas to output approximately i.i.d. copies of another random variable jointly\ndistributed with the two sources and the side information. We are interested in\nthe rate tuples which permit this simulation. This setting can be thought of as\na multi-terminal generalization of the point-to-point channel simulation\nproblem studied by Bennett et al. (2002) and Cuff (2013). General inner and\nouter bounds on the rate region are derived. For the specific case where the\nsources at the encoders are conditionally independent given the\nside-information at the decoder, we completely characterize the rate region.\nOur bounds recover the existing results on function computation over such\nmulti-terminal networks. We then show through an example that an additional\nindependent source of shared randomness between the encoders strictly improves\nthe communication rate requirements, even if the additional randomness is not\navailable to the decoder. Furthermore, we provide inner and outer bounds for\nthis more general setting with independent pairwise shared randomness resources\nbetween all the three possible node pairs.\n"}
+{"abstract": "  A cluster algebra is a commutative algebra whose structure is decided by a\nskew-symmetrizable matrix or a quiver. When a skew-symmetrizable matrix is\ninvariant under an action of a finite group and this action is admissible, the\nfolded cluster algebra is obtained from the original one. Any cluster algebra\nof non-simply-laced affine type can be obtained by folding a cluster algebra of\nsimply-laced affine type with a specific $G$-action. In this paper, we study\nthe combinatorial properties of quivers in the cluster algebra of affine type.\nWe prove that for any quiver of simply-laced affine type, $G$-invariance and\n$G$-admissibility are equivalent. This leads us to prove that the set of\n$G$-invariant seeds forms the folded cluster pattern.\n"}
+{"abstract": "  We consider the decay $B\\to\\ell\\ell\\ell^{\\prime}\\nu$, taking into account the\nleading $1/m_b$ and $q^2$ corrections calculated in the QCD factorization\nframework as well as the soft corrections calculated employing dispersion\nrelations and quark-hadron duality. We extend the existing results for the\nradiative decay $B\\to\\gamma\\ell\\nu$ to the case of non-zero (but small) $q^2$,\nthe invariant mass squared of the dilepton pair $\\ell^+\\ell^-$. This restricts\nus to the case $\\ell\\neq\\ell'$ as otherwise the same sign $\\ell$ and $\\ell'$\ncannot be distinguished. We further study the sensitivity of the results to the\nleading moment of the $B$-meson distribution amplitude and discuss the\npotential to extract this quantity at LHCb and the Belle II experiment.\n"}
+{"abstract": "  We develop a resource theory of symmetric distinguishability, the fundamental\nobjects of which are elementary quantum information sources, i.e., sources that\nemit one of two possible quantum states with given prior probabilities. Such a\nsource can be represented by a classical-quantum state of a composite system\n$XA$, corresponding to an ensemble of two quantum states, with $X$ being\nclassical and $A$ being quantum. We study the resource theory for two different\nclasses of free operations: $(i)$ ${\\rm{CPTP}}_A$, which consists of quantum\nchannels acting only on $A$, and $(ii)$ conditional doubly stochastic (CDS)\nmaps acting on $XA$. We introduce the notion of symmetric distinguishability of\nan elementary source and prove that it is a monotone under both these classes\nof free operations. We study the tasks of distillation and dilution of\nsymmetric distinguishability, both in the one-shot and asymptotic regimes. We\nprove that in the asymptotic regime, the optimal rate of converting one\nelementary source to another is equal to the ratio of their quantum Chernoff\ndivergences, under both these classes of free operations. This imparts a new\noperational interpretation to the quantum Chernoff divergence. We also obtain\ninteresting operational interpretations of the Thompson metric, in the context\nof the dilution of symmetric distinguishability.\n"}
+{"abstract": "  In this study, we investigated the employment status of recent University of\nOttawa physics MSc and PhD graduates, finding that 94% of graduates are either\nemployed or pursuing further physics education one year post-graduation. Our\ndatabase was populated from the public online repository of MSc and PhD theses\nsubmitted between the academic years of 2011 to 2019, with employment\ninformation collected in 2020 from the professional social media platform\nLinkedIn. Our results highlight that graduates primarily find employment\nquickly and in their field of study, with most graduates employed in either\nacademia or physics-related industries. We also found that a significant\nportion of employed graduates, 20%, find employment in non-traditional physics\ncareers, such as business management and healthcare. Graduates with careers in\nacademia tend to have lower online connectivity compared to graduates with\ncareers in industry or non-traditional fields, suggesting a greater importance\nfor online networking for students interested in non-academic careers.\n"}
+{"abstract": "  Bound-systems of $\\Xi^-$--$^{14}_{}{\\rm N}$ are studied via $\\Xi^-$ capture\nat rest followed by emission of a twin single-$\\Lambda$ hypernucleus in the\nemulsion detectors. Two events forming extremely deep $\\Xi^-$ bound states were\nobtained by analysis of a hybrid method in the E07 experiment at J-PARC and\nreanalysis of the E373 experiment at KEK-PS. The decay mode of one event was\nassigned as $\\Xi^-+^{14}_{}{\\rm N}\\to^{5}_{\\Lambda}{\\rm\nHe}$+$^{5}_{\\Lambda}{\\rm He}$+$^{4}_{}{\\rm He}$+n. Since there are no excited\nstates for daughter particles, the binding energy of the $\\Xi^-$ hyperon,\n$B_{\\Xi^-}$, in $^{14}_{}{\\rm N}$ nucleus was uniquely determined to be 6.27\n$\\pm$ 0.27 MeV. Another $\\Xi^-$--$^{14}_{}{\\rm N}$ system via the decay\n$^{9}_{\\Lambda}{\\rm Be}$ + $^{5}_{\\Lambda}{\\rm He}$ + n brings a $B_{\\Xi^-}$\nvalue, 8.00 $\\pm$ 0.77 MeV or 4.96 $\\pm$ 0.77 MeV, where the two possible\nvalues of $B_{\\Xi^-}$ correspond to the ground and the excited states of the\ndaughter $^{9}_{\\Lambda}{\\rm Be}$ nucleus, respectively. Because the\n$B_{\\Xi^-}$ values are larger than those of the previously reported events\n(KISO and IBUKI), which are both interpreted as the nuclear $1p$ state of the\n$\\Xi^-$--$^{14}_{}{\\rm N}$ system, these new events give the first indication\nof the nuclear $1s$ state of the $\\Xi$ hypernucleus, $^{15}_{\\Xi}{\\rm C}$.\n"}
+{"abstract": "  Recent research on disorder effects in topological phases in quasicrystalline\nsystems has received much attention. In this work, by numerically computing the\n(spin) Bott index and the thermal conductance, we reveal the effects of\ndisorder on a class D chiral topological superconductor and a class DIII\ntime-reversal-invariant topological superconductor in a two-dimensional\nAmmann-Beenker tiling quasicrystalline lattice. We demonstrate that both the\ntopologically protected chiral and helical Majorana edge modes are robust\nagainst weak disorder in the quasicrystalline lattice. More fascinating is the\ndiscovery of disorder-induced topologically nontrivial phases exhibiting chiral\nand helical Majorana edge modes in class D and DIII topological superconductor\nsystems, respectively. Our findings open the door for the research on\ndisorder-induced Majorana edge modes in quasicrystalline systems.\n"}
+{"abstract": "  Inverse patchy colloids are nano- to micro-scale particles with a surface\ndivided into differently charged regions. This class of colloids combines\ndirectional, selective bonding with a relatively simple particle design: owing\nto the competitive interplay between the orientation-dependent attraction and\nrepulsion -- induced by the interactions between like/oppositely charged areas\n-- experimentally accessible surface patterns are complex enough to favor the\nstabilization of specific structures of interest. Most important, the behavior\nof heterogeneously charged units can be ideally controlled by means of external\nparameters, such as the pH and the salt concentration. We present a concise\nreview about this class of systems, spanning the range from the synthesis of\nmodel inverse patchy particles to their self-assembly, covering their\ncoarse-grained modeling and the related numerical/analytical treatments.\n"}
+{"abstract": "  With the development of earth observation technology, massive amounts of\nremote sensing (RS) images are acquired. To find useful information from these\nimages, cross-modal RS image-voice retrieval provides a new insight. This paper\naims to study the task of RS image-voice retrieval so as to search effective\ninformation from massive amounts of RS data. Existing methods for RS\nimage-voice retrieval rely primarily on the pairwise relationship to narrow the\nheterogeneous semantic gap between images and voices. However, apart from the\npairwise relationship included in the datasets, the intra-modality and\nnon-paired inter-modality relationships should also be taken into account\nsimultaneously, since the semantic consistency among non-paired representations\nplays an important role in the RS image-voice retrieval task. Inspired by this,\na semantics-consistent representation learning (SCRL) method is proposed for RS\nimage-voice retrieval. The main novelty is that the proposed method takes the\npairwise, intra-modality, and non-paired inter-modality relationships into\naccount simultaneously, thereby improving the semantic consistency of the\nlearned representations for the RS image-voice retrieval. The proposed SCRL\nmethod consists of two main steps: 1) semantics encoding and 2)\nsemantics-consistent representation learning. Firstly, an image encoding\nnetwork is adopted to extract high-level image features with a transfer\nlearning strategy, and a voice encoding network with dilated convolution is\ndevised to obtain high-level voice features. Secondly, a consistent\nrepresentation space is conducted by modeling the three kinds of relationships\nto narrow the heterogeneous semantic gap and learn semantics-consistent\nrepresentations across two modalities. Extensive experimental results on three\nchallenging RS image-voice datasets show the effectiveness of the proposed\nmethod.\n"}
+{"abstract": "  We have analysed the Ca-K images obtained at Kodaikanal Observatory as a\nfunction of latitude and time for the period of 1913 - 2004 covering the Solar\nCycle 15 to 23. We have classified the chromospheric activity into plage,\nEnhanced Network (EN), Active Network (AN), and Quiet Network (QN) areas to\ndifferentiate between large strong active and small weak active regions. The\nstrong active regions represent toroidal and weak active regions poloidal\ncomponent of the magnetic field. We find that plages areas mostly up to 50 deg\nlatitude belt vary with about 11-year Solar Cycle. We also find that weak\nactivity represented by EN, AN and QN varies with about 11-year with\nsignificant amplitude up to about 50 deg latitude in both the hemispheres. The\namplitude of variation is minimum around 50 deg latitude and again increases by\nsmall amount in the polar region. In addition, the plots of plages, EN, AN and\nQN as a function of time indicate the maximum of activity at different latitude\noccur at different epoch. To determine the phase difference for the different\nlatitude belts, we have computed the cross-correlation coefficients of other\nlatitude belts with 35 deg latitude belt. We find that activity shifts from\nmid-latitude belts towards equatorial belts at fast speed at the beginning of\nSolar Cycle and at slower speed as the cycle progresses. The speed of shift\nvaries between approximately 19 and 3 m/s considering all the data for the\nobserved period. This speed can be linked with speed of meridional flows those\nbelieved to occur between convection zone and the surface of the Sun.\n"}
+{"abstract": "  This study discusses the Review Bomb, a phenomenon consisting of a massive\nattack by groups of Internet users on a website that displays users' review on\nproducts. It gained attention, especially on websites that aggregate numerical\nratings. Although this phenomenon can be considered an example of online\nmisinformation, it differs from conventional spam review, which happens within\nlarger time spans. In particular, the Bomb occurs suddenly and for a short\ntime, because in this way it leverages the notorious problem of cold-start: if\nreviews are submitted by a lot of fresh new accounts, it makes hard to justify\npreventative measures. The present research work is focused on the case of The\nLast of Us Part II, a video game published by Sony, that was the target of the\nwidest phenomenon of Review Bomb, occurred in June 2020. By performing an\nobservational analysis of a linguistic corpus of English reviews and the\nfeatures of its users, this study confirms that the Bomb was an ideological\nattack aimed at breaking down the rating system of the platform Metacritic.\nEvidence supports that the bombing had the unintended consequence to induce a\nreaction from users, ending into a consistent polarisation of ratings towards\nextreme values. The results not only display the theory of polarity in online\nreviews, but them also provide insights for the research on the problem of\ncold-start detection of spam review. In particular, it illustrates the\nrelevance of detecting users discussing contextual elements instead of the\nproduct and users with anomalous features.\n"}
+{"abstract": "  Arrays of nanoparticles exploited in light scattering applications commonly\nonly feature either a periodic or a rather random arrangement of its\nconstituents. For the periodic case, light scattering is mostly governed by the\nstrong spatial correlations of the arrangement, expressed by the structure\nfactor. For the random case, structural correlations cancel each other out and\nlight scattering is mostly governed by the scattering properties of the\nindividual scatterer, expressed by the form factor. In contrast to these\nextreme cases, we show here, for the first time, that hyperuniform disorder in\nself-organized large-area arrays of high refractive index nanodisks enables\nboth structure and form factor to impact the resulting scattering pattern,\noffering novel means to tailor light scattering. The scattering response from\nour nearly hyperuniform interfaces can be exploited in a large variety of\napplications and constitutes a novel class of advanced optical materials.\n"}
+{"abstract": "  We study the clustering task under anisotropic Gaussian Mixture Models where\nthe covariance matrices from different clusters are unknown and are not\nnecessarily the identical matrix. We characterize the dependence of\nsignal-to-noise ratios on the cluster centers and covariance matrices and\nobtain the minimax lower bound for the clustering problem. In addition, we\npropose a computationally feasible procedure and prove it achieves the optimal\nrate within a few iterations. The proposed procedure is a hard EM type\nalgorithm, and it can also be seen as a variant of the Lloyd's algorithm that\nis adjusted to the anisotropic covariance matrices.\n"}
+{"abstract": "  Deep learning based generative adversarial networks (GAN) can effectively\nperform image reconstruction with under-sampled MR data. In general, a large\nnumber of training samples are required to improve the reconstruction\nperformance of a certain model. However, in real clinical applications, it is\ndifficult to obtain tens of thousands of raw patient data to train the model\nsince saving k-space data is not in the routine clinical flow. Therefore,\nenhancing the generalizability of a network based on small samples is urgently\nneeded. In this study, three novel applications were explored based on parallel\nimaging combined with the GAN model (PI-GAN) and transfer learning. The model\nwas pre-trained with public Calgary brain images and then fine-tuned for use in\n(1) patients with tumors in our center; (2) different anatomies, including knee\nand liver; (3) different k-space sampling masks with acceleration factors (AFs)\nof 2 and 6. As for the brain tumor dataset, the transfer learning results could\nremove the artifacts found in PI-GAN and yield smoother brain edges. The\ntransfer learning results for the knee and liver were superior to those of the\nPI-GAN model trained with its own dataset using a smaller number of training\ncases. However, the learning procedure converged more slowly in the knee\ndatasets compared to the learning in the brain tumor datasets. The\nreconstruction performance was improved by transfer learning both in the models\nwith AFs of 2 and 6. Of these two models, the one with AF=2 showed better\nresults. The results also showed that transfer learning with the pre-trained\nmodel could solve the problem of inconsistency between the training and test\ndatasets and facilitate generalization to unseen data.\n"}
+{"abstract": "  Deep learning algorithms are a key component of many state-of-the-art vision\nsystems, especially as Convolutional Neural Networks (CNN) outperform most\nsolutions in the sense of accuracy. To apply such algorithms in real-time\napplications, one has to address the challenges of memory and computational\ncomplexity. To deal with the first issue, we use networks with reduced\nprecision, specifically a binary neural network (also known as XNOR). To\nsatisfy the computational requirements, we propose to use highly parallel and\nlow-power FPGA devices. In this work, we explore the possibility of\naccelerating XNOR networks for traffic sign classification. The trained binary\nnetworks are implemented on the ZCU 104 development board, equipped with a Zynq\nUltraScale+ MPSoC device using two different approaches. Firstly, we propose a\ncustom HDL accelerator for XNOR networks, which enables the inference with\nalmost 450 fps. Even better results are obtained with the second method - the\nXilinx FINN accelerator - enabling to process input images with around 550\nframe rate. Both approaches provide over 96% accuracy on the test set.\n"}
+{"abstract": "  This paper presents a novel Sliding Mode Control (SMC) algorithm to handle\nmismatched uncertainties in systems via a novel Self-Learning Disturbance\nObserver (SLDO). A computationally efficient SLDO is developed within a\nframework of feedback-error learning scheme in which a conventional estimation\nlaw and a Neuro-Fuzzy Structure (NFS) work in parallel. In this framework, the\nNFS estimates the mismatched disturbances and becomes the leading disturbance\nestimator while the former feeds the learning error to the NFS to learn system\nbehavior. The simulation results demonstrate that the proposed SMC based on\nSLDO (SMC-SLDO) ensures the robust control performance in the presence of\nmismatched time-varying uncertainties when compared to SMC, integral SMC (ISMC)\nand SMC based on a Basic Nonlinear Disturbance Observer (SMC-BNDO), and also\nremains the nominal control performance in the absence of mismatched\nuncertainties. Additionally, the SMC-SLDO not only counteracts mismatched\ntime-varying uncertainties but also improve the transient response performance\nin the presence of mismatched time-invariant uncertainties. Moreover, the\ncontroller gain of the SMC-SLDO is required to be selected larger than the\nupper bound of the disturbance estimation error rather than the upper bound of\nthe actual disturbance to guarantee the system stability which results in\neliminating the chattering effects on the control signal.\n"}
+{"abstract": "  Graph Neural Networks (GNNs) require a relatively large number of labeled\nnodes and a reliable/uncorrupted graph connectivity structure in order to\nobtain good performance on the semi-supervised node classification task. The\nperformance of GNNs can degrade significantly as the number of labeled nodes\ndecreases or the graph connectivity structure is corrupted by adversarial\nattacks or due to noises in data measurement /collection. Therefore, it is\nimportant to develop GNN models that are able to achieve good performance when\nthere is limited supervision knowledge -- a few labeled nodes and noisy graph\nstructures. In this paper, we propose a novel Dual GNN learning framework to\naddress this challenge task. The proposed framework has two GNN based node\nprediction modules. The primary module uses the input graph structure to induce\nregular node embeddings and predictions with a regular GNN baseline, while the\nauxiliary module constructs a new graph structure through fine-grained spectral\nclusterings and learns new node embeddings and predictions. By integrating the\ntwo modules in a dual GNN learning framework, we perform joint learning in an\nend-to-end fashion. This general framework can be applied on many GNN baseline\nmodels. The experimental results validate that the proposed dual GNN framework\ncan greatly outperform the GNN baseline methods when the labeled nodes are\nscarce and the graph connectivity structure is noisy.\n"}
+{"abstract": "  This study examines habits and perceptions related to pay to publish and open\naccess practices in fields that have attracted little research to date:\nphilosophy and ethics. The study is undertaken in the Spanish context, where\nthe culture of publication and the book and journal publishing industry has\nsome specific characteristics with regard to paying to publish, such as not\noffering open access distribution of books published for a fee. The study draws\non data from a survey of 201 researchers, a public debate with 26 researchers,\nand 14 in-depth interviews. The results reveal some interesting insights on the\ncriteria researchers apply when selecting publishers and journals for their\nwork, the extent of paying to publish (widespread in the case of books and\nmodest for journals) and the debates that arise over the effects it has on\nmanuscript review and unequal access to resources to cover publication fees.\nData on the extent of open access and the researchers views on dissemination of\npublicly funded research are also presented.\n"}
+{"abstract": "  Despite evidence for the existence of dark matter (DM) from very high and low\nredshifts, a moderate amount of DM particle decay remains a valid possibility.\nThis includes both models with very long-lived yet unstable particles or mixed\nscenarios where only a small fraction of dark matter is allowed to decay. In\nthis paper, we investigate how DM particles decaying into radiation affect\nnon-linear structure formation. We look at the power spectrum and its redshift\nevolution, varying both the decay lifetime ($\\tau$) and the fraction of\ndecaying-to-total dark matter ($f$), and we propose a fitting function that\nreaches sub-percent precision below $k\\sim10$ h/Mpc. Based on this fit, we\nperform a forecast analysis for a Euclid-like weak lensing (WL) survey,\nincluding both massive neutrino and baryonic feedback parameters. We find that\nwith WL observations alone, it is possible to rule out decay lifetimes smaller\nthan $\\tau=75$ Gyr (at 95 percent CL) for the case that all DM is unstable.\nThis constraint improves to $\\tau=182$ Gyr if the WL data is combined with CMB\npriors from the Planck satellite and to $\\tau=275$ Gyr if we further assume\nbaryonic feedback to be fully constrained by upcoming Sunyaev-Zeldovich or\nX-ray data. The latter shows a factor of 3.2 improvement compared to\nconstraints from CMB data alone. Regarding the scenario of a strongly decaying\nsub-component of dark matter with $\\tau\\sim 30$ Gyr or lower, it will be\npossible to rule out a decaying-to-total fraction of $f>0.49$, $f>0.21$, and\n$f>0.13$ (at the 95 percent CL) for the same three scenarios. We conclude that\nthe upcoming stage-IV WL surveys will allow us to significantly improve current\nconstraints on the stability of the dark matter sector.\n"}
+{"abstract": "  The inscribed angle theorem, a famous result about the angle subtended by a\nchord within a circle, is well known and commonly taught in school curricula.\nIn this paper, we present a generalisation of this result (and other related\ncircle theorems) to the rectangular hyperbola. The notion of angle is replaced\nby pseudo-angle, defined via the Minkowski inner product. Indeed, in Minkowski\nspace, the unit hyperbola is the set of points a unit metric distance from the\norigin, analogous to the Euclidean unit circle. While this is a result of pure\ngeometrical interest, the connection to Minkowski space allows an\ninterpretation in terms of special relativity where, in the limit $c\\to\\infty$,\nit leads to a familiar result from non-relativistic dynamics. This\nnon-relativistic result can be interpreted as an inscribed angle theorem for\nthe parabola, which we show can also be obtained from the Euclidean inscribed\nangle theorem by taking the limit of a family of ellipses ananlogous to the\nnon-relativistic limit $c\\to\\infty$. This simple result could be used as a\npedagogical example to consolidate understanding of pseudo-angles in\nnon-Euclidean spaces or to demonstrate the power of analytic continuation.\n"}
+{"abstract": "  Human pose estimation is a major computer vision problem with applications\nranging from augmented reality and video capture to surveillance and movement\ntracking. In the medical context, the latter may be an important biomarker for\nneurological impairments in infants. Whilst many methods exist, their\napplication has been limited by the need for well annotated large datasets and\nthe inability to generalize to humans of different shapes and body\ncompositions, e.g. children and infants. In this paper we present a novel\nmethod for learning pose estimators for human adults and infants in an\nunsupervised fashion. We approach this as a learnable template matching problem\nfacilitated by deep feature extractors. Human-interpretable landmarks are\nestimated by transforming a template consisting of predefined body parts that\nare characterized by 2D Gaussian distributions. Enforcing a connectivity prior\nguides our model to meaningful human shape representations. We demonstrate the\neffectiveness of our approach on two different datasets including adults and\ninfants.\n"}
+{"abstract": "  The Rindler spacetime describing a series of accelerating observers is Ricci\nflat, but it still has novel optical effects. In the case of WKB approximation,\nwe derive the light geodesics in the Rindler frame based on the covariant wave\nequation and geodesic equations. Then, we use ABCD matrix optics method to\nexplore the propagation characteristics of Rindler frame, thus link three\ndifferent optical transformation scenes (geometry, gravity and vacuum\nrefractive index) together. Moreover, the propagation characteristics of hollow\nbeam in Rindler spacetime are described analytically. Those characteristics are\nquite different from the ones in the flat spacetime. Based on these\ncalculations, we simply demonstrate the position uncertain relationship between\nthe transverse beam size and the momentum, which surprisingly coincides with\nthe derivation of quantization. We hope that we can provide one simple method\nto analyze the beam propagation in the accelerated frame.\n"}
+{"abstract": "  We present a novel approach for disentangling the content of a text image\nfrom all aspects of its appearance. The appearance representation we derive can\nthen be applied to new content, for one-shot transfer of the source style to\nnew content. We learn this disentanglement in a self-supervised manner. Our\nmethod processes entire word boxes, without requiring segmentation of text from\nbackground, per-character processing, or making assumptions on string lengths.\nWe show results in different text domains which were previously handled by\nspecialized methods, e.g., scene text, handwritten text. To these ends, we make\na number of technical contributions: (1) We disentangle the style and content\nof a textual image into a non-parametric, fixed-dimensional vector. (2) We\npropose a novel approach inspired by StyleGAN but conditioned over the example\nstyle at different resolution and content. (3) We present novel self-supervised\ntraining criteria which preserve both source style and target content using a\npre-trained font classifier and text recognizer. Finally, (4) we also introduce\nImgur5K, a new challenging dataset for handwritten word images. We offer\nnumerous qualitative photo-realistic results of our method. We further show\nthat our method surpasses previous work in quantitative tests on scene text and\nhandwriting datasets, as well as in a user study.\n"}
+{"abstract": "  In this paper we present our system for the detection and classification of\nacoustic scenes and events (DCASE) 2020 Challenge Task 4: Sound event detection\nand separation in domestic environments. We introduce two new models: the\nforward-backward convolutional recurrent neural network (FBCRNN) and the\ntag-conditioned convolutional neural network (CNN). The FBCRNN employs two\nrecurrent neural network (RNN) classifiers sharing the same CNN for\npreprocessing. With one RNN processing a recording in forward direction and the\nother in backward direction, the two networks are trained to jointly predict\naudio tags, i.e., weak labels, at each time step within a recording, given that\nat each time step they have jointly processed the whole recording. The proposed\ntraining encourages the classifiers to tag events as soon as possible.\nTherefore, after training, the networks can be applied to shorter audio\nsegments of, e.g., 200 ms, allowing sound event detection (SED). Further, we\npropose a tag-conditioned CNN to complement SED. It is trained to predict\nstrong labels while using (predicted) tags, i.e., weak labels, as additional\ninput. For training pseudo strong labels from a FBCRNN ensemble are used. The\npresented system scored the fourth and third place in the systems and teams\nrankings, respectively. Subsequent improvements allow our system to even\noutperform the challenge baseline and winner systems in average by,\nrespectively, 18.0% and 2.2% event-based F1-score on the validation set. Source\ncode is publicly available at https://github.com/fgnt/pb_sed.\n"}
+{"abstract": "  Block-sparse signal recovery without knowledge of block sizes and boundaries,\nsuch as those encountered in multi-antenna mmWave channel models, is a hard\nproblem for compressed sensing (CS) algorithms. We propose a novel Sparse\nBayesian Learning (SBL) method for block-sparse recovery based on popular CS\nbased regularizers with the function input variable related to total variation\n(TV). Contrary to conventional approaches that impose the regularization on the\nsignal components, we regularize the SBL hyperparameters. This iterative\nTV-regularized SBL algorithm employs a majorization-minimization approach and\nreduces each iteration to a convex optimization problem, enabling a flexible\nchoice of numerical solvers. The numerical results illustrate that the\nTV-regularized SBL algorithm is robust to the nature of the block structure and\nable to recover signals with both block-patterned and isolated components,\nproving useful for various signal recovery systems.\n"}
+{"abstract": "  Population synthesis studies of binary black-hole mergers often lack robust\nblack-hole spin estimates as they cannot accurately follow tidal spin-up during\nthe late black-hole-Wolf-Rayet evolutionary phase. We provide an analytical\napproximation of the dimensionless second-born black-hole spin given the binary\norbital period and Wolf-Rayet stellar mass at helium depletion or carbon\ndepletion. These approximations are obtained from fitting a sample of around\n$10^5$ detailed MESA simulations that follow the evolution and spin up of close\nblack-hole--Wolf-Rayet systems with metallicities in the range\n$[10^{-4},1.5Z_\\odot]$. Following the potential spin up of the Wolf-Rayet\nprogenitor, the second-born black-hole spin is calculated using up-to-date core\ncollapse prescriptions that account for any potential disk formation in the\ncollapsing Wolf-Rayet star. The fits for second-born black hole spin provided\nin this work can be readily applied to any astrophysical modeling that relies\non rapid population synthesis, and will be useful for the interpretation of\ngravitational-wave sources using such models.\n"}
+{"abstract": "  Vector representations have become a central element in semantic language\nmodelling, leading to mathematical overlaps with many fields including quantum\ntheory. Compositionality is a core goal for such representations: given\nrepresentations for 'wet' and 'fish', how should the concept 'wet fish' be\nrepresented?\n  This position paper surveys this question from two points of view. The first\nconsiders the question of whether an explicit mathematical representation can\nbe successful using only tools from within linear algebra, or whether other\nmathematical tools are needed. The second considers whether semantic vector\ncomposition should be explicitly described mathematically, or whether it can be\na model-internal side-effect of training a neural network.\n  A third and newer question is whether a compositional model can be\nimplemented on a quantum computer. Given the fundamentally linear nature of\nquantum mechanics, we propose that these questions are related, and that this\nsurvey may help to highlight candidate operations for future quantum\nimplementation.\n"}
+{"abstract": "  Combination and aggregation techniques can significantly improve forecast\naccuracy. This also holds for probabilistic forecasting methods where\npredictive distributions are combined. There are several time-varying and\nadaptive weighting schemes such as Bayesian model averaging (BMA). However, the\nquality of different forecasts may vary not only over time but also within the\ndistribution. For example, some distribution forecasts may be more accurate in\nthe center of the distributions, while others are better at predicting the\ntails. Therefore, we introduce a new weighting method that considers the\ndifferences in performance over time and within the distribution. We discuss\npointwise combination algorithms based on aggregation across quantiles that\noptimize with respect to the continuous ranked probability score (CRPS). After\nanalyzing the theoretical properties of pointwise CRPS learning, we discuss B-\nand P-Spline-based estimation techniques for batch and online learning, based\non quantile regression and prediction with expert advice. We prove that the\nproposed fully adaptive Bernstein online aggregation (BOA) method for pointwise\nCRPS online learning has optimal convergence properties. They are confirmed in\nsimulations and a probabilistic forecasting study for European emission\nallowance (EUA) prices.\n"}
+{"abstract": "  This paper presents the definition and implementation of a quantum computer\narchitecture to enable creating a new computational device - a quantum computer\nas an accelerator In this paper, we present explicitly the idea of a quantum\naccelerator which contains the full stack of the layers of an accelerator. Such\na stack starts at the highest level describing the target application of the\naccelerator. Important to realise is that qubits are defined as perfect qubits,\nimplying they do not decohere and perform good quantum gate operations. The\nnext layer abstracts the quantum logic outlining the algorithm that is to be\nexecuted on the quantum accelerator. In our case, the logic is expressed in the\nuniversal quantum-classical hybrid computation language developed in the group,\ncalled OpenQL. We also have to start thinking about how to verify, validate and\ntest the quantum software such that the compiler generates a correct version of\nthe quantum circuit. The OpenQL compiler translates the program to a common\nassembly language, called cQASM. We need to develop a quantum operating system\nthat manages all the hardware of the micro-architecture. The layer below the\nmicro-architecture is responsible of the mapping and routing of the qubits on\nthe topology such that the nearest-neighbour-constraint can be be respected. At\nany moment in the future when we are capable of generating multiple good\nqubits, the compiler can convert the cQASM to generate the eQASM, which is\nexecutable on a particular experimental device incorporating the\nplatform-specific parameters. This way, we are able to distinguish clearly the\nexperimental research towards better qubits, and the industrial and societal\napplications that need to be developed and executed on a quantum device.\n"}
+{"abstract": "  Correspondence-based rotation search and point cloud registration are two\nfundamental problems in robotics and computer vision. However, the presence of\noutliers, sometimes even occupying the great majority of the putative\ncorrespondences, can make many existing algorithms either fail or have very\nhigh computational cost. In this paper, we present RANSIC (RANdom Sampling with\nInvariant Compatibility), a fast and highly robust method applicable to both\nproblems based on a new paradigm combining random sampling with invariance and\ncompatibility. Generally, RANSIC starts with randomly selecting small subsets\nfrom the correspondence set, then seeks potential inliers as graph vertices\nfrom the random subsets through the compatibility tests of invariants\nestablished in each problem, and eventually returns the eligible inliers when\nthere exists at least one K-degree vertex (K is automatically updated depending\non the problem) and the residual errors satisfy a certain termination condition\nat the same time. In multiple synthetic and real experiments, we demonstrate\nthat RANSIC is fast for use, robust against over 95% outliers, and also able to\nrecall approximately 100% inliers, outperforming other state-of-the-art solvers\nfor both the rotation search and the point cloud registration problems.\n"}
+{"abstract": "  Computational models of biological processes provide one of the most powerful\nmethods for a detailed analysis of the mechanisms that drive the behavior of\ncomplex systems. Logic-based modeling has enhanced our understanding and\ninterpretation of those systems. Defining rules that determine how the output\nactivity of biological entities is regulated by their respective inputs has\nproven to be challenging, due to increasingly larger models and the presence of\nnoise in data, allowing multiple model parameterizations to fit the\nexperimental observations.\n  We present several Boolean function metrics that provide modelers with the\nappropriate framework to analyze the impact of a particular model\nparameterization. We demonstrate the link between a semantic characterization\nof a Boolean function and its consistency with the model's underlying\nregulatory structure. We further define the properties that outline such\nconsistency and show that several of the Boolean functions under study violate\nthem, questioning their biological plausibility and subsequent use. We also\nillustrate that regulatory functions can have major differences with regard to\ntheir asymptotic output behavior, with some of them being biased towards\nspecific Boolean outcomes when others are dependent on the ratio between\nactivating and inhibitory regulators.\n  Application results show that in a specific signaling cancer network, the\nfunction bias can be used to guide the choice of logical operators for a model\nthat matches data observations. Moreover, graph analysis indicates that the\nstandardized Boolean function bias becomes more prominent with increasing\nnumbers of regulators, confirming the fact that rule specification can\neffectively determine regulatory outcome despite the complex dynamics of\nbiological networks.\n"}
+{"abstract": "  The application of machine learning(ML) and genetic programming(GP) to the\nimage compression domain has produced promising results in many cases. The need\nfor compression arises due to the exorbitant size of data shared on the\ninternet. Compression is required for text, videos, or images, which are used\nalmost everywhere on web be it news articles, social media posts, blogs,\neducational platforms, medical domain, government services, and many other\nwebsites, need packets for transmission and hence compression is necessary to\navoid overwhelming the network. This paper discusses some of the\nimplementations of image compression algorithms that use techniques such as\nArtificial Neural Networks, Residual Learning, Fuzzy Neural Networks,\nConvolutional Neural Nets, Deep Learning, Genetic Algorithms. The paper also\ndescribes an implementation of Vector Quantization using GA to generate\ncodebook which is used for Lossy image compression. All these approaches prove\nto be very contrasting to the standard approaches to processing images due to\nthe highly parallel and computationally extensive nature of machine learning\nalgorithms. Such non-linear abilities of ML and GP make it widely popular for\nuse in multiple domains. Traditional approaches are also combined with\nartificially intelligent systems, leading to hybrid systems, to achieve better\nresults.\n"}
+{"abstract": "  The thermodynamic properties of Bi-Sn were studied at 600 and 900K using a\nquasi-lattice theory. After successful fitting of Gibbs free energies of mixing\nand thermodynamic activities, the fitting parameters were used to investigate\nthe enthalpy of mixing, the entropy of mixing, concentration fluctuations,\nWarren-Cowley short range order parameter, surface concentrations and surface\ntensions of the binary systems. Positive and symmetrically shaped enthalpies of\nmixing were observed in all composition range, while negative excess entropies\nof mixing were observed. Bi-Sn showed a slight preference for like-atoms as\nnearest neighbours in all composition range. The nature of atomic order in\nBi-Sn at 600 and 900K appeared similar. The highest tendency for\nhomocoordination exists at composition where mole fraction of Bi is about 40%.\nIt was also observed that Bi (whose surface tension is lower than that of Sn)\nhas the highest surface enrichment in the Bi-Sn systems. Unlike many previous\napplications of the quasi-lattice theory where constant values were used to\napproximate coordination numbers, temperature and composition-dependent\ncoordination numbers were applied in this work.\n"}
+{"abstract": "  We have performed ab-initio molecular dynamics simulations to elucidate the\nmechanism of the phase transition at high pressure from hexagonal graphite (HG)\nto hexagonal diamond (HD) or to cubic diamond (CD). The transition from HG to\nHD is found to occur swiftly in very small time of 0.2 ps, with large\ncooperative displacements of all the atoms. We observe that alternate layers of\natoms in HG slide in opposite directions by (1/3, 1/6, 0) and (-1/3, -1/6, 0),\nrespectively, which is about 0.7 {\\AA} along the pm[2, 1, 0] direction, while\nsimultaneously puckering by about pm0.25 {\\AA} perpendicular to the a-b plane.\nThe transition from HG to CD occurred with more complex cooperative\ndisplacements. In this case, six successive HG layers slide in pairs by 1/3\nalong [0, 1, 0], [-1, -1, 0] and [1, 0, 0], respectively along with the\npuckering as above. We have also performed calculations of the phonon spectrum\nin HG at high pressure, which reveal soft phonon modes that may facilitate the\nphase transition involving the sliding and puckering of the HG layers. The\nzero-point vibrational energy and the vibrational entropy are found to have\nimportant role in stabilizing HG up to higher pressures (>10 GPa) and\ntemperatures than that estimated (<6 GPa) from previous enthalpy calculations.\n"}
+{"abstract": "  Recent medical imaging studies have given rise to distinct but inter-related\ndatasets corresponding to multiple experimental tasks or longitudinal visits.\nStandard scalar-on-image regression models that fit each dataset separately are\nnot equipped to leverage information across inter-related images, and existing\nmulti-task learning approaches are compromised by the inability to account for\nthe noise that is often observed in images. We propose a novel joint\nscalar-on-image regression framework involving wavelet-based image\nrepresentations with grouped penalties that are designed to pool information\nacross inter-related images for joint learning, and which explicitly accounts\nfor noise in high-dimensional images via a projection-based approach. In the\npresence of non-convexity arising due to noisy images, we derive non-asymptotic\nerror bounds under non-convex as well as convex grouped penalties, even when\nthe number of voxels increases exponentially with sample size. A projected\ngradient descent algorithm is used for computation, which is shown to\napproximate the optimal solution via well-defined non-asymptotic optimization\nerror bounds under noisy images. Extensive simulations and application to a\nmotivating longitudinal Alzheimer's disease study illustrate significantly\nimproved predictive ability and greater power to detect true signals, that are\nsimply missed by existing methods without noise correction due to the\nattenuation to null phenomenon.\n"}
+{"abstract": "  The availability of multi-omics data has revolutionized the life sciences by\ncreating avenues for integrated system-level approaches. Data integration links\nthe information across datasets to better understand the underlying biological\nprocesses. However, high-dimensionality, correlations and heterogeneity pose\nstatistical and computational challenges. We propose a general framework,\nprobabilistic two-way partial least squares (PO2PLS), which addresses these\nchallenges. PO2PLS models the relationship between two datasets using joint and\ndata-specific latent variables. For maximum likelihood estimation of the\nparameters, we implement a fast EM algorithm and show that the estimator is\nasymptotically normally distributed. A global test for testing the relationship\nbetween two datasets is proposed, and its asymptotic distribution is derived.\nNotably, several existing omics integration methods are special cases of\nPO2PLS. Via extensive simulations, we show that PO2PLS performs better than\nalternatives in feature selection and prediction performance. In addition, the\nasymptotic distribution appears to hold when the sample size is sufficiently\nlarge. We illustrate PO2PLS with two examples from commonly used study designs:\na large population cohort and a small case-control study. Besides recovering\nknown relationships, PO2PLS also identified novel findings. The methods are\nimplemented in our R-package PO2PLS. Supplementary materials for this article\nare available online.\n"}
+{"abstract": "  Hard sphere systems are often used to model simple fluids. The configuration\nspaces of hard spheres in a three-dimensional torus modulo various symmetry\ngroups are comparatively simple, and could provide valuable information about\nthe nature of phase transitions. Specifically, the topological changes in the\nconfiguration space as a function of packing fraction have been conjectured to\nbe related to the onset of first-order phase transitions. The critical\nconfigurations for one to twelve spheres are sampled using a Morse-theoretic\napproach, and are available in an online, interactive database. Explicit\ntriangulations are constructed for the configuration spaces of the two sphere\nsystem, and their topological and geometric properties are studied. The\ncritical configurations are found to be associated with geometric changes to\nthe configuration space that connect previously distant regions and reduce the\nconfiguration space diameter as measured by the commute time and diffusion\ndistances. The number of such critical configurations around the packing\nfraction of the solid-liquid phase transition increases exponentially with the\nnumber of spheres, suggesting that the onset of the first-order phase\ntransition in the thermodynamic limit is associated with a discontinuity in the\nconfiguration space diameter.\n"}
+{"abstract": "  Answering a long standing question, we give an example of a Hilbert module\nand a nonzero bounded right linear map having a kernel with trivial orthogonal\ncomplement. In particular, this kernel is different from its own double\northogonal complement.\n"}
+{"abstract": "  Neural networks often require large amounts of data to generalize and can be\nill-suited for modeling small and noisy experimental datasets. Standard network\narchitectures trained on scarce and noisy data will return predictions that\nviolate the underlying physics. In this paper, we present methods for embedding\neven--odd symmetries and conservation laws in neural networks and propose novel\nextensions and use cases for physical constraint embedded neural networks. We\ndesign an even--odd decomposition architecture for disentangling a neural\nnetwork parameterized function into its even and odd components and demonstrate\nthat it can accurately infer symmetries without prior knowledge. We highlight\nthe noise resilient properties of physical constraint embedded neural networks\nand demonstrate their utility as physics-informed noise regulators. Here we\nemployed a conservation of energy constraint embedded network as a\nphysics-informed noise regulator for a symbolic regression task. We showed that\nour approach returns a symbolic representation of the neural network\nparameterized function that aligns well with the underlying physics while\noutperforming a baseline symbolic regression approach.\n"}
+{"abstract": "  Small form-factor, narrowband, and highly directive antennas are of critical\nimportance in a variety of applications spanning wireless communications,\nremote sensing, Raman spectroscopy, and single photon emission enhancement.\nSurprisingly, we show that the classical directivity limit can be appreciably\nsurpassed for electrically small multilayer spherical antennas excited by a\npoint electric dipole even if limiting ourselves to purely dielectric\nmaterials. Experimentally feasible designs of superdirective antennas are\nestablished by using a stochastic optimization algorithm combined with a\nrigorous analytic solution.\n"}
+{"abstract": "  Recently, [JHEP 20 131 (2020)] obtained (a similar, scaled version of) the\n($a,b$)-phase diagram derived from the Kazakov--Zinn-Justin solution of the\nHermitian two-matrix model with interactions \\[\\mathrm{Tr\\,}\\Big\\{\\frac{a}{4}\n(A^4+B^4)+\\frac{b}{2} ABAB\\Big\\}\\,,\\] starting from Functional Renormalization.\nWe comment on something unexpected: the phase diagram of [JHEP 20 131 (2020)]\nis based on a $\\beta_b$-function that does not have the one-loop structure of\nthe Wetterich-Morris Equation. This raises the question of how to reproduce the\nphase diagram from a set of $\\beta$-functions that is, in its totality,\nconsistent with Functional Renormalization. A non-minimalist, yet simple\ntruncation that could lead to the phase diagram is provided. Additionally, we\nidentify the ensemble for which the result of op. cit. would be entirely\ncorrect.\n"}
+{"abstract": "  Climate change, which is now considered one of the biggest threats to\nhumanity, is also the reason behind various other environmental concerns.\nContinued negligence might lead us to an irreparably damaged environment. After\nthe partial failure of the Paris Agreement, it is quite evident that we as\nindividuals need to come together to bring about a change on a large scale to\nhave a significant impact. This paper discusses our approach towards obtaining\na realistic measure of the carbon footprint index being consumed by a user\nthrough day-to-day activities performed via a smart phone app and offering\nincentives in weekly and monthly leader board rankings along with a reward\nsystem. The app helps ease out decision makings on tasks like travel, shopping,\nelectricity consumption, and gain a different and rather numerical perspective\nover the daily choices.\n"}
+{"abstract": "  In this article we present recent advances on interval methods for rigorous\ncomputation of Poincar\\'e maps. We also discuss the impact of choice of\nPoincar\\'e section and coordinate system on obtained bounds for computing\nPoincar\\'e map nearby fixed points.\n"}
+{"abstract": "  Foraminifera are single-celled marine organisms that construct shells that\nremain as fossils in the marine sediments. Classifying and counting these\nfossils are important in e.g. paleo-oceanographic and -climatological research.\nHowever, the identification and counting process has been performed manually\nsince the 1800s and is laborious and time-consuming. In this work, we present a\ndeep learning-based instance segmentation model for classifying, detecting, and\nsegmenting microscopic foraminifera. Our model is based on the Mask R-CNN\narchitecture, using model weight parameters that have learned on the COCO\ndetection dataset. We use a fine-tuning approach to adapt the parameters on a\nnovel object detection dataset of more than 7000 microscopic foraminifera and\nsediment grains. The model achieves a (COCO-style) average precision of $0.78\n\\pm 0.00$ on the classification and detection task, and $0.80 \\pm 0.00$ on the\nsegmentation task. When the model is evaluated without challenging sediment\ngrain images, the average precision for both tasks increases to $0.84 \\pm 0.00$\nand $0.86 \\pm 0.00$, respectively. Prediction results are analyzed both\nquantitatively and qualitatively and discussed. Based on our findings we\npropose several directions for future work, and conclude that our proposed\nmodel is an important step towards automating the identification and counting\nof microscopic foraminifera.\n"}
+{"abstract": "  For the observational modeling of horizontal abundance distributions and of\nmagnetic geometries in chemically peculiar (CP) stars, Zeeman Doppler mapping\n(ZDM) has become the method of choice. Comparisons between abundance maps\nobtained for CP stars and predictions from numerical simulations of atomic\ndiffusion have always proved unsatisfactory, with the blame routinely put on\ntheory. Expanding a previous study aimed at clarifying the question of the\nuniqueness of ZDM maps, this paper inverts the roles between observational\nmodeling and time-dependent diffusion results, casting a cold eye on essential\nassumptions and algorithms underlying ZDM, in particular the Tikhonov-style\nregularization functionals, from 1D to 3D. We show that these have been\nestablished solely for mathematical convenience, but that they in no way\nreflect the physical reality in the atmospheres of magnetic CP stars.\nRecognizing that the observed strong magnetic fields in most well-mapped stars\nrequire the field geometry to be force-free, we demonstrate that many published\nmaps do not meet this condition. There follows a discussion of the frequent\nchanges in magnetic and abundance maps of well observed stars and a caveat\nconcerning the use of least squares deconvolution in ZDM analyses. It emerges\nthat because of the complexity and non-linearity of the field-dependent\nchemical stratifications, Tikhonov based ZDM inversions cannot recover the true\nabundance and magnetic geometries. As our findings additionally show, there is\nno way to define a physically meaningful 3D regularization functional instead.\nZDM remains dysfunctional and does not provide any observational constraints\nfor the modeling of atomic diffusion.\n"}
+{"abstract": "  In this paper, we propose different algorithms for the solution of a tensor\nlinear discrete ill-posed problem arising in the application of the meshless\nmethod for solving PDEs in three-dimensional space using multiquadric radial\nbasis functions. It is well known that the truncated singular value\ndecomposition (TSVD) is the most common effective solver for ill-conditioned\nsystems, but unfortunately the operation count for solving a linear system with\nthe TSVD is computationally expensive for large-scale matrices. In the present\nwork, we propose algorithms based on the use of the well known Einstein product\nfor two tensors to define the tensor global Arnoldi and the tensor Gloub Kahan\nbidiagonalization algorithms. Using the so-called Tikhonov regularization\ntechnique, we will be able to provide computable approximate regularized\nsolutions in a few iterations.\n"}
+{"abstract": "  Fairness is an important property in data-mining applications, including\nrecommender systems. In this work, we investigate a case where users of a\nrecommender system need (or want) to be fair to a protected group of items. For\nexample, in a job market, the user is the recruiter, an item is the job seeker,\nand the protected attribute is gender or race. Even if recruiters want to use a\nfair talent recommender system, the platform may not provide a fair recommender\nsystem, or recruiters may not be able to ascertain whether the recommender\nsystem's algorithm is fair. In this case, recruiters cannot utilize the\nrecommender system, or they may become unfair to job seekers. In this work, we\npropose methods to enable the users to build their own fair recommender\nsystems. Our methods can generate fair recommendations even when the platform\ndoes not (or cannot) provide fair recommender systems. The key challenge is\nthat a user does not have access to the log data of other users or the latent\nrepresentations of items. This restriction prohibits us from adopting existing\nmethods, which are designed for platforms. The main idea is that a user has\naccess to unfair recommendations provided by the platform. Our methods leverage\nthe outputs of an unfair recommender system to construct a new fair recommender\nsystem. We empirically validate that our proposed method improves fairness\nsubstantially without harming much performance of the original unfair system.\n"}
+{"abstract": "  We provide a categorical interpretation for _escrows_, i.e. trading protocols\nin trustless environment, where the exchange between two agents is mediated by\na third party where the buyer locks the money until they receive the goods they\nwant from the seller. A simplified escrow system can be modeled as a certain\nkind of _optic_ in a monoidal category $\\mathcal M$ (e.g., the category of sets\nwith cartesian product); escrows can be regarded as morphisms of a category\n$\\mathcal E(\\mathcal M)$, with the same objects of $\\mathcal M$, and where the\nhom-objects are $\\langle X , Y \\rangle = \\mathsf{Opt}_{\\mathcal M}(\\left[\n\\begin{smallmatrix} Y \\\\ X \\end{smallmatrix} \\right], \\left[\n\\begin{smallmatrix} X \\\\ Y \\end{smallmatrix} \\right])$. When $X$ is a comonoid\nand $Y$ is a monoid in $\\mathcal M$, $\\mathcal E(\\mathcal M)(X,Y)$ is a monoid\nin $\\mathsf{Set}$ (or in the base of enrichment chosen to model one's specific\nproblem), acting on the set of optics $\\left[ \\begin{smallmatrix} B \\\\ B\n\\end{smallmatrix} \\right] \\to \\left[ \\begin{smallmatrix} X \\\\ Y\n\\end{smallmatrix} \\right]$. Moreover, we define a map $$\\lhd : \\langle Y , X\n\\rangle \\times \\mathsf{Opt}(\\left[ \\begin{smallmatrix} Y \\\\ X \\end{smallmatrix}\n\\right], \\left[ \\begin{smallmatrix} B \\\\ B \\end{smallmatrix} \\right]) \\to\n\\mathsf{Opt}(\\left[ \\begin{smallmatrix} Y \\\\ X \\end{smallmatrix} \\right],\n\\left[ \\begin{smallmatrix}{X\\otimes B}\\\\ {Y\\otimes B} \\end{smallmatrix}\n\\right])$$ having action-like properties. This has the following\ninterpretation: the object $B$ acts as an intermediary in a transaction between\n$X$ and $Y$, modeled by an escrow in $\\langle Y , X \\rangle$.\n"}
+{"abstract": "  We study the mean properties of a large representative sample of 217 galaxies\nshowing CIII] emission at $2<z<4$, selected from a parent sample of $\\sim$750\nmain-sequence star-forming galaxies in the VANDELS survey. These CIII] emitters\nhave a broad range of UV luminosities, thus allowing a detailed stacking\nanalysis to characterize their stellar mass, star formation rate (SFR) and\nstellar metallicity, as a function of the UV emission line ratios, EWs, and the\ncarbon-to-oxygen (C/O) abundance ratio. Reliable CIII] detections represent\n$\\sim$30% of the parent sample. Extreme CIII] emitters\n(EW(CIII])$\\gtrsim$8\\r{A}) are exceedingly rare ($\\sim$3%) in VANDELS. The UV\nline ratios of the sample suggest no ionization source other than massive\nstars. Stacks with larger EW(CIII]) show larger EW(Ly$\\alpha$) and lower\nmetallicity, but not all CIII] emitters are Ly$\\alpha$ emitters. The stellar\nmetallicities of CIII] emitters are not significantly different from that of\nthe parent sample, increasing from $\\sim$10% to $\\sim$40% solar for stellar\nmasses $\\log$(M$_{\\star}$/M$_{\\odot})\\sim$9-10.5. The stellar mass-metallicity\nrelation of the CIII] emitters is consistent with previous works showing strong\nevolution from $z=0$ to $z\\sim3$. The C/O abundances of the sample range\n35%-150% solar, with a noticeable increase with FUV luminosity and a smooth\ndecrease with the CIII] EW. We discuss the CIII] emitters in the C/O-Fe/H and\nthe C/O-O/H planes and find they follow stellar and nebular abundance trends\nconsistent with those of Milky Way halo and thick disc stars and local HII\ngalaxies, respectively. A qualitative agreement is also found with chemical\nevolution models, which suggests that CIII] emitters at $z\\sim$3 are\nexperiencing an active phase of chemical enrichment.\n"}
+{"abstract": "  We describe models focused at the understudied problem of translating between\nmonolingual and code-mixed language pairs. More specifically, we offer a wide\nrange of models that convert monolingual English text into Hinglish (code-mixed\nHindi and English). Given the recent success of pretrained language models, we\nalso test the utility of two recent Transformer-based encoder-decoder models\n(i.e., mT5 and mBART) on the task finding both to work well. Given the paucity\nof training data for code-mixing, we also propose a dependency-free method for\ngenerating code-mixed texts from bilingual distributed representations that we\nexploit for improving language model performance. In particular, armed with\nthis additional data, we adopt a curriculum learning approach where we first\nfinetune the language models on synthetic data then on gold code-mixed data. We\nfind that, although simple, our synthetic code-mixing method is competitive\nwith (and in some cases is even superior to) several standard methods\n(backtranslation, method based on equivalence constraint theory) under a\ndiverse set of conditions. Our work shows that the mT5 model, finetuned\nfollowing the curriculum learning procedure, achieves best translation\nperformance (12.67 BLEU). Our models place first in the overall ranking of the\nEnglish-Hinglish official shared task.\n"}
+{"abstract": "  Open conjectures state that, for every $x\\in[0,1]$, the orbit\n$\\left(x_n\\right)_{n=1}^\\infty$ of the mean-median recursion\n$$x_{n+1}=(n+1)\\cdot\\mathrm{median}\\left(x_1,\\ldots,x_{n}\\right)-\\left(x_1+\\cdots+x_n\\right),\\quad\nn\\geqslant 3,$$ with initial data $\\left(x_1,x_2,x_3\\right)=(0,x,1)$, is\neventually constant, and that its transit time and limit functions (of $x$) are\nunbounded and continuous, respectively. In this paper we prove that, for the\nslightly modified recursion\n$$x_{n+1}=n\\cdot\\mathrm{median}\\left(x_1,\\ldots,x_{n}\\right)-\\left(x_1+\\cdots+x_n\\right),\\quad\nn\\geqslant 3,$$ first suggested by Akiyama, the transit time function is\nunbounded but the limit function is discontinuous.\n"}
+{"abstract": "  The research in Environmental Sound Classification (ESC) has been\nprogressively growing with the emergence of deep learning algorithms. However,\ndata scarcity poses a major hurdle for any huge advance in this domain. Data\naugmentation offers an excellent solution to this problem. While Generative\nAdversarial Networks (GANs) have been successful in generating synthetic speech\nand sounds of musical instruments, they have hardly been applied to the\ngeneration of environmental sounds. This paper presents EnvGAN, the first ever\napplication of GANs for the adversarial generation of environmental sounds. Our\nexperiments on three standard ESC datasets illustrate that the EnvGAN can\nsynthesize audio similar to the ones in the datasets. The suggested method of\naugmentation outshines most of the futuristic techniques for audio\naugmentation.\n"}
+{"abstract": "  We study a weakly-interacting one-dimensional Bose gas with two impurities\ncoupled locally to the boson density. We derive analytical results for the\ninduced interaction between the impurities at arbitrary coupling and separation\n$r$. At $r\\lesssim \\xi$, where $\\xi$ denotes the healing length of the Bose\ngas, the interaction is well described by the mean-field contribution. Its form\nchanges as the coupling is increased, approaching a linear function of $r$ at\nshort distances in the regime of strong coupling. The mean-field contribution\ndecays exponentially at arbitrary coupling for $r\\gg\\xi$. At such long\ndistances, however, the effect of quantum fluctuations becomes important,\ngiving rise to a long-ranged quantum contribution to the induced interaction.\nAt longest distances it behaves as $1/r^3$, while at strong coupling we find an\nintermediate distance regime with a slower decay, $1/r$. The quantum\ncontribution in the crossover regime is also calculated. The induced\ninteraction between impurities (i.e., polarons) is attractive and leads to the\nformation of their bound state, known as bipolaron. We discuss its binding\nenergy.\n"}
+{"abstract": "  We derive symmetric and antisymmetric kernels by symmetrizing and\nantisymmetrizing conventional kernels and analyze their properties. In\nparticular, we compute the feature space dimensions of the resulting polynomial\nkernels, prove that the reproducing kernel Hilbert spaces induced by symmetric\nand antisymmetric Gaussian kernels are dense in the space of symmetric and\nantisymmetric functions, and propose a Slater determinant representation of the\nantisymmetric Gaussian kernel, which allows for an efficient evaluation even if\nthe state space is high-dimensional. Furthermore, we show that by exploiting\nsymmetries or antisymmetries the size of the training data set can be\nsignificantly reduced. The results are illustrated with guiding examples and\nsimple quantum physics and chemistry applications.\n"}
+{"abstract": "  Combinatorial optimization problem (COP) over graphs is a fundamental\nchallenge in optimization. Reinforcement learning (RL) has recently emerged as\na new framework to tackle these problems and has demonstrated promising\nresults. However, most RL solutions employ a greedy manner to construct the\nsolution incrementally, thus inevitably pose unnecessary dependency on action\nsequences and need a lot of problem-specific designs. We propose a general RL\nframework that not only exhibits state-of-the-art empirical performance but\nalso generalizes to a variety class of COPs. Specifically, we define state as a\nsolution to a problem instance and action as a perturbation to this solution.\nWe utilize graph neural networks (GNN) to extract latent representations for\ngiven problem instances for state-action encoding, and then apply deep\nQ-learning to obtain a policy that gradually refines the solution by flipping\nor swapping vertex labels. Experiments are conducted on Maximum $k$-Cut and\nTraveling Salesman Problem and performance improvement is achieved against a\nset of learning-based and heuristic baselines.\n"}
+{"abstract": "  A new connection between structure and dynamics in glass-forming liquids is\npresented. We show how the origin of spatially localized excitations, as\ndefined by dynamical facilitation (DF) theory, can be understood from a\nstructure-based framework. This framework is constructed by associating\nexcitation events in DF theory to hopping events between energy minima in the\npotential energy landscape (PEL). By reducing the PEL to an equal energy well\npicture and applying a harmonic approximation, we develop a field theory to\ndescribe elastic fluctuations about inherent states, which are energy\nminimizing configurations of the PEL. We model an excitation as a shear\ntransformation zone (STZ) inducing a localized pure shear deformation onto an\ninherent state. We connect STZs to T1 transition events that break the elastic\nbonds holding the local structure of an inherent state. A formula for the\nexcitation energy barrier, denoted as $J_\\sigma$, is obtained as a function of\ninherent-state elastic moduli and radial distribution function. The energy\nbarrier from the current theory is compared to one predicted by the DF theory\nwhere good agreement is found in various two-dimensional continuous\npoly-disperse atomistic models of glass formers. These results strengthen the\nrole of structure and elasticity in driving glassy dynamics through the\ncreation and relaxation of localized excitations.\n"}
+{"abstract": "  Interfaces impede heat flow in micro/nanostructured systems. Conventional\ntheories for interfacial thermal transport were derived based on bulk phonon\nproperties of the materials making up the interface without explicitly\nconsidering the atomistic interfacial details, which are found critical to\ncorrectly describing thermal boundary conductance (TBC). Recent theoretical\nstudies predicted the existence of localized phonon modes at the interface\nwhich can play an important role in understanding interfacial thermal\ntransport. However, experimental validation is still lacking. Through a\ncombination of Raman spectroscopy and high-energy resolution electron\nenergy-loss spectroscopy (EELS) in a scanning transmission electron microscope,\nwe report the first experimental observation of localized interfacial phonon\nmodes at ~12 THz at a high-quality epitaxial Si-Ge interface. These modes are\nfurther confirmed using molecular dynamics simulations with a high-fidelity\nneural network interatomic potential, which also yield TBC agreeing well with\nthat measured from time-domain thermoreflectance (TDTR) experiments.\nSimulations find that the interfacial phonon modes have obvious contribution to\nthe total TBC. Our findings may significantly contribute to the understanding\nof interfacial thermal transport physics and have impact on engineering TBC at\ninterfaces in applications such as electronics thermal management and\nthermoelectric energy conversion.\n"}
+{"abstract": "  The diffusion, \"explosion\" and \"evaporation\" of dimers and the subsequent\ncoalescence are treated in a formal way by identifying and solving the\ndifferential equations deduced from the respective behaviors of dimers in the\ndifferent cases. This study leads to analytic formulas allowing to calculate,\nin a simple and fast way, the size statistics obtained after the coalescence of\nthe dimers or their constituents once the dimers have completely disappeared.\nThese formulas are of capital interest to characterize systems in which the\ndimers initially present disappear. 4 Study assumptions. Six assumptions will\nbe made for this study: 1. Initial probabilities are known.\n"}
+{"abstract": "  Quantum synchronizable codes are kinds of quantum error-correcting codes that\ncan not only correct the effects of quantum noise on qubits but also the\nmisalignment in block synchronization. This paper contributes to constructing\ntwo classes of quantum synchronizable codes by the cyclotomic classes of order\ntwo over $\\mathbb{Z}_{2q}$, whose synchronization capabilities can reach the\nupper bound under certain conditions. Moreover, the quantum synchronizable\ncodes possess good error-correcting capability towards bit errors and phase\nerrors.\n"}
+{"abstract": "  In this work, we mainly concern the limiting behavior of the electromagnetic\nfield of two species Vlasov-Maxwell-Botlzmann system in diffusive limits. As\nknudsen numbers goes to zero, the electric magnetic and magnetic field may\nperserve or vanish. We verify rigorously Navier-Stokes, Navier-Stokes-Poisson\nand Navier-Stokes Maxwell limit of the two species Vlasov-Maxwell-Boltzmann\nsystem on the torus in three dimension. The justification is based on the\nunified and uniform estimates of solutions to the dimensionless\nVlasov-Maxwell-Boltzmann. The uniform estimates of solutions are obtained by\nemploying the hypocoercivity of the linear Boltzmann operator and constructing\na equation containing damping term of electric field.\n"}
+{"abstract": "  We consider repair tasks: given a critic (e.g., compiler) that assesses the\nquality of an input, the goal is to train a fixer that converts a bad example\n(e.g., code with syntax errors) into a good one (e.g., code with no syntax\nerrors). Existing works create training data consisting of (bad, good) pairs by\ncorrupting good examples using heuristics (e.g., dropping tokens). However,\nfixers trained on this synthetically-generated data do not extrapolate well to\nthe real distribution of bad inputs. To bridge this gap, we propose a new\ntraining approach, Break-It-Fix-It (BIFI), which has two key ideas: (i) we use\nthe critic to check a fixer's output on real bad inputs and add good (fixed)\noutputs to the training data, and (ii) we train a breaker to generate realistic\nbad code from good code. Based on these ideas, we iteratively update the\nbreaker and the fixer while using them in conjunction to generate more paired\ndata. We evaluate BIFI on two code repair datasets: GitHub-Python, a new\ndataset we introduce where the goal is to repair Python code with AST parse\nerrors; and DeepFix, where the goal is to repair C code with compiler errors.\nBIFI outperforms existing methods, obtaining 90.5% repair accuracy on\nGitHub-Python (+28.5%) and 71.7% on DeepFix (+5.6%). Notably, BIFI does not\nrequire any labeled data; we hope it will be a strong starting point for\nunsupervised learning of various repair tasks.\n"}
+{"abstract": "  The main memory access latency has not much improved for more than two\ndecades while the CPU performance had been exponentially increasing until\nrecently. Approximate memory is a technique to reduce the DRAM access latency\nin return of losing data integrity. It is beneficial for applications that are\nrobust to noisy input and intermediate data such as artificial intelligence,\nmultimedia processing, and graph processing. To obtain reasonable outputs from\napplications on approximate memory, it is crucial to protect critical data\nwhile accelerating accesses to non-critical data. We refer the minimum size of\na continuous memory region that the same error rate is applied in approximate\nmemory to as the approximation granularity. A fundamental limitation of\napproximate memory is that the approximation granularity is as large as a few\nkilo bytes. However, applications may have critical and non-critical data\ninterleaved with smaller granularity. For example, a data structure for graph\nnodes can have pointers (critical) to neighboring nodes and its score\n(non-critical, depending on the use-case). This data structure cannot be\ndirectly mapped to approximate memory due to the gap between the approximation\ngranularity and the granularity of data criticality. We refer to this issue as\nthe granularity gap problem. In this paper, we first show that many\napplications potentially suffer from this problem. Then we propose a framework\nto quantitatively evaluate the performance overhead of a possible method to\navoid this problem using known techniques. The evaluation results show that the\nperformance overhead is non-negligible compared to expected benefit from\napproximate memory, suggesting that the granularity gap problem is a\nsignificant concern.\n"}
+{"abstract": "  While data sharing is crucial for knowledge development, privacy concerns and\nstrict regulation (e.g., European General Data Protection Regulation (GDPR))\nunfortunately limit its full effectiveness. Synthetic tabular data emerges as\nan alternative to enable data sharing while fulfilling regulatory and privacy\nconstraints. The state-of-the-art tabular data synthesizers draw methodologies\nfrom generative Adversarial Networks (GAN) and address two main data types in\nthe industry, i.e., continuous and categorical. In this paper, we develop\nCTAB-GAN, a novel conditional table GAN architecture that can effectively model\ndiverse data types, including a mix of continuous and categorical variables.\nMoreover, we address data imbalance and long-tail issues, i.e., certain\nvariables have drastic frequency differences across large values. To achieve\nthose aims, we first introduce the information loss and classification loss to\nthe conditional GAN. Secondly, we design a novel conditional vector, which\nefficiently encodes the mixed data type and skewed distribution of data\nvariable. We extensively evaluate CTAB-GAN with the state of the art GANs that\ngenerate synthetic tables, in terms of data similarity and analysis utility.\nThe results on five datasets show that the synthetic data of CTAB-GAN\nremarkably resembles the real data for all three types of variables and results\ninto higher accuracy for five machine learning algorithms, by up to 17%.\n"}
+{"abstract": "  This work deals with the topological classification of singular foliation\ngerms on $(\\mathbb C^{2},0)$. Working in a suitable class of foliations we fix\nthe topological invariants given by the separatrix set, the Camacho-Sad indices\nand the projective holonomy representations and we construct a minimal family\nof foliation germs containing all the topological classes and which is\ntopologically complete. We prove factorization properties of equisingular\nfamilies through this family up to topological conjugacy.\n"}
+{"abstract": "  For monolayers of chemically active particles at a fluid interface,\ncollective dynamics are predicted to arise owing to activity-induced Marangoni\nflow even if the particles are not self-propelled. Here we test this prediction\nby employing a monolayer of spherically symmetric active TiO_2 particles\nlocated at an oil-water interface with or without addition of a non-ionic\nsurfactant. Due to the spherical symmetry, an individual particle does not\nself-propel. However, the gradients produced by the photochemical fuel\ndegradation give rise to long-ranged Marangoni flows. For the case in which\nsurfactant is added to the system, we indeed observe the emergence of\ncollective motion, with dynamics dependent on the particle coverage of the\nmonolayer. The experimental observations are discussed within the framework of\na simple theoretical mean field model.\n"}
+{"abstract": "  We consider the problem of making expressive static analyzers interactive.\nFormal static analysis is seeing increasingly widespread adoption as a tool for\nverification and bug-finding, but even with powerful cloud infrastructure it\ncan take minutes or hours to get batch analysis results after a code change.\nWhile existing techniques offer some demand-driven or incremental aspects for\ncertain classes of analysis, the fundamental challenge we tackle is doing both\nfor arbitrary abstract interpreters.\n  Our technique, demanded abstract interpretation, lifts program syntax and\nanalysis state to a dynamically evolving graph structure, in which program\nedits, client-issued queries, and evaluation of abstract semantics are all\ntreated uniformly. The key difficulty addressed by our approach is the\napplication of general incremental computation techniques to the complex,\ncyclic dependency structure induced by abstract interpretation of loops with\nwidening operators. We prove that desirable abstract interpretation\nmeta-properties, including soundness and termination, are preserved in our\napproach, and that demanded analysis results are equal to those computed by a\nbatch abstract interpretation. Experimental results suggest promise for a\nprototype demanded abstract interpretation framework: by combining incremental\nand demand-driven techniques, our framework consistently delivers analysis\nresults at interactive speeds, answering 95% of queries within 1.2 seconds.\n"}
+{"abstract": "  In this paper, we introduce the notion of completely non-trivial module of a\nLie conformal algebra. By this notion, we classify all finite irreducible\nmodules of a class of $\\mathbb{Z}^+$-graded Lie conformal algebras\n$\\mathcal{L}=\\bigoplus_{i=0}^{\\infty} \\mathbb{C}[\\partial]L_i$ satisfying $\n[{L_0}_\\lambda L_0]=(\\partial+2\\lambda)L_0,$ and $[{L_1}_\\lambda L_i]\\neq 0$\nfor any $i\\in \\mathbb{Z}^+$. These Lie conformal algebras include Block type\nLie conformal algebra $\\mathcal{B}(p)$ and map Virasoro Lie conformal algebra\n$\\mathcal{V}(\\mathbb{C}[T])=Vir\\otimes \\mathbb{C}[T]$. As a result, we show\nthat all non-trivial finite irreducible modules of these algebras are free of\nrank one as a $\\mathbb{C}[\\partial]$-module.\n"}
+{"abstract": "  We view disentanglement learning as discovering an underlying structure that\nequivariantly reflects the factorized variations shown in data. Traditionally,\nsuch a structure is fixed to be a vector space with data variations represented\nby translations along individual latent dimensions. We argue this simple\nstructure is suboptimal since it requires the model to learn to discard the\nproperties (e.g. different scales of changes, different levels of abstractness)\nof data variations, which is an extra work than equivariance learning. Instead,\nwe propose to encode the data variations with groups, a structure not only can\nequivariantly represent variations, but can also be adaptively optimized to\npreserve the properties of data variations. Considering it is hard to conduct\ntraining on group structures, we focus on Lie groups and adopt a\nparameterization using Lie algebra. Based on the parameterization, some\ndisentanglement learning constraints are naturally derived. A simple model\nnamed Commutative Lie Group VAE is introduced to realize the group-based\ndisentanglement learning. Experiments show that our model can effectively learn\ndisentangled representations without supervision, and can achieve\nstate-of-the-art performance without extra constraints.\n"}
+{"abstract": "  Large area van der Waals (vdW) thin films are assembled materials consisting\nof a network of randomly stacked nanosheets. The multi-scale structure and the\ntwo-dimensional nature of the building block mean that interfaces naturally\nplay a crucial role in the charge transport of such thin films. While single or\nfew stacked nanosheets (i.e. vdW heterostructures) have been the subject of\nintensive works, little is known about how charges travel through multilayered,\nmore disordered networks. Here we report a comprehensive study of a\nprototypical system given by networks of randomly stacked reduced graphene\noxide 2D nanosheets, whose chemical and geometrical properties can be\ncontrolled independently, permitting to explore percolated networks ranging\nfrom a single nanosheet to some billions with room temperature resistivity\nspanning from 10-5 to 10-1 ohm m. We systematically observe a clear transition\nbetween two different regimes at a critical temperature T*: Efros-Shklovskii\nvariable range hopping (ESVRH) below T* and power law (PL) behavior above.\nFirstly, we demonstrate that the two regimes are strongly correlated with each\nother, both depending on the charge localization length xi, calculated by\nES-VRH model, which corresponds to the characteristic size of overlapping sp2\ndomains belonging to different nanosheets. Thus, we propose a microscopic model\ndescribing the charge transport as a geometrical phase transition, given by the\nmetal-insulator transition associated with the percolation of quasi-1D\nnanofillers with length xi, showing that the charge transport behavior of the\nnetworks is valid for all geometries and defects of the nanosheets, ultimately\nsuggesting a generalized description on vdW and disordered thin films.\n"}
+{"abstract": "  We provide a construction for categorical representation learning and\nintroduce the foundations of \"$\\textit{categorifier}$\". The central theme in\nrepresentation learning is the idea of $\\textbf{everything to vector}$. Every\nobject in a dataset $\\mathcal{S}$ can be represented as a vector in\n$\\mathbb{R}^n$ by an $\\textit{encoding map}$ $E:\n\\mathcal{O}bj(\\mathcal{S})\\to\\mathbb{R}^n$. More importantly, every morphism\ncan be represented as a matrix $E:\n\\mathcal{H}om(\\mathcal{S})\\to\\mathbb{R}^{n}_{n}$. The encoding map $E$ is\ngenerally modeled by a $\\textit{deep neural network}$. The goal of\nrepresentation learning is to design appropriate tasks on the dataset to train\nthe encoding map (assuming that an encoding is optimal if it universally\noptimizes the performance on various tasks). However, the latter is still a\n$\\textit{set-theoretic}$ approach. The goal of the current article is to\npromote the representation learning to a new level via a\n$\\textit{category-theoretic}$ approach. As a proof of concept, we provide an\nexample of a text translator equipped with our technology, showing that our\ncategorical learning model outperforms the current deep learning models by 17\ntimes. The content of the current article is part of the recent US patent\nproposal (patent application number: 63110906).\n"}
+{"abstract": "  We study the number of points in the family of plane curves defined by a\ntrinomial \\[\n  \\mathcal{C}(\\alpha,\\beta)=\n  \\{(x,y)\\in\\mathbb{F}_q^2\\,:\\,\\alpha x^{a_{11}}y^{a_{12}}+\\beta\nx^{a_{21}}y^{a_{22}}=x^{a_{31}}y^{a_{32}}\\} \\] with fixed exponents (not\ncollinear) and varying coefficients over finite fields. We prove that each of\nthese curves has an almost predictable number of points, given by a closed\nformula that depends on the coefficients, exponents, and the field, with a\nsmall error term $N(\\alpha,\\beta)$ that is bounded in absolute value by\n$2\\tilde{g}q^{1/2}$, where $\\tilde{g}$ is a constant that depends only on the\nexponents and the field. A formula for $\\tilde{g}$ is provided, as well as a\ncomparison of $\\tilde{g}$ with the genus $g$ of the projective closure of the\ncurve over $\\overline{\\mathbb{F}_q}$. We also give several linear and quadratic\nidentities for the numbers $N(\\alpha,\\beta)$ that are strong enough to prove\nthe estimate above, and in some cases, to characterize them completely.\n"}
+{"abstract": "  Because of the increasing demand for computation in DNN, researchers develope\nboth hardware and software mechanisms to reduce the compute and memory burden.\nA widely adopted approach is to use mixed precision data types. However, it is\nhard to leverage mixed precision without hardware support because of the\noverhead of data casting. Hardware vendors offer tensorized instructions for\nmixed-precision tensor operations, like Intel VNNI, Tensor Core, and ARM-DOT.\nThese instructions involve a computing idiom that reduces multiple low\nprecision elements into one high precision element. The lack of compilation\ntechniques for this makes it hard to utilize these instructions: Using\nvendor-provided libraries for computationally-intensive kernels is inflexible\nand prevents further optimizations, and manually writing hardware intrinsics is\nerror-prone and difficult for programmers. Some prior works address this\nproblem by creating compilers for each instruction. This requires excessive\neffort when it comes to many tensorized instructions. In this work, we develop\na compiler framework to unify the compilation for these instructions -- a\nunified semantics abstraction eases the integration of new instructions, and\nreuses the analysis and transformations. Tensorized instructions from different\nplatforms can be compiled via UNIT with moderate effort for favorable\nperformance. Given a tensorized instruction and a tensor operation, UNIT\nautomatically detects the applicability, transforms the loop organization of\nthe operation,and rewrites the loop body to leverage the tensorized\ninstruction. According to our evaluation, UNIT can target various mainstream\nhardware platforms. The generated end-to-end inference model achieves 1.3x\nspeedup over Intel oneDNN on an x86 CPU, 1.75x speedup over Nvidia cuDNN on an\nNvidiaGPU, and 1.13x speedup over a carefully tuned TVM solution for ARM DOT on\nan ARM CPU.\n"}
+{"abstract": "  Federated Learning (FL) is a collaborative machine learning technique to\ntrain a global model without obtaining clients' private data. The main\nchallenges in FL are statistical diversity among clients, limited computing\ncapability among clients' equipments, and the excessive communication overhead\nbetween servers and clients. To address these challenges, we propose a novel\nsparse personalized federated learning scheme via maximizing correlation\nFedMac. By incorporating an approximated L1-norm and the correlation between\nclient models and global model into standard FL loss function, the performance\non statistical diversity data is improved and the communicational and\ncomputational loads required in the network are reduced compared with\nnon-sparse FL. Convergence analysis shows that the sparse constraints in FedMac\ndo not affect the convergence rate of the global model, and theoretical results\nshow that FedMac can achieve good sparse personalization, which is better than\nthe personalized methods based on L2-norm. Experimentally, we demonstrate the\nbenefits of this sparse personalization architecture compared with the\nstate-of-the-art personalization methods (e.g. FedMac respectively achieves\n98.95%, 99.37%, 90.90% and 89.06% accuracy on the MNIST, FMNIST, CIFAR-100 and\nSynthetic datasets under non-i.i.d. variants).\n"}
+{"abstract": "  We present the analytical framework for converting projected light\ndistributions with a S\\'ersic profile into three-dimensional light\ndistributions for stellar systems of arbitrary triaxial shape. The main\npractical result is the definition of a simple yet robust measure of intrinsic\ngalaxy size: the median radius $r_\\mathrm{med}$, defined as the radius of a\nsphere that contains 50% of the total luminosity or mass, that is, the median\ndistance of a star to the galaxy center. We examine how $r_\\mathrm{med}$\ndepends on projected size measurements as a function of S\\'ersic index and\nintrinsic axis ratios, and demonstrate its relative independence of these\nparameters. As an application we show that the projected semi-major axis length\nof the ellipse enclosing 50% of the light is an unbiased proxy for\n$r_\\mathrm{med}$, with small galaxy-to-galaxy scatter of $\\sim$10% (1$\\sigma$),\nunder the condition that the variation in triaxiality within the population is\nsmall. For galaxy populations with unknown or a large range in triaxiality an\nunbiased proxy for $r_\\mathrm{med}$ is $1.3\\times R_{e}$, where $R_{e}$ is the\ncircularized half-light radius, with galaxy-to-galaxy scatter of 20-30%\n(1$\\sigma$). We also describe how inclinations can be estimated for individual\ngalaxies based on the measured projected shape and prior knowledge of the\nintrinsic shape distribution of the corresponding galaxy population. We make\nthe numerical implementation of our calculations available.\n"}
+{"abstract": "  We investigate the onset of chaos in a periodically kicked Dicke model (KDM),\nusing the out-of-time-order correlator (OTOC) as a diagnostic tool, in both the\noscillator and the spin subspaces. In the large spin limit, the classical\nHamiltonian map is constructed, which allows us to investigate the\ncorresponding phase space dynamics and to compute the Lyapunov exponent. We\nshow that the growth rate of the OTOC for the canonically conjugate coordinates\nof the oscillator is able to capture the Lyapunov exponent in the chaotic\nregime. The onset of chaos is further investigated using the saturation value\nof the OTOC, that can serve as an alternate indicator of chaos in a generic\ninteracting quantum system. This is also supported by a system independent\neffective random matrix model. We further identify the quantum scars in KDM and\ndetect their dynamical signature by using the OTOC dynamics. The relevance of\nthe present study in the context of ongoing cold atom experiments is also\ndiscussed.\n"}
+{"abstract": "  Identification of parameters in ordinary differential equations (ODEs) is an\nimportant and challenging task when modeling dynamic systems in biomedical\nresearch and other scientific areas, especially with the presence of\ntime-varying parameters. This article proposes a fast and accurate method,\nTVMAGI (Time-Varying MAnifold-constrained Gaussian process Inference), to\nestimate both time-constant and time-varying parameters in the ODE using noisy\nand sparse observation data. TVMAGI imposes a Gaussian process model over the\ntime series of system components as well as time-varying parameters, and\nrestricts the derivative process to satisfy ODE conditions. Consequently,\nTVMAGI completely bypasses numerical integration and achieves substantial\nsavings in computation time. By incorporating the ODE structures through\nmanifold constraints, TVMAGI enjoys a principled statistical construction under\nthe Bayesian paradigm, which further enables it to handle systems with missing\ndata or unobserved components. The Gaussian process prior also alleviates the\nidentifiability issue often associated with the time-varying parameters in ODE.\nUnlike existing approaches, TVMAGI assumes no specific linearity of the ODE\nstructure, and can be applied to general nonlinear systems. We demonstrate the\nrobustness and efficiency of our method through three simulation examples,\nincluding an infectious disease compartmental model.\n"}
+{"abstract": "  We compute topological Hochschild homology of sufficiently structured forms\nof truncated Brown--Peterson spectra with coefficients. In particular, we\ncompute $\\operatorname{THH}_*(\\operatorname{taf}^D;M)$ for $M\\in \\{\nH\\mathbb{Z}_{(3)},k(1),k(2)\\}$ where $\\operatorname{taf}^D$ is the $E_{\\infty}$\nform of $BP\\langle 2\\rangle$ constructed by Hill--Lawson. We compute\n$\\operatorname{THH}_*(\\operatorname{tmf}_1(3);M)$ when $M\\in \\{\nH\\mathbb{Z}_{(2)},k(2)\\}$ where $\\operatorname{tmf}_1(3)$ is the $E_{\\infty}$\nform of $BP\\langle 2\\rangle$ constructed by Lawson--Naumann. We also compute\n$\\operatorname{THH}_*(B\\langle n\\rangle;M)$ for $M=H\\mathbb{Z}_{(p)}$ and\ncertain $E_3$ forms $B\\langle n\\rangle$ of $BP\\langle n\\rangle$. For example at\n$p=2$, this result applies to the $E_3$ forms of $BP\\langle n\\rangle$\nconstructed by Hahn--Wilson.\n"}
+{"abstract": "  This paper proposes an approach that generates multiple 3D human meshes from\ntext. The human shapes are represented by 3D meshes based on the SMPL model.\nThe model's performance is evaluated on the COCO dataset, which contains\nchallenging human shapes and intricate interactions between individuals. The\nmodel is able to capture the dynamics of the scene and the interactions between\nindividuals based on text. We further show how using such a shape as input to\nimage synthesis frameworks helps to constrain the network to synthesize humans\nwith realistic human shapes.\n"}
+{"abstract": "  Here, a physical formalism is proposed for an unconditional microwave quantum\nteleportation of Gaussian states via two-mode squeezed states in lossy\nenvironments. The proposed formalism is controllable to be used in both the\nfridge and free space in case of entanglement between two parties survives.\nSome possible experimental parameters are estimated for the teleportation of\nmicrowave signals with a frequency of 5GHz based on the proposed physical\nframework. This would be helpful for superconducting inter- and intra-fridge\nquantum communication as well as open-air quantum microwave communication,\nwhich can be applied to quantum local area networks (QLANs) and distributed\nquantum computing protocols.\n"}
+{"abstract": "  We study quotients of the Toeplitz C*-algebra of a random walk, similar to\nthose studied by the author and Markiewicz for finite stochastic matrices. We\nintroduce a new Cuntz-type quotient C*-algebra for random walks that have\nconvergent ratios of transition probabilities. These C*-algebras give rise to\nnew notions of ratio limit space and boundary for such random walks, which are\ncomputed by appealing to a companion paper by Woess. Our combined results are\nleveraged to identify a unique smallest symmetry-equivariant quotient\nC*-algebra for any symmetric random walk on a hyperbolic group, shedding light\non a question of Viselter on C*-algebras of subproduct systems.\n"}
+{"abstract": "  We build the bosonic $\\eta$-deformed $AdS_4\\times\\mathbb{CP}^3$ background\ngenerated by an $r$-matrix that satisfies the modified classical Yang-Baxter\nequation. In a special limit we find that it is the gravity dual of the\nnoncommutative ABJM theory.\n"}
+{"abstract": "  We study pure exploration in bandits, where the dimension of the feature\nrepresentation can be much larger than the number of arms. To overcome the\ncurse of dimensionality, we propose to adaptively embed the feature\nrepresentation of each arm into a lower-dimensional space and carefully deal\nwith the induced model misspecifications. Our approach is conceptually very\ndifferent from existing works that can either only handle low-dimensional\nlinear bandits or passively deal with model misspecifications. We showcase the\napplication of our approach to two pure exploration settings that were\npreviously under-studied: (1) the reward function belongs to a possibly\ninfinite-dimensional Reproducing Kernel Hilbert Space, and (2) the reward\nfunction is nonlinear and can be approximated by neural networks. Our main\nresults provide sample complexity guarantees that only depend on the effective\ndimension of the feature spaces in the kernel or neural representations.\nExtensive experiments conducted on both synthetic and real-world datasets\ndemonstrate the efficacy of our methods.\n"}
+{"abstract": "  Oumuamua, the first known object of extrasolar origin seen to enter our Solar\nSystem, has multiple unusual characteristics that, taken together, are very\ndifficult to explain with conventional astronomical entities like asteroids and\ncomets. Consequently, it has been hypothesized that Oumuamua is an interstellar\nprobe that was constructed by an alien civilization. We demonstrate that the\naccomplishments that can be achieved with large space\ntelescopes/interferometers in the alien's planetary system will completely\nquench any motivation for construction and launch of an Oumuamua-like probe.\nThe absence of any such motivation proves that Oumuamua is not an alien\ncreation.\n"}
+{"abstract": "  Despite remarkable progress achieved, most neural architecture search (NAS)\nmethods focus on searching for one single accurate and robust architecture. To\nfurther build models with better generalization capability and performance,\nmodel ensemble is usually adopted and performs better than stand-alone models.\nInspired by the merits of model ensemble, we propose to search for multiple\ndiverse models simultaneously as an alternative way to find powerful models.\nSearching for ensembles is non-trivial and has two key challenges: enlarged\nsearch space and potentially more complexity for the searched model. In this\npaper, we propose a one-shot neural ensemble architecture search (NEAS)\nsolution that addresses the two challenges. For the first challenge, we\nintroduce a novel diversity-based metric to guide search space shrinking,\nconsidering both the potentiality and diversity of candidate operators. For the\nsecond challenge, we enable a new search dimension to learn layer sharing among\ndifferent models for efficiency purposes. The experiments on ImageNet clearly\ndemonstrate that our solution can improve the supernet's capacity of ranking\nensemble architectures, and further lead to better search results. The\ndiscovered architectures achieve superior performance compared with\nstate-of-the-arts such as MobileNetV3 and EfficientNet families under aligned\nsettings. Moreover, we evaluate the generalization ability and robustness of\nour searched architecture on the COCO detection benchmark and achieve a 3.1%\nimprovement on AP compared with MobileNetV3. Codes and models are available at\nhttps://github.com/researchmm/NEAS.\n"}
+{"abstract": "  Optical wave-based computing has enabled the realization of real-time\ninformation processing in both space and time domains. In the past few years,\nanalog computing has experienced rapid development but mostly for a single\nfunction. Motivated by parallel space-time computing and miniaturization, we\nshow that reconfigurable graphene-based metasurfaces offer a promising path\ntowards spatiotemporal computing with integrated functionalities by properly\nengineering both spatial- and temporal-frequency responses. This paper employs\na tunable graphene-based metasurface to enable analog signal and image\nprocessing in both space and time by tuning the electrostatic bias. In the\nfirst part of the paper, we propose a switchable analog computing paradigm in\nwhich the proposed metasurface can switch among defined performances by\nselecting a proper external voltage for graphene monolayers. Spatial isotropic\ndifferentiation and edge detection in the spatial channel and first-order\ntemporal differentiation and metasurface-based phaser with linear group-delay\nresponse in the temporal channel are demonstrated. In the second section of the\npaper, simultaneous and parallel spatiotemporal analog computing is\ndemonstrated. The proposed metasurface processor has almost no static power\nconsumption due to its floating-gate configuration. The spatial- and\ntemporal-frequency transfer functions (TFs) are engineered by using a\ntransmission line (TL) model, and the obtained results are validated with\nfull-wave simulations. Our proposal will enable real-time parallel\nspatiotemporal analog signal and image processing.\n"}
+{"abstract": "  In this paper, we propose a novel SpatioTemporal convolutional Dense Network\n(STDNet) to address the video-based crowd counting problem, which contains the\ndecomposition of 3D convolution and the 3D spatiotemporal dilated dense\nconvolution to alleviate the rapid growth of the model size caused by the\nConv3D layer. Moreover, since the dilated convolution extracts the multiscale\nfeatures, we combine the dilated convolution with the channel attention block\nto enhance the feature representations. Due to the error that occurs from the\ndifficulty of labeling crowds, especially for videos, imprecise or\nstandard-inconsistent labels may lead to poor convergence for the model. To\naddress this issue, we further propose a new patch-wise regression loss (PRL)\nto improve the original pixel-wise loss. Experimental results on three\nvideo-based benchmarks, i.e., the UCSD, Mall and WorldExpo'10 datasets, show\nthat STDNet outperforms both image- and video-based state-of-the-art methods.\nThe source codes are released at \\url{https://github.com/STDNet/STDNet}.\n"}
+{"abstract": "  Multi-level exciton-polariton systems offer an attractive platform for\nstudies of non-linear optical phenomena. However, studies of such consequential\nnon-linear phenomena as polariton condensation and lasing in planar\nmicrocavities have so far been limited to two-level systems, where the\ncondensation takes place in the lowest attainable state. Here, we report\nnon-equilibrium Bose-Einstein condensation of exciton-polaritons and low\nthreshold, dual-wavelength polariton lasing in vertically coupled, double\nplanar microcavities. Moreover, we find that the presence of the non-resonantly\ndriven condensate triggers interbranch exciton-polariton transfer in the form\nof energy-degenerate parametric scattering. Such an effect has so far been\nobserved only under excitation that is strictly resonant in terms of the energy\nand incidence angle. We describe theoretically our time-integrated and\ntime-resolved photoluminescence investigations by a set of rate equations\ninvolving an open-dissipative Gross-Pitaevskii equation. Our platform's\ninherent tunability is promising for construction of planar lattices, enabling\nthree-dimensional polariton hopping and realization of photonic devices, such\nas two-qubit polariton-based logic gates.\n"}
+{"abstract": "  The HAWC Collaboration has observed gamma rays at energies above 56 TeV from\na collection of nine sources. It has been suggested that this emission could be\nhadronic in nature, requiring that these systems accelerate cosmic-ray protons\nor nuclei up to PeV-scale energies. In this paper, we instead show that the\nspectra of these objects favor a leptonic (inverse Compton) origin for their\nemission. More specifically, the gamma-ray emission from these objects can be\nstraightforwardly accommodated within a model in which $\\sim \\mathcal{O}(10\\%)$\nof the host pulsar's spindown power is transferred into the acceleration of\nelectrons and positrons with a power-law spectrum that extends to several\nhundred TeV or higher. The spectral break that is observed among these sources\nis naturally explained within the context of this simple model, and occurs at\nthe energy where the timescale for energy losses matches the age of the pulsar.\nIn contrast, this spectral feature cannot be straightforwardly accommodated in\nhadronic scenarios. Furthermore, hadronic models predict that these sources\nshould produce more emission at GeV-scale energies than is observed. In light\nof these considerations, we conclude that HAWC's highest energy sources should\nbe interpreted as TeV halos or pulsar wind nebulae, which produce their\nemission through inverse Compton scattering, and are powered by the rotational\nkinetic energy of their host pulsar.\n"}
+{"abstract": "  Regional facial image synthesis conditioned on semantic mask has achieved\ngreat success using generative adversarial networks. However, the appearance of\ndifferent regions may be inconsistent with each other when conducting regional\nimage editing. In this paper, we focus on the problem of harmonized regional\nstyle transfer and manipulation for facial images. The proposed approach\nsupports regional style transfer and manipulation at the same time. A\nmulti-scale encoder and style mapping networks are proposed in our work. The\nencoder is responsible for extracting regional styles of real faces. Style\nmapping networks generate styles from random samples for all facial regions. As\nthe key part of our work, we propose a multi-region style attention module to\nadapt the multiple regional style embeddings from a reference image to a target\nimage for generating harmonious and plausible results. Furthermore, we propose\na new metric \"harmony score\" and conduct experiments in a challenging setting:\nthree widely used face datasets are involved and we test the model by\ntransferring the regional facial appearance between datasets. Images in\ndifferent datasets are usually quite different, which makes the inconsistency\nbetween target and reference regions more obvious. Results show that our model\ncan generate reliable style transfer and multi-modal manipulation results\ncompared with SOTAs. Furthermore, we show two face editing applications using\nthe proposed approach.\n"}
+{"abstract": "  We present MoonLight, a tool for monitoring temporal and spatio-temporal\nproperties of mobile and spatially distributed cyber-physical systems (CPS). In\nthe proposed framework, space is represented as a weighted graph, describing\nthe topological configurations in which the single CPS entities (nodes of the\ngraph) are arranged. Both nodes and edges have attributes modelling physical\nand logical quantities that can change in time. MoonLight is implemented in\nJava and supports the monitoring of Spatio-Temporal Reach and Escape Logic\n(STREL). MoonLight can be used as a standalone command line tool, as a Java\nAPI, or via Matlab interface. We provide here some examples using the Matlab\ninterface and we evaluate the tool performance also by comparing with other\ntools specialized in monitoring only temporal properties.\n"}
+{"abstract": "  Numerous improvements for feedback mechanisms have contributed to the great\nprogress in object detection. In this paper, we first present an\nevaluation-feedback module, which is proposed to consist of evaluation system\nand feedback mechanism. Then we analyze and summarize the disadvantages and\nimprovements of traditional evaluation-feedback module. Finally, we focus on\nboth the evaluation system and the feedback mechanism, and propose Control\nDistance IoU and Control Distance IoU loss function (or CDIoU and CDIoU loss\nfor short) without increasing parameters or FLOPs in models, which show\ndifferent significant enhancements on several classical and emerging models.\nSome experiments and comparative tests show that coordinated\nevaluation-feedback module can effectively improve model performance. CDIoU and\nCDIoU loss have different excellent performances in several models such as\nFaster R-CNN, YOLOv4, RetinaNet and ATSS. There is a maximum AP improvement of\n1.9% and an average AP of 0.8% improvement on MS COCO dataset, compared to\ntraditional evaluation-feedback modules.\n"}
+{"abstract": "  Following a tidal disruption event (TDE), the accretion rate can evolve from\nquiescent to near-Eddington levels and back over months - years timescales.\nThis provides a unique opportunity to study the formation and evolution of the\naccretion flow around supermassive black holes (SMBHs). We present two years of\nmulti-wavelength monitoring observations of the TDE AT2018fyk at X-ray, UV,\noptical and radio wavelengths. We identify three distinct accretion states and\ntwo state transitions between them. These appear remarkably similar to the\nbehaviour of stellar-mass black holes in outburst. The X-ray spectral\nproperties show a transition from a soft (thermal-dominated) to a hard\n(power-law dominated) spectral state around L$_{\\rm bol} \\sim $few $ \\times\n10^{-2}$ L$_{\\rm Edd}$, and the strengthening of the corona over time\n$\\sim$100--200 days after the UV/optical peak. Contemporaneously, the spectral\nenergy distribution (in particular, the UV-to-X-ray spectral slope\n$\\alpha_{ox}$) shows a pronounced softening as the outburst progresses. The\nX-ray timing properties also show a marked change, initially dominated by\nvariability at long ($>$day) timescales while a high frequency ($\\sim$10$^{-3}$\nHz) component emerges after the transition into the hard state. At late times\n($\\sim$500 days after peak), a second accretion state transition occurs, from\nthe hard into the quiescent state, as identified by the sudden collapse of the\nbolometric (X-ray+UV) emission to levels below 10$^{-3.4}$ L$_{\\rm Edd}$. Our\nfindings illustrate that TDEs can be used to study the scale (in)variance of\naccretion processes in individual SMBHs. Consequently, they provide a new\navenue to study accretion states over seven orders of magnitude in black hole\nmass, removing limitations inherent to commonly used ensemble studies.\n"}
+{"abstract": "  In this short note we classify the Cartan subalgebras in all von Neumann\nalgebras associated with graph product groups and their free ergodic measure\npreserving actions on probability spaces.\n"}
+{"abstract": "  In this paper, we consider the problem of reducing the semitotal domination\nnumber of a given graph by contracting $k$ edges, for some fixed $k \\geq 1$. We\nshow that this can always be done with at most 3 edge contractions and further\ncharacterise those graphs requiring 1, 2 or 3 edge contractions, respectively,\nto decrease their semitotal domination number. We then study the complexity of\nthe problem for $k=1$ and obtain in particular a complete complexity dichotomy\nfor monogenic classes.\n"}
+{"abstract": "  Bayesian optimization has emerged as a powerful strategy to accelerate\nscientific discovery by means of autonomous experimentation. However, expensive\nmeasurements are required to accurately estimate materials properties, and can\nquickly become a hindrance to exhaustive materials discovery campaigns. Here,\nwe introduce Gemini: a data-driven model capable of using inexpensive\nmeasurements as proxies for expensive measurements by correcting systematic\nbiases between property evaluation methods. We recommend using Gemini for\nregression tasks with sparse data and in an autonomous workflow setting where\nits predictions of expensive to evaluate objectives can be used to construct a\nmore informative acquisition function, thus reducing the number of expensive\nevaluations an optimizer needs to achieve desired target values. In a\nregression setting, we showcase the ability of our method to make accurate\npredictions of DFT calculated bandgaps of hybrid organic-inorganic perovskite\nmaterials. We further demonstrate the benefits that Gemini provides to\nautonomous workflows by augmenting the Bayesian optimizer Phoenics to yeild a\nscalable optimization framework leveraging multiple sources of measurement.\nFinally, we simulate an autonomous materials discovery platform for optimizing\nthe activity of electrocatalysts for the oxygen evolution reaction. Realizing\nautonomous workflows with Gemini, we show that the number of measurements of a\ncomposition space comprising expensive and rare metals needed to achieve a\ntarget overpotential is significantly reduced when measurements from a proxy\ncomposition system with less expensive metals are available.\n"}
+{"abstract": "  Virtual clusters are widely used computing platforms than can be deployed in\nmultiple cloud platforms. The ability to dynamically grow and shrink the number\nof nodes has paved the way for customised elastic computing both for High\nPerformance Computing and High Throughput Computing workloads. However,\nelasticity is typically restricted to a single cloud site, thus hindering the\nability to provision computational resources from multiple geographically\ndistributed cloud sites. To this aim, this paper introduces an architecture of\nopen-source components that coherently deploy a virtual elastic cluster across\nmultiple cloud sites to perform large-scale computing. These hybrid virtual\nelastic clusters are automatically deployed and configured using an\nInfrastructure as Code (IaC) approach on a distributed hybrid testbed that\nspans different organizations, including on-premises and public clouds,\nsupporting automated tunneling of communications across the cluster nodes with\nadvanced VPN topologies. The results indicate that cluster-based computing of\nembarrassingly parallel jobs can benefit from hybrid virtual clusters that\naggregate computing resources from multiple cloud back-ends and bring them\ntogether into a dedicated, albeit virtual network.\n  The work presented in this article has been partially funded by the European\nUnion's (EU) Horizon 2020 research project DEEP Hybrid-DataCloud (grant\nagreement No 777435).\n"}
+{"abstract": "  As an integral part of our culture and way of life, language is intricately\nrelated to migrations of people. To understand whether and how migration shapes\nlanguage formation processes we examine the dynamics of the naming game with\nmigrating agents. (i) When all agents may migrate, the dynamics generates an\neffective surface tension, which drives the coarsening. Such a behaviour is\nvery robust and appears for a wide range of densities of agents and their\nmigration rates. (ii) However, when only multilingual agents are allowed to\nmigrate, monolingual islands are typically formed. In such a case, when the\nmigration rate is sufficiently large, the majority of agents acquire a common\nlanguage, which spontaneously emerges with no indication of the surface-tension\ndriven coarsening. A relatively slow coarsening that takes place in a dense\nstatic population is very fragile, and most likely, an arbitrarily small\nmigration rate can divert the system toward quick formation of monolingual\nislands. Our work shows that migration influences language formation processes\nbut additional details like density, or mobility of agents are needed to\nspecify more precisely this influence.\n"}
+{"abstract": "  Continued fractions are used to give an alternate proof of $e^{x/y}$ is\nirrational.\n"}
+{"abstract": "  We investigate a general formulation for clustering and transductive few-shot\nlearning, which integrates prototype-based objectives, Laplacian regularization\nand supervision constraints from a few labeled data points. We propose a\nconcave-convex relaxation of the problem, and derive a computationally\nefficient block-coordinate bound optimizer, with convergence guarantee. At each\niteration,our optimizer computes independent (parallel) updates for each\npoint-to-cluster assignment. Therefore, it could be trivially distributed for\nlarge-scale clustering and few-shot tasks. Furthermore, we provides a thorough\nconvergence analysis based on point-to-set maps. Were port comprehensive\nclustering and few-shot learning experiments over various data sets, showing\nthat our method yields competitive performances, in term of accuracy and\noptimization quality, while scaling up to large problems. Using standard\ntraining on the base classes, without resorting to complex meta-learning and\nepisodic-training strategies, our approach outperforms state-of-the-art\nfew-shot methods by significant margins, across various models, settings and\ndata sets. Surprisingly, we found that even standard clustering procedures\n(e.g., K-means), which correspond to particular, non-regularized cases of our\ngeneral model, already achieve competitive performances in comparison to the\nstate-of-the-art in few-shot learning. These surprising results point to the\nlimitations of the current few-shot benchmarks, and question the viability of a\nlarge body of convoluted few-shot learning techniques in the recent literature.\n"}
+{"abstract": "  This paper deals with Hensel minimal, non-trivially valued fields $K$ of\nequicharacteristic zero, whose axiomatic theory was introduced in a recent\narticle by Cluckers-Halupczok-Rideau. We additionally require that the standard\nalgebraic language be induced (up to interdefinability) for the imaginary sort\n$RV$. This condition is satisfied by the majority of classical tame structures\non Henselian fields, including Henselian fields with analytic structure. The\nmain purpose is to carry over many results of our previous papers to the above\ngeneral axiomatic settings including, among others, the theorem on existence of\nthe limit, curve selection, the closedness theorem, several non-Archimedean\nversions of the Lojasiewicz inequalities as well as the theorems on extending\ncontinuous definable functions and on existence of definable retractions. We\nestablish an embedding theorem for regular definable spaces and the definable\nultranormality of definable Hausdorff LC-spaces. Also given are examples that\ncurve selection and the closedness theorem, key results for numerous\napplications, may be no longer true after expanding the language for the\nleading term structure $RV$. In the case of Henselian fields with analytic\nstructure, a more precise version of the theorem on existence of the limit (a\nversion of Puiseux's theorem) is provided. Further, we establish definable\nversions of resolution of singularities (hypersurface case) and transformation\nto normal crossings by blowing up, on arbitrary strong analytic manifolds in\nHensel minimal expansions of analytic structures. Also introduced are\nmeromorphous functions, i.e. continuous quotients of strong analytic functions\non strong analytic manifolds. Finally, we prove a finitary meromorphous version\nof the Nullstellensatz.\n"}
+{"abstract": "  In this paper, we prove that the Fechner and Stevens laws are equivalent\n(coincide up to isomorphism). Therefore, the problem does not exist.\n"}
+{"abstract": "  Deep learning has achieved promising segmentation performance on 3D left\natrium MR images. However, annotations for segmentation tasks are expensive,\ncostly and difficult to obtain. In this paper, we introduce a novel\nhierarchical consistency regularized mean teacher framework for 3D left atrium\nsegmentation. In each iteration, the student model is optimized by multi-scale\ndeep supervision and hierarchical consistency regularization, concurrently.\nExtensive experiments have shown that our method achieves competitive\nperformance as compared with full annotation, outperforming other\nstate-of-the-art semi-supervised segmentation methods.\n"}
+{"abstract": "  We present a collection recommender system that can automatically create and\nrecommend collections of items at a user level. Unlike regular recommender\nsystems, which output top-N relevant items, a collection recommender system\noutputs collections of items such that the items in the collections are\nrelevant to a user, and the items within a collection follow a specific theme.\nOur system builds on top of the user-item representations learnt by item\nrecommender systems. We employ dimensionality reduction and clustering\ntechniques along with intuitive heuristics to create collections with their\nratings and titles.\n  We test these ideas in a real-world setting of music recommendation, within a\npopular music streaming service. We find that there is a 2.3x increase in\nrecommendation-driven consumption when recommending collections over items.\nFurther, it results in effective utilization of real estate and leads to\nrecommending a more and diverse set of items. To our knowledge, these are first\nof its kind experiments at such a large scale.\n"}
+{"abstract": "  The number and importance of AI-based systems in all domains is growing. With\nthe pervasive use and the dependence on AI-based systems, the quality of these\nsystems becomes essential for their practical usage. However, quality assurance\nfor AI-based systems is an emerging area that has not been well explored and\nrequires collaboration between the SE and AI research communities. This paper\ndiscusses terminology and challenges on quality assurance for AI-based systems\nto set a baseline for that purpose. Therefore, we define basic concepts and\ncharacterize AI-based systems along the three dimensions of artifact type,\nprocess, and quality characteristics. Furthermore, we elaborate on the key\nchallenges of (1) understandability and interpretability of AI models, (2) lack\nof specifications and defined requirements, (3) need for validation data and\ntest input generation, (4) defining expected outcomes as test oracles, (5)\naccuracy and correctness measures, (6) non-functional properties of AI-based\nsystems, (7) self-adaptive and self-learning characteristics, and (8) dynamic\nand frequently changing environments.\n"}
+{"abstract": "  An action functional is developed for nonlinear dislocation dynamics. This\nserves as a first step towards the application of effective field theory in\nphysics to evaluate its potential in obtaining a macroscopic description of\ndislocation dynamics describing the plasticity of crystalline solids.\nConnections arise between the continuum mechanics and material science of\ndefects in solids, effective field theory techniques in physics, and fracton\ntensor gauge theories.\n"}
+{"abstract": "  Cyber Physical Systems (CPS) are characterized by their ability to integrate\nthe physical and information or cyber worlds. Their deployment in critical\ninfrastructure have demonstrated a potential to transform the world. However,\nharnessing this potential is limited by their critical nature and the far\nreaching effects of cyber attacks on human, infrastructure and the environment.\nAn attraction for cyber concerns in CPS rises from the process of sending\ninformation from sensors to actuators over the wireless communication medium,\nthereby widening the attack surface. Traditionally, CPS security has been\ninvestigated from the perspective of preventing intruders from gaining access\nto the system using cryptography and other access control techniques. Most\nresearch work have therefore focused on the detection of attacks in CPS.\nHowever, in a world of increasing adversaries, it is becoming more difficult to\ntotally prevent CPS from adversarial attacks, hence the need to focus on making\nCPS resilient. Resilient CPS are designed to withstand disruptions and remain\nfunctional despite the operation of adversaries. One of the dominant\nmethodologies explored for building resilient CPS is dependent on machine\nlearning (ML) algorithms. However, rising from recent research in adversarial\nML, we posit that ML algorithms for securing CPS must themselves be resilient.\nThis paper is therefore aimed at comprehensively surveying the interactions\nbetween resilient CPS using ML and resilient ML when applied in CPS. The paper\nconcludes with a number of research trends and promising future research\ndirections. Furthermore, with this paper, readers can have a thorough\nunderstanding of recent advances on ML-based security and securing ML for CPS\nand countermeasures, as well as research trends in this active research area.\n"}
+{"abstract": "  I point out fatal mathematical errors in the paper \"Quantum correlations are\nweaved by the spinors of the Euclidean primitives\" by Joy Christian, published\n(2019) in the journal Royal Society Open Science.\n"}
+{"abstract": "  This article presents an algorithm for reducing measurement uncertainty of\none physical quantity when given oversampled measurements of two physical\nquantities with correlated noise. The algorithm assumes that the aleatoric\nmeasurement uncertainty in both physical quantities follows a Gaussian\ndistribution and relies on sampling faster than it is possible for the\nmeasurand (the true value of the physical quantity that we are trying to\nmeasure) to change (due to the system thermal time constant) to calculate the\nparameters of the noise distribution. In contrast to the Kalman and particle\nfilters, which respectively require state update equations and a map of one\nphysical quality, our algorithm requires only the oversampled sensor\nmeasurements. When applied to temperature-compensated humidity sensors, it\nprovides reduced uncertainty in humidity estimates from correlated temperature\nand humidity measurements. In an experimental evaluation, the algorithm\nachieves average uncertainty reduction of 10.3 %. The algorithm incurs an\nexecution time overhead of 5.3 % when compared to the minimum algorithm\nrequired to measure and calculate the uncertainty. Detailed instruction-level\nemulation of a C-language implementation compiled to the RISC-V architecture\nshows that the uncertainty reduction program required 0.05 % more instructions\nper iteration than the minimum operations required to calculate the\nuncertainty.\n"}
+{"abstract": "  MOA-2006-BLG-074 was selected as one of the most promising planetary\ncandidates in a retrospective analysis of the MOA collaboration: its asymmetric\nhigh-magnification peak can be perfectly explained by a source passing across a\ncentral caustic deformed by a small planet. However, after a detailed analysis\nof the residuals, we have realized that a single lens and a source orbiting\nwith a faint companion provides a more satisfactory explanation for all the\nobserved deviations from a Paczynski curve and the only physically acceptable\ninterpretation. Indeed the orbital motion of the source is constrained enough\nto allow a very good characterization of the binary source from the\nmicrolensing light curve. The case of MOA-2006-BLG-074 suggests that the\nso-called xallarap effect must be taken seriously in any attempts to obtain\naccurate planetary demographics from microlensing surveys.\n"}
+{"abstract": "  Iteratively reweighted least square (IRLS) is a popular approach to solve\nsparsity-enforcing regression problems in machine learning. State of the art\napproaches are more efficient but typically rely on specific coordinate pruning\nschemes. In this work, we show how a surprisingly simple reparametrization of\nIRLS, coupled with a bilevel resolution (instead of an alternating scheme) is\nable to achieve top performances on a wide range of sparsity (such as Lasso,\ngroup Lasso and trace norm regularizations), regularization strength (including\nhard constraints), and design matrices (ranging from correlated designs to\ndifferential operators). Similarly to IRLS, our method only involves linear\nsystems resolutions, but in sharp contrast, corresponds to the minimization of\na smooth function. Despite being non-convex, we show that there is no spurious\nminima and that saddle points are \"ridable\", so that there always exists a\ndescent direction. We thus advocate for the use of a BFGS quasi-Newton solver,\nwhich makes our approach simple, robust and efficient. We perform a numerical\nbenchmark of the convergence speed of our algorithm against state of the art\nsolvers for Lasso, group Lasso, trace norm and linearly constrained problems.\nThese results highlight the versatility of our approach, removing the need to\nuse different solvers depending on the specificity of the ML problem under\nstudy.\n"}
+{"abstract": "  Self-interacting dark matter (SIDM) models offer one way to reconcile\ninconsistencies between observations and predictions from collisionless cold\ndark matter (CDM) models on dwarf-galaxy scales. In order to incorporate the\neffects of both baryonic and SIDM interactions, we study a suite of\ncosmological-baryonic simulations of Milky-Way (MW)-mass galaxies from the\nFeedback in Realistic Environments (FIRE-2) project where we vary the SIDM\nself-interaction cross-section $\\sigma/m$. We compare the shape of the main\ndark matter (DM) halo at redshift $z=0$ predicted by SIDM simulations (at\n$\\sigma/m=0.1$, $1$, and $10$ cm$^2$ g$^{-1}$) with CDM simulations using the\nsame initial conditions. In the presence of baryonic feedback effects, we find\nthat SIDM models do not produce the large differences in the inner structure of\nMW-mass galaxies predicted by SIDM-only models. However, we do find that the\nradius where the shape of the total mass distribution begins to differ from\nthat of the stellar mass distribution is dependent on $\\sigma/m$. This\ntransition could potentially be used to set limits on the SIDM cross-section in\nthe MW.\n"}
+{"abstract": "  We reanalyze the experimental NMC data on the nonsinglet structure function\n$F_2^p-F_2^n$ and E866 data on the nucleon sea asymmetry $\\bar{d}/\\bar{u}$\nusing the truncated moments approach elaborated in our previous papers. With\nhelp of the special truncated sum one can overcome the problem of the\nunavoidable experimental restrictions on the Bjorken $x$ and effectively study\nthe fundamental sum rules for the parton distributions and structure functions.\nUsing only the data from the measured region of $x$, we obtain the Gottfried\nsum $\\int_0^1 F_2^{ns}/x\\, dx$ and the integrated nucleon sea asymmetry\n$\\int_0^1 (\\bar{d}-\\bar{u})\\, dx$. We compare our results with the reported\nexperimental values and with the predictions obtained for different global\nparametrizations for the parton distributions. We also discuss the discrepancy\nbetween the NMC and E866 results on $\\int_0^1 (\\bar{d}-\\bar{u})\\, dx$. We\ndemonstrate that this discrepancy can be resolved by taking into account the\nhigher-twist effects.\n"}
+{"abstract": "  The emission properties of tin plasmas, produced by the irradiation of\npreformed liquid tin targets by several-ns-long 2-$\\mu$m-wavelength laser\npulses, are studied in the extreme ultraviolet (EUV) regime. In a two-pulse\nscheme, a pre-pulse laser is first used to deform tin microdroplets into thin,\nextended disks before the main (2$\\mu$m) pulse creates the EUV-emitting plasma.\nIrradiating 30- to 300-$\\mu$m-diameter targets with 2-$\\mu$m laser pulses, we\nfind that the efficiency in creating EUV light around 13.5nm follows the\nfraction of laser light that overlaps with the target. Next, the effects of a\nchange in 2-$\\mu$m drive laser intensity (0.6-1.8$\\times 10^{11}$W/cm$^2$) and\npulse duration (3.7-7.4ns) are studied. It is found that the angular dependence\nof the emission of light within a 2\\% bandwidth around 13.5nm and within the\nbackward 2$\\pi$ hemisphere around the incoming laser beam is almost independent\nof intensity and duration of the 2-$\\mu$m drive laser. With increasing target\ndiameter, the emission in this 2\\% bandwidth becomes increasingly anisotropic,\nwith a greater fraction of light being emitted into the hemisphere of the\nincoming laser beam. For direct comparison, a similar set of experiments is\nperformed with a 1-$\\mu$m-wavelength drive laser. Emission spectra, recorded in\na 5.5-25.5nm wavelength range, show significant self-absorption of light around\n13.5nm in the 1-$\\mu$m case, while in the 2-$\\mu$m case only an opacity-related\nbroadening of the spectral feature at 13.5nm is observed. This work\ndemonstrates the enhanced capabilities and performance of 2-$\\mu$m-driven\nplasmas produced from disk targets when compared to 1-$\\mu$m-driven plasmas,\nproviding strong motivation for the use of 2-$\\mu$m lasers as drive lasers in\nfuture high-power sources of EUV light.\n"}
+{"abstract": "  Advancements in the digital technologies have enabled researchers to develop\na variety of Computational Music applications. Such applications are required\nto capture, process, and generate data related to music. Therefore, it is\nimportant to digitally represent music in a music theoretic and concise manner.\nExisting approaches for representing music are ineffective in terms of\nutilizing music theory. In this paper, we address the disjoint of music theory\nand computational music by developing an opensource representation tool based\non music theory. Through the wide range of use cases, we run an analysis on the\nclassical music pieces to show the usefulness of the developed music embedding.\n"}
+{"abstract": "  Network function virtualization (NFV) and content caching are two promising\ntechnologies that hold great potential for network operators and designers.\nThis paper optimizes the deployment of NFV and content caching in 5G networks\nand focuses on the associated power consumption savings. In addition, it\nintroduces an approach to combine content caching with NFV in one integrated\narchitecture for energy aware 5G networks. A mixed integer linear programming\n(MILP) model has been developed to minimize the total power consumption by\njointly optimizing the cache size, virtual machine (VM) workload, and the\nlocations of both cache nodes and VMs. The results were investigated under the\nimpact of core network virtual machines (CNVMs) inter-traffic. The result show\nthat the optical line terminal (OLT) access network nodes are the optimum\nlocation for content caching and for hosting VMs during busy times of the day\nwhilst IP over WDM core network nodes are the optimum locations for caching and\nVM placement during off-peak time. Furthermore, the results reveal that a\nvirtualization-only approach is better than a caching-only approach for video\nstreaming services where the virtualization-only approach compared to\ncaching-only approach, achieves a maximum power saving of 7% (average 5%) when\nno CNVMs inter-traffic is considered and 6% (average 4%) with CNVMs\ninter-traffic at 10% of the total backhaul traffic. On the other hand, the\nintegrated approach has a maximum power saving of 15% (average 9%) with and\nwithout CNVMs inter-traffic compared to the virtualization-only approach, and\nit achieves a maximum power saving of 21% (average 13%) without CNVMs\ninter-traffic and 20% (average 12%) when CNVMs inter-traffic is considered\ncompared with the caching-only approach. In order to validate the MILP models\nand achieve real-time operation in our approaches, a heuristic was developed.\n"}
+{"abstract": "  A symbolic method for solving linear recurrences of combinatorial and\nstatistical interest is introduced. This method essentially relies on a\nrepresentation of polynomial sequences as moments of a symbol that looks as the\nframework of a random variable with no reference to any probability space. We\ngive several examples of applications and state an explicit form for the class\nof linear recurrences involving Sheffer sequences satisfying a special initial\ncondition. The results here presented can be easily implemented in a symbolic\nsoftware.\n"}
+{"abstract": "  By Hacon-McKernan-Xu, there is a positive lower bound in each dimension for\nthe volume of all klt varieties with ample canonical class. We show that these\nbounds must go to zero extremely fast as the dimension increases, by\nconstructing a klt $n$-fold with ample canonical class whose volume is less\nthan $1/2^{2^n}$. These examples should be close to optimal.\n  We also construct a klt Fano variety of each dimension $n$ such that\n$H^0(X,-mK_X)=0$ for all $1\\leq m < b$ with $b$ roughly $2^{2^n}$. Here again\nthere is some bound in each dimension, by Birkar's theorem on boundedness of\ncomplements, and we are showing that the bound must increase extremely fast\nwith the dimension.\n"}
+{"abstract": "  The effective low-energy late-time description of many body systems near\nthermal equilibrium provided by classical hydrodynamics in terms of dissipative\ntransport phenomena receives important corrections once the effects of\nstochastic fluctuations are taken into account. One such physical effect is the\noccurrence of long-time power law tails in correlation functions of conserved\ncurrents. In the hydrodynamic regime $\\vec{k} \\rightarrow 0$ this amounts to\nnon-analytic dependence of the correlation functions on the frequency $\\omega$.\nIn this article, we consider a relativistic fluid with a conserved global\n$U(1)$ charge in the presence of a strong background magnetic field, and\ncompute the long-time tails in correlation functions of the stress tensor. The\npresence of the magnetic field renders the system anisotropic. In the absence\nof the magnetic field, there are three out-of-equilibrium transport parameters\nthat arise at the first order in the hydrodynamic derivative expansion, all of\nwhich are dissipative. In the presence of a background magnetic field, there\nare ten independent out-of-equilibrium transport parameters at the first order,\nthree of which are non-dissipative and the rest are dissipative. We provide the\nmost general linearized equations about a given state of thermal equilibrium\ninvolving the various transport parameters in the presence of a magnetic field,\nand use them to compute the long-time tails for the fluid.\n"}
+{"abstract": "  Contending hate speech in social media is one of the most challenging social\nproblems of our time. There are various types of anti-social behavior in social\nmedia. Foremost of them is aggressive behavior, which is causing many social\nissues such as affecting the social lives and mental health of social media\nusers. In this paper, we propose an end-to-end ensemble-based architecture to\nautomatically identify and classify aggressive tweets. Tweets are classified\ninto three categories - Covertly Aggressive, Overtly Aggressive, and\nNon-Aggressive. The proposed architecture is an ensemble of smaller subnetworks\nthat are able to characterize the feature embeddings effectively. We\ndemonstrate qualitatively that each of the smaller subnetworks is able to learn\nunique features. Our best model is an ensemble of Capsule Networks and results\nin a 65.2% F1 score on the Facebook test set, which results in a performance\ngain of 0.95% over the TRAC-2018 winners. The code and the model weights are\npublicly available at\nhttps://github.com/parthpatwa/Hater-O-Genius-Aggression-Classification-using-Capsule-Networks.\n"}
+{"abstract": "  We study the transport properties for a family of geometrically frustrated\nmodels on the triangular lattice with an interaction scale far exceeding the\nsingle-particle bandwidth. Starting from the interaction-only limit, which can\nbe solved exactly, we analyze the transport and thermodynamic behavior as a\nfunction of filling and temperature at the leading non-trivial order in the\nsingle-particle hopping. Over a broad range of intermediate temperatures, we\nfind evidence of a dc resistivity scaling linearly with temperature and with\ntypical values far exceeding the quantum of resistance, $h/e^2$. At a sequence\nof commensurate fillings, the bad-metallic regime eventually crosses over into\ninteraction induced insulating phases in the limit of low temperatures. We\ndiscuss the relevance of our results to experiments in cold-atom and moir\\'e\nheterostructure based platforms.\n"}
+{"abstract": "  The aim of this note is to completely determine the second homology group of\nthe special queer Lie superalgebra $\\mathfrak{sq}_n(R)$ coordinatized by a\nunital associative superalgebra $R$, which will be achieved via an isomorphism\nbetween the special linear Lie superalgebra $\\mathfrak{sl}_{n}(R\\otimes Q_1)$\nand the special queer Lie superalgebra $\\mathfrak{sq}_n(R)$.\n"}
+{"abstract": "  In a multiple linear regression model, the algebraic formula of the\ndecomposition theorem explains the relationship between the univariate\nregression coefficient and partial regression coefficient using geometry. It\nwas found that univariate regression coefficients are decomposed into their\nrespective partial regression coefficients according to the parallelogram rule.\nMulticollinearity is analyzed with the help of the decomposition theorem. It\nwas also shown that it is a sample phenomenon that the partial regression\ncoefficients of important explanatory variables are not significant, but the\nsign expectation deviation cause may be the population structure between the\nexplained variables and explanatory variables or may be the result of sample\nselection. At present, some methods of diagnostic multicollinearity only\nconsider the correlation of explanatory variables, so these methods are\nbasically unreliable, and handling multicollinearity is blind before the causes\nare not distinguished. The increase in the sample size can help identify the\ncauses of multicollinearity, and the difference method can play an auxiliary\nrole.\n"}
+{"abstract": "  As the earliest stage of planet formation, massive, optically thick, and gas\nrich protoplanetary disks provide key insights into the physics of star and\nplanet formation. When viewed edge-on, high resolution images offer a unique\nopportunity to study both the radial and vertical structures of these disks and\nrelate this to vertical settling, radial drift, grain growth, and changes in\nthe midplane temperatures. In this work, we present multi-epoch HST and Keck\nscattered light images, and an ALMA 1.3 mm continuum map for the remarkably\nflat edge-on protoplanetary disk SSTC2DJ163131.2-242627, a young solar-type\nstar in $\\rho$ Ophiuchus. We model the 0.8 $\\mu$m and 1.3 mm images in separate\nMCMC runs to investigate the geometry and dust properties of the disk using the\nMCFOST radiative transfer code. In scattered light, we are sensitive to the\nsmaller dust grains in the surface layers of the disk, while the sub-millimeter\ndust continuum observations probe larger grains closer to the disk midplane. An\nMCMC run combining both datasets using a covariance-based log-likelihood\nestimation was marginally successful, implying insufficient complexity in our\ndisk model. The disk is well characterized by a flared disk model with an\nexponentially tapered outer edge viewed nearly edge-on, though some degree of\ndust settling is required to reproduce the vertically thin profile and lack of\napparent flaring. A colder than expected disk midplane, evidence for dust\nsettling, and residual radial substructures all point to a more complex radial\ndensity profile to be probed with future, higher resolution observations.\n"}
+{"abstract": "  Silicon ferroelectric field-effect transistors (FeFETs) with low-k\ninterfacial layer (IL) between ferroelectric gate stack and silicon channel\nsuffers from high write voltage, limited write endurance and large\nread-after-write latency due to early IL breakdown and charge trapping and\ndetrapping at the interface. We demonstrate low voltage, high speed memory\noperation with high write endurance using an IL-free back-end-of-line (BEOL)\ncompatible FeFET. We fabricate IL-free FeFETs with 28nm channel length and\n126nm width under a thermal budget <400C by integrating 5nm thick Hf0.5Zr0.5O2\ngate stack with amorphous Indium Tungsten Oxide (IWO) semiconductor channel. We\nreport 1.2V memory window and read current window of 10^5 for program and\nerase, write latency of 20ns with +/-2V write pulses, read-after-write latency\n<200ns, write endurance cycles exceeding 5x10^10 and 2-bit/cell programming\ncapability. Array-level analysis establishes IL-free BEOL FeFET as a promising\ncandidate for logic-compatible high-performance on-chip buffer memory and\nmulti-bit weight cell for compute-in-memory accelerators.\n"}
+{"abstract": "  We prove the asymptotic functional Poisson laws in the total variation norm\nand obtain estimates of the corresponding convergence rates for a large class\nof hyperbolic dynamical systems. These results generalize the ones obtained\nbefore in this area. Applications to intermittent solenoids, Axiom A systems,\nH\\'enon attractors and to billiards, are also considered.\n"}
+{"abstract": "  Software engineering educators are continually challenged by rapidly evolving\nconcepts, technologies, and industry demands. Due to the omnipresence of\nsoftware in a digitalized society, higher education institutions (HEIs) have to\neducate the students such that they learn how to learn, and that they are\nequipped with a profound basic knowledge and with latest knowledge about modern\nsoftware and system development. Since industry demands change constantly, HEIs\nare challenged in meeting such current and future demands in a timely manner.\nThis paper analyzes the current state of practice in software engineering\neducation. Specifically, we want to compare contemporary education with\nindustrial practice to understand if frameworks, methods and practices for\nsoftware and system development taught at HEIs reflect industrial practice. For\nthis, we conducted an online survey and collected information about 67 software\nengineering courses. Our findings show that development approaches taught at\nHEIs quite closely reflect industrial practice. We also found that the choice\nof what process to teach is sometimes driven by the wish to make a course\nsuccessful. Especially when this happens for project courses, it could be\nbeneficial to put more emphasis on building learning sequences with other\ncourses.\n"}
+{"abstract": "  This study explores the potential of modern implicit solvers for stochastic\npartial differential equations in the simulation of real-time complex Langevin\ndynamics. Not only do these methods offer asymptotic stability, rendering the\nissue of runaway solution moot, but they also allow us to simulate at\ncomparatively largeLangevin time steps, leading to lower computational cost. We\ncompare different ways of regularizing the underlying path integral and\nestimate the errors introduced due to the finite Langevin time. Based on that\ninsight, we implement benchmark (non-)thermal simulations of the quantum\nanharmonic oscillator on the canonical Schwinger-Keldysh contour of short\nreal-time extent.\n"}
+{"abstract": "  In this article, we consider a class of finite rank perturbations of Toeplitz\noperators that have simple eigenvalues on the unit circle. Under a suitable\nassumption on the behavior of the essential spectrum, we show that such\noperators are power bounded. The problem originates in the approximation of\nhyperbolic partial differential equations with boundary conditions by means of\nfinite difference schemes. Our result gives a positive answer to a conjecture\nby Trefethen, Kreiss and Wu that only a weak form of the so-called Uniform\nKreiss-Lopatinskii Condition is sufficient to imply power boundedness.\n"}
+{"abstract": "  We address the problem of exposure correction of dark, blurry and noisy\nimages captured in low-light conditions in the wild. Classical image-denoising\nfilters work well in the frequency space but are constrained by several factors\nsuch as the correct choice of thresholds, frequency estimates etc. On the other\nhand, traditional deep networks are trained end-to-end in the RGB space by\nformulating this task as an image-translation problem. However, that is done\nwithout any explicit constraints on the inherent noise of the dark images and\nthus produce noisy and blurry outputs. To this end we propose a DCT/FFT based\nmulti-scale loss function, which when combined with traditional losses, trains\na network to translate the important features for visually pleasing output. Our\nloss function is end-to-end differentiable, scale-agnostic, and generic; i.e.,\nit can be applied to both RAW and JPEG images in most existing frameworks\nwithout additional overhead. Using this loss function, we report significant\nimprovements over the state-of-the-art using quantitative metrics and\nsubjective tests.\n"}
+{"abstract": "  The \\textit{node reliability} of a graph $G$ is the probability that at least\none node is operational and that the operational nodes can all communicate in\nthe subgraph that they induce, given that the edges are perfectly reliable but\neach node operates independently with probability $p\\in[0,1]$. We show that\nunlike many other notions of graph reliability, the number of maximal intervals\nof decrease in $[0,1]$ is unbounded, and that there can be arbitrarily many\ninflection points in the interval as well.\n"}
+{"abstract": "  For the Minkowski question mark function $?(x)$ we consider derivative of the\nfunction $f_n(x) = \\underbrace{?(?(...?}_\\text{n times}(x)))$. Apart from\nobvious cases (rational numbers for example) it is non-trivial to find explicit\nexamples of numbers $x$ for which $f'_n(x)=0$. In this paper we present a set\nof irrational numbers, such that for every element $x_0$ of this set and for\nany $n\\in\\mathbb{Z}_+$ one has $f'_n(x_0)=0$.\n"}
+{"abstract": "  Since their inception, learning techniques under the Reservoir Computing\nparadigm have shown a great modeling capability for recurrent systems without\nthe computing overheads required for other approaches. Among them, different\nflavors of echo state networks have attracted many stares through time, mainly\ndue to the simplicity and computational efficiency of their learning algorithm.\nHowever, these advantages do not compensate for the fact that echo state\nnetworks remain as black-box models whose decisions cannot be easily explained\nto the general audience. This work addresses this issue by conducting an\nexplainability study of Echo State Networks when applied to learning tasks with\ntime series, image and video data. Specifically, the study proposes three\ndifferent techniques capable of eliciting understandable information about the\nknowledge grasped by these recurrent models, namely, potential memory, temporal\npatterns and pixel absence effect. Potential memory addresses questions related\nto the effect of the reservoir size in the capability of the model to store\ntemporal information, whereas temporal patterns unveils the recurrent\nrelationships captured by the model over time. Finally, pixel absence effect\nattempts at evaluating the effect of the absence of a given pixel when the echo\nstate network model is used for image and video classification. We showcase the\nbenefits of our proposed suite of techniques over three different domains of\napplicability: time series modeling, image and, for the first time in the\nrelated literature, video classification. Our results reveal that the proposed\ntechniques not only allow for a informed understanding of the way these models\nwork, but also serve as diagnostic tools capable of detecting issues inherited\nfrom data (e.g. presence of hidden bias).\n"}
+{"abstract": "  We point out qualitatively different possibilities on the role of\nCP-conserving processes in generating cosmological particle-antiparticle\nasymmetries, with illustrative examples from models in leptogenesis and\nasymmetric dark matter production. In particular, we consider scenarios in\nwhich the CP-violating and CP-conserving processes are either both decays or\nboth scatterings, thereby being naturally of comparable rates. This is in\ncontrast to the previously considered CP-conserving processes in models of\nleptogenesis in different see-saw mechanisms, in which the CP-conserving\nscatterings typically have lower rates compared to the CP-violating decays, due\nto a Boltzmann suppression. We further point out that the CP-conserving\nprocesses can play a dual role if the asymmetry is generated in the mother\nsector itself, in contrast to the conventional scenarios in which it is\ngenerated in the daughter sector. This is because, the CP-conserving processes\ninitially suppress the asymmetry generation by controlling the\nout-of-equilibrium number densities of the bath particles, but subsequently\nmodify the ratio of particle anti-particle yields at the present epoch by\neliminating the symmetric component of the bath particles through\npair-annihilations, leading to a competing effect stemming from the same\nprocess at different epochs. We find that the asymmetric yields for relevant\nparticle-antiparticle systems can vary by orders of magnitude depending upon\nthe relative size of the CP-conserving and violating reaction rates.\n"}
+{"abstract": "  Magnetic field-line reconnection is a universal plasma process responsible\nfor the conversion of magnetic field energy to the plasma heating and charged\nparticle acceleration. Solar flares and Earth's magnetospheric substorms are\ntwo most investigated dynamical systems where magnetic reconnection is believed\nto be responsible for global magnetic field reconfiguration and energization of\nplasma populations. Such a reconfiguration includes formation of a long-living\ncurrent systems connecting the primary energy release region and cold dense\nconductive plasma of photosphere/ionosphere. In both flares and substorms the\nevolution of this current system correlates with formation and dynamics of\nenergetic particle fluxes. Our study is focused on this similarity between\nflares and substorms. Using a wide range of datasets available for flare and\nsubstorm investigations, we compare qualitatively dynamics of currents and\nenergetic particle fluxes for one flare and one substorm. We showed that there\nis a clear correlation between energetic particle bursts (associated with\nenergy release due to magnetic reconnection) and magnetic field\nreconfiguration/formation of current system. We then discuss how datasets of\nin-situ measurements in the magnetospheric substorm can help in interpretation\nof datasets gathered for the solar flare.\n"}
+{"abstract": "  The design of provably correct controllers for continuous-state stochastic\nsystems crucially depends on approximate finite-state abstractions and their\naccuracy quantification. For this quantification, one generally uses\napproximate stochastic simulation relations, whose constant precision limits\nthe achievable guarantees on the control design. This limitation especially\naffects higher dimensional stochastic systems and complex formal\nspecifications. This work allows for variable precision by defining a\nsimulation relation that contains multiple precision layers. For bi-layered\nsimulation relations, we develop a robust dynamic programming approach yielding\na lower bound on the satisfaction probability of temporal logic specifications.\nWe illustrate the benefit of bi-layered simulation relations for linear\nstochastic systems in an example.\n"}
+{"abstract": "  We are concerned with interior and global gradient estimates for solutions to\na class of singular quasilinear elliptic equations with measure data, whose\nprototype is given by the $p$-Laplace equation $-\\Delta_p u=\\mu$ with $p\\in\n(1,2)$. The cases when $p\\in \\big(2-\\frac 1 n,2\\big)$ and $p\\in\n\\big(\\frac{3n-2}{2n-1},2-\\frac{1}{n}\\big]$ were studied in [9] and [22],\nrespectively. In this paper, we improve the results in [22] and address the\nopen case when $p\\in \\big(1,\\frac{3n-2}{2n-1}\\big]$. Interior and global\nmodulus of continuity estimates of the gradients of solutions are also\nestablished.\n"}
+{"abstract": "  A system of interacting classical oscillators is discussed, similar to a\nquantum mechanical system of a discrete energy level, interacting with the\nenergy quasi-continuum of states considered Fano. The limit of a continuous\nspectrum is analyzed together with the possible connection of the problem under\nstudy with the generation of coherent phonons.\n"}
+{"abstract": "  The sequence of deformation bursts during plastic deformation exhibits\nscale-free features. In addition to the burst or avalanche sizes and the rate\nof avalanches the process is characterized by correlations in the series which\nbecome manifest in the resulting shape of the stress-strain curve. We analyze\nsuch features of plastic deformation with 2D and 3D simulations of discrete\ndislocation dynamics models and we show, that only with severe plastic\ndeformation the ensuing memory effects become negligible. The role of past\ndeformation history and dislocation pinning by disorder are studied. In\ngeneral, the correlations have the effect of reducing the scatter of the\nindividual stress-strain curves around the mean one.\n"}
+{"abstract": "  We introduce the concept of impedance matching to axion dark matter by posing\nthe question of why axion detection is difficult, even though there is enough\npower in each square meter of incident dark-matter flux to energize a LED light\nbulb. By quantifying backreaction on the axion field, we show that a small\naxion-photon coupling does not by itself prevent an order-unity fraction of the\ndark matter from being absorbed through optimal impedance match. We further\nshow, in contrast, that the electromagnetic charges and the self-impedance of\ntheir coupling to photons provide the principal constraint on power absorption\nintegrated across a search band. Using the equations of axion electrodynamics,\nwe demonstrate stringent limitations on absorbed power in linear,\ntime-invariant, passive receivers. Our results yield fundamental constraints,\narising from the photon-electron interaction, on improving integrated power\nabsorption beyond the cavity haloscope technique. The analysis also has\nsignificant practical implications, showing apparent tension with the\nsensitivity projections for a number of planned axion searches. We additionally\nprovide a basis for more accurate signal power calculations and calibration\nmodels, especially for receivers using multi-wavelength open configurations\nsuch as dish antennas and dielectric haloscopes.\n"}
+{"abstract": "  Given a simple connected compact Lie group $K$ and a maximal torus $T$ of\n$K$, the Weyl group $W=N_K(T)/T$ naturally acts on $T$. First, we use the\ncombinatorics of the (extended) affine Weyl group to provide an explicit\n$W$-equivariant triangulation of $T$. We describe the associated cellular\nhomology chain complex and give a formula for the cup product on its dual\ncochain complex, making it a $\\mathbb{Z}[W]$-dg-algebra. Next, remarking that\nthe combinatorics of this dg-algebra is still valid for Coxeter groups, we\nassociate a closed compact manifold $\\mathbf{T}(W)$ to any finite irreducible\nCoxeter group $W$, which coincides with a torus if $W$ is a Weyl group and is\nhyperbolic in other cases. Of course, we focus our study on\nnon-crystallographic groups, which are $I_2(m)$ with $m=5$ or $m\\ge 7$, $H_3$\nand $H_4$. The manifold $\\mathbf{T}(W)$ comes with a $W$-action and an\nequivariant triangulation, whose related $\\mathbb{Z}[W]$-dg-algebra is the one\nmentioned above. We finish by computing the homology of $\\mathbf{T}(W)$, as a\nrepresentation of $W$.\n"}
+{"abstract": "  We present an approach for compressing volumetric scalar fields using\nimplicit neural representations. Our approach represents a scalar field as a\nlearned function, wherein a neural network maps a point in the domain to an\noutput scalar value. By setting the number of weights of the neural network to\nbe smaller than the input size, we achieve compressed representations of scalar\nfields, thus framing compression as a type of function approximation. Combined\nwith carefully quantizing network weights, we show that this approach yields\nhighly compact representations that outperform state-of-the-art volume\ncompression approaches. The conceptual simplicity of our approach enables a\nnumber of benefits, such as support for time-varying scalar fields, optimizing\nto preserve spatial gradients, and random-access field evaluation. We study the\nimpact of network design choices on compression performance, highlighting how\nsimple network architectures are effective for a broad range of volumes.\n"}
+{"abstract": "  The proliferation of resourceful mobile devices that store rich,\nmultidimensional and privacy-sensitive user data motivate the design of\nfederated learning (FL), a machine-learning (ML) paradigm that enables mobile\ndevices to produce an ML model without sharing their data. However, the\nmajority of the existing FL frameworks rely on centralized entities. In this\nwork, we introduce IPLS, a fully decentralized federated learning framework\nthat is partially based on the interplanetary file system (IPFS). By using IPLS\nand connecting into the corresponding private IPFS network, any party can\ninitiate the training process of an ML model or join an ongoing training\nprocess that has already been started by another party. IPLS scales with the\nnumber of participants, is robust against intermittent connectivity and dynamic\nparticipant departures/arrivals, requires minimal resources, and guarantees\nthat the accuracy of the trained model quickly converges to that of a\ncentralized FL framework with an accuracy drop of less than one per thousand.\n"}
+{"abstract": "  In this paper, we introduce a new concept: the Lions tree. These objects\narise in Taylor expansions involving the Lions derivative and prove invaluable\nin classifying the dynamics of mean-field stochastic differential equations.\n  We discuss Lions trees, derive an Algebra spanned by Lions trees and explore\nhow couplings between Lions trees lead to a coupled Hopf algebra. Using this\nframework, we construct a new way to characterise rough signals driving\nmean-field equations: the probabilistic rough path. A comprehensive\ngeneralisation of the ideas first introduced in \\cite{2019arXiv180205882.2B},\nthese ideas promise powerful insights into how interactions with a collective\ndetermine the dynamics of an individual within this collective.\n"}
+{"abstract": "  We consider a Bayesian framework based on \"probability of decision\" for\ndose-finding trial designs. The proposed PoD-BIN design evaluates the posterior\npredictive probabilities of up-and-down decisions. In PoD-BIN, multiple grades\nof toxicity, categorized as the mild toxicity (MT) and dose-limiting toxicity\n(DLT), are modeled simultaneously, and the primary outcome of interests is\ntime-to-toxicity for both MT and DLT. This allows the possibility of enrolling\nnew patients when previously enrolled patients are still being followed for\ntoxicity, thus potentially shortening trial length. The Bayesian decision rules\nin PoD-BIN utilize the probability of decisions to balance the need to speed up\nthe trial and the risk of exposing patients to overly toxic doses. We\ndemonstrate via numerical examples the resulting balance of speed and safety of\nPoD-BIN and compare to existing designs.\n"}
+{"abstract": "  Recently, Blockchain technology adoption has expanded to many application\nareas due to the evolution of smart contracts. However, developing smart\ncontracts is non-trivial and challenging due to the lack of tools and expertise\nin this field. A promising solution to overcome this issue is to use\nModel-Driven Engineering (MDE), however, using models still involves a learning\ncurve and might not be suitable for non-technical users. To tackle this\nchallenge, chatbot or conversational interfaces can be used to assess the\nnon-technical users to specify a smart contract in gradual and interactive\nmanner.\n  In this paper, we propose iContractBot, a chatbot for modeling and developing\nsmart contracts. Moreover, we investigate how to integrate iContractBot with\niContractML, a domain-specific modeling language for developing smart\ncontracts, and instantiate intention models from the chatbot. The iContractBot\nframework provides a domain-specific language (DSL) based on the user intention\nand performs model-to-text transformation to generate the smart contract code.\nA smart contract use case is presented to demonstrate how iContractBot can be\nutilized for creating models and generating the deployment artifacts for smart\ncontracts based on a simple conversation.\n"}
+{"abstract": "  The physical mechanism on meridians (acupuncture lines) is studied and a\ntheoretical model is proposed. The meridians are explained as an alternating\nsystem responsible for the integration and the regulation of life in addition\nto the neuro-humoral regulation. We proposed that the meridian conduction is a\nkind of mechanical waves (soliton) of low frequency along the slits of muscles.\nThe anatomical-physiological and experimental evidences are reviewed. It is\ndemonstrated that the stabilization of the soliton is guaranteed by the\ncoupling between muscle vibration and cell activation. Therefore the\npropagation of mechanical wave dominates the excitation of cell groups along\nthe meridian. The meridian wave equations and its solution are deduced and how\nthese results can be used in studying human healthy is briefly discussed .\n"}
+{"abstract": "  We investigate the dynamics brought on by an impulse perturbation in two\ninfinite-range quantum Ising models coupled to each other and to a dissipative\nbath. We show that, if dissipation is faster the higher the excitation energy,\nthe pulse perturbation cools down the low-energy sector of the system, at the\nexpense of the high-energy one, eventually stabilising a transient\nsymmetry-broken state at temperatures higher than the equilibrium critical one.\nSuch non-thermal quasi-steady state may survive for quite a long time after the\npulse, if the latter is properly tailored.\n"}
+{"abstract": "  Consistent alpha generation, i.e., maintaining an edge over the market,\nunderpins the ability of asset traders to reliably generate profits. Technical\nindicators and trading strategies are commonly used tools to determine when to\nbuy/hold/sell assets, yet these are limited by the fact that they operate on\nknown values. Over the past decades, multiple studies have investigated the\npotential of artificial intelligence in stock trading in conventional markets,\nwith some success. In this paper, we present RCURRENCY, an RNN-based trading\nengine to predict data in the highly volatile digital asset market which is\nable to successfully manage an asset portfolio in a live environment. By\ncombining asset value prediction and conventional trading tools, RCURRENCY\ndetermines whether to buy, hold or sell digital currencies at a given point in\ntime. Experimental results show that, given the data of an interval $t$, a\nprediction with an error of less than 0.5\\% of the data at the subsequent\ninterval $t+1$ can be obtained. Evaluation of the system through backtesting\nshows that RCURRENCY can be used to successfully not only maintain a stable\nportfolio of digital assets in a simulated live environment using real\nhistorical trading data but even increase the portfolio value over time.\n"}
+{"abstract": "  The formation of Uranus' regular moons has been suggested to be linked to the\norigin of its enormous spin axial tilt (~98^o). A giant impact between\nproto-Uranus and a 2-3 M_Earth impactor could lead to a large tilt and to the\nformation of an impact generated disc, where prograde and circular satellites\nare accreted. The most intriguing features of the current regular Uranian\nsatellite system is that it possesses a positive trend in the mass-distance\ndistribution and likely also in the bulk density, implying that viscous\nspreading of the disc after the giant impact plays a crucial role in shaping\nthe architecture of the final system. In this paper, we investigate the\nformation of Uranus' satellites by combining results of SPH simulations for the\ngiant impact, a 1D semi-analytic disc model for viscous spreading of the\npost-impact disc, and N-body simulations for the assembly of satellites from a\ndisc of moonlets. Assuming the condensed rock (i.e., silicate) remains small\nand available to stick onto the relatively rapid growing condensed water-ice,\nwe find that the best case in reproducing the observed mass and bulk\ncomposition of Uranus' satellite system is a pure-rocky impactor with 3 M_Earth\ncolliding with the young Uranus with an impact parameter b = 0.75. Such an\noblique collision could also naturally explain Uranus' large tilt and possibly,\nits low internal heat flux. The giant impact scenario can naturally explain the\nkey features of Uranus and its regular moons. We therefore suggest that the\nUranian satellite system formed as a result of an impact rather than from a\ncircumplanetary disc.\n"}
+{"abstract": "  We investigate a set of techniques for RNN Transducers (RNN-Ts) that were\ninstrumental in lowering the word error rate on three different tasks\n(Switchboard 300 hours, conversational Spanish 780 hours and conversational\nItalian 900 hours). The techniques pertain to architectural changes, speaker\nadaptation, language model fusion, model combination and general training\nrecipe. First, we introduce a novel multiplicative integration of the encoder\nand prediction network vectors in the joint network (as opposed to additive).\nSecond, we discuss the applicability of i-vector speaker adaptation to RNN-Ts\nin conjunction with data perturbation. Third, we explore the effectiveness of\nthe recently proposed density ratio language model fusion for these tasks. Last\nbut not least, we describe the other components of our training recipe and\ntheir effect on recognition performance. We report a 5.9% and 12.5% word error\nrate on the Switchboard and CallHome test sets of the NIST Hub5 2000 evaluation\nand a 12.7% WER on the Mozilla CommonVoice Italian test set.\n"}
+{"abstract": "  The autoregressive (AR) models, such as attention-based encoder-decoder\nmodels and RNN-Transducer, have achieved great success in speech recognition.\nThey predict the output sequence conditioned on the previous tokens and\nacoustic encoded states, which is inefficient on GPUs. The non-autoregressive\n(NAR) models can get rid of the temporal dependency between the output tokens\nand predict the entire output tokens in at least one step. However, the NAR\nmodel still faces two major problems. On the one hand, there is still a great\ngap in performance between the NAR models and the advanced AR models. On the\nother hand, it's difficult for most of the NAR models to train and converge. To\naddress these two problems, we propose a new model named the two-step\nnon-autoregressive transformer(TSNAT), which improves the performance and\naccelerating the convergence of the NAR model by learning prior knowledge from\na parameters-sharing AR model. Furthermore, we introduce the two-stage method\ninto the inference process, which improves the model performance greatly. All\nthe experiments are conducted on a public Chinese mandarin dataset ASIEHLL-1.\nThe results show that the TSNAT can achieve a competitive performance with the\nAR model and outperform many complicated NAR models.\n"}
+{"abstract": "  Mathematical models are formal and simplified representations of the\nknowledge related to a phenomenon. In classical epidemic models, a neglected\naspect is the heterogeneity of disease transmission and progression linked to\nthe viral load of each infectious individual. Here, we attempt to investigate\nthe interplay between the evolution of individuals' viral load and the epidemic\ndynamics from a theoretical point of view. In the framework of multi-agent\nsystems, we propose a particle stochastic model describing the infection\ntransmission through interactions among agents and the individual physiological\ncourse of the disease. Agents have a double microscopic state: a discrete\nlabel, that denotes the epidemiological compartment to which they belong and\nswitches in consequence of a Markovian process, and a microscopic trait,\nrepresenting a normalized measure of their viral load, that changes in\nconsequence of binary interactions or interactions with a background.\nSpecifically, we consider Susceptible--Infected--Removed--like dynamics where\ninfectious individuals may be isolated from the general population and the\nisolation rate may depend on the viral load sensitivity and frequency of tests.\nWe derive kinetic evolution equations for the distribution functions of the\nviral load of the individuals in each compartment, whence, via suitable\nupscaling procedures, we obtain a macroscopic model for the densities and viral\nload momentum. We perform then a qualitative analysis of the ensuing\nmacroscopic model, and we present numerical tests in the case of both constant\nand viral load-dependent isolation control. Also, the matching between the\naggregate trends obtained from the macroscopic descriptions and the original\nparticle dynamics simulated by a Monte Carlo approach is investigated.\n"}
+{"abstract": "  This is a summation of research done in the author's second and third year of\nundergraduate mathematics at The University of Toronto. As the previous details\nwere largely scattered and disorganized; the author decided to rewrite the\ncumulative research. The goal of this paper is to construct a family of\nanalytic functions $\\alpha \\uparrow^n z : (1,e^{1/e}) \\times \\mathbb{C}_{\\Re(z)\n> 0} \\to \\mathbb{C}_{\\Re(z) > 0}$ using methods from fractional calculus. This\nfamily satisfies the hyper-operator chain, $\\alpha \\uparrow^{n-1} \\alpha\n\\uparrow^n z = \\alpha \\uparrow^n (z+1)$; with the initial condition $\\alpha\n\\uparrow^0 z = \\alpha \\cdot z$.\n"}
+{"abstract": "  Fiber-reinforced ceramic-matrix composites are advanced materials resistant\nto high temperatures, with application to aerospace engineering. Their analysis\ndepends on the detection of embedded fibers, with semi-supervised techniques\nusually employed to separate fibers within the fiber beds. Here we present an\nopen computational pipeline to detect fibers in ex-situ X-ray computed\ntomography fiber beds. To separate the fibers in these samples, we tested four\ndifferent architectures of fully convolutional neural networks. When comparing\nour neural network approach to a semi-supervised one, we obtained Dice and\nMatthews coefficients greater than $92.28 \\pm 9.65\\%$, reaching up to $98.42\n\\pm 0.03 \\%$, showing that the network results are close to the\nhuman-supervised ones in these fiber beds, in some cases separating fibers that\nhuman-curated algorithms could not find. The software we generated in this\nproject is open source, released under a permissive license, and can be freely\nadapted and re-used in other domains. All data and instructions on how to\ndownload and use it are also available.\n"}
+{"abstract": "  We have designed a two-stage, 10-step process to give organisations a method\nto analyse small local energy systems (SLES) projects based on their Cyber\nPhysical System components in order to develop future-proof energy systems.\n  SLES are often developed for a specific range of use cases and functions, and\nthese match the specific requirements and needs of the community, location or\nsite under consideration. During the design and commissioning, new and specific\ncyber physical architectures are developed. These are the control and data\nsystems that are needed to bridge the gap between the physical assets, the\ncaptured data and the control signals. Often, the cyber physical architecture\nand infrastructure is focused on functionality and the delivery of the specific\napplications.\n  But we find that technologies and approaches have arisen from other fields\nthat, if used within SLES, could support the flexibility, scalability and\nreusability vital to their success. As these can improve the operational data\nsystems then they can also be used to enhance predictive functions If used and\ndeployed effectively, these new approaches can offer longer term improvements\nin the use and effectiveness of SLES, while allowing the concepts and designs\nto be capitalised upon through wider roll-out and the offering of commercial\nservices or products.\n"}
+{"abstract": "  Mukai varieties are Fano varieties of Picard number one and coindex three. In\ngenus seven to ten they are linear sections of some special homogeneous\nvarieties. We describe the generic automorphism groups of these varieties. When\nthey are expected to be trivial for dimensional reasons, we show they are\nindeed trivial, up to three interesting and unexpected exceptions in genera 7,\n8, 9, and codimension 4, 3, 2 respectively. We conclude in particular that a\ngeneric prime Fano threefold of genus g has no automorphisms for 7 $\\le$ g\n$\\le$ 10. In the Appendix by Y. Prokhorov, the latter statement is extended to\ng = 12.\n"}
+{"abstract": "  Over a decade ago De Loera, Haws and K\\\"oppe conjectured that Ehrhart\npolynomials of matroid polytopes have only positive coefficients and that the\ncoefficients of the corresponding $h^*$-polynomials form a unimodal sequence.\nThe first of these intensively studied conjectures has recently been disproved\nby the first author who gave counterexamples in all ranks greater or equal to\nthree. In this article we complete the picture by showing that Ehrhart\npolynomials of matroids of lower rank have indeed only positive coefficients.\nMoreover, we show that they are coefficient-wise bounded by the Ehrhart\npolynomials of minimal and uniform matroids. We furthermore address the second\nconjecture by proving that $h^*$-polynomials of matroid polytopes of sparse\npaving matroids of rank two are real-rooted and therefore have log-concave and\nunimodal coefficients.\n"}
+{"abstract": "  We present GrammarTagger, an open-source grammar profiler which, given an\ninput text, identifies grammatical features useful for language education. The\nmodel architecture enables it to learn from a small amount of texts annotated\nwith spans and their labels, which 1) enables easier and more intuitive\nannotation, 2) supports overlapping spans, and 3) is less prone to error\npropagation, compared to complex hand-crafted rules defined on\nconstituency/dependency parses. We show that we can bootstrap a grammar\nprofiler model with $F_1 \\approx 0.6$ from only a couple hundred sentences both\nin English and Chinese, which can be further boosted via learning a\nmultilingual model. With GrammarTagger, we also build Octanove Learn, a search\nengine of language learning materials indexed by their reading difficulty and\ngrammatical features. The code and pretrained models are publicly available at\n\\url{https://github.com/octanove/grammartagger}.\n"}
+{"abstract": "  Recently, research on mental health conditions using public online data,\nincluding Reddit, has surged in NLP and health research but has not reported\nuser characteristics, which are important to judge generalisability of\nfindings. This paper shows how existing NLP methods can yield information on\nclinical, demographic, and identity characteristics of almost 20K Reddit users\nwho self-report a bipolar disorder diagnosis. This population consists of\nslightly more feminine- than masculine-gendered mainly young or middle-aged\nUS-based adults who often report additional mental health diagnoses, which is\ncompared with general Reddit statistics and epidemiological studies.\nAdditionally, this paper carefully evaluates all methods and discusses ethical\nissues.\n"}
+{"abstract": "  In the present article, we study the Hawking effect and the bounds on\ngreybody factor in a spacetime with radial deformation. This deformation is\nexpected to carry the imprint of a non-Einsteinian theory of gravity, but\nshares some of the important characteristics of general relativity (GR). In\nparticular, this radial deformation will restore the asymptotic behavior, and\nalso allows for the separation of the scalar field equation in terms of the\nangular and radial coordinates -- making it suitable to study the Hawking\neffect and greybody factors. However, the radial deformation would introduce a\nchange in the locations of the horizon, and therefore, the temperature of the\nHawking effect naturally alters. In fact, we observe that the deformation\nparameter has an enhancing effect on both temperature and bounds on the\ngreybody factor, which introduces a useful distinction with the Kerr spacetime.\nWe discuss these effects elaborately, and broadly study the thermal behavior of\na radially deformed spacetime.\n"}
+{"abstract": "  This paper demonstrates how spectrum up to 1 THz will support mobile\ncommunications beyond 5G in the coming decades. Results of rooftop surrogate\nsatellite/tower base station measurements at 140 GHz show the natural isolation\nbetween terrestrial networks and surrogate satellite systems, as well as\nbetween terrestrial mobile users and co-channel fixed backhaul links. These\nfirst-of-their-kind measurements and accompanying analysis show that by keeping\nthe energy radiated by terrestrial emitters on the horizon (e.g., elevation\nangles $\\leq$15\\textdegree), there will not likely be interference in the same\nor adjacent bands between passive satellite sensors and terrestrial terminals,\nor between mobile links and terrestrial backhaul links at frequencies above 100\nGHz.\n"}
+{"abstract": "  In this paper we discuss applications of the theory developed in [21] and\n[22] in studying certain Galois groups and splitting fields of rational\nfunctions in $\\mathbb Q\\left(X_0(N)\\right)$ using Hilbert's irreducibility\ntheorem and modular forms. We also consider computational aspect of the problem\nusing MAGMA and SAGE.\n"}
+{"abstract": "  We provide the first construction of stationary measures for the open KPZ\nequation on the spatial interval $[0,1]$ with general inhomogeneous Neumann\nboundary conditions at $0$ and $1$ depending on real parameters $u$ and $v$,\nrespectively. When $u+v\\geq 0$ we uniquely characterize the constructed\nstationary measures through their multipoint Laplace transform which we prove\nis given in terms of a stochastic process that we call the continuous dual Hahn\nprocess.\n"}
+{"abstract": "  Generative models are now capable of producing highly realistic images that\nlook nearly indistinguishable from the data on which they are trained. This\nraises the question: if we have good enough generative models, do we still need\ndatasets? We investigate this question in the setting of learning\ngeneral-purpose visual representations from a black-box generative model rather\nthan directly from data. Given an off-the-shelf image generator without any\naccess to its training data, we train representations from the samples output\nby this generator. We compare several representation learning methods that can\nbe applied to this setting, using the latent space of the generator to generate\nmultiple \"views\" of the same semantic content. We show that for contrastive\nmethods, this multiview data can naturally be used to identify positive pairs\n(nearby in latent space) and negative pairs (far apart in latent space). We\nfind that the resulting representations rival those learned directly from real\ndata, but that good performance requires care in the sampling strategy applied\nand the training method. Generative models can be viewed as a compressed and\norganized copy of a dataset, and we envision a future where more and more\n\"model zoos\" proliferate while datasets become increasingly unwieldy, missing,\nor private. This paper suggests several techniques for dealing with visual\nrepresentation learning in such a future. Code is released on our project page:\nhttps://ali-design.github.io/GenRep/\n"}
+{"abstract": "  Generative Adversarial Networks (GANs) currently achieve the state-of-the-art\nsound synthesis quality for pitched musical instruments using a 2-channel\nspectrogram representation consisting of log magnitude and instantaneous\nfrequency (the \"IFSpectrogram\"). Many other synthesis systems use\nrepresentations derived from the magnitude spectra, and then depend on a\nbackend component to invert the output magnitude spectrograms that generally\nresult in audible artefacts associated with the inversion process. However, for\nsignals that have closely-spaced frequency components such as non-pitched and\nother noisy sounds, training the GAN on the 2-channel IFSpectrogram\nrepresentation offers no advantage over the magnitude spectra based\nrepresentations. In this paper, we propose that training GANs on single-channel\nmagnitude spectra, and using the Phase Gradient Heap Integration (PGHI)\ninversion algorithm is a better comprehensive approach for audio synthesis\nmodeling of diverse signals that include pitched, non-pitched, and dynamically\ncomplex sounds. We show that this method produces higher-quality output for\nwideband and noisy sounds, such as pops and chirps, compared to using the\nIFSpectrogram. Furthermore, the sound quality for pitched sounds is comparable\nto using the IFSpectrogram, even while using a simpler representation with half\nthe memory requirements.\n"}
+{"abstract": "  While the anomalous Hall effect can manifest even without an external\nmagnetic field, time reversal symmetry is nonetheless still broken by the\ninternal magnetization of the sample. Recently, it has been shown that certain\nmaterials without an inversion center allow for a nonlinear type of anomalous\nHall effect whilst retaining time reversal symmetry. The effect may arise from\neither Berry curvature or through various asymmetric scattering mechanisms.\nHere, we report the observation of an extremely large $c$-axis nonlinear\nanomalous Hall effect in the non-centrosymmetric T$_d$ phase of MoTe$_2$ and\nWTe$_2$ without intrinsic magnetic order. We find that the effect is dominated\nby skew-scattering at higher temperatures combined with another scattering\nprocess active at low temperatures. Application of higher bias yields an\nextremely large Hall ratio of $E_\\perp /E_\\parallel$=2.47 and corresponding\nanomalous Hall conductivity of order 8x10$^7$S/m.\n"}
+{"abstract": "  Rationalizing which parts of a molecule drive the predictions of a molecular\ngraph convolutional neural network (GCNN) can be difficult. To help, we propose\ntwo simple regularization techniques to apply during the training of GCNNs:\nBatch Representation Orthonormalization (BRO) and Gini regularization. BRO,\ninspired by molecular orbital theory, encourages graph convolution operations\nto generate orthonormal node embeddings. Gini regularization is applied to the\nweights of the output layer and constrains the number of dimensions the model\ncan use to make predictions. We show that Gini and BRO regularization can\nimprove the accuracy of state-of-the-art GCNN attribution methods on artificial\nbenchmark datasets. In a real-world setting, we demonstrate that medicinal\nchemists significantly prefer explanations extracted from regularized models.\nWhile we only study these regularizers in the context of GCNNs, both can be\napplied to other types of neural networks\n"}
+{"abstract": "  Most online multi-object trackers perform object detection stand-alone in a\nneural net without any input from tracking. In this paper, we present a new\nonline joint detection and tracking model, TraDeS (TRAck to DEtect and\nSegment), exploiting tracking clues to assist detection end-to-end. TraDeS\ninfers object tracking offset by a cost volume, which is used to propagate\nprevious object features for improving current object detection and\nsegmentation. Effectiveness and superiority of TraDeS are shown on 4 datasets,\nincluding MOT (2D tracking), nuScenes (3D tracking), MOTS and Youtube-VIS\n(instance segmentation tracking). Project page:\nhttps://jialianwu.com/projects/TraDeS.html.\n"}
+{"abstract": "  For partial, nondeterministic, finite state machines, a new conformance\nrelation called strong reduction is presented. It complements other existing\nconformance relations in the sense that the new relation is well-suited for\nmodel-based testing of systems whose inputs are enabled or disabled, depending\non the actual system state. Examples of such systems are graphical user\ninterfaces and systems with interfaces that can be enabled or disabled in a\nmechanical way. We present a new test generation algorithm producing complete\ntest suites for strong reduction. The suites are executed according to the\ngrey-box testing paradigm: it is assumed that the state-dependent sets of\nenabled inputs can be identified during test execution, while the\nimplementation states remain hidden, as in black-box testing. It is shown that\nthis grey-box information is exploited by the generation algorithm in such a\nway that the resulting best-case test suite size is only linear in the state\nspace size of the reference model. Moreover, examples show that this may lead\nto significant reductions of test suite size in comparison to true black-box\ntesting for strong reduction.\n"}
+{"abstract": "  Residual coherence is a graphical tool for selecting potential second-order\ninteraction terms as functions of a single time series and its lags. This paper\nextends the notion of residual coherence to account for interaction terms of\nmultiple time series. Moreover, an alternative criterion, integrated spectrum,\nis proposed to facilitate this graphical selection.\n  A financial market application shows that new insights can be gained\nregarding implied market volatility.\n"}
+{"abstract": "  V838 Mon erupted in 2002 quickly becoming the prototype of a new type of\nstellar eruptions known today as (luminous) red novae. The red nova outbursts\nare thought to be caused by stellar mergers. The merger in V838 Mon took place\nin a triple or higher system involving two B-type stars. We mapped the merger\nsite with ALMA at a resolution of 25 mas in continuum dust emission and in\nrotational lines of simple molecules, including CO, SiO, SO, SO$_2$, AlOH, and\nH$_2$S. We use radiative transfer calculations to reproduce the remnant's\narchitecture at the epoch of the ALMA observations. For the first time, we\nidentify the position of the B-type companion relative to the outbursting\ncomponent of V838 Mon. The stellar remnant is surrounded by a clumpy wind with\ncharacteristics similar to winds of red supergiants. The merger product is also\nassociated with an elongated structure, $17.6 \\times 7.6$ mas, seen in\ncontinuum emission, and which we interpret as a disk seen at a moderate\ninclination. Maps of continuum and molecular emission show also a complex\nregion of interaction between the B-type star (its gravity, radiation, and\nwind) and the flow of matter ejected in 2002. The remnant's molecular mass is\nabout 0.1 M$_{\\odot}$ and the dust mass is 8.3$\\cdot$10$^{-3}$ M$_{\\odot}$. The\nmass of the atomic component remains unconstrained. The most interesting region\nfor understanding the merger of V838 Mon remains unresolved but appears\nelongated. To study it further in more detail will require even higher angular\nresolutions. ALMA maps show us an extreme form of interaction between the\nmerger ejecta with a distant (250 au) companion. This interaction is similar to\nthat known from the Antares AB system but at a much higher mass loss rate. The\nB-type star not only deflects the merger ejecta but also changes its chemical\ncomposition with an involvement of circumstellar shocks.\n"}
+{"abstract": "  Unmanned aerial vehicles (UAVs) are expected to be an integral part of\nwireless networks. In this paper, we aim to find collision-free paths for\nmultiple cellular-connected UAVs, while satisfying requirements of connectivity\nwith ground base stations (GBSs) in the presence of a dynamic jammer. We first\nformulate the problem as a sequential decision making problem in discrete\ndomain, with connectivity, collision avoidance, and kinematic constraints. We,\nthen, propose an offline temporal difference (TD) learning algorithm with\nonline signal-to-interference-plus-noise ratio (SINR) mapping to solve the\nproblem. More specifically, a value network is constructed and trained offline\nby TD method to encode the interactions among the UAVs and between the UAVs and\nthe environment; and an online SINR mapping deep neural network (DNN) is\ndesigned and trained by supervised learning, to encode the influence and\nchanges due to the jammer. Numerical results show that, without any information\non the jammer, the proposed algorithm can achieve performance levels close to\nthat of the ideal scenario with the perfect SINR-map. Real-time navigation for\nmulti-UAVs can be efficiently performed with high success rates, and collisions\nare avoided.\n"}
+{"abstract": "  A multi-objective optimization problem is $C^r$ weakly simplicial if there\nexists a $C^r$ surjection from a simplex onto the Pareto set/front such that\nthe image of each subsimplex is the Pareto set/front of a subproblem, where\n$0\\leq r\\leq \\infty$. This property is helpful to compute a parametric-surface\napproximation of the entire Pareto set and Pareto front. It is known that all\nunconstrained strongly convex $C^r$ problems are $C^{r-1}$ weakly simplicial\nfor $1\\leq r \\leq \\infty$. In this paper, we show that all unconstrained\nstrongly convex problems are $C^0$ weakly simplicial. The usefulness of this\ntheorem is demonstrated in a sparse modeling application: we reformulate the\nelastic net as a non-differentiable multi-objective strongly convex problem and\napproximate its Pareto set (the set of all trained models with different\nhyper-parameters) and Pareto front (the set of performance metrics of the\ntrained models) by using a B\\'ezier simplex fitting method, which accelerates\nhyper-parameter search.\n"}
+{"abstract": "  Three $q$-versions of Lommel polynomials are studied. Included are explicit\nrepresentations, recurrences, continued fractions, and connections to\nassociated Askey--Wilson polynomials. Combinatorial results are emphasized,\nincluding a general theorem when $R_I$ moments coincide with orthogonal\npolynomial moments. The combinatorial results use weighted Motzkin paths,\nSchr\\\"oder paths, and parallelogram polyominoes.\n"}
+{"abstract": "  Unsupervised time series clustering is a challenging problem with diverse\nindustrial applications such as anomaly detection, bio-wearables, etc. These\napplications typically involve small, low-power devices on the edge that\ncollect and process real-time sensory signals. State-of-the-art time-series\nclustering methods perform some form of loss minimization that is extremely\ncomputationally intensive from the perspective of edge devices. In this work,\nwe propose a neuromorphic approach to unsupervised time series clustering based\non Temporal Neural Networks that is capable of ultra low-power, continuous\nonline learning. We demonstrate its clustering performance on a subset of UCR\nTime Series Archive datasets. Our results show that the proposed approach\neither outperforms or performs similarly to most of the existing algorithms\nwhile being far more amenable for efficient hardware implementation. Our\nhardware assessment analysis shows that in 7 nm CMOS the proposed architecture,\non average, consumes only about 0.005 mm^2 die area and 22 uW power and can\nprocess each signal with about 5 ns latency.\n"}
+{"abstract": "  Various methods for solving the inverse reinforcement learning (IRL) problem\nhave been developed independently in machine learning and economics. In\nparticular, the method of Maximum Causal Entropy IRL is based on the\nperspective of entropy maximization, while related advances in the field of\neconomics instead assume the existence of unobserved action shocks to explain\nexpert behavior (Nested Fixed Point Algorithm, Conditional Choice Probability\nmethod, Nested Pseudo-Likelihood Algorithm). In this work, we make previously\nunknown connections between these related methods from both fields. We achieve\nthis by showing that they all belong to a class of optimization problems,\ncharacterized by a common form of the objective, the associated policy and the\nobjective gradient. We demonstrate key computational and algorithmic\ndifferences which arise between the methods due to an approximation of the\noptimal soft value function, and describe how this leads to more efficient\nalgorithms. Using insights which emerge from our study of this class of\noptimization problems, we identify various problem scenarios and investigate\neach method's suitability for these problems.\n"}
+{"abstract": "  The thermodynamic uncertainty relation originally proven for systems driven\ninto a non-equilibrium steady state (NESS) allows one to infer the total\nentropy production rate by observing any current in the system. This kind of\ninference scheme is especially useful when the system contains hidden degrees\nof freedom or hidden discrete states, which are not accessible to the\nexperimentalist. A recent generalization of the thermodynamic uncertainty\nrelation to arbitrary time-dependent driving allows one to infer entropy\nproduction not only by measuring current-observables but also by observing\nstate variables. A crucial question then is to understand which observable\nyields the best estimate for the total entropy production. In this paper we\naddress this question by analyzing the quality of the thermodynamic uncertainty\nrelation for various types of observables for the generic limiting cases of\nfast driving and slow driving. We show that in both cases observables can be\nfound that yield an estimate of order one for the total entropy production. We\nfurther show that the uncertainty relation can even be saturated in the limit\nof fast driving.\n"}
+{"abstract": "  The KOALA experiment measures the differential cross section of\n(anti)proton-proton elastic scattering over a wide range of four-momentum\ntransfer squared 0.0008 < |t| < 0.1 (GeV/c)$^2$ . The forward scattering\nparameters and the absolute luminosity can be deduced by analyzing the\ncharacteristic shape of the differential cross-section spectrum. The experiment\nis based on fixed target kinematics and uses an internal hydrogen cluster jet\ntarget. The wide range of |t| is achieved by measuring the total kinetic energy\nof the recoil protons near 90{\\deg} with a recoil detector, which consists of\nsilicon and germanium single-sided strip sensors. The energy resolution of the\nrecoil detector is better than 30 keV (FWHM). A forward detector consisting of\ntwo layers of plastic scintillators measures the elastically scattered beam\nparticles in the forward direction close to the beam axis. It helps suppress\nthe large background at small recoil angles and improves the identification of\nelastic scattering events in the low |t| range. The KOALA setup has been\ninstalled and commissioned at COSY in order to validate the detector by\nmeasuring the proton-proton elastic scattering. The results from this\ncommissioning are presented here.\n"}
+{"abstract": "  Nowadays High Energy Physics experiments can accumulate unprecedented\nstatistics of heavy flavour decays that allows to apply new methods, based on\nthe study of very rare phenomena, which used to be just desperate. In this\npaper we propose a new method to measure composition of $K^0$-$\\overline{K}^0$,\nproduced in a decay of heavy hadrons. This composition contains important\ninformation, in particular about weak and strong phases between amplitudes of\nthe produced $K^0$ and $\\overline{K}^0$. We consider possibility to measure\nthese parameters with time-dependent $K^0 \\to \\pi^+ \\pi^-$ analysis. Due to\n$CP$-violation in kaon mixing time-dependent decay rates of $K^0$ and\n$\\overline{K}^0$ differ, and the initial amplitudes revealed in the\n$CP$-violating decay pattern. In particular we consider cases of charmed\nhadrons decays: $D^+ \\to K^0 \\pi^+$, $D_s^+ \\to K^0 K^+$, $\\Lambda_c \\to p K^0$\nand with some assumptions $D^0 \\to K^0 \\pi^0$. This can be used to test the sum\nrule for charmed mesons and to obtain input for the full constraint of the two\nbody amplitudes of $D$-mesons.\n"}
+{"abstract": "  The levitation of a volatile droplet on a highly superheated surface is known\nas the Leidenfrost effect. Wetting state during transition from full wetting of\na surface by a droplet at room temperature to Leidenfrost bouncing, i.e.,\nzero-wetting at high superheating, is not fully understood. Here,\nvisualizations of droplet thermal and wetting footprint in the Leidenfrost\ntransition state are presented using two optical techniques: mid-infrared\nthermography and wetting sensitive total internal reflection imaging under\ncarefully selected experimental conditions, impact Weber number < 10 and\ndroplet diameter < capillary length, using an indium-tin-oxide coated sapphire\nheater. The experimental regime was designed to create relatively stable\ndroplet dynamics, where the effects of oscillatory and capillary instabilities\nwere minimized. The thermography for ethanol droplet in Leidenfrost transition\nstate (superheat range of 82K-97K) revealed thermal footprint with a central\nhot zone surrounded by a cooler periphery, indicative of a partial wetting\nstate during Leidenfrost transition. High-speed total internal reflection\nimaging also confirmed the partial wetting footprint such that there are\nwetting areas around a central non-wetting zone. Result presented here using\nethanol as a test fluid shed light on the geometry and dynamics of a volatile\ndroplet footprint in Leidenfrost transition state.\n"}
+{"abstract": "  We study the null set $N(\\mathcal{P})$ of the Fourier-Laplace transform of a\npolytope $\\mathcal{P} \\subset \\mathbb{R}^d$, and we find that $N(\\mathcal{P})$\ndoes not contain (almost all) circles in $\\mathbb{R}^d$. As a consequence, the\nnull set does not contain the algebraic varieties $\\{z \\in \\mathbb{C}^d \\mid\nz_1^2 + \\dots + z_d^2 = \\alpha^2\\}$ for each fixed $\\alpha \\in \\mathbb{C}$, and\nhence we get an explicit proof that the Pompeiu property is true for all\npolytopes. Our proof uses the Brion-Barvinok theorem, which gives a concrete\nformulation for the Fourier-Laplace transform of a polytope, and it also uses\nproperties of Bessel functions. The original proof that polytopes (as well as\nother bodies) possess the Pompeiu property was given by Brown, Schreiber, and\nTaylor (1973) for dimension 2. Williams (1976) later observed that the same\nproof also works for $d>2$ and, using eigenvalues of the Laplacian, gave\nanother proof valid for $d \\geq 2$ that polytopes have the Pompeiu property.\n"}
+{"abstract": "  In today's world data is being generated at a high rate due to which it has\nbecome inevitable to analyze and quickly get results from this data. Most of\nthe relational databases primarily support SQL querying with a limited support\nfor complex data analysis. Due to this reason, data scientists have no other\noption, but to use a different system for complex data analysis. Due to this,\ndata science frameworks are in huge demand. But to use such a framework, all\nthe data needs to be loaded into it. This requires significant data movement\nacross multiple systems, which can be expensive.\n  We believe that it has become the need of the hour to come up with a single\nsystem which can perform both data analysis tasks and SQL querying. This will\nsave the data scientists from the expensive data transfer operation across\nsystems. In our work, we present DaskDB, a system built over the Python's Dask\nframework, which is a scalable data science system having support for both data\nanalytics and in situ SQL query processing over heterogeneous data sources.\nDaskDB supports invoking any Python APIs as User-Defined Functions (UDF) over\nSQL queries. So, it can be easily integrated with most existing Python data\nscience applications, without modifying the existing code. Since joining two\nrelations is a very vital but expensive operation, so a novel distributed\nlearned index is also introduced to improve the join performance. Our\nexperimental evaluation demonstrates that DaskDB significantly outperforms\nexisting systems.\n"}
+{"abstract": "  We give sufficient conditions on the exponent $p: \\mathbb R^d\\rightarrow\n[1,\\infty)$ for the boundedness of the non-centered Gaussian maximal function\non variable Lebesgue spaces $L^{p(\\cdot)}(\\mathbb R^d, \\gamma_d)$, as well as\nof the new higher order Riesz transforms associated with the Ornstein-Uhlenbeck\nsemigroup, which are the natural extensions of the supplementary first order\nGaussian Riesz transforms defined by A. Nowak and K. Stempak in\n\\cite{nowakstempak}.\n"}
+{"abstract": "  We derive the explicit form of the martingale representation for\nsquare-integrable processes that are martingales with respect to the natural\nfiltration of the super-Brownian motion. This is done by using a weak extension\nof the Dupire derivative for functionals of superprocesses.\n"}
+{"abstract": "  The polymer model framework is a classical tool from statistical mechanics\nthat has recently been used to obtain approximation algorithms for spin systems\non classes of bounded-degree graphs; examples include the ferromagnetic Potts\nmodel on expanders and on the grid. One of the key ingredients in the analysis\nof polymer models is controlling the growth rate of the number of polymers,\nwhich has been typically achieved so far by invoking the bounded-degree\nassumption. Nevertheless, this assumption is often restrictive and obstructs\nthe applicability of the method to more general graphs. For example, sparse\nrandom graphs typically have bounded average degree and good expansion\nproperties, but they include vertices with unbounded degree, and therefore are\nexcluded from the current polymer-model framework.\n  We develop a less restrictive framework for polymer models that relaxes the\nstandard bounded-degree assumption, by reworking the relevant polymer models\nfrom the edge perspective. The edge perspective allows us to bound the growth\nrate of the number of polymers in terms of the total degree of polymers, which\nin turn can be related more easily to the expansion properties of the\nunderlying graph. To apply our methods, we consider random graphs with\nunbounded degrees from a fixed degree sequence (with minimum degree at least 3)\nand obtain approximation algorithms for the ferromagnetic Potts model, which is\na standard benchmark for polymer models. Our techniques also extend to more\ngeneral spin systems.\n"}
+{"abstract": "  In this note, we extend the renormalization horseshoe we have recently\nconstructed with N. Goncharuk for analytic diffeomorphisms of the circle to\ntheir small two-dimensional perturbations. As one consequence, Herman rings\nwith rotation numbers of bounded type survive on a codimension one set of\nparameters under small two-dimensional perturbations.\n"}
+{"abstract": "  Type Ia supernovae (SNe Ia) span a range of luminosities and timescales, from\nrapidly evolving subluminous to slowly evolving overluminous subtypes. Previous\ntheoretical work has, for the most part, been unable to match the entire\nbreadth of observed SNe Ia with one progenitor scenario. Here, for the first\ntime, we apply non-local thermodynamic equilibrium radiative transfer\ncalculations to a range of accurate explosion models of sub-Chandrasekhar-mass\nwhite dwarf detonations. The resulting photometry and spectra are in excellent\nagreement with the range of observed non-peculiar SNe Ia through 15 d after the\ntime of B-band maximum, yielding one of the first examples of a quantitative\nmatch to the entire Phillips (1993) relation. The intermediate-mass element\nvelocities inferred from theoretical spectra at maximum light for the more\nmassive white dwarf explosions are higher than those of bright observed SNe Ia,\nbut these and other discrepancies likely stem from the one-dimensional nature\nof our explosion models and will be improved upon by future non-local\nthermodynamic equilibrium radiation transport calculations of multi-dimensional\nsub-Chandrasekhar-mass white dwarf detonations.\n"}
+{"abstract": "  This article explores the territorial differences in the onset and spread of\nCOVID-19 and the excess mortality associated with the pandemic, across the\nEuropean NUTS3 regions and US counties. Both in Europe and in the US, the\npandemic arrived earlier and recorded higher Rt values in urban regions than in\nintermediate and rural ones. A similar gap is also found in the data on excess\nmortality. In the weeks during the first phase of the pandemic, urban regions\nin EU countries experienced excess mortality of up to 68pp more than rural\nones. We show that, during the initial days of the pandemic, territorial\ndifferences in Rt by the degree of urbanisation can be largely explained by the\nlevel of internal, inbound and outbound mobility. The differences in the spread\nof COVID-19 by rural-urban typology and the role of mobility are less clear\nduring the second wave. This could be linked to the fact that the infection is\nwidespread across territories, to changes in mobility patterns during the\nsummer period as well as to the different containment measures which reverse\nthe causality between mobility and Rt.\n"}
+{"abstract": "  Neural information retrieval systems typically use a cascading pipeline, in\nwhich a first-stage model retrieves a candidate set of documents and one or\nmore subsequent stages re-rank this set using contextualized language models\nsuch as BERT. In this paper, we propose DeepImpact, a new document\nterm-weighting scheme suitable for efficient retrieval using a standard\ninverted index. Compared to existing methods, DeepImpact improves impact-score\nmodeling and tackles the vocabulary-mismatch problem. In particular, DeepImpact\nleverages DocT5Query to enrich the document collection and, using a\ncontextualized language model, directly estimates the semantic importance of\ntokens in a document, producing a single-value representation for each token in\neach document. Our experiments show that DeepImpact significantly outperforms\nprior first-stage retrieval approaches by up to 17% on effectiveness metrics\nw.r.t. DocT5Query, and, when deployed in a re-ranking scenario, can reach the\nsame effectiveness of state-of-the-art approaches with up to 5.1x speedup in\nefficiency.\n"}
+{"abstract": "  Performance metrics are a core component of the evaluation of any machine\nlearning model and used to compare models and estimate their usefulness. Recent\nwork started to question the validity of many performance metrics for this\npurpose in the context of software defect prediction. Within this study, we\nexplore the relationship between performance metrics and the cost saving\npotential of defect prediction models. We study whether performance metrics are\nsuitable proxies to evaluate the cost saving capabilities and derive a theory\nfor the relationship between performance metrics and cost saving potential.\n"}
+{"abstract": "  Many real-life applications involve simultaneously forecasting multiple time\nseries that are hierarchically related via aggregation or disaggregation\noperations. For instance, commercial organizations often want to forecast\ninventories simultaneously at store, city, and state levels for resource\nplanning purposes. In such applications, it is important that the forecasts, in\naddition to being reasonably accurate, are also consistent w.r.t one another.\nAlthough forecasting such hierarchical time series has been pursued by\neconomists and data scientists, the current state-of-the-art models use strong\nassumptions, e.g., all forecasts being unbiased estimates, noise distribution\nbeing Gaussian. Besides, state-of-the-art models have not harnessed the power\nof modern nonlinear models, especially ones based on deep learning. In this\npaper, we propose using a flexible nonlinear model that optimizes quantile\nregression loss coupled with suitable regularization terms to maintain the\nconsistency of forecasts across hierarchies. The theoretical framework\nintroduced herein can be applied to any forecasting model with an underlying\ndifferentiable loss function. A proof of optimality of our proposed method is\nalso provided. Simulation studies over a range of datasets highlight the\nefficacy of our approach.\n"}
+{"abstract": "  The consequences of the attractive, short-range nucleon-nucleon (NN)\ninteraction on the wave functions of the Elliott SU(3) and the proxy-SU(3)\nsymmetry are discussed. The NN interaction favors the most symmetric spatial\nSU(3) irreducible representation, which corresponds to the maximal spatial\noverlap among the fermions. The percentage of the symmetric components out of\nthe total in an SU(3) wave function is introduced, through which it is found,\nthat no SU(3) irrep is more symmetric than the highest weight irrep for a\ncertain number of valence particles in a three dimensional, isotropic, harmonic\noscillator shell. The consideration of the highest weight irreps in nuclei and\nin alkali metal clusters, leads to the prediction of a prolate to oblate shape\ntransition beyond the mid-shell region.\n"}
+{"abstract": "  While deep learning-based 3D face generation has made a progress recently,\nthe problem of dynamic 3D (4D) facial expression synthesis is less\ninvestigated. In this paper, we propose a novel solution to the following\nquestion: given one input 3D neutral face, can we generate dynamic 3D (4D)\nfacial expressions from it? To tackle this problem, we first propose a mesh\nencoder-decoder architecture (Expr-ED) that exploits a set of 3D landmarks to\ngenerate an expressive 3D face from its neutral counterpart. Then, we extend it\nto 4D by modeling the temporal dynamics of facial expressions using a\nmanifold-valued GAN capable of generating a sequence of 3D landmarks from an\nexpression label (Motion3DGAN). The generated landmarks are fed into the mesh\nencoder-decoder, ultimately producing a sequence of 3D expressive faces. By\ndecoupling the two steps, we separately address the non-linearity induced by\nthe mesh deformation and motion dynamics. The experimental results on the CoMA\ndataset show that our mesh encoder-decoder guided by landmarks brings a\nsignificant improvement with respect to other landmark-based 3D fitting\napproaches, and that we can generate high quality dynamic facial expressions.\nThis framework further enables the 3D expression intensity to be continuously\nadapted from low to high intensity. Finally, we show our framework can be\napplied to other tasks, such as 2D-3D facial expression transfer.\n"}
+{"abstract": "  We propose a method to exploit high finesse optical resonators for light\nassisted coherent manipulation of atomic ensembles, overcoming the limit\nimposed by the finite response time of the cavity. The key element of our\nscheme is to rapidly switch the interaction between the atoms and the cavity\nfield with an auxiliary control process as, for example, the light shift\ninduced by an optical beam. The scheme is applicable to many different atomic\nspecies, both in trapped and free fall configurations, and can be adopted to\ncontrol the internal and/or external atomic degrees of freedom. Our method will\nopen new possibilities in cavity-aided atom interferometry and in the\npreparation of highly non-classical atomic states.\n"}
+{"abstract": "  We investigate the quantum transport through Kondo impurity assuming both a\nlarge number of orbital channels $\\mathcal K$$\\gg $$1$ for the itinerant\nelectrons and a semi-classical spin ${\\cal S}$ $\\gg $ $1$ for the impurity. The\nnon-Fermi liquid regime of the Kondo problem is achieved in the overscreened\nsector $\\mathcal K>2\\mathcal{S}$. We show that there exist two distinct\nsemiclassical regimes for the quantum transport through impurity: i) $\\mathcal\nK$ $\\gg$ $\\mathcal S$ $\\gg$ $1$, differential conductance vanishes and ii)\n$\\mathcal S$$/$$\\mathcal K{=}\\mathcal C$ with $ 0$$<$$\\mathcal C$$<$$1/2$,\ndifferential conductance reaches some non-vanishing fraction of its unitary\nvalue. Using conformal field theory approach we analyze behavior of the quantum\ntransport observables and residual entropy in both semiclassical regimes. We\nshow that the semiclassical limit ii) preserves the key features of resonance\nscattering and the most essential fingerprints of the non-Fermi liquid\nbehavior. We discuss possible realization of two semiclassical regimes in\nsemiconductor quantum transport experiments.\n"}
+{"abstract": "  The Multi-voltage Threshold (MVT) method, which samples the signal by certain\nreference voltages, has been well developed as being adopted in pre-clinical\nand clinical digital positron emission tomography(PET) system. To improve its\nenergy measurement performance, we propose a Peak Picking MVT(PP-MVT) Digitizer\nin this paper. Firstly, a sampled Peak Point(the highest point in pulse\nsignal), which carries the values of amplitude feature voltage and amplitude\narriving time, is added to traditional MVT with a simple peak sampling circuit.\nSecondly, an amplitude deviation statistical analysis, which compares the\nenergy deviation of various reconstruction models, is used to select adaptive\nreconstruction models for signal pulses with different amplitudes. After\nprocessing 30,000 randomly-chosen pulses sampled by the oscilloscope with a\n22Na point source, our method achieves an energy resolution of 17.50% within a\n450-650 KeV energy window, which is 2.44% better than the result of traditional\nMVT with same thresholds; and we get a count number at 15225 in the same energy\nwindow while the result of MVT is at 14678. When the PP-MVT involves less\nthresholds than traditional MVT, the advantages of better energy resolution and\nlarger count number can still be maintained, which shows the robustness and the\nflexibility of PP-MVT Digitizer. This improved method indicates that adding\nfeature peak information could improve the performance on signal sampling and\nreconstruction, which canbe proved by the better performance in energy\ndetermination in radiation measurement.\n"}
+{"abstract": "  We present a Python-based renderer built on NVIDIA's OptiX ray tracing engine\nand the OptiX AI denoiser, designed to generate high-quality synthetic images\nfor research in computer vision and deep learning. Our tool enables the\ndescription and manipulation of complex dynamic 3D scenes containing object\nmeshes, materials, textures, lighting, volumetric data (e.g., smoke), and\nbackgrounds. Metadata, such as 2D/3D bounding boxes, segmentation masks, depth\nmaps, normal maps, material properties, and optical flow vectors, can also be\ngenerated. In this work, we discuss design goals, architecture, and\nperformance. We demonstrate the use of data generated by path tracing for\ntraining an object detector and pose estimator, showing improved performance in\nsim-to-real transfer in situations that are difficult for traditional\nraster-based renderers. We offer this tool as an easy-to-use, performant,\nhigh-quality renderer for advancing research in synthetic data generation and\ndeep learning.\n"}
+{"abstract": "  In this paper, we consider visualization of displacement fields via optical\nflow methods in elastographic experiments consisting of a static compression of\na sample. We propose an elastographic optical flow method (EOFM) which takes\ninto account experimental constraints, such as appropriate boundary conditions,\nthe use of speckle information, as well as the inclusion of structural\ninformation derived from knowledge of the background material. We present\nnumerical results based on both simulated and experimental data from an\nelastography experiment in order to demonstrate the relevance of our proposed\napproach.\n"}
+{"abstract": "  Population growth in the last decades has resulted in the production of about\n2.01 billion tons of municipal waste per year. The current waste management\nsystems are not capable of providing adequate solutions for the disposal and\nuse of these wastes. Recycling and reuse have proven to be a solution to the\nproblem, but large-scale waste segregation is a tedious task and on a small\nscale it depends on public awareness. This research used convolutional neural\nnetworks and computer vision to develop a tool for the automation of solid\nwaste sorting. The Fotini10k dataset was constructed, which has more than\n10,000 images divided into the categories of 'plastic bottles', 'aluminum cans'\nand 'paper and cardboard'. ResNet50, MobileNetV1 and MobileNetV2 were retrained\nwith ImageNet weights on the Fotini10k dataset. As a result, top-1 accuracy of\n99% was obtained in the test dataset with all three networks. To explore the\npossible use of these networks in mobile applications, the three nets were\nquantized in float16 weights. By doing so, it was possible to obtain inference\ntimes twice as low for Raspberry Pi and three times as low for computer\nprocessing units. It was also possible to reduce the size of the networks by\nhalf. When quantizing the top-1 accuracy of 99% was maintained with all three\nnetworks. When quantizing MobileNetV2 to int-8, it obtained a top-1 accuracy of\n97%.\n"}
+{"abstract": "  We consider speeding up stochastic gradient descent (SGD) by parallelizing it\nacross multiple workers. We assume the same data set is shared among $N$\nworkers, who can take SGD steps and coordinate with a central server. While it\nis possible to obtain a linear reduction in the variance by averaging all the\nstochastic gradients at every step, this requires a lot of communication\nbetween the workers and the server, which can dramatically reduce the gains\nfrom parallelism. The Local SGD method, proposed and analyzed in the earlier\nliterature, suggests machines should make many local steps between such\ncommunications. While the initial analysis of Local SGD showed it needs $\\Omega\n( \\sqrt{T} )$ communications for $T$ local gradient steps in order for the\nerror to scale proportionately to $1/(NT)$, this has been successively improved\nin a string of papers, with the state of the art requiring $\\Omega \\left( N\n\\left( \\mbox{ poly} (\\log T) \\right) \\right)$ communications. In this paper, we\nsuggest a Local SGD scheme that communicates less overall by communicating less\nfrequently as the number of iterations grows. Our analysis shows that this can\nachieve an error that scales as $1/(NT)$ with a number of communications that\nis completely independent of $T$. In particular, we show that $\\Omega(N)$\ncommunications are sufficient. Empirical evidence suggests this bound is close\nto tight as we further show that $\\sqrt{N}$ or $N^{3/4}$ communications fail to\nachieve linear speed-up in simulations. Moreover, we show that under mild\nassumptions, the main of which is twice differentiability on any neighborhood\nof the optimal solution, one-shot averaging which only uses a single round of\ncommunication can also achieve the optimal convergence rate asymptotically.\n"}
+{"abstract": "  Intent classification and slot filling are two critical tasks for natural\nlanguage understanding. Traditionally the two tasks have been deemed to proceed\nindependently. However, more recently, joint models for intent classification\nand slot filling have achieved state-of-the-art performance, and have proved\nthat there exists a strong relationship between the two tasks. This article is\na compilation of past work in natural language understanding, especially joint\nintent classification and slot filling. We observe three milestones in this\nresearch so far: Intent detection to identify the speaker's intention, slot\nfilling to label each word token in the speech/text, and finally, joint intent\nclassification and slot filling tasks. In this article, we describe trends,\napproaches, issues, data sets, evaluation metrics in intent classification and\nslot filling. We also discuss representative performance values, describe\nshared tasks, and provide pointers to future work, as given in prior works. To\ninterpret the state-of-the-art trends, we provide multiple tables that describe\nand summarise past research along different dimensions, including the types of\nfeatures, base approaches, and dataset domain used.\n"}
+{"abstract": "  This is the user manual for CosmoLattice, a modern package for lattice\nsimulations of the dynamics of interacting scalar and gauge fields in an\nexpanding universe. CosmoLattice incorporates a series of features that makes\nit very versatile and powerful: $i)$ it is written in C++ fully exploiting the\nobject oriented programming paradigm, with a modular structure and a clear\nseparation between the physics and the technical details, $ii)$ it is MPI-based\nand uses a discrete Fourier transform parallelized in multiple spatial\ndimensions, which makes it specially appropriate for probing scenarios with\nwell-separated scales, running very high resolution simulations, or simply very\nlong ones, $iii)$ it introduces its own symbolic language, defining field\nvariables and operations over them, so that one can introduce differential\nequations and operators in a manner as close as possible to the continuum,\n$iv)$ it includes a library of numerical algorithms, ranging from $O(\\delta\nt^2)$ to $O(\\delta t^{10})$ methods, suitable for simulating global and gauge\ntheories in an expanding grid, including the case of `self-consistent'\nexpansion sourced by the fields themselves. Relevant observables are provided\nfor each algorithm (e.g.~energy densities, field spectra, lattice snapshots)\nand we note that remarkably all our algorithms for gauge theories always\nrespect the Gauss constraint to machine precision. In this manual we explain\nhow to obtain and run CosmoLattice in a computer (let it be your laptop,\ndesktop or a cluster). We introduce the general structure of the code and\ndescribe in detail the basic files that any user needs to handle. We explain\nhow to implement any model characterized by a scalar potential and a set of\nscalar fields, either singlets or interacting with $U(1)$ and/or $SU(2)$ gauge\nfields. CosmoLattice is publicly available at www.cosmolattice.net.\n"}
+{"abstract": "  A spin-1/2 Heisenberg model on honeycomb lattice is investigated by doing\ntriplon analysis and quantum Monte Carlo calculations. This model, inspired by\nCu$_2$(pymca)$_3$(ClO$_4$), has three different antiferromagnetic exchange\ninteractions ($J_A$, $J_B$, $J_C$) on three different sets of nearest-neighbour\nbonds which form a kagome superlattice. While the model is bipartite and\nunfrustrated, its quantum phase diagram is found to be dominated by a quantum\nparamagnetic phase that is best described as a spin-gapped hexagonal-singlet\nstate. The N\\'eel antiferromagnetic order survives only in a small region\naround $J_A=J_B=J_C$. The magnetization produced by external magnetic field is\nfound to exhibit plateaus at 1/3 and 2/3 of the saturation value, or at 1/3\nalone, or no plateaus. Notably, the plateaus exist only inside a bounded region\nwithin the hexagonal-singlet phase. This study provides a clear understanding\nof the spin-gapped behaviour and magnetization plateaus observed in\nCu$_2$(pymca)$_3$(ClO$_4$), and also predicts the possible disappearance of 2/3\nplateau under pressure.\n"}
+{"abstract": "  Atomic carbon (CI) has been proposed to be a global tracer of the molecular\ngas as a substitute for CO, however, its utility remains unproven. To evaluate\nthe suitability of CI as the tracer, we performed [CI]$(^3P_1-^3P_0)$\n(hereinafter [CI](1-0)) mapping observations of the northern part of the nearby\nspiral galaxy M83 with the ASTE telescope and compared the distributions of\n[CI](1-0) with CO lines (CO(1-0), CO(3-2), and $^{13}$CO(1-0)), HI, and\ninfrared (IR) emission (70, 160, and 250$ \\mu$m). The [CI](1-0) distribution in\nthe central region is similar to that of the CO lines, whereas [CI](1-0) in the\narm region is distributed outside the CO. We examined the dust temperature,\n$T_{\\rm dust}$, and dust mass surface density, $\\Sigma_{\\rm dust}$, by fitting\nthe IR continuum-spectrum distribution with a single-temperature modified\nblackbody. The distribution of $\\Sigma_{\\rm dust}$ shows a much better\nconsistency with the integrated intensity of CO(1-0) than with that of\n[CI](1-0), indicating that CO(1-0) is a good tracer of the cold molecular gas.\nThe spatial distribution of the [CI] excitation temperature, $T_{\\rm ex}$, was\nexamined using the intensity ratio of the two [CI] transitions. An appropriate\n$T_{\\rm ex}$ at the central, bar, arm, and inter-arm regions yields a constant\n[C]/[H$_2$] abundance ratio of $\\sim7 \\times 10^{-5}$ within a range of 0.1 dex\nin all regions. We successfully detected weak [CI](1-0) emission, even in the\ninter-arm region, in addition to the central, arm, and bar regions, using\nspectral stacking analysis. The stacked intensity of [CI](1-0) is found to be\nstrongly correlated with $T_{\\rm dust}$. Our results indicate that the atomic\ncarbon is a photodissociation product of CO, and consequently, compared to\nCO(1-0), [CI](1-0) is less reliable in tracing the bulk of \"cold\" molecular gas\nin the galactic disk.\n"}
+{"abstract": "  Using Galois theory of functional equations, we give a new proof of the main\nresult of the paper \"Transcendental transcendency of certain functions of\nPoincar\\'e\" by J.F. Ritt, on the differential transcendence of the solutions of\nthe functional equation R(y(t))=y(qt), where R is a rational function with\ncomplex coefficients which verifies R(0)=0, R'(0)=q, where q is a complex\nnumber with |q|>1. We also give a partial result in the case of an algebraic\nfunction R.\n"}
+{"abstract": "  We construct an injection from the set of permutations of length $n$ that\ncontain exactly one copy of the decreasing pattern of length $k$ to the set of\npermutations of length $n+2$ that avoid that pattern. We then prove that the\ngenerating function counting the former is not rational, and in the case when\n$k$ is even and $k\\geq 4$, it is not even algebraic. We extend our injection\nand our nonrationality result to a larger class of patterns.\n"}
+{"abstract": "  This paper proposes a non-autoregressive extension of our previously proposed\nsequence-to-sequence (S2S) model-based voice conversion (VC) methods. S2S\nmodel-based VC methods have attracted particular attention in recent years for\ntheir flexibility in converting not only the voice identity but also the pitch\ncontour and local duration of input speech, thanks to the ability of the\nencoder-decoder architecture with the attention mechanism. However, one of the\nobstacles to making these methods work in real-time is the autoregressive (AR)\nstructure. To overcome this obstacle, we develop a method to obtain a model\nthat is free from an AR structure and behaves similarly to the original S2S\nmodels, based on a teacher-student learning framework. In our method, called\n\"FastS2S-VC\", the student model consists of encoder, decoder, and attention\npredictor. The attention predictor learns to predict attention distributions\nsolely from source speech along with a target class index with the guidance of\nthose predicted by the teacher model from both source and target speech. Thanks\nto this structure, the model is freed from an AR structure and allows for\nparallelization. Furthermore, we show that FastS2S-VC is suitable for real-time\nimplementation based on a sliding-window approach, and describe how to make it\nrun in real-time. Through speaker-identity and emotional-expression conversion\nexperiments, we confirmed that FastS2S-VC was able to speed up the conversion\nprocess by 70 to 100 times compared to the original AR-type S2S-VC methods,\nwithout significantly degrading the audio quality and similarity to target\nspeech. We also confirmed that the real-time version of FastS2S-VC can be run\nwith a latency of 32 ms when run on a GPU.\n"}
+{"abstract": "  A nested coordinate system is a reassigning of independent variables to take\nadvantage of geometric or symmetry properties of a particular application.\nPolar, cylindrical and spherical coordinate systems are primary examples of\nsuch a regrouping that have proved their importance in the separation of\nvariables method for solving partial differential equations. Geometric algebra\noffers powerful complimentary algebraic tools that are unavailable in other\ntreatments.\n"}
+{"abstract": "  The study of the mapping class group of the plane minus a Cantor set uses a\ngraph of loops, which is an analogous of the curve graph in the study of\nmapping class groups of compact surfaces. The Gromov boundary of this loop\ngraph can be described in terms of \"cliques of high-filling rays\": high-filling\nrays are simple geodesics of the surface which are complicated enough to be\ninfinitely far away from any loop in the graph. Moreover, these rays are\narranged in cliques: any two high-filling rays which are both disjoint from a\nthird one are necessarily mutually disjoint. Every such clique is a point of\nthe Gromov-boundary of the loop graph. Some examples of cliques with any finite\nnumber of high-filling rays are already known.\n  In this paper, we construct an infinite clique of high-filling rays.\n"}
+{"abstract": "  We consider the asymptotic expansion of the functional series\n\\[S_{\\mu,\\gamma}(a;\\lambda)=\\sum_{n=1}^\\infty \\frac{n^\\gamma e^{-\\lambda\nn^2/a^2}}{(n^2+a^2)^\\mu}\\] for real values of the parameters $\\gamma$,\n$\\lambda>0$ and $\\mu\\geq0$ as $|a|\\to \\infty$ in the sector $|\\arg\\,a|<\\pi/4$.\nFor general values of $\\gamma$ the expansion is of algebraic type with terms\ninvolving the Riemann zeta function and a terminating confluent hypergeometric\nfunction. Of principal interest in this study is the case corresponding to even\ninteger values of $\\gamma$, where the algebraic-type expansion consists of a\nfinite number of terms together with a contribution comprising an infinite\nsequence of increasingly subdominant exponentially small expansions. This\nsituation is analogous to the well-known Poisson-Jacobi formula corresponding\nto the case $\\mu=\\gamma=0$. Numerical examples are provided to illustrate the\naccuracy of these expansions.\n"}
+{"abstract": "  We present a novel method for predicting accurate depths from monocular\nimages with high efficiency. This optimal efficiency is achieved by exploiting\nwavelet decomposition, which is integrated in a fully differentiable\nencoder-decoder architecture. We demonstrate that we can reconstruct\nhigh-fidelity depth maps by predicting sparse wavelet coefficients. In contrast\nwith previous works, we show that wavelet coefficients can be learned without\ndirect supervision on coefficients. Instead we supervise only the final depth\nimage that is reconstructed through the inverse wavelet transform. We\nadditionally show that wavelet coefficients can be learned in fully\nself-supervised scenarios, without access to ground-truth depth. Finally, we\napply our method to different state-of-the-art monocular depth estimation\nmodels, in each case giving similar or better results compared to the original\nmodel, while requiring less than half the multiply-adds in the decoder network.\nCode at https://github.com/nianticlabs/wavelet-monodepth\n"}
+{"abstract": "  This work presents a novel target-free extrinsic calibration algorithm for a\n3D Lidar and an IMU pair using an Extended Kalman Filter (EKF) which exploits\nthe \\textit{motion based calibration constraint} for state update. The steps\ninclude, data collection by motion excitation of the Lidar Inertial Sensor\nsuite along all degrees of freedom, determination of the inter sensor rotation\nby using rotational component of the aforementioned \\textit{motion based\ncalibration constraint} in a least squares optimization framework, and finally,\nthe determination of inter sensor translation using the \\textit{motion based\ncalibration constraint} for state update in an Extended Kalman Filter (EKF)\nframework. We experimentally validate our method using data collected in our\nlab and open-source (https://github.com/unmannedlab/imu_lidar_calibration) our\ncontribution for the robotics research community.\n"}
+{"abstract": "  The field of quantum simulations in ultra-cold atomic gases has been\nremarkably successful. In principle it allows for an exact treatment of a\nvariety of highly relevant lattice models and their emergent phases of matter.\nBut so far there is a lack in the theoretical literature concerning the\nsystematic study of the effects of the trap potential as well as the finite\nsize of the systems, as numerical studies of such non periodic, correlated\nfermionic lattices models are numerically demanding beyond one dimension. We\nuse the recently introduced real-space truncated unity functional\nrenormalization group to study these boundary and trap effects with a focus on\ntheir impact on the superconducting phase of the $2$D Hubbard model. We find\nthat in the experiments not only lower temperatures need to be reached compared\nto current capabilities, but also system size and trap potential shape play a\ncrucial role to simulate emergent phases of matter.\n"}
+{"abstract": "  The vast majority of semantic segmentation approaches rely on pixel-level\nannotations that are tedious and time consuming to obtain and suffer from\nsignificant inter and intra-expert variability. To address these issues, recent\napproaches have leveraged categorical annotations at the slide-level, that in\ngeneral suffer from robustness and generalization. In this paper, we propose a\nnovel weakly supervised multi-instance learning approach that deciphers\nquantitative slide-level annotations which are fast to obtain and regularly\npresent in clinical routine. The extreme potentials of the proposed approach\nare demonstrated for tumor segmentation of solid cancer subtypes. The proposed\napproach achieves superior performance in out-of-distribution, out-of-location,\nand out-of-domain testing sets.\n"}
+{"abstract": "  The purpose of this report is to look at the measures of importance of\ncomponents in systems in terms of reliability. In the first work of Birnbaum\n(1968) on this subject, many interesting studies were created and important\nindicators were constructed that allowed to organize the components of complex\nsystems. They are helpful in analyzing the reliability of the designed systems,\nestablishing the principles of operation and maintenance. The significance\nmeasures presented here are collected and discussed regarding the motivation\nbehind their creation. They concern an approach in which both elements and\nsystems are binary, and the possibility of generalization to multistate systems\nis only mentioned. Among the discussed is one new proposal using the methods of\ngame theory, combining the sensitivity to the structure of the system and the\noperational effects on the system performance. The presented severity measures\nuse a knowledge of the system structure as well as reliability and wear and\ntear, and whether the components can be repaired and maintained.\n"}
+{"abstract": "  Driven by increased complexity of dynamical systems, the solution of system\nof differential equations through numerical simulation in optimization problems\nhas become computationally expensive. This paper provides a smart data driven\nmechanism to construct low dimensional surrogate models. These surrogate models\nreduce the computational time for solution of the complex optimization problems\nby using training instances derived from the evaluations of the true objective\nfunctions. The surrogate models are constructed using combination of proper\northogonal decomposition and radial basis functions and provides system\nresponses by simple matrix multiplication. Using relative maximum absolute\nerror as the measure of accuracy of approximation, it is shown surrogate models\nwith latin hypercube sampling and spline radial basis functions dominate\nvariable order methods in computational time of optimization, while preserving\nthe accuracy. These surrogate models also show robustness in presence of model\nnon-linearities. Therefore, these computational efficient predictive surrogate\nmodels are applicable in various fields, specifically to solve inverse problems\nand optimal control problems, some examples of which are demonstrated in this\npaper.\n"}
+{"abstract": "  First-order nonadiabatic coupling matrix elements (fo-NACMEs) are the basic\nquantities in theoretical descriptions of electronically nonadiabatic processes\nthat are ubiquitous in molecular physics and chemistry. Given the large size of\nsystems of chemical interests, time-dependent density functional theory (TDDFT)\nis usually the first choice. However, the lack of wave functions in TDDFT\nrenders the formulation of NAC-TDDFT for fo-NACMEs conceptually difficult. The\npresent account aims to analyze the available variants of NAC-TDDFT in a\ncritical but concise manner and meanwhile point out the proper ways for\nimplementation. It can be concluded, from both theoretical and numerical points\nof view, that the equation of motion-based variant of NAC-TDDFT is the right\nchoice. Possible future developments of this variant are also highlighted.\n"}
+{"abstract": "  Immense field enhancement and nanoscale confinement of light are possible\nwithin nanoparticle-on-mirror (NPoM) plasmonic resonators, which enable novel\noptically-activated physical and chemical phenomena, and render these\nnanocavities greatly sensitive to minute structural changes, down to the atomic\nscale. Although a few of these structural parameters, primarily linked to the\nnanoparticle and the mirror morphology, have been identified, the impact of\nmolecular assembly and organization of the spacer layer between them has often\nbeen left uncharacterized. Here, we experimentally investigate how the complex\nand reconfigurable nature of a thiol-based self-assembled monolayer (SAM)\nadsorbed on the mirror surface impacts the optical properties of the NPoMs. We\nfabricate NPoMs with distinct molecular organizations by controlling the\nincubation time of the mirror in the thiol solution. Afterwards, we investigate\nthe structural changes that occur under laser irradiation by tracking the\nbonding dipole plasmon mode, while also monitoring Stokes and anti-Stokes Raman\nscattering from the molecules as a probe of their integrity. First, we find an\neffective decrease in the SAM height as the laser power increases, compatible\nwith an irreversible change of molecule orientation caused by heating. Second,\nwe observe that the nanocavities prepared with a densely packed and more\nordered monolayer of molecules are more prone to changes in their resonance\ncompared to samples with sparser and more disordered SAMs. Our measurements\nindicate that molecular orientation and packing on the mirror surface play a\nkey role in determining the stability of NPoM structures and hence highlight\nthe under-recognized significance of SAM characterization in the development of\nNPoM-based applications.\n"}
+{"abstract": "  Recent literature has demonstrated that the use of per-channel energy\nnormalization (PCEN), has significant performance improvements over traditional\nlog-scaled mel-frequency spectrograms in acoustic sound event detection (SED)\nin a multi-class setting with overlapping events. However, the configuration of\nPCEN's parameters is sensitive to the recording environment, the\ncharacteristics of the class of events of interest, and the presence of\nmultiple overlapping events. This leads to improvements on a class-by-class\nbasis, but poor cross-class performance. In this article, we experiment using\nPCEN spectrograms as an alternative method for SED in urban audio using the\nUrbanSED dataset, demonstrating per-class improvements based on parameter\nconfiguration. Furthermore, we address cross-class performance with PCEN using\na novel method, Multi-Rate PCEN (MRPCEN). We demonstrate cross-class SED\nperformance with MRPCEN, demonstrating improvements to cross-class performance\ncompared to traditional single-rate PCEN.\n"}
+{"abstract": "  We study the Bose polaron problem in a nonequilibrium setting, by considering\nan impurity embedded in a quantum fluid of light realized by exciton-polaritons\nin a microcavity, subject to a coherent drive and dissipation on account of\npump and cavity losses. We obtain the polaron effective mass, the drag force\nacting on the impurity, and determine polaron trajectories at a semiclassical\nlevel. We find different dynamical regimes, originating from the unique\nfeatures of the excitation spectrum of driven-dissipative polariton fluids, in\nparticular a non-trivial regime of acceleration against the flow. Our work\npromotes the study of impurity dynamics as an alternative testbed for probing\nsuperfluidity in quantum fluids of light.\n"}
+{"abstract": "  In this paper, we focus on the performance of vehicle-to-vehicle (V2V)\ncommunication adopting the Dedicated Short Range Communication (DSRC)\napplication in periodic broadcast mode. An analytical model is studied and a\nfixed point method is used to analyze the packet delivery ratio (PDR) and mean\ndelay based on the IEEE 802.11p standard in a fully connected network under the\nassumption of perfect PHY performance. With the characteristics of V2V\ncommunication, we develop the Semi-persistent Contention Density Control\n(SpCDC) scheme to improve the DSRC performance. We use Monte Carlo simulation\nto verify the results obtained by the analytical model. The simulation results\nshow that the packet delivery ratio in SpCDC scheme increases more than 10%\ncompared with IEEE 802.11p in heavy vehicle load scenarios. Meanwhile, the mean\nreception delay decreases more than 50%, which provides more reliable road\nsafety.\n"}
+{"abstract": "  Spectral factorization is a prominent tool with several important\napplications in various areas of applied science. Wiener and Masani proved the\nexistence of matrix spectral factorization. Their theorem has been extended to\nthe multivariable case by Helson and Lowdenslager. Solving the problem\nnumerically is challenging in both situations, and also important due to its\npractical applications. Therefore, several authors have developed algorithms\nfor factorization. The Janashia-Lagvilava algorithm is a relatively new method\nfor matrix spectral factorization which has proved to be useful in several\napplications. In this paper, we extend this method to the multivariable case.\nConsequently, a new numerical algorithm for multivariable matrix spectral\nfactorization is constructed.\n"}
+{"abstract": "  We investigate the torque field and skyrmion movement at an interface between\na ferromagnet hosting a skyrmion and a material with strong spin-orbit\ninteraction. We analyze both semiconductor materials and topological insulators\nusing a Hamiltonian model that includes a linear term. The spin torque inducing\ncurrent is considered to flow in the single band limit therefore a quantum\nmodel of current is used. Skyrmion movement due spin transfer torque proves to\nbe more difficult in presence of spin orbit interaction in the case where only\ninterface in-plane currents are present. However, edge effects in narrow\nnanowires can be used to drive the skyrmion movement and to exert a limited\ncontrol on its movement direction. We also show the differences and\nsimilarities between torque fields due to electric current in the many and in\nthe single band limits.\n"}
+{"abstract": "  For any second-order scalar PDE $\\mathcal{E}$ in one unknown function, that\nwe interpret as a hypersurface of a second-order jet space $J^2$, we construct,\nby means of the characteristics of $\\mathcal{E}$, a sub-bundle of the contact\ndistribution of the underlying contact manifold $J^1$, consisting of conic\nvarieties. We call it the contact cone structure associated with $\\mathcal{E}$.\nWe then focus on symplectic Monge-Amp\\`ere equations in 3 independent\nvariables, that are naturally parametrized by a 13-dimensional real projective\nspace. If we pass to the field of complex numbers $\\mathbb{C}$, this projective\nspace turns out to be the projectivization of the 14-dimensional irreducible\nrepresentation of the simple Lie group $\\mathsf{Sp}(6,\\mathbb{C})$: the\nassociated moment map allows to define a rational map $\\varpi$ from the space\nof symplectic 3D Monge-Amp\\`ere equations to the projectivization of the space\nof quadratic forms on a $6$-dimensional symplectic vector space. We study in\ndetails the relationship between the zero locus of the image of $\\varpi$,\nherewith called the cocharacteristic variety, and the contact cone structure of\na 3D Monge-Amp\\`ere equation $\\mathcal{E}$: under the hypothesis of\nnon-degenerate symbol, we prove that these two constructions coincide. A key\ntool in achieving such a result will be a complete list of mutually\nnon-equivalent quadratic forms on a $6$-dimensional symplectic space, which has\nan interest on its own.\n"}
+{"abstract": "  What do word vector representations reveal about the emotions associated with\nwords? In this study, we consider the task of estimating word-level emotion\nintensity scores for specific emotions, exploring unsupervised, supervised, and\nfinally a self-supervised method of extracting emotional associations from word\nvector representations. Overall, we find that word vectors carry substantial\npotential for inducing fine-grained emotion intensity scores, showing a far\nhigher correlation with human ground truth ratings than achieved by\nstate-of-the-art emotion lexicons.\n"}
+{"abstract": "  Landau suggested that the low-temperature properties of metals can be\nunderstood in terms of long-lived quasiparticles with all complex interactions\nincluded in Fermi-liquid parameters, such as the effective mass $m^{\\star}$.\nDespite its wide applicability, electronic transport in bad or strange metals\nand unconventional superconductors is controversially discussed towards a\npossible collapse of the quasiparticle concept. Here we explore the\nelectrodynamic response of correlated metals at half filling for varying\ncorrelation strength upon approaching a Mott insulator. We reveal persistent\nFermi-liquid behavior with pronounced quadratic dependences of the optical\nscattering rate on temperature and frequency, along with a puzzling elastic\ncontribution to relaxation. The strong increase of the resistivity beyond the\nIoffe-Regel-Mott limit is accompanied by a `displaced Drude peak' in the\noptical conductivity. Our results, supported by a theoretical model for the\noptical response, demonstrate the emergence of a bad metal from resilient\nquasiparticles that are subject to dynamical localization and dissolve near the\nMott transition.\n"}
+{"abstract": "  In his 1935 Gedankenexperiment, Erwin Schr\\\"{o}dinger imagined a poisonous\nsubstance which has a 50% probability of being released, based on the decay of\na radioactive atom. As such, the life of the cat and the state of the poison\nbecome entangled, and the fate of the cat is determined upon opening the box.\nWe present an experimental technique that keeps the cat alive on any account.\nThis method relies on the time-resolved Hong-Ou-Mandel effect: two long,\nidentical photons impinging on a beam splitter always bunch in either of the\noutputs. Interpreting the first photon detection as the state of the poison,\nthe second photon is identified as the state of the cat. Even after the\ncollapse of the first photon's state, we show their fates are intertwined\nthrough quantum interference. We demonstrate this by a sudden phase change\nbetween the inputs, administered conditionally on the outcome of the first\ndetection, which steers the second photon to a pre-defined output and ensures\nthat the cat is always observed alive.\n"}
+{"abstract": "  The exsitance of three-dimensional Hall effect (3DQHE) due to spontaneous\nFermi surface instabilities in strong magnetic field was proposed decades ago,\nand has stimulated recent progress in experiments. The reports in recent\nexperiments show that the Hall plateaus and vanishing transverse\nmagneto-resistivities (TMRs) (which are two main signatures of 3DQHE) are not\neasy to be observed in natural materials. And two main different explanations\nof the slow varying slope like Hall plateaus and non-vanishing\n  TMRs (which can be called as quasi-quantized Hall effect (QQHE)) have been\nproposed. By studying the magneto-transport with a simple effective periodic 3D\nsystem, we show how 3DQHE can be achieved in certain parameter regimes at\nfirst. We find two new mechanisms that may give rise to QQHE. One mechanism is\nthe \"low\" Fermi energy effect, and the other is the \"strong\" impurity effect.\nOur studies also proved that the artificial superlattice is an ideal platform\nfor realizing 3DQHE with high layer barrier periodic potential.\n"}
+{"abstract": "  Presence often is considered the most important quale describing the\nsubjective feeling of being in a computer-generated (virtual) or\ncomputer-mediated environment. The identification and separation of two\northogonal presence components, i.e., the place illusion and the plausibility\nillusion, has been an accepted theoretical model describing Virtual Reality\n(VR) experiences for some time. In this model, immersion is a proposed\ncontributing factor to the place illusion. Lately, copresence and social\npresence illusions have extended this model, and coherence was proposed as a\ncontributing factor to the plausibility illusion. Such factors strive to\nidentify (objectively) measurable characteristics of an experience, e.g.,\nsystems properties that allow controlled manipulations of VR experiences. This\nperspective article challenges this presence-oriented VR theory. First, we\nargue that a place illusion cannot be the major construct to describe the much\nwider scope of Virtual, Augmented, and Mixed Reality (VR, AR, MR: or XR for\nshort). Second, we argue that there is no plausibility illusion but merely\nplausibility, and we derive the place illusion as a consequence of a plausible\ngeneration of spatial cues, and similarly for all of the current model's\nso-defined illusions. Finally, we propose coherence and plausibility to become\nthe central essential conditions in a novel theoretical model describing XR\nexperiences and effects.\n"}
+{"abstract": "  Many teleoperation tasks require three or more tools working together, which\nneed the cooperation of multiple operators. The effectiveness of such schemes\nmay be limited by communication. Trimanipulation by a single operator using an\nartificial third arm controlled together with their natural arms is a promising\nsolution to this issue. Foot-controlled interfaces have previously shown the\ncapability to be used for the continuous control of robot arms. However, the\nuse of such interfaces for controlling a supernumerary robotic limb (SRLs) in\ncoordination with the natural limbs, is not well understood. In this paper, a\nteleoperation task imitating physically coupled hands in a virtual reality\nscene was conducted with 14 subjects to evaluate human performance during\ntri-manipulation. The participants were required to move three limbs together\nin a coordinated way mimicking three arms holding a shared physical object. It\nwas found that after a short practice session, the three-hand tri-manipulation\nusing a single subject's hands and foot was still slower than dyad operation,\nhowever, they displayed similar performance in success rate and higher motion\nefficiency than two person's cooperation.\n"}
+{"abstract": "  The linear frequency modulated (LFM) frequency agile radar (FAR) can\nsynthesize a wide signal bandwidth through coherent processing while keeping\nthe bandwidth of each pulse narrow. In this way, high range resolution profiles\n(HRRP) can be obtained without increasing the hardware system cost.\nFurthermore, the agility provides improved both robustness to jamming and\nspectrum efficiency. Motivated by the Newtonalized orthogonal matching pursuit\n(NOMP) for line spectral estimation problem, the NOMP for the FAR radar termed\nas NOMP-FAR is designed to process each coarse range bin to extract the HRRP\nand velocities of multiple targets, including the guide for determining the\noversampling factor and the stopping criterion. In addition, it is shown that\nthe target will cause false alarm in the nearby coarse range bins, a\npostprocessing algorithm is then proposed to suppress the ghost targets.\nNumerical simulations are conducted to demonstrate the effectiveness of\nNOMP-FAR.\n"}
+{"abstract": "  We present the task description and discussion on the results of the DCASE\n2021 Challenge Task 2. In 2020, we organized an unsupervised anomalous sound\ndetection (ASD) task, identifying whether a given sound was normal or anomalous\nwithout anomalous training data. In 2021, we organized an advanced unsupervised\nASD task under domain-shift conditions, which focuses on the inevitable problem\nof the practical use of ASD systems. The main challenge of this task is to\ndetect unknown anomalous sounds where the acoustic characteristics of the\ntraining and testing samples are different, i.e., domain-shifted. This problem\nfrequently occurs due to changes in seasons, manufactured products, and/or\nenvironmental noise. We received 75 submissions from 26 teams, and several\nnovel approaches have been developed in this challenge. On the basis of the\nanalysis of the evaluation results, we found that there are two types of\nremarkable approaches that TOP-5 winning teams adopted: 1) ensemble approaches\nof ``outlier exposure'' (OE)-based detectors and ``inlier modeling'' (IM)-based\ndetectors and 2) approaches based on IM-based detection for features learned in\na machine-identification task.\n"}
+{"abstract": "  Quantitative information on tumor heterogeneity and cell load could assist in\ndesigning effective and refined personalized treatment strategies. It was\nrecently shown by us that such information can be inferred from the diffusion\nparameter D derived from the diffusion-weighted MRI (DWI) if a relation between\nD and cell density can be established. However, such relation cannot a priori\nbe assumed to be constant for all patients and tumor types. Hence to assist in\nclinical decisions in palliative settings, the relation needs to be established\nwithout tumor resection. It is here demonstrated that biopsies may contain\nsufficient information for this purpose if the localization of biopsies is\nchosen as systematically elaborated in this paper. A superpixel-based method\nfor automated optimal localization of biopsies from the DWI D-map is proposed.\nThe performance of the DWI-guided procedure is evaluated by extensive\nsimulations of biopsies. Needle biopsies yield sufficient histological\ninformation to establish a quantitative relationship between D-value and cell\ndensity, provided they are taken from regions with high, intermediate, and low\nD-value in DWI. The automated localization of the biopsy regions is\ndemonstrated from a NSCLC patient tumor. In this case, even two or three\nbiopsies give a reasonable estimate. Simulations of needle biopsies under\ndifferent conditions indicate that the DWI-guidance highly improves the\nestimation results. Tumor cellularity and heterogeneity in solid tumors may be\nreliably investigated from DWI and a few needle biopsies that are sampled in\nregions of well-separated D-values, excluding adipose tissue. This procedure\ncould provide a way of embedding in the clinical workflow assistance in cancer\ndiagnosis and treatment based on personalized information.\n"}
+{"abstract": "  In this paper we give a classification of the asymptotic expansion of the\n$q$-expansion of reciprocals of Eisenstein series $E_k$ of weight $k$ for the\nmodular group $\\func{SL}_2(\\mathbb{Z})$. For $k \\geq 12$ even, this extends\nresults of Hardy and Ramanujan, and Berndt, Bialek and Yee, utilizing the\nCircle Method on the one hand, and results of Petersson, and Bringmann and\nKane, developing a theory of meromorphic Poincar{\\'e} series on the other. We\nfollow a uniform approach, based on the zeros of the Eisenstein series with the\nlargest imaginary part. These special zeros provide information on the\nsingularities of the Fourier expansion of $1/E_k(z)$ with respect to $q = e^{2\n\\pi i z}$.\n"}
+{"abstract": "  Interactive single-image segmentation is ubiquitous in the scientific and\ncommercial imaging software. In this work, we focus on the single-image\nsegmentation problem only with some seeds such as scribbles. Inspired by the\ndynamic receptive field in the human being's visual system, we propose the\nGaussian dynamic convolution (GDC) to fast and efficiently aggregate the\ncontextual information for neural networks. The core idea is randomly selecting\nthe spatial sampling area according to the Gaussian distribution offsets. Our\nGDC can be easily used as a module to build lightweight or complex segmentation\nnetworks. We adopt the proposed GDC to address the typical single-image\nsegmentation tasks. Furthermore, we also build a Gaussian dynamic pyramid\nPooling to show its potential and generality in common semantic segmentation.\nExperiments demonstrate that the GDC outperforms other existing convolutions on\nthree benchmark segmentation datasets including Pascal-Context, Pascal-VOC\n2012, and Cityscapes. Additional experiments are also conducted to illustrate\nthat the GDC can produce richer and more vivid features compared with other\nconvolutions. In general, our GDC is conducive to the convolutional neural\nnetworks to form an overall impression of the image.\n"}
+{"abstract": "  For any positive integer $r$, we construct a smooth complex projective\nrational surface which has at least $r$ real forms not isomorphic over\n$\\mathbb{R}$.\n"}
+{"abstract": "  We present a very simple form of the supercharges and the Hamiltonian of\n${\\cal N} {=}\\,2$ supersymmetric extension of $n$-particle\nRuijsenaars--Schneider models for three cases of the interaction:\n$1/(x_i-x_j)$, $1/tan(x_i-x_j)$, $1/tanh(x_i-x_j)$. The long \"fermionic tails\"\nof the supercharges and Hamiltonian rolled up in the simple rational functions\ndepending on fermionic bilinears.\n"}
+{"abstract": "  We present a study of the structure and differential capacitance of electric\ndouble layers of aqueous electrolytes. We consider Electric Double Layer\nCapacitors (EDLC) composed of spherical cations and anions in a dielectric\ncontinuum confined between a planar cathode and anode. The model system\nincludes steric as well as Coulombic ion-ion and ion-electrode interactions. We\ncompare results of computationally expensive, but \"exact\", Brownian Dynamics\n(BD) simulations with approximate, but cheap, calculations based on classical\nDensity Functional Theory (DFT). Excellent overall agreement is found for a\nlarge set of system parameters $-$ including variations in concentrations,\nionic size- and valency-asymmetries, applied voltages, and electrode separation\n$-$ provided the differences between the canonical ensemble of the BD\nsimulations and the grand-canonical ensemble of DFT are properly taken into\naccount. In particular a careful distinction is made between the differential\ncapacitance $C_N$ at fixed number of ions and $C_\\mu$ at fixed ionic chemical\npotential. Furthermore, we derive and exploit their thermodynamic relations. In\nthe future these relations are also useful for comparing and contrasting.\n"}
+{"abstract": "  We analyse an extremal question on the degrees of the link graphs of a finite\nregular graph, that is, the subgraphs induced by non-trivial spheres. We show\nthat if $G$ is $d$-regular and connected but not complete then some link graph\nof $G$ has minimum degree at most $\\lfloor 2d/3\\rfloor-1$, and if $G$ is\nsufficiently large in terms of $d$ then some link graph has minimum degree at\nmost $\\lfloor d/2\\rfloor-1$; both bounds are best possible. We also give the\ncorresponding best-possible result for the corresponding problem where\nsubgraphs induced by balls, rather than spheres, are considered.\n  We motivate these questions by posing a conjecture concerning expansion of\nlink graphs in large bounded-degree graphs, together with a heuristic\njustification thereof.\n"}
+{"abstract": "  #P-hardness of computing matrix immanants are proved for each member of a\nbroad class of shapes and restricted sets of matrices. The class is\ncharacterized in the following way. If a shape of size $n$ in it is in form\n$(w,\\mathbf{1}+\\lambda)$ or its conjugate is in that form, where $\\mathbf{1}$\nis the all-$1$ vector, then $|\\lambda|$ is $n^{\\varepsilon}$ for some\n$0<\\varepsilon$, $\\lambda$ can be tiled with $1\\times 2$ dominos and\n$(3w+3h(\\lambda)+1)|\\lambda| \\le n$, where $h(\\lambda)$ is the height of\n$\\lambda$. The problem remains \\#P-hard if the immanants are evaluated on\n$0$-$1$ matrices. We also give hardness proofs of some immanants whose shape\n$\\lambda = (\\mathbf{1}+\\lambda_d)$ has size $n$ such that $|\\lambda_d| =\nn^{\\varepsilon}$ for some $0<\\varepsilon<\\frac{1}{2}$, and for some $w$, the\nshape $\\lambda_d/(w)$ is tilable with $1\\times 2$ dominos. The \\#P-hardness\nresult holds when these immanants are evaluated on adjacency matrices of\nplanar, directed graphs, however, in these cases the edges have small positive\ninteger weights.\n"}
+{"abstract": "  The chromaticity diagram associated with the CIE 1931 color matching\nfunctions is shown to be slightly non-convex. While having no impact on\npractical colorimetric computations, the non-convexity does have a significant\nimpact on the shape of some optimal object color reflectance distributions\nassociated with the outer surface of the object color solid. Instead of the\nusual two-transition Schrodinger form, many optimal colors exhibit higher\ntransition counts. A linear programming formulation is developed and is used to\nlocate where these higher-transition optimal object colors reside on the object\ncolor solid surface. The regions of higher transition count appear to have a\npoint-symmetric complementary structure. The final peer-reviewed version (to\nappear) contains additional material concerning convexification of the\ncolor-matching functions and and additional analysis of modern\n\"physiologically-relevant\" CMFs transformed from cone fundamentals.\n"}
+{"abstract": "  Raman spectroscopy is an advantageous method for studying the local structure\nof materials, but the interpretation of measured spectra is complicated by the\npresence of oblique phonons in polycrystals of polar materials. Whilst group\ntheory considerations and standard ab initio calculations are helpful, they are\noften valid only for single crystals. In this paper, we introduce a method for\ncomputing Raman spectra of polycrystalline materials from first principles. We\nstart from the standard approach based on the (Placzek) rotation invariants of\nthe Raman tensors and extend it to include the effect of the coupling between\nthe lattice vibrations and the induced electric field, and the electro-optic\ncontribution, relevant for polar materials like ferroelectrics. As exemplified\nby applying the method to rhombohedral BaTiO3, AlN, and LiNbO3, such an\nextension brings the simulated Raman spectrum to a much better correspondence\nwith the experimental one. Additional advantages of the method are that it is\ngeneral, permits automation, and thus can be used in high-throughput fashion.\n"}
+{"abstract": "  This paper is a continuation of our article (European J. Math.,\nhttps://doi.org/10.1007/s40879-020-00419-8). The notion of a poor complex\ncompact manifold was introduced there and the group $Aut(X)$ for a $P^1$-bundle\nover such a manifold was proven to be very Jordan. We call a group $G$ very\nJordan if it contains a normal abelian subgroup $G_0$ such that the orders of\nfinite subgroups of the quotient $G/G_0$ are bounded by a constant depending on\n$G$ only.\n  In this paper we provide explicit examples of infinite families of poor\nmanifolds of any complex dimension, namely simple tori of algebraic dimension\nzero. Then we consider a non-trivial holomorphic $P^1$-bundle $(X,p,Y)$ over a\nnon-uniruled complex compact Kaehler manifold $Y$. We prove that $Aut(X)$ is\nvery Jordan provided some additional conditions on the set of sections of $p$\nare met. Applications to $P^1$-bundles over non-algebraic complex tori are\ngiven.\n"}
+{"abstract": "  In this work we obtain a geometric characterization of the measures $\\mu$ in\n$\\mathbb{R}^{n+1}$ with polynomial upper growth of degree $n$ such that the\n$n$-dimensional Riesz transform $\\mathcal{R}\\mu (x) = \\int\n\\frac{x-y}{|x-y|^{n+1}}\\,d\\mu(y)$ belongs to $L^2(\\mu)$, under the assumption\nthat $\\mu$ satisfies the following Wolff energy estimate, for any ball\n$B\\subset\\mathbb{R}^{n+1}$: $$\\int_B \\int_0^\\infty\n\\left(\\frac{\\mu(B(x,r))}{r^{n-\\frac38}}\\right)^2\\,\\frac{dr}r\\,d\\mu(x)\\leq\nM\\,\\bigg(\\frac{\\mu(2B)}{r(B)^{n-\\frac38}}\\bigg)^2\\,\\mu(2B).$$ More precisely,\nwe show that $\\mu$ satisfies the following estimate:\n$$\\|\\mathcal{R}\\mu\\|_{L^2(\\mu)}^2 + \\|\\mu\\|\\approx \\int\\!\\!\\int_0^\\infty\n\\beta_{\\mu,2}(x,r)^2\\,\\frac{\\mu(B(x,r))}{r^n}\\,\\frac{dr}r\\,d\\mu(x) + \\|\\mu\\|,$$\nwhere $\\beta_{\\mu,2}(x,r)^2 = \\inf_L \\frac1{r^n}\\int_{B(x,r)}\n\\left(\\frac{\\mathrm{dist}(y,L)}r\\right)^2\\,d\\mu(y),$ with the infimum taken\nover all affine $n$-planes $L\\subset\\mathbb{R}^{n+1}$. In a companion paper\nwhich relies on the results obtained in this work it is shown that the same\nresult holds without the above assumption regarding the Wolff energy of $\\mu$.\nThis result has important consequences for the Painlev\\'e problem for Lipschitz\nharmonic functions.\n"}
+{"abstract": "  We propose a new approach for trading VIX futures. We assume that the term\nstructure of VIX futures follows a Markov model. Our trading strategy selects a\nposition in VIX futures by maximizing the expected utility for a day-ahead\nhorizon given the current shape and level of the term structure.\nComputationally, we model the functional dependence between the VIX futures\ncurve, the VIX futures positions, and the expected utility as a deep neural\nnetwork with five hidden layers. Out-of-sample backtests of the VIX futures\ntrading strategy suggest that this approach gives rise to reasonable portfolio\nperformance, and to positions in which the investor will be either long or\nshort VIX futures contracts depending on the market environment.\n"}
+{"abstract": "  In this paper we study the connectivity of Fatou components for maps in a\nlarge family of singular perturbations. We prove that, for some parameters\ninside the family, the dynamical planes for the corresponding maps present\nFatou components of arbitrarily large connectivity and we determine precisely\nthese connectivities. In particular, these results extend the ones obtained in\n[Can17, Can18].\n"}
+{"abstract": "  Cross features play an important role in click-through rate (CTR) prediction.\nMost of the existing methods adopt a DNN-based model to capture the cross\nfeatures in an implicit manner. These implicit methods may lead to a\nsub-optimized performance due to the limitation in explicit semantic modeling.\nAlthough traditional statistical explicit semantic cross features can address\nthe problem in these implicit methods, it still suffers from some challenges,\nincluding lack of generalization and expensive memory cost. Few works focus on\ntackling these challenges. In this paper, we take the first step in learning\nthe explicit semantic cross features and propose Pre-trained Cross Feature\nlearning Graph Neural Networks (PCF-GNN), a GNN based pre-trained model aiming\nat generating cross features in an explicit fashion. Extensive experiments are\nconducted on both public and industrial datasets, where PCF-GNN shows\ncompetence in both performance and memory-efficiency in various tasks.\n"}
+{"abstract": "  The number of photographs taken worldwide is growing rapidly and steadily.\nWhile a small subset of these images is annotated and shared by users through\nsocial media platforms, due to the sheer number of images in personal photo\nrepositories (shared or not shared), finding specific images remains\nchallenging. This survey explores existing image retrieval techniques as well\nas photo-organizer applications to highlight their relative strengths in\naddressing this challenge.\n"}
+{"abstract": "  Partitioning graphs into blocks of roughly equal size is widely used when\nprocessing large graphs. Currently there is a gap in the space of available\npartitioning algorithms. On the one hand, there are streaming algorithms that\nhave been adopted to partition massive graph data on small machines. In the\nstreaming model, vertices arrive one at a time including their neighborhood and\nthen have to be assigned directly to a block. These algorithms can partition\nhuge graphs quickly with little memory, but they produce partitions with low\nsolution quality. On the other hand, there are offline (shared-memory)\nmultilevel algorithms that produce partitions with high quality but also need a\nmachine with enough memory. We make a first step to close this gap by\npresenting an algorithm that computes significantly improved partitions of huge\ngraphs using a single machine with little memory in streaming setting. First,\nwe adopt the buffered streaming model which is a more reasonable approach in\npractice. In this model, a processing element can store a buffer, or batch, of\nnodes before making assignment decisions. When our algorithm receives a batch\nof nodes, we build a model graph that represents the nodes of the batch and the\nalready present partition structure. This model enables us to apply multilevel\nalgorithms and in turn compute much higher quality solutions of huge graphs on\ncheap machines than previously possible. To partition the model, we develop a\nmultilevel algorithm that optimizes an objective function that has previously\nshown to be effective for the streaming setting. This also removes the\ndependency on the number of blocks k from the running time compared to the\nprevious state-of-the-art. Overall, our algorithm computes, on average, 75.9%\nbetter solutions than Fennel using a very small buffer size. In addition, for\nlarge values of k our algorithm becomes faster than Fennel.\n"}
+{"abstract": "  Let $X$ be a nonempty set and let $T(X)$ be the full transformation semigroup\non $X$. The main objective of this paper is to study the subsemigroup\n$\\overline{\\Omega}(X, Y)$ of $T(X)$ defined by \\[\\overline{\\Omega}(X, Y) =\n\\{f\\in T(X)\\colon Yf = Y\\},\\] where $Y$ is a fixed nonempty subset of $X$. We\ndescribe regular elements in $\\overline{\\Omega}(X, Y)$ and show that\n$\\overline{\\Omega}(X, Y)$ is regular if and only if $Y$ is finite. We\ncharacterize unit-regular elements in $\\overline{\\Omega}(X, Y)$ and prove that\n$\\overline{\\Omega}(X, Y)$ is unit-regular if and only if $X$ is finite. We\ncharacterize Green's relations on $\\overline{\\Omega}(X, Y)$ and prove that\n$\\mathcal{D} =\\mathcal{J}$ on $\\overline{\\Omega}(X, Y)$ if and only if $Y$ is\nfinite. We also determine ideals of $\\overline{\\Omega}(X, Y)$ and investigate\nits kernel. This paper extends several results appeared in the literature.\n"}
+{"abstract": "  We elaborate on the correspondence between the canonical partition function\nin asymptotically AdS universes and the no-boundary proposal for positive\nvacuum energy. For the case of a pure cosmological constant, the analytic\ncontinuation of the AdS partition function is seen to define the no-boundary\nwave function (in dS) uniquely in the simplest minisuperspace model. A\nconsideration of the AdS gravitational path integral implies that on the dS\nside, saddle points with Hawking-Moss/Coleman-De Luccia-type tunnelling\ngeometries are irrelevant. This implies that simple topology changing\ngeometries do not contribute to the nucleation of the universe. The analytic\nAdS/dS equivalence holds up once tensor fluctuations are added. It also works,\nat the level of the saddle point approximation, when a scalar field with a mass\nterm is included, though in the latter case, it is the mass that must be\nanalytically continued. Our results illustrate the emergence of time from space\nby means of a Stokes phenomenon, in the case of positive vacuum energy.\nFurthermore, we arrive at a new characterisation of the no-boundary condition,\nnamely that there should be no momentum flux at the nucleation of the universe.\n"}
+{"abstract": "  The ultimate detection limit of optical biosensors is often limited by\nvarious noise sources, including those introduced by the optical measurement\nsetup. While sophisticated modifications to instrumentation may reduce noise, a\nsimpler approach that can benefit all sensor platforms is the application of\nsignal processing to minimize the deleterious effects of noise. In this work,\nwe show that applying complex Morlet wavelet convolution to Fabry-P\\'erot\ninterference fringes characteristic of thin film reflectometric biosensors\neffectively filters out white noise and low frequency reflectance variations.\nSubsequent calculation of an average difference in phase between the filtered\nanalyte and reference signals enables a significant reduction in the limit of\ndetection (LOD) enabling closer competition with current state-of-the-art\ntechniques. This method is applied on experimental data sets of thin film\nporous silicon sensors (PSi) in buffered solution and complex media obtained\nfrom two different laboratories. The demonstrated improvement in LOD achieved\nusing wavelet convolution and average phase difference paves the way for PSi\noptical biosensors to operate with clinically relevant detection limits for\nmedical diagnostics, environmental monitoring, and food safety.\n"}
+{"abstract": "  In addition to the well-known gas phase mass-metallicity relation (MZR),\nrecent spatially-resolved observations have shown that local galaxies also obey\na mass-metallicity gradient relation (MZGR) whereby metallicity gradients can\nvary systematically with galaxy mass. In this work, we use our\nrecently-developed analytic model for metallicity distributions in galactic\ndiscs, which includes a wide range of physical processes -- radial advection,\nmetal diffusion, cosmological accretion, and metal-enriched outflows -- to\nsimultaneously analyse the MZR and MZGR. We show that the same physical\nprinciples govern the shape of both: centrally-peaked metal production favours\nsteeper gradients, and this steepening is diluted by the addition of metal-poor\ngas, which is supplied by inward advection for low-mass galaxies and by\ncosmological accretion for massive galaxies. The MZR and the MZGR both bend at\ngalaxy stellar mass $\\sim 10^{10} - 10^{10.5}\\,\\rm{M_{\\odot}}$, and we show\nthat this feature corresponds to the transition of galaxies from the\nadvection-dominated to the accretion-dominated regime. We also find that both\nthe MZR and MZGR strongly suggest that low-mass galaxies preferentially lose\nmetals entrained in their galactic winds. While this metal-enrichment of the\ngalactic outflows is crucial for reproducing both the MZR and the MZGR at the\nlow-mass end, we show that the flattening of gradients in massive galaxies is\nexpected regardless of the nature of their winds.\n"}
+{"abstract": "  Direct Volume Rendering (DVR) using Volumetric Path Tracing (VPT) is a\nscientific visualization technique that simulates light transport with objects'\nmatter using physically-based lighting models. Monte Carlo (MC) path tracing is\noften used with surface models, yet its application for volumetric models is\ndifficult due to the complexity of integrating MC light-paths in volumetric\nmedia with none or smooth material boundaries. Moreover, auxiliary\ngeometry-buffers (G-buffers) produced for volumes are typically very noisy,\nfailing to guide image denoisers relying on that information to preserve image\ndetails. This makes existing real-time denoisers, which take noise-free\nG-buffers as their input, less effective when denoising VPT images. We propose\nthe necessary modifications to an image-based denoiser previously used when\nrendering surface models, and demonstrate effective denoising of VPT images. In\nparticular, our denoising exploits temporal coherence between frames, without\nrelying on noise-free G-buffers, which has been a common assumption of existing\ndenoisers for surface-models. Our technique preserves high-frequency details\nthrough a weighted recursive least squares that handles heterogeneous noise for\nvolumetric models. We show for various real data sets that our method improves\nthe visual fidelity and temporal stability of VPT during classic DVR operations\nsuch as camera movements, modifications of the light sources, and editions to\nthe volume transfer function.\n"}
+{"abstract": "  In the last decade, substantial progress has been made towards standardizing\nthe syntax of graph query languages, and towards understanding their semantics\nand complexity of evaluation. In this paper, we consider temporal property\ngraphs (TPGs) and propose temporal regular path queries (TRPQs) that\nincorporate time into TPG navigation. Starting with design principles, we\npropose a natural syntactic extension of the MATCH clause of popular graph\nquery languages. We then formally present the semantics of TRPQs, and study the\ncomplexity of their evaluation. We show that TRPQs can be evaluated in\npolynomial time if TPGs are time-stamped with time points, and identify\nfragments of the TRPQ language that admit efficient evaluation over a more\nsuccinct interval-annotated representation. Finally, we implement a fragment of\nthe language in a state-of-the-art dataflow framework, and experimentally\ndemonstrate that TRPQ can be evaluated efficiently.\n"}
+{"abstract": "  Let $G$ be a simple undirected graph with vertex set $V(G)=\\{v_1, v_2,\n\\ldots, v_n\\}$ and edge set $E(G)$. The Sombor matrix $\\mathcal{S}(G)$ of a\ngraph $G$ is defined so that its $(i,j)$-entry is equal to $\\sqrt{d_i^2+d_j^2}$\nif the vertices $v_i$ and $v_j$ are adjacent, and zero otherwise, where $d_i$\ndenotes the degree of vertex $v_i$ in $G$. In this paper, lower and upper\nbounds on the spectral radius, energy and Estrada index of the Sombor matrix of\ngraphs are obtained, and the respective extremal graphs are characterized.\n"}
+{"abstract": "  COVID19 has impacted Indian engineering institutions (EIs) enormously. It has\ntightened its knot around EIs that forced their previous half shut shades\ncompletely down to prevent the risk of spreading COVID19. In such a situation,\nfetching new enrollments on EI campuses is a difficult and challenging task, as\nstudents behavior and family preferences have changed drastically due to mental\nstress and emotions attached to them. Consequently, it becomes a prerequisite\nto examine the choice characteristics influencing the selection of EI during\nthe COVID-19 pandemic to make it normal for new enrollments.\n  The purpose of this study is to critically examine choice characteristics\nthat affect students choice for EI and consequently to explore relationships\nbetween institutional characteristics and the suitability of EI during the\nCOVID19 pandemic across students characteristics. The findings of this study\nrevealed dissimilarities across students characteristics regarding the\nsuitability of EIs under pandemic conditions. Regression analysis revealed that\nEI characteristics such as proximity, image and reputation, quality education\nand curriculum delivery have significantly contributed to suitability under\nCOVID19. At the micro level, multiple relationships were noted between EI\ncharacteristics and the suitability of EI under the pandemic across students\ncharacteristics. The study has successfully demonstrated how choice\ncharacteristics can be executed to regulate the suitability of EI under the\nCOVID19 pandemic for the inclusion of diversity. It is useful for policy makers\nand academicians to reposition EIs that fetch diversity during the pandemic.\nThis study is the first to provide insights into the performance of choice\ncharacteristics and their relationship with the suitability of EIs under a\npandemic and can be a yardstick in administering new enrollments.\n"}
+{"abstract": "  We introduce Reflective Hamiltonian Monte Carlo (ReHMC), an HMC-based\nalgorithm, to sample from a log-concave distribution restricted to a convex\nbody. We prove that, starting from a warm start, the walk mixes to a\nlog-concave target distribution $\\pi(x) \\propto e^{-f(x)}$, where $f$ is\n$L$-smooth and $m$-strongly-convex, within accuracy $\\varepsilon$ after\n$\\widetilde O(\\kappa d^2 \\ell^2 \\log (1 / \\varepsilon))$ steps for a\nwell-rounded convex body where $\\kappa = L / m$ is the condition number of the\nnegative log-density, $d$ is the dimension, $\\ell$ is an upper bound on the\nnumber of reflections, and $\\varepsilon$ is the accuracy parameter. We also\ndeveloped an efficient open source implementation of ReHMC and we performed an\nexperimental study on various high-dimensional data-sets. The experiments\nsuggest that ReHMC outperfroms Hit-and-Run and Coordinate-Hit-and-Run regarding\nthe time it needs to produce an independent sample and introduces practical\ntruncated sampling in thousands of dimensions.\n"}
+{"abstract": "  When flipping a fair coin, let $W = L_1L_2...L_N$ with $L_i\\in\\{H,T\\}$ be a\nbinary word of length $N=2$ or $N=3$. In this paper, we establish second- and\nthird-order linear recurrence relations and their generating functions to\ndiscuss the probabilities $p_{W}(n)$ that binary words $W$ appear for the first\ntime after $n$ coin tosses.\n"}
+{"abstract": "  Climate change and global warming are the significant challenges of the new\ncentury. A viable solution to mitigate greenhouse gas emissions is via a\nglobally incentivized market mechanism proposed in the Kyoto protocol. In this\nview, the carbon dioxide (or other greenhouse gases) emission is considered a\ncommodity, forming a carbon trading system. There have been attempts in\ndeveloping this idea in the past decade with limited success. The main\nchallenges of current systems are fragmented implementations, lack of\ntransparency leading to over-crediting and double-spending, and substantial\ntransaction costs that transfer wealth to brokers and agents. We aim to create\na Carbon Credit Ecosystem using smart contracts that operate in conjunction\nwith blockchain technology in order to bring more transparency, accessibility,\nliquidity, and standardization to carbon markets. This ecosystem includes a\ntokenization mechanism to securely digitize carbon credits with clear minting\nand burning protocols, a transparent mechanism for distribution of tokens, a\nfree automated market maker for trading the carbon tokens, and mechanisms to\nengage all stakeholders, including the energy industry, project verifiers,\nliquidity providers, NGOs, concerned citizens, and governments. This approach\ncould be used in a variety of other credit/trading systems.\n"}
+{"abstract": "  We demonstrate a successful navigation and docking control system for the\nJohn Deere Tango autonomous mower, using only a single camera as the input.\nThis vision-only system is of interest because it is inexpensive, simple for\nproduction, and requires no external sensing. This is in contrast to existing\nsystems that rely on integrated position sensors and global positioning system\n(GPS) technologies. To produce our system we combined a state-of-the-art object\ndetection architecture, You Only Look Once (YOLO), with a reinforcement\nlearning (RL) architecture, Double Deep QNetworks (Double DQN). The object\ndetection network identifies features on the mower and passes its output to the\nRL network, providing it with a low-dimensional representation that enables\nrapid and robust training. Finally, the RL network learns how to navigate the\nmachine to the desired spot in a custom simulation environment. When tested on\nmower hardware, the system is able to dock with centimeter-level accuracy from\narbitrary initial locations and orientations.\n"}
+{"abstract": "  Few researches have been proposed specifically for real-time semantic\nsegmentation in rainy environments. However, the demand in this area is huge\nand it is challenging for lightweight networks. Therefore, this paper proposes\na lightweight network which is specially designed for the foreground\nsegmentation in rainy environments, named De-raining Semantic Segmentation\nNetwork (DRSNet). By analyzing the characteristics of raindrops, the\nMultiScaleSE Block is targetedly designed to encode the input image, it uses\nmulti-scale dilated convolutions to increase the receptive field, and SE\nattention mechanism to learn the weights of each channels. In order to combine\nsemantic information between different encoder and decoder layers, it is\nproposed to use Asymmetric Skip, that is, the higher semantic layer of encoder\nemploys bilinear interpolation and the output passes through pointwise\nconvolution, then added element-wise to the lower semantic layer of decoder.\nAccording to the control experiments, the performances of MultiScaleSE Block\nand Asymmetric Skip compared with SEResNet18 and Symmetric Skip respectively\nare improved to a certain degree on the Foreground Accuracy index. The\nparameters and the floating point of operations (FLOPs) of DRSNet is only 0.54M\nand 0.20GFLOPs separately. The state-of-the-art results and real-time\nperformances are achieved on both the UESTC all-day Scenery add rain\n(UAS-add-rain) and the Baidu People Segmentation add rain (BPS-add-rain)\nbenchmarks with the input sizes of 192*128, 384*256 and 768*512. The speed of\nDRSNet exceeds all the networks within 1GFLOPs, and Foreground Accuracy index\nis also the best among the similar magnitude networks on both benchmarks.\n"}
+{"abstract": "  Understanding electrical energy demand at the consumer level plays an\nimportant role in planning the distribution of electrical networks and offering\nof off-peak tariffs, but observing individual consumption patterns is still\nexpensive. On the other hand, aggregated load curves are normally available at\nthe substation level. The proposed methodology separates substation aggregated\nloads into estimated mean consumption curves, called typical curves, including\ninformation given by explanatory variables. In addition, a model-based\nclustering approach for substations is proposed based on the similarity of\ntheir consumers typical curves and covariance structures. The methodology is\napplied to a real substation load monitoring dataset from the United Kingdom\nand tested in eight simulated scenarios.\n"}
+{"abstract": "  The measurements of $V_{us}$ in leptonic $(K_{\\mu 2})$ and semileptonic\n$(K_{l3})$ kaon decays exhibit a $3\\sigma$ disagreement, which could originate\neither from physics beyond the Standard Model or some large unidentified\nStandard Model systematic effects. Clarifying this issue requires a careful\nexamination of all existing Standard Model inputs. Making use of a\nnewly-proposed computational framework and the most recent lattice QCD results,\nwe perform a comprehensive re-analysis of the electroweak radiative corrections\nto the $K_{e3}$ decay rates that achieves an unprecedented level of precision\nof $10^{-4}$, which improves the current best results by almost an order of\nmagnitude. No large systematic effects are found, which suggests that the\nelectroweak radiative corrections should be removed from the ``list of\nculprits'' responsible for the $K_{\\mu 2}$--$K_{l3}$ discrepancy.\n"}
+{"abstract": "  We present a new method to capture detailed human motion, sampling more than\n1000 unique points on the body. Our method outputs highly accurate 4D\n(spatio-temporal) point coordinates and, crucially, automatically assigns a\nunique label to each of the points. The locations and unique labels of the\npoints are inferred from individual 2D input images only, without relying on\ntemporal tracking or any human body shape or skeletal kinematics models.\nTherefore, our captured point trajectories contain all of the details from the\ninput images, including motion due to breathing, muscle contractions and flesh\ndeformation, and are well suited to be used as training data to fit advanced\nmodels of the human body and its motion. The key idea behind our system is a\nnew type of motion capture suit which contains a special pattern with\ncheckerboard-like corners and two-letter codes. The images from our\nmulti-camera system are processed by a sequence of neural networks which are\ntrained to localize the corners and recognize the codes, while being robust to\nsuit stretching and self-occlusions of the body. Our system relies only on\nstandard RGB or monochrome sensors and fully passive lighting and the passive\nsuit, making our method easy to replicate, deploy and use. Our experiments\ndemonstrate highly accurate captures of a wide variety of human poses,\nincluding challenging motions such as yoga, gymnastics, or rolling on the\nground.\n"}
+{"abstract": "  The application of remaining useful life (RUL) prediction has taken great\nimportance in terms of energy optimization, cost-effectiveness, and risk\nmitigation. The existing RUL prediction algorithms mostly constitute deep\nlearning frameworks. In this paper, we implement LSTM and GRU models and\ncompare the obtained results with a proposed genetically trained neural\nnetwork. The current models solely depend on Adam and SGD for optimization and\nlearning. Although the models have worked well with these optimizers, even\nlittle uncertainties in prognostics prediction can result in huge losses. We\nhope to improve the consistency of the predictions by adding another layer of\noptimization using Genetic Algorithms. The hyper-parameters - learning rate and\nbatch size are optimized beyond manual capacity. These models and the proposed\narchitecture are tested on the NASA Turbofan Jet Engine dataset. The optimized\narchitecture can predict the given hyper-parameters autonomously and provide\nsuperior results.\n"}
+{"abstract": "  In this paper, we study the optimal transmission of a multi-quality tiled 360\nvirtual reality (VR) video from a multi-antenna server (e.g., access point or\nbase station) to multiple single-antenna users in a multiple-input\nmultiple-output (MIMO)-orthogonal frequency division multiple access (OFDMA)\nsystem. We minimize the total transmission power with respect to the subcarrier\nallocation constraints, rate allocation constraints, and successful\ntransmission constraints, by optimizing the beamforming vector and subcarrier,\ntransmission power and rate allocation. The formulated resource allocation\nproblem is a challenging mixed discrete-continuous optimization problem. We\nobtain an asymptotically optimal solution in the case of a large antenna array,\nand a suboptimal solution in the general case. As far as we know, this is the\nfirst work providing optimization-based design for 360 VR video transmission in\nMIMO-OFDMA systems. Finally, by numerical results, we show that the proposed\nsolutions achieve significant improvement in performance compared to the\nexisting solutions.\n"}
+{"abstract": "  We discover that deep ReLU neural network classifiers can see a\nlow-dimensional Riemannian manifold structure on data. Such structure comes via\nthe local data matrix, a variation of the Fisher information matrix, where the\nrole of the model parameters is taken by the data variables. We obtain a\nfoliation of the data domain and we show that the dataset on which the model is\ntrained lies on a leaf, the data leaf, whose dimension is bounded by the number\nof classification labels. We validate our results with some experiments with\nthe MNIST dataset: paths on the data leaf connect valid images, while other\nleaves cover noisy images.\n"}
+{"abstract": "  Backhauling services through satellite systems have doubled between 2012 and\n2018. There is an increasing demand for this service for which satellite\nsystems typically allocate a fixed resource. This solution may not help in\noptimizing the usage of the scarce satellite resource.\n  This study measures the relevance of using dynamic resource allocation\nmechanisms for backhaul services through satellite systems. The satellite\nsystem is emulated with OpenSAND, the LTE system with Amarisoft and the\nexperiments are orchestrated by OpenBACH. We compare the relevance of applying\nTCP PEP mechanisms and dynamic resource allocations for different traffic\nservices by measuring the QoE for web browsing, data transfer and VoIP\napplications.\n  The main conclusions are the following. When the system is congested, PEP and\nlayer-2 access mechanisms do not provide significant improvements. When the\nsystem is not congested, data transfer can be greatly improved through\nprotocols and channel access mechanism optimization. Tuning the Constant Rate\nAssignment can help in reducing the cost of the resource and provide QoE\nimprovements when the network is not loaded.\n"}
+{"abstract": "  Grain boundaries (GBs) are planar lattice defects that govern the properties\nof many types of polycrystalline materials. Hence, their structures have been\ninvestigated in great detail. However, much less is known about their chemical\nfeatures, owing to the experimental difficulties to probe these features at the\natomic length scale inside bulk material specimens. Atom probe tomography (APT)\nis a tool capable of accomplishing this task, with an ability to quantify\nchemical characteristics at near-atomic scale. Using APT data sets, we present\nhere a machine-learning-based approach for the automated quantification of\nchemical features of GBs. We trained a convolutional neural network (CNN) using\ntwenty thousand synthesized images of grain interiors, GBs, or triple\njunctions. Such a trained CNN automatically detects the locations of GBs from\nAPT data. Those GBs are then subjected to compositional mapping and analysis,\nincluding revealing their in-plane chemical decoration patterns. We applied\nthis approach to experimentally obtained APT data sets pertaining to three case\nstudies, namely, Ni-P, Pt-Au, and Al-Zn-Mg-Cu alloys. In the first case, we\nextracted GB-specific segregation features as a function of misorientation and\ncoincidence site lattice character. Secondly, we revealed interfacial excesses\nand in-plane chemical features that could not have been found by standard\ncompositional analyses. Lastly, we tracked the temporal evolution of chemical\ndecoration from early-stage solute GB segregation in the dilute limit to\ninterfacial phase separation, characterized by the evolution of complex\ncomposition patterns. This machine-learning-based approach provides\nquantitative, unbiased, and automated access to GB chemical analyses, serving\nas an enabling tool for new discoveries related to interface thermodynamics,\nkinetics, and the associated chemistry-structure-property relations.\n"}
+{"abstract": "  The Fock space $\\mathcal{F}(\\mathbb{C}^n)$ is the space of holomorphic\nfunctions on $\\mathbb{C}^n$ that are square-integrable with respect to the\nGaussian measure on $\\mathbb{C}^n$. This space plays an important role in\nseveral subfields of analysis and representation theory. In particular, it has\nfor a long time been a model to study Toeplitz operators. Esmeral and Maximenko\nshowed in 2016 that radial Toeplitz operators on $\\mathcal{F}(\\mathbb{C})$\ngenerate a commutative $C^*$-algebra which is isometrically isomorphic to the\n$C^*$-algebra $C_{b,u}(\\mathbb{N}_0,\\rho_1)$. In this article, we extend the\nresult to $k$-quasi-radial symbols acting on the Fock space\n$\\mathcal{F}(\\mathbb{C}^n)$. We calculate the spectra of the said Toeplitz\noperators and show that the set of all eigenvalue functions is dense in the\n$C^*$-algebra $C_{b,u}(\\mathbb{N}_0^k,\\rho_k)$ of bounded functions on\n$\\mathbb{N}_0^k$ which are uniformly continuous with respect to the square-root\nmetric. In fact, the $C^*$-algebra generated by Toeplitz operators with\nquasi-radial symbols is $C_{b,u}(\\mathbb{N}_0^k,\\rho_k)$.\n"}
+{"abstract": "  We present optical follow-up imaging obtained with the Katzman Automatic\nImaging Telescope, Las Cumbres Observatory Global Telescope Network, Nickel\nTelescope, Swope Telescope, and Thacher Telescope of the LIGO/Virgo\ngravitational wave (GW) signal from the neutron star-black hole (NSBH) merger\nGW190814. We searched the GW190814 localization region (19 deg$^{2}$ for the\n90th percentile best localization), covering a total of 51 deg$^{2}$ and 94.6%\nof the two-dimensional localization region. Analyzing the properties of 189\ntransients that we consider as candidate counterparts to the NSBH merger,\nincluding their localizations, discovery times from merger, optical spectra,\nlikely host-galaxy redshifts, and photometric evolution, we conclude that none\nof these objects are likely to be associated with GW190814. Based on this\nfinding, we consider the likely optical properties of an electromagnetic\ncounterpart to GW190814, including possible kilonovae and short gamma-ray burst\nafterglows. Using the joint limits from our follow-up imaging, we conclude that\na counterpart with an $r$-band decline rate of 0.68 mag day$^{-1}$, similar to\nthe kilonova AT 2017gfo, could peak at an absolute magnitude of at most $-17.8$\nmag (50% confidence). Our data are not constraining for ''red'' kilonovae and\nrule out ''blue'' kilonovae with $M>0.5 M_{\\odot}$ (30% confidence). We\nstrongly rule out all known types of short gamma-ray burst afterglows with\nviewing angles $<$17$^{\\circ}$ assuming an initial jet opening angle of\n$\\sim$$5.2^{\\circ}$ and explosion energies and circumburst densities similar to\nafterglows explored in the literature. Finally, we explore the possibility that\nGW190814 merged in the disk of an active galactic nucleus, of which we find\nfour in the localization region, but we do not find any candidate counterparts\namong these sources.\n"}
+{"abstract": "  Ultrafast lasers are ideal tools to process transparent materials because\nthey spatially confine the deposition of laser energy within the material's\nbulk via nonlinear photoionization processes. Nonlinear propagation and\nfilamentation were initially regarded as deleterious effects. But in the last\ndecade, they turned out to be benefits to control energy deposition over long\ndistances. These effects create very high aspect ratio structures which have\nfound a number of important applications, particularly for glass separation\nwith non-ablative techniques. This chapter reviews the developments of\nin-volume ultrafast laser processing of transparent materials. We discuss the\nbasic physics of the processes, characterization means, filamentation of\nGaussian and Bessel beams and provide an overview of present applications.\n"}
+{"abstract": "  Hyperspectral imaging at cryogenic temperatures is used to investigate\nexciton and trion propagation in MoSe$_2$ monolayers encapsulated with\nhexagonal boron nitride (hBN). Under a tightly focused, continuous-wave laser\nexcitation, the spatial distribution of neutral excitons and charged trions\nstrongly differ at high excitation densities. Remarkably, in this regime the\ntrion distribution develops a halo shape, similar to that previously observed\nin WS2 monolayers at room temperature and under pulsed excitation. In contrast,\nthe exciton distribution only presents a moderate broadening without the\nappereance of a halo. Spatially and spectrally resolved luminescence spectra\nreveal the buildup of a significant temperature gradient at high excitation\npower, that is attributed to the energy relaxation of photoinduced hot\ncarriers. We show, via a numerical resolution of the transport equations for\nexcitons and trions, that the halo can be interpreted as thermal drift of\ntrions due to a Seebeck term in the particle current. The model shows that the\ndifference between trion and exciton profiles is simply understood in terms of\nthe very different lifetimes of these two quasiparticles.\n"}
+{"abstract": "  The conformational states of a semiflexible polymer enclosed in a volume\n$V:=\\ell^{3}$ are studied as stochastic realizations of paths using the\nstochastic curvature approach developed in [Rev. E 100, 012503 (2019)], in the\nregime whenever $3\\ell/\\ell_ {p}> 1$, where $\\ell_{p}$ is the persistence\nlength. The cases of a semiflexible polymer enclosed in a cube and sphere are\nconsidered. In these cases, we explore the Spakowitz-Wang type polymer shape\ntransition, where the critical persistence length distinguishes between an\noscillating and a monotonic phase at the level of the mean-square end-to-end\ndistance. This shape transition provides evidence of a universal signature of\nthe behavior of a semiflexible polymer confined in a compact domain.\n"}
+{"abstract": "  We construct a cosmological model from the inception of the\nFriedmann-Lem\\^aitre-Robertson-Walker metric into the field equations of the\n$f(R,L_m)$ gravity theory, with $R$ being the Ricci scalar and $L_m$ being the\nmatter lagrangian density. The formalism is developed for a particular\n$f(R,L_m)$ function, namely $R/16\\pi +(1+\\sigma R)L_{m}$, with $\\sigma$ being a\nconstant that carries the geometry-matter coupling. Our solutions are\nremarkably capable of evading the Big-Bang singularity as well as predict the\ncosmic acceleration with no need for the cosmological constant, but simply as a\nconsequence of the geometry-matter coupling terms in the Friedmann-like\nequations.\n"}
+{"abstract": "  Molecular science is governed by the dynamics of electrons, atomic nuclei,\nand their interaction with electromagnetic fields. A reliable physicochemical\nunderstanding of these processes is crucial for the design and synthesis of\nchemicals and materials of economic value. Although some problems in this field\nare adequately addressed by classical mechanics, many require an explicit\nquantum mechanical description. Such quantum problems represented by\nexponentially large wave function should naturally benefit from quantum\ncomputation on a number of logical qubits that scales only linearly with system\nsize. In this perspective, we focus on the potential of quantum computing for\nsolving relevant problems in the molecular sciences -- molecular physics,\nchemistry, biochemistry, and materials science.\n"}
+{"abstract": "  This research discusses multi-criteria decision making (MCDM) using Fuzzy-AHP\nmethods of tourism. The fuzzy-AHP process will rank tourism trends based on\ndata from social media. Social media is one of the channels with the largest\nsource of data input in determining tourism development. The development uses\nsocial media interactions based on the facilities visited, including reviews,\nstories, likes, forums, blogs, and feedback. This experiment aims to prioritize\nfacilities that are the trend of tourism. The priority ranking uses weight\ncriteria and the ranking process. The highest rank is in the attractions of the\nPark/Picnic Area, with the final weight calculation value of 0.6361. Fuzzy-AHP\ncan rank optimally with an MSE value of \\approx 0.0002.\n"}
+{"abstract": "  Despite their recent success on image denoising, the need for deep and\ncomplex architectures still hinders the practical usage of CNNs. Older but\ncomputationally more efficient methods such as BM3D remain a popular choice,\nespecially in resource-constrained scenarios. In this study, we aim to find out\nwhether compact neural networks can learn to produce competitive results as\ncompared to BM3D for AWGN image denoising. To this end, we configure networks\nwith only two hidden layers and employ different neuron models and layer widths\nfor comparing the performance with BM3D across different AWGN noise levels. Our\nresults conclusively show that the recently proposed self-organized variant of\noperational neural networks based on a generative neuron model (Self-ONNs) is\nnot only a better choice as compared to CNNs, but also provide competitive\nresults as compared to BM3D and even significantly surpass it for high noise\nlevels.\n"}
+{"abstract": "  This work investigates the feasibility of using input-output data-driven\ncontrol techniques for building control and their susceptibility to\ndata-poisoning techniques. The analysis is performed on a digital replica of\nthe KTH Livein Lab, a non-linear validated model representing one of the KTH\nLive-in Lab building testbeds. This work is motivated by recent trends showing\na surge of interest in using data-based techniques to control cyber-physical\nsystems. We also analyze the susceptibility of these controllers to\ndata-poisoning methods, a particular type of machine learning threat geared\ntowards finding imperceptible attacks that can undermine the performance of the\nsystem under consideration. We consider the Virtual Reference Feedback Tuning\n(VRFT), a popular data-driven control technique, and show its performance on\nthe KTH Live-In Lab digital replica. We then demonstrate how poisoning attacks\ncan be crafted and illustrate the impact of such attacks. Numerical experiments\nreveal the feasibility of using data-driven control methods for finding\nefficient control laws. However, a subtle change in the datasets can\nsignificantly deteriorate the performance of VRFT.\n"}
+{"abstract": "  Scanning real-life scenes with modern registration devices typically give\nincomplete point cloud representations, mostly due to the limitations of the\nscanning process and 3D occlusions. Therefore, completing such partial\nrepresentations remains a fundamental challenge of many computer vision\napplications. Most of the existing approaches aim to solve this problem by\nlearning to reconstruct individual 3D objects in a synthetic setup of an\nuncluttered environment, which is far from a real-life scenario. In this work,\nwe reformulate the problem of point cloud completion into an object\nhallucination task. Thus, we introduce a novel autoencoder-based architecture\ncalled HyperPocket that disentangles latent representations and, as a result,\nenables the generation of multiple variants of the completed 3D point clouds.\nWe split point cloud processing into two disjoint data streams and leverage a\nhypernetwork paradigm to fill the spaces, dubbed pockets, that are left by the\nmissing object parts. As a result, the generated point clouds are not only\nsmooth but also plausible and geometrically consistent with the scene. Our\nmethod offers competitive performances to the other state-of-the-art models,\nand it enables a~plethora of novel applications.\n"}
+{"abstract": "  Using five year monitoring observations, we did a blind search for pulses for\nrotating radio transient (RRAT) J0139+33 and PSR B0320+39. At the interval \\pm\n1.5m of the time corresponding to the source passing through the meridian, we\ndetected 39377 individual pulses for the pulsar B0320+39 and 1013 pulses for\nRRAT J0139+33. The share of registered pulses from the total number of observed\nperiods for the pulsar B0320+39 is 74%, and for the transient J0139+33 it is\n0.42%. Signal-to-noise ratio (S/N) for the strongest registered pulses is\napproximately equal to: S/N = 262 (for B0320+39) and S/N = 154 (for J0139+33).\n  Distributions of the number of detected pulses in S/N units for the pulsar\nand for the rotating transient are obtained. The distributions could be\napproximated with a lognormal and power dependencies. For B0320+39 pulsar, the\ndependence is lognormal, it turns into a power dependence at high values of\nS/N, and for RRAT J0139+33, the distribution of pulses by energy is described\nby a broken (bimodal) power dependence with an exponent of about 0.4 and 1.8\n(S/N < 19 and S/N > 19).\n  We have not detected regular (pulsar) emission of J0139+33. Analysis of the\nobtained data suggests that RRAT J0139+33 is a pulsar with giant pulses.\n"}
+{"abstract": "  A Banach space X has the SHAI (surjective homomorphisms are injective)\nproperty provided that for every Banach space Y, every continuous surjective\nalgebra homomorphism from the bounded linear operators on X onto the bounded\nlinear operators on Y is injective. The main result gives a sufficient\ncondition for X to have the SHAI property. The condition is satisfied for L^p\n(0, 1) for 1 < p < \\infty, spaces with symmetric bases that have finite cotype,\nand the Schatten p-spaces for 1 < p < \\infty.\n"}
+{"abstract": "  This paper introduces our systems for all three subtasks of SemEval-2021 Task\n4: Reading Comprehension of Abstract Meaning. To help our model better\nrepresent and understand abstract concepts in natural language, we well-design\nmany simple and effective approaches adapted to the backbone model (RoBERTa).\nSpecifically, we formalize the subtasks into the multiple-choice question\nanswering format and add special tokens to abstract concepts, then, the final\nprediction of question answering is considered as the result of subtasks.\nAdditionally, we employ many finetuning tricks to improve the performance.\nExperimental results show that our approaches achieve significant performance\ncompared with the baseline systems. Our approaches achieve eighth rank on\nsubtask-1 and tenth rank on subtask-2.\n"}
+{"abstract": "  How should we understand the social and political effects of the datafication\nof human life? This paper argues that the effects of data should be understood\nas a constitutive shift in social and political relations. We explore how\ndatafication, or quantification of human and non-human factors into binary\ncode, affects the identity of individuals and groups. This fundamental shift\ngoes beyond economic and ethical concerns, which has been the focus of other\nefforts to explore the effects of datafication and AI. We highlight that\ntechnologies such as datafication and AI (and previously, the printing press)\nboth disrupted extant power arrangements, leading to decentralization, and\ntriggered a recentralization of power by new actors better adapted to\nleveraging the new technology. We use the analogy of the printing press to\nprovide a framework for understanding constitutive change. The printing press\nexample gives us more clarity on 1) what can happen when the medium of\ncommunication drastically alters how information is communicated and stored; 2)\nthe shift in power from state to private actors; and 3) the tension of\nsimultaneously connecting individuals while driving them towards narrower\ncommunities through algorithmic analyses of data.\n"}
+{"abstract": "  The concept of an angle is one that often causes difficulties in metrology.\nThese are partly caused by a confusing mixture of several mathematical terms,\npartly by real mathematical difficulties and finally by imprecise terminology.\nThe purpose of this publication is to clarify misunderstandings and to explain\nwhy strict terminology is important. It will also be shown that most\nmisunderstandings regarding the `radian' can be avoided if some simple rules\nare obeyed.\n"}
+{"abstract": "  We explore the parameter space of a U(1) extension of the standard model --\nalso called the super-weak model -- from the point of view of explaining the\nobserved dark matter energy density in the Universe. The new particle spectrum\ncontains a complex scalar singlet and three right-handed neutrinos, among which\nthe lightest one is the dark matter candidate. We explore both freeze-in and\nfreeze-out mechanisms of dark matter production. In both cases, we find regions\nin the plane of the super-weak coupling vs. the mass of the new gauge boson\nthat are not excluded by current experimental constraints. These regions are\ndistinct and the one for freeze-out will be explored in searches for neutral\ngauge boson in the near future.\n"}
+{"abstract": "  This paper focuses on a core task in computational sustainability and\nstatistical ecology: species distribution modeling (SDM). In SDM, the\noccurrence pattern of a species on a landscape is predicted by environmental\nfeatures based on observations at a set of locations. At first, SDM may appear\nto be a binary classification problem, and one might be inclined to employ\nclassic tools (e.g., logistic regression, support vector machines, neural\nnetworks) to tackle it. However, wildlife surveys introduce structured noise\n(especially under-counting) in the species observations. If unaccounted for,\nthese observation errors systematically bias SDMs. To address the unique\nchallenges of SDM, this paper proposes a framework called StatEcoNet.\nSpecifically, this work employs a graphical generative model in statistical\necology to serve as the skeleton of the proposed computational framework and\ncarefully integrates neural networks under the framework. The advantages of\nStatEcoNet over related approaches are demonstrated on simulated datasets as\nwell as bird species data. Since SDMs are critical tools for ecological science\nand natural resource management, StatEcoNet may offer boosted computational and\nanalytical powers to a wide range of applications that have significant social\nimpacts, e.g., the study and conservation of threatened species.\n"}
+{"abstract": "  We study phase contributions of wave functions that occur in the evolution of\nGaussian surface gravity water wave packets with nonzero initial momenta\npropagating in the presence and absence of an effective external linear\npotential. Our approach takes advantage of the fact that in contrast to matter\nwaves, water waves allow us to measure both their amplitudes and phases.\n"}
+{"abstract": "  Non-Hermitian systems show a non-Hermitian skin effect, where the bulk states\nare localized at a boundary of the systems with open boundary conditions. In\nthis paper, we study dependence of the localization length of the eigenstates\non a system size in a specific non-Hermitian model with a critical\nnon-Hermitian skin effect, where the energy spectrum undergoes discontinuous\ntransition in the thermodynamic limit. We analytically show that the\neigenstates exhibit remarkable localization, known as scale-free localization,\nwhere the localization length is proportional to a system size. Our result\ngives a theoretical support for the scale-free localization, which has been\nproposed only numerically in previous works.\n"}
+{"abstract": "  Recently, [8] has proposed that heterogeneity of infectiousness (and\nsusceptibility) across individuals in infectious diseases, plays a major role\nin affecting the Herd Immunity Threshold (HIT). Such heterogeneity has been\nobserved in COVID-19 and is recognized as overdispersion (or\n\"super-spreading\"). The model of [8] suggests that super-spreaders contribute\nsignificantly to the effective reproduction factor, R, and that they are likely\nto get infected and immune early in the process. Consequently, under R_0 = 3\n(attributed to COVID-19), the Herd Immunity Threshold (HIT) is as low as 5%, in\ncontrast to 67% according to the traditional models [1, 2, 4, 10]. This work\nfollows up on [8] and proposes that heterogeneity of infectiousness\n(susceptibility) has two \"faces\" whose mix affects dramatically the HIT: (1)\nPersonal-Trait-, and (2) Event-Based- Infectiousness (Susceptibility). The\nformer is a personal trait of specific individuals (super-spreaders) and is\nnullified once those individuals are immune (as in [8]). The latter is\nevent-based (e.g cultural super-spreading events) and remains effective\nthroughout the process, even after the super-spreaders immune. We extend [8]'s\nmodel to account for these two factors, analyze it and conclude that the HIT is\nvery sensitive to the mix between (1) and (2), and under R_0 = 3 it can vary\nbetween 5% and 67%. Preliminary data from COVID-19 suggests that herd immunity\nis not reached at 5%. We address operational aspects and analyze the effects of\nlockdown strategies on the spread of a disease. We find that herd immunity (and\nHIT) is very sensitive to the lockdown type. While some lockdowns affect\npositively the disease blocking and increase herd immunity, others have adverse\neffects and reduce the herd immunity.\n"}
+{"abstract": "  The quantum operator $\\hat{T}_3$, corresponding to the projection of the\ntoroidal moment on the $z$ axis, admits several self-adjoint extensions, when\ndefined on the whole $\\mathbb{R}^3$ space. $\\hat{T}_3$ commutes with\n$\\hat{L}_3$ (the projection of the angular momentum operator on the $z$ axis)\nand they have a \\textit{natural set of coordinates} $(k,u,\\phi)$ where $\\phi$\nis the azimuthal angle. The second set of \\textit{natural coordinates} is\n$(k_1,k_2,u)$, where $k_1 = k\\cos\\phi$, $k_2 = k\\sin\\phi$. In both sets,\n$\\hat{T}_3 = -i\\hbar\\partial/\\partial u$, so any operator that is a function of\n$k$ and the partial derivatives with respect to the \\textit{natural variables}\n$(k, u, \\phi)$ commute with $\\hat{T}_3$ and $\\hat{L}_3$. Similarly, operators\nthat are functions of $k_1$, $k_2$, and the partial derivatives with respect to\n$k_1$, $k_2$, and $u$ commute with $\\hat{T}_3$. Therefore, we introduce here\nthe operators $\\hat{p}_{k} \\equiv -i \\hbar \\partial/\\partial k$,\n$\\hat{p}^{(k1)} \\equiv -i \\hbar \\partial/\\partial k_1$, and $\\hat{p}^{(k2)}\n\\equiv -i \\hbar \\partial/\\partial k_2$ and express them in the $(x,y,z)$\ncoordinates. One may also invert the relations and write the typical operators,\nlike the momentum $\\hat{\\bf p} \\equiv -i\\hbar {\\bf \\nabla}$ or the kinetic\nenergy $\\hat{H}_0 \\equiv -\\hbar^2\\Delta/(2m)$ in terms of the \"toroidal\"\noperators $\\hat{T}_3$, $\\hat{p}^{(k)}$, $\\hat{p}^{(k1)}$, $\\hat{p}^{(k2)}$,\nand, eventually, $\\hat{L}_3$. The formalism may be applied to specific physical\nsystems, like nuclei, condensed matter systems, or metamaterials. We exemplify\nit by calculating the momentum operator and the free particle Hamiltonian in\nterms of \\textit{natural coordinates} in a thin torus, where the general\nrelations get considerably simplified.\n"}
+{"abstract": "  Electronic states in the gap of a superconductor inherit intriguing many-body\nproperties from the superconductor. Here, we create these in-gap states by\nmanipulating Cr atomic chains on the $\\beta$-Bi$_2$Pd superconductor. We find\nthat the topological properties of the in-gap states can greatly vary depending\non the crafted spin chain. These systems make an ideal platform for non-trivial\ntopological phases because of the large atom-superconductor interactions and\nthe existence of a large Rashba coupling at the Bi-terminated surface. We study\ntwo spin chains, one with atoms two-lattice-parameter apart and one with\nsquare-root-of-two lattice parameters. Of these, only the second one is in a\ntopologically non-trivial phase, in correspondence with the spin interactions\nfor this geometry.\n"}
+{"abstract": "  Using density functional theory combined with nonequilibrium Green's function\nmethod, the transport properties of borophene-based nano gas sensors with gold\nelectrodes are calculated, and comprehensive understandings regarding the\neffects of gas molecules, MoS$_2$ substrate and gold electrodes to the\ntransport properties of borophene are made. Results show that borophene-based\nsensors can be used to detect and distinguish CO, NO, NO$_2$ and NH$_3$ gas\nmolecules, MoS$_2$ substrate leads to a non-linear behavior on the\ncurrent-voltage characteristic, and gold electrodes provide charges to\nborophene and form a potential barrier, which reduced the current values\ncompared to the current of the systems without gold electrodes. Our studies not\nonly provide useful information on the computationally design of\nborophene-based gas sensors, but also help understand the transport behaviors\nand underlying physics of 2D metallic materials with metal electrodes.\n"}
+{"abstract": "  RX J0123.4-7321 is a well-established Be star X-ray binary system (BeXRB) in\nthe Small Magellanic Cloud (SMC). Like many such systems the variable X-ray\nemission is driven by the underlying behaviour of the mass donor Be star.\nPrevious work has shown that the optical and X-ray were characterised by\nregular outbursts at the proposed binary period of 119 d. However around\nFebruary 2008 the optical behaviour changed substantially, with the previously\nregular optical outbursts ending. Reported here are new optical (OGLE) and\nX-ray (Swift) observations covering the period after 2008 which suggest an\nalmost total circumstellar disc loss followed by a gradual recovery. This\nindicates the probable transition of a Be star to a B star, and back again.\nHowever, at the time of the most recent OGLE data (March 2020) the\ncharacteristic periodic outbursts had yet to return to their early state,\nindicating that the disk still had some re-building yet to complete.\n"}
+{"abstract": "  Convection has been discussed in the field of accretion discs for several\ndecades, both as a means of angular momentum transport and also because of its\nrole in controlling discs' vertical structure via heat transport. If the gas is\nsufficiently ionized and threaded by a weak magnetic field, convection might\ninteract in non-trivial ways with the magnetorotational instability (MRI).\nRecently, vertically stratified local simulations of the MRI have reported\nconsiderable variation in the angular momentum transport, as measured by the\nstress to thermal pressure ratio $\\alpha$, when convection is thought to be\npresent. Although MRI turbulence can act as a heat source for convection, it is\nnot clear how the instabilities will interact dynamically. Here we aim to\ninvestigate the interplay between the two instabilities in controlled numerical\nexperiments, and thus isolate the generic features of their interaction. We\nperform vertically stratified, 3D MHD shearing box simulations with a perfect\ngas equation of state with the conservative, finite-volume code PLUTO. We find\ntwo characteristic outcomes of the interaction between the two instabilities:\nstraight MRI and MRI/convective cycles, with the latter exhibiting alternating\nphases of convection-dominated flow (during which the turbulent transport is\nweak) and MRI-dominated flow. During the latter phase we find that $\\alpha$ is\nenhanced by nearly an order of magnitude, reaching peak values of $\\sim 0.08$.\nIn addition, we find that convection in the non-linear phase takes the form of\nlarge-scale and oscillatory convective cells. Convection can also help the MRI\npersist to lower Rm than it would otherwise do. Finally we discuss how our\nresults help interpret simulations of Dwarf Novae.\n"}
+{"abstract": "  In this paper we study Chow motives whose identity map is killed by a natural\nnumber. Examples of such objects were constructed by Gorchinskiy-Orlov. We\nintroduce various invariants of torsion motives, in particular, the $p$-level.\nWe show that this invariant bounds from below the dimension of the variety a\ntorsion motive $M$ is a direct summand of and imposes restrictions on motivic\nand singular cohomology of $M$. We study in more details the $p$-torsion\nmotives of surfaces, in particular, the Godeaux torsion motive. We show that\nsuch motives are in 1-to-1 correspondence with certain Rost cycle submodules of\nfree modules over $H^*_{et}$. This description is parallel to that of mod-$p$\nreduced motives of curves.\n"}
+{"abstract": "  A very useful identity for Parseval frames for Hilbert spaces was obtained by\nBalan, Casazza, Edidin, and Kutyniok. In this paper, we obtain a similar\nidentity for Parseval p-approximate Schauder frames for Banach spaces which\nadmits a homogeneous semi-inner product in the sense of Lumer-Giles.\n"}
+{"abstract": "  We construct the hydrodynamic theory of coherent collective motion\n(\"flocking\") at a solid-liquid interface. The polar order parameter and\nconcentration of a collection of \"active\" (self-propelled) particles at a\nplanar interface between a passive, isotropic bulk fluid and a solid surface\nare dynamically coupled to the bulk fluid. We find that such systems are\nstable, and have long-range orientational order, over a wide range of\nparameters. When stable, these systems exhibit \"giant number fluctuations\",\ni.e., large fluctuations of the number of active particles in a fixed large\narea. Specifically, these number fluctuations grow as the $3/4$th power of the\nmean number within the area. Stable systems also exhibit anomalously rapid\ndiffusion of tagged particles suspended in the passive fluid along any\ndirections in a plane parallel to the solid-liquid interface, whereas the\ndiffusivity along the direction perpendicular to the plane is non-anomalous. In\nother parameter regimes, the system becomes unstable.\n"}
+{"abstract": "  As the fundamental physical process with many astrophysical implications, the\ndiffusion of cosmic rays (CRs) is determined by their interaction with\nmagnetohydrodynamic (MHD) turbulence. We consider the magnetic mirroring effect\narising from MHD turbulence on the diffusion of CRs. Due to the intrinsic\nsuperdiffusion of turbulent magnetic fields, CRs with large pitch angles that\nundergo mirror reflection, i.e., bouncing CRs, are not trapped between magnetic\nmirrors, but move diffusively along the turbulent magnetic field, leading to a\nnew type of parallel diffusion, i.e., mirror diffusion. This mirror diffusion\nis in general slower than the diffusion of non-bouncing CRs with small pitch\nangles that undergo gyroresonant scattering. The critical pitch angle at the\nbalance between magnetic mirroring and pitch-angle scattering is important for\ndetermining the diffusion coefficients of both bouncing and non-bouncing CRs\nand their scalings with the CR energy. We find non-universal energy scalings of\ndiffusion coefficients, depending on the properties of MHD turbulence.\n"}
+{"abstract": "  Floquet engineering is the concept of tailoring a system by a periodic drive.\nIt has been very successful in opening new classes of Hamiltonians to the study\nwith ultracold atoms in optical lattices, such as artificial gauge fields,\ntopological band structures and density-dependent tunneling. Furthermore,\ndriven systems provide new physics without static counterpart such as anomalous\nFloquet topological insulators. In this review article, we provide an overview\nof the exciting developments in the field and discuss the current challenges\nand perspectives.\n"}
+{"abstract": "  Convex clustering is an attractive clustering algorithm with favorable\nproperties such as efficiency and optimality owing to its convex formulation.\nIt is thought to generalize both k-means clustering and agglomerative\nclustering. However, it is not known whether convex clustering preserves\ndesirable properties of these algorithms. A common expectation is that convex\nclustering may learn difficult cluster types such as non-convex ones. Current\nunderstanding of convex clustering is limited to only consistency results on\nwell-separated clusters. We show new understanding of its solutions. We prove\nthat convex clustering can only learn convex clusters. We then show that the\nclusters have disjoint bounding balls with significant gaps. We further\ncharacterize the solutions, regularization hyperparameters, inclusterable cases\nand consistency.\n"}
+{"abstract": "  In this paper we derive sharp lower and upper bounds for the covariance of\ntwo bounded random variables when knowledge about their expected values,\nvariances or both is available. When only the expected values are known, our\nresult can be viewed as an extension of the Bhatia-Davis Inequality for\nvariances. We also provide a number of different ways to standardize\ncovariance. For a binary pair random variables, one of these standardized\nmeasures of covariation agrees with a frequently used measure of dependence\nbetween genetic variants.\n"}
+{"abstract": "  The production, application, and/or measurement of polarised X-/gamma rays\nare key to the fields of synchrotron science and X-/gamma-ray astronomy. The\ndesign, development and optimisation of experimental equipment utilised in\nthese fields typically relies on the use of Monte Carlo radiation transport\nmodelling toolkits such as Geant4. In this work the Geant4 \"G4LowEPPhysics\"\nelectromagnetic physics constructor has been reconfigured to offer a \"best set\"\nof electromagnetic physics models for studies exploring the transport of low\nenergy polarised X-/gamma rays. An overview of the physics models implemented\nin \"G4LowEPPhysics\", and it's experimental validation against Compton X-ray\npolarimetry measurements of the BL38B1 beamline at the SPring-8 synchrotron\n(Sayo, Japan) is reported. \"G4LowEPPhysics\" is shown to be able to reproduce\nthe experimental results obtained at the BL38B1 beamline (SPring-8) to within a\nlevel of accuracy on the same order as Geant4's X-/gamma ray interaction\ncross-sectional data uncertainty (approximately $\\pm$ 5 \\%).\n"}
+{"abstract": "  The overwhelming amount of biomedical scientific texts calls for the\ndevelopment of effective language models able to tackle a wide range of\nbiomedical natural language processing (NLP) tasks. The most recent dominant\napproaches are domain-specific models, initialized with general-domain textual\ndata and then trained on a variety of scientific corpora. However, it has been\nobserved that for specialized domains in which large corpora exist, training a\nmodel from scratch with just in-domain knowledge may yield better results.\nMoreover, the increasing focus on the compute costs for pre-training recently\nled to the design of more efficient architectures, such as ELECTRA. In this\npaper, we propose a pre-trained domain-specific language model, called\nELECTRAMed, suited for the biomedical field. The novel approach inherits the\nlearning framework of the general-domain ELECTRA architecture, as well as its\ncomputational advantages. Experiments performed on benchmark datasets for\nseveral biomedical NLP tasks support the usefulness of ELECTRAMed, which sets\nthe novel state-of-the-art result on the BC5CDR corpus for named entity\nrecognition, and provides the best outcome in 2 over the 5 runs of the 7th\nBioASQ-factoid Challange for the question answering task.\n"}
+{"abstract": "  Payment channel networks are a promising approach to improve the scalability\nof cryptocurrencies: they allow to perform transactions in a peer-to-peer\nfashion, along multi-hop routes in the network, without requiring consensus on\nthe blockchain. However, during the discovery of cost-efficient routes for the\ntransaction, critical information may be revealed about the transacting\nentities.\n  This paper initiates the study of privacy-preserving route discovery\nmechanisms for payment channel networks. In particular, we present LightPIR, an\napproach which allows a source to efficiently discover a shortest path to its\ndestination without revealing any information about the endpoints of the\ntransaction. The two main observations which allow for an efficient solution in\nLightPIR are that: (1) surprisingly, hub labelling algorithms - which were\ndeveloped to preprocess \"street network like\" graphs so one can later\nefficiently compute shortest paths - also work well for the graphs underlying\npayment channel networks, and that (2) hub labelling algorithms can be directly\ncombined with private information retrieval.\n  LightPIR relies on a simple hub labeling heuristic on top of existing hub\nlabeling algorithms which leverages the specific topological features of\ncryptocurrency networks to further minimize storage and bandwidth overheads. In\na case study considering the Lightning network, we show that our approach is an\norder of magnitude more efficient compared to a privacy-preserving baseline\nbased on using private information retrieval on a database that stores all\npairs shortest paths.\n"}
+{"abstract": "  As an essential characteristics of fractional calculus, the memory effect is\nserved as one of key factors to deal with diverse practical issues, thus has\nbeen received extensive attention since it was born. By combining the\nfractional derivative with memory effects and grey modeling theory, this paper\naims to construct an unified framework for the commonly-used fractional grey\nmodels already in place. In particular, by taking different kernel and\nnormalization functions, this framework can deduce some other new fractional\ngrey models. To further improve the prediction performance, the four popular\nintelligent algorithms are employed to determine the emerging coefficients for\nthe UFGM(1,1) model. Two published cases are then utilized to verify the\nvalidity of the UFGM(1,1) model and explore the effects of fractional\naccumulation order and initial value on the prediction accuracy, respectively.\nFinally, this model is also applied to dealing with two real examples so as to\nfurther explain its efficacy and equally show how to use the unified framework\nin practical applications.\n"}
+{"abstract": "  B-splines are widely used in the fields of reverse engineering and\ncomputer-aided design, due to their superior properties. Traditional B-spline\nsurface interpolation algorithms usually assume regularity of the data\ndistribution. In this paper, we introduce a novel B-spline surface\ninterpolation algorithm: KPI, which can interpolate sparsely and non-uniformly\ndistributed data points. As a two-stage algorithm, our method generates the\ndataset out of the sparse data using Kriging, and uses the proposed KPI\n(Key-Point Interpolation) method to generate the control points. Our algorithm\ncan be extended to higher dimensional data interpolation, such as\nreconstructing dynamic surfaces. We apply the method to interpolating the\ntemperature of Shanxi Province. The generated dynamic surface accurately\ninterpolates the temperature data provided by the weather stations, and the\npreserved dynamic characteristics can be useful for meteorology studies.\n"}
+{"abstract": "  Software projects are regularly updated with new functionality and bug fixes\nthrough so-called releases. In recent years, many software projects have been\nshifting to shorter release cycles and this can affect the bug handling\nactivity. Past research has focused on the impact of switching from traditional\nto rapid release cycles with respect to bug handling activity, but the effect\nof the rapid release cycle duration has not yet been studied. We empirically\ninvestigate releases of 420 open source projects with rapid release cycles to\nunderstand the effect of variable and rapid release cycle durations on bug\nhandling activity. We group the releases of these projects into five categories\nof release cycle durations. For each project, we investigate how the sequence\nof releases is related to bug handling activity metrics and we study the effect\nof the variability of cycle durations on bug fixing. Our results did not reveal\nany statistically significant difference for the studied bug handling activity\nmetrics in the presence of variable rapid release cycle durations. This\nsuggests that the duration of fast release cycles does not seem to impact bug\nhandling activity.\n"}
+{"abstract": "  We derive major parts of the eigenvalue spectrum of the operators on the\nsquashed seven-sphere that appear in the compactification of eleven-dimensional\nsupergravity. These spectra determine the mass spectrum of the fields in\n$AdS_4$ and are important for the corresponding ${\\mathcal N} =1$\nsupermultiplet structure. This work is a continuation of the work in [1] where\nthe complete spectrum of irreducible isometry representations of the fields in\n$AdS_4$ was derived for this compactification. Some comments are also made\nconcerning the $G_2$ holonomy and its implications on the structure of the\noperator equations on the squashed seven-sphere.\n"}
+{"abstract": "  We present a quantum error correcting code with dynamically generated logical\nqubits. When viewed as a subsystem code, the code has no logical qubits.\nNevertheless, our measurement patterns generate logical qubits, allowing the\ncode to act as a fault-tolerant quantum memory. Our particular code gives a\nmodel very similar to the two-dimensional toric code, but each measurement is a\ntwo-qubit Pauli measurement.\n"}
+{"abstract": "  Dilatancy associated with fault slip produces a transient pore pressure drop\nwhich increases frictional strength. This effect is analysed in a steadily\npropagating rupture model that includes frictional weakening, slip-dependent\nfault dilation and fluid flow. Dilatancy is shown to increase the stress\nintensity factor required to propagate the rupture tip. With increasing rupture\nspeed, an undrained (strengthened) region develops near the tip and extends\nbeyond the frictionally weakened zone. Away from the undrained region, pore\nfluid diffusion gradually recharges the fault and strength returns to the\ndrained, weakened value. For sufficiently large rupture dimensions, the\ndilation-induced strength increase near the tip is equivalent to an increase in\ntoughness that is proportional to the square root of the rupture speed. In\ngeneral, dilation has the effect of increasing the stress required for rupture\ngrowth by decreasing the stress drop along the crack. Thermal pressurisation\nhas the potential to compensate for the dilatant strengthening effect, at the\nexpense of an increased heating rate, which might lead to premature frictional\nmelting. Using reasonable laboratory parameters, the dilatancy-toughening\neffect leads to rupture dynamics that is quantitatively consistent with the\ndynamics of observed slow slip events in subduction zones.\n"}
+{"abstract": "  We develop a geometrical micro-local analysis of contact Anosov flow, such as\ngeodesic flow on negatively curved manifold. We use the method of wave-packet\ntransform discussed in arXiv:1706.09307 and observe that the transfer operator\nis well approximated (in the high frequency limit) by the quantization of an\ninduced transfer operator acting on sections of some vector bundle on the\ntrapped set. This gives a few important consequences: The discrete eigenvalues\nof the generator of transfer operators, called Ruelle spectrum, are structured\ninto vertical bands. If the right-most band is isolated from the others, most\nof the Ruelle spectrum in it concentrate along a line parallel to the imaginary\naxis and, further, the density satisfies a Weyl law as the imaginary part tend\nto infinity. Some of these results were announced in arXiv:1301.5525.\n"}
+{"abstract": "  While sophisticated Visual Question Answering models have achieved remarkable\nsuccess, they tend to answer questions only according to superficial\ncorrelations between question and answer. Several recent approaches have been\ndeveloped to address this language priors problem. However, most of them\npredict the correct answer according to one best output without checking the\nauthenticity of answers. Besides, they only explore the interaction between\nimage and question, ignoring the semantics of candidate answers. In this paper,\nwe propose a select-and-rerank (SAR) progressive framework based on Visual\nEntailment. Specifically, we first select the candidate answers relevant to the\nquestion or the image, then we rerank the candidate answers by a visual\nentailment task, which verifies whether the image semantically entails the\nsynthetic statement of the question and each candidate answer. Experimental\nresults show the effectiveness of our proposed framework, which establishes a\nnew state-of-the-art accuracy on VQA-CP v2 with a 7.55% improvement.\n"}
+{"abstract": "  The architecture of circuital quantum computers requires computing layers\ndevoted to compiling high-level quantum algorithms into lower-level circuits of\nquantum gates. The general problem of quantum compiling is to approximate any\nunitary transformation that describes the quantum computation, as a sequence of\nelements selected from a finite base of universal quantum gates. The existence\nof an approximating sequence of one qubit quantum gates is guaranteed by the\nSolovay-Kitaev theorem, which implies sub-optimal algorithms to establish it\nexplicitly. Since a unitary transformation may require significantly different\ngate sequences, depending on the base considered, such a problem is of great\ncomplexity and does not admit an efficient approximating algorithm. Therefore,\ntraditional approaches are time-consuming tasks, unsuitable to be employed\nduring quantum computation. We exploit the deep reinforcement learning method\nas an alternative strategy, which has a significantly different trade-off\nbetween search time and exploitation time. Deep reinforcement learning allows\ncreating single-qubit operations in real time, after an arbitrary long training\nperiod during which a strategy for creating sequences to approximate unitary\noperators is built. The deep reinforcement learning based compiling method\nallows for fast computation times, which could in principle be exploited for\nreal-time quantum compiling.\n"}
+{"abstract": "  We solve the large deviations of the Kardar-Parisi-Zhang (KPZ) equation in\none dimension at short time by introducing an approach which combines field\ntheoretical, probabilistic and integrable techniques. We expand the program of\nthe weak noise theory, which maps the large deviations onto a non-linear\nhydrodynamic problem, and unveil its complete solvability through a connection\nto the integrability of the Zakharov-Shabat system. Exact solutions, depending\non the initial condition of the KPZ equation, are obtained using the inverse\nscattering method and a Fredholm determinant framework recently developed.\nThese results, explicit in the case of the droplet geometry, open the path to\nobtain the complete large deviations for general initial conditions.\n"}
+{"abstract": "  Grounding natural language instructions on the web to perform previously\nunseen tasks enables accessibility and automation. We introduce a task and\ndataset to train AI agents from open-domain, step-by-step instructions\noriginally written for people. We build RUSS (Rapid Universal Support Service)\nto tackle this problem. RUSS consists of two models: First, a BERT-LSTM with\npointers parses instructions to ThingTalk, a domain-specific language we design\nfor grounding natural language on the web. Then, a grounding model retrieves\nthe unique IDs of any webpage elements requested in ThingTalk. RUSS may\ninteract with the user through a dialogue (e.g. ask for an address) or execute\na web operation (e.g. click a button) inside the web runtime. To augment\ntraining, we synthesize natural language instructions mapped to ThingTalk. Our\ndataset consists of 80 different customer service problems from help websites,\nwith a total of 741 step-by-step instructions and their corresponding actions.\nRUSS achieves 76.7% end-to-end accuracy predicting agent actions from single\ninstructions. It outperforms state-of-the-art models that directly map\ninstructions to actions without ThingTalk. Our user study shows that RUSS is\npreferred by actual users over web navigation.\n"}
+{"abstract": "  The goal of this paper is to open up a new research direction aimed at\nunderstanding the power of preprocessing in speeding up algorithms that solve\nNP-hard problems exactly. We explore this direction for the classic Feedback\nVertex Set problem on undirected graphs, leading to a new type of graph\nstructure called antler decomposition, which identifies vertices that belong to\nan optimal solution. It is an analogue of the celebrated crown decomposition\nwhich has been used for Vertex Cover. We develop the graph structure theory\naround such decompositions and develop fixed-parameter tractable algorithms to\nfind them, parameterized by the number of vertices for which they witness\npresence in an optimal solution. This reduces the search space of\nfixed-parameter tractable algorithms parameterized by the solution size that\nsolve Feedback Vertex Set.\n"}
+{"abstract": "  Experiments have shown that hepatitis C virus (HCV) infections in vitro\ndisseminate both distally via the release and diffusion of cell-free virus\nthrough the medium, and locally via direct, cell-to-cell transmission. To\ndetermine the relative contribution of each mode of infection to HCV\ndissemination, we developed an agent-based model (ABM) that explicitly\nincorporates both distal and local modes of infection. The ABM tracks the\nconcentration of extracellular infectious virus in the supernatant and the\nnumber of intracellular HCV RNA segments within each infected cell over the\ncourse of simulated in vitro HCV infections. Experimental data for in vitro HCV\ninfections conducted in the presence and absence of free-virus neutralizing\nantibodies was used to validate the ABM and constrain the value of its\nparameters. We found that direct, cell-to-cell infection accounts for 99%\n(84%$-$100%, 95% credible interval) of infection events, making it the dominant\nmode of HCV dissemination in vitro. Yet, when infection via the free-virus\nroute is blocked, a 57% reduction in the number of infection events at 72 hpi\nis observed experimentally; a result consistent with that found by our ABM.\nTaken together, these findings suggest that while HCV spread via cell-free\nvirus contributes little to the total number of infection events in vitro, it\nplays a critical role in enhancing cell-to-cell HCV dissemination by providing\naccess to distant, uninfected areas, away from the already established large\ninfection foci.\n"}
+{"abstract": "  We investigate the second harmonic generation of light field carrying orbital\nangular momentum in bulk $\\chi^{(2)}$ material. We show that due to\nconservation of energy and momentum, the frequency doubled beam light has a\nmodified spatial distribution and mode characteristics. Through rigorous phase\nmatching conditions, we demonstrate efficient mode and frequency conversion\nbased on three wave nonlinear optical mixing.\n"}
+{"abstract": "  We develop two parallel machine-learning pipelines to estimate the\ncontribution of cosmic strings (CSs), conveniently encoded in their tension\n($G\\mu$), to the anisotropies of the cosmic microwave background radiation\nobserved by {\\it Planck}. The first approach is tree-based and feeds on certain\nmap features derived by image processing and statistical tools. The second uses\nconvolutional neural network with the goal to explore possible non-trivial\nfeatures of the CS imprints. The two pipelines are trained on {\\it Planck}\nsimulations and when applied to {\\it Planck} \\texttt{SMICA} map yield the\n$3\\sigma$ upper bound of $G\\mu\\lesssim 8.6\\times 10^{-7}$. We also train and\napply the pipelines to make forecasts for futuristic CMB-S4-like surveys and\nconservatively find their minimum detectable tension to be $G\\mu_{\\rm min}\\sim\n1.9\\times 10^{-7}$.\n"}
+{"abstract": "  We explore quantitative descriptors that herald when a many-particle system\nin $d$-dimensional Euclidean space $\\mathbb{R}^d$ approaches a hyperuniform\nstate as a function of the relevant control parameter. We establish\nquantitative criteria to ascertain the extent of hyperuniform and\nnonhyperuniform distance-scaling regimes n terms of the ratio $B/A$, where $A$\nis \"volume\" coefficient and $B$ is \"surface-area\" coefficient associated with\nthe local number variance $\\sigma^2(R)$ for a spherical window of radius $R$.\nTo complement the known direct-space representation of the coefficient $B$ in\nterms of the total correlation function $h({\\bf r})$, we derive its\ncorresponding Fourier representation in terms of the structure factor $S({\\bf\nk})$, which is especially useful when scattering information is available\nexperimentally or theoretically. We show that the free-volume theory of the\npressure of equilibrium packings of identical hard spheres that approach a\nstrictly jammed state either along the stable crystal or metastable disordered\nbranch dictates that such end states be exactly hyperuniform. Using the ratio\n$B/A$, the hyperuniformity index $H$ and the direct-correlation function length\nscale $\\xi_c$, we study three different exactly solvable models as a function\nof the relevant control parameter, either density or temperature, with end\nstates that are perfectly hyperuniform. We analyze equilibrium hard rods and\n\"sticky\" hard-sphere systems in arbitrary space dimension $d$ as a function of\ndensity. We also examine low-temperature excited states of many-particle\nsystems interacting with \"stealthy\" long-ranged pair interactions as the\ntemperature tends to zero. The capacity to identify hyperuniform scaling\nregimes should be particularly useful in analyzing experimentally- or\ncomputationally-generated samples that are necessarily of finite size.\n"}
+{"abstract": "  We develop an approach to choice principles and their contrapositive\nbar-induction principles as extensionality schemes connecting an \"intensional\"\nor \"effective\" view of respectively ill-and well-foundedness properties to an\n\"extensional\" or \"ideal\" view of these properties. After classifying and\nanalysing the relations between different intensional definitions of\nill-foundedness and well-foundedness, we introduce, for a domain $A$, a\ncodomain $B$ and a \"filter\" $T$ on finite approximations of functions from $A$\nto $B$, a generalised form GDC$_{A,B,T}$ of the axiom of dependent choice and\ndually a generalised bar induction principle GBI$_{A,B,T}$ such that:\n  GDC$_{A,B,T}$ intuitionistically captures the strength of\n  $\\bullet$ the general axiom of choice expressed as $\\forall a\\exists b R(a,\nb) \\Rightarrow\\exists\\alpha\\forall \\alpha R(\\alpha,\\alpha(a))$ when $T$ is a\nfilter that derives point-wise from a relation $R$ on $A \\times B$ without\nintroducing further constraints,\n  $\\bullet$ the Boolean Prime Filter Theorem / Ultrafilter Theorem if $B$ is\nthe two-element set $\\mathbb{B}$ (for a constructive definition of prime\nfilter),\n  $\\bullet$ the axiom of dependent choice if $A = \\mathbb{N}$,\n  $\\bullet$ Weak K{\\\"o}nig's Lemma if $A = \\mathbb{N}$ and $B = \\mathbb{B}$ (up\nto weak classical reasoning)\n  GBI$_{A,B,T}$ intuitionistically captures the strength of\n  $\\bullet$ G{\\\"o}del's completeness theorem in the form validity implies\nprovability for entailment relations if $B = \\mathbb{B}$,\n  $\\bullet$ bar induction when $A = \\mathbb{N}$,\n  $\\bullet$ the Weak Fan Theorem when $A = \\mathbb{N}$ and $B = \\mathbb{B}$.\n  Contrastingly, even though GDC$_{A,B,T}$ and GBI$_{A,B,T}$ smoothly capture\nseveral variants of choice and bar induction, some instances are inconsistent,\ne.g. when $A$ is $\\mathbb{B}^\\mathbb{N}$ and $B$ is $\\mathbb{N}$.\n"}
+{"abstract": "  The Cyber Science Lab (CSL) and Smart Cyber-Physical System (SCPS) Lab at the\nUniversity of Guelph conduct a market study of cybersecurity technology\nadoption and requirements for smart and precision farming in Canada. We\nconducted 17 stakeholder/key opinion leader interviews in Canada and the USA,\nas well as conducting extensive secondary research, to complete this study.\nEach interview generally required 15-20 minutes to complete. Interviews were\nconducted using a client-approved interview guide. Secondary and primary\nresearch focussed on the following areas of investigation: Market size and\nsegmentation Market forecast and growth rate Competitive landscape Market\nchallenges/barriers to entry Market trends/growth drivers\nAdoption/commercialization of the technology\n"}
+{"abstract": "  AGBs and YSOs often share the same domains in IR color-magnitude or\ncolor-color diagrams leading to potential mis-classification. We extracted a\nlist of AGB interlopers from the published YSO catalogues using the periodogram\nanalysis on NEOWISE time series data. YSO IR variability is typically\nstochastic and linked to episodic mass accretion. Furthermore, most variable\nYSOs are at an early evolutionary stage, with significant surrounding envelope\nand/or disk material. In contrast, AGBs are often identified by a well defined\nsinusoidal variability with periods of a few hundreds days. From our\nperiodogram analysis of all known low mass YSOs in the Gould Belt, we find 85\nAGB candidates, out of which 62 were previously classified as late-stage Class\nIII YSOs. Most of these new AGB candidates have similar IR colors to O-rich\nAGBs. We observed 73 of these AGB candidates in the H2O, CH3OH and SiO maser\nlines to further reveal their nature. The SiO maser emission was detected in 10\nsources, confirming them as AGBs since low mass YSOs, especially Class III\nYSOs, do not show such maser emission. The H2O and CH3OH maser lines were\ndetected in none of our targets.\n"}
+{"abstract": "  In multi-component scalar dark matter scenarios, a single $Z_N$ ($N\\geq 4$)\nsymmetry may account for the stability of different dark matter particles. Here\nwe study the case where $N$ is even ($N=2n$) and two species, a complex scalar\nand a real scalar, contribute to the observed dark matter density. We perform a\nphenomenological analysis of three scenarios based on the $Z_4$ and $Z_6$\nsymmetries, characterizing their viable parameter spaces and analyzing their\ndetection prospects. Our results show that, thanks to the new interactions\nallowed by the $Z_{2n}$ symmetry, current experimental constraints can be\nsatisfied over a wider range of dark matter masses, and that these scenarios\nmay lead to observable signals in direct detection experiments. Finally, we\nargue that these three scenarios serve as prototypes for other two-component\n$Z_{2n}$ models with one complex and one real dark matter particle.\n"}
+{"abstract": "  Astrophysical black holes are thought to be the Kerr black holes predicted by\ngeneral relativity, but macroscopic deviations from the Kerr solution can be\nexpected from a number of scenarios involving new physics. In Paper I, we\nstudied the reflection features in NuSTAR and XMM-Newton spectra of the\nsupermassive black hole at the center of the galaxy MCG-06-30-15 and we\nconstrained a set of deformation parameters proposed by Konoplya, Rezzolla &\nZhidenko (Phys. Rev. D93, 064015, 2016). In the present work, we analyze the\nX-ray data of a stellar-mass black hole within the same theoretical framework\nin order to probe a different curvature regime. We consider a NuSTAR\nobservation of the X-ray binary EXO 1846-031 during its outburst in 2019. As in\nthe case of Paper I, all our fits are consistent with the Kerr black hole\nhypothesis, but some deformation parameters cannot be constrained well.\n"}
+{"abstract": "  Intuitively, one would expect the accuracy of a trained neural network's\nprediction on a test sample to correlate with how densely that sample is\nsurrounded by seen training samples in representation space. In this work we\nprovide theory and experiments that support this hypothesis. We propose an\nerror function for piecewise linear neural networks that takes a local region\nin the network's input space and outputs smooth empirical training error, which\nis an average of empirical training errors from other regions weighted by\nnetwork representation distance. A bound on the expected smooth error for each\nregion scales inversely with training sample density in representation space.\nEmpirically, we verify this bound is a strong predictor of the inaccuracy of\nthe network's prediction on test samples. For unseen test sets, including those\nwith out-of-distribution samples, ranking test samples by their local region's\nerror bound and discarding samples with the highest bounds raises prediction\naccuracy by up to 20% in absolute terms, on image classification datasets.\n"}
+{"abstract": "  Current deep learning models for classification tasks in computer vision are\ntrained using mini-batches. In the present article, we take advantage of the\nrelationships between samples in a mini-batch, using graph neural networks to\naggregate information from similar images. This helps mitigate the adverse\neffects of alterations to the input images on classification performance.\nDiverse experiments on image-based object and scene classification show that\nthis approach not only improves a classifier's performance but also increases\nits robustness to image perturbations and adversarial attacks. Further, we also\nshow that mini-batch graph neural networks can help to alleviate the problem of\nmode collapse in Generative Adversarial Networks.\n"}
+{"abstract": "  Two main methods have been proposed to derive the acoustical radiation force\nand torque applied by an arbitrary acoustic field on a particle: The first one\nrelies on the plane wave angular spectrum decomposition of the incident field\n(see [Sapozhnikov and Bailey, J. Acoust. Soc. Am. 133, 661 (2013)] for the\nforce and [Gong and Baudoin, J. Acoust. Soc. Am. 148, 3131 (2020)] for the\ntorque), while the second one relies on the decomposition of the incident field\ninto a sum of spherical waves, the so-called multipole expansion (see [Silva,\nJ. Acoust. Soc. Am. 130, 3541 (2011)] and [Baresh et al., J. Acoust. Soc. Am.\n133, 25 (2013)] for the force, and [Silva et al., EPL 97, 54003 (2012)] and\n[Gong et al., Phys. Rev. Applied 11, 064022 (2019)] for the torque). In this\npaper, we formally establish the equivalence between the expressions obtained\nwith these two methods for both the force and torque.\n"}
+{"abstract": "  B-doped $\\delta$-layers were fabricated in Si(100) using BCl$_{3}$ as a\ndopant precursor in ultrahigh vacuum. BCl$_{3}$ adsorbed readily at room\ntemperature, as revealed by scanning tunneling microscopy (STM) imaging.\nAnnealing at elevated temperatures facilitated B incorporation into the Si\nsubstrate. Secondary ion mass spectrometry (SIMS) depth profiling demonstrated\na peak B concentration $>$ 1.2(1) $\\times$ 10$^{21}$ cm$^{-3}$ with a total\nareal dose of 1.85(1) $\\times$ 10$^{14}$ cm$^{-2}$ resulting from a 30 L\nBCl$_{3}$ dose at 150 $^{\\circ}$C. Hall bar measurements of a similar sample\nwere performed at 3.0 K revealing a sheet resistance of $R_{\\mathrm{s}}$ = 1.91\nk$\\Omega\\square^{-1}$, a hole concentration of $n$ = 1.90 $\\times$ 10$^{14}$\ncm$^{-2}$ and a hole mobility of $\\mu$ = 38.0 cm$^{2}$V$^{-1}$s$^{-1}$ without\nperforming an incorporation anneal. Further, the conductivity of several\nB-doped $\\delta$-layers showed a log dependence on temperature suggestive of a\ntwo-dimensional system. Selective-area deposition of BCl$_{3}$ was also\ndemonstrated using both H- and Cl-based monatomic resists. In comparison to a\ndosed area on bare Si, adsorption selectivity ratios for H and Cl resists were\ndetermined by SIMS to be 310(10):1 and 1529(5):1, respectively, further\nvalidating the use of BCl$_{3}$ as a dopant precursor for atomic precision\nfabrication of acceptor-doped devices in Si.\n"}
+{"abstract": "  This paper proposes a novel way to solve transient linear, and non-linear\nsolid dynamics for compressible, nearly incompressible, and incompressible\nmaterial in the updated Lagrangian framework for tetrahedral unstructured\nfinite elements. It consists of a mixed formulation in both displacement and\npressure, where the momentum equation of the continuum is complemented with a\npressure equation that handles incompresibility inherently. It is obtained\nthrough the deviatoric and volumetric split of the stress, that enables us to\nsolve the problem in the incompressible limit. The Varitaional Multi-Scale\nmethod (VMS) is developed based on the orthogonal decomposition of the\nvariables, which damps out spurious pressure fields for piece wise linear\ntetrahedral elements. Various numerical examples are presented to assess the\nrobustness, accuracy and capabilities of our scheme in bending dominated\nproblems, and for complex geometries.\n"}
+{"abstract": "  Coronary artery disease (CAD) has posed a leading threat to the lives of\ncardiovascular disease patients worldwide for a long time. Therefore, automated\ndiagnosis of CAD has indispensable significance in clinical medicine. However,\nthe complexity of coronary artery plaques that cause CAD makes the automatic\ndetection of coronary artery stenosis in Coronary CT angiography (CCTA) a\ndifficult task. In this paper, we propose a Transformer network (TR-Net) for\nthe automatic detection of significant stenosis (i.e. luminal narrowing > 50%)\nwhile practically completing the computer-assisted diagnosis of CAD. The\nproposed TR-Net introduces a novel Transformer, and tightly combines\nconvolutional layers and Transformer encoders, allowing their advantages to be\ndemonstrated in the task. By analyzing semantic information sequences, TR-Net\ncan fully understand the relationship between image information in each\nposition of a multiplanar reformatted (MPR) image, and accurately detect\nsignificant stenosis based on both local and global information. We evaluate\nour TR-Net on a dataset of 76 patients from different patients annotated by\nexperienced radiologists. Experimental results illustrate that our TR-Net has\nachieved better results in ACC (0.92), Spec (0.96), PPV (0.84), F1 (0.79) and\nMCC (0.74) indicators compared with the state-of-the-art methods. The source\ncode is publicly available from the link (https://github.com/XinghuaMa/TR-Net).\n"}
+{"abstract": "  We introduce large-scale Augmented Granger Causality (lsAGC) as a method for\nconnectivity analysis in complex systems. The lsAGC algorithm combines\ndimension reduction with source time-series augmentation and uses predictive\ntime-series modeling for estimating directed causal relationships among\ntime-series. This method is a multivariate approach, since it is capable of\nidentifying the influence of each time-series on any other time-series in the\npresence of all other time-series of the underlying dynamic system. We\nquantitatively evaluate the performance of lsAGC on synthetic directional\ntime-series networks with known ground truth. As a reference method, we compare\nour results with cross-correlation, which is typically used as a standard\nmeasure of connectivity in the functional MRI (fMRI) literature. Using\nextensive simulations for a wide range of time-series lengths and two different\nsignal-to-noise ratios of 5 and 15 dB, lsAGC consistently outperforms\ncross-correlation at accurately detecting network connections, using Receiver\nOperator Characteristic Curve (ROC) analysis, across all tested time-series\nlengths and noise levels. In addition, as an outlook to possible clinical\napplication, we perform a preliminary qualitative analysis of connectivity\nmatrices for fMRI data of Autism Spectrum Disorder (ASD) patients and typical\ncontrols, using a subset of 59 subjects of the Autism Brain Imaging Data\nExchange II (ABIDE II) data repository. Our results suggest that lsAGC, by\nextracting sparse connectivity matrices, may be useful for network analysis in\ncomplex systems, and may be applicable to clinical fMRI analysis in future\nresearch, such as targeting disease-related classification or regression tasks\non clinical data.\n"}
+{"abstract": "  Frustrated Mott insulators such as, transition metal dichalcogenide\n1T-TaS$_{2}$ present an ideal platform for the experimental realization of\ndisorder induced insulator-metal transition. In this letter we present the\nfirst non perturbative theoretical investigation of the disorder induced\ninsulator-metal transition in copper (Cu) intercalated 1T-TaS$_{2}$, in the\nframework of Anderson-Hubbard model on a triangular lattice. Based on the\nmagnetic, spectroscopic and transport signatures we map out the thermal phase\ndiagram of this system. Our results show that over a regime of moderate\ndisorder strength this material hosts an antiferromagnetic metal. The emergent\nmetal is a non Fermi liquid, governed by resilient quasiparticles, that survive\nas the relevant low energy excitations even after the break down of Fermi\nliquid description. The system undergoes a crossover from a non Fermi liquid\nmetal to a bad metallic phase, as a function of temperature. Our results on\nspectral line shape are found to be in excellent agreement with the\nexperimental observations on Cu intercalated 1T-TaS$_{2}$. The optical and\nspectroscopic signatures discussed in this letter are expected to serve as\nimportant benchmark for future experiments on this and related class of\nmaterials. The numerical technique discussed herein serves as a computational\nbreakthrough to address systems for which most of the existing methods fall\nshort.\n"}
+{"abstract": "  In this work, we propose a Model Predictive Control (MPC)-based Reinforcement\nLearning (RL) method for Autonomous Surface Vehicles (ASVs). The objective is\nto find an optimal policy that minimizes the closed-loop performance of a\nsimplified freight mission, including collision-free path following, autonomous\ndocking, and a skillful transition between them. We use a parametrized\nMPC-scheme to approximate the optimal policy, which considers\npath-following/docking costs and states (position, velocity)/inputs (thruster\nforce, angle) constraints. The Least Squares Temporal Difference (LSTD)-based\nDeterministic Policy Gradient (DPG) method is then applied to update the policy\nparameters. Our simulation results demonstrate that the proposed MPC-LSTD-based\nDPG method could improve the closed-loop performance during learning for the\nfreight mission problem of ASV.\n"}
+{"abstract": "  Pore structures and gas transport properties in porous separators for polymer\nelectrolyte fuel cells are evaluated both experimentally and through\nsimulations. In the experiments, the gas permeabilities of two porous samples,\na conventional sample and one with low electrical resistivity, are measured by\na capillary flow porometer, and the pore size distributions are evaluated with\nmercury porosimetry. Local pore structures are directly observed with micro\nX-ray computed tomography (CT). In the simulations, the effective diffusion\ncoefficients of oxygen and the air permeability in porous samples are\ncalculated using random walk Monte Carlo simulations and computational fluid\ndynamics (CFD) simulations, respectively, based on the X-ray CT images. The\ncalculated porosities and air permeabilities of the porous samples are in good\nagreement with the experimental values. The simulation results also show that\nthe in-plane permeability is twice the through-plane permeability in the\nconventional sample, whereas it is slightly higher in the low-resistivity\nsample. The results of this study show that CFD simulation based on micro X-ray\nCT images makes it possible to evaluate anisotropic gas permeabilities in\nanisotropic porous media.\n"}
+{"abstract": "  We describe how some problems (interpretability,lack of object-orientedness)\nof modern deep networks potentiallycould be solved by adapting a biologically\nplausible saccadicmechanism of perception. A sketch of such a saccadic\nvisionmodel is proposed. Proof of concept experimental results areprovided to\nsupport the proposed approach.\n"}
+{"abstract": "  The knapsack problem for groups was introduced by Miasnikov, Nikolaev, and\nUshakov. It is defined for each finitely generated group $G$ and takes as input\ngroup elements $g_1,\\ldots,g_n,g\\in G$ and asks whether there are\n$x_1,\\ldots,x_n\\ge 0$ with $g_1^{x_1}\\cdots g_n^{x_n}=g$. We study the knapsack\nproblem for wreath products $G\\wr H$ of groups $G$ and $H$. Our main result is\na characterization of those wreath products $G\\wr H$ for which the knapsack\nproblem is decidable. The characterization is in terms of decidability\nproperties of the indiviual factors $G$ and $H$. To this end, we introduce two\ndecision problems, the intersection knapsack problem and its restriction, the\npositive intersection knapsack problem. Moreover, we apply our main result to\n$H_3(\\mathbb{Z})$, the discrete Heisenberg group, and to Baumslag-Solitar\ngroups $\\mathsf{BS}(1,q)$ for $q\\ge 1$. First, we show that the knapsack\nproblem is undecidable for $G\\wr H_3(\\mathbb{Z})$ for any $G\\ne 1$. This\nimplies that for $G\\ne 1$ and for infinite and virtually nilpotent groups $H$,\nthe knapsack problem for $G\\wr H$ is decidable if and only if $H$ is virtually\nabelian and solvability of systems of exponent equations is decidable for $G$.\nSecond, we show that the knapsack problem is decidable for\n$G\\wr\\mathsf{BS}(1,q)$ if and only if solvability of systems of exponent\nequations is decidable for $G$.\n"}
+{"abstract": "  The first of a two-part series, this paper assumes a weak local energy decay\nestimate holds and proves that solutions to the linear wave equation with\nvariable coefficients in $\\mathbb R^{1+3}$, first-order terms, and a potential\ndecay at a rate depending on how rapidly the vector fields of the metric,\nfirst-order terms, and potential decay at spatial infinity. We prove results\nfor both stationary and nonstationary metrics. The proof uses local energy\ndecay to prove an initial decay rate, and then uses the one-dimensional\nreduction repeatedly to achieve the full decay rate.\n"}
+{"abstract": "  The effect of coupling between pairing and quadrupole triaxial shape\nvibrations on the low-energy collective states of $\\gamma$-soft nuclei is\ninvestigated using a model based on the framework of nuclear energy density\nfunctionals (EDFs). Employing a constrained self-consistent mean-field (SCMF)\nmethod that uses universal EDFs and pairing interactions, potential energy\nsurfaces of characteristic $\\gamma$-soft Os and Pt nuclei with $A\\approx190$\nare calculated as functions of the pairing and triaxial quadrupole\ndeformations. Collective spectroscopic properties are computed using a\nnumber-nonconserving interacting boson model (IBM) Hamiltonian, with parameters\ndetermined by mapping the SCMF energy surface onto the expectation value of the\nHamiltonian in the boson condensate state. It is shown that, by simultaneously\nconsidering both the shape and pairing collective degrees of freedom, the\nEDF-based IBM successfully reproduces data on collective structures based on\nlow-energy $0^{+}$ states, as well as $\\gamma$-vibrational bands.\n"}
+{"abstract": "  We consider the capacity of entanglement in models related with the\ngravitational phase transitions. The capacity is labeled by the replica\nparameter which plays a similar role to the inverse temperature in\nthermodynamics. In the end of the world brane model of a radiating black hole\nthe capacity has a peak around the Page time indicating the phase transition\nbetween replica wormhole geometries of different types of topology. Similarly,\nin a moving mirror model describing Hawking radiation the capacity typically\nshows a discontinuity when the dominant saddle switches between two phases,\nwhich can be seen as a formation of island regions. In either case we find the\ncapacity can be an invaluable diagnostic for a black hole evaporation process.\n"}
+{"abstract": "  The tension between inferences of Hubble constant ($H_0$) is found in a large\narray of datasets combinations. Modification to the late expansion history is\nthe most direct solution to this discrepancy. In this work we examine the\nviability of restoring the cosmological concordance within the scenarios of\nlate dark energy. We explore two representative parameterizations: a novel\nversion of transitional dark energy (TDE) and modified emergent dark energy\n(MEDE). We find that, the main anchors for the cosmic distance scale: cosmic\nmicrowave background (CMB), baryon acoustic oscillation (BAO), and SNe Ia\ncalibrated by Cepheids form a ``impossible trinity'', i.e., it's plausible to\nreconcile with any of the two but unlikely to accommodate them all.\nParticularly, the tension between BAO and the calibrated SNe Ia can not be\nreconciled within the scenarios of late dark energy. Nevertheless, we still\nfind a positive evidence for TDE model in analysis of all datasets\ncombinations, while with the the exclusion of BOSS datasets, the tensions with\nSH0ES drops from $3.1\\sigma$ to $1.1\\sigma$. For MEDE model, the tension with\n$H_0$ is much alleviated with the exclusion of SNe dataset. But unfortunately,\nin both TDE and MEDE scenarios, the $S_8$ tension is not relieved nor\nexacerbated.\n"}
+{"abstract": "  The Raman peak position and linewidth provide insight into phonon\nanharmonicity and electron-phonon interactions (EPI) in materials. For\nmonolayer graphene, prior first-principles calculations have yielded decreasing\nlinewidth with increasing temperature, which is opposite to measurement\nresults. Here, we explicitly consider four-phonon anharmonicity, phonon\nrenormalization, and electron-phonon coupling, and find all to be important to\nsuccessfully explain both the $G$ peak frequency shift and linewidths in our\nsuspended graphene sample at a wide temperature range. Four-phonon scattering\ncontributes a prominent linewidth that increases with temperature, while\ntemperature dependence from EPI is found to be reversed above a doping\nthreshold ($\\hbar\\omega_G/2$, with $\\omega_G$ being the frequency of the $G$\nphonon).\n"}
+{"abstract": "  We propose a novel neural network module that transforms an existing\nsingle-frame semantic segmentation model into a video semantic segmentation\npipeline. In contrast to prior works, we strive towards a simple, fast, and\ngeneral module that can be integrated into virtually any single-frame\narchitecture. Our approach aggregates a rich representation of the semantic\ninformation in past frames into a memory module. Information stored in the\nmemory is then accessed through an attention mechanism. In contrast to previous\nmemory-based approaches, we propose a fast local attention layer, providing\ntemporal appearance cues in the local region of prior frames. We further fuse\nthese cues with an encoding of the current frame through a second\nattention-based module. The segmentation decoder processes the fused\nrepresentation to predict the final semantic segmentation. We integrate our\napproach into two popular semantic segmentation networks: ERFNet and PSPNet. We\nobserve an improvement in segmentation performance on Cityscapes by 1.7% and\n2.1% in mIoU respectively, while increasing inference time of ERFNet by only\n1.5ms.\n"}
+{"abstract": "  We study the problem of dynamically trading multiple futures whose underlying\nasset price follows a multiscale central tendency Ornstein-Uhlenbeck (MCTOU)\nmodel. Under this model, we derive the closed-form no-arbitrage prices for the\nfutures contracts. Applying a utility maximization approach, we solve for the\noptimal trading strategies under different portfolio configurations by\nexamining the associated system of Hamilton-Jacobi-Bellman (HJB) equations. The\noptimal strategies depend on not only the parameters of the underlying asset\nprice process but also the risk premia embedded in the futures prices.\nNumerical examples are provided to illustrate the investor's optimal positions\nand optimal wealth over time.\n"}
+{"abstract": "  Let $ K $ be a number field over $ \\mathbb{Q} $ and let $ a_K(m) $ denote the\nnumber of integral ideals of $ K $ of norm equal to $ m\\in\\mathbb{N} $. In this\npaper we obtain asymptotic formulae for sums of the form $ \\sum_{m\\leq X}\na^l_K(m) $ thereby generalizing the previous works on the problem. Previously\nsuch asymptotics were known only in the case when $ K $ is Galois or when $K$\nwas a non normal cubic extension and $ l=2,3 $. The present work subsumes both\nthese cases.\n"}
+{"abstract": "  Automatic transcription of monophonic/polyphonic music is a challenging task\ndue to the lack of availability of large amounts of transcribed data. In this\npaper, we propose a data augmentation method that converts natural speech to\nsinging voice based on vocoder based speech synthesizer. This approach, called\nvoice to singing (V2S), performs the voice style conversion by modulating the\nF0 contour of the natural speech with that of a singing voice. The V2S model\nbased style transfer can generate good quality singing voice thereby enabling\nthe conversion of large corpora of natural speech to singing voice that is\nuseful in building an E2E lyrics transcription system. In our experiments on\nmonophonic singing voice data, the V2S style transfer provides a significant\ngain (relative improvements of 21%) for the E2E lyrics transcription system. We\nalso discuss additional components like transfer learning and lyrics based\nlanguage modeling to improve the performance of the lyrics transcription\nsystem.\n"}
+{"abstract": "  This paper provides a multivariate extension of Bertoin's pathwise\nconstruction of a L\\'evy process conditioned to stay positive/negative. Thus\nobtained processes conditioned to stay in half-spaces are closely related to\nthe original process on a compact time interval seen from its directional\nextremal points. In the case of a correlated Brownian motion the law of the\nconditioned process is obtained by a linear transformation of a standard\nBrownian motion and an independent Bessel-3 process. Further motivation is\nprovided by a limit theorem corresponding to zooming in on a L\\'evy process\nwith a Brownian part at the point of its directional infimum. Applications to\nzooming in at the point furthest from the origin are envisaged.\n"}
+{"abstract": "  The US Census Bureau plans to protect the privacy of 2020 Census respondents\nthrough its Disclosure Avoidance System (DAS), which attempts to achieve\ndifferential privacy guarantees by adding noise to the Census microdata. By\napplying redistricting simulation and analysis methods to DAS-protected 2010\nCensus data, we find that the protected data are not of sufficient quality for\nredistricting purposes. We demonstrate that the injected noise makes it\nimpossible for states to accurately comply with the One Person, One Vote\nprinciple. Our analysis finds that the DAS-protected data are biased against\ncertain areas, depending on voter turnout and partisan and racial composition,\nand that these biases lead to large and unpredictable errors in the analysis of\npartisan and racial gerrymanders. Finally, we show that the DAS algorithm does\nnot universally protect respondent privacy. Based on the names and addresses of\nregistered voters, we are able to predict their race as accurately using the\nDAS-protected data as when using the 2010 Census data. Despite this, the\nDAS-protected data can still inaccurately estimate the number of\nmajority-minority districts. We conclude with recommendations for how the\nCensus Bureau should proceed with privacy protection for the 2020 Census.\n"}
+{"abstract": "  For analytic functions $g$ on the unit disc with non-negative Maclaurin\ncoefficients, we describe the boundedness and compactness of the integral\noperator $T_g(f)(z)=\\int_0^zf(\\zeta)g'(\\zeta)\\,d\\zeta$ from a space $X$ of\nanalytic functions in the unit disc to $H^\\infty$, in terms of neat and useful\nconditions on the Maclaurin coefficients of $g$. The choices of $X$ that will\nbe considered contain the Hardy and the Hardy-Littlewood spaces, the\nDirichlet-type spaces $D^p_{p-1}$, as well as the classical Bloch and BMOA\nspaces.\n"}
+{"abstract": "  We consider a multiphysics model for the flow of Newtonian fluid coupled with\nBiot consolidation equations through an interface, and incorporating total\npressure as an unknown in the poroelastic region. A new mixed-primal finite\nelement scheme is proposed solving for the pairs fluid velocity - pressure and\ndisplacement - total poroelastic pressure using Stokes-stable elements, and\nwhere the formulation does not require Lagrange multipliers to set up the usual\ntransmission conditions on the interface. The stability and well-posedness of\nthe continuous and semi-discrete problems are analysed in detail. Our numerical\nstudy is framed in the context of applicative problems pertaining to\nheterogeneous geophysical flows and to eye poromechanics. For the latter, we\ninvestigate different interfacial flow regimes in Cartesian and axisymmetric\ncoordinates that could eventually help describe early morphologic changes\nassociated with glaucoma development in canine species.\n"}
+{"abstract": "  In this work, we explore macroscopic transport phenomena associated with a\nrotational system in the presence of an external orthogonal electromagnetic\nfield. Simply based on the lowest Landau level approximation, we derive\nnontrivial expressions for chiral density and various currents consistently by\nadopting small angular velocity expansion or Kubo formula. While the generation\nof anomalous electric current is due to the pseudo gauge field effect of the\nspin-rotation coupling, the chiral density and current can be simply explained\nwith the help of Lorentz boosts. Finally, Lorentz covariant forms can be\nobtained by unifying our results and the magnetovorticity effect.\n"}
+{"abstract": "  We find an explicit formula that produces inductively the elliptic stable\nenvelopes of an arbitrary Nakajima variety associated to a quiver Q from the\nones of those Nakajima varieties whose framing vectors are the fundamental\nvectors of the quiver Q, i.e. the dimension vectors with just one unitary\nnonzero entry. The result relies on abelianization of stable envelopes. As an\napplication, we combine our result with Smirnov's formula for the elliptic\nstable envelopes of the Hilbert scheme of points on the plane to produce the\nelliptic stable envelopes of the instanton moduli space.\n"}
+{"abstract": "  Small-scale magnetic fields are not only the fundamental element of the solar\nmagnetism, but also closely related to the structure of the solar atmosphere.\nThe observations have shown that there is a ubiquitous tangled small-scale\nmagnetic field with a strength of 60 $\\sim$ 130\\,G in the canopy forming layer\nof the quiet solar photosphere. On the other hand, the multi-dimensional MHD\nsimulations show that the convective overshooting expels the magnetic field to\nform the magnetic canopies at a height of about 500\\,km in the upper\nphotosphere. However, the distribution of such small-scale ``canopies\" in the\nsolar photosphere cannot be rigorously constrained by either observations and\nnumerical simulations. Based on stellar standard models, we identify that these\nmagnetic canopies can act as a global magnetic-arch splicing layer, and find\nthat the reflections of the solar p-mode oscillations at this magnetic-arch\nsplicing layer results in significant improvement on the discrepancy between\nthe observed and calculated p-mode frequencies. The location of the\nmagnetic-arch splicing layer is determined at a height of about 630\\,km, and\nthe inferred strength of the magnetic field is about 90\\,G. These features of\nthe magnetic-arch splicing layer derived independently in the present study are\nquantitatively in agreement with the presence of small-scale magnetic canopies\nas those obtained by the observations and 3-D MHD simulations.\n"}
+{"abstract": "  In many real-world scenarios, the utility of a user is derived from the\nsingle execution of a policy. In this case, to apply multi-objective\nreinforcement learning, the expected utility of the returns must be optimised.\nVarious scenarios exist where a user's preferences over objectives (also known\nas the utility function) are unknown or difficult to specify. In such\nscenarios, a set of optimal policies must be learned. However, settings where\nthe expected utility must be maximised have been largely overlooked by the\nmulti-objective reinforcement learning community and, as a consequence, a set\nof optimal solutions has yet to be defined. In this paper we address this\nchallenge by proposing first-order stochastic dominance as a criterion to build\nsolution sets to maximise expected utility. We also propose a new dominance\ncriterion, known as expected scalarised returns (ESR) dominance, that extends\nfirst-order stochastic dominance to allow a set of optimal policies to be\nlearned in practice. We then define a new solution concept called the ESR set,\nwhich is a set of policies that are ESR dominant. Finally, we define a new\nmulti-objective distributional tabular reinforcement learning (MOT-DRL)\nalgorithm to learn the ESR set in a multi-objective multi-armed bandit setting.\n"}
+{"abstract": "  This paper presents a theoretical framework for the design and analysis of\ngradient descent-based algorithms for coverage control tasks involving robot\nswarms. We adopt a multiscale approach to analysis and design to ensure\nconsistency of the algorithms in the large-scale limit. First, we represent the\nmacroscopic configuration of the swarm as a probability measure and formulate\nthe macroscopic coverage task as the minimization of a convex objective\nfunction over probability measures. We then construct a macroscopic dynamics\nfor swarm coverage, which takes the form of a proximal descent scheme in the\n$L^2$-Wasserstein space. Our analysis exploits the generalized geodesic\nconvexity of the coverage objective function, proving convergence in the\n$L^2$-Wasserstein sense to the target probability measure. We then obtain a\nconsistent gradient descent algorithm in the Euclidean space that is\nimplementable by a finite collection of agents, via a \"variational\"\ndiscretization of the macroscopic coverage objective function. We establish the\nconvergence properties of the gradient descent and its behavior in the\ncontinuous-time and large-scale limits. Furthermore, we establish a connection\nwith well-known Lloyd-based algorithms, seen as a particular class of\nalgorithms within our framework, and demonstrate our results via numerical\nexperiments.\n"}
+{"abstract": "  The higher-order generalized singular value decomposition (HO-GSVD) is a\nmatrix factorization technique that extends the GSVD to $N \\ge 2$ data\nmatrices, and can be used to identify shared subspaces in multiple large-scale\ndatasets with different row dimensions. The standard HO-GSVD factors $N$\nmatrices $A_i\\in\\mathbb{R}^{m_i\\times n}$ as $A_i=U_i\\Sigma_i V^\\text{T}$, but\nrequires that each of the matrices $A_i$ has full column rank. We propose a\nreformulation of the HO-GSVD that extends its applicability to rank-deficient\ndata matrices $A_i$. If the matrix of stacked $A_i$ has full rank, we show that\nthe properties of the original HO-GSVD extend to our reformulation. The HO-GSVD\ncaptures shared right singular vectors of the matrices $A_i$, and we show that\nour method also identifies directions that are unique to the image of a single\nmatrix. We also extend our results to the higher-order cosine-sine\ndecomposition (HO-CSD), which is closely related to the HO-GSVD. Our extension\nof the standard HO-GSVD allows its application to datasets with $m_i < n$, such\nas are encountered in bioinformatics, neuroscience, control theory or\nclassification problems.\n"}
+{"abstract": "  Multi-frame human pose estimation in complicated situations is challenging.\nAlthough state-of-the-art human joints detectors have demonstrated remarkable\nresults for static images, their performances come short when we apply these\nmodels to video sequences. Prevalent shortcomings include the failure to handle\nmotion blur, video defocus, or pose occlusions, arising from the inability in\ncapturing the temporal dependency among video frames. On the other hand,\ndirectly employing conventional recurrent neural networks incurs empirical\ndifficulties in modeling spatial contexts, especially for dealing with pose\nocclusions. In this paper, we propose a novel multi-frame human pose estimation\nframework, leveraging abundant temporal cues between video frames to facilitate\nkeypoint detection. Three modular components are designed in our framework. A\nPose Temporal Merger encodes keypoint spatiotemporal context to generate\neffective searching scopes while a Pose Residual Fusion module computes\nweighted pose residuals in dual directions. These are then processed via our\nPose Correction Network for efficient refining of pose estimations. Our method\nranks No.1 in the Multi-frame Person Pose Estimation Challenge on the\nlarge-scale benchmark datasets PoseTrack2017 and PoseTrack2018. We have\nreleased our code, hoping to inspire future research.\n"}
+{"abstract": "  Versions of the following problem appear in several topics such as Gamma\nKnife radiosurgery, studying objects with the X-ray transform, the 3SUM\nproblem, and the $k$-linear degeneracy testing. Suppose there are $n$ points on\na plane whose specific locations are unknown. We are given all the lines that\ngo through the points with a given slope. We show that the minimum number of\nslopes needed, in general, to find all the point locations is $n+1$ and we\nprovide an algorithm to do so.\n"}
+{"abstract": "  Speech disorders often occur at the early stage of Parkinson's disease (PD).\nThe speech impairments could be indicators of the disorder for early diagnosis,\nwhile motor symptoms are not obvious. In this study, we constructed a new\nspeech corpus of Mandarin Chinese and addressed classification of patients with\nPD. We implemented classical machine learning methods with ranking algorithms\nfor feature selection, convolutional and recurrent deep networks, and an end to\nend system. Our classification accuracy significantly surpassed\nstate-of-the-art studies. The result suggests that free talk has stronger\nclassification power than standard speech tasks, which could help the design of\nfuture speech tasks for efficient early diagnosis of the disease. Based on\nexisting classification methods and our natural speech study, the automatic\ndetection of PD from daily conversation could be accessible to the majority of\nthe clinical population.\n"}
+{"abstract": "  In this paper we consider convex co-compact subgroups of the projective\nlinear group. We prove that such a group is relatively hyperbolic with respect\nto a collection of virtually Abelian subgroups of rank two if and only if each\nopen face in the ideal boundary has dimension at most one. We also introduce\nthe \"coarse Hilbert dimension\" of a subset of a convex set and use it to\ncharacterize when a naive convex co-compact subgroup is word hyperbolic or\nrelatively hyperbolic with respect to a collection of virtually Abelian\nsubgroups of rank two.\n"}
+{"abstract": "  Let $I(G)^{[k]}$ denote the $k$th squarefree power of the edge ideal of $G$.\nWhen $G$ is a forest, we provide a sharp upper bound for the regularity of\n$I(G)^{[k]}$ in terms of the $k$-admissable matching number of $G$. For any\npositive integer $k$, we classify all forests $G$ such that $I(G)^{[k]}$ has\nlinear resolution. We also give a combinatorial formula for the regularity of\n$I(G)^{[2]}$ for any forest $G$.\n"}
+{"abstract": "  Recently, deep learning approaches have become the main research frontier for\nbiological image reconstruction and enhancement problems thanks to their high\nperformance, along with their ultra-fast inference times. However, due to the\ndifficulty of obtaining matched reference data for supervised learning, there\nhas been increasing interest in unsupervised learning approaches that do not\nneed paired reference data. In particular, self-supervised learning and\ngenerative models have been successfully used for various biological imaging\napplications. In this paper, we overview these approaches from a coherent\nperspective in the context of classical inverse problems, and discuss their\napplications to biological imaging, including electron, fluorescence and\ndeconvolution microscopy, optical diffraction tomography and functional\nneuroimaging.\n"}
+{"abstract": "  Given a graph with a source vertex $s$, the Single Source Replacement Paths\n(SSRP) problem is to compute, for every vertex $t$ and edge $e$, the length\n$d(s,t,e)$ of a shortest path from $s$ to $t$ that avoids $e$. A Single-Source\nDistance Sensitivity Oracle (Single-Source DSO) is a data structure that\nanswers queries of the form $(t,e)$ by returning the distance $d(s,t,e)$. We\nshow how to deterministically compress the output of the SSRP problem on\n$n$-vertex, $m$-edge graphs with integer edge weights in the range $[1,M]$ into\na Single-Source DSO of size $O(M^{1/2}n^{3/2})$ with query time\n$\\widetilde{O}(1)$. The space requirement is optimal (up to the word size) and\nour techniques can also handle vertex failures.\n  Chechik and Cohen [SODA 2019] presented a combinatorial, randomized\n$\\widetilde{O}(m\\sqrt{n}+n^2)$ time SSRP algorithm for undirected and\nunweighted graphs. Grandoni and Vassilevska Williams [FOCS 2012, TALG 2020]\ngave an algebraic, randomized $\\widetilde{O}(Mn^\\omega)$ time SSRP algorithm\nfor graphs with integer edge weights in the range $[1,M]$, where $\\omega<2.373$\nis the matrix multiplication exponent. We derandomize both algorithms for\nundirected graphs in the same asymptotic running time and apply our compression\nto obtain deterministic Single-Source DSOs. The $\\widetilde{O}(m\\sqrt{n}+n^2)$\nand $\\widetilde{O}(Mn^\\omega)$ preprocessing times are polynomial improvements\nover previous $o(n^2)$-space oracles.\n  On sparse graphs with $m=O(n^{5/4-\\varepsilon}/M^{7/4})$ edges, for any\nconstant $\\varepsilon > 0$, we reduce the preprocessing to randomized\n$\\widetilde{O}(M^{7/8}m^{1/2}n^{11/8})=O(n^{2-\\varepsilon/2})$ time. This is\nthe first truly subquadratic time algorithm for building Single-Source DSOs on\nsparse graphs.\n"}
+{"abstract": "  End-to-end DNN architectures have pushed the state-of-the-art in speech\ntechnologies, as well as in other spheres of AI, leading researchers to train\nmore complex and deeper models. These improvements came at the cost of\ntransparency. DNNs are innately opaque and difficult to interpret. We no longer\nunderstand what features are learned, where they are preserved, and how they\ninter-operate. Such an analysis is important for better model understanding,\ndebugging and to ensure fairness in ethical decision making. In this work, we\nanalyze the representations trained within deep speech models, towards the task\nof speaker recognition, dialect identification and reconstruction of masked\nsignals. We carry a layer- and neuron-level analysis on the utterance-level\nrepresentations captured within pretrained speech models for speaker, language\nand channel properties. We study: is this information captured in the learned\nrepresentations? where is it preserved? how is it distributed? and can we\nidentify a minimal subset of network that posses this information. Using\ndiagnostic classifiers, we answered these questions. Our results reveal: (i)\nchannel and gender information is omnipresent and is redundantly distributed\n(ii) complex properties such as dialectal information is encoded only in the\ntask-oriented pretrained network and is localised in the upper layers (iii) a\nminimal subset of neurons can be extracted to encode the predefined property\n(iv) salient neurons are sometimes shared between properties and can highlights\npresence of biases in the network. Our cross-architectural comparison indicates\nthat (v) the pretrained models captures speaker-invariant information and (vi)\nthe pretrained CNNs models are competitive to the Transformers for encoding\ninformation for the studied properties. To the best of our knowledge, this is\nthe first study to investigate neuron analysis on the speech models.\n"}
+{"abstract": "  AOSAT is a python package for the analysis of single-conjugate adaptive\noptics (SCAO) simulation results. Python is widely used in the astronomical\ncommunity these days, and AOSAT may be used stand-alone, integrated into a\nsimulation environment, or can easily be extended according to a user's needs.\nStandalone operation requires the user to provide the residual wavefront frames\nprovided by the SCAO simulation package used, the aperture mask (pupil) used\nfor the simulation, and a custom setup file describing the simulation/analysis\nconfiguration. In its standard form, AOSAT's \"tearsheet\" functionality will\nthen run all standard analyzers, providing an informative plot collection on\nproperties such as the point-spread function (PSF) and its quality, residual\ntip-tilt, the impact of pupil fragmentation, residual optical aberration modes\nboth static and dynamic, the expected high-contrast performance of suitable\ninstrumentation with and without coronagraphs, and the power spectral density\nof residual wavefront errors.\n  AOSAT fills the gap between the simple numerical outputs provided by most\nsimulation packages, and the full-scale deployment of instrument simulators and\ndata reduction suites operating on SCAO residual wavefronts. It enables\ninstrument designers and end-users to quickly judge the impact of design or\nconfiguration decisions on the final performance of down-stream\ninstrumentation.\n"}
+{"abstract": "  It has recently been pointed out that Gaia is capable of detecting a\nstochastic gravitational wave background in the sensitivity band between the\nfrequency of pulsar timing arrays and LISA. We argue that Gaia and THEIA has\ngreat potential for early universe cosmology, since such a frequency range is\nideal for probing phase transitions in asymmetric dark matter, SIMP and the\ncosmological QCD transition. Furthermore, there is the potential for detecting\nprimordial black holes in the solar mass range produced during such an early\nuniverse transition and distinguish them from those expected from the QCD\nepoch. Finally, we discuss the potential for Gaia and THEIA to probe\ntopological defects and the ability of Gaia to potentially shed light on the\nrecent NANOGrav results.\n"}
+{"abstract": "  This paper studies the recovery of a joint piece-wise linear trend from a\ntime series using L1 regularization approach, called L1 trend filtering (Kim,\nKoh and Boyd, 2009). We provide some sufficient conditions under which a L1\ntrend filter can be well-behaved in terms of mean estimation and change point\ndetection. The result is two-fold: for the mean estimation, an almost optimal\nconsistent rate is obtained; for the change point detection, the slope change\nin direction can be recovered in a high probability. In addition, we show that\nthe weak irrepresentable condition, a necessary condition for LASSO model to be\nsign consistent (Zhao and Yu, 2006), is not necessary for the consistent change\npoint detection. The performance of the L1 trend filter is evaluated by some\nfinite sample simulations studies.\n"}
+{"abstract": "  It has recently been shown that superconductivity in magic-angle twisted\ntrilayer graphene survives to in-plane magnetic fields that are well in excess\nof the Pauli limit, and much stronger than the in-plane critical magnetic\nfields of magic-angle twisted bilayer graphene. The difference is surprising\nbecause twisted bilayers and trilayers both support the magic-angle flat bands\nthought to be the fountainhead of twisted graphene superconductivity. We show\nhere that the difference in critical magnetic fields can be traced to a\n$\\mathcal{C}_2 \\mathcal{M}_{h}$ symmetry in trilayers that survives in-plane\nmagnetic fields, and also relative displacements between top and bottom layers\nthat are not under experimental control at present. An gate electric field\nbreaks the $\\mathcal{C}_2 \\mathcal{M}_{h}$ symmetry and therefore limits the\nin-plane critical magnetic field.\n"}
+{"abstract": "  We analyze possibilities of second-order quantifier elimination for formulae\ncontaining parameters -- constants or functions. For this, we use a constraint\nresolution calculus obtained from specializing the hierarchical superposition\ncalculus. If saturation terminates, we analyze possibilities of obtaining\nweakest constraints on parameters which guarantee satisfiability. If the\nsaturation does not terminate, we identify situations in which finite\nrepresentations of infinite saturated sets exist. We identify situations in\nwhich entailment between formulae expressed using second-order quantification\ncan be effectively checked. We illustrate the ideas on a series of examples\nfrom wireless network research.\n"}
+{"abstract": "  In the context of supervised learning of a function by a Neural Network (NN),\nwe claim and empirically justify that a NN yields better results when the\ndistribution of the data set focuses on regions where the function to learn is\nsteeper. We first traduce this assumption in a mathematically workable way\nusing Taylor expansion. Then, theoretical derivations allow to construct a\nmethodology that we call Variance Based Samples Weighting (VBSW). VBSW uses\nlocal variance of the labels to weight the training points. This methodology is\ngeneral, scalable, cost effective, and significantly increases the performances\nof a large class of NNs for various classification and regression tasks on\nimage, text and multivariate data. We highlight its benefits with experiments\ninvolving NNs from shallow linear NN to Resnet or Bert.\n"}
+{"abstract": "  Multiple transition phenomena in divalent Eu compound EuAl$_4$ with the\ntetragonal structure were investigated via the single-crystal time-of-flight\nneutron Laue technique. At 30.0 K below a charge-density-wave (CDW) transition\ntemperature of $T_{\\rm CDW}$ = 140 K, superlattice peaks emerge near nuclear\nBragg peaks described by an ordering vector $q_{\\rm CDW}$=(0 0 ${\\delta}_c$)\nwith ${\\delta}_c{\\sim}$0.19. In contrast, magnetic peaks appear at $q_2 =\n({\\delta}_2 {\\delta}_2 0)$ with ${\\delta}_2$ = 0.085 in a magnetic-ordered\nphase at 13.5 K below $T_{\\rm N1}$ = 15.4 K. By further cooling to below\n$T_{\\rm N3}$ = 12.2 K, the magnetic ordering vector changes into $q_1 =\n({\\delta}_1 0 0)$ with ${\\delta}_1$ = 0.17 at 11.5 K and slightly shifts to\n${\\delta}_1$ = 0.194 at 4.3 K. No distinct change in the magnetic Bragg peak\nwas detected at $T_{\\rm N2}$=13.2 K and $T_{\\rm N4}$=10.0 K. The structural\nmodulation below $T_{\\rm CDW}$ with $q_{\\rm CDW}$ is characterized by the\nabsence of the superlattice peak in the (0 0 $l$) axis. As a similar CDW\ntransition was observed in SrAl$_4$, the structural modulation with $q_{\\rm\nCDW}$ could be mainly ascribed to the displacement of Al ions within the\ntetragonal $ab$-plane. Complex magnetic transitions are in stark contrast to a\nsimple collinear magnetic structure in isovalent EuGa$_4$. This could stem from\ndifferent electronic structures with the CDW transition between two compounds.\n"}
+{"abstract": "  We propose a gauged $B-L$ extension of the standard model (SM) where light\nneutrinos are of Dirac type by virtue of tiny Yukawa couplings with the SM\nHiggs. To achieve leptogenesis, we include additional heavy Majorana fermions\nwithout introducing any $B-L$ violation by two units. An additional scalar\ndoublet with appropriate $B-L$ charge can allow heavy fermion coupling with the\nSM leptons so that out of equilibrium decay of the former can lead to\ngeneration of lepton asymmetry. Due to the $B-L$ gauge interactions of the\ndecaying fermion, the criteria of successful Dirac leptogenesis can also\nconstrain the gauge sector couplings so as to keep the corresponding washout\nprocesses under control. The same $B-L$ gauge sector parameter space can also\nbe constrained from dark matter requirements if the latter is assumed to be a\nSM singlet particle with non-zero $B-L$ charge. The same $B-L$ gauge\ninteractions also lead to additional thermalised relativistic degrees of\nfreedom $\\Delta N_{\\rm eff}$ from light Dirac neutrinos which are tightly\nconstrained by Planck 2018 data. While there exists parameter space from the\ncriteria of successful low scale Dirac leptogenesis, dark matter and $\\Delta\nN_{\\rm eff}$ even after incorporating the latest collider bounds, all the\ncurrently allowed parameters can be probed by future measurements of $\\Delta\nN_{\\rm eff}$.\n"}
+{"abstract": "  In two-dimensional loop models, the scaling properties of critical random\ncurves are encoded in the correlators of connectivity operators. In the dense\nO($n$) loop model, any such operator is naturally associated to a standard\nmodule of the periodic Temperley-Lieb algebra. We introduce a new family of\nrepresentations of this algebra, with connectivity states that have two marked\npoints, and argue that they define the fusion of two standard modules. We\nobtain their decomposition on the standard modules for generic values of the\nparameters, which in turn yields the structure of the operator product\nexpansion of connectivity operators.\n"}
+{"abstract": "  We seek to investigate the scalability of neuromorphic computing for computer\nvision, with the objective of replicating non-neuromorphic performance on\ncomputer vision tasks while reducing power consumption. We convert the deep\nArtificial Neural Network (ANN) architecture U-Net to a Spiking Neural Network\n(SNN) architecture using the Nengo framework. Both rate-based and spike-based\nmodels are trained and optimized for benchmarking performance and power, using\na modified version of the ISBI 2D EM Segmentation dataset consisting of\nmicroscope images of cells. We propose a partitioning method to optimize\ninter-chip communication to improve speed and energy efficiency when deploying\nmulti-chip networks on the Loihi neuromorphic chip. We explore the advantages\nof regularizing firing rates of Loihi neurons for converting ANN to SNN with\nminimum accuracy loss and optimized energy consumption. We propose a percentile\nbased regularization loss function to limit the spiking rate of the neuron\nbetween a desired range. The SNN is converted directly from the corresponding\nANN, and demonstrates similar semantic segmentation as the ANN using the same\nnumber of neurons and weights. However, the neuromorphic implementation on the\nIntel Loihi neuromorphic chip is over 2x more energy-efficient than\nconventional hardware (CPU, GPU) when running online (one image at a time).\nThese power improvements are achieved without sacrificing the task performance\naccuracy of the network, and when all weights (Loihi, CPU, and GPU networks)\nare quantized to 8 bits.\n"}
+{"abstract": "  We put forward the concept of work extraction from thermal noise by\nphase-sensitive (homodyne) measurements of the noisy input followed by\n(outcome-dependent) unitary manipulations of the post-measured state. For\noptimized measurements, noise input with more than one quantum on average is\nshown to yield heat-to-work conversion with efficiency and power that grow with\nthe mean number of input quanta, detector efficiency and its inverse\ntemperature. This protocol is shown to be advantageous compared to common\nmodels of information and heat engines.\n"}
+{"abstract": "  Different regimes of entanglement growth under measurement have been\ndemonstrated for quantum many-body systems, with an entangling phase for low\nmeasurement rates and a disentangling phase for high rates (quantum Zeno\neffect). Here we study entanglement growth on a disordered Bose-Fermi mixture\nwith the bosons playing the role of the effective self-induced measurement for\nthe fermions. Due to the interplay between the disorder and a non-Abelian\nsymmetry, the model features an entanglement growth resonance when the\nboson-fermion interaction strength is varied. With the addition of a magnetic\nfield, the model acquires a dynamical symmetry leading to experimentally\nmeasurable long-time local oscillations. At the entanglement growth resonance,\nwe demonstrate the emergence of the cleanest oscillations. Furthermore, we show\nthat this resonance is distinct from both noise enhanced transport and a\nstandard stochastic resonance. Our work paves the way for experimental\nrealizations of self-induced correlated phases in multi-species systems.\n"}
+{"abstract": "  Saturn's E ring consists of micron-sized particles launched from Enceladus by\nthat moon's geological activity. A variety of small-scale structures in the\nE-ring's brightness have been attributed to tendrils of material recently\nlaunched from Enceladus. However, one of these features occurs at a location\nwhere Enceladus' gravitational perturbations should concentrate background\nE-ring particles into structures known as satellite wakes. While satellite\nwakes have been observed previously in ring material drifting past other moons,\nthese E-ring structures would be the first examples of wakes involving\nparticles following horseshoe orbits near Enceladus' orbit. The predicted\nintensity of these wake signatures are particularly sensitive to the fraction\nE-ring particles' on orbits with low eccentricities and semi-major axes just\noutside of Enceladus' orbit, and so detailed analyses of these and other\nsmall-scale E-ring features should place strong constraints on the orbital\nproperties and evolution of E-ring particles.\n"}
+{"abstract": "  In \\cite{BH20} an elegant choice-free construction of a canonical extension\nof a boolean algebra $B$ was given as the boolean algebra of regular open\nsubsets of the Alexandroff topology on the poset of proper filters of $B$. We\nmake this construction point-free by replacing the Alexandroff space of proper\nfilters of $B$ with the free frame $\\mathcal{L}$ generated by the bounded\nmeet-semilattice of all filters of $B$ (ordered by reverse inclusion) and prove\nthat the booleanization of $\\mathcal{L}$ is a canonical extension of $B$. Our\nmain result generalizes this approach to the category\n$\\boldsymbol{\\mathit{ba}\\ell}$ of bounded archimedean $\\ell$-algebras, thus\nyielding a point-free construction of canonical extensions in\n$\\boldsymbol{\\mathit{ba}\\ell}$. We conclude by showing that the algebra of\nnormal functions on the Alexandroff space of proper archimedean $\\ell$-ideals\nof $A$ is a canonical extension of $A\\in\\boldsymbol{\\mathit{ba}\\ell}$, thus\nproviding a generalization of the result of \\cite{BH20} to\n$\\boldsymbol{\\mathit{ba}\\ell}$.\n"}
+{"abstract": "  We report on the measurement of inclusive charmless semileptonic B decays $B\n\\to X_{u} \\ell \\nu$. The analysis makes use of hadronic tagging and is\nperformed on the full data set of the Belle experiment comprising 772 million\n$B\\bar{B}$ pairs. In the proceedings, the preliminary results of measurements\nof partial branching fractions and the CKM matrix element $|V_{ub}|$ are\npresented.\n"}
+{"abstract": "  Superconductivity and magnetism are generally incompatible because of the\nopposing requirement on electron spin alignment. When combined, they produce a\nmultitude of fascinating phenomena, including unconventional superconductivity\nand topological superconductivity. The emergence of two-dimensional (2D)layered\nsuperconducting and magnetic materials that can form nanoscale junctions with\natomically sharp interfaces presents an ideal laboratory to explore new\nphenomena from coexisting superconductivity and magnetic ordering. Here we\nreport tunneling spectroscopy under an in-plane magnetic field of\nsuperconductor-ferromagnet-superconductor (S/F/S) tunnel junctions that are\nmade of 2D Ising superconductor NbSe2 and ferromagnetic insulator CrBr3. We\nobserve nearly 100% tunneling anisotropic magnetoresistance (AMR), that is,\ndifference in tunnel resistance upon changing magnetization direction from\nout-of-plane to inplane. The giant tunneling AMR is induced by\nsuperconductivity, particularly, a result of interfacial magnetic exchange\ncoupling and spin-dependent quasiparticle scattering. We also observe an\nintriguing magnetic hysteresis effect in superconducting gap energy and\nquasiparticle scattering rate with a critical temperature that is 2 K below the\nsuperconducting transition temperature. Our study paves the path for exploring\nsuperconducting spintronic and unconventional superconductivity in van der\nWaals heterostructures.\n"}
+{"abstract": "  This paper explores how different ideas of racial equity in machine learning,\nin justice settings in particular, can present trade-offs that are difficult to\nsolve computationally. Machine learning is often used in justice settings to\ncreate risk assessments, which are used to determine interventions, resources,\nand punitive actions. Overall aspects and performance of these machine\nlearning-based tools, such as distributions of scores, outcome rates by levels,\nand the frequency of false positives and true positives, can be problematic\nwhen examined by racial group. Models that produce different distributions of\nscores or produce a different relationship between level and outcome are\nproblematic when those scores and levels are directly linked to the restriction\nof individual liberty and to the broader context of racial inequity. While\ncomputation can help highlight these aspects, data and computation are unlikely\nto solve them. This paper explores where values and mission might have to fill\nthe spaces computation leaves.\n"}
+{"abstract": "  Affect recognition based on subjects' facial expressions has been a topic of\nmajor research in the attempt to generate machines that can understand the way\nsubjects feel, act and react. In the past, due to the unavailability of large\namounts of data captured in real-life situations, research has mainly focused\non controlled environments. However, recently, social media and platforms have\nbeen widely used. Moreover, deep learning has emerged as a means to solve\nvisual analysis and recognition problems. This paper exploits these advances\nand presents significant contributions for affect analysis and recognition\nin-the-wild. Affect analysis and recognition can be seen as a dual knowledge\ngeneration problem, involving: i) creation of new, large and rich in-the-wild\ndatabases and ii) design and training of novel deep neural architectures that\nare able to analyse affect over these databases and to successfully generalise\ntheir performance on other datasets. The paper focuses on large in-the-wild\ndatabases, i.e., Aff-Wild and Aff-Wild2 and presents the design of two classes\nof deep neural networks trained with these databases. The first class refers to\nuni-task affect recognition, focusing on prediction of the valence and arousal\ndimensional variables. The second class refers to estimation of all main\nbehavior tasks, i.e. valence-arousal prediction; categorical emotion\nclassification in seven basic facial expressions; facial Action Unit detection.\nA novel multi-task and holistic framework is presented which is able to jointly\nlearn and effectively generalize and perform affect recognition over all\nexisting in-the-wild databases. Large experimental studies illustrate the\nachieved performance improvement over the existing state-of-the-art in affect\nrecognition.\n"}
+{"abstract": "  The Segerdahl process (Segerdahl (1955)), characterized by exponential claims\nand affine drift, has drawn a considerable amount of interest -- see, for\nexample, (Tichy (1984); Avram and Usabel (2008), due to its economic interest\n(it is the simplest risk process which takes into account the effect of\ninterest rates). It is also the simplest non-Levy, non-diffusion example of a\nspectrally negative Markov risk model. Note that for both spectrally negative\nLevy and diffusion processes, first passage theories which are based on\nidentifying two basic monotone harmonic functions/martingales have been\ndevelopped. This means that for these processes many control problems involving\ndividends, capital injections, etc., may be solved explicitly once the two\nbasic functions have been obtained. Furthermore, extensions to general\nspectrally negative Markov processes are possible (Landriault et al. (2017),\nAvram et al. (2018); Avram and Goreac (2019); Avram et al. (2019b).\nUnfortunately, methods for computing the basic functions are still lacking\noutside the Levy and diffusion classes, with the notable exception of the\nSegerdahl process, for which the ruin probability has been computed (Paulsen\nand Gjessing (1997). However, there is a striking lack of numerical results in\nboth cases. This motivated us to review several approaches, with the purpose of\ndrawing attention to connections between them, and underlying open problems.\n"}
+{"abstract": "  Ultralight bosons are possible fundamental building blocks of nature, and\npromising dark matter candidates. They can trigger superradiant instabilities\nof spinning black holes (BHs) and form long-lived \"bosonic clouds\" that slowly\ndissipate energy through the emission of gravitational waves (GWs). Previous\nstudies constrained ultralight bosons by searching for the stochastic\ngravitational wave background (SGWB) emitted by these sources in LIGO data,\nfocusing on the most unstable dipolar and quadrupolar modes. Here we focus on\nscalar bosons and extend previous works by: (i) studying in detail the impact\nof higher modes in the SGWB; (ii) exploring the potential of future proposed\nground-based GW detectors, such as the Neutron Star Extreme Matter Observatory,\nthe Einstein Telescope and Cosmic Explorer, to detect this SGWB. We find that\nhigher modes largely dominate the SGWB for bosons with masses $\\gtrsim\n10^{-12}$ eV, which is particularly relevant for future GW detectors. By\nestimating the signal-to-noise ratio of this SGWB, due to both stellar-origin\nBHs and from a hypothetical population of primordial BHs, we find that future\nground-based GW detectors could observe or constrain bosons in the mass range\n$\\sim [7\\times 10^{-14}, 2\\times 10^{-11}]$ eV and significantly improve on\ncurrent and future constraints imposed by LIGO and Virgo observations.\n"}
+{"abstract": "  Army cadets obtain occupations through a centralized process. Three\nobjectives -- increasing retention, aligning talent, and enhancing trust --\nhave guided reforms to this process since 2006. West Point's mechanism for the\nClass of 2020 exacerbated challenges implementing Army policy aims. We\nformulate these desiderata as axioms and study their implications theoretically\nand with administrative data. We show that the Army's objectives not only\ndetermine an allocation mechanism, but also a specific priority policy, a\nuniqueness result that integrates mechanism and priority design. These results\nled to a re-design of the mechanism, now adopted at both West Point and ROTC.\n"}
+{"abstract": "  We associate a certain tensor product lattice to any primitive integer\nlattice and ask about its typical shape. These lattices are related to the\ntangent bundle of Grassmannians and their study is motivated by Peyre's\nprogramme on \"freeness\" for rational points of bounded height on Fano\nvarieties.\n"}
+{"abstract": "  Static (DC) and dynamic (AC, at 14 MHz and 8 GHz) magnetic susceptibilities\nof single crystals of a ferromagnetic superconductor,\n$\\textrm{EuFe}_{2}(\\textrm{As}_{1-x}\\textrm{P}_{x})_{2}$ (x = 0.23), were\nmeasured in pristine state and after different doses of 2.5 MeV electron or 3.5\nMeV proton irradiation. The superconducting transition temperature, $T_{c}(H)$,\nshows an extraordinarily large decrease. It starts at\n$T_{c}(H=0)\\approx24\\:\\textrm{K}$ in the pristine sample for both AC and DC\nmeasurements, but moves to almost half of that value after moderate irradiation\ndose. Our results suggest that in\n$\\textrm{EuFe}_{2}(\\textrm{As}_{1-x}\\textrm{P}_{x})_{2}$ superconductivity is\naffected by local-moment ferromagnetism mostly via the spontaneous internal\nmagnetic fields induced by the FM subsystem. Another mechanism is revealed upon\nirradiation where magnetic defects created in ordered $\\text{Eu}^{2+}$ lattice\nact as efficient pairbreakers leading to a significant $T_{c}$ reduction upon\nirradiation compared to other 122 compounds. On the other hand, the exchange\ninteractions seem to be weakly screened by the superconducting phase leading to\na modest increase of $T_{m}$ (less than 1 K) after the irradiation drives\n$T_{c}$ to below $T_{m}$. The results suggest that FM and SC phases coexist\nmicroscopically in the same volume.\n"}
+{"abstract": "  We propose a new network architecture, the Fractal Pyramid Networks (PFNs)\nfor pixel-wise prediction tasks as an alternative to the widely used\nencoder-decoder structure. In the encoder-decoder structure, the input is\nprocessed by an encoding-decoding pipeline that tries to get a semantic\nlarge-channel feature. Different from that, our proposed PFNs hold multiple\ninformation processing pathways and encode the information to multiple separate\nsmall-channel features. On the task of self-supervised monocular depth\nestimation, even without ImageNet pretrained, our models can compete or\noutperform the state-of-the-art methods on the KITTI dataset with much fewer\nparameters. Moreover, the visual quality of the prediction is significantly\nimproved. The experiment of semantic segmentation provides evidence that the\nPFNs can be applied to other pixel-wise prediction tasks, and demonstrates that\nour models can catch more global structure information.\n"}
+{"abstract": "  Black-box quantum state preparation is a fundamental primitive in quantum\nalgorithms. Starting from Grover, a series of techniques have been devised to\nreduce the complexity. In this work, we propose to perform black-box state\npreparation using the technique of linear combination of unitaries (LCU). We\nprovide two algorithms based on a different structure of LCU. Our algorithms\nimprove upon the existed best results by reducing the required additional\nqubits and Toffoli gates to 2log(n) and n, respectively, in the bit precision\nn. We demonstrate the algorithms using the IBM Quantum Experience cloud\nservices. The further reduced complexity of the present algorithms brings the\nblack-box quantum state preparation closer to reality.\n"}
+{"abstract": "  Three-dimensional topological insulators (TIs) host helical Dirac surface\nstates at the interface with a trivial insulator. In quasi-one-dimensional TI\nnanoribbon structures the wave function of surface charges extends\nphase-coherently along the perimeter of the nanoribbon, resulting in a\nquantization of transverse surface modes. Furthermore, as the inherent\nspin-momentum locking results in a Berry phase offset of $\\pi$ of\nself-interfering charge carriers an energy gap within the surface state\ndispersion appears and all states become spin-degenerate. We investigate and\ncompare the magnetic field dependent surface state dispersion in selectively\ndeposited Bi$_2$Te$_3$ TI micro- and nanoribbon structures by analysing the\ngate voltage dependent magnetoconductance at cryogenic temperatures. While in\nwide microribbon devices the field effect mainly changes the amount of bulk\ncharges close to the top surface we identify coherent transverse surface states\nalong the perimeter of the nanoribbon devices responding to a change in top\ngate potential. We quantify the energetic spacing in between these quantized\ntransverse subbands by using an electrostatic model that treats an initial\ndifference in charge carrier densities on the top and bottom surface as well as\nremaining bulk charges. In the gate voltage dependent transconductance we find\noscillations that change their relative phase by $\\pi$ at half-integer values\nof the magnetic flux quantum applied coaxial to the nanoribbon, which is a\nsignature for a magnetic flux dependent topological phase transition in narrow,\nselectively deposited TI nanoribbon devices.\n"}
+{"abstract": "  Interference between light waves is one of the widely known phenomena in\nphysics, which is widely used in modern optics, ranging from precise detection\nat the nanoscale to gravitational-wave observation. Akin to light, both\nclassical and quantum interferences between surface plasmon polaritons (SPPs)\nhave been demonstrated. However, to actively control the SPP interference\nwithin subcycle in time (usually less than several femtoseconds in the visible\nrange) is still missing, which hinders the ultimate manipulation of SPP\ninterference on ultrafast time scale. In this paper, the interference between\nSPPs launched by a hole dimer, which was excited by a grazing incident free\nelectron beam without direct contact, was manipulated through both propagation\nand initial phase difference control. Particularly, using cathodoluminescence\nspectroscopy, the appearance of higher-order interference orders was obtained\nthrough propagation phase control by increasing separation distances of the\ndimer. Meanwhile, the peak-valley-peak evolution at a certain wavelength\nthrough changing the accelerating voltages was observed, which originates from\nthe initial phase difference control of hole launched SPPs. In particular, the\ntime resolution of this kind of control is shown to be in the ultrafast\nattosecond (as) region. Our work suggests that fast electron beams can be an\nefficient tool to control polarition interference in subcycle temporal scale,\nwhich can be potentially used in ultrafast optical processing or sensing.\n"}
+{"abstract": "  We investigate Bayesian predictive distributions for Wishart distributions\nunder the Kullback--Leibler divergence. We consider a recently introduced class\nof prior distributions, called the family of enriched standard conjugate prior\ndistributions and compare the Bayesian predictive distributions based on these\nprior distributions. We study the performance of the Bayesian predictive\ndistribution based on the reference prior distribution in the family. We show\nthat there exists a prior distribution in the family that dominates the\nreference prior distribution.\n"}
+{"abstract": "  The construction of an ontology of scientific knowledge objects, presented\nhere, is part of the development of an approach oriented towards the\nvisualization of scientific knowledge. It is motivated by the fact that the\nconcepts that are used to organize scientific knowledge (theorem, law,\nexperience, proof, etc.) appear in existing ontologies but that none of these\nontologies is centered on this topic and presents them in a simple and easily\nunderstandable organization. This ontology has been constructed by 1) selecting\nconcepts that appear in high level ontologies or in ontologies of knowledge\nobjects of specific fields and 2) by interviewing scientists in different\nfields. We have aligned this ontology with some of the sources used, which has\nallowed us to verify its consistency with respect to them. The validation of\nthe ontology consists in using it to formalize knowledge from various sources,\nwhich we have begun to do in the field of physics.\n"}
+{"abstract": "  We consider an input-constrained differential-drive robot with actuator\ndynamics. For this system, we establish asymptotic stability of the origin on\narbitrary compact, convex sets using Model Predictive Control (MPC) without\nstabilizing terminal conditions despite the presence of state constraints and\nactuator dynamics. We note that the problem without those two additional\ningredients was essentially solved beforehand, despite the fact that the\nlinearization is not stabilizable. We propose an approach successfully solving\nthe task at hand by combining the theory of barriers to characterize the\nviability kernel and an MPC framework based on so-called cost controllability.\nMoreover, we present a numerical case study to derive quantitative bounds on\nthe required length of the prediction horizon. To this end, we investigate the\nboundary of the viability kernel and a neighbourhood of the origin, i.e. the\nmost interesting areas.\n"}
+{"abstract": "  Anomaly detection is a crucial and challenging subject that has been studied\nwithin diverse research areas. In this work, we explore the task of log anomaly\ndetection (especially computer system logs and user behavior logs) by analyzing\nlogs' sequential information. We propose LAMA, a multi-head attention based\nsequential model to process log streams as template activity (event) sequences.\n  A next event prediction task is applied to train the model for anomaly\ndetection. Extensive empirical studies demonstrate that our new model\noutperforms existing log anomaly detection methods including statistical and\ndeep learning methodologies, which validate the effectiveness of our proposed\nmethod in learning sequence patterns of log data.\n"}
+{"abstract": "  In this paper, we consider fully connected feed-forward deep neural networks\nwhere weights and biases are independent and identically distributed according\nto Gaussian distributions. Extending previous results (Matthews et al.,\n2018a;b; Yang, 2019) we adopt a function-space perspective, i.e. we look at\nneural networks as infinite-dimensional random elements on the input space\n$\\mathbb{R}^I$. Under suitable assumptions on the activation function we show\nthat: i) a network defines a continuous Gaussian process on the input space\n$\\mathbb{R}^I$; ii) a network with re-scaled weights converges weakly to a\ncontinuous Gaussian process in the large-width limit; iii) the limiting\nGaussian process has almost surely locally $\\gamma$-H\\\"older continuous paths,\nfor $0 < \\gamma <1$. Our results contribute to recent theoretical studies on\nthe interplay between infinitely wide deep neural networks and Gaussian\nprocesses by establishing weak convergence in function-space with respect to a\nstronger metric.\n"}
+{"abstract": "  In this article, we wish to establish some first order differential\nsubordination relations for certain Carath\\'{e}odory functions with nice\ngeometrical properties. Moreover, several implications are determined so that\nthe normalized analytic function belongs to various subclasses of starlike\nfunctions.\n"}
+{"abstract": "  We study the Du Bois complex of a hypersurface $Z$ in a smooth complex\nalgebraic variety in terms of the minimal exponent $\\widetilde{\\alpha}(Z)$ and\ngive various applications. We show that if $\\widetilde{\\alpha}(Z)\\geq p+1$,\nthen the canonical morphism $\\Omega_Z^p\\to \\underline{\\Omega}_Z^p$ is an\nisomorphism. On the other hand, if $Z$ is singular and\n$\\widetilde{\\alpha}(Z)>p\\geq 2$, then ${\\mathcal\nH}^{p-1}(\\underline{\\Omega}_Z^{n-p})\\neq 0$.\n"}
+{"abstract": "  We determine the asymptotic spreading speed of the solutions of a Fisher-KPP\nreaction-diffusion equation, starting from compactly supported initial data,\nwhen the diffusion coefficient is a fixed bounded monotone profile that is\nshifted at a given forcing speed and satisfies a general uniform ellipticity\ncondition. Depending on the monotony of the profile, we are able to\ncharacterize this spreading speed as a function of the forcing speed and the\ntwo linear spreading speeds associated to the asymptotic problems. Most\nnotably, when the profile of the coefficient diffusion is increasing we show\nthat there is an intermediate range for the forcing speed where spreading\nactually occurs at a speed which is larger than the linear speed associated\nwith the homogeneous state around the position of the front. We complement our\nstudy with the construction of strictly monotone traveling front solutions with\nstrong exponential decay near the unstable state when the profile of the\ncoefficient diffusion is decreasing and in the regime where the forcing speed\nis precisely the selected spreading speed.\n"}
+{"abstract": "  In this paper, we study the rainbow Erd\\H{o}s-Rothschild problem with respect\nto 3-term arithmetic progressions. We obtain the asymptotic number of\n$r$-colorings of $[n]$ without rainbow 3-term arithmetic progressions, and we\nshow that the typical colorings with this property are 2-colorings. We also\nprove that $[n]$ attains the maximum number of rainbow 3-term arithmetic\nprogression-free $r$-colorings among all subsets of $[n]$. Moreover, the exact\nnumber of rainbow 3-term arithmetic progression-free $r$-colorings of\n$\\mathbb{Z}_p$ is obtained, where $p$ is any prime and $\\mathbb{Z}_p$ is the\ncyclic group of order $p$.\n"}
+{"abstract": "  In recent times, the Internet has been plagued by a tremendous amount of\nmisinformation. Online markets such as Amazon are also not free from\nmisinformation. In this work, we study the misinformation propagated to\nconsumers through the form of Amazon reviews. There exists a vast underground\nmarket where reviews by real Amazon users are purchased and sold. While such a\npractice violates Amazon's terms of service, we observe that there exists a\ncomplex network consisting of thousands of sellers and agents, who provide a\nrebate to consumers for leaving positive reviews to over $5000$ products. Based\non interviews with members involved in the reviews market, we understand the\nworking of this market, and the tactics used to avoid detection by Amazon. We\nalso present a set of recommendations of features that Amazon and similar\nonline markets can take into consideration to detect such reviews.\n"}
+{"abstract": "  Spatially separated bodies in relative motion through vacuum experience a\ntiny friction force known as quantum friction. This force has so far eluded\nexperimental detection due to its small magnitude and short range. Quantitative\ndetails revealing traces of the quantum friction in the degradation of the\nquantum coherence of a particle are presented. Environmentally induced\ndecoherence for a particle sliding over a dielectric sheet can be decomposed\ninto contributions of different signatures: one solely induced by the\nelectromagnetic vacuum in presence of the dielectric and another induced by\nmotion. As the geometric phase has been proved to be a fruitful venue of\ninvestigation to infer features of the quantum systems, herein we propose to\nuse the accumulated geometric phase acquired by a particle as a quantum\nfriction sensor. Furthermore, an innovative experiment designed to track traces\nof quantum friction by measuring the velocity dependence of corrections to the\ngeometric phase and coherence is proposed. The experimentally viable scheme\npresented can spark renewed optimism for the detection of non-contact friction,\nwith the hope that this non-equilibrium phenomenon can be readily measured\nsoon.\n"}
+{"abstract": "  Multicore fibers can be used for Radio over Fiber transmission of mmwave\nsignals for phased array antennas in 5G networks. The inter-core skew of these\nfibers distort the radiation pattern. We propose an efficient method to\ncompensate the differential delays, without full equalization of the\ntransmission path lengths, reducing the power loss and complexity.\n"}
+{"abstract": "  The interplay between security and reliability is poorly understood. This\npaper shows how triple modular redundancy affects a side-channel attack (SCA).\nOur counterintuitive findings show that modular redundancy can increase SCA\nresiliency.\n"}
+{"abstract": "  An intelligent reflecting surface (IRS) can greatly improve the channel\nquality over a frequency-flat channel, if it is configured to reflect the\nincident signal as a beam towards the receiver. However, the fundamental\nlimitations of the IRS technology become apparent over practical\nfrequency-selective channels, where the same configuration must be used over\nthe entire bandwidth. In this paper, we consider a wideband orthogonal\nfrequency-division multiplexing (OFDM) system that is supported by a fairly\nrealistic IRS setup with two unbalanced states per element and also mutual\ncoupling. We describe the simulation setup considered in the IEEE Signal\nProcessing Cup 2021, propose a low-complexity solution for channel estimation\nand IRS configuration, and evaluate it on that setup.\n"}
+{"abstract": "  In this paper, we propose a novel DNN watermarking method that utilizes a\nlearnable image transformation method with a secret key. The proposed method\nembeds a watermark pattern in a model by using learnable transformed images and\nallows us to remotely verify the ownership of the model. As a result, it is\npiracy-resistant, so the original watermark cannot be overwritten by a pirated\nwatermark, and adding a new watermark decreases the model accuracy unlike most\nof the existing DNN watermarking methods. In addition, it does not require a\nspecial pre-defined training set or trigger set. We empirically evaluated the\nproposed method on the CIFAR-10 dataset. The results show that it was resilient\nagainst fine-tuning and pruning attacks while maintaining a high\nwatermark-detection accuracy.\n"}
+{"abstract": "  For a typical Scene Graph Generation (SGG) method, there is often a large gap\nin the performance of the predicates' head classes and tail classes. This\nphenomenon is mainly caused by the semantic overlap between different\npredicates as well as the long-tailed data distribution. In this paper, a\nPredicate Correlation Learning (PCL) method for SGG is proposed to address the\nabove two problems by taking the correlation between predicates into\nconsideration. To describe the semantic overlap between strong-correlated\npredicate classes, a Predicate Correlation Matrix (PCM) is defined to quantify\nthe relationship between predicate pairs, which is dynamically updated to\nremove the matrix's long-tailed bias. In addition, PCM is integrated into a\nPredicate Correlation Loss function ($L_{PC}$) to reduce discouraging gradients\nof unannotated classes. The proposed method is evaluated on Visual Genome\nbenchmark, where the performance of the tail classes is significantly improved\nwhen built on the existing methods.\n"}
+{"abstract": "  Developers in data science and other domains frequently use computational\nnotebooks to create exploratory analyses and prototype models. However, they\noften struggle to incorporate existing software engineering tooling into these\nnotebook-based workflows, leading to fragile development processes. We\nintroduce Assembl\\'{e}, a new development environment for collaborative data\nscience projects, in which promising code fragments of data science pipelines\ncan be contributed as pull requests to an upstream repository entirely from\nwithin JupyterLab, abstracting away low-level version control tool usage. We\ndescribe the design and implementation of Assembl\\'{e} and report on a user\nstudy of 23 data scientists.\n"}
+{"abstract": "  Predicting the permeability of porous media in saturated and partially\nsaturated conditions is of crucial importance in many geo-engineering areas,\nfrom water resources to vadose zone hydrology or contaminant transport\npredictions. Many models have been proposed in the literature to estimate the\npermeability from properties of the porous media such as porosity, grain size\nor pore size. In this study, we develop a model of the permeability for porous\nmedia saturated by one or two fluid phases with all physically-based parameters\nusing a fractal upscaling technique. The model is related to microstructural\nproperties of porous media such as fractal dimension for pore space, fractal\ndimension for tortuosity, porosity, maximum radius, ratio of minimum pore\nradius and maximum pore radius, water saturation and irreducible water\nsaturation. The model is favorably compared to existing and widely used models\nfrom the literature. Then, comparison with published experimental data for both\nunconsolidated and consolidated samples, we show that the proposed model\nestimate the permeability from the medium properties very well.\n"}
+{"abstract": "  This paper is one in a series that investigates topological measures on\nlocally compact spaces. A topological measure is a set function which is\nfinitely additive on the collection of open and compact sets, inner regular on\nopen sets, and outer regular on closed sets. We examine semisolid sets and give\na way of constructing topological measures from solid-set functions on locally\ncompact, connected, locally connected spaces. For compact spaces our approach\nproduces a simpler method than the current one. We give examples of finite and\ninfinite topological measures on locally compact spaces and present an easy way\nto generate topological measures on spaces whose one-point compactification has\ngenus 0. Results of this paper are necessary for various methods for\nconstructing topological measures, give additional properties of topological\nmeasures, and provide a tool for determining whether two topological measures\nor quasi-linear functionals are the same.\n"}
+{"abstract": "  We propose a novel neural model compression strategy combining data\naugmentation, knowledge transfer, pruning, and quantization for device-robust\nacoustic scene classification (ASC). Specifically, we tackle the ASC task in a\nlow-resource environment leveraging a recently proposed advanced neural network\npruning mechanism, namely Lottery Ticket Hypothesis (LTH), to find a\nsub-network neural model associated with a small amount non-zero model\nparameters. The effectiveness of LTH for low-complexity acoustic modeling is\nassessed by investigating various data augmentation and compression schemes,\nand we report an efficient joint framework for low-complexity multi-device ASC,\ncalled \\emph{Acoustic Lottery}. Acoustic Lottery could compress an ASC model up\nto $1/10^{4}$ and attain a superior performance (validation accuracy of 79.4%\nand Log loss of 0.64) compared to its not compressed seed model. All results\nreported in this work are based on a joint effort of four groups, namely\nGT-USTC-UKE-Tencent, aiming to address the \"Low-Complexity Acoustic Scene\nClassification (ASC) with Multiple Devices\" in the DCASE 2021 Challenge Task\n1a.\n"}
+{"abstract": "  Models of the geomagnetic field rely on magnetic data of high spatial and\ntemporal resolution. The magnetic data from low-Earth orbit satellites of\ndedicated magnetic survey missions such as CHAMP and Swarm play a key role in\nthe construction of such models. Unfortunately, there are no magnetic data from\nsuch satellites after the end of CHAMP in 2010 and before the launch of Swarm\nin late 2013. This limits our ability to recover signals on timescales of 3\nyears and less during this gap period. The magnetic data from platform\nmagnetometers carried by satellites for navigational purposes may help address\nthis data gap provided that they are carefully calibrated. Earlier studies have\ndemonstrated that platform magnetometer data can be calibrated using a fixed\nreference field model. However, this approach can lead to biased calibration\nparameters. An alternative has been developed in the form of a co-estimation\nscheme which consists of simultaneously estimating both the calibration\nparameters and a model of the internal geomagnetic field. Here, we develop a\nscheme, based on the CHAOS framework, that involves the co-estimation of a\ngeomagnetic field model along with calibration parameters of platform\nmagnetometers. Using our implementation, we are able to derive a geomagnetic\nfield model from 2008 to 2018 with satellite magnetic data from CHAMP, Swarm,\nsecular variation data from ground observatories, and platform magnetometer\ndata from CryoSat-2 and GRACE. Through experiments, we explore correlations\nbetween the estimates of the geomagnetic field and the calibration parameters,\nand suggest how these may be avoided. We find that platform magnetometer data\nprovide additional information on the secular acceleration, especially in the\nPacific during the gap period. This study adds to the evidence that it is\nbeneficial to use platform magnetometer data in geomagnetic field modeling.\n"}
+{"abstract": "  We generalize the continuous observation privacy setting from Dwork et al.\n'10 and Chan et al. '11 by allowing each event in a stream to be a subset of\nsome (possibly unknown) universe of items. We design differentially private\n(DP) algorithms for histograms in several settings, including top-$k$\nselection, with privacy loss that scales with polylog$(T)$, where $T$ is the\nmaximum length of the input stream. We present a meta-algorithm that can use\nexisting one-shot top-$k$ DP algorithms as a subroutine to continuously release\nprivate histograms from a stream. Further, we present more practical DP\nalgorithms for two settings: 1) continuously releasing the top-$k$ counts from\na histogram over a known domain when an event can consist of an arbitrary\nnumber of items, and 2) continuously releasing histograms over an unknown\ndomain when an event has a limited number of items.\n"}
+{"abstract": "  Electronic instabilities in transition metal compounds often spontaneously\nform orbital molecules, which consist of orbital-coupled metal ions at low\ntemperature. Recent local structural studies utilizing the pair distribution\nfunction revealed that preformed orbital molecules appear disordered even in\nthe high-temperature paramagnetic phase. However, it is unclear whether\npreformed orbital molecules are dynamic or static. Here, we provide clear\nexperimental evidence of the slow dynamics of disordered orbital molecules\nrealized in the high-temperature paramagnetic phase of LiVS2, which exhibits\nvanadium trimerization upon cooling below 314 K. Unexpectedly, the preformed\norbital molecules appear as a disordered zigzag chain that fluctuate in both\ntime and space under electron irradiation. Our findings should advance studies\non soft matter physics realized in an inorganic material due to disordered\norbital molecules.\n"}
+{"abstract": "  In the classical survey (Chapter 16.2, {\\it Mathematics in industrial\nproblem}, Vol. 24, Springer-Verlag, New York, 1989), A. Friedman proposed an\nopen problem on the collision of two incompressible jets emerging from two\naxially symmetric nozzles. In this paper, we concerned with the mathematical\ntheory on this collision problem, and establish the well-posedness theory on\nhydrodynamic impinging outgoing jets issuing from two coaxial axially symmetric\nnozzles. More precisely, we showed that for any given mass fluxes $M_1>0$ and\n$M_2<0$ in two nozzles respectively, that there exists an incompressible,\ninviscid impinging outgoing jet with contact discontinuity, which issues from\ntwo given semi-infinitely long axially symmetric nozzles and extends to\ninfinity. Moreover, the constant pressure free stream surfaces of the impinging\njet initiate smoothly from the mouths of the two nozzles and shrink to some\nasymptotic conical surface. There exists a smooth surface separating the two\nincompressible fluids and the contact discontinuity occurs on the surface.\nFurthermore, we showed that there is no stagnation point in the flow field and\nits closure, except one point on the symmetric axis. Some asymptotic behavior\nof the impinging jet in upstream and downstream, geometric properties of the\nfree stream surfaces are also obtained. The main results in this paper solved\nthe open problem on the collision of two incompressible axially symmetric jets\nin [24].\n"}
+{"abstract": "  The existence of multiple datasets for sarcasm detection prompts us to apply\ntransfer learning to exploit their commonality. The adversarial neural transfer\n(ANT) framework utilizes multiple loss terms that encourage the source-domain\nand the target-domain feature distributions to be similar while optimizing for\ndomain-specific performance. However, these objectives may be in conflict,\nwhich can lead to optimization difficulties and sometimes diminished transfer.\nWe propose a generalized latent optimization strategy that allows different\nlosses to accommodate each other and improves training dynamics. The proposed\nmethod outperforms transfer learning and meta-learning baselines. In\nparticular, we achieve 10.02% absolute performance gain over the previous state\nof the art on the iSarcasm dataset.\n"}
+{"abstract": "  This is the third series of the lab manuals for virtual teaching of\nintroductory physics classes. This covers fluids, waves, thermodynamics,\noptics, interference, photoelectric effect, atomic spectra, and radiation\nconcepts. A few of these labs can be used within Physics I and a few other labs\nwithin Physics II depending on the syllabi of Physics I and II classes. Virtual\nexperiments in this lab manual and our previous Physics I (arXiv.2012.09151)\nand Physics II (arXiv.2012.13278) lab manuals were designed for 2.45 hrs long\nlab classes (algebra-based and calculus-based). However, all the virtual labs\nin these three series can be easily simplified to align with conceptual type or\nshort time physics lab classes as desired. All the virtual experiments were\nbased on open education resource (OER) type simulations. Virtual experiments\nwere designed to simulate in-person physical laboratory experiments. Student\nlearning outcomes (understand, apply, analyze and evaluate) were studied with\ndetailed lab reports per each experiment and end of the semester written exam\nwhich was based on experiments. Special emphasis was given to study the student\nskill development of computational data analysis.\n"}
+{"abstract": "  A pearl's distinguished beauty and toughness are attributable to the periodic\nstacking of aragonite tablets known as nacre. Nacre has naturally occurring\nmesoscale periodicity that remarkably arises in the absence of discrete\ntranslational symmetry. Gleaning the inspiring biomineral design of a pearl\nrequires quantifying its structural coherence and understanding the stochastic\nprocesses that influence formation. By characterizing the entire structure of\npearls (~3 mm) in cross-section at high resolution, we show nacre has\nmedium-range mesoscale periodicity. Self-correcting growth mechanisms actively\nremedy disorder and topological defects of the tablets and act as a\ncountervailing process to long-range disorder. Nacre has a correlation length\nof roughly 16 tablets (~5.5 um) despite persistent fluctuations and topological\ndefects. For longer distances (> 25 tablets, ~8.5 um), the frequency spectrum\nof nacre tablets follows f^(-1.5) behavior suggesting growth is coupled to\nexternal stochastic processes-a universality found across disparate natural\nphenomena which now includes pearls.\n"}
+{"abstract": "  Let $F$ be a field of characteristic $p$ and let $\\Omega^n(F)$ be the\n$F$-vector space of $n$-differential forms. In this work, we will study the\nannihilator of differential forms, give specific descriptions for special cases\nand show a connection between these annihilators and the kernels of the\nrestriction map $\\Omega^n(F) \\to \\Omega^n(E)$ for purely inseparable field\nextensions $E/F$.\n"}
+{"abstract": "  Despite abundant negotiation strategies in literature, the complexity of\nautomated negotiation forbids a single strategy from being dominant against all\nothers in different negotiation scenarios. To overcome this, one approach is to\nuse mixture of experts, but at the same time, one problem of this method is the\nselection of experts, as this approach is limited by the competency of the\nexperts selected. Another problem with most negotiation strategies is their\nincapability of adapting to dynamic variation of the opponent's behaviour\nwithin a single negotiation session resulting in poor performance. This work\nfocuses on both, solving the problem of expert selection and adapting to the\nopponent's behaviour with our Autonomous Negotiating Agent Framework. This\nframework allows real-time classification of opponent's behaviour and provides\na mechanism to select, switch or combine strategies within a single negotiation\nsession. Additionally, our framework has a reviewer component which enables\nself-enhancement capability by deciding to include new strategies or replace\nold ones with better strategies periodically. We demonstrate an instance of our\nframework by implementing maximum entropy reinforcement learning based\nstrategies with a deep learning based opponent classifier. Finally, we evaluate\nthe performance of our agent against state-of-the-art negotiators under varied\nnegotiation scenarios.\n"}
+{"abstract": "  Software Transactional Memory (STM) algorithms provide programmers with a\nsynchronisation mechanism for concurrent access to shared variables. Basically,\nprogrammers can specify transactions (reading from and writing to shared state)\nwhich execute \"seemingly\" atomic. This property is captured in a correctness\ncriterion called opacity. For model checking opacity of an STM algorithm, we --\nin principle -- need to check opacity for all possible combinations of\ntransactions writing to and reading from potentially unboundedly many\nvariables.\n  To still apply automatic model checking techniques to opacity checking, a so\ncalled small model theorem has been proven which states that model checking on\ntwo variables and two transactions is sufficient for correctness verification\nof STMs. In this paper, we take a fresh look at this small model theorem and\ninvestigate its applicability to opacity checking of STM algorithms.\n"}
+{"abstract": "  The electronic nematic phase emerging with spontaneous rotation symmetry\nbreaking is a central issue of modern condensed matter physics. In particular,\nvarious nematic phases in iron-based superconductors and high-$T_{\\rm c}$\ncuprate superconductors are extensively studied recently. Electric quadrupole\nmoments (EQMs) are one of the order parameters characterizing these nematic\nphases in a unified way, and elucidating EQMs is a key to understanding these\nnematic phases. However, the quantum-mechanical formulation of the EQMs in\ncrystals is a nontrivial issue because the position operators are non-periodic\nand unbound. Recently, the EQMs have been formulated by local thermodynamics,\nand such {\\it thermodynamic EQMs} may be used to characterize the fourfold\nrotation symmetry breaking in materials. In this paper, we calculate the\nthermodynamic EQMs in iron-based superconductors LaFeAsO and FeSe as well as a\ncuprate superconductor La$_2$CuO$_4$ by a first-principles calculation. We show\nthat owing to the orbital degeneracy the EQMs in iron-based superconductors are\nmainly determined by the geometric properties of wave functions. This result is\nin sharp contrast to the cuprate superconductor, in which the EQMs are\ndominated by distortion of the Fermi surface.\n"}
+{"abstract": "  Escape from a potential well through an index-1 saddle can be widely found in\nsome important physical systems. Knowing the criteria and phase space geometry\nthat govern escape events plays an important role in making use of such\nphenomenon, particularly when realistic frictional or dissipative forces are\npresent. We aim to extend the study the escape dynamics around the saddle from\ntwo degrees of freedom to three degrees of freedom, presenting both a\nmethodology and phase space structures. Both the ideal conservative system and\na perturbed, dissipative system are considered. We define the five-dimensional\ntransition region, $\\mathcal{T}_h$, as the set of initial conditions of a given\ninitial energy $h$ for which the trajectories will escape from one side of the\nsaddle to another. Invariant manifold arguments demonstrate that in the\nsix-dimensional phase space, the boundary of the transition region, $\\partial\n\\mathcal{T}_h$, is topologically a four-dimensional hyper-cylinder in the\nconservative system, and a four-dimensional hyper-sphere in the dissipative\nsystem. The transition region $\\mathcal{T}_h$ can be constructed by a solid\nthree-dimensional ellipsoid (solid three-dimensional cylinder) in the\nthree-dimensional configuration space, where at each point, there is a cone of\nvelocity -- the velocity directions leading to transition are given by cones,\nwith velocity magnitude given by the initial energy and the direction by two\nspherical angles with given limits. To illustrate our analysis, we consider an\nexample system which has two potential minima connected by an index 1 saddle.\n"}
+{"abstract": "  Scale is often seen as a given, disturbing factor in many vision tasks. When\ndoing so it is one of the factors why we need more data during learning. In\nrecent work scale equivariance was added to convolutional neural networks. It\nwas shown to be effective for a range of tasks. We aim for accurate\nscale-equivariant convolutional neural networks (SE-CNNs) applicable for\nproblems where high granularity of scale and small kernel sizes are required.\nCurrent SE-CNNs rely on weight sharing and kernel rescaling, the latter of\nwhich is accurate for integer scales only. To reach accurate scale\nequivariance, we derive general constraints under which scale-convolution\nremains equivariant to discrete rescaling. We find the exact solution for all\ncases where it exists, and compute the approximation for the rest. The discrete\nscale-convolution pays off, as demonstrated in a new state-of-the-art\nclassification on MNIST-scale and on STL-10 in the supervised learning setting.\nWith the same SE scheme, we also improve the computational effort of a\nscale-equivariant Siamese tracker on OTB-13.\n"}
+{"abstract": "  Crowdsourcing is widely used to create data for common natural language\nunderstanding tasks. Despite the importance of these datasets for measuring and\nrefining model understanding of language, there has been little focus on the\ncrowdsourcing methods used for collecting the datasets. In this paper, we\ncompare the efficacy of interventions that have been proposed in prior work as\nways of improving data quality. We use multiple-choice question answering as a\ntestbed and run a randomized trial by assigning crowdworkers to write questions\nunder one of four different data collection protocols. We find that asking\nworkers to write explanations for their examples is an ineffective stand-alone\nstrategy for boosting NLU example difficulty. However, we find that training\ncrowdworkers, and then using an iterative process of collecting data, sending\nfeedback, and qualifying workers based on expert judgments is an effective\nmeans of collecting challenging data. But using crowdsourced, instead of expert\njudgments, to qualify workers and send feedback does not prove to be effective.\nWe observe that the data from the iterative protocol with expert assessments is\nmore challenging by several measures. Notably, the human--model gap on the\nunanimous agreement portion of this data is, on average, twice as large as the\ngap for the baseline protocol data.\n"}
+{"abstract": "  We theoretically investigate pattern formation and nonlinear dynamics in an\narray of equally-coupled, optically driven, Kerr nonlinear microresonators. We\nshow that the nonlinear dynamics of the system can be associated with an\neffective two dimensional space due to the multimiode structure of each\nresonator. As a result, two fundamentally different dynamical regimes (elliptic\nand hyperbolic) arise at different regions of the hybridized dispersion\nsurface. We demonstrate the formation of global nonlinear optical patterns in\nboth regimes which correspond to coherent optical frequency combs on the\nindividual resonator level. In addition we show that the presence of an\nadditional dimension leads to the observation of wave collapse.\n"}
+{"abstract": "  We apply the method of differential inequalities for the computation of upper\nbounds for the rate of convergence to the limiting regime for one specific\nclass of (in)homogeneous continuous-time Markov chains. To obtain these\nestimates, we investigate the corresponding forward system of Kolmogorov\ndifferential equations.\n"}
+{"abstract": "  Recently, advances in film synthesis methods have enabled a study of\nextremely overdoped $La_{2-x}Sr_{x}CuO_{4}$. This has revealed a surprising\nbehavior of the superfluid density as a function of doping and temperature, the\nexplanation of which is vividly debated. One popular class of models posits\nelectronic phase separation, where the superconducting phase fraction decreases\nwith doping, while some competing phase (e.g. ferromagnetic) progressively\ntakes over. A problem with this scenario is that all the way up to the dome\nedge the superconducting transition remains sharp, according to mutual\ninductance measurements. However, the physically relevant scale is the Pearl\npenetration depth, $\\Lambda_{P}$, and this technique probes the sample on a\nlength scale $L$ that is much larger than $\\Lambda_{P}$. In the present paper,\nwe use local scanning SQUID measurements that probe the susceptibility of the\nsample on the scale $L << \\Lambda_{P}$. Our SQUID maps show uniform landscapes\nof susceptibility and excellent overall agreement of the local penetration\ndepth data with the bulk measurements. These results contribute an important\npiece to the puzzle of how high-temperature superconductivity vanishes on the\noverdoped side of the cuprates phase diagram.\n"}
+{"abstract": "  Social media often acts as breeding grounds for different forms of offensive\ncontent. For low resource languages like Tamil, the situation is more complex\ndue to the poor performance of multilingual or language-specific models and\nlack of proper benchmark datasets. Based on this shared task, Offensive\nLanguage Identification in Dravidian Languages at EACL 2021, we present an\nexhaustive exploration of different transformer models, We also provide a\ngenetic algorithm technique for ensembling different models. Our ensembled\nmodels trained separately for each language secured the first position in\nTamil, the second position in Kannada, and the first position in Malayalam\nsub-tasks. The models and codes are provided.\n"}
+{"abstract": "  Overnight, Apple has turned its hundreds-of-million-device ecosystem into the\nworld's largest crowd-sourced location tracking network called offline finding\n(OF). OF leverages online finder devices to detect the presence of missing\noffline devices using Bluetooth and report an approximate location back to the\nowner via the Internet. While OF is not the first system of its kind, it is the\nfirst to commit to strong privacy goals. In particular, OF aims to ensure\nfinder anonymity, untrackability of owner devices, and confidentiality of\nlocation reports. This paper presents the first comprehensive security and\nprivacy analysis of OF. To this end, we recover the specifications of the\nclosed-source OF protocols by means of reverse engineering. We experimentally\nshow that unauthorized access to the location reports allows for accurate\ndevice tracking and retrieving a user's top locations with an error in the\norder of 10 meters in urban areas. While we find that OF's design achieves its\nprivacy goals, we discover two distinct design and implementation flaws that\ncan lead to a location correlation attack and unauthorized access to the\nlocation history of the past seven days, which could deanonymize users. Apple\nhas partially addressed the issues following our responsible disclosure.\nFinally, we make our research artifacts publicly available.\n"}
+{"abstract": "  We develop a constitutive model allowing for the description of the rheology\nof two-dimensional soft dense suspensions above jamming. Starting from a\nstatistical description of the particle dynamics, we derive, using a set of\napproximations, a non-linear tensorial evolution equation linking the\ndeviatoric part of the stress tensor to the strain-rate and vorticity tensors.\nThe coefficients appearing in this equation can be expressed in terms of the\npacking fraction and of particle-level parameters. This constitutive equation\nrooted in the microscopic dynamic qualitatively reproduces a number of salient\nfeatures of the rheology of jammed soft suspensions, including the presence of\nyield stresses for the shear component of the stress and for the normal stress\ndifference. More complex protocols like the relaxation after a preshear are\nalso considered, showing a smaller stress after relaxation for a stronger\npreshear.\n"}
+{"abstract": "  We study the MaxCut problem for graphs $G=(V,E)$. The problem is NP-hard,\nthere are two main approximation algorithms with theoretical guarantees: (1)\nthe Goemans \\& Williamson algorithm uses semi-definite programming to provide a\n0.878MaxCut approximation (which, if the Unique Games Conjecture is true, is\nthe best that can be done in polynomial time) and (2) Trevisan proposed an\nalgorithm using spectral graph theory from which a 0.614MaxCut approximation\ncan be obtained. We discuss a new approach using a specific quadratic program\nand prove that its solution can be used to obtain at least a 0.502MaxCut\napproximation. The algorithm seems to perform well in practice.\n"}
+{"abstract": "  In many applications, a large number of features are collected with the goal\nto identify a few important ones. Sometimes, these features lie in a metric\nspace with a known distance matrix, which partially reflects their\nco-importance pattern. Proper use of the distance matrix will boost the power\nof identifying important features. Hence, we develop a new multiple testing\nframework named the Distance Assisted Recursive Testing (DART). DART has two\nstages. In stage 1, we transform the distance matrix into an aggregation tree,\nwhere each node represents a set of features. In stage 2, based on the\naggregation tree, we set up dynamic node hypotheses and perform multiple\ntesting on the tree. All rejections are mapped back to the features. Under mild\nassumptions, the false discovery proportion of DART converges to the desired\nlevel in high probability converging to one. We illustrate by theory and\nsimulations that DART has superior performance under various models compared to\nthe existing methods. We applied DART to a clinical trial in the allogeneic\nstem cell transplantation study to identify the gut microbiota whose abundance\nwill be impacted by the after-transplant care.\n"}
+{"abstract": "  Trusted AI literature to date has focused on the trust needs of users who\nknowingly interact with discrete AIs. Conspicuously absent from the literature\nis a rigorous treatment of public trust in AI. We argue that public distrust of\nAI originates from the under-development of a regulatory ecosystem that would\nguarantee the trustworthiness of the AIs that pervade society. Drawing from\nstructuration theory and literature on institutional trust, we offer a model of\npublic trust in AI that differs starkly from models driving Trusted AI efforts.\nThis model provides a theoretical scaffolding for Trusted AI research which\nunderscores the need to develop nothing less than a comprehensive and visibly\nfunctioning regulatory ecosystem. We elaborate the pivotal role of externally\nauditable AI documentation within this model and the work to be done to ensure\nit is effective, and outline a number of actions that would promote public\ntrust in AI. We discuss how existing efforts to develop AI documentation within\norganizations -- both to inform potential adopters of AI components and support\nthe deliberations of risk and ethics review boards -- is necessary but\ninsufficient assurance of the trustworthiness of AI. We argue that being\naccountable to the public in ways that earn their trust, through elaborating\nrules for AI and developing resources for enforcing these rules, is what will\nultimately make AI trustworthy enough to be woven into the fabric of our\nsociety.\n"}
+{"abstract": "  Consider two batches of independent or interdependent exponentiated\nlocation-scale distributed heterogeneous random variables. This article\ninvestigates ordering results for the second-order statistics from these\nbatches when a vector of parameters is switched to another vector of parameters\nin the specified model. Sufficient conditions for the usual stochastic order\nand the hazard rate order are derived. Some applications of the established\nresults are presented.\n"}
+{"abstract": "  We determine the starspot detection rate in exoplanetary transit light curves\nfor M and K dwarf stars observed by the Transiting Exoplanet Survey Satellite\n(TESS) using various starspot filling factors and starspot distributions. We\nused $3.6\\times10^9$ simulations of planetary transits around spotted stars\nusing the transit-starspot model \\prism. The simulations cover a range of\nstarspot filling factors using one of three distributions: uniform,\npolar-biased, and mid-latitude. After construction of the stellar disc and\nstarspots, we checked the transit cord for starspots and examined the change in\nflux of each starspot to determine whether or not a starspot anomaly would be\ndetected. The results were then compared to predicted planetary detections for\nTESS. The results show that for the case of a uniform starspot distribution,\n$64\\pm9$ M dwarf and $23\\pm4$ K dwarf transit light curves observed by TESS\nwill contain a starspot anomaly. This reduces to $37\\pm6$ M dwarf and $12\\pm2$\nK dwarf light curves for a polar-biased distribution and $47\\pm7$ M dwarf and\n$21\\pm4$ K dwarf light curves for a mid-latitude distribution. Currently there\nare only 17 M dwarf and 10 K dwarf confirmed planetary systems from TESS, none\nof which are confirmed as showing starspot anomalies. All three starspot\ndistributions can explain the current trend. However, with such a small sample,\na firm conclusion can not be made at present. In the coming years when more\nTESS M and K dwarf exoplanetary systems have been detected and characterised,\nit will be possible to determine the dominant starspot distribution.\n"}
+{"abstract": "  In this paper, we propose a novel data augmentation strategy named\nCut-Thumbnail, that aims to improve the shape bias of the network. We reduce an\nimage to a certain size and replace the random region of the original image\nwith the reduced image. The generated image not only retains most of the\noriginal image information but also has global information in the reduced\nimage. We call the reduced image as thumbnail. Furthermore, we find that the\nidea of thumbnail can be perfectly integrated with Mixed Sample Data\nAugmentation, so we put one image's thumbnail on another image while the ground\ntruth labels are also mixed, making great achievements on various computer\nvision tasks. Extensive experiments show that Cut-Thumbnail works better than\nstate-of-the-art augmentation strategies across classification, fine-grained\nimage classification, and object detection. On ImageNet classification,\nResNet-50 architecture with our method achieves 79.21\\% accuracy, which is more\nthan 2.8\\% improvement on the baseline.\n"}
+{"abstract": "  This essay summarizes the efforts required to build a program of a unified,\nlow-dimension topology that allows characterizing all these flat space-times.\nSince spatiotemporal manifolds are topological spaces equipped with metrics,\ntheir properties are characterized by Clifford algebras in hypercomplex rings\nassociative with unity, so that Galileo's transformations are induced by a dual\nnumber; the Lorentz transformations, by a perplexed number and the Euclid\ntransformations, by a complex number. This fact led us to establish an internal\nautomorphism in the ring of hybrid numbers that acts as a map of the manifolds\nand induces the space-time metric based on the quality (characteristic) of the\nassociated hypercomplex unit. From this automorphism, we built hybrid\ntrigonometric functions, which we call Poincar\\'e functions, which allowed us\nto deduce general properties of space-time, hyperbolic, parabolic and\nelliptical geometries and the groups SO (3), SO (4) and SO (1, 3). This\napproach allows us to highlight the global properties of space-time, suggests\nmethods for geodynamic models and allows us to interpret anti-matter as matter\nin a Euclidean space-time where the nature of time is imaginary.\n"}
+{"abstract": "  Global value chains (GVCs) are formed through value-added trade, and some\nregions promote economic integration by concluding regional trade agreements to\npromote these chains. However, there is no way to quantitatively assess the\nscope and extent of economic integration involving various sectors in multiple\ncountries. In this study, we used the World Input--Output Database to create a\ncross-border sector-wise trade in value-added network (international\nvalue-added network (IVAN)) covering the period of 2000--2014 and evaluated\nthem using network science methods. By applying Infomap to the IVAN, we\nconfirmed for the first time the existence of two regional communities: Europe\nand the Pacific Rim. Helmholtz--Hodge decomposition was used to decompose the\nvalue flows within the region into potential and circular flows, and the annual\nevolution of the potential and circular relationships between countries and\nsectors was clarified. The circular flow component of the decomposition was\nused to define an economic integration index, and findings confirmed that the\ndegree of economic integration in Europe declined sharply after the economic\ncrisis in 2009 to a level lower than that in the Pacific Rim. The European\neconomic integration index recovered in 2011 but again fell below that of the\nPacific Rim in 2013. Moreover, sectoral analysis showed that the economic\nintegration index captured the effect of Russian mineral resources, free\nmovement of labor in Europe, and international division of labor in the Pacific\nRim, especially in GVCs for the manufacture of motor vehicles and high-tech\nproducts.\n"}
+{"abstract": "  Unitarity demands that the black-hole final state (what remains inside the\nevent horizon at complete evaporation) must be unique. Assuming a UV theory\nwith infinitely many fields, we propose that the uniqueness of the final state\ncan be achieved via a mechanism analogous to the quantum-mechanical description\nof dissipation.\n"}
+{"abstract": "  \\textit{Resolve} onboard the X-ray satellite XRISM is a cryogenic instrument\nwith an X-ray microcalorimeter in a Dewar. A lid partially transparent to\nX-rays (called gate valve, or GV) is installed at the top of the Dewar along\nthe optical axis. Because observations will be made through the GV for the\nfirst few months, the X-ray transmission calibration of the GV is crucial for\ninitial scientific outcomes. We present the results of our ground calibration\ncampaign of the GV, which is composed of a Be window and a stainless steel\nmesh. For the stainless steel mesh, we measured its transmission using the\nX-ray beamline at ISAS. For the Be window, we used synchrotron facilities to\nmeasure the transmission and modeled the data with (i) photoelectric absorption\nand incoherent scattering of Be, (ii) photoelectric absorption of contaminants,\nand (iii) coherent scattering of Be changing at specific energies. We discuss\nthe physical interpretation of the transmission discontinuity caused by the\nBragg diffraction in poly-crystal Be, which we incorporated into our\ntransmission phenomenological model. We present the X-ray diffraction\nmeasurement on the sample to support our interpretation. The measurements and\nthe constructed model meet the calibration requirements of the GV. We also\nperformed a spectral fitting of the Crab nebula observed with Hitomi SXS and\nconfirmed improvements of the model parameters.\n"}
+{"abstract": "  The uptake of machine learning (ML) approaches in the social and health\nsciences has been rather slow, and research using ML for social and health\nresearch questions remains fragmented. This may be due to the separate\ndevelopment of research in the computational/data versus social and health\nsciences as well as a lack of accessible overviews and adequate training in ML\ntechniques for non data science researchers. This paper provides a meta-mapping\nof research questions in the social and health sciences to appropriate ML\napproaches, by incorporating the necessary requirements to statistical analysis\nin these disciplines. We map the established classification into description,\nprediction, and causal inference to common research goals, such as estimating\nprevalence of adverse health or social outcomes, predicting the risk of an\nevent, and identifying risk factors or causes of adverse outcomes. This\nmeta-mapping aims at overcoming disciplinary barriers and starting a fluid\ndialogue between researchers from the social and health sciences and\nmethodologically trained researchers. Such mapping may also help to fully\nexploit the benefits of ML while considering domain-specific aspects relevant\nto the social and health sciences, and hopefully contribute to the acceleration\nof the uptake of ML applications to advance both basic and applied social and\nhealth sciences research.\n"}
+{"abstract": "  We develop a new three-dimensional time-dependent radiative transfer code,\nTRINITY (Time-dependent Radiative transfer In Near-Infrared TomographY), for\nin-vivo diffuse optical tomography (DOT). The simulation code is based on the\ndesign of long radiation rays connecting boundaries of a computational domain,\nwhich allows us to calculate light propagation with little numerical diffusion.\nWe parallelize the code with MPI using the domain decomposition technique and\nconfirm the high parallelization efficiency so that simulations with a spatial\nresolution of $\\sim 1~\\rm mm$ can be performed in practical time. As a first\napplication, we study the light propagation for a pulse collimated within\n$\\theta \\sim 15^\\circ$ in a phantom, which is a uniform medium made of\npolyurethane mimicking biological tissue. We show that the pulse spreads in all\nforward directions over $\\sim$ a few mm due to the multiple scattering process\nof photons. Our simulations successfully reproduce the time-resolved signals\nmeasured with eight detectors for the phantom. We also introduce the effects of\nreflection and refraction at the boundary of medium with different refractive\nindex and demonstrate the faster propagation of photons in an air hole that is\nan analogue for the respiratory tract.\n"}
+{"abstract": "  Topological phases of materials are characterized by topological invariants\nthat are conventionally calculated by different means according to the\ndimension and symmetry class of the system. For topological materials described\nby Dirac models, we introduce a wrapping number as a unified approach to obtain\nthe topological invariants in arbitrary dimensions and symmetry classes. Given\na unit vector that parametrizes the momentum-dependence of the Dirac model, the\nwrapping number describes the degree of the map from the Brillouin zone torus\nto the sphere formed by the unit vector that we call Dirac sphere. This method\nis gauge-invariant and originates from the intrinsic features of the Dirac\nmodel, and moreover places all known topological invariants, such as Chern\nnumber, winding number, Pfaffian, etc, on equal footing.\n"}
+{"abstract": "  Recently monolayer jacutingaite (Pt2HgSe3), a naturally occurring exfoliable\nmineral, discovered in Brazil in 2008, has been theoretically predicted as a\ncandidate quantum spin Hall system with a 0.5 eV band gap, while the bulk form\nis one of only a few known dual-topological insulators which may host different\nsurface states protected by symmetries. In this work, we systematically\ninvestigate both structure and electronic evolution of bulk Pt2HgSe3 under high\npressure up to 96 GPa. The nontrivial topology persists up to the structural\nphase transition observed in the high-pressure regime. Interestingly, we found\nthat this phase transition is accompanied by the appearance of\nsuperconductivity at around 55 GPa and the critical transition temperature Tc\nincreases with applied pressure. Our results demonstrate that Pt2HgSe3 with\nnontrivial topology of electronic states displays new ground states upon\ncompression and raises potentials in application to the next-generation\nspintronic devices.\n"}
+{"abstract": "  We make two observations on the motion of coupled particles in a periodic\npotential. Coupled pendula, or the space-discretized sine-Gordon equation is an\nexample of this problem. Linearized spectrum of the synchronous motion turns\nout to have a hidden asymptotic periodicity in its dependence on the energy;\nthis is the gist of the first observation. Our second observation is the\ndiscovery of a special property of the purely sinusoidal potentials: the\nlinearization around the synchronous solution is equivalent to the classical\nLam\\`e equation. As a consequence, {\\it all but one instability zones of the\nlinearized equation collapse to a point for the one-harmonic potentials}. This\nprovides a new example where Lam\\'e's finite zone potential arises in the\nsimplest possible setting.\n"}
+{"abstract": "  We present a multi-wavelength investigation of a C-class flaring activity\nthat occurred in the active region NOAA 12734 on 8 March 2019. The\ninvestigation utilises data from AIA and HMI on board the SDO and the\nUdaipur-CALLISTO solar radio spectrograph of the Physical Research Laboratory.\nThis low intensity C1.3 event is characterised by typical features of a long\nduration event (LDE), viz. extended flare arcade, large-scale two-ribbon\nstructures and twin coronal dimmings. The eruptive event occurred in a coronal\nsigmoid and displayed two distinct stages of energy release, manifested in\nterms of temporal and spatial evolution. The formation of twin dimming regions\nare consistent with the eruption of a large flux rope with footpoints lying in\nthe western and eastern edges of the coronal sigmoid. The metric radio\nobservations obtained from Udaipur-CALLISTO reveals a broad-band\n($\\approx$50-180 MHz), stationary plasma emission for $\\approx$7 min during the\nsecond stage of the flaring activity that resemble a type IV radio burst. A\ntype III decametre-hectometre radio bursts with starting frequency of\n$\\approx$2.5 MHz precedes the stationary type IV burst observed by\nUdaipur-CALLISTO by $\\approx$5 min. The synthesis of multi-wavelength\nobservations and Non-Linear Force Free Field (NLFFF) coronal modelling together\nwith magnetic decay index analysis suggests that the sigmoid flux rope\nunderwent a zipping-like uprooting from its western to eastern footpoints in\nresponse to the overlying asymmetric magnetic field confinement. The\nasymmetrical eruption of the flux rope also accounts for the observed\nlarge-scale structures viz. apparent eastward shift of flare ribbons and post\nflare loops along the polarity inversion line (PIL), and provides an evidence\nfor lateral progression of magnetic reconnection site as the eruption proceeds.\n"}
+{"abstract": "  Observations suggest that protoplanetary disks have moderate accretion rates\nonto the central young star, especially at early stages (e.g. HL Tau),\nindicating moderate disk turbulence. However, recent ALMA observations suggest\nthat dust is highly settled, implying weak turbulence. Motivated by such\ntension, we carry out 3D stratified local simulations of self-gravitating\ndisks, focusing on settling of dust particles in actively accreting disks. We\nfind that gravitationally unstable disks can have moderately high accretion\nrates while maintaining a relatively thin dust disk for two reasons. First,\naccretion stress from the self-gravitating spirals (self-gravity stress) can be\nstronger than the stress from turbulence (Reynolds stress) by a factor of 5-20.\nSecond, the strong gravity from the gas to the dust decreases the dust scale\nheight by another factor of $\\sim 2$. Furthermore, the turbulence is slightly\nanisotropic, producing a larger Reynolds stress than the vertical dust\ndiffusion coefficient. Thus, gravitoturbulent disks have unusually high\nvertical Schmidt numbers ($Sc_z$) if we scale the total accretion stress with\nthe vertical diffusion coefficient (e.g. $Sc_z\\sim$ 10-100). The reduction of\nthe dust scale height by the gas gravity, should also operate in\ngravitationally stable disks ($Q>$1). Gravitational forces between particles\nbecome more relevant for the concentration of intermediate dust sizes, forming\ndense clouds of dust. After comparing with HL Tau observations, our results\nsuggest that self-gravity and gravity among different disk components could be\ncrucial for solving the conflict between the protoplanetary disk accretion and\ndust settling, at least at the early stages.\n"}
+{"abstract": "  Non-Hermiticity enriches the 10-fold Altland-Zirnbauer symmetry class into\nthe 38-fold symmetry class, where critical behavior of the Anderson transitions\n(ATs) has been extensively studied recently. Here, we establish a\ncorrespondence of the universality classes of the ATs between Hermitian and\nnon-Hermitian systems. We demonstrate that the critical exponents of the length\nscale in non-Hermitian systems coincide with the critical exponents in the\ncorresponding Hermitian systems with additional chiral symmetry. A remarkable\nconsequence is superuniversality, i.e., the ATs in some different symmetry\nclasses of non-Hermitian systems are characterized by the same critical\nexponent. In addition to the comparisons between the known critical exponents\nfor non-Hermitian systems and their Hermitian counterparts, we obtain the\ncritical exponents in symmetry classes AI, AII, AII$^{\\dagger}$,\nCII$^{\\dagger}$, and DIII in two and three dimensions. Our precise critical\nexponents not only confirm the correspondence, but also predict the unknown\ncritical exponents in Hermitian systems, paving a way to study the ATs of\nHermitian systems by the corresponding non-Hermitian systems.\n"}
+{"abstract": "  We consider strongly convex-concave minimax problems in the federated\nsetting, where the communication constraint is the main bottleneck. When\nclients are arbitrarily heterogeneous, a simple Minibatch Mirror-prox achieves\nthe best performance. As the clients become more homogeneous, using multiple\nlocal gradient updates at the clients significantly improves upon Minibatch\nMirror-prox by communicating less frequently. Our goal is to design an\nalgorithm that can harness the benefit of similarity in the clients while\nrecovering the Minibatch Mirror-prox performance under arbitrary heterogeneity\n(up to log factors). We give the first federated minimax optimization algorithm\nthat achieves this goal. The main idea is to combine (i) SCAFFOLD (an algorithm\nthat performs variance reduction across clients for convex optimization) to\nerase the worst-case dependency on heterogeneity and (ii) Catalyst (a framework\nfor acceleration based on modifying the objective) to accelerate convergence\nwithout amplifying client drift. We prove that this algorithm achieves our\ngoal, and include experiments to validate the theory.\n"}
+{"abstract": "  Thermal power generation plays a dominant role in the world's electricity\nsupply. It consumes large amounts of coal worldwide, and causes serious air\npollution. Optimizing the combustion efficiency of a thermal power generating\nunit (TPGU) is a highly challenging and critical task in the energy industry.\nWe develop a new data-driven AI system, namely DeepThermal, to optimize the\ncombustion control strategy for TPGUs. At its core, is a new model-based\noffline reinforcement learning (RL) framework, called MORE, which leverages\nlogged historical operational data of a TPGU to solve a highly complex\nconstrained Markov decision process problem via purely offline training. MORE\naims at simultaneously improving the long-term reward (increase combustion\nefficiency and reduce pollutant emission) and controlling operational risks\n(safety constraints satisfaction). In DeepThermal, we first learn a data-driven\ncombustion process simulator from the offline dataset. The RL agent of MORE is\nthen trained by combining real historical data as well as carefully filtered\nand processed simulation data through a novel restrictive exploration scheme.\nDeepThermal has been successfully deployed in four large coal-fired thermal\npower plants in China. Real-world experiments show that DeepThermal effectively\nimproves the combustion efficiency of a TPGU. We also report and demonstrate\nthe superior performance of MORE by comparing with the state-of-the-art\nalgorithms on the standard offline RL benchmarks. To the best knowledge of the\nauthors, DeepThermal is the first AI application that has been used to solve\nreal-world complex mission-critical control tasks using the offline RL\napproach.\n"}
+{"abstract": "  Pushed by market forces, software development has become fast-paced. As a\nconsequence, modern development projects are assembled from 3rd-party\ncomponents. Security & privacy assurance techniques once designed for large,\ncontrolled updates over months or years, must now cope with small, continuous\nchanges taking place within a week, and happening in sub-components that are\ncontrolled by third-party developers one might not even know they existed. In\nthis paper, we aim to provide an overview of the current software security\napproaches and evaluate their appropriateness in the face of the changed nature\nin software development. Software security assurance could benefit by switching\nfrom a process-based to an artefact-based approach. Further, security\nevaluation might need to be more incremental, automated and decentralized. We\nbelieve this can be achieved by supporting mechanisms for lightweight and\nscalable screenings that are applicable to the entire population of software\ncomponents albeit there might be a price to pay.\n"}
+{"abstract": "  We study the role of driving in an initial maximally entangled state evolving\nunder the presence of a structured environment in a weak and strong regime. We\nfocus on the enhancement and degradation of maximal Concurrence when the system\nis driven on and out of resonance for a general evolution, as well as the\neffect of adding a transverse coupling among the particles of the model. We\nfurther investigate the role of driving in the acquisition of a geometric phase\nfor the maximally entangled state. As the model studied herein can be used to\nmodel experimental situations such as hybrid quantum classical systems feasible\nwith current technologies, this knowledge can aid the search for physical\nsetups that best retain quantum properties under dissipative dynamics.\n"}
+{"abstract": "  Jacobi's triple product identity is proved from one of Euler's\n$q$-exponential functions in an elementary way.\n"}
+{"abstract": "  The goal of regression is to recover an unknown underlying function that best\nlinks a set of predictors to an outcome from noisy observations. In\nnon-parametric regression, one assumes that the regression function belongs to\na pre-specified infinite dimensional function space (the hypothesis space). In\nthe online setting, when the observations come in a stream, it is\ncomputationally-preferable to iteratively update an estimate rather than\nrefitting an entire model repeatedly. Inspired by nonparametric sieve\nestimation and stochastic approximation methods, we propose a sieve stochastic\ngradient descent estimator (Sieve-SGD) when the hypothesis space is a Sobolev\nellipsoid. We show that Sieve-SGD has rate-optimal MSE under a set of simple\nand direct conditions. We also show that the Sieve-SGD estimator can be\nconstructed with low time expense, and requires almost minimal memory usage\namong all statistically rate-optimal estimators, under some conditions on the\ndistribution of the predictors.\n"}
+{"abstract": "  The objective of this study is to correlate the scaling factor of the\nStandard Model (SM) like Higgs boson and the cross section ratio of the process\n$e^+ e^- \\rightarrow hhf\\overline{f}$ where $f\\neq t$, normalized to SM\npredictions in the type I of the Two Higgs Doublet Model. All calculations have\nbeen performed at $\\sqrt{s}=500$ GeV and $1 \\leq \\tan{\\beta} \\leq 30$ for\nmasses $m_H = m_A = m_{H^\\pm} = 300GeV$ and $m_H=300$ GeV, $m_A=m_{H^\\pm}=500$\nGeV. The working scenario is by taking without alignment limit, that is\n$s_{\\beta-\\alpha}= 0.98$ and $s_{\\beta-\\alpha}= 0.99,$ $0.995$, which gives the\nenhancement in the cross section, particularly a few times greater than the\npredictions of the SM due to resonant-impacts of the additional heavy neutral\nHiggs bosons. This shows that enhancement in cross section occurs on leaving\nthe alignment i.e., $s_{\\beta-\\alpha}=1$, at which all the higgs that couple to\nvector bosons and fermions have the same values as in SM at tree level. A large\nvalue of enhancement factor is obtained at $s_{\\beta-\\alpha}= 0.98$ compared to\n$s_{\\beta-\\alpha}= 0.99,$ $0 .995$. Furthermore, the decrease in the\nenhancement factor is observed for the case when $m_H=300$ GeV,\n$m_A=m_{H^\\pm}=500$ GeV. The behavior of the scaling factor with $\\tan{\\beta}$\nis also studied, which shows that for large values of $\\tan\\beta$, the scaling\nfactor becomes equal to $s_{\\beta-\\alpha}$. Finally a convincing correlation is\nachieved by taking into account, the experimental and theoretical constraints\ne.g, perturbative unitarity, vacuum stability and electroweak oblique\nparameters.\n"}
+{"abstract": "  In this paper, we propose a polar coding based scheme for set reconciliation\nbetween two network nodes. The system is modeled as a well-known Slepian-Wolf\nsetting induced by a fixed number of deletions. The set reconciliation process\nis divided into two phases: 1) a deletion polar code is employed to help one\nnode to identify the possible deletion indices, which may be larger than the\nnumber of genuine deletions; 2) a lossless compression polar code is then\ndesigned to feedback those indices with minimum overhead. Our scheme can be\nviewed as a generalization of polar codes to some emerging network-based\napplications such as the package synchronization in blockchains. Some\nconnections with the existing schemes based on the invertible Bloom lookup\ntables (IBLTs) and network coding are also observed and briefly discussed.\n"}
+{"abstract": "  The two-dimensional regular and chaotic electro-convective flow states of a\ndielectric liquid between two infinite parallel planar electrodes are\ninvestigated using a two-relaxation-time lattice Boltzmann method. Positive\ncharges injected at the metallic planar electrode located at the bottom of the\ndielectric liquid layer are transported towards the grounded upper electrode by\nthe synergy of the flow and the electric field. The various flow states can be\ncharacterized by a non-dimensional parameter, the electric Rayleigh number.\nGradually increasing the electric Rayleigh number, the flow system sequentially\nevolves via quasi-periodic, periodic, and chaotic flow states with five\nidentified bifurcations. The turbulence kinetic energy spectrum is shown to\nfollow the -3 law as the flow approaches turbulence. The spectrum is found to\nfollow a -5 law when the flow is periodic.\n"}
+{"abstract": "  We discuss possibilities to test physics beyond the Standard Model in\n$\\vert\\Delta c\\vert=\\vert\\Delta u\\vert= 1$ semileptonic, hadronic and missing\nenergy decay modes. Clean null test observables such as angular observables,\nCP-asymmetries and lepton universality tests are presented and\nmodel-independent correlations as well as details within flavorful,\nanomaly-free $Z^\\prime$ models are worked out.\n"}
+{"abstract": "  Axion-CMB scenario is an interesting possibility to explain the temperature\nanisotropy of the cosmic microwave background (CMB) by primordial fluctuations\nof the QCD axion \\cite{Iso:2020pzv}. In this scenario, fluctuations of\nradiations are generated by an energy exchange between axions and radiations,\nwhich results in the correlation between the primordial axion fluctuations and\nthe CMB anisotropies. Consequently, the cosmological observations stringently\nconstrain a model of the axion and the early history of the universe. In\nparticular, we need a large energy fraction $\\Omega_A^{}$ of the axion at the\nQCD phase transition, but it must become tiny at the present universe to\nsuppress the isocurvature power spectrum. One of natural cosmological scenarios\nto realize such a situation is the thermal inflation which can sufficiently\ndilute the axion abundance. Thermal inflation occurs in various models. In this\npaper, we focus on a classically conformal (CC) $B$-$L$ model with a QCD axion.\nIn this model, the early universe undergoes a long supercooling era of the\n$B$-$L$ and electroweak symmetries, and thermal inflation naturally occurs.\nThus it can be a good candidate for the axion-CMB scenario. But the axion\nabundance at the QCD transition is shown to be insufficient in the original CC\n$B$-$L$ model. To overcome the situation, we extend the model by introducing\n$N$ scalar fields $S$ (either massive or massless) and consider a novel\ncosmological history such that the $O(N)$ and the $B$-$L$ sectors evolve almost\nseparately in the early universe. We find that all the necessary conditions for\nthe axion-CMB scenario can be satisfied in some parameter regions for massless\n$S$ fields, typically $N\\sim 10^{19}$ and the mass of $B$-$L$ gauge boson\naround $5-10$ TeV.\n"}
+{"abstract": "  We prove that every class of graphs $\\mathscr C$ that is monadically stable\nand has bounded twin-width can be transduced from some class with bounded\nsparse twin-width. This generalizes analogous results for classes of bounded\nlinear cliquewidth and of bounded cliquewidth. It also implies that monadically\nstable classes of bounded twin-widthare linearly $\\chi$-bounded.\n"}
+{"abstract": "  We present a transformer-based image anomaly detection and localization\nnetwork. Our proposed model is a combination of a reconstruction-based approach\nand patch embedding. The use of transformer networks helps to preserve the\nspatial information of the embedded patches, which are later processed by a\nGaussian mixture density network to localize the anomalous areas. In addition,\nwe also publish BTAD, a real-world industrial anomaly dataset. Our results are\ncompared with other state-of-the-art algorithms using publicly available\ndatasets like MNIST and MVTec.\n"}
+{"abstract": "  Small-scale inhomogeneities in the baryon density around recombination have\nbeen proposed as a solution to the tension between local and global\ndeterminations of the Hubble constant. These baryon clumping models make\ndistinct predictions for the cosmic microwave background anisotropy power\nspectra on small angular scales. We use recent data from the Atacama Cosmology\nTelescope to test these predictions. No evidence for baryon clumping is found,\nassuming a range of parameterizations for time-independent baryon density\nprobability distribution functions. The inferred Hubble constant remains in\nsignificant tension with the SH0ES measurement.\n"}
+{"abstract": "  Spontaneous onset of a low temperature topologically ordered phase in a\n2-dimensional (2D) lattice model of uniaxial liquid crystal (LC) was debated\nextensively pointing to a suspected underlying mechanism affecting the RG flow\nnear the topological fixed point. A recent MC study clarified that a prior\ncrossover leads to a transition to nematic phase. The crossover was interpreted\nas due to the onset of a perturbing relevant scaling field originating from the\nextra spin degree of freedom. As a counter example and in support of this\nhypothesis, we now consider V-shaped bent-core molecules with rigid rod-like\nsegments connected at an assigned angle. The two segments of the molecule\ninteract with the segments of all the nearest neighbours on a square lattice,\nprescribed by a biquadratic interaction. We compute equilibrium averages of\ndifferent observables with Monte Carlo techniques as a function of temperature\nand sample size. For the chosen molecular bend angle and symmetric\ninter-segment interaction between neighbouirng molecules, the 2D system shows\ntwo transitions as a function of T: the higher one at T1 leads to a topological\nordering of defects associated with the major molecular axis without a\ncrossover, imparting uniaxial symmetry to the medium described by the first\nfundamental group of the order parameter space $\\pi_{1}$= $Z_{2}$ (inversion\nsymmetry). The second at T2 leads to a medium displaying biaxial symmetry with\n$\\pi_{1}$ = Q (quaternion group). The biaxial phase shows a self-similar\nmicroscopic structure with the three axes showing power law correlations with\nvanishing exponents as the temperature decreases.\n"}
+{"abstract": "  WeChat is the largest social instant messaging platform in China, with 1.1\nbillion monthly active users. \"Top Stories\" is a novel friend-enhanced\nrecommendation engine in WeChat, in which users can read articles based on\npreferences of both their own and their friends. Specifically, when a user\nreads an article by opening it, the \"click\" behavior is private. Moreover, if\nthe user clicks the \"wow\" button, (only) her/his direct connections will be\naware of this action/preference. Based on the unique WeChat data, we aim to\nunderstand user preferences and \"wow\" diffusion in Top Stories at different\nlevels. We have made some interesting discoveries. For instance, the \"wow\"\nprobability of one user is negatively correlated with the number of connected\ncomponents that are formed by her/his active friends, but the click probability\nis the opposite. We further study to what extent users' \"wow\" and click\nbehavior can be predicted from their social connections. To address this\nproblem, we present a hierarchical graph representation learning based model\nDiffuseGNN, which is capable of capturing the structure-based social\nobservations discovered above. Our experiments show that the proposed method\ncan significantly improve the prediction performance compared with alternative\nmethods.\n"}
+{"abstract": "  In recent experiments, electromagnetically induced transparency (EIT) were\nobserved with giant atoms, but nothing unconventional were found from the\ntransmission spectra. In this letter, we show that unconventional EIT does\nexist in giant atoms, and indicate why it has not been observed so far.\nDifferent from these existing works, this letter presents a consistent theory\nincluding a real space method and a time delayed master equation for observing\nunconventional EIT. We discover that this phenomenon is a quantum effect which\ncannot be correctly described in a semi-classical way as those in recent works.\nOur theory shows that it can be observed when the time delay between two\nneighboring coupling points is comparable to the relaxation time of the atom,\nwhich is crucial for a future experimental observation. This new phenomenon\nresults from inherent non-locality of the giant atom, which physically forces\npropagating fields to be standing waves in space and the atom exhibiting\nretardations in time. Our theory establishes a framework for application of\nnonlocal systems to quantum information processing.\n"}
+{"abstract": "  We provide a generalization of the McMullen's algorithm to approximate the\nHausdorff dimension of the limit set for convex-cocompact subgroups of\nisometries of the Complex Hyperbolic Plane.\n"}
+{"abstract": "  Bi$_2$O$_2$Se is a promising material for next-generation semiconducting\nelectronics. It exhibits premature metallicity on the introduction of a tiny\namount of electrons, the physics behind which remains elusive. Here we report\non transport and dielectric measurements in Bi$_2$O$_2$Se single crystals at\nvarious carrier densities. The temperature-dependent resistivity ($\\rho$)\nindicates a smooth evolution from the semiconducting to the metallic state. The\ncritical concentration for the metal-insulator transition (MIT) to occur is\nextraordinarily low ($n_\\textrm{c}\\sim10^{16}$ cm$^{-3}$). The relative\npermittivity of the insulating sample is huge\n($\\epsilon_\\textrm{r}\\approx155(10)$) and varies slowly with temperature.\nCombined with the light effective mass, a long effective Bohr radius\n($a_\\textrm{B}^*\\approx36(2)$ $\\textrm{nm}$) is derived, which provides a\nreasonable interpretation of the metallic prematurity according to Mott's\ncriterion for MITs. The high electron mobility ($\\mu$) at low temperatures may\nresult from the screening of ionized scattering centers due to the huge\n$\\epsilon_\\textrm{r}$. Our findings shed light on the electron dynamics in two\ndimensional (2D) Bi$_2$O$_2$Se devices.\n"}
+{"abstract": "  Discrete Morse theory, a cell complex-analog to smooth Morse theory, has been\ndeveloped over the past few decades since its original formulation by Robin\nForman in 1998. In particular, discrete gradient vector fields on simplicial\ncomplexes capture important features of discrete Morse functions. We prove that\nthe characteristic polynomials of the Laplacian matrices of a simplicial\ncomplex are generating functions for discrete gradient vector fields of\ndiscrete Morse functions when the complex is either a graph or a triangulation\nof an orientable manifold. Furthermore, we provide a full characterization of\nthe correspondence between rooted forests in higher dimensions and discrete\ngradient vector fields.\n"}
+{"abstract": "  Symbolic equations are at the core of scientific discovery. The task of\ndiscovering the underlying equation from a set of input-output pairs is called\nsymbolic regression. Traditionally, symbolic regression methods use\nhand-designed strategies that do not improve with experience. In this paper, we\nintroduce the first symbolic regression method that leverages large scale\npre-training. We procedurally generate an unbounded set of equations, and\nsimultaneously pre-train a Transformer to predict the symbolic equation from a\ncorresponding set of input-output-pairs. At test time, we query the model on a\nnew set of points and use its output to guide the search for the equation. We\nshow empirically that this approach can re-discover a set of well-known\nphysical equations, and that it improves over time with more data and compute.\n"}
+{"abstract": "  Owing to the pandemic caused by the coronavirus disease of 2019 (COVID-19),\nseveral universities have closed their campuses for preventing the spread of\ninfection. Consequently, the university classes are being held over the\nInternet, and students attend these classes from their homes. While the\nCOVID-19 pandemic is expected to be prolonged, the online-centric lifestyle has\nraised concerns about secondary health issues caused by reduced physical\nactivity (PA). However, the actual status of PA among university students has\nnot yet been examined in Japan. Hence, in this study, we collected daily PA\ndata (including the data corresponding to the number of steps taken and the\ndata associated with six types of activities) by employing smartphones and\nthereby analyzed the changes in the PA of university students. The PA data were\ncollected over a period of ten weeks from 305 first-year university students\nwho were attending a mandatory class of physical education at the university.\nThe obtained results indicate that compared to the average number of steps\ntaken before the COVID-19 pandemic (6474.87 steps), the average number of steps\ntaken after the COVID-19 pandemic (3522.5 steps) has decreased by 45.6%.\nFurthermore, the decrease in commuting time (7 AM to 10 AM), classroom time,\nand extracurricular activity time (11 AM to 12 AM) has led to a decrease in PA\non weekdays owing to reduced unplanned exercise opportunities and has caused an\nincrease in the duration of being in the stationary state in the course of\ndaily life.\n"}
+{"abstract": "  We consider a distributed function computation problem in which parties\nobserving noisy versions of a remote source facilitate the computation of a\nfunction of their observations at a fusion center through public communication.\nThe distributed function computation is subject to constraints, including not\nonly reliability and storage but also privacy and secrecy. Specifically, 1) the\nremote source should remain private from an eavesdropper and the fusion center,\nmeasured in terms of the information leaked about the remote source; 2) the\nfunction computed should remain secret from the eavesdropper, measured in terms\nof the information leaked about the arguments of the function, to ensure\nsecrecy regardless of the exact function used. We derive the exact rate regions\nfor lossless and lossy single-function computation and illustrate the lossy\nsingle-function computation rate region for an information bottleneck example,\nin which the optimal auxiliary random variables are characterized for\nbinary-input symmetric-output channels. We extend the approach to lossless and\nlossy asynchronous multiple-function computations with joint secrecy and\nprivacy constraints, in which case inner and outer bounds for the rate regions\ndiffering only in the Markov chain conditions imposed are characterized.\n"}
+{"abstract": "  For a $d$-dimensional random vector $X$, let $p_{n, X}(\\theta)$ be the\nprobability that the convex hull of $n$ independent copies of $X$ contains a\ngiven point $\\theta$. We provide several sharp inequalities regarding $p_{n,\nX}(\\theta)$ and $N_X(\\theta)$ denoting the smallest $n$ for which $p_{n,\nX}(\\theta)\\ge1/2$. As a main result, we derive the totally general inequality\n$1/2 \\le \\alpha_X(\\theta)N_X(\\theta)\\le 3d + 1$, where $\\alpha_X(\\theta)$\n(a.k.a. the Tukey depth) is the minimum probability that $X$ is in a fixed\nclosed halfspace containing the point $\\theta$. We also show several\napplications of our general results: one is a moment-based bound on\n$N_X(\\mathbb{E}[X])$, which is an important quantity in randomized approaches\nto cubature construction or measure reduction problem. Another application is\nthe determination of the canonical convex body included in a random convex\npolytope given by independent copies of $X$, where our combinatorial approach\nallows us to generalize existing results in random matrix community\nsignificantly.\n"}
+{"abstract": "  Finding exact circuit size is a notorious optimization problem in practice.\nWhereas modern computers and algorithmic techniques allow to find a circuit of\nsize seven in blink of an eye, it may take more than a week to search for a\ncircuit of size thirteen. One of the reasons of this behavior is that the\nsearch space is enormous: the number of circuits of size $s$ is\n$s^{\\Theta(s)}$, the number of Boolean functions on $n$ variables is $2^{2^n}$.\n  In this paper, we explore the following natural heuristic idea for decreasing\nthe size of a given circuit: go through all its subcircuits of moderate size\nand check whether any of them can be improved by reducing to SAT. This may be\nviewed as a local search approach: we search for a smaller circuit in a ball\naround a given circuit. We report the results of experiments with various\nsymmetric functions.\n"}
+{"abstract": "  The orientational correlation scheme introduced earlier for tetrahedral\nmolecules is extended for being able to classify orientational correlations\nbetween pairs of high symmetry molecules. While in the original algorithm a\ngiven orientation of a pair of tetrahedral molecules is characterized\nunambiguously by the number of ligand atoms that can be found between two\nplanes that contain each centre and perpendicular to the centre-centre\nconnecting line, in the generalized algorithm, the planes are replaced by\ncones, whose apex angles are set according to the symmetry of each molecule. To\ndemonstrate the applicability of the method, the octahedral-shaped SF$_6$\nmolecule is studied in a wide range of phases (gaseous, supercritical fluid,\nliquid and plastic crystalline) using classical molecular dynamics. By\nanalyzing the orientational correlations, a close-contact region in the first\ncoordination shell and a medium-range order behaviour are identified in the\nnon-crystalline phases. While the former is invariant to changes of the\ndensity, the latter showed longer-ranged correlations as density is raised. In\nthe plastic crystalline state, fluorine atoms are oriented along the lattice\ndirections with higher probability. To test the method for icosahedral\nsymmetries, the crystalline structures of room temperature C$_{60}$ is\ngenerated by three sets of potentials that produce different local\narrangements. The novel analysis provided quantitative result on preferred\narrangements.\n"}
+{"abstract": "  The oxDNA model of DNA has been applied widely to systems in biology,\nbiophysics and nanotechnology. It is currently available via two independent\nopen source packages. Here we present a set of clearly-documented exemplar\nsimulations that simultaneously provide both an introduction to simulating the\nmodel, and a review of the model's fundamental properties. We outline how\nsimulation results can be interpreted in terms of -- and feed into our\nunderstanding of -- less detailed models that operate at larger length scales,\nand provide guidance on whether simulating a system with oxDNA is worthwhile.\n"}
+{"abstract": "  We study the problem of list-decodable mean estimation, where an adversary\ncan corrupt a majority of the dataset. Specifically, we are given a set $T$ of\n$n$ points in $\\mathbb{R}^d$ and a parameter $0< \\alpha <\\frac 1 2$ such that\nan $\\alpha$-fraction of the points in $T$ are i.i.d. samples from a\nwell-behaved distribution $\\mathcal{D}$ and the remaining $(1-\\alpha)$-fraction\nare arbitrary. The goal is to output a small list of vectors, at least one of\nwhich is close to the mean of $\\mathcal{D}$. We develop new algorithms for\nlist-decodable mean estimation, achieving nearly-optimal statistical\nguarantees, with running time $O(n^{1 + \\epsilon_0} d)$, for any fixed\n$\\epsilon_0 > 0$. All prior algorithms for this problem had additional\npolynomial factors in $\\frac 1 \\alpha$. We leverage this result, together with\nadditional techniques, to obtain the first almost-linear time algorithms for\nclustering mixtures of $k$ separated well-behaved distributions,\nnearly-matching the statistical guarantees of spectral methods. Prior\nclustering algorithms inherently relied on an application of $k$-PCA, thereby\nincurring runtimes of $\\Omega(n d k)$. This marks the first runtime improvement\nfor this basic statistical problem in nearly two decades.\n  The starting point of our approach is a novel and simpler near-linear time\nrobust mean estimation algorithm in the $\\alpha \\to 1$ regime, based on a\none-shot matrix multiplicative weights-inspired potential decrease. We\ncrucially leverage this new algorithmic framework in the context of the\niterative multi-filtering technique of Diakonikolas et al. '18, '20, providing\na method to simultaneously cluster and downsample points using one-dimensional\nprojections -- thus, bypassing the $k$-PCA subroutines required by prior\nalgorithms.\n"}
+{"abstract": "  A selection of intellectual goods produced by online communities - e.g. open\nsource software or knowledge bases like Wikipedia - are in daily use by a broad\naudience, and thus their quality impacts the public at large. Yet, it is still\nunclear what contributes to the effectiveness of such online peer production\nsystems: what conditions or social processes help them deliver quality\nproducts. Specifically, while co-contribution (i.e. bipartite networks) are\noften investigated in online collaboration, the role of interpersonal\ncommunication in coordination of online peer-production is much less\ninvestigated. To address this gap we have reconstructed networks of personal\ncommunication (direct messaging) between Wikipedia editors gathered in so\ncalled Wikiprojects - teams of contributors who focus on articles within\nspecific topical areas. We found that effective projects exchange larger volume\nof direct messages and that their communication structure allows for complex\ncoordination: for sharing of information locally through selective ties, and at\nthe same time globally across the whole group. To verify how these network\nmeasures relate to the subjective perception of importance of group\ncommunication we conducted semi-structured interviews with members of selected\nprojects. Our interviewees used direct communication for providing feedback,\nfor maintaining close relations and for tapping on the social capital of the\nWikipedia community. Our results underscore the importance of communication\nstructure in online collaboration: online peer production communities rely on\ninterpersonal communication to coordinate their work and to maintain high\nlevels of engagement. Design of platforms for such communities should allow for\nample group level communication as well as for one-on-one interactions.\n"}
+{"abstract": "  A phenomenological Hamiltonian of a closed (i.e., unitary) quantum system is\nassumed to have an $N$ by $N$ real-matrix form composed of a unperturbed\ndiagonal-matrix part $H^{(N)}_0$ and of a tridiagonal-matrix perturbation\n$\\lambda\\,W^{(N)}(\\lambda)$. The requirement of the unitarity of the evolution\nof the system (i.e., of the diagonalizability and of the reality of the\nspectrum) restricts, naturally, the variability of the matrix elements to a\n\"physical\" domain ${\\cal D}^{[N]} \\subset \\mathbb{R}^d$. We fix the unperturbed\nmatrix (simulating a non-equidistant, square-well-type unperturbed spectrum)\nand we only admit the maximally non-Hermitian antisymmetric-matrix\nperturbations. This yields the hiddenly Hermitian model with the measure of\nperturbation $\\lambda$ and with the $d=N$ matrix elements which are, inside\n${\\cal D}^{[N]}$, freely variable. Our aim is to describe the quantum\nphase-transition boundary $\\partial {\\cal D}^{[N]}$ (alias exceptional-point\nboundary) at which the unitarity of the system is lost. Our main attention is\npaid to the strong-coupling extremes of stability, i.e., to the Kato's\nexceptional points of order $N$ (EPN) and to the (sharply spiked) shape of the\nboundary $\\partial {\\cal D}^{[N]}$ in their vicinity. The feasibility of our\nconstructions is based on the use of the high-precision arithmetics in\ncombination with the computer-assisted symbolic manipulations (including, in\nparticular, the Gr\\\"{o}bner basis elimination technique).\n"}
+{"abstract": "  Stance detection concerns the classification of a writer's viewpoint towards\na target. There are different task variants, e.g., stance of a tweet vs. a full\narticle, or stance with respect to a claim vs. an (implicit) topic. Moreover,\ntask definitions vary, which includes the label inventory, the data collection,\nand the annotation protocol. All these aspects hinder cross-domain studies, as\nthey require changes to standard domain adaptation approaches. In this paper,\nwe perform an in-depth analysis of 16 stance detection datasets, and we explore\nthe possibility for cross-domain learning from them. Moreover, we propose an\nend-to-end unsupervised framework for out-of-domain prediction of unseen,\nuser-defined labels. In particular, we combine domain adaptation techniques\nsuch as mixture of experts and domain-adversarial training with label\nembeddings, and we demonstrate sizable performance gains over strong baselines,\nboth (i) in-domain, i.e., for seen targets, and (ii) out-of-domain, i.e., for\nunseen targets. Finally, we perform an exhaustive analysis of the cross-domain\nresults, and we highlight the important factors influencing the model\nperformance.\n"}
+{"abstract": "  An inspiration at the origin of wavelet analysis (when Grossmann, Morlet,\nMeyer and collaborators were interacting and exploring versions of multiscale\nrepresentations) was provided by the analysis of holomorphic signals, for which\nthe images of the phase of Cauchy wavelets were remarkable in their ability to\nreveal intricate singularities or dynamic structures, such as instantaneous\nfrequency jumps in musical recordings. Our goal is to follow their seminal work\nand introduce recent developments in nonlinear analysis. In particular we\nsketch methods extending conventional Fourier analysis, exploiting both phase\nand amplitudes of holomorphic functions. The Blaschke factors are a key\ningredient, in building analytic tools, starting with the Malmquist Takenaka\northonormal bases of the Hardy space, continuing with \"best\" adapted bases\nobtained through phase unwinding, and concluding with relations to composition\nof Blaschke products and their dynamics. We also remark that the phase of a\nBlaschke product is a one layer neural net with arctan as an activation sigmoid\nand that the composition is a \"Deep Neural Net\" whose depth is the number of\ncompositions. Our results provide a wealth of related library of orthonormal\nbases.\n"}
+{"abstract": "  In autonomous driving, perceiving the driving behaviors of surrounding agents\nis important for the ego-vehicle to make a reasonable decision. In this paper,\nwe propose a neural network model based on trajectories information for driving\nbehavior recognition. Unlike existing trajectory-based methods that recognize\nthe driving behavior using the hand-crafted features or directly encoding the\ntrajectory, our model involves a Multi-Scale Convolutional Neural Network\n(MSCNN) module to automatically extract the high-level features which are\nsupposed to encode the rich spatial and temporal information. Given a\ntrajectory sequence of an agent as the input, firstly, the Bi-directional Long\nShort Term Memory (Bi-LSTM) module and the MSCNN module respectively process\nthe input, generating two features, and then the two features are fused to\nclassify the behavior of the agent. We evaluate the proposed model on the\npublic BLVD dataset, achieving a satisfying performance.\n"}
+{"abstract": "  High load latency that results from deep cache hierarchies and relatively\nslow main memory is an important limiter of single-thread performance. Data\nprefetch helps reduce this latency by fetching data up the hierarchy before it\nis requested by load instructions. However, data prefetching has shown to be\nimperfect in many situations. We propose cache-level prediction to complement\nprefetchers. Our method predicts which memory hierarchy level a load will\naccess allowing the memory loads to start earlier, and thereby saves many\ncycles. The predictor provides high prediction accuracy at the cost of just one\ncycle added latency to L1 misses. Experimental results show speedup of 7.8\\% on\ngeneric, graph, and HPC applications over a baseline with aggressive\nprefetchers.\n"}
+{"abstract": "  Buckling strength estimation of architected materials has mainly been\nrestricted to load cases oriented along symmetry axes. However, realistic load\nscenarios normally exhibit more general stress distributions. In this paper we\npropose a simple yet accurate method to estimate the buckling strength of\nstretch-dominated lattice structures based on individual member analysis. As an\nintegral part of the method, the yield strength is also determined. This\nsimplified model is verified by rigorous numerical analysis. In particular, we\nefficiently compute the complete buckling strength surfaces of an orthotropic\nbulk modulus optimal plate lattice structure and isotropic stiffness optimal\nplate and truss lattice structures subjected to rotated uni-axial loads, where\nthe ratio between the highest and lowest buckling strength is found to be 1.77,\n2.11 and 2.41, respectively. For comparison, we also provide their yield\nstrength surfaces, where the corresponding ratios are 1.84, 1.16 and 1.79.\nFurthermore, we use the knowledge gained from the simplified model to create a\nnew configuration of the isotropic plate lattice structure with a more\nisotropic buckling strength surface and buckling strength ratio of 1.24,\nwithout deterioration of the stiffness or yield strength. The proposed method\nprovides a valuable tool to quickly estimate the microstructural buckling\nstrength of stretch-dominated lattice structures, especially for applications\nwhere the stress state is non-uniform such as infill in additive manufacturing.\n"}
+{"abstract": "  We analyze the phase diagram of a topological insulator model including\nantiferromagnetic interactions in the form of an extended Su-Schrieffer Heeger\nmodel. To this end, we employ a recently introduced operational definition of\ntopological order based on the ability of a system to perform topological error\ncorrection. We show that the necessary error correction statistics can be\nobtained efficiently using a Monte-Carlo sampling of a matrix product state\nrepresentation of the ground state wave function. Specifically, we identify two\ndistinct symmetry-protected topological phases corresponding to two different\nfully dimerized reference states. Finally, we extend the notion of error\ncorrection to classify thermodynamic phases to those exhibiting local order\nparameters, finding a topologically trivial antiferromagnetic phase for\nsufficiently strong interactions.\n"}
+{"abstract": "  Cyber-Physical System (CPS) has made a tremendous progress in recent years\nand also disrupted many technical fields such as smart industries, smart\nhealth, smart transportation etc. to flourish the nations economy. However, CPS\nSecurity is still one of the concerns for wide adoption owing to high number of\ndevices connecting to the internet and the traditional security solutions may\nnot be suitable to protect the advanced, application specific attacks. This\npaper presents a programmable device network layer architecture to combat\nattacks and efficient network monitoring in heterogeneous environment CPS\napplications. We leverage Industrial control systems (ICS) to discuss the\nexisting issues, highlighting the importance of advanced network layer for CPS.\nThe programmable data plane language (P4) is introduced to detect well known\nHELLO Flood attack with minimal efforts in the network level and also used to\nfeaturing the potential solutions for security.\n"}
+{"abstract": "  The news recommender systems are marked by a few unique challenges specific\nto the news domain. These challenges emerge from rapidly evolving readers'\ninterests over dynamically generated news items that continuously change over\ntime. News reading is also driven by a blend of a reader's long-term and\nshort-term interests. In addition, diversity is required in a news recommender\nsystem, not only to keep the reader engaged in the reading process but to get\nthem exposed to different views and opinions. In this paper, we propose a deep\nneural network that jointly learns informative news and readers' interests into\na unified framework. We learn the news representation (features) from the\nheadlines, snippets (body) and taxonomy (category, subcategory) of news. We\nlearn a reader's long-term interests from the reader's click history,\nshort-term interests from the recent clicks via LSTMSs and the diversified\nreader's interests through the attention mechanism. We also apply different\nlevels of attention to our model. We conduct extensive experiments on two news\ndatasets to demonstrate the effectiveness of our approach.\n"}
+{"abstract": "  Two-dimensional (2D) ZrS2 monolayer (ML) has emerged as a promising candidate\nfor thermoelectric (TE) device applications due to its high TE figure of merit,\nwhich is mainly contributed by its inherently low lattice thermal conductivity.\nThis work investigates the effect of the lattice anharmonicity driven by\ntemperature-dependent phonon dispersions on thermal transport of ZrS2 ML. The\ncalculations are based on the self-consistent phonon (SCP) theory to calculate\nthe thermodynamic parameters along with the lattice thermal conductivity. The\nhigher- order (quartic) force constants were extracted by using an efficient\ncompressive sensing lattice dynamics technique, which estimates the necessary\ndata based on the emerging machine learning program as an alternative of\ncomputationally expensive density functional theory calculations. Resolve of\nthe degeneracy and hardening of the vibrational frequencies of low-energy\noptical modes were predicted upon including the quartic anharmonicity. As\ncompared to the conventional Boltzmann transport equation (BTE) approach, the\nlattice thermal conductivity of the optimized ZrS2 ML unit cell within SCP +\nBTE approach is found to be significantly enhanced (e.g., by 21% at 300 K).\nThis enhancement is due to the relatively lower value of phonon linewidth\ncontributed by the anharmonic frequency renormalization included in the SCP\ntheory. Mainly, the conventional BTE approach neglects the temperature\ndependence of the phonon frequencies due to the consideration of harmonic\nlattice dynamics and treats the normal process of three-phonon scattering\nincorrectly due to the use of quasi-particle lifetimes. These limitations are\naddressed in this work within the SCP + BTE approach, which signifies the\nvalidity and accuracy of this approach.\n"}
+{"abstract": "  Let $R$ be a finite commutative ring with identity and $U(R)$ be its group of\nunits. In 2005, El-Kassar and Chehade presented a ring structure for $U(R)$ and\nas a consequence they generalized this group of units to the generalized group\nof units $U^{k}\\left( R\\right) $ defined iteratively as the group of the units\nof $U^{k-1}(R)$, with $U^{1}\\left( R\\right) =U(R) $. In this paper, we examine\nthe structure of this group, when $R=\\mathbb{Z}_{n}.$ We find a decomposition\nof $U^{k}\\left(\\mathbb{Z}_{n}\\right)$ as a direct product of cyclic groups for\nthe general case of any $k$, and we study when these groups are boolean and\ntrivial. We also show that this decomposition structure is directly related to\nthe Pratt Tree primes.\n"}
+{"abstract": "  Cluster-randomized experiments are widely used due to their logistical\nconvenience and policy relevance. To analyze them properly, we must address the\nfact that the treatment is assigned at the cluster level instead of the\nindividual level. Standard analytic strategies are regressions based on\nindividual data, cluster averages, and cluster totals, which differ when the\ncluster sizes vary. These methods are often motivated by models with strong and\nunverifiable assumptions, and the choice among them can be subjective. Without\nany outcome modeling assumption, we evaluate these regression estimators and\nthe associated robust standard errors from a design-based perspective where\nonly the treatment assignment itself is random and controlled by the\nexperimenter. We demonstrate that regression based on cluster averages targets\na weighted average treatment effect, regression based on individual data is\nsuboptimal in terms of efficiency, and regression based on cluster totals is\nconsistent and more efficient with a large number of clusters. We highlight the\ncritical role of covariates in improving estimation efficiency, and illustrate\nthe efficiency gain via both simulation studies and data analysis. Moreover, we\nshow that the robust standard errors are convenient approximations to the true\nasymptotic standard errors under the design-based perspective. Our theory holds\neven when the outcome models are misspecified, so it is model-assisted rather\nthan model-based. We also extend the theory to a wider class of weighted\naverage treatment effects.\n"}
+{"abstract": "  Face anti-spoofing approaches based on domain generalization (DG) have drawn\ngrowing attention due to their robustness for unseen scenarios. Previous\nmethods treat each sample from multiple domains indiscriminately during the\ntraining process, and endeavor to extract a common feature space to improve the\ngeneralization. However, due to complex and biased data distribution, directly\ntreating them equally will corrupt the generalization ability. To settle the\nissue, we propose a novel Dual Reweighting Domain Generalization (DRDG)\nframework which iteratively reweights the relative importance between samples\nto further improve the generalization. Concretely, Sample Reweighting Module is\nfirst proposed to identify samples with relatively large domain bias, and\nreduce their impact on the overall optimization. Afterwards, Feature\nReweighting Module is introduced to focus on these samples and extract more\ndomain-irrelevant features via a self-distilling mechanism. Combined with the\ndomain discriminator, the iteration of the two modules promotes the extraction\nof generalized features. Extensive experiments and visualizations are presented\nto demonstrate the effectiveness and interpretability of our method against the\nstate-of-the-art competitors.\n"}
+{"abstract": "  Quantum channels in free-space, an essential prerequisite for fundamental\ntests of quantum mechanics and quantum technologies in open space, have so far\nbeen based on direct line-of-sight because the predominant approaches for\nphoton-encoding, including polarization and spatial modes, are not compatible\nwith randomly scattered photons. Here we demonstrate a novel approach to\ntransfer and recover quantum coherence from scattered, non-line-of-sight\nphotons analyzed in a multimode and imaging interferometer for time-bins,\ncombined with photon detection based on a 8x8 single-photon-detector-array. The\nobserved time-bin visibility for scattered photons remained at a high $95\\%$\nover a wide scattering angle range of -45 degree to +45 degree, while the\nindividual pixels in the detector array resolve or track an image in its field\nof view of ca. 0.5 degrees. Using our method we demonstrate the viability of\ntwo novel applications. Firstly, using scattered photons as an indirect channel\nfor quantum communication thereby enabling non-line-of-sight quantum\ncommunication with background suppression, and secondly, using the combined\narrival time and quantum coherence to enhance the contrast of low-light imaging\nand laser ranging under high background light. We believe our method will\ninstigate new lines for research and development on applying photon coherence\nfrom scattered signals to quantum sensing, imaging, and communication in\nfree-space environments.\n"}
+{"abstract": "  Aharonov-Bohm interferometry is the most direct probe of anyonic statistics\nin the quantum Hall effect. The technique involves oscillations of the electric\ncurrent as a function of the magnetic field and is not applicable to Kitaev\nspin liquids and other systems without charged quasiparticles. Here, we\nestablish a novel protocol, involving heat transport, for revealing fractional\nstatistics even in the absence of charged excitations, as is the case in\nquantum spin liquids. Specifically, we demonstrate that heat transport in\nKitaev spin liquids through two distinct interferometer geometries, Fabry-Perot\nand Mach-Zehnder, exhibits drastically different behaviors. Therefore, we\npropose the use of heat transport interferometry as a probe of anyonic\nstatistics in charge insulators.\n"}
+{"abstract": "  We consider 5d supersymmetric gauge theories with unitary groups in the\n$\\Omega$-background and study codim-2/4 BPS defects supported on orthogonal\nplanes intersecting at the origin along a circle. The intersecting defects\narise upon implementing the most generic Higgsing (geometric transition) to the\nparent higher dimensional theory, and they are described by pairs of 3d\nsupersymmetric gauge theories with unitary groups interacting through 1d matter\nat the intersection. We explore the relations between instanton and generalized\nvortex calculus, pointing out a duality between intersecting defects subject to\nthe $\\Omega$-background and a deformation of supergroup gauge theories, the\nexact supergroup point being achieved in the self-dual or unrefined limit.\nEmbedding our setup into refined topological strings and in the simplest case\nwhen the parent 5d theory is Abelian, we are able to identify the supergroup\ntheory dual to the intersecting defects as the supergroup version of refined\nChern-Simons theory via open/closed duality. We also discuss the BPS/CFT side\nof the correspondence, finding an interesting large rank duality with\nsuper-instanton counting.\n"}
+{"abstract": "  We designed, constructed and have been operating a system based on\nsingle-crystal synthetic diamond sensors, to monitor the beam losses at the\ninteraction region of the SuperKEKB asymmetric-energy electron-positron\ncollider. The system records the radiation dose-rates in positions close to the\ninner detectors of the Belle II experiment, and protects both the detector and\naccelerator components against destructive beam losses, by participating in the\nbeam-abort system. It also provides complementary information for the dedicated\nstudies of beam-related backgrounds. We describe the performance of the system\nduring the commissioning of the accelerator and during the first physics data\ntaking.\n"}
+{"abstract": "  We study a class of convex-concave saddle-point problems of the form\n$\\min_x\\max_y \\langle Kx,y\\rangle+f_{\\cal{P}}(x)-h^\\ast(y)$ where $K$ is a\nlinear operator, $f_{\\cal{P}}$ is the sum of a convex function $f$ with a\nLipschitz-continuous gradient and the indicator function of a bounded convex\npolytope $\\cal{P}$, and $h^\\ast$ is a convex (possibly nonsmooth) function.\nSuch problem arises, for example, as a Lagrangian relaxation of various\ndiscrete optimization problems. Our main assumptions are the existence of an\nefficient linear minimization oracle ($lmo$) for $f_{\\cal{P}}$ and an efficient\nproximal map for $h^*$ which motivate the solution via a blend of proximal\nprimal-dual algorithms and Frank-Wolfe algorithms. In case $h^*$ is the\nindicator function of a linear constraint and function $f$ is quadratic, we\nshow a $O(1/n^2)$ convergence rate on the dual objective, requiring $O(n \\log\nn)$ calls of $lmo$. If the problem comes from the constrained optimization\nproblem $\\min_{x\\in\\mathbb R^d}\\{f_{\\cal{P}}(x)\\:|\\:Ax-b=0\\}$ then we\nadditionally get bound $O(1/n^2)$ both on the primal gap and on the\ninfeasibility gap. In the most general case, we show a $O(1/n)$ convergence\nrate of the primal-dual gap again requiring $O(n\\log n)$ calls of $lmo$. To the\nbest of our knowledge, this improves on the known convergence rates for the\nconsidered class of saddle-point problems. We show applications to labeling\nproblems frequently appearing in machine learning and computer vision.\n"}
+{"abstract": "  We study the optical properties of the solar gravitational lens (SGL) while\ntreating the Sun as an extended, axisymmetric and rotating body. The\ngravitational field of the Sun is represented using a set of zonal harmonics.\nWe develop an analytical description of the intensity of light that is observed\nin the image plane in the strong interference region of a realistic SGL. This\nformalism makes it possible to model not only the point-spread function of\npoint sources, but also actual observables, images that form in the focal plane\nof an imaging telescope positioned in the image plane. Perturbations of the\nmonopole gravitational field of the Sun are dominated by the solar quadrupole\nmoment, which results in forming an astroid caustic on the image plane.\nConsequently, an imaging telescope placed inside the astroid caustic observes\nfour bright spots, forming the well-known pattern of an Einstein cross. The\nrelative intensities and positions of these spots change as the telescope is\nmoved in the image plane, with spots merging into bright arcs when the\ntelescope approaches the caustic boundary. Outside the astroid caustic, only\ntwo spots remain and the observed pattern eventually becomes indistinguishable\nfrom the imaging pattern of a monopole lens at greater distances from the\noptical axis. We present results from extensive numerical simulations, forming\nthe basis of our ongoing study of prospective exoplanet imaging with the SGL.\nThese results are also applicable to describe a large class of gravitational\nlensing scenarios involving axisymmetric lenses that can be represented using\nzonal harmonics.\n"}
+{"abstract": "  Deep learning affords enormous opportunities to augment the armamentarium of\nbiomedical imaging, albeit its design and implementation have potential flaws.\nFundamentally, most deep learning models are driven entirely by data without\nconsideration of any prior knowledge, which dramatically increases the\ncomplexity of neural networks and limits the application scope and model\ngeneralizability. Here we establish a geometry-informed deep learning framework\nfor ultra-sparse 3D tomographic image reconstruction. We introduce a novel\nmechanism for integrating geometric priors of the imaging system. We\ndemonstrate that the seamless inclusion of known priors is essential to enhance\nthe performance of 3D volumetric computed tomography imaging with ultra-sparse\nsampling. The study opens new avenues for data-driven biomedical imaging and\npromises to provide substantially improved imaging tools for various clinical\nimaging and image-guided interventions.\n"}
+{"abstract": "  We present an exploratory case study describing the design and realisation of\na ''pure mixed reality'' application in a museum setting, where we investigate\nthe potential of using Microsoft's HoloLens for object-centred museum\nmediation. Our prototype supports non-expert visitors observing a sculpture by\noffering interpretation that is linked to visual properties of the museum\nobject. The design and development of our research prototype is based on a\ntwo-stage visitor observation study and a formative study we conducted prior to\nthe design of the application. We present a summary of our findings from these\nstudies and explain how they have influenced our user-centred content creation\nand the interaction design of our prototype. We are specifically interested in\ninvestigating to what extent different constructs of initiative influence the\nlearning and user experience. Thus, we detail three modes of activity that we\nrealised in our prototype. Our case study is informed by research in the area\nof human-computer interaction, the humanities and museum practice. Accordingly,\nwe discuss core concepts, such as gaze-based interaction, object-centred\nlearning, presence, and modes of activity and guidance with a transdisciplinary\nperspective.\n"}
+{"abstract": "  The computational power increases over the past decades havegreatly enhanced\nthe ability to simulate chemical reactions andunderstand ever more complex\ntransformations. Tensor contractions are the fundamental computational building\nblock of these simulations. These simulations have often been tied to one\nplatform and restricted in generality by the interface provided to the user.\nThe expanding prevalence of accelerators and researcher demands necessitate a\nmore general approach which is not tied to specific hardware or requires\ncontortion of algorithms to specific hardware platforms. In this paper we\npresent COMET, a domain-specific programming language and compiler\ninfrastructure for tensor contractions targeting heterogeneous accelerators. We\npresent a system of progressive lowering through multiple layers of abstraction\nand optimization that achieves up to 1.98X speedup for 30 tensor contractions\ncommonly used in computational chemistry and beyond.\n"}
+{"abstract": "  The next generation of galaxy surveys will allow us to test some fundamental\naspects of the standard cosmological model, including the assumption of a\nminimal coupling between the components of the dark sector. In this paper, we\npresent the Javalambre Physics of the Accelerated Universe Astrophysical Survey\n(J-PAS) forecasts on a class of unified models where cold dark matter interacts\nwith a vacuum energy, considering future observations of baryon acoustic\noscillations, redshift-space distortions, and the matter power spectrum. After\nproviding a general framework to study the background and linear perturbations,\nwe focus on a concrete interacting model without momentum exchange by taking\ninto account the contribution of baryons. We compare the J-PAS results with\nthose expected for DESI and Euclid surveys and show that J-PAS is competitive\nto them, especially at low redshifts. Indeed, the predicted errors for the\ninteraction parameter, which measures the departure from a $\\Lambda$CDM model,\ncan be comparable to the actual errors derived from the current data of cosmic\nmicrowave background temperature anisotropies.\n"}
+{"abstract": "  The scaling of different features of stream-wise normal stress profiles\n$\\langle uu\\rangle^+(y^+)$ in turbulent wall-bounded flows, in particular in\ntruly parallel flows, such as channel and pipe flows, is the subject of a long\nrunning debate. Particular points of contention are the scaling of the \"inner\"\nand \"outer\" peaks of $\\langle uu\\rangle^+$ at $y^+\\approxeq 15$ and $y^+\n=\\mathcal{O}(10^3)$, respectively, their infinite Reynolds number limit, and\nthe rate of logarithmic decay in the outer part of the flow. Inspired by the\nlandmark paper of Chen and Sreenivasan (2021), two terms of the inner\nasymptotic expansion of $\\langle uu\\rangle^+$ in the small parameter\n$Re_\\tau^{-1/4}$ are extracted for the first time from a set of direct\nnumerical simulations (DNS) of channel flow. This inner expansion is completed\nby a matching outer expansion, which not only fits the same set of channel DNS\nwithin 1.5\\% of the peak stress, but also provides a good match of laboratory\ndata in pipes and the near-wall part of boundary layers, up to the highest\n$Re_\\tau$'s of order $10^5$. The salient features of the new composite\nexpansion are first, an inner $\\langle uu\\rangle^+$ peak, which saturates at\n11.3 and decreases as $Re_\\tau^{-1/4}$, followed by a short \"wall loglaw\" with\na slope that becomes positive for $Re_\\tau \\gtrapprox 20'000$, leading up to an\nouter peak, and an outer logarithmic overlap with a negative slope continuously\ngoing to zero for $Re_\\tau \\to\\infty$.\n"}
+{"abstract": "  In recent years, two-dimensional van der Waals materials have emerged as an\nimportant platform for the observation of long-range ferromagnetic order in\natomically thin layers. Although heterostructures of such materials can be\nconceived to harness and couple a wide range of magneto-optical and\nmagneto-electrical properties, technologically relevant applications require\nCurie temperatures at or above room-temperature and the ability to grow films\nover large areas. Here we demonstrate the large-area growth of single-crystal\nultrathin films of stoichiometric Fe5GeTe2 on an insulating substrate using\nmolecular beam epitaxy. Magnetic measurements show the persistence of soft\nferromagnetism up to room temperature, with a Curie temperature of 293 K, and a\nweak out-of-plane magnetocrystalline anisotropy. Surface, chemical, and\nstructural characterizations confirm the layer-by-layer growth, 5:1:2 Fe:Ge:Te\nstoichiometric elementary composition, and single crystalline character of the\nfilms.\n"}
+{"abstract": "  \\textbf{Background} Hydrogels are crosslinked polymer networks that can\nabsorb and retain a large fraction of liquid. Near a critical sliding velocity,\nhydrogels pressed against smooth surfaces exhibit time-dependent frictional\nbehavior occurring over multiple timescales, yet the origin of these dynamics\nis unresolved. \\textbf{Objective} Here, we characterize this time-dependent\nregime and show that it is consistent with two distinct molecular processes:\nsliding-induced relaxation and quiescent recovery. \\textbf{Methods} Our\nexperiments use a custom pin-on-disk tribometer to examine poly(acrylic acid)\nhydrogels on smooth poly(methyl methacrylate) surfaces over a variety of\nsliding conditions, from minutes to hours. \\textbf{Results} We show that at a\nfixed sliding velocity, the friction coefficient decays exponentially and\nreaches a steady-state value. The time constant associated with this decay\nvaries exponentially with the sliding velocity, and is sensitive to any\nprecedent frictional shearing of the interface. This process is reversible;\nupon cessation of sliding, the friction coefficient recovers to its original\nstate. We also show that the initial direction of shear can be imprinted as an\nobservable \"memory\", and is visible after 24 hrs of repeated frictional\nshearing. \\textbf{Conclusions} We attribute this behavior to nanoscale\nextension and relaxation dynamics of the near-surface polymer network, leading\nto a model of frictional relaxation and recovery with two parallel timescales.\n"}
+{"abstract": "  It is shown that the equalization of temperatures between our and mirror\nsectors occurs during one Hubble time due to microscopic black hole production\nand evaporation in particle collisions if the temperature of the Universe is\nnear the multidimensional Plank mass. This effect excludes the multidimensional\nPlanck masses smaller than the reheating temperature of the Universe\n($\\sim10^{13}$ GeV) in the mirror matter models, because the primordial\nnucleosynthesis theory requires that the temperature of the mirror world should\nbe lower than ours. In particular, the birth of microscopic black holes in the\nLHC is impossible if the dark matter of our Universe is represented by baryons\nof mirror matter. It excludes some of the possible coexisting options in\nparticle physics and cosmology. Multidimensional models with flat additional\ndimensions are already strongly constrained in maximum temperature due to the\neffect of Kaluza-Klein mode (KK-mode) overproduction. In these models, the\nreheating temperature should be significantly less than the multidimensional\nPlanck mass, so our restrictions in this case are not paramount. The new\nconstraints play a role in multidimensional models in which the spectrum of\nKK-modes does not lead to their overproduction in the early Universe, for\nexample, in theories with hyperbolic additional space.\n"}
+{"abstract": "  Multilingual models have demonstrated impressive cross-lingual transfer\nperformance. However, test sets like XNLI are monolingual at the example level.\nIn multilingual communities, it is common for polyglots to code-mix when\nconversing with each other. Inspired by this phenomenon, we present two strong\nblack-box adversarial attacks (one word-level, one phrase-level) for\nmultilingual models that push their ability to handle code-mixed sentences to\nthe limit. The former uses bilingual dictionaries to propose perturbations and\ntranslations of the clean example for sense disambiguation. The latter directly\naligns the clean example with its translations before extracting phrases as\nperturbations. Our phrase-level attack has a success rate of 89.75% against\nXLM-R-large, bringing its average accuracy of 79.85 down to 8.18 on XNLI.\nFinally, we propose an efficient adversarial training scheme that trains in the\nsame number of steps as the original model and show that it improves model\naccuracy.\n"}
+{"abstract": "  We present a statistical study of the largest bibliographic compilation of\nstellar and orbital parameters of W UMa stars derived by light curve synthesis\nwith Roche models. The compilation includes nearly 700 individually\ninvestigated objects from over 450 distinct publications. Almost 70% of this\nsample is comprised of stars observed in the last decade that have not been\nconsidered in previous statistical studies. We estimate the ages of the\ncataloged stars, model the distributions of their periods, mass ratios,\ntemperatures and other quantities, and compare them with the data from CRTS,\nLAMOST and Gaia archives. As only a small fraction of the sample has radial\nvelocity curves, we examine the reliability of the photometric mass ratios in\ntotally and partially eclipsing systems and find that totally eclipsing W UMa\nstars with photometric mass ratios have the same parameter distributions as\nthose with spectroscopic mass ratios. Most of the stars with reliable\nparameters have mass ratios below 0.5 and orbital periods shorter than 0.5\ndays. Stars with longer periods and temperatures above 7000 K stand out as\noutliers and shouldn't be labeled as W UMa binaries.\n  The collected data is available as an online database at\nhttps://wumacat.aob.rs.\n"}
+{"abstract": "  Among the models of disordered conduction and localization, models with $N$\norbitals per site are attractive both for their mathematical tractability and\nfor their physical realization in coupled disordered grains. However Wegner\nproved that there is no Anderson transition and no localized phase in the $N\n\\rightarrow \\infty$ limit, if the hopping constant $K$ is kept fixed. Here we\nshow that the localized phase is preserved in a different limit where $N$ is\ntaken to infinity and the hopping $K$ is simultaneously adjusted to keep $N \\,\nK$ constant. We support this conclusion with two arguments. The first is\nnumerical computations of the localization length showing that in the $N\n\\rightarrow \\infty$ limit the site-diagonal-disorder model possesses a\nlocalized phase if $N\\,K$ is kept constant, but does not possess that phase if\n$K$ is fixed. The second argument is a detailed analysis of the energy and\nlength scales in a functional integral representation of the gauge invariant\nmodel. The analysis shows that in the $K$ fixed limit the functional integral's\nspins do not exhibit long distance fluctuations, i.e. such fluctuations are\nmassive and therefore decay exponentially, which signals conduction. In\ncontrast the $N\\,K$ fixed limit preserves the massless character of certain\nspin fluctuations, allowing them to fluctuate over long distance scales and\ncause Anderson localization.\n"}
+{"abstract": "  Mutation and drift play opposite roles in genetics. While mutation creates\ndiversity, drift can cause gene variants to disappear, especially when they are\nrare. In the absence of natural selection and migration, the balance between\nthe drift and mutation in a well-mixed population defines its diversity. The\nMoran model captures the effects of these two evolutionary forces and has a\ncounterpart in social dynamics, known as the Voter model with external opinion\ninfluencers. Two extreme outcomes of the Voter model dynamics are consensus and\ncoexistence of opinions, which correspond to low and high diversity in the\nMoran model. Here we use a Shannon's information-theoretic approach to\ncharacterize the smooth transition between the states of consensus and\ncoexistence of opinions in the Voter model. Mapping the Moran into the Voter\nmodel we extend the results to the mutation-drift balance and characterize the\ntransition between low and high diversity in finite populations. Describing the\npopulation as a network of connected individuals we show that the transition\nbetween the two regimes depends on the network topology of the population and\non the possible asymmetries in the mutation rates.\n"}
+{"abstract": "  Granulation of quantum matter -- the formation of persistent small-scale\npatterns -- is realized in the images of quasi-one-dimensional Bose-Einstein\ncondensates perturbed by a periodically modulated interaction. Our present\nanalysis of a mean-field approximation suggests that granulation is caused by\nthe gradual transformation of phase undulations into density undulations. This\nis achieved by a suitably large modulation frequency, while for low enough\nfrequencies the system exhibits a quasi-adiabatic regime. We show that the\npersistence of granulation is a result of the irregular evolution of the phase\nof the wavefunction representing an irreversible process. Our model predictions\nagree with numerical solutions of the Schr\\\"odinger equation and experimental\nobservations. The numerical computations reveal the emergent many-body\ncorrelations behind these phenomena via the multi-configurational\ntime-dependent Hartree theory for bosons (MCTDHB).\n"}
+{"abstract": "  Production high-performance computing systems continue to grow in complexity\nand size. As applications struggle to make use of increasingly heterogeneous\ncompute nodes, maintaining high efficiency (performance per watt) for the whole\nplatform becomes a challenge. Alongside the growing complexity of scientific\nworkloads, this extreme heterogeneity is also an opportunity: as applications\ndynamically undergo variations in workload, due to phases or data/compute\nmovement between devices, one can dynamically adjust power across compute\nelements to save energy without impacting performance. With an aim toward an\nautonomous and dynamic power management strategy for current and future HPC\narchitectures, this paper explores the use of control theory for the design of\na dynamic power regulation method. Structured as a feedback loop, our\napproach-which is novel in computing resource management-consists of\nperiodically monitoring application progress and choosing at runtime a suitable\npower cap for processors. Thanks to a preliminary offline identification\nprocess, we derive a model of the dynamics of the system and a\nproportional-integral (PI) controller. We evaluate our approach on top of an\nexisting resource management framework, the Argo Node Resource Manager,\ndeployed on several clusters of Grid'5000, using a standard memory-bound HPC\nbenchmark.\n"}
+{"abstract": "  We present a data-driven optimization framework for redesigning police patrol\nzones in an urban environment. The objectives are to rebalance police workload\namong geographical areas and to reduce response time to emergency calls. We\ndevelop a stochastic model for police emergency response by integrating\nmultiple data sources, including police incidents reports, demographic surveys,\nand traffic data. Using this stochastic model, we optimize zone redesign plans\nusing mixed-integer linear programming. Our proposed design was implemented by\nthe Atlanta Police Department in March 2019. By analyzing data before and after\nthe zone redesign, we show that the new design has reduced the response time to\nhigh priority 911 calls by 5.8\\% and the imbalance of police workload among\ndifferent zones by 43\\%.\n"}
+{"abstract": "  We present a novel class of projected methods, to perform statistical\nanalysis on a data set of probability distributions on the real line, with the\n2-Wasserstein metric. We focus in particular on Principal Component Analysis\n(PCA) and regression. To define these models, we exploit a representation of\nthe Wasserstein space closely related to its weak Riemannian structure, by\nmapping the data to a suitable linear space and using a metric projection\noperator to constrain the results in the Wasserstein space. By carefully\nchoosing the tangent point, we are able to derive fast empirical methods,\nexploiting a constrained B-spline approximation. As a byproduct of our\napproach, we are also able to derive faster routines for previous work on PCA\nfor distributions. By means of simulation studies, we compare our approaches to\npreviously proposed methods, showing that our projected PCA has similar\nperformance for a fraction of the computational cost and that the projected\nregression is extremely flexible even under misspecification. Several\ntheoretical properties of the models are investigated and asymptotic\nconsistency is proven. Two real world applications to Covid-19 mortality in the\nUS and wind speed forecasting are discussed.\n"}
+{"abstract": "  Task-agnostic knowledge distillation, a teacher-student framework, has been\nproved effective for BERT compression. Although achieving promising results on\nNLP tasks, it requires enormous computational resources. In this paper, we\npropose Extract Then Distill (ETD), a generic and flexible strategy to reuse\nthe teacher's parameters for efficient and effective task-agnostic\ndistillation, which can be applied to students of any size. Specifically, we\nintroduce two variants of ETD, ETD-Rand and ETD-Impt, which extract the\nteacher's parameters in a random manner and by following an importance metric\nrespectively. In this way, the student has already acquired some knowledge at\nthe beginning of the distillation process, which makes the distillation process\nconverge faster. We demonstrate the effectiveness of ETD on the GLUE benchmark\nand SQuAD. The experimental results show that: (1) compared with the baseline\nwithout an ETD strategy, ETD can save 70\\% of computation cost. Moreover, it\nachieves better results than the baseline when using the same computing\nresource. (2) ETD is generic and has been proven effective for different\ndistillation methods (e.g., TinyBERT and MiniLM) and students of different\nsizes. The source code will be publicly available upon publication.\n"}
+{"abstract": "  We present the results regarding the analysis of the fast X-ray/infrared (IR)\nvariability of the black-hole transient MAXI J1535$-$571. The data studied in\nthis work consist of two strictly simultaneous observations performed with\nXMM-Newton (X-rays: 0.7$-$10 keV), VLT/HAWK-I ($K_{\\rm s}$ band, 2.2 $\\mu$m)\nand VLT/VISIR ($M$ and $PAH2$_$2$ bands, 4.85 and 11.88 $\\mu$m respectively).\nThe cross-correlation function between the X-ray and near-IR light curves shows\na strong asymmetric anti-correlation dip at positive lags. We detect a near-IR\nQPO (2.5 $\\sigma$) at $2.07\\pm0.09$ Hz simultaneously with an X-ray QPO at\napproximately the same frequency ($f_0=2.25\\pm0.05$). From the cross-spectral\nanalysis a lag consistent with zero was measured between the two oscillations.\nWe also measure a significant correlation between the average near-IR and\nmid-IR fluxes during the second night, but find no correlation on short\ntimescales. We discuss these results in terms of the two main scenarios for\nfast IR variability (hot inflow and jet powered by internal shocks). In both\ncases, our preliminary modelling suggests the presence of a misalignment\nbetween disk and jet.\n"}
+{"abstract": "  The paper contains an application of van Kampen theorem for groupoids for\ncomputation of homotopy types of certain class of non-compact foliated surfaces\nobtained by gluing at most countably many strips $\\mathbb{R}\\times(0,1)$ with\nboundary intervals in $\\mathbb{R}\\times\\{\\pm1\\}$ along some of those intervals.\n"}
+{"abstract": "  We present randUBV, a randomized algorithm for matrix sketching based on the\nblock Lanzcos bidiagonalization process. Given a matrix $\\bf{A}$, it produces a\nlow-rank approximation of the form ${\\bf UBV}^T$, where $\\bf{U}$ and $\\bf{V}$\nhave orthonormal columns in exact arithmetic and $\\bf{B}$ is block bidiagonal.\nIn finite precision, the columns of both ${\\bf U}$ and ${\\bf V}$ will be close\nto orthonormal. Our algorithm is closely related to the randQB algorithms of\nYu, Gu, and Li (2018) in that the entries of $\\bf{B}$ are incrementally\ngenerated and the Frobenius norm approximation error may be efficiently\nestimated. Our algorithm is therefore suitable for the fixed-accuracy problem,\nand so is designed to terminate as soon as a user input error tolerance is\nreached. Numerical experiments suggest that the block Lanczos method is\ngenerally competitive with or superior to algorithms that use power iteration,\neven when $\\bf{A}$ has significant clusters of singular values.\n"}
+{"abstract": "  We study the transport property of Gaussian measures on Sobolev spaces of\nperiodic functions under the dynamics of the one-dimensional cubic fractional\nnonlinear Schr\\\"{o}dinger equation. For the case of second-order dispersion or\ngreater, we establish an optimal regularity result for the quasi-invariance of\nthese Gaussian measures, following the approach by Debussche and Tsutsumi [15].\nMoreover, we obtain an explicit formula for the Radon-Nikodym derivative and,\nas a corollary, a formula for the two-point function arising in wave turbulence\ntheory. We also obtain improved regularity results in the weakly dispersive\ncase, extending those in [20]. Our proof combines the approach introduced by\nPlanchon, Tzvetkov and Visciglia [47] and that of Debussche and Tsutsumi [15].\n"}
+{"abstract": "  We report on preliminary results of a statistical study of student\nperformance in more than a decade of calculus-based introductory physics\ncourses. Treating average homework and test grades as proxies for student\neffort and comprehension respectively, we plot comprehension versus effort in\nan academic version of the astronomical Hertzsprung-Russell diagram (which\nplots stellar luminosity versus temperature). We study the evolution of this\ndiagram with time, finding that the \"academic main sequence\" has begun to break\ndown in recent years as student achievement on tests has become decoupled from\nhomework grades. We present evidence that this breakdown is likely related to\nthe emergence of easily accessible online solutions to most textbook problems,\nand discuss possible responses and strategies for maintaining and enhancing\nstudent learning in the online era.\n"}
+{"abstract": "  A method for creating a vision-and-language (V&L) model is to extend a\nlanguage model through structural modifications and V&L pre-training. Such an\nextension aims to make a V&L model inherit the capability of natural language\nunderstanding (NLU) from the original language model. To see how well this is\nachieved, we propose to evaluate V&L models using an NLU benchmark (GLUE). We\ncompare five V&L models, including single-stream and dual-stream models,\ntrained with the same pre-training. Dual-stream models, with their higher\nmodality independence achieved by approximately doubling the number of\nparameters, are expected to preserve the NLU capability better. Our main\nfinding is that the dual-stream scores are not much different than the\nsingle-stream scores, contrary to expectation. Further analysis shows that\npre-training causes the performance drop in NLU tasks with few exceptions.\nThese results suggest that adopting a single-stream structure and devising the\npre-training could be an effective method for improving the maintenance of\nlanguage knowledge in V&L extensions.\n"}
+{"abstract": "  We address a state-of-the-art reinforcement learning (RL) control approach to\nautomatically configure robotic prosthesis impedance parameters to enable\nend-to-end, continuous locomotion intended for transfemoral amputee subjects.\nSpecifically, our actor-critic based RL provides tracking control of a robotic\nknee prosthesis to mimic the intact knee profile. This is a significant advance\nfrom our previous RL based automatic tuning of prosthesis control parameters\nwhich have centered on regulation control with a designer prescribed robotic\nknee profile as the target. In addition to presenting the complete tracking\ncontrol algorithm based on direct heuristic dynamic programming (dHDP), we\nprovide an analytical framework for the tracking controller with constrained\ninputs. We show that our proposed tracking control possesses several important\nproperties, such as weight convergence of the learning networks, Bellman\n(sub)optimality of the cost-to-go value function and control input, and\npractical stability of the human-robot system under input constraint. We\nfurther provide a systematic simulation of the proposed tracking control using\na realistic human-robot system simulator, the OpenSim, to emulate how the dHDP\nenables level ground walking, walking on different terrains and at different\npaces. These results show that our proposed dHDP based tracking control is not\nonly theoretically suitable, but also practically useful.\n"}
+{"abstract": "  The high energy Operator Product Expansion for the product of two\nelectromagnetic currents is extended to the sub-eikonal level in a rigorous\nway. I calculate the impact factors for polarized and unpolarized structure\nfunctions, define new distribution functions, and derive the evolution\nequations for unpolarized and polarized structure functions in the flavor\nsinglet and non-singlet case.\n"}
+{"abstract": "  We investigate the diurnal modulation of the event rate for dark matter\nscattering on solid targets arising from the directionally dependent defect\ncreation threshold energy. In particular, we quantify how this effect would\nhelp in separating dark matter signal from the neutrino background. We perform\na benchmark analysis for a germanium detector and compute how the reach of the\nexperiment is affected by including the timing information of the scattering\nevents. We observe that for light dark matter just above the detection\nthreshold the magnitude of the annual modulation is enhanced. In this mass\nrange using either the annual or diurnal modulation information provides a\nsimilar gain in the reach of the experiment, while the additional reach from\nusing both effects remains modest. Furthermore, we demonstrate that if the\nbackground contains a feature exhibiting an annual modulation similar to the\none observed by DAMA experiment, the diurnal modulation provides for an\nadditional handle to separate dark matter signal from the background.\n"}
+{"abstract": "  Quasielastic scattering excitation function at large backward angle has been\nmeasured for the weakly bound system, $^{7}$Li+$^{159}$Tb at energies around\nthe Coulomb barrier. The corresponding quasielastic barrier distribution has\nbeen derived from the excitation function, both including and excluding the\n$\\alpha$-particles produced in the reaction. The centroid of the barrier\ndistribution obtained after inclusion of $\\alpha$-particles was found to be\nshifted higher in energy, compared to the distribution excluding the $\\alpha\n$-particles. The quasielastic data, excluding the $\\alpha$-particles, have been\nanalyzed in the framework of continuum discretized coupled channel\ncalculations. The quasielastic barrier distribution for $^{7}$Li+$^{159}$Tb,\nhas also been compared with the fusion barrier distribution for the system.\n"}
+{"abstract": "  Rectification of interacting Brownian particles is investigated in a\ntwo-dimensional asymmetric channel in the presence of an external periodic\ndriving force. The periodic driving force can break the thermodynamic\nequilibrium and induces rectification of particles (or finite average\nvelocity). The spatial variation in the shape of the channel leads to entropic\nbarriers, which indeed control the rectification of particles. We find that by\nsimply tunning the driving frequency, driving amplitude, and shape of the\nasymmetric channel, the average velocity can be reversed. Moreover, a short\nrange interaction force between the particles further enhances the\nrectification of particles greatly. This interaction force is modeled as the\nlubrication interaction. Interestingly, it is observed that there exists a\ncharacteristic critical frequency $\\Omega_c$ below which the rectification of\nparticles greatly enhances in the positive direction with increasing the\ninteraction strength; whereas, for the frequency above this critical value, it\ngreatly enhances in the negative direction with increasing the interaction\nstrength. Further, there exists an optimal value of the asymmetric parameter of\nthe channel for which the rectification of interacting particles is maximum.\nThese findings are useful in sorting out the particles and understanding the\ndiffusive behavior of small particles or molecules in microfluidic channels,\nmembrane pores, etc.\n"}
+{"abstract": "  Modern deep neural networks (DNNs) achieve highly accurate results for many\nrecognition tasks on overhead (e.g., satellite) imagery. One challenge however\nis visual domain shifts (i.e., statistical changes), which can cause the\naccuracy of DNNs to degrade substantially and unpredictably when tested on new\nsets of imagery. In this work we model domain shifts caused by variations in\nimaging hardware, lighting, and other conditions as non-linear pixel-wise\ntransformations; and we show that modern DNNs can become largely invariant to\nthese types of transformations, if provided with appropriate training data\naugmentation. In general, however, we do not know the transformation between\ntwo sets of imagery. To overcome this problem, we propose a simple real-time\nunsupervised training augmentation technique, termed randomized histogram\nmatching (RHM). We conduct experiments with two large public benchmark datasets\nfor building segmentation and find that RHM consistently yields comparable\nperformance to recent state-of-the-art unsupervised domain adaptation\napproaches despite being simpler and faster. RHM also offers substantially\nbetter performance than other comparably simple approaches that are widely-used\nin overhead imagery.\n"}
+{"abstract": "  Feature-based dynamic pricing is an increasingly popular model of setting\nprices for highly differentiated products with applications in digital\nmarketing, online sales, real estate and so on. The problem was formally\nstudied as an online learning problem [Javanmard & Nazerzadeh, 2019] where a\nseller needs to propose prices on the fly for a sequence of $T$ products based\non their features $x$ while having a small regret relative to the best --\n\"omniscient\" -- pricing strategy she could have come up with in hindsight. We\nrevisit this problem and provide two algorithms (EMLP and ONSP) for stochastic\nand adversarial feature settings, respectively, and prove the optimal\n$O(d\\log{T})$ regret bounds for both. In comparison, the best existing results\nare $O\\left(\\min\\left\\{\\frac{1}{\\lambda_{\\min}^2}\\log{T},\n\\sqrt{T}\\right\\}\\right)$ and $O(T^{2/3})$ respectively, with $\\lambda_{\\min}$\nbeing the smallest eigenvalue of $\\mathbb{E}[xx^T]$ that could be arbitrarily\nclose to $0$. We also prove an $\\Omega(\\sqrt{T})$ information-theoretic lower\nbound for a slightly more general setting, which demonstrates that\n\"knowing-the-demand-curve\" leads to an exponential improvement in feature-based\ndynamic pricing.\n"}
+{"abstract": "  The interplay of different electronic phases underlies the physics of\nunconventional superconductors. One of the most intriguing examples is a\nhigh-Tc superconductor FeTe1-xSex: it undergoes both a topological transition,\nlinked to the electronic band inversion, and an electronic nematic phase\ntransition, associated with rotation symmetry breaking, around the same\ncritical composition xc where superconducting Tc peaks. At this regime, nematic\nfluctuations and symmetry-breaking strain could have an enormous impact, but\nthis is yet to be fully explored. Using spectroscopic-imaging scanning\ntunneling microscopy, we study the electronic nematic transition in FeTe1-xSex\nas a function of composition. Near xc, we reveal the emergence of electronic\nnematicity in nanoscale regions. Interestingly, we discover that\nsuperconductivity is drastically suppressed in areas where static nematic order\nis the strongest. By analyzing atomic displacement in STM topographs, we find\nthat small anisotropic strain can give rise to these strongly nematic localized\nregions. Our experiments reveal a tendency of FeTe1-xSex near x~0.45 to form\npuddles hosting static nematic order, suggestive of nematic fluctuations pinned\nby structural inhomogeneity, and demonstrate a pronounced effect of anisotropic\nstrain on superconductivity in this regime.\n"}
+{"abstract": "  The near vanishing of the cosmological constant is one of the most puzzling\nopen problems in theoretical physics. We consider a system, the so-called\nframid, that features a technically similar problem. Its stress-energy tensor\nhas a Lorentz-invariant expectation value on the ground state, yet there are no\nstandard, symmetry-based selection rules enforcing this, since the ground state\nspontaneously breaks boosts. We verify the Lorentz invariance of the\nexpectation value in question with explicit one-loop computations. These,\nhowever, yield the expected result only thanks to highly nontrivial\ncancellations, which are quite mysterious from the low-energy effective theory\nviewpoint.\n"}
+{"abstract": "  We demonstrate experimentally a method of varying the degree of\ndirectionality in laser-induced molecular rotation. To control the ratio\nbetween the number of clockwise and counter-clockwise rotating molecules (with\nrespect to a fixed laboratory axis), we change the polarization ellipticity of\nthe laser field of an optical centrifuge. The experimental data, supported by\nthe numerical simulations, show that the degree of rotational directionality\ncan be varied in a continuous fashion between unidirectional and bidirectional\nrotation. The control can be executed with no significant loss in the total\nnumber of rotating molecules. The technique could be used for studying the\neffects of orientation of the molecular angular momentum on molecular\ncollisions and chemical reactions. It could also be utilized for controlling\nmagnetic and optical properties of gases, as well as for the enantioselective\ndetection of chiral molecules.\n"}
+{"abstract": "  Based on a general transport theory for non-reciprocal non-Hermitian systems\nand a topological model that encompasses a wide range of previously studied\nmodels, we (i) provide conditions for effects such as reflectionless and\ntransparent transport, lasing, and coherent perfect absorption, (ii) identify\nwhich effects are compatible and linked with each other, and (iii) determine by\nwhich levers they can be tuned independently. For instance, the directed\namplification inherent in the non-Hermitian skin effect does not enter the\nspectral conditions for reflectionless transport, lasing, or coherent perfect\nabsorption, but allows to adjust the transparency of the system. In addition,\nin the topological model the conditions for reflectionless transport depend on\nthe topological phase, but those for coherent perfect absorption do not. This\nthen allows us to establish a number of distinct transport signatures of\nnon-Hermitian, nonreciprocal, and topological behaviour, in particular (I)\nreflectionless transport in a direction that depends on the topological phase,\n(II) invisibility coinciding with the skin-effect phase transition of\ntopological edge states, and (III) coherent perfect absorption in a system that\nis transparent when probed from one side.\n"}
+{"abstract": "  We obtain an upper bound for the number of critical points of the systole\nfunction on $\\mathcal{M}_g$. Besides, we obtain an upper bound for the number\nof those critical points whose systole is smaller than a constant.\n"}
+{"abstract": "  We study the multimessenger signals from the merger of a black hole with a\nmagnetized neutron star using resistive magnetohydrodynamics simulations\ncoupled to full general relativity. We focus on a case with a 5:1 mass ratio,\nwhere only a small amount of the neutron star matter remains post-merger, but\nwe nevertheless find that significant electromagnetic radiation can be powered\nby the interaction of the neutron star's magnetosphere with the black hole. In\nthe lead-up to merger, strong twisting of magnetic field lines from the\ninspiral leads to plasmoid emission and results in a luminosity in excess of\nthat expected from unipolar induction. We find that the strongest emission\noccurs shortly after merger during a transitory period in which magnetic loops\nform and escape the central region. The remaining magnetic field collimates\naround the spin axis of the remnant black hole before dissipating, an\nindication that, in more favorable scenarios (higher black hole spin/lower mass\nratio) with larger accretion disks, a jet would form.\n"}
+{"abstract": "  As a unique perovskite transparent oxide semiconductor, high-mobility\nLa-doped BaSnO3 films have been successfully synthesized by molecular beam\nepitaxy and pulsed laser deposition. However, it remains a big challenge for\nmagnetron sputtering, a widely applied technique suitable for large-scale\nfabrication, to grow high-mobility La-doped BaSnO3 films. Here, we developed a\nmethod to synthesize high-mobility epitaxial La-doped BaSnO3 films (mobility up\nto 121 cm2V-1s-1 at the carrier density ~ 4.0 x 10^20 cm-3 at room temperature)\ndirectly on SrTiO3 single crystal substrates using high-pressure magnetron\nsputtering. The structural and electrical properties of the La-doped BaSnO3\nfilms were characterized by combined high-resolution X-ray diffraction, X-ray\nphotoemission spectroscopy, and temperature-dependent electrical transport\nmeasurements. The room temperature electron mobility of La-doped BaSnO3 films\nin this work is 2 to 4 times higher than the reported values of the films grown\nby magnetron sputtering. Moreover, in the high carrier density range (n > 3 x\n10^20 cm-3), the electron mobility value of 121 cm2V-1s-1 in our work is among\nthe highest values for all reported doped BaSnO3 films. It is revealed that\nhigh argon pressure during sputtering plays a vital role in stabilizing the\nfully relaxed films and inducing oxygen vacancies, which benefit the high\nmobility at room temperature. Our work provides an easy and economical way to\nmassively synthesize high-mobility transparent conducting films for transparent\nelectronics.\n"}
+{"abstract": "  The 16-year old Blaise Pascal found a way to determine if 6 points lie on a\nconic using a straightedge. Nearly 400 years later, we develop a method that\nuses a straightedge to check whether 10 points lie on a plane cubic curve.\n"}
+{"abstract": "  A compact analytic model is proposed to describe the combined orientation\npreference (OP) and ocular dominance (OD) features of simple cells and their\nlayout in the primary visual cortex (V1). This model consists of three parts:\n(i) an anisotropic Laplacian (AL) operator that represents the local neural\nsensitivity to the orientation of visual inputs; (ii) a receptive field (RF)\noperator that models the anisotropic spatial RF that projects to a given V1\ncell over scales of a few tenths of a millimeter and combines with the AL\noperator to give an overall OP operator; and (iii) a map that describes how the\nparameters of these operators vary approximately periodically across V1. The\nparameters of the proposed model maximize the neural response at a given OP\nwith an OP tuning curve fitted to experimental results. It is found that the\nanisotropy of the AL operator does not significantly affect OP selectivity,\nwhich is dominated by the RF anisotropy, consistent with Hubel and Wiesel's\noriginal conclusions that orientation tuning width of V1 simple cell is\ninversely related to the elongation of its RF. A simplified OP-OD map is then\nconstructed to describe the approximately periodic OP-OD structure of V1 in a\ncompact form. Specifically, the map is approximated by retaining its dominant\nspatial Fourier coefficients, which are shown to suffice to reconstruct the\noverall structure of the OP-OD map. This representation is a suitable form to\nanalyze observed maps compactly and to be used in neural field theory of V1.\nApplication to independently simulated V1 structures shows that observed\nirregularities in the map correspond to a spread of dominant coefficients in a\ncircle in Fourier space.\n"}
+{"abstract": "  We propose a scheme comprising an array of anisotropic optical waveguides,\nembedded in a gas of cold atoms, which can be tuned from a Hermitian to an\nodd-PT -- symmetric configuration through the manipulation of control and\nassistant laser fields. We show that the system can be controlled by tuning\nintra -- and inter-cell coupling coefficients, enabling the creation of\ntopologically distinct phases and linear topological edge states. The waveguide\narray, characterized by a quadrimer primitive cell, allows for implementing\ntransitions between Hermitian and odd-PT -symmetric configurations, broken and\nunbroken PT -symmetric phases, topologically trivial and nontrivial phases, as\nwell as transitions between linear and nonlinear regimes. The introduced scheme\ngeneralizes the Rice-Mele Hamiltonian for a nonlinear non-Hermitian quadrimer\narray featuring odd-PT symmetry and makes accessible unique phenomena and\nfunctionalities that emerge from the interplay of non-Hermiticity, topology,\nand nonlinearity. We also show that in the presence of nonlinearity the system\nsustains nonlinear topological edge states bifurcating from the linear\ntopological edge states and the modes without linear limit. Each nonlinear mode\nrepresents a doublet of odd-PT -conjugate states. In the broken PT phase, the\nnonlinear edge states may be effectively stabilized when an additional\nabsorption is introduced into the system.\n"}
+{"abstract": "  Universal register machine, a formal model of computation, can be emulated on\nthe array of the Game of Life, a two-dimensional cellular automaton. We perform\nspectral analysis on the computation dynamical process of the universal\nregister machine on the Game of Life. The array is divided into small sectors\nand the power spectrum is calculated from the evolution in each sector. The\npower spectrum can be classified into four categories by its shape; null, white\nnoise, sharp peaks, and power law. By representing the shape of power spectrum\nby a mark, we can visualize the activity of the sector during the computation\nprocess. For example, the track of pulse moving between components of the\nuniversal register machine and the position of frequently modified registers\ncan be identified. This method can expose the functional difference in each\nregion of computing machine.\n"}
+{"abstract": "  In this note, we investigate a new model theoretical tree property, called\nthe antichain tree property (ATP). We develop combinatorial techniques for ATP.\nFirst, we show that ATP is always witnessed by a formula in a single free\nvariable, and for formulas, not having ATP is closed under disjunction. Second,\nwe show the equivalence of ATP and $k$-ATP, and provide a criterion for\ntheories to have not ATP (being NATP).\n  Using these combinatorial observations, we find algebraic examples of ATP and\nNATP, including pure group, pure fields, and valued fields. More precisely, we\nprove Mekler's construction for groups, Chatzidakis-Ramsey's style criterion\nfor PAC fields, and the AKE-style principle for valued fields preserving NATP.\nAnd we give a construction of an antichain tree in the Skolem arithmetic and\natomless Boolean algebras.\n"}
+{"abstract": "  We extend some classical results of Bousfield on homology localizations and\nnilpotent completions to a presentably symmetric monoidal stable\n$\\infty$-category $\\mathscr{M}$ admitting a multiplicative left-complete\n$t$-structure. If $E$ is a homotopy commutative algebra in $\\mathscr{M}$ we\nshow that $E$-nilpotent completion, $E$-localization, and a suitable formal\ncompletion agree on bounded below objects when $E$ satisfies some reasonable\nconditions.\n"}
+{"abstract": "  In one-shot NAS, sub-networks need to be searched from the supernet to meet\ndifferent hardware constraints. However, the search cost is high and $N$ times\nof searches are needed for $N$ different constraints. In this work, we propose\na novel search strategy called architecture generator to search sub-networks by\ngenerating them, so that the search process can be much more efficient and\nflexible. With the trained architecture generator, given target hardware\nconstraints as the input, $N$ good architectures can be generated for $N$\nconstraints by just one forward pass without re-searching and supernet\nretraining. Moreover, we propose a novel single-path supernet, called unified\nsupernet, to further improve search efficiency and reduce GPU memory\nconsumption of the architecture generator. With the architecture generator and\nthe unified supernet, we propose a flexible and efficient one-shot NAS\nframework, called Searching by Generating NAS (SGNAS). With the pre-trained\nsupernt, the search time of SGNAS for $N$ different hardware constraints is\nonly 5 GPU hours, which is $4N$ times faster than previous SOTA single-path\nmethods. After training from scratch, the top1-accuracy of SGNAS on ImageNet is\n77.1%, which is comparable with the SOTAs. The code is available at:\nhttps://github.com/eric8607242/SGNAS.\n"}
+{"abstract": "  We report here on the discovery with XMM-Newton of pulsations at 22 ms from\nthe central compact source associated with IKT16, a supernova remnant in the\nSmall Magellanic Cloud (SMC). The measured spin period and spin period\nderivative correspond to 21.7661076(2) ms and $2.9(3)\\times10^{-14}$\ns,s$^{-1}$, respectively. Assuming standard spin-down by magnetic dipole\nradiation, the spin-down power corresponds to $1.1\\times10^{38}$,erg,s$^{-1}$\nimplying a Crab-like pulsar. This makes it the most energetic pulsar discovered\nin the SMC so far and a close analogue of PSR J0537--6910, a Crab-like pulsar\nin the Large Magellanic Cloud. The characteristic age of the pulsar is 12 kyr.\nHaving for the first time a period measure for this source, we also searched\nfor the signal in archival data collected in radio with the Parkes telescope\nand in Gamma-rays with the Fermi/LAT, but no evidence for pulsation was found\nin these energy bands.\n"}
+{"abstract": "  For a locally presentable abelian category $\\mathsf B$ with a projective\ngenerator, we construct the projective derived and contraderived model\nstructures on the category of complexes, proving in particular the existence of\nenough homotopy projective complexes of projective objects. We also show that\nthe derived category $\\mathsf D(\\mathsf B)$ is generated, as a triangulated\ncategory with coproducts, by the projective generator of $\\mathsf B$. For a\nGrothendieck abelian category $\\mathsf A$, we construct the injective derived\nand coderived model structures on complexes. Assuming Vopenka's principle, we\nprove that the derived category $\\mathsf D(\\mathsf A)$ is generated, as a\ntriangulated category with products, by the injective cogenerator of $\\mathsf\nA$. More generally, we define the notion of an exact category with an object\nsize function and prove that the derived category of any such exact category\nwith exact $\\kappa$-directed colimits of chains of admissible monomorphisms has\nHom sets. In particular, the derived category of any locally presentable\nabelian category has Hom sets.\n"}
+{"abstract": "  The recent emergence of contrastive learning approaches facilitates the\nresearch on graph representation learning (GRL), introducing graph contrastive\nlearning (GCL) into the literature. These methods contrast semantically similar\nand dissimilar sample pairs to encode the semantics into node or graph\nembeddings. However, most existing works only performed model-level evaluation,\nand did not explore the combination space of modules for more comprehensive and\nsystematic studies. For effective module-level evaluation, we propose a\nframework that decomposes GCL models into four modules: (1) a sampler to\ngenerate anchor, positive and negative data samples (nodes or graphs); (2) an\nencoder and a readout function to get sample embeddings; (3) a discriminator to\nscore each sample pair (anchor-positive and anchor-negative); and (4) an\nestimator to define the loss function. Based on this framework, we conduct\ncontrolled experiments over a wide range of architectural designs and\nhyperparameter settings on node and graph classification tasks. Specifically,\nwe manage to quantify the impact of a single module, investigate the\ninteraction between modules, and compare the overall performance with current\nmodel architectures. Our key findings include a set of module-level guidelines\nfor GCL, e.g., simple samplers from LINE and DeepWalk are strong and robust; an\nMLP encoder associated with Sum readout could achieve competitive performance\non graph classification. Finally, we release our implementations and results as\nOpenGCL, a modularized toolkit that allows convenient reproduction, standard\nmodel and module evaluation, and easy extension.\n"}
+{"abstract": "  With use of the U(1) quantum rotor method in the path integral effective\naction formulation, we have confirmed the mathematical similarity of the phase\nHamiltonian and of the extended Bose-Hubbard model with density-induced\ntunneling (DIT). Moreover, we have shown that the latter model can be mapped to\na pseudospin Hamiltonian that exhibits two coexisting (single-particle and\npair) superfluid phases. Phase separation of the two has also been confirmed,\ndetermining that there exists a range of coefficients in which only pair\ncondensation, and not single-particle superfluidity, is present. The DIT part\nsupports the coherence in the system at high densities and low temperatures,\nbut also has dissipative effects independent of the system's thermal\nproperties.\n"}
+{"abstract": "  Recent advances in Named Entity Recognition (NER) show that document-level\ncontexts can significantly improve model performance. In many application\nscenarios, however, such contexts are not available. In this paper, we propose\nto find external contexts of a sentence by retrieving and selecting a set of\nsemantically relevant texts through a search engine, with the original sentence\nas the query. We find empirically that the contextual representations computed\non the retrieval-based input view, constructed through the concatenation of a\nsentence and its external contexts, can achieve significantly improved\nperformance compared to the original input view based only on the sentence.\nFurthermore, we can improve the model performance of both input views by\nCooperative Learning, a training method that encourages the two input views to\nproduce similar contextual representations or output label distributions.\nExperiments show that our approach can achieve new state-of-the-art performance\non 8 NER data sets across 5 domains.\n"}
+{"abstract": "  Vertical Federated Learning (vFL) allows multiple parties that own different\nattributes (e.g. features and labels) of the same data entity (e.g. a person)\nto jointly train a model. To prepare the training data, vFL needs to identify\nthe common data entities shared by all parties. It is usually achieved by\nPrivate Set Intersection (PSI) which identifies the intersection of training\nsamples from all parties by using personal identifiable information (e.g.\nemail) as sample IDs to align data instances. As a result, PSI would make\nsample IDs of the intersection visible to all parties, and therefore each party\ncan know that the data entities shown in the intersection also appear in the\nother parties, i.e. intersection membership. However, in many real-world\nprivacy-sensitive organizations, e.g. banks and hospitals, revealing membership\nof their data entities is prohibited. In this paper, we propose a vFL framework\nbased on Private Set Union (PSU) that allows each party to keep sensitive\nmembership information to itself. Instead of identifying the intersection of\nall training samples, our PSU protocol generates the union of samples as\ntraining instances. In addition, we propose strategies to generate synthetic\nfeatures and labels to handle samples that belong to the union but not the\nintersection. Through extensive experiments on two real-world datasets, we show\nour framework can protect the privacy of the intersection membership while\nmaintaining the model utility.\n"}
+{"abstract": "  Electroencephalograms (EEG) are noninvasive measurement signals of electrical\nneuronal activity in the brain. One of the current major statistical challenges\nis formally measuring functional dependency between those complex signals. This\npaper, proposes the spectral causality model (SCAU), a robust linear model,\nunder a causality paradigm, to reflect inter- and intra-frequency modulation\neffects that cannot be identifiable using other methods. SCAU inference is\nconducted with three main steps: (a) signal decomposition into frequency bins,\n(b) intermediate spectral band mapping, and (c) dependency modeling through\nfrequency-specific autoregressive models (VAR). We apply SCAU to study complex\ndependencies during visual and lexical fluency tasks (word generation and\nvisual fixation) in 26 participants' EEGs. We compared the connectivity\nnetworks estimated using SCAU with respect to a VAR model. SCAU networks show a\nclear contrast for both stimuli while the magnitude links also denoted a low\nvariance in comparison with the VAR networks. Furthermore, SCAU dependency\nconnections not only were consistent with findings in the neuroscience\nliterature, but it also provided further evidence on the directionality of the\nspatio-spectral dependencies such as the delta-originated and theta-induced\nlinks in the fronto-temporal brain network.\n"}
+{"abstract": "  Let $\\Sigma$ be a closed Riemann surface, $h$ a positive smooth function on\n$\\Sigma$, $\\rho$ and $\\alpha$ real numbers. In this paper, we study a\ngeneralized mean field equation \\begin{align*}\n  -\\Delta u=\\rho\\left(\\dfrac{he^u}{\\int_\\Sigma\nhe^u}-\\dfrac{1}{\\mathrm{Area}\\left(\\Sigma\\right)}\\right)+\\alpha\\left(u-\\fint_{\\Sigma}u\\right),\n\\end{align*} where $\\Delta$ denotes the Laplace-Beltrami operator. We first\nderive a uniform bound for solutions when $\\rho\\in (8k\\pi, 8(k+1)\\pi)$ for some\nnon-negative integer number $k\\in \\mathbb{N}$ and\n$\\alpha\\notin\\mathrm{Spec}\\left(-\\Delta\\right)\\setminus\\set{0}$. Then we obtain\nexistence results for $\\alpha<\\lambda_1\\left(\\Sigma\\right)$ by using the\nLeray-Schauder degree theory and the minimax method, where\n$\\lambda_1\\left(\\Sigma\\right)$ is the first positive eigenvalue for $-\\Delta$.\n"}
+{"abstract": "  Argument mining is often addressed by a pipeline method where segmentation of\ntext into argumentative units is conducted first and proceeded by an argument\ncomponent identification task. In this research, we apply a token-level\nclassification to identify claim and premise tokens from a new corpus of\nargumentative essays written by middle school students. To this end, we compare\na variety of state-of-the-art models such as discrete features and deep\nlearning architectures (e.g., BiLSTM networks and BERT-based architectures) to\nidentify the argument components. We demonstrate that a BERT-based multi-task\nlearning architecture (i.e., token and sentence level classification)\nadaptively pretrained on a relevant unlabeled dataset obtains the best results\n"}
+{"abstract": "  We discuss the essential spectrum of essentially self-adjoint elliptic\ndifferential operators of first order and of Laplace type operators on\nRiemannian vector bundles over geometrically finite orbifolds.\n"}
+{"abstract": "  We analyze the SDSS data to classify the galaxies based on their colour using\na fuzzy set-theoretic method and quantify their environments using the local\ndimension. We find that the fraction of the green galaxies does not depend on\nthe environment and $10\\%-20\\%$ of the galaxies at each environment are in the\ngreen valley depending on the stellar mass range chosen. Approximately $10\\%$\nof the green galaxies at each environment host an AGN. Combining data from the\nGalaxy Zoo, we find that $\\sim 95\\%$ of the green galaxies are spirals and\n$\\sim 5\\%$ are ellipticals at each environment. Only $\\sim 8\\%$ of green\ngalaxies exhibit signs of interactions and mergers, $\\sim 1\\%$ have dominant\nbulge, and $\\sim 6\\%$ host a bar. We show that the stellar mass distributions\nfor the red and green galaxies are quite similar at each environment. Our\nanalysis suggests that the majority of the green galaxies must curtail their\nstar formation using physical mechanism(s) other than interactions, mergers,\nand those driven by bulge, bar and AGN activity. We speculate that these are\nthe massive galaxies that have grown only via smooth accretion and suppressed\nthe star formation primarily through mass driven quenching. Using a\nKolmogorov-Smirnov test, we do not find any statistically significant\ndifference between the properties of green galaxies in different environments.\nWe conclude that the environmental factors play a minor role and the internal\nprocesses play the dominant role in quenching star formation in the green\nvalley galaxies.\n"}
+{"abstract": "  Single sign-on authentication systems such as OAuth 2.0 are widely used in\nweb services. They allow users to use accounts registered with major identity\nproviders such as Google and Facebook to login on multiple services (relying\nparties). These services can both identify users and access a subset of the\nuser's data stored with the provider. We empirically investigate the end-user\nprivacy implications of OAuth 2.0 implementations in relying parties most\nvisited around the world. We collect data on the use of OAuth-based logins in\nthe Alexa Top 500 sites per country for five countries. We categorize user data\nmade available by four identity providers (Google, Facebook, Apple and\nLinkedIn) and evaluate popular services accessing user data from the SSO\nplatforms of these providers. Many services allow users to choose from multiple\nlogin options (with different identity providers). Our results reveal that\nservices request different categories and amounts of personal data from\ndifferent providers, with at least one choice undeniably more\nprivacy-intrusive. These privacy choices (and their privacy implications) are\nhighly invisible to users. Based on our analysis, we also identify areas which\ncould improve user privacy and help users make informed decisions.\n"}
+{"abstract": "  This study explores the Gaussian and the Lorentzian distributed spherically\nsymmetric wormhole solutions in the $f(\\tau, T)$ gravity. The basic idea of the\nGaussian and Lorentzian noncommutative geometries emerges as the physically\nacceptable and substantial notion in quantum physics. This idea of the\nnoncommutative geometries with both the Gaussian and Lorentzian distributions\nbecomes more striking when wormhole geometries in the modified theories of\ngravity are discussed. Here we consider a linear model within $f(\\tau,T)$\ngravity to investigate traversable wormholes. In particular, we discuss the\npossible cases for the wormhole geometries using the Gaussian and the\nLorentzian noncommutative distributions to obtain the exact shape function for\nthem. By incorporating the particular values of the unknown parameters\ninvolved, we discuss different properties of the new wormhole geometries\nexplored here. It is noted that the involved matter violates the weak energy\ncondition for both the cases of the noncommutative geometries, whereas there is\na possibility for a physically viable wormhole solution. By analyzing the\nequilibrium condition, it is found that the acquired solutions are stable.\nFurthermore, we provide the embedded diagrams for wormhole structures under\nGaussian and Lorentzian noncommutative frameworks. Moreover, we present the\ncritical analysis on an anisotropic pressure under the Gaussian and the\nLorentzian distributions.\n"}
+{"abstract": "  We report test results searching for an effect of electrostatic charge on\nweight. For conducting test objects of mass of order 1 kilogram, we found no\neffect on weight, for potentials ranging from 10 V to 200 kV, corresponding to\ncharge states ranging from $10^{-9}$ to over $10^{-5}$ coulombs, and for both\npolarities, to within a measurement precision of 2 grams. While such a result\nmay not be unexpected, this is the first unipolar, high-voltage, meter-scale,\nstatic test for electro-gravitic effects reported in the literature. Our\ninvestigation was motivated by the search for possible coupling to a long-range\nscalar field that could surround the planet, yet go otherwise undetected. The\nlarge buoyancy force predicted within the classical Kaluza theory involving a\nlong-range scalar field is falsified by our results, and this appears to be the\nfirst such experimental test of the classical Kaluza theory in the weak field\nregime where it was otherwise thought identical with known physics. A\nparameterization is suggested to organize the variety of electro-gravitic\nexperiment designs.\n"}
+{"abstract": "  Communication efficiency is a major bottleneck in the applications of\ndistributed networks. To address the problem, the problem of quantized\ndistributed optimization has attracted a lot of attention. However, most of the\nexisting quantized distributed optimization algorithms can only converge\nsublinearly. To achieve linear convergence, this paper proposes a novel\nquantized distributed gradient tracking algorithm (Q-DGT) to minimize a finite\nsum of local objective functions over directed networks. Moreover, we\nexplicitly derive the update rule for the number of quantization levels, and\nprove that Q-DGT can converge linearly even when the exchanged variables are\nrespectively one bit. Numerical results also confirm the efficiency of the\nproposed algorithm.\n"}
+{"abstract": "  The multislice method, which simulates the propagation of the incident\nelectron wavefunction through a crystal, is a well-established method for\nanalyzing the multiple scattering effects that an electron beam may undergo.\nThe inclusion of magnetic effects into this method proves crucial towards\nsimulating magnetic differential phase contrast images at atomic resolution,\nenhanced magnetic interaction of vortex beams with magnetic materials,\ncalculating magnetic Bragg spots, or searching for magnon signatures, to name a\nfew examples. Inclusion of magnetism poses novel challenges to the efficiency\nof the multislice method for larger systems, especially regarding the\nconsistent computation of magnetic vector potentials and magnetic fields over\nlarge supercells. We present in this work a tabulation of parameterized\nmagnetic values for the first three rows of transition metal elements computed\nfrom atomic density functional theory calculations, allowing for the efficient\ncomputation of approximate magnetic vector fields across large crystals using\nonly structural and magnetic moment size and direction information.\nFerromagnetic bcc Fe and tetragonal FePt are chosen as examples in this work to\nshowcase the performance of the parameterization versus directly obtaining\nmagnetic vector fields from the unit cell spin density by density functional\ntheory calculations, both for the quantities themselves and the resulting\nmagnetic signal from their respective use in multislice calculations.\n"}
+{"abstract": "  Recently, several experiments on La$_{2-x}$Sr$_x$CuO$_4$ (LSCO) challenged\nthe Fermi liquid picture for overdoped cuprates, and stimulated intensive\ndebates [1]. In this work, we study the magnetotransport phenomena in such\nsystems based on the Fermi liquid assumption. The Hall coefficient $R_H$ and\nmagnetoresistivity $\\rho_{xx}$ are investigated near the van Hove singularity\n$x_{\\tiny\\text{VHS}}\\approx0.2$ across which the Fermi surface topology changes\nfrom hole- to electron-like. Our main findings are: (1) $R_H$ depends on the\nmagnetic field $B$ and drops from positive to negative values with increasing\n$B$ in the doping regime $x_{\\tiny\\text{VHS}}<x\\lesssim0.3$; (2) $\\rho_{xx}$\ngrows up as $B^2$ at small $B$ and saturates at large $B$, while in the\ntransition regime a \"nearly linear\" behavior shows up. Our results can be\nfurther tested by future magnetotransport experiments in the overdoped LSCO.\n"}
+{"abstract": "  Using the cohomology of the $G_2$-flag manifolds $G_2/U(2)_{\\pm}$, and their\nstructure as a fiber bundle over the homogeneous space $G_2/SO(4)$, we compute\ntheir Borel cohomology and the $\\mathbb{Z}_2$ Fadell-Husseini index of such\nfiber bundles, for the $\\mathbb{Z}_2$ action given by complex conjugation.\n  Considering the orthogonal complement of the tautological bundle $\\gamma$\nover $\\widetilde{G}_{3}( \\mathbb{R}^{7})$, we compute the $\\mathbb{Z}_2$\nFadell-Husseini index of the pullback bundle of $s\\gamma^{\\perp}$ along the\ncomposition of the embedding between $G_2/SO(4)$ and $\\widetilde{G}_{3}(\n\\mathbb{R}^{7})$, and the fiber bundle $ G_2/U(2)_{\\pm} \\to G_2/SO(4)$. Here\n$s\\gamma^{\\perp}$ means the associated sphere bundle of the orthogonal bundle\n$\\gamma^{\\perp}$. Furthermore, we derive a general formula for the $n$-fold\nproduct bundle $(s\\gamma^{\\perp})^n$ for which we make the same computations.\n"}
+{"abstract": "  Identifying \"superspreaders\" of disease is a pressing concern for society\nduring pandemics such as COVID-19. Superspreaders represent a group of people\nwho have much more social contacts than others. The widespread deployment of\nWLAN infrastructure enables non-invasive contact tracing via people's\nubiquitous mobile devices. This technology offers promise for detecting\nsuperspreaders. In this paper, we propose a general framework for\nWLAN-log-based superspreader detection. In our framework, we first use WLAN\nlogs to construct contact graphs by jointly considering human symmetric and\nasymmetric interactions. Next, we adopt three vertex centrality measurements\nover the contact graphs to generate three groups of superspreader candidates.\nFinally, we leverage SEIR simulation to determine groups of superspreaders\namong these candidates, who are the most critical individuals for the spread of\ndisease based on the simulation results. We have implemented our framework and\nevaluate it over a WLAN dataset with 41 million log entries from a large-scale\nuniversity. Our evaluation shows superspreaders exist on university campuses.\nThey change over the first few weeks of a semester, but stabilize throughout\nthe rest of the term. The data also demonstrate that both symmetric and\nasymmetric contact tracing can discover superspreaders, but the latter performs\nbetter with daily contact graphs. Further, the evaluation shows no consistent\ndifferences among three vertex centrality measures for long-term (i.e., weekly)\ncontact graphs, which necessitates the inclusion of SEIR simulation in our\nframework. We believe our proposed framework and these results may provide\ntimely guidance for public health administrators regarding effective testing,\nintervention, and vaccination policies.\n"}
+{"abstract": "  The recent demonstration of MoSi2N4 and its exceptional stability to air,\nwater, acid, and heat has generated intense interest in this family of\ntwo-dimensional (2D) materials. Among these materials, NbSi2N4, VSi2N4, and\nVSi2P4 are semiconducting, easy-plane ferromagnets with negligible in-plane\nmagnetic anisotropy. They thus satisfy a necessary condition for exhibiting a\ndissipationless spin superfluid mode. The Curie temperatures of monolayer\nVSi2P4 and VSi2N4 are determined to be above room temperature based on Monte\nCarlo and density functional theory calculations. The magnetic moments of\nVSi2N4 can be switched from in-plane to out-of-plane by applying tensile\nbiaxial strain or electron doping.\n"}
+{"abstract": "  Being able to parse code-switched (CS) utterances, such as Spanish+English or\nHindi+English, is essential to democratize task-oriented semantic parsing\nsystems for certain locales. In this work, we focus on Spanglish\n(Spanish+English) and release a dataset, CSTOP, containing 5800 CS utterances\nalongside their semantic parses. We examine the CS generalizability of various\nCross-lingual (XL) models and exhibit the advantage of pre-trained XL language\nmodels when data for only one language is present. As such, we focus on\nimproving the pre-trained models for the case when only English corpus\nalongside either zero or a few CS training instances are available. We propose\ntwo data augmentation methods for the zero-shot and the few-shot settings:\nfine-tune using translate-and-align and augment using a generation model\nfollowed by match-and-filter. Combining the few-shot setting with the above\nimprovements decreases the initial 30-point accuracy gap between the zero-shot\nand the full-data settings by two thirds.\n"}
+{"abstract": "  Let $\\mathbf G$ be the finite simple group $\\mathrm{PSL}(2,\\mathbf F_{11})$.\nIt has an irreducible representation $V_{10}$ of dimension 10. In this note, we\nstudy a special trivector $\\sigma\\in \\bigwedge^3V_{10}^\\vee$ which is $\\mathbf\nG$-invariant. Following the construction of Debarre-Voisin, we obtain a smooth\nhyperk\\\"ahler fourfold $X_6^\\sigma\\subset\\mathrm{Gr}(6,V_{10})$ with many\nsymmetries. We will also look at the associated Peskine variety\n$X_1^\\sigma\\subset \\mathbf P(V_{10})$, which is highly symmetric as well and\nadmits 55 isolated singular points. It will help us to understand better the\ngeometry of the special Debarre-Voisin fourfold $X_6^\\sigma$.\n"}
+{"abstract": "  The Electric Network Frequency (ENF) is a signature of power distribution\nnetworks that can be captured by multimedia recordings made in areas where\nthere is electrical activity. This has led to an emergence of several forensic\napplications based on the use of the ENF signature. Examples of such\napplications include estimating or verifying the time-of-recording of a media\nsignal and inferring the power grid associated with the location in which the\nmedia signal was recorded. In this paper, we carry out a feasibility study to\nexamine the possibility of using embedded ENF traces to pinpoint the\nlocation-of-recording of a signal within a power grid. In this study, we\ndemonstrate that it is possible to pinpoint the location-of-recording to a\ncertain geographical resolution using power signal recordings containing strong\nENF traces. To this purpose, a high-passed version of an ENF signal is\nextracted and it is demonstrated that the correlation between two such signals,\nextracted from recordings made in different geographical locations within the\nsame grid, decreases as the distance between the recording locations increases.\nWe harness this property of correlation in the ENF signals to propose\ntrilateration based localization methods, which pinpoint the unknown location\nof a recording while using some known recording locations as anchor locations.\nWe also discuss the challenges that need to be overcome in order to extend this\nwork to using ENF traces in noisier audio/video recordings for such fine\nlocalization purposes.\n"}
+{"abstract": "  We present a collaborative learning method called Mutual Contrastive Learning\n(MCL) for general visual representation learning. The core idea of MCL is to\nperform mutual interaction and transfer of contrastive distributions among a\ncohort of models. Benefiting from MCL, each model can learn extra contrastive\nknowledge from others, leading to more meaningful feature representations for\nvisual recognition tasks. We emphasize that MCL is conceptually simple yet\nempirically powerful. It is a generic framework that can be applied to both\nsupervised and self-supervised representation learning. Experimental results on\nsupervised and self-supervised image classification, transfer learning and\nfew-shot learning show that MCL can lead to consistent performance gains,\ndemonstrating that MCL can guide the network to generate better feature\nrepresentations.\n"}
+{"abstract": "  In this paper, we discuss the educational value of a few mid-size and one\nlarge applied research projects at the Computer Science Department of Okanagan\nCollege (OC) and at the Universities of Paris East Creteil (LACL) and Orleans\n(LIFO) in France. We found, that some freshmen students are very active and\neager to be involved in applied research projects starting from the second\nsemester. They are actively participating in programming competitions and want\nto be involved in applied research projects to compete with sophomore and older\nstudents. Our observation is based on five NSERC Engage College and Applied\nResearch and Development (ARD) grants, and several small applied projects.\nStudent involvement in applied research is a key motivation and success factor\nin our activities, but we are also involved in transferring some results of\napplied research, namely programming techniques, into the parallel programming\ncourses for beginners at the senior- and first-year MSc levels. We illustrate\nthis feedback process with programming notions for beginners, practical tools\nto acquire them and the overall success/failure of students as experienced for\nmore than 10 years in our French University courses.\n"}
+{"abstract": "  We present a new approach to jet definition as an alternative to methods that\nexploit kinematic data directly, such as the anti-$k_T$ scheme; we use the\nkinematics to represent the particles of an event in a new multidimensional\nspace. The latter is constituted by the eigenvectors of a matrix of kinematic\nrelations between particles, and the resulting partition is called spectral\nclustering. After confirming its Infra-Red (IR) safety, we compare its\nperformance to the anti-$k_T$ algorithm in reconstructing relevant final\nstates. We base this on Monte Carlo (MC) samples generated from the following\nprocesses: $qq\\to H_{125\\,\\text{GeV}} \\rightarrow H_{40\\,\\text{GeV}}\nH_{40\\,\\text{GeV}} \\rightarrow b \\bar{b} b \\bar{b}$, $qq\\to H_{500\\,\\text{GeV}}\n\\rightarrow H_{125\\,\\text{GeV}} H_{125\\,\\text{GeV}} \\rightarrow b \\bar{b} b\n\\bar{b}$ and $gg,q\\bar q\\to t\\bar t\\to b\\bar b W^+W^-\\to b\\bar b jj\n\\ell\\nu_\\ell$. Additionally, the impact of pileup on the clustering algorithm\nis demonstrated. Finally, we show that the results for spectral clustering are\nobtained without any change in the algorithm's parameter settings, unlike the\nanti-$k_T$ case, which requires the cone size to be adjusted to the physics\nprocess under study.\n"}
+{"abstract": "  Antireflection coatings are an interesting challenge for multijunction solar\ncells due to their broadband spectrum absorption and the requirement of current\nmatching of each subcell. A new design for multijunction solar cell\nantireflection coatings is presented in this work in which alternative high and\nlow index materials are used to minimize the reflection in a broadband\n(300nm-1800nm). We compared the short circuit current density of high-low\nrefractive index stacks designs with optimum double-layer antireflection\ncoatings by considering two optical materials combinations (MgF2/ZnS and\nAl2O3/TiO2) for the AM0 and AM1.5D spectra. The calculations demonstrate that\nfor lattice-matched triple-junction solar cells and inverted metamorphic\nquadruple-junction solar cells, high-low refractive index stacks outperform the\noptimum double-layer antireflection coatings. The new design philosophy\nrequires no new optical materials because only two materials are used and\nexhibits excellent performance in broadband spectra. The angle performance of\nthese antireflection coatings is slightly better than classical double-layers\nwhereas the analysis for thickness sensitivity shows that small deviations from\ndeposition targets only slightly impact the performance of antireflection\ncoatings. Finally, some technical solutions for depositing these high-low\nrefractive index multilayers are discussed.\n"}
+{"abstract": "  Interaction enables users to navigate large amounts of data effectively,\nsupports cognitive processing, and increases data representation methods.\nHowever, there have been few attempts to empirically demonstrate whether adding\ninteraction to a static visualization improves its function beyond popular\nbeliefs. In this paper, we address this gap. We use a classic Bayesian\nreasoning task as a testbed for evaluating whether allowing users to interact\nwith a static visualization can improve their reasoning. Through two\ncrowdsourced studies, we show that adding interaction to a static Bayesian\nreasoning visualization does not improve participants' accuracy on a Bayesian\nreasoning task. In some cases, it can significantly detract from it. Moreover,\nwe demonstrate that underlying visualization design modulates performance and\nthat people with high versus low spatial ability respond differently to\ndifferent interaction techniques and underlying base visualizations. Our work\nsuggests that interaction is not as unambiguously good as we often believe; a\nwell designed static visualization can be as, if not more, effective than an\ninteractive one.\n"}
+{"abstract": "  As cross-chain technologies make the interactions among different blockchains\n(hereinafter \"chains\") possible, multi-chains consensus is becoming more and\nmore important in blockchain networks. However, more attention has been paid to\nthe single-chain consensus schemes. The multi-chains consensus with trusted\nminers participation has been not considered, thus offering opportunities for\nmalicious users to launch Diverse Miners Behaviors (DMB) attacks on different\nchains. DMB attackers can be friendly in the consensus process of some chains\ncalled mask-chains to enhance trust value, while on other chains called\nkill-chains they engage in destructive behaviors of network. In this paper, we\npropose a multi-chains consensus scheme named as Proof-of-DiscTrust (PoDT) to\ndefend against DMB attacks. Distinctive trust idea (DiscTrust) is introduced to\nevaluate the trust value of each user concerning different chains. A dynamic\nbehaviors prediction scheme is designed to strengthen DiscTrust to prevent\nintensive DMB attackers who maintain high trust by alternately creating true or\nfalse blocks on kill-chains. On this basis, a trusted miners selection\nalgorithm for multi-chains can be achieved at a round of block creation.\nExperimental results show that PoDT is secure against DMB attacks and more\neffective than traditional consensus schemes in multi-chains environments.\n"}
+{"abstract": "  The Eshelby formalism for an inclusion in a solid is of significant\ntheoretical and practical implications in mechanics and other fields of\nheterogeneous media. Eshelby's finding that a uniform eigenstrain prescribed in\na solitary ellipsoidal inclusion in an infinite isotropic medium results in a\nuniform elastic strain field in the inclusion leads to the conjecture that the\nellipsoid is the only inclusion that possesses the so-called Eshelby uniformity\nproperty. Previously, only the weak version of the conjecture has been proved\nfor the isotropic medium, whereas the general validity of the conjecture for\nanisotropic media in three dimensions is yet to be explored. In this work,\nfirstly, we present proofs of the weak version of the generalized Eshelby\nconjecture for anisotropic media that possess cubic, transversely isotropic,\northotropic, and monoclinic symmetries. Secondly, we prove that in these\nanisotropic media, there exist non-ellipsoidal inclusions that can transform\nparticular polynomial eigenstrains of even degrees into polynomial elastic\nstrain fields of the same even degrees in them. These results constitute\ncounter-examples, in the strong sense, to the generalized high-order Eshelby\nconjecture (inverse problem of Eshelby's polynomial conservation theorem) for\npolynomial eigenstrains in both anisotropic media and the isotropic medium\n(quadratic eigenstrain only). These findings reveal striking richness of the\nuniformity between the eigenstrains and the correspondingly induced elastic\nstrains in inclusions in anisotropic media beyond the canonical ellipsoidal\ninclusions. Since the strain fields in embedded and inherently anisotropic\nquantum dot crystals are effective tuning knobs of the quality of the emitted\nphotons by the quantum dots, the results may have implications in the\ntechnology of quantum information, in addition to in mechanics and materials\nscience.\n"}
+{"abstract": "  Recent years have witnessed an upsurge of interest in the problem of anomaly\ndetection on attributed networks due to its importance in both research and\npractice. Although various approaches have been proposed to solve this problem,\ntwo major limitations exist: (1) unsupervised approaches usually work much less\nefficiently due to the lack of supervisory signal, and (2) existing anomaly\ndetection methods only use local contextual information to detect anomalous\nnodes, e.g., one- or two-hop information, but ignore the global contextual\ninformation. Since anomalous nodes differ from normal nodes in structures and\nattributes, it is intuitive that the distance between anomalous nodes and their\nneighbors should be larger than that between normal nodes and their neighbors\nif we remove the edges connecting anomalous and normal nodes. Thus, hop counts\nbased on both global and local contextual information can be served as the\nindicators of anomaly. Motivated by this intuition, we propose a hop-count\nbased model (HCM) to detect anomalies by modeling both local and global\ncontextual information. To make better use of hop counts for anomaly\nidentification, we propose to use hop counts prediction as a self-supervised\ntask. We design two anomaly scores based on the hop counts prediction via HCM\nmodel to identify anomalies. Besides, we employ Bayesian learning to train HCM\nmodel for capturing uncertainty in learned parameters and avoiding overfitting.\nExtensive experiments on real-world attributed networks demonstrate that our\nproposed model is effective in anomaly detection.\n"}
+{"abstract": "  We report on hyperpolarization of quadrupolar (I=3/2) 131Xe via spin-exchange\noptical pumping. Observations of the 131Xe polarization dynamics show that the\neffective alkali-metal/131Xe spin-exchange cross-sections are large enough to\ncompete with 131Xe spin relaxation. 131Xe polarization up to 7.6 p/m 1.5\npercent was achieved in ca. 8.5EE20 spins--a ca. 100-fold improvement in the\ntotal spin angular momentum--enabling applications including measurement of\nspin-dependent neutron-131Xe s-wave scattering and sensitive searches for\ntime-reversal violation in neutron-131Xe interactions beyond the Standard\nModel.\n"}
+{"abstract": "  A translation from Spanish into French of a paper by N. Cuesta published in\n1954. The paper deals mainly with partially, and totally, ordered sets. Two\nsubjects are specially dealt with: Construction of new ordered sets starting\nfrom a family of those. Completion of ordered sets by tools akin to Dedekind\ncuts. Curiously enough, the so-called surreal numbers (later defined by Conway,\nin 1974) are already there, thirty years before.\n"}
+{"abstract": "  We comprehensively study admissible transformations between normal linear\nsystems of second-order ordinary differential equations with an arbitrary\nnumber of dependent variables under several appropriate gauges of the arbitrary\nelements parameterizing these systems. For each class from the constructed\nchain of nested gauged classes of such systems, we single out its singular\nsubclass, which appears to consist of systems being similar to the elementary\n(free particle) system whereas the regular subclass is the complement of the\nsingular one. This allows us to exhaustively describe the equivalence groupoids\nof the above classes as well as of their singular and regular subclasses.\nApplying various algebraic techniques, we establish principal properties of Lie\nsymmetries of the systems under consideration and outline ways for completely\nclassifying these symmetries. In particular, we compute the sharp lower and\nupper bounds for the dimensions of the maximal Lie invariance algebras\npossessed by systems from each of the above classes and subclasses. We also\nshow how equivalence transformations and Lie symmetries can be used for\nreduction of order of such systems and their integration. As an illustrative\nexample of using the theory developed, we solve the complete group\nclassification problems for all these classes in the case of two dependent\nvariables.\n"}
+{"abstract": "  The Hubble parameter inferred from cosmic microwave background observations\nis consistently lower than that from local measurements, which could hint\ntowards new physics. Solutions to the Hubble tension typically require a\nsizable amount of extra radiation $\\Delta N^{}_{\\rm eff}$ during recombination.\nHowever, the amount of $\\Delta N^{}_{\\rm eff}$ in the early Universe is\nunavoidably constrained by Big Bang Nucleosynthesis (BBN), which causes\nproblems for such solutions. We present a possibility to evade this problem by\nintroducing neutrino self-interactions via a simple Majoron-like coupling. The\nscalar is slightly heavier than $1~{\\rm MeV}$ and allowed to be fully\nthermalized throughout the BBN era. The rise of neutrino temperature due to the\nentropy transfer via $\\phi \\to \\nu\\overline{\\nu}$ reactions compensates the\neffect of a large $\\Delta N^{}_{\\rm eff}$ on BBN. Values of $\\Delta N^{}_{\\rm\neff}$ as large as $0.7$ are in this case compatible with BBN. We perform a fit\nto the parameter space of the model.\n"}
+{"abstract": "  Online educational systems running on smart devices have the advantage of\nallowing users to learn online regardless of the location of the users. In\nparticular, data synchronization enables users to cooperate on contents in real\ntime anywhere by sharing their files via cloud storage. However, users cannot\ncollaborate by simultaneously modifying files that are shared with each other.\nIn this paper, we propose a content collaboration method and a history\nmanagement technique that are based on distributed system structure and can\nsynchronize data shared in the cloud for multiple users and multiple devices.\n"}
+{"abstract": "  Face recognition and identification is a very important application in\nmachine learning. Due to the increasing amount of available data, traditional\napproaches based on matricization and matrix PCA methods can be difficult to\nimplement. Moreover, the tensorial approaches are a natural choice, due to the\nmere structure of the databases, for example in the case of color images.\nNevertheless, even though various authors proposed factorization strategies for\ntensors, the size of the considered tensors can pose some serious issues. When\nonly a few features are needed to construct the projection space, there is no\nneed to compute a SVD on the whole data. Two versions of the tensor Golub-Kahan\nalgorithm are considered in this manuscript, as an alternative to the classical\nuse of the tensor SVD which is based on truncated strategies. In this paper, we\nconsider the Tensor Tubal Golub Kahan Principal Component Analysis method which\npurpose is to extract the main features of images using the tensor singular\nvalue decomposition (SVD) based on the tensor cosine product that uses the\ndiscrete cosine transform. This approach is applied for classification and face\nrecognition and numerical tests show its effectiveness.\n"}
+{"abstract": "  Extreme near-field heat transfer between metallic surfaces is a subject of\ndebate as the state-of-the-art theory and experiments are in disagreement on\nthe energy carriers driving heat transport. In an effort to elucidate the\nphysics of extreme near-field heat transfer between metallic surfaces, this\nLetter presents a comprehensive model combining radiation, acoustic phonon and\nelectron transport across sub-10-nm vacuum gaps. The results obtained for gold\nsurfaces show that in the absence of bias voltage, acoustic phonon transport is\ndominant for vacuum gaps smaller than ~2 nm. The application of a bias voltage\nsignificantly affects the dominant energy carriers as it increases the phonon\ncontribution mediated by the long-range Coulomb force and the electron\ncontribution due to a lower potential barrier. For a bias voltage of 0.6 V,\nacoustic phonon transport becomes dominant at a vacuum gap of 5 nm, whereas\nelectron tunneling dominates at sub-1-nm vacuum gaps. The comparison of the\ntheory against experimental data from the literature suggests that\nwell-controlled measurements between metallic surfaces are needed to quantify\nthe contributions of acoustic phonon and electron as a function of the bias\nvoltage.\n"}
+{"abstract": "  Let $V\\subseteq A$ be a conformal inclusion of vertex operator algebras and\nlet $\\mathcal{C}$ be a category of grading-restricted generalized $V$-modules\nthat admits the vertex algebraic braided tensor category structure of\nHuang-Lepowsky-Zhang. We give conditions under which $\\mathcal{C}$ inherits\nsemisimplicity from the category of grading-restricted generalized $A$-modules\nin $\\mathcal{C}$, and vice versa. The most important condition is that $A$ be a\nrigid $V$-module in $\\mathcal{C}$ with non-zero categorical dimension, that is,\nwe assume the index of $V$ as a subalgebra of $A$ is finite and non-zero. As a\nconsequence, we show that if $A$ is strongly rational, then $V$ is also\nstrongly rational under the following conditions: $A$ contains $V$ as a\n$V$-module direct summand, $V$ is $C_2$-cofinite with a rigid tensor category\nof modules, and $A$ has non-zero categorical dimension as a $V$-module. These\nresults are vertex operator algebra interpretations of theorems proved for\ngeneral commutative algebras in braided tensor categories. We also generalize\nthese results to the case that $A$ is a vertex operator superalgebra.\n"}
+{"abstract": "  Machine-learning models contain information about the data they were trained\non. This information leaks either through the model itself or through\npredictions made by the model. Consequently, when the training data contains\nsensitive attributes, assessing the amount of information leakage is paramount.\nWe propose a method to quantify this leakage using the Fisher information of\nthe model about the data. Unlike the worst-case a priori guarantees of\ndifferential privacy, Fisher information loss measures leakage with respect to\nspecific examples, attributes, or sub-populations within the dataset. We\nmotivate Fisher information loss through the Cram\\'{e}r-Rao bound and delineate\nthe implied threat model. We provide efficient methods to compute Fisher\ninformation loss for output-perturbed generalized linear models. Finally, we\nempirically validate Fisher information loss as a useful measure of information\nleakage.\n"}
+{"abstract": "  In this work, considering the background dynamics of flat\nFriedmann-Lemaitre-Robertson-Walker(FLRW) model of the universe, we investigate\na non-canonical scalar field model as dark energy candidate which interacting\nwith the pressureless dust as dark matter in view of dynamical systems\nanalysis. Two interactions from phenomenological point of view are chosen: one\nis depending on Hubble parameter $H$, another is local, independent of Hubble\nparameter. In Interaction model 1, an inverse square form of potential as well\nas coupling function associated with scalar field is chosen and a two\ndimensional autonomous system is obtained. From the 2D autonomous system, we\nobtain scalar field dominated solutions representing late time accelerated\nevolution of the universe. Late time scaling solutions are also realized by the\naccelerated evolution of the universe attracted in quintessence era. Center\nManifold Theory can provide the sufficient conditions on model parameters such\nthat the de Sitter like solutions can be stable attractor at late time in this\nmodel. In the Interaction model 2, potential as well as coupling function are\nconsidered to be evolved exponentially on scalar field and as a result of which\na four dimensional autonomous system is achieved. From the analysis of 4D\nsystem, we obtain non-hyperbolic sets of critical points which are analyzed by\nthe Center Manifold Theory. In this model, de Sitter like solutions represent\nthe transient evolution of the universe.\n"}
+{"abstract": "  This article focuses on different aspects of pedestrian (crowd) modeling and\nsimulation. The review includes: various modeling criteria, such as\ngranularity, techniques, and factors involved in modeling pedestrian behavior,\nand different pedestrian simulation methods with a more detailed look at two\napproaches for simulating pedestrian behavior in traffic scenes. At the end,\nbenefits and drawbacks of different simulation techniques are discussed and\nrecommendations are made for future research.\n"}
+{"abstract": "  We investigate the convergence of the Crouzeix-Raviart finite element method\nfor variational problems with non-autonomous integrands that exhibit\nnon-standard growth conditions. While conforming schemes fail due to the\nLavrentiev gap phenomenon, we prove that the solution of the Crouzeix-Raviart\nscheme converges to a global minimiser. Numerical experiments illustrate the\nperformance of the scheme and give additional analytical insights.\n"}
+{"abstract": "  We present Phrase-Verified Voting, a voter-verifiable remote voting system\nassembled from commercial off-the-shelf software for small private elections.\nThe system is transparent and enables each voter to verify that the tally\nincludes their ballot selection without requiring any understanding of\ncryptography. This paper describes the system and its use in fall 2020, to vote\nremotely in promotion committees in a university. Each voter fills out a form\nin the cloud with their vote V (YES, NO, ABSTAIN) and a passphrase P-two words\nentered by the voter. The system generates a verification prompt of the (P,V)\npairs and a tally of the votes, organized to help visualize how the votes add\nup. After the polls close, each voter verifies that this table lists their\n(P,V) pair and that the tally is computed correctly. The system is especially\nappropriate for any small group making sensitive decisions. Because the system\nwould not prevent a coercer from demanding that their victim use a specified\npassphrase, it is not designed for applications where such malfeasance would be\nlikely or go undetected. Results from 43 voters show that the system was\nwell-accepted, performed effectively for its intended purpose, and introduced\nusers to the concept of voter-verified elections. Compared to the commonly-used\nalternatives of paper ballots or voting by email, voters found the system\neasier to use, and that it provided greater privacy and outcome integrity.\n"}
+{"abstract": "  In this paper we provide results on using integer programming (IP) and\nconstraint programming (CP) to search for sets of mutually orthogonal latin\nsquares (MOLS). Both programming paradigms have previously successfully been\nused to search for MOLS, but solvers for IP and CP solvers have significantly\nimproved in recent years and data on how modern IP and CP solvers perform on\nthe MOLS problem is lacking. Using state-of-the-art solvers as black boxes we\nwere able to quickly find pairs of MOLS (or prove their nonexistence) in all\norders up to ten. Moreover, we improve the effectiveness of the solvers by\nformulating an extended symmetry breaking method as well as an improvement to\nthe straightforward CP encoding. We also analyze the effectiveness of using CP\nand IP solvers to search for triples of MOLS, compare our timings to those\nwhich have been previously published, and estimate the running time of using\nthis approach to resolve the longstanding open problem of determining the\nexistence of a triple of MOLS of order ten.\n"}
+{"abstract": "  We theoretically investigate the anomalous Hall effect (AHE) that requires\nneither a net magnetization nor an external magnetic field in collinear\nantiferromagnets. We show that such an emergent AHE is essentially caused by a\nferroic ordering of the anisotropic magnetic dipole (AMD), which provides an\neffective coupling between ordered magnetic moments and electronic motion in\nthe crystal. We demonstrate that the AMD is naturally induced by the\nantiferromagnetic ordering, in which the magnetic moments have a quadrupole\nspatial distribution. In view of the ferroic AMD ordering, we analyze the\nbehavior of the AHE in the orthorhombic lattice system, where the AHE is\nlargely enhanced by the large coupling between the AMD and the spin-orbit\ninteraction. From these findings, the AMD can be used as a descriptor in\ngeneral to investigate the ferromagnetic-related physical quantities in\nantiferromagnets including noncollinear ones, which is detectable by using the\nx-ray magneto-circular dichroism.\n"}
+{"abstract": "  Students develop and test simple models of the spread of COVID-19. Microsoft\nExcel is used as the modeling platform because it's non-threatening to students\nand because it's widely available. Students develop finite difference models\nand implement them in the cells of preformatted spreadsheets following a\nguided-inquiry pedagogy that introduces new model parameters in a scaffolded\nstep-by-step manner. That approach allows students to investigate the\nimplications of new model parameters in a systematic way. Students fit the\nresulting models to reported cases-per-day data for the United States using\nleast-squares techniques with Excel's Solver. Using their own spreadsheets,\nstudents discover for themselves that the initial exponential growth of\nCOVID-19 can be explained by a simplified unlimited growth model and by the SIR\nmodel. They also discover that the effects of social distancing can be modeled\nusing a Gaussian transition function for the infection rate coefficient and\nthat the summer surge was caused by prematurely relaxing social distancing and\nthen reimposing stricter social distancing. Students then model the effect of\nvaccinations and validate the resulting SIRV model by showing that it\nsuccessfully predicts the reported cases-per-day data from Thanksgiving through\nFebruary 14, 2021. The same SIRV model is then extended and successfully fits\nthe fourth peak up to June 1, 2021, caused by further relaxation of social\ndistancing measures. Finally, students extend the model up to the present day\nand successfully account for the appearance of the delta variant of SARS-CoV-2.\nThe fitted model also predicts that the delta-variant peak will be\ncomparatively short, and the cases-per-day data should begin to fall off in\nearly September 2021 - counter to current expectations. This case study would\nmake an excellent capstone experience for students interested in scientific\nmodeling.\n"}
+{"abstract": "  We report the discovery of diffuse extended Ly-alpha emission from redshift\n3.1 to 4.5, tracing cosmic web filaments on scales of 2.5-4 comoving Mpc. These\nstructures have been observed in overdensities of Ly-alpha emitters in the MUSE\nExtremely Deep Field, a 140 hour deep MUSE observation located in the Hubble\nUltra Deep Field. Among the 22 overdense regions identified, 5 are likely to\nharbor very extended Ly-alpha emission at high significance with an average\nsurface brightness of $\\mathrm{5 \\times 10^{-20} erg s^{-1} cm^{-2}\narcsec^{-2}}$. Remarkably, 70% of the total Ly-alpha luminosity from these\nfilaments comes from beyond the circumgalactic medium of any identified\nLy-alpha emitters. Fluorescent Ly-alpha emission powered by the cosmic UV\nbackground can only account for less than 34% of this emission at z$\\approx$3\nand for not more than 10% at higher redshift. We find that the bulk of this\ndiffuse emission can be reproduced by the unresolved Ly-alpha emission of a\nlarge population of ultra low luminosity Ly-alpha emitters ($\\mathrm{<10^{40}\nerg s^{-1}}$), provided that the faint end of the Ly-alpha luminosity function\nis steep ($\\alpha \\lessapprox -1.8$), it extends down to luminosities lower\nthan $\\mathrm{10^{38} - 10^{37} erg s^{-1}}$ and the clustering of these\nLy-alpha emitters is significant (filling factor $< 1/6$). If these Ly-alpha\nemitters are powered by star formation, then this implies their luminosity\nfunction needs to extend down to star formation rates $\\mathrm{< 10^{-4}\nM_\\odot yr^{-1}}$. These observations provide the first detection of the cosmic\nweb in Ly-alpha emission in typical filamentary environments and the first\nobservational clue for the existence of a large population of ultra low\nluminosity Ly-alpha emitters at high redshift.\n"}
+{"abstract": "  Recently, the selection-recombination equation with a single selected site\nand an arbitrary number of neutral sites was solved by means of the ancestral\nselection-recombination graph. Here, we introduce a more accessible approach,\nnamely the ancestral initiation graph. The construction is based on a\ndiscretisation of the selection-recombination equation. We apply our method to\nsystematically explain a long-standing observation concerning the dynamics of\nlinkage disequilibrium between two neutral loci hitchhiking along with a\nselected one. In particular, this clarifies the nontrivial dependence on the\nposition of the selected site.\n"}
+{"abstract": "  Searching for novel two-dimensional (2D) materials is crucial for the\ndevelopment of the next generation technologies such as electronics,\noptoelectronics, electrochemistry and biomedicine. In this work, we designed a\nseries of 2D materials based on endohedral fullerenes, and revealed that many\nof them integrate different functions in a single system, such as\nferroelectricity with large electric dipole moments, multiple magnetic phases\nwith both strong magnetic anisotropy and high Curie temperature, quantum spin\nHall effect or quantum anomalous Hall effect with robust topologically\nprotected edge states. We further proposed a new style topological field-effect\ntransistor. These findings provide a strategy of using fullerenes as building\nblocks for the synthesis of novel 2D materials which can be easily controlled\nwith a local electric field.\n"}
+{"abstract": "  Large-scale tissue deformation during biological processes such as\nmorphogenesis requires cellular rearrangements. The simplest rearrangement in\nconfluent cellular monolayers involves neighbor exchanges among four cells,\ncalled a T1 transition, in analogy to foams. But unlike foams, cells must\nexecute a sequence of molecular processes, such as endocytosis of adhesion\nmolecules, to complete a T1 transition. Such processes could take a long time\ncompared to other timescales in the tissue. In this work, we incorporate this\nidea by augmenting vertex models to require a fixed, finite time for T1\ntransitions, which we call the \"T1 delay time\". We study how variations in T1\ndelay time affect tissue mechanics, by quantifying the relaxation time of\ntissues in the presence of T1 delays and comparing that to the cell-shape based\ntimescale that characterizes fluidity in the absence of any T1 delays. We show\nthat the molecular-scale T1 delay timescale dominates over the cell shape-scale\ncollective response timescale when the T1 delay time is the larger of the two.\nWe extend this analysis to tissues that become anisotropic under convergent\nextension, finding similar results. Moreover, we find that increasing the T1\ndelay time increases the percentage of higher-fold coordinated vertices and\nrosettes, and decreases the overall number of successful T1s, contributing to a\nmore elastic-like -- and less fluid-like -- tissue response. Our work suggests\nthat molecular mechanisms that act as a brake on T1 transitions could stiffen\nglobal tissue mechanics and enhance rosette formation during morphogenesis.\n"}
+{"abstract": "  We attempt to reveal the geometry, emerged from the entanglement structure of\nany general $N$-party pure quantum many-body state by representing entanglement\nentropies corresponding to all $2^N $ bipartitions of the state by means of a\ngeneralized adjacency matrix. We show this representation is often exact and\nmay lead to a geometry very different than suggested by the Hamiltonian.\nMoreover, in all the cases, it yields a natural entanglement contour, similar\nto previous proposals. The formalism is extended for conformal invariant\nsystems, and a more insightful interpretation of entanglement is presented as a\nflow among different parts of the system.\n"}
+{"abstract": "  In audio processing applications, phase retrieval (PR) is often performed\nfrom the magnitude of short-time Fourier transform (STFT) coefficients.\nAlthough PR performance has been observed to depend on the considered STFT\nparameters and audio data, the extent of this dependence has not been\nsystematically evaluated yet. To address this, we studied the performance of\nthree PR algorithms for various types of audio content and various STFT\nparameters such as redundancy, time-frequency ratio, and the type of window.\nThe quality of PR was studied in terms of objective difference grade and\nsignal-to-noise ratio of the STFT magnitude, to provide auditory- and\nsignal-based quality assessments. Our results show that PR quality improved\nwith increasing redundancy, with a strong relevance of the time-frequency\nratio. The effect of the audio content was smaller but still observable. The\neffect of the window was only significant for one of the PR algorithms.\nInterestingly, for a good PR quality, each of the three algorithms required a\ndifferent set of parameters, demonstrating the relevance of individual\nparameter sets for a fair comparison across PR algorithms. Based on these\nresults, we developed guidelines for optimizing STFT parameters for a given\napplication.\n"}
+{"abstract": "  Motion of a test particle plays an important role in understanding the\nproperties of a spacetime. As a new type of the strong gravity system, boson\nstars could mimic black holes located at the center of galaxies. Studying the\nmotion of a test particle in the spacetime of a rotating boson star will\nprovide the astrophysical observable effects if a boson star is located at the\ncenter of a galaxy. In this paper, we investigate the timelike geodesic of a\ntest particle in the background of a rotating boson star with angular number\n$m=(1, 2, 3)$. With the change of angular number and frequency, a rotating\nboson star will transform from the low rotating state to the highly\nrelativistic rapidly rotating state, the corresponding Lense-Thirring effects\nwill be more and more significant and it should be studied in detail. By\nsolving the four-velocity of a test particle and integrating the geodesics, we\ninvestigate the bound orbits with a zero and nonzero angular momentum. We find\nthat a test particle can stay more longer time in the central region of a boson\nstar when the boson star becomes from low rotating state to highly relativistic\nrotating state. Such behaviors of the orbits are quite different from the\norbits in a Kerr black hole, and the observable effects from these orbits will\nprovide a rule to investigate the astrophysical compact objects in the Galactic\ncenter.\n"}
+{"abstract": "  As the medical usage of computed tomography (CT) continues to grow, the\nradiation dose should remain at a low level to reduce the health risks.\nTherefore, there is an increasing need for algorithms that can reconstruct\nhigh-quality images from low-dose scans. In this regard, most of the recent\nstudies have focused on iterative reconstruction algorithms, and little\nattention has been paid to restoration of the projection measurements, i.e.,\nthe sinogram. In this paper, we propose a novel sinogram interpolation\nalgorithm. The proposed algorithm exploits the self-similarity and smoothness\nof the sinogram. Sinogram self-similarity is modeled in terms of the similarity\nof small blocks extracted from stacked projections. The smoothness is modeled\nvia second-order total variation. Experiments with simulated and real CT data\nshow that sinogram interpolation with the proposed algorithm leads to a\nsubstantial improvement in the quality of the reconstructed image, especially\non low-dose scans. The proposed method can result in a significant reduction in\nthe number of projection measurements. This will reduce the radiation dose and\nalso the amount of data that need to be stored or transmitted, if the\nreconstruction is to be performed in a remote site.\n"}
+{"abstract": "  To address fermion mass hierarchy and flavor mixings in the quark and lepton\nsectors, a minimal flavor structure without any redundant parameters beyond\nphenomenological observables is proposed via decomposition of the Standard\nModel Yukawa mass matrix into a bi-unitary form. After reviewing the roles and\nparameterization of the factorized matrix ${\\bf M}_0^f$ and ${\\bf F}_L^f$ in\nfermion masses and mixings, we generalize the mechanism to up- and down-type\nfermions to unify them into a universal quark/lepton Yukawa interaction. In the\nsame way, a unified form of the description of the quark and lepton Yukawa\ninteractions is also proposed, which shows a similar picture as the unification\nof gauge interactions.\n"}
+{"abstract": "  The multi-link operation (MLO) is a new feature proposed to be part of the\nIEEE 802.11be Extremely High Throughput (EHT) amendment. Through MLO, access\npoints and stations will be provided with the capabilities to transmit and\nreceive data from the same traffic flow over multiple radio interfaces.\nHowever, the question on how traffic flows should be distributed over the\ndifferent interfaces to maximize the WLAN performance is still unresolved. To\nthat end, we evaluate in this article different traffic allocation policies,\nunder a wide variety of scenarios and traffic loads, in order to shed some\nlight on that question. The obtained results confirm that congestion-aware\npolicies outperform static ones. However, and more importantly, the results\nalso reveal that traffic flows become highly vulnerable to the activity of\nneighboring networks when they are distributed across multiple links. As a\nresult, the best performance is obtained when a new arriving flow is simply\nassigned entirely to the emptiest interface.\n"}
+{"abstract": "  This work developed a meta-learning approach that adapts the control policy\non the fly to different changing conditions for robust locomotion. The proposed\nmethod constantly updates the interaction model, samples feasible sequences of\nactions of estimated the state-action trajectories, and then applies the\noptimal actions to maximize the reward. To achieve online model adaptation, our\nproposed method learns different latent vectors of each training condition,\nwhich are selected online given the newly collected data. Our work designs\nappropriate state space and reward functions, and optimizes feasible actions in\nan MPC fashion which are then sampled directly in the joint space considering\nconstraints, hence requiring no prior design of specific walking gaits. We\nfurther demonstrate the robot's capability of detecting unexpected changes\nduring interaction and adapting control policies quickly. The extensive\nvalidation on the SpotMicro robot in a physics simulation shows adaptive and\nrobust locomotion skills under varying ground friction, external pushes, and\ndifferent robot models including hardware faults and changes.\n"}
+{"abstract": "  We use the tridiagonal representation approach to solve the radial\nSchr\\\"odinger equation for an inverse power-law potential of a combined quartic\nand sextic degrees and for all angular momenta. The amplitude of the quartic\nsingularity is larger than that of the sextic but the signs are negative and\npositive, respectively. It turns out that the system has a finite number of\nbound states, which is determined by the larger ratio of the two singularity\namplitudes. The solution is written as a finite series of square integrable\nfunctions written in terms of the Bessel polynomial.\n"}
+{"abstract": "  The interest in the use of the HF band in telecommunication has increased\nsignificantly in the last decade, mainly due to the development of new\nstandards for military telecommunications in HF, as well as the expansion of\ndigital broadcasting in the HF band. More specifically, these new standards\nallow the implementation of links of hundreds or thousands of kilometers at a\nlow cost, which suggests a widespread adoption can occur. In Brazil, this type\nof communication can be used in remote regions or regions of difficult access,\nsuch as the Amazon rain-forest region. In addition to the evolution of\ntechnologies concerning the physical layer of the HF telecommunication systems,\nthere has been a great development of techniques that use machine learning\nalgorithms for audio and image coding. It is believed that all these advances\nwill enable the use of the HF band for communication services in places without\ntelecommunication infrastructure. This work presents recent applications of HF\nradio for digital links in Brazil, describing the challenges present for the\ndevelopment of telecommunication systems in the HF band.\n"}
+{"abstract": "  In this paper we explore the topological properties of self-replicating,\n3-dimensional manifolds, which are modeled by idempotents in the\n(2+1)-cobordism category. We give a classification theorem for all such\nidempotents. Additionally, we characterize biologically interesting ways in\nwhich self-replicating 3-manifolds can embed in $\\mathbb{R}^3$.\n"}
+{"abstract": "  Previous post-processing bias mitigation algorithms on both group and\nindividual fairness don't work on regression models and datasets with\nmulti-class numerical labels. We propose a priority-based post-processing bias\nmitigation on both group and individual fairness with the notion that similar\nindividuals should get similar outcomes irrespective of socio-economic factors\nand more the unfairness, more the injustice. We establish this proposition by a\ncase study on tariff allotment in a smart grid. Our novel framework establishes\nit by using a user segmentation algorithm to capture the consumption strategy\nbetter. This process ensures priority-based fair pricing for group and\nindividual facing the maximum injustice. It upholds the notion of fair tariff\nallotment to the entire population taken into consideration without modifying\nthe in-built process for tariff calculation. We also validate our method and\nshow superior performance to previous work on a real-world dataset in criminal\nsentencing.\n"}
+{"abstract": "  The rise of Internet has made it a major source of information.\nUnfortunately, not all information online is true, and thus a number of\nfact-checking initiatives have been launched, both manual and automatic, to\ndeal with the problem. Here, we present our contribution in this regard:\n\\emph{WhatTheWikiFact}, a system for automatic claim verification using\nWikipedia. The system can predict the veracity of an input claim, and it\nfurther shows the evidence it has retrieved as part of the verification\nprocess. It shows confidence scores and a list of relevant Wikipedia articles,\ntogether with detailed information about each article, including the phrase\nused to retrieve it, the most relevant sentences extracted from it and their\nstance with respect to the input claim, as well as the associated\nprobabilities. The system supports several languages: Bulgarian, English, and\nRussian.\n"}
+{"abstract": "  In this technical report, we present our 1st place solution for the ICDAR\n2021 competition on mathematical formula detection (MFD). The MFD task has\nthree key challenges including a large scale span, large variation of the ratio\nbetween height and width, and rich character set and mathematical expressions.\nConsidering these challenges, we used Generalized Focal Loss (GFL), an\nanchor-free method, instead of the anchor-based method, and prove the Adaptive\nTraining Sampling Strategy (ATSS) and proper Feature Pyramid Network (FPN) can\nwell solve the important issue of scale variation. Meanwhile, we also found\nsome tricks, e.g., Deformable Convolution Network (DCN), SyncBN, and Weighted\nBox Fusion (WBF), were effective in MFD task. Our proposed method ranked 1st in\nthe final 15 teams.\n"}
+{"abstract": "  We determine the tensor rank of all semifields of order 16 over\n$\\mathbb{F}_2$ and of all semifields of order 81 over $\\mathbb{F}_3$. Our\nresults imply that some semifields of order 81 have lower multiplicative\ncomplexity than the finite field $\\mathbb{F}_{81}$ over $\\mathbb{F}_3$. We\nprove new results on the correspondence between linear codes and tensor rank,\nincluding a generalisation of a theorem of Brockett and Dobkin to arbitrary\ntensors, which makes the problem computationally feasible.\n"}
+{"abstract": "  The nonlocal response functions to quantum fluctuations are used to find\nasymptotic expressions for the Casimir free energy and entropy at arbitrarily\nlow temperature in the configuration of two parallel metallic plates. It is\nshown that by introducing an alternative nonlocal response to the\noff-the-mass-shell fluctuations the Lifshitz theory is brought into agreement\nwith the requirements of thermodynamics. According to our results, the Casimir\nentropy calculated using the nonlocal response functions, which take into\naccount dissipation of conduction electrons, remains positive and monotonously\ngoes to zero with vanishing temperature, i.e., satisfies the Nernst heat\ntheorem. This is true for both plates with perfect crystal lattices and for\nlattices with defects of structure. The obtained results are discussed in the\ncontext of the Casimir puzzle.\n"}
+{"abstract": "  We consider a game for a continuum of non-identical players evolving on a\nfinite state space. Their heterogeneous interactions are represented by a\ngraphon, which can be viewed as the limit of a dense random graph. The player's\ntransition rates between the states depend on their own control and the\ninteraction strengths with the other players. We develop a rigorous\nmathematical framework for this game and analyze Nash equilibria. We provide a\nsufficient condition for a Nash equilibrium and prove existence of solutions to\na continuum of fully coupled forward-backward ordinary differential equations\ncharacterizing equilibria. Moreover, we propose a numerical approach based on\nmachine learning tools and show experimental results on different applications\nto compartmental models in epidemiology.\n"}
+{"abstract": "  We introduce and prove the $n$-dimensional Pizza Theorem: Let $\\mathcal{H}$\nbe a hyperplane arrangement in $\\mathbb{R}^{n}$. If $K$ is a measurable set of\nfinite volume, the {pizza quantity} of $K$ is the alternating sum of the\nvolumes of the regions obtained by intersecting $K$ with the arrangement\n$\\mathcal{H}$. We prove that if $\\mathcal{H}$ is a Coxeter arrangement\ndifferent from $A_{1}^{n}$ such that the group of isometries $W$ generated by\nthe reflections in the hyperplanes of $\\mathcal{H}$ contains the map\n$-\\mathrm{id}$, and if $K$ is a translate of a convex body that is stable under\n$W$ and contains the origin, then the pizza quantity of $K$ is equal to zero.\nOur main tool is an induction formula for the pizza quantity involving a\nsubarrangement of the restricted arrangement on hyperplanes of $\\mathcal{H}$\nthat we call the {even restricted arrangement}. More generally, we prove that\nfor a class of arrangements that we call {even} (this includes the Coxeter\narrangements above) and for a {sufficiently symmetric} set $K$, the pizza\nquantity of $K+a$ is polynomial in $a$ for $a$ small enough, for example if $K$\nis convex and $0\\in K+a$. We get stronger results in the case of balls, more\ngenerally, convex bodies bounded by quadratic hypersurfaces. For example, we\nprove that the pizza quantity of the ball centered at $a$ having radius\n$R\\geq\\|a\\|$ vanishes for a Coxeter arrangement $\\mathcal{H}$ with\n$|\\mathcal{H}|-n$ an even positive integer. We also prove the Pizza Theorem for\nthe surface volume: When $\\mathcal{H}$ is a Coxeter arrangement and\n$|\\mathcal{H}| - n$ is a nonnegative even integer, for an $n$-dimensional ball\nthe alternating sum of the $(n-1)$-dimensional surface volumes of the regions\nis equal to zero.\n"}
+{"abstract": "  The R package optimall offers a collection of functions that efficiently\nstreamline the design process of sampling in surveys ranging from simple to\ncomplex. The package's main functions allow users to interactively define and\nadjust strata cut points based on values or quantiles of auxiliary covariates,\nadaptively calculate the optimum number of samples to allocate to each stratum\nusing Neyman or Wright allocation, and select specific IDs to sample based on a\nstratified sampling design. Using real-life epidemiological study examples, we\ndemonstrate how optimall facilitates an efficient workflow for the design and\nimplementation of surveys in R. Although tailored towards multi-wave sampling\nunder two- or three-phase designs, the R package optimall may be useful for any\nsampling survey.\n"}
+{"abstract": "  The orbital observatory Spectrum-Roentgen-Gamma (SRG), equipped with the\ngrazing-incidence X-ray telescopes Mikhail Pavlinsky ART-XC and eROSITA, was\nlaunched by Roscosmos to the Lagrange L2 point of the Sun-Earth system on July\n13, 2019. The launch was carried out from the Baikonur Cosmodrome by a Proton-M\nrocket with a DM-03 upper stage. The German telescope eROSITA was installed on\nSRG under an agreement between Roskosmos and the DLR, the German Aerospace\nAgency. In December 2019, SRG started to perform its main scientific task:\nscanning the celestial sphere to obtain X-ray maps of the entire sky in several\nenergy ranges (from 0.2 to 8 keV with eROSITA, and from 4 to 30 keV with\nART-XC). By mid-June 2021, the third six-month all-sky survey had been\ncompleted. Over a period of four years, it is planned to obtain eight\nindependent maps of the entire sky in each of the energy ranges. The sum of\nthese maps will provide high sensitivity and reveal more than three million\nquasars and over one hundred thousand massive galaxy clusters and galaxy\ngroups. The availability of eight sky maps will enable monitoring of long-term\nvariability (every six months) of a huge number of extragalactic and Galactic\nX-ray sources, including hundreds of thousands of stars with hot coronae. The\nrotation of the satellite around the axis directed toward the Sun with a period\nof four hours enables tracking the faster variability of bright X-ray sources\nduring one day every half year. The chosen strategy of scanning the sky leads\nto the formation of deep survey zones near both ecliptic poles. The paper\npresents sky maps obtained by the telescopes on board SRG during the first\nsurvey of the entire sky and a number of results of deep observations performed\nduring the flight to the L2 point in the frame of the performance verification\nprogram.(Abriged)\n"}
+{"abstract": "  In this paper, we consider the convex, finite-sum minimization problem with\nexplicit convex constraints over strongly connected directed graphs. The\nconstraint is an intersection of several convex sets each being known to only\none node. To solve this problem, we propose a novel decentralized projected\ngradient scheme based on local averaging and prove its convergence using only\nlocal functions' smoothness.\n"}
+{"abstract": "  Immiscible two-phase flow in porous media with mixed wet conditions was\nexamined using a capillary fiber bundle model, which is analytically solvable,\nand a dynamic pore network model. The mixed wettability was implemented in the\nmodels by allowing each tube or link to have a different wetting angle chosen\nrandomly from a given distribution. Both models showed that mixed wettability\ncan have significant influence on the rheology in terms of the dependence of\nthe global volumetric flow rate on the global pressure drop. In the capillary\nfiber bundle model, for small pressure drops when only a small fraction of the\ntubes were open, it was found that the volumetric flow rate depended on the\nexcess pressure drop as a power law with an exponent equal to 3/2 or 2\ndepending on the minimum pressure drop necessary for flow. When all the tubes\nwere open due to a high pressure drop, the volumetric flow rate depended\nlinearly on the pressure drop, independent of the wettability. In the\ntransition region in between where most of the tubes opened, the volumetric\nflow depended more sensitively on the wetting angle distribution function and\nwas in general not a simple power law. The dynamic pore network model results\nalso showed a linear dependence of the flow rate on the pressure drop when the\npressure drop is large. However, out of this limit the dynamic pore network\nmodel demonstrated a more complicated behaviour that depended on the mixed\nwettability condition and the saturation. In particular, the exponent relating\nvolumetric flow rate to the excess pressure drop could take on values anywhere\nbetween 1.0 and 1.8. The values of the exponent were highest for saturations\napproaching 0.5, also, the exponent generally increased when the difference in\nwettability of the two fluids were larger and when this difference was present\nfor a larger fraction of the porous network.\n"}
+{"abstract": "  We consider a data-driven robust hypothesis test where the optimal test will\nminimize the worst-case performance regarding distributions that are close to\nthe empirical distributions with respect to the Wasserstein distance. This\nleads to a new non-parametric hypothesis testing framework based on\ndistributionally robust optimization, which is more robust when there are\nlimited samples for one or both hypotheses. Such a scenario often arises from\napplications such as health care, online change-point detection, and anomaly\ndetection. We study the computational and statistical properties of the\nproposed test by presenting a tractable convex reformulation of the original\ninfinite-dimensional variational problem exploiting Wasserstein's properties\nand characterizing the radii selection for the uncertainty sets. We also\ndemonstrate the good performance of our method on synthetic and real data.\n"}
+{"abstract": "  This paper presents a coding scheme for an insertion deletion substitution\nchannel. We extend a previous scheme for the deletion channel where polar codes\nare modified by adding \"guard bands\" between segments. In the new scheme, each\nguard band is comprised of a middle segment of '1' symbols, and left and right\nsegments of '0' symbols. Our coding scheme allows for a regular hidden-Markov\ninput distribution, and achieves the information rate between the input and\ncorresponding output of such a distribution. Thus, we prove that our scheme can\nbe used to efficiently achieve the capacity of the channel. The probability of\nerror of our scheme decays exponentially in the cube-root of the block length.\n"}
+{"abstract": "  In this letter, two unmanned-aerial-vehicle (UAV) optimal position selection\nschemes are proposed. Based on the proposed schemes, the optimal UAV\ntransmission positions for secure precise wireless transmission (SPWT) are\ngiven, where the maximum secrecy rate (SR) can be achieved without artificial\nnoise (AN). In conventional SPWT schemes, the transmission location is not\nconsidered which impacts the SR a lot. The proposed schemes find the optimal\ntransmission positions based on putting the eavesdropper at the null point.\nThus, the received confidential message energy at the eavesdropper is zero, and\nthe maximum SR achieves. Simulation results show that proposed schemes have\nimproved the SR performance significantly.\n"}
+{"abstract": "  Given two triangles whose angles are all acute, we find a homeomorphism with\nthe smallest Lipschitz constant between them and we give a formula for the\nLipschitz constant of this map. We show that on the set of pairs of acute\ntriangles with fixed area, the function which assigns the logarithm of the\nsmallest Lipschitz constant of Lipschitz maps between them is a symmetric\nmetric. We show that this metric is Finsler, we give a necessary and sufficient\ncondition for a path in this metric space to be geodesic and we determine the\nisometry group of this metric space. This study is motivated by Thurston's\nasymmetric metric on the Teichm{\\\"u}ller space of a hyperbolic surface, and the\nresults in this paper constitute an analysis of a basic Euclidean analogue of\nThurston's hyperbolic theory. Many interesting questions in the Euclidean\nsetting deserve further attention.\n"}
+{"abstract": "  Data subset selection from a large number of training instances has been a\nsuccessful approach toward efficient and cost-effective machine learning.\nHowever, models trained on a smaller subset may show poor generalization\nability. In this paper, our goal is to design an algorithm for selecting a\nsubset of the training data, so that the model can be trained quickly, without\nsignificantly sacrificing on accuracy. More specifically, we focus on data\nsubset selection for L2 regularized regression problems and provide a novel\nproblem formulation which seeks to minimize the training loss with respect to\nboth the trainable parameters and the subset of training data, subject to error\nbounds on the validation set. We tackle this problem using several technical\ninnovations. First, we represent this problem with simplified constraints using\nthe dual of the original training problem and show that the objective of this\nnew representation is a monotone and alpha-submodular function, for a wide\nvariety of modeling choices. Such properties lead us to develop SELCON, an\nefficient majorization-minimization algorithm for data subset selection, that\nadmits an approximation guarantee even when the training provides an imperfect\nestimate of the trained model. Finally, our experiments on several datasets\nshow that SELCON trades off accuracy and efficiency more effectively than the\ncurrent state-of-the-art.\n"}
+{"abstract": "  Platooning of connected and autonomous vehicles (CAVs) is an emerging\ntechnology with a strong potential for throughput improvement and fuel\nreduction. Adequate macroscopic models are critical for system-level efficiency\nand reliability of platooning. In this paper, we consider a hybrid queuing\nmodel for a mixed-autonomy highway section and develop an easy-to-use training\nalgorithm. The model predicts CAV and non-CAV counts according to the traffic\ndemand as well as key parameters of the highway section. The training algorithm\nlearns the highway parameters from observed data in real time. We test the\nmodel and the algorithm in Simulation of Urban Mobility (SUMO) and show that\nthe prediction error is around 15% in a stationary setting and around 25% in a\nnon-stationary setting. We also show that the trained model leads to a platoon\nheadway regulation policy very close to the simulated optimum. The proposed\nmodel and algorithm can directly support model-predictive decision-making for\nplatooning in mixed autonomy.\n"}
+{"abstract": "  This paper proposed the 'Post Triangular Rewiring' method that minimizes the\nsacrifice of planning time and overcomes the limit of Optimality of\nsampling-based algorithm such as Rapidly-exploring Random Tree (RRT) algorithm.\nThe proposed 'Post Triangular Rewiring' method creates a closer to the optimal\npath than RRT algorithm before application through the triangular inequality\nprinciple. The experiments were conducted to verify a performance of the\nproposed method. When the method proposed in this paper are applied to the RRT\nalgorithm, the Optimality efficiency increase compared to the planning time.\n"}
+{"abstract": "  Optical Coherence Tomography (OCT) is a widely used non-invasive biomedical\nimaging modality that can rapidly provide volumetric images of samples. Here,\nwe present a deep learning-based image reconstruction framework that can\ngenerate swept-source OCT (SS-OCT) images using undersampled spectral data,\nwithout any spatial aliasing artifacts. This neural network-based image\nreconstruction does not require any hardware changes to the optical set-up and\ncan be easily integrated with existing swept-source or spectral domain OCT\nsystems to reduce the amount of raw spectral data to be acquired. To show the\nefficacy of this framework, we trained and blindly tested a deep neural network\nusing mouse embryo samples imaged by an SS-OCT system. Using 2-fold\nundersampled spectral data (i.e., 640 spectral points per A-line), the trained\nneural network can blindly reconstruct 512 A-lines in ~6.73 ms using a desktop\ncomputer, removing spatial aliasing artifacts due to spectral undersampling,\nalso presenting a very good match to the images of the same samples,\nreconstructed using the full spectral OCT data (i.e., 1280 spectral points per\nA-line). We also successfully demonstrate that this framework can be further\nextended to process 3x undersampled spectral data per A-line, with some\nperformance degradation in the reconstructed image quality compared to 2x\nspectral undersampling. This deep learning-enabled image reconstruction\napproach can be broadly used in various forms of spectral domain OCT systems,\nhelping to increase their imaging speed without sacrificing image resolution\nand signal-to-noise ratio.\n"}
+{"abstract": "  We prove that the volume measure of the Brownian sphere is equal to a\nconstant multiple of the Hausdorff measure associated with the gauge function\n$h(r)=r^4\\log\\log(1/r)$. This shows in particular that the volume measure of\nthe Brownian sphere is determined by its metric structure. As a key ingredient\nof our proofs, we derive precise estimates on moments of the volume of balls in\nthe Brownian sphere.\n"}
+{"abstract": "  Despite the enormous progress achieved during the past decade, nanoelectronic\ndevices based on two-dimensional (2D) semiconductors still suffer from a\nlimited electrical stability. This limited stability has been shown to result\nfrom the interaction of charge carriers originating from the 2D semiconductors\nwith defects in the surrounding insulating materials. The resulting dynamically\ntrapped charges are particularly relevant in field effect transistors (FETs)\nand can lead to a large hysteresis, which endangers stable circuit operation.\nBased on the notion that charge trapping is highly sensitive to the energetic\nalignment of the channel Fermi-level with the defect band in the insulator, we\npropose to optimize device stability by deliberately tuning the channel\nFermi-level. Our approach aims to minimize the amount of electrically active\nborder traps without modifying the total number of traps in the insulator. We\ndemonstrate the applicability of this idea by using two differently doped\ngraphene layers in otherwise identical FETs with Al$_2$O$_3$ as a gate oxide\nmounted on a flexible substrate. Our results clearly show that by increasing\nthe distance of the Fermi-level to the defect band, the hysteresis is\nsignificantly reduced. Furthermore, since long-term reliability is also very\nsensitive to trapped charges, a corresponding improvement in reliability is\nboth expected theoretically and demonstrated experimentally. Our study paves\nthe way for the construction of more stable and reliable 2D FETs in which the\nchannel material is carefully chosen and tuned to maximize the energetic\ndistance between charge carriers in the channel and the defect bands in the\ninsulator employed.\n"}
+{"abstract": "  In this paper, we study Riemannian maps whose base manifolds admit a Ricci\nsoliton and give a non-trivial example of such Riemannian maps. First, we find\nRiemannian curvature tensor of base manifolds for Riemannian map $F$. Further,\nwe obtain Ricci tensors and calculate scalar curvature of base manifolds.\nMoreover, we obtain necessary conditions for $rangeF_\\ast$ to be Ricci soliton,\nalmost Ricci soliton and Einstein. We also obtain necessary conditions for\n$(rangeF_\\ast)^\\bot$ to be Ricci soliton and Einstein. Also, we calculate\nscalar curvatures of $rangeF_\\ast$ and $(rangeF_\\ast)^\\bot$ by using Ricci\nsoliton. Finally, we study harmonicity and biharmonicity of such Riemannian\nmaps and obtain a necessary and sufficient condition for Riemannian map between\nRiemannian manifolds whose base manifold admits a Ricci soliton to be harmonic.\nWe also obtain necessary and sufficient conditions for Riemannian map from\nRiemannian manifold to space form which admits Ricci soliton to be harmonic and\nbiharmonic. Finally, some applications are presented for further studies on\nbase manifolds of Riemannian maps.\n"}
+{"abstract": "  The leptophilic weakly interacting massive particle (WIMP) is realized in a\nminimal renormalizable model scenario where scalar mediators with lepton number\nestablish the WIMP interaction with the standard model (SM) leptons. We perform\na comprehensive analysis for such a WIMP scenario for two distinct cases with\nan SU(2) doublet or singlet mediator considering all the relevant theoretical,\ncosmological and experimental constraints at present. We show that the\nmono-photon search at near-future lepton collider experiments (ILC, FCC-ee,\nCEPC, etc.) can play a significant role to probe the yet unexplored parameter\nrange allowed by the WIMP relic density constraint. This will complement the\nsearch prospect at the near-future hadron collider experiment (HL-LHC).\nFurthermore, we discuss the combined model scenario including both the doublet\nand singlet mediator. The combined model is capable of explaining the\nlong-standing muon (g-2) anomaly which is an additional advantage. We\ndemonstrate that the allowed region for anomalous muon (g-2) explanation, which\nhas been updated very recently at Fermi National Accelerator Laboratory, can\nalso be probed at the future colliders which will thus be a simultaneous\nauthentication of the model scenario.\n"}
+{"abstract": "  Keyword spotting is an important research field because it plays a key role\nin device wake-up and user interaction on smart devices. However, it is\nchallenging to minimize errors while operating efficiently in devices with\nlimited resources such as mobile phones. We present a broadcasted residual\nlearning method to achieve high accuracy with small model size and\ncomputational load. Our method configures most of the residual functions as 1D\ntemporal convolution while still allows 2D convolution together using a\nbroadcasted-residual connection that expands temporal output to\nfrequency-temporal dimension. This residual mapping enables the network to\neffectively represent useful audio features with much less computation than\nconventional convolutional neural networks. We also propose a novel network\narchitecture, Broadcasting-residual network (BC-ResNet), based on broadcasted\nresidual learning and describe how to scale up the model according to the\ntarget device's resources. BC-ResNets achieve state-of-the-art 98.0% and 98.7%\ntop-1 accuracy on Google speech command datasets v1 and v2, respectively, and\nconsistently outperform previous approaches, using fewer computations and\nparameters.\n"}
+{"abstract": "  In this paper, we study stochastic optimization of areas under\nprecision-recall curves (AUPRC), which is widely used for combating imbalanced\nclassification tasks. Although a few methods have been proposed for maximizing\nAUPRC, stochastic optimization of AUPRC with convergence guarantee remains an\nundeveloped territory. A recent work [42] has proposed a promising approach\ntowards AUPRC based on maximizing a surrogate loss for the average precision,\nand proved an $O(1/\\epsilon^5)$ complexity for finding an $\\epsilon$-stationary\nsolution of the non-convex objective. In this paper, we further improve the\nstochastic optimization of AURPC by (i) developing novel stochastic momentum\nmethods with a better iteration complexity of $O(1/\\epsilon^4)$ for finding an\n$\\epsilon$-stationary solution; and (ii) designing a novel family of stochastic\nadaptive methods with the same iteration complexity of $O(1/\\epsilon^4)$, which\nenjoy faster convergence in practice. To this end, we propose two innovative\ntechniques that are critical for improving the convergence: (i) the biased\nestimators for tracking individual ranking scores are updated in a randomized\ncoordinate-wise manner; and (ii) a momentum update is used on top of the\nstochastic gradient estimator for tracking the gradient of the objective.\nExtensive experiments on various data sets demonstrate the effectiveness of the\nproposed algorithms. Of independent interest, the proposed stochastic momentum\nand adaptive algorithms are also applicable to a class of two-level stochastic\ndependent compositional optimization problems.\n"}
+{"abstract": "  A high energy muon collider can provide new and complementary discovery\npotential to the LHC or future hadron colliders. Leptoquarks are a motivated\nclass of exotic new physics models, with distinct production channels at hadron\nand lepton machines. We study a vector leptoquark model at a muon collider with\n$\\sqrt{s} = 3, 14$ TeV within a set of both UV and phenomenologically motivated\nflavor scenarios. We compute which production mechanism has the greatest reach\nfor various values of the leptoquark mass and the coupling between leptoquark\nand Standard Model fermions. We find that we can probe leptoquark masses up to\nan order of magnitude beyond $\\sqrt{s}$ with perturbative couplings.\nAdditionally, we can also probe regions of parameter space unavailable to\nflavor experiments. In particular, all of the parameter space of interest to\nexplain recent low-energy anomalies in B meson decays would be covered even by\na $\\sqrt{s} = 3$ TeV collider.\n"}
+{"abstract": "  There is evidence of misinformation in the online discourses and discussions\nabout the COVID-19 vaccines. Using a sample of 1.6 million geotagged English\ntweets and the data from the CDC COVID Data Tracker, we conduct a quantitative\nstudy to understand the influence of both misinformation and fact-based news on\nTwitter on the COVID-19 vaccine uptake in the U.S. from April 19 when U.S.\nadults were vaccine eligible to May 7, 2021, after controlling state-level\nfactors such as demographics, education, and the pandemic severity. We identify\nthe tweets related to either misinformation or fact-based news by analyzing the\nURLs. By analyzing the content of the most frequent tweets of these two groups,\nwe find that their structures are similar, making it difficult for Twitter\nusers to distinguish one from another by reading the text alone. The users who\nspread both fake news and fact-based news tend to show a negative attitude\ntowards the vaccines. We further conduct the Fama-MacBeth regression with the\nNewey-West adjustment to examine the effect of fake-news-related and\nfact-related tweets on the vaccination rate, and find marginally negative\ncorrelations.\n"}
+{"abstract": "  We prove the equidistribution of several multistatistics over some classes of\npermutations avoiding a $3$-length pattern. We deduce the equidistribution, on\nthe one hand of inv and foze\" statistics, and on the other hand that of maj and\nmakl statistics, over these classes of pattern avoiding permutations. Here inv\nand maj are the celebrated Mahonian statistics, foze\" is one of the statistics\ndefined in terms of generalized patterns in the 2000 pioneering paper of Babson\nand Steingr\\'imsson, and makl is one of the statistics defined by Clarke,\nSteingr\\'imsson and Zeng in 1997. These results solve several conjectures posed\nby Amini in 2018.\n"}
+{"abstract": "  In this paper we construct a family of holomorphic functions $\\beta_\\lambda\n(s)$ which are solutions to the asymptotic tetration equation. Each\n$\\beta_\\lambda$ satisfies the functional relationship ${\\displaystyle\n\\beta_\\lambda(s+1) = \\frac{e^{\\beta_\\lambda(s)}}{e^{-\\lambda s} + 1}}$; which\nasymptotically converges as $\\log \\beta_\\lambda(s+1) = \\beta_\\lambda (s) +\n\\mathcal{O}(e^{-\\lambda s})$ as $\\Re(\\lambda s) \\to \\infty$. This family of\nasymptotic solutions is used to construct a holomorphic function\n$\\text{tet}_\\beta(s) : \\mathbb{C}/(-\\infty,-2] \\to \\mathbb{C}$ such that\n$\\text{tet}_\\beta(s+1) = e^{\\text{tet}_\\beta(s)}$ and $\\text{tet}_\\beta :\n(-2,\\infty) \\to \\mathbb{R}$ bijectively.\n"}
+{"abstract": "  Pulsars are rapidly spinning highly magnetised neutron stars. Their spin\nperiod is observed to decrease with time. An early analytical model for this\nprocess was the vacuum retarded dipole (VRD) by Deutsch in his 1955 paper \"The\nElectromagnetic Field of an Idealized Star in Rigid Rotation in Vacuo\" (D55).\nThis model assumes an idealised star and it finds that the energy is radiated\naway by the electromagnetic fields. This model has been superseded by more\nrealistic numerical simulations that account for the non-vacuum like\nsurroundings of the neutron star. However, the VRD still provides a reasonable\napproximation and is a useful limiting case that can provide some qualitative\nunderstanding. We provide a detailed derivation of the spin down and related\nfield equations of the VRD. We also correct a typo found in the general field\nequations in D55.\n"}
+{"abstract": "  Labelled data often comes at a high cost as it may require recruiting human\nlabelers or running costly experiments. At the same time, in many practical\nscenarios, one already has access to a partially labelled, potentially biased\ndataset that can help with the learning task at hand. Motivated by such\nsettings, we formally initiate a study of $semi-supervised$ $active$ $learning$\nthrough the frame of linear regression. In this setting, the learner has access\nto a dataset $X \\in \\mathbb{R}^{(n_1+n_2) \\times d}$ which is composed of $n_1$\nunlabelled examples that an algorithm can actively query, and $n_2$ examples\nlabelled a-priori. Concretely, denoting the true labels by $Y \\in\n\\mathbb{R}^{n_1 + n_2}$, the learner's objective is to find $\\widehat{\\beta}\n\\in \\mathbb{R}^d$ such that, \\begin{equation}\n  \\| X \\widehat{\\beta} - Y \\|_2^2 \\le (1 + \\epsilon) \\min_{\\beta \\in\n\\mathbb{R}^d} \\| X \\beta - Y \\|_2^2 \\end{equation} while making as few\nadditional label queries as possible. In order to bound the label queries, we\nintroduce an instance dependent parameter called the reduced rank, denoted by\n$R_X$, and propose an efficient algorithm with query complexity\n$O(R_X/\\epsilon)$. This result directly implies improved upper bounds for two\nimportant special cases: (i) active ridge regression, and (ii) active kernel\nridge regression, where the reduced-rank equates to the statistical dimension,\n$sd_\\lambda$ and effective dimension, $d_\\lambda$ of the problem respectively,\nwhere $\\lambda \\ge 0$ denotes the regularization parameter. For active ridge\nregression we also prove a matching lower bound of $O(sd_\\lambda / \\epsilon)$\non the query complexity of any algorithm. This subsumes prior work that only\nconsidered the unregularized case, i.e., $\\lambda = 0$.\n"}
+{"abstract": "  Context: Using student subjects in empirical studies has been discussed\nextensively from a methodological perspective in Software Engineering (SE), but\nthere is a lack of similar discussion surrounding ethical aspects of doing so.\nAs students are in a subordinate relationship to their instructors, such a\ndiscussion is needed. Objective: We aim to increase the understanding of\npractices and perceptions SE researchers have of ethical issues with student\nparticipation in empirical studies. Method: We conducted a systematic mapping\nstudy of 372 empirical SE studies involving students, following up with a\nsurvey answered by 100 SE researchers regarding their current practices and\nopinions regarding student participation. Results: The mapping study shows that\nthe majority of studies does not report conditions regarding recruitment,\nvoluntariness, compensation, and ethics approval. In contrast, the majority of\nsurvey participants supports reporting these conditions. The survey further\nreveals that less than half of the participants require ethics approval.\nAdditionally, the majority of participants recruit their own students on a\nvoluntary basis, and use informed consent with withdrawal options. There is\ndisagreement among the participants whether course instructors should be\ninvolved in research studies and if should know who participates in a study.\nConclusions: It is a positive sign that mandatory participation is rare, and\nthat informed consent and withdrawal options are standard. However, we see\nimmediate need for action, as study conditions are under-reported, and as\nopinions on ethical practices differ widely. In particular, there is little\nregard in SE on the power relationship between instructors and students.\n"}
+{"abstract": "  The difficulty of obtaining paired data remains a major bottleneck for\nlearning image restoration and enhancement models for real-world applications.\nCurrent strategies aim to synthesize realistic training data by modeling noise\nand degradations that appear in real-world settings. We propose DeFlow, a\nmethod for learning stochastic image degradations from unpaired data. Our\napproach is based on a novel unpaired learning formulation for conditional\nnormalizing flows. We model the degradation process in the latent space of a\nshared flow encoder-decoder network. This allows us to learn the conditional\ndistribution of a noisy image given the clean input by solely minimizing the\nnegative log-likelihood of the marginal distributions. We validate our DeFlow\nformulation on the task of joint image restoration and super-resolution. The\nmodels trained with the synthetic data generated by DeFlow outperform previous\nlearnable approaches on three recent datasets. Code and trained models are\navailable at: https://github.com/volflow/DeFlow\n"}
+{"abstract": "  XRT 201423 is an X-ray transient with a nearly flat plateau lasting 4.1 ks\nfollowed by a steep decay. This feature indicates that it might come from a\nmagnetar formed through a binary neutron star merger, similar to CDF-S XT2 and\nas predicted as a type of electromagnetic counterpart of binary neutron star\nmergers. We test the compliance of the data with this model and use the\nobserved duration and flux of the X-ray signal as well as upper limits of\noptical emission to pose constraints on the parameters of the underlying\nputative magnetar. Both the free-zone and trapped-zone geometric configurations\nare considered. We find that the data are generally consistent with such a\nmodel. The surface dipolar magnetic field and the ellipticity of the magnetar\nshould satisfy $B_p < 7\\times 10^{14}{\\rm G}$ ($B_p < 4.9 \\times 10^{14}{\\rm\nG}$) and $\\epsilon < 1.5 \\times 10^{-3}$ ($\\epsilon < 1.1 \\times 10^{-3}$)\nunder free zone (trapped zone) configurations, respectively. An upper limit on\nthe distance (e.g. $z < 0.55$ with $\\eta_x = 10^{-4}$ or $z < 3.5$ with $\\eta_x\n= 10^{-2}$) can be derived from the X-ray data which depends on the X-ray\ndissipation efficiency $\\eta_x$ of the spin-down luminosity. The non-detection\nof an optical counterpart places a conservative lower limit on the distance of\nthe source, i.e. $z > 0.045$ regardless of the geometric configuration.\n"}
+{"abstract": "  We study the environmental dependence of ultralight scalar dark matter (DM)\nwith linear interactions to the standard model particles. The solution to the\nDM field turns out to be a sum of the cosmic harmonic oscillation term and the\nlocal exponential fluctuation term. The amplitude of the first term depends on\nthe local DM density and the mass of the DM field. The second term is induced\nby the local distribution of matter, such as the Earth. Then, we compute the\nphase shift induced by the DM field in atom interferometers (AIs), through\nsolving the trajectories of atoms. Especially, the AI signal for the violation\nof weak equivalence principle (WEP) caused by the DM field is calculated.\nDepending on the values of the DM coupling parameters, contributions to the WEP\nviolation from the first and second terms of the DM field can be either\ncomparable or one larger than the other. Finally, we give some constraints to\nDM coupling parameters using results from the terrestrial atomic WEP tests.\n"}
+{"abstract": "  With the rise and ever-increasing potential of deep learning techniques in\nrecent years, publicly available medical datasets became a key factor to enable\nreproducible development of diagnostic algorithms in the medical domain.\nMedical data contains sensitive patient-related information and is therefore\nusually anonymized by removing patient identifiers, e.g., patient names before\npublication. To the best of our knowledge, we are the first to show that a\nwell-trained deep learning system is able to recover the patient identity from\nchest X-ray data. We demonstrate this using the publicly available large-scale\nChestX-ray14 dataset, a collection of 112,120 frontal-view chest X-ray images\nfrom 30,805 unique patients. Our verification system is able to identify\nwhether two frontal chest X-ray images are from the same person with an AUC of\n0.9940 and a classification accuracy of 95.55%. We further highlight that the\nproposed system is able to reveal the same person even ten and more years after\nthe initial scan. When pursuing a retrieval approach, we observe an mAP@R of\n0.9748 and a precision@1 of 0.9963. Furthermore, we achieve an AUC of up to\n0.9870 and a precision@1 of up to 0.9444 when evaluating our trained networks\non CheXpert and the COVID-19 Image Data Collection. Based on this high\nidentification rate, a potential attacker may leak patient-related information\nand additionally cross-reference images to obtain more information. Thus, there\nis a great risk of sensitive content falling into unauthorized hands or being\ndisseminated against the will of the concerned patients. Especially during the\nCOVID-19 pandemic, numerous chest X-ray datasets have been published to advance\nresearch. Therefore, such data may be vulnerable to potential attacks by deep\nlearning-based re-identification algorithms.\n"}
+{"abstract": "  In normal times, it is assumed that financial institutions operating in\nnon-overlapping sectors have complementary and distinct outcomes, typically\nreflected in mostly uncorrelated outcomes and asset returns. Such is the\nreasoning behind common \"free lunches\" to be had in investing, like\ndiversifying assets across equity and bond sectors. Unfortunately, the\nrecurrence of crises like the Great Financial Crisis of 2007-2008 demonstrate\nthat such convenient assumptions often break down, with dramatic consequences\nfor all financial actors. In hindsight, the emergence of systemic risk (as\nexemplified by failure in one part of a system spreading to ostensibly\nunrelated parts of the system) has been explained by narratives such as\nderegulation and leverage. But can we diagnose and quantify the ongoing\nemergence of systemic risk in financial systems? In this study, we focus on two\npreviously-documented measures of systemic risk that require only easily\navailable time series data (eg monthly asset returns): cross-correlation and\nprincipal component analysis. We apply these tests to daily and monthly returns\non hedge fund indexes and broad-based market indexes, and discuss their\nresults. We hope that a frank discussion of these simple, non-parametric\nmeasures can help inform legislators, lawmakers, and financial actors of\npotential crises looming on the horizon.\n"}
+{"abstract": "  We present a detailed description of the experiment realising for the first\ntime a protective measurement, a novel measurement protocol which combines weak\ninteractions with a ``protection mechanism'' preserving the measured state\ncoherence during the whole measurement process. Furthermore, protective\nmeasurement allows finding the expectation value of an observable, i.e. an\ninherently statistical quantity, by measuring a single particle, without the\nneed of any statistics. This peculiar property, in sharp contrast with the\nframework of traditional (projective) quantum measurement, might constitute a\ngroundbreaking advance for several quantum technology related fields.\n"}
+{"abstract": "  Temporal-Difference (TD) learning is a general and very useful tool for\nestimating the value function of a given policy, which in turn is required to\nfind good policies. Generally speaking, TD learning updates states whenever\nthey are visited. When the agent lands in a state, its value can be used to\ncompute the TD-error, which is then propagated to other states. However, it may\nbe interesting, when computing updates, to take into account other information\nthan whether a state is visited or not. For example, some states might be more\nimportant than others (such as states which are frequently seen in a successful\ntrajectory). Or, some states might have unreliable value estimates (for\nexample, due to partial observability or lack of data), making their values\nless desirable as targets. We propose an approach to re-weighting states used\nin TD updates, both when they are the input and when they provide the target\nfor the update. We prove that our approach converges with linear function\napproximation and illustrate its desirable empirical behaviour compared to\nother TD-style methods.\n"}
+{"abstract": "  We extend the classical tracking-by-detection paradigm to this\ntracking-any-object task. Solid detection results are first extracted from TAO\ndataset. Some state-of-the-art techniques like \\textbf{BA}lanced-\\textbf{G}roup\n\\textbf{S}oftmax (\\textbf{BAGS}\\cite{li2020overcoming}) and\nDetectoRS\\cite{qiao2020detectors} are integrated during detection. Then we\nlearned appearance features to represent any object by training feature\nlearning networks. We ensemble several models for improving detection and\nfeature representation. Simple linking strategies with most similar appearance\nfeatures and tracklet-level post association module are finally applied to\ngenerate final tracking results. Our method is submitted as \\textbf{AOA} on the\nchallenge website. Code is available at\nhttps://github.com/feiaxyt/Winner_ECCV20_TAO.\n"}
+{"abstract": "  In this paper we study the set of prime ideals in vector lattices and how the\nproperties of the prime ideals structure the vector lattice in question. The\ndifferent properties that will be considered are firstly, that all or none of\nthe prime ideals are order dense, secondly, that there are only finitely many\nprime ideals, thirdly, that every prime ideal is principal, and lastly, that\nevery ascending chain of prime ideals is stationary (a property that we refer\nto as prime Noetherian). We also completely characterize the prime ideals in\nvector lattices of piecewise polynomials, which turns out to be an interesting\nclass of vector lattices for studying principal prime ideals and ascending\nchains of prime ideals.\n"}
+{"abstract": "  For a connected smooth proper rigid space $X$ over a perfectoid field\nextension of $\\mathbb{Q}_p$, we show that the Picard functor of\n$X^\\diamondsuit$ defined on perfectoid test objects is the diamondification of\nthe rigid analytic Picard functor. In particular, it is represented by a rigid\ngroup variety if and only if the rigid analytic Picard functor is.\n  As an application, we determine which line bundles are trivialized by\npro-finite-\\'etale covers, and prove unconditionally that the associated\n\"topological torsion Picard functor\" is represented by a divisible analytic\ngroup. We use this to generalize and geometrize a construction of\nDeninger--Werner in the $p$-adic Simpson correspondence: There is an\nisomorphism of rigid analytic group varieties between the moduli space of\ncontinuous characters of $\\pi_1(X,x)$ and that of pro-finite-\\'etale Higgs line\nbundles on $X$.\n  This article is part II of a series about line bundles on rigid spaces as\ndiamonds.\n"}
+{"abstract": "  We construct a large family of normal $\\kappa$-complete\n$\\mathbb{R}_\\kappa$-embeddable $\\kappa^+$-Aronszajn trees which have no club\nisomorphic subtrees using an instance of the proxy principle of Brodsky-Rinot.\n"}
+{"abstract": "  Recently, Robotic Cooking has been a very promising field. To execute a\nrecipe, a robot has to recognize different objects and their states. Contrary\nto object recognition, state identification has not been explored that much.\nBut it is very important because different recipe might require different state\nof an object. Moreover, robotic grasping depends on the state. Pretrained model\nusually perform very well in this type of tests. Our challenge was to handle\nthis problem without using any pretrained model. In this paper, we have\nproposed a CNN and trained it from scratch. The model is trained and tested on\nthe dataset from cooking state recognition challenge. We have also evaluated\nthe performance of our network from various perspective. Our model achieves\n65.8% accuracy on the unseen test dataset.\n"}
+{"abstract": "  Robust and distributionally robust optimization are modeling paradigms for\ndecision-making under uncertainty where the uncertain parameters are only known\nto reside in an uncertainty set or are governed by any probability distribution\nfrom within an ambiguity set, respectively, and a decision is sought that\nminimizes a cost function under the most adverse outcome of the uncertainty. In\nthis paper, we develop a rigorous and general theory of robust and\ndistributionally robust nonlinear optimization using the language of convex\nanalysis. Our framework is based on a generalized\n`primal-worst-equals-dual-best' principle that establishes strong duality\nbetween a semi-infinite primal worst and a non-convex dual best formulation,\nboth of which admit finite convex reformulations. This principle offers an\nalternative formulation for robust optimization problems that may be\ncomputationally advantageous, and it obviates the need to mobilize the\nmachinery of abstract semi-infinite duality theory to prove strong duality in\ndistributionally robust optimization. We illustrate the modeling power of our\napproach through convex reformulations for distributionally robust optimization\nproblems whose ambiguity sets are defined through general optimal transport\ndistances, which generalize earlier results for Wasserstein ambiguity sets.\n"}
+{"abstract": "  This work derives explicit series reversions for the solution of Calder\\'on's\nproblem. The governing elliptic partial differential equation is\n$\\nabla\\cdot(A\\nabla u)=0$ in a bounded Lipschitz domain and with a\nmatrix-valued coefficient. The corresponding forward map sends $A$ to a\nprojected version of a local Neumann-to-Dirichlet operator, allowing for the\nuse of partial boundary data and finitely many measurements. It is first shown\nthat the forward map is analytic, and subsequently reversions of its Taylor\nseries up to specified orders lead to a family of numerical methods for solving\nthe inverse problem with increasing accuracy. The convergence of these methods\nis shown under conditions that ensure the invertibility of the Fr\\'echet\nderivative of the forward map. The introduced numerical methods are of the same\ncomputational complexity as solving the linearised inverse problem. The\nanalogous results are also presented for the smoothened complete electrode\nmodel.\n"}
+{"abstract": "  Quantum technology is approaching a level of maturity, recently demonstrated\nin space-borne experiments and in-field measurements, which would allow for\nadoption by non-specialist users. Parallel advancements made in\nmicroprocessor-based electronics and database software can be combined to\ncreate robust, versatile and modular experimental monitoring systems. Here, we\ndescribe a monitoring network used across a number of cold atom laboratories\nwith a shared laser system. The ability to diagnose malfunction, unexpected or\nunintended behaviour and passively collect data for key experimental\nparameters, such as vacuum chamber pressure, laser beam power, or resistances\nof important conductors, significantly reduces debugging time. This allows for\nefficient control over a number of experiments and remote control when access\nis limited.\n"}
+{"abstract": "  The Cherenkov Telescope Array (CTA) is an initiative that is currently\nbuilding the largest gamma-ray ground Observatory that ever existed. A Science\nAlert Generation (SAG) system, part of the Array Control and Data Acquisition\n(ACADA) system of the CTA Observatory, analyses online the telescope data -\narriving at an event rate of tens of kHz - to detect transient gamma-ray\nevents. The SAG system also performs an online data quality analysis to assess\nthe instruments' health during the data acquisition: this analysis is crucial\nto confirm good detections. A Python and a C++ software library to perform the\nonline data quality analysis of CTA data, called rta-dq-lib, has been proposed\nfor CTA. The Python version is dedicated to the rapid prototyping of data\nquality use cases. The C++ version is optimized for maximum performance. The\nlibrary allows the user to define, through XML configuration files, the format\nof the input data and, for each data field, which quality checks must be\nperformed and which types of aggregations and transformations must be applied.\nIt internally translates the XML configuration into a direct acyclic\ncomputational graph that encodes the dependencies of the computational tasks to\nbe performed. This model allows the library to easily take advantage of\nparallelization at the thread level and the overall flexibility allow us to\ndevelop generic data quality analysis pipelines that could also be reused in\nother applications.\n"}
+{"abstract": "  The transformer based model (e.g., FusingTF) has been employed recently for\nElectrocardiogram (ECG) signal classification. However, the high-dimensional\nembedding obtained via 1-D convolution and positional encoding can lead to the\nloss of the signal's own temporal information and a large amount of training\nparameters. In this paper, we propose a new method for ECG classification,\ncalled low-dimensional denoising embedding transformer (LDTF), which contains\ntwo components, i.e., low-dimensional denoising embedding (LDE) and transformer\nlearning. In the LDE component, a low-dimensional representation of the signal\nis obtained in the time-frequency domain while preserving its own temporal\ninformation. And with the low dimensional embedding, the transformer learning\nis then used to obtain a deeper and narrower structure with fewer training\nparameters than that of the FusingTF. Experiments conducted on the MIT-BIH\ndataset demonstrates the effectiveness and the superior performance of our\nproposed method, as compared with state-of-the-art methods.\n"}
+{"abstract": "  The climate system is a complex, chaotic system with many degrees of freedom\nand variability on a vast range of temporal and spatial scales. Attaining a\ndeeper level of understanding of its dynamical processes is a scientific\nchallenge of great urgency, especially given the ongoing climate change and the\nevolving climate crisis. In statistical physics, complex, many-particle systems\nare studied successfully using the mathematical framework of Large Deviation\nTheory (LDT). A great potential exists for applying LDT to problems relevant\nfor geophysical fluid dynamics and climate science. In particular, LDT allows\nfor understanding the fundamental properties of persistent deviations of\nclimatic fields from the long-term averages and for associating them to\nlow-frequency, large scale patterns of climatic variability. Additionally, LDT\ncan be used in conjunction with so-called rare events algorithms to explore\nrarely visited regions of the phase space and thus to study special dynamical\nconfigurations of the climate. These applications are of key importance to\nimprove our understanding of high-impact weather and climate events.\nFurthermore, LDT provides powerful tools for evaluating the probability of\nnoise-induced transitions between competing metastable states of the climate\nsystem or of its components. This in turn essential for improving our\nunderstanding of the global stability properties of the climate system and of\nits predictability of the second kind in the sense of Lorenz. The goal of this\nreview is manifold. First, we want to provide an introduction to the derivation\nof large deviation laws in the context of stochastic processes. We then relate\nsuch results to the existing literature showing the current status of\napplications of LDT in climate science and geophysical fluid dynamics. Finally,\nwe propose some possible lines of future investigations.\n"}
+{"abstract": "  We propose leveraging our proficiency for detecting Higgs resonances by using\nthe Higgs as a tagging object for new heavy physics. In particular, we argue\nthat searches for exotic Higgs production from decays of color-singlet fields\nwith electroweak charges could beat current searches at the LHC which look for\ntheir decays to vectors. As an example, we study the production and decay of\nvector-like leptons which admit Yukawa couplings with SM leptons. We find that\nbounds from Run 2 searches are consistent with anywhere from hundreds to many\nthousands of Higgses having been produced in their decays over the same period,\ndepending on the representation. Dedicated searches for these signatures may\nthus be able to significantly improve our reach at the electroweak energy\nfrontier.\n"}
+{"abstract": "  In this paper, we study certifying the robustness of ReLU neural networks\nagainst adversarial input perturbations. To diminish the relaxation error\nsuffered by the popular linear programming (LP) and semidefinite programming\n(SDP) certification methods, we propose partitioning the input uncertainty set\nand solving the relaxations on each part separately. We show that this approach\nreduces relaxation error, and that the error is eliminated entirely upon\nperforming an LP relaxation with an intelligently designed partition. To scale\nthis approach to large networks, we consider courser partitions that take the\nsame form as this motivating partition. We prove that computing such a\npartition that directly minimizes the LP relaxation error is NP-hard. By\ninstead minimizing the worst-case LP relaxation error, we develop a\ncomputationally tractable scheme with a closed-form optimal two-part partition.\nWe extend the analysis to the SDP, where the feasible set geometry is exploited\nto design a two-part partition that minimizes the worst-case SDP relaxation\nerror. Experiments on IRIS classifiers demonstrate significant reduction in\nrelaxation error, offering certificates that are otherwise void without\npartitioning. By independently increasing the input size and the number of\nlayers, we empirically illustrate under which regimes the partitioned LP and\nSDP are best applied.\n"}
+{"abstract": "  Triangle counting is a building block for a wide range of graph applications.\nTraditional wisdom suggests that i) hashing is not suitable for triangle\ncounting, ii) edge-centric triangle counting beats vertex-centric design, and\niii) communication-free and workload balanced graph partitioning is a grand\nchallenge for triangle counting. On the contrary, we advocate that i) hashing\ncan help the key operations for scalable triangle counting on Graphics\nProcessing Units (GPUs), i.e., list intersection and graph partitioning,\nii)vertex-centric design reduces both hash table construction cost and memory\nconsumption, which is limited on GPUs. In addition, iii) we exploit graph and\nworkload collaborative, and hashing-based 2D partitioning to scale\nvertex-centric triangle counting over 1,000 GPUswith sustained scalability. In\nthis work, we present TRUST which performs triangle counting with the hash\noperation and vertex-centric mechanism at the core. To the best of our\nknowledge, TRUSTis the first work that achieves over one trillion Traversed\nEdges Per Second (TEPS) rate for triangle counting.\n"}
+{"abstract": "  The rapid development of reliable Quantum Processing Units (QPU) opens up\nnovel computational opportunities for machine learning. Here, we introduce a\nprocedure for measuring the similarity between graph-structured data, based on\nthe time-evolution of a quantum system. By encoding the topology of the input\ngraph in the Hamiltonian of the system, the evolution produces measurement\nsamples that retain key features of the data. We study analytically the\nprocedure and illustrate its versatility in providing links to standard\nclassical approaches. We then show numerically that this scheme performs well\ncompared to standard graph kernels on typical benchmark datasets. Finally, we\nstudy the possibility of a concrete implementation on a realistic neutral-atom\nquantum processor.\n"}
+{"abstract": "  We established a Spatio-Temporal Neural Network, namely STNN, to forecast the\nspread of the coronavirus COVID-19 outbreak worldwide in 2020. The basic\nstructure of STNN is similar to the Recurrent Neural Network (RNN)\nincorporating with not only temporal data but also spatial features. Two\nimproved STNN architectures, namely the STNN with Augmented Spatial States\n(STNN-A) and the STNN with Input Gate (STNN-I), are proposed, which ensure more\npredictability and flexibility. STNN and its variants can be trained using\nStochastic Gradient Descent (SGD) algorithm and its improved variants (e.g.,\nAdam, AdaGrad and RMSProp). Our STNN models are compared with several classical\nepidemic prediction models, including the fully-connected neural network\n(BPNN), and the recurrent neural network (RNN), the classical curve fitting\nmodels, as well as the SEIR dynamical system model. Numerical simulations\ndemonstrate that STNN models outperform many others by providing more accurate\nfitting and prediction, and by handling both spatial and temporal data.\n"}
+{"abstract": "  Domain-specific neural network accelerators have seen growing interest in\nrecent years due to their improved energy efficiency and inference performance\ncompared to CPUs and GPUs. In this paper, we propose a novel cross-layer\noptimized neural network accelerator called CrossLight that leverages silicon\nphotonics. CrossLight includes device-level engineering for resilience to\nprocess variations and thermal crosstalk, circuit-level tuning enhancements for\ninference latency reduction, and architecture-level optimization to enable\nhigher resolution, better energy-efficiency, and improved throughput. On\naverage, CrossLight offers 9.5x lower energy-per-bit and 15.9x higher\nperformance-per-watt at 16-bit resolution than state-of-the-art photonic deep\nlearning accelerators.\n"}
+{"abstract": "  The rise of digitization of cultural documents offers large-scale contents,\nopening the road for development of AI systems in order to preserve, search,\nand deliver cultural heritage. To organize such cultural content also means to\nclassify them, a task that is very familiar to modern computer science.\nContextual information is often the key to structure such real world data, and\nwe propose to use it in form of a knowledge graph. Such a knowledge graph,\ncombined with content analysis, enhances the notion of proximity between\nartworks so it improves the performances in classification tasks. In this\npaper, we propose a novel use of a knowledge graph, that is constructed on\nannotated data and pseudo-labeled data. With label propagation, we boost\nartwork classification by training a model using a graph convolutional network,\nrelying on the relationships between entities of the knowledge graph. Following\na transductive learning framework, our experiments show that relying on a\nknowledge graph modeling the relations between labeled data and unlabeled data\nallows to achieve state-of-the-art results on multiple classification tasks on\na dataset of paintings, and on a dataset of Buddha statues. Additionally, we\nshow state-of-the-art results for the difficult case of dealing with unbalanced\ndata, with the limitation of disregarding classes with extremely low degrees in\nthe knowledge graph.\n"}
+{"abstract": "  DeepOnets have recently been proposed as a framework for learning nonlinear\noperators mapping between infinite dimensional Banach spaces. We analyze\nDeepOnets and prove estimates on the resulting approximation and generalization\nerrors. In particular, we extend the universal approximation property of\nDeepOnets to include measurable mappings in non-compact spaces. By a\ndecomposition of the error into encoding, approximation and reconstruction\nerrors, we prove both lower and upper bounds on the total error, relating it to\nthe spectral decay properties of the covariance operators, associated with the\nunderlying measures. We derive almost optimal error bounds with very general\naffine reconstructors and with random sensor locations as well as bounds on the\ngeneralization error, using covering number arguments. We illustrate our\ngeneral framework with four prototypical examples of nonlinear operators,\nnamely those arising in a nonlinear forced ODE, an elliptic PDE with variable\ncoefficients and nonlinear parabolic and hyperbolic PDEs. In all these\nexamples, we prove that DeepOnets break the curse of dimensionality, thus\ndemonstrating the efficient approximation of infinite-dimensional operators\nwith this machine learning framework.\n"}
+{"abstract": "  Chaotic quantum systems with Lyapunov exponent $\\lambda_\\mathrm{L}$ obey an\nupper bound $\\lambda_\\mathrm{L}\\leq 2\\pi k_\\mathrm{B}T/\\hbar$ at temperature\n$T$, implying a divergence of the bound in the classical limit $\\hbar\\to 0$.\nFollowing this trend, does a quantum system necessarily become `more chaotic'\nwhen quantum fluctuations are reduced? We explore this question by computing\n$\\lambda_\\mathrm{L}(\\hbar,T)$ in the quantum spherical $p$-spin glass model,\nwhere $\\hbar$ can be continuously varied. We find that quantum fluctuations, in\ngeneral, make paramagnetic phase less chaotic and the spin glass phase more\nchaotic. We show that the approach to the classical limit could be non-trivial,\nwith non-monotonic dependence of $\\lambda_\\mathrm{L}$ on $\\hbar$ close to the\ndynamical glass transition temperature $T_d$. Our results in the classical\nlimit ($\\hbar\\to 0$) naturally describe chaos in super-cooled liquid in\nstructural glasses. We find a crossover from strong to weak chaos substantially\nabove $T_d$, concomitant with the onset of two-step glassy relaxation. We\nfurther show that $\\lambda_\\mathrm{L}\\sim T^\\alpha$, with the exponent $\\alpha$\nvarying between 2 and 1 from quantum to classical limit, at low temperatures in\nthe spin glass phase. Our results reveal intricate interplay between quantum\nfluctuations, glassy dynamics and chaos.\n"}
+{"abstract": "  An exotic rotationally invariant harmonic oscillator (ERIHO) is constructed\nby applying a non-unitary isotropic conformal bridge transformation (CBT) to a\nfree planar particle. It is described by the isotropic harmonic oscillator\nHamiltonian supplemented by a Zeeman type term with a real coupling constant\n$g$. The model reveals the Euclidean ($|g|<1$) and Minkowskian ($|g|>1$) phases\nseparated by the phases $g=+1$ and $g=-1$ of the Landau problem in the\nsymmetric gauge with opposite orientation of the magnetic field. A hidden\nsymmetry emerges in the system at rational values of $g$. Its generators,\ntogether with the Hamiltonian and angular momentum produce non-linearly\ndeformed $\\mathfrak{u}(2)$ and $\\mathfrak{gl}(2,{\\mathbb R})$ algebras in the\ncases of $0<|g|<1$ and $\\infty>|g|>1$, which transmute one into another under\nthe inversion $g\\rightarrow -1/g$. Similarly, the true, $\\mathfrak{u}(2)$, and\nextended conformal, $\\mathfrak{gl}(2,{\\mathbb R})$, symmetries of the isotropic\nEuclidean oscillator ($g=0$) interchange their roles in the isotropic\nMinkowskian oscillator ($|g|=\\infty$), while two copies of the\n$\\mathfrak{gl}(2,{\\mathbb R})$ algebra of analogous symmetries mutually\ntransmute in Landau phases. We show that the ERIHO system is transformed by a\npeculiar unitary transformation into the anisotropic harmonic oscillator\ngenerated, in turn, by anisotropic CBT. The relationship between the ERIHO and\nthe subcritical phases of the harmonically extended Landau problem, as well as\nwith a plane isotropic harmonic oscillator in a uniformly rotating reference\nframe, is established.\n"}
+{"abstract": "  We consider a sequence of variables having multinomial distribution with the\nnumber of trials corresponding to these variables being large and possibly\ndifferent. The multinomial probabilities of the categories are assumed to vary\nrandomly depending on batches. The proposed framework is interesting from the\nperspective of various applications in practice such as predicting the winner\nof an election, forecasting the market share of different brands etc. In this\nwork, first we derive sufficient conditions of asymptotic normality of the\nestimates of the multinomial cell probabilities, and corresponding suitable\ntransformations. Then, we consider a Bayesian setting to implement our model.\nWe consider hierarchical priors using multivariate normal and inverse Wishart\ndistributions, and establish the posterior consistency. Based on this result\nand following appropriate Gibbs sampling algorithms, we can infer about\naggregate data. The methodology is illustrated in detail with two real life\napplications, in the contexts of political election and sales forecasting.\nAdditional insights of effectiveness are also derived through a simulation\nstudy.\n"}
+{"abstract": "  It is widely accepted that both backscattering and dissipation cannot occur\nin topological systems because of the topological protection. Here we show that\nthe thermal dissipation can occur in the quantum Hall (QH) regime in graphene\nin the presence of dissipation sources, although the Hall plateaus and the zero\nlongitudinal resistance still survive. Dissipation appears along the downstream\nchiral flow direction of the constriction in the Hall plateau regime, but it\noccurs mainly in the bulk in the Hall plateau transition regime. In addition,\ndissipation processes are accompanied with the evolution of the energy\ndistribution from non-equilibrium to equilibrium. This indicates that topology\nneither prohibits the appearance of dissipation nor prohibits entropy\nincreasing, which opens a new topic on the dissipation in topological systems.\n"}
+{"abstract": "  Attractor-based end-to-end diarization is achieving comparable accuracy to\nthe carefully tuned conventional clustering-based methods on challenging\ndatasets. However, the main drawback is that it cannot deal with the case where\nthe number of speakers is larger than the one observed during training. This is\nbecause its speaker counting relies on supervised learning. In this work, we\nintroduce an unsupervised clustering process embedded in the attractor-based\nend-to-end diarization. We first split a sequence of frame-wise embeddings into\nshort subsequences and then perform attractor-based diarization for each\nsubsequence. Given subsequence-wise diarization results, inter-subsequence\nspeaker correspondence is obtained by unsupervised clustering of the vectors\ncomputed from the attractors from all the subsequences. This makes it possible\nto produce diarization results of a large number of speakers for the whole\nrecording even if the number of output speakers for each subsequence is\nlimited. Experimental results showed that our method could produce accurate\ndiarization results of an unseen number of speakers. Our method achieved 11.84\n%, 28.33 %, and 19.49 % on the CALLHOME, DIHARD II, and DIHARD III datasets,\nrespectively, each of which is better than the conventional end-to-end\ndiarization methods.\n"}
+{"abstract": "  We study the effects of the flux configurations on the emergent Majorana\nfermions in the $S=1/2$ Kitaev model on a honeycomb lattice, where quantum\nspins are fractionalized into itinerant Majorana fermions and localized fluxes.\nA quantum spin liquid appears as the ground state of the Kitaev model in the\nflux-free sector, which has intensively been investigated so far. In this flux\nsector, the Majorana fermion system has linear dispersions and shows power law\nbehavior in the Majorana correlations. On the other hand, periodically-arranged\nflux configurations yield low-energy excitations in the Majorana fermion\nsystem, which are distinctly different from those in the flux-free state. We\nfind that one of the periodically arranged flux states results in the gapped\nMajorana dispersion and the exponential decay in the Majorana correlations. The\nKitaev system with another flux configuration exhibits a semi-Dirac like\ndispersion, leading to the power law decay with a smaller power than that in\nthe flux-free sector along symmetry axes. We also examine the effect of the\nrandomness in the flux configurations and clarify that the Majorana density of\nstates is filled by increasing the flux density, and power-law decay in the\nMajorana correlations remains. The present results could be important to\ncontrol the motion of Majorana fermions, which carries the spin excitations, in\nthe Kitaev candidate materials.\n"}
+{"abstract": "  Bouncing models are alternatives to inflationary cosmology that replace the\ninitial Big-Bang singularity by a `bouncing' phase. A deeper understanding of\nthe initial conditions of the universe, in these scenarios, requires knowledge\nof quantum aspects of bouncing models. In this work, we propose two classes of\nbouncing models that can be studied with great analytical ease and hence,\nprovide test-bed for investigating more profound problems in quantum cosmology\nof bouncing universes. Our model's two key ingredients enable us to do\nstraightforward analytical calculations: (i) a convenient parametrization of\nthe minisuperspace of FRLW spacetimes and (ii) two distinct choices of the\neffective perfect fluids that source the background geometry of the bouncing\nuniverse. We study the quantum cosmology of these models using both the\nWheeler-de Witt equations and the path integral approach. In particular, we\nfound a bouncing model analogue of the no-boundary wavefunction and presented a\nLorentzian path integral representation for the same. We also discuss the\nintroduction of real scalar perturbations.\n"}
+{"abstract": "  In highway scenarios, an alert human driver will typically anticipate early\ncut-in/cut-out maneuvers of surrounding vehicles using visual cues mainly.\nAutonomous vehicles must anticipate these situations at an early stage too, to\nincrease their safety and efficiency. In this work, lane-change recognition and\nprediction tasks are posed as video action recognition problems. Up to four\ndifferent two-stream-based approaches, that have been successfully applied to\naddress human action recognition, are adapted here by stacking visual cues from\nforward-looking video cameras to recognize and anticipate lane-changes of\ntarget vehicles. We study the influence of context and observation horizons on\nperformance, and different prediction horizons are analyzed. The different\nmodels are trained and evaluated using the PREVENTION dataset. The obtained\nresults clearly demonstrate the potential of these methodologies to serve as\nrobust predictors of future lane-changes of surrounding vehicles proving an\naccuracy higher than 90% in time horizons of between 1-2 seconds.\n"}
+{"abstract": "  The advent of large pre-trained language models has made it possible to make\nhigh-quality predictions on how to add or change a sentence in a document.\nHowever, the high branching factor inherent to text generation impedes the\nability of even the strongest language models to offer useful editing\nsuggestions at a more global or document level. We introduce a new task,\ndocument sketching, which involves generating entire draft documents for the\nwriter to review and revise. These drafts are built from sets of documents that\noverlap in form - sharing large segments of potentially reusable text - while\ndiverging in content. To support this task, we introduce a Wikipedia-based\ndataset of analogous documents and investigate the application of weakly\nsupervised methods, including use of a transformer-based mixture of experts,\ntogether with reinforcement learning. We report experiments using automated and\nhuman evaluation methods and discuss relative merits of these models.\n"}
+{"abstract": "  Quasi-periodic changes of the paleointensity and geomagnetic polarity in the\nintervals of 170 Ma to the present time and of 550 Ma to the present time were\nstudied, respectively. It is revealed that the spectrum of the basic variations\nin the paleointensity and of the duration of the polar intervals is discrete\nand includes quasi-periodic oscillations with characteristic times of 15 Ma, 8\nMa, 5 Ma, and 3 Ma. The characteristic time of these quasi-periodic changes of\nthe geomagnetic field at the beginning and at the end of the Phanerozoic\ndiffered by no more than 10%. The spectral density of quasi-periodic variations\nof the geomagnetic field changed cyclically over geological time. The relation\nbetween the behaviors of the amplitude of paleointensity variations, the\nduration of the polar intervals, and their spectral density was shown.\nQuasi-periodic variations of the paleointensity (geomagnetic activity) had a\nrelatively high spectral density in the interval of (150 - 40) Ma (in the\nCretaceous - Early Paleogene). In this interval, both the amplitude of\npaleointensity variations and the duration of polar intervals increased. In the\nintervals of (170 - 150) Ma and of 30 Ma to the present, a quasi-periodic\nvariation in the paleointensity practically did not detect against the\nbackground of its noise variations. At the same time, the amplitude of the\npaleointensity variations and duration of polar intervals decreased. An\nalternation of time intervals in which the paleointensity variations acquired\neither a quasi-periodic or noise character took place during the geomagnetic\nhistory.\n"}
+{"abstract": "  Object tracking has achieved significant progress over the past few years.\nHowever, state-of-the-art trackers become increasingly heavy and expensive,\nwhich limits their deployments in resource-constrained applications. In this\nwork, we present LightTrack, which uses neural architecture search (NAS) to\ndesign more lightweight and efficient object trackers. Comprehensive\nexperiments show that our LightTrack is effective. It can find trackers that\nachieve superior performance compared to handcrafted SOTA trackers, such as\nSiamRPN++ and Ocean, while using much fewer model Flops and parameters.\nMoreover, when deployed on resource-constrained mobile chipsets, the discovered\ntrackers run much faster. For example, on Snapdragon 845 Adreno GPU, LightTrack\nruns $12\\times$ faster than Ocean, while using $13\\times$ fewer parameters and\n$38\\times$ fewer Flops. Such improvements might narrow the gap between academic\nmodels and industrial deployments in object tracking task. LightTrack is\nreleased at https://github.com/researchmm/LightTrack.\n"}
+{"abstract": "  Solar radio type II bursts serve as early indicators of incoming\ngeo-effective space weather events such as coronal mass ejections (CMEs). In\norder to investigate the origin of high-frequency type II bursts (HF type II\nbursts), we have identified 51 of them (among 180 type II bursts from SWPC\nreports) that are observed by ground-based Compound Astronomical Low-cost\nLow-frequency Instrument for Spectroscopy and Transportable Observatory\n(CALLISTO) spectrometers and whose upper-frequency cutoff (of either\nfundamental or harmonic emission) lies in between 150 MHz-450 MHz during\n2010-2019. We found that 60% of HF type II bursts, whose upper-frequency cutoff\n$\\geq$ 300 MHz originate from the western longitudes. Further, our study finds\na good correlation $\\sim $ 0.73 between the average shock speed derived from\nthe radio dynamic spectra and the corresponding speed from CME data. Also, we\nfound that analyzed HF type II bursts are associated with wide and fast CMEs\nlocated near the solar disk. In addition, we have analyzed the spatio-temporal\ncharacteristics of two of these high-frequency type II bursts and compared the\nderived from radio observations with those derived from multi-spacecraft CME\nobservations from SOHO/LASCO and STEREO coronagraphs.\n"}
+{"abstract": "  We report on the design and whole characterization of low-noise and\naffordable-cost Yb-doped double-clad fiber amplifiers operating at room\ntemperature in the near-infrared spectral region at pulse repetition rate of\n160 MHz. Two different experimental configurations are discussed. In the first\none, a broadband seed radiation with a transform limited pulse duration of 71\nfs, an optical spectrum of 20 nm wide at around 1040 nm, and 20 mW average\npower is adopted. In the second configuration, the seed radiation is\nconstituted by stretched pulses with a time duration as long as 170 ps, with a\n5-nm narrow pulse spectrum centered at 1029 nm and 2 mW average input power. In\nboth cases we obtained transform limited pulse trains with an amplified output\npower exceeding 2 W. Furthermore, relative intensity noise measurements show\nthat no significant noise degradation occurs during the amplification process.\n"}
+{"abstract": "  The mapper construction is a powerful tool from topological data analysis\nthat is designed for the analysis and visualization of multivariate data. In\nthis paper, we investigate a method for stitching a pair of univariate mappers\ntogether into a bivariate mapper, and study topological notions of information\ngains, referred to as topological gains, during such a process. We further\nprovide implementations that visualize such topological gains for mapper\ngraphs.\n"}
+{"abstract": "  Artificial bacteria flagella (ABFs) are magnetic helical micro-swimmers that\ncan be remotely controlled via a uniform, rotating magnetic field. Previous\nstudies have used the heterogeneous response of microswimmers to external\nmagnetic fields for achieving independent control. Here we introduce analytical\nand reinforcement learning control strategies for path planning to a target by\nmultiple swimmers using a uniform magnetic field. The comparison of the two\nalgorithms shows the superiority of reinforcement learning in achieving minimal\ntravel time to a target. The results demonstrate, for the first time, the\neffective independent navigation of realistic micro-swimmers with a uniform\nmagnetic field in a viscous flow field.\n"}
+{"abstract": "  When a chaotic, ergodic Hamiltonian system with $N$ degrees of freedom is\nsubject to sufficiently rapid periodic driving, its energy evolves diffusively.\nWe derive a Fokker-Planck equation that governs the evolution of the system's\nprobability distribution in energy space, and we provide explicit expressions\nfor the energy drift and diffusion rates. Our analysis suggests that the system\ngenerically relaxes to a long-lived \"prethermal\" state characterized by minimal\nenergy absorption, eventually followed by more rapid heating. When $N\\gg 1$,\nthe system ultimately absorbs energy indefinitely from the drive, or at least\nuntil an infinite temperature state is reached.\n"}
+{"abstract": "  Understanding the drift motion and dynamical locking of crystalline clusters\non patterned substrates is important for the diffusion and manipulation of\nnano- and micro-scale objects on surfaces. In a previous work, we studied the\norientational and directional locking of colloidal two-dimensional clusters\nwith triangular structure driven across a triangular substrate lattice. Here we\nshow with experiments and simulations that such locking features arise for\nclusters with arbitrary lattice structure sliding across arbitrary regular\nsubstrates. Similar to triangular-triangular contacts, orientational and\ndirectional locking are strongly correlated via the real- and reciprocal-space\nmoir\\'e patterns of the contacting surfaces. Due to the different symmetries of\nthe surfaces in contact, however the relation between the locking orientation\nand the locking direction becomes more complicated compared to interfaces\ncomposed of identical lattice symmetries. We provide a generalized formalism\nwhich describes the relation between the locking orientation and locking\ndirection with arbitrary lattice symmetries.\n"}
+{"abstract": "  A unification of Klein--Gordon, Dirac, Maxwell, Rarita--Schwinger and\nEinstein equations exact solutions (for the massless fields cases) is\npresented. The method is based on writing all of the relevant dynamical fields\nin terms of products and derivatives of pre--potential functions, which satisfy\nd'Alambert equation. The coupled equations satisfied by the pre--potentials are\nnon-linear. Remarkably, there are particular solutions of (gradient) orthogonal\npre--potentials that satisfy the usual wave equation which may be used to\nconstruct {\\it{exact non--trivial solutions to Klein--Gordon, Dirac, Maxwell,\nRarita--Schwinger and (linearized and full) Einstein equations}}, thus giving\nrise to a unification of the solutions of all massless field equations for any\nspin. Some solutions written in terms of orthogonal pre--potentials are\npresented. Relations of this method to previously developed ones, as well as to\nother subjects in physics are pointed out.\n"}
+{"abstract": "  Soil carbon accounting and prediction play a key role in building decision\nsupport systems for land managers selling carbon credits, in the spirit of the\nParis and Kyoto protocol agreements. Land managers typically rely on\ncomputationally complex models fit using sparse datasets to make these\naccountings and predictions. The model complexity and sparsity of the data can\nlead to over-fitting, leading to inaccurate results using new data or making\npredictions. Modellers address over-fitting by simplifying their models,\nneglecting some soil organic carbon (SOC) components. In this study, we\nintroduce two novel SOC models and a new RothC-like model and investigate how\nthe SOC components and complexity of the SOC models affect the SOC prediction\nin the presence of small and sparse time series data. We develop model\nselection methods that can identify the soil carbon model with the best\npredictive performance, in light of the available data. Through this analysis\nwe reveal that commonly used complex soil carbon models can over-fit in the\npresence of sparse time series data, and our simpler models can produce more\naccurate predictions.\n"}
+{"abstract": "  The observation of a radioactively powered kilonova AT~2017gfo associated\nwith the gravitational wave-event GW170817 from binary neutron star merger\nproves that these events are ideal sites for the production of heavy\n$r$-process elements. The gamma-ray photons produced by the radioactive decay\nof heavy elements are unique probes for the detailed nuclide compositions.\nBasing on the detailed $r$-process nucleosynthesis calculations and considering\nradiative transport calculations for the gamma-rays in different shells, we\nstudy the gamma-ray emission in a merger ejecta on a timescale of a few days.\nIt is found that the total gamma-ray energy generation rate evolution is\nroughly depicted as $\\dot{E}\\propto t^{-1.3}$. For the dynamical ejecta with a\nlow electron fraction ($Y_{\\rm e}\\lesssim0.20$), the dominant contributors of\ngamma-ray energy are the nuclides around the second $r$-process peak\n($A\\sim130$), and the decay chain of $^{132}$Te ($t_{1/2}=3.21$~days)\n$\\rightarrow$ $^{132}$I ($t_{1/2}=0.10$~days) $\\rightarrow$ $^{132}$Xe produces\ngamma-ray lines at $228$ keV, $668$ keV, and $773$ keV. For the case of a wind\nejecta with $Y_{\\rm e}\\gtrsim0.30$, the dominant contributors of gamma-ray\nenergy are the nuclides around the first $r$-process peak ($A\\sim80$), and the\ndecay chain of $^{72}$Zn ($t_{1/2}=1.93$~days) $\\rightarrow$ $^{72}$Ga\n($t_{1/2}=0.59$~days) $\\rightarrow$ $^{72}$Ge produces gamma-ray lines at $145$\nkeV, $834$ keV, $2202$ keV, and $2508$ keV. The peak fluxes of these lines are\n$10^{-9}\\sim 10^{-7}$~ph~cm$^{-2}$ s$^{-1}$, which are marginally detectable\nwith the next-generation MeV gamma-ray detector \\emph{ETCC} if the source is at\na distance of $40$~Mpc.\n"}
+{"abstract": "  This paper addresses reinforcement learning based, direct signal tracking\ncontrol with an objective of developing mathematically suitable and practically\nuseful design approaches. Specifically, we aim to provide reliable and easy to\nimplement designs in order to reach reproducible neural network-based\nsolutions. Our proposed new design takes advantage of two control design\nframeworks: a reinforcement learning based, data-driven approach to provide the\nneeded adaptation and (sub)optimality, and a backstepping based approach to\nprovide closed-loop system stability framework. We develop this work based on\nan established direct heuristic dynamic programming (dHDP) learning paradigm to\nperform online learning and adaptation and a backstepping design for a class of\nimportant nonlinear dynamics described as Euler-Lagrange systems. We provide a\ntheoretical guarantee for the stability of the overall dynamic system, weight\nconvergence of the approximating nonlinear neural networks, and the Bellman\n(sub)optimality of the resulted control policy. We use simulations to\ndemonstrate significantly improved design performance of the proposed approach\nover the original dHDP.\n"}
+{"abstract": "  In this paper, we formalize precisely the sense in which the application of\ncellular automaton to partial configuration is a natural extension of its local\ntransition function through the categorical notion of Kan extension. In fact,\nthe two possible ways to do such an extension and the ingredients involved in\ntheir definition are related through Kan extensions in many ways. These\nrelations provide additional links between computer science and category\ntheory, and also give a new point of view on the famous Curtis-Hedlung theorem\nof cellular automata from the extended topological point of view provided by\ncategory theory. These relations provide additional links between computer\nscience and category theory. No prior knowledge of category theory is assumed.\n"}
+{"abstract": "  Emotion recognition from speech is a challenging task. Re-cent advances in\ndeep learning have led bi-directional recur-rent neural network (Bi-RNN) and\nattention mechanism as astandard method for speech emotion recognition,\nextractingand attending multi-modal features - audio and text, and thenfusing\nthem for downstream emotion classification tasks. Inthis paper, we propose a\nsimple yet efficient neural networkarchitecture to exploit both acoustic and\nlexical informationfrom speech. The proposed framework using multi-scale\ncon-volutional layers (MSCNN) to obtain both audio and text hid-den\nrepresentations. Then, a statistical pooling unit (SPU)is used to further\nextract the features in each modality. Be-sides, an attention module can be\nbuilt on top of the MSCNN-SPU (audio) and MSCNN (text) to further improve the\nperfor-mance. Extensive experiments show that the proposed modeloutperforms\nprevious state-of-the-art methods on IEMOCAPdataset with four emotion\ncategories (i.e., angry, happy, sadand neutral) in both weighted accuracy (WA)\nand unweightedaccuracy (UA), with an improvement of 5.0% and 5.2% respectively\nunder the ASR setting.\n"}
+{"abstract": "  The asymptotic equivalence of canonical and microcanonical ensembles is a\ncentral concept in statistical physics, with important consequences for both\ntheoretical research and practical applications. However, this property breaks\ndown under certain circumstances. The most studied violation of ensemble\nequivalence requires phase transitions, in which case it has a `restricted'\n(i.e. confined to a certain region in parameter space) but `strong' (i.e.\ncharacterized by a difference between the entropies of the two ensembles that\nis of the same order as the entropies themselves) form. However, recent\nresearch on networks has shown that the presence of an extensive number of\nlocal constraints can lead to ensemble nonequivalence even in the absence of\nphase transitions. This occurs in a `weak' (i.e. leading to a subleading\nentropy difference) but remarkably `unrestricted' (i.e. valid in the entire\nparameter space) form. Here we look for more general manifestations of ensemble\nnonequivalence in arbitrary ensembles of matrices with given margins. These\nmodels have widespread applications in the study of spatially heterogeneous\nand/or temporally nonstationary systems, with consequences for the analysis of\nmultivariate financial and neural time-series, multi-platform social activity,\ngene expression profiles and other Big Data. We confirm that ensemble\nnonequivalence appears in `unrestricted' form throughout the entire parameter\nspace due to the extensivity of local constraints. Surprisingly, at the same\ntime it can also exhibit the `strong' form. This novel, simultaneously `strong\nand unrestricted' form of nonequivalence is very robust and imposes a\nprincipled choice of the ensemble. We calculate the proper mathematical\nquantities to be used in real-world applications.\n"}
+{"abstract": "  Privacy-preserving genomic data sharing is prominent to increase the pace of\ngenomic research, and hence to pave the way towards personalized genomic\nmedicine. In this paper, we introduce ($\\epsilon , T$)-dependent local\ndifferential privacy (LDP) for privacy-preserving sharing of correlated data\nand propose a genomic data sharing mechanism under this privacy definition. We\nfirst show that the original definition of LDP is not suitable for genomic data\nsharing, and then we propose a new mechanism to share genomic data. The\nproposed mechanism considers the correlations in data during data sharing,\neliminates statistically unlikely data values beforehand, and adjusts the\nprobability distributions for each shared data point accordingly. By doing so,\nwe show that we can avoid an attacker from inferring the correct values of the\nshared data points by utilizing the correlations in the data. By adjusting the\nprobability distributions of the shared states of each data point, we also\nimprove the utility of shared data for the data collector. Furthermore, we\ndevelop a greedy algorithm that strategically identifies the processing order\nof the shared data points with the aim of maximizing the utility of the shared\ndata. Considering the interdependent privacy risks while sharing genomic data,\nwe also analyze the information gain of an attacker about genomes of a donor's\nfamily members by observing perturbed data of the genome donor and we propose a\nmechanism to select the privacy budget (i.e., $\\epsilon$ parameter of LDP) of\nthe donor by also considering privacy preferences of her family members. Our\nevaluation results on a real-life genomic dataset show the superiority of the\nproposed mechanism compared to the randomized response mechanism (a widely used\ntechnique to achieve LDP).\n"}
+{"abstract": "  In this note we study the contact geometry of symplectic divisors. We show\nthe contact structure induced on the boundary of a divisor neighborhood is\ninvariant under toric and interior blow-ups and blow-downs. We also construct\nan open book decomposition on the boundary of a concave divisor neighborhood\nand apply it to the study of universally tight contact structures of contact\ntorus bundles.\n"}
+{"abstract": "  We study the quantum Riemannian geometry of quantum projective spaces of any\ndimension. In particular we compute the Riemann and Ricci tensors, using\npreviously introduced quantum metrics and quantum Levi-Civita connections. We\nshow that the Riemann tensor is a bimodule map and derive various consequences\nof this fact. We prove that the Ricci tensor is proportional to the quantum\nmetric, giving a quantum analogue of the Einstein condition, and compute the\ncorresponding scalar curvature. Along the way we also prove several results for\nvarious objects related to those mentioned here.\n"}
+{"abstract": "  The hole probability, i.e., the probability that a region is void of\nparticles, is a benchmark of correlations in many body systems. We compute\nanalytically this probability $P(R)$ for a spherical region of radius $R$ in\nthe case of $N$ noninteracting fermions in their ground state in a\n$d$-dimensional trapping potential. Using a connection to the Laguerre-Wishart\nensembles of random matrices, we show that, for large $N$ and in the bulk of\nthe Fermi gas, $P(R)$ is described by a universal scaling function of $k_F R$,\nfor which we obtain an exact formula ($k_F$ being the local Fermi wave-vector).\nIt exhibits a super exponential tail $P(R)\\propto e^{- \\kappa_d (k_F R)^{d+1}}$\nwhere $\\kappa_d$ is a universal amplitude, in good agreement with existing\nnumerical simulations. When $R$ is of the order of the radius of the Fermi gas,\nthe hole probability is described by a large deviation form which is not\nuniversal and which we compute exactly for the harmonic potential. Similar\nresults also hold in momentum space.\n"}
+{"abstract": "  We consider a high-Q microresonator with $\\chi^{(2)}$ nonlinearity under\nconditions when the coupling rates between the sidebands around the pump and\nsecond harmonic exceed the damping rates, implying the strong coupling regime\n(SC). Using the dressed-resonator approach we demonstrate that this regime\nleads to the dominance of the Hermitian part of the operator driving the\nside-band dynamics over its non-Hermitian part responsible for the parametric\ngain. This has allowed us to introduce and apply the cross-area concept of the\npolariton quasi-particles and define their effective masses in the context of\n$\\chi^{(2)}$ ring-microresonators. We further use polaritons to predict the\nmodified spectral response of the resonator to a weak probe field, and to\nreveal splitting of the bare-resonator resonances, avoided crossings, and Rabi\ndynamics. Polariton basis also allows deriving a discrete sequence of the\nparametric thresholds for the generation of sidebands of different orders.\n"}
+{"abstract": "  We use a Wigner distribution-like function based on the strong field\napproximation theory to obtain the time-energy distributions and the ionization\ntime distributions of electrons ionized by an XUV pulse alone and in the\npresence of an infrared (IR) pulse. In the case of a single XUV pulse, although\nthe overall shape of the ionization time distribution resembles the\nXUV-envelope, its detail shows dependence on the emission direction of the\nelectron and the carrier-envelope phase of the pulse, which mainly results from\nthe low-energy interference structure. It is further found that the electron\nfrom the counter-rotating term plays an important role in the interference. In\nthe case of the two-color pulse, both the time-energy distributions and the\nionization time distributions change with varying IR field. Our analysis\ndemonstrates that the IR field not only modifies the final electron kinetic\nenergy but also changes the electron's emission time, which results from the\nchange of the electric field induced by the IR pulse. Moreover, the ionization\ntime distributions of the photoelectrons emitted from atoms with higher\nionization energy are also given, which show less impact of the IR field on the\nelectron dynamics.\n"}
+{"abstract": "  Breathers are localized structures that undergo a periodic oscillation in\ntheir duration and amplitude. Optical microresonators, benefiting from their\nhigh quality factor, provide an ideal test bench for studying the breathing\nphenomena. In the monochromatically pumped microresonator system, intrinsic\nbreathing instabilities are widely observed in the form of temporal dissipative\nKerr solitons which only exist in the effectively red detuned regime. Here, we\nproposed a novel bichromatic pumping scheme to create compulsive breathing\nmicrocombs via respectively distributing two pump lasers at the effectively\nblue and red detuned side of a single resonance. We experimentally discover the\nartificial cnoidal wave breathers and molecular crystal-like breathers in a\nchip-based silicon nitride microresonator, and theoretically describe their\nintriguing temporal dynamics based on the bichromatic pumping Lugiato-Lefever\nequation. In particular, the corresponding breathing microcombs exhibit diverse\ncomb line spacing ranging from 2 to 17 times of the free spectral range of the\nresonator. Our discovery not only provides a simple and robust method to\nproduce microcombs with reconfigurable comb line spacing, but also reveals a\nnew type of breathing waves in driven dissipative nonlinear systems.\n"}
+{"abstract": "  We derive general covariant coupled equations of QCD describing the\ntetraquark in terms of a mix of four-quark states $2q2\\bar q$, and two-quark\nstates $q\\bar q$. The coupling of $2q2\\bar q$ to $q\\bar q$ states is achieved\nby a simple contraction of a four-quark $q\\bar q$-irreducible Green function\ndown to a two-quark $q\\bar q$ Bethe-Salpeter kernel. The resulting tetraquark\nequations are expressed in an exact field theoretic form, and are in agreement\nwith those obtained previously by consideration of disconnected interactions;\nhowever, despite being more general, they have been derived here in a much\nsimpler and more transparent way.\n"}
+{"abstract": "  We show how spectral submanifold theory can be used to construct\nreduced-order models for harmonically excited mechanical systems with internal\nresonances. Efficient calculations of periodic and quasi-periodic responses\nwith the reduced-order models are discussed in this paper and its companion,\nPart II, respectively. The dimension of a reduced-order model is determined by\nthe number of modes involved in the internal resonance, independently of the\ndimension of the full system. The periodic responses of the full system are\nobtained as equilibria of the reduced-order model on spectral submanifolds. The\nforced response curve of periodic orbits then becomes a manifold of equilibria,\nwhich can be easily extracted using parameter continuation. To demonstrate the\neffectiveness and efficiency of the reduction, we compute the forced response\ncurves of several high-dimensional nonlinear mechanical systems, including the\nfinite-element models of a von K\\'arm\\'an beam and a plate.\n"}
+{"abstract": "  We report for the first time the occurrence of superconductivity in the\nquaternary silicide carbide YRe2SiC with Tc = 5.9 K. The emergence of\nsuperconductivity was confirmed by means of magnetic susceptibility, electrical\nresistivity, and heat capacity measurements. The presence of a well developed\nheat capacity feature at Tc confirms that superconductivity is a bulk\nphenomenon, while a second feature in the heat capacity near 0.5 Tc combined\nwith the unusual temperature dependence of the upper critical field Hc2(T)\nindicate the presence of a multiband superconducting state. Additionally, the\nlinear dependence of the lower critical field Hc1 with temperature resemble the\nbehavior found in compounds with unconventional pairing symmetry. Band\nstructure calculations reveal YRe2SiC could harbor a non-trivial topological\nstate and that the low-energy states occupy multiple disconnected sheets at the\nFermi surface, with different degrees of hybridization, nesting, and screening\neffects, therefore making unconventional multiband superconductivity plausible.\n"}
+{"abstract": "  We consider the dynamics of local entropy and nearest neighbor mutual\ninformation of a 1-D lattice of qubits via the repeated application of nearest\nneighbor CNOT quantum gates. This is a quantum version of a cellular automaton.\nWe analyze the entropy dynamics for different initial product states, both for\nopen boundary conditions, periodic boundary conditions and we also consider the\ninfinite lattice thermodynamic limit. The dynamics gives rise to fractal\nbehavior, where we see the appearance of the Sierpinski triangle both for\nstates in the computational basis and for operator dynamics in the Heisenberg\npicture. In the thermodynamics limit, we see equilibration with a time\ndependence controlled by $\\exp(-\\alpha t^{h-1})$ where $h$ is the fractal\ndimension of the Sierpinski triangle, and $\\alpha$ depends on the details of\nthe initial state. We also see log-periodic reductions in the one qubit entropy\nwhere the approach to equilibrium is only power law. For open boundary\nconditions we see time periodic oscillations near the boundary, associated to\nsubalgebras of operators localized near the boundary that are mapped to\nthemselves by the dynamics.\n"}
+{"abstract": "  In an article by Garc\\'ia-Pintos et al. [Rev. Lett. 125, 040601 (2020)] the\nconnection between the charging power of a quantum battery and the fluctuations\nof a \"free energy operator\" whose expectation value characterizes the maximum\nextractable work of the battery is studied. The result of the closed-system\nanalysis shows that for a general charging process the battery will have a\nnonzero charging power if and only if the state of the battery is not an\neigenstate of the free energy operator. In this Comment, we point out a few\nmistakes in the analysis and obtain the correct bound on the charging power.\nConsequently, the result for closed-system dynamics is in general not correct.\n"}
+{"abstract": "  In real-world multi-agent systems, agents with different capabilities may\njoin or leave without altering the team's overarching goals. Coordinating teams\nwith such dynamic composition is challenging: the optimal team strategy varies\nwith the composition. We propose COPA, a coach-player framework to tackle this\nproblem. We assume the coach has a global view of the environment and\ncoordinates the players, who only have partial views, by distributing\nindividual strategies. Specifically, we 1) adopt the attention mechanism for\nboth the coach and the players; 2) propose a variational objective to\nregularize learning; and 3) design an adaptive communication method to let the\ncoach decide when to communicate with the players. We validate our methods on a\nresource collection task, a rescue game, and the StarCraft micromanagement\ntasks. We demonstrate zero-shot generalization to new team compositions. Our\nmethod achieves comparable or better performance than the setting where all\nplayers have a full view of the environment. Moreover, we see that the\nperformance remains high even when the coach communicates as little as 13% of\nthe time using the adaptive communication strategy.\n"}
+{"abstract": "  Let $H\\subset G$ be semisimple Lie groups, $\\Gamma\\subset G$ a lattice and\n$K$ a compact subgroup of $G$. For $n \\in \\mathbb N$, let $\\mathcal O_n$ be the\nprojection to $\\Gamma \\backslash G/K$ of a finite union of closed $H$-orbits in\n$\\Gamma \\backslash G$. In this very general context of homogeneous dynamics, we\nprove an equidistribution theorem for intersections of $\\mathcal O_n$ with an\nanalytic subvariety $S$ of $G/K$ of complementary dimension: if $\\mathcal O_n$\nis equidistributed in $\\Gamma \\backslash G/K$, then the signed intersection\nmeasure of $S \\cap \\mathcal O_n$ normalized by the volume of $\\mathcal O_n$\nconverges to the restriction to $S$ of some $G$-invariant closed form on $G/K$.\nWe give general tools to determine this closed form and compute it in some\nexamples.\n  As our main application, we prove that, if $\\mathbb V$ is a polarized\nvariation of Hodge structure of weight $2$ and Hodge numbers $(q,p,q)$ over a\nbase $S$ of dimension $rq$, then the (non-exceptional) locus where the Picard\nrank is at least $r$ is equidistributed in $S$ with respect to the volume form\n$c_q^r$, where $c_q$ is the $q^{\\textrm{th}}$ Chern form of the Hodge bundle.\nThis generalizes a previous work of the first author which treated the case\n$q=r=1$. We also prove an equidistribution theorem for certain families of CM\npoints in Shimura varieties, and another one for Hecke translates of a divisor\nin $\\mathcal A_g$.\n"}
+{"abstract": "  We consider the problem of binomiality of the steady state ideals of\nbiochemical reaction networks. We are interested in finding polynomial\nconditions on the parameters such that the steady state ideal of a chemical\nreaction network is binomial under every specialisation of the parameters if\nthe conditions on the parameters hold. We approach the binomiality problem\nusing Comprehensive Gr\\\"obner systems. Considering rate constants as\nparameters, we compute comprehensive Gr\\\"obner systems for various reactions.\nIn particular, we make automatic computations on n-site phosphorylations and\nbiomodels from the Biomodels repository using the grobcov library of the\ncomputer algebra system Singular.\n"}
+{"abstract": "  An important component of unsupervised learning by instance-based\ndiscrimination is a memory bank for storing a feature representation for each\ntraining sample in the dataset. In this paper, we introduce 3 improvements to\nthe vanilla memory bank-based formulation which brings massive accuracy gains:\n(a) Large mini-batch: we pull multiple augmentations for each sample within the\nsame batch and show that this leads to better models and enhanced memory bank\nupdates. (b) Consistency: we enforce the logits obtained by different\naugmentations of the same sample to be close without trying to enforce\ndiscrimination with respect to negative samples as proposed by previous\napproaches. (c) Hard negative mining: since instance discrimination is not\nmeaningful for samples that are too visually similar, we devise a novel nearest\nneighbour approach for improving the memory bank that gradually merges\nextremely similar data samples that were previously forced to be apart by the\ninstance level classification loss. Overall, our approach greatly improves the\nvanilla memory-bank based instance discrimination and outperforms all existing\nmethods for both seen and unseen testing categories with cosine similarity.\n"}
+{"abstract": "  Conformal quantum mechanics has been proposed to be the CFT$_1$ dual to\nAdS$_2$. The $N$-point correlation function that satisfy conformal constraints\nhave been constructed from a non-conformal vacuum and the insertion of a\nnon-primary operator. The main goal of this paper is to find an interpretation\nof this oddness. For this purpouse, we study possible gravitational dual models\nand propose a two-dimensional dilaton gravity with a massless fermion for the\ndescription of conformal quantum mechanics. We find a universal correspondence\nbetween states in the conformal quantum mechanics model and two-dimensional\nspacetimes. Moreover, the solutions of the Dirac equation can be interpreted as\nzero modes of a Floquet-Dirac system. Within this system, the oddness of the\nnon-conformal vacuum and non-primary operator is elucidated. As a possible\napplication, we interpret the gauge symmetries of the Floquet-Dirac system as\nthe corresponding infinite symmetries of the Schr\\\"odinger equation which are\nconjectured to be related to higher spin symmetries.\n"}
+{"abstract": "  We investigate the necessary conditions for the two spacetimes, which are\nsolutions to the Einstein field equations with an anisotropic matter source, to\nbe related to each other by means of a conformal transformation. As a result,\nwe obtain that if one of such spacetimes is a generalization of the\nRobertson-Walker solution with vanishing acceleration and vorticity, then the\nother one has to be in this class as well, i.e. the conformal factor will be a\nconstant function on the hypersurfaces orthogonal to the fluid flow lines. The\nevolution equation for this function appears naturally as a direct consequence\nof the conformal transformation of the Ricci tensor.\n"}
+{"abstract": "  A caveat to many applications of the current Deep Learning approach is the\nneed for large-scale data. One improvement suggested by Kolmogorov Complexity\nresults is to apply the minimum description length principle with\ncomputationally universal models. We study the potential gains in sample\nefficiency that this approach can bring in principle. We use polynomial-time\nTuring machines to represent computationally universal models and Boolean\ncircuits to represent Artificial Neural Networks (ANNs) acting on\nfinite-precision digits.\n  Our analysis unravels direct links between our question and Computational\nComplexity results. We provide lower and upper bounds on the potential gains in\nsample efficiency between the MDL applied with Turing machines instead of ANNs.\nOur bounds depend on the bit-size of the input of the Boolean function to be\nlearned. Furthermore, we highlight close relationships between classical open\nproblems in Circuit Complexity and the tightness of these.\n"}
+{"abstract": "  Scrum, the most popular agile method and project management framework, is\nwidely reported to be used, adapted, misused, and abused in practice. However,\nnot much is known about how Scrum actually works in practice, and critically,\nwhere, when, how and why it diverges from Scrum by the book. Through a Grounded\nTheory study involving semi-structured interviews of 45 participants from 30\ncompanies and observations of five teams, we present our findings on how Scrum\nworks in practice as compared to how it is presented in its formative books. We\nidentify significant variations in these practices such as work breakdown,\nestimation, prioritization, assignment, the associated roles and artefacts, and\ndiscuss the underlying rationales driving the variations. Critically, we claim\nthat not all variations are process misuse/abuse and propose a nuanced\nclassification approach to understanding variations as standard, necessary,\ncontextual, and clear deviations for successful Scrum use and adaptation\n"}
+{"abstract": "  Axion-like particles (ALPs) are ubiquitous in models of new physics\nexplaining some of the most pressing puzzles of the Standard Model. However,\nuntil relatively recently, little attention has been paid to its interplay with\nflavour. In this work, we study in detail the phenomenology of ALPs that\nexclusively interact with up-type quarks at the tree-level, which arise in some\nwell-motivated ultra-violet completions such as QCD-like dark sectors or\nFroggatt-Nielsen type models of flavour. Our study is performed in the\nlow-energy effective theory to highlight the key features of these scenarios in\na model independent way. We derive all the existing constraints on these models\nand demonstrate how upcoming experiments at fixed-target facilities and the LHC\ncan probe a vast region of the parameter space, which is currently not excluded\nby cosmological and astrophysical bounds. We also emphasize how a future\nmeasurement of the currently unavailable meson decay $D \\to \\pi +\n\\rm{invisible}$ could complement these upcoming searches and help to probe a\nlarge unexplored region of their parameter space.\n"}
+{"abstract": "  We construct analytical and regular solutions in four-dimensional General\nRelativity which represent multi-black hole systems immersed in external\ngravitational field configurations. The external field background is composed\nby an infinite multipolar expansion, which allows to regularise the conical\nsingularities of an array of collinear static black holes. A stationary\nrotating generalisation is achieved by adding independent angular momenta and\nNUT parameters to each source of the binary configuration. Moreover, a charged\nextension of the binary black hole system at equilibrium is generated. Finally,\nwe show that the binary Majumdar-Papapetrou solution is consistently recovered\nin the vanishing external field limit. All of these solutions reach an\nequilibrium state due to the external gravitational field only, avoiding in\nthis way the presence of any string or strut defect.\n"}
+{"abstract": "  The problem of covariance of physical quantities has not been solved\nfundamentally in the theory of relativity, which has caused a lot of confusion\nin the community; a typical example is the Gordon metric tensor, which was\ndeveloped almost a century ago, and has been widely used to describe the\nequivalent gravitational effect of moving media on light propagation,\npredicting a novel physics of optical black hole. In this paper, it is shown\nthat under Lorentz transformation, a covariant tensor satisfies three rules:\n(1) the tensor keeps invariant in mathematical form in all inertial frames; (2)\nall elements of the tensor have the same physical definitions in all frames;\n(3) the tensor expression in one inertial frame does not include any physical\nquantities defined in other frames. The three rules constitute a criterion for\ntesting the covariance of physical laws, required by Einstein's principle of\nrelativity. Gordon metric does not satisfy Rule (3), and its covariance cannot\nbe identified before a general refractive index is defined. Finally, it is also\nshown as well that in the relativistic quantum mechanics, the Lorentz\ncovariance of Dirac wave equation is not compatible with Einstein's mass-energy\nequivalence.\n"}
+{"abstract": "  In this work, we present a coupled 3D-1D model of solid tumor growth within a\ndynamically changing vascular network to facilitate realistic simulations of\nangiogenesis. Additionally, the model includes erosion of the extracellular\nmatrix, interstitial flow, and coupled flow in blood vessels and tissue. We\nemploy continuum mixture theory with stochastic Cahn--Hilliard type phase-field\nmodels of tumor growth. The interstitial flow is governed by a mesoscale\nversion of Darcy's law. The flow in the blood vessels is controlled by\nPoiseuille flow, and Starling's law is applied to model the mass transfer in\nand out of blood vessels. The evolution of the network of blood vessels is\norchestrated by the concentration of the tumor angiogenesis factors (TAFs);\nblood vessels grow towards the increasing TAFs concentrations. This process is\nnot deterministic, allowing random growth of blood vessels and, therefore, due\nto the coupling of nutrients in tissue and vessels, makes the growth of tumors\nstochastic. We demonstrate the performance of the model by applying it to a\nvariety of scenarios. Numerical experiments illustrate the flexibility of the\nmodel and its ability to generate satellite tumors. Simulations of the effects\nof angiogenesis on tumor growth are presented as well as sample-independent\nfeatures of cancer.\n"}
+{"abstract": "  The behavior of hot carriers in metal-halide perovskites (MHPs) present a\nvaluable foundation for understanding the details of carrier-phonon coupling in\nthe materials as well as the prospective development of highly efficient hot\ncarrier and carrier multiplication solar cells. Whilst the carrier population\ndynamics during cooling have been intensely studied, the evolution of the hot\ncarrier properties, namely the hot carrier mobility, remain largely unexplored.\nTo address this, we introduce a novel ultrafast visible pump - infrared push -\nterahertz probe spectroscopy (PPP-THz) to monitor the real-time conductivity\ndynamics of cooling carriers in methylammonium lead iodide. We find a decrease\nin mobility upon optically depositing energy into the carriers, which is\ntypical of band-transport. Surprisingly, the conductivity recovery dynamics are\nincommensurate with the intraband relaxation measured by an analogous\nexperiment with an infrared probe (PPP- IR), and exhibit a negligible\ndependence on the density of hot carriers. These results and the kinetic\nmodelling reveal the importance of highly-localized lattice heating on the\nmobility of the hot electronic states. This collective polaron-lattice\nphenomenon may contribute to the unusual photophysics observed in MHPs and\nshould be accounted for in devices that utilize hot carriers.\n"}
+{"abstract": "  Balanced weighing matrices with parameters $$\n\\left(1+18\\cdot\\frac{9^{m+1}-1}{8},9^{m+1},4\\cdot 9^m\\right), $$ for each\nnonzero integer $m$ is constructed. This is the first infinite class not\nbelonging to those with classical parameters. It is shown that any balanced\nweighing matrix is equivalent to a five-class association scheme.\n"}
+{"abstract": "  Evaluating image generation models such as generative adversarial networks\n(GANs) is a challenging problem. A common approach is to compare the\ndistributions of the set of ground truth images and the set of generated test\nimages. The Frech\\'et Inception distance is one of the most widely used metrics\nfor evaluation of GANs, which assumes that the features from a trained\nInception model for a set of images follow a normal distribution. In this\npaper, we argue that this is an over-simplified assumption, which may lead to\nunreliable evaluation results, and more accurate density estimation can be\nachieved using a truncated generalized normal distribution. Based on this, we\npropose a novel metric for accurate evaluation of GANs, named TREND (TRuncated\ngEneralized Normal Density estimation of inception embeddings). We demonstrate\nthat our approach significantly reduces errors of density estimation, which\nconsequently eliminates the risk of faulty evaluation results. Furthermore, we\nshow that the proposed metric significantly improves robustness of evaluation\nresults against variation of the number of image samples.\n"}
+{"abstract": "  Unconstrained handwriting recognition is an essential task in document\nanalysis. It is usually carried out in two steps. First, the document is\nsegmented into text lines. Second, an Optical Character Recognition model is\napplied on these line images. We propose the Simple Predict & Align Network: an\nend-to-end recurrence-free Fully Convolutional Network performing OCR at\nparagraph level without any prior segmentation stage. The framework is as\nsimple as the one used for the recognition of isolated lines and we achieve\ncompetitive results on three popular datasets: RIMES, IAM and READ 2016. The\nproposed model does not require any dataset adaptation, it can be trained from\nscratch, without segmentation labels, and it does not require line breaks in\nthe transcription labels. Our code and trained model weights are available at\nhttps://github.com/FactoDeepLearning/SPAN.\n"}
+{"abstract": "  Determining the maximum size of a $t$-intersecting code in $[m]^n$ was a\nlongstanding open problem of Frankl and F\\\"uredi, solved independently by\nAhlswede and Khachatrian and by Frankl and Tokushige. We extend their result to\nthe setting of forbidden intersections, by showing that for any $m>2$ and $n$\nlarge compared with $t$ (but not necessarily $m$) that the same bound holds for\ncodes with the weaker property of being $(t-1)$-avoiding, i.e.\\ having no two\nvectors that agree on exactly $t-1$ coordinates. Our proof proceeds via a junta\napproximation result of independent interest, which we prove via a development\nof our recent theory of global hypercontractivity: we show that any\n$(t-1)$-avoiding code is approximately contained in a $t$-intersecting junta (a\ncode where membership is determined by a constant number of co-ordinates). In\nparticular, when $t=1$ this gives an alternative proof of a recent result of\nEberhard, Kahn, Narayanan and Spirkl that symmetric intersecting codes in\n$[m]^n$ have size $o(m^n)$.\n"}
+{"abstract": "  This paper is directed to the financial community and focuses on the\nfinancial risks associated with climate change. It, specifically, addresses the\nestimate of climate risk embedded within a bank loan portfolio. During the 21st\ncentury, man-made carbon dioxide emissions in the atmosphere will raise global\ntemperatures, resulting in severe and unpredictable physical damage across the\nglobe. Another uncertainty associated with climate, known as the energy\ntransition risk, comes from the unpredictable pace of political and legal\nactions to limit its impact. The Climate Extended Risk Model (CERM) adapts well\nknown credit risk models. It proposes a method to calculate incremental credit\nlosses on a loan portfolio that are rooted into physical and transition risks.\nThe document provides detailed description of the model hypothesis and steps.\nThis work was initiated by the association Green RWA (Risk Weighted Assets). It\nwas written in collaboration with Jean-Baptiste Gaudemet, Anne Gruz, and\nOlivier Vinciguerra (cerm@greenrwa.org), who contributed their financial and\nrisk expertise, taking care of its application to a pilot-portfolio. It extends\nthe model proposed in a first white paper published by Green RWA\n(https://www.greenrwa.org/).\n"}
+{"abstract": "  Recent studies have suggested that low-energy cosmic rays (CRs) may be\naccelerated inside molecular clouds by the shocks associated with star\nformation. We use a Monte Carlo transport code to model the propagation of CRs\naccelerated by protostellar accretion shocks through protostellar cores. We\ncalculate the CR attenuation and energy losses and compute the resulting flux\nand ionization rate as a function of both radial distance from the protostar\nand angular position. We show that protostellar cores have non-uniform CR\nfluxes that produce a broad range of CR ionization rates, with the maximum\nvalue being up to two orders of magnitude higher then the radial average at a\ngiven distance. In particular, the CR flux is focused in the direction of the\noutflow cavity, creating a 'flashlight' effect and allowing CRs to leak out of\nthe core. The radially averaged ionization rates are less than the measured\nvalue for the Milky Way of $\\zeta \\approx 10^{-16} \\rm s^{-1}$; however, within\n$r \\approx 0.03$ pc from the protostar, the maximum ionization rates exceed\nthis value. We show that variation in the protostellar parameters, particularly\nin the accretion rate, may produce ionization rates that are a couple of orders\nof magnitude higher or lower than our fiducial values. Finally, we use a\nstatistical method to model unresolved sub-grid magnetic turbulence in the\ncore. We show that turbulence modifies the CR spectrum and increases the\nuniformity of the CR distribution but does not significantly affect the\nresulting ionization rates.\n"}
+{"abstract": "  We analyze the collision of three identical spin-polarized fermions at zero\ncollision energy, assuming arbitrary finite-range potentials, and define the\ncorresponding three-body scattering hypervolume $D_F$. The scattering\nhypervolume $D$ was first defined for identical bosons in 2008 by one of us. It\nis the three-body analog of the two-body scattering length. We solve the\nthree-body Schr\\\"{o}dinger equation asymptotically when the three fermions are\nfar apart or one pair and the third fermion are far apart, deriving two\nasymptotic expansions of the wave function. Unlike the case of bosons for which\n$D$ has the dimension of length to the fourth power, here the $D_F$ we define\nhas the dimension of length to the eighth power. We then analyze the\ninteraction energy of three such fermions with momenta $\\hbar\\mathbf{k}_1$,\n$\\hbar\\mathbf{k}_2$ and $\\hbar\\mathbf{k}_3$ in a large periodic cubic box. The\nenergy shift due to $D_F$ is proportional to $D_F/\\Omega^2$, where $\\Omega$ is\nthe volume of the box. We also calculate the shifts of energy and pressure of\nspin-polarized Fermi gases due to a nonzero $D_F$ and the three-body\nrecombination rate of spin-polarized ultracold atomic Fermi gases at finite\ntemperatures.\n"}
+{"abstract": "  We present a dual-pathway approach for recognizing fine-grained interactions\nfrom videos. We build on the success of prior dual-stream approaches, but make\na distinction between the static and dynamic representations of objects and\ntheir interactions explicit by introducing separate motion and object detection\npathways. Then, using our new Motion-Guided Attention Fusion module, we fuse\nthe bottom-up features in the motion pathway with features captured from object\ndetections to learn the temporal aspects of an action. We show that our\napproach can generalize across appearance effectively and recognize actions\nwhere an actor interacts with previously unseen objects. We validate our\napproach using the compositional action recognition task from the\nSomething-Something-v2 dataset where we outperform existing state-of-the-art\nmethods. We also show that our method can generalize well to real world tasks\nby showing state-of-the-art performance on recognizing humans assembling\nvarious IKEA furniture on the IKEA-ASM dataset.\n"}
+{"abstract": "  We introduce a new type of inversion-free feedforward hysteresis control with\nthe Preisach operator. The feedforward control has a high-gain integral loop\nstructure with the Preisach operator in negative feedback. This allows\nobtaining a dynamic quantity which corresponds to the inverse hysteresis\noutput, since the loop error tends towards zero for a sufficiently high\nfeedback gain. By analyzing the loop sensitivity function with hysteresis,\nwhich acts as a non-constant phase lag, we show the achievable bandwidth and\naccuracy of the proposed control. Remarkable fact is that the control bandwidth\nis theoretically infinite, provided the integral feedback loop with Preisach\noperator can be implemented with a smooth hysteresis output. Numerical control\nexamples with the Preisach hysteresis model in differential form are shown and\ndiscussed in detail.\n"}
+{"abstract": "  One of the most suitable methods for modeling fully dynamic earthquake cycle\nsimulations is the spectral boundary integral element method (sBIEM), which\ntakes advantage of the fast Fourier transform (FFT) to make a complex numerical\ndynamic rupture tractable. However, this method has the serious drawback of\nrequiring a flat fault geometry due to the FFT approach. Here we present an\nanalytical formulation that extends the sBIEM to a mildly non-planar fault. We\nstart from a regularized boundary element method and apply a small-slope\napproximation of the fault geometry. Making this assumption, it is possible to\nshow that the main effect of non-planar fault geometry is to change the normal\ntraction along the fault, which is controlled by the local curvature along the\nfault. We then convert this space--time boundary integral equation of the\nnormal traction into a spectral-time formulation and incorporate this change in\nnormal traction into the existing sBIEM methodology. This approach allows us to\nmodel fully dynamic seismic cycle simulations on non-planar faults in a\nparticularly efficient way. We then test this method against a regular boundary\nintegral element method for both rough-fault and seamount fault geometries, and\ndemonstrate that this sBIEM maintains the scaling between the fault geometry\nand slip distribution.\n"}
+{"abstract": "  Field-effect transistors made of wide-bandgap semiconductors can operate at\nhigh voltages, temperatures and frequencies with low energy losses, and have\nbeen of increasing importance in power and high-frequency electronics. However,\nthe poor performance of p-channel transistors compared with that of n-channel\ntransistors has constrained the production of energy-efficient complimentary\ncircuits with integrated n- and p-channel transistors. The p-type surface\nconductivity of hydrogen-terminated diamond offers great potential for solving\nthis problem, but surface transfer doping, which is commonly believed to be\nessential for generating the conductivity, limits the performance of\ntransistors made of hydrogen-terminated diamond because it requires the\npresence of ionized surface acceptors, which cause hole scattering. Here, we\nreport on fabrication of a p-channel wide-bandgap heterojunction field-effect\ntransistor consisting of a hydrogen-terminated diamond channel and hexagonal\nboron nitride ($h$-BN) gate insulator, without relying on surface transfer\ndoping. Despite its reduced density of surface acceptors, the transistor has\nthe lowest sheet resistance ($1.4$ k$\\Omega$) and largest on-current ($1600$\n$\\mu$m mA mm$^{-1}$) among p-channel wide-bandgap transistors, owing to the\nhighest hole mobility (room-temperature Hall mobility: $680$\ncm$^2$V$^{-1}$s$^{-1}$). Importantly, the transistor also shows normally-off\nbehavior, with a high on/off ratio exceeding $10^8$. These characteristics are\nsuited for low-loss switching and can be explained on the basis of standard\ntransport and transistor models. This new approach to making diamond\ntransistors paves the way to future wide-bandgap semiconductor electronics.\n"}
+{"abstract": "  A plasmon is a collective excitation of electrons due to the Coulomb\ninteraction. Both plasmons and single-particle excitations (SPEs) are\neigenstates of bulk metallic systems and they are orthogonal to each other.\nHowever, in non-translationally symmetric systems such as nanostructures,\nplasmons and SPEs coherently interact. It has been well discussed that the\nplasmons and SPEs, respectively, can couple with transverse (T) electric field\nin such systems, and also that they are coupled with each other via\nlongitudinal (L) field. However, there has been a missing link in the previous\nstudies: the coherent coupling between the plasmons and SPEs mediated by the T\nfield. Herein, we develop a theoretical framework to describe the\nself-consistent relationship between plasmons and SPEs through both the L and T\nfields. The excitations are described in terms of the charge and current\ndensities in a constitutive equation with a nonlocal susceptibility, where the\ndensities include the L and T components. The electromagnetic fields\noriginating from the densities are described in terms of the Green's function\nin the Maxwell equations. The T field is generated from both densities, whereas\nthe L component is attributed to the charge density only. We introduce a\nfour-vector representation incorporating the vector and scalar potentials in\nthe Coulomb gauge, in which the T and L fields are separated explicitly. The\neigenvalues of the matrix for the self-consistent equations appear as the poles\nof the system excitations. The developed formulation enables to approach\nunknown mechanisms for enhancement of the coherent coupling between plasmons\nand the hot carriers generated by radiative fields.\n"}
+{"abstract": "  Lin and Wang defined a model of random walks on knot diagrams and interprete\nthe Alexnader polynomials and the colored Jones polynomials as Ihara zeta\nfunctions, i.e. zeta functions defined by counting cycles on the knot diagram.\nUsing this explanation, they gave a more conceptual proof for the Melvin-Morton\nconjecture. In this paper, we give an analogous zeta function expression for\nthe twisted Alexander invariants.\n"}
+{"abstract": "  Recent achievements in depth prediction from a single RGB image have powered\nthe new research area of combining convolutional neural networks (CNNs) with\nclassical simultaneous localization and mapping (SLAM) algorithms. The depth\nprediction from a CNN provides a reasonable initial point in the optimization\nprocess in the traditional SLAM algorithms, while the SLAM algorithms further\nimprove the CNN prediction online. However, most of the current CNN-SLAM\napproaches have only taken advantage of the depth prediction but not yet other\nproducts from a CNN. In this work, we explore the use of the outlier mask, a\nby-product from unsupervised learning of depth from video, as a prior in a\nclassical probability model for depth estimate fusion to step up the\noutlier-resistant tracking performance of a SLAM front-end. On the other hand,\nsome of the previous CNN-SLAM work builds on feature-based sparse SLAM methods,\nwasting the per-pixel dense prediction from a CNN. In contrast to these sparse\nmethods, we devise a dense CNN-assisted SLAM front-end that is implementable\nwith TensorFlow and evaluate it on both indoor and outdoor datasets.\n"}
+{"abstract": "  The proposed in J. Math. Phys. v.57,071903 (2016) analytical expansion of\nmonotone (contractive) Riemannian metrics (called also quantum Fisher\ninformation(s)) in terms of moments of the dynamical structure factor (DSF)\nrelative to an original intensive observable is reconsidered and extended. The\nnew approach through the DSF which characterizes fully the set of monotone\nRiemannian metrics on the space of Gibbs thermal states is utilized to obtain\nan extension of the spectral presentation obtained for the Bogoliubov-Kubo-Mori\nmetric (the generalized isothermal susceptibility) on the entire class of\nmonotone Riemannian metrics. The obtained spectral presentation is the main\npoint of our consideration. The last allows to present the one to one\ncorrespondence between monotone Riemannian metrics and operator monotone\nfunctions (which is a statement of the Petz theorem in the quantum information\ntheory) in terms of the linear response theory. We show that monotone\nRiemannian metrics can be determined from the analysis of the infinite chain of\nequations of motion of the retarded Green's functions. Inequalities between the\ndifferent metrics have been obtained as well. It is a demonstration that the\nanalysis of information-theoretic problems has benefited from concepts of\nstatistical mechanics and might cross-fertilize or extend both directions, and\nvice versa. We illustrate the presented approach on the calculation of the\nentire class of monotone (contractive) Riemannian metrics on the examples of\nsome simple but instructive systems employed in various physical problems.\n"}
+{"abstract": "  Mobile apps are increasingly relying on high-throughput and low-latency\ncontent delivery, while the available bandwidth on wireless access links is\ninherently time-varying. The handoffs between base stations and access modes\ndue to user mobility present additional challenges to deliver a high level of\nuser Quality-of-Experience (QoE). The ability to predict the available\nbandwidth and the upcoming handoffs will give applications valuable leeway to\nmake proactive adjustments to avoid significant QoE degradation. In this paper,\nwe explore the possibility and accuracy of realtime mobile bandwidth and\nhandoff predictions in 4G/LTE and 5G networks. Towards this goal, we collect\nlong consecutive traces with rich bandwidth, channel, and context information\nfrom public transportation systems. We develop Recurrent Neural Network models\nto mine the temporal patterns of bandwidth evolution in fixed-route mobility\nscenarios. Our models consistently outperform the conventional univariate and\nmultivariate bandwidth prediction models. For 4G \\& 5G co-existing networks, we\npropose a new problem of handoff prediction between 4G and 5G, which is\nimportant for low-latency applications like self-driving strategy in realistic\n5G scenarios. We develop classification and regression based prediction models,\nwhich achieve more than 80\\% accuracy in predicting 4G and 5G handoffs in a\nrecent 5G dataset.\n"}
+{"abstract": "  We verify the leading order term in the asymptotic expansion conjecture of\nthe relative Reshetikhin-Turaev invariants proposed in \\cite{WY4} for all pairs\n$(M,L)$ satisfying the properties that $M\\setminus L$ is homeomorphic to some\nfundamental shadow link complement and the 3-manifold $M$ is obtained by doing\nrational Dehn filling on some boundary components of the fundamental shadow\nlink complement, under the assumptions that the denominator of the surgery\ncoefficients are odd and the cone angles are sufficiently small. In particular,\nthe asymptotics of the invariants captures the complex volume and the twisted\nReidemeister torsion of the manifold $M\\setminus L$ associated with the\nhyperbolic cone structure determined by the sequence of colorings of the framed\nlink $L$.\n"}
+{"abstract": "  A Leavitt labelled path algebra over a commutative unital ring is associated\nwith a labelled space, generalizing Leavitt path algebras associated with\ngraphs and ultragraphs as well as torsion-free commutative algebras generated\nby idempotents. We show that Leavitt labelled path algebras can be realized as\npartial skew group rings, Steinberg algebras, and Cuntz-Pimsner algebras. Via\nthese realizations we obtain generalized uniqueness theorems, a description of\ndiagonal preserving isomorphisms and we characterize simplicity of Leavitt\nlabelled path algebras. In addition, we prove that a large class of partial\nskew group rings can be realized as Leavitt labelled path algebras.\n"}
+{"abstract": "  We establish the first tight lower bound of $\\Omega(\\log\\log\\kappa)$ on the\nquery complexity of sampling from the class of strongly log-concave and\nlog-smooth distributions with condition number $\\kappa$ in one dimension.\nWhereas existing guarantees for MCMC-based algorithms scale polynomially in\n$\\kappa$, we introduce a novel algorithm based on rejection sampling that\ncloses this doubly exponential gap.\n"}
+{"abstract": "  In this paper, we consider gradient-type methods for convex positively\nhomogeneous optimization problems with relative accuracy. An analogue of the\naccelerated universal gradient-type method for positively homogeneous\noptimization problems with relative accuracy is investigated. The second\napproach is related to subgradient methods with B. T. Polyak stepsize. Result\non the linear convergence rate for some methods of this type with adaptive step\nadjustment is obtained for some class of non-smooth problems. Some\ngeneralization to a special class of non-convex non-smooth problems is also\nconsidered.\n"}
+{"abstract": "  We develop a deep convolutional neural networks(CNNs) to deal with the blurry\nartifacts caused by the defocus of the camera using dual-pixel images.\nSpecifically, we develop a double attention network which consists of\nattentional encoders, triple locals and global local modules to effectively\nextract useful information from each image in the dual-pixels and select the\nuseful information from each image and synthesize the final output image. We\ndemonstrate the effectiveness of the proposed deblurring algorithm in terms of\nboth qualitative and quantitative aspects by evaluating on the test set in the\nNTIRE 2021 Defocus Deblurring using Dual-pixel Images Challenge. The code, and\ntrained models are available at https://github.com/tuvovan/ATTSF.\n"}
+{"abstract": "  As hardware architectures are evolving in the push towards exascale,\ndeveloping Computational Science and Engineering (CSE) applications depend on\nperformance portable approaches for sustainable software development. This\npaper describes one aspect of performance portability with respect to\ndeveloping a portable library of kernels that serve the needs of several CSE\napplications and software frameworks. We describe Kokkos Kernels, a library of\nkernels for sparse linear algebra, dense linear algebra and graph kernels. We\ndescribe the design principles of such a library and demonstrate portable\nperformance of the library using some selected kernels. Specifically, we\ndemonstrate the performance of four sparse kernels, three dense batched\nkernels, two graph kernels and one team level algorithm.\n"}
+{"abstract": "  Recent breakthroughs of Neural Architecture Search (NAS) extend the field's\nresearch scope towards a broader range of vision tasks and more diversified\nsearch spaces. While existing NAS methods mostly design architectures on a\nsingle task, algorithms that look beyond single-task search are surging to\npursue a more efficient and universal solution across various tasks. Many of\nthem leverage transfer learning and seek to preserve, reuse, and refine network\ndesign knowledge to achieve higher efficiency in future tasks. However, the\nenormous computational cost and experiment complexity of cross-task NAS are\nimposing barriers for valuable research in this direction. Existing NAS\nbenchmarks all focus on one type of vision task, i.e., classification. In this\nwork, we propose TransNAS-Bench-101, a benchmark dataset containing network\nperformance across seven tasks, covering classification, regression,\npixel-level prediction, and self-supervised tasks. This diversity provides\nopportunities to transfer NAS methods among tasks and allows for more complex\ntransfer schemes to evolve. We explore two fundamentally different types of\nsearch space: cell-level search space and macro-level search space. With 7,352\nbackbones evaluated on seven tasks, 51,464 trained models with detailed\ntraining information are provided. With TransNAS-Bench-101, we hope to\nencourage the advent of exceptional NAS algorithms that raise cross-task search\nefficiency and generalizability to the next level. Our dataset file will be\navailable at Mindspore, VEGA.\n"}
+{"abstract": "  We consider analytic functions from a reproducing kernel Hilbert space. Given\nthat such a function is of order $\\epsilon$ on a set of discrete data points,\nrelative to its global size, we ask how large can it be at a fixed point\noutside of the data set. We obtain optimal bounds on this error of analytic\ncontinuation and describe its asymptotic behavior in $\\epsilon$. We also\ndescribe the maximizer function attaining the optimal error in terms of the\nresolvent of a positive semidefinite, self-adjoint and finite rank operator.\n"}
+{"abstract": "  In offline reinforcement learning, a policy needs to be learned from a single\npre-collected dataset. Typically, policies are thus regularized during training\nto behave similarly to the data generating policy, by adding a penalty based on\na divergence between action distributions of generating and trained policy. We\npropose a new algorithm, which constrains the policy directly in its weight\nspace instead, and demonstrate its effectiveness in experiments.\n"}
+{"abstract": "  We study the entanglement between soft and hard particles produced in generic\nscattering processes in QED. The reduced density matrix for the hard particles,\nobtained via tracing over the entire spectrum of soft photons, is shown to have\na large eigenvalue, which governs the behavior of the Renyi entropies and of\nthe non-analytic part of the entanglement entropy at low orders in perturbation\ntheory. The leading perturbative entanglement entropy is logarithmically IR\ndivergent. The coefficient of the IR divergence exhibits certain universality\nproperties, irrespectively of the dressing of the asymptotic charged particles\nand the detailed properties of the initial state. In a certain kinematical\nlimit, the coefficient is proportional to the cusp anomalous dimension in QED.\nFor Fock basis computations associated with two-electron scattering, we derive\nan exact expression for the large eigenvalue of the density matrix in terms of\nhard scattering amplitudes, which is valid at any finite order in perturbation\ntheory. As a result, the IR logarithmic divergences appearing in the\nexpressions for the Renyi and entanglement entropies persist at any finite\norder of the perturbative expansion. To all orders, however, the IR logarithmic\ndivergences exponentiate, rendering the large eigenvalue of the density matrix\nIR finite. The all-orders Renyi entropies (per unit time, per particle flux),\nwhich are shown to be proportional to the total inclusive cross-section in the\ninitial state, are also free of IR divergences. The entanglement entropy, on\nthe other hand, retains non-analytic, logarithmic behavior with respect to the\nsize of the box (which provides the IR cutoff) even to all orders in\nperturbation theory.\n"}
+{"abstract": "  We introduce a tamed exponential time integrator which exploits linear terms\nin both the drift and diffusion for Stochastic Differential Equations (SDEs)\nwith a one sided globally Lipschitz drift term. Strong convergence of the\nproposed scheme is proved, exploiting the boundedness of the geometric Brownian\nmotion (GBM) and we establish order 1 convergence for linear diffusion terms.\nIn our implementation we illustrate the efficiency of the proposed scheme\ncompared to existing fixed step methods and utilize it in an adaptive time\nstepping scheme. Furthermore we extend the method to nonlinear diffusion terms\nand show it remains competitive. The efficiency of these GBM based approaches\nare illustrated by considering some well-known SDE models.\n"}
+{"abstract": "  Language models like BERT and SpanBERT pretrained on open-domain data have\nobtained impressive gains on various NLP tasks. In this paper, we probe the\neffectiveness of domain-adaptive pretraining objectives on downstream tasks. In\nparticular, three objectives, including a novel objective focusing on modeling\npredicate-argument relations, are evaluated on two challenging dialogue\nunderstanding tasks. Experimental results demonstrate that domain-adaptive\npretraining with proper objectives can significantly improve the performance of\na strong baseline on these tasks, achieving the new state-of-the-art\nperformances.\n"}
+{"abstract": "  Studies evaluating bikeability usually compute spatial indicators shaping\ncycling conditions and conflate them in a quantitative index. Much research\ninvolves site visits or conventional geospatial approaches, and few studies\nhave leveraged street view imagery (SVI) for conducting virtual audits. These\nhave assessed a limited range of aspects, and not all have been automated using\ncomputer vision (CV). Furthermore, studies have not yet zeroed in on gauging\nthe usability of these technologies thoroughly. We investigate, with\nexperiments at a fine spatial scale and across multiple geographies (Singapore\nand Tokyo), whether we can use SVI and CV to assess bikeability\ncomprehensively. Extending related work, we develop an exhaustive index of\nbikeability composed of 34 indicators. The results suggest that SVI and CV are\nadequate to evaluate bikeability in cities comprehensively. As they\noutperformed non-SVI counterparts by a wide margin, SVI indicators are also\nfound to be superior in assessing urban bikeability, and potentially can be\nused independently, replacing traditional techniques. However, the paper\nexposes some limitations, suggesting that the best way forward is combining\nboth SVI and non-SVI approaches. The new bikeability index presents a\ncontribution in transportation and urban analytics, and it is scalable to\nassess cycling appeal widely.\n"}
+{"abstract": "  Using hydrodynamical simulations, we study how well the underlying\ngravitational potential of a galaxy cluster can be modelled dynamically with\ndifferent types of tracers. In order to segregate different systematics and the\neffects of varying estimator performances, we first focus on applying a generic\nminimal assumption method (oPDF) to model the simulated haloes using the full\n6-D phasespace information. We show that the halo mass and concentration can be\nrecovered in an ensemble unbiased way, with a stochastic bias that varies from\nhalo to halo, mostly reflecting deviations from steady state in the tracer\ndistribution. The typical systematic uncertainty is $\\sim 0.17$ dex in the\nvirial mass and $\\sim 0.17$ dex in the concentration as well when dark matter\nparticles are used as tracers. The dynamical state of satellite galaxies are\nclose to that of dark matter particles, while intracluster stars are less in a\nsteady state, resulting in a $\\sim$ 0.26 dex systematic uncertainty in mass.\nCompared with galactic haloes hosting Milky-Way-like galaxies, cluster haloes\nshow a larger stochastic bias in the recovered mass profiles. We also test the\naccuracy of using intracluster gas as a dynamical tracer modelled through a\ngeneralised hydrostatic equilibrium equation, and find a comparable systematic\nuncertainty in the estimated mass to that using dark matter. Lastly, we\ndemonstrate that our conclusions are largely applicable to other steady-state\ndynamical models including the spherical Jeans equation, by quantitatively\nsegregating their statistical efficiencies and robustness to systematics. We\nalso estimate the limiting number of tracers that leads to the\nsystematics-dominated regime in each case.\n"}
+{"abstract": "  In this paper, a novel intelligent reflecting surface (IRS)-assisted wireless\npowered communication network (WPCN) architecture is proposed for\npower-constrained Internet-of-Things (IoT) smart devices, where IRS is\nexploited to improve the performance of WPCN under imperfect channel state\ninformation (CSI). We formulate a hybrid access point (HAP) transmit energy\nminimization problem by jointly optimizing time allocation, HAP energy\nbeamforming, receiving beamforming, user transmit power allocation, IRS energy\nreflection coefficient and information reflection coefficient under the\nimperfect CSI and non-linear energy harvesting model. On account of the high\ncoupling of optimization variables, the formulated problem is a non-convex\noptimization problem that is difficult to solve directly. To address the\nabove-mentioned challenging problem, alternating optimization (AO) technique is\napplied to decouple the optimization variables to solve the problem.\nSpecifically, through AO, time allocation, HAP energy beamforming, receiving\nbeamforming, user transmit power allocation, IRS energy reflection coefficient\nand information reflection coefficient are divided into three sub-problems to\nbe solved alternately. The difference-of-convex (DC) programming is used to\nsolve the non-convex rank-one constraint in solving IRS energy reflection\ncoefficient and information reflection coefficient. Numerical simulations\nverify the superiority of the proposed optimization algorithm in decreasing HAP\ntransmit energy compared with other benchmark schemes.\n"}
+{"abstract": "  Ranking the participants of a tournament has applications in voting, paired\ncomparisons analysis, sports and other domains. In this paper we introduce\nbipartite tournaments, which model situations in which two different kinds of\nentity compete indirectly via matches against players of the opposite kind;\nexamples include education (students/exam questions) and solo sports\n(golfers/courses). In particular, we look to find rankings via chain graphs,\nwhich correspond to bipartite tournaments in which the sets of adversaries\ndefeated by the players on one side are nested with respect to set inclusion.\nTournaments of this form have a natural and appealing ranking associated with\nthem. We apply chain editing -- finding the minimum number of edge changes\nrequired to form a chain graph -- as a new mechanism for tournament ranking.\nThe properties of these rankings are investigated in a probabilistic setting,\nwhere they arise as maximum likelihood estimators, and through the axiomatic\nmethod of social choice theory. Despite some nice properties, two problems\nremain: an important anonymity axiom is violated, and chain editing is NP-hard.\nWe address both issues by relaxing the minimisation constraint in chain\nediting, and characterise the resulting ranking methods via a greedy\napproximation algorithm.\n"}
+{"abstract": "  Studies on the interplay between the charge order and the $d$-wave\nsuperconductivity in the copper-oxide high $T_{\\rm c}$ superconductors are\nreviewed with a special emphasis on the exploration based on the unconventional\nconcept of the electron fractionalization and its consequences supported by\nsolutions of high-accuracy quantum many-body solvers. Severe competitions\nbetween the superconducting states and the charge inhomogeneity including the\ncharge/spin striped states revealed by the quantum many-body solvers are first\naddressed for the Hubbard models and then for the {\\it ab initio} Hamiltonians\nof the cuprates derived without adjustable parameters to represent the\nlow-energy physics of the cuprates. The charge inhomogeneity and\nsuperconductivity are born out of the same mother, namely, the carrier\nattraction arising from the strong Coulomb repulsion near the Mott insulator\n(Mottness) and accompanied electron fractionalization. The same mother makes\nthe severe competition of the two brothers inevitable. The electron\nfractionalization has a remarkable consequences on the mechanism of the\nsuperconductivity. Recent explorations motivated by the concept of the\nfractionalization and their consequences on experimental observations in\nenergy-momentum resolved spectroscopic measurements including the angle\nresolved photoemission spectroscopy (ARPES) and the resonant inelastic X-ray\nspectroscopy (RIXS) are overviewed, with future vision for the integrated\nspectroscopy to challenge the long-standing difficulties in the cuprates as\nwell as in other strongly correlated matter in general.\n"}
+{"abstract": "  In recent years, speech processing algorithms have seen tremendous progress\nprimarily due to the deep learning renaissance. This is especially true for\nspeech separation where the time-domain audio separation network (TasNet) has\nled to significant improvements. However, for the related task of\nsingle-speaker speech enhancement, which is of obvious importance, it is yet\nunknown, if the TasNet architecture is equally successful. In this paper, we\nshow that TasNet improves state-of-the-art also for speech enhancement, and\nthat the largest gains are achieved for modulated noise sources such as speech.\nFurthermore, we show that TasNet learns an efficient inner-domain\nrepresentation, where target and noise signal components are highly separable.\nThis is especially true for noise in terms of interfering speech signals, which\nmight explain why TasNet performs so well on the separation task. Additionally,\nwe show that TasNet performs poorly for large frame hops and conjecture that\naliasing might be the main cause of this performance drop. Finally, we show\nthat TasNet consistently outperforms a state-of-the-art single-speaker speech\nenhancement system.\n"}
+{"abstract": "  We show that any a-priori possible entropy value is realized by an ergodic\nIRS, in free groups and in SL2(Z). This is in stark contrast to what may happen\nin SLn(Z) for n>2, where only the trivial entropy values can be realized by\nergodic IRSs.\n"}
+{"abstract": "  Intense laser-plasma interactions are an essential tool for the laboratory\nstudy of ion acceleration at a collisionless shock. With two-dimensional\nparticle-in-cell calculations of a multicomponent plasma we observe two\nelectrostatic collisionless shocks at two distinct longitudinal positions when\ndriven with a linearly-polarized laser at normalized laser vector potential a0\nthat exceeds 10. Moreover, these shocks, associated with protons and carbon\nions, show a power-law dependence on a0 and accelerate ions to different\nvelocities in an expanding upstream with higher flux than in a single-component\nhydrogen or carbon plasma. This results from an electrostatic ion two-stream\ninstability caused by differences in the charge-to-mass ratio of different\nions. Particle acceleration in collisionless shocks in multicomponent plasma\nare ubiquitous in space and astrophysics, and these calculations identify the\npossibility for studying these complex processes in the laboratory.\n"}
+{"abstract": "  Computer science has grown rapidly since its inception in the 1950s and the\npioneers in the field are celebrated annually by the A.M. Turing Award. In this\npaper, we attempt to shed light on the path to influential computer scientists\nby examining the characteristics of the 72 Turing Award laureates. To achieve\nthis goal, we build a comprehensive dataset of the Turing Award laureates and\nanalyze their characteristics, including their personal information, family\nbackground, academic background, and industry experience. The FP-Growth\nalgorithm is used for frequent feature mining. Logistic regression plot, pie\nchart, word cloud and map are generated accordingly for each of the interesting\nfeatures to uncover insights regarding personal factors that drive influential\nwork in the field of computer science. In particular, we show that the Turing\nAward laureates are most commonly white, male, married, United States citizen,\nand received a PhD degree. Our results also show that the age at which the\nlaureate won the award increases over the years; most of the Turing Award\nlaureates did not major in computer science; birth order is strongly related to\nthe winners' success; and the number of citations is not as important as one\nwould expect.\n"}
+{"abstract": "  Online misinformation is a prevalent societal issue, with adversaries relying\non tools ranging from cheap fakes to sophisticated deep fakes. We are motivated\nby the threat scenario where an image is used out of context to support a\ncertain narrative. While some prior datasets for detecting image-text\ninconsistency generate samples via text manipulation, we propose a dataset\nwhere both image and text are unmanipulated but mismatched. We introduce\nseveral strategies for automatically retrieving convincing images for a given\ncaption, capturing cases with inconsistent entities or semantic context. Our\nlarge-scale automatically generated NewsCLIPpings Dataset: (1) demonstrates\nthat machine-driven image repurposing is now a realistic threat, and (2)\nprovides samples that represent challenging instances of mismatch between text\nand image in news that are able to mislead humans. We benchmark several\nstate-of-the-art multimodal models on our dataset and analyze their performance\nacross different pretraining domains and visual backbones.\n"}
+{"abstract": "  We review the equation of state of QCD matter at finite densities. We discuss\nthe construction of the equation of state with net baryon number, electric\ncharge, and strangeness using the results of lattice QCD simulations and hadron\nresonance gas models. Its application to the hydrodynamic analyses of\nrelativistic nuclear collisions suggests that the interplay of multiple\nconserved charges is important in the quantitative understanding of the dense\nnuclear matter created at lower beam energies. Several different models of the\nQCD equation of state are discussed for comparison.\n"}
+{"abstract": "  The existence of two novel hybrid two-dimensional (2D) monolayers, 2D B3C2P3\nand 2D B2C4P2, has been predicted based on the density functional theory\ncalculations. It has been shown that these materials possess structural and\nthermodynamic stability. 2D B3C2P3 is a moderate band gap semiconductor, while\n2D B2C4P2 is a zero band gap semiconductor. It has also been shown that 2D\nB3C2P3 has a highly tunable band gap under the effect of strain and substrate\nengineering. Moreover, 2D B3C2P3 produces low barriers for dissociation of\nwater and hydrogen molecules on its surface, and shows fast recovery after\ndesorption of the molecules. The novel materials can be fabricated by carbon\ndoping of boron phosphide, and directly by arc discharge and laser ablation and\nvaporization. Applications of 2D B3C2P3 in renewable energy and straintronic\nnanodevices have been proposed.\n"}
+{"abstract": "  The generalization performance of a machine learning algorithm such as a\nneural network depends in a non-trivial way on the structure of the data\ndistribution. To analyze the influence of data structure on test loss dynamics,\nwe study an exactly solveable model of stochastic gradient descent (SGD) on\nmean square loss which predicts test loss when training on features with\narbitrary covariance structure. We solve the theory exactly for both Gaussian\nfeatures and arbitrary features and we show that the simpler Gaussian model\naccurately predicts test loss of nonlinear random-feature models and deep\nneural networks trained with SGD on real datasets such as MNIST and CIFAR-10.\nWe show that the optimal batch size at a fixed compute budget is typically\nsmall and depends on the feature correlation structure, demonstrating the\ncomputational benefits of SGD with small batch sizes. Lastly, we extend our\ntheory to the more usual setting of stochastic gradient descent on a fixed\nsubsampled training set, showing that both training and test error can be\naccurately predicted in our framework on real data.\n"}
+{"abstract": "  As high-performance organic semiconductors, {\\pi}-conjugated polymers have\nattracted much attention due to their charming advantages including low-cost,\nsolution processability, mechanical flexibility, and tunable optoelectronic\nproperties. During the past several decades, the great advances have been made\nin polymers-based OFETs with p-type, n-type or even ambipolar characterics.\nThrough chemical modification and alignment optimization, lots of conjugated\npolymers exhibited superior mobilities, and some mobilities are even larger\nthan 10 cm2 V-1 s-1 in OFETs, which makes them very promising for the\napplications in organic electronic devices. This review describes the recent\nprogress of the high performance polymers used in OFETs from the aspects of\nmolecular design and assembly strategy. Furthermore, the current challenges and\noutlook in the design and development of conjugated polymers are also\nmentioned.\n"}
+{"abstract": "  We consider convex, black-box objective functions with additive or\nmultiplicative noise with a high-dimensional parameter space and a data space\nof lower dimension, where gradients of the map exist, but may be inaccessible.\nWe investigate Derivative-Free Optimization (DFO) in this setting and propose a\nnovel method, Active STARS (ASTARS), based on STARS (Chen and Wild, 2015) and\ndimension reduction in parameter space via Active Subspace (AS) methods\n(Constantine, 2015). STARS hyperparmeters are inversely proportional to the\nknown dimension of parameter space, resulting in heavy smoothing and small step\nsizes for large dimensions. When possible, ASTARS leverages a lower-dimensional\nAS, defining a set of directions in parameter space causing the majority of the\nvariance in function values. ASTARS iterates are updated with steps only taken\nin the AS, reducing the value of the objective function more efficiently than\nSTARS, which updates iterates in the full parameter space. Computational costs\nmay be reduced further by learning ASTARS hyperparameters and the AS, reducing\nthe total evaluations of the objective function and eliminating the requirement\nthat the user specify hyperparameters, which may be unknown in our setting. We\ncall this method Fully Automated ASTARS (FAASTARS). We show that STARS and\nASTARS will both converge -- with a certain complexity -- even with inexact,\nestimated hyperparemters. We also find that FAASTARS converges with the use of\nestimated AS's and hyperparameters. We explore the effectiveness of ASTARS and\nFAASTARS in numerical examples which compare ASTARS and FAASTARS to STARS.\n"}
+{"abstract": "  While several non-pharmacological measures have been implemented for a few\nmonths in an effort to slow the coronavirus disease (COVID-19) pandemic in the\nUnited States, the disease remains a danger in a number of counties as\nrestrictions are lifted to revive the economy. Making a trade-off between\neconomic recovery and infection control is a major challenge confronting many\nhard-hit counties. Understanding the transmission process and quantifying the\ncosts of local policies are essential to the task of tackling this challenge.\nHere, we investigate the dynamic contact patterns of the populations from\nanonymized, geo-localized mobility data and census and demographic data to\ncreate data-driven, agent-based contact networks. We then simulate the epidemic\nspread with a time-varying contagion model in ten large metropolitan counties\nin the United States and evaluate a combination of mobility reduction, mask\nuse, and reopening policies. We find that our model captures the\nspatial-temporal and heterogeneous case trajectory within various counties\nbased on dynamic population behaviors. Our results show that a decision-making\ntool that considers both economic cost and infection outcomes of policies can\nbe informative in making decisions of local containment strategies for optimal\nbalancing of economic slowdown and virus spread.\n"}
+{"abstract": "  We propose a comprehensive field-based semianalytical method for designing\nfabrication-ready multifunctional periodic metasurfaces (MSs). Harnessing\nrecent work on multielement metagratings based on capacitively-loaded strips,\nwe have extended our previous meta-atom design formulation to generate\nrealistic substrate-supported printed-circuit-board layouts for anomalous\nrefraction MSs. Subsequently, we apply a greedy algorithm for iteratively\noptimizing individual scatterers across the entire macroperiod to achieve\nmultiple design goals for corresponding multiple incidence angles with a single\nMS structure. As verified with commercial solvers, the proposed semianalytical\nscheme, properly accounting for near-field coupling between the various\nscatterers, can reliably produce highly efficient multifunctional MSs on\ndemand, without requiring time-consuming full-wave optimization.\n"}
+{"abstract": "  We consider the quasi-one-dimensional (quasi-1D) model of a sonic black hole\nin a dilute Bose-Einstein condensate. It is shown that an accurate treatment of\nthe dimensional reduction to quasi-1D leads to a finite condensate quantum\ndepletion even for axially infinite systems, and an intrinsic nonthermality of\nthe black hole radiation spectrum. By calculating the depletion, we derive a\n{\\em first-order} many-body signature of the sonic horizon, represented by a\ndistinct peak in the depletion power spectrum. This peak constitutes a readily\nexperimentally accessible tool to detect quantum sonic horizons even when a\nnegligible Hawking radiation flux is created by the black hole.\n"}
+{"abstract": "  The study of strong-lensing systems conventionally involves constructing a\nmass distribution that can reproduce the observed multiply-imaging properties.\nSuch mass reconstructions are generically non-unique. Here, we present an\nalternative strategy: instead of modelling the mass distribution, we search\ncosmological galaxy-formation simulations for plausible matches. In this paper\nwe test the idea on seven well-studied lenses from the SLACS survey. For each\nof these, we first pre-select a few hundred galaxies from the EAGLE\nsimulations, using the expected Einstein radius as an initial criterion. Then,\nfor each of these pre-selected galaxies, we fit for the source light\ndistribution, while using MCMC for the placement and orientation of the lensing\ngalaxy, so as to reproduce the multiple images and arcs. The results indicate\nthat the strategy is feasible, and even yields relative posterior probabilities\nof two different galaxy-formation scenarios, though these are not statistically\nsignificant yet. Extensions to other observables, such as kinematics and\ncolours of the stellar population in the lensing galaxy, is straightforward in\nprinciple, though we have not attempted it yet. Scaling to arbitrarily large\nnumbers of lenses also appears feasible. This will be especially relevant for\nupcoming wide-field surveys, through which the number of galaxy lenses will\nrise possibly a hundredfold, which will overwhelm conventional modelling\nmethods.\n"}
+{"abstract": "  In supervised learning for medical image analysis, sample selection\nmethodologies are fundamental to attain optimum system performance promptly and\nwith minimal expert interactions (e.g. label querying in an active learning\nsetup). In this paper we propose a novel sample selection methodology based on\ndeep features leveraging information contained in interpretability saliency\nmaps. In the absence of ground truth labels for informative samples, we use a\nnovel self supervised learning based approach for training a classifier that\nlearns to identify the most informative sample in a given batch of images. We\ndemonstrate the benefits of the proposed approach, termed\nInterpretability-Driven Sample Selection (IDEAL), in an active learning setup\naimed at lung disease classification and histopathology image segmentation. We\nanalyze three different approaches to determine sample informativeness from\ninterpretability saliency maps: (i) an observational model stemming from\nfindings on previous uncertainty-based sample selection approaches, (ii) a\nradiomics-based model, and (iii) a novel data-driven self-supervised approach.\nWe compare IDEAL to other baselines using the publicly available NIH chest\nX-ray dataset for lung disease classification, and a public histopathology\nsegmentation dataset (GLaS), demonstrating the potential of using\ninterpretability information for sample selection in active learning systems.\nResults show our proposed self supervised approach outperforms other approaches\nin selecting informative samples leading to state of the art performance with\nfewer samples.\n"}
+{"abstract": "  In order to remain competitive, Internet companies collect and analyse user\ndata for the purpose of improving user experiences. Frequency estimation is a\nwidely used statistical tool which could potentially conflict with the relevant\nprivacy regulations. Privacy preserving analytic methods based on differential\nprivacy have been proposed, which either require a large user base or a trusted\nserver; hence may give big companies an unfair advantage while handicapping\nsmaller organizations in their growth opportunity. To address this issue, this\npaper proposes a fair privacy-preserving sampling-based frequency estimation\nmethod and provides a relation between its privacy guarantee, output accuracy,\nand number of participants. We designed decentralized privacy-preserving\naggregation mechanisms using multi-party computation technique and established\nthat, for a limited number of participants and a fixed privacy level, our\nmechanisms perform better than those that are based on traditional perturbation\nmethods; hence, provide smaller companies a fair growth opportunity. We further\npropose an architectural model to support weighted aggregation in order to\nachieve higher accuracy estimate to cater for users with different privacy\nrequirements. Compared to the unweighted aggregation, our method provides a\nmore accurate estimate. Extensive experiments are conducted to show the\neffectiveness of the proposed methods.\n"}
+{"abstract": "  We study directions along which the norms of vectors are preserved under a\nlinear map. In particular, we find families of matrices for which these\ndirections are determined by integer vectors. We consider the two-dimensional\ncase in detail, and also discuss the extension to the three-dimensional case.\n"}
+{"abstract": "  Currently, soil structure interaction in energy piles has not been understood\nthoroughly. One of the important underlying features is the effect of tip and\nhead restraints on displacement, strain and stress in energy piles. This study\nhas investigated thermo-mechanical response of energy piles subjected to\ndifferent end restraints by using recently found analytical solutions, thus\nproviding a fundamental, rational, mechanics-based understanding. End\nrestraints are found to have a substantial effect on thermo-mechanical response\nof energy piles, especially on thermal axial displacement and axial stress in\nthe pile. Head restraint imposed by interaction of an energy pile with the\nsuperstructure led to a decrease in the magnitude of head displacement and\nincrease in axial stress, while decreasing the axial strain. The impact of head\nrestraint was more pronounced in end bearing than in fully floating energy\npiles.\n"}
+{"abstract": "  We model the 21cm power spectrum across the Cosmic Dawn and the Epoch of\nReionization (EoR) in fuzzy dark matter (FDM) cosmologies. The suppression of\nsmall mass halos in FDM models leads to a delay in the onset redshift of these\nepochs relative to cold dark matter (CDM) scenarios. This strongly impacts the\n21cm power spectrum and its redshift evolution. The 21cm power spectrum at a\ngiven stage of the EoR/Cosmic Dawn process is also modified: in general, the\namplitude of 21cm fluctuations is boosted by the enhanced bias factor of galaxy\nhosting halos in FDM. We forecast the prospects for discriminating between CDM\nand FDM with upcoming power spectrum measurements from HERA, accounting for\ndegeneracies between astrophysical parameters and dark matter properties. If\nFDM constitutes the entirety of the dark matter and the FDM particle mass is\n10-21eV, HERA can determine the mass to within 20 percent at 2-sigma\nconfidence.\n"}
+{"abstract": "  We evaluate in closed form several series involving products of Cauchy\nnumbers with other special numbers (harmonic, skew-harmonic, hyperharmonic, and\ncentral binomial). Similar results are obtained with series involving Stirling\nnumbers of the first kind. We focus on several particular cases which give new\nclosed forms for Euler sums of hyperharmonic numbers and products of\nhyperharmonic and harmonic numbers.\n"}
+{"abstract": "  Anisotropic outgassing from comets exerts a torque sufficient to rapidly\nchange the angular momentum of the nucleus, potentially leading to rotational\ninstability. Here, we use empirical measures of spin changes in a sample of\ncomets to characterize the torques and to compare them with expectations from a\nsimple model. Both the data and the model show that the characteristic spin-up\ntimescale, $\\tau_s$, is a strong function of nucleus radius, $r_n$.\nEmpirically, we find that the timescale for comets (most with perihelion 1 to 2\nAU and eccentricity $\\sim$0.5) varies as $\\tau_s \\sim 100 r_n^{2}$, where $r_n$\nis expressed in kilometers and $\\tau_s$ is in years. The fraction of the\nnucleus surface that is active varies as $f_A \\sim 0.1 r_n^{-2}$. We find that\nthe median value of the dimensionless moment arm of the torque is $k_T$ = 0.007\n(i.e. $\\sim$0.7\\% of the escaping momentum torques the nucleus), with weak\n($<$3$\\sigma$) evidence for a size dependence $k_T \\sim 10^{-3} r_n^2$.\nSub-kilometer nuclei have spin-up timescales comparable to their orbital\nperiods, confirming that outgassing torques are quickly capable of driving\nsmall nuclei towards rotational disruption. Torque-induced rotational\ninstability likely accounts for the paucity of sub-kilometer short-period\ncometary nuclei, and for the pre-perihelion destruction of sungrazing comets.\nTorques from sustained outgassing on small active asteroids can rival YORP\ntorques, even for very small ($\\lesssim$1 g s$^{-1}$) mass loss rates. Finally,\nwe highlight the important role played by observational biases in the measured\ndistributions of $\\tau_s$, $f_A$ and $k_T$.\n"}
+{"abstract": "  We study the stochastic bilinear minimax optimization problem, presenting an\nanalysis of the same-sample Stochastic ExtraGradient (SEG) method with constant\nstep size, and presenting variations of the method that yield favorable\nconvergence. In sharp contrasts with the basic SEG method whose last iterate\nonly contracts to a fixed neighborhood of the Nash equilibrium, SEG augmented\nwith iteration averaging provably converges to the Nash equilibrium under the\nsame standard settings, and such a rate is further improved by incorporating a\nscheduled restarting procedure. In the interpolation setting where noise\nvanishes at the Nash equilibrium, we achieve an optimal convergence rate up to\ntight constants. We present numerical experiments that validate our theoretical\nfindings and demonstrate the effectiveness of the SEG method when equipped with\niteration averaging and restarting.\n"}
+{"abstract": "  The polarization properties of the elastic electron scattering on H-like ions\nare investigated within the framework of the relativistic QED theory. The\npolarization properties are determined by a combination of relativistic effects\nand spin exchange between the incident and bound electrons. The scattering of a\npolarized electron on an initially unpolarized ion is fully described by five\nparameters. We study these parameters for non-resonant scattering, as well as\nin the vicinity of LL resonances, where scattering occurs through the formation\nand subsequent decay of intermediate autoionizing states. The study was carried\nout for ions from $\\txt{B}^{4+}$ to $\\txt{Xe}^{53+}$. Special attention was\npaid to the study of asymmetry in electron scattering.\n"}
+{"abstract": "  Dynamical quantum phase transitions (DQPTs), which refer to the criticality\nin time of a quantum many-body system, have attracted much theoretical and\nexperimental research interest recently. Despite DQPTs are defined and\nsignalled by the non-analyticities in the Loschmidt rate, its interrelation\nwith various correlation measures such as the equilibrium order parameters of\nthe system remains unclear. In this work, by considering the quench dynamics in\nan interacting topological model, we find that the equilibrium order parameters\nof the model in general exhibit signatures around the DQPT, in the short time\nregime. The first extrema of the equilibrium order parameters are connected to\nthe first Loschmidt rate peak. By studying the unequal-time two-point\ncorrelation, we also find that the correlation between the nearest neighbors\ndecays while that with neighbors further away builds up as time grows in the\nnon-interacting case, and upon the addition of repulsive intra-cell\ninteractions. On the other hand, the inter-cell interaction tends to suppress\nthe two-site correlations. These findings could provide us insights into the\ncharacteristic of the system around DQPTs, and pave the way to a better\nunderstanding of the dynamics in non-equilibrium quantum many-body systems.\n"}
+{"abstract": "  Ensemble learning methods are designed to benefit from multiple learning\nalgorithms for better predictive performance. The tradeoff of this improved\nperformance is slower speed and larger size of ensemble learning systems\ncompared to single learning systems. In this paper, we present a novel approach\nto deal with this problem in Random Forest (RF) as one of the most powerful\nensemble methods. The method is based on crossbreeding of the best tree\nbranches to increase the performance of RF in space and speed while keeping the\nperformance in the classification measures. The proposed approach has been\ntested on a group of synthetic and real datasets and compared to the standard\nRF approach. Several evaluations have been conducted to determine the effects\nof the Crossbred RF (CRF) on the accuracy and the number of trees in a forest.\nThe results show better performance of CRF compared to RF.\n"}
+{"abstract": "  The structure of finite self-assembling systems depends sensitively on the\nnumber of constituent building blocks. Recently, it was demonstrated that hard\nsphere-like colloidal particles show a magic number effect when confined in\nspherical emulsion droplets. Geometric construction rules permit a few dozen\nmagic numbers that correspond to a discrete series of completely filled\nconcentric icosahedral shells. Here, we investigate the free energy landscape\nof these colloidal clusters as a function of the number of their constituent\nbuilding blocks for system sizes up to several thousand particles. We find that\nminima in the free energy landscape, arising from the presence of filled,\nconcentric shells, are significantly broadened. In contrast to their atomic\nanalogues, colloidal clusters in spherical confinement can flexibly accommodate\nexcess colloids by ordering icosahedrally in the cluster center while changing\nthe structure near the cluster surface. In-between these magic number regions,\nthe building blocks cannot arrange into filled shells. Instead, we observe that\ndefects accumulate in a single wedge and therefore only affect a few\ntetrahedral grains of the cluster. We predict the existence of this wedge by\nsimulation and confirm its presence in experiment using electron tomography.\nThe introduction of the wedge minimizes the free energy penalty by confining\ndefects to small regions within the cluster. In addition, the remaining ordered\ntetrahedral grains can relax internal strain by breaking icosahedral symmetry.\nOur findings demonstrate how multiple defect mechanisms collude to form the\ncomplex free energy landscape of hard sphere-like colloidal clusters.\n"}
+{"abstract": "  We prove the sufficient conditions for convergence of a certain iterative\nprocess of order 2 for solving nonlinear functional equations, which does not\nrequire inverting the derivative. We translate and detail our results for a\nsystem of nonlinear equations, and apply it for some numerical example which\nillustrates our theorems.\n"}
+{"abstract": "  Percolation transition (PT) means the formation of a macroscopic-scale large\ncluster, which exhibits a continuous transition. However, when the growth of\nlarge clusters is globally suppressed, the type of PT is changed to a\ndiscontinuous transition for random networks. A question arises as to whether\nthe type of PT is also changed for scale-free (SF) network, because the\nexistence of hubs incites the formation of a giant cluster. Here, we apply a\nglobal suppression rule to the static model for SF networks, and investigate\nproperties of the PT. We find that even for SF networks with the degree\nexponent $2 < \\lambda <3$, a hybrid PT occurs at a finite transition point\n$t_c$, which we can control by the suppression strength. The order parameter\njumps at $t_c^-$ and exhibits a critical behavior at $t_c^+$.\n"}
+{"abstract": "  Clemm and Trebat-Leder (2014) proved that the number of quadratic number\nfields with absolute discriminant bounded by $x$ over which there exist\nelliptic curves with good reduction everywhere and rational $j$-invariant is\n$\\gg x\\log^{-1/2}(x)$. In this paper, we assume the $abc$-conjecture to show\nthe sharp asymptotic $\\sim cx\\log^{-1/2}(x)$ for this number, obtaining\nformulae for $c$ in both the real and imaginary cases. Our method has three\ningredients:\n  (1) We make progress towards a conjecture of Granville: Given a fixed\nelliptic curve $E/\\mathbb{Q}$ with short Weierstrass equation $y^2 = f(x)$ for\nreducible $f \\in \\mathbb{Z}[x]$, we show that the number of integers $d$, $|d|\n\\leq D$, for which the quadratic twist $dy^2 = f(x)$ has an integral\nnon-$2$-torsion point is at most $D^{2/3+o(1)}$, assuming the $abc$-conjecture.\n  (2) We apply the Selberg--Delange method to obtain a Tauberian theorem which\nallows us to count integers satisfying certain congruences while also being\ndivisible only by certain primes.\n  (3) We show that for a polynomially sparse subset of the natural numbers, the\nnumber of pairs of elements with least common multiple at most $x$ is\n$O(x^{1-\\epsilon})$ for some $\\epsilon > 0$. We also exhibit a matching lower\nbound.\n  If instead of the $abc$-conjecture we assume a particular tail bound, we can\nprove all the aforementioned results and that the coefficient $c$ above is\ngreater in the real quadratic case than in the imaginary quadratic case, in\nagreement with an experimentally observed bias.\n"}
+{"abstract": "  A key challenge towards the goal of multi-part assembly tasks is finding\nrobust sensorimotor control methods in the presence of uncertainty. In contrast\nto previous works that rely on a priori knowledge on whether two parts match,\nwe aim to learn this through physical interaction. We propose a hierarchical\napproach that enables a robot to autonomously assemble parts while being\nuncertain about part types and positions. In particular, our probabilistic\napproach learns a set of differentiable filters that leverage the tactile\nsensorimotor trace from failed assembly attempts to update its belief about\npart position and type. This enables a robot to overcome assembly failure. We\ndemonstrate the effectiveness of our approach on a set of object fitting tasks.\nThe experimental results indicate that our proposed approach achieves higher\nprecision in object position and type estimation, and accomplishes object\nfitting tasks faster than baselines.\n"}
+{"abstract": "  The pairing of two electrons on a Fermi surface due to an infinitesimal\nattraction between them always results in a superconducting instability at zero\ntemperature ($T=0$). The equivalent question of pairing instability on a\nLuttinger surface (LS) -- a contour of zeros of the propagator -- instead leads\nto a quantum critical point (QCP) that separates a non-Fermi liquid (NFL) and\nsuperconductor. A surprising and little understood aspect of pair fluctuations\nat this QCP is that their thermodynamics maps to that of the Sachdev-Ye-Kitaev\n(SYK) model in the strong coupling limit. Here, we offer a simple justification\nfor this mapping by demonstrating that (i) LS models share the\nreparametrization symmetry of the $q\\rightarrow \\infty$ SYK model with $q$-body\ninteractions \\textcolor{black}{close to the LS}, and (ii) the enforcement of\ngauge invariance results in a $\\frac{1}{\\sqrt{\\tau}}$ ($\\tau\\sim T^{-1}$)\nbehavior of the fluctuation propagator near the QCP, as is a feature of the\nfundamental SYK fermion.\n"}
+{"abstract": "  We independently determine the zero-point offset of the Gaia early Data\nRelease-3 (EDR3) parallaxes based on $\\sim 110,000$ W Ursae Majoris (EW)-type\neclipsing binary systems. EWs cover almost the entire sky and are characterized\nby a relatively complete coverage in magnitude and color. They are an excellent\nproxy for Galactic main-sequence stars. We derive a $W1$-band Period-Luminosity\nrelation with a distance accuracy of $7.4\\%$, which we use to anchor the Gaia\nparallax zero-point. The final, global parallax offsets are $-28.6\\pm0.6$\n$\\mu$as and $-25.4\\pm4.0$ $\\mu$as (before correction) and $4.2\\pm0.5$ $\\mu$as\nand $4.6\\pm3.7$ $\\mu$as (after correction) for the five- and six-parameter\nsolutions, respectively. The total systematic uncertainty is $1.8$ $\\mu$as. The\nspatial distribution of the parallax offsets shows that the bias in the\ncorrected Gaia EDR3 parallaxes is less than 10 $\\mu$as across $40\\%$ of the\nsky. Only $15\\%$ of the sky is characterized by a parallax offset greater than\n30 $\\mu$as. Thus, we have provided independent evidence that the parallax\nzero-point correction provided by the Gaia team significantly reduces the\nprevailing bias. Combined with literature data, we find that the overall Gaia\nEDR3 parallax offsets for Galactic stars are $[-20, -30]$ $\\mu$as and 4-10\n$\\mu$as, respectively, before and after correction. For specific regions, an\nadditional deviation of about 10 $\\mu$as is found.\n"}
+{"abstract": "  The lattice Boltzmann method (LBM) has recently emerged as an efficient\nalternative to classical Navier-Stokes solvers. This is particularly true for\nhemodynamics in complex geometries. However, in its most basic formulation,\n{i.e.} with the so-called single relaxation time (SRT) collision operator, it\nhas been observed to have a limited stability domain in the Courant/Fourier\nspace, strongly constraining the minimum time-step and grid size. The\ndevelopment of improved collision models such as the multiple relaxation time\n(MRT) operator in central moments space has tremendously widened the stability\ndomain, while allowing to overcome a number of other well-documented artifacts,\ntherefore opening the door for simulations over a wider range of grid and\ntime-step sizes. The present work focuses on implementing and validating a\nspecific collision operator, the central Hermite moments multiple relaxation\ntime model with the full expansion of the equilibrium distribution function, to\nsimulate blood flows in intracranial aneurysms. The study further proceeds with\na validation of the numerical model through different test-cases and against\nexperimental measurements obtained via stereoscopic particle image velocimetry\n(PIV) and phase-contrast magnetic resonance imaging (PC-MRI). For a\npatient-specific aneurysm both PIV and PC-MRI agree fairly well with the\nsimulation. Finally, low-resolution simulations were shown to be able to\ncapture blood flow information with sufficient accuracy, as demonstrated\nthrough both qualitative and quantitative analysis of the flow field {while\nleading to strongly reduced computation times. For instance in the case of the\npatient-specific configuration, increasing the grid-size by a factor of two led\nto a reduction of computation time by a factor of 14 with very good similarity\nindices still ranging from 0.83 to 0.88.}\n"}
+{"abstract": "  We consider correlations, $p_{n,x}$, arising from measuring a maximally\nentangled state using $n$ measurements with two outcomes each, constructed from\n$n$ projections that add up to $xI$. We show that the correlations $p_{n,x}$\nrobustly self-test the underlying states and measurements. To achieve this, we\nlift the group-theoretic Gowers-Hatami based approach for proving robust\nself-tests to a more natural algebraic framework. A key step is to obtain an\nanalogue of the Gowers-Hatami theorem allowing to perturb an \"approximate\"\nrepresentation of the relevant algebra to an exact one.\n  For $n=4$, the correlations $p_{n,x}$ self-test the maximally entangled state\nof every odd dimension as well as 2-outcome projective measurements of\narbitrarily high rank. The only other family of constant-sized self-tests for\nstrategies of unbounded dimension is due to Fu (QIP 2020) who presents such\nself-tests for an infinite family of maximally entangled states with even local\ndimension. Therefore, we are the first to exhibit a constant-sized self-test\nfor measurements of unbounded dimension as well as all maximally entangled\nstates with odd local dimension.\n"}
+{"abstract": "  Strong lensing of gravitational waves (GWs) is attracting growing attention\nof the community. The event rates of lensed GWs by galaxies were predicted in\nnumerous papers, which used some approximations to evaluate the GW strains\ndetectable by a single detector. The joint-detection of GW signals by a network\nof instruments will increase the detecting ability of fainter and farther GW\nsignals, which could increase the detection rate of the lensed GWs, especially\nfor the 3rd generation detectors, e.g., Einstein Telescope (ET) and Cosmic\nExplorer (CE). Moreover, realistic GW templates will improve the accuracy of\nthe prediction. In this work, we consider the detection of galaxy-scale lensed\nGW events under the 2nd, 2.5th, and 3rd generation detectors with the network\nscenarios and adopt the realistic templates to simulate GW signals. Our\nforecast is based on the Monte Carlo technique which enables us to take Earth's\nrotation into consideration. We find that the overall detection rate is\nimproved, especially for the 3rd generation detector scenarios. More precisely,\nit increases by ~37% adopting realistic templates, and under network detection\nstrategy, further increases by ~58% comparing with adoption of the realistic\ntemplates, and we estimate that the 3rd generation GW detectors will detect\nhundreds lensed events per year. The effect from the Earth's rotation is\nweakened in the detector network strategy.\n"}
+{"abstract": "  Multi-head attention has each of the attention heads collect salient\ninformation from different parts of an input sequence, making it a powerful\nmechanism for sequence modeling. Multilingual and multi-domain learning are\ncommon scenarios for sequence modeling, where the key challenge is to maximize\npositive transfer and mitigate negative transfer across languages and domains.\nIn this paper, we find that non-selective attention sharing is sub-optimal for\nachieving good generalization across all languages and domains. We further\npropose attention sharing strategies to facilitate parameter sharing and\nspecialization in multilingual and multi-domain sequence modeling. Our approach\nautomatically learns shared and specialized attention heads for different\nlanguages and domains to mitigate their interference. Evaluated in various\ntasks including speech recognition, text-to-text and speech-to-text\ntranslation, the proposed attention sharing strategies consistently bring gains\nto sequence models built upon multi-head attention. For speech-to-text\ntranslation, our approach yields an average of $+2.0$ BLEU over $13$ language\ndirections in multilingual setting and $+2.0$ BLEU over $3$ domains in\nmulti-domain setting.\n"}
+{"abstract": "  We give a presentation in terms of generators and relations of the cohomology\nin degree zero of the Campos-Willwacher graph complexes associated to compact\norientable surfaces of genus $g$. The results carry a natural Lie algebra\nstructure, and for $g=1$ we recover Enriquez' elliptic\nGrothendieck-Teichm\\\"uller Lie algebra. In analogy to Willwacher's theorem\nrelating Kontsevich's graph complex to Drinfeld's Grothendieck-Teichm\\\"uller\nLie algebra, we call the results higher genus Grothendieck-Teichm\\\"uller Lie\nalgebras. Moreover, we find that the graph cohomology vanishes in negative\ndegrees.\n"}
+{"abstract": "  Social recommendation aims to fuse social links with user-item interactions\nto alleviate the cold-start problem for rating prediction. Recent developments\nof Graph Neural Networks (GNNs) motivate endeavors to design GNN-based social\nrecommendation frameworks to aggregate both social and user-item interaction\ninformation simultaneously. However, most existing methods neglect the social\ninconsistency problem, which intuitively suggests that social links are not\nnecessarily consistent with the rating prediction process. Social inconsistency\ncan be observed from both context-level and relation-level. Therefore, we\nintend to empower the GNN model with the ability to tackle the social\ninconsistency problem. We propose to sample consistent neighbors by relating\nsampling probability with consistency scores between neighbors. Besides, we\nemploy the relation attention mechanism to assign consistent relations with\nhigh importance factors for aggregation. Experiments on two real-world datasets\nverify the model effectiveness.\n"}
+{"abstract": "  The steering dynamics of re-configurable intelligent surfaces (RIS) have\nhoisted them to the front row of technologies that can be exploited to solve\nskip-zones in wireless communication systems. They can enable a programmable\nwireless environment, turning it into a partially deterministic space that\nplays an active role in determining how wireless signals propagate. However,\nRIS-based communication systems' practical implementation may face challenges\nsuch as noise generated by the RIS structure. Besides, the transmitted signal\nmay face a double-fading effect over the two portions of the channel. This\narticle tackles this double-fading problem in near-terrestrial free-space\noptical (nT-FSO) communication systems using a RIS module based upon\nliquid-crystal (LC) on silicon (LCoS). A doped LC layer can directly amplify a\nlight when placed in an external field. Leveraging on this capacity of a doped\nLC, we mitigate the double-attenuation faced by the transmitted signal. We\nfirst revisit the nT-FSO power loss scenario, then discuss the direct-light\namplification, and consider the system performance. Results show that at 51\ndegrees of the incoming light incidence angle, the proposed LCoS design has\nminimal RIS depth, implying less LC's material. The performance results show\nthat the number of bit per unit bandwidth is upper-bounded and grows with the\nratio of the sub-links distances. Finally, we present and discuss open issues\nto enable new research opportunities towards the use of RIS and amplifying-RIS\nin nT-FSO systems.\n"}
+{"abstract": "  In this paper, we propose a novel iterative encoding algorithm for DNA\nstorage to satisfy both the GC balance and run-length constraints using a\ngreedy algorithm. DNA strands with run-length more than three and the GC\nbalance ratio far from 50\\% are known to be prone to errors. The proposed\nencoding algorithm stores data at high information density with high\nflexibility of run-length at most $m$ and GC balance between $0.5\\pm\\alpha$ for\narbitrary $m$ and $\\alpha$. More importantly, we propose a novel mapping method\nto reduce the average bit error compared to the randomly generated mapping\nmethod, using a greedy algorithm. The proposed algorithm is implemented through\niterative encoding, consisting of three main steps: randomization, M-ary\nmapping, and verification. The proposed algorithm has an information density of\n1.8523 bits/nt in the case of $m=3$ and $\\alpha=0.05$. Also, the proposed\nalgorithm is robust to error propagation, since the average bit error caused by\nthe one nt error is 2.3455 bits, which is reduced by $20.5\\%$, compared to the\nrandomized mapping.\n"}
+{"abstract": "  Decentralized vehicle-to-everything (V2X) networks (i.e., Mode-4 C-V2X and\nMode 2a NR-V2X), rely on periodic Basic Safety Messages (BSMs) to disseminate\ntime-sensitive information (e.g., vehicle position) and has the potential to\nimprove on-road safety. For BSM scheduling, decentralized V2X networks utilize\nsensing-based semi-persistent scheduling (SPS), where vehicles sense radio\nresources and select suitable resources for BSM transmissions at prespecified\nperiodic intervals termed as Resource Reservation Interval (RRI). In this\npaper, we show that such a BSM scheduling (with a fixed RRI) suffers from\nsevere under- and over- utilization of radio resources under varying vehicle\ntraffic scenarios; which severely compromises timely dissemination of BSMs,\nwhich in turn leads to increased collision risks. To address this, we extend\nSPS to accommodate an adaptive RRI, termed as SPS++. Specifically, SPS++ allows\neach vehicle -- (i) to dynamically adjust RRI based on the channel resource\navailability (by accounting for various vehicle traffic scenarios), and then,\n(ii) select suitable transmission opportunities for timely BSM transmissions at\nthe chosen RRI. Our experiments based on Mode-4 C-V2X standard implemented\nusing the ns-3 simulator show that SPS++ outperforms SPS by at least $50\\%$ in\nterms of improved on-road safety performance, in all considered simulation\nscenarios.\n"}
+{"abstract": "  Current gate-based quantum computers have the potential to provide a\ncomputational advantage if algorithms use quantum hardware efficiently. To make\ncombinatorial optimization more efficient, we introduce the Filtering\nVariational Quantum Eigensolver (F-VQE) which utilizes filtering operators to\nachieve faster and more reliable convergence to the optimal solution.\nAdditionally we explore the use of causal cones to reduce the number of qubits\nrequired on a quantum computer. Using random weighted MaxCut problems, we\nnumerically analyze our methods and show that they perform better than the\noriginal VQE algorithm and the Quantum Approximate Optimization Algorithm\n(QAOA). We also demonstrate the experimental feasibility of our algorithms on a\nHoneywell trapped-ion quantum processor.\n"}
+{"abstract": "  We study whether receiving advice from either a human or algorithmic advisor,\naccompanied by five types of Local and Global explanation labelings, has an\neffect on the readiness to adopt, willingness to pay, and trust in a financial\nAI consultant. We compare the differences over time and in various key\nsituations using a unique experimental framework where participants play a\nweb-based game with real monetary consequences. We observed that accuracy-based\nexplanations of the model in initial phases leads to higher adoption rates.\nWhen the performance of the model is immaculate, there is less importance\nassociated with the kind of explanation for adoption. Using more elaborate\nfeature-based or accuracy-based explanations helps substantially in reducing\nthe adoption drop upon model failure. Furthermore, using an autopilot increases\nadoption significantly. Participants assigned to the AI-labeled advice with\nexplanations were willing to pay more for the advice than the AI-labeled advice\nwith a No-explanation alternative. These results add to the literature on the\nimportance of XAI for algorithmic adoption and trust.\n"}
+{"abstract": "  The network-based model of social contagion has revolved around information\non local interactions; its central focus has been on network topological\nproperties shaping the local interactions and, ultimately, social contagion\noutcomes. We extend this approach by introducing information on the global\nstate, or global information, into the network-based model and analyzing how it\nalters social contagion dynamics in six different classes of networks: a\ntwo-dimensional square lattice, small-world networks, Erd\\H{o}s-R\\'{e}nyi\nnetworks, regular random networks, Holme-Kim networks, and Barab\\'{a}si-Albert\nnetworks. We find that there is an optimal amount of global information that\nminimizes the time to reach global cascades in highly clustered networks. We\nalso find that global information prolongs the time to hit the tipping point\nbut substantially compresses the time to reach global cascades after then, so\nthat the overall time to reach global cascades can even be shortened under\ncertain conditions. Finally, we show that random links substitute for global\ninformation in regulating the social contagion dynamics.\n"}
+{"abstract": "  This paper provides a numerical framework for computing the achievable rate\nregion of memoryless multiple access channel (MAC) with a continuous alphabet\nfrom data. In particular, we use recent results on variational lower bounds on\nmutual information and KL-divergence to compute the boundaries of the rate\nregion of MAC using a set of functions parameterized by neural networks. Our\nmethod relies on a variational lower bound on KL-divergence and an upper bound\non KL-divergence based on the f-divergence inequalities. Unlike previous work,\nwhich computes an estimate on mutual information, which is neither a lower nor\nan upper bound, our method estimates a lower bound on mutual information. Our\nnumerical results show that the proposed method provides tighter estimates\ncompared to the MINE-based estimator at large SNRs while being computationally\nmore efficient. Finally, we apply the proposed method to the optical intensity\nMAC and obtain a new achievable rate boundary tighter than prior works.\n"}
+{"abstract": "  Measuring the electrophoretic mobility of molecules is a powerful\nexperimental approach for investigating biomolecular processes. A frequent\nchallenge in the context of single-particle measurements is throughput,\nlimiting the obtainable statistics. Here, we present a molecular force sensor\nand charge detector based on parallelised imaging and tracking of tethered\ndouble-stranded DNA functionalised with charged nanoparticles interacting with\nan externally applied electric field. Tracking the position of the tethered\nparticle with simultaneous nanometre precision and microsecond temporal\nresolution allows us to detect and quantify electrophoretic forces down to the\nsub-piconewton scale. Furthermore, we demonstrate that this approach is capable\nof detecting changes to the particle charge state, as induced by the addition\nof charged biomolecules or changes to pH. Our approach provides an alternative\nroute to studying structural and charge dynamics at the single-molecule level.\n"}
+{"abstract": "  One of the main challenges in real-world reinforcement learning is to learn\nsuccessfully from limited training samples. We show that in certain settings,\nthe available data can be dramatically increased through a form of multi-task\nlearning, by exploiting an invariance property in the tasks. We provide a\ntheoretical performance bound for the gain in sample efficiency under this\nsetting. This motivates a new approach to multi-task learning, which involves\nthe design of an appropriate neural network architecture and a prioritized\ntask-sampling strategy. We demonstrate empirically the effectiveness of the\nproposed approach on two real-world sequential resource allocation tasks where\nthis invariance property occurs: financial portfolio optimization and meta\nfederated learning.\n"}
+{"abstract": "  Graphs are a common model for complex relational data such as social networks\nand protein interactions, and such data can evolve over time (e.g., new\nfriendships) and be noisy (e.g., unmeasured interactions). Link prediction aims\nto predict future edges or infer missing edges in the graph, and has diverse\napplications in recommender systems, experimental design, and complex systems.\nEven though link prediction algorithms strongly depend on the set of edges in\nthe graph, existing approaches typically do not modify the graph topology to\nimprove performance. Here, we demonstrate how simply adding a set of edges,\nwhich we call a \\emph{proposal set}, to the graph as a pre-processing step can\nimprove the performance of several link prediction algorithms. The underlying\nidea is that if the edges in the proposal set generally align with the\nstructure of the graph, link prediction algorithms are further guided towards\npredicting the right edges; in other words, adding a proposal set of edges is a\nsignal-boosting pre-processing step. We show how to use existing link\nprediction algorithms to generate effective proposal sets and evaluate this\napproach on various synthetic and empirical datasets. We find that proposal\nsets meaningfully improve the accuracy of link prediction algorithms based on\nboth neighborhood heuristics and graph neural networks. Code is available at\n\\url{https://github.com/CUAI/Edge-Proposal-Sets}.\n"}
+{"abstract": "  The TianQin space Gravitational Waves (GW) observatory will contain 3\ngeocentric and circularly orbiting spacecraft with an orbital radius of 10^5\nkm, to detect the GW in the milli-hertz frequency band. Each spacecraft pair\nwill establish a 1.7*10^5 km-long laser interferometer immersed in the solar\nwind and the magnetospheric plasmas to measure the phase deviations induced by\nthe GW. GW detection requires a high-precision measurement of the laser phase.\nThe cumulative effects of the long distance and the periodic oscillations of\nthe plasma density may induce an additional phase noise. This paper aims to\nmodel the plasma induced phase deviation of the inter-spacecraft laser signals,\nusing a realistic orbit simulator and the Space Weather Modeling Framework\n(SWMF) model. Preliminary results show that the plasma density oscillation can\ninduce the phase deviations close to 2*10^-6 rad/Hz^1/2 or 0.3pm/Hz^1/2 in the\nmilli-hertz frequency band and it is within the error budget assigned to the\ndisplacement noise of the interferometry. The amplitude spectrum density of\nphases along three arms become more separated when the orbital plane is\nparallel to the Sun-Earth line or during a magnetic storm. Finally, the\ndependence of the phase deviations on the orbital radius is examined.\n"}
+{"abstract": "  We elaborate on the role of higher-derivative curvature invariants as a\nquantum selection mechanism of regular spacetimes in the framework of the\nLorentzian path integral approach to quantum gravity. We show that for a large\nclass of black hole metrics prominently regular there are higher-derivative\ncurvature invariants which are singular. If such terms are included in the\naction, according to the finite action principle applied to a higher-derivative\ngravity model, not only singular spacetimes but also some of the regular ones\ndo not seem to contribute to the path integral.\n"}
+{"abstract": "  This note provides a variational description of the mechanical effects of\nflexural stiffening of a 2D plate glued to an elastic-brittle or an\nelastic-plastic reinforcement. The reinforcement is assumed to be linear\nelastic outside possible free plastic yield lines or free crack. Explicit Euler\nequations and a compliance identity are shown for the reinforcement of a 1D\nbeam.\n"}
+{"abstract": "  The generation of mean flows is a long-standing issue in rotating fluids.\nMotivated by planetary objects, we consider here a rapidly rotating\nfluid-filled spheroid, which is subject to weak perturbations of either the\nboundary (e.g. tides) or the rotation vector (e.g. in direction by precession,\nor in magnitude by longitudinal librations). Using boundary-layer theory, we\ndetermine the mean zonal flows generated by nonlinear interactions within the\nviscous Ekman layer. These flows are of interest because they survive in the\nrelevant planetary regime of both vanishing forcings and viscous effects. We\nextend the theory to take into account (i) the combination of spatial and\ntemporal perturbations, providing new mechanically driven zonal flows (e.g.\ndriven by latitudinal librations), and (ii) the spheroidal geometry relevant\nfor planetary bodies. Wherever possible, our analytical predictions are\nvalidated with direct numerical simulations. The theoretical solutions are in\ngood quantitative agreement with the simulations, with expected discrepancies\n(zonal jets) in the presence of inertial waves generated at the critical\nlatitudes (as for precession). Moreover, we find that the mean zonal flows can\nbe strongly affected in spheroids. Guided by planetary applications, we also\nrevisit the scaling laws for the geostrophic shear layers at the critical\nlatitudes, and the influence of a solid inner core.\n"}
+{"abstract": "  Assigning meaning to parts of image data is the goal of semantic image\nsegmentation. Machine learning methods, specifically supervised learning is\ncommonly used in a variety of tasks formulated as semantic segmentation. One of\nthe major challenges in the supervised learning approaches is expressing and\ncollecting the rich knowledge that experts have with respect to the meaning\npresent in the image data. Towards this, typically a fixed set of labels is\nspecified and experts are tasked with annotating the pixels, patches or\nsegments in the images with the given labels. In general, however, the set of\nclasses does not fully capture the rich semantic information present in the\nimages. For example, in medical imaging such as histology images, the different\nparts of cells could be grouped and sub-grouped based on the expertise of the\npathologist.\n  To achieve such a precise semantic representation of the concepts in the\nimage, we need access to the full depth of knowledge of the annotator. In this\nwork, we develop a novel approach to collect segmentation annotations from\nexperts based on psychometric testing. Our method consists of the psychometric\ntesting procedure, active query selection, query enhancement, and a deep metric\nlearning model to achieve a patch-level image embedding that allows for\nsemantic segmentation of images. We show the merits of our method with\nevaluation on the synthetically generated image, aerial image and histology\nimage.\n"}
+{"abstract": "  We report an infrared spectroscopy study of the axion topological insulator\ncandidate EuIn$_2$As$_2$ for which the Eu moments exhibit an A-type\nantiferromagnetic (AFM) order below $T_N \\simeq 18 \\mathrm{K}$. The low energy\nresponse is composed of a weak Drude peak at the origin, a pronounced\ninfrared-active phonon mode at 185 cm$^{-1}$ and a free carrier plasma edge\naround 600 cm$^{-1}$. The interband transitions start above 800 cm$^{-1}$ and\ngive rise to a series of weak absorption bands at 5\\,000 and 12\\,000 cm$^{-1}$\nand strong ones at 20\\,000, 27\\,500 and 32\\,000 cm$^{-1}$. The AFM transition\ngives rise to pronounced anomalies of the charge response in terms of a\ncusp-like maximum of the free carrier scattering rate around $T_N$ and large\nmagnetic splittings of the interband transitions at 5\\,000 and 12\\,000\ncm$^{-1}$. The phonon mode at 185 cm$^{-1}$ has also an anomalous temperature\ndependence around $T_N$ which suggests that it couples to the fluctuations of\nthe Eu spins. The combined data provide evidence for a strong interaction\namongst the charge, spin and lattice degrees of freedom.\n"}
+{"abstract": "  Backtracking of RNA polymerase (RNAP) is an important pausing mechanism\nduring DNA transcription that is part of the error correction process that\nenhances transcription fidelity. We model the backtracking mechanism of RNA\npolymerase, which usually happens when the polymerase tries to incorporate a\nmismatched nucleotide triphosphate. Previous models have made simplifying\nassumptions such as neglecting the trailing polymerase behind the backtracking\npolymerase or assuming that the trailing polymerase is stationary. We derive\nexact analytic solutions of a stochastic model that includes locally\ninteracting RNAPs by explicitly showing how a trailing RNAP influences the\nprobability that an error is corrected or incorporated by the leading\nbacktracking RNAP. We also provide two related methods for computing the mean\ntimes to error correction or incorporation given an initial local RNAP\nconfiguration.\n"}
+{"abstract": "  Let $G$ be any group and $k\\geq 1$ be an integer number. The ordered\nconfiguration set of $k$ points in $G$ is given by the subset\n$F(G,k)=\\{(g_1,\\ldots,g_k)\\in G\\times \\cdots\\times G: g_i\\neq g_j \\text{ for }\ni\\neq j\\}\\subset G^k$. In this work, we will study the configuration set\n$F(G,k)$ in algebraic terms as a subset of the product $G^k=G\\times\n\\cdots\\times G$. As we will see, we develop practical tools for dealing with\nthe configuration set of $k$ points in $G$, which, to our knowledge, can not be\nfound in literature.\n"}
+{"abstract": "  We propose Scale-aware AutoAug to learn data augmentation policies for object\ndetection. We define a new scale-aware search space, where both image- and\nbox-level augmentations are designed for maintaining scale invariance. Upon\nthis search space, we propose a new search metric, termed Pareto Scale Balance,\nto facilitate search with high efficiency. In experiments, Scale-aware AutoAug\nyields significant and consistent improvement on various object detectors\n(e.g., RetinaNet, Faster R-CNN, Mask R-CNN, and FCOS), even compared with\nstrong multi-scale training baselines. Our searched augmentation policies are\ntransferable to other datasets and box-level tasks beyond object detection\n(e.g., instance segmentation and keypoint estimation) to improve performance.\nThe search cost is much less than previous automated augmentation approaches\nfor object detection. It is notable that our searched policies have meaningful\npatterns, which intuitively provide valuable insight for human data\naugmentation design. Code and models will be available at\nhttps://github.com/Jia-Research-Lab/SA-AutoAug.\n"}
+{"abstract": "  With the incoming 5G network, the ubiquitous Internet of Things (IoT) devices\ncan benefit our daily life, such as smart cameras, drones, etc. With the\nintroduction of the millimeter-wave band and the thriving number of IoT\ndevices, it is critical to design new dynamic spectrum access (DSA) system to\ncoordinate the spectrum allocation across massive devices in 5G. In this paper,\nwe present Hermes, the first decentralized DSA system for massive devices\ndeployment. Specifically, we propose an efficient multi-agent reinforcement\nlearning algorithm and introduce a novel shuffle mechanism, addressing the\ndrawbacks of collision and fairness in existing decentralized systems. We\nimplement Hermes in 5G network via simulations. Extensive evaluations show that\nHermes significantly reduces collisions and improves fairness compared to the\nstate-of-the-art decentralized methods. Furthermore, Hermes is able to adapt\nthe environmental changes within 0.5 seconds, showing its deployment\npracticability in dynamic environment of 5G.\n"}
+{"abstract": "  We report the first-ever calculation of the isovector flavor combination of\nthe chiral-odd twist-3 parton distribution $h_L(x)$ for the proton from lattice\nQCD. We employ gauge configurations with two degenerate light, a strange and a\ncharm quark ($N_f=2+1+1$) of maximally twisted mass fermions with a clover\nimprovement. The lattice has a spatial extent of 3 fm and lattice spacing of\n0.093 fm. The values of the quark masses lead to a pion mass of $260$ MeV. We\nuse a source-sink time separation of 1.12 fm to control contamination from\nexcited states. Our calculation is based on the quasi-distribution approach,\nwith three values for the proton momentum: 0.83 GeV, 1.25 GeV, and 1.67 GeV.\nThe lattice data are renormalized non-perturbatively using the RI$'$ scheme,\nand the final result for $h_L(x)$ is presented in the $\\overline{\\rm MS}$\nscheme at the scale of 2 GeV. Furthermore, we compute in the same setup the\ntransversity distribution, $h_1(x)$, which allows us, in particular, to compare\n$h_L(x)$ to its Wandzura-Wilczek approximation. We also combine results for the\nisovector and isoscalar flavor combinations to disentangle the individual quark\ncontributions for $h_1(x)$ and $h_L(x)$, and address the Wandzura-Wilczek\napproximation in that case as well.\n"}
+{"abstract": "  We present an adaptation of the NPA hierarchy to the setting of synchronous\ncorrelation matrices. Our adaptation improves upon the original NPA hierarchy\nby using smaller certificates and fewer constraints, although it can only be\napplied to certify synchronous correlations. We recover characterizations for\nthe sets of synchronous quantum commuting and synchronous quantum correlations.\nFor applications, we show that the existence of symmetric informationally\ncomplete positive operator-valued measures and maximal sets of mutually\nunbiased bases can be verified or invalidated with only two certificates of our\nadapted NPA hierarchy.\n"}
+{"abstract": "  Nonlinear phononics relies on the resonant optical excitation of\ninfrared-active lattice vibrations to coherently induce targeted structural\ndeformations in solids. This form of dynamical crystal-structure design has\nbeen applied to control the functional properties of many interesting systems,\nincluding magneto-resistive manganites, magnetic materials, superconductors,\nand ferroelectrics. However, phononics has so far been restricted to protocols\nin which structural deformations occur locally within the optically excited\nvolume, sometimes resulting in unwanted heating. Here, we extend nonlinear\nphononics to propagating polaritons, effectively separating in space the\noptical drive from the functional response. Mid-infrared optical pulses are\nused to resonantly drive an 18 THz phonon at the surface of ferroelectric\nLiNbO3. A time-resolved stimulated Raman scattering probe reveals that the\nferroelectric polarization is reduced over the entire 50 micron depth of the\nsample, far beyond the ~ micron depth of the evanescent phonon field. We\nattribute the bulk response of the ferroelectric polarization to the excitation\nof a propagating 2.5 THz soft-mode phonon-polariton. For the highest excitation\namplitudes, we reach a regime in which the polarization is reversed. In this\nthis non-perturbative regime, we expect that the polariton model evolves into\nthat of a solitonic domain wall that propagates from the surface into the\nmaterials at near the speed of light.\n"}
+{"abstract": "  About 5-8% of individuals over the age of 60 have dementia. With our\never-aging population this number is likely to increase, making dementia one of\nthe most important threats to public health in the 21st century. Given the\nphenotypic overlap of individual dementias the diagnosis of dementia is a major\nclinical challenge, even with current gold standard diagnostic approaches.\nHowever, it has been shown that certain dementias show specific structural\ncharacteristics in the brain. Progressive supranuclear palsy (PSP) and multiple\nsystem atrophy (MSA) are prototypical examples of this phenomenon, as they\noften present with characteristic brainstem atrophy. More detailed\ncharacterization of brain atrophy due to individual diseases is urgently\nrequired to select biomarkers and therapeutic targets that are meaningful to\neach disease. Here we present a joint multi-atlas-segmentation and\ndeep-learning-based segmentation method for fast and robust parcellation of the\nbrainstem into its four sub-structures, i.e., the midbrain, pons, medulla, and\nsuperior cerebellar peduncles (SCP), that in turn can provide detailed\nvolumetric information on the brainstem sub-structures affected in PSP and MSA.\nThe method may also benefit other neurodegenerative diseases, such as\nParkinson's disease; a condition which is often considered in the differential\ndiagnosis of PSP and MSA. Comparison with state-of-the-art labeling techniques\nevaluated on ground truth manual segmentations demonstrate that our method is\nsignificantly faster than prior methods as well as showing improvement in\nlabeling the brainstem indicating that this strategy may be a viable option to\nprovide a better characterization of the brainstem atrophy seen in PSP and MSA.\n"}
+{"abstract": "  It is shown that the relativistic invariance plays a key role in the study of\nintegrable systems. Using the relativistically invariant sine-Gordon equation,\nthe Tzitzeica equation, the Toda fields and the second heavenly equation as\ndual relations, some continuous and discrete integrable positive hierarchies\nsuch as the potential modified Korteweg-de Vries hierarchy, the potential\nFordy-Gibbons hierarchies, the potential dispersionless\nKadomtsev-Petviashvili-like (dKPL) hierarchy, the differential-difference dKPL\nhierarchy and the second heavenly hierarchies are converted to the integrable\nnegative hierarchies including the sG hierarchy and the Tzitzeica hierarchy,\nthe two-dimensional dispersionless Toda hierarchy, the two-dimensional Toda\nhierarchies and negative heavenly hierarchy. In (1+1)-dimensional cases the\npositive/negative hierarchy dualities are guaranteed by the dualities between\nthe recursion operators and their inverses. In (2+1)-dimensional cases, the\npositive/negative hierarchy dualities are explicitly shown by using the formal\nseries symmetry approach, the mastersymmetry method and the relativistic\ninvariance of the duality relations. For the 4-dimensional heavenly system, the\nduality problem is studied firstly by formal series symmetry approach. Two\nelegant commuting recursion operators of the heavenly equation appear naturally\nfrom the formal series symmetry approach so that the duality problem can also\nbe studied by means of the recursion operators.\n"}
+{"abstract": "  Predictions of biodiversity trajectories under climate change are crucial in\norder to act effectively in maintaining the diversity of species. In many\necological applications, future predictions are made under various global\nwarming scenarios as described by a range of different climate models. The\noutputs of these various predictions call for a reliable interpretation. We\npropose a interpretable and flexible two step methodology to measure the\nsimilarity between predicted species range maps and cluster the future scenario\npredictions utilizing a spectral clustering technique. We find that clustering\nbased on ecological impact (predicted species range maps) is mainly driven by\nthe amount of warming. We contrast this with clustering based only on predicted\nclimate features, which is driven mainly by climate models. The differences\nbetween these clusterings illustrate that it is crucial to incorporate\necological information to understand the relevant differences between climate\nmodels. The findings of this work can be used to better synthesize forecasts of\nbiodiversity loss under the wide spectrum of results that emerge when\nconsidering potential future biodiversity loss.\n"}
+{"abstract": "  Short-period sub-Neptunes with substantial volatile envelopes are among the\nmost common type of known exoplanets. However, recent studies of the Kepler\npopulation have suggested a dearth of sub-Neptunes on highly irradiated orbits,\nwhere they are vulnerable to atmospheric photoevaporation. Physically, we\nexpect this \"photoevaporation desert\" to depend on the total lifetime X-ray and\nextreme ultraviolet flux, the main drivers of atmospheric escape. In this work,\nwe study the demographics of sub-Neptunes as a function of lifetime exposure to\nhigh energy radiation and host star mass. We find that for a given present day\ninsolation, planets orbiting a 0.3 $M_{sun}$ star experience $\\sim$100 $\\times$\nmore X-ray flux over their lifetimes versus a 1.2 $M_{sun}$ star. Defining the\nphotoevaporation desert as a region consistent with zero occurrence at 2\n$\\sigma$, the onset of the desert happens for integrated X-ray fluxes greater\nthan 1.43 $\\times 10^{22}$ erg/cm$^2$ to 8.23 $\\times 10^{20}$ erg/cm$^2$ as a\nfunction of planetary radii for 1.8 -- 4 $R_{\\oplus}$. We also compare the\nlocation of the photoevaporation desert for different stellar types. We find\nmuch greater variability in the desert onset in bolometric flux space compared\nto integrated X-ray flux space, suggestive of photoevaporation driven by steady\nstate stellar X-ray emissions as the dominant control on desert location.\nFinally, we report tentative evidence for the sub-Neptune valley, first seen\naround Sun-like stars, for M & K dwarfs. The discovery of additional planets\naround low-mass stars from surveys such as the TESS mission will enable\ndetailed exploration of these trends.\n"}
+{"abstract": "  The theoretical maximum efficiency of a solar cell is typically characterized\nby a detailed balance of optical absorption and emission for a semiconductor in\nthe limit of unity radiative efficiency and an ideal step-function response for\nthe density of states and absorbance at the semiconductor band edges, known as\nthe Shockley-Queisser limit. However, real materials have non-abrupt band\nedges, which are typically characterized by an exponential distribution of\nstates, known as an Urbach tail. We develop here a modified detailed balance\nlimit of solar cells with imperfect band edges, using optoelectronic\nreciprocity relations. We find that for semiconductors whose band edges are\nbroader than the thermal energy, kT, there is an effective renormalized bandgap\ngiven by the quasi-Fermi level splitting within the solar cell. This\nrenormalized bandgap creates a Stokes shift between the onset of the absorption\nand photoluminescence emission energies, which significantly reduces the\nmaximum achievable efficiency. The abruptness of the band edge density of\nstates therefore has important implications for the maximum achievable\nphotovoltaic efficiency.\n"}
+{"abstract": "  We study relativistic hydrodynamics in the presence of a non vanishing spin\nchemical potential. Using a variety of techniques we carry out an exhaustive\nanalysis, and identify the constitutive relations for the stress tensor and\nspin current in such a setup, allowing us to write the hydrodynamic equations\nof motion to second order in derivatives. We then solve the equations of motion\nin a perturbative setup and find surprisingly good agreement with measurements\nof global $\\Lambda$-hyperon polarization carried out at RHIC.\n"}
+{"abstract": "  A monopolist wants to sell one item per period to a consumer with evolving\nand persistent private information. The seller sets a price each period\ndepending on the history so far, but cannot commit to future prices. We show\nthat, regardless of the degree of persistence, any equilibrium under a D1-style\nrefinement gives the seller revenue no higher than what she would get from\nposting all prices in advance.\n"}
+{"abstract": "  Development of the new artificial systems with unique characteristics is very\nchallenging task. In this paper the application of the hybrid super\nintelligence concept with object-process methodology to develop unique\nhigh-performance computational systems is considered. The methodological\napproach how to design new intelligent components for existing high-performance\ncomputing development systems is proposed on the example of system requirements\ncreation for \"MicroAI\" and \"Artificial Electronic\" systems.\n"}
+{"abstract": "  We define a class of discrete operators that, in particular, include the\ndelta and nabla fractional operators.\n"}
+{"abstract": "  Existing image segmentation networks mainly leverage large-scale labeled\ndatasets to attain high accuracy. However, labeling medical images is very\nexpensive since it requires sophisticated expert knowledge. Thus, it is more\ndesirable to employ only a few labeled data in pursuing high segmentation\nperformance. In this paper, we develop a data augmentation method for one-shot\nbrain magnetic resonance imaging (MRI) image segmentation which exploits only\none labeled MRI image (named atlas) and a few unlabeled images. In particular,\nwe propose to learn the probability distributions of deformations (including\nshapes and intensities) of different unlabeled MRI images with respect to the\natlas via 3D variational autoencoders (VAEs). In this manner, our method is\nable to exploit the learned distributions of image deformations to generate new\nauthentic brain MRI images, and the number of generated samples will be\nsufficient to train a deep segmentation network. Furthermore, we introduce a\nnew standard segmentation benchmark to evaluate the generalization performance\nof a segmentation network through a cross-dataset setting (collected from\ndifferent sources). Extensive experiments demonstrate that our method\noutperforms the state-of-the-art one-shot medical segmentation methods. Our\ncode has been released at\nhttps://github.com/dyh127/Modeling-the-Probabilistic-Distribution-of-Unlabeled-Data.\n"}
+{"abstract": "  We study the homogenized energy densities of periodic ferromagnetic Ising\nsystems. We prove that, for finite range interactions, the homogenized energy\ndensity, identifying the effective limit, is crystalline, i.e. its Wulff\ncrystal is a polytope, for which we can (exponentially) bound the number of\nvertices. This is achieved by deriving a dual representation of the energy\ndensity through a finite cell formula. This formula also allows easy numerical\ncomputations: we show a few experiments where we compute periodic patterns\nwhich minimize the anisotropy of the surface tension.\n"}
+{"abstract": "  Classical two-sample permutation tests for equality of distributions have\nexact size in finite samples, but they fail to control size for testing\nequality of parameters that summarize each distribution. This paper proposes\npermutation tests for equality of parameters that are estimated at root-n or\nslower rates. Our general framework applies to both parametric and\nnonparametric models, with two samples or one sample split into two subsamples.\nOur tests have correct size asymptotically while preserving exact size in\nfinite samples when distributions are equal. They have no loss in\nlocal-asymptotic power compared to tests that use asymptotic critical values.\nWe propose confidence sets with correct coverage in large samples that also\nhave exact coverage in finite samples if distributions are equal up to a\ntransformation. We apply our theory to four commonly-used hypothesis tests of\nnonparametric functions evaluated at a point. Lastly, simulations show good\nfinite sample properties of our tests.\n"}
+{"abstract": "  The private simultaneous messages model is a non-interactive version of the\nmultiparty secure computation, which has been intensively studied to examine\nthe communication cost of the secure computation. We consider its quantum\ncounterpart, the private simultaneous quantum messages (PSQM) model, and\nexamine the advantages of quantum communication and prior entanglement of this\nmodel. In the PSQM model, $k$ parties $P_1,\\ldots,P_k$ initially share a common\nrandom string (or entangled states in a stronger setting), and they have\nprivate classical inputs $x_1,\\ldots, x_k$. Every $P_i$ generates a quantum\nmessage from the private input $x_i$ and the shared random string (entangled\nstates), and then sends it to the referee $R$. Receiving the messages, $R$\ncomputes $F(x_1,\\ldots,x_k)$. Then, $R$ learns nothing except for\n$F(x_1,\\ldots,x_k)$ as the privacy condition. We obtain the following results\nfor this PSQM model. (1) We demonstrate that the privacy condition inevitably\nincreases the communication cost in the two-party PSQM model as well as in the\nclassical case presented by Applebaum, Holenstein, Mishra, and Shayevitz. In\nparticular, we prove a lower bound $(3-o(1))n$ of the communication complexity\nin PSQM protocols with a shared random string for random Boolean functions of\n$2n$-bit input, which is larger than the trivial upper bound $2n$ of the\ncommunication complexity without the privacy condition. (2) We demonstrate a\nfactor two gap between the communication complexity of PSQM protocols with\nshared entangled states and with shared random strings by designing a\nmultiparty PSQM protocol with shared entangled states for a total function that\nextends the two-party equality function. (3) We demonstrate an exponential gap\nbetween the communication complexity of PSQM protocols with shared entangled\nstates and with shared random strings for a two-party partial function.\n"}
+{"abstract": "  Low-Rank Parity-Check (LRPC) codes are a class of rank metric codes that have\nmany applications specifically in network coding and cryptography. Recently,\nLRPC codes have been extended to Galois rings which are a specific case of\nfinite rings. In this paper, we first define LRPC codes over finite commutative\nlocal rings, which are bricks of finite rings, with an efficient decoder. We\nimprove the theoretical bound of the failure probability of the decoder. Then,\nwe extend the work to arbitrary finite commutative rings. Certain conditions\nare generally used to ensure the success of the decoder. Over finite fields,\none of these conditions is to choose a prime number as the extension degree of\nthe Galois field. We have shown that one can construct LRPC codes without this\ncondition on the degree of Galois extension.\n"}
+{"abstract": "  The isolated susceptibility $\\chi_{\\rm I}$ may be defined as a\n(non-thermodynamic) average over the canonical ensemble, but while it has often\nbeen discussed in the literature, it has not been clearly measured. Here, we\ndemonstrate an unambiguous measurement of $\\chi_{\\rm I}$ at avoided\nnuclear-electronic level crossings in a dilute spin ice system, containing\nwell-separated holmium ions. We show that $\\chi_{\\rm I}$ quantifies the\nsuperposition of quasi-classical spin states at these points, and is a direct\nmeasure of state concurrence and populations.\n"}
+{"abstract": "  The econometric literature on program-evaluation and optimal treatment-choice\ntakes functionals of outcome-distributions as target welfare, and ignores\nprogram-impacts on unobserved utilities, including utilities of those whose\noutcomes may be unaffected by the intervention. We show that in the practically\nimportant setting of discrete-choice, under general preference-heterogeneity\nand income-effects, the distribution of indirect-utility is nonparametrically\nidentified from average demand. This enables cost-benefit analysis and\ntreatment-targeting based on social welfare and planners' distributional\npreferences, while also allowing for general unobserved heterogeneity in\nindividual preferences. We demonstrate theoretical connections between\nutilitarian social welfare and Hicksian compensation. An empirical application\nillustrates our results.\n"}
+{"abstract": "  We demonstrate that multiple higher-order topological transitions can be\ntriggered via the continuous change of the geometry in kagome photonic crystals\ncomposed of three dielectric rods. By tuning a single geometry parameter, the\nphotonic corner and edge states emerge or disappear with the higher-order\ntopological transitions. Two distinct higher-order topological insulator phases\nand a normal insulator phase are revealed. Their topological indices are\nobtained from symmetry representations. A photonic analog of fractional corner\ncharge is introduced to distinguish the two higher-order topological insulator\nphases. Our predictions can be readily realized and verified in configurable\ndielectric photonic crystals.\n"}
+{"abstract": "  A prototype neutron detector has been created through modification to a\ncommercial non-volatile flash memory device. Studies are being performed to\nmodify this prototype into a purpose-built device with greater performance and\nfunctionality. This paper describes a demonstration of this technology using a\nthermal neutron beam produced by a TRIGA research reactor. With a 4x4 array of\n16 prototype devices, the full widths of the beam dimensions at half maximum\nare measured to be 2.2x2.1 cm2.\n"}
+{"abstract": "  Mathematical modelling of ionic electrodiffusion and water movement is\nemerging as a powerful avenue of investigation to provide new physiological\ninsight into brain homeostasis. However, in order to provide solid answers and\nresolve controversies, the accuracy of the predictions is essential. Ionic\nelectrodiffusion models typically comprise non-trivial systems of non-linear\nand highly coupled partial and ordinary differential equations that govern\nphenomena on disparate time scales. Here, we study numerical challenges related\nto approximating these systems. We consider a homogenized model for\nelectrodiffusion and osmosis in brain tissue and present and evaluate different\nassociated finite element-based splitting schemes in terms of their numerical\nproperties, including accuracy, convergence, and computational efficiency for\nboth idealized scenarios and for the physiologically relevant setting of\ncortical spreading depression (CSD). We find that the schemes display optimal\nconvergence rates in space for problems with smooth manufactured solutions.\nHowever, the physiological CSD setting is challenging: we find that the\naccurate computation of CSD wave characteristics (wave speed and wave width)\nrequires a very fine spatial and fine temporal resolution.\n"}
+{"abstract": "  The Chermak-Delgado lattice of a finite group $G$ is a self-dual sublattice\nof the subgroup lattice of $G$. In this paper, we focus on finite groups whose\nChermak-Delgado lattice is a subgroup lattice of an elementary abelian\n$p$-group. We prove that such groups are nilpotent of class $2$. We also prove\nthat, for any elementary abelian $p$-group $E$, there exists a finite group $G$\nsuch that the Chermak-Delgado lattice of $G$ is a subgroup lattice of $E$.\n"}
+{"abstract": "  Let $(X, H)$ be a polarized smooth projective algebraic surface and $E$ is\nglobally generated, stable vector bundle on $X$. Then the Syzygy bundle $M_E$\nassociated to it is defined as the kernel bundle corresponding to the\nevaluation map. In this article we will study the stability property of $M_E$\nwith respect to $H$.\n"}
+{"abstract": "  Contagion arising from clustering of multiple time series like those in the\nstock market indicators can further complicate the nature of volatility,\nrendering a parametric test (relying on asymptotic distribution) to suffer from\nissues on size and power. We propose a test on volatility based on the\nbootstrap method for multiple time series, intended to account for possible\npresence of contagion effect. While the test is fairly robust to distributional\nassumptions, it depends on the nature of volatility. The test is correctly\nsized even in cases where the time series are almost nonstationary. The test is\nalso powerful specially when the time series are stationary in mean and that\nvolatility are contained only in fewer clusters. We illustrate the method in\nglobal stock prices data.\n"}
+{"abstract": "  The collateral choice option gives the collateral posting party the\nopportunity to switch between different collateral currencies which is\nwell-known to impact the asset price. Quantification of the option's value is\nof practical importance but remains challenging under the assumption of\nstochastic rates, as it is determined by an intractable distribution which\nrequires involved approximations. Indeed, many practitioners still rely on\ndeterministic spreads between the rates for valuation. We develop a scalable\nand stable stochastic model of the collateral spreads under the assumption of\nconditional independence. This allows for a common factor approximation which\nadmits analytical results from which further estimators are obtained. We show\nthat in modelling the spreads between collateral rates, a second order model\nyields accurate results for the value of the collateral choice option. The\nmodel remains precise for a wide range of model parameters and is numerically\nefficient even for a large number of collateral currencies.\n"}
+{"abstract": "  In this work we investigate neutron stars (NS) in $f(\\mathtt{R,L_m})$ theory\nof gravity for the case $f(\\mathtt{R,L_m}) = \\mathtt{R} + \\mathtt{L_m} +\n\\sigma\\mathtt{R}\\mathtt{L_m}$, where $\\mathtt{R}$ is the Ricci scalar and\n$\\mathtt{L_m}$ the Lagrangian matter density. In the term\n$\\sigma\\mathtt{R}\\mathtt{L_m}$, $\\sigma$ represents the coupling between the\ngravitational and particles fields. For the first time the hydrostatic\nequilibrium equations in the theory are solved considering realistic equations\nof state and NS masses and radii obtained are subject to joint constrains from\nmassive pulsars, the gravitational wave event GW170817 and from the PSR\nJ0030+0451 mass-radius from NASA's Neutron Star Interior Composition Explorer\n(${\\it NICER}$) data. We show that in this theory of gravity, the mass-radius\nresults can accommodate massive pulsars, while the general theory of relativity\ncan hardly do it. The theory also can explain the observed NS within the radius\nregion constrained by the GW170817 and PSR J0030+0451 observations for masses\naround $1.4~M_{\\odot}$.\n"}
+{"abstract": "  Let $B\\subset A$ be a left or right bounded extension of finite dimensional\nalgebras. We use the Jacobi-Zariski long nearly exact sequence to show that $B$\nsatisfies Han's conjecture if and only if $A$ does, regardless if the extension\nsplits or not. We provide conditions ensuring that an extension by arrows and\nrelations is left or right bounded. Finally we give a structure result for\nextensions of an algebra given by a quiver and admissible relations, and\nexamples of non split left or right bounded extensions.\n"}
+{"abstract": "  The genuine concurrence is a standard quantifier of multipartite\nentanglement, detection and quantification of which still remains a difficult\nproblem from both theoretical and experimental point of view. Although many\nefforts have been devoted toward the detection of multipartite entanglement\n(e.g., using entanglement witnesses), measuring the degree of multipartite\nentanglement, in general, requires some knowledge about an exact shape of a\ndensity matrix of the quantum state. An experimental reconstruction of such\ndensity matrix can be done by full state tomography which amounts to having the\ndistant parties share a common reference frame and well calibrated devices.\nAlthough this assumption is typically made implicitly in theoretical works,\nestablishing a common reference frame, as well as aligning and calibrating\nmeasurement devices in experimental situations are never trivial tasks. It is\ntherefore an interesting and important question whether the requirements of\nhaving a shared reference frame and calibrated devices can be relaxed. In this\nwork we study both theoretically and experimentally the genuine concurrence for\nthe generalized Greenberger-Horne-Zeilinger states under randomly chosen\nmeasurements on a single qubits without a shared frame of reference and\ncalibrated devices. We present the relation between genuine concurrence and\nso-called nonlocal volume, a recently introduced indicator of nonlocality.\n"}
+{"abstract": "  This paper presents dEchorate: a new database of measured multichannel Room\nImpulse Responses (RIRs) including annotations of early echo timings and 3D\npositions of microphones, real sources and image sources under different wall\nconfigurations in a cuboid room. These data provide a tool for benchmarking\nrecent methods in echo-aware speech enhancement, room geometry estimation, RIR\nestimation, acoustic echo retrieval, microphone calibration, echo labeling and\nreflectors estimation. The database is accompanied with software utilities to\neasily access, manipulate and visualize the data as well as baseline methods\nfor echo-related tasks.\n"}
+{"abstract": "  We present X-ray analysis of the ejecta of supernova remnant G350.1$-$0.3\nobserved with Chandra and Suzaku, and clarify the ejecta's kinematics over a\ndecade and obtain a new observational clue to understanding the origin of the\nasymmetric explosion. Two images of Chandra X-ray Observatory taken in 2009 and\n2018 are analyzed in several methods, and enable us to measure the velocities\nin the plane of the sky. A maximum velocity is 4640$\\pm$290 km s$^{-1}$\n(0.218$\\pm$0.014 arcsec yr$^{-1}$) in the eastern region in the remnant. These\nfindings trigger us to scrutinize the Doppler effects in the spectra of the\nthermal emission, and the velocities in the line-of-sight direction are\nestimated to be a thousand km s$^{-1}$. The results are confirmed by analyzing\nthe spectra of Suzaku. Combining the proper motions and line-of-sight\nvelocities, the ejecta's three-dimensional velocities are $\\sim$3000-5000 km\ns$^{-1}$. The center of the explosion is more stringently constrained by\nfinding the optimal time to reproduce the observed spatial expansion. Our\nfindings that the age of the SNR is estimated at most to be 655 years, and the\nCCO is observed as a point source object against the SNR strengthen the\n'hydrodynamical kick' hypothesis on the origin of the remnant.\n"}
+{"abstract": "  We present a derivation-based Atiyah sequence for noncommutative principal\nbundles. Along the way we treat the problem of deciding when a given\n*-automorphism on the quantum base space lifts to a *-automorphism on the\nquantum total space that commutes with the underlying structure group.\n"}
+{"abstract": "  We formulate the Lagrangian of the Newtonian cosmology where the cosmological\nconstant is also introduced. Following the affine quantization procedure, the\nHamiltonian operator is derived. The wave functions of the Newtonian universe\nand the corresponding eigenvalues for the case of matter dominated by a\nnegative cosmological constant are given.\n"}
+{"abstract": "  Learning effective representations in image-based environments is crucial for\nsample efficient Reinforcement Learning (RL). Unfortunately, in RL,\nrepresentation learning is confounded with the exploratory experience of the\nagent -- learning a useful representation requires diverse data, while\neffective exploration is only possible with coherent representations.\nFurthermore, we would like to learn representations that not only generalize\nacross tasks but also accelerate downstream exploration for efficient\ntask-specific training. To address these challenges we propose Proto-RL, a\nself-supervised framework that ties representation learning with exploration\nthrough prototypical representations. These prototypes simultaneously serve as\na summarization of the exploratory experience of an agent as well as a basis\nfor representing observations. We pre-train these task-agnostic representations\nand prototypes on environments without downstream task information. This\nenables state-of-the-art downstream policy learning on a set of difficult\ncontinuous control tasks.\n"}
+{"abstract": "  The paper discusses how robots enable occupant-safe continuous protection for\nstudents when schools reopen. Conventionally, fixed air filters are not used as\na key pandemic prevention method for public indoor spaces because they are\nunable to trap the airborne pathogens in time in the entire room. However, by\ncombining the mobility of a robot with air filtration, the efficacy of cleaning\nup the air around multiple people is largely increased. A disinfection co-robot\nprototype is thus developed to provide continuous and occupant-friendly\nprotection to people gathering indoors, specifically for students in a\nclassroom scenario. In a static classroom with students sitting in a grid\npattern, the mobile robot is able to serve up to 14 students per cycle while\nreducing the worst-case pathogen dosage by 20%, and with higher robustness\ncompared to a static filter. The extent of robot protection is optimized by\ntuning the passing distance and speed, such that a robot is able to serve more\npeople given a threshold of worst-case dosage a person can receive.\n"}
+{"abstract": "  We study orbit codes in the field extension ${\\mathbb F}_{q^n}$. First we\nshow that the automorphism group of a cyclic orbit code is contained in the\nnormalizer of the Singer subgroup if the orbit is generated by a subspace that\nis not contained in a proper subfield of ${\\mathbb F}_{q^n}$. We then\ngeneralize to orbits under the normalizer of the Singer subgroup. In that\nsituation some exceptional cases arise and some open cases remain. Finally we\ncharacterize linear isometries between such codes.\n"}
+{"abstract": "  Optimal design of distributed decision policies can be a difficult task,\nillustrated by the famous Witsenhausen counterexample. In this paper we\ncharacterize the optimal control designs for the vector-valued setting assuming\nthat it results in an internal state that can be described by a continuous\nrandom variable which has a probability density function. More specifically, we\nprovide a genie-aided outer bound that relies on our previous results for\nempirical coordination problems. This solution turns out to be not optimal in\ngeneral, since it consists of a time-sharing strategy between two linear\nschemes of specific power. It follows that the optimal decision strategy for\nthe original scalar Witsenhausen problem must lead to an internal state that\ncannot be described by a continuous random variable which has a probability\ndensity function.\n"}
+{"abstract": "  A user generates n independent and identically distributed data random\nvariables with a probability mass function that must be guarded from a querier.\nThe querier must recover, with a prescribed accuracy, a given function of the\ndata from each of n independent and identically distributed query responses\nupon eliciting them from the user. The user chooses the data probability mass\nfunction and devises the random query responses to maximize distribution\nprivacy as gauged by the (Kullback-Leibler) divergence between the former and\nthe querier's best estimate of it based on the n query responses. Considering\nan arbitrary function, a basic achievable lower bound for distribution privacy\nis provided that does not depend on n and corresponds to worst-case privacy.\nWorst-case privacy equals the logsum cardinalities of inverse atoms under the\ngiven function, with the number of summands decreasing as the querier recovers\nthe function with improving accuracy. Next, upper (converse) and lower\n(achievability) bounds for distribution privacy, dependent on n, are developed.\nThe former improves upon worst-case privacy and the latter does so under\nsuitable assumptions; both converge to it as n grows. The converse and\nachievability proofs identify explicit strategies for the user and the querier.\n"}
+{"abstract": "  We consider the problem of assigning agents to programs in the presence of\ntwo-sided preferences, commonly known as the Hospital Residents problem. In the\nstandard setting each program has a rigid upper-quota which cannot be violated.\nMotivated by applications where quotas are governed by resource availability,\nwe propose and study the problem of computing optimal matchings with\ncost-controlled quotas -- denoted as the CCQ setting. In the CCQ setting we\nhave a cost associated with every program which denotes the cost of matching a\nsingle agent to the program and these costs control the quotas. Our goal is to\ncompute a matching that matches all agents, respects the preference lists of\nagents and programs and is optimal with respect to the cost criteria. We study\ntwo optimization problems with respect to the costs -- minimize the total cost\n(MINSUM) and minimize the maximum cost at a program (MINMAX). We show that\nthere is a sharp contrast in the complexity status of these two problems --\nMINMAX is polynomial time solvable whereas MINSUM is NP-hard and hard to\napproximate within a constant factor unless P = NP even under severe\nrestrictions. On the positive side, we present approximation algorithms for the\nMINSUM for the general case and a special hard case. The special hard case is\ntheoretically challenging as well as practically motivated and we present a\nLinear Programming based algorithm for this case. We also establish the\nconnection of our model with the stable extension problem in an apparently\ndifferent two-round setting of the stable matching problem [Gajulapalli et al.\nFSTTCS 2020]. We show that our results in the CCQ setting generalize the stable\nextension problem.\n"}
+{"abstract": "  One-shot semantic image segmentation aims to segment the object regions for\nthe novel class with only one annotated image. Recent works adopt the episodic\ntraining strategy to mimic the expected situation at testing time. However,\nthese existing approaches simulate the test conditions too strictly during the\ntraining process, and thus cannot make full use of the given label information.\nBesides, these approaches mainly focus on the foreground-background target\nclass segmentation setting. They only utilize binary mask labels for training.\nIn this paper, we propose to leverage the multi-class label information during\nthe episodic training. It will encourage the network to generate more\nsemantically meaningful features for each category. After integrating the\ntarget class cues into the query features, we then propose a pyramid feature\nfusion module to mine the fused features for the final classifier. Furthermore,\nto take more advantage of the support image-mask pair, we propose a\nself-prototype guidance branch to support image segmentation. It can constrain\nthe network for generating more compact features and a robust prototype for\neach semantic class. For inference, we propose a fused prototype guidance\nbranch for the segmentation of the query image. Specifically, we leverage the\nprediction of the query image to extract the pseudo-prototype and combine it\nwith the initial prototype. Then we utilize the fused prototype to guide the\nfinal segmentation of the query image. Extensive experiments demonstrate the\nsuperiority of our proposed approach.\n"}
+{"abstract": "  Digital contents have grown dramatically in recent years, leading to\nincreased attention to copyright. Image watermarking has been considered one of\nthe most popular methods for copyright protection. With the recent advancements\nin applying deep neural networks in image processing, these networks have also\nbeen used in image watermarking. Robustness and imperceptibility are two\nchallenging features of watermarking methods that the trade-off between them\nshould be satisfied. In this paper, we propose to use an end-to-end network for\nwatermarking. We use a convolutional neural network (CNN) to control the\nembedding strength based on the image content. Dynamic embedding helps the\nnetwork to have the lowest effect on the visual quality of the watermarked\nimage. Different image processing attacks are simulated as a network layer to\nimprove the robustness of the model. Our method is a blind watermarking\napproach that replicates the watermark string to create a matrix of the same\nsize as the input image. Instead of diffusing the watermark data into the input\nimage, we inject the data into the feature space and force the network to do\nthis in regions that increase the robustness against various attacks.\nExperimental results show the superiority of the proposed method in terms of\nimperceptibility and robustness compared to the state-of-the-art algorithms.\n"}
+{"abstract": "  This paper examines a continuous time intertemporal consumption and portfolio\nchoice problem with a stochastic differential utility preference of Epstein-Zin\ntype for a robust investor, who worries about model misspecification and seeks\nrobust decision rules. We provide a verification theorem which formulates the\nHamilton-Jacobi-Bellman-Isaacs equation under a non-Lipschitz condition. Then,\nwith the verification theorem, the explicit closed-form optimal robust\nconsumption and portfolio solutions to a Heston model are given. Also we\ncompare our robust solutions with the non-robust ones, and the comparisons\nshown in a few figures coincide with our common sense.\n"}
+{"abstract": "  We study transmission in a system consisting of a curved graphene surface as\nan arc (ripple) of circle connected to two flat graphene sheets on the left and\nright sides. We introduce a mass term in the curved part and study the effect\nof a generated band gap in spectrum on transport properties for spin-up/-down.\nThe tunneling analysis allows us to find all transmission and reflections\nchannels modeled by the band gap. This later acts by decreasing the\ntransmissions with spin-up/-down but increasing with spin opposite, which\nexhibit some behaviors look like bell-shaped curve. We find resonances\nappearing in reflection with the same spin, thus backscattering with a\nspin-up/-down is not null in ripple. We observe huge spatial shifts for the\ntotal conduction in our model and the magnitudes of these shifts can be\nefficiently controlled by adjusting the band gap. This high order tunability of\nthe tunneling effect can be used to design highly accurate devises based on\ngraphene.\n"}
+{"abstract": "  The cells and their spatial patterns in the tumor microenvironment (TME) play\na key role in tumor evolution, and yet the latter remains an understudied topic\nin computational pathology. This study, to the best of our knowledge, is among\nthe first to hybridize local and global graph methods to profile orchestration\nand interaction of cellular components. To address the challenge in\nhematolymphoid cancers, where the cell classes in TME may be unclear, we first\nimplemented cell-level unsupervised learning and identified two new cell\nsubtypes. Local cell graphs or supercells were built for each image by\nconsidering the individual cell's geospatial location and classes. Then, we\napplied supercell level clustering and identified two new cell communities. In\nthe end, we built global graphs to abstract spatial interaction patterns and\nextract features for disease diagnosis. We evaluate the proposed algorithm on\nH&E slides of 60 hematolymphoid neoplasms and further compared it with three\ncell level graph-based algorithms, including the global cell graph, cluster\ncell graph, and FLocK. The proposed algorithm achieved a mean diagnosis\naccuracy of 0.703 with the repeated 5-fold cross-validation scheme. In\nconclusion, our algorithm shows superior performance over the existing methods\nand can be potentially applied to other cancer types.\n"}
+{"abstract": "  Research in the Vision and Language area encompasses challenging topics that\nseek to connect visual and textual information. When the visual information is\nrelated to videos, this takes us into Video-Text Research, which includes\nseveral challenging tasks such as video question answering, video summarization\nwith natural language, and video-to-text and text-to-video conversion. This\npaper reviews the video-to-text problem, in which the goal is to associate an\ninput video with its textual description. This association can be mainly made\nby retrieving the most relevant descriptions from a corpus or generating a new\none given a context video. These two ways represent essential tasks for\nComputer Vision and Natural Language Processing communities, called text\nretrieval from video task and video captioning/description task. These two\ntasks are substantially more complex than predicting or retrieving a single\nsentence from an image. The spatiotemporal information present in videos\nintroduces diversity and complexity regarding the visual content and the\nstructure of associated language descriptions. This review categorizes and\ndescribes the state-of-the-art techniques for the video-to-text problem. It\ncovers the main video-to-text methods and the ways to evaluate their\nperformance. We analyze twenty-six benchmark datasets, showing their drawbacks\nand strengths for the problem requirements. We also show the progress that\nresearchers have made on each dataset, we cover the challenges in the field,\nand we discuss future research directions.\n"}
+{"abstract": "  We study static magnetic susceptibility $\\chi(T, \\mu)$ in $SU(2)$ lattice\ngauge theory with $N_f = 2$ light flavours of dynamical fermions at finite\nchemical potential $\\mu$. Using linear response theory we find that $SU(2)$\ngauge theory exhibits paramagnetic behavior in both the high-temperature\ndeconfined regime and the low-temperature confining regime. Paramagnetic\nresponse becomes stronger at higher temperatures and larger values of the\nchemical potential. For our range of temperatures $0.727 \\leq T/T_c \\leq 2.67$,\nthe first coefficient of the expansion of $\\chi(T, \\mu)$ in even powers of\n$\\mu/T$ around $\\mu=0$ is close to that of free quarks and lies in the range\n$(2 \\ldots 5) \\cdot 10^{-3}$. The strongest paramagnetic response is found in\nthe diquark condensation phase at $\\mu > m_{\\pi}/2$.\n"}
+{"abstract": "  We consider the problem of estimation and structure learning of high\ndimensional signals via a normal sequence model, where the underlying parameter\nvector is piecewise constant, or has a block structure. We develop a Bayesian\nfusion estimation method by using the Horseshoe prior to induce a strong\nshrinkage effect on successive differences in the mean parameters,\nsimultaneously imposing sufficient prior concentration for non-zero values of\nthe same. The proposed method thus facilitates consistent estimation and\nstructure recovery of the signal pieces. We provide theoretical justifications\nof our approach by deriving posterior convergence rates and establishing\nselection consistency under suitable assumptions. We also extend our proposed\nmethod to signal de-noising over arbitrary graphs and develop efficient\ncomputational methods along with providing theoretical guarantees. We\ndemonstrate the superior performance of the Horseshoe based Bayesian fusion\nestimation method through extensive simulations and two real-life examples on\nsignal de-noising in biological and geophysical applications. We also\ndemonstrate the estimation performance of our method on a real-world large\nnetwork for the graph signal de-noising problem.\n"}
+{"abstract": "  Let $G$ be a finite group and ${\\rm cd}(G)$ will be the set of the degrees of\nthe complex irreducible characters of $G$. Also let ${\\rm cod}(G)$ be the set\nof codegrees of the irreducible characters of $G$. The Taketa problem\nconjectures if $G$ is solvable, then ${\\rm dl}(G) \\leq |{\\rm cd}(G)|$, where\n${\\rm dl}(G)$ is the derived length of $G$. In this note, we show that ${\\rm\ndl}(G) \\leq |{\\rm cod}(G)|$ in some cases and we conjecture that this\ninequality holds if $G$ is a finite solvable group.\n"}
+{"abstract": "  In this article, we model Earth's lower small-scale eddies motion in the\natmosphere as a compressible neutral fluid flow on a rotating sphere. To\njustify the model, we carried out a numerical computation of the thermodynamic\nand hydrodynamic properties of the viscous atmospheric motion in two dimensions\nusing Naiver-Stokes dynamics, conservation of atmospheric energy, and\ncontinuity equation. The dynamics of the atmosphere, governed by a partial\ndifferential equation without any approximation , and without considering\nlatitude-dependent acceleration due to gravity. The numerical solution for\nthose governed equations was solved by applying the finite difference method\nwith applying some sort of horizontal air mass density as a perturbation to the\natmosphere at a longitude of $5\\Delta\\lambda$ . Based on this initial boundary\ncondition with taking temperature-dependent transport coefficient into account,\nwe obtain the propagation for each atmospheric parameter and presented it\ngraphically as a function of geometrically position and time. All of the\nparameters oscillating with respect to time and satisfy the characteristics of\nan atmospheric waves.\n  Finally, the effect of the Coriolis force on resultant velocity was also\ndiscussed by plotting contour lines for the resultant velocity for the\ndifferent magnitude of Coriolis force, then we also obtain an interesting wave\nphenomena for the respective rotation of the Coriolis force.\n  ~~~~Keywords: Naiver-Stokes Equations; Finite difference method; Viscous\natmospheric motion; Viscous dissipation; convective motion.\n"}
+{"abstract": "  Microbiota profiles measure the structure of microbial communities in a\ndefined environment (known as microbiomes). In the past decade, microbiome\nresearch has focused on health applications as a result of which the gut\nmicrobiome has been implicated in the development of a broad range of diseases\nsuch as obesity, inflammatory bowel disease, and major depressive disorder. A\nkey goal of many microbiome experiments is to characterise or describe the\nmicrobial community. High-throughput sequencing is used to generate microbiota\nprofiles, but data gathered via this method are extremely challenging to\nanalyse, as the data violate multiple strong assumptions of standard models.\nRough Set Theory (RST) has weak assumptions that are less likely to be\nviolated, and offers a range of attractive tools for extracting knowledge from\ncomplex data. In this paper we present the first application of RST for\ncharacterising microbiomes. We begin with a demonstrative benchmark microbiota\nprofile and extend the approach to gut microbiomes gathered from depressed\nsubjects to enable knowledge discovery. We find that RST is capable of\nexcellent characterisation of the gut microbiomes in depressed subjects and\nidentifying previously undescribed alterations to the microbiome-gut-brain\naxis. An important aspect of the application of RST is that it provides a\npossible solution to an open research question regarding the search for an\noptimal normalisation approach for microbiome census data, as one does not\ncurrently exist.\n"}
+{"abstract": "  Multistability is a common phenomenon which naturally occurs in complex\nnetworks. If coexisting attractors are numerous and their basins of attraction\nare complexly interwoven, the long-term response to a perturbation can be\nhighly uncertain. We examine the uncertainty in the outcome of perturbations to\nthe synchronous state in a Kuramoto-like representation of the British power\ngrid. Based on local basin landscapes which correspond to single-node\nperturbations, we demonstrate that the uncertainty shows strong spatial\nvariability. While perturbations at many nodes only allow for a few outcomes,\nother local landscapes show extreme complexity with more than a hundred basins.\nParticularly complex domains in the latter can be related to unstable invariant\nchaotic sets of saddle type. Most importantly, we show that the characteristic\ndynamics on these chaotic saddles can be associated with certain topological\nstructures of the network. We find that one particular tree-like substructure\nallows for the chaotic response to perturbations at nodes in the north of Great\nBritain. The interplay with other peripheral motifs increases the uncertainty\nin the system response even further.\n"}
+{"abstract": "  Ginzburg algebras associated to triangulated surfaces provide a means to\ncategorify the cluster algebras of these surfaces. As shown by Ivan Smith, the\nfinite derived category of such a Ginzburg algebra can be embedded into the\nFukaya category of the total space of a Lefschetz fibration over the surface.\nInspired by this perspective we provide a description of the full derived\ncategory in terms of a perverse schober. The main novelty is a gluing formalism\ndescribing the Ginzburg algebra as a colimit of certain local Ginzburg algebras\nassociated to discs. As a first application we give a new proof of the derived\ninvariance of these Ginzburg algebras under flips of an edge of the\ntriangulation. Finally, we note that the perverse schober as well as the\nresulting gluing construction can also be defined over the sphere spectrum.\n"}
+{"abstract": "  Fourth-order interference is an information processing primitive for photonic\nquantum technologies. When used in conjunction with post-selection, it forms\nthe basis of photonic controlled logic gates, entangling measurements, and can\nbe used to produce quantum correlations. Here, using classical weak coherent\nstates as inputs, we study fourth-order interference in novel $4 \\times 4$\nmulti-port beam splitters built within multi-core optical fibers. Using two\nmutually incoherent weak laser pulses as inputs, we observe high-quality fourth\norder interference between photons from different cores, as well as\nself-interference of a two-photon wavepacket. In addition, we show that quantum\ncorrelations, in the form of quantum discord, can be maximized by controlling\nthe intensity ratio between the two input weak coherent states. This should\nallow for the exploitation of quantum correlations in future telecommunication\nnetworks.\n"}
+{"abstract": "  Plant phenotyping, that is, the quantitative assessment of plant traits\nincluding growth, morphology, physiology, and yield, is a critical aspect\ntowards efficient and effective crop management. Currently, plant phenotyping\nis a manually intensive and time consuming process, which involves human\noperators making measurements in the field, based on visual estimates or using\nhand-held devices. In this work, methods for automated grapevine phenotyping\nare developed, aiming to canopy volume estimation and bunch detection and\ncounting. It is demonstrated that both measurements can be effectively\nperformed in the field using a consumer-grade depth camera mounted onboard an\nagricultural vehicle.\n"}
+{"abstract": "  Unsupervised domain adaptation (UDA) methods for person re-identification\n(re-ID) aim at transferring re-ID knowledge from labeled source data to\nunlabeled target data. Although achieving great success, most of them only use\nlimited data from a single-source domain for model pre-training, making the\nrich labeled data insufficiently exploited. To make full use of the valuable\nlabeled data, we introduce the multi-source concept into UDA person re-ID\nfield, where multiple source datasets are used during training. However,\nbecause of domain gaps, simply combining different datasets only brings limited\nimprovement. In this paper, we try to address this problem from two\nperspectives, \\ie{} domain-specific view and domain-fusion view. Two\nconstructive modules are proposed, and they are compatible with each other.\nFirst, a rectification domain-specific batch normalization (RDSBN) module is\nexplored to simultaneously reduce domain-specific characteristics and increase\nthe distinctiveness of person features. Second, a graph convolutional network\n(GCN) based multi-domain information fusion (MDIF) module is developed, which\nminimizes domain distances by fusing features of different domains. The\nproposed method outperforms state-of-the-art UDA person re-ID methods by a\nlarge margin, and even achieves comparable performance to the supervised\napproaches without any post-processing techniques.\n"}
+{"abstract": "  We provide high-precision predictions for muon-pair and tau-pair productions\nin a photon-photon collision by considering a complete set of one-loop-level\nscattering amplitudes, i.e., electroweak (EW) corrections together with soft\nand hard QED radiation. Accordingly, we present a detailed numerical discussion\nwith particular emphasis on the pure QED corrections as well as genuinely weak\ncorrections. The effects of angular and initial beam polarisation distributions\non production rates are also discussed. An improvement is observed by a factor\nof two with oppositely polarized photons. Our results indicate that the\none-loop EW radiative corrections enhance the Born cross section and the total\nrelative correction is typically about ten percent for both production\nchannels. It appears that the full EW corrections to $\\gamma \\gamma \\to \\ell^-\n\\ell^+$ are required to match a percent level accuracy.\n"}
+{"abstract": "  The program-over-monoid model of computation originates with Barrington's\nproof that the model captures the complexity class $\\mathsf{NC^1}$. Here we\nmake progress in understanding the subtleties of the model. First, we identify\na new tameness condition on a class of monoids that entails a natural\ncharacterization of the regular languages recognizable by programs over monoids\nfrom the class. Second, we prove that the class known as $\\mathbf{DA}$\nsatisfies tameness and hence that the regular languages recognized by programs\nover monoids in $\\mathbf{DA}$ are precisely those recognizable in the classical\nsense by morphisms from $\\mathbf{QDA}$. Third, we show by contrast that the\nwell studied class of monoids called $\\mathbf{J}$ is not tame. Finally, we\nexhibit a program-length-based hierarchy within the class of languages\nrecognized by programs over monoids from $\\mathbf{DA}$.\n"}
+{"abstract": "  We show that in analytic sub-Riemannian manifolds of rank 2 satisfying a\ncommutativity condition spiral-like curves are not length minimizing near the\ncenter of the spiral. The proof relies upon the delicate construction of a\ncompeting curve.\n"}
+{"abstract": "  We introduce a thermodynamically consistent, minimal stochastic model for\ncomplementary logic gates built with field-effect transistors. We characterize\nthe performance of such gates with tools from information theory and study the\ninterplay between accuracy, speed, and dissipation of computations. With a few\nuniversal building blocks, such as the NOT and NAND gates, we are able to model\narbitrary combinatorial and sequential logic circuits, which are modularized to\nimplement computing tasks. We find generically that high accuracy can be\nachieved provided sufficient energy consumption and time to perform the\ncomputation. However, for low-energy computing, accuracy and speed are coupled\nin a way that depends on the device architecture and task. Our work bridges the\ngap between the engineering of low dissipation digital devices and theoretical\ndevelopments in stochastic thermodynamics, and provides a platform to study\ndesign principles for low dissipation digital devices.\n"}
+{"abstract": "  A low complexity frequency offset estimation algorithm based on all-phase FFT\nfor M-QAM is proposed. Compared with two-stage algorithms such as FFT+CZT and\nFFT+ZoomFFT, our algorithm can lower computational complexity by 73% and 30%\nrespectively, without loss of the estimation accuracy.\n"}
+{"abstract": "  While self-supervised pretraining has proven beneficial for many computer\nvision tasks, it requires expensive and lengthy computation, large amounts of\ndata, and is sensitive to data augmentation. Prior work demonstrates that\nmodels pretrained on datasets dissimilar to their target data, such as chest\nX-ray models trained on ImageNet, underperform models trained from scratch.\nUsers that lack the resources to pretrain must use existing models with lower\nperformance. This paper explores Hierarchical PreTraining (HPT), which\ndecreases convergence time and improves accuracy by initializing the\npretraining process with an existing pretrained model. Through experimentation\non 16 diverse vision datasets, we show HPT converges up to 80x faster, improves\naccuracy across tasks, and improves the robustness of the self-supervised\npretraining process to changes in the image augmentation policy or amount of\npretraining data. Taken together, HPT provides a simple framework for obtaining\nbetter pretrained representations with less computational resources.\n"}
+{"abstract": "  Currently, every 1 in 54 children have been diagnosed with Autism Spectrum\nDisorder (ASD), which is 178% higher than it was in 2000. An early diagnosis\nand treatment can significantly increase the chances of going off the spectrum\nand making a full recovery. With a multitude of physical and behavioral tests\nfor neurological and communication skills, diagnosing ASD is very complex,\nsubjective, time-consuming, and expensive. We hypothesize that the use of\nmachine learning analysis on facial features and social behavior can speed up\nthe diagnosis of ASD without compromising real-world performance. We propose to\ndevelop a hybrid architecture using both categorical data and image data to\nautomate traditional ASD pre-screening, which makes diagnosis a quicker and\neasier process. We created and tested a Logistic Regression model and a Linear\nSupport Vector Machine for Module 1, which classifies ADOS categorical data. A\nConvolutional Neural Network and a DenseNet network are used for module 2,\nwhich classifies video data. Finally, we combined the best performing models, a\nLinear SVM and DenseNet, using three data averaging strategies. We used a\nstandard average, weighted based on number of training data, and weighted based\non the number of ASD patients in the training data to average the results,\nthereby increasing accuracy in clinical applications. The results we obtained\nsupport our hypothesis. Our novel architecture is able to effectively automate\nASD pre-screening with a maximum weighted accuracy of 84%.\n"}
+{"abstract": "  With the Deep Neural Networks (DNNs) as a powerful function approximator,\nDeep Reinforcement Learning (DRL) has been excellently demonstrated on robotic\ncontrol tasks. Compared to DNNs with vanilla artificial neurons, the\nbiologically plausible Spiking Neural Network (SNN) contains a diverse\npopulation of spiking neurons, making it naturally powerful on state\nrepresentation with spatial and temporal information. Based on a hybrid\nlearning framework, where a spike actor-network infers actions from states and\na deep critic network evaluates the actor, we propose a Population-coding and\nDynamic-neurons improved Spiking Actor Network (PDSAN) for efficient state\nrepresentation from two different scales: input coding and neuronal coding. For\ninput coding, we apply population coding with dynamically receptive fields to\ndirectly encode each input state component. For neuronal coding, we propose\ndifferent types of dynamic-neurons (containing 1st-order and 2nd-order neuronal\ndynamics) to describe much more complex neuronal dynamics. Finally, the PDSAN\nis trained in conjunction with deep critic networks using the Twin Delayed Deep\nDeterministic policy gradient algorithm (TD3-PDSAN). Extensive experimental\nresults show that our TD3-PDSAN model achieves better performance than\nstate-of-the-art models on four OpenAI gym benchmark tasks. It is an important\nattempt to improve RL with SNN towards the effective computation satisfying\nbiological plausibility.\n"}
+{"abstract": "  Mode-locking operation and multimode instabilities in Terahertz (THz) quantum\ncascade lasers (QCLs) have been intensively investigated during the last\ndecade. These studies have unveiled a rich phenomenology, owing to the unique\nproperties of these lasers, in particular their ultrafast gain medium. Thanks\nto this, in QCLs a modulation of the intracavity field intensity gives rise to\na strong modulation of the population inversion, directly affecting the laser\ncurrent. In this work we show that this property can be used to study the\nreal-time dynamics of multimode THz QCLs, using a self-detection technique\ncombined with a 60GHz real-time oscilloscope. To demonstrate the potential of\nthis technique we investigate a free-running 4.2THz QCL, and observe a\nself-starting periodic modulation of the laser current, producing trains of\nregularly spaced, ~100ps-long pulses. Depending on the drive current we find\ntwo regimes of oscillation with dramatically different properties: a first\nregime at the fundamental repetition rate, characterised by large amplitude and\nphase noise, with coherence times of a few tens of periods; a much more regular\nsecond-harmonic-comb regime, with typical coherence times of ~105 oscillation\nperiods. We interpret these measurements using a set of effective semiconductor\nMaxwell-Bloch equations that qualitatively reproduce the fundamental features\nof the laser dynamics, indicating that the observed carrier-density and optical\npulses are in antiphase, and appear as a rather shallow modulation on top of a\ncontinuous wave background. Thanks to its simplicity and versatility, the\ndemonstrated technique is a powerful tool for the study of ultrafast dynamics\nin THz QCLs.\n"}
+{"abstract": "  We study the renormalization of Entanglement Entropy in holographic CFTs dual\nto Lovelock gravity. It is known that the holographic EE in Lovelock gravity is\ngiven by the Jacobson-Myers (JM) functional. As usual, due to the divergent\nWeyl factor in the Fefferman-Graham expansion of the boundary metric for\nAsymptotically AdS spaces, this entropy functional is infinite. By considering\nthe Kounterterm renormalization procedure, which utilizes extrinsic boundary\ncounterterms in order to renormalize the on-shell Lovelock gravity action for\nAAdS spacetimes, we propose a new renormalization prescription for the\nJacobson-Myers functional. We then explicitly show the cancellation of\ndivergences in the EE up to next-to-leading order in the holographic radial\ncoordinate, for the case of spherical entangling surfaces. Using this new\nrenormalization prescription, we directly find the $C-$function candidates for\nodd and even dimensional CFTs dual to Lovelock gravity. Our results illustrate\nthe notable improvement that the Kounterterm method affords over other\napproaches, as it is non-perturbative and does not require that the Lovelock\ntheory has limiting Einstein behavior.\n"}
+{"abstract": "  We study identification of linear systems with multiplicative noise from\nmultiple trajectory data. A least-squares algorithm, based on exploratory\ninputs, is proposed to simultaneously estimate the parameters of the nominal\nsystem and the covariance matrix of the multiplicative noise. The algorithm\ndoes not need prior knowledge of the noise or stability of the system, but\nrequires mild conditions of inputs and relatively small length for each\ntrajectory. Identifiability of the noise covariance matrix is studied, showing\nthat there exists an equivalent class of matrices that generate the same\nsecond-moment dynamic of system states. It is demonstrated how to obtain the\nequivalent class based on estimates of the noise covariance. Asymptotic\nconsistency of the algorithm is verified under sufficiently exciting inputs and\nsystem controllability conditions. Non-asymptotic estimation performance is\nalso analyzed under the assumption that system states and noise are bounded,\nproviding vanishing high-probability bounds as the number of trajectories grows\nto infinity. The results are illustrated by numerical simulations.\n"}
+{"abstract": "  Yield farming has been an immensely popular activity for cryptocurrency\nholders since the explosion of Decentralized Finance (DeFi) in the summer of\n2020. In this Systematization of Knowledge (SoK), we study a general framework\nfor yield farming strategies with empirical analysis. First, we summarize the\nfundamentals of yield farming by focusing on the protocols and tokens used by\naggregators. We then examine the sources of yield and translate those into\nthree example yield farming strategies, followed by the simulations of yield\nfarming performance, based on these strategies. We further compare four major\nyield aggregrators -- Idle, Pickle, Harvest and Yearn -- in the ecosystem,\nalong with brief introductions of others. We systematize their strategies and\nrevenue models, and conduct an empirical analysis with on-chain data from\nexample vaults, to find a plausible connection between data anomalies and\nhistorical events. Finally, we discuss the benefits and risks of yield\naggregators.\n"}
+{"abstract": "  We propose a Point-Voxel DeConvolution (PVDeConv) module for 3D data\nautoencoder. To demonstrate its efficiency we learn to synthesize\nhigh-resolution point clouds of 10k points that densely describe the underlying\ngeometry of Computer Aided Design (CAD) models. Scanning artifacts, such as\nprotrusions, missing parts, smoothed edges and holes, inevitably appear in real\n3D scans of fabricated CAD objects. Learning the original CAD model\nconstruction from a 3D scan requires a ground truth to be available together\nwith the corresponding 3D scan of an object. To solve the gap, we introduce a\nnew dedicated dataset, the CC3D, containing 50k+ pairs of CAD models and their\ncorresponding 3D meshes. This dataset is used to learn a convolutional\nautoencoder for point clouds sampled from the pairs of 3D scans - CAD models.\nThe challenges of this new dataset are demonstrated in comparison with other\ngenerative point cloud sampling models trained on ShapeNet. The CC3D\nautoencoder is efficient with respect to memory consumption and training time\nas compared to stateof-the-art models for 3D data generation.\n"}
+{"abstract": "  We investigate quantitative aspects of the LEF property for subgroups of the\ntopological full group $[[ \\sigma ]]$ of a two-sided minimal subshift over a\nfinite alphabet, measured via the LEF growth function. We show that the LEF\ngrowth of $[[ \\sigma ]]^{\\prime}$ may be bounded from above and below in terms\nof the recurrence function and the complexity function of the subshift,\nrespectively. As an application, we construct groups of previously unseen LEF\ngrowth types, and exhibit a continuum of finitely generated LEF groups which\nmay be distinguished from one another by their LEF growth.\n"}
+{"abstract": "  Riordan arrays, denoted by pairs of generating functions (g(z), f(z)), are\ninfinite lower-triangular matrices that are used as combinatorial tools. In\nthis paper, we present Riordan and stochastic Riordan arrays that have\nconnections to the Fibonacci and modified Lucas numbers. Then, we present some\npseudo-involutions in the Riordan group that are based on constructions\nstarting with a certain generating function g(z). We also present a theorem\nthat shows how to construct pseudo-involutions in the Riordan group starting\nwith a certain generating function f(z) whose additive inverse has\ncompositional order 2. The theorem is then used to construct more\npseudo-involutions in the Riordan group where some arrays have connections to\nthe Fibonacci and modified Lucas numbers. A MATLAB algorithm for constructing\nthe pseudo-involutions is also given.\n"}
+{"abstract": "  The measurement of bias in machine learning often focuses on model\nperformance across identity subgroups (such as man and woman) with respect to\ngroundtruth labels. However, these methods do not directly measure the\nassociations that a model may have learned, for example between labels and\nidentity subgroups. Further, measuring a model's bias requires a fully\nannotated evaluation dataset which may not be easily available in practice. We\npresent an elegant mathematical solution that tackles both issues\nsimultaneously, using image classification as a working example. By treating a\nclassification model's predictions for a given image as a set of labels\nanalogous to a bag of words, we rank the biases that a model has learned with\nrespect to different identity labels. We use (man, woman) as a concrete example\nof an identity label set (although this set need not be binary), and present\nrankings for the labels that are most biased towards one identity or the other.\nWe demonstrate how the statistical properties of different association metrics\ncan lead to different rankings of the most \"gender biased\" labels, and conclude\nthat normalized pointwise mutual information (nPMI) is most useful in practice.\nFinally, we announce an open-sourced nPMI visualization tool using TensorBoard.\n"}
+{"abstract": "  Usually, in mechanics, we obtain the trajectory of a particle in a given\nforce field by solving Newton's second law with chosen initial conditions. In\ncontrast, through our work here, we first demonstrate how one may analyse the\nbehaviour of a suitably defined family of trajectories of a given mechanical\nsystem. Such an approach leads us to develop a mechanics analog following the\nwell-known Raychaudhuri equation largely studied in Riemannian geometry and\ngeneral relativity. The idea of geodesic focusing, which is more familiar to a\nrelativist, appears to be analogous to the meeting of trajectories of a\nmechanical system within a finite time. Applying our general results to the\ncase of simple pendula, we obtain relevant quantitative consequences.\nThereafter, we set up and perform a straightforward experiment based on a\nsystem with two pendula. The experimental results on this system are found to\ntally well with our proposed theoretical model. In summary, the simple theory,\nas well as the related experiment, provides us with a way to understand the\nessence of a fairly involved concept in advanced physics from an elementary\nstandpoint.\n"}
+{"abstract": "  Recently it has become essential to search for and retrieve high-resolution\nand efficient images easily due to swift development of digital images, many\npresent annotation algorithms facing a big challenge which is the variance for\nrepresent the image where high level represent image semantic and low level\nillustrate the features, this issue is known as semantic gab. This work has\nbeen used MPEG-7 standard to extract the features from the images, where the\ncolor feature was extracted by using Scalable Color Descriptor (SCD) and Color\nLayout Descriptor (CLD), whereas the texture feature was extracted by employing\nEdge Histogram Descriptor (EHD), the CLD produced high dimensionality feature\nvector therefore it is reduced by Principal Component Analysis (PCA). The\nfeatures that have extracted by these three descriptors could be passing to the\nclassifiers (Naive Bayes and Decision Tree) for training. Finally, they\nannotated the query image. In this study TUDarmstadt image bank had been used.\nThe results of tests and comparative performance evaluation indicated better\nprecision and executing time of Naive Bayes classification in comparison with\nDecision Tree classification.\n"}
+{"abstract": "  According to the O'Nan--Scott Theorem, a finite primitive permutation group\neither preserves a structure of one of three types (affine space, Cartesian\nlattice, or diagonal semilattice), or is almost simple. However, diagonal\ngroups are a much larger class than those occurring in this theorem. For any\npositive integer $m$ and group $G$ (finite or infinite), there is a diagonal\nsemilattice, a sub-semilattice of the lattice of partitions of a set $\\Omega$,\nwhose automorphism group is the corresponding diagonal group. Moreover, there\nis a graph (the diagonal graph), bearing much the same relation to the diagonal\nsemilattice and group as the Hamming graph does to the Cartesian lattice and\nthe wreath product of symmetric groups.\n  Our purpose here, after a brief introduction to this semilattice and graph,\nis to establish some properties of this graph. The diagonal graph\n$\\Gamma_D(G,m)$ is a Cayley graph for the group~$G^m$, and so is\nvertex-transitive. We establish its clique number in general and its chromatic\nnumber in most cases, with a conjecture about the chromatic number in the\nremaining cases. We compute the spectrum of the adjacency matrix of the graph,\nusing a calculation of the M\\\"obius function of the diagonal semilattice. We\nalso compute some other graph parameters and symmetry properties of the graph.\n  We believe that this family of graphs will play a significant role in\nalgebraic graph theory.\n"}
+{"abstract": "  Many Riordan arrays play a significant role in algebraic combinatorics. We\nexplore the inversion of Riordan arrays in this context. We give a general\nconstruct for the inversion of a Riordan array, and study this in the case of\nvarious subgroups of the Riordan group. For instance, we show that the\ninversion of an ordinary Bell matrix is an exponential Riordan array in the\nassociated subgroup. Examples from combinatorics and algebraic combinatorics\nillustrate the usefulness of such inversions. We end with a brief look at the\ninversion of exponential Riordan arrays. A final example places Airey's\nconvergent factor in the context of a simple exponential Riordan array.\n"}
+{"abstract": "  Unveiling point defects concentration in transition metal oxide thin films is\nessential to understand and eventually control their functional properties,\nemployed in an increasing number of applications and devices. Despite this\nunquestionable interest, there is a lack of available experimental techniques\nable to estimate the defect chemistry and equilibrium constants in such oxides\nat intermediate-to-low temperatures. In this study, the defect chemistry of a\nrelevant material such as La1-xSrxFeO3-d (LSF) with (x = 0.2, 0.4 and 0.5\n(LSF20, LSF40 and LSF50 respectively) is obtained by using a novel in situ\nspectroscopic ellipsometry approach applied to thin films. Through this\ntechnique, the concentration of holes in LSF is correlated to measured optical\nproperties and its evolution with temperature and oxygen partial pressure is\ndetermined. In this way, a systematic description of defect chemistry in LSF\nthin films in the temperature range from 350dC to 500dC is obtained for the\nfirst time, which represents a step forward in the understanding of LSF20,\nLSF40 and LSF50 for emerging low temperature applications.\n"}
+{"abstract": "  We use a theorem of P. Berger and D. Turaev to construct an example of a\nFinsler geodesic flow on the 2-torus with a transverse section, such that its\nPoincar\\'e return map has positive metric entropy. The Finsler metric\ngenerating the flow can be chosen to be arbitrarily $C^\\infty$-close to a flat\nmetric.\n"}
+{"abstract": "  In this paper, we characterize the performance of a three-dimensional (3D)\ntwo-hop cellular network in which terrestrial base stations (BSs) coexist with\nunmanned aerial vehicles (UAVs) to serve a set of ground user equipment (UE).\nIn particular, a UE connects either directly to its serving terrestrial BS by\nan access link or connects first to its serving UAV which is then wirelessly\nbackhauled to a terrestrial BS (joint access and backhaul). We consider\nrealistic antenna radiation patterns for both BSs and UAVs using practical\nmodels developed by the third generation partnership project (3GPP). We assume\na probabilistic channel model for the air-to-ground transmission, which\nincorporates both line-of-sight (LoS) and non-line-of-sight (NLoS) links.\nAssuming the max-power association policy, we study the performance of the\nnetwork in both amplify-and-forward (AF) and decode-and-forward (DF) relaying\nprotocols. Using tools from stochastic geometry, we analyze the joint\ndistribution of distance and zenith angle of the closest (and serving) UAV to\nthe origin in a 3D setting. Further, we identify and extensively study key\nmathematical constructs as the building blocks of characterizing the received\nsignal-to-interference-plus-noise ratio (SINR) distribution. Using these\nresults, we obtain exact mathematical expressions for the coverage probability\nin both AF and DF relaying protocols. Furthermore, considering the fact that\nbackhaul links could be quite weak because of the downtilted antennas at the\nBSs, we propose and analyze the addition of a directional uptilted antenna at\nthe BS that is solely used for backhaul purposes. The superiority of having\ndirectional antennas with wirelessly backhauled UAVs is further demonstrated\nvia simulation.\n"}
+{"abstract": "  Edge computing has become one of the key enablers for ultra-reliable and\nlow-latency communications in the industrial Internet of Things in the fifth\ngeneration communication systems, and is also a promising technology in the\nfuture sixth generation communication systems. In this work, we consider the\napplication of edge computing to smart factories for mission-critical task\noffloading through wireless links. In such scenarios, although high end-to-end\ndelays from the generation to completion of tasks happen with low probability,\nthey may incur severe casualties and property loss, and should be seriously\ntreated. Inspired by the risk management theory widely used in finance, we\nadopt the Conditional Value at Risk to capture the tail of the delay\ndistribution. An upper bound of the Conditional Value at Risk is derived\nthrough analysis of the queues both at the devices and the edge computing\nservers. We aim to find out the optimal offloading policy taking into\nconsideration both the average and the worst case delay performance of the\nsystem. Given that the formulated optimization problem is a non-convex mixed\ninteger non-linear programming problem, a decomposition into sub-problems is\nperformed and a two-stage heuristic algorithm is proposed. Simulation results\nvalidate our analysis and indicate that the proposed algorithm can reduce the\nrisk in both the queuing and end-to-end delay.\n"}
+{"abstract": "  For many years, the image databases used in steganalysis have been relatively\nsmall, i.e. about ten thousand images. This limits the diversity of images and\nthus prevents large-scale analysis of steganalysis algorithms.\n  In this paper, we describe a large JPEG database composed of 2 million colour\nand grey-scale images. This database, named LSSD for Large Scale Steganalysis\nDatabase, was obtained thanks to the intensive use of \\enquote{controlled}\ndevelopment procedures. LSSD has been made publicly available, and we aspire it\ncould be used by the steganalysis community for large-scale experiments.\n  We introduce the pipeline used for building various image database versions.\nWe detail the general methodology that can be used to redevelop the entire\ndatabase and increase even more the diversity. We also discuss computational\ncost and storage cost in order to develop images.\n"}
+{"abstract": "  Machine-Learning-as-a-Service providers expose machine learning (ML) models\nthrough application programming interfaces (APIs) to developers. Recent work\nhas shown that attackers can exploit these APIs to extract good approximations\nof such ML models, by querying them with samples of their choosing. We propose\nVarDetect, a stateful monitor that tracks the distribution of queries made by\nusers of such a service, to detect model extraction attacks. Harnessing the\nlatent distributions learned by a modified variational autoencoder, VarDetect\nrobustly separates three types of attacker samples from benign samples, and\nsuccessfully raises an alarm for each. Further, with VarDetect deployed as an\nautomated defense mechanism, the extracted substitute models are found to\nexhibit poor performance and transferability, as intended. Finally, we\ndemonstrate that even adaptive attackers with prior knowledge of the deployment\nof VarDetect, are detected by it.\n"}
+{"abstract": "  Fog computing can be used to offload computationally intensive tasks from\nbattery powered Internet of Things (IoT) devices. Although it reduces energy\nrequired for computations in an IoT device, it uses energy for communications\nwith the fog. This paper analyzes when usage of fog computing is more energy\nefficient than local computing. Detailed energy consumption models are built in\nboth scenarios with the focus set on the relation between energy consumption\nand distortion introduced by a Power Amplifier (PA). Numerical results show\nthat task offloading to a fog is the most energy efficient for short, wideband\nlinks.\n"}
+{"abstract": "  The geometric properties of sigma models with target space a Jacobi manifold\nare investigated. In their basic formulation, these are topological field\ntheories - recently introduced by the authors - which share and generalise\nrelevant features of Poisson sigma models, such as gauge invariance under\ndiffeomorphisms and finite dimension of the reduced phase space. After\nreviewing the main novelties and peculiarities of these models, we perform a\ndetailed analysis of constraints and ensuing gauge symmetries in the\nHamiltonian approach. Contact manifolds as well as locally conformal symplectic\nmanifolds are discussed, as main instances of Jacobi manifolds.\n"}
+{"abstract": "  Using symmetrization techniques, we show that, for every $N \\geq 2$, any\nsecond eigenfunction of the fractional Laplacian in the $N$-dimensional unit\nball with homogeneous Dirichlet conditions is nonradial, and hence its nodal\nset is an equatorial section of the ball.\n"}
+{"abstract": "  In March 2020 the United Kingdom (UK) entered a nationwide lockdown period\ndue to the Covid-19 pandemic. As a result, levels of nitrogen dioxide (NO2) in\nthe atmosphere dropped. In this work, we use 550,134 NO2 data points from 237\nstations in the UK to build a spatiotemporal Gaussian process capable of\npredicting NO2 levels across the entire UK. We integrate several covariate\ndatasets to enhance the model's ability to capture the complex spatiotemporal\ndynamics of NO2. Our numerical analyses show that, within two weeks of a UK\nlockdown being imposed, UK NO2 levels dropped 36.8%. Further, we show that as a\ndirect result of lockdown NO2 levels were 29-38% lower than what they would\nhave been had no lockdown occurred. In accompaniment to these numerical\nresults, we provide a software framework that allows practitioners to easily\nand efficiently fit similar models.\n"}
+{"abstract": "  Locally-rotationally-symmetric Bianchi type-I viscous and non -viscous\ncosmological models are explored in general relativity (GR) and in f(R,T)\ngravity. Solutions are obtained by assuming that the expansion scalar is\nproportional to the shear scalar which yields a constant value for the\ndeceleration parameter (q=2). Constraints are obtained by requiring the\nphysical viability of the solutions. A comparison is made between the viscous\nand non-viscous models, and between the models in GR and in f(R,T) gravity. The\nmetric potentials remain the same in GR and in f(R,T) gravity. Consequently,\nthe geometrical behavior of the $f(R,T)$ gravity models remains the same as the\nmodels in GR. It is found that f(R,T) gravity or bulk viscosity does not affect\nthe behavior of effective matter which acts as a stiff fluid in all models. The\nindividual fluids have very rich behavior. In one of the viscous models, the\nmatter either follows a semi-realistic EoS or exhibits a transition from stiff\nmatter to phantom, depending on the values of the parameter. In another model,\nthe matter describes radiation, dust, quintessence, phantom, and the\ncosmological constant for different values of the parameter. In general, f(R,T)\ngravity diminishes the effect of bulk viscosity.\n"}
+{"abstract": "  We propose TubeR: a simple solution for spatio-temporal video action\ndetection. Different from existing methods that depend on either an off-line\nactor detector or hand-designed actor-positional hypotheses like proposals or\nanchors, we propose to directly detect an action tubelet in a video by\nsimultaneously performing action localization and recognition from a single\nrepresentation. TubeR learns a set of tubelet-queries and utilizes a\ntubelet-attention module to model the dynamic spatio-temporal nature of a video\nclip, which effectively reinforces the model capacity compared to using\nactor-positional hypotheses in the spatio-temporal space. For videos containing\ntransitional states or scene changes, we propose a context aware classification\nhead to utilize short-term and long-term context to strengthen action\nclassification, and an action switch regression head for detecting the precise\ntemporal action extent. TubeR directly produces action tubelets with variable\nlengths and even maintains good results for long video clips. TubeR outperforms\nthe previous state-of-the-art on commonly used action detection datasets AVA,\nUCF101-24 and JHMDB51-21.\n"}
+{"abstract": "  We prove the existence of an extremal function in the\nHardy-Littlewood-Sobolev inequality for the energy associated to an stable\noperator. To this aim we obtain a concentration-compactness principle for\nstable processes in $\\mathbb{R}^N$.\n"}
+{"abstract": "  Smooth interfaces of topological systems are known to host massive surface\nstates along with the topologically protected chiral one. We show that in Weyl\nsemimetals these massive states, along with the chiral Fermi arc, strongly\nalter the form of the Fermi-arc plasmon, Most saliently, they yield further\ncollective plasmonic modes that are absent in a conventional interfaces. The\nplasmon modes are completely anisotropic as a consequence of the underlying\nanisotropy in the surface model and expected to have a clear-cut experimental\nsignature, e.g. in electron-energy loss spectroscopy.\n"}
+{"abstract": "  A two-class Processor-Sharing queue with one impatient class is studied.\nLocal exponential decay rates for its stationary distribution (N, M) are\nestablished in the heavy traffic regime where the arrival rate of impatient\ncustomers grows proportionally to a large factor A. This regime is\ncharacterized by two time-scales, so that no general Large Deviations result is\napplicable. In the framework of singular perturbation methods, we instead\nassume that an asymptotic expansion of the solution of associated Kolmogorov\nequations exists for large A and derive it in the form P(N = Ax, M = Ay) ~\ng(x,y)/A exp(-A H(x,y)) for x > 0 and y > 0 with explicit functions g and H.\n  This result is then applied to the model of mobile networks proposed in a\nprevious work and accounting for the spatial movement of users. We give further\nevidence of a unusual growth behavior in heavy traffic in that the stationary\nmean queue length E(N') and E(M') of each customer-class increases\nproportionally to E(N') ~ E(M') ~ -log(1-rho) with system load rho tending to\n1, instead of the usual 1/(1-rho) growth behavior.\n"}
+{"abstract": "  Loosely bound van der Waals dimers of lanthanide atoms, as might be obtained\nin ultracold atom experiments, are investigated. These molecules are known to\nexhibit a degree of quantum chaos, due to the strong anisotropic mixing of\ntheir angular spin and rotation degrees of freedom. Within a model of these\nmolecules, we identify different realms of this anisotropic mixing, depending\non whether the spin, the rotation, or both, are significantly mixed by the\nanisotropy. These realms are in turn generally correlated with the resulting\nmagnetic moments of the states.\n"}
+{"abstract": "  We have investigated the structural, magnetic and dielectric properties of\nPb-based langasite compound Pb$_3$TeMn$_3$P$_2$O$_{14}$ both experimentally and\ntheoretically in the light of metal-oxygen covalency, and the consequent\ngeneration of multiferroicity. It is known that large covalency between Pb 6$p$\nand O 2$p$ plays instrumental role behind stereochemical lone pair activity of\nPb. The same happens here but a subtle structural phase transition above room\ntemperature changes the degree of such lone pair activity and the system\nbecomes ferroelectric below 310 K. Interestingly, this structural change also\nmodulates the charge densities on different constituent atoms and consequently\nthe overall magnetic response of the system while maintaining global\nparamagnetism behavior of the compound intact. This single origin of modulation\nin polarity and paramagnetism inherently connects both the functionalities and\nthe system exhibits mutiferroicity at room temperature.\n"}
+{"abstract": "  We present a series of models of three-dimensional rotation-symmetric fragile\ntopological insulators in class AI (time-reversal symmetric and spin-orbit-free\nsystems), which have gapless surface states protected by time-reversal ($T$)\nand $n$-fold rotation ($C_n$) symmetries ($n=2,4,6$). Our models are\ngeneralizations of Fu's model of a spinless topological crystalline insulator,\nin which orbital degrees of freedom play the role of pseudo-spins. We consider\nminimal surface Hamiltonian with $C_n$ symmetry in class AI and discuss\npossible symmetry-protected gapless surface states, i.e., a quadratic band\ntouching and multiple Dirac cones with linear dispersion. We characterize\ntopological structure of bulk wave functions in terms of two kinds of\ntopological invariants obtained from Wilson loops: $\\mathbb{Z}_2$ invariants\nprotected by $C_n$ ($n=4,6$) and time-reversal symmetries, and\n$C_2T$-symmetry-protected $\\mathbb{Z}$ invariants (the Euler class) when the\nnumber of occupied bands is two. Accordingly, our models realize two kinds of\nfragile topological insulators. One is a fragile $\\mathbb{Z}$ topological\ninsulator whose only nontrivial topological index is the Euler class that\nspecifies the number of surface Dirac cones. The other is a fragile\n$\\mathbb{Z}_2$ topological insulator having gapless surface states with either\na quadratic band touching or four (six) Dirac cones, which are protected by\ntime-reversal and $C_4$ ($C_6$) symmetries. Finally, we discuss the instability\nof gapless surface states against the addition of $s$-orbital bands and\ndemonstrate that surface states are gapped out through hybridization with\nsurface-localized $s$-orbital bands.\n"}
+{"abstract": "  Complexity of products, volatility in global markets, and the increasingly\nrapid pace of innovations may make it difficult to know how to approach\nchallenging situations in mechatronic design and production. Technical Debt\n(TD) is a metaphor that describes the practical bargain of exchanging\nshort-term benefits for long-term negative consequences. Oftentimes, the scope\nand impact of TD, as well as the cost of corrective measures, are\nunderestimated. Especially for mechatronic teams in the mechanical, electrical,\nand software disciplines, the adverse interdisciplinary ripple effects of TD\nincidents are passed on throughout the life cycle. The analysis of the first\ncomprehensive survey showed that not only do the TD types differ in\ncross-disciplinary comparisons, but different characteristics can also be\nobserved depending on whether a discipline is studied in isolation or in\ncombination with others. To validate the study results and to report on a\ngeneral consciousness of TD in the disciplines, this follow-up study involves\n15 of the 50 experts of the predecessor study and reflects the frequency and\nimpact of technical debt in industrial experts' daily work using a\nquestionnaire. These experts rate 14 TD types, 47 TD causes, and 33 TD symptoms\nin terms of their frequency and impact. Detailed analyses reveal consistent\nresults for the most frequent TD types and causes, yet they show divergent\ncharacteristics in a profound exploration of discipline-specific phenomena.\nThus, this study has the potential to set the foundations for future automated\nTD identification analyses in mechatronics.\n"}
+{"abstract": "  Defect detection at commit check-in time prevents the introduction of defects\ninto software systems. Current defect detection approaches rely on metric-based\nmodels which are not very accurate and whose results are not directly useful\nfor developers. We propose a method to detect bug-inducing commits by comparing\nthe incoming changes with all past commits in the project, considering both\nthose that introduced defects and those that did not. Our method considers\nindividual changes in the commit separately, at the method-level granularity.\nDoing so helps developers as they are informed of specific methods that need\nfurther attention instead of being told that the entire commit is problematic.\nOur approach represents source code as abstract syntax trees and uses tree\nkernels to estimate the similarity of the code with previous commits. We\nexperiment with subtree kernels (STK), subset tree kernels (SSTK), or partial\ntree kernels (PTK). An incoming change is then classified using a K-NN\nclassifier on the past changes. We evaluate our approach on the BigCloneBench\nbenchmark and on the Technical Debt dataset, using the NiCad clone detector as\nthe baseline. Our experiments with the BigCloneBench benchmark show that the\ntree kernel approach can detect clones with a comparable MAP to that of NiCad.\nAlso, on defect detection with the Technical Debt dataset, tree kernels are\nleast as effective as NiCad with MRR, F-score, and Accuracy of 0.87, 0.80, and\n0.82 respectively.\n"}
+{"abstract": "  Particle-In-Cell codes are widely used for plasma physics simulations. It is\noften the case that particles within a computational cell need to be split to\nimprove the statistics or, in the case of non-uniform meshes, to avoid the\ndevelopment of fictitious self-forces. Existing particle splitting methods are\nlargely empirical and their accuracy in preserving the distribution function\nhas not been evaluated in a quantitative way. Here we present a new method\nspecifically designed for codes using adaptive mesh refinement. Although we\npoint out that an exact, distribution function preserving method does exist, it\nrequires a large number of split particles and its practical use is limited. We\nderive instead a method that minimizes the cost function representing the\ndistance between the assignment function of the original particle and that of\nthe sum of split particles. Depending on the interpolation degree and the\ndimension of the problem, we provide tabulated results for the weight and\nposition of the split particles. This strategy represents no overhead in\ncomputing time and for a large enough number of split-particles it\nasymptotically tends to the exact solution.\n"}
+{"abstract": "  Lettericity is a graph parameter introduced by Petkov\\v{s}ek in 2002 in order\nto study well-quasi-orderability under the induced subgraph relation. In the\nworld of permutations, geometric griddability was independently introduced in\n2013 by Albert, Atkinson, Bouvel, Ru\\v{s}kuc and Vatter, partly as an\nenumerative tool. Despite their independent origins, those two notions share a\nconnection: they highlight very similar structural features in their respective\nobjects. The fact that those structural features arose separately on two\ndifferent occasions makes them very interesting to study in their own right.\n  In the present paper, we explore the notion of lettericity through the lens\nof \"minimal obstructions\", i.e., minimal classes of graphs of unbounded\nlettericity, and identify an infinite collection of such classes. We also\ndiscover an intriguing structural hierarchy that arises in the study of\nlettericity and that of griddability.\n"}
+{"abstract": "  Deuterated molecules are good tracers of the evolutionary stage of\nstar-forming cores. During the star formation process, deuterated molecules are\nexpected to be enhanced in cold, dense pre-stellar cores and to deplete after\nprotostellar birth. In this paper we study the deuteration fraction of\nformaldehyde in high-mass star-forming cores at different evolutionary stages\nto investigate whether the deuteration fraction of formaldehyde can be used as\nan evolutionary tracer. Using the APEX SEPIA Band 5 receiver, we extended our\npilot study of the $J$=3$\\rightarrow$2 rotational lines of HDCO and D$_2$CO to\neleven high-mass star-forming regions that host objects at different\nevolutionary stages. High-resolution follow-up observations of eight objects in\nALMA Band 6 were performed to reveal the size of the H$_2$CO emission and to\ngive an estimate of the deuteration fractions HDCO/H$_2$CO and D$_2$CO/HDCO at\nscales of $\\sim$6\" (0.04-0.15 pc at the distance of our targets). Our\nobservations show that singly- and doubly deuterated H$_2$CO are detected\ntoward high-mass protostellar objects (HMPOs) and ultracompact HII regions\n(UCHII regions), the deuteration fraction of H$_2$CO is also found to decrease\nby an order of magnitude from the earlier HMPO phases to the latest\nevolutionary stage (UCHII), from $\\sim$0.13 to $\\sim$0.01. We have not detected\nHDCO and D$_2$CO emission from the youngest sources (high-mass starless cores,\nHMSCs). Our extended study supports the results of the previous pilot study:\nthe deuteration fraction of formaldehyde decreases with evolutionary stage, but\nhigher sensitivity observations are needed to provide more stringent\nconstraints on the D/H ratio during the HMSC phase. The calculated upper limits\nfor the HMSC sources are high, so the trend between HMSC and HMPO phases cannot\nbe constrained.\n"}
+{"abstract": "  In this paper we investigate multi-agent discrete-event systems with partial\nobservation. The agents can be divided into several groups in each of which the\nagents have similar (isomorphic) state transition structures, and thus can be\nrelabeled into the same template. Based on the template a scalable supervisor\nwhose state size and computational cost are independent of the number of agents\nis designed for the case of partial observation. The scalable supervisor under\npartial observation does not need to be recomputed regardless of how many\nagents are added to or removed from the system. We generalize our earlier\nresults to partial observation by proposing sufficient conditions for safety\nand maximal permissiveness of the scalable least restrictive supervisor on the\ntemplate level. An example is provided to illustrate the proposed scalable\nsupervisory synthesis.\n"}
+{"abstract": "  We study best approximations in Banach spaces via Birkhoff-James\northogonality of functionals. To exhibit the usefulness of Birkhoff-James\northogonality techniques in the study of best approximation problems, some\nalgorithms and distance formulae are presented. As an application of our study,\nwe obtain some crucial inequalities, which also strengthen the classical\nH\\\"{o}lder's inequality. The relevance of the algorithms and the inequalities\nare discussed through concrete examples.\n"}
+{"abstract": "  We present an alternative proof of asymptotic freeness of independent sample\ncovariance matrices, when the dimension and the sample size grow at the same\nrate, by embedding these matrices into Wigner matrices of a larger order and\nusing asymptotic freeness of independent Wigner and deterministic matrices.\n"}
+{"abstract": "  Despite the structural resemblance of certain cuprate and nickelate parent\ncompounds there is a striking spread of $T_c$ among such transition metal oxide\nsuperconductors. We adopt a minimal two-orbital $e_g$ model which covers\ncuprates and nickelate heterostructures in different parametric limits, and\nanalyse its superconducting instabilities. The joint consideration of\ninteractions, doping, Fermiology, and in particular the $e_g$ orbital splitting\nallows us to explain the strongly differing pairing propensities in cuprate and\nnickelate superconductors.\n"}
+{"abstract": "  We use the plasma density based on measurements of the probe-to-spacecraft\npotential in combination with magnetic field measurements by MAG to study\nfields and density fluctuations in the solar wind observed by Solar Orbiter\nduring the first perihelion encounter ($\\sim$0.5~AU away from the Sun). In\nparticular we use the polarization of the wave magnetic field, the phase\nbetween the compressible magnetic field and density fluctuations and the\ncompressibility ratio (the ratio of the normalized density fluctuations to the\nnormalized compressible fluctuations of B) to characterize the observed waves\nand turbulence. We find that the density fluctuations are out-of-phase with the\ncompressible component of magnetic fluctuations for intervals of turbulence,\nwhile they are in phase for the circular-polarized waves around the proton\ncyclotron frequency. We analyze in detail two specific events with simultaneous\npresence of left- and right-handed waves at different frequencies. We compare\nobserved wave properties to a prediction of the three-fluid (electrons, protons\nand alphas) model. We find a limit on the observed wavenumbers, $10^{-6} < k <\n7 \\times 10^{-6}$~m$^{-1}$, which corresponds to wavelength $7 \\times 10^6\n>\\lambda > 10^6$~m. We conclude that most likely both the left- and\nright-handed waves correspond to the low-wavenumber part (close to the cut-off\nat $\\Omega_{c\\mathrm{He}++}$) proton-band electromagnetic ion cyclotron\n(left-handed wave in the plasma frame confined to the frequency range\n$\\Omega_{c\\mathrm{He}++} < \\omega < \\Omega_{c\\mathrm{H}+}$) waves propagating\nin the outwards and inwards directions respectively. The fact that both wave\npolarizations are observed at the same time and the identified wave mode has a\nlow group velocity suggests that the double-banded events occur in the source\nregions of the waves.\n"}
+{"abstract": "  Finite players gather information about an uncertain state before making\ndecisions. Each player allocates his limited attention capacity between biased\nsources and the other players, and the resulting stochastic attention network\nfacilitates the transmission of information from primary sources to him either\ndirectly or indirectly through the other players. The scarcity of attention\nleads the player to focus on his own-biased source, resulting in occasional\ncross-cutting exposures but most of the time a reinforcement of his\npredisposition. It also limits his attention to like-minded friends who, by\nattending to the same primary source as his, serve as secondary sources in case\nthe information transmission from the primary source to him is disrupted. A\nmandate on impartial exposures to all biased sources disrupts echo chambers but\nentails ambiguous welfare consequences. Inside an echo chamber, even a small\namount of heterogeneity between players can generate fat-tailed distributions\nof public opinion, and factors affecting the visibility of sources and players\ncould have unintended consequences for public opinion and consumer welfare.\n"}
+{"abstract": "  This report focuses on safety aspects of connected and automated vehicles\n(CAVs). The fundamental question to be answered is how can CAVs improve road\nusers' safety? Using advanced data mining and thematic text analytics tools,\nthe goal is to systematically synthesize studies related to Big Data for safety\nmonitoring and improvement. Within this domain, the report systematically\ncompares Big Data initiatives related to transportation initiatives nationally\nand internationally and provides insights regarding the evolution of Big Data\nscience applications related to CAVs and new challenges. The objectives\naddressed are: 1-Creating a database of Big Data efforts by acquiring reports,\nwhite papers, and journal publications; 2-Applying text analytics tools to\nextract key concepts, and spot patterns and trends in Big Data initiatives;\n3-Understanding the evolution of CAV Big Data in the context of safety by\nquantifying granular taxonomies and modeling entity relations among contents in\nCAV Big Data research initiatives, and 4-Developing a foundation for exploring\nnew approaches to tracking and analyzing CAV Big Data and related innovations.\nThe study synthesizes and derives high-quality information from innovative\nresearch activities undertaken by various research entities through Big Data\ninitiatives. The results can provide a conceptual foundation for developing new\napproaches for guiding and tracking the safety implications of Big Data and\nrelated innovations.\n"}
+{"abstract": "  Performance of recommender systems (RS) relies heavily on the amount of\ntraining data available. This poses a chicken-and-egg problem for early-stage\nproducts, whose amount of data, in turn, relies on the performance of their RS.\nOn the other hand, zero-shot learning promises some degree of generalization\nfrom an old dataset to an entirely new dataset. In this paper, we explore the\npossibility of zero-shot learning in RS. We develop an algorithm, dubbed\nZEro-Shot Recommenders (ZESRec), that is trained on an old dataset and\ngeneralize to a new one where there are neither overlapping users nor\noverlapping items, a setting that contrasts typical cross-domain RS that has\neither overlapping users or items. Different from categorical item indices,\ni.e., item ID, in previous methods, ZESRec uses items' natural-language\ndescriptions (or description embeddings) as their continuous indices, and\ntherefore naturally generalize to any unseen items. In terms of users, ZESRec\nbuilds upon recent advances on sequential RS to represent users using their\ninteractions with items, thereby generalizing to unseen users as well. We study\nthree pairs of real-world RS datasets and demonstrate that ZESRec can\nsuccessfully enable recommendations in such a zero-shot setting, opening up new\nopportunities for resolving the chicken-and-egg problem for data-scarce\nstartups or early-stage products.\n"}
+{"abstract": "  We revisit gravitational particle production from the Stokes phenomenon\nviewpoint, which helps us make a systematic way to understand asymptotic\nbehavior of mode functions in time-dependent background. One of our purposes of\nthis work is to make the method more practical for evaluation of\nnon-perturbative particle production rate. In particular, with several examples\nof time-dependent backgrounds, we introduce some approximation methods that\nmake the analysis more practical. Specifically, we consider particle production\nin simple expanding backgrounds, preheating after $R^2$ inflation, and a\ntransition model with smoothly changing mass. As we find several technical\nissues in analyzing the Stokes phenomenon of each example, we discuss how to\nsimplify the problems while showing the accuracy of analytic estimation under\nthe approximations we make.\n"}
+{"abstract": "  We present new BVR band photometric light curves of BO Aries obtained in 2020\nand combined them with the Transiting Exoplanet Survey Satellite (TESS) light\ncurves. We obtained times of minima based on Gaussian and Cauchy distributions\nand then applied the Monte Carlo Markov Chain (MCMC) method to measure the\namount of uncertainty from our CCD photometry and TESS data. A new ephemeris of\nthe binary system was computed employing 204 times of minimum. The light curves\nwere analyzed using the Wilson-Devinney binary code combined with the Monte\nCarlo (MC) simulation. For this light curve solution, we considered a dark spot\non the primary component. We conclude that this binary is an A-type system with\na mass ratio of q=0.2074+-0.0001, an orbital inclination of i=82.18+-0.02 deg,\nand a fillout factor of f=75.7+-0.8%. Our results for the a(Rsun) and q\nparameters are consistent with the results of the Xu-Dong Zhang and Sheng-Bang\nQian (2020) model. The absolute parameters of the two components were\ncalculated and the distance estimate of the binary system was found to be\n142+-9 pc.\n"}
+{"abstract": "  For Dirichlet $L$-functions in $\\mathbb{F}_q [T]$ we obtain a hybrid\nEuler-Hadamard product formula. We make a splitting conjecture, namely that the\n$2k$-th moment of the Dirichlet $L$-functions at $\\frac{1}{2}$, averaged over\nprimitive characters of modulus $R$, is asymptotic to (as $\\mathrm{deg} R\n\\longrightarrow \\infty$) the $2k$-th moment of the Euler product multiplied by\nthe $2k$-th moment of the Hadamard product. We explicitly obtain the main term\nof the $2k$-th moment of the Euler product, and we conjecture via random matrix\ntheory the main term of the $2k$-th moment of the Hadamard product. With the\nsplitting conjecture, this directly leads to a conjecture for the $2k$-th\nmoment of Dirichlet $L$-functions. Finally, we lend support for the splitting\nconjecture by proving the cases $k=1,2$. This work is the function field\nanalogue of the work of Bui and Keating. A notable difference in the function\nfield setting is that the Euler-Hadamard product formula is exact, in that\nthere is no error term.\n"}
+{"abstract": "  Nonadiabatic holonomic quantum computation (NHQC) provides a method to\nimplement error resilient gates and that has attracted considerable attention\nrecently. Since it was proposed, three-level {\\Lambda} systems have become the\ntypical building block for NHQC and a number of NHQC schemes have been\ndeveloped based on such systems. In this paper, we investigate the realization\nof NHQC beyond the standard three-level setting. The central idea of our\nproposal is to improve NHQC by enlarging the Hilbert space of the building\nblock system and letting it have a bipartite graph structure in order to ensure\npurely holonomic evolution. Our proposal not only improves conventional\nqubit-based NHQC by efficiently reducing its duration, but also provides\nimplementations of qudit-based NHQC. Therefore, our proposal provides a further\ndevelopment of NHQC that can contribute significantly to the physical\nrealization of efficient quantum information processors.\n"}
+{"abstract": "  Bilateral trade, a fundamental topic in economics, models the problem of\nintermediating between two strategic agents, a seller and a buyer, willing to\ntrade a good for which they hold private valuations. Despite the simplicity of\nthis problem, a classical result by Myerson and Satterthwaite (1983) affirms\nthe impossibility of designing a mechanism which is simultaneously efficient,\nincentive compatible, individually rational, and budget balanced. This\nimpossibility result fostered an intense investigation of meaningful trade-offs\nbetween these desired properties. Much work has focused on approximately\nefficient fixed-price mechanisms, i.e., Blumrosen and Dobzinski (2014; 2016),\nColini-Baldeschi et al. (2016), which have been shown to fully characterize\nstrong budget balanced and ex-post individually rational direct revelation\nmechanisms. All these results, however, either assume some knowledge on the\npriors of the seller/buyer valuations, or a black box access to some samples of\nthe distributions, as in D{\\\"u}tting et al. (2021). In this paper, we cast for\nthe first time the bilateral trade problem in a regret minimization framework\nover rounds of seller/buyer interactions, with no prior knowledge on the\nprivate seller/buyer valuations. Our main contribution is a complete\ncharacterization of the regret regimes for fixed-price mechanisms with\ndifferent models of feedback and private valuations, using as benchmark the\nbest fixed price in hindsight. More precisely, we prove the following bounds on\nthe regret:\n  $\\bullet$ $\\widetilde{\\Theta}(\\sqrt{T})$ for full-feedback (i.e., direct\nrevelation mechanisms);\n  $\\bullet$ $\\widetilde{\\Theta}(T^{2/3})$ for realistic feedback (i.e.,\nposted-price mechanisms) and independent seller/buyer valuations with bounded\ndensities;\n  $\\bullet$ $\\Theta(T)$ for realistic feedback and seller/buyer valuations with\nbounded densities;\n  $\\bullet$ $\\Theta(T)$ for realistic feedback and independent seller/buyer\nvaluations;\n  $\\bullet$ $\\Theta(T)$ for the adversarial setting.\n"}
+{"abstract": "  Spontaneous synchronization is a general phenomenon in which a large\npopulation of coupled oscillators of diverse natural frequencies self-organize\nto operate in unison. The phenomenon occurs in physical and biological systems\nover a wide range of spatial and temporal scales, e.g., in electrochemical and\nelectronic oscillators, Josephson junctions, laser arrays, animal flocking,\npedestrians on footbridges, audience clapping, etc. Besides the obvious\nnecessity of the synchronous firings of cardiac cells to keep the heart\nbeating, synchrony is desired in many man-made systems such as parallel\ncomputing, electrical power-grids. On the contrary, synchrony could also be\nhazardous, e.g., in neurons, leading to impaired brain function in Parkinson's\ndisease and epilepsy. Due to this wide range of applications, collective\nsynchrony in networks of oscillators has attracted the attention of physicists,\napplied mathematicians and researchers from many other fields. An essential\naspect of synchronizing systems is that long-range order naturally appear in\nthese systems, which questions the fact whether long-range interactions may be\nparticular suitable to synchronization. In this context, it is interesting to\nremind that long-range interacting system required several adaptations from\nstatistical mechanics \\`a la Gibbs Boltzmann, in order to deal with the\npeculiarities of these systems: negative specific heat, breaking of ergodicity\nor lack of extensivity. As for synchrony, it is still lacking a theoretical\nframework to use the tools from statistical mechanics. The present issue\npresents a collection of exciting recent theoretical developments in the field\nof synchronization and long-range interactions, in order to highlight the\nmutual progresses of these twin areas.\n"}
+{"abstract": "  We construct a toy model for the harmonic oscillator that is neither\nclassical nor quantum. The model features a discrete energy spectrum, a ground\nstate with sharp position and momentum, an eigenstate with non-positive Wigner\nfunction as well as a state that has tunneling properties. The underlying\nformalism exploits that the Wigner-Weyl approach to quantum theory and the\nHamilton formalism in classical theory can be formulated in the same\noperational language, which we then use to construct generalized theories with\nwell-defined phase space. The toy model demonstrates that operational theories\nare a viable alternative to operator-based approaches for building physical\ntheories.\n"}
+{"abstract": "  Extending on ideas of Lewin, Lieb and Seiringer (Phys Rev B, 100, 035127,\n(2019)) we present a modified \"floating crystal\" trial state for Jellium (also\nknown as the classical homogeneous electron gas) with density equal to a\ncharacteristic function. This allows us to show that three definitions of the\nJellium energy coincide in dimensions $d\\geq 2$, thus extending the result of\nCotar and Petrache (arXiv: 1707.07664) and Lewin, Lieb and Seiringer (Phys Rev\nB, 100, 035127, (2019)) that the three definitions coincide in dimension $d\n\\geq 3$. We show that the Jellium energy is also equivalent to a \"renormalized\nenergy\" studied in a series of papers by Serfaty and others and thus, by work\nof B\\'etermin and Sandier (Constr Approx, 47:39-74, (2018)), we relate the\nJellium energy to the order $n$ term in the logarithmic energy of $n$ points on\nthe unit 2-sphere. We improve upon known lower bounds for this renormalized\nenergy. Additionally, we derive formulas for the Jellium energy of periodic\nconfigurations.\n"}
+{"abstract": "  Automated planning enables robots to find plans to achieve complex,\nlong-horizon tasks, given a planning domain. This planning domain consists of a\nlist of actions, with their associated preconditions and effects, and is\nusually manually defined by a human expert, which is very time-consuming or\neven infeasible. In this paper, we introduce a novel method for generating this\ndomain automatically from human demonstrations. First, we automatically segment\nand recognize the different observed actions from human demonstrations. From\nthese demonstrations, the relevant preconditions and effects are obtained, and\nthe associated planning operators are generated. Finally, a sequence of actions\nthat satisfies a user-defined goal can be planned using a symbolic planner. The\ngenerated plan is executed in a simulated environment by the TIAGo robot. We\ntested our method on a dataset of 12 demonstrations collected from three\ndifferent participants. The results show that our method is able to generate\nexecutable plans from using one single demonstration with a 92% success rate,\nand 100% when the information from all demonstrations are included, even for\npreviously unknown stacking goals.\n"}
+{"abstract": "  We investigate the upscaling of diffusive transport parameters as function of\npore scale material structure using a stochastic framework. We focus on sub-REV\n(representative elementary volume) scale where the complexity of pore space\ngeometry leads to a significant scatter of transport observations. We study a\nlarge data set of sub-REV measurements on porosity and transport ability being\na dimensionless parameter representing the ratio of diffusive flow through the\nporous volume and through an empty volume. We characterize transport ability as\nprobability distribution functions (PDFs) of porosity capturing the effect of\npore structure differences among samples. We then investigate domain size\neffects and predict the REV scale. While scatter in porosity observation\ndecrease linearly with increasing sample size, the observed scatter in\ntransport ability converges towards a constant value larger zero. Our results\nconfirm that differences in pore structure topology impact transport parameters\nat all scales. Consequently, the use of PDFs to describe the relationship of\neffective transport coefficients to porosity is advantageous to deterministic\nsemi-empirical functions. We discuss the consequences and advocate the use of\nPDFs for effective parameters in both continuum equations and data\ninterpretation of experimental or computational work. We believe that the\npresented statistics-based upscaling technique of sub-REV microscopy data\nprovides a new tool in understanding, describing and predicting macroscopic\ntransport behavior of micro-porous media.\n"}
+{"abstract": "  The linear noise approximation models the random fluctuations from the mean\nfield model of a chemical reaction that unfolds near the thermodynamic limit.\nSpecifically, the fluctuations obey a linear Langevin equation up to order\n$\\Omega^{-1/2}$, where $\\Omega$ is the size of the chemical system (usually the\nvolume). In the presence of disparate timescales, the linear noise\napproximation admits a quasi-steady-state reduction referred to as the\n\\textit{slow scale} linear noise approximation. However, the slow scale linear\napproximation has only been derived for fast/slow systems that are in Tikhonov\nstandard form. In this work, we derive, for the first time, the slow scale\nlinear noise approximation directly from Fenichel theory, without the need for\na priori scaling and dimensional analysis. In so doing, we also apply, for the\nfirst time, the slow scale linear noise approximation to fast/slow systems that\nare not in standard form. Furthermore, we demonstrate how a priori scaling\nanalysis can in some cases distort the structure of the mean field singular\nperturbation, and this distortion can lead to the incomplete reduction of the\nlinear Langevin equation. In its entirety, our paper not only presents new\nmathematical results to the fields of mathematical biology and physical\nchemistry, it also unifies the several important research results in\ndeterministic and stochastic chemical kinetics.\n"}
+{"abstract": "  Multilingual transformers (XLM, mT5) have been shown to have remarkable\ntransfer skills in zero-shot settings. Most transfer studies, however, rely on\nautomatically translated resources (XNLI, XQuAD), making it hard to discern the\nparticular linguistic knowledge that is being transferred, and the role of\nexpert annotated monolingual datasets when developing task-specific models. We\ninvestigate the cross-lingual transfer abilities of XLM-R for Chinese and\nEnglish natural language inference (NLI), with a focus on the recent\nlarge-scale Chinese dataset OCNLI. To better understand linguistic transfer, we\ncreated 4 categories of challenge and adversarial tasks (totaling 17 new\ndatasets) for Chinese that build on several well-known resources for English\n(e.g., HANS, NLI stress-tests). We find that cross-lingual models trained on\nEnglish NLI do transfer well across our Chinese tasks (e.g., in 3/4 of our\nchallenge categories, they perform as well/better than the best monolingual\nmodels, even on 3/5 uniquely Chinese linguistic phenomena such as idioms, pro\ndrop). These results, however, come with important caveats: cross-lingual\nmodels often perform best when trained on a mixture of English and high-quality\nmonolingual NLI data (OCNLI), and are often hindered by automatically\ntranslated resources (XNLI-zh). For many phenomena, all models continue to\nstruggle, highlighting the need for our new diagnostics to help benchmark\nChinese and cross-lingual models. All new datasets/code are released at\nhttps://github.com/huhailinguist/ChineseNLIProbing.\n"}
+{"abstract": "  For lower arm amputees, robotic prosthetic hands offer the promise to regain\nthe capability to perform fine object manipulation in activities of daily\nliving. Current control methods based on physiological signals such as EEG and\nEMG are prone to poor inference outcomes due to motion artifacts, variability\nof skin electrode junction impedance over time, muscle fatigue, and other\nfactors. Visual evidence is also susceptible to its own artifacts, most often\ndue to object occlusion, lighting changes, variable shapes of objects depending\non view-angle, among other factors. Multimodal evidence fusion using\nphysiological and vision sensor measurements is a natural approach due to the\ncomplementary strengths of these modalities.\n  In this paper, we present a Bayesian evidence fusion framework for grasp\nintent inference using eye-view video, gaze, and EMG from the forearm processed\nby neural network models. We analyze individual and fused performance as a\nfunction of time as the hand approaches the object to grasp it. For this\npurpose, we have also developed novel data processing and augmentation\ntechniques to train neural network components. Our experimental data analyses\ndemonstrate that EMG and visual evidence show complementary strengths, and as a\nconsequence, fusion of multimodal evidence can outperform each individual\nevidence modality at any given time. Specifically, results indicate that, on\naverage, fusion improves the instantaneous upcoming grasp type classification\naccuracy while in the reaching phase by 13.66% and 14.8%, relative to EMG and\nvisual evidence individually. An overall fusion accuracy of 95.3% among 13\nlabels (compared to a chance level of 7.7%) is achieved, and more detailed\nanalysis indicate that the correct grasp is inferred sufficiently early and\nwith high confidence compared to the top contender, in order to allow\nsuccessful robot actuation to close the loop.\n"}
+{"abstract": "  The grade of clear cell renal cell carcinoma (ccRCC) is a critical prognostic\nfactor, making ccRCC nuclei grading a crucial task in RCC pathology analysis.\nComputer-aided nuclei grading aims to improve pathologists' work efficiency\nwhile reducing their misdiagnosis rate by automatically identifying the grades\nof tumor nuclei within histopathological images. Such a task requires precisely\nsegment and accurately classify the nuclei. However, most of the existing\nnuclei segmentation and classification methods can not handle the inter-class\nsimilarity property of nuclei grading, thus can not be directly applied to the\nccRCC grading task. In this paper, we propose a Composite High-Resolution\nNetwork for ccRCC nuclei grading. Specifically, we propose a segmentation\nnetwork called W-Net that can separate the clustered nuclei. Then, we recast\nthe fine-grained classification of nuclei to two cross-category classification\ntasks, based on two high-resolution feature extractors (HRFEs) which are\nproposed for learning these two tasks. The two HRFEs share the same backbone\nencoder with W-Net by a composite connection so that meaningful features for\nthe segmentation task can be inherited for the classification task. Last, a\nhead-fusion block is applied to generate the predicted label of each nucleus.\nFurthermore, we introduce a dataset for ccRCC nuclei grading, containing 1000\nimage patches with 70945 annotated nuclei. We demonstrate that our proposed\nmethod achieves state-of-the-art performance compared to existing methods on\nthis large ccRCC grading dataset.\n"}
+{"abstract": "  Heavy ion collisions provide a unique opportunity to study the nature of\nX(3872) compared with electron-positron and proton-proton (antiproton)\ncollisions. With the abundant charm pairs produced in heavy-ion collisions, the\nproduction of multicharm hadrons and molecules can be enhanced by the\ncombination of charm and anticharm quarks in the medium. We investigate the\ncentrality and momentum dependence of X(3872) in heavy-ion collisions via the\nLangevin equation and instant coalescence model (LICM). When X(3872) is treated\nas a compact tetraquark state, the tetraquarks are produced via the coalescence\nof heavy and light quarks near the quantum chromodynamic (QCD) phase transition\ndue to the restoration of the heavy quark potential at $T\\rightarrow T_c$. In\nthe molecular scenario, loosely bound X(3872) is produced via the coalescence\nof $D^0$-$\\bar D^{*0}$ mesons in a hadronic medium after kinetic freeze-out.\nThe phase space distributions of the charm quarks and D mesons in a bulk medium\nare studied with the Langevin equation, while the coalescence probability\nbetween constituent particles is controlled by the Wigner function, which\nencodes the internal structure of the formed particle.\n  First, we employ the LICM to explain both $D^0$ and $J/\\psi$ production as a\nbenchmark. Then, we give predictions regarding X(3872) production. We find that\nthe total yield of tetraquark is several times larger than the molecular\nproduction in Pb-Pb collisions. Although the geometric size of the molecule is\nhuge, the coalescence probability is small due to strict constraints on the\nrelative momentum between $D^0$ and $\\bar D^{*0}$ in the molecular Wigner\nfunction, which significantly suppresses the molecular yield.\n"}
+{"abstract": "  With a model of a geometric theory in an arbitrary topos, we associate a site\nobtained by endowing a category of generalized elements of the model with a\nGrothendieck topology, which we call the antecedent topology. Then we show that\nthe associated sheaf topos, which we call the over-topos at the given model,\nadmits a canonical totally connected morphism to the given base topos and\nsatisfies a universal property generalizing that of the colocalization of a\ntopos at a point. We first treat the case of the base topos of sets, where\nglobal elements are sufficient to describe our site of definition; in this\ncontext, we also introduce a geometric theory classified by the over-topos,\nwhose models can be identified with the model homomorphisms towards the\n(internalizations of the) model. Then we formulate and prove the general\nstatement over an arbitrary topos, which involves the stack of generalized\nelements of the model. Lastly, we investigate the geometric and 2-categorical\naspects of the over-topos construction, exhibiting it as a bilimit in the\nbicategory of Grothendieck toposes.\n"}
+{"abstract": "  Let F be a non-Archimedean locally compact field of residue characteristic p,\nlet G be an inner form of GL(n,F) with n>0, and let l be a prime number\ndifferent from p. We describe the block decomposition of the category of finite\nlength smooth representations of G with coefficients in an algebraically closed\nfield of characteristic l. Unlike the case of complex representations of an\narbitrary p-adic reductive group and that of l-modular representations of\nGL(n,F), several non-isomorphic supercuspidal supports may correspond to the\nsame block. We describe the (finitely many) supercuspidal supports\ncorresponding to a given block. We also prove that a supercuspidal block is\nequivalent to the principal (that is, the one which contains the trivial\ncharacter) block of the multiplicative group of a suitable division algebra,\nand we determine those irreducible representations having a nontrivial\nextension with a given supercuspidal representation of G.\n"}
+{"abstract": "  Unmanned aerial vehicles (UAVs) are expected to be an integral part of\nwireless networks, and determining collision-free trajectories for multiple\nUAVs while satisfying requirements of connectivity with ground base stations\n(GBSs) is a challenging task. In this paper, we first reformulate the multi-UAV\ntrajectory optimization problem with collision avoidance and wireless\nconnectivity constraints as a sequential decision making problem in the\ndiscrete time domain. We, then, propose a decentralized deep reinforcement\nlearning approach to solve the problem. More specifically, a value network is\ndeveloped to encode the expected time to destination given the agent's joint\nstate (including the agent's information, the nearby agents' observable\ninformation, and the locations of the nearby GBSs). A\nsignal-to-interference-plus-noise ratio (SINR)-prediction neural network is\nalso designed, using accumulated SINR measurements obtained when interacting\nwith the cellular network, to map the GBSs' locations into the SINR levels in\norder to predict the UAV's SINR. Numerical results show that with the value\nnetwork and SINR-prediction network, real-time navigation for multi-UAVs can be\nefficiently performed in various environments with high success rate.\n"}
+{"abstract": "  We report on building of a compact vacuum chamber for spectroscopy of\nultracold thulium and trapping of up to 13 million atoms. Compactness is\nachieved by obviating a classical Zeeman slower section and placing an atomic\noven close to a magneto-optical trap (MOT), specifically at the distance of 11\ncm. In this configuration, we significantly gained in solid angle of an atomic\nbeam, which is affected by MOT laser beams, and reached 1 million atoms loaded\ndirectly in the MOT. By exploiting Zeeman-like deceleration of atoms with an\nadditional laser beam, we increased the number of trapped atoms to 6 million.\nThen we gained an extra factor of 2 by tailoring the MOT magnetic field\ngradient with an additional small magnetic coil. Demonstrated results show\ngreat perspective of the developed setup for realizing a compact\nhigh-performance optical atomic clock based on thulium atoms.\n"}
+{"abstract": "  Among the challenges in discriminating between theoretical approaches to the\nglass transition is obtaining suitable data. In particular, particle--resolved\ndata in liquids supercooled past the mode--coupling crossover has until\nrecently been hard to obtain. Here we combine nano-particle resolved\nexperiments and GPU simulation data which addresses this and investigate the\npredictions of differing theoretical approaches. We find support for both\ndynamic facilitation and thermodynamic approaches. In particular, excitations,\nthe elementary units of relaxation in dynamic facilitation theory follow the\npredicted scaling behaviour and the properties of cooperatively rearranging\nregions (CRRs) are consistent with RFOT theory. At weak supercooling there is\nlimited correlation between particles identified in excitations and CRRs, but\nthis increases very substantially at deep supercooling. We identify a timescale\nrelated to the CRRs which is coupled to the structural relaxation time and thus\ndecoupled from the excitation timescale, which remains microscopic.\n"}
+{"abstract": "  At a first glance the Theory of computation relies on potential infinity and\nan organization aimed at solving a problem. Under such aspect it is like\nMendeleev theory of chemistry. Also its theoretical development reiterates that\nof this scientific theory: it makes use of doubly negated propositions and its\nreasoning proceeds through ad absurdum proofs; a final, universal predicate of\nequivalence of all definitions of a computations is translated into an equality\none, and at the same time intuitionist logic into classical logic. Yet, the\nlast step of this development of current theory includes both a misleading\nnotion of thesis and intuitive notions (e.g. the partial computable function,\nas stressed by some scholars). A program for a rational re-construction of the\ntheory according to the theoretical development of the above mentioned theories\nis sketchy suggested.\n"}
+{"abstract": "  Gaussian Graphical models (GGM) are widely used to estimate the network\nstructures in many applications ranging from biology to finance. In practice,\ndata is often corrupted by latent confounders which biases inference of the\nunderlying true graphical structure. In this paper, we compare and contrast two\nstrategies for inference in graphical models with latent confounders: Gaussian\ngraphical models with latent variables (LVGGM) and PCA-based removal of\nconfounding (PCA+GGM). While these two approaches have similar goals, they are\nmotivated by different assumptions about confounding. In this paper, we explore\nthe connection between these two approaches and propose a new method, which\ncombines the strengths of these two approaches. We prove the consistency and\nconvergence rate for the PCA-based method and use these results to provide\nguidance about when to use each method. We demonstrate the effectiveness of our\nmethodology using both simulations and in two real-world applications.\n"}
+{"abstract": "  We consider the model of the data broker selling information to a single\nagent to maximize his revenue. The agent has private valuation for the\nadditional information, and upon receiving the signal from the data broker, the\nagent can conduct her own experiment to refine her posterior belief on the\nstates with additional costs. In this paper, we show that in the optimal\nmechanism, the agent has no incentive to acquire any additional costly\ninformation under equilibrium. Still, the ability to acquire additional\ninformation distorts the incentives of the agent, and reduces the optimal\nrevenue of the data broker. Moreover, we characterize the optimal mechanism\nwhen the valuation function of the agent is separable. The optimal mechanism in\ngeneral may be complex and contain a continuum of menu entries. However, we\nshow that posting a deterministic price for revealing the states is optimal\nwhen the prior distribution is sufficiently informative or the cost of\nacquiring additional information is sufficiently high, and obtains at least\nhalf of the optimal revenue for arbitrary prior and cost functions.\n"}
+{"abstract": "  Why does the vaccination rate remain low, even in countries where\nlong-established immunization programs exist, and vaccines are provided for\nfree? We study this lower vaccination paradox in the context of India- which\ncontributes to the largest pool of under-vaccinated children in the world and\nabout one-third of all vaccine-preventable deaths globally. We explore the\nimportance of historical events shaping current vaccination practices.\nCombining historical records with survey datasets, we examine the Indian\ngovernment's forced sterilization policy implemented in 1976-77 and find that\ngreater exposure to forced sterilization has had a large negative effect on the\ncurrent vaccination completion rate. We explore the mechanism for this practice\nand find that institutional delivery and antenatal care are low in states where\npolicy exposure was high. Finally, we examine the consequence of lower\nvaccination, suggesting that child mortality is currently high in states with\ngreater sterilization exposure. Together, the evidence suggests that government\npolicies implemented in the past could have persistent impacts on adverse\ndemand for health-seeking behavior, even if the burden is exceedingly high.\n"}
+{"abstract": "  Accurate and robust prediction of patient's response to drug treatments is\ncritical for developing precision medicine. However, it is often difficult to\nobtain a sufficient amount of coherent drug response data from patients\ndirectly for training a generalized machine learning model. Although the\nutilization of rich cell line data provides an alternative solution, it is\nchallenging to transfer the knowledge obtained from cell lines to patients due\nto various confounding factors. Few existing transfer learning methods can\nreliably disentangle common intrinsic biological signals from confounding\nfactors in the cell line and patient data. In this paper, we develop a Coherent\nDeconfounding Autoencoder (CODE-AE) that can extract both common biological\nsignals shared by incoherent samples and private representations unique to each\ndata set, transfer knowledge learned from cell line data to tissue data, and\nseparate confounding factors from them. Extensive studies on multiple data sets\ndemonstrate that CODE-AE significantly improves the accuracy and robustness\nover state-of-the-art methods in both predicting patient drug response and\nde-confounding biological signals. Thus, CODE-AE provides a useful framework to\ntake advantage of in vitro omics data for developing generalized patient\npredictive models. The source code is available at\nhttps://github.com/XieResearchGroup/CODE-AE.\n"}
+{"abstract": "  In [M. Zych et al., Nat. Commun. 2, 505 (2011)], the authors predicted that\nthe interferometric visibility is affected by a gravitational field in way that\ncannot be explained without the general relativistic notion of proper time. In\nthis work, we take a different route and start deriving the same effect using\nthe unitary representation of the local Lorentz transformation in the Newtonian\nLimit. In addition, we show that the effect on the interferometric visibility\ndue to gravity persists in different spacetime geometries. However, the\ninfluence is not necessarily due to the notion of proper time. For instance, by\nconstructing a `astronomical' Mach-Zehnder interferometer in the Schwarzschild\nspacetime, the influence on the interferometric visibility can be due to\nanother general relativistic effect, the geodetic precession. Besides, by using\nthe unitary representation of the local Lorentz transformation, we show that\nthis behavior of the interferometric visibility is general for an arbitrary\nspacetime, provided that we restrict the motion of the quanton to a\ntwo-dimensional spacial plane.\n"}
+{"abstract": "  We define a universal state sum construction which specializes to most\npreviously known state sums (Turaev-Viro, Dijkgraaf-Witten, Crane-Yetter,\nDouglas-Reutter, Witten-Reshetikhin-Turaev surgery formula, Brown-Arf). The\ninput data for the state sum is an n-category satisfying various conditions,\nincluding finiteness, semisimplicity and n-pivotality. From this n-category one\nconstructs an n+1-dimensional TQFT, and applying the TQFT gluing rules to a\nhandle decomposition of an n+1-manifold produces the state sum.\n"}
+{"abstract": "  Transition metal dichalcogenide heterobilayers offer attractive opportunities\nto realize lattices of interacting bosons with several degrees of freedom. Such\nheterobilayers can feature moir\\'e patterns that modulate their electronic band\nstructure, leading to spatial confinement of single interlayer excitons (IXs)\nthat act as quantum emitters with $C_3$ symmetry. However, the narrow emission\nlinewidths of the quantum emitters contrast with a broad ensemble IX emission\nobserved in nominally identical heterobilayers, opening a debate regarding the\norigin of IX emission. Here we report the continuous evolution from a few\ntrapped IXs to an ensemble of IXs with both triplet and singlet spin\nconfigurations in a gate-tunable $2H$-MoSe$_2$/WSe$_2$ heterobilayer. We\nobserve signatures of dipolar interactions in the IX ensemble regime which,\nwhen combined with magneto-optical spectroscopy, reveal that the narrow\nquantum-dot-like and broad ensemble emission originate from IXs trapped in\nmoir\\'e potentials with the same atomic registry. Finally, electron doping\nleads to the formation of three different species of localised negative trions\nwith contrasting spin-valley configurations, among which we observe both\nintervalley and intravalley IX trions with spin-triplet optical transitions.\nOur results identify the origin of IX emission in MoSe$_2$/WSe$_2$\nheterobilayers and highlight the important role of exciton-exciton interactions\nand Fermi-level control in these highly tunable quantum materials.\n"}
+{"abstract": "  Multifractal detrended fluctuation analysis (MFDFA) has become a central\nmethod to characterise the variability and uncertainty in empiric time series.\nExtracting the fluctuations on different temporal scales allows quantifying the\nstrength and correlations in the underlying stochastic properties, their\nscaling behaviour, as well as the level of fractality. Several extensions to\nthe fundamental method have been developed over the years, vastly enhancing the\napplicability of MFDFA, e.g. empirical mode decomposition for the study of\nlong-range correlations and persistence. In this article we introduce an\nefficient, easy-to-use python library for MFDFA, incorporating the most common\nextensions and harnessing the most of multi-threaded processing for very fast\ncalculations.\n"}
+{"abstract": "  The Imry-Ma phenomenon, predicted in 1975 by Imry and Ma and rigorously\nestablished in 1989 by Aizenman and Wehr, states that first-order phase\ntransitions of low-dimensional spin systems are `rounded' by the addition of a\nquenched random field to the quantity undergoing the transition. The phenomenon\napplies to a wide class of spin systems in dimensions $d\\le 2$ and to spin\nsystems possessing a continuous symmetry in dimensions $d\\le 4$.\n  This work provides quantitative estimates for the Imry--Ma phenomenon: In a\ncubic domain of side length $L$, we study the effect of the boundary conditions\non the spatial and thermal average of the quantity coupled to the random field.\nWe show that the boundary effect diminishes at least as fast as an inverse\npower of $\\log\\log L$ for general two-dimensional spin systems and for\nfour-dimensional spin systems with continuous symmetry, and at least as fast as\nan inverse power of $L$ for two- and three-dimensional spin systems with\ncontinuous symmetry. Specific models of interest for the obtained results\ninclude the two-dimensional random-field $q$-state Potts and Edwards-Anderson\nspin glass models, and the $d$-dimensional random-field spin $O(n)$ models\n($n\\ge 2$) in dimensions $d\\le 4$.\n"}
+{"abstract": "  We study the problem of fairly allocating indivisible items to agents with\ndifferent entitlements, which captures, for example, the distribution of\nministries among political parties in a coalition government. Our focus is on\npicking sequences derived from common apportionment methods, including five\ntraditional divisor methods and the quota method. We paint a complete picture\nof these methods in relation to known envy-freeness and proportionality\nrelaxations for indivisible items as well as monotonicity properties with\nrespect to the resource, population, and weights. In addition, we provide\ncharacterizations of picking sequences satisfying each of the fairness notions,\nand show that the well-studied maximum Nash welfare solution fails resource-\nand population-monotonicity even in the unweighted setting. Our results serve\nas an argument in favor of using picking sequences in weighted fair division\nproblems.\n"}
+{"abstract": "  In a surprising recent work, Lemke Oliver and Soundararajan noticed how\nexperimental data exhibits erratic distributions for consecutive pairs of\nprimes in arithmetic progressions, and proposed a heuristic model based on the\nHardy--Littlewood conjectures containing a large secondary term, which fits the\ndata very well. In this paper, we study consecutive pairs of sums of squares in\narithmetic progressions, and develop a similar heuristic model based on the\nHardy--Littlewood conjecture for sums of squares, which also explain the biases\nin the experimental data. In the process, we prove several results related to\naverages of the Hardy--Littlewood constant in the context of sums of two\nsquares.\n"}
+{"abstract": "  We investigate the problem of jointly testing multiple hypotheses and\nestimating a random parameter of the underlying distribution in a sequential\nsetup. The aim is to jointly infer the true hypothesis and the true parameter\nwhile using on average as few samples as possible and keeping the detection and\nestimation errors below predefined levels. Based on mild assumptions on the\nunderlying model, we propose an asymptotically optimal procedure, i.e., a\nprocedure that becomes optimal when the tolerated detection and estimation\nerror levels tend to zero. The implementation of the resulting asymptotically\noptimal stopping rule is computationally cheap and, hence, applicable for\nhigh-dimensional data. We further propose a projected quasi-Newton method to\noptimally chose the coefficients that parameterize the instantaneous cost\nfunction such that the constraints are fulfilled with equality. The proposed\ntheory is validated by numerical examples.\n"}
+{"abstract": "  Background: Most of the stars in the Universe will end their evolution by\nlosing their envelope during the thermally pulsing asymptotic giant branch\n(TP-AGB) phase, enriching the interstellar medium of galaxies with heavy\nelements, partially condensed into dust grains formed in their extended\ncircumstellar envelopes. Among these stars, carbon-rich TP-AGB stars (C-stars)\nare particularly relevant for the chemical enrichment of galaxies. We here\ninvestigated the role of the metallicity in the dust formation process from a\ntheoretical viewpoint. Methods: We coupled an up-to-date description of dust\ngrowth and dust-driven wind, which included the time-averaged effect of shocks,\nwith FRUITY stellar evolutionary tracks. We compared our predictions with\nobservations of C-stars in our Galaxy, in the Magellanic Clouds (LMC and SMC)\nand in the Galactic Halo, characterised by metallicity between solar and 1/10\nof solar. Results: Our models explained the variation of the gas and dust\ncontent around C-stars derived from the IRS Spitzer spectra. The wind speed of\nthe C-stars at varying metallicity was well reproduced by our description. We\npredicted the wind speed at metallicity down to 1/10 of solar in a wide range\nof mass-loss rates.\n"}
+{"abstract": "  Lower-limb prosthesis wearers are more prone to fall than non-amputees.\nPowered prostheses can reduce this instability of passive prostheses. While\nshown to be more stable in practice, powered prostheses generally use\nmodel-independent control methods that lack formal guarantees of stability and\nrely on heuristic tuning. Recent work overcame one of the limitations of\nmodel-based prosthesis control by developing a class of stable prosthesis\nsubsystem controllers independent of the human model, except for its\ninteraction forces with the prosthesis. Our work realizes the first\nmodel-dependent prosthesis controller that uses in-the-loop on-board real-time\nforce sensing at the interface between the human and prosthesis and at the\nground, resulting in stable human-prosthesis walking and increasing the\nvalidity of our formal guarantees of stability. Experimental results\ndemonstrate this controller using force sensors outperforms the controller when\nnot using force sensors with better tracking performance and more consistent\ntracking performance across 4 types of terrain.\n"}
+{"abstract": "  In this paper, we apply a Bayesian perspective to the sampling of\nalternatives for multinomial logit (MNL) and mixed multinomial logit (MMNL)\nmodels. A sampling of alternatives reduces the computational challenge of\nevaluating the denominator of the logit choice probability for large choice\nsets by only using a smaller subset of sampled alternatives including the\nchosen alternative. To correct for the resulting overestimation of the choice\nprobability, a correction factor has to be applied. McFadden (1978) proposes a\ncorrection factor to the utility of each alternative which is based on the\nprobability of sampling the smaller subset of alternatives and that alternative\nbeing chosen. McFadden's correction factor ensures consistency of parameter\nestimates under a wide range of sampling protocols. A special sampling protocol\ndiscussed by McFadden is uniform conditioning, which assigns the same sampling\nprobability and therefore the same correction factor to each alternative in the\nsampled choice set. Since a constant is added to each alternative the\ncorrection factor cancels out, but consistent estimates are still obtained.\nBayesian estimation is focused on describing the full posterior distributions\nof the parameters of interest instead of the consistency of their point\nestimates. We theoretically show that uniform conditioning is sufficient to\nminimise the loss of information from a sampling of alternatives on the\nparameters of interest over the full posterior distribution in Bayesian MNL\nmodels. Minimum loss of information is, however, not guaranteed for other\nsampling protocols. This result extends to Bayesian MMNL models estimated using\nthe principle of data augmentation. The application of uniform conditioning, a\nmore restrictive sampling protocol, is thus sufficient in a Bayesian estimation\ncontext to achieve finite sample properties of MNL and MMNL parameter\nestimates.\n"}
+{"abstract": "  A decision maker is choosing between an active action (e.g., purchase a\nhouse, invest certain stock) and a passive action. The payoff of the active\naction depends on the buyer's private type and also an unknown state of nature.\nAn information seller can design experiments to reveal information about the\nrealized state to the decision maker, and would like to maximize profit from\nselling such information. We characterize, in closed-form, the revenue-optimal\ninformation selling mechanism for the seller. After eliciting the buyer's type,\nthe optimal mechanism charges the buyer an upfront payment and then simply\nreveals whether the realized state exceeds a certain threshold or not. The\noptimal mechanism features both price discrimination and information\ndiscrimination. The special buyer type who is a-priori indifferent between the\nactive and passive action benefits the most from participating in the\nmechanism.\n"}
+{"abstract": "  Two-particle angular correlations are measured in high-multiplicity\nproton-proton collisions at $\\sqrt{s} =13$ TeV by the ALICE Collaboration. The\nyields of particle pairs at short-($\\Delta\\eta$ $\\sim$ 0) and long-range ($1.6\n< |\\Delta\\eta| < 1.8$) in pseudorapidity are extracted on the near-side\n($\\Delta\\varphi$ $\\sim$ 0). They are reported as a function of transverse\nmomentum ($p_{\\mathrm T}$) in the range $1 <p_{\\mathrm T}< 4$ GeV/$c$.\nFurthermore, the event-scale dependence is studied for the first time by\nrequiring the presence of high-$p_{\\rm T}$ leading particles and jets for\nvarying $p_{\\rm T}$ thresholds. The results demonstrate that the long-range\n\"ridge\" yield, possibly related to the collective behavior of the system, is\npresent in events with high-$p_{\\mathrm T}$ processes. The magnitudes of the\nshort- and long-range yields are found to grow with the event scale. The\nresults are compared to EPOS LHC and PYTHIA 8 calculations, with and without\nstring-shoving interactions. It is found that while both models describe the\nqualitative trends in the data, calculations from EPOS LHC show a better\nquantitative agreement, in particular for the $p_{\\rm T}$ and event-scale\ndependencies.\n"}
+{"abstract": "  Recent empirical evidence suggests that the Weston-Watkins support vector\nmachine is among the best performing multiclass extensions of the binary SVM.\nCurrent state-of-the-art solvers repeatedly solve a particular subproblem\napproximately using an iterative strategy. In this work, we propose an\nalgorithm that solves the subproblem exactly using a novel reparametrization of\nthe Weston-Watkins dual problem. For linear WW-SVMs, our solver shows\nsignificant speed-up over the state-of-the-art solver when the number of\nclasses is large. Our exact subproblem solver also allows us to prove linear\nconvergence of the overall solver.\n"}
+{"abstract": "  Tetragonal Mo5PB2 compound, a recently discovered superconductor, belongs to\ntechnologically important class of materials. It is quite surprising to note\nthat a large number of physical properties of Mo5PB2, including elastic\nproperties and their anisotropy, acoustic behavior, electronic (charge density\ndistribution, electron density difference), thermo-physical, bonding\ncharacteristics, and optical properties have not been carried out at all. In\nthe present work we have explored all these properties in details for the first\ntime with density functional theory based first-principles method. Mo5PB2 is\nfound to be a mechanically stable, elastically anisotropic compound with\nductile character. Moreover, the chemical bonding is interpreted by calculating\nthe electronic energy density of states, electron density distribution, elastic\nproperties and Mulliken bond population analysis. Mo5PB2 has a combination of\nmainly ionic, metallic, and some covalent bonding characteristics. The compound\npossesses high level of machinability. The band structure along with a large\nelectronic density of states at the Fermi level reveals metallic character.\nCalculated values of different thermal parameters of Mo5PB2 are closely related\nto the elastic properties. The energy dependent optical parameters show close\nassent to the underlying electronic band structure. The optical absorption and\nreflectivity spectra and the low energy index of refraction of Mo5PB2 show that\nthe compound holds promise to be used in optoelectronic device sector. Unlike\nthe notable anisotropy found in elastic, mechanical properties and minimum\nthermal conductivity, the optical parameters are found to be almost isotropic\nwith respect to the polarization direction of the incident electric field.\n"}
+{"abstract": "  Point cloud upsampling is vital for the quality of the mesh in\nthree-dimensional reconstruction. Recent research on point cloud upsampling has\nachieved great success due to the development of deep learning. However, the\nexisting methods regard point cloud upsampling of different scale factors as\nindependent tasks. Thus, the methods need to train a specific model for each\nscale factor, which is both inefficient and impractical for storage and\ncomputation in real applications. To address this limitation, in this work, we\npropose a novel method called ``Meta-PU\" to firstly support point cloud\nupsampling of arbitrary scale factors with a single model. In the Meta-PU\nmethod, besides the backbone network consisting of residual graph convolution\n(RGC) blocks, a meta-subnetwork is learned to adjust the weights of the RGC\nblocks dynamically, and a farthest sampling block is adopted to sample\ndifferent numbers of points. Together, these two blocks enable our Meta-PU to\ncontinuously upsample the point cloud with arbitrary scale factors by using\nonly a single model. In addition, the experiments reveal that training on\nmultiple scales simultaneously is beneficial to each other. Thus, Meta-PU even\noutperforms the existing methods trained for a specific scale factor only.\n"}
+{"abstract": "  We construct colored lattice models whose partition functions represent\nsymplectic and odd orthogonal Demazure characters and atoms. We show that our\nlattice models are not solvable, but we are able to show the existence of\nsufficiently many solutions of the Yang--Baxter equation that allows us to\ncompute functional equations for the corresponding partition functions. From\nthese functional equations, we determine that the partition function of our\nmodels are the Demazure atoms and characters for the symplectic and odd\northogonal Lie groups. We coin our lattice models as quasi-solvable. We use the\nnatural bijection of admissible states in our models with Proctor patterns to\ngive a right key algorithm for reverse King tableaux and Sundaram tableaux.\n"}
+{"abstract": "  The estimation of entropy rates for stationary discrete-valued stochastic\nprocesses is a well studied problem in information theory. However, estimating\nthe entropy rate for stationary continuous-valued stochastic processes has not\nreceived as much attention. In fact, many current techniques are not able to\naccurately estimate or characterise the complexity of the differential entropy\nrate for strongly correlated processes, such as Fractional Gaussian Noise and\nARFIMA(0,d,0). To the point that some cannot even detect the trend of the\nentropy rate, e.g. when it increases/decreases, maximum, or asymptotic trends,\nas a function of their Hurst parameter. However, a recently developed technique\nprovides accurate estimates at a high computational cost. In this paper, we\ndefine a robust technique for non-parametrically estimating the differential\nentropy rate of a continuous valued stochastic process from observed data, by\nmaking an explicit link between the differential entropy rate and the Shannon\nentropy rate of a quantised version of the original data. Estimation is\nperformed by a Shannon entropy rate estimator, and then converted to a\ndifferential entropy rate estimate. We show that this technique inherits many\nimportant statistical properties from the Shannon entropy rate estimator. The\nestimator is able to provide better estimates than the defined relative\nmeasures and much quicker estimates than known absolute measures, for strongly\ncorrelated processes. Finally, we analyse the complexity of the estimation\ntechnique and test the robustness to non-stationarity, and show that none of\nthe current techniques are robust to non-stationarity, even if they are robust\nto strong correlations.\n"}
+{"abstract": "  Within the MOJAVE VLBA program (Monitoring of Jets in AGN with VLBA\nExperiments), we have accumulated observational data at 15 GHz for hundreds of\njets in $\\gamma$-ray bright active galactic nuclei since the beginning of the\nFermi scientific observations in August 2008. We investigated a time delay\nbetween the flux density of AGN parsec-scale radio emission at 15 GHz and\n0.1$-$300 GeV Fermi LAT photon flux, taken from constructed light curves using\nweekly and adaptive binning. The correlation analysis shows that radio is\nlagging $\\gamma$-ray radiation by up to 8 months in the observer's frame, while\nin the source frame, the typical delay is about 2-3 months. If the jet radio\nemission, excluding the opaque core, is considered, significant correlation is\nfound at greater time lags. We supplement these results with VLBI kinematics\nand core shift data to conclude that the dominant high-energy production zone\nis typically located at a distance of several parsecs from the central nucleus.\nWe also found that quasars have on average more significant correlation peak,\nmore distant $\\gamma$-ray emission region from the central engine and shorter\nvariability time scale compared to those of BL Lacertae objects.\n"}
+{"abstract": "  We investigate a cooperative federated learning framework among devices for\nmobile edge computing, named CFLMEC, where devices co-exist in a shared\nspectrum with interference. Keeping in view the time-average network throughput\nof cooperative federated learning framework and spectrum scarcity, we focus on\nmaximize the admission data to the edge server or the near devices, which fills\nthe gap of communication resource allocation for devices with federated\nlearning. In CFLMEC, devices can transmit local models to the corresponding\ndevices or the edge server in a relay race manner, and we use a decomposition\napproach to solve the resource optimization problem by considering maximum data\nrate on sub-channel, channel reuse and wireless resource allocation in which\nestablishes a primal-dual learning framework and batch gradient decent to learn\nthe dynamic network with outdated information and predict the sub-channel\ncondition. With aim at maximizing throughput of devices, we propose\ncommunication resource allocation algorithms with and without sufficient\nsub-channels for strong reliance on edge servers (SRs) in cellular link, and\ninterference aware communication resource allocation algorithm for less\nreliance on edge servers (LRs) in D2D link. Extensive simulation results\ndemonstrate the CFLMEC can achieve the highest throughput of local devices\ncomparing with existing works, meanwhile limiting the number of the\nsub-channels.\n"}
+{"abstract": "  Underwater robots play an important role in oceanic geological exploration,\nresource exploitation, ecological research, and other fields. However, the\nvisual perception of underwater robots is affected by various environmental\nfactors. The main challenge now is that images captured by underwater robots\nare color-distorted. The hue of underwater images tends to be close to green\nand blue. In addition, the contrast is low and the details are fuzzy. In this\npaper, a new underwater image enhancement algorithm based on deep learning and\nimage formation model is proposed. Experimental results show that the\nadvantages of the proposed method are that it eliminates the influence of\nunderwater environmental factors, enriches the color, enhances details,\nachieves higher scores in PSNR and SSIM metrics, and helps feature key-point\npoint matching get better results. Another significant advantage is that its\ncomputation speed is much faster than other methods.\n"}
+{"abstract": "  In this paper, we compare single-photon qubits and hybrid qubits as\ninformation carriers through quantum teleportation using a Gaussian\ncontinuous-variable channel. A hybrid qubit in our study is in the form of\nentanglement between a coherent state and a single photon. We find that hybrid\nqubits outperform photonic qubits when coherent amplitudes of the hybrid qubits\nare as low as $\\alpha\\lesssim 1$, while photonic qubits yield better results\nfor larger amplitudes. We analyze effects of photon losses, and observe that\nthe overall character of teleportation for different qubits remains the same\nalthough the teleportation fidelities are degraded by photon losses. Our work\nprovides a comparative look at practical quantum information processing with\ndifferent types of qubits.\n"}
+{"abstract": "  Periodic quasars have been suggested as candidates for hosting binary\nsupermassive black holes (SMBHs), although alternative scenarios remain\npossible to explain the optical light curve periodicity. To test the\nalternative hypothesis of precessing radio jet, we present deep 6 GHz radio\nimaging conducted with NSF's Karl G. Jansky Very Large Array (VLA) in its C\nconfiguration for the three candidate periodic quasars, DES\nJ024703.24$-$010032.0, DES J024944.66$-$000036.8, and DES\nJ025214.67$-$002813.7. Our targets were selected based on their optical\nvariability using 20-yr long multi-color light curves from the Dark Energy\nSurvey (DES) and the Sloan Digital Sky Survey (SDSS). The new VLA observations\nshow that all three periodic quasars are radio-quiet with the radio loudness\nparameters measured to be $R\\equiv f_{6\\,{\\rm cm}}/f_{{\\rm 2500}}$ of\n$\\lesssim$1.0$-$1.5 and the $k$-corrected luminosities $\\nu L_\\nu$[6 GHz] of\n$\\lesssim$5$-$21 $\\times$ 10$^{39}$ erg s$^{-1}$. They are in stark contrast to\npreviously known periodic quasars proposed as binary SMBH candidates such as\nthe blazar OJ 287 and PG1302$-$102. Our results rule out optical emission\ncontributed from precessing radio jets as the origin of the optical periodicity\nin the three DES$-$SDSS-selected candidate periodic quasars. Future continued\noptical monitoring and complementary multi-wavelength observations are still\nneeded to further test the binary SMBH hypothesis as well as other competing\nscenarios to explain the optical periodicity.\n"}
+{"abstract": "  As the Internet of Things (IoT) rolls out today to devices whose lifetime may\nwell exceed a decade, conservative threat models should consider attackers with\naccess to quantum computing power. The SUIT standard (specified by the IETF)\ndefines a security architecture for IoT software updates, standardizing the\nmetadata and the cryptographic tools-namely, digital signatures and hash\nfunctions-that guarantee the legitimacy of software updates. While the\nperformance of SUIT has previously been evaluated in the pre-quantum context,\nit has not yet been studied in a post-quantum context. Taking the open-source\nimplementation of SUIT available in RIOT as a case study, we overview\npost-quantum considerations, and quantum-resistant digital signatures in\nparticular, focusing on lowpower, microcontroller-based IoT devices which have\nstringent resource constraints in terms of memory, CPU, and energy consumption.\nWe benchmark a selection of proposed post-quantum signature schemes (LMS,\nFalcon, and Dilithium) and compare them with current pre-quantum signature\nschemes (Ed25519 and ECDSA). Our benchmarks are carried out on a variety of IoT\nhardware including ARM Cortex-M, RISC-V, and Espressif (ESP32), which form the\nbulk of modern 32-bit microcontroller architectures. We interpret our benchmark\nresults in the context of SUIT, and estimate the real-world impact of\npost-quantum alternatives for a range of typical software update categories.\nCCS CONCEPTS $\\bullet$ Computer systems organization $\\rightarrow$ Embedded\nsystems.\n"}
+{"abstract": "  Crowdsourcing is becoming an accepted method of software development for\ndifferent phases in the production lifecycle. Ideally, mass parallel production\nthrough Crowdsourcing could be an option for rapid acquisition in software\nengineering by leveraging infinite worker resource on the internet. It is\nimportant to understand the patterns and strategies of decomposing and\nuploading parallel tasks to maintain a stable worker supply as well as a\nsatisfactory task completion rate.\n  This research report is an empirical analysis of the available tasks'\nlifecycle patterns in crowdsourcing. Following the waterfall model in\nCrowdsourced Software Development (CSD), this research identified four patterns\nfor the sequence of task arrival per project: 1) Prior Cycle, 2) Current Cycle,\n3) Orbit Cycle, and 4) Fresh Cycle.\n"}
+{"abstract": "  For abelian groups $A, B$, $A$ is called $B$-small if the covariant functor\n$Hom(A,-)$ commutes with all direct sums $B^{(\\kappa)}$ and $A$ is self-small\nprovided it is $A$-small. The paper characterizes self-small products applying\ndeveloped closure properties of the classes of relatively small groups. As a\nconsequence, self-small products of finitely generated abelian groups are\ndescribed.\n"}
+{"abstract": "  Boundary samples are special inputs to artificial neural networks crafted to\nidentify the execution environment used for inference by the resulting output\nlabel. The paper presents and evaluates algorithms to generate transparent\nboundary samples. Transparency refers to a small perceptual distortion of the\nhost signal (i.e., a natural input sample). For two established image\nclassifiers, ResNet on FMNIST and CIFAR10, we show that it is possible to\ngenerate sets of boundary samples which can identify any of four tested\nmicroarchitectures. These sets can be built to not contain any sample with a\nworse peak signal-to-noise ratio than 70dB. We analyze the relationship between\nsearch complexity and resulting transparency.\n"}
+{"abstract": "  The rapid progress of noisy intermediate-scale quantum (NISQ) computing\nunderscores the need to test and evaluate new devices and applications. Quantum\nchemistry is a key application area for these devices, and therefore serves as\nan important benchmark for current and future quantum computer performance.\nPrevious benchmarks in this field have focused on variational methods for\ncomputing ground and excited states of various molecules, including a\nbenchmarking suite focused on performance of computing ground states for\nalkali-hydrides under an array of error mitigation methods. Here, we outline\nstate of the art methods to reach chemical accuracy in hybrid quantum-classical\nelectronic structure calculations of alkali hydride molecules on NISQ devices\nfrom IBM. We demonstrate how to extend the reach of variational eigensolvers\nwith new symmetry preserving Ans\\\"atze. Next, we outline how to use quantum\nimaginary time evolution and Lanczos as a complementary method to variational\ntechniques, highlighting the advantages of each approach. Finally, we\ndemonstrate a new error mitigation method which uses systematic error\ncancellation via hidden inverse gate constructions, improving the performance\nof typical variational algorithms. These results show that electronic structure\ncalculations have advanced rapidly, to routine chemical accuracy for simple\nmolecules, from their inception on quantum computers a few short years ago, and\nthey point to further rapid progress to larger molecules as the power of NISQ\ndevices grows.\n"}
+{"abstract": "  We give a non-asymptotic bound on the spectral norm of a $d\\times d$ matrix\n$X$ with centered jointly Gaussian entries in terms of the covariance matrix of\nthe entries. In some cases, this estimate is sharp and removes the $\\sqrt{\\log\nd}$ factor in the noncommutative Khintchine inequality.\n"}
+{"abstract": "  Regulatory compliance is a well-studied area, including research on how to\nmodel, check, analyse, enact, and verify compliance of software. However, while\nthe theoretical body of knowledge is vast, empirical evidence on challenges\nwith regulatory compliance, as faced by industrial practitioners particularly\nin the Software Engineering domain, is still lacking. In this paper, we report\non an industrial case study which aims at providing insights into common\npractices and challenges with checking and analysing regulatory compliance, and\nwe discuss our insights in direct relation to the state of reported evidence.\nOur study is performed at Ericsson AB, a large telecommunications company,\nwhich must comply to both locally and internationally governing regulatory\nentities and standards such as GDPR. The main contributions of this work are\nempirical evidence on challenges experienced by Ericsson that complement the\nexisting body of knowledge on regulatory compliance.\n"}
+{"abstract": "  Layer transfer offers enormous potential for the industrial implementation of\n2D material technology platforms. However, the transfer method used must retain\nas-grown uniformity and cleanliness in the transferred films for the\nfabrication of 2D material-based. Additionally, the method used must be capable\nof large-area transfer to maintain wafer-scale fabrication standards. Here, a\nfacile route to transfer centimeter-scale synthesized 2D TMDCs (3L MoS2, 1L\nWS2) onto various substrates such as sapphire, SiO2/Si, and flexible substrates\n(mica, polyimide) has been developed using a water-soluble layer (Na2S/Na2SO4)\nunderneath the as-grown film. The developed transfer process represents a fast,\nclean, generic, and scalable technique to transfer 2D atomic layers. The key\nstrategy used in this process includes the dissolution of Na2S/Na2SO4 layer due\nto the penetration of NaOH solution between the growth substrate and\nhydrophobic 2D TMDC film. As a proof-of-concept device, a broadband\nphotodetector has been fabricated onto transferred 3L MoS2, which shows\nphotoresponse behavior for a wide range of wavelength ranging from NIR to UV.\nThe enhancement in photocurrent was found to be 100 times and 10 times to dark\ncurrent in the UV and visible region, respectively. This work opens up the\npathway towards flexible electronics and optoelectronics.\n"}
+{"abstract": "  We derive two new forms of the K\\'arm\\'an-Howarth-Monin equation for decaying\ncompressible Hall magnetohydrodynamic (MHD) turbulence. We test them on results\nof a weakly-compressible, two-dimensional, moderate-Reynolds-number Hall MHD\nsimulation and compare them with an isotropic spectral transfer (ST) equation.\nThe KHM and ST equations are automatically satisfied during the whole\nsimulation owing to the periodic boundary conditions and have complementary\ncumulative behavior. They are used here to analyze the onset of turbulence and\nits properties when it is fully developed. These approaches give equivalent\nresults characterizing: the decay of the kinetic + magnetic energy at large\nscales, the MHD and Hall cross-scale energy transfer/cascade, the pressure\ndilatation, and the dissipation. The Hall cascade appears when the MHD one\nbrings the energy close to the ion inertial range and is related to the\nformation of reconnecting current sheets. At later times, the pressure-dilation\nenergy-exchange rate oscillates around zero with no net effect on the\ncross-scale energy transfer when averaged over a period of its oscillations. A\nreduced one-dimensional analysis suggests that all three methods may be useful\nto estimate the energy cascade rate from in situ observations.\n"}
+{"abstract": "  In this paper we study the problem of model reduction by moment matching for\nstochastic systems. We characterize the mathematical object which generalizes\nthe notion of moment to stochastic differential equations and we find a class\nof models which achieve moment matching. However, differently from the\ndeterministic case, these reduced-order models cannot be considered \"simpler\"\nbecause of the high computational cost paid to determine the moment. To\novercome this difficulty, we relax the moment matching problem in two different\nways and we present two classes of reduced-order models which, approximately\nmatching the stochastic moment, are computationally tractable.\n"}
+{"abstract": "  Neural image compression (NIC) is a new coding paradigm where coding\ncapabilities are captured by deep models learned from data. This data-driven\nnature enables new potential functionalities. In this paper, we study the\nadaptability of codecs to custom domains of interest. We show that NIC codecs\nare transferable and that they can be adapted with relatively few target domain\nimages. However, naive adaptation interferes with the solution optimized for\nthe original source domain, resulting in forgetting the original coding\ncapabilities in that domain, and may even break the compatibility with\npreviously encoded bitstreams. Addressing these problems, we propose Codec\nAdaptation without Forgetting (CAwF), a framework that can avoid these problems\nby adding a small amount of custom parameters, where the source codec remains\nembedded and unchanged during the adaptation process. Experiments demonstrate\nits effectiveness and provide useful insights on the characteristics of\ncatastrophic interference in NIC.\n"}
+{"abstract": "  We present a graph neural network to learn graph coloring heuristics using\nreinforcement learning. Our learned deterministic heuristics give better\nsolutions than classical degree-based greedy heuristics and only take seconds\nto evaluate on graphs with tens of thousands of vertices. As our approach is\nbased on policy-gradients, it also learns a probabilistic policy as well. These\nprobabilistic policies outperform all greedy coloring baselines and a machine\nlearning baseline. Our approach generalizes several previous machine-learning\nframeworks, which applied to problems like minimum vertex cover. We also\ndemonstrate that our approach outperforms two greedy heuristics on minimum\nvertex cover.\n"}
+{"abstract": "  Expressing attack-defence trees in a multi-agent setting allows for studying\na new aspect of security scenarios, namely how the number of agents and their\ntask assignment impact the performance, e.g. attack time, of strategies\nexecuted by opposing coalitions. Optimal scheduling of agents' actions, a\nnon-trivial problem, is thus vital. We discuss associated caveats and propose\nan algorithm that synthesises such an assignment, targeting minimal attack time\nand using minimal number of agents for a given attack-defence tree.\n"}
+{"abstract": "  Diferentially private (DP) synthetic datasets are a powerful approach for\ntraining machine learning models while respecting the privacy of individual\ndata providers. The effect of DP on the fairness of the resulting trained\nmodels is not yet well understood. In this contribution, we systematically\nstudy the effects of differentially private synthetic data generation on\nclassification. We analyze disparities in model utility and bias caused by the\nsynthetic dataset, measured through algorithmic fairness metrics. Our first set\nof results show that although there seems to be a clear negative correlation\nbetween privacy and utility (the more private, the less accurate) across all\ndata synthesizers we evaluated, more privacy does not necessarily imply more\nbias. Additionally, we assess the effects of utilizing synthetic datasets for\nmodel training and model evaluation. We show that results obtained on synthetic\ndata can misestimate the actual model performance when it is deployed on real\ndata. We hence advocate on the need for defining proper testing protocols in\nscenarios where differentially private synthetic datasets are utilized for\nmodel training and evaluation.\n"}
+{"abstract": "  In this simple paper, we exhibit a Schur partition giving rise to a\ntriangle-free linear colouring of $K_{1697}$ in 7 colours. Thus we show that\nthe Schur number $S(7) \\ge 1696$ and the multicolour Ramsey number $R_{7}(3)\n\\ge 1698$.\n  We also demonstrate a specific partition of the closed integer interval $[1,\n376]$ which, through repeated use of a specific construction, allows us to\nconclude that $S(k+5) \\ge 376.S(k)+160$ for any positive integer $k$. The\nexistence of this partition, with its specific properties, implies that\n$\\lim_{\\substack{r \\rightarrow \\infty}} R{_r}(3)^{1/r} > 3.273$.\n"}
+{"abstract": "  An square matrix is $k$-Toeplitz if its diagonals are periodic sequences of\nperiod $k$. We find rational formulas for the determinant, the characteristic\npolynomial, and the elements of the inverse of a tridiagonal $k$-Toeplitz\nmatrix (in particular, of any tridiagonal matrix) over any commutative unital\nring, using only elementary linear algebra.\n"}
+{"abstract": "  The increasing availability of data presents an opportunity to calibrate\nunknown parameters which appear in complex models of phenomena in the\nbiomedical, physical and social sciences. However, model complexity often leads\nto parameter-to-data maps which are expensive to evaluate and are only\navailable through noisy approximations. This paper is concerned with the use of\ninteracting particle systems for the solution of the resulting inverse problems\nfor parameters. Of particular interest is the case where the available forward\nmodel evaluations are subject to rapid fluctuations, in parameter space,\nsuperimposed on the smoothly varying large scale parametric structure of\ninterest. Multiscale analysis is used to study the behaviour of interacting\nparticle system algorithms when such rapid fluctuations, which we refer to as\nnoise, pollute the large scale parametric dependence of the parameter-to-data\nmap. Ensemble Kalman methods (which are derivative-free) and Langevin-based\nmethods (which use the derivative of the parameter-to-data map) are compared in\nthis light. The ensemble Kalman methods are shown to behave favourably in the\npresence of noise in the parameter-to-data map, whereas Langevin methods are\nadversely affected. On the other hand, Langevin methods have the correct\nequilibrium distribution in the setting of noise-free forward models, whilst\nensemble Kalman methods only provide an uncontrolled approximation, except in\nthe linear case. Therefore a new class of algorithms, ensemble Gaussian process\nsamplers, which combine the benefits of both ensemble Kalman and Langevin\nmethods, are introduced and shown to perform favourably.\n"}
+{"abstract": "  Scenario-based question answering (SQA) has attracted an increasing research\ninterest. Compared with the well-studied machine reading comprehension (MRC),\nSQA is a more challenging task: a scenario may contain not only a textual\npassage to read but also structured data like tables, i.e., tabular scenario\nbased question answering (TSQA). AI applications of TSQA such as answering\nmultiple-choice questions in high-school exams require synthesizing data in\nmultiple cells and combining tables with texts and domain knowledge to infer\nanswers. To support the study of this task, we construct GeoTSQA. This dataset\ncontains 1k real questions contextualized by tabular scenarios in the geography\ndomain. To solve the task, we extend state-of-the-art MRC methods with TTGen, a\nnovel table-to-text generator. It generates sentences from variously\nsynthesized tabular data and feeds the downstream MRC method with the most\nuseful sentences. Its sentence ranking model fuses the information in the\nscenario, question, and domain knowledge. Our approach outperforms a variety of\nstrong baseline methods on GeoTSQA.\n"}
+{"abstract": "  As a novel layered noble metal dichalcogenide material, palladium diselenide\n(PdSe2) has attracted wide interest due to its excellent optical and electronic\nproperties. In this work, a strong third-order nonlinear optical response of 2D\nPdSe2 films is reported. We conduct both open aperture (OA) and closed-aperture\n(CA) Z scan measurements with a femtosecond pulsed laser at 800 nm to\ninvestigate the nonlinear absorption and nonlinear refraction, respectively. In\nthe OA experiment, we observe optical limiting behaviour originating from large\ntwo photo absorption (TPA) in the PdSe2 film of \\b{eta} = 3.26 x 10-8 m/W. In\nthe CA experiment, we measure a peak-valley response corresponding to a large\nand negative Kerr nonlinearity of n2 = -1.33 x 10-15 m2/W, two orders of\nmagnitude larger than bulk silicon. In addition, the variation of n2 as a\nfunction of laser intensity is also characterized, with n2 decreasing in\nmagnitude when increasing incident laser intensity, becoming saturated at n2 =\n-9.96 x 10-16 m2/W at high intensities. Our results show that the extraordinary\nthird order nonlinear optical properties of PdSe2 have strong potential for\nhigh-performance nonlinear photonic devices.\n"}
+{"abstract": "  We employ a non-compact gauging of four-dimensional maximal supergravity to\nconstruct a two-parameter family of AdS$_4$ J-fold solutions preserving\n$\\mathcal{N}=2$ supersymmetry. All solutions preserve\n$\\mathfrak{u}(1)\\times\\mathfrak{u}(1)$ global symmetry and in special limits we\nrecover the previously known $\\mathfrak{su}(2)\\times\\mathfrak{u}(1)$ invariant\n$\\mathcal{N}=2$ and $\\mathfrak{su}(2)\\times\\mathfrak{su}(2)$ invariant\n$\\mathcal{N}=4$ J-fold solutions. This family of AdS$_4$ backgrounds can be\nuplifted to type IIB string theory and is holographically dual to the conformal\nmanifold of a class of three-dimensional $S$-fold SCFTs obtained from the\n$\\mathcal{N}=4$ $T[\\text{U}(N)]$ theory of Gaiotto-Witten. We find the spectrum\nof supergravity excitations of the AdS$_4$ solutions and use it to study how\nthe operator spectrum of the three-dimensional SCFT depends on the exactly\nmarginal couplings.\n"}
+{"abstract": "  Whistler mode chorus waves are quasi-coherent electromagnetic emissions with\nfrequency chirping. Various models have been proposed to understand the\nchirping mechanism, which is a long-standing problem in space plasmas. Based on\nanalysis of effective wave growth rate and electron phase space dynamics in a\nself-consistent particle simulation, we propose here a phenomenological model\ncalled the \"Trap-Release-Amplify\" (TaRA) model for chorus. In this model, phase\nspace structures of correlated electrons are formed by nonlinear wave particle\ninteractions, which mainly occur in the downstream. When released from the wave\npacket in the upstream, these electrons selectively amplify new emissions which\nsatisfy the phase-locking condition to maximize wave power transfer, leading to\nfrequency chirping. The phase-locking condition at the release point gives a\nfrequency chirping rate that is fully consistent with the one by Helliwell in\ncase of a nonuniform background magnetic field. The nonlinear wave particle\ninteraction part of the TaRA model results in a chirping rate that is\nproportional to wave amplitude, a conclusion originally reached by Vomvoridis\net al. Therefore, the TaRA model unifies two different results from seemingly\nunrelated studies. Furthermore, the TaRA model naturally explains fine\nstructures of chorus waves, including subpackets and bandwidth, and their\nevolution through dynamics of phase-trapped electrons. Finally, we suggest that\nthis model could be applied to explain other related phenomena, including\nfrequency chirping of chorus in a uniform background magnetic field and of\nelectromagnetic ion cyclotron waves in the magnetosphere.\n"}
+{"abstract": "  Probabilistic linear discriminant analysis (PLDA) or cosine similarity have\nbeen widely used in traditional speaker verification systems as back-end\ntechniques to measure pairwise similarities. To make better use of multiple\nenrollment utterances, we propose a novel attention back-end model, which can\nbe used for both text-independent (TI) and text-dependent (TD) speaker\nverification, and employ scaled-dot self-attention and feed-forward\nself-attention networks as architectures that learn the intra-relationships of\nthe enrollment utterances. In order to verify the proposed attention back-end,\nwe conduct a series of experiments on CNCeleb and VoxCeleb datasets by\ncombining it with several sate-of-the-art speaker encoders including TDNN and\nResNet. Experimental results using multiple enrollment utterances on CNCeleb\nshow that the proposed attention back-end model leads to lower EER and minDCF\nscore than the PLDA and cosine similarity counterparts for each speaker encoder\nand an experiment on VoxCeleb indicate that our model can be used even for\nsingle enrollment case.\n"}
+{"abstract": "  Atmospheric retrievals of exoplanetary transmission spectra provide important\nconstraints on various properties such as chemical abundances, cloud/haze\nproperties, and characteristic temperatures, at the day-night atmospheric\nterminator. To date, most spectra have been observed for giant exoplanets due\nto which retrievals typically assume H-rich atmospheres. However, recent\nobservations of mini-Neptunes/super-Earths, and the promise of upcoming\nfacilities including JWST, call for a new generation of retrievals that can\naddress a wide range of atmospheric compositions and related complexities. Here\nwe report Aurora, a next-generation atmospheric retrieval framework that builds\nupon state-of-the-art architectures and incorporates the following key\nadvancements: a) a generalised compositional retrieval allowing for H-rich and\nH-poor atmospheres, b) a generalised prescription for inhomogeneous\nclouds/hazes, c) multiple Bayesian inference algorithms for high-dimensional\nretrievals, d) modular considerations for refraction, forward scattering, and\nMie-scattering, and e) noise modeling functionalities. We demonstrate Aurora on\ncurrent and/or synthetic observations of hot Jupiter HD209458b, mini-Neptune\nK218b, and rocky exoplanet TRAPPIST1d. Using current HD209458b spectra, we\ndemonstrate the robustness of our framework and cloud/haze prescription against\nassumptions of H-rich/H-poor atmospheres, improving on previous treatments.\nUsing real and synthetic spectra of K218b, we demonstrate the agnostic approach\nto confidently constrain its bulk atmospheric composition and obtain precise\nabundance estimates. For TRAPPIST1d, 10 JWST NIRSpec transits can enable\nidentification of the main atmospheric component for cloud-free CO$_2$-rich and\nN$_2$-rich atmospheres, and abundance constraints on trace gases including\ninitial indications of O$_3$ if present at enhanced levels ($\\sim$10-100x Earth\nlevels).\n"}
+{"abstract": "  Many autonomous systems, such as robots and self-driving cars, involve\nreal-time decision making in complex environments, and require prediction of\nfuture outcomes from limited data. Moreover, their decisions are increasingly\nrequired to be interpretable to humans for safe and trustworthy co-existence.\nThis paper is a first step towards interpretable learning-based robot control.\nWe introduce a novel learning problem, called incremental formula and predictor\nlearning, to generate binary classifiers with temporal logic structure from\ntime-series data. The classifiers are represented as pairs of Signal Temporal\nLogic (STL) formulae and predictors for their satisfaction. The incremental\nproperty provides prediction of labels for prefix signals that are revealed\nover time. We propose a boosted decision-tree algorithm that leverages weak,\nbut computationally inexpensive, learners to increase prediction and runtime\nperformance. The effectiveness and classification accuracy of our algorithms\nare evaluated on autonomous-driving and naval surveillance case studies.\n"}
+{"abstract": "  Photoacoustic tomography (PAT) has the potential to recover morphological and\nfunctional tissue properties such as blood oxygenation with high spatial\nresolution and in an interventional setting. However, decades of research\ninvested in solving the inverse problem of recovering clinically relevant\ntissue properties from spectral measurements have failed to produce solutions\nthat can quantify tissue parameters robustly in a clinical setting. Previous\nattempts to address the limitations of model-based approaches with machine\nlearning were hampered by the absence of labeled reference data needed for\nsupervised algorithm training. While this bottleneck has been tackled by\nsimulating training data, the domain gap between real and simulated images\nremains a huge unsolved challenge. As a first step to address this bottleneck,\nwe propose a novel approach to PAT data simulation, which we refer to as\n\"learning to simulate\". Our approach involves subdividing the challenge of\ngenerating plausible simulations into two disjoint problems: (1) Probabilistic\ngeneration of realistic tissue morphology, represented by semantic segmentation\nmaps and (2) pixel-wise assignment of corresponding optical and acoustic\nproperties. In the present work, we focus on the first challenge. Specifically,\nwe leverage the concept of Generative Adversarial Networks (GANs) trained on\nsemantically annotated medical imaging data to generate plausible tissue\ngeometries. According to an initial in silico feasibility study our approach is\nwell-suited for contributing to realistic PAT image synthesis and could thus\nbecome a fundamental step for deep learning-based quantitative PAT.\n"}
+{"abstract": "  A dramatic slowing down of acoustic wave transport in dense fish shoals is\nobserved in open-sea fish cages. By employing a multi-beam ultrasonic antenna,\nwe observe the coherent backscattering (CBS) phenomenon. We extract key\nparameters of wave transport such as the transport mean free path and the\nenergy transport velocity of diffusive waves from diffusion theory fits to the\nexperimental data. The energy transport velocity is found to be about 10 times\nsmaller than the speed of sound in water, a value that is exceptionally low\ncompared with most observations in acoustics. By studying different models of\nthe fish body, we explain the basic mechanism responsible for the observed very\nslow transport of ultrasonic waves in dense fish shoals. Our results show that,\nwhile the fish swim bladder plays an important role in wave scattering, other\norgans have to be considered to explain ultra-low energy transport velocities.\n"}
+{"abstract": "  In recent decades, with the emergence of numerous novel intelligent\noptimization algorithms, many optimization researchers have begun to look for a\nbasic search mechanism for their schemes that provides a more essential\nexplanation of their studies. This paper aims to study the basic mechanism of\nan algorithm for black-box optimization with quantum theory. To achieve this\ngoal, the Schroedinger equation is employed to establish the relationship\nbetween the optimization problem and the quantum system, which makes it\npossible to study the dynamic search behaviors in the evolution process with\nquantum theory. Moreover, to explore the basic behavior of the optimization\nsystem, the optimization problem is assumed to be decomposed and approximated.\nThen, a multilevel approximation quantum dynamics model of the optimization\nalgorithm is established, which provides a mathematical and physical framework\nfor the analysis of the optimization algorithm. Correspondingly, the basic\nsearch behavior based on this model is derived, which is governed by quantum\ntheory. Comparison experiments and analysis between different bare-bones\nalgorithms confirm the existence of the quantum mechanic based basic search\nmechanism of the algorithm on black-box optimization.\n"}
+{"abstract": "  Psycholinguistic studies of human word processing and lexical access provide\nample evidence of the preferred nature of word-initial versus word-final\nsegments, e.g., in terms of attention paid by listeners (greater) or the\nlikelihood of reduction by speakers (lower). This has led to the conjecture --\nas in Wedel et al. (2019b), but common elsewhere -- that languages have evolved\nto provide more information earlier in words than later. Information-theoretic\nmethods to establish such tendencies in lexicons have suffered from several\nmethodological shortcomings that leave open the question of whether this high\nword-initial informativeness is actually a property of the lexicon or simply an\nartefact of the incremental nature of recognition. In this paper, we point out\nthe confounds in existing methods for comparing the informativeness of segments\nearly in the word versus later in the word, and present several new measures\nthat avoid these confounds. When controlling for these confounds, we still find\nevidence across hundreds of languages that indeed there is a cross-linguistic\ntendency to front-load information in words.\n"}
+{"abstract": "  Lentil seeds have been packed in a dielectric barrier device and exposed for\nseveral minutes to a cold atmospheric plasma generated in helium with/without a\nreactive gas (nitrogen or oxygen). While no impact is evidenced on germination\nrates (caping nearly at 100% with/without plasma exposure), seeds vigor is\nclearly improved with a median germination time decreasing from 1850 min (31h)\nto 1500 min (26 h), hence representing a time saving of at least 5 hours. We\nshow that the admixture of nitrogen to helium can further increase this time\nsaving up to 8 hours. Contrarily, we demonstrate that the addition of molecular\noxygen to the helium discharge does not promote seeds vigor. Whatever the\nplasma chemistry utilized, these biological effects are accompanied with strong\nhydrophilization of the seed coating (with a decrease in contact angles from\n118{\\deg} to 25{\\deg}) as well as increased water absorption (water uptakes\nmeasured 8 hours after imbibition are close to 50% for plasma-treated seeds\ninstead of 37% for seeds from the control group). A follow-up of the seeds over\na 45-days ageing period shows the sustainability of the plasma-triggered\nbiological effects: whatever the plasma treatment, seeds vigor remains stable\nand much higher than for seeds unexposed to plasma). For these reasons, the\nseed-packed dielectric barrier device (SP-DBD) supplied with a He-N2 gas\nmixture can be considered as a relevant dry atmospheric priming plasma (DAPP)\nin the same way as those used in routine by seed companies.\n"}
+{"abstract": "  Photoacoustic (PA) imaging is a biomedical imaging modality capable of\nacquiring high-contrast images of optical absorption at depths much greater\nthan traditional optical imaging techniques. However, practical instrumentation\nand geometry limit the number of available acoustic sensors surrounding the\nimaging target, which results in the sparsity of sensor data. Conventional PA\nimage reconstruction methods give severe artifacts when they are applied\ndirectly to the sparse PA data. In this paper, we firstly propose to employ a\nnovel signal processing method to make sparse PA raw data more suitable for the\nneural network, concurrently speeding up image reconstruction. Then we propose\nAttention Steered Network (AS-Net) for PA reconstruction with multi-feature\nfusion. AS-Net is validated on different datasets, including simulated\nphotoacoustic data from fundus vasculature phantoms and experimental data from\nin vivo fish and mice. Notably, the method is also able to eliminate some\nartifacts present in the ground truth for in vivo data. Results demonstrated\nthat our method provides superior reconstructions at a faster speed.\n"}
+{"abstract": "  The current methods for realization of the second with the highest accuracy\nthrough the frequency of the hyperfine transition between two levels of the\nground state of the cesium 133 atom are considered. It is discussed possible\nways for the future redefinition of the SI time unit with the help of optical\nfrequency standards, as well as thorium and super-high frequency standards.\nSeveral constants which can be also used for definitions of the time, mass and\nelectric current units are analyzed.\n"}
+{"abstract": "  Using Infrastructure-to-Vehicle (I2V) information can be of great benefit\nwhen driving autonomously in high-density traffic situations with limited\nvisibility, since the sensing capabilities of the vehicle are enhanced by\nexternal sensors. In this research, a method is introduced to increase the\nvehicle's self-awareness in intersections for one of the largest foreseen\nchallenges when using I2V communication: cyber security. The introduced anomaly\ndetection algorithm, running on the automated vehicle, assesses the health of\nthe I2V communication against multiple cyber security attacks. The analysis is\ndone in a simulation environment, using cyber-attack scenarios from the\nSecredas Project (Cyber Security for Cross Domain Reliable Dependable Automated\nSystems) and provides insights into the limitations the vehicle has when facing\nI2V cyber attacks of different types and amplitudes and when sensor redundancy\nis lost. The results demonstrate that anomalies injected can be robustly\ndetected and mitigated by the autonomous vehicle, allowing it to react more\nsafely and comfortably and maintaining correct object tracking in\nintersections.\n"}
+{"abstract": "  Although close contact represents an important contagion route, the mechanism\nof exposure to exhaled droplets remains insufficiently characterized. In this\nstudy, an integrated risk assessment is presented for SARS-CoV-2 close contact\nexposure between a speaking infectious subject and a susceptible subject. It is\nbased on a three-dimensional transient numerical model for the description of\nexhaled droplet spread once emitted by a speaking person, coupled with a\nrecently proposed SARS-CoV-2 emission approach. Particle image velocimetry\nmeasurements were conducted to validate the numerical model. The contribution\nof large droplets to infection risk is dominant for distances < 0.6 m, whereas\nfor longer distances, the exposure risk depends only on airborne droplets. In\nparticular, for short exposures (10 s) a minimum safety distance of 0.75 m\nshould be maintained to lower the risk below 0.1%; for exposures of 1 and 15\nmin this distance increases to about 1.0 and 1.5 m, respectively. Based on the\ninterpersonal distances across countries reported as a function of interacting\nindividuals, cultural differences, and environmental and sociopsychological\nfactors, the approach presented here revealed that, in addition to intimate and\npersonal distances, particular attention must be paid to exposures longer than\n1 min within social distances (of about 1 m).\n"}
+{"abstract": "  It is possible to implement a certain form of modified gravity inspired by\nloop quantization through non-bijective canonical transformations. The\ncanonical nature might suggest that such modifications are guaranteed to\npreserve general covariance. Here, however, we show that a dedicated space-time\nanalysis is still required, even in the case of a bijective canonical\ntransformation. In addition, a complete global analysis is presented for a\nrecent proposal of a non-bijective transformation, showing that it does not\npreserve general covariance and that the only novel physical effect introduced\nby the modification is the presence of certain time-reversal hypersurfaces\nbetween classical space-time regions. These results provide further insights\ninto the physical interpretation of modified dynamics in models of loop quantum\ngravity.\n"}
+{"abstract": "  We discuss whether the bound nature of multiquark states in quark models\ncould benefit from relativistic effects on the kinetic energy operator. For\nmesons and baryons, relativistic corrections to the kinetic energy lead to\nlower energies, and thus call for a retuning of the parameters of the model.\nFor multiquark states, as well as their respective thresholds, a comparison is\nmade of the results obtained with non-relativistic and relativistic kinetic\nenergy. It is found that the binding energy is lower in the relativistic case.\nIn particular, $QQ\\bar q\\bar q$ tetraquarks with double heavy flavor become\nstable for a larger ratio of the heavy to light quark masses; and the all-heavy\ntetraquarks $QQ\\bar Q\\bar Q$ that are not stable in standard non-relativistic\nquark models remain unstable when a relativistic form of kinetic energy is\nadopted\n"}
+{"abstract": "  We report on precise Doppler measurements of L231-32 (TOI-270), a nearby M\ndwarf ($d=22$ pc, $M_\\star = 0.39$ M$_\\odot$, $R_\\star = 0.38$ R$_\\odot$),\nwhich hosts three transiting planets that were recently discovered using data\nfrom the Transiting Exoplanet Survey Satellite (TESS). The three planets are\n1.2, 2.4, and 2.1 times the size of Earth and have orbital periods of 3.4, 5.7,\nand 11.4 days. We obtained 29 high-resolution optical spectra with the newly\ncommissioned Echelle Spectrograph for Rocky Exoplanet and Stable Spectroscopic\nObservations (ESPRESSO) and 58 spectra using the High Accuracy Radial velocity\nPlanet Searcher (HARPS). From these observations, we find the masses of the\nplanets to be $1.58 \\pm 0.26$, $6.15 \\pm 0.37$, and $4.78 \\pm 0.43$ M$_\\oplus$,\nrespectively. The combination of radius and mass measurements suggests that the\ninnermost planet has a rocky composition similar to that of Earth, while the\nouter two planets have lower densities. Thus, the inner planet and the outer\nplanets are on opposite sides of the `radius valley' -- a region in the\nradius-period diagram with relatively few members, which has been interpreted\nas a consequence of atmospheric photo-evaporation. We place these findings into\nthe context of other small close-in planets orbiting M dwarf stars, and use\nsupport vector machines to determine the location and slope of the M dwarf\n($T_\\mathrm{eff} < 4000$ K) radius valley as a function of orbital period. We\ncompare the location of the M dwarf radius valley to the radius valley observed\nfor FGK stars, and find that its location is a good match to photo-evaporation\nand core-powered mass loss models. Finally, we show that planets below the M\ndwarf radius valley have compositions consistent with stripped rocky cores,\nwhereas most planets above have a lower density consistent with the presence of\na H-He atmosphere.\n"}
+{"abstract": "  We propose a definition for the average indirect effect of a binary treatment\nin the potential outcomes model for causal inference under cross-unit\ninterference. Our definition is analogous to the standard definition of the\naverage direct effect, and can be expressed without needing to compare outcomes\nacross multiple randomized experiments. We show that the proposed indirect\neffect satisfies a decomposition theorem whereby, in a Bernoulli trial, the sum\nof the average direct and indirect effects always corresponds to the effect of\na policy intervention that infinitesimally increases treatment probabilities.\nWe also consider a number of parametric models for interference, and find that\nour (non-parametric) indirect effect remains a natural estimand when\nre-expressed in the context of these models.\n"}
+{"abstract": "  In this work, we have investigated the process $D_s^+\\to K^+ K^- \\pi^+$,\ntaking into account the contributions from the $S$-wave\npseudoscalar-pseudoscalar interaction within the chiral unitary approach, and\nalso the intermediate $\\phi$ resonance. By analyzing the BESIII and {\\it BABAR}\nmeasurements, we conclude that the $f_0(980)$ state, dynamically generated from\nthe $S$-wave pseudoscalar-pseudoscalar interaction, gives the dominant\ncontribution close to the $K^+K^-$ threshold in the $K^+K^-$ invariant mass\ndistribution of the decay $D_s^+\\to K^+ K^- \\pi^+$ in $S$-wave. On the other\nhand, our results imply that the lineshape adopted by BESIII and {\\it BABAR}\nfor the resonances $a_0(980)$ and $f_0(980)$ is not advisable in the fit to the\ndata close to the $K^+K^-$ threshold.\n"}
+{"abstract": "  The Cepheid Period-Luminosity (PL) relation is the key tool for measuring\nastronomical distances and for establishing the extragalactic distance scale.\nIn particular, the local value of the Hubble constant ($H_0$) strongly depends\non Cepheid distance measurements. The recent Gaia Data Releases and other\nparallax measurements from the Hubble Space Telescope (HST) already enabled to\nimprove the accuracy of the slope ($\\alpha$) and intercept ($\\beta$) of the PL\nrelation. However, the dependence of this law on metallicity is still largely\ndebated. In this paper, we combine three samples of Cepheids in the Milky Way\n(MW), the Large Magellanic Cloud (LMC) and the Small Magellanic Cloud (SMC) in\norder to derive the metallicity term (hereafter $\\gamma$) of the PL relation.\nThe recent publication of extremely precise LMC and SMC distances based on\nlate-type detached eclipsing binary systems (DEBs) provides a solid anchor for\nthe Magellanic Clouds. In the MW, we adopt Cepheid parallaxes from the early\nthird Gaia Data Release. We derive the metallicity effect in $V$, $I$, $J$,\n$H$, $K_S$, $W_{VI}$ and $W_{JK}$. In the $K_S$ band we report a metallicity\neffect of $-0.221 \\pm 0.051$ mag/dex, the negative sign meaning that more\nmetal-rich Cepheids are intrinsically brighter than their more metal-poor\ncounterparts of the same pulsation period.\n"}
+{"abstract": "  Unlike the classical Brauer group of a field, the Brauer-Grothendieck group\nof a singular scheme need not be torsion. We show that there exist integral\nnormal projective surfaces over a large field of positive characteristic with\nnon-torsion Brauer group. In contrast, we demonstrate that such examples cannot\nexist over the algebraic closure of a finite field.\n"}
+{"abstract": "  Optical neural networks, employing optical fields and photonic tools to\nperform artificial neural network computations, are rapidly advancing and are\ngenerating a broad interest and sparking new applications. We propose a nascent\napproach for realizing the optical neural network utilizing a single resonator\nnetwork, where the arrival times of optical pulses are interconnected to\nconstruct a synthetic temporal dimension. The set of pulses in each roundtrip\ntherefore provides the sites in each layer in the optical neural network, and\ncan be linearly transformed with splitters and delay lines, including the phase\nmodulators, when pulses circulate inside the network. Such linear\ntransformation can be arbitrarily controlled by applied modulation phases,\nwhich serve as the building block of the neural network together with a\nnonlinear component for pulses. We validate the functionality of the proposed\noptical neural network using an example of a complicated wine classification\nproblem. This proof of principle demonstration opens up an opportunity to\ndevelop a photonics-based machine learning in a single ring network utilizing\nthe concept of synthetic dimensions. Our approach holds flexibility and\neasiness of reconfiguration with potentially complex functionality in achieving\ndesired optical tasks, pointing towards promisingly perform on-chip optical\ncomputations with further miniaturization.\n"}
+{"abstract": "  AC optimal power flow (AC-OPF) problems need to be solved more frequently in\nthe future to maintain stable and economic power system operation. To tackle\nthis challenge, a deep neural network-based voltage-constrained approach\n(DeepOPF-V) is proposed to solve AC-OPF problems with high computational\nefficiency. Its unique design predicts voltages of all buses and then uses them\nto reconstruct the remaining variables without solving non-linear AC power flow\nequations. A fast post-processing process is developed to enforce the box\nconstraints. The effectiveness of DeepOPF-V is validated by simulations on IEEE\n118/300-bus systems and a 2000-bus test system. Compared with existing studies,\nDeepOPF-V achieves decent computation speedup up to four orders of magnitude\nand comparable performance in optimality gap and preserving the feasibility of\nthe solution.\n"}
+{"abstract": "  This paper presents a data-driven approach to the design of predictive\ncontrollers. The prediction matrices utilized in standard model predictive\ncontrol (MPC) algorithms are typically constructed using knowledge of a system\nmodel such as, state-space or input-output models. Instead, we directly\nestimate the prediction matrices relating future outputs with current and\nfuture inputs from measured data, off-line. On-line, the developed data--driven\npredictive controller reduces to solving a quadratic program with a similar\nstructure and complexity as linear MPC. Additionally, we develop a new\nprocedure for estimating prediction matrices from data for predictive\ncontrollers with integral action, corresponding to the rate-based formulation\nof linear MPC. The effectiveness of the developed data-driven predictive\ncontroller is illustrated on position control of a linear motor model.\n"}
+{"abstract": "  Radiation Treatment Planning (RTP) is the process of planning the appropriate\nexternal beam radiotherapy to combat cancer in human patients. RTP is a complex\nand compute-intensive task, which often takes a long time (several hours) to\ncompute. Reducing this time allows for higher productivity at clinics and more\nsophisticated treatment planning, which can materialize in better treatments.\nThe state-of-the-art in medical facilities uses general-purpose processors\n(CPUs) to perform many steps in the RTP process. In this paper, we explore the\nuse of accelerators to reduce RTP calculating time. We focus on the step that\ncalculates the dose using the Graphics Processing Unit (GPU), which we believe\nis an excellent candidate for this computation type. Next, we create a highly\noptimized implementation for a custom Sparse Matrix-Vector Multiplication\n(SpMV) that operates on numerical formats unavailable in state-of-the-art SpMV\nlibraries (e.g., Ginkgo and cuSPARSE). We show that our implementation is\nseveral times faster than the baseline (up-to 4x) and has a higher operational\nintensity than similar (but different) versions such as Ginkgo and cuSPARSE.\n"}
+{"abstract": "  Using first-principles calculations, we show that CsBX$_3$ halides with B=Sn\nor Pb undergo octahedral rotation distortions, while for B=Ge and Si, they\nundergo a ferro-electric rhombohedral distortion accompanied by a rhombohedral\nstretching of the lattice. We show that these are mutually exclusive at their\nequilibrium volume although different distortions may occur as function of\nlattice expansion. The choice between the two distortion modes is in part\ngoverned by the Goldschmidt tolerance factor. However, another factor\nexplaining the difference between Sn and Pb compared with Ge and Si is the\nstronger lone-pair character of Ge and Si when forced to be divalent as is the\ncase in these structures. The lone-pair chemistry is related to the\noff-centering. While the Si-based compounds have not yet been synthesized, the\nGe compounds have been established experimentally. As a final test of the\nimportance of the tolerance factor we consider RbGeX$_3$, which has smaller\ntolerance factor than the corresponding CsGeX$_3$ because Rb is smaller than\nCs. We find that it can lower its energy by both rotations or rhombohedral\noff-centering distortions but the latter lower the energy slightly more\nefficiently.\n"}
+{"abstract": "  Open Source Software (OSS) plays an important role in the digital economy.\nYet although software production is amenable to remote collaboration and its\noutputs are easily shared across distances, software development seems to\ncluster geographically in places such as Silicon Valley, London, or Berlin. And\nwhile recent work indicates that OSS activity creates positive externalities\nwhich accrue locally through knowledge spillovers and information effects,\nup-to-date data on the geographic distribution of active open source developers\nis limited. This presents a significant blindspot for policymakers, who tend to\npromote OSS at the national level as a cost-saving tool for public sector\ninstitutions. We address this gap by geolocating more than half a million\nactive contributors to GitHub in early 2021 at various spatial scales. Compared\nto results from 2010, we find a significant increase in the share of developers\nbased in Asia, Latin America and Eastern Europe, suggesting a more even spread\nof OSS developers globally. Within countries, however, we find significant\nconcentration in regions, exceeding the concentration of workers in high-tech\nfields. Social and economic development indicators predict at most half of\nregional variation in OSS activity in the EU, suggesting that clusters of OSS\nhave idiosyncratic roots. We argue that policymakers seeking to foster OSS\nshould focus locally rather than nationally, using the tools of cluster policy\nto support networks of OSS developers.\n"}
+{"abstract": "  Using the group $G(1)$ of invertible elements and the maximal ideals\n$\\mathfrak{m}_x$ of the commutative algebra $C(X)$ of real-valued functions on\na compact regular space $X$, we define a Borel action of the algebra on the\nmeasure space $(X,\\mu)$ with $\\mu$ a Radon measure. The zero sets $Z(X)$ of the\nalgebra $C(X)$ is used to study the ergodicity of the $G(1)$-action via its\naction on the maximal ideals $\\mathfrak{m}_x$ which defines an action groupoid\n$\\mathcal{G} = \\mathfrak{m}_x \\ltimes G(1)$ trivialized on $X$. The resulting\nmeasure groupoid $(\\mathcal{G},\\mathcal{C})$ is used to define a proper action\non the generalized space $\\mathcal{M}(X)$. The existence of slice at each point\nof $\\mathcal{M}(X)$ present it as a cohomogeneity-one $\\mathcal{G}$-space. The\ndynamical system of the algebra $C(X)$ is defined by the action of the measure\ngroupoid $(\\mathcal{G},\\mathcal{C}) \\times \\mathcal{M}(X) \\to \\mathcal{M}(X)$.\n"}
+{"abstract": "  End-to-end speech recognition systems usually require huge amounts of\nlabeling resource, while annotating the speech data is complicated and\nexpensive. Active learning is the solution by selecting the most valuable\nsamples for annotation. In this paper, we proposed to use a predicted loss that\nestimates the uncertainty of the sample. The CTC (Connectionist Temporal\nClassification) and attention loss are informative for speech recognition since\nthey are computed based on all decoding paths and alignments. We defined an\nend-to-end active learning pipeline, training an ASR/LP (Automatic Speech\nRecognition/Loss Prediction) joint model. The proposed approach was validated\non an English and a Chinese speech recognition task. The experiments show that\nour approach achieves competitive results, outperforming random selection,\nleast confidence, and estimated loss method.\n"}
+{"abstract": "  Transformers with remarkable global representation capacities achieve\ncompetitive results for visual tasks, but fail to consider high-level local\npattern information in input images. In this paper, we present a generic\nDual-stream Network (DS-Net) to fully explore the representation capacity of\nlocal and global pattern features for image classification. Our DS-Net can\nsimultaneously calculate fine-grained and integrated features and efficiently\nfuse them. Specifically, we propose an Intra-scale Propagation module to\nprocess two different resolutions in each block and an Inter-Scale Alignment\nmodule to perform information interaction across features at dual scales.\nBesides, we also design a Dual-stream FPN (DS-FPN) to further enhance\ncontextual information for downstream dense predictions. Without bells and\nwhistles, the proposed DS-Net outperforms DeiT-Small by 2.4% in terms of top-1\naccuracy on ImageNet-1k and achieves state-of-the-art performance over other\nVision Transformers and ResNets. For object detection and instance\nsegmentation, DS-Net-Small respectively outperforms ResNet-50 by 6.4% and 5.5%\nin terms of mAP on MSCOCO 2017, and surpasses the previous state-of-the-art\nscheme, which significantly demonstrates its potential to be a general backbone\nin vision tasks. The code will be released soon.\n"}
+{"abstract": "  Magnetic reconnection can convert magnetic energy into kinetic energy of\nnon-thermal electron beams. We have now characterized the EVDFs generated by 3D\nkinetic magnetic reconnection obtained by numerical simulations utilizing the\nACRONYM particle-in-cell (PIC) code, and their consequences for plasma\ninstabilities which differ from those of 2D kinetic magnetic reconnection,\nsince in 3D unstable waves can propagate in all directions. We found that: (1)\nIn both diffusion region and separatrices of reconnection, EVDFs with positive\nvelocity-space gradients in the direction parallel to the local magnetic field\nare formed. These gradients can cause counter-streaming and bump-on-tail\ninstabilities. (2) In regions with weak magnetic field strength, namely,\nregions near the current sheet midplane, EVDF with positive velocity space\ngradients are generated in the direction perpendicular to the local magnetic\nfield. In particular crescent-shaped EVDFs in the velocity space perpendicular\nto local magnetic field are mainly formed in the diffusion region of\nreconnection. These perpendicular gradients in the EVDFs can cause electron\ncyclotron maser instabilities. (3) As guide-field strength increases, less\nregions in the current sheets feature perpendicular velocity-space gradients in\nthe EVDFs. The formation of EVDFs with positive gradients in the parallel\n(magnetic field-aligned) direction is mainly due to magnetized and adiabatic\nelectrons, while EVDFs with positive gradients in the direction perpendicular\nto the local magnetic field are attributed to unmagnetized, nonadiabatic\nelectrons in the diffusion and outflow region near the reconnection midplane.\n"}
+{"abstract": "  We propose a method for learning linear models whose predictive performance\nis robust to causal interventions on unobserved variables, when noisy proxies\nof those variables are available. Our approach takes the form of a\nregularization term that trades off between in-distribution performance and\nrobustness to interventions. Under the assumption of a linear structural causal\nmodel, we show that a single proxy can be used to create estimators that are\nprediction optimal under interventions of bounded strength. This strength\ndepends on the magnitude of the measurement noise in the proxy, which is, in\ngeneral, not identifiable. In the case of two proxy variables, we propose a\nmodified estimator that is prediction optimal under interventions up to a known\nstrength. We further show how to extend these estimators to scenarios where\nadditional information about the \"test time\" intervention is available during\ntraining. We evaluate our theoretical findings in synthetic experiments and\nusing real data of hourly pollution levels across several cities in China.\n"}
+{"abstract": "  Sparse principal component analysis (PCA) is a popular tool for dimensional\nreduction of high-dimensional data. Despite its massive popularity, there is\nstill a lack of theoretically justifiable Bayesian sparse PCA that is\ncomputationally scalable. A major challenge is choosing a suitable prior for\nthe loadings matrix, as principal components are mutually orthogonal. We\npropose a spike and slab prior that meets this orthogonality constraint and\nshow that the posterior enjoys both theoretical and computational advantages.\nTwo computational algorithms, the PX-CAVI and the PX-EM algorithms, are\ndeveloped. Both algorithms use parameter expansion to deal with the\northogonality constraint and to accelerate their convergence speeds. We found\nthat the PX-CAVI algorithm has superior empirical performance than the PX-EM\nalgorithm and two other penalty methods for sparse PCA. The PX-CAVI algorithm\nis then applied to study a lung cancer gene expression dataset. $\\mathsf{R}$\npackage $\\mathsf{VBsparsePCA}$ with an implementation of the algorithm is\navailable on The Comprehensive R Archive Network.\n"}
+{"abstract": "  The physics goal of the strong interaction program of the NA61/SHINE\nexperiment at the CERN Super Proton Synchrotron (SPS) is to study the phase\ndiagram of hadronic matter by a scan of particle production in collisions of\nnuclei with various sizes at a set of energies covering the SPS energy range.\nThis paper presents differential inclusive spectra of transverse momentum,\ntransverse mass and rapidity of $\\pi^{-}$ mesons produced in $central$\n${}^{40}$Ar+${}^{45}$Sc collisions at beam momenta of 13$A$, 19$A$, 30$A$,\n40$A$, 75$A$ and 150$A$ GeV/$c$. Energy and system size dependence of\nparameters of these distributions -- mean transverse mass, the inverse slope\nparameter of transverse mass spectra, width of the rapidity distribution and\nmean multiplicity -- are presented and discussed. Furthermore, the dependence\nof the ratio of the mean number of produced pions to the mean number of wounded\nnucleons on the collision energy was derived. The results are compared to\npredictions of several models.\n"}
+{"abstract": "  Effective theory framework based on symmetry has recently gained widespread\ninterest in the field of cosmology. In this paper, we apply the same idea on\nthe genesis of the primordial magnetic field and its evolution throughout the\ncosmological universe. Given the broken time-diffeomorphism symmetry by the\ncosmological background, we considered the most general Lagrangian of\nelectromagnetic and metric fluctuation up to second order, which naturally\nbreaks conformal symmetry in the electromagnetic (EM) sector. We also include\nparity violation in the electromagnetic sector with the motivation that has\npotential observational significance. In such a set-up, we explore the\nevolution of EM, scalar, and tensor perturbations considering different\nobservational constraints. In our analysis we emphasize the role played by the\nintermediate reheating phase which has got limited interest in all the previous\nstudies. Assuming the vanishing electrical conductivity during the entire\nperiod of reheating, the well-known Faraday electromagnetic induction has been\nshown to play a crucial role in enhancing the strength of the present-day\nmagnetic field. We show how such physical effects combined with the PLANCK and\nthe large scale magnetic field observation makes a large class of models viable\nand severely restricts the reheating equation of state parameter within a very\nnarrow range of $0.01 < \\omega_\\mathrm{eff} < 0.27$, which is nearly\nindependent of reheating scenarios we have considered.\n"}
+{"abstract": "  In the real world, medical datasets often exhibit a long-tailed data\ndistribution (i.e., a few classes occupy most of the data, while most classes\nhave rarely few samples), which results in a challenging imbalance learning\nscenario. For example, there are estimated more than 40 different kinds of\nretinal diseases with variable morbidity, however with more than 30+ conditions\nare very rare from the global patient cohorts, which results in a typical\nlong-tailed learning problem for deep learning-based screening models. In this\nstudy, we propose class subset learning by dividing the long-tailed data into\nmultiple class subsets according to prior knowledge, such as regions and\nphenotype information. It enforces the model to focus on learning the\nsubset-specific knowledge. More specifically, there are some relational classes\nthat reside in the fixed retinal regions, or some common pathological features\nare observed in both the majority and minority conditions. With those subsets\nlearnt teacher models, then we are able to distill the multiple teacher models\ninto a unified model with weighted knowledge distillation loss. The proposed\nframework proved to be effective for the long-tailed retinal diseases\nrecognition task. The experimental results on two different datasets\ndemonstrate that our method is flexible and can be easily plugged into many\nother state-of-the-art techniques with significant improvements.\n"}
+{"abstract": "  Local feature attribution methods are increasingly used to explain complex\nmachine learning models. However, current methods are limited because they are\nextremely expensive to compute or are not capable of explaining a distributed\nseries of models where each model is owned by a separate institution. The\nlatter is particularly important because it often arises in finance where\nexplanations are mandated. Here, we present DeepSHAP, a tractable method to\npropagate local feature attributions through complex series of models based on\na connection to the Shapley value. We evaluate DeepSHAP across biological,\nhealth, and financial datasets to show that it provides equally salient\nexplanations an order of magnitude faster than existing model-agnostic\nattribution techniques and demonstrate its use in an important distributed\nseries of models setting.\n"}
+{"abstract": "  A definition of a convolution of tensor fields on group manifolds is given,\nwhich is then generalised to generic homogeneous spaces. This is applied to the\nproduct of gauge fields in the context of `gravity $=$ gauge $\\times$ gauge'.\nIn particular, it is shown that the linear Becchi-Rouet-Stora-Tyutin (BRST)\ngauge transformations of two Yang-Mills gauge fields generate the linear BRST\ndiffeomorphism transformations of the graviton. This facilitates the definition\nof the `gauge $\\times$ gauge' convolution product on, for example, the static\nEinstein universe, and more generally for ultrastatic spacetimes with compact\nspatial slices.\n"}
+{"abstract": "  Network dismantling aims to scratch the network into unconnected fragments by\nremoving an optimal set of nodes and has been widely adopted in many real-world\napplications such as epidemic control and rumor containment. However,\nconventional methods often disassemble the system from the perspective of\nclassic networks, which have only pairwise interactions, and often ignored the\nmore ubiquitous and nature group-wise interactions modeled by hypernetwork.\nMoreover, a simple network can't describe the collective behavior of multiple\nobjects, it is necessary to solve related problems through hypernetwork\ndismantling. In this work, we designed a higher order collective influence\nmeasure to identify key node sets in hypernetwork. It comprehensively consider\nthe environment in which the target node is located and its own characteristics\nto determine the importance of the node, so as to dismantle the hypernetwork by\nremoving these selected nodes. Finally, we used the method to carry out a\nseries of real-world hypernetwork dismantling tasks. Experimental results on\nfive real-world hypernetworks demonstrate the effectiveness of our proposed\nmeasure.\n"}
+{"abstract": "  We investigate the sensitivity of the projected TeV muon collider to the\ngauged $L^{}_{\\mu}$-$L^{}_{\\tau}$ model. Two processes are considered:\n$Z'$-mediated two-body scatterings $\\mu^+ \\mu^- \\to \\ell^+ \\ell^-$ with $\\ell =\n\\mu$ or $\\tau$, and scattering with initial state photon emission, $\\mu^+ \\mu^-\n\\to \\gamma Z',~Z' \\to \\ell \\overline{\\ell}$, where $\\ell$ can be $\\mu$, $\\tau$\nor $\\nu_{\\mu/\\tau}$. We quantitatively study the sensitivities of these two\nprocesses by taking into account possible signals and relevant backgrounds in a\nmuon collider experiment with a center-of-mass energy $\\sqrt{s} = 3~{\\rm TeV}$\nand a luminosity $L=1~{\\rm ab^{-1}}$. For two-body scattering one can exclude\n$Z'$ masses $M^{}_{Z'} \\lesssim 100~{\\rm TeV}$ with $\\mathcal{O}(1)$ gauge\ncouplings. When $M^{}_{Z'} \\lesssim 1~{\\rm TeV} <\\sqrt{s}$, one can exclude $g'\n\\gtrsim 2\\times 10^{-2}$. The process with photon emission is more powerful\nthan the two-body scattering if $M^{}_{Z'} < \\sqrt{s}$. For instance, a\nsensitivity of $g' \\simeq 4 \\times 10^{-3}$ can be achieved at $M^{}_{Z'} =\n1~{\\rm TeV}$. The parameter spaces favored by the $(g-2)^{}_{\\mu}$ and $B$\nanomalies with $M^{}_{Z'} > 100~{\\rm GeV}$ are entirely covered by a muon\ncollider.\n"}
+{"abstract": "  A monopolist seller of multiple goods screens a buyer whose type is initially\nunknown to both but drawn from a commonly known distribution. The buyer\nprivately learns about his type via a signal. We derive the seller's optimal\nmechanism in two different information environments. We begin by deriving the\nbuyer-optimal outcome. Here, an information designer first selects a signal,\nand then the seller chooses an optimal mechanism in response; the designer's\nobjective is to maximize consumer surplus. Then, we derive the optimal\ninformationally robust mechanism. In this case, the seller first chooses the\nmechanism, and then nature picks the signal that minimizes the seller's\nprofits. We derive the relation between both problems and show that the optimal\nmechanism in both cases takes the form of pure bundling.\n"}
+{"abstract": "  The task of age transformation illustrates the change of an individual's\nappearance over time. Accurately modeling this complex transformation over an\ninput facial image is extremely challenging as it requires making convincing,\npossibly large changes to facial features and head shape, while still\npreserving the input identity. In this work, we present an image-to-image\ntranslation method that learns to directly encode real facial images into the\nlatent space of a pre-trained unconditional GAN (e.g., StyleGAN) subject to a\ngiven aging shift. We employ a pre-trained age regression network to explicitly\nguide the encoder in generating the latent codes corresponding to the desired\nage. In this formulation, our method approaches the continuous aging process as\na regression task between the input age and desired target age, providing\nfine-grained control over the generated image. Moreover, unlike approaches that\noperate solely in the latent space using a prior on the path controlling age,\nour method learns a more disentangled, non-linear path. Finally, we demonstrate\nthat the end-to-end nature of our approach, coupled with the rich semantic\nlatent space of StyleGAN, allows for further editing of the generated images.\nQualitative and quantitative evaluations show the advantages of our method\ncompared to state-of-the-art approaches.\n"}
+{"abstract": "  Camera pose regression methods apply a single forward pass to the query image\nto estimate the camera pose. As such, they offer a fast and light-weight\nalternative to traditional localization schemes based on image retrieval. Pose\nregression approaches simultaneously learn two regression tasks, aiming to\njointly estimate the camera position and orientation using a single embedding\nvector computed by a convolutional backbone. We propose an attention-based\napproach for pose regression, where the convolutional activation maps are used\nas sequential inputs. Transformers are applied to encode the sequential\nactivation maps as latent vectors, used for camera pose regression. This allows\nus to pay attention to spatially-varying deep features. Using two Transformer\nheads, we separately focus on the features for camera position and orientation,\nbased on how informative they are per task. Our proposed approach is shown to\ncompare favorably to contemporary pose regressors schemes and achieves\nstate-of-the-art accuracy across multiple outdoor and indoor benchmarks. In\nparticular, to the best of our knowledge, our approach is the only method to\nattain sub-meter average accuracy across outdoor scenes. We make our code\npublicly available from here.\n"}
+{"abstract": "  A pervasive design issue of AI systems is their explainability--how to\nprovide appropriate information to help users understand the AI. The technical\nfield of explainable AI (XAI) has produced a rich toolbox of techniques.\nDesigners are now tasked with the challenges of how to select the most suitable\nXAI techniques and translate them into UX solutions. Informed by our previous\nwork studying design challenges around XAI UX, this work proposes a design\nprocess to tackle these challenges. We review our and related prior work to\nidentify requirements that the process should fulfill, and accordingly, propose\na Question-Driven Design Process that grounds the user needs, choices of XAI\ntechniques, design, and evaluation of XAI UX all in the user questions. We\nprovide a mapping guide between prototypical user questions and exemplars of\nXAI techniques to reframe the technical space of XAI, also serving as boundary\nobjects to support collaboration between designers and AI engineers. We\ndemonstrate it with a use case of designing XAI for healthcare adverse events\nprediction, and discuss lessons learned for tackling design challenges of AI\nsystems.\n"}
+{"abstract": "  Assuming time-scale separation, a simple and unified theory of thermodynamics\nand stochastic thermodynamics is constructed for small classical systems\nstrongly interacting with its environment in a controllable fashion. The total\nHamiltonian is decomposed into a bath part and a system part, the latter being\nthe Hamiltonian of mean force. Both the conditional equilibrium of bath and the\nreduced equilibrium of the system are described by canonical ensemble theories\nwith respect to their own Hamiltonians. The bath free energy is independent of\nthe system variables and the control parameter. Furthermore, the weak coupling\ntheory of stochastic thermodynamics becomes applicable almost verbatim, even if\nthe interaction and correlation between the system and its environment are\nstrong and varied externally. Finally, this TSS-based approach also leads to\nsome new insights about the origin of the second law of thermodynamics.\n"}
+{"abstract": "  End-to-end models are favored in automatic speech recognition (ASR) because\nof their simplified system structure and superior performance. Among these\nmodels, Transformer and Conformer have achieved state-of-the-art recognition\naccuracy in which self-attention plays a vital role in capturing important\nglobal information. However, the time and memory complexity of self-attention\nincreases squarely with the length of the sentence. In this paper, a\nprob-sparse self-attention mechanism is introduced into Conformer to sparse the\ncomputing process of self-attention in order to accelerate inference speed and\nreduce space consumption. Specifically, we adopt a Kullback-Leibler divergence\nbased sparsity measurement for each query to decide whether we compute the\nattention function on this query. By using the prob-sparse attention mechanism,\nwe achieve impressively 8% to 45% inference speed-up and 15% to 45% memory\nusage reduction of the self-attention module of Conformer Transducer while\nmaintaining the same level of error rate.\n"}
+{"abstract": "  In this paper, we investigate the nonhomogeneous boundary value problem for\nthe steady Navier-Stokes equations in a helically symmetric spatial domain.\nWhen data is assumed to be helical invariant and satisfies the compatibility\ncondition, we prove this problem has at least one helical invariant solution.\n"}
+{"abstract": "  We use the aggregate information from individual-to-firm and firm-to-firm in\nGaranti BBVA Bank transactions to mimic domestic private demand. Particularly,\nwe replicate the quarterly national accounts aggregate consumption and\ninvestment (gross fixed capital formation) and its bigger components (Machinery\nand Equipment and Construction) in real time for the case of Turkey. In order\nto validate the usefulness of the information derived from these indicators we\ntest the nowcasting ability of both indicators to nowcast the Turkish GDP using\ndifferent nowcasting models. The results are successful and confirm the\nusefulness of Consumption and Investment Banking transactions for nowcasting\npurposes. The value of the Big data information is more relevant at the\nbeginning of the nowcasting process, when the traditional hard data information\nis scarce. This makes this information specially relevant for those countries\nwhere statistical release lags are longer like the Emerging Markets.\n"}
+{"abstract": "  Satterthwaite and Toepke (1970 Phys. Rev. Lett. 25 741) predicted\nhigh-temperature superconductivity in hydrogen-rich metallic alloys, based on\nan idea that these compounds should exhibit high Debye frequency of the proton\nlattice, which boosts the superconducting transition temperature, Tc. The idea\nhas got full confirmation more than four decades later when Drozdov et al (2015\nNature 525 73) experimentally discovered near-room-temperature\nsuperconductivity in highly-compressed sulphur superhydride, H3S. To date, more\nthan a dozen of high-temperature hydrogen-rich superconducting phases in Ba-H,\nPr-H, P-H, Pt-H, Ce-H, Th-H, S-H, Y-H, La-H, and (La,Y)-H systems have been\nsynthesized and, recently, Hong et al (2021 arXiv:2101.02846) reported on the\ndiscovery of C2/m-SnH12 phase with superconducting transition temperature of Tc\n~ 70 K. Here we analyse the magnetoresistance data, R(T,B), of C2/m-SnH12 phase\nand report that this superhydride exhibits the ground state superconducting gap\nof $\\Delta$(0) = 9.2 meV, the ratio of 2$\\Delta$(0)/k$_B$Tc = 3.3, and 0.010 <\nTc/Tf < 0.014 (where Tf is the Fermi temperature) and, thus, C2/m-SnH12 falls\ninto unconventional superconductors band in the Uemura plot.\n"}
+{"abstract": "  Intelligent systems are transforming the world, as well as our healthcare\nsystem. We propose a deep learning-based cough sound classification model that\ncan distinguish between children with healthy versus pathological coughs such\nas asthma, upper respiratory tract infection (URTI), and lower respiratory\ntract infection (LRTI). In order to train a deep neural network model, we\ncollected a new dataset of cough sounds, labelled with clinician's diagnosis.\nThe chosen model is a bidirectional long-short term memory network (BiLSTM)\nbased on Mel Frequency Cepstral Coefficients (MFCCs) features. The resulting\ntrained model when trained for classifying two classes of coughs -- healthy or\npathology (in general or belonging to a specific respiratory pathology),\nreaches accuracy exceeding 84\\% when classifying cough to the label provided by\nthe physicians' diagnosis. In order to classify subject's respiratory pathology\ncondition, results of multiple cough epochs per subject were combined. The\nresulting prediction accuracy exceeds 91\\% for all three respiratory\npathologies. However, when the model is trained to classify and discriminate\namong the four classes of coughs, overall accuracy dropped: one class of\npathological coughs are often misclassified as other. However, if one consider\nthe healthy cough classified as healthy and pathological cough classified to\nhave some kind of pathologies, then the overall accuracy of four class model is\nabove 84\\%. A longitudinal study of MFCC feature space when comparing\npathological and recovered coughs collected from the same subjects revealed the\nfact that pathological cough irrespective of the underlying conditions occupy\nthe same feature space making it harder to differentiate only using MFCC\nfeatures.\n"}
+{"abstract": "  The Tick library allows researchers in market microstructure to simulate and\nlearn Hawkes process in high-frequency data, with optimized parametric and\nnon-parametric learners. But one challenge is to take into account the correct\ncausality of order book events considering latency: the only way one order book\nevent can influence another is if the time difference between them (by the\ncentral order book timestamps) is greater than the minimum amount of time for\nan event to be (i) published in the order book, (ii) reach the trader\nresponsible for the second event, (iii) influence the decision (processing time\nat the trader) and (iv) the 2nd event reach the order book and be processed.\nFor this we can use exponential kernels shifted to the right by the latency\namount. We derive the expression for the log-likelihood to be minimized for the\n1-D and the multidimensional cases, and test this method with simulated data\nand real data. On real data we find that, although not all decays are the same,\nthe latency itself will determine most of the decays. We also show how the\ndecays are related to the latency. Code is available on GitHub at\nhttps://github.com/MarcosCarreira/Hawkes-With-Latency.\n"}
+{"abstract": "  We study the problem of diffeomorphometric geodesic landmark matching where\nthe objective is to find a diffeomorphism that via its group action maps\nbetween two sets of landmarks. It is well-known that the motion of the\nlandmarks, and thereby the diffeomorphism, can be encoded by an initial\nmomentum leading to a formulation where the landmark matching problem can be\nsolved as an optimisation problem over such momenta. The novelty of our work\nlies in the application of a derivative-free Bayesian inverse method for\nlearning the optimal momentum encoding the diffeomorphic mapping between the\ntemplate and the target. The method we apply is the ensemble Kalman filter, an\nextension of the Kalman filter to nonlinear observation operators. We describe\nan efficient implementation of the algorithm and show several numerical results\nfor various target shapes.\n"}
+{"abstract": "  We propose a novel framework for model-order reduction of hyperbolic\ndifferential equations. The approach combines a relaxation formulation of the\nhyperbolic equations with a discretization using shifted base functions.\nModel-order reduction techniques are then applied to the resulting system of\ncoupled ordinary differential equations. On computational examples including in\nparticular the case of shock waves we show the validity of the approach and the\nperformance of the reduced system.\n"}
+{"abstract": "  Quantum computing is poised to dramatically change the computational\nlandscape, worldwide. Quantum computers can solve complex problems that are, at\nleast in some cases, beyond the ability of even advanced future classical-style\ncomputers. In addition to being able to solve these classical\ncomputer-unsolvable problems, quantum computers have demonstrated a capability\nto solve some problems (such as prime factoring) much more efficiently than\nclassical computing. This will create problems for encryption techniques, which\ndepend on the difficulty of factoring for their security. Security, scientific,\nand other applications will require access to quantum computing resources to\naccess their unique capabilities, speed and economic (aggregate computing time\ncost) benefits. Many scientific applications, as well as numerous other ones,\nuse grid computing to provide benefits such as scalability and resource access.\nAs these applications may benefit from quantum capabilities - and some future\napplications may require quantum capabilities - identifying how to integrate\nquantum computing systems into grid computing environments is critical. This\npaper discusses the benefits of grid-connected quantum computers and what is\nrequired to achieve this.\n"}
+{"abstract": "  Moral outrage has become synonymous with social media in recent years.\nHowever, the preponderance of academic analysis on social media websites has\nfocused on hate speech and misinformation. This paper focuses on analyzing\nmoral judgements rendered on social media by capturing the moral judgements\nthat are passed in the subreddit /r/AmITheAsshole on Reddit. Using the labels\nassociated with each judgement we train a classifier that can take a comment\nand determine whether it judges the user who made the original post to have\npositive or negative moral valence. Then, we use this classifier to investigate\nan assortment of website traits surrounding moral judgements in ten other\nsubreddits, including where negative moral users like to post and their posting\npatterns. Our findings also indicate that posts that are judged in a positive\nmanner will score higher.\n"}
+{"abstract": "  A pair-density-wave (PDW) is a novel superconducting state with an\noscillating order parameter. A microscopic mechanism that can give rise to it\nhas been long sought but has not yet been established by any controlled\ncalculation. Here we report a density-matrix renormalization group (DMRG) study\nof an effective $t$-$J$-$V$ model, which is equivalent to the Holstein-Hubbard\nmodel in a strong-coupling limit, on long two-, four- and six-leg triangular\ncylinders. While a state with long-range PDW order is precluded in one\ndimension, we find strong quasi-long-range PDW order with a divergent PDW\nsusceptibility as well as spontaneous breaking of time-reversal and inversion\nsymmetries. Despite the strong interactions, the underlying Fermi surfaces and\nelectron pockets around the $K$ and $K^\\prime$ points in the Brillouin zone can\nbe identified. We conclude that the state is valley-polarized and that the PDW\narises from intra-pocket pairing with an incommensurate center of mass\nmomentum. In the two-leg case, the exponential decay of spin correlations and\nthe measured central charge $c\\approx 1$ are consistent with an unusual\nrealization of a Luther-Emery liquid.\n"}
+{"abstract": "  One-dimensional (1D) materials have attracted significant research interest\ndue to their unique quantum confinement effects and edge-related properties.\nAtomically thin 1D nanoribbon is particularly interesting because it is a\nvaluable platform with physical limits of both thickness and width. Here, we\ndevelop a catalyst-free growth method and achieves the growth of Bi2O2Se\nnanostructures with tunable dimensionality. Significantly, Bi2O2Se nanoribbons\nwith thickness down to 0.65 nm, corresponding to monolayer, are successfully\ngrown for the first time. Electrical and optoelectronic measurements show that\nBi2O2Se nanoribbons possess decent performance in terms of mobility, on/off\nratio, and photoresponsivity, suggesting their promising for devices. This work\nnot only reports a new method for the growth of atomically thin nanoribbons but\nalso provides a platform to study properties and applications of such\nnanoribbon materials at thickness limit.\n"}
+{"abstract": "  In many dynamic systems, decisions on system operation are updated over time,\nand the decision maker requires an online learning approach to optimize its\nstrategy in response to the changing environment. When the loss and constraint\nfunctions are convex, this belongs to the general family of online convex\noptimization (OCO). In existing OCO works, the environment is assumed to vary\nin a time-slotted fashion, while the decisions are updated at each time slot.\nHowever, many wireless communication systems permit only periodic decision\nupdates, i.e., each decision is fixed over multiple time slots, while the\nenvironment changes between the decision epochs. The standard OCO model is\ninadequate for these systems. Therefore, in this work, we consider periodic\ndecision updates for OCO. We aim to minimize the accumulation of time-varying\nconvex loss functions, subject to both short-term and long-term constraints.\nInformation about the loss functions within the current update period may be\nincomplete and is revealed to the decision maker only after the decision is\nmade. We propose an efficient algorithm, termed Periodic Queueing and Gradient\nAggregation (PQGA), which employs novel periodic queues together with possibly\nmulti-step aggregated gradient descent to update the decisions over time. We\nderive upper bounds on the dynamic regret, static regret, and constraint\nviolation of PQGA. As an example application, we study the performance of PQGA\nin a large-scale multi-antenna system shared by multiple wireless service\nproviders. Simulation results show that PQGA converges fast and substantially\noutperforms the known best alternative.\n"}
+{"abstract": "  In this paper, we propose an anchor-free single-stage LiDAR-based 3D object\ndetector -- RangeDet. The most notable difference with previous works is that\nour method is purely based on the range view representation. Compared with the\ncommonly used voxelized or Bird's Eye View (BEV) representations, the range\nview representation is more compact and without quantization error. Although\nthere are works adopting it for semantic segmentation, its performance in\nobject detection is largely behind voxelized or BEV counterparts. We first\nanalyze the existing range-view-based methods and find two issues overlooked by\nprevious works: 1) the scale variation between nearby and far away objects; 2)\nthe inconsistency between the 2D range image coordinates used in feature\nextraction and the 3D Cartesian coordinates used in output. Then we\ndeliberately design three components to address these issues in our RangeDet.\nWe test our RangeDet in the large-scale Waymo Open Dataset (WOD). Our best\nmodel achieves 72.9/75.9/65.8 3D AP on vehicle/pedestrian/cyclist. These\nresults outperform other range-view-based methods by a large margin (~20 3D AP\nin vehicle detection), and are overall comparable with the state-of-the-art\nmulti-view-based methods. Codes will be public.\n"}
+{"abstract": "  Parameter Estimation (PE) and State Estimation (SE) are the most wide-spread\ntasks in the system engineering. They need to be done automatically, fast and\nfrequently, as measurements arrive. Deep Learning (DL) holds the promise of\ntackling the challenge, however in so far, as PE and SE in power systems is\nconcerned, (a) DL did not win trust of the system operators because of the lack\nof the physics of electricity based, interpretations and (b) DL remained\nillusive in the operational regimes were data is scarce. To address this, we\npresent a hybrid scheme which embeds physics modeling of power systems into\nGraphical Neural Networks (GNN), therefore empowering system operators with a\nreliable and explainable real-time predictions which can then be used to\ncontrol the critical infrastructure. To enable progress towards trustworthy DL\nfor PE and SE, we build a physics-informed method, named Power-GNN, which\nreconstructs physical, thus interpretable, parameters within Effective Power\nFlow (EPF) models, such as admittances of effective power lines, and NN\nparameters, representing implicitly unobserved elements of the system. In our\nexperiments, we test the Power-GNN on different realistic power networks,\nincluding these with thousands of loads and hundreds of generators. We show\nthat the Power-GNN outperforms vanilla NN scheme unaware of the EPF physics.\n"}
+{"abstract": "  A constraint satisfaction problem (CSP), $\\textsf{Max-CSP}(\\mathcal{F})$, is\nspecified by a finite set of constraints $\\mathcal{F} \\subseteq \\{[q]^k \\to\n\\{0,1\\}\\}$ for positive integers $q$ and $k$. An instance of the problem on $n$\nvariables is given by $m$ applications of constraints from $\\mathcal{F}$ to\nsubsequences of the $n$ variables, and the goal is to find an assignment to the\nvariables that satisfies the maximum number of constraints. In the\n$(\\gamma,\\beta)$-approximation version of the problem for parameters $0 \\leq\n\\beta < \\gamma \\leq 1$, the goal is to distinguish instances where at least\n$\\gamma$ fraction of the constraints can be satisfied from instances where at\nmost $\\beta$ fraction of the constraints can be satisfied. In this work we\nconsider the approximability of this problem in the context of sketching\nalgorithms and give a dichotomy result. Specifically, for every family\n$\\mathcal{F}$ and every $\\beta < \\gamma$, we show that either a linear\nsketching algorithm solves the problem in polylogarithmic space, or the problem\nis not solvable by any sketching algorithm in $o(\\sqrt{n})$ space.\n"}
+{"abstract": "  Traditionally, origami has been categorized into two groups according to\ntheir kinematics design: rigid and non-rigid origami. However, such\ncategorization can be superficial, and rigid origami can obtain new mechanical\nproperties by intentionally relaxing the rigid-folding kinematics. Based on\nnumerical simulations using the bar-hinge approach and experiments, this study\nexamines the multi-stability of a stacked Miura-origami cellular structure with\ndifferent levels of facet compliance. The simulation and experiment results\nshow that a unit cell in such cellular solid exhibits only two stable states if\nit follows the rigid origami kinematics; however, two more stable states are\nreachable if the origami facets become sufficiently compliant. Moreover, the\nswitch between two certain stable states shows an asymmetric energy barrier,\nmeaning that the unit cell follows fundamentally different deformation paths\nwhen it extends from one state to another compared to the opposite compression\nswitch. As a result, the reaction force required for extending this unit cell\nbetween these two states can be higher than the compression switch. Such\nasymmetric multi-stability can be fine-tuned by tailoring the underlying\norigami design, and it can be extended into cellular solids with carefully\nplaced voids. By showing the benefits of exploiting facet compliance, this\nstudy could foster multi-functional structures and material systems that\ntraditional rigid origami cannot create.\n"}
+{"abstract": "  We prove $L^p$ bounds for the maximal operators associated to an\nAhlfors-regular variant of fractal percolation. Our bounds improve upon those\nobtained by I. {\\L}aba and M. Pramanik and in some cases are sharp up to the\nendpoint. A consequence of our main result is that there exist Ahlfors-regular\nSalem Cantor sets of any dimension $>1/2$ such that the associated maximal\noperator is bounded on $L^2(\\mathbb{R})$. We follow the overall scheme of\n{\\L}aba-Pramanik for the analytic part of the argument, while the probabilistic\npart is instead inspired by our earlier work on intersection properties of\nrandom measures.\n"}
+{"abstract": "  Bayesian Optimization is a popular tool for tuning algorithms in automatic\nmachine learning (AutoML) systems. Current state-of-the-art methods leverage\nRandom Forests or Gaussian processes to build a surrogate model that predicts\nalgorithm performance given a certain set of hyperparameter settings. In this\npaper, we propose a new surrogate model based on gradient boosting, where we\nuse quantile regression to provide optimistic estimates of the performance of\nan unobserved hyperparameter setting, and combine this with a distance metric\nbetween unobserved and observed hyperparameter settings to help regulate\nexploration. We demonstrate empirically that the new method is able to\noutperform some state-of-the art techniques across a reasonable sized set of\nclassification problems.\n"}
+{"abstract": "  Consider a connected graph $G$ and let $T$ be a spanning tree of $G$. Every\nedge $e \\in G-T$ induces a cycle in $T \\cup \\{e\\}$. The intersection of two\ndistinct such cycles is the set of edges of $T$ that belong to both cycles. We\nconsider the problem of finding a spanning tree that has the least number of\nsuch non-empty intersections.\n"}
+{"abstract": "  We consider the task of grasping a target object based on a natural language\ncommand query. Previous work primarily focused on localizing the object given\nthe query, which requires a separate grasp detection module to grasp it. The\ncascaded application of two pipelines incurs errors in overlapping multi-object\ncases due to ambiguity in the individual outputs. This work proposes a model\nnamed Command Grasping Network(CGNet) to directly output command satisficing\ngrasps from RGB image and textual command inputs. A dataset with ground truth\n(image, command, grasps) tuple is generated based on the VMRD dataset to train\nthe proposed network. Experimental results on the generated test set show that\nCGNet outperforms a cascaded object-retrieval and grasp detection baseline by a\nlarge margin. Three physical experiments demonstrate the functionality and\nperformance of CGNet.\n"}
+{"abstract": "  It has been shown that the parallel Lattice Linear Predicate (LLP) algorithm\nsolves many combinatorial optimization problems such as the shortest path\nproblem, the stable marriage problem and the market clearing price problem. In\nthis paper, we give the parallel LLP algorithm for many dynamic programming\nproblems. In particular, we show that the LLP algorithm solves the longest\nsubsequence problem, the optimal binary search tree problem, and the knapsack\nproblem. Furthermore, the algorithm can be used to solve the constrained\nversions of these problems so long as the constraints are lattice linear. The\nparallel LLP algorithm requires only read-write atomicity and no higher-level\natomic instructions.\n"}
+{"abstract": "  We report the first investigation of the performance of EOM-CC4 -- an\napproximate equation-of-motion coupled-cluster model which includes iterative\nquadruple excitations -- for vertical excitation energies in molecular systems.\nBy considering a set of 28 excited states in 10 small molecules for which we\nhave computed CCSDTQP and FCI reference energies, we show that, in the case of\nexcited states with a dominant contribution from the single excitations, CC4\nyields excitation energies with sub-kJ~mol$^{-1}$ accuracy (i.e., error below\n$0.01$ eV), in very close agreement with its more expensive CCSDTQ parent.\nTherefore, if one aims at high accuracy, CC4 stands as a highly competitive\napproximate method to model molecular excited states, with a significant\nimprovement over both CC3 and CCSDT. Our results also evidence that, although\nthe same qualitative conclusions hold, one cannot reach the same level of\naccuracy for transitions with a dominant contribution from the double\nexcitations.\n"}
+{"abstract": "  We present a comprehensive analytic model of a relativistic jet propagation\nin expanding media. This model is the first to cover the entire jet evolution\nfrom early to late times, as well as a range of configurations that are\nrelevant to binary neutron star mergers. These include low and high luminosity\njets, unmagnetized and mildly magnetized jets, time-dependent luminosity jets,\nand Newtonian and relativistic head velocities. We also extend the existing\nsolution of jets in a static medium to power-law density media with index\n$\\alpha<5$. Our model, which is tested and calibrated by a suite of 3D RMHD\nsimulations, provides simple analytic formulae for the jet head propagation and\nbreakout times, as well as a simple breakout criterion which depends only on\nthe jet to ejecta energy ratio and jet opening angle. Assuming a delay time $\nt_d $ between the onset of a homologous ejecta expansion and jet launching, the\nsystem evolution has two main regimes: strong and weak jets. The regime depends\non the ratio between the jet head velocity in the ejecta frame and the local\nejecta velocity, denoted as $ \\eta $. Strong jets start their propagation in\nthe ejecta on a timescale shorter than $t_d$ with $\\eta \\gg 1$, and within\nseveral ejecta dynamical times $\\eta$ drops below unity. Weak jets are unable\nto penetrate the ejecta at first (start with $\\eta \\ll 1$), and breach the\nejecta only after the ejecta expands over a timescale longer than $ t_d $, thus\ntheir evolution is independent of $ t_d $. After enough time, both strong and\nweak jets approach an asymptotic phase where $\\eta$ is constant. Applying our\nmodel to short GRBs, we find that there is most likely a large diversity of\nejecta mass, where mass $ \\lesssim 10^{-3}~{\\rm M}_{\\odot} $ (at least along\nthe poles) is common.\n"}
+{"abstract": "  As gradient descent method in deep learning causes a series of questions,\nthis paper proposes a novel gradient-free deep learning structure. By adding a\nnew module into traditional Self-Organizing Map and introducing residual into\nthe map, a Deep Valued Self-Organizing Map network is constructed. And analysis\nabout the convergence performance of such a deep Valued Self-Organizing Map\nnetwork is proved in this paper, which gives an inequality about the designed\nparameters with the dimension of inputs and the loss of prediction.\n"}
+{"abstract": "  It has been previously shown that a particular nonperturbative\nconstituent-quark model of hadrons describes experimental measurements of\nelectromagnetic form factors of light charged mesons through a small number of\ncommon phenomenological parameters, matching at the same time the\nQuantum-Chromodynamics (QCD) asymptotics for the pi-meson form factor at large\nmomentum transfer. Here we start with the determination of the K0\nelectromagnetic form factor in this approach. Precise measurement of the K0\ncharge radius makes it possible to constrain model parameters with high\naccuracy. Then, with all parameters fixed, we revisit the K+ form factor and\nfind that it matches experimental measurements in the infrared, lattice results\nat moderate momentum transfer and the perturbative QCD asymptotics in the\nultraviolet. In this way we obtain a narrow constraint on the K+ charge radius,\n<r_K+^2> = 0.403 +0.007 -0.006 fm^2, and extend the successful\ninfrared-ultraviolet connection from pi to K mesons.\n"}
+{"abstract": "  The purpose of this paper is to present an inexact version of the scaled\ngradient projection method on a convex set, which is inexact in two sense.\nFirst, an inexact projection on the feasible set is computed, allowing for an\nappropriate relative error tolerance. Second, an inexact non-monotone line\nsearch scheme is employed to compute a step size which defines the next\niteration. It is shown that the proposed method has similar asymptotic\nconvergence properties and iteration-complexity bounds as the usual scaled\ngradient projection method employing monotone line searches.\n"}
+{"abstract": "  We calculate the mass difference between the $\\Upsilon$ and $\\eta_b$ and the\n$\\Upsilon$ leptonic width from lattice QCD using the Highly Improved Staggered\nQuark formalism for the $b$ quark and including $u$, $d$, $s$ and $c$ quarks in\nthe sea. We have results for lattices with lattice spacing as low as 0.03 fm\nand multiple heavy quark masses, enabling us to map out the heavy quark mass\ndependence and determine values at the $b$ quark mass. Our results are:\n$M_{\\Upsilon} -M_{\\eta_b} = 57.5(2.3)(1.0) \\,\\mathrm{MeV}$ (where the second\nuncertainty comes from neglect of quark-line disconnected correlation\nfunctions) and decay constants, $f_{\\eta_b}=724(12)$ MeV and $f_{\\Upsilon}\n=677.2(9.7)$ MeV, giving $\\Gamma(\\Upsilon \\rightarrow e^+e^-) = 1.292(37)(3)\n\\,\\mathrm{keV}$. The hyperfine splitting and leptonic width are both in good\nagreement with experiment, and provide the most accurate lattice QCD results to\ndate for these quantities by some margin. At the same time results for the time\nmoments of the vector-vector correlation function can be compared to values for\nthe $b$ quark contribution to $\\sigma(e^+e^- \\rightarrow \\mathrm{hadrons})$\ndetermined from experiment. Moments 4--10 provide a 2\\% test of QCD and yield a\n$b$ quark contribution to the anomalous magnetic moment of the muon of\n0.300(15)$\\times 10^{-10}$. Our results, covering a range of heavy quark\nmasses, may also be useful to constrain QCD-like composite theories for beyond\nthe Standard Model physics.\n"}
+{"abstract": "  The purpose of this paper is presenting a theoretical basis for the study of\n$\\omega$-Hamiltonian vector fields in a more general approach than the\nclassical one. We introduce the concepts of $\\omega$-symplectic group and\n$\\omega$-semisymplectic group, and describe some of their properties. We show\nthat the Lie algebra of such groups is a useful tool in the recognition of an\n$\\omega$-Hamiltonian vector field defined on a symplectic vector space\n$(V,\\omega)$ with respect to coordinates that are not necessarily symplectic.\n"}
+{"abstract": "  Ineffective fundraising lowers the resources charities can use to provide\ngoods. We combine a field experiment and a causal machine-learning approach to\nincrease a charity's fundraising effectiveness. The approach optimally targets\na fundraising instrument to individuals whose expected donations exceed\nsolicitation costs. Our results demonstrate that machine-learning-based optimal\ntargeting allows the charity to substantially increase donations net of\nfundraising costs relative to uniform benchmarks in which either everybody or\nno one receives the gift. To that end, it (a) should direct its fundraising\nefforts to a subset of past donors and (b) never address individuals who were\npreviously asked but never donated. Further, we show that the benefits of\nmachine-learning-based optimal targeting even materialize when the charity only\nexploits publicly available geospatial information or applies the estimated\noptimal targeting rule to later fundraising campaigns conducted in similar\nsamples. We conclude that charities not engaging in optimal targeting waste\nsignificant resources.\n"}
+{"abstract": "  Spin$-$orbit alignment (SOA; i.e., the vector alignment between the halo spin\nand the orbital angular momentum of neighboring halos) provides an important\nclue to how galactic angular momenta develop. For this study, we extract\nvirial-radius-wise contact halo pairs with mass ratios between 1/10 and 10 from\na set of cosmological $N$-body simulations. In the spin--orbit angle\ndistribution, we find a significant SOA in that 52.7%$\\pm$0.2% of neighbors are\non the prograde orbit. The SOA of our sample is mainly driven by low-mass\ntarget halos ($<10^{11.5}h^{-1}M_{\\odot}$) with close merging neighbors,\ncorroborating the notion that the tidal interaction is one of the physical\norigins of SOA. We also examine the correlation of SOA with the adjacent\nfilament and find that halos closer to the filament show stronger SOA. Most\ninterestingly, we discover for the first time that halos with the spin parallel\nto the filament experience most frequently the prograde-polar interaction\n(i.e., fairly perpendicular but still prograde interaction; spin--orbit angle\n$\\sim$ 70$^{\\circ}$). This instantly invokes the spin-flip event and the\nprograde-polar interaction will soon flip the spin of the halo to align it with\nthe neighbor's orbital angular momentum. We propose that the SOA originates\nfrom the local cosmic flow along the anisotropic large-scale structure,\nespecially that along the filament, and grows further by interactions with\nneighbors.\n"}
+{"abstract": "  The outbreak of the coronavirus disease 2019 (COVID-19) has now spread\nthroughout the globe infecting over 150 million people and causing the death of\nover 3.2 million people. Thus, there is an urgent need to study the dynamics of\nepidemiological models to gain a better understanding of how such diseases\nspread. While epidemiological models can be computationally expensive, recent\nadvances in machine learning techniques have given rise to neural networks with\nthe ability to learn and predict complex dynamics at reduced computational\ncosts. Here we introduce two digital twins of a SEIRS model applied to an\nidealised town. The SEIRS model has been modified to take account of spatial\nvariation and, where possible, the model parameters are based on official virus\nspreading data from the UK. We compare predictions from a data-corrected\nBidirectional Long Short-Term Memory network and a predictive Generative\nAdversarial Network. The predictions given by these two frameworks are accurate\nwhen compared to the original SEIRS model data.\n  Additionally, these frameworks are data-agnostic and could be applied to\ntowns, idealised or real, in the UK or in other countries. Also, more\ncompartments could be included in the SEIRS model, in order to study more\nrealistic epidemiological behaviour.\n"}
+{"abstract": "  This work presents the selection principle $S_1^*(\\tau_x,CD)$ that\ncharacterizes $q$-points. We also discuss the induced topological game\n$G_1^*(\\tau_x,CD)$ and its relations with $W$-points and\n$\\widetilde{W}$-points, as well as with the game $G_1(\\Omega_x,\\Omega_x)$.\n"}
+{"abstract": "  A single layer neural network for the solution of linear equations is\npresented. The proposed circuit is based on the standard Hopfield model albeit\nwith the added flexibility that the interconnection weight matrix need not be\nsymmetric. This results in an asymmetric Hopfield neural network capable of\nsolving linear equations. PSPICE simulation results are given which verify the\ntheoretical predictions. Experimental results for circuits set up to solve\nsmall problems further confirm the operation of the proposed circuit.\n"}
+{"abstract": "  We introduce a simple and effective method for learning VAEs with\ncontrollable inductive biases by using an intermediary set of latent variables.\nThis allows us to overcome the limitations of the standard Gaussian prior\nassumption. In particular, it allows us to impose desired properties like\nsparsity or clustering on learned representations, and incorporate prior\ninformation into the learned model. Our approach, which we refer to as the\nIntermediary Latent Space VAE (InteL-VAE), is based around controlling the\nstochasticity of the encoding process with the intermediary latent variables,\nbefore deterministically mapping them forward to our target latent\nrepresentation, from which reconstruction is performed. This allows us to\nmaintain all the advantages of the traditional VAE framework, while\nincorporating desired prior information, inductive biases, and even topological\ninformation through the latent mapping. We show that this, in turn, allows\nInteL-VAEs to learn both better generative models and representations.\n"}
+{"abstract": "  We shall describe the various activities done by us in Covid Times including\noutreach and educational workshops in Physics and Astronomy. We shall discuss\nthe caveats in virtual teaching of Astronomy and the lessons learnt in the\nprocess.\n"}
+{"abstract": "  We investigate and analyze principles of typical motion planning algorithms.\nThese include traditional planning algorithms, supervised learning, optimal\nvalue reinforcement learning, policy gradient reinforcement learning.\nTraditional planning algorithms we investigated include graph search\nalgorithms, sampling-based algorithms, and interpolating curve algorithms.\nSupervised learning algorithms include MSVM, LSTM, MCTS and CNN. Optimal value\nreinforcement learning algorithms include Q learning, DQN, double DQN, dueling\nDQN. Policy gradient algorithms include policy gradient method, actor-critic\nalgorithm, A3C, A2C, DPG, DDPG, TRPO and PPO. New general criteria are also\nintroduced to evaluate performance and application of motion planning\nalgorithms by analytical comparisons. Convergence speed and stability of\noptimal value and policy gradient algorithms are specially analyzed. Future\ndirections are presented analytically according to principles and analytical\ncomparisons of motion planning algorithms. This paper provides researchers with\na clear and comprehensive understanding about advantages, disadvantages,\nrelationships, and future of motion planning algorithms in robotics, and paves\nways for better motion planning algorithms.\n"}
+{"abstract": "  Missing data is a common problem in clinical data collection, which causes\ndifficulty in the statistical analysis of such data. To overcome problems\ncaused by incomplete data, we propose a new imputation method called projective\nresampling imputation mean estimation (PRIME), which can also address ``the\ncurse of dimensionality\" problem in imputation with less information loss. We\nuse various sample sizes, missing-data rates, covariate correlations, and noise\nlevels in simulation studies, and all results show that PRIME outperformes\nother methods such as iterative least-squares estimation (ILSE), maximum\nlikelihood (ML), and complete-case analysis (CC). Moreover, we conduct a study\nof influential factors in cardiac surgery-associated acute kidney injury\n(CSA-AKI), which show that our method performs better than the other models.\nFinally, we prove that PRIME has a consistent property under some regular\nconditions.\n"}
+{"abstract": "  We study a duality for the $n$-point functions in VEV formalism that we call\nthe ordinary vs fully simple duality. It provides an ultimate generalisation\nand a proper context for the duality between maps and fully simple maps\nobserved by Borot and Garcia-Failde. Our approach allows to transfer the\nalgebraicity properties between the systems of $n$-point functions related by\nthis duality, and gives direct tools for the analysis of singularities. As an\napplication, we give a proof of a recent conjecture of Borot and Garcia-Failde\non topological recursion for fully simple maps.\n"}
+{"abstract": "  Logging is a development practice that plays an important role in the\noperations and monitoring of complex systems. Developers place log statements\nin the source code and use log data to understand how the system behaves in\nproduction. Unfortunately, anticipating where to log during development is\nchallenging. Previous studies show the feasibility of leveraging machine\nlearning to recommend log placement despite the data imbalance since logging is\na fraction of the overall code base. However, it remains unknown how those\ntechniques apply to an industry setting, and little is known about the effect\nof imbalanced data and sampling techniques.\n  In this paper, we study the log placement problem in the code base of Adyen,\na large-scale payment company. We analyze 34,526 Java files and 309,527 methods\nthat sum up +2M SLOC. We systematically measure the effectiveness of five\nmodels based on code metrics, explore the effect of sampling techniques,\nunderstand which features models consider to be relevant for the prediction,\nand evaluate whether we can exploit 388,086 methods from 29 Apache projects to\nlearn where to log in an industry setting.\n  Our best performing model achieves 79% of balanced accuracy, 81% of\nprecision, 60% of recall. While sampling techniques improve recall, they\npenalize precision at a prohibitive cost. Experiments with open-source data\nyield under-performing models over Adyen's test set; nevertheless, they are\nuseful due to their low rate of false positives. Our supporting scripts and\ntools are available to the community.\n"}
+{"abstract": "  Faraday rotation provides a valuable tracer of magnetic fields in the\ninterstellar medium; catalogs of Faraday rotation measures provide key\nobservations for studies of the Galactic magnetic field. We present a new\ncatalog of rotation measures derived from the Canadian Galactic Plane Survey,\ncovering a large region of the Galactic plane spanning 52 deg < l < 192 deg, -3\ndeg < b < 5 deg, along with northern and southern latitude extensions around l\n~ 105 deg. We have derived rotation measures for 2234 sources (4 of which are\nknown pulsars), 75% of which have no previous measurements, over an area of\napproximately 1300 square degrees. These new rotation measures increase the\nmeasurement density for this region of the Galactic plane by a factor of two.\n"}
+{"abstract": "  Multi-Agent Reinforcement Learning (MARL) algorithms show amazing performance\nin simulation in recent years, but placing MARL in real-world applications may\nsuffer safety problems. MARL with centralized shields was proposed and verified\nin safety games recently. However, centralized shielding approaches can be\ninfeasible in several real-world multi-agent applications that involve\nnon-cooperative agents or communication delay. Thus, we propose to combine MARL\nwith decentralized Control Barrier Function (CBF) shields based on available\nlocal information. We establish a safe MARL framework with decentralized\nmultiple CBFs and develop Multi-Agent Deep Deterministic Policy Gradient\n(MADDPG) to Multi-Agent Deep Deterministic Policy Gradient with decentralized\nmultiple Control Barrier Functions (MADDPG-CBF). Based on a collision-avoidance\nproblem that includes not only cooperative agents but obstacles, we demonstrate\nthe construction of multiple CBFs with safety guarantees in theory. Experiments\nare conducted and experiment results verify that the proposed safe MARL\nframework can guarantee the safety of agents included in MARL.\n"}
+{"abstract": "  Carbon is one of the most essential elements to support a sustained human\npresence in space, and more immediately, several large-scale methalox-based\ntransport systems will begin operating in the near future. This raises the\nquestion of whether indigenous carbon on the Moon is abundant and concentrated\nto the extent where it could be used as a viable resource including as\npropellant. Here, I assess potential sources of lunar carbon based on previous\nwork focused on polar water ice. A simplified model is used to estimate the\ntemperature-dependent Carbon Content of Ices at the lunar poles, and this is\ncombined with remote sensing data to estimate the total amount of carbon and\ngenerate a Carbon Favorability Index that highlights promising deposits for\nfuture ground-based prospecting. Hotspots in the index maps are identified, and\nnearby staging areas are analyzed using quantitative models of trafficability\nand solar irradiance. Overall, the Moon is extremely poor in carbon sources\ncompared to more abundant and readily accessible options at Mars. However, a\nhandful of polar regions may contain appreciable amounts of subsurface\ncarbon-bearing ices that could serve as a rich source in the near term, but\nwould be easily exhausted on longer timescales. Four of those regions were\nfound to have safe nearby staging areas with equatorial-like illumination at a\nmodest height above the surface. Any one of these sites could yield enough C, H\nand O to produce propellant for hundreds of refuelings of a large spacecraft.\nOther potential lunar carbon sources including bulk regolith and pyroclastic\nglasses are less viable due to their low carbon concentrations.\n"}
+{"abstract": "  Traffic simulators act as an essential component in the operating and\nplanning of transportation systems. Conventional traffic simulators usually\nemploy a calibrated physical car-following model to describe vehicles'\nbehaviors and their interactions with traffic environment. However, there is no\nuniversal physical model that can accurately predict the pattern of vehicle's\nbehaviors in different situations. A fixed physical model tends to be less\neffective in a complicated environment given the non-stationary nature of\ntraffic dynamics. In this paper, we formulate traffic simulation as an inverse\nreinforcement learning problem, and propose a parameter sharing adversarial\ninverse reinforcement learning model for dynamics-robust simulation learning.\nOur proposed model is able to imitate a vehicle's trajectories in the real\nworld while simultaneously recovering the reward function that reveals the\nvehicle's true objective which is invariant to different dynamics. Extensive\nexperiments on synthetic and real-world datasets show the superior performance\nof our approach compared to state-of-the-art methods and its robustness to\nvariant dynamics of traffic.\n"}
+{"abstract": "  The direction of arrival (DOA) estimation in array signal processing is an\nimportant research area. The effectiveness of the direction of arrival greatly\ndetermines the performance of multi-input multi-output (MIMO) antenna systems.\nThe multiple signal classification (MUSIC) algorithm, which is the most\ncanonical and widely used subspace-based method, has a moderate estimation\nperformance of DOA. However, in hybrid massive MIMO systems, the received\nsignals at the antennas are not sent to the receiver directly, and spatial\ncovariance matrix, which is essential in MUSIC algorithm, is thus unavailable.\nTherefore, the spatial covariance matrix reconstruction is required for the\napplication of MUSIC in hybrid massive MIMO systems. In this article, we\npresent a quantum algorithm for MUSIC-based DOA estimation in hybrid massive\nMIMO systems. Compared with the best-known classical algorithm, our quantum\nalgorithm can achieve an exponential speedup on some parameters and a\npolynomial speedup on others under some mild conditions. In our scheme, we\nfirst present the quantum subroutine for the beam sweeping based spatial\ncovariance matrix reconstruction, where we implement a quantum singular vector\ntransition process to avoid extending the steering vectors matrix into the\nHermitian form. Second, a variational quantum density matrix eigensolver\n(VQDME) is proposed for obtaining signal and noise subspaces, where we design a\nnovel objective function in the form of the trace of density matrices product.\nFinally, a quantum labeling operation is proposed for the direction of arrival\nestimation of the signal.\n"}
+{"abstract": "  Time-frequency concentration operators restrict the integral\nanalysis-synthesis formula for the short-time Fourier transform to a given\ncompact domain. We estimate how much the corresponding eigenvalue counting\nfunction deviates from the Lebesgue measure of the time-frequency domain. For\nwindow functions in the Gelfand-Shilov class, the bounds approximately match\nknown asymptotics. We also consider window functions that decay only\npolynomially in time and frequency.\n"}
+{"abstract": "  The correlation between the event mean-transverse momentum\n$[p_{\\mathrm{T}}]$, and the anisotropic flow magnitude $v_n$,\n$\\rho(v^{2}_{n},[p_{T}])$, has been argued to be sensitive to the initial\nconditions in heavy-ion collisions. We use simulated events generated with the\nAMPT and EPOS models for Au+Au at $\\sqrt{\\textit{s}_{NN}}$ = 200 GeV, to\ninvestigate the model dependence and the response and sensitivity of the\n$\\rho(v^{2}_{2},[p_{T}])$ correlator to collision-system size and shape, and\nthe viscosity of the matter produced in the collisions. We find good\nqualitative agreement between the correlators for the string melting version of\nthe AMPT model and the EPOS model. The model investigations for\nshape-engineered events as well as events with different viscosity ($\\eta/s$),\nindicate that $\\rho(v^{2}_{2},[p_{T}])$ is sensitive to the initial-state\ngeometry of the collision system but is insensitive to sizable changes in\n$\\eta/s$ for the medium produced in the collisions. These findings suggest that\nprecise differential measurements of $\\rho(v^{2}_{2},[p_{T}])$ as a function of\nsystem size, shape, and beam-energy could provide more stringent constraints to\ndiscern between initial-state models and hence, more reliable extractions of\n$\\eta/s$.\n"}
+{"abstract": "  Crowd counting is critical for numerous video surveillance scenarios. One of\nthe main issues in this task is how to handle the dramatic scale variations of\npedestrians caused by the perspective effect. To address this issue, this paper\nproposes a novel convolution neural network-based crowd counting method, termed\nPerspective-guided Fractional-Dilation Network (PFDNet). By modeling the\ncontinuous scale variations, the proposed PFDNet is able to select the proper\nfractional dilation kernels for adapting to different spatial locations. It\nsignificantly improves the flexibility of the state-of-the-arts that only\nconsider the discrete representative scales. In addition, by avoiding the\nmulti-scale or multi-column architecture that used in other methods, it is\ncomputationally more efficient. In practice, the proposed PFDNet is constructed\nby stacking multiple Perspective-guided Fractional-Dilation Convolutions (PFC)\non a VGG16-BN backbone. By introducing a novel generalized dilation convolution\noperation, the PFC can handle fractional dilation ratios in the spatial domain\nunder the guidance of perspective annotations, achieving continuous scales\nmodeling of pedestrians. To deal with the problem of unavailable perspective\ninformation in some cases, we further introduce an effective perspective\nestimation branch to the proposed PFDNet, which can be trained in either\nsupervised or weakly-supervised setting once the branch has been pre-trained.\nExtensive experiments show that the proposed PFDNet outperforms\nstate-of-the-art methods on ShanghaiTech A, ShanghaiTech B, WorldExpo'10,\nUCF-QNRF, UCF_CC_50 and TRANCOS dataset, achieving MAE 53.8, 6.5, 6.8, 84.3,\n205.8, and 3.06 respectively.\n"}
+{"abstract": "  Rikudo is a number-placement puzzle, where the player is asked to complete a\nHamiltonian path on a hexagonal grid, given some clues (numbers already placed\nand edges of the path). We prove that the game is complete for NP, even if the\npuzzle has no hole. When all odd numbers are placed it is in P, whereas it is\nstill NP-hard when all numbers of the form $3k+1$ are placed.\n"}
+{"abstract": "  We have demonstrated the advantage of combining multi-wavelength\nobservations, from the ultraviolet (UV) to near-infrared, to study Kron 3, a\nmassive star cluster in the Small Magellanic Cloud. We have estimated the\nradius of the cluster Kron 3 to be 2.'0 and for the first time, we report the\nidentification of NUV-bright red clump (RC) stars and the extension of the RCin\ncolour and magnitude in the NUV vs (NUV-optical) colour-magnitude diagram\n(CMD). We found that extension of the RC is an intrinsic property of the\ncluster and it is not due to contamination of field stars or differential\nreddening across the field. We studied the spectral energy distribution of the\nRC stars and estimated a small range in temperature ~5000 - 5500K, luminosity\n~60 - 90 Land radius ~8.0 - 11.0 Supporting their RC nature. The range of UV\nmagnitudes amongst the RC stars (~23.3 to 24.8 mag) is likely caused by the\ncombined effect of variable mass loss, variation in initial helium abundance\n(Y_ini=0.23 to 0.28), and a small variation in age (6.5-7.5 Gyr) and\nmetallicity ([Fe/H]=-1.5 to -1.3). Spectroscopic follow-up observations of RC\nstars in Kron 3 are necessary to confirm the cause of the extended RC.\n"}
+{"abstract": "  Image restoration is a typical ill-posed problem, and it contains various\ntasks. In the medical imaging field, an ill-posed image interrupts diagnosis\nand even following image processing. Both traditional iterative and up-to-date\ndeep networks have attracted much attention and obtained a significant\nimprovement in reconstructing satisfying images. This study combines their\nadvantages into one unified mathematical model and proposes a general image\nrestoration strategy to deal with such problems. This strategy consists of two\nmodules. First, a novel generative adversarial net(GAN) with WGAN-GP training\nis built to recover image structures and subtle details. Then, a deep iteration\nmodule promotes image quality with a combination of pre-trained deep networks\nand compressed sensing algorithms by ADMM optimization. (D)eep (I)teration\nmodule suppresses image artifacts and further recovers subtle image details,\n(A)ssisted by (M)ulti-level (O)bey-pixel feature extraction networks\n(D)iscriminator to recover general structures. Therefore, the proposed strategy\nis named DIAMOND.\n"}
+{"abstract": "  This work (Part (I)) together with its companion (Part (II) [45]) develops a\nnew framework for stochastic functional Kolmogorov equations, which are\nnonlinear stochastic differential equations depending on the current as well as\nthe past states. Because of the complexity of the results, it seems to be\ninstructive to divide our contributions to two parts. In contrast to the\nexisting literature, our effort is to advance the knowledge by allowing delay\nand past dependence, yielding essential utility to a wide range of\napplications. A long-standing question of fundamental importance pertaining to\nbiology and ecology is: What are the minimal necessary and sufficient\nconditions for long-term persistence and extinction (or for long-term\ncoexistence of interacting species) of a population? Regardless of the\nparticular applications encountered, persistence and extinction are properties\nshared by Kolmogorov systems. While there are many excellent treaties of\nstochastic-differential-equation-based Kolmogorov equations, the work on\nstochastic Kolmogorov equations with past dependence is still scarce. Our aim\nhere is to answer the aforementioned basic question. This work, Part (I), is\ndevoted to characterization of persistence, whereas its companion, Part (II)\n[45], is devoted to extinction. The main techniques used in this paper include\nthe newly developed functional It^o formula and asymptotic coupling and\nHarris-like theory for infinite dimensional systems specialized to functional\nequations. General theorems for stochastic functional Kolmogorov equations are\ndeveloped first. Then a number of applications are examined to obtain new\nresults substantially covering, improving, and extending the existing\nliterature. Furthermore, these conditions reduce to that of Kolmogorov systems\nwhen there is no past dependence.\n"}
+{"abstract": "  Most softwarized telco services are conveniently framed as Service Function\nChains (SFCs). Indeed, being structured as a combination of interconnected\nnodes, service chains may suffer from the single point of failure problem,\nmeaning that an individual node malfunctioning could compromise the whole chain\noperation. To guarantee \"highly available\" (HA) levels, service providers are\nrequired to introduce redundancy strategies to achieve specific availability\ndemands, where cost constraints have to be taken into account as well. Along\nthese lines we propose HASFC (standing for High Availability SFC), a framework\ndesigned to support, through a dedicated REST interface, the MANO\ninfrastructure in deploying SFCs with an optimal availability-cost trade off.\nOur framework is equipped with: i) an availability model builder aimed to\nconstruct probabilistic models of the SFC nodes in terms of failure and repair\nactions; ii) a chaining and selection module to compose the possible redundant\nSFCs, and extract the best candidates thereof. Beyond providing architectural\ndetails, we demonstrate the functionalities of HASFC through a use case which\nconsiders the IP Multimedia Subsystem, an SFC-like structure adopted to manage\nmultimedia contents within 4G and 5G networks.\n"}
+{"abstract": "  Many computer systems for calculating the proper organization of memory are\namong the most critical issues. Using a tier cache memory (along with branching\nprediction) is an effective means of increasing modern multi-core processors'\nperformance. Designing high-performance processors is a complex task and\nrequires preliminary verification and analysis of the model level, usually used\nin analytical and simulation modeling. The refinement of extreme programming is\nan unfortunate challenge. Few experts disagree with the synthesis of access\npoints. This article demonstrates that Internet QoS and 16-bit architectures\nare always incompatible, but it's the same situation for write-back caches. The\nsolution to this problem can be implemented by analyzing simulation models of\ndifferent complexity in combination with the analytical evaluation of\nindividual algorithms. This work is devoted to designing a multi-parameter\nsimulation model of a multi-process for evaluating the performance of cache\nmemory algorithms and the optimality of the structure. Optimization of the\nstructures and algorithms of the cache memory allows you to accelerate the\ninteraction of the memory process and improve the performance of the entire\nsystem.\n"}
+{"abstract": "  This article investigates the heat kernel of the two-dimensional uniform\nspanning tree. We improve previous work by demonstrating the occurrence of\nlog-logarithmic fluctuations around the leading order polynomial behaviour for\nthe on-diagonal part of the quenched heat kernel. In addition we give two-sided\nestimates for the averaged heat kernel, and we show that the exponents that\nappear in the off-diagonal parts of the quenched and averaged versions of the\nheat kernel differ. Finally, we derive various scaling limits for the heat\nkernel, the implications of which include enabling us to sharpen the known\nasymptotics regarding the on-diagonal part of the averaged heat kernel and the\nexpected distance travelled by the associated simple random walk.\n"}
+{"abstract": "  We extend the weak-strong uniqueness principle to general models of\ncompressible viscous fluids near/on the vacuum. In particular, the physically\nrelevant case of positive density with polynomial decay at infinity is\nconsidered.\n"}
+{"abstract": "  How can a collection of motile cells, each generating contractile nematic\nstresses in isolation, become an extensile nematic at the tissue-level?\nUnderstanding this seemingly contradictory experimental observation, which\noccurs irrespective of whether the tissue is in the liquid or solid states, is\nnot only crucial to our understanding of diverse biological processes, but is\nalso of fundamental interest to soft matter and many-body physics. Here, we\nresolve this cellular to tissue level disconnect in the small fluctuation\nregime by using analytical theories based on hydrodynamic descriptions of\nconfluent tissues, in both liquid and solid states. Specifically, we show that\na collection of microscopic constituents with no inherently nematic extensile\nforces can exhibit active extensile nematic behavior when subject to polar\nfluctuating forces. We further support our findings by performing cell level\nsimulations of minimal models of confluent tissues.\n"}
+{"abstract": "  Internet of Things (IoT) is transforming human lives by paving the way for\nthe management of physical devices on the edge. These interconnected IoT\nobjects share data for remote accessibility and can be vulnerable to open\nattacks and illegal access. Intrusion detection methods are commonly used for\nthe detection of such kinds of attacks but with these methods, the\nperformance/accuracy is not optimal. This work introduces a novel intrusion\ndetection approach based on an ensemble-based voting classifier that combines\nmultiple traditional classifiers as a base learner and gives the vote to the\npredictions of the traditional classifier in order to get the final prediction.\nTo test the effectiveness of the proposed approach, experiments are performed\non a set of seven different IoT devices and tested for binary attack\nclassification and multi-class attack classification. The results illustrate\nprominent accuracies on Global Positioning System (GPS) sensors and weather\nsensors to 96% and 97% and for other machine learning algorithms to 85% and\n87%, respectively. Furthermore, comparison with other traditional machine\nlearning methods validates the superiority of the proposed algorithm.\n"}
+{"abstract": "  We combine observations from ALMA, ATCA, MUSE, andHerschel to study\ngas-to-dust ratios in 15 Fornax cluster galaxies detected in the FIR/sub-mm by\nHerschel and observed by ALMA as part of the ALMA Fornax Cluster Survey\n(AlFoCS). The sample spans a stellar mass range of 8.3 $\\leq$ log (M$_*$ /\nM$_\\odot$) $\\leq$ 11.16, and a variety of morphological types. We use gas-phase\nmetallicities derived from MUSE observations (from the Fornax3D survey) to\nstudy these ratios as a function of metallicity, and to study dust-to-metal\nratios, in a sub-sample of nine galaxies. We find that gas-to-dust ratios in\nFornax galaxies are systematically lower than those in field galaxies at fixed\nstellar mass/metallicity. This implies that a relatively large fraction of the\nmetals in these Fornax systems is locked up in dust, which is possibly due to\naltered chemical evolution as a result of the dense environment. The low ratios\nare not only driven by HI deficiencies, but H$_2$-to-dust ratios are also\nsignificantly decreased. This is different in the Virgo cluster, where low\ngas-to-dust ratios inside the virial radius are driven by low HI-to-dust\nratios, while H$_2$-to-dust ratios are increased. Resolved observations of\nNGC1436 show a radial increase in H$_2$-to-dust ratio, and show that low ratios\nare present throughout the disc. We propose various explanations for the low\nH$_2$-to-dust ratios in the Fornax cluster, including the more efficient\nstripping of H$_2$ compared to dust, more efficient enrichment of dust in the\nstar formation process, and altered ISM physics in the cluster environment.\n"}
+{"abstract": "  It has been established that solutions to the inviscid Proudman-Johnson\nequation subject to a homogeneous three-point boundary condition can develop\nsingularities in finite time. In this paper, we consider the possibility of\nsingularity formation in solutions of the generalized, inviscid\nProudman-Johnson equation with damping subject to the same homogeneous\nthree-point boundary condition. In particular, we derive conditions the initial\ndata must satisfy in order for solutions to blowup in finite time with either\nbounded or unbounded smooth damping term.\n"}
+{"abstract": "  Finding a good query plan is key to the optimization of query runtime. This\nholds in particular for cost-based federation engines, which make use of\ncardinality estimations to achieve this goal. A number of studies compare\nSPARQL federation engines across different performance metrics, including query\nruntime, result set completeness and correctness, number of sources selected\nand number of requests sent. Albeit informative, these metrics are generic and\nunable to quantify and evaluate the accuracy of the cardinality estimators of\ncost-based federation engines. To thoroughly evaluate cost-based federation\nengines, the effect of estimated cardinality errors on the overall query\nruntime performance must be measured. In this paper, we address this challenge\nby presenting novel evaluation metrics targeted at a fine-grained benchmarking\nof cost-based federated SPARQL query engines. We evaluate five cost-based\nfederated SPARQL query engines using existing as well as novel evaluation\nmetrics by using LargeRDFBench queries. Our results provide a detailed analysis\nof the experimental outcomes that reveal novel insights, useful for the\ndevelopment of future cost-based federated SPARQL query processing engines.\n"}
+{"abstract": "  Radiomics is an active area of research in medical image analysis, the low\nreproducibility of radiomics has limited its applicability to clinical\npractice. This issue is especially prominent when radiomic features are\ncalculated from noisy images, such as low dose computed tomography (CT) scans.\nIn this article, we investigate the possibility of improving the\nreproducibility of radiomic features calculated on noisy CTs by using\ngenerative models for denoising.One traditional denoising method - non-local\nmeans - and two generative models - encoder-decoder networks (EDN) and\nconditional generative adversarial networks (CGANs) - were selected as the test\nmodels. We added noise to the sinograms of full dose CTs to mimic low dose CTs\nwith two different levels of noise: low-noise CT and high-noise CT. Models were\ntrained on high-noise CTs and used to denoise low-noise CTs without\nre-training. We also test the performance of our model in real data, using\ndataset of same-day repeat low dose CTs to assess the reproducibility of\nradiomic features in denoised images. The EDN and the CGAN improved the\nconcordance correlation coefficients (CCC) of radiomic features for low-noise\nimages from 0.87 to 0.92 and for high-noise images from 0.68 to 0.92\nrespectively. Moreover, the EDN and the CGAN improved the test-retest\nreliability of radiomic features (mean CCC increased from 0.89 to 0.94) based\non real low dose CTs. The results show that denoising using EDN and CGANs can\nimprove the reproducibility of radiomic features calculated on noisy CTs.\nMoreover, images with different noise levels can be denoised to improve the\nreproducibility using these models without re-training, as long as the noise\nintensity is equal or lower than that in high-noise CTs. To the authors'\nknowledge, this is the first effort to improve the reproducibility of radiomic\nfeatures calculated on low dose CT scans.\n"}
+{"abstract": "  From any location outside the event horizon of a black hole there are an\ninfinite number of trajectories for light to an observer. Each of these paths\ndiffer in the number of orbits revolved around the black hole and in their\nproximity to the last photon orbit. With simple numerical and a perturbed\nanalytical solution to the null-geodesic equation of the Schwarzschild black\nhole we will reaffirm how each additional orbit is a factor $e^{2 \\pi}$ closer\nto the black hole's optical edge. Consequently, the surface of the black hole\nand any background light will be mirrored infinitely in exponentially thinner\nslices around the last photon orbit. Furthermore, the introduced formalism\nproves how the entire trajectories of light in the strong field limit is\nprescribed by a diverging and a converging exponential. Lastly, the existence\nof the exponential family is generalized to the equatorial plane of the Kerr\nblack hole with the exponentials dependence on spin derived. Thereby, proving\nthat the distance between subsequent images increases and decreases for\nrespectively retrograde and prograde images. In the limit of an extremely\nrotating Kerr black hole no logarithmic divergence exists for prograde\ntrajectories.\n"}
+{"abstract": "  We provide a unified, comprehensive treatment of all operators that\ncontribute to the anti-ferromagnetic, ferromagnetic, and charge-density-wave\nstructure factors and order parameters of the hexagonal Hubbard Model. We use\nthe Hybrid Monte Carlo algorithm to perform a systematic, carefully controlled\nanalysis in the temporal Trotter error and of the thermodynamic limit. We\nexpect our findings to improve the consistency of Monte Carlo determinations of\ncritical exponents. We perform a data collapse analysis and determine the\ncritical exponent $\\beta=0.898(37)$ for the semimetal-Mott insulator transition\nin the hexagonal Hubbard Model. Our methods are applicable to a wide range of\nlattice theories of strongly correlated electrons.\n"}
+{"abstract": "  MixUp is a computer vision data augmentation technique that uses convex\ninterpolations of input data and their labels to enhance model generalization\nduring training. However, the application of MixUp to the natural language\nunderstanding (NLU) domain has been limited, due to the difficulty of\ninterpolating text directly in the input space. In this study, we propose MixUp\nmethods at the Input, Manifold, and sentence embedding levels for the\ntransformer architecture, and apply them to finetune the BERT model for a\ndiverse set of NLU tasks. We find that MixUp can improve model performance, as\nwell as reduce test loss and model calibration error by up to 50%.\n"}
+{"abstract": "  We enrich the setting of strongly stable ideals (SSI): We introduce shift\nmodules, a module category encompassing SSI's. The recently introduced duality\non SSI's is given an effective conceptual and computational setting. We study\nstrongly stable ideals in infinite dimensional polynomial rings, where the\nduality is most natural. Finally a new type of resolution for SSI's is\nintroduced. This is the projective resolution in the category of shift modules.\n"}
+{"abstract": "  The states of two electrons in tunnel-coupled semiconductor quantum dots can\nbe effectively described in terms of a two-spin Hamiltonian with an isotropic\nHeisenberg interaction. A similar description needs to be generalized in the\ncase of holes due to their multiband character and spin-orbit coupling, which\nmixes orbital and spin degrees of freedom, and splits $J=3/2$ and $J = 1/2$\nmultiplets. Here we investigate two-hole states in prototypical coupled Si and\nGe quantum dots via different theoretical approaches. Multiband\n$\\boldsymbol{k}\\cdot\\boldsymbol{p}$ and Configuration-Interaction calculations\nare combined with entanglement measures in order to thoroughly characterize the\ntwo-hole states in terms of band mixing and justify the introduction of an\neffective spin representation, which we analytically derive a from generalized\nHubbard model. We find that, in the weak interdot regime, the ground state and\nfirst excited multiplet of the two-hole system display -- unlike their\nelectronic counterparts -- a high degree of $J$-mixing, even in the limit of\npurely heavy-hole states. The light-hole component additionally induces\n$M$-mixing and a weak coupling between spinors characterized by different\npermutational symmetries.\n"}
+{"abstract": "  CT image quality is heavily reliant on radiation dose, which causes a\ntrade-off between radiation dose and image quality that affects the subsequent\nimage-based diagnostic performance. However, high radiation can be harmful to\nboth patients and operators. Several (deep learning-based) approaches have been\nattempted to denoise low dose images. However, those approaches require access\nto large training sets, specifically the full dose CT images for reference,\nwhich can often be difficult to obtain. Self-supervised learning is an emerging\nalternative for lowering the reference data requirement facilitating\nunsupervised learning. Currently available self-supervised CT denoising works\nare either dependent on foreign domain or pretexts are not very task-relevant.\nTo tackle the aforementioned challenges, we propose a novel self-supervised\nlearning approach, namely Self-Supervised Window-Leveling for Image DeNoising\n(SSWL-IDN), leveraging an innovative, task-relevant, simple, yet effective\nsurrogate -- prediction of the window-leveled equivalent. SSWL-IDN leverages\nresidual learning and a hybrid loss combining perceptual loss and MSE, all\nincorporated in a VAE framework. Our extensive (in- and cross-domain)\nexperimentation demonstrates the effectiveness of SSWL-IDN in aggressive\ndenoising of CT (abdomen and chest) images acquired at 5\\% dose level only.\n"}
+{"abstract": "  The wild McKay correspondence, a variant of the McKay correspondence in\npositive characteristics, shows that stringy motives of quotient varieties\nequal some motivic integrals on the moduli space of of the Galois covers of a\nformal disk. In this paper, we determine when the integrals converge for the\nthe case of cyclic groups of prime power order. As an application, we give a\ncriterion for the quotient variety being canonical or log canonical.\n"}
+{"abstract": "  This paper presents a Matlab toolbox to perform basic image processing and\nvisualization tasks, particularly designed for medical image processing. The\nfunctionalities available are similar to basic functions found in other\nnon-Matlab widely used libraries such as the Insight Toolkit (ITK). The toolbox\nis entirely written in native Matlab code, but is fast and flexible.\n  Main use cases for the toolbox are illustrated here, including image\ninput/output, pre-processing, filtering, image registration and visualisation.\nBoth the code and sample data are made publicly available and open source.\n"}
+{"abstract": "  Sound event detection (SED) is a hot topic in consumer and smart city\napplications. Existing approaches based on Deep Neural Networks are very\neffective, but highly demanding in terms of memory, power, and throughput when\ntargeting ultra-low power always-on devices.\n  Latency, availability, cost, and privacy requirements are pushing recent IoT\nsystems to process the data on the node, close to the sensor, with a very\nlimited energy supply, and tight constraints on the memory size and processing\ncapabilities precluding to run state-of-the-art DNNs.\n  In this paper, we explore the combination of extreme quantization to a\nsmall-footprint binary neural network (BNN) with the highly energy-efficient,\nRISC-V-based (8+1)-core GAP8 microcontroller. Starting from an existing CNN for\nSED whose footprint (815 kB) exceeds the 512 kB of memory available on our\nplatform, we retrain the network using binary filters and activations to match\nthese memory constraints. (Fully) binary neural networks come with a natural\ndrop in accuracy of 12-18% on the challenging ImageNet object recognition\nchallenge compared to their equivalent full-precision baselines. This BNN\nreaches a 77.9% accuracy, just 7% lower than the full-precision version, with\n58 kB (7.2 times less) for the weights and 262 kB (2.4 times less) memory in\ntotal. With our BNN implementation, we reach a peak throughput of 4.6 GMAC/s\nand 1.5 GMAC/s over the full network, including preprocessing with Mel bins,\nwhich corresponds to an efficiency of 67.1 GMAC/s/W and 31.3 GMAC/s/W,\nrespectively. Compared to the performance of an ARM Cortex-M4 implementation,\nour system has a 10.3 times faster execution time and a 51.1 times higher\nenergy-efficiency.\n"}
+{"abstract": "  We prove a quantitative $h$-principle statement for subcritical isotropic\nembeddings. As an application, we construct a symplectic homeomorphism that\ntakes a symplectic disc into an isotropic one in dimension at least $6$.\n"}
+{"abstract": "  We construct a family of functions suitable for establishing lower bounds on\nthe oracle complexity of first-order minimization of smooth strongly-convex\nfunctions. Based on this construction, we derive new lower bounds on the\ncomplexity of strongly-convex minimization under various inaccuracy criteria.\nThe new bounds match the known upper bounds up to a constant factor, and when\nthe inaccuracy of a solution is measured by its distance to the solution set,\nthe new lower bound exactly matches the upper bound obtained by the recent\nInformation-Theoretic Exact Method by the same authors, thereby establishing\nthe exact oracle complexity for this class of problems.\n"}
+{"abstract": "  A large class of two dimensional quantum gravity theories of\nJackiw-Teitelboim form have a description in terms of random matrix models.\nSuch models, treated fully non-perturbatively, can give an explicit and\ntractable description of the underlying ``microstate'' degrees of freedom. They\nplay a prominent role in regimes where the smooth geometrical picture of the\nphysics is inadequate. This is shown using a natural tool for extracting the\ndetailed microstate physics, a Fredholm determinant ${\\rm\ndet}(\\mathbf{1}{-}\\mathbf{ K})$. Its associated kernel $K(E,E^\\prime)$ can be\ndefined explicitly for a wide variety of JT gravity theories. To illustrate the\nmethods, the statistics of the first several energy levels of a\nnon-perturbative definition of JT gravity are constructed explicitly using\nnumerical methods, and the full quenched free energy $F_Q(T)$ of the system is\ncomputed for the first time. These results are also of relevance to quantum\nproperties of black holes in higher dimensions.\n"}
+{"abstract": "  Supermassive black hole binaries (SMBHBs) should form frequently in galactic\nnuclei as a result of galaxy mergers. At sub-parsec separations, binaries\nbecome strong sources of low-frequency gravitational waves (GWs), targeted by\nPulsar Timing Arrays (PTAs). We used recent upper limits on continuous GWs from\nthe North American Nanohertz Observatory for Gravitational Waves (NANOGrav)\n11yr dataset to place constraints on putative SMBHBs in nearby massive\ngalaxies. We compiled a comprehensive catalog of ~44,000 galaxies in the local\nuniverse (up to redshift ~0.05) and populated them with hypothetical binaries,\nassuming that the total mass of the binary is equal to the SMBH mass derived\nfrom global scaling relations. Assuming circular equal-mass binaries emitting\nat NANOGrav's most sensitive frequency of 8nHz, we found that 216 galaxies are\nwithin NANOGrav's sensitivity volume. We ranked the potential SMBHBs based on\nGW detectability by calculating the total signal-to-noise ratio (S/N) such\nbinaries would induce within the NANOGrav array. We placed constraints on the\nchirp mass and mass ratio of the 216 hypothetical binaries. For 19 galaxies,\nonly very unequal-mass binaries are allowed, with the mass of the secondary\nless than 10 percent that of the primary, roughly comparable to constraints on\na SMBHB in the Milky Way. Additionally, we were able to exclude binaries\ndelivered by major mergers (mass ratio of at least 1/4) for several of these\ngalaxies. We also derived the first limit on the density of binaries delivered\nby major mergers purely based on GW data.\n"}
+{"abstract": "  We characterize the monodromies of projective structures with fuchsian-type\nsingularities. Namely, any representation from the fundamental group of a\nRiemann surface of finite-type in $PSL_2(\\mathbb{C})$ can be represented as the\nholonomy of branched projective structure with fuchsian-type singularities over\nthe cusps. We made a geometrical/topological study of all local conical\nprojective structures whose Schwarzian derivative admits a simple pole at the\ncusp. Finally, we explore isomonodromic deformations of such projective\nstructures and the problem of minimizing angles.\n"}
+{"abstract": "  The cytoskeleton is a model active matter system that controls diverse\ncellular processes from division to motility. While both active actomyosin\ndynamics and actin-microtubule interactions are key to the cytoskeleton's\nversatility and adaptability, an understanding of their interplay is lacking.\nHere, we couple microscale experiments with mechanistic modeling to elucidate\nhow connectivity, rigidity, and force-generation affect emergent material\nproperties in in vitro composites of actin, tubulin, and myosin. We use\ntime-resolved differential dynamic microscopy and spatial image autocorrelation\nto show that ballistic contraction occurs in composites with sufficient\nflexibility and motor density, but that a critical fraction of microtubules is\nnecessary to sustain controlled dynamics. Our active double-network models\nreveal that percolated actomyosin networks are essential for contraction, but\nthat networks with comparable actin and microtubule densities can uniquely\nresist mechanical stresses while simultaneously supporting substantial\nrestructuring. Our findings provide a much-needed blueprint for designing\ncytoskeleton-inspired materials that couple tunability with resilience and\nadaptability.\n"}
+{"abstract": "  This paper describes the submission of the NiuTrans end-to-end speech\ntranslation system for the IWSLT 2021 offline task, which translates from the\nEnglish audio to German text directly without intermediate transcription. We\nuse the Transformer-based model architecture and enhance it by Conformer,\nrelative position encoding, and stacked acoustic and textual encoding. To\naugment the training data, the English transcriptions are translated to German\ntranslations. Finally, we employ ensemble decoding to integrate the predictions\nfrom several models trained with the different datasets. Combining these\ntechniques, we achieve 33.84 BLEU points on the MuST-C En-De test set, which\nshows the enormous potential of the end-to-end model.\n"}
+{"abstract": "  Video-based person re-identification aims to match pedestrians from video\nsequences across non-overlapping camera views. The key factor for video person\nre-identification is to effectively exploit both spatial and temporal clues\nfrom video sequences. In this work, we propose a novel Spatial-Temporal\nCorrelation and Topology Learning framework (CTL) to pursue discriminative and\nrobust representation by modeling cross-scale spatial-temporal correlation.\nSpecifically, CTL utilizes a CNN backbone and a key-points estimator to extract\nsemantic local features from human body at multiple granularities as graph\nnodes. It explores a context-reinforced topology to construct multi-scale\ngraphs by considering both global contextual information and physical\nconnections of human body. Moreover, a 3D graph convolution and a cross-scale\ngraph convolution are designed, which facilitate direct cross-spacetime and\ncross-scale information propagation for capturing hierarchical spatial-temporal\ndependencies and structural information. By jointly performing the two\nconvolutions, CTL effectively mines comprehensive clues that are complementary\nwith appearance information to enhance representational capacity. Extensive\nexperiments on two video benchmarks have demonstrated the effectiveness of the\nproposed method and the state-of-the-art performance.\n"}
+{"abstract": "  For fixed graphs $F$ and $H$, the generalized Tur\\'an problem asks for the\nmaximum number $ex(n,H,F)$ of copies of $H$ that an $n$-vertex $F$-free graph\ncan have. In this paper, we focus on cases with $F$ being $B_{r,s}$, the graph\nconsisting of two cliques of size $s$ sharing $r$ common vertices. We determine\n$ex(n,K_t,B_{r,0})$, $ex(n,K_{a,b},B_{3,1})$ for any values of $a,b,r,t$ if $n$\nis large enough and $ex(n,K_{r+t},B_{r,s})$ if $2s+t+1<r$ and $n$ is large\nenough.\n"}
+{"abstract": "  Edge-localized stationary states of the focusing nonlinear Schrodinger\nequation on a general quantum graph are considered in the limit of large mass.\nCompared to the previous works, we include arbitrary multi-pulse positive\nstates which approach asymptotically to a composition of N solitons, each\nsitting on a bounded (pendant, looping, or internal) edge. Not only we prove\nthat such states exist in the limit of large mass, but also we compute the\nprecise Morse index (the number of negative eigenvalues in the corresponding\nlinearized operator). In the case of the edge-localized N-soliton states on the\npendant and looping edges, we prove that the Morse index is exactly N. The\ntechnical novelty of this work is achieved by avoiding elliptic functions (and\nrelated exponentially small scalings) and closing the existence arguments in\nterms of the Dirichlet-to-Neumann maps for relevant parts of the given graph.\n"}
+{"abstract": "  We investigate a generalization of Binet's factorial series in the parameter\n$\\alpha$ \\[ \\mu\\left( z\\right) =\\sum_{m=1}^{\\infty}\\frac{b_{m}\\left(\n\\alpha\\right) }{\\prod_{k=0}^{m-1}(z+\\alpha+k)}% \\] for the Binet function \\[\n\\mu\\left( z\\right) =\\log\\Gamma\\left( z\\right) -\\left( z-\\frac{1}% {2}\\right)\n\\log z+z-\\frac{1}{2}\\log\\left( 2\\pi\\right) \\] After a brief review of the Binet\nfunction $\\mu\\left( z\\right) $, several properties of the Binet polynomials\n$b_{m}\\left( \\alpha\\right) $ are presented. We compute the corresponding\nfactorial series for the derivatives of the Binet function and apply those\nseries to the digamma and polygamma functions. We compare Binet's generalized\nfactorial series with Stirling's \\emph{asymptotic} expansion and demonstrate by\na numerical example that, with a same number of terms evaluated, the Binet\ngeneralized factorial series with an optimized value of $\\alpha$ can beat the\nbest possible accuracy of Stirling's expansion. Finally, we extend Binet's\nmethod to factorial series of Laplace transforms.\n"}
+{"abstract": "  The representation dimension of a finite group $G$ is the minimal dimension\nof a faithful complex linear representation of $G$. We prove that the\nrepresentation dimension of any finite group $G$ is at most $\\sqrt{|G|}$ except\nif $G$ is a $2$-group with elementary abelian center of order $8$ and all\nirreducible characters of $G$ whose kernel does not contain $Z(G)$ are fully\nramified with respect to $G/Z(G)$. We also obtain bounds for the representation\ndimension of quotients of $G$ in terms of the representation dimension of $G$,\nand discuss the relation of this invariant with the essential dimension of $G$.\n"}
+{"abstract": "  We compute the scaling dimensions of a family of fixed-charge operators at\nthe infrared fixed point of the $O(N)$ model featuring cubic interactions in\n$d=6-\\epsilon$ for arbitrary $N$ to leading and subleading order in the charge\nbut to all orders in the couplings. The results are used to analyze the\nconjectured equivalence with the $O(N)$ model displaying quartic interactions\nat its ultraviolet fixed point. This is performed by comparing the cubic model\nscaling dimensions against the known large $N$ results for the quartic model\nand demonstrating that they match. Our results reinforce the conjectured\nequivalence and further provide novel information on the finite $N$ physics\nstemming from the computations in the cubic model just below 6 dimensions.\n"}
+{"abstract": "  This paper addresses the degraded discrete-time Poisson wiretap channel\n(DT--PWC) in an optical wireless communication system based on intensity\nmodulation and direct detection. Subject to nonnegativity, peak- and\naverage-intensity as well as bandwidth constraints, we study the\nsecrecy-capacity-achieving input distribution of this wiretap channel and prove\nit to be unique and discrete with a finite number of mass points; one of them\nlocated at the origin. Furthermore, we establish that every point on the\nboundary of the rate-equivocation region of this wiretap channel is also\nobtained by a unique and discrete input distribution with finitely many mass\npoints. In general, the number of mass points of the optimal distributions is\ngreater than two. This is in contrast with the degraded continuous-time PWC\nwhen the signaling bandwidth is not restricted and where the secrecy capacity\nand the entire boundary of the rate-equivocation region are achieved by binary\ndistributions. Furthermore, we extend our analysis to the case where only an\naverage-intensity constraint is active. For this case, we find that the secrecy\ncapacity and the entire boundary of the rate-equivocation region are attained\nby discrete distributions with countably \\textit{infinite} number of mass\npoints, but with finitely many mass points in any bounded interval.\n"}
+{"abstract": "  We generalize solid-state tight-binding techniques for the spectral analysis\nof large superconducting circuits. We find that tight-binding states can be\nbetter suited for approximating the low-energy excitations than charge-basis\nstates, as illustrated for the interesting example of the current-mirror\ncircuit. The use of tight binding can dramatically lower the Hilbert space\ndimension required for convergence to the true spectrum, and allows for the\naccurate simulation of larger circuits that are out of reach of charge basis\ndiagonalization.\n"}
+{"abstract": "  Diffuse phonon scattering strongly affects the phonon transport through a\ndisordered interface. The often-used diffuse mismatch model assumes that\nphonons lose memory of their origin after being scattered by the interface.\nUsing mode-resolved atomic Green's function simulation, we demonstrate that\ndiffuse phonon scattering by a single disordered interface cannot make a phonon\nlose its memory and thus the applicability of diffusive mismatch model is\nlimited. An analytical expression for diffuse scattering probability based on\nthe continuum approximation is also derived and shown to work reasonably well\nat low frequencies.\n"}
+{"abstract": "  Understanding who blames or supports whom in news text is a critical research\nquestion in computational social science. Traditional methods and datasets for\nsentiment analysis are, however, not suitable for the domain of political text\nas they do not consider the direction of sentiments expressed between entities.\nIn this paper, we propose a novel NLP task of identifying directed sentiment\nrelationship between political entities from a given news document, which we\ncall directed sentiment extraction. From a million-scale news corpus, we\nconstruct a dataset of news sentences where sentiment relations of political\nentities are manually annotated. We present a simple but effective approach for\nutilizing a pretrained transformer, which infers the target class by predicting\nmultiple question-answering tasks and combining the outcomes. We demonstrate\nthe utility of our proposed method for social science research questions by\nanalyzing positive and negative opinions between political entities in two\nmajor events: 2016 U.S. presidential election and COVID-19. The newly proposed\nproblem, data, and method will facilitate future studies on interdisciplinary\nNLP methods and applications.\n"}
+{"abstract": "  If S is a structure over (a concrete category over) metric spaces, we say\nthat a group G has property BS if any action on a S-space has bounded orbits.\nExamples of such structures include metric spaces, Hilbert spaces, CAT(0) cube\ncomplexes, connected median graphs, trees or ultra-metric spaces. The\ncorresponding properties BS are respectively Bergman's property, property FH\n(which, for countable groups, is equivalent to the celebrated Kazhdan's\nproperty (T)), property FW (both for CAT(0) cube complexes and for connected\nmedian graphs), property FA and uncountable cofinality (cof $\\neq\\omega$).\n  Our main result is that for a large class of structures S, the wreath product\n$G\\wr_XH$ has property BS if and only if both $G$ and $H$ have property BS and\n$X$ is finite. On one hand, this encompasses in a general setting previously\nknown results for properties FH and FW. On the other hand, this also applies to\nthe Bergman's property. Finally, we also obtain that $G\\wr_XH$ has cof\n$\\neq\\omega$ if and only if both $G$ and $H$ have cof $\\neq\\omega$ and $H$ acts\non $X$ with finitely many orbits.\n"}
+{"abstract": "  We present here results from a survey of intervening C IV absorbers at $z <\n0.16$ conducted using 223 sightlines from the Hubble Spectroscopic Legacy\nArchive. Most systems (83%) out of the total sample of 69 have simple\nkinematics with 1 or 2 C IV components. In the 22 C IV systems with well\nconstrained H I column densities, the temperatures from the $b$-values imply\npredominantly photoionized plasma ($T\\leq 10^5$ K) and non-thermal dynamics.\nThese systems also have solar or higher metallicities. We obtain a C IV line\ndensity of $d\\mathcal{N}/dX = 5.1\\pm 1.0$ for $\\log\n[N(C~IV)~(cm^{-2})]\\geq12.9$, and $\\Omega_{C~IV}=(8.01\\pm 1.62) \\times 10^{-8}$\nfor $12.9 \\leq \\log [N(C~IV)~(cm^{-2})] \\leq 15.0$. The C IV bearing diffuse\ngas in the $z < 0.16$ Universe has a metallicity of\n$(2.07~{\\pm}~0.43)~\\times~10^{-3}$ Z$_{\\odot}$, an order of magnitude more than\nthe metal abundances in the IGM at high redshifts ($z \\gtrsim 5$), and\nconsistent with the slow build-up of metals in the diffuse circum/intergalactic\nspace with cosmic time. For $z<0.015$ (complete above $L>0.01L^\\star$), the\nSloan Digital Sky Survey provides a tentative evidence of declining covering\nfraction for strong C IV ($N>10^{13.5}~cm^{-2}$) with $\\rho$ (impact parameter)\nand $\\rho/R_\\mathrm{vir}$. However, the increase at high separations suggests\nthat strong systems are not necessarily coincident with such galaxies. We also\nfind that strong C IV absorption at $z<0.051$ is not coincident with galaxy\nover-dense regions complete for $L>0.13L^\\star$\n"}
+{"abstract": "  Network anomaly detection aims to find network elements (e.g., nodes, edges,\nsubgraphs) with significantly different behaviors from the vast majority. It\nhas a profound impact in a variety of applications ranging from finance,\nhealthcare to social network analysis. Due to the unbearable labeling cost,\nexisting methods are predominately developed in an unsupervised manner.\nNonetheless, the anomalies they identify may turn out to be data noises or\nuninteresting data instances due to the lack of prior knowledge on the\nanomalies of interest. Hence, it is critical to investigate and develop\nfew-shot learning for network anomaly detection. In real-world scenarios, few\nlabeled anomalies are also easy to be accessed on similar networks from the\nsame domain as of the target network, while most of the existing works omit to\nleverage them and merely focus on a single network. Taking advantage of this\npotential, in this work, we tackle the problem of few-shot network anomaly\ndetection by (1) proposing a new family of graph neural networks -- Graph\nDeviation Networks (GDN) that can leverage a small number of labeled anomalies\nfor enforcing statistically significant deviations between abnormal and normal\nnodes on a network; and (2) equipping the proposed GDN with a new cross-network\nmeta-learning algorithm to realize few-shot network anomaly detection by\ntransferring meta-knowledge from multiple auxiliary networks. Extensive\nevaluations demonstrate the efficacy of the proposed approach on few-shot or\neven one-shot network anomaly detection.\n"}
+{"abstract": "  We compute the expected sensitivity on measurements of optical depth to\nreionization for a ground-based experiment at Teide Observatory. We simulate\npolarized partial sky maps for the GroundBIRD experiment at the frequencies 145\nand 220 GHz. We perform fits for the simulated maps with our pixel-based\nlikelihood to extract the optical depth to reionization. The noise levels of\npolarization maps are estimated as 110 $\\mu\\mathrm{K~arcmin}$ and 780 $\n\\mu\\mathrm{K~arcmin}$ for 145 and 220 GHz, respectively, by assuming a\nthree-year observing campaign and sky coverages of 0.537 for 145 GHz and 0.462\nfor 220 GHz. Our sensitivities for the optical depth to reionization are found\nto be $\\sigma_\\tau$=0.030 with the simulated GroundBIRD maps, and\n$\\sigma_\\tau$=0.012 by combining with the simulated QUIJOTE maps at 11, 13, 17,\n19, 30, and 40 GHz.\n"}
+{"abstract": "  With the growing awareness to fairness in machine learning and the\nrealization of the central role that data representation has in data processing\ntasks, there is an obvious interest in notions of fair data representations.\nThe goal of such representations is that a model trained on data under the\nrepresentation (e.g., a classifier) will be guaranteed to respect some fairness\nconstraints.\n  Such representations are useful when they can be fixed for training models on\nvarious different tasks and also when they serve as data filtering between the\nraw data (known to the representation designer) and potentially malicious\nagents that use the data under the representation to learn predictive models\nand make decisions.\n  A long list of recent research papers strive to provide tools for achieving\nthese goals.\n  However, we prove that this is basically a futile effort. Roughly stated, we\nprove that no representation can guarantee the fairness of classifiers for\ndifferent tasks trained using it; even the basic goal of achieving\nlabel-independent Demographic Parity fairness fails once the marginal data\ndistribution shifts. More refined notions of fairness, like Odds Equality,\ncannot be guaranteed by a representation that does not take into account the\ntask specific labeling rule with respect to which such fairness will be\nevaluated (even if the marginal data distribution is known a priory).\nFurthermore, except for trivial cases, no representation can guarantee Odds\nEquality fairness for any two different tasks, while allowing accurate label\npredictions for both.\n  While some of our conclusions are intuitive, we formulate (and prove) crisp\nstatements of such impossibilities, often contrasting impressions conveyed by\nmany recent works on fair representations.\n"}
+{"abstract": "  We construct a bi-Hamiltonian structure for the holomorphic spin Sutherland\nhierarchy based on collective spin variables. The construction relies on\nPoisson reduction of a bi-Hamiltonian structure on the holomorphic cotangent\nbundle of GL(n,C), which itself arises from the canonical symplectic structure\nand the Poisson structure of the Heisenberg double of the standard GL(n,C)\nPoisson--Lie group. The previously obtained bi-Hamiltonian structures of the\nhyperbolic and trigonometric real forms are recovered on real slices of the\nholomorphic spin Sutherland model.\n"}
+{"abstract": "  We introduce the concept of a Gr\\\"obner nice pair of ideals in a polynomial\nring and we present some applications.\n"}
+{"abstract": "  The use of the nuclear spins surrounding electron spin qubits as quantum\nregisters and long-lived memories opens the way to new applications in quantum\ninformation and biological sensing. Hence, there is a need for generic and\nrobust forms of control of the nuclear registers. Although adiabatic gates are\nwidely used in quantum information, they can become too slow to outpace\ndecoherence. Here, we introduce a technique whereby adiabatic gates arise from\nthe dynamical decoupling protocols that simultaneously extend coherence. We\nillustrate this pulse-based adiabatic control for nuclear spins around NV\ncenters in diamond. We obtain a closed-form expression from Landau-Zener theory\nand show that it reliably describes the dynamics. By identifying robust Floquet\nstates, we show that the technique enables polarisation, one-shot flips and\nstate storage for nuclear spins. These results introduce a new control paradigm\nthat combines dynamical decoupling with adiabatic evolution.\n"}
+{"abstract": "  We prove that in the Cayley graph of any braid group modulo its center\n$B_n/Z(B_n)$, equipped with Garside's generating set, the axes of all\npseudo-Anosov braids are strongly contracting. More generally, we consider a\nGarside group $G$ of finite type with cyclic center. We prove that in the\nCayley graph of $G/Z(G)$, equipped with the Garside generators, the axis of any\nMorse element is strongly contracting. As a consequence, we prove that Morse\nelements act loxodromically on the additional length graph of $G$.\n"}
+{"abstract": "  We establish the existence and uniqueness of weak and renormalized solutions\nto a degenerate, hypoelliptic Mean Field Games system with local coupling. An\nimportant step is to obtain $L^{\\infty}-$bounds for solutions to a degenerate\nFokker-Planck equation with a De-Giorgi type argument. In particular, we show\nexistence and uniqueness of weak solutions to Mean Fields Games systems with\nLipschitz Hamiltonians. Furthermore, we establish existence and uniqueness of\nrenormalized solutions for Hamiltonians with quadratic growth. Our approach\nrelies on the kinetic regularity of hypoelliptic equations obtained by Bouchut\nand the work of Porretta on the existence and uniqueness of renormalized\nsolutions for the Mean Field Game system, in the non-degenerate setting.\n"}
+{"abstract": "  The design of machines and algorithms capable of learning in a dynamically\nchanging environment has become an increasingly topical problem with the\nincrease of the size and heterogeneity of data available to learning systems.\nAs a consequence, the key issue of Continual Learning has become that of\naddressing the stability-plasticity dilemma of connectionist systems, as they\nneed to adapt their model without forgetting previously acquired knowledge.\nWithin this context, rehearsal-based methods i.e., solutions in where the\nlearner exploits memory to revisit past data, has proven to be very effective,\nleading to performance at the state-of-the-art. In our study, we propose an\nanalysis of the memory quantity/quality trade-off adopting various data\nreduction approaches to increase the number of instances storable in memory. In\nparticular, we investigate complex instance compression techniques such as deep\nencoders, but also trivial approaches such as image resizing and linear\ndimensionality reduction. Our findings suggest that the optimal trade-off is\nseverely skewed toward instance quantity, where rehearsal approaches with\nseveral heavily compressed instances easily outperform state-of-the-art\napproaches with the same amount of memory at their disposal. Further, in high\nmemory configurations, deep approaches extracting spatial structure combined\nwith extreme resizing (of the order of $8\\times8$ images) yield the best\nresults, while in memory-constrained configurations where deep approaches\ncannot be used due to their memory requirement in training, Extreme Learning\nMachines (ELM) offer a clear advantage.\n"}
+{"abstract": "  Let $V$ be a finite set. Let $\\mathcal{K}$ be a simplicial complex with its\nvertices in $V$. In this paper, we discuss some differential calculus on $V$.\nWe construct some generalized homology groups of $\\mathcal{K}$ by using the\ndifferential calculus on $V$. Moreover, we define a co-simplicial complex to be\nthe complement of a simplicial complex in the complete hypergraph on $V$. Let\n$\\mathcal{L}$ be a co-simplicial complex with its vertices in $V$. We construct\nsome generalized cohomology groups of $\\mathcal{L}$ by using the differential\ncalculus on $V$.\n"}
+{"abstract": "  In stationary subspace analysis (SSA) one assumes that the observable\np-variate time series is a linear mixture of a k-variate nonstationary time\nseries and a (p-k)-variate stationary time series. The aim is then to estimate\nthe unmixing matrix which transforms the observed multivariate time series onto\nstationary and nonstationary components. In the classical approach multivariate\ndata are projected onto stationary and nonstationary subspaces by minimizing a\nKullback-Leibler divergence between Gaussian distributions, and the method only\ndetects nonstationarities in the first two moments. In this paper we consider\nSSA in a more general multivariate time series setting and propose SSA methods\nwhich are able to detect nonstationarities in mean, variance and\nautocorrelation, or in all of them. Simulation studies illustrate the\nperformances of proposed methods, and it is shown that especially the method\nthat detects all three types of nonstationarities performs well in various time\nseries settings. The paper is concluded with an illustrative example.\n"}
+{"abstract": "  Graph neural networks (GNNs) are a powerful architecture for tackling graph\nlearning tasks, yet have been shown to be oblivious to eminent substructures\nsuch as cycles. We present TOGL, a novel layer that incorporates global\ntopological information of a graph using persistent homology. TOGL can be\neasily integrated into any type of GNN and is strictly more expressive in terms\nof the Weisfeiler-Lehman graph isomorphism test. Augmenting GNNs with our layer\nleads to improved predictive performance for graph and node classification\ntasks, both on synthetic data sets (which can be classified by humans using\ntheir topology but not by ordinary GNNs) and on real-world data.\n"}
+{"abstract": "  This paper introduces the Gene Mover's Distance, a measure of similarity\nbetween a pair of cells based on their gene expression profiles obtained via\nsingle-cell RNA sequencing. The underlying idea of the proposed distance is to\ninterpret the gene expression array of a single cell as a discrete probability\nmeasure. The distance between two cells is hence computed by solving an Optimal\nTransport problem between the two corresponding discrete measures. In the\nOptimal Transport model, we use two types of cost function for measuring the\ndistance between a pair of genes. The first cost function exploits a gene\nembedding, called gene2vec, which is used to map each gene to a high\ndimensional vector: the cost of moving a unit of mass of gene expression from a\ngene to another is set to the Euclidean distance between the corresponding\nembedded vectors. The second cost function is based on a Pearson distance among\npairs of genes. In both cost functions, the more two genes are correlated, the\nlower is their distance. We exploit the Gene Mover's Distance to solve two\nclassification problems: the classification of cells according to their\ncondition and according to their type. To assess the impact of our new metric,\nwe compare the performances of a $k$-Nearest Neighbor classifier using\ndifferent distances. The computational results show that the Gene Mover's\nDistance is competitive with the state-of-the-art distances used in the\nliterature.\n"}
+{"abstract": "  Nearby, low-metallicity dwarf starburst galaxies hosting active galactic\nnuclei (AGNs) offer the best local analogs to study the early evolution of\ngalaxies and their supermassive black holes (BHs). Here we present a detailed\nmulti-wavelength investigation of star formation and BH activity in the\nlow-metallicity dwarf-dwarf galaxy merger Mrk 709. Using Hubble Space Telescope\nH$\\alpha$ and continuum imaging combined with Keck spectroscopy, we determine\nthat the two dwarf galaxies are likely in the early stages of a merger (i.e.,\ntheir first pass) and discover a spectacular $\\sim 10$ kpc-long string of young\nmassive star clusters ($t \\lesssim 10$ Myr; $M_\\star \\gtrsim 10^5~M_\\odot$)\nbetween the galaxies triggered by the interaction. We find that the southern\ngalaxy, Mrk 709 S, is undergoing a clumpy mode of star formation resembling\nthat seen in high-redshift galaxies, with multiple young clusters/clumps having\nstellar masses between $10^7$ and $10^8~M_\\odot$. Furthermore, we present\nadditional evidence for a low-luminosity AGN in Mrk 709 S (first identified by\nReines et al. 2014 (arXiv:1405.0278) using radio and X-ray observations),\nincluding the detection of the coronal [Fe X] optical emission line. The work\npresented here provides a unique glimpse into processes key to hierarchical\ngalaxy formation and BH growth in the early Universe.\n"}
+{"abstract": "  We present a field theoretical description of quarkyonic matter consisting of\nquark, nucleon and ghost fields coupling to mesonic degrees of freedom. The\nghosts are present to cancel over-counting of nucleon states that are Pauli\nblocked by the quark Fermi sea. Such a theory becomes an effective field theory\nof nucleons at low baryon density, and as such will reproduce nucleonic matter\nphenomenology. This theory can accommodate chiral symmetry restoration and the\ndynamical generation of a shell of nucleons at the Fermi surface. It is valid\nfor finite temperature and density. In such a theory, quark-nucleon duality is\naccomplished by inclusion of ghost fields so that the nucleons extra degrees of\nfreedom, that are beyond those of quarks, are compensated by the ghost fields.\n"}
+{"abstract": "  The manuscript contains elegant extensions of the fundamental variational\nprinciples: Br\\o ndsted's and Ekeland's. On the other hand we get general and\nprecise version of the Takahashi and the Cariste fixed point theorems. The\nresults are based on the notion of istance for uniform spaces.\n"}
+{"abstract": "  Topology effects have being extensively studied and confirmed in strongly\ncorrelated condensed matter physics. In the large color number limit of QCD,\nbaryons can be regarded as topological objects -- skyrmions -- and the baryonic\nmatter can be regarded as a skyrmion matter. We review in this paper the\ngeneralized effective field theory for dense compact-star matter constructed\nwith the robust inputs obtained from the skyrmion approach to dense nuclear\nmatter, relying to possible ``emergent\" scale and local flavor symmetries at\nhigh density. All nuclear matter properties from the saturation density $n_0$\nup to several times $n_0$ can be fairly well described. A uniquely novel -- and\nunorthdox -- feature of this theory is the precocious appearance of the\npseudo-conformal sound velocity $v^2_{s}/c^2 \\approx 1/3$, with the\nnon-vanishing trace of the energy momentum tensor of the system. The topology\nchange encoded in the density scaling of low energy constants is interpreted as\nthe quark-hadron continuity in the sense of Cheshire Cat Principle (CCP) at\ndensity $\\gsim 2n_0$ in accessing massive compact stars. We confront the\napproach with the data from GW170817 and GW190425.\n"}
+{"abstract": "  The COVID-19 pandemic is a global crisis that has been testing every society\nand exposing the critical role of local politics in crisis response. In the\nUnited States, there has been a strong partisan divide between the Democratic\nand Republican party's narratives about the pandemic which resulted in\npolarization of individual behaviors and divergent policy adoption across\nregions. As shown in this case, as well as in most major social issues,\nstrongly polarized narrative frameworks facilitate such narratives. To\nunderstand polarization and other social chasms, it is critical to dissect\nthese diverging narratives. Here, taking the Democratic and Republican\npolitical social media posts about the pandemic as a case study, we demonstrate\nthat a combination of computational methods can provide useful insights into\nthe different contexts, framing, and characters and relationships that\nconstruct their narrative frameworks which individual posts source from.\nLeveraging a dataset of tweets from elite politicians in the U.S., we found\nthat the Democrats' narrative tends to be more concerned with the pandemic as\nwell as financial and social support, while the Republicans discuss more about\nother political entities such as China. We then perform an automatic framing\nanalysis to characterize the ways in which they frame their narratives, where\nwe found that the Democrats emphasize the government's role in responding to\nthe pandemic, and the Republicans emphasize the roles of individuals and\nsupport for small businesses. Finally, we present a semantic role analysis that\nuncovers the important characters and relationships in their narratives as well\nas how they facilitate a membership categorization process. Our findings\nconcretely expose the gaps in the \"elusive consensus\" between the two parties.\nOur methodologies may be applied to computationally study narratives in various\ndomains.\n"}
+{"abstract": "  Closed-loop control systems employ continuous sensing and actuation to\nmaintain controlled variables within preset bounds and achieve the desired\nsystem output. Intentional disturbances in the system, such as in the case of\ncyberattacks, can compromise reachability of control goals, and in several\ncases jeopardize safety. The increasing connectivity and exposure of networked\ncontrol to external networks has enabled attackers to compromise these systems\nby exploiting security vulnerabilities. Attacks against safety-critical control\nloops can not only drive the system over a trajectory different from the\ndesired, but also cause fatal consequences to humans. In this paper we present\na physics-based Intrusion Detection System (IDS) aimed at increasing the\nsecurity in control systems. In addition to conventional process state\nestimation for intrusion detection, since the controller cannot be trusted, we\nintroduce a controller state estimator. Additionally, we make our detector\ncontext-aware by utilizing sensor measurements from other control loops, which\nallows to distinguish and characterize disturbances from attacks. We introduce\nadaptive thresholding and adaptive filtering as means to achieve\ncontext-awareness. Together, these methodologies allow detection and\nlocalization of attacks in closed-loop controls. Finally, we demonstrate\nfeasibility of the approach by mounting a series of attacks against a networked\nDirect Current (DC) motor closed-loop speed control deployed on an ECU testbed,\nas well as on a simulated automated lane keeping system. Among other\napplication domains, this set of approaches is key to support security in\nautomotive systems, and ultimately increase road and passenger safety.\n"}
+{"abstract": "  Current dialogue summarization systems usually encode the text with a number\nof general semantic features (e.g., keywords and topics) to gain more powerful\ndialogue modeling capabilities. However, these features are obtained via\nopen-domain toolkits that are dialog-agnostic or heavily relied on human\nannotations. In this paper, we show how DialoGPT, a pre-trained model for\nconversational response generation, can be developed as an unsupervised\ndialogue annotator, which takes advantage of dialogue background knowledge\nencoded in DialoGPT. We apply DialoGPT to label three types of features on two\ndialogue summarization datasets, SAMSum and AMI, and employ pre-trained and non\npre-trained models as our summarizes. Experimental results show that our\nproposed method can obtain remarkable improvements on both datasets and\nachieves new state-of-the-art performance on the SAMSum dataset.\n"}
+{"abstract": "  It is common knowledge that leverage can increase the potential returns of an\ninvestment, at the expense of increased risk. For a passive investor in the\nstock market, leverage can be achieved using margin debt or leveraged-ETFs. We\nperform bootstrapped Monte-Carlo simulations of leveraged (and unleveraged)\nmixed portfolios of stocks and bonds, based on past stock market data, and show\nthat leverage can amplify the potential returns, without significantly\nincreasing the risk for long-term investors.\n"}
+{"abstract": "  In this paper, we inaugurate the field of quantum fair machine learning. We\nundertake a comparative analysis of differences and similarities between\nclassical and quantum fair machine learning algorithms, specifying how the\nunique features of quantum computation alter measures, metrics and remediation\nstrategies when quantum algorithms are subject to fairness constraints. We\npresent the first results in quantum fair machine learning by demonstrating the\nuse of Grover's search algorithm to satisfy statistical parity constraints\nimposed on quantum algorithms. We provide lower-bounds on iterations needed to\nachieve such statistical parity within $\\epsilon$-tolerance. We extend\ncanonical Lipschitz-conditioned individual fairness criteria to the quantum\nsetting using quantum metrics. We examine the consequences for typical measures\nof fairness in machine learning context when quantum information processing and\nquantum data are involved. Finally, we propose open questions and research\nprogrammes for this new field of interest to researchers in computer science,\nethics and quantum computation.\n"}
+{"abstract": "  Constraint-based recommenders support users in the identification of items\n(products) fitting their wishes and needs. Example domains are financial\nservices and electronic equipment. In this paper we show how divide-and-conquer\nbased (direct) diagnosis algorithms (no conflict detection is needed) can be\nexploited in constraint-based recommendation scenarios. In this context, we\nprovide an overview of the MediaWiki-based recommendation environment WeeVis.\n"}
+{"abstract": "  The level of star formation in elliptical galaxies is poorly constrained, due\nto difficulties in quantifying the contamination of flux-based estimates of\nstar formation from unrelated phenomena, such as AGN and old stellar\npopulations. We here utilise core-collapse supernovae (CCSNe) as unambiguous\ntracers of recent star formation in ellipticals within a cosmic volume. We\nfirstly isolate a sample of 421 z < 0.2, r < 21.8 mag CCSNe from the SDSS-II\nSupernova Survey. We then introduce a Bayesian method of identifying\nellipticals via their colours and morphologies in a manner unbiased by redshift\nand yet consistent with manual classification from Galaxy Zoo 1. We find ~ 25 %\nof z < 0.2 r < 20 mag galaxies in the Stripe 82 region are ellipticals (~ 28000\ngalaxies). In total, 36 CCSNe are found to reside in ellipticals. We\ndemonstrate that such early-types contribute a non-negligible fraction of star\nformation to the present-day cosmic budget, at 11.2 $\\pm$ 3.1 (stat)\n$^{+3.0}_{-4.2}$ (sys) %. Coupling this result with the galaxy stellar mass\nfunction of ellipticals, the mean specific star formation rate (SSFR;\n$\\overline{S}$) of these systems is derived. The best-fit slope is given by log\n($\\overline{S}(M)$/yr) = - (0.80 $\\pm$ 0.59) log ($M/10^{10.5}\\rm{M}_{\\odot}$)\n- 10.83 $\\pm$ 0.18. The mean SSFR for all log ($M/\\rm{M}_{\\odot}$) > 10.0\nellipticals is found to be $\\overline{S} = 9.2 \\pm 2.4$ (stat) $^{+2.7}_{-2.3}$\n(sys) $\\times 10^{-12}$ yr$^{-1}$, which is consistent with recent estimates\nvia SED-fitting, and is 11.8 $\\pm$ 3.7 (stat) $^{+3.5}_{-2.9}$ (sys) % of the\nmean SSFR level on the main sequence as also derived from CCSNe. We find the\nmedian optical spectrum of elliptical CCSN hosts is statistically consistent\nwith that of a control sample of ellipticals that do not host CCSNe, implying\nthat these SN-derived results are well-representative of the total low-z\nelliptical population.\n"}
+{"abstract": "  We present a novel Material Point Method (MPM) discretization of surface\ntension forces that arise from spatially varying surface energies. These\nvariations typically arise from surface energy dependence on temperature and/or\nconcentration. Furthermore, since the surface energy is an interfacial property\ndepending on the types of materials on either side of an interface, spatial\nvariation is required for modeling the contact angle at the triple junction\nbetween a liquid, solid and surrounding air. Our discretization is based on the\nsurface energy itself, rather than on the associated traction condition most\ncommonly used for discretization with particle methods. Our energy based\napproach automatically captures surface gradients without the explicit need to\nresolve them as in traction condition based approaches. We include an implicit\ndiscretization of thermomechanical material coupling with a novel\nparticle-based enforcement of Robin boundary conditions associated with\nconvective heating. Lastly, we design a particle resampling approach needed to\nachieve perfect conservation of linear and angular momentum with\nAffineParticle-In-Cell (APIC) [Jiang et al. 2015]. We show that our approach\nenables implicit time stepping for complex behaviors like the Marangoni effect\nand hydrophobicity/hydrophilicity. We demonstrate the robustness and utility of\nour method by simulating materials that exhibit highly diverse degrees of\nsurface tension and thermomechanical effects, such as water, wine and wax.\n"}
+{"abstract": "  We consider the lower-triangular matrix of generating polynomials that\nenumerate $k$-component forests of rooted trees on the vertex set $[n]$\naccording to the number of improper edges (generalizations of the Ramanujan\npolynomials). We show that this matrix is coefficientwise totally positive and\nthat the sequence of its row-generating polynomials is coefficientwise\nHankel-totally positive. More generally, we define the generic rooted-forest\npolynomials by introducing also a weight $m! \\, \\phi_m$ for each vertex with\n$m$ proper children. We show that if the weight sequence $\\phi$ is\nToeplitz-totally positive, then the two foregoing total-positivity results\ncontinue to hold. Our proofs use production matrices and exponential Riordan\narrays.\n"}
+{"abstract": "  The state of the art in video super-resolution (SR) are techniques based on\ndeep learning, but they perform poorly on real-world videos (see Figure 1). The\nreason is that training image-pairs are commonly created by downscaling a\nhigh-resolution image to produce a low-resolution counterpart. Deep models are\ntherefore trained to undo downscaling and do not generalize to super-resolving\nreal-world images. Several recent publications present techniques for improving\nthe generalization of learning-based SR, but are all ill-suited for real-time\napplication.\n  We present a novel approach to synthesizing training data by simulating two\ndigital-camera image-capture processes at different scales. Our method produces\nimage-pairs in which both images have properties of natural images. Training an\nSR model using this data leads to far better generalization to real-world\nimages and videos.\n  In addition, deep video-SR models are characterized by a high\noperations-per-pixel count, which prohibits their application in real-time. We\npresent an efficient CNN architecture, which enables real-time application of\nvideo SR on low-power edge-devices. We split the SR task into two sub-tasks: a\ncontrol-flow which estimates global properties of the input video and adapts\nthe weights and biases of a processing-CNN that performs the actual processing.\nSince the process-CNN is tailored to the statistics of the input, its capacity\nkept low, while retaining effectivity. Also, since video-statistics evolve\nslowly, the control-flow operates at a much lower rate than the video\nframe-rate. This reduces the overall computational load by as much as two\norders of magnitude. This framework of decoupling the adaptivity of the\nalgorithm from the pixel processing, can be applied in a large family of\nreal-time video enhancement applications, e.g., video denoising, local\ntone-mapping, stabilization, etc.\n"}
+{"abstract": "  This article presents a bidirectional type system for the Calculus of\nInductive Constructions (CIC). It introduces a new judgement intermediate\nbetween the usual inference and checking, dubbed constrained inference, to\nhandle the presence of computation in types. The key property of the system is\nits completeness with respect to the usual undirected one, which has been\nformally proven in Coq as a part of the MetaCoq project. Although it plays an\nimportant role in an ongoing completeness proof for a realistic typing\nalgorithm, the interest of bidirectionality is wider, as it gives insights and\nstructure when trying to prove properties on CIC or design variations and\nextensions. In particular, we put forward constrained inference, an\nintermediate between the usual inference and checking judgements, to handle the\npresence of computation in types.\n"}
+{"abstract": "  Federated learning allows distributed medical institutions to collaboratively\nlearn a shared prediction model with privacy protection. While at clinical\ndeployment, the models trained in federated learning can still suffer from\nperformance drop when applied to completely unseen hospitals outside the\nfederation. In this paper, we point out and solve a novel problem setting of\nfederated domain generalization (FedDG), which aims to learn a federated model\nfrom multiple distributed source domains such that it can directly generalize\nto unseen target domains. We present a novel approach, named as Episodic\nLearning in Continuous Frequency Space (ELCFS), for this problem by enabling\neach client to exploit multi-source data distributions under the challenging\nconstraint of data decentralization. Our approach transmits the distribution\ninformation across clients in a privacy-protecting way through an effective\ncontinuous frequency space interpolation mechanism. With the transferred\nmulti-source distributions, we further carefully design a boundary-oriented\nepisodic learning paradigm to expose the local learning to domain distribution\nshifts and particularly meet the challenges of model generalization in medical\nimage segmentation scenario. The effectiveness of our method is demonstrated\nwith superior performance over state-of-the-arts and in-depth ablation\nexperiments on two medical image segmentation tasks. The code is available at\n\"https://github.com/liuquande/FedDG-ELCFS\".\n"}
+{"abstract": "  Molecular adhesion promoters are a central component of modern coating\nsystems for the corrosion protection of structural materials. They are\ninterface active and form ultrathin corrosion inhibiting and adhesion-promoting\nlayers. Here we utilize thiol-based self-assembled monolayers (SAMs) as model\nsystem for demonstrating a comprehensive combinatorial approach to understand\nmolecular level corrosion protection mechanisms under anodic polarization.\nSpecifically, we compare hydrophilic 11-Mercapto-1-undecanol and hydrophobic\n1-Undecanethiol SAMs and their gold-dissolution inhibiting properties. We can\nshow that the intermolecular forces (hydrophobic vs hydrophilic effects)\ncontrol how SAM layers perform under oxidative conditions. Specifically, using\n\\textit{in situ} electrochemical AFM and a scanning-flow cell coupled to an\nICP-MS a complementary view on both corrosion resistance, as well as on changes\nin surface morphology/adhesion of the SAM is possible. Protection from\noxidative dissolution is higher with hydrophobic SAMs, which detach under\nmicelle formation, while the hydrophilic SAM exhibits lower protective effects\non gold dissolution rates, although it stays intact as highly mobile layer\nunder anodic polarization. The developed multi-technique approach will prove\nuseful for studying the interfacial activity and corrosion suppression\nmechanism of inhibiting molecules on other metals and alloys.\n"}
+{"abstract": "  We report on a novel material, namely two-dimensional (2D)\nV$_{1-x}$Pt$_x$Se$_2$ alloy, exhibiting simultaneously ferromagnetic order and\nRashba spin-orbit coupling. While ferromagnetism is absent in 1T-VSe$_2$ due to\nthe competition with the charge density wave phase, we demonstrate\ntheoretically and experimentally that the substitution of vanadium by platinum\nin VSe$_2$ (10-50 %) to form an homogeneous 2D alloy restores ferromagnetic\norder with Curie temperatures of 6 K for 5 monolayers and 25 K for one\nmonolayer of V$_{0.65}$Pt$_{0.35}$Se$_2$. Moreover, the presence of platinum\natoms gives rise to Rashba spin-orbit coupling in (V,Pt)Se$_2$ providing an\noriginal platform to study the interplay between ferromagnetism and spin-orbit\ncoupling in the 2D limit.\n"}
+{"abstract": "  The present work clarifies a failure of the effective-field theory in\npredicting a false spontaneous long-range order and phase transition of Ising\nnanoparticles, nanoislands, nanotubes and nanowires with either zero- or\none-dimensional magnetic dimensionality. It is conjectured that the standard\nformulation of the effective-field theory due to Honmura and Kaneyoshi\ngenerally predicts for the Ising spin systems a spurious spontaneous long-range\norder with nonzero critical temperature regardless of their magnetic\ndimensionality whenever at least one Ising spin has coordination number greater\nthan two. The failure of the effective-field theory is exemplified on a few\nparadigmatic exactly solved examples of zero- and one-dimensional Ising\nnanosystems: star, cube, decorated hexagon, star of David, branched chain,\nsawtooth chain, two-leg and hexagonal ladders. The presented exact solutions\nillustrate eligibility of a few rigorous analytical methods for exact treatment\nof the Ising nanosystems: exact enumeration, graph-theoretical approach,\ntransfer-matrix method and decoration-iteration transformation. The paper also\nprovides a substantial survey of the scientific literature, in which the\neffective-field theory led to a false prediction of the spontaneous long-range\nordering and phase transition.\n"}
+{"abstract": "  Let $L = \\Delta + V$ be Schr{\\\"o}dinger operator with a non-negative\npotential $V$ on a complete Riemannian manifold $M$. We prove that the conical\nsquare functional associated with $L$ is bounded on $L^p$ under different\nassumptions. This functional is defined by $$ \\mathcal{G}_L (f) (x) = \\left(\n\\int_0^\\infty \\int_{B(x,t^{1/2})} |\\nabla e^{-tL} f(y)|^2 + V |e^{-tL} f(y)|^2\n\\frac{\\mathrm{d}t \\mathrm{d}y}{Vol(y,t^{1/2})} \\right)^{1/2}.$$For $p \\in\n[2,+\\infty)$ we show that it is sufficient to assume that the manifold has the\nvolume doubling property whereas for $p \\in (1,2)$ we need extra assumptions of\n$L^p-L^2$ of diagonal estimates for $\\{ \\sqrt{t} \\nabla e^{-tL}, t\\geq 0 \\}$\nand $ \\{ \\sqrt{t} \\sqrt{V} e^{-tL} , t \\geq 0\\}$.Given a bounded holomorphic\nfunction $F$ on some angular sector, we introduce the generalized conical\nvertical square functional$$\\mathcal{G}_L^F (f) (x) = \\left( \\int_0^\\infty\n\\int_{B(x,t^{1/2})} |\\nabla F(tL) f(y)|^2 + V |F(tL) f(y)|^2 \\frac{\\mathrm{d}t\n\\mathrm{d}y}{Vol(y,t^{1/2})} \\right)^{1/2}$$ and prove its boundedness on $L^p$\nif $F$ has sufficient decay at zero and infinity. We also consider conical\nsquare functions associated with the Poisson semigroup, lower bounds, and make\na link with the Riesz transform.\n"}
+{"abstract": "  The use of a policy and a heuristic function for guiding search can be quite\neffective in adversarial problems, as demonstrated by AlphaGo and its\nsuccessors, which are based on the PUCT search algorithm. While PUCT can also\nbe used to solve single-agent deterministic problems, it lacks guarantees on\nits search effort and it can be computationally inefficient in practice.\nCombining the A* algorithm with a learned heuristic function tends to work\nbetter in these domains, but A* and its variants do not use a policy. Moreover,\nthe purpose of using A* is to find solutions of minimum cost, while we seek\ninstead to minimize the search loss (e.g., the number of search steps). LevinTS\nis guided by a policy and provides guarantees on the number of search steps\nthat relate to the quality of the policy, but it does not make use of a\nheuristic function. In this work we introduce Policy-guided Heuristic Search\n(PHS), a novel search algorithm that uses both a heuristic function and a\npolicy and has theoretical guarantees on the search loss that relates to both\nthe quality of the heuristic and of the policy. We show empirically on the\nsliding-tile puzzle, Sokoban, and a puzzle from the commercial game `The\nWitness' that PHS enables the rapid learning of both a policy and a heuristic\nfunction and compares favorably with A*, Weighted A*, Greedy Best-First Search,\nLevinTS, and PUCT in terms of number of problems solved and search time in all\nthree domains tested.\n"}
+{"abstract": "  The two-dimensional thermoelastic problem of an adiabatic cavity in an\ninfinite isotropic homogeneous medium subjected to uniform heat flux is\nstudied, where the shape of the cavity is characterized by the Laurent\npolynomial. By virtue of a novel tactics, the obtained K-M potentials can be\nexplicitly worked out to satisfy the boundary conditions precisely, and the\npossible translation of the cavity is also available. The new and explicit\nanalytical solutions are compared with the those reported in literature and\nsome serious problems are found and corrected. Finally, some discussions on the\nthermal stress concentration around the tips of three typical cavities are\nprovided.\n"}
+{"abstract": "  Recent investigations have been carried out on critical analyses of beginning\nphysics teachers confronted with questionable explanations. These studies raise\nthe question of the choices made by teachers for their teaching once they have\nbecome aware of one (or more) flaw(s) in the explanations analysed. This\npresentation will focus on the possible conflicts, for beginning teachers,\nbetween various selection criteria declared for their explanations, including:\nappropriateness (from the points of view of internal coherence, logical\ncompleteness and compliance with accepted physical laws) and simplicity. It\nwill introduce the definition of \"mathematical efficiency\" as a criterion for\nassessing an explanation, on the basis of a study whose results show that it is\na priority for some EDs even at the cost of coherence and of simplicity. The\narticle concludes with the implications of the issues addressed for research\nand teacher training\n"}
+{"abstract": "  A covariant, scalar-tensor gravity is constructed such that the static,\nspherically symmetric Rezzolla-Zhidenko metric is an exact solution to the\ntheory. The equations describing gravitational perturbations of this spacetime,\nwhich represents a generic black hole possessing an arbitrary number of hairs,\ncan then be derived. This allows for a self-consistent study of the associated\nquasinormal modes. It is shown that mode spectra are tied to not only the\nnon-Einstein parameters in the metric but also to those that appear at the\nlevel of the action, and that different branches of the exact theory can, in\nsome cases, predict significantly different oscillation frequencies and damping\ntimes. For choices which make the theory appear more like general relativity in\nsome precise sense, we find that a nontrivial Rezzolla-Zhidenko parameter space\nis permissible under current constraints on fundamental ringdown modes observed\nby Advanced LIGO.\n"}
+{"abstract": "  We perform a study of $W$-boson production in polarized proton-proton\ncollisions through next-to-next-to-leading order (NNLO) in perturbative QCD.\nThis calculation is required to extend the extraction of polarized parton\ndistribution functions to NNLO accuracy. We present differential distributions\nat $\\sqrt{s}=510$ GeV, relevant for comparison to measurements from the\nRelativistic Heavy Ion Collider (RHIC). The NNLO QCD corrections significantly\nreduce the scale dependence of the cross section. We compare the longitudinal\nsingle-spin asymmetries as a function of lepton pseudorapidity to RHIC data.\nThe asymmetries exhibit excellent stability under perturbative QCD corrections.\n"}
+{"abstract": "  Nisan and Szegedy (CC 1994) showed that any Boolean function\n$f:\\{0,1\\}^n\\rightarrow \\{0,1\\}$ that depends on all its input variables, when\nrepresented as a real-valued multivariate polynomial $P(x_1,\\ldots,x_n)$, has\ndegree at least $\\log n - O(\\log \\log n)$. This was improved to a tight $(\\log\nn - O(1))$ bound by Chiarelli, Hatami and Saks (Combinatorica 2020). Similar\nstatements are also known for other Boolean function complexity measures such\nas Sensitivity (Simon (FCT 1983)), Quantum query complexity, and Approximate\ndegree (Ambainis and de Wolf (CC 2014)).\n  In this paper, we address this question for \\emph{Probabilistic degree}. The\nfunction $f$ has probabilistic degree at most $d$ if there is a random\nreal-valued polynomial of degree at most $d$ that agrees with $f$ at each input\nwith high probability. Our understanding of this complexity measure is\nsignificantly weaker than those above: for instance, we do not even know the\nprobabilistic degree of the OR function, the best-known bounds put it between\n$(\\log n)^{1/2-o(1)}$ and $O(\\log n)$ (Beigel, Reingold, Spielman (STOC 1991);\nTarui (TCS 1993); Harsha, Srinivasan (RSA 2019)).\n  Here we can give a near-optimal understanding of the probabilistic degree of\n$n$-variate functions $f$, \\emph{modulo} our lack of understanding of the\nprobabilistic degree of OR. We show that if the probabilistic degree of OR is\n$(\\log n)^c$, then the minimum possible probabilistic degree of such an $f$ is\nat least $(\\log n)^{c/(c+1)-o(1)}$, and we show this is tight up to $(\\log\nn)^{o(1)}$ factors.\n"}
+{"abstract": "  A preference based multi-objective evolutionary algorithm is proposed for\ngenerating solutions in an automatically detected knee point region. It is\nnamed Automatic Preference based DI-MOEA (AP-DI-MOEA) where DI-MOEA stands for\nDiversity-Indicator based Multi-Objective Evolutionary Algorithm). AP-DI-MOEA\nhas two main characteristics: firstly, it generates the preference region\nautomatically during the optimization; secondly, it concentrates the solution\nset in this preference region. Moreover, the real-world vehicle fleet\nmaintenance scheduling optimization (VFMSO) problem is formulated, and a\ncustomized multi-objective evolutionary algorithm (MOEA) is proposed to\noptimize maintenance schedules of vehicle fleets based on the predicted failure\ndistribution of the components of cars. Furthermore, the customized MOEA for\nVFMSO is combined with AP-DI-MOEA to find maintenance schedules in the\nautomatically generated preference region. Experimental results on\nmulti-objective benchmark problems and our three-objective real-world\napplication problems show that the newly proposed algorithm can generate the\npreference region accurately and that it can obtain better solutions in the\npreference region. Especially, in many cases, under the same budget, the Pareto\noptimal solutions obtained by AP-DI-MOEA dominate solutions obtained by MOEAs\nthat pursue the entire Pareto front.\n"}
+{"abstract": "  Sparse regression has recently been applied to enable transfer learning from\nvery limited data. We study an extension of this approach to unsupervised\nlearning -- in particular, learning word embeddings from unstructured text\ncorpora using low-rank matrix factorization. Intuitively, when transferring\nword embeddings to a new domain, we expect that the embeddings change for only\na small number of words -- e.g., the ones with novel meanings in that domain.\nWe propose a novel group-sparse penalty that exploits this sparsity to perform\ntransfer learning when there is very little text data available in the target\ndomain -- e.g., a single article of text. We prove generalization bounds for\nour algorithm. Furthermore, we empirically evaluate its effectiveness, both in\nterms of prediction accuracy in downstream tasks as well as the\ninterpretability of the results.\n"}
+{"abstract": "  A novel technique for divided-pulse amplification is presented in a\nproof-of-principle experiment. A pulse burst, cut out of the pulse train of a\nmode-locked oscillator, is amplified and temporally combined into a single\npulse. High combination efficiency and excellent pulse contrast are\ndemonstrated. The system is mostly fiber-coupled, enabling a high\ninterferometric stability. This approach provides access to the amplitude and\nphase of the individual pulses in the burst to be amplified, potentially\nallowing the compensation of gain saturation and nonlinear phase mismatches\nwithin the burst. Therefore, this technique enables the scaling of the peak\npower and pulse energy of pulsed laser systems beyond currently prevailing\nlimitations.\n"}
+{"abstract": "  Optical cat state plays an essential role in quantum computation and quantum\nmetrology. Here, we experimentally quantify quantum coherence of an optical cat\nstate by means of relative entropy and l_1 norm of coherence in Fock basis\nbased on the prepared optical cat state at rubidium D1 line. By transmitting\nthe optical cat state through a lossy channel, we also demonstrate the\nrobustness of quantum coherence of optical cat state in the presence of loss,\nwhich is different from the decoherence properties of fidelity and Wigner\nfunction negativity of the optical cat state. Our results confirm that quantum\ncoherence of optical cat states is robust against loss and pave the way for the\napplication with optical cat states.\n"}
+{"abstract": "  Software designers and developers are increasingly relying on application\nframeworks as first-class design concepts. They instantiate the services that\nframeworks provide to implement various architectural tactics and patterns. One\nof the challenges in using frameworks for such tasks is the difficulty of\nlearning and correctly using frameworks' APIs. This paper introduces a\nlearning-based approach called ArCode to help novice programmers correctly use\nframeworks' APIs to implement architectural tactics and patterns. ArCode has\nseveral novel components: a graph-based approach for learning specification of\na framework from a limited number of training software, a program analysis\nalgorithm to eliminate erroneous training data, and a recommender module to\nhelp programmers use APIs correctly and identify API misuses in their programs.\nWe evaluated our technique across two popular frameworks: JAAS security\nframework used for authentication and authorization tactic and Java RMI\nframework used to enable remote method invocation between client and server and\nother object-oriented patterns. Our evaluation results show (i) the feasibility\nof using ArCode to learn the specification of a framework; (ii) ArCode\ngenerates accurate recommendations for finding the next API call to implement\nan architectural tactic/pattern based on the context of the programmer's code;\n(iii) it accurately detects API misuses in the code that implements a\ntactic/pattern and provides fix recommendations. Comparison of ArCode with two\nprior techniques (MAPO and GrouMiner) on API recommendation and misuse\ndetection shows that ArCode outperforms these approaches.\n"}
+{"abstract": "  A bipartite network is a graph structure where nodes are from two distinct\ndomains and only inter-domain interactions exist as edges. A large number of\nnetwork embedding methods exist to learn vectorial node representations from\ngeneral graphs with both homogeneous and heterogeneous node and edge types,\nincluding some that can specifically model the distinct properties of bipartite\nnetworks. However, these methods are inadequate to model multiplex bipartite\nnetworks (e.g., in e-commerce), that have multiple types of interactions (e.g.,\nclick, inquiry, and buy) and node attributes. Most real-world multiplex\nbipartite networks are also sparse and have imbalanced node distributions that\nare challenging to model. In this paper, we develop an unsupervised Dual\nHyperGraph Convolutional Network (DualHGCN) model that scalably transforms the\nmultiplex bipartite network into two sets of homogeneous hypergraphs and uses\nspectral hypergraph convolutional operators, along with intra- and\ninter-message passing strategies to promote information exchange within and\nacross domains, to learn effective node embedding. We benchmark DualHGCN using\nfour real-world datasets on link prediction and node classification tasks. Our\nextensive experiments demonstrate that DualHGCN significantly outperforms\nstate-of-the-art methods, and is robust to varying sparsity levels and\nimbalanced node distributions.\n"}
+{"abstract": "  As AI-based medical devices are becoming more common in imaging fields like\nradiology and histology, interpretability of the underlying predictive models\nis crucial to expand their use in clinical practice. Existing heatmap-based\ninterpretability methods such as GradCAM only highlight the location of\npredictive features but do not explain how they contribute to the prediction.\nIn this paper, we propose a new interpretability method that can be used to\nunderstand the predictions of any black-box model on images, by showing how the\ninput image would be modified in order to produce different predictions. A\nStyleGAN is trained on medical images to provide a mapping between latent\nvectors and images. Our method identifies the optimal direction in the latent\nspace to create a change in the model prediction. By shifting the latent\nrepresentation of an input image along this direction, we can produce a series\nof new synthetic images with changed predictions. We validate our approach on\nhistology and radiology images, and demonstrate its ability to provide\nmeaningful explanations that are more informative than GradCAM heatmaps. Our\nmethod reveals the patterns learned by the model, which allows clinicians to\nbuild trust in the model's predictions, discover new biomarkers and eventually\nreveal potential biases.\n"}
+{"abstract": "  In this survey we present applications of the ideas of complement and\nneighborhood in the theory embeddings of manifolds into Euclidean space (in\ncodimension at least three). We describe how the combination of these ideas\ngives a reduction of embeddability and isotopy problems to algebraic problems.\nWe present a more clarified exposition of the Browder-Levine theorem on\nrealization of normal systems. Most of the survey is accessible to\nnon-specialists in the theory of embeddings.\n"}
+{"abstract": "  We study random quantum circuits and their rate of producing bipartite\nentanglement, specifically with respect to the choice of 2-qubit gates and the\norder (protocol) in which these are applied. The problem is mapped to a\nMarkovian process and proved that there are large spectral equivalence classes\n-- different configurations have the same spectrum. Optimal gates and the\nprotocol that generate entanglement with the fastest theoretically possible\nrate are identified. Relaxation towards the asymptotic thermal entanglement\nproceeds via a series of phase transitions in the local relaxation rate, which\nis a consequence of non-Hermiticity. In particular, non-Hermiticity can cause\nthe rate to be either faster, or, even more interestingly, slower than\npredicted by the matrix eigenvalue gap. This is caused by an exponential in\nsystem size explosion of expansion coefficient sizes resulting in a 'phantom'\neigenvalue, and is due to non-orthogonality of non-Hermitian eigenvectors. We\nnumerically demonstrate that the phenomenon occurs also in random circuits with\nnon-optimal generic gates, random U(4) gates, and also without spatial or\ntemporal randomness, suggesting that it could be of wide importance also in\nother non-Hermitian settings, including correlations.\n"}
+{"abstract": "  Observations made in dusty plasma experiments suggest that an ensemble of\nelectrically charged solid particles, confined in an elongated trap, develops\nstructural inhomogeneities. With narrowing the trap the particles tend to form\nlayers oriented parallel with the trap walls. In this work we present\ntheoretical and numerical results on the structure of three-dimensional\nmany-particle systems with screened Coulomb (Yukawa) inter-particle interaction\nin the strongly coupled liquid phase, confined in one-dimensional harmonic\ntrap, forming quasi-2D configurations. Particle density profiles are calculated\nby means of the hypernetted chain approximation (HNC), showing clear signs of\nlayer formation. The mechanism behind the formation of layer structure is\ndiscussed and a method to predict the number of layers is presented. Molecular\ndynamics (MD) simulations provide validation of the theoretical results and\ndetailed microscopic insights.\n"}
+{"abstract": "  Scientists who study how the brain solves problems have recently verified\nthat, because of stringent limitations in working memory, where the brain\nsolves problems, students must apply facts and algorithms that have previously\nbeen well memorized to reliably solve problems of any complexity. This is a\nparadigm shift: A change in the fundamental understanding of how the brain\nsolves problems and how we can best guide students to learn to solve problems\nin the physical sciences. One implication is that for students, knowledge of\nconcepts and big ideas is not sufficient to solve most problems assigned in\nphysics and chemistry courses for STEM majors. To develop an intuitive sense of\nwhich fundamentals to recall when, first students must make the fundamental\nrelationships of a topic recallable with automaticity then apply those\nfundamentals to solving problems in a variety of distinctive contexts. Based on\nthese findings, cognitive science has identified strategies that speed learning\nand assist in retention of physics and chemistry. Experiments will be suggested\nby which instructors can test science-informed methodologies.\n"}
+{"abstract": "  We report the results of a multi-year spectroscopic and photometric\nmonitoring campaign of two luminous quasars, PG~0923+201 and PG~1001+291, both\nlocated at the high-luminosity end of the broad-line region (BLR)\nsize-luminosity relation with optical luminosities above $10^{45}~{\\rm\nerg~s^{-1}}$. PG~0923+201 is for the first time monitored, and PG~1001+291 was\npreviously monitored but our campaign has a much longer temporal baseline. We\ndetect time lags of variations of the broad H$\\beta$, H$\\gamma$, Fe {\\sc ii}\nlines with respect to those of the 5100~{\\AA} continuum. The velocity-resolved\ndelay map of H$\\beta$ in PG~0923+201 indicates a complicated structure with a\nmix of Keplerian disk-like motion and outflow, and the map of H$\\beta$ in\nPG~1001+291 shows a signature of Keplerian disk-like motion. Assuming a virial\nfactor of $f_{\\rm BLR}=1$ and FWHM line widths, we measure the black hole mass\nto be $118_{-16}^{+11}\\times 10^7 M_{\\odot}$ for PG~0923+201 and\n$3.33_{-0.54}^{+0.62}\\times 10^7 M_{\\odot}$ for PG~1001+291. Their respective\naccretion rates are estimated to be $0.21_{-0.07}^{+0.06} \\times L_{\\rm\nEdd}\\,c^{-2}$ and $679_{-227}^{+259}\\times L_{\\rm Edd}\\,c^{-2}$, indicating\nthat PG~0923+201 is a sub-Eddington accretor and PG~1001+291 is a\nsuper-Eddington accretor. While the H$\\beta$ time lag of PG~0923+201 agrees\nwith the size-luminosity relation, the time lag of PG~1001+291 shows a\nsignificant deviation, confirming that in high-luminosity AGN the BLR size\ndepends on both luminosity and Eddington ratio. Black hole mass estimates from\nsingle AGN spectra will be over-estimated at high luminosities and redshifts if\nthis effect is not taken into account.\n"}
+{"abstract": "  This paper deals with the formation of a bound dineutron in the outgoing\nchannel of the $^{159}$Tb(n,$^2$n)$^{158g}$Tb nuclear reaction followed by\nassumed transformations of this reaction products. Such nuclear processes were\nstudied in detail from the point of view of $^{160}$Tb / $^{160}$Dy /\n$^{160}$Ho radioactivity versus time dependence. Based on some signs of fusion\nprocess between heavier nuclei ($^{158}$Tb and/or $^{158}$Gd) and the deuteron,\nthat is a bound dineutron decay product, the mathematical model, including\nthree systems of differential equations, was developed to describe experimental\ndata. This development requires a reasonable estimate of the half-life of a\nbound dineutron, which was found to be equal 5,877 s as the greatest. We\nmathematically modeled the delayed in time experimentally observed buildup of\n$^{160}$Tb radioactivity with a maximum at about 495 d since a neutron\nirradiation completion of Tb sample, based on the similarity with the\nparent-daughter nuclei radioactivity decay and nuclear accumulation processes.\n"}
+{"abstract": "  In the present paper, we give some characterizations by considering $*$-Ricci\nsoliton as a Kenmotsu metric. We prove that if a Kenmotsu manifold represents\nan almost $*$-Ricci soliton with the potential vector field $V$ is a Jacobi\nalong the Reeb vector field, then it is a steady $*$-Ricci soliton. Next, we\nshow that a Kenmotsu matric endowed an almost $*$-Ricci soliton is Einstein\nmetric if it is $\\eta$-Einstein or the potential vector field $V$ is collinear\nto the Reeb vector field or $V$ is an infinitesimal contact transformation.\n"}
+{"abstract": "  We present a parallel algorithm for permanent mod 2^k of a matrix of\nunivariate integer polynomials. It places the problem in ParityL subset of\nNC^2. This extends the techniques of [Valiant], [Braverman, Kulkarni, Roy] and\n[Bj\\\"orklund, Husfeldt], and yields a (randomized) parallel algorithm for\nshortest 2-disjoint paths improving upon the recent result from (randomized)\npolynomial time.\n  We also recognize the disjoint paths problem as a special case of finding\ndisjoint cycles, and present (randomized) parallel algorithms for finding a\nshortest cycle and shortest 2-disjoint cycles passing through any given fixed\nnumber of vertices or edges.\n"}
+{"abstract": "  In this paper, we present a toolchain to design, execute, and verify robot\nbehaviors. The toolchain follows the guidelines defined by the EU H2020 project\nRobMoSys and encodes the robot deliberation as a Behavior Tree (BT), a directed\ntree where the internal nodes model behavior composition and leaf nodes model\naction or measurement operations. Such leaf nodes take the form of a statechart\n(SC), which runs in separate threads, whose states perform basic arithmetic\noperations and send commands to the robot. The toolchain provides the ability\nto define a runtime monitor for a given system specification that warns the\nuser whenever a given specification is violated.\n  We validated the toolchain in a simulated experiment that we made\nreproducible in an OS-virtualization environment.\n"}
+{"abstract": "  Community search aims at finding densely connected subgraphs for query\nvertices in a graph. While this task has been studied widely in the literature,\nmost of the existing works only focus on finding homogeneous communities rather\nthan heterogeneous communities with different labels. In this paper, we\nmotivate a new problem of cross-group community search, namely Butterfly-Core\nCommunity (BCC), over a labeled graph, where each vertex has a label indicating\nits properties and an edge between two vertices indicates their cross\nrelationship. Specifically, for two query vertices with different labels, we\naim to find a densely connected cross community that contains two query\nvertices and consists of butterfly networks, where each wing of the butterflies\nis induced by a k-core search based on one query vertex and two wings are\nconnected by these butterflies. Indeed, the BCC structure admits the structure\ncohesiveness and minimum diameter, and thus can effectively capture the\nheterogeneous and concise collaborative team. Moreover, we theoretically prove\nthis problem is NP-hard and analyze its non-approximability. To efficiently\ntackle the problem, we develop a heuristic algorithm, which first finds a BCC\ncontaining the query vertices, then iteratively removes the farthest vertices\nto the query vertices from the graph. The algorithm can achieve a\n2-approximation to the optimal solution. To further improve the efficiency, we\ndesign a butterfly-core index and develop a suite of efficient algorithms for\nbutterfly-core identification and maintenance as vertices are eliminated.\nExtensive experiments on eight real-world networks and four novel case studies\nvalidate the effectiveness and efficiency of our algorithms.\n"}
+{"abstract": "  We present analyses of Spitzer observations of 29P/Schwassmann-Wachmann 1\nusing 16 $\\mu$m IRS \"blue\" peak-up (PU) and 24 $\\mu$m and 70 $\\mu$m MIPS images\nobtained on UT 2003 November 23 and 24 that characterize the Centaur's\nlarge-grain (10-100 $\\mu$m) dust coma during a time of non-outbursting\n\"quiescent\" activity. Estimates of $\\epsilon f \\rho$ for each band (16 $\\mu$m\n(2600 $\\pm$ 43 cm), 24 $\\mu$m (5800 $\\pm$ 63 cm), and 70 $\\mu$m (1800 $\\pm$ 900\ncm)) follow the trend between nucleus size vs. $\\epsilon f \\rho$ that was\nobserved for the WISE/NEOWISE comet ensemble. A coma model was used to derive a\ndust production rate in the range of 50-100 kg/s. For the first time, a color\ntemperature map of SW1's coma was constructed using the 16 $\\mu$m and 24 $\\mu$m\nimaging data. With peaks at $\\sim$ 140K, this map implies that coma water ice\ngrains should be slowly sublimating and producing water gas in the coma. We\nanalyzed the persistent 24 $\\mu$m \"wing\" (a curved southwestern coma) feature\nat 352,000 km (90$''$) from the nucleus attributed by Stansberry et al. (2004)\nto nucleus rotation and instead propose that it is largely created by solar\nradiation pressure and gravity acting on micron sized grains. We performed coma\nremoval to the 16 $\\mu$m PU image in order to refine the nucleus' emitted\nthermal flux. A new application of the Near Earth Asteroid Thermal Model\n(NEATM; Harris 1998) at five wavelengths (5.730 $\\mu$m, 7.873 $\\mu$m, 15.80\n$\\mu$m, 23.68 $\\mu$m, and 71.42 $\\mu$m) was then used to refine SW1's effective\nradius measurement to $R = 32.3 \\pm 3.1$ km and infrared beaming parameter to\n$\\eta = 1.1 \\pm 0.2$, respectively.\n"}
+{"abstract": "  Polymers are widely-studied materials with diverse properties and\napplications determined by different molecular structures. It is essential to\nrepresent these structures clearly and explore the full space of achievable\nchemical designs. However, existing approaches are unable to offer\ncomprehensive design models for polymers because of their inherent scale and\nstructural complexity. Here, we present a parametric, context-sensitive grammar\ndesigned specifically for the representation and generation of polymers. As a\ndemonstrative example, we implement our grammar for polyurethanes. Using our\nsymbolic hypergraph representation and 14 simple production rules, our\nPolyGrammar is able to represent and generate all valid polyurethane\nstructures. We also present an algorithm to translate any polyurethane\nstructure from the popular SMILES string format into our PolyGrammar\nrepresentation. We test the representative power of PolyGrammar by translating\na dataset of over 600 polyurethane samples collected from literature.\nFurthermore, we show that PolyGrammar can be easily extended to the other\ncopolymers and homopolymers such as polyacrylates. By offering a complete,\nexplicit representation scheme and an explainable generative model with\nvalidity guarantees, our PolyGrammar takes an important step toward a more\ncomprehensive and practical system for polymer discovery and exploration. As\nthe first bridge between formal languages and chemistry, PolyGrammar also\nserves as a critical blueprint to inform the design of similar grammars for\nother chemistries, including organic and inorganic molecules.\n"}
+{"abstract": "  We investigate the presence of a black hole black string phase transition in\nEinstein Gauss Bonnet (EGB) gravity in the large dimension limit. The merger\npoint is the static spacetime connecting the black string phase with the black\nhole phase. We consider several ranges of the Gauss-Bonnet parameter. We find\nthat there is a range when the Gauss-Bonnet corrections are subordinate to the\nEinstein gravity terms in the large dimension limit, and yet the merger point\ngeometry does not approach a black hole away from the neck. We cannot rule out\na topology changing phase transition as argued by Kol. However as the merger\npoint geometry does not approach the black hole geometry asymptotically it is\nnot obvious that the transition is directly to a black hole phase. We also\ndemonstrate that for another range of the Gauss-Bonnet parameter, the merger\npoint geometry approaches the black hole geometry asymptotically when a certain\nparameter depending on the Gauss-Bonnet parameter $\\alpha$ and on the\nparameters in the Einstein-Gauss-Bonnet black hole metric is small enough.\n"}
+{"abstract": "  NLP community is currently investing a lot more research and resources into\ndevelopment of deep learning models than training data. While we have made a\nlot of progress, it is now clear that our models learn all kinds of spurious\npatterns, social biases, and annotation artifacts. Algorithmic solutions have\nso far had limited success. An alternative that is being actively discussed is\nmore careful design of datasets so as to deliver specific signals. This\nposition paper maps out the arguments for and against data curation, and argues\nthat fundamentally the point is moot: curation already is and will be\nhappening, and it is changing the world. The question is only how much thought\nwe want to invest into that process.\n"}
+{"abstract": "  In this paper we resolve the Alon-Jaeger-Tarsi conjecture for sufficiently\nlarge primes. Namely, we show that for any finite field $\\mathbb{F}$ of size\n$61<|\\mathbb F|\\ne 79$ and any nonsingular matrix $M$ over $\\mathbb{F}$ there\nexists a vector $x$ such that neither $x$ nor $Ax$ has a 0 component.\n"}
+{"abstract": "  State-of-the-art reinforcement learning (RL) algorithms suffer from high\nsample complexity, particularly in the sparse reward case. A popular strategy\nfor mitigating this problem is to learn control policies by imitating a set of\nexpert demonstrations. The drawback of such approaches is that an expert needs\nto produce demonstrations, which may be costly in practice. To address this\nshortcoming, we propose Probabilistic Planning for Demonstration Discovery\n(P2D2), a technique for automatically discovering demonstrations without access\nto an expert. We formulate discovering demonstrations as a search problem and\nleverage widely-used planning algorithms such as Rapidly-exploring Random Tree\nto find demonstration trajectories. These demonstrations are used to initialize\na policy, then refined by a generic RL algorithm. We provide theoretical\nguarantees of P2D2 finding successful trajectories, as well as bounds for its\nsampling complexity. We experimentally demonstrate the method outperforms\nclassic and intrinsic exploration RL techniques in a range of classic control\nand robotics tasks, requiring only a fraction of exploration samples and\nachieving better asymptotic performance.\n"}
+{"abstract": "  Airfoil stall plays a central role in the design of safe and efficient\nlifting surfaces. We typically distinguish between static and dynamic stall\nbased on the unsteady rate of change of an airfoil's angle of attack. Despite\nthe somewhat misleading denotation, the force and flow development of an\nairfoil undergoing static stall are highly unsteady and the boundary with\ndynamic stall is not clearly defined. We experimentally investigate the forces\nacting on a two-dimensional airfoil that is subjected to two manoeuvres leading\nto static stall: a slow continuous increase in angle of attack with a reduced\npitch rate of 1.3e-4 and a step-wise increase in angle of attack from\n14.2{\\deg} to 14.8{\\deg} within 0.04 convective times. We systematically\nquantify the stall reaction delay for many repetitions of these two manoeuvres.\nThe onset of flow stall is marked by the distinct drop in the lift coefficient.\nThe reaction delay for the slow continuous ramp-up manoeuvre is not influenced\nby the blade kinematics and its occurrence histogram is normally distributed\naround 32 convective times. The static reaction delay is compared with dynamic\nstall delays for dynamic ramp-up motions with reduced pitch rates ranging from\n9e-4 to 0.14 and for dynamic sinusoidal pitching motions of different airfoils\nat higher Reynolds numbers up to 1e6. The stall delays for all conditions\nfollows the same power law decrease from 32 convective times for the most\nsteady case down to an asymptotic value of 3 for reduced pitch rates above\n0.04. Static stall is not phenomenologically different than dynamic stall and\nis merely a typical case of stall for low pitch rates. Based on our results, we\nsuggest that conventional measurements of the static stall angle and the static\nload curves should be conducted using a continuous and uniform ramp-up motion\nat a reduced frequency around 1e-4.\n"}
+{"abstract": "  By using the tight-binding model and non-equilibrium Green's function method\n(NEGF), we study the band structures and transport properties of a silicene\nnanoribbon with a line defect where a bulk energy gap is opened due to the\nsublattice symmetry breaking. The flat subband bends downwards or upwards due\nto the effect of the line defect. The spin-orbit coupling induces quantum spin\nHall states. Especially, the energy band depends on the distance between the\nline defect and the edge of the nanoribbon. The effects of the on-site energies\non the band spectra of the two defect configurations are different. There\nalways exists one band gap for different on-site energies for the defect\nconfiguration of case 1. However, a gapless state and a band gap can be\nmodulated by changing the on-site energy, the sublattice potential and\nspin-orbit couplings for the defect configuration of case 2. Accordingly, the\nvariation trends of the conductance including zero conductance can be well\nunderstood in terms of the combined effect of the sublattice potential, the\non-site energy and spin-orbit couplings on the band structures. Thus it is easy\nand effective to modulate the transport property of the silicene nanoribbon\nwith the defect configuration of case 2 by utilizing the sublattice potential,\nthe on-site energy and spin-orbit couplings. This study is of great\nsignificance for the fabrication and the modulation of the transport property\nof silicene-based devices.\n"}
+{"abstract": "  We prove the existence of ground state solutions to critical growth\n$p$-Laplacian and fractional $p$-Laplacian problems that are nonresonant at\nzero.\n"}
+{"abstract": "  Let $S$ be a commutative semigroup, $K$ a quadratically closed commutative\nfield of characteristic different from $2$, $G$ a $2$-cancellative abelian\ngroup and $H$ an abelian group uniquely divisible by $2$. The aim of this paper\nis to determine the general solution $f:S^2\\to K$ of the d'Alembert type\nequation: $$ f(x+y,z+w)+f(x+\\sigma(y),z+\\tau(w)) =2f(x,z)f(y,w),\\quad\\quad\n(x,y,z,w\\in S) $$ the general solution $f:S^2\\to G$ of the Jensen type\nequation: $$ f(x+y,z+w)+f(x+\\sigma(y),z+\\tau(w)) =2f(x,z),\\quad\\quad\n(x,y,z,w\\in S) $$ the general solution $f:S^2\\to H$ of the quadratic type\nequation quation: $$ f(x+y,z+w)+f(x+\\sigma(y),z+\\tau(w))\n=2f(x,z)+2f(y,w),\\quad\\quad (x,y,z,w\\in S) $$ where $\\sigma,\\tau: S\\to S$ are\ntwo involutions.\n"}
+{"abstract": "  In repeated measures factorial designs involving clustered units, parametric\nmethods such as linear mixed effects models are used to handle within subject\ncorrelations. However, assumptions of these parametric models such as\ncontinuity and normality are usually hard to come by in many cases. The\nhomoscedasticity assumption is rather hard to verify in practice. Furthermore,\nthese assumptions may not even be realistic when data are measured in a\nnon-metric scale as commonly happens, for example, in Quality of Life outcomes.\nIn this article, nonparametric effect-size measures for clustered data in\nfactorial designs with pre-post measurements will be introduced. The\neffect-size measures provide intuitively-interpretable and informative\nprobabilistic comparisons of treatment and time effects. The dependence among\nobservations within a cluster can be arbitrary across treatment groups. The\neffect-size estimators along with their asymptotic properties for computing\nconfidence intervals and performing hypothesis tests will be discussed.\nANOVA-type statistics with $\\chi^2$ approximation that retain some of the\noptimal asymptotic behaviors in small samples are investigated. Within each\ntreatment group, we allow some clusters to involve observations measured on\nboth pre and post intervention periods (referred to as complete clusters),\nwhile others to contain observations from either pre or post intervention\nperiod only (referred to as incomplete clusters). Our methods are shown to be,\nparticularly effective in the presence of multiple forms of clustering. The\ndeveloped nonparametric methods are illustrated with data from a three-arm\nRandomized Trial of Indoor Wood Smoke reduction. The study considered two\nactive treatments to improve asthma symptoms of kids living in homes that use\nwood stove for heating.\n"}
+{"abstract": "  Top-quark pair production is central to many facets of LHC physics. At\nleading order, the top and anti-top are produced in a back-to-back topology,\nhowever this topology accounts only for a minority of $t \\bar t$ events with\nTeV-scale momentum transfer. The remaining events instead involve the splitting\nof an initial or final-state gluon to $t \\bar t$. We provide simple\nquantitative arguments that explain why this is the case and examine the\ninterplay between different topologies and a range of variables that\ncharacterise the event hardness. We then develop a method to classify the\ntopologies of individual events and use it to illustrate our findings in the\ncontext of simulated events, using both top partons and suitably defined\nfiducial tops. For events with large $t \\bar t$ invariant mass, we comment on\nadditional features that have important experimental and theoretical\nimplications.\n"}
+{"abstract": "  Cervical cancer is one of the most deadly and common diseases among women\nworldwide. It is completely curable if diagnosed in an early stage, but the\ntedious and costly detection procedure makes it unviable to conduct\npopulation-wise screening. Thus, to augment the effort of the clinicians, in\nthis paper, we propose a fully automated framework that utilizes Deep Learning\nand feature selection using evolutionary optimization for cytology image\nclassification. The proposed framework extracts Deep feature from several\nConvolution Neural Network models and uses a two-step feature reduction\napproach to ensure reduction in computation cost and faster convergence. The\nfeatures extracted from the CNN models form a large feature space whose\ndimensionality is reduced using Principal Component Analysis while preserving\n99% of the variance. A non-redundant, optimal feature subset is selected from\nthis feature space using an evolutionary optimization algorithm, the Grey Wolf\nOptimizer, thus improving the classification performance. Finally, the selected\nfeature subset is used to train an SVM classifier for generating the final\npredictions. The proposed framework is evaluated on three publicly available\nbenchmark datasets: Mendeley Liquid Based Cytology (4-class) dataset, Herlev\nPap Smear (7-class) dataset, and the SIPaKMeD Pap Smear (5-class) dataset\nachieving classification accuracies of 99.47%, 98.32% and 97.87% respectively,\nthus justifying the reliability of the approach. The relevant codes for the\nproposed approach can be found in:\nhttps://github.com/DVLP-CMATERJU/Two-Step-Feature-Enhancement\n"}
+{"abstract": "  We propose a new type of structure for singly heavy baryons of $Qqq\\bar{q}q$\nin addition to the conventional one of $Qqq$. Based on chiral symmetry of the\nlight quarks, we show that the $Qqq\\bar{q}q$ baryon offers a novel picture for\nheavy quark spin-singlet and flavor-antisymmetric baryons. By making use of the\neffective Lagrangian approach, we find $\\Lambda_c(2765)$ and $\\Xi_c(2967)$ are\nmostly $Qqq\\bar{q}q$ while $\\Lambda_c(2286)$ and $\\Xi_c(2470)$ are mostly\n$Qqq$. The masses of negative-parity baryons are predicted. We also derive a\nsum rule and the extended Goldberger-Treiman relation that the masses of the\nbaryons satisfy. Furthermore, a mass degeneracy of parity partners of the\nbaryons with the restoration of chiral symmetry is discussed. These are the\nunique features that the conventional picture of radial excitation in the quark\nmodel does not accommodate. Our findings provide useful information not only\nfor future experiments but also for future lattice simulations on diquarks.\n"}
+{"abstract": "  For more than three decades, nearly free electron elemental metals have been\na topic of debate because the computed bandwidths are significantly wider in\nthe local density approximation to density-functional theory (DFT) than\nindicated by angle-resolved photoemission experiments. Here, we systematically\ninvestigate this using first-principles calculations for alkali and\nalkaline-earth metals using DFT and various beyond-DFT methods such as\nmeta-GGA, G$_0$W$_0$, B3LYP, and DFT+eDMFT. We find that the static non-local\nexchange and correlation, as partly included in the B3LYP hybrid functional,\nsignificantly increase the bandwidths even compared to LDA, while the\nG$_0$W$_0$ bands are only slightly narrower than in LDA. The agreement with the\nARPES is best when the local approximation to the self-energy is used in the\nDFT+eDMFT method. We infer that even moderately correlated systems with\npartially occupied s-orbitals, which were assumed to approximate the uniform\nelectron gas, are very well described in terms of short-range dynamical\ncorrelations that are only local to an atom.\n"}
+{"abstract": "  Multiple Sclerosis (MS) and microvascular leukoencephalopathy are two\ndistinct neurological conditions, the first caused by focal autoimmune\ninflammation in the central nervous system, the second caused by chronic white\nmatter damage from atherosclerotic microvascular disease. Both conditions lead\nto signal anomalies on Fluid Attenuated Inversion Recovery (FLAIR) magnetic\nresonance (MR) images, which can be distinguished by an expert\nneuroradiologist, but which can look very similar to the untrained eye as well\nas in the early stage of both diseases. In this paper, we attempt to train a\n3-dimensional deep neural network to learn the specific features of both\ndiseases in an unsupervised manner. For this manner, in a first step we train a\ngenerative neural network to create artificial MR images of both conditions\nwith approximate explicit density, using a mixed dataset of multiple sclerosis,\nleukoencephalopathy and healthy patients containing in total 5404 volumes of\n3096 patients. In a second step, we distinguish features between the different\ndiseases in the latent space of this network, and use them to classify new\ndata.\n"}
+{"abstract": "  The holographic state of matter exists in the quantum gravitational regime,\nwith black holes as the example. In this essay, we provide a microscopic\nderivation to the holographic thermodynamics and further discuss its\nimplications for quantum gravity. Especially, we establish the link between\nholographic entropy and dimensional reduction. It seems that the fundamental\nphysics behind black holes and the very early universe is $1+1$ dimensional.\n"}
+{"abstract": "  Gravitational waves emitted by black hole binary inspiral and mergers enable\nunprecedented strong-field tests of gravity, requiring accurate theoretical\nmodelling of the expected signals in extensions of General Relativity. In this\npaper we model the gravitational wave emission of inspiraling binaries in\nscalar Gauss-Bonnet gravity theories. Going beyond the weak-coupling\napproximation, we derive the gravitational waveform to first post-Newtonian\norder beyond the quadrupole approximation and calculate new contributions from\nnonlinear curvature terms. We quantify the effect of these terms and provide\nready-to-implement gravitational wave and scalar waveforms as well as the\nFourier domain phase for quasi-circular binaries. We also perform a parameter\nspace study, which indicates that the values of black hole scalar charges play\na crucial role in the detectability of deviation from General Relativity. We\nalso compare the scalar waveforms to numerical relativity simulations to assess\nthe impact of the relativistic corrections to the scalar radiation. Our results\nprovide important foundations for future precision tests of gravity.\n"}
+{"abstract": "  We study reductive subgroups $H$ of a reductive linear algebraic group $G$ --\npossibly non-connected -- such that $H$ contains a regular unipotent element of\n$G$. We show that under suitable hypotheses, such subgroups are $G$-irreducible\nin the sense of Serre. This generalizes results of Malle, Testerman and\nZalesski. We obtain analogous results for Lie algebras and for finite groups of\nLie type. Our proofs are short, conceptual and uniform.\n"}
+{"abstract": "  State-of-the-art cosmological simulations on classical computers are limited\nby time, energy, and memory usage. Quantum computers can perform some\ncalculations exponentially faster than classical computers, using exponentially\nless energy and memory, and may enable extremely large simulations that\naccurately capture the whole dynamic range of structure in the Universe within\nstatistically representative cosmic volumes. However, not all computational\ntasks exhibit a `quantum advantage'. Quantum circuits act linearly on quantum\nstates, so nonlinearities (e.g. self-gravity in cosmological simulations) pose\na significant challenge. Here we outline one potential approach to overcome\nthis challenge and solve the (nonlinear) Schrodinger-Poisson equations for the\nevolution of self-gravitating dark matter, based on a hybrid quantum-classical\nvariational algorithm framework (Lubasch 2020). We demonstrate the method with\na proof-of-concept mock quantum simulation, envisioning a future where quantum\ncomputers will one day lead simulations of dark matter.\n"}
+{"abstract": "  We reformulate known exotic theories (including theories of fractons) on a\nEuclidean spacetime lattice. We write them using the Villain approach and then\nwe modify them to a convenient range of parameters. The new lattice models are\ncloser to the continuum limit than the original lattice versions. In\nparticular, they exhibit many of the recently found properties of the continuum\ntheories including emergent global symmetries and surprising dualities. Also,\nthese new models provide a clear and rigorous formulation to the continuum\nmodels and their singularities. In appendices, we use this approach to review\nwell-studied lattice models and their continuum limits. These include the\nXY-model, the $\\mathbb{Z}_N$ clock-model, and various gauge theories in diverse\ndimensions. This presentation clarifies the relation between the\ncondensed-matter and the high-energy views of these systems. It emphasizes the\nrole of symmetries associated with the topology of field space, duality, and\nvarious anomalies.\n"}
+{"abstract": "  In the Ehrenfest wind tree model, a point particle moves on the plane and\ncollides with randomly placed fixed square obstacles under the usual law of\ngeometric optics. The particle represents the wind and the squares are the\ntrees. We examine the periodic version of the model. Previous authors analyze\nthe dynamical properties of the model using techniques from algebraic topology\nor ergodic theory. In contrast to these works, we adopt a signal processing\nviewpoint. We describe the phenomenon of the long-term trajectories by using a\n3-state hidden Markov model.\n"}
+{"abstract": "  In an interesting recent work, Kuzborskij and Szepesv\\'ari derived a\nconfidence bound for functions of independent random variables, which is based\non an inequality that relates concentration to squared perturbations of the\nchosen function. Kuzborskij and Szepesv\\'ari also established the\nPAC-Bayes-ification of their confidence bound. Two important aspects of their\nwork are that the random variables could be of unbounded range, and not\nnecessarily of an identical distribution. The purpose of this note is to\nadvertise/discuss these interesting results, with streamlined proofs. This\nexpository note is written for persons who, metaphorically speaking, enjoy the\n\"featured movie\" but prefer to skip the preview sequence.\n"}
+{"abstract": "  A tidal disruption event (TDE) involves the tidal shredding of a star in the\nvicinity of a dormant supermassive black hole. The nearby ($\\approx$230\nmega-parsec) radio-quiet (radio luminosity of $4 \\times 10^{38}$ erg s$^{-1}$)\nAT2019dsg is the first TDE potentially associated with a neutrino event. The\norigin of the non-thermal emission in AT2019dsg remains inconclusive;\npossibilities include a relativistic jet or a sub-relativistic outflow.\nDistinguishing between them can address neutrino production mechanisms. High\nresolution very long baseline interferometry monitoring provides uniquely\nconstraining flux densities and proper motion of the ejecta. A non-relativistic\n(outflow velocity of $\\approx$0.1 $c$) decelerated expansion in a relatively\ndense environment is found to produce the radio emission. Neutrino production\nmay be related to the acceleration of protons by the outflow. The present study\nthus helps exclude jet-related origins for the non-thermal emission and\nneutrino production, and constrains non-jetted scenarios.\n"}
+{"abstract": "  Although the black holes are an integral part of the standard model of\nastrophysics and cosmology, their existence poses some serious fundamental\nproblems. In recent years, several horizonless compact object models were\nproposed to address those issues. As the gravitational wave detectors started\nto observe more and more merger events with a large signal-to-noise ratio,\ngravitational wave spectroscopy could hold the key to uncover the existence of\nthese objects. This is because the late time ringdown signals of horizonless\ncompact objects differ from that of the black holes. In this paper, we study\nthe ringdown properties of charged compact objects and compare them with those\nobtained in the black hole scenario. Since the internal structure and the\nequation of state of these compact objects are largely unknown, we employ\nmembrane paradigm to obtain appropriate boundary conditions for the\nperturbations of these objects. This model can describe the ringdown properties\nof a large variety of compact objects.\n"}
+{"abstract": "  We study learning Censor Markov Random Fields (abbreviated CMRFs). These are\nMarkov Random Fields where some of the nodes are censored (not observed). We\npresent an algorithm for learning high-temperature CMRFs within o(n)\ntransportation distance. Crucially our algorithm makes no assumption about the\nstructure of the graph or the number or location of the observed nodes. We\nobtain stronger results for high girth high-temperature CMRFs as well as\ncomputational lower bounds indicating that our results can not be qualitatively\nimproved.\n"}
+{"abstract": "  A graph's spectral wavelet signature determines a filtration, and\nconsequently an associated set of extended persistence diagrams. We propose a\nframework that optimises the choice of wavelet for a dataset of graphs, such\nthat their associated persistence diagrams capture features of the graphs that\nare best suited to a given data science problem. Since the spectral wavelet\nsignature of a graph is derived from its Laplacian, our framework encodes\ngeometric properties of graphs in their associated persistence diagrams and can\nbe applied to graphs without a priori node attributes. We apply our framework\nto graph classification problems and obtain performances competitive with other\npersistence-based architectures. To provide the underlying theoretical\nfoundations, we extend the differentiability result for ordinary persistent\nhomology to extended persistent homology.\n"}
+{"abstract": "  We present SPUX - a modular framework for Bayesian inference enabling\nuncertainty quantification and propagation in linear and nonlinear,\ndeterministic and stochastic models, and supporting Bayesian model selection.\nSPUX can be coupled to any serial or parallel application written in any\nprogramming language, (e.g. including Python, R, Julia, C/C++, Fortran, Java,\nor a binary executable), scales effortlessly from serial runs on a personal\ncomputer to parallel high performance computing clusters, and aims to provide a\nplatform particularly suited to support and foster reproducibility in\ncomputational science. We illustrate SPUX capabilities for a simple yet\nrepresentative random walk model, describe how to couple different types of\nuser applications, and showcase several readily available examples from\nenvironmental sciences. In addition to available state-of-the-art numerical\ninference algorithms including EMCEE, PMCMC (PF) and SABC, the open source\nnature of the SPUX framework and the explicit description of the hierarchical\nparallel SPUX executors should also greatly simplify the implementation and\nusage of other inference and optimization techniques.\n"}
+{"abstract": "  Real scalar fields with attractive self-interaction may form self-bound\nstates, called oscillons. These dense objects are ubiquitous in leading\ntheories of dark matter and inflation; of particular interest are long-lived\noscillons which survive past $14$ Gyr, offering dramatic astrophysical\nsignatures into the present day. We introduce a new formalism for computing the\nproperties of oscillons with improved accuracy, which we apply to study the\ninternal structure of oscillons and to identify the physical mechanisms\nresponsible for oscillon longevity. In particular, we show how imposing\nrealistic boundary conditions naturally selects a near-minimally radiating\nsolution, and how oscillon longevity arises from its geometry. Further, we\nintroduce a natural vocabulary for the issue of oscillon stability, which we\nuse to predict new features in oscillon evolution. This framework allows for\nnew efficient algorithms, which we use to address questions of whether and to\nwhat extent long-lived oscillons are fine-tuned. Finally, we construct a family\nof potentials supporting ultra-long-lived oscillons, with lifetimes in excess\nof $10^{17}$ years.\n"}
+{"abstract": "  In gauge-Higgs unification (GHU), the 4D Higgs boson appears as a part of the\nfifth dimensional component of 5D gauge field. Recently, an $SO(11)$ GUT\ninspired $SO(5)\\times U(1)\\times SU(3)$ GHU model has been proposed. In the\nGHU, Kaluza-Klein (KK) excited states of neutral vector bosons, photon, $Z$\nboson and $Z_R$ boson, appear as neutral massive vector bosons $Z'$s. The $Z'$\nbosons in the GHU couple to quarks and leptons with large parity violation,\nwhich leads to distinctive polarization dependence in, e.g., cross sections and\nforward-backward asymmetries in $e^-e^+\\to\\mu^-\\mu^+,q\\bar{q}$ processes. In\nthe talk, we discuss fermion pair production in $e^-e^+$ linear collider\nexperiments with polarized $e^-$ and $e^+$ beams in the GUT inspired GHU.\nDeviations from the SM are shown in the early stage of planned international\nlinear collider (ILC) with 250 GeV experiments. The deviations can be tested\nfor the KK mass scale up to about 15 TeV. This talk is mainly based on\nPhys.Rev.D102(2020)015029.\n"}
+{"abstract": "  Wheeler's delayed-choice experiment delays the decision to observe either the\nwave or particle behavior of a photon until after it has entered the\ninterferometer, and the quantum delayed-choice experiment provides the\npossibility of observing the wave and particle behavior simultaneously by\nintroducing quantum control device. We here propose a modified quantum\ndelayed-choice experiment without quantum control or entanglement assistance,\nin which a photon can be prepared in a wave-particle superposition state and\nthe morphing behavior of wave-to-particle transition can be observed easily. It\nis demonstrated that the presented scheme can allow us to rule out classical\nhidden variable models in a device-independent manner via violating dimension\nwitness. We also extend the scheme to the situation of two degrees of freedom,\nfirst constructing a hybrid quantum delayed-choice experiment which enables\nsimultaneous observation of a photon's wave and particle behaviors in different\ndegrees of freedom, and then proposing a scheme to prepare the single-photon\nwave-particle entanglement. This study is not only meaningful to explore the\nwave and particle properties of photons, but also provides potential for the\nresearch of the single-particle nonlocality from the perspective of the\nwave-particle degree of freedom.\n"}
+{"abstract": "  We report on finite-size exact-diagonalization calculations in a Hilbert\nspace defined by the continuum-model flat moir\\'e bands of magic angle twisted\nbilayer graphene (MATBG). For moir\\'e band filling $3>|\\nu|>2$, where\nsuperconductivity is strongest, we obtain evidence that the ground state is a\nspin ferromagnet. Near $|\\nu|=3$, we find Chern insulator ground states that\nhave spontaneous spin, valley, and sublattice polarization, and demonstrate\nthat the anisotropy energy in this order-parameter space is strongly\nband-filling-factor dependent. We emphasize that inclusion of the remote band\nself-energy is necessary for a reliable description of MATBG flat band\ncorrelations.\n"}
+{"abstract": "  This paper introduces a novel Russian speech dataset called Golos, a large\ncorpus suitable for speech research. The dataset mainly consists of recorded\naudio files manually annotated on the crowd-sourcing platform. The total\nduration of the audio is about 1240 hours. We have made the corpus freely\navailable to download, along with the acoustic model with CTC loss prepared on\nthis corpus. Additionally, transfer learning was applied to improve the\nperformance of the acoustic model. In order to evaluate the quality of the\ndataset with the beam-search algorithm, we have built a 3-gram language model\non the open Common Crawl dataset. The total word error rate (WER) metrics\nturned out to be about 3.3% and 11.5%.\n"}
+{"abstract": "  We investigate the evaporation process of a Kerr-de Sitter black hole with\nthe Unruh-Hawking-like vacuum state, which is a realistic vacuum state\nmodelling the evaporation process of a black hole originating from\ngravitational collapse. We also compute the greybody factors for gravitons,\nphotons, and conformal-coupling massless scalar particles by using the analytic\nsolutions of the Teukolsky equation in the Kerr-de Sitter background. It turns\nout that the cosmological constant quenches the amplification factor and it\napproaches to zero towards the critical point where the Nariai and extremal\nlimits merge together. We confirm that even near the critical point, the\nsuperradiance of gravitons is more significant than that of photons and scalar\nparticles. Angular momentum is carried out by particles several times faster\nthan the mass energy decreases. This means that a Kerr-de Sitter black hole\nrapidly spins down to a nearly Schwarzschild-de Sitter black hole before it\ncompletely evaporates. We also compute the time evolution of the\nBekenstein-Hawking entropy. The total entropy of the Kerr-de Sitter black hole\nand cosmological horizon increases with time, which is consistent with the\ngeneralized second law of thermodynamics.\n"}
+{"abstract": "  Inspired by investigations of Bose-Einstein condensates (BECs) produced in\nthe Cold Atom Laboratory (CAL) aboard the International Space Station, we\npresent a study of thermodynamic properties of shell-shaped BECs. Within the\ncontext of a spherically symmetric `bubble trap' potential, we study the\nevolution of the system from small filled spheres to hollow, large, thin shells\nvia the tuning of trap parameters. We analyze the bubble trap spectrum and\nstates, and track the distinct changes in spectra between radial and angular\nmodes across the evolution. This separation of the excitation spectrum provides\na basis for quantifying dimensional cross-over to quasi-2D physics at a given\ntemperature. Using the spectral data, for a range of trap parameters, we\ncompute the critical temperature for a fixed number of particles to form a BEC.\nFor a set of initial temperatures, we also evaluate the change in temperature\nthat would occur in adiabatic expansion from small filled sphere to large thin\nshell were the trap to be dynamically tuned. We show that the system cools\nduring this expansion but that the decrease in critical temperature occurs more\nrapidly, thus resulting in depletion of any initial condensate. We contrast our\nspectral methods with standard semiclassical treatments, which we find must be\nused with caution in the thin-shell limit. With regards to interactions, using\nenergetic considerations and corroborated through Bogoliubov treatments, we\ndemonstrate that they would be less important for thin shells due to reduced\ndensity but vortex physics would become more predominant. Finally, we apply our\ntreatments to traps that realistically model CAL experiments and borrow from\nthe thermodynamic insights found in the idealized bubble case during adiabatic\nexpansion.\n"}
+{"abstract": "  Hardly any software development process is used as prescribed by authors or\nstandards. Regardless of company size or industry sector, a majority of project\nteams and companies use hybrid development methods (short: hybrid methods) that\ncombine different development methods and practices. Even though such hybrid\nmethods are highly individualized, a common understanding of how to\nsystematically construct synergetic practices is missing. In this article, we\nmake a first step towards a statistical construction procedure for hybrid\nmethods. Grounded in 1467 data points from a large-scale practitioner survey,\nwe study the question: What are hybrid methods made of and how can they be\nsystematically constructed? Our findings show that only eight methods and few\npractices build the core of modern software development. Using an 85% agreement\nlevel in the participants' selections, we provide examples illustrating how\nhybrid methods can be characterized by the practices they are made of.\nFurthermore, using this characterization, we develop an initial construction\nprocedure, which allows for defining a method frame and enriching it\nincrementally to devise a hybrid method using ranked sets of practice.\n"}
+{"abstract": "  The remarkable progress in the field of laser spectroscopy induced by the\ninvention of the frequency-comb laser has enabled many new high-precision tests\nof fundamental theory and searches for new physics. Extending frequency-comb\nbased spectroscopy techniques to the vacuum (VUV) and extreme ultraviolet (XUV)\nspectral range would enable measurements in e.g. heavier hydrogen-like systems\nand open up new possibilities for tests of quantum electrodynamics and\nmeasurements of fundamental constants. The main approaches rely on\nhigh-harmonic generation (HHG), which is known to induce spurious phase shifts\nfrom plasma formation. After our initial report (Physical Review Letters 123,\n143001 (2019)), we give a detailed account of how the Ramsey-comb technique is\nused to probe the plasma dynamics with high precision, and enables accurate\nspectroscopy in the VUV. A series of Ramsey fringes is recorded to track the\nphase evolution of a superposition state in xenon atoms, excited by two\nup-converted frequency-comb pulses. Phase shifts of up to 1 rad induced by HHG\nwere observed at ns timescales and with mrad-level accuracy at $110$ nm. Such\nphase shifts could be reduced to a negligible level, enabling us to measure the\n$5p^6 \\rightarrow 5p^5 8s~^2[3/2]_1$ transition frequency in $^{132}Xe$ at 110\nnm (seventh harmonic) with sub-MHz accuracy. The obtained value is $10^4$ times\nmore precise than the previous determination and the fractional accuracy of\n$2.3 \\times 10^{-10}$ is $3.6$ times better than the previous best\nspectroscopic measurement using HHG. The isotope shifts between $^{132}Xe$ and\ntwo other isotopes were determined with an accuracy of $420$ kHz. The method\ncan be readily extended to achieve kHz-level accuracy, e.g. to measure the\n$1S-2S$ transition in $He^+$. Therefore, the Ramsey-comb method shows great\npromise for high-precision spectroscopy of targets requiring VUV and XUV\nwavelengths.\n"}
+{"abstract": "  The Large Area Telescope (LAT) on board the \\Fermi Gamma-ray Space Telescope\n(\\Fermi) shows long-lasting high-energy emission in many gamma-ray bursts\n(GRBs), similar to X-ray afterglows observed by the Neil Gehrels Swift\nObservatory \\citep[\\textit{Swift};][]{gehrels2004}. Some LAT light curves (LCs)\nshow a late-time flattening reminiscent of X-ray plateaus. We explore the\npresence of plateaus in LAT temporally extended emission analyzing GRBs from\nthe second \\lat GRB Catalog \\citep[2FLGC;][]{Ajello2019apj} from 2008 to May\n2016 with known redshifts, and check whether they follow closure relations\ncorresponding to 4 distinct astrophysical environments predicted by the\nexternal forward shock (ES) model. We find that three LCs can be fit by the\nsame phenomenological model used to fit X-ray plateaus \\citep{Willingale2007}\nand show tentative evidence for the existence of plateaus in their high-energy\nextended emission. The most favorable scenario is a slow cooling regime,\nwhereas the preferred density profile for each GRBs varies from a constant\ndensity ISM to a $r^{-2}$ wind environment. We also compare the end time of the\nplateaus in $\\gamma$-rays and X-rays using a statistical comparison with 222\n\\textit{Swift} GRBs with plateaus and known redshifts from January 2005 to\nAugust 2019. Within this comparison, the case of GRB 090510 shows an indication\nof chromaticity at the end time of the plateau. Finally, we update the 3-D\nfundamental plane relation among the rest frame end time of the plateau, its\ncorrespondent luminosity, and the peak prompt luminosity for 222 GRBs observed\nby \\textit{Swift}. We find that these three LAT GRBs follow this relation.\n"}
+{"abstract": "  We review the properties of neutron matter in the low-density regime. In\nparticular, we revise its ground state energy and the superfluid neutron\npairing gap, and analyze their evolution from the weak to the strong coupling\nregime. The calculations of the energy and the pairing gap are performed,\nrespectively, within the Brueckner--Hartree--Fock approach of nuclear matter\nand the BCS theory using the chiral nucleon-nucleon interaction of Entem and\nMachleidt at N$^3$LO and the Argonne V18 phenomenological potential. Results\nfor the energy are also shown for a simple Gaussian potential with a strength\nand range adjusted to reproduce the $^1S_0$ neutron-neutron scattering length\nand effective range. Our results are compared with those of quantum Monte Carlo\ncalculations for neutron matter and cold atoms. The Tan contact parameter in\nneutron matter is also calculated finding a reasonable agreement with\nexperimental data with ultra-cold atoms only at very low densities. We find\nthat low-density neutron matter exhibits a behavior close to that of a Fermi\ngas at the unitary limit, although, this limit is actually never reached. We\nalso review the properties (energy, effective mass and quasiparticle residue)\nof a spin-down neutron impurity immersed in a low-density free Fermi gas of\nspin-up neutrons already studied by the author in a recent work where it was\nshown that these properties are very close to those of an attractive Fermi\npolaron in the unitary limit.\n"}
+{"abstract": "  Poetry is one of the most important art forms of human languages. Recently\nmany studies have focused on incorporating some linguistic features of poetry,\nsuch as style and sentiment, into its understanding or generation system.\nHowever, there is no focus on understanding or evaluating the semantics of\npoetry. Therefore, we propose a novel task to assess a model's semantic\nunderstanding of poetry by poem matching. Specifically, this task requires the\nmodel to select one line of Chinese classical poetry among four candidates\naccording to the modern Chinese translation of a line of poetry. To construct\nthis dataset, we first obtain a set of parallel data of Chinese classical\npoetry and modern Chinese translation. Then we retrieve similar lines of poetry\nwith the lines in a poetry corpus as negative choices. We name the dataset\nChinese Classical Poetry Matching Dataset (CCPM) and release it at\nhttps://github.com/THUNLP-AIPoet/CCPM. We hope this dataset can further enhance\nthe study on incorporating deep semantics into the understanding and generation\nsystem of Chinese classical poetry. We also preliminarily run two variants of\nBERT on this dataset as the baselines for this dataset.\n"}
+{"abstract": "  Taking advantage of social media platforms, such as Twitter, this paper\nprovides an effective framework for emotion detection among those who are\nquarantined. Early detection of emotional feelings and their trends help\nimplement timely intervention strategies. Given the limitations of medical\ndiagnosis of early emotional change signs during the quarantine period,\nartificial intelligence models provide effective mechanisms in uncovering early\nsigns, symptoms and escalating trends. Novelty of the approach presented herein\nis a multitask methodological framework of text data processing, implemented as\na pipeline for meaningful emotion detection and analysis, based on the\nPlutchik/Ekman approach to emotion detection and trend detection. We present an\nevaluation of the framework and a pilot system. Results of confirm the\neffectiveness of the proposed framework for topic trends and emotion detection\nof COVID-19 tweets. Our findings revealed Stay-At-Home restrictions result in\npeople expressing on twitter both negative and positive emotional semantics.\nSemantic trends of safety issues related to staying at home rapidly decreased\nwithin the 28 days and also negative feelings related to friends dying and\nquarantined life increased in some days. These findings have potential to\nimpact public health policy decisions through monitoring trends of emotional\nfeelings of those who are quarantined. The framework presented here has\npotential to assist in such monitoring by using as an online emotion detection\ntool kit.\n"}
+{"abstract": "  In this paper, we numerically investigate the propulsive performance of\nthree-dimensional pitching flexible plates with varying flexibility and\ntrailing edge shapes. To eliminate the effect of other geometric parameters,\nonly the trailing edge angle is varied from 45{\\deg} (concave), 90{\\deg}\n(rectangular) to 135{\\deg} (convex) while maintaining the constant area of the\nflexible plate. We examine the impact of the frequency ratio f* defined as the\nratio of the natural frequency of the flexible plate to the actuated pitching\nfrequency. Through our numerical simulations, we find that the global maximum\nmean thrust occurs near f*=1 corresponding to the resonance condition. However,\nthe optimal propulsive efficiency is achieved around f*=1.54 instead of the\nresonance condition. While the convex plate with low and high bending stiffness\nvalues shows the best performance, the rectangular plate with moderate bending\nstiffness is the most efficient propulsion configuration. Through dynamic mode\ndecomposition, we find that the passive deformation can help in redistributing\nthe pressure gradient thus improving the efficiency and thrust production. A\nmomentum-based thrust evaluation approach is adopted to link the instantaneous\nvortical structures with the time-dependent thrust. When the vortices detach\nfrom the trailing edge, the instantaneous thrust shows the largest values due\nto the strong momentum change and convection process. Moderate flexibility and\nconvex shape help transfer momentum to the fluid, thereby improving thrust\ngeneration and promoting the transition from drag to thrust. The increase of\nthe trailing edge angle can broaden the range of flexibility that produces\npositive mean thrust.\n"}
+{"abstract": "  The world is still struggling in controlling and containing the spread of the\nCOVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions\nassociated with SARS-CoV-2 infections have resulted in a surge in the number of\npatients at clinics and hospitals, leading to a significantly increased strain\non healthcare resources. As such, an important part of managing and handling\npatients with SARS-CoV-2 infections within the clinical workflow is severity\nassessment, which is often conducted with the use of chest x-ray (CXR) images.\nIn this work, we introduce COVID-Net CXR-S, a convolutional neural network for\npredicting the airspace severity of a SARS-CoV-2 positive patient based on a\nCXR image of the patient's chest. More specifically, we leveraged transfer\nlearning to transfer representational knowledge gained from over 16,000 CXR\nimages from a multinational cohort of over 15,000 patient cases into a custom\nnetwork architecture for severity assessment. Experimental results with a\nmulti-national patient cohort curated by the Radiological Society of North\nAmerica (RSNA) RICORD initiative showed that the proposed COVID-Net CXR-S has\npotential to be a powerful tool for computer-aided severity assessment of CXR\nimages of COVID-19 positive patients. Furthermore, radiologist validation on\nselect cases by two board-certified radiologists with over 10 and 19 years of\nexperience, respectively, showed consistency between radiologist interpretation\nand critical factors leveraged by COVID-Net CXR-S for severity assessment.\nWhile not a production-ready solution, the ultimate goal for the open source\nrelease of COVID-Net CXR-S is to act as a catalyst for clinical scientists,\nmachine learning researchers, as well as citizen scientists to develop\ninnovative new clinical decision support solutions for helping clinicians\naround the world manage the continuing pandemic.\n"}
+{"abstract": "  The understanding of the ro-vibrational dynamics in molecular\n(near)-atmospheric pressure plasmas is essential to investigate the influence\nof vibrational excited molecules on the discharge properties. In a companion\npaper, results of ro-vibrational coherent anti-Stokes Raman scattering (CARS)\nmeasurements for a nanosecond pulsed plasma jet consisting of two conducting\nmolybdenum electrodes with a gap of 1 mm in nitrogen at 200 mbar are presented.\nHere, those results are discussed and compared to theoretical predictions based\non rate coefficients for the relevant processes found in the literature. It is\nfound, that during the discharge the measured vibrational excitation agrees\nwell with predictions obtained from the rates for resonant electron collisions\ncalculated by Laporta et al [PlasmaSources Science and Technology 23 065002\n(2014)]. The predictions are based on the electric field during the discharge,\nmeasured by EFISH and the electron density which is deduced from the field and\nmobility data calculated with Bolsig+. In the afterglow a simple kinetic\nsimulation for the vibrational subsystem of nitrogen is performed and it is\nfound, that the populations of vibrational excited states develop according to\nvibrational-vibrational transfer on timescales of a few $\\mu$s, while the\ndevelopment on timescales of some hundred $\\mu$s is determined by the losses at\nthe walls. No significant influence of electronically excited states on the\npopulations of the vibrational states visible in the CARS measurements ($v\n\\lesssim 7$) was observed.\n"}
+{"abstract": "  Our life is getting filled by Internet of Things (IoT) devices. These devices\noften rely on closed or poorly documented protocols, with unknown formats and\nsemantics. Learning how to interact with such devices in an autonomous manner\nis the key for interoperability and automatic verification of their\ncapabilities. In this paper, we propose RL-IoT, a system that explores how to\nautomatically interact with possibly unknown IoT devices. We leverage\nreinforcement learning (RL) to recover the semantics of protocol messages and\nto take control of the device to reach a given goal, while minimizing the\nnumber of interactions. We assume to know only a database of possible IoT\nprotocol messages, whose semantics are however unknown. RL-IoT exchanges\nmessages with the target IoT device, learning those commands that are useful to\nreach the given goal. Our results show that RL-IoT is able to solve both simple\nand complex tasks. With properly tuned parameters, RL-IoT learns how to perform\nactions with the target device, a Yeelight smart bulb in our case study,\ncompleting non-trivial patterns with as few as 400 interactions. RL-IoT paves\nthe road for automatic interactions with poorly documented IoT protocols, thus\nenabling interoperable systems.\n"}
+{"abstract": "  Quantifying and verifying the control level in preparing a quantum state are\ncentral challenges in building quantum devices. The quantum state is\ncharacterized from experimental measurements, using a procedure known as\ntomography, which requires a vast number of resources. Furthermore, the\ntomography for a quantum device with temporal processing, which is\nfundamentally different from the standard tomography, has not been formulated.\nWe develop a practical and approximate tomography method using a recurrent\nmachine learning framework for this intriguing situation. The method is based\non repeated quantum interactions between a system called quantum reservoir with\na stream of quantum states. Measurement data from the reservoir are connected\nto a linear readout to train a recurrent relation between quantum channels\napplied to the input stream. We demonstrate our algorithms for quantum learning\ntasks followed by the proposal of a quantum short-term memory capacity to\nevaluate the temporal processing ability of near-term quantum devices.\n"}
+{"abstract": "  This paper describes the short-term competition on the Components\nSegmentation Task of Document Photos that was prepared in the context of the\n16th International Conference on Document Analysis and Recognition (ICDAR\n2021). This competition aims to bring together researchers working in the field\nof identification document image processing and provides them a suitable\nbenchmark to compare their techniques on the component segmentation task of\ndocument images. Three challenge tasks were proposed entailing different\nsegmentation assignments to be performed on a provided dataset. The collected\ndata are from several types of Brazilian ID documents, whose personal\ninformation was conveniently replaced. There were 16 participants whose results\nobtained for some or all the three tasks show different rates for the adopted\nmetrics, like Dice Similarity Coefficient ranging from 0.06 to 0.99. Different\nDeep Learning models were applied by the entrants with diverse strategies to\nachieve the best results in each of the tasks. Obtained results show that the\ncurrently applied methods for solving one of the proposed tasks (document\nboundary detection) are already well established. However, for the other two\nchallenge tasks (text zone and handwritten sign detection) research and\ndevelopment of more robust approaches are still required to achieve acceptable\nresults.\n"}
+{"abstract": "  In this work, the issue of estimation of reachable sets in continuous bimodal\npiecewise affine systems is studied. A new method is proposed, in the framework\nof ellipsoidal bounding, using piecewise quadratic Lyapunov functions. Although\nbimodal piecewise affine systems can be seen as a special class of affine\nhybrid systems, reachability methods developed for affine hybrid systems might\nbe inappropriately complex for bimodal dynamics. This work goes in the\ndirection of exploiting the dynamical structure of the system to propose a\nsimpler approach. More specifically, because of the piecewise nature of the\nLyapunov function, we first derive conditions to ensure that a given quadratic\nfunction is positive on half spaces. Then, we exploit the property of bimodal\npiecewise quadratic functions being continuous on a given hyperplane. Finally,\nlinear matrix characterizations of the estimate of the reachable set are\nderived.\n"}
+{"abstract": "  Magneto-optical parameters of trions in novel large and symmetric InP-based\nquantum dots, uncommon for molecular beam epitaxy grown nanostructures, with\nemission in the third telecom window, are measured in Voigt and Faraday\nconfigurations of external magnetic field. The diamagnetic coefficients are\nfound to be in the range of 1.5-4 {\\mu}eV/{\\T^2}, and 8-15 {\\mu}eV/{\\T^2},\nrespectively out of plane and in plane of the dots. The determined values of\ndiamagnetic shifts are related to the anisotropy of dot sizes. Trion g-factors\nare measured to be relatively small, in the range of 0.3-0.7 and 0.5-1.3, in\nboth configurations respectively. Analysis of single carrier g-factors, based\non the formalism of spin-correlated orbital currents, leads to the similar\nvalues for hole and electron of {\\sim} 0.25 for Voigt and {\\g_e} {\\approx} -5;\n{\\g_h} {\\approx} +6 for Faraday configuration of magnetic field. Values of\ng-factors close to zero measured in Voigt configuration make the investigated\ndots promising for electrical tuning of g-factor sign, required for schemes of\nsingle spin control in qubit applications.\n"}
+{"abstract": "  Edge devices, such as cameras and mobile units, are increasingly capable of\nperforming sophisticated computation in addition to their traditional roles in\nsensing and communicating signals. The focus of this paper is on collaborative\nobject detection, where deep features computed on the edge device from input\nimages are transmitted to the cloud for further processing. We consider the\nimpact of packet loss on the transmitted features and examine several ways for\nrecovering the missing data. In particular, through theory and experiments, we\nshow that methods for image inpainting based on partial differential equations\nwork well for the recovery of missing features in the latent space. The\nobtained results represent the new state of the art for missing data recovery\nin collaborative object detection.\n"}
+{"abstract": "  The long dreamed quantum internet would consist of a network of quantum nodes\n(solid-state or atomic systems) linked by flying qubits, naturally based on\nphotons, travelling over long distances at the speed of light, with negligible\ndecoherence. A key component is a light source, able to provide single or\nentangled photon pairs. Among the different platforms, semiconductor quantum\ndots are very attractive, as they can be integrated with other photonic and\nelectronic components in miniaturized chips. In the early 1990s two approaches\nwere developed to synthetize self-assembled epitaxial semiconductor quantum\ndots (QDs), or artificial atoms, namely the Stranski-Krastanov (SK) and the\ndroplet epitaxy (DE) method. Because of its robustness and simplicity, the SK\nmethod became the workhorse to achieve several breakthroughs in both\nfundamental and technological areas. The need for specific emission wavelengths\nor structural and optical properties has nevertheless motivated further\nresearch on the DE method and its more recent development, the\nlocal-droplet-etching (LDE), as complementary routes to obtain high quality\nsemiconductor nanostructures. The recent reports on the generation of highly\nentangled photon pairs, combined with good photon indistinguishability, suggest\nthat DE and LDE QDs may complement (and sometime even outperform) conventional\nSK InGaAs QDs as quantum emitters. We present here a critical survey of the\nstate of the art of DE and LDE, highlighting the advantages and weaknesses, the\nobtained achievements and the still open challenges, in view of applications in\nquantum communication and technology.\n"}
+{"abstract": "  Hyperdimensional computing (HDC) is a brain-inspired computing paradigm based\non high-dimensional holistic representations of vectors. It recently gained\nattention for embedded smart sensing due to its inherent error-resiliency and\nsuitability to highly parallel hardware implementations. In this work, we\npropose a programmable all-digital CMOS implementation of a fully autonomous\nHDC accelerator for always-on classification in energy-constrained sensor\nnodes. By using energy-efficient standard cell memory (SCM), the design is\neasily cross-technology mappable. It achieves extremely low power, 5 $\\mu W$ in\ntypical applications, and an energy-efficiency improvement over the\nstate-of-the-art (SoA) digital architectures of up to 3$\\times$ in post-layout\nsimulations for always-on wearable tasks such as EMG gesture recognition. As\npart of the accelerator's architecture, we introduce novel hardware-friendly\nembodiments of common HDC-algorithmic primitives, which results in 3.3$\\times$\ntechnology scaled area reduction over the SoA, achieving the same accuracy\nlevels in all examined targets. The proposed architecture also has a fully\nconfigurable datapath using microcode optimized for HDC stored on an integrated\nSCM based configuration memory, making the design \"general-purpose\" in terms of\nHDC algorithm flexibility. This flexibility allows usage of the accelerator\nacross novel HDC tasks, for instance, a newly designed HDC applied to the task\nof ball bearing fault detection.\n"}
+{"abstract": "  The neutron-proton equilibration process in 48 Ca+ 40 Ca at 35 MeV/nucleon\nbombarding energy has been experimentally estimated by means of the isospin\ntransport ratio. Experimental data have been collected with a subset of the\nFAZIA telescope array, which permitted to determine Z and N of detected\nfragments. For the first time, the QP evaporative channel has been compared\nwith the QP break-up one in a homogeneous and consistent way, pointing out to a\ncomparable n-p equilibration which suggests close interaction time between\nprojectile and target independently of the exit channel. Moreover, in the QP\nevaporative channel n-p equilibration has been compared with the prediction of\nthe Antisymmetrized Molecular Dynamics (AMD) model coupled to the GEMINI\nstatistical model as an afterburner, showing a larger probability of proton and\nneutron transfers in the simulation with respect to the experimental data.\n"}
+{"abstract": "  Turbulent flows over wavy surfaces give rise to the formation of ripples,\ndunes and other natural bedforms. To predict how much sediment these flows\ntransport, research has focused mainly on basal shear stress, which peaks\nupstream of the highest topography, and has largely ignored the corresponding\npressure variations. In this article, we reanalyze old literature data, as well\nas more recent wind tunnel results, to shed a new light on pressure induced by\na turbulent flow on a sinusoidal surface. While the Bernoulli effect increases\nthe velocity above crests and reduces it in troughs, pressure exhibits\nvariations that lag behind the topography. We extract the in-phase and\nin-quadrature components from streamwise pressure profiles and compare them to\nhydrodynamic predictions calibrated on shear stress data.\n"}
+{"abstract": "  We report a set of exact formulae for computing Dirac masses, mixings, and\nCP-violation parameter(s) from 3$\\times$3 Yukawa matrices $Y$ valid when $Y\nY^\\dagger \\rightarrow U^\\dagger \\,Y Y^\\dagger \\, U$ under global $U(3)_{Q_L}$\nflavour symmetry transformations $U$. The results apply to the Standard Model\nEffective Field Theory (SMEFT) and its `geometric' realization (geoSMEFT). We\nthereby complete, in the Dirac flavour sector, the catalogue of geoSMEFT\nparameters derived at all orders in the $\\sqrt{2 \\langle H^\\dagger H \\rangle} /\n\\Lambda$ expansion. The formalism is basis-independent, and can be applied to\nmodels with decoupled ultraviolet flavour dynamics, as well as to models whose\ninfrared dynamics are not minimally flavour violating. We highlight these\npoints with explicit examples and, as a further demonstration of the\nformalism's utility, we derive expressions for the renormalization group flow\nof quark masses, mixings, and CP-violation at all mass dimension and\nperturbative loop orders in the (geo)SM(EFT) and beyond.\n"}
+{"abstract": "  An hidden variable (hv) theory is a theory that allows globally dispersion\nfree ensembles. We demonstrate that the Phase Space formulation of Quantum\nMechanics (QM) is an hv theory with the position q, and momentum p as the hv.\n  Comparing the Phase space and Hilbert space formulations of QM we identify\nthe assumption that led von Neumann to the Hilbert space formulation of QM\nwhich, in turn, precludes global dispersion free ensembles within the theory.\nThe assumption, dubbed I, is: \"If a physical quantity $\\mathbf{A}$ has an\noperator $\\hat{A}$ then $f(\\mathbf{A})$ has the operator $f(\\hat{A})$\". This\nassumption does not hold within the Phase Space formulation of QM.\n  The hv interpretation of the Phase space formulation provides novel insight\ninto the interrelation between dispersion and non commutativity of position and\nmomentum (operators) within the Hilbert space formulation of QM and mitigates\nthe criticism against von Neumann's no hidden variable theorem by, virtually,\nthe consensus.\n"}
+{"abstract": "  The extremely low threshold voltage (Vth) of native MOSFETs (Vth~0V@300K) is\nconducive to the design of cryogenic circuits. Previous research on cryogenic\nMOSFETs mainly focused on the standard threshold voltage (SVT) and low\nthreshold voltage (LVT) MOSFETs. In this paper, we characterize native MOSFETs\nwithin the temperature range from 300K to 4.2K. The cryogenic Vth increases up\nto ~0.25V (W/L=10um/10um) and the improved subthreshold swing\n(SS)~14.30mV/dec@4.2K. The off-state current (Ioff) and the gate-induced drain\nleakage (GIDL) effect are ameliorated greatly. The step-up effect caused by the\nsubstrate charge and the transconductance peak effect caused by the energy\nquantization in different sub-bands are also discussed. Based on the EKV model,\nwe modified the mobility calculation equations and proposed a compact model of\nlarge size native MOSFETs suitable for the range of 300K to 4.2K. The\nmobility-related parameters are extracted via a machine learning approach and\nthe temperature dependences of the scattering mechanisms are analyzed. This\nwork is beneficial to both the research on cryogenic MOSFETs modeling and the\ndesign of cryogenic CMOS circuits for quantum chips.\n"}
+{"abstract": "  The majoron, a neutrinophilic pseudo-Goldstone boson conventionally arising\nin the context of neutrino mass models, can damp neutrino free-streaming and\ninject additional energy density into neutrinos prior to recombination. The\ncombination of these effects for an eV-scale mass majoron has been shown to\nameliorate the outstanding $H_0$ tension, however only if one introduces\nadditional dark radiation at the level of $\\Delta N_{\\rm eff} \\sim 0.5$. We\nshow here that models of low-scale leptogenesis can naturally source this dark\nradiation by generating a primordial population of majorons from the decays of\nGeV-scale sterile neutrinos in the early Universe. Using a posterior predictive\ndistribution conditioned on Planck2018+BAO data, we show that the value of\n$H_0$ observed by the SH$_0$ES collaboration is expected to occur at the level\nof $\\sim 10\\%$ in the primordial majoron cosmology (to be compared with $\\sim\n0.1\\%$ in the case of $\\Lambda$CDM). This insight provides an intriguing\nconnection between the neutrino mass mechanism, the baryon asymmetry of the\nUniverse, and the discrepant measurements of $H_0$.\n"}
+{"abstract": "  The classical construction of the Weil representation, that is with complex\ncoefficients, has been expected to be working for more general coefficient\nrings. This paper exhibits a minimal ring $\\mathcal{A}$, being the integral\nclosure of $\\mathbb{Z}[\\frac{1}{p}]$ in a cyclotomic field, and carries the\nconstruction of the Weil representation over $\\mathcal{A}$-algebras. As a\nleitmotiv all along the work, most of the problems can actually be solved over\nthe based ring $\\mathcal{A}$ and transferred for any $\\mathcal{A}$-algebra by\nscalar extension. The most striking fact consists in these many Weil\nrepresentations arising as the scalar extension of a single one with\ncoefficients in $\\mathcal{A}$. In this sense, the Weil module obtained is\nuniversal. Building upon this construction, we are speculating and making\nprognoses about an integral theta correspondence.\n"}
+{"abstract": "  Light curves produced by the Kepler mission demonstrate stochastic brightness\nfluctuations (or \"flicker\") of stellar origin which contribute to the noise\nfloor, limiting the sensitivity of exoplanet detection and characterization\nmethods. In stars with surface convection, the primary driver of these\nvariations on short (sub-eight-hour) timescales is believed to be convective\ngranulation. In this work, we improve existing models of this granular flicker\namplitude, or $F_8$, by including the effect of the Kepler bandpass on measured\nflicker, by incorporating metallicity in determining convective Mach numbers,\nand by using scaling relations from a wider set of numerical simulations. To\nmotivate and validate these changes, we use a recent database of convective\nflicker measurements in Kepler stars, which allows us to more fully detail the\nremaining model--prediction error. Our model improvements reduce the typical\nmisprediction of flicker amplitude from a factor of 2.5 to 2. We rule out\nrotation period and strong magnetic activity as possible explanations for the\nremaining model error, and we show that binary companions may affect convective\nflicker. We also introduce an \"envelope\" model which predicts a range of\nflicker amplitudes for any one star to account for some of the spread in\nnumerical simulations, and we find that this range covers 78% of observed\nstars. We note that the solar granular flicker amplitude is lower than most\nSun-like stars. This improved model of convective flicker amplitude can better\ncharacterize this source of noise in exoplanet studies as well as better inform\nmodels and simulations of stellar granulation.\n"}
+{"abstract": "  The idea of a metapopulation has become canonical in ecology. Its original\nmean field form provides the important intuition that migration and extinction\ninteract to determine the dynamics of a population composed of subpopulations.\nFrom its conception, it has been evident that the very essence of the\nmetapopulation paradigm centers on the process of local extinction. We note\nthat there are two qualitatively distinct types of extinction, gradual and\ncatastrophic, and explore their impact on the dynamics of metapopulation\nformation using discrete iterative maps. First, by modifying the classic\nlogistic map with the addition of the Allee effect, we show that catastrophic\nlocal extinctions in subpopulations are a pre-requisite of metapopulation\nformation. When subpopulations experience gradual extinction, increased\nmigration rates force synchrony and drive the metapopulation below the Allee\npoint resulting in migration induced destabilization of the system across\nparameter space. Second, a sawtooth map (an extension of the Bernoulli bit\nshift map) is employed to simultaneously explore the increasing and decreasing\nmodes of population behavior. We conclude with four generalizations. 1. At low\nmigration rates, a metapopulation may go extinct faster than completely\nunconnected subpopulations. 2. There exists a gradient between stable\nmetapopulation formation and population synchrony, with critical transitions\nfrom no metapopulation to metapopulation to synchronization, the latter\nfrequently inducing metapopulation extinction. 3. Synchronization patterns\nemerge through time, resulting in synchrony groups and chimeric populations\nexisting simultaneously. 4. There are two distinct mechanisms of\nsynchronization: i. extinction and rescue and, ii.) stretch reversals in a\nmodification of the classic chaotic stretching and folding.\n"}
+{"abstract": "  Edge intelligence leverages computing resources on network edge to provide\nartificial intelligence (AI) services close to network users. As it enables\nfast inference and distributed learning, edge intelligence is envisioned to be\nan important component of 6G networks. In this article, we investigate AI\nservice provisioning for supporting edge intelligence. First, we present the\nfeatures and requirements of AI services. Then, we introduce AI service data\nmanagement, and customize network slicing for AI services. Specifically, we\npropose a novel resource pooling method to jointly manage service data and\nnetwork resources for AI services. A trace-driven case study demonstrates the\neffectiveness of the proposed resource pooling method. Through this study, we\nillustrate the necessity, challenge, and potential of AI service provisioning\non network edge.\n"}
+{"abstract": "  We explore the degrees of freedom required to jointly fit projected and\nredshift-space clustering of galaxies selected in three bins of stellar mass\nfrom the Sloan Digital Sky Survey Main Galaxy Sample (SDSS MGS) using a subhalo\nabundance matching (SHAM) model. We employ emulators for relevant clustering\nstatistics in order to facilitate our analysis, leading to large speed gains\nwith minimal loss of accuracy. We are able to simultaneously fit the projected\nand redshift-space clustering of the two most massive galaxy samples that we\nconsider with just two free parameters: scatter in stellar mass at fixed SHAM\nproxy and the dependence of the SHAM proxy on dark matter halo concentration.\nWe find some evidence for models that include velocity bias, but including\norphan galaxies improves our fits to the lower mass samples significantly. We\nalso model the clustering signals of specific star formation rate (SSFR)\nselected samples using conditional abundance matching (CAM). We obtain\nacceptable fits to projected and redshift-space clustering as a function of\nSSFR and stellar mass using two CAM variants, although the fits are worse than\nfor stellar mass-selected samples alone. By incorporating non-unity\ncorrelations between the CAM proxy and SSFR we are able to resolve previously\nidentified discrepancies between CAM predictions and SDSS observations of the\nenvironmental dependence of quenching for isolated central galaxies.\n"}
+{"abstract": "  Recently, AutoRegressive (AR) models for the whole image generation empowered\nby transformers have achieved comparable or even better performance to\nGenerative Adversarial Networks (GANs). Unfortunately, directly applying such\nAR models to edit/change local image regions, may suffer from the problems of\nmissing global information, slow inference speed, and information leakage of\nlocal guidance. To address these limitations, we propose a novel model -- image\nLocal Autoregressive Transformer (iLAT), to better facilitate the locally\nguided image synthesis. Our iLAT learns the novel local discrete\nrepresentations, by the newly proposed local autoregressive (LA) transformer of\nthe attention mask and convolution mechanism. Thus iLAT can efficiently\nsynthesize the local image regions by key guidance information. Our iLAT is\nevaluated on various locally guided image syntheses, such as pose-guided person\nimage synthesis and face editing. Both the quantitative and qualitative results\nshow the efficacy of our model.\n"}
+{"abstract": "  Microwave electromagnetic heating are widely used in many industrial\nprocesses. The mathematics involved is based on the Maxwell's equations coupled\nwith the heat equation. The thermal conductivity is strongly dependent on the\ntemperature, itself an unknown of the system of P.D.E. We propose here a model\nwhich simplifies this coupling using a nonlocal term as the source of heating.\nWe prove that the corresponding mathematical initial-boundary value problem has\nsolutions using the Schauder's fixed point theorem.\n"}
+{"abstract": "  Optical flow estimation with occlusion or large displacement is a problematic\nchallenge due to the lost of corresponding pixels between consecutive frames.\nIn this paper, we discover that the lost information is related to a large\nquantity of motion features (more than 40%) computed from the popular\ndiscriminative cost-volume feature would completely vanish due to invalid\nsampling, leading to the low efficiency of optical flow learning. We call this\nphenomenon the Vanishing Cost Volume Problem. Inspired by the fact that local\nmotion tends to be highly consistent within a short temporal window, we propose\na novel iterative Motion Feature Recovery (MFR) method to address the vanishing\ncost volume via modeling motion consistency across multiple frames. In each MFR\niteration, invalid entries from original motion features are first determined\nbased on the current flow. Then, an efficient network is designed to adaptively\nlearn the motion correlation to recover invalid features for lost-information\nrestoration. The final optical flow is then decoded from the recovered motion\nfeatures. Experimental results on Sintel and KITTI show that our method\nachieves state-of-the-art performances. In fact, MFR currently ranks second on\nSintel public website.\n"}
+{"abstract": "  There is an increasing pressure for lecturers to work with two goals. First,\nthey need to ensure their undergraduate students have a good grasp of the\nknowledge and skills of the intellectual field. In addition, they need to\nprepare graduates and postgraduates for careers both within and outside of\nacademia. The problem addressed by this paper is how assessments may reveal a\nshift of focus from a mastery of knowledge to a work-focused orientation. This\nshift is examined through a case study of physics and the sub-discipline of\ntheoretical physics as intellectual fields. The evidence is assessment tasks\ngiven to students at different points of their studies from first year to\ndoctoral levels. By examining and analysing the assessment tasks using concepts\nfrom Legitimation Code Theory (LCT), we demonstrate how the shifts in the\nassessments incrementally lead students from a pure disciplinary focus to one\nthat enables students to potentially pursue employment both within and outside\nof academia. In doing so, we also highlight the usefulness of LCT as a\nframework for evaluating the preparation of science students for diverse\nworkplaces.\n"}
+{"abstract": "  The fabrication of graphene-silicon (Gr-Si) junction inolves the formation of\na parallel metal-insulator-semiconductor (MIS) structure, which is often\ndisregarded but plays an important role in the optoelectronic properties of the\ndevice. In this work, the transfer of graphene onto a patterned n-type Si\nsubstrate, covered by $Si_3N_4$, produces a Gr-Si device in which the parallel\nMIS consists of a $Gr-Si_3N_4-Si$ structure surrounding the Gr-Si junction. The\nGr-Si device exhibits rectifying behavior with a rectification ratio up to\n$10^4$. The investigation of its temperature behavior is necessary to\naccurately estimate the Schottky barrier height at zero bias, ${\\phi}_{b0}=0.24\neV$, the effective Richardson's constant, $A^*=7 \\cdot 10^{-10}\nAK^{-2}cm^{-2}$, and the diode ideality factor n=2.66 of the Gr-Si junction.\nThe device is operated as a photodetector in both photocurrent and photovoltage\nmode in the visible and infrared (IR) spectral regions. A responsivity up to\n350 mA/W and external quantum efficiency (EQE) up to 75% is achieved in the\n500-1200 nm wavelength range. A decrease of responsivity to 0.4 mA/W and EQE to\n0.03% is observed above 1200 nm, that is in the IR region beyond the silicon\noptical bandgap, in which photoexcitation is driven by graphene. Finally, a\nmodel based on two back-to-back diodes, one for the Gr-Si junction. the other\nfor the $Gr-Si_3N_4-Si$ MIS structure, is proposed to explain the electrical\nbehavior of the Gr-Si device.\n"}
+{"abstract": "  We create an artificial system of agents (attention-based neural networks)\nwhich selectively exchange messages with each-other in order to study the\nemergence of memetic evolution and how memetic evolutionary pressures interact\nwith genetic evolution of the network weights. We observe that the ability of\nagents to exert selection pressures on each-other is essential for memetic\nevolution to bootstrap itself into a state which has both high-fidelity\nreplication of memes, as well as continuing production of new memes over time.\nHowever, in this system there is very little interaction between this memetic\n'ecology' and underlying tasks driving individual fitness - the emergent meme\nlayer appears to be neither helpful nor harmful to agents' ability to learn to\nsolve tasks. Sourcecode for these experiments is available at\nhttps://github.com/GoodAI/memes\n"}
+{"abstract": "  Populism is a political phenomenon of democratic illiberalism centered on the\nfigure of a strong leader. By modeling person/node connections of prominent\nfigures of the recent Colombian political landscape we map, quantify, and\nanalyze the position and influence of Alvaro Uribe as a populist leader. We\nfound that Uribe is a central hub in the political alliances networks, cutting\nthrough traditional party alliances, . but is not the most central figure in\nthe state machinery.\n  The article first presents the framing of the problem, followed by the\nhistorical context of the case in study, the methodology employed and data\ncollection, analysis, conclusions and further research paths. This study has\nimplications for offering a new way of applying quantitative methods to the\nstudies of populist regimes\n"}
+{"abstract": "  The thermodynamic and structural properties of two dimensional dense Yukawa\nliquids are studied with molecular dynamics simulations. The \"exact\"\nthermodynamic properties are simultaneously employed in an advanced scheme for\nthe determination of an equation of state that shows an unprecedented level of\naccuracy for the internal energy, pressure and isothermal compressibility. The\n\"exact\" structural properties are utilized to formulate a novel empirical\ncorrection to the hypernetted-chain approach that leads to a very high accuracy\nlevel in terms of static correlations and thermodynamics.\n"}
+{"abstract": "  We prove that a tournament and its complement contain the same number of\noriented Hamiltonian paths (resp. cycles) of any given type, as a\ngeneralization of Rosenfeld's result proved for antidirected paths.\n"}
+{"abstract": "  The binary alloy of titanium-tungsten (TiW) is an established diffusion\nbarrier in high-power semiconductor devices, owing to its ability to suppress\nthe diffusion of copper from the metallisation scheme into the surrounding\nsilicon substructure. However, little is known about the response of TiW to\nhigh temperature events or its behaviour when exposed to air. Here, a combined\nsoft and hard X-ray photoelectron spectroscopy (XPS) characterisation approach\nis used to study the influence of post-deposition annealing and titanium\nconcentration on the oxidation behaviour of a 300~nm-thick TiW film. The\ncombination of both XPS techniques allows for the assessment of the chemical\nstate and elemental composition across the surface and bulk of the TiW layer.\nThe findings show that in response to high-temperature annealing, titanium\nsegregates out of the mixed metal system and upwardly migrates, accumulating at\nthe TiW/air interface. Titanium shows remarkably rapid diffusion under\nrelatively short annealing timescales and the extent of titanium surface\nenrichment is increased through longer annealing periods or by increasing the\nprecursor titanium concentration. Surface titanium enrichment enhances the\nextent of oxidation both at the surface and in the bulk of the alloy due to the\nstrong gettering ability of titanium. Quantification of the soft X-ray\nphotoelectron spectra highlights the formation of three tungsten oxidation\nenvironments, attributed to WO$_2$, WO$_3$ and a WO$_3$ oxide coordinated with\na titanium environment. This combinatorial characterisation approach provides\nvaluable insights into the thermal and oxidation stability of TiW alloys from\ntwo depth perspectives, aiding the development of future device technologies.\n"}
+{"abstract": "  We consider topological defects for the $\\lambda\\phi^4$ theory in (1+1)\ndimensions with a Lorentz-violating background.\n  It has been shown, by M. Barreto et al. (2006) \\cite{barreto2006defect}, one\ncannot have original effects in (the leading order of) single scalar field\nmodel.\n  Here, we introduce a new Lorentz-violating term, next to leading order which\ncannot be absorbed by any redefinition of the scalar field or coordinates.\n  Our term is the lowest order term which leads to concrete effects on the kink\nproperties. We calculate the corrections to the kink shape and the\ncorresponding mass. Quantization of the kink is performed and the revised modes\nare obtained. We find the bound and continuum states are affected due to this\nLorentz symmetry violation.\n"}
+{"abstract": "  We show that the observed primordial perturbations can be entirely sourced by\na light spectator scalar field with a quartic potential, akin to the Higgs\nboson, provided that the field is sufficiently displaced from vacuum during\ninflation. The framework relies on the indirect modulation of reheating, which\nis implemented without any direct coupling between the spectator field and the\ninflaton and does not require non-renormalisable interactions. The scenario\ngives rise to local non-Gaussianity with $f_{\\rm NL}\\simeq 5$ as the typical\nsignal. As an example model where the indirect modulation mechanism is realised\nfor the Higgs boson, we study the Standard Model extended with right-handed\nneutrinos. For the Standard Model running we find, however, that the scenario\nanalysed does not seem to produce the observed perturbation.\n"}
+{"abstract": "  We present mid-infrared observations of comet P/2016 BA14 (PANSTARRS), which\nwere obtained on UT 2016 March 21.3 at heliocentric and geocentric distances of\n1.012 au and 0.026 au, respectively, approximately 30 hours before its closest\napproach to Earth (0.024 au) on UT 2016 March 22.6. Low-resolution\n($\\lambda$/$\\Delta \\lambda$~250) spectroscopic observations in the N-band and\nimaging observations with four narrow-band filters (centered at 8.8, 12.4, 17.7\nand 18.8 $\\mu$m) in the N- and Q-bands were obtained using the Cooled\nMid-Infrared Camera and Spectrometer (COMICS) mounted on the 8.2-m Subaru\ntelescope atop Maunakea, Hawaii. The observed spatial profiles of P/2016 BA14\nat different wavelengths are consistent with a point-spread function. Owing to\nthe close approach of the comet to the Earth, the observed thermal emission\nfrom the comet is dominated by the thermal emission from its nucleus rather\nthan its dust coma. The observed spectral energy distribution of the nucleus at\nmid-infrared wavelengths is consistent with a Planck function at temperature\nT~350 K, with the effective diameter of P/2016 BA14 estimated as ~0.8 km (by\nassuming an emissivity of 0.97). The normalized emissivity spectrum of the\ncomet exhibits absorption-like features that are not reproduced by the\nanhydrous minerals typically found in cometary dust coma, such as olivine and\npyroxene. Instead, the spectral features suggest the presence of large grains\nof phyllosilicate minerals and organic materials. Thus, our observations\nindicate that an inactive small body covered with these processed materials is\na possible end state of comets.\n"}
+{"abstract": "  Time-domain reflectometry (TDR) is an established means of measuring\nimpedance inhomogeneity of a variety of waveguides, providing critical data\nnecessary to characterize and optimize the performance of high-bandwidth\ncomputational and communication systems. However, TDR systems with both the\nhigh spatial resolution (sub-cm) and voltage resolution (sub-$\\muV$) required\nto evaluate high-performance waveguides are physically large and often\ncost-prohibitive, severely limiting their utility as testing platforms and\ngreatly limiting their use in characterizing and trouble-shooting fielded\nhardware.\n  Consequently, there exists a growing technical need for an electronically\nsimple, portable, and low-cost TDR technology. The receiver of a TDR system\nplays a key role in recording reflection waveforms; thus, such a receiver must\nhave high analog bandwidth, high sampling rate, and high-voltage resolution.\nHowever, these requirements are difficult to meet using low-cost\nanalog-to-digital converters (ADCs). This article describes a new TDR\narchitecture, namely, jitter-based APC (JAPC), which obviates the need for\nexternal components based on an alternative concept, analog-to-probability\nconversion (APC) that was recently proposed. These results demonstrate that a\nfully reconfigurable and highly integrated TDR (iTDR) can be implemented on a\nfield-programmable gate array (FPGA) chip without using any external circuit\ncomponents. Empirical evaluation of the system was conducted using an HDMI\ncable as the device under test (DUT), and the resulting impedance inhomogeneity\npattern (IIP) of the DUT was extracted with spatial and voltage resolutions of\n5 cm and 80 $\\muV$, respectively. These results demonstrate the feasibility of\nusing the prototypical JAPC-based iTDR for real-world waveguide\ncharacterization applications\n"}
+{"abstract": "  Silicon nitride (SiN) waveguides with ultra-low optical loss enable\nintegrated photonic applications including low noise, narrow linewidth lasers,\nchip-scale nonlinear photonics, and microwave photonics. Lasers are key\ncomponents to SiN photonic integrated circuits (PICs), but are difficult to\nfully integrate with low-index SiN waveguides due to their large mismatch with\nthe high-index III-V gain materials. The recent demonstration of multilayer\nheterogeneous integration provides a practical solution and enabled the\nfirst-generation of lasers fully integrated with SiN waveguides. However a\nlaser with high device yield and high output power at telecommunication\nwavelengths, where photonics applications are clustered, is still missing,\nhindered by large mode transition loss, nonoptimized cavity design, and a\ncomplicated fabrication process. Here, we report high-performance lasers on SiN\nwith tens of milliwatts output through the SiN waveguide and sub-kHz\nfundamental linewidth, addressing all of the aforementioned issues. We also\nshow Hertz-level linewidth lasers are achievable with the developed integration\ntechniques. These lasers, together with high-$Q$ SiN resonators, mark a\nmilestone towards a fully-integrated low-noise silicon nitride photonics\nplatform. This laser should find potential applications in LIDAR, microwave\nphotonics and coherent optical communications.\n"}
+{"abstract": "  In this paper, we are tackling the proposal-free referring expression\ngrounding task, aiming at localizing the target object according to a query\nsentence, without relying on off-the-shelf object proposals. Existing\nproposal-free methods employ a query-image matching branch to select the\nhighest-score point in the image feature map as the target box center, with its\nwidth and height predicted by another branch. Such methods, however, fail to\nutilize the contextual relation between the target and reference objects, and\nlack interpretability on its reasoning procedure. To solve these problems, we\npropose an iterative shrinking mechanism to localize the target, where the\nshrinking direction is decided by a reinforcement learning agent, with all\ncontents within the current image patch comprehensively considered. Beside, the\nsequential shrinking process enables to demonstrate the reasoning about how to\niteratively find the target. Experiments show that the proposed method boosts\nthe accuracy by 4.32% against the previous state-of-the-art (SOTA) method on\nthe RefCOCOg dataset, where query sentences are long and complex, with many\ntargets referred by other reference objects.\n"}
+{"abstract": "  This paper considers joint device activity detection and channel estimation\nin Internet of Things (IoT) networks, where a large number of IoT devices exist\nbut merely a random subset of them become active for short-packet transmission\nat each time slot. In particular, we propose to leverage the temporal\ncorrelation in user activity, i.e., a device active at the previous time slot\nis more likely to be still active at the current moment, to improve the\ndetection performance. Despite the temporally-correlated user activity in\nconsecutive time slots, it is challenging to unveil the connection between the\nactivity pattern estimated previously, which is imperfect but the only\navailable side information (SI), and the true activity pattern at the current\nmoment due to the unknown estimation error. In this work, we manage to tackle\nthis challenge under the framework of approximate message passing (AMP).\nSpecifically, thanks to the state evolution, the correlation between the\nactivity pattern estimated by AMP at the previous time slot and the real\nactivity pattern at the previous and current moment is quantified explicitly.\nBased on the well-defined temporal correlation, we further manage to embed this\nuseful SI into the design of the minimum mean-squared error (MMSE) denoisers\nand log-likelihood ratio (LLR) test based activity detectors under the AMP\nframework. Theoretical comparison between the SI-aided AMP algorithm and its\ncounterpart without utilizing temporal correlation is provided. Moreover,\nnumerical results are given to show the significant gain in activity detection\naccuracy brought by the SI-aided algorithm.\n"}
+{"abstract": "  Quantum chromodynamics (QCD) claims that the major source of the nucleon\ninvariant mass is not the Higgs mechanism but the trace anomaly in QCD energy\nmomentum tensor. Although experimental and theoretical results support such\nconclusion, a direct demonstration is still absent. We present the first\nLattice QCD calculation of the quark and gluon trace anomaly contributions to\nthe hadron masses, using the overlap fermion on the 2+1 flavor dynamical Domain\nwall quark ensemble at $m_{\\pi}=340$ MeV and lattice spacing $a=$0.1105 fm. The\nresult shows that the gluon trace anomaly contributes to most of the nucleon\nmass, and the contribution in the pion state is smaller than that in others\nnearly by a factor $\\sim$10 since the gluon trace anomaly density inside pion\nis different from the other hadrons and the magnitude is much smaller. The\ngluon trace anomaly coefficient $\\beta/g^3=-0.056(6)$ we obtained is consistent\nwith its regularization independent leading order value\n$(-11+\\frac{2N_f}{3})/(4\\pi)^2$ perfectly.\n"}
+{"abstract": "  Massive black hole binaries (MBHBs) with masses of ~ 10^4 to ~ 10^10 of solar\nmasses are one of the main targets for currently operating and forthcoming\nspace-borne gravitational wave observatories. In this paper, we explore the\neffect of the stellar host rotation on the bound binary hardening efficiency,\ndriven by three-body stellar interactions. As seen in previous studies, we find\nthat the centre of mass (CoM) of a prograde MBHB embedded in a rotating\nenvironment starts moving on a nearly circular orbit about the centre of the\nsystem shortly after the MBHB binding. In our runs, the oscillation radius is\napproximately 0.25 ( approximately 0.1) times the binary influence radius for\nequal mass MBHBs (MBHBs with mass ratio 1:4). Conversely, retrograde binaries\nremain anchored about the centre of the host. The binary shrinking rate is\ntwice as fast when the binary CoM exhibits a net orbital motion, owing to a\nmore efficient loss cone repopulation even in our spherical stellar systems. We\ndevelop a model that captures the CoM oscillations of prograde binaries; we\nargue that the CoM angular momentum gain per time unit scales with the internal\nbinary angular momentum, so that most of the displacement is induced by stellar\ninteractions occurring around the time of MBHB binding, while the subsequent\nangular momentum enhancement gets eventually quashed by the effect of dynamical\nfriction. The effect of the background rotation on the MBHB evolution may be\nrelevant for LISA sources, that are expected to form in significantly rotating\nstellar systems.\n"}
+{"abstract": "  Time-frequency masking or spectrum prediction computed via short symmetric\nwindows are commonly used in low-latency deep neural network (DNN) based source\nseparation. In this paper, we propose the usage of an asymmetric\nanalysis-synthesis window pair which allows for training with targets with\nbetter frequency resolution, while retaining the low-latency during inference\nsuitable for real-time speech enhancement or assisted hearing applications. In\norder to assess our approach across various model types and datasets, we\nevaluate it with both speaker-independent deep clustering (DC) model and a\nspeaker-dependent mask inference (MI) model. We report an improvement in\nseparation performance of up to 1.5 dB in terms of source-to-distortion ratio\n(SDR) while maintaining an algorithmic latency of 8 ms.\n"}
+{"abstract": "  Gravitational waves (GWs) provide unobscured insight into the birthplace of\nneutron stars (NSs) and black holes in core-collapse supernovae (CCSNe). The\nnuclear equation of state (EOS) describing these dense environments is yet\nuncertain, and variations in its prescription affect the proto-neutron star\n(PNS) and the post-bounce dynamics in CCSNe simulations, subsequently impacting\nthe GW emission. We perform axisymmetric simulations of CCSNe with Skyrme-type\nEOSs to study how the GW signal and PNS convection zone are impacted by two\nexperimentally accessible EOS parameters, (1) the effective mass of nucleons,\n$m^\\star$, which is crucial in setting the thermal dependence of the EOS, and\n(2) the isoscalar incompressibility modulus, $K_{\\rm{sat}}$. While\n$K_{\\rm{sat}}$ shows little impact, the peak frequency of the GWs has a strong\neffective mass dependence due to faster contraction of the PNS for higher\nvalues of $m^\\star$ owing to a decreased thermal pressure. These more compact\nPNSs also exhibit more neutrino heating which drives earlier explosions and\ncorrelates with the GW amplitude via accretion plumes striking the PNS,\nexciting the oscillations. We investigate the spatial origin of the GWs and\nshow the agreement between a frequency-radial distribution of the GW emission\nand a perturbation analysis. We do not rule out overshoot from below via PNS\nconvection as another moderately strong excitation mechanism in our\nsimulations. We also study the combined effect of effective mass and rotation.\nIn all our simulations we find evidence for a power gap near $\\sim$1250 Hz, we\ninvestigate its origin and report its EOS dependence.\n"}
+{"abstract": "  NASA's Great Observatories have opened up the electromagnetic spectrum from\nspace, providing sustained access to wavelengths not accessible from the\nground. Together, Hubble, Compton, Chandra, and Spitzer have provided the\nscientific community with an agile and powerful suite of telescopes with which\nto attack broad scientific questions, and react to a rapidly changing\nscientific landscape. As the existing Great Observatories age, or are\ndecommissioned, community access to these wavelengths will diminish, with an\naccompanying loss of scientific capability. This report, commissioned by the\nNASA Cosmic Origins, Physics of the Cosmos and Exoplanet Exploration Program\nAnalysis Groups (PAGs), analyzes the importance of multi-wavelength\nobservations from space during the epoch of the Great Observatories, providing\nexamples that span a broad range of astrophysical investigations.\n"}
+{"abstract": "  We present a parametric family of semi-implicit second order accurate\nnumerical methods for non-conservative and conservative advection equation for\nwhich the numerical solutions can be obtained in a fixed number of forward and\nbackward alternating substitutions. The methods use a novel combination of\nimplicit and explicit time discretizations for one-dimensional case and the\nStrang splitting method in several dimensional case. The methods are described\nfor advection equations with a continuous variable velocity that can change its\nsign inside of computational domain. The methods are unconditionally stable in\nthe non-conservative case for variable velocity and for variable numerical\nparameter. Several numerical experiments confirm the advantages of presented\nmethods including an involvement of differential programming to find optimized\nvalues of the variable numerical parameter.\n"}
+{"abstract": "  Change detection (CD) in remote sensing images has been an ever-expanding\narea of research. To date, although many methods have been proposed using\nvarious techniques, accurately identifying changes is still a great challenge,\nespecially in the high resolution or heterogeneous situations, due to the\ndifficulties in effectively modeling the features from ground objects with\ndifferent patterns. In this paper, a novel CD method based on the graph\nconvolutional network (GCN) and multiscale object-based technique is proposed\nfor both homogeneous and heterogeneous images. First, the object-wise high\nlevel features are obtained through a pre-trained U-net and the multiscale\nsegmentations. Treating each parcel as a node, the graph representations can be\nformed and then, fed into the proposed multiscale graph convolutional network\nwith each channel corresponding to one scale. The multiscale GCN propagates the\nlabel information from a small number of labeled nodes to the other ones which\nare unlabeled. Further, to comprehensively incorporate the information from the\noutput channels of multiscale GCN, a fusion strategy is designed using the\nfather-child relationships between scales. Extensive Experiments on optical,\nSAR and heterogeneous optical/SAR data sets demonstrate that the proposed\nmethod outperforms some state-of the-art methods in both qualitative and\nquantitative evaluations. Besides, the Influences of some factors are also\ndiscussed.\n"}
+{"abstract": "  The objective of this study was to investigate the importance of multiple\ncounty-level features in the trajectory of COVID-19. We examined feature\nimportance across 2,787 counties in the United States using a data-driven\nmachine learning model. We trained random forest models using 23 features\nrepresenting six key influencing factors affecting pandemic spread: social\ndemographics of counties, population activities, mobility within the counties,\nmovement across counties, disease attributes, and social network structure.\nAlso, we categorized counties into multiple groups according to their\npopulation densities, and we divided the trajectory of COVID-19 into three\nstages: the outbreak stage, the social distancing stage, and the reopening\nstage. The study aims to answer two research questions: (1) The extent to which\nthe importance of heterogeneous features evolves in different stages; (2) The\nextent to which the importance of heterogeneous features varies across counties\nwith different characteristics. We fitted a set of random forest models to\ndetermine weekly feature importance. The results showed that: (1) Social\ndemographic features, such as gross domestic product, population density, and\nminority status maintained high-importance features throughout stages of\nCOVID-19 across the 2787 studied counties; (2) Within-county mobility features\nhad the highest importance in county clusters with higher population densities;\n(3) The feature reflecting the social network structure (Facebook, social\nconnectedness index), had higher importance in the models for counties with\nhigher population densities. The results show that the data-driven machine\nlearning models could provide important insights to inform policymakers\nregarding feature importance for counties with various population densities and\nin different stages of a pandemic life cycle.\n"}
+{"abstract": "  A fuzzy programming approach is used in this article for solving the piece\nselection problem in P2P network with multiple objectives, in which some of the\nfactors are fuzzy in nature. A piece selection problem has been prepared as a\nfuzzy mixed integer goal programming piece selection problem that includes\nthree primary goals: minimizing the download cost and download time and\nmaximizing speed and useful information transmission subject to realistic\nconstraints regarding peer's demand, peer's capacity, peer's dynamicity, etc.\nThe proposed approach has the ability to handle practical situations in a fuzzy\nenvironment and offers a better decision tool for the piece selection decision\nin a dynamic P2P network. An extensive simulation is carried out to demonstrate\nthe effectiveness of the proposed model. The proposed mechanism has the\ncapability to handle practical situations in a fuzzy environment and offers a\nbetter decision tool for the piece selection decision in a decentralized P2P\nnetwork.\n"}
+{"abstract": "  We study the properties {of a conformal field theory} (CFT) driven\nperiodically with a continuous protocol characterized by a frequency\n$\\omega_D$. Such a drive, in contrast to its discrete counterparts (such as\nsquare pulses or periodic kicks), does not admit exact analytical solution for\nthe evolution operator $U$. In this work, we develop a Floquet perturbation\ntheory which provides an analytic, albeit perturbative, result for $U$ that\nmatches exact numerics in the large drive amplitude limit. We find that the\ndrive yields the well-known heating (hyperbolic) and non-heating (elliptic)\nphases separated by transition lines (parabolic phase boundary). Using this and\nstarting from a primary state of the CFT, we compute the return probability\n($P_n$), equal ($C_n$) and unequal ($G_n$) time two-point primary correlators,\nenergy density($E_n$), and the $m^{\\rm th}$ Renyi entropy ($S_n^m$) after $n$\ndrive cycles. Our results show that below a crossover stroboscopic time scale\n$n_c$, $P_n$, $E_n$ and $G_n$ exhibits universal power law behavior as the\ntransition is approached either from the heating or the non-heating phase; this\ncrossover scale diverges at the transition. We also study the emergent spatial\nstructure of $C_n$, $G_n$ and $E_n$ for the continuous protocol and find\nemergence of spatial divergences of $C_n$ and $G_n$ in both the heating and\nnon-heating phases. We express our results for $S_n^m$ and $C_n$ in terms of\nconformal blocks and provide analytic expressions for these quantities in\nseveral limiting cases. Finally we relate our results to those obtained from\nexact numerics of a driven lattice model.\n"}
+{"abstract": "  We comment on recent claims that recoil in the final stages of Hawking\nevaporation gives black hole remnants large velocities, rendering them inviable\nas a dark matter candidate. We point out that due to cosmic expansion, such\nlarge velocities at the final stages of evaporation are not in tension with the\ncold dark matter paradigm so long as they are attained at sufficiently early\ntimes. In particular, the predicted recoil velocities are robustly compatible\nwith observations if the remnants form before the epoch of big bang\nnucleosynthesis, a requirement which is already imposed by the physics of\nnucleosynthesis itself.\n"}
+{"abstract": "  We present reconstructed convergence maps, \\textit{mass maps}, from the Dark\nEnergy Survey (DES) third year (Y3) weak gravitational lensing data set. The\nmass maps are weighted projections of the density field (primarily dark matter)\nin the foreground of the observed galaxies. We use four reconstruction methods,\neach is a \\textit{maximum a posteriori} estimate with a different model for the\nprior probability of the map: Kaiser-Squires, null B-mode prior, Gaussian\nprior, and a sparsity prior. All methods are implemented on the celestial\nsphere to accommodate the large sky coverage of the DES Y3 data. We compare the\nmethods using realistic $\\Lambda$CDM simulations with mock data that are\nclosely matched to the DES Y3 data. We quantify the performance of the methods\nat the map level and then apply the reconstruction methods to the DES Y3 data,\nperforming tests for systematic error effects. The maps are compared with\noptical foreground cosmic-web structures and are used to evaluate the lensing\nsignal from cosmic-void profiles. The recovered dark matter map covers the\nlargest sky fraction of any galaxy weak lensing map to date.\n"}
+{"abstract": "  In this paper we prove a H\\\"older regularity estimate for viscosity solutions\nof inhomogeneous equations governed by the infinite Laplace operator relative\nto a frame of vector fields.\n"}
+{"abstract": "  Recent experiments (Guan et al. 2016a,b) showed many interesting phenomena on\ndynamic contact angle hysteresis while there is still a lack of complete\ntheoretical interpretation. In this work, we study the time averaging of the\napparent advancing and receding contact angles on surfaces with periodic\nchemical patterns. We first derive a Cox-type boundary condition for the\napparent dynamic contact angle on homogeneous surfaces using Onsager\nvariational principle. Based on this condition, we propose a reduced model for\nsome typical moving contact line problems on chemically inhomogeneous surfaces\nin two dimensions. Multiscale expansion and averaging techniques are employed\nto approximate the model for asymptotically small chemical patterns. We obtain\na quantitative formula for the averaged dynamic contact angles. It gives\nexplicitly how the advancing and receding contact angles depend on the velocity\nand the chemical inhomogeneity of the substrate. The formula is a\ncoarse-graining version of the Cox-type boundary condition on inhomogeneous\nsurfaces. Numerical simulations are presented to validate the analytical\nresults. The numerical results also show that the formula characterizes very\nwell the complicated behaviour of dynamic contact angle hysteresis observed in\nthe experiments.\n"}
+{"abstract": "  Analysing research trends and predicting their impact on academia and\nindustry is crucial to gain a deeper understanding of the advances in a\nresearch field and to inform critical decisions about research funding and\ntechnology adoption. In the last years, we saw the emergence of several\npublicly-available and large-scale Scientific Knowledge Graphs fostering the\ndevelopment of many data-driven approaches for performing quantitative analyses\nof research trends. This chapter presents an innovative framework for\ndetecting, analysing, and forecasting research topics based on a large-scale\nknowledge graph characterising research articles according to the research\ntopics from the Computer Science Ontology. We discuss the advantages of a\nsolution based on a formal representation of topics and describe how it was\napplied to produce bibliometric studies and innovative tools for analysing and\npredicting research dynamics.\n"}
+{"abstract": "  The fish target detection algorithm lacks a good quality data set, and the\nalgorithm achieves real-time detection with lower power consumption on embedded\ndevices, and it is difficult to balance the calculation speed and\nidentification ability. To this end, this paper collected and annotated a data\nset named \"Aquarium Fish\" of 84 fishes containing 10042 images, and based on\nthis data set, proposed a multi-scale input fast fish target detection network\n(BTP-yoloV3) and its optimization method. The experiment uses Depthwise\nconvolution to redesign the backbone of the yoloV4 network, which reduces the\namount of calculation by 94.1%, and the test accuracy is 92.34%. Then, the\ntraining model is enhanced with MixUp, CutMix, and mosaic to increase the test\naccuracy by 1.27%; Finally, use the mish, swish, and ELU activation functions\nto increase the test accuracy by 0.76%. As a result, the accuracy of testing\nthe network with 2000 fish images reached 94.37%, and the computational\ncomplexity of the network BFLOPS was only 5.47. Comparing the YoloV3~4,\nMobileNetV2-yoloV3, and YoloV3-tiny networks of migration learning on this data\nset. The results show that BTP-Yolov3 has smaller model parameters, faster\ncalculation speed, and lower energy consumption during operation while ensuring\nthe calculation accuracy. It provides a certain reference value for the\npractical application of neural network.\n"}
+{"abstract": "  We describe the ongoing Relativistic Binary programme (RelBin), a part of the\nMeerTime large survey project with the MeerKAT radio telescope. RelBin is\nprimarily focused on observations of relativistic effects in binary pulsars to\nenable measurements of neutron star masses and tests of theories of gravity. We\nselected 25 pulsars as an initial high priority list of targets based on their\ncharacteristics and observational history with other telescopes. In this paper,\nwe provide an outline of the programme, present polarisation calibrated pulse\nprofiles for all selected pulsars as a reference catalogue along with updated\ndispersion measures. We report Faraday rotation measures for 24 pulsars, twelve\nof which have been measured for the first time. More than a third of our\nselected pulsars show a flat position angle swing confirming earlier\nobservations. We demonstrate the ability of the Rotating Vector Model (RVM),\nfitted here to seven binary pulsars, including the Double Pulsar (PSR\nJ0737$-$3039A), to obtain information about the orbital inclination angle. We\npresent a high time resolution light curve of the eclipse of PSR J0737$-$3039A\nby the companion's magnetosphere, a high-phase resolution position angle swing\nfor PSR J1141$-$6545, an improved detection of the Shapiro delay of PSR\nJ1811$-$2405, and pulse scattering measurements for PSRs J1227$-$6208,\nJ1757$-$1854, and J1811$-$1736. Finally, we demonstrate that timing\nobservations with MeerKAT improve on existing data sets by a factor of,\ntypically, 2-3, sometimes by an order of magnitude.\n"}
+{"abstract": "  Device-edge co-inference opens up new possibilities for resource-constrained\nwireless devices (WDs) to execute deep neural network (DNN)-based applications\nwith heavy computation workloads. In particular, the WD executes the first few\nlayers of the DNN and sends the intermediate features to the edge server that\nprocesses the remaining layers of the DNN. By adapting the model splitting\ndecision, there exists a tradeoff between local computation cost and\ncommunication overhead. In practice, the DNN model is re-trained and updated\nperiodically at the edge server. Once the DNN parameters are regenerated, part\nof the updated model must be placed at the WD to facilitate on-device\ninference. In this paper, we study the joint optimization of the model\nplacement and online model splitting decisions to minimize the energy-and-time\ncost of device-edge co-inference in presence of wireless channel fading. The\nproblem is challenging because the model placement and model splitting\ndecisions are strongly coupled, while involving two different time scales. We\nfirst tackle online model splitting by formulating an optimal stopping problem,\nwhere the finite horizon of the problem is determined by the model placement\ndecision. In addition to deriving the optimal model splitting rule based on\nbackward induction, we further investigate a simple one-stage look-ahead rule,\nfor which we are able to obtain analytical expressions of the model splitting\ndecision. The analysis is useful for us to efficiently optimize the model\nplacement decision in a larger time scale. In particular, we obtain a\nclosed-form model placement solution for the fully-connected multilayer\nperceptron with equal neurons. Simulation results validate the superior\nperformance of the joint optimal model placement and splitting with various DNN\nstructures.\n"}
+{"abstract": "  We use Feireisl-Lions theory to deduce the existence of weak solutions to a\nsystem describing the dynamics of a linear oscillator containing a Newtonian\ncompressible fluid. The appropriate Navier-Stokes equation is considered on a\ndomain whose movement has one degree of freedom. The equation is paired with\nthe Newton law and we assume a no-slip boundary condition.\n"}
+{"abstract": "  Wavelength transduction of single-photon signals is indispensable to\nnetworked quantum applications, particularly those incorporating quantum\nmemories. Lithium niobate nanophotonic devices have demonstrated favorable\nlinear, nonlinear, and electro-optical properties to deliver this crucial\nfunction while offering superiror efficiency, integrability, and scalability.\nYet, their quantum noise level--an crucial metric for any single-photon based\napplication--has yet to be understood. In this work, we report the first study\nwith the focus on telecom to near-visible conversion driven by a telecom pump\nof small detuning, for practical considerations in distributed quantum\nprocessing over fiber networks. Our results find the noise level to be on the\norder of $10^{-4}$ photons per time-frequency mode for high conversion,\nallowing faithful pulsed operations. Through carefully analyzing the origins of\nsuch noise and each's dependence on the pump power and wavelength detuning, we\nhave also identified a formula for noise suppression to $10^{-5}$ photons per\nmode. Our results assert a viable, low-cost, and modular approach to networked\nquantum processing and beyond using lithium niobate nanophotonics.\n"}
+{"abstract": "  Walking while using a smartphone is becoming a major pedestrian safety\nconcern as people may unknowingly bump into various obstacles that could lead\nto severe injuries. In this paper, we propose ObstacleWatch, an acoustic-based\nobstacle collision detection system to improve the safety of pedestrians who\nare engaged in smartphone usage while walking. ObstacleWatch leverages the\nadvanced audio hardware of the smartphone to sense the surrounding obstacles\nand infers fine-grained information about the frontal obstacle for collision\ndetection. In particular, our system emits well-designed inaudible beep signals\nfrom the smartphone built-in speaker and listens to the reflections with the\nstereo recording of the smartphone. By analyzing the reflected signals received\nat two microphones, ObstacleWatch is able to extract fine-grained information\nof the frontal obstacle including the distance, angle, and size for detecting\nthe possible collisions and to alert users. Our experimental evaluation under\ntwo real-world environments with different types of phones and obstacles shows\nthat ObstacleWatch achieves over 92% accuracy in predicting obstacle collisions\nwith distance estimation errors at about 2 cm. Results also show that\nObstacleWatch is robust to different sizes of objects and is compatible with\ndifferent phone models with low energy consumption.\n"}
+{"abstract": "  In this work we look into adding a new language to a multilingual NMT system\nin an unsupervised fashion. Under the utilization of pre-trained cross-lingual\nword embeddings we seek to exploit a language independent multilingual sentence\nrepresentation to easily generalize to a new language. While using\ncross-lingual embeddings for word lookup we decode from a yet entirely unseen\nsource language in a process we call blind decoding. Blindly decoding from\nPortuguese using a basesystem containing several Romance languages we achieve\nscores of 36.4 BLEU for Portuguese-English and 12.8 BLEU for Russian-English.\nIn an attempt to train the mapping from the encoder sentence representation to\na new target language we use our model as an autoencoder. Merely training to\ntranslate from Portuguese to Portuguese while freezing the encoder we achieve\n26 BLEU on English-Portuguese, and up to 28 BLEU when adding artificial noise\nto the input. Lastly we explore a more practical adaptation approach through\nnon-iterative backtranslation, exploiting our model's ability to produce high\nquality translations through blind decoding. This yields us up to 34.6 BLEU on\nEnglish-Portuguese, attaining near parity with a model adapted on real\nbilingual data.\n"}
+{"abstract": "  We report here on experiments and simulations examining the effect of\nchanging wall friction on the gravity-driven flow of spherical particles in a\nvertical hopper. In 2D experiments and simulations, we observe that the\nexponent of the expected power-law scaling of mass flow rate with opening size\n(known as Beverloo's law) decreases as the coefficient of friction between\nparticles and wall increases, whereas Beverloo scaling works as expected in 3D.\nIn our 2D experiments, we find that wall friction plays the biggest role in a\nregion near the outlet comparable in height to the largest opening size.\nHowever, wall friction is not the only factor determining a constant rate of\nflow, as we observe a near-constant mass outflow rate in the 2D simulations\neven when wall friction is set to zero. We show in our simulations that an\nincrease in wall friction leaves packing fractions relatively unchanged, while\naverage particle velocities become independent of opening size as the\ncoefficient of friction increases. We track the spatial pattern of\ntime-averaged particle velocities and accelerations inside the hopper. We\nobserve that the hemisphere-like region above the opening where particles begin\nto accelerate is largely independent of opening size at finite wall friction.\nHowever, the magnitude of particle accelerations decreases significantly as\nwall friction increases, which in turn results in mean sphere velocities that\nno longer scale with opening size, consistent with our observations of mass\nflow rate scaling. The case of zero wall friction is anomalous, in that most of\nthe acceleration takes place near the outlet.\n"}
+{"abstract": "  The analytical and numerical analysis of the dynamics of charged particles in\nthe field of an intensive transverse electromagnetic wave in a vacuum presented\nin the article. Identifies the conditions for resonant acceleration of\nparticles. These conditions are formulated. The features and the mechanism of\nthis acceleration are discussed.\n"}
+{"abstract": "  The outbreak of COVID-19 led to a record-breaking race to develop a vaccine.\nHowever, the limited vaccine capacity creates another massive challenge: how to\ndistribute vaccines to mitigate the near-end impact of the pandemic? In the\nUnited States in particular, the new Biden administration is launching mass\nvaccination sites across the country, raising the obvious question of where to\nlocate these clinics to maximize the effectiveness of the vaccination campaign.\nThis paper tackles this question with a novel data-driven approach to optimize\nCOVID-19 vaccine distribution. We first augment a state-of-the-art\nepidemiological model, called DELPHI, to capture the effects of vaccinations\nand the variability in mortality rates across age groups. We then integrate\nthis predictive model into a prescriptive model to optimize the location of\nvaccination sites and subsequent vaccine allocation. The model is formulated as\na bilinear, non-convex optimization model. To solve it, we propose a coordinate\ndescent algorithm that iterates between optimizing vaccine distribution and\nsimulating the dynamics of the pandemic. As compared to benchmarks based on\ndemographic and epidemiological information, the proposed optimization approach\nincreases the effectiveness of the vaccination campaign by an estimated $20\\%$,\nsaving an extra $4000$ extra lives in the United States over a three-month\nperiod. The proposed solution achieves critical fairness objectives -- by\nreducing the death toll of the pandemic in several states without hurting\nothers -- and is highly robust to uncertainties and forecast errors -- by\nachieving similar benefits under a vast range of perturbations.\n"}
+{"abstract": "  In this paper, we present the use of Model Predictive Control (MPC) based on\nReinforcement Learning (RL) to find the optimal policy for a multi-agent\nbattery storage system. A time-varying prediction of the power price and\nproduction-demand uncertainty are considered. We focus on optimizing an\neconomic objective cost while avoiding very low or very high state of charge,\nwhich can damage the battery. We consider the bounded power provided by the\nmain grid and the constraints on the power input and state of each agent. A\nparametrized MPC-scheme is used as a function approximator for the\ndeterministic policy gradient method and RL optimizes the closed-loop\nperformance by updating the parameters. Simulation results demonstrate that the\nproposed method is able to tackle the constraints and deliver the optimal\npolicy.\n"}
+{"abstract": "  The mass contained in an arbitrary spacetime in general relativity is not\nwell defined. However, for asymptotically flat spacetimes various definitions\nof mass have been proposed. In this paper I consider eight masses and show that\nsome of them correspond to the active gravitational mass while the others\ncorrespond to the inertial mass. For example, the ADM mass corresponds to the\ninertial mass while the M$\\o$ller mass corresponds to the active gravitational\nmass. In general the inertial and active gravitational masses are not equal. If\nthe spacetime is vacuum at large $r$ the Einstein equations force the inertial\nand active gravitational masses to be the same. The Einstein equations also\nforce the masses to be the same if any matter that extends out to large $r$\nsatisfies the weak, strong or dominant energy condition. I also examine the\ncontributions of the inertial and active gravitational masses to the\ngravitational redshift, the deflection of light, the Shapiro time delay, the\nprecession of perihelia and to the motion of test bodies in the spacetime.\n"}
+{"abstract": "  We test the adequacy of ultraviolet (UV) spectra for characterizing the outer\nstructure of Type Ia supernova (SN) ejecta. For this purpose, we perform\nspectroscopic analysis for ASASSN-14lp, a normal SN Ia showing low continuum in\nthe mid-UV regime. To explain the strong UV suppression, two possible origins\nhave been investigated by mapping the chemical profiles over a significant part\nof their ejecta. We fit the spectral time series with mid-UV coverage obtained\nbefore and around maximum light by HST, supplemented with ground-based optical\nobservations for the earliest epochs. The synthetic spectra are calculated with\nthe one dimensional MC radiative-transfer code TARDIS from self-consistent\nejecta models. Among several physical parameters, we constrain the abundance\nprofiles of nine chemical elements. We find that a distribution of $^{56}$Ni\n(and other iron-group elements) that extends toward the highest velocities\nreproduces the observed UV flux well. The presence of radioactive material in\nthe outer layers of the ejecta, if confirmed, implies strong constraints on the\npossible explosion scenarios. We investigate the impact of the inferred\n$^{56}$Ni distribution on the early light curves with the radiative transfer\ncode TURTLS, and confront the results with the observed light curves of\nASASSN-14lp. The inferred abundances are not in conflict with the observed\nphotometry. We also test whether the UV suppression can be reproduced if the\nradiation at the photosphere is significantly lower in the UV regime than the\npure Planck function. In this case, solar metallicity might be sufficient\nenough at the highest velocities to reproduce the UV suppression.\n"}
+{"abstract": "  Medical image registration and segmentation are two of the most frequent\ntasks in medical image analysis. As these tasks are complementary and\ncorrelated, it would be beneficial to apply them simultaneously in a joint\nmanner. In this paper, we formulate registration and segmentation as a joint\nproblem via a Multi-Task Learning (MTL) setting, allowing these tasks to\nleverage their strengths and mitigate their weaknesses through the sharing of\nbeneficial information. We propose to merge these tasks not only on the loss\nlevel, but on the architectural level as well. We studied this approach in the\ncontext of adaptive image-guided radiotherapy for prostate cancer, where\nplanning and follow-up CT images as well as their corresponding contours are\navailable for training. The study involves two datasets from different\nmanufacturers and institutes. The first dataset was divided into training (12\npatients) and validation (6 patients), and was used to optimize and validate\nthe methodology, while the second dataset (14 patients) was used as an\nindependent test set. We carried out an extensive quantitative comparison\nbetween the quality of the automatically generated contours from different\nnetwork architectures as well as loss weighting methods. Moreover, we evaluated\nthe quality of the generated deformation vector field (DVF). We show that MTL\nalgorithms outperform their Single-Task Learning (STL) counterparts and achieve\nbetter generalization on the independent test set. The best algorithm achieved\na mean surface distance of $1.06 \\pm 0.3$ mm, $1.27 \\pm 0.4$ mm, $0.91 \\pm 0.4$\nmm, and $1.76 \\pm 0.8$ mm on the validation set for the prostate, seminal\nvesicles, bladder, and rectum, respectively. The high accuracy of the proposed\nmethod combined with the fast inference speed, makes it a promising method for\nautomatic re-contouring of follow-up scans for adaptive radiotherapy.\n"}
+{"abstract": "  Transductive zero-shot learning (T-ZSL) which could alleviate the domain\nshift problem in existing ZSL works, has received much attention recently.\nHowever, an open problem in T-ZSL: how to effectively make use of unseen-class\nsamples for training, still remains. Addressing this problem, we first\nempirically analyze the roles of unseen-class samples with different degrees of\nhardness in the training process based on the uneven prediction phenomenon\nfound in many ZSL methods, resulting in three observations. Then, we propose\ntwo hardness sampling approaches for selecting a subset of diverse and hard\nsamples from a given unseen-class dataset according to these observations. The\nfirst one identifies the samples based on the class-level frequency of the\nmodel predictions while the second enhances the former by normalizing the class\nfrequency via an approximate class prior estimated by an explored prior\nestimation algorithm. Finally, we design a new Self-Training framework with\nHardness Sampling for T-ZSL, called STHS, where an arbitrary inductive ZSL\nmethod could be seamlessly embedded and it is iteratively trained with\nunseen-class samples selected by the hardness sampling approach. We introduce\ntwo typical ZSL methods into the STHS framework and extensive experiments\ndemonstrate that the derived T-ZSL methods outperform many state-of-the-art\nmethods on three public benchmarks. Besides, we note that the unseen-class\ndataset is separately used for training in some existing transductive\ngeneralized ZSL (T-GZSL) methods, which is not strict for a GZSL task. Hence,\nwe suggest a more strict T-GZSL data setting and establish a competitive\nbaseline on this setting by introducing the proposed STHS framework to T-GZSL.\n"}
+{"abstract": "  Let $(\\xi_k,\\eta_k)_{k\\in\\mathbb{N}}$ be independent identically distributed\nrandom vectors with arbitrarily dependent positive components. We call a\n(globally) perturbed random walk a random sequence $T:=(T_k)_{k\\in\\mathbb{N}}$\ndefined by $T_k:=\\xi_1+\\ldots+\\xi_{k-1}+\\eta_k$ for $k\\in\\mathbb{N}$. Consider\na general branching process generated by $T$ and denote by $N_j(t)$ the number\nof the $j$th generation individuals with birth times $\\leq t$. We treat early\ngenerations, that is, fixed generations $j$ which do not depend on $t$. In this\nsetting we prove counterparts for $\\mathbb{E}N_j$ of the Blackwell theorem and\nthe key renewal theorem, prove a strong law of large numbers for $N_j$, find\nthe first-order asymptotics for the variance of $N_j$. Also, we prove a\nfunctional limit theorem for the vector-valued process $(N_1(ut),\\ldots,\nN_j(ut))_{u\\geq 0}$, properly normalized and centered, as $t\\to\\infty$. The\nlimit is a vector-valued Gaussian process whose components are integrated\nBrownian motions.\n"}
+{"abstract": "  The augmented Lagrangian method (ALM) is one of the most useful methods for\nconstrained optimization. Its convergence has been well established under\nconvexity assumptions or smoothness assumptions, or under both assumptions. ALM\nmay experience oscillations and divergence when the underlying problem is\nsimultaneously nonconvex and nonsmooth. In this paper, we consider the linearly\nconstrained problem with a nonconvex (in particular, weakly convex) and\nnonsmooth objective. We modify ALM to use a Moreau envelope of the augmented\nLagrangian and establish its convergence under conditions that are weaker than\nthose in the literature. We call it the Moreau envelope augmented Lagrangian\n(MEAL) method. We also show that the iteration complexity of MEAL is\n$o(\\varepsilon^{-2})$ to yield an $\\varepsilon$-accurate first-order stationary\npoint. We establish its whole sequence convergence (regardless of the initial\nguess) and a rate when a Kurdyka-Lojasiewicz property is assumed. Moreover,\nwhen the subproblem of MEAL has no closed-form solution and is difficult to\nsolve, we propose two practical variants of MEAL, an inexact version called\niMEAL with an approximate proximal update, and a linearized version called\nLiMEAL for the constrained problem with a composite objective. Their\nconvergence is also established.\n"}
+{"abstract": "  In this effort, a novel operator theoretic framework is developed for\ndata-driven solution of optimal control problems. The developed methods focus\non the use of trajectories (i.e., time-series) as the fundamental unit of data\nfor the resolution of optimal control problems in dynamical systems. Trajectory\ninformation in the dynamical systems is embedded in a reproducing kernel\nHilbert space (RKHS) through what are called occupation kernels. The occupation\nkernels are tied to the dynamics of the system through the densely defined\nLiouville operator. The pairing of Liouville operators and occupation kernels\nallows for lifting of nonlinear finite-dimensional optimal control problems\ninto the space of infinite-dimensional linear programs over RKHSs.\n"}
+{"abstract": "  The fifth generation (5G) wireless technology is primarily designed to\naddress a wide range of use cases categorized into the enhanced mobile\nbroadband (eMBB), ultra-reliable and low latency communication (URLLC), and\nmassive machine-type communication (mMTC). Nevertheless, there are a few other\nuse cases which are in-between these main use cases such as industrial wireless\nsensor networks, video surveillance, or wearables. In order to efficiently\nserve such use cases, in Release 17, the 3rd generation partnership project\n(3GPP) introduced the reduced capability NR devices (NR-RedCap) with lower cost\nand complexity, smaller form factor and longer battery life compared to regular\nNR devices. However, one key potential consequence of device cost and\ncomplexity reduction is the coverage loss. In this paper, we provide a\ncomprehensive evaluation of NR RedCap coverage for different physical channels\nand initial access messages to identify the channels/messages that are\npotentially coverage limiting for RedCap UEs. We perform the coverage\nevaluations for RedCap UEs operating in three different scenarios, namely\nRural, Urban and Indoor with carrier frequencies 700 MHz, 2.6 GHz and 28 GHz,\nrespectively. Our results confirm that for all the considered scenarios, the\namounts of required coverage recovery for RedCap channels are either less than\n1 dB or can be compensated by considering smaller data rate targets for RedCap\nuse cases.\n"}
+{"abstract": "  Using a extended version of the Quantum Hadrodynamics (QHD), I propose a new\nmicroscopic equation of state (EoS) able to correctly reproduce the main\nproperties of symmetric nuclear matter at the saturation density, as well\nproduce massive neutron stars and satisfactory results for the radius and the\ntidal parameter $\\Lambda$. I show that even when hyperons are present, this EoS\nis able to reproduce at least 2.00 solar masses neutron star. The constraints\nabout the radius of a $2.00M_\\odot$ and the minimum mass that enable direct\nUrca effect are also checked.\n"}
+{"abstract": "  This paper is concerned with high moment and pathwise error estimates for\nboth velocity and pressure approximations of the Euler-Maruyama scheme for time\ndiscretization and its two fully discrete mixed finite element discretizations.\nThe main idea for deriving the high moment error estimates for the velocity\napproximation is to use a bootstrap technique starting from the second moment\nerror estimate. The pathwise error estimate, which is sub-optimal in the energy\nnorm, is obtained by using Kolmogorov's theorem based on the high moment error\nestimates. Unlike for the velocity error estimate, the higher moment and\npathwise error estimates for the pressure approximation are derived in a\ntime-averaged norm. In addition, the impact of noise types on the rates of\nconvergence for both velocity and pressure approximations is also addressed.\n"}
+{"abstract": "  Machine Learning (ML) models are known to be vulnerable to adversarial inputs\nand researchers have demonstrated that even production systems, such as\nself-driving cars and ML-as-a-service offerings, are susceptible. These systems\nrepresent a target for bad actors. Their disruption can cause real physical and\neconomic harm. When attacks on production ML systems occur, the ability to\nattribute the attack to the responsible threat group is a critical step in\nformulating a response and holding the attackers accountable. We pose the\nfollowing question: can adversarially perturbed inputs be attributed to the\nparticular methods used to generate the attack? In other words, is there a way\nto find a signal in these attacks that exposes the attack algorithm, model\narchitecture, or hyperparameters used in the attack? We introduce the concept\nof adversarial attack attribution and create a simple supervised learning\nexperimental framework to examine the feasibility of discovering attributable\nsignals in adversarial attacks. We find that it is possible to differentiate\nattacks generated with different attack algorithms, models, and hyperparameters\non both the CIFAR-10 and MNIST datasets.\n"}
+{"abstract": "  In steady-state, the plasma loss rate to the chamber wall is balanced by the\nionization rate in the hot-filament discharges. This balance in the loss rate\nand ionization rate maintains the quasi--neutrality of the bulk plasma. In this\nreport, we have studied the properties of bulk plasma in the presence of an\nauxiliary (additional) biased metal disk, which is working as a sink, in\nlow-pressure helium plasma. A single Langmuir probe and emissive probe are used\nto characterize the plasma for various biases (positive and negative) to the\nmetal disk, which was placed along the discharge axis inside the plasma. It is\nobserved that only a positively biased disk increases the plasma potential,\nelectron temperature, and plasma density. Moreover, the plasma parameters\nremain unaltered when the disk is negatively biased. The observed results for\ntwo different-sized positively metal disks are compared with an available\ntheoretical model and found an opposite behavior of plasma density variation\nwith the disk bias voltages at given discharge condition. The role of the\nprimary energetic electron population in determining the plasma parameters is\ndiscussed. These experimental results are qualitatively explained on the basis\nof electrostatic confinement arising due to the loss of electrons to a biased\nmetal disk electrode.\n"}
+{"abstract": "  Language models are the foundation of current neural network-based models for\nnatural language understanding and generation. However, research on the\nintrinsic performance of language models on African languages has been\nextremely limited, which is made more challenging by the lack of large or\nstandardised training and evaluation sets that exist for English and other\nhigh-resource languages. In this paper, we evaluate the performance of\nopen-vocabulary language models on low-resource South African languages, using\nbyte-pair encoding to handle the rich morphology of these languages. We\nevaluate different variants of n-gram models, feedforward neural networks,\nrecurrent neural networks (RNNs), and Transformers on small-scale datasets.\nOverall, well-regularized RNNs give the best performance across two isiZulu and\none Sepedi datasets. Multilingual training further improves performance on\nthese datasets. We hope that this research will open new avenues for research\ninto multilingual and low-resource language modelling for African languages.\n"}
+{"abstract": "  We argue that a superconducting state with a Fermi-surface of Bogoliubov\nquasiparticles, a Bogoliubov Fermi-surface (BG-FS), can be identified by the\ndependence of physical quantities on disorder. In particular, we show that a\nlinear dependence of the residual density of states at weak disorder\ndistinguishes a BG-FS state from other nodal superconducting states. We further\ndemonstrate the stability of supercurrent against impurities and a\ncharacteristic Drude-like behavior of the optical conductivity. Our results can\nbe directly applied to electron irradiation experiments on candidate materials\nof BG-FSs, including Sr$_2$RuO$_4$, FeSe$_{1-x}$S$_x$, and UBe$_{13}$.\n"}
+{"abstract": "  We study the effects of stochastic resetting on geometric Brownian motion\n(GBM), a canonical stochastic multiplicative process for non-stationary and\nnon-ergodic dynamics. Resetting is a sudden interruption of a process, which\nconsecutively renews its dynamics. We show that, although resetting renders GBM\nstationary, the resulting process remains non-ergodic. Quite surprisingly, the\neffect of resetting is pivotal in manifesting the non-ergodic behavior. In\nparticular, we observe three different long-time regimes: a quenched state, an\nunstable and a stable annealed state depending on the resetting strength.\nNotably, in the last regime, the system is self-averaging and thus the sample\naverage will always mimic ergodic behavior establishing a stand alone feature\nfor GBM under resetting. Crucially, the above-mentioned regimes are well\nseparated by a self-averaging time period which can be minimized by an optimal\nresetting rate. Our results can be useful to interpret data emanating from\nstock market collapse or reconstitution of investment portfolios.\n"}
+{"abstract": "  Markov Decision Processes are classically solved using Value Iteration and\nPolicy Iteration algorithms. Recent interest in Reinforcement Learning has\nmotivated the study of methods inspired by optimization, such as gradient\nascent. Among these, a popular algorithm is the Natural Policy Gradient, which\nis a mirror descent variant for MDPs. This algorithm forms the basis of several\npopular Reinforcement Learning algorithms such as Natural actor-critic, TRPO,\nPPO, etc, and so is being studied with growing interest. It has been shown that\nNatural Policy Gradient with constant step size converges with a sublinear rate\nof O(1/k) to the global optimal. In this paper, we present improved finite time\nconvergence bounds, and show that this algorithm has geometric (also known as\nlinear) asymptotic convergence rate. We further improve this convergence result\nby introducing a variant of Natural Policy Gradient with adaptive step sizes.\nFinally, we compare different variants of policy gradient methods\nexperimentally.\n"}
+{"abstract": "  In 2017 Skabelund constructed two new examples of maximal curves\n$\\tilde{\\mathcal{S}}_q$ and $\\tilde{\\mathcal{R}}_q$ as covers of the Suzuki and\nRee curves, respectively. The resulting Skabelund curves are analogous to the\nGiulietti-Korchm\\'aros cover of the Hermitian curve. In this paper a complete\ncharacterization of all Galois subcovers of the Skabelund curves\n$\\tilde{\\mathcal{S}}_q$ and $\\tilde{\\mathcal{R}}_q$ is given. Calculating the\ngenera of the corresponding curves, we find new additions to the list of known\ngenera of maximal curves over finite fields.\n"}
+{"abstract": "  Efficient and precise prediction of plasticity by data-driven models relies\non appropriate data preparation and a well-designed model. Here we introduce an\nunsupervised machine learning-based data preparation method to maximize the\ntrainability of crystal orientation evolution data during deformation. For\nTaylor model crystal plasticity data, the preconditioning procedure improves\nthe test score of an artificial neural network from 0.831 to 0.999, while\ndecreasing the training iterations by an order of magnitude. The efficacy of\nthe approach was further improved with a recurrent neural network. Electron\nbackscattered (EBSD) lab measurements of crystal rotation during rolling were\ncompared with the results of the surrogate model, and despite error introduced\nby Taylor model simplifying assumptions, very reasonable agreement between the\nsurrogate model and experiment was observed. Our method is foundational for\nfurther data-driven studies, enabling the efficient and precise prediction of\ntexture evolution from experimental and simulated crystal plasticity results.\n"}
+{"abstract": "  Cyber threat and attack intelligence information are available in\nnon-standard format from heterogeneous sources. Comprehending them and\nutilizing them for threat intelligence extraction requires engaging security\nexperts. Knowledge graphs enable converting this unstructured information from\nheterogeneous sources into a structured representation of data and factual\nknowledge for several downstream tasks such as predicting missing information\nand future threat trends. Existing large-scale knowledge graphs mainly focus on\ngeneral classes of entities and relationships between them. Open-source\nknowledge graphs for the security domain do not exist. To fill this gap, we've\nbuilt \\textsf{TINKER} - a knowledge graph for threat intelligence\n(\\textbf{T}hreat \\textbf{IN}telligence \\textbf{K}nowl\\textbf{E}dge\ng\\textbf{R}aph). \\textsf{TINKER} is generated using RDF triples describing\nentities and relations from tokenized unstructured natural language text from\n83 threat reports published between 2006-2021. We built \\textsf{TINKER} using\nclasses and properties defined by open-source malware ontology and using\nhand-annotated RDF triples. We also discuss ongoing research and challenges\nfaced while creating \\textsf{TINKER}.\n"}
+{"abstract": "  We generalize standard credal set models for imprecise probabilities to\ninclude higher order credal sets -- confidences about confidences. In doing so,\nwe specify how an agent's higher order confidences (credal sets) update upon\nobserving an event. Our model begins to address standard issues with imprecise\nprobability models, like Dilation and Belief Inertia. We conjecture that when\nhigher order credal sets contain all possible probability functions, then in\nthe limiting case the highest order confidences converge to form a uniform\ndistribution over the first order credal set, where we define uniformity in\nterms of the statistical distance metric (total variation distance). Finite\nsimulation supports the conjecture. We further suggest that this convergence\npresents the total-variation-uniform distribution as a natural, privileged\nprior for statistical hypothesis testing.\n"}
+{"abstract": "  We consider the inverse Seesaw scenario for neutrino masses with the\napproximate Lepton number symmetry broken dynamically by a scalar with Lepton\nnumber two. We show that the Majoron associated to the spontaneous symmetry\nbreaking can alleviate the Hubble tension through its contribution to $\\Delta\nN_\\text{eff}$ and late decays to neutrinos. Among the additional fermionic\nstates required for realizing the inverse Seesaw mechanism, sterile neutrinos\nat the keV-MeV scale can account for all the dark matter component of the\nUniverse if produced via freeze-in from the decays of heavier degrees of\nfreedom.\n"}
+{"abstract": "  In any type II superstring background, the supergravity vertex operators in\nthe pure spinor formalism are described by a gauge superfield. In this paper,\nwe obtain for the first time an explicit expression for this superfield in an\n$AdS_5 \\times S^5$ background. Previously, the vertex operators were only known\nclose to the boundary of $AdS_5$ or in the minus eight picture. Our strategy\nfor the computation was to apply eight picture raising operators in the minus\neight picture vertices. In the process, a huge number of terms are generated\nand we have developed numerical techniques to perform intermediary\nsimplifications. Alternatively, the same numerical techniques can be used to\ncompute the vertices directly in the zero picture by constructing a basis of\ninvariants and fitting for the coefficients. One motivation for constructing\nthe vertex operators is the computation of $AdS_5 \\times S^5$ string\namplitudes.\n"}
+{"abstract": "  In this article we investigate the fibers of relative $D$-modules. In general\nwe prove that there exists an open, Zariski dense subset of the vanishing set\nof the annihilator over which the fibers of a cyclic relative $D$-module are\nnon-zero. Next we restrict our attention to relatively holonomic $D$-modules.\nFor this class we prove that the fiber over every point in the vanishing set of\nthe annihilator is non-zero. As a consequence we obtain new proofs of a\nconjecture of Budur which was recently proven by Budur, van der Veer, Wu and\nZhou, as well as a new proof of a theorem of Maisonobe. Moreover, we also\nobtain a diagonal specialization result for Bernstein-Sato ideals.\n"}
+{"abstract": "  Riemannian manifolds provide a principled way to model nonlinear geometric\nstructure inherent in data. A Riemannian metric on said manifolds determines\ngeometry-aware shortest paths and provides the means to define statistical\nmodels accordingly. However, these operations are typically computationally\ndemanding. To ease this computational burden, we advocate probabilistic\nnumerical methods for Riemannian statistics. In particular, we focus on\nBayesian quadrature (BQ) to numerically compute integrals over normal laws on\nRiemannian manifolds learned from data. In this task, each function evaluation\nrelies on the solution of an expensive initial value problem. We show that by\nleveraging both prior knowledge and an active exploration scheme, BQ\nsignificantly reduces the number of required evaluations and thus outperforms\nMonte Carlo methods on a wide range of integration problems. As a concrete\napplication, we highlight the merits of adopting Riemannian geometry with our\nproposed framework on a nonlinear dataset from molecular dynamics.\n"}
+{"abstract": "  Gravitational instabilities can drive small-scale turbulence and large-scale\nspiral arms in massive gaseous disks under conditions of slow radiative\ncooling. These motions affect the observed disk morphology, its mass accretion\nrate and variability, and could control the process of planet formation via\ndust grain concentration, processing, and collisional fragmentation. We study\ngravito-turbulence and its associated spiral structure in thin gaseous disks\nsubject to a prescribed cooling law. We characterize the morphology, coherence,\nand propagation of the spirals and examine when the flow deviates from viscous\ndisk models. We used the finite-volume code Pluto to integrate the equations of\nself-gravitating hydrodynamics in three-dimensional spherical geometry. The gas\nwas cooled over longer-than-orbital timescales to trigger the gravitational\ninstability and sustain turbulence. We ran models for various disk masses and\ncooling rates. In all cases considered, the turbulent gravitational stress\ntransports angular momentum outward at a rate compatible with viscous disk\ntheory. The dissipation of orbital energy happens via shocks in spiral density\nwakes, heating the disk back to a marginally stable thermal equilibrium. These\nwakes drive vertical motions and contribute to mix material from the disk with\nits corona. They are formed and destroyed intermittently, and they nearly\ncorotate with the gas at every radius. As a consequence, large-scale spiral\narms exhibit no long-term global coherence, and energy thermalization is an\nessentially local process. In the absence of radial substructures or tidal\nforcing, and provided a local cooling law, gravito-turbulence reduces to a\nlocal phenomenon in thin gaseous disks.\n"}
+{"abstract": "  For many reinforcement learning (RL) applications, specifying a reward is\ndifficult. This paper considers an RL setting where the agent obtains\ninformation about the reward only by querying an expert that can, for example,\nevaluate individual states or provide binary preferences over trajectories.\nFrom such expensive feedback, we aim to learn a model of the reward that allows\nstandard RL algorithms to achieve high expected returns with as few expert\nqueries as possible. To this end, we propose Information Directed Reward\nLearning (IDRL), which uses a Bayesian model of the reward and selects queries\nthat maximize the information gain about the difference in return between\nplausibly optimal policies. In contrast to prior active reward learning methods\ndesigned for specific types of queries, IDRL naturally accommodates different\nquery types. Moreover, it achieves similar or better performance with\nsignificantly fewer queries by shifting the focus from reducing the reward\napproximation error to improving the policy induced by the reward model. We\nsupport our findings with extensive evaluations in multiple environments and\nwith different query types.\n"}
+{"abstract": "  The growing popularity of online fundraising (aka \"crowdfunding\") has\nattracted significant research on the subject. In contrast to previous studies\nthat attempt to predict the success of crowdfunded projects based on specific\ncharacteristics of the projects and their creators, we present a more general\napproach that focuses on crowd dynamics and is robust to the particularities of\ndifferent crowdfunding platforms. We rely on a multi-method analysis to\ninvestigate the correlates, predictive importance, and quasi-causal effects of\nfeatures that describe crowd dynamics in determining the success of crowdfunded\nprojects. By applying a multi-method analysis to a study of fundraising in\nthree different online markets, we uncover general crowd dynamics that\nultimately decide which projects will succeed. In all analyses and across the\nthree different platforms, we consistently find that funders' behavioural\nsignals (1) are significantly correlated with fundraising success; (2)\napproximate fundraising outcomes better than the characteristics of projects\nand their creators such as credit grade, company valuation, and subject domain;\nand (3) have significant quasi-causal effects on fundraising outcomes while\ncontrolling for potentially confounding project variables. By showing that\nuniversal features deduced from crowd behaviour are predictive of fundraising\nsuccess on different crowdfunding platforms, our work provides design-relevant\ninsights about novel types of collective decision-making online. This research\ninspires thus potential ways to leverage cues from the crowd and catalyses\nresearch into crowd-aware system design.\n"}
+{"abstract": "  We use a recent numerical security proof technique to analyse three different\npostselection strategies for continuous-variable quantum key distribution\nprotocols (CV-QKD) with quadrature phase-shift keying modulation. For all\npostselection strategies studied we provide novel analytical results for the\noperators that define the respective regions in phase space. In both, the\nuntrusted and trusted detector model, a new, cross-shaped postselection\nstrategy, clearly outperforms a state-of-the-art radial postselection scheme\nfor higher transmission distances and higher values of noise. Motivated by the\nhigh computational effort for the error-correction phase we also studied the\ncase when a large fraction of the raw key is eliminated by postselection: We\nobserve that the secure key rate in case that only $20\\%$ of the raw key passes\nthe cross-shaped postselection is still roughly $80\\%$ of the secure key rate\nwithout postselection for low values of excess noise and roughly $95\\%$ for\nhigher values of excess noise. Furthermore, we examine a strategy with radial\nand angular postselection that combines both the advantages of state-of-the-art\nradial postselection schemes and our cross-shaped strategy at the cost of\nhigher complexity. Employing the cross-shaped postselection strategy, that can\neasily be introduced in the data processing, both new and existing CV-QKD\nsystems can improve the achievable secure key rates significantly.\n"}
+{"abstract": "  Subaru Strategic Program with the Hyper-Suprime Cam (HSC-SSP) has proven to\nbe successful with its extremely-wide area coverage in past years. Taking\nadvantages of this feature, we report initial results from exploration and\nresearch of expansive over- and under-dense structures at $z=$ 0.3-1 based on\nthe second Public Data Release where optical 5-band photometric data for $\\sim$\neight million sources with $i<23$ mag are available over $\\sim360$ square\ndegrees. We not only confirm known superclusters but also find candidates of\ntitanic over- and under-dense regions out to $z=1$. The mock data analysis\nsuggests that the density peaks would involve one or more massive dark matter\nhaloes ($>10^{14}$ M$_\\odot$) of the redshift, and the density troughs tend to\nbe empty of massive haloes over $>10$ comoving Mpc. Besides, the density peaks\nand troughs at $z<0.6$ are in part identified as positive and negative weak\nlensing signals respectively, in mean tangential shear profiles, showing a good\nagreement with those inferred from the full-sky weak lensing simulation. The\ncoming extensive spectroscopic surveys will be able to resolve these colossal\nstructures in three-dimensional space. The number density information over the\nentire survey field will be available as grid-point data on the website of the\nHSC-SSP data release (https://hsc.mtk.nao.ac.jp/ssp/data-release/).\n"}
+{"abstract": "  Pre-training and fine-tuning have achieved remarkable success in many\ndownstream natural language processing (NLP) tasks. Recently, pre-training\nmethods tailored for information retrieval (IR) have also been explored, and\nthe latest success is the PROP method which has reached new SOTA on a variety\nof ad-hoc retrieval benchmarks. The basic idea of PROP is to construct the\n\\textit{representative words prediction} (ROP) task for pre-training inspired\nby the query likelihood model. Despite its exciting performance, the\neffectiveness of PROP might be bounded by the classical unigram language model\nadopted in the ROP task construction process. To tackle this problem, we\npropose a bootstrapped pre-training method (namely B-PROP) based on BERT for\nad-hoc retrieval. The key idea is to use the powerful contextual language model\nBERT to replace the classical unigram language model for the ROP task\nconstruction, and re-train BERT itself towards the tailored objective for IR.\nSpecifically, we introduce a novel contrastive method, inspired by the\ndivergence-from-randomness idea, to leverage BERT's self-attention mechanism to\nsample representative words from the document. By further fine-tuning on\ndownstream ad-hoc retrieval tasks, our method achieves significant improvements\nover baselines without pre-training or with other pre-training methods, and\nfurther pushes forward the SOTA on a variety of ad-hoc retrieval tasks.\n"}
+{"abstract": "  By analyzing photometric and spectroscopic time series in this paper, we show\nthat the pulsator V764 Mon, assumed to be the brightest RR Lyrae star in the\nsky, is in fact a rapidly rotating delta Scuti star with an unusually long\ndominant period (P1=0.29 d). Our spectroscopy confirmed the discovery of the\nGaia satellite about the binarity of V764 Mon. In the case of HY Com, a `bona\nfide' RRc star, we present its first complete radial velocity curve.\nAdditionally, we found that the star continues its strong phase variation\nreported before.\n"}
+{"abstract": "  Quantum computing can efficiently simulate Hamiltonian dynamics of many-body\nquantum physics, a task that is generally intractable with classical computers.\nThe hardness lies at the ubiquitous anti-commutative relations of quantum\noperators, in corresponding with the notorious negative sign problem in\nclassical simulation. Intuitively, Hamiltonians with more commutative terms are\nalso easier to simulate on a quantum computer, and anti-commutative relations\ngenerally cause more errors, such as in the product formula method. Here, we\ntheoretically explore the role of anti-commutative relation in Hamiltonian\nsimulation. We find that, contrary to our intuition, anti-commutative relations\ncould also reduce the hardness of Hamiltonian simulation. Specifically,\nHamiltonians with mutually anti-commutative terms are easy to simulate, as what\nhappens with ones consisting of mutually commutative terms. Such a property is\nfurther utilized to reduce the algorithmic error or the gate complexity in the\ntruncated Taylor series quantum algorithm for general problems. Moreover, we\npropose two modified linear combinations of unitaries methods tailored for\nHamiltonians with different degrees of anti-commutation. We numerically verify\nthat the proposed methods exploiting anti-commutative relations could\nsignificantly improve the simulation accuracy of electronic Hamiltonians. Our\nwork sheds light on the roles of commutative and anti-commutative relations in\nsimulating quantum systems.\n"}
+{"abstract": "  The deep extension of the restricted Boltzmann machine (RBM), known as the\ndeep Boltzmann machine (DBM), is an expressive family of machine learning\nmodels which can serve as compact representations of complex probability\ndistributions. However, jointly training DBMs in the unsupervised setting has\nproven to be a formidable task. A recent technique we have proposed, called\nmode-assisted training, has shown great success in improving the unsupervised\ntraining of RBMs. Here, we show that the performance gains of the mode-assisted\ntraining are even more dramatic for DBMs. In fact, DBMs jointly trained with\nthe mode-assisted algorithm can represent the same data set with orders of\nmagnitude lower number of total parameters compared to state-of-the-art\ntraining procedures and even with respect to RBMs, provided a fan-in network\ntopology is also introduced. This substantial saving in number of parameters\nmakes this training method very appealing also for hardware implementations.\n"}
+{"abstract": "  Probability forecasts for binary events play a central role in many\napplications. Their quality is commonly assessed with proper scoring rules,\nwhich assign forecasts a numerical score such that a correct forecast achieves\na minimal expected score. In this paper, we construct e-values for testing the\nstatistical significance of score differences of competing forecasts in\nsequential settings. E-values have been proposed as an alternative to p-values\nfor hypothesis testing, and they can easily be transformed into conservative\np-values by taking the multiplicative inverse. The e-values proposed in this\narticle are valid in finite samples without any assumptions on the data\ngenerating processes. They also allow optional stopping, so a forecast user may\ndecide to interrupt evaluation taking into account the available data at any\ntime and still draw statistically valid inference, which is generally not true\nfor classical p-value based tests. In a case study on postprocessing of\nprecipitation forecasts, state-of-the-art forecasts dominance tests and\ne-values lead to the same conclusions.\n"}
+{"abstract": "  We prove an upper bound on the rank of the abelianised revised fundamental\ngroup (called \"revised first Betti number\") of a compact $RCD^{*}(K,N)$ space,\nin the same spirit of the celebrated Gromov-Gallot upper bound on the first\nBetti number for a smooth compact Riemannian manifold with Ricci curvature\nbounded below. When the synthetic lower Ricci bound is close enough to\n(negative) zero and the aforementioned upper bound on the revised first Betti\nnumber is saturated (i.e. equal to the integer part of $N$, denoted by $\\lfloor\nN \\rfloor$), then we establish a torus stability result stating that the space\nis $\\lfloor N \\rfloor$-rectifiable as a metric measure space, and a finite\ncover must be mGH-close to an $\\lfloor N \\rfloor$-dimensional flat torus;\nmoreover, in case $N$ is an integer, we prove that the space itself is\nbi-H\\\"older homeomorphic to a flat torus. This second result extends to the\nclass of non-smooth $RCD^{*}(-\\delta, N)$ spaces a celebrated torus stability\ntheorem by Colding (later refined by Cheeger-Colding).\n"}
+{"abstract": "  Customers of machine learning systems demand accountability from the\ncompanies employing these algorithms for various prediction tasks.\nAccountability requires understanding of system limit and condition of\nerroneous predictions, as customers are often interested in understanding the\nincorrect predictions, and model developers are absorbed in finding methods\nthat can be used to get incremental improvements to an existing system.\nTherefore, we propose an accountable error characterization method, AEC, to\nunderstand when and where errors occur within the existing black-box models.\nAEC, as constructed with human-understandable linguistic features, allows the\nmodel developers to automatically identify the main sources of errors for a\ngiven classification system. It can also be used to sample for the set of most\ninformative input points for a next round of training. We perform error\ndetection for a sentiment analysis task using AEC as a case study. Our results\non the sample sentiment task show that AEC is able to characterize erroneous\npredictions into human understandable categories and also achieves promising\nresults on selecting erroneous samples when compared with the uncertainty-based\nsampling.\n"}
+{"abstract": "  The paper deals with the H2-norm and associated energy or power measurements\nfor a class of processes known as CSVIU (Control and State Variation Increase\nUncertainty). These are system models for which a stochastic process conveys\nthe underlying uncertainties, and are able to give rise to cautious controls.\nThe paper delves into the non-controlled version and fundamental system and\nnorms notions associated with stochastic stability and mean-square convergence.\nOne pillar of the study is the connection between the finiteness of one of\nthese norms or a limited energy measurement growth with the corresponding\nstochastic stability notions. A detectability concept ties these notions, and\nthe analysis of linear-positive operators plays a fundamental role. The\nintroduction of various H2-norms and energy measurement performance criteria\nallows one to span the focus from transient to long-run behavior. As the\ndiscount parameter turns into a counter-discount, the criteria enforce stricter\nrequirements on the second-moment steady state errors and on the exponential\nconvergence rate. A tidy connection among this H2-performance measures cast\nemploys a unifying vanishing discount reasoning.\n"}
+{"abstract": "  A nilmanifold is a (left) quotient of a nilpotent Lie group by a cocompact\nlattice. A hypercomplex structure on a manifold is a triple of complex\nstructure operators satisfying the quaternionic relations. A hypercomplex\nnilmanifold is a compact quotient of a nilpotent Lie group equipped with a\nleft-invariant hypercomplex structure. Such a manifold admits a whole\n2-dimensional sphere $S^2$ of complex structures induced by quaternions. We\nprove that for any hypercomplex nilmanifold $M$ and a generic complex structure\n$L\\in S^2$, the complex manifold $(M,L)$ has algebraic dimension 0. A stronger\nresult is proven when the hypercomplex nilmanifold is abelian. Consider the Lie\nalgebra of left-invariant vector fields of Hodge type (1,0) on the\ncorresponding nilpotent Lie group with respect to some complex structure $I\\in\nS^2$. A hypercomplex nilmanifold is called abelian when this Lie algebra is\nabelian. We prove that all complex subvarieties of $(M,L)$ for generic $L\\in\nS^2$ on a hypercomplex abelian nilmanifold are also hypercomplex nilmanifolds.\n"}
+{"abstract": "  Given the increasing data collection capabilities and limited computing\nresources of future collider experiments, interest in using generative neural\nnetworks for the fast simulation of collider events is growing. In our previous\nstudy, the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture for\ngenerating photon showers in a high-granularity calorimeter showed a high\naccuracy modeling of various global differential shower distributions. In this\nwork, we investigate how the BIB-AE encodes this physics information in its\nlatent space. Our understanding of this encoding allows us to propose methods\nto optimize the generation performance further, for example, by altering latent\nspace sampling or by suggesting specific changes to hyperparameters. In\nparticular, we improve the modeling of the shower shape along the particle\nincident axis.\n"}
+{"abstract": "  Recent progress in nanofabrication has led to the emergence of\nthree-dimensional magnetic nanostructures as a vibrant field of research. This\nincludes the study of three-dimensional arrays of interconnected magnetic\nnanowires with tunable artificial spin-ice properties. Prominent examples of\nsuch structures are magnetic buckyball nanoarchitectures, which consist of\nferromagnetic nanowires connected at vertex positions corresponding to those of\na C60 molecule. These structures can be regarded as prototypes for the study of\nthe transition from two- to three-dimensional spin-ice lattices. In spite of\ntheir significance for three-dimensional nanomagnetism, little is known about\nthe micromagnetic properties of buckyball nanostructures. By means of\nfinite-element micromagnetic simulations, we investigate the magnetization\nstructures and the hysteretic properties of several sub-micron-sized magnetic\nbuckyballs. Similar to ordinary artificial spin ice lattices, the array can be\nmagnetized in a variety of zero-field states with vertices exhibiting different\ndegrees of magnetic frustration. Remarkably, and unlike planar geometries,\nmagnetically frustrated states can be reversibly created and dissolved by\napplying an external magnetic field. This easiness to insert and remove\ndefect-like magnetic charges, made possible by the angle-selectivity of the\nfield-induced switching of individual nanowires, demonstrates a potentially\nsignificant advantage of three-dimensional nanomagnetism compared to planar\ngeometries. The control provided by the ability to switch between ice-rule\nobeying and magnetically frustrated structures could be an important feature of\nfuture applications, including magnonic devices exploiting differences in the\nfundamental frequencies of these configurations.\n"}
+{"abstract": "  This perspective article elucidates both the importance and the implications\nof relativistic spacetime crystals as well as the renormalized blended\ncoordinates transformation. It alludes to possible applications in materials\nscience, condensed matter physics and quantum gravity.\n"}
+{"abstract": "  The task of assigning semantic classes and track identities to every pixel in\na video is called video panoptic segmentation. Our work is the first that\ntargets this task in a real-world setting requiring dense interpretation in\nboth spatial and temporal domains. As the ground-truth for this task is\ndifficult and expensive to obtain, existing datasets are either constructed\nsynthetically or only sparsely annotated within short video clips. To overcome\nthis, we introduce a new benchmark encompassing two datasets, KITTI-STEP, and\nMOTChallenge-STEP. The datasets contain long video sequences, providing\nchallenging examples and a test-bed for studying long-term pixel-precise\nsegmentation and tracking under real-world conditions. We further propose a\nnovel evaluation metric Segmentation and Tracking Quality (STQ) that fairly\nbalances semantic and tracking aspects of this task and is more appropriate for\nevaluating sequences of arbitrary length. Finally, we provide several baselines\nto evaluate the status of existing methods on this new challenging dataset. We\nhave made our datasets, metric, benchmark servers, and baselines publicly\navailable, and hope this will inspire future research.\n"}
+{"abstract": "  Third-party software, or skills, are essential components in Smart Personal\nAssistants (SPA). The number of skills has grown rapidly, dominated by a\nchanging environment that has no clear business model. Skills can access\npersonal information and this may pose a risk to users. However, there is\nlittle information about how this ecosystem works, let alone the tools that can\nfacilitate its study. In this paper, we present the largest systematic\nmeasurement of the Amazon Alexa skill ecosystem to date. We study developers'\npractices in this ecosystem, including how they collect and justify the need\nfor sensitive information, by designing a methodology to identify\nover-privileged skills with broken privacy policies. We collect 199,295 Alexa\nskills and uncover that around 43% of the skills (and 50% of the developers)\nthat request these permissions follow bad privacy practices, including\n(partially) broken data permissions traceability. In order to perform this kind\nof analysis at scale, we present SkillVet that leverages machine learning and\nnatural language processing techniques, and generates high-accuracy prediction\nsets. We report a number of concerning practices including how developers can\nbypass Alexa's permission system through account linking and conversational\nskills, and offer recommendations on how to improve transparency, privacy and\nsecurity. Resulting from the responsible disclosure we have conducted,13% of\nthe reported issues no longer pose a threat at submission time.\n"}
+{"abstract": "  We introduce a new network marker for climate network analysis. It is based\nupon an available special definition of local clustering coefficient for\nweighted correlation networks, which was previously introduced in the\nneuroscience context and aimed at compensating for uninformative correlations\ncaused by indirect interactions. We modify this definition further by replacing\nPearson's pairwise correlation coefficients and Pearson's three-way partial\ncorrelation coefficients by the respective Kendall's rank correlations. This\nreduces statistical sample size requirements to compute the correlations, which\ntranslates into the possibility of using shorter time windows and hence into\nshorter response time of the real-time climate network analysis. We compare\nthis proposed network marker to the conventional local clustering coefficient\nbased on unweighted networks obtained by thresholding the correlation matrix.\nWe show several examples where the new marker is found to be better associated\nto tropical cyclones than the unweighted local clustering coefficient.\n"}
+{"abstract": "  A previously published covariant decomposition of the Levi-Civita tensor has\ndemonstrated the strong mathematical parallel between gravity and $N=2$\nYang-Mills theory. We use this to argue that that the Lorentz gauge theory has\na condensate vacuum of lower energy than the perturbative vacuum. Adapting our\npreviously published Clairaut-based treatment of QCD, we go on to study the\nimplications for second quantisation.\n"}
+{"abstract": "  Decentralized Finance (DeFi), a blockchain powered peer-to-peer financial\nsystem, is mushrooming. One and a half years ago the total value locked in DeFi\nsystems was approximately $700$m USD, now, as of September 2021, it stands at\naround $100$bn USD. The frenetic evolution of the ecosystem has created\nchallenges in understanding the basic principles of these systems and their\nsecurity risks. In this Systematization of Knowledge (SoK) we delineate the\nDeFi ecosystem along the following axes: its primitives, its operational\nprotocol types and its security. We provide a distinction between technical\nsecurity, which has a healthy literature, and economic security, which is\nlargely unexplored, connecting the latter with new models and thereby\nsynthesizing insights from computer science, economics and finance. Finally, we\noutline the open research challenges in the ecosystem across these security\ntypes.\n"}
+{"abstract": "  10 {\\mu}m lasing is studied in a compact CO2-He cell pressurized up to 15 atm\nwhen optically pumped by a ~50 mJ Fe:ZnSe laser tunable around 4.3 {\\mu}m. The\noptimal pump wavelength and partial pressure of CO2 for generating 10 {\\mu}m\npulses are found to be ~4.4 {\\mu}m and 0.75 atm, respectively. Without cavity\noptimization, the optical-to-optical conversion efficiency reached ~10% at a\ntotal pressure of 7 atm. The gain lifetime is measured to be ~1 {\\mu}s at\npressures above 10 atm, indicating the feasibility of using high-pressure\noptically pumped CO2 for the efficient amplification of picosecond 10 {\\mu}m\npulses.\n"}
+{"abstract": "  We present a unified framework for minimizing average completion time for\nmany seemingly disparate online scheduling problems, such as the traveling\nrepairperson problems (TRP), dial-a-ride problems (DARP), and scheduling on\nunrelated machines.\n  We construct a simple algorithm that handles all these scheduling problems,\nby computing and later executing auxiliary schedules, each optimizing a certain\nfunction on already seen prefix of the input. The optimized function resembles\na prize-collecting variant of the original scheduling problem. By a careful\nanalysis of the interplay between these auxiliary schedules, and later\nemploying the resulting inequalities in a factor-revealing linear program, we\nobtain improved bounds on the competitive ratio for all these scheduling\nproblems.\n  In particular, our techniques yield a $4$-competitive deterministic algorithm\nfor all previously studied variants of online TRP and DARP, and a\n$3$-competitive one for the scheduling on unrelated machines (also with\nprecedence constraints). This improves over currently best ratios for these\nproblems that are $5.14$ and $4$, respectively. We also show how to use\nrandomization to further reduce the competitive ratios to $1+2/\\ln 3 < 2.821$\nand $1+1/\\ln 2 < 2.443$, respectively. The randomized bounds also substantially\nimprove the current state of the art. Our upper bound for DARP contradicts the\nlower bound of 3 given by Fink et al. (Inf. Process. Lett. 2009); we pinpoint a\nflaw in their proof.\n"}
+{"abstract": "  This paper introduces the Simulated Jet Engine Bracket Dataset (SimJEB): a\nnew, public collection of crowdsourced mechanical brackets and accompanying\nstructural simulations. SimJEB is applicable to a wide range of geometry\nprocessing tasks; the complexity of the shapes in SimJEB offer a challenge to\nautomated geometry cleaning and meshing, while categorical labels and\nstructural simulations facilitate classification and regression (i.e.\nengineering surrogate modeling). In contrast to existing shape collections,\nSimJEB's models are all designed for the same engineering function and thus\nhave consistent structural loads and support conditions. On the other hand,\nSimJEB models are more complex, diverse, and realistic than the synthetically\ngenerated datasets commonly used in parametric surrogate model evaluation. The\ndesigns in SimJEB were derived from submissions to the GrabCAD Jet Engine\nBracket Challenge: an open engineering design competition with over 700\nhand-designed CAD entries from 320 designers representing 56 countries. Each\nmodel has been cleaned, categorized, meshed, and simulated with finite element\nanalysis according to the original competition specifications. The result is a\ncollection of 381 diverse, high-quality and application-focused designs for\nadvancing geometric deep learning, engineering surrogate modeling, automated\ncleaning and related geometry processing tasks.\n"}
+{"abstract": "  Several governments introduced or promoted the use of contact-tracing apps\nduring the ongoing COVID-19 pandemic. In Germany, the related app is called\nCorona-Warn-App, and by end of 2020, it had 22.8 million downloads. Contact\ntracing is a promising approach for containing the spread of the novel\ncoronavirus. It is only effective if there is a large user base, which brings\nnew challenges like app users unfamiliar with using smartphones or apps. As\nCorona-Warn-App is voluntary to use, reaching many users and gaining a positive\npublic perception is crucial for its effectiveness. Based on app reviews and\ntweets, we are analyzing the public perception of Corona-Warn-App. We collected\nand analyzed all 78,963 app reviews for the Android and iOS versions from\nrelease (June 2020) to beginning of February 2021, as well as all original\ntweets until February 2021 containing #CoronaWarnApp (43,082). For the reviews,\nthe most common words and n-grams point towards technical issues, but it\nremains unclear, to what extent this is due to the app itself, the used\nExposure Notification Framework, system settings on the user's phone, or the\nuser's misinterpretations of app content. For Twitter data, overall, based on\ntweet content, frequent hashtags, and interactions with tweets, we conclude\nthat the German Twitter-sphere widely reports adopting the app and promotes its\nuse.\n"}
+{"abstract": "  The proximity effect from a spin-triplet $p_x$-wave superconductor to a dirty\nnormal-metal has been shown to result in various unusual electromagnetic\nproperties, reflecting a cooperative relation between topologically protected\nzero-energy quasiparticles and odd-frequency Cooper pairs. However, because of\na lack of candidate materials for spin-triplet $p_x$-wave superconductors,\nobserving this effect has been difficult. In this paper, we demonstrate that\nthe anomalous proximity effect, which is essentially equivalent to that of a\nspin-triplet $p_x$-wave superconductor, can occur in a semiconductor/high-$T_c$\ncuprate superconductor hybrid device in which two potentials coexist: a\nspin-singlet $d$-wave pair potential and a spin--orbit coupling potential\nsustaining the persistent spin-helix state. As a result, we propose an\nalternative and promising route to observe the anomalous proximity effect\nrelated to the profound nature of topologically protected quasiparticles and\nodd-frequency Cooper pairs.\n"}
+{"abstract": "  The dipole anisotropy in the Cosmic Microwave Background Radiation (CMBR) has\ngiven a peculiar velocity vector 370 km s$^{-1}$ along\n$l=264^\\circ,b=48^\\circ$. However, some other dipoles, for instance, from the\nnumber counts, sky brightness or redshift distributions in large samples of\ndistant Active Galactic Nuclei (AGNs), have yielded values of the peculiar\nvelocity many times larger than that from the CMBR, though surprisingly, in all\ncases the directions agreed with the CMBR dipole. Here we determine our\npeculiar motion from a sample of ~0.28 million AGNs, selected from the Mid\nInfra Red Active Galactic Nuclei (MIRAGN) sample comprising more than a million\nsources. From this, we find a peculiar velocity, which is more than four times\nthe CMBR value, although the direction seems to be within $\\sim 2\\sigma$ of the\nCMBR dipole. A genuine value of the solar peculiar velocity should be the same\nirrespective of the data or the technique employed to estimate it. Therefore,\nsuch discordant dipole amplitudes, might mean that the explanation for these\ndipoles, including that of the CMBR, might in fact be something else. But, the\nobserved fact that the direction in all cases, is the same, though obtained\nfrom completely independent surveys using different instruments and techniques,\nby different sets of people employing different computing routines, might\nnonetheless indicate that these dipoles are not merely due to some systematics,\notherwise why would they all be pointing along the same direction. It might\ninstead suggest a preferred direction in the Universe, implying a genuine\nanisotropy, which would violate the Cosmological Principle, the core of the\nmodern cosmology.\n"}
+{"abstract": "  We present 3D general relativistic magnetohydrodynamic(GRMHD) simulations of\nzero angular momentum accretion around a rapidly rotating black hole, modified\nby the presence of initially uniform magnetic fields. We consider serveral\nangles between the magnetic field direction and the black hole spin. In the\nresulting flows, the midplane dynamics are governed by magnetic\nreconnection-driven turbulence in a magnetically arrested (or a nearly\narrested) state. Electromagnetic jets with outflow efficiencies ~10-200% occupy\nthe polar regions, reaching several hundred gravitational radii before they\ndissipate due to the kink instability. The jet directions fluctuate in time and\ncan be tilted by as much as ~30 degrees with respect to black hole spin, but\nthis tilt does not depend strongly on the tilt of the initial magnetic field. A\njet forms even when there is no initial net vertical magnetic flux since\nturbulent, horizon-scale fluctuations can generate a net vertical field\nlocally. Peak jet power is obtained for an initial magnetic field tilted by\n40-80 degrees with respect to the black hole spin because this maximizes the\namount of magnetic flux that can reach the black hole. These simulations may be\na reasonable model for low luminosity black hole accretion flows such as Sgr A*\nor M87.\n"}
+{"abstract": "  Large Pre-trained Language Models (PLMs) have become ubiquitous in the\ndevelopment of language understanding technology and lie at the heart of many\nartificial intelligence advances. While advances reported for English using\nPLMs are unprecedented, reported advances using PLMs in Hebrew are few and far\nbetween. The problem is twofold. First, Hebrew resources available for training\nNLP models are not at the same order of magnitude as their English\ncounterparts. Second, there are no accepted tasks and benchmarks to evaluate\nthe progress of Hebrew PLMs on. In this work we aim to remedy both aspects.\nFirst, we present AlephBERT, a large pre-trained language model for Modern\nHebrew, which is trained on larger vocabulary and a larger dataset than any\nHebrew PLM before. Second, using AlephBERT we present new state-of-the-art\nresults on multiple Hebrew tasks and benchmarks, including: Segmentation,\nPart-of-Speech Tagging, full Morphological Tagging, Named-Entity Recognition\nand Sentiment Analysis. We make our AlephBERT model publicly available,\nproviding a single point of entry for the development of Hebrew NLP\napplications.\n"}
+{"abstract": "  The goal of this minireview is restricted to describe how the\nKolmogorov-Johnson-Mehl-Avrami model has evolved from its birth up to the\npresent day. The model, which dates back to the late of 1930s, has the purpose\nof describing the kinetics of a phase transformation. Given the nature of this\narticle, although there are hundreds (if not thousands) of experimental data\nconcerning the most disparate topics, which are interpreted on the basis of the\nKJMA model, no arguments relating to these, will be touched upon. Starting from\nthe ingenious concept of phantom nuclei, firstly introduced by Avrami to get\nthe exact kinetics, we review theoretical approaches which overcome such\nconcept. We will show how spatial correlation among nuclei plays a fundamental\nrole in these modelings.\n"}
+{"abstract": "  The layered perovskite YBaCuFeO5 (YBCFO) is considered one of the best\ncandidates to high-temperature chiral multiferroics with strong magnetoelectric\ncoupling. In RBaCuFeO5 perovskites (R: rare-earth or Y) A-site cations are\nfully ordered whereas their magnetic properties strongly depend on the\npreparation process. They exhibit partial cationic disorder at the B-site that\ngenerates a magnetic spiral stabilized through directionally assisted long\nrange coupling between canted locally frustrated spins. Moreover the\norientation of its magnetic spiral can be critical for the magnetoelectric\nresponse of this chiral magnetic oxide. We have synthesized and studied\nYBaCuFe1-xMnxO5 samples doped with Mn, with the aim of increasing spin-orbit\ncoupling effects, and found that the overall Fe/Cu cation disorder at the\nB-sites can be increased by doping without changing the sample preparation\nprocess. In YBaCuFe1-xMnxO5 samples prepared under the same conditions, the T-x\nmagnetic phase diagram have been constructed in the range 10K-500K combining\nmagnetometry, X-ray and neutron powder diffraction measurements. The tilting\nangles of the spins in the collinear, {\\theta}col , and spiral phases,\n{\\theta}spiral, barely vary with temperature. In the collinear phase\n{\\theta}col is also independent of the Mn content. In contrast, the presence of\nMn produces a progressive reorientation of the plane of the magnetic helix in\nthe incommensurate phase, capable to transform the helicoidal spin ordering\ninto a cycloidal one, which may critically determine the ferroelectric and\nmagnetoelectric behavior in these compounds. Some of the observations are of\ninterest for engineering and developing this family of potential\nhigh-temperature multiferroics.\n"}
+{"abstract": "  Despite their consequential applications, metastable states of antibranes in\nwarped throats are not yet fully understood. In this thesis, we provide new\ninformation on various aspects of these metastable antibranes through\napplications of the blackfold effective theory for higher-dimensional black\nholes. As concrete examples, we study the conjectured metastable state of\npolarised anti-D3 branes at the tip of the Klebanov-Strassler (KS) throat in\ntype IIB supergravity and the analogous state of polarised anti-M2 branes at\nthe tip of the Cvetic-Gibbons-Lu-Pope (CGLP) throat in eleven-dimensional\nsupergravity.\n  For anti-D3 branes in KS throat, we provide novel evidence for the existence\nof the metastable state exactly where no-go theorems are lifted. In the\nextremal limit, we recover directly in supergravity the metastable states\noriginally discovered by Kachru, Pearson, and Verlinde (KPV). Away from\nextremality, we uncover a metastable wrapped black NS5 state. We observe that\nsuch metastability is lost when the wrapped NS5 is heated sufficiently that its\nhorizon geometry resembles that of a black anti-D3. We study the classical\nstability of the KPV state under generic long-wavelength deformations. We\nobserve that, with regards to considered perturbations and regime of\nparameters, the state is classically stable. A study of anti-M2 branes in CGLP\nthroat reveals many similarities to that of the anti-D3 branes. We recover\ndirectly in supergravity the Klebanov-Pufu (KP) state at extremality, and our\nfinite temperature results fit suggestively well with known, complementary\nno-go theorems. However, we discover an unexpected, exotic pattern of thermal\ntransitions of the KP state different from that of the KPV.\n  This thesis contains also a pedagogical introduction to the blackfold\nformalism, focusing on aspects immediately relevant to applications to\nmetastable antibranes.\n"}
+{"abstract": "  In this paper, a relay-aided two-phase transmission protocol for the smart\nfactory scenario is proposed. This protocol aims at enabling all robots'\nultra-reliable target number of uplink critical data transmission within a\nlatency constraint by jointly optimizing the relay selection, resource block\n(RB) assignment, and transmit power allocation. Such protocol design is\nformulated as a mixed-integer and strictly non-convex problem where\noptimization variables are mutual coupling, which is definitely challenging.\nInstead of conventional methods designed for solving the problem, we leverage\nthe properties of the relative entropy function to equivalently transform the\nproblem without introducing extra constraints. As the packet error probability\nrequirements of each robot under two possible transmission modes are coupled in\none overall reliability constraint, the big-M technique is applied to decouple\nit into two corresponding reliability constraints. One is for direct\ntransmission mode, and the other is for cooperative transmission mode.\nMoreover, both non-convex penalty (NCP) and quadratic penalty (QP) approaches\nare utilized to deal with the binary indicator constraints. Based on such\npenalty methods, a sequence of penalized approximated convex problems can be\niteratively solved for sub-optimal solutions. Numerical results demonstrate the\nefficiency of such two penalty methods from the perspectives of sub-optimal\nvalues of total transmit power and convergence rate. Further, the impacts of\nreliability, the number and location of relays, the number of robots, the\ntarget number of data bits on the total power consumption are analyzed.\n"}
+{"abstract": "  We analytically study shock wave in the Josephson transmission line (JTL) in\nthe presence of ohmic dissipation. When ohmic resistors shunt the Josephson\njunctions (JJ) or are introduced in series with the ground capacitors the shock\nis broadened. When ohmic resistors are in series with the JJ, the shock remains\nsharp, same as it was in the absence of dissipation. In all the cases\nconsidered, ohmic resistors don't influence the shock propagation velocity. We\nstudy an alternative to the shock wave - an expansion fan - in the framework of\nthe simple wave approximation for the dissipationless JTL and formulate the\ngeneralization of the approximation for the JTL with ohmic dissipation.\n"}
+{"abstract": "  The world is facing a tough situation due to the catastrophic pandemic caused\nby novel coronavirus (COVID-19). The number people affected by this virus are\nincreasing exponentially day by day and the number has already crossed 6.4\nmillion. As no vaccine has been discovered yet, the early detection of patients\nand isolation is the only and most effective way to reduce the spread of the\nvirus. Detecting infected persons from chest X-Ray by using Deep Neural\nNetworks, can be applied as a time and laborsaving solution. In this study, we\ntried to detect Covid-19 by classification of Covid-19, pneumonia and normal\nchest X-Rays. We used five different Convolutional Pre-Trained Neural Network\nmodels (VGG16, VGG19, Xception, InceptionV3 and Resnet50) and compared their\nperformance. VGG16 and VGG19 shows precise performance in classification. Both\nmodels can classify between three kinds of X-Rays with an accuracy over 92%.\nAnother part of our study was to find the impact of weather factors\n(temperature, humidity, sun hour and wind speed) on this pandemic using\nDecision Tree Regressor. We found that temperature, humidity and sun-hour\njointly hold 85.88% impact on escalation of Covid-19 and 91.89% impact on death\ndue to Covid-19 where humidity has 8.09% impact on death. We also tried to\npredict the death of an individual based on age, gender, country, and location\ndue to COVID-19 using the LogisticRegression, which can predict death of an\nindividual with a model accuracy of 94.40%.\n"}
+{"abstract": "  Single-particle x-ray diffractive imaging (SPI) of small (bio-)nanoparticles\n(NPs) requires optimized injectors to collect sufficient diffraction patterns\nto reconstruct the NP structure with high resolution. Typically,\naerodynamic-lens-stack injectors are used for single NP injection. However,\ncurrent injectors were developed for larger NPs ($\\gg\\!100$ nm) and their\nability to generate high-density NP beams suffers with decreasing NP size.\nHere, an aerodynamic-lens-stack injector with variable geometry and the\ngeometry-optimization procedure are presented. The optimization for 50 nm gold\nNP (AuNP) injection using a numerical simulation infrastructure capable of\ncalculating the carrier gas flow and the particle trajectories through the\ninjector is introduced. The simulations are experimentally validated using\nspherical AuNPs and sucrose NPs. In addition, the optimized injector is\ncompared to the standard-installation \"Uppsala-injector\" for AuNPs and results\nfor these heavy particles show a shift in the particle-beam focus position\nrather than a change in beam size, which results in a lower gas background for\nthe optimized injector. Optimized aerodynamic-lens stack injectors will allow\nto increase NP beam density, reduce the gas background, discover the limits of\ncurrent injectors, and contribute to structure determination of small NPs using\nSPI.\n"}
+{"abstract": "  Despite significant improvements over the last few years, cloud-based\nhealthcare applications continue to suffer from poor adoption due to their\nlimitations in meeting stringent security, privacy, and quality of service\nrequirements (such as low latency). The edge computing trend, along with\ntechniques for distributed machine learning such as federated learning, have\ngained popularity as a viable solution in such settings. In this paper, we\nleverage the capabilities of edge computing in medicine by analyzing and\nevaluating the potential of intelligent processing of clinical visual data at\nthe edge allowing the remote healthcare centers, lacking advanced diagnostic\nfacilities, to benefit from the multi-modal data securely. To this aim, we\nutilize the emerging concept of clustered federated learning (CFL) for an\nautomatic diagnosis of COVID-19. Such an automated system can help reduce the\nburden on healthcare systems across the world that has been under a lot of\nstress since the COVID-19 pandemic emerged in late 2019. We evaluate the\nperformance of the proposed framework under different experimental setups on\ntwo benchmark datasets. Promising results are obtained on both datasets\nresulting in comparable results against the central baseline where the\nspecialized models (i.e., each on a specific type of COVID-19 imagery) are\ntrained with central data, and improvements of 16\\% and 11\\% in overall\nF1-Scores have been achieved over the multi-modal model trained in the\nconventional Federated Learning setup on X-ray and Ultrasound datasets,\nrespectively. We also discuss in detail the associated challenges,\ntechnologies, tools, and techniques available for deploying ML at the edge in\nsuch privacy and delay-sensitive applications.\n"}
+{"abstract": "  The goal of this paper is to propose a framework for representing and\nreasoning about the rules governing a combinatorial exchange. Such a framework\nis at first interest as long as we want to build up digital marketplaces based\non auction, a widely used mechanism for automated transactions. Combinatorial\nexchange is the most general case of auctions, mixing the double and\ncombinatorial variants: agents bid to trade bundles of goods. Hence the\nframework should fulfill two requirements: (i) it should enable bidders to\nexpress their bids on combinations of goods and (ii) it should allow describing\nthe rules governing some market, namely the legal bids, the allocation and\npayment rules. To do so, we define a logical language in the spirit of the Game\nDescription Language: the Combinatorial Exchange Description Language is the\nfirst language for describing combinatorial exchange in a logical framework.\nThe contribution is two-fold: first, we illustrate the general dimension by\nrepresenting different kinds of protocols, and second, we show how to reason\nabout auction properties in this machine-processable language.\n"}
+{"abstract": "  Active learning continues to remain significant in the industry since it is\ndata efficient. Not only is it cost effective on a constrained budget,\ncontinuous refinement of the model allows for early detection and resolution of\nfailure scenarios during the model development stage. Identifying and fixing\nfailures with the model is crucial as industrial applications demand that the\nunderlying model performs accurately in all foreseeable use cases. One popular\nstate-of-the-art technique that specializes in continuously refining the model\nvia failure identification is Learning Loss. Although simple and elegant, this\napproach is empirically motivated. Our paper develops a foundation for Learning\nLoss which enables us to propose a novel modification we call LearningLoss++.\nWe show that gradients are crucial in interpreting how Learning Loss works,\nwith rigorous analysis and comparison of the gradients between Learning Loss\nand LearningLoss++. We also propose a convolutional architecture that combines\nfeatures at different scales to predict the loss. We validate LearningLoss++\nfor regression on the task of human pose estimation (using MPII and LSP\ndatasets), as done in Learning Loss. We show that LearningLoss++ outperforms in\nidentifying scenarios where the model is likely to perform poorly, which on\nmodel refinement translates into reliable performance in the open world.\n"}
+{"abstract": "  We study voltage controllable superconducting state in multi-terminal bridge\ncomposed of the dirty superconductor/pure normal metal (SN) bilayer and pure\nnormal metal. In the proposed system small control current $I_{ctrl}$ flows via\nnormal bridge, creates voltage drop $V$ and modifies distribution function of\nelectrons in connected SN bilayer. In case of long normal bridge the voltage\ninduced nonequilibrium effects could be interpreted in terms of increased local\nelectron temperature. In this limit we experimentally find large sensitivity of\ncritical curent $I_c$ of Cu/MoN/Pt-Cu bridge to $I_{ctrl}$ and relatively large\ncurrent gain which originate from steep dependence of $I_c$ on temperature and\nlarge $I_c$ (comparable with theoretical depairing current of superconducting\nbridge). In the short normal bridge deviation from equilibrium cannot be\ndescribed by simple increase of local temperature but we also theoretically\nfind large sensitivity of $I_c$ to control current/voltage. In this limit we\npredict existence at finite $V$ of so called in-plane Fulde-Ferrell state with\nspontaneous currents in SN bilayer. We argue that its appearance is connected\nwith voltage induced paramagnetic response in N layer.\n"}
+{"abstract": "  A unified explicit form for difference formulas to approximate the fractional\nand classical derivatives is presented. The formula gives finite difference\napproximations for any classical derivatives with a desired order of accuracy\nat nodal point in the computational domain. It also gives Gr\\\"unwald type\napproximations for fractional derivatives with arbitrary order of approximation\nat any point. Thus, this explicit unifies approximations of both types of\nderivatives. Moreover, for classical derivatives, it provides various finite\ndifference formulas such as forward, backward, central, staggered, compact,\nnon-compact etc. Efficient computations of the coefficients of the difference\nformulas are also presented that lead to automating the solution process of\ndifferential equations with a given higher order accuracy. Some basic\napplications are presented to demonstrate the usefulness of this unified\nformulation.\n"}
+{"abstract": "  Recent strategies achieved ensembling \"for free\" by fitting concurrently\ndiverse subnetworks inside a single base network. The main idea during training\nis that each subnetwork learns to classify only one of the multiple inputs\nsimultaneously provided. However, the question of how to best mix these\nmultiple inputs has not been studied so far. In this paper, we introduce MixMo,\na new generalized framework for learning multi-input multi-output deep\nsubnetworks. Our key motivation is to replace the suboptimal summing operation\nhidden in previous approaches by a more appropriate mixing mechanism. For that\npurpose, we draw inspiration from successful mixed sample data augmentations.\nWe show that binary mixing in features - particularly with rectangular patches\nfrom CutMix - enhances results by making subnetworks stronger and more diverse.\nWe improve state of the art for image classification on CIFAR-100 and Tiny\nImageNet datasets. Our easy to implement models notably outperform data\naugmented deep ensembles, without the inference and memory overheads. As we\noperate in features and simply better leverage the expressiveness of large\nnetworks, we open a new line of research complementary to previous works.\n"}
+{"abstract": "  Effects from nonstandard corrections to Newtonian gravity, at large scale,\ncan be investigated using the cosmological structure formation. In particular,\nit is possible to show if and how a logarithmic correction (as that induced\nfrom nonlocal gravity) modifies the clustering properties of galaxies and of\nclusters of galaxies. The thermodynamics of such systems can be used to obtain\nimportant information about the effects of such modification on clustering. We\nwill compare its effects with observational data and it will be demonstrated\nthat the observations seem to point to a characteristic scale where such a\nlogarithmic correction might be in play at galactic scales. However, at larger\nscales such statistical inferences are much weaker, so that a fully reliable\nstatistical evidence for this kind of corrections cannot be stated without\nfurther investigations and the use of more varied and precise cosmological and\nastrophysical probes.\n"}
+{"abstract": "  Recent research has shown that non-additive image steganographic frameworks\neffectively improve security performance through adjusting distortion\ndistribution. However, as far as we know, all of the existing non-additive\nproposals are based on handcrafted policies, and can only be applied to a\nspecific image domain, which heavily prevent non-additive steganography from\nreleasing its full potentiality. In this paper, we propose an automatic\nnon-additive steganographic distortion learning framework called MCTSteg to\nremove the above restrictions. Guided by the reinforcement learning paradigm,\nwe combine Monte Carlo Tree Search (MCTS) and steganalyzer-based environmental\nmodel to build MCTSteg. MCTS makes sequential decisions to adjust distortion\ndistribution without human intervention. Our proposed environmental model is\nused to obtain feedbacks from each decision. Due to its self-learning\ncharacteristic and domain-independent reward function, MCTSteg has become the\nfirst reported universal non-additive steganographic framework which can work\nin both spatial and JPEG domains. Extensive experimental results show that\nMCTSteg can effectively withstand the detection of both hand-crafted\nfeature-based and deep-learning-based steganalyzers. In both spatial and JPEG\ndomains, the security performance of MCTSteg steadily outperforms the state of\nthe art by a clear margin under different scenarios.\n"}
+{"abstract": "  State-of-the-art approaches to calculate the electron-phonon and the\nphonon-electron self-energy are based on a mean-field approximation for the\ninteracting electronic system. This approach introduces an overscreening error\nwhich results in an underestimation of the electron-phonon coupling strength.\nWe introduce a theoretical and numerical approach for the calculation of the\nphonon-electron self-energy without the overscreening error. Starting from the\nout-of-equilibrium Kadanoff-Baym equations for the phonon propagator, we\ndiscuss and compare the ovescreened (i.e., symmetrically screened) and\noverscreening--free (i.e., asymmetrically screened) cases. We point out that\nthe difficulty in treating the latter stems from the static approximation to\nthe dielectric function and from the need to obtain a self-energy that\npreserves the elementary scattering processes. We solve both problems in the\nequilibrium case by considering a manifestly symmetric form of the correct\nself-energy which can be easily calculated numerically and yields an\noverscreening--free coupling strength. Finally, we describe the numerical\nimplementation of this treatment into the first--principles Yambo code for the\ncalculations of phonon linewidths.\n"}
+{"abstract": "  Ab initio molecular dynamics (AIMD) with hybrid density functionals and plane\nwave basis is computationally expensive due to the high computational cost of\nexact exchange energy evaluation. Recently, we proposed a strategy to combine\nadaptively compressed exchange (ACE) operator formulation and multiple time\nstep (MTS) integration scheme to reduce the computational cost significantly\n[J. Chem. Phys. 151, 151102 (2019)]. However, it was found that the\nconstruction of the ACE operator, which has to be done at least once in every\nMD time step, is computationally expensive. In the present work, systematic\nimprovements are introduced to further speed-up by employing localized orbitals\nfor the construction of the ACE operator. By this, we could achieve a\ncomputational speed-up of an order of magnitude for a periodic system\ncontaining 32-water molecules. Benchmark calculations were carried out to show\nthe accuracy and efficiency of the method in predicting the structural and\ndynamical properties of bulk water. To demonstrate the applicability,\ncomputationally intensive free energy computations at the level of hybrid\ndensity functional theory were performed to investigate (a) methyl formate\nhydrolysis reaction in neutral aqueous medium and (b) proton transfer reaction\nwithin the active site residues of class-C $\\beta$-lactamase enzyme.\n"}
+{"abstract": "  We theoretically investigate the apparent contact angle of droplets on liquid\ninfused surfaces as a function of the relative size of the wetting ridge and\nthe deposited droplet. We provide an intuitive geometrical interpretation\nwhereby the variation in the apparent contact angle is due to the rotation of\nthe Neumann triangle. We also derive linear and quadratic corrections to the\napparent contact angle as power series expansion in terms of pressure\ndifferences between the lubricant, droplet and gas phases. These expressions\nare much simpler and more compact compared to those previously derived by\nSemprebon et al. [Soft Matter, 2017, 13, 101-110].\n"}
+{"abstract": "  Self-attention has the promise of improving computer vision systems due to\nparameter-independent scaling of receptive fields and content-dependent\ninteractions, in contrast to parameter-dependent scaling and\ncontent-independent interactions of convolutions. Self-attention models have\nrecently been shown to have encouraging improvements on accuracy-parameter\ntrade-offs compared to baseline convolutional models such as ResNet-50. In this\nwork, we aim to develop self-attention models that can outperform not just the\ncanonical baseline models, but even the high-performing convolutional models.\nWe propose two extensions to self-attention that, in conjunction with a more\nefficient implementation of self-attention, improve the speed, memory usage,\nand accuracy of these models. We leverage these improvements to develop a new\nself-attention model family, HaloNets, which reach state-of-the-art accuracies\non the parameter-limited setting of the ImageNet classification benchmark. In\npreliminary transfer learning experiments, we find that HaloNet models\noutperform much larger models and have better inference performance. On harder\ntasks such as object detection and instance segmentation, our simple local\nself-attention and convolutional hybrids show improvements over very strong\nbaselines. These results mark another step in demonstrating the efficacy of\nself-attention models on settings traditionally dominated by convolutional\nmodels.\n"}
+{"abstract": "  We introduce an additive basis of the integral cohomology ring of the\nPeterson variety which reflects the geometry of certain subvarieties of the\nPeterson variety. We explain the positivity of the structure constants from a\ngeometric viewpoint, and provide a manifestly positive combinatorial formula\nfor them. We also prove that our basis coincides with the additive basis\nintroduced by Harada-Tymoczko.\n"}
+{"abstract": "  We study non-linear optical effects in electron systems with and without\ninversion symmetry in a Fabry-Perot cavity. General photon up- and\ndown-conversion processes are modeled by the coupling of a noninteracting\nlattice model to two modes of the quantized light field. Effective descriptions\nretaining the most relevant states are devised via downfolding and a\ngeneralized Householder transformation. These models are used to relate the\ntransition amplitudes for even order photon-conversion processes to the shift\nvector, a topological quantity describing the difference in polarization\nbetween the valence and conduction band in non-centrosymmetric systems. We also\ndemonstrate that the truncated models, despite their small Hilbert space,\ncapture correlation effects induced by the photons in the electronic subsystem.\n"}
+{"abstract": "  In recent years, blockchain has grown in popularity due to its singular\nattributes, enabling the development of new innovative decentralized\napplications. But when companies consider leveraging blockchain for their\napplications, the plethora of possible choices and the difficulty of\nintegrating blockchain into architectures can hinder its adoption. Our research\nproject aims to ease the adoption of blockchain into companies, notably with\nthe construction of an automated decision process to solve this issue in which\nrequirements are first-class citizens, a knowledge base containing\narchitectural patterns and blockchains refined over time, and an architecture\ngenerator able to process outputs into architectural stubs. This paper will\nalso present our current progression on this decision process, by introducing\nthe preliminary version that is able to choose the most suitable blockchain\nbetween multiple choices and our process-driven benchmarking tool.\n"}
+{"abstract": "  A leader-follower framework is proposed for multi-robot navigation of large\nscale teams where the leader agents corral the follower agents. A group of\nleaders is modeled as a 2D deformable object where discrete masses (i.e.,\nleader robots) are interconnected by springs and dampers. A time-varying domain\nis defined by the positions of leaders while the external forces induce\ndeformations of the domain from its nominal configuration. The team of\nfollowers is performing coverage over the time-varying domain by employing a\nperspective transformation that maps between the nominal and deformed\nconfigurations. A decentralized control strategy is proposed where a leader\nonly takes local sensing information and information about its neighbors\n(connected by virtual springs and dampers), and a follower only needs partial\ninformation about leaders and information about its Delaunay neighbors.\n"}
+{"abstract": "  In the era of digitization, different actors in agriculture produce numerous\ndata. Such data contains already latent historical knowledge in the domain.\nThis knowledge enables us to precisely study natural hazards within global or\nlocal aspects, and then improve the risk prevention tasks and augment the\nyield, which helps to tackle the challenge of growing population and changing\nalimentary habits. In particular, French Plants Health Bulletins (BSV, for its\nname in French Bulletin de Sant{\\'e} du V{\\'e}g{\\'e}tal) give information about\nthe development stages of phytosanitary risks in agricultural production.\nHowever, they are written in natural language, thus, machines and human cannot\nexploit them as efficiently as it could be. Natural language processing (NLP)\ntechnologies aim to automatically process and analyze large amounts of natural\nlanguage data. Since the 2010s, with the increases in computational power and\nparallelization, representation learning and deep learning methods became\nwidespread in NLP. Recent advancements Bidirectional Encoder Representations\nfrom Transformers (BERT) inspire us to rethink of knowledge representation and\nnatural language understanding in plant health management domain. The goal in\nthis work is to propose a BERT-based approach to automatically classify the BSV\nto make their data easily indexable. We sampled 200 BSV to finetune the\npretrained BERT language models and classify them as pest or/and disease and we\nshow preliminary results.\n"}
+{"abstract": "  The chiral Hamiltonian for twisted graphene bilayers is written as a\n$2\\times2$ matrix operator by a renormalization of the Hamiltonian that takes\ninto account the particle-hole symmetry. This results in an effective\nHamiltonian with an average field plus and effective non-Abelian gauge\npotential. The action of the proposed renormalization maps the zero-mode region\ninto the ground state. Modes near zero energy have an antibonding nature in a\ntriangular lattice. This leads to a phase-frustration effect associated with\nmassive degeneration, and makes flat-bands modes similar to confined modes\nobserved in other bipartite lattices. Suprisingly, the proposed Hamiltonian\nrenormalization suggests that flat-bands at magic angles are akin to\nfloppy-mode bands in flexible crystals or glasses, making an unexpected\nconnection between rigidity topological theory and magic angle twisted\ntwo-dimensional heterostructures physics.\n"}
+{"abstract": "  While the original goal for developing robots is replacing humans in\ndangerous and tedious tasks, the final target shall be completely mimicking the\nhuman cognitive and motor behaviour. Hence, building detailed computational\nmodels for the human brain is one of the reasonable ways to attain this. The\ncerebellum is one of the key players in our neural system to guarantee\ndexterous manipulation and coordinated movements as concluded from lesions in\nthat region. Studies suggest that it acts as a forward model providing\nanticipatory corrections for the sensory signals based on observed\ndiscrepancies from the reference values. While most studies consider providing\nthe teaching signal as error in joint-space, few studies consider the error in\ntask-space and even fewer consider the spiking nature of the cerebellum on the\ncellular-level. In this study, a detailed cellular-level forward cerebellar\nmodel is developed, including modeling of Golgi and Basket cells which are\nusually neglected in previous studies. To preserve the biological features of\nthe cerebellum in the developed model, a hyperparameter optimization method\ntunes the network accordingly. The efficiency and biological plausibility of\nthe proposed cerebellar-based controller is then demonstrated under different\nrobotic manipulation tasks reproducing motor behaviour observed in human\nreaching experiments.\n"}
+{"abstract": "  We compute exactly the statistics of the number of records in a discrete-time\nrandom walk model on a line where the walker stays at a given position with a\nnonzero probability $0\\leq p \\leq 1$, while with the complementary probability\n$1-p$, it jumps to a new position with a jump length drawn from a continuous\nand symmetric distribution $f_0(\\eta)$. We have shown that, for arbitrary $p$,\nthe statistics of records up to step $N$ is completely universal, i.e.,\nindependent of $f_0(\\eta)$ for any $N$. We also compute the connected two-time\ncorrelation function $C_p(m_1, m_2)$ of the record-breaking events at times\n$m_1$ and $m_2$ and show it is also universal for all $p$. Moreover, we\ndemonstrate that $C_p(m_1, m_2)< C_0(m_1, m_2)$ for all $p>0$, indicating that\na nonzero $p$ induces additional anti-correlations between record events. We\nfurther show that these anti-correlations lead to a drastic reduction in the\nfluctuations of the record numbers with increasing $p$. This is manifest in the\nFano factor, i.e. the ratio of the variance and the mean of the record number,\nwhich we compute explicitly. We also show that an interesting scaling limit\nemerges when $p \\to 1$, $N \\to \\infty$ with the product $t = (1-p)\\, N$ fixed.\nWe compute exactly the associated universal scaling functions for the mean,\nvariance and the Fano factor of the number of records in this scaling limit. .\n"}
+{"abstract": "  Large organizations such as social media companies continually release data,\nfor example user images. At the same time, these organizations leverage their\nmassive corpora of released data to train proprietary models that give them an\nedge over their competitors. These two behaviors can be in conflict as an\norganization wants to prevent competitors from using their own data to\nreplicate the performance of their proprietary models. We solve this problem by\ndeveloping a data poisoning method by which publicly released data can be\nminimally modified to prevent others from train-ing models on it. Moreover, our\nmethod can be used in an online fashion so that companies can protect their\ndata in real time as they release it.We demonstrate the success of our approach\nonImageNet classification and on facial recognition.\n"}
+{"abstract": "  The Born cross sections are measured for the first time for the processes\n$e^+e^-\\to D_s^{*+}D_{s0}^*(2317)^- +c.c.$ and $e^+e^-\\to\nD_s^{*+}D_{s1}(2460)^- +c.c.$ at the center-of-mass energy $\\sqrt{s}=$\n4.600~GeV, 4.612~GeV, 4.626~GeV, 4.640~GeV, 4.660~GeV, 4.68~GeV, and 4.700~GeV,\nand for $e^+e^-\\to D_s^{*+}D_{s1}(2536)^- +c.c.$ at $\\sqrt{s}=$ 4.660~GeV,\n4.680~GeV, and 4.700~GeV, using data samples collected with the BESIII detector\nat the BEPCII collider. No structures are observed in cross-section\ndistributions for any of the processes.\n"}
+{"abstract": "  The autoencoder model uses an encoder to map data samples to a lower\ndimensional latent space and then a decoder to map the latent space\nrepresentations back to the data space. Implicitly, it relies on the encoder to\napproximate the inverse of the decoder network, so that samples can be mapped\nto and back from the latent space faithfully. This approximation may lead to\nsub-optimal latent space representations. In this work, we investigate a\ndecoder-only method that uses gradient flow to encode data samples in the\nlatent space. The gradient flow is defined based on a given decoder and aims to\nfind the optimal latent space representation for any given sample through\noptimisation, eliminating the need of an approximate inversion through an\nencoder. Implementing gradient flow through ordinary differential equations\n(ODE), we leverage the adjoint method to train a given decoder. We further show\nempirically that the costly integrals in the adjoint method may not be entirely\nnecessary. Additionally, we propose a $2^{nd}$ order ODE variant to the method,\nwhich approximates Nesterov's accelerated gradient descent, with faster\nconvergence per iteration. Commonly used ODE solvers can be quite sensitive to\nthe integration step-size depending on the stiffness of the ODE. To overcome\nthe sensitivity for gradient flow encoding, we use an adaptive solver that\nprioritises minimising loss at each integration step. We assess the proposed\nmethod in comparison to the autoencoding model. In our experiments, GFE showed\na much higher data-efficiency than the autoencoding model, which can be crucial\nfor data scarce applications.\n"}
+{"abstract": "  New computing technologies inspired by the brain promise fundamentally\ndifferent ways to process information with extreme energy efficiency and the\nability to handle the avalanche of unstructured and noisy data that we are\ngenerating at an ever-increasing rate. To realise this promise requires a brave\nand coordinated plan to bring together disparate research communities and to\nprovide them with the funding, focus and support needed. We have done this in\nthe past with digital technologies; we are in the process of doing it with\nquantum technologies; can we now do it for brain-inspired computing?\n"}
+{"abstract": "  In this paper we characterize the value set $\\Delta$ of the $R$-modules of\nthe form $R+zR$ for the local ring $R$ associated to a germ $\\xi$ of an\nirreducible plane curve singularity with one Puiseux pair. In the particular\ncase of the module of K\\\"ahler differentials attached to $\\xi$, we recover some\nresults of Delorme. From our characterization of $\\Delta$ we introduce a proper\nsubset of semimodules over the value semigroup of the ring $R$. Moreover, we\nprovide a geometric algorithm to construct all possible semimodules in this\nsubset for a given value semigroup.\n"}
+{"abstract": "  Chemotaxis, the directional locomotion of cells towards a source of a\nchemical gradient, is an integral part of many biological processes - for\nexample, bacteria motion, single-cell or multicellular organisms development,\nimmune response, etc. Chemotaxis directs bacteria's movement to find food\n(e.g., glucose) by swimming toward the highest concentration of food molecules.\nIn multicellular organisms, chemotaxis is critical to early development (e.g.,\nmovement of sperm towards the egg during fertilization). Chemotaxis also helps\nmobilize phagocytic and immune cells at sites of infection, tissue injury, and\nthus facilitates immune reactions. In this paper, we study a PDE system that\ndescribes such biological processes in one dimension, which may correspond to a\nthin channel, the setting relevant in many applications: for example,\nspermatozoa progression to the ovum inside a Fallopian tube or immune response\nin a blood vessel.\n"}
+{"abstract": "  This study evaluates a suspension design of a passenger car to obtain maximum\nrider's comfort when the vehicle is subjected to different road profile or road\nsurface condition. The challenge will be on finding a balance between the\nrider's comfort and vehicle handling to optimize design parameters. The study\nuses a simple passive suspension system and an active suspension model\nintegrated with a pneumatic actuator controlled by proportional integral\nderivative (PID) controller in both quarter car and full car models having a\ndifferent degree of freedoms (DOF) and increasing degrees of complexities. The\nquarter car considered as a 2-DOF model, while the full car model is a 7-DOF\nmodel. The design process set to optimise the spring stiffnesses, damping\ncoefficients and actuator PID controller gains. For optimisation, the research\nfeatured genetic algorithm optimisation technique to obtain a balanced response\nof the vehicle as evaluated from the displacement, velocity and acceleration of\nsprung and unsprung masses along with different human comfort and vehicle\nperformance criteria. The results revealed that the active suspension system\nwith optimised spring stiffness, damping coefficients and PID gains\ndemonstrated the superior riding comfort and road holding compared to a passive\nsuspension system.\n"}
+{"abstract": "  Prompt isolated leptons are essential in many analyses in high-energy\nparticle physics but are subject to fake-lepton background, i.e. objects that\nmimic the lepton signature. The fake-lepton background is difficult to estimate\nfrom simulation and is often directly determined from data. A popular method is\nthe matrix method, which however suffers from several limitations. This paper\nrecapitulates an alternative approach based on a likelihood with Poisson\nconstraints and reformulates the problem from a different starting point in the\nframework of Bayesian statistics. The equality of both approaches is shown and\nseveral cases are studied in which the matrix method is limited. In addition,\nthe fake lepton background is recalculated and compared to the estimate with\nthe matrix method in an example top-quark measurement.\n"}
+{"abstract": "  Training vision-based Urban Autonomous driving models is a challenging\nproblem, which is highly researched in recent times. Training such models is a\ndata-intensive task requiring the storage and processing of vast volumes of\n(possibly redundant) driving video data. In this paper, we study the problem of\ndeveloping data-efficient autonomous driving systems. In this context, we study\nthe problem of multi-criteria online video frame subset selection. We study\nconvex optimization-based solutions and show that they are unable to provide\nsolutions with high weightage to the loss of selected video frames. We design a\nnovel convex optimization-based multi-criteria online subset selection\nalgorithm that uses a thresholded concave function of selection variables. We\nalso propose and study a submodular optimization-based algorithm. Extensive\nexperiments using the driving simulator CARLA show that we are able to drop 80%\nof the frames while succeeding to complete 100% of the episodes w.r.t. the\nmodel trained on 100% data, in the most difficult task of taking turns. This\nresults in a training time of less than 30% compared to training on the whole\ndataset. We also perform detailed experiments on prediction performances of\nvarious affordances used by the Conditional Affordance Learning (CAL) model and\nshow that our subset selection improves performance on the crucial affordance\n\"Relative Angle\" during turns.\n"}
+{"abstract": "  Thinking aloud is an effective meta-cognitive strategy human reasoners apply\nto solve difficult problems. We suggest to improve the reasoning ability of\npre-trained neural language models in a similar way, namely by expanding a\ntask's context with problem elaborations that are dynamically generated by the\nlanguage model itself. Our main result is that dynamic problem elaboration\nsignificantly improves the zero-shot performance of GPT-2 in a deductive\nreasoning and natural language inference task: While the model uses a syntactic\nheuristic for predicting an answer, it is capable (to some degree) of\ngenerating reasoned additional context which facilitates the successful\napplication of its heuristic. We explore different ways of generating\nelaborations, including fewshot learning, and find that their relative\nperformance varies with the specific problem characteristics (such as problem\ndifficulty). Moreover, the effectiveness of an elaboration can be explained in\nterms of the degree to which the elaboration semantically coheres with the\ncorresponding problem. In particular, elaborations that are most faithful to\nthe original problem description may boost accuracy by up to 24%.\n"}
+{"abstract": "  We establish a novel approach to computing $G$-equivariant cohomology for a\nfinite group $G$, and demonstrate it in the case that $G = C_{p^n}$.\n  For any commutative ring spectrum $R$, we prove a symmetric monoidal\nreconstruction theorem for genuine $G$-$R$-modules, which records them in terms\nof their geometric fixedpoints as well as gluing maps involving their Tate\ncohomologies. This reconstruction theorem follows from a symmetric monoidal\nstratification (in the sense of \\cite{AMR-strat}); here we identify the gluing\nfunctors of this stratification in terms of Tate cohomology.\n  Passing from genuine $G$-spectra to genuine $G$-$\\mathbb{Z}$-modules (a.k.a.\nderived Mackey functors) provides a convenient intermediate category for\ncalculating equivariant cohomology. Indeed, as $\\mathbb{Z}$-linear Tate\ncohomology is far simpler than $\\mathbb{S}$-linear Tate cohomology, the above\nreconstruction theorem gives a particularly simple algebraic description of\ngenuine $G$-$\\mathbb{Z}$-modules. We apply this in the case that $G = C_{p^n}$\nfor an odd prime $p$, computing the Picard group of genuine\n$G$-$\\mathbb{Z}$-modules (and therefore that of genuine $G$-spectra) as well as\nthe $RO(G)$-graded and Picard-graded $G$-equivariant cohomology of a point.\n"}
+{"abstract": "  Spin defects in silicon carbide (SiC) have attracted increasing interest due\nto their excellent optical and spin properties, which are useful in quantum\ninformation processing. In this paper, we systematically investigate the\ntemperature dependence of the spin properties of divacancy defects in implanted\n4\\emph{H}-SiC. The zero-field splitting parameter $D$, the inhomogeneous\ndephasing time $T_2^{*}$, the coherence time $T_2$, and the depolarization time\n$T_1$ are extensively explored in a temperature range from 5 to 300 K. Two\nsamples implanted with different nitrogen molecule ion fluences ($\\rm\n{N_2}^{+}$, $1\\times 10^{14}/\\rm cm^{2}$ and $1\\times 10^{13}/\\rm cm^{2}$) are\ninvestigated, whose spin properties are shown to have similar\ntemperature-dependent behaviors. Still, the sample implanted with a lower ion\nfluence has longer $T_{2}$ and $T_{1}$. We provide possible theoretical\nexplanations for the observed temperature-dependent dynamics. Our work promotes\nthe understanding of the temperature dependence of spin properties in\nsolid-state systems, which can be helpful for constructing wide\ntemperature-range thermometers based on the mature semiconductor material.\n"}
+{"abstract": "  In addition to regular Schwabe cycles (~ 11 years), solar activity also shows\nlonger periods of enhanced or reduced activity. Of these, reconstructions of\nthe Dalton Minimum provide controversial sunspot group numbers and limited\nsunspot positions, partially due to limited source record accessibility. We\nanalysed Stephan Prantner's sunspot observations from 1804--1844, the values of\nwhich had only been known through estimates despite their notable chronological\ncoverage during the Dalton Minimum. We identified his original manuscript in\nStiftsarchiv Wilten, near Innsbruck, Austria. We reviewed his biography\n(1782--1873) and located his observational sites at Wilten and Waidring, which\nhoused the principal telescopes for his early and late observations: a 3.5-inch\nastronomical telescope and a Reichenbach 4-feet achromatic erecting telescope,\nrespectively. We identified 215 days of datable sunspot observations, which are\ntwice as much data as his estimated data in the existing database (= 115 days).\nPrantner counted up to 7--9 sunspot groups per day and measured sunspot\npositions, which show their distributions in both solar hemispheres. These\nresults strikingly emphasise the difference between the Dalton Minimum and the\nMaunder Minimum as well as the similarity between the Dalton Minimum and the\nmodern solar cycles.\n"}
+{"abstract": "  We present algebraic and geometric classifications of the $4$-dimensional\ncomplex nilpotent right alternative algebras. Specifically, we find that, up to\nisomorphism, there are only $9$ non-isomorphic nontrivial nilpotent right\nalternative algebras. The corresponding geometric variety has dimension $13$\nand it is determined by the Zariski closure of $4$ rigid algebras and one\none-parametric family of algebras.\n"}
+{"abstract": "  This paper investigates algebraic objects equipped with an operator, such as\noperated monoids, operated algebras etc. Various free object functors in these\noperated contexts are explicitly constructed. For operated algebras whose\noperator satisfies a set $\\Phi$ of relations (usually called operated\npolynomial identities (aka. OPIs)), Guo defined free objects, called free\n$\\Phi$-algebras, via universal algebra. Free $\\Phi$-algebras over algebras are\nstudied in details. A mild sufficient condition is found such that $\\Phi$\ntogether with a Gr\\\"obner-Shirshov basis of an algebra $A$ form a\nGr\\\"obner-Shirshov basis of the free $\\Phi$-algebra over algebra $A$ in the\nsense of Guo et al.. Ample examples for which this condition holds are\nprovided, such as all Rota-Baxter type OPIs, a class of differential type OPIs,\naveraging OPIs and Reynolds OPI.\n"}
+{"abstract": "  Neural networks trained with SGD were recently shown to rely preferentially\non linearly-predictive features and can ignore complex, equally-predictive\nones. This simplicity bias can explain their lack of robustness out of\ndistribution (OOD). The more complex the task to learn, the more likely it is\nthat statistical artifacts (i.e. selection biases, spurious correlations) are\nsimpler than the mechanisms to learn. We demonstrate that the simplicity bias\ncan be mitigated and OOD generalization improved. We train a set of similar\nmodels to fit the data in different ways using a penalty on the alignment of\ntheir input gradients. We show theoretically and empirically that this induces\nthe learning of more complex predictive patterns. OOD generalization\nfundamentally requires information beyond i.i.d. examples, such as multiple\ntraining environments, counterfactual examples, or other side information. Our\napproach shows that we can defer this requirement to an independent model\nselection stage. We obtain SOTA results in visual recognition on biased data\nand generalization across visual domains. The method - the first to evade the\nsimplicity bias - highlights the need for a better understanding and control of\ninductive biases in deep learning.\n"}
+{"abstract": "  Multi-Source Domain Adaptation (MSDA) focuses on transferring the knowledge\nfrom multiple source domains to the target domain, which is a more practical\nand challenging problem compared to the conventional single-source domain\nadaptation. In this problem, it is essential to utilize the labeled source data\nand the unlabeled target data to approach the conditional distribution of\nsemantic label on target domain, which requires the joint modeling across\ndifferent domains and also an effective domain combination scheme. The\ngraphical structure among different domains is useful to tackle these\nchallenges, in which the interdependency among various instances/categories can\nbe effectively modeled. In this work, we propose two types of graphical\nmodels,i.e. Conditional Random Field for MSDA (CRF-MSDA) and Markov Random\nField for MSDA (MRF-MSDA), for cross-domain joint modeling and learnable domain\ncombination. In a nutshell, given an observation set composed of a query sample\nand the semantic prototypes i.e. representative category embeddings) on various\ndomains, the CRF-MSDA model seeks to learn the joint distribution of labels\nconditioned on the observations. We attain this goal by constructing a\nrelational graph over all observations and conducting local message passing on\nit. By comparison, MRF-MSDA aims to model the joint distribution of\nobservations over different Markov networks via an energy-based formulation,\nand it can naturally perform label prediction by summing the joint likelihoods\nover several specific networks. Compared to the CRF-MSDA counterpart, the\nMRF-MSDA model is more expressive and possesses lower computational cost. We\nevaluate these two models on four standard benchmark data sets of MSDA with\ndistinct domain shift and data complexity, and both models achieve superior\nperformance over existing methods on all benchmarks.\n"}
+{"abstract": "  We prove that a simple knot in the lens space $L(p,q)$ fibers if and only if\nits order in homology does not divide any remainder occurring in the Euclidean\nalgorithm applied to the pair $(p,q)$. One corollary is that if $p=m^2$ is a\nperfect square, then any simple knot of order $m$ fibers, answering a question\nof Cebanu. More generally, we compute the leading coefficient of the Alexander\npolynomial of a simple knot, and we describe how to construct a minimum\ncomplexity Seifert surface for one. The methods are direct, combinatorial, and\ngeometric.\n"}
+{"abstract": "  In Veltman's original view, the Standard Model with a large Higgs particle\nmass of about 1 TeV was the natural completion of non-renormalizable Glashow\nmodel. This mass was thus a second threshold for weak interactions, as the W\nmass was for the non-renormalizable 4-fermion V-A theory. Today, after the\nobservation of the narrow scalar resonance at 125 GeV, Veltman's large-mass\nidea seems to be ruled out. Yet, this is not necessarily true. Depending on the\ndescription of SSB in $\\Phi^4$ theory, and by combining analytic calculations\nand lattice simulations, besides the known particle at 125 GeV, a new resonance\nof the Higgs field may also show up around 700 GeV. The peculiarity, though, is\nthat this heavier state would couple to longitudinal vector bosons with the\nsame typical strength of the low-mass state and thus represent a relatively\nnarrow resonance. In this way, such hypothetical new resonance would naturally\nfit with some excess of 4-lepton events observed by ATLAS around 680 GeV.\nAnalogous data from CMS are needed to confirm or disprove this interpretation.\nImplications of a two-mass structure for radiative corrections are also\ndiscussed.\n"}
+{"abstract": "  The understanding of an offense is subjective and people may have different\nopinions about the offensiveness of a comment. Moreover, offenses and hate\nspeech may occur through sarcasm, which hides the real intention of the comment\nand makes the decision of the annotators more confusing. Therefore, providing a\nwell-structured annotation process is crucial to a better understanding of hate\nspeech and offensive language phenomena, as well as supplying better\nperformance for machine learning classifiers. In this paper, we describe a\ncorpus annotation process proposed by a linguist, a hate speech specialist, and\nmachine learning engineers in order to support the identification of hate\nspeech and offensive language on social media. In addition, we provide the\nfirst robust dataset of this kind for the Brazilian Portuguese language. The\ncorpus was collected from Instagram posts of political personalities and\nmanually annotated, being composed by 7,000 annotated documents according to\nthree different layers: a binary classification (offensive versus non-offensive\nlanguage), the level of offense (highly offensive, moderately offensive, and\nslightly offensive messages), and the identification regarding the target of\nthe discriminatory content (xenophobia, racism, homophobia, sexism, religious\nintolerance, partyism, apology to the dictatorship, antisemitism, and\nfatphobia). Each comment was annotated by three different annotators and\nachieved high inter-annotator agreement. The proposed annotation approach is\nalso language and domain-independent nevertheless it is currently customized\nfor Brazilian Portuguese.\n"}
+{"abstract": "  We describe the addition of Lyman-alpha resonant line transfer to our dust\ncontinuum radiation transfer code SKIRT, verifying our implementation with\npublished results for spherical problems and using some self-designed\nthree-dimensional setups. We specifically test spatial discretization through\nvarious grid types, including hierarchical octree grids and unstructured\nVoronoi tessellations. We then use a radiation transfer post-processing model\nfor one of the spiral galaxies produced by the Auriga cosmological zoom\nsimulations to investigate the effect of spatial discretization on the\nsynthetic observations. We find that the calculated Lyman-alpha line profiles\nexhibit an extraordinarily strong dependence on the type and resolution of the\nspatial grid, rendering the results untrustworthy at best. We attribute this\neffect to the large gradients in the hydrogen density distribution over small\ndistances, which remain significantly under-resolved in the input model. We\ntherefore argue that further research is needed to determine the required\nspatial resolution of a hydrodynamical simulation snapshot to enable meaningful\nLyman-alpha line transfer post-processing.\n"}
+{"abstract": "  In modern data science, it is often not enough to obtain only a data-driven\nmodel with a good prediction quality. On the contrary, it is more interesting\nto understand the properties of the model, which parts could be replaced to\nobtain better results. Such questions are unified under machine learning\ninterpretability questions, which could be considered one of the area's raising\ntopics. In the paper, we use multi-objective evolutionary optimization for\ncomposite data-driven model learning to obtain the algorithm's desired\nproperties. It means that whereas one of the apparent objectives is precision,\nthe other could be chosen as the complexity of the model, robustness, and many\nothers. The method application is shown on examples of multi-objective learning\nof composite models, differential equations, and closed-form algebraic\nexpressions are unified and form approach for model-agnostic learning of the\ninterpretable models.\n"}
+{"abstract": "  Multi-messenger astrophysics is becoming a major avenue to explore the\nUniverse, with the potential to span a vast range of redshifts. The growing\nsynergies between different probes is opening new frontiers, which promise\nprofound insights into several aspects of fundamental physics and cosmology. In\nthis context, THESEUS will play a central role during the 2030s in detecting\nand localizing the electromagnetic counterparts of gravitational wave and\nneutrino sources that the unprecedented sensitivity of next generation\ndetectors will discover at much higher rates than the present. Here, we review\nthe most important target signals from multi-messenger sources that THESEUS\nwill be able to detect and characterize, discussing detection rate expectations\nand scientific impact.\n"}
+{"abstract": "  We adopt a geometric perspective on Fock space to provide two complementary\ninsights into the eigenstates in many-body-localized fermionic systems. On the\none hand, individual many-bodylocalized eigenstates are well approximated by a\nSlater determinant of single-particle orbitals. On the other hand, the orbitals\nof different eigenstates in a given system display a varying, and generally\nimperfect, degree of compatibility, as we quantify by a measure based on the\nprojectors onto the corresponding single-particle subspaces. We study this\nincompatibility between states of fixed and differing particle number, as well\nas inside and outside the many-body-localized regime. This gives detailed\ninsights into the emergence and strongly correlated nature of\nquasiparticle-like excitations in many-body localized systems, revealing\nintricate correlations between states of different particle number down to the\nlevel of individual realizations.\n"}
+{"abstract": "  In applications such as participatory sensing and crowd sensing,\nself-interested agents exert costly effort towards achieving an objective for\nthe system operator. We study such a setup where a principal incentivizes\nmultiple agents of different types who can collude with each other to derive\nrent. The principal cannot observe the efforts exerted directly, but only the\noutcome of the task, which is a noisy function of the effort. The type of each\nagent influences the effort cost and task output. For a duopoly in which agents\nare coupled in their payments, we show that if the principal and the agents\ninteract finitely many times, the agents can derive rent by colluding even if\nthe principal knows the types of the agents. However, if the principal and the\nagents interact infinitely often, the principal can disincentivize agent\ncollusion through a suitable data-driven contract.\n"}
+{"abstract": "  We establish an exact formulation for wave scattering of a massless field\nwith spin and charge by a Kerr-Newman-de Sitter black hole. Our formulation is\nbased on the exact solution of the Teukolsky equation in terms of the local\nHeun function, and does not require any approximation. It serves as simple\nexact formulae with arbitrary high precision, which realize fast calculation\nwithout restrictions on model parameters. We highlight several applications\nincluding quasinormal modes, cross section, reflection/absorption rate, and\nGreen function.\n"}
+{"abstract": "  The Sloan Digital Sky Survey provides colors for more than 100 000 moving\nobjects, among which around 10 000 have albedos determined. Here we combined\ncolors and albedo in order to perform a cluster analysis on the small bodies\npopulation, and identify a C-cluster, a group of asteroid related to C-type as\ndefined in earlier work. Members of this C-cluster are in fair agreement with\nthe color boundaries of B and C-type defined in DeMeo and Carry (2013). We then\ncompare colors of C-cluster asteroids to those of carbonaceous chondrites\npowders, while taking into account the effect of phase angle. We show that only\nCM chondrites have colors in the range of C-cluster asteroids, CO, CR and CV\nchondrites being significantly redder. Also, CM chondrites powders are on\naverage slightly redder than the average C-cluster. The colors of C-cluster\nmembers are further investigated by looking at color variations as a function\nof asteroid diameter. We observe that the visible slope becomes bluer with\ndecreasing asteroids diameter, and a transition seems to be present around 20\nkm. We discuss the origin of this variation and, if not related to a bias in\nthe dataset - analysis, we conclude that it is related to the surface texture\nof the objects, smaller objects being covered by rocks, while larger objects\nare covered by a particulate surface. The blueing is interpreted by an\nincreased contribution of the first reflection in the case of rock-dominated\nsurfaces, which can scatter light in a Rayleigh-like manner. We do not have\nunambiguous evidence of space weathering within the C-cluster based on this\nanalysis, however the generally bluer nature of C-cluster objects compared to\nCM chondrites could be to some extent related to space weathering.\n"}
+{"abstract": "  An intersecting $r$-uniform straight line system is an intersecting linear\nsystem whose lines consist of $r$ points on straight line segment of\n$\\mathbb{R}^2$ and any two lines share a point. Recently, the author [A.\nV\\'azquez-\\'Avila, \\emph{On intersecting straight line systems}, J. Discret.\nMath. Sci. Cryptogr. Accepted] proved that any intersecting $r$-uniform\nstraight line system $(P,\\mathcal{L})$ has transversal number at most\n$\\nu_2-1$, with $r\\geq\\nu_2$, where $\\nu_2$ is the maximum cardinality of a\nsubset of lines $R\\subseteq\\mathcal{L}$ such that every triplet of different\nelements of $R$ does not have a common point.\n  In this paper, we improve such upper bound if the intersecting $r$-uniform\nstraight line system satisfies $r=\\nu_2$. This result has immediate\nconsequences for some questions given by Oliveros et al. [D. Oliveros, C.\nO'Neill and S. Zerbib, \\emph{The geometry and combinatorics of discrete line\nsegment hypergraphs}, Discrete Math. {\\bf 343} (2020), no. 6, 111825].\n"}
+{"abstract": "  The effect of polyvalent molecular cations, such as spermine, on the\ncondensation of DNA into very well-defined toroidal shapes have been well\nstudied and understood. However, a great effort has been made trying to obtain\nsimilar condensed structures from either ssRNA or dsRNA, which the latter\ncarries similar negative charge density as dsDNA, although it adopts a\ndifferent helical form. But the analogous condensation of RNA molecules into\nwell-defined structures has so far been elusive. In this work, we show that\nssRNA molecules can easily be condensed into nanoring structures on a mice\nsurface, where each nanoring structure is formed mostly by a single RNA\nmolecule. The condensation occurs in a concentration range of different atomic\ncations, from monovalent to trivalent. The structures of the RNA nanorings on\nmica surfaces were oberved by atomic force microscopy (AFM). The samples were\nobserved in tapping mode and were prepared by drop evaporation of a solution of\nRNA in the presence of one type of the different cations used. As far as we\nknow, this is the first time that nanorings or any other well-defined condensed\nRNA structures have been reported. The RNA nanorings formation can be\nunderstood by an energy competition between the hydrogen bonding forming\nhairpin stems, weakened by the salts, and hairpin loops. This results may have\nan important biological relevance, since it has been proposed that RNA is the\noldest genome coding molecule and the formation of these structures could have\ngiven it stability against degradation in primeval times. Even more, the\nnanoring structures could have the potential to be used as biosensors and\nfunctionalized nanodevices.\n"}
+{"abstract": "  We explore the feasibility of combining Graph Neural Network-based policy\narchitectures with Deep Reinforcement Learning as an approach to problems in\nsystems. This fits particularly well with operations on networks, which\nnaturally take the form of graphs. As a case study, we take the idea of\ndata-driven routing in intradomain traffic engineering, whereby the routing of\ndata in a network can be managed taking into account the data itself. The\nparticular subproblem which we examine is minimising link congestion in\nnetworks using knowledge of historic traffic flows. We show through experiments\nthat an approach using Graph Neural Networks (GNNs) performs at least as well\nas previous work using Multilayer Perceptron architectures. GNNs have the added\nbenefit that they allow for the generalisation of trained agents to different\nnetwork topologies with no extra work. Furthermore, we believe that this\ntechnique is applicable to a far wider selection of problems in systems\nresearch.\n"}
+{"abstract": "  We seek to design experimentally feasible broadband, temporally multiplexed\noptical quantum memory with near-term applications to telecom bands.\nSpecifically, we devise dispersion compensation for an impedance-matched\nnarrow-band quantum memory by exploiting Raman processes over two three-level\natomic subensembles, one for memory and the other for dispersion compensation.\nDispersion compensation provides impedance matching over more than a full\ncavity linewidth. Combined with one second spin-coherence lifetime the memory\ncould be capable of power efficiency exceeding 90% leading to 106 modes for\ntemporal multiplexing. Our design could lead to significant multiplexing\nenhancement for quantum repeaters to be used for telecom quantum networks.\n"}
+{"abstract": "  The flux of cosmic rays (CRs) in the heliosphere is subjected to remarkable\ntime variations caused by the 11-year cycle of solar activity. To help the\nstudy of this effect, we have developed a web application (Heliophysics Virtual\nObservatory) that collects real-time data on solar activity, interplanetary\nplasma, and charged radiation from several space missions or observatories. As\nwe will show, our application can be used to visualize, manipulate, and\ndownload updated data on sunspots, heliospheric magnetic fields, solar wind,\nand neutron monitors counting rates. Data and calculations are automatically\nupdated on daily basis. A nowcasting for the energy spectrum of CR protons\nnear-Earth is also provided using calculations and real-time neutron monitor\ndata as input.\n"}
+{"abstract": "  Flickering is a universal phenomenon in accreting astronomical systems which\nstill defies detailed physical understanding. It is particularly evident in\ncataclysmic variables (CVs). Attempting to define boundary conditions for\nmodels, the strength of the flickering is measured in several thousand light\ncurves of more than 100 CVs. The flickering amplitude is parameterized by the\nFWHM of a Gaussian fit to the magnitude distribution of data points in a light\ncurve. This quantity requires several corrections before a comparison between\ndifferent sources can be made. While no correlations of the flickering strength\nwith simple parameters such as component masses, orbital inclination or period\nwere detected, a dependence on the absolute magnitude of the primary component\nand on the CV subtype is found. In particular, flickering in VY Scl tpye\nnovalike variables is systematically stronger than in UX UMa type novalikes.\nThe broadband spectrum of the flickering light source can be fit by simple\nmodels but shows excess in the $U$ band. When the data permitted to investigate\nthe flickering strength as a function of orbital phase in eclipsing CVs, such a\ndependence was found, but it is different for different systems. Surprisingly,\nthere are also indications for variations of the flickering strength with the\nsuperhump phase in novalike variables with permanent superhumps. In dwarf\nnovae, the flickering amplitude is high during quiescence, drops quickly at an\nintermediate magnitude when the system enters into (or returns from) an\noutburst and, on average, remains constant above a given brightness threshold.\n"}
+{"abstract": "  Training a multi-speaker Text-to-Speech (TTS) model from scratch is\ncomputationally expensive and adding new speakers to the dataset requires the\nmodel to be re-trained. The naive solution of sequential fine-tuning of a model\nfor new speakers can cause the model to have poor performance on older\nspeakers. This phenomenon is known as catastrophic forgetting. In this paper,\nwe look at TTS modeling from a continual learning perspective where the goal is\nto add new speakers without forgetting previous speakers. Therefore, we first\npropose an experimental setup and show that serial fine-tuning for new speakers\ncan result in the forgetting of the previous speakers. Then we exploit two\nwell-known techniques for continual learning namely experience replay and\nweight regularization and we reveal how one can mitigate the effect of\ndegradation in speech synthesis diversity in sequential training of new\nspeakers using these methods. Finally, we present a simple extension to improve\nthe results in extreme setups.\n"}
+{"abstract": "  The difficulty of optimal control problems has classically been characterized\nin terms of system properties such as minimum eigenvalues of\ncontrollability/observability gramians. We revisit these characterizations in\nthe context of the increasing popularity of data-driven techniques like\nreinforcement learning (RL), and in control settings where input observations\nare high-dimensional images and transition dynamics are unknown. Specifically,\nwe ask: to what extent are quantifiable control and perceptual difficulty\nmetrics of a task predictive of the performance and sample complexity of\ndata-driven controllers? We modulate two different types of partial\nobservability in a cartpole \"stick-balancing\" problem -- (i) the height of one\nvisible fixation point on the cartpole, which can be used to tune fundamental\nlimits of performance achievable by any controller, and by (ii) the level of\nperception noise in the fixation point position inferred from depth or RGB\nimages of the cartpole. In these settings, we empirically study two popular\nfamilies of controllers: RL and system identification-based $H_\\infty$ control,\nusing visually estimated system state. Our results show that the fundamental\nlimits of robust control have corresponding implications for the\nsample-efficiency and performance of learned perception-based controllers.\nVisit our project website https://jxu.ai/rl-vs-control-web for more\ninformation.\n"}
+{"abstract": "  We introduce and apply a new approach to probe the response of galactic\nstellar haloes to the interplay between cosmological merger histories and\ngalaxy formation physics. We perform dark-matter-only, zoomed simulations of\ntwo Milky Way-mass hosts and make targeted, controlled changes to their\ncosmological histories using the genetic modification technique. Populating\neach history's stellar halo with a semi-empirical, particle-tagging approach\nthen enables a controlled study, with all instances converging to the same\nlarge-scale structure, dynamical and stellar mass at $z=0$ as their reference.\nThese related merger scenarios alone generate an extended spread in stellar\nhalo mass fractions (1.5 dex) comparable to the observed population. Largest\nscatter is achieved by growing late ($z\\leq1$) major mergers that spread out\nexisting stars to create massive, in-situ dominated stellar haloes. Increasing\na last major merger at $z\\sim2$ brings more accreted stars into the inner\nregions, resulting in smaller scatter in the outskirts which are predominantly\nbuilt by subsequent minor events. Exploiting the flexibility of our\nsemi-empirical approach, we show that the diversity of stellar halo masses\nacross scenarios is reduced by allowing shallower slopes in the stellar\nmass--halo mass relation for dwarf galaxies, while it remains conserved when\ncentral stars are born with hotter kinematics across cosmic time. The\nmerger-dependent diversity of stellar haloes thus responds distinctly to\nassumptions in modelling the central and dwarf galaxies respectively, opening\nexciting prospects to constrain star formation and feedback at different\ngalactic mass-scales with the coming generation of deep, photometric\nobservatories.\n"}
+{"abstract": "  This paper presents a method for riggable 3D face reconstruction from\nmonocular images, which jointly estimates a personalized face rig and per-image\nparameters including expressions, poses, and illuminations. To achieve this\ngoal, we design an end-to-end trainable network embedded with a differentiable\nin-network optimization. The network first parameterizes the face rig as a\ncompact latent code with a neural decoder, and then estimates the latent code\nas well as per-image parameters via a learnable optimization. By estimating a\npersonalized face rig, our method goes beyond static reconstructions and\nenables downstream applications such as video retargeting. In-network\noptimization explicitly enforces constraints derived from the first principles,\nthus introduces additional priors than regression-based methods. Finally,\ndata-driven priors from deep learning are utilized to constrain the ill-posed\nmonocular setting and ease the optimization difficulty. Experiments demonstrate\nthat our method achieves SOTA reconstruction accuracy, reasonable robustness\nand generalization ability, and supports standard face rig applications.\n"}
+{"abstract": "  Object recognition has made great advances in the last decade, but\npredominately still relies on many high-quality training examples per object\ncategory. In contrast, learning new objects from only a few examples could\nenable many impactful applications from robotics to user personalization. Most\nfew-shot learning research, however, has been driven by benchmark datasets that\nlack the high variation that these applications will face when deployed in the\nreal-world. To close this gap, we present the ORBIT dataset and benchmark,\ngrounded in the real-world application of teachable object recognizers for\npeople who are blind/low-vision. The dataset contains 3,822 videos of 486\nobjects recorded by people who are blind/low-vision on their mobile phones. The\nbenchmark reflects a realistic, highly challenging recognition problem,\nproviding a rich playground to drive research in robustness to few-shot,\nhigh-variation conditions. We set the benchmark's first state-of-the-art and\nshow there is massive scope for further innovation, holding the potential to\nimpact a broad range of real-world vision applications including tools for the\nblind/low-vision community. We release the dataset at\nhttps://doi.org/10.25383/city.14294597 and benchmark code at\nhttps://github.com/microsoft/ORBIT-Dataset.\n"}
+{"abstract": "  The recent inflow of empirical data about the collective behaviour of\nstrongly correlated biological systems has brought field theory and the\nrenormalization group into the biophysical arena. Experiments on bird flocks\nand insect swarms show that social forces act on the particles' velocity\nthrough the generator of its rotations, namely the spin, indicating that\nmode-coupling field theories are necessary to reproduce the correct dynamical\nbehaviour. Unfortunately, a theory for three coupled fields - density, velocity\nand spin - has a prohibitive degree of intricacy. A simplifying path consists\nin getting rid of density fluctuations by studying incompressible systems. This\nrequires imposing a solenoidal constraint on the primary field, an unsolved\nproblem even for equilibrium mode-coupling theories. Here, we perform an\nequilibrium dynamic renormalization group analysis of a mode-coupling field\ntheory subject to a solenoidal constraint; using the classification of Halperin\nand Hohenberg, we can dub this case as a solenoidal Model G. We demonstrate\nthat the constraint produces a new vertex that mixes static and dynamical\ncoupling constants, and that this vertex is essential to grant the closure of\nthe renormalization group structure and the consistency of dynamics with\nstatics. Interestingly, although the solenoidal constraint leads to a\nmodification of the static universality class, we find that it does not change\nthe dynamical universality class, a result that seems to represent an exception\nto the general rule that dynamical universality classes are narrower than\nstatic ones. Our results constitute a solid stepping stone in the admittedly\nlarge chasm towards developing an off-equilibrium mode-coupling theory of\nbiological groups.\n"}
+{"abstract": "  Besides magnetic and charge order, regular arrangements of orbital occupation\nconstitute a fundamental order parameter of condensed matter physics. Even\nthough orbital order is difficult to identify directly in experiments, its\npresence was firmly established in a number of strongly correlated,\nthree-dimensional Mott insulators. Here, reporting resonant X-ray scattering\nexperiments on the layered Van der Waals compound $1T$-TiSe$_2$, we establish\nthe emergence of orbital order in a weakly correlated, quasi-two-dimensional\nmaterial. Our experimental scattering results are consistent with\nfirst-principles calculations that bring to the fore a generic mechanism of\nclose interplay between charge redistribution, lattice displacements, and\norbital order. It demonstrates the essential role that orbital degrees of\nfreedom play in TiSe$_2$, and their importance throughout the family of\ncorrelated Van der Waals materials.\n"}
+{"abstract": "  Existing radar sensors can be classified into automotive and scanning radars.\nWhile most radar odometry (RO) methods are only designed for a specific type of\nradar, our RO method adapts to both scanning and automotive radars. Our RO is\nsimple yet effective, where the pipeline consists of thresholding,\nprobabilistic submap building, and an NDT-based radar scan matching. The\nproposed RO has been tested on two public radar datasets: the Oxford Radar\nRobotCar dataset and the nuScenes dataset, which provide scanning and\nautomotive radar data respectively. The results show that our approach\nsurpasses state-of-the-art RO using either automotive or scanning radar by\nreducing translational error by 51% and 30%, respectively, and rotational error\nby 17% and 29%, respectively. Besides, we show that our RO achieves\ncentimeter-level accuracy as lidar odometry, and automotive and scanning RO\nhave similar accuracy.\n"}
+{"abstract": "  Periodically-driven systems are ubiquitous in science and technology. In\nquantum dynamics, even a small number of periodically-driven spins leads to\ncomplicated dynamics. Hence, it is of interest to understand what constraints\nsuch dynamics must satisfy. We derive a set of constraints for each number of\ncycles. For pure initial states, the observable being constrained is the\nrecurrence probability. We use our constraints for detecting undesired coupling\nto unaccounted environments and drifts in the driving parameters. To illustrate\nthe relevance of these results for modern quantum systems we demonstrate our\nfindings experimentally on a trapped-ion quantum computer, and on various IBM\nquantum computers. Specifically, we provide two experimental examples where\nthese constraints surpass fundamental bounds associated with known one-cycle\nconstraints. This scheme can potentially be used to detect the effect of the\nenvironment in quantum circuits that cannot be classically simulated. Finally,\nwe show that, in practice, testing an $n$-cycle constraint requires executing\nonly $O(\\sqrt{n})$ cycles, which makes the evaluation of constraints associated\nwith hundreds of cycles realistic.\n"}
+{"abstract": "  We study one-loop bulk entanglement entropy in even spacetime dimensions\nusing the heat kernel method, which captures the universal piece of\nentanglement entropy, a logarithmically divergent term in even dimensions. In\nfour dimensions, we perform explicit calculations for various shapes of\nboundary subregions. In particular, for a cusp subregion with an arbitrary\nopening angle, we find that the bulk entanglement entropy always encodes the\nsame universal information about the boundary theories as the leading\nentanglement entropy in the large N limit, up to a fixed proportional constant.\nBy smoothly deforming a circle in the boundary, we find that to leading order\nof the deformations, the bulk entanglement entropy shares the same shape\ndependence as the leading entanglement entropy and hence the same physical\ninformation can be extracted from both cases. This establishes an interesting\nlocal/nonlocal duality for holographic $\\mm{CFT}_3$. However, the result does\nnot hold for higher dimensional holographic theories.\n"}
+{"abstract": "  Using the nonabilinization procedure, we find an integrable matrix version of\nthe Euler top on $\\mathfrak{so}_3$\n"}
+{"abstract": "  Linear-response time-dependent density functional theory (LR-TDDFT) for core\nlevel spectroscopy using standard local functionals suffers from\nself-interaction error and a lack of orbital relaxation upon creation of the\ncore hole. As a result, LR-TDDFT calculated X-ray absorption near edge\nstructure (XANES) spectra need to be shifted along the energy axis to match\nexperimental data. We propose a correction scheme based on many body\nperturbation theory to calculate the shift from first principles. The\nionization potential of the core donor state is first computed and then\nsubstituted for the corresponding Kohn--Sham orbital energy, thus emulating\nKoopmans' condition. Both self-interaction error and orbital relaxation are\ntaken into account. The method exploits the localized nature of core states for\nefficiency and integrates seamlessly in our previous implementation of core\nlevel LR-TDDFT, yielding corrected spectra in a single calculation. We\nbenchmark the correction scheme on molecules at the K- and L-edges as well as\nfor core binding energies and report accuracies comparable to higher order\nmethods. We also demonstrate applicability in large and extended systems and\ndiscuss efficient approximations.\n"}
+{"abstract": "  A giant impact origin for the Moon is generally accepted, but many aspects of\nlunar formation remain poorly understood and debated. \\'Cuk et al. (2016)\nproposed that an impact that left the Earth-Moon system with high obliquity and\nangular momentum could explain the Moon's orbital inclination and isotopic\nsimilarity to Earth. In this scenario, instability during the Laplace Plane\ntransition, when the Moon's orbit transitions from the gravitational influence\nof Earth's figure to that of the Sun, would both lower the system's angular\nmomentum to its present-day value and generate the Moon's orbital inclination.\nRecently, Tian and Wisdom (2020) discovered new dynamical constraints on the\nLaplace Plane transition and concluded that the Earth-Moon system could not\nhave evolved from an initial state with high obliquity. Here we demonstrate\nthat the Earth-Moon system with an initially high obliquity can evolve into the\npresent state, and we identify a spin-orbit secular resonance as a key\ndynamical mechanism in the later stages of the Laplace Plane transition. Some\nof the simulations by Tian and Wisdom (2020) did not encounter this late\nsecular resonance, as their model suppressed obliquity tides and the resulting\ninclination damping. Our results demonstrate that a giant impact that left\nEarth with high angular momentum and high obliquity ($\\theta > 61^{\\circ}$) is\na promising scenario for explaining many properties of the Earth-Moon system,\nincluding its angular momentum and obliquity, the geochemistry of Earth and the\nMoon, and the lunar inclination.\n"}
+{"abstract": "  Transmission electron microscopes use electrons with wavelengths of a few\npicometers, potentially capable of imaging individual atoms in solids at a\nresolution ultimately set by the intrinsic size of an atom. Unfortunately, due\nto imperfections in the imaging lenses and multiple scattering of electrons in\nthe sample, the image resolution reached is 3 to 10 times worse. Here, by\ninversely solving the multiple scattering problem and overcoming the\naberrations of the electron probe using electron ptychography to recover a\nlinear phase response in thick samples, we demonstrate an instrumental blurring\nof under 20 picometers. The widths of atomic columns in the measured\nelectrostatic potential are now no longer limited by the imaging system, but\ninstead by the thermal fluctuations of the atoms. We also demonstrate that\nelectron ptychography can potentially reach a sub-nanometer depth resolution\nand locate embedded atomic dopants in all three dimensions with only a single\nprojection measurement.\n"}
+{"abstract": "  The question of whether to use one classifier or a combination of classifiers\nis a central topic in Machine Learning. We propose here a method for finding an\noptimal linear combination of classifiers derived from a bias-variance\nframework for the classification task.\n"}
+{"abstract": "  By fitting stellar populations to SDSS-IV MaNGA survey observations of ~7000\nsuitably-weighted individual galaxies, we reconstruct the star-formation\nhistory of the Universe, which we find to be in reasonable agreement with\nprevious studies. Dividing the galaxies by their present-day stellar mass, we\ndemonstrate the downsizing phenomenon, whereby the more massive galaxies hosted\nthe most star-formation at earlier times. Further dividing the galaxy sample by\ncolour and morphology, we find that a galaxy's present-day colour tells us more\nabout its historical contribution to the cosmic star formation history than its\ncurrent morphology. We show that downsizing effects are greatest among galaxies\ncurrently in the blue cloud, but that the level of downsizing in galaxies of\ndifferent morphologies depends quite sensitively on the morphological\nclassification used, due largely to the difficulty in classifying the smaller\nlow-mass galaxies from their ground-based images. Nevertheless, we find\nagreement that among galaxies with stellar masses\n$M_{\\star}>6\\times10^{9}\\,M_{\\odot}$, downsizing is most significant in\nspirals. However, there are complicating factors. For example, for more massive\ngalaxies, we find that colour and morphology are predictors of the past star\nformation over a longer timescale than in less massive systems. Presumably this\neffect is reflecting the longer period of evolution required to alter these\nlarger galaxies' physical properties, but shows that conclusions based on any\nsingle property don't tell the full story.\n"}
+{"abstract": "  Model predictive control (MPC) schemes are commonly designed with fixed,\ni.e., time-invariant, horizon length and cost functions. If no stabilizing\nterminal ingredients are used, stability can be guaranteed via a sufficiently\nlong horizon. A suboptimality index can be derived that gives bounds on the\nperformance of the MPC law over an infinite-horizon (IH). While for\ntime-invariant schemes such index can be computed offline, less attention has\nbeen paid to time-varying strategies with adapting cost function which can be\nfound, e.g., in learning-based optimal control. This work addresses the\nperformance bounds of nonlinear MPC with stabilizing horizon and time-varying\nterminal cost. A scheme is proposed that uses the decay of the optimal\nfinite-horizon cost and convolutes a history stack to predict the bounds on the\nIH performance. Based on online information on the decay rate, the performance\nbound estimate is improved while the terminal cost is adapted using methods\nfrom adaptive dynamic programming. The adaptation of the terminal cost leads to\nperformance improvement over a time-invariant scheme with the same horizon\nlength. The approach is demonstrated in a case study.\n"}
+{"abstract": "  Microlensing is a powerful tool for discovering cold exoplanets, and the The\nRoman Space Telescope microlensing survey will discover over 1000 such planets.\nRapid, automated classification of Roman's microlensing events can be used to\nprioritize follow-up observations of the most interesting events. Machine\nlearning is now often used for classification problems in astronomy, but the\nsuccess of such algorithms can rely on the definition of appropriate features\nthat capture essential elements of the observations that can map to parameters\nof interest. In this paper, we introduce tools that we have developed to\ncapture features in simulated Roman light curves of different types of\nmicrolensing events, and evaluate their effectiveness in classifying\nmicrolensing light curves. These features are quantified as parameters that can\nbe used to decide the likelihood that a given light curve is due to a specific\ntype of microlensing event. This method leaves us with a list of parameters\nthat describe features like the smoothness of the peak, symmetry, the number of\npeaks, and width and height of small deviations from the main peak. This will\nallow us to quickly analyze a set of microlensing light curves and later use\nthe resulting parameters as input to machine learning algorithms to classify\nthe events.\n"}
+{"abstract": "  An effective email search engine can facilitate users' search tasks and\nimprove their communication efficiency. Users could have varied preferences on\nvarious ranking signals of an email, such as relevance and recency based on\ntheir tasks at hand and even their jobs. Thus a uniform matching pattern is not\noptimal for all users. Instead, an effective email ranker should conduct\npersonalized ranking by taking users' characteristics into account. Existing\nstudies have explored user characteristics from various angles to make email\nsearch results personalized. However, little attention has been given to users'\nsearch history for characterizing users. Although users' historical behaviors\nhave been shown to be beneficial as context in Web search, their effect in\nemail search has not been studied and remains unknown. Given these\nobservations, we propose to leverage user search history as query context to\ncharacterize users and build a context-aware ranking model for email search. In\ncontrast to previous context-dependent ranking techniques that are based on raw\ntexts, we use ranking features in the search history. This frees us from\npotential privacy leakage while giving a better generalization power to unseen\nusers. Accordingly, we propose a context-dependent neural ranking model (CNRM)\nthat encodes the ranking features in users' search history as query context and\nshow that it can significantly outperform the baseline neural model without\nusing the context. We also investigate the benefit of the query context vectors\nobtained from CNRM on the state-of-the-art learning-to-rank model LambdaMart by\nclustering the vectors and incorporating the cluster information. Experimental\nresults show that significantly better results can be achieved on LambdaMart as\nwell, indicating that the query clusters can characterize different users and\neffectively turn the ranking model personalized.\n"}
+{"abstract": "  Can a regulated, legal market for wildlife products protect species\nthreatened by poaching? It is one of the most controversial ideas in\nbiodiversity conservation. Perhaps the most convincing reason for legalizing\nwildlife trade is that trade revenue could fund the protection and conservation\nof poached species. In this paper, we examine the possible poacher-population\ndynamic consequences of legal trade funding conservation. The model consists of\na manager scavenging carcasses for wildlife products, who then sells the\nproducts, and directs a portion of the revenue towards funding anti-poaching\nlaw enforcement. Through a global analysis of the model, we derive the critical\nproportion of product the manager must scavenge, and the critical proportion of\ntrade revenue the manager must allocate towards increased enforcement, in order\nfor legal trade to lead to abundant long-term wildlife populations. We\nillustrate how the model could inform management with parameter values derived\nfrom the African elephant literature, under a hypothetical scenario where a\nmanager scavenges elephant carcasses to sell ivory. We find that there is a\nlarge region of parameter space where populations go extinct under legal trade,\nunless a significant portion of trade revenue is directed towards protecting\npopulations from poaching. The model is general and therefore can be used as a\nstarting point for exploring the consequences of funding many conservation\nprograms using wildlife trade revenue.\n"}
+{"abstract": "  Brain-inspired computing and neuromorphic hardware are promising approaches\nthat offer great potential to overcome limitations faced by current computing\nparadigms based on traditional von-Neumann architecture. In this regard,\ninterest in developing memristor crossbar arrays has increased due to their\nability to natively perform in-memory computing and fundamental synaptic\noperations required for neural network implementation. For optimal efficiency,\ncrossbar-based circuits need to be compatible with fabrication processes and\nmaterials of industrial CMOS technologies. Herein, we report a complete\nCMOS-compatible fabrication process of TiO2-based passive memristor crossbars\nwith 700 nm wide electrodes. We show successful bottom electrode fabrication by\na damascene process, resulting in an optimised topography and a surface\nroughness as low as 1.1 nm. DC sweeps and voltage pulse programming yield\nstatistical results related to synaptic-like multilevel switching. Both\ncycle-to-cycle and device-to-device variability are investigated. Analogue\nprogramming of the conductance using sequences of 200 ns voltage pulses suggest\nthat the fabricated memories have a multilevel capacity of at least 3 bits due\nto the cycle-to-cycle reproducibility.\n"}
+{"abstract": "  We study the effects of bond and site disorder in the classical\n$J_{1}$-$J_{2}$ Heisenberg model on a square lattice in the order-by-disorder\nfrustrated regime $2J_{2}>\\left|J_{1}\\right|$. Combining symmetry arguments,\nnumerical energy minimization and large scale Monte Carlo simulations, we\nestablish that the finite temperature Ising-like transition of the clean system\nis destroyed in the presence of any finite concentration of impurities. We\nexplain this finding via a random-field mechanism which generically emerges in\nsystems where disorder locally breaks the same real-space symmetry\nspontaneously globally broken by the associated order parameter. We also\ndetermine that the phase replacing the clean one is a paramagnet polarized in\nthe nematic glass order with non-trivial magnetic response. This is because\ndisorder also induces non-collinear spin-vortex-crystal order and produces a\nconjugated transverse dipolar random field. As a result of these many competing\neffects, the associated magnetic susceptibilities are non-monotonic functions\nof the temperature. As a further application of our methods, we show the\ngeneration of random axes in other frustrated magnets with broken SU(2)\nsymmetry. We also discuss the generality of our findings and their relevance to\nexperiments.\n"}
+{"abstract": "  Motivated by a recent first principles prediction of an anisotropic cubic\nDirac semi-metal in a real material Tl(TeMo)$_3$, we study the behavior of\nelectrons tunneling through a potential barrier in such systems. To clearly\ninvestigate effects from different contributions to the Hamiltonian we study\nthe model in various limits. First, in the limit of a very thin material where\nthe linearly dispersive $z$-direction is frozen out at zero momentum and the\ndispersion in the $x$-$y$ plane is rotationally symmetric. In this limit we\nfind a Klein tunneling reminiscent of what is observed in single layer graphene\nand linearly dispersive Dirac semi-metals. Second, an increase in thickness of\nthe material leads to the possibility of a non-zero momentum eigenvalue $k_z$\nthat acts as an effective mass term in the Hamiltonian. We find that these lead\nto a suppression of Klein tunneling. Third, the inclusion of an anisotropy\nparameter $\\lambda\\neq 1$ leads to a breaking of rotational invariance.\nFurthermore, we observed that for different values of incident angle $\\theta$\nand anisotropy parameter $\\lambda$ the Hamiltonian supports different numbers\nof modes propagating to infinity. We display this effect in form of a diagram\nthat is similar to a phase diagram of a distant detector. Fourth, we consider\ncoexistence of both anisotropy and non-zero $k_z$ but do not find any effect\nthat is unique to the interplay between non-zero momentum $k_z$ and anisotropy\nparameter $\\lambda$. Last, we studied the case of a barrier that was placed in\nthe linearly dispersive direction and found Klein tunneling $T-1\\propto\n\\theta^6+\\mathcal{O}(\\theta^8)$ that is enhanced when compared to the Klein\ntunneling in linear Dirac semi-metals or graphene where $T-1\\propto\n\\theta^2+\\mathcal{O}(\\theta^4)$.\n"}
+{"abstract": "  Let $G$ be an irreducible imprimitive subgroup of\n$\\operatorname{GL}_n(\\mathbb{F})$, where $\\mathbb{F}$ is a field. Any system of\nimprimitivity for $G$ can be refined to a nonrefinable system of imprimitivity,\nand we consider the question of when such a refinement is unique. Examples show\nthat $G$ can have many nonrefinable systems of imprimitivity, and even the\nnumber of components is not uniquely determined. We consider the case where $G$\nis the wreath product of an irreducible primitive $H \\leq\n\\operatorname{GL}_d(\\mathbb{F})$ and transitive $K \\leq S_k$, where $n = dk$.\nWe show that $G$ has a unique nonrefinable system of imprimitivity, except in\nthe following special case: $d = 1$, $n = k$ is even, $|H| = 2$, and $K$ is a\ntransitive subgroup of $C_2 \\wr S_{n/2}$. As a simple application, we prove\nresults about inclusions between wreath product subgroups.\n"}
+{"abstract": "  We report a configuration strategy for improving the thermoelectric (TE)\nperformance of two-dimensional (2D) transition metal dichalcogenide (TMDC) WS2\nbased on the experimentally prepared WS2/WSe2 lateral superlattice (LS)\ncrystal. On the basis of density function theory combined with Boltzmann\ntransport equation, we show that the TE figure of merit zT of monolayer WS2 is\nremarkably enhanced when forming into a WS2/WSe2 LS crystal. This is primarily\nascribed to the almost halved lattice thermal conductivity due to the enhanced\nanharmonic processes. Electronic transport properties parallel (xx) and\nperpendicular (yy) to the superlattice period are highly symmetric for both p-\nand n-doped LS owing to the nearly isotropic lifetime of charger carriers. The\nspin-orbital effect causes a significant split of conduction band and leads to\nthree-fold degenerate sub-bands and high density of states (DOS), which offers\nopportunity to obtain the high n-type Seebeck coefficient (S). Interestingly,\nthe separated degenerate sub-bands and upper conduction band in monolayer WS2\nform a remarkable stairlike DOS, yielding a higher S. The hole carriers with\nmuch higher mobility than electrons reveal the high p-type power factor and the\npotential to be good p-type TE materials with optimal zT exceeds 1 at 400K in\nWS2/WSe2 LS.\n"}
+{"abstract": "  The flux ratios of high-ionization lines are commonly assumed to indicate the\nmetallicity of the broad emission line region in luminous quasars. When\naccounting for the variation in their kinematic profiles, we show that the\nNV/CIV, (SiIV+OIV])/CIV and NV/Lya line ratios do not vary as a function of the\nquasar continuum luminosity, black hole mass, or accretion rate. Using\nphotoionization models from CLOUDY , we further show that the observed changes\nin these line ratios can be explained by emission from gas with solar\nabundances, if the physical conditions of the emitting gas are allowed to vary\nover a broad range of densities and ionizing fluxes. The diversity of broad\nline emission in quasar spectra can be explained by a model with emission from\ntwo kinematically distinct regions, where the line ratios suggest that these\nregions have either very different metallicity or density. Both simplicity and\ncurrent galaxy evolution models suggest that near-solar abundances, with parts\nof the spectrum forming in high-density clouds, are more likely. Within this\nparadigm, objects with stronger outflow signatures show stronger emission from\ngas which is denser and located closer to the ionizing source, at radii\nconsistent with simulations of line-driven disc-winds. Studies using broad-line\nratios to infer chemical enrichment histories should consider changes in\ndensity and ionizing flux before estimating metallicities.\n"}
+{"abstract": "  Given the coordinates of the terminals $ \\{(x_j,y_j)\\}_{j=1}^n $ of the full\nEuclidean Steiner tree, its length equals $$ \\left| \\sum_{j=1}^n z_j U_j\n\\right| \\, , $$ where $ \\{z_j:=x_j+ \\mathbf i y_j\\}_{j=1}^n $ and $\n\\{U_j\\}_{j=1}^n $ are suitably chosen $ 6 $th roots of unity. We also extend\nthis result for the cost of the optimal Weber networks which are topologically\nequivalent to some full Steiner trees.\n"}
+{"abstract": "  The Inverse First Ionization Potential (FIP) Effect, the depletion in coronal\nabundance of elements like Fe, Mg, and Si that are ionized in the solar\nchromosphere relative to those that are neutral, has been identified in several\nsolar flares. We give a more detailed discussion of the mechanism of\nfractionation by the ponderomotive force associated with magnetohydrodynamic\nwaves, paying special attention to the conditions in which Inverse FIP\nfractionation arises in order to better understand its relation to the usual\nFIP Effect, i.e. the enhancement of coronal abundance of Fe, Mg, Si, etc. The\nFIP Effect is generated by parallel propagating Alfv\\'en waves, with either\nphotospheric, or more likely coronal, origins. The Inverse FIP Effect arises as\nupward propagating fast mode waves with an origin in the photosphere or below,\nrefract back downwards in the chromosphere where the Alfv\\'en speed is\nincreasing with altitude. We give a more physically motivated picture of the\nFIP fractionation, based on the wave refraction around inhomogeneities in the\nsolar atmosphere, and inspired by previous discussions of analogous phenomena\nin the optical trapping of particles by laser beams. We apply these insights to\nmodeling the fractionation and find good agreement with the observations of\nKatsuda et al. (2020; arXiv:2001.10643) and Dennis et al. (2015;\narXiv:1503.01602).\n"}
+{"abstract": "  We work out axioms for the duals $G\\subset U_N^+$ of the finite quantum\npermutation groups, $F\\subset S_N^+$ with $|F|<\\infty$, and we discuss how the\nbasic theory of such quantum permutation groups partly simplifies in the dual\nsetting. We discuss as well some potential extensions to the infinite case, in\nconnection with the well-known question of axiomatizing the discrete quantum\ngroup actions on the infinite graphs.\n"}
+{"abstract": "  The standard electrocardiogram (ECG) is a point-wise evaluation of the body\npotential at certain given locations. These locations are subject to\nuncertainty and may vary from patient to patient or even for a single patient.\nIn this work, we estimate the uncertainty in the ECG induced by uncertain\nelectrode positions when the ECG is derived from the forward bidomain model. In\norder to avoid the high computational cost associated to the solution of the\nbidomain model in the entire torso, we propose a low-rank approach to solve the\nuncertainty quantification (UQ) problem. More precisely, we exploit the\nsparsity of the ECG and the lead field theory to translate it into a set of\ndeterministic, time-independent problems, whose solution is eventually used to\nevaluate expectation and covariance of the ECG. We assess the approach with\nnumerical experiments in a simple geometry.\n"}
+{"abstract": "  Quantum materials with non-trivial band topology and bulk superconductivity\nare considered superior materials to realize topological superconductivity. In\nthis regard, we report detailed Density Functional Theory (DFT) calculations\nand Z2 invaraints for the NbC superconductor, exhibiting its band structure to\nbe topologically non-trivial. Bulk superconductivity at 8.9K is confirmed\nthrough DC magnetization measurements under Field Cooled (FC) and Zero Field\nCooled (ZFC) protocols. This superconductivity is found to be of type-II nature\nas revealed by isothermal M-H measurements and thus calculated the\nGinzberg-Landau parameter. A large intermediate state is evident from the phase\ndiagram, showing NbC to be a strong type-II superconductor. Comparing with\nearlier reports on superconducting NbC, a non-monotonic relationship of\ncritical temperature with lattice parameters is seen. In conclusion, NbC is a\ntype-II around 10K superconductor with topological non-trivial surface states.\n"}
+{"abstract": "  Graph transaction processing raises many unique challenges such as random\ndata access due to the irregularity of graph structures, low throughput and\nhigh abort rate due to the relatively large read/write sets in graph\ntransactions. To address these challenges, we present G-Tran -- an RDMA-enabled\ndistributed in-memory graph database with serializable and snapshot isolation\nsupport. First, we propose a graph-native data store to achieve good data\nlocality and fast data access for transactional updates and queries. Second,\nG-Tran adopts a fully decentralized architecture that leverages RDMA to process\ndistributed transactions with the MPP model, which can achieve high performance\nby utilizing all computing resources. In addition, we propose a new MV-OCC\nimplementation with two optimizations to address the issue of large read/write\nsets in graph transactions. Extensive experiments show that G-Tran achieves\ncompetitive performance compared with other popular graph databases on\nbenchmark workloads.\n"}
+{"abstract": "  We present new observations of the odd $z=0.96$ weak-line quasar PG1407+265,\nand report the discovery of CXOU J140927.9+261813, a $z=0.68$ X-ray cluster.\nArchival X-ray photometry spanning nearly four decades reveals that PG1407+265\nis variable at the 1 dex level on a timescale of years. V-band variability is\npresent with an amplitude less than 0.1 mag. The emission-line properties of\nPG1407+265 also reveal clear evidence for a powerful inflow or outflow due to\nnear- or super-Eddington accretion, having a mechanical luminosity of order\n$10^{48}$ erg s$^{-1}$. Our follow-up {\\sl Chandra} exposure centered on this\nobject reveal a foreground $z=0.68$ cluster roughly 1' x 1'.5 in extent, offset\nto the east of PG1407+265, roughly coincident with the $z=0.68$ radio galaxy\nFIRST J140927.8+261818. This non-cool-core cluster contributes about 10\\% of\nthe X-ray flux of PG1407+265, has a mass of $(0.6- 5.5)\\times10^{14} M_\\odot$,\nand an X-ray gas temperature of ($2.2-4.3$) keV. Because the projected position\nof the quasar lies at about twice that of the cluster's inferred Einstein\nradius, lensing by the cluster is unlikely to explain the quasar's unusual\nproperties. We also discuss the evidence for a second cluster centered on and\nat the redshift of the quasar.\n"}
+{"abstract": "  Determining habitable zones in binary star systems can be a challenging task\ndue to the combination of perturbed planetary orbits and varying stellar\nirradiation conditions. The concept of \"dynamically informed habitable zones\"\nallows us, nevertheless, to make predictions on where to look for habitable\nworlds in such complex environments. Dynamically informed habitable zones have\nbeen used in the past to investigate the habitability of circumstellar planets\nin binary systems and Earth-like analogs in systems with giant planets. Here,\nwe extend the concept to potentially habitable worlds on circumbinary orbits.\nWe show that habitable zone borders can be found analytically even when another\ngiant planet is present in the system. By applying this methodology to\nKepler-16, Kepler-34, Kepler-35, Kepler-38, Kepler-64, Kepler-413, Kepler-453,\nKepler-1647 and Kepler-1661 we demonstrate that the presence of the known giant\nplanets in the majority of those systems does not preclude the existence of\npotentially habitable worlds. Among the investigated systems Kepler-35,\nKepler-38 and Kepler-64 currently seem to offer the most benign environment. In\ncontrast, Kepler-16 and Kepler-1647 are unlikely to host habitable worlds.\n"}
+{"abstract": "  Ferroelectric materials are spontaneous symmetry breaking systems\ncharacterized by ordered electric polarizations. Similar to its ferromagnetic\ncounterpart, a ferroelectric domain wall can be regarded as a soft interface\nseparating two different ferroelectric domains. Here we show that two bound\nstate excitations of electric polarization (polar wave), or the vibration and\nbreathing modes, can be hosted and propagate within the ferroelectric domain\nwall. Specially, the vibration polar wave has zero frequency gap, thus is\nconstricted deeply inside ferroelectric domain wall, and can propagate even in\nthe presence of local pinnings. The ferroelectric domain wall waveguide as\ndemonstrated here, offers new paradigm in developing ferroelectric information\nprocessing units.\n"}
+{"abstract": "  We present a tight RMR complexity lower bound for the recoverable mutual\nexclusion (RME) problem, defined by Golab and Ramaraju \\cite{GR2019a}. In\nparticular, we show that any $n$-process RME algorithm using only atomic read,\nwrite, fetch-and-store, fetch-and-increment, and compare-and-swap operations,\nhas an RMR complexity of $\\Omega(\\log n/\\log\\log n)$ on the CC and DSM model.\nThis lower bound covers all realistic synchronization primitives that have been\nused in RME algorithms and matches the best upper bounds of algorithms\nemploying swap objects (e.g., [5,6,10]).\n  Algorithms with better RMR complexity than that have only been obtained by\neither (i) assuming that all failures are system-wide [7], (ii) employing\nfetch-and-add objects of size $(\\log n)^{\\omega(1)}$ [12], or (iii) using\nartificially defined synchronization primitives that are not available in\nactual systems [6,9].\n"}
+{"abstract": "  By using a sharp isoperimetric inequality and an anisotropic symmetrization\nargument, we establish Morrey-Sobolev and Hardy-Sobolev inequalities on\n$n$-dimensional Finsler manifolds having nonnegative $n$-Ricci curvature; in\nsome cases we also discuss the sharpness of these functional inequalities. As\napplications, by using variational arguments, we guarantee the\nexistence/multiplicity of solutions for certain eigenvalue problems and\nelliptic PDEs involving the Finsler-Laplace operator. Our results are also new\nin the Riemannian setting.\n"}
+{"abstract": "  The topological Hall effect is used extensively to study chiral spin textures\nin various materials. However, the factors controlling its magnitude in\ntechnologically-relevant thin films remain uncertain. Using variable\ntemperature magnetotransport and real-space magnetic imaging in a series of\nIr/Fe/Co/Pt heterostructures, here we report that the chiral spin fluctuations\nat the phase boundary between isolated skyrmions and a disordered skyrmion\nlattice result in a power-law enhancement of the topological Hall resistivity\nby up to three orders of magnitude. Our work reveals the dominant role of\nskyrmion stability and configuration in determining the magnitude of the\ntopological Hall effect.\n"}
+{"abstract": "  We exhibit a Finsler metric on the 2-sphere whose systolic (Holmes-Thompson)\nratio is $\\frac{4{\\pi}}{3}$. This is bigger than the conjectured maximal\nRiemannian systolic ratio of $2\\sqrt{3}$ achieved by the Calabi-Croke metric.\nThe construction of the Finsler metric is heavily inspired by a paper of\nCossarini-Sabourau.\n"}
+{"abstract": "  Let $d \\ge 1$. We study a subspace of the space of automorphic forms of\n$\\mathrm{GL}_d$ over a global field of positive characteristic (or, a function\nfield of a curve over a finite field). We fix a place $\\infty$ of $F$, and we\nconsider the subspace $\\mathcal{A}_{\\mathrm{St}}$ consisting of automorphic\nforms such that the local component at $\\infty$ of the associated automorphic\nrepresentation is the Steinberg representation (to be made precise in the\ntext).\n  We have two results.\n  One theorem (Theorem 16) describes the constituents of\n$\\mathcal{A}_{\\mathrm{St}}$ as automorphic representation and gives a\nmultiplicity one type statement.\n  For the other theorem (Theorem 12), we construct, using the geometry of the\nBruhat-Tits building, an analogue of modular symbols in\n$\\mathcal{A}_{\\mathrm{St}}$ integrally (that is, in the space of\n$\\mathbb{Z}$-valued automorphic forms). We show that the quotient is finite and\ngive a bound on the exponent of this quotient.\n"}
+{"abstract": "  Coronavirus disease 2019 (COVID-19) has caused global disruption and a\nsignificant loss of life. Existing treatments that can be repurposed as\nprophylactic and therapeutic agents could reduce the pandemic's devastation.\nEmerging evidence of potential applications in other therapeutic contexts has\nled to the investigation of dietary supplements and nutraceuticals for\nCOVID-19. Such products include vitamin C, vitamin D, omega 3 polyunsaturated\nfatty acids, probiotics, and zinc, all of which are currently under clinical\ninvestigation. In this review, we critically appraise the evidence surrounding\ndietary supplements and nutraceuticals for the prophylaxis and treatment of\nCOVID-19. Overall, further study is required before evidence-based\nrecommendations can be formulated, but nutritional status plays a significant\nrole in patient outcomes, and these products could help alleviate deficiencies.\nFor example, evidence indicates that vitamin D deficiency may be associated\nwith greater incidence of infection and severity of COVID-19, suggesting that\nvitamin D supplementation may hold prophylactic or therapeutic value. A growing\nnumber of scientific organizations are now considering recommending vitamin D\nsupplementation to those at high risk of COVID-19. Because research in vitamin\nD and other nutraceuticals and supplements is preliminary, here we evaluate the\nextent to which these nutraceutical and dietary supplements hold potential in\nthe COVID-19 crisis.\n"}
+{"abstract": "  We show that the baryon asymmetry of the universe can be explained in models\nwhere the Higgs couples to the Chern-Simons term of the hypercharge group and\nis away from the late-time minimum of its potential during inflation. The Higgs\nthen relaxes toward this minimum once inflation ends which leads to the\nproduction of (hyper)magnetic helicity. We discuss the conditions under which\nthis helicity can be approximately conserved during its joint evolution with\nthe thermal plasma. At the electroweak phase transition the helicity is then\nconverted into a baryon asymmetry by virtue of the chiral anomaly in the\nstandard model. We propose a simple model which realizes this mechanism and\nshow that the observed baryon asymmetry of the universe can be reproduced.\n"}
+{"abstract": "  We present a novel expression for an integrated correlation function of four\nsuperconformal primaries in $SU(N)$ $\\mathcal{N}=4$ SYM. This integrated\ncorrelator, which is based on supersymmetric localisation, has been the subject\nof several recent developments. The correlator is re-expressed as a sum over a\ntwo dimensional lattice that is valid for all $N$ and all values of the complex\nYang-Mills coupling $\\tau$. In this form it is manifestly invariant under\n$SL(2,\\mathbb{Z})$ Montonen-Olive duality. Furthermore, it satisfies a\nremarkable Laplace-difference equation that relates the $SU(N)$ to the\n$SU(N+1)$ and $SU(N-1)$ correlators. For any fixed value of $N$ the correlator\nis an infinite series of non-holomorphic Eisenstein series,\n$E(s;\\tau,\\bar\\tau)$ with $s\\in \\mathbb{Z}$, and rational coefficients. The\nperturbative expansion of the integrated correlator is asymptotic and the\n$n$-loop coefficient is a rational multiple of $\\zeta(2n+1)$. The $n=1$ and\n$n=2$ terms agree precisely with results determined directly by integrating the\nexpressions in one- and two-loop perturbative SYM. Likewise, the charge-$k$\ninstanton contributions have an asymptotic, but Borel summable, series of\nperturbative corrections. The large-$N$ expansion of the correlator with fixed\n$\\tau$ is a series in powers of $N^{1/2-\\ell}$ ($\\ell\\in \\mathbb{Z}$) with\ncoefficients that are rational sums of $E_s$ with $s\\in \\mathbb{Z}+1/2$. This\ngives an all orders derivation of the form of the recently conjectured\nexpansion. We further consider 't Hooft large-$N$ Yang-Mills theory. The\ncoefficient of each order can be expanded as a convergent series in $\\lambda$.\nFor large $\\lambda$ this becomes an asymptotic series with coefficients that\nare again rational multiples of odd zeta values. The large-$\\lambda$ series is\nnot Borel summable, and its resurgent non-perturbative completion is\n$O(\\exp(-2\\sqrt{\\lambda}))$.\n"}
+{"abstract": "  In some conditions, bacteria self-organise into biofilms, supracellular\nstructures made of a self-produced embedding matrix, mainly composed on\npolysaccharides, DNA, proteins and lipids. It is known that bacteria change\ntheir colony/matrix ratio in the presence of external stimuli such as\nhydrodynamic stress. However, little is still known about the molecular\nmechanisms driving this self-adaptation. In this work, we monitor structural\nfeatures of Pseudomonas fluorescens biofilms grown with and without\nhydrodynamic stress. Our measurements show that the hydrodynamic stress\nconcomitantly increases the cell density population and the matrix production.\nAt short growth timescales, the matrix mediates a weak cell-cell attractive\ninteraction due to the depletion forces originated by the polymer constituents.\nUsing a population dynamics model, we conclude that hydrodynamic stress causes\na faster diffusion of nutrients and a higher incorporation of planktonic\nbacteria to the already formed microcolonies. This results in the formation of\nmore mechanically stable biofilms due to an increase of the number of\ncrosslinks, as shown by computer simulations. The mechanical stability also\nlies on a change in the chemical compositions of the matrix, which becomes\nenriched in carbohydrates, known to display adhering properties. Overall, we\ndemonstrate that bacteria are capable of self-adapting to hostile hydrodynamic\nstress by tailoring the biofilm chemical composition, thus affecting both the\nmesoscale structure of the matrix and its viscoelastic properties that\nultimately regulate the bacteria-polymer interactions.\n"}
+{"abstract": "  Table Structure Recognition is an essential part of end-to-end tabular data\nextraction in document images. The recent success of deep learning model\narchitectures in computer vision remains to be non-reflective in table\nstructure recognition, largely because extensive datasets for this domain are\nstill unavailable while labeling new data is expensive and time-consuming.\nTraditionally, in computer vision, these challenges are addressed by standard\naugmentation techniques that are based on image transformations like color\njittering and random cropping. As demonstrated by our experiments, these\ntechniques are not effective for the task of table structure recognition. In\nthis paper, we propose TabAug, a re-imagined Data Augmentation technique that\nproduces structural changes in table images through replication and deletion of\nrows and columns. It also consists of a data-driven probabilistic model that\nallows control over the augmentation process. To demonstrate the efficacy of\nour approach, we perform experimentation on ICDAR 2013 dataset where our\napproach shows consistent improvements in all aspects of the evaluation\nmetrics, with cell-level correct detections improving from 92.16% to 96.11%\nover the baseline.\n"}
+{"abstract": "  Recommender systems have achieved great success in modeling user's\npreferences on items and predicting the next item the user would consume.\nRecently, there have been many efforts to utilize time information of users'\ninteractions with items to capture inherent temporal patterns of user behaviors\nand offer timely recommendations at a given time. Existing studies regard the\ntime information as a single type of feature and focus on how to associate it\nwith user preferences on items. However, we argue they are insufficient for\nfully learning the time information because the temporal patterns of user\npreference are usually heterogeneous. A user's preference for a particular item\nmay 1) increase periodically or 2) evolve over time under the influence of\nsignificant recent events, and each of these two kinds of temporal pattern\nappears with some unique characteristics. In this paper, we first define the\nunique characteristics of the two kinds of temporal pattern of user preference\nthat should be considered in time-aware recommender systems. Then we propose a\nnovel recommender system for timely recommendations, called TimelyRec, which\njointly learns the heterogeneous temporal patterns of user preference\nconsidering all of the defined characteristics. In TimelyRec, a cascade of two\nencoders captures the temporal patterns of user preference using a proposed\nattention module for each encoder. Moreover, we introduce an evaluation\nscenario that evaluates the performance on predicting an interesting item and\nwhen to recommend the item simultaneously in top-K recommendation (i.e.,\nitem-timing recommendation). Our extensive experiments on a scenario for item\nrecommendation and the proposed scenario for item-timing recommendation on\nreal-world datasets demonstrate the superiority of TimelyRec and the proposed\nattention modules.\n"}
+{"abstract": "  It was shown recently that the f-diagonal tensor in the T-SVD factorization\nmust satisfy some special properties. Such f-diagonal tensors are called\ns-diagonal tensors. In this paper, we show that such a discussion can be\nextended to any real invertible linear transformation. We show that two\nEckart-Young like theorems hold for a third order real tensor, under any doubly\nreal-preserving unitary transformation. The normalized Discrete Fourier\nTransformation (DFT) matrix, an arbitrary orthogonal matrix, the product of the\nnormalized DFT matrix and an arbitrary orthogonal matrix are examples of doubly\nreal-preserving unitary transformations. We use tubal matrices as a tool for\nour study. We feel that the tubal matrix language makes this approach more\nnatural.\n"}
+{"abstract": "  Observational astronomers survey the sky in great detail to gain a better\nunderstanding of many types of astronomical phenomena. In particular, the\nformation and evolution of galaxies, including our own, is a wide field of\nresearch. Three dimensional (spatial 3D) scientific visualisation is typically\nlimited to simulated galaxies, due to the inherently two dimensional spatial\nresolution of Earth-based observations. However, with appropriate means of\nreconstruction, such visualisation can also be used to bring out the inherent\n3D structure that exists in 2D observations of known galaxies, providing new\nviews of these galaxies and visually illustrating the spatial relationships\nwithin galaxy groups that are not obvious in 2D. We present a novel approach to\nreconstruct and visualise 3D representations of nearby galaxies based on\nobservational data using the scientific visualisation software Splotch. We\napply our approach to a case study of the nearby barred spiral galaxy known as\nM83, presenting a new perspective of the M83 local group and highlighting the\nsimilarities between our reconstructed views of M83 and other known galaxies of\nsimilar inclinations.\n"}
+{"abstract": "  In the univariate setting, using the kernel spectral representation is an\nappealing approach for generating stationary covariance functions. However,\nperforming the same task for multiple-output Gaussian processes is\nsubstantially more challenging. We demonstrate that current approaches to\nmodelling cross-covariances with a spectral mixture kernel possess a critical\nblind spot. For a given pair of processes, the cross-covariance is not\nreproducible across the full range of permitted correlations, aside from the\nspecial case where their spectral densities are of identical shape. We present\na solution to this issue by replacing the conventional Gaussian components of a\nspectral mixture with block components of finite bandwidth (i.e. rectangular\nstep functions). The proposed family of kernel represents the first\nmulti-output generalisation of the spectral mixture kernel that can approximate\nany stationary multi-output kernel to arbitrary precision.\n"}
+{"abstract": "  Post-hoc explanation methods are an important class of approaches that help\nunderstand the rationale underlying a trained model's decision. But how useful\nare they for an end-user towards accomplishing a given task? In this vision\npaper, we argue the need for a benchmark to facilitate evaluations of the\nutility of post-hoc explanation methods. As a first step to this end, we\nenumerate desirable properties that such a benchmark should possess for the\ntask of debugging text classifiers. Additionally, we highlight that such a\nbenchmark facilitates not only assessing the effectiveness of explanations but\nalso their efficiency.\n"}
+{"abstract": "  Underwater image restoration is of significant importance in unveiling the\nunderwater world. Numerous techniques and algorithms have been developed in the\npast decades. However, due to fundamental difficulties associated with\nimaging/sensing, lighting, and refractive geometric distortions, in capturing\nclear underwater images, no comprehensive evaluations have been conducted of\nunderwater image restoration. To address this gap, we have constructed a\nlarge-scale real underwater image dataset, dubbed `HICRD' (Heron Island Coral\nReef Dataset), for the purpose of benchmarking existing methods and supporting\nthe development of new deep-learning based methods. We employ accurate water\nparameter (diffuse attenuation coefficient) in generating reference images.\nThere are 2000 reference restored images and 6003 original underwater images in\nthe unpaired training set. Further, we present a novel method for underwater\nimage restoration based on unsupervised image-to-image translation framework.\nOur proposed method leveraged contrastive learning and generative adversarial\nnetworks to maximize the mutual information between raw and restored images.\nExtensive experiments with comparisons to recent approaches further demonstrate\nthe superiority of our proposed method. Our code and dataset are publicly\navailable at GitHub.\n"}
+{"abstract": "  A classical observation of Deligne shows that, for any prime $p \\geq 5$, the\ndivisor polynomial of the Eisenstein series $E_{p-1}(z)$ mod $p$ is closely\nrelated to the supersingular polynomial at $p$, $$S_p(x) :=\n\\prod_{E/\\overline{\\mathbb{F}}_p \\text{ supersingular}}(x-j(E)) \\in\n\\mathbb{F}_p[x].$$ Deuring, Hasse, and Kaneko and Zagier found other families\nof modular forms which also give the supersingular polynomial at $p$. In a new\napproach, we prove an analogue of Deligne's result for the Hecke trace forms\n$T_k(z)$ defined by the Hecke action on the space of cusp forms $S_k$. We use\nthe Eichler-Selberg trace formula to identify congruences between trace forms\nof different weights mod $p$, and then relate their divisor polynomials to\n$S_p(x)$ using Deligne's observation.\n"}
+{"abstract": "  One of the most important barriers toward a widespread use of mobile robots\nin unstructured and human populated work environments is the ability to plan a\nsafe path. In this paper, we propose to delegate this activity to a human\noperator that walks in front of the robot marking with her/his footsteps the\npath to be followed. The implementation of this approach requires a high degree\nof robustness in locating the specific person to be followed (the leader). We\npropose a three phase approach to fulfil this goal: 1. identification and\ntracking of the person in the image space, 2. sensor fusion between camera data\nand laser sensors, 3. point interpolation with continuous curvature curves. The\napproach is described in the paper and extensively validated with experimental\nresults.\n"}
+{"abstract": "  The aim of this note is to provoke discussion concerning arithmetic\nproperties of function $p_{d}(n)$ counting partitions of an positive integer\n$n$ into $d$-th powers, where $d\\geq 2$. Besides results concerning the\nasymptotic behavior of $p_{d}(n)$ a little is known. In the first part of the\npaper, we prove certain congruences involving functions counting various types\nof partitions into $d$-th powers. The second part of the paper has experimental\nnature and contains questions and conjectures concerning arithmetic behavior of\nthe sequence $(p_{d}(n))_{n\\in\\N}$. They based on our computations of\n$p_{d}(n)$ for $n\\leq 10^5$ in case of $d=2$, and $n\\leq 10^{6}$ for $d=3, 4,\n5$.\n"}
+{"abstract": "  We report the synthesis, crystal structure, and magnetic properties of two\nnew quantum antiferromagnets A3ReO5Cl2 (A = Sr and Ba). The crystal structure\nis isostructural with the mineral pinalite Pb3WO5Cl2, in which the Re6+ ion is\nsquare-pyramidally coordinated by five oxide atoms, and forms an anisotropic\ntriangular lattice (ATL) made of S = 1/2 spins. The magnetic interactions J and\nJ' in the ATL are estimated from magnetic susceptibilities to be 19.5 (44.9)\nand 9.2 (19.3) K, respectively, with J'/J = 0.47 (0.43) for A = Ba (Sr). For\neach compound, heat capacity at low temperatures shows a large T-linear\ncomponent with no signature of long-range magnetic order above 2 K, which\nsuggests a gapless spin liquid state of one-dimensional character of the J\nchains in spite of the significantly large J' couplings. This is a consequence\nof one-dimensionalization by geometrical frustration in the ATL magnet; a\nsimilar phenomenon has been observed in two compounds with slightly smaller\nJ'/J values: Cs2CuCl4 (J'/J = 0.3) and the related compound Ca3ReO5Cl2 (0.32).\nOur findings demonstrate that 5d mixed-anion compounds provide a unique\nopportunity to explore novel quantum magnetism.\n"}
+{"abstract": "  As robots move from the laboratory into the real world, motion planning will\nneed to account for model uncertainty and risk. For robot motions involving\nintermittent contact, planning for uncertainty in contact is especially\nimportant, as failure to successfully make and maintain contact can be\ncatastrophic. Here, we model uncertainty in terrain geometry and friction\ncharacteristics, and combine a risk-sensitive objective with chance constraints\nto provide a trade-off between robustness to uncertainty and constraint\nsatisfaction with an arbitrarily high feasibility guarantee. We evaluate our\napproach in two simple examples: a push-block system for benchmarking and a\nsingle-legged hopper. We demonstrate that chance constraints alone produce\ntrajectories similar to those produced using strict complementarity\nconstraints; however, when equipped with a robust objective, we show the chance\nconstraints can mediate a trade-off between robustness to uncertainty and\nstrict constraint satisfaction. Thus, our study may represent an important step\ntowards reasoning about contact uncertainty in motion planning.\n"}
+{"abstract": "  Hovey introduced $A$-cordial labelings as a generalization of cordial and\nharmonious labelings \\cite{Hovey}. If $A$ is an Abelian group, then a labeling\n$f \\colon V (G) \\rightarrow A$ of the vertices of some graph $G$ induces an\nedge labeling on $G$; the edge $uv$ receives the label $f (u) + f (v)$. A graph\n$G$ is $A$-cordial if there is a vertex-labeling such that (1) the vertex label\nclasses differ in size by at most one and (2) the induced edge label classes\ndiffer in size by at most one.\n  Patrias and Pechenik studied the larger class of finite abelian groups $A$\nsuch that all path graphs are $A$-cordial. They posed a conjecture that all but\nfinitely many paths graphs are $A$-cordial for any Abelian group $A$. In this\npaper we solve this conjecture. Moreover we show that all cycle graphs are\n$A$-cordial for any Abelian group $A$ of odd order.\n"}
+{"abstract": "  Efficient and accurate object detection in video and image analysis is one of\nthe major beneficiaries of the advancement in computer vision systems with the\nhelp of deep learning. With the aid of deep learning, more powerful tools\nevolved, which are capable to learn high-level and deeper features and thus can\novercome the existing problems in traditional architectures of object detection\nalgorithms. The work in this thesis aims to achieve high accuracy in object\ndetection with good real-time performance.\n  In the area of computer vision, a lot of research is going into the area of\ndetection and processing of visual information, by improving the existing\nalgorithms. The binarized neural network has shown high performance in various\nvision tasks such as image classification, object detection, and semantic\nsegmentation. The Modified National Institute of Standards and Technology\ndatabase (MNIST), Canadian Institute for Advanced Research (CIFAR), and Street\nView House Numbers (SVHN) datasets are used which is implemented using a\npre-trained convolutional neural network (CNN) that is 22 layers deep.\nSupervised learning is used in the work, which classifies the particular\ndataset with the proper structure of the model. In still images, to improve\naccuracy, Googlenet is used. The final layer of the Googlenet is replaced with\nthe transfer learning to improve the accuracy of the Googlenet. At the same\ntime, the accuracy in moving images can be maintained by transfer learning\ntechniques. Hardware is the main backbone for any model to obtain faster\nresults with a large number of datasets. Here, Nvidia Jetson Nano is used which\nis a graphics processing unit (GPU), that can handle a large number of\ncomputations in the process of object detection. Results show that the accuracy\nof objects detected by the transfer learning method is more when compared to\nthe existing methods.\n"}
+{"abstract": "  As weak lensing surveys are becoming deeper and cover larger areas,\ninformation will be available on small angular scales down to the arcmin level.\nTo extract this extra information, accurate modelling of baryonic effects is\nnecessary. In this work, we adopt a baryonic correction model, which includes\ngas both bound inside and ejected from dark matter (DM) haloes, a central\ngalaxy, and changes in the DM profile induced by baryons. We use this model to\nincorporate baryons into a large suite of DM-only $N$-body simulations,\ncovering a grid of 75 cosmologies in the $\\Omega_\\mathrm{m}-\\sigma_8$ parameter\nspace. We investigate how baryons affect Gaussian and non-Gaussian weak lensing\nstatistics and the cosmological parameter inferences from these statistics. Our\nresults show that marginalizing over baryonic parameters degrades the\nconstraints in $\\Omega_\\mathrm{m}-\\sigma_8$ space by a factor of $2-4$ compared\nto those with baryonic parameters fixed. We investigate the contribution of\neach baryonic component to this degradation, and find that the distance to\nwhich gas is ejected (from AGN feedback) has the largest impact due to its\ndegeneracy with cosmological parameters. External constraints on this\nparameter, either from other datasets or from a better theoretical\nunderstanding of AGN feedback, can significantly mitigate the impact of baryons\nin an HSC-like survey.\n"}
+{"abstract": "  In this letter we study how fast the energy density of a quantum gas can\nincrease in time, when the inter-atomic interaction characterized by the\n$s$-wave scattering length $a_\\text{s}$ is increased from zero with arbitrary\ntime dependence. We show that, at short time, the energy density can at most\nincrease as $\\sqrt{t}$, which can be achieved when the time dependence of\n$a_\\text{s}$ is also proportional to $\\sqrt{t}$, and especially, a universal\nmaximum energy growth rate can be reached when $a_\\text{s}$ varies as\n$2\\sqrt{\\hbar t/(\\pi m)}$. If $a_\\text{s}$ varies faster or slower than\n$\\sqrt{t}$, it is respectively proximate to the quench process and the\nadiabatic process, and both result in a slower energy growth rate. These\nresults are obtained by analyzing the short time dynamics of the short-range\nbehavior of the many-body wave function characterized by the contact, and are\nalso confirmed by numerical solving an example of interacting bosons with\ntime-dependent Bogoliubov theory. These results can also be verified\nexperimentally in ultracold atomic gases.\n"}
+{"abstract": "  In this note we continue our study of unidirectional solutions to\nhydrodynamic Euler alignment systems with strongly singular communication\nkernels $\\phi(x):=|x|^{-(n+\\alpha)}$ for $\\alpha\\in(0,2)$. Here, we consider\nthe critical case $\\alpha=1$ and establish a couple of global existence results\nof smooth solutions, together with a full description of their long time\ndynamics. The first one is obtained via Schauder-type estimates under a null\ninitial entropy condition and the other is a small data result. In fact, using\nDuhamel's approach we get that any solution is almost Lipschitz-continuous in\nspace. We extend the notion of weak solution for $\\alpha\\in[1,2)$ and prove the\nexistence of global Leray-Hopf solutions. Furthermore, we give an anisotropic\nOnsager-type criteria for the validity of the natural energy law for weak\nsolutions of the system. Finally, we provide a series of quantitative estimates\nthat show how far the density of the limiting flock is from a uniform\ndistribution depending solely on the size of the initial entropy.\n"}
+{"abstract": "  A method for active learning of hyperspectral images (HSI) is proposed, which\ncombines deep learning with diffusion processes on graphs. A deep variational\nautoencoder extracts smoothed, denoised features from a high-dimensional HSI,\nwhich are then used to make labeling queries based on graph diffusion\nprocesses. The proposed method combines the robust representations of deep\nlearning with the mathematical tractability of diffusion geometry, and leads to\nstrong performance on real HSI.\n"}
+{"abstract": "  Dynamic pricing schemes were introduced as an alternative to posted-price\nmechanisms. In contrast to static models, the dynamic setting allows to update\nthe prices between buyer-arrivals based on the remaining sets of items and\nbuyers, and so it is capable of maximizing social welfare without the need for\na central coordinator. In this paper, we study the existence of optimal dynamic\npricing schemes in combinatorial markets. In particular, we concentrate on\nmulti-demand valuations, a natural extension of unit-demand valuations. The\nproposed approach is based on computing an optimal dual solution of the maximum\nsocial welfare problem with distinguished structural properties.\n  Our contribution is twofold. By relying on an optimal dual solution, we show\nthe existence of optimal dynamic prices in unit-demand markets and in\nmulti-demand markets up to three buyers, thus giving new interpretations of\nresults of Cohen-Addad et al. and Berger et al. , respectively. Furthermore, we\nprovide an optimal dynamic pricing scheme for bi-demand valuations with an\narbitrary number of buyers. In all cases, our proofs also provide efficient\nalgorithms for determining the optimal dynamic prices.\n"}
+{"abstract": "  We propose effective scheme of deep learning method for high-order nonlinear\nsoliton equation and compare the activation function for high-order soliton\nequation. The neural network approximates the solution of the equation under\nthe conditions of differential operator, initial condition and boundary\ncondition. We apply this method to high-order nonlinear soliton equation, and\nverify its efficiency by solving the fourth-order Boussinesq equation and the\nfifth-order Korteweg de Vries equation. The results show that deep learning\nmethod can solve the high-order nonlinear soliton equation and reveal the\ninteraction between solitons.\n"}
+{"abstract": "  Circumplanetary discs can be linearly unstable to the growth of disc tilt in\nthe tidal potential of the star-planet system. We use three-dimensional\nhydrodynamical simulations to characterize the disc conditions needed for\ninstability, together with its long term evolution. Tilt growth occurs for disc\naspect ratios, evaluated near the disc outer edge, of $H/r\\gtrsim 0.05$, with a\nweak dependence on viscosity in the wave-like regime of warp propagation. Lower\nmass giant planets are more likely to have circumplanetary discs that satisfy\nthe conditions for instability. We show that the tilt instability can excite\nthe inclination to above the threshold where the circumplanetary disc becomes\nunstable to Kozai--Lidov (KL) oscillations. Dissipation in the Kozai--Lidov\nunstable regime caps further tilt growth, but the disc experiences large\noscillations in both inclination and eccentricity. Planetary accretion occurs\nin episodic accretion events. We discuss implications of the joint tilt--KL\ninstability for the detectability of circumplanetary discs, for the obliquity\nevolution of forming giant planets, and for the formation of satellite systems.\n"}
+{"abstract": "  Using fully-resolved simulations, we investigate the torque experienced by a\nfinite-length circular cylinder rotating steadily perpendicularly to its\nsymmetry axis. The aspect ratio $\\chi$, i.e. the ratio of the length of the\ncylinder to its diameter, is varied from 1 to 15. In the creeping-flow regime,\nwe employ the slender-body theory to derive the expression of the torque up to\norder 4 with respect to the small parameter $1/\\ln(2\\chi)$. Numerical results\nagree well with the corresponding predictions for $\\chi\\gtrsim3$. We introduce\nan \\textit{ad hoc} modification in the theoretical prediction to fit the\nnumerical results obtained with shorter cylinders, and a second modification to\naccount for the increase of the torque resulting from finite inertial effects.\nIn strongly inertial regimes, a prominent wake pattern made of two pairs of\ncounter-rotating vortices takes place. Nevertheless the flow remains stationary\nand exhibits two distinct symmetries, one of which implies that the\ncontributions to the torque arising from the two cylinder ends are identical.\nWe build separate empirical formulas for the contributions of pressure and\nviscous stress to the torque provided by the lateral surface and the cylinder\nends. We show that, in each contribution, the dominant scaling law may be\ninferred from simple physical arguments. This approach eventually results in an\nempirical formula for the rotation-induced torque valid throughout the range of\ninertial regimes and aspect ratios considered in the simulations.\n"}
+{"abstract": "  Intent classification is an important task in natural language understanding\nsystems. Existing approaches have achieved perfect scores on the benchmark\ndatasets. However they are not suitable for deployment on low-resource devices\nlike mobiles, tablets, etc. due to their massive model size. Therefore, in this\npaper, we present a novel light-weight architecture for intent classification\nthat can run efficiently on a device. We use character features to enrich the\nword representation. Our experiments prove that our proposed model outperforms\nexisting approaches and achieves state-of-the-art results on benchmark\ndatasets. We also report that our model has tiny memory footprint of ~5 MB and\nlow inference time of ~2 milliseconds, which proves its efficiency in a\nresource-constrained environment.\n"}
+{"abstract": "  In certain pulsar timing experiments, where observations are scheduled\napproximately periodically (e.g. daily), timing models with significantly\ndifferent frequencies (including but not limited to glitch models with\ndifferent frequency increments) return near-equivalent timing residuals. The\naverage scheduling aperiodicity divided by the phase error due to\ntime-of-arrival uncertainties is a useful indicator of when the degeneracy is\nimportant. Synthetic data are used to explore the effect of this degeneracy\nsystematically. It is found that phase-coherent tempo2 or temponest-based\napproaches are biased sometimes toward reporting small glitch sizes regardless\nof the true glitch size. Local estimates of the spin frequency alleviate this\nbias. A hidden Markov model is free from bias towards small glitches and\nannounces explicitly the existence of multiple glitch solutions but sometimes\nfails to recover the correct glitch size. Two glitches in the UTMOST public\ndata release are re-assessed, one in PSR J1709$-$4429 at MJD 58178 and the\nother in PSR J1452$-$6036 at MJD 58600. The estimated fractional frequency jump\nin PSR J1709$-$4429 is revised upward from $\\Delta f/f = (54.6\\pm 1.0) \\times\n10^{-9}$ to $\\Delta f/f = (2432.2 \\pm 0.1) \\times 10^{-9}$ with the aid of\nadditional data from the Parkes radio telescope. We find that the available\nUTMOST data for PSR J1452$-$6036 are consistent with $\\Delta f/f = 270 \\times\n10^{-9} + N/(fT)$ with $N = 0,1,2$, where $T \\approx 1\\,\\text{sidereal day}$ is\nthe observation scheduling period. Data from the Parkes radio telescope can be\nincluded, and the $N = 0$ case is selected unambiguously with a combined\ndataset.\n"}
+{"abstract": "  Contact tracing has been extensively studied from different perspectives in\nrecent years. However, there is no clear indication of why this intervention\nhas proven effective in some epidemics (SARS) and mostly ineffective in some\nothers (COVID-19). Here, we perform an exhaustive evaluation of random testing\nand contact tracing on novel superspreading random networks to try to identify\nwhich epidemics are more containable with such measures. We also explore the\nsuitability of positive rates as a proxy of the actual infection statuses of\nthe population. Moreover, we propose novel ideal strategies to explore the\npotential limits of both testing and tracing strategies. Our study counsels\ncaution, both at assuming epidemic containment and at inferring the actual\nepidemic progress, with current testing or tracing strategies. However, it also\nbrings a ray of light for the future, with the promise of the potential of\nnovel testing strategies that can achieve great effectiveness.\n"}
+{"abstract": "  Although ground robotic autonomy has gained widespread usage in structured\nand controlled environments, autonomy in unknown and off-road terrain remains a\ndifficult problem. Extreme, off-road, and unstructured environments such as\nundeveloped wilderness, caves, and rubble pose unique and challenging problems\nfor autonomous navigation. To tackle these problems we propose an approach for\nassessing traversability and planning a safe, feasible, and fast trajectory in\nreal-time. Our approach, which we name STEP (Stochastic Traversability\nEvaluation and Planning), relies on: 1) rapid uncertainty-aware mapping and\ntraversability evaluation, 2) tail risk assessment using the Conditional\nValue-at-Risk (CVaR), and 3) efficient risk and constraint-aware kinodynamic\nmotion planning using sequential quadratic programming-based (SQP) model\npredictive control (MPC). We analyze our method in simulation and validate its\nefficacy on wheeled and legged robotic platforms exploring extreme terrains\nincluding an abandoned subway and an underground lava tube.\n"}
+{"abstract": "  We present and characterize the classes of Grothendieck toposes having enough\nsupercompact objects or enough compact objects. In the process, we examine the\nsubcategories of supercompact objects and compact objects within such toposes\nand classes of geometric morphism which interact well with these objects. We\nalso present canonical classes of sites generating such toposes.\n"}
+{"abstract": "  For a hereditary graph class $\\mathcal{H}$, the $\\mathcal{H}$-elimination\ndistance of a graph $G$ is the minimum number of rounds needed to reduce $G$ to\na member of $\\mathcal{H}$ by removing one vertex from each connected component\nin each round. The $\\mathcal{H}$-treewidth of a graph $G$ is the minimum, taken\nover all vertex sets $X$ for which each connected component of $G - X$ belongs\nto $\\mathcal{H}$, of the treewidth of the graph obtained from $G$ by replacing\nthe neighborhood of each component of $G-X$ by a clique and then removing $V(G)\n\\setminus X$. These parameterizations recently attracted interest because they\nare simultaneously smaller than the graph-complexity measures treedepth and\ntreewidth, respectively, and the vertex-deletion distance to $\\mathcal{H}$. For\nthe class $\\mathcal{H}$ of bipartite graphs, we present non-uniform\nfixed-parameter tractable algorithms for testing whether the\n$\\mathcal{H}$-elimination distance or $\\mathcal{H}$-treewidth of a graph is at\nmost $k$. Along the way, we also provide such algorithms for all graph classes\n$\\mathcal{H}$ defined by a finite set of forbidden induced subgraphs.\n"}
+{"abstract": "  We adapt the direct approach to the semiclassical Bergman kernel asymptotics,\ndeveloped recently by A. Deleporte, J. Sj\\\"ostrand, and the first-named author\nfor real analytic exponential weights, to the smooth case. Similar to that\nwork, our approach avoids the use of the Kuranishi trick and it allows us to\nconstruct the amplitude of the asymptotic Bergman projection by means of an\nasymptotic inversion of an explicit Fourier integral operator.\n"}
+{"abstract": "  Deep learning has become the most powerful machine learning tool in the last\ndecade. However, how to efficiently train deep neural networks remains to be\nthoroughly solved. The widely used minibatch stochastic gradient descent (SGD)\nstill needs to be accelerated. As a promising tool to better understand the\nlearning dynamic of minibatch SGD, the information bottleneck (IB) theory\nclaims that the optimization process consists of an initial fitting phase and\nthe following compression phase. Based on this principle, we further study\ntypicality sampling, an efficient data selection method, and propose a new\nexplanation of how it helps accelerate the training process of the deep\nnetworks. We show that the fitting phase depicted in the IB theory will be\nboosted with a high signal-to-noise ratio of gradient approximation if the\ntypicality sampling is appropriately adopted. Furthermore, this finding also\nimplies that the prior information of the training set is critical to the\noptimization process and the better use of the most important data can help the\ninformation flow through the bottleneck faster. Both theoretical analysis and\nexperimental results on synthetic and real-world datasets demonstrate our\nconclusions.\n"}
+{"abstract": "  We propose a fully asynchronous networked aggregative game (Asy-NAG) where\neach player minimizes a cost function that depends on its local action and the\naggregate of all players' actions. In sharp contrast to the existing NAGs, each\nplayer in our Asy-NAG can compute an estimate of the aggregate action at any\nwall-clock time by only using (possibly stale) information from nearby players\nof a directed network. Such an asynchronous update does not require any\ncoordination among players. Moreover, we design a novel distributed algorithm\nwith an aggressive mechanism for each player to adaptively adjust the\noptimization stepsize per update. Particularly, the slow players in terms of\nupdating their estimates smartly increase their stepsizes to catch up with the\nfast ones. Then, we develop an augmented system approach to address the\nasynchronicity and the information delays between players, and rigorously show\nthe convergence to a Nash equilibrium of the Asy-NAG via a perturbed coordinate\nalgorithm which is also of independent interest. Finally, we evaluate the\nperformance of the distributed algorithm through numerical simulations.\n"}
+{"abstract": "  An implicit and conservative numerical scheme is proposed for the isotropic\nquantum Fokker-Planck equation describing the evolution of degenerate electrons\nsubject to elastic collisions with other electrons and ions. The electron-ion\nand electron-electron collision operators are discretized using a discontinuous\nGalerkin method, and the electron energy distribution is updated by an implicit\ntime integration method. The numerical scheme is designed to satisfy all\nconservation laws exactly. Numerical tests and comparisons with other modeling\napproaches are shown to demonstrate the accuracy and conservation properties of\nthe proposed method.\n"}
+{"abstract": "  Traditional laws of friction believe that the friction coefficient of two\nspecific solids takes constant value. However, molecular simulations revealed\nthat the friction coefficient of nanosized asperity depends strongly on contact\nsize and asperity radius. Since contacting surfaces are always rough consisting\nof asperities varying dramatically in geometric size, a theoretical model is\ndeveloped to predict the friction behavior of fractal rough surfaces in this\nwork. The result of atomic-scale simulations of sphere-on-flat friction is\nsummarized into a uniform expression. Then, the size dependent feature of\nfriction at nanoscale is incorporated into the analysis of fractal rough\nsurfaces. The obtained results display the dependence of friction coefficient\non roughness, material properties and load. It is revealed that the friction\ncoefficient decreases with increasing contact area or external load. This model\ngives a theoretical guideline for the prediction of friction coefficient and\nthe design of friction pairs.\n"}
+{"abstract": "  Building a benchmark dataset for hate speech detection presents various\nchallenges. Firstly, because hate speech is relatively rare, random sampling of\ntweets to annotate is very inefficient in finding hate speech. To address this,\nprior datasets often include only tweets matching known \"hate words\". However,\nrestricting data to a pre-defined vocabulary may exclude portions of the\nreal-world phenomenon we seek to model. A second challenge is that definitions\nof hate speech tend to be highly varying and subjective. Annotators having\ndiverse prior notions of hate speech may not only disagree with one another but\nalso struggle to conform to specified labeling guidelines. Our key insight is\nthat the rarity and subjectivity of hate speech are akin to that of relevance\nin information retrieval (IR). This connection suggests that well-established\nmethodologies for creating IR test collections can be usefully applied to\ncreate better benchmark datasets for hate speech. To intelligently and\nefficiently select which tweets to annotate, we apply standard IR techniques of\n{\\em pooling} and {\\em active learning}. To improve both consistency and value\nof annotations, we apply {\\em task decomposition} and {\\em annotator rationale}\ntechniques. We share a new benchmark dataset for hate speech detection on\nTwitter that provides broader coverage of hate than prior datasets. We also\nshow a dramatic drop in accuracy of existing detection models when tested on\nthese broader forms of hate. Annotator rationales we collect not only justify\nlabeling decisions but also enable future work opportunities for\ndual-supervision and/or explanation generation in modeling. Further details of\nour approach can be found in the supplementary materials.\n"}
+{"abstract": "  The laser-driven generation of relativistic electron beams in plasma and\ntheir acceleration to high energies with GV/m-gradients has been successfully\ndemonstrated. Now, it is time to focus on the application of laser-plasma\naccelerated (LPA) beams. The \"Accelerator Technology HElmholtz iNfrAstructure\"\n(ATHENA) of the Helmholtz Association fosters innovative particle accelerators\nand high-power laser technology. As part of the ATHENAe pillar several\ndifferent applications driven by LPAs are to be developed, such as a compact\nFEL, medical imaging and the first realization of LPA-beam injection into a\nstorage ring. The latter endeavour is conducted in close collaboration between\nDeutsches Elektronen-Synchrotron (DESY), Karlsruhe Institute of Technology\n(KIT) and Helmholtz Institute Jena (HIJ). In the cSTART project at KIT, a\ncompact storage ring optimized for short bunches and suitable to accept\nLPA-based electron bunches is in preparation. In this conference contribution\nwe will introduce the 50 MeV LPA-based injector and give an overview about the\nproject goals. The key parameters of the plasma injector will be presented.\nFinally, the current status of the project will be summarized.\n"}
+{"abstract": "  Perturbations are ubiquitous in metabolism. A central tool to understand and\ncontrol their influence on metabolic networks is sensitivity analysis, which\ninvestigates how the network responds to external perturbations. We follow here\na structural approach: the analysis is based on the network stoichiometry only\nand it does not require any quantitative knowledge of the reaction rates. We\nconsider perturbations of reaction rates and metabolite concentrations, at\nequilibrium, and we investigate the responses in the network. For general\nmetabolic systems, this paper focuses on the sign of the responses, i.e.\nwhether a response is positive, negative or whether its sign depends on the\nparameters of the system. In particular, we identify and describe the\nsubnetworks that are the main players in the sign description. These\nsubnetworks are associated to certain kernel vectors of the stoichiometric\nmatrix and are thus independent from the chosen kinetics.\n"}
+{"abstract": "  Learning high-level navigation behaviors has important implications: it\nenables robots to build compact visual memory for repeating demonstrations and\nto build sparse topological maps for planning in novel environments. Existing\napproaches only learn discrete, short-horizon behaviors. These standalone\nbehaviors usually assume a discrete action space with simple robot dynamics,\nthus they cannot capture the intricacy and complexity of real-world\ntrajectories. To this end, we propose Composable Behavior Embedding (CBE), a\ncontinuous behavior representation for long-horizon visual navigation. CBE is\nlearned in an end-to-end fashion; it effectively captures path geometry and is\nrobust to unseen obstacles. We show that CBE can be used to performing\nmemory-efficient path following and topological mapping, saving more than an\norder of magnitude of memory than behavior-less approaches.\n"}
+{"abstract": "  Though models with the radiative neutrino mass generation are\nphenomenologically attractive, the complicated relationship between the flavour\nstructure of additional Yukawa matrices and the neutrino mass matrix sometimes\nis a barrier to explore the models. We introduce a simple prescription to\nanalyze the relation in a class of models with the asymmetric Yukawa structure.\nWe then apply the treatment to the Zee-Babu model as a concrete example of the\nclass and discuss the phenomenological consequences of the model. The combined\nstudies among the neutrino physics, the lepton flavour violation, and the\nsearch for the new particles at the collider experiments provide the anatomy of\nthe Zee-Babu model.\n"}
+{"abstract": "  Let $G$ be a finite simple graph with Laplacian polynomial\n$\\psi(G,\\lambda)=\\sum_{k=0}^n(-1)^{n-k}c_k\\lambda^k$. In an earlier paper, the\ncoefficients $c_{n-4}$ and $c_{n-5}$ for tree with respect to some degree-based\ngraph invariants were computed. The aim of this paper is to continue this work\nby giving an exact formula for the coefficients $c_{n-6}$. As a consequence of\nthis work, the Laplacian coefficients $c_{n-k}$ of a forest $F$, $1\\leq k \\leq\n6$, are computed in terms of the number of closed walks in $F$ and its line\ngraph.\n"}
+{"abstract": "  A system of linear equations $L$ over $\\mathbb{F}_q$ is common if the number\nof monochromatic solutions to $L$ in any two-colouring of $\\mathbb{F}_q^n$ is\nasymptotically at least the expected number of monochromatic solutions in a\nrandom two-colouring of $\\mathbb{F}_q^n$. Motivated by existing results for\nspecific systems (such as Schur triples and arithmetic progressions), as well\nas extensive research on common and Sidorenko graphs, the systematic study of\ncommon systems of linear equations was recently initiated by Saad and Wolf.\n  Building upon earlier work of Cameron, Cilleruelo and Serra, as well as Saad\nand Wolf, common linear equations have recently been fully characterised by\nFox, Pham and Zhao, who asked about common \\emph{systems} of equations. In this\npaper we move towards a classification of common systems of two or more linear\nequations. In particular we prove that any system containing an arithmetic\nprogression of length four is uncommon, confirming a conjecture of Saad and\nWolf. This follows from a more general result which allows us to deduce the\nuncommonness of a general system from certain properties of one- or\ntwo-equation subsystems.\n"}
+{"abstract": "  Let $(\\xi_1, \\eta_1)$, $(\\xi_2, \\eta_2),\\ldots$ be independent identically\ndistributed $\\mathbb{R}^2$-valued random vectors. We prove a strong law of\nlarge numbers, a functional central limit theorem and a law of the iterated\nlogarithm for convergent perpetuities $\\sum_{k\\geq\n0}b^{\\xi_1+\\ldots+\\xi_k}\\eta_{k+1}$ as $b\\to 1-$. Under the standard actuarial\ninterpretation, these results correspond to the situation when the actuarial\nmarket is close to the customer-friendly scenario of no risk.\n"}
+{"abstract": "  We prove that supports of a wide class of temperate distributions with\nuniformly discrete support and spectrum on Euclidean spaces are finite unions\nof translations of full-rank lattices. This result is a generalization of the\ncorresponding theorem for Fourier quasicrystals, and its proof uses the\ntechnique of almost periodic distributions.\n"}
+{"abstract": "  Long-range correlation plays an important role in analyses of pionic\nBose-Einstein correlations (BECs). In many cases, such correlations are\nphenomenologically introduced. In this investigation, we propose an analytic\nform. By making use of the form, we analyze the OPAL BEC and the L3 BEC at\n$Z^0$-pole and the CMS BEC at 0.9 and 7 TeV using our formulas and the\n$\\tau$-model. The parameters estimated by both approaches are found to be\nconsistent. Utilizing the Fourier transform in four-dimensional Euclidean\nspace, a number of pion-pair density distributions are also studied.\n"}
+{"abstract": "  In this paper, we study the convergence analysis for a robust stochastic\nstructure-preserving Lagrangian numerical scheme in computing effective\ndiffusivity of time-dependent chaotic flows, which are modeled by stochastic\ndifferential equations (SDEs). Our numerical scheme is based on a splitting\nmethod to solve the corresponding SDEs in which the deterministic subproblem is\ndiscretized using structure-preserving schemes while the random subproblem is\ndiscretized using the Euler-Maruyama scheme. We obtain a sharp and\nuniform-in-time convergence analysis for the proposed numerical scheme that\nallows us to accurately compute long-time solutions of the SDEs. As such, we\ncan compute the effective diffusivity for time-dependent flows. Finally, we\npresent numerical results to demonstrate the accuracy and efficiency of the\nproposed method in computing effective diffusivity for the time-dependent\nArnold-Beltrami-Childress (ABC) flow and Kolmogorov flow in three-dimensional\nspace.\n"}
+{"abstract": "  Recently, there has been rapid and significant progress on image dehazing.\nMany deep learning based methods have shown their superb performance in\nhandling homogeneous dehazing problems. However, we observe that even if a\ncarefully designed convolutional neural network (CNN) can perform well on\nlarge-scaled dehazing benchmarks, the network usually fails on the\nnon-homogeneous dehazing datasets introduced by NTIRE challenges. The reasons\nare mainly in two folds. Firstly, due to its non-homogeneous nature, the\nnon-uniformly distributed haze is harder to be removed than the homogeneous\nhaze. Secondly, the research challenge only provides limited data (there are\nonly 25 training pairs in NH-Haze 2021 dataset). Thus, learning the mapping\nfrom the domain of hazy images to that of clear ones based on very limited data\nis extremely hard. To this end, we propose a simple but effective approach for\nnon-homogeneous dehazing via ensemble learning. To be specific, we introduce a\ntwo-branch neural network to separately deal with the aforementioned problems\nand then map their distinct features by a learnable fusion tail. We show\nextensive experimental results to illustrate the effectiveness of our proposed\nmethod.\n"}
+{"abstract": "  We study permutations over the set of $\\ell$-grams, that are feasible in the\nsense that there is a sequence whose $\\ell$-gram frequency has the same ranking\nas the permutation. Codes, which are sets of feasible permutations, protect\ninformation stored in DNA molecules using the rank-modulation scheme, and read\nusing the shotgun sequencing technique. We construct systematic codes with an\nefficient encoding algorithm, and show that they are optimal in size. The\nlength of the DNA sequences that correspond to the codewords is shown to be\npolynomial in the code parameters. Non-systematic with larger size are also\nconstructed.\n"}
+{"abstract": "  A promising channel for producing binary black hole mergers is the\nLidov-Kozai orbital resonance in hierarchical triple systems. While this\nmechanism has been studied in isolation, the distribution of such mergers in\ntime and across star-forming environments is not well characterized. In this\nwork, we explore Lidov-Kozai-induced black hole mergers in open clusters,\ncombining semi-analytic and Monte Carlo methods to calculate merger rates and\ndelay times for eight different population models. We predict a merger rate\ndensity of $\\sim$1--10\\,Gpc$^{-3}$\\,yr$^{-1}$ for the Lidov-Kozai channel in\nthe local universe, and all models yield delay-time distributions in which a\nsignificant fraction of binary black hole mergers (e.g., $\\sim$20\\%--50\\% in\nour baseline model) occur during the open cluster phase. Our findings suggest\nthat a substantial fraction of mergers from hierarchical triples occur within\nstar-forming regions in spiral galaxies.\n"}
+{"abstract": "  Automated diagnosis using deep neural networks in chest radiography can help\nradiologists detect life-threatening diseases. However, existing methods only\nprovide predictions without accurate explanations, undermining the\ntrustworthiness of the diagnostic methods. Here, we present XProtoNet, a\nglobally and locally interpretable diagnosis framework for chest radiography.\nXProtoNet learns representative patterns of each disease from X-ray images,\nwhich are prototypes, and makes a diagnosis on a given X-ray image based on the\npatterns. It predicts the area where a sign of the disease is likely to appear\nand compares the features in the predicted area with the prototypes. It can\nprovide a global explanation, the prototype, and a local explanation, how the\nprototype contributes to the prediction of a single image. Despite the\nconstraint for interpretability, XProtoNet achieves state-of-the-art\nclassification performance on the public NIH chest X-ray dataset.\n"}
+{"abstract": "  The paper deals with dynamics of expanding Lorenz maps, which appear in a\nnatural way as Poincar\\`e maps in geometric models of well known Lorenz\nattractor. We study connections between periodic points, completely invariant\nsets and renormalizations. We show that in general, renormalization cannot be\nfully characterized by a completely invariant set, however there are various\nsituations when such characterization is possible. This way we provide a better\ninsight into the structure of renormalizations in Lorenz maps, correcting some\ngaps existing in the literature and completing to some extent the description\nof possible dynamics in this important field of study.\n"}
+{"abstract": "  We propose a novel approach to few-shot action recognition, finding\ntemporally-corresponding frame tuples between the query and videos in the\nsupport set. Distinct from previous few-shot works, we construct class\nprototypes using the CrossTransformer attention mechanism to observe relevant\nsub-sequences of all support videos, rather than using class averages or single\nbest matches. Video representations are formed from ordered tuples of varying\nnumbers of frames, which allows sub-sequences of actions at different speeds\nand temporal offsets to be compared.\n  Our proposed Temporal-Relational CrossTransformers (TRX) achieve\nstate-of-the-art results on few-shot splits of Kinetics, Something-Something V2\n(SSv2), HMDB51 and UCF101. Importantly, our method outperforms prior work on\nSSv2 by a wide margin (12%) due to the its ability to model temporal relations.\nA detailed ablation showcases the importance of matching to multiple support\nset videos and learning higher-order relational CrossTransformers.\n"}
+{"abstract": "  We present results on global very long baseline interferometry (VLBI)\nobservations at 327 MHz of eighteen compact steep-spectrum (CSS) and GHz-peaked\nspectrum (GPS) radio sources from the 3C and the Peacock & Wall catalogues.\nAbout 80 per cent of the sources have a 'double/triple' structure. The radio\nemission at 327 MHz is dominated by steep-spectrum extended structures, while\ncompact regions become predominant at higher frequencies. As a consequence, we\ncould unambiguously detect the core region only in three sources, likely due to\nself-absorption affecting its emission at this low frequency. Despite their low\nsurface brightness, lobes store the majority of the source energy budget, whose\ncorrect estimate is a key ingredient in tackling the radio source evolution.\nLow-frequency VLBI observations able to disentangle the lobe emission from that\nof other regions are therefore the best way to infer the energetics of these\nobjects. Dynamical ages estimated from energy budget arguments provide values\nbetween 2x10^3 and 5x10^4 yr, in agreement with the radiative ages estimated\nfrom the fit of the integrated synchrotron spectrum, further supporting the\nyouth of these objects. A discrepancy between radiative and dynamical ages is\nobserved in a few sources where the integrated spectrum is dominated by\nhotspots. In this case the radiative age likely represents the time spent by\nthe particles in these regions, rather than the source age.\n"}
+{"abstract": "  The design and application of an instrumented particle for the lagrangian\ncharacterization of turbulent free surface flows is presented in this study.\nThis instrumented particle constitutes a local measurement device capable of\nmeasuring both its instantaneous 3D translational acceleration and angular\nvelocity components, as well as recording them on an embarked removeable memory\ncard. A lithium ion polymer battery provides the instrumented particle with up\nto 8 hours of autonomous operation. Entirely composed of commercial off the\nshelf electronic components, it features accelerometer and gyroscope sensors\nwith a resolution of 16 bits for each individual axis, and maximum data\nacquisition rates of 1 and 8 kHz, respectively, as well as several user\nprogrammable dynamic ranges. Its ABS 3D printed body takes the form of a 36 mm\ndiameter hollow sphere, and has a total mass of (19.6 $\\pm$ 0.5) g. Controlled\nexperiments, carried out to calibrate and validate its performance showed good\nagreement when compared to reference techniques. In order to assess the\npracticality of the instrumented particle, we apply it to the statistical\ncharacterization of floater dynamics in experiments of surface wave turbulence.\nIn this feasibility study, we focused our attention on the distribution of\nacceleration and angular velocity fluctuations as a function of the forcing\nintensity. The IP's motion is also simultaneously registered by a 3D particle\ntracking velocimetry (PTV) system, for the purposes of comparison. Beyond the\nresults particular to this study case, it constitutes a proof of both the\nfeasibility and potentiality of the IP as a tool for the experimental\ncharacterization of particle dynamics in such flows.\n"}
+{"abstract": "  A realistic communication system model is critical in power system studies\nemphasizing the cyber and physical intercoupling. In this paper, we provide\ncharacteristics that could be used in modeling the underlying cyber network for\npower grid models. A real utility communication network and a simplified\ninter-substation connectivity model are studied, and their statistics could be\nused to fulfill the requirements for different modeling resolutions.\n"}
+{"abstract": "  We present a novel large-context end-to-end automatic speech recognition\n(E2E-ASR) model and its effective training method based on knowledge\ndistillation. Common E2E-ASR models have mainly focused on utterance-level\nprocessing in which each utterance is independently transcribed. On the other\nhand, large-context E2E-ASR models, which take into account long-range\nsequential contexts beyond utterance boundaries, well handle a sequence of\nutterances such as discourses and conversations. However, the transformer\narchitecture, which has recently achieved state-of-the-art ASR performance\namong utterance-level ASR systems, has not yet been introduced into the\nlarge-context ASR systems. We can expect that the transformer architecture can\nbe leveraged for effectively capturing not only input speech contexts but also\nlong-range sequential contexts beyond utterance boundaries. Therefore, this\npaper proposes a hierarchical transformer-based large-context E2E-ASR model\nthat combines the transformer architecture with hierarchical encoder-decoder\nbased large-context modeling. In addition, in order to enable the proposed\nmodel to use long-range sequential contexts, we also propose a large-context\nknowledge distillation that distills the knowledge from a pre-trained\nlarge-context language model in the training phase. We evaluate the\neffectiveness of the proposed model and proposed training method on Japanese\ndiscourse ASR tasks.\n"}
+{"abstract": "  Error-bounded lossy compression is becoming an indispensable technique for\nthe success of today's scientific projects with vast volumes of data produced\nduring the simulations or instrument data acquisitions. Not only can it\nsignificantly reduce data size, but it also can control the compression errors\nbased on user-specified error bounds. Autoencoder (AE) models have been widely\nused in image compression, but few AE-based compression approaches support\nerror-bounding features, which are highly required by scientific applications.\nTo address this issue, we explore using convolutional autoencoders to improve\nerror-bounded lossy compression for scientific data, with the following three\nkey contributions. (1) We provide an in-depth investigation of the\ncharacteristics of various autoencoder models and develop an error-bounded\nautoencoder-based framework in terms of the SZ model. (2) We optimize the\ncompression quality for main stages in our designed AE-based error-bounded\ncompression framework, fine-tuning the block sizes and latent sizes and also\noptimizing the compression efficiency of latent vectors. (3) We evaluate our\nproposed solution using five real-world scientific datasets and comparing them\nwith six other related works. Experiments show that our solution exhibits a\nvery competitive compression quality from among all the compressors in our\ntests. In absolute terms, it can obtain a much better compression quality (100%\n~ 800% improvement in compression ratio with the same data distortion) compared\nwith SZ2.1 and ZFP in cases with a high compression ratio.\n"}
+{"abstract": "  Recent millimeter and infrared observations have shown that gap and ring-like\nstructures are common in both dust thermal emission and scattered-light of\nprotoplanetary disks. We investigate the impact of the so-called Thermal Wave\nInstability (TWI) on the millimeter and infrared scattered-light images of\ndisks. We perform 1+1D simulations of the TWI and confirm that the TWI operates\nwhen the disk is optically thick enough for stellar light, i.e.,\nsmall-grain-to-gas mass ratio of $\\gtrsim0.0001$. The mid-plane temperature\nvaries as the waves propagate and hence gap and ring structures can be seen in\nboth millimeter and infrared emission. The millimeter substructures can be\nobserved even if the disk is fully optically thick since it is induced by the\ntemperature variation, while density-induced substructures would disappear in\nthe optically thick regime. The fractional separation between TWI-induced ring\nand gap is $\\Delta r/r \\sim$ 0.2-0.4 at $\\sim$ 10-50 au, which is comparable to\nthose found by ALMA. Due to the temperature variation, snow lines of volatile\nspecies move radially and multiple snow lines are observed even for a single\nspecies. The wave propagation velocity is as fast as $\\sim$ 0.6 ${\\rm\nau~yr^{-1}}$, which can be potentially detected with a multi-epoch observation\nwith a time separation of a few years.\n"}
+{"abstract": "  Click-through rate (CTR) prediction is a critical problem in web search,\nrecommendation systems and online advertisement displaying. Learning good\nfeature interactions is essential to reflect user's preferences to items. Many\nCTR prediction models based on deep learning have been proposed, but\nresearchers usually only pay attention to whether state-of-the-art performance\nis achieved, and ignore whether the entire framework is reasonable. In this\nwork, we use the discrete choice model in economics to redefine the CTR\nprediction problem, and propose a general neural network framework built on\nself-attention mechanism. It is found that most existing CTR prediction models\nalign with our proposed general framework. We also examine the expressive power\nand model complexity of our proposed framework, along with potential extensions\nto some existing models. And finally we demonstrate and verify our insights\nthrough some experimental results on public datasets.\n"}
+{"abstract": "  Numerical simulation is an essential tool for many applications involving\nsubsurface flow and transport, yet often suffers from computational challenges\ndue to the multi-physics nature, highly non-linear governing equations,\ninherent parameter uncertainties, and the need for high spatial resolutions to\ncapture multi-scale heterogeneity. We developed CCSNet, a general-purpose\ndeep-learning modeling suite that can act as an alternative to conventional\nnumerical simulators for carbon capture and storage (CCS) problems where CO$_2$\nis injected into saline aquifers in 2d-radial systems. CCSNet consists of a\nsequence of deep learning models producing all the outputs that a numerical\nsimulator typically provides, including saturation distributions, pressure\nbuildup, dry-out, fluid densities, mass balance, solubility trapping, and sweep\nefficiency. The results are 10$^3$ to 10$^4$ times faster than conventional\nnumerical simulators. As an application of CCSNet illustrating the value of its\nhigh computational efficiency, we developed rigorous estimation techniques for\nthe sweep efficiency and solubility trapping.\n"}
+{"abstract": "  We present systematic and efficient solutions for both observability\nenhancement and root-cause diagnosis of post-silicon System-on-Chips (SoCs)\nvalidation with diverse usage scenarios. We model specification of interacting\nflows in typical applications for message selection. Our method for message\nselection optimizes flow specification coverage and trace buffer utilization.\nWe define the diagnosis problem as identifying buggy traces as outliers and\nbug-free traces as inliers/normal behaviors, for which we use unsupervised\nlearning algorithms for outlier detection. Instead of direct application of\nmachine learning algorithms over trace data using the signals as raw features,\nwe use feature engineering to transform raw features into more sophisticated\nfeatures using domain specific operations. The engineered features are highly\nrelevant to the diagnosis task and are generic to be applied across any\nhardware designs. We present debugging and root cause analysis of subtle\npost-silicon bugs in industry-scale OpenSPARC T2 SoC. We achieve a trace buffer\nutilization of 98.96\\% with a flow specification coverage of 94.3\\% (average).\nOur diagnosis method was able to diagnose up to 66.7\\% more bugs and took up to\n847$\\times$ less diagnosis time as compared to the manual debugging with a\ndiagnosis precision of 0.769.\n"}
+{"abstract": "  We present an open-source Python package, Orbits from Radial Velocity,\nAbsolute, and/or Relative Astrometry (orvara), to fit Keplerian orbits to any\ncombination of radial velocity, relative astrometry, and absolute astrometry\ndata from the Hipparcos-Gaia Catalog of Accelerations. By combining these three\ndata types, one can measure precise masses and sometimes orbital parameters\neven when the observations cover a small fraction of an orbit. orvara achieves\nits computational performance with an eccentric anomaly solver five to ten\ntimes faster than commonly used approaches, low-level memory management to\navoid python overheads, and by analytically marginalizing out parallax,\nbarycenter proper motion, and the instrument-specific radial velocity zero\npoints. Through its integration with the Hipparcos and Gaia intermediate\nastrometry package htof, orvara can properly account for the epoch astrometry\nmeasurements of Hipparcos and the measurement times and scan angles of\nindividual Gaia epochs. We configure orvara with modifiable .ini configuration\nfiles tailored to any specific stellar or planetary system. We demonstrate\norvara with a case study application to a recently discovered white dwarf/main\nsequence (WD/MS) system, HD 159062. By adding absolute astrometry to literature\nRV and relative astrometry data, our comprehensive MCMC analysis improves the\nprecision of HD 159062B's mass by more than an order of magnitude to\n$0.6083^{+0.0083}_{-0.0073}\\,M_\\odot$. We also derive a low eccentricity and\nlarge semimajor axis, establishing HD 159062AB as a system that did not\nexperience Roche lobe overflow.\n"}
+{"abstract": "  It has recently been established that cluster-like states -- states that are\nin the same symmetry-protected topological phase as the cluster state --\nprovide a family of resource states that can be utilized for Measurement-Based\nQuantum Computation. In this work, we ask whether it is possible to prepare\ncluster-like states in finite time without breaking the symmetry protecting the\nresource state. Such a symmetry-preserving protocol would benefit from\ntopological protection to errors in the preparation. We answer this question in\nthe positive by providing a Hamiltonian in one higher dimension whose\nfinite-time evolution is a unitary that acts trivially in the bulk, but pumps\nthe desired cluster state to the boundary. Examples are given for both the 1D\ncluster state protected by a global symmetry, and various 2D cluster states\nprotected by subsystem symmetries. We show that even if unwanted symmetric\nperturbations are present in the driving Hamiltonian, projective measurements\nin the bulk along with post-selection is sufficient to recover a cluster-like\nstate. For a resource state of size $N$, failure to prepare the state is\nnegligible if the size of the perturbations are much smaller than $N^{-1/2}$.\n"}
+{"abstract": "  Detecting anomalies has been a fundamental approach in detecting potentially\nfraudulent activities. Tasked with detection of illegal timber trade that\nthreatens ecosystems and economies and association with other illegal\nactivities, we formulate our problem as one of anomaly detection. Among other\nchallenges annotations are unavailable for our large-scale trade data with\nheterogeneous features (categorical and continuous), that can assist in\nbuilding automated systems to detect fraudulent transactions. Modelling the\ntask as unsupervised anomaly detection, we propose a novel model Contrastive\nLearning based Heterogeneous Anomaly Detector to address shortcomings of prior\nmodels. Our model uses an asymmetric autoencoder that can effectively handle\nlarge arity categorical variables, but avoids assumptions about structure of\ndata in low-dimensional latent space and is robust to changes to\nhyper-parameters. The likelihood of data is approximated through an estimator\nnetwork, which is jointly trained with the autoencoder,using negative sampling.\nFurther the details and intuition for an effective negative sample generation\napproach for heterogeneous data are outlined. We provide a qualitative study to\nshowcase the effectiveness of our model in detecting anomalies in timber trade.\n"}
+{"abstract": "  We often use perturbations to regularize neural models. For neural\nencoder-decoders, previous studies applied the scheduled sampling (Bengio et\nal., 2015) and adversarial perturbations (Sato et al., 2019) as perturbations\nbut these methods require considerable computational time. Thus, this study\naddresses the question of whether these approaches are efficient enough for\ntraining time. We compare several perturbations in sequence-to-sequence\nproblems with respect to computational time. Experimental results show that the\nsimple techniques such as word dropout (Gal and Ghahramani, 2016) and random\nreplacement of input tokens achieve comparable (or better) scores to the\nrecently proposed perturbations, even though these simple methods are faster.\nOur code is publicly available at\nhttps://github.com/takase/rethink_perturbations.\n"}
+{"abstract": "  A neutron decays into a proton, an electron, and an anti-neutrino through the\nbeta-decay process. The decay lifetime ($\\sim$880 s) is an important parameter\nin the weak interaction. For example, the neutron lifetime is a parameter used\nto determine the |$V_{\\rm ud}$| parameter of the CKM quark mixing matrix. The\nlifetime is also one of the input parameters for the Big Bang Nucleosynthesis,\nwhich predicts light element synthesis in the early universe. However,\nexperimental measurements of the neutron lifetime today are significantly\ndifferent (8.4 s or 4.0$\\sigma$) depending on the methods. One is a bottle\nmethod measuring surviving neutron in the neutron storage bottle. The other is\na beam method measuring neutron beam flux and neutron decay rate in the\ndetector. There is a discussion that the discrepancy comes from unconsidered\nsystematic error or undetectable decay mode, such as dark decay. A new type of\nbeam experiment is performed at the BL05 MLF J-PARC. This experiment measured\nneutron flux and decay rate simultaneously with a time projection chamber using\na pulsed neutron beam. We will present the world situation of neutron lifetime\nand the latest results at J-PARC.\n"}
+{"abstract": "  For two-dimensional percolation on a domain with the topology of a disc, we\nintroduce a nested-path operator (NP) and thus a continuous family of one-point\nfunctions $W_k \\equiv \\langle \\mathcal{R} \\cdot k^\\ell \\rangle $, where $\\ell$\nis the number of independent nested closed paths surrounding the center, $k$ is\na path fugacity, and $\\mathcal{R}$ projects on configurations having a cluster\nconnecting the center to the boundary. At criticality, we observe a power-law\nscaling $W_k \\sim L^{X_{\\rm NP}}$, with $L$ the linear system size, and we\ndetermine the exponent $X_{\\rm NP}$ as a function of $k$. On the basis of our\nnumerical results, we conjecture an analytical formula, $X_{\\rm NP} (k) =\n\\frac{3}{4}\\phi^2 -\\frac{5}{48}\\phi^2/ (\\phi^2-\\frac{2}{3})$ where $k = 2\n\\cos(\\pi \\phi)$, which reproduces the exact results for $k=0,1$ and agrees with\nthe high-precision estimate of $X_{\\rm NP}$ for other $k$ values. In addition,\nwe observe that $W_2(L)=1$ for site percolation on the triangular lattice with\nany size $L$, and we prove this identity for all self-matching lattices.\n"}
+{"abstract": "  A key functionality of emerging connected autonomous systems such as smart\ntransportation systems, smart cities, and the industrial Internet-of-Things, is\nthe ability to process and learn from data collected at different physical\nlocations. This is increasingly attracting attention under the terms of\ndistributed learning and federated learning. However, in this setup data\ntransfer takes place over communication resources that are shared among many\nusers and tasks or subject to capacity constraints. This paper examines\nalgorithms for efficiently allocating resources to linear regression tasks by\nexploiting the informativeness of the data. The algorithms developed enable\nadaptive scheduling of learning tasks with reliable performance guarantees.\n"}
+{"abstract": "  Blazars emit a highly-variable non-thermal spectrum. It is usually assumed\nthat the same non-thermal electrons are responsible for the IR-optical-UV\nemission (via synchrotron) and the gamma-ray emission (via inverse Compton).\nHence, the light curves in the two bands should be correlated. Orphan gamma-ray\nflares (i.e., lacking a luminous low-frequency counterpart) challenge our\ntheoretical understanding of blazars. By means of large-scale two-dimensional\nradiative particle-in-cell simulations, we show that orphan gamma-ray flares\nmay be a self-consistent by-product of particle energization in turbulent\nmagnetically-dominated pair plasmas. The energized particles produce the\ngamma-ray flare by inverse Compton scattering an external radiation field,\nwhile the synchrotron luminosity is heavily suppressed since the particles are\naccelerated nearly along the direction of the local magnetic field. The ratio\nof inverse Compton to synchrotron luminosity is sensitive to the initial\nstrength of turbulent fluctuations (a larger degree of turbulent fluctuations\nweakens the anisotropy of the energized particles, thus increasing the\nsynchrotron luminosity). Our results show that the anisotropy of the\nnon-thermal particle population is key to modeling the blazar emission.\n"}
+{"abstract": "  We present a high fidelity snapshot spectroscopic radio imaging study of a\nweak type I solar noise storm which took place during an otherwise\nexceptionally quiet time. Using high fidelity images from the Murchison\nWidefield Array, we track the observed morphology of the burst source for 70\nminutes and identify multiple instances where its integrated flux density and\narea are strongly anti-correlated with each other. The type I radio emission is\nbelieved to arise due to electron beams energized during magnetic reconnection\nactivity. The observed anti-correlation is interpreted as evidence for presence\nof MHD sausage wave modes in the magnetic loops and strands along which these\nelectron beams are propagating. Our observations suggest that the sites of\nthese small scale reconnections are distributed along the magnetic flux tube.\nWe hypothesise that small scale reconnections produces electron beams which\nquickly get collisionally damped. Hence, the plasma emission produced by them\nspan only a narrow bandwidth and the features seen even a few MHz apart must\narise from independent electron beams.\n"}
+{"abstract": "  This study investigated how social interaction among robotic agents changes\ndynamically depending on the individual belief of action intention. In a set of\nsimulation studies, we examine dyadic imitative interactions of robots using a\nvariational recurrent neural network model. The model is based on the free\nenergy principle such that a pair of interacting robots find themselves in a\nloop, attempting to predict and infer each other's actions using active\ninference. We examined how regulating the complexity term to minimize free\nenergy determines the dynamic characteristics of networks and interactions.\nWhen one robot trained with tighter regulation and another trained with looser\nregulation interact, the latter tends to lead the interaction by exerting\nstronger action intention, while the former tends to follow by adapting to its\nobservations. The study confirms that the dyadic imitative interaction becomes\nsuccessful by achieving a high synchronization rate when a leader and a\nfollower are determined by developing action intentions with strong belief and\nweak belief, respectively.\n"}
+{"abstract": "  We reveal the microscopic origin of electric polarization $\\vec{P}$ induced\nby noncollinear magnetic order. We show that in Mott insulators, such $\\vec{P}$\nis given by all possible combinations of position operators $\\hat{\\vec{r}}_{ij}\n= (\\vec{r}_{ij}^{\\, 0},\\vec{\\boldsymbol{r}}_{ij}^{\\phantom{0}})$ and transfer\nintegrals $\\hat{t}_{ij} = (t_{ij}^{0},\\boldsymbol{t}_{ij}^{\\phantom{0}})$ in\nthe bonds, where $\\vec{r}_{ij}^{\\, 0}$ and $t_{ij}^{0}$ are spin-independent\ncontributions in the basis of Kramers doublet states, while\n$\\vec{\\boldsymbol{r}}_{ij}^{\\phantom{0}}$ and\n$\\boldsymbol{t}_{ij}^{\\phantom{0}}$ stem solely from the spin-orbit\ninteraction. Among them, the combination $t_{ij}^{0}\n\\vec{\\boldsymbol{r}}_{ij}^{\\phantom{0}}$, which couples to the spin current,\nremains finite in the centrosymmetric bonds, thus yielding finite $\\vec{P}$ in\nthe case of noncollinear arrangement of spins. The form of the magnetoelectric\ncoupling, which is controlled by $\\vec{\\boldsymbol{r}}_{ij}^{\\phantom{0}}$,\nappears to be rich and is not limited to the phenomenological law $\\vec{P} \\sim\n\\boldsymbol{\\epsilon}_{ij} \\times [\\boldsymbol{e}_{i} \\times\n\\boldsymbol{e}_{j}]$ with $\\boldsymbol{\\epsilon}_{ij}$ being the bond vector\nconnecting the spins $\\boldsymbol{e}_{i}$ and $\\boldsymbol{e}_{j}$. Using\ndensity-functional theory, we illustrate how the proposed mechanism work in the\nspiral magnets CuCl$_2$, CuBr$_2$, CuO, and $\\alpha$-Li$_2$IrO$_3$, providing\nconsistent explanation to available experimental data.\n"}
+{"abstract": "  Methods for constructing synthetic multidimensional electron hole equilibria\nwithout using particle simulation are investigated. Previous approaches have\nvarious limitations and approximations that make them unsuitable within the\ncontext of expected velocity diffusion near the trapped-passing boundary. An\nadjustable model of the distribution function is introduced that avoids\nunphysical singularities there, and yet is sufficiently tractable analytically\nto enable prescription of the potential spatial profiles. It is shown why\nsimple models of the charge density as being a function only of potential\ncannot give solitary multidimensional electron holes, in contradiction of prior\nsuppositions. Fully self-consistent axisymmetric electron holes in the\ndrift-kinetic limit of electron motion (negligible gyro-radius) are constructed\nand their properties relevant to observational interpretation and\nfinite-gyro-radius theory are discussed.\n"}
+{"abstract": "  In this paper we introduce the notions of phi-contractive parent-child\ninfinite iterated function system (pcIIFS) and orbital phi-contractive infinite\niterated function system (oIIFS) and we prove that the corresponding fractal\noperator is weakly Picard. The corresponding notions of shift space, canonical\nprojection and their properties are also treated.\n"}
+{"abstract": "  Delays are ubiquitous in modern hybrid systems, which exhibit both continuous\nand discrete dynamical behaviors. Induced by signal transmission, conversion,\nthe nature of plants, and so on, delays may appear either in the continuous\nevolution of a hybrid system such that the evolution depends not only on the\npresent state but also on its execution history, or in the discrete switching\nbetween its different control modes. In this paper we come up with a new model\nof hybrid systems, called \\emph{delay hybrid automata}, to capture the dynamics\nof systems with the aforementioned two kinds of delays. Furthermore, based upon\nthis model we study the robust switching controller synthesis problem such that\nthe controlled delay system is able to satisfy the specified safety properties\nregardless of perturbations. To the end, a novel method is proposed to\nsynthesize switching controllers based on the computation of differential\ninvariants for continuous evolution and backward reachable sets of discrete\njumps with delays. Finally, we implement a prototypical tool of our approach\nand demonstrate it on some case studies.\n"}
+{"abstract": "  We study the molecular gas content of 24 star-forming galaxies at $z=3-4$,\nwith a median stellar mass of $10^{9.1}$ M$_{\\odot}$, from the MUSE Hubble\nUltra Deep Field (HUDF) Survey. Selected by their Lyman-alpha-emission and\nH-band magnitude, the galaxies show an average EW $\\approx 20$ angstrom, below\nthe typical selection threshold for Lyman Alpha Emitters (EW $> 25$ angstrom),\nand a rest-frame UV spectrum similar to Lyman Break Galaxies. We use rest-frame\noptical spectroscopy from KMOS and MOSFIRE, and the UV features observed with\nMUSE, to determine the systemic redshifts, which are offset from Lyman alpha by\n346 km s$^{-1}$, with a 100 to 600 km s$^{-1}$ range. Stacking CO(4-3) and\n[CI](1-0) (and higher-$J$ CO lines) from the ALMA Spectroscopic Survey of the\nHUDF (ASPECS), we determine $3\\sigma$ upper limits on the line luminosities of\n$4.0\\times10^{8}$ K km s$^{-1}$pc$^{2}$ and $5.6\\times10^{8}$ K km\ns$^{-1}$pc$^{2}$, respectively (for a 300 km s$^{-1}$ linewidth). Stacking the\n1.2 mm and 3 mm dust continuum flux densities, we find a $3\\sigma$ upper limits\nof 9 $\\mu$Jy and $1.2$ $\\mu$Jy, respectively. The inferred gas fractions, under\nthe assumption of a 'Galactic' CO-to-H$_{2}$ conversion factor and gas-to-dust\nratio, are in tension with previously determined scaling relations. This\nimplies a substantially higher $\\alpha_{\\rm CO} \\ge 10$ and $\\delta_{\\rm GDR}\n\\ge 1200$, consistent with the sub-solar metallicity estimated for these\ngalaxies ($12 + \\log(O/H) \\approx 7.8 \\pm 0.2$). The low metallicity of $z \\ge\n3$ star-forming galaxies may thus make it very challenging to unveil their cold\ngas through CO or dust emission, warranting further exploration of alternative\ntracers, such as [CII].\n"}
+{"abstract": "  Multi-layer feedforward networks have been used to approximate a wide range\nof nonlinear functions. An important and fundamental problem is to understand\nthe learnability of a network model through its statistical risk, or the\nexpected prediction error on future data. To the best of our knowledge, the\nrate of convergence of neural networks shown by existing works is bounded by at\nmost the order of $n^{-1/4}$ for a sample size of $n$. In this paper, we show\nthat a class of variation-constrained neural networks, with arbitrary width,\ncan achieve near-parametric rate $n^{-1/2+\\delta}$ for an arbitrarily small\npositive constant $\\delta$. It is equivalent to $n^{-1 +2\\delta}$ under the\nmean squared error. This rate is also observed by numerical experiments. The\nresult indicates that the neural function space needed for approximating smooth\nfunctions may not be as large as what is often perceived. Our result also\nprovides insight to the phenomena that deep neural networks do not easily\nsuffer from overfitting when the number of neurons and learning parameters\nrapidly grow with $n$ or even surpass $n$. We also discuss the rate of\nconvergence regarding other network parameters, including the input dimension,\nnetwork layer, and coefficient norm.\n"}
+{"abstract": "  This paper presents the components of a newly developed Malaysian SMEs -\nSoftware Process Improvement model (MSME-SPI) that can assess SMEs soft-ware\ndevelopment industry in managing and improving their software processes\ncapability. The MSME-SPI is developed in response to practitioner needs that\nwere highlighted in an empirical study with the Malaysian SME software\ndevelopment industry. After the model development, there is a need for\nindependent feedback to show that the model meets its objectives. Consequently,\nthe validation phase is performed by involving a group of software process\nimprovement experts in examining the MSME-SPI model components. Besides, the\neffectiveness of the MSME-SPI model is validated using an expert panel. Three\ncriteria were used to evaluate the effectiveness of the model namely:\nusefulness, verifiability, and structure. The results show the model effective\nto be used by SMEs with minor modifications. The validation phase contributes\ntowards a better understanding and use of the MSME-SPI model by the\npractitioners in the field.\n"}
+{"abstract": "  Deep learning (DL) models for image-based malware detection have exhibited\ntheir capability in producing high prediction accuracy. But model\ninterpretability is posing challenges to their widespread application in\nsecurity and safety-critical application domains. This paper aims for designing\nan Interpretable Ensemble learning approach for image-based Malware Detection\n(IEMD). We first propose a Selective Deep Ensemble Learning-based (SDEL)\ndetector and then design an Ensemble Deep Taylor Decomposition (EDTD) approach,\nwhich can give the pixel-level explanation to SDEL detector outputs.\nFurthermore, we develop formulas for calculating fidelity, robustness and\nexpressiveness on pixel-level heatmaps in order to assess the quality of EDTD\nexplanation. With EDTD explanation, we develop a novel Interpretable Dropout\napproach (IDrop), which establishes IEMD by training SDEL detector. Experiment\nresults exhibit the better explanation of our EDTD than the previous\nexplanation methods for image-based malware detection. Besides, experiment\nresults indicate that IEMD achieves a higher detection accuracy up to 99.87%\nwhile exhibiting interpretability with high quality of prediction results.\nMoreover, experiment results indicate that IEMD interpretability increases with\nthe increasing detection accuracy during the construction of IEMD. This\nconsistency suggests that IDrop can mitigate the tradeoff between model\ninterpretability and detection accuracy.\n"}
+{"abstract": "  For robots to navigate and interact more richly with the world around them,\nthey will likely require a deeper understanding of the world in which they\noperate. In robotics and related research fields, the study of understanding is\noften referred to as semantics, which dictates what does the world \"mean\" to a\nrobot, and is strongly tied to the question of how to represent that meaning.\nWith humans and robots increasingly operating in the same world, the prospects\nof human-robot interaction also bring semantics and ontology of natural\nlanguage into the picture. Driven by need, as well as by enablers like\nincreasing availability of training data and computational resources, semantics\nis a rapidly growing research area in robotics. The field has received\nsignificant attention in the research literature to date, but most reviews and\nsurveys have focused on particular aspects of the topic: the technical research\nissues regarding its use in specific robotic topics like mapping or\nsegmentation, or its relevance to one particular application domain like\nautonomous driving. A new treatment is therefore required, and is also timely\nbecause so much relevant research has occurred since many of the key surveys\nwere published. This survey therefore provides an overarching snapshot of where\nsemantics in robotics stands today. We establish a taxonomy for semantics\nresearch in or relevant to robotics, split into four broad categories of\nactivity, in which semantics are extracted, used, or both. Within these broad\ncategories we survey dozens of major topics including fundamentals from the\ncomputer vision field and key robotics research areas utilizing semantics,\nincluding mapping, navigation and interaction with the world. The survey also\ncovers key practical considerations, including enablers like increased data\navailability and improved computational hardware, and major application areas\nwhere...\n"}
+{"abstract": "  We introduce a differential extension of algebraic K-theory of an algebra\nusing Karoubi's Chern character. In doing so, we develop a necessary theory of\nsecondary transgression forms as well as a differential refinement of the\nsmooth Serre--Swan correspondence. Our construction subsumes the differential\nK-theory of a smooth manifold when the algebra is complex-valued smooth\nfunctions. Furthermore, our construction fits into a noncommutative\ndifferential cohomology hexagon diagram.\n"}
+{"abstract": "  While the LCDM framework has been incredibly successful for modern cosmology,\nit requires the admission of two mysterious substances as a part of the\nparadigm, dark energy and dark matter. Although this framework adequately\nexplains most of the large-scale properties of the Universe (i.e., existence\nand structure of the CMB, the large-scale structure of galaxies, the abundances\nof light elements and the accelerating expansion), it has failed to make\nsignificant predictions on smaller scale features such as the kinematics of\ngalaxies and their formation. In particular, the rotation curves of disk\ngalaxies (the original observational discovery of dark matter) are better\nrepresented by non-Newtonian models of gravity that challenge our understanding\nof motion in the low acceleration realm (much as general relativity provided an\nextension of gravity into the high acceleration realm e.g., blackholes). The\ntension between current cold dark matter scenarios and proposed new\nformulations of gravity in the low energy regime suggests an upcoming paradigm\nshift in cosmology. And, if history is a guide, observations will lead the way.\n"}
+{"abstract": "  As artificial intelligence (AI) systems are increasingly deployed, principles\nfor ethical AI are also proliferating. Certification offers a method to both\nincentivize adoption of these principles and substantiate that they have been\nimplemented in practice. This paper draws from management literature on\ncertification and reviews current AI certification programs and proposals.\nSuccessful programs rely on both emerging technical methods and specific design\nconsiderations. In order to avoid two common failures of certification, program\ndesigns should ensure that the symbol of the certification is substantially\nimplemented in practice and that the program achieves its stated goals. The\nreview indicates that the field currently focuses on self-certification and\nthird-party certification of systems, individuals, and organizations - to the\nexclusion of process management certifications. Additionally, the paper\nconsiders prospects for future AI certification programs. Ongoing changes in AI\ntechnology suggest that AI certification regimes should be designed to\nemphasize governance criteria of enduring value, such as ethics training for AI\ndevelopers, and to adjust technical criteria as the technology changes.\nOverall, certification can play a valuable mix in the portfolio of AI\ngovernance tools.\n"}
+{"abstract": "  We perform the analysis of the focusing nonlinear Schr\\\"odinger equation on\nthe half-line with time-dependent boundary conditions along the lines of the\nnonlinear method of images with the help of B\\\"acklund transformations. The\ndifficulty arising from having such time-dependent boundary conditions at $x=0$\nis overcome by changing the viewpoint of the method and fixing the B\\\"acklund\ntransformation at infinity as well as relating its value at $x=0$ to a\ntime-dependent reflection matrix. The interplay between the various aspects of\nintegrable boundary conditions is reviewed in detail to brush a picture of the\narea. We find two possible classes of solutions. One is very similar to the\ncase of Robin boundary conditions whereby solitons are reflected at the\nboundary, as a result of an effective interaction with their images on the\nother half-line. The new regime of solutions supports the existence of one\nsoliton that is not reflected at the boundary but can be either absorbed or\nemitted by it. We demonstrate that this is a unique feature of time-dependent\nintegrable boundary conditions.\n"}
+{"abstract": "  In this paper, we study the problem of exact community recovery in the\nsymmetric stochastic block model, where a graph of $n$ vertices is randomly\ngenerated by partitioning the vertices into $K \\ge 2$ equal-sized communities\nand then connecting each pair of vertices with probability that depends on\ntheir community memberships. Although the maximum-likelihood formulation of\nthis problem is discrete and non-convex, we propose to tackle it directly using\nprojected power iterations with an initialization that satisfies a partial\nrecovery condition. Such an initialization can be obtained by a host of\nexisting methods. We show that in the logarithmic degree regime of the\nconsidered problem, the proposed method can exactly recover the underlying\ncommunities at the information-theoretic limit. Moreover, with a qualified\ninitialization, it runs in $\\mathcal{O}(n\\log^2n/\\log\\log n)$ time, which is\ncompetitive with existing state-of-the-art methods. We also present numerical\nresults of the proposed method to support and complement our theoretical\ndevelopment.\n"}
+{"abstract": "  Large optimization problems with hard constraints arise in many settings, yet\nclassical solvers are often prohibitively slow, motivating the use of deep\nnetworks as cheap \"approximate solvers.\" Unfortunately, naive deep learning\napproaches typically cannot enforce the hard constraints of such problems,\nleading to infeasible solutions. In this work, we present Deep Constraint\nCompletion and Correction (DC3), an algorithm to address this challenge.\nSpecifically, this method enforces feasibility via a differentiable procedure,\nwhich implicitly completes partial solutions to satisfy equality constraints\nand unrolls gradient-based corrections to satisfy inequality constraints. We\ndemonstrate the effectiveness of DC3 in both synthetic optimization tasks and\nthe real-world setting of AC optimal power flow, where hard constraints encode\nthe physics of the electrical grid. In both cases, DC3 achieves near-optimal\nobjective values while preserving feasibility.\n"}
+{"abstract": "  I recount my personal experience interacting with Roman Jackiw in the 1980s,\nwhen we both worked on Chern-Simons theories in three dimensions.\n"}
+{"abstract": "  The Minimum Linear Arrangement problem (MLA) consists of finding a mapping\n$\\pi$ from vertices of a graph to distinct integers that minimizes\n$\\sum_{\\{u,v\\}\\in E}|\\pi(u) - \\pi(v)|$. In that setting, vertices are often\nassumed to lie on a horizontal line and edges are drawn as semicircles above\nsaid line. For trees, various algorithms are available to solve the problem in\npolynomial time in $n=|V|$. There exist variants of the MLA in which the\narrangements are constrained. Iordanskii, and later Hochberg and Stallmann\n(HS), put forward $O(n)$-time algorithms that solve the problem when\narrangements are constrained to be planar (also known as one-page book\nembeddings). We also consider linear arrangements of rooted trees that are\nconstrained to be projective (planar embeddings where the root is not covered\nby any edge). Gildea and Temperley (GT) sketched an algorithm for projective\narrangements which they claimed runs in $O(n)$ but did not provide any\njustification of its cost. In contrast, Park and Levy claimed that GT's\nalgorithm runs in $O(n \\log d_{max})$ where $d_{max}$ is the maximum degree but\ndid not provide sufficient detail. Here we correct an error in HS's algorithm\nfor the planar case, show its relationship with the projective case, and derive\nsimple algorithms for the projective and planar cases that run without a doubt\nin $O(n)$ time.\n"}
+{"abstract": "  It is well known that a quantum circuit on $N$ qubits composed of Clifford\ngates with the addition of $k$ non Clifford gates can be simulated on a\nclassical computer by an algorithm scaling as $\\text{poly}(N)\\exp(k)$[1]. We\nshow that, for a quantum circuit to simulate quantum chaotic behavior, it is\nboth necessary and sufficient that $k=O(N)$. This result implies the\nimpossibility of simulating quantum chaos on a classical computer.\n"}
+{"abstract": "  We present an algorithm for computing $\\epsilon$-coresets for $(k,\n\\ell)$-median clustering of polygonal curves in $\\mathbb{R}^d$ under the\nFr\\'echet distance. This type of clustering is an adaption of Euclidean\n$k$-median clustering: we are given a set of $n$ polygonal curves in\n$\\mathbb{R}^d$, each of complexity (number of vertices) at most $m$, and want\nto compute $k$ median curves such that the sum of distances from the given\ncurves to their closest median curve is minimal. Additionally, we restrict the\ncomplexity of the median curves to be at most $\\ell$ each, to suppress\noverfitting, a problem specific for sequential data. Our algorithm has running\ntime linear in $n$, sub-quartic in $m$ and quadratic in $\\epsilon^{-1}$. With\nhigh probability it returns $\\epsilon$-coresets of size quadratic in\n$\\epsilon^{-1}$ and logarithmic in $n$ and $m$. We achieve this result by\napplying the improved $\\epsilon$-coreset framework by Langberg and Feldman to a\ngeneralized $k$-median problem over an arbitrary metric space. Later we combine\nthis result with the recent result by Driemel et al. on the VC dimension of\nmetric balls under the Fr\\'echet distance. Furthermore, our framework yields\n$\\epsilon$-coresets for any generalized $k$-median problem where the range\nspace induced by the open metric balls of the underlying space has bounded VC\ndimension, which is of independent interest. Finally, we show that our\n$\\epsilon$-coresets can be used to improve the running time of an existing\napproximation algorithm for $(1,\\ell)$-median clustering.\n"}
+{"abstract": "  In this paper, we enable automated property verification of deliberative\ncomponents in robot control architectures. We focus on formalizing the\nexecution context of Behavior Trees (BTs) to provide a scalable, yet formally\ngrounded, methodology to enable runtime verification and prevent unexpected\nrobot behaviors. To this end, we consider a message-passing model that\naccommodates both synchronous and asynchronous composition of parallel\ncomponents, in which BTs and other components execute and interact according to\nthe communication patterns commonly adopted in robotic software architectures.\nWe introduce a formal property specification language to encode requirements\nand build runtime monitors. We performed a set of experiments, both on\nsimulations and on the real robot, demonstrating the feasibility of our\napproach in a realistic application and its integration in a typical robot\nsoftware architecture. We also provide an OS-level virtualization environment\nto reproduce the experiments in the simulated scenario.\n"}
+{"abstract": "  Aided by recent advances in Deep Learning, Image Caption Generation has seen\ntremendous progress over the last few years. Most methods use transfer learning\nto extract visual information, in the form of image features, with the help of\npre-trained Convolutional Neural Network models followed by transformation of\nthe visual information using a Caption Generator module to generate the output\nsentences. Different methods have used different Convolutional Neural Network\nArchitectures and, to the best of our knowledge, there is no systematic study\nwhich compares the relative efficacy of different Convolutional Neural Network\narchitectures for extracting the visual information. In this work, we have\nevaluated 17 different Convolutional Neural Networks on two popular Image\nCaption Generation frameworks: the first based on Neural Image Caption (NIC)\ngeneration model and the second based on Soft-Attention framework. We observe\nthat model complexity of Convolutional Neural Network, as measured by number of\nparameters, and the accuracy of the model on Object Recognition task does not\nnecessarily co-relate with its efficacy on feature extraction for Image Caption\nGeneration task.\n"}
+{"abstract": "  We present a computational study of the behaviour of a lipid-coated SonoVue\nmicrobubble with initial radius $1 \\, \\mu \\text{m} \\leq R_0 \\leq 2 \\, \\mu\n\\text{m}$, excited at frequencies (200-1500 kHz) significantly below the linear\nresonance frequency and pressure amplitudes of up to 1500 kPa, an excitation\nregime used in many focused ultrasound applications. The bubble dynamics are\nsimulated using the Rayleigh-Plesset equation and the Gilmore equation, in\nconjunction with the Marmottant model for the lipid monolayer coating. Also, a\nnew continuously differentiable variant of the Marmottant model is introduced.\nBelow the onset of inertial cavitation, a linear regime is identified in which\nthe maximum pressure at the bubble wall is linearly proportional to the\nexcitation pressure amplitude and, likewise, the mechanical index. This linear\nregime is bounded by the Blake pressure and, in line with recent in vitro\nexperiments, the onset of inertial cavitation is found to occur approximately\nat an excitation pressure amplitude of 130-190 kPa, dependent on the initial\nbubble size. In the nonlinear regime the maximum pressure at the bubble wall is\nfound to be readily predicted by the maximum bubble radius and both the\nRayleigh-Plesset and Gilmore equations are shown to predict the onset of sub-\nand ultraharmonic frequencies of the acoustic emissions compared to in vitro\nexperiments. Neither the surface dilatational viscosity of the lipid monolayer\nnor the compressibility of the liquid have a discernible influence on the\nstudied quantities, yet accounting for the lipid coating is critical for the\naccurate prediction of the bubble behaviour. The Gilmore equation is shown to\nbe valid for the considered bubbles and excitation regime, and the\nRayleigh-Plesset equation also provides accurate qualitative predictions, even\nthough it is outside its range of validity for many of the considered cases.\n"}
+{"abstract": "  We consider the problem of private distributed matrix multiplication under\nlimited resources. Coded computation has been shown to be an effective solution\nin distributed matrix multiplication, both providing privacy against the\nworkers and boosting the computation speed by efficiently mitigating\nstragglers. In this work, we propose the use of recently-introduced bivariate\npolynomial codes to further speed up private distributed matrix multiplication\nby exploiting the partial work done by the stragglers rather than completely\nignoring them. We show that the proposed approach reduces the average\ncomputation time of private distributed matrix multiplication compared to its\ncompetitors in the literature while improving the upload communication cost and\nthe workers' storage efficiency.\n"}
+{"abstract": "  In this paper, a novel three-dimensional (3D) non-stationary geometry-based\nstochastic model (GBSM) for the fifth generation (5G) and beyond 5G (B5G)\nsystems is proposed. The proposed B5G channel model (B5GCM) is designed to\ncapture various channel characteristics in (B)5G systems such as\nspace-time-frequency (STF) non-stationarity, spherical wavefront (SWF), high\ndelay resolution, time-variant velocities and directions of motion of the\ntransmitter, receiver, and scatterers, spatial consistency, etc. By combining\ndifferent channel properties into a general channel model framework, the\nproposed B5GCM is able to be applied to multiple frequency bands and multiple\nscenarios, including massive multiple-input multiple-output (MIMO),\nvehicle-to-vehicle (V2V), high-speed train (HST), and millimeter wave-terahertz\n(mmWave-THz) communication scenarios. Key statistics of the proposed B5GCM are\nobtained and compared with those of standard 5G channel models and\ncorresponding measurement data, showing the generalization and usefulness of\nthe proposed model.\n"}
+{"abstract": "  Logit dynamics is a form of randomized game dynamics where players have a\nbias towards strategic deviations that give a higher improvement in cost. It is\nused extensively in practice. In congestion (or potential) games, the dynamics\nconverges to the so-called Gibbs distribution over the set of all strategy\nprofiles, when interpreted as a Markov chain. In general, logit dynamics might\nconverge slowly to the Gibbs distribution, but beyond that, not much is known\nabout their algorithmic aspects, nor that of the Gibbs distribution. In this\nwork, we are interested in the following two questions for congestion games: i)\nIs there an efficient algorithm for sampling from the Gibbs distribution? ii)\nIf yes, do there also exist natural randomized dynamics that converges quickly\nto the Gibbs distribution?\n  We first study these questions in extension parallel congestion games, a\nwell-studied special case of symmetric network congestion games. As our main\nresult, we show that there is a simple variation on the logit dynamics (in\nwhich we in addition are allowed to randomly interchange the strategies of two\nplayers) that converges quickly to the Gibbs distribution in such games. This\nanswers both questions above affirmatively. We also address the first question\nfor the class of so-called capacitated $k$-uniform congestion games.\n  To prove our results, we rely on the recent breakthrough work of Anari, Liu,\nOveis-Gharan and Vinzant (2019) concerning the approximate sampling of the base\nof a matroid according to strongly log-concave probability distribution.\n"}
+{"abstract": "  The ability to recognize the position and order of the floor-level lines that\ndivide adjacent building floors can benefit many applications, for example,\nurban augmented reality (AR). This work tackles the problem of locating\nfloor-level lines in street-view images, using a supervised deep learning\napproach. Unfortunately, very little data is available for training such a\nnetwork $-$ current street-view datasets contain either semantic annotations\nthat lack geometric attributes, or rectified facades without perspective\npriors. To address this issue, we first compile a new dataset and develop a new\ndata augmentation scheme to synthesize training samples by harassing (i) the\nrich semantics of existing rectified facades and (ii) perspective priors of\nbuildings in diverse street views. Next, we design FloorLevel-Net, a multi-task\nlearning network that associates explicit features of building facades and\nimplicit floor-level lines, along with a height-attention mechanism to help\nenforce a vertical ordering of floor-level lines. The generated segmentations\nare then passed to a second-stage geometry post-processing to exploit\nself-constrained geometric priors for plausible and consistent reconstruction\nof floor-level lines. Quantitative and qualitative evaluations conducted on\nassorted facades in existing datasets and street views from Google demonstrate\nthe effectiveness of our approach. Also, we present context-aware image overlay\nresults and show the potentials of our approach in enriching AR-related\napplications.\n"}
+{"abstract": "  Natural language processing (NLP) has been applied to various fields\nincluding text classification and sentiment analysis. In the shared task of\nsentiment analysis of code-mixed tweets, which is a part of the SemEval-2020\ncompetition~\\cite{patwa2020sentimix}, we preprocess datasets by replacing emoji\nand deleting uncommon characters and so on, and then fine-tune the\nBidirectional Encoder Representation from Transformers(BERT) to perform the\nbest. After exhausting top3 submissions, Our team MeisterMorxrc achieves an\naveraged F1 score of 0.730 in this task, and and our codalab username is\nMeisterMorxrc.\n"}
+{"abstract": "  In this paper, we illustrate how to fine-tune the entire Retrieval Augment\nGeneration (RAG) architecture in an end-to-end manner. We highlighted the main\nengineering challenges that needed to be addressed to achieve this objective.\nWe also compare how end-to-end RAG architecture outperforms the original RAG\narchitecture for the task of question answering. We have open-sourced our\nimplementation in the HuggingFace Transformers library.\n"}
+{"abstract": "  We study solutions of the Bethe ansatz equations associated to the\northosymplectic Lie superalgebras $\\mathfrak{osp}_{2m+1|2n}$ and\n$\\mathfrak{osp}_{2m|2n}$. Given a solution, we define a reproduction procedure\nand use it to construct a family of new solutions which we call a population.\nTo each population we associate a symmetric rational pseudo-differential\noperator $\\mathcal R$. Under some technical assumptions, we show that the\nsuperkernel $W$ of $\\mathcal R$ is a self-dual superspace of rational\nfunctions, and the population is in a canonical bijection with the variety of\nisotropic full superflags in $W$ and with the set of symmetric complete\nfactorizations of $\\mathcal R$. In particular, our results apply to the case of\neven Lie algebras of type D${}_m$ corresponding to\n$\\mathfrak{osp}_{2m|0}=\\mathfrak{so}_{2m}$.\n"}
+{"abstract": "  Astounding results from Transformer models on natural language tasks have\nintrigued the vision community to study their application to computer vision\nproblems. Among their salient benefits, Transformers enable modeling long\ndependencies between input sequence elements and support parallel processing of\nsequence as compared to recurrent networks e.g., Long short-term memory (LSTM).\nDifferent from convolutional networks, Transformers require minimal inductive\nbiases for their design and are naturally suited as set-functions. Furthermore,\nthe straightforward design of Transformers allows processing multiple\nmodalities (e.g., images, videos, text and speech) using similar processing\nblocks and demonstrates excellent scalability to very large capacity networks\nand huge datasets. These strengths have led to exciting progress on a number of\nvision tasks using Transformer networks. This survey aims to provide a\ncomprehensive overview of the Transformer models in the computer vision\ndiscipline. We start with an introduction to fundamental concepts behind the\nsuccess of Transformers i.e., self-attention, large-scale pre-training, and\nbidirectional encoding. We then cover extensive applications of transformers in\nvision including popular recognition tasks (e.g., image classification, object\ndetection, action recognition, and segmentation), generative modeling,\nmulti-modal tasks (e.g., visual-question answering, visual reasoning, and\nvisual grounding), video processing (e.g., activity recognition, video\nforecasting), low-level vision (e.g., image super-resolution, image\nenhancement, and colorization) and 3D analysis (e.g., point cloud\nclassification and segmentation). We compare the respective advantages and\nlimitations of popular techniques both in terms of architectural design and\ntheir experimental value. Finally, we provide an analysis on open research\ndirections and possible future works.\n"}
+{"abstract": "  Volcanic ash clouds often become multilayered and thin with distance from the\nvent. We explore one mechanism for development of this layered structure. We\nreview data on the characteristics of turbulence layering in the free\natmosphere, as well as examples of observations of layered clouds both\nnear-vent and distally. We then explore and contrast the output of volcanic ash\ntransport and dispersal models with models that explicitly use the observed\nlayered structure of atmospheric turbulence. The results suggest that the\nalternation of turbulent and quiescent atmospheric layers provides one\nmechanism for development of multilayered ash clouds by modulating the manner\nin which settling occurs.\n"}
+{"abstract": "  Differences in gait patterns of children with Duchenne muscular dystrophy\n(DMD) and typically developing (TD) peers are visible to the eye, but\nquantification of those differences outside of the gait laboratory has been\nelusive. We measured vertical, mediolateral, and anteroposterior acceleration\nusing a waist-worn iPhone accelerometer during ambulation across a typical\nrange of velocities. Six TD and six DMD children from 3-15 years of age\nunderwent seven walking/running tasks, including five 25m walk/run tests at a\nslow walk to running speeds, a 6-minute walk test (6MWT), and a\n100-meter-run/walk (100MRW). We extracted temporospatial clinical gait features\n(CFs) and applied multiple Artificial Intelligence (AI) tools to differentiate\nbetween DMD and TD control children using extracted features and raw data.\nExtracted CFs showed reduced step length and a greater mediolateral component\nof total power (TP) consistent with shorter strides and Trendelenberg-like gait\ncommonly observed in DMD. AI methods using CFs and raw data varied\nineffectiveness at differentiating between DMD and TD controls at different\nspeeds, with an accuracy of some methods exceeding 91%. We demonstrate that by\nusing AI tools with accelerometer data from a consumer-level smartphone, we can\nidentify DMD gait disturbance in toddlers to early teens.\n"}
+{"abstract": "  Detecting changes in COVID-19 disease transmission over time is a key\nindicator of epidemic growth.Near real-time monitoring of the pandemic growth\nis crucial for policy makers and public health officials who need to make\ninformed decisions about whether to enforce lockdowns or allow certain\nactivities. The effective reproduction number Rt is the standard index used in\nmany countries for this goal. However, it is known that due to the delays\nbetween infection and case registration, its use for decision making is\nsomewhat limited. In this paper a near real-time COVINDEX is proposed for\nmonitoring the evolution of the pandemic. The index is computed from\npredictions obtained from a GAM beta regression for modelling the test positive\nrate as a function of time. The proposal is illustrated using data on COVID-19\npandemic in Italy and compared with Rt. A simple chart is also proposed for\nmonitoring local and national outbreaks by policy makers and public health\nofficials.\n"}
+{"abstract": "  The dynamics of cross-diffusion models leads to a high computational\ncomplexity for implicit difference schemes, turning them unsuitable for tasks\nthat require results in real-time. We propose the use of two operator splitting\nschemes for nonlinear cross-diffusion processes in order to lower the\ncomputational load, and establish their stability properties using discrete\n$L^2$ energy methods. Furthermore, by attaining a stable factorization of the\nsystem matrix as a forward-backward pass, corresponding to the Thomas algorithm\nfor self-diffusion processes, we show that the use of implicit cross-diffusion\ncan be competitive in terms of execution time, widening the range of viable\ncross-diffusion coefficients for \\textit{on-the-fly} applications.\n"}
+{"abstract": "  Classical supervised methods commonly used often suffer from the requirement\nof an abudant number of training samples and are unable to generalize on unseen\ndatasets. As a result, the broader application of any trained model is very\nlimited in clinical settings. However, few-shot approaches can minimize the\nneed for enormous reliable ground truth labels that are both labor intensive\nand expensive. To this end, we propose to exploit an optimization-based\nimplicit model agnostic meta-learning {iMAML} algorithm in a few-shot setting\nfor medical image segmentation. Our approach can leverage the learned weights\nfrom a diverse set of training samples and can be deployed on a new unseen\ndataset. We show that unlike classical few-shot learning approaches, our method\nhas improved generalization capability. To our knowledge, this is the first\nwork that exploits iMAML for medical image segmentation. Our quantitative\nresults on publicly available skin and polyp datasets show that the proposed\nmethod outperforms the naive supervised baseline model and two recent few-shot\nsegmentation approaches by large margins.\n"}
+{"abstract": "  The non-Debye, \\textit{i.e.,} non-exponential, behavior characterizes a large\nplethora of dielectric relaxation phenomena. Attempts to find their theoretical\nexplanation are dominated either by considerations rooted in the stochastic\nprocesses methodology or by the so-called \\textsl{fractional dynamics} based on\nequations involving fractional derivatives which mimic the non-local time\nevolution and as such may be interpreted as describing memory effects. Using\nthe recent results coming from the stochastic approach we link memory functions\nwith the Laplace (characteristic) exponents of infinitely divisible probability\ndistributions and show how to relate the latter with experimentally measurable\nspectral functions characterizing relaxation in the frequency domain. This\nenables us to incorporate phenomenological knowledge into the evolution laws.\nTo illustrate our approach we consider the standard Havriliak-Negami and\nJurlewicz-Weron-Stanislavsky models for which we derive well-defined evolution\nequations. Merging stochastic and fractional dynamics approaches sheds also new\nlight on the analysis of relaxation phenomena which description needs going\nbeyond using the single evolution pattern. We determine sufficient conditions\nunder which such description is consistent with general requirements of our\napproach.\n"}
+{"abstract": "  Context: Individuals' personality traits have been shown to influence their\nbehavior during team work. In particular, positive group attitudes are said to\nbe essential for distributed and global software development efforts where\ncollaboration is critical to project success. Objective: Given this, we have\nsought to study the influence of global software practitioners' personality\nprofiles from a psycholinguistic perspective. Method: Artifacts from ten teams\nwere selected from the IBM Rational Jazz repository and mined. We employed\nsocial network analysis (SNA) techniques to identify and group practitioners\ninto two clusters based on the numbers of messages they communicated, Top\nMembers and Others, and used standard statistical techniques to assess\npractitioners' engagement in task changes associated with work items. We then\nperformed psycholinguistic analysis on practitioners' messages using linguistic\ndimensions of the LIWC tool that had been previously correlated with the Big\nFive personality profiles. Results: For our sample of 146 practitioners, we\nfound that the Top Members demonstrated more openness to experience than the\nOther practitioners. Additionally, practitioners involved in usability-related\ntasks were found to be highly extroverted, and coders were most neurotic and\nconscientious. Conclusion: High levels of organizational and inter-personal\nskills may be useful for those operating in distributed settings, and\npersonality diversity is likely to boost team performance.\n"}
+{"abstract": "  Electron dynamics in water are of fundamental importance for a broad range of\nphenomena, but their real-time study faces numerous conceptual and\nmethodological challenges. Here, we introduce attosecond size-resolved cluster\nspectroscopy and build up a molecular-level understanding of the attosecond\nelectron dynamics in water. We measure the effect that the addition of single\nwater molecules has on the photoionization time delays of water clusters. We\nfind a continuous increase of the delay for clusters containing up to 4-5\nmolecules and little change towards larger clusters. We show that these delays\nare proportional to the spatial extension of the created electron hole, which\nfirst increases with cluster size and then partially localizes through the\nonset of structural disorder that is characteristic of large clusters and bulk\nliquid water. These results establish a previously unknown sensitivity of\nphotoionization delays to electron-hole delocalization and reveal a direct link\nbetween electronic structure and attosecond photoemission dynamics. Our results\noffer novel perspectives for studying electron/hole delocalization and its\nattosecond dynamics.\n"}
+{"abstract": "  General circulation models are essential tools in weather and hydrodynamic\nsimulation. They solve discretized, complex physical equations in order to\ncompute evolutionary states of dynamical systems, such as the hydrodynamics of\na lake. However, high-resolution numerical solutions using such models are\nextremely computational and time consuming, often requiring a high performance\ncomputing architecture to be executed satisfactorily. Machine learning\n(ML)-based low-dimensional surrogate models are a promising alternative to\nspeed up these simulations without undermining the quality of predictions. In\nthis work, we develop two examples of fast, reliable, low-dimensional surrogate\nmodels to produce a 36 hour forecast of the depth-averaged hydrodynamics at\nLake George NY, USA. Our ML approach uses two widespread artificial neural\nnetwork (ANN) architectures: fully connected neural networks and long\nshort-term memory. These ANN architectures are first validated in the\ndeterministic and chaotic regimes of the Lorenz system and then combined with\nproper orthogonal decomposition (to reduce the dimensionality of the incoming\ninput data) to emulate the depth-averaged hydrodynamics of a flow simulator\ncalled SUNTANS. Results show the ANN-based reduced order models have promising\naccuracy levels (within 6% of the prediction range) and advocate for further\ninvestigation into hydrodynamic applications.\n"}
+{"abstract": "  Ground surface detection in point cloud is widely used as a key module in\nautonomous driving systems. Different from previous approaches which are mostly\ndeveloped for lidars with high beam resolution, e.g. Velodyne HDL-64, this\npaper proposes ground detection techniques applicable to much sparser point\ncloud captured by lidars with low beam resolution, e.g. Velodyne VLP-16. The\napproach is based on the RANSAC scheme of plane fitting. Inlier verification\nfor plane hypotheses is enhanced by exploiting the point-wise tangent, which is\na local feature available to compute regardless of the density of lidar beams.\nGround surface which is not perfectly planar is fitted by multiple\n(specifically 4 in our implementation) disjoint plane regions. By assuming\nthese plane regions to be rectanglar and exploiting the integral image\ntechnique, our approach approximately finds the optimal region partition and\nplane hypotheses under the RANSAC scheme with real-time computational\ncomplexity.\n"}
+{"abstract": "  A large eddy simulation (LES) study of the flow around a 1/4 scale squareback\nAhmed body at $Re_H=33,333$ is presented. The study consists of both\nwall-resolved (WRLES) and wall-modelled (WMLES) simulations, and investigates\nthe bimodal switching of the wake between different horizontal positions.\nWithin a non-dimensional time-window of 1050 convective flow units, both WRLES\nand WMLES simulations, for which only the near-wall region of the turbulent\nboundary layer is treated in a Reynolds-averaged sense, are able to capture\nhorizontal (spanwise) shifts in the wake's cross-stream orientation.\nEquilibrium wall-models in the form of Spalding's law and the log-law of the\nwall are successfully used. Once these wall-models are, however, applied to a\nvery coarse near-wall WMLES mesh, in which a portion of the turbulent boundary\nlayer's outer region dynamics is treated in a Reynolds-averaged manner as well,\nlarge-scale horizontal shifts in the wake's orientation are no longer detected.\nThis suggests larger-scale flow structures found within the turbulent boundary\nlayer's outer domain are responsible for generating the critical amount of flow\nintermittency needed to trigger a bimodal switching event. By looking at mean\nflow structures, instantaneous flow features and their associated turbulent\nkinetic energy (TKE) production, it becomes clear that the front separation\nbubbles just aft of the Ahmed body nose generate high levels of TKE through the\nshedding of large hairpin vortices. Only in the reference WRLES and\n(relatively) fine near-wall mesh WMLES simulations are these features present,\nexemplifying their importance in triggering a bimodal event. This motivates\nstudies on the suppression of wake bimodality by acting upon the front\nseparation bubbles.\n"}
+{"abstract": "  We show under some natural smoothness assumptions that pure in-plane drill\nrotations as deformation mappings of a $C^2$-smooth regular shell surface to\nanother one parametrized over the same domain are impossible provided that the\nrotations are fixed at a portion of the boundary. Put otherwise, if the tangent\nvectors of the new surface are obtained locally by only rotating the given\ntangent vectors, and if these rotations have a rotation axis which coincides\neverywhere with the normal of the initial surface, then the two surfaces are\nequal provided they coincide at a portion of the boundary. In the language of\ndifferential geometry of surfaces we show that any isometry which leaves\nnormals invariant and which coincides with the given surface at a portion of\nthe boundary, is the identity mapping.\n"}
+{"abstract": "  Digitalization opens up new opportunities in the collection, analysis, and\npresentation of data which can contribute to the achievement of the 2030 Agenda\nand its Sustainable Development Goals (SDGs). In particular, the access to and\ncontrol of environmental and geospatial data is fundamental to identify and\nunderstand global issues and trends. Also immediate crises such as the COVID-19\npandemic demonstrate the importance of accurate health data such as infection\nstatistics and the relevance of digital tools like video conferencing\nplatforms. However, today much of the data is collected and processed by\nprivate actors. Thus, governments and researchers depend on data platforms and\nproprietary systems of big tech companies such as Google or Microsoft. The\nmarket capitalization of the seven largest US and Chinese big tech companies\nhas grown to 8.7tn USD in recent years, about twice the size of Germany's gross\ndomestic product (GDP). Therefore, their market power is enormous, allowing\nthem to dictate many rules of the digital space and even interfere with\nlegislations. Based on a literature review and nine expert interviews this\nstudy presents a framework that identifies the risks and consequences along the\nworkflow of collecting, processing, storing, using of data. It also includes\nsolutions that governmental and multilateral actors can strive for to alleviate\nthe risks. Fundamental to this framework is the novel concept of \"data\ncolonialism\" which describes today's trend of private companies appropriating\nthe digital sphere. Historically, colonial nations used to grab indigenous land\nand exploit the cheap labor of slave workers. In a similar way, today's big\ntech corporations use cheap data of their users to produce valuable services\nand thus create enormous market power.\n"}
+{"abstract": "  To reveal the detail of the internal structure, the relationship between\nchromospheric activity and the Rossby number, N_R (= rotational period P /\nconvective turnover time tau_c), has been extensively examined for\nmain-sequence stars. The goal of our work is to apply the same methods to\npre-main-sequence (PMS) stars and identify the appropriate model of tau_c for\nthem. Yamashita et al. (2020) investigated the relationship between N_R and\nstrengths of the Ca II infrared triplet (IRT; lambda 8498, 8542, 8662 A)\nemission lines of 60 PMS stars. Their equivalent widths are converted into the\nemission line to stellar bolometric luminosity ratio (R'). The 54 PMS stars\nhave N_R < 10^{-1.0} and show R' \\sim 10^{-4.2} as large as the maximum R' of\nthe zero-age main-sequence (ZAMS) stars. However, because all R' was saturated\nagainst N_R, it was not possible to estimate the appropriate tau_c model for\nthe PMS stars. We noticed that Mg I emission lines at 8808 A is an optically\nthin chromospheric line, appropriate for determination of the adequate tau_c\nfor PMS stars. Using the archive data of the Anglo-Australian Telescope\n(AAT)/the University College London Echelle Spectrograph (UCLES), we\ninvestigated the Mg I line of 52 ZAMS stars. After subtracting photospheric\nabsorption component, the Mg I line is detected as an emission line in 45 ZAMS\nstars, whose R' is between 10^{-5.9} and 10^{-4.1}. The Mg I line is not\nsaturated yet in \"the saturated regime for the Ca II emission lines\", i.e.\n10^{-1.6} < N_R < 10^{-0.8}. Therefore, the adequate tau_c for PMS stars can be\ndetermined by measuring of their R' values.\n"}
+{"abstract": "  Time series imputation is a fundamental task for understanding time series\nwith missing data. Existing methods either do not directly handle\nirregularly-sampled data or degrade severely with sparsely observed data. In\nthis work, we reformulate time series as permutation-equivariant sets and\npropose a novel imputation model NRTSI that does not impose any recurrent\nstructures. Taking advantage of the permutation equivariant formulation, we\ndesign a principled and efficient hierarchical imputation procedure. In\naddition, NRTSI can directly handle irregularly-sampled time series, perform\nmultiple-mode stochastic imputation, and handle data with partially observed\ndimensions. Empirically, we show that NRTSI achieves state-of-the-art\nperformance across a wide range of time series imputation benchmarks.\n"}
+{"abstract": "  The point-splitting renormalization method offers a prescription to calculate\nfinite expectation values of quadratic operators constructed from quantum\nfields in a general curved spacetime. It has been recently shown by Levi and\nOri that when the background metric possesses an isometry, like stationary or\nspherically symmetric black holes, the method can be upgraded into a pragmatic\nprocedure of renormalization that produces efficient numerical calculations. In\nthis note we show that when the background enjoys three-dimensional spatial\nsymmetries, like homogeneous expanding universes, the above pragmatic\nregularization technique reduces to the well established adiabatic\nregularization method.\n"}
+{"abstract": "  Merging beliefs depends on the relative reliability of their sources. When\nunknown, assuming equal reliability is unwarranted. The solution proposed in\nthis article is that every reliability profile is possible, and only what holds\naccording to all is accepted. Alternatively, one source is completely reliable,\nbut which one is unknown. These two cases motivate two existing forms of\nmerging: maxcons-based merging and arbitration.\n"}
+{"abstract": "  Pest and disease control plays a key role in agriculture since the damage\ncaused by these agents are responsible for a huge economic loss every year.\nBased on this assumption, we create an algorithm capable of detecting rust\n(Hemileia vastatrix) and leaf miner (Leucoptera coffeella) in coffee leaves\n(Coffea arabica) and quantify disease severity using a mobile application as a\nhigh-level interface for the model inferences. We used different convolutional\nneural network architectures to create the object detector, besides the OpenCV\nlibrary, k-means, and three treatments: the RGB and value to quantification,\nand the AFSoft software, in addition to the analysis of variance, where we\ncompare the three methods. The results show an average precision of 81,5% in\nthe detection and that there was no significant statistical difference between\ntreatments to quantify the severity of coffee leaves, proposing a\ncomputationally less costly method. The application, together with the trained\nmodel, can detect the pest and disease over different image conditions and\ninfection stages and also estimate the disease infection stage.\n"}
+{"abstract": "  Next generation mobile networks need to expand towards uncharted territories\nin order to enable the digital transformation of society. In this context,\naerial devices such as unmanned aerial vehicles (UAVs) are expected to address\nthis gap in hard-to-reach locations. However, limited battery-life is an\nobstacle for the successful spread of such solutions. Reconfigurable\nintelligent surfaces (RISs) represent a promising solution addressing this\nchallenge since on-board passive and lightweight controllable devices can\nefficiently reflect the signal propagation from the ground BSs towards specific\ntarget areas. In this paper, we focus on air-to-ground networks where UAVs\nequipped with RIS can fly over selected areas to provide connectivity. In\nparticular, we study how to optimally compensate flight effects and propose\nRiFe as well as its practical implementation Fair-RiFe that automatically\nconfigure RIS parameters accounting for undesired UAV oscillations due to\nadverse atmospheric conditions. Our results show that both algorithms provide\nrobustness and reliability while outperforming state-of-the-art solutions in\nthe multiple conditions studied.\n"}
+{"abstract": "  We experimentally studied a clusterization process in a system of polyamide\ntracers that are used for visualizing the flow of liquids on their surface. It\nwas shown that in a surface structure system appearing on the water surface a\nPareto distribution is formed for normalized cluster density and it is well\ndescribed by a C/x^(-n) power-law function. One can suggest that the growth of\na surface structure is defined by the action of surface tension forces, and the\nnumber of surface structures decreases exponentially, while maintaining their\ntotal surface area. We show experimentally the significant role of background\nliquid flows on the surface in the clusterization process\n"}
+{"abstract": "  Fault Localization (FL) is an important first step in software debugging and\nis mostly manual in the current practice. Many methods have been proposed over\nyears to automate the FL process, including information retrieval (IR)-based\ntechniques. These methods localize the fault based on the similarity of the\nreported bug report and the source code. Newer variations of IR-based FL (IRFL)\ntechniques also look into the history of bug reports and leverage them during\nthe localization. However, all existing IRFL techniques limit themselves to the\ncurrent project's data (local data). In this study, we introduce Globug, which\nis an IRFL framework consisting of methods that use models pre-trained on the\nglobal data (extracted from open-source benchmark projects). In Globug, we\ninvestigate two heuristics: a) the effect of global data on a state-of-the-art\nIR-FL technique, namely BugLocator, and b) the application of a Word Embedding\ntechnique (Doc2Vec) together with global data. Our large scale experiment on 51\nsoftware projects shows that using global data improves BugLocator on average\n6.6% and 4.8% in terms of MRR (Mean Reciprocal Rank) and MAP (Mean Average\nPrecision), with over 14% in a majority (64% and 54% in terms of MRR and MAP,\nrespectively) of the cases. This amount of improvement is significant compared\nto the improvement rates that five other state-of-the-art IRFL tools provide\nover BugLocator. In addition, training the models globally is a one-time\noffline task with no overhead on BugLocator's run-time fault localization. Our\nstudy, however, shows that a Word Embedding-based global solution did not\nfurther improve the results.\n"}
+{"abstract": "  We theoretically investigate high harmonic generation (HHG) from silicon thin\nfilms with thicknesses from a few atomic layers to a few hundreds of\nnanometers, to determine the most efficient thickness for producing intense HHG\nin the reflected and transmitted pulses. For this purpose, we employ a few\ntheoretical and computational methods. The most sophisticated method is the ab\ninitio time-dependent density functional theory coupled with the Maxwell\nequations in a common spatial resolution. This enables us to explore such\neffects as the surface electronic structure and light propagation, as well as\nelectronic motion in the energy band in a unified manner. We also utilize a\nmultiscale method that is applicable to thicker films. Two-dimensional\napproximation is introduced to obtain an intuitive understanding of the\nthickness dependence of HHG. From these ab initio calculations, we find that\nthe HHG signals are the strongest in films with thicknesses of 2-15 nm, which\nis determined by the bulk conductivity of silicon. We also find that the HHG\nsignals in the reflected and transmitted pulses are identical in such thin\nfilms. In films whose thicknesses are comparable to the wavelength in the\nmedium, the intensity of HHG signals in the reflected (transmitted) pulse is\nfound to correlate with the magnitude of the electric field at the front (back)\nsurface of the thin film.\n"}
+{"abstract": "  Time series data play an important role in many applications and their\nanalysis reveals crucial information for understanding the underlying\nprocesses. Among the many time series learning tasks of great importance, we\nhere focus on semi-supervised learning based on a graph representation of the\ndata. Two main aspects are involved in this task. A suitable distance measure\nto evaluate the similarities between time series, and a learning method to make\npredictions based on these distances. However, the relationship between the two\naspects has never been studied systematically in the context of graph-based\nlearning. We describe four different distance measures, including (Soft) DTW\nand MPDist, a distance measure based on the Matrix Profile, as well as four\nsuccessful semi-supervised learning methods, including the graph Allen--Cahn\nmethod and a Graph Convolutional Neural Network. We then compare the\nperformance of the algorithms on binary classification data sets. In our\nfindings we compare the chosen graph-based methods using all distance measures\nand observe that the results vary strongly with respect to the accuracy. As\npredicted by the ``no free lunch'' theorem, no clear best combination to employ\nin all cases is found. Our study provides a reproducible framework for future\nwork in the direction of semi-supervised learning for time series with a focus\non graph representations.\n"}
+{"abstract": "  It is widely perceived that leveraging the success of modern machine learning\ntechniques to mobile devices and wireless networks has the potential of\nenabling important new services. This, however, poses significant challenges,\nessentially due to that both data and processing power are highly distributed\nin a wireless network. In this paper, we develop a learning algorithm and an\narchitecture that make use of multiple data streams and processing units, not\nonly during the training phase but also during the inference phase. In\nparticular, the analysis reveals how inference propagates and fuses across a\nnetwork. We study the design criterion of our proposed method and its bandwidth\nrequirements. Also, we discuss implementation aspects using neural networks in\ntypical wireless radio access; and provide experiments that illustrate benefits\nover state-of-the-art techniques.\n"}
+{"abstract": "  In this paper, we study the nonlinear inverse problem of estimating the\nspectrum of a system matrix, that drives a finite-dimensional affine dynamical\nsystem, from partial observations of a single trajectory data. In the noiseless\ncase, we prove an annihilating polynomial of the system matrix, whose roots are\na subset of the spectrum, can be uniquely determined from data. We then study\nwhich eigenvalues of the system matrix can be recovered and derive various\nsufficient and necessary conditions to characterize the relationship between\nthe recoverability of each eigenvalue and the observation locations. We propose\nvarious reconstruction algorithms, with theoretical guarantees, generalizing\nthe classical Prony method, ESPIRIT, and matrix pencil method. We test the\nalgorithms over a variety of examples with applications to graph signal\nprocessing, disease modeling and a real-human motion dataset. The numerical\nresults validate our theoretical results and demonstrate the effectiveness of\nthe proposed algorithms, even when the data did not follow an exact linear\ndynamical system.\n"}
+{"abstract": "  The interplay between strong electron correlation and band topology is at the\nforefront of condensed matter research. As a direct consequence of correlation,\nmagnetism enriches topological phases and also has promising functional\napplications. However, the influence of topology on magnetism remains unclear,\nand the main research effort has been limited to ground state magnetic orders.\nHere we report a novel order above the magnetic transition temperature in\nmagnetic Weyl semimetal (WSM) CeAlGe. Such order shows a number of anomalies in\nelectrical and thermal transport, and neutron scattering measurements. We\nattribute this order to the coupling of Weyl fermions and magnetic fluctuations\noriginating from a three-dimensional Seiberg-Witten monopole, which\nqualitatively agrees well with the observations. Our work reveals a prominent\nrole topology may play in tailoring electron correlation beyond ground state\nordering, and offers a new avenue to investigate emergent electronic properties\nin magnetic topological materials.\n"}
+{"abstract": "  Modifying Serre's arguments, Cojocaru showed that for any elliptic curve\n$E/\\mathbb{Q}$ and any integer $m$ co-prime to $30,$ the induced Galois\nrepresentation $$\\rho_{E,m}: \\text{Gal}(\\overline{\\mathbb{Q}}/\\mathbb{Q})\n\\longrightarrow \\text{GL}_{2}(\\mathbb{Z}/m\\mathbb{Z})$$ is surjective if and\nonly if $\\rho_{E,\\ell}$ is surjective for any prime $\\ell|m.$ In this article,\nwe shall first study an analog of the same problem over arbitrary number\nfields. Later, we shall extend this to the product of elliptic curves and\nabelian varieties. At the end we shall discuss this local-global phenomenon for\na larger family of linear algebraic groups.\n"}
+{"abstract": "  In a recent paper on a study of the Sylow 2-subgroups of the symmetric group\nwith 2^n elements it has been show that the growth of the first (n-2)\nconsecutive indices of a certain normalizer chain is linked to the sequence of\npartitions of integers into distinct parts. Unrefinable partitions into\ndistinct parts are those in which no part x can be replaced with integers whose\nsum is x obtaining a new partition into distinct parts. We prove here that the\n(n-1)-th index of the previously mentioned chain is related to the number of\nunrefinable partitions into distinct parts satisfying a condition on the\nminimal excludant.\n"}
+{"abstract": "  It is known that the ordered Bell numbers count all the ordered partitions of\nthe set $[n]=\\{1,2,\\dots,n\\}$. In this paper, we introduce the deranged Bell\nnumbers that count the total number of deranged partitions of $[n]$. We first\nstudy the classical properties of these numbers (generating function, explicit\nformula, convolutions, etc.), we then present an asymptotic behavior of the\nderanged Bell numbers. Finally, we give some brief results for their\n$r$-versions.\n"}
+{"abstract": "  We propose methods that augment existing numerical schemes for the simulation\nof hyperbolic balance laws with Dirichlet boundary conditions to allow for the\nsimulation of a broad class of differential algebraic conditions. Our approach\nis similar to that of Thompson (1987), where the boundary values were simulated\nby combining characteristic equations with the time derivative of the algebraic\nconditions, but differs in two important regards. Firstly, when the boundary is\na characteristic of one of the fields Thompson's method can fail to produce\nreasonable values. We propose a method of combining the characteristic\nequations with extrapolation which ensures convergence. Secondly, the\napplication of algebraic conditions can suffer from $O(1)$ drift-off error, and\nwe discuss projective time-stepping algorithms designed to converge for this\ntype of system. Test problems for the shallow water equations are presented to\ndemonstrate the result of simulating with and without the modifications\ndiscussed, illustrating their necessity for certain problems.\n"}
+{"abstract": "  This survey presents an overview of integrating prior knowledge into machine\nlearning systems in order to improve explainability. The complexity of machine\nlearning models has elicited research to make them more explainable. However,\nmost explainability methods cannot provide insight beyond the given data,\nrequiring additional information about the context. We propose to harness prior\nknowledge to improve upon the explanation capabilities of machine learning\nmodels. In this paper, we present a categorization of current research into\nthree main categories which either integrate knowledge into the machine\nlearning pipeline, into the explainability method or derive knowledge from\nexplanations. To classify the papers, we build upon the existing taxonomy of\ninformed machine learning and extend it from the perspective of explainability.\nWe conclude with open challenges and research directions.\n"}
+{"abstract": "  Retrieving keywords (bidwords) with the same intent as query, referred to as\nclose variant keywords, is of prime importance for effective targeted search\nadvertising. For head and torso search queries, sponsored search engines use a\nhuge repository of same intent queries and keywords, mined ahead of time.\nOnline, this repository is used to rewrite the query and then lookup the\nrewrite in a repository of bid keywords contributing to significant revenue.\nRecently generative retrieval models have been shown to be effective at the\ntask of generating such query rewrites. We observe two main limitations of such\ngenerative models. First, rewrites generated by these models exhibit low\nlexical diversity, and hence the rewrites fail to retrieve relevant keywords\nthat have diverse linguistic variations. Second, there is a misalignment\nbetween the training objective - the likelihood of training data, v/s what we\ndesire - improved quality and coverage of rewrites. In this work, we introduce\nCLOVER, a framework to generate both high-quality and diverse rewrites by\noptimizing for human assessment of rewrite quality using our diversity-driven\nreinforcement learning algorithm. We use an evaluation model, trained to\npredict human judgments, as the reward function to finetune the generation\npolicy. We empirically show the effectiveness of our proposed approach through\noffline experiments on search queries across geographies spanning three major\nlanguages. We also perform online A/B experiments on Bing, a large commercial\nsearch engine, which shows (i) better user engagement with an average increase\nin clicks by 12.83% accompanied with an average defect reduction by 13.97%, and\n(ii) improved revenue by 21.29%.\n"}
+{"abstract": "  Supervised deep convolutional neural networks (DCNNs) are currently one of\nthe best computational models that can explain how the primate ventral visual\nstream solves object recognition. However, embodied cognition has not been\nconsidered in the existing visual processing models. From the ecological\nstandpoint, humans learn to recognize objects by interacting with them,\nallowing better classification, specialization, and generalization. Here, we\nask if computational models under the embodied learning framework can explain\nmechanisms underlying object recognition in the primate visual system better\nthan the existing supervised models? To address this question, we use\nreinforcement learning to train neural network models to play a 3D computer\ngame and we find that these reinforcement learning models achieve neural\nresponse prediction accuracy scores in the early visual areas (e.g., V1 and V2)\nin the levels that are comparable to those accomplished by the supervised\nneural network model. In contrast, the supervised neural network models yield\nbetter neural response predictions in the higher visual areas, compared to the\nreinforcement learning models. Our preliminary results suggest the future\ndirection of visual neuroscience in which deep reinforcement learning should be\nincluded to fill the missing embodiment concept.\n"}
+{"abstract": "  Estimation of a precision matrix (i.e., inverse covariance matrix) is widely\nused to exploit conditional independence among continuous variables. The\ninfluence of abnormal observations is exacerbated in a high dimensional setting\nas the dimensionality increases. In this work, we propose robust estimation of\nthe inverse covariance matrix based on an $l_1$ regularized objective function\nwith a weighted sample covariance matrix. The robustness of the proposed\nobjective function can be justified by a nonparametric technique of the\nintegrated squared error criterion. To address the non-convexity of the\nobjective function, we develop an efficient algorithm in a similar spirit of\nmajorization-minimization. Asymptotic consistency of the proposed estimator is\nalso established. The performance of the proposed method is compared with\nseveral existing approaches via numerical simulations. We further demonstrate\nthe merits of the proposed method with application in genetic network\ninference.\n"}
+{"abstract": "  Motivated by the recently observed unconventional Hall effect in ultra-thin\nfilms of ferromagnetic SrRuO$_3$ (SRO) we investigate the effect of\nstrain-induced oxygen octahedral distortion in the electronic structure and\nanomalous Hall response of the SRO ultra-thin films by virtue of density\nfunctional theory calculations. Our findings reveal that the ferromagnetic SRO\nfilms grown on SrTiO$_3$ (in-plane strain of $-$0.47$\\%$) have an orthorhombic\n(both tilting and rotation) distorted structure and with an increasing amount\nof substrate-induced compressive strain the octahedral tilting angle is found\nto be suppressed gradually, with SRO films grown on NdGaO$_3$ (in-plane strain\nof $-$1.7$\\%$) stabilized in the tetragonal distorted structure (with zero\ntilting). Our Berry curvature calculations predict a positive value of the\nanomalous Hall conductivity of $+$76\\,S/cm at $-$1.7$\\%$ strain, whereas it is\nfound to be negative ($-$156\\,S/cm) at $-$0.47$\\%$ strain. We attribute the\nfound behavior of the anomalous Hall effect to the nodal point dynamics in the\nelectronic structure arising in response to tailoring the oxygen octahedral\ndistortion driven by the substrate-induced strain. We also calculate\nstrain-mediated anomalous Hall conductivity as a function of reduced\nmagnetization obtained by scaling down the magnitude of the exchange field\ninside Ru atoms finding good qualitative agreement with experimental\nobservations, which indicates a strong impact of longitudinal thermal\nfluctuations of Ru spin moments on the anomalous Hall effect in this system.\n"}
+{"abstract": "  Widely deployed deep neural network (DNN) models have been proven to be\nvulnerable to adversarial perturbations in many applications (e.g., image,\naudio and text classifications). To date, there are only a few adversarial\nperturbations proposed to deviate the DNN models in video recognition systems\nby simply injecting 2D perturbations into video frames. However, such attacks\nmay overly perturb the videos without learning the spatio-temporal features\n(across temporal frames), which are commonly extracted by DNN models for video\nrecognition. To our best knowledge, we propose the first black-box attack\nframework that generates universal 3-dimensional (U3D) perturbations to subvert\na variety of video recognition systems. U3D has many advantages, such as (1) as\nthe transfer-based attack, U3D can universally attack multiple DNN models for\nvideo recognition without accessing to the target DNN model; (2) the high\ntransferability of U3D makes such universal black-box attack easy-to-launch,\nwhich can be further enhanced by integrating queries over the target model when\nnecessary; (3) U3D ensures human-imperceptibility; (4) U3D can bypass the\nexisting state-of-the-art defense schemes; (5) U3D can be efficiently generated\nwith a few pre-learned parameters, and then immediately injected to attack\nreal-time DNN-based video recognition systems. We have conducted extensive\nexperiments to evaluate U3D on multiple DNN models and three large-scale video\ndatasets. The experimental results demonstrate its superiority and\npracticality.\n"}
+{"abstract": "  We propose optical longitudinal conductivity as a realistic observable to\ndetect light-induced Floquet band gaps in graphene. These gaps manifest as\nresonant features in the conductivity, when resolved with respect to the\nprobing frequency and the driving field strength. We demonstrate these features\nvia a dissipative master equation approach which gives access to a frequency-\nand momentum-resolved electron distribution. This distribution follows the\nlight-induced Floquet-Bloch bands, resulting in a natural interpretation as\noccupations of these bands. Furthermore, we show that there are population\ninversions of the Floquet-Bloch bands at the band gaps for sufficiently strong\ndriving field strengths. This strongly reduces the conductivity at the\ncorresponding frequencies. Therefore our proposal puts forth not only an\nunambiguous demonstration of light-induced Floquet-Bloch bands, which advances\nthe field of Floquet engineering in solids, but also points out the control of\ntransport properties via light, that derives from the electron distribution on\nthese bands.\n"}
+{"abstract": "  We propose to predict the future trajectories of observed agents (e.g.,\npedestrians or vehicles) by estimating and using their goals at multiple time\nscales. We argue that the goal of a moving agent may change over time, and\nmodeling goals continuously provides more accurate and detailed information for\nfuture trajectory estimation. In this paper, we present a novel recurrent\nnetwork for trajectory prediction, called Stepwise Goal-Driven Network (SGNet).\nUnlike prior work that models only a single, long-term goal, SGNet estimates\nand uses goals at multiple temporal scales. In particular, the framework\nincorporates an encoder module that captures historical information, a stepwise\ngoal estimator that predicts successive goals into the future, and a decoder\nmodule that predicts future trajectory. We evaluate our model on three\nfirst-person traffic datasets (HEV-I, JAAD, and PIE) as well as on two bird's\neye view datasets (ETH and UCY), and show that our model outperforms the\nstate-of-the-art methods in terms of both average and final displacement errors\non all datasets. Code has been made available at:\nhttps://github.com/ChuhuaW/SGNet.pytorch.\n"}
+{"abstract": "  We construct an equivalence between the 2-categories VMonCat of rigid\nV-monoidal categories for a braided monoidal category V and VModTens of oplax\nbraided functors from V into the Drinfeld centers of ordinary rigid monoidal\ncategories. The 1-cells in each are the respective lax monoidal functors, and\nthe 2-cells are the respective monoidal natural transformations. Our proof also\ngives an equivalence in the case that we consider only strong monoidal 1-cells\non both sides. The 2-categories VMonCat and VModTens have G-graded analogues.\nWe also get an equivalence of 2-categories between G-extensions of some fixed\nV-monoidal category A, and G-extensions of some fixed V-module tensor category\n(A, F_A^Z).\n"}
+{"abstract": "  The Jiangmen Underground Neutrino Observatory (JUNO) is an experiment\ndesigned to study neutrino oscillations. Determination of neutrino mass\nordering and precise measurement of neutrino oscillation parameters $\\sin^2\n2\\theta_{12}$, $\\Delta m^2_{21}$ and $\\Delta m^2_{32}$ are the main goals of\nthe experiment. A rich physical program beyond the oscillation analysis is also\nforeseen. The ability to accurately reconstruct particle interaction events in\nJUNO is of great importance for the success of the experiment. In this work we\npresent a few machine learning approaches applied to the vertex and the energy\nreconstruction. Multiple models and architectures were compared and studied,\nincluding Boosted Decision Trees (BDT), Deep Neural Networks (DNN), a few kinds\nof Convolution Neural Networks (CNN), based on ResNet and VGG, and a Graph\nNeural Network based on DeepSphere. Based on a study, carried out using the\ndataset, generated by the official JUNO software, we demonstrate that machine\nlearning approaches achieve the necessary level of accuracy for reaching the\nphysical goals of JUNO: $\\sigma_E=3\\%$ at $E_\\text{vis}=1~\\text{MeV}$ for the\nenergy and $\\sigma_{x,y,z}=10~\\text{cm}$ at $E_\\text{vis}=1~\\text{MeV}$ for the\nposition.\n"}
+{"abstract": "  In temporal action localization methods, temporal downsampling operations are\nwidely used to extract proposal features, but they often lead to the aliasing\nproblem, due to lacking consideration of sampling rates. This paper aims to\nverify the existence of aliasing in TAL methods and investigate utilizing low\npass filters to solve this problem by inhibiting the high-frequency band.\nHowever, the high-frequency band usually contains large amounts of specific\ninformation, which is important for model inference. Therefore, it is necessary\nto make a tradeoff between anti-aliasing and reserving high-frequency\ninformation. To acquire optimal performance, this paper learns different cutoff\nfrequencies for different instances dynamically. This design can be plugged\ninto most existing temporal modeling programs requiring only one additional\ncutoff frequency parameter. Integrating low pass filters to the downsampling\noperations significantly improves the detection performance and achieves\ncomparable results on THUMOS'14, ActivityNet~1.3, and Charades datasets.\nExperiments demonstrate that anti-aliasing with low pass filters in TAL is\nadvantageous and efficient.\n"}
+{"abstract": "  3D Lidar imaging can be a challenging modality when using multiple\nwavelengths, or when imaging in high noise environments (e.g., imaging through\nobscurants). This paper presents a hierarchical Bayesian algorithm for the\nrobust reconstruction of multispectral single-photon Lidar data in such\nenvironments. The algorithm exploits multi-scale information to provide robust\ndepth and reflectivity estimates together with their uncertainties to help with\ndecision making. The proposed weight-based strategy allows the use of available\nguide information that can be obtained by using state-of-the-art learning based\nalgorithms. The proposed Bayesian model and its estimation algorithm are\nvalidated on both synthetic and real images showing competitive results\nregarding the quality of the inferences and the computational complexity when\ncompared to the state-of-the-art algorithms.\n"}
+{"abstract": "  Intrusion detection is an essential task in the cyber threat environment.\nMachine learning and deep learning techniques have been applied for intrusion\ndetection. However, most of the existing research focuses on the model work but\nignores the fact that poor data quality has a direct impact on the performance\nof a machine learning system. More attention should be paid to the data work\nwhen building a machine learning-based intrusion detection system. This article\nfirst summarizes existing machine learning-based intrusion detection systems\nand the datasets used for building these systems. Then the data preparation\nworkflow and quality requirements for intrusion detection are discussed. To\nfigure out how data and models affect machine learning performance, we\nconducted experiments on 11 HIDS datasets using seven machine learning models\nand three deep learning models. The experimental results show that BERT and GPT\nwere the best algorithms for HIDS on all of the datasets. However, the\nperformance on different datasets varies, indicating the differences between\nthe data quality of these datasets. We then evaluate the data quality of the 11\ndatasets based on quality dimensions proposed in this paper to determine the\nbest characteristics that a HIDS dataset should possess in order to yield the\nbest possible result. This research initiates a data quality perspective for\nresearchers and practitioners to improve the performance of machine\nlearning-based intrusion detection.\n"}
+{"abstract": "  Parallel tempering (PT) is a class of Markov chain Monte Carlo algorithms\nthat constructs a path of distributions annealing between a tractable reference\nand an intractable target, and then interchanges states along the path to\nimprove mixing in the target. The performance of PT depends on how quickly a\nsample from the reference distribution makes its way to the target, which in\nturn depends on the particular path of annealing distributions. However, past\nwork on PT has used only simple paths constructed from convex combinations of\nthe reference and target log-densities. This paper begins by demonstrating that\nthis path performs poorly in the setting where the reference and target are\nnearly mutually singular. To address this issue, we expand the framework of PT\nto general families of paths, formulate the choice of path as an optimization\nproblem that admits tractable gradient estimates, and propose a flexible new\nfamily of spline interpolation paths for use in practice. Theoretical and\nempirical results both demonstrate that our proposed methodology breaks\npreviously-established upper performance limits for traditional paths.\n"}
+{"abstract": "  How does one compute the Bondi mass on an arbitrary cut of null infinity\n$\\scri$ when it is not presented in a Bondi system? What then is the correct\ndefinition of the mass aspect? How does one normalise an asymptotic translation\ncomputed on a cut which is not equipped with the unit-sphere metric? These are\nquestions which need to be answered if one wants to calculate the Bondi-Sachs\nenergy-momentum for a space-time which has been determined numerically. Under\nsuch conditions there is not much control over the presentation of $\\scri$ so\nthat most of the available formulations of the Bondi energy-momentum simply do\nnot apply. The purpose of this article is to provide the necessary background\nfor a manifestly conformally invariant and gauge independent formulation of the\nBondi energy-momentum. To this end we introduce a conformally invariant version\nof the GHP formalism to rephrase all the well-known formulae. This leads us to\nnatural definitions for the space of asymptotic translations with its\nLorentzian metric, for the Bondi news and the mass-aspect. A major role in\nthese developments is played by the \"co-curvature\", a naturally appearing\nquantity closely related to the Gau{\\ss} curvature on a cut of~$\\scri$.\n"}
+{"abstract": "  The surface topography of diamond coatings strongly affects surface\nproperties such as adhesion, friction, wear, and biocompatibility. However, the\nunderstanding of multi-scale topography, and its effect on properties, has been\nhindered by conventional measurement methods, which capture only a single\nlength scale. Here, four different polycrystalline diamond coatings are\ncharacterized using transmission electron microscopy to assess the roughness\ndown to the sub-nanometer scale. Then these measurements are combined, using\nthe power spectral density (PSD), with conventional methods (stylus\nprofilometry and atomic force microscopy) to characterize all scales of\ntopography. The results demonstrate the critical importance of measuring\ntopography across all length scales, especially because their PSDs cross over\none another, such that a surface that is rougher at a larger scale may be\nsmoother at a smaller scale and vice versa. Furthermore, these measurements\nreveal the connection between multi-scale topography and grain size, with\ncharacteristic scaling behavior at and slightly below the mean grain size, and\nself-affine fractal-like roughness at other length scales. At small (subgrain)\nscales, unpolished surfaces exhibit a common form of residual roughness that is\nself-affine in nature but difficult to detect with conventional methods. This\napproach of capturing topography from the atomic- to the macro-scale is termed\ncomprehensive topography characterization, and all of the topography data from\nthese surfaces has been made available for further analysis by experimentalists\nand theoreticians. Scientifically, this investigation has identified four\ncharacteristic regions of topography scaling in polycrystalline diamond\nmaterials.\n"}
+{"abstract": "  In this article we analyse the oil-food price co-movement and its\ndeterminants in both time and frequency domains, using the wavelet analysis\napproach. Our results show that the significant local correlation between food\nand oil is only apparent. This is mainly due to the activity of commodity index\ninvestments and, to a lower extent, to the increasing demand from emerging\neconomies. Furthermore, we employ the wavelet entropy to assess the\npredictability of the time series under consideration. We find that some\nvariables share with both food and oil a similar predictability structure.\nThese variables are those that mostly co-move with both oil and food. Some\npolicy implications can be derived from our results, the most relevant being\nthat the activity of commodity index investments is able to increase\ncorrelation between food and oil. This activity generates highly integrated\nmarkets and an increasing risk of joint price movements which is potentially\ndangerous in periods of economic downturn and financial stress. In our work we\nsuggest that governments should also provide subsidy packages based on the\ncommodity traded in commodity indices to protect producers and consumers from\nadverse price movements due to financial activity rather than lack of supply or\ndemand.\n"}
+{"abstract": "  Facebook and Twitter recently announced community-based review platforms to\naddress misinformation. We provide an overview of the potential affordances of\nsuch community-based approaches to content moderation based on past research\nand preliminary analysis of Twitter's Birdwatch data. While our analysis\ngenerally supports a community-based approach to content moderation, it also\nwarns against potential pitfalls, particularly when the implementation of the\nnew infrastructure focuses on crowd-based \"validation\" rather than\n\"collaboration.\" We call for multidisciplinary research utilizing methods from\ncomplex systems studies, behavioural sociology, and computational social\nscience to advance the research on crowd-based content moderation.\n"}
+{"abstract": "  We report a $\\approx 400$-hour Giant Metrewave Radio Telescope (GMRT) search\nfor HI 21 cm emission from star-forming galaxies at $z = 1.18-1.39$ in seven\nfields of the DEEP2 Galaxy Survey. Including data from an earlier 60-hour GMRT\nobserving run, we co-added the HI 21 cm emission signals from 2,841 blue\nstar-forming galaxies that lie within the full-width at half-maximum of the\nGMRT primary beam. This yielded a $5.0\\sigma$ detection of the average HI 21 cm\nsignal from the 2,841 galaxies at an average redshift $\\langle z \\rangle\n\\approx 1.3$, only the second detection of HI 21 cm emission at $z\\ge1$. We\nobtain an average HI mass of $\\langle {\\rm M_{HI}} \\rangle=(3.09 \\pm 0.61)\n\\times 10^{10}\\ {\\rm M}_\\odot$ and an HI-to-stellar mass ratio of $2.6\\pm0.5$,\nboth significantly higher than values in galaxies with similar stellar masses\nin the local Universe. We also stacked the 1.4 GHz continuum emission of the\ngalaxies to obtain a median star-formation rate (SFR) of $14.5\\pm1.1\\ {\\rm\nM}_\\odot \\textrm{yr}^{-1}$. This implies an average HI depletion timescale of\n$\\approx 2$ Gyr for blue star-forming galaxies at $z\\approx 1.3$, a factor of\n$\\approx 3.5$ lower than that of similar local galaxies. Our results suggest\nthat the HI content of galaxies towards the end of the epoch of peak cosmic SFR\ndensity is insufficient to sustain their high SFR for more than $\\approx 2$\nGyr. Insufficient gas accretion to replenish the HI could then explain the\nobserved decline in the cosmic SFR density at $z< 1$.\n"}
+{"abstract": "  Single-particle resonances in the continuum are crucial for studies of exotic\nnuclei. In this study, the Green's function approach is employed to search for\nsingle-particle resonances based on the relativistic-mean-field model. Taking\n$^{120}$Sn as an example, we identify single-particle resonances and determine\nthe energies and widths directly by probing the extrema of the Green's\nfunctions. In contrast to the results found by exploring for the extremum of\nthe density of states proposed in our recent study [Chin. Phys. C, 44:084105\n(2020)], which has proven to be very successful, the same resonances as well as\nvery close energies and widths are obtained. By comparing the Green's functions\nplotted in different coordinate space sizes, we also found that the results\nvery slightly depend on the space size. These findings demonstrate that the\napproach by exploring for the extremum of the Green's function is also very\nreliable and effective for identifying resonant states, regardless of whether\nthey are wide or narrow.\n"}
+{"abstract": "  We study the problem of a Herbig-Haro jet with a uniformly accelerating\nejection velocity, travelling into a uniform environment. For the ejection\ndensity we consider two cases: a time-independent density, and a\ntime-independent mass loss rate. For these two cases, we obtain analytic\nsolutions for the motion of the jet head using a ram-pressure balance and a\ncenter of mass equation of motion. We also compute axisymmetric numerical\nsimulations of the same flow, and compare the time-dependent positions of the\nleading working surface shocks with the predictions of the two analytic models.\nWe find that if the jet is over-dense and over-pressured (with respect to the\nenvironment) during its evolution, a good agreement is obtained with the\nanalytic models, with the flow initially following the center of mass analytic\nsolution, and (for the constant ejection density case) at later times\napproaching the ram-pressure balance solution.\n"}
+{"abstract": "  We conduct an analysis of the quasi-normal modes for generic spin\nperturbations of the Kerr black hole using the isomonodromic method. The\nstrategy consists of solving the Riemann-Hilbert map relating the accessory\nparameters of the differential equations involved to monodromy properties of\nthe solutions, using the $\\tau$-function for the Painlev\\'e V transcendent. We\nshow good accordance of the method with the literature for generic rotation\nparameter $a<M$. In the extremal limit, we determined the dependence of the\nmodes with the black hole temperature and establish that the extremal values of\nthe modes are obtainable from the Painlev\\'e V and III transcendents.\n"}
+{"abstract": "  We study finite temperature string scale $AdS_3$ backgrounds. One background\nis $AdS_3 \\times S^1 \\times T^2$ in which the anti-de Sitter space-time and the\ncircle are at the radius $\\sqrt{\\alpha'}$. Using path integral techniques, we\nshow that the bulk spectrum includes a continuum of states as well as\nRamond-Ramond ground states that agree with those of the symmetric orbifold of\nthe two-torus after second quantization. We also examine the one-loop free\nenergy of the background $AdS_3 \\times S^1$ at curvature radius $\\sqrt{2\n\\alpha'/3}$. In the space-time NSNS sector, the string theory spontaneously\nbreaks conformal symmetry as well as R-charge conjugation symmetry. We prove\nthat the minimum in the boundary energy is reached for a singly wound string.\nIn the RR sector, we classify the infinite set of ground states with fractional\nR-charges. Moreover, we remark on the behaviour of critical temperatures as the\ncurvature scale becomes smaller than the string scale. In an appendix, we\nderive the Hawking-Page transition in string theory by integrating a world\nsheet one-point function.\n"}
+{"abstract": "  We develop a new phenomenological model that addresses current tensions\nbetween observations of the early and late Universe. Our scenario features: (i)\na decaying dark energy fluid (DDE), which undergoes a transition at $z \\sim\n5,000$, to raise today's value of the Hubble parameter -- addressing the $H_0$\ntension, and (ii) an ultra-light axion (ULA), which starts oscillating at\n$z\\gtrsim 10^4$, to suppress the matter power spectrum -- addressing the $S_8$\ntension. Our Markov Chain Monte Carlo analyses show that such a Dark Sector\nmodel fits a combination of Cosmic Microwave Background (CMB), Baryon Acoustic\nOscillations, and Large Scale Structure (LSS) data slightly better than the\n$\\Lambda$CDM model, while importantly reducing both the $H_0$ and $S_8$\ntensions with late universe probes ($\\lesssim 3\\sigma$). Combined with\nmeasurements from cosmic shear surveys, we find that the discrepancy on $S_8$\nis reduced to the $1.4\\sigma$ level, and the value of $H_0$ is further raised.\nAdding local supernovae measurements, we find that the $H_0$ and $S_8$ tensions\nare reduced to the $1.4\\sigma$ and $1.2\\sigma$ level respectively, with a\nsignificant improvement $\\Delta\\chi^2\\simeq -18$ compared to the $\\Lambda$CDM\nmodel. With this complete dataset, the DDE and ULA are detected at\n$\\simeq4\\sigma$ and $\\simeq2\\sigma$, respectively. We discuss a possible\nparticle physics realization of this model, with a dark confining gauge sector\nand its associated axion, although embedding the full details within\nmicrophysics remains an urgent open question. Our scenario will be decisively\nprobed with future CMB and LSS surveys.\n"}
+{"abstract": "  We consider a wide range of UV scenarios with the aim of informing searches\nfor CP violation at the TeV scale using effective field theory techniques. We\ndemonstrate that broad theoretical assumptions about the nature of UV dynamics\nresponsible for CP violation map out a small subset of relevant operators at\nthe TeV scale. Concretely, this will allow us to reduce the number of free\nparameters that need to be considered in experimental investigations, thus\nenhancing analyses' sensitivities. In parallel, reflecting the UV dynamics'\nWilson coefficient hierarchy will enable a streamlined theoretical\ninterpretation of such analyses in the future. We demonstrate a minimal\napproach to analysing CP violation in this context using a Monte Carlo study of\na combination of weak boson fusion Higgs and electroweak diboson production,\nwhich provide complementary information on the relevant EFT operators.\n"}
+{"abstract": "  Neural decoders were introduced as a generalization of the classic Belief\nPropagation (BP) decoding algorithms, where the Trellis graph in the BP\nalgorithm is viewed as a neural network, and the weights in the Trellis graph\nare optimized by training the neural network. In this work, we propose a novel\nneural decoder for cyclic codes by exploiting their cyclically invariant\nproperty. More precisely, we impose a shift invariant structure on the weights\nof our neural decoder so that any cyclic shift of inputs results in the same\ncyclic shift of outputs. Extensive simulations with BCH codes and punctured\nReed-Muller (RM) codes show that our new decoder consistently outperforms\nprevious neural decoders when decoding cyclic codes. Finally, we propose a list\ndecoding procedure that can significantly reduce the decoding error probability\nfor BCH codes and punctured RM codes. For certain high-rate codes, the gap\nbetween our list decoder and the Maximum Likelihood decoder is less than\n$0.1$dB. Code available at\nhttps://github.com/cyclicallyneuraldecoder/CyclicallyEquivariantNeuralDecoders\n"}
+{"abstract": "  The INvestigating Stellar Population In RElics is an on-going project\ntargeting 52 ultra-compact massive galaxies at 0.1<z<0.5 with the X-Shooter@VLT\nspectrograph (XSH). These objects are the perfect candidates to be 'relics',\nmassive red-nuggets formed at high-z (z>2) through a short and intense star\nformation burst, that evolved passively and undisturbed until the present-day.\nRelics provide a unique opportunity to study the mechanisms of star formation\nat high-z. In this paper, we present the first INSPIRE Data Release, comprising\n19 systems with observations completed in 2020. We use the methods already\npresented in the INSPIRE Pilot, but revisiting the 1D spectral extraction. For\nthese 19 systems, we obtain an estimate of the stellar velocity dispersion,\nfitting separately the two UVB and VIS XSH arms at their original resolution.\nWe estimate [Mg/Fe] abundances via line-index strength and mass-weighted\nintegrated stellar ages and metallicities with full spectral fitting on the\ncombined spectrum. Ages are generally old, in agreement with the photometric\nones, and metallicities are almost always super-solar, confirming the\nmass-metallicity relation. The [Mg/Fe] ratio is also larger than solar for the\ngreat majority of the galaxies, as expected. We find that 10 objects have\nformed more than 75% of their stellar mass (M*) within 3 Gyr from the Big Bang\nand classify them as relics. Among these, we identify 4 galaxies which had\nalready fully assembled their M* by that time. They are therefore `extreme\nrelics' of the ancient Universe. The INSPIRE DR1 catalogue of 10 known relics\nto-date augment by a factor of 3.3 the total number of confirmed relics, also\nenlarging the redshift window. It is therefore the largest publicly available\ncollection. Thanks to the larger number of systems, we can also better quantify\nthe existence of a 'degree of relicness', already hinted at the Pilot Paper.\n"}
+{"abstract": "  In recent years, 5G is widely used in parallel with IoT networks to enable\nmassive data connectivity and exchange with ultra-reliable and low latency\ncommunication (URLLC) services. The internet requirements from user's\nperspective have shifted from simple human to human interactions to different\ncommunication paradigms and information-centric networking (ICN). ICN\ndistributes the content among the users based on their trending requests. ICN\nis responsible not only for the routing and caching but also for naming the\nnetwork's content. ICN considers several parameters such as cache-hit ratio,\ncontent diversity, content redundancy, and stretch to route the content. ICN\nenables name-based caching of the required content according to the user's\nrequest based on the router's interest table. The stretch shows the path\ncovered while retrieving the content from producer to consumer. Reduction in\npath length also leads to a reduction in end-to-end latency and better data\nrate availability. ICN routers must have the minimum stretch to obtain a better\nsystem efficiency. Reinforcement learning (RL) is widely used in networks\nenvironment to increase agent efficiency to make decisions. In ICN, RL can aid\nto increase caching and stretch efficiency. This paper investigates a stretch\nreduction strategy for ICN routers by formulating the stretch reduction problem\nas a Markov decision process. The evaluation of the proposed stretch reduction\nstrategy's accuracy is done by employing Q-Learning, an RL technique. The\nsimulation results indicate that by using the optimal parameters for the\nproposed stretch reduction strategy.\n"}
+{"abstract": "  Characterising the atmospheres of exoplanets is key to understanding their\nnature and provides hints about their formation and evolution. High-resolution\nmeasurements of the helium triplet, He(2$^{3}$S), absorption of highly\nirradiated planets have been recently reported, which provide a new mean to\nstudy their atmospheric escape. In this work, we study the escape of the upper\natmospheres of HD 189733 b and GJ 3470 b by analysing high-resolution\nHe(2$^{3}$S) absorption measurements and using a 1D hydrodynamic model coupled\nwith a non-LTE model for the He(2$^{3}$S) state. We also use the H density\nderived from Ly$\\alpha$ observations to further constrain their temperatures,\nT, mass-loss rates,$\\dot M$, and H/He ratios. We have significantly improved\nour knowledge of the upper atmospheres of these planets. While HD 189733 b has\na rather compressed atmosphere and small gas radial velocities, GJ 3470 b, with\na gravitational potential ten times smaller, exhibits a very extended\natmosphere and large radial outflow velocities. Hence, although GJ 3470 b is\nmuch less irradiated in the XUV, and its upper atmosphere is much cooler, it\nevaporates at a comparable rate. In particular, we find that the upper\natmosphere of HD 189733 b is compact and hot, with a maximum T of\n12400$^{+400}_{-300}$ K, with very low mean molecular mass\n(H/He=(99.2/0.8)$\\pm0.1$), almost fully ionised above 1.1 R$_p$, and with $\\dot\nM$=(1.1$\\pm0.1$)$\\times$10$^{11}$ g/s. In contrast, the upper atmosphere of GJ\n3470 b is highly extended and relatively cold, with a maximum T of 5100$\\pm900$\nK, also with very low mean molecular mass (H/He=(98.5/1.5)$^{+1.0}_{-1.5}$),\nnot strongly ionised and with $\\dot M$=(1.9$\\pm1.1$)$\\times$10$^{11}$ g/s.\nFurthermore, our results suggest that the upper atmospheres of giant planets\nundergoing hydrodynamic escape tend to have very low mean molecular mass\n(H/He$\\gtrsim$97/3).\n"}
+{"abstract": "  In this paper, a new lattice concept called the locally symmetric lattice is\nproposed for storage ring light sources. In this new lattice, beta functions\nare made locally symmetric about two mirror planes of the lattice cell, and the\nphase advances between the two mirror planes satisfy the condition of nonlinear\ndynamics cancellation. There are two kinds of locally symmetric lattices,\ncorresponding to two symmetric representations of lattice cell. In a locally\nsymmetric lattice, main nonlinear effects caused by sextupoles can be\neffectively cancelled within one lattice cell, and generally there can also be\nmany knobs of sextupoles available for further optimizing the nonlinear\ndynamics. Two kinds of locally symmetric lattices are designed for a 2.2 GeV\ndiffraction-limited storage ring to demonstrate the lattice concept.\n"}
+{"abstract": "  We derive a new model for neutrino-plasma interactions in an expanding\nuniverse that incorporates the collective effects of the neutrinos on the\nplasma constituents. We start from the kinetic description of a multi-species\nplasma in the flat Friedmann-Robertson-Walker metric, where the particles are\ncoupled to neutrinos through the charged- and neutral-current forms of the weak\ninteraction. We then derive the fluid equations and specialize our model to (a)\nthe lepton epoch, where we consider a pair electron-positron plasma interacting\nwith electron (anti-)neutrinos, and (b) after the electron-positron\nannihilation, where we model an electron-proton plasma and take the limit of\nslow ions and inertia-less electrons to obtain a set of neutrino-electron\nmagnetohydrodynamics (NEMHD) equations. In both models, the dynamics of the\nplasma is affected by the neutrino motion through a ponderomotive force and, as\na result, new terms appear in the induction equation that can act as a source\nfor magnetic field generation in the early universe. A brief discussion on the\npossible applications of our model is proposed.\n"}
+{"abstract": "  We introduce a spacetime discretization of the Dirac equation that has the\nform of a quantum automaton and that is invariant upon changing the\nrepresentation of the Clifford algebra, as the Dirac equation itself. Our\nderivation follows Dirac's original one: We required that the square of the\ndiscrete Dirac scheme be a discretization of the Klein-Gordon equation.\nContrary to standard lattice gauge theory in discrete time, in which unitarity\nneeds to be proven, we show that the quantum automaton delivers naturally\nunitary Wilson fermions for any choice of Wilson's parameter.\n"}
+{"abstract": "  In this paper, we study the dynamic regret of online linear quadratic\nregulator (LQR) control with time-varying cost functions and disturbances. We\nconsider the case where a finite look-ahead window of cost functions and\ndisturbances is available at each stage. The online control algorithm studied\nin this paper falls into the category of model predictive control (MPC) with a\nparticular choice of terminal costs to ensure the exponential stability of MPC.\nIt is proved that the regret of such an online algorithm decays exponentially\nfast with the length of predictions. The impact of inaccurate prediction on\ndisturbances is also investigated in this paper.\n"}
+{"abstract": "  We investigate the contributions of the hadronic structure of the neutron to\nradiative $O(\\alpha E_e/m_N)$ corrections (or the inner $O(\\alpha E_e/m_N)$ RC)\nto the neutron beta decay, where $\\alpha$, $E_e$ and $m_N$ are the\nfine-structure constant, the electron energy and the nucleon mass,\nrespectively. We perform the calculation within the effective quantum field\ntheory of strong low-energy pion-nucleon interactions described by the linear\n$\\sigma$-model with chiral $SU(2) \\times SU(2)$ symmetry and electroweak\nhadron-hadron, hadron-lepton and lepton-lepton interactions for the\nelectron-lepton family with $SU(2)_L \\times U(1)_Y$ symmetry of the Standard\nElectroweak Theory (Ivanov et al., Phys. Rev. D99, 093006 (2019)). We show that\nafter renormalization, carried out in accordance with Sirlin's prescription\n(Sirlin, Phys. Rev. 164, 1767 (1967)), the inner $O(\\alpha E_e/m_N)$ RC are of\nthe order of a few parts of $10^{-5} - 10^{-4}$. This agrees well with the\nresults obtained in (Ivanov et al., Phys. Rev. D99, 093006 (2019)).\n"}
+{"abstract": "  This paper presents a novel distributed active set method for model\npredictive control of linear systems. The method combines a primal active set\nstrategy with a decentralized conjugate gradient method to solve convex\nquadratic programs. An advantage of the proposed method compared to existing\ndistributed model predictive algorithms is the primal feasibility of the\niterates. Numerical results show that the proposed method can compete with the\nalternating direction method of multipliers in terms of communication\nrequirements for a chain of masses example.\n"}
+{"abstract": "  Olivine and pyroxene are important mineral end-members for studying the\nsur-face material compositions of mafic bodies. The profiles of visible and\nnear-infraredspectra of olivine-orthopyroxene mixtures systematically varied\nwith their compositionratios. In our experiments, we combine the RELAB spectral\ndatabase with a new spec-tral data obtained from some assembled\nolivine-orthopyroxene mixtures. We found thatthe commonly-used band area ratio\n(BAR, Cloutis et al. 1986) does not work well onour newly obtained spectral\ndata. To investigate this issue, an empirical procedure basedon fitted results\nby modified Gaussian model is proposed to analyze the spectral curves.Following\nthe new empirical procedure, the end-member abundances can be estimatedwith a\n15% accuracy with some prior mineral absorption features. In addition, the\nmix-ture samples configured in our experiments are also irradiated by pulsed\nlasers to simulateand investigate the space weathering effects. Spectral\ndeconvolution results confirm thatlow-content olivine on celestial bodies are\ndifficult to measure and estimate. Therefore,the olivine abundance of space\nweathered materials may be underestimated from remotesensing data. This study\nmay be used to quantify the spectral relationship of olivine-orthopyroxene\nmixtures and further reveal their correlation between the spectra of ordi-nary\nchondrites and silicate asteroids.\n"}
+{"abstract": "  Bilinear pairing is a fundamental operation that is widely used in\ncryptographic algorithms (e.g., identity-based cryptographic algorithms) to\nsecure IoT applications. Nonetheless, the time complexity of bilinear pairing\nis $O(n^3)$, making it a very time-consuming operation, especially for\nresource-constrained IoT devices. Secure outsourcing of bilinear pairing has\nbeen studied in recent years to enable computationally weak devices to securely\noutsource the bilinear pairing to untrustworthy cloud servers. However, the\nstate-of-art algorithms often require to pre-compute and store some values,\nwhich results in storage burden for devices. In the Internet of Things, devices\nare generally with very limited storage capacity. Thus, the existing algorithms\ndo not fit the IoT well. In this paper, we propose a secure outsourcing\nalgorithm of bilinear pairings, which does not require pre-computations. In the\nproposed algorithm, the outsourcer side's efficiency is significantly improved\ncompared with executing the original bilinear pairing operation. At the same\ntime, the privacy of the input and output is ensured. Also, we apply the\nEthereum blockchain in our outsourcing algorithm to enable fair payments, which\nensures that the cloud server gets paid only when he correctly accomplished the\noutsourced work. The theoretical analysis and experimental results show that\nthe proposed algorithm is efficient and secure.\n"}
+{"abstract": "  Transformer networks are able to capture patterns in data coming from many\ndomains (text, images, videos, proteins, etc.) with little or no change to\narchitecture components. We perform a theoretical analysis of the core\ncomponent responsible for signal propagation between elements, i.e. the\nself-attention matrix. In practice, this matrix typically exhibits two\nproperties: (1) it is sparse, meaning that each token only attends to a small\nsubset of other tokens; and (2) it changes dynamically depending on the input\nto the module. With these considerations in mind, we ask the following\nquestion: Can a fixed self-attention module approximate arbitrary sparse\npatterns depending on the input? How small is the hidden size $d$ required for\nsuch approximation? We make progress in answering this question and show that\nthe self-attention matrix can provably approximate sparse matrices, where\nsparsity is in terms of a bounded number of nonzero elements in each row and\ncolumn. While the parameters of self-attention are fixed, various sparse\nmatrices can be approximated by only modifying the inputs. Our proof is based\non the random projection technique and uses the seminal Johnson-Lindenstrauss\nlemma. Our proof is constructive, enabling us to propose an algorithm for\nfinding adaptive inputs and fixed self-attention parameters in order to\napproximate a given matrix. In particular, we show that, in order to\napproximate any sparse matrix up to a given precision defined in terms of\npreserving matrix element ratios, $d$ grows only logarithmically with the\nsequence length $L$ (i.e. $d = O(\\log L)$).\n"}
+{"abstract": "  We study the optical appearance of a thin accretion disk around compact\nobjects within the Einstein-Gauss-Bonnet gravity. Considering static\nspherically symmetric black holes and naked singularities we search for\ncharacteristic signatures which can arise in the observable images due to the\nmodification of general relativity. While the images of the Gauss-Bonnet black\nholes closely resemble the Schwarzschild black hole, naked singularities\npossess a distinctive feature. A series of bright rings are formed in the\ncentral part of the images with observable radiation $10^3$ times larger than\nthe rest of the flux making them observationally significant. We elucidate the\nphysical mechanism, which causes the appearance of the central rings, showing\nthat the image is determined by the light ring structure of the spacetime. In a\ncertain region of the parametric space the Gauss-Bonnet naked singularities\npossess a stable and an unstable light ring. In addition the gravitational\nfield becomes repulsive in a certain neighbourhood of the singularity. This\ncombination of features leads to the formation of the central rings implying\nthat the effect is not specific for the Einstein-Gauss-Bonnet gravity but would\nalso appear for any other compact object with the same characteristics of the\nphoton dynamics.\n"}
+{"abstract": "  The classical Mountain Pass Lemma of Ambrosetti-Rabinowitz has been studied,\nextended and modified in several directions. Notable examples would certainly\ninclude the generalization to locally Lipschitz functionals by K.C. Chang,\nanalyzing the structure of the critical set in the mountain pass theorem in the\nworks of Hofer, Pucci-Serrin and Tian, and the extension by Ghoussoub-Preiss to\nclosed subsets in a Banach space with recent variations. In this paper, we\nutilize the generalized gradient of Clarke and Ekeland's variatonal principle\nto generalize the Ghoussoub-Preiss's Theorem in the setting of locally\nLipschitz functionals. We give an application to periodic solutions of\nHamiltonian systems.\n"}
+{"abstract": "  The stochastic gravitational wave background (SGWB) created by astrophysical\nsources in the nearby universe is likely to be anisotropic. Upper limits on\nSGWB anisotropy have been produced for all major data taking runs by the\nground-based laser interferometric detectors. However, due to the challenges\ninvolved in numerically inverting the pixel-to-pixel noise covariance matrix,\nwhich is necessary for setting upper limits, the searches accounted for angular\ncorrelations in the map by using the spherical harmonic basis, where\nregularization was relatively easier. This approach is better suited though for\nextended sources.Moreover, the upper limit maps produced in the two different\nbases are seemingly different. While the upper limits may be consistent within\nstatistical errors, it was important to check whether the results would remain\nconsistent if the full noise covariance matrix was used in the pixel basis.\nHere, we use the full pixel-to-pixel Fisher information matrix to create upper\nlimit maps of SGWB anisotropy. We first perform an unmodeled search for\npersistent, directional gravitational wave sources using folded data from the\nfirst (O1) and second (O2) observing runs of Advanced LIGO and show that the\nresults are consistent with the upper limits published by the LIGO-Virgo\nCollaboration (LVC). We then explore various ways to account for the\npixel-to-pixel Fisher information matrix using singular value decomposition and\nBayesian regularization schemes. We also account for the bias arising from\nregularization in the likelihood. We do not find evidence for any SGWB signal\nin the data, consistent with the LVC results and, though the upper limits\ndiffer significantly. Through an injection study we show that they are all\nvalid $95\\%$ upper limits, that is, the upper limit in a pixel is less than the\ninjected signal strength in less than $5\\%$ of the pixels.\n"}
+{"abstract": "  The 331 model with right-handed neutrinos is re-assessed to investigate the\nCP violation in the quark sector. After the spontaneous symmetry breaking, the\nmasses and physical fields of the particle content are obtained. The fermions\ncontent of the 331 model is enlarged to include exotic quarks with known\nelectric charge and with masses defined at the TeV scale. The existence of\nthese exotic quarks induces extra CP violations via couplings with quarks of\nthe Standard Model mediated by charged gauge boson with mass fixed at the TeV\nscale. An extra discrete $\\mathbb{Z}_{2}$ symmetry is introduced in the 331\nmodel to get a stable scalar field that can be a candidate to the dark matter\ncontent. The new scalar field interacts at tree level with the $Z^{\\prime}$\ngauge boson that works as a dark matter portal. The relic density associated\nwith the scalar field is calculated to yield the solution mass that satisfies\nthe observed dark matter. The region allowed on the parameter space of the dark\nmatter mass versus $Z^{\\prime}$ mass is obtained to include the bounds of\nPANDAX2017, XENON1T(2t.y) and LUX experiments.\n"}
+{"abstract": "  Recent years have seen spectacular progress in the development of innovative\nacceleration methods that are not based on traditional RF accelerating\nstructures. These novel developments are at the interface of laser, plasma and\naccelerator physics and may potentially lead to much more compact and\ncost-effective accelerator facilities. While primarily focusing on the ability\nto accelerate charged particles with much larger gradients than traditional RF\nstructures, these new techniques have yet to demonstrate comparable\nperformances to RF structures in terms of both beam parameters and\nreproducibility. To guide the developments beyond the necessary basic R&D and\nconcept validations, a common understanding and definition of required\nperformance and beam parameters for an operational user facility is now needed.\nThese innovative user facilities can include \"table-top\" light sources, medical\naccelerators, industrial accelerators or even high-energy colliders. This paper\nwill review the most promising developments in new acceleration methods and it\nwill present the status of on-going projects.\n"}
+{"abstract": "  We analyze the features of strongly interacting matter in the presence of\nnonzero isospin chemical potential $\\mu_I$, within a nonlocal two-flavor\nPolyakov-Nambu-Jona-Lasinio (PNJL) model. For a system at finite temperature\n$T$, we describe the behavior of various thermodynamic quantities and study the\nphase diagram in the $\\mu_I - T$ plane. In particular, it is found that for\nvalues of $\\mu_I$ larger than the pion mass and temperatures lower than a\ncritical value of about 170 MeV the system lies in an isospin symmetry broken\nphase signaled by the presence of a nonzero pion condensate. Our results for\nthe phase diagram are found to be in better agreement with those arising from\nlattice QCD calculations, as compared to the predictions from other theoretical\napproaches like the local PNJL model.\n"}
+{"abstract": "  In dynamical systems governed by differential equations, a guarantee that\ntrajectories emanating from a given set of initial conditions do not enter\nanother given set can be obtained by constructing a barrier function that\nsatisfies certain inequalities on phase space. Often these inequalities amount\nto nonnegativity of polynomials and can be enforced using sum-of-squares\nconditions, in which case barrier functions can be constructed computationally\nusing convex optimization over polynomials. To study how well such computations\ncan characterize sets of initial conditions in a chaotic system, we use the\nundamped double pendulum as an example and ask which stationary initial\npositions do not lead to flipping of the pendulum within a chosen time window.\nComputations give semialgebraic sets that are close inner approximations to the\nfractal set of all such initial positions.\n"}
+{"abstract": "  Most available semantic parsing datasets, comprising of pairs of natural\nutterances and logical forms, were collected solely for the purpose of training\nand evaluation of natural language understanding systems. As a result, they do\nnot contain any of the richness and variety of natural-occurring utterances,\nwhere humans ask about data they need or are curious about. In this work, we\nrelease SEDE, a dataset with 12,023 pairs of utterances and SQL queries\ncollected from real usage on the Stack Exchange website. We show that these\npairs contain a variety of real-world challenges which were rarely reflected so\nfar in any other semantic parsing dataset, propose an evaluation metric based\non comparison of partial query clauses that is more suitable for real-world\nqueries, and conduct experiments with strong baselines, showing a large gap\nbetween the performance on SEDE compared to other common datasets.\n"}
+{"abstract": "  General-purpose Markov Chain Monte Carlo sampling algorithms suffer from a\ndramatic reduction in efficiency as the system being studied is driven towards\na critical point. Recently, a series of seminal studies suggested that\nnormalizing flows - a class of deep generative models - can form the basis of a\nsampling strategy that does not suffer from this 'critical slowing down'. The\ncentral idea is to use machine learning techniques to build (approximate)\ntrivializing maps, i.e. field transformations that map the theory of interest\ninto a 'simpler' theory in which the degrees of freedom decouple, and where the\nstatistical weight in the path integral is given by a distribution from which\nsampling is easy. No separate process is required to generate training data for\nsuch models, and convergence to the desired distribution is guaranteed through\na reweighting procedure such as a Metropolis test. In a proof-of-principle\ndemonstration on two-dimensional $\\phi^4$ theory, Albergo et al.\n(arXiv:1904.12072) modelled the trivializing map as a sequence of pointwise\naffine transformations. We pick up this thread, with the aim of quantifying how\nwell we can expect this approach to scale as we increase the number of degrees\nof freedom in the system. We make several modifications to the original design\nthat allow our models learn more efficient representations of trivializing maps\nusing much smaller neural networks, which leads to a large reduction in the\ncomputational cost required to train models of equivalent quality. After making\nthese changes, we find that sampling efficiency is almost entirely dictated by\nhow extensively a model has been trained, while being unresponsive to further\nalterations that increase model flexibility. However, as we move towards the\ncontinuum limit the training costs scale extremely quickly, which urgently\nrequires further work to fully understand and mitigate.\n"}
+{"abstract": "  Nowadays, the bulk of Internet traffic uses TCP protocol for reliable\ntransmission. But the standard TCP's performance is very poor in High Speed\nNetworks (HSN) and hence the core gigabytes links are usually underutilization.\nThis problem has roots in conservative nature of TCP, especially in its\nAdditive Increase Multiplicative Decrease (AIMD) phase. In other words, since\nTCP can't figure out precisely the congestion status of the network, it follows\na conservative strategy to keep the network from overwhelming. We believe that\nprecisely congestion estimation in the network can solve this problem by\navoiding unnecessary conservation. To this end, this paper proposes an\nalgorithm which considers packet loss and delay information jointly and employs\na probabilistic approach to accurately estimation of congestion status in the\nnetwork. To examine the proposed scheme performance, extensive simulations have\nbeen performed in the NS-2 environment. Simulation results reveal that the\nproposed algorithm has better performance than existing algorithms in terms of\nbottleneck utilization, stability, throughput and fairness.\n"}
+{"abstract": "  Learning sentence embeddings often requires a large amount of labeled data.\nHowever, for most tasks and domains, labeled data is seldom available and\ncreating it is expensive. In this work, we present a new state-of-the-art\nunsupervised method based on pre-trained Transformers and Sequential Denoising\nAuto-Encoder (TSDAE) which outperforms previous approaches by up to 6.4 points.\nIt can achieve up to 93.1% of the performance of in-domain supervised\napproaches. Further, we show that TSDAE is a strong domain adaptation and\npre-training method for sentence embeddings, significantly outperforming other\napproaches like Masked Language Model.\n  A crucial shortcoming of previous studies is the narrow evaluation: Most work\nmainly evaluates on the single task of Semantic Textual Similarity (STS), which\ndoes not require any domain knowledge. It is unclear if these proposed methods\ngeneralize to other domains and tasks. We fill this gap and evaluate TSDAE and\nother recent approaches on four different datasets from heterogeneous domains.\n"}
+{"abstract": "  We have produced persistent currents of ultracold fermionic atoms trapped in\na toroidal geometry with lifetimes greater than 10 seconds in the\nstrongly-interacting limit. These currents remain stable well into the BCS\nlimit at sufficiently low temperature. We drive a circulating BCS superfluid\ninto the normal phase and back by changing the interaction strength and find\nthat the probability for quantized superflow to reappear is remarkably\ninsensitive to the time spent in the normal phase and the minimum interaction\nstrength. After ruling out the Kibble-Zurek mechanism for our experimental\nconditions, we argue that the reappearance of superflow is due to long-lived\nnormal currents and the Hess-Fairbank effect.\n"}
+{"abstract": "  We prove that torsion in the abelianizations of open normal subgroups in\nfinitely presented pro-$p$ groups can grow arbitrarily fast. By way of contrast\nin $\\mathbb Z_p$- analytic groups the torsion growth is at most polynomial.\n"}
+{"abstract": "  Employing the the stellar evolution code (Modules for Experiments in Stellar\nAstrophysics), we calculate yields of heavy elements from massive stars via\nstellar wind and core-collapse supernovae (CCSN) ejecta to interstellar medium\n(ISM). In our models, the initial masses ($M_{\\rm ini}$) of massive stars are\ntaken from 13 to 80 $M_\\odot$, their initial rotational velocities (V) are 0,\n300 and 500 km s$^{-1}$, and their metallicities are [Fe/H] = -3, -2, -1, and\n0. The yields of heavy elements coming from stellar winds are mainly affected\nby the stellar rotation which changes the chemical abundances of stellar\nsurfaces via chemically homogeneous evolution, and enhances mass-loss rate. We\nestimate that the stellar wind can produce heavy element yields of about\n$10^{-2}$ (for low metallicity models) to several $M_\\odot$ (for low\nmetallicity and rapid rotation models) mass. The yields of heavy element\nproduced by CCSN ejecta also depend on the remnant mass of massive mass which\nis mainly determined by the mass of CO-core. Our models calculate that the\nyields of heavy elements produced by CCSN ejecta can get up to several\n$M_\\odot$. Compared with stellar wind, CCSN ejecta has a greater contribution\nto the heavy elements in ISM. We also compare the $^{56}$Ni yields by\ncalculated in this work with observational estimate. Our models only explain\nthe $^{56}$Ni masses produced by faint SNe or normal SNe with progenitor mass\nlower than about 25 $M_\\odot$, and greatly underestimate the $^{56}$Ni masses\nproduced by stars with masses higher than about 30 $M_\\odot$.\n"}
+{"abstract": "  While remarkable advances have been made in Computed Tomography (CT),\ncapturing CT images with non-standardized protocols causes low reproducibility\nregarding radiomic features, forming a barrier on CT image analysis in a large\nscale. RadiomicGAN is developed to effectively mitigate the discrepancy caused\nby using non-standard reconstruction kernels. RadiomicGAN consists of hybrid\nneural blocks including both pre-trained and trainable layers adopted to learn\nradiomic feature distributions efficiently. A novel training approach, called\nDynamic Window-based Training, has been developed to smoothly transform the\npre-trained model to the medical imaging domain. Model performance evaluated\nusing 1401 radiomic features show that RadiomicGAN clearly outperforms the\nstate-of-art image standardization models.\n"}
+{"abstract": "  Cross domain recommender systems have been increasingly valuable for helping\nconsumers identify useful items in different applications. However, existing\ncross-domain models typically require large number of overlap users, which can\nbe difficult to obtain in some applications. In addition, they did not consider\nthe duality structure of cross-domain recommendation tasks, thus failing to\ntake into account bidirectional latent relations between users and items and\nachieve optimal recommendation performance. To address these issues, in this\npaper we propose a novel cross-domain recommendation model based on dual\nlearning that transfers information between two related domains in an iterative\nmanner until the learning process stabilizes. We develop a novel latent\northogonal mapping to extract user preferences over multiple domains while\npreserving relations between users across different latent spaces. Furthermore,\nwe combine the dual learning method with the metric learning approach, which\nallows us to significantly reduce the required common user overlap across the\ntwo domains and leads to even better cross-domain recommendation performance.\nWe test the proposed model on two large-scale industrial datasets and six\ndomain pairs, demonstrating that it consistently and significantly outperforms\nall the state-of-the-art baselines. We also show that the proposed model works\nwell with very few overlap users to obtain satisfying recommendation\nperformance comparable to the state-of-the-art baselines that use many overlap\nusers.\n"}
+{"abstract": "  Answer selection is a task to choose the positive answers from a pool of\ncandidate answers for a given question. In this paper, we propose a novel\nstrategy for answer selection, called hierarchical ranking. We introduce three\nlevels of ranking: point-level ranking, pair-level ranking, and list-level\nranking. They formulate their optimization objectives by employing supervisory\ninformation from different perspectives to achieve the same goal of ranking\ncandidate answers. Therefore, the three levels of ranking are related and they\ncan promote each other. We take the well-performed compare-aggregate model as\nthe backbone and explore three schemes to implement the idea of applying the\nhierarchical rankings jointly: the scheme under the Multi-Task Learning (MTL)\nstrategy, the Ranking Integration (RI) scheme, and the Progressive Ranking\nIntegration (PRI) scheme. Experimental results on two public datasets, WikiQA\nand TREC-QA, demonstrate that the proposed hierarchical ranking is effective.\nOur method achieves state-of-the-art (non-BERT) performance on both TREC-QA and\nWikiQA.\n"}
+{"abstract": "  Video Question Answering (Video QA) is a powerful testbed to develop new AI\ncapabilities. This task necessitates learning to reason about objects,\nrelations, and events across visual and linguistic domains in space-time.\nHigh-level reasoning demands lifting from associative visual pattern\nrecognition to symbol-like manipulation over objects, their behavior and\ninteractions. Toward reaching this goal we propose an object-oriented reasoning\napproach in that video is abstracted as a dynamic stream of interacting\nobjects. At each stage of the video event flow, these objects interact with\neach other, and their interactions are reasoned about with respect to the query\nand under the overall context of a video. This mechanism is materialized into a\nfamily of general-purpose neural units and their multi-level architecture\ncalled Hierarchical Object-oriented Spatio-Temporal Reasoning (HOSTR) networks.\nThis neural model maintains the objects' consistent lifelines in the form of a\nhierarchically nested spatio-temporal graph. Within this graph, the dynamic\ninteractive object-oriented representations are built up along the video\nsequence, hierarchically abstracted in a bottom-up manner, and converge toward\nthe key information for the correct answer. The method is evaluated on multiple\nmajor Video QA datasets and establishes new state-of-the-arts in these tasks.\nAnalysis into the model's behavior indicates that object-oriented reasoning is\na reliable, interpretable and efficient approach to Video QA.\n"}
+{"abstract": "  Finite frieze patterns with entries in\n$\\mathbb{Z}[\\lambda_{p_1},\\ldots,\\lambda_{p_s}]$ where $\\{p_1,\\ldots,p_s\\}\n\\subseteq \\mathbb{Z}_{\\geq 3}$ and $\\lambda_p = 2 \\cos(\\pi/p)$ were shown to\nhave a connection to dissected polygons by Holm and Jorgensen. We extend their\nwork by studying the connection between infinite frieze patterns with such\nentries and dissections of annuli and once-punctured discs. We give an\nalgorithm to determine whether a frieze pattern with entries in\n$\\mathbb{Z}[\\lambda_{p_1},\\ldots,\\lambda_{p_s}]$, finite or infinite, comes\nfrom a dissected surface. We introduce quotient dissections as a realization\nfor some frieze patterns unrealizable by an ordinary dissection. We also\nintroduce two combinatorial interpretations for entries of frieze patterns from\ndissected surfaces. These interpretations are a generalization of matchings\nintroduced by Broline, Crowe, and Isaacs for finite frieze patterns over\n$\\mathbb{Z}$.\n"}
+{"abstract": "  Position $n$ points uniformly at random in the unit square $S$, and consider\nthe Voronoi tessellation of $S$ corresponding to the set $\\eta$ of points. Toss\na fair coin for each cell in the tessellation to determine whether to colour\nthe cell red or blue. Let $H_S$ denote the event that there exists a red\nhorizontal crossing of $S$ in the resulting colouring. In 1999, Benjamini,\nKalai and Schramm conjectured that knowing the tessellation, but not the\ncolouring, asymptotically gives no information as to whether the event $H_S$\nwill occur or not. More precisely, since $H_S$ occurs with probability $1/2$,\nby symmetry, they conjectured that the conditional probabilities\n$\\mathbb{P}(H_S|\\eta)$ converge in probability to 1/2, as $n\\to\\infty$. This\nconjecture was settled in 2016 by Ahlberg, Griffiths, Morris and Tassion. In\nthis paper we derive a stronger bound on the rate at which\n$\\mathbb{P}(H_S|\\eta)$ approaches its mean. As a consequence we strengthen the\nconvergence in probability to almost sure convergence.\n"}
+{"abstract": "  Nonlocal games are extensions of Bell inequalities, aimed at demonstrating\nquantum advantage. These games are well suited for noisy quantum computers\nbecause they only require the preparation of a shallow circuit, followed by the\nmeasurement of non-commuting observable. Here, we consider the minimal\nimplementation of the nonlocal game proposed in Science 362, 308 (2018). We\ntest this game by preparing a 6-qubit cluster state using quantum computers on\nthe cloud by IBM, Ionq, and Honeywell. Our approach includes several levels of\noptimization, such as circuit identities and error mitigation and allows us to\ncross the classical threshold and demonstrate quantum advantage in one quantum\ncomputer. We conclude by introducing a different inequality that allows us to\nobserve quantum advantage in less accurate quantum computers, at the expense of\nprobing a larger number of circuits.\n"}
+{"abstract": "  We prove bounds for the number of solutions to\n  $$a_1 + \\dots + a_k = a_1' + \\dots + a_k'$$ over $N$-element sets of reals,\nwhich are sufficiently convex or near-convex. A near-convex set will be the\nimage of a set with small additive doubling under a convex function with\nsufficiently many strictly monotone derivatives. We show, roughly, that every\ntime the number of terms in the equation is doubled, an additional saving of\n$1$ in the exponent of the trivial bound $N^{2k-1}$ is made, starting from the\ntrivial case $k=1$. In the context of near-convex sets we also provide explicit\ndependencies on the additive doubling parameters.\n  Higher convexity is necessary for such bounds to hold, as evinced by sets of\nperfect powers of consecutive integers. We exploit these stronger assumptions\nusing an idea of Garaev, rather than the ubiquitous Szemer\\'edi-Trotter\ntheorem, which has not been adapted in earlier results to embrace higher\nconvexity.\n  As an application we prove small improvements for the best known bounds for\nsumsets of convex sets under additional convexity assumptions.\n"}
+{"abstract": "  Estimating the unknown causal dependencies among graph-connected time series\nplays an important role in many applications, such as sensor network analysis,\nsignal processing over cyber-physical systems, and finance engineering.\nInference of such causal dependencies, often know as topology identification,\nis not well studied for non-linear non-stationary systems, and most of the\nexisting methods are batch-based which are not capable of handling streaming\nsensor signals. In this paper, we propose an online kernel-based algorithm for\ntopology estimation of non-linear vector autoregressive time series by solving\na sparse online optimization framework using the composite objective mirror\ndescent method. Experiments conducted on real and synthetic data sets show that\nthe proposed algorithm outperforms the state-of-the-art methods for topology\nestimation.\n"}
+{"abstract": "  A mixed graph is obtained by orienting some edges of a simple graph. The\npositive inertia index of a mixed graph is defined as the number of positive\neigenvalues of its Hermitian adjacency matrix, including multiplicities. This\nmatrix was introduced by Liu and Li, independently by Guo and Mohar, in the\nstudy of graph energy. Recently, Yuan et al. characterized the mixed graphs\nwith exactly one positive eigenvalue. In this paper, we study the positive\ninertia indices of mixed graphs and characterize the mixed graphs with cut\nvertices having positive inertia index 2.\n"}
+{"abstract": "  In this paper, we propose and analyse a system that can automatically detect,\nlocalise and classify polyps from colonoscopy videos. The detection of frames\nwith polyps is formulated as a few-shot anomaly classification problem, where\nthe training set is highly imbalanced with the large majority of frames\nconsisting of normal images and a small minority comprising frames with polyps.\nColonoscopy videos may contain blurry images and frames displaying feces and\nwater jet sprays to clean the colon -- such frames can mistakenly be detected\nas anomalies, so we have implemented a classifier to reject these two types of\nframes before polyp detection takes place. Next, given a frame containing a\npolyp, our method localises (with a bounding box around the polyp) and\nclassifies it into five different classes. Furthermore, we study a method to\nimprove the reliability and interpretability of the classification result using\nuncertainty estimation and classification calibration. Classification\nuncertainty and calibration not only help improve classification accuracy by\nrejecting low-confidence and high-uncertain results, but can be used by doctors\nto decide how to decide on the classification of a polyp. All the proposed\ndetection, localisation and classification methods are tested using large data\nsets and compared with relevant baseline approaches.\n"}
+{"abstract": "  Lexical Semantics is concerned with how words encode mental representations\nof the world, i.e., concepts . We call this type of concepts, classification\nconcepts . In this paper, we focus on Visual Semantics , namely on how humans\nbuild concepts representing what they perceive visually. We call this second\ntype of concepts, substance concepts . As shown in the paper, these two types\nof concepts are different and, furthermore, the mapping between them is\nmany-to-many. In this paper we provide a theory and an algorithm for how to\nbuild substance concepts which are in a one-to-one correspondence with\nclassifications concepts, thus paving the way to the seamless integration\nbetween natural language descriptions and visual perception. This work builds\nupon three main intuitions: (i) substance concepts are modeled as visual\nobjects , namely sequences of similar frames, as perceived in multiple\nencounters ; (ii) substance concepts are organized into a visual subsumption\nhierarchy based on the notions of Genus and Differentia ; (iii) the human\nfeedback is exploited not to name objects, but, rather, to align the hierarchy\nof substance concepts with that of classification concepts. The learning\nalgorithm is implemented for the base case of a hierarchy of depth two. The\nexperiments, though preliminary, show that the algorithm manages to acquire the\nnotions of Genus and Differentia with reasonable accuracy, this despite seeing\na small number of examples and receiving supervision on a fraction of them.\n"}
+{"abstract": "  In this article we argue that in quantum mechanics, and in opposition to\nclassical physics, it is impossible to say that an isolated quantum system\n\"owns\" a physical property. Some properties of the system, its mass for\nexample, belong to it in a sense close to that of classical physics; but most\noften a property must be attributed to the system within a context. We give\nsimple motivations for adopting this point of view, and show that it clarifies\nmany issues in quantum physics.\n"}
+{"abstract": "  YouTube has revolutionized the way people discover and consume video.\nAlthough YouTube facilitates easy access to hundreds of well-produced and\ntrustworthy videos, abhorrent, misinformative, and mistargeted content is also\ncommon. The platform is plagued by various types of problematic content: 1)\ndisturbing videos targeting young children; 2) hateful and misogynistic\ncontent; and 3) pseudoscientific misinformation. While YouTube's recommendation\nalgorithm plays a vital role in increasing user engagement and YouTube's\nmonetization, its role in unwittingly promoting problematic content is not\nentirely understood. In this thesis, we shed some light on the degree of\nproblematic content on YouTube and the role of the recommendation algorithm in\nthe dissemination of such content. Following a data-driven quantitative\napproach, we analyze thousands of videos on YouTube, to shed light on: 1) the\nrisks of YouTube media consumption by young children; 2) the role of the\nrecommendation algorithm in the dissemination of misogynistic content, by\nfocusing on the Involuntary Celibates (Incels) community; and 3) user exposure\nto pseudoscientific content on various parts of the platform and how this\nexposure changes based on the user's watch history. Our analysis reveals that\nyoung children are likely to encounter disturbing content when they randomly\nbrowse the platform. By analyzing the Incel community on YouTube, we find that\nIncel activity is increasing over time and that platforms may play an active\nrole in steering users towards extreme content. Finally, when studying\npseudoscientific misinformation, we find that YouTube suggests more\npseudoscientific content regarding traditional pseudoscientific topics (e.g.,\nflat earth) than for emerging ones (like COVID-19) and that these\nrecommendations are more common on the search results page than on a user's\nhomepage or the video recommendations section.\n"}
+{"abstract": "  Neural Arithmetic Logic Modules have become a growing area of interest,\nthough remain a niche field. These units are small neural networks which aim to\nachieve systematic generalisation in learning arithmetic operations such as {+,\n-, *, \\} while also being interpretive in their weights. This paper is the\nfirst in discussing the current state of progress of this field, explaining key\nworks, starting with the Neural Arithmetic Logic Unit (NALU). Focusing on the\nshortcomings of NALU, we provide an in-depth analysis to reason about design\nchoices of recent units. A cross-comparison between units is made on experiment\nsetups and findings, where we highlight inconsistencies in a fundamental\nexperiment causing the inability to directly compare across papers. We finish\nby providing a novel discussion of existing applications for NALU and research\ndirections requiring further exploration.\n"}
+{"abstract": "  We propose a novel planning technique for satisfying tasks specified in\ntemporal logic in partially revealed environments. We define high-level actions\nderived from the environment and the given task itself, and estimate how each\naction contributes to progress towards completing the task. As the map is\nrevealed, we estimate the cost and probability of success of each action from\nimages and an encoding of that action using a trained neural network. These\nestimates guide search for the minimum-expected-cost plan within our model. Our\nlearned model is structured to generalize across environments and task\nspecifications without requiring retraining. We demonstrate an improvement in\ntotal cost in both simulated and real-world experiments compared to a\nheuristic-driven baseline.\n"}
+{"abstract": "  We investigate the electronic properties in the Bloch electron on a square\nlattice with vacancies in the uniform magnetic field. We show that a single\nvacancy site introduced to the system creates a defect energy level in every\nsingle innumerable fractal energy gap in the Hofstadter butterfly. The\nwavefunctions of different defect levels have all different localization\nlengths depending on their fractal generations, and they can be described by a\nsingle universal function after an appropriate fractal scaling. We also show\nthat each defect state has its own characteristic orbital magnetic moment,\nwhich is exactly correlated to the gradient of the energy level in the\nHofstadter diagram. Probing the spatial nature of the defect-localized states\nprovides a powerful way to elucidate the fractal nature of the Hofstadter\nbutterfly.\n"}
+{"abstract": "  We define and study 'non-abelian' Poincar\\'e series for the group\n$G=\\mathrm{SU} (2,1)$, i.e. Poincar\\'e series attached to a Stone-Von Neumann\nrepresentation of the unipotent subgroup $N$ of $G$. Such Poincar\\'e series\nhave in general exponential growth. In this study we use results on abelian and\nnon-abelian Fourier term modules obtained in arXiv:1912.01334. We compute the\ninner product of truncations of these series and those associated to unitary\ncharacters of $N$ with square integrable automorphic forms, in connection with\ntheir Fourier expansions. As a consequence, we obtain general completeness\nresults that, in particular, generalize those valid for the classical\nholomorphic (and antiholomorphic) Poincar\\'e series for\n$\\mathrm{SL}(2,\\mathbb{R})$.\n"}
+{"abstract": "  In this paper, we consider static parameter estimation for a class of\ncontinuous-time state-space models. Our goal is to obtain an unbiased estimate\nof the gradient of the log-likelihood (score function), which is an estimate\nthat is unbiased even if the stochastic processes involved in the model must be\ndiscretized in time. To achieve this goal, we apply a doubly randomized scheme,\nthat involves a novel coupled conditional particle filter (CCPF) on the second\nlevel of randomization. Our novel estimate helps facilitate the application of\ngradient-based estimation algorithms, such as stochastic-gradient Langevin\ndescent. We illustrate our methodology in the context of stochastic gradient\ndescent (SGD) in several numerical examples and compare with the Rhee & Glynn\nestimator.\n"}
+{"abstract": "  The sensitivity of light and matter-wave interferometers to rotations is\nbased on the Sagnac effect and increases with the area enclosed by the\ninterferometer. In the case of light, the latter can be enlarged by forming\nmultiple fibre loops, whereas the equivalent for matter-wave interferometers\nremains an experimental challenge. We present a concept for a multi-loop atom\ninterferometer with a scalable area formed by light pulses. Our method will\noffer sensitivities as high as $2\\cdot10^{-11}$ rad/s at 1 s in combination\nwith the respective long-term stability as required for Earth rotation\nmonitoring.\n"}
+{"abstract": "  Modern classification models tend to struggle when the amount of annotated\ndata is scarce. To overcome this issue, several neural few-shot classification\nmodels have emerged, yielding significant progress over time, both in Computer\nVision and Natural Language Processing. In the latter, such models used to rely\non fixed word embeddings before the advent of transformers. Additionally, some\nmodels used in Computer Vision are yet to be tested in NLP applications. In\nthis paper, we compare all these models, first adapting those made in the field\nof image processing to NLP, and second providing them access to transformers.\nWe then test these models equipped with the same transformer-based encoder on\nthe intent detection task, known for having a large number of classes. Our\nresults reveal that while methods perform almost equally on the ARSC dataset,\nthis is not the case for the Intent Detection task, where the most recent and\nsupposedly best competitors perform worse than older and simpler ones (while\nall are given access to transformers). We also show that a simple baseline is\nsurprisingly strong. All the new developed models, as well as the evaluation\nframework, are made publicly available.\n"}
+{"abstract": "  Recent ground-based deep observations of the Universe have discovered large\npopulations of massive quiescent galaxies at z~3-5. With the launch of the\nJames Webb Space Telescope (JWST), the on-board NIRSpec instrument will provide\ncontinuous 0.6-5.3 $\\mu$ m spectroscopic coverage of these galaxies. Here we\nshow that NIRSpec/CLEAR spectroscopy is ideal to probe the completeness of\nphotometrically selected massive quiescent galaxies such as the ones presented\nby Schreiber et al. (2018b). Using a subset of the Schreiber et al. (2018b)\nsample with deep Keck/MOSFIRE spectroscopy presented by Esdaile et al. (2020),\nwe perform a suite of mock JWST/NIRSpec observations to determine optimal\nobserving strategies to efficiently recover the star-formation histories\n(SFHs), element abundances, and kinematics of these massive quiescent galaxies.\nWe find that at z~3, medium resolution G235M/FL170LP NIRSpec observations could\nrecover element abundances at an accuracy of ~15%, which is comparable to local\nglobular clusters. Mimicking ZFOURGE COSMOS photometry, we perform mock\nspectrophotometric fitting with Prospector to show that the overall shape of\nthe SFHs of our mock galaxies can be recovered well, albeit with a dependency\non the number of non-parametric SFH bins. We show that deep high-resolution\nG235H/FL170LP integral field spectroscopy with a S/N~7 per spaxel is required\nto constrain the rotational properties of our sample at >2$\\sigma$ confidence.\nThus, through optimal grism/filter choices, JWST/NIRSpec slit and integral\nfield spectroscopy observations would provide tight constraints to galaxy\nevolution in the early Universe.\n"}
+{"abstract": "  Since physics of the dark sector components of the Universe is not yet\nwell-understood, the phenomenological studies of non-minimal interaction in the\ndark sector could possibly pave the way to theoretical and experimental\nprogress in this direction. Therefore, in this work, we intend to explore some\nfeatures and consequences of a phenomenological interaction in the dark sector.\nWe use the Planck 2018, BAO, JLA, KiDS and HST data to investigate two\nextensions of the base $\\Lambda$CDM model, viz., (i) we allow the interaction\namong vacuum energy and dark matter, namely the I$\\Lambda$CDM model, wherein\nthe interaction strength is proportional to the vacuum energy density and\nexpansion rate of the Universe, and (ii) the I$\\Lambda$CDM scenario with free\neffective neutrino mass and number, namely the $\\nu$I$\\Lambda$CDM model. We\nalso present comparative analyses of the interaction models with the companion\nmodels, namely, $\\Lambda$CDM, $\\nu\\Lambda$CDM, $w$CDM and $\\nu w$CDM. In both\nthe interaction models, we find non-zero coupling in the dark sector up to 99\\%\nCL with energy transfer from dark matter to vacuum energy, and observe a\nphantom-like behavior of the effective dark energy without actual ``phantom\ncrossing\". The well-known tensions on the cosmological parameters $H_0$ and\n$\\sigma_8$, prevailing within the $\\Lambda$CDM cosmology, are relaxed\nsignificantly in these models wherein the $\\nu$I$\\Lambda$CDM model shows\nconsistency with the standard effective neutrino mass and number. Both the\ninteraction models find a better fit to the combined data compared to the\ncompanion models under consideration.\n"}
+{"abstract": "  Transformer has been widely adopted in Neural Machine Translation (NMT)\nbecause of its large capacity and parallel training of sequence generation.\nHowever, the deployment of Transformer is challenging because different\nscenarios require models of different complexities and scales. Naively training\nmultiple Transformers is redundant in terms of both computation and memory. In\nthis paper, we propose a novel Scalable Transformers, which naturally contains\nsub-Transformers of different scales and have shared parameters. Each\nsub-Transformer can be easily obtained by cropping the parameters of the\nlargest Transformer. A three-stage training scheme is proposed to tackle the\ndifficulty of training the Scalable Transformers, which introduces additional\nsupervisions from word-level and sequence-level self-distillation. Extensive\nexperiments were conducted on WMT EN-De and En-Fr to validate our proposed\nScalable Transformers.\n"}
+{"abstract": "  It is shown that the Ablowitz-Kaup-Newell-Segur (AKNS) integrable hierarchy\ncan be obtained as the dynamical equations of three-dimensional General\nRelativity with a negative cosmological constant. This geometrization of the\nAKNS system is possible through the construction of novel boundary conditions\nfor the gravitational field. These are invariant under an asymptotic symmetry\ngroup characterized by an infinite set of AKNS commuting conserved charges.\nGravitational configurations are studied by means of $SL(2,\\mathbb{R})$\nconjugacy classes. Conical singularities and black hole solutions are included\nin the boundary conditions.\n"}
+{"abstract": "  We show that if $(X,d)$ is a metric space which admits a consistent covex\ngeodesic bicombing, then we can construct a conical bicombing on $CB(X)$, the\nhyperspace of nonempty, closed, bounded, and convex subsets of $X$ (with the\nHausdorff metric). If $X$ is a normed space, this same method produces a\nconsistent convex bicombing on $CB(X)$. We follow this by examining a geodesic\nbicombing on the nonempty compact subsets of $X$, assuming $X$ is a proper\nmetric space.\n"}
+{"abstract": "  In order to solve the recent defect in garbage classification - including low\nlevel of intelligence, low accuracy and high cost of equipment, this paper\npresents a series of methods in identification and judgment in intelligent\ngarbage classification, including a material identification based on thermal\nprinciple and non-destructive laser irradiation, another material\nidentification based on optical diffraction and phase analysis, a profile\nidentification which utilizes a scenery thermal image after PCA and histogram\ncorrection, another profile identification which utilizes computer vision with\ninnovated data sets and algorithms. Combining AHP and Bayesian formula, the\npaper innovates a coupling algorithm which helps to make a comprehensive\njudgment of the garbage sort, based on the material and profile identification.\nThis paper also proposes a method for real-time space measurement of garbage\ncans, which based on the characteristics of air as fluid, and analyses the\nfunctions of air cleaning and particle disposing. Instead of the single use of\ngarbage image recognition, this paper provides a comprehensive method to judge\nthe garbage sort by material and profile identifications, which greatly\nenhancing the accuracy and intelligence in garbage classification.\n"}
+{"abstract": "  We study the entanglement dynamics generated by quantum quenches in the\nquantum cellular automaton Rule $54$. We consider the evolution from a recently\nintroduced class of solvable initial states. States in this class relax\n(locally) to a one-parameter family of Gibbs states and the thermalisation\ndynamics of local observables can be characterised exactly by means of an\nevolution in space. Here we show that the latter approach also gives access to\nthe entanglement dynamics and derive exact formulas describing the asymptotic\nlinear growth of all R\\'enyi entropies in the thermodynamic limit and their\neventual saturation for finite subsystems. While in the case of von Neumann\nentropy we recover exactly the predictions of the quasiparticle picture, we\nfind no physically meaningful quasiparticle description for other R\\'enyi\nentropies. Our results apply to both homogeneous and inhomogeneous quenches.\n"}
+{"abstract": "  The Virtual Brain (TVB) is now available as open-source cloud ecosystem on\nEBRAINS, a shared digital research platform for brain science. It offers\nservices for constructing, simulating and analysing brain network models (BNMs)\nincluding the TVB network simulator; magnetic resonance imaging (MRI)\nprocessing pipelines to extract structural and functional connectomes;\nmultiscale co-simulation of spiking and large-scale networks; a domain specific\nlanguage for automatic high-performance code generation from user-specified\nmodels; simulation-ready BNMs of patients and healthy volunteers; Bayesian\ninference of epilepsy spread; data and code for mouse brain simulation; and\nextensive educational material. TVB cloud services facilitate reproducible\nonline collaboration and discovery of data assets, models, and software\nembedded in scalable and secure workflows, a precondition for research on large\ncohort data sets, better generalizability and clinical translation.\n"}
+{"abstract": "  We analyze the Bianchi I cosmology in the presence of a massless scalar field\nand describe its dynamics via a semiclassical and quantum polymer approach. We\nstudy the morphology of the Big Bounce by adopting three different sets of\nconfigurational variables: the Ashtekar connections, a set of anisotropic\nvolume-like coordinates and the Universe volume plus two anisotropy coordinates\n(the latter two sets of variables would coincide in the case of an isotropic\nUniverse). In the semiclassical analysis we demonstrate that the value of the\ncritical matter energy density depends on the Cauchy problem for the dynamics\nwhen adopting the Ashtekar connections or the anisotropic volume-like\ncoordinates. On the contrary, when the Universe volume is considered as a\nconfigurational coordinate, we are able to derive a polymer-modified Friedmann\nequation for the Bianchi I model, from which the expression of the critical\nenergy density can be derived. This analysis shows that the Big Bounce has\nuniversal features only when the Universe volume is defined on the polymer\nlattice. Then, a cosmological constant is included in the Ashtekar connections'\nformulation and some interesting results are mentioned making a comparison\nbetween the synchronous dynamics and that one when the scalar field is taken as\na relational time. From a pure quantum point of view, we investigate the\nBianchi I dynamics in terms of the Ashtekar connections. We apply the ADM\nreduction of the variational principle and then we quantize the system. We\nstudy the resulting Schr\\\"{o}dinger dynamics, stressing that the behavior of\nthe wave packet peak over time singles out common features with the\nsemiclassical trajectories, confirming the non-universal character of the\nemerging Big Bounce also on a quantum level.\n"}
+{"abstract": "  We use the stochastic series expansion quantum Monte Carlo method, together\nwith the eigenstate-to-Hamiltonian mapping approach, to map the localized\nground states of the disordered two-dimensional Heisenberg model, to excited\nstates of a target Hamiltonian. The localized nature of the ground state is\nestablished by studying the spin stiffness, local entanglement entropy, and\nlocal magnetization. This construction allows us to define many body localized\nstates in an energy resolved phase diagram thereby providing concrete numerical\nevidence for the existence of a many-body localized phase in two dimensions.\n"}
+{"abstract": "  There is a large ongoing research effort towards obtaining a quantum\nadvantage in the solution of combinatorial optimization problems on near-term\nquantum devices. A particularly promising platform for testing and developing\nquantum optimization algorithms are arrays of trapped neutral atoms,\nlaser-coupled to highly excited Rydberg states. However, encoding combinatorial\noptimization problems in atomic arrays is challenging due to the limited\ninter-qubit connectivity given by their native finite-range interactions. Here\nwe propose and analyze a fast, high fidelity four-body Rydberg parity gate,\nenabling a direct and straightforward implementation of the\nLechner-Hauke-Zoller (LHZ) scheme and its recent generalization, the parity\narchitecture, a scalable architecture for encoding arbitrarily connected\ninteraction graphs. Our gate relies on onetime-optimized adiabatic laser pulses\nand is fully programmable by adjusting two hold-times during operation. We\nnumerically demonstrate an implementation of the quantum approximate\noptimization algorithm (QAOA) for a small scale test problem. Our approach\nallows for efficient execution of variational optimization steps with a\nconstant number of system manipulations, independent of the system size, thus\npaving the way for experimental investigations of QAOA beyond the reach of\nnumerical simulations.\n"}
+{"abstract": "  In contrast to the generic object, aerial targets are often non-axis aligned\nwith arbitrary orientations having the cluttered surroundings. Unlike the\nmainstreamed approaches regressing the bounding box orientations, this paper\nproposes an effective adaptive points learning approach to aerial object\ndetection by taking advantage of the adaptive points representation, which is\nable to capture the geometric information of the arbitrary-oriented instances.\nTo this end, three oriented conversion functions are presented to facilitate\nthe classification and localization with accurate orientation. Moreover, we\npropose an effective quality assessment and sample assignment scheme for\nadaptive points learning toward choosing the representative oriented reppoints\nsamples during training, which is able to capture the non-axis aligned features\nfrom adjacent objects or background noises. A spatial constraint is introduced\nto penalize the outlier points for roust adaptive learning. Experimental\nresults on four challenging aerial datasets including DOTA, HRSC2016, UCAS-AOD\nand DIOR-R, demonstrate the efficacy of our proposed approach. The source code\nis availabel at: https://github.com/LiWentomng/OrientedRepPoints.\n"}
+{"abstract": "  Color symmetry implies that the colors of geometrical objects are assigned\naccording to their symmetry properties. It is defined by associating the\nelements of the symmetry group with a color permutation. I use this concept for\ngenerative art and apply symmetry-consistent color distortions to images of\npaintings by Johannes Vermeer. The color permutations are realized as mappings\nof the HSV color space onto itself.\n"}
+{"abstract": "  Survival outcomes are common in comparative effectiveness studies and require\nunique handling because they are usually incompletely observed due to\nright-censoring. A ``once for all'' approach for causal inference with survival\noutcomes constructs pseudo-observations and allows standard methods such as\npropensity score weighting to proceed as if the outcomes are completely\nobserved. For a general class of model-free causal estimands with survival\noutcomes on user-specified target populations, we develop corresponding\npropensity score weighting estimators based on the pseudo-observations and\nestablish their asymptotic properties. In particular, utilizing the functional\ndelta-method and the von Mises expansion, we derive a new closed-form variance\nof the weighting estimator that takes into account the uncertainty due to both\npseudo-observation calculation and propensity score estimation. This allows\nvalid and computationally efficient inference without resampling. We also prove\nthe optimal efficiency property of the overlap weights within the class of\nbalancing weights for survival outcomes. The proposed methods are applicable to\nboth binary and multiple treatments. Extensive simulations are conducted to\nexplore the operating characteristics of the proposed method versus other\ncommonly used alternatives. We apply the proposed method to compare the causal\neffects of three popular treatment approaches for prostate cancer patients.\n"}
+{"abstract": "  During the planning phase of industrial robot workplaces, hazard analyses are\nrequired so that potential hazards for human workers can be identified and\nappropriate safety measures can be implemented. Existing hazard analysis\nmethods use human reasoning, checklists and/or abstract system models, which\nlimit the level of detail. We propose a new approach that frames hazard\nanalysis as a search problem in a dynamic simulation environment. Our goal is\nto identify workplace hazards by searching for simulation sequences that result\nin hazardous situations. We solve this search problem by placing virtual humans\ninto workplace simulation models. These virtual humans act in an adversarial\nmanner: They learn to provoke unsafe situations, and thereby uncover workplace\nhazards. Although this approach cannot replace a thorough hazard analysis, it\ncan help uncover hazards that otherwise may have been overlooked, especially in\nearly development stages. Thus, it helps to prevent costly re-designs at later\ndevelopment stages. For validation, we performed hazard analyses in six\ndifferent example scenarios that reflect typical industrial robot workplaces.\n"}
+{"abstract": "  In the past, the axion-nucleon coupling has been calculated in the framework\nof SU(2) heavy baryon chiral perturbation theory up to third order in the\nchiral power counting. Here, we extend these earlier studies to the case of\nheavy baryon chiral perturbation theory with SU(3) flavor symmetry and derive\nthe axion coupling to the full SU(3) baryon octet, showing that the axion also\nsignificantly couples to hyperons. As studies on dense nuclear matter suggest\nthe possible existence of hyperons in stellar objects such as neutron stars,\nour results should have phenomenological implications related to the so-called\naxion window.\n"}
+{"abstract": "  Hardware performance counters (HPCs) that measure low-level architectural and\nmicroarchitectural events provide dynamic contextual information about the\nstate of the system. However, HPC measurements are error-prone due to non\ndeterminism (e.g., undercounting due to event multiplexing, or OS\ninterrupt-handling behaviors). In this paper, we present BayesPerf, a system\nfor quantifying uncertainty in HPC measurements by using a domain-driven\nBayesian model that captures microarchitectural relationships between HPCs to\njointly infer their values as probability distributions. We provide the design\nand implementation of an accelerator that allows for low-latency and low-power\ninference of the BayesPerf model for x86 and ppc64 CPUs. BayesPerf reduces the\naverage error in HPC measurements from 40.1% to 7.6% when events are being\nmultiplexed. The value of BayesPerf in real-time decision-making is illustrated\nwith a simple example of scheduling of PCIe transfers.\n"}
+{"abstract": "  COVID-19 has disrupted normal life and has enforced a substantial change in\nthe policies, priorities and activities of individuals, organisations and\ngovernments. These changes are proving to be a catalyst for technology and\ninnovation. In this paper, we discuss the pandemic's potential impact on the\nadoption of the Internet of Things (IoT) in various broad sectors namely\nhealthcare, smart homes, smart buildings, smart cities, transportation and\nindustrial IoT. Our perspective and forecast of this impact on IoT adoption is\nbased on a thorough research literature review, a careful examination of\nreports from leading consulting firms and interactions with several industry\nexperts. For each of these sectors, we also provide the details of notable IoT\ninitiatives taken in wake of COVID-19. We also highlight the challenges that\nneed to be addressed and important research directions that will facilitate\naccelerated IoT adoption.\n"}
+{"abstract": "  In this (partly expository) paper we give a short overview about the close\nrelationship between the sequence of Catalan numbers and Hankel determinants\nfrom the point of view of orthogonal polynomials and show that an analogous\nsituation exists for more general sequences.\n"}
+{"abstract": "  Ultra-hot Jupiters are defined as giant planets with equilibrium temperatures\nlarger than 2000 K. Most of them are found orbiting bright A-F type stars,\nmaking them extremely suitable objects to study their atmospheres using\nhigh-resolution spectroscopy. Recent studies show a variety of atoms and\nmolecules detected in the atmospheres of this type of planets. Here we present\nour analysis of the newly discovered ultra-hot Jupiter TOI-1431b/MASCARA-5b,\nusing two transit observations with the HARPS-N spectrograph and one transit\nobservation with the EXPRES spectrograph. Analysis of the Rossiter-McLaughlin\neffect shows that the planet is in a polar orbit, with a projected obliquity $\n\\lambda = -155^{+20}_{-10}$ degrees. Combining the nights and applying both\ncross-correlation methods and transmission spectroscopy, we find no evidences\nof CaI, FeI, FeII, MgI, NaI, VI, TiO, VO or H$\\alpha$ in the atmosphere of the\nplanet. Our most likely explanation for the lack of atmospheric features is the\nlarge surface gravity of the planet.\n"}
+{"abstract": "  We present an exact computation of effective Hamiltonians for an elementary\nmodel obtained from the Yukawa theory by going to the limit of bare fermions\nbeing infinitely heavy and bare bosons being at rest with respect to the\nfermions that emit or absorb them. The coupling constant can be arbitrarily\nlarge. The Hamiltonians are computed by solving the differential equation of\nthe renormalization group procedure for effective particles (RGPEP). Physical\nfermions, defined in the model as eigenstates of the effective Hamiltonians,\nare obtained in the form of an effective fermion dressed with a coherent state\nof effective bosons. The model computation illustrates the method that can be\nused in perturbative computations of effective Hamiltonians for realistic\ntheories. It shows the mechanism by which the perturbative expansion and\nTamm-Dancoff approximation increase in accuracy along the RGPEP evolution.\n"}
+{"abstract": "  Blended organic thin films have been studied during the last decades due to\ntheir applicability in organic solar cells. Although their optical and\nelectronic features have been examined intensively, there is still lack of\ndetailed knowledge about their growth processes and resulting morphologies,\nwhich play a key role for the efficiency of optoelectronic devices such as\norganic solar cells. In this study, pure and blended thin films of copper\nphthalocyanine (CuPc) and the Buckminster fullerene (C60) were grown by vacuum\ndeposition onto a native silicon oxide substrate at two different substrate\ntemperatures, 310 K and 400 K. The evolution of roughness was followed by\nin-situ real-time X-ray reflectivity. Crystal orientation, island densities and\nmorphology were examined after the growth by X-ray diffraction experiments and\nmicroscopy techniques. The formation of a smooth wetting layer followed by\nrapid roughening was found in pure CuPc thin films, whereas C60 shows a fast\nformation of distinct islands at a very early stage of growth. The growth of\nneedle-like CuPc crystals loosing their alignment with the substrate was\nidentified in co-deposited thin films. Furthermore, the data demonstrates that\nstructural features become larger and more pronounced and that the island\ndensity decreases by a factor of four when going from 310 K to 400 K. Finally,\nthe key parameters roughness and island density were well reproduced on a\nsmaller scale by kinetic Monte-Carlo simulations of a generic, binary lattice\nmodel with simple nearest-neighbor interaction energies.\n"}
+{"abstract": "  Improving the clock stability is of fundamental importance for the\ndevelopment of quantum-enhanced metrology. One of the main limitations arises\nfrom the randomly-fluctuating local oscillator (LO) frequency, which introduces\n\"phase slips\" for long interrogation times and hence failure of the\nfrequency-feedback loop. Here we propose a strategy to improve the stability of\natomic clocks by interrogating two out-of-phase state sharing the same LO.\nWhile standard Ramsey interrogation can only determine phases unambiguously in\nthe interval $[-\\pi/2,\\pi/2]$, the joint interrogation allows for an extension\nto $[-\\pi,\\pi]$, resulting in a relaxed restriction of the Ramsey time and\nimprovement of absolute clock stability. Theoretical predictions are supported\nby ab-initio numerical simulation for white and correlated LO noise. While our\nbasic protocol uses uncorrelated atoms, we have further extended it to include\nspin-squeezing and further improving the scaling of clock stability with the\nnumber of atoms. Our protocol can be readily tested in current state-of-the-art\nexperiments.\n"}
+{"abstract": "  Rational Krylov subspace projection methods are one of successful methods in\nMOR, mainly because some order derivatives of the approximate and original\ntransfer functions are the same. This is the well known moments matching\nresult. However, the properties of points which are far from the interpolating\npoints are little known. In this paper, we obtain the error's explicit\nexpression which involves shifts and Ritz values. The advantage of our result\nover than the known moments matches theory is, to some extent, similar to the\none of Lagrange type remainder formula over than Peano Type remainder formula\nin Taylor theorem. Expect for the proof, we also provide three explanations for\nthe error formula. One explanation shows that in the Gauss-Christoffel\nquadrature sense, the error is the Gauss quadrature remainder, when the Gauss\nquadrature formula is applied onto the resolvent function. By using the error\nformula, we propose some greedy algorithms for the interpolatory $H_{\\infty}$\nnorm MOR.\n"}
+{"abstract": "  Let $s(n):= \\sum_{d\\mid n,~d<n} d$ denote the sum of the proper divisors of\n$n$. It is natural to conjecture that for each integer $k\\ge 2$, the\nequivalence \\[ \\text{$n$ is $k$th powerfree} \\Longleftrightarrow \\text{$s(n)$\nis $k$th powerfree} \\] holds almost always (meaning, on a set of asymptotic\ndensity $1$). We prove this for $k\\ge 4$.\n"}
+{"abstract": "  In recent years, Graph Neural Networks has received enormous attention from\nacademia for its huge potential of modeling the network traits such as\nmacrostructure and single node attributes. However, prior mainstream works\nmainly focus on homogeneous network and lack the capacity to characterize the\nnetwork heterogeneous property. Besides, most previous literature cannot model\nthe influence under microscope vision, making it infeasible to model the joint\nrelation between the heterogeneity and mutual interaction within multiple\nrelation type. In this paper, we propose an Influence Self-attention network to\naddress the difficulties mentioned above. To model heterogeneity and mutual\ninteraction, we redesign attention mechanism with influence factor on the\nsingle-type relation level, which learns the importance coefficient from its\nadjacent neighbors under the same meta-path based patterns. To incorporate the\nheterogeneous meta-path in a unified dimension, we developed a self-attention\nbased framework for meta-path relation fusion according to the learned\nmeta-path coefficient. Our experimental results demonstrate that our framework\nnot only achieve higher results than current state-of-the-art baselines, but\nalso show promising vision on depicting heterogeneous interactive relations\nunder complicated network structure.\n"}
+{"abstract": "  Stencil computation is one of the most important kernels in various\nscientific and engineering applications. A variety of work has focused on\nvectorization techniques, aiming at exploiting the in-core data parallelism.\nBriefly, they either incur data alignment conflicts or hurt the data locality\nwhen integrated with tiling. In this paper, a novel transpose layout is devised\nto preserve the data locality for tiling in the data space and reduce the data\nreorganization overhead for vectorization simultaneously. We then propose an\napproach of temporal computation folding designed to further reduce the\nredundancy of arithmetic calculations by exploiting the register reuse,\nalleviating the increased register pressure, and deducing generalization with a\nlinear regression model. Experimental results on the AVX-2 and AVX-512 CPUs\nshow that our approach obtains a competitive performance.\n"}
+{"abstract": "  In the last few years, deep learning classifiers have shown promising results\nin image-based medical diagnosis. However, interpreting the outputs of these\nmodels remains a challenge. In cancer diagnosis, interpretability can be\nachieved by localizing the region of the input image responsible for the\noutput, i.e. the location of a lesion. Alternatively, segmentation or detection\nmodels can be trained with pixel-wise annotations indicating the locations of\nmalignant lesions. Unfortunately, acquiring such labels is labor-intensive and\nrequires medical expertise. To overcome this difficulty, weakly-supervised\nlocalization can be utilized. These methods allow neural network classifiers to\noutput saliency maps highlighting the regions of the input most relevant to the\nclassification task (e.g. malignant lesions in mammograms) using only\nimage-level labels (e.g. whether the patient has cancer or not) during\ntraining. When applied to high-resolution images, existing methods produce\nlow-resolution saliency maps. This is problematic in applications in which\nsuspicious lesions are small in relation to the image size. In this work, we\nintroduce a novel neural network architecture to perform weakly-supervised\nsegmentation of high-resolution images. The proposed model selects regions of\ninterest via coarse-level localization, and then performs fine-grained\nsegmentation of those regions. We apply this model to breast cancer diagnosis\nwith screening mammography, and validate it on a large clinically-realistic\ndataset. Measured by Dice similarity score, our approach outperforms existing\nmethods by a large margin in terms of localization performance of benign and\nmalignant lesions, relatively improving the performance by 39.6% and 20.0%,\nrespectively. Code and the weights of some of the models are available at\nhttps://github.com/nyukat/GLAM\n"}
+{"abstract": "  High-dimensional distributed semantic spaces have proven useful and effective\nfor aggregating and processing visual, auditory, and lexical information for\nmany tasks related to human-generated data. Human language makes use of a large\nand varying number of features, lexical and constructional items as well as\ncontextual and discourse-specific data of various types, which all interact to\nrepresent various aspects of communicative information. Some of these features\nare mostly local and useful for the organisation of e.g. argument structure of\na predication; others are persistent over the course of a discourse and\nnecessary for achieving a reasonable level of understanding of the content.\nThis paper describes a model for high-dimensional representation for utterance\nand text level data including features such as constructions or contextual\ndata, based on a mathematically principled and behaviourally plausible approach\nto representing linguistic information. The implementation of the\nrepresentation is a straightforward extension of Random Indexing models\npreviously used for lexical linguistic items. The paper shows how the\nimplemented model is able to represent a broad range of linguistic features in\na common integral framework of fixed dimensionality, which is computationally\nhabitable, and which is suitable as a bridge between symbolic representations\nsuch as dependency analysis and continuous representations used e.g. in\nclassifiers or further machine-learning approaches. This is achieved with\noperations on vectors that constitute a powerful computational algebra,\naccompanied with an associative memory for the vectors. The paper provides a\ntechnical overview of the framework and a worked through implemented example of\nhow it can be applied to various types of linguistic features.\n"}
+{"abstract": "  The TRAPPIST-1 system is a priority target for terrestrial exoplanet\ncharacterization. TRAPPIST-1e, residing in the habitable zone, will be observed\nduring the JWST GTO Program. Here, we assess the prospects of differentiating\nbetween prebiotic and modern Earth scenarios for TRAPPIST-1e via transmission\nspectroscopy. Using updated TRAPPIST-1 stellar models from the Mega-MUSCLES\nsurvey, we compute self-consistent model atmospheres for a 1 bar prebiotic\nEarth scenario and two modern Earth scenarios (1 and 0.5 bar eroded\natmosphere). Our modern and prebiotic high-resolution transmission spectra (0.4\n- 20 $\\mu$m at $R \\sim$ 100,000) are made available online. We conduct a\nBayesian atmospheric retrieval analysis to ascertain the molecular\ndetectability, abundance measurements, and temperature constraints achievable\nfor both scenarios with JWST. We demonstrate that JWST can differentiate\nbetween our prebiotic and modern Earth scenarios within 20 NIRSpec Prism\ntransits via CH$_4$ abundance measurements. However, JWST will struggle to\ndetect O$_3$ for our modern Earth scenario to $> 2\\,\\sigma$ confidence within\nthe nominal mission lifetime ($\\sim$ 80 transits over 5 years). The agnostic\ncombination of N$_2$O and/or O$_3$ offers better prospects, with a predicted\ndetection significance of $2.7\\,\\sigma$ with 100 Prism transits. We show that\ncombining MIRI LRS transits with Prism data provides little improvement to\natmospheric constraints compared to observing additional Prism transits. Though\nbiosignatures will be challenging to detect for TRAPPIST-1e with JWST, the\nabundances for several important molecules - CO$_2$, CH$_4$, and H$_2$O - can\nbe measured to a precision of $\\lesssim$ 0.7 dex (a factor of 5) within a 20\nPrism transit JWST program.\n"}
+{"abstract": "  We present results of a multi-line study of the filamentary infrared dark\ncloud G351.78-0.54 in the 1.3 and 0.8 mm wavelength bands. The lines of the\nthree isotopologues of carbon monoxide CO, N$_2$H$^+$, CH$_3$CCH and HNCO were\nobserved. The aim was to study the general structure of the filamentary cloud,\nits fragmentation and physical parameters with the emphasis on properties of\ndense clumps in this cloud. Several dense clumps are identified from the\nN$_2$H$^+$ (3-2) data, their masses and virial parameters are determined using\nthe C$^{18}$O (2-1) line. Temperatures of some clumps are estimated from the\nCH$_3$CCH and HNCO data. Almost all clumps appear to be gravitationally\nunstable. The density estimates obtained from the C$^{18}$O (3-2)/(2-1) and\nN$_2$H$^+$ (3-2)/(1-0) intensity ratios are in the range $n \\sim (0.3-3)\\times\n10^5$ cm$^{-2}$. The HNCO emission is detected exclusively toward the first\nclump which contains the luminous IR source IRAS 17233-3606, and indicates an\neven higher density. It is observed in the outflow, too. The velocity shift of\nthe higher excitation HNCO lines may indicate a movement of the hot core\nrelative the surrounding medium. In some clumps there is a velocity shift $\\sim\n1$ km s$^{-1}$ between N$_2$H$^+$ (3-2) and CO isotopologues. The large widths\nof the N$_2$H$^+$ (3-2) line in the clumps indicate an increase of the velocity\ndispersion in their dense interiors, which may be related to the star formation\nprocess. The N$_2$H$^+$ abundance drops toward the luminous IR source.\n"}
+{"abstract": "  Dimension four provides a peculiarly idiosyncratic setting for the interplay\nbetween scalar curvature and differential topology. Here we will explain some\nof the peculiarities of the four-dimensional realm via a careful discussion of\nthe Yamabe invariant (or sigma constant). In the process, we will also prove\nsome new results, and point out open problems that continue to represent key\nchallenges in the subject.\n"}
+{"abstract": "  Rate splitting (RS) has emerged as a valuable technology for wireless\ncommunications systems due to its capability to deal with uncertainties in the\nchannel state information at the transmitter (CSIT). RS with linear and\nnon-linear precoders, such as the Tomlinson-Harashima (THP) precoder, have been\nexplored in the downlink (DL) of multiuser multi antenna systems. In this work,\nwe propose a multi-branch (MB) scheme for a RS-based multiple-antenna system,\nwhich creates patterns to order the transmitted symbols and enhances the\noverall sum rate performance compared to existing approaches. Closed-form\nexpressions are derived for the sum rate through statistical analysis.\nSimulation results show that the proposed MB-THP for RS outperforms\nconventional THP and MB-THP schemes.\n"}
+{"abstract": "  Recently, the Siamese-based method has stood out from multitudinous tracking\nmethods owing to its state-of-the-art (SOTA) performance. Nevertheless, due to\nvarious special challenges in UAV tracking, \\textit{e.g.}, severe occlusion and\nfast motion, most existing Siamese-based trackers hardly combine superior\nperformance with high efficiency. To this concern, in this paper, a novel\nattentional Siamese tracker (SiamAPN++) is proposed for real-time UAV tracking.\nBy virtue of the attention mechanism, we conduct a special attentional\naggregation network (AAN) consisting of self-AAN and cross-AAN for raising the\nrepresentation ability of features eventually. The former AAN aggregates and\nmodels the self-semantic interdependencies of the single feature map via\nspatial and channel dimensions. The latter aims to aggregate the\ncross-interdependencies of two different semantic features including the\nlocation information of anchors. In addition, the anchor proposal network based\non dual features is proposed to raise its robustness of tracking objects with\nvarious scales. Experiments on two well-known authoritative benchmarks are\nconducted, where SiamAPN++ outperforms its baseline SiamAPN and other SOTA\ntrackers. Besides, real-world tests onboard a typical embedded platform\ndemonstrate that SiamAPN++ achieves promising tracking results with real-time\nspeed.\n"}
+{"abstract": "  In this paper, we introduce a sequential variational mode decomposition\nmethod to separate non-stationary mixed signals successively. This method is\ninspired by the variational method, and can precisely recover the original\ncomponents one by one from the raw mixture without prior knowing or assuming\nthe number of components. And in such a way, the mode number also can be\ndetermined during the separation procedure. Such character brings great\nconvenience for real application and differs from the current VMD method.\nFurthermore, we also conduct a principal elongation for the mixture signal\nbefore the decomposing operation. By applying such an approach, the end effect\ncan be reduced to a low level compared with the VMD method. To obtain higher\naccuracy, a refinement process has been introduced after gross extraction.\nCombined these techniques together, the final decomposition result implies a\nsignificant improvement compared with the VMD method and EMD method.\n"}
+{"abstract": "  A full Bayesian approach to the estimation of Vaccine Efficacy is presented,\nwhich is an improvement over the currently used exact method conditional on the\ntotal number of cases. As an example, we reconsider the statistical sections of\nthe BioNTech/Pfizer protocol, which in 2020 has led to the first approved\nanti-Covid-19 vaccine.\n"}
+{"abstract": "  Generalized (non-Markovian) diffusion equations with different memory kernels\nand subordination schemes based on random time change in the Brownian diffusion\nprocess are popular mathematical tools for description of a variety of\nnon-Fickian diffusion processes in physics, biology and earth sciences. Some of\nsuch processes (notably, the fluid limits of continuous time random walks)\nallow for either kind of description, but other ones do not. In the present\nwork we discuss the conditions under which a generalized diffusion equation\ndoes correspond to a subordination scheme, and the conditions under which a\nsubordination scheme does possess the corresponding generalized diffusion\nequation. Moreover, we discuss examples of random processes for which only one,\nor both kinds of description are applicable.\n"}
+{"abstract": "  We consider a planar SIS-type Josephson junction between diffusive\nsuperconductors (S) through an insulating tunnel interface (I). We construct\nfully self-consistent perturbation theory with respect to the interface\nconductance. As a result, we find correction to the first Josephson harmonic\nand calculate the second Josephson harmonic. At arbitrary temperatures, we\ncorrect previous results for the nonsinusoidal current-phase relation in\nJosephson tunnel junctions, which were obtained with the help of conjectured\nform of solution. Our perturbation theory also describes the difference between\nthe phases of the order parameter and of the anomalous Green functions.\n"}
+{"abstract": "  We show that the uniform radius of spatial analyticity $\\sigma(t)$ of\nsolution at time $t$ for the fifth order KdV-BBM equation cannot decay faster\nthan $1/t$ for large $t>0$, given initial data that is analytic with fixed\nradius $\\sigma_0$. This significantly improves a recent result by Carvajal and\nPanthee, where they established an exponential decay of $\\sigma(t)$ for large\n$t$.\n"}
+{"abstract": "  Knowledge about the locations of keypoints of an object in an image can\nassist in fine-grained classification and identification tasks, particularly\nfor the case of objects that exhibit large variations in poses that greatly\ninfluence their visual appearance, such as wild animals. However, supervised\ntraining of a keypoint detection network requires annotating a large image\ndataset for each animal species, which is a labor-intensive task. To reduce the\nneed for labeled data, we propose to learn simultaneously keypoint heatmaps and\npose invariant keypoint representations in a semi-supervised manner using a\nsmall set of labeled images along with a larger set of unlabeled images.\nKeypoint representations are learnt with a semantic keypoint consistency\nconstraint that forces the keypoint detection network to learn similar features\nfor the same keypoint across the dataset. Pose invariance is achieved by making\nkeypoint representations for the image and its augmented copies closer together\nin feature space. Our semi-supervised approach significantly outperforms\nprevious methods on several benchmarks for human and animal body landmark\nlocalization.\n"}
+{"abstract": "  The paper is concerned with the time-periodic (T-periodic) problem of the\nfractal Burgers equation with a T-periodic force on the real line. Based on the\nGalerkin approximates and Fourier series (transform) methods, we first prove\nthe existence of T-periodic solution to a linearized version. Then, the\nexistence and uniqueness of T-periodic solution to the nonlinear equation are\nestablished by the contraction mapping argument. Furthermore, we show that the\nunique T-periodic solution is asymptotically stable. This analysis, which is\ncarried out in energy space $ H^{1}(0,T;H^{\\frac{\\alpha}{2}}(R))\\cap\nL^{2}(0,T;\\dot{H}^{\\alpha})$ with $1<\\alpha<\\frac{3}{2}$, extends the\nT-periodic viscid Burgers equation in \\cite{5} to the T-periodic fractional\ncase.\n"}
+{"abstract": "  In this paper we introduce models of short wave-long wave interactions in the\nrelativistic setting. In this context the nonlinear Schr\\\"odinger equation is\nno longer adequate for describing short waves and is replaced by a nonlinear\nDirac equation. Two specific examples are considered: the case where the long\nwaves are governed by a scalar conservation law; and the case where the long\nwaves are governed by the augmented Born-Infeld equations in electromagnetism.\n"}
+{"abstract": "  Aging affects almost all aspects of an organism -- its morphology, its\nphysiology, its behavior. Isolating which biological mechanisms are regulating\nthese changes, however, has proven difficult, potentially due to our inability\nto characterize the full repertoire of an animal's behavior across the\nlifespan. Using data from fruit flies (D. melanogaster) we measure the full\nrepertoire of behaviors as a function of age. We observe a sexually dimorphic\npattern of changes in the behavioral repertoire during aging. Although the\nstereotypy of the behaviors and the complexity of the repertoire overall\nremains relatively unchanged, we find evidence that the observed alterations in\nbehavior can be explained by changing the fly's overall energy budget,\nsuggesting potential connections between metabolism, aging, and behavior.\n"}
+{"abstract": "  The strange visual appearance of objects is one of the puzzling predictions\nof Einstein's relativity. This is mainly due to the distinction between\nmeasuring and seeing, where the former is described by the Lorentz\nTransformation and the latter considers the time light rays (emitted by each\npoint on the object) take to reach the observer. We compute the apparent\nposition of a point given its velocity, initial position, and observation time.\nThe apparent speed of a point is calculated, and we obtain that it exceeds the\nspeed of light when approaching the observer, similar to superluminal motion.\nFor parameterizable surfaces, we analyze properties (such as curvature and\ntorsion) of apparent shapes. The observation that a sphere retains its circular\nsilhouette when transformed to its apparent shape, independent of the initial\nconditions, is proved mathematically. Plots describing the apparent speed and\nlength of objects are made, and the metric tensor for a distorted sphere is\ncalculated. A generalized equation for the Doppler effect and relativistic\naberration is derived to analyze regions of redshift and blueshift. Using the\nBorn-rigidity conditions, we compute the hyperbolic trajectories of each point\non an extended object given an initial velocity, position, and proper\nacceleration for any reference point. The claim that a rigid body, accelerating\nin Special Relativity, cannot exceed a given length in certain circumstances is\njustified. We obtain many non-trivial results, which are proved algebraically\nand using light cones, that are tested by taking the limit of acceleration\napproaching 0 to retrieve results in the constant velocity scenario. In\nconclusion, these visualizations may be used by teachers to explain SR\nintuitively. Finally, we provide an overview of extending the same problem to\ncurved spacetime and explain the potential applications of this project.\n"}
+{"abstract": "  The problem of graph Reachability is to decide whether there is a path from\none vertex to another in a given graph. In this paper, we study the\nReachability problem on three distinct graph families - intersection graphs of\nJordan regions, unit contact disk graphs (penny graphs), and chordal graphs.\nFor each of these graph families, we present space-efficient algorithms for the\nReachability problem.\n  For intersection graphs of Jordan regions, we show how to obtain a \"good\"\nvertex separator in a space-efficient manner and use it to solve the\nReachability in polynomial time and $O(m^{1/2}\\log n)$ space, where $n$ is the\nnumber of Jordan regions, and $m$ is the total number of crossings among the\nregions. We use a similar approach for chordal graphs and obtain a\npolynomial-time and $O(m^{1/2}\\log n)$ space algorithm, where $n$ and $m$ are\nthe number of vertices and edges, respectively. However, we use a more involved\ntechnique for unit contact disk graphs (penny graphs) and obtain a better\nalgorithm. We show that for every $\\epsilon> 0$, there exists a polynomial-time\nalgorithm that can solve Reachability in an $n$ vertex directed penny graph,\nusing $O(n^{1/4+\\epsilon})$ space. We note that the method used to solve penny\ngraphs does not extend naturally to the class of geometric intersection graphs\nthat include arbitrary size cliques.\n"}
+{"abstract": "  Differentiable architecture search (DAS) has made great progress in searching\nfor high-performance architectures with reduced computational cost. However,\nDAS-based methods mainly focus on searching for a repeatable cell structure,\nwhich is then stacked sequentially in multiple stages to form the networks.\nThis configuration significantly reduces the search space, and ignores the\nimportance of connections between the cells. To overcome this limitation, in\nthis paper, we propose a Hierarchical Differentiable Architecture Search\n(H-DAS) that performs architecture search both at the cell level and at the\nstage level. Specifically, the cell-level search space is relaxed so that the\nnetworks can learn stage-specific cell structures. For the stage-level search,\nwe systematically study the architectures of stages, including the number of\ncells in each stage and the connections between the cells. Based on insightful\nobservations, we design several search rules and losses, and mange to search\nfor better stage-level architectures. Such hierarchical search space greatly\nimproves the performance of the networks without introducing expensive search\ncost. Extensive experiments on CIFAR10 and ImageNet demonstrate the\neffectiveness of the proposed H-DAS. Moreover, the searched stage-level\narchitectures can be combined with the cell structures searched by existing DAS\nmethods to further boost the performance. Code is available at:\nhttps://github.com/MalongTech/research-HDAS\n"}
+{"abstract": "  In the race to achieve climate goals, many governments and organizations are\nencouraging the local development of Renewable Energy Technology (RET). The\nspatial innovation dynamics of the development of a technology partly depends\non the characteristics of the knowledge base on which this technology builds,\nin particular the analyticity and cumulativeness of knowledge. Theoretically,\ngreater analyticity and lesser cumulativeness are positively associated with\nmore widespread development. In this study, we first empirically evaluate these\nrelations for general technology and then systematically determine the\nknowledge base characteristics for a set of 14 different RETs. We find that,\nwhile several RETs (photovoltaics, fuel-cells, energy storage) have a highly\nanalytic knowledge base and develop more widespread, there are also important\nRETs (wind turbines, solar thermal, geothermal and hydro energy) for which the\nknowledge base is less analytic and which develop less widespread. Likewise,\nthe technological cumulativeness tends to be lower for the former than for the\nlatter group. This calls for regional and country-level policies to be specific\nfor different RETs, taking for a given RET into account both the type of\nknowledge it builds on as well as the local presence of this knowledge.\n"}
+{"abstract": "  One of the fundamental concerns in the operation of modern power systems is\nthe assessment of their frequency stability in case of inertia-reduction\ninduced by the large share of power electronic interfaced resources. Within\nthis context, the paper proposes a framework that, by making use of linear\nmodels of the frequency response of different types of power plants, including\nalso grid--forming and grid-following converters, is capable to infer a\nnumerically tractable dynamical model to be used in frequency stability\nassessment. Furthermore, the proposed framework makes use of models defined in\na way such that their parameters can be inferred from real-time measurements\nfeeding a classical least squares estimator. The paper validates the proposed\nframework using a full-replica of the dynamical model of the IEEE 39 bus system\nsimulated in a real-time platform.\n"}
+{"abstract": "  Adversarial attacks expose important blind spots of deep learning systems.\nWhile word- and sentence-level attack scenarios mostly deal with finding\nsemantic paraphrases of the input that fool NLP models, character-level attacks\ntypically insert typos into the input stream. It is commonly thought that these\nare easier to defend via spelling correction modules. In this work, we show\nthat both a standard spellchecker and the approach of Pruthi et al. (2019),\nwhich trains to defend against insertions, deletions and swaps, perform poorly\non the character-level benchmark recently proposed in Eger and Benz (2020)\nwhich includes more challenging attacks such as visual and phonetic\nperturbations and missing word segmentations. In contrast, we show that an\nuntrained iterative approach which combines context-independent character-level\ninformation with context-dependent information from BERT's masked language\nmodeling can perform on par with human crowd-workers from Amazon Mechanical\nTurk (AMT) supervised via 3-shot learning.\n"}
+{"abstract": "  We considered the multiphoton resonance in the periodically driven quantum\noscillator with Kerr nonlinearity in the presence of weak high-order\nnonlinearities. Multiphoton resonance leads to the emergence of peaks and dips\nin the dependence of the stationary occupations of the stable states on\ndetuning. We demonstrated that due to high-order nonlinearities, these peaks\nand dips acquire additional fine structure and split into several closely\nspaced ones. Quasiclassically, multiphoton resonance is treated as tunneling\nbetween the regions of the oscillator phase portrait, and the fine structure of\nthe multiphoton resonance is a consequence of a special quasienergy dependence\nof the tunneling rate between different regions of the classical phase\nportrait. For different values of damping and high-order nonlinearity\ncoefficients, we identified the domain of quasienergies where tunneling\nstrongly influences the system kinetics. The corresponding tunneling term in\nthe Fokker-Planck equation in quasienergy space was derived directly from the\nquantum master equation.\n"}
+{"abstract": "  Active learning promises to alleviate the massive data needs of supervised\nmachine learning: it has successfully improved sample efficiency by an order of\nmagnitude on traditional tasks like topic classification and object\nrecognition. However, we uncover a striking contrast to this promise: across 5\nmodels and 4 datasets on the task of visual question answering, a wide variety\nof active learning approaches fail to outperform random selection. To\nunderstand this discrepancy, we profile 8 active learning methods on a\nper-example basis, and identify the problem as collective outliers -- groups of\nexamples that active learning methods prefer to acquire but models fail to\nlearn (e.g., questions that ask about text in images or require external\nknowledge). Through systematic ablation experiments and qualitative\nvisualizations, we verify that collective outliers are a general phenomenon\nresponsible for degrading pool-based active learning. Notably, we show that\nactive learning sample efficiency increases significantly as the number of\ncollective outliers in the active learning pool decreases. We conclude with a\ndiscussion and prescriptive recommendations for mitigating the effects of these\noutliers in future work.\n"}
+{"abstract": "  Let $C/\\mathbb{Q}$ be a hyperelliptic curve with an affine model of the form\n$y^2=x^p+a$. We explicitly determine the root number of the Jacobian of $C$,\nwith particular focus on the local root number at $p$ where $C$ has wild\nramification.\n"}
+{"abstract": "  Cooperative multi-agent reinforcement learning (MARL) has achieved\nsignificant results, most notably by leveraging the representation learning\nabilities of deep neural networks. However, large centralized approaches\nquickly become infeasible as the number of agents scale, and fully\ndecentralized approaches can miss important opportunities for information\nsharing and coordination. Furthermore, not all agents are equal - in some\ncases, individual agents may not even have the ability to send communication to\nother agents or explicitly model other agents. This paper considers the case\nwhere there is a single, powerful, central agent that can observe the entire\nobservation space, and there are multiple, low powered, local agents that can\nonly receive local observations and cannot communicate with each other. The job\nof the central agent is to learn what message to send to different local\nagents, based on the global observations, not by centrally solving the entire\nproblem and sending action commands, but by determining what additional\ninformation an individual agent should receive so that it can make a better\ndecision. After explaining our MARL algorithm, hammer, and where it would be\nmost applicable, we implement it in the cooperative navigation and multi-agent\nwalker domains. Empirical results show that 1) learned communication does\nindeed improve system performance, 2) results generalize to multiple numbers of\nagents, and 3) results generalize to different reward structures.\n"}
+{"abstract": "  I show that any locally Cartesian left localisation of a presentable\ninfinity-category admits a right proper model structure in which all morphisms\nare cofibrations, and obtain a Koszul duality classification of its fibrations.\nBy a simple criterion in terms of generators for a localisation to be locally\nCartesian, this applies to any nullification functor. In particular, it\nincludes examples with non-trivial \"homotopical content.\"\n  I further describe, and provide examples from, the set of fibrations in three\ncontexts: the higher categorical Thomason model structure of Mazel-Gee, where\nfibrations are local systems; Morel-Voevodsky A1-localisation, where they are a\nhigher analogue of A1-covering spaces; and the Quillen plus construction, where\nthey are related to loop space modules trivialised over the universal acyclic\nextension.\n"}
+{"abstract": "  Quantum simulation has shown great potential in many fields due to its\npowerful computational capabilities. However, the limited fidelity can lead to\na severe limitation on the number of gate operations, which requires us to find\noptimized algorithms. Trotter decomposition and high order Trotter\ndecompositions are widely used in quantum simulations. We find that they can be\nsignificantly improved by force-gradient integrator in lattice QCD. By using\ntwo applications as examples, we show that the force-gradient decomposition can\nreduce the number of gate operations up to about a third of those using high\norder Trotter decompositions. Therefore, force-gradient decomposition shows a\ngreat prospective in future applications of quantum simulation.\n"}
+{"abstract": "  Powder X-ray Diffraction (PXRD) and Pair Distribution Function (PDF) analysis\nare well-established techniques for investigation of atomic configurations in\ncrystalline materials, and the two are related by a Fourier transformation. In\nPXRD experiments, structural information, such as crystallite size and strain,\nis contained within the peak profile function of the diffraction peaks.\nHowever, the effects of the PXRD peak profile function on the PDF are not fully\nunderstood. Here, all the effects from a Voigt diffraction peak profile are\nsolved analytically and verified experimentally through a high-quality X-ray\ntotal scattering measurements on strained Ni powder. The Lorentzian\ncontribution to strain broadening is found to result in Voigt shaped PDF peaks.\nFurthermore, it is demonstrated that an improper description of the Voigt shape\nduring model refinement leads to overestimation of the atomic displacement\nparameter.\n"}
+{"abstract": "  The analysis of plaque deposits in the coronary vasculature is an important\ntopic in current clinical research. From a technical side mostly new algorithms\nfor different sub tasks - e.g. centerline extraction or vessel/plaque\nsegmentation - are proposed. However, to enable clinical research with the help\nof these algorithms, a software solution, which enables manual correction,\ncomprehensive visual feedback and tissue analysis capabilities, is needed.\nTherefore, we want to present such an integrated software solution. It is able\nto perform robust automatic centerline extraction and inner and outer vessel\nwall segmentation, while providing easy to use manual correction tools. Also,\nit allows for annotation of lesions along the centerlines, which can be further\nanalyzed regarding their tissue composition. Furthermore, it enables research\nin upcoming technologies and research directions: it does support dual energy\nCT scans with dedicated plaque analysis and the quantification of the fatty\ntissue surrounding the vasculature, also in automated set-ups.\n"}
+{"abstract": "  Under mild conditions on the noise level of the measurements, rotation\naveraging satisfies strong duality, which enables global solutions to be\nobtained via semidefinite programming (SDP) relaxation. However, generic\nsolvers for SDP are rather slow in practice, even on rotation averaging\ninstances of moderate size, thus developing specialised algorithms is vital. In\nthis paper, we present a fast algorithm that achieves global optimality called\nrotation coordinate descent (RCD). Unlike block coordinate descent (BCD) which\nsolves SDP by updating the semidefinite matrix in a row-by-row fashion, RCD\ndirectly maintains and updates all valid rotations throughout the iterations.\nThis obviates the need to store a large dense semidefinite matrix. We\nmathematically prove the convergence of our algorithm and empirically show its\nsuperior efficiency over state-of-the-art global methods on a variety of\nproblem configurations. Maintaining valid rotations also facilitates\nincorporating local optimisation routines for further speed-ups. Moreover, our\nalgorithm is simple to implement; see supplementary material for a\ndemonstration program.\n"}
+{"abstract": "  This paper proposes a deep learning-based method to identify the segments of\na clinical note corresponding to ICD-9 broad categories which are further\ncolor-coded with respect to 17 ICD-9 categories. The proposed Medical Segment\nColorer (MSC) architecture is a pipeline framework that works in three stages:\n(1) word categorization, (2) phrase allocation, and (3) document\nclassification. MSC uses gated recurrent unit neural networks (GRUs) to map\nfrom an input document to word multi-labels to phrase allocations, and uses\nstatistical median to map phrase allocation to document multi-label. We compute\nvariable length segment coloring from overlapping phrase allocation\nprobabilities. These cross-level bidirectional contextual links identify\nadaptive context and then produce segment coloring. We train and evaluate MSC\nusing the document labeled MIMIC-III clinical notes. Training is conducted\nsolely using document multi-labels without any information on phrases,\nsegments, or words. In addition to coloring a clinical note, MSC generates as\nbyproducts document multi-labeling and word tagging -- creation of ICD9\ncategory keyword lists based on segment coloring. Performance comparison of MSC\nbyproduct document multi-labels versus methods whose purpose is to produce\njustifiable document multi-labels is 64% vs 52.4% micro-average F1-score\nagainst the CAML (CNN attention multi label) method. For evaluation of MSC\nsegment coloring results, medical practitioners independently assigned the\ncolors to broad ICD9 categories given a sample of 40 colored notes and a sample\nof 50 words related to each category based on the word tags. Binary scoring of\nthis evaluation has a median value of 83.3% and mean of 63.7%.\n"}
+{"abstract": "  Short-term precipitation forecasting is essential for planning of human\nactivities in multiple scales, ranging from individuals' planning, urban\nmanagement to flood prevention. Yet the short-term atmospheric dynamics are\nhighly nonlinear that it cannot be easily captured with classical time series\nmodels. On the other hand, deep learning models are good at learning nonlinear\ninteractions, but they are not designed to deal with the seasonality in time\nseries. In this study, we aim to develop a forecasting model that can both\nhandle the nonlinearities and detect the seasonality hidden within the daily\nprecipitation data. To this end, we propose a seasonally-integrated autoencoder\n(SSAE) consisting of two long short-term memory (LSTM) autoencoders: one for\nlearning short-term dynamics, and the other for learning the seasonality in the\ntime series. Our experimental results show that not only does the SSAE\noutperform various time series models regardless of the climate type, but it\nalso has low output variance compared to other deep learning models. The\nresults also show that the seasonal component of the SSAE helped improve the\ncorrelation between the forecast and the actual values from 4% at horizon 1 to\n37% at horizon 3.\n"}
+{"abstract": "  We give a new, simpler proof of the fractional Korn's inequality for subsets\nof $\\mathbb{R}^d$. We also show a framework for obtaining Korn's inequality\ndirectly from the appropriate Hardy-type inequality.\n"}
+{"abstract": "  The structure of the icy shells of ocean worlds is important for\nunderstanding the stability of their underlying oceans as it controls the rate\nat which heat can be transported outward and radiated to space. Future\nspacecraft exploration of the ocean worlds (e.g., by NASA's Europa Clipper\nmission) will allow for higher-resolution measurements of gravity and shape\nthan currently available.\n  In this paper, we study the sensitivity of gravity-topography admittance to\nthe structure of icy shells in preparation for future data analysis. An\nanalytical viscous relaxation model is used to predict admittance spectra given\ndifferent shell structures determined by the temperature-dependent viscosity of\na tidally heated, conductive shell. We apply these methods to the ocean worlds\nof Europa and Enceladus. We find that admittance is sensitive to the mechanisms\nof topography support at different wavelengths and estimate the required\ngravity performance to resolve transitions between these mechanisms. We find\nthat the Airy isostatic model is unable to accurately describe admittance\nuniversally across all wavelengths when the shell thickness is a significant\nfraction of body's radius. Our models suggest that measurements of admittance\nat low spherical harmonic degrees are more sensitive to thick shells with high\ntidal dissipation, and may complement ice-penetrating radar measurements in\nconstraining shell thickness. Finally, we find that admittance may be used to\nconstrain the tidal dissipation within the icy shell, which would be\ncomplementary to a more demanding measurement of the tidal phase lag.\n"}
+{"abstract": "  Future direct imaging missions will primarily observe planets that have been\npreviously detected, mostly via the radial velocity (RV) technique, to\ncharacterize planetary atmospheres. In the meantime, direct imaging may\ndiscover new planets within existing planetary systems that have bright enough\nreflected flux, yet with insufficient signals for other methods to detect.\nHere, we investigate the parameter space within which planets are unlikely to\nbe detected by RV in the near future due to precision limitations, but could be\ndiscovered through reflected light with future direct imaging missions. We use\nthe HD 134987 system as a working example, combine RV and direct imaging\ndetection limit curves in the same parameter space through various assumptions,\nand insert a fictitious planet into the system while ensuring it lies between\nthe RV and imaging detection limits. Planet validity tested through dynamical\nsimulations and retrieval tests revealed that the planet could indeed be\ndetected by imaging while remaining hidden from RV surveys. Direct imaging\nretrieval was carried out using starshade simulations for two mission concepts:\nthe Starshade Rendezvous Probe that could be coupled with the Nancy Grace Roman\nSpace Telescope, and the Habitable Exoplanet Observatory. This method is\napplicable to any other systems and high contrast direct imaging instruments,\nand could help inform future imaging observations and data analysis on the\ndiscovery of new exoplanets.\n"}
+{"abstract": "  Recent works highlight the importance of stellar X-rays on the evolution of\nthe circumstellar disks of young stellar objects, especially for disk\nphotoevaporation. A signature of this process may be seen in the so far\ntentatively observed dependence of stellar accretion rates on X-ray\nluminosities. According to models of X-ray driven photoevaporation, stars with\nhigher X-ray luminosities should show lower accretion rates, on average, in a\nsample with similar masses and ages. To this aim, we have analyzed X-ray\nproperties of young stars in the Orion Nebula Cluster determined with Chandra\nduring the COUP observation as well as accretion data obtained from the\nphotometric catalog of the HST Treasury Program. With these data, we have\nperformed a statistical analysis of the relation between X-ray activity and\naccretion rates using partial linear regression analysis. The initial\nanticorrelation found with a sample of 332 young stars is considerably weaker\ncompared to previous studies. However, excluding flaring activity or limiting\nthe X-ray luminosity to the soft band (0.5 - 2.0 keV) leads to a stronger\nanticorrelation, which is statistically more significant. Furthermore, we have\nfound a weak positive correlation between the higher component of the plasma\ntemperature gained in the X-ray spectral fitting and the accretion rates,\nindicating that the hardness of the X-ray spectra may influence the accretion\nprocess. There is evidence for a weak anticorrelation, as predicted by\ntheoretical models, suggesting that X-ray photoevaporation modulates the\naccretion rate through the inner disk at late stages of disk evolution, leading\nto a phase of photoevaporation-starved accretion.\n"}
+{"abstract": "  As the number of novel data-driven approaches to material science continues\nto grow, it is crucial to perform consistent quality, reliability and\napplicability assessments of model performance. In this paper, we benchmark the\nMaterials Optimal Descriptor Network (MODNet) method and architecture against\nthe recently released MatBench v0.1, a curated test suite of materials\ndatasets. MODNet is shown to outperform current leaders on 6 of the 13 tasks,\nwhilst closely matching the current leaders on a further 2 tasks; MODNet\nperforms particularly well when the number of samples is below 10,000.\nAttention is paid to two topics of concern when benchmarking models. First, we\nencourage the reporting of a more diverse set of metrics as it leads to a more\ncomprehensive and holistic comparison of model performance. Second, an equally\nimportant task is the uncertainty assessment of a model towards a target\ndomain. Significant variations in validation errors can be observed, depending\non the imbalance and bias in the training set (i.e., similarity between\ntraining and application space). By using an ensemble MODNet model, confidence\nintervals can be built and the uncertainty on individual predictions can be\nquantified. Imbalance and bias issues are often overlooked, and yet are\nimportant for successful real-world applications of machine learning in\nmaterials science and condensed matter.\n"}
+{"abstract": "  Social media became popular and percolated almost all aspects of our daily\nlives. While online posting proves very convenient for individual users, it\nalso fosters fast-spreading of various rumors. The rapid and wide percolation\nof rumors can cause persistent adverse or detrimental impacts. Therefore,\nresearchers invest great efforts on reducing the negative impacts of rumors.\nTowards this end, the rumor classification system aims to detect, track, and\nverify rumors in social media. Such systems typically include four components:\n(i) a rumor detector, (ii) a rumor tracker, (iii) a stance classifier, and (iv)\na veracity classifier. In order to improve the state-of-the-art in rumor\ndetection, tracking, and verification, we propose VRoC, a tweet-level\nvariational autoencoder-based rumor classification system. VRoC consists of a\nco-train engine that trains variational autoencoders (VAEs) and rumor\nclassification components. The co-train engine helps the VAEs to tune their\nlatent representations to be classifier-friendly. We also show that VRoC is\nable to classify unseen rumors with high levels of accuracy. For the PHEME\ndataset, VRoC consistently outperforms several state-of-the-art techniques, on\nboth observed and unobserved rumors, by up to 26.9%, in terms of macro-F1\nscores.\n"}
+{"abstract": "  Turing machines and register machines have been used for decades in\ntheoretical computer science as abstract models of computation. Also the\n$\\lambda$-calculus has played a central role in this domain as it allows to\nfocus on the notion of functional computation, based on the substitution\nmechanism, while abstracting away from implementation details. The present\narticle starts from the observation that the equivalence between these\nformalisms is based on the Church-Turing Thesis rather than an actual encoding\nof $\\lambda$-terms into Turing (or register) machines. The reason is that these\nmachines are not well-suited for modelling \\lam-calculus programs.\n  We study a class of abstract machines that we call \\emph{addressing machine}\nsince they are only able to manipulate memory addresses of other machines. The\noperations performed by these machines are very elementary: load an address in\na register, apply a machine to another one via their addresses, and call the\naddress of another machine. We endow addressing machines with an operational\nsemantics based on leftmost reduction and study their behaviour. The set of\naddresses of these machines can be easily turned into a combinatory algebra. In\norder to obtain a model of the full untyped $\\lambda$-calculus, we need to\nintroduce a rule that bares similarities with the $\\omega$-rule and the rule\n$\\zeta_\\beta$ from combinatory logic.\n"}
+{"abstract": "  A smooth rigidity inequalitiy provides an explicit lower bound for the\n$(d+1)$-st derivatives of a smooth function $f$, which holds, if $f$ exhibits\ncertain patterns, forbidden for polynomials of degree $d$. The main goal of the\npresent paper is twofold: first, we provide an overview of some recent results\nand questions related to smooth rigidity, which recently were obtained in\nSingularity Theory, in Approximation Theory, and in Whitney smooth extensions.\nSecond, we prove some new results, specifically, a new Remez-type inequality,\nand on this base we obtain a new rigidity inequality. In both parts of the\npaper we stress the topology of the level sets, as the input information. Here\nare the main new results of the paper:\n  \\smallskip\n  Let $B^n$ be the unit $n$-dimensional ball. For a given integer $d$ let\n$Z\\subset B^n$ be a smooth compact hypersurface with $N=(d-1)^n+1$ connected\ncomponents $Z_j$. Let $\\mu_j$ be the $n$-volume of the interior of $Z_j$, and\nput $\\mu=\\min \\mu_j, \\ j=1,\\ldots, N$. Then for each polynomial $P$ of degree\n$d$ on ${\\mathbb R}^n$ we have $$ \\frac{\\max_{B^n}|P|}{\\max_{Z}|P|}\\le\n(\\frac{4n}{\\mu})^d. $$ As a consequence, we provide an explicit lower bound for\nthe $(d+1)$-st derivatives of any smooth function $f$, which vanishes on $Z$,\nwhile being of order $1$ on $B^n$ (smooth rigidity)}: $$ ||f^{(d+1)}||\\ge\n\\frac{1}{(d+1)!}(\\frac{4n}{\\mu})^d. $$ We also provide an interpretation, in\nterms of smooth rigidity, of one of the simplest versions of the results in\n\\cite{Ler.Ste}.\n"}
+{"abstract": "  We prove radial symmetry for bounded nonnegative solutions of a weighted\nanisotropic problem.\n  Given the anisotropic setting that we deal with, the term \"radial\" is\nunderstood in the Finsler framework.\n  In the whole space, J. Serra obtained the symmetry result in the isotropic\nunweighted setting. In this case we provide the extension of his result to the\nanisotropic setting. This provides a generalization to the anisotropic setting\nof a celebrated result due to Gidas-Ni-Nirenberg and such a generalization is\nnew even for in the case of linear operators whenever the dimension is greater\nthan 2.\n  In proper cones, the results presented are new even in the isotropic and\nunweighted setting for suitable nonlinear cases. Even for the previously known\ncase of unweighted isotropic setting, the present paper provides an approach to\nthe problem by exploiting integral (in)equalities which is new for $N>2$: this\ncomplements the corresponding symmetry result obtained via the moving planes\nmethod by Berestycki-Pacella.\n"}
+{"abstract": "  In this paper, we introduce weighted fractional generalized cumulative past\nentropy of a nonnegative absolutely continuous random variable with bounded\nsupport. Various properties of the proposed weighted fractional measure are\nstudied. Bounds and stochastic orderings are derived. A connection between the\nproposed measure and the left-sided Riemann-Liouville fractional integral is\nestablished. Further, the proposed measure is studied for the proportional\nreversed hazard rate models. Next, a nonparametric estimator of the weighted\nfractional generalized cumulative past entropy is suggested based on the\nempirical distribution function. Various examples with a real life data set are\nconsidered for the illustration purposes. Finally, large sample properties of\nthe proposed empirical estimator are studied.\n"}
+{"abstract": "  The BERT model has shown significant success on various natural language\nprocessing tasks. However, due to the heavy model size and high computational\ncost, the model suffers from high latency, which is fatal to its deployments on\nresource-limited devices. To tackle this problem, we propose a dynamic\ninference method on BERT via trainable gate variables applied on input tokens\nand a regularizer that has a bi-modal property. Our method shows reduced\ncomputational cost on the GLUE dataset with a minimal performance drop.\nMoreover, the model adjusts with a trade-off between performance and\ncomputational cost with the user-specified hyperparameter.\n"}
+{"abstract": "  In introductory level electromagnetism courses the calculation of\nelectrostatic potential and electric field in an arbitrary point is a very\ncommon exercise. One of the most viewed cases is the calculation of\nelectrostatic potential and electric field in the symmetry axis of a centered\nring and it has been widely studied the potential off the axis of a charged\nring centered in the origin coordinate. In this work, we calculated the\nelectrostatic potential and electric field in the $z$ axis of a non centered\ncharged ring using elliptic integrals as an pedagogical example of the\napplication of special functions in electromagnetism.\n"}
+{"abstract": "  Nova outbursts play an important role in the chemical evolution of galaxies,\nespecially they are the main source of synthetic $^{13}\\rm C$, $^{15}\\rm N$,\n$^{17}\\rm O$ and some radioactive isotopes like $^{22}\\rm Na$ and $^{26}\\rm\nAl$. The enrichment of He in nova ejecta indicates that the accreted material\nmay mix with the He-shell (He-mixing). The purpose of this work is to\ninvestigate how the He-mixing affects the nova outbursts in a systematic way.\nWe evolved a series of accreting WD models, and found that the mass fraction of\nH and He in nova ejecta can be influenced by different He-mixing fractions\nsignificantly. We also found that both the nova cycle duration and ejected mass\nincrease with the He-mixing fractions. Meanwhile, the nuclear energy production\nfrom $p$-$p$ chains decreases with the He-mixing fraction during the nova\noutbursts, whereas the CNO-cycle increases. The present work can reproduce the\nchemical abundances in the ejecta of some novae, such as GQ Mus, ASASSN-18fv,\nHR Del, T Aur and V443 Sct. This implies that the He-mixing process cannot be\nneglected when studying nova outbursts. This study also develops a He-mixing\nmeter (i.e. $\\rm He/H$) that can be used to estimate the He-mixing fraction in\nclassical nova systems.\n"}
+{"abstract": "  We study the stochastic gravitational waves from string gas cosmology. With\nthe help of the Lambert W function, we derive the exact energy density spectrum\nof the stochastic gravitational waves in term of tensor-to-scalar. New feathers\nwith the spectrum are found. First, the non-Hagedorn phase can be ruled out by\nthe current B-mode polarization in the cosmic microwave background. Second, the\nexact spectrum from the Hagedorn phase with a logarithmic term is shown to be\nunique in the measurable frequency range. Third, which is the most important,\nwe find the string length can be constrained to be lower than 7 $\\sim$ orders\nof that Planck scale.\n"}
+{"abstract": "  We argue against the use of generally weighted moving average (GWMA) control\ncharts. Our primary reasons are the following: 1) There is no recursive formula\nfor the GWMA control chart statistic, so all previous data must be stored and\nused in the calculation of each chart statistic. 2) The Markovian property does\nnot apply to the GWMA statistics, so computer simulation must be used to\ndetermine control limits and the statistical performance. 3) An appropriately\ndesigned, and much simpler, exponentially weighted moving average (EWMA) chart\nprovides as good or better statistical performance. 4) In some cases the GWMA\nchart gives more weight to past data values than to current values.\n"}
+{"abstract": "  Modern online services rely on data stores that replicate their data across\ngeographically distributed data centers. Providing strong consistency in such\ndata stores results in high latencies and makes the system vulnerable to\nnetwork partitions. The alternative of relaxing consistency violates crucial\ncorrectness properties. A compromise is to allow multiple consistency levels to\ncoexist in the data store. In this paper we present UniStore, the first\nfault-tolerant and scalable data store that combines causal and strong\nconsistency. The key challenge we address in UniStore is to maintain liveness\ndespite data center failures: this could be compromised if a strong transaction\ntakes a dependency on a causal transaction that is later lost because of a\nfailure. UniStore ensures that such situations do not arise while paying the\ncost of durability for causal transactions only when necessary. We evaluate\nUniStore on Amazon EC2 using both microbenchmarks and a sample application. Our\nresults show that UniStore effectively and scalably combines causal and strong\nconsistency.\n"}
+{"abstract": "  Sequences of events including infectious disease outbreaks, social network\nactivities, and crimes are ubiquitous and the data on such events carry\nessential information about the underlying diffusion processes between\ncommunities (e.g., regions, online user groups). Modeling diffusion processes\nand predicting future events are crucial in many applications including\nepidemic control, viral marketing, and predictive policing. Hawkes processes\noffer a central tool for modeling the diffusion processes, in which the\ninfluence from the past events is described by the triggering kernel. However,\nthe triggering kernel parameters, which govern how each community is influenced\nby the past events, are assumed to be static over time. In the real world, the\ndiffusion processes depend not only on the influences from the past, but also\nthe current (time-evolving) states of the communities, e.g., people's awareness\nof the disease and people's current interests. In this paper, we propose a\nnovel Hawkes process model that is able to capture the underlying dynamics of\ncommunity states behind the diffusion processes and predict the occurrences of\nevents based on the dynamics. Specifically, we model the latent dynamic\nfunction that encodes these hidden dynamics by a mixture of neural networks.\nThen we design the triggering kernel using the latent dynamic function and its\nintegral. The proposed method, termed DHP (Dynamic Hawkes Processes), offers a\nflexible way to learn complex representations of the time-evolving communities'\nstates, while at the same time it allows to computing the exact likelihood,\nwhich makes parameter learning tractable. Extensive experiments on four\nreal-world event datasets show that DHP outperforms five widely adopted methods\nfor event prediction.\n"}
+{"abstract": "  Form understanding is a challenging problem which aims to recognize semantic\nentities from the input document and their hierarchical relations. Previous\napproaches face significant difficulty dealing with the complexity of the task,\nthus treat these objectives separately. To this end, we present a novel deep\nneural network to jointly perform both entity detection and link prediction in\nan end-to-end fashion. Our model extends the Multi-stage Attentional U-Net\narchitecture with the Part-Intensity Fields and Part-Association Fields for\nlink prediction, enriching the spatial information flow with the additional\nsupervision from entity linking. We demonstrate the effectiveness of the model\non the Form Understanding in Noisy Scanned Documents (FUNSD) dataset, where our\nmethod substantially outperforms the original model and state-of-the-art\nbaselines in both Entity Labeling and Entity Linking task.\n"}
+{"abstract": "  IV-VI materials are some of the most efficient bulk thermoelectric materials\ndue to their proximity to soft-mode phase transitions, which leads to low\nlattice thermal conductivity. It has been shown that the lattice thermal\nconductivity of PbTe can be considerably reduced by bringing PbTe closer to the\nphase transition e.g. via lattice expansion. However, the effect of soft phonon\nmodes on the electronic thermoelectric properties of such system remains\nunknown. Using first principles calculations, we show that the soft zone center\ntransverse optical phonons do not deteriorate the electronic thermoelectric\nproperties of PbTe driven closer to the phase transition via lattice expansion\ndue to external stress, and thus enhance the thermoelectric figure of merit. We\nfind that the optical deformation potentials change very weakly as the\nproximity to the phase transition increases, but the population and scattering\nphase space of soft phonon modes increase. Nevertheless, scattering between\nelectronic states near the band edge and soft optical phonons remains\nrelatively weak even very near the phase transition.\n"}
+{"abstract": "  In micro- and nano-scale systems, particles can be moved by using an external\nforce like gravity or a magnetic field. In the presence of adhesive particles\nthat can attach to each other, the challenge is to decide whether a shape is\nconstructible. Previous work provides a class of shapes for which\nconstructibility can be decided efficiently, when particles move maximally into\nthe same direction induced by a global signal. In this paper we consider the\nsingle step model, i.e., each particle moves one unit step into the given\ndirection. We prove that deciding constructibility is NP-complete for\nthree-dimensional shapes, and that a maximum constructible shape can be\napproximated. The same approximation algorithm applies for 2D. We further\npresent linear-time algorithms to decide whether or not a tree-shape in 2D or\n3D is constructible. Scaling a shape yields constructibility; in particular we\nshow that the $2$-scaled copy of every non-degenerate polyomino is\nconstructible. In the three-dimensional setting we show that the $3$-scaled\ncopy of every non-degenerate polycube is constructible.\n"}
+{"abstract": "  Information transmission over a multiple-input-multiple-output (MIMO) fading\nchannel with imperfect channel state information (CSI) is investigated, under a\nnew receiver architecture which combines the recently proposed generalized\nnearest neighbor decoding rule (GNNDR) and a successive procedure in the spirit\nof successive interference cancellation (SIC). Recognizing that the channel\ninput-output relationship is a nonlinear mapping under imperfect CSI, the GNNDR\nis capable of extracting the information embedded in the joint observation of\nchannel output and imperfect CSI more efficiently than the conventional linear\nscheme, as revealed by our achievable rate analysis via generalized mutual\ninformation (GMI). Numerical results indicate that the proposed scheme achieves\nperformance close to the channel capacity with perfect CSI, and significantly\noutperforms the conventional pilot-assisted scheme, which first estimates the\nCSI and then uses the estimated CSI as the true one for coherent decoding.\n"}
+{"abstract": "  This paper presents our approach to address the EACL WANLP-2021 Shared Task\n1: Nuanced Arabic Dialect Identification (NADI). The task is aimed at\ndeveloping a system that identifies the geographical location(country/province)\nfrom where an Arabic tweet in the form of modern standard Arabic or dialect\ncomes from. We solve the task in two parts. The first part involves\npre-processing the provided dataset by cleaning, adding and segmenting various\nparts of the text. This is followed by carrying out experiments with different\nversions of two Transformer based models, AraBERT and AraELECTRA. Our final\napproach achieved macro F1-scores of 0.216, 0.235, 0.054, and 0.043 in the four\nsubtasks, and we were ranked second in MSA identification subtasks and fourth\nin DA identification subtasks.\n"}
+{"abstract": "  The prominence of figurative language devices, such as sarcasm and irony,\nposes serious challenges for Arabic Sentiment Analysis (SA). While previous\nresearch works tackle SA and sarcasm detection separately, this paper\nintroduces an end-to-end deep Multi-Task Learning (MTL) model, allowing\nknowledge interaction between the two tasks. Our MTL model's architecture\nconsists of a Bidirectional Encoder Representation from Transformers (BERT)\nmodel, a multi-task attention interaction module, and two task classifiers. The\noverall obtained results show that our proposed model outperforms its\nsingle-task counterparts on both SA and sarcasm detection sub-tasks.\n"}
+{"abstract": "  Atomic probe tomography (APT), based on the work of Erwin Mueller, is able to\ngenerate three-dimensional chemical maps in atomic resolution. The required\ninstruments for APT have evolved over the last 20 years from an experimental to\nan established method of materials analysis. Here, we describe the realization\nof a new instrument concept that allows the direct attachment of APT to a dual\nbeam SEM microscope with the main achievement of fast and direct sample\ntransfer. New operational modes are enabled regarding sample geometry,\nalignment of tips and microelectrode. The instrument is optimized to handle\ncryo-samples at all stages of preparation and storage. The instrument comes\nwith its own software for evaluation and reconstruction. The performance in\nterms of mass resolution, aperture angle, and detection efficiency is\ndemonstrated with a few application examples.\n"}
+{"abstract": "  We consider systems that require timely monitoring of sources over a\ncommunication network, where the cost of delayed information is unknown,\ntime-varying and possibly adversarial. For the single source monitoring\nproblem, we design algorithms that achieve sublinear regret compared to the\nbest fixed policy in hindsight. For the multiple source scheduling problem, we\ndesign a new online learning algorithm called\nFollow-the-Perturbed-Whittle-Leader and show that it has low regret compared to\nthe best fixed scheduling policy in hindsight, while remaining computationally\nfeasible. The algorithm and its regret analysis are novel and of independent\ninterest to the study of online restless multi-armed bandit problems. We\nfurther design algorithms that achieve sublinear regret compared to the best\ndynamic policy when the environment is slowly varying. Finally, we apply our\nalgorithms to a mobility tracking problem. We consider non-stationary and\nadversarial mobility models and illustrate the performance benefit of using our\nonline learning algorithms compared to an oblivious scheduling policy.\n"}
+{"abstract": "  Population aging in Brazil and in the world occurs at the same time of\nadvances and evolutions in technology. Thus, opportunities for new solutions\narise for the elderly, such as innovations in Home Care. With the Internet of\nThings, it is possible to improve the elderly autonomy, safety and quality of\nlife. However, the design of IoT solutions for elderly Home Care poses new\nchallenges. In this context, this technical report aims to detail activities\ndeveloped as a case study to evaluate the IoT-PMHCS Method, which was developed\nin the context of the Master's program in Computer Science at UNIFACCAMP,\nBrazil. This report includes the planning and results of interviews,\nparticipatory workshops, validations, simulation of solutions, among other\nactivities. This document reports the practical experience of applying the\nIoT-PMHCS Method.\n  --\n  O envelhecimento populacional no Brasil e no mundo ocorre ao mesmo tempo que\nos avan\\c{c}os e evolu\\c{c}\\~oes na tecnologia. Desta forma, surgem\noportunidades de novas solu\\c{c}\\~oes para o p\\'ublico idoso, tais como\ninova\\c{c}\\~oes em Home Care. Com a Internet das Coisas \\'e poss\\'ivel promover\nmaior autonomia, seguran\\c{c}a e qualidade de vida aos idosos. Entretanto, o\ndesign de solu\\c{c}\\~oes de IoT para Home Care de pessoas idosas traz novos\ndesafios. Diante disto, este relat\\'orio t\\'ecnico tem o objetivo de detalhar\natividades desenvolvidas como estudo de caso para avalia\\c{c}\\~ao do M\\'etodo\nIoT-PMHCS, desenvolvido no contexto do programa de Mestrado em Ci\\^encia da\nComputa\\c{c}\\~ao da UNIFACCAMP, Brasil. O relat\\'orio inclui o planejamento e\nresultados de entrevistas, workshops participativos, pesquisas de\nvalida\\c{c}\\~ao, simula\\c{c}\\~ao de solu\\c{c}\\~oes, dentre outras atividades.\nEste documento relata a experi\\^encia pr\\'atica da aplica\\c{c}\\~ao do M\\'etodo\nIoT-PMHCS.\n"}
+{"abstract": "  The Stokes-Brinkman equations model fluid flow in highly heterogeneous porous\nmedia. In this paper, we consider the numerical solution of the Stokes-Brinkman\nequations with stochastic permeabilities, where the permeabilities in\nsubdomains are assumed to be independent and uniformly distributed within a\nknown interval. We employ a truncated anchored ANOVA decomposition alongside\nstochastic collocation to estimate the moments of the velocity and pressure\nsolutions. Through an adaptive procedure selecting only the most important\nANOVA directions, we reduce the number of collocation points needed for\naccurate estimation of the statistical moments. However, for even modest\nstochastic dimensions, the number of collocation points remains too large to\nperform high-fidelity solves at each point. We use reduced basis methods to\nalleviate the computational burden by approximating the expensive high-fidelity\nsolves with inexpensive approximate solutions on a low-dimensional space. We\nfurthermore develop and analyze rigorous a posteriori error estimates for the\nreduced basis approximation. We apply these methods to 2D problems considering\nboth isotropic and anisotropic permeabilities.\n"}
+{"abstract": "  Semantic Segmentation is a crucial component in the perception systems of\nmany applications, such as robotics and autonomous driving that rely on\naccurate environmental perception and understanding. In literature, several\napproaches are introduced to attempt LiDAR semantic segmentation task, such as\nprojection-based (range-view or birds-eye-view), and voxel-based approaches.\nHowever, they either abandon the valuable 3D topology and geometric relations\nand suffer from information loss introduced in the projection process or are\ninefficient. Therefore, there is a need for accurate models capable of\nprocessing the 3D driving-scene point cloud in 3D space. In this paper, we\npropose S3Net, a novel convolutional neural network for LiDAR point cloud\nsemantic segmentation. It adopts an encoder-decoder backbone that consists of\nSparse Intra-channel Attention Module (SIntraAM), and Sparse Inter-channel\nAttention Module (SInterAM) to emphasize the fine details of both within each\nfeature map and among nearby feature maps. To extract the global contexts in\ndeeper layers, we introduce Sparse Residual Tower based upon sparse convolution\nthat suits varying sparsity of LiDAR point cloud. In addition, geo-aware\nanisotrophic loss is leveraged to emphasize the semantic boundaries and\npenalize the noise within each predicted regions, leading to a robust\nprediction. Our experimental results show that the proposed method leads to a\nlarge improvement (12\\%) compared to its baseline counterpart (MinkNet42\n\\cite{choy20194d}) on SemanticKITTI \\cite{DBLP:conf/iccv/BehleyGMQBSG19} test\nset and achieves state-of-the-art mIoU accuracy of semantic segmentation\napproaches.\n"}
+{"abstract": "  In this paper, we present a hybrid deep learning framework named CTNet which\ncombines convolutional neural network and transformer together for the\ndetection of COVID-19 via 3D chest CT images. It consists of a CNN feature\nextractor module with SE attention to extract sufficient features from CT\nscans, together with a transformer model to model the discriminative features\nof the 3D CT scans. Compared to previous works, CTNet provides an effective and\nefficient method to perform COVID-19 diagnosis via 3D CT scans with data\nresampling strategy. Advanced results on a large and public benchmarks,\nCOV19-CT-DB database was achieved by the proposed CTNet, over the\nstate-of-the-art baseline approachproposed together with the dataset.\n"}
+{"abstract": "  We report analysis of sub-Alfv\\'enic magnetohydrodynamic (MHD) perturbations\nin the low-\\b{eta} radial-field solar wind using the Parker Solar Probe\nspacecraft data from 31 October to 12 November 2018. We calculate wave vectors\nusing the singular value decomposition method and separate the MHD\nperturbations into three types of linear eigenmodes (Alfv\\'en, fast, and slow\nmodes) to explore the properties of the sub-Alfv\\'enic perturbations and the\nrole of compressible perturbations in solar wind heating. The MHD perturbations\nthere show a high degree of Alfv\\'enicity in the radial-field solar wind, with\nthe energy fraction of Alfv\\'en modes dominating (~45%-83%) over those of fast\nmodes (~16%-43%) and slow modes (~1%-19%). We present a detailed analysis of a\nrepresentative event on 10 November 2018. Observations show that fast modes\ndominate magnetic compressibility, whereas slow modes dominate density\ncompressibility. The energy damping rate of compressible modes is comparable to\nthe heating rate, suggesting the collisionless damping of compressible modes\ncould be significant for solar wind heating. These results are valuable for\nfurther studies of the imbalanced turbulence near the Sun and possible heating\neffects of compressible modes at MHD scales in low-\\b{eta} plasma.\n"}
+{"abstract": "  We compute the partition function for 6d $\\mathcal{N}=1$ $SO(2N)$ gauge\ntheories compactified on a circle with $\\mathbb{Z}_2$ outer automorphism twist.\nWe perform the computation based on 5-brane webs with two O5-planes using\ntopological vertex with two O5-planes. As representative examples, we consider\n6d $SO(8)$ and $SU(3)$ gauge theories with $\\mathbb{Z}_2$ twist. We confirm\nthat these partition functions obtained from the topological vertex with\nO5-planes indeed agree with the elliptic genus computations.\n"}
+{"abstract": "  Due to the brittle feature of carbon fiber reinforced plastic laminates,\nmechanical multi-joint within these composite components show uneven load\ndistribution for each bolt, which weaken the strength advantage of composite\nlaminates. In order to reduce this defect and achieve the goal of even load\ndistribution in mechanical joints, we propose a machine learning-based\nframework as an optimization method. Since that the friction effect has been\nproven to be a significant factor in determining bolt load distribution, our\nframework aims at providing optimal parameters including bolt-hole clearances\nand tightening torques for a minimum unevenness of bolt load. A novel circuit\nmodel is established to generate data samples for the training of artificial\nnetworks at a relatively low computational cost. A database for all the\npossible inputs in the design space is built through the machine learning\nmodel. The optimal dataset of clearances and torques provided by the database\nis validated by both the finite element method, circuit model, and an\nexperimental measurement based on the linear superposition principle, which\nshows the effectiveness of this general framework for the optimization problem.\nThen, our machine learning model is further compared and worked in\ncollaboration with commonly used optimization algorithms, which shows the\npotential of greatly increasing computational efficiency for the inverse design\nproblem.\n"}
+{"abstract": "  An ultra-light bosonic particle of mass around $10^{-22}\\,\\mathrm{eV}/c^2$ is\nof special interest as a dark matter candidate, as it both has particle physics\nmotivations, and may give rise to notable differences in the structures on\nhighly non-linear scales due to the manifestation of quantum-physical wave\neffects on macroscopic scales, which could address a number of contentious\nsmall-scale tensions in the standard cosmological model, $\\Lambda$CDM. Using a\nspectral technique, we here discuss simulations of such fuzzy dark matter\n(FDM), including the full non-linear wave dynamics, with a comparatively large\ndynamic range and for larger box sizes than considered previously. While the\nimpact of suppressed small-scale power in the initial conditions associated\nwith FDM has been studied before, the characteristic FDM dynamics are often\nneglected; in our simulations, we instead show the impact of the full\nnon-linear dynamics on physical observables. We focus on the evolution of the\nmatter power spectrum, give first results for the FDM halo mass function\ndirectly based on full FDM simulations, and discuss the computational\nchallenges associated with the FDM equations. FDM shows a pronounced\nsuppression of power on small scales relative to cold dark matter (CDM), which\ncan be understood as a damping effect due to 'quantum pressure'. In certain\nregimes, however, the FDM power can exceed that of CDM, which may be\ninterpreted as a reflection of order-unity density fluctuations occurring in\nFDM. In the halo mass function, FDM shows a significant abundance reduction\nbelow a characteristic mass scale only. This could in principle alleviate the\nneed to invoke very strong feedback processes in small galaxies to reconcile\n$\\Lambda$CDM with the observed galaxy luminosity function, but detailed studies\nthat also include baryons will be needed to ultimately judge the viability of\nFDM.\n"}
+{"abstract": "  We introduce a class of systems of Hamilton-Jacobi equations that\ncharacterize critical points of functionals associated to centroidal\ntessellations of domains, i.e. tessellations where generators and centroids\ncoincide,\n  such as centroidal Voronoi tessellations and centroidal power diagrams. An\nappropriate version of the Lloyd algorithm, combined with a Fast Marching\nmethod on unstructured grids for the Hamilton-Jacobi equation, allows computing\nthe solution of the system. We propose various numerical examples to illustrate\nthe features of the technique.\n"}
+{"abstract": "  Neural dependency parsing has achieved remarkable performance for many\ndomains and languages. The bottleneck of massive labeled data limits the\neffectiveness of these approaches for low resource languages. In this work, we\nfocus on dependency parsing for morphological rich languages (MRLs) in a\nlow-resource setting. Although morphological information is essential for the\ndependency parsing task, the morphological disambiguation and lack of powerful\nanalyzers pose challenges to get this information for MRLs. To address these\nchallenges, we propose simple auxiliary tasks for pretraining. We perform\nexperiments on 10 MRLs in low-resource settings to measure the efficacy of our\nproposed pretraining method and observe an average absolute gain of 2 points\n(UAS) and 3.6 points (LAS). Code and data available at:\nhttps://github.com/jivnesh/LCM\n"}
+{"abstract": "  The band structure of bilayer graphene is tunable by introducing a relative\ntwist angle between the two layers, unlocking exotic phases, such as\nsuperconductor and Mott insulator, and providing a fertile ground for new\nphysics. At intermediate twist angles around 10{\\deg}, highly degenerate\nelectronic transitions hybridize to form excitonic states, a quite unusual\nphenomenon in a metallic system. We probe the bright exciton mode using\nresonant Raman scattering measurements to track the evolution of the intensity\nof the graphene Raman G peak, corresponding to the E2g phonon. By cryogenically\ncooling the sample, we are able to resolve both the incoming and outgoing\nresonance in the G peak intensity evolution as a function of excitation energy,\na prominent manifestation of the bright exciton serving as the intermediate\nstate in the Raman process. For a sample with twist angle 8.6{\\deg}, we report\na weakly temperature dependent resonance broadening ${\\gamma}$ ${\\approx}$ 0.07\neV. In the limit of small inhomogeneous broadening, the observed ${\\gamma}$\nplaces a lower bound for the bright exciton scattering lifetime at 10 fs in the\npresence of charges and excitons excited by the light pulse for Raman\nmeasurement, limited by the rapid exciton-exciton and exciton-charge scattering\nin graphene.\n"}
+{"abstract": "  We consider a dynamic assortment selection problem where a seller has a fixed\ninventory of $N$ substitutable products and faces an unknown demand that\narrives sequentially over $T$ periods. In each period, the seller needs to\ndecide on the assortment of products (of cardinality at most $K$) to offer to\nthe customers. The customer's response follows an unknown multinomial logit\nmodel (MNL) with parameters $v$. The goal of the seller is to maximize the\ntotal expected revenue given the fixed initial inventory of $N$ products. We\ngive a policy that achieves a regret of $\\tilde O\\left(K \\sqrt{K N T}\\left(1 +\n\\frac{\\sqrt{v_{\\max}}}{q_{\\min}}\\text{OPT}\\right) \\right)$ under a mild\nassumption on the model parameters. In particular, our policy achieves a\nnear-optimal $\\tilde O(\\sqrt{T})$ regret in the large inventory setting.\n  Our policy builds upon the UCB-based approach for MNL-bandit without\ninventory constraints in [1] and addresses the inventory constraints through an\nexponentially sized LP for which we present a tractable approximation while\nkeeping the $\\tilde O(\\sqrt{T})$ regret bound.\n"}
+{"abstract": "  The extensive use of medical CT has raised a public concern over the\nradiation dose to the patient. Reducing the radiation dose leads to increased\nCT image noise and artifacts, which can adversely affect not only the\nradiologists judgement but also the performance of downstream medical image\nanalysis tasks. Various low-dose CT denoising methods, especially the recent\ndeep learning based approaches, have produced impressive results. However, the\nexisting denoising methods are all downstream-task-agnostic and neglect the\ndiverse needs of the downstream applications. In this paper, we introduce a\nnovel Task-Oriented Denoising Network (TOD-Net) with a task-oriented loss\nleveraging knowledge from the downstream tasks. Comprehensive empirical\nanalysis shows that the task-oriented loss complements other task agnostic\nlosses by steering the denoiser to enhance the image quality in the task\nrelated regions of interest. Such enhancement in turn brings general boosts on\nthe performance of various methods for the downstream task. The presented work\nmay shed light on the future development of context-aware image denoising\nmethods.\n"}
+{"abstract": "  In the dynamics of open quantum systems, the backflow of information to the\nreduced system under study has been suggested as the actual physical mechanism\ninducing memory and thus leading to non-Markovian quantum dynamics. To this\naim, the trace-distance or Bures-distance revivals between distinct evolved\nsystem states have been shown to be subordinated to the establishment of\nsystem-environment correlations or changes in the environmental state. We show\nthat this interpretation can be substantiated also for a class of entropic\nquantifiers. We exploit a suitably regularized version of Umegaki's quantum\nrelative entropy, known as telescopic relative entropy, that is tightly\nconnected to the quantum Jensen-Shannon divergence. In particular, we derive\ngeneral upper bounds on the telescopic relative entropy revivals conditioned\nand determined by the formation of correlations and changes in the environment.\nWe illustrate our findings by means of examples, considering the\nJaynes-Cummings model and a two-qubit dynamics.\n"}
+{"abstract": "  Transmission eigenfunctions are certain interior resonant modes that are of\ncentral importance to the wave scattering theory. In this paper, we present the\ndiscovery of novel global rigidity properties of the transmission\neigenfunctions associated with the Maxwell system. It is shown that the\ntransmission eigenfunctions carry the geometrical and topological information\nof the underlying domain. We present both analytical and numerical results of\nthese intriguing rigidity properties. As an interesting application, we propose\nan illusion scheme of artificially generating a mirage image of any given\noptical object.\n"}
+{"abstract": "  We consider the ten confidently detected gravitational wave signals in the\nGWTC-1 catalog [1] which are consistent with mergers of binary black hole\nsystems, and re-analyze them with waveform models that contain subdominant\nspherical harmonic modes. This analysis is based on the current (fourth)\ngeneration of the IMRPhenom family of phenomenological waveform models, which\nconsists of the IMRPhenomX frequency-domain models [2-5] and the IMRPhenomT\ntime-domain models [6-8]. We find overall consistent results, with all\nJensen-Shannon divergences between the previous results using IMRPhenomPv2 and\nour default IMRPhenomXPHM posterior results below 0.045 bits. Effects of\nsubdominant harmonics are however visible for several events, and for GW170729\nour new time domain IMRPhenomTPHM model provides the best fit and shifts the\nposterior further toward more unequal masses and a higher primary mass of\n$57.3^{+12.0}_{-10.9}$ solar masses at the lower end of the PISN mass gap.\n"}
+{"abstract": "  The first detection of gravitational waves from the binary neutron star\nmerger GW170817 by the LIGO-Virgo Collaboration has provided fundamental new\ninsights into the astrophysical site for r-process nucleosynthesis and on the\nnature of dense neutron-star matter. The detected gravitational wave signal\ndepends upon the tidal distortion of the neutron stars as they approach merger.\nWe report on relativistic numerical simulations of the approach to binary\nmerger in the conformally flat, quasi-circular orbit approximation. We show\nthat this event serves as a calibration to the quasi-circular approximation and\na confirmation of the validity of the conformally flat approximation to the\nthree-metric. We then examine how the detected chirp depends upon the adopted\nequation of state. This establishes a new efficient means to constrain the\nnuclear equation of state in binary neutron star mergers.\n"}
+{"abstract": "  We propose a new class of robust and Fisher-consistent estimators for mixture\nmodels. These estimators can be used to construct robust model-based clustering\nprocedures. We study in detail the case of multivariate normal mixtures and\npropose a procedure that uses S estimators of multivariate location and\nscatter. We develop an algorithm to compute the estimators and to build the\nclusters which is quite similar to the EM algorithm. An extensive Monte Carlo\nsimulation study shows that our proposal compares favorably with other robust\nand non robust model-based clustering procedures. We apply ours and alternative\nprocedures to a real data set and again find that the best results are obtained\nusing our proposal.\n"}
+{"abstract": "  The EXperiment for Cryogenic Large-Aperture Intensity Mapping (EXCLAIM) is a\ncryogenic balloon-borne instrument that will map carbon monoxide and\nsingly-ionized carbon emission lines across redshifts from 0 to 3.5, using an\nintensity mapping approach. EXCLAIM will broaden our understanding of these\nelemental and molecular gases and the role they play in star formation\nprocesses across cosmic time scales. The focal plane of EXCLAIM's cryogenic\ntelescope features six {\\mu}-Spec spectrometers. {\\mu}-Spec is a compact,\nintegrated grating-analog spectrometer, which uses meandered superconducting\nniobium microstrip transmission lines on a single-crystal silicon dielectric to\nsynthesize the grating. It features superconducting aluminum microwave kinetic\ninductance detectors (MKIDs), also in a microstrip architecture. The\nspectrometers for EXCLAIM couple to the telescope optics via a hybrid planar\nantenna coupled to a silicon lenslet. The spectrometers operate from 420 to 540\nGHz with a resolving power R={\\lambda}/{\\Delta}{\\lambda}=512 and employ an\narray of 355 MKIDs on each spectrometer. The spectrometer design targets a\nnoise equivalent power (NEP) of 2x10-18W/\\sqrt{Hz} (defined at the input to the\nmain lobe of the spectrometer lenslet beam, within a 9-degree half width),\nenabled by the cryogenic telescope environment, the sensitive MKID detectors,\nand the low dielectric loss of single-crystal silicon. We report on these\nspectrometers under development for EXCLAIM, providing an overview of the\nspectrometer and component designs, the spectrometer fabrication process,\nfabrication developments since previous prototype demonstrations, and the\ncurrent status of their development for the EXCLAIM mission.\n"}
+{"abstract": "  The eigenstate thermalization hypothesis provides to date the most successful\ndescription of thermalization in isolated quantum systems by conjecturing\nstatistical properties of matrix elements of typical operators in the\n(quasi-)energy eigenbasis. Here we study the distribution of matrix elements\nfor a class of operators in dual-unitary quantum circuits in dependence of the\nfrequency associated with the corresponding eigenstates. We provide an exact\nasymptotic expression for the spectral function, i.e., the second moment of\nthis frequency resolved distribution. The latter is obtained from the decay of\ndynamical correlations between local operators which can be computed exactly\nfrom the elementary building blocks of the dual-unitary circuits. Comparing the\nasymptotic expression with results obtained by exact diagonalization we find\nexcellent agreement. Small fluctuations at finite system size are explicitly\nrelated to dynamical correlations at intermediate times and the deviations from\ntheir asymptotical dynamics. Moreover, we confirm the expected Gaussian\ndistribution of the matrix elements by computing higher moments numerically.\n"}
+{"abstract": "  The Boussinesq $abcd$ system arises in the modeling of long wave small\namplitude water waves in a channel, where the four parameters $(a,b,c,d)$\nsatisfy one constraint. In this paper we focus on the solitary wave solutions\nto such a system. In particular we work in two parameter regimes where the\nsystem does not admit a Hamiltonian structure (corresponding to $b \\ne d$). We\nprove via analytic global bifurcation techniques the existence of solitary\nwaves in such parameter regimes. Some qualitative properties of the solutions\nare also derived, from which sharp results can be obtained for the global\nsolution curves. Specifically, we first construct solutions bifurcating from\nthe stationary waves, and obtain a global continuous curve of solutions that\nexhibits a loss of ellipticity in the limit. The second family of solutions\nbifurcate from the classical Boussinesq supercritical waves. We show that the\ncurve associated to the second class either undergoes a loss of ellipticity in\nthe limit or becomes arbitrarily close to having a stagnation point.\n"}
+{"abstract": "  The deep theory of approximate subgroups establishes 3-step product growth\nfor subsets of finite simple groups $G$ of Lie type of bounded rank. In this\npaper we obtain 2-step growth results for representations of such groups $G$\n(including those of unbounded rank), where products of subsets are replaced by\ntensor products of representations.\n  Let $G$ be a finite simple group of Lie type and $\\chi$ a character of $G$.\nLet $|\\chi|$ denote the sum of the squares of the degrees of all (distinct)\nirreducible characters of $G$ which are constituents of $\\chi$. We show that\nfor all $\\delta>0$ there exists $\\epsilon>0$, independent of $G$, such that if\n$\\chi$ is an irreducible character of $G$ satisfying $|\\chi| \\le\n|G|^{1-\\delta}$, then $|\\chi^2| \\ge |\\chi|^{1+\\epsilon}$. We also obtain\nresults for reducible characters, and establish faster growth in the case where\n$|\\chi| \\le |G|^{\\delta}$.\n  In another direction, we explore covering phenomena, namely situations where\nevery irreducible character of $G$ occurs as a constituent of certain products\nof characters. For example, we prove that if $|\\chi_1| \\cdots |\\chi_m|$ is a\nhigh enough power of $|G|$, then every irreducible character of $G$ appears in\n$\\chi_1\\cdots\\chi_m$. Finally, we obtain growth results for compact semisimple\nLie groups.\n"}
+{"abstract": "  We study in this paper lower bounds for the generalization error of models\nderived from multi-layer neural networks, in the regime where the size of the\nlayers is commensurate with the number of samples in the training data. We show\nthat unbiased estimators have unacceptable performance for such nonlinear\nnetworks in this regime. We derive explicit generalization lower bounds for\ngeneral biased estimators, in the cases of linear regression and of two-layered\nnetworks. In the linear case the bound is asymptotically tight. In the\nnonlinear case, we provide a comparison of our bounds with an empirical study\nof the stochastic gradient descent algorithm. The analysis uses elements from\nthe theory of large random matrices.\n"}
+{"abstract": "  We construct and study a new class $\\mathscr{M}=\\{\\mathscr{M}_n\\}_{n\\ge 4}$\nof compact hyperbolic $3$-manifolds with totally geodesic boundary. The members\nof $\\mathscr{M}_n$ are defined via triples of pairwise compatible Eulerian\ncycles in $4$-regular $n$-vertex graphs. We show that each $M$ in\n$\\mathscr{M}_n$ is of Matveev complexity $n$ and has a unique minimal ideal\ntriangulation, which consists of $n$ tetrahedra. We exploit these properties to\nshow that $n!\\,4^n > |\\mathscr{M}_n| > n!$ for each sufficiently large\n$n\\in\\mathbb{N}$.\n"}
+{"abstract": "  Accelerated degradation tests are used to provide accurate estimation of\nlifetime properties of highly reliable products within a relatively short\ntesting time. There data from particular tests at high levels of stress\n(e.\\,g.\\ temperature, voltage, or vibration) are extrapolated, through a\nphysically meaningful model, to obtain estimates of lifetime quantiles under\nnormal use conditions. In this work, we consider repeated measures accelerated\ndegradation tests with multiple stress variables, where the degradation paths\nare assumed to follow a linear mixed effects model which is quite common in\nsettings when repeated measures are made. We derive optimal experimental\ndesigns for minimizing the asymptotic variance for estimating the median\nfailure time under normal use conditions when the time points for measurements\nare either fixed in advance or are also to be optimized.\n"}
+{"abstract": "  Classical turnpikes correspond to optimal steady states which are attractors\nof optimal control problems. In this paper, motivated by mechanical systems\nwith symmetries, we generalize this concept to manifold turnpikes.\nSpecifically, the necessary optimality conditions on a symmetry-induced\nmanifold coincide with those of a reduced-order problem under certain\nconditions. We also propose sufficient conditions for the existence of manifold\nturnpikes based on a tailored notion of dissipativity with respect to\nmanifolds. We show how the classical Legendre transformation between\nEuler-Lagrange and Hamilton formalisms can be extended to the adjoint\nvariables. Finally, we draw upon the Kepler problem to illustrate our findings.\n"}
+{"abstract": "  For the Helmholtz equation posed in the exterior of a Dirichlet obstacle, we\nprove that if there exists a family of quasimodes (as is the case when the\nexterior of the obstacle has stable trapped rays), then there exist near-zero\neigenvalues of the standard variational formulation of the exterior Dirichlet\nproblem (recall that this formulation involves truncating the exterior domain\nand applying the exterior Dirichlet-to-Neumann map on the truncation boundary).\n  Our motivation for proving this result is that a) the finite-element method\nfor computing approximations to solutions of the Helmholtz equation is based on\nthe standard variational formulation, and b) the location of eigenvalues, and\nespecially near-zero ones, plays a key role in understanding how iterative\nsolvers such as the generalised minimum residual method (GMRES) behave when\nused to solve linear systems, in particular those arising from the\nfinite-element method. The result proved in this paper is thus the first step\ntowards rigorously understanding how GMRES behaves when applied to\ndiscretisations of high-frequency Helmholtz problems under strong trapping (the\nsubject of the companion paper [Marchand, Galkowski, Spence, Spence, 2021]).\n"}
+{"abstract": "  Let $X$ be a finite set, $Z \\subseteq X$ and $y \\notin X$. Marcel Ern\\'{e}\nshowed in 1981, that the number of posets on $X$ containing $Z$ as an antichain\nequals the number of posets $R$ on $X \\cup \\{ y \\}$ in which the points of $Z\n\\cup \\{ y \\}$ are exactly the maximal points of $R$. We prove the following\ngeneralization: For every poset $Q$ with carrier $Z$, the number of posets on\n$X$ containing $Q$ as an induced sub-poset equals the number of posets $R$ on\n$X \\cup \\{ y \\}$ which contain $Q^d + A_y$ as an induced sub-poset and in which\nthe maximal points of $Q^d + A_y$ are exactly the maximal points of $R$. Here,\n$Q^d$ is the dual of $Q$, $A_y$ is the singleton-poset on $y$, and $Q^d + A_y$\ndenotes the direct sum of $Q^d$ and $A_y$.\n"}
+{"abstract": "  We present a new analysis of the light curve of the young planet-hosting star\nTOI 451 in the light of new observations from TESS Cycle 3. Our joint analysis\nof the transits of all three planets, using all available TESS data, results in\nan improved ephemeris for TOI 451 b and TOI 451 c, which will help to plan\nfollow-up observations. The updated mid-transit times are\n$\\textrm{BJD}-2,457\\,000=$ $1410.9896_{ - 0.0029 }^{ + 0.0032 }$,\n$1411.7982_{-0.0020}^{+0.0022}$, and $1416.63407_{-0.00100}^{+0.00096}$ for TOI\n451 b, c, and d, respectively, and the periods are\n$1.8587028_{-10e-06}^{+08e-06}$, $9.192453_{-3.3e-05}^{+4.1e-05}$, and\n$16.364932_{-3.5e-05}^{+3.6e-05 }$ days. We also model the out-of-transit light\ncurve using a Gaussian Process with a quasi-periodic kernel and infer a change\nin the properties of the active regions on the surface of TOI 451 between TESS\nCycles 1 and 3.\n"}
+{"abstract": "  Multi-relational graph is a ubiquitous and important data structure, allowing\nflexible representation of multiple types of interactions and relations between\nentities. Similar to other graph-structured data, link prediction is one of the\nmost important tasks on multi-relational graphs and is often used for knowledge\ncompletion. When related graphs coexist, it is of great benefit to build a\nlarger graph via integrating the smaller ones. The integration requires\npredicting hidden relational connections between entities belonged to different\ngraphs (inter-domain link prediction). However, this poses a real challenge to\nexisting methods that are exclusively designed for link prediction between\nentities of the same graph only (intra-domain link prediction). In this study,\nwe propose a new approach to tackle the inter-domain link prediction problem by\nsoftly aligning the entity distributions between different domains with optimal\ntransport and maximum mean discrepancy regularizers. Experiments on real-world\ndatasets show that optimal transport regularizer is beneficial and considerably\nimproves the performance of baseline methods.\n"}
+{"abstract": "  For a single server system, Shortest Remaining Processing Time (SRPT) is an\noptimal size-based policy. In this paper, we discuss scheduling a single-server\nsystem when exact information about the jobs' processing times is not\navailable. When the SRPT policy uses estimated processing times, the\nunderestimation of large jobs can significantly degrade performance. We propose\na simple heuristic, Size Estimate Hedging (SEH), that only uses jobs' estimated\nprocessing times for scheduling decisions. A job's priority is increased\ndynamically according to an SRPT rule until it is determined that it is\nunderestimated, at which time the priority is frozen. Numerical results suggest\nthat SEH has desirable performance when estimation errors are not unreasonably\nlarge.\n"}
+{"abstract": "  We propose to use ultra-high intensity laser pulses with wavefront rotation\n(WFR) to produce short, ultra-intense surface plasma waves (SPW) on grating\ntargets for electron acceleration. Combining a smart grating design with\noptimal WFR conditions identified through simple analytical modeling and\nparticle-in-cell simulation allows to decrease the SPW duration (down to few\noptical cycles) and increase its peak amplitude. In the relativistic regime,\nfor $I\\lambda_0^2=3.4 \\times 10^{19}{\\rm W/cm^2\\mu m^2}$, such SPW are found to\naccelerate high-charge (few 10's of pC), high-energy (up to 70 MeV) and\nultra-short (few fs) electron bunches.\n"}
+{"abstract": "  On this note we show how one specific proposal of solution to the problem of\n`closing' the well motivated 331 model to the Standard Model actually implies a\nlower bound for the otherwise theoretically free vacuum expectation value\n$v_\\chi$.\n"}
+{"abstract": "  We show that while orbital magnetic field and disorder, acting individually\nweaken superconductivity, acting together they produce an intriguing evolution\nof a two-dimensional type-II s-wave superconductor. For weak disorder, the\ncritical field H_c at which the superfluid density collapses is coincident with\nthe field at which the superconducting energy gap gets suppressed. However,\nwith increasing disorder these two fields diverge from each other creating a\npseudogap region. The nature of vortices also transform from Abrikosov vortices\nwith a metallic core for weak disorder to Josephson vortices with gapped and\ninsulating cores for higher disorder. Our results naturally explain two\noutstanding puzzles: (1) the gigantic magnetoresistance peak observed as a\nfunction of magnetic field in thin disordered superconducting films; and (2)\nthe disappearance of the celebrated zero-bias Caroli-de Gennes-Matricon peak in\ndisordered superconductors.\n"}
+{"abstract": "  Various applications which run on the machines in a network such as\nInternet-of-Things require different bandwidths. So each machine may select one\nof its multiple Radio Frequency (RF) interfaces for machine-to-machine or\nmachine-to base-station communications according to required bandwidth. We have\nproposed a generalized framework for joint dynamic optimal RF interface setting\nand next-hop selection, which is suitable for networks with multiple base\nstations, and source nodes that have the same requests for bandwidth.\nSimulation results show average data rate of the source nodes may be increased\nup to 117%.\n"}
+{"abstract": "  Wavelength-sized microdisk resonators were fabricated on a single crystalline\n4H-silicon-carbide-oninsulator platform (4H-SiCOI). By carrying out\nmicrophotoluminescence measurements at room temperature, we show that the\nmicrodisk resonators support whispering-gallery modes (WGMs) with quality\nfactors up to $5.25 \\times 10^3$ and mode volumes down to $2.69 \\times(\\lambda\n/n)^3$ at the visible and near-infrared wavelengths. Moreover, the demonstrated\nwavelength-sized microdisk resonators exhibit WGMs whose resonant wavelengths\ncompatible with the zero-phonon lines of spin defects in 4H-SiCOI, making them\na promising candidate for applications in cavity quantum electrodynamics and\nintegrated quantum photonic circuits.\n"}
+{"abstract": "  Yield stress fluids (YSFs) display a dual nature highlighted by the existence\nof a yield stress such that YSFs are solid below the yield stress, whereas they\nflow like liquids above it. Under an applied shear rate $\\dot\\gamma$, the\nsolid-to-liquid transition is associated with a complex spatiotemporal\nscenario. Still, the general phenomenology reported in the literature boils\ndown to a simple sequence that can be divided into a short-time response\ncharacterized by the so-called \"stress overshoot\", followed by stress\nrelaxation towards a steady state. Such relaxation can be either long-lasting,\nwhich usually involves the growth of a shear band that can be only transient or\nthat may persist at steady-state, or abrupt, in which case the solid-to-liquid\ntransition resembles the failure of a brittle material, involving avalanches.\nHere we use a continuum model based on a spatially-resolved fluidity approach\nto rationalize the complete scenario associated with the shear-induced yielding\nof YSFs. Our model provides a scaling for the coordinates of the stress maximum\nas a function of $\\dot\\gamma$, which shows excellent agreement with\nexperimental and numerical data extracted from the literature. Moreover, our\napproach shows that such a scaling is intimately linked to the growth dynamics\nof a fluidized boundary layer in the vicinity of the moving boundary. Yet, such\nscaling is independent of the fate of that layer, and of the long-term behavior\nof the YSF. Finally, when including the presence of \"long-range\" correlations,\nwe show that our model displays a ductile to brittle transition, i.e., the\nstress overshoot reduces into a sharp stress drop associated with avalanches,\nwhich impacts the scaling of the stress maximum with $\\dot\\gamma$. Our work\noffers a unified picture of shear-induced yielding in YSFs, whose complex\nspatiotemporal dynamics are deeply connected to non-local effects.\n"}
+{"abstract": "  We propose a novel IaaS composition framework that selects an optimal set of\nconsumer requests according to the provider's qualitative preferences on\nlong-term service provisions. Decision variables are included in the temporal\nconditional preference networks (TempCP-net) to represent qualitative\npreferences for both short-term and long-term consumers. The global preference\nranking of a set of requests is computed using a \\textit{k}-d tree indexing\nbased temporal similarity measure approach. We propose an extended\nthree-dimensional Q-learning approach to maximize the global preference\nranking. We design the on-policy based sequential selection learning approach\nthat applies the length of request to accept or reject requests in a\ncomposition. The proposed on-policy based learning method reuses historical\nexperiences or policies of sequential optimization using an agglomerative\nclustering approach. Experimental results prove the feasibility of the proposed\nframework.\n"}
+{"abstract": "  Motivated by the theoretical interest in reconstructing long 3D trajectories\nof individual birds in large flocks, we developed CoMo, a co-moving camera\nsystem of two synchronized high speed cameras coupled with rotational stages,\nwhich allow us to dynamically follow the motion of a target flock. With the\nrotation of the cameras we overcome the limitations of standard static systems\nthat restrict the duration of the collected data to the short interval of time\nin which targets are in the cameras common field of view, but at the same time\nwe change in time the external parameters of the system, which have then to be\ncalibrated frame-by-frame. We address the calibration of the external\nparameters measuring the position of the cameras and their three angles of yaw,\npitch and roll in the system \"home\" configuration (rotational stage at an angle\nequal to 0deg and combining this static information with the time dependent\nrotation due to the stages. We evaluate the robustness and accuracy of the\nsystem by comparing reconstructed and measured 3D distances in what we call 3D\ntests, which show a relative error of the order of 1%. The novelty of the work\npresented in this paper is not only on the system itself, but also on the\napproach we use in the tests, which we show to be a very powerful tool in\ndetecting and fixing calibration inaccuracies and that, for this reason, may be\nrelevant for a broad audience.\n"}
+{"abstract": "  We revisit an algorithm constructing elliptic tori, that was originally\ndesigned for applications to planetary hamiltonian systems. The scheme is\nadapted to properly work with models of chains of $N+1$ particles interacting\nvia anharmonic potentials, thus covering also the case of FPU chains. After\nhaving preliminarily settled the Hamiltonian in a suitable way, we perform a\nsequence of canonical transformations removing the undesired perturbative terms\nby an iterative procedure. This is done by using the Lie series approach, that\nis explicitly implemented in a programming code with the help of a software\npackage, which is especially designed for computer algebra manipulations. In\nthe cases of FPU chains with $N=4,\\, 8$, we successfully apply our new\nalgorithm to the construction of elliptic tori for wide sets of the parameter\nruling the size of the perturbation, i.e., the total energy of the system.\nMoreover, we explore the stability regions surrounding 1D elliptic tori. We\ncompare our semi-analytical results with those provided by numerical\nexplorations of the FPU-model dynamics, where the latter ones are obtained by\nusing techniques based on the so called frequency analysis. We find that our\nprocedure works up to values of the total energy that are of the same order of\nmagnitude with respect to the maximal ones, for which elliptic tori are\ndetected by numerical methods.\n"}
+{"abstract": "  Finding an effective formula for describing a discriminant of a quadrinomial\n(a formula which can be easily computed for high values of degrees of\nquadrinomials) is a difficult problem. In 2018 Otake and Shaska using advanced\nmatrix operations found an explicit expression of $\\Delta(x^n+t(x^2+ax+b))$. In\nthis paper we focus on deriving similar results, taking advantage of\nalternative elementary approach, for quadrinomials of the form $x^n+ax^k+bx+c$,\nwhere $ k \\in \\{2,3,n-1\\}$. Moreover, we make some notes about\n$\\Delta(x^{2n}+ax^n+bx^l+c)$ such that $n>2l$.\n"}
+{"abstract": "  We analyse the near infrared colour magnitude diagram of a field including\nthe giant molecular cloud G0.253+0.016 (a.k.a. The Brick) observed at high\nspatial resolution, with HAWK-I at the VLT. The distribution of red clump stars\nin a line of sight crossing the cloud, compared with that in a direction just\nbeside it, and not crossing it, allow us to measure the distance of the cloud\nfrom the Sun to be 7.20, with a statistical uncertainty of +/-0.16 and a\nsystematic error of +/-0.20 kpc. This is significantly closer than what is\ngenerally assumed, i.e., that the cloud belongs to the near side of the central\nmolecular zone, at 60 pc from the Galactic center. This assumption was based on\ndynamical models of the central molecular zone, observationally constrained\nuniquely by the radial velocity of this and other clouds. Determining the true\nposition of the Brick cloud is relevant because this is the densest cloud of\nthe Galaxy not showing any ongoing star formation. This puts the cloud off by 1\norder of magnitude from the Kennicutt-Schmidt relation between the density of\nthe dense gas and the star formation rate. Several explanations have been\nproposed for this absence of star formation, most of them based on the\ndynamical evolution of this and other clouds, within the Galactic center\nregion. Our result emphasizes the need to include constraints coming from\nstellar observations in the interpretation of our Galaxy central molecular\nzone.\n"}
+{"abstract": "  We present a compressive radar design that combines multitone linear\nfrequency modulated (LFM) waveforms in the transmitter with a classical stretch\nprocessor and sub-Nyquist sampling in the receiver. The proposed compressive\nillumination scheme has fewer random elements resulting in reduced storage and\ncomplexity for implementation than previously proposed compressive radar\ndesigns based on stochastic waveforms. We analyze this illumination scheme for\nthe task of a joint range-angle of arrival estimation in the multi-input and\nmulti-output (MIMO) radar system. We present recovery guarantees for the\nproposed illumination technique. We show that for a sufficiently large number\nof modulating tones, the system achieves high-resolution in range and\nsuccessfully recovers the range and angle-of-arrival of targets in a sparse\nscene. Furthermore, we present an algorithm that estimates the target range,\nangle of arrival, and scattering coefficient in the continuum. Finally, we\npresent simulation results to illustrate the recovery performance as a function\nof system parameters.\n"}
+{"abstract": "  Telehealth helps to facilitate access to medical professionals by enabling\nremote medical services for the patients. These services have become gradually\npopular over the years with the advent of necessary technological\ninfrastructure. The benefits of telehealth have been even more apparent since\nthe beginning of the COVID-19 crisis, as people have become less inclined to\nvisit doctors in person during the pandemic. In this paper, we focus on\nfacilitating the chat sessions between a doctor and a patient. We note that the\nquality and efficiency of the chat experience can be critical as the demand for\ntelehealth services increases. Accordingly, we develop a smart auto-response\ngeneration mechanism for medical conversations that helps doctors respond to\nconsultation requests efficiently, particularly during busy sessions. We\nexplore over 900,000 anonymous, historical online messages between doctors and\npatients collected over nine months. We implement clustering algorithms to\nidentify the most frequent responses by doctors and manually label the data\naccordingly. We then train machine learning algorithms using this preprocessed\ndata to generate the responses. The considered algorithm has two steps: a\nfiltering (i.e., triggering) model to filter out infeasible patient messages\nand a response generator to suggest the top-3 doctor responses for the ones\nthat successfully pass the triggering phase. The method provides an accuracy of\n83.28\\% for precision@3 and shows robustness to its parameters.\n"}
+{"abstract": "  A high-order finite element method is proposed to solve the nonlinear\nconvection-diffusion equation on a time-varying domain whose boundary is\nimplicitly driven by the solution of the equation. The method is semi-implicit\nin the sense that the boundary is traced explicitly with a high-order\nsurface-tracking algorithm, while the convection-diffusion equation is solved\nimplicitly with high-order backward differentiation formulas and\nfictitious-domain finite element methods. By two numerical experiments for\nseverely deforming domains, we show that optimal convergence orders are\nobtained in energy norm for third-order and fourth-order methods.\n"}
+{"abstract": "  The continual success of superconducting photon-detection technologies in\nquantum photonics asserts cryogenic-compatible systems as a cornerstone of full\nquantum photonic integration. Here, we present a way to reversibly fine-tune\nthe optical properties of individual waveguide structures through local changes\nto their geometry using solidified xenon. Essentially, we remove the need for\nadditional on-chip calibration elements, effectively zeroing the power\nconsumption tied to reconfigurable elements, with virtually no detriment to\nphotonic device performance. We enable passive circuit tuning in\npressure-controlled environments, locally manipulating the cladding thickness\nover portions of optical waveguides. We realize this in a cryogenic\nenvironment, through controlled deposition of xenon gas and precise tuning of\nits thickness using sublimation, triggered by on-chip resistive heaters. $\\pi$\nphase shifts occur over a calculated length of just $L_{\\pi}$ = 12.3$\\pm$0.3\n$\\mu m$. This work paves the way towards the integration of compact,\nreconfigurable photonic circuits alongside superconducting detectors, devices,\nor otherwise.\n"}
+{"abstract": "  Recovering badly damaged face images is a useful yet challenging task,\nespecially in extreme cases where the masked or damaged region is very large.\nOne of the major challenges is the ability of the system to generalize on faces\noutside the training dataset. We propose to tackle this extreme inpainting task\nwith a conditional Generative Adversarial Network (GAN) that utilizes\nstructural information, such as edges, as a prior condition. Edge information\ncan be obtained from the partially masked image and a structurally similar\nimage or a hand drawing. In our proposed conditional GAN, we pass the\nconditional input in every layer of the encoder while maintaining consistency\nin the distributions between the learned weights and the incoming conditional\ninput. We demonstrate the effectiveness of our method with badly damaged face\nexamples.\n"}
+{"abstract": "  The SPAdes assembler for metagenome assembly is a long-running application\ncommonly used at the NERSC supercomputing site. However, NERSC, like many other\nsites, has a 48-hour limit on resource allocations. The solution is to chain\ntogether multiple resource allocations in a single run, using\ncheckpoint-restart. This case study provides insights into the \"pain points\" in\napplying a well-known checkpointing package (DMTCP: Distributed MultiThreaded\nCheckPointing) to long-running production workloads of SPAdes. This work has\nexposed several bugs and limitations of DMTCP, which were fixed to support the\nlarge memory and fragmented intermediate files of SPAdes. But perhaps more\ninteresting for other applications, this work reveals a tension between the\ntransparency goals of DMTCP and performance concerns due to an I/O bottleneck\nduring the checkpointing process when supporting large memory and many files.\nSuggestions are made for overcoming this I/O bottleneck, which provides\nimportant \"lessons learned\" for similar applications.\n"}
+{"abstract": "  We report the results of our experimental studies on the magnetic, transport\nand thermoelectric properties of the ferromagnetic metal CoMnSb. Sizable\nanomalous Hall conductivity $\\sigma_{yx}$ and transverse thermoelectric\nconductivity $\\alpha_{yx}$ are found experimentally and comparable in size to\nthe values estimated from density-functional theory. Our experiment further\nreveals that CoMnSb exhibits $-T\\ln T$ critical behavior in $\\alpha_{yx}(T)$,\ndeviating from Fermi liquid behavior $\\alpha_{yx}\\sim T$ over a decade of\ntemperature between 10 K to 400 K, similar to ferromagnetic Weyl and nodal-line\nsemimetals. Our theoretical calculation for CoMnSb also predicts the $-T\\ln T$\nbehavior when the Fermi energy locates near the Weyl nodes in momentum space.\n"}
+{"abstract": "  We propose a framework to use Nesterov's accelerated method for constrained\nconvex optimization problems. Our approach consists of first reformulating the\noriginal problem as an unconstrained optimization problem using a continuously\ndifferentiable exact penalty function. This reformulation is based on replacing\nthe Lagrange multipliers in the augmented Lagrangian of the original problem by\nLagrange multiplier functions. The expressions of these Lagrange multiplier\nfunctions, which depend upon the gradients of the objective function and the\nconstraints, can make the unconstrained penalty function non-convex in general\neven if the original problem is convex. We establish sufficient conditions on\nthe objective function and the constraints of the original problem under which\nthe unconstrained penalty function is convex. This enables us to use Nesterov's\naccelerated gradient method for unconstrained convex optimization and achieve a\nguaranteed rate of convergence which is better than the state-of-the-art\nfirst-order algorithms for constrained convex optimization. Simulations\nillustrate our results.\n"}
+{"abstract": "  Despite the growing availability of high-quality public datasets, the lack of\ntraining samples is still one of the main challenges of deep-learning for skin\nlesion analysis. Generative Adversarial Networks (GANs) appear as an enticing\nalternative to alleviate the issue, by synthesizing samples indistinguishable\nfrom real images, with a plethora of works employing them for medical\napplications. Nevertheless, carefully designed experiments for skin-lesion\ndiagnosis with GAN-based data augmentation show favorable results only on\nout-of-distribution test sets. For GAN-based data anonymization $-$ where the\nsynthetic images replace the real ones $-$ favorable results also only appear\nfor out-of-distribution test sets. Because of the costs and risks associated\nwith GAN usage, those results suggest caution in their adoption for medical\napplications.\n"}
+{"abstract": "  Human motion recognition (HMR) based on wireless sensing is a low-cost\ntechnique for scene understanding. Current HMR systems adopt support vector\nmachines (SVMs) and convolutional neural networks (CNNs) to classify radar\nsignals. However, whether a deeper learning model could improve the system\nperformance is currently not known. On the other hand, training a machine\nlearning model requires a large dataset, but data gathering from experiment is\ncost-expensive and time-consuming. Although wireless channel models can be\nadopted for dataset generation, current channel models are mostly designed for\ncommunication rather than sensing. To address the above problems, this paper\nproposes a deep spectrogram network (DSN) by leveraging the residual mapping\ntechnique to enhance the HMR performance. Furthermore, a primitive based\nautoregressive hybrid (PBAH) channel model is developed, which facilitates\nefficient training and testing dataset generation for HMR in a virtual\nenvironment. Experimental results demonstrate that the proposed PBAH channel\nmodel matches the actual experimental data very well and the proposed DSN\nachieves significantly smaller recognition error than that of CNN.\n"}
+{"abstract": "  Tropical toric varieties are partial compactifications of finite dimensional\nreal vector spaces associated with rational polyhedral fans. We introduce\nplurisubharmonic functions and a Bedford--Taylor product for Lagerberg currents\non open subsets of a tropical toric variety. The resulting tropical toric\npluripotential theory provides the link to give a canonical correspondence\nbetween complex and non-archimedean pluripotential theories of invariant\nplurisubharmonic functions on toric varieties. We will apply this\ncorrespondence to solve invariant non-archimedean Monge--Amp\\`ere equations on\ntoric and abelian varieties over arbitrary non-archimedean fields.\n"}
+{"abstract": "  We discuss our recent theoretical work on vibronic coupling mechanisms in a\nmodel energy transfer system in the context of previous 2DEV experiments on a\nnatural light-harvesting system, light-harvesting complex II (LHCII), where\nvibronic signatures were suggested to be involved in energy transfer. In this\ncomparison, we directly assign the vibronic coupling mechanism in LHCII as\narising from Herzberg-Teller activity and show how this coupling modulates the\nenergy transfer dynamics in this photosynthetic system.\n"}
+{"abstract": "  We investigate universal estimate of finite Morse index solutions to\npolyharmonic equation in a proper open subset of $\\mathbb{R}^n$. Differently to\nprevious works \\cite{DDF, fa, H1, WY} , we propose here a direct proof under\nlarge superlinear and subcritical growth conditions on the term source where we\nshow that the universal constant evolves as a polynomial function of the Morse\nindex. To do so, we introduce a new interpolation inequality and we make use of\nPohozaev's identity and a delicate boot strap argument. Thanks to our\ninterpolation inequality, we improve previous nonexistence results \\cite{H1,\nFH} dealing with stable at infinity weak solutions to the $p$-polyharmonic\nequation in the subcritical range.\n"}
+{"abstract": "  In this paper, based on the classical K. Yano's formula, we first establish\nan optimal integral inequality for compact Lagrangian submanifolds in the\ncomplex space forms, which involves the Ricci curvature in the direction\n$J\\vec{H}$ and the norm of the covariant differentiation of the second\nfundamental form $h$, where $J$ is the almost complex structure and $\\vec{H}$\nis the mean curvature vector field. Second and analogously, for compact\nLegendrian submanifolds in the Sasakian space forms with Sasakian structure\n$(\\varphi,\\xi,\\eta,g)$, we also establish an optimal integral inequality\ninvolving the Ricci curvature in the direction $\\varphi\\vec{H}$ and the norm of\nthe modified covariant differentiation of the second fundamental form. The\nintegral inequality is optimal in the sense that all submanifolds attaining the\nequality are completely classified. As direct consequences, we obtain new and\nglobal characterizations for the Whitney spheres in complex space forms as well\nas the contact Whitney spheres in Sasakian space forms. Finally, we show that,\njust as the Whitney spheres in complex space forms, the contact Whitney spheres\nin Sasakian space forms are locally conformally flat manifolds with sectional\ncurvatures non-constant.\n"}
+{"abstract": "  In this paper, we investigate how the COVID-19 pandemics and more precisely\nthe lockdown of a sector of the economy may have changed our habits and,\nthere-fore, altered the demand of some goods even after the re-opening. In a\ntwo-sector infinite horizon economy, we show that the demand of the goods\nproduced by the sector closed during the lockdown could shrink or expand with\nrespect to their pre-pandemic level depending on the length of the lockdown and\nthe relative strength of the satiation effect and the substitutability effect.\nWe also provide conditions under which this sector could remain inactive even\nafter the lockdown as well as an insight on the policy which should be adopted\nto avoid this outcome.\n"}
+{"abstract": "  In order to create user-centric and personalized privacy management tools,\nthe underlying models must account for individual users' privacy expectations,\npreferences, and their ability to control their information sharing activities.\nExisting studies of users' privacy behavior modeling attempt to frame the\nproblem from a request's perspective, which lack the crucial involvement of the\ninformation owner, resulting in limited or no control of policy management.\nMoreover, very few of them take into the consideration the aspect of\ncorrectness, explainability, usability, and acceptance of the methodologies for\neach user of the system. In this paper, we present a methodology to formally\nmodel, validate, and verify personalized privacy disclosure behavior based on\nthe analysis of the user's situational decision-making process. We use a model\nchecking tool named UPPAAL to represent users' self-reported privacy disclosure\nbehavior by an extended form of finite state automata (FSA), and perform\nreachability analysis for the verification of privacy properties through\ncomputation tree logic (CTL) formulas. We also describe the practical use cases\nof the methodology depicting the potential of formal technique towards the\ndesign and development of user-centric behavioral modeling. This paper, through\nextensive amounts of experimental outcomes, contributes several insights to the\narea of formal methods and user-tailored privacy behavior modeling.\n"}
+{"abstract": "  Non-reciprocal plasmons in current-driven, isotropic, and homogenous graphene\nwith proximal metallic gates is theoretically explored. Nearby metallic gates\nscreen the Coulomb interactions, leading to linearly dispersive acoustic\nplasmons residing close to its particle-hole continuum counterpart. We show\nthat the applied bias leads to spectral broadband focused plasmons whose\nresonance linewidth is dependent on the angular direction relative to the\ncurrent flow due to Landau damping. We predict that forward focused\nnon-reciprocal plasmons are possible with accessible experimental parameters\nand setup.\n"}
+{"abstract": "  We theoretically study the superconductivity in multiorbital superconductors\nbased on a three-orbital tight-banding model. With appropriate values of the\nnearest-neighbour exchange $J_{1}^{\\alpha \\beta}$ and the\nnext-nearest-neighbour exchange $J_{2}^{\\alpha \\beta}$, we find a two-dome\nstructure in the $T_{c}-n$ phase diagram: one dome in the doping range $n<3.9$\nwhere the superconducting (SC) state is mainly $s_{x^{2} y^{2}}$ component\ncontributed by inter-orbital pairing, the other dome in the doping range\n$3.9<n<4.46$ where the SC state is mainly $s_{x^{2} y^{2}}+s_{x^{2}+y^{2}}$\ncomponents contributed by intra-orbital pairing. We find that the competition\nbetween different orbital pairing leads to two-dome SC phase diagrams in\nmultiorbital superconductors, and different matrix elements of $J_{1}$ and\n$J_{2}$ considerably affect the boundary of two SC domes.\n"}
+{"abstract": "  We survey recent developments in the study of Hodge theoretic aspects of\nAlexander-type invariants associated with smooth complex algebraic varieties.\n"}
+{"abstract": "  For fixed integers $D \\geq 0$ and $c \\geq 3$, we demonstrate how to use\n$2$-adic valuation trees of sequences to analyze Diophantine equations of the\nform $x^2+D=2^cy$ and $x^3+D=2^cy$, for $y$ odd. Further, we show for what\nvalues $D \\in \\mathbb{Z}^+$, the numbers $x^3+D$ will generate infinite\nvaluation trees, which lead to infinite solutions to the above Diophantine\nequations.\n"}
+{"abstract": "  This paper introduces a new specification for the nonparametric\nproduction-frontier based on Data Envelopment Analysis (DEA) when dealing with\ndecision-making units whose economic performances are correlated with those of\nthe neighbors (spatial dependence). To illustrate the bias reduction that the\nSpDEA provides with respect to standard DEA methods, an analysis of the\nregional production frontiers for the NUTS-2 European regions during the period\n2000-2014 was carried out. The estimated SpDEA scores show a bimodal\ndistribution do not detected by the standard DEA estimates. The results confirm\nthe crucial role of space, offering important new insights on both the causes\nof regional disparities in labour productivity and the observed polarization of\nthe European distribution of per capita income.\n"}
+{"abstract": "  In this paper we consider the relationship between monomial-size and\nbit-complexity in Sums-of-Squares (SOS) in Polynomial Calculus Resolution over\nrationals (PCR/$\\mathbb{Q}$). We show that there is a set of polynomial\nconstraints $Q_n$ over Boolean variables that has both SOS and PCR/$\\mathbb{Q}$\nrefutations of degree 2 and thus with only polynomially many monomials, but for\nwhich any SOS or PCR/$\\mathbb{Q}$ refutation must have exponential\nbit-complexity, when the rational coefficients are represented with their\nreduced fractions written in binary.\n"}
+{"abstract": "  Automatic speaker verification (ASV), one of the most important technology\nfor biometric identification, has been widely adopted in security-critical\napplications, including transaction authentication and access control. However,\nprevious work has shown that ASV is seriously vulnerable to recently emerged\nadversarial attacks, yet effective countermeasures against them are limited. In\nthis paper, we adopt neural vocoders to spot adversarial samples for ASV. We\nuse the neural vocoder to re-synthesize audio and find that the difference\nbetween the ASV scores for the original and re-synthesized audio is a good\nindicator for discrimination between genuine and adversarial samples. This\neffort is, to the best of our knowledge, among the first to pursue such a\ntechnical direction for detecting adversarial samples for ASV, and hence there\nis a lack of established baselines for comparison. Consequently, we implement\nthe Griffin-Lim algorithm as the detection baseline. The proposed approach\nachieves effective detection performance that outperforms all the baselines in\nall the settings. We also show that the neural vocoder adopted in the detection\nframework is dataset-independent. Our codes will be made open-source for future\nworks to do comparison.\n"}
+{"abstract": "  Loop compilation for Tightly Coupled Processor Arrays (TCPAs), a class of\nmassively parallel loop accelerators, entails solving NP-hard problems, yet\ndepends on the loop bounds and number of available processing elements (PEs),\nparameters known only at runtime because of dynamic resource management and\ninput sizes. Therefore, this article proposes a two-phase approach called\nsymbolic loop compilation: At compile time, the necessary NP-complete problems\nare solved and the solutions compiled into a space-efficient symbolic\nconfiguration. At runtime, a concrete configuration is generated from the\nsymbolic configuration according to the parameters values. We show that the\nlatter phase, called instantiation, runs in polynomial time with its most\ncomplex step, program instantiation, not depending on the number of PEs. As\nvalidation, we performed symbolic loop compilation on real-world loops and\nmeasured time and space requirements. Our experiments confirm that a symbolic\nconfiguration is space-efficient and suited for systems with little memory --\noften, a symbolic configuration is smaller than a single concrete configuration\n-- and that program instantiation scales well with the number of PEs -- for\nexample, when instantiating a symbolic configuration of a matrix-matrix\nmultiplication, the execution time is similar for $4\\times 4$ and $32\\times 32$\nPEs.\n"}
+{"abstract": "  Learning behavior mechanism is widely anticipated in managed settings through\nthe formal syllabus. However, heading for learning stimulus whilst daily\nmobility practices through urban transit is the novel feature in learning\nsciences. Theory of planned behavior (TPB), technology acceptance model (TAM),\nand service quality of transit are conceptualized to assess the learning\nbehavioral intention (LBI) of commuters in Greater Kuala Lumpur. An online\nsurvey was conducted to understand the LBI of 117 travelers who use the\ntechnology to engage in the informal learning process during daily commuting.\nThe results explored that all the model variables i.e., perceived ease of use,\nperceived usefulness, service quality, and subjective norms are significant\npredictors of LBI. The perceived usefulness of learning during traveling and\ntransit service quality has a vibrant impact on LBI. The research will support\nthe informal learning mechanism from commuters point of view. The study is a\nnovel contribution to transport and learning literature that will open the new\nprospect of research in urban mobility and its connotation with personal\nlearning and development.\n"}
+{"abstract": "  Diffuse interface models are widely used to describe evolution of multi-phase\nsystems of different nature. Dispersed \"inclusions\", described by the phase\nfield distribution, are usually three dimensional objects. When describing\nelastic fracture evolution, elements of the dispersed phase are effectively 2d\nobjects. An example of the model which governs evolution of effectively 1d\ndispersed inclusions is phase field model for electric breakdown in solids.\nPhase field model is defined by appropriate free energy functional, which\ndepends on phase field and its derivatives. In this work we show that\ncodimension of the dispersed \"inclusion\" significantly restrict the functional\ndependency of system energy on the derivatives of the problem state variables.\nIt is shown that free energy of any phase field model suitable to describe\ncodimension 2 diffuse objects necessary depends on higher order derivatives of\nthe phase field or need an additional smoothness of the solution - it should\nhave first derivatives integrable with a power greater then two. To support\ntheoretical discussion, some numerical experiments are presented.\n"}
+{"abstract": "  Giant radio pulses (GRPs) are sporadic bursts emitted by some pulsars,\nlasting a few microseconds. GRPs are hundreds to thousands of times brighter\nthan regular pulses from these sources. The only GRP-associated emission\noutside radio wavelengths is from the Crab Pulsar, where optical emission is\nenhanced by a few percent during GRPs. We observed the Crab Pulsar\nsimultaneously at X-ray and radio wavelengths, finding enhancement of the X-ray\nemission by $3.8\\pm0.7\\%$ (a 5.4$\\sigma$ detection) coinciding with GRPs. This\nimplies that the total emitted energy from GRPs is tens to hundreds of times\nhigher than previously known. We discuss the implications for the pulsar\nemission mechanism and extragalactic fast radio bursts.\n"}
+{"abstract": "  In transition metal compounds, due to the interplay of charge, spin, lattice\nand orbital degrees of freedom, many intertwined orders exist with close\nenergies. One of the commonly observed states is the so-called nematic electron\nstate, which breaks the in-plane rotational symmetry. This nematic state\nappears in cuprates, iron-based superconductor, etc. Nematicity may coexist,\naffect, cooperate or compete with other orders. Here we show the anisotropic\nin-plane electronic state and superconductivity in a recently discovered kagome\nmetal CsV$_3$Sb$_5$ by measuring $c$-axis resistivity with the in-plane\nrotation of magnetic field. We observe a twofold symmetry of superconductivity\nin the superconducting state and a unique in-plane nematic electronic state in\nnormal state when rotating the in-plane magnetic field. Interestingly these two\norders are orthogonal to each other in terms of the field direction of the\nminimum resistivity. Our results shed new light in understanding non-trivial\nphysical properties of CsV$_3$Sb$_5$.\n"}
+{"abstract": "  CSI (Channel State Information) of WiFi systems contains the environment\nchannel response between the transmitter and the receiver, so the\npeople/objects and their movement in between can be sensed. To get CSI, the\nreceiver performs channel estimation based on the pre-known training field of\nthe transmitted WiFi signal. CSI related technology is useful in many cases,\nbut it also brings concerns on privacy and security. In this paper, we open\nsourced a CSI fuzzer to enhance the privacy and security of WiFi CSI\napplications. It is built and embedded into the transmitter of openwifi, which\nis an open source full-stack WiFi chip design, to prevent unauthorized sensing\nwithout sacrificing the WiFi link performance. The CSI fuzzer imposes an\nartificial channel response to the signal before it is transmitted, so the CSI\nseen by the receiver will indicate the actual channel response combined with\nthe artificial response. Only the authorized receiver, that knows the\nartificial response, can calculate the actual channel response and perform the\nCSI sensing. Another potential application of the CSI fuzzer is covert channels\nbased on a set of pre-defined artificial response patterns. Our work resolves\nthe pain point of implementing the anti-sensing idea based on the commercial\noff-the-shelf WiFi devices.\n"}
+{"abstract": "  Evidence that visual communication preceded written language and provided a\nbasis for it goes back to prehistory, in forms such as cave and rock paintings\ndepicting traces of our distant ancestors. Emergent communication research has\nsought to explore how agents can learn to communicate in order to\ncollaboratively solve tasks. Existing research has focused on language, with a\nlearned communication channel transmitting sequences of discrete tokens between\nthe agents. In this work, we explore a visual communication channel between\nagents that are allowed to draw with simple strokes. Our agents are\nparameterised by deep neural networks, and the drawing procedure is\ndifferentiable, allowing for end-to-end training. In the framework of a\nreferential communication game, we demonstrate that agents can not only\nsuccessfully learn to communicate by drawing, but with appropriate inductive\nbiases, can do so in a fashion that humans can interpret. We hope to encourage\nfuture research to consider visual communication as a more flexible and\ndirectly interpretable alternative of training collaborative agents.\n"}
+{"abstract": "  Machine learning has been increasingly used as a first line of defense for\nWindows malware detection. Recent work has however shown that learning-based\nmalware detectors can be evaded by carefully-perturbed input malware samples,\nreferred to as adversarial EXEmples, thus demanding for tools that can ease and\nautomate the adversarial robustness evaluation of such detectors. To this end,\nwe present secml-malware, the first Python library for computing adversarial\nattacks on Windows malware detectors. \\secmlmalware implements state-of-the-art\nwhite-box and black-box attacks on Windows malware classifiers, by leveraging a\nset of feasible manipulations that can be applied to Windows programs while\npreserving their functionality. The library can be used to perform the\npenetration testing and assessment of the adversarial robustness of Windows\nmalware detectors, and it can be easily extended to include novel attack\nstrategies. Our library is available at\nhttps://github.com/pralab/secml_malware.\n"}
+{"abstract": "  Motivated by the need for estimating the 3D pose of arbitrary objects, we\nconsider the challenging problem of class-agnostic object viewpoint estimation\nfrom images only, without CAD model knowledge. The idea is to leverage features\nlearned on seen classes to estimate the pose for classes that are unseen, yet\nthat share similar geometries and canonical frames with seen classes. We train\na direct pose estimator in a class-agnostic way by sharing weights across all\nobject classes, and we introduce a contrastive learning method that has three\nmain ingredients: (i) the use of pre-trained, self-supervised, contrast-based\nfeatures; (ii) pose-aware data augmentations; (iii) a pose-aware contrastive\nloss. We experimented on Pascal3D+, ObjectNet3D and Pix3D in a cross-dataset\nfashion, with both seen and unseen classes. We report state-of-the-art results,\nincluding against methods that additionally use CAD models as input.\n"}
+{"abstract": "  This paper deals with the classification of groups G such that power graphs\nand proper power graphs of G are line graphs. In fact, We classify all finite\nnilpotent groups whose power graphs are line graphs. Also we categorize all\nfinite nilpotent groups (except non abelian 2-groups) whose proper power graph\nare line graphs. Moreover, we study that the proper power graphs of generalized\nquaternion groups are line graphs. Besides, we derive a condition on the order\nof the dihedral groups for which the proper power graphs of the dihedral groups\nare line graphs.\n"}
+{"abstract": "  With the development of IoT, the sensor usage has been elevated to a new\nlevel, and it becomes more crucial to maintain reliable sensor networks. In\nthis paper, we provide how to efficiently and reliably manage the sensor\nmonitoring system for securing fresh data at the data center (DC). A sensor\ntransmits its sensing information regularly to the DC, and the freshness of the\ninformation at the DC is characterized by the age of information (AoI) that\nquantifies the timeliness of information. By considering the effect of the AoI\nand the spatial distance from the sensor on the information error at the DC, we\nnewly define an error-tolerable sensing (ETS) coverage as the area that the\nestimated information is with smaller error than the target value. We then\nderive the average AoI and the AoI violation probability of the sensor\nmonitoring system, and finally present the {\\eta}-coverage probability, which\nis the probability that the ETS coverage is greater than {\\eta} ratio of the\nmaximum sensor coverage. We also provide the optimal transmission power of the\nsensor, which minimizes the average energy consumption while guaranteeing\ncertain level of the {\\eta}-coverage probability. Numerical results validate\nthe theoretical analysis and show the tendency of the optimal transmission\npower according to the maximum number of retransmissions. This paper can pave\nthe way to efficient design of the AoI-sensitive sensor networks for IoT.\n"}
+{"abstract": "  Music emotion recognition is an important task in MIR (Music Information\nRetrieval) research. Owing to factors like the subjective nature of the task\nand the variation of emotional cues between musical genres, there are still\nsignificant challenges in developing reliable and generalizable models. One\nimportant step towards better models would be to understand what a model is\nactually learning from the data and how the prediction for a particular input\nis made. In previous work, we have shown how to derive explanations of model\npredictions in terms of spectrogram image segments that connect to the\nhigh-level emotion prediction via a layer of easily interpretable perceptual\nfeatures. However, that scheme lacks intuitive musical comprehensibility at the\nspectrogram level. In the present work, we bridge this gap by merging audioLIME\n-- a source-separation based explainer -- with mid-level perceptual features,\nthus forming an intuitive connection chain between the input audio and the\noutput emotion predictions. We demonstrate the usefulness of this method by\napplying it to debug a biased emotion prediction model.\n"}
+{"abstract": "  The fractional dark energy (FDE) model describes the accelerated expansion of\nthe Universe through a nonrelativistic gas of particles with a noncanonical\nkinetic term. This term is proportional to the absolute value of the\nthree-momentum to the power of $3w$, where $w$ is simply the dark energy\nequation of state parameter, and the corresponding energy leads to an energy\ndensity that mimics the cosmological constant. In this paper we expand the\nfractional dark energy model considering a non-zero chemical potential and we\nshow that it may thermodynamically describe a phantom regime. The Planck\nconstraints on the equation of state parameter put upper limits on the allowed\nvalue of the ratio of the chemical potential to the temperature. In the second\npart, we investigate the system of fractional dark energy particles with\nnegative absolute temperatures (NAT). NAT are possible in quantum systems and\nin cosmology, if there exists an upper bound on the energy. This maximum energy\nis one ingredient of the FDE model and indicates a connection between FDE and\nNAT, if FDE is composed of fermions. In this scenario, the equation of state\nparameter is equal to minus one and, using cosmological observations, we find\nthat the transition from positive to negative temperatures is allowed at any\nredshift larger than one.\n"}
+{"abstract": "  With the depletion of spectrum, wireless communication systems turn to\nexploit large antenna arrays to achieve the degree of freedom in space domain,\nsuch as millimeter wave massive multi-input multioutput (MIMO), reconfigurable\nintelligent surface assisted communications and cell-free massive MIMO. In\nthese systems, how to acquire accurate channel state information (CSI) is\ndifficult and becomes a bottleneck of the communication links. In this article,\nwe introduce the concept of channel extrapolation that relies on a small\nportion of channel parameters to infer the remaining channel parameters. Since\nthe substance of channel extrapolation is a mapping from one parameter subspace\nto another, we can resort to deep learning (DL), a powerful learning\narchitecture, to approximate such mapping function. Specifically, we first\nanalyze the requirements, conditions and challenges for channel extrapolation.\nThen, we present three typical extrapolations over the antenna dimension, the\nfrequency dimension, and the physical terminal, respectively. We also\nillustrate their respective principles, design challenges and DL strategies. It\nwill be seen that channel extrapolation could greatly reduce the transmission\noverhead and subsequently enhance the performance gains compared with the\ntraditional strategies. In the end, we provide several potential research\ndirections on channel extrapolation for future intelligent communications\nsystems.\n"}
+{"abstract": "  The signature of noncommutativity on various measures of entanglement has\nbeen observed by considering the holographic dual of noncommutative super\nYang-Mills theory. We have followed a systematic analytical approach in order\nto compute the holographic entanglement entropy corresponding to a strip like\nsubsystem of length $l$. The relationship between the subsystem size (in\ndimensionless form) $\\frac{l}{a}$ and the turning point (in dimensionless form)\nintroduces a critical length scale $\\frac{l_c}{a}$ which leads to three domains\nin the theory, namely, the deep UV domain ($l< l_c$; $au_{t}\\gg 1$, $au_{t}\\sim\nau_{b}$), deep noncommutative domain ($l> l_c,~au_{b}>au_t\\gg 1$) and deep IR\ndomain ($l> l_c,~au_t\\ll 1$). This in turn means that the length scale $l_c$\ndistinctly points out the UV/IR mixing property of the non-local theory under\nconsideration. We have carried out the holographic study of entanglement\nentropy for each of these domains by employing both analytical and numerical\ntechniques. The broken Lorentz symmetry induced by noncommutativity has\nmotivated us to redefine the entropic $c$-function. We have obtained the\nnoncommutative correction to the $c$-function upto leading order in the\nnoncommutative parameter. We then move on to compute the minimal cross-section\narea of the entanglement wedge by considering two disjoint subsystems $A$ and\n$B$. On the basis of $E_P = E_W$ duality, this leads to the holographic\ncomputation of the entanglement of purification. The correlation between two\nsubsystems, namely, the holographic mutual information $I(A:B)$ has also been\ncomputed. Moreover, the computations of $E_W$ and $I(A:B)$ has been done for\neach of the domains in the theory. Finally, we consider a black hole geometry\nwith a noncommutative parameter and study the influence of both\nnoncommutativity and finite temperature on the various measures of quantum\nentanglement.\n"}
+{"abstract": "  We introduce Hausdorff operators over the unit disc and give conditions for\nboundedness of such operator in Bloch, Bergman, and Hardy spaces on the disc.\nIdentity approximation by Hausdorff operators is also considered.\n"}
+{"abstract": "  Restarting a deterministic process always impedes its completion. However, it\nis known that restarting a random process can also lead to an opposite outcome\n-- expediting completion. Hence, the effect of restart is contingent on the\nunderlying statistical heterogeneity of the process' completion times. To\nquantify this heterogeneity we bring a novel approach to restart: the\nmethodology of inequality indices, which is widely applied in economics and in\nthe social sciences to measure income and wealth disparity. Using this approach\nwe establish an `inequality roadmap' for the mean-performance of sharp restart:\na whole new set of universal inequality criteria that determine when restart\nwith sharp timers (i.e. with fixed deterministic timers) decreases/increases\nmean completion. The criteria are based on a host of inequality indices\nincluding Bonferroni, Gini, Pietra, and other Lorenz-curve indices; each index\ncaptures a different angle of the restart-inequality interplay. Utilizing the\nfact that sharp restart can match the mean-performance of any general restart\nprotocol, we prove -- with unprecedented precision and resolution -- the\nvalidity of the following statement: restart impedes/expedites mean completion\nwhen the underlying statistical heterogeneity is low/high.\n"}
+{"abstract": "  In the continual effort to improve product quality and decrease operations\ncosts, computational modeling is increasingly being deployed to determine\nfeasibility of product designs or configurations. Surrogate modeling of these\ncomputer experiments via local models, which induce sparsity by only\nconsidering short range interactions, can tackle huge analyses of complicated\ninput-output relationships. However, narrowing focus to local scale means that\nglobal trends must be re-learned over and over again. In this article, we\npropose a framework for incorporating information from a global sensitivity\nanalysis into the surrogate model as an input rotation and rescaling\npreprocessing step. We discuss the relationship between several sensitivity\nanalysis methods based on kernel regression before describing how they give\nrise to a transformation of the input variables. Specifically, we perform an\ninput warping such that the \"warped simulator\" is equally sensitive to all\ninput directions, freeing local models to focus on local dynamics. Numerical\nexperiments on observational data and benchmark test functions, including a\nhigh-dimensional computer simulator from the automotive industry, provide\nempirical validation.\n"}
+{"abstract": "  Astrophysical observations show complex organic molecules (COMs) in the gas\nphase of protoplanetary disks. X-rays emitted from the central young stellar\nobject (YSO) that irradiate interstellar ices in the disk, followed by the\nejection of molecules in the gas phase, are a possible route to explain the\nabundances observed in the cold regions. This process, known as X-ray\nphotodesorption, needs to be quantified for methanol-containing ices. This\npaper I focuses on the case of X-ray photodesorption from pure methanol ices.\nWe aim at experimentally measuring X-ray photodesorption yields of methanol and\nits photo-products from pure CH$_3$OH ices, and to shed light on the mechanisms\nresponsible for the desorption process. We irradiated methanol ices at 15 K\nwith X-rays in the 525 - 570 eV range. The release of species in the gas phase\nwas monitored by quadrupole mass spectrometry, and photodesorption yields were\nderived. Under our experimental conditions, the CH$_3$OH X-ray photodesorption\nyield from pure methanol ice is 10$^{-2}$ molecule/photon at 564 eV.\nPhoto-products such as CH$_4$, H$_2$CO, H$_2$O, CO$_2$ , and CO also desorb at\nincreasing efficiency. X-ray photodesorption of larger COMs, which can be\nattributed to either ethanol, dimethyl ether, and/or formic acid, is also\ndetected. The physical mechanisms at play are discussed and must likely involve\nthe thermalization of Auger electrons in the ice, thus indicating that its\ncomposition plays an important role. Finally, we provide desorption yields\napplicable to protoplanetary disk environments for astrochemical models. The\nX-rays are shown to be a potential candidate to explain gas-phase abundances of\nmethanol in disks. However, more relevant desorption yields derived from\nexperiments on mixed ices are mandatory to properly support the role played by\nX-rays in nonthermal desorption of methanol (see paper II).\n"}
+{"abstract": "  We consider the global Morrey-type spaces with variable exponents and general\nfunction defining these spaces. In the case of unbounded sets, we prove\nboundedness of the Hardy-Littlewood maximal operator, potential type operator\nin these spaces.\n"}
+{"abstract": "  The Galactic black hole transient GRS1915+105 is famous for its markedly\nvariable X-ray and radio behaviour, and for being the archetypal galactic\nsource of relativistic jets. It entered an X-ray outburst in 1992 and has been\nactive ever since. Since 2018 GRS1915+105 has declined into an extended\nlow-flux X-ray plateau, occasionally interrupted by multi-wavelength flares.\nHere we report the radio and X-ray properties of GRS1915+105 collected in this\nnew phase, and compare the recent data to historic observations. We find that\nwhile the X-ray emission remained unprecedentedly low for most of the time\nfollowing the decline in 2018, the radio emission shows a clear mode change\nhalf way through the extended X-ray plateau in 2019 June: from low flux (~3mJy)\nand limited variability, to marked flaring with fluxes two orders of magnitude\nlarger. GRS1915+105 appears to have entered a low-luminosity canonical hard\nstate, and then transitioned to an unusual accretion phase, characterised by\nheavy X-ray absorption/obscuration. Hence, we argue that a local absorber hides\nfrom the observer the accretion processes feeding the variable jet responsible\nfor the radio flaring. The radio-X-ray correlation suggests that the current\nlow X-ray flux state may be a signature of a super-Eddington state akin to the\nX-ray binaries SS433 or V404 Cyg.\n"}
+{"abstract": "  We show that for any finite set $A$ and an arbitrary $\\varepsilon>0$ there is\n$k=k(\\varepsilon)$ such that the higher energy ${\\mathsf{E}}_k(A)$ is at most\n$|A|^{k+\\varepsilon}$ unless $A$ has a very specific structure. As an\napplication we obtain that any finite subset $A$ of the real numbers or the\nprime field either contains an additive Sidon--type subset of size\n$|A|^{1/2+c}$ or a multiplicative Sidon--type subset of size $|A|^{1/2+c}$.\n"}
+{"abstract": "  Skin blemishes and diseases have attracted increasing research interest in\nrecent decades, due to their growing frequency of occurrence and the severity\nof related diseases. Various laser treatment approaches have been introduced\nfor the alleviation and removal of skin pigmentation. The treatments' effects\nhighly depend on the experience and prognosis of the relevant operators. But,\nthe operation process lacks real-time feedback, which may directly reflect the\nextent of the treatment. In this manuscript, we report a photoacoustic-guided\nlaser treatment method with a feasibility study, specifically for laser\ntreatment targeting the tattoo's removal. The results well validated the\nfeasibility of the proposed method through the experiments on phantoms and ex\nvivo pig skin samples.\n"}
+{"abstract": "  Rate-Splitting Multiple Access (RSMA) is an emerging flexible and powerful\nmultiple access for downlink multiantenna networks. In this paper, we introduce\nthe concept of RSMA into short-packet downlink communications. We design\noptimal linear precoders that maximize the sum rate with Finite Blocklength\n(FBL) constraints. The relations between the sum rate and blocklength of RSMA\nare investigated for a wide range of network loads and user deployments.\nNumerical results demonstrate that RSMA can achieve the same transmission rate\nas Non-Orthogonal Multiple Access (NOMA) and Space Division Multiple Access\n(SDMA) with shorter blocklengths (and therefore lower latency), especially in\noverloaded multi-antenna networks. Hence, we conclude that RSMA is a promising\nmultiple access for low-latency communications.\n"}
+{"abstract": "  Due to a lack of treatments and universal vaccine, early forecasts of Dengue\nare an important tool for disease control. Neural networks are powerful\npredictive models that have made contributions to many areas of public health.\nIn this systematic review, we provide an introduction to the neural networks\nrelevant to Dengue forecasting and review their applications in the literature.\nThe objective is to help inform model design for future work. Following the\nPRISMA guidelines, we conduct a systematic search of studies that use neural\nnetworks to forecast Dengue in human populations. We summarize the relative\nperformance of neural networks and comparator models, model architectures and\nhyper-parameters, as well as choices of input features. Nineteen papers were\nincluded. Most studies implement shallow neural networks using historical\nDengue incidence and meteorological input features. Prediction horizons tend to\nbe short. Building on the strengths of neural networks, most studies use\ngranular observations at the city or sub-national level. Performance of neural\nnetworks relative to comparators such as Support Vector Machines varies across\nstudy contexts. The studies suggest that neural networks can provide good\npredictions of Dengue and should be included in the set of candidate models.\nThe use of convolutional, recurrent, or deep networks is relatively unexplored\nbut offers promising avenues for further research, as does the use of a broader\nset of input features such as social media or mobile phone data.\n"}
+{"abstract": "  Quantum uncertainty relations are typically analyzed for a pair of\nincompatible observables, however, the concept per se naturally extends to\nsituations of more than two observables. In this work, we obtain tripartite\nquantum memory-assisted entropic uncertainty relations and show that the lower\nbounds of these relations have three terms that depend on the complementarity\nof the observables, the conditional von-Neumann entropies, the Holevo\nquantities, and the mutual information. The saturation of these inequalities is\nanalyzed.\n"}
+{"abstract": "  A new technique that utilizes surface integrals to find the force, torque and\npotential energy between two non-spherical, rigid bodies is presented. The\nmethod is relatively fast, and allows us to solve the full rigid two-body\nproblem for pairs of spheroids and ellipsoids with 12 degrees of freedom. We\ndemonstrate the method with two dimensionless test scenarios, one where\ntumbling motion develops, and one where the motion of the bodies resemble\nspinning tops. We also test the method on the asteroid binary (66391) 1999 KW4,\nwhere both components are modelled either as spheroids or ellipsoids. The two\ndifferent shape models have negligible effects on the eccentricity and\nsemi-major axis, but have a larger impact on the angular velocity along the\n$z$-direction. In all cases, energy and total angular momentum is conserved,\nand the simulation accuracy is kept at the machine accuracy level.\n"}
+{"abstract": "  Online debates are often characterised by extreme polarisation and heated\ndiscussions among users. The presence of hate speech online is becoming\nincreasingly problematic, making necessary the development of appropriate\ncountermeasures. In this work, we perform hate speech detection on a corpus of\nmore than one million comments on YouTube videos through a machine learning\nmodel fine-tuned on a large set of hand-annotated data. Our analysis shows that\nthere is no evidence of the presence of \"serial haters\", intended as active\nusers posting exclusively hateful comments. Moreover, coherently with the echo\nchamber hypothesis, we find that users skewed towards one of the two categories\nof video channels (questionable, reliable) are more prone to use inappropriate,\nviolent, or hateful language within their opponents community. Interestingly,\nusers loyal to reliable sources use on average a more toxic language than their\ncounterpart. Finally, we find that the overall toxicity of the discussion\nincreases with its length, measured both in terms of number of comments and\ntime. Our results show that, coherently with Godwin's law, online debates tend\nto degenerate towards increasingly toxic exchanges of views.\n"}
+{"abstract": "  Brain storm optimization (BSO) is a newly proposed population-based\noptimization algorithm, which uses a logarithmic sigmoid transfer function to\nadjust its search range during the convergent process. However, this adjustment\nonly varies with the current iteration number and lacks of flexibility and\nvariety which makes a poor search effciency and robustness of BSO. To alleviate\nthis problem, an adaptive step length structure together with a success memory\nselection strategy is proposed to be incorporated into BSO. This proposed\nmethod, adaptive step length based on memory selection BSO, namely ASBSO,\napplies multiple step lengths to modify the generation process of new\nsolutions, thus supplying a flexible search according to corresponding problems\nand convergent periods. The novel memory mechanism, which is capable of\nevaluating and storing the degree of improvements of solutions, is used to\ndetermine the selection possibility of step lengths. A set of 57 benchmark\nfunctions are used to test ASBSO's search ability, and four real-world problems\nare adopted to show its application value. All these test results indicate the\nremarkable improvement in solution quality, scalability, and robustness of\nASBSO.\n"}
+{"abstract": "  We construct almost holomorphic and holomorphic modular forms by considering\ntheta series for quadratic forms of signature $(n-1,1)$. We include homogeneous\nand spherical polynomials in the definition of the theta series (generalizing a\nconstruction of the second author) to obtain holomorphic, almost holomorphic\nand modular theta series. We give a criterion for these series to coincide,\nenabling us to construct almost holomorphic and holomorphic cusp forms on\ncongruence subgroups of the modular group. Further, we provide numerous\nexplicit examples.\n"}
+{"abstract": "  We measure chemical abundances for over 20 elements of 15 N-rich field stars\nwith high resolution ($R \\sim 30000$) optical spectra. We find that Na, Mg, Al,\nSi, and Ca abundances of our N-rich field stars are mostly consistent with\nthose of stars from globular clusters (GCs). Seven stars are estimated to have\n[Al/Fe$]>0.5$, which is not found in most GC \"first generation\" stars. On the\nother hand, $\\alpha$ element abundances (especially Ti) could show\ndistinguishable differences between in situ stars and accreted stars. We\ndiscover that one interesting star, with consistently low [Mg/Fe], [Si/Fe],\n[Ca/Fe], [Ti/Fe], [Sc/Fe], [V/Fe], and [Co/Fe], show similar kinematic and\n[Ba/Eu] as other stars from the dissolved dwarf galaxy\n\"$Gaia$-Sausage-Enceladus\". The $\\alpha$-element abundances and the iron-peak\nelement abundances of the N-rich field stars with metallicities $-1.25 \\le {\\rm\n[Fe/H]} \\le -0.95$ show consistent values with Milky Way field stars rather\nthan stars from dwarf galaxies, indicating that they were formed in situ. In\naddition, the neutron capture elements of N-rich field stars show that most of\nthem could be enriched by asymptotic giant branch (AGB) stars with masses\naround $3 - 5\\, M_{\\odot}$.\n"}
+{"abstract": "  Lithium niobate on insulator (LNOI), regarded as an important candidate\nplatform for optical integration due to its excellent nonlinear, electro-optic\nand other physical properties, has become a research hotspot. Light source, as\nan essential component for integrated optical system, is urgently needed. In\nthis paper, we reported the realization of 1550-nm band on-chip LNOI\nmicrolasers based on erbium-doped LNOI ring cavities with loaded quality\nfactors higher than one million, which were fabricated by using electron beam\nlithography and inductively coupled plasma reactive ion etching processes.\nThese microlasers demonstrated a low pump threshold of ~20 {\\mu}W and stable\nperformance under the pump of a 980-nm band continuous laser. Comb-like laser\nspectra spanning from 1510 nm to 1580 nm were observed in high pump power\nregime, which lays the foundation of the realization of pulsed laser and\nfrequency combs on rare-earth ion doped LNOI platform. This work has\neffectively promoted the development of on-chip integrated active LNOI devices.\n"}
+{"abstract": "  In systems where interactions couple a central degree of freedom and a bath,\none would expect signatures of the bath's phase to be reflected in the dynamics\nof the central degree of freedom. This has been recently explored in connection\nwith many-body localized baths coupled with a central qubit or a single cavity\nmode -- systems with growing experimental relevance in various platforms. Such\nmodels also have an interesting connection with Floquet many-body localization\nvia quantizing the external drive, although this has been relatively\nunexplored. Here we adapt the multilayer multiconfigurational time-dependent\nHartree (ML-MCTDH) method, a well-known tree tensor network algorithm, to\nnumerically simulate the dynamics of a central degree of freedom, represented\nby a $d$-level system (qudit), coupled to a disordered interacting 1D spin\nbath. ML-MCTDH allows us to reach $\\approx 10^2$ lattice sites, a far larger\nsystem size than what is feasible with exact diagonalization or kernel\npolynomial methods. From the intermediate time dynamics, we find a well-defined\nthermodynamic limit for the qudit dynamics upon appropriate rescaling of the\nsystem-bath coupling. The spin system shows similar scaling collapse in the\nEdward-Anderson spin glass order parameter or entanglement entropy at\nrelatively short times. At longer time scales, we see slow growth of the\nentanglement, which may arise from dephasing mechanisms in the localized system\nor long-range interactions mediated by the central degree of freedom. Similar\nsigns of localization are shown to appear as well with unscaled system-bath\ncoupling.\n"}
+{"abstract": "  With the rapid growth of online services, the number of online accounts\nproliferates. The security of a single user account no longer depends merely on\nits own service provider but also the accounts on other service platforms(We\nrefer to this online account environment as Online Account Ecosystem). In this\npaper, we first uncover the vulnerability of Online Account Ecosystem, which\nstems from the defective multi-factor authentication (MFA), specifically the\nones with SMS-based verification, and dependencies among accounts on different\nplatforms. We propose Chain Reaction Attack that exploits the weakest point in\nOnline Account Ecosystem and can ultimately compromise the most secure\nplatform. Furthermore, we design and implement ActFort, a systematic approach\nto detect the vulnerability of Online Account Ecosystem by analyzing the\nauthentication credential factors and sensitive personal information as well as\nevaluating the dependency relationships among online accounts. We evaluate our\nsystem on hundreds of representative online services listed in Alexa in\ndiversified fields. Based on the analysis from ActFort, we provide several\npragmatic insights into the current Online Account Ecosystem and propose\nseveral feasible countermeasures including the online account exposed\ninformation protection mechanism and the built-in authentication to fortify the\nsecurity of Online Account Ecosystem.\n"}
+{"abstract": "  We propose Sliceable Monolith, a new methodology for developing microservice\narchitectures and perform their integration testing by leveraging most of the\nsimplicity of a monolith: a single codebase and a local execution environment\nthat simulates distribution. Then, a tool compiles a codebase for each\nmicroservice and a cloud deployment configuration. The key enabler of our\napproach is the technology-agnostic service definition language offered by\nJolie.\n"}
+{"abstract": "  We introduce a proper display calculus for first-order logic, of which we\nprove soundness, completeness, conservativity, subformula property and cut\nelimination via a Belnap-style metatheorem. All inference rules are closed\nunder uniform substitution and are without side conditions.\n"}
+{"abstract": "  Contextual multi-armed bandit has shown to be an effective tool in\nrecommender systems. In this paper, we study a novel problem of multi-facet\nbandits involving a group of bandits, each characterizing the users' needs from\none unique aspect. In each round, for the given user, we need to select one arm\nfrom each bandit, such that the combination of all arms maximizes the final\nreward. This problem can find immediate applications in E-commerce, healthcare,\netc. To address this problem, we propose a novel algorithm, named MuFasa, which\nutilizes an assembled neural network to jointly learn the underlying reward\nfunctions of multiple bandits. It estimates an Upper Confidence Bound (UCB)\nlinked with the expected reward to balance between exploitation and\nexploration. Under mild assumptions, we provide the regret analysis of MuFasa.\nIt can achieve the near-optimal $\\widetilde{ \\mathcal{O}}((K+1)\\sqrt{T})$\nregret bound where $K$ is the number of bandits and $T$ is the number of played\nrounds. Furthermore, we conduct extensive experiments to show that MuFasa\noutperforms strong baselines on real-world data sets.\n"}
+{"abstract": "  As is well known, there are various mass limits for compact stars. For\nexample, the maximum mass for non-rotating white dwarfs is given by the famous\nChandrasekhar limit about $1.4 M_\\odot$ (solar masses). Although the mass limit\nfor neutron stars is not so clear to date, one of the widely accepted values is\nabout $2.1 M_\\odot\\,$. Recently, challenges to these mass limits appeared.\nMotivated by the super-Chandrasekhar mass white dwarfs with masses up to $2.4\n\\sim 2.8 M_\\odot\\,$, and compact objects (probably neutron stars) in the mass\ngap (from $2.5 M_\\odot$ or $3 M_\\odot$ to $5 M_\\odot$) inferred from\ngravitational waves detected by LIGO/Virgo in the third observing run (O3), we\nreconsider the mass limits for compact stars in the present work. Without\ninvoking strong magnetic field and/or exotic equation of state (EOS), we try to\nincrease the mass limits for compact stars in modified gravity theory. In this\nwork, we propose an inverse chameleon mechanism, and show that the fifth-force\nmediated by the scalar field can evade the severe tests on earth, in solar\nsystem and universe, but manifest itself in compact stars such as white dwarfs\nand neutron stars. The mass limits for compact stars in the inverse chameleon\nmechanism can be easily increased to $3 M_\\odot\\,$, $5 M_\\odot$ or even larger.\nWe argue that the inverse chameleon mechanism might be constrained by the\nobservations of exoplanets orbiting compact stars (such as white dwarfs and\nneutron stars), and gravitational waves from the last stage of binary compact\nstar coalescence.\n"}
+{"abstract": "  We study the connection between extreme events of thermal and kinetic energy\ndissipation rates in the bulk of three-dimensional Rayleigh-B\\'{e}nard\nconvection and the wall shear stress patterns at the top and the bottom planes\nthat enclose the layer. Zero points of this two-dimensional vector field stand\nfor detachments of strong thermal plumes. If their position at the opposite\nplanes and a given time is close then they can be considered as precursors for\nhigh-amplitude bulk dissipation events triggered by plume collisions or close\npassings. This scenario requires a breaking of the synchronicity of the\nboundary layer dynamics at both plates which is found to be in line with a\ntransition of the bulk derivative statistics from Gaussian to intermittent. Our\nstudies are based on three-dimensional high-resolution direct numerical\nsimulations for moderate Rayleigh numbers between $Ra=10^4$ and $5\\times 10^5$.\n"}
+{"abstract": "  Virtual reality (VR) head-mounted displays (HMD) have recently been used to\nprovide an immersive, first-person vision/view in real-time for manipulating\nremotely-controlled unmanned ground vehicles (UGV). The teleoperation of UGV\ncan be challenging for operators when it is done in real time. One big\nchallenge is for operators to perceive quickly and rapidly the distance of\nobjects that are around the UGV while it is moving. In this research, we\nexplore the use of monoscopic and stereoscopic views and display types\n(immersive and non-immersive VR) for operating vehicles remotely. We conducted\ntwo user studies to explore their feasibility and advantages. Results show a\nsignificantly better performance when using an immersive display with\nstereoscopic view for dynamic, real-time navigation tasks that require avoiding\nboth moving and static obstacles. The use of stereoscopic view in an immersive\ndisplay in particular improved user performance and led to better usability.\n"}
+{"abstract": "  We discuss various aspects of anisotropic gravity in $(d+D)$-dimensional\nspacetime where $D$ dimensions are treated as extra dimensions. It is based on\nthe foliation preserving diffeomorphism invariance and anisotropic conformal\ninvariance. The anisotropy is embodied by introducing a factor $z$ which\ndiscriminates the scaling degree of the extra $D$ dimensions against the\n$d$-dimensional base spacetime and Weyl scalar field which mediates the\nanisotropic scaling symmetry. There is no intrinsic scale but a physical scale\n$M_*$ emerges as a consequence of spontaneous conformal symmetry breaking. Some\nvacuum solutions are obtained and we discuss an issue of `size separation'\nbetween the base spacetime and the extra dimensions. The size separation means\nlarge hierarchy between the scales appearing in the base spacetime and the\nextra dimensions respectively. We also discuss interesting theories obtained\nfrom our model. In the case of (4,1), we propose a resolution of hierarchy\nproblem and discuss comparison with the results of the brane-world model. In a\n$(d,D)=(2,2)$ case, we suggest a UV-complete unitary quantum gravity which\nmight become Einstein gravity in IR. In a certain (2,1) case, we obtain\nCGHS-model.\n"}
+{"abstract": "  In many mechanistic medical, biological, physical and engineered\nspatiotemporal dynamic models the numerical solution of partial differential\nequations (PDEs) can make simulations impractically slow. Biological models\nrequire the simultaneous calculation of the spatial variation of concentration\nof dozens of diffusing chemical species. Machine learning surrogates, neural\nnetworks trained to provide approximate solutions to such complicated numerical\nproblems, can often provide speed-ups of several orders of magnitude compared\nto direct calculation. PDE surrogates enable use of larger models than are\npossible with direct calculation and can make including such simulations in\nreal-time or near-real time workflows practical. Creating a surrogate requires\nrunning the direct calculation tens of thousands of times to generate training\ndata and then training the neural network, both of which are computationally\nexpensive. We use a Convolutional Neural Network to approximate the stationary\nsolution to the diffusion equation in the case of two equal-diameter, circular,\nconstant-value sources located at random positions in a two-dimensional square\ndomain with absorbing boundary conditions. To improve convergence during\ntraining, we apply a training approach that uses roll-back to reject stochastic\nchanges to the network that increase the loss function. The trained neural\nnetwork approximation is about 1e3 times faster than the direct calculation for\nindividual replicas. Because different applications will have different\ncriteria for acceptable approximation accuracy, we discuss a variety of loss\nfunctions and accuracy estimators that can help select the best network for a\nparticular application.\n"}
+{"abstract": "  Peer-to-peer (p2p) content delivery is promising to provide benefits like\ncost-saving and scalable peak-demand handling in comparison with conventional\ncontent delivery networks (CDNs) and complement the decentralized storage\nnetworks such as Filecoin. However, reliable p2p delivery requires proper\nenforcement of delivery fairness, i.e., the deliverers should be rewarded\naccording to their in-time delivery. Unfortunately, most existing studies on\ndelivery fairness are based on non-cooperative game-theoretic assumptions that\nare arguably unrealistic in the ad-hoc p2p setting. We for the first time put\nforth the expressive yet still minimalist securities for p2p content delivery,\nand give two efficient solutions FairDownload and FairStream via the blockchain\nfor p2p downloading and p2p streaming scenarios, respectively. Our designs not\nonly guarantee delivery fairness to ensure deliverers be paid (nearly)\nproportional to his in-time delivery, but also ensure the content consumers and\ncontent providers to be fairly treated. The fairness of each party can be\nguaranteed when the other two parties collude to arbitrarily misbehave.\nMoreover, the systems are efficient in the sense of attaining asymptotically\noptimal on-chain costs and optimal deliverer communication. We implement the\nprotocols to build the prototype systems atop the Ethereum Ropsten network.\nExtensive experiments done in LAN and WAN settings showcase their high\npracticality.\n"}
+{"abstract": "  We provide a variational approximation of Ambrosio-Tortorelli type for\nbrittle fracture energies of piecewise-rigid solids. Our result covers both the\ncase of geometrically nonlinear elasticity and that of linearised elasticity.\n"}
+{"abstract": "  Inspired by natural evolution, evolutionary search algorithms have proven\nremarkably capable due to their dual abilities to radiantly explore through\ndiverse populations and to converge to adaptive pressures. A large part of this\nbehavior comes from the selection function of an evolutionary algorithm, which\nis a metric for deciding which individuals survive to the next generation. In\ndeceptive or hard-to-search fitness landscapes, greedy selection often fails,\nthus it is critical that selection functions strike the correct balance between\ngradient-exploiting adaptation and exploratory diversification. This paper\nintroduces Sel4Sel, or Selecting for Selection, an algorithm that searches for\nhigh-performing neural-network-based selection functions through a\nmeta-evolutionary loop. Results on three distinct bitstring domains indicate\nthat Sel4Sel networks consistently match or exceed the performance of both\nfitness-based selection and benchmarks explicitly designed to encourage\ndiversity. Analysis of the strongest Sel4Sel networks reveals a general\ntendency to favor highly novel individuals early on, with a gradual shift\ntowards fitness-based selection as deceptive local optima are bypassed.\n"}
+{"abstract": "  Recently, channel state information (CSI) at the physical-layer has been\nutilized to detect spoofing attacks in wireless communications. However, due to\nhardware impairments and communication noise, the CSI cannot be estimated\naccurately, which significantly degrades the attack detection performance.\nBesides, the reliability of CSI based detection schemes is challenged by\ntime-varying scenarios. To address these issues, we propose an adaptive Kalman\nbased detection scheme. By utilizing the knowledge of the predicted channel we\neliminate the channel estimation error, especially the random phase error which\noccurs due to the lack of synchronization between transmitter and receiver.\nFurthermore, we define a Kalman residual based test statistic for attack\ndetection. Simulation results show that our proposed scheme makes the detection\nmore robust at low signal-to-noise ratio (SNR) and in dynamic scenarios.\n"}
+{"abstract": "  In this work, we solve the problem of finding non-intersecting paths between\npoints on a plane with a new approach by borrowing ideas from geometric\ntopology, in particular, from the study of polygonal schema in mathematics. We\nuse a topological transformation on the 2-dimensional planar routing\nenvironment that simplifies the routing problem into a problem of connecting\npoints on a circle with straight line segments that do not intersect in the\ninterior of the circle. These points are either the points that need to be\nconnected by non-intersecting paths or special `reference' points that\nparametrize the topology of the original environment prior to the\ntransformation. When all the necessary points on the circle are fully\nconnected, the transformation is reversed such that the line segments combine\nto become the non-intersecting paths that connect the start and end points in\nthe original environment. We interpret the transformed environment in which the\nrouting problem is solved as a new data structure where any routing problem can\nbe solved efficiently. We perform experiments and show that the routing time\nand success rate of the new routing algorithm outperforms the ones for the\nA*-algorithm.\n"}
+{"abstract": "  Massive Open Online Courses (MOOCs) have been used by students as a low-cost\nand low-touch educational credential in a variety of fields. Understanding the\ngrading mechanisms behind these course assignments is important for evaluating\nMOOC credentials. A common approach to grading free-response assignments is\nmassive scale peer-review, especially used for assignments that are not easy to\ngrade programmatically. It is difficult to assess these approaches since the\nresponses typically require human evaluation. Here we link data from public\ncode repositories on GitHub and course grades for a large massive-online open\ncourse to study the dynamics of massive scale peer review. This has important\nimplications for understanding the dynamics of difficult to grade assignments.\nSince the research was not hypothesis-driven, we described the results in an\nexploratory framework. We find three distinct clusters of repeated peer-review\nsubmissions and use these clusters to study how grades change in response to\nchanges in code submissions. Our exploration also leads to an important\nobservation that massive scale peer-review scores are highly variable,\nincrease, on average, with repeated submissions, and changes in scores are not\nclosely tied to the code changes that form the basis for the re-submissions.\n"}
+{"abstract": "  State estimators are crucial components of anomaly detectors that are used to\nmonitor cyber-physical systems. Many frequently-used state estimators are\nsusceptible to model risk as they rely critically on the availability of an\naccurate state-space model. Modeling errors make it more difficult to\ndistinguish whether deviations from expected behavior are due to anomalies or\nsimply a lack of knowledge about the system dynamics. In this research, we\naccount for model uncertainty through a multiplicative noise framework.\nSpecifically, we propose to use the multiplicative noise LQG based compensator\nin this setting to hedge against the model uncertainty risk. The size of the\nresidual from the estimator can then be compared against a threshold to detect\nanomalies. Finally, the proposed detector is validated using numerical\nsimulations. Extension of state-of-the-art anomaly detection in cyber-physical\nsystems to handle model uncertainty represents the main novel contribution of\nthe present work.\n"}
+{"abstract": "  In this article, we prove that if $(\\mathcal A ,\\mathcal B,\\mathcal C)$ is a\nrecollement of extriangulated categories, then torsion pairs in $\\mathcal A$\nand $\\mathcal C$ can induce torsion pairs in $\\mathcal B$, and the converse\nholds under natural assumptions. Besides, we give mild conditions on a cluster\ntilting subcategory on the middle category of a recollement of extriangulated\ncategories, for the corresponding abelian quotients to form a recollement of\nabelian categories.\n"}
+{"abstract": "  In this paper, we establish blow-up results for the semilinear wave equation\nin generalized Einstein-de Sitter spacetime with nonlinearity of derivative\ntype. Our approach is based on the integral representation formula for the\nsolution to the corresponding linear problem in the one-dimensional case, that\nwe will determine through Yagdjian's Integral Transform approach. As upper\nbound for the exponent of the nonlinear term, we discover a Glassey-type\nexponent which depends both on the space dimension and on the Lorentzian metric\nin the generalized Einstein-de Sitter spacetime.\n"}
+{"abstract": "  The dairy industry uses clover and grass as fodder for cows. Accurate\nestimation of grass and clover biomass yield enables smart decisions in\noptimizing fertilization and seeding density, resulting in increased\nproductivity and positive environmental impact. Grass and clover are usually\nplanted together, since clover is a nitrogen-fixing plant that brings nutrients\nto the soil. Adjusting the right percentages of clover and grass in a field\nreduces the need for external fertilization. Existing approaches for estimating\nthe grass-clover composition of a field are expensive and time consuming -\nrandom samples of the pasture are clipped and then the components are\nphysically separated to weigh and calculate percentages of dry grass, clover\nand weeds in each sample. There is growing interest in developing novel deep\nlearning based approaches to non-destructively extract pasture phenotype\nindicators and biomass yield predictions of different plant species from\nagricultural imagery collected from the field. Providing these indicators and\npredictions from images alone remains a significant challenge. Heavy occlusions\nin the dense mixture of grass, clover and weeds make it difficult to estimate\neach component accurately. Moreover, although supervised deep learning models\nperform well with large datasets, it is tedious to acquire large and diverse\ncollections of field images with precise ground truth for different biomass\nyields. In this paper, we demonstrate that applying data augmentation and\ntransfer learning is effective in predicting multi-target biomass percentages\nof different plant species, even with a small training dataset. The scheme\nproposed in this paper used a training set of only 261 images and provided\npredictions of biomass percentages of grass, clover, white clover, red clover,\nand weeds with mean absolute error of 6.77%, 6.92%, 6.21%, 6.89%, and 4.80%\nrespectively.\n"}
+{"abstract": "  With the increasing scale and diversification of interaction behaviors in\nE-commerce, more and more researchers pay attention to multi-behavior\nrecommender systems that utilize interaction data of other auxiliary behaviors\nsuch as view and cart. To address these challenges in heterogeneous scenarios,\nnon-sampling methods have shown superiority over negative sampling methods.\nHowever, two observations are usually ignored in existing state-of-the-art\nnon-sampling methods based on binary regression: (1) users have different\npreference strengths for different items, so they cannot be measured simply by\nbinary implicit data; (2) the dependency across multiple behaviors varies for\ndifferent users and items. To tackle the above issue, we propose a novel\nnon-sampling learning framework named \\underline{C}riterion-guided\n\\underline{H}eterogeneous \\underline{C}ollaborative \\underline{F}iltering\n(CHCF). CHCF introduces both upper and lower bounds to indicate selection\ncriteria, which will guide user preference learning. Besides, CHCF integrates\ncriterion learning and user preference learning into a unified framework, which\ncan be trained jointly for the interaction prediction on target behavior. We\nfurther theoretically demonstrate that the optimization of Collaborative Metric\nLearning can be approximately achieved by CHCF learning framework in a\nnon-sampling form effectively. Extensive experiments on two real-world datasets\nshow that CHCF outperforms the state-of-the-art methods in heterogeneous\nscenarios.\n"}
+{"abstract": "  Using the econometric models, this paper addresses the ability of Albanian\nSmall and Medium-sized Enterprises (SMEs) to identify the risks they face. To\nwrite this paper, we studied SMEs operating in the Gjirokastra region. First,\nqualitative data gathered through a questionnaire was used. Next, the 5-level\nLikert scale was used to measure it. Finally, the data was processed through\nstatistical software SPSS version 21, using the binary logistic regression\nmodel, which reveals the probability of occurrence of an event when all\nindependent variables are included. Logistic regression is an integral part of\na category of statistical models, which are called General Linear Models.\nLogistic regression is used to analyze problems in which one or more\nindependent variables interfere, which influences the dichotomous dependent\nvariable. In such cases, the latter is seen as the random variable and is\ndependent on them. To evaluate whether Albanian SMEs can identify risks, we\nanalyzed the factors that SMEs perceive as directly affecting the risks they\nface. At the end of the paper, we conclude that Albanian SMEs can identify risk\n"}
+{"abstract": "  The Patterson-Sullivan construction is proved almost surely to recover a\nBergman function from its values on a random discrete subset sampled with the\ndeterminantal point process induced by the Bergman kernel on the unit ball\n$\\mathbb{D}_d$ in $\\mathbb{C}^d$. For super-critical weighted Bergman spaces,\nthe interpolation is uniform when the functions range over the unit ball of the\nweighted Bergman space. As main results, we obtain a necessary and sufficient\ncondition for interpolation of a fixed pluriharmonic function in the complex\nhyperbolic space of arbitrary dimension (cf. Theorem 1.4 and Theorem 4.11);\noptimal simultaneous uniform interpolation for weighted Bergman spaces (cf.\nTheorem 1.8, Proposition 1.9 and Theorem 4.13); strong simultaneous uniform\ninterpolation for weighted harmonic Hardy spaces (cf. Theorem 1.11 and Theorem\n4.15); and establish the impossibility of the uniform simultaneous\ninterpolation for the Bergman space $A^2(\\mathbb{D}_d)$ on $\\mathbb{D}_d$ (cf.\nTheorem 1.12 and Theorem 6.7).\n"}
+{"abstract": "  The exponential growth of the number of multihomed mobile devices is changing\nthe way how we can connect to the Internet. Our mobile devices are demanding\nfor more network resources, in terms of traffic volume and QoS requirements.\nUnfortunately, it is very hard to a multihomed device to be simultaneously\nconnected to the network through multiple links. The current work enhances the\nnetwork access of multihomed devices agnostically to the deployed access\ntechnologies. This enhancement is achieved by using simultaneously all of the\nmobile devices interfaces, and by routing each individual data flow through the\nmost convenient access technology. The proposed solution is only deployed at\nthe network side and it extends Proxy Mobile IPv6 with flow mobility in a\ncompletely transparent way to mobile nodes. In fact, it gives particular\nattention to the handover mechanisms, by improving the detection and attachment\nof nodes in the network, with the inclusion of the IEEE 802.21 standard in the\nsolution. This provides the necessary implementation and integration details to\nextend a network topology with femtocell devices. Each femtocell is equipped\nwith various network interfaces supporting a diverse set of access\ntechnologies. There is also a decision entity that manages individually each\ndata flow according to its QoS / QoE requisites. The proposed solution has been\ndeveloped and extensively tested with a real prototype. Evaluation results\nevidence that the overhead for using the solution is negligible as compared to\nthe offered advantages such as: the support of flow mobility, the fulfil of\nVoIP functional requisites, the session continuity in spite of flows mobility,\nits low overhead, its high scalability, and the complete transparency of the\nproposed solution to the user terminals.\n"}
+{"abstract": "  Brain-inspired machine learning is gaining increasing consideration,\nparticularly in computer vision. Several studies investigated the inclusion of\ntop-down feedback connections in convolutional networks; however, it remains\nunclear how and when these connections are functionally helpful. Here we\naddress this question in the context of object recognition under noisy\nconditions. We consider deep convolutional networks (CNNs) as models of\nfeed-forward visual processing and implement Predictive Coding (PC) dynamics\nthrough feedback connections (predictive feedback) trained for reconstruction\nor classification of clean images. To directly assess the computational role of\npredictive feedback in various experimental situations, we optimize and\ninterpret the hyper-parameters controlling the network's recurrent dynamics.\nThat is, we let the optimization process determine whether top-down connections\nand predictive coding dynamics are functionally beneficial. Across different\nmodel depths and architectures (3-layer CNN, ResNet18, and EfficientNetB0) and\nagainst various types of noise (CIFAR100-C), we find that the network\nincreasingly relies on top-down predictions as the noise level increases; in\ndeeper networks, this effect is most prominent at lower layers. In addition,\nthe accuracy of the network implementing PC dynamics significantly increases\nover time-steps, compared to its equivalent forward network. All in all, our\nresults provide novel insights relevant to Neuroscience by confirming the\ncomputational role of feedback connections in sensory systems, and to Machine\nLearning by revealing how these can improve the robustness of current vision\nmodels.\n"}
+{"abstract": "  The induced surface charges appear to diverge when dielectric particles form\nclose contacts. Resolving this singularity numerically is prohibitively\nexpensive because high spatial resolution is needed. We show that the strength\nof this singularity is logarithmic in both inter-particle separation and\ndielectric permittivity. A regularization scheme is proposed to isolate this\nsingularity, and to calculate the exact cohesive energy for clusters of\ncontacting dielectric particles. The results indicate that polarization energy\nstabilizes clusters of open configurations when permittivity is high, in\nagreement with the behavior of conducting particles, but stabilizes the compact\nconfigurations when permittivity is low.\n"}
+{"abstract": "  We introduce the dueling teams problem, a new online-learning setting in\nwhich the learner observes noisy comparisons of disjoint pairs of $k$-sized\nteams from a universe of $n$ players. The goal of the learner is to minimize\nthe number of duels required to identify, with high probability, a Condorcet\nwinning team, i.e., a team which wins against any other disjoint team (with\nprobability at least $1/2$). Noisy comparisons are linked to a total order on\nthe teams. We formalize our model by building upon the dueling bandits setting\n(Yue et al.2012) and provide several algorithms, both for stochastic and\ndeterministic settings. For the stochastic setting, we provide a reduction to\nthe classical dueling bandits setting, yielding an algorithm that identifies a\nCondorcet winning team within $\\mathcal{O}((n + k \\log (k)) \\frac{\\max(\\log\\log\nn, \\log k)}{\\Delta^2})$ duels, where $\\Delta$ is a gap parameter. For\ndeterministic feedback, we additionally present a gap-independent algorithm\nthat identifies a Condorcet winning team within $\\mathcal{O}(nk\\log(k)+k^5)$\nduels.\n"}
+{"abstract": "  Detecting which parts of a sentence contribute to that sentence's toxicity --\nrather than providing a sentence-level verdict of hatefulness -- would increase\nthe interpretability of models and allow human moderators to better understand\nthe outputs of the system. This paper presents our team's, UTNLP, methodology\nand results in the SemEval-2021 shared task 5 on toxic spans detection. We test\nmultiple models and contextual embeddings and report the best setting out of\nall. The experiments start with keyword-based models and are followed by\nattention-based, named entity-based, transformers-based, and ensemble models.\nOur best approach, an ensemble model, achieves an F1 of 0.684 in the\ncompetition's evaluation phase.\n"}
+{"abstract": "  We continue the constructive program about tensor field theory through the\nnext natural model, namely the rank five tensor theory with quartic melonic\ninteractions and propagator inverse of the Laplacian on $U(1)^5$. We make a\nfirst step towards its construction by establishing its power counting,\nidentifiying the divergent graphs and performing a careful study of (a slight\nmodification of) its RG flow. Thus we give strong evidence that this just\nrenormalizable tensor field theory is non perturbatively asymptotically free.\n"}
+{"abstract": "  Distributed delay equations have been used to model situations in which there\nis some sort of delay whose duration is uncertain. However, the interpretation\nof a distributed delay equation is actually very different from that of a delay\ndifferential equation with a random delay. This work explicitly highlights this\ndistinction as it is an important consideration to make when modeling delayed\nsystems in which the delay can take on several values.\n"}
+{"abstract": "  We characterize the kinematic and chemical properties of 589 Galactic\nAnticenter Substructure Stars (GASS) with K-/M- giants in Integrals-of-Motion\nspace. These stars likely include members of previously identified\nsubstructures such as Monoceros, A13, and the Triangulum-Andromeda cloud\n(TriAnd). We show that these stars are on nearly circular orbits on both sides\nof the Galactic plane. We can see velocity($V_{Z}$) gradient along Y-axis\nespecially for the south GASS members. Our GASS members have similar energy and\nangular momentum distributions to thin disk stars. Their location in\n[$\\alpha$/M] vs. [M/H] space is more metal poor than typical thin disk stars,\nwith [$\\alpha$/M] \\textbf{lower} than the thick disk. We infer that our GASS\nmembers are part of the outer metal-poor disk stars, and the outer-disk extends\nto 30 kpc. Considering the distance range and $\\alpha$-abundance features, GASS\ncould be formed after the thick disk was formed due to the molecular cloud\ndensity decreased in the outer disk where the SFR might be less efficient than\nthe inner disk.\n"}
+{"abstract": "  Database management has become an enormous tool for on-demand content\ndistribution services, proffering required information and providing custom\nservices to the user. Also plays a major role for the platforms to manage their\ndata in such a way that data redundancy is minimized. This paper emphasizes\nimproving the user experience for the platform by efficiently managing data.\nKeeping in mind all the new age requirements, especially after COVID-19 the\nsudden surge in subscription has led the stakeholders to try new things to lead\nthe OTT market. Collection of shows being the root of the tree here, this paper\nimprovises the currently existing branches via various tables and suggests some\nnew features on how the data collected can be utilized for introducing new and\nmuch-required query results for the consumer.\n"}
+{"abstract": "  The ratio of the $B^0_s$ and $B^0$ fragmentation fractions, $f_s/f_d$, in\nproton-proton collisions at the LHC, is obtained as a function of $B$-meson\ntransverse momentum and collision centre-of-mass energy from the combined\nanalysis of different $B$-decay channels measured by the LHCb experiment. The\nresults are described by a linear function of the meson transverse momentum, or\nwith a function inspired by Tsallis statistics. Precise measurements of the\nbranching fractions of the $B^0_s \\to J/\\psi \\phi$ and $B^0_s \\to D^-_s \\pi^+$\ndecays are performed, reducing their uncertainty by about a factor of two with\nrespect to previous world averages. Numerous $B^0_s$ decay branching fractions,\nmeasured at the LHCb experiment, are also updated using the new values of\n$f_s/f_d$ and branching fractions of normalisation channels. These results\nreduce a major source of systematic uncertainty in several searches for new\nphysics performed through measurements of $B^0_s$ branching fractions.\n"}
+{"abstract": "  We present the Mathematica package QMeS-Derivation. It derives symbolic\nfunctional equations from a given master equation. The latter include\nfunctional renormalisation group equations, Dyson-Schwinger equations,\nSlavnov-Taylor and Ward identities and their modifications in the presence of\nmomentum cutoffs. The modules allow to derive the functional equations, take\nfunctional derivatives, trace over field space, apply a given truncation\nscheme, and do momentum routings while keeping track of prefactors and signs\nthat arise from fermionic commutation relations. The package furthermore\ncontains an installer as well as Mathematica notebooks with showcase examples.\n"}
+{"abstract": "  The pion structure is represented by Generelazed parton distribution\nfunctions (GPDs). The momentum transfer dependence of GPDs of the pion was\nobtained on the basis of the form of GPDs of the nucleon in the framework of\nthe high energy generalized structure (HEGS) model. To this end, different\nforms of PDFs of the pion of various Collaborations were examined with taking\ninto account the available experimental data on the pion form factors. As a\nresult, the electromagnetic and gravitomagnetic form factors of the pion were\ncalculated. They were used in the framework of the HEGS model with the\nelectromagnetic and gravitomagnetic form factors of the proton for describing\npion-nucleon elastic scattering in a wide energy and momentum transfer region\nwith a minimum of fitting parameters. The properties of the obtained scattering\namplitude were analyzed.\n"}
+{"abstract": "  Non-linear dimensionality reduction can be performed by \\textit{manifold\nlearning} approaches, such as Stochastic Neighbour Embedding (SNE), Locally\nLinear Embedding (LLE) and Isometric Feature Mapping (ISOMAP). These methods\naim to produce two or three latent embeddings, primarily to visualise the data\nin intelligible representations. This manuscript proposes extensions of\nStudent's t-distributed SNE (t-SNE), LLE and ISOMAP, for dimensionality\nreduction and visualisation of multi-view data. Multi-view data refers to\nmultiple types of data generated from the same samples. The proposed multi-view\napproaches provide more comprehensible projections of the samples compared to\nthe ones obtained by visualising each data-view separately. Commonly\nvisualisation is used for identifying underlying patterns within the samples.\nBy incorporating the obtained low-dimensional embeddings from the multi-view\nmanifold approaches into the K-means clustering algorithm, it is shown that\nclusters of the samples are accurately identified. Through the analysis of real\nand synthetic data the proposed multi-SNE approach is found to have the best\nperformance. We further illustrate the applicability of the multi-SNE approach\nfor the analysis of multi-omics single-cell data, where the aim is to visualise\nand identify cell heterogeneity and cell types in biological tissues relevant\nto health and disease.\n"}
+{"abstract": "  The paper considers the system of pressureless gas dynamics in one space\ndimension. The question of solvability of the initial-boundary value problem is\naddressed. Using the method of generalized potentials and characteristic\ntriangles, extended to the boundary value case, an explicit way of constructing\nmeasure-valued solutions is presented. The prescription of boundary data is\nshown to depend on the behavior of the generalized potentials at the boundary.\nWe show that the constructed solution satisfies an entropy condition and it\nconserves mass, whereby mass may accumulate at the boundary. Conservation of\nmomentum again depends on the behavior of the generalized boundary potentials.\nThere is a large amount of literature where the initial value problem for the\npressureless gas dynamics model has been studied. To our knowledge, this paper\nis the first one which considers the initial-boundary value problem.\n"}
+{"abstract": "  Recently, the object detection based on deep learning has proven to be\nvulnerable to adversarial patch attacks. The attackers holding a specially\ncrafted patch can hide themselves from the state-of-the-art person detectors,\ne.g., YOLO, even in the physical world. This kind of attack can bring serious\nsecurity threats, such as escaping from surveillance cameras. In this paper, we\ndeeply explore the detection problems about the adversarial patch attacks to\nthe object detection. First, we identify a leverageable signature of existing\nadversarial patches from the point of the visualization explanation. A fast\nsignature-based defense method is proposed and demonstrated to be effective.\nSecond, we design an improved patch generation algorithm to reveal the risk\nthat the signature-based way may be bypassed by the techniques emerging in the\nfuture. The newly generated adversarial patches can successfully evade the\nproposed signature-based defense. Finally, we present a novel\nsignature-independent detection method based on the internal content semantics\nconsistency rather than any attack-specific prior knowledge. The fundamental\nintuition is that the adversarial object can appear locally but disappear\nglobally in an input image. The experiments demonstrate that the\nsignature-independent method can effectively detect the existing and improved\nattacks. It has also proven to be a general method by detecting unforeseen and\neven other types of attacks without any attack-specific prior knowledge. The\ntwo proposed detection methods can be adopted in different scenarios, and we\nbelieve that combining them can offer a comprehensive protection.\n"}
+{"abstract": "  Highly dynamic environments, with moving objects such as cars or humans, can\npose a performance challenge for LiDAR SLAM systems that assume largely static\nscenes. To overcome this challenge and support the deployment of robots in real\nworld scenarios, we propose a complete solution for a dynamic object aware\nLiDAR SLAM algorithm. This is achieved by leveraging a real-time capable neural\nnetwork that can detect dynamic objects, thus allowing our system to deal with\nthem explicitly. To efficiently generate the necessary training data which is\nkey to our approach, we present a novel end-to-end occupancy grid based\npipeline that can automatically label a wide variety of arbitrary dynamic\nobjects. Our solution can thus generalize to different environments without the\nneed for expensive manual labeling and at the same time avoids assumptions\nabout the presence of a predefined set of known objects in the scene. Using\nthis technique, we automatically label over 12000 LiDAR scans collected in an\nurban environment with a large amount of pedestrians and use this data to train\na neural network, achieving an average segmentation IoU of 0.82. We show that\nexplicitly dealing with dynamic objects can improve the LiDAR SLAM odometry\nperformance by 39.6% while yielding maps which better represent the\nenvironments. A supplementary video as well as our test data are available\nonline.\n"}
+{"abstract": "  Let $r>0, n\\in\\mathbb{N}, {\\bf k}\\in\\mathbb{N}$. Consider the delay\ndifferential equation $$ x'(t)=g(x(t-d_1(Lx_t)),\\ldots,x(t-d_{{\\bf k}}(Lx_t)))\n$$ for $g:(\\mathbb{R}^n)^{{\\bf k}}\\supset V\\to\\mathbb{R}^n$ continuously\ndifferentiable, $L$ a continuous linear map from $C([-r,0],\\mathbb{R}^n)$ into\na finite-dimensional vectorspace $F$, each $d_k:F\\supset W\\to[0,r]$,\n$k=1,\\ldots,{\\bf k}$, continuously differentiable, and $x_t(s)=x(t+s)$. The\nsolutions define a semiflow of continuously differentiable solution operators\non the submanifold $X_f\\subset C^1([-r,0],\\mathbb{R}^n)$ which is given by the\ncompatibility condition $\\phi'(0)=f(\\phi)$ with $$\nf(\\phi)=g(\\phi(-d_1(L\\phi)),\\ldots,\\phi(-d_{{\\bf k}}(L\\phi))). $$ We prove that\n$X_f$ has a finite atlas of at most $2^{{\\bf k}}$ manifold charts, whose\ndomains are almost graphs over $X_0$. The size of the atlas depends solely on\nthe zerosets of the delay functions $d_k$.\n"}
+{"abstract": "  We elaborate on the structure of the conformal anomaly effective action up to\n4-th order, in an expansion in the gravitational fluctuations $(h)$ of the\nbackground metric, in the flat spacetime limit. For this purpose we discuss the\nrenormalization of 4-point functions containing insertions of stress-energy\ntensors (4T), in conformal field theories in four spacetime dimensions with the\ngoal of identifying the structure of the anomaly action. We focus on a\nseparation of the correlator into its transverse/traceless and longitudinal\ncomponents, applied to the trace and conservation Ward identities (WI) in\nmomentum space. These are sufficient to identify, from their hierarchical\nstructure, the anomaly contribution, without the need to proceed with a\ncomplete determination of all of its independent form factors. Renormalization\ninduces sequential bilinear graviton-scalar mixings on single, double and\nmultiple trace terms, corresponding to $R\\square^{-1}$ interactions of the\nscalar curvature, with intermediate virtual massless exchanges. These\ndilaton-like terms couple to the conformal anomaly, as for the chiral anomalous\nWIs. We show that at 4T level a new traceless component appears after\nrenormalization. We comment on future extensions of this result to more general\nbackgrounds, with possible applications to non local cosmologies.\n"}
+{"abstract": "  Interfacial segregation can stabilize grain structures and even lead to grain\nboundary complexion transitions. However, understanding of the complexity of\nsuch phenomena in polycrystalline materials is limited, as most studies focus\non bicrystal geometries. In this work, we investigate interfacial segregation\nand subsequent complexion transitions in polycrystalline Cu-Zr alloys using\nhybrid Monte Carlo/molecular dynamics simulations. No significant change in the\ngrain size or structure is observed upon Zr dopant addition to a pure Cu\npolycrystal at moderate temperature, where grain boundary segregation is the\ndominant behavior. Segregation within the boundary network is inhomogeneous,\nwith some boundaries having local concentrations that are an order of magnitude\nlarger than the global value and others having almost no segregation, and\nchanges to physical parameters such as boundary free volume and energy are\nfound to correlate with dopant concentration. Further, another alloy sample is\ninvestigated at a higher temperature to probe the occurrence of widespread\ntransitions in interfacial structure, where a significant fraction of the\noriginally ordered boundaries transition to amorphous complexions,\ndemonstrating the coexistence of multiple complexion types, each with their own\ndistribution of boundary chemical composition. Overall, this work highlights\nthat interfacial segregation and complexion structure can be diverse in a\npolycrystalline network. The findings shown here complement existing\ncomputational and experimental studies of individual interfaces and help pave\nthe way for unraveling the complexity of interfacial structure in realistic\nmicrostructures.\n"}
+{"abstract": "  Performance of neural models for named entity recognition degrades over time,\nbecoming stale. This degradation is due to temporal drift, the change in our\ntarget variables' statistical properties over time. This issue is especially\nproblematic for social media data, where topics change rapidly. In order to\nmitigate the problem, data annotation and retraining of models is common.\nDespite its usefulness, this process is expensive and time-consuming, which\nmotivates new research on efficient model updating. In this paper, we propose\nan intuitive approach to measure the potential trendiness of tweets and use\nthis metric to select the most informative instances to use for training. We\nconduct experiments on three state-of-the-art models on the Temporal Twitter\nDataset. Our approach shows larger increases in prediction accuracy with less\ntraining data than the alternatives, making it an attractive, practical\nsolution.\n"}
+{"abstract": "  As important data carriers, the drastically increasing number of multimedia\nvideos often brings many duplicate and near-duplicate videos in the top results\nof search. Near-duplicate video retrieval (NDVR) can cluster and filter out the\nredundant contents. In this paper, the proposed NDVR approach extracts the\nframe-level video representation based on convolutional neural network (CNN)\nfeatures from fully-connected layer and aggregated intermediate convolutional\nlayers. Unsupervised metric learning is used for similarity measurement and\nfeature matching. An efficient re-ranking algorithm combined with k-nearest\nneighborhood fuses the retrieval results from two levels of features and\nfurther improves the retrieval performance. Extensive experiments on the widely\nused CC\\_WEB\\_VIDEO dataset shows that the proposed approach exhibits superior\nperformance over the state-of-the-art.\n"}
+{"abstract": "  Accurate cosmological parameter estimates using polarization data of the\ncosmic microwave background (CMB) put stringent requirements on map\ncalibration, as highlighted in the recent results from the Planck satellite. In\nthis paper, we point out that a model-dependent determination of polarization\ncalibration can be achieved by the joint fit of the TE and EE CMB power\nspectra. This provides a valuable cross-check to band-averaged polarization\nefficiency measurements determined using other approaches. We demonstrate that,\nin $\\Lambda$CDM, the combination of the TE and EE constrain polarization\ncalibration with sub-percent uncertainty with Planck data and 2% uncertainty\nwith SPTpol data. We arrive at similar conclusions when extending $\\Lambda$CDM\nto include the amplitude of lensing $A_{\\rm L}$, the number of relativistic\nspecies $N_{\\rm eff}$, or the sum of the neutrino masses $\\sum m_{\\nu}$. The\nuncertainties on cosmological parameters are minimally impacted when\nmarginalizing over polarization calibration, except, as can be expected, for\nthe uncertainty on the amplitude of the primordial scalar power spectrum\n$\\ln(10^{10} A_{\\rm s})$, which increases by $20-50$%. However, this\ninformation can be fully recovered by adding TT data. For current and future\nground-based experiments, SPT-3G and CMB-S4, we forecast the cosmological\nparameter uncertainties to be minimally degraded when marginalizing over\npolarization calibration parameters. In addition, CMB-S4 could constrain its\npolarization calibration at the level of $\\sim$0.2% by combining TE and EE, and\nreach $\\sim$0.06% by also including TT. We therefore conclude that relying on\ncalibrating against Planck polarization maps, whose statistical uncertainty is\nlimited to $\\sim$0.5%, would be insufficient for upcoming experiments.\n"}
+{"abstract": "  We investigate the dynamics of kicked pseudo-spin-1/2 Bose-Einstein\ncondensates (BECs) with spin-orbit coupling (SOC) in a tightly confined\ntoroidal trap. The system exhibits different dynamical behaviors depending on\nthe competition among SOC, kick strength, kick period and interatomic\ninteraction. For weak kick strength, with the increase of SOC the density\nprofiles of two components evolve from overlapped symmetric distributions into\nstaggered antisymmetric distributions, and the evolution of energy experiences\na transition from quasiperiodic motion to modulated quantum beating. For large\nkick strength, when the SOC strength increases, the overlapped symmetric\ndensity distributions become staggered irregular patterns, and the energy\nevolution undergoes a transition from quasiperiodic motion to dynamical\nlocalization. Furthermore, in the case of weak SOC, the increase of kick period\nleads to a transition of the system from quantum beating to Rabi oscillation,\nwhile for the case of strong SOC the system demonstrates complex quasiperiodic\nmotion.\n"}
+{"abstract": "  Recent advances in Reinforcement Learning (RL) combined with Deep Learning\n(DL) have demonstrated impressive performance in complex tasks, including\nautonomous driving. The use of RL agents in autonomous driving leads to a\nsmooth human-like driving experience, but the limited interpretability of Deep\nReinforcement Learning (DRL) creates a verification and certification\nbottleneck. Instead of relying on RL agents to learn complex tasks, we propose\nHPRL - Hierarchical Program-triggered Reinforcement Learning, which uses a\nhierarchy consisting of a structured program along with multiple RL agents,\neach trained to perform a relatively simple task. The focus of verification\nshifts to the master program under simple guarantees from the RL agents,\nleading to a significantly more interpretable and verifiable implementation as\ncompared to a complex RL agent. The evaluation of the framework is demonstrated\non different driving tasks, and NHTSA precrash scenarios using CARLA, an\nopen-source dynamic urban simulation environment.\n"}
+{"abstract": "  The DiCOVA challenge aims at accelerating research in diagnosing COVID-19\nusing acoustics (DiCOVA), a topic at the intersection of speech and audio\nprocessing, respiratory health diagnosis, and machine learning. This challenge\nis an open call for researchers to analyze a dataset of sound recordings\ncollected from COVID-19 infected and non-COVID-19 individuals for a two-class\nclassification. These recordings were collected via crowdsourcing from multiple\ncountries, through a website application. The challenge features two tracks,\none focusing on cough sounds, and the other on using a collection of breath,\nsustained vowel phonation, and number counting speech recordings. In this\npaper, we introduce the challenge and provide a detailed description of the\ntask, and present a baseline system for the task.\n"}
+{"abstract": "  We study the evolution of six exoplanetary systems with the stellar\nevolutionary code MESA and conclude that they will likely spin-up the envelope\nof their parent stars on the red giant branch (RGB) or later on the asymptotic\ngiant branch (AGB) to the degree that the mass loss process might become\nnon-spherical. We choose six observed exoplanetary systems where the semi-major\naxis is ~1-2AU, and use the binary mode of MESA to follow the evolution of the\nsystems. In four systems the star engulfs the planet on the RGB, and in two\nsystems on the AGB, and the systems enter a common envelope evolution (CEE). In\ntwo systems where the exoplanet masses are Mp~10MJ, where MJ is Jupiter mass,\nthe planet spins-up the envelope to about 10% of the break-up velocity. Such\nenvelopes are likely to have significant non-spherical mass loss geometry. In\nthe other four systems where Mp~MJ the planet spins-up the envelope to values\nof ~1-2% of break-up velocity. Magnetic activity in the envelope that\ninfluences dust formation might lead to a small departure from spherical mass\nloss even in these cases. In the two cases of CEE on the AGB the planet\ndeposits energy to the envelope that amounts to >10% of the envelope binding\nenergy. We expect this to cause a non-spherical mass loss that will shape an\nelliptical planetary nebula in each case.\n"}
+{"abstract": "  We modify the standard model of price competition with horizontally\ndifferentiated products, imperfect information, and search frictions by\nallowing consumers to flexibly acquire information about a product's match\nvalue during their visits. We characterize a consumer's optimal search and\ninformation acquisition protocol and analyze the pricing game between firms.\nNotably, we establish that in search markets there are fundamental differences\nbetween search frictions and information frictions, which affect market prices,\nprofits, and consumer welfare in markedly different ways. Although higher\nsearch costs beget higher prices (and profits for firms), higher information\nacquisition costs lead to lower prices and may benefit consumers. We discuss\nimplications of our findings for policies concerning disclosure rules and\nhidden fees.\n"}
+{"abstract": "  Pharmaceutical companies regularly need to make decisions about drug\ndevelopment programs based on the limited knowledge from early stage clinical\ntrials. In this situation, eliciting the judgements of experts is an attractive\napproach for synthesising evidence on the unknown quantities of interest. When\ncalculating the probability of success for a drug development program, multiple\nquantities of interest - such as the effect of a drug on different endpoints -\nshould not be treated as unrelated.\n  We discuss two approaches for establishing a multivariate distribution for\nseveral related quantities within the SHeffield ELicitation Framework (SHELF).\nThe first approach elicits experts' judgements about a quantity of interest\nconditional on knowledge about another one. For the second approach, we first\nelicit marginal distributions for each quantity of interest. Then, for each\npair of quantities, we elicit the concordance probability that both lie on the\nsame side of their respective elicited medians. This allows us to specify a\ncopula to obtain the joint distribution of the quantities of interest.\n  We show how these approaches were used in an elicitation workshop that was\nperformed to assess the probability of success of the registrational program of\nan asthma drug. The judgements of the experts, which were obtained prior to\ncompletion of the pivotal studies, were well aligned with the final trial\nresults.\n"}
+{"abstract": "  In this paper, we release an open-source library, called TextBox, to provide\na unified, modularized, and extensible text generation framework. TextBox aims\nto support a broad set of text generation tasks and models. In our library, we\nimplement 21 text generation models on 9 benchmark datasets, covering the\ncategories of VAE, GAN, and pretrained language models. Meanwhile, our library\nmaintains sufficient modularity and extensibility by properly decomposing the\nmodel architecture, inference, and learning process into highly reusable\nmodules, which allows users to easily incorporate new models into our\nframework. The above features make TextBox specially suitable for researchers\nand practitioners to quickly reproduce baseline models and develop new models.\nTextBox is implemented based on PyTorch, and released under Apache License 2.0\nat https://github.com/RUCAIBox/TextBox.\n"}
+{"abstract": "  The detection of voiced speech, the estimation of the fundamental frequency,\nand the tracking of pitch values over time are crucial subtasks for a variety\nof speech processing techniques. Many different algorithms have been developed\nfor each of the three subtasks. We present a new algorithm that integrates the\nthree subtasks into a single procedure. The algorithm can be applied to\npre-recorded speech utterances in the presence of considerable amounts of\nbackground noise. We combine a collection of standard metrics, such as the\nzero-crossing rate, for example, to formulate an unsupervised voicing\nclassifier. The estimation of pitch values is accomplished with a hybrid\nautocorrelation-based technique. We propose a forward-backward Kalman filter to\nsmooth the estimated pitch contour. In experiments, we are able to show that\nthe proposed method compares favorably with current, state-of-the-art pitch\ndetection algorithms.\n"}
+{"abstract": "  Stream graphs are a very useful mode of representation for temporal network\ndata, whose richness offers a wide range of possible approaches. The various\nmethods aimed at generalising the classical approaches applied to static\nnetworks are constantly being improved. In this paper, we describe a framework\nthat extend to stream graphs iterative weighted-rich-clubs characterisation for\nstatic networks proposed in [1]. The general principle is that we no longer\nconsider the membership of a node to one of the weighted-rich-clubs for the\nwhole time period, but each node is associated with a temporal profile which is\nthe concatenation of the successive memberships of the node to the\nweighted-rich-clubs that appear, disappear and change all along the period. A\nclustering of these profiles gives the possibility to establish a reduced list\nof typical temporal profiles and so a more in-depth understanding of the\ntemporal structure of the network. This approach is tested on real world data\nproduced by recording the interactions between different students within their\nrespective schools. [1] M. Djellabi, B. Jouve, and F. Amblard. Dense and sparse\nvertex connectivity in networks. Journal of Complex Networks, 8(3), 2020.\n"}
+{"abstract": "  We post-process galaxies in the IllustrisTNG simulations with SKIRT radiative\ntransfer calculations to make predictions for the rest-frame near-infrared\n(NIR) and far-infrared (FIR) properties of galaxies at $z\\geq 4$. The\nrest-frame $K$- and $z$-band galaxy luminosity functions from TNG are overall\nconsistent with observations, despite a $\\sim 0.4\\,\\mathrm{dex}$\nunderprediction at $z=4$ for $M_{\\rm z}\\lesssim -24$. Predictions for the JWST\nMIRI observed galaxy luminosity functions and number counts are given. We show\nthat the next-generation survey conducted by JWST can detect 500 (30) galaxies\nin F1000W in a survey area of $500\\,{\\rm arcmin}^{2}$ at $z=6$ ($z=8$). As\nopposed to the consistency in the UV, optical and NIR, we find that TNG,\ncombined with our dust modelling choices, significantly underpredicts the\nabundance of most dust-obscured and thus most luminous FIR galaxies. As a\nresult, the obscured cosmic star formation rate density (SFRD) and the SFRD\ncontributed by optical/NIR dark objects are underpredicted. The discrepancies\ndiscovered here could provide new constraints on the sub-grid feedback models,\nor the dust contents, of simulations. Meanwhile, although the TNG predicted\ndust temperature and its relations with IR luminosity and redshift are\nqualitatively consistent with observations, the peak dust temperature of $z\\geq\n6$ galaxies are overestimated by about $20\\,{\\rm K}$. This could be related to\nthe limited mass resolution of our simulations to fully resolve the porosity of\nthe interstellar medium (or specifically its dust content) at these redshifts.\n"}
+{"abstract": "  We compute the leading corrections to the differential cross section for\ntop-pair production via gluon fusion due to dimension-six operators at leading\norder in QCD. The Standard Model fields are assumed to couple only weakly to\nthe hypothetical new sector. A systematic approach then suggests treating\nsingle insertions of the operator class containing gluon field strength tensors\non the same footing as expli\\-citly loop suppressed contributions from\nfour-fermion operators. This is in particular the case for the chromomagnetic\noperator $Q_{(uG)}$ and the purely bosonic operators $Q_{(G)}$ and $Q_{(\\varphi\nG)}$. All leading order dimension-six contributions are consequently suppressed\nwith a loop factor $1/16\\pi^2$.\n"}
+{"abstract": "  Finding out the differences and commonalities between the knowledge of two\nparties is an important task. Such a comparison becomes necessary, when one\nparty wants to determine how much it is worth to acquire the knowledge of the\nsecond party, or similarly when two parties try to determine, whether a\ncollaboration could be beneficial. When these two parties cannot trust each\nother (for example, due to them being competitors) performing such a comparison\nis challenging as neither of them would be willing to share any of their\nassets. This paper addresses this problem for knowledge graphs, without a need\nfor non-disclosure agreements nor a third party during the protocol.\n  During the protocol, the intersection between the two knowledge graphs is\ndetermined in a privacy preserving fashion. This is followed by the computation\nof various metrics, which give an indication of the potential gain from\nobtaining the other parties knowledge graph, while still keeping the actual\nknowledge graph contents secret. The protocol makes use of blind signatures and\n(counting) Bloom filters to reduce the amount of leaked information. Finally,\nthe party who wants to obtain the other's knowledge graph can get a part of\nsuch in a way that neither party is able to know beforehand which parts of the\ngraph are obtained (i.e., they cannot choose to only get or share the good\nparts). After inspection of the quality of this part, the Buyer can decide to\nproceed with the transaction.\n  The analysis of the protocol indicates that the developed protocol is secure\nagainst malicious participants. Further experimental analysis shows that the\nresource consumption scales linear with the number of statements in the\nknowledge graph.\n"}
+{"abstract": "  Glacier calving front position (CFP) is an important glaciological variable.\nTraditionally, delineating the CFPs has been carried out manually, which was\nsubjective, tedious and expensive. Automating this process is crucial for\ncontinuously monitoring the evolution and status of glaciers. Recently, deep\nlearning approaches have been investigated for this application. However, the\ncurrent methods get challenged by a severe class-imbalance problem. In this\nwork, we propose to mitigate the class-imbalance between the calving front\nclass and the non-calving front class by reformulating the segmentation problem\ninto a pixel-wise regression task. A Convolutional Neural Network gets\noptimized to predict the distance values to the glacier front for each pixel in\nthe image. The resulting distance map localizes the CFP and is further\npost-processed to extract the calving front line. We propose three\npost-processing methods, one method based on statistical thresholding, a second\nmethod based on conditional random fields (CRF), and finally the use of a\nsecond U-Net. The experimental results confirm that our approach significantly\noutperforms the state-of-the-art methods and produces accurate delineation. The\nSecond U-Net obtains the best performance results, resulting in an average\nimprovement of about 21% dice coefficient enhancement.\n"}
+{"abstract": "  We present HandGAN (H-GAN), a cycle-consistent adversarial learning approach\nimplementing multi-scale perceptual discriminators. It is designed to translate\nsynthetic images of hands to the real domain. Synthetic hands provide complete\nground-truth annotations, yet they are not representative of the target\ndistribution of real-world data. We strive to provide the perfect blend of a\nrealistic hand appearance with synthetic annotations. Relying on image-to-image\ntranslation, we improve the appearance of synthetic hands to approximate the\nstatistical distribution underlying a collection of real images of hands. H-GAN\ntackles not only the cross-domain tone mapping but also structural differences\nin localized areas such as shading discontinuities. Results are evaluated on a\nqualitative and quantitative basis improving previous works. Furthermore, we\nrelied on the hand classification task to claim our generated hands are\nstatistically similar to the real domain of hands.\n"}
+{"abstract": "  Ideals in BIT speciale varieties are characterized. In particular, it is\nproved that, for any finitary BIT speciale variety, there is a finite set of\nideal terms determining ideals. Several ideal term sets of this kind are given.\nFor the variety of groups one of these sets consists of the terms $y_1y_2$,\n$xyx^{-1}$, $x^{-1}y^{-1}x$ (and $y$ which can be ignored), while for rings it\nconsists of the terms $y_1+y_2$, $-y$, $xy$, $yx$. For each of the following\nvarieties -- groups with multiple operators, semi-loops, and divisible\ninvolutory groupoids -- one of the term sets found in this paper almost\ncoincides with the term sets found earlier for these particular varieties by\nresp. Higgins, B\\v{e}lohl\\'{a}vek and Chajda, and Hala\\v{s}. The coincidence is\nprecise in the case of divisible involutory groupoids. For loops (resp. loops\nwith operators), the intersection of one of the term sets found in this paper\nwith the term sets found earlier for loops (resp. for loops with operators) by\nBruck (resp. by Higgins) forms the major part of both sets.\n"}
+{"abstract": "  Counting the number of nodes in Anonymous Dynamic Networks is enticing from\nan algorithmic perspective: an important computation in a restricted platform\nwith promising applications. Starting with Michail, Chatzigiannakis, and\nSpirakis [19], a flurry of papers sped up the running time guarantees from\ndoubly-exponential to polynomial [16]. There is a common theme across all those\nworks: a distinguished node is assumed to be present, because Counting cannot\nbe solved deterministically without at least one. In the present work we study\nchallenging questions that naturally follow: how to efficiently count with more\nthan one distinguished node, or how to count without any distinguished node.\nMore importantly, what is the minimal information needed about these\ndistinguished nodes and what is the best we can aim for (count precision,\nstochastic guarantees, etc.) without any. We present negative and positive\nresults to answer these questions. To the best of our knowledge, this is the\nfirst work that addresses them.\n"}
+{"abstract": "  Convolutional networks (ConvNets) have shown impressive capability to solve\nvarious vision tasks. Nevertheless, the trade-off between performance and\nefficiency is still a challenge for a feasible model deployment on\nresource-constrained platforms. In this paper, we introduce a novel concept\ntermed multi-path fully connected pattern (MPFC) to rethink the\ninterdependencies of topology pattern, accuracy and efficiency for ConvNets.\nInspired by MPFC, we further propose a dual-branch module named dynamic clone\ntransformer (DCT) where one branch generates multiple replicas from inputs and\nanother branch reforms those clones through a series of difference vectors\nconditional on inputs itself to produce more variants. This operation allows\nthe self-expansion of channel-wise information in a data-driven way with little\ncomputational cost while providing sufficient learning capacity, which is a\npotential unit to replace computationally expensive pointwise convolution as an\nexpansion layer in the bottleneck structure.\n"}
+{"abstract": "  An exciting recent development is the uptake of deep learning in many\nscientific fields, where the objective is seeking novel scientific insights and\ndiscoveries. To interpret a learning outcome, researchers perform hypothesis\ntesting for explainable features to advance scientific domain knowledge. In\nsuch a situation, testing for a blackbox learner poses a severe challenge\nbecause of intractable models, unknown limiting distributions of parameter\nestimates, and high computational constraints. In this article, we derive two\nconsistent tests for the feature relevance of a blackbox learner. The first one\nevaluates a loss difference with perturbation on an inference sample, which is\nindependent of an estimation sample used for parameter estimation in model\nfitting. The second further splits the inference sample into two but does not\nrequire data perturbation. Also, we develop their combined versions by\naggregating the order statistics of the $p$-values based on repeated sample\nsplitting. To estimate the splitting ratio and the perturbation size, we\ndevelop adaptive splitting schemes for suitably controlling the Type \\rom{1}\nerror subject to computational constraints. By deflating the\n\\textit{bias-sd-ratio}, we establish asymptotic null distributions of the test\nstatistics and their consistency in terms of statistical power. Our theoretical\npower analysis and simulations indicate that the one-split test is more\npowerful than the two-split test, though the latter is easier to apply for\nlarge datasets. Moreover, the combined tests are more stable while compensating\nfor a power loss by repeated sample splitting. Numerically, we demonstrate the\nutility of the proposed tests on two benchmark examples. Accompanying this\npaper is our Python library {\\tt dnn-inference}\nhttps://dnn-inference.readthedocs.io/en/latest/ that implements the proposed\ntests.\n"}
+{"abstract": "  Fast radio bursts (FRBs) can be scattered by ionized gas in their local\nenvironments, host galaxies, intervening galaxies along their lines-of-sight,\nthe intergalactic medium, and the Milky Way. The relative contributions of\nthese different media depend on their geometric configuration and the internal\nproperties of the gas. When these relative contributions are well understood,\nFRB scattering is a powerful probe of density fluctuations along the\nline-of-sight. The precise scattering measurements for FRB 121102 and FRB\n180916 allow us to place an upper limit on the amount of scattering contributed\nby the Milky Way halo to these FRBs. The scattering time $\\tau\\propto(\\tilde{F}\n\\times {\\rm DM}^2) A_\\tau$, where ${\\rm DM}$ is the dispersion measure,\n$\\tilde{F}$ quantifies electron density variations with $\\tilde{F}=0$ for a\nsmooth medium, and the dimensionless constant $A_\\tau$ quantifies the\ndifference between the mean scattering delay and the $1/e$ scattering time\ntypically measured. A likelihood analysis of the observed scattering and halo\nDM constraints finds that $\\tilde{F}$ is at least an order of magnitude smaller\nin the halo than in the Galactic disk. The maximum pulse broadening from the\nhalo is $\\tau\\lesssim12$ $\\mu$s at 1 GHz. We compare our analysis of the Milky\nWay halo with other galaxy haloes by placing limits on the scattering\ncontributions from haloes intersecting the lines-of-sight to FRB 181112 and FRB\n191108. Our results are consistent with haloes making negligible or very small\ncontributions to the scattering times of these FRBs.\n"}
+{"abstract": "  Using the Feynman-Kac formula, a work fluctuation theorem for a Brownian\nparticle in a nonconfining potential, e.g., a potential well with finite depth,\nis derived. The theorem yields aninequality that puts a lower bound on the\naverage work needed to change the potential in time.In comparison to the\nJarzynski equality, which holds for confining potentials, an additional\ntermdescribing a form of energy related to the never ending diffusive expansion\nappears.\n"}
+{"abstract": "  A necessary first step for dust removal in protoplanetary disc winds is the\ndelivery of dust from the disc to the wind. In the case of ionized winds, the\ndisc and wind are sharply delineated by a narrow ionization front where the gas\ndensity and temperature vary by more than an order of magnitude. Using a novel\nmethod that is able to model the transport of dust across the ionization front\nin the presence of disc turbulence, we revisit the problem of dust delivery.\nOur results show that the delivery of dust to the wind is determined by the\nvertical gas flow through the disc induced by the mass loss, rather than\nturbulent diffusion (unless the turbulence is strong, i.e. $\\alpha \\gtrsim\n0.01$). Using these results we provide a simple relation between the maximum\nsize of particle that can be delivered to the wind and the local mass-loss rate\nper unit area from the wind. This relation is independent of the physical\norigin of the wind and predicts typical sizes in the 0.01 -- $1\\,\\mu m$ range\nfor EUV or X-ray driven winds. These values are a factor $\\sim 10$ smaller than\nthose obtained when considering only whether the wind is able to carry away the\ngrains.\n"}
+{"abstract": "  Exoplanetary science continues to excite and surprise with its rich\ndiversity. We discuss here some key aspects potentially influencing the range\nof exoplanetary terrestrial-type atmospheres which could exist in nature. We\nare motivated by newly emerging observations, refined approaches to address\ndata degeneracies, improved theories for key processes affecting atmospheric\nevolution and a new generation of atmospheric models which couple physical\nprocesses from the deep interior through to the exosphere and consider the\nplanetary-star system as a whole. Using the Solar System as our guide we first\nsummarize the main processes which sculpt atmospheric evolution then discuss\ntheir potential interactions in the context of exoplanetary environments. We\nsummarize key uncertainties and consider a diverse range of atmospheric\ncompositions discussing their potential occurrence in an exoplanetary context.\n"}
+{"abstract": "  In this paper, we propose methods for improving the modeling performance of a\nTransformer-based non-autoregressive text-to-speech (TNA-TTS) model. Although\nthe text encoder and audio decoder handle different types and lengths of data\n(i.e., text and audio), the TNA-TTS models are not designed considering these\nvariations. Therefore, to improve the modeling performance of the TNA-TTS model\nwe propose a hierarchical Transformer structure-based text encoder and audio\ndecoder that are designed to accommodate the characteristics of each module.\nFor the text encoder, we constrain each self-attention layer so the encoder\nfocuses on a text sequence from the local to the global scope. Conversely, the\naudio decoder constrains its self-attention layers to focus in the reverse\ndirection, i.e., from global to local scope. Additionally, we further improve\nthe pitch modeling accuracy of the audio decoder by providing sentence and\nword-level pitch as conditions. Various objective and subjective evaluations\nverified that the proposed method outperformed the baseline TNA-TTS.\n"}
+{"abstract": "  Lattice sums of cuboidal lattices, which connect the face-centered with the\nmean-centered and the body-centered cubic lattices through parameter dependent\nlattice vectors, are evaluated by decomposing them into two separate lattice\nsums related to a scaled cubic lattice and a scaled Madelung constant. Using\ntheta functions we were able to derive fast converging series in terms of\nBessel functions. Analytical continuations of these lattice sums are discussed\nin detail.\n"}
+{"abstract": "  An Integral Equation (IE) based electromagnetic field solver using\nmetasurface susceptibility tensors is proposed and validated using variety of\nnumerical examples in 2D. The proposed method solves for fields generated by\nthe metasurface which are represented as spatial discontinuities satisfying the\nGeneralized Sheet Transition Conditions (GSTCs), and described using tensorial\nsurface susceptibility densities, $\\bar{\\bar{\\chi}}$. For the first time, the\ncomplete tensorial representation of susceptibilities is incorporated in this\nintegrated IE-GSTC framework, where the normal surface polarizabilities and\ntheir spatial derivatives along the metasurface are rigorously taken into\naccount. The proposed field equation formulation further utilizes a local\nco-ordinate system which enables modeling metasurfaces with arbitrary\norientations and geometries. The proposed 2D BEM-GSTC framework is successfully\ntested using variety of examples including infinite and finite sized\nmetasurfaces, periodic metasurfaces and complex shaped structures, showing\ncomparisons with both analytical results and a commercial full-wave solver. It\nis shown that the zero-thickness sheet model with complete tensorial\nsusceptibilities can very accurately reproduce the macroscopic fields,\naccounting for their angular field scattering response and the edge diffraction\neffects in finite-sized surfaces.\n"}
+{"abstract": "  We study the electronic structures of chiral, Eshelby-twisted van der Waals\natomic layers with a particular focus on a chiral twisted graphite (CTG), a\ngraphene stack with a constant twist angle $\\theta$ between successive layers.\nWe show that each CTG can host infinitely many resonant states which arise from\nthe interaction between the degenerate monolayer states of the constituent\nlayers. Each resonant state has a screw rotational symmetry, and may have a\nsmaller reduced Brillouin zone than other non-resonant states in the same\nstructure. And each CTG can have the resonant states with up to four different\nscrew symmetries. We derive the energies and wave functions of the resonant\nstates in a universal form of a one-dimensional chain regardless of $\\theta$,\nand show that these states exhibit a clear optical selection rule for\ncircularly polarized light. Finally, we discuss the uniqueness and existence of\nthe exact center of the lattice and the self-similarity of the wave amplitudes\nof the resonant states.\n"}
+{"abstract": "  We present the second public data release of the Dark Energy Survey, DES DR2,\nbased on optical/near-infrared imaging by the Dark Energy Camera mounted on the\n4-m Blanco telescope at Cerro Tololo Inter-American Observatory in Chile. DES\nDR2 consists of reduced single-epoch and coadded images, a source catalog\nderived from coadded images, and associated data products assembled from 6\nyears of DES science operations. This release includes data from the DES\nwide-area survey covering ~5000 deg2 of the southern Galactic cap in five broad\nphotometric bands, grizY. DES DR2 has a median delivered point-spread function\nfull-width at half maximum of g= 1.11, r= 0.95, i= 0.88, z= 0.83, and Y= 0.90\narcsec photometric uniformity with a standard deviation of < 3 mmag with\nrespect to Gaia DR2 G-band, a photometric accuracy of ~10 mmag, and a median\ninternal astrometric precision of ~27 mas. The median coadded catalog depth for\na 1.95 arcsec diameter aperture at S/N= 10 is g= 24.7, r= 24.4, i= 23.8, z=\n23.1 and Y= 21.7 mag. DES DR2 includes ~691 million distinct astronomical\nobjects detected in 10,169 coadded image tiles of size 0.534 deg2 produced from\n76,217 single-epoch images. After a basic quality selection, benchmark galaxy\nand stellar samples contain 543 million and 145 million objects, respectively.\nThese data are accessible through several interfaces, including interactive\nimage visualization tools, web-based query clients, image cutout servers and\nJupyter notebooks. DES DR2 constitutes the largest photometric data set to date\nat the achieved depth and photometric precision.\n"}
+{"abstract": "  We recently proposed DOVER-Lap, a method for combining overlap-aware speaker\ndiarization system outputs. DOVER-Lap improved upon its predecessor DOVER by\nusing a label mapping method based on globally-informed greedy search. In this\npaper, we analyze this label mapping in the framework of a maximum orthogonal\ngraph partitioning problem, and present three inferences. First, we show that\nDOVER-Lap label mapping is exponential in the input size, which poses a\nchallenge when combining a large number of hypotheses. We then revisit the\nDOVER label mapping algorithm and propose a modification which performs similar\nto DOVER-Lap while being computationally tractable. We also derive an\napproximation bound for the algorithm in terms of the maximum number of\nhypotheses speakers. Finally, we describe a randomized local search algorithm\nwhich provides a near-optimal $(1-\\epsilon)$-approximate solution to the\nproblem with high probability. We empirically demonstrate the effectiveness of\nour methods on the AMI meeting corpus. Our code is publicly available:\nhttps://github.com/desh2608/dover-lap.\n"}
+{"abstract": "  In this work, we study the computational complexity of determining whether a\nmachine learning model that perfectly fits the training data will generalizes\nto unseen data. In particular, we study the power of a malicious agent whose\ngoal is to construct a model g that fits its training data and nothing else,\nbut is indistinguishable from an accurate model f. We say that g strongly\nspoofs f if no polynomial-time algorithm can tell them apart. If instead we\nrestrict to algorithms that run in $n^c$ time for some fixed $c$, we say that g\nc-weakly spoofs f. Our main results are\n  1. Under cryptographic assumptions, strong spoofing is possible and 2. For\nany c> 0, c-weak spoofing is possible unconditionally\n  While the assumption of a malicious agent is an extreme scenario (hopefully\ncompanies training large models are not malicious), we believe that it sheds\nlight on the inherent difficulties of blindly trusting large proprietary models\nor data.\n"}
+{"abstract": "  With the advancement of machine learning (ML) and its growing awareness, many\norganizations who own data but not ML expertise (data owner) would like to pool\ntheir data and collaborate with those who have expertise but need data from\ndiverse sources to train truly generalizable models (model owner). In such\ncollaborative ML, the data owner wants to protect the privacy of its training\ndata, while the model owner desires the confidentiality of the model and the\ntraining method which may contain intellectual properties. However, existing\nprivate ML solutions, such as federated learning and split learning, cannot\nmeet the privacy requirements of both data and model owners at the same time.\n  This paper presents Citadel, a scalable collaborative ML system that protects\nthe privacy of both data owner and model owner in untrusted infrastructures\nwith the help of Intel SGX. Citadel performs distributed training across\nmultiple training enclaves running on behalf of data owners and an aggregator\nenclave on behalf of the model owner. Citadel further establishes a strong\ninformation barrier between these enclaves by means of zero-sum masking and\nhierarchical aggregation to prevent data/model leakage during collaborative\ntraining. Compared with the existing SGX-protected training systems, Citadel\nenables better scalability and stronger privacy guarantees for collaborative\nML. Cloud deployment with various ML models shows that Citadel scales to a\nlarge number of enclaves with less than 1.73X slowdown caused by SGX.\n"}
+{"abstract": "  With updated experimental data and improved theoretical calculations, several\nsignificant deviations are being observed between the Standard Model\npredictions and the experimental measurements of the branching ratios of\n$\\bar{B}_{(s)}^0\\to D_{(s)}^{(*)+} L^-$ decays, where $L$ is a light meson from\nthe set $\\{\\pi,\\rho,K^{(\\ast)}\\}$. Especially for the two channels\n$\\bar{B}^0\\to D^{+}K^-$ and $\\bar{B}_{s}^0\\to D_{s}^{+}\\pi^-$, both of which\nare free of the weak annihilation contribution, the deviations observed can\neven reach 4-5$\\sigma$. Here we exploit possible new-physics effects in these\nclass-I non-leptonic $B$-meson decays within the framework of QCD\nfactorization. Firstly, we perform a model-independent analysis of the effects\nfrom twenty linearly independent four-quark operators that can contribute,\neither directly or through operator mixing, to the quark-level $b\\to c\\bar{u}\nd(s)$ transitions. It is found that, under the combined constraints from the\ncurrent experimental data, the deviations observed could be well explained at\nthe $1\\sigma$ level by the new-physics four-quark operators with\n$\\gamma^{\\mu}(1-\\gamma_5)\\otimes\\gamma_{\\mu} (1-\\gamma_5)$ structure, and also\nat the $2\\sigma$ level by the operators with $(1+\\gamma_5)\\otimes(1-\\gamma_5)$\nand $(1+\\gamma_5)\\otimes(1+\\gamma_5)$ structures. However, the new-physics\nfour-quark operators with other Dirac structures fail to provide a consistent\ninterpretation, even at the $2\\sigma$ level. Then, as two specific examples of\nmodel-dependent considerations, we discuss the case where the new-physics\nfour-quark operators are generated by either a colorless charged gauge boson or\na colorless charged scalar, with their masses fixed both at the $1$~TeV.\nConstraints on the effective coefficients describing the couplings of these\nmediators to the relevant quarks are obtained by fitting to the current\nexperimental data.\n"}
+{"abstract": "  Motivation: Cryo-Electron Tomography (cryo-ET) is a 3D bioimaging tool that\nvisualizes the structural and spatial organization of macromolecules at a\nnear-native state in single cells, which has broad applications in life\nscience. However, the systematic structural recognition and recovery of\nmacromolecules captured by cryo-ET are difficult due to high structural\ncomplexity and imaging limits. Deep learning based subtomogram classification\nhave played critical roles for such tasks. As supervised approaches, however,\ntheir performance relies on sufficient and laborious annotation on a large\ntraining dataset.\n  Results: To alleviate this major labeling burden, we proposed a Hybrid Active\nLearning (HAL) framework for querying subtomograms for labelling from a large\nunlabeled subtomogram pool. Firstly, HAL adopts uncertainty sampling to select\nthe subtomograms that have the most uncertain predictions. Moreover, to\nmitigate the sampling bias caused by such strategy, a discriminator is\nintroduced to judge if a certain subtomogram is labeled or unlabeled and\nsubsequently the model queries the subtomogram that have higher probabilities\nto be unlabeled. Additionally, HAL introduces a subset sampling strategy to\nimprove the diversity of the query set, so that the information overlap is\ndecreased between the queried batches and the algorithmic efficiency is\nimproved. Our experiments on subtomogram classification tasks using both\nsimulated and real data demonstrate that we can achieve comparable testing\nperformance (on average only 3% accuracy drop) by using less than 30% of the\nlabeled subtomograms, which shows a very promising result for subtomogram\nclassification task with limited labeling resources.\n"}
+{"abstract": "  Motivated by the complexity of network data, we propose a directed hybrid\nrandom network that mixes preferential attachment (PA) rules with uniform\nattachment (UA) rules. When a new edge is created, with probability $p\\in\n[0,1]$, it follows the PA rule. Otherwise, this new edge is added between two\nuniformly chosen nodes. Such mixture makes the in- and out-degrees of a fixed\nnode grow at a slower rate, compared to the pure PA case, thus leading to\nlighter distributional tails. Useful inference methods for the proposed hybrid\nmodel are then provided and applied to both synthetic and real datasets. We see\nthat with extra flexibility given by the parameter $p$, the hybrid random\nnetwork provides a better fit to real-world scenarios, where lighter tails from\nin- and out-degrees are observed.\n"}
+{"abstract": "  The sine-Gordon expansion method; which is a transformation of the\nsine-Gordon equation has been applied to the potential-YTSF equation of\ndimension (3+1) and the reaction-diffusion equation. We obtain new solitons of\nthis equation in the form hyperbolic, complex and trigonometric function by\nusing this method. We plot 2D and 3D graphics of these solutions using symbolic\nsoftware.\n"}
+{"abstract": "  We consider a gravitational theory with an additional non-minimal coupling\nbetween baryonic matter fields and geometry. The coupling is second order in\nthe energy momentum tensor and can be seen as a generalization of the\nenergy-momentum squared gravity model. We will add a constraint through a\nLagrange multiplier to ensure the conservation of the energy-momentum tensor.\nBackground cosmological implications together with its dynamical system\nanalysis will be investigated in details. Also we will consider the growth of\nmatter perturbation at first order, and estimate the model parameter from\nobservations on $H$ and also $f\\sigma_8$. We will show that the model parameter\nshould be small and positive in 2$\\sigma$ confidence interval. The theory is\nshown to be in a good agreement with observational data.\n"}
+{"abstract": "  The advancement in the world of global satellite systems has enhanced the\naccuracy and reliability between various constellations. However, these\nenhancements have made numerous challenges for the receivers designer. For\ninstance, comparing the acquisition and tracking of the Galileo signals turn\ninto relatively complex processes after the utilization of Alternate Binary\nOffset Carrier AltBOC modulation scheme. This paper presents an efficient and\nunique method for comparing baseband signal processing of the complex receiver\nstructure of the Galileo E5 AltBOC signal. More specifically, the data\ndemodulation has attained after comparing the noisy satellite data sets with\nthe clean data sets. Moreover, the paper presents the implementation of signal\nacquisition, code tracking, multipath noise characteristics, and carrier\ntracking for various datasets of the satellite after the eradication of noise.\nThe results obtained in the paper are promising and provide the through\ntreatment to the problem.\n"}
+{"abstract": "  The simultaneous detection of organic molecules of the form\nC$_2$H$_{\\text{n}}$O, such as ketene (CH$_2$CO), acetaldehyde (CH$_3$CHO), and\nethanol (CH$_3$CH$_2$OH), toward early star-forming regions offers hints of\nshared chemical history. Several reaction routes have been proposed and\nexperimentally verified under various interstellar conditions to explain the\nformation pathways involved. Most noticeably, the non-energetic processing of\nC$_2$H$_2$ ice with OH-radicals and H-atoms was shown to provide formation\nroutes to ketene, acetaldehyde, ethanol, and vinyl alcohol (CH$_2$CHOH) along\nthe H$_2$O formation sequence on grain surfaces. In this work, the\nnon-energetic formation scheme is extended with laboratory measurements\nfocusing on the energetic counterpart, induced by cosmic rays penetrating the\nH$_2$O-rich ice mantle. The focus here is on the H$^+$ radiolysis of\ninterstellar C$_2$H$_2$:H$_2$O ice analogs at 17 K. Ultra-high vacuum\nexperiments were performed to investigate the 200 keV H$^+$ radiolysis\nchemistry of predeposited C$_2$H$_2$:H$_2$O ices, both as mixed and layered\ngeometries. Fourier-transform infrared spectroscopy was used to monitor in situ\nnewly formed species as a function of the accumulated energy dose (or H$^+$\nfluence). The infrared (IR) spectral assignments are further confirmed in\nisotope labeling experiments using H$_2$$^{18}$O. The energetic processing of\nC$_2$H$_2$:H$_2$O ice not only results in the formation of (semi-) saturated\nhydrocarbons (C$_2$H$_4$ and C$_2$H$_6$) and polyynes as well as cumulenes\n(C$_4$H$_2$ and C$_4$H$_4$), but it also efficiently forms O-bearing COMs,\nincluding vinyl alcohol, ketene, acetaldehyde, and ethanol, for which the\nreaction cross-section and product composition are derived. A clear composition\ntransition of the product, from H-poor to H-rich species, is observed as a\nfunction of the accumulated energy dose.\n"}
+{"abstract": "  We propose a new approach to obtain the momentum expectation value of an\nelectron in a high-intensity laser, including multiple photon emissions and\nloops. We find a recursive formula that allows us to obtain the\n$\\mathcal{O}(\\alpha^n)$ term from $\\mathcal{O}(\\alpha^{n-1})$, which can also\nbe expressed as an integro-differential equation. In the classical limit we\nobtain the solution to the Landau-Lifshitz equation to all orders. We show how\nspin-dependent quantum radiation reaction can be obtained by resumming both the\nenergy expansion as well as the $\\alpha$ expansion.\n"}
+{"abstract": "  In this paper the determination of material properties such as Sieverts'\nconstant (solubility) and diffusivity (transport rate) via so-called gas\nrelease experiments is discussed. In order to simulate the time-dependent\nhydrogen fluxes and concentration profiles efficiently, we make use of an\nanalytical method, namely we provide an analytical solution for the\ncorresponding diffusion equations on a cylindrical specimen and a cylindrical\ncontainer for three boundary conditions. These conditions occur in three phases\n-- loading phase, evacuation phase and gas release phase. In the loading phase\nthe specimen is charged with hydrogen assuring a constant partial pressure of\nhydrogen. Then the gas will be quickly removed by a vacuum pump in the second\nphase, and finally in the third time interval, the hydrogen is released from\nthe specimen to the gaseous phase, where the pressure increase will be measured\nby an equipment which is attached to the cylindrical container. The\ninvestigated diffusion equation in each phase is a simple homogeneous equation,\nbut due to the complex time-dependent boundary conditions which include the\nSieverts' constant and the pressure, we transform the homogeneous equations to\nthe non-homogeneous ones with a zero Dirichlet boundary condition. Compared\nwith the time consuming numerical methods our analytical approach has an\nadvantage that the flux of desorbed hydrogen can be explicitly given and\ntherefore can be evaluated efficiently. Our analytical solution also assures\nthat the time-dependent boundary conditions are exactly satisfied and\nfurthermore that the interaction between specimen and container is correctly\ntaken into account.\n"}
+{"abstract": "  Vehicular Ad hoc Network (VANET) is a new sort of wireless ad-hoc network.\nVehicle-to-Vehicle (V2V) communication is one of the main communication\nparadigms that provide a level of safety and convenience to drivers and\npassengers on the road. In such an environment, routing data packets is\nchallenging due to frequent changes of network topology because of the highly\ndynamic nature of vehicles. Thus, routing in VANETs requires efficient\nprotocols that guarantee message transmission among vehicles. Numerous routing\nprotocols and algorithms have been proposed or enhanced to solve the\naforementioned problems. Many position-based routing protocols have been\ndeveloped for routing messages that have been identified to be appropriate for\nVANETs. This work explores the performances of selected unicast non-delay\ntolerant overlay position-based routing protocols. The evaluation has been\nconducted in highway and urban environments in two different scenarios. The\nevaluation metrics that are used are Packet Delivery Ratio (PDR), Void Problem\nOccurrence (VPO), and Average Hop Count (AHC).\n"}
+{"abstract": "  A Lifshitz black brane at generic dynamical critical exponent $z > 1$, with\nnon-zero linear momentum along the boundary, provides a holographic dual\ndescription of a non-equilibrium steady state in a quantum critical fluid, with\nLifshitz scale invariance but without boost symmetry. We consider moving\nLifshitz branes in Einstein-Maxwell-Dilaton gravity and obtain the\nnon-relativistic stress tensor complex of the dual field theory via a suitable\nholographic renormalisation procedure. The resulting black brane hydrodynamics\nand thermodynamics are a concrete holographic realization of a Lifshitz perfect\nfluid with a generic dynamical critical exponent.\n"}
+{"abstract": "  Local volatility is an important quantity in option pricing, portfolio\nhedging, and risk management. It is not directly observable from the market;\nhence calibrations of local volatility models are necessary using observable\nmarket data. Unlike most existing point-estimate methods, we cast the\nlarge-scale nonlinear inverse problem into the Bayesian framework, yielding a\nposterior distribution of the local volatility, which naturally quantifies its\nuncertainty. This extra uncertainty information enables traders and risk\nmanagers to make better decisions. To alleviate the computational cost, we\napply Karhunen--L\\`oeve expansion to reduce the dimensionality of the Gaussian\nProcess prior for local volatility. A modified two-stage adaptive Metropolis\nalgorithm is applied to sample the posterior probability distribution, which\nfurther reduces computational burdens caused by repetitive numerical forward\noption pricing model solver and time of heuristic tuning. We demonstrate our\nmethodology with both synthetic and market data.\n"}
+{"abstract": "  A fundamental objective in quantum information science is to determine the\ncost in classical resources of simulating a particular quantum system. The\nclassical simulation cost is quantified by the signaling dimension which\nspecifies the minimum amount of classical communication needed to perfectly\nsimulate a channel's input-output correlations when unlimited shared randomness\nis held between encoder and decoder. This paper provides a collection of\ndevice-independent tests that place lower and upper bounds on the signaling\ndimension of a channel. Among them, a single family of tests is shown to\ndetermine when a noisy classical channel can be simulated using an amount of\ncommunication strictly less than either its input or its output alphabet size.\nIn addition, a family of eight Bell inequalities is presented that completely\ncharacterize when any four-outcome measurement channel, such as a Bell\nmeasurement, can be simulated using one communication bit and shared\nrandomness. Finally, we bound the signaling dimension for all partial replacer\nchannels in $d$ dimensions. The bounds are found to be tight for the special\ncase of the erasure channel.\n"}
+{"abstract": "  This paper provides a critical overview of Georg Kreisel's method of informal\nrigour, most famously presented in his 1967 paper `Informal rigour and\ncompleteness proofs'. After first considering Kreisel's own characterization in\nhistorical context, we then present two schemas under which we claim his\nvarious examples of informal rigour can be subsumed. We then present detailed\nreconstructions of his three original examples: his squeezing argument in favor\nof the adequacy of the model theoretic analysis of logical validity, his\nargument for the determinacy of the Continuum Hypothesis, and his refutation of\nMarkov's principle in intuitionistic analysis. We conclude by offering a\ncomparison of Kreisel's understanding of informal rigour with Carnap's method\nof explication. In an appendix, we also offer briefer reconstructions of\nKreisel's attempts to apply informal rigour to the discovery of set theoretic\naxioms, the distinction between standard and nonstandard models of arithmetic,\nand the concepts of finitist proof, predicative definability, and\nintuitionistic validity.\n"}
+{"abstract": "  To scale neural speech synthesis to various real-world languages, we present\na multilingual end-to-end framework that maps byte inputs to spectrograms, thus\nallowing arbitrary input scripts. Besides strong results on 40+ languages, the\nframework demonstrates capabilities to adapt to new languages under extreme\nlow-resource and even few-shot scenarios of merely 40s transcribed recording,\nwithout the need of per-language resources like lexicon, extra corpus,\nauxiliary models, or linguistic expertise, thus ensuring scalability. While it\nretains satisfactory intelligibility and naturalness matching rich-resource\nmodels. Exhaustive comparative and ablation studies are performed to reveal the\npotential of the framework for low-resource languages. Furthermore, we propose\na novel method to extract language-specific sub-networks in a multilingual\nmodel for a better understanding of its mechanism.\n"}
+{"abstract": "  In this paper, we consider the downlink (DL) of a zero-forcing (ZF) precoded\nextra-large scale massive MIMO (XL-MIMO) system. The base-station (BS) operates\nwith limited number of radio-frequency (RF) transceivers due to high cost,\npower consumption and interconnection bandwidth associated to the fully digital\nimplementation. The BS, which is implemented with a subarray switching\narchitecture, selects groups of active antennas inside each subarray to\ntransmit the DL signal. This work proposes efficient resource allocation (RA)\nprocedures to perform joint antenna selection (AS) and power allocation (PA) to\nmaximize the DL spectral efficiency (SE) of an XL-MIMO system operating under\ndifferent loading settings. Two metaheuristic RA procedures based on the\ngenetic algorithm (GA) are assessed and compared in terms of performance,\ncoordination data size and computational complexity. One algorithm is based on\na quasi-distributed methodology while the other is based on the conventional\ncentralized processing. Numerical results demonstrate that the\nquasi-distributed GA-based procedure results in a suitable trade-off between\nperformance, complexity and exchanged coordination data. At the same time, it\noutperforms the centralized procedures with appropriate system operation\nsettings.\n"}
+{"abstract": "  Knowledge of longitudinal electron bunch profiles is vital to optimize the\nperformance of plasma wakefield accelerators and x-ray free electron laser\nlinacs. Because of their importance to these novel applications, noninvasive\nfrequency domain techniques are often employed to reconstruct longitudinal\nbunch profiles from coherent synchrotron, transition, or undulator radiation\nmeasurements. In this paper, we detail several common reconstruction techniques\ninvolving the Kramers-Kronig phase relationship and Gerchberg-Saxton algorithm.\nThrough statistical analysis, we draw general conclusions about the accuracy of\nthese reconstruction techniques and the most suitable candidate for\nlongitudinal bunch reconstruction from spectroscopic data.\n"}
+{"abstract": "  In this paper we present a conservative cell-centered Lagrangian finite\nvolume scheme for the solution of the hyper-elasticity equations on\nunstructured multidimensional grids. The starting point of the new method is\nthe Eucclhyd scheme, which is here combined with the a posteriori\nMultidimensional Optimal Order Detection (MOOD) limiting strategy to ensure\nrobustness and stability at shock waves with piece-wise linear spatial\nreconstruction. The ADER (Arbitrary high order schemes using DERivatives)\napproach is adopted to obtain second-order of accuracy in time as well. This\nmethod has been tested in an hydrodynamics context and the present work aims at\nextending it to the case of hyper-elasticity models. Such models are presented\nin a fully Lagrangian framework and the dedicated Lagrangian numerical scheme\nis derived in terms of nodal solver, GCL compliance, subcell forces and\ncompatible discretization. The Lagrangian numerical method is implemented in 3D\nunder MPI parallelization framework allowing to handle genuinely large meshes.\nA relative large set of numerical test cases is presented to assess the ability\nof the method to achieve effective second order of accuracy on smooth flows,\nmaintaining an essentially non-oscillatory behavior and general robustness\nacross discontinuities and ensuring at least physical admissibility of the\nsolution where appropriate. Pure elastic neo-Hookean and non-linear materials\nare considered for our benchmark test problems in 2D and 3D. These test cases\nfeature material bending, impact, compression, non-linear deformation and\nfurther bouncing/detaching motions.\n"}
+{"abstract": "  This paper presents a novel method, Zero-Reference Deep Curve Estimation\n(Zero-DCE), which formulates light enhancement as a task of image-specific\ncurve estimation with a deep network. Our method trains a lightweight deep\nnetwork, DCE-Net, to estimate pixel-wise and high-order curves for dynamic\nrange adjustment of a given image. The curve estimation is specially designed,\nconsidering pixel value range, monotonicity, and differentiability. Zero-DCE is\nappealing in its relaxed assumption on reference images, i.e., it does not\nrequire any paired or even unpaired data during training. This is achieved\nthrough a set of carefully formulated non-reference loss functions, which\nimplicitly measure the enhancement quality and drive the learning of the\nnetwork. Despite its simplicity, we show that it generalizes well to diverse\nlighting conditions. Our method is efficient as image enhancement can be\nachieved by an intuitive and simple nonlinear curve mapping. We further present\nan accelerated and light version of Zero-DCE, called Zero-DCE++, that takes\nadvantage of a tiny network with just 10K parameters. Zero-DCE++ has a fast\ninference speed (1000/11 FPS on a single GPU/CPU for an image of size\n1200*900*3) while keeping the enhancement performance of Zero-DCE. Extensive\nexperiments on various benchmarks demonstrate the advantages of our method over\nstate-of-the-art methods qualitatively and quantitatively. Furthermore, the\npotential benefits of our method to face detection in the dark are discussed.\nThe source code will be made publicly available at\nhttps://li-chongyi.github.io/Proj_Zero-DCE++.html.\n"}
+{"abstract": "  The partial (up to 7 %) substitution of Cd for Zn in the Yb-based\nheavy-fermion material YbFe$_2$Zn$_{20}$ is known to induce a slight ($\\sim 20$\n%) reduction of the Sommerfeld specific heat coefficient $\\gamma$ and a huge\n(up to two orders of magnitude) reduction of the $T^2$ resistivity coefficient\n$A$, corresponding to a drastic and unexpected reduction of the Kadowaki-Woods\nratio $A/\\gamma ^2$. Here, Yb $L_{3}$-edge X-ray absorption spectroscopy shows\nthat the Yb valence state is close to $3+$ for all $x$, whereas X-ray\ndiffraction reveals that Cd replace the Zn ions only at the $16c$ site of the\n$Fd\\bar{3}m$ cubic structure, leaving the $48f$ and $96g$ sites with full Zn\noccupation. Ab-initio electronic structure calculations in pure and Cd-doped\nmaterials, carried out without considering correlations, show multiple\nconduction bands with only minor modifications of the band dispersions near the\nFermi level and therefore do not explain the resistivity drop introduced by Cd\nsubstitution. We propose that the site-selective Cd substitution introduces\nlight conduction bands with substantial contribution of Cd($16c$) $5p$ levels\nthat have weak coupling to the Yb$^{3+}$ $4f$ moments. These light fermions\ncoexist with heavy fermions originated from other conduction bands with larger\nparticipation of Zn($48f$ and $96g$) $4p$ levels that remain strongly coupled\nwith the Yb$^{3+}$ local moments.\n"}
+{"abstract": "  We propose a novel generic trust management framework for crowdsourced IoT\nservices. The framework exploits a multi-perspective trust model that captures\nthe inherent characteristics of crowdsourced IoT services. Each perspective is\ndefined by a set of attributes that contribute to the perspective's influence\non trust. The attributes are fed into a machine-learning-based algorithm to\ngenerate a trust model for crowdsourced services in IoT environments. We\ndemonstrate the effectiveness of our approach by conducting experiments on\nreal-world datasets.\n"}
+{"abstract": "  We investigated the radio properties of the host galaxy of X-ray flash,\nXRF020903, which is the best example for investigating of the off-axis origin\nof gamma-ray bursts(GRBs). Dust continuum at 233 GHz and CO are observed using\nthe Atacama Large millimeter/submillimeter array. The molecular gas mass\nderived by applying the metalicity-dependent CO-to-H$_{2}$ conversion factor\nmatches the global trend along the redshift and stellar mass of the GRB host\ngalaxies. The estimated gas depletion timescale (pertaining to the potential\ncritical characteristics of GRB host galaxies) is equivalent to those of GRBs\nand super-luminous supernova hosts in the same redshift range. These properties\nof the XRF020903 host galaxy observed in radio resemble those of GRB host\ngalaxies, thereby supporting the identical origin of XRF020903 and GRBs.\n"}
+{"abstract": "  It was recently argued that the pigeonhole principle, which states that if\nthree pigeons are put into two pigeonholes then at least one pigeonhole must\ncontain more than one pigeon, is violated in quantum systems [Y. Aharonov et\nal., PNAS 113, 532 (2016)]. An experimental verification of this effect was\nrecently reported [M.-C. Chen et al., PNAS 116, 1549 (2019)]. In another recent\nexperimental work, it was argued that two entities were observed to exchange\nproperties without meeting each other [Z.-H. Liu et al., Nat. Commun. 11, 3006\n(2020)]. Here we describe all these proposals and experiments as simple quantum\ninterference effects, where no such dramatic conclusions appear. Besides\ndemystifying some of the conclusions of the cited works, we also present\nphysical insights for some interesting behaviors present in these treatments.\nFor instance, we associate the anomalous particles behaviors in the quantum\npigeonhole effect to a quantum interference of force.\n"}
+{"abstract": "  We have studied the three-body recombination rates on both sides of the\ninterspecies d-wave Feshbach resonance in the $^{85}$Rb\\,-$^{87}$Rb-$^{87}$Rb\nsystem using the $R$-matrix propagation method in the hyperspherical coordinate\nframe. Two different mechanisms of recombination rate enhancement for positive\nand negative $^{85}$Rb\\,-$^{87}$Rb d-wave scattering lengths are analyzed. On\nthe positive scattering length side, the recombination rate enhancement occurs\ndue to the existence of three-body shape resonance, while on the negative\nscattering length side, the coupling between the lowest entrance channel and\nthe highest recombination channel is crucial to the appearance of the\nenhancement. In addition, our study shows that the intraspecies interaction\nplays a significant role in determining the emergence of recombination rate\nenhancements. Compared to the case in which the three pairwise interactions are\nall in d-wave resonance, when the $^{87}$Rb-$^{87}$Rb interaction is near the\nd-wave resonance, the values of the interspecies scattering length that produce\nthe recombination enhancement shift. In particular, when the\n$^{87}$Rb-$^{87}$Rb interaction is away from the d-wave resonance, the\nenhancement disappears on the negative interspecies scattering length side.\n"}
+{"abstract": "  We discuss the quantum chemical nature of the Lead(II) valence basins,\nsometime called the Lead \"lone pair\". Using various chemical interpretation\ntools such as the molecular orbital analysis, Natural Bond Orbitals (NBO),\nNatural Population Analysis (NPA) and Electron Localization Function (ELF)\ntopological analysis, we study a variety of Lead(II) complexes. A careful\nanalysis of the results show that the optimal structures of the lead complexes\nare only govern by the 6s and 6p subshells whereas no involvement of the 5d\norbitals is found. Similarly, we do not find any significant contribution of\nthe 6d. Therefore, the Pb(II) complexation with its ligand can be explained\nthrough the interaction of the 6s2 electrons and the accepting 6p orbitals. We\ndetail the potential structural and dynamical consequences of such electronic\nstructure organization of the Pb (II) valence domain.\n"}
+{"abstract": "  $f(P)$ gravity is a novel extension of ECG in which the Ricci scalar in the\naction is replaced by a function of the curvature invariant $P$ which\nrepresents the contractions of the Riemann tensor at the cubic order \\cite{p}.\nThe present work is concentrated on bounding some $f(P)$ gravity models using\nthe concept of energy conditions where the functional forms of $f(P)$ are\nrepresented as \\textbf{a)} $f(P) = \\alpha \\sqrt{P}$, and \\textbf{b)} $f(P) =\n\\alpha \\exp (P)$, where $\\alpha$ is the sole model parameter. Energy conditions\nare interesting linear relationships between pressure and density and have been\nextensively employed to derive interesting results in Einstein's gravity, and\nare also an excellent tool to impose constraints on any cosmological model. To\nplace the bounds, we ensured that the energy density must remain positive, the\npressure must remain negative, and the EoS parameter must attain a value close\nto $-1$ to make sure that the bounds respect the accelerated expansion of the\nUniverse and are also in harmony with the latest observational data. We report\nthat for both the models, suitable parameter spaces exist which satisfy the\naforementioned conditions and therefore posit the $f(P)$ theory of gravity to\nbe a promising modified theory of gravitation.\n"}
+{"abstract": "  In convolutional neural network-based character recognition, pooling layers\nplay an important role in dimensionality reduction and deformation\ncompensation. However, their kernel shapes and pooling operations are\nempirically predetermined; typically, a fixed-size square kernel shape and max\npooling operation are used. In this paper, we propose a meta-learning framework\nfor pooling layers. As part of our framework, a parameterized pooling layer is\nproposed in which the kernel shape and pooling operation are trainable using\ntwo parameters, thereby allowing flexible pooling of the input data. We also\npropose a meta-learning algorithm for the parameterized pooling layer, which\nallows us to acquire a suitable pooling layer across multiple tasks. In the\nexperiment, we applied the proposed meta-learning framework to character\nrecognition tasks. The results demonstrate that a pooling layer that is\nsuitable across character recognition tasks was obtained via meta-learning, and\nthe obtained pooling layer improved the performance of the model in both\nfew-shot character recognition and noisy image recognition tasks.\n"}
+{"abstract": "  Fu and Kane have discovered that a topological insulator with induced s-wave\nsuperconductivity (gap $\\Delta_0$, Fermi velocity $v_{\\rm F}$, Fermi energy\n$\\mu$) supports chiral Majorana modes propagating on the surface along the edge\nwith a magnetic insulator. We show that the direction of motion of the Majorana\nfermions can be inverted by the counterflow of supercurrent, when the Cooper\npair momentum along the boundary exceeds $\\Delta_0^2/\\mu v_{\\rm F}$. The\nchirality inversion is signaled by a doubling of the thermal conductance of a\nchannel parallel to the supercurrent. Moreover, the inverted edge can transport\na nonzero electrical current, carried by a Dirac mode that appears when the\nMajorana mode switches chirality. The chirality inversion is a unique signature\nof Majorana fermions in a spinful topological superconductor: it does not exist\nfor spinless chiral p-wave pairing.\n"}
+{"abstract": "  Transport phenomena plays an important role in science and technology. In the\nwide variety of applications both advection and diffusion may appear. Regarding\ndiffusion, for long times, different type of decay rates are possible for\ndifferent non-equilibrium systems. After summarizing the existing solutions of\nthe regular diffusion equation, we present not so well known solution derived\nfrom three different trial functions, as a key point we present a family of\nsolutions for the case of infinite horizon. By this we tried to make a step\ntoward understanding the different long time decays for different diffusive\nsystems.\n"}
+{"abstract": "  The Theory of Functional Connections (TFC) is a general methodology for\nfunctional interpolation that can embed a set of user-specified linear\nconstraints. The functionals derived from this method, called \\emph{constrained\nexpressions}, analytically satisfy the imposed constraints and can be leveraged\nto transform constrained optimization problems to unconstrained ones. By\nsimplifying the optimization problem, this technique has been shown to produce\na numerical scheme that is faster, more accurate, and robust to poor\ninitialization. The content of this dissertation details the complete\ndevelopment of the Theory of Functional Connections. First, the seminal paper\non the Theory of Functional Connections is discussed and motivates the\ndiscovery of a more general formulation of the constrained expressions.\nLeveraging this formulation, a rigorous structure of the constrained expression\nis produced with associated mathematical definitions, claims, and proofs.\nFurthermore, the second part of this dissertation explains how this technique\ncan be used to solve ordinary differential equations providing a wide variety\nof examples compared to the state-of-the-art. The final part of this work\nfocuses on unitizing the techniques and algorithms produced in the prior\nsections to explore the feasibility of using the Theory of Functional\nConnections to solve real-time optimal control problems, namely optimal landing\nproblems.\n"}
+{"abstract": "  The evolution of quadrupole and octupole collectivity and their coupling is\ninvestigated in a series of even-even isotopes of the actinide Ra, Th, U, Pu,\nCm, and Cf with neutron number in the interval $130\\leqslant N\\leqslant 150$.\nThe Hartree-Fock-Bogoliubov approximation, based on the parametrization D1M of\nthe Gogny energy density functional, is employed to generate potential energy\nsurfaces depending upon the axially-symmetric quadrupole and octupole shape\ndegrees of freedom. The mean-field energy surface is then mapped onto the\nexpectation value of the $sdf$ interacting-boson-model Hamiltonian in the boson\ncondensate state as to determine the strength parameters of the boson\nHamiltonian. Spectroscopic properties related to the octupole degree of freedom\nare produced by diagonalizing the mapped Hamiltonian. Calculated low-energy\nnegative-parity spectra, $B(E3;3^{-}_{1}\\to 0^{+}_{1})$ reduced transition\nrates, and effective octupole deformation suggest that the transition from\nnearly spherical to stable octupole-deformed, and to octupole vibrational\nstates occurs systematically in the actinide region.\n"}
+{"abstract": "  We consider a conjecture that identifies two types of base point free\ndivisors on $\\bar{M}_{0,n}$. The first arises from Gromov-Witten theory of a\nGrassmannian. The second comes from first Chern classes of vector bundles\nassociated to simple Lie algebras in type A. Here we reduce this conjecture on\n$\\bar{M}_{0,n}$ to the same statement for $n=4$. A reinterpretation leads to a\nproof of the conjecture on $\\bar{M}_{0,n}$ for a large class, and we give\nsufficient conditions for the non-vanishing of these divisors.\n"}
+{"abstract": "  Many modern software-intensive systems employ artificial intelligence /\nmachine-learning (AI/ML) components and are, thus, inherently data-centric. The\nbehaviour of such systems depends on typically large amounts of data processed\nat run-time rendering such non-deterministic systems as complex. This\ncomplexity growth affects our understanding on needs and practices in\nRequirements Engineering (RE). There is, however, still little guidance on how\nto handle requirements for such systems effectively: What are, for example,\ntypical quality requirements classes? What modelling concepts do we rely on or\nwhich levels of abstraction do we need to consider? In fact, how to integrate\nsuch concepts into approaches for a more traditional RE still needs profound\ninvestigations. In this research preview paper, we report on ongoing efforts to\nestablish an artefact-based RE approach for the development of datacentric\nsystems (DCSs). To this end, we sketch a DCS development process with the newly\nproposed requirements categories and data-centric artefacts and briefly report\non an ongoing investigation of current RE challenges in industry developing\ndata-centric systems.\n"}
+{"abstract": "  Inspired by the fact that human eyes continue to develop tracking ability in\nearly and middle childhood, we propose to use tracking as a proxy task for a\ncomputer vision system to learn the visual representations. Modelled on the\nCatch game played by the children, we design a Catch-the-Patch (CtP) game for a\n3D-CNN model to learn visual representations that would help with video-related\ntasks. In the proposed pretraining framework, we cut an image patch from a\ngiven video and let it scale and move according to a pre-set trajectory. The\nproxy task is to estimate the position and size of the image patch in a\nsequence of video frames, given only the target bounding box in the first\nframe. We discover that using multiple image patches simultaneously brings\nclear benefits. We further increase the difficulty of the game by randomly\nmaking patches invisible. Extensive experiments on mainstream benchmarks\ndemonstrate the superior performance of CtP against other video pretraining\nmethods. In addition, CtP-pretrained features are less sensitive to domain gaps\nthan those trained by a supervised action recognition task. When both trained\non Kinetics-400, we are pleasantly surprised to find that CtP-pretrained\nrepresentation achieves much higher action classification accuracy than its\nfully supervised counterpart on Something-Something dataset. Code is available\nonline: github.com/microsoft/CtP.\n"}
+{"abstract": "  The method recently introduced in arXiv:2011.10115 realizes a deep neural\nnetwork with just a single nonlinear element and delayed feedback. It is\napplicable for the description of physically implemented neural networks. In\nthis work, we present an infinite-dimensional generalization, which allows for\na more rigorous mathematical analysis and a higher flexibility in choosing the\nweight functions. Precisely speaking, the weights are described by Lebesgue\nintegrable functions instead of step functions. We also provide a functional\nback-propagation algorithm, which enables gradient descent training of the\nweights. In addition, with a slight modification, our concept realizes\nrecurrent neural networks.\n"}
+{"abstract": "  Order dispatch is one of the central problems to ride-sharing platforms.\nRecently, value-based reinforcement learning algorithms have shown promising\nperformance on this problem. However, in real-world applications, the\nnon-stationarity of the demand-supply system poses challenges to re-utilizing\ndata generated in different time periods to learn the value function. In this\nwork, motivated by the fact that the relative relationship between the values\nof some states is largely stable across various environments, we propose a\npattern transfer learning framework for value-based reinforcement learning in\nthe order dispatch problem. Our method efficiently captures the value patterns\nby incorporating a concordance penalty. The superior performance of the\nproposed method is supported by experiments.\n"}
+{"abstract": "  The missing data problem pervasively exists in statistical applications. Even\nas simple as the count data in mortality projections, it may not be available\nfor certain age-and-year groups due to the budget limitations or difficulties\nin tracing research units, resulting in the follow-up estimation and prediction\ninaccuracies. To circumvent this data-driven challenge, we extend the Poisson\nlog-normal Lee-Carter model to accommodate a more flexible time structure, and\ndevelop the new sampling algorithm that improves the MCMC convergence when\ndealing with incomplete mortality data. Via the overdispersion term and Gibbs\nsampler, the extended model can be re-written as the dynamic linear model so\nthat both Kalman and sequential Kalman filters can be incorporated into the\nsampling scheme. Additionally, our meticulous prior settings can avoid the\nre-scaling step in each MCMC iteration, and allow model selection\nsimultaneously conducted with estimation and prediction. The proposed method is\napplied to the mortality data of Chinese males during the period 1995-2016 to\nyield mortality rate forecasts for 2017-2039. The results are comparable to\nthose based on the imputed data set, suggesting that our approach could handle\nincomplete data well.\n"}
+{"abstract": "  Vanadium tetracyanoethylene (V[TCNE]$_{x}$, $x\\approx 2$) is an organic-based\nferrimagnet with a high magnetic ordering temperature $\\mathrm{T_C>600 ~K}$,\nlow magnetic damping, and growth compatibility with a wide variety of\nsubstrates. However, similar to other organic-based materials, it is sensitive\nto air. Although encapsulation of V[TCNE]$_{x}$ with glass and epoxy extends\nthe film lifetime from an hour to a few weeks, what is limiting its lifetime\nremains poorly understood. Here we characterize encapsulated V[TCNE]$_{x}$\nfilms using confocal microscopy, Raman spectroscopy, ferromagnetic resonance\nand SQUID magnetometry. We identify the relevant features in the Raman spectra\nin agreement with \\textit{ab initio} theory, reproducing $\\mathrm{C=C,C\\equiv\nN}$ vibrational modes. We correlate changes in the effective dynamic\nmagnetization with changes in Raman intensity and in photoluminescence. Based\non changes in Raman spectra, we hypothesize possible structural changes and\naging mechanisms in V[TCNE]$_x$. These findings enable a local optical probe of\nV[TCNE]$_{x}$ film quality, which is invaluable in experiments where assessing\nfilm quality with local magnetic characterization is not possible.\n"}
+{"abstract": "  We provide new necessary and sufficient conditions for the convergence of\npositive series developing Bertran-De Morgan and Cauchy type tests given in [M.\nMartin, Bull. Amer. Math. Soc. 47(1941), 452-457] and [L. Bourchtein et al,\nInt. J. Math. Anal. 6(2012), 1847-1869]. The obtained result enables us to\nextend the known conditions for recurrence and transience of birth-and-death\nprocesses given in [V. M. Abramov, Amer. Math. Monthly 127(2020) 444-448].\n"}
+{"abstract": "  We propose an anti-parity-time (anti-PT ) symmetric non-Hermitian\nSu-Schrieffer-Heeger (SSH) model, where the large non-Hermiticity\nconstructively creates nontrivial topology and greatly expands the topological\nphase. In the anti-PT -symmetric SSH model, the gain and loss are alternatively\narranged in pairs under the inversion symmetry. The appearance of degenerate\npoint at the center of the Brillouin zone determines the topological phase\ntransition, while the exceptional points unaffect the band topology. The large\nnon-Hermiticity leads to unbalanced wavefunction distribution in the broken\nanti-PT -symmetric phase and induces the nontrivial topology. Our findings can\nbe verified through introducing dissipations in every another two sites of the\nstandard SSH model even in its trivial phase, where the nontrivial topology is\nsolely induced by the dissipations.\n"}
+{"abstract": "  The trustworthiness of Robots and Autonomous Systems (RAS) has gained a\nprominent position on many research agendas towards fully autonomous systems.\nThis research systematically explores, for the first time, the key facets of\nhuman-centered AI (HAI) for trustworthy RAS. In this article, five key\nproperties of a trustworthy RAS initially have been identified. RAS must be (i)\nsafe in any uncertain and dynamic surrounding environments; (ii) secure, thus\nprotecting itself from any cyber-threats; (iii) healthy with fault tolerance;\n(iv) trusted and easy to use to allow effective human-machine interaction\n(HMI), and (v) compliant with the law and ethical expectations. Then, the\nchallenges in implementing trustworthy autonomous system are analytically\nreviewed, in respects of the five key properties, and the roles of AI\ntechnologies have been explored to ensure the trustiness of RAS with respects\nto safety, security, health and HMI, while reflecting the requirements of\nethics in the design of RAS. While applications of RAS have mainly focused on\nperformance and productivity, the risks posed by advanced AI in RAS have not\nreceived sufficient scientific attention. Hence, a new acceptance model of RAS\nis provided, as a framework for requirements to human-centered AI and for\nimplementing trustworthy RAS by design. This approach promotes human-level\nintelligence to augment human's capacity. while focusing on contributions to\nhumanity.\n"}
+{"abstract": "  The pandemic of novel Coronavirus Disease 2019 (COVID-19) is widespread all\nover the world causing serious health problems as well as serious impact on the\nglobal economy. Reliable and fast testing of the COVID-19 has been a challenge\nfor researchers and healthcare practitioners. In this work we present a novel\nmachine learning (ML) integrated X-ray device in Healthcare Cyber-Physical\nSystem (H-CPS) or smart healthcare framework (called CoviLearn) to allow\nhealthcare practitioners to perform automatic initial screening of COVID-19\npatients. We propose convolutional neural network (CNN) models of X-ray images\nintegrated into an X-ray device for automatic COVID-19 detection. The proposed\nCoviLearn device will be useful in detecting if a person is COVID-19 positive\nor negative by considering the chest X-ray image of individuals. CoviLearn will\nbe useful tool doctors to detect potential COVID-19 infections instantaneously\nwithout taking more intrusive healthcare data samples, such as saliva and\nblood. COVID-19 attacks the endothelium tissues that support respiratory tract,\nX-rays images can be used to analyze the health of a patient lungs. As all\nhealthcare centers have X-ray machines, it could be possible to use proposed\nCoviLearn X-rays to test for COVID-19 without the especial test kits. Our\nproposed automated analysis system CoviLearn which has 99% accuracy will be\nable to save valuable time of medical professionals as the X-ray machines come\nwith a drawback as it needed a radiology expert.\n"}
+{"abstract": "  We introduce the notion of a nonlinear splitting on a fibre bundle as a\ngeneralization of an Ehresmann connection. We present its basic properties and\nwe pay attention to the special cases of affine, homogeneous and principal\nnonlinear splittings. We explain where nonlinear splittings appear in the\ncontext of Lagrangian systems and Finsler geometry and we show their relation\nto Routh symmetry reduction, submersive second-order differential equations and\nunreduction. We define a curvature map for a nonlinear splitting, and we\nindicate where this concept appears in the context of nonholonomic systems with\naffine constraints and Lagrangian systems of magnetic type.\n"}
+{"abstract": "  Although board games and video games have been studied for decades in\nartificial intelligence research, challenging word games remain relatively\nunexplored. Word games are not as constrained as games like chess or poker.\nInstead, word game strategy is defined by the players' understanding of the way\nwords relate to each other. The word game Codenames provides a unique\nopportunity to investigate common sense understanding of relationships between\nwords, an important open challenge. We propose an algorithm that can generate\nCodenames clues from the language graph BabelNet or from any of several\nembedding methods - word2vec, GloVe, fastText or BERT. We introduce a new\nscoring function that measures the quality of clues, and we propose a weighting\nterm called DETECT that incorporates dictionary-based word representations and\ndocument frequency to improve clue selection. We develop BabelNet-Word\nSelection Framework (BabelNet-WSF) to improve BabelNet clue quality and\novercome the computational barriers that previously prevented leveraging\nlanguage graphs for Codenames. Extensive experiments with human evaluators\ndemonstrate that our proposed innovations yield state-of-the-art performance,\nwith up to 102.8% improvement in precision@2 in some cases. Overall, this work\nadvances the formal study of word games and approaches for common sense\nlanguage understanding.\n"}
+{"abstract": "  Significant number of researches have been developed recently around\nintelligent system for traffic management, especially, OCR based license plate\nrecognition, as it is considered as a main step for any automatic traffic\nmanagement system. Good quality data sets are increasingly needed and produced\nby the research community to improve the performance of those algorithms.\nFurthermore, a special need of data is noted for countries having special\ncharacters on their licence plates, like Morocco, where Arabic Alphabet is\nused. In this work, we present a labeled open data set of circulation plates\ntaken in Morocco, for different type of vehicles, namely cars, trucks and\nmotorcycles. This data was collected manually and consists of 705 unique and\ndifferent images. Furthermore this data was labeled for plate segmentation and\nfor matriculation number OCR. Also, As we show in this paper, the data can be\nenriched using data augmentation techniques to create training sets with few\nthousands of images for different machine leaning and AI applications. We\npresent and compare a set of models built on this data. Also, we publish this\ndata as an open access data to encourage innovation and applications in the\nfield of OCR and image processing for traffic control and other applications\nfor transportation and heterogeneous vehicle management.\n"}
+{"abstract": "  High hardware cost and high power consumption of massive multiple-input and\nmultiple output (MIMO) are still two challenges for the future wireless\ncommunications including beyond 5G. Adopting the low-resolution\nanalog-to-digital converter (ADC) is viewed as a promising solution.\nAdditionally, the direction of arrival (DOA) estimation is an indispensable\ntechnology for beam alignment and tracking in massive MIMO systems. Thus, in\nthis paper, the performance of DOA estimation for massive MIMO receive array\nwith mixed-ADC structure is first investigated, where one part of radio\nfrequency (RF) chains are connected with high-resolution ADCs and the remaining\nones are connected with low-resolution ADCs. Moreover, the Cramer-Rao lower\nbound (CRLB) for this architecture is derived based on the additive\nquantization noise model approximation for the effect of low-resolution ADCs.\nThen, the root-MUSIC method is designed for such a receive structure.\nEventually, a performance loss factor and the associated energy efficiency\nfactor is defined for analysis in detail. Simulation results find that a\nmixed-ADC architecture can strike a good balance among RMSE performance,\ncircuit cost and energy efficiency. More importantly, just 1-4 bits of\nlow-resolution ADCs can achieve a satisfactory performance for DOA measurement.\n"}
+{"abstract": "  We use Direct Numerical Simulations (DNS) of the forced Navier-Stokes\nequation for a 3-dimensional incompressible fluid in order to test recent\ntheoretical predictions. We study the two- and three-point spatio-temporal\ncorrelation functions of the velocity field in stationary, isotropic and\nhomogeneous turbulence. We compare our numerical results to the predictions\nfrom the Functional Renormalization Group (FRG) which were obtained in the\nlarge wavenumber limit. DNS are performed at various Reynolds numbers and the\ncorrelations are analyzed in different time regimes focusing on the large\nwavenumbers. At small time delays, we find that the two-point correlation\nfunction decays as a Gaussian in the variable $kt$ where $k$ is the wavenumber\nand $t$ the time delay. The three-point correlation function, determined from\nthe time-dependent advection-velocity correlations, also follows a Gaussian\ndecay at small $t$ with the same prefactor as the one of the two-point\nfunction. These behaviors are in precise agreement with the FRG results, and\ncan be simply understood as a consequence of sweeping. At large time delays,\nthe FRG predicts a crossover to an exponential in $k^2 t$, which we were not\nable to resolve in our simulations. However, we analyze the two-point\nspatio-temporal correlations of the modulus of the velocity, and show that they\nexhibit this crossover from a Gaussian to an exponential decay, although we\nlack of a theoretical understanding in this case. This intriguing phenomenon\ncalls for further theoretical investigation.\n"}
+{"abstract": "  We derive a precise asymptotic formula for the density of the small singular\nvalues of the real Ginibre matrix ensemble shifted by a complex parameter $z$\nas the dimension tends to infinity. For $z$ away from the real axis the formula\ncoincides with that for the complex Ginibre ensemble we derived earlier in\n[arXiv:1908.01653]. On the level of the one-point function of the low lying\nsingular values we thus confirm the transition from real to complex Ginibre\nensembles as the shift parameter $z$ becomes genuinely complex; the analogous\nphenomenon has been well known for eigenvalues. We use the superbosonization\nformula [arXiv:0707.2929] in a regime where the main contribution comes from a\nthree dimensional saddle manifold.\n"}
+{"abstract": "  We develop a variational Bayesian (VB) approach for estimating large-scale\ndynamic network models in the network autoregression framework. The VB approach\nallows for the automatic identification of the dynamic structure of such a\nmodel and obtains a direct approximation of the posterior density. Compared to\nMarkov Chain Monte Carlo (MCMC) based sampling approaches, the VB approach\nachieves enhanced computational efficiency without sacrificing estimation\naccuracy. In the simulation study conducted here, the proposed VB approach\ndetects various types of proper active structures for dynamic network models.\nCompared to the alternative approach, the proposed method achieves similar or\nbetter accuracy, and its computational time is halved. In a real data analysis\nscenario of day-ahead natural gas flow prediction in the German gas\ntransmission network with 51 nodes between October 2013 and September 2015, the\nVB approach delivers promising forecasting accuracy along with clearly detected\nstructures in terms of dynamic dependence.\n"}
+{"abstract": "  This paper studies the design of a finite-dimensional output feedback\ncontroller for the stabilization of a reaction-diffusion equation in the\npresence of a sector nonlinearity in the boundary input. Due to the input\nnonlinearity, classical approaches relying on the transfer of the control from\nthe boundary into the domain with explicit occurrence of the time-derivative of\nthe control cannot be applied. In this context, we first demonstrate using\nLyapunov direct method how a finite-dimensional observer-based controller can\nbe designed, without using the time derivative of the boundary input as an\nauxiliary command, in order to achieve the boundary stabilization of general\n1-D reaction-diffusion equations with Robin boundary conditions and a\nmeasurement selected as a Dirichlet trace. We extend this approach to the case\nof a control applying at the boundary through a sector nonlinearity. We show\nfrom the derived stability conditions the existence of a size of the sector (in\nwhich the nonlinearity is confined) so that the stability of the closed-loop\nsystem is achieved when selecting the dimension of the observer large enough.\n"}
+{"abstract": "  This is a short review of the Kadomtsev-Petviashvili hierarchies of types B\nand C. The main objects are the $L$-operator, the wave operator, the auxiliary\nlinear problems for the wave function, the bilinear identity for the wave\nfunction and the tau-function. All of them are discussed in the paper. The\nconnections with the usual Kadomtsev-Petviashvili hierarchy (of the type A) are\nclarified. Examples of soliton solutions and the dispersionless limit of the\nhierarchies are also considered.\n"}
+{"abstract": "  In this article, we introduce a framework for entanglement characterization\nby time-resolved single-photon counting with measurement operators defined in\nthe time domain. For a quantum system with unitary dynamics, we generate\ntime-continuous measurements by shifting from the Schr\\\"odinger picture to the\nHeisenberg representation. In particular, we discuss this approach in reference\nto photonic tomography. To make the measurement scheme realistic, we impose\ntiming uncertainty on photon counts along with the Poisson noise. Then, the\nframework is tested numerically on quantum tomography of qubits. Next, we\ninvestigate the accuracy of the model for polarization-entangled photon pairs.\nEntanglement detection and precision of state reconstruction are quantified by\nfigures of merit and presented on graphs versus the amount of time uncertainty.\n"}
+{"abstract": "  Magnetic skyrmions are stable topological spin textures with significant\npotential for spintronics applications. Merons, as half-skyrmions, have been\ndiscovered by recent observations, which have also raised the upsurge of\nresearch. The main purpose of this work is to study further the lattice forms\nof merons and skyrmions. We study a classical spin model with\nDzyaloshinskii-Moriya interaction, easy-axis, and in-plane magnetic\nanisotropies on the honeycomb lattice via Monte Carlo simulations. This model\ncould also describe the low-energy behaviors of a two-component bosonic model\nwith a synthetic spin-orbit coupling in the deep Mott insulating region or\ntwo-dimensional materials with strong spin-orbit coupling. The results\ndemonstrate the emergence of different sizes of spiral phases, skyrmion and\nvortex superlattice in absence of magnetic field, furthered the emergence of\nfield-induced meron and skyrmion superlattice. In particular, we give the\nsimulated evolution of the spin textures driven by the magnetic field, which\ncould further reveal the effect of the magnetic field for inducing meron and\nskyrmion superlattice.\n"}
+{"abstract": "  We construct Fock and MacMahon modules for the quantum toroidal superalgebra\n$\\mathcal{E}_\\mathbf{s}$ associated with the Lie superalgebra\n$\\mathfrak{gl}_{m|n}$ and parity $\\mathbf{s}$. The bases of the Fock and\nMacMahon modules are labeled by super-analogs of partitions and plane\npartitions with various boundary conditions, while the action of generators of\n$\\mathcal{E}_\\mathbf{s}$ is given by Pieri type formulas. We study the\ncorresponding characters.\n"}
+{"abstract": "  We consider the exact rogue periodic wave (rogue wave on the periodic\nbackground) and periodic wave solutions for the Chen-Lee-Liu equation via the\nodd-th order Darboux transformation. Then, the multi-layer physics-informed\nneural networks (PINNs) deep learning method is applied to research the\ndata-driven rogue periodic wave, breather wave, soliton wave and periodic wave\nsolutions of well-known Chen-Lee-Liu equation. Especially, the data-driven\nrogue periodic wave is learned for the first time to solve the partial\ndifferential equation. In addition, using image simulation, the relevant\ndynamical behaviors and error analysis for there solutions are presented. The\nnumerical results indicate that the rogue periodic wave, breather wave, soliton\nwave and periodic wave solutions for Chen-Lee-Liu equation can be generated\nwell by PINNs deep learning method.\n"}
+{"abstract": "  Predicting vulnerable road user behavior is an essential prerequisite for\ndeploying Automated Driving Systems (ADS) in the real-world. Pedestrian\ncrossing intention should be recognized in real-time, especially for urban\ndriving. Recent works have shown the potential of using vision-based deep\nneural network models for this task. However, these models are not robust and\ncertain issues still need to be resolved. First, the global spatio-temproal\ncontext that accounts for the interaction between the target pedestrian and the\nscene has not been properly utilized. Second, the optimum strategy for fusing\ndifferent sensor data has not been thoroughly investigated. This work addresses\nthe above limitations by introducing a novel neural network architecture to\nfuse inherently different spatio-temporal features for pedestrian crossing\nintention prediction. We fuse different phenomena such as sequences of RGB\nimagery, semantic segmentation masks, and ego-vehicle speed in an optimum way\nusing attention mechanisms and a stack of recurrent neural networks. The\noptimum architecture was obtained through exhaustive ablation and comparison\nstudies. Extensive comparative experiments on the JAAD pedestrian action\nprediction benchmark demonstrate the effectiveness of the proposed method,\nwhere state-of-the-art performance was achieved. Our code is open-source and\npublicly available.\n"}
+{"abstract": "  The likelihood ratio for a continuous gravitational wave signal is viewed\ngeometrically as a function of the orientation of two vectors; one representing\nthe optimal signal-to-noise ratio, the other representing the maximised\nlikelihood ratio or $\\mathcal{F}$-statistic. Analytic marginalisation over the\nangle between the vectors yields a marginalised likelihood ratio which is a\nfunction of the $\\mathcal{F}$-statistic. Further analytic marginalisation over\nthe optimal signal-to-noise ratio is explored using different choices of prior.\nMonte-Carlo simulations show that the marginalised likelihood ratios have\nidentical detection power to the $\\mathcal{F}$-statistic. This approach\ndemonstrates a route to viewing the $\\mathcal{F}$-statistic in a Bayesian\ncontext, while retaining the advantages of its efficient computation.\n"}
+{"abstract": "  Photonic metamaterials with properties unattainable in base materials are\nalready beginning to revolutionize optical component design. However, their\nexceptional characteristics are often static, as artificially engineered into\nthe material during the fabrication process. This limits their application for\nin-operando adjustable optical devices and active optics in general. Here, for\na hybrid material consisting of a liquid crystal-infused nanoporous solid, we\ndemonstrate active and dynamic control of its meta-optics by applying\nalternating electric fields parallel to the long axes of its cylindrical pores.\nFirst-harmonic Pockels and second-harmonic Kerr birefringence responses,\nstrongly depending on the excitation frequency- and temperature, are observed\nin a frequency range from 50 Hz to 50 kHz. This peculiar behavior is\nquantitatively traced by a Landau-De Gennes free energy analysis to an\norder-disorder orientational transition of the rod-like mesogens and intimately\nrelated changes in the molecular mobilities and polar anchoring at the solid\nwalls on the single-pore, meta-atomic scale. Thus, our study evidences that\nliquid crystal-infused nanopores exhibit integrated multi-physical couplings\nand reversible phase changes that make them particularly promising for the\ndesign of photonic metamaterials with thermo-electrically tunable birefringence\nin the emerging field of spacetime metamaterials aiming at a full\nspatio-temporal control of light.\n"}
+{"abstract": "  Graph Convolutional Networks (GCNs) are powerful models for node\nrepresentation learning tasks. However, the node representation in existing GCN\nmodels is usually generated by performing recursive neighborhood aggregation\nacross multiple graph convolutional layers with certain sampling methods, which\nmay lead to redundant feature mixing, needless information loss, and extensive\ncomputations. Therefore, in this paper, we propose a novel architecture named\nNon-Recursive Graph Convolutional Network (NRGCN) to improve both the training\nefficiency and the learning performance of GCNs in the context of node\nclassification. Specifically, NRGCN proposes to represent different hops of\nneighbors for each node based on inner-layer aggregation and layer-independent\nsampling. In this way, each node can be directly represented by concatenating\nthe information extracted independently from each hop of its neighbors thereby\navoiding the recursive neighborhood expansion across layers. Moreover, the\nlayer-independent sampling and aggregation can be precomputed before the model\ntraining, thus the training process can be accelerated considerably. Extensive\nexperiments on benchmark datasets verify that our NRGCN outperforms the\nstate-of-the-art GCN models, in terms of the node classification performance\nand reliability.\n"}
+{"abstract": "  Learning a sequence of tasks without access to i.i.d. observations is a\nwidely studied form of continual learning (CL) that remains challenging. In\nprinciple, Bayesian learning directly applies to this setting, since recursive\nand one-off Bayesian updates yield the same result. In practice, however,\nrecursive updating often leads to poor trade-off solutions across tasks because\napproximate inference is necessary for most models of interest. Here, we\ndescribe an alternative Bayesian approach where task-conditioned parameter\ndistributions are continually inferred from data. We offer a practical deep\nlearning implementation of our framework based on probabilistic\ntask-conditioned hypernetworks, an approach we term posterior meta-replay.\nExperiments on standard benchmarks show that our probabilistic hypernetworks\ncompress sequences of posterior parameter distributions with virtually no\nforgetting. We obtain considerable performance gains compared to existing\nBayesian CL methods, and identify task inference as our major limiting factor.\nThis limitation has several causes that are independent of the considered\nsequential setting, opening up new avenues for progress in CL.\n"}
+{"abstract": "  We introduce a geometric approach of integral curves for functional\ninequalities involving directional derivatives in the general context of\ndifferentiable manifolds that are equipped with a volume form. We focus on\nHardy-type inequalities and the explicit optimal Hardy potentials that are\ninduced by this method. We then apply the method to retrieve some known\ninequalities and establish some new ones.\n"}
+{"abstract": "  In this paper, we devise a distributional framework on actor-critic as a\nsolution to distributional instability, action type restriction, and conflation\nbetween samples and statistics. We propose a new method that minimizes the\nCram\\'er distance with the multi-step Bellman target distribution generated\nfrom a novel Sample-Replacement algorithm denoted SR($\\lambda$), which learns\nthe correct value distribution under multiple Bellman operations.\nParameterizing a value distribution with Gaussian Mixture Model further\nimproves the efficiency and the performance of the method, which we name GMAC.\nWe empirically show that GMAC captures the correct representation of value\ndistributions and improves the performance of a conventional actor-critic\nmethod with low computational cost, in both discrete and continuous action\nspaces using Arcade Learning Environment (ALE) and PyBullet environment.\n"}
+{"abstract": "  Advances in integrated photonics open exciting opportunities for\nbatch-fabricated optical sensors using high quality factor nanophotonic\ncavities to achieve ultra-high sensitivities and bandwidths. The sensitivity\nimproves with higher optical power, however, localized absorption and heating\nwithin a micrometer-scale mode volume prominently distorts the cavity\nresonances and strongly couples the sensor response to thermal dynamics,\nlimiting the sensitivity and hindering the measurement of broadband\ntime-dependent signals. Here, we derive a frequency-dependent photonic sensor\ntransfer function that accounts for thermo-optical dynamics and quantitatively\ndescribes the measured broadband optomechanical signal from an integrated\nphotonic atomic-force-microscopy nanomechanical probe. Using this transfer\nfunction, the probe can be operated in the high optical power, strongly\nthermo-optically nonlinear regime, reaching a sensitivity of $\\approx$ 0.4\nfm/Hz$^{1/2}$, an improvement of $\\approx 10\\times$ relative to the best\nperformance in the linear regime. Counterintuitively, we discover that higher\ntransduction gain and sensitivity are obtained with lower quality factor\noptical modes for low signal frequencies. Not limited to optomechanical\ntransducers, the derived transfer function is generally valid for describing\nsmall-signal dynamic response of a broad range of technologically important\nphotonic sensors subject to the thermo-optical effect.\n"}
+{"abstract": "  Understanding and improving mobile broadband deployment is critical to\nbridging the digital divide and targeting future investments. Yet accurately\nmapping mobile coverage is challenging. In 2019, the Federal Communications\nCommission (FCC) released a report on the progress of mobile broadband\ndeployment in the United States. This report received a significant amount of\ncriticism with claims that the cellular coverage, mainly available through\nLong-Term Evolution (LTE), was over-reported in some areas, especially those\nthat are rural and/or tribal [12]. We evaluate the validity of this criticism\nusing a quantitative analysis of both the dataset from which the FCC based its\nreport and a crowdsourced LTE coverage dataset. Our analysis is focused on the\nstate of New Mexico, a region characterized by diverse mix of\ndemographics-geography and poor broadband access. We then performed a\ncontrolled measurement campaign in northern New Mexico during May 2019. Our\nfindings reveal significant disagreement between the crowdsourced dataset and\nthe FCC dataset regarding the presence of LTE coverage in rural and tribal\ncensus blocks, with the FCC dataset reporting higher coverage than the\ncrowdsourced dataset. Interestingly, both the FCC and the crowdsourced data\nreport higher coverage compared to our on-the-ground measurements. Based on\nthese findings, we discuss our recommendations for improved LTE coverage\nmeasurements, whose importance has only increased in the COVID-19 era of\nperforming work and school from home, especially in rural and tribal areas.\n"}
+{"abstract": "  We propose a new approach to probe neutral-current non-standard neutrino\ninteraction parameter $\\varepsilon_{\\mu\\tau}$ using the oscillation dip and\noscillation valley. Using the simulated ratio of upward-going and\ndownward-going reconstructed muon events at the upcoming ICAL detector, we\ndemonstrate that the presence of non-zero $\\varepsilon_{\\mu\\tau}$ would result\nin the shift in the dip location as well as the bending of the oscillation\nvalley. Thanks to the charge identification capability of ICAL, the opposite\nshifts in the locations of oscillation dips as well as the contrast in the\ncurvatures of oscillation valleys for $\\mu^-$ and $\\mu^+$ is used to constrain\n$|\\varepsilon_{\\mu\\tau}|$ at 90% C.L. to about 2% using 500 kt$\\cdot$yr\nexposure. Our procedure incorporates statistical fluctuations, uncertainties in\noscillation parameters, and systematic errors.\n"}
+{"abstract": "  In this letter, we present an intelligent reflecting surface (IRS) selection\nstrategy for multiple IRSs aided multiuser multiple-input single-output (MISO)\nsystems. In particular, we pose the IRS selection problem as a stable matching\nproblem. A two stage user-IRS assignment algorithm is proposed, where the main\nobjective is to carry out a stable user-IRS matching, such that the sum rate of\nthe system is improved. The first stage of the proposed algorithm employs a\nwell-known Gale Shapley matching designed for the stable marriage problem.\nHowever, due to interference in multiuser systems, the matching obtained after\nthe first stage may not be stable. To overcome this issue, one-sided (i.e.,\nonly IRSs) blocking pairs (BPs) are identified in the second stage of the\nproposed algorithm, where the BP is a pair of IRSs which are better off after\nexchanging their partners. Thus, the second stage validates the stable matching\nin the proposed algorithm. Numerical results show that the proposed assignment\nachieves better sum rate performance compared to distance-based and random\nmatching algorithms.\n"}
+{"abstract": "  Let $V$ be a simple vertex operator superalgebra and $G$ a finite\nautomorphism group of $V$ containing the canonical automorphism $\\sigma$ such\nthat $V^G$ is regular. It is proved that every irreducible $V^G$-module occurs\nin an irreducible $g$-twisted $V$-module for some $g\\in G$ and the irreducible\n$V^G$-modules are classified. Moreover, the quantum dimensions of irreducible\n$V^G$-modules are determined, a global dimension formula for $V$ in terms of\ntwisted modules is obtained and a super quantum Galois theory is established.\nIn addition, the $S$-matrix of $V^G$ is computed\n"}
+{"abstract": "  In imitation learning from observation IfO, a learning agent seeks to imitate\na demonstrating agent using only observations of the demonstrated behavior\nwithout access to the control signals generated by the demonstrator. Recent\nmethods based on adversarial imitation learning have led to state-of-the-art\nperformance on IfO problems, but they typically suffer from high sample\ncomplexity due to a reliance on data-inefficient, model-free reinforcement\nlearning algorithms. This issue makes them impractical to deploy in real-world\nsettings, where gathering samples can incur high costs in terms of time,\nenergy, and risk. In this work, we hypothesize that we can incorporate ideas\nfrom model-based reinforcement learning with adversarial methods for IfO in\norder to increase the data efficiency of these methods without sacrificing\nperformance. Specifically, we consider time-varying linear Gaussian policies,\nand propose a method that integrates the linear-quadratic regulator with path\nintegral policy improvement into an existing adversarial IfO framework. The\nresult is a more data-efficient IfO algorithm with better performance, which we\nshow empirically in four simulation domains: using far fewer interactions with\nthe environment, the proposed method exhibits similar or better performance\nthan the existing technique.\n"}
+{"abstract": "  Floating photovoltaics (FPV) is an emerging technology that is gaining\nattention worldwide. However, little information is still available on its\npossible impacts in the aquatic ecosystems, as well as on the durability of its\ncomponents. Therefore, this work intends to provide a contribution to this\nfield, analysing possible obstacles that can compromise the performance of this\ntechnology, adding to an increase of its reliability and assessing possible\nimpacts. The problem under study is related to the potential submersion of\nphotovoltaic cables, that can lead to a degradation of its electrical\ninsulation capabilities and, consequently, higher energy production losses and\nwater contamination. In the present study, the submersion of photovoltaic\ncables (with two different insulation materials) in freshwater and artificial\nseawater was tested, in order to replicate real life conditions, when FPV\nsystems are located in reservoirs or in the marine environment. Electrical\ninsulation tests were carried out weekly to assess possible cable degradation,\nthe physical-chemical characteristics of the water were also periodically\nmonitored, complemented by analysis to detect traces of copper and\nmicroplastics in the water. The results showed that the submersion of\nphotovoltaic cables with rubber sheath in saltwater can lead to a cable\naccelerated degradation, with reduction of its electrical insulation and,\nconsequently, copper release into the aquatic environment.\n"}
+{"abstract": "  The COVID-19 pandemic has impacted billions of people around the world. To\ncapture some of these impacts in the United States, we are conducting a\nnationwide longitudinal survey collecting information about activity and\ntravel-related behaviors and attitudes before, during, and after the COVID-19\npandemic. The survey questions cover a wide range of topics including\ncommuting, daily travel, air travel, working from home, online learning,\nshopping, and risk perception, along with attitudinal, socioeconomic, and\ndemographic information. The survey is deployed over multiple waves to the same\nrespondents to monitor how behaviors and attitudes evolve over time. Version\n1.0 of the survey contains 8,723 Wave 1 responses that are publicly available.\nThis article details the methodology adopted for the collection, cleaning, and\nprocessing of the data. In addition, the data are weighted to be representative\nof national and regional demographics. This survey dataset can aid researchers,\npolicymakers, businesses, and government agencies in understanding both the\nextent of behavioral shifts and the likelihood that changes in behaviors will\npersist after COVID-19.\n"}
+{"abstract": "  This paper shows that, in the definition of Alexandrov space with lower\n([BGP]) or upper ([AKP]) curvature bound, the original conditions can be\nreplaced with much weaker ones, which can be viewed as comparison versions of\nthe second variation formula in Riemannian geometry (and thus if we define\nAlexandrov spaces using these weakened conditions, then the original definition\nwill become a local version of Toponogov's Comparison Theorem on such spaces).\nAs an application, we give a new proof for the Doubling Theorem by Perel'man.\n"}
+{"abstract": "  The thinness of a graph is a width parameter that generalizes some properties\nof interval graphs, which are exactly the graphs of thinness one. Graphs with\nthinness at most two include, for example, bipartite convex graphs. Many\nNP-complete problems can be solved in polynomial time for graphs with bounded\nthinness, given a suitable representation of the graph. Proper thinness is\ndefined analogously, generalizing proper interval graphs, and a larger family\nof NP-complete problems are known to be polynomially solvable for graphs with\nbounded proper thinness. It is known that the thinness of a graph is at most\nits pathwidth plus one. In this work, we prove that the proper thinness of a\ngraph is at most its bandwidth, for graphs with at least one edge. It is also\nknown that boxicity is a lower bound for the thinness. The main results of this\nwork are characterizations of 2-thin and 2-proper thin graphs as intersection\ngraphs of rectangles in the plane with sides parallel to the Cartesian axes and\nother specific conditions. We also bound the bend number of graphs with low\nthinness as vertex intersection graphs of paths on a grid ($B_k$-VPG graphs are\nthe graphs that have a representation in which each path has at most $k$\nbends). We show that 2-thin graphs are a subclass of $B_1$-VPG graphs and,\nmoreover, of monotone L-graphs, and that 3-thin graphs are a subclass of\n$B_3$-VPG graphs. We also show that $B_0$-VPG graphs may have arbitrarily large\nthinness, and that not every 4-thin graph is a VPG graph. Finally, we\ncharacterize 2-thin graphs by a set of forbidden patterns for a vertex order.\n"}
+{"abstract": "  This paper contributes with a new formal method of spatial discretization of\na class of nonlinear distributed parameter systems that allow a\nport-Hamiltonian representation over a one dimensional manifold. A specific\nfinite dimensional port-Hamiltonian element is defined that enables a structure\npreserving discretization of the infinite dimensional model that inherits the\nDirac structure, the underlying energy balance and matches the Hamiltonian\nfunction on any, possibly nonuniform mesh of the spatial geometry.\n"}
+{"abstract": "  The identification of stellar-mass black-hole mergers with up to 80 Msun as\npowerful sources of gravitational wave radiation led to increased interest in\nthe physics of the most massive stars. The largest sample of possible\nprogenitors of such objects, very massive stars (VMS) with masses up to 300\nMsun, have been identified in the 30 Dor star-forming region in the Large\nMagellanic Cloud (LMC). The physics and evolution of VMS is highly uncertain,\nmainly due to their proximity to the Eddington limit. In this work we\ninvestigate the two most important effects that are thought to occur near the\nEddington limit. Enhanced mass loss through optically thick winds, and the\nformation of radially inflated stellar envelopes. We compute evolutionary\nmodels for VMS at LMC metallicity and perform a population synthesis of the\nyoung stellar population in 30 Dor. We find that enhanced mass loss and\nenvelope inflation have a dominant effect on the evolution of the most massive\nstars. While the observed mass-loss properties and the associated surface\nHe-enrichment are well described by our new models, the observed O-star\nmass-loss rates are found to cover a much larger range than theoretically\npredicted, with particularly low mass-loss rates for the youngest objects.\nAlso, the (rotational) surface enrichment in the O-star regime appears to be\nnot well understood. The positions of the most massive stars in the\nHertzsprung-Russell Diagram (HRD) are affected by mass loss and envelope\ninflation. For instance, the majority of luminous B-supergiants in 30 Dor, and\nthe lack thereof at the highest luminosities, can be explained through the\ncombination of envelope inflation and mass loss. Finally, we find that the\nupper limit for the inferred initial stellar masses in the greater 30 Dor\nregion is significantly lower than in its central cluster R 136, implying a\nvariable upper limit for the masses of stars.\n"}
+{"abstract": "  The first half of the paper is devoted to description and implementation of\nstatistical tests arguing for the presence of a Brownian component in the\ninventories and wealth processes of individual traders. We use intra-day data\nfrom the Toronto Stock Exchange to provide empirical evidence of this claim. We\nwork with regularly spaced time intervals, as well as with asynchronously\nobserved data. The tests reveal with high significance the presence of a\nnon-zero Brownian motion component. The second half of the paper is concerned\nwith the analysis of trader behaviors throughout the day. We extend the\ntheoretical analysis of an existing optimal execution model to accommodate the\npresence of It\\^o inventory processes, and we compare empirically the optimal\nbehavior of traders in such fitted models, to their actual behavior as inferred\nfrom the data.\n"}
+{"abstract": "  We study $b$-property of a sublattice (or an order ideal) $F$ of a vector\nlattice $E$. In particular, $b$-property of $E$ in $E^\\delta$, the Dedekind\ncompletion of $E$, $b$-property of $E$ in $E^u$, the universal completion of\n$E$, and $b$-property of $E$ in $\\hat{E}(\\hat{\\tau})$, the completion of $E$.\n"}
+{"abstract": "  We describe explicitly the chamber structure of the movable cone for a\ngeneral smooth complete intersection Calabi-Yau threefold $X$ of Picard number\ntwo in certain Pr-ruled Fano manifold and hence verify the Morrison-Kawamata\ncone conjecture for such $X$. Moreover, all birational minimal models of such\nCalabi-Yau threefolds are found, whose number is finite up to isomorphism.\n"}
+{"abstract": "  Recently, learning a model that generalizes well on out-of-distribution (OOD)\ndata has attracted great attention in the machine learning community. In this\npaper, after defining OOD generalization via Wasserstein distance, we\ntheoretically show that a model robust to input perturbation generalizes well\non OOD data. Inspired by previous findings that adversarial training helps\nimprove input-robustness, we theoretically show that adversarially trained\nmodels have converged excess risk on OOD data, and empirically verify it on\nboth image classification and natural language understanding tasks. Besides, in\nthe paradigm of first pre-training and then fine-tuning, we theoretically show\nthat a pre-trained model that is more robust to input perturbation provides a\nbetter initialization for generalization on downstream OOD data. Empirically,\nafter fine-tuning, this better-initialized model from adversarial pre-training\nalso has better OOD generalization.\n"}
+{"abstract": "  Strong gravitational lensing is a gravitational wave (GW) propagation effect\nthat influences the inferred GW source parameters and the cosmological\nenvironment. Identifying strongly-lensed GW images is challenging as waveform\namplitude magnification is degenerate with a shift in the source intrinsic mass\nand redshift. However, even in the geometric-optics limit, type II\nstrongly-lensed images cannot be fully matched by type I (or unlensed) waveform\ntemplates, especially with large binary mass ratios and orbital inclination\nangles. We propose to use this mismatch to distinguish individual type II\nimages. Using planned noise spectra of Cosmic Explorer, Einstein Telescope and\nLIGO Voyager, we show that a significant fraction of type II images can be\ndistinguished from unlensed sources, given sufficient SNR ($\\sim 30$).\nIncorporating models on GW source population and lens population, we predict\nthat the yearly detection rate of lensed GW sources with detectable type II\nimages is 172.2, 118.2 and 27.4 for CE, ET and LIGO Voyager, respectively.\nAmong these detectable events, 33.1%, 7.3% and 0.22% will be distinguishable\nvia their type II images with a log Bayes factor larger than 10. We conclude\nthat such distinguishable events are likely to appear in the third-generation\ndetector catalog; our strategy will significantly supplement existing strong\nlensing search strategies.\n"}
+{"abstract": "  A/B experimentation is a known technique for data-driven product development\nand has demonstrated its value in web-facing businesses. With the\ndigitalisation of the automotive industry, the focus in the industry is\nshifting towards software. For automotive embedded software to continuously\nimprove, A/B experimentation is considered an important technique. However, the\nadoption of such a technique is not without challenge. In this paper, we\npresent an architecture to enable A/B testing in automotive embedded software.\nThe design addresses challenges that are unique to the automotive industry in a\nsystematic fashion. Going from hypothesis to practice, our architecture was\nalso applied in practice for running online experiments on a considerable\nscale. Furthermore, a case study approach was used to compare our proposal with\nstate-of-practice in the automotive industry. We found our architecture design\nto be relevant and applicable in the efforts of adopting continuous A/B\nexperiments in automotive embedded software.\n"}
+{"abstract": "  Maintaining security and privacy in real-world enterprise networks is\nbecoming more and more challenging. Cyber actors are increasingly employing\npreviously unreported and state-of-the-art techniques to break into corporate\nnetworks. To develop novel and effective methods to thwart these sophisticated\ncyberattacks, we need datasets that reflect real-world enterprise scenarios to\na high degree of accuracy. However, precious few such datasets are publicly\navailable. Researchers still predominantly use the decade-old KDD datasets,\nhowever, studies showed that these datasets do not adequately reflect modern\nattacks like Advanced Persistent Threats(APT). In this work, we analyze the\nusefulness of the recently introduced DARPA Operationally Transparent Cyber\n(OpTC) dataset in this regard. We describe the content of the dataset in detail\nand present a qualitative analysis. We show that the OpTC dataset is an\nexcellent candidate for advanced cyber threat detection research while also\nhighlighting its limitations. Additionally, we propose several research\ndirections where this dataset can be useful.\n"}
+{"abstract": "  In this paper, we propose a scheme that utilizes the optimization ability of\nartificial intelligence (AI) for optimal transceiver-joint equalization in\ncompensating for the optical filtering impairments caused by wavelength\nselective switches (WSS). In contrast to adding or replacing a certain module\nof existing digital signal processing (DSP), we exploit the similarity between\na communication system and a neural network (NN). By mapping a communication\nsystem to an NN, in which the equalization modules correspond to the\nconvolutional layers and other modules can been regarded as static layers, the\noptimal transceiver-joint equalization coefficients can be obtained. In\nparticular, the DSP structure of the communication system is not changed.\nExtensive numerical simulations are performed to validate the performance of\nthe proposed method. For a 65 GBaud 16QAM signal, it can achieve a 0.76 dB gain\nwhen the number of WSSs is 16 with a -6 dB bandwidth of 73 GHz.\n"}
+{"abstract": "  We present a novel framework for designing multiplierless kernel machines\nthat can be used on resource-constrained platforms like intelligent edge\ndevices. The framework uses a piecewise linear (PWL) approximation based on a\nmargin propagation (MP) technique and uses only addition/subtraction, shift,\ncomparison, and register underflow/overflow operations. We propose a\nhardware-friendly MP-based inference and online training algorithm that has\nbeen optimized for a Field Programmable Gate Array (FPGA) platform. Our FPGA\nimplementation eliminates the need for DSP units and reduces the number of\nLUTs. By reusing the same hardware for inference and training, we show that the\nplatform can overcome classification errors and local minima artifacts that\nresult from the MP approximation. Using the FPGA platform, we also show that\nthe proposed multiplierless MP-kernel machine demonstrates superior performance\nin terms of power, performance, and area compared to other comparable\nimplementations.\n"}
+{"abstract": "  This paper is devoted to studying impedance eigenvalues (that is, eigenvalues\nof a particular Dirichlet-to-Neumann map) for the time harmonic linear elastic\nwave problem, and their potential use as target-signatures for fluid-solid\ninteraction problems. We first consider several possible families of\neigenvalues of the elasticity problem, focusing on certain impedance\neigenvalues that are an analogue of Steklov eigenvalues. We show that one of\nthese families arises naturally in inverse scattering. We also analyse their\napproximation from far field measurements of the scattered pressure field in\nthe fluid, and illustrate several alternative methods of approximation in the\ncase of an isotropic elastic disk.\n"}
+{"abstract": "  For a finite group $G,$ we define the concept of $G$-partial permutation and\nuse it to show that the structure coefficients of the center of the wreath\nproduct $G\\wr \\mathcal{S}_n$ algebra are polynomials in $n$ with non-negative\ninteger coefficients. Our main tool is a combinatorial algebra which projects\nonto the center of the group $G\\wr \\mathcal{S}_n$ algebra for every $n.$ This\ngeneralizes the Ivanov and Kerov method to prove the polynomiality property for\nthe structure coefficients of the center of the symmetric group algebra.\n"}
+{"abstract": "  In this paper, closed-loop entry guidance in a randomly perturbed atmosphere,\nusing bank angle control, is posed as a stochastic optimal control problem. The\nentry trajectory, as well as the closed-loop controls, are both modeled as\nrandom processes with statistics determined by the entry dynamics, the entry\nguidance, and the probabilistic structure of altitude-dependent atmospheric\ndensity variations. The entry guidance, which is parameterized as a sequence of\nlinear feedback gains, is designed to steer the probability distribution of the\nentry trajectories while satisfying bounds on the allowable control inputs and\non the maximum allowable state errors. Numerical simulations of a Mars entry\nscenario demonstrate improved range targeting performance when using the\ndeveloped stochastic guidance scheme as compared to the existing Apollo final\nphase algorithm.\n"}
+{"abstract": "  Graph neural networks (GNNs) have received tremendous attention due to their\npower in learning effective representations for graphs. Most GNNs follow a\nmessage-passing scheme where the node representations are updated by\naggregating and transforming the information from the neighborhood. Meanwhile,\nthey adopt the same strategy in aggregating the information from different\nfeature dimensions. However, suggested by social dimension theory and spectral\nembedding, there are potential benefits to treat the dimensions differently\nduring the aggregation process. In this work, we investigate to enable\nheterogeneous contributions of feature dimensions in GNNs. In particular, we\npropose a general graph feature gating network (GFGN) based on the graph signal\ndenoising problem and then correspondingly introduce three graph filters under\nGFGN to allow different levels of contributions from feature dimensions.\nExtensive experiments on various real-world datasets demonstrate the\neffectiveness and robustness of the proposed frameworks.\n"}
+{"abstract": "  The purpose of this paper is to introduce the notion of noncommutative\nBiHom-pre-Poisson algebra. Also we establish the bimodules and matched pairs of\nnoncommutative BiHom-(pre)-Poisson algebras and related relevant properties are\nalso given. Finally, we exploit the notion of $\\mathcal{O}$-operator to\nillustrate the relations existing between noncommutative BiHom-Poisson and\nnoncommutative BiHom pre-Poisson algebras.\n"}
+{"abstract": "  Classical Cepheids (DCEPs) are the most important primary indicators for the\nextragalactic distance scale, but they are also important objects per se,\nallowing us to put constraints on the physics of intermediate-mass stars and\nthe pulsation theories. We have investigated the peculiar DCEP HD 344787, which\nis known to exhibit the fastest positive period change among DCEPs along with a\nquenching amplitude of the light variation. We have used high-resolution\nspectra obtained with HARPS-N@TNG for HD 344787 and the more famous Polaris\nDCEP, to infer their detailed chemical abundances. Results from the analysis of\nnew time-series photometry of HD 344787 obtained by the TESS satellite are also\nreported. The double mode nature of HD344787 pulsation is confirmed by analysis\nof the TESS light curve, although with rather tiny amplitudes of a few tens of\nmillimag. This is an indication that HD344787 is on the verge of quenching the\npulsation. Analysis of the HARPS-N@TNG spectra reveals an almost solar\nabundance and no depletion of carbon and oxygen. Hence, the star appears to\nhave not gone through the first dredge-up. Similar results are obtained for\nPolaris. Polaris and HD344787 are confirmed to be both most likely at their\nfirst crossing of the instability strip (IS). The two stars are likely at the\nopposite borders of the IS for first overtone DCEPs with metal abundance\nZ=0.008. A comparison with other DCEPs which are also thought to be at their\nfirst crossing allows us to speculate that the differences we see in the\nHertzsprung-Russell diagram might be due to differences in the properties of\nthe DCEP progenitors during the main sequence phase.\n"}
+{"abstract": "  Deep neural networks for automatic image colorization often suffer from the\ncolor-bleeding artifact, a problematic color spreading near the boundaries\nbetween adjacent objects. Such color-bleeding artifacts debase the reality of\ngenerated outputs, limiting the applicability of colorization models in\npractice. Although previous approaches have attempted to address this problem\nin an automatic manner, they tend to work only in limited cases where a high\ncontrast of gray-scale values are given in an input image. Alternatively,\nleveraging user interactions would be a promising approach for solving this\ncolor-breeding artifacts. In this paper, we propose a novel edge-enhancing\nnetwork for the regions of interest via simple user scribbles indicating where\nto enhance. In addition, our method requires a minimal amount of effort from\nusers for their satisfactory enhancement. Experimental results demonstrate that\nour interactive edge-enhancing approach effectively improves the color-bleeding\nartifacts compared to the existing baselines across various datasets.\n"}
+{"abstract": "  We consider the distributed training of large-scale neural networks that\nserve as PDE solvers producing full field outputs. We specifically consider\nneural solvers for the generalized 3D Poisson equation over megavoxel domains.\nA scalable framework is presented that integrates two distinct advances. First,\nwe accelerate training a large model via a method analogous to the multigrid\ntechnique used in numerical linear algebra. Here, the network is trained using\na hierarchy of increasing resolution inputs in sequence, analogous to the 'V',\n'W', 'F', and 'Half-V' cycles used in multigrid approaches. In conjunction with\nthe multi-grid approach, we implement a distributed deep learning framework\nwhich significantly reduces the time to solve. We show the scalability of this\napproach on both GPU (Azure VMs on Cloud) and CPU clusters (PSC Bridges2). This\napproach is deployed to train a generalized 3D Poisson solver that scales well\nto predict output full-field solutions up to the resolution of 512x512x512 for\na high dimensional family of inputs.\n"}
+{"abstract": "  This paper presents a novel supervised approach to detecting the chorus\nsegments in popular music. Traditional approaches to this task are mostly\nunsupervised, with pipelines designed to target some quality that is assumed to\ndefine \"chorusness,\" which usually means seeking the loudest or most frequently\nrepeated sections. We propose to use a convolutional neural network with a\nmulti-task learning objective, which simultaneously fits two temporal\nactivation curves: one indicating \"chorusness\" as a function of time, and the\nother the location of the boundaries. We also propose a post-processing method\nthat jointly takes into account the chorus and boundary predictions to produce\nbinary output. In experiments using three datasets, we compare our system to a\nset of public implementations of other segmentation and chorus-detection\nalgorithms, and find our approach performs significantly better.\n"}
+{"abstract": "  Motivated by the recent LHCb announcement of a $3.1\\sigma$ violation of\nlepton-flavor universality in the ratio $R_K=\\Gamma(B\\to\nK\\mu^+\\mu^-)/\\Gamma(B\\to K e^+ e^-)$, we present an updated, comprehensive\nanalysis of the flavor anomalies seen in both neutral-current ($b\\to\ns\\ell^+\\ell^-$) and charged-current ($b\\to c\\tau\\bar\\nu$) decays of $B$ mesons.\nOur study starts from a model-independent effective field-theory approach and\nthen considers both a simplified model and a UV-complete extension of the\nStandard Model featuring a vector leptoquark $U_1$ as the main mediator of the\nanomalies. We show that the new LHCb data corroborate the emerging pattern of a\nnew, predominantly left-handed, semileptonic current-current interaction with a\nflavor structure respecting a (minimally) broken $U(2)^5$ flavor symmetry. New\naspects of our analysis include a combined analysis of the semileptonic\noperators involving tau leptons, including in particular the important\nconstraint from $B_s$--$\\bar B_s$ mixing, a systematic study of the effects of\nright-handed leptoquark couplings and of deviations from minimal\nflavor-symmetry breaking, a detailed analysis of various rare $B$-decay modes\nwhich would provide smoking-gun signatures of this non-standard framework (LFV\ndecays, di-tau modes, and $B\\to K^{(*)}\\nu\\bar\\nu$), and finally an updated\nanalysis of collider bounds on the leptoquark mass and couplings.\n"}
+{"abstract": "  In this paper, the interference mitigation for Frequency Modulated Continuous\nWave (FMCW) radar system with a dechirping receiver is investigated. After\ndechirping operation, the scattered signals from targets result in beat\nsignals, i.e., the sum of complex exponentials while the interferences lead to\nchirp-like short pulses. Taking advantage of these different time and frequency\nfeatures between the useful signals and the interferences, the interference\nmitigation is formulated as an optimization problem: a sparse and low-rank\ndecomposition of a Hankel matrix constructed by lifting the measurements. Then,\nan iterative optimization algorithm is proposed to tackle it by exploiting the\nAlternating Direction of Multipliers (ADMM) scheme. Compared to the existing\nmethods, the proposed approach does not need to detect the interference and\nalso improves the estimation accuracy of the separated useful signals. Both\nnumerical simulations with point-like targets and experiment results with\ndistributed targets (i.e., raindrops) are presented to demonstrate and verify\nits performance. The results show that the proposed approach is generally\napplicable for interference mitigation in both stationary and moving target\nscenarios.\n"}
+{"abstract": "  RGB-D salient object detection(SOD) demonstrates its superiority on detecting\nin complex environments due to the additional depth information introduced in\nthe data. Inevitably, an independent stream is introduced to extract features\nfrom depth images, leading to extra computation and parameters. This\nmethodology which sacrifices the model size to improve the detection accuracy\nmay impede the practical application of SOD problems. To tackle this dilemma,\nwe propose a dynamic distillation method along with a lightweight framework,\nwhich significantly reduces the parameters. This method considers the factors\nof both teacher and student performance within the training stage and\ndynamically assigns the distillation weight instead of applying a fixed weight\non the student model. Extensive experiments are conducted on five public\ndatasets to demonstrate that our method can achieve competitive performance\ncompared to 10 prior methods through a 78.2MB lightweight structure.\n"}
+{"abstract": "  Here we prove a global existence theorem for the solutions of the semi-linear\nwave equation with critical non-linearity admitting a positive definite\nHamiltonian. Formulating a parametrix for the wave equation in a globally\nhyperbolic curved spacetime, we derive an apriori pointwise bound for the\nsolution of the nonlinear wave equation in terms of the initial energy, from\nwhich the global existence follows in a straightforward way. This is\naccomplished in two steps. First, based on Moncrief's light cone formulation we\nderive an expression for the scalar field in terms of integrals over the past\nlight cone from an arbitrary spacetime point to an `initial', Cauchy\nhypersurface and additional integrals over the intersection of this cone with\nthe initial hypersurface. Secondly, we obtain apriori estimates for the energy\nassociated with three quasi-local approximate time-like conformal Killing and\none approximate Killing vector fields. Utilizing these naturally defined\nenergies associated with the physical stress-energy tensor together with the\nintegral equation, we show that the spacetime $L^{\\infty}$ norm of the scalar\nfield remains bounded in terms of the initial data and continues to be so as\nlong as the spacetime remains singularity/Cauchy-horizon free.\n"}
+{"abstract": "  An analytic formula is given for the total scattering cross section of an\nelectron and a photon at order $\\alpha^3$. This includes both the\ndouble-Compton scattering real-emission contribution as well as the virtual\nCompton scattering part. When combined with the recent analytic result for the\npair-production cross section, the complete $\\alpha^3$ cross section is now\nknown. Both the next-to-leading order calculation as well as the\npair-production cross section are computed using modern multiloop calculation\ntechniques, where cut diagrams are decomposed into a set of master integrals\nthat are then computed using differential equations.\n"}
+{"abstract": "  Kernel maximum moment restriction (KMMR) recently emerges as a popular\nframework for instrumental variable (IV) based conditional moment restriction\n(CMR) models with important applications in conditional moment (CM) testing and\nparameter estimation for IV regression and proximal causal learning. The\neffectiveness of this framework, however, depends critically on the choice of a\nreproducing kernel Hilbert space (RKHS) chosen as a space of instruments. In\nthis work, we presents a systematic way to select the instrument space for\nparameter estimation based on a principle of the least identifiable instrument\nspace (LIIS) that identifies model parameters with the least space complexity.\nOur selection criterion combines two distinct objectives to determine such an\noptimal space: (i) a test criterion to check identifiability; (ii) an\ninformation criterion based on the effective dimension of RKHSs as a complexity\nmeasure. We analyze the consistency of our method in determining the LIIS, and\ndemonstrate its effectiveness for parameter estimation via simulations.\n"}
+{"abstract": "  We report the first detection in space of the two doubly deuterated\nisotopologues of methyl acetylene. The species CHD2CCH and CH2DCCD were\nidentified in the dense core L483 through nine and eight, respectively,\nrotational lines in the 72-116 GHz range using the IRAM 30m telescope. The\nastronomical frequencies observed here were combined with laboratory\nfrequencies from the literature measured in the 29-47 GHz range to derive more\naccurate spectroscopic parameters for the two isotopologues. We derive\nbeam-averaged column densities of (2.7 +/- 0.5)e12 cm-2 for CHD2CCH and (2.2\n+/- 0.4)e12 cm-2 for CH2DCCD, which translate to abundance ratios\nCH3CCH/CHD2CCH = 34 +/- 10 and CH3CCH/CH2DCCD = 42 +/- 13. The doubly\ndeuterated isotopologues of methyl acetylene are only a few times less abundant\nthan the singly deuterated ones, concretely around 2.4 times less abundant than\nCH3CCD. The abundances of the different deuterated isotopologues with respect\nto CH3CCH are reasonably accounted for by a gas-phase chemical model in which\ndeuteration occurs from the precursor ions C3H6D+ and C3H5D+, when the\northo-to-para ratio of molecular hydrogen is sufficiently low. This points to\ngas-phase chemical reactions, rather than grain-surface processes, as\nresponsible for the formation and deuterium fractionation of CH3CCH in L483.\nThe abundance ratios CH2DCCH/CH3CCD = 3.0 +/- 0.9 and CHD2CCH/CH2DCCD = 1.25\n+/- 0.37 observed in L483 are consistent with the statistically expected values\nof three and one, respectively, with the slight overabundance of CHD2CCH\ncompared to CH2DCCD being well explained by the chemical model.\n"}
+{"abstract": "  Significant experimental progress has been made recently for observing\nlong-sought supersolid-like states in Bose-Einstein condensates, where spatial\ntranslational symmetry is spontaneously broken by anisotropic interactions to\nform a stripe order. Meanwhile, the superfluid stripe ground state was also\nobserved by applying a weak optical lattice that forces the symmetry breaking.\nDespite of the similarity of the ground states, here we show that these two\nsymmetry breaking mechanisms can be distinguished by their collective\nexcitation spectra. In contrast to gapless Goldstone modes of the\n\\textit{spontaneous} stripe state, we propose that the excitation spectra of\nthe \\textit{forced} stripe phase can provide direct experimental evidence for\nthe long-sought gapped pseudo-Goldstone modes. We characterize the\npseudo-Goldstone mode of such lattice-induced stripe phase through its\nexcitation spectrum and static structure factor. Our work may pave the way for\nexploring spontaneous and forced/approximate symmetry breaking mechanisms in\ndifferent physical systems.\n"}
+{"abstract": "  Inertia effects in magnetization dynamics are theoretically shown to result\nin a different type of spin waves, i.e. nutation surface spin waves, which\npropagate at terahertz frequencies in in-plane magnetized ferromagnetic thin\nfilms. Considering the magnetostatic limit, i.e. neglecting exchange coupling,\nwe calculate dispersion relation and group velocity, which we find to be slower\nthan the velocity of conventional (precession) spin waves. In addition, we find\nthat the nutation surface spin waves are backward spin waves. Furthermore, we\nshow that inertia causes a decrease of the frequency of the precession spin\nwaves, namely magnetostatic surface spin waves and backward volume\nmagnetostatic spin waves. The magnitude of the decrease depends on the magnetic\nproperties of the film and its geometry.\n"}
+{"abstract": "  This paper introduces a node formulation for multistage stochastic programs\nwith endogenous (i.e., decision-dependent) uncertainty. Problems with such\nstructure arise when the choices of the decision maker determine a change in\nthe likelihood of future random events. The node formulation avoids an explicit\nstatement of non-anticipativity constraints, and as such keeps the dimension of\nthe model sizeable. An exact solution algorithm for a special case is\nintroduced and tested on a case study. Results show that the algorithm\noutperforms a commercial solver as the size of the instances increases.\n"}
+{"abstract": "  Human activities are hugely restricted by COVID-19, recently. Robots that can\nconduct inter-floor navigation attract much public attention, since they can\nsubstitute human workers to conduct the service work. However, current robots\neither depend on human assistance or elevator retrofitting, and fully\nautonomous inter-floor navigation is still not available. As the very first\nstep of inter-floor navigation, elevator button segmentation and recognition\nhold an important position. Therefore, we release the first large-scale\npublicly available elevator panel dataset in this work, containing 3,718 panel\nimages with 35,100 button labels, to facilitate more powerful algorithms on\nautonomous elevator operation. Together with the dataset, a number of deep\nlearning based implementations for button segmentation and recognition are also\nreleased to benchmark future methods in the community. The dataset will be\navailable at \\url{https://github.com/zhudelong/elevator_button_recognition\n"}
+{"abstract": "  The Reeb space of a continuous map is the space of all (elements\nrepresenting) connected components of preimages endowed with the quotient\ntopology induced from the natural equivalence relation on the domain. These\nobjects are strong tools in (differential) topological theory of Morse\nfunctions, fold maps, which are their higher dimensional variants, and so on:\nthey are in general polyhedra whose dimensions are same as those of the\ntargets. In suitable cases Reeb spaces inherit topological information such as\nhomology groups, cohomology rings, and so on, of the manifolds.\n  This presents the following problem: what are global topologies of Reeb\nspaces of these smooth maps of suitable classes like? The present paper\npresents families of stable fold maps having Reeb spaces with non-trivial top\nhomology groups with their (co)homology groups (and rings). Related studies on\nthe global topologies from the viewpoints of the singularity theory of\ndifferentiable maps and differential topology have been presented by various\nresearchers including the author. The author previously constructed families of\nfold maps with Reeb spaces with non-trivial top homology groups and with good\ntopological properties. This paper presents new families, especially,\ngeneralized situations of some known situations.\n"}
+{"abstract": "  Let G be a split, simple, simply connected, algebraic group over Q. The\ndegree 4, weight 2 motivic cohomology group of the classifying space BG of G is\nidentified with Z. We construct cocycles representing the generator of this\ngroup, known as the second universal motivic Chern class.\n  If G = SL(m), there is a canonical cocycle, defined by the first author\n(1993). For any group G, we define a collection of cocycles parametrised by\ncluster coordinate systems on the space of G-orbits on the cube of the\nprincipal affine space G/U. Cocycles for different clusters are related by\nexplicit coboundaries, constructed using cluster transformations relating the\nclusters.\n  The cocycle has three components. The construction of the last one is\ncanonical and elementary; it does not use clusters, and provides a canonical\ncocycle for the motivic generator of the degree 3 cohomology class of the\ncomplex manifold G(C). However to lift this component to the whole cocycle we\nneed cluster coordinates: the construction of the first two components uses\ncrucially the cluster structure of the moduli spaces A(G,S) related to the\nmoduli space of G-local systems on S. In retrospect, it partially explains why\nthe cluster coordinates on the space A(G,S) should exist.\n  This construction has numerous applications, including an explicit\nconstruction of the universal extension of the group G by K_2, the line bundle\non Bun(G) generating its Picard group, Kac-Moody groups, etc. Another\napplication is an explicit combinatorial construction of the second motivic\nChern class of a G-bundle. It is a motivic analog of the work of\nGabrielov-Gelfand-Losik (1974), for any G.\n"}
+{"abstract": "  Reconfigurable intelligent surface (RIS) is an emerging technique employing\nmetasurface to reflect the signal from the source node to the destination node\nwithout consuming any energy. Not only the spectral efficiency but also the\nenergy efficiency can be improved through RIS. Essentially, RIS can be\nconsidered as a passive relay between the source and destination node. On the\nother hand, a relay node in a traditional relay network has to be active, which\nindicates that it will consume energy when it is relaying the signal or\ninformation between the source and destination nodes. In this paper, we compare\nthe performances between RIS and active relay for a general multiple-input\nmultiple-output (MIMO) system. To make the comparison fair and comprehensive,\nboth the performances of RIS and active relay are optimized with best-effort.\nIn terms of the RIS, transmit beamforming and reflecting coefficient at the RIS\nare jointly optimized so as to maximize the end-to-end throughput. Although the\noptimization problem is non-convex, it is transformed equivalently to a\nweighted mean-square error (MSE) minimization problem and an alternating\noptimization problem is proposed, which can ensure the convergence to a\nstationary point. In terms of active relay, both half duplex relay (HDR) and\nfull duplex relay (FDR) are considered. End-to-end throughput is maximized via\nan alternating optimization method. Numerical results are presented to\ndemonstrate the effectiveness of the proposed algorithm. Finally, comparisons\nbetween RIS and relays are investigated from the perspective of system model,\nperformance, deployment and controlling method.\n"}
+{"abstract": "  We study theoretical neutrino signals from core-collapse supernova (CCSN)\ncomputed using axisymmetric CCSN simulations that cover the post-bounce phase\nup to $\\sim 4$~s. We provide basic quantities of the neutrino signals such as\nevent rates, energy spectra, and cumulative number of events at some\nterrestrial neutrino detectors, and then discuss some new features in the late\nphase that emerge in our models. Contrary to popular belief, neutrino emissions\nin the late phase are not always steady, but rather have temporal fluctuations,\nthe vigor of which hinges on the CCSN model and neutrino flavor. We find that\nsuch temporal variations are not primarily driven by proto-neutron star (PNS)\nconvection, but by fallback accretion in exploding models. We assess the\ndetectability of these temporal variations, and find that IceCube is the most\npromising detector with which to resolve them. We also update fitting formulae\nfirst proposed in our previous paper for which the total neutrino energy (TONE)\nemitted at the CCSN source is estimated from the cumulative number of events in\neach detector. This will be a powerful technique with which to analyze real\nobservations, particularly for low-statistics data.\n"}
+{"abstract": "  Model-based evaluation in cybersecurity has a long history. Attack Graphs\n(AGs) and Attack Trees (ATs) were the earlier developed graphical security\nmodels for cybersecurity analysis. However, they have limitations (e.g.,\nscalability problem, state-space explosion problem, etc.) and lack the ability\nto capture other security features (e.g., countermeasures). To address the\nlimitations and to cope with various security features, a graphical security\nmodel named attack countermeasure tree (ACT) was developed to perform security\nanalysis by taking into account both attacks and countermeasures. In our\nresearch, we have developed different variants of a hierarchical graphical\nsecurity model to solve the complexity, dynamicity, and scalability issues\ninvolved with security models in the security analysis of systems. In this\npaper, we summarize and classify security models into the following;\ngraph-based, tree-based, and hybrid security models. We discuss the development\nof a hierarchical attack representation model (HARM) and different variants of\nthe HARM, its applications, and usability in a variety of domains including the\nInternet of Things (IoT), Cloud, Software-Defined Networking, and Moving Target\nDefenses. We provide the classification of the security metrics, including\ntheir discussions. Finally, we highlight existing problems and suggest future\nresearch directions in the area of graphical security models and applications.\nAs a result of this work, a decision-maker can understand which type of HARM\nwill suit their network or security analysis requirements.\n"}
+{"abstract": "  For any positive regularity parameter $\\beta < \\frac 12$, we construct\nnon-conservative weak solutions of the 3D incompressible Euler equations which\nlie in $H^{\\beta}$ uniformly in time. In particular, we construct solutions\nwhich have an $L^2$-based regularity index \\emph{strictly larger} than $\\frac\n13$, thus deviating from the $H^{\\frac{1}{3}}$-regularity corresponding to the\nKolmogorov-Obhukov $\\frac 53$ power spectrum in the inertial range.\n"}
+{"abstract": "  Using spin-assisted ab-initio random structure searches, we explore an\nexhaustive quantum phase diagram of archetypal interfaced Mott insulators, i.e.\nlanthanum-iron and lanthanum-titanium oxides. In particular, we report that the\ncharge transfer induced by the interfacial electronic reconstruction stabilises\na high spin ferrous Fe2+ state. We provide a pathway to control the strength of\ncorrelation in this electronic state by tuning the epitaxial strain, yielding a\nmanifold of quantum electronic phases, i.e. Mott-Hubbard, charge transfer and\nSlater insulating states. Furthermore we report that the electronic\ncorrelations are closely related to the structural oxygen octahedral rotations,\nwhose control is able to stabilise the low spin state of Fe2+ at low pressure\npreviously observed only under the extreme high pressure conditions in the\nEarth's lower mantle. Thus we provide avenues for magnetic switching via THz\nradiations which have crucial implications for next generation of spintronics\ntechnologies.\n"}
+{"abstract": "  The unprecedented worldwide spread of coronavirus disease has significantly\nsped up the development of technology-based solutions to prevent, combat,\nmonitor, or predict pandemics and/or its evolution. The omnipresence of smart\nInternet-of-things (IoT) devices can play a predominant role in designing\nadvanced techniques helping in minimizing the risk of contamination. In this\npaper, we propose a practical framework that uses the Social IoT (SIoT) concept\nto improve pedestrians safely navigate through a real-wold map of a smart city.\nThe objective is to mitigate the risks of exposure to the virus in high-dense\nareas where social distancing might not be well-practiced. The proposed routing\napproach recommends pedestrians' route in a real-time manner while considering\nother devices' mobility. First, the IoT devices are clustered into communities\naccording to two SIoT relations that consider the devices' locations and the\nfriendship levels among their owners. Accordingly, the city map roads are\nassigned weights representing their safety levels. Afterward, a navigation\nalgorithm, namely the Dijkstra algorithm, is applied to recommend the safest\nroute to follow. Simulation results applied on a real-world IoT data set have\nshown the ability of the proposed approach in achieving trade-offs between both\nsafest and shortest paths according to the pedestrian preference.\n"}
+{"abstract": "  Although van der Waals (vdW) layered MoS2 shows the phase transformation from\nthe semiconducting 2H-phase to the metallic 1T-phase through chemical lithium\nintercalation, vdW MoTe2 is thermodynamically reversible between the 2H- and\n1T'-phases, and can be further transformed by energetics, laser irradiation,\nstrain or pressure, and electrical doping. Here, thickness- and\ntemperature-dependent optical properties of 1T'-MoTe2 thin films grown by\nchemical vapor depsition are investigated via Fourier-transformed infrared\nspectroscopy. An optical gap of 28 +/- 2 meV in a 3-layer (or 2 nm) thick\n1T'-MoTe2 is clearly observed at a low temperature region below 50K. No\ndiscernible optical bandgap is observed in samples thicker than ~4 nm. The\nobserved thickness-dependent bandgap results agree with the measured dc\nresistivity data; the thickness-dependent 1T'-MoTe2 clearly demonstrates the\nmetal-semiconductor transition at a crossover below the 2 nm-thick sample.\n"}
+{"abstract": "  With the advent of quantum computers, many quantum computing algorithms are\nbeing developed. Solving linear system is one of the most fundamental problems\nin almost all of science and engineering. Harrow-Hassidim-Lloyd algorithm, a\nmonumental quantum algorithm for solving linear systems on the gate model\nquantum computers, was invented and several advanced variations have been\ndeveloped. For a given square matrix $A$ and a vector $\\vec{b}$, we will find\nunconstrained binary optimization (QUBO) models for a vector $\\vec{x}$ that\nsatisfies $A \\vec{x} = \\vec{b}$. To formulate QUBO models for a linear system\nsolving problem,we make use of a linear least-square problem with binary\nrepresentation of the solution. We validate those QUBO models on the D-Wave\nsystem and discuss the results. For a simple system, We provide a python code\nto calculate the matrix characterizing the relationship between the variables\nand to print the test code that can be used directly in D-Wave system.\n"}
+{"abstract": "  We consider the reducibility problem of cocycles by isometries of Gromov\nhyperbolic metric spaces in the Livsic setting. We show that provided that the\nboundary cocycle (that acts on a compact space) is reducible in a suitable\nH\\\"older class, then the original cocycle by isometries (that acts on an\nunbounded space) is also reducible.\n"}
+{"abstract": "  We present the electron tunneling transport and spectroscopic characters of a\nsuperconducting Josephson junction with a barrier of single Kitaev quantum spin\nliquid (QSL) layer. We find that the dynamical spin correlation features are\nwell reflected in the direct current differential conductance dI_c/dV of the\nsingle-particle tunneling with an energy shift of superconducting gap sum\n2{\\Delta}, including the unique spin gap and dressed itinerant Majorana\ndispersive band, which can be regarded as evidence of the Kitaev QSL. However,\nthe zero-voltage Josephson current I_s only displays some residual features of\ndynamical spin susceptibility in the Kitaev QSL due to the spin singlet of\nCooper pairs. These results pave a new way to measure the dynamical spinon or\nMajorana fermion spectroscopy of the Kitaev and other spin liquid materials.\n"}
+{"abstract": "  We can only allow human-robot-cooperation in a common work cell if the human\nintegrity is guaranteed. A surveillance system with multiple cameras can detect\ncollisions without contact to the human collaborator. A failure safe system\nneeds to optimally cover the important areas of the robot work cell with safety\noverlap. We propose an efficient algorithm for optimally placing and orienting\nthe cameras in a 3D CAD model of the work cell. In order to evaluate the\nquality of the camera constellation in each step, our method simulates the\nvision system using a z-buffer rendering technique for image acquisition, a\nvoxel space for the overlap and a refined visual hull method for a conservative\nhuman reconstruction. The simulation allows to evaluate the quality with\nrespect to the distortion of images and advanced image analysis in the presence\nof static and dynamic visual obstacles such as tables, racks, walls, robots and\npeople. Our method is ideally suited for maximizing the coverage of multiple\ncameras or minimizing an error made by the visual hull and can be extended to\nprobabilistic space carving.\n"}
+{"abstract": "  In this article, we introduce the notion of Lie triple centralizer as\nfollows. Let $\\mathcal{A}$ be an algebra, and $\\phi:\\mathcal{A}\\to\\mathcal{A}$\nbe a linear mapping. we say $\\phi$ is a Lie triple centralizer whenever\n$\\phi([[a,b],c])=[[\\phi(a),b],c]$ for all $a,b,c\\in\\mathcal{A}$. Then we\ncharacterize the general form of Lie triple centralizers on generalized matrix\nalgebra $\\mathcal{U}$ and under some mild conditions on $\\mathcal{U}$, we\npresent the necessary and sufficient conditions for Lie triple centralizers to\nbe proper. As an application of our results, we characterize generalized Lie\ntriple derivations on generalized matrix algebras.\n"}
+{"abstract": "  The purpose of this Conference is to present the main lines of base projects\nthat are founded on research already begun in previous years. In this sense,\nthis manuscript will present the main lines of research in Diabetes Mellitus\ntype 1 and Machine Learning techniques in an Internet of Things environment, so\nthat we can summarize the future lines to be developed as follows: data\ncollection through biosensors, massive data processing in the cloud,\ninterconnection of biodevices, local computing vs. cloud computing, and\npossibilities of machine learning techniques to predict blood glucose values,\nincluding both variable selection algorithms and predictive techniques.\n"}
+{"abstract": "  We introduce reachability analysis for the formal examination of robots. We\npropose a novel identification method, which preserves reachset conformance of\nlinear systems. We additionally propose a simultaneous identification and\ncontrol synthesis scheme to obtain optimal controllers with formal guarantees.\nIn a case study, we examine the effectiveness of using reachability analysis to\nsynthesize a state-feedback controller, a velocity observer, and an output\nfeedback controller.\n"}
+{"abstract": "  We present a classification of strict limits of planar BV homeomorphisms. The\nauthors and S. Hencl showed in a previous work \\cite{CHKR} that such mappings\nallow for cavitations and fractures singularities but fulfill a suitable\ngeneralization of the INV condition. As pointed out by J. Ball \\cite{B}, these\nfeatures are physically expected by limit configurations of elastic\ndeformations. In the present work we develop a suitable generalization of the\n\\emph{no-crossing} condition introduced by De Philippis and Pratelli in\n\\cite{PP} to describe weak limits of planar Sobolev homeomorphisms that we call\n\\emph{BV no-crossing} condition, and we show that a planar mapping satisfies\nthis property if and only if it can be approximated strictly by homeomorphisms\nof bounded variations.\n"}
+{"abstract": "  Magnetic properties of A2BB'O6 (A = rare or alkaline earth ions; B,B' =\ntransition metal ions) double perovskites are of great interest due to their\npotential spintronic applications. Particularly fascinating is the zero field\ncooled exchange bias (ZEB) effect observed for the hole doped La2-xAxCoMnO6\npolycrystalline samples. In this work we synthesize La2CoMnO6,\nLa1.5Ca0.5CoMnO6, and La1.5Sr0.5CoMnO6 single crystals by the floating zone\nmethod and study their magnetic behavior. The three materials are\nferromagnetic. Surprisingly, we observe no zero or even conventional exchange\nbias effect for the Ca and Sr doped single crystals, in sharp contrast to\npolycrystalline samples. This absence indicates that the lack of grain\nboundaries and spin glass-like behavior, not observed in our samples, might be\nkey ingredients for the spontaneous exchange bias phenomena seen in\npolycrystalline samples.\n"}
+{"abstract": "  Low-rank Tucker and CP tensor decompositions are powerful tools in data\nanalytics. The widely used alternating least squares (ALS) method, which solves\na sequence of over-determined least squares subproblems, is costly for large\nand sparse tensors. We propose a fast and accurate sketched ALS algorithm for\nTucker decomposition, which solves a sequence of sketched rank-constrained\nlinear least squares subproblems. Theoretical sketch size upper bounds are\nprovided to achieve $O(\\epsilon)$ relative error for each subproblem with two\nsketching techniques, TensorSketch and leverage score sampling. Experimental\nresults show that this new ALS algorithm, combined with a new initialization\nscheme based on randomized range finder, yields up to $22.0\\%$ relative\ndecomposition residual improvement compared to the state-of-the-art sketched\nrandomized algorithm for Tucker decomposition of various synthetic and real\ndatasets. This Tucker-ALS algorithm is further used to accelerate CP\ndecomposition, by using randomized Tucker compression followed by CP\ndecomposition of the Tucker core tensor. Experimental results show that this\nalgorithm not only converges faster, but also yields more accurate CP\ndecompositions.\n"}
+{"abstract": "  The accelerated penetration rate of renewable energy sources (RES) brings\nenvironmental benefits at the expense of increasing operation cost and\nundermining the satisfaction of the N-1 security criterion. To address the\nlatter issue, this paper envisions N-1 security control in RES dominated power\nsystems through stochastic multi-period AC security constrained optimal power\nflow (SCOPF). The paper extends the state-of-the-art, i.e. deterministic and\nsingle time period AC SCOPF, to capture two new dimensions, RES stochasticity\nand multiple time periods, as well as emerging sources of flexibility such as\nflexible loads (FL) and energy storage systems (ESS). Accordingly, the paper\nproposes and solves for the first time a new problem formulation in the form of\nstochastic multi-period AC SCOPF (S-MP-SCOPF). The S-MP-SCOPF is formulated as\na non-linear programming (NLP) problem. It computes optimal setpoints of\nflexibility resources and other conventional control means for congestion\nmanagement and voltage control in day-ahead operation. Another salient feature\nof this paper is the comprehensive and accurate modelling, using: AC power flow\nmodel for both pre-contingency and post-contingency states, inter-temporal\nconstraints for resources such as FL and ESS in a 24-hours time horizon and RES\nuncertainties. The importance and performances of the proposed model through a\ndirect approach, pushing the problem size up to the solver limit, are\nillustrated on two test systems of 5 nodes and 60 nodes, respectively, while\nfuture work will develop a tractable algorithm.\n"}
+{"abstract": "  The determination of efficient collective variables is crucial to the success\nof many enhanced sampling methods. As inspired by previous discrimination\napproaches, we first collect a set of data from the different metastable\nbasins. The data are then projected with the help of a neural network into a\nlow-dimensional manifold in which data from different basins are well\ndiscriminated. This is here guaranteed by imposing that the projected data\nfollows a preassigned distribution. The collective variables thus obtained lead\nto an efficient sampling and often allow reducing the number of collective\nvariables in a multi-basin scenario. We first check the validity of the method\nin two-state systems. We then move to multi-step chemical processes. In the\nlatter case, at variance with previous approaches, one single collective\nvariable suffices, leading not only to computational efficiency but to a very\nclear representation of the reaction free energy profile.\n"}
+{"abstract": "  We construct the gauge-invariant electric and magnetic charges in Yang-Mills\ntheory coupled to cosmological General Relativity (or any other geometric\ngravity), extending the flat spacetime construction of Abbott and Deser. For\nnon-vanishing background gauge fields, the charges receive non-trivial\ncontribution from the gravity part. In addition, we study the constraints on\nthe first order perturbation theory and establish the conditions for\nlinearization instability: that is the validity of the first order perturbation\ntheory.\n"}
+{"abstract": "  In the band theory, first-principles calculations, the tight-binding method\nand the effective $k\\cdot p$ model are usually employed to investigate the\nelectronic structure of condensed matters. The effective $k\\cdot p$ model has a\ncompact form with a clear physical picture, and first-principles calculations\ncan give more accurate results. Nowadays, it has been widely recognized to\ncombine the $k\\cdot p$ model and first-principles calculations to explore\ntopological materials. However, the traditional method to derive the $k\\cdot p$\nHamiltonian is complicated and time-consuming by hand. In this work, we\nindependently develop a programmable algorithm to construct effective $k\\cdot\np$ Hamiltonians. Symmetries and orbitals are used as the input information to\nproduce the one-/two-/three-dimension $k\\cdot p$ Hamiltonian in our method, and\nthe open-source code can be directly downloaded online. At last, we also\ndemonstrate the application to MnBi$_2$Te$_4$-family magnetic topological\nmaterials.\n"}
+{"abstract": "  In successful enterprise attacks, adversaries often need to gain access to\nadditional machines beyond their initial point of compromise, a set of internal\nmovements known as lateral movement. We present Hopper, a system for detecting\nlateral movement based on commonly available enterprise logs. Hopper constructs\na graph of login activity among internal machines and then identifies\nsuspicious sequences of loginsthat correspond to lateral movement. To\nunderstand the larger context of each login, Hopper employs an inference\nalgorithm to identify the broader path(s) of movement that each login belongs\nto and the causal user responsible for performing a path's logins. Hopper then\nleverages this path inference algorithm, in conjunction with a set of detection\nrules and a new anomaly scoring algorithm, to surface the login paths most\nlikely to reflect lateral movement. On a 15-month enterprise dataset consisting\nof over 780 million internal logins, Hop-per achieves a 94.5% detection rate\nacross over 300 realistic attack scenarios, including one red team attack,\nwhile generating an average of <9 alerts per day. In contrast, to detect the\nsame number of attacks, prior state-of-the-art systems would need to generate\nnearly 8x as many false positives.\n"}
+{"abstract": "  In this paper, we prove the existence of infinite Gibbs Delaunay Potts\ntessellations for marked particle configurations. The particle systems has two\ntypes of interaction, a so-called \\emph{background potential} ensures that\nsmall and large triangles are excluded in the Delaunay tessellation, and is\nsimilar to the so-called hardcore potential introduced in \\cite{Der08}.\nParticles carry one of $q$ separate marks. Our main result is that for large\nactivities and high \\emph{type interaction} strength the model has at least $\nq$ distinct translation-invariant Gibbs Delaunay Potts tessellations. The main\ntechnique is a coarse-graining procedure using the scales in the system\nfollowed by comparison with site percolation on $ \\Z^2 $.\n"}
+{"abstract": "  Many special classes of simplicial sets, such as the nerves of categories or\ngroupoids, the 2-Segal sets of Dyckerhoff and Kapranov, and the (discrete)\ndecomposition spaces of G\\'{a}lvez, Kock, and Tonks, are characterized by the\nproperty of sending certain commuting squares in the simplex category $\\Delta$\nto pullback squares of sets. We introduce weaker analogues of these properties\ncalled completeness conditions, which require squares in $\\Delta$ to be sent to\nweak pullbacks of sets, defined similarly to pullback squares but without the\nuniqueness property of induced maps. We show that some of these completeness\nconditions provide a simplicial set with lifts against certain subsets of\nsimplices first introduced in the theory of database design. We also provide\nreduced criteria for checking these properties using factorization results for\npushouts squares in $\\Delta$, which we characterize completely, along with\nseveral other classes of squares in $\\Delta$. Examples of simplicial sets with\ncompleteness conditions include quasicategories, Kan complexes, many of the\ncompositories and gleaves of Flori and Fritz, and bar constructions for\nalgebras of certain classes of monads. The latter is our motivating example\nwhich we discuss in a companion paper.\n"}
+{"abstract": "  A novel and compact dual band planar antenna for 2.4/5.2/5.8-GHz wireless\nlocal area network(WLAN) applications is proposed and studied in this paper.\nThe antenna comprises of a T-shaped and a F-shaped element to generate two\nresonant modes for dual band operation. The two elements can independently\ncontrol the operating frequencies of the two excited resonant modes. The\nT-element which is fed directly by a 50 $\\Omega$ microstrip line generates a\nfrequency band at around 5.2 GHz and the antenna parameters can be adjusted to\ngenerate a frequency band at 5.8 GHz as well, thus covering the two higher\nbands of WLAN systems individually. By couple-feeding the F-element through the\nT-element, a frequency band can be generated at 2.4 GHz to cover the lower band\nof WLAN system. Hence, the two elements together are very compact with a total\narea of only 11$\\times$6.5 mm$^{2}$. A thorough parametric study of key\ndimensions in the design has been performed and the results obtained have been\nused to present a generalized design approach. Plots of the return loss and\nradiation pattern have been given and discussed in detail to show that the\ndesign is a very promising candidate for WLAN applications.\n"}
+{"abstract": "  In this study, we perform a two-dimensional axisymmetric simulation to assess\nthe flow characteristics and understand the film cooling process in a dual bell\nnozzle. The secondary stream with low temperature is injected at three\ndifferent axial locations on the nozzle wall, and the simulations are carried\nout to emphasize the impact of injection location (secondary flow) on film\ncooling of the dual bell nozzle. The cooling effect is demonstrated through the\ntemperature and pressure distributions on the nozzle wall or, in-turn, the\nseparation point movement. Downstream of the injection point, the Mach number\nand temperature profiles document the mixing of the main flow and secondary\nflow. The inflection region is observed to be the most promising location for\nthe injection of the secondary flow. We have further investigated the effect of\nMach number of the secondary stream. The current study demonstrates that one\ncan control the separation point in a dual bell nozzle with the help of\nsecondary injection (Mach number) so that an optimum amount of thrust can be\nachieved.\n"}
+{"abstract": "  We systematically explored the phase behavior of the hard-core two-scale ramp\nmodel suggested by Jagla[E. A. Jagla, Phys. Rev. E 63, 061501 (2001)] using a\ncombination of the nested sampling and free energy methods. The sampling\nrevealed that the phase diagram of the Jagla potential is significantly richer\nthan previously anticipated, and we identified a family of new crystalline\nstructures, which is stable over vast regions in the phase diagram. We showed\nthat the new melting line is located at considerably higher temperature than\nthe boundary between the low- and high-density liquid phases, which was\npreviously suggested to lie in a thermodynamically stable region. The newly\nidentified crystalline phases show unexpectedly complex structural features,\nsome of which are shared with the high-pressure ice VI phase.\n"}
+{"abstract": "  dentifying associations among biological variables is a major challenge in\nmodern quantitative biological research, particularly given the systemic and\nstatistical noise endemic to biological systems. Drug sensitivity data has\nproven to be a particularly challenging field for identifying associations to\ninform patient treatment. To address this, we introduce two semi-parametric\nvariations on the commonly used concordance index: the robust concordance index\nand the kernelized concordance index (rCI, kCI), which incorporate measurements\nabout the noise distribution from the data. We demonstrate that common\nstatistical tests applied to the concordance index and its variations fail to\ncontrol for false positives, and introduce efficient implementations to compute\np-values using adaptive permutation testing. We then evaluate the statistical\npower of these coefficients under simulation and compare with Pearson and\nSpearman correlation coefficients. Finally, we evaluate the various statistics\nin matching drugs across pharmacogenomic datasets. We observe that the rCI and\nkCI are better powered than the concordance index in simulation and show some\nimprovement on real data. Surprisingly, we observe that the Pearson correlation\nwas the most robust to measurement noise among the different metrics.\n"}
+{"abstract": "  We discuss significant improvements to our calculation of electroweak (EW)\n$t\\bar{t}$ hadroproduction in extensions of the Standard Model (SM) with extra\nheavy neutral and charged spin-1 resonances using the Recola2 package. We allow\nfor flavour-non-diagonal $Z'$ couplings and take into account non-resonant\nproduction in the SM and beyond including the contributions with t-channel $W$-\nand $W'$-bosons. We include next-to-leading order (NLO) QCD corrections and\nconsistently match to parton showers with the POWHEG method fully taking into\naccount the interference effects between SM and new physics amplitudes. We\nbriefly describe the Contour method and give some information about the Rivet\nrepository which catalogues particle-level measurements subsequently used by\nContur to set limits on beyond the SM (BSM) theories. We explain how we use our\ncalculation within Contour in order to set limits on models with additional\nheavy gauge bosons using LHC measurements, and illustrate this with an example\nusing the leptophobic Topcolour (TC) model.\n"}
+{"abstract": "  The OSIRIS-REx spacecraft encountered the asteroid (101955) Bennu on December\n3, 2018, and has since acquired extensive data from the payload of scientific\ninstruments on board. In 2019, the OSIRIS-REx team selected primary and backup\nsample collection sites, called Nightingale and Osprey, respectively. On\nOctober 20, 2020, OSIRIS-REx successfully collected material from Nightingale.\nIn this work, we apply an unsupervised machine learning classification through\nthe K-Means algorithm to spectrophotometrically characterize the surface of\nBennu, and in particular Nightingale and Osprey. We first analyze a global\nmosaic of Bennu, from which we find four clusters scattered across the surface,\nreduced to three when we normalize the images at 550 nm. The three spectral\nclusters are associated with boulders and show significant differences in\nspectral slope and UV value. We do not see evidence of latitudinal\nnon-uniformity, which suggests that Bennu's surface is well-mixed. In our\nhigher-resolution analysis of the primary and backup sample sites, we find\nthree representative normalized clusters, confirming an inverse correlation\nbetween reflectance and spectral slope (the darkest areas being the reddest\nones) and between b' normalized reflectance and slope. Nightingale and Osprey\nare redder than the global surface of Bennu by more than $1\\sigma$ from\naverage, consistent with previous findings, with Nightingale being the reddest\n($S' = (- 0.3 \\pm 1.0) \\times 10^{- 3}$ percent per thousand angstroms). We see\nhints of a weak absorption band at 550 nm at the candidate sample sites and\nglobally, which lends support to the proposed presence of magnetite on Bennu.\n"}
+{"abstract": "  RR Tel is an interacting binary system in which a hot white dwarf (WD)\naccretes matter from a Mira-type variable star via gravitational capture of its\nstellar wind. This symbiotic nova shows intense Raman-scattered O VI 1032\\r{A}\nand 1038\\r{A} features at 6825\\r{A} and 7082\\r{A}. We present high-resolution\noptical spectra of RR Tel taken in 2016 and 2017 with the Magellan Inamori\nKyocera Echelle (MIKE) spectrograph at Magellan-Clay telescope, Chile. We aim\nto study the stellar wind accretion in RR Tel from the profile analysis of\nRaman O VI features. With an asymmetric O VI disk model, we derive a\nrepresentative Keplerian speed of $> 35{\\rm km~s^{-1}}$, and the corresponding\nscale < 0.8 au. The best-fit for the Raman profiles is obtained with a mass\nloss rate of the Mira ${\\dot M}\\sim2\\times10^{-6}~{\\rm M_{\\odot}~yr^{-1}}$ and\na wind terminal velocity $v_{\\infty}\\sim 20~{\\rm km~s^{-1}}$. We compare the\nMIKE data with an archival spectrum taken in 2003 with the Fibre-fed Extended\nRange Optical Spectrograph (FEROS) at the MPG/ESO 2.2m telescope. It allows us\nto highlight the profile variation of the Raman O VI features, indicative of a\nchange in the density distribution of the O VI disk in the last two decades. We\nalso report the detection of O VI recombination lines at 3811\\r{A} and\n3834\\r{A}, which are blended with other emission lines. Our profile\ndecomposition suggests that the recombination of O VII takes place nearer to\nthe WD than the O VI 1032\\r{A} and 1038\\r{A} emission region.\n"}
+{"abstract": "  We analysed publicly available data on place of occurrence of COVID-19 deaths\nfrom national statistical agencies in the UK between March 9 2020 and February\n28 2021. We introduce a modified Weibull model that describes the deaths due to\nCOVID-19 at a national and place of occurrence level. We observe similar trends\nin the UK where deaths due to COVID-19 first peak in Homes, followed by\nHospitals and Care Homes 1-2 weeks later in the first and second waves. This is\nin line with the infectious period of the disease, indicating a possible\ntransmission vehicle between the settings. Our results show that the first wave\nis characterised by fast growth and a slow reduction after the peak in deaths\ndue to COVID-19. The second and third waves have the converse property, with\nslow growth and a rapid decrease from the peak. This difference may result from\nbehavioural changes in the population (social distancing, masks, etc). Finally,\nwe introduce a double logistic model to describe the dynamic proportion of\nCOVID-19 deaths occurring in each setting. This analysis reveals that the\nproportion of COVID-19 deaths occurring in Care Homes increases from the start\nof the pandemic and past the peak in total number of COVID-19 deaths in the\nfirst wave. After the catastrophic impact in the first wave, the proportion of\nCOVID-19 deaths occurring in Care Homes gradually decreased from is maximum\nafter the first wave indicating residence were better protected in the second\nand third waves compared to the first.\n"}
+{"abstract": "  The membership determination for open clusters in noisy environments of the\nMilky Way is still an open problem. In this paper, our main aim is provide the\nmembership probability of stars using proper motions and parallax values of\nstars using Gaia EDR3 astrometry. Apart from the Gaia astrometry, we have also\nused other photometric data sets like UKIDSS, WISE, APASS and Pan-STARRS1 in\norder to understand cluster properties from optical to mid-infrared regions. We\nselected 438 likely members with membership probability higher than $50\\%$ and\nG$\\le$20 mag. We obtained the mean value of proper motion as\n$\\mu_{x}=1.27\\pm0.001$ and $\\mu_{y}=-0.73\\pm0.002$ mas yr$^{-1}$. The cluster's\nradius is determined as 7.5 arcmin (5.67 pc) using radial density profile. Our\nanalysis suggests that NGC 1348 is located at a distance of $2.6\\pm0.05$ kpc.\nThe mass function slope is found to be $1.30\\pm0.18$ in the mass range\n1.0$-$4.1 $M_\\odot$, which is in fair agreement with Salpeter's value within\nthe 1$\\sigma$ uncertainty. The present study validates that NGC 1348 is a\ndynamically relaxed cluster. We computed the apex coordinates $(A, D)$ for NGC\n1348 as $(A_\\circ, D_\\circ)$ = $(-23^{\\textrm{o}}.815\\pm 0^{\\textrm{o}}.135$,\n$-22^{\\textrm{o}}.228\\pm 0^{\\textrm{o}}.105)$. In addition, calculations of the\nvelocity ellipsoid parameters (VEPs), matrix elements $\\mu_{ij}$, direction\ncosines ($l_j$, $m_j$, $n_j$) and the Galactic longitude of the vertex have\nbeen also conducted in this analysis.\n"}
+{"abstract": "  Galaxies are the basic structural element of the universe; galaxy formation\ntheory seeks to explain how these structures came to be. I trace some of the\nfoundational ideas in galaxy formation, with emphasis on the need for\nnon-baryonic cold dark matter. Many elements of early theory did not survive\ncontact with observations of low surface brightness galaxies, leading to the\nneed for auxiliary hypotheses like feedback. The failure points often trace to\nthe surprising predictive successes of an alternative to dark matter, the\nModified Newtonian Dynamics (MOND). While dark matter models are flexible in\naccommodating observations, they do not provide the predictive capacity of\nMOND. If the universe is made of cold dark matter, why does MOND get any\npredictions right?\n"}
+{"abstract": "  We give a short and insightful proof of Gerry Leversha's elegant theorem\nregarding the isogonal conjugates of each of the vertices of a non-cyclic\nquadrilateral with respect to the triangle formed by the other three. It uses\nthe Maple package RENE.txt, available from .\nhttp://www.math.rutgers.edu/~zeilberg/tokhniot/RENE.txt\n"}
+{"abstract": "  Hackathons are events in which diverse teams work together to explore, and\ndevelop solutions, software or even ideas. Hackathons have been recognized not\nonly as public events for hacking, but also as a corporate mechanism for\ninnovation. Hackathons are a way for established companies to achieve increased\nemployee wellbeing as well as being a curator for innovation and developing new\nproducts. Sudden transition to the work-from-home mode caused by the COVID-19\npandemic first put many corporate events requiring collocation, such as\nhackathons, temporarily on hold and then motivated companies to find ways to\nhold these events virtually. In this paper, we report our findings from\ninvestigating hackathons in the context of a large agile company by first\nexploring the general benefits and challenges of hackathons and then trying to\nunderstand how they were affected by the virtual setup. We conducted nine\ninterviews, surveyed 23 employees and analyzed a hackathon demo. We found that\nhackathons provide both individual and organizational benefits of innovation,\npersonal interests, and acquiring new skills and competences. Several\nchallenges such as added stress due to stopping the regular work, employees\nfearing not having enough contribution to deliver and potential mismatch\nbetween individual and organizational goals were also found. With respect to\nthe virtual setup, we found that virtual hackathons are not diminishing the\ninnovation benefits, however, some negative effect surfaced on the social and\nnetworking side.\n"}
+{"abstract": "  Quantum systems governed by non-Hermitian Hamiltonians with $\\PT$ symmetry\nare special in having real energy eigenvalues bounded below and unitary time\nevolution. We argue that $\\PT$ symmetry may also be important and present at\nthe level of Hermitian quantum field theories because of the process of\nrenormalisation. In some quantum field theories renormalisation leads to\n$\\PT$-symmetric effective Lagrangians. We show how $\\PT$ symmetry may allow\ninterpretations that evade ghosts and instabilities present in an\ninterpretation of the theory within a Hermitian framework. From the study of\nexamples $\\PT$-symmetric interpretation is naturally built into a path integral\nformulation of quantum field theory; there is no requirement to calculate\nexplicitly the $\\PT$ norm that occurs in Hamiltonian quantum theory. We discuss\nexamples where $\\PT$-symmetric field theories emerge from Hermitian field\ntheories due to effects of renormalization. We also consider the effects of\nrenormalization on field theories that are non-Hermitian but $\\PT$-symmetric\nfrom the start.\n"}
+{"abstract": "  This paper follows the generalisation of the classical theory of Diophantine\napproximation to subspaces of $\\mathbb{R}^n$ established by W. M. Schmidt in\n1967. Let $A$ and $B$ be two subspaces of $\\mathbb{R}^n$ of respective\ndimensions $d$ and $e$ with $d+e\\leqslant n$. The proximity between $A$ and $B$\nis measured by $t=\\min(d,e)$ canonical angles $0\\leqslant \\theta_1\\leqslant\n\\cdots\\leqslant \\theta_t\\leqslant \\pi/2$; we set $\\psi_j(A,B)=\\sin\\theta_j$. If\n$B$ is a rational subspace, his complexity is measured by its height\n$H(B)=\\mathrm{covol}(B\\cap\\mathbb{Z}^n)$. We denote by $\\mu_n(A\\vert e)_j$ the\nexponent of approximation defined as the upper bound (possibly equal to\n$+\\infty$) of the set of $\\beta>0$ such that the inequality\n$\\psi_j(A,B)\\leqslant H(B)^{-\\beta}$ holds for infinitely many rational\nsubspaces $B$ of dimension $e$. We are interested in the minimal value\n$\\mathring{\\mu}_n(d\\vert e)_j$ taken by $\\mu_n(A\\vert e)_j$ when $A$ ranges\nthrough the set of subspaces of dimension $d$ of $\\mathbb{R}^n$ such that for\nall rational subspaces $B$ of dimension $e$ one has $\\dim (A\\cap B)<j$. We show\nthat $\\mathring{\\mu}_4(2\\vert 2)_1=3$, $\\mathring{\\mu}_5(3\\vert 2)_1\\le 6$ and\n$\\mathring{\\mu}_{2d}(d\\vert \\ell)_1\\leqslant 2d^2/(2d-\\ell)$. We also prove a\nlower bound in the general case, which implies that $\\mathring{\\mu}_n(d\\vert\nd)_d\\xrightarrow[n\\to+\\infty]{} 1/d$.\n"}
+{"abstract": "  Complex and spinorial techniques of general relativity are used to determine\nall the states of the $SU(2)$ invariant quantum mechanical systems in which the\nequality holds in the uncertainty relations for the components of the angular\nmomentum vector operator in two given directions. The expectation values depend\non a discrete `quantum number' and two parameters, one of them is the angle\nbetween the two angular momentum components and the other is the quotient of\nthe two standard deviations. Allowing the angle between the two angular\nmomentum components to be arbitrary, \\emph{a new genuine quantum mechanical\nphenomenon emerges}: It is shown that although the standard deviations change\ncontinuously, one of the expectation values changes \\emph{discontinuously} on\nthis parameter space. Since physically neither of the angular momentum\ncomponents is distinguished over the other, this discontinuity suggests that\nthe genuine parameter space must be a \\emph{double cover} of this classical\none: It must be a \\emph{Riemann surface} known in connection with the complex\nfunction $\\sqrt{z}$. Moreover, the angle between the angular momentum\ncomponents plays the role of the parameter of an interpolation between the\ncontinuous range of the expectation values found in the special case of the\northogonal angular momentum components by Aragone \\emph{et al} (J. Phys. A.\n{\\bf 7} L149 (1974)) and the discrete point spectrum of one angular momentum\ncomponent. The consequences in the \\emph{simultaneous} measurements of these\nangular momentum components are also discussed briefly.\n"}
+{"abstract": "  The discovery that many classical novae produce detectable GeV $\\gamma$-ray\nemission has raised the question of the role of shocks in nova eruptions. Here\nwe use radio observations of nova V809 Cep (Nova Cep 2013) with the Jansky Very\nLarge Array to show that it produced non-thermal emission indicative of\nparticle acceleration in strong shocks for more than a month starting about six\nweeks into the eruption, quasi-simultaneous with the production of dust.\nBroadly speaking, the radio emission at late times -- more than a six months or\nso into the eruption -- is consistent with thermal emission from $10^{-4}\nM_\\odot$ of freely expanding, $10^4$~K ejecta. At 4.6 and 7.4 GHz, however, the\nradio light-curves display an initial early-time peak 76 days after the\ndiscovery of the eruption in the optical ($t_0$). The brightness temperature at\n4.6 GHz on day 76 was greater than $10^5 K$, an order of magnitude above what\nis expected for thermal emission. We argue that the brightness temperature is\nthe result of synchrotron emission due to internal shocks within the ejecta.\nThe evolution of the radio spectrum was consistent with synchrotron emission\nthat peaked at high frequencies before low frequencies, suggesting that the\nsynchrotron from the shock was initially subject to free-free absorption by\noptically thick ionized material in front of the shock. Dust formation began\naround day 37, and we suggest that internal shocks in the ejecta were\nestablished prior to dust formation and caused the nucleation of dust.\n"}
+{"abstract": "  The process of momentum and energy transfer from a massive body moving\nthrough a background medium, known as dynamical friction (DF), is key to our\nunderstanding of many astrophysical systems. We present a series of\nhigh-resolution simulations of gaseous DF using Lagrangian hydrodynamics\nsolvers, in the state-of-the-art multi-physics code, GIZMO. The numerical setup\nis chosen to allow direct comparison to analytic predictions for DF in the\nrange of Mach 0.2<M<3. We investigate, in detail, the DF drag force, the radial\nstructure of the wake, and their time evolution. The subsonic forces are shown\nto be well resolved, except at Mach numbers close to M=1. The supersonic cases,\nclose to M=1, significantly under-shoot the predicted force. We find that for\nscenarios with 0.7<M<2, between 10%-50% of the expected DF force is missing.\nThe origin of this deficit is mostly related to the wake structure close to the\nperturber, where the density profile of the Mach cone face shows significant\nsmoothing, which does not improve with time. The spherical expanding\nperturbation of the medium is captured well by the hydro scheme, but it is the\nsharp density structure, at the transition from Mach cone to average density,\nthat introduces the mismatch. However, we find a general improvement of the\nforce deficit with time, though significant differencesremain, in agreement\nwith other studies. This is due to (1) the structure of the far field wake\nbeing reproduced well, and (2) the fraction of total drag from the far field\nwake increasing with time. Dark matter sub-haloes, in typical cosmological\nsimulations, occupy parameters similar to those tested here, suggesting that\nthe DF which these sub-haloes experience is significantly underestimated, and\nhence their merger rate. Dynamical friction is a relevant benchmark and should\nbe included as one of the standard hydro tests for astrophysical simulations.\n"}
+{"abstract": "  The JOREK extended magneto-hydrodynamic (MHD) code is a widely used\nsimulation code for studying the non-linear dynamics of large-scale\ninstabilities in divertor tokamak plasmas. Due to the large scale-separation\nintrinsic to these phenomena both in space and time, the computational costs\nfor simulations in realistic geometry and with realistic parameters can be very\nhigh, motivating the investment of considerable effort for optimization. In\nthis article, a set of developments regarding the JOREK solver and\npreconditioner is described, which lead to overall significant benefits for\nlarge production simulations. This comprises in particular enhanced convergence\nin highly non-linear scenarios and a general reduction of memory consumption\nand computational costs. The developments include faster construction of\npreconditioner matrices, a domain decomposition of preconditioning matrices for\nsolver libraries that can handle distributed matrices, interfaces for\nadditional solver libraries, an option to use matrix compression methods, and\nthe implementation of a complex solver interface for the preconditioner. The\nmost significant development presented consists in a generalization of the\nphysics based preconditioner to \"mode groups\", which allows to account for the\ndominant interactions between toroidal Fourier modes in highly non-linear\nsimulations. At the cost of a moderate increase of memory consumption, the\ntechnique can strongly enhance convergence in suitable cases allowing to use\nsignificantly larger time steps. For all developments, benchmarks based on\ntypical simulation cases demonstrate the resulting improvements.\n"}
+{"abstract": "  Optimal use and distribution of Covid-19 vaccines involves adjustments of\ndosing. Due to the rapidly-evolving pandemic, such adjustments often need to be\nintroduced before full efficacy data are available. As demonstrated in other\nareas of drug development, quantitative systems pharmacology (QSP) is well\nplaced to guide such extrapolation in a rational and timely manner. Here we\npropose for the first time how QSP can be applied real time in the context of\nCOVID-19 vaccine development.\n"}
+{"abstract": "  End-to-end optimization capability offers neural image compression (NIC)\nsuperior lossy compression performance. However, distinct models are required\nto be trained to reach different points in the rate-distortion (R-D) space. In\nthis paper, we consider the problem of R-D characteristic analysis and modeling\nfor NIC. We make efforts to formulate the essential mathematical functions to\ndescribe the R-D behavior of NIC using deep network and statistical modeling.\nThus continuous bit-rate points could be elegantly realized by leveraging such\nmodel via a single trained network. In this regard, we propose a plugin-in\nmodule to learn the relationship between the target bit-rate and the binary\nrepresentation for the latent variable of auto-encoder. Furthermore, we model\nthe rate and distortion characteristic of NIC as a function of the coding\nparameter $\\lambda$ respectively. Our experiments show our proposed method is\neasy to adopt and obtains competitive coding performance with fixed-rate coding\napproaches, which would benefit the practical deployment of NIC. In addition,\nthe proposed model could be applied to NIC rate control with limited bit-rate\nerror using a single network.\n"}
+{"abstract": "  Simulation-based inference enables learning the parameters of a model even\nwhen its likelihood cannot be computed in practice. One class of methods uses\ndata simulated with different parameters to infer an amortized estimator for\nthe likelihood-to-evidence ratio, or equivalently the posterior function. We\nshow that this approach can be formulated in terms of mutual information\nmaximization between model parameters and simulated data. We use this\nequivalence to reinterpret existing approaches for amortized inference and\npropose two new methods that rely on lower bounds of the mutual information. We\napply our framework to the inference of parameters of stochastic processes and\nchaotic dynamical systems from sampled trajectories, using artificial neural\nnetworks for posterior prediction. Our approach provides a unified framework\nthat leverages the power of mutual information estimators for inference.\n"}
+{"abstract": "  This paper describes our contribution to the WASSA 2021 shared task on\nEmpathy Prediction and Emotion Classification. The broad goal of this task was\nto model an empathy score, a distress score and the overall level of emotion of\nan essay written in response to a newspaper article associated with harm to\nsomeone. We have used the ELECTRA model abundantly and also advanced deep\nlearning approaches like multi-task learning. Additionally, we also leveraged\nstandard machine learning techniques like ensembling. Our system achieves a\nPearson Correlation Coefficient of 0.533 on sub-task I and a macro F1 score of\n0.5528 on sub-task II. We ranked 1st in Emotion Classification sub-task and 3rd\nin Empathy Prediction sub-task\n"}
+{"abstract": "  In this paper, we introduce the \\textit{Layer-Peeled Model}, a nonconvex yet\nanalytically tractable optimization program, in a quest to better understand\ndeep neural networks that are trained for a sufficiently long time. As the name\nsuggests, this new model is derived by isolating the topmost layer from the\nremainder of the neural network, followed by imposing certain constraints\nseparately on the two parts of the network. We demonstrate that the\nLayer-Peeled Model, albeit simple, inherits many characteristics of\nwell-trained neural networks, thereby offering an effective tool for explaining\nand predicting common empirical patterns of deep learning training. First, when\nworking on class-balanced datasets, we prove that any solution to this model\nforms a simplex equiangular tight frame, which in part explains the recently\ndiscovered phenomenon of neural collapse \\cite{papyan2020prevalence}. More\nimportantly, when moving to the imbalanced case, our analysis of the\nLayer-Peeled Model reveals a hitherto unknown phenomenon that we term\n\\textit{Minority Collapse}, which fundamentally limits the performance of deep\nlearning models on the minority classes. In addition, we use the Layer-Peeled\nModel to gain insights into how to mitigate Minority Collapse. Interestingly,\nthis phenomenon is first predicted by the Layer-Peeled Model before being\nconfirmed by our computational experiments.\n"}
+{"abstract": "  In empirical game-theoretic analysis (EGTA), game models are extended\niteratively through a process of generating new strategies based on learning\nfrom experience with prior strategies. The strategy exploration problem in EGTA\nis how to direct this process so to construct effective models with minimal\niteration. A variety of approaches have been proposed in the literature,\nincluding methods based on classic techniques and novel concepts. Comparing the\nperformance of these alternatives can be surprisingly subtle, depending\nsensitively on criteria adopted and measures employed. We investigate some of\nthe methodological considerations in evaluating strategy exploration, defining\nkey distinctions and identifying a few general principles based on examples and\nexperimental observations. In particular, we emphasize the fact that empirical\ngames create a space of strategies that should be evaluated as a whole. Based\non this fact, we suggest that the minimum regret constrained profile (MRCP)\nprovides a particularly robust basis for evaluating a space of strategies, and\npropose a local search method for MRCP that outperforms previous approaches.\nHowever, the computation of MRCP is not always feasible especially in large\ngames. In this scenario, we highlight consistency considerations for comparing\nacross different approaches. Surprisingly, we find that recent works violate\nthese considerations that are necessary for evaluation, which may result in\nmisleading conclusions on the performance of different approaches. For proper\nevaluation, we propose a new evaluation scheme and demonstrate that our scheme\ncan reveal the true learning performance of different approaches compared to\nprevious evaluation methods.\n"}
+{"abstract": "  Fourier expansion of the integrand in the path integral formula for the\npartition function of quantum systems leads to a deterministic expression\nwhich, though still quite complex, is easier to process than the original\nfunctional integral. It therefore can give access to problems that eluded\nsolution so far. Here we derive the formula; a first application is presented\nin \"Simultaneous occurrence of off-diagonal long-range order and infinite\npermutation cycles in systems of interacting atoms\", arXiv:2108.02659\n[math-ph].\n"}
+{"abstract": "  MomentClosure.jl is a Julia package providing automated derivation of the\ntime-evolution equations of the moments of molecule numbers for virtually any\nchemical reaction network using a wide range of moment closure approximations.\nIt extends the capabilities of modelling stochastic biochemical systems in\nJulia and can be particularly useful when exact analytic solutions of the\nchemical master equation are unavailable and when Monte Carlo simulations are\ncomputationally expensive.\n  MomentClosure.jl is freely accessible under the MIT license. Source code and\ndocumentation are available at https://github.com/augustinas1/MomentClosure.jl\n"}
+{"abstract": "  Micrometer scale colloidal particles that propel in a deterministic fashion\nin response to local environmental cues are useful analogs to self-propelling\nentities found in nature. Both natural and synthetic active colloidal systems\nare often near boundaries or are located in crowded environments. Herein, we\ndescribe experiments in which we measured the influence of hydrogen peroxide\nconcentration and dispersed polyethylene glycol (PEG) on the clustering\nbehavior of 5 micrometer catalytic active Janus particles at low concentration.\nWe found the extent to which clustering occurred in ensembles of active Janus\nparticles grew with hydrogen peroxide concentration in the absence of PEG. Once\nPEG was added, clustering was slightly enhanced at low PEG volume fractions,\nbut was reduced at higher PEG volumes fractions. The region in which clustering\nwas mitigated at higher PEG volume fractions corresponded to the region in\nwhich propulsion was previously found to be quenched. Complementary agent based\nsimulations showed that clustering grew with nominal speed. These data support\nthe hypothesis that growth of living crystals is enhanced with increases in\npropulsion speed, but the addition of PEG will tend to mitigate cluster\nformation as a consequence of quenched propulsion at these conditions.\n"}
+{"abstract": "  It was recently suggested that certain UV-completable supersymmetric actions\ncan be characterized by the solutions to an auxiliary non-linear sigma-model\nwith special asymptotic boundary conditions. The space-time of this sigma-model\nis the scalar field space of these effective theories while the target space is\na coset space. We study this sigma-model without any reference to a potentially\nunderlying geometric description. Using a holographic approach reminiscent of\nthe bulk reconstruction in the AdS/CFT correspondence, we then derive its\nnear-boundary solutions for a two-dimensional space-time. Specifying a set of $\nSl(2,\\mathbb{R})$ boundary data we show that the near-boundary solutions are\nuniquely fixed after imposing a single bulk-boundary matching condition. The\nreconstruction exploits an elaborate set of recursion relations introduced by\nCattani, Kaplan, and Schmid in the proof of the $Sl(2)$-orbit theorem. We\nexplicitly solve these recursion relations for three sets of simple boundary\ndata and show that they model asymptotic periods of a Calabi--Yau threefold\nnear the conifold point, the large complex structure point, and the Tyurin\ndegeneration.\n"}
+{"abstract": "  We study the time evolution of molecular clouds across three Milky Way-like\nisolated disc galaxy simulations at a temporal resolution of 1 Myr, and at a\nrange of spatial resolutions spanning two orders of magnitude in spatial scale\nfrom ~10 pc up to ~1 kpc. The cloud evolution networks generated at the highest\nspatial resolution contain a cumulative total of ~80,000 separate molecular\nclouds in different galactic-dynamical environments. We find that clouds\nundergo mergers at a rate proportional to the crossing time between their\ncentroids, but that their physical properties are largely insensitive to these\ninteractions. Below the gas disc scale-height, the cloud lifetime obeys a\nscaling relation of the form $\\tau_{\\rm life} \\propto \\ell^{-0.3}$ with the\ncloud size $\\ell$, consistent with over-densities that collapse, form stars,\nand are dispersed by stellar feedback. Above the disc scale-height, these\nself-gravitating regions are no longer resolved, so the scaling relation\nflattens to a constant value of ~13 Myr, consistent with the turbulent crossing\ntime of the gas disc, as observed in nearby disc galaxies.\n"}
+{"abstract": "  We continue our investigation, from \\cite{dh}, of the ring-theoretic\ninfiniteness properties of ultrapowers of Banach algebras, studying in this\npaper the notion of being purely infinite. It is well known that a\n$C^*$-algebra is purely infinite if and only if any of its ultrapower is. We\nfind examples of Banach algebras, as algebras of operators on Banach spaces,\nwhich do have purely infinite ultrapowers. Our main contribution is the\nconstruction of a \"Cuntz-like\" Banach $*$-algebra which is purely infinite, but\ndoes not have purely infinite ultrapowers. Our proof of being purely infinite\nis combinatorial, but direct, and so differs from the proof for the Cuntz\nalgebra. We use an indirect method (and not directly computing norm estimates)\nto show that this algebra does not have purely infinite ultrapowers.\n"}
+{"abstract": "  The Eisenbud--Goto conjecture states that $\\operatorname{reg}\nX\\le\\operatorname{deg} X -\\operatorname{codim} X+1$ for a nondegenerate\nirreducible projective variety $X$ over an algebraically closed field. While\nthis conjecture is known to be false in general, it has been proven in several\nspecial cases, including when $X$ is a projective toric variety of codimension\n$2$. We classify the projective toric varieties of codimension $2$ having\nmaximal regularity, that is, for which equality holds in the Eisenbud--Goto\nbound. We also give combinatorial characterizations of the arithmetically\nCohen--Macaulay toric varieties of maximal regularity in characteristic $0$.\n"}
+{"abstract": "  Instrumental variable methods are among the most commonly used causal\ninference approaches to account for unmeasured confounders in observational\nstudies. The presence of invalid instruments is a major concern for practical\napplications and a fast-growing area of research is inference for the causal\neffect with possibly invalid instruments. The existing inference methods rely\non correctly separating valid and invalid instruments in a data dependent way.\nIn this paper, we illustrate post-selection problems of these existing methods.\nWe construct uniformly valid confidence intervals for the causal effect, which\nare robust to the mistakes in separating valid and invalid instruments. Our\nproposal is to search for the causal effect such that a sufficient amount of\ncandidate instruments can be taken as valid. We further devise a novel sampling\nmethod, which, together with searching, lead to a more precise confidence\ninterval. Our proposed searching and sampling confidence intervals are shown to\nbe uniformly valid under the finite-sample majority and plurality rules. We\ncompare our proposed methods with existing inference methods over a large set\nof simulation studies and apply them to study the effect of the triglyceride\nlevel on the glucose level over a mouse data set.\n"}
+{"abstract": "  Explaining the decisions of an Artificial Intelligence (AI) model is\nincreasingly critical in many real-world, high-stake applications. Hundreds of\npapers have either proposed new feature attribution methods, discussed or\nharnessed these tools in their work. However, despite humans being the target\nend-users, most attribution methods were only evaluated on proxy\nautomatic-evaluation metrics (Zhang et al. 2018; Zhou et al. 2016; Petsiuk et\nal. 2018). In this paper, we conduct the first user study to measure\nattribution map effectiveness in assisting humans in ImageNet classification\nand Stanford Dogs fine-grained classification, and when an image is natural or\nadversarial (i.e., contains adversarial perturbations). Overall, feature\nattribution is surprisingly not more effective than showing humans nearest\ntraining-set examples. On a harder task of fine-grained dog categorization,\npresenting attribution maps to humans does not help, but instead hurts the\nperformance of human-AI teams compared to AI alone. Importantly, we found\nautomatic attribution-map evaluation measures to correlate poorly with the\nactual human-AI team performance. Our findings encourage the community to\nrigorously test their methods on the downstream human-in-the-loop applications\nand to rethink the existing evaluation metrics.\n"}
+{"abstract": "  Recently, High-Efficiency Video Coding (HEVC/H.265) has been chosen to\nreplace previous video coding standards, such as H.263 and H.264. Despite the\nefficiency of HEVC, it still lacks reliable and practical functionalities to\nsupport authentication and copyright applications. In order to provide this\nsupport, several watermarking techniques have been proposed by many researchers\nduring the last few years. However, those techniques are still suffering from\nmany issues that need to be considered for future designs. In this paper, a\nSystematic Literature Review (SLR) is introduced to identify HEVC challenges\nand potential research directions for interested researchers and developers.\nThe time scope of this SLR covers all research articles published during the\nlast six years starting from January 2014 up to the end of April 2020.\nForty-two articles have met the criteria of selection out of 343 articles\npublished in this area during the mentioned time scope. A new classification\nhas been drawn followed by an identification of the challenges of implementing\nHEVC watermarking techniques based on the analysis and discussion of those\nchosen articles. Eventually, recommendations for HEVC watermarking techniques\nhave been listed to help researchers to improve the existing techniques or to\ndesign new efficient ones.\n"}
+{"abstract": "  Collective migration of cells and animals often relies on a specialised set\nof \"leaders\", whose role is to steer a population of naive followers towards\nsome target. We formulate a continuous model to understand the dynamics and\nstructure of such groups, splitting a population into separate follower and\nleader types with distinct orientation responses. We incorporate \"leader\ninfluence\" via three principal mechanisms: a bias in the orientation of leaders\naccording to the destination, distinct speeds of movement and distinct levels\nof conspicuousness. Using a combination of analysis and numerical computation\non a sequence of models of increasing complexity, we assess the extent to which\nleaders successfully shepherd the swarm. While all three mechanisms can lead to\na successfully steered swarm, parameter regime is crucial with non successful\nchoices generating a variety of unsuccessful attempts, including movement away\nfrom the target, swarm splitting or swarm dispersal.\n"}
+{"abstract": "  We develop an algebro-geometric formulation for neural networks in machine\nlearning using the moduli space of framed quiver representations. We find\nnatural Hermitian metrics on the universal bundles over the moduli which are\ncompatible with the GIT quotient construction by the general linear group, and\nshow that their Ricci curvatures give a K\\\"ahler metric on the moduli.\nMoreover, we use toric moment maps to construct activation functions, and prove\nthe universal approximation theorem for the multi-variable activation function\nconstructed from the complex projective space.\n"}
+{"abstract": "  As internet related challenges increase such as cyber-attacks, the need for\nsafe practises among users to maintain computer system's health and online\nsecurity have become imperative, and this is known as cyber-hygiene. Poor\ncyber-hygiene among internet users is a very critical issue undermining the\ngeneral acceptance and adoption of internet technology. It has become a global\nissue and concern in this digital era when virtually all business transactions,\nlearning, communication and many other activities are performed online. Virus\nattack, poor authentication technique, improper file backups and the use of\ndifferent social engineering approaches by cyber-attackers to deceive internet\nusers into divulging their confidential information with the intention to\nattack them have serious negative implications on the industries and\norganisations, including educational institutions. Moreover, risks associated\nwith these ugly phenomena are likely to be more in developing countries such as\nNigeria. Thus, authors of this paper undertook an online pilot study among\nstudents and employees of University of Nigeria, Nsukka and a total of 145\nresponses were received and used for the study. The survey seeks to find out\nthe effect of age and level of education on the cyber hygiene knowledge and\nbehaviour of the respondents, and in addition, the type of devices used and\nactivities they engage in while on the internet. Our findings show wide\nadoption of internet in institution of higher learning, whereas, significant\nnumber of the internet users do not have good cyber hygiene knowledge and\nbehaviour. Hence, our findings can instigate an organised training for students\nand employees of higher institutions in Nigeria.\n"}
+{"abstract": "  Andrews, Lewis and Lovejoy introduced the partition function $PD(n)$ as the\nnumber of partitions of $n$ with designated summands. A bipartition of $n$ is\nan ordered pair of partitions $(\\pi_1, \\pi_2)$ with the sum of all of the parts\nbeing $n$. In this paper, we introduce a generalized crank named the $pd$-crank\nfor bipartitions with designated summands and give some inequalities for the\n$pd$-crank of bipartitions with designated summands modulo 2 and 3. We also\ndefine the $pd$-crank moments weighted by the parity of $pd$-cranks\n$\\mu_{2k,bd}(-1,n)$ and show the positivity of $(-1)^n\\mu_{2k,bd}(-1,n)$. Let\n$M_{bd}(m,n)$ denote the number of bipartitions of $n$ with designated summands\nwith $pd$-crank $m$. We prove a monotonicity property of $pd$-cranks of\nbipartitions with designated summands and find that the sequence\n$\\{M_{bd}(m,n)\\}_{|m|\\leq n}$ is unimodal for $n\\not= 1,5,7$.\n"}
+{"abstract": "  The future communication will be characterized by ubiquitous connectivity and\nsecurity. These features will be essential requirements for the efficient\nfunctioning of the futuristic applications. In this paper, in order to\nhighlight the impact of blockchain and 6G on the future communication systems,\nwe categorize these application requirements into two broad groups. In the\nfirst category, called Requirement Group I \\mbox{(RG-I)}, we include the\nperformance-related needs on data rates, latency, reliability and massive\nconnectivity, while in the second category, called Requirement Group II\n\\mbox{(RG-II)}, we include the security-related needs on data integrity,\nnon-repudiability, and auditability. With blockchain and 6G, the network\ndecentralization and resource sharing would minimize resource under-utilization\nthereby facilitating RG-I targets. Furthermore, through appropriate selection\nof blockchain type and consensus algorithms, RG-II needs of 6G applications can\nalso be readily addressed. Through this study, the combination of blockchain\nand 6G emerges as an elegant solution for secure and ubiquitous future\ncommunication.\n"}
+{"abstract": "  A singular perturbation problem from the artificial compressible system to\nthe incompressible system is considered for a doubly diffusive convection when\na Hopf bifurcation from the motionless state occurs in the incompressible\nsystem. It is proved that the Hopf bifurcation also occurs in the artificial\ncompressible system for small singular perturbation parameter, called the\nartificial Mach number. The time periodic solution branch of the artificial\ncompressible system is shown to converge to the corresponding bifurcating\nbranch of the incompressible system in the singular limit of vanishing\nartificial Mach number.\n"}
+{"abstract": "  We present vir, an R package for variational inference with shrinkage priors.\nOur package implements variational and stochastic variational algorithms for\nlinear and probit regression models, the use of which is a common first step in\nmany applied analyses. We review variational inference and show how the\nderivation for a Gibbs sampler can be easily modified to derive a corresponding\nvariational or stochastic variational algorithm. We provide simulations showing\nthat, at least for a normal linear model, variational inference can lead to\nsimilar uncertainty quantification as the corresponding Gibbs samplers, while\nestimating the model parameters at a fraction of the computational cost. Our\ntiming experiments show situations in which our algorithms converge faster than\nthe frequentist LASSO implementations in glmnet while simultaneously providing\nsuperior parameter estimation and variable selection. Hence, our package can be\nutilized to quickly explore different combinations of predictors in a linear\nmodel, while providing accurate uncertainty quantification in many applied\nsituations. The package is implemented natively in R and RcppEigen, which has\nthe benefit of bypassing the substantial operating system specific overhead of\nlinking external libraries to work efficiently with R.\n"}
+{"abstract": "  The use of Cauchy Markov random field priors in statistical inverse problems\ncan potentially lead to posterior distributions which are non-Gaussian,\nhigh-dimensional, multimodal and heavy-tailed. In order to use such priors\nsuccessfully, sophisticated optimization and Markov chain Monte Carlo (MCMC)\nmethods are usually required. In this paper, our focus is largely on reviewing\nrecently developed Cauchy difference priors, while introducing interesting new\nvariants, whilst providing a comparison. We firstly propose a one-dimensional\nsecond order Cauchy difference prior, and construct new first and second order\ntwo-dimensional isotropic Cauchy difference priors. Another new Cauchy prior is\nbased on the stochastic partial differential equation approach, derived from\nMat\\'{e}rn type Gaussian presentation. The comparison also includes Cauchy\nsheets. Our numerical computations are based on both maximum a posteriori and\nconditional mean estimation.We exploit state-of-the-art MCMC methodologies such\nas Metropolis-within-Gibbs, Repelling-Attracting Metropolis, and No-U-Turn\nsampler variant of Hamiltonian Monte Carlo. We demonstrate the models and\nmethods constructed for one-dimensional and two-dimensional deconvolution\nproblems. Thorough MCMC statistics are provided for all test cases, including\npotential scale reduction factors.\n"}
+{"abstract": "  Theoretical treatments of periodically-driven quantum thermal machines\n(PD-QTMs) are largely focused on the limit-cycle stage of operation\ncharacterized by a periodic state of the system. Yet, this regime is not\nimmediately accessible for experimental verification. Here, we present a\ngeneral thermodynamic framework that can handle the performance of PD-QTMs both\nbefore and during the limit-cycle stage of operation. It is achieved by\nobserving that periodicity may break down at the ensemble average level, even\nin the limit-cycle phase. With this observation, and using conventional\nthermodynamic expressions for work and heat, we find that a complete\ndescription of the first law of thermodynamics for PD-QTMs requires a new\ncontribution, which vanishes only in the limit-cycle phase under rather weak\nsystem-bath couplings. Significantly, this contribution is substantial at\nstrong couplings even at limit cycle, thus largely affecting the behavior of\nthe thermodynamic efficiency. We demonstrate our framework by simulating a\nquantum Otto engine building upon a driven resonant level model. Our results\nprovide new insights towards a complete description of PD-QTMs, from turn-on to\nthe limit-cycle stage and, particularly, shed light on the development of\nquantum thermodynamics at strong coupling.\n"}
+{"abstract": "  Segmentation of pathological images is essential for accurate disease\ndiagnosis. The quality of manual labels plays a critical role in segmentation\naccuracy; yet, in practice, the labels between pathologists could be\ninconsistent, thus confusing the training process. In this work, we propose a\nnovel label re-weighting framework to account for the reliability of different\nexperts' labels on each pixel according to its surrounding features. We further\ndevise a new attention heatmap, which takes roughness as prior knowledge to\nguide the model to focus on important regions. Our approach is evaluated on the\npublic Gleason 2019 datasets. The results show that our approach effectively\nimproves the model's robustness against noisy labels and outperforms\nstate-of-the-art approaches.\n"}
+{"abstract": "  We present a new method and a large-scale database to detect audio-video\nsynchronization(A/V sync) errors in tennis videos. A deep network is trained to\ndetect the visual signature of the tennis ball being hit by the racquet in the\nvideo stream. Another deep network is trained to detect the auditory signature\nof the same event in the audio stream. During evaluation, the audio stream is\nsearched by the audio network for the audio event of the ball being hit. If the\nevent is found in audio, the neighboring interval in video is searched for the\ncorresponding visual signature. If the event is not found in the video stream\nbut is found in the audio stream, A/V sync error is flagged. We developed a\nlarge-scaled database of 504,300 frames from 6 hours of videos of tennis\nevents, simulated A/V sync errors, and found our method achieves high accuracy\non the task.\n"}
+{"abstract": "  The so-called improved soft-aided bit-marking algorithm was recently proposed\nfor staircase codes (SCCs) in the context of fiber optical communications. This\nalgorithm is known as iSABM-SCC. With the help of channel soft information, the\niSABM-SCC decoder marks bits via thresholds to deal with both miscorrections\nand failures of hard-decision (HD) decoding. In this paper, we study iSABM-SCC\nfocusing on the parameter optimization of the algorithm and its performance\nanalysis, in terms of the gap to the achievable information rates (AIRs) of HD\ncodes and the fiber reach enhancement. We show in this paper that the marking\nthresholds and the number of modified component decodings heavily affect the\nperformance of iSABM-SCC, and thus, they need to be carefully optimized. By\nreplacing standard decoding with the optimized iSABM-SCC decoding, the gap to\nthe AIRs of HD codes can be reduced to 0.26-1.02 dB for code rates of 0.74-0.87\nin the additive white Gaussian noise channel with 8-ary pulse amplitude\nmodulation. The obtained reach increase is up to 22% for data rates between 401\nGbps and 468 Gbps in an optical fiber channel.\n"}
+{"abstract": "  The Electron-Ion Collider (EIC) Yellow Report specified parameters for the\ngeneral-purpose detector that can deliver the scientific goals delineated by\nthe EIC White Paper and NAS report. These parameters dictate the tracking\nmomentum resolution, secondary-vertex resolutions, calorimeter energy\nresolutions, as well as $\\pi/K/p$ ID. We have incorporated these parameters\ninto a configuration card for Delphes, which is a widely used \"C++ framework,\nfor performing a fast multipurpose detector response simulation\". We include\nboth the 1.5 T and 3.0 T scenarios. We also show the expected performance for\nhigh-level quantities such as jets, missing transverse energy, charm tagging,\nand others. These parametrizations can be easily updated with more refined\nGeant4 studies, which provides an efficient way to perform simulations to\nbenchmark a variety of observables using state-of-the art event generators such\nas Pythia8.\n"}
+{"abstract": "  Building on a result by Tao, we show that a certain type of simple closed\ncurve in the plane given by the union of the graphs of two $1$-Lipschitz\nfunctions inscribes a square whose sidelength is bounded from below by a\nuniversal constant times the maximum of the difference of the two functions.\n"}
+{"abstract": "  In this paper the tracking problem of multi-agent systems, in a particular\nscenario where a segment of agents entering a sensing-denied environment or\nbehaving as non-cooperative targets, is considered. The focus is on determining\nthe optimal sensor precisions while simultaneously promoting sparseness in the\nsensor measurements to guarantee a specified estimation performance. The\nproblem is formulated in the discrete-time centralized Kalman filtering\nframework. A semi-definite program subject to linear matrix inequalities is\nsolved to minimize the trace of precision matrix which is defined to be the\ninverse of sensor noise covariance matrix. Simulation results expose a\ntrade-off between sensor precisions and sensing frequency.\n"}
+{"abstract": "  Physical systems characterized by a shallow two-body bound or virtual state\nare governed at large distances by a continuous-scale invariance, which is\nbroken to a discrete one when three or more particles come into play. This\nsymmetry induces a universal behavior for different systems, independent of the\ndetails of the underlying interaction, rooted in the smallness of the ratio\n$\\ell/a_B \\ll 1$, where the length $a_B$ is associated to the binding energy of\nthe two-body system $E_2=\\hbar^2/m a_B^2$ and $\\ell$ is the natural length\ngiven by the interaction range. Efimov physics refers to this universal\nbehavior, which is often hidden by the on-set of system-specific non-universal\neffects. In this work we identify universal properties by providing an explicit\nlink of physical systems to their unitary limit, in which\n$a_B\\rightarrow\\infty$, and show that nuclear systems belong to this class of\nuniversality.\n"}
+{"abstract": "  An attention matrix of a transformer self-attention sublayer can provably be\ndecomposed into two components and only one of them (effective attention)\ncontributes to the model output. This leads us to ask whether visualizing\neffective attention gives different conclusions than interpretation of standard\nattention. Using a subset of the GLUE tasks and BERT, we carry out an analysis\nto compare the two attention matrices, and show that their interpretations\ndiffer. Effective attention is less associated with the features related to the\nlanguage modeling pretraining such as the separator token, and it has more\npotential to illustrate linguistic features captured by the model for solving\nthe end-task. Given the found differences, we recommend using effective\nattention for studying a transformer's behavior since it is more pertinent to\nthe model output by design.\n"}
+{"abstract": "  We propose an affine-mapping based variational Ensemble Kalman filter for\nsequential Bayesian filtering problems with generic observation models.\nSpecifically, the proposed method is formulated as to construct an affine\nmapping from the prior ensemble to the posterior one, and the affine mapping is\ncomputed via a variational Bayesian formulation, i.e., by minimizing the\nKullback-Leibler divergence between the transformed distribution through the\naffine mapping and the actual posterior. Some theoretical properties of\nresulting optimization problem are studied and a gradient descent scheme is\nproposed to solve the resulting optimization problem. With numerical examples\nwe demonstrate that the method has competitive performance against existing\nmethods.\n"}
+{"abstract": "  In this work we present a novel, robust transition generation technique that\ncan serve as a new tool for 3D animators, based on adversarial recurrent neural\nnetworks. The system synthesizes high-quality motions that use\ntemporally-sparse keyframes as animation constraints. This is reminiscent of\nthe job of in-betweening in traditional animation pipelines, in which an\nanimator draws motion frames between provided keyframes. We first show that a\nstate-of-the-art motion prediction model cannot be easily converted into a\nrobust transition generator when only adding conditioning information about\nfuture keyframes. To solve this problem, we then propose two novel additive\nembedding modifiers that are applied at each timestep to latent representations\nencoded inside the network's architecture. One modifier is a time-to-arrival\nembedding that allows variations of the transition length with a single model.\nThe other is a scheduled target noise vector that allows the system to be\nrobust to target distortions and to sample different transitions given fixed\nkeyframes. To qualitatively evaluate our method, we present a custom\nMotionBuilder plugin that uses our trained model to perform in-betweening in\nproduction scenarios. To quantitatively evaluate performance on transitions and\ngeneralizations to longer time horizons, we present well-defined in-betweening\nbenchmarks on a subset of the widely used Human3.6M dataset and on LaFAN1, a\nnovel high quality motion capture dataset that is more appropriate for\ntransition generation. We are releasing this new dataset along with this work,\nwith accompanying code for reproducing our baseline results.\n"}
+{"abstract": "  The use of machine learning to develop intelligent software tools for\ninterpretation of radiology images has gained widespread attention in recent\nyears. The development, deployment, and eventual adoption of these models in\nclinical practice, however, remains fraught with challenges. In this paper, we\npropose a list of key considerations that machine learning researchers must\nrecognize and address to make their models accurate, robust, and usable in\npractice. Namely, we discuss: insufficient training data, decentralized\ndatasets, high cost of annotations, ambiguous ground truth, imbalance in class\nrepresentation, asymmetric misclassification costs, relevant performance\nmetrics, generalization of models to unseen datasets, model decay, adversarial\nattacks, explainability, fairness and bias, and clinical validation. We\ndescribe each consideration and identify techniques to address it. Although\nthese techniques have been discussed in prior research literature, by freshly\nexamining them in the context of medical imaging and compiling them in the form\nof a laundry list, we hope to make them more accessible to researchers,\nsoftware developers, radiologists, and other stakeholders.\n"}
+{"abstract": "  The transverse field in the quantum Ising chain is linearly ramped from the\npara- to the ferromagnetic phase across the quantum critical point at a rate\ncharacterized by a quench time $\\tau_Q$. We calculate a connected kink-kink\ncorrelator in the final state at zero transverse field. The correlator is a sum\nof two terms: a negative (anti-bunching) Gaussian that depends on the\nKibble-Zurek (KZ) correlation length only and a positive term that depends on a\nsecond longer scale of length. The second length is made longer by dephasing of\nthe state excited near the critical point during the following ramp across the\nferromagnetic phase. This interpretation is corroborated by considering a\nlinear ramp that is halted in the ferromagnetic phase for a finite waiting time\nand then continued at the same rate as before the halt. The extra time\navailable for dephasing increases the second scale of length that\nasymptotically grows linearly with the waiting time. The dephasing also\nsuppresses magnitude of the second term making it negligible for waiting times\nmuch longer than $\\tau_Q$. The same dephasing can be obtained with a smooth\nramp that slows down in the ferromagnetic phase. Assuming sufficient dephasing\nwe obtain also higher order kink correlators and the ferromagnetic correlation\nfunction.\n"}
+{"abstract": "  A Sidon space is a subspace of an extension field over a base field in which\nthe product of any two elements can be factored uniquely, up to constants. This\npaper proposes a new public-key cryptosystem of the multivariate type which is\nbased on Sidon spaces, and has the potential to remain secure even if quantum\nsupremacy is attained. This system, whose security relies on the hardness of\nthe well-known MinRank problem, is shown to be resilient to several\nstraightforward algebraic attacks. In particular, it is proved that the two\npopular attacks on the MinRank problem, the kernel attack, and the minor\nattack, succeed only with exponentially small probability. The system is\nimplemented in software, and its hardness is demonstrated experimentally.\n"}
+{"abstract": "  Mortality risk is a major concern to patients have just been discharged from\nthe intensive care unit (ICU). Many studies have been directed to construct\nmachine learning models to predict such risk. Although these models are highly\naccurate, they are less amenable to interpretation and clinicians are typically\nunable to gain further insights into the patients' health conditions and the\nunderlying factors that influence their mortality risk. In this paper, we use\npatients' profiles extracted from the MIMIC-III clinical database to construct\nrisk calculators based on different machine learning techniques such as\nlogistic regression, decision trees, random forests and multilayer perceptrons.\nWe perform an extensive benchmarking study that compares the most salient\nfeatures as predicted by various methods. We observe a high degree of agreement\nacross the considered machine learning methods; in particular, the cardiac\nsurgery recovery unit, age, and blood urea nitrogen levels are commonly\npredicted to be the most salient features for determining patients' mortality\nrisks. Our work has the potential for clinicians to interpret risk predictions.\n"}
+{"abstract": "  Photon detection at microwave frequency is of great interest due to its\napplication in quantum computation information science and technology. Herein\nare results from studying microwave response in a topological superconducting\nquantum interference device (SQUID) realized in Dirac semimetal Cd3As2. The\ntemperature dependence and microwave power dependence of the SQUID junction\nresistance are studied, from which we obtain an effective temperature at each\nmicrowave power level. It is observed the effective temperature increases with\nthe microwave power. This observation of microwave response may pave the way\nfor single photon detection at the microwave frequency in topological quantum\nmaterials.\n"}
+{"abstract": "  Probabilistic programming languages aim to describe and automate Bayesian\nmodeling and inference. Modern languages support programmable inference, which\nallows users to customize inference algorithms by incorporating guide programs\nto improve inference performance. For Bayesian inference to be sound, guide\nprograms must be compatible with model programs. One pervasive but challenging\ncondition for model-guide compatibility is absolute continuity, which requires\nthat the model and guide programs define probability distributions with the\nsame support.\n  This paper presents a new probabilistic programming language that guarantees\nabsolute continuity, and features general programming constructs, such as\nbranching and recursion. Model and guide programs are implemented as coroutines\nthat communicate with each other to synchronize the set of random variables\nthey sample during their execution. Novel guide types describe and enforce\ncommunication protocols between coroutines. If the model and guide are\nwell-typed using the same protocol, then they are guaranteed to enjoy absolute\ncontinuity. An efficient algorithm infers guide types from code so that users\ndo not have to specify the types. The new programming language is evaluated\nwith an implementation that includes the type-inference algorithm and a\nprototype compiler that targets Pyro. Experiments show that our language is\ncapable of expressing a variety of probabilistic models with nontrivial control\nflow and recursion, and that the coroutine-based computation does not introduce\nsignificant overhead in actual Bayesian inference.\n"}
+{"abstract": "  In this paper we propose a novel data augmentation approach for visual\ncontent domains that have scarce training datasets, compositing synthetic 3D\nobjects within real scenes. We show the performance of the proposed system in\nthe context of object detection in thermal videos, a domain where 1) training\ndatasets are very limited compared to visible spectrum datasets and 2) creating\nfull realistic synthetic scenes is extremely cumbersome and expensive due to\nthe difficulty in modeling the thermal properties of the materials of the\nscene. We compare different augmentation strategies, including state of the art\napproaches obtained through RL techniques, the injection of simulated data and\nthe employment of a generative model, and study how to best combine our\nproposed augmentation with these other techniques.Experimental results\ndemonstrate the effectiveness of our approach, and our single-modality detector\nachieves state-of-the-art results on the FLIR ADAS dataset.\n"}
+{"abstract": "  We investigate a Sobolev map $f$ from a finite dimensional RCD space $(X,\n\\dist_X, \\meas_X)$ to a finite dimensional non-collapsed compact RCD space $(Y,\n\\dist_Y, \\mathcal{H}^N)$. If the image $f(X)$ is smooth in a weak sense (which\nis satisfied if $f_{\\sharp}\\meas_X$ is absolutely continuous with respect to\nthe Hausdorff measure $\\mathcal{H}^N$, or if $(Y, \\dist_Y, \\mathcal{H}^N)$ is\nsmooth in a weak sense), then the pull-back $f^*g_Y$ of the Riemannian metric\n$g_Y$ of $(Y, \\dist_Y, \\mathcal{H}^N)$ is well-defined as an $L^1$-tensor on\n$X$, the minimal weak upper gradient $G_f$ of $f$ can be written by using\n$f^*g_Y$, and it coincides with the local slope for $\\meas_X$-almost everywhere\npoints in $X$ when $f$ is Lipschitz. In particular the last statement gives a\nnonlinear analogue of Cheeger's differentiability theorem for Lipschitz\nfunctions on metric measure spaces. Moreover these results allow us to define\nthe energy of $f$. The energy coincides with the Korevaar-Schoen energy.In\norder to achieve this, we use a smoothing of $g_Y$ via the heat kernel\nembedding $\\Phi_t:Y \\hookrightarrow L^2(Y, \\mathcal{H}^N)$, which is\nestablished by Ambrosio-Portegies-Tewodrose and the first named author.\nMoreover we improve the regularity of $\\Phi_t$, which plays a key role. We show\nalso that $(Y, \\dist_Y)$ is isometric to the $N$-dimensional standard unit\nsphere in $\\mathbb{R}^{N+1}$ and $f$ is a minimal isometric immersion if and\nonly if $(X, \\dist_X, \\meas_X)$ is non-collapsed up to a multiplication of a\nconstant to $\\meas_X$, and $f$ is an eigenmap whose eigenvalues coincide with\nthe essential dimension of $(X, \\dist_X, \\meas_X)$, which gives a positive\nanswer to a remaining problem from a previous work by the first named author.\n"}
+{"abstract": "  Graph-based analyses have gained a lot of relevance in the past years due to\ntheir high potential in describing complex systems by detailing the actors\ninvolved, their relations and their behaviours. Nevertheless, in scenarios\nwhere these aspects are evolving over time, it is not easy to extract valuable\ninformation or to characterize correctly all the actors. In this study, a two\nphased approach for exploiting the potential of graph structures in the\ncybersecurity domain is presented. The main idea is to convert a network\nclassification problem into a graph-based behavioural one. We extract these\ngraph structures that can represent the evolution of both normal and attack\nentities and apply a temporal dissection approach in order to highlight their\nmicro-dynamics. Further, three clustering techniques are applied to the normal\nentities in order to aggregate similar behaviours, mitigate the imbalance\nproblem and reduce noisy data. Our approach suggests the implementation of two\npromising deep learning paradigms for entity classification based on Graph\nConvolutional Networks.\n"}
+{"abstract": "  Within Transformer, self-attention is the key module to learn powerful\ncontext-aware representations. However, self-attention suffers from quadratic\nmemory requirements with respect to the sequence length, which limits us to\nprocess longer sequence on GPU. In this work, we propose sequence parallelism,\na memory efficient parallelism method to help us break input sequence length\nlimitation and train with longer sequence on GPUs. Compared with existing\nparallelism, our approach no longer requires a single device to hold the whole\nsequence. Specifically, we split the input sequence into multiple chunks and\nfeed each chunk into its corresponding device (i.e. GPU). To compute the\nattention output, we communicate attention embeddings among GPUs. Inspired by\nring all-reduce, we integrated ring-style communication with self-attention\ncalculation and proposed Ring Self-Attention (RSA). Our implementation is fully\nbased on PyTorch. Without extra compiler or library changes, our approach is\ncompatible with data parallelism and pipeline parallelism. Experiments show\nthat sequence parallelism performs well when scaling with batch size and\nsequence length. Compared with tensor parallelism, our approach achieved\n$13.7\\times$ and $3.0\\times$ maximum batch size and sequence length\nrespectively when scaling up to 64 NVIDIA P100 GPUs. We plan to integrate our\nsequence parallelism with data, pipeline and tensor parallelism to further\ntrain large-scale models with 4D parallelism in our future work.\n"}
+{"abstract": "  Even as the understanding of the mechanism behind correlated insulating\nstates in magic-angle twisted bilayer graphene converges towards various kinds\nof spontaneous symmetry breaking, the metallic \"normal state\" above the\ninsulating transition temperature remains mysterious, with its excessively high\nentropy and linear-in-temperature resistivity. In this work, we focus on the\neffects of fluctuations of the order-parameters describing correlated\ninsulating states at integer fillings of the low-energy flat bands on charge\ntransport. Motivated by the observation of heterogeneity in the order-parameter\nlandscape at zero magnetic field in certain samples, we conjecture the\nexistence of frustrating extended range interactions in an effective Ising\nmodel of the order-parameters on a triangular lattice. The competition between\nshort-distance ferromagnetic interactions and frustrating extended range\nantiferromagnetic interactions leads to an emergent length scale that forms\nstripe-like mesoscale domains above the ordering transition. The gapless\nfluctuations of these heterogeneous configurations are found to be responsible\nfor the linear-in-temperature resistivity as well as the enhanced low\ntemperature entropy. Our insights link experimentally observed\nlinear-in-temperature resistivity and enhanced entropy to the strength of\nfrustration, or equivalently, to the emergence of mesoscopic length scales\ncharacterizing order-parameter domains.\n"}
+{"abstract": "  The Controller Area Network (CAN) bus works as an important protocol in the\nreal-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust\narchitecture. The risk of IVN devices has still been insecure and vulnerable\ndue to the complex data-intensive architectures which greatly increase the\naccessibility to unauthorized networks and the possibility of various types of\ncyberattacks. Therefore, the detection of cyberattacks in IVN devices has\nbecome a growing interest. With the rapid development of IVNs and evolving\nthreat types, the traditional machine learning-based IDS has to update to cope\nwith the security requirements of the current environment. Nowadays, the\nprogression of deep learning, deep transfer learning, and its impactful outcome\nin several areas has guided as an effective solution for network intrusion\ndetection. This manuscript proposes a deep transfer learning-based IDS model\nfor IVN along with improved performance in comparison to several other existing\nmodels. The unique contributions include effective attribute selection which is\nbest suited to identify malicious CAN messages and accurately detect the normal\nand abnormal activities, designing a deep transfer learning-based LeNet model,\nand evaluating considering real-world data. To this end, an extensive\nexperimental performance evaluation has been conducted. The architecture along\nwith empirical analyses shows that the proposed IDS greatly improves the\ndetection accuracy over the mainstream machine learning, deep learning, and\nbenchmark deep transfer learning models and has demonstrated better performance\nfor real-time IVN security.\n"}
+{"abstract": "  Symmetry is among the most fundamental and powerful concepts in nature, whose\nexistence is usually taken as given, without explanation. We explore whether\nsymmetry can be derived from more fundamental principles from the perspective\nof quantum information. Starting with a two-qubit system, we show there are\nonly two minimally entangling logic gates: the Identity and the SWAP, where\nSWAP interchanges the two states of the qubits. We further demonstrate that,\nwhen viewed as an entanglement operator in the spin-space, the $S$-matrix in\nthe two-body scattering of fermions in the $s$-wave channel is uniquely\ndetermined by unitarity and rotational invariance to be a linear combination of\nthe Identity and the SWAP. Realizing a minimally entangling $S$-matrix would\ngive rise to global symmetries, as exemplified in Wigner's spin-flavor symmetry\nand Schr\\\"odinger's conformal invariance in low energy Quantum Chromodynamics.\nFor $N_q$ species of qubit, the Identity gate is associated with an\n$[SU(2)]^{N_q}$ symmetry, which is enlarged to $SU(2N_q)$ when there is a\nspecies-universal coupling constant.\n"}
+{"abstract": "  The presence of A/F-type {\\it Kepler} hybrid stars extending across the\nentire $\\delta$ Sct-$\\gamma$ Dor instability strips and beyond remains largely\nunexplained. In order to better understand these particular stars, we performed\na multi-epoch spectroscopic study of 49 candidate A/F-type hybrid stars and one\ncool(er) hybrid object detected by the {\\it Kepler} mission. We determined a\nlower limit of 27 % for the multiplicity fraction. For six spectroscopic\nsystems, we also reported long-term variations of the time delays. For four\nsystems, the time delay variations are fully coherent with those of the radial\nvelocities and can be attributed to orbital motion. We aim to improve the\norbital solutions for those systems with long orbital periods (order of 4-6\nyears) among the {\\it Kepler} hybrid stars. The orbits are computed based on a\nsimultaneous modelling of the RVs obtained with high-resolution spectrographs\nand the photometric time delays derived from time-dependent frequency analyses\nof the {\\it Kepler} light curves. We refined the orbital solutions of four\nspectroscopic systems with A/F-type {\\it Kepler} hybrid component stars: KIC\n4480321, 5219533, 8975515 and KIC 9775454. Simultaneous modelling of both data\ntypes analysed together enabled us to improve the orbital solutions, obtain\nmore robust and accurate information on the mass ratio, and identify the\ncomponent with the short-period $\\delta$ Sct-type pulsations. In several cases,\nwe were also able to derive new constraints for the minimum component masses.\nFrom a search for regular frequency patterns in the high-frequency regime of\nthe Fourier transforms of each system, we found no evidence of tidal splitting\namong the triple systems with close (inner) companions. However, some systems\nexhibit frequency spacings which can be explained by the mechanism of\nrotational splitting.\n"}
+{"abstract": "  Multi-type recurrent events are often encountered in medical applications\nwhen two or more different event types could repeatedly occur over an\nobservation period. For example, patients may experience recurrences of\nmulti-type nonmelanoma skin cancers in a clinical trial for skin cancer\nprevention. The aims in those applications are to characterize features of the\nmarginal processes, evaluate covariate effects, and quantify both the\nwithin-subject recurrence dependence and the dependence among different event\ntypes. We use copula-frailty models to analyze correlated recurrent events of\ndifferent types. Parameter estimation and inference are carried out by using a\nMonte Carlo expectation-maximization (MCEM) algorithm, which can handle a\nrelatively large (i.e., three or more) number of event types. Performances of\nthe proposed methods are evaluated via extensive simulation studies. The\ndeveloped methods are used to model the recurrences of skin cancer with\ndifferent types.\n"}
+{"abstract": "  We introduce AOT, an anonymous communication system based on mix network\narchitecture that uses oblivious transfer (OT) to deliver messages. Using OT to\ndeliver messages helps AOT resist blending ($n-1$) attacks and helps AOT\npreserve receiver anonymity, even if a covert adversary controls all nodes in\nAOT. AOT comprises three levels of nodes, where nodes at each level perform a\ndifferent function and can scale horizontally. The sender encrypts their\npayload and a tag, derived from a secret shared between the sender and\nreceiver, with the public key of a Level-2 node and sends them to a Level-1\nnode. On a public bulletin board, Level-3 nodes publish tags associated with\nmessages ready to be retrieved. Each receiver checks the bulletin board,\nidentifies tags, and receives the associated messages using OT. A receiver can\nreceive their messages even if the receiver is offline when messages are ready.\nThrough what we call a \"handshake\" process, communicants can use the AOT\nprotocol to establish shared secrets anonymously. Users play an active role in\ncontributing to the unlinkability of messages: periodically, users initiate\nrequests to AOT to receive dummy messages, such that an adversary cannot\ndistinguish real and dummy requests.\n"}
+{"abstract": "  With the continuous rise of malicious campaigns and the exploitation of new\nattack vectors, it is necessary to assess the efficacy of the defensive\nmechanisms used to detect them. To this end, the contribution of our work is\ntwofold. First, it introduces a new method for obfuscating malicious code to\nbypass all static checks of multi-engine scanners, such as VirusTotal.\nInterestingly, our approach to generating the malicious executables is not\nbased on introducing a new packer but on the augmentation of the capabilities\nof an existing and widely used tool for packaging Python, PyInstaller but can\nbe used for all similar packaging tools. As we prove, the problem is deeper and\ninherent in almost all antivirus engines and not PyInstaller specific. Second,\nour work exposes significant issues of well-known sandboxes that allow malware\nto evade their checks. As a result, we show that stealth and evasive malware\ncan be efficiently developed, bypassing with ease state of the art malware\ndetection tools without raising any alert.\n"}
+{"abstract": "  We present a toolkit of directed distances between quantile functions. By\nemploying this, we solve some new optimal transport (OT) problems which e.g.\nconsiderably flexibilize some prominent OTs expressed through Wasserstein\ndistances.\n"}
+{"abstract": "  Creating safe concurrent algorithms is challenging and error-prone. For this\nreason, a formal verification framework is necessary especially when those\nconcurrent algorithms are used in safety-critical systems. The goal of this\nguide is to provide resources for beginners to get started in their journey of\nformal verification using the powerful tool Iris. The difference between this\nguide and many others is that it provides (i) an in-depth explanation of\nexamples and tactics, (ii) an explicit discussion of separation logic, and\n(iii) a thorough coverage of Iris and Coq. References to other guides and to\npapers are included throughout to provide readers with resources through which\nto continue their learning.\n"}
+{"abstract": "  We consider the problem of identity testing of Markov chains based on a\nsingle trajectory of observations under the distance notion introduced by\nDaskalakis et al. [2018a] and further analyzed by Cherapanamjeri and Bartlett\n[2019]. Both works made the restrictive assumption that the Markov chains under\nconsideration are symmetric. In this work we relax the symmetry assumption to\nthe more natural assumption of reversibility, still assuming that both the\nreference and the unknown Markov chains share the same stationary distribution.\n"}
+{"abstract": "  This article considers the optimal control of the SIR model with both\ntransmission and treatment uncertainty. It follows the model presented in Gatto\nand Schellhorn (2021). We make four significant improvements on the latter\npaper. First, we prove the existence of a solution to the model. Second, our\ninterpretation of the control is more realistic: while in Gatto and Schellhorn\nthe control $\\alpha$ is the proportion of the population that takes a basic\ndose of treatment, so that $\\alpha >1$ occurs only if some patients take more\nthan a basic dose, in our paper, $\\alpha$ is constrained between zero and one,\nand represents thus the proportion of the population undergoing treatment.\nThird, we provide a complete solution for the moderate infection regime (with\nconstant treatment). Finally, we give a thorough interpretation of the control\nin the moderate infection regime, while Gatto and Schellhorn focussed on the\ninterpretation of the low infection regime. Finally, we compare the efficiency\nof our control to curb the COVID-19 epidemic to other types of control.\n"}
+{"abstract": "  Double sided auctions are widely used in financial markets to match demand\nand supply. Prior works on double sided auctions have focused primarily on\nsingle quantity trade requests. We extend various notions of double sided\nauctions to incorporate multiple quantity trade requests and provide fully\nformalized matching algorithms for double sided auctions with their correctness\nproofs. We establish new uniqueness theorems that enable automatic detection of\nviolations in an exchange program by comparing its output with that of a\nverified program. All proofs are formalized in the Coq proof assistant without\nadding any axiom to the system. We extract verified OCaml and Haskell programs\nthat can be used by the exchanges and the regulators of the financial markets.\nWe demonstrate the practical applicability of our work by running the verified\nprogram on real market data from an exchange to automatically check for\nviolations in the exchange algorithm.\n"}
+{"abstract": "  In this paper, we study the Combinatorial Pure Exploration problem with the\nBottleneck reward function (CPE-B) under the fixed-confidence (FC) and\nfixed-budget (FB) settings. In CPE-B, given a set of base arms and a collection\nof subsets of base arms (super arms) following a certain combinatorial\nconstraint, a learner sequentially plays a base arm and observes its random\nreward, with the objective of finding the optimal super arm with the maximum\nbottleneck value, defined as the minimum expected reward of the base arms\ncontained in the super arm. CPE-B captures a variety of practical scenarios\nsuch as network routing in communication networks, and its \\emph{unique\nchallenges} fall on how to utilize the bottleneck property to save samples and\nachieve the statistical optimality. None of the existing CPE studies (most of\nthem assume linear rewards) can be adapted to solve such challenges, and thus\nwe develop brand-new techniques to handle them. For the FC setting, we propose\nnovel algorithms with optimal sample complexity for a broad family of instances\nand establish a matching lower bound to demonstrate the optimality (within a\nlogarithmic factor). For the FB setting, we design an algorithm which achieves\nthe state-of-the-art error probability guarantee and is the first to run\nefficiently on fixed-budget path instances, compared to existing CPE\nalgorithms. Our experimental results on the top-$k$, path and matching\ninstances validate the empirical superiority of the proposed algorithms over\ntheir baselines.\n"}
+{"abstract": "  The visible light communication (VLC) by LED is one of the important\ncommunication methods because LED can work as high speed and VLC sends the\ninformation by high flushing LED. We use the pulse wave modulation for the VLC\nwith LED because LED can be controlled easily by the microcontroller, which has\nthe digital output pins. At the pulse wave modulation, deciding the high and\nlow voltage by the middle voltage when the receiving signal level is amplified\nis equal to deciding it by the threshold voltage without amplification. In this\npaper, we proposed two methods that adjust the threshold value using counting\nthe slot number and measuring the signal level. The number of signal slots is\nconstant per one symbol when we use Pulse Position Modulation (PPM). If the\nnumber of received signal slots per one symbol time is less than the\ntheoretical value, that means the threshold value is higher than the optimal\nvalue. If it is more than the theoretical value, that means the threshold value\nis lower. So, we can adjust the threshold value using the number of received\nsignal slots. At the second proposed method, the average received signal level\nis not equal to the signal level because there is a ratio between the number of\nhigh slots and low slots. So, we can calculate the threshold value from the\naverage received signal level and the slot ratio. We show these performances as\nreal experiments.\n"}
+{"abstract": "  Numerous works have been proposed to generate random graphs preserving the\nsame properties as real-life large scale networks. However, many real networks\nare better represented by hypergraphs. Few models for generating random\nhypergraphs exist and no general model allows to both preserve a power-law\ndegree distribution and a high modularity indicating the presence of\ncommunities. We present a dynamic preferential attachment hypergraph model\nwhich features partition into communities. We prove that its degree\ndistribution follows a power-law and we give theoretical lower bounds for its\nmodularity. We compare its characteristics with a real-life co-authorship\nnetwork and show that our model achieves good performances. We believe that our\nhypergraph model will be an interesting tool that may be used in many research\ndomains in order to reflect better real-life phenomena.\n"}
+{"abstract": "  We introduce $\\varepsilon$-approximate versions of the notion of Euclidean\nvector bundle for $\\varepsilon \\geq 0$, which recover the classical notion of\nEuclidean vector bundle when $\\varepsilon = 0$. In particular, we study\n\\v{C}ech cochains with coefficients in the orthogonal group that satisfy an\napproximate cocycle condition. We show that $\\varepsilon$-approximate vector\nbundles can be used to represent classical vector bundles when $\\varepsilon >\n0$ is sufficiently small. We also introduce distances between approximate\nvector bundles and use them to prove that sufficiently similar approximate\nvector bundles represent the same classical vector bundle. This gives a way of\nspecifying vector bundles over finite simplicial complexes using a finite\namount of data, and also allows for some tolerance to noise when working with\nvector bundles in an applied setting. As an example, we prove a reconstruction\ntheorem for vector bundles from finite samples. We give algorithms for the\neffective computation of low-dimensional characteristic classes of vector\nbundles directly from discrete and approximate representations and illustrate\nthe usage of these algorithms with computational examples.\n"}
+{"abstract": "  Many Gibbs measures with mean field interactions are known to be chaotic, in\nthe sense that any collection of $k$ particles in the $n$-particle system are\nasymptotically independent, as $n\\to\\infty$ with $k$ fixed or perhaps $k=o(n)$.\nThis paper quantifies this notion for a class of continuous Gibbs measures on\nEuclidean space with pairwise interactions, with main examples being systems\ngoverned by convex interactions and uniformly convex confinement potentials.\nThe distance between the marginal law of $k$ particles and its limiting product\nmeasure is shown to be $O((k/n)^{c \\wedge 2})$, with $c$ proportional to the\nsquared temperature. In the high temperature case, this improves upon prior\nresults based on subadditivity of entropy, which yield $O(k/n)$ at best. The\nbound $O((k/n)^2)$ cannot be improved, as a Gaussian example demonstrates. The\nresults are non-asymptotic, and distance is quantified via relative Fisher\ninformation, relative entropy, or the squared quadratic Wasserstein metric. The\nmethod relies on an a priori functional inequality for the limiting measure,\nused to derive an estimate for the $k$-particle distance in terms of the\n$(k+1)$-particle distance.\n"}
+{"abstract": "  We have designed and fabricated a microfluidic-based platform for sensing\nmechanical forces generated by cardiac microtissues in a highly-controlled\nmicroenvironment. Our fabrication approach combines Direct Laser Writing (DLW)\nlithography with soft lithography. At the center of our platform is a\ncylindrical volume, divided into two chambers by a cylindrical\npolydimethylsiloxane (PDMS) shell. Cells are seeded into the inner chamber from\na top opening, and the microtissue assembles onto tailor-made attachment sites\non the inner walls of the cylindrical shell. The outer chamber is electrically\nand fluidically isolated from the inner one by the cylindrical shell and is\ndesigned for actuation and sensing purposes. Externally applied pressure waves\nto the outer chamber deform parts of the cylindrical shell and thus allow us to\nexert time-dependent forces on the microtissue. Oscillatory forces generated by\nthe microtissue similarly deform the cylindrical shell and change the volume of\nthe outer chamber, resulting in measurable electrical conductance changes. We\nhave used this platform to study the response of cardiac microtissues derived\nfrom human induced pluripotent stem cells (hiPSC) under prescribed mechanical\nloading and pacing.\n"}
+{"abstract": "  In this work, we introduce a new preprocessing step applicable to UAV bird's\neye view imagery, which we call Adaptive Resizing. It is constructed to adjust\nthe vast variances in objects' scales, which are naturally inherent to UAV data\nsets. Furthermore, it improves inference speed by four to five times on\naverage. We test this extensively on UAVDT, VisDrone, and on a new data set, we\ncaptured ourselves. On UAVDT, we achieve more than 100 % relative improvement\nin AP50. Moreover, we show how this method can be applied to a general UAV\nobject detection task. Additionally, we successfully test our method on a\ndomain transfer task where we train on some interval of altitudes and test on a\ndifferent one. Code will be made available at our website.\n"}
+{"abstract": "  Our goal in this work is to present some mean value type theorems that are\nnot studied in classic calculus and analysis courses. They are simple theorems\nyet with large applicability in mathematical analysis (for example, in the\nstudy of functional equations and integral operators), computational\nmathematics, economics among other areas.\n"}
+{"abstract": "  Depression is a public health issue which severely affects one's well being\nand cause negative social and economic effect for society. To rise awareness of\nthese problems, this publication aims to determine if long lasting effects of\ndepression can be determined from electoencephalographic (EEG) signals. The\narticle contains accuracy comparison for SVM, LDA, NB, kNN and D3 binary\nclassifiers which were trained using linear (relative band powers, APV, SASI)\nand non-linear (HFD, LZC, DFA) EEG features. The age and gender matched dataset\nconsisted of 10 healthy subjects and 10 subjects with depression diagnosis at\nsome point in their lifetime. Several of the proposed feature selection and\nclassifier combinations reached accuracy of 90% where all models where\nevaluated using 10-fold cross validation and averaged over 100 repetitions with\nrandom sample permutations.\n"}
+{"abstract": "  The amount and variety of data is increasing drastically for several years.\nThese data are often represented as networks, which are then explored with\napproaches arising from network theory. Recent years have witnessed the\nextension of network exploration methods to leverage more complex and richer\nnetwork frameworks. Random walks, for instance, have been extended to explore\nmultilayer networks. However, current random walk approaches are limited in the\ncombination and heterogeneity of network layers they can handle. New analytical\nand numerical random walk methods are needed to cope with the increasing\ndiversity and complexity of multilayer networks. We propose here MultiXrank, a\nPython package that enables Random Walk with Restart (RWR) on any kind of\nmultilayer network with an optimized implementation. This package is supported\nby a universal mathematical formulation of the RWR. We evaluated MultiXrank\nwith leave-one-out cross-validation and link prediction, and introduced\nprotocols to measure the impact of the addition or removal of multilayer\nnetwork data on prediction performances. We further measured the sensitivity of\nMultiXrank to input parameters by in-depth exploration of the parameter space.\nFinally, we illustrate the versatility of MultiXrank with different use-cases\nof unsupervised node prioritization and supervised classification in the\ncontext of human genetic diseases.\n"}
+{"abstract": "  We consider the point evaluation of the solution to interface problems with\ngeometric uncertainties, where the uncertainty in the obstacle is described by\na high-dimensional parameter $\\boldsymbol{y}\\in[-1,1]^d$, $d\\in\\mathbb{N}$. We\nfocus in particular on an elliptic interface problem and a Helmholtz\ntransmission problem. Point values of the solution in the physical domain\ndepend in general non-smoothly on the high-dimensional parameter, posing a\nchallenge when one is interested in building surrogates. Indeed, high-order\nmethods show poor convergence rates, while methods which are able to track\ndiscontinuities usually suffer from the so-called curse of dimensionality. For\nthis reason, in this work we propose to build surrogates for point evaluation\nusing deep neural networks. We provide a theoretical justification for why we\nexpect neural networks to provide good surrogates. Furthermore, we present\nextensive numerical experiments showing their good performance in practice. We\nobserve in particular that neural networks do not suffer from the curse of\ndimensionality, and we study the dependence of the error on the number of point\nevaluations (that is, the number of discontinuities in the parameter space), as\nwell as on several modeling parameters, such as the contrast between the two\nmaterials and, for the Helmholtz transmission problem, the wavenumber.\n"}
+{"abstract": "  Starting from 2003, a large number of the so-called exotic hadrons, such as\n$X(3872)$ and $D_{s0}^*(2317)$, were discovered experimentally. Since then,\nunderstanding the nature of these states has been a central issue both\ntheoretically and experimentally. As many of these states are located close to\ntwo hadron thresholds, they are believed to be molecular states or at least\ncontain large molecular components. We argue that if they are indeed molecular\nstates, in the way that the deuteron is a bound state of proton and neutron,\nthen molecular states of three or more hadrons are likely, in the sense that\natomic nuclei are bound states of nucleons. Following this conjecture, we study\nthe likely existence of $DDK$, $D\\bar{D}K$, and $D\\bar{D}^{*}K$ molecular\nstates. We show that within the theoretical uncertainties of the two-body\ninteractions deduced, they most likely exist. Furthermore, we predict their\nstrong decays to help guide future experimental searches. In addition, we show\nthat the same approach can indeed reproduce some of the known three-body\nsystems from the two-body inputs, such as the deuteron-triton and the\n$\\Lambda(1405)$-$\\bar{K}NN$ systems.\n"}
+{"abstract": "  The integration of energy systems such as electricity and gas grids and power\nand thermal grids can bring significant benefits in terms of system security,\nreliability, and reduced emissions. Another alternative coupling of sectors\nwith large potential benefits is the power and transportation networks. This is\nprimarily due to the increasing use of electric vehicles (EV) and their demand\non the power grid. Besides, the production and operating costs of EVs and\nbattery technologies are steadily decreasing, while tax credits for EV purchase\nand usage are being offered to users in developed countries. The power grid is\nalso undergoing major upgrades and changes with the aim of ensuring\nenvironmentally sustainable grids. These factors influence our work. We present\na new operating model for an integrated EV-grid system that incorporates a set\nof aggregators (owning a fleet of EVs) with partial access to the distribution\ngrid. Then, the Cooperative Game Theory is used to model the behavior of the\nsystem. The Core is used to describe the stability of the interaction between\nthese aggregators, and the Shapley value is used to assign costs to them. The\nresults obtained show the benefit of cooperation, which could lead to an\noverall reduction in energy consumption, reduced operating costs for electric\nvehicles and the distribution grid, and, in some cases, the additional monetary\nbudget available to reinforce the transmission and grid infrastructures.\n"}
+{"abstract": "  We investigate classes of shear-free cosmological dust models with\nirrotational fluid flows within the framework of $f(T)$ gravity. In particular,\nwe use the $1 + 3$ covariant formalism and present the covariant linearised\nevolution and constraint equations describing such models. We then derive the\nintegrability conditions describing a consistent evolution of the linearised\nfield equations of these quasi-Newtonian universes in the $f(T)$ gravitational\ntheory. Finally, we derive the evolution equations for the density and velocity\nperturbations of the quasi-Newtonian universe. We explore the behaviour of the\nmatter density contrast for two models - $f(T)= \\mu T_{0}(T/T_{0})^{n}$ and the\nmore generalised case, where $f(T)= T+ \\mu T_{0} (T/T_{0})^{n}$, with and\nwithout the application of the quasi-static approximation. Our numerical\nsolutions show that these $f(T)$ theories can be suitable alternatives to study\nthe background dynamics, whereas the growth of energy density fluctuations\nchange dramatically from the expected $\\Lambda$CDM behaviour even for small\ndeviations away from the general relativistic limits of the underlying $f(T)$\ntheory. Moreover, applying the so-called quasi-static approximation yields\nexact-solution results that are orders of magnitude different from the\nnumerically integrated solutions of the full system, suggesting that these\napproximations are not applicable here.\n"}
+{"abstract": "  Dual-energy X-ray tomography is considered in a context where the target\nunder imaging consists of two distinct materials. The materials are assumed to\nbe possibly intertwined in space, but at any given location there is only one\nmaterial present. Further, two X-ray energies are chosen so that there is a\nclear difference in the spectral dependence of the attenuation coefficients of\nthe two materials. A novel regularizer is presented for the inverse problem of\nreconstructing separate tomographic images for the two materials. A combination\nof two things, (a) non-negativity constraint, and (b) penalty term containing\nthe inner product between the two material images, promotes the presence of at\nmost one material in a given pixel. A preconditioned interior point method is\nderived for the minimization of the regularization functional. Numerical tests\nwith digital phantoms suggest that the new algorithm outperforms the baseline\nmethod, Joint Total Variation regularization, in terms of correctly\nmaterial-characterized pixels. While the method is tested only in a\ntwo-dimensional setting with two materials and two energies, the approach\nreadily generalizes to three dimensions and more materials. The number of\nmaterials just needs to match the number of energies used in imaging.\n"}
+{"abstract": "  We consider random Hermitian matrices with independent upper triangular\nentries. Wigner's semicircle law says that under certain additional\nassumptions, the empirical spectral distribution converges to the semicircle\ndistribution. We characterize convergence to semicircle in terms of the\nvariances of the entries, under natural assumptions such as the Lindeberg\ncondition. The result extends to certain matrices with entries having infinite\nsecond moments. As a corollary, another characterization of semicircle\nconvergence is given in terms of convergence in distribution of the row sums to\nthe standard normal distribution.\n"}
+{"abstract": "  With strong marketing advocacy of the benefits of cannabis use for improved\nmental health, cannabis legalization is a priority among legislators. However,\npreliminary scientific research does not conclusively associate cannabis with\nimproved mental health. In this study, we explore the relationship between\ndepression and consumption of cannabis in a targeted social media corpus\ninvolving personal use of cannabis with the intent to derive its potential\nmental health benefit. We use tweets that contain an association among three\ncategories annotated by domain experts - Reason, Effect, and Addiction. The\nstate-of-the-art Natural Langauge Processing techniques fall short in\nextracting these relationships between cannabis phrases and the depression\nindicators. We seek to address the limitation by using domain knowledge;\nspecifically, the Drug Abuse Ontology for addiction augmented with Diagnostic\nand Statistical Manual of Mental Disorders lexicons for mental health. Because\nof the lack of annotations due to the limited availability of the domain\nexperts' time, we use supervised contrastive learning in conjunction with GPT-3\ntrained on a vast corpus to achieve improved performance even with limited\nsupervision. Experimental results show that our method can significantly\nextract cannabis-depression relationships better than the state-of-the-art\nrelation extractor. High-quality annotations can be provided using a nearest\nneighbor approach using the learned representations that can be used by the\nscientific community to understand the association between cannabis and\ndepression better.\n"}
+{"abstract": "  Sequential recommendation aims to leverage users' historical behaviors to\npredict their next interaction. Existing works have not yet addressed two main\nchallenges in sequential recommendation. First, user behaviors in their rich\nhistorical sequences are often implicit and noisy preference signals, they\ncannot sufficiently reflect users' actual preferences. In addition, users'\ndynamic preferences often change rapidly over time, and hence it is difficult\nto capture user patterns in their historical sequences. In this work, we\npropose a graph neural network model called SURGE (short for SeqUential\nRecommendation with Graph neural nEtworks) to address these two issues.\nSpecifically, SURGE integrates different types of preferences in long-term user\nbehaviors into clusters in the graph by re-constructing loose item sequences\ninto tight item-item interest graphs based on metric learning. This helps\nexplicitly distinguish users' core interests, by forming dense clusters in the\ninterest graph. Then, we perform cluster-aware and query-aware graph\nconvolutional propagation and graph pooling on the constructed graph. It\ndynamically fuses and extracts users' current activated core interests from\nnoisy user behavior sequences. We conduct extensive experiments on both public\nand proprietary industrial datasets. Experimental results demonstrate\nsignificant performance gains of our proposed method compared to\nstate-of-the-art methods. Further studies on sequence length confirm that our\nmethod can model long behavioral sequences effectively and efficiently.\n"}
+{"abstract": "  The multiple lobes of high order Hermite-Gaussian (HG) laser modes differ in\nterms of shape, size, and optical energy distribution. Here, we introduce a\ngeneric numerical method that redistributes optical energy among the lobes of\nhigh order HG modes such that all the identical low intense lobes become both\nmoderate or high intense lobes and vice-versa, in a controlled manner. Further,\nthe modes which consist of only two types of intensity distribution among its\nmultiple lobes are transformed together into all high intense lobes.\nFurthermore, in some cases, moderate intense lobes together with high intense\nlobes become high intense lobes, and moderate intense lobes together with low\nintense lobes become high intense lobes. Such controlled modulation of optical\nenergy may offer efficient and selective utilization of each lobe of HG modes\nin most applications like particle manipulation, optical lithography, and the\nmethod can be used in other fields like nonlinear frequency conversion and\nshaping ultrafast optical pulses.\n"}
+{"abstract": "  Internet of Things (IoT) is now omnipresent in all aspects of life and\nprovides a large number of potentially critical services. For this, Internet of\nThings relies on the data collected by objects. Data integrity is therefore\nessential. Unfortunately, this integrity is threatened by a type of attack\nknown as False Data Injection Attack. This consists of an attacker who injects\nfabricated data into a system to modify its behaviour. In this work, we dissect\nand present a method that uses a Domain-Specific Language (DSL) to generate\naltered data, allowing these attacks to be simulated and tested.\n"}
+{"abstract": "  We consider the problem of empirical risk minimization given a database,\nusing the gradient descent algorithm. We note that the function to be optimized\nmay be non-convex, consisting of saddle points which impede the convergence of\nthe algorithm. A perturbed gradient descent algorithm is typically employed to\nescape these saddle points. We show that this algorithm, that perturbs the\ngradient, inherently preserves the privacy of the data. We then employ the\ndifferential privacy framework to quantify the privacy hence achieved. We also\nanalyze the change in privacy with varying parameters such as problem dimension\nand the distance between the databases.\n"}
+{"abstract": "  In situ generation of a high-energy, high-current, spin-polarized electron\nbeam is an outstanding scientific challenge to the development of plasma-based\naccelerators for high-energy colliders. In this Letter we show how such a\nspin-polarized relativistic beam can be produced by ionization injection of\nelectrons of certain atoms with a circularly polarized laser field into a\nbeam-driven plasma wakefield accelerator, providing a much desired one-step\nsolution to this challenge. Using time-dependent Schr\\\"odinger equation (TDSE)\nsimulations, we show the propensity rule of spin-dependent ionization of xenon\natoms can be reversed in the strong-field multi-photon regime compared with the\nnon-adiabatic tunneling regime, leading to high total spin-polarization.\nFurthermore, three-dimensional particle-in-cell (PIC) simulations are\nincorporated with TDSE simulations, providing start-to-end simulations of\nspin-dependent strong-field ionization of xenon atoms and subsequent trapping,\nacceleration, and preservation of electron spin-polarization in lithium plasma.\nWe show the generation of a high-current (0.8 kA),\nultra-low-normalized-emittance (~37 nm), and high-energy (2.7 GeV) electron\nbeam within just 11 cm distance, with up to ~31% net spin polarization. Higher\ncurrent, energy, and net spin-polarization beams are possible by optimizing\nthis concept, thus solving a long-standing problem facing the development of\nplasma accelerators.\n"}
+{"abstract": "  Angular path integration is the ability of a system to estimate its own\nheading direction from potentially noisy angular velocity (or increment)\nobservations. Non-probabilistic algorithms for angular path integration, which\nrely on a summation of these noisy increments, do not appropriately take into\naccount the reliability of such observations, which is essential for\nappropriately weighing one's current heading direction estimate against\nincoming information. In a probabilistic setting, angular path integration can\nbe formulated as a continuous-time nonlinear filtering problem (circular\nfiltering) with observed state increments. The circular symmetry of heading\ndirection makes this inference task inherently nonlinear, thereby precluding\nthe use of popular inference algorithms such as Kalman filters and rendering\nthe problem analytically inaccessible. Here, we derive an approximate solution\nto circular continuous-time filtering, which integrates state increment\nobservations while maintaining a fixed representation through both state\npropagation and observational updates. Specifically, we extend the established\nprojection-filtering method to account for observed state increments and apply\nthis framework to the circular filtering problem. We further propose a\ngenerative model for continuous-time angular-valued direct observations of the\nhidden state, which we integrate seamlessly into the projection filter.\nApplying the resulting scheme to a model of probabilistic angular path\nintegration, we derive an algorithm for circular filtering, which we term the\ncircular Kalman filter. Importantly, this algorithm is analytically accessible,\ninterpretable, and outperforms an alternative filter based on a Gaussian\napproximation.\n"}
+{"abstract": "  Semidefinite programs (SDPs) can be solved in polynomial time by interior\npoint methods. However, when the dimension of the problem gets large, interior\npoint methods become impractical both in terms of computational time and memory\nrequirements. First order methods, such as Alternating Direction Methods of\nMultipliers (ADMMs), turned out to be suitable algorithms to deal with large\nscale SDPs and gained growing attention during the past decade. In this paper,\nwe focus on an ADMM designed for SDPs in standard form and extendit to deal\nwith inequalities when solving SDPs in general form. This allows to handle SDP\nrelaxations of classical combinatorial problems such as the graph coloring\nproblem and the maximum clique problem, that we consider in our extensive\nnumerical experience. The numerical results show the comparison of the method\nproposed equipped with a post-processing procedure with the state-of-the-art\nsolver SDPNAL+.\n"}
+{"abstract": "  Non-Gaussian continuous variable states play a central role both in the\nfoundations of quantum theory and for emergent quantum technologies. In\nparticular, \"cat states\", i.e., two-component macroscopic quantum\nsuperpositions, embody quantum coherence in an accessible way and can be\nharnessed for fundamental tests and quantum information tasks alike. Degenerate\noptical parametric oscillators can naturally produce single-mode cat states and\nthus represent a promising platform for their realization and harnessing. We\nshow that a dissipative coupling between degenerate optical parametric\noscillators extends this to two-mode entangled cat states, i.e., two-mode\nentangled cat states are naturally produced under such dissipative coupling.\nWhile overcoming single-photon loss still represents a major challenge towards\nthe realization of sufficiently pure single-mode cat states in degenerate\noptical parametric oscillators, we show that the generation of two-mode\nentangled cat states under such dissipative coupling can then be achieved\nwithout additional hurdles. We numerically explore the parameter regime for the\nsuccessful generation of transient two-mode entangled cat states in two\ndissipatively coupled degenerate optical parametric oscillators. To certify the\ncat-state entanglement, we employ a tailored, variance-based entanglement\ncriterion, which can robustly detect cat-state entanglement under realistic\nconditions.\n"}
+{"abstract": "  The interpolation of spatial data can be of tremendous value in various\napplications, such as forecasting weather from only a few measurements of\nmeteorological or remote sensing data. Existing methods for spatial\ninterpolation, such as variants of kriging and spatial autoregressive models,\ntend to suffer from at least one of the following limitations: (a) the\nassumption of stationarity, (b) the assumption of isotropy, and (c) the\ntrade-off between modelling local or global spatial interaction. Addressing\nthese issues in this work, we propose the use of Markov reward processes (MRPs)\nas a spatial interpolation method, and we introduce three variants thereof: (i)\na basic static discount MRP (SD-MRP), (ii) an accurate but mostly theoretical\noptimised MRP (O-MRP), and (iii) a transferable weight prediction MRP (WP-MRP).\nAll variants of MRP interpolation operate locally, while also implicitly\naccounting for global spatial relationships in the entire system through\nrecursion. Additionally, O-MRP and WP-MRP no longer assume stationarity and are\nrobust to anisotropy. We evaluated our proposed methods by comparing the mean\nabsolute errors of their interpolated grid cells to those of 7 common\nbaselines, selected from models based on spatial autocorrelation, (spatial)\nregression, and deep learning.\n  We performed detailed evaluations on two publicly available datasets (local\nGDP values, and COVID-19 patient trajectory data). The results from these\nexperiments clearly show the competitive advantage of MRP interpolation, which\nachieved significantly lower errors than the existing methods in 23 out of 40\nexperimental conditions, or 35 out of 40 when including O-MRP.\n"}
+{"abstract": "  Metallography is crucial for a proper assessment of material's properties. It\ninvolves mainly the investigation of spatial distribution of grains and the\noccurrence and characteristics of inclusions or precipitates. This work\npresents an holistic artificial intelligence model for Anomaly Detection that\nautomatically quantifies the degree of anomaly of impurities in alloys. We\nsuggest the following examination process: (1) Deep semantic segmentation is\nperformed on the inclusions (based on a suitable metallographic database of\nalloys and corresponding tags of inclusions), producing inclusions masks that\nare saved into a separated database. (2) Deep image inpainting is performed to\nfill the removed inclusions parts, resulting in 'clean' metallographic images,\nwhich contain the background of grains. (3) Grains' boundaries are marked using\ndeep semantic segmentation (based on another metallographic database of\nalloys), producing boundaries that are ready for further inspection on the\ndistribution of grains' size. (4) Deep anomaly detection and pattern\nrecognition is performed on the inclusions masks to determine spatial, shape\nand area anomaly detection of the inclusions. Finally, the system recommends to\nan expert on areas of interests for further examination. The performance of the\nmodel is presented and analyzed based on few representative cases. Although the\nmodels presented here were developed for metallography analysis, most of them\ncan be generalized to a wider set of problems in which anomaly detection of\ngeometrical objects is desired. All models as well as the data-sets that were\ncreated for this work, are publicly available at\nhttps://github.com/Scientific-Computing-Lab-NRCN/MLography.\n"}
+{"abstract": "  The intensity of the Cosmic UV background (UVB), coming from all sources of\nionising photons such as star-forming galaxies and quasars, determines the\nthermal evolution and ionization state of the intergalactic medium (IGM) and\nis, therefore, a critical ingredient for models of cosmic structure formation.\nMost of the previous estimates are based on the comparison between observed and\nsimulated Lyman-$\\alpha$ forest. We present the results of an independent\nmethod to constrain the product of the UVB photoionisation rate and the\ncovering fraction of Lyman limit systems (LLSs) by searching for the\nfluorescent Lyman-$\\alpha$ emission produced by self-shielded clouds. Because\nthe expected surface brightness is well below current sensitivity limits for\ndirect imaging, we developed a new method based on three-dimensional stacking\nof the IGM around Lyman-$\\alpha$ emitting galaxies (LAEs) between 2.9<z<6.6\nusing deep MUSE observations. Combining our results with covering fractions of\nLLSs obtained from mock cubes extracted from the EAGLE simulation, we obtain\nnew and independent constraints on the UVB at z>3 that are consistent with\nprevious measurements, with a preference for relatively low UVB intensities at\nz=3, and which suggest a non-monotonic decrease of $\\Gamma$HI with increasing\nredshift between 3<z<5. This could suggest a possible tension between some UVB\nmodels and current observations which however require deeper and wider\nobservations in Lyman-$\\alpha$ emission and absorption to be confirmed.\nAssuming instead a value of UVB from current models, our results constrain the\ncovering fraction of LLSs at 3<z<4.5 to be less than 25% within 150kpc from\nLAEs.\n"}
+{"abstract": "  The ability to accurately detect and localize objects is recognized as being\nthe most important for the perception of self-driving cars. From 2D to 3D\nobject detection, the most difficult is to determine the distance from the\nego-vehicle to objects. Expensive technology like LiDAR can provide a precise\nand accurate depth information, so most studies have tended to focus on this\nsensor showing a performance gap between LiDAR-based methods and camera-based\nmethods. Although many authors have investigated how to fuse LiDAR with RGB\ncameras, as far as we know there are no studies to fuse LiDAR and stereo in a\ndeep neural network for the 3D object detection task. This paper presents\nSLS-Fusion, a new approach to fuse data from 4-beam LiDAR and a stereo camera\nvia a neural network for depth estimation to achieve better dense depth maps\nand thereby improves 3D object detection performance. Since 4-beam LiDAR is\ncheaper than the well-known 64-beam LiDAR, this approach is also classified as\na low-cost sensors-based method. Through evaluation on the KITTI benchmark, it\nis shown that the proposed method significantly improves depth estimation\nperformance compared to a baseline method. Also, when applying it to 3D object\ndetection, a new state of the art on low-cost sensor based method is achieved.\n"}
+{"abstract": "  We experimentally demonstrate that when three single photons transmit through\ntwo polarization channels, in a well-defined pre- and postselected ensemble,\nthere are no two photons in the same polarization channel by weak-strength\nmeasurement, a counter-intuitive quantum counting effect called quantum\npigeonhole paradox. We further show that this effect breaks down in\nsecond-order measurement. These results indicate the existence of quantum\npigeonhole paradox and its operating regime.\n"}
+{"abstract": "  HCI and NLP traditionally focus on different evaluation methods. While HCI\ninvolves a small number of people directly and deeply, NLP traditionally relies\non standardized benchmark evaluations that involve a larger number of people\nindirectly. We present five methodological proposals at the intersection of HCI\nand NLP and situate them in the context of ML-based NLP models. Our goal is to\nfoster interdisciplinary collaboration and progress in both fields by\nemphasizing what the fields can learn from each other.\n"}
+{"abstract": "  We study distribution dependent stochastic differential equation driven by a\ncontinuous process, without any specification on its law, following the\napproach initiated in [16]. We provide several criteria for existence and\nuniqueness of solutions which go beyond the classical globally Lipschitz\nsetting. In particular we show well-posedness of the equation, as well as\nalmost sure convergence of the associated particle system, for drifts\nsatisfying either Osgood-continuity, monotonicity, local Lipschitz or Sobolev\ndifferentiability type assumptions.\n"}
+{"abstract": "  We demonstrate Raman sideband thermometry of single carbyne chains confined\nin double-walled carbon nanotubes. Our results show that carbyne's record-high\nRaman scattering cross section enables anti-Stokes Raman measurements at the\nsingle chain level. Using laser irradiation as a heating source, we exploit the\ntemperature dependence of the anti-Stokes/Stokes ratio for local temperature\nsensing. Due to its molecular size and its large Raman cross section carbyne is\nan efficient probe for local temperature monitoring, with applications ranging\nfrom nanoelectronics to biology.\n"}
+{"abstract": "  In this paper we present our preliminary work on model-based behavioral\nanalysis of horse motion. Our approach is based on the SMAL model, a 3D\narticulated statistical model of animal shape. We define a novel SMAL model for\nhorses based on a new template, skeleton and shape space learned from $37$\nhorse toys. We test the accuracy of our hSMAL model in reconstructing a horse\nfrom 3D mocap data and images. We apply the hSMAL model to the problem of\nlameness detection from video, where we fit the model to images to recover 3D\npose and train an ST-GCN network on pose data. A comparison with the same\nnetwork trained on mocap points illustrates the benefit of our approach.\n"}
+{"abstract": "  This paper continues discussions in the author's previous paper about the\nMisiurewicz polynomials defined for a family of degree $d \\ge 2$ rational maps\nwith an automorphism group containing the cyclic group of order $d$. In\nparticular, we extend the sufficient conditions that the Misiurewicz\npolynomials are irreducible over $\\mathbb{Q}$. We also prove that the\nMisiurewicz polynomials always have an irreducible factor of large degree.\n"}
+{"abstract": "  If devices are physically accessible optical fault injection attacks pose a\ngreat threat since the data processed as well as the operation flow can be\nmanipulated. Successful physical attacks may lead not only to leakage of secret\ninformation such as cryptographic private keys, but can also cause economic\ndamage especially if as a result of such a manipulation a critical\ninfrastructure is successfully attacked. Laser based attacks exploit the\nsensitivity of CMOS technologies to electromagnetic radiation in the visible or\nthe infrared spectrum. It can be expected that radiation-hard designs,\nspecially crafted for space applications, are more robust not only against\nhigh-energy particles and short electromagnetic waves but also against optical\nfault injection attacks. In this work we investigated the sensitivity of\nradiation-hard JICG shift registers to optical fault injection attacks. In our\nexperiments, we were able to trigger bit-set and bit-reset repeatedly changing\nthe data stored in single JICG flip-flops despite their high-radiation fault\ntolerance.\n"}
+{"abstract": "  Geometrical chirality is a universal phenomenon that is encountered on many\ndifferent length scales ranging from geometrical shapes of various living\norganisms to protein and DNA molecules. Interaction of chiral matter with\nchiral light - that is, electromagnetic field possessing a certain handedness -\nunderlies our ability to discriminate enantiomers of chiral molecules. In this\ncontext, it is often desired to have an optical cavity that would efficiently\ncouple to only a specific (right or left) molecular enantiomer, and not couple\nto the opposite one. Here, we demonstrate a single-handedness chiral optical\ncavity supporting only an eigenmode of a given handedness without the presence\nof modes of other helicity. Resonant excitation of the cavity with light of\nappropriate handedness enables formation of a helical standing wave with a\nuniform chirality density, while the opposite handedness does not cause any\nresonant effects. Furthermore, only chiral emitters of the matching handedness\nefficiently interact with such a chiral eigenmode, enabling the\nhandedness-selective coupling light-matter strength. The proposed system\nexpands the set of tools available for investigations of chiral matter and\nopens the door to studies of chiral electromagnetic vacuum.\n"}
+{"abstract": "  In this article, we consider the estimation of the marginal distributions for\npairs of data are recorded, with unobserved order in each pair. New estimators\nare proposed and their asymptotic properties are established, by proving a\nGlivenko-Cantelli theorem and a functional central limit result. Results from a\nsimulation study are included and we illustrate the applicability of the method\non the homologous chromosomes data.\n"}
+{"abstract": "  Pitting corrosion is a much-studied and technologically relevant subject.\nHowever, the fundamental mechanisms responsible for the breakdown of the\npassivating oxide layer are still subjects of debate. Chloride anions are known\nto accelerate corrosion; relevant hypotheses include Cl insertion into\npositively charged oxygen vacancies in the oxide film, and Cl adsorption on\npassivating oxide surfaces, substituting for surface hydroxyl groups. In this\nwork, we conduct large-scale first principles modeling of explicit\nmetal/Al(2)O(3) interfaces to investigate the energetics and electronic\nstructures associated with these hypotheses. The explicit interface models\nallow electron transfer that mimics electrochemical events, and the\nestablishment of the relation between atomic structures at different interfaces\nand the electronic band alignment. For multiple model interfaces, we find that\ndoubly charged oxygen vacancies, which are key ingredients of the point defect\nmodel (PDM) often used to analyze corrosion data, can only occur in the\npresence of a potential gradient that raises the voltage. Cl-insertion into\noxide films can be energetically favorable in some oxygen vacancy sites,\ndepending on the voltage. We also discuss the challenges associated with\nexplicit DFT modeling of these complex interfaces.\n"}
+{"abstract": "  The LHC is undergoing a high luminosity upgrade, which is set to increase the\ninstantaneous luminosity by at least a factor of five, resulting in a higher\nmuon flux rate in the forward region, which will overwhelm the current trigger\nsystem of the CMS experiment. The ME0, a gas electron multiplier detector, is\nproposed for the Phase-2 Muon System Upgrade to help increase the muon\nacceptance and to control the Level 1 muon trigger rate. To lower the\nprobability of HV discharges, the ME0 was designed with GEM foils that are\nsegmented on both sides. Initial testing of the ME0 showed substantial\ncrosstalk between readout sectors. Here, we investigate, characterize, and\nquantify the crosstalk in the detector, and estimate the performance of the\nchamber as a result of this crosstalk via simulation of the detector dead time,\nefficiency loss, and frontend electronics response. The results of crosstalk\nvia signals produced by applying a square voltage pulse directly on the readout\nstrips of the detector with a pulser are summarized, and the efficacy of\nvarious mitigation strategies are presented. The crosstalk is a result of\ncapacitive coupling between the readout strips on the readout board and between\nthe readout strips and the bottom of GEM3. The crosstalk also generally follows\na pattern where the largest magnitude of crosstalk is within the same azimuthal\nreadout segment in the detector and in the nearest horizontal segments. The use\nof bypass capacitors and larger HV segments successfully reduce the crosstalk:\nwe observe a maximum decrease of crosstalk in sectors previously experiencing\ncrosstalk from $(1.66\\pm0.03)\\%$ to $(1.11\\pm0.02)\\%$ with all HV segments\nconnected in parallel on the bottom of GEM3, with an HV low-pass filter, and an\nHV divider. These mitigation strategies slightly increase crosstalk\n$\\big(\\hspace{-0.1cm}\\lessapprox 0.4\\%\\big)$ in readout sectors farther away.\n"}
+{"abstract": "  High-dimensional black-box optimisation remains an important yet notoriously\nchallenging problem. Despite the success of Bayesian optimisation methods on\ncontinuous domains, domains that are categorical, or that mix continuous and\ncategorical variables, remain challenging. We propose a novel solution -- we\ncombine local optimisation with a tailored kernel design, effectively handling\nhigh-dimensional categorical and mixed search spaces, whilst retaining sample\nefficiency. We further derive convergence guarantee for the proposed approach.\nFinally, we demonstrate empirically that our method outperforms the current\nbaselines on a variety of synthetic and real-world tasks in terms of\nperformance, computational costs, or both.\n"}
+{"abstract": "  Tournaments are a widely used mechanism to rank alternatives in a noisy\nenvironment. This paper investigates a fundamental issue of economics in\ntournament design: what is the best usage of limited resources, that is, how\nshould the alternatives be compared pairwise to best approximate their true but\nlatent ranking. We consider various formats including knockout tournaments,\nmulti-stage championships consisting of round-robin groups followed by single\nelimination, and the Swiss-system. They are evaluated via Monte-Carlo\nsimulations under six different assumptions on winning probabilities. Comparing\nthe same pair of alternatives multiple times turns out to be an inefficacious\npolicy. While seeding can increase the efficacy of the knockout and group-based\ndesigns, its influence remains marginal unless one has an unrealistically good\nestimation on the true ranking of the players. The Swiss-system is found to be\nthe most accurate among all these tournament formats, especially in its ability\nto rank all participants. A possible explanation is that it does not eliminate\na player after a single loss, while it takes the history of the comparisons\ninto account. The results can be especially interesting for emerging esports,\nwhere the tournament designs are not yet solidified.\n"}
+{"abstract": "  Context: Software startups develop innovative, software-intensive products.\nGiven the uncertainty associated with such an innovative context,\nexperimentation is a valuable approach for these companies, especially in the\nearly stages of the development, when implementing unnecessary features\nrepresents a higher risk for companies' survival. Nevertheless, researchers\nhave argued that the lack of clearly defined practices led to limited adoption\nof experimentation. In this regard, the first step is to define the hypotheses\nbased on which teams will create experiments. Objective: We aim to develop a\nsystematic technique to identify hypotheses for early-stage software startups.\nMethods: We followed a Design Science approach consisted of three cycles in the\nconstruction phase, that involved seven startups in total, and an evaluation of\nthe final artifact within three startups. Results: We developed the HyMap, a\nhypotheses elicitation technique based on cognitive mapping. It consists of a\nvisual language to depict a cognitive map representing the founder's\nunderstanding of the product, and a process to elicit this map consisted of a\nseries of questions the founder must answer. Our evaluation showed that the\nartifacts are clear, easy to use, and useful leading to hypotheses and\nfacilitating founders to visualize their idea. Conclusion: Our study\ncontributes to both descriptive and prescriptive bodies of knowledge. Regarding\nthe first, it provides a better understanding of the guidance founders use to\ndevelop their startups and, for the latter, a technique to identify hypotheses\nin early-stage software startups.\n"}
+{"abstract": "  We prove an algebraic version of the Hamilton-Tian Conjecture for all log\nFano pairs. More precisely, we show that any log Fano pair admits a canonical\ntwo-step degeneration to a reduced uniformly Ding stable triple, which admits a\nK\\\"ahler-Ricci soliton when the ground field $\\mathbb{k}=\\mathbb{C}$.\n"}
+{"abstract": "  In the text processing context, most ML models are built on word embeddings.\nThese embeddings are themselves trained on some datasets, potentially\ncontaining sensitive data. In some cases this training is done independently,\nin other cases, it occurs as part of training a larger, task-specific model. In\neither case, it is of interest to consider membership inference attacks based\non the embedding layer as a way of understanding sensitive information leakage.\nBut, somewhat surprisingly, membership inference attacks on word embeddings and\ntheir effect in other natural language processing (NLP) tasks that use these\nembeddings, have remained relatively unexplored.\n  In this work, we show that word embeddings are vulnerable to black-box\nmembership inference attacks under realistic assumptions. Furthermore, we show\nthat this leakage persists through two other major NLP applications:\nclassification and text-generation, even when the embedding layer is not\nexposed to the attacker. We show that our MI attack achieves high attack\naccuracy against a classifier model and an LSTM-based language model. Indeed,\nour attack is a cheaper membership inference attack on text-generative models,\nwhich does not require the knowledge of the target model or any expensive\ntraining of text-generative models as shadow models.\n"}
+{"abstract": "  In this work we study the time complexity for the search of local minima in\nrandom graphs whose vertices have i.i.d. cost values. We show that, for\nErd\\\"os-R\\'enyi graphs with connection probability given by $\\lambda/n^\\alpha$\n(with $\\lambda > 0$ and $0 < \\alpha < 1$), a family of local algorithms that\napproximate a gradient descent find local minima faster than the full gradient\ndescent. Furthermore, we find a probabilistic representation for the running\ntime of these algorithms leading to asymptotic estimates of the mean running\ntimes.\n"}
+{"abstract": "  Spatially separating electrons of different spins and efficiently generating\nspin currents are crucial steps towards building practical spintronics devices.\nTransverse magnetic focusing is a potential technique to accomplish both those\ntasks. In a material where there is significant Rashba spin-orbit interaction,\nelectrons of different spins will traverse different paths in the presence of\nan external magnetic field. Experiments have demonstrated the viability of this\ntechnique by measuring conductance spectra that indicate the separation of\nspin-up and spin-down electrons. However the effect that the geometry of the\nleads has on these measurements is not well understood. We show that the\nresolution of features in the conductance spectra is affected by the shape,\nseparation and width of the leads. Furthermore, the number of subbands occupied\nby the electrons in the leads affects the ratio between the amplitudes of the\nspin-split peaks in the spectra. We simulated devices with random onsite\npotentials and observed that transverse magnetic focusing devices are sensitive\nto disorder. Ultimately we show that careful choice and characterisation of\ndevice geometry is crucial for correctly interpreting the results of transverse\nmagnetic focusing experiments.\n"}
+{"abstract": "  Predicting materials properties from composition or structure is of great\ninterest to the materials science community. Deep learning has recently\ngarnered considerable interest in materials predictive tasks with low model\nerrors when dealing with large materials data. However, deep learning models\nsuffer in the small data regime that is common in materials science. Here we\nleverage the transfer learning concept and the graph network deep learning\nframework and develop the AtomSets machine learning framework for consistent\nhigh model accuracy at both small and large materials data. The AtomSets models\ncan work with both compositional and structural materials data. By combining\nwith transfer learned features from graph networks, they can achieve\nstate-of-the-art accuracy from using small compositional data (<400) to large\nstructural data (>130,000). The AtomSets models show much lower errors than the\nstate-of-the-art graph network models at small data limits and the classical\nmachine learning models at large data limits. They also transfer better in the\nsimulated materials discovery process where the targeted materials have\nproperty values out of the training data limits. The models require minimal\ndomain knowledge inputs and are free from feature engineering. The presented\nAtomSets model framework opens new routes for machine learning-assisted\nmaterials design and discovery.\n"}
+{"abstract": "  We present an analytical model to identify thin discs in galaxies, and apply\nthis model to a sample of SDSS MaNGA galaxies. This model fits the velocity and\nvelocity dispersion fields of galaxies with regular kinematics. By introducing\ntwo parameters $\\zeta$ related to the comparison of the model's asymmetric\ndrift correction to the observed gas kinematics and $\\eta$ related to the\ndominant component of a galaxy, we classify the galaxies in the sample as\n\"disc-dominated\", \"non-disc-dominated\", or \"disc-free\" indicating galaxies with\na dominating thin disc, a non-dominating thin disc, or no thin disc detection\nwith our method, respectively. The dynamical mass resulting from our model\ncorrelates with stellar mass, and we investigate discrepancies by including gas\nmass and variation of the initial mass function. As expected, most spiral\ngalaxies in the sample are disc-dominated, while ellipticals are predominantly\ndisc-free. Lenticular galaxies show a dichotomy in their kinematic\nclassification, which is related to their different star formation rates and\ngas fractions. We propose two possible scenarios to explain these results. In\nthe first scenario, disc-free lenticulars formed in more violent processes than\ndisc-dominated ones, while in the second scenario, the quenching processes in\nlenticulars lead to a change in their kinematic structures as disc-dominated\nlenticulars evolve to disc-free ones.\n"}
+{"abstract": "  Motivated by applications from computer vision to bioinformatics, the field\nof shape analysis deals with problems where one wants to analyze geometric\nobjects, such as curves, while ignoring actions that preserve their shape, such\nas translations, rotations, or reparametrizations. Mathematical tools have been\ndeveloped to define notions of distances, averages, and optimal deformations\nfor geometric objects. One such framework, which has proven to be successful in\nmany applications, is based on the square root velocity (SRV) transform, which\nallows one to define a computable distance between spatial curves regardless of\nhow they are parametrized. This paper introduces a supervised deep learning\nframework for the direct computation of SRV distances between curves, which\nusually requires an optimization over the group of reparametrizations that act\non the curves. The benefits of our approach in terms of computational speed and\naccuracy are illustrated via several numerical experiments.\n"}
+{"abstract": "  Perhaps the most explored hypothesis for the accelerated cosmic expansion\nrate arise in the context of extra fields or modifications to General\nRelativity. A prevalent approach is to parameterise the expansion history\nthrough the equation of state, $\\omega(z)$. We present a parametric form for\n$\\omega(z)$ that can reproduce the generic behaviour of the most widely used\nphysical models for accelerated expansion with infrared corrections. The\npresent proposal has at most 3 free parameters which can be mapped back to\nspecific archetypal models for dark energy. We analyze in detail how different\ncombinations of data can constrain the specific cases embedded in our form for\n$\\omega(z)$. We implement our parametric equation for $\\omega(z)$ to\nobservations from CMB, luminous distance of SNeIa, cosmic chronometers, and\nbaryon acoustic oscillations identified in galaxies and in the Lymann-$\\alpha$\nforest. We find that the parameters can be well constrained by using different\nobservational data sets. Our findings point to an oscillatory behaviour which\nis consistent with an $f(R)$-like model or an unknown combination of scalar\nfields. When we let the three parameters vary freely, we find an EOS which\noscillates around the phantom-dividing line, and, with over 99$\\%$ of\nconfidence, the cosmological constant solution is disfavored.\n"}
+{"abstract": "  We introduce the Macaulay2 package $\\mathtt{LinearTruncations}$ for finding\nand studying the truncations of a multigraded module over a standard\nmultigraded ring that have linear resolutions.\n"}
+{"abstract": "  As a critical component for online advertising and marking, click-through\nrate (CTR) prediction has draw lots of attentions from both industry and\nacademia field. Recently, the deep learning has become the mainstream\nmethodological choice for CTR. Despite of sustainable efforts have been made,\nexisting approaches still pose several challenges. On the one hand, high-order\ninteraction between the features is under-explored. On the other hand,\nhigh-order interactions may neglect the semantic information from the low-order\nfields. In this paper, we proposed a novel prediction method, named FINT, that\nemploys the Field-aware INTeraction layer which captures high-order feature\ninteractions while retaining the low-order field information. To empirically\ninvestigate the effectiveness and robustness of the FINT, we perform extensive\nexperiments on the three realistic databases: KDD2012, Criteo and Avazu. The\nobtained results demonstrate that the FINT can significantly improve the\nperformance compared to the existing methods, without increasing the amount of\ncomputation required. Moreover, the proposed method brought about 2.72\\%\nincrease to the advertising revenue of a big online video app through A/B\ntesting. To better promote the research in CTR field, we released our code as\nwell as reference implementation at: https://github.com/zhishan01/FINT.\n"}
+{"abstract": "  In this paper, the zero-forcing (ZF) precoder with max-min power allocation\nis proposed for cell-free millimeter wave (mmWave) massive multiple-input\nmultiple-output (MIMO) systems using low-resolution digital-to-analog\nconverters (DACs) with limited-capacity fronthaul links. The proposed power\nallocation aims to achieve max-min fairness on the achievable rate lower bounds\nof the users obtained by the additive quantization noise model (AQNM), which\nmimics the effect of low-resolution DACs. To solve the max-min power allocation\nproblem, an alternating optimization (AO) method is proposed, which is\nguaranteed to converge because the global optima of the subproblems that\nconstitute the original problem are attained at each AO iteration. The\nperformance of cell-free and small-cell systems is explored in the simulation\nresults, which suggest that not-too-small fronthaul capacity suffices for\ncell-free systems to outperform small-cell systems.\n"}
+{"abstract": "  We consider scattering theory of the Laplace Beltrami operator on\ndifferential forms on a Riemannian manifold that is Euclidean near infinity.\nAllowing for compact boundaries of low regularity we prove a Birman-Krein\nformula on the space of co-closed differential forms. In the case of dimension\nthree this reduces to a Birman-Krein formula in Maxwell scattering.\n"}
+{"abstract": "  The evolution by horizontal mean curvature flow (HMCF) is a partial\ndifferential equation in a sub-Riemannian setting with application in IT and\nneurogeometry (see Citti-Franceschiello-Sanguinetti-Sarti, 2016). Unfortunately\nthis equation is difficult to study, since the horizontal normal is not always\nwell defined. To overcome this problem the Riemannian approximation was\nintroduced. In this article we define a stochastic representation of the\nsolution of the approximated Riemannian mean curvature using the Riemannian\napproximation and we will prove that it is a solution in the viscosity sense of\nthe approximated mean curvature flow, generalizing the result of\nDirr-Dragoni-von Renesse, 2010.\n"}
+{"abstract": "  The advent of the transformer has sparked a quick growth in the size of\nlanguage models, far outpacing hardware improvements. (Dense) transformers are\nexpected to reach the trillion-parameter scale in the near future, for which\ntraining requires thousands or even tens of thousands of GPUs. We investigate\nthe challenges of training at this scale and beyond on commercially available\nhardware. In particular, we analyse the shortest possible training time for\ndifferent configurations of distributed training, leveraging empirical scaling\nlaws for language models to estimate the optimal (critical) batch size.\nContrary to popular belief, we find no evidence for a memory wall, and instead\nargue that the real limitation -- other than the cost -- lies in the training\nduration.\n  In addition to this analysis, we introduce two new methods, \\textit{layered\ngradient accumulation} and \\textit{modular pipeline parallelism}, which\ntogether cut the shortest training time by half. The methods also reduce data\nmovement, lowering the network requirement to a point where a fast InfiniBand\nconnection is not necessary. This increased network efficiency also improve on\nthe methods introduced with the ZeRO optimizer, reducing the memory usage to a\ntiny fraction of the available GPU memory.\n"}
+{"abstract": "  We investigated the out-of-plane transport properties of parent and\nchemically substituted BaFe$_{2}$As$_{2}$ for various types of substitution.\nBased on the studies of Hall coefficient and chemical-substitution effect, we\nhave clarified the origin for the unusual temperature dependence of\nout-of-plane resistivity $\\rho_c(T)$ in the high-temperature\nparamagnetic-tetragonal phase. Electron (hole) carriers have an incoherent\n(coherent) character, which is responsible for non-metallic (metallic)\n$\\rho_c(T)$. Although both of electron and hole contributions are almost\ncomparable, a slightly larger contribution comes from electrons at high\ntemperatures, while from holes at low temperatures, resulting in a maximum in\n$\\rho_c(T)$. In the low-temperature antiferromagnetic-orthorhombic phase, the\nmajor effect of substitution is to increase the residual-resistivity component,\nas in the case for the in-plane transport. In particular, Co atoms substituted\nfor Fe give rise to strong scattering with large $\\mathit{ac}$ anisotropy. We\nfound that K substitution induces a non-metallic behavior in $\\rho_c(T)$ at low\ntemperatures, which is likely due to a weakly localized nature along the\n$c$-axis direction.\n"}
+{"abstract": "  We study the problem of determining elements of the Selberg class by\ninformation on the coefficents of the Dirichlet series at the squares of\nprimes, or information about the zeroes of the functions.\n"}
+{"abstract": "  Clinicians often do not sufficiently adhere to evidence-based clinical\nguidelines in a manner sensitive to the context of each patient. It is\nimportant to detect such deviations, typically including redundant or missing\nactions, even when the detection is performed retrospectively, so as to inform\nboth the attending clinician and policy makers. Furthermore, it would be\nbeneficial to detect such deviations in a manner proportional to the level of\nthe deviation, and not to simply use arbitrary cut-off values. In this study,\nwe introduce a new approach for automated guideline-based quality assessment of\nthe care process, the bidirectional knowledge-based assessment of compliance\n(BiKBAC) method. Our BiKBAC methodology assesses the degree of compliance when\napplying clinical guidelines, with respect to multiple different aspects of the\nguideline (e.g., the guideline's process and outcome objectives). The\nassessment is performed through a highly detailed, automated quality-assessment\nretrospective analysis, which compares a formal representation of the guideline\nand of its process and outcome intentions (we use the Asbru language for that\npurpose) with the longitudinal electronic medical record of its continuous\napplication over a significant time period, using both a top-down and a\nbottom-up approach, which we explain in detail. Partial matches of the data to\nthe process and to the outcome objectives are resolved using fuzzy temporal\nlogic. We also introduce the DiscovErr system, which implements the BiKBAC\napproach, and present its detailed architecture. The DiscovErr system was\nevaluated in a separate study in the type 2 diabetes management domain, by\ncomparing its performance to a panel of three clinicians, with highly\nencouraging results with respect to the completeness and correctness of its\ncomments.\n"}
+{"abstract": "  The presence of a small concentration of in-plane Fe dopants in\nLa$_{1.87}$Sr$_{0.13}$Cu$_{0.99}$Fe$_{0.01}$O$_4$ is known to enhance\nstripe-like spin and charge density wave (SDW and CDW) order, and suppress the\nsuperconducting $T_c$. Here, we show that it also induces highly\ntwo-dimensional (2D) superconducting correlations that have been argued to be\nsignatures of a new form of superconducting order, so-called pair-density-wave\n(PDW) order. In addition, using the resonant soft x-ray scattering, we find\nthat the 2D superconducting fluctuation is strongly associated with the CDW\nstripe. In particular, the PDW signature first appears when the correlation\nlength of the CDW stripe grows over eight times the lattice unit ($\\sim$ 8$a$).\nThese results provide critical conditions for the formation of PDW order.\n"}
+{"abstract": "  Hyperbolic metamaterials (HMMs) are highly anisotropic optical materials that\nbehave as metals or as dielectrics depending on the direction of propagation of\nlight. They are becoming essential for a plethora of applications, ranging from\naerospace to automotive, from wireless to medical and IoT. These applications\noften work in harsh environments or may sustain remarkable external stresses.\nThis calls for materials that show enhanced optical properties as well as\ntailorable mechanical properties. Depending on their specific use, both hard\nand ultrasoft materials could be required, although the combination with\noptical hyperbolic response is rarely addressed. Here, we demonstrate the\npossibility to combine optical hyperbolicity and tunable mechanical properties\nin the same (meta)material, focusing on the case of extreme mechanical\nhardness. Using high-throughput calculations from first principles and\neffective medium theory, we explored a large class of layered materials with\nhyperbolic optical activity in the near-IR and visible range, and we identified\na reduced number of ultrasoft and hard HMMs among more than 1800 combinations\nof transition metal rocksalt crystals. Once validated by the experiments, this\nnew class of metamaterials may foster previously unexplored optical/mechanical\napplications.\n"}
+{"abstract": "  Interpretability of learning algorithms is crucial for applications involving\ncritical decisions, and variable importance is one of the main interpretation\ntools. Shapley effects are now widely used to interpret both tree ensembles and\nneural networks, as they can efficiently handle dependence and interactions in\nthe data, as opposed to most other variable importance measures. However,\nestimating Shapley effects is a challenging task, because of the computational\ncomplexity and the conditional expectation estimates. Accordingly, existing\nShapley algorithms have flaws: a costly running time, or a bias when input\nvariables are dependent. Therefore, we introduce SHAFF, SHApley eFfects via\nrandom Forests, a fast and accurate Shapley effect estimate, even when input\nvariables are dependent. We show SHAFF efficiency through both a theoretical\nanalysis of its consistency, and the practical performance improvements over\ncompetitors with extensive experiments. An implementation of SHAFF in C++ and R\nis available online.\n"}
+{"abstract": "  In the present work, we report the dynamics and geometrical features of the\nplasma plume formed by the laser ablation of copper and graphite (carbon)\ntargets in the presence of different transverse magnetic field. This work\nemphasizes on the effect of atomic mass of the plume species on the diamagnetic\nbehaviour and geometrical aspect of the expanding plasma plume in the magnetic\nfield. The time-resolved analysis of the simultaneously captured two\ndirectional images in orthogonal to the expansion axis is carried out for the\ncomparative study of projected three-dimensional structure of copper and carbon\nplasma plume. In the presence of magnetic field, sharp differences are observed\nbetween the copper and carbon plasma plumes in terms of formation of\ndiamagnetic cavity and structure formation. An elliptical cavity-like structure\nis observed in case of copper plasma plume which attains the sharp conical\nshape with increasing the time delay or magnetic field strength. On the other\nhand, splitted carbon plasma plume appears as a Y-shape structure in the\npresence of magnetic field where the cavity-like structure is not observed for\nthe considered time and magnetic field. Based on the modified energy balance\nrelation for the elliptic cylindrical geometry, we have also simulated the\ndynamics of the plume which is in close agreement with observed plasma\nexpansion in diamagnetic and non-diamagnetic regions.\n"}
+{"abstract": "  Large labeled data sets are one of the essential basics of modern deep\nlearning techniques. Therefore, there is an increasing need for tools that\nallow to label large amounts of data as intuitively as possible. In this paper,\nwe introduce SALT, a tool to semi-automatically annotate RGB-D video sequences\nto generate 3D bounding boxes for full six Degrees of Freedom (DoF) object\nposes, as well as pixel-level instance segmentation masks for both RGB and\ndepth. Besides bounding box propagation through various interpolation\ntechniques, as well as algorithmically guided instance segmentation, our\npipeline also provides built-in pre-processing functionalities to facilitate\nthe data set creation process. By making full use of SALT, annotation time can\nbe reduced by a factor of up to 33.95 for bounding box creation and 8.55 for\nRGB segmentation without compromising the quality of the automatically\ngenerated ground truth.\n"}
+{"abstract": "  Several language applications often require word semantics as a core part of\ntheir processing pipeline, either as precise meaning inference or semantic\nsimilarity. Multi-sense embeddings (M-SE) can be exploited for this important\nrequirement. M-SE seeks to represent each word by their distinct senses in\norder to resolve the conflation of meanings of words as used in different\ncontexts. Previous works usually approach this task by training a model on a\nlarge corpus and often ignore the effect and usefulness of the semantic\nrelations offered by lexical resources. However, even with large training data,\ncoverage of all possible word senses is still an issue. In addition, a\nconsiderable percentage of contextual semantic knowledge are never learned\nbecause a huge amount of possible distributional semantic structures are never\nexplored. In this paper, we leverage the rich semantic structures in WordNet\nusing a graph-theoretic walk technique over word senses to enhance the quality\nof multi-sense embeddings. This algorithm composes enriched texts from the\noriginal texts. Furthermore, we derive new distributional semantic similarity\nmeasures for M-SE from prior ones. We adapt these measures to word sense\ndisambiguation (WSD) aspect of our experiment. We report evaluation results on\n11 benchmark datasets involving WSD and Word Similarity tasks and show that our\nmethod for enhancing distributional semantic structures improves embeddings\nquality on the baselines. Despite the small training data, it achieves\nstate-of-the-art performance on some of the datasets.\n"}
+{"abstract": "  Score-based generative models provide state-of-the-art quality for image and\naudio synthesis. Sampling from these models is performed iteratively, typically\nemploying a discretized series of noise levels and a predefined scheme. In this\nnote, we first overview three common sampling schemes for models trained with\ndenoising score matching. Next, we focus on one of them, consistent annealed\nsampling, and study its hyper-parameter boundaries. We then highlight a\npossible formulation of such hyper-parameter that explicitly considers those\nboundaries and facilitates tuning when using few or a variable number of steps.\nFinally, we highlight some connections of the formulation with other sampling\nschemes.\n"}
+{"abstract": "  Larger language models have higher accuracy on average, but are they better\non every single instance (datapoint)? Some work suggests larger models have\nhigher out-of-distribution robustness, while other work suggests they have\nlower accuracy on rare subgroups. To understand these differences, we\ninvestigate these models at the level of individual instances. However, one\nmajor challenge is that individual predictions are highly sensitive to noise in\nthe randomness in training. We develop statistically rigorous methods to\naddress this, and after accounting for pretraining and finetuning noise, we\nfind that our BERT-Large is worse than BERT-Mini on at least 1-4% of instances\nacross MNLI, SST-2, and QQP, compared to the overall accuracy improvement of\n2-10%. We also find that finetuning noise increases with model size and that\ninstance-level accuracy has momentum: improvement from BERT-Mini to BERT-Medium\ncorrelates with improvement from BERT-Medium to BERT-Large. Our findings\nsuggest that instance-level predictions provide a rich source of information;\nwe therefore, recommend that researchers supplement model weights with model\npredictions.\n"}
+{"abstract": "  We consider an architecture of confidential cloud-based control synthesis\nbased on Homomorphic Encryption (HE). Our study is motivated by the recent\nsurge of data-driven control such as deep reinforcement learning, whose heavy\ncomputational requirements often necessitate an outsourcing to the third party\nserver. To achieve more flexibility than Partially Homomorphic Encryption (PHE)\nand less computational overhead than Fully Homomorphic Encryption (FHE), we\nconsider a Reinforcement Learning (RL) architecture over Leveled Homomorphic\nEncryption (LHE). We first show that the impact of the encryption noise under\nthe Cheon-Kim-Kim-Song (CKKS) encryption scheme on the convergence of the\nmodel-based tabular Value Iteration (VI) can be analytically bounded. We also\nconsider secure implementations of TD(0), SARSA(0) and Z-learning algorithms\nover the CKKS scheme, where we numerically demonstrate that the effects of the\nencryption noise on these algorithms are also minimal.\n"}
+{"abstract": "  Recently, surface electromyography (sEMG) emerged as a novel biometric\nauthentication method. Since EMG system parameters, such as the feature\nextraction methods and the number of channels, have been known to affect system\nperformances, it is important to investigate these effects on the performance\nof the sEMG-based biometric system to determine optimal system parameters. In\nthis study, three robust feature extraction methods, Time-domain (TD) feature,\nFrequency Division Technique (FDT), and Autoregressive (AR) feature, and their\ncombinations were investigated while the number of channels varying from one to\neight. For these system parameters, the performance of sixteen static wrist and\nhand gestures was systematically investigated in two authentication modes:\nverification and identification. The results from 24 participants showed that\nthe TD features significantly (p<0.05) and consistently outperformed FDT and AR\nfeatures for all channel numbers. The results also showed that the performance\nof a four-channel setup was not significantly different from those with higher\nnumber of channels. The average equal error rate (EER) for a four-channel sEMG\nverification system was 4% for TD features, 5.3% for FDT features, and 10% for\nAR features. For an identification system, the average Rank-1 error (R1E) for a\nfour-channel configuration was 3% for TD features, 12.4% for FDT features, and\n36.3% for AR features. The electrode position on the flexor carpi ulnaris (FCU)\nmuscle had a critical contribution to the authentication performance. Thus, the\ncombination of the TD feature set and a four-channel sEMG system with one of\nthe electrodes positioned on the FCU are recommended for optimal authentication\nperformance.\n"}
+{"abstract": "  Let \"Faulhaber's formula\" refer to an expression for the sum of powers of\nintegers written with terms in n(n+1)/2. Initially, the author used Faulhaber's\nformula to explain why odd Bernoulli numbers are equal to zero. Next, Cereceda\ngave alternate proofs of that result and then proved the converse, if odd\nBernoulli numbers are equal to zero then we can derive Faulhaber's formula.\nHere, the original author will give a new proof of the converse.\n"}
+{"abstract": "  Following the recent discovery of the Lauricella string scattering amplitudes\n(LSSA) and their associated exact SL(K+3,C) symmetry, we give a brief comment\non Gross conjecture regarding \"High energy symmetry of string theory\".\n"}
+{"abstract": "  A method to compute optimal collision avoidance maneuvers for short-term\nencounters is presented. The maneuvers are modeled as multiple-impulses to\nhandle impulsive cases and to approximate finite burn arcs associated either\nwith short alert times or the use of low-thrust propulsion. The maneuver design\nis formulated as a sequence of convex optimization problems solved in\npolynomial time by state-of-the-art primal-dual interior-point algorithms. The\nproposed approach calculates optimal solutions without assumptions about the\nthrust arc structure and thrust direction. The execution time is fraction of a\nsecond for an optimization problem with hundreds of variables and constraints,\nmaking it suitable for autonomous calculations.\n"}
+{"abstract": "  Microgrids with energy storage systems and distributed renewable energy\nsources play a crucial role in reducing the consumption from traditional power\nsources and the emission of $CO_2$. Connecting multi microgrid to a\ndistribution power grid can facilitate a more robust and reliable operation to\nincrease the security and privacy of the system. The proposed model consists of\nthree layers, smart grid layer, independent system operator (ISO) layer and\npower grid layer. Each layer aims to maximise its benefit. To achieve these\nobjectives, an intelligent multi-microgrid energy management method is proposed\nbased on the multi-objective reinforcement learning (MORL) techniques, leading\nto a Pareto optimal set. A non-dominated solution is selected to implement a\nfair design in order not to favour any particular participant. The simulation\nresults demonstrate the performance of the MORL and verify the viability of the\nproposed approach.\n"}
+{"abstract": "  At ambient pressure, lithium molybdenum purple bronze (Li0.9Mo6O17) is a\nquasi-one dimensional solid in which the anisotropic crystal structure and the\nlinear dispersion of the underlying bands produced by electronic correlations\npossibly bring about a rare experimental realization of Tomomaga-Luttinger\nliquid physics. It is also the sole member of the broader purple molybdenum\nbronzes family where a Peierls instability has not been identified at low\ntemperatures. The present study reports a pressure-induced series of phase\ntransitions between 0 and 12 GPa. These transitions are strongly reflected in\ninfrared spectroscopy, Raman spectroscopy, and x-ray diffraction. The most\ndramatic effect seen in optical conductivity is the metallization of the\nc-axis, concomitant to the decrease of conductivity along the b-axis. This\nindicates that high pressure drives the material away from its quasi-one\ndimensional behavior at ambient pressure. While the first pressure-induced\nstructure of the series is resolved, the identification of the underlying\nmechanisms driving the dimensional change in the physics remains a challenge.\n"}
+{"abstract": "  Optimizing the confinement and transport of fast ions is an important\nconsideration in the design of modern fusion reactors. For spherical tokamaks\nin particular, fast ions can significantly influence global plasma behavior\nbecause their large drift orbits often sample both core and scrape-off-layer\n(SOL) plasma conditions. Their Larmor radii are also comparable to the SOL\nwidth, rendering the commonly chosen guiding center approximations\ninappropriate. Accurately modeling the behavior of fast ions therefore requires\nretaining a complete description of the fast ion orbit including its Larmor\nmotion. Here, we introduce the Scrape-Off-Layer Fast Ion (SOLFI) code, which is\na new and versatile full-orbit Monte Carlo particle tracer being developed to\nfollow fast ion orbits inside and outside the separatrix. We benchmark SOLFI in\na simple straight mirror geometry and show that the code (i) conserves particle\nenergy and magnetic moment, (ii) obtains the correct passing boundary for\nparticles moving in magnetic mirror field with an imposed electrostatic field,\nand (iii) correctly observes equal ion and electron current at the ambipolar\npotential predicted from analytical theory.\n"}
+{"abstract": "  Automated testing tools typically create test cases that are different from\nwhat human testers create. This often makes the tools less effective, the\ncreated tests harder to understand, and thus results in tools providing less\nsupport to human testers. Here, we propose a framework based on cognitive\nscience and, in particular, an analysis of approaches to problem-solving, for\nidentifying cognitive processes of testers. The framework helps map test design\nsteps and criteria used in human test activities and thus to better understand\nhow effective human testers perform their tasks. Ultimately, our goal is to be\nable to mimic how humans create test cases and thus to design more human-like\nautomated test generation systems. We posit that such systems can better\naugment and support testers in a way that is meaningful to them.\n"}
+{"abstract": "  We obtain a type of Ay\\'{o}n-Beato-Garc\\'{\\i}a (ABG) related black hole\nsolutions with five parameters: the mass $m$, the charge $q$, and three\ndimensionless parameters $\\alpha$, $\\beta$ and $\\gamma$ associated with\nnonlinear electrodynamics. We find that this type of black holes is regular\nunder the conditions: $\\alpha \\gamma \\geqslant 6$, $\\beta \\gamma \\geqslant 8$,\nand $\\gamma >0$. Here we focus on the saturated case: $\\alpha={6}/{\\gamma}$ and\n$\\beta ={8}/{\\gamma }$, such that only three parameters $m$, $q$ and $\\gamma$\nremain, which leads to a new family of ABG black holes. For such a family of\nblack holes, we investigate the influence of the charge $q$ and the parameter\n$\\gamma$ on the horizon radius and the Hawking temperature. In addition, we\ncalculate the quasinormal mode frequencies of massless scalar field\nperturbations by using the sixth-order WKB approximation method and the\nunstable circular null geodesic method in the eikonal limit. We also compute\nthe shadow radius for the new family of ABG black holes and use the shadow data\nof the $M87^{*}$ black hole detected by the Event Horizon Telescope to provide\nan upper limit on the charge $q$ of the new black holes. Using the shadow data\nof the $M87^{*}$ black hole, we find that the upper limit of the charge $q$\nincreases rapidly at first and then slowly but does not exceed the mass of the\n$M87^{*}$ black hole at last when the parameter $\\gamma$ is increasing and\ngoing to infinity, and that the data restrict the frequency range of the\nfundamental mode with $l=1$ to $1.4\\times 10^{-6}Hz\\sim 1.9\\times 10^{-6} Hz$.\n"}
+{"abstract": "  This paper describes the development needed to support the functional and\nteaching requirements of iRead, a 4-year EU-funded project which produced an\naward-winning serious game utilising lexical and syntactical game content. The\nmain functional requirement was that the game should retain different profiles\nfor each student, encapsulating both the respective language model (which\nlanguage features should be taught/used in the game first, before moving on to\nmore advanced ones) and the user model (mastery level for each feature, as\nreported by the student's performance in the game). In addition to this,\nresearchers and stakeholders stated additional requirements related to learning\nobjectives and strategies to make the game more interesting and successful;\nthese were implemented as a set of selection rules which take into account not\nonly the mastery level for each feature, but also respect the priorities set by\nteachers, helping avoid repetition of content and features, and maintaining a\nbalance between new content and revision of already mastered features to give\nstudents the sense of progress, while also reinforcing learning.\n"}
+{"abstract": "  Surface ion traps are among the most promising technologies for scaling up\nquantum computing machines, but their complicated multi-electrode geometry can\nmake some tasks, including compensation for stray electric fields, challenging\nboth at the level of modeling and of practical implementation. Here we\ndemonstrate the compensation of stray electric fields using a gradient descent\nalgorithm and a machine learning technique, which trained a deep learning\nnetwork. We show automated dynamical compensation tested against induced\nelectric charging from UV laser light hitting the chip trap surface. The\nresults show improvement in compensation using gradient descent and the machine\nlearner over manual compensation. This improvement is inferred from an increase\nof the fluorescence rate of 78% and 96% respectively, for a trapped\n$^{171}$Yb$^+$ ion driven by a laser tuned to -7.8 MHz of the\n$^2$S$_{1/2}\\leftrightarrow^2$P$_{1/2}$ Doppler cooling transition at 369.5 nm.\n"}
+{"abstract": "  Active learning is usually applied to acquire labels of informative data\npoints in supervised learning, to maximize accuracy in a sample-efficient way.\nHowever, maximizing the accuracy is not the end goal when the results are used\nfor decision-making, for example in personalized medicine or economics. We\nargue that when acquiring samples sequentially, separating learning and\ndecision-making is sub-optimal, and we introduce an active learning strategy\nwhich takes the down-the-line decision problem into account. Specifically, we\nintroduce a novel active learning criterion which maximizes the expected\ninformation gain on the posterior distribution of the optimal decision. We\ncompare our targeted active learning strategy to existing alternatives on both\nsimulated and real data, and show improved performance in decision-making\naccuracy.\n"}
+{"abstract": "  We show that there is a red-blue colouring of $[N]$ with no blue 3-term\narithmetic progression and no red arithmetic progression of length $e^{C(\\log\nN)^{3/4}(\\log \\log N)^{1/4}}$. Consequently, the two-colour van der Waerden\nnumber $w(3,k)$ is bounded below by $k^{b(k)}$, where $b(k) = c \\big(\n\\frac{\\log k}{\\log\\log k} \\big)^{1/3}$. Previously it had been speculated,\nsupported by data, that $w(3,k) = O(k^2)$.\n"}
+{"abstract": "  We prove that, for $g\\geq19$ the mapping class group of a nonorientable\nsurface of genus $g$, $\\textrm{Mod}(N_g)$, can be generated by two elements,\none of which is of order $g$. We also prove that for $g\\geq26$,\n$\\textrm{Mod}(N_g)$ can be generated by three involutions if $g\\geq26$.\n"}
+{"abstract": "  Advances in both instrumentation and data analysis software are now enabling\nthe first ultra-high-resolution microcalorimeter gamma spectrometers designed\nfor implementation in nuclear facilities and analytical laboratories. With\napproximately ten times better energy resolution than high-purity germanium\ndetectors, these instruments can overcome important uncertainty limits.\nMicrocalorimeter gamma spectroscopy is intended to provide nondestructive\nisotopic analysis capabilities with sufficient precision and accuracy to reduce\nthe need for sampling, chemical separations, and mass spectrometry to meet\nsafeguards and security goals. Key milestones were the development of the SOFIA\ninstrument (Spectrometer Optimized for Facility Integrated Applications) and\nthe SAPPY software (Spectral Analysis Program in PYthon). SOFIA is a compact\ninstrument that combines advances in large multiplexed transition-edge sensor\narrays with optimized cryogenic performance to overcome many practical\nlimitations of previous systems. With a 256-pixel multiplexed detector array\ncapable of 5000 counts per second, measurement time can be comparable to\nhigh-purity germanium detectors. SAPPY was developed to determine isotopic\nratios in data from SOFIA and other microcalorimeter instruments with an\napproach similar to the widely-used FRAM software. SAPPY provides a flexible\nframework with rigorous uncertainty analysis for both microcalorimeter and HPGe\ndata, allowing direct comparison. We present current results from the SOFIA\ninstrument, preliminary isotopic analysis using SAPPY, and describe how the\ntechnology is being used to explore uncertainty limits of nondestructive\nisotopic characterization, inform safeguards models, and extract improved\nnuclear data including gamma-ray branching ratios.\n"}
+{"abstract": "  This dissertation studies the quantum anomalous effects on the description of\nhigh energy electrodynamics. We argue that on the temperatures comparable to\nthe electroweak scale, characteristic for the early Universe and objects like\nneutron stars, the description of electromagnetic fields in conductive plasmas\nneeds to be extended to include the effects of chiral anomaly. It is\ndemonstrated that chiral effects can have a significant influence on the\nevolution of magnetic fields, tending to produce exponential amplification,\ncreation of magnetic helicity from initially non-helical fields, and can lead\nto an inverse energy transfer. We further discuss the modified\nmagnetohydrodynamic equations around the electroweak transition. The obtained\nsolutions demonstrate that the asymmetry between right-handed and left-handed\ncharged fermions of negligible mass typically grows with time when approaching\nthe electroweak crossover from higher temperatures, until it undergoes a fast\ndecrease at the transition, and then eventually gets damped at lower\ntemperatures in the broken phase. At the same time, the dissipation of magnetic\nfields gets slower due to the chiral effects. We furthermore report some first\nanalytical attempts in the study of chiral magnetohydrodynamic turbulence.\nUsing the analysis of simplified regimes and qualitative arguments, it is shown\nthat anomalous effects can strongly support turbulent inverse cascade and lead\nto a faster growth of the correlation length, when compared to the evolution\npredicted by the non-chiral magnetohydrodynamics. Finally, the discussion of\nrelaxation towards minimal energy states in the chiral magnetohydrodynamic\nturbulence is also presented.\n"}
+{"abstract": "  The Covid-19 pandemic introduces new challenges and constraints for return to\nwork business planning. We describe a space allocation problem that\nincorporates social distancing constraints while optimising the number of\navailable safe workspaces in a return to work scenario. We propose and\ndemonstrate a graph based approach that solves the optimisation problem via\nmodelling as a bipartite graph of disconnected components over a graph of\nconstraints. We compare results obtained with a constrained random walk and a\nlinear programming approach.\n"}
+{"abstract": "  We provide a new analysis technique to measure the effect of the isotropic\npolarization rotation, induced by e.g. the isotropic cosmic birefringence from\naxion-like particles and a miscalibration of CMB polarization angle, via mode\ncoupling in the cosmic microwave background (CMB). Several secondary effects\nsuch as gravitational lensing and CMB optical-depth anisotropies lead to mode\ncoupling in observed CMB anisotropies, i.e., non-zero off-diagonal elements in\nthe observed CMB covariance. To derive the mode coupling, however, we usually\nassume no parity violation in the observed CMB anisotropies. We first derive a\nnew contribution to the CMB mode coupling arising from parity violation in\nobserved CMB. Since the isotropic polarization rotation leads to parity\nviolation in the observed CMB anisotropies, we then discuss the use of the new\nmode coupling for constraining the isotropic polarization angle. We find that\nconstraints on the isotropic polarization angle by measuring the new\nmode-coupling contribution are comparable to that using the $EB$ cross-power\nspectrum in future high-sensitivity polarization experiments such as CMB-S4 and\nLiteBIRD. Thus, this technique can be used to cross-check results obtained by\nthe use of the $EB$ cross-power spectrum.\n"}
+{"abstract": "  What breathes life into an embodied agent or avatar? While body motions such\nas facial expressions, speech and gestures have been well studied, relatively\nlittle attention has been applied to subtle changes due to underlying\nphysiology. We argue that subtle pulse signals are important for creating more\nlifelike and less disconcerting avatars. We propose a method for animating\nblood flow patterns, based on a data-driven physiological model that can be\nused to directly augment the appearance of synthetic avatars and\nphoto-realistic faces. While the changes are difficult for participants to\n\"see\", they significantly more frequently select faces with blood flow as more\nanthropomorphic and animated than faces without blood flow. Furthermore, by\nmanipulating the frequency of the heart rate in the underlying signal we can\nchange the perceived arousal of the character.\n"}
+{"abstract": "  During software evolution, inexperienced developers may introduce design\nanti-patterns when they modify their software systems to fix bugs or to add new\nfunctionalities based on changes in requirements. Developers may also use\ndesign patterns to promote software quality or as a possible cure for some\ndesign anti-patterns. Thus, design patterns and design anti-patterns are\nintroduced, removed, and mutated from one another by developers.\n  Many studies investigated the evolution of design patterns and design\nanti-patterns and their impact on software development. However, they\ninvestigated design patterns or design anti-patterns in isolation and did not\nconsider their mutations and the impact of these mutations on software quality.\nTherefore, we report our study of bidirectional mutations between design\npatterns and design anti-patterns and the impacts of these mutations on\nsoftware change- and fault-proneness.\n  We analyzed snapshots of seven Java software systems with diverse sizes,\nevolution histories, and application domains. We built Markov models to capture\nthe probability of occurrences of the different design patterns and design\nanti-patterns mutations. Results from our study show that (1) design patterns\nand design anti-patterns mutate into other design patterns and/or design\nanti-patterns. They also show that (2) some change types primarily trigger\nmutations of design patterns and design anti-patterns (renaming and changes to\ncomments, declarations, and operators), and (3) some mutations of design\nanti-patterns and design patterns are more faulty in specific contexts. These\nresults provide important insights into the evolution of design patterns and\ndesign anti-patterns and its impact on the change- and fault-proneness of\nsoftware systems.\n"}
+{"abstract": "  In order to study the phenomenon of regional economic development and urban\nexpansion from the perspective of night-light remote sensing images,\nresearchers use NOAA-provided night-light remote sensing image data (data from\n1992 to 2013) along with ArcGIS software to process image information, obtain\nthe basic pixel information data of specific areas of the image, and analyze\nthese data from the space-time domain for presentation of the trend of regional\neconomic development in China in recent years, and tries to explore the\nurbanization effect brought by the rapid development of China's economy.\nThrough the analysis and study of the data, the results show that the\nurbanization development speed in China is still at its peak, and has great\ndevelopment potential and space. But at the same time, people also need to pay\nattention to the imbalance of regional development.\n"}
+{"abstract": "  Specific heat and linear thermal expansivity are fundamental thermal dynamics\nand have been proven as interesting relaxing quantities to investigate in glass\ntransition and glassy state. However, their possibility has much less been\nexploited compared to mechanical and dielectric susceptibilities due to the\nlimited spectroscopy bandwidth. This work reports on simultaneous spectroscopy\nof the two by making use of ultrafast time-resolved thermal lens (TL)\nspectroscopy. Detailed modeling of the thermoelastic transients of a relaxing\nsystem subjected to ultrashort laser heating is presented to describe the TL\nresponse. The model has been applied to analyze a set of experimentally\nrecorded TL waveforms, allowing the determination of relaxation strength and\nrelaxation frequency from sub-kilohertz to sub-100 MHz and in a wide\ntemperature range from 200-280 K.\n"}
+{"abstract": "  This paper proposes an efficient video summarization framework that will give\na gist of the entire video in a few key-frames or video skims. Existing video\nsummarization frameworks are based on algorithms that utilize computer vision\nlow-level feature extraction or high-level domain level extraction. However,\nbeing the ultimate user of the summarized video, humans remain the most\nneglected aspect. Therefore, the proposed paper considers human's role in\nsummarization and introduces human visual attention-based summarization\ntechniques. To understand human attention behavior, we have designed and\nperformed experiments with human participants using electroencephalogram (EEG)\nand eye-tracking technology. The EEG and eye-tracking data obtained from the\nexperimentation are processed simultaneously and used to segment frames\ncontaining useful information from a considerable video volume. Thus, the frame\nsegmentation primarily relies on the cognitive judgments of human beings. Using\nour approach, a video is summarized by 96.5% while maintaining higher precision\nand high recall factors. The comparison with the state-of-the-art techniques\ndemonstrates that the proposed approach yields ceiling-level performance with\nreduced computational cost in summarising the videos.\n"}
+{"abstract": "  Let $\\mathbf{X}\\in\\mathbb{C}^{m\\times n}$ ($m\\geq n$) be a random matrix with\nindependent rows each distributed as complex multivariate Gaussian with zero\nmean and {\\it single-spiked} covariance matrix $\\mathbf{I}_n+ \\eta\n\\mathbf{u}\\mathbf{u}^*$, where $\\mathbf{I}_n$ is the $n\\times n$ identity\nmatrix, $\\mathbf{u}\\in\\mathbb{C}^{n\\times n}$ is an arbitrary vector with a\nunit Euclidean norm, $\\eta\\geq 0$ is a non-random parameter, and $(\\cdot)^*$\nrepresents conjugate-transpose. This paper investigates the distribution of the\nrandom quantity $\\kappa_{\\text{SC}}^2(\\mathbf{X})=\\sum_{k=1}^n\n\\lambda_k/\\lambda_1$, where $0<\\lambda_1<\\lambda_2<\\ldots<\\lambda_n<\\infty$ are\nthe ordered eigenvalues of $\\mathbf{X}^*\\mathbf{X}$ (i.e., single-spiked\nWishart matrix). This random quantity is intimately related to the so called\n{\\it scaled condition number} or the Demmel condition number (i.e.,\n$\\kappa_{\\text{SC}}(\\mathbf{X})$) and the minimum eigenvalue of the fixed trace\nWishart-Laguerre ensemble (i.e., $\\kappa_{\\text{SC}}^{-2}(\\mathbf{X})$). In\nparticular, we use an orthogonal polynomial approach to derive an exact\nexpression for the probability density function of\n$\\kappa_{\\text{SC}}^2(\\mathbf{X})$ which is amenable to asymptotic analysis as\nmatrix dimensions grow large. Our asymptotic results reveal that, as\n$m,n\\to\\infty$ such that $m-n$ is fixed and when $\\eta$ scales on the order of\n$1/n$, $\\kappa_{\\text{SC}}^2(\\mathbf{X})$ scales on the order of $n^3$. In this\nrespect we establish simple closed-form expressions for the limiting\ndistributions.\n"}
+{"abstract": "  Millimeter wave multiple-input multiple-output (mmWave-MIMO) systems with\nsmall number of radio-frequency (RF) chains have limited multiplexing gain.\nSpatial path index modulation (SPIM) is helpful in improving this gain by\nutilizing additional signal bits modulated by the indices of spatial paths. In\nthis paper, we introduce model-based and model-free frameworks for beamformer\ndesign in multi-user SPIM-MIMO systems. We first design the beamformers via\nmodel-based manifold optimization algorithm. Then, we leverage federated\nlearning (FL) with dropout learning (DL) to train a learning model on the local\ndataset of users, who estimate the beamformers by feeding the model with their\nchannel data. The DL randomly selects different set of model parameters during\ntraining, thereby further reducing the transmission overhead compared to\nconventional FL. Numerical experiments show that the proposed framework\nexhibits higher spectral efficiency than the state-of-the-art SPIM-MIMO methods\nand mmWave-MIMO, which relies on the strongest propagation path. Furthermore,\nthe proposed FL approach provides at least 10 times lower transmission overhead\nthan the centralized learning techniques.\n"}
+{"abstract": "  Out-of-town recommendation is designed for those users who leave their\nhome-town areas and visit the areas they have never been to before. It is\nchallenging to recommend Point-of-Interests (POIs) for out-of-town users since\nthe out-of-town check-in behavior is determined by not only the user's\nhome-town preference but also the user's travel intention. Besides, the user's\ntravel intentions are complex and dynamic, which leads to big difficulties in\nunderstanding such intentions precisely. In this paper, we propose a\nTRAvel-INtention-aware Out-of-town Recommendation framework, named TRAINOR. The\nproposed TRAINOR framework distinguishes itself from existing out-of-town\nrecommenders in three aspects. First, graph neural networks are explored to\nrepresent users' home-town check-in preference and geographical constraints in\nout-of-town check-in behaviors. Second, a user-specific travel intention is\nformulated as an aggregation combining home-town preference and generic travel\nintention together, where the generic travel intention is regarded as a mixture\nof inherent intentions that can be learned by Neural Topic Model (NTM). Third,\na non-linear mapping function, as well as a matrix factorization method, are\nemployed to transfer users' home-town preference and estimate out-of-town POI's\nrepresentation, respectively. Extensive experiments on real-world data sets\nvalidate the effectiveness of the TRAINOR framework. Moreover, the learned\ntravel intention can deliver meaningful explanations for understanding a user's\ntravel purposes.\n"}
+{"abstract": "  Cavity optomechanical systems have become a popular playground for studies of\ncontrollable nonlinear interactions between light and motion. Owing to the\nlarge speed of light, realizing cavity optomechanics in the microwave frequency\nrange requires cavities up to several mm in size, hence making it hard to embed\nseveral of them on the same chip. An alternative scheme with much smaller\nfootprint is provided by magnomechanics, where the electromagnetic cavity is\nreplaced by a magnet undergoing ferromagnetic resonance, and the optomechanical\ncoupling originates from magnetic shape anisotropy. Here, we consider the\nmagnomechanical interaction occurring in a suspended magnetic beam -- a scheme\nin which both magnetic and mechanical modes physically overlap and can also be\ndriven individually. We show that a sizable interaction can be produced if the\nbeam has some initial static deformation, as is often the case due to unequal\nstrains in the constituent materials. We also show how the magnetism affects\nthe magnetomotive detection of the vibrations, and how the magnomechanics\ninteraction can be used in microwave signal amplification. Finally, we discuss\nexperimental progress towards realizing the scheme.\n"}
+{"abstract": "  This paper studies offline Imitation Learning (IL) where an agent learns to\nimitate an expert demonstrator without additional online environment\ninteractions. Instead, the learner is presented with a static offline dataset\nof state-action-next state transition triples from a potentially less\nproficient behavior policy. We introduce Model-based IL from Offline data\n(MILO): an algorithmic framework that utilizes the static dataset to solve the\noffline IL problem efficiently both in theory and in practice. In theory, even\nif the behavior policy is highly sub-optimal compared to the expert, we show\nthat as long as the data from the behavior policy provides sufficient coverage\non the expert state-action traces (and with no necessity for a global coverage\nover the entire state-action space), MILO can provably combat the covariate\nshift issue in IL. Complementing our theory results, we also demonstrate that a\npractical implementation of our approach mitigates covariate shift on benchmark\nMuJoCo continuous control tasks. We demonstrate that with behavior policies\nwhose performances are less than half of that of the expert, MILO still\nsuccessfully imitates with an extremely low number of expert state-action pairs\nwhile traditional offline IL method such as behavior cloning (BC) fails\ncompletely. Source code is provided at https://github.com/jdchang1/milo.\n"}
+{"abstract": "  This position paper examines potential pitfalls on the way towards achieving\nhuman-AI co-creation with generative models in a way that is beneficial to the\nusers' interests. In particular, we collected a set of nine potential pitfalls,\nbased on the literature and our own experiences as researchers working at the\nintersection of HCI and AI. We illustrate each pitfall with examples and\nsuggest ideas for addressing it. Reflecting on all pitfalls, we discuss and\nconclude with implications for future research directions. With this\ncollection, we hope to contribute to a critical and constructive discussion on\nthe roles of humans and AI in co-creative interactions, with an eye on related\nassumptions and potential side-effects for creative practices and beyond.\n"}
+{"abstract": "  We give an explicit description of the generator of finitely presented\nobjects of the coslice of a locally finitely presentable category under a given\nobject, as consisting of all pushouts of finitely presented maps under this\nobject. Then we prove that the comma category under the direct image part of a\nmorphism of locally finitely presentable category is still locally finitely\npresentable, and we give again an explicit description of its generator of\nfinitely presented objects. We finally deduce that 2-category $\\LFP$ has comma\nobjects computed in $\\Cat$.\n"}
+{"abstract": "  Genome-wide association studies (GWAS) have identified thousands of genetic\nvariants associated with complex traits. Many complex traits are found to have\nshared genetic etiology. Genetic covariance is defined as the underlying\ncovariance of genetic values and can be used to measure the shared genetic\narchitecture. The data of two outcomes may be collected from the same group or\ndifferent groups of individuals and the outcomes can be of different types or\ncollected based on different study designs. This paper proposes a unified\napproach to robust estimation and inference for genetic covariance of general\noutcomes that may be associated with genetic variants nonlinearly. We provide\nthe asymptotic properties of the proposed estimator and show that our proposal\nis robust under certain model mis-specification. Our method under linear\nworking models provides robust inference for the narrow-sense genetic\ncovariance, even when both linear models are mis-specified. Various numerical\nexperiments are performed to support the theoretical results. Our method is\napplied to an outbred mice GWAS data set to study the overlapping genetic\neffects between the behavioral and physiological phenotypes. The real data\nresults demonstrate the robustness of the proposed method and reveal\ninteresting genetic covariance among different mice developmental traits.\n"}
+{"abstract": "  We study unirationality of a Del Pezzo surface of degree two over a given\n(non algebraically closed) field, under the assumption that it admits at least\none rational double point over an algebraic closure of the base field. As\ncorollaries of our main results, we find that over a finite field, it is\nunirational if the cardinality of the field is greater than or equal to nine\nand we also find that over an infinite field, which is not necessarily perfect,\nit is unirational if and only if the rational points are Zariski dense over the\nfield.\n"}
+{"abstract": "  Input perturbation methods occlude parts of an input to a function and\nmeasure the change in the function's output. Recently, input perturbation\nmethods have been applied to generate and evaluate saliency maps from\nconvolutional neural networks. In practice, neutral baseline images are used\nfor the occlusion, such that the baseline image's impact on the classification\nprobability is minimal. However, in this paper we show that arguably neutral\nbaseline images still impact the generated saliency maps and their evaluation\nwith input perturbations. We also demonstrate that many choices of\nhyperparameters lead to the divergence of saliency maps generated by input\nperturbations. We experimentally reveal inconsistencies among a selection of\ninput perturbation methods and find that they lack robustness for generating\nsaliency maps and for evaluating saliency maps as saliency metrics.\n"}
+{"abstract": "  We introduce a novel fine-grained dataset and benchmark, the Danish Fungi\n2020 (DF20). The dataset, constructed from observations submitted to the Atlas\nof Danish Fungi, is unique in its taxonomy-accurate class labels, small number\nof errors, highly unbalanced long-tailed class distribution, rich observation\nmetadata, and well-defined class hierarchy. DF20 has zero overlap with\nImageNet, allowing unbiased comparison of models fine-tuned from publicly\navailable ImageNet checkpoints. The proposed evaluation protocol enables\ntesting the ability to improve classification using metadata -- e.g. precise\ngeographic location, habitat, and substrate, facilitates classifier calibration\ntesting, and finally allows to study the impact of the device settings on the\nclassification performance. Experiments using Convolutional Neural Networks\n(CNN) and the recent Vision Transformers (ViT) show that DF20 presents a\nchallenging task. Interestingly, ViT achieves results superior to CNN baselines\nwith 80.45% accuracy and 0.743 macro F1 score, reducing the CNN error by 9% and\n12% respectively. A simple procedure for including metadata into the decision\nprocess improves the classification accuracy by more than 2.95 percentage\npoints, reducing the error rate by 15%. The source code for all methods and\nexperiments is available at https://sites.google.com/view/danish-fungi-dataset.\n"}
+{"abstract": "  In the present work, the European option pricing SWIFT method is extended for\nHeston model calibration. The computation of the option price gradient is\nsimplified thanks to the knowledge of the characteristic function in closed\nform. The proposed calibration machinery appears to be extremely fast, in\nparticular for a single expiry and multiples strikes, outperforming the\nstate-of-the-art method we compare with. Further, the a priori knowledge of\nSWIFT parameters makes possible a reliable and practical implementation of the\npresented calibration method. A wide set of stress, speed and convergence\nnumerical experiments is carried out, with deep in-the-money, at-the-money and\ndeep out-of-the-money options for very short and very long maturities.\n"}
+{"abstract": "  We note that a strongly minimal Steiner $k$-Steiner system $(M,R)$ from\n(Baldwin-Paolini 2020) can be `coordinatized' in the sense of (Gantner-Werner\n1975) by a quasigroup if $k$ is a prime-power. But for the basic construction\nthis coordinatization is never definable in $(M,R)$. Nevertheless, by refining\nthe construction, if $k$ is a prime power there is a $(2,k)$-variety of\nquasigroups which is strongly minimal and definably coordinatizes a Steiner\n$k$-system.\n"}
+{"abstract": "  The chromosphere is a partially ionized layer of the solar atmosphere, the\ntransition between the photosphere where the gas motion is determined by the\ngas pressure and the corona dominated by the magnetic field. We study the\neffect of partial ionization for 2D wave propagation in a gravitationally\nstratified, magnetized atmosphere with properties similar to the solar\nchromosphere. We adopt an oblique uniform magnetic field in the plane of\npropagation with strength suitable for a quiet sun region. The theoretical\nmodel used is a single fluid magnetohydrodynamic approximation, where\nion-neutral interaction is modeled by the ambipolar diffusion term. Magnetic\nenergy can be converted into internal energy through the dissipation of the\nelectric current produced by the drift between ions and neutrals. We use\nnumerical simulations where we continuously drive fast waves at the bottom of\nthe atmosphere. The collisional coupling between ions and neutrals decreases\nwith the decrease of the density and the ambipolar effect becomes important.\nFast waves excited at the base of the atmosphere reach the equipartition layer\nand reflect or transmit as slow waves. While the waves propagate through the\natmosphere and the density drops, the waves steepen into shocks. The main\neffect of ambipolar diffusion is damping of the waves. We find that for the\nparameters chosen in this work, the ambipolar diffusion affects the fast wave\nbefore it is reflected, with damping being more pronounced for waves which are\nlaunched in a direction perpendicular to the magnetic field. Slow waves are\nless affected by ambipolar effects. The damping increases for shorter periods\nand larger magnetic field strengths. Small scales produced by the nonlinear\neffects and the superposition of different types of waves created at the\nequipartition height are efficiently damped by ambipolar diffusion.\n"}
+{"abstract": "  In this paper we obtain a parametric solution of the hitherto unsolved\ndiophantine equation $(x_1^5+x_2^5)(x_3^5+x_4^5)=(y_1^5+y_2^5)(y_3^5+y_4^5)$.\nFurther, we show, using elliptic curves, that there exist infinitely many\nparametric solutions of the aforementioned diophantine equation, and they can\nbe effectively computed.\n"}
+{"abstract": "  Topological Dirac semimetals are a class of semimetals that host\nsymmetry-protected Dirac points near the Fermi level, which arise due to a band\ninversion of the conduction and valence bands. In this work, we study the less\nexplored class of \\emph{noncentrosymmetric} topological Dirac semimetals in\nthree dimensions. We identify the noncentrosymmetric crystallographic point\ngroups required to stabilize fourfold degenerate band crossings and derive\nmodel Hamiltonians for all distinct types of band inversions allowed by\nsymmetry. Using these model Hamiltonians, which emphasize the physical nature\nof the allowed couplings, we establish the generic electronic phase diagram\nnoncentrosymmetric Dirac semimetals and show that it generically includes\nphases with coexistent Weyl point nodes or Weyl line nodes. In particular, for\none specific type of band inversion in sixfold symmetric systems we show that\nWeyl line nodes are always present. Based on first-principles calculations, we\npredict that BiPd$_2$O$_4$ is a noncentrosymmetric Dirac semimetal under 20 Gpa\npressure and hosts topological type-II Dirac points on the fourfold rotation\naxis. Furthermore, we propose that the hexagonal polar alloy\nLiZnSb$_{x}$Bi$_{1-x}$ realizes a Dirac semimetal with coexistent Weyl points.\nInterestingly, the emergence and location of the Weyl points is highly tunable\nand can be controlled by the alloy concentration $x$. More generally, our\nresults not only establish band-inverted noncentrosymmetric systems as a broad\nand versatile class of topological semimetals, but also provide a framework for\nstudying the quantum nonlinear Hall effect and nonlinear optical properties in\nthe Dirac semimetals.\n"}
+{"abstract": "  The ability to engineer the properties of quantum optical states is essential\nfor quantum information processing applications. Here, we demonstrate tunable\ncontrol of spatial correlations between photon pairs produced by spontaneous\nparametric down-conversion. By shaping the spatial pump beam profile in a\ntype-I collinear configuration, we tailor the spatial structure of coincidences\nbetween photon pairs entangled in high dimensions, without effect on intensity.\nThe results highlight fundamental aspects of spatial coherence and hold\npotential for the development of quantum technologies based on high-dimensional\nspatial entanglement.\n"}
+{"abstract": "  The arithmetic of N, Z, Q, R can be extended to a graph arithmetic where N is\nthe semiring of finite simple graphs and where Z and Q are integral domains,\nculminating in a Banach algebra R. A single network completes to the Wiener\nalgebra. We illustrate the compatibility with topology and spectral theory.\nMultiplicative linear functionals like Euler characteristic, the Poincare\npolynomial or the zeta functions can be extended naturally. These functionals\ncan also help with number theoretical questions. The story of primes is a bit\ndifferent as the integers are not a unique factorization domain, because there\nare many additive primes. Most graphs are multiplicative primes.\n"}
+{"abstract": "  An ultrafast laser based on coherent beam combination of four ytterbium-doped\nstep-index fiber amplifiers is presented. The system delivers an average power\nof 3.5 kW and a pulse duration of 430 fs at 80 MHz repetition rate. The beam\nquality is excellent (M2<1.24x1.10) and the relative intensity noise is as low\nas 1% in the frequency span from 1 Hz to 1 MHz. The system is turn-key operable\nas it features an automated spatial and temporal alignment of the\ninterferometric amplification channels.\n"}
+{"abstract": "  The recently described pushframe imager, a parallelized single pixel camera\ncapturing with a pushbroom-like motion, is intrinsically suited to both\nremote-sensing and compressive sampling. It optically applies a 2D mask to the\nimaged scene, before performing light integration along a single spatial axis,\nbut previous work has not made use of the architecture's potential for taking\nmeasurements sparsely. In this paper we develop a strongly performing static\nbinarized noiselet compressive sampling mask design, tailored to pushframe\nhardware, allowing both a single exposure per motion time-step, and retention\nof 2D correlations in the scene. Results from simulated and real-world captures\nare presented, with performance shown to be similar to that of immobile -- and\nhence inappropriate for satellite use -- whole-scene imagers. A particular\nfeature of our sampling approach is that the degree of compression can be\nvaried without altering the pattern, and we demonstrate the utility of this for\nefficiently storing and transmitting multi-spectral images.\n"}
+{"abstract": "  When network products and services become more valuable as their userbase\ngrows (network effects), this tendency can become a major determinant of how\nthey compete with each other in the market and how the market is structured.\nNetwork effects are traditionally linked to high market concentration,\nearly-mover advantages, and entry barriers, and in the cryptoasset market they\nhave been used as a valuation tool too. The recent resurgence of Bitcoin has\nbeen partly attributed to network effects too. We study the existence of\nnetwork effects in six cryptoassets from their inception to obtain a high-level\noverview of the application of network effects in the cryptoasset market. We\nshow that contrary to the usual implications of network effects, they do not\nserve to concentrate the cryptoasset market, nor do they accord any one\ncryptoasset a definitive competitive advantage, nor are they consistent enough\nto be reliable valuation tools. Therefore, while network effects do occur in\ncryptoasset networks, they are not a defining feature of the cryptoasset market\nas a whole.\n"}
+{"abstract": "  We consider electronic and magnetic properties of chromium, a well-known\nitinerant antiferromagnet, by a combination of density functional theory (DFT)\nand dynamical mean-field theory (DMFT). We find that electronic correlation\neffects in chromium, in contrast to its neighbours in the periodic table, are\nweak, leading to the quasiparticle mass enhancement factor ${m^*/m \\approx\n1.2}$. Our results for local spin-spin correlation functions and distribution\nof weigths of atomic configurations indicate that the local magnetic moments\nare not formed. Similarly to previous results of DFT at ambient pressure, the\nnon-uniform magnetic susceptibility as a function of momentum possesses close\nto the wave vector ${{\\mathbf Q}_{\\rm H}=(0,0,2\\pi/a)}$ ($a$ is the lattice\nconstant) sharp maxima, corresponding to Kohn anomalies. We find that these\nmaxima are preserved by the interaction and are not destroyed by pressure. Our\ncalculations qualitatively capture a decrease of the N\\'eel temperature with\npressure and a breakdown of itinerant antiferomagnetism at pressure of $\\sim$9\nGPa in agreement with experimental data, although the N\\'eel temperature is\nsignificantly overestimated because of the mean-field nature of DMFT.\n"}
+{"abstract": "  While most existing segmentation methods usually combined the powerful\nfeature extraction capabilities of CNNs with Conditional Random Fields (CRFs)\npost-processing, the result always limited by the fault of CRFs . Due to the\nnotoriously slow calculation speeds and poor efficiency of CRFs, in recent\nyears, CRFs post-processing has been gradually eliminated. In this paper, an\nimproved Generative Adversarial Networks (GANs) for image semantic segmentation\ntask (semantic segmentation by GANs, Seg-GAN) is proposed to facilitate further\nsegmentation research. In addition, we introduce Convolutional CRFs (ConvCRFs)\nas an effective improvement solution for the image semantic segmentation task.\nTowards the goal of differentiating the segmentation results from the ground\ntruth distribution and improving the details of the output images, the proposed\ndiscriminator network is specially designed in a full convolutional manner\ncombined with cascaded ConvCRFs. Besides, the adversarial loss aggressively\nencourages the output image to be close to the distribution of the ground\ntruth. Our method not only learns an end-to-end mapping from input image to\ncorresponding output image, but also learns a loss function to train this\nmapping. The experiments show that our method achieves better performance than\nstate-of-the-art methods.\n"}
+{"abstract": "  We propose a relativistic quantum Otto cycle between an entangled state of\ntwo qubits and their composite excited (or ground) state whose efficiency can\nbe greater than the usual single qubit quantum Otto engine. The hot and cold\nreservoirs are constructed by providing uniform accelerations to these qubits\nalong with the interaction between the background field and individual qubits.\nThe efficiency, as measured from one of the qubits' frame, not only depends on\nthe energy gap of the states but also the relative acceleration between them.\nFor lower acceleration of our observer's qubit compared to the other one, the\ncycle is more efficient than the single qubit quantum Otto engine. Furthermore,\na complete protocol to construct such a cycle is being provided.\n"}
+{"abstract": "  In recent years, trends towards studying simulated games have gained momentum\nin the fields of artificial intelligence, cognitive science, psychology, and\nneuroscience. The intersections of these fields have also grown recently, as\nresearchers increasing study such games using both artificial agents and human\nor animal subjects. However, implementing games can be a time-consuming\nendeavor and may require a researcher to grapple with complex codebases that\nare not easily customized. Furthermore, interdisciplinary researchers studying\nsome combination of artificial intelligence, human psychology, and animal\nneurophysiology face additional challenges, because existing platforms are\ndesigned for only one of these domains. Here we introduce Modular\nObject-Oriented Games, a Python task framework that is lightweight, flexible,\ncustomizable, and designed for use by machine learning, psychology, and\nneurophysiology researchers.\n"}
+{"abstract": "  We use a hybrid k dot p theory - tight binding (HkpTB) model to describe\ninterlayer coupling simultaneously in both Bernal and twisted graphene\nstructures. For Bernal-aligned interfaces, HkpTB is parametrized using the full\nSlonczewski-Weiss-McClure (SWMcC) Hamiltonian of graphite, which is then used\nto refine the commonly used minimal model for twisted interfaces, by deriving\nadditional terms that reflect all details of the full SWMcC model of graphite.\nWe find that these terms introduce some electron-hole asymmetry in the band\nstructure of twisted bilayers, but in twistronic multilayer graphene, they\nproduce only a subtle change of moire miniband spectra, confirming the broad\napplicability of the minimal model for implementing the twisted interface\ncoupling in such systems.\n"}
+{"abstract": "  What will the future of UAV cellular communications be? In this tutorial\narticle, we address such a compelling yet difficult question by embarking on a\njourney from 5G to 6G and sharing a large number of realistic case studies\nsupported by original results. We start by overviewing the status quo on UAV\ncommunications from an industrial standpoint, providing fresh updates from the\n3GPP and detailing new 5G NR features in support of aerial devices. We then\nshow the potential and the limitations of such features. In particular, we\ndemonstrate how sub-6 GHz massive MIMO can successfully tackle cell selection\nand interference challenges, we showcase encouraging mmWave coverage\nevaluations in both urban and suburban/rural settings, and we examine the\npeculiarities of direct device-to-device communications in the sky. Moving on,\nwe sneak a peek at next-generation UAV communications, listing some of the use\ncases envisioned for the 2030s. We identify the most promising 6G enablers for\nUAV communication, those expected to take the performance and reliability to\nthe next level. For each of these disruptive new paradigms (non-terrestrial\nnetworks, cell-free architectures, artificial intelligence, reconfigurable\nintelligent surfaces, and THz communications), we gauge the prospective\nbenefits for UAVs and discuss the main technological hurdles that stand in the\nway. All along, we distil our numerous findings into essential takeaways, and\nwe identify key open problems worthy of further study.\n"}
+{"abstract": "  Composite minimization is a powerful framework in large-scale convex\noptimization, based on decoupling of the objective function into terms with\nstructurally different properties and allowing for more flexible algorithmic\ndesign. In this work, we introduce a new algorithmic framework for\ncomplementary composite minimization, where the objective function decouples\ninto a (weakly) smooth and a uniformly convex term. This particular form of\ndecoupling is pervasive in statistics and machine learning, due to its link to\nregularization.\n  The main contributions of our work are summarized as follows. First, we\nintroduce the problem of complementary composite minimization in general normed\nspaces; second, we provide a unified accelerated algorithmic framework to\naddress broad classes of complementary composite minimization problems; and\nthird, we prove that the algorithms resulting from our framework are\nnear-optimal in most of the standard optimization settings. Additionally, we\nshow that our algorithmic framework can be used to address the problem of\nmaking the gradients small in general normed spaces. As a concrete example, we\nobtain a nearly-optimal method for the standard $\\ell_1$ setup (small gradients\nin the $\\ell_\\infty$ norm), essentially matching the bound of Nesterov (2012)\nthat was previously known only for the Euclidean setup. Finally, we show that\nour composite methods are broadly applicable to a number of regression\nproblems, leading to complexity bounds that are either new or match the best\nexisting ones.\n"}
+{"abstract": "  The formation of the most massive quasars observed at high redshifts requires\nextreme inflows of gas down to the length scales of the central compact object.\nHere, we estimate the maximum inflow rate allowed by gravity down to the\nsurface of supermassive stars, the possible progenitors of these supermassive\nblack holes. We use the continuity equation and the assumption of free-fall to\nderive maximum allowed inflow rates for various density profiles. We apply our\napproach to the mass-radius relation of rapidly accreting supermassive stars to\nestimate an upper limit to the accretion rates allowed during the formation of\nthese objects. We find that the maximum allowed rate $\\dot M_{\\rm max}$ is\ngiven uniquely by the compactness of the accretor. For the compactness of\nrapidly accreting supermassive stars, $\\dot M_{\\rm max}$ is related to the\nstellar mass $M$ by a power-law $\\dot M_{\\rm max}\\propto M^{3/4}$. The rates of\natomically cooled halos (0.1 -- 10 M$_\\odot$ yr$^{-1}$) are allowed as soon as\n$M\\gtrsim1$ M$_\\odot$. The largest rates expected in galaxy mergers\n($10^4-10^5$ M$_\\odot$ yr$^{-1}$) become accessible once the accretor is\nsupermassive ($M\\gtrsim10^4$ M$_\\odot$). These results suggest that\nsupermassive stars can accrete up to masses $>10^6$ M$_\\odot$ before they\ncollapse via the general-relativistic instability. At such masses, the collapse\nis expected to lead to the direct formation of a supermassive black hole even\nwithin metal-rich gas, resulting in a black hole seed that is significantly\nheavier than in conventional direct collapse models for atomic cooling halos.\n"}
+{"abstract": "  We give a new proof of the uniformization theorem of the leaves of a\nlamination by surfaces of hyperbolic conformal type. We use a laminated version\nof the Ricci flow to prove the existence of a laminated Riemannian metric\n(smooth on the leaves, transversaly continuous) with leaves of constant\nGaussian curvature equal to -1, which is conformally equivalent to the original\nmetric.\n"}
+{"abstract": "  How to handle gender with machine learning is a controversial topic. A\ngrowing critical body of research brought attention to the numerous issues\ntransgender communities face with the adoption of current automatic gender\nrecognition (AGR) systems. In contrast, we explore how such technologies could\npotentially be appropriated to support transgender practices and needs,\nespecially in non-Western contexts like Japan. We designed a virtual makeup\nprobe to assist transgender individuals with passing, that is to be perceived\nas the gender they identify as. To understand how such an application might\nsupport expressing transgender individuals gender identity or not, we\ninterviewed 15 individuals in Tokyo and found that in the right context and\nunder strict conditions, AGR based systems could assist transgender passing.\n"}
+{"abstract": "  The background magnetic-field formalism of Lattice QCD has been used recently\nto calculate the magnetic polarizability of the charged pion. These $n_f = 2 +\n1$ numerical simulations are electro-quenched, such that the virtual sea-quarks\nof the QCD vacuum do not interact with the background field. To understand the\nimpact of this, we draw on partially quenched chiral perturbation theory. In\nthis case, the leading term proportional to $1/M_\\pi$ arises at tree level from\n$\\mathcal{L}_4$. To describe the results from lattice QCD, while maintaining\nthe exact leading terms of chiral perturbation theory, we introduce a Pad\\'e\napproximant designed to reproduce the slow variation observed in the lattice\nQCD results. Two-loop contributions are introduced to assess the systematic\nuncertainty associated with higher-order terms of the expansion. Upon\nextrapolation, the magnetic polarizability of the charged pion at the physical\npion mass is found to be $\\beta_{\\pi^\\pm}=-1.70\\,(14)_{\\rm stat}(25)_{\\rm\nsyst}\\times 10^{-4}$ fm$^3$, in good agreement with the recent experimental\nmeasurement.\n"}
+{"abstract": "  In this paper, we focus on the fairness issues regarding unsupervised outlier\ndetection. Traditional algorithms, without a specific design for algorithmic\nfairness, could implicitly encode and propagate statistical bias in data and\nraise societal concerns. To correct such unfairness and deliver a fair set of\npotential outlier candidates, we propose Deep Clustering based Fair Outlier\nDetection (DCFOD) that learns a good representation for utility maximization\nwhile enforcing the learnable representation to be subgroup-invariant on the\nsensitive attribute. Considering the coupled and reciprocal nature between\nclustering and outlier detection, we leverage deep clustering to discover the\nintrinsic cluster structure and out-of-structure instances. Meanwhile, an\nadversarial training erases the sensitive pattern for instances for fairness\nadaptation. Technically, we propose an instance-level weighted representation\nlearning strategy to enhance the joint deep clustering and outlier detection,\nwhere the dynamic weight module re-emphasizes contributions of likely-inliers\nwhile mitigating the negative impact from outliers. Demonstrated by experiments\non eight datasets comparing to 17 outlier detection algorithms, our DCFOD\nmethod consistently achieves superior performance on both the outlier detection\nvalidity and two types of fairness notions in outlier detection.\n"}
+{"abstract": "  Presence of large-scale surface magnetic field in early-type stars leads to\nseveral unique electromagnetic phenomena producing radiation over X-ray to\nradio bands. Among them, the rarest type of emission is electron cyclotron\nmaser emission (ECME) observed as periodic, circularly polarized radio pulses.\nThe phenomenon was first discovered in the hot magnetic star CU Vir. Past\nobservations of this star led to the consensus that the star produces only\nright circularly polarized ECME, suggesting that only one magnetic hemisphere\ntakes part in the phenomenon. Here we present the first ultra-wideband (0.4$-$4\nGHz) study of this star using the upgraded Giant Metrewave Radio telescope and\nthe Karl G. Jansky Very Large Array, which led to the surprising discovery of\nECME of both circular polarizations up to around 1.5 GHz. The GHz observations\nalso allowed us to infer that the upper ECME cut-off frequency is at $\\gtrsim\n5\\,\\mathrm{GHz}$. The sub-GHz observation led to the unexpected observation of\nmore than two pairs of ECME pulses per rotation cycle. In addition, we report\nthe discovery of a `giant pulse', and transient enhancements, which are\npotentially the first observational evidence of `centrifugal breakout' of\nplasma from the innermost part of the stellar magnetosphere. The stark contrast\nbetween the star's behavior at GHz and sub-GHz frequencies could either be due\nto propagation effects, a manifestation of varying magnetic field topology as a\nfunction of height, or a signature of an additional `ECME engine'.\n"}
+{"abstract": "  Sub-Terahertz frequencies (frequencies above 100 GHz) have the potential to\nsatisfy the unprecedented demand on data rate on the order of hundreds of Gbps\nfor sixth-generation (6G) wireless communications and beyond. Accurate beam\ntracking and rapid beam selection are increasingly important since antenna\narrays with more elements generate narrower beams to compensate for additional\npath loss within the first meter of propagation distance at sub-THz\nfrequencies. Realistic channel models for above 100 GHz are needed, and should\ninclude spatial consistency to model the spatial and temporal channel evolution\nalong the user trajectory. This paper introduces recent outdoor urban microcell\n(UMi) propagation measurements at 142 GHz along a 39 m $\\times$ 12 m\nrectangular route (102 m long), where each consecutive and adjacent receiver\nlocation is 3 m apart from each other. The measured power delay profiles and\nangular power spectrum at each receiver location are used to study spatial\nautocorrelation properties of various channel parameters such as shadow fading,\ndelay spread, and angular spread along the track. Compared to the correlation\ndistances reported in the 3GPP TR 38.901 for frequencies below 100 GHz, the\nmeasured correlation distance of shadow fading at 142 GHz (3.8 m) is much\nshorter than the 10-13 m as specified in 3GPP; the measured correlation\ndistances of delay spread and angular spread at 142 GHz (both 12 m) are\ncomparable to the 7-10 m as specified in 3GPP. This result may guide the\ndevelopment of a statistical spatially consistent channel model for frequencies\nabove 100 GHz in the UMi street canyon environment.\n"}
+{"abstract": "  We derive a general expression for the absorptive part of the one-loop photon\npolarization tensor in a strongly magnetized quark-gluon plasma at nonzero\nbaryon chemical potential. To demonstrate the application of the main result in\nthe context of heavy-ion collisions, we study the effect of a nonzero baryon\nchemical potential on the photon emission rate. The rate and the ellipticity of\nphoton emission are studied numerically as a function the transverse momentum\n(energy) for several values of temperature and chemical potential. When the\nchemical potential is small compared to the temperature, the rates of the quark\nand antiquark splitting processes (i.e., $q\\rightarrow q +\\gamma$ and\n$\\bar{q}\\rightarrow \\bar{q} +\\gamma$, respectively) are approximately the same.\nHowever, the quark splitting gradually becomes the dominant process with\nincreasing the chemical potential. We also find that increasing the chemical\npotential leads to a growing total photon production rate but has only a small\neffect on the ellipticity of photon emission. The quark-antiquark annihilation\n($q+\\bar{q}\\rightarrow \\gamma$) also contributes to the photon production, but\nits contribution remains relatively small for a wide range of temperatures and\nchemical potentials investigated.\n"}
+{"abstract": "  Topic trajectory information provides crucial insight into the dynamics of\ntopics and their evolutionary relationships over a given time. Also, this\ninformation can help to improve our understanding on how new topics have\nemerged or formed through a sequential or interrelated events of emergence,\nmodification and integration of prior topics. Nevertheless, the implementation\nof the existing methods for topic trajectory identification is rarely available\nas usable software. In this paper, we present TopicTracker, a platform for\ntopic trajectory identification and visualisation. The key of Topic Tracker is\nthat it can represent the three facets of information together, given two kinds\nof input: a time-stamped topic profile consisting of the set of the underlying\ntopics over time, and the evolution strength matrix among them: evolutionary\npathways of dynamic topics, evolution states of the topics, and topic\nimportance. TopicTracker is a publicly available software implemented using the\nR software.\n"}
+{"abstract": "  Mechanical strength of amyloid beta fibrils has been known to be correlated\nwith neuronal cell death. Here, we resorted to steered molecular dynamics (SMD)\nsimulations to mechanically stretch a single S-shape amyloid beta Abeta11-42\ndodecamer fibril in vacuum. It was found that the weakest sites at which the\nfibril was ruptured due to mechanical extension were exclusively at the\ninterfaces of alanine and glutamic acid distributed throughout the fibril. It\nwas also revealed that the free energy required to unfold the fibril to form a\nlong linear conformation is equivalent to ~ 210 eV, being several thousand\ntimes larger than thermal voltage at room temperature. As a consequence, within\nsolution a larger free energy is needed for such a maximal stretching based on\nthe fact that amyloid beta fibrils are structurally more stable in solution due\nto the interplay between their hydrophobic cores and solution's entropy.\n"}
+{"abstract": "  Multivariate functional data can be intrinsically multivariate like movement\ntrajectories in 2D or complementary like precipitation, temperature, and wind\nspeeds over time at a given weather station. We propose a multivariate\nfunctional additive mixed model (multiFAMM) and show its application to both\ndata situations using examples from sports science (movement trajectories of\nsnooker players) and phonetic science (acoustic signals and articulation of\nconsonants). The approach includes linear and nonlinear covariate effects and\nmodels the dependency structure between the dimensions of the responses using\nmultivariate functional principal component analysis. Multivariate functional\nrandom intercepts capture both the auto-correlation within a given function and\ncross-correlations between the multivariate functional dimensions. They also\nallow us to model between-function correlations as induced by e.g.\\ repeated\nmeasurements or crossed study designs. Modeling the dependency structure\nbetween the dimensions can generate additional insight into the properties of\nthe multivariate functional process, improves the estimation of random effects,\nand yields corrected confidence bands for covariate effects. Extensive\nsimulation studies indicate that a multivariate modeling approach is more\nparsimonious than fitting independent univariate models to the data while\nmaintaining or improving model fit.\n"}
+{"abstract": "  In this paper we prove the existence and uniqueness of strong solution of the\nincompressible Navier-Stokes equations with damping $\\alpha\n(e^{\\beta|u|^2}-1)u$.\n"}
+{"abstract": "  The hypothesis that strange quark matter is the true ground state of matter\nhas been investigated for almost four decades, but only a few works have\nexplored the dynamics of binary systems of quark stars. This is partly due to\nthe numerical challenges that need to be faced when modelling the large\ndiscontinuities at the surface of these stars. We here present a novel\ntechnique in which the EOS of a quark star is suitably rescaled to produce a\nsmooth change of the specific enthalpy across a very thin crust. The\nintroduction of the crust has been carefully tested by considering the\noscillation properties of isolated quark stars, showing that the response of\nthe simulated quark stars matches accurately the perturbative predictions.\nUsing this technique, we have carried out the first fully general-relativistic\nsimulations of the merger of quark-star binaries finding several important\ndifferences between quark-star binaries and hadronic-star binaries with the\nsame mass and comparable tidal deformability. In particular, we find that\ndynamical mass loss is significantly suppressed in quark-star binaries. In\naddition, quark-star binaries have merger and post-merger frequencies that obey\nthe same quasi-universal relations derived from hadron stars if expressed in\nterms of the tidal deformability, but not when expressed in terms of the\naverage stellar compactness. Hence, it may be difficult to distinguish the two\nclasses of stars if no information on the stellar radius is available. Finally,\ndifferences are found in the distributions in velocity and entropy of the\nejected matter, for which quark-stars have much smaller tails. Whether these\ndifferences in the ejected matter will leave an imprint in the electromagnetic\ncounterpart and nucleosynthetic yields remains unclear, calling for the\nconstruction of an accurate model for the evaporation of the ejected quarks\ninto nucleons.\n"}
+{"abstract": "  We introduce a new benchmark dataset, namely VinDr-RibCXR, for automatic\nsegmentation and labeling of individual ribs from chest X-ray (CXR) scans. The\nVinDr-RibCXR contains 245 CXRs with corresponding ground truth annotations\nprovided by human experts. A set of state-of-the-art segmentation models are\ntrained on 196 images from the VinDr-RibCXR to segment and label 20 individual\nribs. Our best performing model obtains a Dice score of 0.834 (95% CI,\n0.810--0.853) on an independent test set of 49 images. Our study, therefore,\nserves as a proof of concept and baseline performance for future research.\n"}
+{"abstract": "  Incentive mechanism design is crucial for enabling federated learning. We\ndeal with clustering problem of agents contributing to federated learning\nsetting. Assuming agents behave selfishly, we model their interaction as a\nstable coalition partition problem using hedonic games where agents and\nclusters are the players and coalitions, respectively. We address the following\nquestion: is there a family of hedonic games ensuring a Nash-stable coalition\npartition? We propose the Nash-stable set which determines the family of\nhedonic games possessing at least one Nash-stable partition, and analyze the\nconditions of non-emptiness of the Nash-stable set. Besides, we deal with the\ndecentralized clustering. We formulate the problem as a non-cooperative game\nand prove the existence of a potential game.\n"}
+{"abstract": "  Magnetic resonance imaging (MRI) acquisition, reconstruction, and\nsegmentation are usually processed independently in the conventional practice\nof MRI workflow. It is easy to notice that there are significant relevances\namong these tasks and this procedure artificially cuts off these potential\nconnections, which may lead to losing clinically important information for the\nfinal diagnosis. To involve these potential relations for further performance\nimprovement, a sequential multi-task joint learning network model is proposed\nto train a combined end-to-end pipeline in a differentiable way, aiming at\nexploring the mutual influence among those tasks simultaneously. Our design\nconsists of three cascaded modules: 1) deep sampling pattern learning module\noptimizes the $k$-space sampling pattern with predetermined sampling rate; 2)\ndeep reconstruction module is dedicated to reconstructing MR images from the\nundersampled data using the learned sampling pattern; 3) deep segmentation\nmodule encodes MR images reconstructed from the previous module to segment the\ninterested tissues. The proposed model retrieves the latently interactive and\ncyclic relations among those tasks, from which each task will be mutually\nbeneficial. The proposed framework is verified on MRB dataset, which achieves\nsuperior performance on other SOTA methods in terms of both reconstruction and\nsegmentation.\n"}
+{"abstract": "  Clustering is one of the most fundamental tasks in machine learning.\nRecently, deep clustering has become a major trend in clustering techniques.\nRepresentation learning often plays an important role in the effectiveness of\ndeep clustering, and thus can be a principal cause of performance degradation.\nIn this paper, we propose a clustering-friendly representation learning method\nusing instance discrimination and feature decorrelation. Our\ndeep-learning-based representation learning method is motivated by the\nproperties of classical spectral clustering. Instance discrimination learns\nsimilarities among data and feature decorrelation removes redundant correlation\namong features. We utilize an instance discrimination method in which learning\nindividual instance classes leads to learning similarity among instances.\nThrough detailed experiments and examination, we show that the approach can be\nadapted to learning a latent space for clustering. We design novel\nsoftmax-formulated decorrelation constraints for learning. In evaluations of\nimage clustering using CIFAR-10 and ImageNet-10, our method achieves accuracy\nof 81.5% and 95.4%, respectively. We also show that the softmax-formulated\nconstraints are compatible with various neural networks.\n"}
+{"abstract": "  The phase stability and equilibria of carbon dioxide is investigated from 125\n-- 325K and 1 -- 10,000 atm using extensive molecular dynamics (MD) simulations\nand the Two-Phase Thermodynamics (2PT) method. We devise a direct approach for\ncalculating phase diagrams in general, by considering the separate chemical\npotentials of the isolated phase at specific points on the P-T diagram. The\nunique ability of 2PT to accurately and efficiently approximate the entropy and\nGibbs energy of liquids thus allows for assignment of phase boundaries from\nrelatively short ($\\mathrm{\\sim}$ 100ps) MD simulations. We validate our\napproach by calculating the critical properties of the flexible Elementary\nPhysical Model 2 (FEPM2), showing good agreement with previous results. We\nshow, however, that the incorrect description of the short-range Pauli force\nand the lack of molecular charge polarization leads to deviations from\nexperiments at high pressures. We thus develop a many-body, fluctuating charge\nmodel for CO${}_{2}$, termed CO${}_{2}$-Fq, from high level quantum mechanics\n(QM) calculations, that accurately captures the condensed phase vibrational\nproperties of the solid (including the Fermi resonance at 1378 cm${}^{-1}$) as\nwell as the diffusional properties of the liquid, leading to overall excellent\nagreement with experiments over the entire phase diagram. This work provides an\nefficient computational approach for determining phase diagrams of arbitrary\nsystems and underscore the critical role of QM charge reorganization physics in\nmolecular phase stability.\n"}
+{"abstract": "  K\\\"ahler's geometric approach in which relativistic fermion fields are\ntreated as differential forms is applied in three spacetime dimensions. It is\nshown that the resulting continuum theory is invariant under global\nU($N)\\otimes$U($N)$ field transformations, and has a parity-invariant mass\nterm, both symmetries shared in common with staggered lattice fermions. The\nformalism is used to construct a version of the Thirring model with contact\ninteractions between conserved Noether currents. Under reasonable assumptions\nabout field rescaling after quantum corrections, a more general interaction\nterm is derived, sharing the same symmetries but now including terms which\nentangle spin and taste degrees of freedom, which exactly coincides with the\nleading terms in the staggered lattice Thirring model in the long-wavelength\nlimit. Finally truncated versions of the theory are explored; it is found that\nexcluding scalar and pseudoscalar components leads to a theory of six-component\nfermion fields describing particles with spin 1, with fermion and antifermion\ncorresponding to states with definite circular polarisation. In the UV limit\nonly transverse states with just four non-vanishing components propagate.\nImplications for the description of dynamics at a strongly interacting\nrenormalisation-group fixed point are discussed.\n"}
+{"abstract": "  Chiral optical effects are generally quantified along some specific incident\ndirections of exciting waves (especially for extrinsic chiralities of achiral\nstructures) or defined as direction-independent properties by averaging the\nresponses among all structure orientations. Though of great significance for\nvarious applications, chirality extremization (maximized or minimized) with\nrespect to incident directions or structure orientations have not been\nexplored, especially in a systematic manner. In this study we examine the\nchiral responses of open photonic structures from perspectives of quasi-normal\nmodes and polarization singularities of their far-field radiations. The\nnontrivial topology of the momentum sphere secures the existence of singularity\ndirections along which mode radiations are either circularly or linearly\npolarized. When plane waves are incident along those directions, the\nreciprocity ensures ideal maximization and minimization of optical chiralities,\nfor corresponding mode radiations of circular and linear polarizations\nrespectively. For directions of general elliptical polarizations, we have\nunveiled the subtle equality of a Stokes parameter and the circular dichroism,\nshowing that an intrinsically chiral structure can unexpectedly exhibit no\nchirality at all or even chiralities of opposite handedness for different\nincident directions. The framework we establish can be applied to not only\nfinite scattering bodies but also infinite periodic structures, encompassing\nboth intrinsic and extrinsic optical chiralities. We have effectively merged\ntwo vibrant disciplines of chiral and singular optics, which can potentially\ntrigger more optical chirality-singularity related interdisciplinary studies.\n"}
+{"abstract": "  The semantic matching capabilities of neural information retrieval can\nameliorate synonymy and polysemy problems of symbolic approaches. However,\nneural models' dense representations are more suitable for re-ranking, due to\ntheir inefficiency. Sparse representations, either in symbolic or latent form,\nare more efficient with an inverted index. Taking the merits of the sparse and\ndense representations, we propose an ultra-high dimensional (UHD)\nrepresentation scheme equipped with directly controllable sparsity. UHD's large\ncapacity and minimal noise and interference among the dimensions allow for\nbinarized representations, which are highly efficient for storage and search.\nAlso proposed is a bucketing method, where the embeddings from multiple layers\nof BERT are selected/merged to represent diverse linguistic aspects. We test\nour models with MS MARCO and TREC CAR, showing that our models outperforms\nother sparse models\n"}
+{"abstract": "  We explore the physics of topological lattice models in c-QED architectures\nfor arbitrary coupling strength, and the possibility of using the cavity\ntransmission as a topological marker. For this, we develop an approach\ncombining the input-output formalism with Mean-Field theory, which includes\nself-consistency and quantum fluctuations to first order, and allows to go\nbeyond the small-coupling regime. We apply our formalism to the case of a\nfermionic Su-Schrieffer-Heeger (SSH) chain. Our findings confirm that the\ncavity can indeed act as a quantum sensor for topological phases, where the\ninitial state preparation plays a crutial role. Additionally, we discuss the\npersistence of topological features when the coupling strength increases, in\nterms of an effective Hamiltonian, and calculate the entanglement entropy. Our\napproach can be applied to other fermionic systems, opening a route to the\ncharacterization of their topological properties in terms of experimental\nobservables.\n"}
+{"abstract": "  Hyperactive comets have high water production rates, with inferred\nsublimation areas of order the surface area of the nucleus. Comets 46P/Wirtanen\nand 103P/Hartley 2 are two examples of this cometary class. Based on\nobservations of comet Hartley 2 by the Deep Impact spacecraft, hyperactivity\nappears to be caused by the ejection of water-ice grains and/or water-ice rich\nchunks of nucleus into the coma. These materials increase the sublimating\nsurface area, and yield high water production rates. The historic close\napproach of comet Wirtanen to Earth in 2018 afforded an opportunity to test\nHartley 2 style hyperactivity in a second Jupiter-family comet. We present high\nspatial resolution, near-infrared spectroscopy of the inner coma of Wirtanen.\nNo evidence for the 1.5- or 2.0-$\\mu$m water-ice absorption bands is found in\nsix 0.8-2.5 $\\mu$m spectra taken around perihelion and closest approach to\nEarth. In addition, the strong 3.0-$\\mu$m water-ice absorption band is absent\nin a 2.0-5.3 $\\mu$m spectrum taken near perihelion. Using spectroscopic and\nsublimation lifetime models we set constraints on the physical properties of\nthe ice grains in the coma, assuming they are responsible for the comet's\nhyperactivity. We rule out pure water-ice grains of any size, given their long\nlifetime. Instead, the hyperactivity of the nucleus and lack of water-ice\nabsorption features in our spectra can be explained either by icy grains on the\norder of 1 $\\mu$m in size with a small amount of low albedo dust (greater than\n0.5% by volume), or large chunks containing significant amounts of water ice.\n"}
+{"abstract": "  Human movement disorders or paralysis lead to the loss of control of muscle\nactivation and thus motor control. Functional Electrical Stimulation (FES) is\nan established and safe technique for contracting muscles by stimulating the\nskin above a muscle to induce its contraction. However, an open challenge\nremains on how to restore motor abilities to human limbs through FES, as the\nproblem of controlling the stimulation is unclear. We are taking a robotics\nperspective on this problem, by developing robot learning algorithms that\ncontrol the ultimate humanoid robot, the human body, through electrical muscle\nstimulation. Human muscles are not trivial to control as actuators due to their\nforce production being non-stationary as a result of fatigue and other internal\nstate changes, in contrast to robot actuators which are well-understood and\nstationary over broad operation ranges. We present our Deep Reinforcement\nLearning approach to the control of human muscles with FES, using a recurrent\nneural network for dynamic state representation, to overcome the unobserved\nelements of the behaviour of human muscles under external stimulation. We\ndemonstrate our technique both in neuromuscular simulations but also\nexperimentally on a human. Our results show that our controller can learn to\nmanipulate human muscles, applying appropriate levels of stimulation to achieve\nthe given tasks while compensating for advancing muscle fatigue which arises\nthroughout the tasks. Additionally, our technique can learn quickly enough to\nbe implemented in real-world human-in-the-loop settings.\n"}
+{"abstract": "  Let $(\\kappa_n(a))_{n\\geq 1}$ denote the sequence of free cumulants of a\nrandom variable $a$ in a non-commutative probability space\n$(\\mathcal{A},\\varphi)$. Based on some considerations on bipartite graphs, we\nprovide a formula to compute the cumulants $(\\kappa_n(ab+ba))_{n\\geq 1}$ in\nterms of $(\\kappa_n(a))_{n\\geq 1}$ and $(\\kappa_n(b))_{n\\geq 1}$, where $a$ and\n$b$ are freely independent. Our formula expresses the $n$-th free cumulant of\n$ab+ba$ as a sum indexed by partitions in the set $\\mathcal{Y}_{2n}$ of\nnon-crossing partitions of the form\n  \\[ \\sigma=\\{B_1,B_3,\\dots, B_{2n-1},E_1,\\dots,E_r\\}, \\quad \\text{with }r\\geq\n0, \\]\n  such that $i\\in B_{i}$ for $i=1,3,\\dots,2n-1$ and $|E_j|$ even for $j\\leq r$.\nTherefore, by studying the sets $\\mathcal{Y}_{2n}$ we obtain new results\nregarding the distribution of $ab+ba$. For instance, the size\n$|\\mathcal{Y}_{2n}|$ is closely related to the case when $a,b$ are free Poisson\nrandom variables of parameter 1. Our formula can also be expressed in terms of\ncacti graphs. This graph theoretic approach suggests a natural generalization\nthat allows us to study quadratic forms in $k$ free random variables.\n"}
+{"abstract": "  Deep reinforcement learning (DRL) is applied in safety-critical domains such\nas robotics and autonomous driving. It achieves superhuman abilities in many\ntasks, however whether DRL agents can be shown to act safely is an open\nproblem. Atari games are a simple yet challenging exemplar for evaluating the\nsafety of DRL agents and feature a diverse portfolio of game mechanics. The\nsafety of neural agents has been studied before using methods that either\nrequire a model of the system dynamics or an abstraction; unfortunately, these\nare unsuitable to Atari games because their low-level dynamics are complex and\nhidden inside their emulator. We present the first exact method for analysing\nand ensuring the safety of DRL agents for Atari games. Our method only requires\naccess to the emulator. First, we give a set of 43 properties that characterise\n\"safe behaviour\" for 30 games. Second, we develop a method for exploring all\ntraces induced by an agent and a game and consider a variety of sources of game\nnon-determinism. We observe that the best available DRL agents reliably satisfy\nonly very few properties; several critical properties are violated by all\nagents. Finally, we propose a countermeasure that combines a bounded\nexplicit-state exploration with shielding. We demonstrate that our method\nimproves the safety of all agents over multiple properties.\n"}
+{"abstract": "  The phase structure of baryonic matter is investigated with focus on the role\nof fluctuations beyond the mean-field approximation. The prototype test case\nstudied is the chiral nucleon-meson model, with added comments on the chiral\nquark-meson model. Applications to the liquid-gas phase transition in nuclear\nmatter and extensions to dense matter are performed. The role of vacuum\nfluctuations and thermal excitations is systematically explored. It is pointed\nout that such fluctuations tend to stabilise the hadronic phase characterised\nby spontaneously broken chiral symmetry, shifting the chiral restoration\ntransition to very high densities. This stabilisation effect is shown to be\nfurther enhanced by additional dynamical fluctuations treated with functional\nrenormalisation group methods.\n"}
+{"abstract": "  Deep learning (DL) has recently attracted increasing interest to improve\nobject type classification for automotive radar.In addition to high accuracy,\nit is crucial for decision making in autonomous vehicles to evaluate the\nreliability of the predictions; however, decisions of DL networks are\nnon-transparent. Current DL research has investigated how uncertainties of\npredictions can be quantified, and in this article, we evaluate the potential\nof these methods for safe, automotive radar perception. In particular we\nevaluate how uncertainty quantification can support radar perception under (1)\ndomain shift, (2) corruptions of input signals, and (3) in the presence of\nunknown objects. We find that in agreement with phenomena observed in the\nliterature,deep radar classifiers are overly confident, even in their wrong\npredictions. This raises concerns about the use of the confidence values for\ndecision making under uncertainty, as the model fails to notify when it cannot\nhandle an unknown situation. Accurate confidence values would allow optimal\nintegration of multiple information sources, e.g. via sensor fusion. We show\nthat by applying state-of-the-art post-hoc uncertainty calibration, the quality\nof confidence measures can be significantly improved,thereby partially\nresolving the over-confidence problem. Our investigation shows that further\nresearch into training and calibrating DL networks is necessary and offers\ngreat potential for safe automotive object classification with radar sensors.\n"}
+{"abstract": "  ESA's INTEGRAL space mission has achieved unique results for solar and\nterrestrial physics, although spacecraft operations nominally excluded the\npossibility to point at the Sun or the Earth. The Earth avoidance was, however,\nexceptionally relaxed for special occultation observations of the Cosmic X-ray\nBackground (CXB), which on some occasions allowed the detection of strong X-ray\nauroral emission. In addition, the most intense solar flares can be bright\nenough to be detectable from outside the field of view of the main instruments.\nThis article presents for the first time the auroral observations by INTEGRAL\nand reviews earlier studies of the most intense solar flares. We end by briefly\nsummarising the studies of the Earth's radiation belts, which can be considered\nas another topic of serendipitous science with INTEGRAL.\n"}
+{"abstract": "  Non-invasive therapeutic ultrasound methods, such as high-intensity focused\nultrasound (HIFU), have limited access to tissue targets shadowed by bones or\npresence of gas. This study demonstrates that an ultrasonically actuated\nmedical needle can be used to translate nanoparticles and fluids under the\naction of nonlinear phenomena, potentially overcoming some limitations of HIFU.\nA simulation study was first conducted to study the delivery of a tracer with\nan ultrasonically actuated needle (33 kHz) inside a porous medium acting as a\nmodel for soft tissue. The model was then validated experimentally in different\nconcentrations of agarose gel showing a close match with the experimental\nresults, when diluted soot nanoparticles (diameter < 150 nm) were employed as\ndelivered entity. An additional simulation study demonstrated a threefold\nincrease of the volume covered by the delivered agent in liver under a constant\ninjection rate, when compared to without ultrasound. This method, if developed\nto its full potential, could serve as a cost effective way to improve safety\nand efficacy of drug therapies by maximizing the concentration of delivered\nentities within e.g. a small lesion, while minimizing exposure outside the\nlesion.\n"}
+{"abstract": "  Cone Beam Computed Tomography(CBCT) is a now known method to conduct CT\nimaging. Especially, The Low Dose CT imaging is one of possible options to\nprotect organs of patients when conducting CT imaging. Therefore Low Dose CT\nimaging can be an alternative instead of Standard dose CT imaging. However Low\nDose CT imaging has a fundamental issue with noises within results compared to\nStandard Dose CT imaging. Currently, there are lots of attempts to erase the\nnoises. Most of methods with artificial intelligence have many parameters and\nunexplained layers or a kind of black-box methods. Therefore, our research has\npurposes related to these issues. Our approach has less parameters than usual\nmethods by having Iterative learn-able bilateral filtering approach with Deep\nreinforcement learning. And we applied The Iterative learn-able filtering\napproach with deep reinforcement learning to sinograms and reconstructed volume\ndomains. The method and the results of the method can be much more explainable\nthan The other black box AI approaches. And we applied the method to Helical\nCone Beam Computed Tomography(CBCT), which is the recent CBCT trend. We tested\nthis method with on 2 abdominal scans(L004, L014) from Mayo Clinic TCIA\ndataset. The results and the performances of our approach overtake the results\nof the other previous methods.\n"}
+{"abstract": "  In this paper, we consider the energy conservation and regularity of the weak\nsolution $u$ to the Navier-Stokes equations in the endpoint case. We first\nconstruct a divergence-free field $u(t,x)$ which satisfies $\\lim_{t\\to\nT}\\sqrt{T-t}||u(t)||_{BMO}<\\infty$ and $\\lim_{t\\to\nT}\\sqrt{T-t}||u(t)||_{L^\\infty}=\\infty$ to demonstrate that the Type II\nsingularity is admissible in the endpoint case $u\\in L^{2,\\infty}(BMO)$.\nSecondly, we prove that if a suitable weak solution $u(t,x)$ satisfying\n$||u||_{L^{2,\\infty}([0,T];BMO(\\Omega))}<\\infty$ for arbitrary\n$\\Omega\\subseteq\\mathbb{R}^3$ then the local energy equality is valid on\n$[0,T]\\times\\Omega$. As a corollary, we also prove\n$||u||_{L^{2,\\infty}([0,T];BMO(\\mathbb{R}^3))}<\\infty$ implies the global\nenergy equality on $[0,T]$. Thirdly, we show that as the solution $u$\napproaches a finite blowup time $T$, the norm $||u(t)||_{BMO}$ must blow up at\na rate faster than $\\frac{c}{\\sqrt{T-t}}$ with some absolute constant $c>0$.\nFurthermore, we prove that if\n$||u_3||_{L^{2,\\infty}([0,T];BMO(\\mathbb{R}^3))}=M<\\infty$ then there exists a\nsmall constant $c_M$ depended on $M$ such that if\n$||u_h||_{L^{2,\\infty}([0,T];BMO(\\mathbb{R}^3))}\\leq c_M$ then $u$ is regular\non $(0,T]\\times\\mathbb{R}^3$.\n"}
+{"abstract": "  We propose a series-based nonparametric specification test for a regression\nfunction when data are spatially dependent, the `space' being of a general\neconomic or social nature. Dependence can be parametric, parametric with\nincreasing dimension, semiparametric or any combination thereof, thus covering\na vast variety of settings. These include spatial error models of varying types\nand levels of complexity. Under a new smooth spatial dependence condition, our\ntest statistic is asymptotically standard normal. To prove the latter property,\nwe establish a central limit theorem for quadratic forms in linear processes in\nan increasing dimension setting. Finite sample performance is investigated in a\nsimulation study, with a bootstrap method also justified and illustrated, and\nempirical examples illustrate the test with real-world data.\n"}
+{"abstract": "  Inelastic neutron scattering (INS) is a key method for studying magnetic\nexcitations in spin systems, including molecular spin clusters. The method has\nsignificantly advanced in recent years and now permits to probe the scattering\nintensity as a function of the energy transfer and the momentum-transfer vector\nQ. It was recently shown that high molecular symmetry facilitates the analysis\nof spectra. Point-group symmetry imposes selection rules in isotropic as well\nas anisotropic spin models. Furthermore, the Q-dependence of the INS intensity\nmay be completely determined by the point-group symmetry of the states involved\nin a transition, thereby affording a clear separation of dynamics (energies,\ntransition strengths) and geometrical features (Q-dependencies). The present\nwork addresses this issue for anisotropic spin models. We identify a number of\ncases where the Q-dependence is completely fixed by the point-group symmetry.\nFor six- and eight-membered planar spin rings and two polyhedra (the cube and\nthe icosahedron) we tabulate and plot the corresponding powder-averaged\nuniversal intensity functions. The outlined formalism straightforwardly applies\nto other highly-symmetric systems and should be useful for future analyses of\nINS spectra by focusing on those features that contain information on either\nspin dynamics or the point-group symmetry of states.\n"}
+{"abstract": "  We report the detection of CH$_3$OH emission in comet 46P/Wirtanen on UT 2018\nDecember 8 and 9 using the Atacama Compact Array (ACA), part of the Atacama\nLarge Millimeter/Submillimeter Array (ALMA). These interferometric measurements\nof CH$_3$OH along with continuum emission from dust probed the inner coma\n($<$2000 km from the nucleus) of 46P/Wirtanen approximately one week before its\nclosest approach to Earth ($\\Delta$ = 0.089 -- 0.092 au), revealing rapidly\nvarying and anisotropic CH$_3$OH outgassing during five separate ACA executions\nbetween UT 23:57 December 7 and UT 04:55 December 9, with a clear progression\nin the spectral line profiles over a timescale of minutes. We present\nspectrally integrated flux maps, production rates, rotational temperatures, and\nspectral line profiles of CH$_3$OH during each ACA execution. The variations in\nCH$_3$OH outgassing are consistent with Wirtanen's 9 hr nucleus rotational\nperiod derived from optical and millimeter wavelength measurements and thus are\nlikely coupled to the changing illumination of active sites on the nucleus. The\nconsistent blue offset of the line center indicates enhanced CH$_3$OH\nsublimation from the sunward hemisphere of the comet, perhaps from icy grains.\nThese results demonstrate the exceptional capabilities of the ACA for\ntime-resolved measurements of comets such as 46P/Wirtanen.\n"}
+{"abstract": "  Direct numerical simulations have been performed for heat and momentum\ntransfer in internally heated turbulent shear flow with constant bulk mean\nvelocity and temperature, $u_{b}$ and $\\theta_{b}$, between parallel,\nisothermal, no-slip and permeable walls. The wall-normal transpiration velocity\non the walls $y=\\pm h$ is assumed to be proportional to the local pressure\nfluctuations, i.e. $v=\\pm \\beta p/\\rho$ (Jim\\'enez et al., J. Fluid Mech., vol.\n442, 2001, pp.89-117). The temperature is supposed to be a passive scalar, and\nthe Prandtl number is set to unity. Turbulent heat and momentum transfer in\npermeable-channel flow for $\\beta u_{b}=0.5$ has been found to exhibit distinct\nstates depending on the Reynolds number $Re_b=2h u_b/\\nu$. At $Re_{b}\\lesssim\n10^4$, the classical Blasius law of the friction coefficient and its similarity\nto the Stanton number, $St\\approx c_{f}\\sim Re_{b}^{-1/4}$, are observed,\nwhereas at $Re_{b}\\gtrsim 10^4$, the so-called ultimate scaling, $St\\sim\nRe_b^0$ and $c_{f}\\sim Re_b^0$, is found. The ultimate state is attributed to\nthe appearance of large-scale intense spanwise rolls with the length scale of\n$O(h)$ arising from the Kelvin-Helmholtz type of shear-layer instability over\nthe permeable walls. The large-scale rolls can induce large-amplitude velocity\nfluctuations of $O(u_b)$ as in free shear layers, so that the Taylor\ndissipation law $\\epsilon\\sim u_{b}^{3}/h$ (or equivalently $c_{f}\\sim Re_b^0$)\nholds. In spite of strong turbulence promotion there is no flow separation, and\nthus large-amplitude temperature fluctuations of $O(\\theta_b)$ can also be\ninduced similarly. As a consequence, the ultimate heat transfer is achieved,\ni.e., a wall heat flux scales with $u_{b}\\theta_{b}$ (or equivalently $St\\sim\nRe_b^0$) independent of thermal diffusivity, although the heat transfer on the\nwalls is dominated by thermal conduction.\n"}
+{"abstract": "  This paper considers the problem of nonstationary process monitoring under\nfrequently varying operating conditions. Traditional approaches generally\nmisidentify the normal dynamic deviations as faults and thus lead to high false\nalarms. Besides, they generally consider single relatively steady operating\ncondition and suffer from the catastrophic forgetting issue when learning\nsuccessive operating conditions. In this paper, recursive cointegration\nanalysis (RCA) is first proposed to distinguish the real faults from normal\nsystems changes, where the model is updated once a new normal sample arrives\nand can adapt to slow change of cointegration relationship. Based on the\nlong-term equilibrium information extracted by RCA, the remaining short-term\ndynamic information is monitored by recursive principal component analysis\n(RPCA). Thus a comprehensive monitoring framework is built. When the system\nenters a new operating condition, the RCA-RPCA model is rebuilt to deal with\nthe new condition. Meanwhile, elastic weight consolidation (EWC) is employed to\nsettle the `catastrophic forgetting' issue inherent in RPCA, where significant\ninformation of influential parameters is enhanced to avoid the abrupt\nperformance degradation for similar modes. The effectiveness of the proposed\nmethod is illustrated by a practical industrial system.\n"}
+{"abstract": "  In this paper, we prove that a Sasakian pseudo-metric manifold which admits\nan $\\eta-$Ricci soliton is an $\\eta-$Einstein manifold, and if the potential\nvector field of the $\\eta-$Ricci soliton is not a Killing vector field then the\nmanifold is $\\mathcal{D}-$homothetically fixed, and the vector field leaves the\nstructure tensor field invariant. Next, we prove that a $K-$contact\npseudo-metric manifold with a gradient $\\eta-$Ricci soliton metric is\n$\\eta-$Einstein. Moreover, we study contact pseudo-metric manifolds admitting\nan $\\eta-$Ricci soliton with a potential vector field point-wise colinear with\nthe Reeb vector field. Finally, we study gradient $\\eta-$Ricci solitons on\n$(\\kappa, \\mu)$-contact pseudo-metric manifolds.\n"}
+{"abstract": "  Self-supervised learning for depth estimation possesses several advantages\nover supervised learning. The benefits of no need for ground-truth depth,\nonline fine-tuning, and better generalization with unlimited data attract\nresearchers to seek self-supervised solutions. In this work, we propose a new\nself-supervised framework for stereo matching utilizing multiple images\ncaptured at aligned camera positions. A cross photometric loss, an\nuncertainty-aware mutual-supervision loss, and a new smoothness loss are\nintroduced to optimize the network in learning disparity maps end-to-end\nwithout ground-truth depth information. To train this framework, we build a new\nmultiscopic dataset consisting of synthetic images rendered by 3D engines and\nreal images captured by real cameras. After being trained with only the\nsynthetic images, our network can perform well in unseen outdoor scenes. Our\nexperiment shows that our model obtains better disparity maps than previous\nunsupervised methods on the KITTI dataset and is comparable to supervised\nmethods when generalized to unseen data. Our source code and dataset are\navailable at https://sites.google.com/view/multiscopic.\n"}
+{"abstract": "  Resource Public Key Infrastructure (RPKI) is vital to the security of\ninter-domain routing. However, RPKI enables Regional Internet Registries (RIRs)\nto unilaterally takedown IP prefixes - indeed, such attacks have been launched\nby nation-state adversaries. The threat of IP prefix takedowns is one of the\nfactors hindering RPKI adoption.\n  In this work, we propose the first distributed RPKI system, based on\nthreshold signatures, that requires the coordination of a number of RIRs to\nmake changes to RPKI objects; hence, preventing unilateral prefix takedown. We\nperform extensive evaluations using our implementation demonstrating the\npracticality of our solution. Furthermore, we show that our system is scalable\nand remains efficient even when RPKI is widely deployed.\n"}
+{"abstract": "  Dujmovi\\'{c}, Joret, Micek, Morin, Ueckerdt and Wood recently in [Planar\ngraphs have bounded queue-number, Journal of the ACM, Volume 67, Issue 4,\nArticle No.: 22, August 2020] showed some attractive graph product structure\ntheorems for planar graphs. By using the product structure, they proved that\nplanar graphs have bounded queue-number $48$; in [Planar graphs have bounded\nnonrepetitive chromatic number, Advances in Combinatorics, 5, 11 pp, 2020], the\nauthors proved that planar graphs have bounded nonrepetitive chromatic number\n$768$.\n  In this paper, still by using some product structure theorem, we improve the\nupper bound of queue-number of planar graphs to $27$ and the non-repetitive\nchromatic number to $320$. We also study powers of trees. We show a graph\nproduct structure theorem of the $k$-th power $T^k$ of tree $T$, then use it\ngiving an upper bound of the nonrepetitive~chromatic~number of $T^k$. We also\ngive an asymptotically tight upper bound of the queue-number of $T^k$.\n"}
+{"abstract": "  We study sets of recurrence, in both measurable and topological settings, for\nactions of $(\\mathbb{N},\\times)$ and $(\\mathbb{Q}^{>0},\\times)$. In particular,\nwe show that autocorrelation sequences of positive functions arising from\nmultiplicative systems have positive additive averages. We also give criteria\nfor when sets of the form $\\{(an+b)^{\\ell}/(cn+d)^{\\ell}: n \\in \\mathbb{N}\\}$\nare sets of multiplicative recurrence, and consequently we recover two recent\nresults in number theory regarding completely multiplicative functions and the\nOmega function.\n"}
+{"abstract": "  The interaction between magnetic and acoustic excitations have recently\ninspired many interdisciplinary studies ranging from fundamental physics to\ncircuit implementation. Specifically, the exploration of their coherent\ninterconversion enabled via the magnetoelastic coupling opens a new playground\ncombining straintronics and spintronics, and provides a unique platform for\nbuilding up on-chip coherent information processing networks with miniaturized\nmagnonic and acoustic devices. In this Perspective, we will focus on the recent\nprogress of magnon-phonon coupled dynamic systems, including materials,\ncircuits, imaging and new physics. In particular, we highlight the unique\nfeatures such as nonreciprocal acoustic wave propagation and strong coupling\nbetween magnons and phonons in magnetic thin-film systems, which provides a\nunique platform for their coherent manipulation and transduction. We will also\nreview the frontier of surface acoustic wave resonators in coherent quantum\ntransduction and discuss how the novel acoustic circuit design can be applied\nin microwave spintronics.\n"}
+{"abstract": "  We conjecture and verify a set of universal relations between global\nparameters of hot and fast-rotating compact stars, including a relation\nconnecting the masses of the mass-shedding (Kepler) and static configurations.\nWe apply these relations to the GW170817 event by adopting the scenario in\nwhich a hypermassive compact star remnant formed in a merger evolves into a\nsupramassive compact star that collapses into a black hole once the stability\nline for such stars is crossed. We deduce an upper limit on the maximum mass of\nstatic, cold neutron stars $ 2.15^{+0.10}_{-0.07}\\le M^\\star_{\\mathrm{TOV}} \\le\n2.24^{+0.12}_{-0.10} $ for the typical range of entropy per baryon $2 \\le S/A\n\\le 3$ and electron fraction $Y_e = 0.1$ characterizing the hot hypermassive\nstar. Our result implies that accounting for the finite temperature of the\nmerger remnant relaxes previously derived constraints on the value of the\nmaximum mass of a cold, static compact star.\n"}
+{"abstract": "  For distant iris recognition, a long focal length lens is generally used to\nensure the resolution ofiris images, which reduces the depth of field and leads\nto potential defocus blur. To accommodate users at different distances, it is\nnecessary to control focus quickly and accurately. While for users in motion,\nit is expected to maintain the correct focus on the iris area continuously. In\nthis paper, we introduced a novel rapid autofocus camera for active refocusing\nofthe iris area ofthe moving objects using a focus-tunable lens. Our end-to-end\ncomputational algorithm can predict the best focus position from one single\nblurred image and generate a lens diopter control signal automatically. This\nscene-based active manipulation method enables real-time focus tracking of the\niris area ofa moving object. We built a testing bench to collect real-world\nfocal stacks for evaluation of the autofocus methods. Our camera has reached an\nautofocus speed ofover 50 fps. The results demonstrate the advantages of our\nproposed camera for biometric perception in static and dynamic scenes. The code\nis available at https://github.com/Debatrix/AquulaCam.\n"}
+{"abstract": "  We minimize the stray electric field in a linear Paul trap quickly and\naccurately, by applying interferometry pulse sequences to a trapped ion optical\nqubit. The interferometry sequences are sensitive to the change of ion\nequilibrium position when the trap stiffness is changed, and we use this to\ndetermine the stray electric field. The simplest pulse sequence is a two-pulse\nRamsey sequence, and longer sequences with multiple pulses offer a higher\nprecision. The methods allow the stray field strength to be minimized beyond\nstate-of-the-art levels, with only modest experimental requirements. Using a\nsequence of nine pulses we reduce the 2D stray field strength to\n$(10.5\\pm0.8)\\,\\mathrm{mV\\,m^{-1}}$ in $11\\,\\mathrm{s}$ measurement time. The\npulse sequences are easy to implement and automate, and they are robust against\nlaser detuning and pulse area errors.\n  We use interferometry sequences with different lengths and precisions to\nmeasure the stray field with an uncertainty below the standard quantum limit.\nThis marks a real-world case in which quantum metrology offers a significant\nenhancement. Also, we minimize micromotion in 2D using a single probe laser, by\nusing an interferometry method together with the resolved sideband method; this\nis useful for experiments with restricted optical access.\n  Furthermore, a technique presented in this work is related to quantum\nprotocols for synchronising clocks; we demonstrate these protocols here.\n"}
+{"abstract": "  We learn an interactive vision-based driving policy from pre-recorded driving\nlogs via a model-based approach. A forward model of the world supervises a\ndriving policy that predicts the outcome of any potential driving trajectory.\nTo support learning from pre-recorded logs, we assume that the world is on\nrails, meaning neither the agent nor its actions influence the environment.\nThis assumption greatly simplifies the learning problem, factorizing the\ndynamics into a nonreactive world model and a low-dimensional and compact\nforward model of the ego-vehicle. Our approach computes action-values for each\ntraining trajectory using a tabular dynamic-programming evaluation of the\nBellman equations; these action-values in turn supervise the final vision-based\ndriving policy. Despite the world-on-rails assumption, the final driving policy\nacts well in a dynamic and reactive world. At the time of writing, our method\nranks first on the CARLA leaderboard, attaining a 25% higher driving score\nwhile using 40 times less data. Our method is also an order of magnitude more\nsample-efficient than state-of-the-art model-free reinforcement learning\ntechniques on navigational tasks in the ProcGen benchmark.\n"}
+{"abstract": "  We present new radio observations of the binary neutron star merger GW170817\ncarried out with the Karl G. Jansky Very large Array (VLA) more than 3\\,yrs\nafter the merger. Our combined dataset is derived by co-adding more than\n$\\approx32$\\,hours of VLA time on-source, and as such provides the deepest\ncombined observation (rms sensitivity $\\approx 0.99\\,\\mu$Jy) of the GW170817\nfield obtained to date at 3\\,GHz. We find no evidence for a late-time radio\nre-brightening at a mean epoch of $t\\approx 1200$\\,d since merger, in contrast\nto a $\\approx 2.1\\,\\sigma$ excess observed at X-ray wavelengths at the same\nmean epoch. Our measurements agree with expectations from the post-peak decay\nof the radio afterglow of the GW170817 structured jet. Using these results, we\nconstrain the parameter space of models that predict a late-time radio\nre-brightening possibly arising from the high-velocity tail of the GW170817\nkilonova ejecta, which would dominate the radio and X-ray emission years after\nthe merger (once the structured jet afterglow fades below detection level). Our\nresults point to a steep energy-speed distribution of the kilonova ejecta (with\nenergy-velocity power law index $\\alpha \\gtrsim 5$). We suggest possible\nimplications of our radio analysis, when combined with the recent tentative\nevidence for a late-time re-brightening in the X-rays, and highlight the need\nfor continued radio-to-X-ray monitoring to test different scenarios.\n"}
+{"abstract": "  We have obtained new observations of the absorption system at\n$z_\\mathrm{abs}=0.48$ toward QSO Q0454-220, which we use to constrain its\nchemical and physical conditions. The system features metal-enriched gas and\npreviously unknown low-metallicity gas detected $\\sim 200 \\, \\mathrm{km \\,\ns^{-1}}$ blueward of the metal-enriched gas. The low-metallicity gas is\ndetected in multiple Lyman series lines but is not detected in any metal lines.\nOur analysis includes low-ionization (e.g., Fe II, Mg II) metal lines,\nhigh-ionization (e.g., C IV, O VI, N V) metal lines, and several Lyman series\nlines. We use new UV spectra taken with HST/COS along with data taken from\nHST/STIS, Keck/HIRES, and VLT/UVES. We find that the absorption system can be\nexplained with a photoionized low-ionization phase with $\\mathrm{[Fe/H]} \\sim\n-0.5$ and $n_\\mathrm{H} \\sim 10^{-2.3} \\, \\mathrm{cm}^{-3}$, a photoionized\nhigh-ionization phase with a conservative lower limit of $-3.3 <\n\\mathrm{[Fe/H]}$ and $n_\\mathrm{H} \\sim 10^{-3.8} \\, \\mathrm{cm}^{-3}$, and a\nlow-metallicity component with a conservative upper limit of $\\mathrm{[Fe/H]} <\n-2.5$ that may be photoionized or collisionally ionized. We suggest that the\nlow-ionization phase may be due to cold-flow accretion via large-scale\nfilamentary structure or due to recycled accretion while the high-ionization\nphase is the result of ancient outflowing material from a nearby galaxy. The\nlow-metallicity component may come from pristine accretion. The velocity spread\nand disparate conditions among the absorption system's components suggest a\ncombination of gas arising near galaxies along with gas arising from intergroup\nmaterial.\n"}
+{"abstract": "  Minimally invasive surgery mainly consists of a series of sub-tasks, which\ncan be decomposed into basic gestures or contexts. As a prerequisite of\nautonomic operation, surgical gesture recognition can assist motion planning\nand decision-making, and build up context-aware knowledge to improve the\nsurgical robot control quality. In this work, we aim to develop an effective\nsurgical gesture recognition approach with an explainable feature extraction\nprocess. A Bidirectional Multi-Layer independently RNN (BML-indRNN) model is\nproposed in this paper, while spatial feature extraction is implemented via\nfine-tuning of a Deep Convolutional Neural Network(DCNN) model constructed\nbased on the VGG architecture. To eliminate the black-box effects of DCNN,\nGradient-weighted Class Activation Mapping (Grad-CAM) is employed. It can\nprovide explainable results by showing the regions of the surgical images that\nhave a strong relationship with the surgical gesture classification results.\nThe proposed method was evaluated based on the suturing task with data obtained\nfrom the public available JIGSAWS database. Comparative studies were conducted\nto verify the proposed framework. Results indicated that the testing accuracy\nfor the suturing task based on our proposed method is 87.13%, which outperforms\nmost of the state-of-the-art algorithms.\n"}
+{"abstract": "  R Coronae Borealis (RCB) stars are rare hydrogen-deficient carbon-rich\nvariable supergiants thought to be the result of dynamically unstable white\ndwarf mergers. We attempt to model RCBs through all the relevant timescales by\nsimulating a merger event in Octo-tiger, a 3D adaptive mesh refinement (AMR)\nhydrodynamics code and mapping the post-merger object into MESA, a 1D stellar\nevolution code. We then post-process the nucleosynthesis on a much larger\nnuclear reaction network to study the enhancement of s-process elements. We\npresent models that match observations or previous studies in most surface\nabundances, isotopic ratios, early evolution and lifetimes. We also observe\nsimilar mixing behavior as previous modeling attempts which result in the\npartial He-burning products visible on the surface in observations. However, we\ndo note that our sub-solar models lack any enhancement in s-process elements,\nwhich we attribute to a lack of hydrogen in the envelope. We also find that the\nOxygen-16/Oxygen-18 isotopic ratio is very sensitive to initial hydrogen\nabundance and increases outside of the acceptable range with a hydrogen mass\nfraction greater than $10^{-4}$.\n"}
+{"abstract": "  We report a detailed theoretical study of a coherent macroscopic\nquantum-mechanical phenomenon - quantum beats of a single magnetic fluxon\ntrapped in a two-cell SQUID of high kinetic inductance. We calculate\nnumerically and analytically the low-lying energy levels of the fluxon, and\nexplore their dependence on externally applied magnetic fields. The quantum\ndynamics of the fluxon shows quantum beats originating from its coherent\nquantum tunneling between the SQUID cells. We analyze the experimental setup\nbased on a three-cell SQUID, allowing for time-resolved measurements of quantum\nbeats of the fluxon.\n"}
+{"abstract": "  We present an approach for continual learning (CL) that is based on fully\nprobabilistic (or generative) models of machine learning. In contrast to, e.g.,\nGANs that are \"generative\" in the sense that they can generate samples, fully\nprobabilistic models aim at modeling the data distribution directly.\nConsequently, they provide functionalities that are highly relevant for\ncontinual learning, such as density estimation (outlier detection) and sample\ngeneration. As a concrete realization of generative continual learning, we\npropose Gaussian Mixture Replay (GMR). GMR is a pseudo-rehearsal approach using\na Gaussian Mixture Model (GMM) instance for both generator and classifier\nfunctionalities. Relying on the MNIST, FashionMNIST and Devanagari benchmarks,\nwe first demonstrate unsupervised task boundary detection by GMM density\nestimation, which we also use to reject untypical generated samples. In\naddition, we show that GMR is capable of class-conditional sampling in the way\nof a cGAN. Lastly, we verify that GMR, despite its simple structure, achieves\nstate-of-the-art performance on common class-incremental learning problems at\nvery competitive time and memory complexity.\n"}
+{"abstract": "  We report measurements of the parity-conserving beam-normal single-spin\nelastic scattering asymmetries $B_n$ on $^{12}$C and $^{27}$Al, obtained with\nan electron beam polarized transverse to its momentum direction. These\nmeasurements add an additional kinematic point to a series of previous\nmeasurements of $B_n$ on $^{12}$C and provide a first measurement on $^{27}$Al.\nThe experiment utilized the Qweak apparatus at Jefferson Lab with a beam energy\nof 1.158 GeV. The average lab scattering angle for both targets was 7.7\ndegrees, and the average $Q^2$ for both targets was 0.02437 GeV$^2$ (Q=0.1561\nGeV). The asymmetries are $B_n$ = -10.68 $\\pm$ 0.90 stat) $\\pm$ 0.57 (syst) ppm\nfor $^{12}$C and $B_n$ = -12.16 $\\pm$ 0.58 (stat) $\\pm$ 0.62 (syst) ppm for\n$^{27}$Al. The results are consistent with theoretical predictions, and are\ncompared to existing data. When scaled by Z/A, the Q-dependence of all the\nfar-forward angle (theta < 10 degrees) data from $^{1}$H to $^{27}$Al can be\ndescribed by the same slope out to $Q \\approx 0.35$ GeV. Larger-angle data from\nother experiments in the same Q range are consistent with a slope about twice\nas steep.\n"}
+{"abstract": "  Multi Layer Capacitors MLCs are considered as the most promising refrigerant\nelements to design and develop electrocaloric cooling devices. Recently, the\nheat transfer of these MLCs has been considered. However, the heat exchange\nwith the surrounding environment has been poorly, if not, addressed. In this\nwork, we measure by infrared thermography the temperature change versus time in\nfour different heat exchange configurations. Depending on the configurations,\nNewtonian and non-Newtonian regimes with their corresponding Biot number are\ndetermined allowing to provide useful thermal characteristics. Indeed, in case\nof large area thermal pad contacts, heat transfer coefficients up to 3400 W m-2\nK-1 are obtained showing that the standard MLCs already reach the needs for\ndesigning efficient prototypes. We also determine the ideal Brayton cooling\npower in case of thick wires contact which varies between 3.4 mW and 9.8 mW for\noperating frequencies varying from 0.25 Hz to 1 Hz. While only heat conduction\nis considered here, our work provides some design rules for improving heat\nexchanges in future devices.\n"}
+{"abstract": "  Complex plasmas consist of microparticles embedded in a low-temperature\nplasma and allow investigating various effects by tracing the motion of these\nmicroparticles. Dust density waves appear in complex plasmas as self-excited\nacoustic waves in the microparticle fluid at low neutral gas pressures. Here we\nshow that various properties of these waves depend on the position of the\nmicroparticle cloud with respect to the plasma sheath and explain this finding\nin terms of the underlying ion-drift instability. These results may be helpful\nin better understanding the propagation of dust density waves in complex\nplasmas and beyond, for instance, in astrophysical dusty plasmas.\n"}
+{"abstract": "  In the second of two papers on the peculiar interacting transient AT 2016jbu,\nwe present the bolometric lightcurve, identification and analysis of the\nprogenitor candidate, as well as preliminary modelling to help elucidate the\nnature of this event. We identify the progenitor candidate for AT 2016jbu in\nquiescence, and find it to be consistent with a $\\sim$20 M$_{\\odot}$ yellow\nhypergiant surrounded by a dusty circumstellar shell. We see evidence for\nsignificant photometric variability in the progenitor, as well as strong\nH$\\alpha$ emission consistent with pre-existing circumstellar material. The age\nof the resolved stellar population surrounding AT 2016jbu, as well as\nintegral-field unit spectra of the region support a progenitor age of >16 Myr,\nagain consistent with a progenitor mass of $\\sim$20 M$_{\\odot}$. Through a\njoint analysis of the velocity evolution of AT 2016jbu, and the photospheric\nradius inferred from the bolometric lightcurve, we find that the transient is\nconsistent with two successive outbursts or explosions. The first outburst\nejected a shell of material with velocity 650 km $s^{-1}$, while the second\nmore energetic event ejected material at 4500 km $s^{-1}$. Whether the latter\nis the core-collapse of the progenitor remains uncertain, as the required\nejecta mass is relatively low (few tenths of M$_{\\odot}$). We also place a\nrestrictive upper limit on the ejected $^{56}$Ni mass of <0.016 M$_{\\odot}$.\nUsing the BPASS code, we explore a wide range of possible progenitor systems,\nand find that the majority of these are in binaries, some of which are\nundergoing mass transfer or common envelope evolution immediately prior to\nexplosion. Finally, we use the SNEC code to demonstrate that the low-energy\nexplosion of some of these systems together with sufficient CSM can reproduce\nthe overall morphology of the lightcurve of AT 2016jbu.\n"}
+{"abstract": "  In the present paper, we introduce a concept of Ricci curvature on\nhypergraphs for a nonlinear Laplacian. We prove that our definition of the\nRicci curvature is a generalization of Lin-Lu-Yau coarse Ricci curvature for\ngraphs to hypergraphs. We also show a lower bound of nonzero eigenvalues of\nLaplacian, gradient estimate of heat flow, and diameter bound of Bonnet-Myers\ntype for our curvature notion. This research leads to understanding how\nnonlinearity of Laplacian causes complexity of curvatures.\n"}
+{"abstract": "  The Jahn-Teller theorem constitutes one of the most popular and stringent\nconcepts, applicable to all fields of chemistry. In open shell transition\nelements chemistry and physics, 3d4, 3d9, and 3d7(low-spin) configurations in\noctahedral complexes serve as particular illustrative and firm examples, where\na striking change (distortion) in local geometry is associated to a substantial\nreduction of electronic energy. However, there has been a lasting debate, about\nthe fact that the octahedra are found to exclusively elongate, (at least for eg\nelectrons). Against this background, the title compound displays two marked\nfeatures, (1) the octahedron of oxygen atoms around Os6+ (d2) is drastically\ncompressed, in contrast to the standard JT expectations, and (2) the splitting\nof the t2g set induced by this compression is extreme, such that a diamagnetic\nground state results. What we see is obviously a Jahn-Teller distortion\nresulting in a compression of the respective octahedron and acting on the t2g\nset of orbitals. Both these issues are unprecedented. Noteworthy, the splitting\ninto a lower dxy (hosting two d electrons with opposite spin) and two higher\ndxz and dyz orbitals is so large that for the first time ever the Hund's\ncoupling for t2g electrons is overcome. We show that these effects are not\nforced by structural frustration, the structure offers sufficient space for Os\nto shift the apical oxygen atoms to a standard distance. Local electronic\neffects appear to be responsible, instead. The relevance of these findings is\nfar reaching, since they provide insights in the hierarchy of perturbations\ndefining ground states of open shell electronic systems. The system studied\nhere, offers substantially more structural and compositional degrees of\nfreedom, such that a configuration could form that enables Os6+ to adopt its\napparently genuine diamagnetic ground state.\n"}
+{"abstract": "  There are several methods for model selection in cosmology which have at\nleast two major goals, that of finding the correct model or predicting well. In\nthis work we discuss through a study of well-known model selection methods like\nAkaike information criterion (AIC), Bayesian information criterion (BIC),\ndeviance information criterion (DIC) and Bayesian evidence, how these different\ngoals are pursued in each paradigm. We also apply another method for model\nselection which less seen in cosmological literature, the Cross-validation\nmethod. Using these methods we will compare two different scenarios in\ncosmology, $\\Lambda$CDM model and dynamical dark energy. We show that each of\nthe methods tends to different results in model selection. While BIC and\nBayesian evidence overrule the dynamical dark energy scenarios with 2 or 3\nextra degree of freedom, the DIC and cross-validation method prefer these\ndynamical models to $\\Lambda$CDM model. Assuming the numerical results of\ndifferent analysis and combining cosmological and statistical aspects of the\nsubject, we propose cross-validation as an interesting method for model\nselection in cosmology that can lead to different results in comparison with\nusual methods of model selection.\n"}
+{"abstract": "  Sound event detection is a core module for acoustic environmental analysis.\nSemi-supervised learning technique allows to largely scale up the dataset\nwithout increasing the annotation budget, and recently attracts lots of\nresearch attention. In this work, we study on two advanced semi-supervised\nlearning techniques for sound event detection. Data augmentation is important\nfor the success of recent deep learning systems. This work studies the\naudio-signal random augmentation method, which provides an augmentation\nstrategy that can handle a large number of different audio transformations. In\naddition, consistency regularization is widely adopted in recent\nstate-of-the-art semi-supervised learning methods, which exploits the\nunlabelled data by constraining the prediction of different transformations of\none sample to be identical to the prediction of this sample. This work finds\nthat, for semi-supervised sound event detection, consistency regularization is\nan effective strategy, especially the best performance is achieved when it is\ncombined with the MeanTeacher model.\n"}
+{"abstract": "  We derive the non-relativistic limit of a massive vector field. We show that\nthe Cartesian spatial components of the vector behave as three identical,\nnon-interacting scalar fields. We find classes of spherical, cylindrical, and\nplanar self-gravitating vector solitons in the Newtonian limit. The\ngravitational properties of the lowest-energy vector solitons$\\mathrm{-}$the\ngravitational potential and density field$\\mathrm{-}$depend only on the net\nmass of the soliton and the vector particle mass. In particular, these\nself-gravitating, ground-state vector solitons are independent of the\ndistribution of energy across the vector field components, and are\nindistinguishable from their scalar-field counterparts. Fuzzy Vector Dark\nMatter models can therefore give rise to halo cores with identical\nobservational properties to the ones in scalar Fuzzy Dark Matter models. We\nalso provide novel hedgehog vector soliton solutions, which cannot be observed\nin scalar-field theories. The gravitational binding of the lowest-energy\nhedgehog halo is about three times weaker than the ground-state vector soliton.\nFinally, we show that no spherically symmetric solitons exist with a\ndivergence-free vector field.\n"}
+{"abstract": "  Given two algebraic groups $G$, $H$ over a field $k$, we investigate the\nrepresentability of the functor of morphisms (of schemes) $\\mathbf{Hom}(G,H)$\nand the subfunctor of homomorphisms (of algebraic groups) $\\mathbf{Hom}_{\\rm\ngp}(G,H)$. We show that $\\mathbf{Hom}(G,H)$ is represented by a group scheme,\nlocally of finite type, if the $k$-vector space $\\mathcal{O}(G)$ is\nfinite-dimensional; the converse holds if $H$ is not \\'etale. When $G$ is\nlinearly reductive and $H$ is smooth, we show that $\\mathbf{Hom}_{\\rm gp}(G,H)$\nis represented by a smooth scheme $M$; moreover, every orbit of $H$ acting by\nconjugation on $M$ is open.\n"}
+{"abstract": "  Modern power systems face a grand challenge in grid management due to\nincreased electricity demand, imminent disturbances, and uncertainties\nassociated with renewable generation, which can compromise grid security. The\nsecurity assessment is directly connected to the robustness of the operating\ncondition and is evaluated by analyzing proximity to the power flow solution\nspace's boundary. Calculating location of such a boundary is a computationally\nchallenging task, linked to the power flow equations' non-linear nature,\npresence of technological constraints, and complicated network topology. In\nthis paper we introduce a general framework to characterize points on the power\nflow solution space boundary in terms of auxiliary variables subject to\nalgebraic constraints. Then we develop an adaptive continuation algorithm to\ntrace 1-dimensional sections of boundary curves which exhibits robust\nperformance and computational tractability. Implementation of the algorithm is\ndescribed in detail, and its performance is validated on different test\nnetworks.\n"}
+{"abstract": "  We investigate optimal states of photon pairs to excite a target transition\nin a multilevel quantum system. With the help of coherent control theory for\ntwo-photon absorption with quantum light, we infer the maximal population\nachievable by optimal entangled vs. separable states of light. Interference\nbetween excitation pathways, as well as the presence of nearby states, may\nhamper the selective excitation of a particular target state, but we show that\nquantum correlations can help to overcome this problem, and enhance the\nachievable \"selectivity\" between two energy levels, i.e. the relative\ndifference in population transferred into each of them. We find that the added\nvalue of optimal entangled states of light increases with broadening linewidths\nof the target states.\n"}
+{"abstract": "  In non-centrosymmetric metals, spin-orbit coupling (SOC) induces\nmomentum-dependent spin polarization at the Fermi surfaces. This is exemplified\nby the valley-contrasting spin polarization in monolayer transition metal\ndichalcogenides (TMDCs) with in-plane inversion asymmetry. However, the valley\nconfiguration of massive Dirac fermions in TMDCs is fixed by the graphene-like\nstructure, which limits the variety of spin-valley coupling. Here, we show that\nthe layered polar metal BaMn$X_2$ ($X =$Bi, Sb) hosts tunable\nspin-valley-coupled Dirac fermions, which originate from the distorted $X$\nsquare net with in-plane lattice polarization. We found that in spite of the\nlarger SOC, BaMnBi$_2$ has approximately one-tenth the lattice distortion of\nBaMnSb$_2$, from which a different configuration of spin-polarized Dirac\nvalleys is theoretically predicted. This was experimentally observed as a clear\ndifference in the Shubnikov-de Haas oscillation at high fields between the two\nmaterials. The chemically tunable spin-valley coupling in BaMn$X_2$ makes it a\npromising material for various spin-valleytronic devices.\n"}
+{"abstract": "  Traditionally, for most machine learning settings, gaining some degree of\nexplainability that tries to give users more insights into how and why the\nnetwork arrives at its predictions, restricts the underlying model and hinders\nperformance to a certain degree. For example, decision trees are thought of as\nbeing more explainable than deep neural networks but they lack performance on\nvisual tasks. In this work, we empirically demonstrate that applying methods\nand architectures from the explainability literature can, in fact, achieve\nstate-of-the-art performance for the challenging task of domain generalization\nwhile offering a framework for more insights into the prediction and training\nprocess. For that, we develop a set of novel algorithms including DivCAM, an\napproach where the network receives guidance during training via gradient based\nclass activation maps to focus on a diverse set of discriminative features, as\nwell as ProDrop and D-Transformers which apply prototypical networks to the\ndomain generalization task, either with self-challenging or attention\nalignment. Since these methods offer competitive performance on top of\nexplainability, we argue that the proposed methods can be used as a tool to\nimprove the robustness of deep neural network architectures.\n"}
+{"abstract": "  Static analysis tools typically address the problem of excessive false\npositives by requiring programmers to explicitly annotate their code. However,\nwhen faced with incomplete annotations, many analysis tools are either too\nconservative, yielding false positives, or too optimistic, resulting in unsound\nanalysis results. In order to flexibly and soundly deal with\npartially-annotated programs, we propose to build upon and adapt the gradual\ntyping approach to abstract-interpretation-based program analyses.\nSpecifically, we focus on null-pointer analysis and demonstrate that a gradual\nnull-pointer analysis hits a sweet spot, by gracefully applying static analysis\nwhere possible and relying on dynamic checks where necessary for soundness. In\naddition to formalizing a gradual null-pointer analysis for a core imperative\nlanguage, we build a prototype using the Infer static analysis framework, and\npresent preliminary evidence that the gradual null-pointer analysis reduces\nfalse positives compared to two existing null-pointer checkers for Infer.\nFurther, we discuss ways in which the gradualization approach used to derive\nthe gradual analysis from its static counterpart can be extended to support\nmore domains. This work thus provides a basis for future analysis tools that\ncan smoothly navigate the tradeoff between human effort and run-time overhead\nto reduce the number of reported false positives.\n"}
+{"abstract": "  In the absence of dissipation a non-rotating magnetic nanoparticle can be\nstably levitated in a static magnetic field as a consequence of the spin origin\nof its magnetization. Here, we study the effects of dissipation on the\nstability of the system, considering the interaction with the background gas\nand the intrinsic Gilbert damping of magnetization dynamics. We find that\ndissipation limits the time over which a particle can be stably levitated. At\nlarge applied magnetic fields we identify magnetization switching induced by\nGilbert damping as the key limiting factor for stable levitation. At low\napplied magnetic fields and for small particle dimensions magnetization\nswitching is prevented due to the strong coupling of rotation and magnetization\ndynamics, and the stability is mainly limited by the gas-induced dissipation.\nIn this latter case, high vacuum should be sufficient to extend stable\nlevitation over experimentally relevant timescales. Our results demonstrate the\npossibility to experimentally observe the phenomenon of quantum spin stabilized\nmagnetic levitation.\n"}
+{"abstract": "  Cryptocurrencies are increasingly popular. Even people who are not experts\nhave started to invest in these securities, and nowadays, cryptocurrency\nexchanges process transactions for over 100 billion US dollars per month. In\nspite of this, many cryptocurrencies have low liquidity, and therefore, they\nare highly prone to market manipulation. This paper performs an in-depth\nanalysis of two market manipulations organized by communities over the\nInternet: The pump and dump and the crowd pump. The pump and dump scheme is a\nfraud as old as the stock market. Now, it got new vitality in the loosely\nregulated market of cryptocurrencies. Groups of highly coordinated people\narrange this scam, usually on Telegram and Discord. We monitored these groups\nfor more than 3 years detecting around 900 individual events. We analyze how\nthese communities are organized and how they carry out the fraud. We report on\nthree case studies of pump and dump. Then, we leverage our unique dataset of\nthe verified pump and dumps to build a machine learning model able to detect a\npump and dump in 25 seconds from the moment it starts, achieving the results of\n94.5% of F1-score. Then, we move on to the crowd pump, a new phenomenon that\nhit the news in the first months of 2021, when a Reddit community inflates the\nprice of the GameStop stocks (GME) of over 1,900% on Wall Street, the world's\nlargest stock exchange. Later, other Reddit communities replicate the operation\non the cryptocurrency markets. The targets were Dogecoin (DOGE) and Ripple\n(XRP). We reconstruct how these operations developed, and we discuss\ndifferences and analogies with the standard pump and dump. Lastly, we\nillustrate how it is possible to leverage our classifier to detect this kind of\noperation too.\n"}
+{"abstract": "  Two-dimensional (2D) magnetic materials with strong magnetostriction are\ninteresting systems for strain-tuning the magnetization, enabling potential for\nrealizing spintronic and nanomagnetic devices. Realizing this potential\nrequires understanding of the magneto-mechanical coupling in the 2D limit. In\nthis work, we suspend thin Cr$_2$Ge$_2$Te$_6$ layers, creating nanomechanical\nmembrane resonators. We probe its mechanical and magnetic properties as a\nfunction of temperature and strain. Pronounced signatures of magneto-elastic\ncoupling are observed in the temperature-dependent resonance frequency of these\nmembranes near $T_{\\rm C}$. We further utilize Cr$_2$Ge$_2$Te$_6$ in\nheterostructures with thin layers of WSe$_2$ and FePS$_3$, which have positive\nthermal expansion coefficients, to compensate the negative thermal expansion\ncoefficient of Cr$_2$Ge$_2$Te$_6$ and quantitatively probe the corresponding\n$T_{\\rm C}$. Finally, we induce a strain of $0.016\\%$ in a suspended\nheterostructure via electrostatic force and demonstrate a resulting enhancement\nof $T_{\\rm C}$ by $2.5 \\pm 0.6$ K in the absence of an external magnetic field.\n"}
+{"abstract": "  Progress on the study of synchronisation in quantum systems has been largely\ndriven by specific examples which resulted in several examples of frequency\nentrainment as well as mutual synchronisation. Here we study quantum\nsynchronisation by utilising Liouville space perturbation theory. We begin by\nclarifying the role of centers, symmetries and oscillating coherences in the\ncontext of quantum synchronisation. We then analyse the eigenspectrum of the\nLiouville superoperator generating the dynamics of the quantum system and\ndetermine the conditions under which synchronisation arises. We apply our\nframework to derive a powerful relationship between energy conservation,\ndegeneracies and synchronisation in quantum systems. Finally, we demonstrate\nour approach by analysing two mutually coupled thermal machines and the close\nrelationship between synchronisation and thermodynamic quantities.\n"}
+{"abstract": "  Structural optimization (topology, shapes, sizing) is an important tool for\nfacilitating the emergence of new concepts in structural design. Normally,\ntopology optimization is carried out at the early stage of design and then\nshape and sizing design are performed sequentially. Unlike traditional topology\noptimization method, explicit methodologies have attracted a great deal of\nattention because of the advantages of shortcuting the costly CAD/CAE processes\nwhile dealing with low order number of design variables compared to implicit\nmethod (such as SIMP). This paper aims at presenting an adaptation of a\nflow-inspired approach so-called Moving Node Approach (MNA) in topology\noptimization. In this approach, the discretization is decoupled from the\nmaterial distribution and the final objective is to recognize the best beam\nassembly while minimizing compliance. The paradigm has here changed and new\ndesign variables are used such as nodes location, elements length/orientation\nand size providing a lower number of design variables than pixels-based. The\nmethodology is validated using 2 classical testcases in the field of Topology\nOptimization: the Cantilever beam and the L-Shape problem.\n"}
+{"abstract": "  The increase in the sensitivity of gravitational wave interferometers will\nbring additional detections of binary black hole and double neutron star\nmergers. It will also very likely add many merger events of black hole -\nneutron star binaries. Distinguishing mixed binaries from binary black holes\nmergers for high mass ratios could be challenging because in this situation the\nneutron star coalesces with the black hole without experiencing significant\ndisruption. To investigate the transition of mixed binary mergers into those\nbehaving more like binary black hole coalescences, we present results from\nmerger simulations for different mass ratios. We show how the degree of\ndeformation and disruption of the neutron star impacts the inspiral and merger\ndynamics, the properties of the final black hole, the accretion disk formed\nfrom the circularization of the tidal debris, the gravitational waves, and the\nstrain spectrum and mismatches. The results also show the effectiveness of the\ninitial data method that generalizes the Bowen-York initial data for black hole\npunctures to the case of binaries with neutron star companions.\n"}
+{"abstract": "  Pre-trained contextualized language models (PrLMs) have led to strong\nperformance gains in downstream natural language understanding tasks. However,\nPrLMs can still be easily fooled by adversarial word substitution, which is one\nof the most challenging textual adversarial attack methods. Existing defence\napproaches suffer from notable performance loss and complexities. Thus, this\npaper presents a compact and performance-preserved framework, Anomaly Detection\nwith Frequency-Aware Randomization (ADFAR). In detail, we design an auxiliary\nanomaly detection classifier and adopt a multi-task learning procedure, by\nwhich PrLMs are able to distinguish adversarial input samples. Then, in order\nto defend adversarial word substitution, a frequency-aware randomization\nprocess is applied to those recognized adversarial input samples. Empirical\nresults show that ADFAR significantly outperforms those newly proposed defense\nmethods over various tasks with much higher inference speed. Remarkably, ADFAR\ndoes not impair the overall performance of PrLMs. The code is available at\nhttps://github.com/LilyNLP/ADFAR\n"}
+{"abstract": "  We consider the renormalization-group evolution of the matrix element of\n$\\langle 0| \\bar{q}(z)_\\beta [z,0]b(0)_\\alpha| \\bar{B}\\rangle$, which can be\nused to define the distribution amplitudes for $B$ meson and widely applied in\nstudies of $B$ meson decays. The contribution to the renormalization constant\nof the non-local operator $\\bar{q}(z)_\\beta [z,0]b(0)_\\alpha$ is considered up\nto one-loop order in QCD. Since the quark fields in this operator are not\ndirectly coupled fields, momentum can not flow freely through this non-local\noperator. Momentum involved in this operator can be treated stringently in\ncoordinate space. We find that the ultraviolet divergences regulated by\ndimensional parameter $\\epsilon$ cancel with each other, and the evolution\neffect vanishes. The matrix element $\\langle 0| \\bar{q}(z)_\\beta\n[z,0]b(0)_\\alpha| \\bar{B}\\rangle$ escapes from the renormalization-group\nevolution. We then apply the matrix element in calculating $B\\to\\pi$ transition\nform factor, where the matrix element is obtained by using the $B$ meson wave\nfunction calculated in QCD-inspired potential model. By comparing with\nexperimental data for the semileptonic decay of $B\\to \\pi \\ell\\nu$ and\nlight-cone sum rule calculation, we analyse the perturbative and\nnon-perturbative contributions to $B\\to\\pi$ transition form factor in the frame\nwork of perturbative QCD approach. We find that the effectiveness of the\nsuppression of Sudakov factor to soft contribution depends on the end-point\nbehavior of $B$ meson wave function, and with the $B$-meson wave function used\nin this work, soft contribution can not be well suppressed. The hard\ncontribution to the $B\\to\\pi$ transition form factor is about 59\\%, and soft\ncontribution can be as large as 41\\% in the naive calculation. To make the\nperturbative calculation reliable, a soft momentum cutoff in the calculation\nand soft form factor have to be introduced.\n"}
+{"abstract": "  It has been shown that temperature cycles on airless bodies of our Solar\nSystem can cause damaging of surface materials. Nevertheless, propagation\nmechanisms in the case of space objects are still poorly understood. Present\nwork combines a thermoelasticity model together with linear elastic fracture\nmechanics theory to predict fracture propagation in the presence of thermal\ngradients generated by diurnal temperature cycling and under conditions similar\nto those existing on the asteroid Bennu. The crack direction is computed using\nthe maximal strain energy release rate criterion, which is implemented using\nfinite elements and the so-called G$\\theta$ method (Uribe-Su\\'arez et al. 2020.\nEng. Fracture Mech. 227:106918). Using the implemented methodology, crack\npropagation direction for an initial crack tip in different positions and for\ndifferent orientations is computed. It is found that cracks preferentially\npropagate in the North to South (N-S), in the North-East to South-West (NE-SW)\nand in the North-West to South-East (NW-SE) directions. Finally, thermal\nfatigue analysis was performed in order to estimate the crack growth rate.\nComputed value is in good agreement with available experimental evidence.\n"}
+{"abstract": "  We establish sharp dynamical implications of convexity on symmetric spheres\nthat do not follow from dynamical convexity. It allows us to show the existence\nof elliptic and non-hyperbolic periodic orbits and to furnish new examples of\ndynamically convex contact forms, in any dimension, that are not equivalent to\nconvex ones via contactomorphisms that preserve the symmetry. Moreover, these\nexamples are $C^1$-stable in the sense that they are actually not equivalent to\nconvex ones via contactomorphisms that are $C^1$-close to those preserving the\nsymmetry. We also show the multiplicity of symmetric non-hyperbolic and\nsymmetric (not necessarily non-hyperbolic) closed Reeb orbits under suitable\npinching conditions.\n"}
+{"abstract": "  We introduce a geometric operation, which we call the relative Whitney trick,\nthat removes a single double point between properly immersed surfaces in a\n$4$-manifold with boundary. Using the relative Whitney trick we prove that\nevery link in a homology sphere is homotopic to a link that is topologically\nslice in a contractible topological $4$-manifold. We further prove that any\nlink in a homology sphere is order $k$ Whitney tower concordant to a link in\n$S^3$ for all $k$. Finally, we explore the minimum Gordian distance from a link\nin $S^3$ to a homotopically trivial link. Extending this notion to links in\nhomology spheres, we use the relative Whitney trick to make explicit\ncomputations for 3-component links and establish bounds in general.\n"}
+{"abstract": "  We use relative hyperbolicity of mapping tori and Dehn fillings of relatively\nhyperbolic groups to solve the conjugacy problem between certain outer\nautomorphisms. We reduce this problem to algorithmic problems entirely\nexpressed in terms of the parabolic subgroups of the mapping tori. As an\nimmediate application, we solve the conjugacy problem for outer automorphisms\nof free groups, whose polynomial part is piecewise inner. This proposes a path\ntoward a full solution to the conjugacy problem for $\\mathrm{Out}(F_n)$.\n"}
+{"abstract": "  We investigate how the choice of equation of state (EOS) and resolution\nconspire to affect the outcomes of giant impact (GI) simulations. We focus on\nthe simple case of equal mass collisions of two Earth-like $0.5\\,M_\\oplus$\nproto-planets showing that the choice of EOS has a profound impact on the\noutcome of such collisions as well as on the numerical convergence with\nresolution. In simulations where the Tillotson EOS is used, impacts generate an\nexcess amount of vapour due to the lack of a thermodynamically consistent\ntreatment of phase transitions and mixtures. In oblique collisions this\nenhances the artificial angular momentum (AM) transport from the planet to the\ncircum-planetary disc reducing the planet's rotation period over time. Even at\na resolution of $1.3 \\times 10^6$ particles the result is not converged. In\nhead-on collisions the lack of a proper treatment of the solid/liquid-vapour\nphase transition allows the bound material to expand to very low densities\nwhich in turn results in very slow numerical convergence of the critical\nspecific impact energy for catastrophic disruption $Q_{RD}^*$ with increasing\nresolution as reported in prior work. The simulations where ANEOS is used for\noblique impacts are already converged at a modest resolution of $10^5$\nparticles, while head-on collisions converge when they evidence the post-shock\nformation of a dense iron-rich ring, which promotes gravitational\nre-accumulation of material. Once sufficient resolution is reached to resolve\nthe liquid-vapour phase transition of iron in the ANEOS case, and this ring is\nresolved, the value of $Q_{RD}^*$ has then converged.\n"}
+{"abstract": "  We studied theoretically the effect of a low concentration of adsorbed polar\nmolecules on the optical conductivity of graphene, within the Kubo linear\nresponse approximation. Our analysis is based on a continuum model\napproximation that includes up to next to nearest neighbors in the pristine\ngraphene effective Hamiltonian, thus extending the field-theoretical analysis\ndeveloped in Refs.[1,2]. Our results show that the conductivity can be\nexpressed in terms of renormalized quasiparticle parameters $\\tilde{v}_F$,\n$\\tilde{M}$ and $\\tilde{\\mu}$ that include the effect of the molecular surface\nconcentration $n_{dip}$ and dipolar moment $\\boldsymbol{\\mathcal{P}}$, thus\nproviding an analytical model for a graphene-based chemical sensor.\n"}
+{"abstract": "  Learning an empirically effective model with generalization using limited\ndata is a challenging task for deep neural networks. In this paper, we propose\na novel learning framework called PurifiedLearning to exploit task-irrelevant\nfeatures extracted from task-irrelevant labels when training models on\nsmall-scale datasets. Particularly, we purify feature representations by using\nthe expression of task-irrelevant information, thus facilitating the learning\nprocess of classification. Our work is built on solid theoretical analysis and\nextensive experiments, which demonstrate the effectiveness of PurifiedLearning.\nAccording to the theory we proved, PurifiedLearning is model-agnostic and\ndoesn't have any restrictions on the model needed, so it can be combined with\nany existing deep neural networks with ease to achieve better performance. The\nsource code of this paper will be available in the future for reproducibility.\n"}
+{"abstract": "  Virtual-reality (VR) and augmented-reality (AR) technology is increasingly\ncombined with eye-tracking. This combination broadens both fields and opens up\nnew areas of application, in which visual perception and related cognitive\nprocesses can be studied in interactive but still well controlled settings.\nHowever, performing a semantic gaze analysis of eye-tracking data from\ninteractive three-dimensional scenes is a resource-intense task, which so far\nhas been an obstacle to economic use. In this paper we present a novel approach\nwhich minimizes time and information necessary to annotate volumes of interest\n(VOIs) by using techniques from object recognition. To do so, we train\nconvolutional neural networks (CNNs) on synthetic data sets derived from\nvirtual models using image augmentation techniques. We evaluate our method in\nreal and virtual environments, showing that the method can compete with\nstate-of-the-art approaches, while not relying on additional markers or\npreexisting databases but instead offering cross-platform use.\n"}
+{"abstract": "  On a compact K\\\"ahler manifold $X$, Toeplitz operators determine a\ndeformation quantization $(\\operatorname{C}^\\infty(X, \\mathbb{C})[[\\hbar]],\n\\star)$ with separation of variables [10] with respect to transversal complex\npolarizations $T^{1, 0}X, T^{0, 1}X$ as $\\hbar \\to 0^+$ [15]. The analogous\nstatement is proved for compact symplectic manifolds with transversal\nnon-singular real polarizations [13].\n  In this paper, we establish the analogous result for transversal singular\nreal polarizations on compact toric symplectic manifolds $X$. Due to toric\nsingularities, half-form correction and localization of our Toeplitz operators\nare essential. Via norm estimations, we show that these Toeplitz operators\ndetermine a star product on $X$ as $\\hbar \\to 0^+$.\n"}
+{"abstract": "  We construct analogues of the Hecke operators for the moduli space of\nG-bundles on a curve X over a local field F with parabolic structures at\nfinitely many points. We conjecture that they define commuting compact normal\noperators on the Hilbert space of half-densities on this moduli space. In the\ncase F=C, we also conjecture that their joint spectrum is in a natural\nbijection with the set of opers on X for the Langlands dual group with real\nmonodromy. This may be viewed as an analytic version of the Langlands\ncorrespondence for complex curves. Furthermore, we conjecture an explicit\nformula relating the eigenvalues of the Hecke operators and the global\ndifferential operators studied in our previous paper arXiv:1908.09677. Assuming\nthe compactness conjecture, this formula follows from a certain system of\ndifferential equations satisfied by the Hecke operators, which we prove in this\npaper for G=PGL(n).\n"}
+{"abstract": "  The potential of using millimeter-wave (mmWave) to encounter the current\nbandwidth shortage has motivated packing more antenna elements in the same\nphysical size which permits the advent of massive\nmultiple-input-multiple-output (MIMO) for mmWave communication. However, with\nincreasing number of antenna elements, the ability of allocating a single\nRF-chain per antenna becomes infeasible and unaffordable. As a cost-effective\nalternative, the design of hybrid precoding has been considered where the\nlimited-scattering signals are captured by a high-dimensional RF precoder\nrealized by an analog phase-shifter network followed by a low-dimensional\ndigital precoder at baseband. In this paper, the max-min fair problem is\nconsidered to design a low-complexity hybrid precoder for multi-group\nmulticasting systems in mmWave channels. The problem is non-trivial due to two\nmain reasons: the original max-min problem for multi-group multicasting for a\nfully-digital precoder is non-convex, and the analog precoder places constant\nmodules constraint which restricts the feasible set of the precoders in the\ndesign problem. Therefore, we consider a low complexity hybrid precoder design\nto tackle and benefit from the mmWave channel structure. Each analog beamformer\nwas designed to maximize the minimum matching component for users within a\ngiven group. Once obtained, the digital precoder was attained by solving the\nmax-min problem of the equivalent channel.\n"}
+{"abstract": "  Background: To prevent future outbreaks of COVID-19, Australia is pursuing a\nmass-vaccination approach in which a targeted group of the population\ncomprising healthcare workers, aged-care residents and other individuals at\nincreased risk of exposure will receive a highly effective priority vaccine.\nThe rest of the population will instead have access to a less effective\nvaccine.\n  Methods: We apply a large-scale agent-based model of COVID-19 in Australia to\ninvestigate the possible implications of this hybrid approach to\nmass-vaccination. The model is calibrated to recent epidemiological and\ndemographic data available in Australia, and accounts for several components of\nvaccine efficacy.\n  Findings: Within a feasible range of vaccine efficacy values, our model\nsupports the assertion that complete herd immunity due to vaccination is not\nlikely in the Australian context. For realistic scenarios in which herd\nimmunity is not achieved, we simulate the effects of mass-vaccination on\nepidemic growth rate, and investigate the requirements of lockdown measures\napplied to curb subsequent outbreaks. In our simulations, Australia's\nvaccination strategy can feasibly reduce required lockdown intensity and\ninitial epidemic growth rate by 43% and 52%, respectively. The severity of\nepidemics, as measured by the peak number of daily new cases, decreases by up\nto two orders of magnitude under plausible mass-vaccination and lockdown\nstrategies.\n  Interpretation: The study presents a strong argument for a large-scale\nvaccination campaign in Australia, which would substantially reduce both the\nintensity of future outbreaks and the stringency of non-pharmaceutical\ninterventions required for their suppression.\n"}
+{"abstract": "  The study of network structural controllability focuses on the minimum number\nof driver nodes needed to control a whole network. Despite intensive studies on\nthis topic, most of them consider static networks only. It is well-known,\nhowever, that real networks are growing, with new nodes and links added to the\nsystem. Here, we analyze controllability of evolving networks and propose a\ngeneral rule for the change of driver nodes. We further apply the rule to solve\nthe problem of network augmentation subject to the controllability constraint.\nThe findings fill a gap in our understanding of network controllability and\nshed light on controllability of real systems.\n"}
+{"abstract": "  This article presents the results of investigations using topic modeling of\nthe Voynich Manuscript (Beinecke MS408). Topic modeling is a set of\ncomputational methods which are used to identify clusters of subjects within\ntext. We use latent dirichlet allocation, latent semantic analysis, and\nnonnegative matrix factorization to cluster Voynich pages into `topics'. We\nthen compare the topics derived from the computational models to clusters\nderived from the Voynich illustrations and from paleographic analysis. We find\nthat computationally derived clusters match closely to a conjunction of scribe\nand subject matter (as per the illustrations), providing further evidence that\nthe Voynich Manuscript contains meaningful text.\n"}
+{"abstract": "  Sparsely-gated Mixture of Experts networks (MoEs) have demonstrated excellent\nscalability in Natural Language Processing. In Computer Vision, however, almost\nall performant networks are \"dense\", that is, every input is processed by every\nparameter. We present a Vision MoE (V-MoE), a sparse version of the Vision\nTransformer, that is scalable and competitive with the largest dense networks.\nWhen applied to image recognition, V-MoE matches the performance of\nstate-of-the-art networks, while requiring as little as half of the compute at\ninference time. Further, we propose an extension to the routing algorithm that\ncan prioritize subsets of each input across the entire batch, leading to\nadaptive per-image compute. This allows V-MoE to trade-off performance and\ncompute smoothly at test-time. Finally, we demonstrate the potential of V-MoE\nto scale vision models, and train a 15B parameter model that attains 90.35% on\nImageNet.\n"}
+{"abstract": "  A recent proposal for a superdeterministic account of quantum mechanics,\nnamed Invariant-set theory, appears to bring ideas from several diverse fields\nlike chaos theory, number theory and dynamical systems to quantum foundations.\nHowever, a clear cut hidden-variable model has not been developed, which makes\nit difficult to assess the proposal from a quantum foundational perspective. In\nthis article, we first build a hidden-variable model based on the proposal, and\nthen critically analyse several aspects of the proposal using the model. We\nshow that several arguments related to counter-factual measurements,\nnonlocality, non-commutativity of quantum observables, measurement independence\netcetera that appear to work in the proposal fail when considered in our model.\nWe further show that our model is not only superdeterministic but also\nnonlocal, with an ontic quantum state. We argue that the bit string defined in\nthe model is a hidden variable and that it contains redundant information.\nLastly, we apply the analysis developed in a previous work (Proc. R. Soc. A,\n476(2243):20200214, 2020) to illustrate the issue of superdeterministic\nconspiracy in the model. Our results lend further support to the view that\nsuperdeterminism is unlikely to solve the puzzle posed by the Bell\ncorrelations.\n"}
+{"abstract": "  We consider statistical models arising from the common set of solutions to a\nsparse polynomial system with general coefficients. The maximum likelihood\ndegree counts the number of critical points of the likelihood function\nrestricted to the model.\n  We prove the maximum likelihood degree of a sparse polynomial system is\ndetermined by its Newton polytopes and equals the mixed volume of a related\nLagrange system of equations. As a corollary, we find that the algebraic degree\nof several optimization problems is equal to a similar mixed volume.\n"}
+{"abstract": "  Single crystals of SrMn2P2 and CaMn2P2 were grown using Sn flux and\ncharacterized by single-crystal x-ray diffraction, electrical resistivity rho,\nheat capacity Cp, and magnetic susceptibility chi = M/H measurements versus\ntemperature T and magnetization M versus applied magnetic field H isotherm\nmeasurements. The x-ray diffraction results show that both compounds adopt the\ntrigonal CaAl2Si2-type structure. The rho(T) measurements demonstrate\ninsulating ground states for both compounds. The chi(T) and Cp(T) data reveal a\nweak first-order antiferromagnetic (AFM) transition at the Neel temperature TN\n= 53(1) K for SrMn2P2 and a strong first-order AFM transition at TN = 69.8(3) K\nfor CaMn2P2. Both compounds show an isotropic and nearly T-independent chi(T <\nTN). {31}P NMR measurements confirm the strong first-order transition in\nCaMn2P2 but show critical slowing down near TN for SrMn2P2 thus evidencing\nsecond-order character. The NMR measurements also indicate that the AFM\nstructure of CaMn2P2 is commensurate with the lattice whereas that of SrMn2P2\nis incommensurate. These first-order AFM transitions are unique among the class\nof trigonal (Ca, Sr, Ba)Mn2(P, As, Sb, Bi)2 compounds which otherwise exhibit\nsecond-order AFM transitions. This result presents a challenge to understand\nthe systematics of magnetic ordering in this class of materials in which\nmagnetically-frustrated antiferromagnetism is quasi-two-dimensional.\n"}
+{"abstract": "  We used a convolutional neural network to infer stellar rotation periods from\na set of synthetic light curves simulated with realistic spot evolution\npatterns. We convolved these simulated light curves with real TESS light curves\ncontaining minimal intrinsic astrophysical variability to allow the network to\nlearn TESS systematics and estimate rotation periods despite them. In addition\nto periods, we predict uncertainties via heteroskedastic regression to estimate\nthe credibility of the period predictions. In the most credible half of the\ntest data, we recover 10%-accurate periods for 46% of the targets, and\n20%-accurate periods for 69% of the targets. Using our trained network, we\nsuccessfully recover periods of real stars with literature rotation\nmeasurements, even past the 13.7-day limit generally encountered by TESS\nrotation searches using conventional period-finding techniques. Our method also\ndemonstrates resistance to half-period aliases. We present the neural network\nand simulated training data, and introduce the software butterpy used to\nsynthesize the light curves using realistic star spot evolution.\n"}
+{"abstract": "  We pursue a novel strategy towards a first detection of continuous\ngravitational waves from rapidly-rotating deformed neutron stars. Computational\npower is focused on a narrow region of signal parameter space selected by a\nstrategically-chosen benchmark. We search data from the 2nd observing run of\nthe LIGO Observatory with an optimised analysis run on graphics processing\nunits. While no continuous waves are detected, the search achieves a\nsensitivity to gravitational wave strain of $h_0 = 1.01{\\times}10^{-25}$ at 90%\nconfidence, 24% to 69% better than past searches of the same parameter space.\nConstraints on neutron star deformity are within theoretical maxima, thus a\ndetection by this search was not inconceivable.\n"}
+{"abstract": "  A high imbalance exists between technical debt and non-technical debt source\ncode comments. Such imbalance affects Self-Admitted Technical Debt (SATD)\ndetection performance, and existing literature lacks empirical evidence on the\nchoice of balancing technique. In this work, we evaluate the impact of multiple\nbalancing techniques, including Data level, Classifier level, and Hybrid, for\nSATD detection in Within-Project and Cross-Project setup. Our results show that\nthe Data level balancing technique SMOTE or Classifier level Ensemble\napproaches Random Forest or XGBoost are reasonable choices depending on whether\nthe goal is to maximize Precision, Recall, F1, or AUC-ROC. We compared our\nbest-performing model with the previous SATD detection benchmark\n(cost-sensitive Convolution Neural Network). Interestingly the top-performing\nXGBoost with SMOTE sampling improved the Within-project F1 score by 10% but\nfell short in Cross-Project set up by 9%. This supports the higher\ngeneralization capability of deep learning in Cross-Project SATD detection, yet\nwhile working within individual projects, classical machine learning algorithms\ncan deliver better performance. We also evaluate and quantify the impact of\nduplicate source code comments in SATD detection performance. Finally, we\nemploy SHAP and discuss the interpreted SATD features. We have included the\nreplication package and shared a web-based SATD prediction tool with the\nbalancing techniques in this study.\n"}
+{"abstract": "  Quantum dots are arguably one of the best platforms for optically accessible\nspin based qubits. The paramount demand of extended qubit storage time can be\nmet by using quantum-dot-confined dark exciton: a longlived electron-hole pair\nwith parallel spins. Despite its name the dark exciton reveals weak\nluminescence that can be directly measured. The origins of this optical\nactivity remain largely unexplored. In this work, using the atomistic\ntight-binding method combined with configuration-interaction approach, we\ndemonstrate that atomic-scale randomness strongly affects oscillator strength\nof dark excitons confined in self-assembled cylindrical InGaAs quantum dots\nwith no need for faceting or shape-elongation. We show that this process is\nmediated by two mechanisms: mixing dark and bright configurations by exchange\ninteraction, and equally important appearance of non-vanishing optical\ntransition matrix elements that otherwise correspond to nominally forbidden\ntransitions in a non-alloyed case. The alloy randomness has essential impact on\nboth bright and dark exciton states, including their energy, emission\nintensity, and polarization angle. We conclude that, due to the atomic-scale\nalloy randomness, finding dots with desired dark exciton properties may require\nexploration of a large ensemble, similarly to how dots with low bright exciton\nsplitting are selected for entanglement generation.\n"}
+{"abstract": "  Superpixel is generated by automatically clustering pixels in an image into\nhundreds of compact partitions, which is widely used to perceive the object\ncontours for its excellent contour adherence. Although some works use the\nConvolution Neural Network (CNN) to generate high-quality superpixel, we\nchallenge the design principles of these networks, specifically for their\ndependence on manual labels and excess computation resources, which limits\ntheir flexibility compared with the traditional unsupervised segmentation\nmethods. We target at redefining the CNN-based superpixel segmentation as a\nlifelong clustering task and propose an unsupervised CNN-based method called\nLNS-Net. The LNS-Net can learn superpixel in a non-iterative and lifelong\nmanner without any manual labels. Specifically, a lightweight feature embedder\nis proposed for LNS-Net to efficiently generate the cluster-friendly features.\nWith those features, seed nodes can be automatically assigned to cluster pixels\nin a non-iterative way. Additionally, our LNS-Net can adapt the sequentially\nlifelong learning by rescaling the gradient of weight based on both channel and\nspatial context to avoid overfitting. Experiments show that the proposed\nLNS-Net achieves significantly better performance on three benchmarks with\nnearly ten times lower complexity compared with other state-of-the-art methods.\n"}
+{"abstract": "  In the May-Leonard model of three cyclically competing species, we analyze\nthe statistics of rare events in which all three species go extinct due to\nstrong but rare fluctuations. These fluctuations are from the tails of the\nprobability distribution of species concentrations. They render a coexistence\nof three populations unstable even if the coexistence is stable in the\ndeterministic limit. We determine the mean time to extinction (MTE) by using a\nWKB-ansatz in the master equation that represents the stochastic description of\nthis model. This way, the calculation is reduced to a problem of classical\nmechanics and amounts to solving a Hamilton-Jacobi equation with zero-energy\nHamiltonian. We solve the corresponding Hamilton's equations of motion in\nsix-dimensional phase space numerically by using the Iterative Action\nMinimization Method. This allows to project on the optimal path to extinction,\nstarting from a parameter choice where the three-species coexistence-fixed\npoint undergoes a Hopf bifurcation and becomes stable. Specifically for our\nsystem of three species, extinction events can be triggered along various paths\nto extinction, differing in their intermediate steps. We compare our analytical\npredictions with results from Gillespie simulations for two-species\nextinctions, complemented by an analytical calculation of the MTE in which the\nremaining third species goes extinct. From Gillespie simulations we also\nanalyze how the distributions of times to extinction change upon varying the\nbifurcation parameter. Even within the same model and the same dynamical\nregime, the MTE depends on the distance from the bifurcation point in a way\nthat contains the system size dependence in the exponent. It is challenging and\nworthwhile to quantify how rare the rare events of extinction are.\n"}
+{"abstract": "  Extracting critical behavior in the wake of quantum quenches has recently\nbeen at the forefront of theoretical and experimental investigations in\ncondensed matter physics and quantum synthetic matter, with particular emphasis\non experimental feasibility. Here, we investigate the potential of single-site\nobservables in probing equilibrium phase transitions and dynamical criticality\nin short-range transverse-field Ising chains. For integrable and\nnear-integrable models, our exact and mean-field-theory analyses reveal a truly\nout-of-equilibrium universal scaling exponent in the vicinity of the transition\nthat is independent of the initial state and the location of the probe site so\nlong as the latter is sufficiently close to the edge of the chain. Signature of\na dynamical crossover survives when integrability is strongly broken. Our work\nprovides a robust scheme for the experimental detection of quantum critical\npoints and dynamical scaling laws in short-range interacting models using\nmodern ultracold-atom setups.\n"}
+{"abstract": "  A common concern in observational studies focuses on properly evaluating the\ncausal effect, which usually refers to the average treatment effect or the\naverage treatment effect on the treated. In this paper, we propose a data\npreprocessing method, the Kernel-distance-based covariate balancing, for\nobservational studies with binary treatments. This proposed method yields a set\nof unit weights for the treatment and control groups, respectively, such that\nthe reweighted covariate distributions can satisfy a set of pre-specified\nbalance conditions. This preprocessing methodology can effectively reduce\nconfounding bias of subsequent estimation of causal effects. We demonstrate the\nimplementation and performance of Kernel-distance-based covariate balancing\nwith Monte Carlo simulation experiments and a real data analysis.\n"}
+{"abstract": "  Apart from the role the clustering coefficient plays in the definition of the\nsmall-world phenomena, it also has great relevance for practical problems\ninvolving networked dynamical systems. To study the impact of the clustering\ncoefficient on dynamical processes taking place on networks, some authors have\nfocused on the construction of graphs with tunable clustering coefficients.\nThese constructions are usually realized through a stochastic process, either\nby growing a network through the preferential attachment procedure, or by\napplying a random rewiring process. In contrast, we consider here several\nfamilies of static graphs whose clustering coefficients can be determined\nexplicitly. The basis for these families is formed by the $k$-regular graphs on\n$N$ nodes, that belong to the family of so-called circulant graphs denoted by\n$C_{N,k}$. We show that the expression for the clustering coefficient of\n$C_{N,k}$ reported in literature, only holds for sufficiently large $N$. Next,\nwe consider three generalizations of the circulant graphs, either by adding\nsome pendant links to $C_{N,k}$, or by connecting, in two different ways, an\nadditional node to some nodes of $C_{N,k}$. For all three generalizations, we\nderive explicit expressions for the clustering coefficient. Finally, we\nconstruct a family of pairs of generalized circulant graphs, with the same\nnumber of nodes and links, but with different clustering coefficients.\n"}
+{"abstract": "  In this paper we study lumpy black holes with AdS${}_p \\times S^q$\nasymptotics, where the isometry group coming from the sphere factor is broken\ndown to SO($q$). Depending on the values of $p$ and $q$, these are solutions to\na certain Supergravity theory with a particular gauge field. We have considered\nthe values $(p,q) = (5,5)$ and $(p,q) = (4,7)$, corresponding to type IIB\nsupergravity in ten dimensions and eleven-dimensional supergravity\nrespectively. These theories presumably contain an infinite spectrum of\nfamilies of lumpy black holes, labeled by a harmonic number $\\ell$, whose\nendpoints in solution space merge with another type of black holes with\ndifferent horizon topology. We have numerically constructed the first four\nfamilies of lumpy solutions, corresponding to $\\ell = 1, 2^+, 2^-$ and $3$. We\nshow that the geometry of the horizon near the merger is well-described by a\ncone over a triple product of spheres, thus extending Kol's local model to the\npresent asymptotics. Interestingly, the presence of non-trivial fluxes in the\ninternal sphere implies that the cone is no longer Ricci flat. This conical\nmanifold accounts for the geometry and the behavior of the physical quantities\nof the solutions sufficiently close to the critical point. Additionally, we\nshow that the vacuum expectation values of the dual scalar operators approach\ntheir critical values with a power law whose exponents are dictated by the\nlocal cone geometry in the bulk.\n"}
+{"abstract": "  We propose an efficient framework of reversible data hiding to preserve\ncompatibility between normal printing and printing with a special color ink by\nusing a single common image. The special color layer is converted to a binary\nimage by digital halftoning and losslessly compressed using JBIG2. Then, the\ncompressed information of the binarized special color layer is reversibly\nembedded into the general color layer without significant distortion. Our\nexperimental results show the availability of the proposed method in terms of\nthe marked image quality.\n"}
+{"abstract": "  Two-dimensional (2D) palladium ditelluride (PdTe2) and platinum ditelluride\n(PtTe2) are two Dirac semimetals which demonstrate fascinating quantum\nproperties such as superconductivity, magnetism and topological order,\nillustrating promising applications in future nanoelectronics and\noptoelectronics. However, the synthesis of their monolayers is dramatically\nhindered by strong interlayer coupling and orbital hybridization. In this\nstudy, an efficient synthesis method for monolayer PdTe2 and PtTe2 is\ndemonstrated. Taking advantages of the surface reaction, epitaxial growth of\nlarge-area and high quality monolayers of PdTe2 and patterned PtTe2 is achieved\nby direct tellurization of Pd(111) and Pt(111). A well-ordered PtTe2 pattern\nwith Kagome lattice formed by Te vacancy arrays is successfully grown.\nMoreover, multilayer PtTe2 can be also obtained and potential excitation of\nDirac plasmons is observed. The simple and reliable growth procedure of\nmonolayer PdTe2 and patterned PtTe2 gives unprecedented opportunities for\ninvestigating new quantum phenomena and facilitating practical applications in\noptoelectronics.\n"}
+{"abstract": "  We present Sandwich Batch Normalization (SaBN), a frustratingly easy\nimprovement of Batch Normalization (BN) with only a few lines of code changes.\nSaBN is motivated by addressing the inherent feature distribution heterogeneity\nthat one can be identified in many tasks, which can arise from data\nheterogeneity (multiple input domains) or model heterogeneity (dynamic\narchitectures, model conditioning, etc.). Our SaBN factorizes the BN affine\nlayer into one shared sandwich affine layer, cascaded by several parallel\nindependent affine layers. Concrete analysis reveals that, during optimization,\nSaBN promotes balanced gradient norms while still preserving diverse gradient\ndirections -- a property that many application tasks seem to favor. We\ndemonstrate the prevailing effectiveness of SaBN as a drop-in replacement in\nfour tasks: conditional image generation, neural architecture search (NAS),\nadversarial training, and arbitrary style transfer. Leveraging SaBN immediately\nachieves better Inception Score and FID on CIFAR-10 and ImageNet conditional\nimage generation with three state-of-the-art GANs; boosts the performance of a\nstate-of-the-art weight-sharing NAS algorithm significantly on NAS-Bench-201;\nsubstantially improves the robust and standard accuracies for adversarial\ndefense; and produces superior arbitrary stylized results. We also provide\nvisualizations and analysis to help understand why SaBN works. Codes are\navailable at: https://github.com/VITA-Group/Sandwich-Batch-Normalization.\n"}
+{"abstract": "  Flat band moir\\'e superlattices have recently emerged as unique platforms for\ninvestigating the interplay between strong electronic correlations, nontrivial\nband topology, and multiple isospin 'flavor' symmetries. Twisted\nmonolayer-bilayer graphene (tMBG) is an especially rich system owing to its low\ncrystal symmetry and the tunability of its bandwidth and topology with an\nexternal electric field. Here, we find that orbital magnetism is abundant\nwithin the correlated phase diagram of tMBG, giving rise to the anomalous Hall\neffect (AHE) in correlated metallic states nearby most odd integer fillings of\nthe flat conduction band, as well as correlated Chern insulator states\nstabilized in an external magnetic field. The behavior of the states at zero\nfield appears to be inconsistent with simple spin and valley polarization for\nthe specific range of twist angles we investigate, and instead may plausibly\nresult from an intervalley coherent (IVC) state with an order parameter that\nbreaks time reversal symmetry. The application of a magnetic field further\ntunes the competition between correlated states, in some cases driving\nfirst-order topological phase transitions. Our results underscore the rich\ninterplay between closely competing correlated ground states in tMBG, with\npossible implications for probing exotic IVC ordering.\n"}
+{"abstract": "  We consider a magnetic skyrmion crystal formed at the surface of a\ntopological insulator. Incorporating the exchange interaction between the\nhelical Dirac surface states and the periodic N\\'eel or Bloch skyrmion texture,\nwe obtain the resulting electronic band structures. We discuss the properties\nof the reconstructed skyrmion bands, namely the impact of symmetries on the\nenergies and Berry curvature. We find substantive qualitative differences\nbetween the N\\'eel and Bloch cases, with the latter generically permitting a\nlow-energy tight-binding representation whose parameters are tightly\nconstrained by symmetries. We explicitly construct the associated Wannier\norbitals, which resemble the ring-like chiral bound states of helical Dirac\nfermions coupled to a single skyrmion in a ferromagnetic background. We\nconstruct a two-band tight-binding model with complex nearest-neighbor hoppings\nwhich captures the salient topological features of the low-energy bands. Our\nresults are relevant to magnetic topological insulators (TIs), as well as to\nTI-magnetic thin film heterostructures, in which skyrmion crystals may be\nstabilized.\n"}
+{"abstract": "  This paper underlines a subtle property of batch-normalization (BN):\nSuccessive batch normalizations with random linear transformations make hidden\nrepresentations increasingly orthogonal across layers of a deep neural network.\nWe establish a non-asymptotic characterization of the interplay between depth,\nwidth, and the orthogonality of deep representations. More precisely, under a\nmild assumption, we prove that the deviation of the representations from\northogonality rapidly decays with depth up to a term inversely proportional to\nthe network width. This result has two main implications: 1) Theoretically, as\nthe depth grows, the distribution of the representation -- after the linear\nlayers -- contracts to a Wasserstein-2 ball around an isotropic Gaussian\ndistribution. Furthermore, the radius of this Wasserstein ball shrinks with the\nwidth of the network. 2) In practice, the orthogonality of the representations\ndirectly influences the performance of stochastic gradient descent (SGD). When\nrepresentations are initially aligned, we observe SGD wastes many iterations to\northogonalize representations before the classification. Nevertheless, we\nexperimentally show that starting optimization from orthogonal representations\nis sufficient to accelerate SGD, with no need for BN.\n"}
+{"abstract": "  Planet formation via core accretion requires the production of km-sized\nplanetesimals from cosmic dust. This process must overcome barriers to simple\ncollisional growth, for which the Streaming Instability (SI) is often invoked.\nDust evolution is still required to create particles large enough to undergo\nvigorous instability. The SI has been studied primarily with single size dust,\nand the role of the full evolved dust distribution is largely unexplored. We\nsurvey the Polydispserse Streaming Instability (PSI) with physical parameters\ncorresponding to plausible conditions in protoplanetary discs. We consider a\nfull range of particle stopping times, generalized dust size distributions, and\nthe effect of turbulence. We find that, while the PSI grows in many cases more\nslowly with a interstellar power-law dust distribution than with a single size,\nreasonable collisional dust evolution, producing an enhancement of the largest\ndust sizes, produces instability behaviour similar to the monodisperse case.\nConsidering turbulent diffusion the trend is similar. We conclude that if fast\nlinear growth of PSI is required for planet formation, then dust evolution\nproducing a distribution with peak stopping times on the order of 0.1 orbits\nand an enhancement of the largest dust significantly above the single power-law\ndistribution produced by a fragmentation cascade is sufficient, along with\nlocal enhancement of the dust to gas volume mass density ratio to order unity.\n"}
+{"abstract": "  In today's networked society, many real-world problems can be formalized as\npredicting links in networks, such as Facebook friendship suggestions,\ne-commerce recommendations, and the prediction of scientific collaborations in\ncitation networks. Increasingly often, link prediction problem is tackled by\nmeans of network embedding methods, owing to their state-of-the-art\nperformance. However, these methods lack transparency when compared to simpler\nbaselines, and as a result their robustness against adversarial attacks is a\npossible point of concern: could one or a few small adversarial modifications\nto the network have a large impact on the link prediction performance when\nusing a network embedding model? Prior research has already investigated\nadversarial robustness for network embedding models, focused on classification\nat the node and graph level. Robustness with respect to the link prediction\ndownstream task, on the other hand, has been explored much less.\n  This paper contributes to filling this gap, by studying adversarial\nrobustness of Conditional Network Embedding (CNE), a state-of-the-art\nprobabilistic network embedding model, for link prediction. More specifically,\ngiven CNE and a network, we measure the sensitivity of the link predictions of\nthe model to small adversarial perturbations of the network, namely changes of\nthe link status of a node pair. Thus, our approach allows one to identify the\nlinks and non-links in the network that are most vulnerable to such\nperturbations, for further investigation by an analyst. We analyze the\ncharacteristics of the most and least sensitive perturbations, and empirically\nconfirm that our approach not only succeeds in identifying the most vulnerable\nlinks and non-links, but also that it does so in a time-efficient manner thanks\nto an effective approximation.\n"}
+{"abstract": "  We calculate Galactic Chemical Evolution (GCE) of Mo and Ru by taking into\naccount the contribution from $\\nu p$-process nucleosynthesis. We estimate\nyields of $p$-nuclei such as $^{92,94}\\mathrm{Mo}$ and $^{96,98}\\mathrm{Ru}$\nthrough the $\\nu p$-process in various supernova (SN) progenitors based upon\nrecent models. In particular, the $\\nu p$-process in energetic hypernovae\nproduces a large amount of $p$-nuclei compared to the yield in ordinary\ncore-collapse SNe. Because of this the abundances of $^{92,94}\\mathrm{Mo}$ and\n$^{96,98}\\mathrm{Ru}$ in the Galaxy are significantly enhanced at [Fe/H]=0 by\nthe $\\nu p$-process. We find that the $\\nu p$-process in hypernovae is the main\ncontributor to the elemental abundance of $^{92}$Mo at low metallicity\n[Fe/H$]<-2$. Our theoretical prediction of the elemental abundances in\nmetal-poor stars becomes more consistent with observational data when the $\\nu\np$-process in hypernovae is taken into account.\n"}
+{"abstract": "  Photo-Induced Enhanced Raman Spectroscopy (PIERS) is a new surface enhanced\nRaman spectroscopy (SERS) modality with an order-of-magnitude Raman signal\nenhancement of adsorbed analytes over that of typical SERS substrates. Despite\nthe impressive PIERS enhancement factors and explosion in recent demonstrations\nof its utility, the detailed enhancement mechanism remains undetermined. Using\na range of optical and X-ray spectroscopies, supported by density functional\ntheory calculations, we elucidate the chemical and atomic-scale mechanism\nbehind the PIERS enhancement. Stable PIERS substrates with enhancement factors\nof 10^6 were fabricated using self-organized hexagonal arrays of TiO2 nanotubes\nthat were defect-engineered via annealing in inert atmospheres, and silver\nnanoparticles were deposited by magnetron sputtering and subsequent thermal\ndewetting. We identified the key source of the enhancement of PIERS vs. SERS in\nthese structures as an increase in the Raman polarizability of the adsorbed\nprobe molecule upon photo-induced charge transfer. A balance between\ncrystallinity, which enhances charge transfer due to higher electron mobility\nin anatase-rutile heterostructures but decreases visible light absorption, and\noxygen vacancy defects, which increase visible light absorption and\nphoto-induced electron transfers, was critical to achieve high PIERS\nenhancements.\n"}
+{"abstract": "  The optical phase shifter that constantly rotates the local oscillator phase\nis a necessity in continuous-variable quantum key distribution systems with\nheterodyne detection. In previous experimental implementations, the optical\nphase shifter is generally regarded as an ideal passive optical device that\nperfectly rotates the phase of the electromagnetic wave of $90^\\circ$. However,\nthe optical phase shifter in practice introduces imperfections, mainly the\nmeasurement angular error, which inevitably deteriorates the security of the\npractical systems. Here, we will give a concrete interpretation of measurement\nangular error in practical systems and the corresponding entanglement-based\ndescription. Subsequently, from the parameter estimation, we deduce the\noverestimated excess noise and the underestimated transmittance, which lead to\na reduction in the final secret key rate. Simultaneously, we propose an\nestimation method of the measurement angular error. Next, the practical\nsecurity analysis is provided in detail, and the effect of the measurement\nangular error and its corresponding compensation scheme are demonstrated. We\nconclude that measurement angular error severely degrades the security, but the\nproposed calibration and compensation method can significantly help improve the\nperformance of the practical CV-QKD systems.\n"}
+{"abstract": "  Deep energy renovation of building stock came more into focus in the European\nUnion due to energy efficiency related directives. Many buildings that must\nundergo deep energy renovation are old and may lack design/renovation\ndocumentation, or possible degradation of materials might have occurred in\nbuilding elements over time. Thermal transmittance (i.e. U-value) is one of the\nmost important parameters for determining the transmission heat losses through\nbuilding envelope elements. It depends on the thickness and thermal properties\nof all the materials that form a building element. In-situ U-value can be\ndetermined by ISO 9869-1 standard (Heat Flux Method - HFM). Still, measurement\nduration is one of the reasons why HFM is not widely used in field testing\nbefore the renovation design process commences. This paper analyzes the\npossibility of reducing the measurement time by conducting parallel\nmeasurements with one heat-flux sensor. This parallelization could be achieved\nby applying a specific class of the Artificial Neural Network (ANN) on HFM\nresults to predict unknown heat flux based on collected interior and exterior\nair temperatures. After the satisfying prediction is achieved, HFM sensor can\nbe relocated to another measuring location. Paper shows a comparison of four\nANN cases applied to HFM results for a measurement held on one multi-layer wall\n- multilayer perceptron with three neurons in one hidden layer, long short-term\nmemory with 100 units, gated recurrent unit with 100 units and combination of\n50 long short-term memory units and 50 gated recurrent units. The analysis gave\npromising results in term of predicting the heat flux rate based on the two\ninput temperatures. Additional analysis on another wall showed possible\nlimitations of the method that serves as a direction for further research on\nthis topic.\n"}
+{"abstract": "  HR 8799 hosts four directly imaged giant planets, but none has a mass\nmeasured from first principles. We present the first dynamical mass measurement\nin this planetary system, finding that the innermost planet HR~8799~e has a\nmass of $9.6^{+1.9}_{-1.8} \\, M_{\\rm Jup}$. This mass results from combining\nthe well-characterized orbits of all four planets with a new astrometric\nacceleration detection (5$\\sigma$) from the Gaia EDR3 version of the\nHipparcos-Gaia Catalog of Accelerations. We find with 95\\% confidence that\nHR~8799~e is below $13\\, M_{\\rm Jup}$, the deuterium-fusing mass limit. We\nderive a hot-start cooling age of $42^{+24}_{-16}$\\,Myr for HR~8799~e that\nagrees well with its hypothesized membership in the Columba association but is\nalso consistent with an alternative suggested membership in the\n$\\beta$~Pictoris moving group. We exclude the presence of any additional\n$\\gtrsim$5-$M_{\\rm Jup}$ planets interior to HR~8799~e with semi-major axes\nbetween $\\approx$3-16\\,au. We provide proper motion anomalies and a matrix\nequation to solve for the mass of any of the planets of HR~8799 using only mass\nratios between the planets.\n"}
+{"abstract": "  In this paper, we explore methods for computing wall-normal derivatives used\nfor calculating wall skin friction and heat transfer over a solid wall in\nunstructured simplex-element (triangular/tetrahedral) grids generated by\nanisotropic grid adaptation. Simplex-element grids are considered as efficient\nand suitable for automatic grid generation and adaptation, but present a\nchallenge to accurately predict wall-normal derivatives. For example,\nwall-normal derivatives computed by a simple finite-difference approximation,\nas typically done in practical fluid-dynamics simulation codes, are often\ncontaminated with numerical noise. To address this issue, we propose an\nimproved method based on a common step-length for the finite-difference\napproximation, which is otherwise random due to grid irregularity and thus\nexpected to smooth the wall-normal derivative distribution over a boundary.\nAlso, we consider using least-squares gradients to compute the wall-normal\nderivatives and discuss their possible improvements. Numerical results show\nthat the improved methods greatly reduce the noise in the wall-normal\nderivatives for irregular simplex-element grids.\n"}
+{"abstract": "  In this study we extract the deep features and investigate the compression of\nthe Mg II k spectral line profiles observed in quiet Sun regions by NASA's IRIS\nsatellite. The data set of line profiles used for the analysis was obtained on\nApril 20th, 2020, at the center of the solar disc, and contains almost 300,000\nindividual Mg II k line profiles after data cleaning. The data are separated\ninto train and test subsets. The train subset was used to train the autoencoder\nof the varying embedding layer size. The early stopping criterion was\nimplemented on the test subset to prevent the model from overfitting. Our\nresults indicate that it is possible to compress the spectral line profiles\nmore than 27 times (which corresponds to the reduction of the data\ndimensionality from 110 to 4) while having a 4 DN average reconstruction error,\nwhich is comparable to the variations in the line continuum. The mean squared\nerror and the reconstruction error of even statistical moments sharply decrease\nwhen the dimensionality of the embedding layer increases from 1 to 4 and almost\nstop decreasing for higher numbers. The observed occasional improvements in\ntraining for values higher than 4 indicate that a better compact embedding may\npotentially be obtained if other training strategies and longer training times\nare used. The features learned for the critical four-dimensional case can be\ninterpreted. In particular, three of these four features mainly control the\nline width, line asymmetry, and line dip formation respectively. The presented\nresults are the first attempt to obtain a compact embedding for spectroscopic\nline profiles and confirm the value of this approach, in particular for feature\nextraction, data compression, and denoising.\n"}
+{"abstract": "  The J-PARC muon $g - 2$/EDM experiment aims to measure the muon magnetic\nmoment anomaly $a_{\\mu} = (g -2) / 2 ~ $ and the muon electric dipole moment\n(EDM) $d_{\\mu}$. The target sensitivity for $a_{\\mu}$ is a statistical\nuncertainty of $450 \\times 10^{-9}$, and for $d_{\\mu}$ it is $1.5 \\times\n10^{-21}$ $e \\cdot \\rm{cm}$. The readout electronics require a DC to DC\nconverter to provide 1.5 V and 3.3 V electric voltage within a specific\nenvironment. To capture the positron decay from the muon, the detector\ncomponents are placed in the muon storage area where a 3 T magnetic field is\napplied in a vacuum. The extra magnetic field from the converter should be less\nthan 30 $\\mu$T to reach the aforementioned sensitivity on $a_{\\mu}$. In\naddition, the electric field produced by the converter has to be as small as 1\nV/m to also reach the target sensitivity on $d_{\\mu}$. For this purpose, we\ndevelop a step-down converter. We discuss the development process and the\nperformance of our DC to DC converter for the J-PARC muon $g - 2$/EDM\nexperiment.\n"}
+{"abstract": "  Few-shot Named Entity Recognition (NER) exploits only a handful of\nannotations to identify and classify named entity mentions. Prototypical\nnetwork shows superior performance on few-shot NER. However, existing\nprototypical methods fail to differentiate rich semantics in other-class words,\nwhich will aggravate overfitting under few shot scenario. To address the issue,\nwe propose a novel model, Mining Undefined Classes from Other-class (MUCO),\nthat can automatically induce different undefined classes from the other class\nto improve few-shot NER. With these extra-labeled undefined classes, our method\nwill improve the discriminative ability of NER classifier and enhance the\nunderstanding of predefined classes with stand-by semantic knowledge.\nExperimental results demonstrate that our model outperforms five\nstate-of-the-art models in both 1-shot and 5-shots settings on four NER\nbenchmarks. We will release the code upon acceptance. The source code is\nreleased on https: //github.com/shuaiwa16/OtherClassNER.git.\n"}
+{"abstract": "  We present a new trainable system for physically plausible markerless 3D\nhuman motion capture, which achieves state-of-the-art results in a broad range\nof challenging scenarios. Unlike most neural methods for human motion capture,\nour approach, which we dub physionical, is aware of physical and environmental\nconstraints. It combines in a fully differentiable way several key innovations,\ni.e., 1. a proportional-derivative controller, with gains predicted by a neural\nnetwork, that reduces delays even in the presence of fast motions, 2. an\nexplicit rigid body dynamics model and 3. a novel optimisation layer that\nprevents physically implausible foot-floor penetration as a hard constraint.\nThe inputs to our system are 2D joint keypoints, which are canonicalised in a\nnovel way so as to reduce the dependency on intrinsic camera parameters -- both\nat train and test time. This enables more accurate global translation\nestimation without generalisability loss. Our model can be finetuned only with\n2D annotations when the 3D annotations are not available. It produces smooth\nand physically principled 3D motions in an interactive frame rate in a wide\nvariety of challenging scenes, including newly recorded ones. Its advantages\nare especially noticeable on in-the-wild sequences that significantly differ\nfrom common 3D pose estimation benchmarks such as Human 3.6M and MPI-INF-3DHP.\nQualitative results are available at\nhttp://gvv.mpi-inf.mpg.de/projects/PhysAware/\n"}
+{"abstract": "  It is known that if $f\\colon {\\mathbb R}^2 \\to {\\mathbb R}$ is a polynomial\nin each variable, then $f$ is a polynomial. We present generalizations of this\nfact, when ${\\mathbb R}^2$ is replaced by $G\\times H$, where $G$ and $H$ are\ntopological Abelian groups. We show, e.g., that the conclusion holds (with\ngeneralized polynomials in place of polynomials) if $G$ is a connected Baire\nspace and $H$ has a dense subgroup of finite rank or, for continuous functions,\nif $G$ and $H$ are connected Baire spaces. The condition of continuity can be\nomitted if $G$ and $H$ are locally compact or complete metric spaces. We\npresent several examples showing that the results are not far from being\noptimal.\n"}
+{"abstract": "  We introduce a new sequential methodology to calibrate the fixed parameters\nand track the stochastic dynamical variables of a state-space system. The\nproposed method is based on the nested hybrid filtering (NHF) framework of [1],\nthat combines two layers of filters, one inside the other, to compute the joint\nposterior probability distribution of the static parameters and the state\nvariables. In particular, we explore the use of deterministic sampling\ntechniques for Gaussian approximation in the first layer of the algorithm,\ninstead of the Monte Carlo methods employed in the original procedure. The\nresulting scheme reduces the computational cost and so makes the algorithms\npotentially better-suited for high-dimensional state and parameter spaces. We\ndescribe a specific instance of the new method and then study its performance\nand efficiency of the resulting algorithms for a stochastic Lorenz 63 model\nwith uncertain parameters.\n"}
+{"abstract": "  We present an Extended Kalman Filter framework for system identification and\ncontrol of a stochastic high-dimensional epidemic model. The scale and severity\nof the COVID-19 emergency have highlighted the need for accurate forecasts of\nthe state of the pandemic at a high resolution. Mechanistic compartmental\nmodels are widely used to produce such forecasts and assist in the design of\ncontrol and relief policies. Unfortunately, the scale and stochastic nature of\nmany of these models often makes the estimation of their parameters difficult.\nWith the goal of calibrating a high dimensional COVID-19 model using low-level\nmobility data, we introduce a method for tractable maximum likelihood\nestimation that combines tools from Bayesian inference with scalable\noptimization techniques from machine learning. The proposed approach uses\nautomatic backward-differentiation to directly compute the gradient of the\nlikelihood of COVID-19 incidence and death data. The likelihood of the\nobservations is estimated recursively using an Extended Kalman Filter and can\nbe easily optimized using gradient-based methods to compute maximum likelihood\nestimators. Our compartmental model is trained using GPS mobility data that\nmeasures the mobility patterns of millions of mobile phones across the United\nStates. We show that, after calibrating against incidence and deaths data from\nthe city of Philadelphia, our model is able to produce an accurate 30-day\nforecast of the evolution of the pandemic.\n"}
+{"abstract": "  We propose a minimal model based on Lepton number symmetry (and violation),\nto address a common origin of baryon asymmetry, dark matter (DM) and neutrino\nmass generation, having one-to-one correspondence. The {\\em unnatural}\nlargeness and smallness of the parameters required to describe correct\nexperimental limits are attributed to lepton number violation. Allowed\nparameter space of the model is illustrated via numerical scan.\n"}
+{"abstract": "  In 2019, The Centers for Medicare and Medicaid Services (CMS) launched an\nArtificial Intelligence (AI) Health Outcomes Challenge seeking solutions to\npredict risk in value-based care for incorporation into CMS Innovation Center\npayment and service delivery models. Recently, modern language models have\nplayed key roles in a number of health related tasks. This paper presents, to\nthe best of our knowledge, the first application of these models to patient\nreadmission prediction. To facilitate this, we create a dataset of 1.2 million\nmedical history samples derived from the Limited Dataset (LDS) issued by CMS.\nMoreover, we propose a comprehensive modeling solution centered on a deep\nlearning framework for this data. To demonstrate the framework, we train an\nattention-based Transformer to learn Medicare semantics in support of\nperforming downstream prediction tasks thereby achieving 0.91 AUC and 0.91\nrecall on readmission classification. We also introduce a novel data\npre-processing pipeline and discuss pertinent deployment considerations\nsurrounding model explainability and bias.\n"}
+{"abstract": "  Reconstructing the shape and appearance of real-world objects using measured\n2D images has been a long-standing problem in computer vision. In this paper,\nwe introduce a new analysis-by-synthesis technique capable of producing\nhigh-quality reconstructions through robust coarse-to-fine optimization and\nphysics-based differentiable rendering.\n  Unlike most previous methods that handle geometry and reflectance largely\nseparately, our method unifies the optimization of both by leveraging image\ngradients with respect to both object reflectance and geometry. To obtain\nphysically accurate gradient estimates, we develop a new GPU-based Monte Carlo\ndifferentiable renderer leveraging recent advances in differentiable rendering\ntheory to offer unbiased gradients while enjoying better performance than\nexisting tools like PyTorch3D and redner. To further improve robustness, we\nutilize several shape and material priors as well as a coarse-to-fine\noptimization strategy to reconstruct geometry. We demonstrate that our\ntechnique can produce reconstructions with higher quality than previous methods\nsuch as COLMAP and Kinect Fusion.\n"}
+{"abstract": "  This paper derives the CS decomposition for orthogonal tensors (T-CSD) and\nthe generalized singular value decomposition for two tensors (T-GSVD) via the\nT-product. The structures of the two decompositions are analyzed in detail and\nare consistent with those for matrix cases. Then the corresponding algorithms\nare proposed respectively. Finally, T-GSVD can be used to give the explicit\nexpression for the solution of tensor Tikhonov regularization. Numerical\nexamples demonstrate the effectiveness of T-GSVD in solving image restoration\nproblems.\n"}
+{"abstract": "  While there have been many results on lower bounds for Max Cut in unweighted\ngraphs, the only lower bound for non-integer weights is that by Poljak and\nTurzik (1986). In this paper, we launch an extensive study of lower bounds for\nMax Cut in weighted graphs. We introduce a new approach for obtaining lower\nbounds for Weighted Max Cut. Using it, Probabilistic Method, Vizing's chromatic\nindex theorem, and other tools, we obtain several lower bounds for arbitrary\nweighted graphs, weighted graphs of bounded girth and triangle-free weighted\ngraphs. We pose conjectures and open questions.\n"}
+{"abstract": "  This paper addresses the problem of creating abstract transformers\nautomatically. The method we present provides the basis for creating a tool to\nautomate the construction of program analyzers in a fashion similar to the way\nyacc automates the construction of parsers. Our method treats the problem as a\nprogram-synthesis problem. The user provides specifications of (i) the concrete\nsemantics of a given operation O, (ii) the abstract domain A to be used by the\nanalyzer, and (iii) the semantics of a domain-specific language L in which the\nabstract transformer is to be expressed. As output, our method creates an\nabstract transformer for O for abstract domain A, expressed in DSL L. We\nimplemented our method, and used it to create a set of replacement abstract\ntransformers for those used in an existing analyzer, and obtained essentially\nidentical performance. However, when we compared the existing transformers with\nthe generated transformers, we discovered that two of the existing transformers\nwere unsound, which demonstrates the risk of using manually created\ntransformers.\n"}
+{"abstract": "  Autonomous vehicles (AVs) need to interact with other traffic participants\nwho can be either cooperative or aggressive, attentive or inattentive. Such\ndifferent characteristics can lead to quite different interactive behaviors.\nHence, to achieve safe and efficient autonomous driving, AVs need to be aware\nof such uncertainties when they plan their own behaviors. In this paper, we\nformulate such a behavior planning problem as a partially observable Markov\nDecision Process (POMDP) where the cooperativeness of other traffic\nparticipants is treated as an unobservable state. Under different\ncooperativeness levels, we learn the human behavior models from real traffic\ndata via the principle of maximum likelihood. Based on that, the POMDP problem\nis solved by Monte-Carlo Tree Search. We verify the proposed algorithm in both\nsimulations and real traffic data on a lane change scenario, and the results\nshow that the proposed algorithm can successfully finish the lane changes\nwithout collisions.\n"}
+{"abstract": "  The perfect soliton crystal (PSC) was recently discovered as an extraordinary\nKerr soliton state with regularly distributed soliton pulses and enhanced comb\nline power spaced by multiples of the cavity free spectral ranges (FSRs). The\nmodulation of continuous-wave excitation in optical microresonators and the\ntunable repetition rate characteristic will significantly enhance and extend\nthe application potential of soliton microcombs for self-referencing comb\nsource, terahertz wave generation, and arbitrary waveform generation. However,\nthe reported PSC spectrum is generally narrow. Here, we demonstrate the\ndeterministic accessing of versatile perfect soliton crystals in the AlN\nmicroresonators (FSR ~374 GHz), featuring a broad spectral range up to 0.96 of\nan octave-span (1170-2300 nm) and terahertz repetition rates (up to ~1.87 THz).\nThe measured 60-fs short pulses and low-noise characteristics confirms the high\ncoherence of the PSCs\n"}
+{"abstract": "  Superconducting and topological states are two quantum phenomena attracting\nmuch interest. Their coexistence may lead to topological superconductivity\nsought-after for Majorana-based quantum computing. However, there is no causal\nrelationship between the two, since superconductivity is a many-body effect due\nto electron-electron interaction while topology is a single-particle\nmanifestation of electron band structure. Here, we demonstrate a novel form of\nFulde-Ferrell-Larkin-Ovchinnikov (FFLO) pairing, induced by topological Weyl\nnodal lines in Ising Bardeen-Cooper-Schrieffer (IBCS) superconductors. Based on\nfirst-principles calculations and analyses, we predict that the nonmagnetic\nmetals of MA$_2$Z$_4$ family, including ${\\alpha}_1$-TaSi$_2$P$_4$,\n${\\alpha}_1$-TaSi$_2$N$_4$, ${\\alpha}_1$-NbSi$_2$P$_4$,\n${\\alpha}_2$-TaGe$_2$P$_4$, and ${\\alpha}_2$-NbGe$_2$P$_4$ monolayers, are all\nsuperconductors. While the intrinsic IBCS paring arises in these\nnon-centrosymmetric systems, the extrinsic FFLO pairing is revealed to be\nevoked by the Weyl nodal lines under magnetic field, facilitating the formation\nof Cooper pairs with nonzero momentum in their vicinity. Moreover, we show that\nthe IBCS pairing alone will enhance the in-plane critical field $B_c$ to ~10-50\ntimes of Pauli paramagnetic limit $B_p$, and additional FFLO pairing can\nfurther triple the $B_c/B_p$ ratio. It therefore affords an effective approach\nto enhance the robustness of superconductivity. Also, the topology induced\nsuperconductivity renders naturally the possible existence of topological\nsuperconducting state.\n"}
+{"abstract": "  Motion artifacts are a common occurrence in the Magnetic Resonance Imaging\n(MRI) exam. Motion during acquisition has a profound impact on workflow\nefficiency, often requiring a repeat of sequences. Furthermore, motion\nartifacts may escape notice by technologists, only to be revealed at the time\nof reading by the radiologists, affecting their diagnostic quality. Designing a\ncomputer-aided tool for automatic motion detection and elimination can improve\nthe diagnosis, however, it needs a deep understanding of motion\ncharacteristics. Motion artifacts in MRI have a complex nature and it is\ndirectly related to the k-space sampling scheme. In this study we investigate\nthe effect of three conventional k-space samplers, including Cartesian, Uniform\nSpiral and Radial on motion induced image distortion. In this regard, various\nsynthetic motions with different trajectories of displacement and rotation are\napplied to T1 and T2-weighted MRI images, and a convolutional neural network is\ntrained to show the difficulty of motion classification. The results show that\nthe spiral k-space sampling method get less effect of motion artifact in image\nspace as compared to radial k-space sampled images, and radial k-space sampled\nimages are more robust than Cartesian ones. Cartesian samplers, on the other\nhand, are the best in terms of deep learning motion detection because they can\nbetter reflect motion.\n"}
+{"abstract": "  This is a commentary on, and critique of, Latif Salum's paper titled\n\"Tractability of One-in-three $\\mathrm{3SAT}$: $\\mathrm{P} = \\mathrm{NP}$.\"\nSalum purports to give a polynomial-time algorithm that solves the\n$\\mathrm{NP}$-complete problem $\\mathrm{X3SAT}$, thereby claiming $\\mathrm{P} =\n\\mathrm{NP}$. The algorithm, in short, fixes the polarity of a variable,\ncarries out simplifications over the resulting formula to decide whether to\nkeep the value assigned or flip the polarity, and repeats with the remaining\nvariables. One thing this algorithm does not do is backtrack. We give an\nillustrative counterexample showing why the lack of backtracking makes this\nalgorithm flawed.\n"}
+{"abstract": "  The neural text generation suffers from the text degeneration issue such as\nrepetition. Traditional stochastic sampling methods only focus on truncating\nthe unreliable \"tail\" of the distribution, and do not address the \"head\" part,\nwhich we show might contain tedious or even repetitive candidates with high\nprobability that lead to repetition loops. They also do not consider the issue\nthat human text does not always favor high-probability words. Inspired by\nthese, in this work we propose a heuristic sampling method. We propose to use\ninterquartile range of the predicted distribution to determine the \"head\" part,\nthen permutate and rescale the \"head\" with inverse probability. This aims at\ndecreasing the probability for the tedious and possibly repetitive candidates\nwith higher probability, and increasing the probability for the rational but\nmore surprising candidates with lower probability. The proposed algorithm\nprovides a reasonable permutation on the predicted distribution which enhances\ndiversity without compromising rationality of the distribution. We use\npre-trained language model to compare our algorithm with traditional methods.\nResults show that our algorithm can effectively increase the diversity of\ngenerated samples while achieving close resemblance to human text.\n"}
+{"abstract": "  On a smooth, closed Riemannian manifold, we study the question of\nproportionality of components, also called synchronization, of vector-valued\nsolutions to nonlinear elliptic Schr\\\"odinger systems with constant\ncoefficients. In particular, we obtain bifurcation results showing the\nexistence of branches of non-synchronized solutions emanating from the constant\nsolutions.\n"}
+{"abstract": "  It is proved that for any mapping of a unit segment to a unit square, there\nis a pair of points of the segment for which the square of the Euclidean\ndistance between their images exceeds the distance between them on the segment\nby at least $3\\frac58$ times. And the additional condition that the images of\nthe beginning and end of the segment belong to opposite sides of the square\nincreases the estimate to $4+\\varepsilon$.\n"}
+{"abstract": "  The so-called aromatic infrared bands are attributed to emission of\npolycyclic aromatic hydrocarbons. The observed variations toward different\nregions in space are believed to be caused by contributions of different\nclasses of PAH molecules, i.e. with respect to their size, structure, and\ncharge state. Laboratory spectra of members of these classes are needed to\ncompare them to observations and to benchmark quantum-chemically computed\nspectra of these species. In this paper we present the experimental infrared\nspectra of three different PAH dications, naphthalene$^{2+}$,\nanthracene$^{2+}$, and phenanthrene$^{2+}$, in the vibrational fingerprint\nregion 500-1700~cm$^{-1}$. The dications were produced by electron impact\nionization of the vapors with 70 eV electrons, and they remained stable against\ndissociation and Coulomb explosion. The vibrational spectra were obtained by IR\npredissociation of the PAH$^{2+}$ complexed with neon in a 22-pole cryogenic\nion trap setup coupled to a free-electron infrared laser at the Free-Electron\nLasers for Infrared eXperiments (FELIX) Laboratory. We performed anharmonic\ndensity-functional theory calculations for both singly and doubly charged\nstates of the three molecules. The experimental band positions showed excellent\nagreement with the calculated band positions of the singlet electronic ground\nstate for all three doubly charged species, indicating its higher stability\nover the triplet state. The presence of several strong combination bands and\nadditional weaker features in the recorded spectra, especially in the\n10-15~$\\mu$m region of the mid-IR spectrum, required anharmonic calculations to\nunderstand their effects on the total integrated intensity for the different\ncharge states. These measurements, in tandem with theoretical calculations,\nwill help in the identification of this specific class of doubly-charged PAHs\nas carriers of AIBs.\n"}
+{"abstract": "  Distributed software development is more difficult than co-located software\ndevelopment. One of the main reasons is that communication is more difficult in\ndistributed settings. Defined processes and artifacts help, but cannot cover\nall information needs. Not communicating important project information,\ndecisions and rationales can result in duplicate or extra work, delays or even\nproject failure. Planning and managing a distributed project from an\ninformation flow perspective helps to facilitate available communication\nchannels right from the start - beyond the documents and artifacts which are\ndefined for a given development process. In this paper we propose FLOW Mapping,\na systematic approach for planning and managing information flows in\ndistributed projects. We demonstrate the feasibility of our approach with a\ncase study in a distributed agile class room project. FLOW Mapping is\nsufficient to plan communication and to measure conformance to the\ncommunication strategy. We also discuss cost and impact of our approach.\n"}
+{"abstract": "  A new ultra-hard rhombohedral carbon rh-C4 (or hexagonal h-C12) is reported\nas derived from 3R graphite through crystal chemistry construction and ground\nstate energy within the density functional theory. An extended hexagonal\nthree-dimensional network of h-C12 is formed of C4 tetrahedra alike in h-C4\nlonsdaleite (hexagonal diamond). The electronic band structure of rh-C4 is\ncharacteristic of insulator with Egap = 4 eV similarly to diamond. From the set\nof elastic constants a larger value of bulk modulus versus lonsdaleite, and the\nlargest Vickers hardness (HV) versus both forms of diamond were derived.\n"}
+{"abstract": "  Multifunctional-therapeutic 3D scaffolds have been prepared. These\nbiomaterials are able to destroy the S. aureus bacteria biofilm and to allow\nbone regeneration at the same time. The present study is focused on the design\nof pH sensitive 3D hierarchical meso-macroporous scaffolds based on MGHA\nnanocomposite formed by a mesostructured glassy network with embedded\nhydroxyapatite nanoparticles, whose mesopores have been loaded with\nlevofloxacin as antibacterial agent. These 3D platforms exhibit controlled and\npH-dependent levofloxacin release, sustained over time at physiological pH\n(7.4) and notably increased at infection pH (6.7 and 5.5), which is due to the\ndifferent interaction rate between diverse levofloxacin species and the silica\nmatrix. These 3D systems are able to inhibit the S. aureus growth and to\ndestroy the bacterial biofilm without cytotoxic effects on human osteoblasts\nand allowing an adequate colonization and differentiation of preosteoblastic\ncells on their surface. These findings suggest promising applications of these\nhierarchical MGHA nanocomposite scaffolds for the treatment and prevention of\nbone infection.\n"}
+{"abstract": "  We investigate the role of higher-form symmetries and two kinds of 't Hooft\nanomalies in non-equilibrium systems. To aid our investigation, we extend the\ncoset construction to account for $p$-form symmetries at zero and finite\ntemperature. One kind of anomaly arises when a $p$-form symmetry is\nspontaneously broken: in a $d+1$-dimensional spacetime there often exists an\nemergent $d-p-1$-form symmetry with mixed 't Hooft anomaly. That is, the\n$p$-form and $d-p-1$-form symmetries cannot be gauged simultaneously. At the\nlevel of the coset construction, this mixed anomaly prevents the Goldstones for\nthe $p$- and $d-p-1$-form symmetries from appearing in the same Maurer-Cartan\nform. As a result, whenever such a mixed anomaly exists, we find the emergence\nof dual theories -- one involving the $p$-form Goldstone and the other\ninvolving the $d-p-1$-form Goldstone -- that are related to each other by a\nkind of Legendre transform. Such an anomaly can exist at zero and finite\ntemperature. The other kind of 't Hooft anomaly can only arise in\nnon-equilibrium systems; we therefore term it the non-equilibrium 't Hoof\nanomaly. In this case, an exact symmetry of the non-equilibrium effective\naction fails to have a non-trivial, conserved Noether current. This anomalous\nbehavior arises when a global symmetry cannot be gauged in the non-equilibrium\neffective action and can arise in both open and closed systems. We construct\nactions for a number of systems including chemically reacting fluids,\nYang-Mills theory, Chern-Simons theory, magnetohydrodynamic systems, and dual\nsuperfluid and solid theories. Finally, we find that the interplay of these two\nkinds of anomalies has a surprising result: in non-equilibrium systems, whether\nor not a symmetry appears spontaneously broken can depend on the time-scale\nover which the system is observed.\n"}
+{"abstract": "  We present a brief review of Friedmann's impact on cosmology from a\nhistorical and a physical perspective.\n"}
+{"abstract": "  Multi-agent behavior modeling aims to understand the interactions that occur\nbetween agents. We present a multi-agent dataset from behavioral neuroscience,\nthe Caltech Mouse Social Interactions (CalMS21) Dataset. Our dataset consists\nof trajectory data of social interactions, recorded from videos of freely\nbehaving mice in a standard resident-intruder assay. To help accelerate\nbehavioral studies, the CalMS21 dataset provides benchmarks to evaluate the\nperformance of automated behavior classification methods in three settings: (1)\nfor training on large behavioral datasets all annotated by a single annotator,\n(2) for style transfer to learn inter-annotator differences in behavior\ndefinitions, and (3) for learning of new behaviors of interest given limited\ntraining data. The dataset consists of 6 million frames of unlabeled tracked\nposes of interacting mice, as well as over 1 million frames with tracked poses\nand corresponding frame-level behavior annotations. The challenge of our\ndataset is to be able to classify behaviors accurately using both labeled and\nunlabeled tracking data, as well as being able to generalize to new settings.\n"}
+{"abstract": "  Synchronization, cooperation, and chaos are ubiquitous phenomena in nature.\nIn a population composed of many distinct groups of individuals playing the\nprisoner's dilemma game, there exists a migration dilemma: No cooperator would\nmigrate to a group playing the prisoner's dilemma game lest it should be\nexploited by a defector; but unless the migration takes place, there is no\nchance of the entire population's cooperator-fraction to increase. Employing a\nrandomly rewired coupled map lattice of chaotic replicator maps, modelling\nreplication-selection evolutionary game dynamics, we demonstrate that the\ncooperators -- evolving in synchrony -- overcome the migration dilemma to\nproliferate across the population when altruism is mildly incentivized making\nfew of the demes play the leader game.\n"}
+{"abstract": "  We present large-eddy simulations (LESs) of riverine flow in a study reach in\nthe Sacramento River, California. The riverbed bathymetry was surveyed in\nhigh-resolution using a multibeam echosounder to construct the computational\nmodel of the study area, while the topographies were defined using aerial\nphotographs taken by an Unmanned Aircraft System (UAS). In a series of field\ncampaigns, we measured the flow field of the river using the acoustic Doppler\ncurrent profiler (ADCP) and estimated using large-scale particle velocimetry of\nthe videos taken during the operation UAS. We used the measured data of the\nriver flow field to evaluate the accuracy of the LES-computed hydrodynamics.\nThe propagation of uncertainties in the LES results due to the variations in\nthe effective roughness height of the riverbed and the inflow discharge of the\nriver was studied using uncertainty quantification (UQ) analyses. The\npolynomial chaos expansion (PCE) method was used to develop a surrogate model,\nwhich was randomly sampled sufficiently by the Monte Carlo Sampling (MCS)\nmethod to generate confidence intervals for the LES-computed velocity field.\nAlso, Sobol indices derived from the PCE coefficients were calculated to help\nunderstand the relative influence of different input parameters on the global\nuncertainty of the results. The UQ analysis showed that uncertainties of LES\nresults in the shallow near bank regions of the river were mainly related to\nthe roughness, while the variation of inflow discharge leads to uncertainty in\nthe LES results throughout the river, indiscriminately.\n"}
+{"abstract": "  Directly manipulating the atomic structure to achieve a specific property is\na long pursuit in the field of materials. However, hindered by the disordered,\nnon-prototypical glass structure and the complex interplay between structure\nand property, such inverse design is dauntingly hard for glasses. Here,\ncombining two cutting-edge techniques, graph neural networks and swap Monte\nCarlo, we develop a data-driven, property-oriented inverse design route that\nmanaged to improve the plastic resistance of Cu-Zr metallic glasses in a\ncontrollable way. Swap Monte Carlo, as \"sampler\", effectively explores the\nglass landscape, and graph neural networks, with high regression accuracy in\npredicting the plastic resistance, serves as \"decider\" to guide the search in\nconfiguration space. Via an unconventional strengthening mechanism, a\ngeometrically ultra-stable yet energetically meta-stable state is unraveled,\ncontrary to the common belief that the higher the energy, the lower the plastic\nresistance. This demonstrates a vast configuration space that can be easily\noverlooked by conventional atomistic simulations. The data-driven techniques,\nstructural search methods and optimization algorithms consolidate to form a\ntoolbox, paving a new way to the design of glassy materials.\n"}
+{"abstract": "  In this paper a novel non-negative finite volume discretization scheme is\nproposed for certain first order nonlinear partial differential equations\ndescribing conservation laws arising in traffic flow modelling. The spatially\ndiscretized model is shown to preserve several fundamentally important\nanalytical properties of the conservation law (e.g., conservativeness,\ncapacity) giving rise to a set of (second order) polynomial ODEs. Furthermore,\nit is shown that the discretized traffic flow model is formally kinetic and\nthat it can be interpreted in a compartmental context. As a consequence,\ntraffic networks can be represented as reaction graphs. It is shown that the\nmodel can be equipped with on- and off- ramps in a physically meaningful way,\nstill preserving the advantageous properties of the discretization. Numerical\ncase studies include empirical convergence tests, and the stability analysis\npresented in the paper paves the way to scalable observer and controller\ndesign.\n"}
+{"abstract": "  We continue our study of initial-value problems for fully nonlinear systems\nexhibiting strong or weak defects of hyperbolicity. We prove that, regardless\nof the initial Sobolev regularity, the initial-value problem has no local $H^s$\nsolutions, for $s > s_0 + d/2,$ if the principal symbol has a strong, or even\nweak, defect of hyperbolicity, and the purely imaginary eigenvalues of the\nprincipal symbol are semi-simple and have constant multiplicity. The index $s_0\n> 0$ depends on the severity of the defect of hyperbolicity. These results\nrecover and extend previous work from G. M\\'etivier [{\\it Remarks on the well\nposedness of the nonlinear Cauchy problem,} 2005], N.Lerner, Y. Morimoto, C.-J.\nXu [{\\it Instability of the Cauchy-Kovalevskaya solution for a class of\nnon-linear systems}, 2010] and N. Lerner, T. Nguyen, B. Texier, {\\it The onset\nof instability in first-order systems}, 2018]\n"}
+{"abstract": "  In this paper, we present a new objective prediction model for synthetic\nspeech naturalness. It can be used to evaluate Text-To-Speech or Voice\nConversion systems and works language independently. The model is trained\nend-to-end and based on a CNN-LSTM network that previously showed to give good\nresults for speech quality estimation. We trained and tested the model on 16\ndifferent datasets, such as from the Blizzard Challenge and the Voice\nConversion Challenge. Further, we show that the reliability of deep\nlearning-based naturalness prediction can be improved by transfer learning from\nspeech quality prediction models that are trained on objective POLQA scores.\nThe proposed model is made publicly available and can, for example, be used to\nevaluate different TTS system configurations.\n"}
+{"abstract": "  The (poset) cube Ramsey number $R(Q_n,Q_n)$ is defined as the least~$m$ such\nthat any 2-coloring of the $m$-dimensional cube $Q_m$ admits a monochromatic\ncopy of $Q_n$. The trivial lower bound $R(Q_n,Q_n)\\ge 2n$ was improved by Cox\nand Stolee, who showed $R(Q_n,Q_n)\\ge 2n+1$ for $3\\le n\\le 8$ and $n\\ge 13$\nusing a probabilistic existence proof. In this paper, we provide an explicit\nconstruction that establishes $R(Q_n,Q_n)\\ge 2n+1$ for all $n\\ge 3$.\n"}
+{"abstract": "  In this article, we present a visual introduction to Gaussian Belief\nPropagation (GBP), an approximate probabilistic inference algorithm that\noperates by passing messages between the nodes of arbitrarily structured factor\ngraphs. A special case of loopy belief propagation, GBP updates rely only on\nlocal information and will converge independently of the message schedule. Our\nkey argument is that, given recent trends in computing hardware, GBP has the\nright computational properties to act as a scalable distributed probabilistic\ninference framework for future machine learning systems.\n"}
+{"abstract": "  Materials property predictions have improved from advances in\nmachine-learning algorithms, delivering materials discoveries and novel\ninsights through data-driven models of structure-property relationships. Nearly\nall available models rely on featurization of materials composition, however,\nwhether the exclusive use of structural knowledge in such models has the\ncapacity to make comparable predictions remains unknown. Here we employ a deep\nneural network (DNN) model, deepKNet, to learn structure-property relationships\nin crystalline materials without explicit chemical compositions, focusing on\nclassification of crystal systems, mechanical elasticity, electrical behavior,\nand phase stability. The deepKNet model utilizes a three-dimensional (3D)\nmomentum space representation of structure from elastic X-ray scattering theory\nand simultaneously exhibits rotation and permutation invariance. We find that\nthe spatial symmetry of the 3D point cloud, which reflects crystalline symmetry\noperations, is more important than the point intensities contained within,\nwhich correspond to various planar electron densities, for making a successful\nmetal-insulator classification. In contrast, the intensities are more important\nfor predicting bulk moduli. Phase stability, however, relies more upon chemical\ncomposition information, where our structure-based model exhibits limited\npredictability. We find learning the materials structural genome in the form of\na chemistry-agnostic DNN demonstrates that some crystal structures inherently\nhost high propensities for optimal materials properties, which enables the\ndecoupling of structure and composition for future co-design of\nmultifunctionality.\n"}
+{"abstract": "  As a consequence of the classification of finite simple groups, the\nclassification of permutation groups of prime degree is complete, apart from\nthe question of when the natural degree $(q^n-1)/(q-1)$ of ${\\rm PSL}_n(q)$ is\nprime. We present heuristic arguments and computational evidence based on the\nBateman-Horn Conjecture to support a conjecture that for each prime $n\\ge 3$\nthere are infinitely many primes of this form, even if one restricts to prime\nvalues of $q$. Similar arguments and results apply to the parameters of the\nsimple groups ${\\rm PSL}_n(q)$, ${\\rm PSU}_n(q)$ and ${\\rm PSp}_{2n}(q)$ which\narise in the work of Dixon and Zalesskii on linear groups of prime degree.\n"}
+{"abstract": "  It is shown that $c=-29/16$ is the unique rational number of smallest\ndenominator, and the unique rational number of smallest numerator, for which\nthe map $f_c(x) = x^2+c$ has a rational periodic point of period $3$. Several\narithmetic conditions on the set of all such rational numbers $c$ and the\nrational orbits of $f_c(x)$ are proved. A graph on the numerators of the\nrational $3$-periodic points of maps $f_c$ is considered which reflects\nconnections between solutions of norm equations from the cubic field of\ndiscriminant $-23$.\n"}
+{"abstract": "  The emergence of deep learning has been accompanied by privacy concerns\nsurrounding users' data and service providers' models. We focus on private\ninference (PI), where the goal is to perform inference on a user's data sample\nusing a service provider's model. Existing PI methods for deep networks enable\ncryptographically secure inference with little drop in functionality; however,\nthey incur severe latency costs, primarily caused by non-linear network\noperations (such as ReLUs). This paper presents Sphynx, a ReLU-efficient\nnetwork design method based on micro-search strategies for convolutional cell\ndesign. Sphynx achieves Pareto dominance over all existing private inference\nmethods on CIFAR-100. We also design large-scale networks that support\ncryptographically private inference on Tiny-ImageNet and ImageNet.\n"}
+{"abstract": "  Electronic health record (EHR) coding is the task of assigning ICD codes to\neach EHR. Most previous studies either only focus on the frequent ICD codes or\ntreat rare and frequent ICD codes in the same way. These methods perform well\non frequent ICD codes but due to the extremely unbalanced distribution of ICD\ncodes, the performance on rare ones is far from satisfactory. We seek to\nimprove the performance for both frequent and rare ICD codes by using a\ncontrastive graph-based EHR coding framework, CoGraph, which re-casts EHR\ncoding as a few-shot learning task. First, we construct a heterogeneous EHR\nword-entity (HEWE) graph for each EHR, where the words and entities extracted\nfrom an EHR serve as nodes and the relations between them serve as edges. Then,\nCoGraph learns similarities and dissimilarities between HEWE graphs from\ndifferent ICD codes so that information can be transferred among them. In a\nfew-shot learning scenario, the model only has access to frequent ICD codes\nduring training, which might force it to encode features that are useful for\nfrequent ICD codes only. To mitigate this risk, CoGraph devises two graph\ncontrastive learning schemes, GSCL and GECL, that exploit the HEWE graph\nstructures so as to encode transferable features. GSCL utilizes the\nintra-correlation of different sub-graphs sampled from HEWE graphs while GECL\nexploits the inter-correlation among HEWE graphs at different clinical stages.\nExperiments on the MIMIC-III benchmark dataset show that CoGraph significantly\noutperforms state-of-the-art methods on EHR coding, not only on frequent ICD\ncodes, but also on rare codes, in terms of several evaluation indicators. On\nfrequent ICD codes, GSCL and GECL improve the classification accuracy and F1 by\n1.31% and 0.61%, respectively, and on rare ICD codes CoGraph has more obvious\nimprovements by 2.12% and 2.95%.\n"}
+{"abstract": "  Semiconductor nanowire networks are essential elements for a variety of\ngate-tunable quantum applications. Their relevance, however, depends critically\non the material quality. In this work we study selective area growth (SAG) of\nhighly lattice-mismatched InAs/In$_x$Ga$_{1-x}$As nanowires on insulating\nGaAs(001) substrates and address two key challenges: crystalline quality and\ncompositional uniformity. We introduce optimization steps and show how misfit\ndislocations are guided away from the InAs active region and how Ga-In\nintermixing is kinetically limited with growth temperature. The optimization\nprocess leads to a more than twofold increase in electron mobility and shows an\nadvancement toward realizing high quality gatable quantum wire networks.\n"}
+{"abstract": "  This article is mostly based on a talk I gave at the March 2021 meeting\n(virtual) of the American Physical Society on the occasion of receiving the\nDannie Heineman prize for Mathematical Physics from the American Institute of\nPhysics and the American Physical Society. I am greatly indebted to many\ncolleagues for the results leading to this award. To name them all would take\nup all the space allotted to this article. (I have had more than 200\ncollaborators so far), I will therefore mention just a few: Michael Aizenman,\nBernard Derrida, Shelly Goldstein, Elliott Lieb, Oliver Penrose, Errico\nPresutti, Gene Speer and Herbert Spohn. I am grateful to all of my\ncollaborators, listed and unlisted. I would also like to acknowledge here long\ntime support form the AFOSR and the NSF.\n"}
+{"abstract": "  We investigate the possibility of gravitationally generated particle\nproduction via the mechanism of nonminimal torsion--matter coupling. An\nintriguing feature of this theory is that the divergence of the matter\nenergy--momentum tensor does not vanish identically. We explore the physical\nand cosmological implications of the nonconservation of the energy--momentum\ntensor by using the formalism of irreversible thermodynamics of open systems in\nthe presence of matter creation/annihilation. The particle creation rates,\npressure, and the expression of the comoving entropy are obtained in a\ncovariant formulation and discussed in detail. Applied together with the\ngravitational field equations, the thermodynamics of open systems lead to a\ngeneralization of the standard $\\Lambda$CDM cosmological paradigm, in which the\nparticle creation rates and pressures are effectively considered as components\nof the cosmological fluid energy--momentum tensor. We consider specific models,\nand we show that cosmology with a torsion--matter coupling can almost perfectly\nreproduce the $\\Lambda$CDM scenario, while it additionally gives rise to\nparticle creation rates, creation pressures, and entropy generation through\ngravitational matter production in both low and high redshift limits.\n"}
+{"abstract": "  We present the identification of the COCONUTS-2 system, composed of the M3\ndwarf L 34-26 and the T9 dwarf WISEPA J075108.79$-$763449.6. Given their common\nproper motions and parallaxes, these two field objects constitute a physically\nbound pair with a projected separation of 594$\"$ (6471 au). The primary star\nCOCONUTS-2A has strong stellar activity (H$\\alpha$, X-ray, and UV emission) and\nis rapidly rotating ($P_{\\rm rot} = 2.83$ days), from which we estimate an age\nof 150-800 Myr. Comparing equatorial rotational velocity derived from the TESS\nlight curve to spectroscopic $v\\sin{i}$, we find COCONUTS-2A has a nearly\nedge-on inclination. The wide exoplanet COCONUTS-2b has an effective\ntemperature of $T_{\\rm eff}=434 \\pm 9$ K, a surface gravity of $\\log{g} =\n4.11^{+0.11}_{-0.18}$ dex, and a mass of $M=6.3^{+1.5}_{-1.9}$ $M_{\\rm Jup}$\nbased on hot-start evolutionary models, leading to a $0.016^{+0.004}_{-0.005}$\nmass ratio for the COCONUTS-2 system. COCONUTS-2b is the second coldest (after\nWD 0806$-$661B) and the second widest (after TYC 9486-927-1 b) exoplanet imaged\nto date. Comparison of COCONUTS-2b's infrared photometry with ultracool model\natmospheres suggests the presence of both condensate clouds and non-equilibrium\nchemistry in its photosphere. Similar to 51 Eri b, COCONUTS-2b has a\nsufficiently low luminosity ($\\log{(L_{\\rm bol}/L_{\\odot})} = -6.384 \\pm 0.028$\ndex) to be consistent with the cold-start process that may form gas-giant\n(exo)planets, though its large separation means such formation would not have\noccurred in situ. Finally, at a distance of 10.9 pc, COCONUTS-2b is the nearest\nimaged exoplanet to Earth known to date.\n"}
+{"abstract": "  Magnetic insulators are important materials for a range of next generation\nmemory and spintronic applications. Structural constraints in this class of\ndevices generally require a clean heterointerface that allows effective\nmagnetic coupling between the insulating layer and the conducting layer.\nHowever, there are relatively few examples of magnetic insulators which can be\nsynthesized with surface qualities that would allow these smooth interfaces and\nprecisely tuned interfacial magnetic exchange coupling which might be\napplicable at room temperature. In this work, we demonstrate an example of how\nthe configurational complexity in the magnetic insulator layer can be used to\nrealize these properties. The entropy-assisted synthesis is used to create\nsingle crystal (Mg0.2Ni0.2Fe0.2Co0.2Cu0.2)Fe2O4 films on substrates spanning a\nrange of strain states. These films show smooth surfaces, high resistivity, and\nstrong magnetic responses at room temperature. Local and global magnetic\nmeasurements further demonstrate how strain can be used to manipulate magnetic\ntexture and anisotropy. These findings provide insight into how precise\nmagnetic responses can be designed using compositionally complex materials that\nmay find application in next generation magnetic devices.\n"}
+{"abstract": "  We characterize the essential spectrum of the plasmonic problem for polyhedra\nin $\\mathbb{R}^3$. The description is particularly simple for convex polyhedra\nand permittivities $\\epsilon < - 1$. The plasmonic problem is interpreted as a\nspectral problem through a boundary integral operator, the direct value of the\ndouble layer potential, also known as the Neumann--Poincar\\'e operator. We\ntherefore study the spectral structure of the the double layer potential for\npolyhedral cones and polyhedra.\n"}
+{"abstract": "  Lattice simulations of the QCD correlation functions in the Landau gauge have\nestablished two remarkable facts. First, the coupling constant in the gauge\nsector remains finite and moderate at all scales, suggesting that some kind of\nperturbative description should be valid down to infrared momenta. Second, the\ngluon propagator reaches a finite nonzero value at vanishing momentum,\ncorresponding to a gluon screening mass. We review recent studies which aim at\ndescribing the long-distance properties of Landau gauge QCD by means of the\nperturbative Curci-Ferrari model. The latter is the simplest deformation of the\nFaddeev-Popov Lagrangian in the Landau gauge that includes a gluon screening\nmass at tree-level. There are, by now, strong evidences that this approach\nsuccessfully describes many aspects of the infrared QCD dynamics. In\nparticular, several correlation functions were computed at one- and two-loop\norders and compared with {\\it ab-initio} lattice simulations. The typical error\nis of the order of ten percent for a one-loop calculation and drops to few\npercents at two loops. We review such calculations in the quenched\napproximation as well as in the presence of dynamical quarks. In the latter\ncase, the spontaneous breaking of the chiral symmetry requires to go beyond a\ncoupling expansion but can still be described in a controlled approximation\nscheme in terms of small parameters. We also review applications of the\napproach to nonzero temperature and chemical potential.\n"}
+{"abstract": "  Two-band model works well for Hall effect in topological insulators. It turns\nout to be non-Hermitian when the system is subjected to environments, and its\ntopology characterized by Chern numbers has received extensive studies in the\npast decades. However, how a non-Hermitian system responses to an electric\nfield and what is the connection of the response to the Chern number defined\nvia the non-Hermitian Hamiltonian remain barely explored. In this paper,\nfocusing on a k-dependent decay rate, we address this issue by studying the\nresponse of such a non-Hermitian Chern insulator to an external electric field.\nTo this aim, we first derive an effective non-Hermitian Hamiltonian to describe\nthe system and give a specific form of k-dependent decay rate. Then we\ncalculate the response of the non-Hermitian system to a constant electric\nfield. We observe that the environment leads the Hall conductance to be a\nweighted integration of curvature of the ground band and hence the conductance\nis no longer quantized in general. And the environment induces a delay in the\nresponse of the system to the electric field. A discussion on the validity of\nthe non-Hermitian model compared with the master equation description is also\npresented.\n"}
+{"abstract": "  An appearance or disappearance of QPOs associated with the variation of X-ray\nflux can be used to decipher the accretion ejection mechanism of black hole\nX-ray sources. We searched and studied such rapid transitions in H1743-322\nusing RXTE archival data and found eight such events, where QPO vanishes\nsuddenly along with the variation of X-ray flux. The appearance/disappearance\nof QPOs were associated to the four events exhibiting type-B QPOs at $\\sim$ 4.5\nHz, one with type-A QPO at $\\nu$ $\\sim$ 3.5 Hz, and the remaining three were\nconnected to type-C QPOs at $\\sim$ 9.5 Hz. Spectral studies of the data\nunveiled that an inner disk radius remained at the same location around 2-9\nr$_g$ , depending on the used model but power-law indices were varying,\nindicating that either corona or jet is responsible for the events. The\nprobable ejection radii of corona were estimated to be around 4.2-15.4 r$_g$\nbased on the plasma ejection model. Our X-ray and quasi-simultaneous radio\ncorrelation studies suggest that the type-B QPOs are probably related to the\nprecession of a weak jet though a small and weak corona is present at its base\nand the type-C QPOs are associated to the base of a relatively strong jet which\nis acting like a corona.\n"}
+{"abstract": "  Solutions to the $\\mu$ problem in supersymmetry based on the Kim-Nilles\nmechanism naturally feature a Dine-Fischler-Srednicki-Zhitnitsky (DFSZ) axion\nwith decay constant of order the geometric mean of the Planck and TeV scales,\nconsistent with astrophysical limits. We investigate minimal models of this\ntype with two gauge-singlet fields that break a Peccei-Quinn symmetry, and\nextensions with extra vectorlike quark and lepton supermultiplets consistent\nwith gauge coupling unification. We show that there are many anomaly-free\ndiscrete symmetries, depending on the vectorlike matter content, that protect\nthe Peccei-Quinn symmetry to sufficiently high order to solve the strong CP\nproblem. We study the axion couplings in this class of models. Models of this\ntype that are automatically free of the domain wall problem require at least\none pair of strongly interacting vectorlike multiplets with mass at the\nintermediate scale, and predict axion couplings that are greatly enhanced\ncompared to the minimal supersymmetric DFSZ models, putting them within reach\nof proposed axion searches.\n"}
+{"abstract": "  In this paper, we focus on the $(\\si,\\t)$-derivation theory of Lie conformal\nsuperalgebras. Firstly, we study the fundamental properties of conformal\n$(\\si,\\t)$-derivations. Secondly, we mainly research the interiors of conformal\n$G$-derivations. Finally, we discuss the relationships between the conformal\n$(\\si,\\t)$-derivations and some generalized conformal derivations of Lie\nconformal superalgebras.\n"}
+{"abstract": "  The thermodynamic activities of all components in Ga-In-Tl system have been\npredicted at 1073, 1173 and 1273 K, using Molecular interaction volume model\n(MIVM). The infinite dilute activity coefficients for Ga-In and Ga-Tl binary\nsubsystems, which were needed for the determination of thermodynamic activities\nof all components in Ga-In-Tl, have been predicted by using a method that is\nbased on Complex formation model for liquid alloys. The computed thermodynamic\nactivities of Ga in Ga-In-Tl were observed to satisfactorily agree with the\navailable experimental data when the newly computed coefficients were applied\nin MIVM. The satisfactory prediction of the activity of Ga led to the\nprediction of the activities of the remaining two components (In and Tl).\nIso-activities of all components (Ga, In and Tl) were plotted, and they reveal\nthe dependence of the nature of chemical short range order in Ga-In-Tl system\non composition.\n"}
+{"abstract": "  The Ni-based superalloy Alloy 718 is used in aircraft engines as\nhigh-pressure turbine discs and must endure challenging demands on\nhigh-temperature yield strength, creep-, and oxidation-resistance. Nanoscale\n$\\gamma^{\\prime}$- and $\\gamma^{\\prime \\prime}$-precipitates commonly found in\nduplet and triplet co-precipitate morphologies provide high-temperature\nstrength under these harsh operating conditions. Direct ageing of Alloy 718 is\nan attractive alternative manufacturing route known to increase the yield\nstrength at 650 $^{\\deg}$C by at least +10 $\\%$, by both retaining high\ndislocation densities and changing the nanoscale co-precipitate morphology.\nHowever, the detailed nucleation and growth mechanisms of the duplet and\ntriplet co-precipitate morphologies of $\\gamma^{\\prime}$ and $\\gamma^{\\prime\n\\prime}$ during the direct ageing process remain unknown. We provide a\ncorrelative high-resolution microscopy approach using transmission electron\nmicroscopy, high-angle annular dark-field imaging, and atom probe microscopy to\nreveal the early stages of precipitation during direct ageing of Alloy 718.\nQuantitative stereological analyses of the $\\gamma^{\\prime}$- and\n$\\gamma^{\\prime \\prime}$-precipitate dispersions as well as their chemical\ncompositions have allowed us to propose a qualitative model of the\nmicrostructural evolution. It is shown that fine $\\gamma^{\\prime}$- and\n$\\gamma^{\\prime \\prime}$-precipitates nucleate homogeneously and grow\ncoherently. However, $\\gamma^{\\prime \\prime}$-precipitates also nucleate\nheterogeneously on dislocations and experience accelerated growth due to Nb\npipe diffusion. Moreover, the co-precipitation reactions are largely influenced\nby solute availability and the potential for enrichment of Nb and rejection of\nAl+Ti.\n"}
+{"abstract": "  We study systems of String Equations where block variables need to be\nassigned strings so that their concatenation gives a specified target string.\nWe investigate this problem under a multivariate complexity framework,\nsearching for tractable special cases such as systems of equations with few\nblock variables or few equations. Our main results include a polynomial-time\nalgorithm for size-2 equations, and hardness for size-3 equations, as well as\nhardness for systems of two equations, even with tight constraints on the block\nvariables. We also study a variant where few deletions are allowed in the\ntarget string, and give XP algorithms in this setting when the number of block\nvariables is constant.\n"}
+{"abstract": "  The inverse renormalization group is studied based on the image\nsuper-resolution using the deep convolutional neural networks. We consider the\nimproved correlation configuration instead of spin configuration for the spin\nmodels, such as the two-dimensional Ising and three-state Potts models. We\npropose a block-cluster transformation as an alternative to the block-spin\ntransformation in dealing with the improved estimators. In the framework of the\ndual Monte Carlo algorithm, the block-cluster transformation is regarded as a\ntransformation in the graph degrees of freedom, whereas the block-spin\ntransformation is that in the spin degrees of freedom. We demonstrate that the\nrenormalized improved correlation configuration successfully reproduces the\noriginal configuration at all the temperatures by the super-resolution scheme.\nUsing the rule of enlargement, we repeatedly make inverse renormalization\nprocedure to generate larger correlation configurations. To connect\nthermodynamics, an approximate temperature rescaling is discussed. The enlarged\nsystems generated using the super-resolution satisfy the finite-size scaling.\n"}
+{"abstract": "  Although much progress has been made on the physics of magic angle twisted\nbilayer graphene at integer fillings, little attention has been given to\nfractional fillings. Here we show that the three-peak structure of Wannier\norbitals, dictated by the symmetry and topology of flat bands, facilitates the\nemergence of a novel state at commensurate fractional filling of $\\nu = n \\pm\n1/3$. We dub this state a \"fractional correlated insulator\". Specifically for\nthe filling of $\\pm 1/3$ electrons per moir\\'{e} unit cell, we show that\nshort-range interactions alone imply an approximate extensive entropy due to\nthe \"breathing\" degree of freedom of an irregular honeycomb lattice that\nemerges through defect lines. The leading further-range interaction lifts this\ndegeneracy and selects a novel ferromagnetic nematic state that breaks AB/BA\nsublattice symmetry. The proposed fractional correlated insulating state might\nunderlie the suppression of superconductivity at $\\nu = 2-1/3$ filling observed\nin arXiv:2004.04148. Further investigation of the proposed fractional\ncorrelated insulating state would open doors to new regimes of correlation\neffects in MATBG.\n"}
+{"abstract": "  Self-supervised learning presents a remarkable performance to utilize\nunlabeled data for various video tasks. In this paper, we focus on applying the\npower of self-supervised methods to improve semi-supervised action proposal\ngeneration. Particularly, we design an effective Self-supervised\nSemi-supervised Temporal Action Proposal (SSTAP) framework. The SSTAP contains\ntwo crucial branches, i.e., temporal-aware semi-supervised branch and\nrelation-aware self-supervised branch. The semi-supervised branch improves the\nproposal model by introducing two temporal perturbations, i.e., temporal\nfeature shift and temporal feature flip, in the mean teacher framework. The\nself-supervised branch defines two pretext tasks, including masked feature\nreconstruction and clip-order prediction, to learn the relation of temporal\nclues. By this means, SSTAP can better explore unlabeled videos, and improve\nthe discriminative abilities of learned action features. We extensively\nevaluate the proposed SSTAP on THUMOS14 and ActivityNet v1.3 datasets. The\nexperimental results demonstrate that SSTAP significantly outperforms\nstate-of-the-art semi-supervised methods and even matches fully-supervised\nmethods. Code is available at https://github.com/wangxiang1230/SSTAP.\n"}
+{"abstract": "  We consider the Keller-Segel system of consumption type coupled with an\nincompressible fluid equation. The system describes the dynamics of oxygen and\nbacteria densities evolving within a fluid. We establish local well-posedness\nof the system in Sobolev spaces for partially inviscid and fully inviscid\ncases. In the latter, additional assumptions on the initial data are required\nwhen either the oxygen or bacteria density touches zero. Even though the oxygen\ndensity satisfies a maximum principle due to consumption, we prove finite time\nblow-up of its $C^{2}$--norm with certain initial data.\n"}
+{"abstract": "  Magnetic impurities in $s$-wave superconductors lead to spin-polarized\nYu-Shiba-Rusinov (YSR) in-gap states. Chains of magnetic impurities offer one\nof the most viable routes for the realization of Majorana bound states which\nhold a promise for topological quantum computing. However, this ambitious goal\nlooks distant since no quantum coherent degrees of freedom have yet been\nidentified in these systems. To fill this gap we propose an effective two-level\nsystem, a YSR qubit, stemming from two nearby impurities. Using a\ntime-dependent wave-function approach, we derive an effective Hamiltonian\ndescribing the YSR qubit evolution as a function of distance between the\nimpurity spins, their relative orientations, and their dynamics. We show that\nthe YSR qubit can be controlled and read out using the state-of-the-art\nexperimental techniques for manipulation of the spins. Finally, we address the\neffect of the spin noises on the coherence properties of the YSR qubit, and\nshow a robust behaviour for a wide range of experimentally relevant parameters.\nLooking forward, the YSR qubit could facilitate the implementation of a\nuniversal set of quantum gates in hybrid systems where they are coupled to\ntopological Majorana qubits.\n"}
+{"abstract": "  We consider the problem of finding, through adaptive sampling, which of $n$\noptions (arms) has the largest mean. Our objective is to determine a rule which\nidentifies the best arm with a fixed minimum confidence using as few\nobservations as possible, i.e. this is a fixed-confidence (FC) best arm\nidentification (BAI) in multi-armed bandits. We study such problems under the\nBayesian setting with both Bernoulli and Gaussian arms. We propose to use the\nclassical \"vector at a time\" (VT) rule, which samples each remaining arm once\nin each round. We show how VT can be implemented and analyzed in our Bayesian\nsetting and be improved by early elimination. Our analysis show that these\nalgorithms guarantee an optimal strategy under the prior. We also propose and\nanalyze a variant of the classical \"play the winner\" (PW) algorithm. Numerical\nresults show that these rules compare favorably with state-of-art algorithms.\n"}
+{"abstract": "  The dust production in debris discs by grinding collisions of planetesimals\nrequires their orbits to be stirred. However, stirring levels remain largely\nunconstrained, and consequently the stirring mechanisms as well. This work\nshows how the sharpness of the outer edge of discs can be used to constrain the\nstirring levels. Namely, the sharper the edge is the lower the eccentricity\ndispersion must be. For a Rayleigh distribution of eccentricities ($e$), I find\nthat the disc surface density near the outer edge can be parametrised as\n$\\tanh[(r_{\\max}-r)/l_{\\rm out}]$, where $r_{\\max}$ approximates the maximum\nsemi-major axis and $l_{\\rm out}$ defines the edge smoothness. If the\nsemi-major axis distribution has sharp edges $e_\\mathrm{rms}$ is roughly $1.2\nl_{\\rm out}/r_{\\max}$, or $e_\\mathrm{rms}=0.77 l_{\\rm out}/r_{\\max}$ if\nsemi-major axes have diffused due to self-stirring. This model is fitted to\nALMA data of five wide discs: HD 107146, HD 92945, HD 206893, AU Mic and HR\n8799. The results show that HD 107146, HD 92945 and AU Mic have the sharpest\nouter edges, corresponding to $e_\\mathrm{rms}$ values of $0.121\\pm0.05$,\n$0.15^{+0.07}_{-0.05}$ and $0.10\\pm0.02$ if their discs are self-stirred,\nsuggesting the presence of Pluto-sized objects embedded in the disc. Although\nthese stirring values are larger than typically assumed, the radial stirring of\nHD 92945 is in good agreement with its vertical stirring constrained by the\ndisc height. HD 206893 and HR~8799, on the other hand, have smooth outer edges\nthat are indicative of scattered discs since both systems have massive inner\ncompanions.\n"}
+{"abstract": "  Entangled quantum states play an important role in quantum information\nscience and also in quantum mechanics fundamental investigations.\nImplementation and characterization of techniques allowing for easy preparation\nof entangled states are important steps in such fields. Here we generated\nentangled quantum states encoded in photons transversal paths, obtained by\npumping a non-linear crystal with multiple transversal Gaussian beams. Such\napproach allows us to generate entangled states of two qubits and two qutrits\nencoded in Gaussian transversal path of twin photons. We make a theoretical\nanalyses of this source, considering the influence of the pump angular spectrum\non the generated states, further characterizing those by their purity and\nentanglement degree. Our experimental results reveals that the generated states\npresents both high purity and entanglement, and the theoretical analysis\nelucidates how the pump beams profile can be used to manipulate such photonic\nstates.\n"}
+{"abstract": "  The logic of Bunched Implications (BI) combines both additive and\nmultiplicative connectives, which include two primitive intuitionistic\nimplications. As a consequence, contexts in the sequent presentation are not\nlists, nor multisets, but rather tree-like structures called bunches. This\nadditional complexity notwithstanding, the logic has a well-behaved metatheory\nadmitting all the familiar forms of semantics and proof systems. However, the\npresentation of an effective proof-search procedure has been elusive since the\nlogic's debut. We show that one can reduce the proof-search space for any given\nsequent to a primitive recursive set, the argument generalizing Gentzen's\ndecidability argument for classical propositional logic and combining key\nfeatures of Dyckhoff's contraction-elimination argument for intuitionistic\nlogic. An effective proof-search procedure, and hence decidability of\nprovability, follows as a corollary.\n"}
+{"abstract": "  Cell-free translational strategies are needed to accelerate the repair of\nmineralised tissues, particularly large bone defects, using minimally invasive\napproaches. Regenerative bone scaffolds should ideally mimic aspects of the\ntissue's ECM over multiple length scales and enable surgical handling and\nfixation during implantation in vivo. Leveraging the knowledge gained with\nbioactive self-assembling peptides (SAPs) and SAP-enriched electrospun fibres,\nwe presented a cell free approach for promoting mineralisation via apatite\ndeposition and crystal growth, in vitro, of SAP-enriched nonwoven scaffolds.\nThe nonwoven scaffold was made by electrospinning poly(epsilon-caprolactone)\n(PCL) in the presence of either peptide P11-4 (Ac-QQRFEWEFEQQ-Am) or P11-8\n(Ac-QQRFOWOFEQQ-Am), in light of the polymer's fibre forming capability and its\nhydrolytic degradability as well as the well-known apatite nucleating\ncapability of SAPs. The 11-residue family of peptides (P11-X) has the ability\nto self-assemble into beta-sheet ordered structures at the nano-scale and to\ngenerate hydrogels at the macroscopic scale, some of which are capable of\npromoting biomineralisation due to their apatite-nucleating capability. Both\nvariants of SAP-enriched nonwoven used in this study were proven to be\nbiocompatible with murine fibroblasts and supported nucleation and growth of\napatite minerals in simulated body fluid (SBF) in vitro. The fibrous nonwoven\nprovided a structurally robust scaffold, with the capability to control SAP\nrelease behaviour. Up to 75% of P11-4 and 45% of P11-8 were retained in the\nfibres after 7-day incubation in aqueous solution at pH 7.4. The encapsulation\nof SAP in a nonwoven system with apatite-forming as well as localised and\nlong-term SAP delivery capabilities is appealing as a potential means of\nachieving cost-effective bone repair therapy for critical size defects.\n"}
+{"abstract": "  Min-max saddle point games have recently been intensely studied, due to their\nwide range of applications, including training Generative Adversarial Networks\n(GANs). However, most of the recent efforts for solving them are limited to\nspecial regimes such as convex-concave games. Further, it is customarily\nassumed that the underlying optimization problem is solved either by a single\nmachine or in the case of multiple machines connected in centralized fashion,\nwherein each one communicates with a central node. The latter approach becomes\nchallenging, when the underlying communications network has low bandwidth. In\naddition, privacy considerations may dictate that certain nodes can communicate\nwith a subset of other nodes. Hence, it is of interest to develop methods that\nsolve min-max games in a decentralized manner. To that end, we develop a\ndecentralized adaptive momentum (ADAM)-type algorithm for solving min-max\noptimization problem under the condition that the objective function satisfies\na Minty Variational Inequality condition, which is a generalization to\nconvex-concave case. The proposed method overcomes shortcomings of recent\nnon-adaptive gradient-based decentralized algorithms for min-max optimization\nproblems that do not perform well in practice and require careful tuning. In\nthis paper, we obtain non-asymptotic rates of convergence of the proposed\nalgorithm (coined DADAM$^3$) for finding a (stochastic) first-order Nash\nequilibrium point and subsequently evaluate its performance on training GANs.\nThe extensive empirical evaluation shows that DADAM$^3$ outperforms recently\ndeveloped methods, including decentralized optimistic stochastic gradient for\nsolving such min-max problems.\n"}
+{"abstract": "  Artificial Intelligence (AI), along with the recent progress in biomedical\nlanguage understanding, is gradually changing medical practice. With the\ndevelopment of biomedical language understanding benchmarks, AI applications\nare widely used in the medical field. However, most benchmarks are limited to\nEnglish, which makes it challenging to replicate many of the successes in\nEnglish for other languages. To facilitate research in this direction, we\ncollect real-world biomedical data and present the first Chinese Biomedical\nLanguage Understanding Evaluation (CBLUE) benchmark: a collection of natural\nlanguage understanding tasks including named entity recognition, information\nextraction, clinical diagnosis normalization, single-sentence/sentence-pair\nclassification, and an associated online platform for model evaluation,\ncomparison, and analysis. To establish evaluation on these tasks, we report\nempirical results with the current 11 pre-trained Chinese models, and\nexperimental results show that state-of-the-art neural models perform by far\nworse than the human ceiling. Our benchmark is released at\n\\url{https://tianchi.aliyun.com/dataset/dataDetail?dataId=95414&lang=en-us}.\n"}
+{"abstract": "  For $m, d \\in {\\mathbb N}$, a jittered sampling point set $P$ having $N =\nm^d$ points in $[0,1)^d$ is constructed by partitioning the unit cube $[0,1)^d$\ninto $m^d$ axis-aligned cubes of equal size and then placing one point\nindependently and uniformly at random in each cube. We show that there are\nconstants $c \\ge 0$ and $C$ such that for all $d$ and all $m \\ge d$ the\nexpected non-normalized star discrepancy of a jittered sampling point set\nsatisfies \\[c \\,dm^{\\frac{d-1}{2}} \\sqrt{1 + \\log(\\tfrac md)} \\le {\\mathbb E}\nD^*(P) \\le C\\, dm^{\\frac{d-1}{2}} \\sqrt{1 + \\log(\\tfrac md)}.\\]\n  This discrepancy is thus smaller by a factor of\n$\\Theta\\big(\\sqrt{\\frac{1+\\log(m/d)}{m/d}}\\,\\big)$ than the one of a uniformly\ndistributed random point set of $m^d$ points. This result improves both the\nupper and the lower bound for the discrepancy of jittered sampling given by\nPausinger and Steinerberger (Journal of Complexity (2016)). It also removes the\nasymptotic requirement that $m$ is sufficiently large compared to $d$.\n"}
+{"abstract": "  Magnetic field evolution in neutron-star crusts is driven by the Hall effect\nand Ohmic dissipation, for as long as the crust is sufficiently strong to\nabsorb Maxwell stresses exerted by the field and thus make the momentum\nequation redundant. For the strongest neutron-star fields, however, stresses\nbuild to the point of crustal failure, at which point the standard evolution\nequations are no longer valid. Here, we study the evolution of the magnetic\nfield of the crust up to and beyond crustal failure, whence the crust begins to\nflow plastically. We perform global axisymmetric evolutions, exploring\ndifferent types of failure affecting a limited region of the crust. We find\nthat a plastic flow does not simply suppress the Hall effect even in the regime\nof a low plastic viscosity, but it rather leads to non-trivial evolution -- in\nsome cases even overreacting and enhancing the impact of the Hall effect. Its\nimpact is more pronouced in the toroidal field, with the differences on the\npoloidal field being less substantial. We argue that both the nature of\nmagnetar bursts and their spindown evolution will be affected by plastic flow,\nso that observations of these phenomena may help to constrain the way the crust\nfails.\n"}
+{"abstract": "  We study best approximations to compact operators between Banach spaces and\nHilbert spaces, from the point of view of Birkhoff-James orthogonality and\nsemi-inner-products. As an application of the present study, some distance\nformulae are presented in the space of compact operators. The special case of\nbounded linear functionals as compact operators is treated separately and some\napplications to best approximations in reflexive, strictly convex and smooth\nBanach spaces are discussed. An explicit example is presented in $ \\ell_p^{n} $\nspaces, where $ 1 < p < \\infty, $ to illustrate the applicability of the\nmethods developed in this article. A comparative analysis of the results\npresented in this article with the well-known classical duality principle in\napproximation theory is conducted to demonstrate the advantage in the former\ncase, from a computational point of view.\n"}
+{"abstract": "  We study Krasnoselskii-Mann style iterative algorithms for approximating\nfixpoints of asymptotically weakly contractive mappings, with a focus on\nproviding generalised convergence proofs along with explicit rates of\nconvergence. More specifically, we define a new notion of being asymptotically\n$\\psi$-weakly contractive with modulus, and present a series of abstract\nconvergence theorems which both generalise and unify known results from the\nliterature. Rates of convergence are formulated in terms of our modulus of\ncontractivity, in conjunction with other moduli and functions which form\nquantitative analogues of additional assumptions that are required in each\ncase. Our approach makes use of ideas from proof theory, in particular our\nemphasis on abstraction and on formulating our main results in a quantitative\nmanner. As such, the paper can be seen as a contribution to the proof mining\nprogram.\n"}
+{"abstract": "  From nutrient uptake, to chemoreception, to synaptic transmission, many\nsystems in cell biology depend on molecules diffusing and binding to membrane\nreceptors. Mathematical analysis of such systems often neglects the fact that\nreceptors process molecules at finite kinetic rates. A key example is the\ncelebrated formula of Berg and Purcell for the rate that cell surface receptors\ncapture extracellular molecules. Indeed, this influential result is only valid\nif receptors transport molecules through the cell wall at a rate much faster\nthan molecules arrive at receptors. From a mathematical perspective, ignoring\nreceptor kinetics is convenient because it makes the diffusing molecules\nindependent. In contrast, including receptor kinetics introduces correlations\nbetween the diffusing molecules since, for example, bound receptors may be\ntemporarily blocked from binding additional molecules. In this work, we present\na modeling framework for coupling bulk diffusion to surface receptors with\nfinite kinetic rates. The framework uses boundary homogenization to couple the\ndiffusion equation to nonlinear ordinary differential equations on the\nboundary. We use this framework to derive an explicit formula for the cellular\nuptake rate and show that the analysis of Berg and Purcell significantly\noverestimates uptake in some typical biophysical scenarios. We confirm our\nanalysis by numerical simulations of a many particle stochastic system.\n"}
+{"abstract": "  Minimizing the bending energy within knot classes leads to the concept of\nelastic knots which has been initiated in [von der Mosel, Asymptot. Anal.\n1998]. Motivated by numerical experiments in arxiv:1804.02206\n(doi:10.1090/mcom/3633) we prescribe dihedral symmetry and establish existence\nof dihedrally symmetric elastic knots for knot classes admitting this type of\nsymmetry. Among other results we prove that the dihedral elastic trefoil is the\nunion of two circles that form a (planar) figure-eight. We also discuss some\ngeneralizations and limitations regarding other symmetries and knot classes.\n"}
+{"abstract": "  We employed the log-periodic power law singularity (LPPLS) methodology to\nsystematically investigate the 2020 stock market crash in the U.S. equities\nsectors with different levels of total market capitalizations through four\nmajor U.S. stock market indexes, including the Wilshire 5000 Total Market\nindex, the S&P 500 index, the S&P MidCap 400 index, and the Russell 2000 index,\nrepresenting the stocks overall, the large capitalization stocks, the middle\ncapitalization stocks and the small capitalization stocks, respectively. During\nthe 2020 U.S. stock market crash, all four indexes lost more than a third of\ntheir values within five weeks, while both the middle capitalization stocks and\nthe small capitalization stocks have suffered much greater losses than the\nlarge capitalization stocks and stocks overall. Our results indicate that the\nprice trajectories of these four stock market indexes prior to the 2020 stock\nmarket crash have clearly featured the obvious LPPLS bubble pattern and were\nindeed in a positive bubble regime. Contrary to the popular belief that the\nCOVID-19 led to the 2020 stock market crash, the 2020 U.S. stock market crash\nwas endogenous, stemming from the increasingly systemic instability of the\nstock market itself. We also performed the complementary post-mortem analysis\nof the 2020 U.S. stock market crash. Our analyses indicate that the 2020 U.S.\nstock market crash originated from a bubble which began to form as early as\nSeptember 2018; and the bubbles in stocks with different levels of total market\ncapitalizations have significantly different starting time profiles. This study\nnot only sheds new light on the making of the 2020 U.S. stock market crash but\nalso creates a novel pipeline for future real-time crash detection and\nmechanism dissection of any financial market and/or economic index.\n"}
+{"abstract": "  It is important to estimate the errors of probabilistic inference algorithms.\nExisting diagnostics for Markov chain Monte Carlo methods assume inference is\nasymptotically exact, and are not appropriate for approximate methods like\nvariational inference or Laplace's method. This paper introduces a diagnostic\nbased on repeatedly simulating datasets from the prior and performing inference\non each. The central observation is that it is possible to estimate a symmetric\nKL-divergence defined over these simulations.\n"}
+{"abstract": "  Although distance learning presents a number of interesting educational\nadvantages as compared to in-person instruction, it is not without its\ndownsides. We first assess the educational challenges presented by distance\nlearning as a whole and identify 4 main challenges that distance learning\ncurrently presents as compared to in-person instruction: the lack of social\ninteraction, reduced student engagement and focus, reduced comprehension and\ninformation retention, and the lack of flexible and customizable instructor\nresources. After assessing each of these challenges in-depth, we examine how\nAR/VR technologies might serve to address each challenge along with their\ncurrent shortcomings, and finally outline the further research that is required\nto fully understand the potential of AR/VR technologies as they apply to\ndistance learning.\n"}
+{"abstract": "  An important goal of medical imaging is to be able to precisely detect\npatterns of disease specific to individual scans; however, this is challenged\nin brain imaging by the degree of heterogeneity of shape and appearance.\nTraditional methods, based on image registration to a global template,\nhistorically fail to detect variable features of disease, as they utilise\npopulation-based analyses, suited primarily to studying group-average effects.\nIn this paper we therefore take advantage of recent developments in generative\ndeep learning to develop a method for simultaneous classification, or\nregression, and feature attribution (FA). Specifically, we explore the use of a\nVAE-GAN translation network called ICAM, to explicitly disentangle class\nrelevant features from background confounds for improved interpretability and\nregression of neurological phenotypes. We validate our method on the tasks of\nMini-Mental State Examination (MMSE) cognitive test score prediction for the\nAlzheimer's Disease Neuroimaging Initiative (ADNI) cohort, as well as brain age\nprediction, for both neurodevelopment and neurodegeneration, using the\ndeveloping Human Connectome Project (dHCP) and UK Biobank datasets. We show\nthat the generated FA maps can be used to explain outlier predictions and\ndemonstrate that the inclusion of a regression module improves the\ndisentanglement of the latent space. Our code is freely available on Github\nhttps://github.com/CherBass/ICAM.\n"}
+{"abstract": "  It is well-known that there is no spherical/topologically spherical\ngravitational waves in vacuum space in general relativity. We show that a\ndeviation from general relativity leads to exact vacuum spherical gravitational\nwaves, no matter how tiny this deviation is. We also discuss the related\ntopics, including Vaidya-like metric in $f(R)$ gravity. We demonstrate that the\nexistence of spherical gravitational wave is a non perturbative property for\ngravities. We investigate energy carried by this nonperturbative wave. We first\nfind the wave solution from investigations of Vaidya-like metric in $f(R)$\ngravity, which has only one longitude polarization. We further extend it to a\nmetric with two transverse polarizations by directly solving the field\nequation.\n"}
+{"abstract": "  We introduce a novel primal-dual flow for affine constrained convex\noptimization problem. As a modification of the standard saddle-point system,\nour primal-dual flow is proved to possesses the exponential decay property, in\nterms of a tailored Lyapunov function. Then a class of primal-dual methods for\nthe original optimization problem are obtained from numerical discretizations\nof the continuous flow, and with a unified discrete Lyapunov function,\nnonergodic convergence rates are established. Among those algorithms, we can\nrecover the (linearized) augmented Lagrangian method and the quadratic penalty\nmethod with continuation technique. Also, new methods with a special inner\nproblem, that is a linear symmetric positive definite system or a nonlinear\nequation which may be solved efficiently via the semi-smooth Newton method,\nhave been proposed as well. Especially, numerical tests on the linearly\nconstrained $l_1$-$l_2$ minimization show that our method outperforms the\naccelerated linearized Bregman method.\n"}
+{"abstract": "  We present 3 mm and 2 mm band simultaneously spectroscopic observations of\nHCN 1-0, HCO$^{+}$ 1-0, HNC 1-0, and CS 3-2 with the IRAM 30 meter telescope,\ntoward a sample of 70 sources as nearby galaxies with infrared luminosities\nranging from several 10$^{5}L_{\\odot}$ to more than 10$^{12}L_{\\odot}$. After\ncombining HCN 1-0, HCO$^{+}$ 1-0 and HNC 1-0 data from literature with our\ndetections, relations between luminosities of dense gas tracers (HCN 1-0,\nHCO$^{+}$ 1-0 and HNC 1-0) and infrared luminosities are derived, with tight\nlinear correlations for all tracers. Luminosities of CS 3-2 with only our\nobservations also show tight linear correlation with infrared luminosities. No\nsystematic difference is found for tracing dense molecular gas among these\ntracers. Star formation efficiencies for dense gas with different tracers also\ndo not show any trend along different infrared luminosities. Our study also\nshows that HCN/HCO$^{+}$ line ratio might not be a good indicator to diagnose\nobscured AGN in galaxies.\n"}
+{"abstract": "  We investigate a model of one-to-one matching with transferable utility and\ngeneral unobserved heterogeneity. Under a separability assumption that\ngeneralizes Choo and Siow (2006), we first show that the equilibrium matching\nmaximizes a social gain function that trades off exploiting complementarities\nin observable characteristics and matching on unobserved characteristics. We\nuse this result to derive simple closed-form formulae that identify the joint\nmatching surplus and the equilibrium utilities of all participants, given any\nknown distribution of unobserved heterogeneity. We provide efficient algorithms\nto compute the stable matching and to estimate parametric versions of the\nmodel. Finally, we revisit Choo and Siow's empirical application to illustrate\nthe potential of our more general approach.\n"}
+{"abstract": "  Graph filtering is a fundamental tool in graph signal processing. Polynomial\ngraph filters (PGFs), defined as polynomials of a fundamental graph operator,\ncan be implemented in the vertex domain, and usually have a lower complexity\nthan frequency domain filter implementations. In this paper, we focus on the\ndesign of filters for graphs with graph Fourier transform (GFT) corresponding\nto a discrete trigonometric transform (DTT), i.e., one of 8 types of discrete\ncosine transforms (DCT) and 8 discrete sine transforms (DST). In this case, we\nshow that multiple sparse graph operators can be identified, which allows us to\npropose a generalization of PGF design: multivariate polynomial graph filter\n(MPGF). First, for the widely used DCT-II (type-2 DCT), we characterize a set\nof sparse graph operators that share the DCT-II matrix as their common\neigenvector matrix. This set contains the well-known connected line graph.\nThese sparse operators can be viewed as graph filters operating in the DCT\ndomain, which allows us to approximate any DCT graph filter by a MPGF, leading\nto a design with more degrees of freedom than the conventional PGF approach.\nThen, we extend those results to all of the 16 DTTs as well as their 2D\nversions, and show how their associated sets of multiple graph operators can be\ndetermined. We demonstrate experimentally that ideal low-pass and exponential\nDCT/DST filters can be approximated with higher accuracy with similar runtime\ncomplexity. Finally, we apply our method to transform-type selection in a video\ncodec, AV1, where we demonstrate significant encoding time savings, with a\nnegligible compression loss.\n"}
+{"abstract": "  We derive Boltzmann equations for massive spin-1/2 fermions with local and\nnonlocal collision terms from the Kadanoff--Baym equation in the\nSchwinger--Keldysh formalism, properly accounting for the spin degrees of\nfreedom. The Boltzmann equations are expressed in terms of matrix-valued spin\ndistribution functions, which are the building blocks for the quasi-classical\nparts of the Wigner functions. Nonlocal collision terms appear at\nnext-to-leading order in $\\hbar$ and are sources for the polarization part of\nthe matrix-valued spin distribution functions. The Boltzmann equations for the\nmatrix-valued spin distribution functions pave the way for simulating\nspin-transport processes involving spin-vorticity couplings from first\nprinciples.\n"}
+{"abstract": "  There is growing concern about image privacy due to the popularity of social\nmedia and photo devices, along with increasing use of face recognition systems.\nHowever, established image de-identification techniques are either too subject\nto re-identification, produce photos that are insufficiently realistic, or\nboth. To tackle this, we present a novel approach for image obfuscation by\nmanipulating latent spaces of an unconditionally trained generative model that\nis able to synthesize photo-realistic facial images of high resolution. This\nmanipulation is done in a way that satisfies the formal privacy standard of\nlocal differential privacy. To our knowledge, this is the first approach to\nimage privacy that satisfies $\\varepsilon$-differential privacy \\emph{for the\nperson.}\n"}
+{"abstract": "  It turns out that the standard application of the four-vector SR formalism\ndoes not include the concept of relative velocity. Only the absolute velocity\nis described by the four-vector, and even the Lorentz transformation parameters\nis described by the three-dimensional velocity.\n  This gap in the development of the SR formalism reflects the lack of some\nsignificant velocity subtraction operations. The differential application of\nthese operations leads to a relativistic acceleration.\n"}
+{"abstract": "  We study the problem of off-policy evaluation in the multi-armed bandit model\nwith bounded rewards, and develop minimax rate-optimal procedures under three\nsettings. First, when the behavior policy is known, we show that the Switch\nestimator, a method that alternates between the plug-in and importance sampling\nestimators, is minimax rate-optimal for all sample sizes. Second, when the\nbehavior policy is unknown, we analyze performance in terms of the competitive\nratio, thereby revealing a fundamental gap between the settings of known and\nunknown behavior policies. When the behavior policy is unknown, any estimator\nmust have mean-squared error larger -- relative to the oracle estimator\nequipped with the knowledge of the behavior policy -- by a multiplicative\nfactor proportional to the support size of the target policy. Moreover, we\ndemonstrate that the plug-in approach achieves this worst-case competitive\nratio up to a logarithmic factor. Third, we initiate the study of the partial\nknowledge setting in which it is assumed that the minimum probability taken by\nthe behavior policy is known. We show that the plug-in estimator is optimal for\nrelatively large values of the minimum probability, but is sub-optimal when the\nminimum probability is low. In order to remedy this gap, we propose a new\nestimator based on approximation by Chebyshev polynomials that provably\nachieves the optimal estimation error. Numerical experiments on both simulated\nand real data corroborate our theoretical findings.\n"}
+{"abstract": "  Mixed-frequency Vector AutoRegressions (MF-VAR) model the dynamics between\nvariables recorded at different frequencies. However, as the number of series\nand high-frequency observations per low-frequency period grow, MF-VARs suffer\nfrom the \"curse of dimensionality\". We curb this curse through a regularizer\nthat permits various hierarchical sparsity patterns by prioritizing the\ninclusion of coefficients according to the recency of the information they\ncontain. Additionally, we investigate the presence of nowcasting relations by\nsparsely estimating the MF-VAR error covariance matrix. We study predictive\nGranger causality relations in a MF-VAR for the U.S. economy and construct a\ncoincident indicator of GDP growth.\n"}
+{"abstract": "  An indoor localization approach uses Wi-Fi Access Points (APs) to estimate\nthe Direction of Arrival (DoA) of the WiFi signals. This paper demonstrates\nFIND, a tool for Fine INDoor localization based on a software-defined radio,\nwhich receives Wi-Fi frames in the 80 MHz band with four antennas. To the best\nof our knowledge, it is the first-ever prototype that extracts from such frames\ndata in both frequency and time domains to calculate the DoA of Wi-Fi signals\nin real-time. Apart from other prototypes, we retrieve from frames\ncomprehensive information that could be used to DoA estimation: all preamble\nfields in the time domain, Channels State Information, and signal-to-noise\nratio. Using our device, we collect a dataset for comparing different\nalgorithms estimating the angle of arrival in the same scenario. Furthermore,\nwe propose a novel calibration method, eliminating the constant phase shift\nbetween receiving paths caused by hardware imperfections. All calibration data,\nas well as a gathered dataset with various DoA in an anechoic chamber and in a\nclassroom, are provided to facilitate further research in the area of indoor\nlocalization, intelligence surfaces, and multi-user transmissions in dense\ndeployments.\n"}
+{"abstract": "  In this paper we describe a computational model for the simulation of\nfluid-structure interaction problems based on a fictitious domain approach. We\nsummarize the results presented over the last years when our research evolved\nfrom the Finite Element Immersed Boundary Method (FE-IBM) to the actual Finite\nElement Distributed Lagrange Multiplier method (FE-DLM). We recall the\nwell-posedness of our formulation at the continuous level in a simplified\nsetting. We describe various time semi-discretizations that provide\nunconditionally stable schemes. Finally we report the stability analysis for\nthe finite element space discretization where some improvements and\ngeneralizations of the previous results are obtained.\n"}
+{"abstract": "  Defects in solid state materials provide an ideal, robust platform for\nquantum sensing. To deliver maximum sensitivity, a large ensemble of\nnon-interacting defects hosting coherent quantum states are required. Control\nof such an ensemble is challenging due to the spatial variation in both the\ndefect energy levels and in any control field across a macroscopic sample. In\nthis work we experimentally demonstrate that we can overcome these challenges\nusing Floquet theory and optimal control optimization methods to efficiently\nand coherently control a large defect ensemble, suitable for sensing. We apply\nour methods experimentally to a spin ensemble of up to 4 $\\times$ 10$^9$\nnitrogen vacancy (NV) centers in diamond. By considering the physics of the\nsystem and explicitly including the hyperfine interaction in the optimization,\nwe design shaped microwave control pulses that can outperform conventional\n($\\pi$-) pulses when applied to sensing of temperature or magnetic field, with\na potential sensitivity improvement between 11 and 78\\%. Through dynamical\nmodelling of the behaviour of the ensemble, we shed light on the physical\nbehaviour of the ensemble system and propose new routes for further\nimprovement.\n"}
+{"abstract": "  t-distributed stochastic neighbor embedding (t-SNE) is a well-established\nvisualization method for complex high-dimensional data. However, the original\nt-SNE method is nonparametric, stochastic, and often cannot well prevserve the\nglobal structure of data as it emphasizes local neighborhood. With t-SNE as a\nreference, we propose to combine the deep neural network (DNN) with the\nmathematical-grounded embedding rules for high-dimensional data embedding. We\nfirst introduce a deep embedding network (DEN) framework, which can learn a\nparametric mapping from high-dimensional space to low-dimensional embedding.\nDEN has a flexible architecture that can accommodate different input data\n(vector, image, or tensor) and loss functions. To improve the embedding\nperformance, a recursive training strategy is proposed to make use of the\nlatent representations extracted by DEN. Finally, we propose a two-stage loss\nfunction combining the advantages of two popular embedding methods, namely,\nt-SNE and uniform manifold approximation and projection (UMAP), for optimal\nvisualization effect. We name the proposed method Deep Recursive Embedding\n(DRE), which optimizes DEN with a recursive training strategy and two-stage\nlosse. Our experiments demonstrated the excellent performance of the proposed\nDRE method on high-dimensional data embedding, across a variety of public\ndatabases. Remarkably, our comparative results suggested that our proposed DRE\ncould lead to improved global structure preservation.\n"}
+{"abstract": "  The quiet solar corona consists of myriads of loop-like features, with\nmagnetic fields originating from network and internetwork regions on the solar\nsurface. The continuous interaction between these different magnetic patches\nleads to transient brightenings or bursts that might contribute to the heating\nof the solar atmosphere. However, it remains unclear whether such transients,\nwhich are mostly observed in the EUV, play a significant role in atmospheric\nheating. We revisit the open question of these bursts as a prelude to the new\nhigh-resolution EUV imagery expected from the recently launched Solar Orbiter.\nWe use EUV images recorded by the SDO/AIA to investigate statistical properties\nof the bursts. We detect the bursts in the 171 {\\AA} filter images of AIA in an\nautomated way through a pixel-wise analysis by imposing different intensity\nthresholds. By exploiting the high cadence (12 s) of the AIA observations, we\nfind that the distribution of lifetimes of these events peaks at about 120 s.\nThe sizes of the detected bursts are limited by the spatial resolution, which\nindicates that a larger number of events might be hidden in the AIA data. We\nestimate that about 100 new bursts appear per second on the whole Sun. The\ndetected bursts have nanoflare-like energies of $10^{24}$\\,erg per event. Based\non this, we estimate that at least 100 times more events of a similar nature\nwould be required to account for the energy that is required to heat the\ncorona. When AIA observations are considered alone, the EUV bursts discussed\nhere therefore play no significant role in the coronal heating of the quiet\nSun. If the coronal heating of the quiet Sun is mainly bursty, then the\nhigh-resolution EUV observations from Solar Orbiter may be able to reduce the\ndeficit in the number of EUV bursts seen with SDO/AIA at least partly by\ndetecting more such events.\n"}
+{"abstract": "  Group testing can help maintain a widespread testing program using fewer\nresources amid a pandemic. In group testing, we are given $n$ samples, one per\nindividual. These samples are arranged into $m < n$ pooled samples, where each\npool is obtained by mixing a subset of the $n$ individual samples. Infected\nindividuals are then identified using a group testing algorithm. In this paper,\nwe use side information (SI) collected from contact tracing (CT) within\nnonadaptive/single-stage group testing algorithms. We generate CT SI data by\nincorporating characteristics of disease spread between individuals. These data\nare fed into two signal and measurement models for group testing, and numerical\nresults show that our algorithms provide improved sensitivity and specificity.\nWe also show how to incorporate CT SI into the design of the pooling matrix.\nThat said, our numerical results suggest that the utilization of SI in the\npooling matrix design does not yield significant performance gains beyond the\nincorporation of SI in the group testing algorithm.\n"}
+{"abstract": "  A search for pair production of third-generation scalar leptoquarks decaying\ninto a top quark and a $\\tau$-lepton is presented. The search is based on a\ndataset of $pp$ collisions at $\\sqrt{s}=13$ TeV recorded with the ATLAS\ndetector during Run 2 of the Large Hadron Collider, corresponding to an\nintegrated luminosity of 139 fb$^{-1}$. Events are selected if they have one\nlight lepton (electron or muon) and at least one hadronically decaying\n$\\tau$-lepton, or at least two light leptons. In addition, two or more jets, at\nleast one of which must be identified as containing $b$-hadrons, are required.\nSix final states, defined by the multiplicity and flavour of lepton candidates,\nare considered in the analysis. Each of them is split into multiple event\ncategories to simultaneously search for the signal and constrain several\nleading backgrounds. The signal-rich event categories require at least one\nhadronically decaying $\\tau$-lepton candidate and exploit the presence of\nenergetic final-state objects, which is characteristic of signal events. No\nsignificant excess above the Standard Model expectation is observed in any of\nthe considered event categories, and 95% CL upper limits are set on the\nproduction cross section as a function of the leptoquark mass, for different\nassumptions about the branching fractions into $t\\tau$ and $b\\nu$. Scalar\nleptoquarks decaying exclusively into $t\\tau$ are excluded up to masses of 1.43\nTeV while, for a branching fraction of 50% into $t\\tau$, the lower mass limit\nis 1.22 TeV.\n"}
+{"abstract": "  We describe a robust method for determining Pipek-Mezey (PM) Wannier\nfunctions (WF), recently introduced by J\\'onsson et al. (J. Chem. Theor. Chem.\n2017, 13, 460), which provide some formal advantages over the more common Boys\n(also known as maximally-localized) Wannier functions. The\nBroyden-Fletcher-Goldfarb-Shanno (BFGS) based PMWF solver is demonstrated to\nyield dramatically faster convergence compared to the alternatives (steepest\nascent and conjugate gradient) in a variety of 1-, 2-, and 3-dimensional solids\n(including some with vanishing gaps), and can be used to obtain Wannier\nfunctions robustly in supercells with thousands of atoms. Evaluation of the PM\nfunctional and its gradient in periodic LCAO representation used a particularly\nsimple definition of atomic charges obtained by Moore-Penrose pseudoinverse\nprojection onto the minimal atomic orbital basis. An automated \"Canonicalize\nPhase then Randomize\" (CPR) method for generating the initial guess for WFs\ncontributes significantly to the robustness of the solver.\n"}
+{"abstract": "  The system under study is the $\\Lambda$-Kantowski-Sachs universe. Its\ncanonical quantization is provided based on a recently developed method: the\nsingular minisuperspace Lagrangian describing the system, is reduced to a\nregular (by inserting into the dynamical equations the lapse dictated by the\nquadratic constraint) possessing an explicit (though arbitrary) time\ndependence; thus a time-covariant Schr\\\"{o}dinger equation arises.\nAdditionally, an invariant (under transformations $t=f(\\tilde{t})$) decay\nprobability is defined and thus ``observers'' which correspond to different\ngauge choices obtain, by default, the same results. The time of decay for a\nGaussian wave packet localized around the point $a=0$ (where $a$ the radial\nscale factor) is calculated to be of the order $\\sim\n10^{-42}-10^{-41}\\mathrm{s}$. The acquired value is near the end of the Planck\nera (when comparing to a FLRW universe), during which the quantum effects are\nmost prominent. Some of the results are compared to those obtained by following\nthe well known canonical quantization of cosmological systems, i.e. the\nsolutions of the Wheeler-DeWitt equation.\n"}
+{"abstract": "  A class of analytical solutions of axially symmetric vacuum initial data for\na self-gravitating system has been found. The active region of the constructed\ngravitational wave is a thin torus around which the solution is conformally\nflat. For higher values of gravitational wave amplitude the resulting\nhypersurface contains apparent horizons.\n"}
+{"abstract": "  Automatic garbage collection (GC) prevents certain kinds of bugs and reduces\nprogramming overhead. GC techniques for sequential programs are based on\nreachability analysis. However, testing reachability from a root set is\ninadequate for determining whether an actor is garbage: Observe that an\nunreachable actor may send a message to a reachable actor. Instead, it is\nsufficient to check termination (sometimes also called quiescence): an actor is\nterminated if it is not currently processing a message and cannot receive a\nmessage in the future. Moreover, many actor frameworks provide all actors with\naccess to file I/O or external storage; without inspecting an actor's internal\ncode, it is necessary to check that the actor has terminated to ensure that it\nmay be garbage collected in these frameworks. Previous algorithms to detect\nactor garbage require coordination mechanisms such as causal message delivery\nor nonlocal monitoring of actors for mutation. Such coordination mechanisms\nadversely affect concurrency and are therefore expensive in distributed\nsystems. We present a low-overhead reference listing technique (called DRL) for\ntermination detection in actor systems. DRL is based on asynchronous local\nsnapshots and message-passing between actors. This enables a decentralized\nimplementation and transient network partition tolerance. The paper provides a\nformal description of DRL, shows that all actors identified as garbage have\nindeed terminated (safety), and that all terminated actors--under certain\nreasonable assumptions--will eventually be identified (liveness).\n"}
+{"abstract": "  Single-molecule memory device based on a single-molecule magnet (SMM) is one\nof the ultimate goals of semiconductor nanofabrication technologies. Here, we\nstudy how to manipulate and readout the SMM's two spin-state of stored\ninformation that characterized by the maximum and minimum average value of the\n$Z$-component of the total spin of the SMM and the conduction-electron, which\nare recognized as the information bits \"$1$\" and \"$0$\". We demonstrate that the\nswitching time depends on both the sequential tunneling gap $\\varepsilon_{se}$\nand the spin-selection-rule allowed transition-energy $\\varepsilon_{trans}$,\nwhich can be tuned by the gate voltage. In particular, when the external bias\nvoltage is turned off, in the cases of the unoccupied and doubly-occupied\nground eigenstates, the time derivative of the transport current can be used to\nread out the SMM's two spin-state of stored information. Moreover, the\ntunneling strength of and the asymmetry of the SMM-electrode coupling have a\nstrong influence on the switching time, but that have a slight influence on the\nreadout time that being on the order of nanoseconds. Our results suggest a\nSMM-based memory device, and provide fundamental insight into the electrical\ncontrollable manipulation and readout of the SMM's two spin-state of stored\ninformation.\n"}
+{"abstract": "  The plastic deformation mechanisms of tungsten carbide at room and elevated\ntemperatures influence the wear and fracture properties of WC-Co hardmetal\ncomposite materials. The relationship between residual defect structures,\nincluding glissile and sessile dislocations and stacking faults, and the slip\ndeformation activity, which produce slip traces, is not clear. Part 1 of this\nstudy showed that {10-10} was the primary slip plane at all measured\ntemperatures and orientations, but secondary slip on the basal plane was\nactivated at 600 {\\deg}C, which suggests that <a> dislocations can cross-slip\nonto the basal plane at 600 {\\deg}C. In the present work, Part 2, lattice\nrotation axis analysis of deformed WC micropillar mid-sections was used to\ndiscriminate <a> prismatic slip from multiple <c+a> prismatic slip in WC, which\nenabled the dislocation types contributing to plastic slip to be distinguished,\nindependently of TEM residual defect analysis. Prismatic-oriented micropillars\ndeformed primarily by multiple <c+a> prismatic slip at room temperature, but by\n<a> prismatic slip at 600 {\\deg}C. Deformation in the near-basal oriented\npillar at 600 {\\deg}C can be modelled as prismatic slip along <c> constrained\nby the indenter face and pillar base. Secondary <a> basal slip, which was\nobserved near the top of the pillar, was activated to maintain deformation\ncompatibility with the indenter face. The lattice rotations, buckled pillar\nshape, mechanical data, and slip traces observed in the pillar are all\nconsistent with this model.\n"}
+{"abstract": "  Video classification and analysis is always a popular and challenging field\nin computer vision. It is more than just simple image classification due to the\ncorrelation with respect to the semantic contents of subsequent frames brings\ndifficulties for video analysis. In this literature review, we summarized some\nstate-of-the-art methods for multi-label video classification. Our goal is\nfirst to experimentally research the current widely used architectures, and\nthen to develop a method to deal with the sequential data of frames and perform\nmulti-label classification based on automatic content detection of video.\n"}
+{"abstract": "  The generation-defining Vera C. Rubin Observatory will make state-of-the-art\nmeasurements of both the static and transient universe through its Legacy\nSurvey for Space and Time (LSST). With such capabilities, it is immensely\nchallenging to optimize the LSST observing strategy across the survey's wide\nrange of science drivers. Many aspects of the LSST observing strategy relevant\nto the LSST Dark Energy Science Collaboration, such as survey footprint\ndefinition, single visit exposure time and the cadence of repeat visits in\ndifferent filters, are yet to be finalized. Here, we present metrics used to\nassess the impact of observing strategy on the cosmological probes considered\nmost sensitive to survey design; these are large-scale structure, weak lensing,\ntype Ia supernovae, kilonovae and strong lens systems (as well as photometric\nredshifts, which enable many of these probes). We evaluate these metrics for\nover 100 different simulated potential survey designs. Our results show that\nmultiple observing strategy decisions can profoundly impact cosmological\nconstraints with LSST; these include adjusting the survey footprint, ensuring\nrepeat nightly visits are taken in different filters and enforcing regular\ncadence. We provide public code for our metrics, which makes them readily\navailable for evaluating further modifications to the survey design. We\nconclude with a set of recommendations and highlight observing strategy factors\nthat require further research.\n"}
+{"abstract": "  This paper reexamines the seminal Lagrange multiplier test for cross-section\nindependence in a large panel model where both the number of cross-sectional\nunits n and the number of time series observations T can be large. The first\ncontribution of the paper is an enlargement of the test with two extensions:\nfirstly the new asymptotic normality is derived in a simultaneous limiting\nscheme where the two dimensions (n, T) tend to infinity with comparable\nmagnitudes; second, the result is valid for general error distribution (not\nnecessarily normal). The second contribution of the paper is a new test\nstatistic based on the sum of the fourth powers of cross-section correlations\nfrom OLS residuals, instead of their squares used in the Lagrange multiplier\nstatistic. This new test is generally more powerful, and the improvement is\nparticularly visible against alternatives with weak or sparse cross-section\ndependence. Both simulation study and real data analysis are proposed to\ndemonstrate the advantages of the enlarged Lagrange multiplier test and the\npower enhanced test in comparison with the existing procedures.\n"}
+{"abstract": "  In recent years, knowledge distillation has been proved to be an effective\nsolution for model compression. This approach can make lightweight student\nmodels acquire the knowledge extracted from cumbersome teacher models. However,\nprevious distillation methods of detection have weak generalization for\ndifferent detection frameworks and rely heavily on ground truth (GT), ignoring\nthe valuable relation information between instances. Thus, we propose a novel\ndistillation method for detection tasks based on discriminative instances\nwithout considering the positive or negative distinguished by GT, which is\ncalled general instance distillation (GID). Our approach contains a general\ninstance selection module (GISM) to make full use of feature-based,\nrelation-based and response-based knowledge for distillation. Extensive results\ndemonstrate that the student model achieves significant AP improvement and even\noutperforms the teacher in various detection frameworks. Specifically,\nRetinaNet with ResNet-50 achieves 39.1% in mAP with GID on COCO dataset, which\nsurpasses the baseline 36.2% by 2.9%, and even better than the ResNet-101 based\nteacher model with 38.1% AP.\n"}
+{"abstract": "  Quadratic Unconstrained Binary Optimization models are useful for solving a\ndiverse range of optimization problems. Constraints can be added by\nincorporating quadratic penalty terms into the objective, often with the\nintroduction of slack variables needed for conversion of inequalities. This\ntransformation can lead to a significant increase in the size and density of\nthe problem. Herein, we propose an efficient approach for recasting inequality\nconstraints that reduces the number of linear and quadratic variables.\nExperimental results illustrate the efficacy.\n"}
+{"abstract": "  The spin-$1/2$ XXZ chain is an integrable lattice model and parts of its spin\ncurrent can be protected by local conservation laws for anisotropies\n$-1<\\Delta<1$. In this case, the Drude weight $D(T)$ is non-zero at finite\ntemperatures $T$. Here we obtain analytical results for $D(T)$ at low\ntemperatures for zero external magnetic field and anisotropies\n$\\Delta=\\cos(n\\pi/m)$ with $n,m$ coprime integers, using the thermodynamic\nBethe ansatz. We show that to leading orders\n$D(T)=D(0)-a(\\Delta)T^{2K-2}-b_1(\\Delta)T^2$ where $K$ is the Luttinger\nparameter and the prefactor $a(\\Delta)$, obtained in closed form, has a fractal\nstructure as function of anisotropy $\\Delta$. The prefactor $b_1(\\Delta)$, on\nthe other hand, does not have a fractal structure and can be obtained in a\nstandard field-theoretical approach. Including both temperature corrections, we\nobtain an analytic result for the low-temperature asymptotics of the Drude\nweight in the entire regime $-1<\\Delta=\\cos(n\\pi/m)<1$.\n"}
+{"abstract": "  Silicon-germanium heterojunction bipolar transistors (HBTs) are of interest\nas low-noise microwave amplifiers due to their competitive noise performance\nand low cost relative to III-V devices. The fundamental noise performance\nlimits of HBTs are thus of interest, and several studies report that\nquasiballistic electron transport across the base is a mechanism leading to\ncryogenic non-ideal IV characteristics that affects these limits. However, this\nconclusion has not been rigorously tested against theoretical predictions\nbecause prior studies modeled electron transport with empirical approaches or\napproximate solutions of the Boltzmann equation. Here, we study non-diffusive\ntransport in narrow-base SiGe HBTs using an exact, semi-analytic solution of\nthe Boltzmann equation based on an asymptotic expansion approach. We find that\nthe computed transport characteristics are inconsistent with experiment,\nimplying that quasiballistic electron transport is unlikely to be the origin of\ncryogenic non-ideal IV characteristics. Our work helps to identify the\nmechanisms governing the lower limits of the microwave noise figure of\ncryogenic HBT amplifiers.\n"}
+{"abstract": "  The NLC2CMD Competition hosted at NeurIPS 2020 aimed to bring the power of\nnatural language processing to the command line. Participants were tasked with\nbuilding models that can transform descriptions of command line tasks in\nEnglish to their Bash syntax. This is a report on the competition with details\nof the task, metrics, data, attempted solutions, and lessons learned.\n"}
+{"abstract": "  We perform a detailed numerical study of diffusion in the $\\varepsilon$\nstadium of Bunimovich, and propose an empirical model of the local and global\ndiffusion for various values of $\\varepsilon$ with the following conclusions:\n(i) the diffusion is normal for all values of $\\varepsilon \\leq(0.3)$ and all\ninitial conditions, (ii) the diffusion constant is a parabolic function of the\nmomentum (i.e., we have inhomogeneous diffusion), (iii) the model describes the\ndiffusion very well including the boundary effects, (iv) the approach to the\nasymptotic equilibrium steady state is exponential, (v) the so-called random\nmodel (Robnik et al., 1997) is confirmed to apply very well, (vi) the diffusion\nconstant extracted from the distribution function in momentum space and the one\nderived from the second moment agree very well. The classical transport time,\nan important parameter in quantum chaos, is thus determined.\n"}
+{"abstract": "  The group testing problem consists of determining a small set of defective\nitems from a larger set of items based on a number of possibly-noisy tests, and\nhas numerous practical applications. One of the defining features of group\ntesting is whether the tests are adaptive (i.e., a given test can be chosen\nbased on all previous outcomes) or non-adaptive (i.e., all tests must be chosen\nin advance). In this paper, building on the success of binary splitting\ntechniques in noiseless group testing (Hwang, 1972), we introduce noisy group\ntesting algorithms that apply noisy binary search as a subroutine. We provide\nthree variations of this approach with increasing complexity, culminating in an\nalgorithm that succeeds using a number of tests that matches the best known\npreviously (Scarlett, 2019), while overcoming fundamental practical limitations\nof the existing approach, and more precisely capturing the dependence of the\nnumber of tests on the error probability. We provide numerical experiments\ndemonstrating that adaptive group testing strategies based on noisy binary\nsearch can be highly effective in practice, using significantly fewer tests\ncompared to state-of-the-art non-adaptive strategies.\n"}
+{"abstract": "  To realize autonomous collaborative robots, it is important to increase the\ntrust that users have in them. Toward this goal, this paper proposes an\nalgorithm which endows an autonomous agent with the ability to explain the\ntransition from the current state to the target state in a Markov decision\nprocess (MDP). According to cognitive science, to generate an explanation that\nis acceptable to humans, it is important to present the minimum information\nnecessary to sufficiently understand an event. To meet this requirement, this\nstudy proposes a framework for identifying important elements in the\ndecision-making process using a prediction model for the world and generating\nexplanations based on these elements. To verify the ability of the proposed\nmethod to generate explanations, we conducted an experiment using a grid\nenvironment. It was inferred from the result of a simulation experiment that\nthe explanation generated using the proposed method was composed of the minimum\nelements important for understanding the transition from the current state to\nthe target state. Furthermore, subject experiments showed that the generated\nexplanation was a good summary of the process of state transition, and that a\nhigh evaluation was obtained for the explanation of the reason for an action.\n"}
+{"abstract": "  An interpretable system for open-domain reasoning needs to express its\nreasoning process in a transparent form. Natural language is an attractive\nrepresentation for this purpose -- it is both highly expressive and easy for\nhumans to understand. However, manipulating natural language statements in\nlogically consistent ways is hard: models must cope with variation in how\nmeaning is expressed while remaining precise. In this paper, we describe\nParaPattern, a method for building models to generate deductive inferences from\ndiverse natural language inputs without direct human supervision. We train\nBART-based models (Lewis et al., 2020) to generate the result of applying a\nparticular logical operation to one or more premise statements. Crucially, we\ndevelop a largely automated pipeline for constructing suitable training\nexamples from Wikipedia. We evaluate our models using out-of-domain sentence\ncompositions from the QASC (Khot et al., 2020) and EntailmentBank (Dalvi et\nal., 2021) datasets as well as targeted perturbation sets. Our results show\nthat our models are substantially more accurate and flexible than baseline\nsystems. ParaPattern achieves 85% validity on examples of the 'substitution'\noperation from EntailmentBank without the use of any in-domain training data,\nmatching the performance of a model fine-tuned for EntailmentBank. The full\nsource code for our method is publicly available.\n"}
+{"abstract": "  We present a uniform (and unambiguous) procedure for scaling the matter\nfields in implementing the conformal method to parameterize and construct\nsolutions of Einstein constraint equations with coupled matter sources. The\napproach is based on a phase space representation of the space-time matter\nfields after a careful $n+1$ decomposition into spatial fields $B$ and\nconjugate momenta $\\Pi_B$, which are specified directly and are conformally\ninvariant quantities. We show that if the Einstein-matter field theory is\nspecified by a Lagrangian which is diffeomorphism invariant and involves no\ndependence on derivatives of the space-time metric in the matter portion of the\nLagrangian, then fixing $B$ and $\\Pi_B$ results in conformal constraint\nequations that, for constant-mean curvature initial data, semi-decouple just as\nthey do for the vacuum Einstein conformal constraint equations. We prove this\nresult by establishing a structural property of the Einstein momentum\nconstraint that is independent of the conformal method: For an Einstein-matter\nfield theory which satisfies the conditions just stated, if $B$ and $\\Pi_B$\nsatisfy the matter Euler-Lagrange equations, then (in suitable form) the\nright-hand side of the momentum constraint on each spatial slice depends only\non $B$ and $\\Pi_B$ and is independent of the space-time metric. We discuss the\ndetails of our construction in the special cases of the following models:\nEinstein-Maxwell-charged scalar field, Einstein-Proca, Einstein-perfect fluid,\nand Einstein-Maxwell-charged dust. In these examples we find that our technique\ngives a theoretical basis for scaling rules, such as those for\nelectromagnetism, that have worked pragmatically in the past, but also\ngenerates new equations with advantageous features for perfect fluids that\nallow direct specification of total rest mass and total charge in any spatial\nregion.\n"}
+{"abstract": "  We obtain an array of consistency results concerning trees and stationary\nreflection at double successors of regular cardinals $\\kappa$, updating some\nclassical constructions in the process. This includes models of\n$\\mathsf{CSR}(\\kappa^{++})\\wedge \\mathsf{TP}(\\kappa^{++})$ (both with and\nwithout $\\mathsf{AP}(\\kappa^{++})$) and models of the conjunctions\n$\\mathsf{SR}(\\kappa^{++}) \\wedge \\mathsf{wTP}(\\kappa^{++}) \\wedge\n\\mathsf{AP}(\\kappa^{++})$ and $\\neg \\mathsf{AP}(\\kappa^{++}) \\wedge\n\\mathsf{SR}(\\kappa^{++})$ (the latter was originally obtained in joint work by\nKrueger and the first author \\cite{GilKru:8fold}, and is here given using\ndifferent methods). Analogs of these results with the failure of\n$\\mathsf{SH}(\\kappa^{++})$ are given as well. Finally, we obtain all of our\nresults with an arbitrarily large $2^\\kappa$, applying recent joint work by\nHonzik and the third author.\n"}
+{"abstract": "  Nonlinear distortion of an OFDM signal is a serious problem when it comes to\nenergy-efficient Power Amplifier(PA) utilization. Typically, Peak-to-Average\nPower Ratio(PAPR) reduction algorithms and digital predistortion algorithms are\nused independently to fight the same phenomenon. This paper proposes an\nAmplifier-Coupled Tone Reservation (ACTR)algorithm for the reduction of\nnonlinear distortion power, utilizing knowledge on thep redistorted PA\ncharacteristic. The optimization problem is defined. Its convexity is proved. A\ncomputationally-efficient solution is presented. Finally, its performance is\ncompared against two state-of-the-art TR algorithms by means of simulations and\nmeasurements. The results show the proposed solution is advantageous, both in\nterms of nonlinear distortion power and the required number of computations.\n"}
+{"abstract": "  We proposed a convolutional neural network for vertex classification on\n3-dimensional dental meshes, and used it to detect teeth margins. An expanding\nlayer was constructed to collect statistic values of neighbor vertex features\nand compute new features for each vertex with convolutional neural networks. An\nend-to-end neural network was proposed to take vertex features, including\ncoordinates, curvatures and distance, as input and output each vertex\nclassification label. Several network structures with different parameters of\nexpanding layers and a base line network without expanding layers were designed\nand trained by 1156 dental meshes. The accuracy, recall and precision were\nvalidated on 145 dental meshes to rate the best network structures, which were\nfinally tested on another 144 dental meshes. All networks with our expanding\nlayers performed better than baseline, and the best one achieved an accuracy of\n0.877 both on validation dataset and test dataset.\n"}
+{"abstract": "  Dynamic graph representation learning is a task to learn node embeddings over\ndynamic networks, and has many important applications, including knowledge\ngraphs, citation networks to social networks. Graphs of this type are usually\nlarge-scale but only a small subset of vertices are related in downstream\ntasks. Current methods are too expensive to this setting as the complexity is\nat best linear-dependent on both the number of nodes and edges. In this paper,\nwe propose a new method, namely Dynamic Personalized PageRank Embedding\n(\\textsc{DynamicPPE}) for learning a target subset of node representations over\nlarge-scale dynamic networks. Based on recent advances in local node embedding\nand a novel computation of dynamic personalized PageRank vector (PPV),\n\\textsc{DynamicPPE} has two key ingredients: 1) the per-PPV complexity is\n$\\mathcal{O}(m \\bar{d} / \\epsilon)$ where $m,\\bar{d}$, and $\\epsilon$ are the\nnumber of edges received, average degree, global precision error respectively.\nThus, the per-edge event update of a single node is only dependent on $\\bar{d}$\nin average; and 2) by using these high quality PPVs and hash kernels, the\nlearned embeddings have properties of both locality and global consistency.\nThese two make it possible to capture the evolution of graph structure\neffectively. Experimental results demonstrate both the effectiveness and\nefficiency of the proposed method over large-scale dynamic networks. We apply\n\\textsc{DynamicPPE} to capture the embedding change of Chinese cities in the\nWikipedia graph during this ongoing COVID-19 pandemic\n(https://en.wikipedia.org/wiki/COVID-19_pandemic). Our results show that these\nrepresentations successfully encode the dynamics of the Wikipedia graph.\n"}
+{"abstract": "  The ability to map and estimate the activity of radiological source\ndistributions in unknown three-dimensional environments has applications in the\nprevention and response to radiological accidents or threats as well as the\nenforcement and verification of international nuclear non-proliferation\nagreements. Such a capability requires well-characterized detector response\nfunctions, accurate time-dependent detector position and orientation data, a\ndigitized representation of the surrounding 3D environment, and appropriate\nimage reconstruction and uncertainty quantification methods. We have previously\ndemonstrated 3D mapping of gamma-ray emitters with free-moving detector systems\non a relative intensity scale using a technique called Scene Data Fusion (SDF).\nHere we characterize the detector response of a multi-element gamma-ray imaging\nsystem using experimentally benchmarked Monte Carlo simulations and perform 3D\nmapping on an absolute intensity scale. We present experimental reconstruction\nresults from hand-carried and airborne measurements with point-like and\ndistributed sources in known configurations, demonstrating quantitative SDF in\ncomplex 3D environments.\n"}
+{"abstract": "  We study the prime-to-$p$ Hecke action on the projective limit of the sets of\nconnected components of Shimura varieties with fixed parahoric or Bruhat--Tits\nlevel at $p$. In particular, we construct infinitely many Shimura varieties for\nCM unitary groups in odd variables for which the considering actions are not\ntransitive. We prove this result by giving negative examples on the question of\nBruhat--Colliot-Th\\'el\\`ene--Sansuc--Tits or its variant, which is related to\nthe weak approximation on tori over $\\mathbb{Q}$.\n"}
+{"abstract": "  We formulate a nonlinear optimal control problem for intra-day operation of a\nnatural gas pipeline network that includes storage reservoirs. The dynamics of\ncompressible gas flow through pipes, compressors, reservoirs, and wells are\nconsidered. In particular, a reservoir is modeled as a rigid, hollow container\nthat stores gas under isothermal conditions and uniform density, and a well is\nmodeled as a vertical pipe. For each pipe, flow dynamics are described by a\ncoupled partial differential equation (PDE) system in density and mass flux\nvariables, with momentum dissipation modeled using the Darcy-Wiesbach friction\napproximation. Compressors are modeled as scaling up the pressure of gas\nbetween inlet and outlet. The governing equations for all network components\nare spatially discretized and assembled into a nonlinear differential-algebraic\nequation (DAE) system, which synthesizes above-ground pipeline and subsurface\nreservoir dynamics into a single reduced-order model. We seek to maximize an\nobjective function that quantifies economic profit and network efficiency\nsubject to the flow equations and inequalities that represent operating\nlimitations. The problem is solved using a primal-dual interior point solver\nand the solutions are validated in computational experiments and simulations on\nseveral pipeline test networks to demonstrate the effectiveness of the proposed\nmethodology.\n"}
+{"abstract": "  There is still a limited understanding of the necessary skill, talent, and\nexpertise to manage digital technologies as a crucial enabler of the hospitals\nability to adequately sense and respond to patient needs and wishes, i.e.,\npatient agility. Therefore, this investigates how hospital departments can\nleverage a digital dy-namic capability to enable the departments patient\nagility. This study embraces the dynamic capabilities theory, develops a\nresearch model, and tests it accordingly using data from 90 clinical hospital\ndepartments from the Netherlands through an online survey. The model's\nhypothesized relationships are tested using structural equation modeling (SEM).\nThe outcomes demonstrate the significance of digital dynamic capability in\ndeveloping patient sensing and responding capabili-ties that, in turn,\npositively influence patient service performance. Outcomes are very relevant\nfor the hospital practice now, as hospitals worldwide need to trans-form\nhealthcare delivery processes using digital technologies and increase clinical\nproductivity.\n"}
+{"abstract": "  The knowledge of a deep learning model may be transferred to a student model,\nleading to intellectual property infringement or vulnerability propagation.\nDetecting such knowledge reuse is nontrivial because the suspect models may not\nbe white-box accessible and/or may serve different tasks. In this paper, we\npropose ModelDiff, a testing-based approach to deep learning model similarity\ncomparison. Instead of directly comparing the weights, activations, or outputs\nof two models, we compare their behavioral patterns on the same set of test\ninputs. Specifically, the behavioral pattern of a model is represented as a\ndecision distance vector (DDV), in which each element is the distance between\nthe model's reactions to a pair of inputs. The knowledge similarity between two\nmodels is measured with the cosine similarity between their DDVs. To evaluate\nModelDiff, we created a benchmark that contains 144 pairs of models that cover\nmost popular model reuse methods, including transfer learning, model\ncompression, and model stealing. Our method achieved 91.7% correctness on the\nbenchmark, which demonstrates the effectiveness of using ModelDiff for model\nreuse detection. A study on mobile deep learning apps has shown the feasibility\nof ModelDiff on real-world models.\n"}
+{"abstract": "  Affine rank minimization problem is the generalized version of low rank\nmatrix completion problem where linear combinations of the entries of a low\nrank matrix are observed and the matrix is estimated from these measurements.\nWe propose a trainable deep neural network by unrolling a popular iterative\nalgorithm called the singular value thresholding (SVT) algorithm to perform\nthis generalized matrix completion which we call Learned SVT (LSVT). We show\nthat our proposed LSVT with fixed layers (say T) reconstructs the matrix with\nlesser mean squared error (MSE) compared with that incurred by SVT with fixed\n(same T) number of iterations and our method is much more robust to the\nparameters which need to be carefully chosen in SVT algorithm.\n"}
+{"abstract": "  For a connected reductive group $G$ defined over $\\mathbb{F}_q$ and equipped\nwith the induced Frobenius endomorphism $F$, we study the relation among the\nfollowing three $\\mathbb{Z}$-algebras: (i) the $\\mathbb{Z}$-model\n$\\mathsf{E}_G$ of endomorphism algebras of Gelfand-Graev representations of\n$G^F$; (ii) the Grothendieck group $\\mathsf{K}_{G^\\ast}$ of the category of\nrepresentations of $G^{\\ast F^\\ast}$ over $\\overline{\\mathbb{F}_q}$\n(Deligne-Lusztig dual side); (iii) the ring $\\mathsf{B}_{G^\\vee}$ of the scheme\n$(T^\\vee/\\!\\!/ W)^{F^\\vee}$ over $\\mathbb{Z}$ (Langlands dual side). The\ncomparison between (i) and (iii) is motivated by recent advances in the local\nLanglands program.\n"}
+{"abstract": "  Whilst contrastive learning has recently brought notable benefits to deep\nclustering of unlabelled images by learning sample-specific discriminative\nvisual features, its potential for explicitly inferring class decision\nboundaries is less well understood. This is because its instance discrimination\nstrategy is not class sensitive, therefore, the clusters derived on the\nresulting sample-specific feature space are not optimised for corresponding to\nmeaningful class decision boundaries. In this work, we solve this problem by\nintroducing Semantic Contrastive Learning (SCL). SCL imposes explicitly\ndistance-based cluster structures on unlabelled training data by formulating a\nsemantic (cluster-aware) contrastive learning objective. Moreover, we introduce\na clustering consistency condition to be satisfied jointly by both instance\nvisual similarities and cluster decision boundaries, and concurrently\noptimising both to reason about the hypotheses of semantic ground-truth classes\n(unknown/unlabelled) on-the-fly by their consensus. This semantic contrastive\nlearning approach to discovering unknown class decision boundaries has\nconsiderable advantages to unsupervised learning of object recognition tasks.\nExtensive experiments show that SCL outperforms state-of-the-art contrastive\nlearning and deep clustering methods on six object recognition benchmarks,\nespecially on the more challenging finer-grained and larger datasets.\n"}
+{"abstract": "  Let $H$ be a digraph possibly with loops and $D$ a loopless digraph whose\narcs are colored with the vertices of $H$ ($D$ is said to be an $H-$colored\ndigraph). If $W=(x_{0},\\ldots,x_{n})$ is an open walk in $D$ and $i\\in\n\\{1,\\ldots,n-1\\}$, we say that there is an obstruction on $x_{i}$ whenever\n$(color(x_{i-1},x_{i}),color(x_{i},x_{i+1}))\\notin A(H)$. A $(k,l,H)$-kernel by\nwalks in an $H$-colored digraph $D$ ($k\\geq 2$, $l\\geq 1$), is a subset $S$ of\nvertices of $D$, such that, for every pair of different vertices in $S$, every\nwalk between them has at least $k-1$ obstructions, and for every $x\\in\nV(D)\\setminus S$ there exists an $xS$-walk with at most $l-1$ obstructions.\nThis concept generalize the concepts of kernel, $(k,l)$-kernel, kernel by\nmonochromatic paths, and kernel by $H$-walks. If $D$ is an $H$-colored digraph,\nan $H$-class partition is a partition $\\mathscr{F}$ of $A(D)$ such that, for\nevery $\\{(u,v),(v,w)\\}\\subseteq A(D)$, $(color(u,v),color(v,w))\\in A(H)$ iff\nthere exists $F\\in \\mathscr{F}$ such that $\\{(u,v),(v,w)\\}\\subseteq F$. The\n$H$-class digraph relative to $\\mathscr{F}$, denoted by $C_{\\mathscr{F}}(D)$,\nis the digraph such that $V(C_{\\mathscr{F}}(D))=\\mathscr{F}$, and $(F,G)\\in\nA(C_{\\mathscr{F}}(D))$ iff there exist $(u,v)\\in F$ and $(v,w)\\in G$ with\n$\\{u,v,w\\}\\subseteq V(D)$.\n  We will show sufficient conditions on $\\mathscr{F}$ and $C_{\\mathscr{F}}(D)$\nto guarantee the existence of $(k,l,H)$-kernels by walks in $H$-colored\ndigraphs, and we will show that some conditions are tight. For instance, we\nwill show that if an $H$-colored digraph $D$ has an $H$-class partition in\nwhich every class induces a strongly connected digraph, and has a\nobstruction-free vertex, then for every $k\\geq 2$, $D$ has a $(k,k-1,H)$-kernel\nby walks. Despite finding $(k,l)$-kernels is a $NP$-complete problem, some\nhypothesis presented in this paper can be verified in polynomial time.\n"}
+{"abstract": "  We use an iteration procedure propped up by a a classical form of the maximum\nprinciple to show the existence of solutions to a nonlinear Poisson equation\nwith Dirichlet boundary conditions. These methods can be applied to the case of\nspecial unbounded domains, and can be adapted to show the existence of\nnontrivial solutions to systems, which we show via some examples.\n"}
+{"abstract": "  The rapid growth of the e-commerce market in Indonesia, making various\ne-commerce companies appear and there has been high competition among them.\nMarketing intelligence is an important activity to measure competitive\nposition. One element of marketing intelligence is to assess customer\nsatisfaction. Many Indonesian customers express their sense of satisfaction or\ndissatisfaction towards the company through social media. Hence, using social\nmedia data provides a new practical way to measure marketing intelligence\neffort. This research performs sentiment analysis using the naive bayes\nclassifier classification method with TF-IDF weighting. We compare the\nsentiments towards of top-3 e-commerce sites visited companies, are Bukalapak,\nTokopedia, and Elevenia. We use Twitter data for sentiment analysis because\nit's faster, cheaper, and easier from both the customer and the researcher\nside. The purpose of this research is to find out how to process the huge\ncustomer sentiment Twitter to become useful information for the e-commerce\ncompany, and which of those top-3 e-commerce companies has the highest level of\ncustomer satisfaction. The experiment results show the method can be used to\nclassify customer sentiments in social media Twitter automatically and Elevenia\nis the highest e-commerce with customer satisfaction.\n"}
+{"abstract": "  Marchenko methods are based on integral representations which express Green's\nfunctions for virtual sources and/or receivers in the subsurface in terms of\nthe reflection response at the surface. An underlying assumption is that inside\nthe medium the wave field can be decomposed into downgoing and upgoing waves\nand that evanescent waves can be neglected. We present a new derivation of\nGreen's function representations which circumvents these assumptions, both for\nthe acoustic and the elastodynamic situation. These representations form the\nbasis for research into new Marchenko methods which have the potential to\nhandle refracted and evanescent waves and to more accurately image steeply\ndipping reflectors.\n"}
+{"abstract": "  We investigate fast and communication-efficient algorithms for the classic\nproblem of minimizing a sum of strongly convex and smooth functions that are\ndistributed among $n$ different nodes, which can communicate using a limited\nnumber of bits. Most previous communication-efficient approaches for this\nproblem are limited to first-order optimization, and therefore have\n\\emph{linear} dependence on the condition number in their communication\ncomplexity. We show that this dependence is not inherent:\ncommunication-efficient methods can in fact have sublinear dependence on the\ncondition number. For this, we design and analyze the first\ncommunication-efficient distributed variants of preconditioned gradient descent\nfor Generalized Linear Models, and for Newton's method. Our results rely on a\nnew technique for quantizing both the preconditioner and the descent direction\nat each step of the algorithms, while controlling their convergence rate. We\nalso validate our findings experimentally, showing fast convergence and reduced\ncommunication.\n"}
+{"abstract": "  We propose to accelerate existing linear bandit algorithms to achieve\nper-step time complexity sublinear in the number of arms $K$. The key to\nsublinear complexity is the realization that the arm selection in many linear\nbandit algorithms reduces to the maximum inner product search (MIPS) problem.\nCorrespondingly, we propose an algorithm that approximately solves the MIPS\nproblem for a sequence of adaptive queries yielding near-linear preprocessing\ntime complexity and sublinear query time complexity. Using the proposed MIPS\nsolver as a sub-routine, we present two bandit algorithms (one based on UCB,\nand the other based on TS) that achieve sublinear time complexity. We\nexplicitly characterize the tradeoff between the per-step time complexity and\nregret, and show that our proposed algorithms can achieve $O(K^{1-\\alpha(T)})$\nper-step complexity for some $\\alpha(T) > 0$ and $\\widetilde O(\\sqrt{T})$\nregret, where $T$ is the time horizon. Further, we present the theoretical\nlimit of the tradeoff, which provides a lower bound for the per-step time\ncomplexity. We also discuss other choices of approximate MIPS algorithms and\nother applications to linear bandit problems.\n"}
+{"abstract": "  Lokshtanov et al.~[STOC 2017] introduced \\emph{lossy kernelization} as a\nmathematical framework for quantifying the effectiveness of preprocessing\nalgorithms in preserving approximation ratios. \\emph{$\\alpha$-approximate\nreduction rules} are a central notion of this framework. We propose that\ncarefully crafted $\\alpha$-approximate reduction rules can yield improved\napproximation ratios in practice, while being easy to implement as well. This\nis distinctly different from the (theoretical) purpose for which Lokshtanov et\nal. designed $\\alpha$-approximate Reduction Rules. As evidence in support of\nthis proposal we present a new 2-approximate reduction rule for the\n\\textsc{Dominating Set} problem. This rule, when combined with an approximation\nalgorithm for \\textsc{Dominating Set}, yields significantly better\napproximation ratios on a variety of benchmark instances as compared to the\nlatter algorithm alone.\n  The central thesis of this work is that $\\alpha$-approximate reduction rules\ncan be used as a tool for designing approximation algorithms which perform\nbetter in practice. To the best of our knowledge, ours is the first exploration\nof the use of $\\alpha$-approximate reduction rules as a design technique for\npractical approximation algorithms. We believe that this technique could be\nuseful in coming up with improved approximation algorithms for other\noptimization problems as well.\n"}
+{"abstract": "  This paper studies the convergence of three temporal semi-discretizations for\na backward semilinear stochastic evolution equation. For general terminal value\nand general coefficient with Lipschitz continuity, the convergence of three\nEuler type temporal semi-discretizations is established without regularity\nassumption on the solution. Moreover, the third temporal semi-discretization is\napplied to a stochastic linear quadratic control problem, and an explicit\nconvergence rate is derived.\n"}
+{"abstract": "  Discrete mechanics is presented as an alternative to the equations of fluid\nmechanics, in particular to the Navier-Stokes equation. The derivation of the\ndiscrete equation of motion is built from the intuitions of Galileo, the\nprinciples of Galilean equivalence and relativity. Other more recent concepts\nsuch as the equivalence between mass and energy and the Helmholtz-Hodge\ndecomposition complete the formal framework used to write a fundamental law of\nmotion such as the conservation of accelerations, the intrinsic acceleration of\nthe material medium, and the sum of the accelerations applied to it. The two\nscalar and vector potentials of the acceleration resulting from the\ndecomposition into two contributions, to curl-free and to divergence-free,\nrepresent the energies per unit of mass of compression and shear.\n  The solutions obtained by the incompressible Navier-Stokes equation and the\ndiscrete equation of motion are the same, with constant physical properties.\nThis new formulation of the equation of motion makes it possible to\nsignificantly modify the treatment of surface discontinuities, thanks to the\nintrinsic properties established from the outset for a discrete geometrical\ndescription directly linked to the decomposition of acceleration. The treatment\nof the jump conditions of density, viscosity and capillary pressure is\nexplained in order to understand the two-phase flows. The choice of the\nexamples retained, mainly of the exact solutions of the continuous equations,\nserves to show that the treatment of the conditions of jumps does not affect\nthe precision of the method of resolution.\n"}
+{"abstract": "  Federated Learning (FL) enables multiple distributed clients (e.g., mobile\ndevices) to collaboratively train a centralized model while keeping the\ntraining data locally on the client. Compared to traditional centralized\nmachine learning, FL offers many favorable features such as offloading\noperations which would usually be performed by a central server and reducing\nrisks of serious privacy leakage. However, Byzantine clients that send\nincorrect or disruptive updates due to system failures or adversarial attacks\nmay disturb the joint learning process, consequently degrading the performance\nof the resulting model. In this paper, we propose to mitigate these failures\nand attacks from a spatial-temporal perspective. Specifically, we use a\nclustering-based method to detect and exclude incorrect updates by leveraging\ntheir geometric properties in the parameter space. Moreover, to further handle\nmalicious clients with time-varying behaviors, we propose to adaptively adjust\nthe learning rate according to momentum-based update speculation. Extensive\nexperiments on 4 public datasets demonstrate that our algorithm achieves\nenhanced robustness comparing to existing methods under both cross-silo and\ncross-device FL settings with faulty/malicious clients.\n"}
+{"abstract": "  Ensemble Kalman inversion is a parallelizable derivative-free method to solve\ninverse problems. The method uses an ensemble that follows the Kalman update\nformula iteratively to solve an optimization problem. The ensemble size is\ncrucial to capture the correct statistical information in estimating the\nunknown variable of interest. Still, the ensemble is limited to a size smaller\nthan the unknown variable's dimension for computational efficiency. This study\nproposes a strategy to correct the sampling error due to a small ensemble size,\nwhich improves the performance of the ensemble Kalman inversion. This study\nvalidates the efficiency and robustness of the proposed strategy through a\nsuite of numerical tests, including compressive sensing, image deblurring,\nparameter estimation of a nonlinear dynamical system, and a PDE-constrained\ninverse problem.\n"}
+{"abstract": "  On edge devices, data scarcity occurs as a common problem where transfer\nlearning serves as a widely-suggested remedy. Nevertheless, transfer learning\nimposes a heavy computation burden to resource-constrained edge devices.\nExisting task allocation works usually assume all submitted tasks are equally\nimportant, leading to inefficient resource allocation at a task level when\ndirectly applied in Multi-task Transfer Learning (MTL). To address these\nissues, we first reveal that it is crucial to measure the impact of tasks on\noverall decision performance improvement and quantify \\emph{task importance}.\nWe then show that task allocation with task importance for MTL (TATIM) is a\nvariant of the NP-complete Knapsack problem, where the complicated computation\nto solve this problem needs to be conducted repeatedly under varying contexts.\nTo solve TATIM with high computational efficiency, we propose a Data-driven\nCooperative Task Allocation (DCTA) approach. Finally, we evaluate the\nperformance of DCTA by not only a trace-driven simulation, but also a new\ncomprehensive real-world AIOps case study that bridges model and practice via a\nnew architecture and main components design within the AIOps system. Extensive\nexperiments show that our DCTA reduces 3.24 times of processing time, and saves\n48.4\\% energy consumption compared with the state-of-the-art when solving\nTATIM.\n"}
+{"abstract": "  The one-dimensional Bose-Hubbard model in large-$U$ limit has been studied\nvia reducing and mapping the Hamiltonian to a simpler one. The eigenstates and\neigenvalues have been obtained exactly in the subspaces with fixed numbers of\nsingle- and double-occupancies but without multiple-occupancies, and the\nthermodynamic properties of the system have been calculated further. These\neigenstates and eigenvalues also enable us to develop a new perturbation\ntreatment of the model, with which the ground-state energy has been calculated\nexactly to first order in $1/U$.\n"}
+{"abstract": "  In magnetic Cataclysmic Variables (mCVs), X-ray radiation originates from the\nshock heated multi-temperature plasma in the post-shock region near the white\ndwarf surface. These X-rays are modified by a complex distribution of absorbers\nin the pre-shock region. The presence of photo-ionized lines and warm absorber\nfeatures in the soft X-ray spectra of these mCVs suggests that these absorbers\nare ionized. We developed the ionized complex absorber model zxipab, which is\nrepresented by a power-law distribution of ionized absorbers in the pre-shock\nflow. Using the ionized absorber model zxipab along with a cooling flow model\nand a reflection component, we model the broadband Chandra/HETG and NuSTAR\nspectra of two IPs: NY Lup and V1223 Sgr. We find that this model describes\nwell many of the H and He like emission lines from medium Z elements, which\narises from the collisionally excited plasma. However the model fails to\naccount for some of the He like triplets from medium Z elements, which points\ntowards its photo-ionization origin. We do not find a compelling evidence for a\nblackbody component to model the soft excess seen in the residuals of the\nChandra/HETG spectra, which could be due to the uncertainties in estimation of\nthe interstellar absorption of these sources using Chandra/HETG data and/or\nexcess fluxes seen in some photo-ionized emission lines which are not accounted\nby the cooling flow model. We describe the implications of this model with\nrespect to the geometry of the pre-shock region in these two IPs.\n"}
+{"abstract": "  This text is written based on the author's publications during the period\nfrom 1991 to 2001. The work is devoted to the theory of Markov intertwining\noperators and joinings of measure-preserving group actions, as well as to their\napplications to study asymptotic properties of dynamical systems. Special\nattention is paid to Rokhlin's problems on multiple mixing and multiple\nspectrum. The development of these topics over the past twenty years has not\nbeen discussed. In fact many results on joinings have frozen in time, many\nquestions have remained open without losing their relevance, but probably have\nceased to excite interest due to difficulties. For example, it is not known\nwhether the minimal self-joinings of order 2 imply all orders? Is there a\nnon-trivial pairwise independent joining for a weakly mixing system of zero\nentropy? What can be said about such joinings for transformations with small\nlocal rank? These questions are ripe for a long time, and the author reminds\nthe reader about them, combining his story with numerous partial and related\nresults.\n"}
+{"abstract": "  Let $ \\sigma$ be a partition of the set of all primes and $\\mathfrak{F}$ be a\nhereditary formation. We described all formations $\\mathfrak{F}$ for which the\n$\\mathfrak{F}$-hypercenter and the intersection of weak\n$K$-$\\mathfrak{F}$-subnormalizers of all Sylow subgroups coincide in every\ngroup. In particular the formation of all $\\sigma$-nilpotent groups has this\nproperty. With the help of our results we solve a particular case of\nL.A.~Shemetkov's problem about the intersection of $\\mathfrak{F}$-maximal\nsubgroups and the $\\mathfrak{F}$-hypercenter. As corollaries we obtained P.\nHall's and R. Baer's classical results about the hypercenter. We proved that\nthe non-$\\sigma$-nilpotent graph of a group is connected and its diameter is at\nmost 3.\n"}
+{"abstract": "  Andrews' $(k, i)$-singular overpartition function $\\overline{C}_{k, i}(n)$\ncounts the number of overpartitions of $n$ in which no part is divisible by $k$\nand only parts $\\equiv \\pm i\\pmod{k}$ may be overlined. In recent times,\ndivisibility of $\\overline{C}_{3\\ell, \\ell}(n)$, $\\overline{C}_{4\\ell,\n\\ell}(n)$ and $\\overline{C}_{6\\ell, \\ell}(n)$ by $2$ and $3$ are studied for\ncertain values of $\\ell$. In this article, we study divisibility of\n$\\overline{C}_{3\\ell, \\ell}(n)$, $\\overline{C}_{4\\ell, \\ell}(n)$ and\n$\\overline{C}_{6\\ell, \\ell}(n)$ by primes $p\\geq 5$. For all positive integer\n$\\ell$ and prime divisors $p\\geq 5$ of $\\ell$, we prove that\n$\\overline{C}_{3\\ell, \\ell}(n)$, $\\overline{C}_{4\\ell, \\ell}(n)$ and\n$\\overline{C}_{6\\ell, \\ell}(n)$ are almost always divisible by arbitrary powers\nof $p$. For $s\\in \\{3, 4, 6\\}$, we next show that the set of those $n$ for\nwhich $\\overline{C}_{s\\cdot\\ell, \\ell}(n) \\not\\equiv 0\\pmod{p_i^k}$ is\ninfinite, where $k$ is a positive integer satisfying $p_i^{k-1}\\geq \\ell$. We\nfurther improve a result of Gordon and Ono on divisibility of $\\ell$-regular\npartitions by powers of certain primes. We also improve a result of Ray and\nChakraborty on divisibility of $\\ell$-regular overpartitions by powers of\ncertain primes.\n"}
+{"abstract": "  Affordance detection refers to identifying the potential action possibilities\nof objects in an image, which is an important ability for robot perception and\nmanipulation. To empower robots with this ability in unseen scenarios, we\nconsider the challenging one-shot affordance detection problem in this paper,\ni.e., given a support image that depicts the action purpose, all objects in a\nscene with the common affordance should be detected. To this end, we devise a\nOne-Shot Affordance Detection (OS-AD) network that firstly estimates the\npurpose and then transfers it to help detect the common affordance from all\ncandidate images. Through collaboration learning, OS-AD can capture the common\ncharacteristics between objects having the same underlying affordance and learn\na good adaptation capability for perceiving unseen affordances. Besides, we\nbuild a Purpose-driven Affordance Dataset (PAD) by collecting and labeling 4k\nimages from 31 affordance and 72 object categories. Experimental results\ndemonstrate the superiority of our model over previous representative ones in\nterms of both objective metrics and visual quality. The benchmark suite is at\nProjectPage.\n"}
+{"abstract": "  Federated learning has emerged as a popular technique for distributing\nmachine learning (ML) model training across the wireless edge. In this paper,\nwe propose two timescale hybrid federated learning (TT-HF), a\nsemi-decentralized learning architecture that combines the conventional\ndevice-to-server communication paradigm for federated learning with\ndevice-to-device (D2D) communications for model training. In TT-HF, during each\nglobal aggregation interval, devices (i) perform multiple stochastic gradient\ndescent iterations on their individual datasets, and (ii) aperiodically engage\nin consensus procedure of their model parameters through cooperative,\ndistributed D2D communications within local clusters. With a new general\ndefinition of gradient diversity, we formally study the convergence behavior of\nTT-HF, resulting in new convergence bounds for distributed ML. We leverage our\nconvergence bounds to develop an adaptive control algorithm that tunes the step\nsize, D2D communication rounds, and global aggregation period of TT-HF over\ntime to target a sublinear convergence rate of O(1/t) while minimizing network\nresource utilization. Our subsequent experiments demonstrate that TT-HF\nsignificantly outperforms the current art in federated learning in terms of\nmodel accuracy and/or network energy consumption in different scenarios where\nlocal device datasets exhibit statistical heterogeneity. Finally, our numerical\nevaluations demonstrate robustness against outages caused by fading channels,\nas well favorable performance with non-convex loss functions.\n"}
+{"abstract": "  The ALMA Spectroscopic Survey in the Hubble Ultra Deep Field (ASPECS) Band 6\nscan (212-272 GHz) covers potential [CII] emission in galaxies at $6\\leq z\n\\leq8$ throughout a 2.9 arcmin$^2$ area. By selecting on known Lyman-$\\alpha$\nemitters (LAEs) and photometric dropout galaxies in the field, we perform\ntargeted searches down to a 5$\\sigma$ [CII] luminosity depth\n$L_{\\mathrm{[CII]}}\\sim2.0\\times10^8$ L$_{\\odot}$, corresponding roughly to\nstar formation rates (SFRs) of $10$-$20$ M$_{\\odot}$ yr$^{-1}$ when applying a\nlocally calibrated conversion for star-forming galaxies, yielding zero\ndetections. While the majority of galaxies in this sample are characterized by\nlower SFRs, the resulting upper limits on [CII] luminosity in these sources are\nconsistent with the current literature sample of targeted ALMA observations of\n$z=6$-$7$ LAEs and Lyman-break galaxies (LBGs), as well as the locally\ncalibrated relations between $L_{\\mathrm{[CII]}}$ and SFR -- with the exception\nof a single [CII]-deficient, UV luminous LBG. We also perform a blind search\nfor [CII]-bright galaxies that may have been missed by optical selections,\nresulting in an upper limit on the cumulative number density of [CII] sources\nwith $L_{\\mathrm{[CII]}}>2.0\\times10^8$ L$_{\\odot}$ ($5\\sigma $) to be less\nthan $1.8\\times10^{-4}$ Mpc$^{-3}$ (90% confidence level). At this luminosity\ndepth and volume coverage, we present an observed evolution of the [CII]\nluminosity function from $z=6$-$8$ to $z\\sim0$ by comparing the ASPECS\nmeasurement to literature results at lower redshift.\n"}
+{"abstract": "  We construct two-dimensional non-commutative topological quantum field\ntheories (TQFTs), one for each Hecke algebra corresponding to a finite Coxeter\nsystem. These TQFTs associate an invariant to each ciliated surface, which is a\nLaurent polynomial for punctured surfaces. There is a graphical way to compute\nthe invariant using minimal colored graphs. We give explicit formulas in terms\nof the Schur elements of the Hecke algebra and prove positivity properties for\nthe invariants when the Coxeter group is of classical type, or one of the\nexceptional types $H_3$, $E_6$ and $E_7$.\n"}
+{"abstract": "  Let $\\mathbf{P} \\subset [H_0,H]$ be a set of primes, where $\\log H_0 \\geq\n(\\log H)^{2/3 + \\epsilon}$. Let $\\mathscr{L} = \\sum_{p \\in \\mathbf{P}} 1/p$.\nLet $N$ be such that $\\log H \\leq (\\log N)^{1/2-\\epsilon}$. We show there\nexists a subset $\\mathscr{X} \\subset (N, 2N]$ of density close to $1$ such that\nall the eigenvalues of the linear operator $$(A_{|\\mathscr{X}} f)(n) =\n\\sum_{\\substack{p \\in \\mathbf{P} : p | n \\\\ n, n \\pm p \\in \\mathscr{X}}} f(n\n\\pm p) \\; - \\sum_{\\substack{p \\in\\mathbf{P} \\\\ n, n \\pm p \\in \\mathscr{X}}}\n\\frac{f(n \\pm p)}{p}$$ are $O(\\sqrt{\\mathscr{L}})$. This bound is optimal up to\na constant factor. In other words, we prove that a graph describing\ndivisibility by primes is a strong local expander almost everywhere, and indeed\nwithin a constant factor of being \"locally Ramanujan\" (a.e.).\n  Specializing to $f(n) = \\lambda(n)$ with $\\lambda(n)$ the Liouville function,\nand using an estimate by Matom\\\"aki, Radziwi{\\l}{\\l} and Tao on the average of\n$\\lambda(n)$ in short intervals, we derive that \\[\\frac{1}{\\log x} \\sum_{n\\leq\nx} \\frac{\\lambda(n) \\lambda(n+1)}{n} = O\\Big(\\frac{1}{\\sqrt{\\log \\log\nx}}\\Big),\\]\n  improving on a result of Tao's. We also prove that $\\sum_{N<n\\leq 2 N}\n\\lambda(n) \\lambda(n+1)=o(N)$ at almost all scales with a similar error term,\nimproving on a result by Tao and Ter\\\"av\\\"ainen. (Tao and Tao-Ter\\\"av\\\"ainen\nfollowed a different approach, based on entropy, not expansion; significantly,\nwe can take a much larger value of $H$, and thus consider many more primes.)\n  We can also prove sharper results with ease. For instance: let $S_{N,k}$ the\nset of all $N<n\\leq 2N$ such that $\\Omega(n) = k$. Then, for any fixed value of\n$k$ with $k = \\log \\log N + O(\\sqrt{\\log \\log N})$ (that is, any \"popular\"\nvalue of $k$) the average of $\\lambda(n+1)$ over $S_{N,k}$ is $o(1)$ at almost\nall scales.\n"}
+{"abstract": "  Coronavirus disease (COVID-19) pandemic has changed various aspects of\npeople's lives and behaviors. At this stage, there are no other ways to control\nthe natural progression of the disease than adopting mitigation strategies such\nas wearing masks, watching distance, and washing hands. Moreover, at this time\nof social distancing, social media plays a key role in connecting people and\nproviding a platform for expressing their feelings. In this study, we tap into\nsocial media to surveil the uptake of mitigation and detection strategies, and\ncapture issues and concerns about the pandemic. In particular, we explore the\nresearch question, \"how much can be learned regarding the public uptake of\nmitigation strategies and concerns about COVID-19 pandemic by using natural\nlanguage processing on Reddit posts?\" After extracting COVID-related posts from\nthe four largest subreddit communities of North Carolina over six months, we\nperformed NLP-based preprocessing to clean the noisy data. We employed a custom\nNamed-entity Recognition (NER) system and a Latent Dirichlet Allocation (LDA)\nmethod for topic modeling on a Reddit corpus. We observed that 'mask', 'flu',\nand 'testing' are the most prevalent named-entities for \"Personal Protective\nEquipment\", \"symptoms\", and \"testing\" categories, respectively. We also\nobserved that the most discussed topics are related to testing, masks, and\nemployment. The mitigation measures are the most prevalent theme of discussion\nacross all subreddits.\n"}
+{"abstract": "  Motivated by the mathematical beauty and the recent experimental realizations\nof fractal systems, we study the spin-$1/2$ antiferromagnetic Heisenberg model\non a Sierpi\\'nski gasket. The fractal porous feature generates new kinds of\nfrustration to exhibit exotic quantum states. Using advanced tensor network\ntechniques, we identify a quantum gapless-spin-liquid ground state in\nfractional spatial dimension. This fractal spin system also demonstrates\nnontrivial non-local properties. While the extremely short-range correlation\ncauses a highly degenerate spin form factor, the entanglement in this fractal\nsystem suggests scaling behaviors significantly different from those in integer\ndimensions. We also study the dynamic structure factor and clearly identify the\ngapless excitation with a stable corner excitation emerged from the\nground-state entanglement. Our results unambiguously point out multiple\nessential properties of this fractal spin system, and open a new route to\nexplore spin liquid and frustrated magnetism.\n"}
+{"abstract": "  Injection locking of diode lasers is commonly used to amplify low power laser\nlight, but is extremely sensitive to perturbations in the laser current and\ntemperature. To counter such perturbations, active stabilization is often\napplied to the current of the injection locked diode. We observe that the diode\nlaser's polarization extinction ratio (PER) greatly increases when injection\nlocked, and therefore the PER provides a measure of injection lock quality. We\nreport robust active stabilization of a diode laser injection lock based on the\nPER, demonstrating the technique at 399 nm wavelength where injection locking\nis typically less stable than at longer wavelengths. The PER provides a\nfeedback error signal that is compatible with standard PID servo controllers,\nrequires no additional optical components beyond the optical isolator typically\nused in injection locking, and enables a large feedback bandwidth.\n"}
+{"abstract": "  Layered ternary transition-metal chalcogenides have been focused as a vein of\nexploration for superconductors. In this study, TiGeTe$_{6}$ single crystals\nwere synthesized and characterized by structural and valence state analyses and\nelectrical transport measurements. The transport properties were measured under\nvarious pressures up to 71 GPa. The activation energy gets smaller as the\napplied pressure increases, and a signature of a pressure-induced metallization\nwas observed under around 8.4 GPa. Under 13 GPa, pressure-induced\nsuperconductivity was discovered in this compound for the first time, with\nsuccessive drops at 3 K and 6 K in the resistance, indicating the presence of\nmultiple superconducting transitions. The superconducting transition\ntemperature kept increasing as we further applied the pressure to the\nTiGeTe$_{6}$ single crystal in the performed pressure range, reaching as high\nas 8.1 K under 71 GPa.\n"}
+{"abstract": "  In this paper we study the existence and uniqueness of the random periodic\nsolution for a stochastic differential equation with a one-sided Lipschitz\ncondition (also known as monotonicity condition) and the convergence of its\nnumerical approximation via the backward Euler-Maruyama method. The existence\nof the random periodic solution is shown as the limits of the pull-back flows\nof the SDE and discretized SDE respectively. We establish a convergence rate of\nthe strong error for the backward Euler-Maruyama method and obtain the weak\nconvergence result for the approximation of the periodic measure.\n"}
+{"abstract": "  This report is dedicated to a short motivation and description of our\ncontribution to the AAPM DL-Sparse-View CT Challenge (team name:\n\"robust-and-stable\"). The task is to recover breast model phantom images from\nlimited view fanbeam measurements using data-driven reconstruction techniques.\nThe challenge is distinctive in the sense that participants are provided with a\ncollection of ground truth images and their noiseless, subsampled sinograms (as\nwell as the associated limited view filtered backprojection images), but not\nwith the actual forward model. Therefore, our approach first estimates the\nfanbeam geometry in a data-driven geometric calibration step. In a subsequent\ntwo-step procedure, we design an iterative end-to-end network that enables the\ncomputation of near-exact solutions.\n"}
+{"abstract": "  Accurately forecasting air quality is critical to protecting general public\nfrom lung and heart diseases. This is a challenging task due to the complicated\ninteractions among distinct pollution sources and various other influencing\nfactors. Existing air quality forecasting methods cannot effectively model the\ndiffusion processes of air pollutants between cities and monitoring stations,\nwhich may suddenly deteriorate the air quality of a region. In this paper, we\npropose HighAir, i.e., a hierarchical graph neural network-based air quality\nforecasting method, which adopts an encoder-decoder architecture and considers\ncomplex air quality influencing factors, e.g., weather and land usage.\nSpecifically, we construct a city-level graph and station-level graphs from a\nhierarchical perspective, which can consider city-level and station-level\npatterns, respectively. We design two strategies, i.e., upper delivery and\nlower updating, to implement the inter-level interactions, and introduce\nmessage passing mechanism to implement the intra-level interactions. We\ndynamically adjust edge weights based on wind direction to model the\ncorrelations between dynamic factors and air quality. We compare HighAir with\nthe state-of-the-art air quality forecasting methods on the dataset of Yangtze\nRiver Delta city group, which covers 10 major cities within 61,500 km2. The\nexperimental results show that HighAir significantly outperforms other methods.\n"}
+{"abstract": "  Computationally efficient and accurate quantum mechanical approximations to\nsolve the many-electron Schr\\\"odinger equation are at the heart of\ncomputational materials science. In that respect the coupled cluster hierarchy\nof methods plays a central role in molecular quantum chemistry because of its\nsystematic improvability and computational efficiency. In this hierarchy,\ncoupled cluster singles and doubles (CCSD) is one of the most important steps\nin moving towards chemical accuracy and, in recent years, its scope has\nsuccessfully been expanded to the study of insulating surfaces and solids.\nHere, we show that CCSD theory can also be applied to real metals. In so doing,\nwe overcome the limitation of needing extremely large supercells to capture\nlong range electronic correlation effects. An effective Hamiltonian can be\nfound using the transition structure factor--a map of electronic excitations\nfrom the Hartree--Fock wavefunction--which has fewer finite size effects than\nconventional periodic boundary conditions. This not only paves the way of\napplying coupled cluster methods to real metals but also reduces the\ncomputational cost by two orders of magnitude compared to previous methods. Our\napplications to phases of lithium and silicon show a resounding success in\nreaching the thermodynamic limit, taking the first step towards a truly\nuniversal quantum chemical treatment of solids.\n"}
+{"abstract": "  We perform an extensive analysis of the statistics of axion masses and\ninteractions in compactifications of type IIB string theory, and we show that\nblack hole superradiance excludes some regions of Calabi-Yau moduli space.\nRegardless of the cosmological model, a theory with an axion whose mass falls\nin a superradiant band can be probed by the measured properties of\nastrophysical black holes, unless the axion self-interaction is large enough to\ndisrupt formation of a condensate. We study a large ensemble of\ncompactifications on Calabi-Yau hypersurfaces, with $1 \\leq h^{1,1} \\leq 491$\nclosed string axions, and determine whether the superradiance conditions on the\nmasses and self-interactions are fulfilled. The axion mass spectrum is largely\ndetermined by the K\\\"ahler parameters, for mild assumptions about the\ncontributing instantons, and takes a nearly-universal form when $h^{1,1} \\gg\n1$. When the K\\\"ahler moduli are taken at the tip of the stretched K\\\"ahler\ncone, the fraction of geometries excluded initially grows with $h^{1,1}$, to a\nmaximum of $\\approx 0.5$ at $h^{1,1} \\approx 160$, and then falls for larger\n$h^{1,1}$. Further inside the K\\\"ahler cone, the superradiance constraints are\nfar weaker, but for $h^{1,1} \\gg 100$ the decay constants are so small that\nthese geometries may be in tension with astrophysical bounds, depending on the\nrealization of the Standard Model.\n"}
+{"abstract": "  Writers such as journalists often use automatic tools to find relevant\ncontent to include in their narratives. In this paper, we focus on supporting\nwriters in the news domain to develop event-centric narratives. Given an\nincomplete narrative that specifies a main event and a context, we aim to\nretrieve news articles that discuss relevant events that would enable the\ncontinuation of the narrative. We formally define this task and propose a\nretrieval dataset construction procedure that relies on existing news articles\nto simulate incomplete narratives and relevant articles. Experiments on two\ndatasets derived from this procedure show that state-of-the-art lexical and\nsemantic rankers are not sufficient for this task. We show that combining those\nwith a ranker that ranks articles by reverse chronological order outperforms\nthose rankers alone. We also perform an in-depth quantitative and qualitative\nanalysis of the results that sheds light on the characteristics of this task.\n"}
+{"abstract": "  This paper proposes a multi-task learning network with phoneme-aware and\nchannel-wise attentive learning strategies for text-dependent Speaker\nVerification (SV). In the proposed structure, the frame-level multi-task\nlearning along with the segment-level adversarial learning is adopted for\nspeaker embedding extraction. The phoneme-aware attentive pooling is exploited\non frame-level features in the main network for speaker classifier, with the\ncorresponding posterior probability for the phoneme distribution in the\nauxiliary subnet. Further, the introduction of Squeeze and Excitation\n(SE-block) performs dynamic channel-wise feature recalibration, which improves\nthe representational ability. The proposed method exploits speaker\nidiosyncrasies associated with pass-phrases, and is further improved by the\nphoneme-aware attentive pooling and SE-block from temporal and channel-wise\naspects, respectively. The experiments conducted on RSR2015 Part 1 database\nconfirm that the proposed system achieves outstanding results for textdependent\nSV.\n"}
+{"abstract": "  In this article we apply proper splittings of matrices to develop an\niterative process to approximate solutions of matrix equations of the form TX =\nW. Moreover, by using the partial order induced by positive semidefinite\nmatrices, we obtain equivalent conditions to the convergence of this process.\nWe also include some speed comparison results of the convergence of this\nmethod. In addition, for all matrix T we propose a proper splitting based on\nthe polar decomposition of T.\n"}
+{"abstract": "  Adversarial attack arises due to the vulnerability of deep neural networks to\nperceive input samples injected with imperceptible perturbations. Recently,\nadversarial attack has been applied to visual object tracking to evaluate the\nrobustness of deep trackers. Assuming that the model structures of deep\ntrackers are known, a variety of white-box attack approaches to visual tracking\nhave demonstrated promising results. However, the model knowledge about deep\ntrackers is usually unavailable in real applications. In this paper, we propose\na decision-based black-box attack method for visual object tracking. In\ncontrast to existing black-box adversarial attack methods that deal with static\nimages for image classification, we propose IoU attack that sequentially\ngenerates perturbations based on the predicted IoU scores from both current and\nhistorical frames. By decreasing the IoU scores, the proposed attack method\ndegrades the accuracy of temporal coherent bounding boxes (i.e., object\nmotions) accordingly. In addition, we transfer the learned perturbations to the\nnext few frames to initialize temporal motion attack. We validate the proposed\nIoU attack on state-of-the-art deep trackers (i.e., detection based,\ncorrelation filter based, and long-term trackers). Extensive experiments on the\nbenchmark datasets indicate the effectiveness of the proposed IoU attack\nmethod. The source code is available at\nhttps://github.com/VISION-SJTU/IoUattack.\n"}
+{"abstract": "  The amplitude (Higgs) mode near the two-dimensional superfluid-Mott glass\nquantum phase transition is studied. We map the Bose-Hubbard Hamiltonian of\ndisordered interacting bosons onto an equivalent classical XY model in (2+1)\ndimensions and compute the scalar susceptibility of the order parameter\namplitude via Monte Carlo simulation. Analytic continuation of the scalar\nsusceptibilities from imaginary to real frequency to obtain the spectral\ndensities is performed by a modified maximum entropy technique. Our results\nshow that the introduction of disorder into the system leads to unconventional\ndynamical behavior of the Higgs mode that violates naive scaling,despite the\nunderlying thermodynamics of the transition being of conventional power-law\ntype. The computed spectral densities exhibit a broad, non-critical response\nfor all energies, and a momentum-independent dispersion for long-wavelengths,\nindicating strong evidence for the localization of the Higgs mode for all\ndilutions.\n"}
+{"abstract": "  If $H$ is a Hilbert space, the Stiefel manifold $St(n,H)$ is formed by all\nthe independent $n$-tuples in $H$. In this article, we contribute to the\ntopological study of Stiefel manifolds by proving density and\npath-connectedness-related results. Regarding the density aspect, we generalize\nthe fact that $St(n,H)$ is dense in $H^n$ and prove that $St(n,H) \\cap S$ is\ndense in $S$ whenever $S \\subseteq H^n$ is connected by polynomial paths of\nfinite degree to some $\\Theta \\in St(n,H) \\cap S$. We provide special examples\nof such sets $S$ in the context of finite-dimensional continuous frames (we set\n$H := L^2(X,\\mu;\\mathbb{F})$ and we identify $St(n,H)$ with\n$\\mathcal{F}_{(X,\\mu),n}^\\mathbb{F}$) which are constructed from the inverse\nimage of singletons by some familiar linear and pseudo-quadratic functions. In\nthe second part devoted to path-connectedness, we prove that the intersection\nof translates of $St(n,H)$ is path-connected under a condition on the\ncodimension of the span of the components of the translating $n$-tuples. These\nresults are also a contribution to the topological theory of Hilbert space\nframes which is presently an active area of research.\n"}
+{"abstract": "  Massive black holes often exist within dwarf galaxies, and both simulations\nand observations have shown that a substantial fraction of these may be\noff-center with respect to their hosts. We trace the evolution of off-center\nmassive black holes (MBHs) in dwarf galaxies using cosmological hydrodynamical\nsimulations, and show that the reason for off-center locations is mainly due to\ngalaxy-galaxy mergers. We calculate dynamical timescales and show that\noff-center MBHs are unlikely to sink to their galaxys' centers within a Hubble\ntime, due to the shape of the hosts' potential wells and low stellar densities.\nThese wandering MBHs are unlikely to be detected electromagnetically, nor is\nthere a measurable dynamical effect on the galaxy's stellar population. We\nconclude that off-center MBHs may be common in dwarfs, especially if the mass\nof the MBH is small or the stellar mass of the host galaxy is large. However\ndetecting them is extremely challenging, because their accretion luminosities\nare very low and they do not measurably alter the dynamics of their host\ngalaxies.\n"}
+{"abstract": "  We discuss the interplay of wave package decoherence and decoherence induced\nby quantum gravity via interactions with spacetime foam for high energy\nastrophysical neutrinos. In this context we point out a compelling consequence\nof the expectation that quantum gravity should break global symmetries, namely\nthat quantum-gravity induced decoherence can provide both a powerful tool for\nthe search for new particles, including totally decoupled backgrounds\ninteracting only gravitationally, and at the same time a window into the\nintricacies of black hole information processing.\n"}
+{"abstract": "  Semi-Supervised Learning (SSL) has seen success in many application domains,\nbut this success often hinges on the availability of task-specific unlabeled\ndata. Knowledge distillation (KD) has enabled effective optimization of compact\nneural nets, achieving the best results when the knowledge of an expensive\nnetwork is distilled via fresh task-specific unlabeled data. However,\ntask-specific unlabeled data can be challenging to find, especially for NLP. We\ninvestigate the use of generative models in synthesizing unlabeled data and\npresent a simple and general framework called \"generate, annotate, and learn\n(GAL)\". A language model (LM) is used to synthesize in-domain unlabeled data.\nThen, a classifier is used to annotate such data. Finally, synthetically\ngenerated and annotated data is used to advance SSL, KD, and few-shot learning\non NLP and tabular tasks. To obtain a strong task-specific LM, we either\nfine-tune a large LM on inputs from a specific task, or prompt a large LM with\na few input examples and conditionally generate more unlabeled examples. It\nalso yields a new state-of-the-art for 6-layer transformers on the GLUE\nleaderboard. Finally, self-training with GAL offers large gains on four tabular\ntasks from the UCI repository.\n"}
+{"abstract": "  This chapter is written for the welfare of the society, questioning and\nenlightening the effects of the increment or decrement in the percentage of\nquality of air causing pollution due to the rise in the traffic post lockdown\ndue to COVID 19 in metro cities, specifically in Delhi. In this chapter, we\naddress the question about people's preference in moving in the shared taxis to\ntheir workplaces or their reluctance and denial of the idea of moving in the\nshared vehicle because of the fear of getting infected. The sensitivity of the\nsituation will compel the people to move in a single occupied vehicle (SOV).\nThe rise in the number of vehicles on the roads will result in traffic jams and\ndifferent kinds of pollution where people battling with the pandemic will\ninevitably get exposed to other health related issues. We use a BPR (Bureau of\nPublic Roads) model to combat this issue endangering the environment and public\nhealth. We exploit the BPR function to relate average travel time to the\nestimated number of commuters travelling by car. We collect mode share data\nfrom the NITI Ayog, the State Resource Centre and other authentic sources,\nwhich gives unique figures of the impact of shared mobility in India and how,\nin its absence, various sectors will get affected. Using the given data and the\nBPR, we evaluate increased vehicle volumes on the road if different portions of\ntransit and carpool users switch to single occupancy vehicles and its effect on\nmultiple other factors. Based on the study of densely populated city, Delhi, we\npredict that cities with significant transit ridership are at risk for extreme\ntraffic and pollution unless transit systems can resume safe with effective\nprotocols.\n"}
+{"abstract": "  Motivated by packet routing in computer networks, online queuing systems are\ncomposed of queues receiving packets at different rates. Repeatedly, they send\npackets to servers, each of them treating only at most one packet at a time. In\nthe centralized case, the number of accumulated packets remains bounded (i.e.,\nthe system is \\textit{stable}) as long as the ratio between service rates and\narrival rates is larger than $1$. In the decentralized case, individual\nno-regret strategies ensures stability when this ratio is larger than $2$. Yet,\nmyopically minimizing regret disregards the long term effects due to the\ncarryover of packets to further rounds. On the other hand, minimizing long term\ncosts leads to stable Nash equilibria as soon as the ratio exceeds\n$\\frac{e}{e-1}$. Stability with decentralized learning strategies with a ratio\nbelow $2$ was a major remaining question. We first argue that for ratios up to\n$2$, cooperation is required for stability of learning strategies, as selfish\nminimization of policy regret, a \\textit{patient} notion of regret, might\nindeed still be unstable in this case. We therefore consider cooperative queues\nand propose the first learning decentralized algorithm guaranteeing stability\nof the system as long as the ratio of rates is larger than $1$, thus reaching\nperformances comparable to centralized strategies.\n"}
+{"abstract": "  In this paper we study the transformation of surface envelope solitons\ntravelling over a bottom step in water of a finite depth. Using the\ntransformation coefficients earlier derived in the linear approximation, we\nfind the parameters of transmitted pulses and subsequent evolution of the\npulses in the course of propagation. Relying on the weakly nonlinear theory,\nthe analytic formulae are derived which describe the maximum attainable wave\namplitude in the neighbourhood of the step and in the far zone. Solitary waves\nmay be greatly amplified (within the weakly nonlinear theory formally even\nwithout a limit) when propagating from relatively shallow water to the deeper\ndomain due to the constructive interference between the newly emerging envelope\nsolitons and the residual quasi-linear waves. The theoretical results are in a\ngood agreement with the data of direct numerical modelling of soliton\ntransformation. In particular, more than double wave amplification is\ndemonstrated in the performed simulations.\n"}
+{"abstract": "  The dramatic growth of big datasets presents a new challenge to data storage\nand analysis. Data reduction, or subsampling, that extracts useful information\nfrom datasets is a crucial step in big data analysis. We propose an orthogonal\nsubsampling (OSS) approach for big data with a focus on linear regression\nmodels. The approach is inspired by the fact that an orthogonal array of two\nlevels provides the best experimental design for linear regression models in\nthe sense that it minimizes the average variance of the estimated parameters\nand provides the best predictions. The merits of OSS are three-fold: (i) it is\neasy to implement and fast; (ii) it is suitable for distributed parallel\ncomputing and ensures the subsamples selected in different batches have no\ncommon data points; and (iii) it outperforms existing methods in minimizing the\nmean squared errors of the estimated parameters and maximizing the efficiencies\nof the selected subsamples. Theoretical results and extensive numerical results\nshow that the OSS approach is superior to existing subsampling approaches. It\nis also more robust to the presence of interactions among covariates and, when\nthey do exist, OSS provides more precise estimates of the interaction effects\nthan existing methods. The advantages of OSS are also illustrated through\nanalysis of real data.\n"}
+{"abstract": "  The design of feedback control systems to block observability in a network\nsynchronization model, i.e. to make the dynamics unobservable from measurements\nat a subset of the network's nodes, is studied. First, a general design\nalgorithm is presented for blocking observability at any specified group of $m$\nnodes, by applying state feedback controls at $m+2$ specified actuation nodes.\nThe algorithm is based on a method for eigenstructure assignment, which allows\nsurgical modification of particular eigenvectors to block observability while\npreserving the remaining open-loop eigenstructure. Next, the topological\nstructure of the network is exploited to reduce the number of controllers\nrequired for blocking observability; the result is based on blocking\nobservability on the nodes associated with a vertex-cutset separating the\nactuation and measurement locations. Also, the design is modified to encompass\nregional feedback controls, which only use data from a subset of accessible\nnodes. The regional feedback design does not maintain the open-loop\neigenstructure, but can be guaranteed to preserve stability via a time-scale\nargument. The results are illustrated with numerical examples.\n"}
+{"abstract": "  Context: In the light of the swift and iterative nature of Agile Software\nDevelopment (ASD) practices, establishing deeper insights into capability\nmeasurement within the context of team formation is crucial, as the capability\nof individuals and teams can affect team performance and productivity. Although\na former Systematic Literature Review (SLR) synthesized the state of the art in\nrelation to capability measurement in ASD with a focus on selecting individuals\nto agile teams, and capabilities related to team performance and success,\ndetermining to what degree the SLR's results apply to practice can provide\nprogressive insights to both research and practice.\n  Objective: Our study investigates how agile practitioners perceive the\nrelevance of individual and team level measures for characterizing the\ncapability of an agile team and its members. Furthermore, to scrutinize\nvariations in practitioners' perceptions, our study further analyzes\nperceptions across stratified demographic groups.\n  Method: We undertook a Web-based survey using a questionnaire built based on\nthe capability measures identified from a previously conducted SLR.\n  Results: Our survey responses (60) indicate that 127 individual and 28 team\ncapability measures were considered as relevant by the majority of\npractitioners. We also identified seven individual and one team capability\nmeasure that have not been previously characterized by our SLR. The surveyed\npractitioners suggested that an agile team member's responsibility and\nquestioning skills significantly represent the member's capability.\n  Conclusion: Results from our survey align with our SLR's findings. Measures\nassociated with social aspects were observed to be dominant compared to\ntechnical and innovative aspects. Our results can support agile practitioners\nin their team composition decisions.\n"}
+{"abstract": "  Early speculations about the existence of heavy hadron molecules were\ngrounded on the idea that light-meson exchanges forces could lead to binding.\nIn analogy to the deuteron, the light-mesons usually considered include the\npion, sigma, rho and omega, but not the axial meson $a_1(1260)$. Though it has\nbeen argued in the past that the coupling of the axial meson to the nucleons is\nindeed strong, its mass is considerably heavier than that of the vector mesons\nand thus its exchange ends up being suppressed. Yet, this is not necessarily\nthe case in heavy hadrons molecules: we find that even though the contribution\nto binding from the axial meson is modest, it cannot be neglected in the\nisovector sector where vector meson exchange cancels out. This might provide a\nnatural binding mechanism for molecular candidates such as the $Z_c(3900)$,\n$Z_c(4020)$ or the more recently observed $Z_{cs}(3985)$. However the\n$Z_{cs}(3985)$ is more dependent on a mixture of different factors, which\n(besides axial meson exchange) include $\\eta$ exchange and the nature of scalar\nmeson exchange. Together they point towards the existence of two\n$Z_{cs}(3985)$-like resonances instead of one, while the observations about the\nrole of scalar meson exchange in the $Z_{cs}(3985)$ might be relevant for the\n$P_{cs}(4459)$. Finally, the combination of axial meson exchange and flavor\nsymmetry breaking effects indicates that the isovector $J^{PC} = 0^{++}$\n$D^*\\bar{D}^*$ and the strange $J^P = 2^{+}$ $D^*\\bar{D}_s^*$ molecules are the\nmost attractive configurations and thus the most likely molecular partners of\nthe $Z_c(3900)$, $Z_c(4020)$ and $Z_{cs}(3985)$.\n"}
+{"abstract": "  The 44.7 ms X-ray pulsar in the supernova remnant G12.82-0.02/HESS J1813-178\nhas the second highest spin-down luminosity of known pulsars in the Galaxy,\nwith E-dot=5.6e37 erg/s. Using the Green Bank Telescope, we have detected radio\npulsations from PSR J1813-1749 at 4.4-10.2 GHz. The pulse is highly scattered,\nwith an exponential decay timescale \\tau longer than that of any other pulsar\nat these frequencies. A point source detected at this position by Dzib et al.\nin several observations with the Jansky Very Large Array can be attributed to\nthe pulsed emission. The steep dependence of \\tau on observing frequency\nexplains why all previous pulsation searches at lower frequencies failed\n(\\tau~0.25 s at 2 GHz). The large dispersion measure, DM=1087 pc/cc, indicates\na distance of either 6.2 or 12 kpc according to two widely used models of the\nelectron density distribution in the Galaxy. These disfavor a previously\nsuggested association with a young stellar cluster at the closer distance of\n4.8 kpc. The high X-ray measured column density of ~1e23/cm^2 also supports a\nlarge distance. If at d~12 kpc, HESS J1813-178 would be one of the most\nluminous TeV sources in the Galaxy.\n"}
+{"abstract": "  Two recent papers (Renou et al., arXiv:2101.10873, and Chen et al.,\narXiv:2103.08123) have indicated that complex numbers are necessary for quantum\ntheory. This short note is a comment on their result.\n"}
+{"abstract": "  This work analyzes the optical properties of a localized surface plasmon\n(LSP) spaser made of a dielectric active wire coated with a graphene monolayer.\nOur theoretical results, obtained by using rigorous electromagnetic methods,\nillustrate the non-radiative transfer between the active medium and the\nlocalized surface plasmons of the graphene. In particular, we focus on the\nlasing conditions and the tunability of the LSP spaser in two cases: when the\nwire is made of an infrared/THz transparent dielectric material and when it is\nmade of a metal-like material. We analyze the results by comparing them with\nanalytical expressions obtained by us using the quasistatic approximation. We\nshow that the studied systems present a high tunability of the spaser\nresonances with the geometrical parameters as well as with the chemical\npotential of the graphene.\n"}
+{"abstract": "  We prove positive mass theorems for asymptotically hyperbolic and\nasymptotically locally hyperbolic Riemannian manifolds with black-hole-type\nboundaries.\n"}
+{"abstract": "  Progress in making neural networks more robust against adversarial attacks is\nmostly marginal, despite the great efforts of the research community. Moreover,\nthe robustness evaluation is often imprecise, making it difficult to identify\npromising approaches. We analyze the classification decisions of 19 different\nstate-of-the-art neural networks trained to be robust against adversarial\nattacks. Our findings suggest that current untargeted adversarial attacks\ninduce misclassification towards only a limited amount of different classes.\nAdditionally, we observe that both over- and under-confidence in model\npredictions result in an inaccurate assessment of model robustness. Based on\nthese observations, we propose a novel loss function for adversarial attacks\nthat consistently improves attack success rate compared to prior loss functions\nfor 19 out of 19 analyzed models.\n"}
+{"abstract": "  Estimation of permutation entropy (PE) using Bayesian statistical methods is\npresented for systems where the ordinal pattern sampling follows an\nindependent, multinomial distribution. It is demonstrated that the PE posterior\ndistribution is closely approximated by a standard Beta distribution, whose\nhyperparameters can be estimated directly from moments computed analytically\nfrom observed ordinal pattern counts. Equivalence with expressions derived\npreviously using frequentist methods is also demonstrated. Because Bayesian\nestimation of PE naturally incorporates uncertainty and prior information, the\northodox requirement that $N \\gg D!$ is effectively circumvented, allowing PE\nto be estimated even for very short time series. Self-similarity tests on PE\nposterior distributions computed for a semiconductor laser with optical\nfeedback (SLWOF) system show its PE to vary periodically over time.\n"}
+{"abstract": "  Unconventional fermions, such as three-fold, four-fold, six-fold, and\neight-fold fermions have attracted intense attention in recent years. However,\nthe concrete materials hosting unconventional fermions are still in urgent\nscarcity. In this work, based first-principle calculations and symmetry\nanalysis, we reveal rich unconventional fermions in existing compound Re2C3 (Re\n= Y, La, Ce, Pr, Nd, Sm, Tb, Dy, Ho, Er, Tm, Yb, Lu). We show that these\ncompounds host quadratic dispersive three-fold (TP), linear dispersive\nfour-fold (FP) and six-fold points (SP) near the Fermi level in their electric\nband structures when spin-orbital coupling (SOC) is not included. Notably, the\nFP is charge-2 Dirac-like point. More importantly, among compound Re2C3, the\ncompound Yb2C3 has very clean band structure, and its unconventional fermions\nare closed to the Fermi level. We also find that a uniaxial strain can\ntransform the unconventional fermions into other types fermions, depending on\nthe directions of strain. When SOC is considered, a SP transform to an\neightfold degenerate point and a fourfold degenerate point. Overall, our work\nprovides a family of realistic materials to study the unconventional fermions.\n"}
+{"abstract": "  We condition a Brownian motion with arbitrary starting point $y \\in\n\\mathbb{R}$ on spending at most $1$ time unit below $0$ and provide an explicit\ndescription of the resulting process. In particular, we provide explicit\nformulas for the distributions of its last zero $g=g^y$ and of its occupation\ntime $\\Gamma=\\Gamma^y$ below $0$ as functions of $y$. This generalizes a result\nof Benjamini and Berestycki from 2011, which covers the special case $y=0$.\nAdditionally, we study the behavior of the distributions of $g^y$ and\n$\\Gamma^y$, respectively, for $y \\to \\pm\\infty$.\n"}
+{"abstract": "  Assessing sensitivity to unmeasured confounding is an important step in\nobservational studies, which typically estimate effects under the assumption\nthat all confounders are measured. In this paper, we develop a sensitivity\nanalysis framework for balancing weights estimators, an increasingly popular\napproach that solves an optimization problem to obtain weights that directly\nminimizes covariate imbalance. In particular, we adapt a sensitivity analysis\nframework using the percentile bootstrap for a broad class of balancing weights\nestimators. We prove that the percentile bootstrap procedure can, with only\nminor modifications, yield valid confidence intervals for causal effects under\nrestrictions on the level of unmeasured confounding. We also propose an\namplification to allow for interpretable sensitivity parameters in the\nbalancing weights framework. We illustrate our method through extensive real\ndata examples.\n"}
+{"abstract": "  Topological band theory has achieved great success in the high-throughput\nsearch for topological band structures both in paramagnetic and magnetic\ncrystal materials. However, a significant proportion of materials are\ntopologically trivial insulators at the Fermi level. In this paper, we show\nthat, remarkably, for a subset of the topologically trivial insulators, knowing\nonly their electron number and the Wyckoff positions of the atoms we can\nseparate them into two groups: the obstructed atomic insulator (OAI) and the\natomic insulator (AI). The interesting group, the OAI, have a center of charge\nnot localized on the atoms. Using the theory of topological quantum chemistry,\nin this work we first derive the necessary and sufficient conditions for a\ntopologically trivial insulator to be a filling enforced obstructed atomic\ninsulator (feOAI) in the 1651 Shubnikov space groups. Remarkably, the filling\nenforced criteria enable the identification of obstructed atomic bands without\nknowing the representations of the band structures. Hence, no ab-initio\ncalculations are needed for the filling enforced criteria, although they are\nneeded to obtain the band gaps. With the help of the Topological Quantum\nChemistry website, we have performed a high-throughput search for feOAIs and\nhave found that 957 ICSD entries (638 unique materials) are paramagnetic\nfeOAIs, among which 738 (475) materials have an indirect gap. The metallic\nobstructed surface states of feOAIs are also showcased by several material\nexamples.\n"}
+{"abstract": "  Orthogonal drawings, i.e., embeddings of graphs into grids, are a classic\ntopic in Graph Drawing. Often the goal is to find a drawing that minimizes the\nnumber of bends on the edges. A key ingredient for bend minimization algorithms\nis the existence of an orthogonal representation that describes such drawings\ncombinatorially by only listing the angles between the edges around each vertex\nand the directions of bends on the edges, but neglecting any kind of geometric\ninformation such as vertex coordinates or edge lengths.\n  We generalize this idea to ortho-radial representations of ortho-radial\ndrawings, which are embeddings into an ortho-radial grid, whose gridlines are\nconcentric circles around the origin and straight-line spokes emanating from\nthe origin but excluding the origin itself. Unlike the orthogonal case, there\nexist ortho-radial representations that do not admit a corresponding drawing,\nfor example so-called strictly monotone cycles. An ortho-radial drawing is\ncalled valid if it does not contain a strictly monotone cycle. Our first result\nis that an ortho-radial representation admits a corresponding drawing if and\nonly if it is valid. Previously such a characterization was only known for\northo-radial drawings of paths, cycles, and theta graphs, and in the special\ncase of rectangular drawings of cubic graphs, where the contour of each face is\nrequired to be a rectangle. Further, we give a quadratic-time algorithm that\ntests for an ortho-radial representation whether it is valid, and we show how\nto draw a valid ortho-radial representation in the same running time.\n  Altogether, this reduces the problem of computing a minimum-bend ortho-radial\ndrawing to the task of computing a valid ortho-radial representation with the\nminimum number of bends, and hence establishes an ortho-radial analogue of the\ntopology-shape-metrics framework for planar orthogonal drawings by Tamassia.\n"}
+{"abstract": "  Engineering materials with high thermal conductivity are of fundamental\ninterest for efficiently dissipating heat in micro/nanoelectronics. Using first\nprinciples computations we report an ultra-high thermal conductivity of 2090\nWm-1K-1 (1395 Wm-1K-1) for hexagonal pure (natural) BC6N(h-BC6N). This value is\namong the highest thermal conductivities known after diamond and cubic boron\narsenide. This ultra-high lattice thermal conductivity (k) is mainly attributed\nwith high phonon group velocities of both acoustic and optical phonons arising\nfrom strong C-C and B-N bonds as well as the light atomic mass of the\nconstituent elements such as boron (B), carbon (C) and nitrogen (N). We also\nreport size dependent thermal conductivity of h-BC6N nanostructures by\nincluding boundary scattering. At room temperature (300 K) and at nanoscale\nlength (L) of 100 nm, a high k value of 175 Wm-1K-1 is observed (higher than\nthe bulk k value of silicon). Optical phonons with large group velocities are\nmainly responsible for this high thermal conductivity in h-BC6N nanostructures.\nHigh thermal conductivity of h-BC6N makes it a candidate material for heat\ndissipation in micro/nano thermal management applications.\n"}
+{"abstract": "  The generic fluidity observed in the nature of political protest movements\nacross the world during the last decade weigh heavily with the presence of\nsocial media. As such, there is a possibility to study the contemporary\nmovements with an interdisciplinary approach combining computational analytics\nwith social science perspectives. The present study has put efforts to\nunderstand such dynamics in the context of the ongoing nationwide movement in\nIndia opposing the NRC-CAA enactment. The transformative nature of individual\ndiscontent into collective mobilization, especially with a reflective\nintervention in social media across a sensitive region of the nation state, is\npresented here with a combination of qualitative (fieldwork) and quantitative\n(computing) techniques. The study is augmented further by the primary data\ngeneration coupled with real-time application of analytical approaches.\n"}
+{"abstract": "  We explore the constraints on the three-nucleon force (3NF) of chiral\neffective field theory ($\\chi$EFT) that are provided by bound-state observables\nin the $A=3$ and $A=4$ sectors. Our statistically rigorous analysis\nincorporates experimental error, computational method uncertainty, and the\nuncertainty due to truncation of the $\\chi$EFT expansion at\nnext-to-next-to-leading order. A consistent solution for the ${}^3$H binding\nenergy, the ${}^4$He binding energy and radius, and the ${}^3$H $\\beta$-decay\nrate can only be obtained if $\\chi$EFT truncation errors are included in the\nanalysis. All of these except the $\\beta$-decay rate give essentially\ndegenerate constraints on the 3NF low-energy constants, so it is crucial for\nestimating these parameters. We use eigenvector continuation for fast and\naccurate emulation of No-Core Shell Model calculations of the considered\nfew-nucleon observables. This facilitates sampling of the posterior probability\ndistribution, allowing us to also determine the distributions of the\nhyperparameters that quantify the truncation error. We find a $\\chi$EFT\nexpansion parameter of $Q=0.33 \\pm 0.06$ for these observables.\n"}
+{"abstract": "  An instance of the multiperiod binary knapsack problem (MPBKP) is given by a\nhorizon length $T$, a non-decreasing vector of knapsack sizes $(c_1, \\ldots,\nc_T)$ where $c_t$ denotes the cumulative size for periods $1,\\ldots,t$, and a\nlist of $n$ items. Each item is a triple $(r, q, d)$ where $r$ denotes the\nreward of the item, $q$ its size, and $d$ its time index (or, deadline). The\ngoal is to choose, for each deadline $t$, which items to include to maximize\nthe total reward, subject to the constraints that for all $t=1,\\ldots,T$, the\ntotal size of selected items with deadlines at most $t$ does not exceed the\ncumulative capacity of the knapsack up to time $t$. We also consider the\nmultiperiod binary knapsack problem with soft capacity constraints (MPBKP-S)\nwhere the capacity constraints are allowed to be violated by paying a penalty\nthat is linear in the violation. The goal is to maximize the total profit,\ni.e., the total reward of selected items less the total penalty. Finally, we\nconsider the multiperiod binary knapsack problem with soft stochastic capacity\nconstraints (MPBKP-SS), where the non-decreasing vector of knapsack sizes\n$(c_1, \\ldots, c_T)$ follow some arbitrary joint distribution but we are given\naccess to the profit as an oracle, and we choose a subset of items to maximize\nthe total expected profit, i.e., the total reward less the total expected\npenalty. For MPBKP, we exhibit a fully polynomial-time approximation scheme\nwith runtime\n$\\tilde{\\mathcal{O}}\\left(\\min\\left\\{n+\\frac{T^{3.25}}{\\epsilon^{2.25}},n+\\frac{T^{2}}{\\epsilon^{3}},\\frac{nT}{\\epsilon^2},\\frac{n^2}{\\epsilon}\\right\\}\\right)$\nthat achieves $(1+\\epsilon)$ approximation; for MPBKP-S, the $(1+\\epsilon)$\napproximation can be achieved in $\\mathcal{O}\\left(\\frac{n\\log\nn}{\\epsilon}\\cdot\\min\\left\\{\\frac{T}{\\epsilon},n\\right\\}\\right)$; for MPBKP-SS,\na greedy algorithm is a 2-approximation when items have the same size.\n"}
+{"abstract": "  This manuscript focus on in the transmission problem for one dimensional\nwaves with nonlinear weights on the frictional damping and time-varying delay.\nWe prove global existence of solutions using Kato's variable norm technique and\nwe show the exponential stability by the energy method with the construction of\na suitable Lyapunov functional.\n"}
+{"abstract": "  Predictive state representations (PSRs) are models of controlled non-Markov\nobservation sequences which exhibit the same generative process governing POMDP\nobservations without relying on an underlying latent state. In that respect, a\nPSR is indistinguishable from the corresponding POMDP. However, PSRs\nnotoriously ignore the notion of rewards, which undermines the general utility\nof PSR models for control, planning, or reinforcement learning. Therefore, we\ndescribe a sufficient and necessary accuracy condition which determines whether\na PSR is able to accurately model POMDP rewards, we show that rewards can be\napproximated even when the accuracy condition is not satisfied, and we find\nthat a non-trivial number of POMDPs taken from a well-known third-party\nrepository do not satisfy the accuracy condition. We propose reward-predictive\nstate representations (R-PSRs), a generalization of PSRs which accurately\nmodels both observations and rewards, and develop value iteration for R-PSRs.\nWe show that there is a mismatch between optimal POMDP policies and the optimal\nPSR policies derived from approximate rewards. On the other hand, optimal R-PSR\npolicies perfectly match optimal POMDP policies, reconfirming R-PSRs as\naccurate state-less generative models of observations and rewards.\n"}
+{"abstract": "  This paper presents Gem, a model-agnostic approach for providing\ninterpretable explanations for any GNNs on various graph learning tasks.\nSpecifically, we formulate the problem of providing explanations for the\ndecisions of GNNs as a causal learning task. Then we train a causal explanation\nmodel equipped with a loss function based on Granger causality. Different from\nexisting explainers for GNNs, Gem explains GNNs on graph-structured data from a\ncausal perspective. It has better generalization ability as it has no\nrequirements on the internal structure of the GNNs or prior knowledge on the\ngraph learning tasks. In addition, Gem, once trained, can be used to explain\nthe target GNN very quickly. Our theoretical analysis shows that several recent\nexplainers fall into a unified framework of additive feature attribution\nmethods. Experimental results on synthetic and real-world datasets show that\nGem achieves a relative increase of the explanation accuracy by up to $30\\%$\nand speeds up the explanation process by up to $110\\times$ as compared to its\nstate-of-the-art alternatives.\n"}
+{"abstract": "  We report new precision measurements of the elastic electron-proton\nscattering cross section for momentum transfer squared (Q$^2$) up to\n15.75~\\gevsq. These data allow for improved extraction of the proton magnetic\nform factor at high Q$^2$ and nearly double the Q$^2$ range of direct\nlongitudinal/transverse separated cross sections. A comparison of our results\nto polarization measurements establishes the presence of hard two-photon\nexchange in the $e$-$p$ elastic scattering cross section at greater than 95\\%\nconfidence level for Q$^2$ up to 8 (GeV/c)$^2$.\n"}
+{"abstract": "  Intuition dictates that a very long, very thin cavity (e.g., a fiber optic\ncable) could perhaps be modeled as an approximately one dimensional system. In\nthis paper we rigorously explore the validity of such intuition from the\nperspective of a localized probe coupling to a quantum field inside a cavity\n(e.g., an atom or an Unruh-DeWitt particle detector in a fiber optic cable). To\ndo so, we introduce the notion of subfield decomposition in which a $D+1$\ndimensional quantum field in an axially-symmetric cavity can be reduced to an\ninfinite collection of uncoupled, massive $1+1$ dimensional fields. We show\nthat the ability to approximate a higher-dimensional scenario by a $1+1$\ndimensional model is equivalent to making a certain change of the probe's shape\nin the higher-dimensional space. The approximation is justified whenever this\nchange of shape is \"small enough\". In this light, we identify the dynamically\nrelevant norm by which the magnitude of these changes in probe shape ought to\nbe judged. Finally, we explore this approximation in particular setups\ncorresponding to quantum optics and superconducting circuits.\n"}
+{"abstract": "  Sound localization aims to find the source of the audio signal in the visual\nscene. However, it is labor-intensive to annotate the correlations between the\nsignals sampled from the audio and visual modalities, thus making it difficult\nto supervise the learning of a machine for this task. In this work, we propose\nan iterative contrastive learning framework that requires no data annotations.\nAt each iteration, the proposed method takes the 1) localization results in\nimages predicted in the previous iteration, and 2) semantic relationships\ninferred from the audio signals as the pseudo-labels. We then use the\npseudo-labels to learn the correlation between the visual and audio signals\nsampled from the same video (intra-frame sampling) as well as the association\nbetween those extracted across videos (inter-frame relation). Our iterative\nstrategy gradually encourages the localization of the sounding objects and\nreduces the correlation between the non-sounding regions and the reference\naudio. Quantitative and qualitative experimental results demonstrate that the\nproposed framework performs favorably against existing unsupervised and\nweakly-supervised methods on the sound localization task.\n"}
+{"abstract": "  We examine adaptive strategies adopted by vehicles for route selection\nen-route in transportation networks. By studying a model of two-dimensional\ncellular automata, we model vehicles characterized by a parameter called\npath-greediness, which corresponds to the tendency for them to travel to their\ndestinations via the shortest path. The path-greediness of each individual\nvehicle is updated based on the local traffic conditions, to either keep the\nvehicle travels via a shorter path in an un-congested region or to explore\nlonger diverted paths in a congested region. We found that the optimal number\nof steps to trigger an update of path-greediness is dependent on the density of\nvehicles, and the magnitude of path-greediness increment affects the\nmacroscopic traffic conditions of the system. To better coordinate vehicles in\ndenser networks, the update on the tendency for vehicles to travel via the\nshorter paths should be gradual and less frequent.\n"}
+{"abstract": "  We realize and investigate a nonlinear metasurface taking advantage of\nintersubband transitions in ultranarrow GaN/AlN multi-quantum well\nheterostructures. Owing to huge band offsets, the structures offer resonant\ntransitions in the telecom window around 1.55 $\\mu$m. These heterostructures\nare functionalized with an array of plasmonic antennas featuring\ncross-polarized resonances at these near-infrared wavelengths and their second\nharmonic. This kind of nonlinear metasurface allows for substantial\nsecond-harmonic generation at normal incidence which is completely absent for\nan antenna array without the multi-quantum well structure underneath. While the\nsecond harmonic is originally radiated only into the plane of the quantum\nwells, a proper geometrical arrangement of the plasmonic elements permits to\nredirect the second-harmonic light to free-space radiation, which is emitted\nperpendicular to the surface.\n"}
+{"abstract": "  This work deals with thick branes in bulk with a single extra dimension\nmodeled by a two-field configuration. We first consider the inclusion of the\ncuscuton to also control the dynamics of one of the fields and investigate how\nit contributes to change the internal structure of the configuration in two\ndistinct situations, with the standard and the asymmetric Bloch brane. The\nresults show that the branes get a rich internal structure, with the geometry\npresenting a novel behavior which is also governed by the parameter that\ncontrols the strength of the cuscuton term. We also study the case where the\ndynamics of one of the two fields is only described by the cuscuton. All the\nmodels support analytical solutions which are stable against fluctuations in\nthe metric, and the main results unveil significant modifications in the warp\nfactor and energy density of the branes.\n"}
+{"abstract": "  We study the metric backreaction of mass and angular momentum accretion on\nblack holes. We first develop the formalism of monopole and dipole linear\ngravitational perturbations around the Schwarzschild black holes in the\nEddington-Finkelstein coordinates against the generic time-dependent matters.\nWe derive the relation between the time dependence of the mass and angular\nmomentum of the black hole and the energy-momentum tensors of accreting\nmatters. As a concrete example, we apply our formalism to the Blandford-Znajek\nprocess around the slowly rotating black holes. We find that the time\ndependence of the monopole and dipole perturbations can be interpreted as the\nslowly rotating Kerr metric with time-dependent mass and spin parameters, which\nare determined from the energy and angular momentum extraction rates of the\nBlandford-Znajek process. We also show that the Komar angular momentum and the\narea of the apparent horizon are decreasing and increasing in time,\nrespectively, while they are consistent with the Blandford-Znajek argument of\nenergy extraction in term of black hole mechanics if we regard the\ntime-dependent mass parameter as the energy of the black hole.\n"}
+{"abstract": "  Offline reinforcement learning (RL) enables learning policies using\npre-collected datasets without environment interaction, which provides a\npromising direction to make RL usable in real-world systems. Although recent\noffline RL studies have achieved much progress, existing methods still face\nmany practical challenges in real-world system control tasks, such as\ncomputational restriction during agent training and the requirement of extra\ncontrol flexibility. Model-based planning framework provides an attractive\nsolution for such tasks. However, most model-based planning algorithms are not\ndesigned for offline settings. Simply combining the ingredients of offline RL\nwith existing methods either provides over-restrictive planning or leads to\ninferior performance. We propose a new light-weighted model-based offline\nplanning framework, namely MOPP, which tackles the dilemma between the\nrestrictions of offline learning and high-performance planning. MOPP encourages\nmore aggressive trajectory rollout guided by the behavior policy learned from\ndata, and prunes out problematic trajectories to avoid potential\nout-of-distribution samples. Experimental results show that MOPP provides\ncompetitive performance compared with existing model-based offline planning and\nRL approaches.\n"}
+{"abstract": "  The design of metamaterials which support unique optical responses is the\nbasis for most thin-film nanophotonics applications. In practice this inverse\ndesign problem can be difficult to solve systematically due to the large design\nparameter space associated with general multi-layered systems. We apply\nconvolutional neural networks, a subset of deep machine learning, as a tool to\nsolve this inverse design problem for metamaterials composed of stacks of thin\nfilms. We demonstrate the remarkable ability of neural networks to probe the\nlarge global design space (up to $10^{12}$ possible parameter combinations) and\nresolve all relationships between metamaterial structure and corresponding\nellipsometric and reflectance / transmittance spectra. The applicability of the\napproach is further expanded to include the inverse design of synthetic\nengineered spectra in general design scenarios. Furthermore, this approach is\ncompared with traditional optimization methods. We find an increase in the\nrelative optimization efficiency of the networks with increasing total layer\nnumber, revealing the advantage of the machine learning approach in\nmany-layered systems where traditional methods become impractical.\n"}
+{"abstract": "  This paper addresses the challenge of learning to do procedural reasoning\nover text to answer \"What if...\" questions. We propose a novel relational\ngating network that learns to filter the key entities and relationships and\nlearns contextual and cross representations of both procedure and question for\nfinding the answer. Our relational gating network contains an entity gating\nmodule, relation gating module, and contextual interaction module. These\nmodules help in solving the \"What if...\" reasoning problem. We show that\nmodeling pairwise relationships helps to capture higher-order relations and\nfind the line of reasoning for causes and effects in the procedural\ndescriptions. Our proposed approach achieves the state-of-the-art results on\nthe WIQA dataset.\n"}
+{"abstract": "  We present MISTIQS, a Multiplatform Software for Time-dependent Quantum\nSimulations. MISTIQS delivers end-to-end functionality for simulating the\nquantum many-body dynamics of systems governed by time-dependent Heisenberg\nHamiltonians across multiple quantum computing platforms. It provides\nhigh-level programming functionality for generating intermediate\nrepresentations of quantum circuits which can be translated into a variety of\nindustry-standard representations. Furthermore, it offers a selection of\ncircuit compilation and optimization methods and facilitates execution of the\nquantum circuits on currently available cloud-based quantum computing backends.\nMISTIQS serves as an accessible and highly flexible research and education\nplatform, allowing a broader community of scientists and students to perform\nquantum many-body dynamics simulations on current quantum computers.\n"}
+{"abstract": "  Learning a generative model of visual information with sparse and\ncompositional features has been a challenge for both theoretical neuroscience\nand machine learning communities. Sparse coding models have achieved great\nsuccess in explaining the receptive fields of mammalian primary visual cortex\nwith sparsely activated latent representation. In this paper, we focus on a\nrecently proposed model, sparse coding variational autoencoder (SVAE) (Barello\net al., 2018), and show that the end-to-end training scheme of SVAE leads to a\nlarge group of decoding filters not fully optimized with noise-like receptive\nfields. We propose a few heuristics to improve the training of SVAE and show\nthat a unit $L_2$ norm constraint on the decoder is critical to produce sparse\ncoding filters. Such normalization can be considered as local lateral\ninhibition in the cortex. We verify this claim empirically on both natural\nimage patches and MNIST dataset and show that projection of the filters onto\nunit norm drastically increases the number of active filters. Our results\nhighlight the importance of weight normalization for learning sparse\nrepresentation from data and suggest a new way of reducing the number of\ninactive latent components in VAE learning.\n"}
+{"abstract": "  In this article we derive Biot-Savar law for exterior 2D domain. No-slip\ncondition in terms of vorticity is expressed in integral form. For\nnon-stationary fluid dynamics equations no-slip condition generates affine\ninvariant manifold and no-slip integral relations on vorticity can be\ntransferred to Robin-type boundary condition.\n"}
+{"abstract": "  We consider the semilinear heat equation $$\\partial_t u -\\Delta u =f(u),\n\\quad (x,t)\\in \\mathbb{R}^N\\times [0,T),\\qquad (1)$$ with\n$f(u)=|u|^{p-1}u\\log^a (2+u^2)$, where $p>1$ is Sobolev subcritical and $a\\in\n\\mathbb{R}$. We first show an upper bound for any blow-up solution of (1).\nThen, using this estimate and the logarithmic property, we prove that the exact\nblow-up rate of any singular solution of (1) is given by the ODE solution\nassociated with (1), namely $u' =|u|^{p-1}u\\log^a (2+u^2)$. In other terms, all\nblow-up solutions in the Sobolev subcritical range are Type I solutions. Up to\nour knowledge, this is the first determination of the blow-up rate for a\nsemilinear heat equation where the main nonlinear term is not homogeneous.\n"}
+{"abstract": "  In the future the International Linear Collider (ILC), a helical\nundulator-based polarized positron source, is expected to be chosen. A high\nenergy electron beam passes through a superconducting helical undulator in\norder to create circularly polarized photons which will be directed to a\nconversion target, resulting in electron-positron pairs. The resulting positron\nbeam is longitudinally polarized. Since the photons are produced with an\nopening angle and pass through a long superconducting helical undulator, some\nof these photons will strike the undulator walls. Therefore photon masks must\nbe placed along the undulator line in order to keep the power deposited in the\nundulator walls below the acceptable limit of 1W/m. The baseline design of the\nILC is focused on 250 GeV center-of-mass energy and upgrade to center-of-mass\nenergies of 350 and 500 GeV is foreseen. This paper shows a detailed study of\nthe ideal power deposited along the masks for both 350 and 500 GeV\ncenter-of-mass energies.\n"}
+{"abstract": "  The list of physically motivated urn models that can be solved in terms of\nclassical orthogonal polynomials is very small. It includes a model proposed by\nD. Bernoulli and further analyzed by S. Laplace and a model proposed by P. and\nT. Ehrenfest and eventually connected with the Krawtchouk and Hahn polynomials.\nThis connection was reversed recently in the case of the Jacobi polynomials\nwhere a rather contrived, and later a simpler urn model was proposed. Here we\nconsider an urn model going with the Jacobi-Pi\\~neiro multiple orthogonal\npolynomials. These polynomials have recently been put forth in connection with\na stochastic matrix.\n"}
+{"abstract": "  In this article, we take into account our previous calculations based on the\nQCD sum rules, and tentatively assign the $X(4630)$ as the\n$D_s^*\\bar{D}_{s1}-D_{s1}\\bar{D}_s^*$ tetraquark molecular state or\n$[cs]_P[\\bar{c}\\bar{s}]_A+[cs]_A[\\bar{c}\\bar{s}]_P$ tetraquark state with the\n$J^{PC}=1^{-+}$, and assign the $X(3915)$ and $X(4500)$ as the 1S and 2S\n$[cs]_A[\\bar{c}\\bar{s}]_A$ tetraquark states respectively with the\n$J^{PC}=0^{++}$. Then we extend our previous works to investigate the LHCb's\nnew tetraquark candidate $X(4685)$ as the first radial excited state of the\n$X(4140)$ with the QCD sum rules, and obtain the mass\n$M_{X}=4.70\\pm0.12\\,\\rm{GeV}$, which is in very good agreement with the\nexperimental value $4684 \\pm 7 {}^{+13}_{-16}\\,\\rm{MeV}$. Furthermore, we\ninvestigate the two-meson scattering state contributions in details, and\nobserve that the two-meson scattering states alone cannot saturate the QCD sum\nrules, the contributions of the tetraquark states play an un-substitutable\nrole, we can saturate the QCD sum rules with or without the two-meson\nscattering states.\n"}
+{"abstract": "  An anytime decoding algorithm for tree codes using Monte-Carlo tree search is\nproposed. The meaning of anytime decoding here is twofold: 1) the decoding\nalgorithm is an anytime algorithm, whose decoding performance improves as more\ncomputational resource, measured by decoding time, is allowed, and 2) the\nproposed decoding algorithm can approximate the maximum-likelihood sequence\ndecoding of tree codes, which has the anytime reliability when the code is\nproperly designed. The above anytime properties are demonstrated through\nexperiments. The proposed method may be extended to the decoding of\nconvolutional codes and block codes by Monte-Carlo trellis search, to enable\nsmooth complexity-performance trade-offs in these decoding tasks. Some other\nextensions and possible improvements are also discussed.\n"}
+{"abstract": "  Dynamic systems that consist of a set of interacting elements can be\nabstracted as temporal networks. Recently, higher-order patterns that involve\nmultiple interacting nodes have been found crucial to indicate domain-specific\nlaws of different temporal networks. This posts us the challenge of designing\nmore sophisticated hypergraph models for these higher-order patterns and the\nassociated new learning algorithms. Here, we propose the first model, named\nHIT, for higher-order pattern prediction in temporal hypergraphs. Particularly,\nwe focus on predicting three types of common but important interaction patterns\ninvolving three interacting elements in temporal networks, which could be\nextended to even higher-order patterns. HIT extracts the structural\nrepresentation of a node triplet of interest on the temporal hypergraph and\nuses it to tell what type of, when, and why the interaction expansion could\nhappen in this triplet. HIT could achieve significant improvement(averaged 20%\nAUC gain to identify the interaction type, uniformly more accurate time\nestimation) compared to both heuristic and other neural-network-based baselines\non 5 real-world large temporal hypergraphs. Moreover, HIT provides a certain\ndegree of interpretability by identifying the most discriminatory structural\nfeatures on the temporal hypergraphs for predicting different higher-order\npatterns.\n"}
+{"abstract": "  Factorizing speech as disentangled speech representations is vital to achieve\nhighly controllable style transfer in voice conversion (VC). Conventional\nspeech representation learning methods in VC only factorize speech as speaker\nand content, lacking controllability on other prosody-related factors.\nState-of-the-art speech representation learning methods for more speechfactors\nare using primary disentangle algorithms such as random resampling and ad-hoc\nbottleneck layer size adjustment,which however is hard to ensure robust speech\nrepresentationdisentanglement. To increase the robustness of highly\ncontrollable style transfer on multiple factors in VC, we propose a\ndisentangled speech representation learning framework based on adversarial\nlearning. Four speech representations characterizing content, timbre, rhythm\nand pitch are extracted, and further disentangled by an adversarial\nMask-And-Predict (MAP)network inspired by BERT. The adversarial network is used\ntominimize the correlations between the speech representations,by randomly\nmasking and predicting one of the representationsfrom the others. Experimental\nresults show that the proposedframework significantly improves the robustness\nof VC on multiple factors by increasing the speech quality MOS from 2.79 to3.30\nand decreasing the MCD from 3.89 to 3.58.\n"}
+{"abstract": "  Data augmentation is an effective way to improve the performance of many\nneural text generation models. However, current data augmentation methods need\nto define or choose proper data mapping functions that map the original samples\ninto the augmented samples. In this work, we derive an objective to formulate\nthe problem of data augmentation on text generation tasks without any use of\naugmented data constructed by specific mapping functions. Our proposed\nobjective can be efficiently optimized and applied to popular loss functions on\ntext generation tasks with a convergence rate guarantee. Experiments on five\ndatasets of two text generation tasks show that our approach can approximate or\neven surpass popular data augmentation methods.\n"}
+{"abstract": "  Biomolecular condensates are small droplets forming spontaneously in\nbiological cells via phase separation. They play a role in many cellular\nprocesses, but it is unclear how cells control them. Cellular regulation often\nrelies on post-translational modifications of proteins. For biomolecular\ncondensates, such chemical modifications could alter the molecular interaction\nof key condensate components. We here test this idea using a theoretical model\nbased on non-equilibrium thermodynamics. In particular, we describe the\nchemical reactions using transition-state theory, which accounts for the\nnon-ideality of phase separation. We identify that fast control, like in cell\nsignaling, is only possible when external energy input drives the reaction out\nof equilibrium. If this reaction differs inside and outside the droplet, it is\neven possible to control droplet sizes. Such an imbalance in the reaction could\nbe created by enzymes localizing to the droplet. Since this situation is\ntypical inside cells, we speculate that our proposed mechanism is used to\nstabilize multiple droplets with independently controlled size and count. Our\nmodel provides a novel and thermodynamically consistent framework for\ndescribing droplets subject to non-equilibrium chemical reactions.\n"}
+{"abstract": "  Fe, Mg, and O are among the most abundant elements in terrestrial planets.\nWhile the behavior of the Fe-O, Mg-O, and Fe-Mg binary systems under pressure\nhave been investigated, there are still very few studies of the Fe-Mg-O ternary\nsystem at relevant Earth's core and super-Earth's mantle pressures. Here, we\nuse the adaptive genetic algorithm (AGA) to study ternary Fe$_x$Mg$_y$O$_z$\nphases in a wide range of stoichiometries at 200 GPa and 350 GPa. We discovered\nthree dynamically stable phases with stoichiometries FeMg$_2$O$_4$,\nFe$_2$MgO$_4$, and FeMg$_3$O$_4$ with lower enthalpy than any known combination\nof Fe-Mg-O high-pressure compounds at 350 GPa. With the discovery of these\nphases, we construct the Fe-Mg-O ternary convex hull. We further clarify the\ncomposition- and pressure-dependence of structural motifs with the analysis of\nthe AGA-found stable and metastable structures. Analysis of binary and ternary\nstable phases suggest that O, Mg, or both could stabilize a BCC iron alloy at\ninner core pressures.\n"}
+{"abstract": "  Contemporary grasp detection approaches employ deep learning to achieve\nrobustness to sensor and object model uncertainty. The two dominant approaches\ndesign either grasp-quality scoring or anchor-based grasp recognition networks.\nThis paper presents a different approach to grasp detection by treating it as\nkeypoint detection in image-space. The deep network detects each grasp\ncandidate as a pair of keypoints, convertible to the grasp representationg =\n{x, y, w, {\\theta}} T , rather than a triplet or quartet of corner points.\nDecreasing the detection difficulty by grouping keypoints into pairs boosts\nperformance. To promote capturing dependencies between keypoints, a non-local\nmodule is incorporated into the network design. A final filtering strategy\nbased on discrete and continuous orientation prediction removes false\ncorrespondences and further improves grasp detection performance. GKNet, the\napproach presented here, achieves a good balance between accuracy and speed on\nthe Cornell and the abridged Jacquard datasets (96.9% and 98.39% at 41.67 and\n23.26 fps). Follow-up experiments on a manipulator evaluate GKNet using 4 types\nof grasping experiments reflecting different nuisance sources: static grasping,\ndynamic grasping, grasping at varied camera angles, and bin picking. GKNet\noutperforms reference baselines in static and dynamic grasping experiments\nwhile showing robustness to varied camera viewpoints and moderate clutter. The\nresults confirm the hypothesis that grasp keypoints are an effective output\nrepresentation for deep grasp networks that provide robustness to expected\nnuisance factors.\n"}
+{"abstract": "  One of the challenges faced by many video providers is the heterogeneity of\nnetwork specifications, user requirements, and content compression performance.\nThe universal solution of a fixed bitrate ladder is inadequate in ensuring a\nhigh quality of user experience without re-buffering or introducing annoying\ncompression artifacts. However, a content-tailored solution, based on\nextensively encoding across all resolutions and over a wide quality range is\nhighly expensive in terms of computational, financial, and energy costs.\nInspired by this, we propose an approach that exploits machine learning to\npredict a content-optimized bitrate ladder. The method extracts spatio-temporal\nfeatures from the uncompressed content, trains machine-learning models to\npredict the Pareto front parameters, and, based on that, builds the ladder\nwithin a defined bitrate range. The method has the benefit of significantly\nreducing the number of encodes required per sequence. The presented results,\nbased on 100 HEVC-encoded sequences, demonstrate a reduction in the number of\nencodes required when compared to an exhaustive search and an\ninterpolation-based method, by 89.06% and 61.46%, respectively, at the cost of\nan average Bj{\\o}ntegaard Delta Rate difference of 1.78% compared to the\nexhaustive approach. Finally, a hybrid method is introduced that selects either\nthe proposed or the interpolation-based method depending on the sequence\nfeatures. This results in an overall 83.83% reduction of required encodings at\nthe cost of an average Bj{\\o}ntegaard Delta Rate difference of 1.26%.\n"}
+{"abstract": "  We will discuss about a possible method of using the cubit rod by the\narchitects and the surveyors of Ancient Egypt to measure and draw lengths,\ncomparing it with the other interpretations present in Literature. Instead of\nthe modern decimal notation, which sees the use of comma to represent a number\nor a measure, at that time there was a wide use of fractions in calculations.\nThe current work proposes that, through the cubit rod and its partitions of the\nfinger into fractions, it could be possible to obtain very accurate\nmeasurements.\n"}
+{"abstract": "  We propose a roadmap for bootstrapping conformal field theories (CFTs)\ndescribed by gauge theories in dimensions $d>2$. In particular, we provide a\nsimple and workable answer to the question of how to detect the gauge group in\nthe bootstrap calculation. Our recipe is based on the notion of\n\\emph{decoupling operator}, which has a simple (gauge) group theoretical\norigin, and is reminiscent of the null operator of $2d$ Wess-Zumino-Witten CFTs\nin higher dimensions. Using the decoupling operator we can efficiently detect\nthe rank (i.e. color number) of gauge groups, e.g., by imposing gap conditions\nin the CFT spectrum. We also discuss the physics of the equation of motion,\nwhich has interesting consequences in the CFT spectrum as well. As an\napplication of our recipes, we study a prototypical critical gauge theory,\nnamely the scalar QED which has a $U(1)$ gauge field interacting with critical\nbosons. We show that the scalar QED can be solved by conformal bootstrap,\nnamely we have obtained its kinks and islands in both $d=3$ and $d=2+\\epsilon$\ndimensions.\n"}
+{"abstract": "  Self-Attention has become prevalent in computer vision models. Inspired by\nfully connected Conditional Random Fields (CRFs), we decompose self-attention\ninto local and context terms. They correspond to the unary and binary terms in\nCRF and are implemented by attention mechanisms with projection matrices. We\nobserve that the unary terms only make small contributions to the outputs, and\nmeanwhile standard CNNs that rely solely on the unary terms achieve great\nperformances on a variety of tasks. Therefore, we propose Locally Enhanced\nSelf-Attention (LESA), which enhances the unary term by incorporating it with\nconvolutions, and utilizes a fusion module to dynamically couple the unary and\nbinary operations. In our experiments, we replace the self-attention modules\nwith LESA. The results on ImageNet and COCO show the superiority of LESA over\nconvolution and self-attention baselines for the tasks of image recognition,\nobject detection, and instance segmentation. The code is made publicly\navailable.\n"}
+{"abstract": "  In this work we establish that the Inozemtsev system is the Seiberg-Witten\nintegrable system encoding the Coulomb branch physics of 4d $\\mathcal{N}=2$\nUSp(2N) gauge theory with four fundamental and (for $N \\geq 2$) one\nantisymmetric tensor hypermultiplets. We describe the transformation from the\nspectral curves and canonical one-form of the Inozemtsev system in the $N=1$\nand $N=2$ cases to the Seiberg-Witten curves and differentials explicitly,\nalong with the explicit matching of the modulus of the elliptic curve of\nspectral parameters to the gauge coupling of the field theory, and of the\ncouplings of the Inozemtsev system to the field theory mass parameters. This\nresult is a particular instance of a more general correspondence between\ncrystallographic elliptic Calogero-Moser systems with Seiberg-Witten integrable\nsystems, which will be explored in future work.\n"}
+{"abstract": "  We present a multi-camera 3D pedestrian detection method that does not need\nto train using data from the target scene. We estimate pedestrian location on\nthe ground plane using a novel heuristic based on human body poses and person's\nbounding boxes from an off-the-shelf monocular detector. We then project these\nlocations onto the world ground plane and fuse them with a new formulation of a\nclique cover problem. We also propose an optional step for exploiting\npedestrian appearance during fusion by using a domain-generalizable person\nre-identification model. We evaluated the proposed approach on the challenging\nWILDTRACK dataset. It obtained a MODA of 0.569 and an F-score of 0.78, superior\nto state-of-the-art generalizable detection techniques.\n"}
+{"abstract": "  Inefficient traffic control may cause numerous problems such as traffic\ncongestion and energy waste. This paper proposes a novel multi-agent\nreinforcement learning method, named KS-DDPG (Knowledge Sharing Deep\nDeterministic Policy Gradient) to achieve optimal control by enhancing the\ncooperation between traffic signals. By introducing the knowledge-sharing\nenabled communication protocol, each agent can access to the collective\nrepresentation of the traffic environment collected by all agents. The proposed\nmethod is evaluated through two experiments respectively using synthetic and\nreal-world datasets. The comparison with state-of-the-art reinforcement\nlearning-based and conventional transportation methods demonstrate the proposed\nKS-DDPG has significant efficiency in controlling large-scale transportation\nnetworks and coping with fluctuations in traffic flow. In addition, the\nintroduced communication mechanism has also been proven to speed up the\nconvergence of the model without significantly increasing the computational\nburden.\n"}
+{"abstract": "  We analyze reflection positive representations in terms of positive Hankel\noperators. This is motivated by the fact that positive Hankel operators are\ndescribed in terms of their Carleson measures, whereas the compatibility\ncondition between representations and reflection positive Hilbert spaces is\nquite intricate. This leads us to the concept of a Hankel positive\nrepresentation of triples $(G,S,\\tau)$, where $G$ is a group, $\\tau$ an\ninvolutive automorphism of $G$ and $S \\subseteq G$ a subsemigroup with $\\tau(S)\n= S^{-1}$. For the triples $(\\mathbb Z,\\mathbb N,-id_{\\mathbb Z})$,\ncorresponding to reflection positive operators, and $(\\mathbb R,\\mathbb\nR_+,-id_{\\mathbb R})$, corresponding to reflection positive one-parameter\ngroups, we show that every Hankel positive representation can be made\nreflection positive by a slight change of the scalar product. A key method\nconsists in using the measure $\\mu_H$ on $\\mathbb R_+$ defined by a positive\nHankel operator $H$ on $H^2(\\mathbb C_+)$ to define a Pick function whose\nimaginary part, restricted to the imaginary axis, provides an operator symbol\nfor $H$.\n"}
+{"abstract": "  The goal of metric learning is to learn a function that maps samples to a\nlower-dimensional space where similar samples lie closer than dissimilar ones.\nParticularly, deep metric learning utilizes neural networks to learn such a\nmapping. Most approaches rely on losses that only take the relations between\npairs or triplets of samples into account, which either belong to the same\nclass or two different classes. However, these methods do not explore the\nembedding space in its entirety. To this end, we propose an approach based on\nmessage passing networks that takes all the relations in a mini-batch into\naccount. We refine embedding vectors by exchanging messages among all samples\nin a given batch allowing the training process to be aware of its overall\nstructure. Since not all samples are equally important to predict a decision\nboundary, we use an attention mechanism during message passing to allow samples\nto weigh the importance of each neighbor accordingly. We achieve\nstate-of-the-art results on clustering and image retrieval on the CUB-200-2011,\nCars196, Stanford Online Products, and In-Shop Clothes datasets. To facilitate\nfurther research, we make available the code and the models at\nhttps://github.com/dvl-tum/intra_batch_connections.\n"}
+{"abstract": "  The Transformer model is widely used in natural language processing for\nsentence representation. However, the previous Transformer-based models focus\non function words that have limited meaning in most cases and could merely\nextract high-level semantic abstraction features. In this paper, two approaches\nare introduced to improve the performance of Transformers. We calculated the\nattention score by multiplying the part-of-speech weight vector with the\ncorrelation coefficient, which helps extract the words with more practical\nmeaning. The weight vector is obtained by the input text sequence based on the\nimportance of the part-of-speech. Furthermore, we fuse the features of each\nlayer to make the sentence representation results more comprehensive and\naccurate. In experiments, we demonstrate the effectiveness of our model\nTransformer-F on three standard text classification datasets. Experimental\nresults show that our proposed model significantly boosts the performance of\ntext classification as compared to the baseline model. Specifically, we obtain\na 5.28% relative improvement over the vanilla Transformer on the simple tasks.\n"}
+{"abstract": "  The anisotropic optical response of the layered, nodal-line semimetal ZrSiS\nat ambient and high pressure is investigated by frequency-dependent\nreflectivity measurements for the polarization along and perpendicular to the\nlayers. The highly anisotropic optical conductivity is in very good agreement\nwith results from density functional theory calculations and confirms the\nanisotropic character of ZrSiS. Whereas the in-plane optical conductivity shows\nonly modest pressure-induced changes, we found strong effects on the\nout-of-plane optical conductivity spectrum of ZrSiS, with the appearance of two\nprominent excitations. These pronounced pressure-induced effects can neither be\nattributed to a structural phase transition according to our single-crystal\nx-ray diffraction measurements, nor can they be explained by electronic\ncorrelation and electron-hole pairing effects, as revealed by theoretical\ncalculations. Our findings are discussed in the context of the recently\nproposed excitonic insulator phase in ZrSiS.\n"}
+{"abstract": "  Thanks to the availability of powerful computing resources, big data and deep\nlearning algorithms, we have made great progress on computer vision in the last\nfew years. Computer vision systems begin to surpass humans in some tasks, such\nas object recognition, object detection, face recognition and pose estimation.\nLots of computer vision algorithms have been deployed to real world\napplications and started to improve our life quality. However, big data and\nlabels are not always available. Sometimes we only have very limited labeled\ndata, such as medical images which requires experts to label them. In this\npaper, we study few shot image classification, in which we only have very few\nlabeled data. Machine learning with little data is a big challenge. To tackle\nthis challenge, we propose two methods and test their effectiveness thoroughly.\nOne method is to augment image features by mixing the style of these images.\nThe second method is applying spatial attention to explore the relations\nbetween patches of images. We also find that domain shift is a critical issue\nin few shot learning when the training domain and testing domain are different.\nSo we propose a more realistic cross-domain few-shot learning with unlabeled\ndata setting, in which some unlabeled data is available in the target domain.\nWe propose two methods in this setting. Our first method transfers the style\ninformation of the unlabeled target dataset to the samples in the source\ndataset and trains a model with stylized images and original images. Our second\nmethod proposes a unified framework to fully utilize all the data. Both of our\nmethods surpass the baseline method by a large margin.\n"}
+{"abstract": "  We study the temporal evolution of the circuit complexity after the local\nquench where two harmonic chains are suddenly joined, choosing the initial\nstate as the reference state. We discuss numerical results for the complexity\nfor the entire chain and the subsystem complexity for a block of consecutive\nsites, obtained by exploiting the Fisher information geometry of the covariance\nmatrices. The qualitative behaviour of the temporal evolutions of the subsystem\ncomplexity depends on whether the joining point is inside the subsystem. The\nrevivals and a logarithmic growth observed during these temporal evolutions are\ndiscussed. When the joining point is outside the subsystem, the temporal\nevolutions of the subsystem complexity and of the corresponding entanglement\nentropy are qualitatively similar.\n"}
+{"abstract": "  The $SU(N)$--invariant matrix model potential is written as a sum of squares\nwith only four frequencies (whose multiplicities and simple $N$--dependence are\ncalculated).\n"}
+{"abstract": "  Codes based on sparse matrices have good performance and can be efficiently\ndecoded by belief-propagation (BP). Decoding binary stabilizer codes needs a\nquaternary BP for (additive) codes over GF(4), which has a higher check-node\ncomplexity compared to a binary BP for codes over GF(2). Moreover, BP decoding\nof stabilizer codes suffers a performance loss from the short cycles in the\nunderlying Tanner graph. In this paper, we propose a refined BP algorithm for\ndecoding quantum codes by passing scalar messages. For a given error syndrome,\nthis algorithm decodes to the same output as the conventional quaternary BP but\nwith a check-node complexity the same as binary BP. As every message is a\nscalar, the message normalization can be naturally applied to improve the\nperformance. Another observation is that the message-update schedule affects\nthe BP decoding performance against short cycles. We show that running BP with\nmessage normalization according to a serial schedule (or other schedules) may\nsignificantly improve the decoding performance and error-floor in computer\nsimulation.\n"}
+{"abstract": "  Structural and magnetic transitions in a double perovskite hosting 5d1 Re\nions are discussed on the basis of recently published high-resolution x-ray\ndiffraction patterns [D. Hirai, et al., Phys. Rev. Res. 2, 022063(R) (2020)]. A\nreported structural transition below room temperature, from cubic to tetragonal\nsymmetry, appears not to be driven by T2g-type quadrupoles, as suggested. A\nmagnetic motif at lower temperature is shown to be composed of two order\nparameters, associated with propagation vectors k = (0, 0, 1) and k = (0, 0,\n0). Findings from our studies, for structural and magnetic properties of\nBa2MgReO6, surface in predicted amplitudes for x-ray diffraction at rhenium L2\nand L3 absorption edges, and magnetic neutron Bragg diffraction. Specifically,\nentanglement of anapole and spatial degrees of freedom creates a quadrupole in\nthe neutron scattering amplitude. It would be excluded in an unexpected\nscenario whereby the rhenium atomic state is a manifold. Also, a chiral\nsignature visible in resonant x-ray diffraction will be one consequence of\npredicted electronic quadrupole and magnetic dipole orders. A model Re wave\nfunction consistent with all current knowledge is a guide to electronic and\nmagnetic multipoles engaged in x-ray and neutron diffraction investigations.\n"}
+{"abstract": "  Aiming at facilitating a real-world, ever-evolving and scalable autonomous\ndriving system, we present a large-scale dataset for standardizing the\nevaluation of different self-supervised and semi-supervised approaches by\nlearning from raw data, which is the first and largest dataset to date.\nExisting autonomous driving systems heavily rely on `perfect' visual perception\nmodels (i.e., detection) trained using extensive annotated data to ensure\nsafety. However, it is unrealistic to elaborately label instances of all\nscenarios and circumstances (i.e., night, extreme weather, cities) when\ndeploying a robust autonomous driving system. Motivated by recent advances of\nself-supervised and semi-supervised learning, a promising direction is to learn\na robust detection model by collaboratively exploiting large-scale unlabeled\ndata and few labeled data. Existing datasets either provide only a small amount\nof data or covers limited domains with full annotation, hindering the\nexploration of large-scale pre-trained models. Here, we release a Large-Scale\n2D Self/semi-supervised Object Detection dataset for Autonomous driving, named\nas SODA10M, containing 10 million unlabeled images and 20K images labeled with\n6 representative object categories. To improve diversity, the images are\ncollected within 27833 driving hours under different weather conditions,\nperiods and location scenes of 32 different cities. We provide extensive\nexperiments and deep analyses of existing popular self/semi-supervised\napproaches, and give some interesting findings in autonomous driving scope.\nExperiments show that SODA10M can serve as a promising pre-training dataset for\ndifferent self-supervised learning methods, which gives superior performance\nwhen fine-tuning with different downstream tasks (i.e., detection,\nsemantic/instance segmentation) in autonomous driving domain. More information\ncan refer to https://soda-2d.github.io.\n"}
+{"abstract": "  In this paper, we propose a novel, convolutional neural network model to\nextract highly precise depth maps from missing viewpoints, especially well\napplicable to generate holographic 3D contents. The depth map is an essential\nelement for phase extraction which is required for synthesis of\ncomputer-generated hologram (CGH). The proposed model called the HDD Net uses\nMSE for the better performance of depth map estimation as loss function, and\nutilizes the bilinear interpolation in up sampling layer with the Relu as\nactivation function. We design and prepare a total of 8,192 multi-view images,\neach resolution of 640 by 360 for the deep learning study. The proposed model\nestimates depth maps through extracting features, up sampling. For quantitative\nassessment, we compare the estimated depth maps with the ground truths by using\nthe PSNR, ACC, and RMSE. We also compare the CGH patterns made from estimated\ndepth maps with ones made from ground truths. Furthermore, we demonstrate the\nexperimental results to test the quality of estimated depth maps through\ndirectly reconstructing holographic 3D image scenes from the CGHs.\n"}
+{"abstract": "  We study propagation effects due to the finite speed of light in ionization\nof extended systems. We present a general quantitative theory of these effects\nand show under which conditions such effects should appear. The finite speed of\nlight propagation effects are encoded in the non-dipole terms of the\ntime-dependent Shr\\\"odinger equation and display themselves in the\nphotoelectron momentum distribution projected on the molecular axis. Our\nnumerical modeling for the \\Hp molecular ion and the \\Ne dimer shows that the\nfinite light propagation time from one atomic center to another can be\naccurately determined in a table top laser experiment which is much more\nreadily affordable than an earlier synchrotron measurement by Grundmann {\\em et\nal} [Science 370, 339 (2020)]\n"}
+{"abstract": "  Chest X-rays are an important and accessible clinical imaging tool for the\ndetection of many thoracic diseases. Over the past decade, deep learning, with\na focus on the convolutional neural network (CNN), has become the most powerful\ncomputer-aided diagnosis technology for improving disease identification\nperformance. However, training an effective and robust deep CNN usually\nrequires a large amount of data with high annotation quality. For chest X-ray\nimaging, annotating large-scale data requires professional domain knowledge and\nis time-consuming. Thus, existing public chest X-ray datasets usually adopt\nlanguage pattern based methods to automatically mine labels from reports.\nHowever, this results in label uncertainty and inconsistency. In this paper, we\npropose many-to-one distribution learning (MODL) and K-nearest neighbor\nsmoothing (KNNS) methods from two perspectives to improve a single model's\ndisease identification performance, rather than focusing on an ensemble of\nmodels. MODL integrates multiple models to obtain a soft label distribution for\noptimizing the single target model, which can reduce the effects of original\nlabel uncertainty. Moreover, KNNS aims to enhance the robustness of the target\nmodel to provide consistent predictions on images with similar medical\nfindings. Extensive experiments on the public NIH Chest X-ray and CheXpert\ndatasets show that our model achieves consistent improvements over the\nstate-of-the-art methods.\n"}
+{"abstract": "  Fluorescence microscopy images contain several channels, each indicating a\nmarker staining the sample. Since many different marker combinations are\nutilized in practice, it has been challenging to apply deep learning based\nsegmentation models, which expect a predefined channel combination for all\ntraining samples as well as at inference for future application. Recent work\ncircumvents this problem using a modality attention approach to be effective\nacross any possible marker combination. However, for combinations that do not\nexist in a labeled training dataset, one cannot have any estimation of\npotential segmentation quality if that combination is encountered during\ninference. Without this, not only one lacks quality assurance but one also does\nnot know where to put any additional imaging and labeling effort. We herein\npropose a method to estimate segmentation quality on unlabeled images by (i)\nestimating both aleatoric and epistemic uncertainties of convolutional neural\nnetworks for image segmentation, and (ii) training a Random Forest model for\nthe interpretation of uncertainty features via regression to their\ncorresponding segmentation metrics. Additionally, we demonstrate that including\nthese uncertainty measures during training can provide an improvement on\nsegmentation performance.\n"}
+{"abstract": "  Features of the response of pickup ions (PUIs) to the Kelvin--Helmholtz\ninstability (KHI) on the heliopause (HP) are examined by means of\ntwo-dimensional hybrid simulations. We assume the supersonic neutral solar wind\nas the source of PUIs gyrating about the magnetic field in the outer\nheliosheath. These PUIs become energetic neutral atoms (ENAs) via charge\nexchange with interstellar hydrogen, and a portion of these ENAs are detected\nby spacecraft such as the Interstellar Boundary Explorer (IBEX}. To evaluate\nthe possibility of identifying the KHI on HP from ENA observations, we assume\nthat an imprint of the KHI may be displayed in spatial and temporal variations\nin the observed ENA profile. As an alternative to ENA, the column density of\nPUIs integrated across the HP is calculated. The KH-inducing vortex forces not\nonly background protons but also PUIs to roll up deep in the inner heliosheath.\nThe KH vortex also results in the emission of magnetosonic pulses that sweep\nPUIs in the outer heliosheath and lead to their local confinement. These\neffects elongate the PUIs spatial distribution in the direction normal to the\nHP. The appearance of the local confined structure in the PUIs column density\nis consequently confirmed, and this feature can be confirmed as the KHI\nevolution. Although the simulation cannot be quantitatively compared with the\nobservations currently available because its resolution is too small, we expect\nthat the derived properties will be useful for diagnosing the nature of HP\nfluctuation in future missions.\n"}
+{"abstract": "  Carpet-type structures constitute an ideal laboratory to study and analyze\nthe robustness of the interference process that underlies this phenomenon\nagainst the harmful effects of decoherence. Here, without losing any\ngenerality, for simplicity, the case of a particle with a mass m is considered\nand described by a localized state corresponding to the ground state of a\nsquare box of width w, which is released inside a wider cavity (with a width L\n> w). The effects of decoherence are then numerically investigated by means of\na simple dynamical model that captures the essential features of the phenomenon\nunder Markovian conditions, leaving aside extra complications associated with a\nmore detailed dynamical description of the system-environment interaction. As\nit is shown, this model takes into account and reproduces the fact that\ndecoherence effects are stronger as energy levels become more separated (in\nenergy), which translates into a progressive collapse of the energy density\nmatrix to its main diagonal. However, because energy dissipation is not\nconsidered, an analogous behavior is not observed in the position\nrepresentation, where a proper spatial localization of the probability density\ndoes not take place, but rather a delocalized distribution. This result\nemphasizes the fact that classicality is reached only if both decoherence and\ndissipation coexist; otherwise, non-classical traits might still persist.\nActually, as it is also shown, in the position representation some off-diagonal\ncorrelations indeed survive unless an additional spatial-type factor is\nincluded in the model. This makes evident the rather complex nature of the\ndecoherence phenomenon and hence the importance to have a familiarity with how\nit manifests in different representations, particularly with the purpose to\ndetermine and design reliable control mechanisms.\n"}
+{"abstract": "  Unmanned Aerial Vehicle (UAV) has already demonstrated its potential in many\ncivilian applications, and the fa\\c{c}ade inspection is among the most\npromising ones. In this paper, we focus on enabling the autonomous perception\nand control of a small UAV for a fa\\c{c}ade inspection task. Specifically, we\nconsider the perception as a planar object pose estimation problem by\nsimplifying the building structure as concatenation of planes, and the control\nas an optimal reference tracking control problem. First, a vision based\nadaptive observer is proposed which can realize stable plane pose estimation\nunder very mild observation conditions. Second, a model predictive controller\nis designed to achieve stable tracking and smooth transition in a multi-plane\nscenario, while the persistent excitation (PE) condition of the observer and\nthe maneuver constraints of the UAV are satisfied. The proposed autonomous\nplane pose estimation and plane tracking methods are tested in both simulation\nand practical building fas\\c{c}ade inspection scenarios, which demonstrate\ntheir effectiveness and practicability.\n"}
+{"abstract": "  It is well-known that combining life annuities and death benefits introduce\nopposite effects in payments with respect to the mortality risk on the lifetime\nof the insured. In a general multi-state framework with multiple product types,\nsuch joint effects are less trivial. In this paper, we consider a multivariate\npayment process in multi-state life insurance, where the components are defined\nin terms of the same Markovian state process. The multivariate present value of\nfuture payments is introduced, and we derive differential equations and product\nintegral representations of its conditional moments and moment generating\nfunction. Special attention is given to pair-wise covariances between two\npresent values, where results closely connected to Hattendorff type of results\nfor the variance are derived. The results are illustrated in a numerical\nexample in a disability model.\n"}
+{"abstract": "  We propose a new algorithm for joint dereverberation and blind source\nseparation (DR-BSS). Our work builds upon the IRLMA-T framework that applies a\nunified filter combining dereverberation and separation. One drawback of this\nframework is that it requires several matrix inversions, an operation\ninherently costly and with potential stability issues. We leverage the recently\nintroduced iterative source steering (ISS) updates to propose two algorithms\nmitigating this issue. Albeit derived from first principles, the first\nalgorithm turns out to be a natural combination of weighted prediction error\n(WPE) dereverberation and ISS-based BSS, applied alternatingly. In this case,\nwe manage to reduce the number of matrix inversion to only one per iteration\nand source. The second algorithm updates the ILRMA-T matrix using only\nsequential ISS updates requiring no matrix inversion at all. Its implementation\nis straightforward and memory efficient. Numerical experiments demonstrate that\nboth methods achieve the same final performance as ILRMA-T in terms of several\nrelevant objective metrics. In the important case of two sources, the number of\niterations required is also similar.\n"}
+{"abstract": "  We propose HyperDynamics, a dynamics meta-learning framework that conditions\non an agent's interactions with the environment and optionally its visual\nobservations, and generates the parameters of neural dynamics models based on\ninferred properties of the dynamical system. Physical and visual properties of\nthe environment that are not part of the low-dimensional state yet affect its\ntemporal dynamics are inferred from the interaction history and visual\nobservations, and are implicitly captured in the generated parameters. We test\nHyperDynamics on a set of object pushing and locomotion tasks. It outperforms\nexisting dynamics models in the literature that adapt to environment variations\nby learning dynamics over high dimensional visual observations, capturing the\ninteractions of the agent in recurrent state representations, or using\ngradient-based meta-optimization. We also show our method matches the\nperformance of an ensemble of separately trained experts, while also being able\nto generalize well to unseen environment variations at test time. We attribute\nits good performance to the multiplicative interactions between the inferred\nsystem properties -- captured in the generated parameters -- and the\nlow-dimensional state representation of the dynamical system.\n"}
+{"abstract": "  We give on the 2-torus a characterization of smooth conjugacy of special\nAnosov endomorphisms with their linearizations in terms of the regularity of\nthe stable and unstable foliations. This regularity condition is the uniform\nbounded density (UBD) property, which is the uniform version absolute\ncontinuity for foliations.\n"}
+{"abstract": "  Several high energy $e^{+}e^{-}$ colliders are proposed as Higgs factories by\nthe international high energy physics community. One of the most important\ngoals of these projects is to study the Higgs properties, such as its\ncouplings, mass, width, and production rate, with unprecedented precision.\nPrecision studies of the Higgs boson should be the priority and drive the\ndesign and optimization of detectors. A global analysis approach based on the\nmultinomial distribution and Machine Learning techniques is proposed to realize\nan ``end-to-end'' analysis and to enhance the precision of all accessible decay\nbranching fractions of the Higgs significantly. A proof-of-principle Monte\nCarlo simulation study is performed to show the feasibility. This approach\nshows that the statistical uncertainties of all branching fractions are\nproportional to a single parameter, which can be used as a metric to optimize\nthe detector design, reconstruction algorithms, and data analyses. In the Higgs\nfactories, the global analysis approach is valuable both to the Higgs\nmeasurements and detector R & D, because it has the potential for superior\nprecision and makes detector optimization single-purpose.\n"}
+{"abstract": "  A Kempe swap in a proper coloring interchanges the colors on some maximal\nconnected 2-colored subgraph. Two $k$-colorings are $k$-equivalent if we can\ntransform one into the other using Kempe swaps. The triangulated toroidal grid,\n$T[m\\times n]$, is formed from (a toroidal embedding of) the Cartesian product\nof $C_m$ and $C_n$ by adding parallel diagonals inside all 4-faces. Mohar and\nSalas showed that not all 4-colorings of $T[m\\times n]$ are 4-equivalent. In\ncontrast, Bonamy, Bousquet, Feghali, and Johnson showed that all 6-colorings of\n$T[m\\times n]$ are 6-equivalent. They asked whether the same is true for\n5-colorings. We answer their question affirmatively when $m,n\\ge 6$. Further,\nwe show that if $G$ is 6-regular with a toroidal embedding where every\nnon-contractible cycle has length at least 7, then all 5-colorings of $G$ are\n5-equivalent. Our results relate to the antiferromagnetic Pott's model, in\nstatistical mechanics.\n"}
+{"abstract": "  Analytic continuation from Minkowski space to $(2,2)$ split signature\nspacetime has proven to be a powerful tool for the study of scattering\namplitudes. Here we show that, under this continuation, null infinity becomes\nthe product of a null interval with a celestial torus (replacing the celestial\nsphere) and has only one connected component. Spacelike and timelike infinity\nare time-periodic quotients of AdS$_3$. These three components of infinity\ncombine to an $S^3$ represented as a toric fibration over the interval.\nPrivileged scattering states of scalars organize into $SL(2,\\mathbb{R})_L\n\\times SL(2,\\mathbb{R})_R$ conformal primary wave functions and their\ndescendants with real integral or half-integral conformal weights, giving the\nnormally continuous scattering problem a discrete character.\n"}
+{"abstract": "  In this article, we perform a sensitivity study of an un-binned angular\nanalysis of the $B\\to D^*\\ell \\nu_\\ell$ decay, including the contributions from\nthe right-handed current. We show that the angular observable can constrain\nvery strongly the right-handed current without the intervention of the yet\nunsolved $V_{cb}$ puzzle.\n"}
+{"abstract": "  We describe a novel application of the end-to-end deep learning technique to\nthe task of discriminating top quark-initiated jets from those originating from\nthe hadronization of a light quark or a gluon. The end-to-end deep learning\ntechnique combines deep learning algorithms and low-level detector\nrepresentation of the high-energy collision event. In this study, we use\nlow-level detector information from the simulated CMS Open Data samples to\nconstruct the top jet classifiers. To optimize classifier performance we\nprogressively add low-level information from the CMS tracking detector,\nincluding pixel detector reconstructed hits and impact parameters, and\ndemonstrate the value of additional tracking information even when no new\nspatial structures are added. Relying only on calorimeter energy deposits and\nreconstructed pixel detector hits, the end-to-end classifier achieves an AUC\nscore of 0.975$\\pm$0.002 for the task of classifying boosted top quark jets.\nAfter adding derived track quantities, the classifier AUC score increases to\n0.9824$\\pm$0.0013, serving as the first performance benchmark for these CMS\nOpen Data samples. We additionally provide a timing performance comparison of\ndifferent processor unit architectures for training the network.\n"}
+{"abstract": "  We study the thermodynamic properties of topological Josephson junctions\nusing a quantum spin Hall (QSH) insulator-based junction as an example. In\nparticular, we propose that phase-dependent measurements of the heat capacity\noffer an alternative to Josephson-current measurements to demonstrate key\ntopological features. Even in an equilibrium situation, where the fermion\nparity is not conserved, the heat capacity exhibits a pronounced double peak in\nits phase dependence as a signature of the protected zero-energy crossing in\nthe Andreev spectrum. This double-peak feature is robust against changes of the\ntunneling barrier and thus allows one to distinguish between topological and\ntrivial junctions. At short time scales fermion parity is conserved and the\nheat capacity is $4\\pi$-periodic in the superconducting phase difference. We\npropose a dispersive setup coupling the Josephson junction to a tank LC circuit\nto measure the heat capacity of the QSH-based Josephson junction sufficiently\nfast to detect the $4\\pi$-periodicity. Although explicitly calculated for a\nshort QSH-based Josephson junction, our results are also applicable to long as\nwell as nanowire-based topological Josephson junctions.\n"}
+{"abstract": "  We compare the ground-state features of alternating ferrimagnetic chains\n$(1/2, S)$ with $S=1,3/2,2,5/2$ in a magnetic field and the corresponding\nHolstein-Primakoff bosonic models up to order $\\sqrt{s/S}$, with $s=1/2$,\nconsidering the fully polarized magnetization as the boson vacuum. {The\nsingle-particle Hamiltonian is a Rice-Mele model with uniform hopping and\nmodified boundaries, while the interactions have a correlated\n(density-dependent) hopping term and magnon-magnon repulsion.} The\nmagnon-magnon repulsion increases the many-magnon energy and the\ndensity-dependent hopping decreases the kinetic energy. We use density matrix\nrenormalization group calculations to investigate the effects of these two\ninteraction terms in the bosonic model{, and display the quantitative agreement\nbetween the results from the spin model and the full bosonic approximation. In\nparticular, we verify the good accordance in the behavior of the edge states,\nassociated with the ferrimagnetic plateau, from the spin and from the bosonic\nmodels. Furthermore, we show that the boundary magnon density strongly depends\non the interactions and particle statistics.\n"}
+{"abstract": "  Stephenson~(2018) established annealed local convergence of Boltzmann planar\nmaps conditioned to be large. The present work uses results on rerooted\nmulti-type branching trees to prove a quenched version of this limit.\n"}
+{"abstract": "  We present the open-source Bayesian Atmospheric Radiative Transfer (BART)\nretrieval package, which produces estimates and uncertainties for an\natmosphere's thermal profile and chemical abundances from observations. Several\nBART components are also stand-alone packages, including the parallel\nMulti-Core Markov chain Monte Carlo (MC3), which implements several Bayesian\nsamplers; a line-by-line radiative-transfer model, transit; a code that\ncalculates Thermochemical Equilibrium Abundances, TEA; and a test suite for\nverifying radiative-transfer and retrieval codes, BARTTest. The codes are in\nPython and C. BART and TEA are under a Reproducible Research (RR) license,\nwhich requires reviewed-paper authors to publish a compendium of all inputs,\ncodes, and outputs supporting the paper's scientific claims. BART and TEA\nproduce the compendium's content. Otherwise, these codes are under permissive\nopen-source terms, as are MC3 and BARTTest, for any purpose. This paper\npresents an overview of the code, BARTTest, and an application to eclipse data\nfor exoplanet HD 189733 b. Appendices address RR methodology for accelerating\nscience, a reporting checklist for retrieval papers, the spectral resolution\nrequired for synthetic tests, and a derivation of the effective sample size\nrequired to estimate any Bayesian posterior distribution to a given precision,\nwhich determines how many iterations to run. Paper II, by Cubillos et al.,\npresents the underlying radiative-transfer scheme and an application to transit\ndata for exoplanet HAT-P-11b. Paper III, by Blecic et al., discusses the\ninitialization and post-processing routines, with an application to eclipse\ndata for exoplanet WASP-43b. We invite the community to use and improve BART\nand its components at http://GitHub.com/ExOSPORTS/BART/.\n"}
+{"abstract": "  We study a class of power-law stored energy functions for spherically\nsymmetric elastic bodies that includes well-known material models, such as the\nSaint Venant-Kirchhoff, Hadamard, Signorini and John models. We identify a\nfinite subclass of these stored energy functions, which we call Lam\\'e type,\nthat depend on no more material parameters than the bulk modulus $\\kappa>0$ and\nthe Poisson ratio $-1<\\nu\\leq1/2$. A general theorem proving the existence of\nstatic self-gravitating elastic balls for some power-law materials has been\ngiven elsewhere. In this paper numerical evidence is provided that some\nhypotheses in this theorem are necessary, while others are not.\n"}
+{"abstract": "  A polycentric approach to ecosystem service (ES) governance that combines\nindividual incentives for interdependent ES providers with collective action is\na promising lever to overcome the decline in ES and generate win-win solutions\nin agricultural landscapes. In this study, we explored the effectiveness of\nsuch an approach by focusing on incentives for managed pollination targeting\neither beekeepers or farmers who were either in communication with each other\nor not. We used a stylized bioeconomic model to simulate (i) the mutual\ninterdependency through pollination in intensive agricultural landscapes and\n(ii) the economic and ecological impacts of introducing two beekeeping\nsubsidies and one pesticide tax. The findings showed that incentives generated\na spillover effect, affecting not only targeted stakeholders but non-targeted\nstakeholders as well as the landscape, and that this effect was amplified by\ncommunication. However, none of the simulated types of polycentric ES\ngovernance proved sustainable overall: subsidies showed excellent economic but\nlow environmental performance, while the tax led to economic losses but was\nbeneficial for the landscape. Based on these results, we identified three\nconditions for sustainable ES governance based on communication between\nstakeholders and incentives: (i) strong mutual interdependency (i.e. few\nalternatives exist for stakeholders), (ii) the benefits of communication\noutweigh the costs, and (iii) the incentivized ES drivers are not detrimental\nto other ES. Further research is needed to systematize which combination of\nindividual payments and collaboration are sustainable in which conditions.\n"}
+{"abstract": "  Inductive logic programming (ILP) is a form of logic-based machine learning.\nThe goal is to induce a hypothesis (a logic program) that generalises given\ntraining examples. As ILP turns 30, we review the last decade of research. We\nfocus on (i) new meta-level search methods, (ii) techniques for learning\nrecursive programs, (iii) new approaches for predicate invention, and (iv) the\nuse of different technologies. We conclude by discussing current limitations of\nILP and directions for future research.\n"}
+{"abstract": "  Diffusion of atoms in solids is one of the most fundamental kinetic processes\nthat ultimately governs many materials properties. Here, we report on a\ncombined first-principles and kinetic Monte Carlo study of macroscopic\ndiffusion properties of disordered Ti-Ta alloys over the entire composition\nrange. Using simple cluster expansion model Hamiltonians parametrized on\ndensity functional theory data, we compute transport properties explicitly\nincluding local interactions between the two atomic species and compare them\nwith the non-interacting diffusion model for disordered, random alloys.\nSurprisingly, we find that although these alloys thermodynamically behave as\nnearly random solid solutions, their kinetic properties deviate significantly\nfrom the behavior predicted by diffusion models for non-interacting systems. We\nattribute these differences in transport properties to the local interactions\nthat create a rather corrugated potential energy landscape and consequently\ngive rise to energetically non-degenerate end-states of diffusion processes\nwhich cannot be realized in a non-interacting disordered or other simpler\ndiffusion models. The findings emphasize the limitations of the widely known\nnon-interacting disordered diffusion model for such systems. Furthermore, we\nexplain that changes in mobility in these alloys is predominantly due to\nchanges in the correlation factor caused by the local interactions. Our work\nthus highlights the importance of explicitly including local interactions when\nassessing the transport properties of thermodynamically nearly disordered\nalloys.\n"}
+{"abstract": "  In the context of collaborative robotics, distributed situation awareness is\nessential for supporting collective intelligence in teams of robots and human\nagents where it can be used for both individual and collective decision\nsupport. This is particularly important in applications pertaining to emergency\nrescue and crisis management. During operational missions, data and knowledge\nis gathered incrementally and in different ways by heterogeneous robots and\nhumans. We describe this as the creation of \\emph{Hastily Formed Knowledge\nNetworks} (HFKNs). The focus of this paper is the specification and prototyping\nof a general distributed system architecture that supports the creation of\nHFKNs by teams of robots and humans. The information collected ranges from\nlow-level sensor data to high-level semantic knowledge, the latter represented\nin part as RDF Graphs. The framework includes a synchronization protocol and\nassociated algorithms that allow for the automatic distribution and sharing of\ndata and knowledge between agents. This is done through the distributed\nsynchronization of RDF Graphs shared between agents. High-level semantic\nqueries specified in SPARQL can be used by robots and humans alike to acquire\nboth knowledge and data content from team members. The system is empirically\nvalidated and complexity results of the proposed algorithms are provided.\nAdditionally, a field robotics case study is described, where a 3D mapping\nmission has been executed using several UAVs in a collaborative emergency\nrescue scenario while using the full HFKN Framework.\n"}
+{"abstract": "  State-of-the-art performance for many emerging edge applications is achieved\nby deep neural networks (DNNs). Often, these DNNs are location and time\nsensitive, and the parameters of a specific DNN must be delivered from an edge\nserver to the edge device rapidly and efficiently to carry out time-sensitive\ninference tasks. We introduce AirNet, a novel training and analog transmission\nmethod that allows efficient wireless delivery of DNNs. We first train the DNN\nwith noise injection to counter the wireless channel noise. We also employ\npruning to reduce the channel bandwidth necessary for transmission, and perform\nknowledge distillation from a larger model to achieve satisfactory performance,\ndespite the channel perturbations. We show that AirNet achieves significantly\nhigher test accuracy compared to digital alternatives under the same bandwidth\nand power constraints. It also exhibits graceful degradation with channel\nquality, which reduces the requirement for accurate channel estimation.\n"}
+{"abstract": "  It is shown that the infinite tower of tree-level soft graviton symmetries in\nasymptotically flat 4D quantum gravity can be organized into a single chiral 2D\nKac-Moody symmetry based on the wedge algebra of w(1+infinity). The infinite\ntowers of soft photon or gluon symmetries also transform irreducibly under\nw(1+infinity).\n"}
+{"abstract": "  We propose Asymptotic Expansion Conjectures of the relative\nReshetikhin-Turaev invariants, of the relative Turaev-Viro invariants and of\nthe discrete Fourier transforms of the quantum 6j-symbols, and prove them for\nfamilies of special cases. The significance of these expansions is that we do\nnot specify the way that the sequence of the colorings converges to the limit.\nAs a consequence, the terms in the expansion will have to depend on the index\nr, but the dependence is in a way that the terms are purely geometric\ninvariants of the metrics on the underlying manifold and only the metrics vary\nwith r.\n"}
+{"abstract": "  Quantum cosmology with quintom dark energy model has been investigated in the\npresent work using symmetry analysis of the underlying physical system. In the\nbackground of the flat FLRW model quintom cosmological model has been studied\nusing Noether symmetry and appropriate conserved charge is obtained. The\nWheeler-DeWitt (WD) equation is constructed on the minisuperspace and solutions\nare obtained using conserved charge.\n"}
+{"abstract": "  We develop a new orbit equivalence framework for holomorphically mating the\ndynamics of complex polynomials with that of Kleinian surface groups. We show\nthat the only torsion-free Fuchsian groups that can be thus mated are punctured\nsphere groups. We describe a new class of maps that are topologically orbit\nequivalent to Fuchsian punctured sphere groups. We call these higher\nBowen-Series maps. The existence of this class ensures that the Teichm\\\"uller\nspace of matings is disconnected. Further, they also show that, unlike in\nhigher dimensions, topological orbit equivalence rigidity fails for Fuchsian\ngroups acting on the circle. We also classify the collection of Kleinian Bers'\nboundary groups that are mateable in our framework.\n"}
+{"abstract": "  The kagome lattice, whose electronic valence band (VB) structure includes two\nDirac bands and one flat band, offers a rich space to realise tuneable\ntopological and strongly correlated electronic phases in two-dimensional (2D)\nand layered materials. While strong electron correlations have been observed in\ninorganic kagome crystals, they remain elusive in organic systems. The latter\noffer versatile synthesis protocols via molecular self-assembly and\nmetal-ligand coordination. Here, we report the synthesis of a 2D metal-organic\nframework (MOF) where di-cyano-anthracene (DCA) molecules form a kagome\narrangement via coordination with copper (Cu) atoms on a silver surface\n[Ag(111)]. Low-temperature scanning tunnelling spectroscopy revealed Kondo\nscreening--by the Ag(111) conduction electrons--of magnetic moments localised\nat Cu and DCA sites of the MOF. Density functional theory and mean-field\nHubbard modelling show that these local moments emerge from strong interactions\nbetween kagome MOF electrons, enabling organic 2D materials as viable platforms\nfor strongly correlated nanoelectronics and spintronics.\n"}
+{"abstract": "  Compositional generalization is the ability to generalize systematically to a\nnew data distribution by combining known components. Although humans seem to\nhave a great ability to generalize compositionally, state-of-the-art neural\nmodels struggle to do so. In this work, we study compositional generalization\nin classification tasks and present two main contributions. First, we study\nways to convert a natural language sequence-to-sequence dataset to a\nclassification dataset that also requires compositional generalization. Second,\nwe show that providing structural hints (specifically, providing parse trees\nand entity links as attention masks for a Transformer model) helps\ncompositional generalization.\n"}
+{"abstract": "  Some 6G use cases include augmented reality and high-fidelity holograms, with\nthis information flowing through the network. Hence, it is expected that 6G\nsystems can feed machine learning algorithms with such context information to\noptimize communication performance. This paper focuses on the simulation of 6G\nMIMO systems that rely on a 3-D representation of the environment as captured\nby cameras and eventually other sensors. We present new and improved Raymobtime\ndatasets, which consist of paired MIMO channels and multimodal data. We also\ndiscuss tradeoffs between speed and accuracy when generating channels via\nray-tracing. We finally provide results of beam selection and channel\nestimation to assess the impact of the improvements in the ray-tracing\nsimulation methodology.\n"}
+{"abstract": "  Several large stellar spectroscopic surveys are producing overwhelming\namounts of data that can be used for determining stellar atmospheric parameters\nand chemical abundances. Nonetheless, the accuracy achieved in the derived\nastrophysical parameters is still insufficient, mainly because of the paucity\nof adequate calibrators, particularly in the metal-poor regime ([Fe/H] $\\leq\n-$1.0). Here, we introduce the Titans metal-poor reference stars: a sample of\n41 dwarf and subgiant stars with accurate parameters. Effective temperatures\n(Teff) were derived by fitting observed H$\\alpha$ profiles with synthetic lines\ncomputed using 3D hydrodynamic NLTE models. Surface gravities (logg) were\ncomputed using evolutionary tracks and parallaxes from Gaia EDR3. The same\nmethods recover the Teff values of the Gaia benchmark stars, which are mostly\nbased on interferometric measurements, with a 1$\\sigma$ dispersion of $\\pm 50$\nK. We assume this to be the accuracy of the H$\\alpha$ profiles computed from 3D\nnon-LTE models for metal-poor dwarfs and subgiants. We achieved an internal\nprecision typically between 30-40 K, these errors dominated by instrumental\neffects. The final total uncertainty for the Teff values of the Titans are thus\nestimated to be of the order of $1\\%$. The typical error for logg is $\\leq$\n0.04 dex. In addition, we identified a few members of Gaia-Enceladus, of\nSequoia, and of the Helmi stream in our sample. These stars can pave the way\nfor the accurate chemical characterization of these Galactic substructures.\nUsing the Titans as reference, large stellar surveys will be able to improve\nthe internal calibration of their astrophysical parameters. Ultimately, this\nsample will help users of data from Gaia and large surveys in reaching their\ngoal of redefining our understanding of stars, stellar systems, and the Milky\nWay.\n"}
+{"abstract": "  We investigate the nonperturbative structure of Jackiw-Teitelboim gravity at\nfinite cutoff, as given by its proposed formulation in terms of a\n$T\\bar{T}$-deformed Schwarzian quantum mechanics. Our starting point is a\ncareful computation of the disk partition function to all orders in the\nperturbative expansion in the cutoff parameter. We show that the perturbative\nseries is asymptotic and that it admits a precise completion exploiting the\nanalytical properties of its Borel transform, as prescribed by resurgence\ntheory. The final result is then naturally interpreted in terms of the\nnonperturbative branch of the $T\\bar{T}$-deformed spectrum. The finite-cutoff\ntrumpet partition function is computed by applying the same strategy. In the\nsecond part of the paper, we propose an extension of this formalism to\narbitrary topologies, using the basic gluing rules of the undeformed case. The\nWeil-Petersson integrations can be safely performed due to the nonperturbative\ncorrections and give results that are compatible with the flow equation\nassociated with the $T\\bar{T}$ deformation. We derive exact expressions for\ngeneral topologies and show that these are captured by a suitable deformation\nof the Eynard-Orantin topological recursion. Finally, we study the \"slope\" and\n\"ramp\" regimes of the spectral form factor as functions of the cutoff\nparameter.\n"}
+{"abstract": "  Given a {features, target} dataset, we introduce an incremental algorithm\nthat constructs an aggregate regressor, using an ensemble of neural networks.\nIt is well known that ensemble methods suffer from the multicollinearity issue,\nwhich is the manifestation of redundancy arising mainly due to the common\ntraining-dataset. In the present incremental approach, at each stage we\noptimally blend the aggregate regressor with a newly trained neural network\nunder a convexity constraint which, if necessary, induces negative\ncorrelations. Under this framework, collinearity issues do not arise at all,\nrendering so the method both accurate and robust.\n"}
+{"abstract": "  We prove lower bound for the first closed or Neumann nonzero eigenvalue of\nthe Laplacian on a compact quaternion-K\\\"ahler manifold in terms of dimension,\ndiameter, and scalar curvature lower bound. It is derived as large time\nimplication of the modulus of continuity estimates for solutions of the heat\nequation. We also establish lower bound for the first Dirichlet eigenvalue in\nterms of geometric data, via a Laplace comparison theorem for the distance to\nthe boundary function.\n"}
+{"abstract": "  We investigate spherically symmetric gravitational collapse of thick matter\nshell without radiation in the Einstein gravity with cosmological constant. The\norbit of the infalling thick matter is determined by imposing an equation of\nstate for the matter near interface, where pressure constituted of the\ntransverse component and the longitudinal one is proportional to energy\ndensity. We present analytic solutions for the equation of state and discuss\nparameter region free from shell crossing singularity. We finally show that\nadopting the definition presented in arXiv:2005.13233 the total energy in this\ntime-dependent system is invariant under the given time evolution.\n"}
+{"abstract": "  Video portraits relighting is critical in user-facing human photography,\nespecially for immersive VR/AR experience. Recent advances still fail to\nrecover consistent relit result under dynamic illuminations from monocular RGB\nstream, suffering from the lack of video consistency supervision. In this\npaper, we propose a neural approach for real-time, high-quality and coherent\nvideo portrait relighting, which jointly models the semantic, temporal and\nlighting consistency using a new dynamic OLAT dataset. We propose a hybrid\nstructure and lighting disentanglement in an encoder-decoder architecture,\nwhich combines a multi-task and adversarial training strategy for\nsemantic-aware consistency modeling. We adopt a temporal modeling scheme via\nflow-based supervision to encode the conjugated temporal consistency in a cross\nmanner. We also propose a lighting sampling strategy to model the illumination\nconsistency and mutation for natural portrait light manipulation in real-world.\nExtensive experiments demonstrate the effectiveness of our approach for\nconsistent video portrait light-editing and relighting, even using mobile\ncomputing.\n"}
+{"abstract": "  The development of nanomaterials with a large nonlinear susceptibility is\nessential for nonlinear nanophotonics. We show that\ntransition-metal-dichalcogenide (TMD) nanotriangles have a large effective\nsecond-order susceptibility [$\\chi^{(2)}$] at mid-infrared to near-infrared\nfrequencies owing to their broken centrosymmetry. $\\chi^{(2)}$ is calculated\nwithin the density-matrix formalism that accounts for dissipation and\nscreening. $\\chi^{(2)}$ peaks in the vicinity of both two-photon resonances\n(specified by the geometry) and plasmon resonances (tunable via the carrier\ndensity). Aligning the resonances yields the values of $\\chi^{(2)}$ as high as\n$10^{-6}$ m/V. These findings underscore the potential of TMD nanotriangles for\nnonlinear nanophotonics, particularly second-harmonic generation.\n"}
+{"abstract": "  We study the mean curvature flow in 3-dimensional null hypersurfaces. In a\nspacetime a hypersurface is called null, if its induced metric is degenerate.\nThe speed of the mean curvature flow of spacelike surfaces in a null\nhypersurface is the projection of the codimension-two mean curvature vector\nonto the null hypersurface. We impose fairly mild conditions on the null\nhypersurface. Then for an outer un-trapped initial surface, a condition which\nresembles the mean-convexity of a surface in Euclidean space, we prove that the\nmean curvature flow exists for all times and converges smoothly to a marginally\nouter trapped surface (MOTS). As an application we obtain the existence of a\nglobal foliation of the past of an outermost MOTS, provided the null\nhypersurface admits an un-trapped foliation asymptotically.\n"}
+{"abstract": "  The circumcentered-reflection method (CRM) has been applied for solving\nconvex feasibility problems. CRM iterates by computing a circumcenter upon a\ncomposition of reflections with respect to convex sets. Since reflections are\nbased on exact projections, their computation might be costly. In this regard,\nwe introduce the circumcentered approximate-reflection method (CARM), whose\nreflections rely on outer-approximate projections. The appeal of CARM is that,\nin rather general situations, the approximate projections we employ are\navailable under low computational cost. We derive convergence of CARM and\nlinear convergence under an error bound condition. We also present successful\ntheoretical and numerical comparisons of CARM to the original CRM, to the\nclassical method of alternating projections (MAP) and to a correspondent\nouter-approximate version of MAP, referred to as MAAP. Along with our results\nand numerical experiments, we present a couple of illustrative examples.\n"}
+{"abstract": "  Existing slot filling models can only recognize pre-defined in-domain slot\ntypes from a limited slot set. In the practical application, a reliable\ndialogue system should know what it does not know. In this paper, we introduce\na new task, Novel Slot Detection (NSD), in the task-oriented dialogue system.\nNSD aims to discover unknown or out-of-domain slot types to strengthen the\ncapability of a dialogue system based on in-domain training data. Besides, we\nconstruct two public NSD datasets, propose several strong NSD baselines, and\nestablish a benchmark for future work. Finally, we conduct exhaustive\nexperiments and qualitative analysis to comprehend key challenges and provide\nnew guidance for future directions.\n"}
+{"abstract": "  Stereo matching is one of the most popular techniques to estimate dense depth\nmaps by finding the disparity between matching pixels on two, synchronized and\nrectified images. Alongside with the development of more accurate algorithms,\nthe research community focused on finding good strategies to estimate the\nreliability, i.e. the confidence, of estimated disparity maps. This information\nproves to be a powerful cue to naively find wrong matches as well as to improve\nthe overall effectiveness of a variety of stereo algorithms according to\ndifferent strategies. In this paper, we review more than ten years of\ndevelopments in the field of confidence estimation for stereo matching. We\nextensively discuss and evaluate existing confidence measures and their\nvariants, from hand-crafted ones to the most recent, state-of-the-art learning\nbased methods. We study the different behaviors of each measure when applied to\na pool of different stereo algorithms and, for the first time in literature,\nwhen paired with a state-of-the-art deep stereo network. Our experiments,\ncarried out on five different standard datasets, provide a comprehensive\noverview of the field, highlighting in particular both strengths and\nlimitations of learning-based strategies.\n"}
+{"abstract": "  In Silico Clinical Trials (ISTC), i.e., clinical experimental campaigns\ncarried out by means of computer simulations, hold the promise to decrease time\nand cost for the safety and efficacy assessment of pharmacological treatments,\nreduce the need for animal and human testing, and enable precision medicine. In\nthis paper we present methods and an algorithm that, by means of extensive\ncomputer simulation--based experimental campaigns (ISTC) guided by intelligent\nsearch, optimise a pharmacological treatment for an individual patient\n(precision medicine). e show the effectiveness of our approach on a case study\ninvolving a real pharmacological treatment, namely the downregulation phase of\na complex clinical protocol for assisted reproduction in humans.\n"}
+{"abstract": "  In a recent article a breakthrough was made in quantum computer science which\nestablished the existence of a phenomenon commonly known as $\\mathsf{MIP}^\\ast\n= \\mathsf{RE}$. We show that the existence of this phenomenon implies that the\nsupremum of the real parts of the zeroes of the Riemann zeta function is less\nthan one. Our main tool is an assertion about the representation theory of the\nrank two free group that we refer to as the charged mean ergodic theorem. Our\nmethod also involves the construction of novel modular functions we refer to as\ncomplete electromagnetic functions.\n"}
+{"abstract": "  Contamination of interloper galaxies due to misidentified emission lines can\nbe a big issue in the spectroscopic galaxy clustering surveys, especially in\nfuture high-precision observations. We propose a statistical method based on\nthe cross-correlations of the observational data itself between two redshift\nbins to efficiently reduce this effect, and it also can derive the interloper\nfraction f_i in a redshift bin with a high level of accuracy. The ratio of\ncross and auto angular correlation functions or power spectra between redshift\nbins are suggested to estimate f_i, and the key equations are derived for\ntheoretical discussion. In order to explore and prove the feasibility and\neffectiveness of this method, we also run simulations, generate mock data, and\nperform cosmological constraints considering systematics based on the\nobservation of the China Space Station Telescope (CSST). We find that this\nmethod can effectively reduce the interloper effect, and accurately constrain\nthe cosmological parameters for f_i<1%~10%, which is suitable for most future\nsurveys. This method also can be applied to other kinds of galaxy clustering\nsurveys like line intensity mapping.\n"}
+{"abstract": "  In this article we focus on evolving information systems. First a\ndelimitation of the concept of evolution is provided, resulting in a first\nattempt to a general theory for such evolutions. The theory makes a distinction\nbetween the underlying information structure at the conceptual level, its\nevolution on the one hand, and the description and semantics of operations on\nthe information structure and its population on the other hand. Main issues\nwithin this theory are object typing, type relatedness and identification of\nobjects. In terms of these concepts, we propose some axioms on the\nwell-formedness of evolution. In this general theory, the underlying data model\nis a parameter, making the theory applicable for a wide range of modelling\ntechniques, including object-role modelling and object oriented techniques.\n"}
+{"abstract": "  This paper shows that, in the critical strip, the Riemann zeta function\n$\\zeta(s)$ have the same set of zeros as $F(s):=\\int_0^\\infty\nt^{s-1}(e^t+1)^{-1}dt$, and then discusses the behavior of $F(s)$.\n"}
+{"abstract": "  Out-of-domain (OOD) generalization is a significant challenge for machine\nlearning models. Many techniques have been proposed to overcome this challenge,\noften focused on learning models with certain invariance properties. In this\nwork, we draw a link between OOD performance and model calibration, arguing\nthat calibration across multiple domains can be viewed as a special case of an\ninvariant representation leading to better OOD generalization. Specifically, we\nshow that under certain conditions, models which achieve \\emph{multi-domain\ncalibration} are provably free of spurious correlations. This leads us to\npropose multi-domain calibration as a measurable and trainable surrogate for\nthe OOD performance of a classifier. We therefore introduce methods that are\neasy to apply and allow practitioners to improve multi-domain calibration by\ntraining or modifying an existing model, leading to better performance on\nunseen domains. Using four datasets from the recently proposed WILDS OOD\nbenchmark, as well as the Colored MNIST dataset, we demonstrate that training\nor tuning models so they are calibrated across multiple domains leads to\nsignificantly improved performance on unseen test domains. We believe this\nintriguing connection between calibration and OOD generalization is promising\nfrom both a practical and theoretical point of view.\n"}
+{"abstract": "  In quantum computation theory, quantum random walks have been utilized by\nmany quantum search algorithms which provide improved performance over their\nclassical counterparts. However, due to the importance of the quantum\ndecoherence phenomenon, decoherent quantum walks and their applications have\nbeen studied on a wide variety of structures. Recently, a unified\ntime-inhomogeneous coin-turning random walk with rescaled limiting\ndistributions, Bernoulli, uniform, arcsine and semi-circle laws as parameter\nvaries have been obtained. In this paper we study the quantum analogue of these\nmodels. We obtained a representation theorem for time-inhomogeneous quantum\nwalk on discrete infinite state space. Additionally, the convergence of the\ndistributions of the decoherent quantum walks are numerically estimated as an\napplication of the representation theorem, and the convergence in distribution\nof the quantum analogues of Bernoulli, uniform, arcsine and semicircle laws are\nstatistically analyzed.\n"}
+{"abstract": "  A detailed X-ray Soft Excess of the narrow-line Seyfert-1 galaxy Tonantzintla\nS180 (Ton s180) present. The study used four XMM-Newton observations for period\nfrom the year 2000 to the year 2016 taken from the XMM-Newton archive. Two\ndifferent models were used to treat the X-ray Soft Excess for each data\nseparately, the first model is power law component and the second one is two\nblack body components. We found that, for all observations, the spectra fitted\nby using a power law component are poor and the output parameters much\ndifferent from the previous studies, this model unsuccessfully reproduces the\ncontinuum shape of the spectrum, including the soft excess, of all observations\n(Nh = 3.62 +-1020 cm^{-2} and photon index for the hard and soft bands are 3.31\nand 1.57). The study proved that it can rely upon the two black body components\nin treating X-ray Soft Excess of the narrow-line Seyfert-1 galaxy (Nh= 1.395 +-\n10^20 cm^{-2} and The temperature of the two black-body components (KT) are\n0.075 and 0.17 keV).\n"}
+{"abstract": "  Object tracking becomes critical especially when similar objects are present\nin the same area. Recent state-of-the-art (SOTA) approaches are proposed based\non taking a matching network with a heavy structure to distinguish the target\nfrom other objects in the area which indeed drastically downgrades the\nperformance of the tracker in terms of speed. Besides, several candidates are\nconsidered and processed to localize the intended object in a region of\ninterest for each frame which is time-consuming. In this article, a special\narchitecture is proposed based on which in contrast to the previous approaches,\nit is possible to identify the object location in a single shot while taking\nits template into account to distinguish it from the similar objects in the\nsame area. In brief, first of all, a window containing the object with twice\nthe target size is considered. This window is then fed into a fully\nconvolutional neural network (CNN) to extract a region of interest (RoI) in a\nform of a matrix for each of the frames. In the beginning, a template of the\ntarget is also taken as the input to the CNN. Considering this RoI matrix, the\nnext movement of the tracker is determined based on a simple and fast method.\nMoreover, this matrix helps to estimate the object size which is crucial when\nit changes over time. Despite the absence of a matching network, the presented\ntracker performs comparatively with the SOTA in challenging situations while\nhaving a super speed compared to them (up to $120 FPS$ on 1080ti). To\ninvestigate this claim, a comparison study is carried out on the GOT-10k\ndataset. Results reveal the outstanding performance of the proposed method in\nfulfilling the task.\n"}
+{"abstract": "  A sound and complete algorithm for nominal unification of higher-order\nexpressions with a recursive let is described, and shown to run in\nnondeterministic polynomial time. We also explore specializations like nominal\nletrec-matching for expressions, for DAGs, and for garbage-free expressions and\ndetermine their complexity. Finally, we also provide a nominal unification\nalgorithm for higher-order expressions with recursive let and atom-variables,\nwhere we show that it also runs in nondeterministic polynomial time.\n"}
+{"abstract": "  We develop a detailed theoretical model of photo-induced proton-coupled\nelectron transfer (PPCET) processes, which are at the basis of solar energy\nharvesting in biological systems and photovoltaic materials. Our model enables\nto analyze the dynamics and the efficiency of a PPCET reaction under the\ninfluence of a thermal environment by disentangling the contribution of the\nfundamental electron transfer (ET) and proton transfer (PT) steps. In order to\nstudy quantum dynamics of the PPCET process under an interaction with\nnon-Markovian environment we employ the hierarchical equations of motion\n(HEOM). We calculate transient absorption spectroscopy (TAS) and a newly\ndefined two-dimensional resonant electronic-vibrational spectroscopy (2DREVS)\nsignals in order to study the nonequilibrium reaction dynamics. Our results\nshow that different transition pathways can be separated by TAS and 2DREVS.\n"}
+{"abstract": "  We introduce two new notions of stratifications in valued fields:\nt$^2$-stratifications and arc-wise analytic t-stratifications. We show the\nexistence of arc-wise analytic t-stratifications in algebraically closed valued\nfields with analytic structure in the sense of R. Cluckers and L. Lipshitz. We\nprove that arc-wise analytic t-stratifications are t$^2$-stratifications and,\nmoreover, that t$^2$-stratifications are valuative Lipschitz stratifications as\ndefined by the second author and Y. Yin (the latter ones being closely related\nto Lipschitz stratifications in the sense of Mostowski). Finally, we introduce\na combinatorial invariant associated to a t-stratification which we call the\ncritical value function. We explain how the critical value function of arc-wise\nanalytic t-stratifications can be used to formulate programatic conjectural\nbounds for the Nash-Semple conjecture.\n"}
+{"abstract": "  This paper analyzes difference-in-differences setups with a continuous\ntreatment. We show that treatment effect on the treated-type parameters can be\nidentified under a generalized parallel trends assumption that is similar to\nthe binary treatment setup. However, interpreting differences in these\nparameters across different values of the treatment can be particularly\nchallenging due to treatment effect heterogeneity. We discuss alternative,\ntypically stronger, assumptions that alleviate these challenges. We also\nprovide a variety of treatment effect decomposition results, highlighting that\nparameters associated with popular two-way fixed-effect specifications can be\nhard to interpret, even when there are only two time periods. We introduce\nalternative estimation strategies that do not suffer from these drawbacks. Our\nresults also cover cases where (i) there is no available untreated comparison\ngroup and (ii) there are multiple periods and variation in treatment timing,\nwhich are both common in empirical work.\n"}
+{"abstract": "  While past information technology (IT) advances have transformed society,\nfuture advances hold even greater promise. For example, we have only just begun\nto reap the changes from artificial intelligence (AI), especially machine\nlearning (ML). Underlying IT's impact are the dramatic improvements in computer\nhardware, which deliver performance that unlock new capabilities. For example,\nrecent successes in AI/ML required the synergy of improved algorithms and\nhardware architectures (e.g., general-purpose graphics processing units).\nHowever, unlike in the 20th Century and early 2000s, tomorrow's performance\naspirations must be achieved without continued semiconductor scaling formerly\nprovided by Moore's Law and Dennard Scaling. How will one deliver the next 100x\nimprovement in capability at similar or less cost to enable great value? Can we\nmake the next AI leap without 100x better hardware?\n  This whitepaper argues for a multipronged effort to develop new computing\napproaches beyond Moore's Law to advance the foundation that computing provides\nto US industry, education, medicine, science, and government. This impact\nextends far beyond the IT industry itself, as IT is now central for providing\nvalue across society, for example in semi-autonomous vehicles, tele-education,\nhealth wearables, viral analysis, and efficient administration. Herein we draw\nupon considerable visioning work by CRA's Computing Community Consortium (CCC)\nand the IEEE Rebooting Computing Initiative (IEEE RCI), enabled by thought\nleader input from industry, academia, and the US government.\n"}
+{"abstract": "  Symmetry-protected topological edge modes are one of the most remarkable\nphenomena in topological physics. Here, we formulate and quantitatively examine\nthe effect of a quantum bath on these topological edge modes. Using the density\nmatrix renormalization group method, we study the ground state of a composite\nsystem of spin-1 quantum chain, where the system and the bath degrees of\nfreedom are treated on the same footing. We focus on the dependence of these\nedge modes on the global/partial symmetries of system-bath coupling and on the\nfeatures of the quantum bath. It is shown that the time-reversal symmetry(TRS)\nplays a special role for an open quantum system, where an emergent partial TRS\nbreaking will result in a TRS-protected topological mode diffusing from the\nsystem edge into the bath, thus make it useless for quantum computation.\n"}
+{"abstract": "  We revise the sunspot observations made by Galileo Galilei and Christoph\nScheiner in the context of their controversy on the nature of sunspots. Their\nsunspot records not included in the current sunspot group database, used as a\nbasis to calculate the sunspot group number, are analyzed. Within the\ndocumentary sources consulted in this work, we can highlight the sunspot\nobservations by Scheiner included in the letters sent under the pseudonym\nApelles to Marcus Welser and the first sunspot observations made by Galileo,\nwhich can be consulted in Le opere di Galileo Galilei. These sunspot\nobservations would extend the temporal coverage for these two observers and\nfilling some gaps in the current group database in the earliest period where\nthe data available is sparse. Moreover, we have detected changes in the quality\nof the sunspot drawings made by Galileo and Scheiner in their observation\nseries affecting to the number of groups recorded by the two observers. We also\ncompare these records with sunspot observations made by other astronomers of\nthat time. According to this comparison and regarding the same observation\ndays, Scheiner was generally the astronomer who reported more sunspot groups\nwhile Harriot, Cigoli, and Galileo recorded a similar number of groups. We\nconclude these differences are mainly because of the observational method used\nby the observers.\n"}
+{"abstract": "  When compact manifolds $X$ and $Y$ are both even dimensional, their Euler\ncharacteristics obey the K\\\"unneth formula $\\chi(X\\times Y)=\\chi(X) \\chi(Y)$.\nIn terms of the Betti numbers $b_p(X)$, $\\chi(X)=\\sum_{p}(-1)^p b_p(X)$,\nimplying that $\\chi(X)=0$ when $X$ is odd dimensional. We seek a linear\ncombination of Betti numbers, called $\\rho$, that obeys an analogous formula\n$\\rho(X\\times Y)=\\chi(X) \\rho(Y)$ when $Y$ is odd dimensional. The unique\nsolution is $\\rho(Y)=-\\sum_{p}(-1)^p p b_p(Y)$. Physical applications include:\n(1) $\\rho \\rightarrow (-1)^m \\rho $ under a generalized mirror map in $d=2m+1$\ndimensions, in analogy with $\\chi \\rightarrow (-1)^m \\chi $ in $d=2m$; (2)\n$\\rho$ appears naturally in compactifications of M-theory. For example, the\n4-dimensional Weyl anomaly for M-theory on $X^4 \\times Y^7$ is given by\n$\\chi(X^4)\\rho(Y^7)=\\rho(X^4 \\times Y^7) $ and hence vanishes when $Y^7$ is\nself-mirror. Since, in particular, $\\rho(Y\\times S^1)=\\chi(Y)$, this is\nconsistent with the corresponding anomaly for Type IIA on $X^4 \\times Y^6$,\ngiven by $\\chi(X^4)\\chi(Y^6)=\\chi(X^4 \\times Y^6)$, which vanishes when $Y^6$\nis self-mirror; (3) In the partition function of $p$-form gauge fields, $\\rho$\nappears in odd dimensions as $\\chi$ does in even.\n"}
+{"abstract": "  In this article we study measurement circuit effects in three-terminal\nelectrical transport measurements arising from finite line impedances. We\nprovide exact expressions relating the measured voltages and differential\nconductances to their values at the device under test, which allow for spurious\nvoltage divider effects to be corrected. Finally, we test the implementation of\nthese corrections with experimental measurements.\n"}
+{"abstract": "  We prove that the Khovanov spectra associated to links and tangles are\nfunctorial up to homotopy and sign.\n"}
+{"abstract": "  Understanding influencing factors is essential for the surveillance and\nprevention of infectious diseases, and the factors are likely to vary spatially\nand temporally as the disease progresses. Taking daily cases and deaths data\nduring the coronavirus disease 2019 (COVID-19) outbreak in the U.S. as a case\nstudy, we develop a mobility-augmented geographically and temporally weighted\nregression (M-GTWR) model to quantify the spatiotemporal impacts of\nsocial-demographic factors and human activities on the COVID-19 dynamics.\nDifferent from the base GTWR model, we incorporate a mobility-adjusted distance\nweight matrix where travel mobility is used in addition to the spatial\nadjacency to capture the correlations among local observations. The model\nresiduals suggest that the proposed model achieves a substantial improvement\nover other benchmark methods in addressing the spatiotemporal nonstationarity.\nOur results reveal that the impacts of social-demographic and human activity\nvariables present significant spatiotemporal heterogeneity. In particular, a 1%\nincrease in population density may lead to 0.63% and 0.71% more daily cases and\ndeaths, and a 1% increase in the mean commuting time may result in 0.22% and\n0.95% increases in daily cases and deaths. Although increased human activities\nwill, in general, intensify the disease outbreak, we report that the effects of\ngrocery and pharmacy-related activities are insignificant in areas with high\npopulation density. And activities at the workplace and public transit are\nfound to either increase or decrease the number of cases and deaths, depending\non particular locations. The results of our study establish a quantitative\nframework for identifying influencing factors during a disease outbreak, and\nthe obtained insights may have significant implications in guiding the\npolicy-making against infectious diseases.\n"}
+{"abstract": "  We examine the Kerr/CFT correspondence in Einstein gravity extended with\nquadratic curvature invariants. We consider two explicit examples in four and\nfive dimensions and compute the central charges of the asymptotic symmetry\nalgebras of the near horizon geometries, using the improved version of the BBC\nformalism that encompasses the information of the Lagrangian. We find that the\nresulting Cardy entropy differs from the Wald entropy, caused by the\nRiemann-squared $R^{\\mu\\nu\\rho\\sigma}R_{\\mu\\nu\\rho\\sigma}$ term.\n"}
+{"abstract": "  We examine the use of linear and non-linear dimensionality reduction\nalgorithms for extracting low-rank feature representations for speech emotion\nrecognition. Two feature sets are used, one based on low-level descriptors and\ntheir aggregations (IS10) and one modeling recurrence dynamics of speech (RQA),\nas well as their fusion. We report speech emotion recognition (SER) results for\nlearned representations on two databases using different classification\nmethods. Classification with low-dimensional representations yields performance\nimprovement in a variety of settings. This indicates that dimensionality\nreduction is an effective way to combat the curse of dimensionality for SER.\nVisualization of features in two dimensions provides insight into\ndiscriminatory abilities of reduced feature sets.\n"}
+{"abstract": "  We investigate special structures due to automorphisms in isogeny graphs of\nprincipally polarized abelian varieties, and abelian surfaces in particular. We\ngive theoretical and experimental results on the spectral and statistical\nproperties of (2, 2)-isogeny graphs of superspecial abelian surfaces, including\nstationary distributions for random walks, bounds on eigenvalues and diameters,\nand a proof of the connectivity of the Jacobian subgraph of the (2, 2)-isogeny\ngraph. Our results improve our understanding of the performance and security of\nsome recently-proposed cryptosystems, and are also a concrete step towards a\nbetter understanding of general superspecial isogeny graphs in arbitrary\ndimension.\n"}
+{"abstract": "  Controlling magnetism using voltage is highly desired for applications, but\nremains challenging due to fundamental contradiction between polarity and\nmagnetism. Here we propose a mechanism to manipulate magnetic domain walls in\nferrimagnetic or ferromagnetic multiferroics using electric field. Different\nfrom those studies based on static domain-level couplings, here the\nmagnetoelectric coupling relies on the collaborative spin dynamics around\ndomain walls. Accompanying the reversal of spin chirality driven by\npolarization switching, a \"rolling-downhill\"-like motion of domain wall is\nachieved at the nanoscale, which tunes the magnetization locally. Our mechanism\nopens an alternative route to pursuit practical and fast converse\nmagnetoelectric functions via spin dynamics.\n"}
+{"abstract": "  Fourth-order differential equations play an important role in many\napplications in science and engineering. In this paper, we present a\nthree-field mixed finite-element formulation for fourth-order problems, with a\nfocus on the effective treatment of the different boundary conditions that\narise naturally in a variational formulation. Our formulation is based on\nintroducing the gradient of the solution as an explicit variable, constrained\nusing a Lagrange multiplier. The essential boundary conditions are enforced\nweakly, using Nitsche's method where required. As a result, the problem is\nrewritten as a saddle-point system, requiring analysis of the resulting\nfinite-element discretization and the construction of optimal linear solvers.\nHere, we discuss the analysis of the well-posedness and accuracy of the\nfinite-element formulation. Moreover, we develop monolithic multigrid solvers\nfor the resulting linear systems. Two and three-dimensional numerical results\nare presented to demonstrate the accuracy of the discretization and efficiency\nof the multigrid solvers proposed.\n"}
+{"abstract": "  Context: Open Source Software (OSS) projects rely on a continuous stream of\nnew contributors for sustainable livelihood. Recent studies reported that new\ncontributors experience many barriers in their first contribution. One of the\ncritical barriers is the social barrier. Although a number of studies\ninvestigated the social barriers to new contributors, to the best of our\nknowledge, the relationship between the first response to the first\ncontributions and their future contributions has not been studied\ncomprehensively.\n  Objective: In this registered report, we introduce the study protocols that\ninvestigate the correlation between the first response given to the first\ncontributions and the future contribution. First, we performed a preliminary\nsurvey to manually explore the sentiments of the first response. Preliminary\nanalysis confirms that the first responses are mainly neutral.\n  Method: Our execution plan includes both qualitative and quantitative\napproaches with three research questions. We inspect the first response of the\nfirst contributions, investigate the effects of characteristics of the first\nresponse to the interaction between first-time-contributor and project\ncontributors, and find the impact of the interactions between other\ncontributors.\n"}
+{"abstract": "  Automating a robotic task, e.g., robotic suturing can be very complex and\ntime-consuming. Learning a task model to autonomously perform the task is\ninvaluable making the technology, robotic surgery, accessible for a wider\ncommunity. The data of robotic surgery can be easily logged where the collected\ndata can be used to learn task models. This will result in reduced time and\ncost of robotic surgery in which a surgeon can supervise the robot operation or\ngive high-level commands instead of low-level control of the tools. We present\na data-set of needle insertion in soft tissue with two arms where Arm 1 inserts\nthe needle into the tissue and Arm 2 actively manipulate the soft tissue to\nensure the desired and actual exit points are the same. This is important in\nreal-surgery because suturing without active manipulation of tissue may yield\nfailure of the suturing as the stitch may not grip enough tissue to resist the\nforce applied for the suturing. We present a needle insertion dataset including\n60 successful trials recorded by 3 pair of stereo cameras. Moreover, we present\nDeep-robot Learning from Demonstrations that predicts the desired state of the\nrobot at the time step after t (which the optimal action taken at t yields) by\nlooking at the video of the past time steps, i.e. n step time history where N\nis the memory time window, of the task execution. The experimental results\nillustrate our proposed deep model architecture is outperforming the existing\nmethods. Although the solution is not yet ready to be deployed on a real robot,\nthe results indicate the possibility of future development for real robot\ndeployment.\n"}
+{"abstract": "  This paper deals with the issue of concept drift in supervised machine\nlearn-ing. We make use of graphical models to elicit the visible structure of\nthe dataand we infer from there changes in the hidden context. Differently from\nprevious concept-drift detection methods, this application does not depend on\nthe supervised machine learning model in use for a specific target variable,\nbut it tries to assess the concept drift as independent characteristic of the\nevolution of a dataset. Specifically, we investigate how a graphical model\nevolves by looking at the creation of new links and the disappearing of\nexisting ones in different time periods. The paper suggests a method that\nhighlights the changes and eventually produce a metric to evaluate the\nstability over time. The paper evaluate the method with real world data on the\nAustralian Electric market.\n"}
+{"abstract": "  Strong Parallel Repetition for Unique Games on Small Set Expanders\n  The strong parallel repetition problem for unique games is to efficiently\nreduce the 1-delta vs. 1-C*delta gap problem of Boolean unique games (where C>1\nis a sufficiently large constant) to the 1-epsilon vs. epsilon gap problem of\nunique games over large alphabet. Due to its importance to the Unique Games\nConjecture, this problem garnered a great deal of interest from the research\ncommunity. There are positive results for certain easy unique games (e.g.,\nunique games on expanders), and an impossibility result for hard unique games.\n  In this paper we show how to bypass the impossibility result by enlarging the\nalphabet sufficiently before repetition. We consider the case of unique games\non small set expanders for two setups: (i) Strong small set expanders that\nyield easy unique games. (ii) Weaker small set expanders underlying possibly\nhard unique games as long as the game is mildly fortified. We show how to\nfortify unique games in both cases, i.e., how to transform the game so\nsufficiently large induced sub-games have bounded value. We then prove strong\nparallel repetition for the fortified games. Prior to this work fortification\nwas known for projection games but seemed hopeless for unique games.\n"}
+{"abstract": "  Absorption induced transparency is an optical phenomenon that occurs in\nmetallic arrays of nanoholes when materials featuring narrow lines in their\nabsorption spectra are deposited on top of it. First reported in the visible\nrange, using dye lasers as cover materials, it has been described as\ntransmission peaks unexpectedly close to the absorption energies of the dye\nlaser. In this work, amplification of light is demonstrated in the active\nregime of absorption induced transparency. Amplification of stimulated emission\ncan be achieved when the dye laser behaves as a gain material. Intense\nillumination can modify the dielectric constant of the gain material, which in\nturn, changes the propagation properties of the plasmonic modes excited in the\nhole arrays, providing both less damping to light and further feedback,\nenhancing the stimulated emission process.\n"}
+{"abstract": "  We construct the semi-infinite tensor structure on the semiderived category\nof quasi-coherent torsion sheaves on an ind-scheme endowed with a flat affine\nmorphism into an ind-Noetherian ind-scheme with a dualizing complex. The\nsemitensor product is \"a mixture of\" the cotensor product along the base and\nthe derived tensor product along the fibers. The inverse image of the dualizing\ncomplex is the unit object. This construction is a partial realization of the\nSemi-infinite Algebraic Geometry program, as outlined in the introduction to\narXiv:1504.00700.\n"}
+{"abstract": "  We study reinforcement learning (RL) with no-reward demonstrations, a setting\nin which an RL agent has access to additional data from the interaction of\nother agents with the same environment. However, it has no access to the\nrewards or goals of these agents, and their objectives and levels of expertise\nmay vary widely. These assumptions are common in multi-agent settings, such as\nautonomous driving. To effectively use this data, we turn to the framework of\nsuccessor features. This allows us to disentangle shared features and dynamics\nof the environment from agent-specific rewards and policies. We propose a\nmulti-task inverse reinforcement learning (IRL) algorithm, called \\emph{inverse\ntemporal difference learning} (ITD), that learns shared state features,\nalongside per-agent successor features and preference vectors, purely from\ndemonstrations without reward labels. We further show how to seamlessly\nintegrate ITD with learning from online environment interactions, arriving at a\nnovel algorithm for reinforcement learning with demonstrations, called $\\Psi\n\\Phi$-learning (pronounced `Sci-Fi'). We provide empirical evidence for the\neffectiveness of $\\Psi \\Phi$-learning as a method for improving RL, IRL,\nimitation, and few-shot transfer, and derive worst-case bounds for its\nperformance in zero-shot transfer to new tasks.\n"}
+{"abstract": "  We show that general parity-violating 3d conformal field theories show a\ndouble copy structure for momentum space 3-point functions of conserved\ncurrents, stress tensor and marginal scalar operators. Splitting up the CFT\ncorrelator into two parts - called homogeneous and non-homogeneous - we show\nthat double copy relations exist for each part separately. We arrive at similar\nconclusions regarding double copy structures using tree-level correlators of\nmassless fields in $dS_4$. We also discuss the flat space limit of these\ncorrelators. We further extend the double copy analysis to correlators\ninvolving higher-spin conserved currents, which suggests that the spin-$s$\ncurrent correlator can be thought of as $s$ copies of the spin one current\ncorrelator.\n"}
+{"abstract": "  We consider matter density effects in theories with a false ground state.\nLarge and dense systems, such as stars, can destabilize a metastable minimum\nand allow for the formation of bubbles of the true minimum. We derive the\nconditions under which these bubbles form, as well as the conditions under\nwhich they either remain confined to the dense region or escape to infinity.\nThe latter case leads to a phase transition in the universe at star formation.\nWe explore the phenomenological consequences of such seeded phase transitions.\n"}
+{"abstract": "  The modification of the rotating vector model in the case of magnetars are\ncalculated. Magnetars may have twisted magnetic field compared with normal\npulsars. The polarization position angle of magnetars will change in the case\nof a twisted magnetic field. For a twisted dipole field, we found that the\nposition angle will change both vertically and horizontally. During the\nuntwisting process of the magnetar magnetosphere, the modifications of the\nposition angle will evolve with time monotonously. This may explain the\nevolution of the position angle in magnetar PSR J1622-4950 and XTE J1810-197.\nThe relation between the emission point and the line of sight will also change.\nWe suggest every magnetospheric models of magnetars also calculate the\ncorresponding changes of position angle in their models. Order of magnitude\nestimation formula for doing this is given. This opens the possibility to\nextract the magnetic field geometry of magnetars from their radio polarization\nobservations.\n"}
+{"abstract": "  Although vital to computer vision systems, few-shot action recognition is\nstill not mature despite the wide research of few-shot image classification.\nPopular few-shot learning algorithms extract a transferable embedding from seen\nclasses and reuse it on unseen classes by constructing a metric-based\nclassifier. One main obstacle to applying these algorithms in action\nrecognition is the complex structure of videos. Some existing solutions sample\nframes from a video and aggregate their embeddings to form a video-level\nrepresentation, neglecting important temporal relations. Others perform an\nexplicit sequence matching between two videos and define their distance as\nmatching cost, imposing too strong restrictions on sequence ordering. In this\npaper, we propose Compromised Metric via Optimal Transport (CMOT) to combine\nthe advantages of these two solutions. CMOT simultaneously considers semantic\nand temporal information in videos under Optimal Transport framework, and is\ndiscriminative for both content-sensitive and ordering-sensitive tasks. In\ndetail, given two videos, we sample segments from them and cast the calculation\nof their distance as an optimal transport problem between two segment\nsequences. To preserve the inherent temporal ordering information, we\nadditionally amend the ground cost matrix by penalizing it with the positional\ndistance between a pair of segments. Empirical results on benchmark datasets\ndemonstrate the superiority of CMOT.\n"}
+{"abstract": "  For open and singular varieties in positive characteristic p we study the\nexistence of an integral p-adic cohomology theory which is finitely generated,\ncompatible with log crystalline cohomology and rationally compatible with rigid\ncohomology. We develop such a theory under certain assumptions of resolution of\nsingularities in positive characteristic, by using cdp- and rh-topologies.\n  Without resolution of singularities in positive characteristic, we prove the\nexistence of a good p-adic cohomology theory for open and singular varieties in\ncohomological degree 1, by using split proper generically \\'etale\nhypercoverings. This is a slight generalisation of a result due to\nAndreatta--Barbieri-Viale. We also prove that this approach does not work for\nhigher cohomological degrees.\n"}
+{"abstract": "  Context. Centaurs go around the Sun between the orbits of Jupiter and\nNeptune. Only a fraction of the known centaurs have been found to display\ncomet-like features. Comet 29P/Schwassmann-Wachmann 1 is the most remarkable\nactive centaur. It orbits the Sun just beyond Jupiter in a nearly circular\npath. Only a handful of known objects follow similar trajectories.\n  Aims. We present photometric observations of 2020 MK4, a recently found\ncentaur with an orbit not too different from that of 29P, and we perform a\npreliminary exploration of its dynamical evolution.\n  Methods. We analyzed broadband Cousins R and Sloan g', r', and i' images of\n2020 MK4 acquired with the Jacobus Kapteyn Telescope and the IAC80 telescope to\nsearch for cometary-like activity, and to derive its surface colors and size.\nIts orbital evolution was studied using direct N-body simulations.\n  Results. Centaur 2020 MK4 is neutral-gray in color and has a faint, compact\ncometary-like coma. The values of its color indexes, (g'-r')=0.42+/-0.04 and\n(r'-i')=0.17+/-0.04, are similar to the solar ones. A lower limit for the\nabsolute magnitude of the nucleus is Hg=11.30+/-0.03 mag which, for an albedo\nin the range of 0.1-0.04, gives an upper limit for its size in the interval\n(23, 37) km. Its orbital evolution is very chaotic and 2020 MK4 may be ejected\nfrom the Solar System during the next 200 kyr. Comet 29P experienced relatively\nclose flybys with 2020 MK4 in the past, sometimes when they were temporary\nJovian satellites.\n  Conclusions. We confirm the presence of a coma of material around a central\nnucleus. Its surface colors place this centaur among the most extreme members\nof the gray group. Although its past, present, and future dynamical evolution\nresembles that of 29P, more data are required to confirm or reject a possible\nconnection between the two objects and perhaps others.\n"}
+{"abstract": "  The action of four-dimensional Horndeski gravity coupled to Born-Infeld\nelectromagnetic fields is given via the Kaluza-Klein process. Dyonic black hole\nsolution of the theory is constructed. The metric is devoid of singularity at\nthe origin independent of the parameter selections, this property is different\nfrom the one of Einstein-Born-Infeld black holes. Thermodynamics of the black\nhole is studied, thermodynamic quantities are calculated and the first law is\nchecked to be satisfied. Thermodynamic phase transitions of the black holes are\nstudied in extended phase space.\n"}
+{"abstract": "  A flow view is the graph of a measurable conjugacy between a substitution or\nS-adic subshift and an exchange of infinitely many intervals (IIET) in [0,1].\nThe natural refining sequence of partitions of the sequence space is\ntransferred to [0,1] using a canonical addressing scheme, a fixed dual\nsubstitution, and a shift-invariant probability measure. On the flow view, a\nsequence is shown horizontally at a height given by the conjugacy.\n  The IIET is well approximated by exchanges of finitely many intervals, making\nnumeric and graphic methods possible. The graphs of the IIETs show forms of\nself-similarity, a special case of which is proved. The spectral type of the\nconjugacy itself is of particular interest. As an example of utility, some\nspectral results for constant-length substitutions are included.\n"}
+{"abstract": "  Existing deep neural network (DNN) based wireless localization approaches\ntypically do not capture uncertainty inherent in their estimates. In this work,\nwe propose and evaluate variational and scalable DNN approaches to measure the\nuncertainty as a result of changing propagation conditions and the finite\nnumber of training samples. Furthermore, we show that data uncertainty is\nsufficient to capture the uncertainty due to non-line-of-sight (NLOS) and,\nmodel uncertainty improves the overall reliability. To assess the robustness\ndue to channel conditions and out-of-set regions, we evaluate the methods on\nchallenging massive multiple-input multiple-output (MIMO) scenarios.\n"}
+{"abstract": "  We have formulated elastic seismic full waveform inversion (FWI) within a\ndeep learning environment. In our formulation, a recurrent neural network is\nset up with rules enforcing elastic wave propagation, with the wavefield\nprojected onto a measurement surface acting as the synthetic data to be\ncompared with observed seismic data. Gradients for iterative updating of an\nelastic model, with a variety of parameterizations and misfit functionals, can\nbe efficiently constructed within the network through the automatic\ndifferential method. With this method, the inversion based on complex misfits\ncan be calculated. We survey the impact of different complex misfits (based on\nthe l2, l1 ) with high order total variation (TV) regulations on multiparameter\nelastic FWI recovery of models within velocity/density, modulus/density, and\nstiffness parameter/density parameterizations. We analyze parameter cross-talk.\nInversion results on simple and complex models show that the RNN elastic FWI\nwith high order TV regulation using l1 norm can help mitigate cross-talk issues\nwith gradient-based optimization methods.\n"}
+{"abstract": "  All pandemics are local; so learning about the impacts of pandemics on public\nhealth and related societal issues at granular levels is of great interest.\nCOVID-19 is affecting everyone in the globe and mask wearing is one of the few\nprecautions against it. To quantify people's perception of mask effectiveness\nand to prevent the spread of COVID-19 for small areas, we use Understanding\nAmerica Study's (UAS) survey data on COVID-19 as our primary data source. Our\ndata analysis shows that direct survey-weighted estimates for small areas could\nbe highly unreliable. In this paper we develop a synthetic estimation method to\nestimate proportions of mask effectiveness for small areas using a logistic\nmodel that combines information from multiple data sources. We select our\nworking model using an extensive data analysis facilitated by a new variable\nselection criterion for survey data and benchmarking ratios. We propose a\nJackknife method to estimate variance of our proposed estimator. From our data\nanalysis. it is evident that our proposed synthetic method outperforms direct\nsurvey-weighted estimator with respect to commonly used evaluation measures.\n"}
+{"abstract": "  Semantics has become a key topic of research in Genetic Programming (GP).\nSemantics refers to the outputs (behaviour) of a GP individual when this is run\non a data set. The majority of works that focus on semantic diversity in\nsingle-objective GP indicates that it is highly beneficial in evolutionary\nsearch. Surprisingly, there is minuscule research conducted in semantics in\nMulti-objective GP (MOGP). In this work we make a leap beyond our understanding\nof semantics in MOGP and propose SDO: Semantic-based Distance as an additional\ncriteriOn. This naturally encourages semantic diversity in MOGP. To do so, we\nfind a pivot in the less dense region of the first Pareto front (most promising\nfront). This is then used to compute a distance between the pivot and every\nindividual in the population. The resulting distance is then used as an\nadditional criterion to be optimised to favour semantic diversity. We also use\ntwo other semantic-based methods as baselines, called Semantic Similarity-based\nCrossover and Semantic-based Crowding Distance. Furthermore, we also use the\nNSGA-II and the SPEA2 for comparison too. We use highly unbalanced binary\nclassification problems and consistently show how our proposed SDO approach\nproduces more non-dominated solutions and better diversity, leading to better\nstatistically significant results, using the hypervolume results as evaluation\nmeasure, compared to the rest of the other four methods.\n"}
+{"abstract": "  In this paper, we prove the Khavinson conjecture for hyperbolic harmonic\nfunctions on the unit ball. This conjecture was partially solved in\n\\cite{JKM2020}.\n"}
+{"abstract": "  We calculate the nucleon matrix element of Weinberg's dimension-6 CP-odd\ngluon operator $f^{abc} (\\tilde F_{\\mu\\nu})^a (F^{\\mu\\rho})^b\n(F^{\\nu}_{\\;\\;\\rho})^c$ in the instanton vacuum. In leading order of the\ninstanton packing fraction, the dimension-6 operator is effectively\nproportional to the topological charge density $(\\tilde F_{\\mu\\nu})^a\n(F^{\\mu\\nu})^a$, whose nucleon matrix element is given by the flavor-singlet\naxial charge and constrained by the $U(1)_A$ anomaly. The nucleon matrix\nelement of the dimension-6 operator is obtained substantially larger than in\nother estimates, because of the strong localization of the nonperturbative\ngluon fields in the instanton vacuum. We argue that the neutron electric dipole\nmoment induced by the dimension-6 operator is nevertheless of conventional\nsize.\n"}
+{"abstract": "  Recently, the power of unconditional image synthesis has significantly\nadvanced through the use of Generative Adversarial Networks (GANs). The task of\ninverting an image into its corresponding latent code of the trained GAN is of\nutmost importance as it allows for the manipulation of real images, leveraging\nthe rich semantics learned by the network. Recognizing the limitations of\ncurrent inversion approaches, in this work we present a novel inversion scheme\nthat extends current encoder-based inversion methods by introducing an\niterative refinement mechanism. Instead of directly predicting the latent code\nof a given real image using a single pass, the encoder is tasked with\npredicting a residual with respect to the current estimate of the inverted\nlatent code in a self-correcting manner. Our residual-based encoder, named\nReStyle, attains improved accuracy compared to current state-of-the-art\nencoder-based methods with a negligible increase in inference time. We analyze\nthe behavior of ReStyle to gain valuable insights into its iterative nature. We\nthen evaluate the performance of our residual encoder and analyze its\nrobustness compared to optimization-based inversion and state-of-the-art\nencoders.\n"}
+{"abstract": "  Information theory can be used to analyze the cost-benefit of visualization\nprocesses. However, the current measure of benefit contains an unbounded term\nthat is neither easy to estimate nor intuitive to interpret. In this work, we\npropose to revise the existing cost-benefit measure by replacing the unbounded\nterm with a bounded one. We examine a number of bounded measures that include\nthe Jenson-Shannon divergence and a new divergence measure formulated as part\nof this work. We describe the rationale for proposing a new divergence measure.\nAs the first part of comparative evaluation, we use visual analysis to support\nthe multi-criteria comparison, narrowing the search down to several options\nwith better mathematical properties. The theoretical discourse and conceptual\nevaluation in this paper provide the basis for further comparative evaluation\nthrough synthetic and experimental case studies, which are to be reported in a\nseparate paper.\n"}
+{"abstract": "  We present a class of models of interacting lattice bosons which show\ncomplete Bose-Einstein condensation for the ground state.\n"}
+{"abstract": "  The recent progress of deep convolutional neural networks has enabled great\nsuccess in single image super-resolution (SISR) and many other vision tasks.\nTheir performances are also being increased by deepening the networks and\ndeveloping more sophisticated network structures. However, finding an optimal\nstructure for the given problem is a difficult task, even for human experts.\nFor this reason, neural architecture search (NAS) methods have been introduced,\nwhich automate the procedure of constructing the structures. In this paper, we\nexpand the NAS to the super-resolution domain and find a lightweight densely\nconnected network named DeCoNASNet. We use a hierarchical search strategy to\nfind the best connection with local and global features. In this process, we\ndefine a complexity-based penalty for solving image super-resolution, which can\nbe considered a multi-objective problem. Experiments show that our DeCoNASNet\noutperforms the state-of-the-art lightweight super-resolution networks designed\nby handcraft methods and existing NAS-based design.\n"}
+{"abstract": "  A 2-distance list k-coloring of a graph is a proper coloring of the vertices\nwhere each vertex has a list of at least k available colors and vertices at\ndistance at most 2 cannot share the same color. We prove the existence of a\n2-distance list $(\\Delta + 3)$-coloring for graphs with maximum average degree\nless than $\\frac83$ and maximum degree $\\Delta\\geq 4$ as well as graphs with\nmaximum average degree less than $\\frac{14}5$ and maximum degree $\\Delta\\geq\n6$.\n"}
+{"abstract": "  The recent surge in the adoption of machine learning techniques for materials\ndesign, discovery, and characterization has resulted in an increased interest\nand application of Image Driven Machine Learning (IDML) approaches. In this\nwork, we review the application of IDML to the field of materials\ncharacterization. A hierarchy of six action steps is defined which\ncompartmentalizes a problem statement into well-defined modules. The studies\nreviewed in this work are analyzed through the decisions adopted by them at\neach of these steps. Such a review permits a granular assessment of the field,\nfor example the impact of IDML on materials characterization at the nanoscale,\nthe number of images in a typical dataset required to train a semantic\nsegmentation model on electron microscopy images, the prevalence of transfer\nlearning in the domain, etc. Finally, we discuss the importance of\ninterpretability and explainability, and provide an overview of two emerging\ntechniques in the field: semantic segmentation and generative adversarial\nnetworks.\n"}
+{"abstract": "  Path finding is a well-studied problem in AI, which is often framed as graph\nsearch. Any-angle path finding is a technique that augments the initial graph\nwith additional edges to build shorter paths to the goal. Indeed, optimal\nalgorithms for any-angle path finding in static environments exist. However,\nwhen dynamic obstacles are present and time is the objective to be minimized,\nthese algorithms can no longer guarantee optimality. In this work, we elaborate\non why this is the case and what techniques can be used to solve the problem\noptimally. We present two algorithms, grounded in the same idea, that can\nobtain provably optimal solutions to the considered problem. One of them is a\nnaive algorithm and the other one is much more involved. We conduct a thorough\nempirical evaluation showing that, in certain setups, the latter algorithm\nmight be as fast as the previously-known greedy non-optimal solver while\nproviding solutions of better quality. In some (rare) cases, the difference in\ncost is up to 76%, while on average it is lower than one percent (the same cost\ndifference is typically observed between optimal and greedy any-angle solvers\nin static environments).\n"}
+{"abstract": "  We study the backward stability of running a backward stable eigenstructure\nsolver on a pencil $S(\\lambda)$ that is a strong linearization of a rational\nmatrix $R(\\lambda)$ expressed in the form $R(\\lambda)=D(\\lambda)+ C(\\lambda\nI_\\ell-A)^{-1}B$, where $D(\\lambda)$ is a polynomial matrix and $C(\\lambda\nI_\\ell-A)^{-1}B$ is a minimal state-space realization. We consider the family\nof block Kronecker linearizations of $R(\\lambda)$, which are highly structured\npencils. Backward stable eigenstructure solvers applied to $S(\\lambda)$ will\ncompute the exact eigenstructure of a perturbed pencil $\\widehat\nS(\\lambda):=S(\\lambda)+\\Delta_S(\\lambda)$ and the special structure of\n$S(\\lambda)$ will be lost. In order to link this perturbed pencil with a nearby\nrational matrix, we construct a strictly equivalent pencil $\\widetilde\nS(\\lambda)$ to $\\widehat S(\\lambda)$ that restores the original structure, and\nhence is a block Kronecker linearization of a perturbed rational matrix\n$\\widetilde R(\\lambda) = \\widetilde D(\\lambda)+ \\widetilde C(\\lambda I_\\ell-\n\\widetilde A)^{-1} \\widetilde B$, where $\\widetilde D(\\lambda)$ is a polynomial\nmatrix with the same degree as $D(\\lambda)$. Moreover, we bound appropriate\nnorms of $\\widetilde D(\\lambda)- D(\\lambda)$, $\\widetilde C - C$, $\\widetilde A\n- A$ and $\\widetilde B - B$ in terms of an appropriate norm of\n$\\Delta_S(\\lambda)$. These bounds may be inadmissibly large, but we also\nintroduce a scaling that allows us to make them satisfactorily tiny. Thus, for\nthis scaled representation, we prove that the staircase and the $QZ$ algorithms\ncompute the exact eigenstructure of a rational matrix $\\widetilde R(\\lambda)$\nthat can be expressed in exactly the same form as $R(\\lambda)$ with the\nparameters defining the representation very near to those of $R(\\lambda)$. This\nshows that this approach is backward stable in a structured sense.\n"}
+{"abstract": "  We present MultiBodySync, a novel, end-to-end trainable multi-body motion\nsegmentation and rigid registration framework for multiple input 3D point\nclouds. The two non-trivial challenges posed by this multi-scan multibody\nsetting that we investigate are: (i) guaranteeing correspondence and\nsegmentation consistency across multiple input point clouds capturing different\nspatial arrangements of bodies or body parts; and (ii) obtaining robust\nmotion-based rigid body segmentation applicable to novel object categories. We\npropose an approach to address these issues that incorporates spectral\nsynchronization into an iterative deep declarative network, so as to\nsimultaneously recover consistent correspondences as well as motion\nsegmentation. At the same time, by explicitly disentangling the correspondence\nand motion segmentation estimation modules, we achieve strong generalizability\nacross different object categories. Our extensive evaluations demonstrate that\nour method is effective on various datasets ranging from rigid parts in\narticulated objects to individually moving objects in a 3D scene, be it\nsingle-view or full point clouds.\n"}
+{"abstract": "  Sliced Stein discrepancy (SSD) and its kernelized variants have demonstrated\npromising successes in goodness-of-fit tests and model learning in high\ndimensions. Despite their theoretical elegance, their empirical performance\ndepends crucially on the search of optimal slicing directions to discriminate\nbetween two distributions. Unfortunately, previous gradient-based optimisation\napproaches for this task return sub-optimal results: they are computationally\nexpensive, sensitive to initialization, and they lack theoretical guarantees\nfor convergence. We address these issues in two steps. First, we provide\ntheoretical results stating that the requirement of using optimal slicing\ndirections in the kernelized version of SSD can be relaxed, validating the\nresulting discrepancy with finite random slicing directions. Second, given that\ngood slicing directions are crucial for practical performance, we propose a\nfast algorithm for finding such slicing directions based on ideas of active\nsub-space construction and spectral decomposition. Experiments on\ngoodness-of-fit tests and model learning show that our approach achieves both\nimproved performance and faster convergence. Especially, we demonstrate a\n14-80x speed-up in goodness-of-fit tests when comparing with gradient-based\nalternatives.\n"}
+{"abstract": "  Approximate Bayesian Computation (ABC) now serves as one of the major\nstrategies to perform model choice and parameter inference on models with\nintractable likelihoods. An essential component of ABC involves comparing a\nlarge amount of simulated data with the observed data through summary\nstatistics. To avoid the curse of dimensionality, summary statistic selection\nis of prime importance, and becomes even more critical when applying ABC to\nmechanistic network models. Indeed, while many summary statistics can be used\nto encode network structures, their computational complexity can be highly\nvariable. For large networks, computation of summary statistics can quickly\ncreate a bottleneck, making the use of ABC difficult. To reduce this\ncomputational burden and make the analysis of mechanistic network models more\npractical, we investigated two questions in a model choice framework. First, we\nstudied the utility of cost-based filter selection methods to account for\ndifferent summary costs during the selection process. Second, we performed\nselection using networks generated with a smaller number of nodes to reduce the\ntime required for the selection step. Our findings show that computationally\ninexpensive summary statistics can be efficiently selected with minimal impact\non classification accuracy. Furthermore, we found that networks with a smaller\nnumber of nodes can only be employed to eliminate a moderate number of\nsummaries. While this latter finding is network specific, the former is general\nand can be adapted to any ABC application.\n"}
+{"abstract": "  In [OW2,OW3], the authors studied the nonlinear elliptic system $$\n\\overline{\\partial}^\\pi w = 0, \\, d(w^*\\lambda \\circ j) = 0 $$ without\ninvolving symplectization for each given contact triad $(Q,\\lambda, J)$, and\nestablished the a priori $W^{k,2}$ elliptic estimates and proved the asymptotic\n(subsequence) convergence of the map $w: \\dot \\Sigma \\to Q$ for any solution,\ncalled a contact instanton, on $\\dot \\Sigma$ under the hypothesis\n$\\|w^*\\lambda\\|_{C^0} < \\infty$ and $d^\\pi w \\in L^2 \\cap L^4$. The asymptotic\nlimit of a contact instanton is a `spiraling' instanton along a `rotating' Reeb\norbit near each puncture on a punctured Riemann surface $\\dot \\Sigma$. Each\nlimiting Reeb orbit carries a `charge' arising from the integral of $w^*\\lambda\n\\circ j$. In this article, we further develop analysis of contact instantons,\nespecially the $W^{1,p}$ estimate for $p > 2$ (or the $C^1$-estimate), which is\nessential for the study of compactfication of the moduli space and the relevant\nFredholm theory for contact instantons. In particular, we define a Hofer-type\noff-shell energy $E^\\lambda(j,w)$ for any pair $(j,w)$ with a smooth map $w$\nsatisfying $d(w^*\\lambda \\circ j) = 0$, and develop the bubbling-off analysis\nand prove an $\\epsilon$-regularity result. We also develop the relevant\nFredholm theory and carry out index calculations (for the case of vanishing\ncharge).\n"}
+{"abstract": "  2019 marked the 75th anniversary of the publication of Erwin\nSchr\\\"{o}dinger's \"What is Life?\", a short book described by Roger Penrose in\nhis preface to a reprint of this classic as \"among the most influential\nscientific writings of the 20th century.\" In this article, I review the long\nargument made by Schr\\\"{o}dinger as he mused on how the laws of physics could\nhelp us understand \"the events in space and time which take place within the\nspatial boundary of a living organism.\" Though Schr\\\"{o}dinger's book is often\nhailed for its influence on some of the titans who founded molecular biology,\nthis article takes a different tack. Instead of exploring the way the book\ntouched biologists such as James Watson and Francis Crick, as well as its\ncritical reception by others such as Linus Pauling and Max Perutz, I argue that\nSchr\\\"{o}dinger's classic is a timeless manifesto, rather than a dated\nhistorical curiosity. \"What is Life?\" is full of timely outlooks and approaches\nto understanding the mysterious living world that includes and surrounds us and\ncan instead be viewed as a call to arms to tackle the great unanswered\nchallenges in the study of living matter that remain for 21$^{st}$ century\nscience.\n"}
+{"abstract": "  We prove decay estimates for generalized eigenfunctions of discrete\nSchr\\\"odinger operators on weighted infinite graphs in the spirit of Agmon.\n"}
+{"abstract": "  This is an extended introduction to discrete valuation rings and Dedekind\ndomains. Some natural generalizations of Dedekind domains are also (briefly)\ndiscussed including \"almost Dedekind domains\", Pr\\\"ufer domains, Krull domains,\nand singular integral domains of dimension 1.\n"}
+{"abstract": "  The magnetospheres of the outer planets exhibit turbulent phenomena in an\nenvironment which is qualitatively different compared to the solar wind or the\ninterstellar medium. The key differences are the finite sizes of the\nmagnetospheres limited by their physical boundaries, the presence of a strong\nplanetary background magnetic field and spatially very inhomogeneous plasma\nproperties within the magnetospheres. Typical turbulent fluctuations possess\namplitudes much smaller than the background field and are characterized by\nAlfv\\'en times, which can be smaller than the non-linear interaction time\nscales. The magnetospheres of the outer planets are thus interesting\nlaboratories of plasma turbulence. In Jupiter's and Saturn's magnetospheres,\nturbulence is well established thanks to the in-situ measurements by several\nspacecraft, in particular by the Galileo and Cassini orbiter. In contrast, the\nfluctuations in Uranus' and Neptune's magnetospheres are poorly understood due\nto the lack of sufficient data. Turbulence in the outer planets' magnetospheres\nhave important effects on the systems as a whole. The dissipation of the\nturbulent fluctuations through wave-particle interaction is a significant heat\nsource, which can explain the large magnetospheric plasma temperatures.\n"}
+{"abstract": "  We prove that in the non-orientable setting, the minimal stretch factor of a\npseudo-Anosov homeomorphism of a surface of genus $g$ with a fixed number of\npunctures is asymptotically on the order of $\\frac{1}{g}$. Our result adapts\nthe work of Yazdi to non-orientable surfaces. We include the details of\nThurston's theory of fibered faces for non-orientable 3-manifolds.\n"}
+{"abstract": "  The promise constraint satisfaction problem (PCSP) is a recently introduced\nvast generalisation of the constraint satisfaction problem (CSP) that captures\napproximability of satisfiable instances. A PCSP instance comes with two forms\nof each constraint: a strict one and a weak one. Given the promise that a\nsolution exists using the strict constraints, the task is to find a solution\nusing the weak constraints. While there are by now several dichotomy results\nfor fragments of PCSPs, they all consider (in some way) symmetric PCSPs.\n  1-in-3-SAT and Not-All-Equal-3-SAT are classic examples of Boolean symmetric\n(non-promise) CSPs. While both problems are NP-hard, Brakensiek and Guruswami\nshowed [SODA'18] that given a satisfiable instance of 1-in-3-SAT one can find a\nsolution to the corresponding instance of (weaker) Not-All-Equal-3-SAT. In\nother words, the PCSP template (1-in-3,NAE) is tractable.\n  We focus on non-symmetric PCSPs. In particular, we study PCSP templates\nobtained from the Boolean template (t-in-k,NAE) by either adding tuples to\nt-in-k or removing tuples from NAE. For the former, we classify all templates\nas either tractable or not solvable by the currently strongest known algorithm\nfor PCSPs, the combined basic LP and affine IP relaxation of Brakensiek and\nGuruswami [SODA'20]. For the latter, we classify all templates as either\ntractable or NP-hard.\n"}
+{"abstract": "  Automatic dialogue coherence evaluation has attracted increasing attention\nand is crucial for developing promising dialogue systems. However, existing\nmetrics have two major limitations: (a) they are mostly trained in a simplified\ntwo-level setting (coherent vs. incoherent), while humans give Likert-type\nmulti-level coherence scores, dubbed as \"quantifiable\"; (b) their predicted\ncoherence scores cannot align with the actual human rating standards due to the\nabsence of human guidance during training. To address these limitations, we\npropose Quantifiable Dialogue Coherence Evaluation (QuantiDCE), a novel\nframework aiming to train a quantifiable dialogue coherence metric that can\nreflect the actual human rating standards. Specifically, QuantiDCE includes two\ntraining stages, Multi-Level Ranking (MLR) pre-training and Knowledge\nDistillation (KD) fine-tuning. During MLR pre-training, a new MLR loss is\nproposed for enabling the model to learn the coarse judgement of coherence\ndegrees. Then, during KD fine-tuning, the pretrained model is further finetuned\nto learn the actual human rating standards with only very few human-annotated\ndata. To advocate the generalizability even with limited fine-tuning data, a\nnovel KD regularization is introduced to retain the knowledge learned at the\npre-training stage. Experimental results show that the model trained by\nQuantiDCE presents stronger correlations with human judgements than the other\nstate-of-the-art metrics.\n"}
+{"abstract": "  We investigate the classical active pure exploration problem in Markov\nDecision Processes, where the agent sequentially selects actions and, from the\nresulting system trajectory, aims at identifying the best policy as fast as\npossible. We propose a problem-dependent lower bound on the average number of\nsteps required before a correct answer can be given with probability at least\n$1-\\delta$. We further provide the first algorithm with an instance-specific\nsample complexity in this setting. This algorithm addresses the general case of\ncommunicating MDPs; we also propose a variant with a reduced exploration rate\n(and hence faster convergence) under an additional ergodicity assumption. This\nwork extends previous results relative to the \\emph{generative\nsetting}~\\cite{pmlr-v139-marjani21a}, where the agent could at each step query\nthe random outcome of any (state, action) pair. In contrast, we show here how\nto deal with the \\emph{navigation constraints}, induced by the \\emph{online\nsetting}. Our analysis relies on an ergodic theorem for non-homogeneous Markov\nchains which we consider of wide interest in the analysis of Markov Decision\nProcesses.\n"}
+{"abstract": "  Many COVID-19 patients developed prolonged symptoms after the infection,\nincluding fatigue, delirium, and headache. The long-term health impact of these\nconditions is still not clear. It is necessary to develop a way to follow up\nwith these patients for monitoring their health status to support timely\nintervention and treatment. In the lack of sufficient human resources to follow\nup with patients, we propose a novel smart chatbot solution backed with machine\nlearning to collect information (i.e., generating digital diary) in a\npersonalized manner. In this article, we describe the design framework and\ncomponents of our prototype.\n"}
+{"abstract": "  Interpretability in machine learning models is important in high-stakes\ndecisions, such as whether to order a biopsy based on a mammographic exam.\nMammography poses important challenges that are not present in other computer\nvision tasks: datasets are small, confounding information is present, and it\ncan be difficult even for a radiologist to decide between watchful waiting and\nbiopsy based on a mammogram alone. In this work, we present a framework for\ninterpretable machine learning-based mammography. In addition to predicting\nwhether a lesion is malignant or benign, our work aims to follow the reasoning\nprocesses of radiologists in detecting clinically relevant semantic features of\neach image, such as the characteristics of the mass margins. The framework\nincludes a novel interpretable neural network algorithm that uses case-based\nreasoning for mammography. Our algorithm can incorporate a combination of data\nwith whole image labelling and data with pixel-wise annotations, leading to\nbetter accuracy and interpretability even with a small number of images. Our\ninterpretable models are able to highlight the classification-relevant parts of\nthe image, whereas other methods highlight healthy tissue and confounding\ninformation. Our models are decision aids, rather than decision makers, aimed\nat better overall human-machine collaboration. We do not observe a loss in mass\nmargin classification accuracy over a black box neural network trained on the\nsame data.\n"}
+{"abstract": "  We quantify evolution in the cluster scale stellar mass - halo mass (SMHM)\nrelation's parameters using 2323 clusters and brightest central galaxies (BCGs)\nover the redshift range $0.03 \\le z \\le 0.60$. The precision on inferred SMHM\nparameters is improved by including the magnitude gap ($\\rm m_{gap}$) between\nthe BCG and fourth brightest cluster member (M14) as a third parameter in the\nSMHM relation. At fixed halo mass, accounting for $\\rm m_{gap}$, through a\nstretch parameter, reduces the SMHM relation's intrinsic scatter. To explore\nthis redshift range, we use clusters, BCGs, and cluster members identified\nusing the Sloan Digital Sky Survey C4 and redMaPPer cluster catalogs and the\nDark Energy Survey redMaPPer catalog. Through this joint analysis, we detect no\nsystematic differences in BCG stellar mass, $\\rm m_{gap}$, and cluster mass\n(inferred from richness) between the datsets. We utilize the Pareto function to\nquantify each parameter's evolution. We confirm prior findings of negative\nevolution in the SMHM relation's slope (3.5$\\sigma$) and detect negative\nevolution in the stretch parameter (4.0$\\sigma$) and positive evolution in the\noffset parameter (5.8$\\sigma$). This observed evolution, combined with the\nabsence of BCG growth, when stellar mass is measured within 50kpc, suggests\nthat this evolution results from changes in the cluster's $\\rm m_{gap}$. For\nthis to occur, late-term growth must be in the intra-cluster light surrounding\nthe BCG. We also compare the observed results to Illustris TNG 300-1\ncosmological hydrodynamic simulations and find modest qualitative agreement.\nHowever, the simulations lack the evolutionary features detected in the real\ndata.\n"}
+{"abstract": "  Following the approach of [E1, M1, M2, S1, S2, SZJV] for reaction diffusion\nsystems, we justify rigorously the Eckhaus stability criterion for stability of\nconvective Turing patterns, as derived formally by complex Ginzburg-Landau\napproximation [SS, NW, WZ]. Notably, our analysis includes also higher-order,\nnonlocal, and even certain semilinear hyperbolic systems.\n"}
+{"abstract": "  We introduce a methodology for robust Bayesian estimation with robust\ndivergence (e.g., density power divergence or {\\gamma}-divergence), indexed by\na single tuning parameter. It is well known that the posterior density induced\nby robust divergence gives highly robust estimators against outliers if the\ntuning parameter is appropriately and carefully chosen. In a Bayesian\nframework, one way to find the optimal tuning parameter would be using evidence\n(marginal likelihood). However, we numerically illustrate that evidence induced\nby the density power divergence does not work to select the optimal tuning\nparameter since robust divergence is not regarded as a statistical model. To\novercome the problems, we treat the exponential of robust divergence as an\nunnormalized statistical model, and we estimate the tuning parameter via\nminimizing the Hyvarinen score. We also provide adaptive computational methods\nbased on sequential Monte Carlo (SMC) samplers, which enables us to obtain the\noptimal tuning parameter and samples from posterior distributions\nsimultaneously. The empirical performance of the proposed method through\nsimulations and an application to real data are also provided.\n"}
+{"abstract": "  Magnetic reconnection is invoked as one of the primary mechanisms to produce\nenergetic particles. We employ large-scale three-dimensional (3D)\nparticle-in-cell simulations of reconnection in magnetically-dominated\n($\\sigma=10$) pair plasmas to study the energization physics of high-energy\nparticles. We identify a novel acceleration mechanism that only operates in 3D.\nFor weak guide fields, 3D plasmoids / flux ropes extend along the $z$ direction\nof the electric current for a length comparable to their cross-sectional\nradius. Unlike in 2D simulations, where particles are buried in plasmoids, in\n3D we find that a fraction of particles with $\\gamma\\gtrsim 3\\sigma$ can escape\nfrom plasmoids by moving along $z$, and so they can experience the large-scale\nfields in the upstream region. These \"free\" particles preferentially move in\n$z$ along Speiser-like orbits sampling both sides of the layer, and are\naccelerated linearly in time -- their Lorentz factor scales as $\\gamma\\propto\nt$, in contrast to $\\gamma\\propto \\sqrt{t}$ in 2D. The energy gain rate\napproaches $\\sim eE_{\\rm rec}c$, where $E_{\\rm rec}\\simeq 0.1 B_0$ is the\nreconnection electric field and $B_0$ the upstream magnetic field. The spectrum\nof free particles is hard, $dN_{\\rm free}/d\\gamma\\propto \\gamma^{-1.5}$,\ncontains $\\sim 20\\%$ of the dissipated magnetic energy independently of domain\nsize, and extends up to a cutoff energy scaling linearly with box size. Our\nresults demonstrate that relativistic reconnection in GRB and AGN jets may be a\npromising mechanism for generating ultra-high-energy cosmic rays.\n"}
+{"abstract": "  Self-supervised pretraining has been shown to yield powerful representations\nfor transfer learning. These performance gains come at a large computational\ncost however, with state-of-the-art methods requiring an order of magnitude\nmore computation than supervised pretraining. We tackle this computational\nbottleneck by introducing a new self-supervised objective, contrastive\ndetection, which tasks representations with identifying object-level features\nacross augmentations. This objective extracts a rich learning signal per image,\nleading to state-of-the-art transfer accuracy on a variety of downstream tasks,\nwhile requiring up to 10x less pretraining. In particular, our strongest\nImageNet-pretrained model performs on par with SEER, one of the largest\nself-supervised systems to date, which uses 1000x more pretraining data.\nFinally, our objective seamlessly handles pretraining on more complex images\nsuch as those in COCO, closing the gap with supervised transfer learning from\nCOCO to PASCAL.\n"}
+{"abstract": "  Chimera states, states of coexistence of synchronous and asynchronous motion,\nhave been a subject of extensive research since they were first given a name in\n2004. Increased interest has lead to their discovery in ever new settings, both\ntheoretical and experimental. Less well-discussed is the fact that successive\nresults have also broadened the notion of what actually constitutes a chimera\nstate. In this article, we critically examine how the results for different\nmodel types and coupling schemes, as well as varying implicit interpretations\nof terms such as coexistence, synchrony and incoherence, have influenced the\ncommon understanding of what constitutes a chimera. We cover both theoretical\nand experimental systems, address various chimera-derived terms that have\nemerged over the years and finally reflect on the question of chimera states in\nreal-world contexts.\n"}
+{"abstract": "  We study the changes in the short-distance quark structure of the\nNambu-Goldstone boson when the long-distance symmetry-breaking scales are\ndepleted controllably. We achieve this by studying the valence Parton\nDistribution Function (PDF) of pion in 2+1 dimensional two-color QCD, with the\nnumber $N$ of massless quarks as the tunable parameter that slides the theory\nfrom being strongly broken for $N=0$ to the conformal window for $N>4$, where\nthe theory is gapped by the fixed finite volume. We perform our study\nnon-perturbatively using lattice simulations with $N=0,2,4,8$ flavors of nearly\nmassless two-component Wilson-Dirac sea quarks and employ the leading-twist\nformalism (LaMET/SDF) to compute the PDF of pion at a fixed valence mass. We\nfind that the relative variations in the first few PDF moments are only mild\ncompared to the changes in decay constant, but the shape of the reconstructed\n$x$-dependent PDF itself dramatically changes from being broad in the\nscale-broken sector to being sharply peaked in the near-conformal region, best\nreflected in PDF shape observables such as the cumulants.\n"}
+{"abstract": "  Inferring the connectivity structure of networked systems from data is an\nextremely important task in many areas of science. Most of real-world networks\nexhibit sparsely connected topologies, with links between nodes that in some\ncases may be even associated to a binary state (0 or 1, denoting respectively\nthe absence or the existence of a connection). Such un-weighted topologies are\nelusive to classical reconstruction methods such as Lasso or Compressed Sensing\ntechniques. We here introduce a novel approach called signal Lasso, where the\nestimation of the signal parameter is subjected to 0 or 1 values. The\ntheoretical properties and algorithm of proposed method are studied in detail.\nApplications of the method are illustrated to an evolutionary game and\nsynchronization dynamics in several synthetic and empirical networks, where we\nshow that the novel strategy is reliable and robust, and outperform the\nclassical approaches in terms of accuracy and mean square errors.\n"}
+{"abstract": "  We introduce a two-dimensional electronic insulator that possesses a toric\ncode topological order enriched by translation symmetry. This state can be\nrealized from disordering a weak topological superconductor by double-vortex\ncondensation. It is termed the toric code insulator, whose anyonic excitations\nconsist of a charge-$e$ chargon, a neutral fermion and two types of visons.\nThere are two types of visons because they have constrained motion as a\nconsequence of the fractional Josephson effect of one-dimensional topological\nsuperconductor. Importantly, these two types of visons are related by a\ndiscrete translation symmetry and have a mutual semionic braiding statistics,\nleading to a symmetry-enrichment akin to the type in Wen's plaquette model and\nKitaev's honeycomb model. We construct this state using a three-fluid\ncoupled-wire model, and analyze the anyon spectrum and braiding statistics in\ndetail to unveil the nature of symmetry-enrichment due to translation. We also\ndiscuss potential material realizations and present a band-theoretic\nunderstanding of the state, fitting it into a general framework for studying\nfractionalizaton in strongly-interacting weak topological phases.\n"}
+{"abstract": "  The plasma contributing to emission from the Sun between the cool\nchromosphere ($\\le 10^4$K) and hot corona ($\\ge 10^6$K) has been subjected to\nmany different interpretations. Here we look at the magnetic structure of this\ntransition region (TR) plasma, based upon the implications of CLASP2 data of an\nactive region recently published by Ishikawa et al., and earlier IRIS and SDO\ndata of quiet regions. Ishikawa et al. found that large areas of sunspot plages\nare magnetically unipolar as measured in the cores of \\ion{Mg}{2} resonance\nlines, formed in the lower transition region under low plasma-$\\beta$\nconditions. Here we show that IRIS images in the line cores have fibrils which\nwell aligned with the overlying coronal loop segments seen in the 171 \\AA{}\nchannel of SDO. When the TR emission in active regions arise from plasma\nmagnetically and thermally connected to the corona, then the line cores can\nprovide the first credible magnetic boundary conditions for force-free\ncalculations extended to the corona. We also re-examine IRIS images of dynamic\nTR cool loops previously reported as a major contributor to transition region\nemission from the quiet Sun. Dynamic cool loops contribute only a small\nfraction of the total TR emission from the quiet Sun.\n"}
+{"abstract": "  The mutual information method has demonstrated to be very useful for deriving\nthe potential order parameter of a system. Although the method suggests some\nconstraints which help to define this quantity, there is still some freedom in\nthe definition. The method then results inefficient for cases where we have\norder parameters with a large number of constants in the expansion, which\nhappens when we have many degenerate vacuums. Here we introduce some additional\nconstraints based on the existence of broken symmetries, which help us to\nreduce the arbitrariness in the definitions of the order parameter in the\nproposed mutual information method.\n"}
+{"abstract": "  Autoregressive models have been widely used in unsupervised text style\ntransfer. Despite their success, these models still suffer from the content\npreservation problem that they usually ignore part of the source sentence and\ngenerate some irrelevant words with strong styles. In this paper, we propose a\nNon-Autoregressive generator for unsupervised text Style Transfer (NAST), which\nalleviates the problem from two aspects. First, we observe that most words in\nthe transferred sentence can be aligned with related words in the source\nsentence, so we explicitly model word alignments to suppress irrelevant words.\nSecond, existing models trained with the cycle loss align sentences in two\nstylistic text spaces, which lacks fine-grained control at the word level. The\nproposed non-autoregressive generator focuses on the connections between\naligned words, which learns the word-level transfer between styles. For\nexperiments, we integrate the proposed generator into two base models and\nevaluate them on two style transfer tasks. The results show that NAST can\nsignificantly improve the overall performance and provide explainable word\nalignments. Moreover, the non-autoregressive generator achieves over 10x\nspeedups at inference. Our codes are available at\nhttps://github.com/thu-coai/NAST.\n"}
+{"abstract": "  Skrzypulec (2021) raises the question whether motor activation by masked\ncolor primes is based on the same type of color representation as conscious\nvision. He postulates that the literature on color processing without awareness\nmakes an implicit assumption that \"conscious\" and \"unconscious\" color\nrepresentations have the same properties, in which case priming by masked color\nstimuli would indeed indicate that the same complex representation of surface\ncolor can be conscious as well as unconscious. I review some evidence from\nresponse priming by lightness stimuli in the context of a visual illusion that\nalters the perceived lightness of the primes (Schmidt et al., 2010). Those\nresults clearly show that response priming is not driven by color-constant\ninformation but instead by local image contrast, making it unlikely that rapid\nresponse activation by color primes is based on a color-constant representation\nof surface color.\n"}
+{"abstract": "  During planet formation, numerous small impacting bodies result in cratering\nimpacts on large target bodies. A fraction of the target surface is eroded,\nwhile a fraction of the impactor material accretes onto the surface. These\nfractions depend upon the impact velocities, the impact angles, and the escape\nvelocities of the target. This study uses smoothed particle hydrodynamics\nsimulations to model cratering impacts onto a planar icy target for which\ngravity is the dominant force and material strength is neglected. By evaluating\nnumerical results, scaling laws are derived for the escape mass of the target\nmaterial and the accretion mass of the impactor material onto the target\nsurface. Together with recently derived results for rocky bodies in a companion\nstudy, a conclusion is formulated that typical cratering impacts on terrestrial\nplanets, except for those on Mercury, led to a net accretion, while those on\nthe moons of giant planets, e.g., Rhea and Europa, led to a net erosion. Our\nnewly derived scaling laws would be useful for predicting the erosion of the\ntarget body and the accretion of the impactor for a variety of cratering\nimpacts that would occur on large rocky and icy planetary bodies during planet\nformation and collisional evolution from ancient times to today.\n"}
+{"abstract": "  Safety in reinforcement learning (RL) is a key property in both training and\nexecution in many domains such as autonomous driving or finance. In this paper,\nwe formalize it with a constrained RL formulation in the distributional RL\nsetting. Our general model accepts various definitions of safety(e.g., bounds\non expected performance, CVaR, variance, or probability of reaching bad\nstates). To ensure safety during learning, we extend a safe policy optimization\nmethod to solve our problem. The distributional RL perspective leads to a more\nefficient algorithm while additionally catering for natural safe constraints.\nWe empirically validate our propositions on artificial and real domains against\nappropriate state-of-the-art safe RL algorithms.\n"}
+{"abstract": "  The memory-based diffusion systems have wide applications in practice. Hopf\nbifurcations are observed from such systems. To meet the demand for computing\nthe normal forms of the Hopf bifurcations of such systems, we develop an\neffective new algorithm where the memory delay is treated as the perturbation\nparameter. To illustrate the effectiveness of the algorithm, we consider a\ndiffusive predator-prey system with memory-based diffusion and Holling type-II\nfunctional response. By employing this newly developed procedure, we\ninvestigate the direction and stability of the delay-induced mode-1 and mode-2\nHopf bifurcations. Numerical simulations confirm our theoretical findings, that\nis the existence of stable spatially inhomogeneous periodic solutions with\nmode-1 and mode-2 spatial patterns, and the transition from the unstable mode-2\nspatially inhomogeneous periodic solution to the stable mode-1 spatially\ninhomogeneous periodic solution.\n"}
+{"abstract": "  We prove that the best so far known constant $c_p=\\frac{p^{-p}}{1-p},\\,\np\\in(0,1)$ of a domination inequality, which originates to Lenglart, is sharp.\nIn particular, we solve an open question posed by Revuz and Yor. Motivated by\nthe application to maximal inequalities, like e.g. the Burkholder-Davis-Gundy\ninequality, we also study the domination inequality under an additional\nmonotonicity assumption. In this special case, a constant which stays bounded\nfor $p$ near $1$ was proven by Pratelli and Lenglart. We provide the sharp\nconstant for this case.\n"}
+{"abstract": "  The globalization of the electronics supply chain requires effective methods\nto thwart reverse engineering and IP theft. Logic locking is a promising\nsolution, but there are still several open concerns. First, even when applied\nat a higher level of abstraction, locking has significant overhead without\nimproving the security metric. Second, optimizing a security metric is\napplication-dependent and designers must evaluate and compare alternative\nsolutions. We propose a framework to optimize the use of behavioral locking\nduring the high-level synthesis (HLS) of IP cores. Our method operates on\nchip's specification (before HLS) and it is compatible with all HLS tools,\ncomplementing industrial EDA flows. The framework supports different\nmeta-heuristics to explore the design space and to select points to lock\nautomatically. Our method optimizes a given security metric better than\ntopological locking: 1) we always identify a valid solution that optimizes the\nsecurity metric; 2) we minimize the number of bits used for locking; and 3) we\nmake a better use of hardware resources.\n"}
+{"abstract": "  Automated Audio Captioning is a cross-modal task, generating natural language\ndescriptions to summarize the audio clips' sound events. However, grounding the\nactual sound events in the given audio based on its corresponding caption has\nnot been investigated. This paper contributes an AudioGrounding dataset, which\nprovides the correspondence between sound events and the captions provided in\nAudiocaps, along with the location (timestamps) of each present sound event.\nBased on such, we propose the text-to-audio grounding (TAG) task, which\ninteractively considers the relationship between audio processing and language\nunderstanding. A baseline approach is provided, resulting in an event-F1 score\nof 28.3% and a Polyphonic Sound Detection Score (PSDS) score of 14.7%.\n"}
+{"abstract": "  The digital transformation is creating basically a digital version of our\nphysical world and the currency in that digital space is data. Massive amount\nof data has been generated ranging from wearable devices monitoring our\nphysical health every single millisecond to autonomous vehicles generating\nroughly 5Tb hourly to even astronomical activities producing an order of\nExabytes on daily basis and then ultra-broadband Internet comes into play,\nmoving such data to the cloud. Internet traffic therefore has been experiencing\nexplosive growth and in this context, optical transport networks forming the\nbackbone of the Internet are pushed for transformation in system capacity.\nWhile the intuitive solution of deploying multiple fibers can address the\npressing demand for increased capacity, doing so does not bring improvement in\neconomic of scales in terms of cost, power consumption and spectral efficiency.\nThis necessitates for a different approach so that the fiber capacity could be\nutilized in a more efficient manner. In this paper, we focus on innovative\ntechniques, that is, photonic network coding and partial protection, to reduce\nthe effective traffic load in order to achieve greater capacity efficiency for\noptical transport networks. Specifically, the application of network coding is\nexamined by upgrading the functionalities of intermediate nodes with\nall-optical processing (i.e., encoding and decoding) capabilities. Besides,\npartial protection relying on the premise of providing just enough bandwidth in\ncase of failure events is investigated for saving the redundant protection\ncapacity. That it takes two to tango, combining photonic network coding and\npartial protection therefore bring to light new opportunities and challenges.\nIn mining such new avenue, we present insights on how to derive compounding\ngains to maximize spectral efficiency via a case study.\n"}
+{"abstract": "  At modern construction sites, utilizing GNSS (Global Navigation Satellite\nSystem) to measure the real-time location and orientation (i.e. pose) of\nconstruction machines and navigate them is very common. However, GNSS is not\nalways available. Replacing GNSS with on-board cameras and visual simultaneous\nlocalization and mapping (visual SLAM) to navigate the machines is a\ncost-effective solution. Nevertheless, at construction sites, multiple\nconstruction machines will usually work together and side-by-side, causing\nlarge dynamic occlusions in the cameras' view. Standard visual SLAM cannot\nhandle large dynamic occlusions well. In this work, we propose a motion\nsegmentation method to efficiently extract static parts from crowded dynamic\nscenes to enable robust tracking of camera ego-motion. Our method utilizes\nsemantic information combined with object-level geometric constraints to\nquickly detect the static parts of the scene. Then, we perform a two-step\ncoarse-to-fine ego-motion tracking with reference to the static parts. This\nleads to a novel dynamic visual SLAM formation. We test our proposals through a\nreal implementation based on ORB-SLAM2, and datasets we collected from real\nconstruction sites. The results show that when standard visual SLAM fails, our\nmethod can still retain accurate camera ego-motion tracking in real-time.\nComparing to state-of-the-art dynamic visual SLAM methods, ours shows\noutstanding efficiency and competitive result trajectory accuracy.\n"}
+{"abstract": "  Quantum simulation on emerging quantum hardware is a topic of intense\ninterest. While many studies focus on computing ground state properties or\nsimulating unitary dynamics of closed systems, open quantum systems are an\ninteresting target of study owing to their ubiquity and rich physical behavior.\nHowever, their non-unitary dynamics are also not natural to simulate on digital\nquantum devices. Here, we report algorithms for the digital quantum simulation\nof the dynamics of open quantum systems governed by a Lindblad equation using\nadaptations of the quantum imaginary time evolution (QITE) algorithm. We\ndemonstrate the algorithms on IBM Quantum's hardware with simulations of the\nspontaneous emission of a two level system and the dissipative transverse field\nIsing model. Our work advances efforts to simulate the dynamics of open quantum\nsystems on quantum hardware.\n"}
+{"abstract": "  This study compares the lexical structure of articles titles and abstracts of\ntwo extremes in MB (management-business research): the AMJ (Academy of\nManagement Journal), one of its most revered periodicals, and Espacios, the one\nthat unveiled a structural problem in Latin-American MB. Results showed\nsignificant differences in the median of titles length and abstracts\nreadability and diversity as AMJ titles length was longer and abstracts both\nmore diverse and readability-demanding.\n"}
+{"abstract": "  In this paper, a family of novel diffusion adaptive estimation algorithm is\nproposed from the asymmetric cost function perspective by combining diffusion\nstrategy and the linear-linear cost (LLC), quadratic-quadratic cost (QQC), and\nlinear-exponential cost (LEC), at all distributed network nodes, and named\ndiffusion LLCLMS (DLLCLMS), diffusion QQCLMS (DQQCLMS), and diffusion LECLMS\n(DLECLMS), respectively. Then the stability of mean estimation error and\ncomputational complexity of those three diffusion algorithms are analyzed\ntheoretically. Finally, several experiment simulation results are designed to\nverify the superiority of those three proposed diffusion algorithms.\nExperimental simulation results show that DLLCLMS, DQQCLMS, and DLECLMS\nalgorithms are more robust to the input signal and impulsive noise than the\nDSELMS, DRVSSLMS, and DLLAD algorithms. In brief, theoretical analysis and\nexperiment results show that those proposed DLLCLMS, DQQCLMS, and DLECLMS\nalgorithms have superior performance when estimating the unknown linear system\nunder the changeable impulsive noise environments and different types of input\nsignals.\n"}
+{"abstract": "  Abel's quadratures for integrable Hamiltonian systems are defined up to a\ngroup law of the corresponding Abelian variety $A$. If $A$ is isogenous to a\ndirect product of Abelian varieties $A\\cong A_1\\times\\cdots\\times A_k$, the\ngroup law can be used to construct various Lax matrices on the factors\n$A_1,\\ldots,A_k$. As an example, we discuss 2-dimensional reducible Abelian\nvariety $A=E_+\\times E_-$, which is a product of 1-dimensional varieties\n$E_\\pm$ obtained by Euler in his study of the two fixed centres problem, and\nthe Lax matrices on the factors $E_\\pm$.\n"}
+{"abstract": "  Communications system design has been traditionally guided by task-agnostic\nprinciples, which aim at efficiently transmitting as many correct bits as\npossible through a given channel. However, in the era of cyber-physical\nsystems, the effectiveness of communications is not dictated simply by the bit\nrate, but most importantly by the efficient completion of the task in hand,\ne.g., controlling remotely a robot, automating a production line or\ncollaboratively sensing through a drone swarm. In parallel, it is projected\nthat by 2023, half of the worldwide network connections will be among machines\nrather than humans. In this context, it is crucial to establish a new paradigm\nfor designing communications strategies for multi-agent cyber-physical systems.\nThis is a daunting task, since it requires a combination of principles from\ninformation, communication, control theories and computer science in order to\nformalize a general framework for task-oriented communication design. In this\ndirection, this paper reviews and structures the relevant theoretical work\nacross a wide range of scientific communities. Subsequently, it proposes a\ngeneral conceptual framework for task-oriented communication design, along with\nits specializations according to the targeted use case. Furthermore, it\nprovides a survey of relevant contributions in dominant applications, such as\nindustrial internet of things, multi-UAV systems, tactile internet, autonomous\nvehicles, distributed learning systems, smart manufacturing plants and 5G and\nbeyond self-organizing networks. Finally, it highlights the most important open\nresearch topics from both the theoretical framework and application points of\nview.\n"}
+{"abstract": "  In this paper we prove that the $r$-th ADO polynomial of a knot, for $r$ a\npower of prime number, can be expanded as Vassiliev invariants with values in\n$\\mathbb{Z}$. Nevertheless this expansion is not unique and not easily\ncomputable. We can obtain a unique computable expansion, but we only get $r$\nadic topological Vassiliev invariants as coefficients. To do so, we exploit the\nfact that the colored Jones polynomials can be decomposed as Vassiliev\ninvariants and we tranpose it to ADO using the unified knot invariant\nrecovering both ADO and colored Jones defined in arXiv:2003.09854. Finally we\nprove some asymptotic behavior of the ADO polynomials modulo $r$ as $r$ goes to\ninfinity.\n"}
+{"abstract": "  The excess of $\\gamma$ rays in the data measured by Fermi-LAT from the\nGalactic center region is one of the most intriguing mysteries in Astroparticle\nPhysics. This Galactic center excess (GCE), has been measured with respect to\ndifferent interstellar emission models (IEMs), source catalogs, data selections\nand techniques. Although several proposed interpretations have appeared in the\nliterature, there are not firm conclusions as to its origin. The main\ndifficulty in solving this puzzle lies in modeling a region of such complexity\nand thus precisely measuring the characteristics of the GCE. In this paper, we\nuse 11 years of Fermi-LAT data, state of the art IEMs, and the newest 4FGL\nsource catalog to provide precise measurements of the energy spectrum, spatial\nmorphology, position, and sphericity of the GCE. We find that the GCE has a\nspectrum which is peaked at a few GeV and is well fit with a log-parabola. The\nnormalization of the spectrum changes by roughly $60\\%$ when using different\nIEMs, data selections and analysis techniques. The spatial distribution of the\nGCE is compatible with a dark matter (DM) template produced with a generalized\nNFW density profile with slope $\\gamma = 1.2-1.3$. No energy evolution is\nmeasured for the GCE morphology between $0.6-30$ GeV at a level larger than\n$10\\%$ of the $\\gamma$ average value, which is 1.25. The analysis of the GCE\nmodeled with a DM template divided into quadrants shows that the spectrum and\nspatial morphology of the GCE is similar in different regions around the\nGalactic center. Finally, the GCE centroid is compatible with the Galactic\ncenter, with best-fit position between\n$l=[-0.3^{\\circ},0.0^{\\circ}],b=[-0.1^{\\circ},0.0^{\\circ}]$, and it is\ncompatible with a spherical symmetric morphology. In particular, fitting the DM\nspatial profile with an ellipsoid gives a major-to-minor axis ratio between\n0.8-1.2.\n"}
+{"abstract": "  Recent observations of extrasolar gas giants suggest super-stellar C/O ratios\nin planetary atmospheres, while interior models of observed extrasolar giant\nplanets additionally suggest high heavy element contents. Furthermore, recent\nobservations of protoplanetary disks revealed super-solar C/H ratios, which are\nexplained by inward drifting and evaporating pebbles, enhancing the volatile\ncontent of the disk. We investigate how the inward drift and evaporation of\nvolatile rich pebbles influences the atmospheric C/O ratio and heavy element\ncontent of giant planets growing by pebble and gas accretion. To achieve this\ngoal, we perform semi analytical 1D models of protoplanetary disks including\nthe treatment of viscous evolution and heating, pebble drift and simple\nchemistry to simulate the growth of planets from planetary embryos to Jupiter\nmass objects by accretion of pebbles and gas while they migrate through the\ndisk. Our simulations show that the composition of the planetary gas atmosphere\nis dominated by the accretion of vapour, originating from inward drifting\nevaporating pebbles. This process allows the giant planets to harbour large\nheavy element contents. In addition, our model reveals that giant planets\noriginating further away from the central star have a higher C/O ratio on\naverage due to the evaporation of methane rich pebbles in the outer disk. These\nplanets can then also harbour super-solar C/O ratios, in line with exoplanet\nobservations. However, planets formed in the outer disk harbour a smaller heavy\nelement content, due to a smaller vapour enrichment of the outer disk. Our\nmodel predicts that giant planets with low/large atmospheric C/O should harbour\na large/low total heavy element content. We further conclude that the inclusion\nof pebble evaporation at evaporation lines is a key ingredient to determine the\nheavy element content and composition of giant planets.\n"}
+{"abstract": "  We assume the anisotropic model of the Universe in the framework of varying\nspeed of light $c$ and varying gravitational constant $G$ theories and study\ndifferent types of singularities. For the singularity models, we write the\nscale factors in terms of cosmic time and found some conditions for possible\nsingularities. For future singularities, we assume the forms of varying speed\nof light and varying gravitational constant. For regularizing big bang\nsingularity, we assume two forms of scale factors: sine model and tangent\nmodel. For both the models, we examine the validity of null energy condition\nand strong energy condition. Start from the first law of thermodynamics, we\nstudy the thermodynamic behaviours of $n$ number of Universes (i.e.,\nMultiverse) for (i) varying $c$, (ii) varying $G$ and (iii) both varying $c$\nand $G$ models. We found the total entropies for all the cases in the\nanisotropic Multiverse model. We also found the nature of the Multiverse if\ntotal entropy is constant.\n"}
+{"abstract": "  A new federated learning (FL) framework enabled by large-scale wireless\nconnectivity is proposed for designing the autonomous controller of connected\nand autonomous vehicles (CAVs). In this framework, the learning models used by\nthe controllers are collaboratively trained among a group of CAVs. To capture\nthe varying CAV participation in the FL training process and the diverse local\ndata quality among CAVs, a novel dynamic federated proximal (DFP) algorithm is\nproposed that accounts for the mobility of CAVs, the wireless fading channels,\nas well as the unbalanced and nonindependent and identically distributed data\nacross CAVs. A rigorous convergence analysis is performed for the proposed\nalgorithm to identify how fast the CAVs converge to using the optimal\nautonomous controller. In particular, the impacts of varying CAV participation\nin the FL process and diverse CAV data quality on the convergence of the\nproposed DFP algorithm are explicitly analyzed. Leveraging this analysis, an\nincentive mechanism based on contract theory is designed to improve the FL\nconvergence speed. Simulation results using real vehicular data traces show\nthat the proposed DFP-based controller can accurately track the target CAV\nspeed over time and under different traffic scenarios. Moreover, the results\nshow that the proposed DFP algorithm has a much faster convergence compared to\npopular FL algorithms such as federated averaging (FedAvg) and federated\nproximal (FedProx). The results also validate the feasibility of the\ncontract-theoretic incentive mechanism and show that the proposed mechanism can\nimprove the convergence speed of the DFP algorithm by 40% compared to the\nbaselines.\n"}
+{"abstract": "  According to \"Social Disorganization\" theory, criminal activity increases if\nthe societal institutions that might be responsible for maintaining order are\nweakened. Do large apartment buildings, which often have fairly transient\npopulations and low levels of community involvement, have disproportionately\nhigh rates of crime? Do these rates differ during the daytime or nighttime,\ndepending when residents are present, or away from their property? This study\nexamines four types of \"acquisitive\" crime in Milwaukee during 2014. Overall,\nnighttime crimes are shown to be more dispersed than daytime crimes. A spatial\nregression estimation finds that the density of multiunit housing is positively\nrelated to all types of crime except burglaries, but not for all times of day.\nDaytime robberies, in particular, increase as the density of multiunit housing\nincreases.\n"}
+{"abstract": "  In this paper we present the theory of oscillation numbers and dual\noscillation numbers for continuous Lagrangian paths in $\\mathbb{R}^{2n}$. Our\nmain results include a connection of the oscillation numbers of the given\nLagrangian path with the Lidskii angles of a special symplectic orthogonal\nmatrix. We also present Sturmian type comparison and separation theorems for\nthe difference of the oscillation numbers of two continuous Lagrangian paths.\nThese results, as well as the definition of the oscillation number itself, are\nbased on the comparative index theory (Elyseeva, 2009). The applications of\nthese results are directed to the theory of Maslov index of two continuous\nLagrangian paths. We derive a formula for the Maslov index via the Lidskii\nangles of a special symplectic orthogonal matrix, and hence we express the\nMaslov index as the oscillation number of a certain transformed Lagrangian\npath. The results and methods are based on a generalization of the recently\nintroduced oscillation numbers and dual oscillation numbers for conjoined bases\nof linear Hamiltonian systems (Elyseeva, 2019 and 2020) and on the connection\nbetween the comparative index and Lidskii angles of symplectic matrices\n(\\v{S}epitka and \\v{S}imon Hilscher, 2020).\n"}
+{"abstract": "  We employ the periodic Anderson model with superconducting correlations in\nthe conduction band at half filling to study the behavior of the in-gap bands\nin a heterostructure consisting of a molecular layer deposited on the surface\nof a conventional superconductor. We use the dynamical mean-field theory to map\nthe lattice model on the superconducting single impurity model with\nself-consistently determined bath and use the continuous-time hybridization\nexpansion (CT-HYB) quantum Monte Carlo and the iterative perturbation theory\n(IPT) as solvers for the impurity problem. We present phase diagrams for square\nand triangular lattice that both show two superconducting phases that differ by\nthe sign of the induced pairing, in analogy to the $0$ and $\\pi$ phases of the\nsuperconducting single impurity Anderson model and discuss the evolution of the\nspectral function in the vicinity of the transition. We also discuss the\nfailure of the IPT for superconducting models with spinful ground state and the\nbehavior of the average expansion order of the CT-HYB simulation.\n"}
+{"abstract": "  We consider the problem of service placement at the network edge, in which a\ndecision maker has to choose between $N$ services to host at the edge to\nsatisfy the demands of customers. Our goal is to design adaptive algorithms to\nminimize the average service delivery latency for customers. We pose the\nproblem as a Markov decision process (MDP) in which the system state is given\nby describing, for each service, the number of customers that are currently\nwaiting at the edge to obtain the service. However, solving this $N$-services\nMDP is computationally expensive due to the curse of dimensionality. To\novercome this challenge, we show that the optimal policy for a single-service\nMDP has an appealing threshold structure, and derive explicitly the Whittle\nindices for each service as a function of the number of requests from customers\nbased on the theory of Whittle index policy.\n  Since request arrival and service delivery rates are usually unknown and\npossibly time-varying, we then develop efficient learning augmented algorithms\nthat fully utilize the structure of optimal policies with a low learning\nregret. The first of these is UCB-Whittle, and relies upon the principle of\noptimism in the face of uncertainty. The second algorithm, Q-learning-Whittle,\nutilizes Q-learning iterations for each service by using a two time scale\nstochastic approximation. We characterize the non-asymptotic performance of\nUCB-Whittle by analyzing its learning regret, and also analyze the convergence\nproperties of Q-learning-Whittle. Simulation results show that the proposed\npolicies yield excellent empirical performance.\n"}
+{"abstract": "  This work presents a naive algorithm for parameter transfer between different\narchitectures with a computationally cheap injection technique (which does not\nrequire data). The primary objective is to speed up the training of neural\nnetworks from scratch. It was found in this study that transferring knowledge\nfrom any architecture was superior to Kaiming and Xavier for initialization. In\nconclusion, the method presented is found to converge faster, which makes it a\ndrop-in replacement for classical methods. The method involves: 1) matching:\nthe layers of the pre-trained model with the targeted model; 2) injection: the\ntensor is transformed into a desired shape. This work provides a comparison of\nsimilarity between the current SOTA architectures (ImageNet), by utilising TLI\n(Transfer Learning by Injection) score.\n"}
+{"abstract": "  The majority of current approaches in autonomous driving rely on\nHigh-Definition (HD) maps which detail the road geometry and surrounding area.\nYet, this reliance is one of the obstacles to mass deployment of autonomous\nvehicles due to poor scalability of such prior maps. In this paper, we tackle\nthe problem of online road map extraction via leveraging the sensory system\naboard the vehicle itself. To this end, we design a structured model where a\ngraph representation of the road network is generated in a hierarchical fashion\nwithin a fully convolutional network. The method is able to handle complex road\ntopology and does not require a user in the loop.\n"}
+{"abstract": "  Understanding the critical condition and mechanism of the droplet wetting\ntransition between Cassie-Baxter state and Wenzel state triggered by an\nexternal electric field is of considerable importance because of its numerous\napplications in industry and engineering. However, such a wetting transition on\na patterned surface is still not fully understood, e.g., the effects of\nelectro-wetting number, geometry of the patterned surfaces, and droplet volume\non the transition have not been systematically investigated. In this paper, we\npropose a theoretical model for the Cassie-Baxter- Wenzel wetting transition\ntriggered by applying an external voltage on a droplet placed on a\nmircopillared surface or a porous substrate. It is found that the transition is\nrealized by lowering the energy barrier created by the intermediate composite\nstate considerably, which enables the droplet to cross the energy barrier and\ncomplete the transition process. Our calculations also indicate that for fixed\ndroplet volume, the critical electrowetting number (voltage) will increase\n(decrease) along with the surface roughness for a micro-pillar patterned\n(porous) surface, and if the surface roughness is fixed, a small droplet tends\nto ease the critical electrowetting condition for the transition. Besides,\nthree dimensional phase diagrams in terms of electrowetting number, surface\nroughness, and droplet volume are constructed to illustrate the\nCassie-Baxter-Wenzel wetting transition. Our theoretical model can be used to\nexplain the previous experimental results about the Cassie-Baxter-Wenzel\nwetting transition reported in the literature.\n"}
+{"abstract": "  Under unexpected conditions or scenarios, autonomous vehicles (AV) are more\nlikely to follow abnormal unplanned actions, due to the limited set of rules or\namount of experience they possess at that time. Enabling AV to measure the\ndegree at which their movements are novel in real-time may help to decrease any\npossible negative consequences. We propose a method based on the Local Outlier\nFactor (LOF) algorithm to quantify this novelty measure. We extracted features\nfrom the inertial measurement unit (IMU) sensor's readings, which captures the\nvehicle's motion. We followed a novelty detection approach in which the model\nis fitted only using the normal data. Using datasets obtained from real-world\nvehicle missions, we demonstrate that the suggested metric can quantify to some\nextent the degree of novelty. Finally, a performance evaluation of the model\nconfirms that our novelty metric can be practical.\n"}
+{"abstract": "  The minimal integral Mahler measure of a number field $K$,\n$M(\\mathcal{O}_K)$, is the minimal Mahler measure of a non-torsion primitive\nelement of $\\mathcal{O}_K$. Upper and lower bounds, which depend on the\ndiscriminant, are known. We show that for cubics, the lower bounds are sharp\nwith respect to its growth as a function of discriminant. We construct an\nalgorithm to compute $M(\\mathcal{O}_K)$ for all cubics with absolute value of\nthe discriminant bounded by $N$.\n"}
+{"abstract": "  Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory\nsyndrome coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a\ndetrimental effect on the global economy and health. A positive chest X-ray of\ninfected patients is a crucial step in the battle against COVID-19. Early\nresults suggest that abnormalities exist in chest X-rays of patients suggestive\nof COVID-19. This has led to the introduction of a variety of deep learning\nsystems and studies have shown that the accuracy of COVID-19 patient detection\nthrough the use of chest X-rays is strongly optimistic. Deep learning networks\nlike convolutional neural networks (CNNs) need a substantial amount of training\ndata. Because the outbreak is recent, it is difficult to gather a significant\nnumber of radiographic images in such a short time. Therefore, in this\nresearch, we present a method to generate synthetic chest X-ray (CXR) images by\ndeveloping an Auxiliary Classifier Generative Adversarial Network (ACGAN) based\nmodel called CovidGAN. In addition, we demonstrate that the synthetic images\nproduced from CovidGAN can be utilized to enhance the performance of CNN for\nCOVID-19 detection. Classification using CNN alone yielded 85% accuracy. By\nadding synthetic images produced by CovidGAN, the accuracy increased to 95%. We\nhope this method will speed up COVID-19 detection and lead to more robust\nsystems of radiology.\n"}
+{"abstract": "  In the past decades, continuous Doppler radar sensor-based bio-signal\ndetections have attracted many research interests. A typical example is the\nDoppler heartbeat detection. While significant progresses have been achieved,\nreliable, time-domain accurate demodulation of bio-signals in the presence of\nunavoidable DC offsets remains a technical challenge. Aiming to overcome this\ndifficulty, we propose in this paper a novel demodulation algorithm that does\nnot need to trace and eliminate dynamic DC offsets based on approximating\nsegmented arcs in a quadrature constellation of sampling data to directional\nchords. Assisted by the principal component analysis, such chords and their\ndirections can be deterministically determined. Simulations and experimental\nvalidations showed fully recovery of micron-level pendulum movements and\nstrongly noised human heartbeats, verifying the effectiveness and accuracy of\nthe proposed approach.\n"}
+{"abstract": "  Scene text retrieval aims to localize and search all text instances from an\nimage gallery, which are the same or similar to a given query text. Such a task\nis usually realized by matching a query text to the recognized words, outputted\nby an end-to-end scene text spotter. In this paper, we address this problem by\ndirectly learning a cross-modal similarity between a query text and each text\ninstance from natural images. Specifically, we establish an end-to-end\ntrainable network, jointly optimizing the procedures of scene text detection\nand cross-modal similarity learning. In this way, scene text retrieval can be\nsimply performed by ranking the detected text instances with the learned\nsimilarity. Experiments on three benchmark datasets demonstrate our method\nconsistently outperforms the state-of-the-art scene text spotting/retrieval\napproaches. In particular, the proposed framework of joint detection and\nsimilarity learning achieves significantly better performance than separated\nmethods. Code is available at: https://github.com/lanfeng4659/STR-TDSL.\n"}
+{"abstract": "  Glass-like objects such as windows, bottles, and mirrors exist widely in the\nreal world. Sensing these objects has many applications, including robot\nnavigation and grasping. However, this task is very challenging due to the\narbitrary scenes behind glass-like objects. This paper aims to solve the\nglass-like object segmentation problem via enhanced boundary learning. In\nparticular, we first propose a novel refined differential module that outputs\nfiner boundary cues. We then introduce an edge-aware point-based graph\nconvolution network module to model the global shape along the boundary. We use\nthese two modules to design a decoder that generates accurate and clean\nsegmentation results, especially on the object contours. Both modules are\nlightweight and effective: they can be embedded into various segmentation\nmodels. In extensive experiments on three recent glass-like object segmentation\ndatasets, including Trans10k, MSD, and GDD, our approach establishes new\nstate-of-the-art results. We also illustrate the strong generalization\nproperties of our method on three generic segmentation datasets, including\nCityscapes, BDD, and COCO Stuff. Code and models is available at\n\\url{https://github.com/hehao13/EBLNet}.\n"}
+{"abstract": "  Recent advance in diffusion models incorporates the Stochastic Differential\nEquation (SDE), which brings the state-of-the art performance on image\ngeneration tasks. This paper improves such diffusion models by analyzing the\nmodel at the zero diffusion time. In real datasets, the score function diverges\nas the diffusion time ($t$) decreases to zero, and this observation leads an\nargument that the score estimation fails at $t=0$ with any neural network\nstructure. Subsequently, we introduce Unbounded Diffusion Model (UDM) that\nresolves the score diverging problem with an easily applicable modification to\nany diffusion models. Additionally, we introduce a new SDE that overcomes the\ntheoretic and practical limitations of Variance Exploding SDE. On top of that,\nthe introduced Soft Truncation method improves the sample quality by mitigating\nthe loss scale issue that happens at $t=0$. We further provide a theoretic\nresult of the proposed method to uncover the behind mechanism of the diffusion\nmodels.\n"}
+{"abstract": "  This paper proposes a dual-stage, low complexity, and reconfigurable\ntechnique to enhance the speech contaminated by various types of noise sources.\nDriven by input data and audio contents, the proposed dual-stage speech\nenhancement approach performs a coarse and fine processing in the first-stage\nand second-stage, respectively. In this paper, we demonstrate that the proposed\nspeech enhancement solution significantly enhances the metrics of 3-fold\nQUality Evaluation of Speech in Telecommunication (3QUEST) consisting of speech\nmean-opinion-score (SMOS) and noise MOS (NMOS) for near-field and far-field\napplications. Moreover, the proposed speech enhancement approach greatly\nimproves both the signal-to-noise ratio (SNR) and subjective listening\nexperience. For comparisons, the traditional speech enhancement methods reduce\nthe SMOS although they increase NMOS and SNR. In addition, the proposed speech\nenhancement scheme can be easily adopted in both capture path and speech render\npath for speech communication and conferencing systems, and voice-trigger\napplications.\n"}
+{"abstract": "  An isolated positive wedge disclination deforms an initially flat elastic\nsheet into a perfect cone when the sheet is of infinite extent and is\nelastically inextensible. The latter requires the elastic stretching strains to\nbe vanishingly small. In this paper, rigorous analytical and numerical results\nare obtained for the disclination induced deformed shape and stress field of a\nbounded F{\\\"o}ppl-von K{\\'a}rm{\\'a}n elastic sheet with finite extensibility,\nwhile emphasising the deviations from the perfect cone solution. In particular,\nthe Gaussian curvature field is no longer localised as a Dirac singularity at\nthe defect location whenever elastic extensibility is allowed and is\nnecessarily negative in large regions away from the defect. The stress field,\nsimilarly, has no Dirac singularity in the presence of elastic extensibility.\nHowever, with increasing Young's modulus of the sheet, while keeping the\nbending modulus and the domain size fixed, both of these fields tend to develop\na Dirac singularity. Noticeably, in this limiting behaviour, inextensibility\neludes the bounded elastic sheet due to persisting regions of non-trivial\nGaussian curvature away from the defect. Other results in the paper include\nstudying the effect of specific boundary conditions (free, simply supported, or\npartially clamped) on the Gaussian curvature field away from the defect and on\nthe buckling transition from the flat to a conical solution.\n"}
+{"abstract": "  The aim of this work is to present the optimization of the gate trench module\nfor use in vertical GaN devices in terms of cleaning process of the etched\nsurface of the gate trench, thickness of gate dielectric and magnesium\nconcentration of the p-GaN layer. The analysis was carried out by comparing the\nmain DC parameters of devices that differ in surface cleaning process of the\ngate trench, gate dielectric thickness, and body layer doping. . On the basis\nof experimental results, we report that: (i) a good cleaning process of the\netched GaN surface of the gate trench is a key factor to enhance the device\nperformance, (ii) a gate dielectric >35-nm SiO2 results in a narrow\ndistribution for DC characteristics, (iii) lowering the p-doping in the body\nlayer improves the ON-resistance (RON). Gate capacitance measurements are\nperformed to further confirm the results. Hypotheses on dielectric\ntrapping/detrapping mechanisms under positive and negative gate bias are\nreported.\n"}
+{"abstract": "  This paper aims to derive a definition of complexity for a dynamic spherical\nsystem in the background of self-interacting Brans-Dicke gravity. We measure\ncomplexity of the structure in terms of inhomogeneous energy density,\nanisotropic pressure and massive scalar field. For this purpose, we formulate\nstructure scalars by orthogonally splitting the Riemann tensor. We show that\nself-gravitating models collapsing homologously follow the simplest mode of\nevolution. Furthermore, we demonstrate the effect of scalar field on the\ncomplexity and evolution of non-dissipative as well as dissipative systems. The\ncriteria under which the system deviates from the initial state of zero\ncomplexity is also discussed. It is concluded that complexity of the sphere\nincreases in self-interacting Brans-Dicke gravity because the homologous model\nis not shear-free.\n"}
+{"abstract": "  The strut based injector has been found to be one of the most promising\ninjector designs for supersonic combustor, offering en-hanced mixing of fuel\nand air. The mixing and flow field characteristics of the straight (SS) &\ntapered strut (TS), with fixed ramp an-gle and height at freestream Mach number\n2 in conjunction with fuel injection at Mach 2.3 have been investigated\nnumerically and reported. In the present investigation, hydrogen (H2) and\nethylene (C2H4) are injected in oncoming supersonic flow from the back of the\nstrut, where jet to freestream momentum ratio is maintained at 0.79 and 0.69\nfor H2 & C2H4, respectively. The predicted wall static pressure and species\nmole fractions at various downstream locations are compared with the\nexperimental data for TS case with 0.6 mm jet diameter and found to be in good\nagreement. Further, the effect of jet diameter and strut geometry on the near\nfield mixing in strut ramp configuration is discussed for both the fuel. The\nnumerical results are assessed based on various parameters for the performance\nevaluation of different strut ramp configurations. The SS configuration for\nboth the injectant is found to be an optimum candidate, also it is observed\nthat for higher jet diameter larger combustor length is required to achieve\nsatisfactory near field mixing.\n"}
+{"abstract": "  Sequence alignment supports numerous tasks in bioinformatics, natural\nlanguage processing, pattern recognition, social sciences, and others fields.\nWhile the alignment of two sequences may be performed swiftly in many\napplications, the simultaneous alignment of multiple sequences proved to be\nnaturally more intricate. Although most multiple sequence alignment (MSA)\nformulations are NP-hard, several approaches have been developed, as they can\noutperform pairwise alignment methods or are necessary for some applications.\n  Taking into account not only similarities but also the lengths of the\ncompared sequences (i.e. normalization) can provide better alignment results\nthan both unnormalized or post-normalized approaches. While some normalized\nmethods have been developed for pairwise sequence alignment, none have been\nproposed for MSA. This work is a first effort towards the development of\nnormalized methods for MSA.\n  We discuss multiple aspects of normalized multiple sequence alignment (NMSA).\nWe define three new criteria for computing normalized scores when aligning\nmultiple sequences, showing the NP-hardness and exact algorithms for solving\nthe NMSA using those criteria. In addition, we provide approximation algorithms\nfor MSA and NMSA for some classes of scoring matrices.\n"}
+{"abstract": "  Reasoning is one of the major challenges of Human-like AI and has recently\nattracted intensive attention from natural language processing (NLP)\nresearchers. However, cross-modal reasoning needs further research. For\ncross-modal reasoning, we observe that most methods fall into shallow feature\nmatching without in-depth human-like reasoning.The reason lies in that existing\ncross-modal tasks directly ask questions for a image. However, human reasoning\nin real scenes is often made under specific background information, a process\nthat is studied by the ABC theory in social psychology. We propose a shared\ntask named \"Premise-based Multimodal Reasoning\" (PMR), which requires\nparticipating models to reason after establishing a profound understanding of\nbackground information. We believe that the proposed PMR would contribute to\nand help shed a light on human-like in-depth reasoning.\n"}
+{"abstract": "  Erd\\H{o}s, Harary, and Tutte defined the dimension of a graph $G$ as the\nsmallest natural number $n$ such that $G$ can be embedded in $\\mathbb{R}^n$\nwith each edge a straight line segment of length 1.\n  Since the proposal of this definition, little has been published on how to\ncompute the exact dimension of graphs and almost nothing has been published on\ngraphs that are minor minimal with respect to dimension. This paper develops\nboth of these areas. In particular, it (1) establishes certain conditions under\nwhich computing the dimension of graph sums is easy and (2) constructs three\ninfinitely-large classes of graphs that are minor minimal with respect to their\ndimension.\n"}
+{"abstract": "  (1) If $R$ is an affine algebra of dimension $d\\geq 4$ over\n$\\overline{\\mathbb{F}}_{p}$ with $p>3$, then the group structure on ${\\rm\nUm}_d(R)/{\\rm E}_d(R)$ is nice. (2) If $R$ is a commutative noetherian ring of\ndimension $d\\geq 2$ such that ${\\rm E}_{d+1}(R)$ acts transitively on ${\\rm\nUm}_{d+1}(R),$ then the group structure on ${\\rm Um}_{d+1}(R[X])/{\\rm\nE}_{d+1}(R[X])$ is nice.\n"}
+{"abstract": "  Transformers have shown improved performance when compared to previous\narchitectures for sequence processing such as RNNs. Despite their sizeable\nperformance gains, as recently suggested, the model is computationally\nexpensive to train and with a high parameter budget. In light of this, we\nexplore parameter-sharing methods in Transformers with a specific focus on\ngenerative models. We perform an analysis of different parameter\nsharing/reduction methods and develop the Subformer. Our model combines\nsandwich-style parameter sharing, which overcomes naive cross-layer parameter\nsharing in generative models, and self-attentive embedding factorization\n(SAFE). Experiments on machine translation, abstractive summarization and\nlanguage modeling show that the Subformer can outperform the Transformer even\nwhen using significantly fewer parameters.\n"}
+{"abstract": "  Accurate short range weather forecasting has significant implications for\nvarious sectors. Machine learning based approaches, e.g., deep learning, have\ngained popularity in this domain where the existing numerical weather\nprediction (NWP) models still have modest skill after a few days. Here we use a\nConvLSTM network to develop a deep learning model for precipitation\nforecasting. The crux of the idea is to develop a forecasting model which\ninvolves convolution based feature selection and uses long term memory in the\nmeteorological fields in conjunction with gradient based learning algorithm.\nPrior to using the input data, we explore various techniques to overcome\ndataset difficulties. We follow a strategic approach to deal with missing\nvalues and discuss the models fidelity to capture realistic precipitation. The\nmodel resolution used is (25 km). A comparison between 5 years of predicted\ndata and corresponding observational records for 2 days lead time forecast show\ncorrelation coefficients of 0.67 and 0.42 for lead day 1 and 2 respectively.\nThe patterns indicate higher correlation over the Western Ghats and Monsoon\ntrough region (0.8 and 0.6 for lead day 1 and 2 respectively). Further, the\nmodel performance is evaluated based on skill scores, Mean Square Error,\ncorrelation coefficient and ROC curves. This study demonstrates that the\nadopted deep learning approach based only on a single precipitation variable,\nhas a reasonable skill in the short range. Incorporating multivariable based\ndeep learning has the potential to match or even better the short range\nprecipitation forecasts based on the state of the art NWP models.\n"}
+{"abstract": "  We investigate $(0,1)$-matrices that are {\\em convex}, which means that the\nones are consecutive in every row and column. These matrices occur in discrete\ntomography. The notion of ranked essential sets, known for permutation\nmatrices, is extended to convex sets. We show a number of results for the class\n$\\mc{C}(R,S)$ of convex matrices with given row and column sum vectors $R$ and\n$S$. Also, it is shown that the ranked essential set uniquely determines a\nmatrix in $\\mc{C}(R,S)$.\n"}
+{"abstract": "  Identifying objects in an image and their mutual relationships as a scene\ngraph leads to a deep understanding of image content. Despite the recent\nadvancement in deep learning, the detection and labeling of visual object\nrelationships remain a challenging task. This work proposes a novel\nlocal-context aware architecture named relation transformer, which exploits\ncomplex global objects to object and object to edge (relation) interactions.\nOur hierarchical multi-head attention-based approach efficiently captures\ncontextual dependencies between objects and predicts their relationships. In\ncomparison to state-of-the-art approaches, we have achieved an overall mean\n\\textbf{4.85\\%} improvement and a new benchmark across all the scene graph\ngeneration tasks on the Visual Genome dataset.\n"}
+{"abstract": "  Quantum imaginary time evolution is a powerful algorithm to prepare ground\nstates and thermal states on near-term quantum devices. However, algorithmic\nerrors induced by Trotterization and local approximation severely hinder its\nperformance. Here we propose a deep-reinforcement-learning-based method to\nsteer the evolution and mitigate these errors. In our scheme, the well-trained\nagent can find the subtle evolution path where most algorithmic errors cancel\nout, enhancing the recovering fidelity significantly. We verified the validity\nof the method with the transverse-field Ising model and graph maximum cut\nproblem. Numerical calculations and experiments on a nuclear magnetic resonance\nquantum computer illustrated the efficacy. The philosophy of our method,\neliminating errors with errors, sheds new light on error reduction on near-term\nquantum devices.\n"}
+{"abstract": "  Application developers, in our experience, tend to hesitate when dealing with\nlinked data technologies. To reduce their initial hurdle and enable rapid\nprototyping, we propose in this paper a framework for building linked data\napplications. Our approach especially considers the participation of web\ndevelopers and non-technical users without much prior knowledge about linked\ndata concepts. Web developers are supported with bidirectional RDF to JSON\nconversions and suitable CRUD endpoints. Non-technical users can browse\nwebsites generated from JSON data by means of a template language. A\nprototypical open source implementation demonstrates its capabilities.\n"}
+{"abstract": "  ccc-Autoevolutes are closed constant curvature space curves which are their\nown evolutes. A modified Frenet equation produces constant curvature curves\nsuch that the curve on $[0, \\pi]$ is congruent to the evolute on $[\\pi, 2\\pi]$\nand vice versa. Closed curves are then congruent to their evolutes. If the\nruled surface spanned by the principal normals between curve and evolute is a\nM\\\"obius band then the curve is its own evolute. We use symmetries to construct\nclosed curves by solving 2-parameter problems numerically. The smallest\nautoevolute which we found is a trefoil knot parametrized by three periods $[0,\n6\\pi]$.Our smallest closed solution of the ODE is parametrized by two periods.\n"}
+{"abstract": "  We study a correspondence between the multifractal model of turbulence and\nthe Navier-Stokes equations in $d$ spatial dimensions by comparing their\nrespective dissipation length scales. In Kolmogorov's 1941 theory the key\nparameter $h$, which is an exponent in the Navier-Stokes invariance scaling, is\nfixed at $h=1/3$ but is allowed a spectrum of values in multifractal theory.\nTaking into account all derivatives of the Navier-Stokes equations, it is found\nthat for this correspondence to hold the multifractal spectrum $C(h)$ must be\nbounded from below such that $C(h) \\geq 1-3h$, which is consistent with the\nfour-fifths law. Moreover, $h$ must also be bounded from below such that $h\n\\geq (1-d)/3$. When $d=3$ the allowed range of $h$ is given by $h \\geq -2/3$\nthereby bounding $h$ away from $h=-1$. The implications of this are discussed.\n"}
+{"abstract": "  We consider the stochastic scheduling problem of minimizing the expected\nmakespan on $m$ parallel identical machines. While the (adaptive) list\nscheduling policy achieves an approximation ratio of $2$, any (non-adaptive)\nfixed assignment policy has performance guarantee $\\Omega\\left(\\frac{\\log\nm}{\\log \\log m}\\right)$. Although the performance of the latter class of\npolicies are worse, there are applications in which non-adaptive policies are\ndesired. In this work, we introduce the two classes of $\\delta$-delay and\n$\\tau$-shift policies whose degree of adaptivity can be controlled by a\nparameter. We present a policy - belonging to both classes - which is an\n$\\mathcal{O}(\\log \\log m)$-approximation for reasonably bounded parameters. In\nother words, an exponential improvement on the performance of any fixed\nassignment policy can be achieved when allowing a small degree of adaptivity.\nMoreover, we provide a matching lower bound for any $\\delta$-delay and\n$\\tau$-shift policy when both parameters, respectively, are in the order of the\nexpected makespan of an optimal non-anticipatory policy.\n"}
+{"abstract": "  Decision trees are among the most popular machine learning models and are\nused routinely in applications ranging from revenue management and medicine to\nbioinformatics. In this paper, we consider the problem of learning optimal\nbinary classification trees. Literature on the topic has burgeoned in recent\nyears, motivated both by the empirical suboptimality of heuristic approaches\nand the tremendous improvements in mixed-integer optimization (MIO) technology.\nYet, existing MIO-based approaches from the literature do not leverage the\npower of MIO to its full extent: they rely on weak formulations, resulting in\nslow convergence and large optimality gaps. To fill this gap in the literature,\nwe propose an intuitive flow-based MIO formulation for learning optimal binary\nclassification trees. Our formulation can accommodate side constraints to\nenable the design of interpretable and fair decision trees. Moreover, we show\nthat our formulation has a stronger linear optimization relaxation than\nexisting methods. We exploit the decomposable structure of our formulation and\nmax-flow/min-cut duality to derive a Benders' decomposition method to speed-up\ncomputation. We propose a tailored procedure for solving each decomposed\nsubproblem that provably generates facets of the feasible set of the MIO as\nconstraints to add to the main problem. We conduct extensive computational\nexperiments on standard benchmark datasets on which we show that our proposed\napproaches are 31 times faster than state-of-the art MIO-based techniques and\nimprove out of sample performance by up to 8%.\n"}
+{"abstract": "  Polymers are among the most important materials in the modern society being\nfound almost in every activity of our daily life. Understanding their chemical\nand physical properties lead to improvements of their usage. The correlation\nfunctions are one of most important quantities to understand a physical system.\nThe characteristic way it behaves describe how the system fluctuates, and much\nof the progress achieved to understand complex systems has been due to their\nstudy. Of particular interest in polymer science are the space correlations\nwhich describe its mechanical behavior. In this work I study the stiffness of a\npolymer immersed in a magnetic medium and trapped in an optical tweezers. Using\nMonte Carlo simulations the correlation function along the chain and the force\nin the tweezers are obtained as a function of temperature and density of\nmagnetic particles. The results show that the correlation decay has two\nregimes: an initial very fast decay of order the monomer-monomer spacing and a\npower law in the long distance regime. The power law exponent has a minimum at\na temperature $T_{min}$ for any non zero density of magnetic particles\nindicating that the system is more correlated in this region. Using a formula\nfor the persistence length derived from the WLC theory one observed that it has\na maximum at the same temperature. These results suggest that the correlations\nin the system may be a combination of exponential and power law.\n"}
+{"abstract": "  This paper proposes a novel evolutionary algorithm called Epistocracy which\nincorporates human socio-political behavior and intelligence to solve complex\noptimization problems. The inspiration of the Epistocracy algorithm originates\nfrom a political regime where educated people have more voting power than the\nuneducated or less educated. The algorithm is a self-adaptive, and\nmulti-population optimizer in which the evolution process takes place in\nparallel for many populations led by a council of leaders. To avoid stagnation\nin poor local optima and to prevent a premature convergence, the algorithm\nemploys multiple mechanisms such as dynamic and adaptive leadership based on\ngravitational force, dynamic population allocation and diversification,\nvariance-based step-size determination, and regression-based leadership\nadjustment. The algorithm uses a stratified sampling method called Latin\nHypercube Sampling (LHS) to distribute the initial population more evenly for\nexploration of the search space and exploitation of the accumulated knowledge.\nTo investigate the performance and evaluate the reliability of the algorithm,\nwe have used a set of multimodal benchmark functions, and then applied the\nalgorithm to the MNIST dataset to further verify the accuracy, scalability, and\nrobustness of the algorithm. Experimental results show that the Epistocracy\nalgorithm outperforms the tested state-of-the-art evolutionary and swarm\nintelligence algorithms in terms of performance, precision, and convergence.\n"}
+{"abstract": "  We formalize the notion of vector semi-inner products and introduce a class\nof vector seminorms which are built from these maps. The classical Pythagorean\ntheorem and parallelogram law are then generalized to vector seminorms that\nhave a geometric mean closed vector lattice for codomain. In the special case\nthat this codomain is a square root closed, semiprime $f$-algebra, we provide a\nsharpening of the triangle inequality as well as a condition for equality.\n"}
+{"abstract": "  Reconfigurable Intelligent Surface (RIS) composed of programmable actuators\nis a promising technology, thanks to its capability in manipulating\nElectromagnetic (EM) wavefronts. In particular, RISs have the potential to\nprovide significant performance improvements for wireless networks. However, to\ndo so, a proper configuration of the reflection coefficients of the unit cells\nin the RIS is required. RISs are sophisticated platforms so the design and\nfabrication complexity might be uneconomical for single-user scenarios while a\nRIS that can service multi-users justifies the costs. For the first time, we\npropose an efficient reconfiguration technique providing the multi-beam\nradiation pattern. Thanks to the analytical model the reconfiguration profile\nis at hand compared to time-consuming optimization techniques. The outcome can\npave the wave for commercial use of multi-user communication beyond 5G\nnetworks. We analyze the performance of our proposed RIS technology for indoor\nand outdoor scenarios, given the broadcast mode of operation. The aforesaid\nscenarios encompass some of the most challenging scenarios that wireless\nnetworks encounter. We show that our proposed technique provisions sufficient\ngains in the observed channel capacity when the users are close to the RIS in\nthe indoor office environment scenario. Further, we report more than one order\nof magnitude increase in the system throughput given the outdoor environment.\nThe results prove that RIS with the ability to communicate with multiple users\ncan empower wireless networks with great capacity.\n"}
+{"abstract": "  We develop a system-level design for the provision of Ancillary Service (AS)\nfor control of electric power grids by in-vehicle batteries, suitably applied\nto Electric Vehicles (EVs) operated in a sharing service. The provision is\ncalled in this paper the multi-objective AS: primary frequency control in a\ntransmission grid and voltage amplitude regulation in a distribution grid\nconnected to EVs. The design is based on the ordinary differential equation\nmodel of distribution voltage, which has been recently introduced as a new\nphysics-based model, and is utilized in this paper for assessing and regulating\nthe impact of spatiotemporal charging/charging of a large population of EVs to\na distribution grid. Effectiveness of the autonomous V2G design is evaluated\nwith numerical simulations of realistic models for transmission and\ndistribution grids with synthetic operation data on EVs in a sharing service.\nIn addition, we present a hardware-in-the-loop test for evaluating its\nfeasibility in a situation where inevitable latency is involved due to power,\ncontrol, and communication equipments.\n"}
+{"abstract": "  We present a reanalysis of GW151226, the second binary black hole merger\ndiscovered by the LIGO-Virgo Collaboration. Previous analysis showed that the\nbest-fit waveform for this event corresponded to the merger of a $\\sim 14 \\,\nM_\\odot$ black hole with a $\\sim 7.5 \\, M_\\odot$ companion. In this work, we\nperform parameter estimation using a waveform model that includes the effects\nof orbital precession and higher-order radiative multipoles, and find that the\nmass and spin parameters of GW151226 have bimodal posterior distributions. The\ntwo modes are separated in mass ratio, $q$: the high-$q$ mode ($0.4 \\lesssim q\n< 1$) is consistent with the results reported in the literature. On the other\nhand, the low-$q$ mode ($q \\lesssim 0.4$), which describes a binary with\ncomponent masses of $\\sim 29 \\, M_\\odot$ and $\\sim \\, 4.3 M_\\odot$, is new. The\nlow-$q$ mode has several interesting properties: (a) the secondary black hole\nmass may fall in the lower mass gap of astrophysical black hole population; and\n(b) orbital precession is driven by the primary black hole spin, which has a\ndimensionless magnitude as large as $\\sim 0.88$ and is tilted away from the\norbital angular momentum at an angle of $\\sim 47^\\circ$. The new low-$q$ mode\nhas a log likelihood that is about six points higher than that of the high-$q$\nmode, and can therefore affect the astrophysical interpretation of GW151226.\nCrucially, we show that the low-$q$ mode disappears if we neglect either higher\nmultipoles or orbital precession in the parameter estimation. More generally,\nthis work highlights how incorporating additional physical effects into\nwaveform models used in parameter estimations can alter the interpretation of\ngravitational-wave sources.\n"}
+{"abstract": "  Let H be a tree. It was proved by Rodl that graphs that do not contain H as\nan induced subgraph, and do not contain the complete bipartite graph $K_{t,t}$\nas a subgraph, have bounded chromatic number. Kierstead and Penrice\nstrengthened this, showing that such graphs have bounded degeneracy. Here we\ngive a further strengthening, proving that for every tree H, the degeneracy is\nat most polynomial in t. This answers a question of Bonamy, Pilipczuk,\nRzazewski, Thomasse and Walczak.\n"}
+{"abstract": "  Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural\nnetworks, have attracted great attentions from researchers and industry. The\nmost efficient way to train deep SNNs is through ANN-SNN conversion. However,\nthe conversion usually suffers from accuracy loss and long inference time,\nwhich impede the practical application of SNN. In this paper, we theoretically\nanalyze ANN-SNN conversion and derive sufficient conditions of the optimal\nconversion. To better correlate ANN-SNN and get greater accuracy, we propose\nRate Norm Layer to replace the ReLU activation function in source ANN training,\nenabling direct conversion from a trained ANN to an SNN. Moreover, we propose\nan optimal fit curve to quantify the fit between the activation value of source\nANN and the actual firing rate of target SNN. We show that the inference time\ncan be reduced by optimizing the upper bound of the fit curve in the revised\nANN to achieve fast inference. Our theory can explain the existing work on fast\nreasoning and get better results. The experimental results show that the\nproposed method achieves near loss less conversion with VGG-16,\nPreActResNet-18, and deeper structures. Moreover, it can reach 8.6x faster\nreasoning performance under 0.265x energy consumption of the typical method.\nThe code is available at\nhttps://github.com/DingJianhao/OptSNNConvertion-RNL-RIL.\n"}
+{"abstract": "  We consider the problem of detecting signals in the rank-one\nsignal-plus-noise data matrix models that generalize the spiked Wishart\nmatrices. We show that the principal component analysis can be improved by\npre-transforming the matrix entries if the noise is non-Gaussian. As an\nintermediate step, we prove a sharp phase transition of the largest eigenvalues\nof spiked rectangular matrices, which extends the Baik-Ben Arous-P\\'ech\\'e\n(BBP) transition. We also propose a hypothesis test to detect the presence of\nsignal with low computational complexity, based on the linear spectral\nstatistics, which minimizes the sum of the Type-I and Type-II errors when the\nnoise is Gaussian.\n"}
+{"abstract": "  We generate the perturbative expansion of the single-particle Green's\nfunction and related self-energy for a half-filled single-band Hubbard model on\na square lattice. We invoke algorithmic Matsubara integration to evaluate\nsingle-particle quantities for real and Matsubara frequencies and verify\nresults through comparison to existing data on the Matsubara axis. With low\norder expansions at weak-coupling we observe a number of outcomes expected at\nhigher orders: the opening of a gap, pseudogap behavior, and Fermi-surface\nreconstruction. Based on low-order perturbations we consider the phase diagram\nthat arises from truncated expansions of the self-energy and Green's function\nand their relation via the Dyson equation. From Matsubara axis data we observe\ninsulating behavior in direct expansions of the Green's function, while the\nsame order of truncation of the self-energy produces metallic behavior. This\nobservation is supported by additional calculations for real frequencies. We\nattribute this difference to the order in which diagrams are implicitly summed\nin the Dyson series. By separating the reducible and irreducible contributions\nat each order we show that the reducible diagrams implicitly summed in the\nDyson equation lead to incorrect physics in the half-filled Hubbard model. Our\nobservations for this particular case lead us to question the utility of the\nDyson equation for any problem that shows a disparity between reducible and\nirreducible contributions to the expansion of the Green's function.\n"}
+{"abstract": "  Assuming the existence of Siegel zeros, we prove that there exists an\nincreasing sequence of positive integers for which Chowla's Conjecture on\n$k$-point correlations of the Liouville function holds. This extends work of\nGerm\\'an and K\\'atai, where they studied the case $k=2$ under identical\nhypotheses.\n  An immediate corollary, which follows from a well-known argument due to\nSarnak, is that Sarnak's Conjecture on M\\\"obius disjointness holds. More\nprecisely, assuming the existence of Siegel zeros, there exists a subsequence\nof the natural numbers for which the Liouville function is asymptotically\northogonal to any sequence of topological entropy zero.\n"}
+{"abstract": "  Modifications to the distribution of charged particles with respect to high\ntransverse momentum ($p_\\mathrm{T}$) jets passing through a quark-gluon plasma\nare explored using the CMS detector. Back-to-back dijets are analyzed in\nlead-lead and proton-proton collisions at $\\sqrt{s_\\mathrm{NN}} =$ 5.02 TeV via\ncorrelations of charged particles in bins of relative pseudorapidity and\nangular distance from the leading and subleading jet axes. In comparing the\nlead-lead and proton-proton collision results, modifications to the\ncharged-particle relative distance distribution and to the momentum\ndistributions around the jet axis are found to depend on the dijet momentum\nbalance $x_j$, which is the ratio between the subleading and leading jet\n$p_\\mathrm{T}$. For events with $x_j$ $\\approx$ 1, these modifications are\nobserved for both the leading and subleading jets. However, while subleading\njets show significant modifications for events with a larger dijet momentum\nimbalance, much smaller modifications are found for the leading jets in these\nevents.\n"}
+{"abstract": "  A new type of nonlinear dust pulse structures has been observed in afterglow\ncomplex plasma under microgravity condition on board the International Space\nStation (ISS). The dust pulses are triggered spontaneously as the plasma is\nswitched off and the particles start to flow through each other\n(uni-directional or counter-streaming) in the presence of a low-frequency\nexternal electric excitation. The pulses are oblique with respect to the\nmicroparticle cloud and appear to be symmetric with respect to the central\naxis. A possible explanation of this observation with the spontaneous\ndevelopment of a double layer in the afterglow of complex plasma is described.\n"}
+{"abstract": "  Using the gravitational potential and source multipole moments bilinear in\nthe spins, first computed to next-to-leading order (NLO) in the post-Newtonian\n(PN) expansion within the effective field theory (EFT) framework, we complete\nhere the derivation of the dynamical invariants and flux-balance equations,\nincluding energy and angular momentum. We use these results to calculate\nspin-spin effects in the orbital frequency and accumulated phase to NLO for\ncircular orbits. We also derive the linear momentum and center-of-mass fluxes\nand associated kick-velocity, to the highest relevant PN order. We explicitly\ndemonstrate the equivalence between the quadratic-in-spin source multipoles\nobtained using the EFT formalism and those rederived later with more\ntraditional tools, leading to perfect agreement for spin-spin radiative\nobservables to NLO among both approaches.\n"}
+{"abstract": "  In this paper, we study asynchronous federated learning (FL) in a wireless\ndistributed learning network (WDLN). To allow each edge device to use its local\ndata more efficiently via asynchronous FL, transmission scheduling in the WDLN\nfor asynchronous FL should be carefully determined considering system\nuncertainties, such as time-varying channel and stochastic data arrivals, and\nthe scarce radio resources in the WDLN. To address this, we propose a metric,\ncalled an effectivity score, which represents the amount of learning from\nasynchronous FL. We then formulate an Asynchronous Learning-aware transmission\nScheduling (ALS) problem to maximize the effectivity score and develop three\nALS algorithms, called ALSA-PI, BALSA, and BALSA-PO, to solve it. If the\nstatistical information about the uncertainties is known, the problem can be\noptimally and efficiently solved by ALSA-PI. Even if not, it can be still\noptimally solved by BALSA that learns the uncertainties based on a Bayesian\napproach using the state information reported from devices. BALSA-PO\nsuboptimally solves the problem, but it addresses a more restrictive WDLN in\npractice, where the AP can observe a limited state information compared with\nthe information used in BALSA. We show via simulations that the models trained\nby our ALS algorithms achieve performances close to that by an ideal benchmark\nand outperform those by other state-of-the-art baseline scheduling algorithms\nin terms of model accuracy, training loss, learning speed, and robustness of\nlearning. These results demonstrate that the adaptive scheduling strategy in\nour ALS algorithms is effective to asynchronous FL.\n"}
+{"abstract": "  We investigate the existence of constant-round post-quantum black-box\nzero-knowledge protocols for $\\mathbf{NP}$. As a main result, we show that\nthere is no constant-round post-quantum black-box zero-knowledge argument for\n$\\mathbf{NP}$ unless $\\mathbf{NP}\\subseteq \\mathbf{BQP}$. As constant-round\nblack-box zero-knowledge arguments for $\\mathbf{NP}$ exist in the classical\nsetting, our main result points out a fundamental difference between\npost-quantum and classical zero-knowledge protocols. Combining previous\nresults, we conclude that unless $\\mathbf{NP}\\subseteq \\mathbf{BQP}$,\nconstant-round post-quantum zero-knowledge protocols for $\\mathbf{NP}$ exist if\nand only if we use non-black-box techniques or relax certain security\nrequirements such as relaxing standard zero-knowledge to\n$\\epsilon$-zero-knowledge. Additionally, we also prove that three-round and\npublic-coin constant-round post-quantum black-box $\\epsilon$-zero-knowledge\narguments for $\\mathbf{NP}$ do not exist unless $\\mathbf{NP}\\subseteq\n\\mathbf{BQP}$.\n"}
+{"abstract": "  In graph theory, an independent set is a subset of nodes where there are no\ntwo adjacent nodes. The independent set is maximal if no node outside the\nindependent set can join it. In network applications, maximal independent sets\ncan be used as cluster heads in ad hoc and wireless sensor networks. In order\nto deal with any failure in networks, self-stabilizing algorithms have been\nproposed in the literature to calculate the maximal independent set under\ndifferent hypotheses. In this paper, we propose a self-stabilizing algorithm to\ncompute a maximal independent set where nodes of the independent set are far\nfrom each other at least with distance 3. We prove the correctness and the\nconvergence of the proposed algorithm. Simulation tests show the ability of our\nalgorithm to find a reduced number of nodes in large scale networks which\nallows strong control of networks\n"}
+{"abstract": "  Cuprates, a member of high-Tc superconductors, have been on the long-debate\non their superconducting mechanism, so that predicting the critical temperature\nof cuprates still remains elusive. Herein, using machine learning and first\nprinciple calculations, we predict the maximum superconducting transition\ntemperature (Tc,max) of hole-doped cuprates and suggest the explicit functional\nform for Tc,max with the root-mean-square-error of 3.705 K and the coefficient\nof determination R2 of 0.969. We employed two machine learning models; one is a\nparametric brute force searching method and another is a non-parametric random\nforest regression model. We have found that material dependent parameters such\nas the Bader charge of apical oxygen, the bond strength between apical atoms,\nand the number of superconducting layers are important features to estimate\nTc,max. Furthermore, we predict the Tc,max of hypothetical cuprates generated\nby replacing apical cations with other elements. When Ga is an apical cation,\nthe predicted Tc,max is the highest among the hypothetical structures with 71,\n117, and 131 K for one, two, and three CuO2 layers, respectively. These\nfindings suggest that machine learning could guide the design of new high-Tc\nsuperconductors in the future.\n"}
+{"abstract": "  Defining templates of galaxy spectra is useful to quickly characterise new\nobservations and organise databases from surveys. These templates are usually\nbuilt from a pre-defined classification based on other criteria. Aims. We\npresent an unsupervised classification of 702248 spectra of galaxies and\nquasars with redshifts smaller than 0.25 that were retrieved from the Sloan\nDigital Sky Survey (SDSS) database, release 7. The spectra were first corrected\nfor redshift, then wavelet-filtered to reduce the noise, and finally binned to\nobtain about 1437 wavelengths per spectrum. The unsupervised clustering\nalgorithm Fisher-EM, relying on a discriminative latent mixture model, was\napplied on these corrected spectra. The full set and several subsets of 100000\nand 300000 spectra were analysed. The optimum number of classes given by a\npenalised likelihood criterion is 86 classes, of which the 37 most populated\ngather 99% of the sample. These classes are established from a subset of 302214\nspectra. Using several cross-validation techniques we find that this\nclassification agrees with the results obtained on the other subsets with an\naverage misclassification error of about 15%. The large number of very small\nclasses tends to increase this error rate. In this paper, we do an initial\nquick comparison of our classes with literature templates. This is the first\ntime that an automatic, objective and robust unsupervised classification is\nestablished on such a large number of galaxy spectra. The mean spectra of the\nclasses can be used as templates for a large majority of galaxies in our\nUniverse.\n"}
+{"abstract": "  This paper is a modified chapter of the author's Ph.D. thesis. We introduce\nthe notions of sequentially approximated types and sequentially approximated\nKeisler measures. As the names imply, these are types which can be approximated\nby a sequence of realized types and measures which can be approximated by a\nsequence of `averaging measures' on tuples of realized types. We show that both\ngenerically stable types (in arbitrary theories) and Keisler measures which are\nfinitely satisfiable over a countable model (in NIP theories) are sequentially\napproximated. We also introduce the notion of a smooth sequence in a measure\nover a model and give an equivalent characterization of generically stable\nmeasures (in NIP theories) via this definition. In the last section, we take\nthe opportunity to generalize the main result of [8].\n"}
+{"abstract": "  In this paper, a novel multiagent based state transition optimization\nalgorithm with linear convergence rate named MASTA is constructed. It first\ngenerates an initial population randomly and uniformly. Then, it applies the\nbasic state transition algorithm (STA) to the population and generates a new\npopulation. After that, It computes the fitness values of all individuals and\nfinds the best individuals in the new population. Moreover, it performs an\neffective communication operation and updates the population. With the above\niterative process, the best optimal solution is found out. Experimental results\nbased on some common benchmark functions and comparison with some\nstat-of-the-art optimization algorithms, the proposed MASTA algorithm has shown\nvery superior and comparable performance.\n"}
+{"abstract": "  Open-domain neural dialogue models have achieved high performance in response\nranking and evaluation tasks. These tasks are formulated as a binary\nclassification of responses given in a dialogue context, and models generally\nlearn to make predictions based on context-response content similarity.\nHowever, over-reliance on content similarity makes the models less sensitive to\nthe presence of inconsistencies, incorrect time expressions and other factors\nimportant for response appropriateness and coherence. We propose approaches for\nautomatically creating adversarial negative training data to help ranking and\nevaluation models learn features beyond content similarity. We propose\nmask-and-fill and keyword-guided approaches that generate negative examples for\ntraining more robust dialogue systems. These generated adversarial responses\nhave high content similarity with the contexts but are either incoherent,\ninappropriate or not fluent. Our approaches are fully data-driven and can be\neasily incorporated in existing models and datasets. Experiments on\nclassification, ranking and evaluation tasks across multiple datasets\ndemonstrate that our approaches outperform strong baselines in providing\ninformative negative examples for training dialogue systems.\n"}
+{"abstract": "  Molecular modeling is an important topic in drug discovery. Decades of\nresearch have led to the development of high quality scalable molecular force\nfields. In this paper, we show that neural networks can be used to train a\nuniversal approximator for energy potential functions. By incorporating a fully\nautomated training process we have been able to train smooth, differentiable,\nand predictive potential functions on large-scale crystal structures. A variety\nof tests have also been performed to show the superiority and versatility of\nthe machine-learned model.\n"}
+{"abstract": "  Collecting training data from untrusted sources exposes machine learning\nservices to poisoning adversaries, who maliciously manipulate training data to\ndegrade the model accuracy. When trained on offline datasets, poisoning\nadversaries have to inject the poisoned data in advance before training, and\nthe order of feeding these poisoned batches into the model is stochastic. In\ncontrast, practical systems are more usually trained/fine-tuned on sequentially\ncaptured real-time data, in which case poisoning adversaries could dynamically\npoison each data batch according to the current model state. In this paper, we\nfocus on the real-time settings and propose a new attacking strategy, which\naffiliates an accumulative phase with poisoning attacks to secretly (i.e.,\nwithout affecting accuracy) magnify the destructive effect of a (poisoned)\ntrigger batch. By mimicking online learning and federated learning on MNIST and\nCIFAR-10, we show that model accuracy significantly drops by a single update\nstep on the trigger batch after the accumulative phase. Our work validates that\na well-designed but straightforward attacking strategy can dramatically amplify\nthe poisoning effects, with no need to explore complex techniques.\n"}
+{"abstract": "  Society is showing signs of strong ideological polarization. When pushed to\nseek perspectives different from their own, people often reject diverse ideas\nor find them unfathomable. Work has shown that framing controversial issues\nusing the values of the audience can improve understanding of opposing views.\nIn this paper, we present our work designing systems for addressing ideological\ndivision through educating U.S. news consumers to engage using a framework of\nfundamental human values known as Moral Foundations. We design and implement a\nseries of new features that encourage users to challenge their understanding of\nopposing views, including annotation of moral frames in news articles,\ndiscussion of those frames via inline comments, and recommendations based on\nrelevant moral frames. We describe two versions of features -- the first\ncovering a suite of ways to interact with moral framing in news, and the second\ntailored towards collaborative annotation and discussion. We conduct a field\nevaluation of each design iteration with 71 participants in total over a period\nof 6-8 days, finding evidence suggesting users learned to re-frame their\ndiscourse in moral values of the opposing side. Our work provides several\ndesign considerations for building systems to engage with moral framing.\n"}
+{"abstract": "  The construction of approximate replication strategies for pricing and\nhedging of derivative contracts in incomplete markets is a key problem of\nfinancial engineering. Recently Reinforcement Learning algorithms for hedging\nunder realistic market conditions have attracted significant interest. While\nresearch in the derivatives area mostly focused on variations of $Q$-learning,\nin artificial intelligence Monte Carlo Tree Search is the recognized\nstate-of-the-art method for various planning problems, such as the games of\nHex, Chess, Go,... This article introduces Monte Carlo Tree Search as a method\nto solve the stochastic optimal control problem behind the pricing and hedging\ntasks. As compared to $Q$-learning it combines Reinforcement Learning with tree\nsearch techniques. As a consequence Monte Carlo Tree Search has higher sample\nefficiency, is less prone to over-fitting to specific market models and\ngenerally learns stronger policies faster. In our experiments we find that\nMonte Carlo Tree Search, being the world-champion in games like Chess and Go,\nis easily capable of maximizing the utility of investor's terminal wealth\nwithout setting up an auxiliary mathematical framework.\n"}
+{"abstract": "  Voter eligibility in United States elections is determined by a patchwork of\nstate databases containing information about which citizens are eligible to\nvote. Administrators at the state and local level are faced with the\nexceedingly difficult task of ensuring that each of their jurisdictions is\nproperly managed, while also monitoring for improper modifications to the\ndatabase. Monitoring changes to Voter Registration Files (VRFs) is crucial,\ngiven that a malicious actor wishing to disrupt the democratic process in the\nUS would be well-advised to manipulate the contents of these files in order to\nachieve their goals. In 2020, we saw election officials perform admirably when\nfaced with administering one of the most contentious elections in US history,\nbut much work remains to secure and monitor the election systems Americans rely\non. Using data created by comparing snapshots taken of VRFs over time, we\npresent a set of methods that make use of machine learning to ease the burden\non analysts and administrators in protecting voter rolls. We first evaluate the\neffectiveness of multiple unsupervised anomaly detection methods in detecting\nVRF modifications by modeling anomalous changes as sparse additive noise. In\nthis setting we determine that statistical models comparing administrative\ndistricts within a short time span and non-negative matrix factorization are\nmost effective for surfacing anomalous events for review. These methods were\ndeployed during 2019-2020 in our organization's monitoring system and were used\nin collaboration with the office of the Iowa Secretary of State. Additionally,\nwe propose a newly deployed model which uses historical and demographic\nmetadata to label the likely root cause of database modifications. We hope to\nuse this model to predict which modifications have known causes and therefore\nbetter identify potentially anomalous modifications.\n"}
+{"abstract": "  A family $\\mathcal{F}$ of elliptic curves defined over number fields is said\nto be typically bounded in torsion if the torsion subgroups $E(F)[$tors$]$ of\nthose elliptic curves $E_{/F}\\in \\mathcal{F}$ can be made uniformly bounded\nafter removing from $\\mathcal{F}$ those whose number field degrees lie in a\nsubset of $\\mathbb{Z}^+$ with arbitrarily small upper density. For every number\nfield $F$, we prove unconditionally that the family $\\mathcal{E}_F$ of elliptic\ncurves defined over number fields and with $F$-rational $j$-invariant is\ntypically bounded in torsion. For any integer $d\\in\\mathbb{Z}^+$, we also\nstrengthen a result on typically bounding torsion for the family\n$\\mathcal{E}_d$ of elliptic curves defined over number fields and with degree\n$d$ $j$-invariant.\n"}
+{"abstract": "  A popular method of improving the throughput of blockchain systems is by\nrunning smaller side blockchains that push the hashes of their blocks onto a\ntrusted blockchain. Side blockchains are vulnerable to stalling attacks where a\nside blockchain node pushes the hash of a block to the trusted blockchain but\nmakes the block unavailable to other side blockchain nodes. Recently, Sheng et\nal. proposed a data availability oracle based on LDPC codes and a data\ndispersal protocol as a solution to the above problem. While showing\nimprovements, the codes and dispersal protocol were designed disjointly which\nmay not be optimal in terms of the communication cost associated with the\noracle. In this paper, we provide a tailored dispersal protocol and specialized\nLDPC code construction based on the Progressive Edge Growth (PEG) algorithm,\ncalled the dispersal-efficient PEG (DE-PEG) algorithm, aimed to reduce the\ncommunication cost associated with the new dispersal protocol. Our new code\nconstruction reduces the communication cost and, additionally, is less\nrestrictive in terms of system design.\n"}
+{"abstract": "  Every 19 years, Saturn passes through Jupiter's 'flapping' magnetotail. Here,\nwe report Chandra X-ray observations of Saturn planned to coincide with this\nrare planetary alignment and to analyse Saturn's magnetospheric response when\ntransitioning to this unique parameter space. We analyse three Director's\nDiscretionary Time (DDT) observations from the High Resolution Camera (HRC-I)\non-board Chandra, taken on November 19, 21 and 23 2020 with the aim to find\nauroral and/or disk emissions. We infer the conditions in the kronian system by\nlooking at coincident soft X-ray solar flux data from the Geostationary\nOperational Environmental Satellite (GOES) and Hubble Space Telescope (HST)\nobservations of Saturn's ultraviolet (UV) auroral emissions. The large\nSaturn-Sun-Earth angle during this time would mean that most flares from the\nEarth-facing side of the Sun would not have impacted Saturn. We find no\nsignificant detection of Saturn's disk or auroral emissions in any of our\nobservations. We calculate the 3$\\sigma$ upper band energy flux of Saturn\nduring this time to be 0.9 - 3.04 $\\times$ 10$^{14}$ erg cm$^{-2}$ s$^{-1}$\nwhich agrees with fluxes found from previous modelled spectra of the disk\nemissions. We conclude by discussing the implications of this non-detection and\nhow it is imperative that the next fleet of X-ray telescope (such as Athena and\nthe Lynx mission concept) continue to observe Saturn with their improved\nspatial and spectral resolution and very enhanced sensitivity to help us\nfinally solve the mysteries behind Saturn's apparently elusive X-ray aurora.\n"}
+{"abstract": "  The discovery of gravitational wave radiation from merging black holes (BHs)\nalso uncovered BHs with masses in the range of ~20-160 Msun. In contrast, the\nmost massive Galactic stellar-mass BH currently known has a mass ~21 Msun.\nWhile low-mass X-ray binaries (LMXBs) will never independently evolve into a\nbinary BH system, and binary evolution effects can play an important role\nexplaining the different BH masses found through studies of X-ray binaries and\ngravitational wave events, (electromagnetic) selection effects may also play a\nrole in this discrepancy. Assuming BH LMXBs originate in the Galactic Plane, we\nshow that the spatial distribution of the current sample of confirmed and\ncandidate BH LMXBs are both biased to sources that lie at a large distance from\nthe Plane. Specifically, most of the confirmed and candidate BH LMXBs are found\nat a Galactic height larger than 3 times the scale height for massive star\nformation. In addition, the confirmed BH LMXBs are found at larger distances to\nthe Galactic Center than the candidate BH LMXBs. Interstellar absorption makes\ncandidate BH LMXBs in the Plane and those in the Bulge too faint for a\ndynamical mass measurement using current instrumentation. Given the observed\nand theoretical evidence for BH natal and/or Blaauw kicks, their relation with\nBH mass and binary orbital period, and the relation between outburst recurrence\ntime and BH mass, the observational selection effects imply that the current\nsample of confirmed BH LMXBs is biased against the most massive BHs.\n"}
+{"abstract": "  We describe a new code and approach using particle-level information to\nrecast the recent CMS disappearing track searches including all run 2 data.\nNotably, the simulation relies on knowledge of the detector geometry, and we\nalso include the simulation of pileup events directly rather than as an\nefficiency function. We validate it against provided acceptances and cutflows,\nand use it in combination with heavy stable charged particle searches to place\nlimits on winos with any proper decay length above a centimetre. We also\nprovide limits for a simple model of a charged scalar that is only produced in\npairs, that decays to electrons plus an invisible fermion.\n"}
+{"abstract": "  The study of the classifier's design and it's usage is one of the most\nimportant machine learning areas. With the development of automatic machine\nlearning methods, various approaches are used to build a robust classifier\nmodel. Due to some difficult implementation and customization complexity,\ngenetic programming (GP) methods are not often used to construct classifiers.\nGP classifiers have several limitations and disadvantages. However, the concept\nof \"soft\" genetic programming (SGP) has been developed, which allows the\nlogical operator tree to be more flexible and find dependencies in datasets,\nwhich gives promising results in most cases. This article discusses a method\nfor constructing binary classifiers using the SGP technique. The test results\nare presented. Source code - https://github.com/survexman/sgp_classifier.\n"}
+{"abstract": "  The repetitive tracking task for time-varying systems (TVSs) with\nnon-repetitive time-varying parameters, which is also called non-repetitive\nTVSs, is realized in this paper using iterative learning control (ILC). A\nmachine learning (ML) based nominal model update mechanism, which utilizes the\nlinear regression technique to update the nominal model at each ILC trial only\nusing the current trial information, is proposed for non-repetitive TVSs in\norder to enhance the ILC performance. Given that the ML mechanism forces the\nmodel uncertainties to remain within the ILC robust tolerance, an ILC update\nlaw is proposed to deal with non-repetitive TVSs. How to tune parameters inside\nML and ILC algorithms to achieve the desired aggregate performance is also\nprovided. The robustness and reliability of the proposed method are verified by\nsimulations. Comparison with current state-of-the-art demonstrates its superior\ncontrol performance in terms of controlling precision. This paper broadens ILC\napplications from time-invariant systems to non-repetitive TVSs, adopts ML\nregression technique to estimate non-repetitive time-varying parameters between\ntwo ILC trials and proposes a detailed parameter tuning mechanism to achieve\ndesired performance, which are the main contributions.\n"}
+{"abstract": "  The goal of this paper is to provide a complete representation of regional\nlinguistic variation on a global scale. To this end, the paper focuses on\nremoving three constraints that have previously limited work within\ndialectology/dialectometry. First, rather than assuming a fixed and incomplete\nset of variants, we use Computational Construction Grammar to provide a\nreplicable and falsifiable set of syntactic features. Second, rather than\nassuming a specific area of interest, we use global language mapping based on\nweb-crawled and social media datasets to determine the selection of national\nvarieties. Third, rather than looking at a single language in isolation, we\nmodel seven major languages together using the same methods: Arabic, English,\nFrench, German, Portuguese, Russian, and Spanish. Results show that models for\neach language are able to robustly predict the region-of-origin of held-out\nsamples better using Construction Grammars than using simpler syntactic\nfeatures. These global-scale experiments are used to argue that new methods in\ncomputational sociolinguistics are able to provide more generalized models of\nregional variation that are essential for understanding language variation and\nchange at scale.\n"}
+{"abstract": "  We consider a family of free multiplicative Brownian motions $b_{s,\\tau}$\nparametrized by a real variance parameter $s$ and a complex covariance\nparameter $\\tau.$ We compute the Brown measure $\\mu_{s,\\tau}$ of $ub_{s,\\tau\n},$ where $u$ is a unitary element freely independent of $b_{s,\\tau}.$ We find\nthat $\\mu_{s,\\tau}$ has a simple structure, with a density in logarithmic\ncoordinates that is constant in the $\\tau$-direction. These results generalize\nthose of Driver-Hall-Kemp and Ho-Zhong for the case $\\tau=s.$ We also establish\na remarkable \"model variation phenomenon,'' stating that all the Brown measures\nwith $s$ fixed and $\\tau$ varying are related by push-forward under a natural\nfamily of maps. Our proofs use a first-order nonlinear PDE of Hamilton-Jacobi\ntype satisfied by the regularized log potential of the Brown measures. Although\nthis approach is inspired by the PDE method introduced by Driver-Hall-Kemp, our\nmethods are substantially different at both the technical and conceptual level.\n"}
+{"abstract": "  We propose a new transmit antenna selection (TAS) technique that can be\nbeneficial for physical layer security purposes. Specifically, we show that the\nconventional TAS criterion based on the legitimate channel state information\n(CSI) is not recommended when the average signal-to-noise ratio for the\nillegitimate user becomes comparable or superior to that of the legitimate\nuser. We illustrate that an eavesdropper's based antenna selection technique\noutperforms conventional TAS, without explicit knowledge of the eavesdropper's\ninstantaneous CSI. Analytical expressions and simulation results to support\nthis comparison are given, showing how this new TAS scheme is a better choice\nin scenarios with a strong eavesdropper.\n"}
+{"abstract": "  The repeating fast radio burst (FRB) localized to a globular cluster in M81\nchallenges our understanding of FRB models. In this Letter, we explore\ndynamical formation scenarios for objects in old globular clusters that may\nplausibly power FRBs. Using N-body simulations, we demonstrate that young\nneutron stars may form in globular clusters at a rate of up to\n$\\sim50\\,\\rm{Gpc}^{-3}\\,\\rm{yr}^{-1}$ through a combination of binary white\ndwarf mergers, white dwarf--neutron star mergers, binary neutron star mergers,\nand accretion induced collapse of massive white dwarfs in binary systems. We\nconsider two FRB emission mechanisms: First, we show that a\nmagnetically-powered source (e.g., a magnetar with field strength\n$\\gtrsim10^{14}\\,$G) is viable for radio emission efficiencies\n$\\gtrsim10^{-4}$. This would require magnetic activity lifetimes longer than\nthe associated spin-down timescales and longer than empirically-constrained\nlifetimes of Galactic magnetars. Alternatively, if these dynamical formation\nchannels produce young rotation-powered neutron stars with spin periods of\n$\\sim10\\,$ms and magnetic fields of $\\sim10^{11}\\,$G (corresponding to\nspin-down lifetimes of $\\gtrsim10^5\\,$yr), the inferred event rate and\nenergetics can be reasonably reproduced for order unity duty cycles.\nAdditionally, we show that recycled millisecond pulsars or low-mass X-ray\nbinaries similar to those well-observed in Galactic globular clusters may also\nbe plausible channels, but only if their duty cycle for producing bursts\nsimilar to the M81 FRB is small.\n"}
+{"abstract": "  There has been an intense recent activity in embedding of very high\ndimensional and nonlinear data structures, much of it in the data science and\nmachine learning literature. We survey this activity in four parts. In the\nfirst part we cover nonlinear methods such as principal curves,\nmultidimensional scaling, local linear methods, ISOMAP, graph based methods and\nkernel based methods. The second part is concerned with topological embedding\nmethods, in particular mapping topological properties into persistence\ndiagrams. Another type of data sets with a tremendous growth is very\nhigh-dimensional network data. The task considered in part three is how to\nembed such data in a vector space of moderate dimension to make the data\namenable to traditional techniques such as cluster and classification\ntechniques. The final part of the survey deals with embedding in\n$\\mathbb{R}^2$, which is visualization. Three methods are presented: $t$-SNE,\nUMAP and LargeVis based on methods in parts one, two and three, respectively.\nThe methods are illustrated and compared on two simulated data sets; one\nconsisting of a triple of noisy Ranunculoid curves, and one consisting of\nnetworks of increasing complexity and with two types of nodes.\n"}
+{"abstract": "  We study Martsinkovsky-Russell torsion modules [MaRu20] with pure embeddings\nas an abstract elementary class. We give a model-theoretic characterization of\nthe pure-injective and the $\\Sigma$-pure-injective modules relative to the\nclass of torsion modules assuming that the ring is right semihereditary. Our\ncharacterization of relative $\\Sigma$-pure-injective modules strictly extends\nthe classical charactetization of [GrJe76] and [Zim, 3.6].\n  We study the limit models of the class and determine when the class is\nsuperstable assuming that the ring is right semihereditary. As a corollary, we\nshow that the class of torsion abelian groups with pure embeddings is strictly\nstable, i.e., stable not superstable.\n"}
+{"abstract": "  Axions and axion-like particles are bosonic quantum fields. They are often\nassumed to follow classical field equations due to their high degeneracy in the\nphase space. In this work, we explore the disparity between classical and\nquantum field treatments in the context of density and velocity fields of\naxions. Once the initial density and velocity field are specified, the\nevolution of the axion fluid is unique in the classical field treatment.\nHowever, in the quantum field treatment, there are many quantum states\nconsistent with the given initial density and velocity field. We show that\nevolutions of the density perturbations for these quantum states are not\nnecessarily identical and, in general, differ from the unique classical\nevolution. To illustrate the underlying physics, we consider a system of large\nnumber of bosons in a one-dimensional box, moving under the gravitational\npotential of a heavy static point-mass. We ignore the self-interactions between\nthe bosons here. Starting with homogeneous number density and zero velocity\nfield, we determine the density perturbations in the linear regime in both\nquantum and classical field theories. We find that classical and quantum\nevolutions are identical in the linear regime if only one single-particle state\nis occupied by all the bosons and the self-interaction is absent. If more than\none single-particle states are occupied, the density perturbations in quantum\nevolutions differ from the classical prediction after a certain time which\ndepends upon the parameters of the system.\n"}
+{"abstract": "  This note is a continuation of [CMZ21]. We shall show that an ancient Ricci\nflow with uniformly bounded Nash entropy must also have uniformly bounded\n$\\nu$-functional. Consequently, on such an ancient solution there are uniform\nlogarithmic Sobolev and Sobolev inequalities. We emphasize that the main\ntheorem in this paper is true so long as the theory in [Bam20c] is valid, and\nin particular, when the underlying manifold is closed.\n"}
+{"abstract": "  The long-term optical, X-ray and $\\gamma$-ray data of blazar 3C 279 have been\ncompiled from $Swift$-XRT, $RXTE$ PCA, $Fermi$-LAT, SMARTS and literature. The\nsource exhibits strong variability on long time scales. Since 1980s to now, the\noptical $R$ band light curve spans above 32 yr, and a possible 5.6-yr-long\nquasi-periodic variation component has been found in it. The optical spectral\nbehavior has been investigated. In the optical band, the mean spectral index is\n-1.71. The source exhibits an obvious special spectral behavior. In the low\nstate, the source shows a clear bluer-when-brighter behavior in a sense that\nthe optical spectrum turns harder (flatter) when the brightness increases.\nWhile in the high state, the optical spectrum is stable, that means the source\nspectral index does not vary with the brightness. The correlation analysis has\nbeen performed among optical, X-ray and $\\gamma$-ray energy bands. The result\nindicates that the variations of $\\gamma$-ray and X-ray bands are well\ncorrelated without time delay on the time scale of days, and their variations\nexhibit weak correlations with those of optical band. The variations, especial\noutbursts, are simultaneous, but the magnitude of variations is\ndisproportionate. The detailed analysis reveals that the main outbursts exhibit\nstrong correlations in different $\\gamma$-ray, X-ray and optical bands.\n"}
+{"abstract": "  We study the generation of harmonics from graphene under the influence of an\nartificial magnetic field, generated via bending of a graphene flake. We show\nhow the Landau level structure induced by the pseudomagnetic field breaks the\ncentrosymmetry of graphene, thus allowing the generation of even harmonics. We\nalso show, that depending on the impinging pulse duration, the nonlinear signal\ndoes not only contain the integer harmonics of the impinging pulse, but also\nits half-integer ones, due to the peculiar square-root-like nature of Landau\nlevels in graphene.\n"}
+{"abstract": "  In the present paper, we give new Frenet formulas for the Bertrand partner\ncurve by taking the advantage of relations between curvatures and a curve\nitself. Then making use of these formulas we write the differential equations\nand sufficient conditions of harmonicity of the Bertrand partner curve in terms\nof the main curve. Finally, we exemplify our assertions on the curve helix to\nsee how the formulas we developed work.\n"}
+{"abstract": "  Since 2014, the NIH funded iDASH (integrating Data for Analysis,\nAnonymization, SHaring) National Center for Biomedical Computing has hosted\nyearly competitions on the topic of private computing for genomic data. For one\ntrack of the 2020 iteration of this competition, participants were challenged\nto produce an approach to federated learning (FL) training of genomic cancer\nprediction models using differential privacy (DP), with submissions ranked\naccording to held-out test accuracy for a given set of DP budgets. More\nprecisely, in this track, we are tasked with training a supervised model for\nthe prediction of breast cancer occurrence from genomic data split between two\nvirtual centers while ensuring data privacy with respect to model transfer via\nDP. In this article, we present our 3rd place submission to this competition.\nDuring the competition, we encountered two main challenges discussed in this\narticle: i) ensuring correctness of the privacy budget evaluation and ii)\nachieving an acceptable trade-off between prediction performance and privacy\nbudget.\n"}
+{"abstract": "  The present work revisits the classical Wulff problem restricted to\ncrystalline integrands, a class of surface energies that gives rise to finitely\nfaceted crystals. The general proof of the Wulff theorem was given by J.E.\nTaylor (1978) by methods of Geometric Measure Theory. This work follows a\nsimpler and direct way through Minkowski Theory by taking advantage of the\nconvex properties of the considered Wulff shapes.\n"}
+{"abstract": "  We consider the communication complexity of the Hamming distance of two\nstrings. Bille et al. [SPIRE 2018] considered the communication complexity of\nthe longest common prefix (LCP) problem in the setting where the two parties\nhave their strings in a compressed form, i.e., represented by the Lempel-Ziv 77\nfactorization (LZ77) with/without self-references. We present a randomized\npublic-coin protocol for a joint computation of the Hamming distance of two\nstrings represented by LZ77 without self-references. While our scheme is\nheavily based on Bille et al.'s LCP protocol, our complexity analysis is\noriginal which uses Crochemore's C-factorization and Rytter's AVL-grammar. As a\nbyproduct, we also show that LZ77 with/without self-references are not\nmonotonic in the sense that their sizes can increase by a factor of 4/3 when a\nprefix of the string is removed.\n"}
+{"abstract": "  Soft robotics has been a trending topic within the robotics community for\nalmost two decades. However, the available tools for the community to model and\nanalyze soft robotics artifacts are still limited. This paper presents the\ndevelopment of a user-friendly MATLAB toolbox, SoRoSim, that integrates the\nGeometric Variable Strain model to facilitate the modeling, analysis, and\nsimulation of hybrid rigid-soft open-chain robotic systems. The toolbox\nimplements a recursive, two-level nested quadrature scheme to solve the model.\nWe demonstrate several examples and applications to validate the toolbox and\nexplore the toolbox's capabilities to efficiently model a vast range of robotic\nsystems, considering different actuators and external loads, including the\nfluid-structure interactions. We think that the soft-robotics research\ncommunity will benefit from the SoRoSim toolbox for a wide variety of\napplications.\n"}
+{"abstract": "  When designing large-scale distributed controllers, the information-sharing\nconstraints between sub-controllers, as defined by a communication topology\ninterconnecting them, are as important as the controller itself. Controllers\nimplemented using dense topologies typically outperform those implemented using\nsparse topologies, but it is also desirable to minimize the cost of controller\ndeployment. Motivated by the above, we introduce a compact but expressive graph\nrecurrent neural network (GRNN) parameterization of distributed controllers\nthat is well suited for distributed controller and communication topology\nco-design. Our proposed parameterization enjoys a local and distributed\narchitecture, similar to previous Graph Neural Network (GNN)-based\nparameterizations, while further naturally allowing for joint optimization of\nthe distributed controller and communication topology needed to implement it.\nWe show that the distributed controller/communication topology co-design task\ncan be posed as an $\\ell_1$-regularized empirical risk minimization problem\nthat can be efficiently solved using stochastic gradient methods. We run\nextensive simulations to study the performance of GRNN-based distributed\ncontrollers and show that (a) they achieve performance comparable to GNN-based\ncontrollers while having fewer free parameters, and (b) our method allows for\nperformance/communication density tradeoff curves to be efficiently\napproximated.\n"}
+{"abstract": "  This work aims to empirically clarify a recently discovered perspective that\nlabel smoothing is incompatible with knowledge distillation. We begin by\nintroducing the motivation behind on how this incompatibility is raised, i.e.,\nlabel smoothing erases relative information between teacher logits. We provide\na novel connection on how label smoothing affects distributions of semantically\nsimilar and dissimilar classes. Then we propose a metric to quantitatively\nmeasure the degree of erased information in sample's representation. After\nthat, we study its one-sidedness and imperfection of the incompatibility view\nthrough massive analyses, visualizations and comprehensive experiments on Image\nClassification, Binary Networks, and Neural Machine Translation. Finally, we\nbroadly discuss several circumstances wherein label smoothing will indeed lose\nits effectiveness. Project page:\nhttp://zhiqiangshen.com/projects/LS_and_KD/index.html.\n"}
+{"abstract": "  The present study focuses on identifying the parameters from the Weather\nResearch and Forecasting (WRF) model that strongly influence the prediction of\ntropical cyclones over the Bay of Bengal (BoB) region. Three global sensitivity\nanalysis (SA) methods namely the Morris One-at-A-Time (MOAT), Multivariate\nAdaptive Regression Splines (MARS), and surrogate-based Sobol' are employed to\nidentify the most sensitive parameters out of 24 tunable parameters\ncorresponding to seven parameterization schemes of the WRF model. Ten tropical\ncyclones across different categories, such as cyclonic storms, severe cyclonic\nstorms, and very severe cyclonic storms over BoB between 2011 and 2018, are\nselected in this study. The sensitivity scores of 24 parameters are evaluated\nfor eight meteorological variables. The parameter sensitivity results are\nconsistent across three SA methods for all the variables, and 8 out of the 24\nparameters contribute 80%-90% to the overall sensitivity scores. It is found\nthat the Sobol' method with Gaussian progress regression as a surrogate model\ncan produce reliable sensitivity results when the available samples exceed 200.\nThe parameters with which the model simulations have the least RMSE values when\ncompared with the observations are considered as the optimal parameters.\nComparing observations and model simulations with the default and optimal\nparameters shows that predictions with the optimal set of parameters yield a\n19.65% improvement in surface wind, 6.5% in surface temperature, and 13.2% in\nprecipitation predictions, compared to the default set of parameters.\n"}
+{"abstract": "  Twisted bilayer graphene (TBG) aligned with hexagonal boron nitride (h-BN)\nsubstrate can exhibit an anomalous Hall effect at 3/4 filling due to the\nspontaneous valley polarization in valley resolved moir\\'e bands with opposite\nChern number [Science 367, 900 (2020), Science 365, 605 (2019)]. It was\nobserved that a small DC current is able to switch the valley polarization and\nreverse the sign of the Hall conductance [Science 367, 900 (2020), Science 365,\n605 (2019)]. Here, we discuss the mechanism of the current switching of valley\npolarization near the transition temperature, where bulk dissipative transport\ndominates. We show that for a sample with rotational symmetry breaking, a DC\ncurrent may generate an electron density difference between the two valleys\n(valley density difference). The current induced valley density difference in\nturn induces a first order transition in the valley polarization. We emphasize\nthat the inter-valley scattering plays a central role since it is the channel\nfor exchanging electrons between the two valleys. We further estimate the\nvalley density difference in the TBG/h-BN system with a microscopic model, and\nfind a significant enhancement of the effect in the magic angle regime.\n"}
+{"abstract": "  Bosonic qubits are a promising route to building fault-tolerant quantum\ncomputers on a variety of physical platforms. Studying the performance of\nbosonic qubits under realistic gates and measurements is challenging with\nexisting analytical and numerical tools. We present a novel formalism for\nsimulating classes of states that can be represented as linear combinations of\nGaussian functions in phase space. This formalism allows us to analyze and\nsimulate a wide class of non-Gaussian states, transformations and measurements.\nWe demonstrate how useful classes of bosonic qubits --\nGottesman-Kitaev-Preskill (GKP), cat, and Fock states -- can be simulated using\nthis formalism, opening the door to investigating the behaviour of bosonic\nqubits under Gaussian channels and measurements, non-Gaussian transformations\nsuch as those achieved via gate teleportation, and important non-Gaussian\nmeasurements such as threshold and photon-number detection. Our formalism\nenables simulating these situations with levels of accuracy that are not\nfeasible with existing methods. Finally, we use a method informed by our\nformalism to simulate circuits critical to the study of fault-tolerant quantum\ncomputing with bosonic qubits but beyond the reach of existing techniques.\nSpecifically, we examine how finite-energy GKP states transform under realistic\nqubit phase gates; interface with a CV cluster state; and transform under\nnon-Clifford T gate teleportation using magic states. We implement our\nsimulation method as a part of the open-source Strawberry Fields Python\nlibrary.\n"}
+{"abstract": "  We start by studying the subgroup structures underlying stabilizer circuits\nand we use our results to propose a new normal form for stabilizer circuits.\nThis normal form is computed by induction using simple conjugation rules in the\nClifford group. It has shape CX-CZ-P-H-CZ-P-H, where CX (resp. CZ) denotes a\nlayer of $\\cnot$ (resp. $\\cz$) gates, P a layer of phase gates and H a layer of\nHadamard gates. Then we consider a normal form for stabilizer states and we\nshow how to reduce the two-qubit gate count in circuits implementing graph\nstates. Finally we carry out a few numerical tests on classical and quantum\ncomputers in order to show the practical utility of our methods. All the\nalgorithms described in the paper are implemented in the C language as a Linux\ncommand available on GitHub.\n"}
+{"abstract": "  This paper proposes a forecast-centric adaptive learning model that engages\nwith the past studies on the order book and high-frequency data, with\napplications to hypothesis testing. In line with the past literature, we\nproduce brackets of summaries of statistics from the high-frequency bid and ask\ndata in the CSI 300 Index Futures market and aim to forecast the one-step-ahead\nprices. Traditional time series issues, e.g. ARIMA order selection,\nstationarity, together with potential financial applications are covered in the\nexploratory data analysis, which pave paths to the adaptive learning model. By\ndesigning and running the learning model, we found it to perform well compared\nto the top fixed models, and some could improve the forecasting accuracy by\nbeing more stable and resilient to non-stationarity. Applications to hypothesis\ntesting are shown with a rolling window, and further potential applications to\nfinance and statistics are outlined.\n"}
+{"abstract": "  Let $\\Omega_n$ denote the class of $n \\times n$ doubly stochastic matrices\n(each such matrix is entrywise nonnegative and every row and column sum is 1).\nWe study the diagonals of matrices in $\\Omega_n$. The main question is: which\n$A \\in \\Omega_n$ are such that the diagonals in $A$ that avoid the zeros of $A$\nall have the same sum of their entries. We give a characterization of such\nmatrices, and establish several classes of patterns of such matrices.\n"}
+{"abstract": "  We report an all-optical radio-frequency (RF) spectrum analyzer with a\nbandwidth greater than 5 terahertz (THz), based on a 50-cm long spiral\nwaveguide in a CMOS-compatible high-index doped silica platform. By carefully\nmapping out the dispersion profile of the waveguides for different thicknesses,\nwe identify the optimal design to achieve near zero dispersion in the C-band.\nTo demonstrate the capability of the RF spectrum analyzer, we measure the\noptical output of a femtosecond fiber laser with an ultrafast optical RF\nspectrum in the terahertz regime.\n"}
+{"abstract": "  During an infectious disease pandemic, it is critical to share electronic\nmedical records or models (learned from these records) across regions. Applying\none region's data/model to another region often have distribution shift issues\nthat violate the assumptions of traditional machine learning techniques.\nTransfer learning can be a solution. To explore the potential of deep transfer\nlearning algorithms, we applied two data-based algorithms (domain adversarial\nneural networks and maximum classifier discrepancy) and model-based transfer\nlearning algorithms to infectious disease detection tasks. We further studied\nwell-defined synthetic scenarios where the data distribution differences\nbetween two regions are known. Our experiments show that, in the context of\ninfectious disease classification, transfer learning may be useful when (1) the\nsource and target are similar and the target training data is insufficient and\n(2) the target training data does not have labels. Model-based transfer\nlearning works well in the first situation, in which case the performance\nclosely matched that of the data-based transfer learning models. Still, further\ninvestigation of the domain shift in real world research data to account for\nthe drop in performance is needed.\n"}
+{"abstract": "  Structure formation in our Universe creates non-Gaussian random fields that\nwill soon be observed over almost the entire sky by the Euclid satellite, the\nVera-Rubin observatory, and the Square Kilometre Array. An unsolved problem is\nhow to analyze best such non-Gaussian fields, e.g. to infer the physical laws\nthat created them. This problem could be solved if a parametric non-Gaussian\nsampling distribution for such fields were known, as this distribution could\nserve as likelihood during inference. We therefore create a sampling\ndistribution for non-Gaussian random fields. Our approach is capable of\nhandling strong non-Gaussianity, while perturbative approaches such as the\nEdgeworth expansion cannot. To imitate cosmological structure formation, we\nenforce our fields to be (i) statistically isotropic, (ii) statistically\nhomogeneous, and (iii) statistically independent at large distances. We\ngenerate such fields via a Monte Carlo Markov Chain technique and find that\neven strong non-Gaussianity is not necessarily visible to the human eye. We\nalso find that sampled marginals for pixel pairs have an almost generic\nGauss-like appearance, even if the joint distribution of all pixels is markedly\nnon-Gaussian. This apparent Gaussianity is a consequence of the high\ndimensionality of random fields. We conclude that vast amounts of non-Gaussian\ninformation can be hidden in random fields that appear nearly Gaussian in\nsimple tests, and that it would be short-sighted not to try and extract it.\n"}
+{"abstract": "  The subject of space charge in ionization detectors is reviewed, showing how\nthe observations and the formalism used to describe the effects have evolved,\nstarting with applications to calorimeters and reaching recent, large-size time\nprojection chambers. General scaling laws, and different ways to present and\nmodel the effects are presented. The relation between space-charge effects and\nthe boundary conditions imposed on the side faces of the detector are\ndiscussed, together with a design solution that mitigates part of the effects.\nThe implications of the relative size of drift length and transverse detector\nsize are illustrated. Calibration methods are briefly discussed.\n"}
+{"abstract": "  We have utilized the finite-difference approach to explore electron-tunneling\nproperties in gapped graphene through various electrostatic-potential barriers\nchanging from Gaussian to a triangular envelope function in comparison with a\nsquare potential barrier. Transmission coefficient is calculated numerically\nfor each case and applied to corresponding tunneling conductance. It is well\nknown that Klein tunneling in graphene will be greatly reduced in a gapped\ngraphene. Our results further demonstrate that such a decrease of transmission\ncan be significantly enhanced for spatially-modulated potential barriers.\nMoreover, we investigate the effect from a bias field applied to those barrier\nprofiles, from which we show that it enables the control of electron flow under\nnormal incidence. Meanwhile, the suppression of Klein tunneling is found more\nsevere for a non-square barrier and exhibits a strong dependence on bias-field\npolarity for all kinds of barriers. Finally, roles of a point impurity on\nelectron transmission and conductance are analyzed with a sharp peak appearing\nin electron conductance as the impurity atom is placed at the middle of a\nsquare barrier. For narrow triangular and Gaussian barriers, however, the\nconductance peaks become significantly broadened, associated with an\nenhancement in tunneling conductance.\n"}
+{"abstract": "  Transformers are powerful neural architectures that allow integrating\ndifferent modalities using attention mechanisms. In this paper, we leverage the\nneural transformer architectures for multi-channel speech recognition systems,\nwhere the spectral and spatial information collected from different microphones\nare integrated using attention layers. Our multi-channel transformer network\nmainly consists of three parts: channel-wise self attention layers (CSA),\ncross-channel attention layers (CCA), and multi-channel encoder-decoder\nattention layers (EDA). The CSA and CCA layers encode the contextual\nrelationship within and between channels and across time, respectively. The\nchannel-attended outputs from CSA and CCA are then fed into the EDA layers to\nhelp decode the next token given the preceding ones. The experiments show that\nin a far-field in-house dataset, our method outperforms the baseline\nsingle-channel transformer, as well as the super-directive and neural\nbeamformers cascaded with the transformers.\n"}
+{"abstract": "  In this paper, we study quantum vacuum fluctuation effects on the mass\ndensity of a classical liquid arising from the conical topology of an effective\nidealized cosmic string spacetime, as well as from the mixed, Dirichlet, and\nNeumann boundary conditions in Minkowski spacetime. In this context, we\nconsider a phonon field representing quantum excitations of the liquid density,\nwhich obeys an effective Klein-Gordon equation with the sound velocity replaced\nby the light velocity. In the idealized cosmic string spacetime, the phonon\nfield is subject to a quasi-periodic condition. Moreover, in Minkowski\nspacetime, the Dirichlet and Neumann boundary conditions are applied on one and\nalso two parallel planes. We, thus, in each case, obtain closed analytic\nexpressions for the two-point function and the renormalized mean-squared\ndensity fluctuation of the liquid. We point out specific characteristics of the\nlatter by plotting their graphs.\n"}
+{"abstract": "  Bayesian Networks (BNs) have become a powerful technology for reasoning under\nuncertainty, particularly in areas that require causal assumptions that enable\nus to simulate the effect of intervention. The graphical structure of these\nmodels can be determined by causal knowledge, learnt from data, or a\ncombination of both. While it seems plausible that the best approach in\nconstructing a causal graph involves combining knowledge with machine learning,\nthis approach remains underused in practice. We implement and evaluate 10\nknowledge approaches with application to different case studies and BN\nstructure learning algorithms available in the open-source Bayesys structure\nlearning system. The approaches enable us to specify pre-existing knowledge\nthat can be obtained from heterogeneous sources, to constrain or guide\nstructure learning. Each approach is assessed in terms of structure learning\neffectiveness and efficiency, including graphical accuracy, model fitting,\ncomplexity, and runtime; making this the first paper that provides a\ncomparative evaluation of a wide range of knowledge approaches for BN structure\nlearning. Because the value of knowledge depends on what data are available, we\nillustrate the results both with limited and big data. While the overall\nresults show that knowledge becomes less important with big data due to higher\nlearning accuracy rendering knowledge less important, some of the knowledge\napproaches are actually found to be more important with big data. Amongst the\nmain conclusions is the observation that reduced search space obtained from\nknowledge does not always imply reduced computational complexity, perhaps\nbecause the relationships implied by the data and knowledge are in tension.\n"}
+{"abstract": "  A branching process in a Markovian environment consists of an irreducible\nMarkov chain on a set of \"environments\" together with an offspring distribution\nfor each environment. At each time step the chain transitions to a new random\nenvironment, and one individual is replaced by a random number of offspring\nwhose distribution depends on the new environment. We give a first moment\ncondition that determines whether this process survives forever with positive\nprobability. On the event of survival we prove a law of large numbers and a\ncentral limit theorem for the population size. We also define a matrix-valued\ngenerating function for which the extinction matrix (whose entries are the\nprobability of extinction in state j given that the initial state is i) is a\nfixed point, and we prove that iterates of the generating function starting\nwith the zero matrix converge to the extinction matrix.\n"}
+{"abstract": "  The kinematics on spatially flat FLRW space-times is presented for the first\ntime in co-moving local charts with physical coordinates, i. e. the cosmic time\nand Painlev\\' e-type Cartesian space coordinates. It is shown that there exists\na conserved momentum which determines the form of the covariant four-momentum\non geodesics in terms of physical coordinates. Moreover, with the help of the\nconserved momentum one identifies the peculiar momentum separating the peculiar\nand recessional motions without ambiguities. It is shown that the energy and\npeculiar momentum satisfy the mass-shell condition of special relativity while\nthe recessional momentum does not produce energy. In this framework, the\nmeasurements of the kinetic quantities along geodesic performed by different\nobservers are analysed pointing out an energy loss of the massive particles\nsimilar to that giving the photon redshift. The examples of the kinematics on\nthe de Sitter expanding universe and a new Milne-type space-time are\nextensively analysed.\n"}
+{"abstract": "  Analog quantum simulation offers a hardware-specific approach to studying\nquantum dynamics, but mapping a model Hamiltonian onto the available device\nparameters requires matching the hardware dynamics. We introduce a paradigm for\nquantum Hamiltonian simulation that leverages digital decomposition techniques\nand optimal control to perform analog simulation. We validate this approach by\nconstructing the optimal analog controls for a superconducting transmon device\nto emulate the dynamics of an extended Bose-Hubbard model. We demonstrate the\nrole of control time, digital error, and pulse complexity, and we explore the\naccuracy and robustness of these controls. We conclude by discussing the\nopportunity for implementing this paradigm in near-term quantum devices.\n"}
+{"abstract": "  Deterministic classical dynamical systems have an ergodic hierarchy, from\nergodic through mixing, to Bernoulli systems that are \"as random as a\ncoin-toss\". Dual-unitary circuits have been recently introduced as solvable\nmodels of many-body nonintegrable quantum chaotic systems having a hierarchy of\nergodic properties. We extend this to include the apex of a putative quantum\nergodic hierarchy which is Bernoulli, in the sense that correlations of single\nand two-particle observables vanish at space-time separated points. We derive a\ncondition based on the entangling power $e_p(U)$ of the basic two-particle\nunitary building block, $U$, of the circuit, that guarantees mixing, and when\nmaximized, corresponds to Bernoulli circuits. Additionally we show, both\nanalytically and numerically, how local-averaging over random realizations of\nthe single-particle unitaries, $u_i$ and $v_i$ such that the building block is\n$U^\\prime = (u_1 \\otimes u_2 ) U (v_1 \\otimes v_2 )$ leads to an identification\nof the average mixing rate as being determined predominantly by the entangling\npower $e_p(U)$. Finally we provide several, both analytical and numerical, ways\nto construct dual-unitary operators covering the entire possible range of\nentangling power. We construct a coupled quantum cat map which is dual-unitary\nfor all local dimensions and a 2-unitary or perfect tensor for odd local\ndimensions, and can be used to build Bernoulli circuits.\n"}
+{"abstract": "  In this paper, we focus on the weighted Bergman spaces $A_{\\varphi}^{p}$ in\n$\\mathbb{D}$ with $\\varphi\\in\\mathcal{W}_{0}$. We first give characterizations\nof those finite positive Borel measures $\\mu$ in $\\mathbb{D}$ such that the\nembedding $A_{\\varphi}^{p}\\subset L_{\\mu}^{q}$ is bounded or compact for\n$0<p,q<\\infty$. Then we describe bounded or compact Toeplitz operators\n$T_{\\mu}$ from one Bergman space $A_{\\varphi}^{p}$ to another $A_{\\varphi}^{q}$\nfor all possible $0<p,q<\\infty$. Finally, we characterize Schatten class\nToeplitz operators on $A_{\\varphi}^{2}$.\n"}
+{"abstract": "  Whereas the positive equilibrium of a mass-action system with deficiency zero\nis always globally stable, for deficiency-one networks there are many different\nscenarios, mainly involving oscillatory behaviour. We present several examples,\nwith centers or multiple limit cycles.\n"}
+{"abstract": "  Climate change has largely impacted our daily lives. As one of its\nconsequences, we are experiencing more wildfires. In the year 2020, wildfires\nburned a record number of 8,888,297 acres in the US. To awaken people's\nattention to climate change, and to visualize the current risk of wildfires, We\ndeveloped RtFPS, \"Real-Time Fire Prediction System\". It provides a real-time\nprediction visualization of wildfire risk at specific locations base on a\nMachine Learning model. It also provides interactive map features that show the\nhistorical wildfire events with environmental info.\n"}
+{"abstract": "  We propose a method of neural evolution structures (NESs) combining\nartificial neural networks (ANNs) and evolutionary algorithms (EAs) to generate\nHigh Entropy Alloys (HEAs) structures. Our inverse design approach is based on\npair distribution functions and atomic properties and allows one to train a\nmodel on smaller unit cells and then generate a larger cell. With a speed-up\nfactor of approximately 1000 with respect to the SQSs, the NESs dramatically\nreduces computational costs and time, making possible the generation of very\nlarge structures (over 40,000 atoms) in few hours. Additionally, unlike the\nSQSs, the same model can be used to generate multiple structures with the same\nfractional composition.\n"}
+{"abstract": "  The connection between the Stellar Velocity Ellipsoid (SVE) and the dynamical\nevolution of galaxies has been a matter of debate in the last years and there\nis no clear consensus whether different heating agents (e.g. spiral arms, giant\nmolecular clouds, bars and mergers) leave clear detectable signatures in the\npresent day kinematics. Most of these results are based on a single and global\nSVE and have not taken into account that these agents do not necessarily\nequally affect all regions of the stellar disc.We study the 2D spatial\ndistribution of the SVE across the stellar discs of Auriga galaxies, a set of\nhigh resolution magneto-hydrodynamical cosmological zoom-in simulations, to\nunveil the connection between local and global kinematic properties in the disc\nregion. We find very similar, global, $\\sigma_{z}/\\sigma_{r}$= 0.80$\\pm$ 0.08\nvalues for galaxies of different Hubble types. This shows that the global\nproperties of the SVE at z=0 are not a good indicator of the heating and\ncooling events experienced by galaxies. We also find that similar\n$\\sigma_{z}/\\sigma_{r}$radial profiles are obtained through different\ncombinations of $\\sigma_{z}$ and $\\sigma_{r}$ trends: at a local level, the\nvertical and radial components can evolve differently, leading to similar\n$\\sigma_{z}/\\sigma_{r}$ profiles at z=0. By contrast, the 2D spatial\ndistribution of the SVE varies a lot more from galaxy to galaxy. Present day\nfeatures in the SVE spatial distribution may be associated with specific\ninteractions such as fly-by encounters or the accretion of low mass satellites\neven in the cases when the global SVE is not affected. The stellar populations\ndecomposition reveals that young stellar populations present colder and less\nisotropic SVEs and more complex 2D distributions than their older and hotter\ncounterparts.\n"}
+{"abstract": "  We use a five percent sample of Americans' credit bureau data, combined with\na regression discontinuity approach, to estimate the effect of universal health\ninsurance at age 65-when most Americans become eligible for Medicare-at the\nnational, state, and local level. We find a 30 percent reduction in debt\ncollections-and a two-thirds reduction in the geographic variation in\ncollections-with limited effects on other financial outcomes. The areas that\nexperienced larger reductions in collections debt at age 65 were concentrated\nin the Southern United States, and had higher shares of black residents, people\nwith disabilities, and for-profit hospitals.\n"}
+{"abstract": "  Beyond standard model (BSM) particles should be included in effective field\ntheory in order to compute the scattering amplitudes involving these extra\nparticles. We formulate an extension of Higgs effective field theory which\ncontains arbitrary number of scalar and fermion fields with arbitrary electric\nand chromoelectric charges. The BSM Higgs sector is described by using the\nnon-linear sigma model in a manner consistent with the spontaneous electroweak\nsymmetry breaking. The chiral order counting rule is arranged consistently with\nthe loop expansion. The leading order Lagrangian is organized in accord with\nthe chiral order counting rule. We use a geometrical language to describe the\nparticle interactions. The parametrization redundancy in the effective\nLagrangian is resolved by describing the on-shell scattering amplitudes only\nwith the covariant quantities in the scalar/fermion field space. We introduce a\nuseful coordinate (normal coordinate), which simplifies the computations of the\non-shell amplitudes significantly. We show the high energy behaviors of the\nscattering amplitudes determine the \"curvature tensors\" in the scalar/fermion\nfield space. The massive spinor-wavefunction formalism is shown to be useful in\nthe computations of on-shell helicity amplitudes.\n"}
+{"abstract": "  The QCD axion mass may receive contributions from small-size instantons or\nother Peccei-Quinn breaking effects. We show that it is possible for such a\nheavy QCD axion to induce slow-roll inflation if the potential is sufficiently\nflat near its maximum by balancing the small instanton contribution with\nanother Peccei-Quinn symmetry breaking term. There are two classes of such\naxion hilltop inflation, each giving a different relation between the axion\nmass at the minimum and the decay constant. The first class predicts the\nrelation $m_\\phi \\sim 10^{-6}f_\\phi$, and the axion can decay via the gluon\ncoupling and reheat the universe. Most of the predicted parameter region will\nbe covered by various experiments such as CODEX, DUNE, FASER, LHC, MATHUSLA,\nand NA62 where the production and decay proceed through the same coupling that\ninduced reheating. The second class predicts the relation $m_\\phi \\sim 10^{-6}\nf^2_\\phi/M_{\\rm pl}$. In this case, the axion mass is much lighter than in the\nprevious case, and one needs another mechanism for successful reheating. The\nviable decay constant is restricted to be $10^8\\,{\\rm GeV}\\lesssim f_\\phi\n\\lesssim 10^{10}\\,{\\rm GeV}$, which will be probed by future experiments on the\nelectric dipole moment of nucleons. In both cases, requiring the axion hilltop\ninflation results in the strong CP phase that is close to zero.\n"}
+{"abstract": "  Developing of an effective flow control algorithm to avoid congestion is a\nhot topic in computer network society. This document gives a mathematical model\nfor general network at the beginning, and then discrete control theory is\nproposed as a key tool to design a new flow control algorithm to avoid\ncongestion in the high-speed computer network, the proposed algorithm ensures\nstability of network system. The results of the simulation show that the\nproposed method can adjust the sending speed and the queue level in the buffer\nquickly and effectively. In addition, the method is easy to implement and apply\nto high-speed computer network.\n"}
+{"abstract": "  Most cosmological structures in the universe spin. Although structures in the\nuniverse form on a wide variety of scales from small dwarf galaxies to large\nsuper clusters, the generation of angular momentum across these scales is\npoorly understood. We have investigated the possibility that filaments of\ngalaxies - cylindrical tendrils of matter hundreds of millions of light-years\nacross, are themselves spinning. By stacking thousands of filaments together\nand examining the velocity of galaxies perpendicular to the filament's axis\n(via their red and blue shift), we have found that these objects too display\nmotion consistent with rotation making them the largest objects known to have\nangular momentum. The strength of the rotation signal is directly dependent on\nthe viewing angle and the dynamical state of the filament. Just as it is\neasiest to measure rotation in a spinning disk galaxy viewed edge on, so too is\nfilament rotation clearly detected under similar geometric alignment.\nFurthermore, the mass of the haloes that sit at either end of the filaments\nalso increases the spin speed. The more massive the haloes, the more rotation\nis detected. These results signify that angular momentum can be generated on\nunprecedented scales.\n"}
+{"abstract": "  We show that if a non-amenable, quasi-transitive, unimodular graph $G$ has\nall degrees even then it has a factor-of-iid balanced orientation, meaning each\nvertex has equal in- and outdegree. This result involves extending earlier\nspectral-theoretic results on Bernoulli shifts to the Bernoulli graphings of\nquasi-transitive, unimodular graphs. As a consequence, we also obtain that when\n$G$ is regular (of either odd or even degree) and bipartite, it has a\nfactor-of-iid perfect matching. This generalizes a result of Lyons and Nazarov\nbeyond transitive graphs.\n"}
+{"abstract": "  The process of selecting points for training a machine learning model is\noften a challenging task. Many times, we will have a lot of data, but for\ntraining, we require the labels and labeling is often costly. So we need to\nselect the points for training in an efficient manner so that the model trained\non the points selected will be better than the ones trained on any other\ntraining set. We propose a novel method to select the nodes in graph datasets\nusing the concept of graph centrality. Two methods are proposed - one using a\nsmart selection strategy, where the model is required to be trained only once\nand another using active learning method. We have tested this idea on three\npopular graph datasets - Cora, Citeseer and Pubmed- and the results are found\nto be encouraging.\n"}
+{"abstract": "  Quantitative predictions of the release of volatile radiocontaminants of\nruthenium (Ru) in the environment from either nuclear power plants (NPP) or\nfuel recycling accidents present significant uncertainties while estimated by\nsevere accidents nuclear analysis codes. Observations of Ru from either\nexperimental or modeling works suggest that the main limitations relate to the\npoor evaluation of the kinetics of gaseous Ru in the form of RuO$_3$ and\nRuO$_4$. This work presents relativistic correlated quantum chemical\ncalculations performed to determine the possible reactions pathways leading to\nthe formation of gaseous Ru oxides under NPP severe accident conditions, as a\nresult of reactions of RuO$_2$ gaseous with air radiolysis products, namely\nnitrous and nitrogen oxides. The geometries of the relevant species were\noptimized with the TPSSh-5%HF functional of the density, while the total\nelectronic energies were computed at the CCSD(T) level with extrapolations to\nthe complete basis set CBS limit. The reaction pathways were fully\ncharacterized by localizing the transition states and all intermediate\nstructures using the internal coordinate reaction algorithm (IRC). The rate\nconstants were determined over the temperature range 250-2500 K. It is revealed\nthat the less kinetically limiting pathway to form Ru gaseous fraction is the\noxidation of Ru by nitrogen oxide, corroborating experimental observations.\n"}
+{"abstract": "  Translationally invariant finetuned single-particle lattice Hamiltonians host\nflat bands only. Suitable short-range many-body interactions result in complete\nsuppression of particle transport due to local constraints and Many-Body\nFlatband Localization. Heat can still flow between spatially locked charges. We\nshow that heat transport is forbidden in dimension one. In higher dimensions\nheat transport can be unlocked by tuning filling fractions across a percolation\ntransition for suitable lattice geometries. Transport in percolation clusters\nis additionally affected by effective bulk disorder and edge scattering induced\nby the local constraints, which work in favor of arresting the heat flow. We\ndiscuss explicit examples in one and two dimensions.\n"}
+{"abstract": "  The zero-dimensional (0D) metal halides comprise periodically distributed and\nisolated metal-halide polyhedra, which act as the smallest inorganic quantum\nsystems and can accommodate quasi-localized Frenkel excitons. These excitons\nexhibit unique photophysics including broadband photon emission, huge Stokes\nshift, and long decay lifetime. The polyhedra can have different symmetries due\nto the coordination degree of the metal ions. Little is known about how the\npolyhedron type affects the characteristics of the 0D metal halide crystals. We\nsynthesize and comparatively study three novel kinds of 0D organic-inorganic\nhybrid tin halide compounds. They are efficient light emitters with a highest\nquantum yield of 92.3%. Although they have the same compositional organic\ngroup, the most stable phases are composed of octahedra for the bromide and\niodide but disphenoids (see-saw structures) for the chloride. They separately\nexhibit biexponential and monoexponential luminescence decays due to different\nsymmetries (Ci group for octahedra and C2 group for disphenoids) and\ncorresponding different electronic structures. The chloride has the largest\nabsorption photon energy among the three halides, but it has the smallest\nemission photon energy. A model regarding the unoccupied energy band degeneracy\nis proposed based on the experiments and density functional theory\ncalculations, which explains well the experimental phenomena and reveals the\ncrucial role of polyhedron type in determining the optical properties of the 0D\ntin halide compounds.\n"}
+{"abstract": "  In this note, we introduce and study a notion of bi-exactness for creation\noperators acting on full, symmetric and anti-symmetric Fock spaces. This is a\ngeneralization of our previous work, in which we studied the case of\nanti-symmetric Fock spaces. As a result, we obtain new examples of solid\nactions as well as new proofs for some known solid actions. We also study free\nwreath product groups in the same context.\n"}
+{"abstract": "  We investigate the structure of the fixed-point algebra of $\\mathcal{O}_n$\nunder the action of the cyclic permutation of the generating isometries. We\nprove that it is $*$-isomorphic with $\\mathcal{O}_n$, thus generalizing a\nresult of Choi and Latr\\'emoli\\`ere on $\\mathcal{O}_2$. As an application of\nthe technique employed, we also describe the fixed-point algebra of\n$\\mathcal{O}_{2n}$ under the exchange automorphism.\n"}
+{"abstract": "  Nuclear reactions of interest for astrophysics and applications often rely on\nstatistical model calculations for nuclear reaction rates, particularly for\nnuclei far from $\\beta$-stability. However, statistical model parameters are\noften poorly constrained, where experimental constraints are particularly\nsparse for exotic nuclides. For example, our understanding of the breakout from\nthe NiCu cycle in the astrophysical rp-process is currently limited by\nuncertainties in the statistical properties of the proton-rich nucleus\n$^{60}$Zn. We have determined the nuclear level density of $^{60}$Zn using\nneutron evaporation spectra from $^{58}$Ni($^3$He, n) measured at the Edwards\nAccelerator Laboratory. We compare our results to a number of theoretical\npredictions, including phenomenological, microscopic, and shell model based\napproaches. Notably, we find the $^{60}$Zn level density is somewhat lower than\nexpected for excitation energies populated in the\n$^{59}$Cu(p,$\\gamma$)$^{60}$Zn reaction under rp-process conditions. This\nincludes a level density plateau from roughly 5-6 MeV excitation energy, which\nis counter to the usual expectation of exponential growth and all theoretical\npredictions that we explore. A determination of the spin-distribution at the\nrelevant excitation energies in $^{60}$Zn is needed to confirm that the\nHauser-Feshbach formalism is appropriate for the $^{59}$Cu(p,$\\gamma$)$^{60}$Zn\nreaction rate at X-ray burst temperatures.\n"}
+{"abstract": "  Reconfigurable Intelligent Surfaces (RISs) are recently attracting a wide\ninterest due to their capability of tuning wireless propagation environments in\norder to increase the system performance of wireless networks. In this paper, a\nmultiuser wireless network assisted by a RIS is studied and resource allocation\nalgorithms are presented for several scenarios. First of all, the problem of\nchannel estimation is considered, and an algorithm that permits separate\nestimation of the mobile user-to-RIS and RIS-to-base stations components is\nproposed. Then, for the special case of a single-user system, three possible\napproaches are shown in order to optimize the Signal-to-Noise Ratio with\nrespect to the beamformer used at the base station and to the RIS phase shifts.\nNext, for a multiuser system with two cells, assuming channel-matched\nbeamforming, the geometric mean of the downlink Signal-to-Interference plus\nNoise Ratios across users is maximized with respect to the base stations\ntransmit powers and RIS phase shifts configurations. In this scenario, the RIS\nis placed at the cell-edge and some users are jointly served by two base\nstations to increase the system performance. Numerical results show that the\nproposed procedures are effective and that the RIS brings substantial\nperformance improvements to wireless system.\n"}
+{"abstract": "  Permanently deformed objects in binary systems can experience complex\nrotation evolution, arising from the extensively studied effect of spin-orbit\ncoupling as well as more nuanced dynamics arising from spin-spin interactions.\nThe ability of an object to sustain an aspheroidal shape largely determines\nwhether or not it will exhibit non-trivial rotational behavior. In this work,\nwe adopt a simplified model of a gravitationally interacting primary and\nsatellite pair, where each body's quadrupole moment is approximated by two\ndiametrically opposed point masses. After calculating the net gravitational\ntorque on the satellite from the primary, and the associated equations of\nmotion, we employ a Hamiltonian formalism which allows for a perturbative\ntreatment of the spin-orbit and retrograde and prograde spin-spin coupling\nstates. By analyzing the resonances individually and collectively, we determine\nthe criteria for resonance overlap and the onset of chaos, as a function of\norbital and geometric properties of the binary. We extend the 2D planar\ngeometry to calculate the obliquity evolution, and find that satellites in\nspin-spin resonances undergo precession when inclined out of the plane, but do\nnot tumble. We apply our resonance overlap criteria to the contact binary\nsystem (216) Kleopatra, and find that its satellites, Cleoselene and\nAlexhelios, may plausibly be exhibiting chaotic rotational dynamics from the\noverlap of the spin-orbit and retrograde spin-spin resonances. While this model\nis by construction generalizable to any binary system, it will be particularly\nuseful to study small bodies in the solar system, whose irregular shapes make\nthem ideal candidates for exotic rotational states.\n"}
+{"abstract": "  In many applications, the integrals and derivatives of signals carry valuable\ninformation (e.g., cumulative success over a time window, the rate of change)\nregarding the behavior of the underlying system. In this paper, we extend the\nexpressiveness of Signal Temporal Logic (STL) by introducing predicates that\ncan define rich properties related to the integral and derivative of a signal.\nFor control synthesis, the new predicates are encoded into mixed-integer linear\ninequalities and are used in the formulation of a mixed-integer linear program\nto find a trajectory that satisfies an STL specification. We discuss the\nbenefits of using the new predicates and illustrate them in a case study\nshowing the influence of the new predicates on the trajectories of an\nautonomous robot.\n"}
+{"abstract": "  The extensive computer-aided search applied in [arXiv:2010.10519] to find the\nminimal charge sourced by the fluxes that stabilize all the (flux-stabilizable)\nmoduli of a smooth K3xK3 compactification uses differential evolutionary\nalgorithms supplemented by local searches. We present these algorithms in\ndetail and show that they can also solve our minimization problem for other\nlattices. Our results support the Tadpole Conjecture: The minimal charge grows\nlinearly with the dimension of the lattice and, for K3xK3, this charge is\nlarger than allowed by tadpole cancelation.\n  Even if we are faced with an NP-hard lattice-reduction problem at every step\nin the minimization process, we find that differential evolution is a good\ntechnique for identifying the regions of the landscape where the fluxes with\nthe lowest tadpole can be found. We then design a \"Spider Algorithm,\" which is\nvery efficient at exploring these regions and producing large numbers of\nminimal-tadpole configurations.\n"}
+{"abstract": "  Graph Neural Networks (GNNs) have received significant attention due to their\nstate-of-the-art performance on various graph representation learning tasks.\nHowever, recent studies reveal that GNNs are vulnerable to adversarial attacks,\ni.e. an attacker is able to fool the GNNs by perturbing the graph structure or\nnode features deliberately. While being able to successfully decrease the\nperformance of GNNs, most existing attacking algorithms require access to\neither the model parameters or the training data, which is not practical in the\nreal world.\n  In this paper, we develop deeper insights into the Mettack algorithm, which\nis a representative grey-box attacking method, and then we propose a\ngradient-based black-box attacking algorithm. Firstly, we show that the Mettack\nalgorithm will perturb the edges unevenly, thus the attack will be highly\ndependent on a specific training set. As a result, a simple yet useful strategy\nto defense against Mettack is to train the GNN with the validation set.\nSecondly, to overcome the drawbacks, we propose the Black-Box Gradient Attack\n(BBGA) algorithm. Extensive experiments demonstrate that out proposed method is\nable to achieve stable attack performance without accessing the training sets\nof the GNNs. Further results shows that our proposed method is also applicable\nwhen attacking against various defense methods.\n"}
+{"abstract": "  Model Order Reduction (MOR) methods enable the generation of\nreal-time-capable digital twins, which can enable various novel value streams\nin industry. While traditional projection-based methods are robust and accurate\nfor linear problems, incorporating Machine Learning to deal with nonlinearity\nbecomes a new choice for reducing complex problems. Such methods usually\nconsist of two steps. The first step is dimension reduction by projection-based\nmethod, and the second is the model reconstruction by Neural Network. In this\nwork, we apply some modifications for both steps respectively and investigate\nhow they are impacted by testing with three simulation models. In all cases\nProper Orthogonal Decomposition (POD) is used for dimension reduction. For this\nstep, the effects of generating the input snapshot database with constant input\nparameters is compared with time-dependent input parameters. For the model\nreconstruction step, two types of neural network architectures are compared:\nMultilayer Perceptron (MLP) and Runge-Kutta Neural Network (RKNN). The MLP\nlearns the system state directly while RKNN learns the derivative of system\nstate and predicts the new state as a Runge-Kutta integrator.\n"}
+{"abstract": "  We prove a removal lemma for induced ordered hypergraphs, simultaneously\ngeneralizing Alon--Ben-Eliezer--Fischer's removal lemma for ordered graphs and\nthe induced hypergraph removal lemma. That is, we show that if an ordered\nhypergraph $(V,G,<)$ has few induced copies of a small ordered hypergraph\n$(W,H,\\prec)$ then there is a small modification $G'$ so that $(V,G',<)$ has no\ninduced copies of $(W,H,\\prec)$. (Note that we do \\emph{not} need to modify the\nordering $<$.)\n  We give our proof in the setting of an ultraproduct (that is, a Keisler\ngraded probability space), where we can give an abstract formulation of\nhypergraph removal in terms of sequences of $\\sigma$-algebras. We then show\nthat ordered hypergraphs can be viewed as hypergraphs where we view the\nintervals as an additional notion of a ``very structured'' set. Along the way\nwe give an explicit construction of the bijection between the ultraproduct\nlimit object and the corresponding hyerpgraphon.\n"}
+{"abstract": "  The availability of biomedical text data and advances in natural language\nprocessing (NLP) have made new applications in biomedical NLP possible.\nLanguage models trained or fine tuned using domain specific corpora can\noutperform general models, but work to date in biomedical NLP has been limited\nin terms of corpora and tasks. We present BioALBERT, a domain-specific\nadaptation of A Lite Bidirectional Encoder Representations from Transformers\n(ALBERT), trained on biomedical (PubMed and PubMed Central) and clinical\n(MIMIC-III) corpora and fine tuned for 6 different tasks across 20 benchmark\ndatasets. Experiments show that BioALBERT outperforms the state of the art on\nnamed entity recognition (+11.09% BLURB score improvement), relation extraction\n(+0.80% BLURB score), sentence similarity (+1.05% BLURB score), document\nclassification (+0.62% F1-score), and question answering (+2.83% BLURB score).\nIt represents a new state of the art in 17 out of 20 benchmark datasets. By\nmaking BioALBERT models and data available, our aim is to help the biomedical\nNLP community avoid computational costs of training and establish a new set of\nbaselines for future efforts across a broad range of biomedical NLP tasks.\n"}
+{"abstract": "  In this paper, we justify the convergence from the two-species\nVlasov-Poisson-Boltzmann (in briefly,VPB) system to the two-fluid\nincompressible Navier-Stokes-Fourier-Poisson (in briefly, NSFP) system with\nOhm's law in the context of classical solutions. We prove the uniform estimates\nwith respect to the Knudsen number $\\varepsilon$ for the solutions to the\ntwo-species VPB system near equilibrium by treating the strong interspecies\ninteractions. Consequently, we prove the convergence to the two-fluid\nincompressible NSFP as $\\varepsilon$ go to 0.\n"}
+{"abstract": "  Inverse design arises in a variety of areas in engineering such as acoustic,\nmechanics, thermal/electronic transport, electromagnetism, and optics. Topology\noptimization is a major form of inverse design, where we optimize a designed\ngeometry to achieve targeted properties and the geometry is parameterized by a\ndensity function. This optimization is challenging, because it has a very high\ndimensionality and is usually constrained by partial differential equations\n(PDEs) and additional inequalities. Here, we propose a new deep learning method\n-- physics-informed neural networks with hard constraints (hPINNs) -- for\nsolving topology optimization. hPINN leverages the recent development of PINNs\nfor solving PDEs, and thus does not rely on any numerical PDE solver. However,\nall the constraints in PINNs are soft constraints, and hence we impose hard\nconstraints by using the penalty method and the augmented Lagrangian method. We\ndemonstrate the effectiveness of hPINN for a holography problem in optics and a\nfluid problem of Stokes flow. We achieve the same objective as conventional\nPDE-constrained optimization methods based on adjoint methods and numerical PDE\nsolvers, but find that the design obtained from hPINN is often simpler and\nsmoother for problems whose solution is not unique. Moreover, the\nimplementation of inverse design with hPINN can be easier than that of\nconventional methods.\n"}
+{"abstract": "  This paper is an extension of the paper by Del Popolo, Chan, and Mota (2020)\nto take account the effect of dynamical friction. We show how dynamical\nfriction changes the threshold of collapse, $\\delta_c$, and the turn-around\nradius, $R_t$. We find numerically the relationship between the turnaround\nradius, $R_{\\rm t}$, and mass, $M_{\\rm t}$, in $\\Lambda$CDM, in dark energy\nscenarios, and in a $f(R)$ modified gravity model. Dynamical friction gives\nrise to a $R_{\\rm t}-M_{\\rm t}$ relation differing from that of the standard\nspherical collapse. In particular, dynamical friction amplifies the effect of\nshear, and vorticity already studied in Del Popolo, Chan, and Mota (2020). A\ncomparison of the $R_{\\rm t}-M_{\\rm t}$ relationship for the $\\Lambda$CDM, and\nthose for the dark energy, and modified gravity models shows, that the $R_{\\rm\nt}-M_{\\rm t}$ relationship of the $\\Lambda$CDM is similar to that of the dark\nenergy models, and small differences are seen when comparing with the $f(R)$\nmodels. The effect of shear, rotation, and dynamical friction is particularly\nevident at galactic scales, giving rise to a difference between the $R_{\\rm\nt}-M_{\\rm t}$ relation of the standard spherical collapse of the order of\n$\\simeq 60\\%$. Finally, we show how the new values of the $R_{\\rm t}-M_{\\rm t}$\ninfluence the constraints to the $w$ parameter of the equation of state.\n"}
+{"abstract": "  The synthesis of control laws for interacting agent-based dynamics and their\nmean-field limit is studied. A linearization-based approach is used for the\ncomputation of sub-optimal feedback laws obtained from the solution of\ndifferential matrix Riccati equations. Quantification of dynamic performance of\nsuch control laws leads to theoretical estimates on suitable linearization\npoints of the nonlinear dynamics. Subsequently, the feedback laws are embedded\ninto nonlinear model predictive control framework where the control is updated\nadaptively in time according to dynamic information on moments of linear\nmean-field dynamics. The performance and robustness of the proposed methodology\nis assessed through different numerical experiments in collective dynamics.\n"}
+{"abstract": "  We present an application of the well-known Mask R-CNN approach to the\ncounting of different types of bacterial colony forming units that were\ncultured in Petri dishes. Our model was made available to lab technicians in a\nmodern SPA (Single-Page Application). Users can upload images of dishes, after\nwhich the Mask R-CNN model that was trained and tuned specifically for this\ntask detects the number of BVG- and BVG+ colonies and displays these in an\ninteractive interface for the user to verify. Users can then check the model's\npredictions, correct them if deemed necessary, and finally validate them. Our\nadapted Mask R-CNN model achieves a mean average precision (mAP) of 94\\% at an\nintersection-over-union (IoU) threshold of 50\\%. With these encouraging\nresults, we see opportunities to bring the benefits of improved accuracy and\ntime saved to related problems, such as generalising to other bacteria types\nand viral foci counting.\n"}
+{"abstract": "  This paper develops a non-asymptotic, local approach to quantitative\npropagation of chaos for a wide class of mean field diffusive dynamics. For a\nsystem of $n$ interacting particles, the relative entropy between the marginal\nlaw of $k$ particles and its limiting product measure is shown to be\n$O((k/n)^2)$ at each time, as long as the same is true at time zero. A simple\nGaussian example shows that this rate is optimal. The main assumption is that\nthe limiting measure obeys a certain functional inequality, which is shown to\nencompass many potentially irregular but not too singular finite-range\ninteractions, as well as some infinite-range interactions. This unifies the\npreviously disparate cases of Lipschitz versus bounded measurable interactions,\nimproving the best prior bounds of $O(k/n)$ which were deduced from global\nestimates involving all $n$ particles. We also cover a class of models for\nwhich qualitative propagation of chaos and even well-posedness of the\nMcKean-Vlasov equation were previously unknown. At the center of a new approach\nis a differential inequality, derived from a form of the BBGKY hierarchy, which\nbounds the $k$-particle entropy in terms of the $(k+1)$-particle entropy.\n"}
+{"abstract": "  Intrinsic nonlinear elasticity deals with the deformations of elastic bodies\nas isometric immersions of Riemannian manifolds into the Euclidean spaces (see\nCiarlet [9,10]). In this paper, we study the rigidity and continuity properties\nof elastic bodies for the intrinsic approach to nonlinear elasticity. We first\nestablish a geometric rigidity estimate for mappings from Riemannian manifolds\nto spheres (in the spirit of Friesecke-James-M\\\"{u}ller [23]), which is the\nfirst result of this type for the non-Euclidean case as far as we know. Then we\nprove the asymptotic rigidity of elastic membranes under suitable geometric\nconditions. Finally, we provide a simplified geometric proof of the continuous\ndependence of deformations of elastic bodies on the Cauchy-Green tensors and\nsecond fundamental forms, which extends the Ciarlet-Mardare theorem in [18] to\narbitrary dimensions and co-dimensions.\n"}
+{"abstract": "  We study the nonlinear steady-state transport of spinless fermions through a\nquantum dot with a local two-particle interaction. The dot degree of freedom is\nin addition coupled to a phonon mode. This setup combines the nonequilibrium\nphysics of the interacting resonant level model and that of the\nAnderson-Holstein model. The fermion-fermion interaction defies a perturbative\ntreatment. We mainly focus on the antiadiabatic limit, with the phonon\nfrequency being larger than the lead-dot tunneling rate. In this regime also\nthe fermion-boson coupling cannot be treated perturbatively. Our goal is\ntwo-fold. We investigate the competing roles of the fermion-fermion and\nfermion-boson interactions on the emergent low-energy scale $T_{\\rm K}$ and\nshow how $T_{\\rm K}$ manifests in the transport coefficients as well as the\ncurrent-voltage characteristics. For small to intermediate interactions, the\nlatter is in addition directly affected by both interactions independently.\nWith increasing fermion-boson interaction the Franck-Condon blockade suppresses\nthe current at small voltages and the emission of phonons leads to shoulders or\nsteps at multiples of the phonon frequency, while the local fermion-fermion\ninteraction implies a negative differential conductance at voltages larger than\n$T_{\\rm K}$. We, in addition, use the model to investigate the limitations of\nour low-order truncated functional renormalization group approach on the\nKeldysh contour. In particular, we quantify the role of the broken current\nconservation.\n"}
+{"abstract": "  In this review we introduce computer modelling and simulation techniques\nwhich are used for ferrimagnetic materials. We focus on models where thermal\neffects are accounted for, atomistic spin dynamics and finite temperature\nmacrospin approaches. We survey the literature of two of the most commonly\nmodelled ferrimagnets in the field of spintronics--the amorphous alloy GdFeCo\nand the magnetic insulator yttrium iron garnet. We look at how generic models\nand material specific models have been applied to predict and understand\nspintronic experiments, focusing on the fields of ultrafast magnetisation\ndynamics, spincaloritronics and magnetic textures dynamics and give an outlook\nfor modelling in ferrimagnetic spintronics.\n"}
+{"abstract": "  The coexistence of diverse services with heterogeneous requirements is a\nfundamental feature of 5G. This necessitates efficient radio access network\n(RAN) slicing, defined as sharing of the wireless resources among diverse\nservices while guaranteeing their respective throughput, timing, and/or\nreliability requirements. In this paper, we investigate RAN slicing for an\nuplink scenario in the form of multiple access schemes for two user types: (1)\nbroadband users with throughput requirements and (2) intermittently active\nusers with timing requirements, expressed as either latency-reliability (LR) or\nPeak Age of Information (PAoI). Broadband users transmit data continuously,\nhence, are allocated non-overlapping parts of the spectrum. We evaluate the\ntrade-offs between the achievable throughput of a broadband user and the timing\nrequirements of an intermittent user under Orthogonal Multiple Access (OMA) and\nNon-Orthogonal Multiple Access (NOMA), considering capture. Our analysis shows\nthat NOMA, in combination with packet-level coding, is a superior strategy in\nmost cases for both LR and PAoI, achieving a similar LR with only slight 2%\ndecrease in throughput with respect to the upper bound in performance. However,\nthere are extreme cases where OMA achieves a slightly greater throughput than\nNOMA at the expense of an increased PAoI.\n"}
+{"abstract": "  This paper presents an extremum seeking control algorithm with an adaptive\nstep-size that adjusts the aggressiveness of the controller based on the\nquality of the gradient estimate. The adaptive step-size ensures that the\nintegral-action produced by the gradient descent does not destabilize the\nclosed-loop system. To quantify the quality of the gradient estimate, we\npresent a batch least squares estimator with a novel weighting and show that it\nproduces bounded estimation errors, where the uncertainty is due to the\ncurvature of the unknown cost function. The adaptive step-size then maximizes\nthe decrease of the combined plant and controller Lyapunov function for the\nworst-case estimation error. We prove that our ESC is input-to-state stable\nwith respect to the dither signal. Finally, we demonstrate our proposed ESC\nthrough five numerical examples; one illustrative, one practical, and three\nbenchmarks.\n"}
+{"abstract": "  Here we review the understanding of the classical hydrogen atom in classical\nelectromagnetic zero-point radiation, and emphasize the importance of special\nrelativity. The crucial missing ingredient in earlier calculational attempts\n(both numerical and analytic) is the use of valid approximations to the full\nrelativistic analysis. It is pointed out that the nonrelativistic time Fourier\nexpansion coefficients given by Landau and Lifshitz are in error as the\nelectromagnetic description of a charged particle in a Coulomb potential, and,\nbecause of this error, Marshall and Claverie's conclusion regarding the failure\nof radiation balance is invalid. Rather, using Marshall and Claverie's\ncalculations, but restricted to lowest nonvanishing order in the orbital\neccentricity (where the nonrelativistic orbit is a valid approximation to the\nfully relativistic electromagnetic orbit) radiation balance for classical\nelectromagnetic zero-point radiation is shown to hold at the fundamental\nfrequencies and associated first overtones.\n"}
+{"abstract": "  We study a linear high-dimensional regression model in a semi-supervised\nsetting, where for many observations only the vector of covariates $X$ is given\nwith no response $Y$. We do not make any sparsity assumptions on the vector of\ncoefficients, and aim at estimating $\\mathrm{Var}(Y|X)$. We propose an\nestimator, which is unbiased, consistent, and asymptotically normal. This\nestimator can be improved by adding zero-estimators arising from the unlabelled\ndata. Adding zero-estimators does not affect the bias and potentially can\nreduce variance.\n  In order to achieve optimal improvement, many zero-estimators should be used,\nbut this raises the problem of estimating many parameters. Therefore, we\nintroduce covariate selection algorithms that identify which zero-estimators\nshould be used in order to improve the above estimator.\n  We further illustrate our approach for other estimators, and present an\nalgorithm that improves estimation for any given variance estimator. Our\ntheoretical results are demonstrated in a simulation study.\n"}
+{"abstract": "  We present a comprehensive study on building and adapting RNN transducer\n(RNN-T) models for spoken language understanding(SLU). These end-to-end (E2E)\nmodels are constructed in three practical settings: a case where verbatim\ntranscripts are available, a constrained case where the only available\nannotations are SLU labels and their values, and a more restrictive case where\ntranscripts are available but not corresponding audio. We show how RNN-T SLU\nmodels can be developed starting from pre-trained automatic speech recognition\n(ASR) systems, followed by an SLU adaptation step. In settings where real audio\ndata is not available, artificially synthesized speech is used to successfully\nadapt various SLU models. When evaluated on two SLU data sets, the ATIS corpus\nand a customer call center data set, the proposed models closely track the\nperformance of other E2E models and achieve state-of-the-art results.\n"}
+{"abstract": "  Loop scheduling techniques aim to achieve load-balanced executions of\nscientific applications. Dynamic loop self-scheduling (DLS) libraries for\ndistributed-memory systems are typically MPI-based and employ a centralized\nchunk calculation approach (CCA) to assign variably-sized chunks of loop\niterations. We present a distributed chunk calculation approach (DCA) that\nsupports various types of DLS techniques. Using both CCA and DCA, twelve DLS\ntechniques are implemented and evaluated in different CPU slowdown scenarios.\nThe results show that the DLS techniques implemented using DCA outperform their\ncorresponding ones implemented with CCA, especially in extreme system slowdown\nscenarios.\n"}
+{"abstract": "  The Off-plane Grating Rocket Experiment (OGRE) is a soft X-ray grating\nspectrometer to be flown on a suborbital rocket. The payload is designed to\nobtain the highest-resolution soft X-ray spectrum of Capella to date with a\nresolution goal of $R(\\lambda/\\Delta\\lambda)>2000$ at select wavelengths in its\n10--55 Angstrom bandpass of interest. The optical design of the spectrometer\nrealizes a theoretical maximum resolution of $R\\approx5000$, but this\nperformance does not consider the finite performance of the individual\nspectrometer components, misalignments between components, and in-flight\npointing errors. These errors all degrade the performance of the spectrometer\nfrom its theoretical maximum. A comprehensive line-spread function (LSF) error\nbudget has been constructed for the OGRE spectrometer to identify contributions\nto the LSF, to determine how each of these affects the LSF, and to inform\nperformance requirements and alignment tolerances for the spectrometer. In this\ndocument, the comprehensive LSF error budget for the OGRE spectrometer is\npresented, the resulting errors are validated via raytrace simulations, and the\nimplications of these results are discussed.\n"}
+{"abstract": "  We investigate the possibility to induce double peaks of gravitational\nwave(GW) spectrum from primordial scalar perturbations in inflationary models\nwith three inflection points.Where the inflection points can be generated from\na polynomial potential or generated from Higgs like $\\phi^4$ potential with the\nrunning of quartic coupling.In such models, the inflection point at large\nscales predicts the scalar spectral index and tensor-to-scalar ratio consistent\nwith current CMB constraints, and the other two inflection points generate two\nlarge peaks in the scalar power spectrum at small scales, which can induce GWs\nwith double peaks energy spectrum. We find that for some choices parameters the\ndouble peaks spectrum can be detected by future GW detectors, and one of the\npeaks around $f\\simeq10^{-9}\\sim10^{-8}$Hz can also explain the recent NANOGrav\nsignal. Moreover, the peaks of power spectrum allow for the generation of\nprimordial black holes, which account for a significant fraction of dark\nmatter.\n"}
+{"abstract": "  In this work, we investigate Gaussian process regression used to recover a\nfunction based on noisy observations. We derive upper and lower error bounds\nfor Gaussian process regression with possibly misspecified correlation\nfunctions. The optimal convergence rate can be attained even if the smoothness\nof the imposed correlation function exceeds that of the true correlation\nfunction and the sampling scheme is quasi-uniform. As byproducts, we also\nobtain convergence rates of kernel ridge regression with misspecified kernel\nfunction, where the underlying truth is a deterministic function. The\nconvergence rates of Gaussian process regression and kernel ridge regression\nare closely connected, which is aligned with the relationship between sample\npaths of Gaussian process and the corresponding reproducing kernel Hilbert\nspace.\n"}
+{"abstract": "  Let $G$ be a graph with vertex set $V(G)$ and edge set $E(G)$, and $L(G)$ be\nthe line graph of $G$, which has vertex set $E(G)$ and two vertices $e$ and $f$\nof $L(G)$ is adjacent if $e$ and $f$ is incident in $G$. The vertex-edge graph\n$M(G)$ of $G$ has vertex set $V(G)\\cup E(G)$ and edge set $E(L(G))\\cup\n\\{ue,ve|\\ \\forall\\ e=uv\\in E(G)\\}$. In this paper, by a combinatorial\ntechnique, we show that if $G$ is a connected cubic graph with an even number\nof edges, then the number of dimer coverings of $M(G)$ equals\n$2^{|V(G)|/2+1}3^{|V(G)|/4}$. As an application, we obtain the exact solution\nof the dimer problem of the weighted solicate network obtained from the\nhexagonal lattice in the context of statistical physics.\n"}
+{"abstract": "  Online exams have become widely used to evaluate students' performance in\nmastering knowledge in recent years, especially during the pandemic of\nCOVID-19. However, it is challenging to conduct proctoring for online exams due\nto the lack of face-to-face interaction. Also, prior research has shown that\nonline exams are more vulnerable to various cheating behaviors, which can\ndamage their credibility. This paper presents a novel visual analytics approach\nto facilitate the proctoring of online exams by analyzing the exam video\nrecords and mouse movement data of each student. Specifically, we detect and\nvisualize suspected head and mouse movements of students in three levels of\ndetail, which provides course instructors and teachers with convenient,\nefficient and reliable proctoring for online exams. Our extensive evaluations,\nincluding usage scenarios, a carefully-designed user study and expert\ninterviews, demonstrate the effectiveness and usability of our approach.\n"}
+{"abstract": "  For $M$ a compact Riemannian manifold Brandenbursky and Marcinkowski\nconstructed a transfer map $H_b^*(\\pi_1(M))\\to H_b^*(Homeo_{vol,0}(M))$ and\nused it to show that for certain $M$ the space\n$\\overline{EH}_b^3(Homeo_{vol,0}(M))$ is infinite-dimensional. Kimura adapted\nthe argument to $Diff_{vol}(D^2,\\partial D^2)$. We extend both results to the\nhigher degrees $\\overline{EH}_b^{2n}$, $n\\geq 1$. We also show that for certain\n$M$ the ordinary cohomology $H^*(Homeo_{vol,0}(M))$ is non-trivial in all\ndegrees. In our computations we view the transfer map as being induced by a\ncoupling of groups.\n"}
+{"abstract": "  We define several sorts of mappings on a poset like monotone, strictly\nmonotone, upper cone preserving and variants of these. Our aim is to\ncharacterize posets in which some of these mappings coincide. We define special\nmappings determined by two elements and investigate when these are strictly\nmonotone or upper cone preserving. If the considered poset is a semilattice\nthen its monotone mappings coincide with semilattice homomorphisms if and only\nif the poset is a chain. Similarly, we study posets which need not be\nsemilattices but whose upper cones have a minimal element. We extend this\ninvestigation to posets that are direct products of chains or an ordinal sum of\nan antichain and a finite chain. We characterize equivalence relations induced\nby strongly monotone mappings and show that the quotient set of a poset by such\nan equivalence relation is a poset again.\n"}
+{"abstract": "  In this article, we study several closely related invariants associated to\nDirac operators on odd-dimensional manifolds with boundary with an action of\nthe compact group $H$ of isometries. In particular, the equality between\nequivariant winding numbers, equivariant spectral flow, and equivariant Maslov\nindices is established. We also study equivariant $\\eta$-invariants which play\na fundamental role in the equivariant analog of Getzler's spectral flow\nformula. As a consequence, we establish a relation between equivariant\n$\\eta$-invariants and equivariant Maslov triple indices in the splitting of\nmanifolds.\n"}
+{"abstract": "  The radio source 1146+596 is hosted by an elliptical/S0 galaxy NGC\\,3894,\nwith a low-luminosity active nucleus. The radio structure is compact,\nsuggesting a very young age of the jets in the system. Recently, the source has\nbeen confirmed as a high-energy (HE, $>0.1$\\,GeV) $\\gamma$-ray emitter, in the\nmost recent accumulation of the {\\it Fermi} Large Area Telescope (LAT) data.\nHere we report on the analysis of the archival {\\it Chandra} X-ray Observatory\ndata for the central part of the galaxy, consisting of a single 40\\,ksec-long\nexposure. We have found that the core spectrum is best fitted by a combination\nof an ionized thermal plasma with the temperature of $\\simeq 0.8$\\,keV, and a\nmoderately absorbed power-law component (photon index $\\Gamma = 1.4\\pm 0.4$,\nhydrogen column density $N_{\\rm H}/10^{22}$\\,cm$^{-2}$\\,$= 2.4\\pm 0.7$). We\nhave also detected the iron K$\\alpha$ line at $6.5\\pm 0.1$\\,keV, with a large\nequivalent width of EW\\,$= 1.0_{-0.5}^{+0.9}$\\,keV. Based on the simulations of\nthe {\\it Chandra}'s Point Spread Function (PSF), we have concluded that, while\nthe soft thermal component is extended on the scale of the galaxy host, the\nhard X-ray emission within the narrow photon energy range 6.0--7.0\\,keV\noriginates within the unresolved core (effectively the central kpc radius). The\nline is therefore indicative of the X-ray reflection from a cold neutral gas in\nthe central regions of NGC\\,3894. We discuss the implications of our findings\nin the context of the X-ray Baldwin effect. NGC\\,3894 is the first young radio\ngalaxy detected in HE $\\gamma$-rays with the iron K$\\alpha$ line.\n"}
+{"abstract": "  Particle production from secondary proton-proton collisions, commonly\nreferred to as pile-up, impair the sensitivity of both new physics searches and\nprecision measurements at LHC experiments. We propose a novel algorithm, PUMA,\nfor identifying pile-up objects with the help of deep neural networks based on\nsparse transformers. These attention mechanisms were developed for natural\nlanguage processing but have become popular in other applications. In a\nrealistic detector simulation, our method outperforms classical benchmark\nalgorithms for pile-up mitigation in key observables. It provides a perspective\nfor mitigating the effects of pile-up in the high luminosity era of the LHC,\nwhere up to 200 proton-proton collisions are expected to occur simultaneously.\n"}
+{"abstract": "  We formulate a procedure to obtain a gauge-invariant tunneling rate at zero\ntemperature using the recently developed tunneling potential approach. This\nprocedure relies on a consistent power counting in gauge coupling and a\nderivative expansion. The tunneling potential approach, while numerically more\nefficient than the standard bounce solution method, inherits the\ngauge-dependence of the latter when naively implemented. Using the Abelian\nHiggs model, we show how to obtain a tunneling rate whose residual\ngauge-dependence arises solely from the polynomial approximations adopted in\nthe tunneling potential computation.\n"}
+{"abstract": "  We consider the two-loop corrections to the $HW^+W^-$ vertex at order\n$\\alpha\\alpha_s$. We construct a canonical basis for the two-loop integrals\nusing the Baikov representation and the intersection theory. By solving the\n$\\epsilon$-form differential equations, we obtain fully analytic expressions\nfor the master integrals in terms of multiple polylogarithms, which allow fast\nand accurate numeric evaluation for arbitrary configurations of external\nmomenta. We apply our analytic results to the decay process $H \\to \\nu_e e W$,\nand study both the integrated and differential decay rates. Our results can\nalso be applied to the Higgs production process via $W$ boson fusion.\n"}
+{"abstract": "  There have been considerable research efforts devoted to quantum simulations\nof Fermi-Hubbard model with ultracold atoms loaded in optical lattices. In such\nexperiments, the antiferromagnetically ordered quantum state has been achieved\nat half filling in recent years. The atomic lattice away from half filling is\nexpected to host d-wave superconductivity, but its low temperature phases have\nnot been reached. In a recent work, we proposed an approach of incommensurate\nquantum adiabatic doping, using quantum adiabatic evolution of an\nincommensurate lattice for preparation of the highly correlated many-body\nground state of the doped Fermi-Hubbard model starting from a unit-filling band\ninsulator. Its feasibility has been demonstrated with numerical simulations of\nthe adiabatic preparation for certain incommensurate particle-doping fractions,\nwhere the major problem to circumvent is the atomic localization in the\nincommensurate lattice. Here we carry out a systematic study of the quantum\nadiabatic doping for a wide range of doping fractions from particle-doping to\nhole-doping, including both commensurate and incommensurate cases. We find that\nthere is still a localization-like slowing-down problem at commensurate\nfillings, and that it becomes less harmful in the hole-doped regime. With\ninteractions, the adiabatic preparation is found to be more efficient for that\ninteraction effect destabilizes localization. For both free and interacting\ncases, we find the adiabatic doping has better performance in the hole-doped\nregime than the particle-doped regime. We also study adiabatic doping starting\nfrom the half-filling Mott insulator, which is found to be more efficient for\ncertain filling fractions.\n"}
+{"abstract": "  We present results from seven cosmological simulations that have been\nextended beyond the present era as far as redshift $z=-0.995$ or\n$t\\approx96\\,{\\rm Gyr}$, using the Enzo simulation code. We adopt the\ncalibrated star formation and feedback prescriptions from our previous work on\nreproducing the Milky Way with Enzo with modifications to the simulation code,\nchemistry and cooling library. We then consider the future behaviour of the\nhalo mass function (HMF), the equation of state (EOS) of the IGM, and the\ncosmic star formation history (SFH). Consistent with previous work, we find a\nfreeze-out in the HMF at $z\\approx-0.6$. The evolution of the EOS of the IGM\npresents an interesting case study of the cosmological coincidence problem,\nwhere there is a sharp decline in the IGM temperature immediately after $z=0$.\nFor the SFH, the simulations produce a peak and a subsequent decline into the\nfuture. However, we do find a turnaround in the SFH after $z\\approx-0.98$ in\nsome simulations, probably due to the limitations of the criteria used for star\nformation. By integrating the SFH in time up to $z=-0.92$, the simulation with\nthe best spatial resolution predicts an asymptotic total stellar mass that is\nvery close to that obtained from extrapolating the fit of the observed SFR.\nLastly, we investigate the future evolution of the partition of baryons within\na Milky Way-sized galaxy, using both a zoom and a box simulation. Despite\nvastly different resolutions, these simulations predict individual haloes\ncontaining an equal fraction of baryons in stars and gas at the time of\nfreeze-out ($t\\approx30\\,{\\rm Gyr}$).\n"}
+{"abstract": "  We study algebraic curves that are envelopes of families of polygons\nsupported on the unit circle T. We address, in particular, a characterization\nof such curves of minimal class and show that all realizations of these curves\nare essentially equivalent and can be described in terms of orthogonal\npolynomials on the unit circle (OPUC), also known as Szeg\\H{o} polynomials. Our\nresults have connections to classical results from algebraic and projective\ngeometry, such as theorems of Poncelet, Darboux, and Kippenhahn; numerical\nranges of a class of matrices; and Blaschke products and disk functions.\n  This paper contains new results, some old results presented from a different\nperspective or with a different proof, and a formal foundation for our\nanalysis. We give a rigorous definition of the Poncelet property, of curves\ntangent to a family of polygons, and of polygons associated with Poncelet\ncurves. As a result, we are able to clarify some misconceptions that appear in\nthe literature and present counterexamples to some existing assertions along\nwith necessary modifications to their hypotheses to validate them. For\ninstance, we show that curves inscribed in some families of polygons supported\non T are not necessarily convex, can have cusps, and can even intersect the\nunit circle.\n  Two ideas play a unifying role in this work. The first is the utility of OPUC\nand the second is the advantage of working with tangent coordinates. This\nlatter idea has been previously exploited in the works of B. Mirman, whose\ncontribution we have tried to put in perspective.\n"}
+{"abstract": "  We compute the two-loop four-point form factor of a length-3 half-BPS\noperator in planar N=4 SYM, which belongs to the class of two-loop five-point\nscattering observables with one off-shell color-singlet leg. A new\nbootstrapping strategy is developed to obtain this result by starting with an\nansatz expanded in terms of master integrals and then solving the master\ncoefficients via various physical constraints. We find that consistency\nconditions of infrared divergences and collinear limits, together with the\ncancellation of spurious poles, can fix a significant part of the ansatz. The\nremaining degrees of freedom can be fixed by one simple type of two-double\nunitarity cut. Full analytic results in terms of both symbol and Goncharov\npolylogarithms are provided.\n"}
+{"abstract": "  We reconsider the problem of calculating the vacuum free energy (density) of\nQCD and the shift of the quark condensates in the presence of a uniform\nbackground magnetic field using two-and-three-flavor chiral perturbation theory\n($\\chi$PT). Using the free energy, we calculate the degenerate, light quark\ncondensates in the two-flavor case and the up, down and strange quark\ncondensates in the three-flavor case. We also use the vacuum free energy to\ncalculate the (renormalized) magnetization of the QCD vacuum, which shows that\nit is paramagnetic. We find that the three-flavor light-quark condensates and\n(renormalized) magnetization are improvements on the two-flavor results. We\nalso find that the average light quark condensate is in agreement with the\nlattice up to $eB=0.2 {\\rm\\ GeV^{2}}$, and the (renormalized) magnetization is\nin agreement up to $eB=0.3 {\\rm\\ GeV^{2}}$, while three-flavor $\\chi$PT, which\ngives a non-zero shift in the difference between the light quark condensates\nunlike two-flavor $\\chi$PT, underestimates the difference compared to lattice\nQCD.\n"}
+{"abstract": "  We propose that a broad class of excited-state quantum phase transitions\n(ESQPTs) gives rise to two different excited-state quantum phases. These phases\nare identified by means of an operator, $\\hat{\\mathcal{C}}$, which is a\nconstant of motion only in one of them. Hence, the ESQPT critical energy splits\nthe spectrum into one phase where the equilibirium expectation values of\nphysical observables crucially depend on this constant of motion, and another\nphase where the energy is the only relevant thermodynamic magnitude. The\ntrademark feature of this operator is that it has two different eigenvalues,\n$\\pm1$, and therefore it acts as a discrete symmetry in the first of these two\nphases. This scenario is observed in systems with and without an additional\ndiscrete symmetry; in the first case, $\\hat{\\mathcal{C}}$ explains the change\nfrom degenerate doublets to non-degenerate eigenlevels upon crossing the\ncritical line. We present stringent numerical evidence in the Rabi and Dicke\nmodels, suggesting that this result is exact in the thermodynamic limit, with\nfinite-size corrections that decrease as a power-law.\n"}
+{"abstract": "  Several years ago, in the context of the physics of hysteresis in magnetic\nmaterials, a simple stochastic model has been introduced: the ABBM model.\nLater, the ABBM model has been advocated as a paradigm for a broad class of\ndiverse phenomena, baptised \"crackling noise phenomena\". The model reproduces\nmany statistical features of such intermittent signals, as the statistics of\nburst (or avalanche) durations and sizes, with their power law exponents that\nwould characterise the dynamics as critical. Beyond such \"critical exponents\",\nthe measure of the average shape of the avalanche has also been proposed. Here,\nthe exact calculation of average and fluctuations of the avalanche shape for\nthe ABBM model is presented, showing that its normalised shape is independent\nfrom the external drive. Moreover, average and fluctuations of the\nmulti-avalanche shape, that is a sequence of avalanches of fixed total\nduration, is also computed. Surprisingly, the two quantities (avalanche and\nmulti-avalanche normalised shapes) are identical. This result is obtained using\nthe exact solution of the ABBM model, obtained leveraging the equivalence with\nthe Cox-Ingersoll-Ross process (CIR), through an exact \"time change\". A\npresentation of this and other known exact results is provided: notably the\ncorrespondence of the ABBM/CIR model with the generalised Bessel process,\ndescribing the dynamics of the modulus of the multi dimensional\nOrnstein-Uhlenbeck process. As a consequence, the correspondence between the\nexcursion (avalanche) and bridge (multi-avalanche) shape distributions, turns\nto apply to all the aforementioned stochastic processes. In simple words:\nconsidering the distance from the origin of such diffusive particles, the\n(normalised) average shape (and fluctuations) of its trajectory until a return\nin a time T is the same, whether it has returned before T or not.\n"}
+{"abstract": "  In this paper, we propose a fast second-order approximation to the\nvariable-order (VO) Caputo fractional derivative, which is developed based on\n$L2$-$1_\\sigma$ formula and the exponential-sum-approximation technique. The\nfast evaluation method can achieve the second-order accuracy and further reduce\nthe computational cost and the acting memory for the VO Caputo fractional\nderivative. This fast algorithm is applied to construct a relevant fast\ntemporal second-order and spatial fourth-order scheme ($FL2$-$1_{\\sigma}$\nscheme) for the multi-dimensional VO time-fractional sub-diffusion equations.\nTheoretically, $FL2$-$1_{\\sigma}$ scheme is proved to fulfill the similar\nproperties of the coefficients as those of the well-studied $L2$-$1_\\sigma$\nscheme. Therefore, $FL2$-$1_{\\sigma}$ scheme is strictly proved to be\nunconditionally stable and convergent. A sharp decrease in the computational\ncost and the acting memory is shown in the numerical examples to demonstrate\nthe efficiency of the proposed method.\n"}
+{"abstract": "  Direct Delta Mush is a novel skinning deformation technique introduced by Le\nand Lewis (2019). It generalizes the iterative Delta Mush algorithm of\nMancewicz et al (2014), providing a direct solution with improved efficiency\nand control. Compared to Linear Blend Skinning, Direct Delta Mush offers better\nquality of deformations and ease of authoring at comparable performance.\nHowever, Direct Delta Mush does not handle non-rigid joint transformations\ncorrectly which limits its application for most production environments. This\npaper presents an extension to Direct Delta Mush that integrates the non-rigid\npart of joint transformations into the algorithm. In addition, the paper also\ndescribes practical considerations for computing the orthogonal component of\nthe transformation and stability issues observed during the implementation and\ntesting.\n"}
+{"abstract": "  Geodesic orbit spaces (or g.o. spaces) are defined as those homogeneous\nRiemannian spaces $(M=G/H,g)$ whose geodesics are orbits of one-parameter\nsubgroups of $G$. The corresponding metric $g$ is called a geodesic orbit\nmetric. We study the geodesic orbit spaces of the form $(\\Sp(n)/\\Sp(n_1)\\times\n\\cdots \\times \\Sp(n_s), g)$, with $0<n_1+\\cdots +n_s\\leq n$. Such spaces\ninclude spheres, quaternionic Stiefel manifolds, Grassmann manifolds and\nquaternionic flag manifolds. The present work is a contribution to the study of\ng.o. spaces $(G/H,g)$ with $H$ semisimple.\n"}
+{"abstract": "  Assessing population-level effects of vaccines and other infectious disease\nprevention measures is important to the field of public health. In infectious\ndisease studies, one person's treatment may affect another individual's\noutcome, i.e., there may be interference between units. For example, use of bed\nnets to prevent malaria by one individual may have an indirect or spillover\neffect to other individuals living in close proximity. In some settings,\nindividuals may form groups or clusters where interference only occurs within\ngroups, i.e., there is partial interference. Inverse probability weighted\nestimators have previously been developed for observational studies with\npartial interference. Unfortunately, these estimators are not well suited for\nstudies with large clusters. Therefore, in this paper, the parametric g-formula\nis extended to allow for partial interference. G-formula estimators are\nproposed of overall effects, spillover effects when treated, and spillover\neffects when untreated. The proposed estimators can accommodate large clusters\nand do not suffer from the g-null paradox that may occur in the absence of\ninterference. The large sample properties of the proposed estimators are\nderived, and simulation studies are presented demonstrating the finite-sample\nperformance of the proposed estimators. The Demographic and Health Survey from\nthe Democratic Republic of the Congo is then analyzed using the proposed\ng-formula estimators to assess the overall and spillover effects of bed net use\non malaria.\n"}
+{"abstract": "  We present graph-based translation models which translate source graphs into\ntarget strings. Source graphs are constructed from dependency trees with extra\nlinks so that non-syntactic phrases are connected. Inspired by phrase-based\nmodels, we first introduce a translation model which segments a graph into a\nsequence of disjoint subgraphs and generates a translation by combining\nsubgraph translations left-to-right using beam search. However, similar to\nphrase-based models, this model is weak at phrase reordering. Therefore, we\nfurther introduce a model based on a synchronous node replacement grammar which\nlearns recursive translation rules. We provide two implementations of the model\nwith different restrictions so that source graphs can be parsed efficiently.\nExperiments on Chinese--English and German--English show that our graph-based\nmodels are significantly better than corresponding sequence- and tree-based\nbaselines.\n"}
+{"abstract": "  Most eCommerce applications, like web-shops have millions of products. In\nthis context, the identification of similar products is a common sub-task,\nwhich can be utilized in the implementation of recommendation systems, product\nsearch engines and internal supply logistics. Providing this data set, our goal\nis to boost the evaluation of machine learning methods for the prediction of\nthe category of the retail products from tuples of images and descriptions.\n"}
+{"abstract": "  In this paper, we discuss pre-transformed RM-Polar codes and cyclic\nredundancy check (CRC) concatenated pre-transformed RM-Polar codes. The\nsimulation results show that the pre-transformed RM-Polar (256, 128+9) code\nconcatenated with 9-bit CRC can achieve a frame error rate (FER) of 0.001 at\nEb/No=1.95dB under the list decoding, which is about 0.05dB from the RCU bound.\nThe pre-transformed RM-Polar (512, 256+9) concatenated with 9-bit CRC can\nachieve a FER of 0.001 at Eb/No=1.6dB under the list decoding, which is 0.15dB\nfrom the RCU bound.\n"}
+{"abstract": "  In literature, extensive research has been done with respect to synthesis of\nsupervisory controllers. Such synthesized supervisors can be distributed for\nimplementation on multiple physical controllers. This paper discusses a method\nfor distributing a synthesized supervisory controller. In this method,\ndependency structure matrices are used to distribute a system, the supervisor\nis then distributed accordingly, using existing localization theory. The\nexistence of communication delays between supervisory controllers is\nunavoidable in a distributed application. The influence of these delays on the\nbehavior of a supervisor is therefore studied using delay robustness theory.\nThis paper introduces the use of mutex algorithms to make the distributed\nsupervisor delay-robust. A case study is used to demonstrate the method and\nhardware in the loop testing is used to validate the resulting distributed\nsupervisor.\n"}
+{"abstract": "  For the fourth-order teleparallel $f\\left(T,B\\right) $ theory of gravity, we\ninvestigate the cosmological evolution for the universe in the case of a\nspatially flat Friedmann--Lema\\^{\\i}tre--Robertson--Walker background space. We\nfocus on the case for which $f\\left(T,B\\right) $ is separable, that is,\n$f\\left(T,B\\right) _{,TB}=0$ and $f\\left(T,B\\right) $ is a nonlinear function\non the scalars $T$ and $B$. For this fourth-order theory we use a Lagrange\nmultiplier to introduce a scalar field function which attributes the\nhigher-order derivatives. In order to perform the analysis of the dynamics we\nuse dimensionless variables which allow the Hubble function to change sign. The\nstationary points of the dynamical system are investigated both in the finite\nand infinite regimes. The physical properties of the asymptotic solutions and\ntheir stability characteristics are discussed.\n"}
+{"abstract": "  In this paper, we develop a zonal-based flexible bus services (ZBFBS) by\nconsidering both passenger demands spatial (origin-destination or OD) and\nvolume stochastic variations. Service requests are grouped by zonal OD pairs\nand number of passengers per request, and aggregated into demand categories\nwhich follow certain probability distributions. A two-stage stochastic program\nis formulated to minimize the expected operating cost of ZBFBS, in which the\nzonal visit sequences of vehicles are determined in Stage-1, whereas in\nStage-2, service requests are assigned to either regular routes determined in\nStage-1 or ad hoc services that incur additional costs. Demand volume\nreliability and detour time reliability are introduced to ensure quality of the\nservices and separate the problem into two phases for efficient solutions. In\nphase-1, given the reliability requirements, we minimize the cost of operating\nthe regular services. In phase-2, we optimize the passenger assignment to\nvehicles to minimize the expected ad hoc service cost. The reliabilities are\nthen optimized by a gradient-based approach to minimize the sum of the regular\nservice operating cost and expected ad hoc service cost. We conduct numerical\nstudies on vehicle capacity, detour time limit and demand volume to demonstrate\nthe potential of ZBFBS, and apply the model to Chengdu, China, based on real\ndata to illustrate its applicability.\n"}
+{"abstract": "  Here we revisit an initial orbit determination method introduced by O. F.\nMossotti employing four geocentric sky-plane observations and a linear equation\nto compute the angular momentum of the observed body. We then extend the method\nto topocentric observations, yielding a quadratic equation for the angular\nmomentum. The performance of the two versions are compared through numerical\ntests with synthetic asteroid data using different time intervals between\nconsecutive observations and different astrometric errors. We also show a\ncomparison test with Gauss's method using simulated observations with the\nexpected cadence of the VRO-LSST telescope.\n"}
+{"abstract": "  We propose the attraction Indian buffet distribution (AIBD), a distribution\nfor binary feature matrices influenced by pairwise similarity information.\nBinary feature matrices are used in Bayesian models to uncover latent variables\n(i.e., features) that explain observed data. The Indian buffet process (IBP) is\na popular exchangeable prior distribution for latent feature matrices. In the\npresence of additional information, however, the exchangeability assumption is\nnot reasonable or desirable. The AIBD can incorporate pairwise similarity\ninformation, yet it preserves many properties of the IBP, including the\ndistribution of the total number of features. Thus, much of the interpretation\nand intuition that one has for the IBP directly carries over to the AIBD. A\ntemperature parameter controls the degree to which the similarity information\naffects feature-sharing between observations. Unlike other nonexchangeable\ndistributions for feature allocations, the probability mass function of the\nAIBD has a tractable normalizing constant, making posterior inference on\nhyperparameters straight-forward using standard MCMC methods. A novel posterior\nsampling algorithm is proposed for the IBP and the AIBD. We demonstrate the\nfeasibility of the AIBD as a prior distribution in feature allocation models\nand compare the performance of competing methods in simulations and an\napplication.\n"}
+{"abstract": "  The words we use to talk about the current epidemiological crisis on social\nmedia can inform us on how we are conceptualizing the pandemic and how we are\nreacting to its development. This paper provides an extensive explorative\nanalysis of how the discourse about Covid-19 reported on Twitter changes\nthrough time, focusing on the first wave of this pandemic. Based on an\nextensive corpus of tweets (produced between 20th March and 1st July 2020)\nfirst we show how the topics associated with the development of the pandemic\nchanged through time, using topic modeling. Second, we show how the sentiment\npolarity of the language used in the tweets changed from a relatively positive\nvalence during the first lockdown, toward a more negative valence in\ncorrespondence with the reopening. Third we show how the average subjectivity\nof the tweets increased linearly and fourth, how the popular and frequently\nused figurative frame of WAR changed when real riots and fights entered the\ndiscourse.\n"}
+{"abstract": "  We investigate the distribution of partisanship in a cross-section of ten\ndiverse States to elucidate how votes translate into seats won and other\nmetrics. Markov chain simulations taking into account partisanship distribution\nagree surprisingly well with a simple model covering only equal voting\npopulation-weighted distributions of precinct results containing no spatial\ninformation. We find asymmetries where Democrats win fewer precincts than\nRepublicans but do so with large marjorities. This skew accounts for persistent\nRepublican control of State Legislatures and Congressional seats even in some\nstates with statewide vote majorities for Democrats.\n  Despite overall results showing Republican advantages in many states based on\nmean results from simulations covering many random scenarios, the simulations\nyield a wide range in metrics, suggesting bias can be minimized better by\nselecting districting plans with low values for efficiency gap than by\nselecting plans with values near the means for the ensemble of random\nsimulations.\n  We examine constraints on county splits to achieve higher compactness and\ninvestigate policies requiring cohesiveness for communities of interest as to\nscreen out the most obvious gerrymanders. Minimizing county splits does not\nnecessarily reduce partisan bias, except for Pennsylvania, where limiting\ncounty splits appears to reduce bias.\n"}
+{"abstract": "  In recent years, studying and predicting alternative mobility (e.g., sharing\nservices) patterns in urban environments has become increasingly important as\naccurate and timely information on current and future vehicle flows can\nsuccessfully increase the quality and availability of transportation services.\nThis need is aggravated during the current pandemic crisis, which pushes\npolicymakers and private citizens to seek social-distancing compliant urban\nmobility services, such as electric bikes and scooter sharing offerings.\nHowever, predicting the number of incoming and outgoing vehicles for different\ncity areas is challenging due to the nonlinear spatial and temporal\ndependencies typical of urban mobility patterns. In this work, we propose\nSTREED-Net, a novel deep learning network with a multi-attention (spatial and\ntemporal) mechanism that effectively captures and exploits complex spatial and\ntemporal patterns in mobility data. The results of a thorough experimental\nanalysis using real-life data are reported, indicating that the proposed model\nimproves the state-of-the-art for this task.\n"}
+{"abstract": "  Detection and classification of objects in overhead images are two important\nand challenging problems in computer vision. Among various research areas in\nthis domain, the task of fine-grained classification of objects in overhead\nimages has become ubiquitous in diverse real-world applications, due to recent\nadvances in high-resolution satellite and airborne imaging systems. The small\ninter-class variations and the large intra class variations caused by the fine\ngrained nature make it a challenging task, especially in low-resource cases. In\nthis paper, we introduce COFGA a new open dataset for the advancement of\nfine-grained classification research. The 2,104 images in the dataset are\ncollected from an airborne imaging system at 5 15 cm ground sampling distance,\nproviding higher spatial resolution than most public overhead imagery datasets.\nThe 14,256 annotated objects in the dataset were classified into 2 classes, 15\nsubclasses, 14 unique features, and 8 perceived colors a total of 37 distinct\nlabels making it suitable to the task of fine-grained classification more than\nany other publicly available overhead imagery dataset. We compare COFGA to\nother overhead imagery datasets and then describe some distinguished fine-grain\nclassification approaches that were explored during an open data-science\ncompetition we have conducted for this task.\n"}
+{"abstract": "  A new instrument is required to accommodate the need for increased\nportability and accuracy in laser power measurement above 100 W. Reflection and\nabsorption of laser light provide a measurable force from photon momentum\nexchange that is directly proportional to laser power, which can be measured\nwith an electrostatic balance traceable to the SI. We aim for a relative\nuncertainty of $10^{-3}$ with coverage factor $k=2$. For this purpose, we have\ndesigned a monolithic parallelogram 4-bar linkage incorporating elastic\ncircular notch flexure hinges. The design is optimized to address the main\nfactors driving force measurement uncertainty from the balance mechanism:\ncorner loading errors, balance stiffness, stress in the flexure hinges,\nsensitivity to vibration, and sensitivity to thermal gradients. Parasitic\nrotations in the free end of the 4-bar linkage during arcuate motion are\nconstrained by machining tolerances. An analytical model shows this affects the\nforce measurement less than 0.01 percent. Incorporating an inverted pendulum\nreduces the stiffness of the system without unduly increasing tilt sensitivity.\nFinite element modeling of the flexures is used to determine the hinge\norientation that minimizes stress which is therefore expected to minimize\nhysteresis. Thermal effects are mitigated using an external enclosure to\nminimize temperature gradients, although a quantitative analysis of this effect\nis not carried out. These analyses show the optimized mechanism is expected to\ncontribute less than $10^{-3}$ relative uncertainty in the final laser power\nmeasurement.\n"}
+{"abstract": "  We give a simple and short proof of the fact that the board game of Y cannot\nend in a draw. Our proof, based on the analogous result for the game of Hex\n(the so-called 'Hex Theorem'), is purely topological and does not depend on the\nshape of the board. We also include a simplified version of Gale's proof of Hex\nTheorem.\n"}
+{"abstract": "  We present a Bayesian treatment for deep regression using an\nErrors-in-Variables model which accounts for the uncertainty associated with\nthe input to the employed neural network. It is shown how the treatment can be\ncombined with already existing approaches for uncertainty quantification that\nare based on variational inference. Our approach yields a decomposition of the\npredictive uncertainty into an aleatoric and epistemic part that is more\ncomplete and, in many cases, more consistent from a statistical perspective. We\nillustrate and discuss the approach along various toy and real world examples.\n"}
+{"abstract": "  To effectively control large-scale distributed systems online, model\npredictive control (MPC) has to swiftly solve the underlying high-dimensional\noptimization. There are multiple techniques applied to accelerate the solving\nprocess in the literature, mainly attributed to software-based algorithmic\nadvancements and hardware-assisted computation enhancements. However, those\nmethods focus on arithmetic accelerations and overlook the benefits of the\nunderlying system's structure. In particular, the existing decoupled\nsoftware-hardware algorithm design that naively parallelizes the arithmetic\noperations by the hardware does not tackle the hardware overheads such as\nCPU-GPU and thread-to-thread communications in a principled manner. Also, the\nadvantages of parallelizable subproblem decomposition in distributed MPC are\nnot well recognized and exploited. As a result, we have not reached the full\npotential of hardware acceleration for MPC. In this paper, we explore those\nopportunities by leveraging GPU to parallelize the distributed and localized\nMPC (DLMPC) algorithm. We exploit the locality constraints embedded in the\nDLMPC formulation to reduce the hardware-intrinsic communication overheads. Our\nparallel implementation achieves up to 50x faster runtime than its CPU\ncounterparts under various parameters. Furthermore, we find that the\nlocality-aware GPU parallelization could halve the optimization runtime\ncomparing to the naive acceleration. Overall, our results demonstrate the\nperformance gains brought by software-hardware co-design with the information\nexchange structure in mind.\n"}
+{"abstract": "  Close-in co-orbital planets (in a 1:1 mean motion resonance) can experience\nstrong tidal interactions with the central star. Here, we develop an analytical\nmodel adapted to the study of the tidal evolution of those systems. We use a\nHamiltonian version of the constant time-lag tidal model, which extends the\nHamiltonian formalism developed for the point-mass case. We show that\nco-orbital systems undergoing tidal dissipation either favour the Lagrange or\nthe anti-Lagrange configurations, depending on the system parameters. However,\nfor all range of parameters and initial conditions, both configurations become\nunstable, although the timescale for the destruction of the system can be\nlarger than the lifetime of the star. We provide an easy-to-use criterion to\ndetermine if an already known close-in exoplanet may have an undetected\nco-orbital companion.\n"}
+{"abstract": "  The frustrated magnet $\\alpha$-RuCl$_3$ constitutes a fascinating quantum\nmaterial platform that harbors the intriguing Kitaev physics. However, a\nconsensus on its intricate spin interactions and field-induced quantum phases\nhas not been reached yet. Here we exploit multiple state-of-the-art many-body\nmethods and determine the microscopic spin model that quantitatively explains\nmajor observations in $\\alpha$-RuCl$_3$, including the zigzag order,\ndouble-peak specific heat, magnetic anisotropy, and the characteristic M-star\ndynamical spin structure, etc. According to our model simulations, the in-plane\nfield drives the system into the polarized phase at about 7 T and a thermal\nfractionalization occurs at finite temperature, reconciling observations in\ndifferent experiments. Under out-of-plane fields, the zigzag order is\nsuppressed at 35 T, above which, and below a polarization field of 100 T level,\nthere emerges a field-induced quantum spin liquid. The fractional entropy and\nalgebraic low-temperature specific heat unveil the nature of a gapless spin\nliquid, which can be explored in high-field measurements on $\\alpha$-RuCl$_3$.\n"}
+{"abstract": "  Justification of tight-binding model from Schroedinger formalism for various\ntopologies of position-based semiconductor qubits is presented in this work.\nSimplistic tight-binding model allows for description of single-electron\ndevices at large integration scale. However it is due to the fact that\ntight-binding model omits the integro-differential equations that arise from\nelectron-electron interaction in Schroedinger model. Two approaches are given\nin derivation of tight-binding model from Schroedinger equation. First approach\nis conducted by usage of Green functions obtained from Schroedinger equation.\nSecond approach is given by usage of Taylor expansion applied to Schroedinger\nequation. The obtained results can be extended for the case of many Wannier\nqubits with more than one electron and can be applied to 2 and 3 dimensional\nmodel. Furthermore various correlation functions are proposed in Schroedinger\nformalism that can account for static and time-dependent electric and magnetic\nfield polarizing given Wannier qubit system. One of the central results of\npresented work relies on the emergence of dissipation processes during smooth\nbending of semiconductor nanowires both in the case of classical and quantum\npicture. Presented results give the base for physical description of\nelectrostatic Q-Swap gate of any topology using open loop nanowires. We observe\nstrong localization of wavepacket due to nanowire bending.\n"}
+{"abstract": "  This is a short technical report introducing the solution of the Team\nTCParser for Short-video Face Parsing Track of The 3rd Person in Context (PIC)\nWorkshop and Challenge at CVPR 2021. In this paper, we introduce a strong\nbackbone which is cross-window based Shuffle Transformer for presenting\naccurate face parsing representation. To further obtain the finer segmentation\nresults, especially on the edges, we introduce a Feature Alignment Aggregation\n(FAA) module. It can effectively relieve the feature misalignment issue caused\nby multi-resolution feature aggregation. Benefiting from the stronger backbone\nand better feature aggregation, the proposed method achieves 86.9519% score in\nthe Short-video Face Parsing track of the 3rd Person in Context (PIC) Workshop\nand Challenge, ranked the first place.\n"}
+{"abstract": "  We present a learning-based method for estimating 4D reflectance field of a\nperson given video footage illuminated under a flat-lit environment of the same\nsubject. For training data, we use one light at a time to illuminate the\nsubject and capture the reflectance field data in a variety of poses and\nviewpoints. We estimate the lighting environment of the input video footage and\nuse the subject's reflectance field to create synthetic images of the subject\nilluminated by the input lighting environment. We then train a deep\nconvolutional neural network to regress the reflectance field from the\nsynthetic images. We also use a differentiable renderer to provide feedback for\nthe network by matching the relit images with the input video frames. This\nsemi-supervised training scheme allows the neural network to handle unseen\nposes in the dataset as well as compensate for the lighting estimation error.\nWe evaluate our method on the video footage of the real Holocaust survivors and\nshow that our method outperforms the state-of-the-art methods in both realism\nand speed.\n"}
+{"abstract": "  Particle acceleration and heating at mildly relativistic magnetized shocks in\nelectron-ion plasma are investigated with unprecedentedly high-resolution\ntwo-dimensional particle-in-cell simulations that include ion-scale shock\nrippling. Electrons are super-adiabatically heated at the shock, and most of\nthe energy transfer from protons to electrons takes place at or downstream of\nthe shock. We are the first to demonstrate that shock rippling is crucial for\nthe energization of electrons at the shock. They remain well below\nequipartition with the protons. The downstream electron spectra are\napproximately thermal with a limited supra-thermal power-law component. Our\nresults are discussed in the context of wakefield acceleration and the\nmodelling of electromagnetic radiation from blazar cores.\n"}
+{"abstract": "  This paper introduces a general framework for solving constrained convex\nquaternion optimization problems in the quaternion domain. To soundly derive\nthese new results, the proposed approach leverages the recently developed\ngeneralized $\\mathbb{HR}$-calculus together with the equivalence between the\noriginal quaternion optimization problem and its augmented real-domain\ncounterpart. This new framework simultaneously provides rigorous theoretical\nfoundations as well as elegant, compact quaternion-domain formulations for\noptimization problems in quaternion variables. Our contributions are threefold:\n(i) we introduce the general form for convex constrained optimization problems\nin quaternion variables, (ii) we extend fundamental notions of convex\noptimization to the quaternion case, namely Lagrangian duality and optimality\nconditions, (iii) we develop the quaternion alternating direction method of\nmultipliers (Q-ADMM) as a general purpose quaternion optimization algorithm.\nThe relevance of the proposed methodology is demonstrated by solving two\ntypical examples of constrained convex quaternion optimization problems arising\nin signal processing. Our results open new avenues in the design, analysis and\nefficient implementation of quaternion-domain optimization procedures.\n"}
+{"abstract": "  Given a database schema, Text-to-SQL aims to translate a natural language\nquestion into the corresponding SQL query. Under the setup of cross-domain,\ntraditional semantic parsing models struggle to adapt to unseen database\nschemas. To improve the model generalization capability for rare and unseen\nschemas, we propose a new architecture, ShadowGNN, which processes schemas at\nabstract and semantic levels. By ignoring names of semantic items in databases,\nabstract schemas are exploited in a well-designed graph projection neural\nnetwork to obtain delexicalized representation of question and schema. Based on\nthe domain-independent representations, a relation-aware transformer is\nutilized to further extract logical linking between question and schema.\nFinally, a SQL decoder with context-free grammar is applied. On the challenging\nText-to-SQL benchmark Spider, empirical results show that ShadowGNN outperforms\nstate-of-the-art models. When the annotated data is extremely limited (only\n10\\% training set), ShadowGNN gets over absolute 5\\% performance gain, which\nshows its powerful generalization ability. Our implementation will be\nopen-sourced at \\url{https://github.com/WowCZ/shadowgnn}.\n"}
+{"abstract": "  We construct an infinite family of genus one open book decompositions\nsupporting Stein-fillable contact structures and show that their monodromies do\nnot admit positive factorisations. This extends a line of counterexamples in\nhigher genera and establishes that a correspondence between Stein fillings and\npositive factorisations only exists for planar open book decompositions.\n"}
+{"abstract": "  In heavy atoms and ions, nuclear structure effects are significantly enhanced\ndue to the overlap of the electron wave functions with the nucleus. This\noverlap rapidly increases with the nuclear charge $Z$. We study the energy\nlevel shifts induced by the electric dipole and electric quadrupole nuclear\npolarization effects in atoms and ions with $Z \\geq 20$. The electric dipole\npolarization effect is enhanced by the nuclear giant dipole resonance. The\nelectric quadrupole polarization effect is enhanced because the electrons in a\nheavy atom or ion move faster than the rotation of the deformed nucleus, thus\nexperiencing significant corrections to the conventional approximation in which\nthey `see' an averaged nuclear charge density. The electric nuclear\npolarization effects are computed numerically for $1s$, $2s$, $2p_{1/2}$ and\nhigh $ns$ electrons. The results are fitted with elementary functions of\nnuclear parameters (nuclear charge, mass number, nuclear radius and\ndeformation). We construct an effective potential which models the energy level\nshifts due to nuclear polarization. This effective potential, when added to the\nnuclear Coulomb interaction, may be used to find energy level shifts in\nmulti-electron ions, atoms and molecules. The fitting functions and effective\npotentials of the nuclear polarization effects are important for the studies of\nisotope shifts and nonlinearity in the King plot which are now used to search\nfor new interactions and particles.\n"}
+{"abstract": "  We present online algorithms for directed spanners and Steiner forests. These\nproblems fall under the unifying framework of online covering linear\nprogramming formulations, developed by Buchbinder and Naor (MOR, 34, 2009),\nbased on primal-dual techniques. Our results include the following:\n  For the pairwise spanner problem, in which the pairs of vertices to be\nspanned arrive online, we present an efficient randomized\n$\\tilde{O}(n^{4/5})$-competitive algorithm for graphs with general lengths,\nwhere $n$ is the number of vertices. With uniform lengths, we give an efficient\nrandomized $\\tilde{O}(n^{2/3+\\epsilon})$-competitive algorithm, and an\nefficient deterministic $\\tilde{O}(k^{1/2+\\epsilon})$-competitive algorithm,\nwhere $k$ is the number of terminal pairs. These are the first online\nalgorithms for directed spanners. In the offline setting, the current best\napproximation ratio with uniform lengths is $\\tilde{O}(n^{3/5 + \\epsilon})$,\ndue to Chlamtac, Dinitz, Kortsarz, and Laekhanukit (TALG 2020).\n  For the directed Steiner forest problem with uniform costs, in which the\npairs of vertices to be connected arrive online, we present an efficient\nrandomized $\\tilde{O}(n^{2/3 + \\epsilon})$-competitive algorithm. The\nstate-of-the-art online algorithm for general costs is due to Chakrabarty, Ene,\nKrishnaswamy, and Panigrahi (SICOMP 2018) and is $\\tilde{O}(k^{1/2 +\n\\epsilon})$-competitive. In the offline version, the current best approximation\nratio with uniform costs is $\\tilde{O}(n^{26/45 + \\epsilon})$, due to Abboud\nand Bodwin (SODA 2018).\n  A small modification of the online covering framework by Buchbinder and Naor\nimplies a polynomial-time primal-dual approach with separation oracles, which a\npriori might perform exponentially many calls. We convert the online spanner\nproblem and the online Steiner forest problem into online covering problems and\nround in a problem-specific fashion.\n"}
+{"abstract": "  We report on the search for very-high-energy gamma-ray emission from the\nregions around three nearby supersonic pulsars (PSR B0355+54, PSR J0357+3205\nand PSR J1740+1000) that exhibit long X-ray tails. To date there is no clear\ndetection of TeV emission from any pulsar tail that is prominent in X-ray or\nradio. We provide upper limits on the TeV flux, and luminosity, and also\ncompare these limits with other pulsar wind nebulae detected in X-rays and the\ntail emission model predictions. We find that at least one of the three tails\nis likely to be detected in observations that are a factor of 2-3 more\nsensitive. The analysis presented here also has implications for deriving the\nproperties of pulsar tails, for those pulsars whose tails could be detected in\nTeV.\n"}
+{"abstract": "  Spanning trees are widely used in networks for broadcasting, fault-tolerance,\nand securely delivering messages. Hexagonal interconnection networks have a\nnumber of real life applications. Examples are cellular networks, computer\ngraphics, and image processing. Eisenstein-Jacobi (EJ) networks are a\ngeneralization of hexagonal mesh topology. They have a wide range of potential\napplications, and thus they have received researchers' attention in different\nareas among which interconnection networks and coding theory. In this paper, we\npresent two spanning trees' constructions for Eisenstein-Jacobi (EJ). The first\nconstructs three edge-disjoint node-independent spanning trees, while the\nsecond constructs six node-independent spanning trees but not edge disjoint.\nBased on the constructed trees, we develop routing algorithms that can securely\ndeliver a message and tolerate a number of faults in point-to-point or in\nbroadcast communications. The proposed work is also applied on higher\ndimensional EJ networks.\n"}
+{"abstract": "  Unfamiliar or esoteric visual forms arise in many areas of visualization.\nWhile such forms can be intriguing, it can be unclear how to make effective use\nof them without long periods of practice or costly user studies. In this work\nwe analyze the table cartogram-a graphic which visualizes tabular data by\nbringing the areas of a grid of quadrilaterals into correspondence with the\ninput data, like a heat map that has been \"area-ed\" rather than colored.\nDespite having existed for several years, little is known about its appropriate\nusage. We mend this gap by using Algebraic Visualization Design to show that\nthey are best suited to relatively small tables with ordinal axes for some\ncomparison and outlier identification tasks. In doing so we demonstrate a\ndiscount theory-based analysis that can be used to cheaply determine best\npractices for unknown visualizations.\n"}
+{"abstract": "  It is now well established from a variety of studies that there is a\nsignificant benefit from combining video and audio data in detecting active\nspeakers. However, either of the modalities can potentially mislead audiovisual\nfusion by inducing unreliable or deceptive information. This paper outlines\nactive speaker detection as a multi-objective learning problem to leverage best\nof each modalities using a novel self-attention, uncertainty-based multimodal\nfusion scheme. Results obtained show that the proposed multi-objective learning\narchitecture outperforms traditional approaches in improving both mAP and AUC\nscores. We further demonstrate that our fusion strategy surpasses, in active\nspeaker detection, other modality fusion methods reported in various\ndisciplines. We finally show that the proposed method significantly improves\nthe state-of-the-art on the AVA-ActiveSpeaker dataset.\n"}
+{"abstract": "  The XENON collaboration recently reported an excess of electron recoil events\nin the low energy region with a significance of around $3.3\\sigma$. An\nexplanation of this excess in terms of thermal dark matter seems challenging.\nWe propose a scenario where dark matter in the Milky Way halo gets boosted as a\nresult of scattering with the diffuse supernova neutrino background. This\ninteraction can accelerate the dark-matter to semi-relativistic velocities, and\nthis flux, in turn, can scatter with the electrons in the detector, thereby\nproviding a much better fit to the data. We identify regions in the parameter\nspace of dark-matter mass and interaction cross-section which satisfy the\nexcess. Furthermore, considering the data only hypothesis, we also impose\nbounds on the dark-matter scattering cross-section, which are competitive with\nbounds from other experiments.\n"}
+{"abstract": "  Deep neural networks are known to have security issues. One particular threat\nis the Trojan attack. It occurs when the attackers stealthily manipulate the\nmodel's behavior through Trojaned training samples, which can later be\nexploited.\n  Guided by basic neuroscientific principles we discover subtle -- yet critical\n-- structural deviation characterizing Trojaned models. In our analysis we use\ntopological tools. They allow us to model high-order dependencies in the\nnetworks, robustly compare different networks, and localize structural\nabnormalities. One interesting observation is that Trojaned models develop\nshort-cuts from input to output layers.\n  Inspired by these observations, we devise a strategy for robust detection of\nTrojaned models. Compared to standard baselines it displays better performance\non multiple benchmarks.\n"}
+{"abstract": "  A factor copula model is proposed in which factors are either simulable or\nestimable from exogenous information. Point estimation and inference are based\non a simulated methods of moments (SMM) approach with non-overlapping\nsimulation draws. Consistency and limiting normality of the estimator is\nestablished and the validity of bootstrap standard errors is shown. Doing so,\nprevious results from the literature are verified under low-level conditions\nimposed on the individual components of the factor structure. Monte Carlo\nevidence confirms the accuracy of the asymptotic theory in finite samples and\nan empirical application illustrates the usefulness of the model to explain the\ncross-sectional dependence between stock returns.\n"}
+{"abstract": "  For compact complex surfaces (M^4, J) of Kaehler type, it was previously\nshown that the sign of the Yamabe invariant Y(M) only depends on the Kodaira\ndimension Kod (M, J). In this paper, we prove that this pattern in fact extends\nto all compact complex surfaces except those of class VII. In the process, we\ngive a simplified proof of a result that explains why the exclusion of class\nVII is essential here.\n"}
+{"abstract": "  In literature, there are two different definitions of elliptic divisibility\nsequences. The first one says that a sequence of integers $\\{h_n\\}_{n\\geq 0}$\nis an elliptic divisibility sequence if it verifies the recurrence relation\n$h_{m+n}h_{m-n}h_{r}^2=h_{m+r}h_{m-r}h_{n}^2-h_{n+r}h_{n-r}h_{m}^2$ for every\nnatural number $m\\geq n\\geq r$. The second definition says that a sequence of\nintegers $\\{\\beta_n\\}_{n\\geq 0}$ is an elliptic divisibility sequence if it is\nthe sequence of the square roots (chosen with an appropriate sign) of the\ndenominators of the abscissas of the iterates of a point on a rational elliptic\ncurve. It is well-known that the two sequences are not equivalent. Hence, given\na sequence of the denominators $\\{\\beta_n\\}_{n\\geq 0}$, in general does not\nhold\n$\\beta_{m+n}\\beta_{m-n}\\beta_{r}^2=\\beta_{m+r}\\beta_{m-r}\\beta_{n}^2-\\beta_{n+r}\\beta_{n-r}\\beta_{m}^2$\nfor $m\\geq n\\geq r$. We will prove that the recurrence relation above holds for\n$\\{\\beta_n\\}_{n\\geq 0}$ under some conditions on the indexes $m$, $n$, and $r$.\n"}
+{"abstract": "  In this article we study smooth asymptotically conical self shrinkers in\n$\\mathbb{R}^4$ with Colding-Minicozzi entropy bounded above by $\\Lambda_{1}$.\n"}
+{"abstract": "  We study the irreversibility \\`a la Maxwell from a quantum point of view,\ninvolving an arbitrarily large ensemble of independent particles, with a\ndaemonic potential that is capable of inducing asymmetries in the evolution,\nexhibiting new perspectives on how Maxwell's apparent paradox is posed and\nresolved dynamically. In addition, we design an electromagnetic cavity, to\nwhich dielectrics are added, fulfilling the function of a daemon. Thereby, this\nphysical system is capable of cooling and ordering incident electromagnetic\nradiation. This setting can be generalized to many types of waves, without\nrelying on the concept of measurement in quantum mechanics.\n"}
+{"abstract": "  We study the inflationary period driven by a fermionic field which is\nnon-minimally coupled to gravity in the context of the constant-roll approach.\nWe consider the model for a specific form of coupling and perform the\ncorresponding inflationary analysis. By comparing the result with the Planck\nobservations coming from CMB anisotropies, we find the observational\nconstraints on the parameters space of the model and also the predictions the\nmodel. We find that the values of $r$ and $n_{s}$ for $-1.5<\\beta\\leq-0.9$ are\nin good agreement with the observations when $|\\xi|=0.1$ and $N=60$.\n"}
+{"abstract": "  Organic semiconductor/ferromagnetic bilayer thin films can exhibit novel\nproperties due to the formation of the spinterface at the interface.\nBuckminsterfullerene (C60) has been shown to exhibit ferromagnetism at the\ninterface when it is placed next to a ferromagnet (FM) such as Fe or Co.\nFormation of spinterface occurs due to the orbital hybridization and spin\npolarized charge transfer at the interface. In this work, we have demonstrated\nthat one can enhance the magnetic anisotropy of the low Gilbert damping alloy\nCoFeB by introducing a C60 layer. We have shown that anisotropy increases by\nincreasing the thickness of C60 which might be a result of the formation of\nspinterface. However, the magnetic domain structure remains same in the bilayer\nsamples as compared to the reference CoFeB film.\n"}
+{"abstract": "  The current trends towards vehicle-sharing, electrification, and autonomy are\npredicted to transform mobility. Combined appropriately, they have the\npotential of significantly improving urban mobility. However, what will come\nafter most vehicles are shared, electric, and autonomous remains an open\nquestion, especially regarding the interactions between vehicles and how these\ninteractions will impact system-level behaviour. Inspired by nature and\nsupported by swarm robotics and vehicle platooning models, this paper proposes\na future mobility in which shared, electric, and autonomous vehicles behave as\na bio-inspired collaborative system. The collaboration between vehicles will\nlead to a system-level behaviour analogous to natural swarms. Natural swarms\ncan divide tasks, cluster, build together, or transport cooperatively. In this\nfuture mobility, vehicles will cluster by connecting either physically or\nvirtually, which will enable the possibility of sharing energy, data or\ncomputational power, provide services or transfer cargo, among others. Vehicles\nwill collaborate either with vehicles that are part of the same fleet, or with\nany other vehicle on the road, by finding mutualistic relationships that\nbenefit both parties. The field of swarm robotics has already translated some\nof the behaviours from natural swarms to artificial systems and, if we further\ntranslate these concepts into urban mobility, exciting ideas emerge. Within\nmobility-related research, the coordinated movement proposed in vehicle\nplatooning models can be seen as a first step towards collaborative mobility.\nThis paper contributes with the proposal of a framework for future mobility\nthat integrates current research and mobility trends in a novel and unique way.\n"}
+{"abstract": "  Coronavirus Disease 2019 (COVID-19) demonstrated the need for accurate and\nfast diagnosis methods for emergent viral diseases. Soon after the emergence of\nCOVID-19, medical practitioners used X-ray and computed tomography (CT) images\nof patients' lungs to detect COVID-19. Machine learning methods are capable of\nimproving the identification accuracy of COVID-19 in X-ray and CT images,\ndelivering near real-time results, while alleviating the burden on medical\npractitioners. In this work, we demonstrate the efficacy of a support vector\nmachine (SVM) classifier, trained with a combination of deep convolutional and\nhandcrafted features extracted from X-ray chest scans. We use this combination\nof features to discriminate between healthy, common pneumonia, and COVID-19\npatients. The performance of the combined feature approach is compared with a\nstandard convolutional neural network (CNN) and the SVM trained with\nhandcrafted features. We find that combining the features in our novel\nframework improves the performance of the classification task compared to the\nindependent application of convolutional and handcrafted features.\nSpecifically, we achieve an accuracy of 0.988 in the classification task with\nour combined approach compared to 0.963 and 0.983 accuracy for the handcrafted\nfeatures with SVM and CNN respectively.\n"}
+{"abstract": "  A convex polygon $Q$ is inscribed in a convex polygon $P$ if every side of\n$P$ contains at least one vertex of $Q$. We present algorithms for finding a\nminimum area and a minimum perimeter convex polygon inscribed in any given\nconvex $n$-gon in $O(n)$ and $O(n^3)$ time, respectively. We also investigate\nother variants of this problem.\n"}
+{"abstract": "  We consider conformal deformations within a class of incomplete Riemannian\nmetrics which generalize conic orbifold singularities by allowing both warping\nand any compact manifold (not just quotients of the sphere) to be the \"link\" of\nthe singular set. Within this class of \"conic metrics,\" we determine\nobstructions to the existence of conformal deformations to constant scalar\ncurvature of any sign (positive, negative, or zero). For conic metrics with\nnegative scalar curvature, we determine sufficient conditions for the existence\nof a conformal deformation to a conic metric with constant scalar curvature -1;\nmoreover, we show that this metric is unique within its conformal class of\nconic metrics. Our work is in dimensions three and higher.\n"}
+{"abstract": "  We study the robustness of reinforcement learning (RL) with adversarially\nperturbed state observations, which aligns with the setting of many adversarial\nattacks to deep reinforcement learning (DRL) and is also important for rolling\nout real-world RL agent under unpredictable sensing noise. With a fixed agent\npolicy, we demonstrate that an optimal adversary to perturb state observations\ncan be found, which is guaranteed to obtain the worst case agent reward. For\nDRL settings, this leads to a novel empirical adversarial attack to RL agents\nvia a learned adversary that is much stronger than previous ones. To enhance\nthe robustness of an agent, we propose a framework of alternating training with\nlearned adversaries (ATLA), which trains an adversary online together with the\nagent using policy gradient following the optimal adversarial attack framework.\nAdditionally, inspired by the analysis of state-adversarial Markov decision\nprocess (SA-MDP), we show that past states and actions (history) can be useful\nfor learning a robust agent, and we empirically find a LSTM based policy can be\nmore robust under adversaries. Empirical evaluations on a few continuous\ncontrol environments show that ATLA achieves state-of-the-art performance under\nstrong adversaries. Our code is available at\nhttps://github.com/huanzhang12/ATLA_robust_RL.\n"}
+{"abstract": "  A general class of models is proposed that is able to estimate the whole\npredictive distribution of a dependent variable $Y$ given a vector of\nexplanatory variables $\\xb$. The models exploit that the strength of\nexplanatory variables to distinguish between low and high values of the\ndependent variable may vary across the thresholds that are used to define low\nand high. Simple linear versions of the models are generalizations of classical\nlinear regression models but also of widely used ordinal regression models.\nThey allow to visualize the effect of explanatory variables in the form of\nparameter functions. More general models are based on efficient nonparametric\napproaches like random forests, which are more flexible and are strong\nprediction tools. A general estimation method is given that can use all the\nestimation tools that have been proposed for binary regression, including\nselection methods like the lasso or elastic net. For linearly structured models\nmaximum likelihood estimates are derived. The usefulness of the models is\nillustrated by simulations and several real data set.\n"}
+{"abstract": "  Synchrotron radiation from hot gas near a black hole results in a polarized\nimage. The image polarization is determined by effects including the\norientation of the magnetic field in the emitting region, relativistic motion\nof the gas, strong gravitational lensing by the black hole, and parallel\ntransport in the curved spacetime. We explore these effects using a simple\nmodel of an axisymmetric, equatorial accretion disk around a Schwarzschild\nblack hole. By using an approximate expression for the null geodesics derived\nby Beloborodov (2002) and conservation of the Walker-Penrose constant, we\nprovide analytic estimates for the image polarization. We test this model using\ncurrently favored general relativistic magnetohydrodynamic simulations of M87*,\nusing ring parameters given by the simulations. For a subset of these with\nmodest Faraday effects, we show that the ring model broadly reproduces the\npolarimetric image morphology. Our model also predicts the polarization\nevolution for compact flaring regions, such as those observed from Sgr A* with\nGRAVITY. With suitably chosen parameters, our simple model can reproduce the\nEVPA pattern and relative polarized intensity in Event Horizon Telescope images\nof M87*. Under the physically motivated assumption that the magnetic field\ntrails the fluid velocity, this comparison is consistent with the clockwise\nrotation inferred from total intensity images.\n"}
+{"abstract": "  This paper is concerned with a novel deep learning method for variational\nproblems with essential boundary conditions. To this end, we first reformulate\nthe original problem into a minimax problem corresponding to a feasible\naugmented Lagrangian, which can be solved by the augmented Lagrangian method in\nan infinite dimensional setting. Based on this, by expressing the primal and\ndual variables with two individual deep neural network functions, we present an\naugmented Lagrangian deep learning method for which the parameters are trained\nby the stochastic optimization method together with a projection technique.\nCompared to the traditional penalty method, the new method admits two main\nadvantages: i) the choice of the penalty parameter is flexible and robust, and\nii) the numerical solution is more accurate in the same magnitude of\ncomputational cost. As typical applications, we apply the new approach to solve\nelliptic problems and (nonlinear) eigenvalue problems with essential boundary\nconditions, and numerical experiments are presented to show the effectiveness\nof the new method.\n"}
+{"abstract": "  We propose in this work a multi-view learning approach for audio and music\nclassification. Considering four typical low-level representations (i.e.\ndifferent views) commonly used for audio and music recognition tasks, the\nproposed multi-view network consists of four subnetworks, each handling one\ninput types. The learned embedding in the subnetworks are then concatenated to\nform the multi-view embedding for classification similar to a simple\nconcatenation network. However, apart from the joint classification branch, the\nnetwork also maintains four classification branches on the single-view\nembedding of the subnetworks. A novel method is then proposed to keep track of\nthe learning behavior on the classification branches and adapt their weights to\nproportionally blend their gradients for network training. The weights are\nadapted in such a way that learning on a branch that is generalizing well will\nbe encouraged whereas learning on a branch that is overfitting will be slowed\ndown. Experiments on three different audio and music classification tasks show\nthat the proposed multi-view network not only outperforms the single-view\nbaselines but also is superior to the multi-view baselines based on\nconcatenation and late fusion.\n"}
+{"abstract": "  Following a field-theoretical approach, we study the scalar Casimir effect\nupon a perfectly conducting cylindrical shell in the presence of spontaneous\nLorentz symmetry breaking. The scalar field is modeled by a Lorentz-breaking\nextension of the theory for a real scalar quantum field in the bulk regions.\nThe corresponding Green's functions satisfying Dirichlet boundary conditions on\nthe cylindrical shell are derived explicitly. We express the Casimir pressure\n(i.e. the vacuum expectation value of the normal-normal component of the\nstress-energy tensor) as a suitable second-order differential operator acting\non the corresponding Green's functions at coincident arguments. The divergences\nare regulated by making use of zeta function techniques, and our results are\nsuccessfully compared with the Lorentz invariant case. Numerical calculations\nare carried out for the Casimir pressure as a function of the Lorentz-violating\ncoefficient, and an approximate analytical expression for the force is\npresented as well. It turns out that the Casimir pressure strongly depends on\nthe Lorentz-violating coefficient and it tends to diminish the force.\n"}
+{"abstract": "  Person search aims to simultaneously localize and identify a query person\nfrom realistic, uncropped images, which can be regarded as the unified task of\npedestrian detection and person re-identification (re-id). Most existing works\nemploy two-stage detectors like Faster-RCNN, yielding encouraging accuracy but\nwith high computational overhead. In this work, we present the Feature-Aligned\nPerson Search Network (AlignPS), the first anchor-free framework to efficiently\ntackle this challenging task. AlignPS explicitly addresses the major\nchallenges, which we summarize as the misalignment issues in different levels\n(i.e., scale, region, and task), when accommodating an anchor-free detector for\nthis task. More specifically, we propose an aligned feature aggregation module\nto generate more discriminative and robust feature embeddings by following a\n\"re-id first\" principle. Such a simple design directly improves the baseline\nanchor-free model on CUHK-SYSU by more than 20% in mAP. Moreover, AlignPS\noutperforms state-of-the-art two-stage methods, with a higher speed. Code is\navailable at https://github.com/daodaofr/AlignPS\n"}
+{"abstract": "  With the growth of natural language processing techniques and demand for\nimproved software engineering efficiency, there is an emerging interest in\ntranslating intention from human languages to programming languages. In this\nsurvey paper, we attempt to provide an overview of the growing body of research\nin this space. We begin by reviewing natural language semantic parsing\ntechniques and draw parallels with program synthesis efforts. We then consider\nsemantic parsing works from an evolutionary perspective, with specific analyses\non neuro-symbolic methods, architecture, and supervision. We then analyze\nadvancements in frameworks for semantic parsing for code generation. In\nclosing, we present what we believe are some of the emerging open challenges in\nthis domain.\n"}
+{"abstract": "  Data sampling acts as a pivotal role in training deep learning models.\nHowever, an effective sampling schedule is difficult to learn due to the\ninherently high dimension of parameters in learning the sampling schedule. In\nthis paper, we propose an AutoSampling method to automatically learn sampling\nschedules for model training, which consists of the multi-exploitation step\naiming for optimal local sampling schedules and the exploration step for the\nideal sampling distribution. More specifically, we achieve sampling schedule\nsearch with shortened exploitation cycle to provide enough supervision. In\naddition, we periodically estimate the sampling distribution from the learned\nsampling schedules and perturb it to search in the distribution space. The\ncombination of two searches allows us to learn a robust sampling schedule. We\napply our AutoSampling method to a variety of image classification tasks\nillustrating the effectiveness of the proposed method.\n"}
+{"abstract": "  Standard quantum mechanics has been formulated with complex-valued\nSchrodinger equations, wave functions, operators, and Hilbert spaces. However,\nprevious work has shown possible to simulate quantum systems using only real\nnumbers by adding extra qubits and exploiting an enlarged Hilbert space. A\nfundamental question arises: are the complex numbers really necessary for the\nquantum mechanical description of nature? To answer this question, a non-local\ngame has been developed to reveal a contradiction between a multiqubit quantum\nexperiment and a player using only real numbers. Here, based on deterministic\nand high-fidelity entanglement swapping with superconducting qubits, we\nexperimentally implement the Bell-like game and observe a quantum score of\n8.09(1), which beats the real number bound of 7.66 by 43 standard deviations.\nOur results disprove the real-number description of nature and establish the\nindispensable role of complex numbers in quantum mechanics.\n"}
+{"abstract": "  We initiate the work towards a comprehensive picture of the smoothed\nsatisfaction of voting axioms, to provide a finer and more realistic foundation\nfor comparing voting rules. We adopt the smoothed social choice framework,\nwhere an adversary chooses arbitrarily correlated \"ground truth\" preferences\nfor the agents, on top of which random noises are added. We focus on\ncharacterizing the smoothed satisfaction of two well-studied voting axioms:\nCondorcet criterion and participation. We prove that for any fixed number of\nalternatives, when the number of voters $n$ is sufficiently large, the smoothed\nsatisfaction of the Condorcet criterion under a wide range of voting rules is\n$1$, $1-\\exp(-\\Theta(n))$, $\\Theta(n^{-0.5})$, $ \\exp(-\\Theta(n))$, or being\n$\\Theta(1)$ and $1-\\Theta(1)$ at the same time; and the smoothed satisfaction\nof participation is $1-\\Theta(n^{-0.5})$. Our results address open questions by\nBerg and Lepelley in 1994 for these rules, and also confirm the following\nhigh-level message: the Condorcet criterion is a bigger concern than\nparticipation under realistic models.\n"}
+{"abstract": "  The deep-learning-based image restoration and fusion methods have achieved\nremarkable results. However, the existing restoration and fusion methods paid\nlittle research attention to the robustness problem caused by dynamic\ndegradation. In this paper, we propose a novel dynamic image restoration and\nfusion neural network, termed as DDRF-Net, which is capable of solving two\nproblems, i.e., static restoration and fusion, dynamic degradation. In order to\nsolve the static fusion problem of existing methods, dynamic convolution is\nintroduced to learn dynamic restoration and fusion weights. In addition, a\ndynamic degradation kernel is proposed to improve the robustness of image\nrestoration and fusion. Our network framework can effectively combine image\ndegradation with image fusion tasks, provide more detailed information for\nimage fusion tasks through image restoration loss, and optimize image\nrestoration tasks through image fusion loss. Therefore, the stumbling blocks of\ndeep learning in image fusion, e.g., static fusion weight and specifically\ndesigned network architecture, are greatly mitigated. Extensive experiments\nshow that our method is more superior compared with the state-of-the-art\nmethods.\n"}
+{"abstract": "  In the present paper, we introduce a special function on the Drinfeld period\ndomain $\\Omega^{r}$ for $r\\geq 2$ which gives the false Eisenstein series of\nGekeler when $r=2$. We also study its functional equation and relation with\nquasi-periodic functions of a Drinfeld module as well as transcendence of its\nvalues at CM points.\n"}
+{"abstract": "  Multifractal systems usually have singularity spectra defined on bounded sets\nof H\\\"older exponents. As a consequence, their associated multifractal scaling\nexponents are expected to depend linearly upon statistical moment orders at\nhigh enough orders -- a phenomenon referred to as the {\\it{linearization\neffect}}. Motivated by general ideas taken from models of turbulent\nintermittency and focusing on the case of two-dimensional systems, we\ninvestigate the issue within the framework of Gaussian multiplicative chaos. As\nverified by means of Monte Carlo simulations, it turns out that the\nlinearization effect can be accounted for by Liouville-like random measures\ndefined in terms of upper-bounded scalar fields. The coarse-grained statistical\nproperties of Gaussian multiplicative chaos are furthermore found to be\npreserved in the linear regime of the scaling exponents. As a related\napplication, we look at the problem of turbulent circulation statistics, and\nobtain a remarkably accurate evaluation of circulation statistical moments,\nrecently determined with the help of massive numerical simulations.\n"}
+{"abstract": "  This paper outlines two approaches|based on counterexample-guided abstraction\nrefinement (CEGAR) and counterexample-guided inductive synthesis (CEGIS),\nrespectively to the automated synthesis of finite-state probabilistic models\nand programs. Our CEGAR approach iteratively partitions the design space\nstarting from an abstraction of this space and refines this by a light-weight\nanalysis of verification results. The CEGIS technique exploits critical\nsubsystems as counterexamples to prune all programs behaving incorrectly on\nthat input. We show the applicability of these synthesis techniques to\nsketching of probabilistic programs, controller synthesis of POMDPs, and\nsoftware product lines.\n"}
+{"abstract": "  The increasing performance requirements of modern applications place a\nsignificant burden on software-based packet processing. Most of today's\nsoftware input/output accelerations achieve high performance at the expense of\nreserving CPU resources dedicated to continuously poll the Network Interface\nCard. This is specifically the case with DPDK (Data Plane Development Kit),\nprobably the most widely used framework for software-based packet processing\ntoday. The approach presented in this paper, descriptively called Metronome,\nhas the dual goals of providing CPU utilization proportional to the load, and\nallowing flexible sharing of CPU resources between I/O tasks and applications.\nMetronome replaces DPDK's continuous polling with an intermittent sleep&wake\nmode, and revolves around a new multi-threaded operation, which improves\nservice continuity. Since the proposed operation trades CPU usage with\nbuffering delay, we propose an analytical model devised to dynamically adapt\nthe sleep&wake parameters to the actual traffic load, meanwhile providing a\ntarget average latency. Our experimental results show a significant reduction\nof the CPU cycles, improvements in power usage, and robustness to CPU sharing\neven when challenged with CPU-intensive applications.\n"}
+{"abstract": "  In logistics warehouse, since many objects are randomly stacked on shelves,\nit becomes difficult for a robot to safely extract one of the objects without\nother objects falling from the shelf. In previous works, a robot needed to\nextract the target object after rearranging the neighboring objects. In\ncontrast, humans extract an object from a shelf while supporting other\nneighboring objects. In this paper, we propose a bimanual manipulation planner\nbased on collapse prediction trained with data generated from a physics\nsimulator, which can safely extract a single object while supporting the other\nobject. We confirmed that the proposed method achieves more than 80% success\nrate for safe extraction by real-world experiments using a dual-arm\nmanipulator.\n"}
+{"abstract": "  For many practical computer vision applications, the learned models usually\nhave high performance on the datasets used for training but suffer from\nsignificant performance degradation when deployed in new environments, where\nthere are usually style differences between the training images and the testing\nimages. An effective domain generalizable model is expected to be able to learn\nfeature representations that are both generalizable and discriminative. In this\npaper, we design a novel Style Normalization and Restitution module (SNR) to\nsimultaneously ensure both high generalization and discrimination capability of\nthe networks. In the SNR module, particularly, we filter out the style\nvariations (e.g, illumination, color contrast) by performing Instance\nNormalization (IN) to obtain style normalized features, where the discrepancy\namong different samples and domains is reduced. However, such a process is\ntask-ignorant and inevitably removes some task-relevant discriminative\ninformation, which could hurt the performance. To remedy this, we propose to\ndistill task-relevant discriminative features from the residual (i.e, the\ndifference between the original feature and the style normalized feature) and\nadd them back to the network to ensure high discrimination. Moreover, for\nbetter disentanglement, we enforce a dual causality loss constraint in the\nrestitution step to encourage the better separation of task-relevant and\ntask-irrelevant features. We validate the effectiveness of our SNR on different\ncomputer vision tasks, including classification, semantic segmentation, and\nobject detection. Experiments demonstrate that our SNR module is capable of\nimproving the performance of networks for domain generalization (DG) and\nunsupervised domain adaptation (UDA) on many tasks. Code are available at\nhttps://github.com/microsoft/SNR.\n"}
+{"abstract": "  Multi-agent simulations provide a scalable environment for learning policies\nthat interact with rational agents. However, such policies may fail to\ngeneralize to the real-world where agents may differ from simulated\ncounterparts due to unmodeled irrationality and misspecified reward functions.\nWe introduce Epsilon-Robust Multi-Agent Simulation (ERMAS), a robust\noptimization framework for learning AI policies that are robust to such\nmultiagent sim-to-real gaps. While existing notions of multi-agent robustness\nconcern perturbations in the actions of agents, we address a novel robustness\nobjective concerning perturbations in the reward functions of agents. ERMAS\nprovides this robustness by anticipating suboptimal behaviors from other\nagents, formalized as the worst-case epsilon-equilibrium. We show empirically\nthat ERMAS yields robust policies for repeated bimatrix games and optimal\ntaxation problems in economic simulations. In particular, in the two-level RL\nproblem posed by the AI Economist (Zheng et al., 2020) ERMAS learns tax\npolicies that are robust to changes in agent risk aversion, improving social\nwelfare by up to 15% in complex spatiotemporal simulations.\n"}
+{"abstract": "  Feshbach resonances are an invaluable tool in atomic physics, enabling\nprecise control of interactions and the preparation of complex quantum phases\nof matter. Here, we theoretically analyze a solid-state analogue of a Feshbach\nresonance in two dimensional semiconductor heterostructures. In the presence of\ninter-layer electron tunneling, the scattering of excitons and electrons\noccupying different layers can be resonantly enhanced by tuning an applied\nelectric field. The emergence of an inter-layer Feshbach molecule modifies the\noptical excitation spectrum, and can be understood in terms of Fermi polaron\nformation. We discuss potential implications for the realization of correlated\nBose-Fermi mixtures in bilayer semiconductors.\n"}
+{"abstract": "  Precise in-situ measurements of the neutron flux in underground laboratories\nis crucial for direct dark matter searches, as neutron induced backgrounds can\nmimic the typical dark matter signal. The development of a novel neutron\nspectroscopy technique using Spherical Proportional Counters is investigated.\nThe detector is operated with nitrogen and is sensitive to both fast and\nthermal neutrons through the $^{14}$N(n, $\\alpha$)$^{11}$B and $^{14}$N(n,\np)$^{14}$C reactions. This method holds potential to be a safe, inexpensive,\neffective, and reliable alternative to $^3$He-based detectors. Measurements of\nfast and thermal neutrons from an Am-Be source with a Spherical Proportional\nCounter operated at pressures up to 2 bar at Birmingham are discussed.\n"}
+{"abstract": "  Electrostatic reaction inhibition in heterogeneous catalysis emerges if\ncharged reactants and products are adsorbed on the catalyst and thus repel the\napproaching reactants. In this work, we study the effects of electrostatic\ninhibition on the reaction rate of unimolecular reactions catalyzed on the\nsurface of a spherical model nanoparticle by using particle-based\nreaction-diffusion simulations. Moreover, we derive closed rate equations based\non approximate Debye-Smoluchowski rate theory, valid for diffusion-controlled\nreactions, and a modified Langmuir adsorption isotherm, relevant for\nreaction-controlled reactions, to account for electrostatic inhibition in the\nDebye-H\\\"uckel limit. We study the kinetics of reactions ranging from low to\nhigh adsorptions on the nanoparticle surface and from the surface- to\ndiffusion-controlled limits for charge valencies 1 and 2. In the\ndiffusion-controlled limit, electrostatic inhibition drastically slows down the\nreactions for strong adsorption and low ionic concentration, which is well\ndescribed by our theory. In particular, the rate decreases with adsorption\naffinity, because in this case the inhibiting products are generated at high\nrate. In the (slow) reaction-controlled limit, the effect of electrostatic\ninhibition is much weaker, as semi-quantitatively reproduced by our\nelectrostatic-modified Langmuir theory. We finally propose and verify a simple\ninterpolation formula that describes electrostatic inhibition for all reaction\nspeeds (`diffusion-influenced' reactions) in general.\n"}
+{"abstract": "  This paper addresses outdoor terrain mapping using overhead images obtained\nfrom an unmanned aerial vehicle. Dense depth estimation from aerial images\nduring flight is challenging. While feature-based localization and mapping\ntechniques can deliver real-time odometry and sparse points reconstruction, a\ndense environment model is generally recovered offline with significant\ncomputation and storage. This paper develops a joint 2D-3D learning approach to\nreconstruct local meshes at each camera keyframe, which can be assembled into a\nglobal environment model. Each local mesh is initialized from sparse depth\nmeasurements. We associate image features with the mesh vertices through camera\nprojection and apply graph convolution to refine the mesh vertices based on\njoint 2-D reprojected depth and 3-D mesh supervision. Quantitative and\nqualitative evaluations using real aerial images show the potential of our\nmethod to support environmental monitoring and surveillance applications.\n"}
+{"abstract": "  Intermediate band solar cells (IBSCs) pursue the increase in efficiency by\nabsorbing below-bandgap energy photons while preserving the output voltage.\nExperimental IBSCs based on quantum dots have already demonstrated that both\nbelow-bandgap photon absorption and the output voltage preservation, are\npossible. However, the experimental work has also revealed that the\nbelow-bandgap absorption of light is weak and insufficient to boost the\nefficiency of the solar cells. The objective of this work is to contribute to\nthe study of this absorption by manufacturing and characterizing a quantum dot\nintermediate band solar cell with a single quantum dot layer with and without\nlight trapping elements. Using one-dimensional substrate texturing, our results\nshow a three-fold increase in the absorption of below bandgap energy photons in\nthe lowest energy region of the spectrum, a region not previously explored\nusing this approach. Furthermore, we also measure, at 9K, a distinguished split\nof quasi-Fermi levels between the conduction and intermediate bands, which is a\nnecessary condition to preserve the output voltage of the cell.\n"}
+{"abstract": "  We present a randomized $O(m \\log^2 n)$ work, $O(\\text{polylog } n)$ depth\nparallel algorithm for minimum cut. This algorithm matches the work bounds of a\nrecent sequential algorithm by Gawrychowski, Mozes, and Weimann [ICALP'20], and\nimproves on the previously best parallel algorithm by Geissmann and Gianinazzi\n[SPAA'18], which performs $O(m \\log^4 n)$ work in $O(\\text{polylog } n)$ depth.\n  Our algorithm makes use of three components that might be of independent\ninterest. Firstly, we design a parallel data structure that efficiently\nsupports batched mixed queries and updates on trees. It generalizes and\nimproves the work bounds of a previous data structure of Geissmann and\nGianinazzi and is work efficient with respect to the best sequential algorithm.\nSecondly, we design a parallel algorithm for approximate minimum cut that\nimproves on previous results by Karger and Motwani. We use this algorithm to\ngive a work-efficient procedure to produce a tree packing, as in Karger's\nsequential algorithm for minimum cuts. Lastly, we design an efficient parallel\nalgorithm for solving the minimum $2$-respecting cut problem.\n"}
+{"abstract": "  In this paper, the security-aware robust resource allocation in energy\nharvesting cognitive radio networks is considered with cooperation between two\ntransmitters while there are uncertainties in channel gains and battery energy\nvalue. To be specific, the primary access point harvests energy from the green\nresource and uses time switching protocol to send the energy and data towards\nthe secondary access point (SAP). Using power-domain non-orthogonal multiple\naccess technique, the SAP helps the primary network to improve the security of\ndata transmission by using the frequency band of the primary network. In this\nregard, we introduce the problem of maximizing the proportional-fair energy\nefficiency (PFEE) considering uncertainty in the channel gains and battery\nenergy value subject to the practical constraints. Moreover, the channel gain\nof the eavesdropper is assumed to be unknown. Employing the decentralized\npartially observable Markov decision process, we investigate the solution of\nthe corresponding resource allocation problem. We exploit multi-agent with\nsingle reward deep deterministic policy gradient (MASRDDPG) and recurrent\ndeterministic policy gradient (RDPG) methods. These methods are compared with\nthe state-of-the-art ones like multi-agent and single-agent DDPG. Simulation\nresults show that both MASRDDPG and RDPG methods, outperform the\nstate-of-the-art methods by providing more PFEE to the network.\n"}
+{"abstract": "  Nanophononic materials are promising to control the transport of sound in the\nGHz range and heat in the THz range. Here we are interested in the influence of\na dendritic shape of inclusion on acoustic attenuation. We investigate a Finite\nElement numerical simulation of the transient propagation of an acoustic\nwave-packet in 2D nanophononic materials with circular or dendritic inclusions\nperiodically distributed in matrix. By measuring the penetration length,\ndiffusivity, and instantaneous wave velocity, we find that the multi-branching\ntree-like form of dendrites provides a continuous source of phonon-interface\nscattering leading to an increasing acoustic attenuation. When the wavelength\nis far less than the inter-inclusion distance, we report a strong attenuation\nprocess in the dendritic case which can be fitted by a compressed exponential\nfunction with $\\beta>1$.\n"}
+{"abstract": "  We review the formation and evolution of fossil groups and clusters from both\nthe theoretical and the observational points of view. In the optical band,\nthese systems are dominated by the light of the central galaxy. They were\ninterpreted as old systems that had enough time to merge all the M* galaxies\nwithin the central one. During the last two decades many observational studies\nwere performed to prove the old and relaxed state of fossil systems. The\nmajority of these studies, that spans a wide range of topics including halos\nglobal scaling relations, dynamical substructures, stellar populations, and\ngalaxy luminosity functions, seem to challenge this scenario. The general\npicture that can be obtained by reviewing all the observational works is that\nthe fossil state could be transitional. Indeed, the formation of the large\nmagnitude gap observed in fossil systems could be related to internal processes\nrather than an old formation.\n"}
+{"abstract": "  Nonlinear fractional differential equations have gained a significant place\nin mathematical physics. Finding the solutions to these equations has emerged\nas a field of study that has attracted a lot of attention lately. In this work,\nsemi inverse variation method of He and the ansatz method have been applied to\nfind the soliton solutions for fractional Korteweg de Vries equation,\nfractional equal width equation, and fractional modified equal width equation\ndefined by conformable derivative of Atangana (beta derivative). These two\nmethods are effective methods employed to get the soliton solutions of these\nnonlinear equations. All of the calculations in this work have been obtained\nusing the Maple program and the solutions have been replaced in the equations\nand their accuracy has been confirmed. In addition, graphics of some of the\nsolutions are also included. The found solutions in this study have the\npotential to be useful in mathematical physics and engineering.\n"}
+{"abstract": "  Designing a speech-to-intent (S2I) agent which maps the users' spoken\ncommands to the agents' desired task actions can be challenging due to the\ndiverse grammatical and lexical preference of different users. As a remedy, we\ndiscuss a user-taught S2I system in this paper. The user-taught system learns\nfrom scratch from the users' spoken input with action demonstration, which\nensure it is fully matched to the users' way of formulating intents and their\narticulation habits. The main issue is the scarce training data due to the user\neffort involved. Existing state-of-art approaches in this setting are based on\nnon-negative matrix factorization (NMF) and capsule networks. In this paper we\ncombine the encoder of an end-to-end ASR system with the prior NMF/capsule\nnetwork-based user-taught decoder, and investigate whether pre-training\nmethodology can reduce training data requirements for the NMF and capsule\nnetwork. Experimental results show the pre-trained ASR-NMF framework\nsignificantly outperforms other models, and also, we discuss limitations of\npre-training with different types of command-and-control(C&C) applications.\n"}
+{"abstract": "  Deep reinforcement Learning for end-to-end driving is limited by the need of\ncomplex reward engineering. Sparse rewards can circumvent this challenge but\nsuffers from long training time and leads to sub-optimal policy. In this work,\nwe explore full-control driving with only goal-constrained sparse reward and\npropose a curriculum learning approach for end-to-end driving using only\nnavigation view maps that benefit from small virtual-to-real domain gap. To\naddress the complexity of multiple driving policies, we learn concurrent\nindividual policies selected at inference by a navigation system. We\ndemonstrate the ability of our proposal to generalize on unseen road layout,\nand to drive significantly longer than in the training.\n"}
+{"abstract": "  We systematically study linear and nonlinear wave propagation in a chain\ncomposed of piecewise-linear bistable springs. Such bistable systems are ideal\ntestbeds for supporting nonlinear wave dynamical features including transition\nand (supersonic) solitary waves. We show that bistable chains can support the\npropagation of subsonic wavepackets which in turn can be trapped by a\nlow-energy phase to induce energy localization. The spatial distribution of\nthese energy foci strongly affects the propagation of linear waves, typically\ncausing scattering, but, in special cases, leading to a reflectionless mode\nanalogous to the Ramsauer-Townsend (RT) effect. Further, we show that the\npropagation of nonlinear waves can spontaneously generate or remove additional\nfoci, which act as effective \"impurities\". This behavior serves as a novel\nmechanism for reversibly programming the dynamic response of bistable chains.\n"}
+{"abstract": "  Today we have quite stringent constraints on possible violations of the Weak\nEquivalence Principle from the comparison of the acceleration of test-bodies of\ndifferent composition in Earth's gravitational field. In the present paper, we\npropose a test of the Weak Equivalence Principle in the strong gravitational\nfield of black holes. We construct a relativistic reflection model in which\neither the massive particles of the gas of the accretion disk or the photons\nemitted by the disk may not follow the geodesics of the spacetime. We employ\nour model to analyze the reflection features of a NuSTAR spectrum of the black\nhole binary EXO 1846-031 and we constrain two parameters that quantify a\npossible violation of the Weak Equivalence Principle by massive particles and\nX-ray photons, respectively.\n"}
+{"abstract": "  Internet of Things (IoT) allowed smart homes to improve the quality and the\ncomfort of our daily lives. However, these conveniences introduced several\nsecurity concerns that increase rapidly. IoT devices, smart home hubs, and\ngateway raise various security risks. The smart home gateways act as a\ncentralized point of communication between the IoT devices, which can create a\nbackdoor into network data for hackers. One of the common and effective ways to\ndetect such attacks is intrusion detection in the network traffic. In this\npaper, we proposed an intrusion detection system (IDS) to detect anomalies in a\nsmart home network using a bidirectional long short-term memory (BiLSTM) and\nconvolutional neural network (CNN) hybrid model. The BiLSTM recurrent behavior\nprovides the intrusion detection model to preserve the learned information\nthrough time, and the CNN extracts perfectly the data features. The proposed\nmodel can be applied to any smart home network gateway.\n"}
+{"abstract": "  In this paper, a dynamical process in a statistical thermodynamic system of\nspins exhibiting a phase transition is described on a contact manifold, where\nsuch a dynamical process is a process that a metastable equilibrium state\nevolves into the most stable symmetry broken equilibrium state. Metastable and\nequilibrium states in the symmetry broken phase or ordered phase are assumed to\nbe described as pruned projections of Legendre submanifolds of contact\nmanifolds, where these pruned projections of the submanifolds express\nhysteresis and pseudo-free energy curves. Singularities associated with phase\ntransitions are naturally arose in this framework as has been suggested by\nLegendre singularity theory. Then a particular contact Hamiltonian vector field\nis proposed so that a pruned segment of the projected Legendre submanifold is a\nstable fixed point set in a region of a contact manifold, and that another\npruned segment is a unstable fixed point set. This contact Hamiltonian vector\nfield is identified with a dynamical process departing from a metastable\nequilibrium state to the most stable equilibrium one. To show the statements\nabove explicitly an Ising type spin model with long-range interactions, called\nthe Husimi-Temperley model, is focused, where this model exhibits a phase\ntransition.\n"}
+{"abstract": "  Face presentation attack detection plays a critical role in the modern face\nrecognition pipeline. A face presentation attack detection model with good\ngeneralization can be obtained when it is trained with face images from\ndifferent input distributions and different types of spoof attacks. In reality,\ntraining data (both real face images and spoof images) are not directly shared\nbetween data owners due to legal and privacy issues. In this paper, with the\nmotivation of circumventing this challenge, we propose a Federated Face\nPresentation Attack Detection (FedPAD) framework that simultaneously takes\nadvantage of rich fPAD information available at different data owners while\npreserving data privacy. In the proposed framework, each data center locally\ntrains its own fPAD model. A server learns a global fPAD model by iteratively\naggregating model updates from all data centers without accessing private data\nin each of them. To equip the aggregated fPAD model in the server with better\ngeneralization ability to unseen attacks from users, following the basic idea\nof FedPAD, we further propose a Federated Generalized Face Presentation Attack\nDetection (FedGPAD) framework. A federated domain disentanglement strategy is\nintroduced in FedGPAD, which treats each data center as one domain and\ndecomposes the fPAD model into domain-invariant and domain-specific parts in\neach data center. Two parts disentangle the domain-invariant and\ndomain-specific features from images in each local data center, respectively. A\nserver learns a global fPAD model by only aggregating domain-invariant parts of\nthe fPAD models from data centers and thus a more generalized fPAD model can be\naggregated in server. We introduce the experimental setting to evaluate the\nproposed FedPAD and FedGPAD frameworks and carry out extensive experiments to\nprovide various insights about federated learning for fPAD.\n"}
+{"abstract": "  We present how we formalize the waiting tables task in a restaurant as a\nrobot planning problem. This formalization was used to test our recently\ndeveloped algorithms that allow for optimal planning for achieving multiple\nindependent tasks that are partially observable and evolve over time [1], [2].\n"}
+{"abstract": "  Recently, a number of backdoor attacks against Federated Learning (FL) have\nbeen proposed. In such attacks, an adversary injects poisoned model updates\ninto the federated model aggregation process with the goal of manipulating the\naggregated model to provide false predictions on specific adversary-chosen\ninputs. A number of defenses have been proposed; but none of them can\neffectively protect the FL process also against so-called multi-backdoor\nattacks in which multiple different backdoors are injected by the adversary\nsimultaneously without severely impacting the benign performance of the\naggregated model. To overcome this challenge, we introduce FLGUARD, a poisoning\ndefense framework that is able to defend FL against state-of-the-art backdoor\nattacks while simultaneously maintaining the benign performance of the\naggregated model. Moreover, FL is also vulnerable to inference attacks, in\nwhich a malicious aggregator can infer information about clients' training data\nfrom their model updates. To thwart such attacks, we augment FLGUARD with\nstate-of-the-art secure computation techniques that securely evaluate the\nFLGUARD algorithm. We provide formal argumentation for the effectiveness of our\nFLGUARD and extensively evaluate it against known backdoor attacks on several\ndatasets and applications (including image classification, word prediction, and\nIoT intrusion detection), demonstrating that FLGUARD can entirely remove\nbackdoors with a negligible effect on accuracy. We also show that private\nFLGUARD achieves practical runtimes.\n"}
+{"abstract": "  To demonstrate the ability in standard arithmetic operations to perform a\nvariety of digit manipulation tasks, a closed-form representation of the Conway\nBase-13 Function over the integers is given.\n"}
+{"abstract": "  Missing data is a challenge in many applications, including intelligent\ntransportation systems (ITS). In this paper, we study traffic speed and travel\ntime estimations in ITS, where portions of the collected data are missing due\nto sensor instability and communication errors at collection points. These\npractical issues can be remediated by missing data analysis, which are mainly\ncategorized as either statistical or machine learning(ML)-based approaches.\nStatistical methods require the prior probability distribution of the data\nwhich is unknown in our application. Therefore, we focus on an ML-based\napproach, Multi-Directional Recurrent Neural Network (M-RNN). M-RNN utilizes\nboth temporal and spatial characteristics of the data. We evaluate the\neffectiveness of this approach on a TomTom dataset containing spatio-temporal\nmeasurements of average vehicle speed and travel time in the Greater Toronto\nArea (GTA). We evaluate the method under various conditions, where the results\ndemonstrate that M-RNN outperforms existing solutions,e.g., spline\ninterpolation and matrix completion, by up to 58% decreases in Root Mean Square\nError (RMSE).\n"}
+{"abstract": "  When baryon-quark continuity is formulated in terms of a topology change\nwithout invoking \"explicit \" QCD degrees of freedom at a density higher than\ntwice the nuclear matter density $n_0$ the core of massive compact stars can be\ndescribed in terms of fractionally charged particles, behaving neither like\npure baryons nor deconfined quarks. Hidden symmetries, both local gauge and\npseudo-conformal (or broken scale), lead to the pseudo-conformal (PC) sound\nvelocity $v_{pcs}^2/c^2\\approx 1/3$ at $\\gsim 3n_0$ in compact stars. We argue\nthese symmetries are \"emergent\" from strong nuclear correlations and conjecture\nthat they reflect hidden symmetries in QCD proper exposed by nuclear\ncorrelations. We establish a possible link between the quenching of $g_A$ in\nsuperallowed Gamow-Teller transitions in nuclei and the precocious onset at\n$n\\gsim 3n_0$ of the PC sound velocity predicted at the dilaton limit fixed\npoint. We propose that bringing in explicit quark degrees of freedom as is done\nin terms of the \"quarkyonic\" and other hybrid hadron-quark structure and our\ntopology-change strategy represent the \"hadron-quark duality\" formulated in\nterms of the Cheshire-Cat mechanism~\\cite{CC} for the smooth cross-over between\nhadrons and quarks. Confrontation with currently available experimental\nobservations is discussed to support this notion.\n"}
+{"abstract": "  The particle momentum anisotropy ($v_n$) produced in relativistic nuclear\ncollisions is considered to be a response of the initial geometry or the\nspatial anisotropy $\\epsilon_n$ of the system formed in these collisions. The\nlinear correlation between $\\epsilon_n$ and $v_n$ quantifies the efficiency at\nwhich the initial spatial eccentricity is converted to final momentum\nanisotropy in heavy ion collisions. We study the transverse momentum, collision\ncentrality, and beam energy dependence of this correlation for different\ncharged particles using a hydrodynamical model framework. The ($\\epsilon_n\n-v_n$) correlation is found to be stronger for central collisions and also for\nn=2 compared to that for n=3 as expected. However, the transverse momentum\n($p_T$) dependent correlation coefficient shows interesting features which\nstrongly depends on the mass as well as $p_T$ of the emitted particle. The\ncorrelation strength is found to be larger for lighter particles in the lower\n$p_T$ region. We see that the relative fluctuation in anisotropic flow depends\nstrongly in the value of $\\eta/s$ specially in the region $p_T <1$ GeV unlike\nthe correlation coefficient which does not show significant dependence on\n$\\eta/s$.\n"}
+{"abstract": "  Multi-party computation (MPC) is promising for privacy-preserving machine\nlearning algorithms at edge networks, like federated learning. Despite their\npotential, existing MPC algorithms fail short of adapting to the limited\nresources of edge devices. A promising solution, and the focus of this work, is\ncoded computation, which advocates the use of error-correcting codes to improve\nthe performance of distributed computing through \"smart\" data redundancy. In\nthis paper, we focus on coded privacy-preserving computation using Shamir's\nsecret sharing. In particular, we design novel coded privacy-preserving\ncomputation mechanisms; MatDot coded MPC (MatDot-CMPC) and PolyDot coded MPC\n(PolyDot-CMPC) by employing recently proposed coded computation algorithms;\nMatDot and PolyDot. We take advantage of the \"garbage terms\" that naturally\narise when polynomials are constructed in the design of MatDot-CMPC and\nPolyDot-CMPC to reduce the number of workers needed for privacy-preserving\ncomputation. Also, we analyze MatDot-CMPC and PolyDot-CMPC in terms of their\ncomputation, storage, communication overhead as well as recovery threshold, so\nthey can easily adapt to the limited resources of edge devices.\n"}
+{"abstract": "  In label-noise learning, the transition matrix plays a key role in building\nstatistically consistent classifiers. Existing consistent estimators for the\ntransition matrix have been developed by exploiting anchor points. However, the\nanchor-point assumption is not always satisfied in real scenarios. In this\npaper, we propose an end-to-end framework for solving label-noise learning\nwithout anchor points, in which we simultaneously optimize two objectives: the\ncross entropy loss between the noisy label and the predicted probability by the\nneural network, and the volume of the simplex formed by the columns of the\ntransition matrix. Our proposed framework can identify the transition matrix if\nthe clean class-posterior probabilities are sufficiently scattered. This is by\nfar the mildest assumption under which the transition matrix is provably\nidentifiable and the learned classifier is statistically consistent.\nExperimental results on benchmark datasets demonstrate the effectiveness and\nrobustness of the proposed method.\n"}
+{"abstract": "  We study the dimer model on subgraphs of the square lattice in which vertices\non a prescribed part of the boundary (the free boundary) are possibly\nunmatched. Each such unmatched vertex is called a monomer and contributes a\nfixed multiplicative weight $z>0$ to the total weight of the configuration. A\nbijection described by Giuliani, Jauslin and Lieb relates this model to a\nstandard dimer model but on a non-bipartite graph. The Kasteleyn matrix of this\ndimer model describes a walk with transition weights that are negative along\nthe free boundary. Yet under certain assumptions, which are in particular\nsatisfied in the infinite volume limit in the upper half-plane, we prove an\neffective, true random walk representation for the inverse Kasteleyn matrix. In\nthis case we further show that, independently of the value of $z>0$, the\nscaling limit of the height function is the Gaussian free field with Neumann\n(or free) boundary conditions, thereby answering a question of Giuliani et al.\n"}
+{"abstract": "  In this paper, we present a deep learning model that exploits the power of\nself-supervision to perform 3D point cloud completion, estimating the missing\npart and a context region around it. Local and global information are encoded\nin a combined embedding. A denoising pretext task provides the network with the\nneeded local cues, decoupled from the high-level semantics and naturally shared\nover multiple classes. On the other hand, contrastive learning maximizes the\nagreement between variants of the same shape with different missing portions,\nthus producing a representation which captures the global appearance of the\nshape. The combined embedding inherits category-agnostic properties from the\nchosen pretext tasks. Differently from existing approaches, this allows to\nbetter generalize the completion properties to new categories unseen at\ntraining time. Moreover, while decoding the obtained joint representation, we\nbetter blend the reconstructed missing part with the partial shape by paying\nattention to its known surrounding region and reconstructing this frame as\nauxiliary objective. Our extensive experiments and detailed ablation on the\nShapeNet dataset show the effectiveness of each part of the method with new\nstate of the art results. Our quantitative and qualitative analysis confirms\nhow our approach is able to work on novel categories without relying neither on\nclassification and shape symmetry priors, nor on adversarial training\nprocedures.\n"}
+{"abstract": "  In the largest, currently known, class of one Quadrillion globally consistent\nF-theory Standard Models with gauge coupling unification and no chiral exotics,\nthe vector-like spectra are counted by cohomologies of root bundles. In this\nwork, we apply a previously proposed method to identify toric base 3-folds,\nwhich are promising to establish F-theory Standard Models with exactly three\nquark-doublets and no vector-like exotics in this representation. The base\nspaces in question are obtained from triangulations of 708 polytopes. By\nstudying root bundles on the quark doublet curve\n$C_{(\\mathbf{3},\\mathbf{2})_{1/6}}$ and employing well-known results about\ndesingularizations of toric K3-surfaces, we derive a \\emph{triangulation\nindependent lower bound} $\\check{N}_P^{(3)}$ for the number $N_P^{(3)}$ of root\nbundles on $C_{(\\mathbf{3},\\mathbf{2})_{1/6}}$ with exactly three sections. The\nratio $\\check{N}_P^{(3)} / N_P$, where $N_P$ is the total number of roots on\n$C_{(\\mathbf{3},\\mathbf{2})_{1/6}}$, is largest for base spaces associated with\ntriangulations of the 8-th 3-dimensional polytope $\\Delta^\\circ_8$ in the\nKreuzer-Skarke list. For each of these $\\mathcal{O}( 10^{15} )$ 3-folds, we\nexpect that many root bundles on $C_{(\\mathbf{3},\\mathbf{2})_{1/6}}$ are\ninduced from F-theory gauge potentials and that at least every 3000th root on\n$C_{(\\mathbf{3},\\mathbf{2})_{1/6}}$ has exactly three global sections and thus\nno exotic vector-like quark-doublet modes.\n"}
+{"abstract": "  Thomson's multitaper method estimates the power spectrum of a signal from $N$\nequally spaced samples by averaging $K$ tapered periodograms. Discrete prolate\nspheroidal sequences (DPSS) are used as tapers since they provide excellent\nprotection against spectral leakage. Thomson's multitaper method is widely used\nin applications, but most of the existing theory is qualitative or asymptotic.\nFurthermore, many practitioners use a DPSS bandwidth $W$ and number of tapers\nthat are smaller than what the theory suggests is optimal because the\ncomputational requirements increase with the number of tapers. We revisit\nThomson's multitaper method from a linear algebra perspective involving\nsubspace projections. This provides additional insight and helps us establish\nnonasymptotic bounds on some statistical properties of the multitaper spectral\nestimate, which are similar to existing asymptotic results. We show using\n$K=2NW-O(\\log(NW))$ tapers instead of the traditional $2NW-O(1)$ tapers better\nprotects against spectral leakage, especially when the power spectrum has a\nhigh dynamic range. Our perspective also allows us to derive an\n$\\epsilon$-approximation to the multitaper spectral estimate which can be\nevaluated on a grid of frequencies using $O(\\log(NW)\\log\\tfrac{1}{\\epsilon})$\nFFTs instead of $K=O(NW)$ FFTs. This is useful in problems where many samples\nare taken, and thus, using many tapers is desirable.\n"}
+{"abstract": "  Low frequency gravitational waves (GWs) are keys to understanding\ncosmological inflation and super massive blackhole (SMBH) formation via\nblackhole mergers, while it is difficult to identify the low frequency GWs with\nground-based GW experiments such as the advanced LIGO (aLIGO) and VIRGO due to\nthe seismic noise. Although quasi-stellar object (QSO) proper motions produced\nby the low frequency GWs are measured by pioneering studies of very long\nbaseline interferometry (VLBI) observations with good positional accuracy, the\nlow frequency GWs are not strongly constrained by the small statistics with 711\nQSOs (Darling et al. 2018). Here we present the proper motion field map of\n400,894 QSOs of the Sloan Digital Sky Survey (SDSS) with optical {\\it Gaia}\nEDR3 proper motion measurements whose positional accuracy is $< 0.4$\nmilli-arcsec comparable with the one of the radio VLBI observations. We obtain\nthe best-fit spherical harmonics with the typical field strength of\n$\\mathcal{O}(0.1)\\, \\mu$arcsec, and place a tight constraint on the energy\ndensity of GWs, $\\Omega_{\\rm gw}=(0.964 \\pm 3.804) \\times 10^{-4}$ (95 \\%\nconfidence level), that is significantly stronger than the one of the previous\nVLBI study by two orders of magnitude at the low frequency regime of $f\n<10^{-9}\\,{\\rm [Hz]}\\simeq (30\\,{\\rm yr})^{-1}$ unexplored by the pulsar timing\ntechnique. Our upper limit rules out the existence of SMBH binary systems at\nthe distance $r < 400$ kpc from the Earth where the Milky Way center and local\ngroup galaxies are included. Demonstrating the limit given by our optical QSO\nstudy, we claim that astrometric satellite data including the forthcoming {\\it\nGaia} DR5 data with small systematic errors are powerful to constrain low\nfrequency GWs.\n"}
+{"abstract": "  When reasoning about tasks that involve large amounts of data, a common\napproach is to represent data items as objects in the Hamming space where\noperations can be done efficiently and effectively. Object similarity can then\nbe computed by learning binary representations (hash codes) of the objects and\ncomputing their Hamming distance. While this is highly efficient, each bit\ndimension is equally weighted, which means that potentially discriminative\ninformation of the data is lost. A more expressive alternative is to use\nreal-valued vector representations and compute their inner product; this allows\nvarying the weight of each dimension but is many magnitudes slower. To fix\nthis, we derive a new way of measuring the dissimilarity between two objects in\nthe Hamming space with binary weighting of each dimension (i.e., disabling\nbits): we consider a field-agnostic dissimilarity that projects the vector of\none object onto the vector of the other. When working in the Hamming space,\nthis results in a novel projected Hamming dissimilarity, which by choice of\nprojection, effectively allows a binary importance weighting of the hash code\nof one object through the hash code of the other. We propose a variational\nhashing model for learning hash codes optimized for this projected Hamming\ndissimilarity, and experimentally evaluate it in collaborative filtering\nexperiments. The resultant hash codes lead to effectiveness gains of up to +7%\nin NDCG and +14% in MRR compared to state-of-the-art hashing-based\ncollaborative filtering baselines, while requiring no additional storage and no\ncomputational overhead compared to using the Hamming distance.\n"}
+{"abstract": "  We report muon spin rotation ($\\mu$SR) and neutron diffraction on the\nrare-earth based magnets (Mo$_{2/3}$RE$_{1/3}$)$_2$AlC, also predicted as\nparent materials for 2D derivatives, where RE = Nd, Gd (only ($\\mu$SR), Tb, Dy,\nHo and Er. By crossing information between the two techniques, we determine the\nmagnetic moment ($m$), structure, and dynamic properties of all compounds. We\nfind that only for RE = Nd and Gd the moments are frozen on a microsecond time\nscale. Out of these two, the most promising compound for a potential 2D high\n($m$) magnet is the Gd variant, since the parent crystals are pristine with $m\n= 6.5 \\pm 0.5 \\mu_B$, N\\'eel temperature of $29 \\pm 1$ K, and the magnetic\nanisotropy between in and out of plane coupling is smaller than $10^{-8}$. This\nresult suggests that magnetic ordering in the Gd variant is dominated by\nin-plane magnetic interactions and should therefore remain stable if exfoliated\ninto 2D sheets.\n"}
+{"abstract": "  learning algorithms. In this paper, we review the classification algorithms\nused in the health care system (chronic diseases) and present the neural\nnetwork-based Ensemble learning method. We briefly describe the commonly used\nalgorithms and describe their critical properties. Materials and Methods: In\nthis study, modern classification algorithms used in healthcare, examine the\nprinciples of these methods and guidelines, and to accurately diagnose and\npredict chronic diseases, superior machine learning algorithms with the neural\nnetwork-based ensemble learning Is used. To do this, we use experimental data,\nreal data on chronic patients (diabetes, heart, cancer) available on the UCI\nsite. Results: We found that group algorithms designed to diagnose chronic\ndiseases can be more effective than baseline algorithms. It also identifies\nseveral challenges to further advancing the classification of machine learning\nin the diagnosis of chronic diseases. Conclusion: The results show the high\nperformance of the neural network-based Ensemble learning approach for the\ndiagnosis and prediction of chronic diseases, which in this study reached 98.5,\n99, and 100% accuracy, respectively.\n"}
+{"abstract": "  Video salient object detection (VSOD) aims to locate and segment the most\nattractive object by exploiting both spatial cues and temporal cues hidden in\nvideo sequences. However, spatial and temporal cues are often unreliable in\nreal-world scenarios, such as low-contrast foreground, fast motion, and\nmultiple moving objects. To address these problems, we propose a new framework\nto adaptively capture available information from spatial and temporal cues,\nwhich contains Confidence-guided Adaptive Gate (CAG) modules and Dual\nDifferential Enhancement (DDE) modules. For both RGB features and optical flow\nfeatures, CAG estimates confidence scores supervised by the IoU between\npredictions and the ground truths to re-calibrate the information with a gate\nmechanism. DDE captures the differential feature representation to enrich the\nspatial and temporal information and generate the fused features. Experimental\nresults on four widely used datasets demonstrate the effectiveness of the\nproposed method against thirteen state-of-the-art methods.\n"}
+{"abstract": "  We study a dynamic model of Bayesian persuasion in sequential decision-making\nsettings. An informed principal observes an external parameter of the world and\nadvises an uninformed agent about actions to take over time. The agent takes\nactions in each time step based on the current state, the principal's\nadvice/signal, and beliefs about the external parameter. The action of the\nagent updates the state according to a stochastic process. The model arises\nnaturally in many applications, e.g., an app (the principal) can advice the\nuser (the agent) on possible choices between actions based on additional\nreal-time information the app has. We study the problem of designing a\nsignaling strategy from the principal's point of view. We show that the\nprincipal has an optimal strategy against a myopic agent, who only optimizes\ntheir rewards locally, and the optimal strategy can be computed in polynomial\ntime. In contrast, it is NP-hard to approximate an optimal policy against a\nfar-sighted agent. Further, we show that if the principal has the power to\nthreaten the agent by not providing future signals, then we can efficiently\ndesign a threat-based strategy. This strategy guarantees the principal's payoff\nas if playing against an agent who is far-sighted but myopic to future signals.\n"}
+{"abstract": "  Scalable quantum information processing requires the ability to tune\nmulti-qubit interactions. This makes the precise manipulation of quantum states\nparticularly difficult for multi-qubit interactions because tunability\nunavoidably introduces sensitivity to fluctuations in the tuned parameters,\nleading to erroneous multi-qubit gate operations. The performance of quantum\nalgorithms may be severely compromised by coherent multi-qubit errors. It is\ntherefore imperative to understand how these fluctuations affect multi-qubit\ninteractions and, more importantly, to mitigate their influence. In this study,\nwe demonstrate how to implement dynamical-decoupling techniques to suppress the\ntwo-qubit analogue of the dephasing on a superconducting quantum device\nfeaturing a compact tunable coupler, a trending technology that enables the\nfast manipulation of qubit--qubit interactions. The pure-dephasing time shows\nan up to ~14 times enhancement on average when using robust sequences. The\nresults are in good agreement with the noise generated from room-temperature\ncircuits. Our study further reveals the decohering processes associated with\ntunable couplers and establishes a framework to develop gates and sequences\nrobust against two-qubit errors.\n"}
+{"abstract": "  The Davis-Chandrasekhar-Fermi (DCF) method is widely used to indirectly\nestimate the magnetic field strength from the plane-of-sky field orientation.\nIn this work, we present a set of 3D MHD simulations and synthetic polarization\nimages using radiative transfer of clustered massive star-forming regions. We\napply the DCF method on the synthetic polarization maps to investigate its\nreliability in high-density molecular clumps and dense cores where self-gravity\nis significant. We investigate the validity of the assumptions of the DCF\nmethod step by step and compare the model and estimated field strength to\nderive the correction factors for the estimated uniform and total (rms)\nmagnetic field strength at clump and core scales. The correction factors in\ndifferent situations are catalogued. We find the DCF method works well in\nstrong field cases. However, the magnetic field strength in weak field cases\ncould be significantly overestimated by the DCF method when the turbulent\nmagnetic energy is smaller than the turbulent kinetic energy. We investigate\nthe accuracy of the angular dispersion function (ADF, a modified DCF method)\nmethod on the effects that may affect the measured angular dispersion and find\nthat the ADF method correctly accounts for the ordered field structure, the\nbeam-smoothing, and the interferometric filtering, but may not be applicable to\naccount for the signal integration along the line of sight in most cases. Our\nresults suggest that the DCF methods should be avoided to be applied below\n$\\sim$0.1 pc scales if the effect of line-of-sight signal integration is not\nproperly addressed.\n"}
+{"abstract": "  Pedestrian attribute recognition aims to assign multiple attributes to one\npedestrian image captured by a video surveillance camera. Although numerous\nmethods are proposed and make tremendous progress, we argue that it is time to\nstep back and analyze the status quo of the area. We review and rethink the\nrecent progress from three perspectives. First, given that there is no explicit\nand complete definition of pedestrian attribute recognition, we formally define\nand distinguish pedestrian attribute recognition from other similar tasks.\nSecond, based on the proposed definition, we expose the limitations of the\nexisting datasets, which violate the academic norm and are inconsistent with\nthe essential requirement of practical industry application. Thus, we propose\ntwo datasets, PETA\\textsubscript{$ZS$} and RAP\\textsubscript{$ZS$}, constructed\nfollowing the zero-shot settings on pedestrian identity. In addition, we also\nintroduce several realistic criteria for future pedestrian attribute dataset\nconstruction. Finally, we reimplement existing state-of-the-art methods and\nintroduce a strong baseline method to give reliable evaluations and fair\ncomparisons. Experiments are conducted on four existing datasets and two\nproposed datasets to measure progress on pedestrian attribute recognition.\n"}
+{"abstract": "  This paper uses both experimental and numerical approaches to revisit the\nconcept of current transfer length (CTL) in second-generation high-temperature\nsuperconductor coated conductors with a current flow diverter (CFD)\narchitecture. The CFD architecture has been implemented on eight commercial\ncoated conductors samples from THEVA. In order to measure the 2-D current\ndistribution in the silver stabilizer layer of the samples, we first used a\ncustom-made array of 120 voltage taps to measure the surface potential\ndistribution. Then, the so-called \"static\" CTL ($\\lambda_s$) was extracted\nusing a semi-analytical model that fitted well the experimental data. As\ndefined in this paper, the static CTL on a 2-D domain is a generalization of\nthe definition commonly used in literature. In addition, we used a 3-D finite\nelement model to simulate the normal zone propagation in our CFD samples, in\norder to quantify their \"dynamic\" CTL ($\\lambda_d$), a new concept introduced\nin this paper and defined as the CTL observed during the propagation of a\nquenched region. The results show that, for a CFD architecture, $\\lambda_d$ is\nalways larger than $\\lambda_s$, whereas $\\lambda_d = \\lambda_s$ when the\ninterfacial resistance between the stabilizer and the superconductor layers is\nthe same everywhere. We proved that the cause of these different behaviors is\nrelated to the shape of the normal zone, which is curved for the CFD\narchitecture, and rectangular otherwise. Finally, we showed that the NZPV is\nproportional to $\\lambda_d$, not with $\\lambda_s$, which suggests that the\ndynamic CTL $\\lambda_d$ is the most general definition of the CTL and should\nalways be used when current crowding and non-uniform heat generation occurs\naround a normal zone.\n"}
+{"abstract": "  Understanding the dynamics of a quantum bit's environment is essential for\nthe realization of practical systems for quantum information processing and\nmetrology. We use single nitrogen-vacancy (NV) centers in diamond to study the\ndynamics of a disordered spin ensemble at the diamond surface. Specifically, we\ntune the density of \"dark\" surface spins to interrogate their contribution to\nthe decoherence of shallow NV center spin qubits. When the average surface spin\nspacing exceeds the NV center depth, we find that the surface spin contribution\nto the NV center free induction decay can be described by a stretched\nexponential with variable power n. We show that these observations are\nconsistent with a model in which the spatial positions of the surface spins are\nfixed for each measurement, but some of them reconfigure between measurements.\nIn particular, we observe a depth-dependent critical time associated with a\ndynamical transition from Gaussian (n=2) decay to n=2/3, and show that this\ntransition arises from the competition between the small decay contributions of\nmany distant spins and strong coupling to a few proximal spins at the surface.\nThese observations demonstrate the potential of a local sensor for\nunderstanding complex systems and elucidate pathways for improving and\ncontrolling spin qubits at the surface.\n"}
+{"abstract": "  Let $a$ and $b$ be relatively prime positive integers. In this paper the\nweighted sum $\\sum_{n\\in{\\rm NR}(a,b)}\\lambda^{n-1}n^m$ is given explicitly or\nin terms of the Apostol-Bernoulli numbers, where $m$ is a nonnegative integer,\nand ${\\rm NR}(a,b)$ denotes the set of positive integers nonrepresentable in\nterms of $a$ and $b$.\n"}
+{"abstract": "  A recently developed model chemistry (jun-Cheap) has been slightly modified\nand proposed as an effective, reliable and parameter-free scheme for the\ncomputation of accurate reaction rates with special reference to astrochemical\nand atmospheric processes. Benchmarks with different sets of state-of-the-art\nenergy barriers spanning a wide range of values show that, in the absence of\nstrong multi-reference contributions, the proposed model outperforms the most\nwell-known model chemistries, reaching a sub-chemical accuracy without any\nempirical parameter and with affordable computer times. Some test cases show\nthat geometries, energy barriers, zero point energies and thermal contributions\ncomputed at this level can be used in the framework of the master equation\napproach based on ab-initio transition state theory (AITSTME) for obtaining\naccurate reaction rates.\n"}
+{"abstract": "  Recent research in differential privacy demonstrated that (sub)sampling can\namplify the level of protection. For example, for $\\epsilon$-differential\nprivacy and simple random sampling with sampling rate $r$, the actual privacy\nguarantee is approximately $r\\epsilon$, if a value of $\\epsilon$ is used to\nprotect the output from the sample. In this paper, we study whether this\namplification effect can be exploited systematically to improve the accuracy of\nthe privatized estimate. Specifically, assuming the agency has information for\nthe full population, we ask under which circumstances accuracy gains could be\nexpected, if the privatized estimate would be computed on a random sample\ninstead of the full population. We find that accuracy gains can be achieved for\ncertain regimes. However, gains can typically only be expected, if the\nsensitivity of the output with respect to small changes in the database does\nnot depend too strongly on the size of the database. We only focus on\nalgorithms that achieve differential privacy by adding noise to the final\noutput and illustrate the accuracy implications for two commonly used\nstatistics: the mean and the median. We see our research as a first step\ntowards understanding the conditions required for accuracy gains in practice\nand we hope that these findings will stimulate further research broadening the\nscope of differential privacy algorithms and outputs considered.\n"}
+{"abstract": "  It is well known that a universal set of gates for classical computation\naugmented with the Hadamard gate results in universal quantum computing. While\nthis requires the addition of a genuine quantum element to the set of passive\nclassical gates, here we ask the following: can the same result be attained by\nadding a quantum control unit while keeping the circuit itself completely\nclassical? In other words, can we get universal quantum computation by\ncoherently controlling classical operations? In this work we provide an\naffirmative answer to this question, by considering a computational model that\nconsists of $2n$ target bits together with a set of classical gates, controlled\nby log$(2n+1)$ ancillary qubits. We show that this model is equivalent to a\nquantum computer operating on $n$ qubits. Furthermore, we show that even a\nprimitive computer that is capable of implementing only SWAP gates, can be\nlifted to universal quantum computing, if aided with an appropriate quantum\ncontrol of logarithmic size. Our results thus exemplify the information\nprocessing power brought forth by the quantum control system.\n"}
+{"abstract": "  We establish new examples of augmentations of Legendrian twist knots that\ncannot be induced by orientable Lagrangian fillings. To do so, we use a version\nof the Seidel-Ekholm-Dimitroglou Rizell isomorphism with local coefficients to\nshow that any Lagrangian filling point in the augmentation variety of a\nLegendrian knot must lie in the injective image of an algebraic torus with\ndimension equal to the first Betti number of the filling. This is a\nFloer-theoretic version of a result from microlocal sheaf theory. For the\naugmentations in question, we show that no such algebraic torus can exist.\n"}
+{"abstract": "  Departure time choice models play a crucial role in determining the traffic\nload in transportation systems. This paper introduces a new framework to model\nand analyze the departure time user equilibrium (DTUE) problem based on the\nso-called Mean Field Games (MFGs) theory. The proposed framework is the\ncombination of two main components including (i) the reaction of travelers to\nthe traffic congestion by choosing their departure times to optimize their\ntravel cost; and (ii) the aggregation of the actions of the travelers, which\ndetermines the system level of service. In this paper, we first present a\ncontinuous departure time choice model and investigate the equilibria of the\nsystem. Specifically, we demonstrate the existence of the equilibrium and\ncharacterize the DTUE. Then, a discrete approximation of the system is provided\nbased on deterministic differential game models to numerically obtain the\nequilibrium of the system. To examine the efficiency of the proposed model, we\ncompare it with the departure time choice models in the literature. We apply\nour framework to a standard test case and observe that the solutions obtained\nbased on our model are 5.6\\% better in terms of relative cost compared to the\nsolutions determined based on models in the literature. Moreover, our proposed\nmodel converges with less number of iterations than the reference solution\nmethod in the literature. Finally, the model is scaled up to the real test case\ncorresponding to the whole Lyon Metropolis with real demand pattern. The\nresults show that the proposed framework is able to tackle much larger test\ncase than usual to includes multiple preferred travel times and heterogeneous\ntrip lengths more accurately than existing models in the literature.\n"}
+{"abstract": "  The $S=1$ Haldane state is constructed from a product of local singlet dimers\nin the bulk and topological states at the edges of a chain. It is a fundamental\nrepresentative of topological quantum matter. Its well-known representative,\nthe quasi-one-dimensional SrNi$_2$V$_2$O$_8$ shows both conventional as well as\nunconventional magnetic Raman scattering. The former is observed as one- and\ntwo-triplet excitations with small linewidths and energies corresponding to the\nHaldane gap $\\Delta_H$ and the exchange coupling $J_c$ along the chain,\nrespectively. Well-defined magnetic quasiparticles are assumed to be stabilized\nby interchain interactions and uniaxial single-ion anisotropy. Unconventional\nscattering exists as broad continua of scattering with an intensity $I(T)$ that\nshows a mixed bosonic / fermionic statistic. Such a mixed statistic has also\nbeen observed in Kitaev spin liquids and could point to a non-Abelian symmetry.\nAs the ground state in the bulk of SrNi$_2$V$_2$O$_8$ is topologically trivial,\nwe suggest its fractionalization to be due to light-induced interchain exchange\nprocesses. These processes are supposed to be enhanced due to a proximity to an\nIsing ordered state with a quantum critical point. A comparison with\nSrCo$_2$V$_2$O$_8$, the $S=1/2$ analogue to our title compound, supports these\nstatements.\n"}
+{"abstract": "  We present a \"learning to learn\" approach for automatically constructing\nwhite-box classification loss functions that are robust to label noise in the\ntraining data. We parameterize a flexible family of loss functions using Taylor\npolynomials, and apply evolutionary strategies to search for noise-robust\nlosses in this space. To learn re-usable loss functions that can apply to new\ntasks, our fitness function scores their performance in aggregate across a\nrange of training dataset and architecture combinations. The resulting\nwhite-box loss provides a simple and fast \"plug-and-play\" module that enables\neffective noise-robust learning in diverse downstream tasks, without requiring\na special training procedure or network architecture. The efficacy of our\nmethod is demonstrated on a variety of datasets with both synthetic and real\nlabel noise, where we compare favourably to previous work.\n"}
+{"abstract": "  In 2003, Deutsch and Elizalde defined bijective maps between Dyck paths which\nare beneficial in investigating some statistics distributions of Dyck paths and\npattern-avoiding permutations. In this paper, we give a generalization of the\nmaps so that they are generated by permutations in $S_{2n}$. The construction\ninduces several novel ways to partition $S_{2n}$ which give a new\ninterpretation of an existing combinatorial identity involving double\nfactorials and a new integer sequence. Although the generalization does not in\ngeneral retain bijectivity, we are able to characterize a class of permutations\nthat generates bijections and furthermore imposes an algebraic structure to a\ncertain class of bijections. As a result, we introduce statistics of a Dyck\npath involving the number of unpaired steps in some subpath whose distribution\nis identical to other well-known height statistics.\n"}
+{"abstract": "  We investigate the spectrum of linearized excitations of global vortices in\n$2+1$ dimensions. After identifying the existence of localized excitation\nmodes, we compute the decay time scale of the first two and compare the results\nto the numerical evolution of the full non-linear equations. We show\nnumerically how the interaction of vortices with an external source of\nradiation or other vortices can excite these modes dynamically. We then\nsimulate the formation of vortices in a phase transition and their interaction\nwith a thermal bath estimating the amplitudes of these modes in each case.\nThese numerical experiments indicate that even though, in principle, vortices\nare capable of storing a large amount of energy in these internal excitations,\nthis does not seem to happen dynamically. We then explore the evolution of a\nnetwork of vortices in an expanding (2+1) dimensional background, in particular\nin a radiation dominated universe. We find that vortices are still excited\nafter the course of the cosmological evolution but again the level of\nexcitation is very small. The extra energy in the vortices in these\ncosmological simulations never exceeds the $1\\%$ level of the total mass of the\ncore of the vortex.\n"}
+{"abstract": "  In this paper we show that if S is a simple classical group, a group G is\ncontained in inner-diagonal automorphisms of S and contains S, and H is a\nsolvable Hall subgroup of G, then there exists five conjugates of H, whose\nintersection is trivial.\n"}
+{"abstract": "  We study Markov-modulated affine processes (abbreviated MMAPs), a class of\nMarkov processes that are created from affine processes by allowing some of\ntheir coefficients to be a function of an exogenous Markov process. MMAPs allow\nfor richer models in various applications. At the same time MMAPs largely\npreserve the tractability of standard affine processes, as their characteristic\nfunction has a computationally convenient functional form. Our setup is a\nsubstantial generalization of earlier work, since we consider the case where\nthe generator of the exogenous process $X$ is an unbounded operator (as is the\ncase for diffusions or jump processes with infinite activity). We prove\nexistence of MMAPs via a martingale problem approach, we derive the formula for\ntheir characteristic function and we study various mathematical properties of\nMMAPs. The paper closes with a discussion of several applications of MMAPs in\nfinance.\n"}
+{"abstract": "  The vision community is witnessing a modeling shift from CNNs to\nTransformers, where pure Transformer architectures have attained top accuracy\non the major video recognition benchmarks. These video models are all built on\nTransformer layers that globally connect patches across the spatial and\ntemporal dimensions. In this paper, we instead advocate an inductive bias of\nlocality in video Transformers, which leads to a better speed-accuracy\ntrade-off compared to previous approaches which compute self-attention globally\neven with spatial-temporal factorization. The locality of the proposed video\narchitecture is realized by adapting the Swin Transformer designed for the\nimage domain, while continuing to leverage the power of pre-trained image\nmodels. Our approach achieves state-of-the-art accuracy on a broad range of\nvideo recognition benchmarks, including on action recognition (84.9 top-1\naccuracy on Kinetics-400 and 86.1 top-1 accuracy on Kinetics-600 with ~20x less\npre-training data and ~3x smaller model size) and temporal modeling (69.6 top-1\naccuracy on Something-Something v2). The code and models will be made publicly\navailable at https://github.com/SwinTransformer/Video-Swin-Transformer.\n"}
+{"abstract": "  As an autonomous vehicles, Unmanned Aerial Vehicles (UAVs) are subjected to\nseveral challenges. One of the challenges is for UAV to be able to avoid\ncollision. Many collision avoidance methods have been proposed to address this\nissue. Furthermore, in a multi-UAV system, it is also important to address\ncommunication issue among UAVs for cooperation and collaboration. This issue\ncan be addressed by setting up an ad-hoc network among UAVs. There is also a\nneed to consider the challenges in the deployment of UAVs, as well as, in the\ndevelopment of collision avoidance methods and the establishment of\ncommunication for cooperation and collaboration in a multi-UAV system. In this\npaper, we present general challenges in the deployment of UAV and comparison of\nUAV communication services based on its operating frequency. We also present\nmajor collision avoidance approaches, and specifically discuss collision\navoidance approaches that are suitable for indoor applications. We also present\nthe Flying Ad-hoc Networks (FANET) network architecture, communication and\nrouting protocols for each Open System Interconnection (OSI) communication\nlayers.\n"}
+{"abstract": "  Purpose: To update and extend the Carleton Laboratory for Radiotherapy\nPhysics (CLRP) Eye Plaque (EP) dosimetry database for low-energy\nphoton-emitting brachytherapy sources using egs_brachy, an open-source EGSnrc\napplication. The previous database, CLRP_EPv1, contained datasets for the\nCollaborative Ocular Melanoma Study (COMS) plaques (2008). The new database,\nCLRP EPv2, consists of newly-calculated 3D dose distributions for 17 plaques [8\nCOMS, 5 Eckert & Ziegler BEBIG, and 4 other representative models] for Pd-103,\nI-125, and Cs-131 seeds.\n  Methods: Plaque models are developed with egs_brachy, based on\npublished/manufacturer dimensions and material data. The BEBIG plaques are\nidentical in dimensions to COMS plaques but differ in elemental composition\nand/or density. Eye plaques and seeds are simulated at the centre of\nfull-scatter water phantoms, scoring in (0.05 cm)^3 voxels spanning the eye for\nscenarios: (i) HOMO: simulated TG43 conditions; (ii) HETERO: eye plaques and\nseeds fully modelled; (iii) HETsi (BEBIG only): one seed is active at a time\nwith other seed geometries present but not emitting photons (inactive). For\nvalidation, doses are compared to those from CLRP_EPv1 and published data.\n  Data Format and Access: Data are available at https://physics.carleton.ca/\nclrp/eye_plaque_v2, http://doi.org/10.22215/clrp/EPv2. The data consist of 3D\ndose distributions (text-based EGSnrc 3ddose file) and graphical presentations\nof the comparisons to previously published data.\n  Potential Applications: The CLRP EPv2 database provides accurate reference 3D\ndose distributions to advance ocular brachytherapy dose evaluations. The\nfully-benchmarked eye plaque models will be freely-distributed with egs brachy,\nsupporting adoption of model-based dose evaluations as recommended by TG-129,\nTG-186, and TG-221.\n"}
+{"abstract": "  In recent years, deep neural networks (DNNs) were studied as an alternative\nto traditional acoustic echo cancellation (AEC) algorithms. The proposed models\nachieved remarkable performance for the separate tasks of AEC and residual echo\nsuppression (RES). A promising network topology is a fully convolutional\nrecurrent network (FCRN) structure, which has already proven its performance on\nboth noise suppression and AEC tasks, individually. However, the combination of\nAEC, postfiltering, and noise suppression to a single network typically leads\nto a noticeable decline in the quality of the near-end speech component due to\nthe lack of a separate loss for echo estimation. In this paper, we propose a\ntwo-stage model (Y$^2$-Net) which consists of two FCRNs, each with two inputs\nand one output (Y-Net). The first stage (AEC) yields an echo estimate, which -\nas a novelty for a DNN AEC model - is further used by the second stage to\nperform RES and noise suppression. While the subjective listening test of the\nInterspeech 2021 AEC Challenge mostly yielded results close to the baseline,\nthe proposed method scored an average improvement of 0.46 points over the\nbaseline on the blind testset in double-talk on the instrumental metric DECMOS,\nprovided by the challenge organizers.\n"}
+{"abstract": "  Convolutional Neural Networks have achieved unprecedented success in image\nclassification, recognition, or detection applications. However, their\nlarge-scale deployment in embedded devices is still limited by the huge\ncomputational requirements, i.e., millions of MAC operations per layer. In this\narticle, MinConvNets where the multiplications in the forward propagation are\napproximated by minimum comparator operations are introduced. Hardware\nimplementation of minimum operation is much simpler than multipliers. Firstly,\na methodology to find approximate operations based on statistical correlation\nis presented. We show that it is possible to replace multipliers by minimum\noperations in the forward propagation under certain constraints, i.e. given\nsimilar mean and variances of the feature and the weight vectors. A modified\ntraining method which guarantees the above constraints is proposed. And it is\nshown that equivalent precision can be achieved during inference with\nMinConvNets by using transfer learning from well trained exact CNNs.\n"}
+{"abstract": "  The existence of soliton families in non-parity-time-symmetric complex\npotentials remains poorly understood, especially in two spatial dimensions. In\nthis article, we analytically investigate the bifurcation of soliton families\nfrom linear modes in one- and two-dimensional nonlinear Schr\\\"odinger equations\nwith localized Wadati-type non-parity-time-symmetric complex potentials. By\nutilizing the conservation law of the underlying non-Hamiltonian wave system,\nwe convert the complex soliton equation into a new real system. For this new\nreal system, we perturbatively construct a continuous family of low-amplitude\nsolitons bifurcating from a linear eigenmode to all orders of the small soliton\namplitude. Hence, the emergence of soliton families in these\nnon-parity-time-symmetric complex potentials is analytically explained. We also\ncompare these analytically constructed soliton solutions with high-accuracy\nnumerical solutions in both one and two dimensions, and the asymptotic accuracy\nof these perturbation solutions is confirmed.\n"}
+{"abstract": "  We study imaging of point sources with a quadrupole gravitational lens while\nfocusing on the formation and evolution of the Einstein cross formed on the\nimage sensor of an imaging telescope. We use a new type of a diffraction\nintegral that we developed to study generic, opaque, weakly aspherical\ngravitational lenses. To evaluate this integral, we use the method of\nstationary phase that yields a quartic equation with respect to a Cartesian\nprojection of the observer's position vector with respect to the vector of the\nimpact parameter. The resulting quartic equation can be solved analytically\nusing the method first published by Cardano in 1545. We find that the resulting\nsolution provides a good approximation of the electromagnetic (EM) field almost\neverywhere in the image plane, yielding the well-known astroid caustic of the\nquadrupole lens. The sole exception is the immediate vicinity of the caustic\nboundary, where a numerical treatment of the diffraction integral yields better\nresults. We also convolve the quartic solution for the EM field on the image\nplane with the point-spread function of a thin lens imaging telescope. By doing\nso, we are able to explore the direct relationship between the algebraic\nproperties of the quartic solution for the EM field, the geometry of the\nastroid caustic, and the geometry and shape of the resulting Einstein-cross\nthat appear on the image plane of the thin lens telescope. The new quartic\nsolution leads to significant improvements in numerical modeling as evaluation\nof this solution is computationally far less expensive than a direct numerical\ntreatment of the new diffraction integral. In the case of the solar\ngravitational lens (SGL), the new results drastically improve the speed of\nnumerical simulations related to sensitivity analysis performed in the context\nof high-resolution imaging of exoplanets.\n"}
+{"abstract": "  The Lipschitz constant of neural networks plays an important role in several\ncontexts of deep learning ranging from robustness certification and\nregularization to stability analysis of systems with neural network\ncontrollers. Obtaining tight bounds of the Lipschitz constant is therefore\nimportant. We introduce LipBaB, a branch and bound framework to compute\ncertified bounds of the local Lipschitz constant of deep neural networks with\nReLU activation functions up to any desired precision. We achieve this by\nbounding the norm of the Jacobians, corresponding to different activation\npatterns of the network caused within the input domain. Our algorithm can\nprovide provably exact computation of the Lipschitz constant for any p-norm.\n"}
+{"abstract": "  Understanding the fluctuations of observables is one of the main goals in\nscience, be it theoretical or experimental, quantum or classical. We\ninvestigate such fluctuations when only a subregion of the full system can be\nobserved, focusing on geometries with sharp corners. We report that the\ndependence on the opening angle is super-universal: up to a numerical\nprefactor, this function does not depend on anything, provided the system under\nstudy is uniform, isotropic, and correlations do not decay too slowly. The\nprefactor contains important physical information: we show in particular that\nit gives access to the long-wavelength limit of the structure factor. We\nillustrate our findings with several examples, including fractional quantum\nHall states, scale invariant quantum critical theories, and metals. Finally, we\ndiscuss connections with quantum entanglement, extensions to three dimensions,\nas well as experiments to probe the geometry of fluctuations.\n"}
+{"abstract": "  Interior permanent magnet synchronous machine drives are widely employed in\nelectric traction systems and various industrial processes. However, prolonged\nexposure to high temperatures while operating can demagnetize the permanent\nmagnets to the point of irreversible demagnetization. In addition, direct\nmeasurements with infrared sensors or contact-type sensors with wireless\ncommunication can be expensive and intrusive to the motor drive systems. This\npaper thus proposes a nonintrusive thermal monitoring scheme for the permanent\nmagnets inside the direct-torque-controlled interior permanent magnet\nsynchronous machines. By applying an external high-frequency rotating flux or\ntorque signal to the hysteresis torque controller in the motor drive, the\nhigh-frequency currents can be injected into the stator windings. The permanent\nmagnet temperature can thus be monitored based on the induced high-frequency\nresistance. The nonintrusive nature of the method is indicated by the\nelimination of the extra sensors and no hardware change to the existing system.\nFinally, the effectiveness of the proposed method is validated with\nexperimental results.\n"}
+{"abstract": "  In domains where users tend to develop long-term preferences that do not\nchange too frequently, the stability of recommendations is an important factor\nof the perceived quality of a recommender system. In such cases, unstable\nrecommendations may lead to poor personalization experience and distrust,\ndriving users away from a recommendation service. We propose an incremental\nlearning scheme that mitigates such problems through the dynamic modeling\napproach. It incorporates a generalized matrix form of a partial differential\nequation integrator that yields a dynamic low-rank approximation of\ntime-dependent matrices representing user preferences. The scheme allows\nextending the famous PureSVD approach to time-aware settings and significantly\nimproves its stability without sacrificing the accuracy in standard top-$n$\nrecommendations tasks.\n"}
+{"abstract": "  In a recent paper, the last three authors showed that a game-theoretic\n$p$-harmonic function $v$ is characterized by an asymptotic mean value property\nwith respect to a kind of mean value $\\nu_p^r[v](x)$ defined variationally on\nballs $B_r(x)$. In this paper, in a domain $\\Om\\subset\\RR^N$, $N\\ge 2$, we\nconsider the operator $\\mu_p^\\ve$, acting on continuous functions on\n$\\ol{\\Om}$, defined by the formula $\\mu_p^\\ve[v](x)=\\nu^{r_\\ve(x)}_p[v](x)$,\nwhere $r_\\ve(x)=\\min[\\ve,\\dist(x,\\Ga)]$ and $\\Ga$ denotes the boundary of\n$\\Omega$. We first derive various properties of $\\mu^\\ve_p$ such as continuity\nand monotonicity. Then, we prove the existence and uniqueness of a function\n$u^\\ve\\in C(\\ol{\\Om})$ satisfying the Dirichlet-type problem: $$\nu(x)=\\mu_p^\\ve[u](x) \\ \\mbox{ for every } \\ x\\in\\Om,\\quad u=g \\ \\mbox{ on } \\\n\\Ga, $$ for any given function $g\\in C(\\Ga)$. This result holds, if we assume\nthe existence of a suitable notion of barrier for all points in $\\Ga$. That\n$u^\\ve$ is what we call the \\textit{variational} $p$-harmonious function with\nDirichlet boundary data $g$, and is obtained by means of a Perron-type method\nbased on a comparison principle. \\par We then show that the family $\\{\nu^\\ve\\}_{\\ve>0}$ gives an approximation scheme for the viscosity solution $u\\in\nC(\\ol{\\Om})$ of $$ \\De_p^G u=0 \\ \\mbox{ in }\\Om, \\quad u=g \\ \\mbox{ on } \\ \\Ga,\n$$ where $\\De_p^G$ is the so-called game-theoretic (or homogeneous) $p$-Laplace\noperator. In fact, we prove that $u^\\ve$ converges to $u$, uniformly on\n$\\ol{\\Om}$ as $\\ve\\to 0$.\n"}
+{"abstract": "  We carry out a detailed large-scale data analysis of price response functions\nin the spot foreign exchange market for different years and different time\nscales. Such response functions provide quantitative information on the\ndeviation from Markovian behavior. The price response functions show an\nincrease to a maximum followed by a slow decrease as the time lag grows, in\ntrade time scale and in physical time scale, for all analyzed years.\nFurthermore, we use a price increment point (pip) bid-ask spread definition to\ngroup different foreign exchange pairs and analyze the impact of the spread in\nthe price response functions. We find that large pip spreads have a stronger\nimpact on the response. This is similar to what has been found in stock\nmarkets.\n"}
+{"abstract": "  We consider the asymmetric simple exclusion process (ASEP) on $\\mathbb{Z}$\nstarted from step initial data and obtain the exact Lyapunov exponents for\n$H_0(t)$, the integrated current of ASEP. As a corollary, we derive an explicit\nformula for the upper-tail large deviation rate function for $-H_0(t)$. Our\nresult matches with the rate function for the integrated current of the totally\nasymmetric simple exclusion process (TASEP) obtained in [Johansson\n00](arXiv:math/9903134).\n"}
+{"abstract": "  Given two-dimensional Riemannian manifolds $\\mathcal{M},\\mathcal{N}$, we\nprove a lower bound on the distortion of embeddings $\\mathcal{M} \\to\n\\mathcal{N}$, in terms of the areas' discrepancy\n$V_{\\mathcal{N}}/V_{\\mathcal{M}}$, for a certain class of distortion\nfunctionals. For $V_{\\mathcal{N}}/V_{\\mathcal{M}} \\ge 1/4$, homotheties,\nprovided they exist, are the unique energy minimizing maps attaining the bound,\nwhile for $V_{\\mathcal{N}}/V_{\\mathcal{M}} \\le 1/4$, there are non-homothetic\nminimizers. We characterize the maps attaining the bound, and construct\nexplicit non-homothetic minimizers between disks. We then prove stability\nresults for the two regimes. We end by analyzing other families of distortion\nfunctionals. In particular we characterize a family of functionals where no\nphase transition in the minimizers occurs; homotheties are the energy\nminimizers for all values of $V_{\\mathcal{N}}/V_{\\mathcal{M}}$, provided they\nexist.\n"}
+{"abstract": "  We consider nonlinear scalar conservation laws posed on a network. We\nestablish $L^1$ stability, and thus uniqueness, for weak solutions satisfying\nthe entropy condition. We apply standard finite volume methods and show\nstability and convergence to the unique entropy solution, thus establishing\nexistence of a solution in the process. Both our existence and\nstability/uniqueness theory is centred around families of stationary states for\nthe equation. In one important case -- for monotone fluxes with an upwind\ndifference scheme -- we show that the set of (discrete) stationary solutions is\nindeed sufficiently large to suit our general theory. We demonstrate the\nmethod's properties through several numerical experiments.\n"}
+{"abstract": "  Lattice thermal conductivity ($\\kappa_{lat}$) of MgSiO$_3$ postperovskite\n(MgPPv) under the Earth's lower mantle high pressure-temperature conditions is\nstudied using the phonon quasiparticle approach by combing \\textit{ab initio}\nmolecular dynamics and lattice dynamics simulations. Phonon lifetimes are\nextracted from the phonon quasiparticle calculations, and the phonon group\nvelocities are computed from the anharmonic phonon dispersions, which in\nprinciple capture full anharmonicity. It is found that throughout the lowermost\nmantle, including the D\" region, $\\kappa_{lat}$ of MgPPv is ~25% larger than\nthat of MgSiO$_3$ perovskite (MgPv), mainly due to MgPPv's higher phonon\nvelocities. Such a difference in phonon velocities between the two phases\noriginates in the MgPPv's relatively smaller primitive cell. Systematic results\nof temperature and pressure dependences of both MgPPv's and MgPv's\n$\\kappa_{lat}$ are demonstrated.\n"}
+{"abstract": "  We propose a new scheme for an improved determination of the Newtonian\ngravitational constant G and evaluate it by numerical simulations. Cold atoms\nin free fall are probed by atom interferometry measurements to characterize the\ngravitational field generated by external source masses. Two source mass\nconfigurations having different geometry and using different materials are\ncompared to identify an optimized experimental setup for the G measurement. The\neffects of the magnetic fields used to manipulate the atoms and to control the\ninterferometer phase are also characterized.\n"}
+{"abstract": "  Extended Berkeley Packet Filter (BPF) is a language and run-time system that\nallows non-superusers to extend the Linux and Windows operating systems by\ndownloading user code into the kernel. To ensure that user code is safe to run\nin kernel context, BPF relies on a static analyzer that proves properties about\nthe code, such as bounded memory access and the absence of operations that\ncrash. The BPF static analyzer checks safety using abstract interpretation with\nseveral abstract domains. Among these, the domain of tnums (tristate numbers)\nis a key domain used to reason about the bitwise uncertainty in program values.\nThis paper formally specifies the tnum abstract domain and its arithmetic\noperators. We provide the first proofs of soundness and optimality of the\nabstract arithmetic operators for tnum addition and subtraction used in the BPF\nanalyzer. Further, we describe a novel sound algorithm for multiplication of\ntnums that is more precise and efficient (runs 33% faster on average) than the\nLinux kernel's algorithm. Our tnum multiplication is now merged in the Linux\nkernel.\n"}
+{"abstract": "  We study the role of ensemble averaging in holography by exploring the\nrelation between the universal late time behavior of the spectral form factor\nand the second Renyi entropy of a thermal mixed state of the doubled system.\nBoth quantities receive contributions from wormhole saddle-points: in the\nformer case they lead to non-factorization while in the latter context they\nquantify decoherence due to the interaction with an environment. Our reasoning\nindicates that the space-time continuity responsible for non-factorization and\nspace-time continuity through entanglement are in direct competition with each\nother. In an accompanying paper, we examine this dual relationship in a general\nclass of 1D quantum systems with the help of a simple geometric path integral\nprescription.\n"}
+{"abstract": "  Recent advances in unsupervised domain adaptation have seen considerable\nprogress in semantic segmentation. Existing methods either align different\ndomains with adversarial training or involve the self-learning that utilizes\npseudo labels to conduct supervised training. The former always suffers from\nthe unstable training caused by adversarial training and only focuses on the\ninter-domain gap that ignores intra-domain knowledge. The latter tends to put\noverconfident label prediction on wrong categories, which propagates errors to\nmore samples. To solve these problems, we propose a two-stage adaptive semantic\nsegmentation method based on the local Lipschitz constraint that satisfies both\ndomain alignment and domain-specific exploration under a unified principle. In\nthe first stage, we propose the local Lipschitzness regularization as the\nobjective function to align different domains by exploiting intra-domain\nknowledge, which explores a promising direction for non-adversarial adaptive\nsemantic segmentation. In the second stage, we use the local Lipschitzness\nregularization to estimate the probability of satisfying Lipschitzness for each\npixel, and then dynamically sets the threshold of pseudo labels to conduct\nself-learning. Such dynamical self-learning effectively avoids the error\npropagation caused by noisy labels. Optimization in both stages is based on the\nsame principle, i.e., the local Lipschitz constraint, so that the knowledge\nlearned in the first stage can be maintained in the second stage. Further, due\nto the model-agnostic property, our method can easily adapt to any CNN-based\nsemantic segmentation networks. Experimental results demonstrate the excellent\nperformance of our method on standard benchmarks.\n"}
+{"abstract": "  In this paper we consider a binaural hearing aid setup, where in addition to\nthe head-mounted microphones an external microphone is available. For this\nsetup, we investigate the performance of several relative transfer function\n(RTF) vector estimation methods to estimate the direction of arrival(DOA) of\nthe target speaker in a noisy and reverberant acoustic environment. More in\nparticular, we consider the state-of-the-art covariance whitening (CW) and\ncovariance subtraction (CS) methods, either incorporating the external\nmicrophone or not, and the recently proposed spatial coherence (SC) method,\nrequiring the external microphone. To estimate the DOA from the estimated RTF\nvector, we propose to minimize the frequency-averaged Hermitian angle between\nthe estimated head-mounted RTF vector and a database of prototype head-mounted\nRTF vectors. Experimental results with stationary and moving speech sources in\na reverberant environment with diffuse-like noise show that the SC method\noutperforms the CS method and yields a similar DOA estimation accuracy as the\nCW method at a lower computational complexity.\n"}
+{"abstract": "  We show that some hard to detect properties of quadratic ODEs (eg certain\npreserved integrals and measures) can be deduced more or less algorithmically\nfrom their Kahan discretization, using Darboux Polynomials (DPs). Somewhat\nsimilar results hold for ODEs of higher degree/order using certain other\nbirational discretization methods.\n"}
+{"abstract": "  We discuss the Sherrington-Kirkpatrick mean-field version of a spin glass\nwithin the distributional zeta-function method (DZFM). In the DZFM, since the\ndominant contribution to the average free energy is written as a series of\nmoments of the partition function of the model, the spin-glass multivalley\nstructure is obtained. Also, an exact expression for the saddle points\ncorresponding to each valley and a global critical temperature showing the\nexistence of many stables or at least metastables equilibrium states is\npresented. Near the critical point we obtain analytical expressions of the\norder parameters that are in agreement with phenomenological results. We\nevaluate the linear and nonlinear susceptibility and we find the expected\nsingular behavior at the spin-glass critical temperature. Furthermore, we\nobtain a positive definite expression for the entropy and we show that\nground-state entropy tends to zero as the temperature goes to zero. We show\nthat our solution is stable for each term in the expansion. Finally, we analyze\nthe behavior of the overlap distribution, where we find a general expression\nfor each moment of the partition function.\n"}
+{"abstract": "  Astrophysical observations show complex organic molecules (COMs) in the gas\nphase of protoplanetary disks. X-rays emitted from the central young stellar\nobject (YSO) that irradiate interstellar ices in the disk, followed by the\nejection of molecules in the gas phase, are a possible route to explain the\nabundances observed in the cold regions. This process, known as X-ray\nphotodesorption, needs to be quantified for methanol-containing ices. We aim at\nexperimentally measuring X-ray photodesorption yields of methanol and its\nphoto-products from binary mixed ices: $^{13}$CO:CH$_3$OH ice and\nH$_2$O:CH$_3$OH ice. We irradiated these ices at 15 K with X-rays in the 525 -\n570 eV range. The release of species in the gas phase was monitored by\nquadrupole mass spectrometry, and photodesorption yields were derived. For\n$^{13}$CO:CH$_3$OH ice, CH$_3$OH X-ray photodesorption yield is estimated to be\n10$^{-2}$ molecule/photon at 564 eV. X-ray photodesorption of larger COMs,\nwhich can be attributed to either ethanol, dimethyl ether, and/or formic acid,\nis detected with a yield of 10$^{-3}$ molecule/photon. When methanol is mixed\nwith water, X-ray photodesorption of methanol and of the previous COMs is not\ndetected. X-ray induced chemistry, dominated by low-energy secondary electrons,\nis found to be the main mechanism that explains these results. We also provide\ndesorption yields that are applicable to protoplanetary disk environments for\nastrochemical models. The X-ray emission from YSOs should participate in the\nenrichment of the protoplanetary disk gas phase with COMs such as methanol in\nthe cold and X-ray dominated regions because of X-ray photodesorption from\nmethanol-containing ices.\n"}
+{"abstract": "  We consider fractional parabolic equations with variable coefficients and\nestablish maximal $L_{q}$-regularity in Bessel potential spaces of arbitrary\nnomnegative order. As an application, we show higher order regularity and\ninstantaneous smoothing for the fractional porous medium equation and for a\nnonlocal Kirchhoff equation.\n"}
+{"abstract": "  Cluster randomized controlled trials (cRCTs) are designed to evaluate\ninterventions delivered to groups of individuals. A practical limitation of\nsuch designs is that the number of available clusters may be small, resulting\nin an increased risk of baseline imbalance under simple randomization.\nConstrained randomization overcomes this issue by restricting the allocation to\na subset of randomization schemes where sufficient overall covariate balance\nacross comparison arms is achieved with respect to a pre-specified balance\nmetric. However, several aspects of constrained randomization for the design\nand analysis of multi-arm cRCTs have not been fully investigated. Motivated by\nan ongoing multi-arm cRCT, we provide a comprehensive evaluation of the\nstatistical properties of model-based and randomization-based tests under both\nsimple and constrained randomization designs in multi-arm cRCTs, with varying\ncombinations of design and analysis-based covariate adjustment strategies. In\nparticular, as randomization-based tests have not been extensively studied in\nmulti-arm cRCTs, we additionally develop most-powerful permutation tests under\nthe linear mixed model framework for our comparisons. Our results indicate that\nunder constrained randomization, both model-based and randomization-based\nanalyses could gain power while preserving nominal type I error rate, given\nproper analysis-based adjustment for the baseline covariates. The choice of\nbalance metrics and candidate set size and their implications on the testing of\nthe pairwise and global hypotheses are also discussed. Finally, we caution\nagainst the design and analysis of multi-arm cRCTs with an extremely small\nnumber of clusters, due to insufficient degrees of freedom and the tendency to\nobtain an overly restricted randomization space.\n"}
+{"abstract": "  Minimum spanning trees (MSTs) are used in a variety of fields, from computer\nscience to geography. Infectious disease researchers have used them to infer\nthe transmission pathway of certain pathogens. However, these are often the\nMSTs of sample networks, not population networks, and surprisingly little is\nknown about what can be inferred about a population MST from a sample MST. We\nprove that if $n$ nodes (the sample) are selected uniformly at random from a\ncomplete graph with $N$ nodes and unique edge weights (the population), the\nprobability that an edge is in the population graph's MST given that it is in\nthe sample graph's MST is $\\frac{n}{N}$. We use simulation to investigate this\nconditional probability for $G(N,p)$ graphs, Barab\\'{a}si-Albert (BA) graphs,\ngraphs whose nodes are distributed in $\\mathbb{R}^2$ according to a bivariate\nstandard normal distribution, and an empirical HIV genetic distance network.\nBroadly, results for the complete, $G(N,p)$, and normal graphs are similar, and\nresults for the BA and empirical HIV graphs are similar. We recommend that\nresearchers use an edge-weighted random walk to sample nodes from the\npopulation so that they maximize the probability that an edge is in the\npopulation MST given that it is in the sample MST.\n"}
+{"abstract": "  We study Bergman kernels $K_\\Pi$ and projections $P_\\Pi$ in unbounded planar\ndomains $\\Pi$, which are periodic in one dimension. In the case $\\Pi$ is simply\nconnected we write the kernel $K_\\Pi$ in terms of a Riemann mapping $\\varphi$\nrelated to the bounded periodic cell $\\varpi$ of the domain $\\Pi$. We also\nintroduce and adapt to the Bergman space setting the Floquet transform\ntechnique, which is a standard tool for elliptic spectral problems in periodic\ndomains. We investigate the boundedness properties of the Floquet transform\noperators in Bergman spaces and derive a general formula connecting $P_\\Pi$ to\na projection on a bounded domain. We show how this theory can be used to\nreproduce the above kernel formula for $K_\\Pi$. Finally, we consider weighted\n$L^p$-estimates for $P_\\Pi$ in periodic domains.\n"}
+{"abstract": "  The bifurcation theory of ordinary differential equations (ODEs), and its\napplication to deterministic population models, are by now well established. In\nthis article, we begin to develop a complementary theory for diffusion-like\nperturbations of dynamical systems, with the goal of understanding the space\nand time scales of fluctuations near bifurcation points of the underlying\ndeterministic system. To do so we describe the limit processes that arise in\nthe vicinity of the bifurcation point. In the present article we focus on the\none-dimensional case.\n"}
+{"abstract": "  Successful innovations achieve large geographical coverage by spreading\nacross settlements and distances. For decades, spatial diffusion has been\nargued to take place along the urban hierarchy such that the innovation first\nspreads from large to medium cities then later from medium to small cities.\nYet, the role of geographical distance, the other major factor of spatial\ndiffusion, was difficult to identify in hierarchical diffusion due to missing\ndata on spreading events. In this paper, we exploit spatial patterns of\nindividual invitations on a social media platform sent from registered users to\nnew users over the entire life cycle of the platform. This enables us to\ndisentangle the role of urban hierarchy and the role of distance by observing\nthe source and target locations of flows over an unprecedented timescale. We\ndemonstrate that hierarchical diffusion greatly overlaps with diffusion to\nclose distances and these factors co-evolve over the life cycle; thus, their\njoint analysis is necessary. Then, a regression framework is applied to\nestimate the number of invitations sent between pairs of towns by years in the\nlife cycle with the population sizes of the source and target towns, their\ncombinations, and the distance between them. We confirm that hierarchical\ndiffusion prevails initially across large towns only but emerges in the full\nspectrum of settlements in the middle of the life cycle when adoption\naccelerates. Unlike in previous gravity estimations, we find that after an\nintensifying role of distance in the middle of the life cycle a surprisingly\nweak distance effect characterizes the last years of diffusion. Our results\nstress the dominance of urban hierarchy in spatial diffusion and inform future\npredictions of innovation adoption at local scales.\n"}
+{"abstract": "  A variety of supergravity and string based models contain hidden sectors\nwhich can play a role in particle physics phenomena and in cosmology. In this\nnote we discuss the possibility that the visible sector and the hidden sectors\nin general live in different heat baths. Further, it is entirely possible that\ndark matter resides partially or wholly in hidden sectors in the form of dark\nDirac fermions, dark neutralinos or dark photons. A proper analysis of dark\nmatter and of dark forces in this case requires that one deals with a\nmulti-temperature universe. We discuss the basic formalism which includes the\nmulti-temperature nature of visible and hidden sectors in the analysis of\nphenomena observable in the visible sectors. Specifically we discuss the\napplication of the formalism for explaining the velocity dependence of dark\nmatter cross sections as one extrapolates from galaxy scales to scales of\ngalaxy clusters. Here the dark photon exchange among dark fermions can produce\nthe desired velocity dependent cross sections consistent with existing galactic\ncross section data indicating the existence of a new fifth (dark) force. We\nalso discuss the possibility that the dark photon may constitute a significant\nportion of dark matter. We demonstrate a realization of this possibility in a\nuniverse with two hidden sectors and with the visible sector and the hidden\nsectors in different heat baths which allows a satisfaction of the constraints\nthat the dark photon have a lifetime larger than the age of the universe and\nthat its relic density be consistent with Planck data. Future directions for\nfurther work are discussed.\n"}
+{"abstract": "  Intensity maps of the 21cm emission line of neutral hydrogen are lensed by\nintervening large-scale structure, similar to the lensing of the cosmic\nmicrowave background temperature map. We extend previous work by calculating\nthe lensing contribution to the full-sky 21cm bispectrum in redshift space. The\nlensing contribution tends to peak when equal-redshift fluctuations are lensed\nby a lower redshift fluctuation. At high redshift, lensing effects can become\ncomparable to the contributions from density and redshift-space distortions.\n"}
+{"abstract": "  A finite-dimensional quantum system is coupled to a bath of oscillators in\nthermal equilibrium at temperature $T>0$. We show that for fixed, small values\nof the coupling constant $\\lambda$, the true reduced dynamics of the system is\napproximated by the completely positive, trace preserving Markovian semigroup\ngenerated by the Davies-Lindblad generator. The difference between the true and\nthe Markovian dynamics is $O(|\\lambda|^{1/4})$ for all times, meaning that the\nsolution of the Gorini-Kossakowski-Sudarshan-Lindblad master equation is\napproximating the true dynamics to accuracy $O(|\\lambda|^{1/4})$ for all times.\nOur method is based on a recently obtained expansion of the full system-bath\npropagator. It applies to reservoirs with correlation functions decaying in\ntime as $1/t^{4}$ or faster, which is a significant improvement relative to the\npreviously required exponential decay.\n"}
+{"abstract": "  A pressure-driven two-layer channel flow of a Newtonian fluid with constant\nviscosity (top layer) and a fluid with a time-dependent viscosity (bottom\nlayer) is numerically investigated. The bottom layer goes through an aging\nprocess in which its viscosity increases due to the formation of internal\nstructure, which is represented by a Coussot-type relationship. The resultant\nflow dynamics is the consequence of the competition between structuration and\ndestructuration, as characterized by the dimensionless timescale for\nstructuration (tau) and the dimensionless material property (beta) of the\nbottom fluid. The development of Kelvin-Helmholtz type instabilities (roll-up\nstructures) observed in the Newtonian constant viscosity case was found to be\nsuppressed as the viscosity of the bottom layer increased over time. It is\nfound that, for the set of parameters considered in the present study, the\nbottom layer almost behaves like a Newtonian fluid with constant viscosity for\ntau greater than 10 and beta greater than 1. It is also shown that decreasing\nthe value of the Froude number stabilizes the interfacial instabilities. The\nwavelength of the interfacial wave increases as the capillary number increases.\n"}
+{"abstract": "  This memo describes NTR/TSU winning submission for Low Resource ASR challenge\nat Dialog2021 conference, language identification track.\n  Spoken Language Identification (LID) is an important step in a multilingual\nAutomated Speech Recognition (ASR) system pipeline. Traditionally, the ASR task\nrequires large volumes of labeled data that are unattainable for most of the\nworld's languages, including most of the languages of Russia. In this memo, we\nshow that a convolutional neural network with a Self-Attentive Pooling layer\nshows promising results in low-resource setting for the language identification\ntask and set up a SOTA for the Low Resource ASR challenge dataset.\n  Additionally, we compare the structure of confusion matrices for this and\nsignificantly more diverse VoxForge dataset and state and substantiate the\nhypothesis that whenever the dataset is diverse enough so that the other\nclassification factors, like gender, age etc. are well-averaged, the confusion\nmatrix for LID system bears the language similarity measure.\n"}
+{"abstract": "  Custom hardware accelerators for Deep Neural Networks are increasingly\npopular: in fact, the flexibility and performance offered by FPGAs are\nwell-suited to the computational effort and low latency constraints required by\nmany image recognition and natural language processing tasks. The gap between\nhigh-level Machine Learning frameworks (e.g., Tensorflow, Pytorch) and\nlow-level hardware design in Verilog/VHDL creates a barrier to widespread\nadoption of FPGAs, which can be overcome with the help of High-Level Synthesis.\nhls4ml is a framework that translates Deep Neural Networks into annotated C++\ncode for High-Level Synthesis, offering a complete and user-friendly design\nprocess that has been enthusiastically adopted in physics research. We analyze\nthe strengths and weaknesses of hls4ml, drafting a plan to enhance its core\nlibrary of components in order to allow more advanced optimizations, target a\nwider selection of FPGAs, and support larger Neural Network models.\n"}
+{"abstract": "  Designing new 2D systems with tunable properties is an important subject for\nscience and technology. Starting from graphene, we developed an algorithm to\nsystematically generate 2D carbon crystals belonging to the family of\ngraphdiynes (GDYs) and having different structures and sp/sp2 carbon ratio. We\nanalyze how structural and topological effects can tune the relative stability\nand the electronic behavior, to propose a rationale for the development of new\nsystems with tailored properties. A total of 26 structures have been generated,\nincluding the already known polymorphs such as {\\alpha}-, \\b{eta}- and\n{\\gamma}-GDY. Periodic density functional theory calculations have been\nemployed to optimize the 2D crystal structures and to compute the total energy,\nthe band structure, and the density of states. Relative energies with respect\nto graphene have been found to increase when the values of carbon sp/sp2 ratio\nincrease, following however different trends based on the peculiar topologies\npresent in the crystals. These topologies also influence the band structure\ngiving rise to semiconductors with a finite bandgap, zero-gap semiconductors\ndisplaying Dirac cones, or metallic systems. The different trends allow\nidentifying some topological effects as possible guidelines in the design of\nnew 2D carbon materials beyond graphene.\n"}
+{"abstract": "  A $P_4$-free graph is called a cograph. In this paper we partially\ncharacterize finite groups whose power graph is a cograph. As we will see, this\nproblem is a generalization of the determination of groups in which every\nelement has prime power order, first raised by Graham Higman in 1957 and fully\nsolved very recently.\n  First we determine all groups $G$ and $H$ for which the power graph of\n$G\\times H$ is a cograph. We show that groups whose power graph is a cograph\ncan be characterised by a condition only involving elements whose orders are\nprime or the product of two (possibly equal) primes. Some important graph\nclasses are also taken under consideration. For finite simple groups we show\nthat in most of the cases their power graphs are not cographs: the only ones\nfor which the power graphs are cographs are certain groups PSL$(2,q)$ and\nSz$(q)$ and the group PSL$(3,4)$. However, a complete determination of these\ngroups involves some hard number-theoretic problems.\n"}
+{"abstract": "  In this paper, we mainly investigate the Cauchy problem of the non-viscous\nMHD equations with magnetic diffusion. We first establish the local\nwell-posedness (existence,~uniqueness and continuous dependence) with initial\ndata $(u_0,b_0)$ in critical Besov spaces $\n{B}^{\\frac{d}{p}+1}_{p,1}\\times{B}^{\\frac{d}{p}}_{p,1}$ with $1\\leq\np\\leq\\infty$, and give a lifespan $T$ of the solution which depends on the norm\nof the Littlewood-Paley decomposition of the initial data. Then, we prove the\nglobal existence in critical Besov spaces. In particular, the results of global\nexistence also hold in Sobolev space $ C([0,\\infty);\n{H}^{s}(\\mathbb{S}^2))\\times \\Big(C([0,\\infty);{H}^{s-1}(\\mathbb{S}^2))\\cap\nL^2\\big([0,\\infty);{H}^{s}(\\mathbb{S}^2)\\big)\\Big)$ with $s>2$, when the\ninitial data satisfies $\\int_{\\mathbb{S}^2}b_0dx=0$ and\n$\\|u_0\\|_{B^1_{\\infty,1}(\\mathbb{S}^2)}+\\|b_0\\|_{{B}^{0}_{\\infty,1}(\\mathbb{S}^2)}\\leq\n\\epsilon$. It's worth noting that our results imply some large and low\nregularity initial data for the global existence, which improves considerably\nthe recent results in \\cite{weishen}.\n"}
+{"abstract": "  We consider a collection of statistically identical two-state continuous time\nMarkov chains (channels). A controller continuously selects a channel with the\nview of maximizing infinite horizon average reward. A switching cost is paid\nupon channel changes. We consider two cases: full observation (all channels\nobserved simultaneously) and partial observation (only the current channel\nobserved). We analyze the difference in performance between these cases for\nvarious policies. For the partial observation case with two channels, or an\ninfinite number of channels, we explicitly characterize an optimal threshold\nfor two sensible policies which we name \"call-gapping\" and \"cool-off\". Our\nresults present a qualitative view on the interaction of the number of\nchannels, the available information, and the switching costs.\n"}
+{"abstract": "  We study the cyclotomic exponent sequence of a numerical semigroup $S,$ and\nwe compute its values at the gaps of $S,$ the elements of $S$ with unique\nrepresentations in terms of minimal generators, and the Betti elements $b\\in S$\nfor which the set $\\{a \\in \\operatorname{Betti}(S) : a \\le_{S}b\\}$ is totally\nordered with respect to $\\le_S$ (we write $a \\le_S b$ whenever $a - b \\in S,$\nwith $a,b\\in S$). This allows us to characterize certain semigroup families,\nsuch as Betti-sorted or Betti-divisible numerical semigroups, as well as\nnumerical semigroups with a unique Betti element, in terms of their cyclotomic\nexponent sequences. Our results also apply to cyclotomic numerical semigroups,\nwhich are numerical semigroups with a finitely supported cyclotomic exponent\nsequence. We show that cyclotomic numerical semigroups with certain cyclotomic\nexponent sequences are complete intersections, thereby making progress towards\nproving the conjecture of Ciolan, Garc\\'ia-S\\'anchez and Moree (2016) stating\nthat $S$ is cyclotomic if and only if it is a complete intersection.\n"}
+{"abstract": "  Ice Ih, the common form of ice in the biosphere, contains proton disorder.\nIts proton-ordered counterpart, ice XI, is thermodynamically stable below 72 K.\nHowever, even below this temperature the formation of ice XI is kinetically\nhindered and experimentally it is obtained by doping ice with KOH. Doping\ncreates ionic defects that promote the migration of protons and the associated\nchange in proton configuration. In this article, we mimic the effect of doping\nin molecular dynamics simulations using a bias potential that enhances the\nformation of ionic defects. The recombination of the ions thus formed proceeds\nthrough fast migration of the hydroxide and results in the jump of protons\nalong a hydrogen bond loop. This provides a physical and expedite way to change\nthe proton configuration, and to accelerate diffusion in proton configuration\nspace. A key ingredient of this approach is a machine learning potential\ntrained with density functional theory data and capable of modeling molecular\ndissociation. We exemplify the usefulness of this idea by studying the\norder-disorder transition using an appropriate order parameter to distinguish\nthe proton environments in ice Ih and XI. We calculate the changes in free\nenergy, enthalpy, and entropy associated with the transition. Our estimated\nentropy agrees with experiment within the error bars of our calculation.\n"}
+{"abstract": "  In this work, we present a novel sampling-based path planning method, called\nSPRINT. The method finds solutions for high dimensional path planning problems\nquickly and robustly. Its efficiency comes from minimizing the number of\ncollision check samples. This reduction in sampling relies on heuristics that\npredict the likelihood that samples will be useful in the search process.\nSpecifically, heuristics (1) prioritize more promising search regions; (2) cull\nsamples from local minima regions; and (3) steer the search away from\npreviously observed collision states. Empirical evaluations show that our\nmethod finds shorter or comparable-length solution paths in significantly less\ntime than commonly used methods. We demonstrate that these performance gains\ncan be largely attributed to our approach to achieve sample efficiency.\n"}
+{"abstract": "  The recent gravity field measurements of Jupiter (Juno) and Saturn (Cassini)\nconfirm the existence of deep zonal flows reaching to a depth of 5\\% and 15\\%\nof the respective radius. Relating the zonal wind induced density perturbations\nto the gravity moments has become a major tool to characterise the interior\ndynamics of gas giants. Previous studies differ with respect to the assumptions\nmade on how the wind velocity relates to density anomalies, on the functional\nform of its decay with depth, and on the continuity of antisymmetric winds\nacross the equatorial plane. Most of the suggested vertical structures exhibit\na rather smooth radial decay of the zonal wind, which seems at odds with the\nobserved secular variation of the magnetic field and the prevailing geostrophy\nof the zonal winds. Moreover, the results relied on an artificial equatorial\nregularisation or ignored the equatorial discontinuity altogether. We favour an\nalternative structure, where the equatorially antisymmetric zonal wind in an\nequatorial latitude belt between $\\pm 21^\\circ$ remains so shallow that it does\nnot contribute to the gravity signal. The winds at higher latitudes suffice to\nconvincingly explain the measured gravity moments. Our results indicate that\nthe winds are geostrophic, i.e. constant along cylinders, in the outer $3000\\,$\nkm and decay rapidly below. The preferred wind structure is 50\\% deeper than\npreviously thought, agrees with the measured gravity moment, is compliant with\nthe magnetic constraints and the requirement of an adiabatic atmosphere and\nunbiased by the treatment of the equatorial discontinuity.\n"}
+{"abstract": "  Introduction: One of the most important tasks in the Emergency Department\n(ED) is to promptly identify the patients who will benefit from hospital\nadmission. Machine Learning (ML) techniques show promise as diagnostic aids in\nhealthcare. Material and methods: We investigated the following features\nseeking to investigate their performance in predicting hospital admission:\nserum levels of Urea, Creatinine, Lactate Dehydrogenase, Creatine Kinase,\nC-Reactive Protein, Complete Blood Count with differential, Activated Partial\nThromboplastin Time, D Dimer, International Normalized Ratio, age, gender,\ntriage disposition to ED unit and ambulance utilization. A total of 3,204 ED\nvisits were analyzed. Results: The proposed algorithms generated models which\ndemonstrated acceptable performance in predicting hospital admission of ED\npatients. The range of F-measure and ROC Area values of all eight evaluated\nalgorithms were [0.679-0.708] and [0.734-0.774], respectively. Discussion: The\nmain advantages of this tool include easy access, availability, yes/no result,\nand low cost. The clinical implications of our approach might facilitate a\nshift from traditional clinical decision-making to a more sophisticated model.\nConclusion: Developing robust prognostic models with the utilization of common\nbiomarkers is a project that might shape the future of emergency medicine. Our\nfindings warrant confirmation with implementation in pragmatic ED trials.\n"}
+{"abstract": "  We show that an effective action of the one-dimensional torus $\\mathbb{G}_m$\non a normal affine algebraic variety $X$ can be extended to an effective action\nof a semi-direct product $\\mathbb{G}_m\\rightthreetimes\\mathbb{G}_a$ with the\nsame general orbit closures if and only if there is a divisor $D$ on $X$ that\nconsists of $\\mathbb{G}_m$-fixed points. This result is applied to the study of\norbits of the automorphism group $\\text{Aut}(X)$ on $X$.\n"}
+{"abstract": "  We study the amplification of rotation velocity with the Sagnac\ninterferometer based on the concept of weak-value amplification. By using a\ndifferent scheme to perform the Sagnac interferometer with the probe in\nmomentum space, we have demonstrated the new weak measure protocol to detect\nthe small rotation velocity by amplifying the phase shift of the Sagnac effect.\nAt the given the maximum incident intensity of the initial spectrum, the\ndetection limit of the intensity of the spectrometer and the accuracy of\nangular velocity measurement, we can theoretical give the appropriate\npotselection and the minimum of optical path area before experiment. In\naddition, we put forward a new optical design to increase the optical path area\nand decrease the size of the interferometer to overcome the limit of instrument\nsize. Finally, our modified Sagnac's interferometer based on weak measurement\nis innovative and efficient probing the small rotation velocity signal.\n"}
+{"abstract": "  We consider the task of learning to control a linear dynamical system under\nfixed quadratic costs, known as the Linear Quadratic Regulator (LQR) problem.\nWhile model-free approaches are often favorable in practice, thus far only\nmodel-based methods, which rely on costly system identification, have been\nshown to achieve regret that scales with the optimal dependence on the time\nhorizon T. We present the first model-free algorithm that achieves similar\nregret guarantees. Our method relies on an efficient policy gradient scheme,\nand a novel and tighter analysis of the cost of exploration in policy space in\nthis setting.\n"}
+{"abstract": "  We develop a gauge covariant neural network for four dimensional non-abelian\ngauge theory, which realizes a map between rank-2 tensor valued vector fields.\nWe find that the conventional smearing procedure and gradient flow for gauge\nfields can be regarded as known neural networks, residual networks and neural\nordinal differential equations for rank-2 tensors with fixed parameters. In\nterms of machine learning context, projection or normalization functions in the\nsmearing schemes correspond to an activation function in neural networks. Using\nthe locality of the activation function, we derive the backpropagation for the\ngauge covariant neural network. Consequently, the smeared force in hybrid Monte\nCarlo (HMC) is naturally derived with the backpropagation. As a demonstration,\nwe develop the self-learning HMC (SLHMC) with covariant neural network\napproximated action for non-abelian gauge theory with dynamical fermions, and\nwe observe SLHMC reproduces results from HMC.\n"}
+{"abstract": "  For arbitrary four-dimensional quantum field theories with scalars and\nfermions, renormalisation group equations in the $\\overline{\\text{MS}}$ scheme\nare investigated at three-loop order in perturbation theory. Collecting\nliterature results, general expressions are obtained for field anomalous\ndimensions, Yukawa interactions, as well as fermion masses. The renormalisation\ngroup evolution of scalar quartic, cubic and mass terms is determined up to a\nfew unknown coefficients. The combined results are applied to compute the\nrenormalisation group evolution of the gaugeless Litim-Sannino model.\n"}
+{"abstract": "  Our work focuses on modeling security of systems from their component-level\ndesigns. Towards this goal we develop a categorical formalism to model attacker\nactions. Equipping the categorical formalism with algebras produces two\ninteresting results for security modeling. First, using the Yoneda lemma, we\nare able to model attacker reconnaissance missions. In this context, the Yoneda\nlemma formally shows us that if two system representations, one being complete\nand the other being the attacker's incomplete view, agree at every possible\ntest, then they behave the same. The implication is that attackers can still\nsuccessfully exploit the system even with incomplete information. Second, we\nmodel the possible changes that can occur to the system via an exploit. An\nexploit either manipulates the interactions between system components, for\nexample, providing the wrong values to a sensor, or changes the components\nthemselves, for example, manipulating the firmware of a global positioning\nsystem (GPS). One additional benefit of using category theory is that\nmathematical operations can be represented as formal diagrams, which is useful\nfor applying this analysis in a model-based design setting. We illustrate this\nmodeling framework using a cyber-physical system model of an unmanned aerial\nvehicle (UAV). We demonstrate and model two types of attacks (1) a rewiring\nattack, which violates data integrity, and (2) a rewriting attack, which\nviolates availability.\n"}
+{"abstract": "  Connected and Automated Hybrid Electric Vehicles have the potential to reduce\nfuel consumption and travel time in real-world driving conditions. The\neco-driving problem seeks to design optimal speed and power usage profiles\nbased upon look-ahead information from connectivity and advanced mapping\nfeatures. Recently, Deep Reinforcement Learning (DRL) has been applied to the\neco-driving problem. While the previous studies synthesize simulators and\nmodel-free DRL to reduce online computation, this work proposes a Safe\nOff-policy Model-Based Reinforcement Learning algorithm for the eco-driving\nproblem. The advantages over the existing literature are three-fold. First, the\ncombination of off-policy learning and the use of a physics-based model\nimproves the sample efficiency. Second, the training does not require any\nextrinsic rewarding mechanism for constraint satisfaction. Third, the\nfeasibility of trajectory is guaranteed by using a safe set approximated by\ndeep generative models.\n  The performance of the proposed method is benchmarked against a baseline\ncontroller representing human drivers, a previously designed model-free DRL\nstrategy, and the wait-and-see optimal solution. In simulation, the proposed\nalgorithm leads to a policy with a higher average speed and a better fuel\neconomy compared to the model-free agent. Compared to the baseline controller,\nthe learned strategy reduces the fuel consumption by more than 21\\% while\nkeeping the average speed comparable.\n"}
+{"abstract": "  In this paper, we study slow manifolds for infinite-dimensional evolution\nequations. We compare two approaches: an abstract evolution equation framework\nand a finite-dimensional spectral Galerkin approximation. We prove that the\nslow manifolds constructed within each approach are asymptotically close under\nsuitable conditions. The proof is based upon Lyapunov-Perron methods and a\ncomparison of the local graphs for the slow manifolds in scales of Banach\nspaces. In summary, our main result allows us to change between different\ncharacterizations of slow invariant manifolds, depending upon the technical\nchallenges posed by particular fast-slow systems.\n"}
+{"abstract": "  We propose a nonlocal field theory for gravity in presence of matter\nconsistent with perturbative unitarity, quantum finiteness, and other essential\nclassical properties that we are going to list below. First, the theory exactly\nreproduces the same tree-level scattering amplitudes of Einstein's gravity\ncoupled to matter insuring no violation of macro-causality. Second, all the\nexact solutions of the Einstein's theory are also exact solutions of the\nnonlocal theory. Finally, and most importantly, the linear and nonlinear\nstability analysis of the exact solutions in nonlocal gravity (with or without\nmatter) is in one to one correspondence with the same analysis in General\nRelativity. Therefore, all the exact solutions stable in the Einstein's theory\nare also stable in nonlocal gravity in presence of matter at any perturbative\norder.\n"}
+{"abstract": "  Every small monoidal category with universal (finite) joins of central\nidempotents is monoidally equivalent to the category of global sections of a\nsheaf of (sub)local monoidal categories on a topological space. Every small\nstiff monoidal category monoidally embeds into such a category of global\nsections. These representation results are functorial and subsume the\nLambek-Moerdijk-Awodey sheaf representation for toposes, the Stone\nrepresentation of Boolean algebras, and the Takahashi representation of Hilbert\nmodules as continuous fields of Hilbert spaces. Many properties of a monoidal\ncategory carry over to the stalks of its sheaf, including having a trace,\nhaving exponential objects, having dual objects, having limits of some shape,\nand the central idempotents forming a Boolean algebra.\n"}
+{"abstract": "  We propose a 3+1 Higgs Doublet Model based on the $\\Delta(27)$ family\nsymmetry supplemented by several auxiliary cyclic symmetries leading to viable\nYukawa textures for the Standard Model fermions, consistent with the observed\npattern of fermion masses and mixings. The charged fermion mass hierarchy and\nthe quark mixing pattern is generated by the spontaneous breaking of the\ndiscrete symmetries due to flavons that act as Froggatt-Nielsen fields. The\ntiny neutrino masses arise from a radiative seesaw mechanism at one loop level,\nthanks to a preserved $Z_2^{\\left( 1\\right)}$ discrete symmetry, which also\nleads to stable scalar and fermionic dark matter candidates. The leptonic\nsector features the predictive cobimaximal mixing pattern, consistent with the\nexperimental data from neutrino oscillations. For the scenario of normal\nneutrino mass hierarchy, the model predicts an effective Majorana neutrino mass\nparameter in the range $3$~meV$\\lesssim m_{\\beta\\beta}\\lesssim$ $18$ meV, which\nis within the declared range of sensitivity of modern experiments. The model\npredicts Flavour Changing Neutral Currents which constrain the model, for\ninstance Kaon mixing and $\\mu \\to e$ nuclear conversion processes, the latter\nwhich are found to be within the reach of the forthcoming experiments.\n"}
+{"abstract": "  The constant increase in the amount and complexity of information obtained\nfrom IT data networkelements, for its correct monitoring and management, is a\nreality. The same happens to data net-works in electrical systems that provide\neffective supervision and control of substations and hydro-electric plants.\nContributing to this fact is the growing number of installations and new\nenvironmentsmonitored by such data networks and the constant evolution of the\ntechnologies involved. This sit-uation potentially leads to incomplete and/or\ncontradictory data, issues that must be addressed inorder to maintain a good\nlevel of monitoring and, consequently, management of these systems. Inthis\npaper, a prototype of an expert system is developed to monitor the status of\nequipment of datanetworks in electrical systems, which deals with\ninconsistencies without trivialising the inferences.This is accomplished in the\ncontext of the remote control of hydroelectric plants and substationsby a\nRegional Operation Centre (ROC). The expert system is developed with algorithms\ndefinedupon a combination of Fuzzy logic and Paraconsistent Annotated Logic\nwith Annotation of TwoValues (PAL2v) in order to analyse uncertain signals and\ngenerate the operating conditions (faulty,normal, unstable or inconsistent /\nindeterminate) of the equipment that are identified as importantfor the remote\ncontrol of hydroelectric plants and substations. A prototype of this expert\nsystemwas installed on a virtualised server with CLP500 software (from the\nEFACEC manufacturer) thatwas applied to investigate scenarios consisting of a\nRegional (Brazilian) Operation Centre, with aGeneric Substation and a Generic\nHydroelectric Plant, representing a remote control environment.\n"}
+{"abstract": "  We present a class of one-to-one matching models with perfectly transferable\nutility. We discuss identification and inference in these separable models, and\nwe show how their comparative statics are readily analyzed.\n"}
+{"abstract": "  Digital holography is one of the most widely used label-free microscopy\ntechniques in biomedical imaging. Recovery of the missing phase information of\na hologram is an important step in holographic image reconstruction. Here we\ndemonstrate a convolutional recurrent neural network (RNN) based phase recovery\napproach that uses multiple holograms, captured at different sample-to-sensor\ndistances to rapidly reconstruct the phase and amplitude information of a\nsample, while also performing autofocusing through the same network. We\ndemonstrated the success of this deep learning-enabled holography method by\nimaging microscopic features of human tissue samples and Papanicolaou (Pap)\nsmears. These results constitute the first demonstration of the use of\nrecurrent neural networks for holographic imaging and phase recovery, and\ncompared with existing methods, the presented approach improves the\nreconstructed image quality, while also increasing the depth-of-field and\ninference speed.\n"}
+{"abstract": "  An important paradigm of natural language processing consists of large-scale\npre-training on general domain data and adaptation to particular tasks or\ndomains. As we pre-train larger models, full fine-tuning, which retrains all\nmodel parameters, becomes less feasible. Using GPT-3 175B as an example --\ndeploying independent instances of fine-tuned models, each with 175B\nparameters, is prohibitively expensive. We propose Low-Rank Adaptation, or\nLoRA, which freezes the pre-trained model weights and injects trainable rank\ndecomposition matrices into each layer of the Transformer architecture, greatly\nreducing the number of trainable parameters for downstream tasks. Compared to\nGPT-3 175B fine-tuned with Adam, LoRA can reduce the number of trainable\nparameters by 10,000 times and the GPU memory requirement by 3 times. LoRA\nperforms on-par or better than fine-tuning in model quality on RoBERTa,\nDeBERTa, GPT-2, and GPT-3, despite having fewer trainable parameters, a higher\ntraining throughput, and, unlike adapters, no additional inference latency. We\nalso provide an empirical investigation into rank-deficiency in language model\nadaptation, which sheds light on the efficacy of LoRA. We release a package\nthat facilitates the integration of LoRA with PyTorch models and provide our\nimplementations and model checkpoints for RoBERTa, DeBERTa, and GPT-2 at\nhttps://github.com/microsoft/LoRA.\n"}
+{"abstract": "  Video Instance Segmentation (VIS) is a multi-task problem performing\ndetection, segmentation, and tracking simultaneously. Extended from image set\napplications, video data additionally induces the temporal information, which,\nif handled appropriately, is very useful to identify and predict object\nmotions. In this work, we design a unified model to mutually learn these tasks.\nSpecifically, we propose two modules, named Temporally Correlated Instance\nSegmentation (TCIS) and Bidirectional Tracking (BiTrack), to take the benefit\nof the temporal correlation between the object's instance masks across adjacent\nframes. On the other hand, video data is often redundant due to the frame's\noverlap. Our analysis shows that this problem is particularly severe for the\nYoutubeVOS-VIS2021 data. Therefore, we propose a Multi-Source Data (MSD)\ntraining mechanism to compensate for the data deficiency. By combining these\ntechniques with a bag of tricks, the network performance is significantly\nboosted compared to the baseline, and outperforms other methods by a\nconsiderable margin on the YoutubeVOS-VIS 2019 and 2021 datasets.\n"}
+{"abstract": "  MAELAS is a computer program for the calculation of magnetocrystalline\nanisotropy energy, anisotropic magnetostrictive coefficients and magnetoelastic\nconstants in an automated way. The method originally implemented in version 1.0\nof MAELAS was based on the length optimization of the unit cell, proposed by Wu\nand Freeman, to calculate the anisotropic magnetostrictive coefficients. We\npresent here a revised and updated version (v2.0) of MAELAS, where we added a\nnew methodology to compute anisotropic magnetoelastic constants from a linear\nfitting of the energy versus applied strain. We analyze and compare the\naccuracy of both methods showing that the new approach is more reliable and\nrobust than the one implemented in version 1.0, especially for non-cubic\ncrystal symmetries. This analysis also help us to find that the accuracy of the\nmethod implemented in version 1.0 could be improved by using deformation\ngradients derived from the equilibrium magnetoelastic strain tensor, as well as\npotential future alternative methods like the strain optimization method.\nAdditionally, we clarify the role of the demagnetized state in the fractional\nchange in length, and derive the expression for saturation magnetostriction for\npolycrystals with trigonal, tetragonal and orthorhombic crystal symmetry. In\nthis new version, we also fix some issues related to trigonal crystal symmetry\nfound in version 1.0.\n"}
+{"abstract": "  In this paper, we address the problem of speaker verification in conditions\nunseen or unknown during development. A standard method for speaker\nverification consists of extracting speaker embeddings with a deep neural\nnetwork and processing them through a backend composed of probabilistic linear\ndiscriminant analysis (PLDA) and global logistic regression score calibration.\nThis method is known to result in systems that work poorly on conditions\ndifferent from those used to train the calibration model. We propose to modify\nthe standard backend, introducing an adaptive calibrator that uses duration and\nother automatically extracted side-information to adapt to the conditions of\nthe inputs. The backend is trained discriminatively to optimize binary\ncross-entropy. When trained on a number of diverse datasets that are labeled\nonly with respect to speaker, the proposed backend consistently and, in some\ncases, dramatically improves calibration, compared to the standard PLDA\napproach, on a number of held-out datasets, some of which are markedly\ndifferent from the training data. Discrimination performance is also\nconsistently improved. We show that joint training of the PLDA and the adaptive\ncalibrator is essential -- the same benefits cannot be achieved when freezing\nPLDA and fine-tuning the calibrator. To our knowledge, the results in this\npaper are the first evidence in the literature that it is possible to develop a\nspeaker verification system with robust out-of-the-box performance on a large\nvariety of conditions.\n"}
+{"abstract": "  In this study, we propose a laboratory-scale methodology, based on X-ray\nmicro-tomography and caesium (Cs) as a contrast agent, to advance the\nunderstanding of cracking due to alkali-silica reaction (ASR) in concrete. The\nmethodology allows achieving a completely non-destructive and time-lapse\ncharacterization of the spatial-temporal patterns of both the cracks and the\nASR products. While Cs addition slightly accelerated the ASR kinetics, the\ncrack pat-terns, with and without Cs addition, were statistically equivalent.\nCracks with ASR products appeared first in the aggregates, close to the\ninterface with the cement paste. They propagated afterwards towards the\naggregates interior. Some products were then extruded for several mm into air\nvoids and cracks in the cement paste. This process suggests that, in the early\nstage, the ASR products may be a low-viscosity gel that can flow away from the\nsource aggregate and may settle later elsewhere as a rigid phase, upon calcium\nuptake.\n"}
+{"abstract": "  In this paper, we combined linguistic complexity and (dis)fluency features\nwith pretrained language models for the task of Alzheimer's disease detection\nof the 2021 ADReSSo (Alzheimer's Dementia Recognition through Spontaneous\nSpeech) challenge. An accuracy of 83.1% was achieved on the test set, which\namounts to an improvement of 4.23% over the baseline model. Our best-performing\nmodel that integrated component models using a stacking ensemble technique\nperformed equally well on cross-validation and test data, indicating that it is\nrobust against overfitting.\n"}
+{"abstract": "  Recently, random walks on dynamic graphs have been studied because of its\nadaptivity to dynamical settings including real network analysis. However,\nprevious works showed a tremendous gap between static and dynamic networks for\nthe cover time of a lazy simple random walk: Although $O(n^3)$ cover time was\nshown for any static graphs of $n$ vertices, there is an edge-changing dynamic\ngraph with an exponential cover time.\n  We study a lazy Metropolis walk of Nonaka, Ono, Sadakane, and Yamashita\n(2010), which is a weighted random walk using local degree information. We show\nthat this walk is robust to an edge-changing in dynamic networks: For any\nconnected edge-changing graphs of $n$ vertices, the lazy Metropolis walk has\nthe $O(n^2)$ hitting time, the $O(n^2\\log n)$ cover time, and the $O(n^2)$\ncoalescing time, while those times can be exponential for lazy simple random\nwalks. All of these bounds are tight up to a constant factor. At the heart of\nthe proof, we give upper bounds of those times for any reversible random walks\nwith a time-homogeneous stationary distribution.\n"}
+{"abstract": "  Camera localization aims to estimate 6 DoF camera poses from RGB images.\nTraditional methods detect and match interest points between a query image and\na pre-built 3D model. Recent learning-based approaches encode scene structures\ninto a specific convolutional neural network (CNN) and thus are able to predict\ndense coordinates from RGB images. However, most of them require re-training or\nre-adaption for a new scene and have difficulties in handling large-scale\nscenes due to limited network capacity. We present a new method for scene\nagnostic camera localization using dense scene matching (DSM), where a cost\nvolume is constructed between a query image and a scene. The cost volume and\nthe corresponding coordinates are processed by a CNN to predict dense\ncoordinates. Camera poses can then be solved by PnP algorithms. In addition,\nour method can be extended to temporal domain, which leads to extra performance\nboost during testing time. Our scene-agnostic approach achieves comparable\naccuracy as the existing scene-specific approaches, such as KFNet, on the\n7scenes and Cambridge benchmark. This approach also remarkably outperforms\nstate-of-the-art scene-agnostic dense coordinate regression network SANet. The\nCode is available at https://github.com/Tangshitao/Dense-Scene-Matching.\n"}
+{"abstract": "  Loops, seamlessly repeatable musical segments, are a cornerstone of modern\nmusic production. Contemporary artists often mix and match various sampled or\npre-recorded loops based on musical criteria such as rhythm, harmony and\ntimbral texture to create compositions. Taking such criteria into account, we\npresent LoopNet, a feed-forward generative model for creating loops conditioned\non intuitive parameters. We leverage Music Information Retrieval (MIR) models\nas well as a large collection of public loop samples in our study and use the\nWave-U-Net architecture to map control parameters to audio. We also evaluate\nthe quality of the generated audio and propose intuitive controls for composers\nto map the ideas in their minds to an audio loop.\n"}
+{"abstract": "  Spin defects in solid-state materials are strong candidate systems for\nquantum information technology and sensing applications. Here we explore in\ndetails the recently discovered negatively charged boron vacancies ($V_B^-$) in\nhexagonal boron nitride (hBN) and demonstrate their use as atomic scale sensors\nfor temperature, magnetic fields and externally applied pressure. These\napplications are possible due to the high-spin triplet ground state and bright\nspin-dependent photoluminescence (PL) of the $V_B^-$. Specifically, we find\nthat the frequency shift in optically detected magnetic resonance (ODMR)\nmeasurements is not only sensitive to static magnetic fields, but also to\ntemperature and pressure changes which we relate to crystal lattice parameters.\nOur work is important for the future use of spin-rich hBN layers as intrinsic\nsensors in heterostructures of functionalized 2D materials.\n"}
+{"abstract": "  What does it mean today to study a problem from a computational point of\nview? We focus on parameterized complexity and on Column 16 \"Graph Restrictions\nand Their Effect\" of D. S. Johnson's Ongoing guide, where several puzzles were\nproposed in a summary table with 30 graph classes as rows and 11 problems as\ncolumns. Several of the 330 entries remain unclassified into Polynomial or\nNP-complete after 35 years. We provide a full dichotomy for the Steiner Tree\ncolumn by proving that the problem is NP-complete when restricted to Undirected\nPath graphs. We revise Johnson's summary table according to the granularity\nprovided by the parameterized complexity for NP-complete problems.\n"}
+{"abstract": "  We present the one-loop electroweak renormalization of the CP-violating\n2-Higgs-Doublet Model with softly broken $\\mathbb{Z}_2$ symmetry (C2HDM). The\nexistence of CP violation in the scalar sector of the model leads to a quite\nunique process of renormalization, since it requires the introduction of\nseveral non-physical parameters. The C2HDM will thus have more independent\ncounterterms than independent renormalized parameters. As a consequence,\ndifferent combinations of counterterms can be taken as independent for the same\nset of independent renormalized parameters. We compare the behaviour of\nselected combinations in specific NLO processes, which are assured to be gauge\nindependent via a simple prescription. FeynMaster 2 is used to derive the\nFeynman rules, counterterms and one-loop processes in a simultaneously\nautomatic and flexible way. This illustrates its use as an ideal tool to\nrenormalize models such as the C2HDM and investigate them at NLO.\n"}
+{"abstract": "  The problems regarding spectrum sensing are studied by exploiting a priori\nand a posteriori in information of the received noise variance. First, the\ntraditional Average Likelihood Ratio (ALR) and the General Likelihood Ratio\nTest (GLRT) detectors are investigated under a Gamma distributed function as a\nchannel noise, for the first time, under the availability of a priori\nstatistical distribution about the noise variance. Then, two robust detectors\nare proposed using the exiting excess bandwidth to deliver a posteriori\nprobability on the received noise variance uncertainty. The first proposed\ndetector that is based on traditional ALR employs marginal distribution of the\nobservation under available a priori and a posteriori of the received signal,\nwhile the second proposed detector employs the Maximum a posteriori (MAP)\nestimation of the inverse of the noise power under the same hypothesizes as the\nfirst detector. In addition, analytical expressions for the performance of the\nproposed detectors are obtained in terms of the false-alarm and detection\nprobabilities. The simulation results exhibit the superiority of the proposed\ndetectors over the traditional counterparts.\n"}
+{"abstract": "  We consider state changes in quantum theory due to \"conditional action\" and\nrelate these to the discussion of entropy decrease due to interventions of\n\"intelligent beings\" and the principles of Szilard and Landauer/Bennett. The\nmathematical theory of conditional actions is a special case of the theory of\n\"instruments\" which describes changes of state due to general measurements and\nwill therefore be briefly outlined in the present paper. As a detailed example\nwe consider the imperfect erasure of a qubit that can also be viewed as a\nconditional action and will be realized by the coupling of a spin to another\nsmall spin system in its ground state.\n"}
+{"abstract": "  We consider the distribution in residue classes modulo primes $p$ of Euler's\ntotient function $\\phi(n)$ and the sum-of-proper-divisors function\n$s(n):=\\sigma(n)-n$. We prove that the values $\\phi(n)$, for $n\\le x$, that are\ncoprime to $p$ are asymptotically uniformly distributed among the $p-1$ coprime\nresidue classes modulo $p$, uniformly for $5 \\le p \\le (\\log{x})^A$ (with $A$\nfixed but arbitrary). We also show that the values of $s(n)$, for $n$\ncomposite, are uniformly distributed among all $p$ residue classes modulo every\n$p\\le (\\log{x})^A$. These appear to be the first results of their kind where\nthe modulus is allowed to grow substantially with $x$.\n"}
+{"abstract": "  Born in the aftermath of core collapse supernovae, neutron stars contain\nmatter under extraordinary conditions of density and temperature that are\ndifficult to reproduce in the laboratory. In recent years, neutron star\nobservations have begun to yield novel insights into the nature of strongly\ninteracting matter in the high-density regime where current theoretical models\nare challenged. At the same time, chiral effective field theory has developed\ninto a powerful framework to study nuclear matter properties with quantified\nuncertainties in the moderate-density regime for modeling neutron stars. In\nthis article, we review recent developments in chiral effective field theory\nand focus on many-body perturbation theory as a computationally efficient tool\nfor calculating the properties of hot and dense nuclear matter. We also\ndemonstrate how effective field theory enables statistically meaningful\ncomparisons between nuclear theory predictions, nuclear experiments, and\nobservational constraints on the nuclear equation of state.\n"}
+{"abstract": "  The Semantic Web research community understood since its beginning how\ncrucial it is to equip practitioners with methods to transform non-RDF\nresources into RDF. Proposals focus on either engineering content\ntransformations or accessing non-RDF resources with SPARQL. Existing solutions\nrequire users to learn specific mapping languages (e.g. RML), to know how to\nquery and manipulate a variety of source formats (e.g. XPATH, JSON-Path), or to\ncombine multiple languages (e.g. SPARQL Generate). In this paper, we explore an\nalternative solution and contribute a general-purpose meta-model for converting\nnon-RDF resources into RDF: Facade-X. Our approach can be implemented by\noverriding the SERVICE operator and does not require to extend the SPARQL\nsyntax. We compare our approach with the state of art methods RML and SPARQL\nGenerate and show how our solution has lower learning demands and cognitive\ncomplexity, and it is cheaper to implement and maintain, while having\ncomparable extensibility and efficiency.\n"}
+{"abstract": "  In tokamak plasmas, the interaction among the micro-turbulence, zonal flows\n(ZFs) and energetic particles (EPs) can affect the turbulence saturation level\nand the consequent confinement quality and thus, is important for future\nburning plasmas. In this work, the EP anisotropy effects on the ZF residual\nlevel are studied by using anisotropic EP distributions with dependence on\npitch. Significant effects on the long wavelength ZFs have been found when\nsmall to moderate width around the dominant pitch in the EP distribution\nfunction is assumed. In addition, it is found that ZF residual level is\nenhanced by barely passing/trapped and/or deeply trapped EPs, but it is\nsuppressed by well passing and/or intermediate trapped EPs. Numerical\ncalculation shows that for ASDEX Upgrade plasmas, typical EP distribution\nfunctions can bring in -3%~+5.5% mitigation/enhancement in ZF residual level,\ndepending on the EP distribution functions.\n"}
+{"abstract": "  It was conjectured by White in 1980 that the toric ring associated to a\nmatroid is defined by symmetric exchange relations. This conjecture was\nextended to discrete polymatroids by Herzog and Hibi, and they prove that the\nconjecture holds for polymatroids with the so called strong exchange property.\nIn this paper we generalize their result to polymatroids that are products of\npolymatroids with the strong exchange property. This also extends a result by\nConca on transversal polymatroids.\n"}
+{"abstract": "  From paints to food products, solvent evaporation is ubiquitous and\ncritically impacts product rheological properties. It affects Newtonian fluids\nby concentrating any non-volatile components and viscoelastic materials, which\nhardens up. In both of these cases, solvent evaporation leads to a change in\nthe sample volume, which makes any rheological measurements particularly\nchallenging with traditional shear geometries. Here we show that the\nrheological properties of a sample experiencing `slow' evaporation can be\nmonitored in a time-resolved fashion by using a zero normal-force controlled\nprotocol in a parallel-plate geometry. Solvent evaporation from the sample\nleads to a decrease of the normal force, which is compensated at all times by a\ndecrease of the gap height between the plates. As a result, the sample\nmaintains a constant contact area with the plates despite the significant\ndecrease of its volume. We validate the method under both oscillatory and\ncontinuous shear by accurately monitoring the viscosity of water-glycerol\nmixtures experiencing evaporation and a relative volume decrease as large as\n70%. Moreover, we apply this protocol to dehydrating suspensions. Specifically,\nwe monitor a dispersion of charged silica nanoparticles undergoing a glass\ntransition induced by evaporation. While the decrease in gap height provides a\ndirect estimate of the increasing particle volume fraction, oscillatory and\ncontinuous shear measurements allow us to monitor the suspension's evolving\nviscoelastic properties in real-time. Overall, our study shows that a zero\nnormal-force protocol provides a simple approach to bulk and time-resolved\nrheological characterization for systems experiencing slow volume variations.\n"}
+{"abstract": "  We propose a cross-modal transformer-based neural correction models that\nrefines the output of an automatic speech recognition (ASR) system so as to\nexclude ASR errors. Generally, neural correction models are composed of\nencoder-decoder networks, which can directly model sequence-to-sequence mapping\nproblems. The most successful method is to use both input speech and its ASR\noutput text as the input contexts for the encoder-decoder networks. However,\nthe conventional method cannot take into account the relationships between\nthese two different modal inputs because the input contexts are separately\nencoded for each modal. To effectively leverage the correlated information\nbetween the two different modal inputs, our proposed models encode two\ndifferent contexts jointly on the basis of cross-modal self-attention using a\ntransformer. We expect that cross-modal self-attention can effectively capture\nthe relationships between two different modals for refining ASR hypotheses. We\nalso introduce a shallow fusion technique to efficiently integrate the\nfirst-pass ASR model and our proposed neural correction model. Experiments on\nJapanese natural language ASR tasks demonstrated that our proposed models\nachieve better ASR performance than conventional neural correction models.\n"}
+{"abstract": "  Let $\\Gamma$ be the Fuchsian group of the first kind. For an even integer\n$m\\ge 4$, we study $m/2$-holomorphic differentials in terms of space of\n(holomorphic) cuspidal modular forms $S_m(\\Gamma)$. We also give in depth study\nof Wronskians of cuspidal modular forms and their divisors.\n"}
+{"abstract": "  In this paper we introduce a wide class of space-fractional and\ntime-fractional fractional semidiscrete Dirac operators of L\\'evy-Leblond type\non the semidiscrete space-time $h\\mathbb{Z}^n\\times[0,\\infty)$ ($h>0$),\nresembling to fractional semidiscrete counterparts of the so-called parabolic\nDirac operators. The cornerstone this approach is centered around the study of\nthe null solutions of the underlying space-fractional resp. time-fractional\nfractional semidiscrete Dirac operators, and on the study of representation of\nthe null solutions for both operators with the aid of the analytic fractional\nsemidiscrete semigroup $\\{\\exp(-te^{i\\theta}(-\\Delta_h)^{\\alpha})\\}_{t\\geq 0}$\ncarrying the parameter constraints $0<\\alpha\\leq 1$ and $|\\theta|\\leq\n\\frac{\\alpha \\pi}{2}$. The results obtained involve the study of Cauchy\nproblems on $h\\mathbb{Z}^n\\times[0,\\infty)$.\n"}
+{"abstract": "  Simultaneous vibration control and energy harvesting of vehicle suspensions\nhave attracted significant research attention over the past decades. However,\nexisting energy harvesting shock absorbers (EHSAs) are mainly designed based on\nthe principle of linear resonance, thereby compromising suspension performance\nfor high-efficiency energy harvesting and being only responsive to narrow\nbandwidth vibrations. In this paper, we propose a new EHSA design -- inerter\npendulum vibration absorber (IPVA) -- that integrates an electromagnetic rotary\nEHSA with a nonlinear pendulum vibration absorber. We show that this design\nsimultaneously improves ride comfort and energy harvesting efficiency by\nexploiting the nonlinear effects of pendulum inertia. To further improve the\nperformance, we develop a novel stochastic linearization model predictive\ncontrol (SL-MPC) approach in which we employ stochastic linearization to\napproximate the nonlinear dynamics of EHSA that has superior accuracy compared\nto standard linearization. In particular, we develop a new stochastic\nlinearization method with guaranteed stabilizability, which is a prerequisite\nfor control designs. This leads to an MPC problem that is much more\ncomputationally efficient than the nonlinear MPC counterpart with no major\nperformance degradation. Extensive simulations are performed to show the\nsuperiority of the proposed new nonlinear EHSA and to demonstrate the efficacy\nof the proposed SL-MPC.\n"}
+{"abstract": "  We investigate the Minimal Theory of Massive Gravity (MTMG) in the light of\ndifferent observational data sets which are in tension within the $\\Lambda$CDM\ncosmology. In particular, we analyze MTMG model, for the first time, with the\nPlanck-CMB data, and how these precise measurements affect the free parameters\nof the theory. The MTMG model can affect the CMB power spectrum at large\nangular scales and cause a suppression on the amplitude of the matter power\nspectrum. We find that on adding Planck-CMB data, the graviton has a small,\npositive, but non-zero mass at 68\\% confidence level, and from this\nperspective, we show that the tension between redshift space distortions\nmeasurements and Planck-CMB data in the parametric space $S_8 - \\Omega_m$ can\nbe resolved within the MTMG scenario. Through a robust and accurate analysis,\nwe find that the $H_0$ tension between the CMB and the local distance ladder\nmeasurements still remains but can be reduced to $\\sim3.5\\sigma$ within the\nMTMG theory. The MTMG is very well consistent with the CMB observations, and\nundoubtedly, it can serve as a viable candidate amongst other modified gravity\ntheories.\n"}
+{"abstract": "  We present a protocol to construct your own depth validation dataset for\nnavigation. This protocol, called RDC for Rigid Depth Constructor, aims at\nbeing more accessible and cheaper than already existing techniques, requiring\nonly a camera and a Lidar sensor to get started. We also develop a test suite\nto get insightful information from the evaluated algorithm. Finally, we take\nthe example of UAV videos, on which we test two depth algorithms that were\ninitially tested on KITTI and show that the drone context is dramatically\ndifferent from in-car videos. This shows that a single context benchmark should\nnot be considered reliable, and when developing a depth estimation algorithm,\none should benchmark it on a dataset that best fits one's particular needs,\nwhich often means creating a brand new one. Along with this paper we provide\nthe tool with an open source implementation and plan to make it as\nuser-friendly as possible, to make depth dataset creation possible even for\nsmall teams. Our key contributions are the following: We propose a complete,\nopen-source and almost fully automatic software application for creating\nvalidation datasets with densely annotated depth, adaptable to a wide variety\nof image, video and range data. It includes selection tools to adapt the\ndataset to specific validation needs, and conversion tools to other dataset\nformats. Using this application, we propose two new real datasets, outdoor and\nindoor, readily usable in UAV navigation context. Finally as examples, we show\nan evaluation of two depth prediction algorithms, using a collection of\ncomprehensive (e.g. distribution based) metrics.\n"}
+{"abstract": "  Despite the superior performance in modeling complex patterns to address\nchallenging problems, the black-box nature of Deep Learning (DL) methods impose\nlimitations to their application in real-world critical domains. The lack of a\nsmooth manner for enabling human reasoning about the black-box decisions hinder\nany preventive action to unexpected events, in which may lead to catastrophic\nconsequences. To tackle the unclearness from black-box models, interpretability\nbecame a fundamental requirement in DL-based systems, leveraging trust and\nknowledge by providing ways to understand the model's behavior. Although a\ncurrent hot topic, further advances are still needed to overcome the existing\nlimitations of the current interpretability methods in unsupervised DL-based\nmodels for Anomaly Detection (AD). Autoencoders (AE) are the core of\nunsupervised DL-based for AD applications, achieving best-in-class performance.\nHowever, due to their hybrid aspect to obtain the results (by requiring\nadditional calculations out of network), only agnostic interpretable methods\ncan be applied to AE-based AD. These agnostic methods are computationally\nexpensive to process a large number of parameters. In this paper we present the\nRXP (Residual eXPlainer), a new interpretability method to deal with the\nlimitations for AE-based AD in large-scale systems. It stands out for its\nimplementation simplicity, low computational cost and deterministic behavior,\nin which explanations are obtained through the deviation analysis of\nreconstructed input features. In an experiment using data from a real\nheavy-haul railway line, the proposed method achieved superior performance\ncompared to SHAP, demonstrating its potential to support decision making in\nlarge scale critical systems.\n"}
+{"abstract": "  We study the Hall effect in square, planar type-II superconductors using\nnumerical simulations of time dependent Ginzburg-Landau (TDGL) equations. The\nHall field in some type-II superconductors displays sign-change behavior at\nsome magnetic fields due to the induced field of vortex flow, when its\ncontribution is strong enough to reverse the field direction. In this work, we\nuse modified TDGL equations which couple an externally applied current, and\nalso incorporate normal-state and flux-flow Hall effects. We obtain the profile\nof Hall angle as a function of applied magnetic field for four different sizes\n(l\\times l) of the superconductor: l/ \\xi belongs to {3, 5, 15, 20}. We obtain\nvastly different profiles for each size, proving that size is an important\nparameter that determines Hall behavior. We find that electric field dynamics\nprovides an insight into several anomalous features including signchange of\nHall angle, and leads us to the precise transient behavior of order parameter\nresponsible for them.\n"}
+{"abstract": "  This paper considers a probabilistic model for floating-point computation in\nwhich the roundoff errors are represented by bounded random variables with mean\nzero. Using this model, a probabilistic bound is derived for the forward error\nof the computed sum of n real numbers. This work improves upon existing\nprobabilistic bounds by holding to all orders, and as a result provides\ninformative bounds for larger problem sizes.\n"}
+{"abstract": "  We consider those elements of the Schwartz algebra of entire functions which\nare Fourier-Laplace transforms of invertible distributions with compact\nsupports on the real line. These functions are called invertible in the sense\nof Ehrenpreis. The presented result concerns with the properties of zero\nsubsets of invertible in the sense of Ehrenpreis function f. Namely, we\nestablish some properties of the zero subset formed by zeros of f laying not\nfar from the real axis.\n"}
+{"abstract": "  After the discovery of the Higgs boson in 2012, particle physics has entered\nan exciting era. An important question is whether the Standard Model of\nparticle physics correctly describes the scalar sector realized by nature, or\nwhether it is part of a more extended model, featuring additional particle\ncontent. A prime way to test this is to probe models with extended scalar\nsectors at future collider facilities. We here discuss such models in the\ncontext of high-luminosity LHC, a possible proton-proton collider with 27 and\n100 TeV center-of-mass energy, as well as future lepton colliders with various\ncenter-of-mass energies.\n"}
+{"abstract": "  Single-channel speech enhancement (SE) is an important task in speech\nprocessing. A widely used framework combines an analysis/synthesis filterbank\nwith a mask prediction network, such as the Conv-TasNet architecture. In such\nsystems, the denoising performance and computational efficiency are mainly\naffected by the structure of the mask prediction network. In this study, we aim\nto improve the sequential modeling ability of Conv-TasNet architectures by\nintegrating Conformer layers into a new mask prediction network. To make the\nmodel computationally feasible, we extend the Conformer using linear complexity\nattention and stacked 1-D dilated depthwise convolution layers. We trained the\nmodel on 3,396 hours of noisy speech data, and show that (i) the use of linear\ncomplexity attention avoids high computational complexity, and (ii) our model\nachieves higher scale-invariant signal-to-noise ratio than the improved\ntime-dilated convolution network (TDCN++), an extended version of Conv-TasNet.\n"}
+{"abstract": "  Mechanical properties of transition metal dichalcogenides (TMDCs) are\nrelevant to their prospective applications in flexible electronics. So far, the\nfocus has been on the semiconducting TMDCs, mostly MoX2 and WX2 (X=S, Se) due\nto their potential in optoelectronics. A comprehensive understanding of the\nelastic properties of metallic TMDCs is needed to complement the semiconducting\nTMDCs in flexible optoelectronics. Thus, mechanical testing of metallic TMDCs\nis pertinent to the realization of the applications. Here, we report on the\natomic force microscopy-based nano-indentation measurements on ultra-thin\n2H-TaS2 crystals to elucidate the stretching and breaking of the metallic\nTMDCs. We explored the elastic properties of 2H-TaS2 at different thicknesses\nranging from 3.5 nm to 12.6 nm and find that the Young's modulus is independent\nof the thickness at a value of 85.9 +- 10.6 GPa, which is lower than the\nsemiconducting TMDCs reported so far. We determined the breaking strength as\n5.07 4- 0.10 GPa which is 6% of the Young's modulus. This value is comparable\nto that of other TMDCs. We used ab initio calculations to provide an insight to\nthe high elasticity measured in 2H-TaS2. We also performed measurements on a\nsmall number of 1T-TaTe2, 3R-NbS2 and 1T-NbTe2 samples and extended our ab\ninitio calculations to these materials to gain a deeper understanding on the\nelastic and breaking properties of metallic TMDCs. This work illustrates that\nthe studied metallic TMDCs are suitable candidates to be used as additives in\ncomposites as functional and structural elements and for flexible conductive\nelectronic devices.\n"}
+{"abstract": "  One billion people live in informal settlements worldwide. The complex and\nmultilayered spaces that characterize this unplanned form of urbanization pose\na challenge to traditional approaches to mapping and morphological analysis.\nThis study proposes a methodology to study the morphological properties of\ninformal settlements based on terrestrial LiDAR (Light Detection and Ranging)\ndata collected in Rocinha, the largest favela in Rio de Janeiro, Brazil. Our\nanalysis operates at two resolutions, including a \\emph{global} analysis\nfocused on comparing different streets of the favela to one another, and a\n\\emph{local} analysis unpacking the variation of morphological metrics within\nstreets. We show that our methodology reveals meaningful differences and\ncommonalities both in terms of the global morphological characteristics across\nstreets and their local distributions. Finally, we create morphological maps at\nhigh spatial resolution from LiDAR data, which can inform urban planning\nassessments of concerns related to crowding, structural safety, air quality,\nand accessibility in the favela. The methods for this study are automated and\ncan be easily scaled to analyze entire informal settlements, leveraging the\nincreasing availability of inexpensive LiDAR scanners on portable devices such\nas cellphones.\n"}
+{"abstract": "  We determine the ability of Cosmic Explorer, a proposed third-generation\ngravitational-wave observatory, to detect eccentric binary neutron stars and to\nmeasure their eccentricity. We find that for a matched-filter search, template\nbanks constructed using binaries in quasi-circular orbits are effectual for\neccentric neutron star binaries with $e_{7} \\leq 0.004$ ($e_{7} \\leq 0.003$)\nfor CE1 (CE2), where $e_7$ is the binary's eccentricity at a gravitational-wave\nfrequency of 7~Hz. We show that stochastic template placement can be used to\nconstruct a matched-filter search for binaries with larger eccentricities and\nconstruct an effectual template bank for binaries with $e_{7} \\leq 0.05$. We\nshow that the computational cost of both the search for binaries in\nquasi-circular orbits and eccentric orbits is not significantly larger for\nCosmic Explorer than for Advanced LIGO and is accessible with present-day\ncomputational resources. We investigate Cosmic Explorer's ability to\ndistinguish between circular and eccentric binaries. We estimate that for a\nbinary with a signal-to-noise ratio of 8 (800), Cosmic Explorer can distinguish\nbetween a circular binary and a binary with eccentricity $e_7 \\gtrsim 10^{-2}$\n($10^{-3}$) at 90% confidence.\n"}
+{"abstract": "  Understanding the spread of false or dangerous beliefs through a population\nhas never seemed so urgent. Network science researchers have often taken a page\nfrom epidemiologists, and modeled the spread of false beliefs as similar to how\na disease spreads through a social network. However, absent from those\ndisease-inspired models is an internal model of an individual's set of current\nbeliefs, where cognitive science has increasingly documented how the\ninteraction between mental models and incoming messages seems to be crucially\nimportant for their adoption or rejection. Some computational social science\nmodelers analyze agent-based models where individuals do have simulated\ncognition, but they often lack the strengths of network science, namely in\nempirically-driven network structures. We introduce a cognitive cascade model\nthat combines a network science belief cascade approach with an internal\ncognitive model of the individual agents as in opinion diffusion models as a\npublic opinion diffusion (POD) model, adding media institutions as agents which\nbegin opinion cascades. We conduct an analysis of the cognitive cascade model\nwith our simple cognitive function across various graph topologies and\ninstitutional messaging patterns. We argue from our results that\npopulation-level aggregate outcomes of the model qualitatively match what has\nbeen reported in COVID-related public opinion polls, and that the model\ndynamics lend insights as to how to address the spread of problematic beliefs.\nThe overall model sets up a framework with which social science misinformation\nresearchers and computational opinion diffusion modelers can join forces to\nunderstand, and hopefully learn how to best counter, the spread of\ndisinformation and \"alternative facts.\"\n"}
+{"abstract": "  This paper aims to invalidate the hypothesis that consciousness is necessary\nfor the quantum measurement process. To achieve this target, I propose a\nconsiderable modification of the Schroedinger cat and the Dead-Alive Physicist\nthought experiments, called 'PIAR', short for Physicist Inside the Ambiguous\nRoom. A specific strategy has enabled me to plan the experiment in such a way\nas to logically justify the inconsistency of the above hypothesis and to oblige\nits supporters to rely on an alternative interpretation of quantum mechanics in\nwhich the real-world phenomena exist independently of our conscious mind and\nwhere observers play no special role. Moreover, in my description, the\nmeasurement apparatus will be complete, in the sense that the experiment, given\nthat it includes also the experimenter, will begin and end exclusively within a\nsealed room. Hence, my analysis provides a logical explanation of the\nrelationship between the observer and the objects of her/his experimental\nobservation; this and a few other implications are discussed in the fifth\nsection and the conclusions.\n"}
+{"abstract": "  Nature-inspired swarm-based algorithms have been widely applied to tackle\nhigh-dimensional and complex optimization problems across many disciplines.\nThey are general purpose optimization algorithms, easy to use and implement,\nflexible and assumption-free. A common drawback of these algorithms is\npremature convergence and the solution found is not a global optimum. We\nprovide sufficient conditions for an algorithm to converge almost surely (a.s.)\nto a global optimum. We then propose a general, simple and effective strategy,\ncalled Perturbation-Projection (PP), to enhance an algorithm's exploration\ncapability so that our convergence conditions are guaranteed to hold. We\nillustrate this approach using three widely used nature-inspired swarm-based\noptimization algorithms: particle swarm optimization (PSO), bat algorithm (BAT)\nand competitive swarm optimizer (CSO). Extensive numerical experiments show\nthat each of the three algorithms with the enhanced PP strategy outperforms the\noriginal version in a number of notable ways.\n"}
+{"abstract": "  Understanding pedestrian crossing behavior is an essential goal in\nintelligent vehicle development, leading to an improvement in their security\nand traffic flow. In this paper, we developed a method called IntFormer. It is\nbased on transformer architecture and a novel convolutional video\nclassification model called RubiksNet. Following the evaluation procedure in a\nrecent benchmark, we show that our model reaches state-of-the-art results with\ngood performance ($\\approx 40$ seq. per second) and size ($8\\times $smaller\nthan the best performing model), making it suitable for real-time usage. We\nalso explore each of the input features, finding that ego-vehicle speed is the\nmost important variable, possibly due to the similarity in crossing cases in\nPIE dataset.\n"}
+{"abstract": "  We show that the minimal Type-I Seesaw mechanism can successfully account for\nthe observed dark matter abundance in the form of a keV sterile neutrino. This\npopulation can be produced by the decay of the heavier neutral leptons, with\nmasses above the Higgs mass scale, while they are in thermal equilibrium in the\nearly Universe (freeze-in). Moreover, the implementation of the relevant\nphenomenological constraints (relic abundance, indirect detection and structure\nformation) on this model automatically selects a region of the parameter space\nfeaturing an approximate lepton number symmetry.\n"}
+{"abstract": "  Normalizing flows learn a diffeomorphic mapping between the target and base\ndistribution, while the Jacobian determinant of that mapping forms another\nreal-valued function. In this paper, we show that the Jacobian determinant\nmapping is unique for the given distributions, hence the likelihood objective\nof flows has a unique global optimum. In particular, the likelihood for a class\nof flows is explicitly expressed by the eigenvalues of the auto-correlation\nmatrix of individual data point, and independent of the parameterization of\nneural network, which provides a theoretical optimal value of likelihood\nobjective and relates to probabilistic PCA. Additionally, Jacobian determinant\nis a measure of local volume change and is maximized when MLE is used for\noptimization. To stabilize normalizing flows training, it is required to\nmaintain a balance between the expansiveness and contraction of volume, meaning\nLipschitz constraint on the diffeomorphic mapping and its inverse. With these\ntheoretical results, several principles of designing normalizing flow were\nproposed. And numerical experiments on highdimensional datasets (such as\nCelebA-HQ 1024x1024) were conducted to show the improved stability of training.\n"}
+{"abstract": "  Unobserved confounding is one of the main challenges when estimating causal\neffects. We propose a novel causal reduction method that replaces an arbitrary\nnumber of possibly high-dimensional latent confounders with a single latent\nconfounder that lives in the same space as the treatment variable without\nchanging the observational and interventional distributions entailed by the\ncausal model. After the reduction, we parameterize the reduced causal model\nusing a flexible class of transformations, so-called normalizing flows. We\npropose a learning algorithm to estimate the parameterized reduced model\njointly from observational and interventional data. This allows us to estimate\nthe causal effect in a principled way from combined data. We perform a series\nof experiments on data simulated using nonlinear causal mechanisms and find\nthat we can often substantially reduce the number of interventional samples\nwhen adding observational training samples without sacrificing accuracy. Thus,\nadding observational data may help to more accurately estimate causal effects\neven in the presence of unobserved confounders.\n"}
+{"abstract": "  Blindspots in APIs can cause software engineers to introduce vulnerabilities,\nbut such blindspots are, unfortunately, common. We study the effect APIs with\nblindspots have on developers in two languages by replicating an 109-developer,\n24-Java-API controlled experiment. Our replication applies to Python and\ninvolves 129 new developers and 22 new APIs. We find that using APIs with\nblindspots statistically significantly reduces the developers' ability to\ncorrectly reason about the APIs in both languages, but that the effect is more\npronounced for Python. Interestingly, for Java, the effect increased with\ncomplexity of the code relying on the API, whereas for Python, the opposite was\ntrue. Whether the developers considered API uses to be more difficult, less\nclear, and less familiar did not have an effect on their ability to correctly\nreason about them. Developers with better long-term memory recall were more\nlikely to correctly reason about APIs with blindspots, but short-term memory,\nprocessing speed, episodic memory, and memory span had no effect. Surprisingly,\nprofessional experience and expertice did not improve the developers' ability\nto reason about APIs with blindspots across both languages, with long-term\nprofessionals with many years of experience making mistakes as often as\nrelative novices. Finally, personality traits did not significantly affect the\nPython developers' ability to reason about APIs with blindspots, but less\nextraverted and more open developers were better at reasoning about Java APIs\nwith blindspots. Overall, our findings suggest that blindspots in APIs are a\nserious problem across languages, and that experience and education alone do\nnot overcome that problem, suggesting that tools are needed to help developers\nrecognize blindspots in APIs as they write code that uses those APIs.\n"}
+{"abstract": "  We present three families of minimal border rank tensors: they come from\nhighest weight vectors, smoothable algebras, or monomial algebras. We analyse\nthem using Strassen's laser method and obtain an upper bound $2.431$ on\n$\\omega$. We also explain how in certain monomial cases using the laser method\ndirectly is less profitable than first degenerating. Our results form possible\npaths in the search for valuable tensors for the laser method away from\nCoppersmith-Winograd tensors.\n"}
+{"abstract": "  We use the 21 cm emission line data from the DINGO-VLA project to study the\natomic hydrogen gas H\\,{\\textsc i} of the Universe at redshifts $z<0.1$.\nResults are obtained using a stacking analysis, combining the H\\,{\\textsc i}\nsignals from 3622 galaxies extracted from 267 VLA pointings in the G09 field of\nthe Galaxy and Mass Assembly Survey (GAMA). Rather than using a traditional\none-dimensional spectral stacking method, a three-dimensional cubelet stacking\nmethod is used to enable deconvolution and the accurate recovery of average\ngalaxy fluxes from this high-resolution interferometric dataset. By probing\ndown to galactic scales, this experiment also overcomes confusion corrections\nthat have been necessary to include in previous single dish studies. After\nstacking and deconvolution, we obtain a $30\\sigma$ H\\,{\\textsc i} mass\nmeasurement from the stacked spectrum, indicating an average H\\,{\\textsc i}\nmass of $M_{\\rm H\\,{\\textsc i}}=(1.674\\pm 0.183)\\times 10^{9}~{\\Msun}$. The\ncorresponding cosmic density of neutral atomic hydrogen is $\\Omega_{\\rm\nH\\,{\\textsc i}}=(0.377\\pm 0.042)\\times 10^{-3}$ at redshift of $z=0.051$. These\nvalues are in good agreement with earlier results, implying there is no\nsignificant evolution of $\\Omega_{\\rm H\\,{\\textsc i}}$ at lower redshifts.\n"}
+{"abstract": "  Mobile robotics is a research area that has witnessed incredible advances for\nthe last decades. Robot navigation is an essential task for mobile robots. Many\nmethods are proposed for allowing robots to navigate within different\nenvironments. This thesis studies different deep learning-based approaches,\nhighlighting the advantages and disadvantages of each scheme. In fact, these\napproaches are promising that some of them can navigate the robot in unknown\nand dynamic environments. In this thesis, one of the deep learning methods\nbased on convolutional neural network (CNN) is realized by software\nimplementations. There are different preparation studies to complete this\nthesis such as introduction to Linux, robot operating system (ROS), C++,\npython, and GAZEBO simulator. Within this work, we modified the drone network\n(namely, DroNet) approach to be used in an indoor environment by using a ground\nrobot in different cases. Indeed, the DroNet approach suffers from the absence\nof goal-oriented motion. Therefore, this thesis mainly focuses on tackling this\nproblem via mapping using simultaneous localization and mapping (SLAM) and path\nplanning techniques using Dijkstra. Afterward, the combination between the\nDroNet ground robot-based, mapping, and path planning leads to a goal-oriented\nmotion, following the shortest path while avoiding the dynamic obstacle.\nFinally, we propose a low-cost approach, for indoor applications such as\nrestaurants, museums, etc, on the base of using a monocular camera instead of a\nlaser scanner.\n"}
+{"abstract": "  Inspired by humans' remarkable ability to master arithmetic and generalize to\nunseen problems, we present a new dataset, HINT, to study machines' capability\nof learning generalizable concepts at three different levels: perception,\nsyntax, and semantics. In particular, concepts in HINT, including both digits\nand operators, are required to learn in a weakly-supervised fashion: Only the\nfinal results of handwriting expressions are provided as supervision. Learning\nagents need to reckon how concepts are perceived from raw signals such as\nimages (i.e., perception), how multiple concepts are structurally combined to\nform a valid expression (i.e., syntax), and how concepts are realized to afford\nvarious reasoning tasks (i.e., semantics). With a focus on systematic\ngeneralization, we carefully design a five-fold test set to evaluate both the\ninterpolation and the extrapolation of learned concepts. To tackle this\nchallenging problem, we propose a neural-symbolic system by integrating neural\nnetworks with grammar parsing and program synthesis, learned by a novel\ndeduction--abduction strategy. In experiments, the proposed neural-symbolic\nsystem demonstrates strong generalization capability and significantly\noutperforms end-to-end neural methods like RNN and Transformer. The results\nalso indicate the significance of recursive priors for extrapolation on syntax\nand semantics.\n"}
+{"abstract": "  The lack of publicly available evaluation data for low-resource languages\nlimits progress in Spoken Language Understanding (SLU). As key tasks like\nintent classification and slot filling require abundant training data, it is\ndesirable to reuse existing data in high-resource languages to develop models\nfor low-resource scenarios. We introduce xSID, a new benchmark for\ncross-lingual Slot and Intent Detection in 13 languages from 6 language\nfamilies, including a very low-resource dialect. To tackle the challenge, we\npropose a joint learning approach, with English SLU training data and\nnon-English auxiliary tasks from raw text, syntax and translation for transfer.\nWe study two setups which differ by type and language coverage of the\npre-trained embeddings. Our results show that jointly learning the main tasks\nwith masked language modeling is effective for slots, while machine translation\ntransfer works best for intent classification.\n"}
+{"abstract": "  The convolutional neural network (CNN) remains an essential tool in solving\ncomputer vision problems. Standard convolutional architectures consist of\nstacked layers of operations that progressively downscale the image. Aliasing\nis a well-known side-effect of downsampling that may take place: it causes\nhigh-frequency components of the original signal to become indistinguishable\nfrom its low-frequency components. While downsampling takes place in the\nmax-pooling layers or in the strided-convolutions in these models, there is no\nexplicit mechanism that prevents aliasing from taking place in these layers.\nDue to the impressive performance of these models, it is natural to suspect\nthat they, somehow, implicitly deal with this distortion. The question we aim\nto answer in this paper is simply: \"how and to what extent do CNNs counteract\naliasing?\" We explore the question by means of two examples: In the first, we\nassess the CNNs capability of distinguishing oscillations at the input, showing\nthat the redundancies in the intermediate channels play an important role in\nsucceeding at the task; In the second, we show that an image classifier CNN\nwhile, in principle, capable of implementing anti-aliasing filters, does not\nprevent aliasing from taking place in the intermediate layers.\n"}
+{"abstract": "  A key issue in the solution of partial differential equations via integral\nequation methods is the evaluation of possibly singular integrals involving the\nGreen's function and its derivatives multiplied by simple functions over\ndiscretized representations of the boundary. For the Helmholtz equation, while\nmany authors use numerical quadrature to evaluate these boundary integrals, we\npresent analytical expressions for such integrals over flat polygons in the\nform of infinite series. These can be efficiently truncated based on the\naccurate error bounds, which is key to their integration in methods such as the\nFast Multipole Method.\n"}
+{"abstract": "  This paper introduces Fast Linearized Adaptive Policy (FLAP), a new\nmeta-reinforcement learning (meta-RL) method that is able to extrapolate well\nto out-of-distribution tasks without the need to reuse data from training, and\nadapt almost instantaneously with the need of only a few samples during\ntesting. FLAP builds upon the idea of learning a shared linear representation\nof the policy so that when adapting to a new task, it suffices to predict a set\nof linear weights. A separate adapter network is trained simultaneously with\nthe policy such that during adaptation, we can directly use the adapter network\nto predict these linear weights instead of updating a meta-policy via gradient\ndescent, such as in prior meta-RL methods like MAML, to obtain the new policy.\nThe application of the separate feed-forward network not only speeds up the\nadaptation run-time significantly, but also generalizes extremely well to very\ndifferent tasks that prior Meta-RL methods fail to generalize to. Experiments\non standard continuous-control meta-RL benchmarks show FLAP presenting\nsignificantly stronger performance on out-of-distribution tasks with up to\ndouble the average return and up to 8X faster adaptation run-time speeds when\ncompared to prior methods.\n"}
+{"abstract": "  Solutions of a linear equation b=ax in a homomorphic image of a commutative\nBezout domain of stable range 1.5 is developed. It is proved that the set of\nsolutions of a solvable linear equation contains at least one solution that\ndivides the rest, which is called a generating solution. Generating solutions\nare pairwise associates. Using this result, the structure of elements of the\nZelisko group is investigated.\n"}
+{"abstract": "  With recent high-throughput technology we can synthesize large heterogeneous\ncollections of DNA structures, and also read them all out precisely in a single\nprocedure. Can we use these tools, not only to do things faster, but also to\ndevise new techniques and algorithms? In this paper we examine some DNA\nalgorithms that assume high-throughput synthesis and sequencing. We aim to\nmonitor, record, and read out the order in which a number $N$ of events occur,\nusing $N^2$ redundant detectors, and (after sequencing) reconstructing the\norder by transitive reduction.\n"}
+{"abstract": "  Label noise and long-tailed distributions are two major challenges in\ndistantly supervised relation extraction. Recent studies have shown great\nprogress on denoising, but pay little attention to the problem of long-tailed\nrelations. In this paper, we introduce constraint graphs to model the\ndependencies between relation labels. On top of that, we further propose a\nnovel constraint graph-based relation extraction framework(CGRE) to handle the\ntwo challenges simultaneously. CGRE employs graph convolution networks (GCNs)\nto propagate information from data-rich relation nodes to data-poor relation\nnodes, and thus boosts the representation learning of long-tailed relations. To\nfurther improve the noise immunity, a constraint-aware attention module is\ndesigned in CGRE to integrate the constraint information. Experimental results\non a widely-used benchmark dataset indicate that our approach achieves\nsignificant improvements over the previous methods for both denoising and\nlong-tailed relation extraction. Our dataset and codes are available at\nhttps://github.com/tmliang/CGRE.\n"}
+{"abstract": "  For a discrete function $f\\left( x\\right) $ on a discrete set, the finite\ndifference can be either forward and backward. However, we observe that if $\nf\\left( x\\right) $ is a sum of two functions $f\\left( x\\right) =f_{1}\\left(\nx\\right) +f_{2}\\left( x\\right) $ defined on the discrete set, the first order\ndifference of $\\Delta f\\left( x\\right) $ is equivocal for we may have $ \\Delta\n^{f}f_{1}\\left( x\\right) +\\Delta ^{b}f_{2}\\left( x\\right) $ where $ \\Delta\n^{f}$ and $\\Delta ^{b}$ denotes the forward and backward difference\nrespectively. Thus, the first order variation equation for this function $\nf\\left( x\\right) $ gives many solutions which include both true and false one.\nA proper formalism of the discrete calculus of variations is proposed to single\nout the true one by examination of the second order variations, and is capable\nof yielding the exact form of the distributions for Boltzmann, Bose and Fermi\nsystem without requiring the numbers of particle to be infinitely large. The\nadvantage and peculiarity of our formalism are explicitly illustrated by the\nderivation of the Bose distribution.\n"}
+{"abstract": "  The polynomial $f_{2n}(x)=1+x+\\cdots+x^{2n}$ and its minimizer on the real\nline $x_{2n}=\\operatorname{arg\\,inf} f_{2n}(x)$ for $n\\in\\Bbb N$ are studied.\nResults show that $x_{2n}$ exists, is unique, corresponds to $\\partial_x\nf_{2n}(x)=0$, and resides on the interval $[-1,-1/2]$ for all $n$. It is\nfurther shown that $\\inf f_{2n}(x)=(1+2n)/(1+2n(1-x_{2n}))$ and $\\inf\nf_{2n}(x)\\in[1/2,3/4]$ for all $n$ with an exact solution for $x_{2n}$ given in\nthe form of a finite sum of hypergeometric functions of unity argument.\nPerturbation theory is applied to generate rapidly converging and\nasymptotically exact approximations to $x_{2n}$. Numerical studies are carried\nout to show how many terms of the perturbation expansion for $x_{2n}$ are\nneeded to obtain suitably accurate approximations to the exact value.\n"}
+{"abstract": "  Transition metal oxides (TMOs) like MoOx are increasingly explored as hole\ntransport layers for perovskite-based solar cells. Due to their large work\nfunction, the hole collection mechanism of such solar cells are fundamentally\ndifferent from other materials like PEDOT: PSS, and the associated device\noptimizations are not well elucidated. In addition, the prospects of such\narchitectures against the challenges posed by ion migration are yet to be\nexplored - which we critically examine in this contribution through detailed\nnumerical simulations. Curiously, we find that, for similar ion densities and\ninterface recombination velocities, ion migration is more detrimental for\nPerovskite solar cells with TMO contact layers with much lower achievable\nefficiency limits (21%). The insights shared by this work should be of broad\ninterest to the community in terms of long-term stability, efficiency\ndegradation and hence could help critically evaluate the promises and prospects\nof TMOs as hole contact layers for perovskite solar cells.\n"}
+{"abstract": "  In this paper, we propose a novel methodology for better performing\nuncertainty and sensitivity analysis for complex mathematical models under\nconstraints and/or with dependent input variables, including correlated\nvariables. Our approach allows for assessing the single, overall and\ninteractions effects of any subset of input variables, that account for the\ndependencies structures inferred by the constraints. Using the variance as\nimportance measure among others, we define the main-effect and total\nsensitivity indices of input(s) with the former index less than the latter. We\nalso derive the consistent estimators and asymptotic distributions of such\nindices by distinguishing the case of the multivariate and/or functional\noutputs, including spatio-temporal models and dynamic models.\n"}
+{"abstract": "  We exhibit very small eigenvalues of the quadratic form associated to the\nWeil explicit formulas restricted to test functions whose support is within a\nfixed interval with upper bound S. We show both numerically and conceptually\nthat the associated eigenvectors are obtained by a simple arithmetic operation\nof finite sum using prolate spheroidal wave functions associated to the scale\nS. Then we use these functions to condition the canonical spectral triple of\nthe circle of length L=2 Log(S) in such a way that they belong to the kernel of\nthe perturbed Dirac operator. We give numerical evidence that, when one varies\nL, the low lying spectrum of the perturbed spectral triple resembles the low\nlying zeros of the Riemann zeta function. We justify conceptually this result\nand show that, for each eigenvalue, the coincidence is perfect for the special\nvalues of the length L of the circle for which the two natural ways of\nrealizing the perturbation give the same eigenvalue. This fact is tested\nnumerically by reproducing the first thirty one zeros of the Riemann zeta\nfunction from our spectral side, and estimate the probability of having\nobtained this agreement at random, as a very small number whose first fifty\ndecimal places are all zero. The theoretical concept which emerges is that of\nzeta cycle and our main result establishes its relation with the critical zeros\nof the Riemann zeta function and with the spectral realization of these zeros\nobtained by the first author.\n"}
+{"abstract": "  In this paper we solve two problems of Esperet, Kang and Thomasse as well as\nLi concerning (i) induced bipartite subgraphs in triangle-free graphs and (ii)\nvan der Waerden numbers. Each time random greedy algorithms allow us to go\nbeyond the Lovasz Local Lemma or alteration method used in previous work,\nillustrating the power of the algorithmic approach to the probabilistic method.\n"}
+{"abstract": "  Recently, the convolutional weighted power minimization distortionless\nresponse (WPD) beamformer was proposed, which unifies multi-channel weighted\nprediction error dereverberation and minimum power distortionless response\nbeamforming. To optimize the convolutional filter, the desired speech component\nis modeled with a time-varying Gaussian model, which promotes the sparsity of\nthe desired speech component in the short-time Fourier transform domain\ncompared to the noisy microphone signals. In this paper we generalize the\nconvolutional WPD beamformer by using an lp-norm cost function, introducing an\nadjustable shape parameter which enables to control the sparsity of the desired\nspeech component. Experiments based on the REVERB challenge dataset show that\nthe proposed method outperforms the conventional convolutional WPD beamformer\nin terms of objective speech quality metrics.\n"}
+{"abstract": "  The expected number of secondary infections arising from each index case, the\nreproduction number, or $R$ number is a vital summary statistic for\nunderstanding and managing epidemic diseases. There are many methods for\nestimating $R$; however, few of these explicitly model heterogeneous disease\nreproduction, which gives rise to superspreading within the population. Here we\npropose a parsimonious discrete-time branching process model for epidemic\ncurves that incorporates heterogeneous individual reproduction numbers. Our\nBayesian approach to inference illustrates that this heterogeneity results in\nless certainty on estimates of the time-varying cohort reproduction number\n$R_t$. Leave-future-out cross-validation evaluates the predictive performance\nof the proposed model, allowing us to assess epidemic curves for evidence of\nsuperspreading. We apply these methods to a COVID-19 epidemic curve for the\nRepublic of Ireland and find some support for heterogeneous disease\nreproduction. We conclude that the 10\\% most infectious index cases account for\napproximately 40-80\\% of the expected secondary infections. Our analysis\nhighlights the difficulties in identifying heterogeneous disease reproduction\nfrom epidemic curves and that heterogeneity is a vital consideration when\nestimating $R_t$.\n"}
+{"abstract": "  Fast and high-order accurate algorithms for three dimensional elastic\nscattering are of great importance when modeling physical phenomena in\nmechanics, seismic imaging, and many other fields of applied science. In this\npaper, we develop a novel boundary integral formulation for the three\ndimensional elastic scattering based on the Helmholtz decomposition of elastic\nfields, which converts the Navier equation to a coupled system consisted of\nHelmholtz and Maxwell equations. An FFT-accelerated separation of variables\nsolver is proposed to efficiently invert boundary integral formulations of the\ncoupled system for elastic scattering from axisymmetric rigid bodies. In\nparticular, by combining the regularization properties of the singular boundary\nintegral operators and the FFT-based fast evaluation of modal Green's\nfunctions, our numerical solver can rapidly solve the resulting integral\nequations with a high-order accuracy. Several numerical examples are provided\nto demonstrate the efficiency and accuracy of the proposed algorithm, including\ngeometries with corners at different wave number.\n"}
+{"abstract": "  We study generalized spin waves in graphene under a strong magnetic field\nwhen the Landau-level filling factor is $\\nu=\\pm 1$. In this case, the ground\nstate is a particular SU(4) quantum Hall ferromagnet, in which not only the\nphysical spin is fully polarized but also the pseudo-spin associated with the\nvalley degree of freedom. The nature of the ground state and the spin-valley\npolarization depend on explicit symmetry breaking terms that are also reflected\nin the generalised spin-wave spectrum. In addition to pure spin waves, one\nencounters valley-pseudo-spin waves as well as more exotic entanglement waves\nthat have a mixed spin-valley character. Most saliently, the SU(4)\nsymmetry-breaking terms do not only yield gaps in the spectra, but under\ncertain circumstances, namely in the case of residual ground-state symmetries,\nrender the originally quadratic (in the wave vector) spin-wave dispersion\nlinear.\n"}
+{"abstract": "  In this paper we provide two new semantics for proofs in the constructive\nmodal logics CK and CD. The first semantics is given by extending the syntax of\ncombinatorial proofs for propositional intuitionistic logic, in which proofs\nare factorised in a linear fragment (arena net) and a parallel\nweakening-contraction fragment (skew fibration). In particular we provide an\nencoding of modal formulas by means of directed graphs (modal arenas), and an\nencoding of linear proofs as modal arenas equipped with vertex partitions\nsatisfying topological criteria. The second semantics is given by means of\nwinning innocent strategies of a two-player game over modal arenas. This is\ngiven by extending the Heijltjes-Hughes-Stra{\\ss}burger correspondence between\nintuitionistic combinatorial proofs and winning innocent strategies in a\nHyland-Ong arena. Using our first result, we provide a characterisation of\nwinning strategies for games on a modal arena corresponding to proofs with\nmodalities.\n"}
+{"abstract": "  Floquet engineering, modulating quantum systems in a time periodic way, lies\nat the central part for realizing novel topological dynamical states. Thanks to\nthe Floquet engineering, various new realms on experimentally simulating\ntopological materials have emerged. Conventional Floquet engineering, however,\nonly applies to time periodic non-dissipative Hermitian systems, and for the\nquantum systems in reality, non-Hermitian process with dissipation usually\noccurs. So far, it remains unclear how to characterize topological phases of\nperiodically driven non-Hermitian systems via the frequency space Floquet\nHamiltonian. Here, we propose the non-Floquet theory to identify different\nFloquet topological phases of time periodic non-Hermitian systems via the\ngeneration of Floquet band gaps in frequency space. In non-Floquet theory, the\neigenstates of non-Hermitian Floquet Hamiltonian are temporally deformed to be\nof Wannier-Stark localization. Remarkably, we show that different choices of\nstarting points of driving period can result to different localization\nbehavior, which effect can reversely be utilized to design detectors of quantum\nphases in dissipative oscillating fields. Our protocols establish a fundamental\nrule for describing topological features in non-Hermitian dynamical systems and\ncan find its applications to construct new types of Floquet topological\nmaterials.\n"}
+{"abstract": "  We study the convective and absolute forms of azimuthal magnetorotational\ninstability (AMRI) in a Taylor-Couette (TC) flow with an imposed azimuthal\nmagnetic field. We show that the domain of the convective AMRI is wider than\nthat of the absolute AMRI. Actually, it is the absolute instability which is\nthe most relevant and important for magnetic TC flow experiments. The absolute\nAMRI, unlike the convective one, stays in the device, displaying a sustained\ngrowth that can be experimentally detected. We also study the global AMRI in a\nTC flow of finite height using DNS and find that its emerging butterfly-type\nstructure -- a spatio-temporal variation in the form of upward and downward\ntraveling waves -- is in a very good agreement with the linear stability\nanalysis, which indicates the presence of two dominant absolute AMRI modes in\nthe flow giving rise to this global butterfly pattern.\n"}
+{"abstract": "  Whilst ``slingshot'' prominences have been observed on M-dwarfs, most if not\nall theoretical studies have focused on solar-like stars. We present an\ninvestigation into stellar prominences around rapidly rotating young M-dwarfs.\nWe have extrapolated the magnetic field in the corona from Zeeman-Doppler maps\nand determined the sites of mechanical stability where prominences may form. We\nanalyse the prominence mass that could be supported and the latitude range over\nwhich this material is distributed. We find that for these maps, much of this\nprominence mass may be invisible to observation - typically <1\\% transits the\nstellar disc. On the rapidly-rotating M-dwarf V374 Peg (P$_{\\rm rot}$ = 0.45\ndays) where prominences have been observed, we find the visible prominence mass\nto be around only 10\\% of the total mass supported. The mass loss rate per unit\narea for prominences scales with the X-ray surface flux as $\\dot{M}/A \\propto$\n$F_X^{1.32}$ which is very close to the observationally-derived value for\nstellar winds. This suggests that prominence ejection may contribute\nsignificantly to the overall stellar wind loss and spin down. A planet in an\nequatorial orbit in the habitable zone of these stars may experience\nintermittent enhancements of the stellar wind due to prominence ejections. On\nsome stars, this may occur throughout 20\\% of the orbit.\n"}
+{"abstract": "  Let $g$ be a metric on hemisphere $S^{n}_{+}$ ($n\\geq 3$) which is conformal\nto the standard round metric $g_0$. Suppose its $Q$-curvature $Q_g$ is bounded\nbelow by $Q_0$, we show that $g$ is isometric to $g_0$, provided that the\ninduced metric on $\\partial S^{n}_{+}$ coincides with $g_0$ up to certain\norder.\n"}
+{"abstract": "  The discovery of superconductivity in copper oxide compounds has attracted\nconsiderable attention over the past three decades. The high transition\ntemperature in these compounds, exhibiting proximity to an antiferromagnetic\norder in their phase diagrams, remains one of the main areas of research. The\npresent study attempts to introduce Fe, Co and Ni magnetic impurities into the\nsuperconducting Y-123 with the aim of exploring the transition temperature\nbehavior. The solid-state synthesis is exploited to prepare fully oxygenated\nY1-xMxBa2Cu3O7 (M = Co, Fe, Ni) samples with low levels of doping (0< x <\n0.03). Systematic measurements are then employed to assess the synthesized\nsamples using AC magnetic susceptibility, electrical resistivity and X-ray\ndiffraction. The measurements revealed an increase in Tc as a result of\nmagnetic substitution for Y. However, the study of non-magnetic dopings on the\nfully oxygenated Y1-xM'xBa2Cu3O7 (M' = Ca, Sr) samples showed a decrease in Tc.\nQuantitative XRD analysis further suggested that the internal pressure could\nhave minor effects on the increase in Tc. The normal state resistivity vs\ntemperature showed a linear profile, confirming that the samples are at an\noptimal doping of the carrier concentration.\n"}
+{"abstract": "  We give a new sufficient condition which allows to test primality of Fermat's\nnumbers. This characterization uses uniquely values at most equal to tested\nFermat number. The robustness of this result is due to a strict use of\nelementary arithmetic technical tools and it will be susceptible to open gates\nfor revolutionary statement that all Fermat's numbers are all decomposable.\n"}
+{"abstract": "  Recovering 3D phase features of complex, multiple-scattering biological\nsamples traditionally sacrifices computational efficiency and processing time\nfor physical model accuracy and reconstruction quality. This trade-off hinders\nthe rapid analysis of living, dynamic biological samples that are often of\ngreatest interest to biological research. Here, we overcome this bottleneck by\ncombining annular intensity diffraction tomography (aIDT) with an\napproximant-guided deep learning framework. Using a novel physics model\nsimulator-based learning strategy trained entirely on natural image datasets,\nwe show our network can robustly reconstruct complex 3D biological samples of\narbitrary size and structure. This approach highlights that large-scale\nmultiple-scattering models can be leveraged in place of acquiring experimental\ndatasets for achieving highly generalizable deep learning models. We devise a\nnew model-based data normalization pre-processing procedure for homogenizing\nthe sample contrast and achieving uniform prediction quality regardless of\nscattering strength. To achieve highly efficient training and prediction, we\nimplement a lightweight 2D network structure that utilizes a multi-channel\ninput for encoding the axial information. We demonstrate this framework's\ncapabilities on experimental measurements of epithelial buccal cells and\nCaenorhabditis elegans worms. We highlight the robustness of this approach by\nevaluating dynamic samples on a living worm video, and we emphasize our\napproach's generalizability by recovering algae samples evaluated with\ndifferent experimental setups. To assess the prediction quality, we develop a\nnovel quantitative evaluation metric and show that our predictions are\nconsistent with our experimental measurements and multiple-scattering physics.\n"}
+{"abstract": "  We propose a deep learning approach to predicting audio event onsets in\nelectroencephalogram (EEG) recorded from users as they listen to music. We use\na publicly available dataset containing ten contemporary songs and concurrently\nrecorded EEG. We generate a sequence of onset labels for the songs in our\ndataset and trained neural networks (a fully connected network (FCN) and a\nrecurrent neural network (RNN)) to parse one second windows of input EEG to\npredict one second windows of onsets in the audio. We compare our RNN network\nto both the standard spectral-flux based novelty function and the FCN. We find\nthat our RNN was able to produce results that reflected its ability to\ngeneralize better than the other methods.\n  Since there are no pre-existing works on this topic, the numbers presented in\nthis paper may serve as useful benchmarks for future approaches to this\nresearch problem.\n"}
+{"abstract": "  Methods for extracting the $\\psi(3770)\\to e^+e^-$ decay width from the data\non the reaction cross section $e^+e^-\\to D\\bar D$ are discussed. Attention is\ndrawn to the absence of the generally accepted method for determining\n$\\Gamma_{\\psi(3770)e^+e^-}$ in the presence of interference between the\ncontributions of the $\\psi(3770)$ resonance and background. It is shown that\nthe model for the experimentally measured $D$ meson form factor, which\nsatisfies the requirement of the Watson theorem and takes into account the\ncontribution of the complex of the mixed $\\psi(3770)$ and $\\psi(2S)$\nresonances, allows uniquely determine the value of $\\Gamma_{\\psi(3770)e^+e^-}$\nby fitting. The $\\Gamma_{\\psi(3770)e^+ e^-}$ values found from the data\nprocessing are compared with the estimates in the potential models.\n"}
+{"abstract": "  A novel reformulation of D=4, N=1 supergravity action in the language of\nintegral forms is given. We illustrate the construction of the Berezinian in\nthe supergeometric framework, providing a useful dictionary between mathematics\nand physics. We present a unified framework for Berezin-Lebesgue integrals for\nfunctions and for integral forms. As an application, we discuss Volkov-Akulov\ntheory and its coupling to supergravity from this new perspective.\n"}
+{"abstract": "  This article provides the unconditional security of a semi quantum key\ndistribution (SQKD) protocol based on 3-dimensional quantum states. By deriving\na lower bound for the key rate, in the asymptotic scenario, as a function of\nthe quantum channel's noise, we find that this protocol has improved secret key\nrate with much more tolerance for noise compared to the previous 2-dimensional\nSQKD protocol. Our results highlight that, similar to the fully quantum key\ndistribution protocol, increasing the dimension of the system can increase the\nnoise tolerance in the semi-quantum key distribution, as well.\n"}
+{"abstract": "  Evolving out of a gender-neutral framing of an involuntary celibate identity,\nthe concept of `incels' has come to refer to an online community of men who\nbear antipathy towards themselves, women, and society-at-large for their\nperceived inability to find and maintain sexual relationships. By exploring\nincel language use on Reddit, a global online message board, we contextualize\nthe incel community's online expressions of misogyny and real-world acts of\nviolence perpetrated against women. After assembling around three million\ncomments from incel-themed Reddit channels, we analyze the temporal dynamics of\na data driven rank ordering of the glossary of phrases belonging to an emergent\nincel lexicon. Our study reveals the generation and normalization of an\nextensive coded misogynist vocabulary in service of the group's identity.\n"}
+{"abstract": "  We present a new higher-order accurate finite difference explicit jump\nImmersed Interface Method (HEJIIM) for solving two-dimensional elliptic\nproblems with singular source and discontinuous coefficients in the irregular\nregion on a compact Cartesian mesh. We propose a new strategy for discretizing\nthe solution at irregular points on a nine point compact stencil such that the\nhigher-order compactness is maintained throughout the whole computational\ndomain. The scheme is employed to solve four problems embedded with circular\nand star shaped interfaces in a rectangular region having analytical solutions\nand varied discontinuities across the interface in source and the coefficient\nterms. We also simulate a plethora of fluid flow problems past bluff bodies in\ncomplex flow situations, which are governed by the Navier-Stokes equations;\nthey include problems involving multiple bodies immersed in the flow as well.\nIn the process, we show the superiority of the proposed strategy over the EJIIM\nand other existing IIM methods by establishing the rate of convergence and grid\nindependence of the computed solutions. In all the cases our computed results\nextremely close to the available numerical and experimental results.\n"}
+{"abstract": "  Pressure and temperature profile are key data for safe production in oil and\ngas wells. In this paper, a bucket-brigade inspired sensor network protocol is\nproposed which can be used to extract sensed data profile from the nanoscale up\nto kilometer long structures. The PHY/MAC layers are discussed. This protocol\nis best suited for low data rate exchanges in small fixed-size packets, named\nbuckets, transmitted as time-domain bursts among high-precision smart sensors\ndeployed as a queue. There is only one coordinator, which is not directly\naccessible by most of the sensor nodes. The coordinator is responsible for\ncollecting the measurement profile and send it to a supervisory node. There is\nno need for complex routing mechanism, as the network topology is determined\nduring deployment. There are many applications which require sensors to be\ndeployed as a long queue and sensed data could be transmitted at low data\nrates. Examples of such monitoring applications are: neural connected\nartificial skin, oil/gas/water pipeline integrity, power transmission line\ntower integrity, (rail)road/highway lighting and integrity, individualized\nmonitoring in vineyard or re-foresting or plantation, underwater\ntelecommunications cable integrity, oil/gas riser integrity, oil/gas well\ntemperature and pressure profile, among others. For robustness and reduced\nelectromagnetic interference, wired network is preferred. Besides in some harsh\nenvironment wireless is not feasible. To reduce wiring, communications can be\ncarried out in the same cable used to supply electrical power.\n"}
+{"abstract": "  The task of image segmentation is inherently noisy due to ambiguities\nregarding the exact location of boundaries between anatomical structures. We\nargue that this information can be extracted from the expert annotations at no\nextra cost, and when integrated into state-of-the-art neural networks, it can\nlead to improved calibration between soft probabilistic predictions and the\nunderlying uncertainty. We built upon label smoothing (LS) where a network is\ntrained on 'blurred' versions of the ground truth labels which has been shown\nto be effective for calibrating output predictions. However, LS is not taking\nthe local structure into account and results in overly smoothed predictions\nwith low confidence even for non-ambiguous regions. Here, we propose Spatially\nVarying Label Smoothing (SVLS), a soft labeling technique that captures the\nstructural uncertainty in semantic segmentation. SVLS also naturally lends\nitself to incorporate inter-rater uncertainty when multiple labelmaps are\navailable. The proposed approach is extensively validated on four clinical\nsegmentation tasks with different imaging modalities, number of classes and\nsingle and multi-rater expert annotations. The results demonstrate that SVLS,\ndespite its simplicity, obtains superior boundary prediction with improved\nuncertainty and model calibration.\n"}
+{"abstract": "  Variational Quantum Algorithms (VQAs) have received considerable attention\ndue to their potential for achieving near-term quantum advantage. However, more\nwork is needed to understand their scalability. One known scaling result for\nVQAs is barren plateaus, where certain circumstances lead to exponentially\nvanishing gradients. It is common folklore that problem-inspired ansatzes avoid\nbarren plateaus, but in fact, very little is known about their gradient\nscaling. In this work we employ tools from quantum optimal control to develop a\nframework that can diagnose the presence or absence of barren plateaus for\nproblem-inspired ansatzes. Such ansatzes include the Quantum Alternating\nOperator Ansatz (QAOA), the Hamiltonian Variational Ansatz (HVA), and others.\nWith our framework, we prove that avoiding barren plateaus for these ansatzes\nis not always guaranteed. Specifically, we show that the gradient scaling of\nthe VQA depends on the controllability of the system, and hence can be\ndiagnosed trough the dynamical Lie algebra $\\mathfrak{g}$ obtained from the\ngenerators of the ansatz. We analyze the existence of barren plateaus in QAOA\nand HVA ansatzes, and we highlight the role of the input state, as different\ninitial states can lead to the presence or absence of barren plateaus. Taken\ntogether, our results provide a framework for trainability-aware ansatz design\nstrategies that do not come at the cost of extra quantum resources. Moreover,\nwe prove no-go results for obtaining ground states with variational ansatzes\nfor controllable system such as spin glasses. We finally provide evidence that\nbarren plateaus can be linked to dimension of $\\mathfrak{g}$.\n"}
+{"abstract": "  We discuss the usefulness and theoretical consistency of different entropy\nvariables used in the literature to describe isocurvature perturbations in\nmultifield inflationary models with a generic curved field space. We clarify\nwhich is the proper entropy variable to be used to match the evolution of\nisocurvature modes during inflation to the one after the reheating epoch in\norder to compare with observational constraints. In particular, we find that\ncommonly used variables, as the relative entropy perturbation or the one\nassociated to the decomposition in tangent and normal perturbations with\nrespect to the inflationary trajectory, even if more useful to perform\nnumerical studies, can lead to results which are wrong by several orders of\nmagnitude, or even to apparent destabilisation effects which are unphysical for\ncases with light kinetically coupled spectator fields.\n"}
+{"abstract": "  This paper proposes a controller for stable grasping of unknown-shaped\nobjects by two robotic fingers with tactile fingertips. The grasp is stabilised\nby rolling the fingertips on the contact surface and applying a desired\ngrasping force to reach an equilibrium state. The validation is both in\nsimulation and on a fully-actuated robot hand (the Shadow Modular Grasper)\nfitted with custom-built optical tactile sensors (based on the BRL TacTip). The\ncontroller requires the orientations of the contact surfaces, which are\nestimated by regressing a deep convolutional neural network over the tactile\nimages. Overall, the grasp system is demonstrated to achieve stable equilibrium\nposes on various objects ranging in shape and softness, with the system being\nrobust to perturbations and measurement errors. This approach also has promise\nto extend beyond grasping to stable in-hand object manipulation with multiple\nfingers.\n"}
+{"abstract": "  Surveillance cameras are widely applied for indoor occupancy measurement and\nhuman movement perception, which benefit for building energy management and\nsocial security. To address the challenges of limited view angle of single\ncamera as well as lacking of inter-camera collaboration, this study presents a\nnon-overlapping multi-camera system to enlarge the surveillance area and\ndevotes to retrieve the same person appeared from different camera views. The\nsystem is deployed in an office building and four-day videos are collected. By\ntraining a deep convolutional neural network, the proposed system first\nextracts the appearance feature embeddings of each personal image, which\ndetected from different cameras, for similarity comparison. Then, a stochastic\ninter-camera transition matrix is associated with appearance feature for\nfurther improving the person re-identification ranking results. Finally, a\nnoise-suppression explanation is given for analyzing the matching improvements.\nThis paper expands the scope of indoor movement perception based on\nnon-overlapping multiple cameras and improves the accuracy of pedestrian\nre-identification without introducing additional types of sensors.\n"}
+{"abstract": "  This paper proposes semi-discrete and fully discrete hybridizable\ndiscontinuous Galerkin (HDG) methods for the Burgers' equation in two and three\ndimensions. In the spatial discretization, we use piecewise polynomials of\ndegrees $ k \\ (k \\geq 1), k-1$ and $ l \\ (l=k-1; k) $ to approximate the scalar\nfunction, flux variable and the interface trace of scalar function,\nrespectively. In the full discretization method, we apply a backward Euler\nscheme for the temporal discretization. Optimal a priori error estimates are\nderived. Numerical experiments are presented to support the theoretical\nresults.\n"}
+{"abstract": "  The Kepler mission has provided a wealth of data, revealing new insights in\ntime-domain astronomy. However, Kepler's single band-pass has limited studies\nto a single wavelength. In this work we build a data-driven, pixel-level model\nfor the Pixel Response Function (PRF) of Kepler targets, modeling the image\ndata from the spacecraft. Our model is sufficiently flexible to capture known\ndetector effects, such as non-linearity, intra-pixel sensitivity variations,\nand focus change. In theory, the shape of the Kepler PRF should also be weakly\nwavelength dependent, due to optical chromatic aberration and wavelength\ndependent detector response functions. We are able to identify these predicted\nshape changes to the PRF using the residuals between Kepler data and our model.\nIn this work, we show that these PRF changes correspond to wavelength\nvariability in Kepler targets using a small sample of eclipsing binaries. Using\nour model, we demonstrate that pixel-level light curves of eclipsing binaries\nshow variable eclipse depths, ellipsoidal modulation and limb darkening. These\nchanges at the pixel level are consistent with multi-wavelength photometry. Our\nwork suggests each pixel in the Kepler data of a single target has a different\neffective wavelength, ranging from $\\approx$ 550-750 $nm$. In this proof of\nconcept, we demonstrate our model, and discuss possible use cases for the\nwavelength dependent Pixel Response Function of Kepler. These use cases include\ncharacterizing variable systems, and vetting exoplanet discoveries at the pixel\nlevel. The chromatic PRF of Kepler is due to weak wavelength dependence in the\noptical systems and detector of the telescope, and similar chromatic PRFs are\nexpected in other similar telescopes, notably the NASA TESS telescope.\n"}
+{"abstract": "  Quantum mechanics is well known to accelerate statistical sampling processes\nover classical techniques. In quantitative finance, statistical samplings arise\nbroadly in many use cases. Here we focus on a particular one of such use cases,\ncredit valuation adjustment (CVA), and identify opportunities and challenges\ntowards quantum advantage for practical instances. To improve the depths of\nquantum circuits for solving such problem, we draw on various heuristics that\nindicate the potential for significant improvement over well-known techniques\nsuch as reversible logical circuit synthesis. In minimizing the resource\nrequirements for amplitude amplification while maximizing the speedup gained\nfrom the quantum coherence of a noisy device, we adopt a recently developed\nBayesian variant of quantum amplitude estimation using engineered likelihood\nfunctions (ELF). We perform numerical analyses to characterize the prospect of\nquantum speedup in concrete CVA instances over classical Monte Carlo\nsimulations.\n"}
+{"abstract": "  A variational formula for the asymptotic variance of general Markov processes\nis obtained. As application, we get a upper bound of the mean exit time of\nreversible Markov processes, and some comparison theorems between the\nreversible and non-reversible diffusion processes.\n"}
+{"abstract": "  We derive new variants of the quantitative Borel--Cantelli lemma and apply\nthem to analysis of statistical properties for some dynamical systems. We\nconsider intermittent maps of $(0,1]$ which have absolutely continuous\ninvariant probability measures. In particular, we prove that every sequence of\nintervals with left endpoints uniformly separated from zero is the strong\nBorel--Cantelli sequence with respect to such map and invariant measure.\n"}
+{"abstract": "  The debate surrounding fast magnetic energy dissipation by magnetic\nreconnection has remained a fundamental topic in the plasma universe, not only\nin the Earth's magnetosphere but in astrophysical objects such as pulsar\nmagnetospheres and magnetars, for more than half a century. Recently,\nnonthermal particle acceleration and plasma heating during reconnection have\nbeen extensively studied, and it has been argued that rapid energy dissipation\ncan occur for a collisionless \"thin\" current sheet, the thickness of which is\nof the order of the particle gyro-radius. However, it is an intriguing enigma\nas to how the fast energy dissipation can occur for a \"thick\" current sheet\nwith thickness larger than the particle gyro-radius. Here we demonstrate, using\na high-resolution particle-in-cell simulation for a pair plasma, that an\nexplosive reconnection can emerge with the enhancement of the inertia\nresistivity due to the magnetization of the meandering particles by the\nreconnecting magnetic field and the shrinkage of the current sheet. In\naddition, regardless of the initial thickness of the current sheet, the time\nscale of the nonlinear explosive reconnection is tens of the Alfv\\'{e}n transit\ntime.\n"}
+{"abstract": "  Let $E$ be an elliptic curve over $\\mathbb{Q}$ with discriminant $\\Delta_E$.\nFor primes $p$ of good reduction, let $N_p$ be the number of points modulo $p$\nand write $N_p=p+1-a_p$. In 1965, Birch and Swinnerton-Dyer formulated a\nconjecture which implies $$\\lim_{x\\to\\infty}\\frac{1}{\\log\nx}\\sum_{\\substack{p\\leq x\\\\ p\\nmid \\Delta_{E}}}\\frac{a_p\\log\np}{p}=-r+\\frac{1}{2},$$ where $r$ is the order of the zero of the $L$-function\n$L_{E}(s)$ of $E$ at $s=1$, which is predicted to be the Mordell-Weil rank of\n$E(\\mathbb{Q})$. We show that if the above limit exits, then the limit equals\n$-r+1/2$. We also relate this to Nagao's conjecture.\n"}
+{"abstract": "  In the Internet of Things, learning is one of most prominent tasks. In this\npaper, we consider an Internet of Things scenario where federated learning is\nused with simultaneous transmission of model data and wireless power. We\ninvestigate the trade-off between the number of communication rounds and\ncommunication round time while harvesting energy to compensate the energy\nexpenditure. We formulate and solve an optimization problem by considering the\nnumber of local iterations on devices, the time to transmit-receive the model\nupdates, and to harvest sufficient energy. Numerical results indicate that\nmaximum ratio transmission and zero-forcing beamforming for the optimization of\nthe local iterations on devices substantially boost the test accuracy of the\nlearning task. Moreover, maximum ratio transmission instead of zero-forcing\nprovides the best test accuracy and communication round time trade-off for\nvarious energy harvesting percentages. Thus, it is possible to learn a model\nquickly with few communication rounds without depleting the battery.\n"}
+{"abstract": "  Following the success in advancing natural language processing and\nunderstanding, transformers are expected to bring revolutionary changes to\ncomputer vision. This work provides the first and comprehensive study on the\nrobustness of vision transformers (ViTs) against adversarial perturbations.\nTested on various white-box and transfer attack settings, we find that ViTs\npossess better adversarial robustness when compared with convolutional neural\nnetworks (CNNs). This observation also holds for certified robustness. We\nsummarize the following main observations contributing to the improved\nrobustness of ViTs:\n  1) Features learned by ViTs contain less low-level information and are more\ngeneralizable, which contributes to superior robustness against adversarial\nperturbations.\n  2) Introducing convolutional or tokens-to-token blocks for learning low-level\nfeatures in ViTs can improve classification accuracy but at the cost of\nadversarial robustness.\n  3) Increasing the proportion of transformers in the model structure (when the\nmodel consists of both transformer and CNN blocks) leads to better robustness.\nBut for a pure transformer model, simply increasing the size or adding layers\ncannot guarantee a similar effect.\n  4) Pre-training on larger datasets does not significantly improve adversarial\nrobustness though it is critical for training ViTs.\n  5) Adversarial training is also applicable to ViT for training robust models.\n  Furthermore, feature visualization and frequency analysis are conducted for\nexplanation. The results show that ViTs are less sensitive to high-frequency\nperturbations than CNNs and there is a high correlation between how well the\nmodel learns low-level features and its robustness against different\nfrequency-based perturbations.\n"}
+{"abstract": "  Hypothesis Control of capillary flow through porous media has broad practical\nimplications. However, achieving accurate and reliable control of such\nprocesses by tuning the pore size or by modification of interface wettability\nremains challenging. Here we propose that the flow of liquid by capillary\npenetration can be accurately adjusted by tuning the geometry of porous media\nand develop numerical method to achieve this. Methodologies On the basis of\nDarcys law, a general framework is proposed to facilitate the control of\ncapillary flow in porous systems by tailoring the geometric shape of porous\nstructures. A numerical simulation approach based on finite element method is\nalso employed to validate the theoretical prediction. Findings A basic\ncapillary component with a tunable velocity gradient is designed according to\nthe proposed framework. By using the basic component, two functional capillary\nelements, namely, (i) flow amplifier and (ii) flow resistor, are demonstrated.\nThen, multi functional fluidic devices with controllable capillary flow are\nrealized by integrating the designed capillary elements. All the theoretical\ndesigns are validated by numerical simulations. Finally, it is shown that the\nproposed model can be extended to three dimensional designs of porous media\n"}
+{"abstract": "  Logical theories in the form of ontologies and similar artefacts in computing\nand IT are used for structuring, annotating, and querying data, among others,\nand therewith influence data analytics regarding what is fed into the\nalgorithms. Algorithmic bias is a well-known notion, but what does bias mean in\nthe context of ontologies that provide a structuring mechanism for an\nalgorithm's input? What are the sources of bias there and how would they\nmanifest themselves in ontologies? We examine and enumerate types of bias\nrelevant for ontologies, and whether they are explicit or implicit. These eight\ntypes are illustrated with examples from extant production-level ontologies and\nsamples from the literature. We then assessed three concurrently developed\nCOVID-19 ontologies on bias and detected different subsets of types of bias in\neach one, to a greater or lesser extent. This first characterisation aims\ncontribute to a sensitisation of ethical aspects of ontologies primarily\nregarding representation of information and knowledge.\n"}
+{"abstract": "  We show that anagram-free vertex colouring a $2\\times n$ square grid requires\na number of colours that increases with $n$. This answers an open question in\nWilson's thesis and shows that even graphs of pathwidth $2$ do not have\nanagram-free colourings with a bounded number of colours.\n"}
+{"abstract": "  Despite their success, large pre-trained multilingual models have not\ncompletely alleviated the need for labeled data, which is cumbersome to collect\nfor all target languages. Zero-shot cross-lingual transfer is emerging as a\npractical solution: pre-trained models later fine-tuned on one transfer\nlanguage exhibit surprising performance when tested on many target languages.\nEnglish is the dominant source language for transfer, as reinforced by popular\nzero-shot benchmarks. However, this default choice has not been systematically\nvetted. In our study, we compare English against other transfer languages for\nfine-tuning, on two pre-trained multilingual models (mBERT and mT5) and\nmultiple classification and question answering tasks. We find that other\nhigh-resource languages such as German and Russian often transfer more\neffectively, especially when the set of target languages is diverse or unknown\na priori. Unexpectedly, this can be true even when the training sets were\nautomatically translated from English. This finding can have immediate impact\non multilingual zero-shot systems, and should inform future benchmark designs.\n"}
+{"abstract": "  Recently, chance-constrained stochastic electricity market designs have been\nproposed to address the shortcomings of scenario-based stochastic market\ndesigns. In particular, the use of chance-constrained market-clearing avoids\ntrading off in-expectation and per-scenario characteristics and yields unique\nenergy and reserves prices. However, current formulations rely on symmetric\ncontrol policies based on the aggregated system imbalance, which restricts\nbalancing reserve providers in their energy and reserve commitments. This paper\nextends existing chance-constrained market-clearing formulations by leveraging\nnode-to-node and asymmetric balancing reserve policies and deriving the\nresulting energy and reserve prices. The proposed node-to-node policy allows\nfor relating the remuneration of balancing reserve providers and payment of\nuncertain resources using a marginal cost-based approach. Further, we introduce\nasymmetric balancing reserve policies into the chance-constrained electricity\nmarket design and show how this additional degree of freedom affects market\noutcomes.\n"}
+{"abstract": "  Public transport ridership around the world has been hit hard by the COVID-19\npandemic. Travellers are likely to adapt their behaviour to avoid the risk of\ntransmission and these changes may even be sustained after the pandemic. To\nevaluate travellers' behaviour in public transport networks during these times\nand assess how they will respond to future changes in the pandemic, we conduct\na stated choice experiment with train travellers in the Netherlands. We\nspecifically assess behaviour related to three criteria affecting the risk of\nCOVID-19 transmission: (i) crowding, (ii) exposure duration, and (iii)\nprevalent infection rate.\n  Observed choices are analysed using a latent class choice model which reveals\ntwo, nearly equally sized traveller segments: 'COVID Conscious' and 'Infection\nIndifferent'. The former has a significantly higher valuation of crowding,\naccepting, on average 8.75 minutes extra waiting time to reduce one person\non-board. Moreover, they demonstrate a strong desire to sit without anybody in\ntheir neighbouring seat and are quite sensitive to changes in the prevalent\ninfection rate. By contrast, Infection Indifferent travellers' value of\ncrowding (1.04 waiting time minutes/person) is only slightly higher than\npre-pandemic estimates and they are relatively unaffected by infection rates.\nWe find that older and female travellers are more likely to be COVD Conscious\nwhile those reporting to use the trains more frequently during the pandemic\ntend to be Infection Indifferent. Further analysis also reveals differences\nbetween the two segments in attitudes towards the pandemic and self-reported\nrule-following behaviour. The behavioural insights from this study will not\nonly contribute to better demand forecasting for service planning but will also\ninform public transport policy decisions aimed at curbing the shift to private\nmodes.\n"}
+{"abstract": "  There are only few very-high-energy sources in our Galaxy which might\naccelerate particles up to the knee of the cosmic-ray spectrum. To understand\nthe mechanisms of particle acceleration in these PeVatron candidates,\n\\textit{Fermi}-LAT and H.E.S.S. observations are essential to characterize\ntheir $\\gamma$-ray emission. HESS J1640$-$465 and the PeVatron candidate HESS\nJ1641$-$463 are two neighboring (\\ang[astroang]{0.25}) $\\gamma$-ray sources,\nspatially coincident with the radio supernova remnants (SNRs) G338.3$-$0.0 and\nG338.5+0.1. Detected both by H.E.S.S. and \\textit{Fermi}-LAT, we present here a\nmorphological and spectral analysis of these two sources using 8 years of\n\\textit{Fermi}-LAT data between 200 \\si{\\mega\\electronvolt} and 1\n\\si{\\tera\\electronvolt} with multi-wavelength observations to assess their\nnature. The morphology of HESS J1640$-$465 is described by a 2D Gaussian\n($\\sigma=$ \\ang[astroang]{0.053} $\\pm$ \\ang[astroang]{0.011}$_{stat}$ $ \\pm$\n\\ang[astroang]{0.03}$_{syst}$) and its spectrum is modeled by a power-law with\na spectral index $\\Gamma = 1.8\\pm0.1_{\\rm stat}\\pm0.2_{\\rm syst}$. HESS\nJ1641$-$463 is detected as a point-like source and its GeV emission is\ndescribed by a logarithmic-parabola spectrum with $\\alpha = 2.7 \\pm 0.1_ {\\rm\nstat} \\pm 0.2_ {\\rm syst} $ and significant curvature of $\\beta = 0.11 \\pm\n0.03_ {\\rm stat} \\pm 0.05_ {\\rm syst} $. Radio and X-ray flux upper limits were\nderived. We investigated scenarios to explain their emission, namely the\nemission from accelerated particles within the SNRs spatially coincident with\neach source, molecular clouds illuminated by cosmic rays from the close-by\nSNRs, and a pulsar/PWN origin. Our new \\emph{Fermi}-LAT results and the radio\nand flux X-ray upper limits pose severe constraints on some of these models.\n"}
+{"abstract": "  We consider discrete Schr\\\"odinger operators with aperiodic potentials given\nby a Sturmian word, which is a natural generalisation of the Fibonacci\nHamiltonian. We introduce the finite section method, which is often used to\nsolve operator equations approximately, and apply it first to periodic\nSchr\\\"odinger operators. It turns out that the applicability of the method is\nalways guaranteed for integer-valued potentials provided that the operator is\ninvertible. By using periodic approximations, we find a necessary and\nsufficient condition for the applicability of the finite section method for\naperiodic Schr\\\"odinger operators and a numerical method to check it.\n"}
+{"abstract": "  Quantum integrated photonics requires large-scale linear optical circuitry,\nand for many applications it is desirable to have a universally programmable\ncircuit, able to implement an arbitrary unitary transformation on a number of\nmodes. This has been achieved using the Reck scheme, consisting of a network of\nMach Zehnder interferometers containing a variable phase shifter in one path,\nas well as an external phase shifter after each Mach Zehnder. It subsequently\nbecame apparent that with symmetric Mach Zehnders containing a phase shift in\nboth paths, the external phase shifts are redundant, resulting in a more\ncompact circuit. The rectangular Clements scheme improves on the Reck scheme in\nterms of circuit depth, but it has been thought that an external phase-shifter\nwas necessary after each Mach Zehnder. Here, we show that the Clements scheme\ncan be realised using symmetric Mach Zehnders, requiring only a small number of\nexternal phase-shifters that do not contribute to the depth of the circuit.\nThis will result in a significant saving in the length of these devices,\nallowing more complex circuits to fit onto a photonic chip, and reducing the\npropagation losses associated with these circuits. We also discuss how similar\nsavings can be made to alternative schemes which have robustness to imbalanced\nbeam-splitters.\n"}
+{"abstract": "  The consistency of a response to a given post at semantic-level and\nemotional-level is essential for a dialogue system to deliver human-like\ninteractions. However, this challenge is not well addressed in the literature,\nsince most of the approaches neglect the emotional information conveyed by a\npost while generating responses. This article addresses this problem by\nproposing a unifed end-to-end neural architecture, which is capable of\nsimultaneously encoding the semantics and the emotions in a post for generating\nmore intelligent responses with appropriately expressed emotions. Extensive\nexperiments on real-world data demonstrate that the proposed method outperforms\nthe state-of-the-art methods in terms of both content coherence and emotion\nappropriateness.\n"}
+{"abstract": "  The Isolation Lemma of Mulmuley, Vazirani and Vazirani [Combinatorica'87]\nprovides a self-reduction scheme that allows one to assume that a given\ninstance of a problem has a unique solution, provided a solution exists at all.\nSince its introduction, much effort has been dedicated towards derandomization\nof the Isolation Lemma for specific classes of problems. So far, the focus was\nmainly on problems solvable in polynomial time.\n  In this paper, we study a setting that is more typical for\n$\\mathsf{NP}$-complete problems, and obtain partial derandomizations in the\nform of significantly decreasing the number of required random bits. In\nparticular, motivated by the advances in parameterized algorithms, we focus on\nproblems on decomposable graphs. For example, for the problem of detecting a\nHamiltonian cycle, we build upon the rank-based approach from [Bodlaender et\nal., Inf. Comput.'15] and design isolation schemes that use\n  - $O(t\\log n + \\log^2{n})$ random bits on graphs of treewidth at most $t$;\n  - $O(\\sqrt{n})$ random bits on planar or $H$-minor free graphs; and\n  - $O(n)$-random bits on general graphs.\n  In all these schemes, the weights are bounded exponentially in the number of\nrandom bits used. As a corollary, for every fixed $H$ we obtain an algorithm\nfor detecting a Hamiltonian cycle in an $H$-minor-free graph that runs in\ndeterministic time $2^{O(\\sqrt{n})}$ and uses polynomial space; this is the\nfirst algorithm to achieve such complexity guarantees. For problems of more\nlocal nature, such as finding an independent set of maximum size, we obtain\nisolation schemes on graphs of treedepth at most $d$ that use $O(d)$ random\nbits and assign polynomially-bounded weights.\n  We also complement our findings with several unconditional and conditional\nlower bounds, which show that many of the results cannot be significantly\nimproved.\n"}
+{"abstract": "  The evolution of epidemiological parameters, such as instantaneous\nreproduction number Rt, is important for understanding the transmission\ndynamics of infectious diseases. Current estimates of time-varying\nepidemiological parameters often face problems such as lagging observations,\naveraging inference, and improper quantification of uncertainties. To address\nthese problems, we propose a Bayesian data assimilation framework for\ntime-varying parameter estimation. Specifically, this framework is applied to\nRt estimation, resulting in the state-of-the-art DARt system. With DARt, time\nmisalignment caused by lagging observations is tackled by incorporating\nobservation delays into the joint inference of infections and Rt; the drawback\nof averaging is overcome by instantaneously updating upon new observations and\ndeveloping a model selection mechanism that captures abrupt changes; the\nuncertainty is quantified and reduced by employing Bayesian smoothing. We\nvalidate the performance of DARt and demonstrate its power in revealing the\ntransmission dynamics of COVID-19. The proposed approach provides a promising\nsolution for accurate and timely estimating transmission dynamics from reported\ndata.\n"}
+{"abstract": "  While domain adaptation has been used to improve the performance of object\ndetectors when the training and test data follow different distributions,\nprevious work has mostly focused on two-stage detectors. This is because their\nuse of region proposals makes it possible to perform local adaptation, which\nhas been shown to significantly improve the adaptation effectiveness. Here, by\ncontrast, we target single-stage architectures, which are better suited to\nresource-constrained detection than two-stage ones but do not provide region\nproposals. To nonetheless benefit from the strength of local adaptation, we\nintroduce an attention mechanism that lets us identify the important regions on\nwhich adaptation should focus. Our method gradually adapts the features from\nglobal, image-level to local, instance-level. Our approach is generic and can\nbe integrated into any single-stage detector. We demonstrate this on standard\nbenchmark datasets by applying it to both SSD and YOLOv5. Furthermore, for\nequivalent single-stage architectures, our method outperforms the\nstate-of-the-art domain adaptation techniques even though they were designed\nfor specific detectors.\n"}
+{"abstract": "  Here we present a computational tool for optical tweezers which calculates\nthe particle tracking signal measured with a quadrant detector and the\nshot-noise limit to position resolution. The tool is a piece of Matlab code\nwhich functions within the freely available Optical Tweezers Toolbox. It allows\nthe measurements performed in most optical tweezers experiments to be\ntheoretically characterized in a fast and easy manner. The code supports\nparticles with arbitrary size, any optical fields and any combination of\nobjective and condenser, and performs a full vector calculation of the relevant\nfields. Example calculations are presented which show the tracking signals for\ndifferent particles, and the shot noise limit to position sensitivity as a\nfunction of the effective condenser NA.\n"}
+{"abstract": "  The validity of our already proposed conjecture -- horizon creates a local\ninstability which acts as the source of the quantum temperature of black hole\n-- is being tested here for Kerr black hole. Earlier this has been explicitly\nshown for spherically symmetric static black hole (SSS BH). The more realistic\nsituation like Kerr spacetime, being stationary and axisymmetric, is a\nnon-trivial example to analyze. We show that for a chargeless massless\nparticle, the near horizon radial motion in Kerr spacetime, like SSS BH, can be\nlocally unstable. The radial contribution in the corresponding Hamiltonian is\n$\\sim xp$ kind, where $p$ is the canonical momentum and $x$ is its conjugate\nposition of particle. Finally we show that the horizon thermalization can be\nexplained through this Hamiltonian when one dose a semi-classical analysis. It\nagain confirms that near horizon instability is liable for its own temperature\nand moreover generalizes the validity of our conjectured mechanism for the\nblack hole horizon thermalization.\n"}
+{"abstract": "  Auto-Encoder (AE)-based deep subspace clustering (DSC) methods have achieved\nimpressive performance due to the powerful representation extracted using deep\nneural networks while prioritizing categorical separability. However,\nself-reconstruction loss of an AE ignores rich useful relation information and\nmight lead to indiscriminative representation, which inevitably degrades the\nclustering performance. It is also challenging to learn high-level similarity\nwithout feeding semantic labels. Another unsolved problem facing DSC is the\nhuge memory cost due to $n\\times n$ similarity matrix, which is incurred by the\nself-expression layer between an encoder and decoder. To tackle these problems,\nwe use pairwise similarity to weigh the reconstruction loss to capture local\nstructure information, while a similarity is learned by the self-expression\nlayer. Pseudo-graphs and pseudo-labels, which allow benefiting from uncertain\nknowledge acquired during network training, are further employed to supervise\nsimilarity learning. Joint learning and iterative training facilitate to obtain\nan overall optimal solution. Extensive experiments on benchmark datasets\ndemonstrate the superiority of our approach. By combining with the $k$-nearest\nneighbors algorithm, we further show that our method can address the\nlarge-scale and out-of-sample problems.\n"}
+{"abstract": "  In this paper, we present a network manipulation algorithm based on an\nalternating minimization scheme from (Nesterov 2020). In our context, the\nlatter mimics the natural behavior of agents and organizations operating on a\nnetwork. By selecting starting distributions, the organizations determine the\nshort-term dynamics of the network. While choosing an organization in\naccordance with their manipulation goals, agents are prone to errors. This\nrational inattentive behavior leads to discrete choice probabilities. We extend\nthe analysis of our algorithm to the inexact case, where the corresponding\nsubproblems can only be solved with numerical inaccuracies. The parameters\nreflecting the imperfect behavior of agents and the credibility of\norganizations, as well as the condition number of the network transition matrix\nhave a significant impact on the convergence of our algorithm. Namely, they\nturn out not only to improve the rate of convergence, but also to reduce the\naccumulated errors. From the mathematical perspective, this is due to the\ninduced strong convexity of an appropriate potential function.\n"}
+{"abstract": "  Hole spins in semiconductor quantum dots represent a viable route for the\nimplementation of electrically controlled qubits. In particular, the qubit\nimplementation based on Si pMOSFETs offers great potentialities in terms of\nintegration with the control electronics and long-term scalability. Moreover,\nthe future down scaling of these devices will possibly improve the performance\nof both the classical (control) and quantum components of such monolithically\nintegrated circuits. Here we use a multi-scale approach to simulate a hole-spin\nqubit in a down scaled Si-channel pMOSFET, whose structure is based on a\ncommercial 22nm fully-depleted silicon-on-insulator device. Our calculations\nshow the formation of well defined hole quantum dots within the Si channel, and\nthe possibility of a general electrical control, with Rabi frequencies of the\norder of 100 MHz for realistic field values. Our calculations demonstrate the\ncrucial role of the channel aspect ratio, and the presence of a favorable\nparameter range for the qubit manipulation.\n"}
+{"abstract": "  This study examined a simulated confined space modelled as a hospital waiting\narea, where people who could have underlying conditions congregate and mix with\npotentially infectious individuals. It further investigated the impact of the\nvolume of the waiting area, the number of people in the room, the placement of\nthem as well as their weight. The simulation is an agent-based model (ABM).\n"}
+{"abstract": "  Bipolar resistive switching (BRS) phenomenon has been demonstrated in Mn3O4\nusing Al (Aluminum)/Mn3O4/FTO (Fluorine doped Tin Oxide) Resistive Random\nAccess Memory (RRAM) device. The fabricated RRAM device shows good retention,\nnon volatile behavior and forming free BRS. The Current-Voltage (I-V)\ncharacteristics and the temperature dependence of the resistance (R-T)\nmeasurements were used to explore conduction mechanisms and the thermal\nactivation energy (Ea). The resistance ratio of high resistance state (HRS) to\nlow resistance state (LRS) is ~102. The fabricated RRAM device shows different\nconduction mechanisms in LRS and HRS state such as ohmic conduction and space\ncharge limited conduction (SCLC). The rupture and formation of conducting\nfilaments (CF) of oxygen vacancies take place by changing the polarity of\nexternal voltage, which may be responsible for resistive switching\ncharacteristics in the fabricated RRAM device. This fabricated RRAM device is\nsuitable for application in future high density non-volatile memory (NVM) RRAM\ndevices.\n"}
+{"abstract": "  FourPhonon is a computational package that can calculate four-phonon\nscattering rates in crystals. It is built within ShengBTE framework, which is a\nwell-recognized lattice thermal conductivity solver based on Boltzmann\ntransport equation. An adaptive energy broadening scheme is implemented for the\ncalculation of four-phonon scattering rates. In analogy with $thirdorder.py$ in\nShengBTE, we also provide a separate python script, $Fourthorder.py$, to\ncalculate fourth-order interatomic force-constants. The extension module\npreserves all the nice features of the well-recognized lattice thermal\nconductivity solver ShengBTE, including good parallelism and straightforward\nworkflow. In this paper, we discuss the general theory, program design, and\nexample calculations on Si, BAs and $\\mathrm{LiCoO_2}$.\n"}
+{"abstract": "  In this paper, we propose a linear polarization coding scheme (LPC) combined\nwith the phase conjugated twin signals (PCTS) technique, referred to as\nLPC-PCTS, for fiber nonlinearity mitigation in coherent optical orthogonal\nfrequency division multiplexing (CO-OFDM) systems. The LPC linearly combines\nthe data symbols on the adjacent subcarriers of the OFDM symbol, one at full\namplitude and the other at half amplitude. The linearly coded data is then\ntransmitted as phase conjugate pairs on the same subcarriers of the two OFDM\nsymbols on the two orthogonal polarizations. The nonlinear distortions added to\nthese subcarriers are essentially anti-correlated, since they carry phase\nconjugate pairs of data. At the receiver, the coherent superposition of the\ninformation symbols received on these pairs of subcarriers eventually leads to\nthe cancellation of the nonlinear distortions. We conducted numerical\nsimulation of a single channel 200 Gb/s CO-OFDM system employing the LPCPCTS\ntechnique. The results show that a Q-factor improvement of 2.3 dB and 1.7 dB\nwith and without the dispersion symmetry, respectively, when compared to the\nrecently proposed phase conjugated subcarrier coding (PCSC) technique, at an\naverage launch power of 3 dBm. In addition, our proposed LPCPCTS technique\nshows a significant performance improvement when compared to the 16-quadrature\namplitude modulation (QAM) with phase conjugated twin waves (PCTW) scheme, at\nthe same spectral efficiency, for an uncompensated transmission distance of\n2800 km.\n"}
+{"abstract": "  Accurate simulations of flows in stellar interiors are crucial to improving\nour understanding of stellar structure and evolution. Because the typically\nslow flows are merely tiny perturbations on top of a close balance between\ngravity and the pressure gradient, such simulations place heavy demands on\nnumerical hydrodynamics schemes. We demonstrate how discretization errors on\ngrids of reasonable size can lead to spurious flows orders of magnitude faster\nthan the physical flow. Well-balanced numerical schemes can deal with this\nproblem. Three such schemes were applied in the implicit, finite-volume\nSeven-League Hydro (SLH) code in combination with a low-Mach-number numerical\nflux function. We compare how the schemes perform in four numerical experiments\naddressing some of the challenges imposed by typical problems in stellar\nhydrodynamics. We find that the $\\alpha$-$\\beta$ and deviation well-balancing\nmethods can accurately maintain hydrostatic solutions provided that\ngravitational potential energy is included in the total energy balance. They\naccurately conserve minuscule entropy fluctuations advected in an isentropic\nstratification, which enables the methods to reproduce the expected scaling of\nconvective flow speed with the heating rate. The deviation method also\nsubstantially increases accuracy of maintaining stationary orbital motions in a\nKeplerian disk on long timescales. The Cargo-LeRoux method fares substantially\nworse in our tests, although its simplicity may still offer some merits in\ncertain situations. Overall, we find the well-balanced treatment of gravity in\ncombination with low Mach number flux functions essential to reproducing\ncorrect physical solutions to challenging stellar slow-flow problems on\naffordable collocated grids.\n"}
+{"abstract": "  Improving the performance of deep neural networks (DNNs) is important to both\nthe compiler and neural architecture search (NAS) communities. Compilers apply\nprogram transformations in order to exploit hardware parallelism and memory\nhierarchy. However, legality concerns mean they fail to exploit the natural\nrobustness of neural networks. In contrast, NAS techniques mutate networks by\noperations such as the grouping or bottlenecking of convolutions, exploiting\nthe resilience of DNNs. In this work, we express such neural architecture\noperations as program transformations whose legality depends on a notion of\nrepresentational capacity. This allows them to be combined with existing\ntransformations into a unified optimization framework. This unification allows\nus to express existing NAS operations as combinations of simpler\ntransformations. Crucially, it allows us to generate and explore new tensor\nconvolutions. We prototyped the combined framework in TVM and were able to find\noptimizations across different DNNs, that significantly reduce inference time -\nover 3$\\times$ in the majority of cases.\n  Furthermore, our scheme dramatically reduces NAS search time. Code is\navailable\nat~\\href{https://github.com/jack-willturner/nas-as-program-transformation-exploration}{this\nhttps url}.\n"}
+{"abstract": "  This work presents a data-driven reduced-order modeling framework to\naccelerate the computations of $N$-body dynamical systems and their pair-wise\ninteractions. The proposed framework differs from traditional acceleration\nmethods, like the Barnes-Hut method, which requires online tree building of the\nstate space, or the fast-multipole method, which requires rigorous $a$ $priori$\nanalysis of governing kernels and online tree building. Our approach combines\nBarnes-Hut hierarchical decomposition, dimensional compression via the\nleast-squares Petrov-Galerkin (LSPG) projection, and hyper-reduction by way of\nthe Gauss-Newton with approximated tensor (GNAT) approach. The resulting\n$projection-tree$ reduced order model (PTROM) enables a drastic reduction in\noperational count complexity by constructing sparse hyper-reduced pairwise\ninteractions of the $N$-body dynamical system. As a result, the presented\nframework is capable of achieving an operational count complexity that is\nindependent of $N$, the number of bodies in the numerical domain. Capabilities\nof the PTROM method are demonstrated on the two-dimensional fluid-dynamic\nBiot-Savart kernel within a parametric and reproductive setting. Results show\nthe PTROM is capable of achieving over 2000$\\times$ wall-time speed-up with\nrespect to the full-order model, where the speed-up increases with $N$. The\nresulting solution delivers quantities of interest with errors that are less\nthan 0.1$\\%$ with respect to full-order model.\n"}
+{"abstract": "  Recently, we introduced the \"Newman-Penrose map,\" a novel correspondence\nbetween a certain class of solutions of Einstein's equations and self-dual\nsolutions of the vacuum Maxwell equations, which we showed was closely related\nto the classical double copy. Here, we give an alternative definition of this\ncorrespondence in terms of quantities that are naturally defined on both\nspacetime and twistor space. The advantage of this reformulation is that it is\npurely geometrical in nature, being manifestly invariant under both spacetime\ndiffeomorphisms and projective transformations on twistor space. While the\noriginal formulation of the map may be more convenient for most explicit\ncalculations, the twistorial formulation we present here may be of greater\ntheoretical utility.\n"}
+{"abstract": "  The ultimate performance of any wireless communication system is limited by\nelectromagnetic principles and mechanisms. Motivated by this, we start from the\nfirst principles of wave propagation and consider a multiple-input\nmultiple-output (MIMO) representation of a communication system between two\nspatially-continuous volumes of arbitrary shape and position. This is the\nconcept of holographic MIMO communications. The analysis takes into account the\nelectromagnetic noise field, generated by external sources, and the constraint\non the physical radiated power. The electromagnetic MIMO model is\nparticularized for a system with parallel linear sources and receivers in\nline-of-sight conditions. Inspired by orthogonal-frequency\ndivision-multiplexing, we assume that the spatially-continuous transmit\ncurrents and received fields are represented using the Fourier basis functions.\nIn doing so, a wavenumber-division multiplexing (WDM) scheme is obtained whose\nproperties are studied with the conventional tools of linear systems theory.\nParticularly, the interplay among the different system parameters (e.g.,\ntransmission range, wavelength, and sizes of source and receiver) in terms of\nnumber of communication modes and level of interference is studied. Due to the\nnon-finite support of the electromagnetic channel, we prove that the\ninterference-free condition can only be achieved when the receiver size grows\nto infinity. The spectral efficiency of WDM is evaluated via the singular-value\ndecomposition architecture with water-filling and compared to that of a\nsimplified architecture, which uses linear processing at the receiver and\nsuboptimal power allocation.\n"}
+{"abstract": "  The computational prediction of atomistic structure is a long-standing\nproblem in physics, chemistry, materials, and biology. Within conventional\nforce-field or {\\em ab initio} calculations, structure is determined through\nenergy minimization, which is either approximate or computationally demanding.\nAlas, the accuracy-cost trade-off prohibits the generation of synthetic big\ndata records with meaningful energy based conformational search and structure\nrelaxation output. Exploiting implicit correlations among relaxed structures,\nour kernel ridge regression model, dubbed Graph-To-Structure (G2S), generalizes\nacross chemical compound space, enabling direct predictions of relaxed\nstructures for out-of-sample compounds, and effectively bypassing the energy\noptimization task. After training on constitutional and compositional isomers\n(no conformers) G2S infers atomic coordinates relying solely on stoichiometry\nand bond-network information as input (Our numerical evidence includes closed\nand open shell molecules, transition states, and solids). For all data\nconsidered, G2S learning curves reach mean absolute interatomic distance\nprediction errors of less than 0.2 {\\AA} for less than eight thousand training\nstructures -- on par or better than popular empirical methods. Applicability\ntest of G2S include meaningful structures of molecules for which standard\nmethods require manual intervention, improved initial guesses for subsequent\nconventional {\\em ab initio} based relaxation, and input for structural based\nrepresentations commonly used in quantum machine learning models, (bridging the\ngap between graph and structure based models).\n"}
+{"abstract": "  This paper studies a generalized busy-time scheduling model on heterogeneous\nmachines. The input to the model includes a set of jobs and a set of machine\ntypes. Each job has a size and a time interval during which it should be\nprocessed. Each job is to be placed on a machine for execution. Different types\nof machines have distinct capacities and cost rates. The total size of the jobs\nrunning on a machine must always be kept within the machine's capacity, giving\nrise to placement restrictions for jobs of various sizes among the machine\ntypes. Each machine used is charged according to the time duration in which it\nis busy, i.e., it is processing jobs. The objective is to schedule the jobs\nonto machines to minimize the total cost of all the machines used. We develop\nan $O(1)$-approximation algorithm in the offline setting and an\n$O(\\mu)$-competitive algorithm in the online setting (where $\\mu$ is the\nmax/min job length ratio), both of which are asymptotically optimal.\n"}
+{"abstract": "  We give a new simple proof of boundedness of the family of semistable sheaves\nwith fixed numerical invariants on a fixed smooth projective variety. In\ncharacteristic zero our method gives a quick proof of Bogomolov's inequality\nfor semistable sheaves on a smooth projective variety of any dimension $\\ge 2$\nwithout using any restriction theorems.\n"}
+{"abstract": "  The standard for Deep Reinforcement Learning in games, following Alpha Zero,\nis to use residual networks and to increase the depth of the network to get\nbetter results. We propose to improve mobile networks as an alternative to\nresidual networks and experimentally show the playing strength of the networks\naccording to both their width and their depth. We also propose a generalization\nof the PUCT search algorithm that improves on PUCT.\n"}
+{"abstract": "  We study the Hall response of topologically-trivial mobile impurities (Fermi\npolarons) interacting weakly with majority fermions forming a Chern-insulator\nbackground. This setting involves a rich interplay between the genuine\nmany-body character of the polaron problem and the topological nature of the\nsurrounding cloud. When the majority fermions are accelerated by an external\nfield, a transverse impurity current can be induced. To quantify this polaronic\nHall effect, we compute the drag transconductivity, employing controlled\ndiagrammatic perturbation theory in the impurity-fermion interaction. We show\nthat the impurity Hall drag is not simply proportional to the Chern number\ncharacterizing the topological transport of the insulator on its own - it also\ndepends continuously on particle-hole breaking terms, to which the Chern number\nis insensitive. However, when the insulator is tuned across a topological phase\ntransition, a sharp jump of the impurity Hall drag results, for which we derive\nan analytical expression. We describe how the Hall drag and jump can be\nextracted from a circular dichroic measurement of impurity excitation rates,\nparticularly suited for ultracold gas experiments.\n"}
+{"abstract": "  Modern optical satellite sensors enable high-resolution stereo reconstruction\nfrom space. But the challenging imaging conditions when observing the Earth\nfrom space push stereo matching to its limits. In practice, the resulting\ndigital surface models (DSMs) are fairly noisy and often do not attain the\naccuracy needed for high-resolution applications such as 3D city modeling.\nArguably, stereo correspondence based on low-level image similarity is\ninsufficient and should be complemented with a-priori knowledge about the\nexpected surface geometry beyond basic local smoothness. To that end, we\nintroduce ResDepth, a convolutional neural network that learns such an\nexpressive geometric prior from example data. ResDepth refines an initial, raw\nstereo DSM while conditioning the refinement on the images. I.e., it acts as a\nsmart, learned post-processing filter and can seamlessly complement any stereo\nmatching pipeline. In a series of experiments, we find that the proposed method\nconsistently improves stereo DSMs both quantitatively and qualitatively. We\nshow that the prior encoded in the network weights captures meaningful\ngeometric characteristics of urban design, which also generalize across\ndifferent districts and even from one city to another. Moreover, we demonstrate\nthat, by training on a variety of stereo pairs, ResDepth can acquire a\nsufficient degree of invariance against variations in imaging conditions and\nacquisition geometry.\n"}
+{"abstract": "  In this paper we investigate the critical efficiency of detectors in order to\nsee Bell nonlocality using multiple copies of the two-qubit maximally entangled\nstate and local Pauli measurements which act in the corresponding qubit\nsubspaces. It is known that for the two-qubit maximally entangled state a\nsymmetric detection efficiency of $82.84\\%$ can be tolerated using the\nClauser-Horne-Shimony-Holt (CHSH) Bell test. We show that this threshold can be\nlowered by using multiple copies of the two-qubit maximally entangled state. We\nget the upper bounds $80.86\\%$, $73.99\\%$ and $69.29\\%$ on the symmetric\ndetection efficiency threshold for two, three and four copies of the state,\nwhere the respective number of measurements per party are 4, 8 and 16. However,\nin the case of four copies the result is partly due to a heuristic method. In\norder to get the corresponding Bell inequalities we made use of linear\nprogramming for two copies of the state and convex optimization based on\nGilbert algorithm for three and four copies of the state.\n"}
+{"abstract": "  Unsupervised Domain Adaptation (UDA) methods for person Re-Identification\n(Re-ID) rely on target domain samples to model the marginal distribution of the\ndata. To deal with the lack of target domain labels, UDA methods leverage\ninformation from labeled source samples and unlabeled target samples. A\npromising approach relies on the use of unsupervised learning as part of the\npipeline, such as clustering methods. The quality of the clusters clearly plays\na major role in methods performance, but this point has been overlooked. In\nthis work, we propose a multi-step pseudo-label refinement method to select the\nbest possible clusters and keep improving them so that these clusters become\ncloser to the class divisions without knowledge of the class labels. Our\nrefinement method includes a cluster selection strategy and a camera-based\nnormalization method which reduces the within-domain variations caused by the\nuse of multiple cameras in person Re-ID. This allows our method to reach\nstate-of-the-art UDA results on DukeMTMC-Market1501 (source-target). We surpass\nstate-of-the-art for UDA Re-ID by 3.4% on Market1501-DukeMTMC datasets, which\nis a more challenging adaptation setup because the target domain (DukeMTMC) has\neight distinct cameras. Furthermore, the camera-based normalization method\ncauses a significant reduction in the number of iterations required for\ntraining convergence.\n"}
+{"abstract": "  This paper addresses Monte Carlo algorithms for calculating the\nShapley-Shubik power index in weighted majority games. First, we analyze a\nnaive Monte Carlo algorithm and discuss the required number of samples. We then\npropose an efficient Monte Carlo algorithm and show that our algorithm reduces\nthe required number of samples as compared to the naive algorithm.\n"}
+{"abstract": "  CRISPR-Cas is an adaptive immune mechanism that has been harnessed for a\nvariety of genetic engineering applications: the Cas9 protein recognises a\n2-5nt DNA motif, known as the PAM, and a programmable crRNA binds a target DNA\nsequence that is then cleaved. While off-target activity is undesirable, it\noccurs because cross-reactivity was beneficial in the immune system on which\nthe machinery is based. Here, a stochastic model of the target recognition\nreaction was derived to study the specificity of the innate immune mechanism in\nbacteria. CRISPR systems with Cas9 proteins that recognised PAMs of varying\nlengths were tested on self and phage DNA. The model showed that the energy\nassociated with PAM binding impacted mismatch tolerance, cleavage probability,\nand cleavage time. Small PAMs allowed the CRISPR to balance catching mutant\nphages, avoiding self-targeting, and quickly dissociating from critically\nnon-matching sequences. Additionally, the results revealed a lower tolerance to\nmismatches in the PAM and a PAM-proximal region known as the seed, as seen in\nexperiments. This work illustrates the role that the Cas9 protein has in\ndictating the specificity of DNA cleavage that can aid in preventing off-target\nactivity in biotechnology applications.\n"}
+{"abstract": "  We study the complexity of approximating the number of answers to a small\nquery $\\varphi$ in a large database $\\mathcal{D}$. We establish an exhaustive\nclassification into tractable and intractable cases if $\\varphi$ is a\nconjunctive query with disequalities and negations:\n  $\\bullet$ If there is a constant bound on the arity of $\\varphi$, and if the\nrandomised Exponential Time Hypothesis (rETH) holds, then the problem has a\nfixed-parameter tractable approximation scheme (FPTRAS) if and only if the\ntreewidth of $\\varphi$ is bounded.\n  $\\bullet$ If the arity is unbounded and we allow disequalities only, then the\nproblem has an FPTRAS if and only if the adaptive width of $\\varphi$ (a width\nmeasure strictly more general than treewidth) is bounded; the lower bound\nrelies on the rETH as well.\n  Additionally we show that our results cannot be strengthened to achieve a\nfully polynomial randomised approximation scheme (FPRAS): We observe that,\nunless $\\mathrm{NP} =\\mathrm{RP}$, there is no FPRAS even if the treewidth (and\nthe adaptive width) is $1$. However, if there are neither disequalities nor\nnegations, we prove the existence of an FPRAS for queries of bounded fractional\nhypertreewidth, strictly generalising the recently established FPRAS for\nconjunctive queries with bounded hypertreewidth due to Arenas, Croquevielle,\nJayaram and Riveros (STOC 2021).\n"}
+{"abstract": "  Pentadiamond is a recently proposed new carbon allotrope consisting of a\nnetwork of pentagonal rings where both sp$^2$ and sp$^3$ hybridization are\npresent. In this work we investigated the mechanical and electronic properties,\nas well as, the thermal stability of pentadiamond using DFT and fully atomistic\nreactive molecular dynamics (MD) simulations. We also investigated its\nproperties beyond the elastic regime for three different deformation modes:\ncompression, tensile and shear. The behavior of pentadiamond under compressive\ndeformation showed strong fluctuations in the atomic positions which are\nresponsible for the strain softening at strains beyond the linear regime, which\ncharacterizes the plastic flow. As we increase temperature, as expected,\nYoung's modulus values decrease, but this variation (up to 300 K) is smaller\nthan 10\\% (from 347.5 to 313.6 GPa), but the fracture strain is very sensitive,\nvarying from $\\sim$44\\% at 1K to $\\sim$5\\% at 300K.\n"}
+{"abstract": "  We investigate structural and transport properties of highly Ru-deficient\nSrRu0.7O3 thin films prepared by molecular beam epitaxy on (001) SrTiO3\nsubstrates. To distinguish the influence of the two types of disorders in the\nfilms, Ru vacancies within lattices and disorders near the interface, SrRu0.7O3\nthin films with various thicknesses (t = 1-60 nm) were prepared. It was found\nthat the influence of the former dominates the electrical and magnetic\nproperties when t > 5-10 nm, while that of the latter does when t < 5-10 nm.\nStructural characterizations revealed that the crystallinity, in terms of the\nSr and O sublattices, of SrRu0.7O3 thin films, is as high as that of the\nultrahigh-quality SrRuO3 ones. The Curie temperature (TC) analysis elucidated\nthat SrRu0.7O3 (TC = 140 K) is a material distinct from SrRuO3 (TC = 150 K).\nDespite the large Ru deficiency (30%), the SrRu0.7O3 films showed metallic\nconduction when t > 5 nm. In high-field magnetoresistance measurements, the\nfascinating phenomenon of Weyl fermion transport was not observed for the\nSrRu0.7O3 thin films irrespective of thickness, which is in contrast to the\nstoichiometric SrRuO3 films. The (magneto)transport properties suggest that a\npicture of carrier scattering due to the Ru vacancies is appropriate for\nSrRu0.7O3, and also that proper stoichiometry control is a prerequisite to\nutilizing the full potential of SrRuO3 as a magnetic Weyl semimetal and\ntwo-dimensional spin-polarized system. Nevertheless, the large tolerance in Ru\ncomposition (30 %) to metallic conduction is advantageous for some practical\napplications where SrRu1-xO3 is exploited as an epitaxial conducting layer.\n"}
+{"abstract": "  Recently, mT5 - a massively multilingual version of T5 - leveraged a unified\ntext-to-text format to attain state-of-the-art results on a wide variety of\nmultilingual NLP tasks. In this paper, we investigate the impact of\nincorporating parallel data into mT5 pre-training. We find that multi-tasking\nlanguage modeling with objectives such as machine translation during\npre-training is a straightforward way to improve performance on downstream\nmultilingual and cross-lingual tasks. However, the gains start to diminish as\nthe model capacity increases, suggesting that parallel data might not be as\nessential for larger models. At the same time, even at larger model sizes, we\nfind that pre-training with parallel data still provides benefits in the\nlimited labelled data regime.\n"}
+{"abstract": "  The effect of the self-energy on the photon-proton elastic scattering is\ninvestigated for the backward and the forward directions. The shape of the\nThomson scattering at the photon energy $\\omega \\to 0$ is broken by taking into\naccount the self-energy of proton. The electromagnetic polarizabilities\n$\\bar{\\alpha}\\pm\\bar{\\beta}$ are calculated by the lowest-order perturbative\ntreatment.\n"}
+{"abstract": "  We derive the nucleon-nucleon interaction from the Skyrme model using second\norder perturbation theory and the dipole approximation to skyrmion dynamics.\nUnlike previous derivations, our derivation accounts for the non-trivial\nkinetic and potential parts of the skyrmion-skyrmion interaction lagrangian and\nhow they couple in the quantum calculation. We derive the eight low energy\ninteraction potentials and compare them with the phenomenological Paris model,\nfinding qualitative agreement in seven cases.\n"}
+{"abstract": "  This is an expository paper on tensor products where the standard approaches\nfor constructing concrete instances of algebraic tensor products of linear\nspaces, via quotient spaces or via linear maps of bilinear maps, are reviewed\nby reducing them to different but isomorphic interpretations of an abstract\nnotion, viz., the universal property, which is based on a pair of axioms.\n"}
+{"abstract": "  This paper is concerned with a novel method allowing communication between\nFRET nanonetworks and nerve cells. It is focused on two system components:\nfluorophores and channelrhodopsins which serve as transmitters and receivers,\nrespectively. Channelrhodopsins are used here also as a FRET signal-to-voltage\nconverter. The trade-off between throughput and bit error rate is also\ninvestigated.\n"}
+{"abstract": "  The performance of grant-free random access (GF-RA) is limited by the number\nof accessible random access resources (RRs) due to the absence of collision\nresolution. Compressive sensing (CS)-based RA schemes scale up the RRs at the\nexpense of increased non-orthogonality among transmitted signals. This paper\npresents the design of multi-sequence spreading random access (MSRA) which\nemploys multiple spreading sequences to spread the different symbols of a user\nas opposed to the conventional schemes in which a user employs the same\nspreading sequence for each symbol. We show that MSRA provides code diversity,\nenabling the multi-user detection (MUD) to be modeled into a well-conditioned\nmultiple measurement vector (MMV) CS problem. The code diversity is quantified\nby the decrease in the average Babel mutual coherence among the spreading\nsequences. Moreover, we present a two-stage active user detection (AUD) scheme\nfor both wideband and narrowband implementation. Our theoretical analysis shows\nthat with MSRA activity misdetection falls exponentially while the size of\nGF-RA frame is increased. Finally, the simulation results show that about 82%\nincrease in utilization of RRs, i.e., more active users, is supported by MSRA\nthan the conventional schemes while achieving the RA failure rate lower bound\nset by random access collision.\n"}
+{"abstract": "  In eDiscovery, it is critical to ensure that each page produced in legal\nproceedings conforms with the requirements of court or government agency\nproduction requests. Errors in productions could have severe consequences in a\ncase, putting a party in an adverse position. The volume of pages produced\ncontinues to increase, and tremendous time and effort has been taken to ensure\nquality control of document productions. This has historically been a manual\nand laborious process. This paper demonstrates a novel automated production\nquality control application which leverages deep learning-based image\nrecognition technology to extract Bates Number and Confidentiality Stamping\nfrom legal case production images and validate their correctness. Effectiveness\nof the method is verified with an experiment using a real-world production\ndata.\n"}
+{"abstract": "  Acute respiratory distress syndrome (ARDS) is a life-threatening condition\nthat is often undiagnosed or diagnosed late. ARDS is especially prominent in\nthose infected with COVID-19. We explore the automatic identification of ARDS\nindicators and confounding factors in free-text chest radiograph reports. We\npresent a new annotated corpus of chest radiograph reports and introduce the\nHierarchical Attention Network with Sentence Objectives (HANSO) text\nclassification framework. HANSO utilizes fine-grained annotations to improve\ndocument classification performance. HANSO can extract ARDS-related information\nwith high performance by leveraging relation annotations, even if the annotated\nspans are noisy. Using annotated chest radiograph images as a gold standard,\nHANSO identifies bilateral infiltrates, an indicator of ARDS, in chest\nradiograph reports with performance (0.87 F1) comparable to human annotations\n(0.84 F1). This algorithm could facilitate more efficient and expeditious\nidentification of ARDS by clinicians and researchers and contribute to the\ndevelopment of new therapies to improve patient care.\n"}
+{"abstract": "  Current deep reinforcement learning (RL) algorithms are still highly\ntask-specific and lack the ability to generalize to new environments. Lifelong\nlearning (LLL), however, aims at solving multiple tasks sequentially by\nefficiently transferring and using knowledge between tasks. Despite a surge of\ninterest in lifelong RL in recent years, the lack of a realistic testbed makes\nrobust evaluation of LLL algorithms difficult. Multi-agent RL (MARL), on the\nother hand, can be seen as a natural scenario for lifelong RL due to its\ninherent non-stationarity, since the agents' policies change over time. In this\nwork, we introduce a multi-agent lifelong learning testbed that supports both\nzero-shot and few-shot settings. Our setup is based on Hanabi -- a\npartially-observable, fully cooperative multi-agent game that has been shown to\nbe challenging for zero-shot coordination. Its large strategy space makes it a\ndesirable environment for lifelong RL tasks. We evaluate several recent MARL\nmethods, and benchmark state-of-the-art LLL algorithms in limited memory and\ncomputation regimes to shed light on their strengths and weaknesses. This\ncontinual learning paradigm also provides us with a pragmatic way of going\nbeyond centralized training which is the most commonly used training protocol\nin MARL. We empirically show that the agents trained in our setup are able to\ncoordinate well with unseen agents, without any additional assumptions made by\nprevious works. The code and all pre-trained models are available at\nhttps://github.com/chandar-lab/Lifelong-Hanabi.\n"}
+{"abstract": "  Based on the spectator expansion of the multiple scattering series we employ\na chiral next-to-next-to-leading order (NNLO) nucleon-nucleon interaction on\nthe same footing in the structure as well as in the reaction calculation to\nobtain an in leading-order consistent effective potential for nucleon-nucleus\nelastic scattering, which includes the spin of the struck target nucleon. As an\nexample we present proton scattering off $^{12}$C.\n"}
+{"abstract": "  For modules over group rings we introduce the following numerical parameter.\nWe say that a module A over a ring R has finite r-generator property if each\nf.g. (finitely generated) R-submodule of A can be generated exactly by r\nelements and there exists a f.g. R-submodule D of A, which has a minimal\ngenerating subset, consisting exactly of r elements. Let FG be the group\nalgebra of a finite group G over a field F. In the present paper modules over\nthe algebra FG having finite generator property are described.\n"}
+{"abstract": "  The unique optoelectronic properties of black phosphorus (BP) have triggered\ngreat interest in its applications in areas not fulfilled by other layered\nmaterials (LMs). However, its poor stability (fast degradation, i.e. <<1 h for\nmonolayers) under ambient conditions restricts its practical application. We\ndemonstrate here, by an experimental-theoretical approach, that the\nincorporation of nitrogen molecules (N2) into the BP structure results in a\nrelevant improvement of its stability in air, up to 8 days without optical\ndegradation signs. Our strategy involves the generation of defects (phosphorus\nvacancies) by electron-beam irradiation, followed by their healing with N2\nmolecules. As an additional route, N2 plasma treatment is presented as an\nalternative for large area application. Our first principles calculations\nelucidate the mechanisms involved in the nitrogen incorporation as well as on\nthe stabilization of the modified BP, which corroborates with our experimental\nobservations. This stabilization approach can be applied in the processing of\nBP, allowing for its use in environmentally stable van der Waals\nheterostructures with other LMs as well as in optoelectronic and wearable\ndevices.\n"}
+{"abstract": "  The paper studies distributed binary hypothesis testing over a two-hop relay\nnetwork where both the relay and the receiver decide on the hypothesis. Both\ncommunication links are subject to expected rate constraints, which differs\nfrom the classical assumption of maximum rate constraints. We exactly\ncharacterize the set of type-II error exponent pairs at the relay and the\nreceiver when both type-I error probabilities are constrained by the same value\n$\\epsilon>0$. No tradeoff is observed between the two exponents, i.e., one can\nsimultaneously attain maximum type-II error exponents both at the relay and at\nthe receiver. For $\\epsilon_1 \\neq \\epsilon_2$, we present an achievable\nexponents region, which we obtain with a scheme that applies different versions\nof a basic two-hop scheme that is optimal under maximum rate constraints. We\nuse the basic two-hop scheme with two choices of parameters and rates,\ndepending on the transmitter's observed sequence. For $\\epsilon_1=\\epsilon_2$,\na single choice is shown to be sufficient. Numerical simulations indicate that\nextending to three or more parameter choices is never beneficial.\n"}
+{"abstract": "  The important recent book by G. Schurz appreciates that the no-free-lunch\ntheorems (NFL) have major implications for the problem of (meta) induction.\nHere I review the NFL theorems, emphasizing that they do not only concern the\ncase where there is a uniform prior -- they prove that there are \"as many\npriors\" (loosely speaking) for which any induction algorithm $A$\nout-generalizes some induction algorithm $B$ as vice-versa. Importantly though,\nin addition to the NFL theorems, there are many \\textit{free lunch} theorems.\nIn particular, the NFL theorems can only be used to compare the\n\\textit{marginal} expected performance of an induction algorithm $A$ with the\nmarginal expected performance of an induction algorithm $B$. There is a rich\nset of free lunches which instead concern the statistical correlations among\nthe generalization errors of induction algorithms. As I describe, the\nmeta-induction algorithms that Schurz advocate as a \"solution to Hume's\nproblem\" are just an example of such a free lunch based on correlations among\nthe generalization errors of induction algorithms. I end by pointing out that\nthe prior that Schurz advocates, which is uniform over bit frequencies rather\nthan bit patterns, is contradicted by thousands of experiments in statistical\nphysics and by the great success of the maximum entropy procedure in inductive\ninference.\n"}
+{"abstract": "  At present, there is still no officially accepted and extensively verified\nimplementation of computing the gamma difference distribution allowing unequal\nshape parameters. We explore four computational ways of the gamma difference\ndistribution with the different shape parameters resulting from time series\nkriging, a forecasting approach based on the best linear unbiased prediction,\nand linear mixed models. The results of our numerical study, with emphasis on\nusing open data science tools, demonstrate that our open tool implemented in\nhigh-performance Python(with Numba) is exponentially fast, highly accurate, and\nvery reliable. It combines numerical inversion of the characteristic function\nand the trapezoidal rule with the double exponential oscillatory transformation\n(DE quadrature). At the double 53-bit precision, our tool outperformed the\nspeed of the analytical computation based on Tricomi's $U(a, b, z)$ function in\nCAS software (commercial Mathematica, open SageMath) by 1.5-2 orders. At the\nprecision of scientific numerical computational tools, it exceeded open SciPy,\nNumPy, and commercial MATLAB 5-10 times. The potential future application of\nour tool for a mixture of characteristic functions could open new possibilities\nfor fast data analysis based on exact probability distributions in areas like\nmultidimensional statistics, measurement uncertainty analysis in metrology as\nwell as in financial mathematics and risk analysis.\n"}
+{"abstract": "  Advanced machine learning techniques have been used in remote sensing (RS)\napplications such as crop mapping and yield prediction, but remain\nunder-utilized for tracking crop progress. In this study, we demonstrate the\nuse of agronomic knowledge of crop growth drivers in a Long Short-Term\nMemory-based, domain-guided neural network (DgNN) for in-season crop progress\nestimation. The DgNN uses a branched structure and attention to separate\nindependent crop growth drivers and capture their varying importance throughout\nthe growing season. The DgNN is implemented for corn, using RS data in Iowa for\nthe period 2003-2019, with USDA crop progress reports used as ground truth.\nState-wide DgNN performance shows significant improvement over sequential and\ndense-only NN structures, and a widely-used Hidden Markov Model method. The\nDgNN had a 4.0% higher Nash-Sutfliffe efficiency over all growth stages and 39%\nmore weeks with highest cosine similarity than the next best NN during test\nyears. The DgNN and Sequential NN were more robust during periods of abnormal\ncrop progress, though estimating the Silking-Grainfill transition was difficult\nfor all methods. Finally, Uniform Manifold Approximation and Projection\nvisualizations of layer activations showed how LSTM-based NNs separate crop\ngrowth time-series differently from a dense-only structure. Results from this\nstudy exhibit both the viability of NNs in crop growth stage estimation (CGSE)\nand the benefits of using domain knowledge. The DgNN methodology presented here\ncan be extended to provide near-real time CGSE of other crops.\n"}
+{"abstract": "  In this paper, we wish to investigate the dynamics of information transfer in\nevolutionary dynamics. We use information theoretic tools to track how much\ninformation an evolving population has obtained and managed to retain about\ndifferent environments that it is exposed to. By understanding the dynamics of\ninformation gain and loss in a static environment, we predict how that same\nevolutionary system would behave when the environment is fluctuating.\nSpecifically, we anticipate a cross-over between the regime in which\nfluctuations improve the ability of the evolutionary system to capture\nenvironmental information and the regime in which the fluctuations inhibit it,\ngoverned by a cross-over in the timescales of information gain and decay.\n"}
+{"abstract": "  Vision transformer (ViT) models exhibit substandard optimizability. In\nparticular, they are sensitive to the choice of optimizer (AdamW vs. SGD),\noptimizer hyperparameters, and training schedule length. In comparison, modern\nconvolutional neural networks are easier to optimize. Why is this the case? In\nthis work, we conjecture that the issue lies with the patchify stem of ViT\nmodels, which is implemented by a stride-p p*p convolution (p=16 by default)\napplied to the input image. This large-kernel plus large-stride convolution\nruns counter to typical design choices of convolutional layers in neural\nnetworks. To test whether this atypical design choice causes an issue, we\nanalyze the optimization behavior of ViT models with their original patchify\nstem versus a simple counterpart where we replace the ViT stem by a small\nnumber of stacked stride-two 3*3 convolutions. While the vast majority of\ncomputation in the two ViT designs is identical, we find that this small change\nin early visual processing results in markedly different training behavior in\nterms of the sensitivity to optimization settings as well as the final model\naccuracy. Using a convolutional stem in ViT dramatically increases optimization\nstability and also improves peak performance (by ~1-2% top-1 accuracy on\nImageNet-1k), while maintaining flops and runtime. The improvement can be\nobserved across the wide spectrum of model complexities (from 1G to 36G flops)\nand dataset scales (from ImageNet-1k to ImageNet-21k). These findings lead us\nto recommend using a standard, lightweight convolutional stem for ViT models in\nthis regime as a more robust architectural choice compared to the original ViT\nmodel design.\n"}
+{"abstract": "  Image composition plays an important role in the quality of a photo. However,\nnot every camera user possesses the knowledge and expertise required for\ncapturing well-composed photos. While post-capture cropping can improve the\ncomposition sometimes, it does not work in many common scenarios in which the\nphotographer needs to adjust the camera view to capture the best shot. To\naddress this issue, we propose a deep learning-based approach that provides\nsuggestions to the photographer on how to adjust the camera view before\ncapturing. By optimizing the composition before a photo is captured, our system\nhelps photographers to capture better photos. As there is no publicly-available\ndataset for this task, we create a view adjustment dataset by repurposing\nexisting image cropping datasets. Furthermore, we propose a two-stage\nsemi-supervised approach that utilizes both labeled and unlabeled images for\ntraining a view adjustment model. Experiment results show that the proposed\nsemi-supervised approach outperforms the corresponding supervised alternatives,\nand our user study results show that the suggested view adjustment improves\nimage composition 79% of the time.\n"}
+{"abstract": "  We consider a parabolic sine-Gordon model with periodic boundary conditions.\nWe prove a fundamental maximum principle which gives a priori uniform control\nof the solution. In the one-dimensional case we classify all bounded steady\nstates and exhibit some explicit solutions. For the numerical discretization we\nemploy first order IMEX, and second order BDF2 discretization without any\nadditional stabilization term. We rigorously prove the energy stability of the\nnumerical schemes under nearly sharp and quite mild time step constraints. We\ndemonstrate the striking similarity of the parabolic sine-Gordon model with the\nstandard Allen-Cahn equations with double well potentials.\n"}
+{"abstract": "  Every spacetime is defined by its metric, the mathematical object which\nfurther defines the spacetime curvature. From the relativity principle, we have\nthe freedom to choose which coordinate system to write our metric in. Some\ncoordinate systems, however, are better than others. In this text, we begin\nwith a brief introduction into general relativity, Einstein's masterpiece\ntheory of gravity. We then discuss some physically interesting spacetimes and\nthe coordinate systems that the metrics of these spacetimes can be expressed\nin. More specifically, we discuss the existence of the rather useful unit-lapse\nforms of these spacetimes. Using the metric written in this form then allows us\nto conduct further analysis of these spacetimes, which we discuss.\n"}
+{"abstract": "  Coronary X-ray angiography is a crucial clinical procedure for the diagnosis\nand treatment of coronary artery disease, which accounts for roughly 16% of\nglobal deaths every year. However, the images acquired in these procedures have\nlow resolution and poor contrast, making lesion detection and assessment\nchallenging. Accurate coronary artery segmentation not only helps mitigate\nthese problems, but also allows the extraction of relevant anatomical features\nfor further analysis by quantitative methods. Although automated segmentation\nof coronary arteries has been proposed before, previous approaches have used\nnon-optimal segmentation criteria, leading to less useful results. Most methods\neither segment only the major vessel, discarding important information from the\nremaining ones, or segment the whole coronary tree based mostly on contrast\ninformation, producing a noisy output that includes vessels that are not\nrelevant for diagnosis. We adopt a better-suited clinical criterion and segment\nvessels according to their clinical relevance. Additionally, we simultaneously\nperform catheter segmentation, which may be useful for diagnosis due to the\nscale factor provided by the catheter's known diameter, and is a task that has\nnot yet been performed with good results. To derive the optimal approach, we\nconducted an extensive comparative study of encoder-decoder architectures\ntrained on a combination of focal loss and a variant of generalized dice loss.\nBased on the EfficientNet and the UNet++ architectures, we propose a line of\nefficient and high-performance segmentation models using a new decoder\narchitecture, the EfficientUNet++, whose best-performing version achieved\naverage dice scores of 0.8904 and 0.7526 for the artery and catheter classes,\nrespectively, and an average generalized dice score of 0.9234.\n"}
+{"abstract": "  Certain biomaterials are capable of inducing the secretion of Vascular\nEndothelial Growth Factor (VEGF) from cells exposed to their biochemical\ninfluence, which plays a vital role in stimulating angiogenesis. Looking for\nthis capacity, in this study three porous glasses were synthesized and\ncharacterized. The objective of this study was to determine the concentration\nof the glass particles that, being out of the cytotoxic range, could increase\nVEGF secretion. The viability of cultivated bone marrow stromal cells (ST-2)\nwas assessed. The samples were examined with light microscopy (LM) after the\nhistochemical staining for haematoxylin and eosin (HE). The biological activity\nof glasses was evaluated in terms of the influence of the Cu2+ and Sr2+ ions on\nthe cells. The dissolution products of CuSr-1 and CuSr-2.5 produced the highest\nsecretion of VEGF from ST-2 cells after 48 h of incubation. The combination of\nCu2+ and Sr2+ lays the foundation for engineering a bioactive glass than can\nlead to vascularized, functional bone tissue when used in bone regeneration\napplications.\n"}
+{"abstract": "  We use the data of modern digital sky surveys (PanSTARRS-1, SDSS) combined\nwith HI-line and far ultraviolet (GALEX) surveys to reclassify 165 early-type\ngalaxies from the Catalog of Isolated Galaxies (KIG). As a result, the number\nof E- and S0-type galaxies reduced to 91. Our search for companions of\nearly-type KIG galaxies revealed 90 companions around 45 host galaxies with\nline-of-sight velocity differences $|dV| < 500$ km s$^{-1}$ and linear\nprojected separations $R_{p} < 750$ kpc. We found no appreciable differences in\neither integrated luminosity or color of galaxies associated with the presence\nor absence of close neighbors. We found a characteristic orbital\nmass-to-luminosity ratio for 26 systems \"KIG galaxy--companion\" to be\n$M_{\\odot}/L_{K} = (74\\pm26) M_{\\odot}/L_{\\odot}$, which is consistent with the\n$M_{\\rm orb}/L_{K}$ estimates for early-type isolated galaxies in the 2MIG\ncatalog ($63 M_{\\odot}/L_{\\odot}$), and also with the $M_{\\rm orb}/L_{K}$\nestimates for E- and S0-type galaxies in the Local Volume: $38\\pm22$ (NGC\n3115), $82\\pm26$ (NGC 5128), $65\\pm20$ (NGC 4594). The high halo-to-stellar\nmass ratio for E- and S0-type galaxies compared to the average $(20\\pm3)\nM_{\\odot}/L_{\\odot}$ ratio for bulgeless spiral galaxies is indicative of a\nsignificant difference between the dynamic evolution of early- and late-type\ngalaxies.\n"}
+{"abstract": "  Forced oscillation (FO) is a significant concern threating the power system\nstability. Its mechanisms are mostly studied via linear models. However, FO\namplitude is increasing, e.g., Nordic and Western American FOs, which can\nstimulate power system nonlinearity. Hence, this paper incorporates\nnonlinearity in FO mechanism analysis. The multi-scale technique is employed in\nsolving the forced oscillation equation to handle the quadratic nonlinearity.\nThe amplitude-frequency characteristic curves and first-order approximate\nexpressions are derived. The frequency deviation and jumping phenomenon caused\nby nonlinearity are discovered and further analyzed by comparing with linear\nmodels. This paper provides a preliminary research for nonlinear FOs of power\nsystem, and more characteristics should be further analysis in the near future.\n"}
+{"abstract": "  Within integrated tokamak plasma modelling, turbulent transport codes are\ntypically the computational bottleneck limiting their routine use outside of\npost-discharge analysis. Neural network (NN) surrogates have been used to\naccelerate these calculations while retaining the desired accuracy of the\nphysics-based models. This paper extends a previous NN model, known as\nQLKNN-hyper-10D, by incorporating the impact of impurities, plasma rotation and\nmagnetic equilibrium effects. This is achieved by adding a light impurity\nfractional density ($n_{imp,light} / n_e$) and its normalized gradient, the\nnormalized pressure gradient ($\\alpha$), the toroidal Mach number ($M_{tor}$)\nand the normalized toroidal flow velocity gradient. The input space was sampled\nbased on experimental data from the JET tokamak to avoid the curse of\ndimensionality. The resulting networks, named QLKNN-jetexp-15D, show good\nagreement with the original QuaLiKiz model, both by comparing individual\ntransport quantity predictions as well as comparing its impact within the\nintegrated model, JINTRAC. The profile-averaged RMS of the integrated modelling\nsimulations is <10% for each of the 5 scenarios tested. This is non-trivial\ngiven the potential numerical instabilities present within the highly nonlinear\nsystem of equations governing plasma transport, especially considering the\nnovel addition of momentum flux predictions to the model proposed here. An\nevaluation of all 25 NN output quantities at one radial location takes\n$\\sim$0.1 ms, $10^4$ times faster than the original QuaLiKiz model. Within the\nJINTRAC integrated modelling tests performed in this study, using\nQLKNN-jetexp-15D resulted in a speed increase of only 60 - 100 as other physics\nmodules outside of turbulent transport become the bottleneck.\n"}
+{"abstract": "  Research in Natural Language Processing is making rapid advances, resulting\nin the publication of a large number of research papers. Finding relevant\nresearch papers and their contribution to the domain is a challenging problem.\nIn this paper, we address this challenge via the SemEval 2021 Task 11:\nNLPContributionGraph, by developing a system for a research paper\ncontributions-focused knowledge graph over Natural Language Processing\nliterature. The task is divided into three sub-tasks: extracting contribution\nsentences that show important contributions in the research article, extracting\nphrases from the contribution sentences, and predicting the information units\nin the research article together with triplet formation from the phrases. The\nproposed system is agnostic to the subject domain and can be applied for\nbuilding a knowledge graph for any area. We found that transformer-based\nlanguage models can significantly improve existing techniques and utilized the\nSciBERT-based model. Our first sub-task uses Bidirectional LSTM (BiLSTM)\nstacked on top of SciBERT model layers, while the second sub-task uses\nConditional Random Field (CRF) on top of SciBERT with BiLSTM. The third\nsub-task uses a combined SciBERT based neural approach with heuristics for\ninformation unit prediction and triplet formation from the phrases. Our system\nachieved F1 score of 0.38, 0.63 and 0.76 in end-to-end pipeline testing, phrase\nextraction testing and triplet extraction testing respectively.\n"}
+{"abstract": "  Every year, the Robocup@Home competition challenges teams and robots'\nabilities. In 2020, the RoboCup@Home Education challenge was organized online,\naltering the usual competition rules. In this paper, we present the latest\ndevelopments that lead the RoboBreizh team to win the contest. These\ndevelopments include several modules linked to each other allowing the Pepper\nrobot to understand, act and adapt itself to a local environment. Up-to-date\navailable technologies have been used for navigation and dialogue. First\ncontribution includes combining object detection and pose estimation techniques\nto detect user's intention. Second contribution involves using Learning by\nDemonstrations to easily learn new movements that improve the Pepper robot's\nskills. This proposal won the best performance award of the 2020 RoboCup@Home\nEducation challenge.\n"}
+{"abstract": "  Although significant progress in automatic learning of steganographic cost\nhas been achieved recently, existing methods designed for spatial images are\nnot well applicable to JPEG images which are more common media in daily life.\nThe difficulties of migration mostly lie in the unique and complicated JPEG\ncharacteristics caused by 8x8 DCT mode structure. To address the issue, in this\npaper we extend an existing automatic cost learning scheme to JPEG, where the\nproposed scheme called JEC-RL (JPEG Embedding Cost with Reinforcement Learning)\nis explicitly designed to tailor the JPEG DCT structure. It works with the\nembedding action sampling mechanism under reinforcement learning, where a\npolicy network learns the optimal embedding policies via maximizing the rewards\nprovided by an environment network. The policy network is constructed following\na domain-transition design paradigm, where three modules including pixel-level\ntexture complexity evaluation, DCT feature extraction, and mode-wise\nrearrangement, are proposed. These modules operate in serial, gradually\nextracting useful features from a decompressed JPEG image and converting them\ninto embedding policies for DCT elements, while considering JPEG\ncharacteristics including inter-block and intra-block correlations\nsimultaneously. The environment network is designed in a gradient-oriented way\nto provide stable reward values by using a wide architecture equipped with a\nfixed preprocessing layer with 8x8 DCT basis filters. Extensive experiments and\nablation studies demonstrate that the proposed method can achieve good security\nperformance for JPEG images against both advanced feature based and modern CNN\nbased steganalyzers.\n"}
+{"abstract": "  Back propagation based visualizations have been proposed to interpret deep\nneural networks (DNNs), some of which produce interpretations with good visual\nquality. However, there exist doubts about whether these intuitive\nvisualizations are related to the network decisions. Recent studies have\nconfirmed this suspicion by verifying that almost all these modified\nback-propagation visualizations are not faithful to the model's decision-making\nprocess. Besides, these visualizations produce vague \"relative importance\nscores\", among which low values can't guarantee to be independent of the final\nprediction. Hence, it's highly desirable to develop a novel back-propagation\nframework that guarantees theoretical faithfulness and produces a quantitative\nattribution score with a clear understanding. To achieve the goal, we resort to\nmutual information theory to generate the interpretations, studying how much\ninformation of output is encoded in each input neuron. The basic idea is to\nlearn a source signal by back-propagation such that the mutual information\nbetween input and output should be as much as possible preserved in the mutual\ninformation between input and the source signal. In addition, we propose a\nMutual Information Preserving Inverse Network, termed MIP-IN, in which the\nparameters of each layer are recursively trained to learn how to invert. During\nthe inversion, forward Relu operation is adopted to adapt the general\ninterpretations to the specific input. We then empirically demonstrate that the\ninverted source signal satisfies completeness and minimality property, which\nare crucial for a faithful interpretation. Furthermore, the empirical study\nvalidates the effectiveness of interpretations generated by MIP-IN.\n"}
+{"abstract": "  Matrix powering is a fundamental computational primitive in linear algebra.\nIt has widespread applications in scientific computing and engineering, and\nunderlies the solution of time-homogeneous linear ordinary differential\nequations, simulation of discrete-time Markov chains, or discovering the\nspectral properties of matrices with iterative methods. In this paper, we\ninvestigate the possibility of speeding up matrix powering of sparse stable\nHermitian matrices on a quantum computer. We present two quantum algorithms\nthat can achieve speedup over the classical matrix powering algorithms -- (i)\nan adaption of quantum-walk based fast forwarding algorithm (ii) an algorithm\nbased on Hamiltonian simulation. Furthermore, by mapping the N-bit parity\ndetermination problem to a matrix powering problem, we provide no-go theorems\nthat limit the quantum speedups achievable in powering non-Hermitian matrices.\n"}
+{"abstract": "  In an adversarial environment, a hostile player performing a task may behave\nlike a non-hostile one in order not to reveal its identity to an opponent. To\nmodel such a scenario, we define identity concealment games: zero-sum\nstochastic reachability games with a zero-sum objective of identity\nconcealment. To measure the identity concealment of the player, we introduce\nthe notion of an average player. The average player's policy represents the\nexpected behavior of a non-hostile player. We show that there exists an\nequilibrium policy pair for every identity concealment game and give the\noptimality equations to synthesize an equilibrium policy pair. If the player's\nopponent follows a non-equilibrium policy, the player can hide its identity\nbetter. For this reason, we study how the hostile player may learn the\nopponent's policy. Since learning via exploration policies would quickly reveal\nthe hostile player's identity to the opponent, we consider the problem of\nlearning a near-optimal policy for the hostile player using the game runs\ncollected under the average player's policy. Consequently, we propose an\nalgorithm that provably learns a near-optimal policy and give an upper bound on\nthe number of sample runs to be collected.\n"}
+{"abstract": "  A celebrated theorem of Lind states that a positive real number is equal to\nthe spectral radius of some integral primitive matrix, if and only if, it is a\nPerron algebraic integer. Given a Perron number $p$, we prove that there is an\nintegral irreducible matrix with spectral radius $p$, and with dimension\nbounded above in terms of the algebraic degree, the ratio of the first two\nlargest Galois conjugates, and arithmetic information about the ring of\nintegers of its number field. This arithmetic information can be taken to be\neither the discriminant or the minimal Hermite-like thickness. Equivalently,\ngiven a Perron number $p$, there is an irreducible shift of finite type with\nentropy $\\log(p)$ defined as an edge shift on a graph whose number of vertices\nis bounded above in terms of the aforementioned data.\n"}
+{"abstract": "  Neuromorphic computing aims to mimic the architecture of the human brain to\ncarry out computational tasks that are challenging and much more energy\nconsuming for standard hardware. Despite progress in several fields of physics\nand engineering, the realization of artificial neural networks which combine\nhigh operating speeds with fast and low-energy adaptability remains a\nchallenge. Here we demonstrate an opto-magnetic neural network capable of\nlearning and classification of digitized 3x3 characters exploiting local\nstorage in the magnetic material. Using picosecond laser pulses, we find that\nmicrometer sized synapses absorb well below 100 picojoule per synapse per laser\npulse, with favorable scaling to smaller spatial dimensions. We thus succeeded\nin combining the speed and low-dissipation of optical networks with the\nlow-energy adaptability and non-volatility of magnetism, providing a promising\napproach to fast and energy-efficient neuromorphic computing.\n"}
+{"abstract": "  Gauge invariance, a core principle in electrodynamics, has two separate\nmeanings. One concept treats the photon as the gauge particle for\nelectrodynamics. It is based on symmetries of the Lagrangian, and requires no\nmention of electric or magnetic fields. The second concept depends directly on\nthe electric and magnetic fields, and how they can be represented by potential\nfunctions that are not unique. A general proof that potentials are more\nfundamental than fields serves to resolve discrepancies. Physical symmetries,\nhowever, are altered by gauge transformations and strongly limit gauge freedom.\nA new constraint on the form of allowable gauge transformations must be\nintroduced that applies to both gauge concepts.\n"}
+{"abstract": "  Although matter contributions to the graviton self-energy\n$-i[\\mbox{}^{\\mu\\nu} \\Sigma^{\\rho\\sigma}](x;x')$ must be separately conserved\non $x^{\\mu}$ and ${x'}^{\\mu}$, graviton contributions obey the weaker\nconstraint of the Ward identity, which involves a divergence on both\ncoordinates. On a general homogeneous and isotropic background this leads to\njust four structure functions for matter contributions but nine structure\nfunctions for graviton contributions. We propose a convenient parameterization\nfor these nine structure functions. We also apply the formalism to explicit one\nloop computations of $-i[\\mbox{}^{\\mu\\nu} \\Sigma^{\\rho\\sigma}](x;x')$ on de\nSitter background, one of the contributions from a massless, minimally coupled\nscalar and the other for the contribution from gravitons in the simplest gauge.\nWe also specialize the linearized, quantum-corrected Einstein equation to the\ngraviton mode function and to the gravitational response to a point mass.\n"}
+{"abstract": "  The large-scale integration of converter-interfaced resources in electrical\npower systems raises new stability threats which call for a new theoretic\nframework for modelling and analysis. Here we present the theory of\npower-communication isomorphism to solve this grand challenge. It is revealed\nthat an intrinsic communication mechanism governs the synchronisation of all\napparatus in power systems based on which a unified representation for\nheterogeneous apparatus and behaviours is established. We develop the\nmathematics to model the dynamic interaction within a power-communication\nisomorphic system which yield a simple stability criterion for complex systems\nthat can be intuitively interpreted and thus conveniently applied in practice.\n"}
+{"abstract": "  Bundles of C*-algebras can be used to represent limits of physical theories\nwhose algebraic structure depends on the value of a parameter. The primary\nexample is the $\\hbar\\to 0$ limit of the C*-algebras of physical quantities in\nquantum theories, represented in the framework of strict deformation\nquantization. In this paper, we understand such limiting procedures in terms of\nthe extension of a bundle of C*-algebras to some limiting value of a parameter.\nWe prove existence and uniqueness results for such extensions. Moreover, we\nshow that such extensions are functorial for the C*-product, dynamical\nautomorphisms, and the Lie bracket (in the $\\hbar\\to 0$ case) on the fiber\nC*-algebras.\n"}
+{"abstract": "  We provide a theoretical analysis of the nature of the orbital angular\nmomentum (OAM) modal fields in a multilayered fiber, such as the step-index\nfiber and the ring-core fiber. In a detailed study of the vector field\nsolutions of the step-index fiber (in the exponential basis), we discover that\nthe polarization-induced field component is a modified scalar OAM field (as\nopposed to a standard OAM scalar field) with a shifted intensity pattern in the\nweakly guiding approximation (WGA); the familiar intensity donut pattern is\nreduced or increased in radius depending upon whether it is a case of\nspin-alignment or anti-alignment with the OAM. Such a shift in the intensity\npattern appears to be a general feature of the field of a multilayered fiber as\nseen from an extension to the ring-core fiber. Additionally, we derive a\ngeneral expression for the polarization-correction to the scalar propagation\nconstant, which includes, for the first time, the contribution of the\npolarization-induced field. All the analytic expressions are illustrated and\nvalidated numerically with application to a step-index fiber, whose analytic\nsolutions are well-known.\n"}
+{"abstract": "  In online advertising, auto-bidding has become an essential tool for\nadvertisers to optimize their preferred ad performance metrics by simply\nexpressing high-level campaign objectives and constraints. Previous works\ndesigned auto-bidding tools from the view of single-agent, without modeling the\nmutual influence between agents. In this paper, we instead consider this\nproblem from a distributed multi-agent perspective, and propose a general\n$\\underline{M}$ulti-$\\underline{A}$gent reinforcement learning framework for\n$\\underline{A}$uto-$\\underline{B}$idding, namely MAAB, to learn the\nauto-bidding strategies. First, we investigate the competition and cooperation\nrelation among auto-bidding agents, and propose a temperature-regularized\ncredit assignment to establish a mixed cooperative-competitive paradigm. By\ncarefully making a competition and cooperation trade-off among agents, we can\nreach an equilibrium state that guarantees not only individual advertiser's\nutility but also the system performance (i.e., social welfare). Second, to\navoid the potential collusion behaviors of bidding low prices underlying the\ncooperation, we further propose bar agents to set a personalized bidding bar\nfor each agent, and then alleviate the revenue degradation due to the\ncooperation. Third, to deploy MAAB in the large-scale advertising system with\nmillions of advertisers, we propose a mean-field approach. By grouping\nadvertisers with the same objective as a mean auto-bidding agent, the\ninteractions among the large-scale advertisers are greatly simplified, making\nit practical to train MAAB efficiently. Extensive experiments on the offline\nindustrial dataset and Alibaba advertising platform demonstrate that our\napproach outperforms several baseline methods in terms of social welfare and\nrevenue.\n"}
+{"abstract": "  This paper investigates an end-to-end neural diarization (EEND) method for an\nunknown number of speakers. In contrast to the conventional pipeline approach\nto speaker diarization, EEND methods are better in terms of speaker overlap\nhandling. However, EEND still has a disadvantage in that it cannot deal with a\nflexible number of speakers. To remedy this problem, we introduce\nencoder-decoder-based attractor calculation module (EDA) to EEND. Once\nframe-wise embeddings are obtained, EDA sequentially generates speaker-wise\nattractors on the basis of a sequence-to-sequence method using an LSTM\nencoder-decoder. The attractor generation continues until a stopping condition\nis satisfied; thus, the number of attractors can be flexible. Diarization\nresults are then estimated as dot products of the attractors and embeddings.\nThe embeddings from speaker overlaps result in larger dot product values with\nmultiple attractors; thus, this method can deal with speaker overlaps. Because\nthe maximum number of output speakers is still limited by the training set, we\nalso propose an iterative inference method to remove this restriction. Further,\nwe propose a method that aligns the estimated diarization results with the\nresults of an external speech activity detector, which enables fair comparison\nagainst pipeline approaches. Extensive evaluations on simulated and real\ndatasets show that EEND-EDA outperforms the conventional pipeline approach.\n"}
+{"abstract": "  We study the effect of disorder and doping on the metal-insulator transition\nin a repulsive Hubbard model on a square lattice using the determinant quantum\nMonte Carlo method. First, with the aim of making our results reliable, we\ncompute the sign problem with various parameters such as temperature, disorder,\non-site interactions, and lattice size. We show that in the presence of\nrandomness in the hopping elements, the metal-insulator transition occurs and\nthe critical disorder strength differs at different fillings. We also\ndemonstrate that doping is a driving force behind the metal-insulator\ntransition.\n"}
+{"abstract": "  The symmetry energy and its density dependence are crucial inputs for many\nnuclear physics and astrophysics applications, as they determine properties\nranging from the neutron-skin thickness of nuclei to the crust thickness and\nthe radius of neutron stars. Recently, PREX-II reported a value of $0.283 \\pm\n0.071$ fm for the neutron-skin thickness of $^{208}$Pb, implying a slope\nparameter $L = 106 \\pm 37$ MeV, larger than most ranges obtained from\nmicroscopic calculations and other nuclear experiments. We use a nonparametric\nequation of state representation based on Gaussian processes to constrain the\nsymmetry energy $S_0$, $L$, and $R_\\mathrm{skin}^{^{208}\\mathrm{Pb}}$ directly\nfrom observations of neutron stars with minimal modeling assumptions. The\nresulting astrophysical constraints from heavy pulsar masses, LIGO/Virgo, and\nNICER clearly favor smaller values of the neutron skin and $L$, as well as\nnegative symmetry incompressibilities. Combining astrophysical data with\nPREX-II and chiral effective field theory constraints yields $S_0 =\n33.0^{+2.0}_{-1.8}$ MeV, $L=53^{+14}_{-15}$ MeV, and\n$R_\\mathrm{skin}^{^{208}\\mathrm{Pb}}=0.17^{+0.04}_{-0.04}$ fm.\n"}
+{"abstract": "  The shortage of highly qualified high school physics teachers is a national\nproblem. The Mitchell Institute Physics Enhancement Program (MIPEP) is a\ntwo-week professional development program for in-service high school physics\nteachers with a limited background in the subject area. MIPEP, which started in\n2012, includes intense training in both subject matter and research-based\ninstructional strategies. Content and materials used in the program fulfill\nstate curriculum requirements. The MIPEP curriculum is taught by Texas A&M\nUniversity faculty from the Department of Physics & Astronomy along with two\nmaster high school physics teachers. In this paper we present the design and\nimplementation of MIPEP. We report on assessment of knowledge and confidence of\n2014-2018 MIPEP cohorts. We also present the results of the 2020 program that\nwas delivered remotely due to the pandemic. Analysis of these assessments\nshowed that the majority of MIPEP participants increased their physics\nknowledge and their confidence in that knowledge during both traditional and\nvirtual program deliveries.\n"}
+{"abstract": "  We report the detection and analysis of a radio flare observed on 17 April\n2014 from Sgr A* at $9$ GHz using the VLA in its A-array configuration. This is\nthe first reported simultaneous radio observation of Sgr A* across $16$\nfrequency windows between $8$ and $10$ GHz. We cross correlate the lowest and\nhighest spectral windows centered at $8.0$ and $9.9$ GHz, respectively, and\nfind the $8.0$ GHz light curve lagging $18.37^{+2.17}_{-2.18}$ minutes behind\nthe $9.9$ GHz light curve. This is the first time lag found in Sgr A*'s light\ncurve across a narrow radio frequency bandwidth. We separate the quiescent and\nflaring components of Sgr A* via flux offsets at each spectral window. The\nemission is consistent with an adiabatically-expanding synchrotron plasma,\nwhich we fit to the light curves to characterize the two components. The\nflaring emission has an equipartition magnetic field strength of $2.2$ Gauss,\nsize of $14$ Schwarzschild radii, average speed of $12000$ km s$^{-1}$, and\nelectron energy spectrum index ($N(E)\\propto E^{-p}$), $p = 0.18$. The peak\nflare flux at $10$ GHz is approximately $25$% of the quiescent emission. This\nflare is abnormal as the inferred magnetic field strength and size are\ntypically about $10$ Gauss and few Schwarzschild radii. The properties of this\nflare are consistent with a transient warm spot in the accretion flow at a\ndistance of $10$-$100$ Schwarzschild radii from Sgr A*. Our analysis allows for\nindependent characterization of the variable and quiescent components, which is\nsignificant for studying temporal variations in these components.\n"}
+{"abstract": "  General partners (GP) are sometimes paid on a deal-by-deal basis and other\ntimes on a whole-portfolio basis. When is one method of payment better than the\nother? I show that when assets (projects or firms) are highly correlated or\nwhen GPs have low reputation, whole-portfolio contracting is superior to\ndeal-by-deal contracting. In this case, by bundling payouts together,\nwhole-portfolio contracting enhances incentives for GPs to exert effort.\nTherefore, it is better suited to alleviate the moral hazard problem which is\nstronger than the adverse selection problem in the case of high correlation of\nassets or low reputation of GPs. In contrast, for low correlation of assets or\nhigh reputation of GPs, information asymmetry concerns dominate and\ndeal-by-deal contracts become optimal, as they can efficiently weed out bad\nprojects one by one. These results shed light on recent empirical findings on\nthe relationship between investors and venture capitalists.\n"}
+{"abstract": "  Consensus about the universality of the power law feature in complex networks\nis experiencing profound challenges. To shine fresh light on this controversy,\nwe propose a generic theoretical framework in order to examine the power law\nproperty. First, we study a class of birth-and-death networks that is\nubiquitous in the real world, and calculate its degree distributions. Our\nresults show that the tails of its degree distributions exhibits a distinct\npower law feature, providing robust theoretical support for the ubiquity of the\npower law feature. Second, we suggest that in the real world two important\nfactors, network size and node disappearance probability, point to the\nexistence of the power law feature in the observed networks. As network size\nreduces, or as the probability of node disappearance increases, then the power\nlaw feature becomes increasingly difficult to observe. Finally, we suggest that\nan effective way of detecting the power law property is to observe the\nasymptotic (limiting) behaviour of the degree distribution within its effective\nintervals.\n"}
+{"abstract": "  In the search for small exoplanets orbiting cool stars whose spectral energy\ndistributions peak in the near infrared, the strong absorption of radiation in\nthis region due to water vapour in the atmosphere is a particularly adverse\neffect for the ground-based observations of cool stars. To achieve the\nphotometric precision required to detect exoplanets in the near infrared, it is\nnecessary to mitigate the impact of variable precipitable water vapour (PWV) on\nradial-velocity and photometric measurements. The aim is to enable global PWV\ncorrection by monitoring the amount of precipitable water vapour at zenith and\nalong the line of sight of any visible target. We developed an open source\nPython package that uses Geostationary Operational Environmental Satellites\n(GOES) imagery data, which provides temperature and relative humidity at\ndifferent pressure levels to compute near real-time PWV above any ground-based\nobservatory covered by GOES every 5 minutes or 10 minutes depending on the\nlocation. We computed PWV values on selected days above Cerro Paranal (Chile)\nand San Pedro M\\'artir (Mexico) to benchmark the procedure. We also simulated\ndifferent pointing at test targets as observed from the sites to compute the\nPWV along the line of sight. To asses the accuracy of our method, we compared\nour results with the on-site radiometer measurements obtained from Cerro\nParanal. Our results show that our publicly-available code proves to be a good\nsupporting tool for measuring the local PWV for any ground-based facility\nwithin the GOES coverage, which will help in reducing correlated noise\ncontributions in near-infrared ground-based observations that do not benefit\nfrom on-site PWV measurements.\n"}
+{"abstract": "  \"Pay-per-last-$N$-shares\" (PPLNS) is one of the most common payout strategies\nused by mining pools in Proof-of-Work (PoW) cryptocurrencies. As with any\npayment scheme, it is imperative to study issues of incentive compatibility of\nminers within the pool. For PPLNS this question has only been partially\nanswered; we know that reasonably-sized miners within a PPLNS pool prefer\nfollowing the pool protocol over employing specific deviations. In this paper,\nwe present a novel modification to PPLNS where we randomise the protocol in a\nnatural way. We call our protocol \"Randomised pay-per-last-$N$-shares\"\n(RPPLNS), and note that the randomised structure of the protocol greatly\nsimplifies the study of its incentive compatibility. We show that RPPLNS\nmaintains the strengths of PPLNS (i.e., fairness, variance reduction, and\nresistance to pool hopping), while also being robust against a richer class of\nstrategic mining than what has been shown for PPLNS.\n"}
+{"abstract": "  Accurate knowledge of the thermodynamic properties of zero-temperature,\nhigh-density quark matter plays an integral role in attempts to constrain the\nbehavior of the dense QCD matter found inside neutron-star cores, irrespective\nof the phase realized inside the stars. In this Letter, we consider the\nweak-coupling expansion of the dense QCD equation of state and compute the\nnext-to-next-to-next-to-leading-order contribution arising from the non-Abelian\ninteractions among long-wavelength, dynamically screened gluonic fields.\nAccounting for these interactions requires an all-loop resummation, which can\nbe performed using hard-thermal-loop (HTL) kinematic approximations.\nConcretely, we perform a full two-loop computation using the HTL effective\ntheory, valid for the long-wavelegth, or soft, modes. We find that the soft\nsector is well-behaved within cold quark matter, contrary to the case\nencountered at high temperatures, and find that the new contribution decreases\nthe renormalization-scale dependence of the equation of state at high density.\n"}
+{"abstract": "  We present the first study of nearby M dwarfs with the ROentgen Survey with\nan Imaging Telescope Array (eROSITA) on board the Russian\nSpektrum-Roentgen-Gamma mission (SRG). To this end we extracted the Gaia DR2\ndata for the ~9000 nearby M dwarfs in the superblink proper motion catalog and\ncalculated their stellar parameters from empirical relations with optical-IR\ncolors. We cross-matched this catalog with the eROSITA Final Equatorial Depth\nSurvey (eFEDS) and the first eROSITA all-sky survey (eRASS1). Our sample\nconsists of 704 stars (SpT = K5-M7). This unprecedented data base for X-ray\nemitting M dwarfs allowed to quantitatively constrain the mass dependence of\nthe X-ray luminosity, and to determine the change in the activity level with\nrespect to pre-main-sequence stars. We also combined these data with the\nTransiting Exoplanet Survey Satellite (TESS) observations that are available\nfor 501 of 704 X-ray detected M dwarfs and determined the rotation period for\n180 of them. With the joint eROSITA-TESS sample, and combining it with our\nhistorical X-ray and rotation data for M dwarfs, we examined the mass\ndependence in the saturated regime of the rotation-activity relation. A first\ncomparison of eROSITA hardness ratios and spectra shows that 65% of our X-ray\ndetected M dwarfs have coronal temperatures of $\\sim 0.5$ keV. We investigated\ntheir long-term X-ray variability by comparing the eRASS1 and ROSAT all-sky\nsurvey (RASS) measurements. Evidence for X-ray flares is found in various parts\nof our analysis: directly from inspection of the eFEDS light curves, in the\nrelation between RASS and eRASS1 X-ray luminosities, and in stars displaying\nX-ray emission hotter than the bulk of the sample according to the hardness\nratios. Finally, we point out the need of X-ray spectroscopy for more M dwarfs\nto study the coronal temperature-luminosity relation, not well constrained by\nour eFEDS results.\n"}
+{"abstract": "  The generating function of a Hamiltonian $H$ is defined as $F(t)=\\langle\ne^{-itH}\\rangle$, where $t$ is the time and where the expectation value is\ntaken on a given initial quantum state. This function gives access to the\ndifferent moments of the Hamiltonian $\\langle H^{K}\\rangle$ at various orders\n$K$. The real and imaginary parts of $F(t)$ can be respectively evaluated on\nquantum computers using one extra ancillary qubit with a set of measurement for\neach value of the time $t$. The low cost in terms of qubits renders it very\nattractive in the near term period where the number of qubits is limited.\nAssuming that the generating function can be precisely computed using quantum\ndevices, we show how the information content of this function can be used a\nposteriori on classical computers to solve quantum many-body problems. Several\nmethods of classical post-processing are illustrated with the aim to predict\napproximate ground or excited state energies and/or approximate long-time\nevolutions. This post-processing can be achieved using methods based on the\nKrylov space and/or on the $t$-expansion approach that is closely related to\nthe imaginary time evolution. Hybrid quantum-classical calculations are\nillustrated in many-body interacting systems using the pairing and\nFermi-Hubbard models.\n"}
+{"abstract": "  Reliable predictions of the behaviour of chemical systems are essential\nacross many industries, from nanoscale engineering over validation of advanced\nmaterials to nanotoxicity assessment in health and medicine. For the future we\ntherefore envision a paradigm shift for the design of chemical simulations\nacross all length scales from a prescriptive to a predictive and quantitative\nscience. This paper presents an integrative perspective about the\nstate-of-the-art of modelling in computational and theoretical chemistry with\nexamples from data- and equation-based models. Extension to include reliable\nrisk assessments and quality control are discussed. To specify and broaden the\nconcept of chemical accuracy in the design cycle of reliable and robust\nmolecular simulations the fields of computational chemistry, physics,\nmathematics, visualisation science, and engineering are bridged. Methods from\nelectronic structure calculations serve as examples to explain how\nuncertainties arise: through assumed mechanisms in form of equations, model\nparameters, algorithms, and numerical implementations. We provide a full\nclassification of uncertainties throughout the chemical modelling cycle and\ndiscuss how the associated risks can be mitigated. Further, we apply our\nstatements to molecular dynamics and partial differential equations based\napproaches. An overview of methods from numerical mathematics and statistics\nprovides strategies to analyse risks and potential errors in the design of new\nmaterials and compounds. We also touch on methods for validation and\nverification. In the conclusion we address cross-disciplinary open challenges.\nIn future the quantitative analysis of where simulations and their prognosis\nfail will open doors towards predictive materials engineering and chemical\nmodelling.\n"}
+{"abstract": "  This paper studies a federated learning (FL) system, where \\textit{multiple}\nFL services co-exist in a wireless network and share common wireless resources.\nIt fills the void of wireless resource allocation for multiple simultaneous FL\nservices in the existing literature. Our method designs a two-level resource\nallocation framework comprising \\emph{intra-service} resource allocation and\n\\emph{inter-service} resource allocation. The intra-service resource allocation\nproblem aims to minimize the length of FL rounds by optimizing the bandwidth\nallocation among the clients of each FL service. Based on this, an\ninter-service resource allocation problem is further considered, which\ndistributes bandwidth resources among multiple simultaneous FL services. We\nconsider both cooperative and selfish providers of the FL services. For\ncooperative FL service providers, we design a distributed bandwidth allocation\nalgorithm to optimize the overall performance of multiple FL services,\nmeanwhile cater to the fairness among FL services and the privacy of clients.\nFor selfish FL service providers, a new auction scheme is designed with the FL\nservice owners as the bidders and the network provider as the auctioneer. The\ndesigned auction scheme strikes a balance between the overall FL performance\nand fairness. Our simulation results show that the proposed algorithms\noutperform other benchmarks under various network conditions.\n"}
+{"abstract": "  Among various quantum key distribution (QKD) protocols, the round-robin\ndifferential-phase-shift (RRDPS) protocol has a unique feature that its\nsecurity is guaranteed without monitoring any statistics. Moreover, this\nprotocol has a remarkable property of being robust against source imperfections\nassuming that the emitted pulses are independent. Unfortunately, some\nexperiments confirmed the violation of the independence due to pulse\ncorrelations, and therefore the lack of a security proof without taking into\naccount this effect is an obstacle for the security. In this paper, we prove\nthat the RRDPS protocol is secure against any source imperfections by\nestablishing a proof with the pulse correlations. Our proof is simple in the\nsense that we make only three experimentally simple assumptions for the source.\nOur numerical simulation based on the proof shows that the long-range pulse\ncorrelation does not cause a significant impact on the key rate, which reveals\nanother striking feature of the RRDPS protocol. Our security proof is thus\neffective and applicable to wide range of practical sources and paves the way\nto realize truly secure QKD in high-speed systems.\n"}
+{"abstract": "  We consider a pursuit-evasion problem with a heterogeneous team of multiple\npursuers and multiple evaders. Although both the pursuers (robots) and the\nevaders are aware of each others' control and assignment strategies, they do\nnot have exact information about the other type of agents' location or action.\nUsing only noisy on-board sensors the pursuers (or evaders) make probabilistic\nestimation of positions of the evaders (or pursuers). Each type of agent use\nMarkov localization to update the probability distribution of the other type. A\nsearch-based control strategy is developed for the pursuers that intrinsically\ntakes the probability distribution of the evaders into account. Pursuers are\nassigned using an assignment algorithm that takes redundancy (i.e., an excess\nin the number of pursuers than the number of evaders) into account, such that\nthe total or maximum estimated time to capture the evaders is minimized. In\nthis respect we assume the pursuers to have clear advantage over the evaders.\nHowever, the objective of this work is to use assignment strategies that\nminimize the capture time. This assignment strategy is based on a modified\nHungarian algorithm as well as a novel algorithm for determining assignment of\nredundant pursuers. The evaders, in order to effectively avoid the pursuers,\npredict the assignment based on their probabilistic knowledge of the pursuers\nand use a control strategy to actively move away from those pursues. Our\nexperimental evaluation shows that the redundant assignment algorithm performs\nbetter than an alternative nearest-neighbor based assignment algorithm.\n"}
+{"abstract": "  The drastic increase of data quantity often brings the severe decrease of\ndata quality, such as incorrect label annotations, which poses a great\nchallenge for robustly training Deep Neural Networks (DNNs). Existing learning\n\\mbox{methods} with label noise either employ ad-hoc heuristics or restrict to\nspecific noise assumptions. However, more general situations, such as\ninstance-dependent label noise, have not been fully explored, as scarce studies\nfocus on their label corruption process. By categorizing instances into\nconfusing and unconfusing instances, this paper proposes a simple yet universal\nprobabilistic model, which explicitly relates noisy labels to their instances.\nThe resultant model can be realized by DNNs, where the training procedure is\naccomplished by employing an alternating optimization algorithm. Experiments on\ndatasets with both synthetic and real-world label noise verify that the\nproposed method yields significant improvements on robustness over\nstate-of-the-art counterparts.\n"}
+{"abstract": "  We propose a new hybrid quantum algorithm based on the classical Ant Colony\nOptimization algorithm to produce approximate solutions for NP-hard problems,\nin particular optimization problems. First, we discuss some previously proposed\nQuantum Ant Colony Optimization algorithms, and based on them, we develop an\nimproved algorithm that can be truly implemented on near-term quantum\ncomputers. Our iterative algorithm codifies only the information about the\npheromones and the exploration parameter in the quantum state, while\nsubrogating the calculation of the numerical result to a classical computer. A\nnew guided exploration strategy is used in order to take advantage of the\nquantum computation power and generate new possible solutions as a\nsuperposition of states. This approach is specially useful to solve constrained\noptimization problems, where we can implement efficiently the exploration of\nnew paths without having to check the correspondence of a path to a solution\nbefore the measurement of the state. As an example of a NP-hard problem, we\nchoose to solve the Quadratic Assignment Problem. The benchmarks made by\nsimulating the noiseless quantum circuit and the experiments made on IBM\nquantum computers show the validity of the algorithm.\n"}
+{"abstract": "  The Information Bottleneck (IB) provides an information theoretic principle\nfor representation learning, by retaining all information relevant for\npredicting label while minimizing the redundancy. Though IB principle has been\napplied to a wide range of applications, its optimization remains a challenging\nproblem which heavily relies on the accurate estimation of mutual information.\nIn this paper, we present a new strategy, Variational Self-Distillation (VSD),\nwhich provides a scalable, flexible and analytic solution to essentially\nfitting the mutual information but without explicitly estimating it. Under\nrigorously theoretical guarantee, VSD enables the IB to grasp the intrinsic\ncorrelation between representation and label for supervised training.\nFurthermore, by extending VSD to multi-view learning, we introduce two other\nstrategies, Variational Cross-Distillation (VCD) and Variational\nMutual-Learning (VML), which significantly improve the robustness of\nrepresentation to view-changes by eliminating view-specific and task-irrelevant\ninformation. To verify our theoretically grounded strategies, we apply our\napproaches to cross-modal person Re-ID, and conduct extensive experiments,\nwhere the superior performance against state-of-the-art methods are\ndemonstrated. Our intriguing findings highlight the need to rethink the way to\nestimate mutual\n"}
+{"abstract": "  In this report, we investigate (element-based) inconsistency measures for\nmultisets of business rule bases. Currently, related works allow to assess\nindividual rule bases, however, as companies might encounter thousands of such\ninstances daily, studying not only individual rule bases separately, but rather\nalso their interrelations becomes necessary, especially in regard to\ndetermining suitable re-modelling strategies. We therefore present an approach\nto induce multiset-measures from arbitrary (traditional) inconsistency\nmeasures, propose new rationality postulates for a multiset use-case, and\ninvestigate the complexity of various aspects regarding multi-rule base\ninconsistency measurement.\n"}
+{"abstract": "  In this paper, we try to improve exploration in Blackbox methods,\nparticularly Evolution strategies (ES), when applied to Reinforcement Learning\n(RL) problems where intermediate waypoints/subgoals are available. Since\nEvolutionary strategies are highly parallelizable, instead of extracting just a\nscalar cumulative reward, we use the state-action pairs from the trajectories\nobtained during rollouts/evaluations, to learn the dynamics of the agent. The\nlearnt dynamics are then used in the optimization procedure to speed-up\ntraining. Lastly, we show how our proposed approach is universally applicable\nby presenting results from experiments conducted on Carla driving and UR5\nrobotic arm simulators.\n"}
+{"abstract": "  UDDSKETCH is a recent algorithm for accurate tracking of quantiles in data\nstreams, derived from the DDSKETCH algorithm. UDDSKETCH provides accuracy\nguarantees covering the full range of quantiles independently of the input\ndistribution and greatly improves the accuracy with regard to DDSKETCH. In this\npaper we show how to compress and fuse data streams (or datasets) by using\nUDDSKETCH data summaries that are fused into a new summary related to the union\nof the streams (or datasets) processed by the input summaries whilst preserving\nboth the error and size guarantees provided by UDDSKETCH. This property of\nsketches, known as mergeability, enables parallel and distributed processing.\nWe prove that UDDSKETCH is fully mergeable and introduce a parallel version of\nUDDSKETCH suitable for message-passing based architectures. We formally prove\nits correctness and compare it to a parallel version of DDSKETCH, showing\nthrough extensive experimental results that our parallel algorithm almost\nalways outperforms the parallel DDSKETCH algorithm with regard to the overall\naccuracy in determining the quantiles.\n"}
+{"abstract": "  Analysing whether neural language models encode linguistic information has\nbecome popular in NLP. One method of doing so, which is frequently cited to\nsupport the claim that models like BERT encode syntax, is called probing;\nprobes are small supervised models trained to extract linguistic information\nfrom another model's output. If a probe is able to predict a particular\nstructure, it is argued that the model whose output it is trained on must have\nimplicitly learnt to encode it. However, drawing a generalisation about a\nmodel's linguistic knowledge about a specific phenomena based on what a probe\nis able to learn may be problematic: in this work, we show that semantic cues\nin training data means that syntactic probes do not properly isolate syntax. We\ngenerate a new corpus of semantically nonsensical but syntactically well-formed\nJabberwocky sentences, which we use to evaluate two probes trained on normal\ndata. We train the probes on several popular language models (BERT, GPT, and\nRoBERTa), and find that in all settings they perform worse when evaluated on\nthese data, for one probe by an average of 15.4 UUAS points absolute. Although\nin most cases they still outperform the baselines, their lead is reduced\nsubstantially, e.g. by 53% in the case of BERT for one probe. This begs the\nquestion: what empirical scores constitute knowing syntax?\n"}
+{"abstract": "  The growing political polarization of the American electorate over the last\nseveral decades has been widely studied and documented. During the\nadministration of President Donald Trump, charges of \"fake news\" made social\nand news media not only the means but, to an unprecedented extent, the topic of\npolitical communication. Using data from before the November 3rd, 2020 US\nPresidential election, recent work has demonstrated the viability of using\nYouTube's social media ecosystem to obtain insights into the extent of US\npolitical polarization as well as the relationship between this polarization\nand the nature of the content and commentary provided by different US news\nnetworks. With that work as background, this paper looks at the sharp\ntransformation of the relationship between news consumers and here-to-fore\n\"fringe\" news media channels in the 64 days between the US presidential\nelection and the violence that took place at US Capitol on January 6th. This\npaper makes two distinct types of contributions. The first is to introduce a\nnovel methodology to analyze large social media data to study the dynamics of\nsocial political news networks and their viewers. The second is to provide\ninsights into what actually happened regarding US political social media\nchannels and their viewerships during this volatile 64 day period.\n"}
+{"abstract": "  Age-related macular degeneration (AMD) may cause severe loss of vision or\nblindness particularly in elderly people. Exudative AMD is characterized by\nangiogenesis of blood vessels growing from underneath the macula, crossing the\nblood-retina barrier (that comprise Bruch's membrane, BM, and the retinal\npigmentation epithelium RPE), leaking blood and fluid into the retina and\nknocking off photoreceptors. Here, we simulate a computational model of\nangiogenesis from the choroid blood vessels via a cellular Potts model, as well\nas BM, RPE cells, drusen deposits and photoreceptors. Our results indicate that\nimproving AMD may require fixing the impaired lateral adhesion between RPE\ncells and with BM, as well as diminishing Vessel Endothelial Growth Factor\n(VEGF) and Jagged proteins that affect the Notch signaling pathway. Our\nnumerical simulations suggest that anti-VEGF and anti-Jagged therapies could\ntemporarily halt exudative AMD while addressing impaired cellular adhesion\ncould be more effective on a longer time span.\n"}
+{"abstract": "  The study is based on a principle of laser physics so that a (coherent) laser\nlight whose wavelength is shorter than a feature under inspection (like\nsub-cellular component) can interact with such specific feature (or textural\nfeatures) and generates laser speckle patterns which can characterize those\nspecific features. By the method we have managed to detect differences at\nsub-cellular scales such as genetic modification, cellular shape deformation,\netc. with 87% accuracy. In this study red laser is used whose wavelength (6.5\nmicrons) is shorter than a plant cell (~60 microns) that is suitable to\ninteract with sub-cellular features. The work is assumed to be an initial stage\nof further application on human cellular changes observation that would be\nutilized for development of more accurate methods such as better drug delivery\nassessments, systemic diseases early diagnosis, etc.\n"}
+{"abstract": "  Monitoring the network performance experienced by the end-user is crucial for\nmanagers of wireless networks as it can enable them to remotely modify the\nnetwork parameters to improve the end-user experience. Unfortunately, for\nperformance monitoring, managers are typically limited to the logs of the\nAccess Points (APs) that they manage. This information does not directly\ncapture factors that can hinder station (STA) side transmissions. Consequently,\nstate-of-the-art methods to measure such metrics primarily involve active\nmeasurements. Unfortunately, such active measurements increase traffic load and\nif used regularly and for all the STAs can potentially disrupt user traffic,\nthereby worsening performance for other users in the network and draining the\nbattery of mobile devices.\n  This thesis enables passive AP-side network analytics. In the first part of\nthe thesis, I present virtual speed test, a measurement based framework that\nenables an AP to estimate speed test results for any of its associated clients\nsolely based on AP-side observables. Next, I present Uplink Latency Microscope\n(uScope), an AP-side framework for estimation of WLAN uplink latency for any of\nthe associated STAs and decomposition into its constituent components. Similar\nto virtual speed test, uScope makes estimations solely based on passive AP-side\nobservations. We implement both frameworks on a commodity hardware platform and\nconduct extensive field trials on a university campus and in a residential\napartment complex. In over 1 million tests, the two proposed frameworks\ndemonstrate an estimation accuracy with errors under 10%.\n"}
+{"abstract": "  We propose a theoretical scheme to enhance the phase sensitivity by\nintroducing a Kerr nonlinear phase shift into the traditional SU(1,1)\ninterferometer with a coherent state input and homodyne detection. We\ninvestigate the realistic effects of photon losses on phase sensitivity and\nquantum Fisher information. The results show that compared with the linear\nphase shift in SU(1,1) interferometer, the Kerr nonlinear case can not only\nenhance the phase sensitivity and quantum Fisher information, but also\nsignificantly suppress the photon losses. We also observe that at the same\naccessible parameters, internal losses have a greater influence on the phase\nsensitivity than the external ones. It is interesting that, our scheme shows an\nobvious advantage of low-cost input resources to obtain higher phase\nsensitivity and larger quantum Fisher information due to the introduction of\nnonlinear phase element.\n"}
+{"abstract": "  Deep generative models of 3D shapes have received a great deal of research\ninterest. Yet, almost all of them generate discrete shape representations, such\nas voxels, point clouds, and polygon meshes. We present the first 3D generative\nmodel for a drastically different shape representation --- describing a shape\nas a sequence of computer-aided design (CAD) operations. Unlike meshes and\npoint clouds, CAD models encode the user creation process of 3D shapes, widely\nused in numerous industrial and engineering design tasks. However, the\nsequential and irregular structure of CAD operations poses significant\nchallenges for existing 3D generative models. Drawing an analogy between CAD\noperations and natural language, we propose a CAD generative network based on\nthe Transformer. We demonstrate the performance of our model for both shape\nautoencoding and random shape generation. To train our network, we create a new\nCAD dataset consisting of 178,238 models and their CAD construction sequences.\nWe have made this dataset publicly available to promote future research on this\ntopic.\n"}
+{"abstract": "  Scientific and engineering problems often require the use of artificial\nintelligence to aid understanding and the search for promising designs. While\nGaussian processes (GP) stand out as easy-to-use and interpretable learners,\nthey have difficulties in accommodating big datasets, categorical inputs, and\nmultiple responses, which has become a common challenge for a growing number of\ndata-driven design applications. In this paper, we propose a GP model that\nutilizes latent variables and functions obtained through variational inference\nto address the aforementioned challenges simultaneously. The method is built\nupon the latent variable Gaussian process (LVGP) model where categorical\nfactors are mapped into a continuous latent space to enable GP modeling of\nmixed-variable datasets. By extending variational inference to LVGP models, the\nlarge training dataset is replaced by a small set of inducing points to address\nthe scalability issue. Output response vectors are represented by a linear\ncombination of independent latent functions, forming a flexible kernel\nstructure to handle multiple responses that might have distinct behaviors.\nComparative studies demonstrate that the proposed method scales well for large\ndatasets with over 10^4 data points, while outperforming state-of-the-art\nmachine learning methods without requiring much hyperparameter tuning. In\naddition, an interpretable latent space is obtained to draw insights into the\neffect of categorical factors, such as those associated with building blocks of\narchitectures and element choices in metamaterial and materials design. Our\napproach is demonstrated for machine learning of ternary oxide materials and\ntopology optimization of a multiscale compliant mechanism with aperiodic\nmicrostructures and multiple materials.\n"}
+{"abstract": "  The research presents an overhead view of 10 important objects and follows\nthe general formatting requirements of the most popular machine learning task:\ndigit recognition with MNIST. This dataset offers a public benchmark extracted\nfrom over a million human-labelled and curated examples. The work outlines the\nkey multi-class object identification task while matching with prior work in\nhandwriting, cancer detection, and retail datasets. A prototype deep learning\napproach with transfer learning and convolutional neural networks (MobileNetV2)\ncorrectly identifies the ten overhead classes with an average accuracy of\n96.7%. This model exceeds the peak human performance of 93.9%. For upgrading\nsatellite imagery and object recognition, this new dataset benefits diverse\nendeavors such as disaster relief, land use management, and other traditional\nremote sensing tasks. The work extends satellite benchmarks with new\ncapabilities to identify efficient and compact algorithms that might work\non-board small satellites, a practical task for future multi-sensor\nconstellations. The dataset is available on Kaggle and Github.\n"}
+{"abstract": "  Conformal Predictors (CP) are wrappers around ML models, providing error\nguarantees under weak assumptions on the data distribution. They are suitable\nfor a wide range of problems, from classification and regression to anomaly\ndetection. Unfortunately, their very high computational complexity limits their\napplicability to large datasets. In this work, we show that it is possible to\nspeed up a CP classifier considerably, by studying it in conjunction with the\nunderlying ML method, and by exploiting incremental&decremental learning. For\nmethods such as k-NN, KDE, and kernel LS-SVM, our approach reduces the running\ntime by one order of magnitude, whilst producing exact solutions. With similar\nideas, we also achieve a linear speed up for the harder case of bootstrapping.\nFinally, we extend these techniques to improve upon an optimization of k-NN CP\nfor regression. We evaluate our findings empirically, and discuss when methods\nare suitable for CP optimization.\n"}
+{"abstract": "  The quantum approximate optimization algorithm (QAOA) is a variational method\nfor noisy, intermediate-scale quantum computers to solve combinatorial\noptimization problems. Quantifying performance bounds with respect to specific\nproblem instances provides insight into when QAOA may be viable for solving\nreal-world applications. Here, we solve every instance of MaxCut on\nnon-isomorphic unweighted graphs with nine or fewer vertices by numerically\nsimulating the pure-state dynamics of QAOA. Testing up to three layers of QAOA\ndepth, we find that distributions of the approximation ratio narrow with\nincreasing depth while the probability of recovering the maximum cut generally\nbroadens. We find QAOA exceeds the Goemans-Williamson approximation ratio bound\nfor most graphs. We also identify consistent patterns within the ensemble of\noptimized variational circuit parameters that offer highly efficient heuristics\nfor solving MaxCut with QAOA. The resulting data set is presented as a\nbenchmark for establishing empirical bounds on QAOA performance that may be\nused to test on-going experimental realizations.\n"}
+{"abstract": "  The proof of origin of logs is becoming increasingly important. In the\ncontext of Industry 4.0 and to combat illegal logging there is an increasing\nmotivation to track each individual log. Our previous works in this field\nfocused on log tracking using digital log end images based on methods inspired\nby fingerprint and iris-recognition. This work presents a convolutional neural\nnetwork (CNN) based approach which comprises a CNN-based segmentation of the\nlog end combined with a final CNN-based recognition of the segmented log end\nusing the triplet loss function for CNN training. Results show that the\nproposed two-stage CNN-based approach outperforms traditional approaches.\n"}
+{"abstract": "  Sentiment prediction remains a challenging and unresolved task in various\nresearch fields, including psychology, neuroscience, and computer science. This\nstems from its high degree of subjectivity and limited input sources that can\neffectively capture the actual sentiment. This can be even more challenging\nwith only text-based input. Meanwhile, the rise of deep learning and an\nunprecedented large volume of data have paved the way for artificial\nintelligence to perform impressively accurate predictions or even human-level\nreasoning. Drawing inspiration from this, we propose a coverage-based sentiment\nand subsentence extraction system that estimates a span of input text and\nrecursively feeds this information back to the networks. The predicted\nsubsentence consists of auxiliary information expressing a sentiment. This is\nan important building block for enabling vivid and epic sentiment delivery\n(within the scope of this paper) and for other natural language processing\ntasks such as text summarisation and Q&A. Our approach outperforms the\nstate-of-the-art approaches by a large margin in subsentence prediction (i.e.,\nAverage Jaccard scores from 0.72 to 0.89). For the evaluation, we designed\nrigorous experiments consisting of 24 ablation studies. Finally, our learned\nlessons are returned to the community by sharing software packages and a public\ndataset that can reproduce the results presented in this paper.\n"}
+{"abstract": "  Nano-graphene /polymer composites can functionas pressure induced\nelectro-switches, at concentrations around their conductivity percolation\nthreshold. Close to the critical point, the pressure dependence of the electron\ntunneling through the polymer barrier separating nanon-graphenes results from\nthecompetition among shorteningof the tunneling length and the increase of the\npolymer's polarizability. Such switching behaviorwas recentlyobserved\ninpolyvinyl alcohol (PVA) loaded withnano-graphene platelets (NGPs). In this\nwork, PVA is blended withh alpha-poly(vinylidene fluoride) (PVdF)and NGPs.\nCoaxial mechanical stress and electric field render the nano-composite\npiezoelectric. We investigate the influence of heterogeneity, thermal\nproperties, phase transitions and kinetic processes occurring in the polymer\nmatrix on the macroscopicelectrical conductivity and interfacial polarization\nin casted specimens. Furthermore, the effect of electro-activity of PVdF grains\non the electric and thermal properties are comparatively studied. Broadband\nDielectricspectroscopy is employed to resolve and inspect electron transport\nand trapping with respect to thermal transitions and kineticprocessestraced via\nDifferential Scanning Calorimetry. The harmonic electric field applied during a\nBDS sweep induces volume modifications of the electro-active PVdF grains,\nwhile, electro-activity of PVdF grains can disturb the internal electric field\nthat free (or bound) electric. The dc conductivity and dielectric relaxation\nwas found to exhibit weakdependencies.\n"}
+{"abstract": "  Social recommendation is effective in improving the recommendation\nperformance by leveraging social relations from online social networking\nplatforms. Social relations among users provide friends' information for\nmodeling users' interest in candidate items and help items expose to potential\nconsumers (i.e., item attraction). However, there are two issues haven't been\nwell-studied: Firstly, for the user interests, existing methods typically\naggregate friends' information contextualized on the candidate item only, and\nthis shallow context-aware aggregation makes them suffer from the limited\nfriends' information. Secondly, for the item attraction, if the item's past\nconsumers are the friends of or have a similar consumption habit to the\ntargeted user, the item may be more attractive to the targeted user, but most\nexisting methods neglect the relation enhanced context-aware item attraction.\nTo address the above issues, we proposed DICER (Dual Side Deep Context-aware\nModulation for SocialRecommendation). Specifically, we first proposed a novel\ngraph neural network to model the social relation and collaborative relation,\nand on top of high-order relations, a dual side deep context-aware modulation\nis introduced to capture the friends' information and item attraction.\nEmpirical results on two real-world datasets show the effectiveness of the\nproposed model and further experiments are conducted to help understand how the\ndual context-aware modulation works.\n"}
+{"abstract": "  Energy management is a critical aspect of risk assessment for Uncrewed Aerial\nVehicle (UAV) flights, as a depleted battery during a flight brings almost\nguaranteed vehicle damage and a high risk of human injuries or property damage.\nPredicting the amount of energy a flight will consume is challenging as\nrouting, weather, obstacles, and other factors affect the overall consumption.\nWe develop a deep energy model for a UAV that uses Temporal Convolutional\nNetworks to capture the time varying features while incorporating static\ncontextual information. Our energy model is trained on a real world dataset and\ndoes not require segregating flights into regimes. We illustrate an improvement\nin power predictions by $29\\%$ on test flights when compared to a\nstate-of-the-art analytical method. Using the energy model, we can predict the\nenergy usage for a given trajectory and evaluate the risk of running out of\nbattery during flight. We propose using Conditional Value-at-Risk (CVaR) as a\nmetric for quantifying this risk. We show that CVaR captures the risk\nassociated with worst-case energy consumption on a nominal path by transforming\nthe output distribution of Monte Carlo forward simulations into a risk space.\nComputing the CVaR on the risk-space distribution provides a metric that can\nevaluate the overall risk of a flight before take-off. Our energy model and\nrisk evaluation method can improve flight safety and evaluate the coverage area\nfrom a proposed takeoff location.\n  The video and codebase are available at https://youtu.be/PHXGigqilOA and\nhttps://git.io/cvar-risk .\n"}
+{"abstract": "  Localizing and counting large ungulates -- hoofed mammals like cows and elk\n-- in very high-resolution satellite imagery is an important task for\nsupporting ecological studies. Prior work has shown that this is feasible with\ndeep learning based methods and sub-meter multi-spectral satellite imagery. We\nextend this line of work by proposing a baseline method, CowNet, that\nsimultaneously estimates the number of animals in an image (counts), as well as\npredicts their location at a pixel level (localizes). We also propose an\nmethodology for evaluating such models on counting and localization tasks\nacross large scenes that takes the uncertainty of noisy labels and the\ninformation needed by stakeholders in ecological monitoring tasks into account.\nFinally, we benchmark our baseline method with state of the art vision methods\nfor counting objects in scenes. We specifically test the temporal\ngeneralization of the resulting models over a large landscape in Point Reyes\nSeashore, CA. We find that the LC-FCN model performs the best and achieves an\naverage precision between 0.56 and 0.61 and an average recall between 0.78 and\n0.92 over three held out test scenes.\n"}
+{"abstract": "  Various processes in academic organizations include the decision points where\nselecting people through their assessment and ranking is performed, and the\nimpact of wrong or right choices can be very high. How do we simultaneously\nensure that these selection decisions are well balanced, fair, and unbiased by\nsatisfying the key stakeholders' wishes? How much and what kinds of evidence\nmust be used to make them? How can both the evidence and the procedures be made\ntransparent and unambitious for everyone? In this paper, we suggest a set of\nso-called deep evidence-based analytics, which is applied on top of the\ncollective awareness platform (portal for managing higher education processes).\nThe deep evidence, in addition to the facts about the individual academic\nachievements of personnel, includes the evidence about individual rewards.\nHowever, what is more important is that such evidence also includes explicit\nindividual value systems (formalized personal preferences in the\nself-assessment of both achievements and the rewards). We provide formalized\nprocedures that can be used to drive the academic assessment and selection\nprocesses within universities based on advanced (deep) evidence and with\ndifferent balances of decision power between administrations and personnel. We\nalso show how this analytics enables computational evidence for some abstract\nproperties of an academic organization related to its organizational culture,\nsuch as organizational democracy, justice, and work passion. We present the\nanalytics together with examples of its actual use within Ukrainian higher\neducation at the Trust portal.\n"}
+{"abstract": "  Analyticity and crossing properties of four point function are investigated\nin conformal field theories in the frameworks of Wightman axioms. A Hermitian\nscalar conformal field, satisfying the Wightman axioms, is considered. The\ncrucial role of microcausality in deriving analyticity domains is discussed and\ndomains of analyticity are presented. A pair of permuted Wightman functions are\nenvisaged. The crossing property is derived by appealing to the technique of\nanalytic completion for the pair of permuted Wightman functions. The operator\nproduct expansion of a pair of scalar fields is studied and analyticity\nproperty of the matrix elements of composite fields, appearing in the operator\nproduct expansion, is investigated. An integral representation is presented for\nthe commutator of composite fields where microcausality is a key ingredient.\nThree fundamental theorems of axiomatic local field theories; namely, PCT\ntheorem, the theorem proving equivalence between PCT theorem and weak local\ncommutativity and the edge-of-the-wedge theorem are invoked to derive a\nconformal bootstrap equation rigorously.\n"}
+{"abstract": "  Bohm and Bell's approaches to the foundations of quantum mechanics share\nnotable features with the contemporary Primitive Ontology perspective and\nEsfeld and Deckert minimalist ontology. For instance, all these programs\nconsider ontological clarity a necessary condition to be met by every\ntheoretical framework, promote scientific realism also in the quantum domain\nand strengthen the explanatory power of quantum theory. However, these\napproaches remarkably diverge from one another, since they employ different\nmetaphysical principles leading to conflicting Weltanschaaungen. The principal\naim of this essay is to spell out the relations as well as the main differences\nexisting among such programs, which unfortunately remain often unnoticed in\nliterature. Indeed, it is not uncommon to see Bell's views conflated with the\nPO programme, and the latter with Esfeld and Deckert's proposal. It will be our\ntask to clear up this confusion.\n"}
+{"abstract": "  The recent growth of web video sharing platforms has increased the demand for\nsystems that can efficiently browse, retrieve and summarize video content.\nQuery-aware multi-video summarization is a promising technique that caters to\nthis demand. In this work, we introduce a novel Query-Aware Hierarchical\nPointer Network for Multi-Video Summarization, termed DeepQAMVS, that jointly\noptimizes multiple criteria: (1) conciseness, (2) representativeness of\nimportant query-relevant events and (3) chronological soundness. We design a\nhierarchical attention model that factorizes over three distributions, each\ncollecting evidence from a different modality, followed by a pointer network\nthat selects frames to include in the summary. DeepQAMVS is trained with\nreinforcement learning, incorporating rewards that capture representativeness,\ndiversity, query-adaptability and temporal coherence. We achieve\nstate-of-the-art results on the MVS1K dataset, with inference time scaling\nlinearly with the number of input video frames.\n"}
+{"abstract": "  Written language contains stylistic cues that can be exploited to\nautomatically infer a variety of potentially sensitive author information.\nAdversarial stylometry intends to attack such models by rewriting an author's\ntext. Our research proposes several components to facilitate deployment of\nthese adversarial attacks in the wild, where neither data nor target models are\naccessible. We introduce a transformer-based extension of a lexical replacement\nattack, and show it achieves high transferability when trained on a weakly\nlabeled corpus -- decreasing target model performance below chance. While not\ncompletely inconspicuous, our more successful attacks also prove notably less\ndetectable by humans. Our framework therefore provides a promising direction\nfor future privacy-preserving adversarial attacks.\n"}
+{"abstract": "  This paper details the Leibniz generalization of Lie-theoretic results from\nPeggy Batten's 1993 dissertation. We first show that the multiplier of a\nLeibniz algebra is characterized by its second cohomology group with\ncoefficients in the field. We then establish criteria for when the center of a\ncover maps onto the center of the algebra. Along the way, we obtain a\ncollection of exact sequences and a brief theory of unicentral Leibniz\nalgebras.\n"}
+{"abstract": "  Generalized correlation analysis (GCA) is concerned with uncovering linear\nrelationships across multiple datasets. It generalizes canonical correlation\nanalysis that is designed for two datasets. We study sparse GCA when there are\npotentially multiple generalized correlation tuples in data and the loading\nmatrix has a small number of nonzero rows. It includes sparse CCA and sparse\nPCA of correlation matrices as special cases. We first formulate sparse GCA as\ngeneralized eigenvalue problems at both population and sample levels via a\ncareful choice of normalization constraints. Based on a Lagrangian form of the\nsample optimization problem, we propose a thresholded gradient descent\nalgorithm for estimating GCA loading vectors and matrices in high dimensions.\nWe derive tight estimation error bounds for estimators generated by the\nalgorithm with proper initialization. We also demonstrate the prowess of the\nalgorithm on a number of synthetic datasets.\n"}
+{"abstract": "  We present a detailed study of the Bose-Hubbard model in a $p$-band\ntriangular lattice by focusing on the evolution of orbital order across the\nsuperfluid-Mott insulator transition. Two distinct phases are found in the\nsuperfluid regime. One of these phases adiabatically connects the weak\ninteracting limit. This phase is characterized by the intertwining of axial\n$p_\\pm=p_x \\pm ip_y$ and in-plane $p_\\theta=\\cos\\theta p_x+\\sin\\theta p_y$\norbital orders, which break the time-reversal symmetry and lattice symmetries\nsimultaneously. In addition, the calculated Bogoliubov excitation spectrum gaps\nthe original Dirac points in the single-particle spectrum but exhibits emergent\nDirac points. The other superfluid phase in close proximity to the Mott\ninsulator with unit boson filling shows a detwined in-plane ferro-orbital\norder. Finally, an orbital exchange model is constructed for the Mott insulator\nphase. Its classical ground state has an emergent SO$(2)$ rotational symmetry\nin the in-plane orbital space and therefore enjoys an infinite degeneracy,\nwhich is ultimately lifted by the orbital fluctuation via the order by disorder\nmechanism. Our systematic analysis suggests that the in-plane ferro-orbital\norder in the Mott insulator phase agrees with and likely evolves from the\nlatter superfluid phase.\n"}
+{"abstract": "  Recent studies have shown that the majority of published computational models\nin systems biology and physiology are not repeatable or reproducible. There are\na variety of reasons for this. One of the most likely reasons is that given how\nbusy modern researchers are and the fact that no credit is given to authors for\npublishing repeatable work, it is inevitable that this will be the case. The\nsituation can only be rectified when government agencies, universities and\nother research institutions change policies and that journals begin to insist\nthat published work is in fact at least repeatable if not reproducible. In this\nchapter guidelines are described that can be used by researchers to help make\nsure their work is repeatable. A scoring system is suggested that authors can\nuse to determine how well they are doing.\n"}
+{"abstract": "  We study the effect of torsional deformations on the electronic properties of\nsingle-walled transition metal dichalcogenide (TMD) nanotubes. In particular,\nconsidering forty-five select armchair and zigzag TMD nanotubes, we perform\nsymmetry-adapted Kohn-Sham density functional theory calculations to determine\nthe variation in bandgap and effective mass of charge carriers with twist. We\nfind that metallic nanotubes remain so even after deformation, whereas\nsemiconducting nanotubes experience a decrease in bandgap with twist --\noriginally direct bandgaps become indirect -- resulting in semiconductor to\nmetal transitions. In addition, the effective mass of holes and electrons\ncontinuously decrease and increase with twist, respectively, resulting in\nn-type to p-type semiconductor transitions. We find that this behavior is\nlikely due to rehybridization of orbitals in the metal and chalcogen atoms,\nrather than charge transfer between them. Overall, torsional deformations\nrepresent a powerful avenue to engineer the electronic properties of\nsemiconducting TMD nanotubes, with applications to devices like sensors and\nsemiconductor switches.\n"}
+{"abstract": "  Our all electron (DFBG) calculations show differences between relativistic\nand non-relativistic calculations for the structure of the isomers of Og(CO)6\n"}
+{"abstract": "  We discover the formation of a temporal boundary soliton (TBS) in the close\nproximity of a temporal boundary, moving in a nonlinear optical medium, upon\nhigh-intensity pulse collision with the boundary. We show that the TBS\nexcitation causes giant intensity fluctuations in reflection (transmission)\nfrom (through) the temporal boundary even for very modest input pulse intensity\nfluctuations. We advance a statistical theory of the phenomenon and show that\nthe TBS emerges as an extremely rare event in a nonintegrable nonlinear system,\nheralded by colossal intensity fluctuations with unprecedented magnitudes of\nthe normalized intensity autocorrelation function of the reflected/transmitted\npulse ensemble.\n"}
+{"abstract": "  We introduce Automorphism-based graph neural networks (Autobahn), a new\nfamily of graph neural networks. In an Autobahn, we decompose the graph into a\ncollection of subgraphs and apply local convolutions that are equivariant to\neach subgraph's automorphism group. Specific choices of local neighborhoods and\nsubgraphs recover existing architectures such as message passing neural\nnetworks. Our formalism also encompasses novel architectures: as an example, we\nintroduce a graph neural network that decomposes the graph into paths and\ncycles. The resulting convolutions reflect the natural way that parts of the\ngraph can transform, preserving the intuitive meaning of convolution without\nsacrificing global permutation equivariance. We validate our approach by\napplying Autobahn to molecular graphs, where it achieves state-of-the-art\nresults.\n"}
+{"abstract": "  This paper discusses prime numbers that are (resp. are not) congruent\nnumbers. Particularly the only case not fully covered by earlier results,\nnamely primes of the form $p=8k+1$, receives attention.\n"}
+{"abstract": "  We describe methods for simulating general second-quantized Hamiltonians\nusing the compact encoding, in which qubit states encode only the occupied\nmodes in physical occupation number basis states. These methods apply to\nsecond-quantized Hamiltonians composed of a constant number of interactions,\ni.e., linear combinations of ladder operator monomials of fixed form. Compact\nencoding leads to qubit requirements that are optimal up to logarithmic\nfactors. We show how to use sparse Hamiltonian simulation methods for\nsecond-quantized Hamiltonians in compact encoding, give explicit\nimplementations for the required oracles, and analyze the methods. We also\ndescribe several example applications including the free boson and fermion\ntheories, the $\\phi^4$-theory, and the massive Yukawa model, all in both\nequal-time and light-front quantization. Our methods provide a general-purpose\ntool for simulating second-quantized Hamiltonians, with optimal or near-optimal\nscaling with error and model parameters.\n"}
+{"abstract": "  The neutron drop is firstly investigated in an axially symmetric harmonic\noscillator (ASHO) field, whose potential strengths of different directions can\nbe controlled artificially. The shape of the neutron drop will change from\nspherical to oblate or prolate according to the anisotropy of the external\nfield. With the potential strength increasing in the axial direction, the\nneutron prefers to occupy the orbital perpendicular to the symmetry axis. On\nthe contrary, the neutron likes to stay in the orbital parallel to the symmetry\naxis when the potential strength increases in the radial direction. Meanwhile,\nwhen the potential strength of one direction disappears, the neutron drop\ncannot bind together. These investigations are not only helpful to simulate the\nproperties of neutrons in finite nuclei but also provide the theoretical\npredictions to the future artificial operations on the nuclei like the\nultracold atom system, for a deeper realization of quantum many-body systems.\n"}
+{"abstract": "  Derived from a biophysical model for the motion of a crawling cell, the\nsystem \\[(*)~\\begin{cases}u_t=\\Delta u-\\nabla\\cdot(u\\nabla v)\\\\0=\\Delta\nv-kv+u\\end{cases}\\] is investigated in a finite domain\n$\\Omega\\subset\\mathbb{R}^n$, $n\\geq2$, with $k\\geq0$. While a comprehensive\nliterature is available for cases with $(*)$ describing chemotaxis systems and\nhence being accompanied by homogeneous Neumann-type boundary conditions, the\npresently considered modeling context, besides yet requiring the flux\n$\\partial_\\nu u-u\\partial_\\nu v$ to vanish on $\\partial\\Omega$, inherently\ninvolves homogeneous Dirichlet conditions for the attractant $v$, which in the\ncurrent setting corresponds to the cell's cytoskeleton being free of pressure\nat the boundary.\n  This modification in the boundary setting is shown to go along with a\nsubstantial change with respect to the potential to support the emergence of\nsingular structures: It is, inter alia, revealed that in contexts of radial\nsolutions in balls there exist two critical mass levels, distinct from each\nother whenever $k>0$ or $n\\geq3$, that separate ranges within which (i) all\nsolutions are global in time and remain bounded, (ii) both global bounded and\nexploding solutions exist, or (iii) all nontrivial solutions blow up in finite\ntime. While critical mass phenomena distinguishing between regimes of type (i)\nand (ii) belong to the well-understood characteristics of $(*)$ when posed\nunder classical no-flux boundary conditions in planar domains, the discovery of\na distinct secondary critical mass level related to the occurrence of (iii)\nseems to have no nearby precedent.\n  In the planar case with the domain being a disk, the analytical results are\nsupplemented with some numerical illustrations, and it is discussed how the\nfindings can be interpreted biophysically for the situation of a cell on a flat\nsubstrate.\n"}
+{"abstract": "  Our knowledge of white dwarf planetary systems predominately arises from the\nregion within a few Solar radii of the white dwarfs, where minor planets break\nup, form rings and discs, and accrete onto the star. The entry location, angle\nand speed into this Roche sphere has rarely been explored but crucially\ndetermines the initial geometry of the debris, accretion rates onto the\nphotosphere, and ultimately the composition of the minor planet. Here we evolve\na total of over 10^5 asteroids with single-planet N-body simulations across the\ngiant branch and white dwarf stellar evolution phases to quantify the geometry\nof asteroid injection into the white dwarf Roche sphere as a function of\nplanetary mass and eccentricity. We find that lower planetary masses increase\nthe extent of anisotropic injection and decrease the probability of head-on\n(normal to the Roche sphere) encounters. Our results suggest that one can use\ndynamical activity within the Roche sphere to make inferences about the hidden\narchitectures of these planetary systems.\n"}
+{"abstract": "  We report the observation of discrete vortex bound states with the energy\nlevels deviating from the widely believed ratio of 1:3:5 in the vortices of an\niron based superconductor KCa2Fe4As4F2 through scanning tunneling microcopy\n(STM). Meanwhile Friedel oscillations of vortex bound states are also observed\nfor the first time in related vortices. By doing self-consistent calculations\nof Bogoliubov-de Gennes equations, we find that at extreme quantum limit, the\nsuperconducting order parameter exhibits a Friedel-like oscillation, which\nmodifies the energy levels of the vortex bound states and explains why it\ndeviates from the ratio of 1:3:5. The observed Friedel oscillations of the\nbound states can also be roughly interpreted by the theoretical calculations,\nhowever some features at high energies could not be explained. We attribute\nthis discrepancy to the high energy bound states with the influence of nearby\nimpurities. Our combined STM measurement and the self-consistent calculations\nillustrate a generalized feature of the vortex bound states in type-II\nsuperconductors.\n"}
+{"abstract": "  The removal of organic micropollutants (OMPs) has been investigated in\nconstructed wetlands (CWs) operated as bioelectrochemical systems (BES). The\noperation of CWs as BES (CW-BES), either in the form of microbial fuel cells\n(MFC) or microbial electrolysis cells (MEC), has only been investigated in\nrecent years. The presented experiment used CW meso-scale systems applying a\nrealistic horizontal flow regime and continuous feeding of real urban\nwastewater spiked with four OMPs (pharmaceuticals), namely carbamazepine (CBZ),\ndiclofenac (DCF), ibuprofen (IBU) and naproxen (NPX). The study evaluated the\nremoval efficiency of conventional CW systems (CW-control) as well as CW\nsystems operated as closed-circuit MFCs (CW-MFCs) and MECs (CW-MECs). Although\na few positive trends were identified for the CW-BES compared to the CW-control\n(higher average CBZ, DCF and NPX removal by 10-17% in CW-MEC and 5% in CW-MFC),\nthese proved to be not statistically significantly different. Mesoscale\nexperiments with real wastewater could thus not confirm earlier positive\neffects of CW-BES found under strictly controlled laboratory conditions with\nsynthetic wastewaters.\n"}
+{"abstract": "  We have studied the effect of nitriding on the humidity sensing properties of\nhydrogenated amorphous carbon (a-C:H) films. The films were prepared in two\nstages combining the techniques of physical deposition in vapor phase\nevaporation (PAPVD) and plasma pulsed nitriding. By deconvolution of the Raman\nspectrum we identified two peaks corresponding to the D and G modes\ncharacteristic of a-C:H. After the N$_2$-H$_2$ plasma treating, the peaks\nnarrowed and shifted to the right, which we associated with the incorporation\nof N into the structure. We compared the sensitivity to the relative humidity\n(RH) of the films before and after the N$_2$-H$_2$ plasma treatment. The\nnitriding improved the humidity sensitivity measured as the low frequency\nresistance.\n  By impedance spectroscopy we studied the frequency dependence of the AC\nconductivity $\\sigma$ at different RH conditions. Before nitriding\n$\\sigma(\\omega)\\sim A \\omega^s$, it seemed to have the universal behaviour seen\nin other amorphous systems. The humidity changed the overall scale $A$. After\nnitriding, the exponent $s$ increased, and became RH dependent. We associated\nthis behaviour to the change of the interaction mechanism between the water\nmolecule and the substrate when the samples were nitriding.\n"}
+{"abstract": "  Stealing attack against controlled information, along with the increasing\nnumber of information leakage incidents, has become an emerging cyber security\nthreat in recent years. Due to the booming development and deployment of\nadvanced analytics solutions, novel stealing attacks utilize machine learning\n(ML) algorithms to achieve high success rate and cause a lot of damage.\nDetecting and defending against such attacks is challenging and urgent so that\ngovernments, organizations, and individuals should attach great importance to\nthe ML-based stealing attacks. This survey presents the recent advances in this\nnew type of attack and corresponding countermeasures. The ML-based stealing\nattack is reviewed in perspectives of three categories of targeted controlled\ninformation, including controlled user activities, controlled ML model-related\ninformation, and controlled authentication information. Recent publications are\nsummarized to generalize an overarching attack methodology and to derive the\nlimitations and future directions of ML-based stealing attacks. Furthermore,\ncountermeasures are proposed towards developing effective protections from\nthree aspects -- detection, disruption, and isolation.\n"}
+{"abstract": "  Small-scale Mixed-Integer Quadratic Programming (MIQP) problems often arise\nin embedded control and estimation applications. Driven by the need for\nalgorithmic simplicity to target computing platforms with limited memory and\ncomputing resources, this paper proposes a few approaches to solving MIQPs,\neither to optimality or suboptimally. We specialize an existing Accelerated\nDual Gradient Projection (GPAD) algorithm to effectively solve the Quadratic\nProgramming (QP) relaxation that arise during Branch and Bound (B&B) and\npropose a generic framework to warm-start the binary variables which reduces\nthe number of QP relaxations. Moreover, in order to find an integer feasible\ncombination of the binary variables upfront, two heuristic approaches are\npresented: ($i$) without using B&B, and ($ii$) using B&B with a significantly\nreduced number of QP relaxations. Both heuristic approaches return an integer\nfeasible solution that may be suboptimal but involve a much reduced computation\neffort. Such a feasible solution can be either implemented directly or used to\nset an initial upper bound on the optimal cost in B&B. Through different hybrid\ncontrol and estimation examples involving binary decision variables, we show\nthat the performance of the proposed methods, although very simple to code, is\ncomparable to that of state-of-the-art MIQP solvers.\n"}
+{"abstract": "  We propose a new model for networks of time series that influence each other.\nGraph structures among time series are found in diverse domains, such as web\ntraffic influenced by hyperlinks, product sales influenced by recommendation,\nor urban transport volume influenced by road networks and weather. There has\nbeen recent progress in graph modeling and in time series forecasting,\nrespectively, but an expressive and scalable approach for a network of series\ndoes not yet exist. We introduce Radflow, a novel model that embodies three key\nideas: a recurrent neural network to obtain node embeddings that depend on\ntime, the aggregation of the flow of influence from neighboring nodes with\nmulti-head attention, and the multi-layer decomposition of time series. Radflow\nnaturally takes into account dynamic networks where nodes and edges change over\ntime, and it can be used for prediction and data imputation tasks. On\nreal-world datasets ranging from a few hundred to a few hundred thousand nodes,\nwe observe that Radflow variants are the best performing model across a wide\nrange of settings. The recurrent component in Radflow also outperforms N-BEATS,\nthe state-of-the-art time series model. We show that Radflow can learn\ndifferent trends and seasonal patterns, that it is robust to missing nodes and\nedges, and that correlated temporal patterns among network neighbors reflect\ninfluence strength. We curate WikiTraffic, the largest dynamic network of time\nseries with 366K nodes and 22M time-dependent links spanning five years. This\ndataset provides an open benchmark for developing models in this area, with\napplications that include optimizing resources for the web. More broadly,\nRadflow has the potential to improve forecasts in correlated time series\nnetworks such as the stock market, and impute missing measurements in\ngeographically dispersed networks of natural phenomena.\n"}
+{"abstract": "  We need intelligent robots for mobile construction, the process of navigating\nin an environment and modifying its structure according to a geometric design.\nIn this task, a major robot vision and learning challenge is how to exactly\nachieve the design without GPS, due to the difficulty caused by the\nbi-directional coupling of accurate robot localization and navigation together\nwith strategic environment manipulation. However, many existing robot vision\nand learning tasks such as visual navigation and robot manipulation address\nonly one of these two coupled aspects. To stimulate the pursuit of a generic\nand adaptive solution, we reasonably simplify mobile construction as a\npartially observable Markov decision process (POMDP) in 1/2/3D grid worlds and\nbenchmark the performance of a handcrafted policy with basic localization and\nplanning, and state-of-the-art deep reinforcement learning (RL) methods. Our\nextensive experiments show that the coupling makes this problem very\nchallenging for those methods, and emphasize the need for novel task-specific\nsolutions.\n"}
+{"abstract": "  Analogous to the case of the binomial random graph $G(d+1,p)$, it is known\nthat the behaviour of a random subgraph of a $d$-dimensional hypercube, where\nwe include each edge independently with probability $p$, which we denote by\n$Q^d_p$, undergoes a phase transition around the critical value of\n$p=\\frac{1}{d}$. More precisely, standard arguments show that significantly\nbelow this value of $p$, with probability tending to one as $d \\to \\infty$ (whp\nfor short) all components of this graph have order $O(d)$, whereas Ajtai,\nKoml\\'{o}s and Szemer\\'{e}di showed that significantly above this value, in the\n\\emph{supercritical regime}, whp there is a unique `giant' component of order\n$\\Theta\\left(2^d\\right)$. In $G(d+1,p)$ much more is known about the complex\nstructure of the random graph which emerges in this supercritical regime. For\nexample, it is known that in this regime whp $G(d+1,p)$ contains paths and\ncycles of length $\\Omega(d)$, as well as complete minors of order\n$\\Omega\\left(\\sqrt{d}\\right)$. In this paper we obtain analogous results in\n$Q^d_p$. In particular, we show that for supercritical $p$, i.e., when\n$p=\\frac{1+\\epsilon}{d}$ for a positive constant $\\epsilon$, whp $Q^d_p$\ncontains a cycle of length $\\Omega\\left(\\frac{2^d}{d^3(\\log d)^3} \\right)$ and\na complete minor of order $\\Omega\\left(\\frac{2^{\\frac{d}{2}}}{d^3(\\log d)^3\n}\\right)$. In order to prove these results, we show that whp the largest\ncomponent of $Q^d_p$ has good edge-expansion properties, a result of\nindependent interest. We also consider the genus of $Q^d_p$ and show that, in\nthis regime of $p$, whp the genus is $\\Omega\\left(2^d\\right)$.\n"}
+{"abstract": "  We develop the concept of quasi-phasematching (QPM) by implementing it in the\nrecently proposed Josephson traveling-wave parametric amplifier (JTWPA) with\nthree-wave mixing (3WM). The amplifier is based on a ladder transmission line\nconsisting of flux-biased radio-frequency SQUIDs whose nonlinearity is of\n$\\chi^{(2)}$-type. QPM is achieved in the 3WM process,\n$\\omega_p=\\omega_s+\\omega_i$ (where $\\omega_p$, $\\omega_s$, and $\\omega_i$ are\nthe pump, signal, and idler frequencies, respectively) due to designing the\nJTWPA to include periodically inverted groups of these SQUIDs that reverse the\nsign of the nonlinearity. Modeling shows that the JTWPA bandwidth is relatively\nlarge (ca. $0.4\\omega_p$) and flat, while unwanted modes, including\n$\\omega_{2p}=2\\omega_p$, $\\omega_+=\\omega_p +\\omega_s$, $\\omega_- = 2\\omega_p -\n\\omega_s$, etc., are strongly suppressed with the help of engineered\ndispersion.\n"}
+{"abstract": "  A sequence of point configurations on a compact complex manifold is\nasymptotically Fekete if it is close to maximizing a sequence of Vandermonde\ndeterminants. These Vandermonde determinants are defined by tensor powers of a\nHermitian ample line bundle and the point configurations in the sequence\npossess good sampling properties with respect to sections of the line bundle.\nIn this paper, given a collection of toric Hermitian ample line bundles, we\ngive necessary and sufficient condition for existence of a sequence of point\nconfigurations which is asymptotically Fekete (and hence possess good sampling\nproperties) with respect to each one of the line bundles. When they exist, we\nalso present a way of constructing such sequences. As a byproduct we get a new\nequidistribution property for maximizers of products of Vandermonde\ndeterminants.\n"}
+{"abstract": "  Recently, the study of graph neural network (GNN) has attracted much\nattention and achieved promising performance in molecular property prediction.\nMost GNNs for molecular property prediction are proposed based on the idea of\nlearning the representations for the nodes by aggregating the information of\ntheir neighbor nodes (e.g. atoms). Then, the representations can be passed to\nsubsequent layers to deal with individual downstream tasks. Therefore, the\narchitectures of GNNs can be considered as being composed of two core parts:\ngraph-related layers and task-specific layers. Facing real-world molecular\nproblems, the hyperparameter optimization for those layers are vital.\nHyperparameter optimization (HPO) becomes expensive in this situation because\nevaluating candidate solutions requires massive computational resources to\ntrain and validate models. Furthermore, a larger search space often makes the\nHPO problems more challenging. In this research, we focus on the impact of\nselecting two types of GNN hyperparameters, those belonging to graph-related\nlayers and those of task-specific layers, on the performance of GNN for\nmolecular property prediction. In our experiments. we employed a\nstate-of-the-art evolutionary algorithm (i.e., CMA-ES) for HPO. The results\nreveal that optimizing the two types of hyperparameters separately can gain the\nimprovements on GNNs' performance, but optimising both types of hyperparameters\nsimultaneously will lead to predominant improvements. Meanwhile, our study also\nfurther confirms the importance of HPO for GNNs in molecular property\nprediction problems.\n"}
+{"abstract": "  In a complete metric space equipped with a doubling measure supporting a\n$p$-Poincar\\'e inequality, we prove sharp growth and integrability results for\n$p$-harmonic Green functions and their minimal $p$-weak upper gradients. We\nshow that these properties are determined by the growth of the underlying\nmeasure near the singularity. Corresponding results are obtained also for more\ngeneral $p$-harmonic functions with poles, as well as for singular solutions of\nelliptic differential equations in divergence form on weighted $\\mathbf{R}^n$\nand on manifolds.\n  The proofs are based on a new general capacity estimate for annuli, which\nimplies precise pointwise estimates for $p$-harmonic Green functions. The\ncapacity estimate is valid under considerably milder assumptions than above. We\nalso use it, under these milder assumptions, to characterize singletons of zero\ncapacity and the $p$-parabolicity of the space. This generalizes and improves\nearlier results that have been important especially in the context of\nRiemannian manifolds.\n"}
+{"abstract": "  A number of recent approaches have been proposed for pruning neural network\nparameters at initialization with the goal of reducing the size and\ncomputational burden of models while minimally affecting their training\ndynamics and generalization performance. While each of these approaches have\nsome amount of well-founded motivation, a rigorous analysis of the effect of\nthese pruning methods on network training dynamics and their formal\nrelationship to each other has thus far received little attention. Leveraging\nrecent theoretical approximations provided by the Neural Tangent Kernel, we\nunify a number of popular approaches for pruning at initialization under a\nsingle path-centric framework. We introduce the Path Kernel as the\ndata-independent factor in a decomposition of the Neural Tangent Kernel and\nshow the global structure of the Path Kernel can be computed efficiently. This\nPath Kernel decomposition separates the architectural effects from the\ndata-dependent effects within the Neural Tangent Kernel, providing a means to\npredict the convergence dynamics of a network from its architecture alone. We\nanalyze the use of this structure in approximating training and generalization\nperformance of networks in the absence of data across a number of\ninitialization pruning approaches. Observing the relationship between input\ndata and paths and the relationship between the Path Kernel and its natural\nnorm, we additionally propose two augmentations of the SynFlow algorithm for\npruning at initialization.\n"}
+{"abstract": "  Common modal decomposition techniques for flowfield analysis, data-driven\nmodeling and flow control, such as proper orthogonal decomposition (POD) and\ndynamic mode decomposition (DMD) are usually performed in an Eulerian (fixed)\nframe of reference with snapshots from measurements or evolution equations. The\nEulerian description poses some difficulties, however, when the domain or the\nmesh deforms with time as, for example, in fluid-structure interactions. For\nsuch cases, we first formulate a Lagrangian modal analysis (LMA) ansatz by a\nposteriori transforming the Eulerian flow fields into Lagrangian flow maps\nthrough an orientation and measure-preserving domain diffeomorphism. The\ndevelopment is then verified for Lagrangian variants of POD and DMD using\ndirect numerical simulations (DNS) of two canonical flow configurations at Mach\n0.5, the lid-driven cavity and flow past a cylinder, representing internal and\nexternal flows, respectively, at pre- and post-bifurcation Reynolds numbers.\nThe LMA is demonstrated for several situations encompassing unsteady flow\nwithout and with boundary and mesh deformation as well as non-uniform base\nflows that are steady in Eulerian but not in Lagrangian frames. We show that\nLMA application to steady nonuniform base flow yields insights into flow\nstability and post-bifurcation dynamics. LMA naturally leads to Lagrangian\ncoherent flow structures and connections with finite-time Lyapunov exponents\n(FTLE). We examine the mathematical link between FTLE and LMA by considering a\ndouble-gyre flow pattern. Dynamically important flow features in the Lagrangian\nsense are recovered by performing LMA with forward and backward (adjoint) time\nprocedures.\n"}
+{"abstract": "  Network models provide an efficient way to represent many real life problems\nmathematically. In the last few decades, the field of network optimization has\nwitnessed an upsurge of interest among researchers and practitioners. The\nnetwork models considered in this thesis are broadly classified into four types\nincluding transportation problem, shortest path problem, minimum spanning tree\nproblem and maximum flow problem. Quite often, we come across situations, when\nthe decision parameters of network optimization problems are not precise and\ncharacterized by various forms of uncertainties arising from the factors, like\ninsufficient or incomplete data, lack of evidence, inappropriate judgements and\nrandomness. Considering the deterministic environment, there exist several\nstudies on network optimization problems. However, in the literature, not many\ninvestigations on single and multi objective network optimization problems are\nobserved under diverse uncertain frameworks. This thesis proposes seven\ndifferent network models under different uncertain paradigms. Here, the\nuncertain programming techniques used to formulate the uncertain network models\nare (i) expected value model, (ii) chance constrained model and (iii) dependent\nchance constrained model. Subsequently, the corresponding crisp equivalents of\nthe uncertain network models are solved using different solution methodologies.\nThe solution methodologies used in this thesis can be broadly categorized as\nclassical methods and evolutionary algorithms. The classical methods, used in\nthis thesis, are Dijkstra and Kruskal algorithms, modified rough Dijkstra\nalgorithm, global criterion method, epsilon constraint method and fuzzy\nprogramming method. Whereas, among the evolutionary algorithms, we have\nproposed the varying population genetic algorithm with indeterminate crossover\nand considered two multi objective evolutionary algorithms.\n"}
+{"abstract": "  The wavelet analysis technique is a powerful tool and is widely used in broad\ndisciplines of engineering, technology, and sciences. In this work, we present\na novel scheme of constructing continuous wavelet functions, in which the\nwavelet functions are obtained by taking the first derivative of smoothing\nfunctions with respect to the scale parameter. Due to this wavelet constructing\nscheme, the inverse transforms are only one-dimensional integrations with\nrespect to the scale parameter, and hence the continuous wavelet transforms\nconstructed in this way are more ready to use than the usual scheme. We then\napply the Gaussian-derived wavelet constructed by our scheme to computations of\nthe density power spectrum for dark matter, the velocity power spectrum and the\nkinetic energy spectrum for baryonic fluid. These computations exhibit the\nconvenience and strength of the continuous wavelet transforms. The transforms\nare very easy to perform, and we believe that the simplicity of our wavelet\nscheme will make continuous wavelet transforms very useful in practice.\n"}
+{"abstract": "  We prove the irreducibility of integer polynomials $f(X)$ whose roots lie\ninside an Apollonius circle associated to two points on the real axis with\ninteger abscisae $a$ and $b$, with ratio of the distances to these points\ndepending on the canonical decomposition of $f(a)$ and $f(b)$. In particular,\nwe obtain irreducibility criteria for the case where $f(a)$ and $f(b)$ have few\nprime factors, and $f$ is either an Enestr\\\"om-Kakeya polynomial, or has a\nlarge leading coefficient. Analogous results are also provided for multivariate\npolynomials over arbitrary fields, in a non-Archimedean setting.\n"}
+{"abstract": "  We construct the complete set of boundary states of two-dimensional fermionic\nCFTs using that of the bosonic counterpart. We see that there are two groups of\nboundary conditions, which contributes to the open-string partition function by\ncharacters with integer coefficients, or with $\\sqrt{2}$ times integer\ncoefficients. We argue that, using the argument of [JHEP 09 (2020) 018], this\n$\\sqrt{2}$ indicates a single unpaired Majorana zero mode, and that these two\ngroups of boundary conditions are mutually incompatible. We end the paper by\nmentioning a possible interpretation of the result in terms of the entanglement\nentropy.\n"}
+{"abstract": "  We develop a dynamical density functional theory based model for the drying\nof colloidal films on planar surfaces. We consider mixtures of two different\nsizes of hard-sphere colloids. Depending on the solvent evaporation rate and\nthe initial concentrations of the two species, we observe varying degrees of\nstratification in the final dried films. Our model predicts the various\nstructures described in the literature previously from experiments and computer\nsimulations, in particular the small-on-top stratified films. Our model also\nincludes the influence of adsorption of particles to the interfaces.\n"}
+{"abstract": "  A perennial objection against Bayes factor point-null hypothesis tests is\nthat the point-null hypothesis is known to be false from the outset. Following\nMorey and Rouder (2011) we examine the consequences of approximating the sharp\npoint-null hypothesis by a hazy `peri-null' hypothesis instantiated as a narrow\nprior distribution centered on the point of interest. The peri-null Bayes\nfactor then equals the point-null Bayes factor multiplied by a correction term\nwhich is itself a Bayes factor. For moderate sample sizes, the correction term\nis relatively inconsequential; however, for large sample sizes the correction\nterm becomes influential and causes the peri-null Bayes factor to be\ninconsistent and approach a limit that depends on the ratio of prior ordinates\nevaluated at the maximum likelihood estimate. We characterize the asymptotic\nbehavior of the peri-null Bayes factor and discuss how to construct peri-null\nBayes factor hypothesis tests that are also consistent.\n"}
+{"abstract": "  NonlinearSchrodinger.jl is a Julia package with a simple interface for\nstudying solutions of nonlinear Schr\\\"odinger equations (NLSEs). In\napproximately ten lines of code, one can perform a simulation of the cubic NLSE\nusing one of 32 algorithms, including symplectic and Runge-Kutta-Nystr\\\"om\nintegrators up to eighth order. Furthermore, it is possible to compute\nanalytical solutions via a numerical implementation of the Darboux\ntransformation for extended NLSEs up to fifth order, with an equally simple\ninterface. In what follows, we review the fundamentals of solving this class of\nequations numerically and analytically, discuss the implementation, and provide\nseveral examples.\n"}
+{"abstract": "  In a 2004 paper by V. M. Buchstaber and D. V. Leykin, published in\n\"Functional Analysis and Its Applications,\" for each $g > 0$, a system of $2g$\nmultidimensional heat equations in a nonholonomic frame was constructed. The\nsigma function of the universal hyperelliptic curve of genus $g$ is a solution\nof this system. In the work arXiv:2007.08966 explicit expressions for the\nSchr\\\"odinger operators that define the equations of the system considered were\nobtained in the hyperelliptic case.\n  In this work we use these results to show that if the initial condition of\nthe system considered is polynomial, then the solution of the system is\nuniquely determined up to a constant factor. This has important applications in\nthe well-known problem of series expansion for the hyperelliptic sigma\nfunction. We give an explicit description of the connection of such solutions\nto well-known Burchnall-Chaundy polynomials and Adler-Moser polynomials. We\nfind a system of linear second-order differential equations that determines the\ncorresponding Adler-Moser polynomial.\n"}
+{"abstract": "  Photon-mediated interactions between atomic systems are the cornerstone of\nquantum information transfer. They can arise via coupling to a common\nelectromagnetic mode or by quantum interference. This can manifest in\ncooperative light-matter coupling, yielding collective rate enhancements such\nas those at the heart of superradiance, or remote entanglement via\nmeasurement-induced path erasure. Here, we report coherent control of\ncooperative emission arising from two distant but indistinguishable solid-state\nemitters due to path erasure. The primary signature of cooperative emission,\nthe emergence of \"bunching\" at zero-delay in an intensity correlation\nexperiment, is used to characterise the indistinguishability of the emitters,\ntheir dephasing, and the degree of correlation in the joint system which can be\ncoherently controlled. In a stark departure from a pair of uncorrelated\nemitters, we observe photon statistics resembling that of a weak coherent state\nin Hong-Ou-Mandel type interference measurements. Our experiments establish new\ntechniques to control and characterize cooperative behaviour between matter\nqubits using the full quantum optics toolbox, a key stepping stone on the route\nto realising large-scale quantum photonic networks.\n"}
+{"abstract": "  Galaxy clusters are considered to be gigantic reservoirs of cosmic rays\n(CRs). Some of the clusters are found with extended radio emission, which\nprovides evidence for the existence of magnetic fields and CR electrons in the\nintra-cluster medium (ICM). The mechanism of radio halo (RH) emission is still\nunder debate, and it has been believed that turbulent reacceleration plays an\nimportant role. In this paper, we study the reacceleration of CR protons and\nelectrons in detail by numerically solving the Fokker-Planck equation, and show\nhow radio and gamma-ray observations can be used to constrain CR distributions\nand resulting high-energy emission for the Coma cluster. We take into account\nthe radial diffusion of CRs and follow the time evolution of their\none-dimensional distribution, by which we investigate the radial profile of the\nCR injection that is consistent with the observed RH surface brightness. We\nfind that the required injection profile is non-trivial, depending on whether\nCR electrons have the primary or secondary origin. Although the secondary CR\nelectron scenario predicts larger gamma-ray and neutrino fluxes, it is in\ntension with the observed RH spectrum. In either scenario, we find that galaxy\nclusters can make a sizable contribution to the all-sky neutrino intensity if\nthe CR energy spectrum is nearly flat.\n"}
+{"abstract": "  This letter presents an energy- and memory-efficient pattern-matching engine\nfor a network intrusion detection system (NIDS) in the Internet of Things.\nTightly coupled architecture and circuit co-designs are proposed to fully\nexploit the statistical behaviors of NIDS pattern matching. The proposed engine\nperforms pattern matching in three phases, where the phase-1 prefix matching\nemploys reconfigurable pipelined automata processing to minimize memory\nfootprint without loss of throughput and efficiency. The processing elements\nutilize 8-T content-addressable memory (CAM) cells for dual-port search by\nleveraging proposed fixed-1s encoding. A 65-nm prototype demonstrates\nbest-in-class 1.54-fJ energy per search per pattern byte and 0.9-byte memory\nusage per pattern byte.\n"}
+{"abstract": "  As part of a programme to develop parton showers with controlled logarithmic\naccuracy, we consider the question of collinear spin correlations within the\nPanScales family of parton showers. We adapt the well-known Collins-Knowles\nspin-correlation algorithm to PanScales antenna and dipole showers, using an\napproach with similarities to that taken by Richardson and Webster. To study\nthe impact of spin correlations, we develop Lund-declustering based observables\nthat are sensitive to spin-correlation effects both within and between jets and\nextend the MicroJets collinear single-logarithmic resummation code to include\nspin correlations. Together with a 3-point energy correlation observable\nproposed recently by Chen, Moult and Zhu, this provides a powerful set of\nconstraints for validating the logarithmic accuracy of our shower results. The\nnew observables and their resummation further open the pathway to\nphenomenological studies of these important quantum mechanical effects.\n"}
+{"abstract": "  In this paper, we demonstrate how Hyperledger Fabric, one of the most popular\npermissioned blockchains, can benefit from network-attached acceleration. The\nscalability and peak performance of Fabric is primarily limited by the\nbottlenecks present in its block validation/commit phase. We propose Blockchain\nMachine, a hardware accelerator coupled with a hardware-friendly communication\nprotocol, to act as the validator peer. It can be adapted to applications and\ntheir smart contracts, and is targeted for a server with network-attached FPGA\nacceleration card. The Blockchain Machine retrieves blocks and their\ntransactions in hardware directly from the network interface, which are then\nvalidated through a configurable and efficient block-level and\ntransaction-level pipeline. The validation results are then transferred to the\nhost CPU where non-bottleneck operations are executed. From our implementation\nintegrated with Fabric v1.4 LTS, we observed up to 12x speedup in block\nvalidation when compared to software-only validator peer, with commit\nthroughput of up to 68,900 tps. Our work provides an acceleration platform that\nwill foster further research on hardware acceleration of permissioned\nblockchains.\n"}
+{"abstract": "  We propose multirate training of neural networks: partitioning neural network\nparameters into \"fast\" and \"slow\" parts which are trained simultaneously using\ndifferent learning rates. By choosing appropriate partitionings we can obtain\nlarge computational speed-ups for transfer learning tasks. We show that for\nvarious transfer learning applications in vision and NLP we can fine-tune deep\nneural networks in almost half the time, without reducing the generalization\nperformance of the resulting model. We also discuss other splitting choices for\nthe neural network parameters which are beneficial in enhancing generalization\nperformance in settings where neural networks are trained from scratch.\nFinally, we propose an additional multirate technique which can learn different\nfeatures present in the data by training the full network on different time\nscales simultaneously. The benefits of using this approach are illustrated for\nResNet architectures on image data. Our paper unlocks the potential of using\nmultirate techniques for neural network training and provides many starting\npoints for future work in this area.\n"}
+{"abstract": "  Investigations of magnetically ordered phases on the femtosecond timescale\nhave provided significant insights into the influence of charge and lattice\ndegrees of freedom on the magnetic sub-system. However, short-range magnetic\ncorrelations occurring in the absence of long-range order, for example in\nspin-frustrated systems, are inaccessible to many ultrafast techniques. Here,\nwe show how time-resolved resonant inelastic X-ray scattering (trRIXS) is\ncapable of probing such short-ranged magnetic dynamics in a charge-transfer\ninsulator through the detection of a Zhang-Rice singlet exciton. Utilizing\ntrRIXS measurements at the O K-edge, and in combination with model\ncalculations, we probe the short-range spin-correlations in the frustrated spin\nchain material CuGeO3 following photo-excitation, revealing a strong coupling\nbetween the local lattice and spin sub-systems.\n"}
+{"abstract": "  Many modern workloads, such as neural networks, databases, and graph\nprocessing, are fundamentally memory-bound. For such workloads, the data\nmovement between main memory and CPU cores imposes a significant overhead in\nterms of both latency and energy. A major reason is that this communication\nhappens through a narrow bus with high latency and limited bandwidth, and the\nlow data reuse in memory-bound workloads is insufficient to amortize the cost\nof main memory access. Fundamentally addressing this data movement bottleneck\nrequires a paradigm where the memory system assumes an active role in computing\nby integrating processing capabilities. This paradigm is known as\nprocessing-in-memory (PIM).\n  Recent research explores different forms of PIM architectures, motivated by\nthe emergence of new 3D-stacked memory technologies that integrate memory with\na logic layer where processing elements can be easily placed. Past works\nevaluate these architectures in simulation or, at best, with simplified\nhardware prototypes. In contrast, the UPMEM company has designed and\nmanufactured the first publicly-available real-world PIM architecture.\n  This paper provides the first comprehensive analysis of the first\npublicly-available real-world PIM architecture. We make two key contributions.\nFirst, we conduct an experimental characterization of the UPMEM-based PIM\nsystem using microbenchmarks to assess various architecture limits such as\ncompute throughput and memory bandwidth, yielding new insights. Second, we\npresent PrIM, a benchmark suite of 16 workloads from different application\ndomains (e.g., linear algebra, databases, graph processing, neural networks,\nbioinformatics).\n"}
+{"abstract": "  Let $(\\Omega, \\mathcal{A}, \\mu)$ be a probability space. The classical\nBorel-Cantelli Lemma states that for any sequence of $\\mu$-measurable sets\n$E_i$ ($i=1,2,3,\\dots$), if the sum of their measures converges then the\ncorresponding $\\limsup$ set $E_\\infty$ is of measure zero. In general the\nconverse statement is false. However, it is well known that the divergence\ncounterpart is true under various additional 'independence' hypotheses. In this\npaper we revisit these hypotheses and establish both sufficient and necessary\nconditions for $E_\\infty$ to have either positive or full measure.\n"}
+{"abstract": "  Automatic speech recognition (ASR) in Sanskrit is interesting, owing to the\nvarious linguistic peculiarities present in the language. The Sanskrit language\nis lexically productive, undergoes euphonic assimilation of phones at the word\nboundaries and exhibits variations in spelling conventions and in\npronunciations. In this work, we propose the first large scale study of\nautomatic speech recognition (ASR) in Sanskrit, with an emphasis on the impact\nof unit selection in Sanskrit ASR. In this work, we release a 78 hour ASR\ndataset for Sanskrit, which faithfully captures several of the linguistic\ncharacteristics expressed by the language. We investigate the role of different\nacoustic model and language model units in ASR systems for Sanskrit. We also\npropose a new modelling unit, inspired by the syllable level unit selection,\nthat captures character sequences from one vowel in the word to the next vowel.\nWe also highlight the importance of choosing graphemic representations for\nSanskrit and show the impact of this choice on word error rates (WER). Finally,\nwe extend these insights from Sanskrit ASR for building ASR systems in two\nother Indic languages, Gujarati and Telugu. For both these languages, our\nexperimental results show that the use of phonetic based graphemic\nrepresentations in ASR results in performance improvements as compared to ASR\nsystems that use native scripts.\n"}
+{"abstract": "  We classify connected graphs $G$ whose binomial edge ideal is Gorenstein. The\nproof uses methods in prime characteristic.\n"}
+{"abstract": "  We provide a comprehensive study of the energy transfer phenomenon --\npopulating a given energy level -- in 3- and 4-level quantum systems coupled to\ntwo thermal baths. In particular, we examine the effects of an external\nperiodic driving and the coherence induced by the baths on the efficiency of\nthe energy transfer. We consider the Floquet-Lindblad and the Floquet-Redfield\nscenarios, which both are in the Born-Markov, weak-coupling regime but differ\nin the treatment of the secular approximation, and for the latter, we develop\nan appropriate Floquet-type master equation by employing a partial secular\napproximation. Throughout the whole analysis we keep Lamb-shift corrections in\nthe master equations. We observe that, especially in the Floquet-Redfield\nscenario, the driving field can enhance the energy transfer efficiency compared\nto the nondriven scenario. In addition, unlike degenerate systems where\nLamb-shift corrections do not contribute significantly on the energy transfer,\nin the Redfield and the Floquet-Redfield scenarios these corrections have\nnonnegligible effects.\n"}
+{"abstract": "  For years, the extragalactic community has divided galaxies in two distinct\npopulations. One of them, featuring blue colours, is actively forming stars,\nwhile the other is made up of \"red-and-dead\" objects with negligible star\nformation. Yet, are these galaxies really dead? Here we would like to highlight\nthat, as previously reported by several independent groups, state-of-the-art\ncosmological numerical simulations predict the existence of a large number of\nquenched galaxies that have not formed any star over the last few Gyr. In\ncontrast, observational measurements of large galaxy samples in the nearby\nUniverse suggest that even the most passive systems still form stars at some\nresidual level close to $sSFR\\sim10^{-12}~\\text{yr}^{-1}$. Unfortunately,\nextremely low star formation poses a challenge for both approaches. We conclude\nthat, at present, the fraction of truly dead galaxies is still an important\nopen question that must be addressed in order to understand galaxy formation\nand evolution.\n"}
+{"abstract": "  Magneto-optical traps (MOTs) are widely used for laser cooling of atoms. We\nhave developed a high-flux compact cold-atom source based on a pyramid MOT with\na unique adjustable aperture that is highly suitable for portable quantum\ntechnology devices, including space-based experiments. The adjustability\nenabled an investigation into the previously unexplored impact of aperture size\non the atomic flux, and optimisation of the aperture size allowed us to\ndemonstrate a higher flux than any reported cold-atom sources that use a\npyramid, LVIS, 3D-MOT or grating MOT. We achieved 2.0(1)x10^10 atoms/s of 87-Rb\nwith a mean velocity of 32(1)m/s, FWHM of 27.6(9)m/s and divergence of\n58(3)mrad. Halving the total optical power to 195mW caused only a 26% reduction\nof the flux, and a 33% decrease in mean velocity. Methods to further decrease\nthe velocity as required have been identified. The low power consumption and\nsmall size make this design suitable for a wide range of cold-atom\ntechnologies.\n"}
+{"abstract": "  The third version of the Hypertext Transfer Protocol (HTTP) is currently in\nits final standardization phase by the IETF. Besides better security and\nincreased flexibility, it promises benefits in terms of performance. HTTP/3\nadopts a more efficient header compression schema and replaces TCP with QUIC, a\ntransport protocol carried over UDP, originally proposed by Google and\ncurrently under standardization too. Although HTTP/3 early implementations\nalready exist and some websites announce its support, it has been subject to\nfew studies. In this work, we provide a first measurement study on HTTP/3. We\ntestify how, during 2020, it has been adopted by some of the leading Internet\ncompanies such as Google, Facebook and Cloudflare. We run a large-scale\nmeasurement campaign toward thousands of websites adopting HTTP/3, aiming at\nunderstanding to what extent it achieves better performance than HTTP/2. We\nfind that adopting websites often host most web page objects on third-party\nservers, which support only HTTP/2 or even HTTP/1.1. Our experiments show that\nHTTP/3 provides sizable benefits only in scenarios with high latency or very\npoor bandwidth. Despite the adoption of QUIC, we do not find benefits in case\nof high packet loss, but we observe large diversity across website providers'\ninfrastructures.\n"}
+{"abstract": "  We present a novel method of performing spelling correction on short input\nstrings, such as search queries or individual words. At its core lies a\nprocedure for generating artificial typos which closely follow the error\npatterns manifested by humans. This procedure is used to train the production\nspelling correction model based on a transformer architecture. This model is\ncurrently served in the HubSpot product search. We show that our approach to\ntypo generation is superior to the widespread practice of adding noise, which\nignores human patterns. We also demonstrate how our approach may be extended to\nresource-scarce settings and train spelling correction models for Arabic,\nGreek, Russian, and Setswana languages, without using any labeled data.\n"}
+{"abstract": "  Fair clustering is the process of grouping similar entities together, while\nsatisfying a mathematically well-defined fairness metric as a constraint. Due\nto the practical challenges in precise model specification, the prescribed\nfairness constraints are often incomplete and act as proxies to the intended\nfairness requirement, leading to biased outcomes when the system is deployed.\nWe examine how to identify the intended fairness constraint for a problem based\non limited demonstrations from an expert. Each demonstration is a clustering\nover a subset of the data.\n  We present an algorithm to identify the fairness metric from demonstrations\nand generate clusters using existing off-the-shelf clustering techniques, and\nanalyze its theoretical properties. To extend our approach to novel fairness\nmetrics for which clustering algorithms do not currently exist, we present a\ngreedy method for clustering. Additionally, we investigate how to generate\ninterpretable solutions using our approach. Empirical evaluation on three\nreal-world datasets demonstrates the effectiveness of our approach in quickly\nidentifying the underlying fairness and interpretability constraints, which are\nthen used to generate fair and interpretable clusters.\n"}
+{"abstract": "  The median webpage has increased in size by more than 80% in the last 4\nyears. This extra complexity allows for a rich browsing experience, but it\nhurts the majority of mobile users which still pay for their traffic. This has\nmotivated several data-saving solutions, which aim at reducing the complexity\nof webpages by transforming their content. Despite each method being unique,\nthey either reduce user privacy by further centralizing web traffic through\ndata-saving middleboxes or introduce web compatibility (Webcompat) issues by\nremoving content that breaks pages in unpredictable ways. In this paper, we\nargue that data-saving is still possible without impacting either users privacy\nor Webcompat. Our main observation is that Web images make up a large portion\nof Web traffic and have negligible impact on Webcompat. To this end we make two\nmain contributions. First, we quantify the potential savings that image\nmanipulation, such as dimension resizing, quality compression, and transcoding,\nenables at large scale: 300 landing and 880 internal pages. Next, we design and\nbuild Browselite, an entirely client-side tool that achieves such data savings\nthrough opportunistically instrumenting existing server-side tooling to perform\nimage compression, while simultaneously reducing the total amount of image data\nfetched. The effect of Browselite on the user experience is quantified using\nstandard page load metrics and a real user study of over 200 users across 50\noptimized web pages. Browselite allows for similar savings to middlebox\napproaches, while offering additional security, privacy, and Webcompat\nguarantees.\n"}
+{"abstract": "  Neural volumetric representations such as Neural Radiance Fields (NeRF) have\nemerged as a compelling technique for learning to represent 3D scenes from\nimages with the goal of rendering photorealistic images of the scene from\nunobserved viewpoints. However, NeRF's computational requirements are\nprohibitive for real-time applications: rendering views from a trained NeRF\nrequires querying a multilayer perceptron (MLP) hundreds of times per ray. We\npresent a method to train a NeRF, then precompute and store (i.e. \"bake\") it as\na novel representation called a Sparse Neural Radiance Grid (SNeRG) that\nenables real-time rendering on commodity hardware. To achieve this, we\nintroduce 1) a reformulation of NeRF's architecture, and 2) a sparse voxel grid\nrepresentation with learned feature vectors. The resulting scene representation\nretains NeRF's ability to render fine geometric details and view-dependent\nappearance, is compact (averaging less than 90 MB per scene), and can be\nrendered in real-time (higher than 30 frames per second on a laptop GPU).\nActual screen captures are shown in our video.\n"}
+{"abstract": "  The recent advancements in multicore machines highlight the need to simplify\nconcurrent programming in order to leverage their computational power. One way\nto achieve this is by designing efficient concurrent data structures (e.g.\nstacks, queues, hash-tables, etc.) and synchronization techniques (e.g. locks,\ncombining techniques, etc.) that perform well in machines with large amounts of\ncores. In contrast to ordinary, sequential data-structures, the concurrent\ndata-structures allow multiple threads to simultaneously access and/or modify\nthem.\n  Synch is an open-source framework that not only provides some common\nhigh-performant concurrent data-structures, but it also provides researchers\nwith the tools for designing and benchmarking high performant concurrent\ndata-structures. The Synch framework contains a substantial set of concurrent\ndata-structures such as queues, stacks, combining-objects, hash-tables, locks,\netc. and it provides a user-friendly runtime for developing and benchmarking\nconcurrent data-structures. Among other features, the provided runtime provides\nfunctionality for creating threads easily (both POSIX and user-level threads),\ntools for measuring performance, etc. Moreover, the provided concurrent\ndata-structures and the runtime are highly optimized for contemporary NUMA\nmultiprocessors such as AMD Epyc and Intel Xeon.\n"}
+{"abstract": "  The exponential functional link network (EFLN) has been recently investigated\nand applied to nonlinear filtering. This brief proposes an adaptive EFLN\nfiltering algorithm based on a novel inverse square root (ISR) cost function,\ncalled the EFLN-ISR algorithm, whose learning capability is robust under\nimpulsive interference. The steady-state performance of EFLN-ISR is rigorously\nderived and then confirmed by numerical simulations. Moreover, the validity of\nthe proposed EFLN-ISR algorithm is justified by the actually experimental\nresults with the application to hysteretic nonlinear system identification.\n"}
+{"abstract": "  Cerebral hematoma grows rapidly in 6-24 hours and misprediction of the growth\ncan be fatal if it is not operated by a brain surgeon. There are two types of\ncerebral hematomas: one that grows rapidly and the other that does not grow\nrapidly. We are developing the technique of artificial intelligence to\ndetermine whether the CT image includes the cerebral hematoma which leads to\nthe rapid growth. This problem has various difficulties: the few positive cases\nin this classification problem of cerebral hematoma and the targeted hematoma\nhas deformable object. Other difficulties include the imbalance classification,\nthe covariate shift, the small data, and the spurious correlation problems. It\nis difficult with the plain CNN classification such as VGG. This paper proposes\nthe joint learning of semantic segmentation and classification and evaluate the\nperformance of this.\n"}
+{"abstract": "  We have followed up two ultra-diffuse galaxies (UDGs), detected adjacent to\nstellar streams, with Hubble Space Telescope (HST) imaging and HI mapping with\nthe Jansky Very Large Array (VLA) in order to investigate the possibility that\nthey might have a tidal origin. With the HST F814W and F555W images we measure\nthe globular cluster (GC) counts for NGC 2708-Dw1 and NGC 5631-Dw1 as\n$2^{+1}_{-1}$ and $5^{+1}_{-2}$, respectively. NGC 2708-Dw1 is undetected in HI\ndown to a 3$\\sigma$ limit of $\\log (M_\\mathrm{HI}/\\mathrm{M_\\odot}) = 7.3$, and\nthere is no apparent HI associated with the nearby stellar stream. There is a\n2$\\sigma$ HI feature coincident with NGC 5631-Dw1. However, this emission is\nblended with a large gaseous tail emanating from NGC 5631 and is not\nnecessarily associated with the UDG. The presence of any GCs and the lack of\nclear HI connections between the UDGs and their parent galaxies strongly\ndisfavor a tidal dwarf galaxy origin, but cannot entirely rule it out. The GC\ncounts are consistent with those of normal dwarf galaxies, and the most\nprobable formation mechanism is one where these UDGs were born as normal dwarfs\nand were later tidally stripped and heated. We also identify an over-luminous\n($M_\\mathrm{V} = -11.1$) GC candidate in NGC 2708-Dw1, which may be a nuclear\nstar cluster transitioning to an ultra-compact dwarf as the surrounding dwarf\ngalaxy gets stripped of stars.\n"}
+{"abstract": "  Hierarchical classification is significant for complex tasks by providing\nmulti-granular predictions and encouraging better mistakes. As the label\nstructure decides its performance, many existing approaches attempt to\nconstruct an excellent label structure for promoting the classification\nresults. In this paper, we consider that different label structures provide a\nvariety of prior knowledge for category recognition, thus fusing them is\nhelpful to achieve better hierarchical classification results. Furthermore, we\npropose a multi-task multi-structure fusion model to integrate different label\nstructures. It contains two kinds of branches: one is the traditional\nclassification branch to classify the common subclasses, the other is\nresponsible for identifying the heterogeneous superclasses defined by different\nlabel structures. Besides the effect of multiple label structures, we also\nexplore the architecture of the deep model for better hierachical\nclassification and adjust the hierarchical evaluation metrics for multiple\nlabel structures. Experimental results on CIFAR100 and Car196 show that our\nmethod obtains significantly better results than using a flat classifier or a\nhierarchical classifier with any single label structure.\n"}
+{"abstract": "  Classical machine learning approaches are sensitive to non-stationarity.\nTransfer learning can address non-stationarity by sharing knowledge from one\nsystem to another, however, in areas like machine prognostics and defense, data\nis fundamentally limited. Therefore, transfer learning algorithms have little,\nif any, examples from which to learn. Herein, we suggest that these constraints\non algorithmic learning can be addressed by systems engineering. We formally\ndefine transfer distance in general terms and demonstrate its use in\nempirically quantifying the transferability of models. We consider the use of\ntransfer distance in the design of machine rebuild procedures to allow for\ntransferable prognostic models. We also consider the use of transfer distance\nin predicting operational performance in computer vision. Practitioners can use\nthe presented methodology to design and operate systems with consideration for\nthe learning theoretic challenges faced by component learning systems.\n"}
+{"abstract": "  This paper considers the inverse problem of recovering both the unknown,\nspatially-dependent conductivity $a(x)$ and the nonlinear reaction term $f(u)$\nin a reaction-diffusion equation from overposed data. These measurements can\nconsist of: the value of two different solution measurements taken at a later\ntime $T$; time-trace profiles from two solutions; or both final time and\ntime-trace measurements from a single forwards solve data run. We prove both\nuniqueness results and the convergence of iteration schemes designed to recover\nthese coefficients. The last section of the paper shows numerical\nreconstructions based on these algorithms.\n"}
+{"abstract": "  Einstein equivalence principle (EEP), as one of the foundations of general\nrelativity, is a fundamental test of gravity theories. In this paper, we\npropose a new method to test the EEP of electromagnetic interactions through\nobservations of black hole photon rings, which naturally extends the scale of\nNewtonian and post-Newtoian gravity where the EEP violation through a variable\nfine structure constant has been well constrained to that of stronger gravity.\nWe start from a general form of Lagrangian that violates EEP, where a specific\nEEP violation model could be regarded as one of the cases of this Lagrangian.\nWithin the geometrical optical approximation, we find that the dispersion\nrelation of photons is modified: for photons moving in circular orbit, the\ndispersion relation simplifies, and behaves such that photons with different\nlinear polarizations perceive different gravitational potentials. This makes\nthe size of black hole photon ring depend on polarization. Further assuming\nthat the EEP violation is small, we derive an approximate analytic expression\nfor spherical black holes showing that the change in size of the photon ring is\nproportional to the violation parameters. We also discuss several cases of this\nanalytic expression for specific models. Finally, we explore the effects of\nblack hole rotation and derive a modified proportionality relation between the\nchange in size of photon ring and the violation parameters. The numerical and\nanalytic results show that the influence of black hole rotation on the\nconstraints of EEP violation is relatively weak for small magnitude of EEP\nviolation and small rotation speed of black holes.\n"}
+{"abstract": "  One of the important and widely used classes of models for non-Gaussian time\nseries is the generalized autoregressive model average models (GARMA), which\nspecifies an ARMA structure for the conditional mean process of the underlying\ntime series. However, in many applications one often encounters conditional\nheteroskedasticity. In this paper we propose a new class of models, referred to\nas GARMA-GARCH models, that jointly specify both the conditional mean and\nconditional variance processes of a general non-Gaussian time series. Under the\ngeneral modeling framework, we propose three specific models, as examples, for\nproportional time series, nonnegative time series, and skewed and heavy-tailed\nfinancial time series. Maximum likelihood estimator (MLE) and quasi Gaussian\nMLE (GMLE) are used to estimate the parameters. Simulation studies and three\napplications are used to demonstrate the properties of the models and the\nestimation procedures.\n"}
+{"abstract": "  In this paper we consider the second eigenfunction of the Laplacian with\nDirichlet boundary conditions in convex domains. If the domain has \\emph{large\neccentricity} then the eigenfunction has \\emph{exactly} two nondegenerate\ncritical points (of course they are one maximum and one minimum). The proof\nuses some estimates proved by Jerison ([Jer95a]) and Grieser-Jerison ([GJ96])\njointly with a topological degree argument. Analogous results for higher order\neigenfunctions are proved in rectangular-like domains considered in [GJ09].\n"}
+{"abstract": "  Transient field-resolved spectroscopy enables studies of ultrafast dynamics\nin molecules, nanostructures, or solids with sub-cycle resolution, but previous\nwork has so far concentrated on extracting the dielectric response at\nfrequencies below 50\\,THz. Here, we implemented transient field-resolved\nreflectometry at 50-100\\,THz (3-6\\,$\\mu$m) with MHz repetition rate employing\n800\\,nm few-cycle excitation pulses that provide sub-10\\,fs temporal\nresolution. The capabilities of the technique are demonstrated in studies of\nultrafast photorefractive changes in the semiconductors Ge and GaAs, where the\nhigh frequency range permitted to explore the resonance-free Drude response.\nThe extended frequency range in transient field-resolved spectroscopy can\nfurther enable studies with so far inaccessible transitions, including\nintramolecular vibrations in a large range of systems.\n"}
+{"abstract": "  The decoupling of heavy fields as required by the Appelquist-Carazzone\ntheorem plays a fundamental role in the construction of any effective field\ntheory. However, it is not a trivial task to implement a renormalization\nprescription that produces the expected decoupling of massive fields, and it is\neven more difficult in curved spacetime. Focused on this idea, we consider the\nrenormalization of the one-loop effective action for the Yukawa interaction\nwith a background scalar field in curved space. We compute the beta functions\nwithin a generalized DeWitt-Schwinger subtraction procedure and discuss the\ndecoupling in the running of the coupling constants. For the case of a\nquantized scalar field, all the beta function exhibit decoupling, including\nalso the gravitational ones. For a quantized Dirac field, decoupling appears\nalmost for all the beta functions. We obtain the anomalous result that the mass\nof the background scalar field does not decouple.\n"}
+{"abstract": "  Baryon-to-meson and baryon-to-photon transition distribution amplitudes\n(TDAs) arise in the collinear factorized description of a class of hard\nexclusive reactions characterized by the exchange of a non-zero baryon number\nin the cross channel. These TDAs extend the concepts of generalized parton\ndistributions (GPDs) and baryon distribution amplitudes (DAs). In this review\nwe discuss the general properties and physical interpretation of\nbaryon-to-meson and baryon-to-photon TDAs. We argue that these non-perturbative\nobjects are a convenient complementary tool to explore the structure of baryons\nat the partonic level. We present an overview of hard exclusive reactions\nadmitting a description in terms of TDAs. We discuss the first signals from\nhard exclusive backward meson electroproduction at JLab with the 6 GeV electron\nbeam and explore further experimental opportunities to access TDAs at JLab@12\nGeV, PANDA and J-PARC.\n"}
+{"abstract": "  We study homogeneous nucleation in the two-dimensional $q-$state Potts model\nfor $q=3,5,10,20$ and ferromagnetic couplings $J_{ij} \\propto \\Theta (R -\n|i-j|)$, by means of Monte Carlo simulations employing heat bath dynamics.\nMetastability is induced in the low temperature phase through an instantaneous\nquench of the magnetic field coupled to one of the $q$ spin states. The quench\ndepth is adjusted, depending on the value of temperature $T$, interaction range\n$R$, and number of states $q$, in such a way that a constant nucleation time is\nalways obtained. In this setup we analyze the crossover between the classical\ncompact droplet regime occurring in presence of short range interactions $R\n\\sim 1$, and the long-range regime $R\\gg 1$ where the properties of nucleation\nare influenced by the presence of a mean-field spinodal singularity. We\nevaluate the metastable susceptibility of the order parameter as well as\nvarious critical droplet properties, which along with the evolution of the\nquench depth as a function of $q,T$ and $R$, are then compared with the field\ntheoretical predictions valid in the large $R$ limit in order to find the onset\nof spinodal-assisted nucleation. We find that, with a mild dependence on the\nvalues of $q$ and $T$ considered, spinodal scaling holds for interaction ranges\n$R\\gtrsim 8-10$, and that signatures of the presence of a pseudo-spinodal are\nalready visible for remarkably small interaction ranges $R\\sim 4-5$. The\ninfluence of spinodal singularities on the occurrence of multi-step nucleation\nis also discussed.\n"}
+{"abstract": "  Consider any network of $n$ identical Kuramoto oscillators in which each\noscillator is coupled bidirectionally with unit strength to at least $\\mu\n(n-1)$ other oscillators. There is a critical value of the connectivity,\n$\\mu_c$, such that whenever $\\mu>\\mu_c$, the system is guaranteed to converge\nto the all-in-phase synchronous state for almost all initial conditions, but\nwhen $\\mu<\\mu_c$, there are networks with other stable states. The precise\nvalue of the critical connectivity remains unknown, but it has been conjectured\nto be $\\mu_c=0.75$. In 2020, Lu and Steinerberger proved that $\\mu_c\\leq\n0.7889$, and Yoneda, Tatsukawa, and Teramae proved in 2021 that $\\mu_c >\n0.6838$. In this paper, we prove that $\\mu_c\\leq 0.75$ and explain why this is\nthe best upper bound that one can obtain by a purely linear stability analysis.\n"}
+{"abstract": "  Integrated circuits (ICs) that can operate at high temperature have a wide\nvariety of applications in the fields of automotive, aerospace, space\nexploration, and deep-well drilling. Conventional silicon-based complementary\nmetal-oxide-semiconductor (CMOS) circuits cannot work at higher than 200\n$^\\circ$C, leading to the use of wide bandgap semiconductor, especially silicon\ncarbide (SiC). However, high-density defects at an oxide-SiC interface make it\nimpossible to predict electrical characteristics of SiC CMOS logic gates in a\nwide temperature range and high supply voltage (typically ${\\geqq 15}$ V) is\nrequired to compensate their large logic threshold voltage shift. Here, we show\nthat SiC complementary logic gates composed of p- and n-channel junction\nfield-effect transistors (JFETs) operate at 300 $^\\circ$C with a supply voltage\nas low as 1.4 V. The logic threshold voltage shift of the complementary JFET\n(CJFET) inverter is 0.2 V from room temperature to 300 $^\\circ$C. Furthermore,\ntemperature dependencies of the static and dynamic characteristics of the CJFET\ninverter are well explained by a simple analytical model of SiC JFETs. This\nallows us to perform electronic circuit simulation, leading to superior\ndesignability of complex circuits or memories based on SiC CJFET technology,\nwhich operate within a wide temperature range.\n"}
+{"abstract": "  The Istanbul options were first introduced by Michel Jacques in 1997. These\nderivatives are considered as an extension of the Asian options. In this paper,\nwe propose an analytical approximation formula for a geometric Istanbul call\noption (GIC) under the Black-Scholes model. Our approximate pricing formula is\nobtained in closed-form using a second-order Taylor expansion. We compare our\ntheoretical results with those of Monte-Carlo simulations using the control\nvariates method. Finally, we study the effects of changes in the price of the\nunderlying asset on the value of GIC.\n"}
+{"abstract": "  We study three-terminal thermoelectric transport in a two-dimensional Quantum\nPoint Contact (QPC) connected to left and right electronic reservoirs, as well\nas a third one represented by a scanning probe tip. The latter acts as a\nvoltage probe exchanging heat with the system but no charges on average. The\nthermoelectric coefficients are calculated numerically within the\nLandauer-B\\\"uttiker formalism in the low-temperature and linear response\nregimes. We find tip-induced oscillations of the local and non-local\nthermopowers and study their dependence on the QPC opening. If the latter is\ntuned on a conductance plateau, the system behaves as a perfect thermoelectric\ndiode: for some tip positions the charge current through the QPC, driven by a\nlocal Seebeck effect, can flow in one direction only.\n"}
+{"abstract": "  Identifying harmful instances, whose absence in a training dataset improves\nmodel performance, is important for building better machine learning models.\nAlthough previous studies have succeeded in estimating harmful instances under\nsupervised settings, they cannot be trivially extended to generative\nadversarial networks (GANs). This is because previous approaches require that\n(1) the absence of a training instance directly affects the loss value and that\n(2) the change in the loss directly measures the harmfulness of the instance\nfor the performance of a model. In GAN training, however, neither of the\nrequirements is satisfied. This is because, (1) the generator's loss is not\ndirectly affected by the training instances as they are not part of the\ngenerator's training steps, and (2) the values of GAN's losses normally do not\ncapture the generative performance of a model. To this end, (1) we propose an\ninfluence estimation method that uses the Jacobian of the gradient of the\ngenerator's loss with respect to the discriminator's parameters (and vice\nversa) to trace how the absence of an instance in the discriminator's training\naffects the generator's parameters, and (2) we propose a novel evaluation\nscheme, in which we assess harmfulness of each training instance on the basis\nof how GAN evaluation metric (e.g., inception score) is expect to change due to\nthe removal of the instance. We experimentally verified that our influence\nestimation method correctly inferred the changes in GAN evaluation metrics.\nFurther, we demonstrated that the removal of the identified harmful instances\neffectively improved the model's generative performance with respect to various\nGAN evaluation metrics.\n"}
+{"abstract": "  We present a post-training weight pruning method for deep neural networks\nthat achieves accuracy levels tolerable for the production setting and that is\nsufficiently fast to be run on commodity hardware such as desktop CPUs or edge\ndevices. We propose a data-free extension of the approach for computer vision\nmodels based on automatically-generated synthetic fractal images. We obtain\nstate-of-the-art results for data-free neural network pruning, with ~1.5% top@1\naccuracy drop for a ResNet50 on ImageNet at 50% sparsity rate. When using real\ndata, we are able to get a ResNet50 model on ImageNet with 65% sparsity rate in\n8-bit precision in a post-training setting with a ~1% top@1 accuracy drop. We\nrelease the code as a part of the OpenVINO(TM) Post-Training Optimization tool.\n"}
+{"abstract": "  We show that, in a weakly regular $p$-adic Lie group $G$, the subgroup $G_u$\nspanned by the one-parameter subgroups of $G$ admits a Levi decomposition. As a\nconsequence, there exists a regular open subgroup of $G$ which contains $G_u$.\n"}
+{"abstract": "  A motion-blurred image is the temporal average of multiple sharp frames over\nthe exposure time. Recovering these sharp video frames from a single blurred\nimage is nontrivial, due to not only its strong ill-posedness, but also various\ntypes of complex motion in reality such as rotation and motion in depth. In\nthis work, we report a generalized video extraction method using the affine\nmotion modeling, enabling to tackle multiple types of complex motion and their\nmixing. In its workflow, the moving objects are first segemented in the alpha\nchannel. This allows separate recovery of different objects with different\nmotion. Then, we reduce the variable space by modeling each video clip as a\nseries of affine transformations of a reference frame, and introduce the\n$l0$-norm total variation regularization to attenuate the ringing artifact. The\ndifferentiable affine operators are employed to realize gradient-descent\noptimization of the affine model, which follows a novel coarse-to-fine strategy\nto further reduce artifacts. As a result, both the affine parameters and sharp\nreference image are retrieved. They are finally input into stepwise affine\ntransformation to recover the sharp video frames. The stepwise retrieval\nmaintains the nature to bypass the frame order ambiguity. Experiments on both\npublic datasets and real captured data validate the state-of-the-art\nperformance of the reported technique.\n"}
+{"abstract": "  We study multicomponent coagulation via the Smoluchowski coagulation equation\nunder non-equilibrium stationary conditions induced by a source of small\nclusters. The coagulation kernel can be very general, merely satisfying certain\npower law asymptotic bounds in terms of the total number of monomers in a\ncluster. The bounds are characterized by two parameters and we extend previous\nresults for one-component systems to classify the parameter values for which\nthe above stationary solutions do or do not exist. Moreover, we also obtain\ncriteria for the existence or non-existence of solutions which yield a constant\nflux of mass towards large clusters.\n"}
+{"abstract": "  Ultraviolet (UV) exposure significantly contributes to non-melanoma skin\ncancer. In the context of health, UV exposure is the product of time and the UV\nIndex (UVI), a weighted sum of the irradiance I(lambda) over all wavelengths\nfrom lambda = 250 to 400nm. In our analysis of the United States Environmental\nProtection Agency's UV-Net database of over four-hundred thousand spectral\nirradiance measurements taken over several years, we found that the UVI is well\nestimated by UVI = 77 I(310nm). To better understand this result, we applied an\noptical atmospheric model of the terrestrial irradiance spectra and found that\nit applies across a wide range of conditions.\n"}
+{"abstract": "  The use of the full potential of stellar seismology is made difficult by the\nimproper modeling of the upper-most layers of solar-like stars and their\ninfluence on the modeled frequencies. Our knowledge on these \\emph{surface\neffects} has improved thanks to the use of 3D hydrodynamical simulations but\nthe calculation of eigenfrequencies relies on empirical models for the\ndescription of the Lagrangian perturbation of turbulent pressure: the\nreduced-$\\Gamma_1$ model (RGM) and the gas-$\\Gamma_1$ model (GGM). Starting\nfrom the fully compressible turbulence equations, we derive both the GGM and\nRGM models using a closure to model the flux of turbulent kinetic energy. It is\nfound that both models originate from two terms: the source of turbulent\npressure due to compression produced by the oscillations and the divergence of\nthe flux of turbulent pressure. It is also demonstrated that they are both\ncompatible with the adiabatic approximation but also imply a number of\nquestionable assumptions mainly regarding mode physics. Among others\nhypothesis, one has to neglect the Lagrangian perturbation of the dissipation\nof turbulent kinetic energy into heat and the Lagrangian perturbation of\nbuoyancy work.\n"}
+{"abstract": "  We give a link criterion for normal embeddings of definable sets in o-minimal\nstructures. Namely, we prove that given a definable germ $(X, 0)\\subset\n(\\mathbb{R}^n,0)$ with $(X\\setminus\\{0\\},0)$ connected and a continuous\ndefinable function $\\rho: (X,0) \\to \\mathbb{R}_{\\geq 0}$ such that $\\rho(x)\n\\sim \\|x\\|$, then $(X,0)$ is Lipschitz normally embedded (LNE) if and only if\n$(X,0)$ is link Lipschitz normally embedded (LLNE) with respect to $\\rho$\n(i.e., for $r>0$ small enough, $X\\cap \\rho^{-1}(r)$ is Lipschitz normally\nembedded and its LNE constant is bounded by a constant $C$ independent of $r$).\nThis is a generalization of Mendes--Sampaio's result for the subanalytic case.\nAs an application, we give a counterexample to a question on the relation\nbetween Lipschitz normal embedding and MD Homology asked by Bobadilla et al in\ntheir paper about Moderately Discontinuous Homology.\n"}
+{"abstract": "  We say that a random integer variable $X$ is monotone if the modulus of the\ncharacteristic function of $X$ is decreasing on $[0,\\pi]$. This is the case for\nmany commonly encountered variables, e.g., Bernoulli, Poisson and geometric\nrandom variables. In this note, we provide estimates for the probability that\nthe sum of independent monotone integer variables attains precisely a specific\nvalue. We do not assume that the variables are identically distributed. Our\nestimates are sharp when the specific value is close to the mean, but they are\nnot useful further out in the tail. By combining with the trick of\n\\emph{exponential tilting}, we obtain sharp estimates for the point\nprobabilities in the tail under a slightly stronger assumption on the random\ninteger variables which we call strong monotonicity.\n"}
+{"abstract": "  The lack of stability guarantee restricts the practical use of learning-based\nmethods in core control problems in robotics. We develop new methods for\nlearning neural control policies and neural Lyapunov critic functions in the\nmodel-free reinforcement learning (RL) setting. We use sample-based approaches\nand the Almost Lyapunov function conditions to estimate the region of\nattraction and invariance properties through the learned Lyapunov critic\nfunctions. The methods enhance stability of neural controllers for various\nnonlinear systems including automobile and quadrotor control.\n"}
+{"abstract": "  Let $T$ be a linear operator on an $\\mathbb{F}_q$-vector space $V$ of\ndimension $n$. For any divisor $m$ of $n$, an $m$-dimensional subspace $W$ of\n$V$ is $T$-splitting if\n  $$ V =W\\oplus TW\\oplus \\cdots \\oplus T^{d-1}W, $$ where $d=n/m$. Let\n$\\sigma(m,d;T)$ denote the number of $m$-dimensional $T$-splitting subspaces.\nDetermining $\\sigma(m,d;T)$ for an arbitrary operator $T$ is an open problem.\nThis problem is closely related to another open problem on Krylov spaces. We\ndiscuss this connection and give explicit formulae for $\\sigma(m,d;T)$ in the\ncase where the invariant factors of $T$ satisfy certain degree conditions. A\nconnection with another enumeration problem on polynomial matrices is also\ndiscussed.\n"}
+{"abstract": "  We study the probability distribution of the number of particle and\nantiparticle pairs produced via the Schwinger effect when a uniform but\ntime-dependent electric field is applied to noninteracting scalars or spinors\ninitially at a thermodynamic equilibrium. We derive the formula for the\ncharacteristic function by employing techniques in mesoscopic physics,\nreflecting a close analogy between the Schwinger effect and mesoscopic\ntunneling transports. In particular, we find that the pair production in a\nmedium is enhanced (suppressed) for scalars (spinors) due to the Bose\nstimulation (Pauli blocking). Furthermore, in addition to the production of\naccelerated pairs by the electric field, the annihilation of decelerated pairs\nis found to take place in a medium. Our formula allows us to extract the\nprobability distributions in various situations, such as those obeying the\ngeneralized trinomial statistics for spin-momentum resolved counting and the\nbidirectional Poisson statistics for spin-momentum unresolved counting.\n"}
+{"abstract": "  The Fornax dwarf spheroidal galaxy has an anomalous number of globular\nclusters, five, for its stellar mass. There is a longstanding debate about a\npotential sixth globular cluster (Fornax~6) that has recently been\n`rediscovered' in DECam imaging. We present new Magellan/M2FS spectroscopy of\nthe Fornax~6 cluster and Fornax dSph. Combined with literature data we identify\n$\\sim15-17$ members of the Fornax~6 cluster that this overdensity is indeed a\nstar cluster and associated with the Fornax dSph. The cluster is significantly\nmore metal-rich (mean metallicity of $\\overline{\\rm [Fe/H]}=-0.71\\pm0.05$) than\nthe other five Fornax globular clusters ($-2.5<[Fe/H]<-1.4$) and more\nmetal-rich than the bulk of Fornax. We measure a velocity dispersion of\n$5.6_{-1.6}^{+2.0}\\,{\\rm km \\, s^{-1}}$ corresponding to anomalously high\nmass-to-light of 15$<$M/L$<$258 at 90\\% confidence when calculated assuming\nequilibrium. Two stars inflate this dispersion and may be either Fornax field\nstars or as yet unresolved binary stars. Alternatively the Fornax~6 cluster may\nbe undergoing tidal disruption. Based on its metal-rich nature, the Fornax 6\ncluster is likely younger than the other Fornax clusters, with an estimated age\nof $\\sim2$ Gyr when compared to stellar isochrones. The chemodynamics and star\nformation history of Fornax shows imprints of major events such as infall into\nthe Milky Way, multiple pericenter passages, star formation bursts, and/or\npotential mergers or interactions. Any of these events may have triggered the\nformation of the Fornax~6 cluster.\n"}
+{"abstract": "  Two-dimensional (2D) van der Waals (vdW) magnets provide an ideal platform\nfor exploring, on the fundamental side, new microscopic mechanisms and for\ndeveloping, on the technological side, ultra-compact spintronic applications.\nSo far, bilinear spin Hamiltonians have been commonly adopted to investigate\nthe magnetic properties of 2D magnets, neglecting higher order magnetic\ninteractions. However, we here provide quantitative evidence of giant\nbiquadratic exchange interactions in monolayer NiX2 (X=Cl, Br and I), by\ncombining first-principles calculations and the newly developed machine\nlearning method for constructing Hamiltonian. Interestingly, we show that the\nferromagnetic ground state within NiCl2 single layers cannot be explained by\nmeans of bilinear Heisenberg Hamiltonian; rather, the nearest-neighbor\nbiquadratic interaction is found to be crucial. Furthermore, using a\nthree-orbitals Hubbard model, we propose that the giant biquadratic exchange\ninteraction originates from large hopping between unoccupied and occupied\norbitals on neighboring magnetic ions. On a general framework, our work\nsuggests biquadratic exchange interactions to be important in 2D magnets with\nedge-shared octahedra.\n"}
+{"abstract": "  The Abstraction and Reasoning Corpus (ARC) is a challenging program induction\ndataset that was recently proposed by Chollet (2019). Here, we report the first\nset of results collected from a behavioral study of humans solving a subset of\ntasks from ARC (40 out of 1000). Although this subset of tasks contains\nconsiderable variation, our results showed that humans were able to infer the\nunderlying program and generate the correct test output for a novel test input\nexample, with an average of 80% of tasks solved per participant, and with 65%\nof tasks being solved by more than 80% of participants. Additionally, we find\ninteresting patterns of behavioral consistency and variability within the\naction sequences during the generation process, the natural language\ndescriptions to describe the transformations for each task, and the errors\npeople made. Our findings suggest that people can quickly and reliably\ndetermine the relevant features and properties of a task to compose a correct\nsolution. Future modeling work could incorporate these findings, potentially by\nconnecting the natural language descriptions we collected here to the\nunderlying semantics of ARC.\n"}
+{"abstract": "  This paper proposes a comparison of four popular interface capturing methods\n: the volume of fluid (VOF), the standard level set (SLS), the accurate\nconservative level set (ACLS) and the coupled level set and volume of fluid\n(CLSVOF). All methods are embedded into a unified low-Mach framework based on a\nCartesian-grid finite-volume discretization. This framework includes a sharp\ntransport of the interface, a wellbalanced surface tension discretization and a\nconsistent mass and momentum transport which allows capillary-driven\nsimulations with high density ratio. The comparison relies on shared metrics\nfor geometrical accuracy, mass and momentum conservation which exposes the\nweakness and strengths of each method. Finally, the versatility and\ncapabilities of the proposed solver are demonstrated on the simulation of a 3D\nhead-on collision of two water droplets. Overall, all methods manage to\nretrieve reasonable results for all test cases presented. VOF, CLSVOF and ACLS\ntend to artificially create little structures while SLS suffers from\nconservation issues in the mesh resolution limit. This study leads us to the\nconclusion that CLSVOF is the most promising method for two-phase flow\nsimulations in our specific framework because of its inherent conservation\nproperties and topology accuracy.\n"}
+{"abstract": "  In a few years, space telescopes will investigate our Galaxy to detect\nevidence of life, mainly by observing rocky planets. In the last decade, the\nobservation of exoplanet atmospheres and the theoretical works on biosignature\ngasses have experienced a considerable acceleration. The~most attractive\nfeature of the realm of exoplanets is that 40\\% of M dwarfs host super-Earths\nwith a minimum mass between 1 and 30 Earth masses, orbital periods shorter than\n50 days, and radii between those of the Earth and Neptune (1--3.8 R$_\\oplus$).\nMoreover, the recent finding of cyanobacteria able to use far-red (FR) light\nfor oxygenic photosynthesis due to the synthesis of chlorophylls $d$ and $f$,\nextending in vivo light absorption up to 750\\ nm, suggests the possibility of\nexotic photosynthesis in planets around M dwarfs. Using innovative laboratory\ninstrumentation, we exposed different cyanobacteria to an M dwarf star\nsimulated irradiation, comparing their responses to those under solar and FR\nsimulated lights.~As expected, in FR light, only the cyanobacteria able to\nsynthesize chlorophyll $d$ and $f$ could grow. Surprisingly, all strains, both\nable or unable to use FR light, grew and photosynthesized under the M dwarf\ngenerated spectrum in a similar way to the solar light and much more\nefficiently than under the FR one. Our findings highlight the importance of\nsimulating both the visible and FR light components of an M dwarf spectrum to\ncorrectly evaluate the photosynthetic performances of oxygenic organisms\nexposed under such an exotic light~condition.\n"}
+{"abstract": "  Virtual Reality (VR) has become more and more popular with dropping prices\nfor systems and a growing number of users. However, the issue of accessibility\nin VR has been hardly addressed so far and no uniform approach or standard\nexists at this time. In this position paper, we propose a customisable toolkit\nimplemented at the system-level and discuss the potential benefits of this\napproach and challenges that will need to be overcome for a successful\nimplementation.\n"}
+{"abstract": "  The task of image-based virtual try-on aims to transfer a target clothing\nitem onto the corresponding region of a person, which is commonly tackled by\nfitting the item to the desired body part and fusing the warped item with the\nperson. While an increasing number of studies have been conducted, the\nresolution of synthesized images is still limited to low (e.g., 256x192), which\nacts as the critical limitation against satisfying online consumers. We argue\nthat the limitation stems from several challenges: as the resolution increases,\nthe artifacts in the misaligned areas between the warped clothes and the\ndesired clothing regions become noticeable in the final results; the\narchitectures used in existing methods have low performance in generating\nhigh-quality body parts and maintaining the texture sharpness of the clothes.\nTo address the challenges, we propose a novel virtual try-on method called\nVITON-HD that successfully synthesizes 1024x768 virtual try-on images.\nSpecifically, we first prepare the segmentation map to guide our virtual try-on\nsynthesis, and then roughly fit the target clothing item to a given person's\nbody. Next, we propose ALIgnment-Aware Segment (ALIAS) normalization and ALIAS\ngenerator to handle the misaligned areas and preserve the details of 1024x768\ninputs. Through rigorous comparison with existing methods, we demonstrate that\nVITON-HD highly surpasses the baselines in terms of synthesized image quality\nboth qualitatively and quantitatively. Code is available at\nhttps://github.com/shadow2496/VITON-HD.\n"}
+{"abstract": "  Symmetries naturally occur in real-world networks and can significantly\ninfluence the observed dynamics. For instance, many synchronization patterns\nresult from the underlying network symmetries, and high symmetries are known to\nincrease the stability of synchronization. Yet, here we find that general\nmacroscopic features of network solutions such as regularity can be induced by\nbreaking their symmetry of interactions. We demonstrate this effect in an\necological multilayer network where the topological asymmetries occur\nnaturally. These asymmetries rescue the system from chaotic oscillations by\nestablishing stable periodic orbits and equilibria. We call this phenomenon\nasymmetry-induced order and uncover its mechanism by analyzing both\nanalytically and numerically the suppression of dynamics on the system's\nsynchronization manifold. Moreover, the bifurcation scenario describing the\nroute from chaos to order is also disclosed. We demonstrate that this result\nalso holds for generic node dynamics by analyzing coupled paradigmatic\nR\\\"ossler and Lorenz systems.\n"}
+{"abstract": "  This paper describes the submission to the IWSLT 2021 offline speech\ntranslation task by the UPC Machine Translation group. The task consists of\nbuilding a system capable of translating English audio recordings extracted\nfrom TED talks into German text. Submitted systems can be either cascade or\nend-to-end and use a custom or given segmentation. Our submission is an\nend-to-end speech translation system, which combines pre-trained models\n(Wav2Vec 2.0 and mBART) with coupling modules between the encoder and decoder,\nand uses an efficient fine-tuning technique, which trains only 20% of its total\nparameters. We show that adding an Adapter to the system and pre-training it,\ncan increase the convergence speed and the final result, with which we achieve\na BLEU score of 27.3 on the MuST-C test set. Our final model is an ensemble\nthat obtains 28.22 BLEU score on the same set. Our submission also uses a\ncustom segmentation algorithm that employs pre-trained Wav2Vec 2.0 for\nidentifying periods of untranscribable text and can bring improvements of 2.5\nto 3 BLEU score on the IWSLT 2019 test set, as compared to the result with the\ngiven segmentation.\n"}
+{"abstract": "  Three dimensional space is said to be spherically symmetric if it admits\nSO(3) as the group of isometries. Under this symmetry condition, the Einsteins\nField equations for vacuum, yields the Schwarzschild Metric as the unique\nsolution, which essentially is the statement of the well known Birkhoffs\nTheorem. Geometrically speaking this theorem claims that the pseudo-Riemanian\nspace-times provide more isometries than expected from the original metric\nholonomy/ansatz. In this paper we use the method of Lie Symmetry Analysis to\nanalyze the Einsteins Vacuum Field Equations so as to obtain the Symmetry\nGenerators of the corresponding Differential Equation. Additionally, applying\nthe Noether Point Symmetry method we have obtained the conserved quantities\ncorresponding to the generators of the Schwarzschild Lagrangian and paving way\nto reformulate the Birkhoffs Theorem from a different approach.\n"}
+{"abstract": "  Scanning quantum dot microscopy is a recently developed high-resolution\nmicroscopy technique that is based on atomic force microscopy and is capable of\nimaging the electrostatic potential of nanostructures like molecules or single\natoms. Recently, it could be shown that it not only yields qualitatively but\nalso quantitatively cutting edge images even on an atomic level. In this paper\nwe present how control is a key enabling element to this. The developed control\napproach consists of a two-degree-of-freedom control framework that comprises a\nfeedforward and a feedback part. For the latter we design two tailored feedback\ncontrollers. The feedforward part generates a reference for the current scanned\nline based on the previously scanned one. We discuss in detail various aspects\nof the presented control approach and its implications for scanning quantum dot\nmicroscopy. We evaluate the influence of the feedforward part and compare the\ntwo proposed feedback controllers. The proposed control algorithms speed up\nscanning quantum dot microscopy by more than a magnitude and enable to scan\nlarge sample areas.\n"}
+{"abstract": "  Low resolution fine-grained classification has widespread applicability for\napplications where data is captured at a distance such as surveillance and\nmobile photography. While fine-grained classification with high resolution\nimages has received significant attention, limited attention has been given to\nlow resolution images. These images suffer from the inherent challenge of\nlimited information content and the absence of fine details useful for\nsub-category classification. This results in low inter-class variations across\nsamples of visually similar classes. In order to address these challenges, this\nresearch proposes a novel attribute-assisted loss, which utilizes ancillary\ninformation to learn discriminative features for classification. The proposed\nloss function enables a model to learn class-specific discriminative features,\nwhile incorporating attribute-level separability. Evaluation is performed on\nmultiple datasets with different models, for four resolutions varying from\n32x32 to 224x224. Different experiments demonstrate the efficacy of the\nproposed attributeassisted loss for low resolution fine-grained classification.\n"}
+{"abstract": "  What is the power of constant-depth circuits with $MOD_m$ gates, that can\ncount modulo $m$? Can they efficiently compute MAJORITY and other symmetric\nfunctions? When $m$ is a constant prime power, the answer is well understood:\nRazborov and Smolensky proved in the 1980s that MAJORITY and $MOD_m$ require\nsuper-polynomial-size $MOD_q$ circuits, where $q$ is any prime power not\ndividing $m$. However, relatively little is known about the power of $MOD_m$\ncircuits for non-prime-power $m$. For example, it is still open whether every\nproblem in $EXP$ can be computed by depth-$3$ circuits of polynomial size and\nonly $MOD_6$ gates.\n  We shed some light on the difficulty of proving lower bounds for $MOD_m$\ncircuits, by giving new upper bounds. We construct $MOD_m$ circuits computing\nsymmetric functions with non-prime power $m$, with size-depth tradeoffs that\nbeat the longstanding lower bounds for $AC^0[m]$ circuits for prime power $m$.\nOur size-depth tradeoff circuits have essentially optimal dependence on $m$ and\n$d$ in the exponent, under a natural circuit complexity hypothesis.\n  For example, we show for every $\\varepsilon > 0$ that every symmetric\nfunction can be computed with depth-3 $MOD_m$ circuits of\n$\\exp(O(n^{\\varepsilon}))$ size, for a constant $m$ depending only on\n$\\varepsilon > 0$. That is, depth-$3$ $CC^0$ circuits can compute any symmetric\nfunction in \\emph{subexponential} size. This demonstrates a significant\ndifference in the power of depth-$3$ $CC^0$ circuits, compared to other models:\nfor certain symmetric functions, depth-$3$ $AC^0$ circuits require\n$2^{\\Omega(\\sqrt{n})}$ size [H{\\aa}stad 1986], and depth-$3$ $AC^0[p^k]$\ncircuits (for fixed prime power $p^k$) require $2^{\\Omega(n^{1/6})}$ size\n[Smolensky 1987]. Even for depth-two $MOD_p \\circ MOD_m$ circuits,\n$2^{\\Omega(n)}$ lower bounds were known [Barrington Straubing Th\\'erien 1990].\n"}
+{"abstract": "  We discuss stiffening of matter in quark-hadron continuity. We introduce a\nmodel that relates quark wave functions in a baryon and the occupation\nprobability of states for baryons and quarks in dense matter. In a dilute\nregime, the confined quarks contribute to the energy density through the masses\nof baryons, but do not directly contribute to the pressure; hence, the\nequations of state are very soft. This dilute regime continues until the low\nmomentum states for quarks get saturated; this may happen even before baryons\nfully overlap, possibly at density slightly above the nuclear saturation\ndensity. After the saturation the pressure grows rapidly while changes in\nenergy density are modest, producing a peak in the speed of sound. If we use\nbaryonic descriptions for quark distributions near the Fermi surface, we reach\na description similar to the quarkyonic matter model of McLerran and Reddy.\nWith a simple adjustment of quark interactions to get the nucleon mass, our\nmodel becomes consistent with the constraints from 1.4-solar mass neutron\nstars, but the high density part is too soft to account for two-solar mass\nneutron stars. We delineate the relation between the saturation effects and\nshort range interactions of quarks, suggesting interactions that leave low\ndensity equations of state unchanged but stiffen the high density part.\n"}
+{"abstract": "  The coherent superposition of non-orthogonal fermionic Gaussian states has\nbeen shown to be an efficient approximation to the ground states of quantum\nimpurity problems [Bravyi and Gosset,Comm. Math. Phys.,356 451 (2017)]. We\npresent a practical approach for performing a variational calculation based on\nsuch states. Our method is based on approximate imaginary-time equations of\nmotion that decouple the dynamics of each Gaussian state forming the ansatz. It\nis independent of the lattice connectivity of the model and the implementation\nis highly parallelizable. To benchmark our variational method, we calculate the\nspin-spin correlation function and R\\'enyi entanglement entropy of an Anderson\nimpurity, allowing us to identify the screening cloud and compare to density\nmatrix renormalization group calculations. Secondly, we study the screening\ncloud of the two-channel Kondo model, a problem difficult to tackle using\nexisting numerical tools.\n"}
+{"abstract": "  The production of polarized proton beams with multi-GeV energies in\nultra-intense laser interaction with targets is studied with three-dimensional\nParticle-In-Cell simulations. A near-critical density plasma target with\npre-polarized proton and tritium ions is considered for the proton\nacceleration. The pre-polarized protons are initially accelerated by laser\nradiation pressure before injection and further acceleration in a bubble-like\nwakefield. The temporal dynamics of proton polarization is tracked via the\nT-BMT equation, and it is found that the proton polarization state can be\naltered both by the laser field and the magnetic component of the wakefield.\nThe dependence of the proton acceleration and polarization on the ratio of the\nion species is determined, and it is found that the protons can be efficiently\naccelerated as long as their relative fraction is less than 20%, in which case\nthe bubble size is large enough for the protons to obtain sufficient energy to\novercome the bubble injection threshold.\n"}
+{"abstract": "  This work presents the derivation of a model for the heating process of the\nair of a glass dome, where an indoor swimming pool is located in the bottom of\nthe dome. The problem can be reduced from a three dimensional to a two\ndimensional one. The main goal is the formulation of a proper optimization\nproblem for computing the optimal heating of the air after a given time. For\nthat, the model of the heating process as a partial differential equation is\nformulated as well as the optimization problem subject to the time-dependent\npartial differential equation. This yields the optimal heating of the air under\nthe glass dome such that the desired temperature distribution is attained after\na given time. The discrete formulation of the optimization problem and a proper\nnumerical method for it, the projected gradient method, are discussed. Finally,\nnumerical experiments are presented which show the practical performance of the\noptimal control problem and its numerical solution method discussed.\n"}
+{"abstract": "  Constituted with a massive black hole and a stellar mass compact object,\nExtreme Mass Ratio Inspiral (EMRI) events hold unique opportunity for the study\nof massive black holes, such as by measuring and checking the relations among\nthe mass, spin and quadrupole moment of a massive black hole, putting the\nno-hair theorem to test. TianQin is a planned space-based gravitational wave\nobservatory and EMRI is one of its main types of sources. It is important to\nestimate the capacity of TianQin on testing the no-hair theorem with EMRIs. In\nthis work, we use the analytic kludge waveform with quadrupole moment\ncorrections and study how the quadrupole moment can be constrained with\nTianQin. We find that TianQin can measure the dimensionless quadrupole moment\nparameter with accuracy to the level of $10^{-5}$ under suitable scenarios. The\nchoice of the waveform cutoff is found to have significant effect on the\nresult: if the Schwarzschild cutoff is used, the accuracy depends strongly on\nthe mass of the massive black hole, while the spin has negligible impact; if\nthe Kerr cutoff is used, however, the dependence on the spin is more\nsignificant. We have also analyzed the cases when TianQin is observing\nsimultaneously with other detectors such as LISA.\n"}
+{"abstract": "  In this paper, we investigate the Dirac equation with the Killingbeck\npotential under the external magnetic field in non-commutative space.\nCorresponding to the expressions of the energy level and wave functions in spin\nsymmetry limit and pseudo-spin symmetry limit are derived by using the Bethe\nansatz method. The parameter B associated with the external magnetic field and\nnon-commutative parameter {\\theta} make to modify the energy level for\nconsidered systems.\n"}
+{"abstract": "  A 360{\\deg} perception of scene geometry is essential for automated driving,\nnotably for parking and urban driving scenarios. Typically, it is achieved\nusing surround-view fisheye cameras, focusing on the near-field area around the\nvehicle. The majority of current depth estimation approaches focus on employing\njust a single camera, which cannot be straightforwardly generalized to multiple\ncameras. The depth estimation model must be tested on a variety of cameras\nequipped to millions of cars with varying camera geometries. Even within a\nsingle car, intrinsics vary due to manufacturing tolerances. Deep learning\nmodels are sensitive to these changes, and it is practically infeasible to\ntrain and test on each camera variant. As a result, we present novel\ncamera-geometry adaptive multi-scale convolutions which utilize the camera\nparameters as a conditional input, enabling the model to generalize to\npreviously unseen fisheye cameras. Additionally, we improve the distance\nestimation by pairwise and patchwise vector-based self-attention encoder\nnetworks. We evaluate our approach on the Fisheye WoodScape surround-view\ndataset, significantly improving over previous approaches. We also show a\ngeneralization of our approach across different camera viewing angles and\nperform extensive experiments to support our contributions. To enable\ncomparison with other approaches, we evaluate the front camera data on the\nKITTI dataset (pinhole camera images) and achieve state-of-the-art performance\namong self-supervised monocular methods. An overview video with qualitative\nresults is provided at https://youtu.be/bmX0UcU9wtA. Baseline code and dataset\nwill be made public.\n"}
+{"abstract": "  We report on the data set, data handling, and detailed analysis techniques of\nthe first neutrino-mass measurement by the Karlsruhe Tritium Neutrino (KATRIN)\nexperiment, which probes the absolute neutrino-mass scale via the $\\beta$-decay\nkinematics of molecular tritium. The source is highly pure, cryogenic T$_2$\ngas. The $\\beta$ electrons are guided along magnetic field lines toward a\nhigh-resolution, integrating spectrometer for energy analysis. A silicon\ndetector counts $\\beta$ electrons above the energy threshold of the\nspectrometer, so that a scan of the thresholds produces a precise measurement\nof the high-energy spectral tail. After detailed theoretical studies,\nsimulations, and commissioning measurements, extending from the molecular\nfinal-state distribution to inelastic scattering in the source to subtleties of\nthe electromagnetic fields, our independent, blind analyses allow us to set an\nupper limit of 1.1 eV on the neutrino-mass scale at a 90\\% confidence level.\nThis first result, based on a few weeks of running at a reduced source\nintensity and dominated by statistical uncertainty, improves on prior limits by\nnearly a factor of two. This result establishes an analysis framework for\nfuture KATRIN measurements, and provides important input to both particle\ntheory and cosmology.\n"}
+{"abstract": "  Formed by using laser inter-satellite links (LISLs) among satellites in\nupcoming low Earth orbit and very low Earth orbit satellite constellations,\noptical wireless satellite networks (OWSNs), also known as free-space optical\nsatellite networks, can provide a better alternative to existing optical fiber\nterrestrial networks (OFTNs) for long-distance inter-continental data\ncommunications. The LISLs operate at the speed of light in vacuum in space,\nwhich gives OWSNs a crucial advantage over OFTNs in terms of latency. In this\npaper, we employ the satellite constellation for Phase I of Starlink and LISLs\nbetween satellites to simulate an OWSN. Then, we compare the network latency of\nthis OWSN and the OFTN under three different scenarios for long-distance\ninter-continental data communications. The results show that the OWSN performs\nbetter than the OFTN in all scenarios. It is observed that the longer the\nlength of the inter-continental connection between the source and the\ndestination, the better the latency improvement offered by the OWSN compared to\nOFTN.\n"}
+{"abstract": "  We propose a useful integral representation of the quenched free energy which\nis applicable to any random systems. Our formula involves the generating\nfunction of multi-boundary correlators, which can be interpreted on the bulk\ngravity side as spacetime D-branes introduced by Marolf and Maxfield in\n[arXiv:2002.08950]. As an example, we apply our formalism to the Airy limit of\nthe random matrix model and compute its quenched free energy under certain\napproximations of the generating function of correlators. It turns out that the\nresulting quenched free energy is a monotonically decreasing function of the\ntemperature, as expected.\n"}
+{"abstract": "  Let $\\phi:X\\rightarrow \\mathbb{P}^n$ be a morphism of varieties. Given a\nhyperplane $H$ in $\\mathbb{P}^n$, there is a Gysin map from the compactly\nsupported cohomology of $\\phi^{-1}(H)$ to that of $X$. We give conditions on\nthe degree of the cohomology under which this map is an isomorphism for all but\na low-dimensional set of hyperplanes, generalizing results due to Skorobogatov,\nBenoist, and Poonen-Slavov. Our argument is based on Beilinson's theory of\nsingular supports for \\'etale sheaves.\n"}
+{"abstract": "  We present FedScale, a diverse set of challenging and realistic benchmark\ndatasets to facilitate scalable, comprehensive, and reproducible federated\nlearning (FL) research. FedScale datasets are large-scale, encompassing a\ndiverse range of important FL tasks, such as image classification, object\ndetection, word prediction, and speech recognition. For each dataset, we\nprovide a unified evaluation protocol using realistic data splits and\nevaluation metrics. To meet the pressing need for reproducing realistic FL at\nscale, we have also built an efficient evaluation platform to simplify and\nstandardize the process of FL experimental setup and model evaluation. Our\nevaluation platform provides flexible APIs to implement new FL algorithms and\nincludes new execution backends with minimal developer efforts. Finally, we\nperform in-depth benchmark experiments on these datasets. Our experiments\nsuggest fruitful opportunities in heterogeneity-aware co-optimizations of the\nsystem and statistical efficiency under realistic FL characteristics. FedScale\nis open-source with permissive licenses and actively maintained, and we welcome\nfeedback and contributions from the community.\n"}
+{"abstract": "  State-of-the-art motor vehicles are able to break for pedestrians in an\nemergency. We investigate what it would take to issue an early warning to the\ndriver so he/she has time to react. We have identified that predicting the\nintention of a pedestrian reliably by position is a particularly hard\nchallenge. This paper describes an early pedestrian warning demonstration\nsystem.\n"}
+{"abstract": "  This paper concerns the verification of continuous-time polynomial spline\ntrajectories against linear temporal logic specifications (LTL without 'next').\nEach atomic proposition is assumed to represent a state space region described\nby a multivariate polynomial inequality. The proposed approach samples a\ntrajectory strategically, to capture every one of its region transitions. This\nyields a discrete word called a trace, which is amenable to established formal\nmethods for path checking. The original continuous-time trajectory is shown to\nsatisfy the specification if and only if its trace does. General topological\nconditions on the sample points are derived that ensure a trace is recorded for\narbitrary continuous paths, given arbitrary region descriptions. Using\ntechniques from computer algebra, a trace generation algorithm is developed to\nsatisfy these conditions when the path and region boundaries are defined by\npolynomials. The proposed PolyTrace algorithm has polynomial complexity in the\nnumber of atomic propositions, and is guaranteed to produce a trace of any\npolynomial path. Its performance is demonstrated via numerical examples and a\ncase study from robotics.\n"}
+{"abstract": "  As a part of science of science (SciSci) research, the evolution of\nscientific disciplines has been attracting a great deal of attention recently.\nThis kind of discipline level analysis not only give insights of one particular\nfield but also shed light on general principles of scientific enterprise. In\nthis paper we focus on graphene research, a fast growing field covers both\ntheoretical and applied study. Using co-clustering method, we split graphene\nliterature into two groups and confirm that one group is about theoretical\nresearch (T) and another corresponds to applied research (A). We analyze the\nproportion of T/A and found applied research becomes more and more popular\nafter 2007. Geographical analysis demonstrated that countries have different\npreference in terms of T/A and they reacted differently to research trend. The\ninteraction between two groups has been analyzed and shows that T extremely\nrelies on T and A heavily relies on A, however the situation is very stable for\nT but changed markedly for A. No geographic difference is found for the\ninteraction dynamics. Our results give a comprehensive picture of graphene\nresearch evolution and also provide a general framework which is able to\nanalyze other disciplines.\n"}
+{"abstract": "  Free-space optical communication is emerging as a low-power, low-cost, and\nhigh data rate alternative to radio-frequency communication in short-to\nmedium-range applications. However, it requires a close-to-line-of-sight link\nbetween the transmitter and the receiver. This paper proposes a robust $\\cHi$\ncontrol law for free-space optical (FSO) beam pointing error systems under\ncontrolled weak turbulence conditions. The objective is to maintain the\ntransmitter-receiver line, which means the center of the optical beam as close\nas possible to the center of the receiving aperture within a prescribed\ndisturbance attenuation level. First, we derive an augmented nonlinear\ndiscrete-time model for pointing error loss due to misalignment caused by weak\natmospheric turbulence. We then investigate the $\\cHi$-norm optimization\nproblem that guarantees the closed-loop pointing error is stable and ensures\nthe prescribed weak disturbance attenuation. Furthermore, we evaluate the\nclosed-loop outage probability error and bit error rate (BER) that quantify the\nfree-space optical communication performance in fading channels. Finally, the\npaper concludes with a numerical simulation of the proposed approach to the FSO\nlink's error performance.\n"}
+{"abstract": "  Fake news has now grown into a big problem for societies and also a major\nchallenge for people fighting disinformation. This phenomenon plagues\ndemocratic elections, reputations of individual persons or organizations, and\nhas negatively impacted citizens, (e.g., during the COVID-19 pandemic in the US\nor Brazil). Hence, developing effective tools to fight this phenomenon by\nemploying advanced Machine Learning (ML) methods poses a significant challenge.\nThe following paper displays the present body of knowledge on the application\nof such intelligent tools in the fight against disinformation. It starts by\nshowing the historical perspective and the current role of fake news in the\ninformation war. Proposed solutions based solely on the work of experts are\nanalysed and the most important directions of the application of intelligent\nsystems in the detection of misinformation sources are pointed out.\nAdditionally, the paper presents some useful resources (mainly datasets useful\nwhen assessing ML solutions for fake news detection) and provides a short\noverview of the most important R&D projects related to this subject. The main\npurpose of this work is to analyse the current state of knowledge in detecting\nfake news; on the one hand to show possible solutions, and on the other hand to\nidentify the main challenges and methodological gaps to motivate future\nresearch.\n"}
+{"abstract": "  We study adaptive methods for differentially private convex optimization,\nproposing and analyzing differentially private variants of a Stochastic\nGradient Descent (SGD) algorithm with adaptive stepsizes, as well as the\nAdaGrad algorithm. We provide upper bounds on the regret of both algorithms and\nshow that the bounds are (worst-case) optimal. As a consequence of our\ndevelopment, we show that our private versions of AdaGrad outperform adaptive\nSGD, which in turn outperforms traditional SGD in scenarios with non-isotropic\ngradients where (non-private) Adagrad provably outperforms SGD. The major\nchallenge is that the isotropic noise typically added for privacy dominates the\nsignal in gradient geometry for high-dimensional problems; approaches to this\nthat effectively optimize over lower-dimensional subspaces simply ignore the\nactual problems that varying gradient geometries introduce. In contrast, we\nstudy non-isotropic clipping and noise addition, developing a principled\ntheoretical approach; the consequent procedures also enjoy significantly\nstronger empirical performance than prior approaches.\n"}
+{"abstract": "  In this paper we show that wormholes in (2+1) dimensions (3-D) cannot be\nsourced solely by both Casimir energy and tension, differently from what\nhappens in a 4-D scenario, in which case it has been shown recently, by the\ndirect computation of the exact shape and redshift functions of a wormhole\nsolution, that this is possible. We show that in a 3-D spacetime the same is\nnot true since the arising of at least an event horizon is inevitable. We do\nthe analysis for massive and massless fermions, as well as for scalar fields,\nconsidering quasi-periodic boundary conditions and find that a possibility to\ncircumvent such a restriction is to introduce, besides the 3-D Casimir energy\ndensity and tension, a cosmological constant, embedding the surface in a 4-D\nmanifold and applying a perpendicular weak magnetic field. This causes an\nadditional tension on it, which contributes to the formation of the wormhole.\nFinally, we discuss the possibility of producing the condensed matter analogous\nof this wormhole in a graphene sheet and analyze the electronic transport\nthrough it.\n"}
+{"abstract": "  We propose the Automatic-differentiated Physics-Informed Echo State Network\n(API-ESN). The network is constrained by the physical equations through the\nreservoir's exact time-derivative, which is computed by automatic\ndifferentiation. As compared to the original Physics-Informed Echo State\nNetwork, the accuracy of the time-derivative is increased by up to seven orders\nof magnitude. This increased accuracy is key in chaotic dynamical systems,\nwhere errors grows exponentially in time. The network is showcased in the\nreconstruction of unmeasured (hidden) states of a chaotic system. The API-ESN\neliminates a source of error, which is present in existing physics-informed\necho state networks, in the computation of the time-derivative. This opens up\nnew possibilities for an accurate reconstruction of chaotic dynamical states.\n"}
+{"abstract": "  The aerodynamic performance of the high-lift configuration greatly influences\nthe safety and economy of commercial aircraft. Accurately predicting the\naerodynamic performance of the high-lift configuration, especially the stall\nbehavior, is important for aircraft design. However, the complex flow phenomena\nof high-lift configurations pose substantial difficulties to current turbulence\nmodels. In this paper, a three-equation k-(v^2)-{\\omega} turbulence model for\nthe Reynolds-averaged Navier-Stokes equations is used to compute the stall\nbehavior of high-lift configurations. A separated shear layer fixed function is\nimplemented in the turbulence model to better capture the nonequilibrium\ncharacteristics of turbulence. Different high-lift configurations, including\nthe two-dimensional multielement NLR7301 and Omar airfoils and a complex\nfull-configuration model (JAXA Standard Model), are numerically tested. The\nresults indicate that the effect of the nonequilibrium characteristics of\nturbulence is significant in the free shear layer, which is key to accurately\npredicting the stall behavior of high-lift devices. The modified SPF k-(v^2\n)-{\\omega} model is more accurate in predicting stall behavior than the\nSpalart-Allmaras, shear stress transport, and original k-(v^2)-{\\omega} models\nfor the full high-lift configuration. The relative errors in the predicted\nmaximum lift coefficients are within 3% of the experimental data.\n"}
+{"abstract": "  Aberration-corrected scanning electron microscopy (AC-STEM) can provide\nvaluable information on the atomic structure of nanoclusters, an essential\ninput for gaining an understanding of their physical and chemical properties. A\nsystematic method is presented here for the extraction of atom coordinates from\nan AC-STEM image in a way that is general enough to be applicable to irregular\nstructures. The two-dimensional information from the image is complemented with\nan approximate description of the atomic interactions so as to construct a\nthree-dimensional structure and, at a final stage, the structure is refined\nusing electron density functional theory (DFT) calculations. The method is\napplied to an AC-STEM image of Au55. Analysis of the local structure shows that\nthe cluster is a combination of a part with icosahedral structure elements and\na part with local atomic arrangement characteristic of crystal packing,\nincluding a segment of a flat surface facet. The energy landscape of the\ncluster is explored in calculations of minimum energy paths between the optimal\nfit structure and other candidates generated in the analysis. This reveals low\nenergy barriers for conformational changes, showing that such transitions can\noccur on laboratory timescale even at room temperature and lead to large\nchanges in the AC-STEM image. The paths furthermore reveal additional cluster\nconfigurations, some with lower DFT energy and providing nearly as good fit to\nthe experimental image.\n"}
+{"abstract": "  We present a method for exploring analogue Hawking radiation using a laser\npulse propagating through an underdense plasma. The propagating fields in the\nHawking effect are local perturbations of the plasma density and laser\namplitude. We derive the dependence of the resulting Hawking temperature on the\ndimensionless amplitude of the laser and the behaviour of the spot area of the\nlaser at the analogue event horizon. We demonstrate one possible way of\nobtaining the analogue Hawking temperature in terms of the plasma wavelength,\nand our analysis shows that for a high intensity near-IR laser the analogue\nHawking temperature is less than approximately 25K for a reasonable choice of\nparameters.\n"}
+{"abstract": "  Ionic polymer-metal composites consist in a thin film of electro-active\npolymers (Nafion R for example) sandwiched between two metallic electrodes.\nThey can be used as sensors or actuators. The polymer is saturated with water,\nwhich causes a complete dissociation and the release of small cations. The\nstrip undergoes large bending motions when it is submitted to an orthogonal\nelectric field and vice versa. We used a continuous medium approach and a\ncoarse grain model; the system is depicted as a deformable porous medium in\nwhich flows an ionic solution. We write microscale balance laws and\nthermodynamic relations for each phase, then for the complete material using an\naverage technique. Entropy production, then constitutive equations are deduced\n: a Kelvin-Voigt stress-strain relation, generalized Fourier's and Darcy's laws\nand a Nernst-Planck equation. We applied this model to a cantilever E.A.P.\nstrip undergoing a continuous potential difference (static case); a shear force\nmay be applied to the free end to prevent its displacement. Applied forces and\ndeflection are calculated using a beam model in large displacements. The\nresults obtained are in good agreement with the experimental data published in\nthe literature.\n"}
+{"abstract": "  Learning representation for source code is a foundation of many program\nanalysis tasks. In recent years, neural networks have already shown success in\nthis area, but most existing models did not make full use of the unique\nstructural information of programs. Although abstract syntax tree-based neural\nmodels can handle the tree structure in the source code, they cannot capture\nthe richness of different types of substructure in programs. In this paper, we\npropose a modular tree network (MTN) which dynamically composes different\nneural network units into tree structures based on the input abstract syntax\ntree. Different from previous tree-structural neural network models, MTN can\ncapture the semantic differences between types of ASTsubstructures. We evaluate\nour model on two tasks: program classification and code clone detection.\nOurmodel achieves the best performance compared with state-of-the-art\napproaches in both tasks, showing the advantage of leveraging more elaborate\nstructure information of the source code.\n"}
+{"abstract": "  Motivated by the recent surge of criminal activities with\ncross-cryptocurrency trades, we introduce a new topological perspective to\nstructural anomaly detection in dynamic multilayer networks. We postulate that\nanomalies in the underlying blockchain transaction graph that are composed of\nmultiple layers are likely to also be manifested in anomalous patterns of the\nnetwork shape properties. As such, we invoke the machinery of clique persistent\nhomology on graphs to systematically and efficiently track evolution of the\nnetwork shape and, as a result, to detect changes in the underlying network\ntopology and geometry. We develop a new persistence summary for multilayer\nnetworks, called stacked persistence diagram, and prove its stability under\ninput data perturbations. We validate our new topological anomaly detection\nframework in application to dynamic multilayer networks from the Ethereum\nBlockchain and the Ripple Credit Network, and demonstrate that our stacked PD\napproach substantially outperforms state-of-art techniques.\n"}
+{"abstract": "  Consider a deterministically growing surface of any dimension, where the\ngrowth at a point is an arbitrary nonlinear function of the heights at that\npoint and its neighboring points. Assuming that this nonlinear function is\nmonotone, invariant under the symmetries of the lattice, equivariant under\nconstant shifts, and twice continuously differentiable, it is shown that any\nsuch growing surface approaches a solution of the deterministic KPZ equation in\na suitable space-time scaling limit.\n"}
+{"abstract": "  Extracting the interaction rules of biological agents from movement sequences\npose challenges in various domains. Granger causality is a practical framework\nfor analyzing the interactions from observed time-series data; however, this\nframework ignores the structures and assumptions of the generative process in\nanimal behaviors, which may lead to interpretational problems and sometimes\nerroneous assessments of causality. In this paper, we propose a new framework\nfor learning Granger causality from multi-animal trajectories via augmented\ntheory-based behavioral models with interpretable data-driven models. We adopt\nan approach for augmenting incomplete multi-agent behavioral models described\nby time-varying dynamical systems with neural networks. For efficient and\ninterpretable learning, our model leverages theory-based architectures\nseparating navigation and motion processes, and the theory-guided\nregularization for reliable behavioral modeling. This can provide interpretable\nsigns of Granger-causal effects over time, i.e., when specific others cause the\napproach or separation. In experiments using synthetic datasets, our method\nachieved better performance than various baselines. We then analyzed\nmulti-animal datasets of mice, flies, birds, and bats, which verified our\nmethod and obtained novel biological insights.\n"}
+{"abstract": "  While attention-based encoder-decoder (AED) models have been successfully\nextended to the online variants for streaming automatic speech recognition\n(ASR), such as monotonic chunkwise attention (MoChA), the models still have a\nlarge label emission latency because of the unconstrained end-to-end training\nobjective. Previous works tackled this problem by leveraging alignment\ninformation to control the timing to emit tokens during training. In this work,\nwe propose a simple alignment-free regularization method, StableEmit, to\nencourage MoChA to emit tokens earlier. StableEmit discounts the selection\nprobabilities in hard monotonic attention for token boundary detection by a\nconstant factor and regularizes them to recover the total attention mass during\ntraining. As a result, the scale of the selection probabilities is increased,\nand the values can reach a threshold for token emission earlier, leading to a\nreduction of emission latency and deletion errors. Moreover, StableEmit can be\ncombined with methods that constraint alignments to further improve the\naccuracy and latency. Experimental evaluations with LSTM and Conformer encoders\ndemonstrate that StableEmit significantly reduces the recognition errors and\nthe emission latency simultaneously. We also show that the use of alignment\ninformation is complementary in both metrics.\n"}
+{"abstract": "  This paper describes a test and case study of self-evaluation of online\ncourses during the pandemic time. Due to the Covid-19, the whole world needs to\nsit on lockdown in different periods. Many things need to be done in all kinds\nof business including the education sector of countries. To sustain the\neducation development teaching methods had to switch from traditional\nface-to-face teaching to online courses. The government made decisions in a\nshort time and educational institutions had no time to prepare the materials\nfor the online teaching. All courses of the Mongolian University of\nPharmaceutical Sciences switched to online lessons. Challenges were raised\nbefore professors and tutors during online teaching. Our university did not\nhave a specific learning management system for online teaching and e-learning.\nTherefore professors used different platforms for their online teaching such as\nZoom, Microsoft teams for instance. Moreover, different social networking\nplatforms played an active role in communication between students and\nprofessors. The situation is very difficult for professors and students. To\nmeasure the quality of online courses and to figure out the positive and weak\npoints of online teaching we need an evaluation of e-learning. The focus of\nthis paper is to share the evaluation process of e-learning based on a\nstructure-oriented evaluation model.\n"}
+{"abstract": "  This report contains the description of two novel job shop scheduling\nbenchmarks that resemble instances of real scheduling problem as they appear in\nindustry. In particular, the aim was to provide large-scale benchmarks (up to 1\nmillion operations) to test the state-of-the-art scheduling solutions on\nproblems that are closer to what occurs in a real industrial context. The first\nbenchmark is an extension of the well known Taillard benchmark (1992), while\nthe second is a collection of scheduling instances with a known-optimum\nsolution.\n"}
+{"abstract": "  Sentiment analysis can provide a suitable lead for the tools used in software\nengineering along with the API recommendation systems and relevant libraries to\nbe used. In this context, the existing tools like SentiCR, SentiStrength-SE,\netc. exhibited low f1-scores that completely defeats the purpose of deployment\nof such strategies, thereby there is enough scope for performance improvement.\nRecent advancements show that transformer based pre-trained models (e.g., BERT,\nRoBERTa, ALBERT, etc.) have displayed better results in the text classification\ntask. Following this context, the present research explores different\nBERT-based models to analyze the sentences in GitHub comments, Jira comments,\nand Stack Overflow posts. The paper presents three different strategies to\nanalyse BERT based model for sentiment analysis, where in the first strategy\nthe BERT based pre-trained models are fine-tuned; in the second strategy an\nensemble model is developed from BERT variants, and in the third strategy a\ncompressed model (Distil BERT) is used. The experimental results show that the\nBERT based ensemble approach and the compressed BERT model attain improvements\nby 6-12% over prevailing tools for the F1 measure on all three datasets.\n"}
+{"abstract": "  Neural networks notoriously suffer from the problem of catastrophic\nforgetting, the phenomenon of forgetting the past knowledge when acquiring new\nknowledge. Overcoming catastrophic forgetting is of significant importance to\nemulate the process of \"incremental learning\", where the model is capable of\nlearning from sequential experience in an efficient and robust way.\nState-of-the-art techniques for incremental learning make use of knowledge\ndistillation towards preventing catastrophic forgetting. Therein, one updates\nthe network while ensuring that the network's responses to previously seen\nconcepts remain stable throughout updates. This in practice is done by\nminimizing the dissimilarity between current and previous responses of the\nnetwork one way or another. Our work contributes a novel method to the arsenal\nof distillation techniques. In contrast to the previous state of the art, we\npropose to firstly construct low-dimensional manifolds for previous and current\nresponses and minimize the dissimilarity between the responses along the\ngeodesic connecting the manifolds. This induces a more formidable knowledge\ndistillation with smooth properties which preserves the past knowledge more\nefficiently as observed by our comprehensive empirical study.\n"}
+{"abstract": "  One of the problems of conventional visual quality evaluation criteria such\nas PSNR and MSE is the lack of appropriate standards based on the human visual\nsystem (HVS). They are calculated based on the difference of the corresponding\npixels in the original and manipulated image. Hence, they practically do not\nprovide a correct understanding of the image quality. Watermarking is an image\nprocessing application in which the image's visual quality is an essential\ncriterion for its evaluation. Watermarking requires a criterion based on the\nHVS that provides more accurate values than conventional measures such as PSNR.\nThis paper proposes a weighted fuzzy-based criterion that tries to find\nessential parts of an image based on the HVS. Then these parts will have larger\nweights in computing the final value of PSNR. We compare our results against\nstandard PSNR, and our experiments show considerable consequences.\n"}
+{"abstract": "  Hermite best approximation vectors of a real number $\\theta$ were introduced\nby Lagarias. A nonzero vector (p, q) $\\in$ Z x N is a Hermite best\napproximation vector of $\\theta$ if there exists $\\Delta$ > 0 such that (p --\nq$\\theta$) 2 + q 2 /$\\Delta$ $\\le$ (a -- b$\\theta$) 2 + b 2 /$\\Delta$ for all\nnonzero (a, b) $\\in$ Z 2. Hermite observed that if q > 0 then the fraction p/q\nmust be a convergent of the continued fraction expansion of $\\theta$ and\nLagarias pointed out that some convergents are not associated with a Hermite\nbest approximation vectors. In this note we show that the almost sure\nproportion of Hermite best approximation vectors among convergents is ln 3/ ln\n4. The main tool of the proof is the natural extension of the Gauss map x\n$\\in$]0, 1[$\\rightarrow$ {1/x}.\n"}
+{"abstract": "  To make off-screen interaction without specialized hardware practical, we\ninvestigate using deep learning methods to process the common built-in IMU\nsensor (accelerometers and gyroscopes) on mobile phones into a useful set of\none-handed interaction events. We present the design, training, implementation\nand applications of TapNet, a multi-task network that detects tapping on the\nsmartphone. With phone form factor as auxiliary information, TapNet can jointly\nlearn from data across devices and simultaneously recognize multiple tap\nproperties, including tap direction and tap location. We developed two datasets\nconsisting of over 135K training samples, 38K testing samples, and 32\nparticipants in total. Experimental evaluation demonstrated the effectiveness\nof the TapNet design and its significant improvement over the state of the art.\nAlong with the datasets,\n(https://sites.google.com/site/michaelxlhuang/datasets/tapnet-dataset), and\nextensive experiments, TapNet establishes a new technical foundation for\noff-screen mobile input.\n"}
+{"abstract": "  In this report we are aiming at introducing a global measure of\nnon-classicality of the state space of $N$-level quantum systems and estimating\nit in the limit of large $N$. For this purpose we employ the Wigner function\nnegativity as a non-classicality criteria. Thus, the specific volume of the\nsupport of negative values of Wigner function is treated as a measure of\nnon-classicality of an individual state. Assuming that the states of an\n$N$-level quantum system are distributed by Hilbert-Schmidt measure\n(Hilbert-Schmidt ensemble), we define the global measure as the average\nnon-classicality of the individual states over the Hilbert-Schmidt ensemble. We\npresent the numerical estimate of this quantity as a result of random\ngeneration of states, and prove a proposition claiming its exact value in the\nlimit of $N\\to \\infty$\n"}
+{"abstract": "  While language identification is a fundamental speech and language processing\ntask, for many languages and language families it remains a challenging task.\nFor many low-resource and endangered languages this is in part due to resource\navailability: where larger datasets exist, they may be single-speaker or have\ndifferent domains than desired application scenarios, demanding a need for\ndomain and speaker-invariant language identification systems. This year's\nshared task on robust spoken language identification sought to investigate just\nthis scenario: systems were to be trained on largely single-speaker speech from\none domain, but evaluated on data in other domains recorded from speakers under\ndifferent recording circumstances, mimicking realistic low-resource scenarios.\nWe see that domain and speaker mismatch proves very challenging for current\nmethods which can perform above 95% accuracy in-domain, which domain adaptation\ncan address to some degree, but that these conditions merit further\ninvestigation to make spoken language identification accessible in many\nscenarios.\n"}
+{"abstract": "  We establish an uncountable amenable ergodic Roth theorem, in which the\nacting group is not assumed to be countable and the space need not be\nseparable. This extends a previous result of Bergelson, McCutcheon and Zhang.\nUsing this uncountable Roth theorem, we establish the following two additional\nresults.\n  [(i)] We establish a combinatorial application about triangular patterns in\ncertain subsets of the Cartesian square of arbitrary amenable groups, extending\na result of Bergelson, McCutcheon and Zhang for countable amenable groups.\n  [(ii)] We establish a uniform bound on the lower Banach density of the set of\ndouble recurrence times along all $\\Gamma$-systems, where $\\Gamma$ is any group\nin a class of uniformly amenable groups. As a special case, we obtain this\nuniformity over all $\\mathbb{Z}$-systems, and our result seems to be novel\nalready in this particular case.\n  Our uncountable Roth theorem is crucial in the proof of both of these\nresults.\n"}
+{"abstract": "  We consider a class of anisotropic spin-$\\frac{1}{2}$ models with competing\nferro- and antiferromagnetic interactions on two-dimensional Tasaki and kagome\nlattices consisting of corner sharing triangles. For certain values of the\ninteractions the ground state is macroscopically degenerated in zero magnetic\nfield. In this case the ground state manifold consists of isolated magnons as\nwell as the bound magnon complexes. The ground state degeneracy is estimated\nusing a special form of exact wave function which admits arrow configuration\nrepresentation on two-dimensional lattice. The comparison of this estimate with\nthe result for some special exactly solved models shows that the used approach\ndetermines the number of the ground states with exponential accuracy. It is\nshown that the main contribution to the ground state degeneracy and the\nresidual entropy is given by the bound magnon complexes.\n"}
+{"abstract": "  We introduce a method where particle physics processes in cosmology may be\ncalculated by the usual perturbative flat space quantum field theory through an\neffective Minkowski space description at small time intervals provided that the\nrunning of the effective particle masses are sufficiently slow. We discuss the\nnecessary conditions for the applicability of this method and illustrate the\nmethod through a simple example. This method has the advantage of avoiding the\neffects of gravitational particle creation in the calculation of rates and\ncross sections i.e. giving directly the rates and the cross sections due to the\nscatterings or the decay processes.\n"}
+{"abstract": "  Complex linear differential equations with entire coefficients are studied in\nthe situation where one of the coefficients is an exponential polynomial and\ndominates the growth of all the other coefficients. If such an equation has an\nexponential polynomial solution $f$, then the order of $f$ and of the dominant\ncoefficient are equal, and the two functions possess a certain duality\nproperty. The results presented in this paper improve earlier results by some\nof the present authors, and the paper adjoins with two open problems.\n"}
+{"abstract": "  In this article, we study the logarithm of the central value\n$L\\left(\\frac{1}{2}, \\chi_D\\right)$ in the symplectic family of Dirichlet\n$L$-functions associated with the hyperelliptic curve of genus $\\delta$ over a\nfixed finite field $\\mathbb{F}_q$ in the limit as $\\delta\\to \\infty$.\nUnconditionally, we show that the distribution of $\\log\n\\big|L\\left(\\frac{1}{2}, \\chi_D\\right)\\big|$ is asymptotically bounded above by\nthe Gaussian distribution of mean $\\frac{1}{2}\\log \\deg(D)$ and variance $\\log\n\\deg(D)$. Assuming a mild condition on the distribution of the low-lying zeros\nin this family, we obtain the full Gaussian distribution.\n"}
+{"abstract": "  In this article, we obtain a complete list of inequivalent irreducible\nrepresentations of the compact quantum group $U_q(2)$ for non-zero complex\ndeformation parameters $q$, which are not roots of unity. The matrix\ncoefficients of these representations are described in terms of the little\n$q$-Jacobi polynomials. The Haar state is shown to be faithful and an\northonormal basis of $L^2(U_q(2))$ is obtained. Thus, we have an explicit\ndescription of the Peter-Weyl decomposition of $U_q(2)$. As an application, we\ndiscuss the Fourier transform and establish the Plancherel formula. We also\ndescribe the decomposition of the tensor product of two irreducible\nrepresentations into irreducible components. Finally, we classify the compact\nquantum groups $U_q(2)$.\n"}
+{"abstract": "  In this work, the $\\overline{\\partial}$ steepest descent method is employed\nto investigate the soliton resolution for the Hirota equation with the initial\nvalue belong to weighted Sobolev space $H^{1,1}(\\mathbb{R})=\\{f\\in\nL^{2}(\\mathbb{R}): f',xf\\in L^{2}(\\mathbb{R})\\}$. The long-time asymptotic\nbehavior of the solution $q(x,t)$ is derived in any fixed space-time cone\n$C(x_{1},x_{2},v_{1},v_{2})=\\left\\{(x,t)\\in \\mathbb{R}\\times\\mathbb{R}:\nx=x_{0}+vt ~\\text{with}~ x_{0}\\in[x_{1},x_{2}]\\right\\}$. We show that solution\nresolution conjecture of the Hirota equation is characterized by the leading\norder term $\\mathcal {O}(t^{-1/2})$ in the continuous spectrum, $\\mathcal\n{N}(\\mathcal {I})$ soliton solutions in the discrete spectrum and error order\n$\\mathcal {O}(t^{-3/4})$ from the $\\overline{\\partial}$ equation.\n"}
+{"abstract": "  We present the first 3D radiation-hydrodynamic simulations on the formation\nand evolution of born-again planetary nebulae (PNe), with particular emphasis\nto the case of HuBi1, the inside-out PN. We use the extensively-tested GUACHO\ncode to simulate the formation of HuBi1 adopting mass-loss and stellar wind\nterminal velocity estimates obtained from observations presented by our group.\nWe found that, if the inner shell of HuBi1 was formed by an explosive very late\nthermal pulse (VLTP) ejecting material with velocities of $\\sim$300 km\ns$^{-1}$, the age of this structure is consistent with that of $\\simeq$200 yr\nderived from multi-epoch narrow-band imaging. Our simulations predict that, as\na consequence of the dramatic reduction of the stellar wind velocity and photon\nionizing flux during the VLTP, the velocity and pressure structure of the outer\nH-rich nebula are affected creating turbulent ionized structures surrounding\nthe inner shell. These are indeed detected in Gran Telescopio Canarias MEGARA\noptical observations. Furthermore, we demonstrate that the current relatively\nlow ionizing photon flux from the central star of HuBi1 is not able to\ncompletely ionize the inner shell, which favors previous suggestions that its\nexcitation is dominated by shocks. Our simulations suggest that the kinetic\nenergy of the H-poor ejecta of HuBi1 is at least 30 times that of the clumps\nand filaments in the evolved born-again PNe A30 and A78, making it a truly\nunique VLTP event.\n"}
+{"abstract": "  Here we study the effect of an additional interfacial spin-transfer torque,\nas well as the well established spin-orbit torque and bulk spin-transfer\ntorque, on skyrmion collections - group of skyrmions dense enough that they are\nnot isolated from on another - in ultrathin heavy metal / ferromagnetic\nmultilayers, by comparing modelling with experimental results. Using a skyrmion\ncollection with a range of skyrmion diameters and landscape disorder, we study\nthe dependence of the skyrmion Hall angle on diameter and velocity, as well as\nthe velocity as a function of diameter. We show the experimental results are in\ngood agreement with modelling when including the interfacial spin-transfer\ntorque, and cannot be reproduced by using the spin-orbit torque alone. We also\nshow that for skyrmion collections the velocity is approximately independent of\ndiameter, in marked contrast to the motion of isolated skyrmions, as the group\nof skyrmions move together at an average group velocity. Moreover, the\ncalculated skyrmion velocities are comparable to those obtained in experiments\nwhen the interfacial spin-transfer torque in included, whilst modelling using\nthe spin-orbit torque alone shows large discrepancies with the experimental\ndata. Our results thus show the significance of the interfacial spin-transfer\ntorque in ultrathin magnetic multilayers, which is of similar strength to the\nspin-orbit torque, and both significantly larger than the bulk spin-transfer\ntorque. Due to the good agreement with experiments, we conclude that the\ninterfacial spin-transfer torque should be included in numerical modelling for\ncorrect reproduction of experimental results.\n"}
+{"abstract": "  A method is demonstrated to optimize a stellarator's geometry to eliminate\nmagnetic islands and achieve other desired physics properties at the same time.\nFor many physics quantities that have been used in stellarator optimization,\nincluding quasisymmetry, neoclassical transport, and magnetohydrodynamic\nstability, it is convenient to use a magnetic equilibrium representation that\nassures the existence of magnetic surfaces. However, this representation hides\nthe possible presence of magnetic islands, which are typically undesirable. To\ninclude both surface-based objectives and island widths in a single\noptimization, two fixed-boundary equilibrium calculations are run at each\niteration of the optimization: one that enforces the existence of magnetic\nsurfaces (VMEC [S. P. Hirshman and J. C. Whitson, Phys. Fluids 26, 3553\n(1983)]), and one that does not (SPEC [S. R. Hudson, et al, Phys. Plasmas 19,\n112502 (2012)]). By penalizing the island residues in the objective function,\nthe two magnetic field representations are brought into agreement during the\noptimization. An example is presented in which, particularly on the surface\nwhere quasisymmetry was targeted, quasisymmetry is achieved more accurately\nthan in previously published examples.\n"}
+{"abstract": "  This paper discusses relational operations in the first-order logical\nenvironment {FOLE}. Here we demonstrate how FOLE expresses the relational\noperations of database theory in a clear and implementable representation. An\nanalysis of the representation of database tables/relations in FOLE reveals a\nprincipled way to express the relational operations. This representation is\nexpressed in terms of a distinction between basic components versus composite\nrelational operations. The 9 basic components fall into three categories:\nreflection (2), Booleans or basic operations (3), and adjoint flow (4). Adjoint\nflow is given for signatures (2) and for type domains (2), which are then\ncombined into full adjoint flow. The basic components are used to express\nvarious composite operations, where we illustrate each of these with a\nflowchart. Implementation of the composite operations is then expressed in an\ninput/output table containing four parts: constraint, construction, input, and\noutput. We explain how limits and colimits are constructed from diagrams of\ntables, and then classify composite relational operations into three\ncategories: limit-like, colimit-like and unorthodox.\n"}
+{"abstract": "  In this paper, the generation of magnetic fields in a nonuniformly rotating\nlayer of finite thickness of an electrically conducting fluid by thermomagnetic\n(TM) instability. This instability arises due to the temperature gradient\n$\\nabla T_0$ and thermoelectromotive coefficient gradient $\\nabla\\alpha $. The\ninfluence of the generation of a toroidal magnetic field by TM instability on\nconvective instability in a nonuniformly rotating layer of an electrically\nconductive fluid in the presence of a vertical constant magnetic field\n${\\bf{B}}_0 \\| {\\rm OZ}$ is established. As a result of applying the method of\nperturbation theory for the small parameter $ \\epsilon = \\sqrt {(\\textrm\n{Ra}-\\textrm {Ra}_c) / \\textrm {Ra}_c} $ of supercriticality of the stationary\nRayleigh number $\\textrm {Ra}_c$ a nonlinear equation of the Ginzburg-Landau\ntype was obtained. This equation describes the evolution of the finite\namplitude of perturbations. Numerical solutions of this equation made it\npossible to determine the heat transfer in the fluid layer with and without TM\neffects. It is shown that the amplitude of the stationary toroidal magnetic\nfield noticeably increases with allowance for TM effects.\n"}
+{"abstract": "  Head and Neck Squamous Cell Carcinoma (HNSCC) is one of cancer type that is\nmost distressing leading to acute pain, effecting speech and primary survival\nfunctions such as swallowing and breathing. The morbidity and mortality of\nHNSCC patients have not significantly improved even tough there has been\nadvancement in surgical and radiotherapy treatments. The high mortality may be\nattributed to the complexity and significant changes in the clinical outcomes.\nTherefore, it is important to increase the accuracy of predicting the outcome\nof cancer survival. Few cancer survival prediction models of HNSCC have been\nproposed so far. In this study, genomic data (whole exome sequencing) are\nintegrated with clinical data to improve the performance of prediction model.\nThe somatic mutations of every patient is processed using Multifractal\nDeterended Fluctuation Analysis (MFDFA) algorithm and the parameter values of\nFractal Dimension (Dq) is included along with clinical data for cancer survival\nprediction. Feature ranking proves that the new engineered feature is one of\nthe important feature in prediction model. In order to improve the performance\nindex of models, hyperparameters were also tuned in all the classifiers\nconsidered. 10-Fold cross validation is implemented and XGBoost (98% AUROC, 94%\nprecision, and 93% recall) proves to be best model classifier followed by\nRandom Forest 93% AUROC, 93% precision, and 93% recall), Support Vector Machine\n(84% AUCROC, 79% precision, and 79% recall) and Logistic Regression (80% AUROC,\n77% precision, and 76% recall).\n"}
+{"abstract": "  In this paper, the formation of primordial black holes (PBHs) is\nreinvestigated using inflationary $\\alpha$-attractors. Instead of using the\nconventional Press-Schechter theory to compute the abundance, the optimized\npeaks theory is used, which was developed in Ref.\n\\cite{Yoo:2018kvb,Yoo:2020dkz}. This method takes into account how curvature\nperturbations play a r\\^{o}le in modifying the mass of primordial black holes.\nAnalyzing the model proposed in \\cite{Mahbub:2019uhl} it is seen that the\nhorizon mass of the collapsed Hubble patch is larger by $\\mathcal{O}(10)$\ncompared to the usual computation. Moreover, PBHs can be formed from curvature\npower spectrum, $\\mathcal{P}_{\\zeta}(k)$, peaked at lower values using\nnumerically favored threshold overdensities. As a result of the generally\nlarger masses predicted, the peak of the power spectrum can be placed at larger\n$k$ modes than that is typical with which potential future constraints on the\nprimordial power spectrum through gravitational waves (GWs) can be evaded.\n"}
+{"abstract": "  The Einstein field equations for a class of irrotational non-orthogonally\ntransitive $G_{2}$ cosmologies are written down as a system of partial\ndifferential equations. The equilibrium points are self-similar and can be\nwritten as a one-parameter, five-dimensional, ordinary differential equation.\nThe corresponding cosmological models both evolve and have one-dimension of\ninhomogeneity. The major mathematical features of this ordinary differential\nequation are derived, and a cosmological interpretation is given. The\nrelationship to the exceptional Bianchi models is explained and exploited to\nprovide a conjecture about future generalizations.\n"}
+{"abstract": "  Polarimetric observations of Fast Radio Bursts (FRBs) are a powerful resource\nfor better understanding these mysterious sources by directly probing the\nemission mechanism of the source and the magneto-ionic properties of its\nenvironment. We present a pipeline for analysing the polarized signal of FRBs\ncaptured by the triggered baseband recording system operating on the FRB survey\nof The Canadian Hydrogen Intensity Mapping Experiment (CHIME/FRB). Using a\ncombination of simulated and real FRB events, we summarize the main features of\nthe pipeline and highlight the dominant systematics affecting the polarized\nsignal. We compare parametric (QU-fitting) and non-parametric (rotation measure\nsynthesis) methods for determining the Faraday rotation measure (RM) and find\nthe latter method susceptible to systematic errors from known instrumental\neffects of CHIME/FRB observations. These errors include a leakage artefact that\nappears as polarized signal near $\\rm{RM\\sim 0 \\; rad \\, m^{-2}}$ and an RM\nsign ambiguity introduced by path length differences in the system's\nelectronics. We apply the pipeline to a bright burst previously reported by\n\\citet[FRB 20191219F;][]{Leung2021}, detecting an $\\mathrm{RM}$ of $\\rm{+6.074\n\\pm 0.006 \\pm 0.050 \\; rad \\, m^{-2}}$ with a significant linear polarized\nfraction ($\\gtrsim0.87$) and strong evidence for a non-negligible circularly\npolarized component. Finally, we introduce an RM search method that employs a\nphase-coherent de-rotation algorithm to correct for intra-channel\ndepolarization in data that retain electric field phase information, and\nsuccessfully apply it to an unpublished FRB, FRB 20200917A, measuring an\n$\\mathrm{RM}$ of $\\rm{-1294.47 \\pm 0.10 \\pm 0.05 \\; rad \\, m^{-2}}$ (the second\nlargest unambiguous RM detection from any FRB source observed to date).\n"}
+{"abstract": "  We propose a projected Wasserstein gradient descent method (pWGD) for\nhigh-dimensional Bayesian inference problems. The underlying density function\nof a particle system of WGD is approximated by kernel density estimation (KDE),\nwhich faces the long-standing curse of dimensionality. We overcome this\nchallenge by exploiting the intrinsic low-rank structure in the difference\nbetween the posterior and prior distributions. The parameters are projected\ninto a low-dimensional subspace to alleviate the approximation error of KDE in\nhigh dimensions. We formulate a projected Wasserstein gradient flow and analyze\nits convergence property under mild assumptions. Several numerical experiments\nillustrate the accuracy, convergence, and complexity scalability of pWGD with\nrespect to parameter dimension, sample size, and processor cores.\n"}
+{"abstract": "  In this paper, we investigate the computational intelligibility of Boolean\nclassifiers, characterized by their ability to answer XAI queries in polynomial\ntime. The classifiers under consideration are decision trees, DNF formulae,\ndecision lists, decision rules, tree ensembles, and Boolean neural nets. Using\n9 XAI queries, including both explanation queries and verification queries, we\nshow the existence of large intelligibility gap between the families of\nclassifiers. On the one hand, all the 9 XAI queries are tractable for decision\ntrees. On the other hand, none of them is tractable for DNF formulae, decision\nlists, random forests, boosted decision trees, Boolean multilayer perceptrons,\nand binarized neural networks.\n"}
+{"abstract": "  An approach that extends equilibrium thermodynamics principles to\nout-of-equilibrium systems is based on the local equilibrium hypothesis.\nHowever, the validity of the a priori assumption of local equilibrium has been\nquestioned due to the lack of sufficient experimental evidence. In this paper,\nwe present experimental results obtained from a pure thermodynamic study of the\nnon-turbulent Rayleigh-B\\'enard convection at steady-state to verify the\nvalidity of the local equilibrium hypothesis. A non-turbulent Rayleigh-B\\'enard\nconvection at steady-state is an excellent `model thermodynamic system' in\nwhich local measurements provide no insights about the spatial heterogeneity\npresent in the macroscopic thermodynamic landscape. Indeed, the onset of\nconvection leads to the emergence of spatially stable hot and cold domains. Our\nresults indicate that these domains while break spatial symmetry\nmacroscopically, preserves it locally that exhibit room temperature\nequilibrium-like statistics. Furthermore, the role of the emergent heat flux is\ninvestigated and a linear relationship is observed between the heat flux and\nthe external driving force following the onset of thermal convection. Finally,\ntheoretical and conceptual implications of these results are discussed which\nopens up new avenues in the study non-equilibrium steady-states, especially in\ncomplex, soft, and active-matter systems.\n"}
+{"abstract": "  Graphical languages are symmetric monoidal categories presented by generators\nand equations. The string diagrams notation allows to transform numerous axioms\ninto low dimension topological rules we are comfortable with as three\ndimensional space citizens. This aspect is often referred to by the Only\nTopology Matters paradigm (OTM). However OTM remains quite informal and its\nexact meaning in terms of rewriting rules is ambiguous. In this paper we define\nthree precise aspects of the OTM paradigm, namely flexsymmetry, flexcyclicity\nand flexibility of Frobenius algebras. We investigate how this new framework\ncan simplify the presentation of known graphical languages based on Frobenius\nalgebras.\n"}
+{"abstract": "  We show that there exist K\\\"ahler-Einstein metrics on two exceptional\nPasquier's two-orbits varieties. As an application, we will provide a new\nexample of K-unstable Fano manifold with Picard number one.\n"}
+{"abstract": "  In this letter, we investigate the changes in the quantum vacuum energy\ndensity of a massless scalar field inside a Casimir cavity that orbits a\nwormhole, by considering the cosmological model with an isotropic form of the\nMorris-Thorne wormhole, embedded in the FLRW universe. In this sense, we\nexamine the effects of its global curvature and scale factor in an instant of\nthe cosmic history, besides the influences of the local geometry as well as of\ninertial forces, on the Casimirenergy density. We also study the behavior of\nthis quantity when each plate is fixed without rotation at the opposite sides\nof the wormhole throat, at zero and finite temperatures, taking into account\nthe effective distance between the plates through the wormhole throat.\n"}
+{"abstract": "  Determination of the neutrino mass ordering (NMO) is one of the biggest\npriorities in the intensity frontier of high energy particle physics. To\naccomplish that goal a lot of efforts are being put together with the\natmospheric, solar, reactor, and accelerator neutrinos. In the standard\n3-flavor framework, NMO is defined to be normal if $m_1<m_2<m_3$, and inverted\nif $m_3<m_1<m_2$, where $m_1$, $m_2$, and $m_3$ are the masses of the three\nneutrino mass eigenstates $\\nu_1$, $\\nu_2$, and $\\nu_3$ respectively.\nInterestingly, two long-baseline experiments T2K and NO$\\nu$A are playing a\nleading role in this direction and provide a $\\sim2.4\\sigma$ indication in\nfavor of normal ordering (NO) which we find in this work. In addition, we\nexamine how the situation looks like in presence of non-standard interactions\n(NSI) of neutrinos with a special focus on the non-diagonal flavor changing\ntype $\\varepsilon_{e\\tau}$ and $\\varepsilon_{e\\mu}$. We find that the present\nindication of NO in the standard 3-flavor framework gets completely vanished in\nthe presence of NSI of the flavor changing type involving the $e-\\tau$ flavors.\n"}
+{"abstract": "  The current Siamese network based on region proposal network (RPN) has\nattracted great attention in visual tracking due to its excellent accuracy and\nhigh efficiency. However, the design of the RPN involves the selection of the\nnumber, scale, and aspect ratios of anchor boxes, which will affect the\napplicability and convenience of the model. Furthermore, these anchor boxes\nrequire complicated calculations, such as calculating their\nintersection-over-union (IoU) with ground truth bounding boxes.Due to the\nproblems related to anchor boxes, we propose a simple yet effective anchor-free\ntracker (named Siamese corner networks, SiamCorners), which is end-to-end\ntrained offline on large-scale image pairs. Specifically, we introduce a\nmodified corner pooling layer to convert the bounding box estimate of the\ntarget into a pair of corner predictions (the bottom-right and the top-left\ncorners). By tracking a target as a pair of corners, we avoid the need to\ndesign the anchor boxes. This will make the entire tracking algorithm more\nflexible and simple than anchorbased trackers. In our network design, we\nfurther introduce a layer-wise feature aggregation strategy that enables the\ncorner pooling module to predict multiple corners for a tracking target in deep\nnetworks. We then introduce a new penalty term that is used to select an\noptimal tracking box in these candidate corners. Finally, SiamCorners achieves\nexperimental results that are comparable to the state-of-art tracker while\nmaintaining a high running speed. In particular, SiamCorners achieves a 53.7%\nAUC on NFS30 and a 61.4% AUC on UAV123, while still running at 42 frames per\nsecond (FPS).\n"}
+{"abstract": "  Information exchange is a crucial component of many real-world multi-agent\nsystems. However, the communication between the agents involves two major\nchallenges: the limited bandwidth, and the shared communication medium between\nthe agents, which restricts the number of agents that can simultaneously\nexchange information. While both of these issues need to be addressed in\npractice, the impact of the latter problem on the performance of the\nmulti-agent systems has often been neglected. This becomes even more important\nwhen the agents' information or observations have different importance, in\nwhich case the agents require different priorities for accessing the medium and\nsharing their information. Representing the agents' priorities by fairness\nweights and normalizing each agent's share by the assigned fairness weight, the\ngoal can be expressed as equalizing the agents' normalized shares of the\ncommunication medium. To achieve this goal, we adopt a queueing theoretic\napproach and propose a distributed fair scheduling algorithm for providing\nweighted fairness in single-hop networks. Our proposed algorithm guarantees an\nupper-bound on the normalized share disparity among any pair of agents. This\ncan particularly improve the short-term fairness, which is important in\nreal-time applications. Moreover, our scheduling algorithm adjusts itself\ndynamically to achieve a high throughput at the same time. The simulation\nresults validate our claims and comparisons with the existing methods show our\nalgorithm's superiority in providing short-term fairness, while achieving a\nhigh throughput.\n"}
+{"abstract": "  We show that quantum state tomography with perfect knowledge of the\nmeasurement apparatus proves to be, in some instances, inferior to strategies\ndiscarding all information about the measurement at hand, as in the case of\ndata pattern tomography. In those scenarios, the larger uncertainty about the\nmeasurement is traded for the smaller uncertainty about the reconstructed\nsignal. This effect is more pronounced for minimal or nearly minimal\ninformationally complete measurement settings, which are of utmost practical\nimportance.\n"}
+{"abstract": "  This letter gives a credit to a pioneering paper that is almost unknown to\nscientific community. On the basis of Transmission Electron Microscopy images\nand X-ray Ray Diffraction patterns of carbon multi-layer tubular crystals the\nauthors suggested a model of nanotube structure formation and hypothesis on\nvarious chirality of carbon nanotubes.\n"}
+{"abstract": "  Subspace-valued functions arise in a wide range of problems, including\nparametric reduced order modeling (PROM). In PROM, each parameter point can be\nassociated with a subspace, which is used for Petrov-Galerkin projections of\nlarge system matrices. Previous efforts to approximate such functions use\ninterpolations on manifolds, which can be inaccurate and slow. To tackle this,\nwe propose a novel Bayesian nonparametric model for subspace prediction: the\nGaussian Process Subspace regression (GPS) model. This method is extrinsic and\nintrinsic at the same time: with multivariate Gaussian distributions on the\nEuclidean space, it induces a joint probability model on the Grassmann\nmanifold, the set of fixed-dimensional subspaces. The GPS adopts a simple yet\ngeneral correlation structure, and a principled approach for model selection.\nIts predictive distribution admits an analytical form, which allows for\nefficient subspace prediction over the parameter space. For PROM, the GPS\nprovides a probabilistic prediction at a new parameter point that retains the\naccuracy of local reduced models, at a computational complexity that does not\ndepend on system dimension, and thus is suitable for online computation. We\ngive four numerical examples to compare our method to subspace interpolation,\nas well as two methods that interpolate local reduced models. Overall, GPS is\nthe most data efficient, more computationally efficient than subspace\ninterpolation, and gives smooth predictions with uncertainty quantification.\n"}
+{"abstract": "  Scheduling computational tasks represented by directed acyclic graphs (DAGs)\nis challenging because of its complexity. Conventional scheduling algorithms\nrely heavily on simple heuristics such as shortest job first (SJF) and critical\npath (CP), and are often lacking in scheduling quality. In this paper, we\npresent a novel learning-based approach to scheduling DAG tasks. The algorithm\nemploys a reinforcement learning agent to iteratively add directed edges to the\nDAG, one at a time, to enforce ordering (i.e., priorities of execution and\nresource allocation) of \"tricky\" job nodes. By doing so, the original DAG\nscheduling problem is dramatically reduced to a much simpler proxy problem, on\nwhich heuristic scheduling algorithms such as SJF and CP can be efficiently\nimproved. Our approach can be easily applied to any existing heuristic\nscheduling algorithms. On the benchmark dataset of TPC-H, we show that our\nlearning based approach can significantly improve over popular heuristic\nalgorithms and consistently achieves the best performance among several methods\nunder a variety of settings.\n"}
+{"abstract": "  The fused deposition modeling is one of the most rapidly developing 3D\nprinting techniques, with numerous applications, also in the field of applied\nelectrochemistry. Here, utilization of conductive polylactic acid (C-PLA) for\n3D printouts is the most promising, due to its biodegradability, commercial\navailability, and ease of processing. To use C-PLA as an electrode material, an\nactivation process must be performed, removing the polymer matrix and\nuncovering the electroactive filler. The most popular chemical or\nelectrochemical activation routes are done in solvents. In this manuscript, we\npresent a novel, alternative approach towards C-PLA activation with Nd:YAG\n(lambda = 1064 nm) laser ablation. We present and discuss the activation\nefficiency based on various laser source operating conditions, and the gas\nmatrix. The XPS, contact angle, and Raman analyses were performed for\nevaluation of the surface chemistry and to discuss the mechanism of the\nactivation process. The ablation process carried out in the inert gas matrix\n(helium) delivers a highly electroactive C-PLA electrode surface, while the\nresultant charge transfer process is hindered when activated in the air. This\nis due to thermally induced oxide layers formation. The electroanalytical\nperformance of laser-treated C-PLA in He atmosphere was confirmed through\ncaffeine detection, offering detection limits of 0.49 and 0.40 microM (S/N = 3)\nbased on CV and DPV studies, respectively.\n"}
+{"abstract": "  Supernova properties in radio strongly depend on their circumstellar\nenvironment and they are an important probe to investigate the mass loss of\nsupernova progenitors. Recently, core-collapse supernova observations in radio\nhave been assembled and the rise time and peak luminosity distribution of\ncore-collapse supernovae at 8.4 GHz has been estimated. In this paper, we\nconstrain the mass-loss prescriptions for red supergiants by using the rise\ntime and peak luminosity distribution of Type II supernovae in radio. We take\nthe de Jager and van Loon mass-loss rates for red supergiants, calculate the\nrise time and peak luminosity distribution based on them, and compare the\nresults with the observed distribution. We found that the de Jager mass-loss\nrate explains the widely spread radio rise time and peak luminosity\ndistribution of Type II supernovae well, while the van Loon mass-loss rate\npredicts a relatively narrow range for the rise time and peak luminosity. We\nconclude that the mass-loss prescriptions of red supergiants should have strong\ndependence on the luminosity as in the de Jager mass-loss rate to reproduce the\nwidely spread distribution of the rise time and peak luminosity in radio\nobserved in Type II supernovae.\n"}
+{"abstract": "  gComm is a step towards developing a robust platform to foster research in\ngrounded language acquisition in a more challenging and realistic setting. It\ncomprises a 2-d grid environment with a set of agents (a stationary speaker and\na mobile listener connected via a communication channel) exposed to a\ncontinuous array of tasks in a partially observable setting. The key to solving\nthese tasks lies in agents developing linguistic abilities and utilizing them\nfor efficiently exploring the environment. The speaker and listener have access\nto information provided in different modalities, i.e. the speaker's input is a\nnatural language instruction that contains the target and task specifications\nand the listener's input is its grid-view. Each must rely on the other to\ncomplete the assigned task, however, the only way they can achieve the same, is\nto develop and use some form of communication. gComm provides several tools for\nstudying different forms of communication and assessing their generalization.\n"}
+{"abstract": "  Autonomous driving highly depends on capable sensors to perceive the\nenvironment and to deliver reliable information to the vehicles' control\nsystems. To increase its robustness, a diversified set of sensors is used,\nincluding radar sensors. Radar is a vital contribution of sensory information,\nproviding high resolution range as well as velocity measurements. The increased\nuse of radar sensors in road traffic introduces new challenges. As the so far\nunregulated frequency band becomes increasingly crowded, radar sensors suffer\nfrom mutual interference between multiple radar sensors. This interference must\nbe mitigated in order to ensure a high and consistent detection sensitivity. In\nthis paper, we propose the use of Complex-Valued Convolutional Neural Networks\n(CVCNNs) to address the issue of mutual interference between radar sensors. We\nextend previously developed methods to the complex domain in order to process\nradar data according to its physical characteristics. This not only increases\ndata efficiency, but also improves the conservation of phase information during\nfiltering, which is crucial for further processing, such as angle estimation.\nOur experiments show, that the use of CVCNNs increases data efficiency, speeds\nup network training and substantially improves the conservation of phase\ninformation during interference removal.\n"}
+{"abstract": "  Using a total of $5.25~{\\rm fb}^{-1}$ of $e^{+}e^{-}$ collision data with\ncenter-of-mass energies from 4.236 to 4.600 GeV, we report the first\nobservation of the process $e^{+}e^{-}\\to \\eta\\psi(2S)$ with a statistical\nsignificance of $5\\sigma$. The data sets were collected by the BESIII detector\noperating at the BEPCII storage ring. We measure the yield of events integrated\nover center-of-mass energies and also present the energy dependence of the\nmeasured cross section.\n"}
+{"abstract": "  The ongoing trend of moving data and computation to the cloud is met with\nconcerns regarding privacy and protection of intellectual property. Cloud\nService Providers (CSP) must be fully trusted to not tamper with or disclose\nprocessed data, hampering adoption of cloud services for many sensitive or\ncritical applications. As a result, CSPs and CPU manufacturers are rushing to\nfind solutions for secure outsourced computation in the Cloud. While enclaves,\nlike Intel SGX, are strongly limited in terms of throughput and size, AMD's\nSecure Encrypted Virtualization (SEV) offers hardware support for transparently\nprotecting code and data of entire VMs, thus removing the performance, memory\nand software adaption barriers of enclaves. Through attestation of boot code\nintegrity and means for securely transferring secrets into an encrypted VM,\nCSPs are effectively removed from the list of trusted entities. There have been\nseveral attacks on the security of SEV, by abusing I/O channels to encrypt and\ndecrypt data, or by moving encrypted code blocks at runtime. Yet, none of these\nattacks have targeted the attestation protocol, the core of the secure\ncomputing environment created by SEV. We show that the current attestation\nmechanism of Zen 1 and Zen 2 architectures has a significant flaw, allowing us\nto manipulate the loaded code without affecting the attestation outcome. An\nattacker may abuse this weakness to inject arbitrary code at startup -- and\nthus take control over the entire VM execution, without any indication to the\nVM's owner. Our attack primitives allow the attacker to do extensive\nmodifications to the bootloader and the operating system, like injecting spy\ncode or extracting secret data. We present a full end-to-end attack, from the\ninitial exploit to leaking the key of the encrypted disk image during boot,\ngiving the attacker unthrottled access to all of the VM's persistent data.\n"}
+{"abstract": "  We prove the well-posedness of entropy solutions for a wide class of nonlocal\ntransport equations with nonlinear mobility in one spatial dimension. The\nsolution is obtained as the limit of approximations constructed via a\ndeterministic system of interacting particles that exhibits a gradient flow\nstructure. At the same time, we expose a rigorous gradient flow structure for\nthis class of equations in terms of an Energy-Dissipation balance, which we\nobtain via the asymptotic convergence of functionals.\n"}
+{"abstract": "  In autonomous microgrids frequency regulation (FR) is a critical issue,\nespecially with a high level of penetration of the photovoltaic (PV)\ngeneration. In this study, a novel virtual synchronous generator (VSG) control\nfor PV generation was introduced to provide frequency support without energy\nstorage. PV generation reserve a part of the active power in accordance with\nthe pre-defined power versus voltage curve. Based on the similarities of the\nsynchronous generator power-angle characteristic curve and the PV array\ncharacteristic curve, PV voltage Vpv can be analogized to the power angle\n{\\delta}. An emulated governor (droop control) and the swing equation control\nis designed and applied to the DC-DC converter. PV voltage deviation is\nsubsequently generated and the pre-defined power versus voltage curve is\nmodified to provide the primary frequency and inertia support. A simulation\nmodel of an autonomous microgrid with PV, storage, and diesel generator was\nbuilt. The feasibility and effectiveness of the proposed VSG strategy are\nexamined under different operating conditions.\n"}
+{"abstract": "  One of the paramount advantages of multi-level cache-enabled (MLCE) networks\nis pushing contents proximity to the network edge and proactively caching them\nat multiple transmitters (i.e., small base-stations (SBSs), unmanned aerial\nvehicles (UAVs), and cache-enabled device-to-device (CE-D2D) users). As such,\nthe fronthaul congestion between a core network and a large number of\ntransmitters is alleviated. For this objective, we exploit network coding (NC)\nto schedule a set of users to the same transmitter. Focusing on this, we\nconsider the throughput maximization problem that optimizes jointly the\nnetwork-coded user scheduling and power allocation, subject to fronthaul\ncapacity, transmit power, and NC constraints. Given the intractability of the\nproblem, we decouple it into two separate subproblems. In the first subproblem,\nwe consider the network-coded user scheduling problem for the given power\nallocation, while in the second subproblem, we use the NC resulting user\nschedule to optimize the power levels. We design an innovative\n\\textit{two-layered rate-aware NC (RA-IDNC)} graph to solve the first\nsubproblem and evaluate the second subproblem using an iterative function\nevaluation (IFE) approach. Simulation results are presented to depict the\nthroughput gain of the proposed approach over the existing solutions.\n"}
+{"abstract": "  Latent alignment objectives such as CTC and AXE significantly improve\nnon-autoregressive machine translation models. Can they improve autoregressive\nmodels as well? We explore the possibility of training autoregressive machine\ntranslation models with latent alignment objectives, and observe that, in\npractice, this approach results in degenerate models. We provide a theoretical\nexplanation for these empirical results, and prove that latent alignment\nobjectives are incompatible with teacher forcing.\n"}
+{"abstract": "  Graph convolutional networks (GCNs) are widely used in graph-based\napplications such as graph classification and segmentation. However, current\nGCNs have limitations on implementation such as network architectures due to\ntheir irregular inputs. In contrast, convolutional neural networks (CNNs) are\ncapable of extracting rich features from large-scale input data, but they do\nnot support general graph inputs. To bridge the gap between GCNs and CNNs, in\nthis paper we study the problem of how to effectively and efficiently map\ngeneral graphs to 2D grids that CNNs can be directly applied to, while\npreserving graph topology as much as possible. We therefore propose two novel\ngraph-to-grid mapping schemes, namely, {\\em graph-preserving grid layout\n(GPGL)} and its extension {\\em Hierarchical GPGL (H-GPGL)} for computational\nefficiency. We formulate the GPGL problem as integer programming and further\npropose an approximate yet efficient solver based on a penalized Kamada-Kawai\nmethod, a well-known optimization algorithm in 2D graph drawing. We propose a\nnovel vertex separation penalty that encourages graph vertices to lay on the\ngrid without any overlap. Along with this image representation, even extra 2D\nmaxpooling layers contribute to the PointNet, a widely applied point-based\nneural network. We demonstrate the empirical success of GPGL on general graph\nclassification with small graphs and H-GPGL on 3D point cloud segmentation with\nlarge graphs, based on 2D CNNs including VGG16, ResNet50 and multi-scale maxout\n(MSM) CNN.\n"}
+{"abstract": "  The input of almost every machine learning algorithm targeting the properties\nof matter at the atomic scale involves a transformation of the list of\nCartesian atomic coordinates into a more symmetric representation. Many of\nthese most popular representations can be seen as an expansion of the\nsymmetrized correlations of the atom density, and differ mainly by the choice\nof basis. Here we discuss how to build an adaptive, optimal numerical basis\nthat is chosen to represent most efficiently the structural diversity of the\ndataset at hand. For each training dataset, this optimal basis is unique, and\ncan be computed at no additional cost with respect to the primitive basis by\napproximating it with splines. We demonstrate that this construction yields\nrepresentations that are accurate and computationally efficient, presenting\nexamples that involve both molecular and condensed-phase machine-learning\nmodels.\n"}
+{"abstract": "  We formulate a plausible conjecture for the optimal Ehrhard-type inequality\nfor convex symmetric sets with respect to the Gaussian measure. Namely, letting\n$J_{k-1}(s)=\\int^s_0 t^{k-1} e^{-\\frac{t^2}{2}}dt$ and\n$c_{k-1}=J_{k-1}(+\\infty)$, we conjecture that the function\n$F:[0,1]\\rightarrow\\mathbb{R},$ given by $$F(a)= \\sum_{k=1}^n 1_{a\\in\nE_k}\\cdot(\\beta_k J_{k-1}^{-1}(c_{k-1} a)+\\alpha_k)$$ (with an appropriate\nchoice of a decomposition $[0,1]=\\cup_{i} E_i$ and coefficients $\\alpha_i,\n\\beta_i$) satisfies, for all symmetric convex sets $K$ and $L,$ and any\n$\\lambda\\in[0,1]$, $$ F\\left(\\gamma(\\lambda K+(1-\\lambda)L)\\right)\\geq \\lambda\nF\\left(\\gamma(K)\\right)+(1-\\lambda) F\\left(\\gamma(L)\\right). $$ We explain that\nthis conjecture is ``the most optimistic possible'', and is equivalent to the\nfact that for any symmetric convex set $K,$ its \\emph{Gaussian concavity power}\n$p^s(K,\\gamma)$ is greater than or equal to $p_s(RB^k_2\\times\n\\mathbb{R}^{n-k},\\gamma),$ for some $k\\in \\{1,...,n\\}$. We call the sets\n$RB^k_2\\times \\mathbb{R}^{n-k}$ round $k$-cylinders; they also appear as the\nconjectured Gaussian isoperimetric minimizers for symmetric sets, see Heilman\n\\cite{Heilman}. In this manuscript, we make progress towards this question, and\nprove certain inequality for which the round k-cylinders are the only equality\ncases. As an auxiliary result on the way to the equality case characterization,\nwe characterize the equality cases in the ``convex set version'' of the\nBrascamp-Lieb inequality, and moreover, obtain a quantitative stability version\nin the case of the standard Gaussian measure; this may be of independent\ninterest.\n"}
+{"abstract": "  Federated learning (FL) is a distributed machine learning architecture that\nleverages a large number of workers to jointly learn a model with decentralized\ndata. FL has received increasing attention in recent years thanks to its data\nprivacy protection, communication efficiency and a linear speedup for\nconvergence in training (i.e., convergence performance increases linearly with\nrespect to the number of workers). However, existing studies on linear speedup\nfor convergence are only limited to the assumptions of i.i.d. datasets across\nworkers and/or full worker participation, both of which rarely hold in\npractice. So far, it remains an open question whether or not the linear speedup\nfor convergence is achievable under non-i.i.d. datasets with partial worker\nparticipation in FL. In this paper, we show that the answer is affirmative.\nSpecifically, we show that the federated averaging (FedAvg) algorithm (with\ntwo-sided learning rates) on non-i.i.d. datasets in non-convex settings\nachieves a convergence rate $\\mathcal{O}(\\frac{1}{\\sqrt{mKT}} + \\frac{1}{T})$\nfor full worker participation and a convergence rate\n$\\mathcal{O}(\\frac{\\sqrt{K}}{\\sqrt{nT}} + \\frac{1}{T})$ for partial worker\nparticipation, where $K$ is the number of local steps, $T$ is the number of\ntotal communication rounds, $m$ is the total worker number and $n$ is the\nworker number in one communication round if for partial worker participation.\nOur results also reveal that the local steps in FL could help the convergence\nand show that the maximum number of local steps can be improved to $T/m$ in\nfull worker participation. We conduct extensive experiments on MNIST and\nCIFAR-10 to verify our theoretical results.\n"}
+{"abstract": "  We consider rather a general class of multi-level optimization problems,\nwhere a convex objective function is to be minimized, subject to constraints to\noptima of a nested convex optimization problem. As a special case, we consider\na trilevel optimization problem, where the objective of the two lower layers\nconsists of a sum of a smooth and a non-smooth term. Based on fixed-point\ntheory and related arguments, we present a natural first-order algorithm and\nanalyze its convergence and rates of convergence in several regimes of\nparameters.\n"}
+{"abstract": "  The aim of this paper is to generalize results known for the symplectic\ninvolutions on K3 surfaces to the order 3 symplectic automorphisms on K3\nsurfaces. In particular, we will explicitly describe the action induced on the\nlattice $\\Lambda_{K3}$, isometric to the second cohomology group of a K3\nsurface, by a symplectic automorphism of order 3; we exhibit the maps $\\pi_*$\nand $\\pi^*$ induced in cohomology by the rational quotient map\n$\\pi:X\\dashrightarrow Y$, where $X$ is a K3 surface admitting an order 3\nsymplectic automorphism $\\sigma$ and $Y$ is the minimal resolution of the\nquotient $X/\\sigma$; we deduce the relation between the N\\'eron--Severi group\nof $X$ and the one of $Y$. Applying these results we describe explicit\ngeometric examples and generalize the Shioda--Inose structures, relating\nAbelian surfaces admitting order 3 endomorphisms with certain specific K3\nsurfaces admitting particular order 3 symplectic automorphisms.\n"}
+{"abstract": "  This book develops an effective theory approach to understanding deep neural\nnetworks of practical relevance. Beginning from a first-principles\ncomponent-level picture of networks, we explain how to determine an accurate\ndescription of the output of trained networks by solving layer-to-layer\niteration equations and nonlinear learning dynamics. A main result is that the\npredictions of networks are described by nearly-Gaussian distributions, with\nthe depth-to-width aspect ratio of the network controlling the deviations from\nthe infinite-width Gaussian description. We explain how these effectively-deep\nnetworks learn nontrivial representations from training and more broadly\nanalyze the mechanism of representation learning for nonlinear models. From a\nnearly-kernel-methods perspective, we find that the dependence of such models'\npredictions on the underlying learning algorithm can be expressed in a simple\nand universal way. To obtain these results, we develop the notion of\nrepresentation group flow (RG flow) to characterize the propagation of signals\nthrough the network. By tuning networks to criticality, we give a practical\nsolution to the exploding and vanishing gradient problem. We further explain\nhow RG flow leads to near-universal behavior and lets us categorize networks\nbuilt from different activation functions into universality classes.\nAltogether, we show that the depth-to-width ratio governs the effective model\ncomplexity of the ensemble of trained networks. By using information-theoretic\ntechniques, we estimate the optimal aspect ratio at which we expect the network\nto be practically most useful and show how residual connections can be used to\npush this scale to arbitrary depths. With these tools, we can learn in detail\nabout the inductive bias of architectures, hyperparameters, and optimizers.\n"}
+{"abstract": "  Programming efficiently heterogeneous systems is a major challenge, due to\nthe complexity of their architectures. Intel oneAPI, a new and powerful\nstandards-based unified programming model, built on top of SYCL, addresses\nthese issues. In this paper, oneAPI is provided with co-execution strategies to\nrun the same kernel between different devices, enabling the exploitation of\nstatic and dynamic policies. On top of that, static and dynamic load-balancing\nalgorithms are integrated and analyzed.\n  This work evaluates the performance and energy efficiency for a well-known\nset of regular and irregular HPC benchmarks, using an integrated GPU and CPU.\nExperimental results show that co-execution is worthwhile when using dynamic\nalgorithms, improving efficiency even more when using unified shared memory.\n"}
+{"abstract": "  Open Radio Access Network (ORAN) is being developed with an aim to\ndemocratise access and lower the cost of future mobile data networks,\nsupporting network services with various QoS requirements, such as massive IoT\nand URLLC. In ORAN, network functionality is dis-aggregated into remote units\n(RUs), distributed units (DUs) and central units (CUs), which allows flexible\nsoftware on Commercial-Off-The-Shelf (COTS) deployments. Furthermore, the\nmapping of variable RU requirements to local mobile edge computing centres for\nfuture centralized processing would significantly reduce the power consumption\nin cellular networks. In this paper, we study the RU-DU resource assignment\nproblem in an ORAN system, modelled as a 2D bin packing problem. A deep\nreinforcement learning-based self-play approach is proposed to achieve\nefficient RU-DU resource management, with AlphaGo Zero inspired neural\nMonte-Carlo Tree Search (MCTS). Experiments on representative 2D bin packing\nenvironment and real sites data show that the self-play learning strategy\nachieves intelligent RU-DU resource assignment for different network\nconditions.\n"}
+{"abstract": "  This paper presents a novel solution paradigm of general optimization under\nboth exogenous and endogenous uncertainties. This solution paradigm consists of\na probability distribution (PD)-free method of obtaining deterministic\nequivalents and an innovative approach of scenario reduction. First, dislike\nthe existing methods that use scenarios sampled from pre-known PD functions,\nthe PD-free method uses historical measurements of uncertain variables as input\nto convert the logical models into a type of deterministic equivalents called\nGeneral Scenario Program (GSP). Our contributions to the PD-free deterministic\nequivalent construction reside in generalization (making it applicable to\ngeneral optimization under uncertainty rather than just chance-constrained\noptimization) and extension (enabling it to the problems under endogenous\nuncertainty via developing an iterative and a non-iterative frameworks).\nSecond, this paper reveals some unknown properties of the PD-free deterministic\nequivalent construction, such as the characteristics of active scenarios and\nrepeated scenarios. Base on this discoveries, we propose a concept and methods\nof strategic scenario selection which can effectively reduce the required\nnumber of scenarios as demonstrated in both mathematical analysis and numerical\nexperiments. Numerical experiments are conducted on two typical smart grid\noptimization problems under exogenous and endogenous uncertainties.\n"}
+{"abstract": "  The detection of spatial or temporal variations in very thin samples has\nimportant applications in the biological sciences. For example, cellular\nmembranes exhibit changes in lipid composition and order, which in turn\nmodulate their function in space and time. Simultaneous measurement of\nthickness and refractive index would be one way to observe these variations,\nyet doing it noninvasively remains an elusive goal. Here we present a\nmicroscopic-imaging technique to simultaneously measure the thickness and\nrefractive index of thin layers in a spatially resolved manner using\nreflectometry. The heterodyne-detected interference between a light field\nreflected by the sample and a reference field allows measurement of the\namplitude and phase of the reflected field and thus determination of the\ncomplex reflection coefficient. Comparing the results with the simulated\nreflection of a thin layer under coherent illumination of high numerical\naperture by the microscope objective, the refractive index and thickness of the\nlayer can be determined. We present results on a layer of polyvinylacetate\n(PVA) with a thickness of approximately 80~nm. These results have a precision\nbetter than 10\\% in the thickness and better than 1\\% in the refractive index\nand are consistent within error with measurements by quantitative differential\ninterference contrast (qDIC) and literature values. We discuss the significance\nof these results, and the possibility of performing accurate measurements on\nnanometric layers. Notably, the shot-noise limit of the technique is below\n0.5~nm in thickness and 0.0005 in refractive index for millisecond measurement\ntimes.\n"}
+{"abstract": "  We consider the scenario where human-driven/autonomous vehicles with low/high\noccupancy are sharing a segment of highway and autonomous vehicles are capable\nof increasing the traffic throughput by preserving a shorter headway than\nhuman-driven vehicles. We propose a toll lane framework where a lane on the\nhighway is reserved freely for autonomous vehicles with high occupancy, which\nhave the greatest capability to increase social mobility, and the other three\nclasses of vehicles can choose to use the toll lane with a toll or use the\nother regular lanes freely. All vehicles are assumed to be only interested in\nminimizing their own travel costs. We explore the resulting lane choice\nequilibria under the framework and establish desirable properties of the\nequilibria, which implicitly compare high-occupancy vehicles with autonomous\nvehicles in terms of their capabilities to increase social mobility. We further\nuse numerical examples in the optimal toll design, the occupancy threshold\ndesign, and the policy design problems to clarify the various potential\napplications of this toll lane framework that unites high-occupancy vehicles\nand autonomous vehicles. To our best knowledge, this is the first work that\nsystematically studies a toll lane framework that unites autonomous vehicles\nand high-occupancy vehicles on the roads.\n"}
+{"abstract": "  We explore explicit virtual resolutions, as introduced by Berkesch, Erman,\nand Smith, for ideals of sets of points in $\\mathbb{P}^1 \\times \\mathbb{P}^1$.\nSpecifically, we describe a virtual resolution for a sufficiently general set\nof points $X$ in $\\mathbb{P}^1 \\times \\mathbb{P}^1$ that only depends on $|X|$.\nWe also improve an existence result of Berkesch, Erman, and Smith in the\nspecial case of points in $\\mathbb{P}^1 \\times \\mathbb{P}^1$; more precisely,\nwe give an effective bound for their construction that gives a virtual\nresolution of length two for any set of points in $\\mathbb{P}^1 \\times\n\\mathbb{P}^1$.\n"}
+{"abstract": "  Many defenses have emerged with the development of adversarial attacks.\nModels must be objectively evaluated accordingly. This paper systematically\ntackles this concern by proposing a new parameter-free benchmark we coin RoBIC.\nRoBIC fairly evaluates the robustness of image classifiers using a new\nhalf-distortion measure. It gauges the robustness of the network against white\nand black box attacks, independently of its accuracy. RoBIC is faster than the\nother available benchmarks. We present the significant differences in the\nrobustness of 16 recent models as assessed by RoBIC.\n"}
+{"abstract": "  The automorphism groups of the Fano-Mukai fourfold of genus 10 were studied\nin our previous paper [arXiv:1706.04926]. In particular, we found in\n[arXiv:1706.04926] the neutral components of these groups. In the present paper\nwe finish the description of the discrete parts.\n  Up to isomorphism, there are two special Fano--Mukai fourfold of genus 10\nwith the automorphism groups $GL_2(k)\\rtimes\\mathbb{Z}/2\\mathbb{Z}$ and\n$(\\mathbb{G}_a\\times\\mathbb{G}m)\\rtimes\\mathbb{Z}/2\\mathbb{Z}$, respectively.\nFor any other Fano-Mukai fourfold $V$ of genus 10 one has\n$\\mathrm{Aut}(V)=\\mathbb{G}_m^2\\rtimes \\mathbb{Z}/2\\mathbb{Z}$, except for\nexactly one of them with $\\mathrm{Aut}(V)=\\mathbb{G}_m^2\\rtimes \\mathbb{Z}/6\n\\mathbb{Z}$.\n"}
+{"abstract": "  Piezo ion channels underlie many forms of mechanosensation in vertebrates,\nand have been found to bend the membrane into strongly curved dome shapes. We\ndevelop here a methodology describing the self-assembly of lipids and Piezo\ninto polyhedral bilayer vesicles. We validate this methodology for bilayer\nvesicles formed from bacterial mechanosensitive channels of small conductance,\nfor which experiments found a polyhedral arrangement of proteins with snub cube\nsymmetry and a well-defined characteristic vesicle size. On this basis, we\ncalculate the self-assembly diagram for polyhedral bilayer vesicles formed from\nPiezo. We find that the radius of curvature of the Piezo dome provides a\ncritical control parameter for the self-assembly of Piezo vesicles, with high\nabundances of Piezo vesicles with octahedral, icosahedral, and snub cube\nsymmetry with increasing Piezo dome radius of curvature.\n"}
+{"abstract": "  Heart rate variability (HRV), defined as the variability between consecutive\nheartbeats, is a surrogate measure of cardiac vagal tone. It is widely accepted\nthat a decreased HRV is associated to several risk factors and cardiovascular\ndiseases. However, a possible association between HRV and altered cerebral\nhemodynamics is still debated, suffering from HRV short-term measures and the\npaucity of high-resolution deep cerebral data. We propose a computational\napproach to evaluate the deep cerebral and central hemodynamics subject to\nphysiological alterations of HRV in an ideal young healthy patient at rest. The\ncardiovascular-cerebral model was validated and recently exploited to\nunderstand the hemodynamic mechanisms between cardiac arrythmia and cognitive\ndeficit. Three configurations (baseline, increased HRV, and decreased HRV) are\nbuilt based on the standard deviation (SDNN) of RR beats. In the cerebral\ncirculation, our results show that HRV has overall a stronger impact on\npressure than flow rate mean values but similarly alters pressure and flow rate\nin terms of extreme events. By comparing reduced and increased HRV, this latter\ninduces a higher probability of altered mean and extreme values, and is\ntherefore more detrimental at distal cerebral level. On the contrary, at\ncentral level a decreased HRV induces a higher cardiac effort without improving\nthe mechano-contractile performance, thus overall reducing the heart\nefficiency. Present results suggest that: (i) the increase of HRV per se does\nnot seem to be sufficient to trigger a better cerebral hemodynamic response;\n(ii) by accounting for both central and cerebral circulations, the optimal HRV\nconfiguration is found at baseline. Given the relation inversely linking HRV\nand HR, the presence of this optimal condition can contribute to explain why\nthe mean HR of the general population settles around the baseline value (70\nbpm).\n"}
+{"abstract": "  This paper is concerned with the localized behaviors of the solution $u$ to\nthe Navier-Stokes equations near the potential singular points. We establish\nthe concentration rate for the $L^{p,\\infty}$ norm of $u$ with $3\\leq\np\\leq\\infty$. Namely, we show that if $z_0=(t_0,x_0)$ is a singular point, then\nfor any $r>0$, it holds \\begin{align} \\limsup_{t\\to\nt_0^-}||u(t,x)-u(t)_{x_0,r}||_{L^{3,\\infty}(B_r(x_0))}>\\delta^*,\\notag\n\\end{align} and \\begin{align} \\limsup_{t\\to\nt_0^-}(t_0-t)^{\\frac{1}{\\mu}}r^{\\frac{2}{\\nu}-\\frac{3}{p}}||u(t)||_{L^{p,\\infty}(B_r(x_0))}>\\delta^*\\notag\nfor~3<p\\leq\\infty,\n~\\frac{1}{\\mu}+\\frac{1}{\\nu}=\\frac{1}{2}~and~2\\leq\\nu\\leq\\frac{2}{3}p,\\notag\n\\end{align}where $\\delta^*$ is a positive constant independent of $p$ and\n$\\nu$. Our main tools are some $\\varepsilon$-regularity criteria in\n$L^{p,\\infty}$ spaces and an embedding theorem from $L^{p,\\infty}$ space into a\nMorrey type space. These are of independent interests.\n"}
+{"abstract": "  Multiple Kernel Learning is a conventional way to learn the kernel function\nin kernel-based methods. MKL algorithms enhance the performance of kernel\nmethods. However, these methods have a lower complexity compared to deep\nlearning models and are inferior to these models in terms of recognition\naccuracy. Deep learning models can learn complex functions by applying\nnonlinear transformations to data through several layers. In this paper, we\nshow that a typical MKL algorithm can be interpreted as a one-layer neural\nnetwork with linear activation functions. By this interpretation, we propose a\nNeural Generalization of Multiple Kernel Learning (NGMKL), which extends the\nconventional multiple kernel learning framework to a multi-layer neural network\nwith nonlinear activation functions. Our experiments on several benchmarks show\nthat the proposed method improves the complexity of MKL algorithms and leads to\nhigher recognition accuracy.\n"}
+{"abstract": "  The density power divergence (DPD) and related measures have produced many\nuseful statistical procedures which provide a good balance between model\nefficiency on one hand, and outlier stability or robustness on the other. The\nlarge number of citations received by the original DPD paper (Basu et al.,\n1998) and its many demonstrated applications indicate the popularity of these\ndivergences and the related methods of inference. The estimators that are\nderived from this family of divergences are all M-estimators where the defining\n$\\psi$ function is based explicitly on the form of the model density. The\nsuccess of the minimum divergence estimators based on the density power\ndivergence makes it imperative and meaningful to look for other, similar\ndivergences in the same spirit. The logarithmic density power divergence (Jones\net al., 2001), a logarithmic transform of the density power divergence, has\nalso been very successful in producing inference procedures with a high degree\nof efficiency simultaneously with a high degree of robustness. This further\nstrengthens the motivation to look for statistical divergences that are\ntransforms of the density power divergence, or, alternatively, members of the\nfunctional density power divergence class. This note characterizes the\nfunctional density power divergence class, and thus identifies the available\ndivergence measures within this construct that may possibly be explored for\nrobust and efficient statistical inference.\n"}
+{"abstract": "  Experiments observe an enhanced superconducting gap over impurities as\ncompared to the clean-bulk value. In order to shed more light on this\nphenomenon, we perform simulations within the framework of Bogoliubov-deGennes\ntheory applied to the attractive Hubbard model. The simulations qualitatively\nreproduce the experimentally observed enhancement effect; it can be traced back\nto an increased particle density in the metal close to the impurity site. In\naddition, the simulations display significant differences between a thin (2D)\nand a very thick (3D) film. In 2D pronounced Friedel oscillations can be\nobserved, which decay much faster in (3D) and therefore are more difficult to\nresolve. Also this feature is in qualitative agreement with the experiment.\n"}
+{"abstract": "  Supermassive black hole (SMBH) binaries represent the main target for\nmissions such as the Laser Interferometer Space Antenna and Pulsar Timing\nArrays. The understanding of their dynamical evolution prior to coalescence is\ntherefore crucial to improving detection strategies and for the astrophysical\ninterpretation of the gravitational wave data. In this paper, we use\nhigh-resolution $N$-body simulations to model the merger of two equal-mass\ngalaxies hosting a central SMBH. In our models, all binaries are initially\nprograde with respect to the galaxy sense of rotation. But, binaries that form\nwith a high eccentricity, $e\\gtrsim 0.7$, quickly reverse their sense of\nrotation and become almost perfectly retrograde at the moment of binary\nformation. The evolution of these binaries proceeds towards larger\neccentricities, as expected for a binary hardening in a counter-rotating\nstellar distribution. Binaries that form with lower eccentricities remain\nprograde and at comparatively low eccentricities. We study the origin of the\norbital flip by using an analytical model that describes the early stages of\nbinary evolution. This model indicates that the orbital plane flip is due to\nthe torque from the triaxial background mass distribution that naturally arises\nfrom the galactic merger process. Our results imply the existence of a\npopulation of SMBH binaries with a high eccentricity and could have significant\nimplications for the detection of the gravitational wave signal emitted by\nthese systems.\n"}
+{"abstract": "  In this work, we deal with the problem of rating in sports, where the skills\nof the players/teams are inferred from the observed outcomes of the games. Our\nfocus is on the online rating algorithms which estimate the skills after each\nnew game by exploiting the probabilistic models of the relationship between the\nskills and the game outcome. We propose a Bayesian approach which may be seen\nas an approximate Kalman filter and which is generic in the sense that it can\nbe used with any skills-outcome model and can be applied in the individual --\nas well as in the group-sports. We show how the well-know algorithms (such as\nthe Elo, the Glicko, and the TrueSkill algorithms) may be seen as instances of\nthe one-fits-all approach we propose. In order to clarify the conditions under\nwhich the gains of the Bayesian approach over the simpler solutions can\nactually materialize, we critically compare the known and the new algorithms by\nmeans of numerical examples using the synthetic as well as the empirical data.\n"}
+{"abstract": "  Our experiments demonstrate that alloying the cubic--phase YbN into the\nwurtzite--phase AlN results in clear mechanical softening and enhanced\nelectromechanical coupling of AlN. The first principle calculations reproduce\nexperimental results well, and predict a maximum of 270% increase in\nelectromechanical coupling coefficient caused by 1) enhanced piezoelectric\nresponse induced by the local strain of Yb ions and 2) structural flexibility\nof the YbAlN alloy. Extensive calculations suggest that the substitutional\nneighbor Yb--Yb pairs in wurtzite AlN are energetically stable along c axis,\nand avoid to form on the basal plane of wurtzite structure due to the repulsion\nbetween them, which explains that YbAlN films with high Yb concentrations are\ndifficult to fabricate in our sputtering experiments. Moreover, the neighbor\nYb--Yb pair interactions also promote structural flexibility of YbAlN, and are\nconsidered a cause for mechanical softening of YbAlN.\n"}
+{"abstract": "  In the simplest game-theoretic formulation of Schelling's model of\nsegregation on graphs, agents of two different types each select their own\nvertex in a given graph such as to maximize the fraction of agents of their\ntype in their occupied neighborhood. Two ways of modeling agent movement here\nare either to allow two agents to swap their vertices or to allow an agent to\njump to a free vertex. The contributions of this paper are twofold. First, we\nprove that deciding the existence of a swap-equilibrium and a jump-equilibrium\nin this simplest model of Schelling games is NP-hard, thereby answering\nquestions left open by Agarwal et al. [AAAI '20] and Elkind et al. [IJCAI '19].\nSecond, we introduce a measure for the robustness of equilibria in Schelling\ngames in terms of the minimum number of edges that need to be deleted to make\nan equilibrium unstable. We prove tight lower and upper bounds on the\nrobustness of swap-equilibria in Schelling games on different graph classes.\n"}
+{"abstract": "  In this work, we study the problem of co-optimize communication,\npre-computing, and computation cost in one-round multi-way join evaluation. We\npropose a multi-way join approach ADJ (Adaptive Distributed Join) for complex\njoin which finds one optimal query plan to process by exploring cost-effective\npartial results in terms of the trade-off between pre-computing, communication,\nand computation.We analyze the input relations for a given join query and find\none optimal over a set of query plans in some specific form, with high-quality\ncost estimation by sampling. Our extensive experiments confirm that ADJ\noutperforms the existing multi-way join methods by up to orders of magnitude.\n"}
+{"abstract": "  This study delves into semi-supervised object detection (SSOD) to improve\ndetector performance with additional unlabeled data. State-of-the-art SSOD\nperformance has been achieved recently by self-training, in which training\nsupervision consists of ground truths and pseudo-labels. In current studies, we\nobserve that class imbalance in SSOD severely impedes the effectiveness of\nself-training. To address the class imbalance, we propose adaptive\nclass-rebalancing self-training (ACRST) with a novel memory module called\nCropBank. ACRST adaptively rebalances the training data with foreground\ninstances extracted from the CropBank, thereby alleviating the class imbalance.\nOwing to the high complexity of detection tasks, we observe that both\nself-training and data-rebalancing suffer from noisy pseudo-labels in SSOD.\nTherefore, we propose a novel two-stage filtering algorithm to generate\naccurate pseudo-labels. Our method achieves satisfactory improvements on\nMS-COCO and VOC benchmarks. When using only 1\\% labeled data in MS-COCO, our\nmethod achieves 17.02 mAP improvement over supervised baselines, and 5.32 mAP\nimprovement compared with state-of-the-art methods.\n"}
+{"abstract": "  Cyclotron line scattering features are detected in a few tens of X-ray\npulsars (XRPs) and used as direct indicators of a strong magnetic field at the\nsurface of accreting neutron stars (NSs). In a few cases, cyclotron lines are\nknown to be variable with accretion luminosity of XRPs. It is accepted that the\nobserved variations of cyclotron line scattering features are related to\nvariations of geometry and dynamics of accretion flow above the magnetic poles\nof a NS. A positive correlation between the line centroid energy and luminosity\nis typical for sub-critical XRPs, where the accretion results in hot spots at\nthe magnetic poles. The negative correlation was proposed to be a specific\nfeature of bright super-critical XRPs, where radiation pressure supports\naccretion columns above the stellar surface. Cyclotron line in spectra of\nBe-transient X-ray pulsar GRO J1008-57 is detected at energies from $\\sim 75\n-90$ keV, the highest observed energy of cyclotron line feature in XRPs. We\nreport the peculiar relation of cyclotron line centroid energies with\nluminosity in GRO J1008-57 during the Type II outburst in August 2017 observed\nby Insight-HXMT. The cyclotron line energy was detected to be negatively\ncorrelated with the luminosity at $3.2\\times 10^{37}\\,\\ergs<L<4.2\\times\n10^{37}\\,\\ergs$, and positively correlated at $L\\gtrsim 5\\times\n10^{37}\\,\\ergs$. We speculate that the observed peculiar behavior of a\ncyclotron line would be due to variations of accretion channel geometry.\n"}
+{"abstract": "  Reconfiguration aims at recovering a system from a fault by automatically\nadapting the system configuration, such that the system goal can be reached\nagain. Classical approaches typically use a set of pre-defined faults for which\ncorresponding recovery actions are defined manually. This is not possible for\nmodern hybrid systems which are characterized by frequent changes. Instead,\nAI-based approaches are needed which leverage on a model of the non-faulty\nsystem and which search for a set of reconfiguration operations which will\nestablish a valid behavior again.\n  This work presents a novel algorithm which solves three main challenges: (i)\nOnly a model of the non-faulty system is needed, i.e. the faulty behavior does\nnot need to be modeled. (ii) It discretizes and reduces the search space which\noriginally is too large -- mainly due to the high number of continuous system\nvariables and control signals. (iii) It uses a SAT solver for propositional\nlogic for two purposes: First, it defines the binary concept of validity.\nSecond, it implements the search itself -- sacrificing the optimal solution for\na quick identification of an arbitrary solution. It is shown that the approach\nis able to reconfigure faults on simulated process engineering systems.\n"}
+{"abstract": "  We study the $T\\bar T$ deformation on a multi-quantum mechanical systems. By\nintroducing the dynamical coordinate transformation, we obtain the deformed\ntheory as well as the solution. We further study the thermo-field-double state\nunder the $T\\bar T$ deformation on these systems, including conformal quantum\nmechanical system, the Sachdev-Ye-Kitaev model, and the model satisfying\nEigenstate Thermalization Hypothesis. We find common regenesis phenomena where\nthe signal injected into one local system can regenerate from the other local\nsystem. From the bulk picture, we study the deformation on Jackiw-Teitelboim\ngravity governed by Schwarzian action and find that the regenesis phenomena\nhere are not related to the causal structure of semi-classical wormhole.\n"}
+{"abstract": "  Labelled networks are an important class of data, naturally appearing in\nnumerous applications in science and engineering. A typical inference goal is\nto determine how the vertex labels (or features) affect the network's\nstructure. In this work, we introduce a new generative model, the feature-first\nblock model (FFBM), that facilitates the use of rich queries on labelled\nnetworks. We develop a Bayesian framework and devise a two-level Markov chain\nMonte Carlo approach to efficiently sample from the relevant posterior\ndistribution of the FFBM parameters. This allows us to infer if and how the\nobserved vertex-features affect macro-structure. We apply the proposed methods\nto a variety of network data to extract the most important features along which\nthe vertices are partitioned. The main advantages of the proposed approach are\nthat the whole feature-space is used automatically and that features can be\nrank-ordered implicitly according to impact.\n"}
+{"abstract": "  This paper concerns a new optimization problem arising in the management of a\nmulti-object spectrometer with a configurable slit unit. The field of view of\nthe spectrograph is divided into contiguous and parallel spatial bands, each\none associated with two opposite sliding metal bars that can be positioned to\nobserve one astronomical object. Thus several objects can be analyzed\nsimultaneously within a configuration of the bars called a mask. Due to the\nhigh demand from astronomers, pointing the spectrograph's field of view to the\nsky, rotating it, and selecting the objects to conform a mask is a crucial\noptimization problem for the efficient use of the spectrometer. The paper\ndescribes this problem, presents a Mixed Integer Linear Programming formulation\nfor the case where the rotation angle is fixed, presents a non-convex\nformulation for the case where the rotation angle is unfixed, describes a\nheuristic approach for the general problem, and discusses computational results\non real-world and randomly-generated instances.\n"}
+{"abstract": "  In this work, the electrical and spin properties of monolayer MoSi2X4 (X= N,\nP, As, and Sb) under vertical strain are investigated. The band structures\nstate that MoSi2N4 is an indirect semiconductor, whereas other compounds are\ndirect semiconductors. The vertical strain has been selected to modify the\nelectrical properties. The bandgap shows a maximum and decreases for both\ntensile and compressive strains. The valence band at K-point displays a large\nspin-splitting, whereas the conduction band has a negligible splitting. On the\nother hand, the second conduction band has a large spin-splitting and moves\ndown under vertical strain which leads to a large spin-splitting in both\nconduction and valence bands edges. The projected density of states along with\nthe projected band structure clarifies the origin of these large\nspin-splittings. These three spin-splittings can be controlled by vertical\nstrain.\n"}
+{"abstract": "  Let $\\Gamma$ be a graph with vertex set $V$, and let $a$ and $b$ be\nnonnegative integers. A subset $C$ of $V$ is called an $(a,b)$-regular set in\n$\\Gamma$ if every vertex in $C$ has exactly $a$ neighbors in $C$ and every\nvertex in $V\\setminus C$ has exactly $b$ neighbors in $C$. In particular, $(0,\n1)$-regular sets and $(1, 1)$-regular sets in $\\Ga$ are called perfect codes\nand total perfect codes in $\\Ga$, respectively. A subset $C$ of a group $G$ is\nsaid to be an $(a,b)$-regular set of $G$ if there exists a Cayley graph of $G$\nwhich admits $C$ as an $(a,b)$-regular set. In this paper we prove that, for\nany generalized dihedral group $G$ or any group $G$ of order $4p$ or $pq$ for\nsome primes $p$ and $q$, if a nontrivial subgroup $H$ of $G$ is a $(0,\n1)$-regular set of $G$, then it must also be an $(a,b)$-regular set of $G$ for\nany $0\\leqslant a\\leqslant|H|-1$ and $0\\leqslant b\\leqslant |H|$ such that $a$\nis even when $|H|$ is odd. A similar result involving $(1, 1)$-regular sets of\nsuch groups is also obtained in the paper.\n"}
+{"abstract": "  We consider the convex geometry of the cone of nonnegative quadratics over\nStanley-Reisner varieties. Stanley-Reisner varieties (which are unions of\ncoordinate planes) are amongst the simplest real projective varieties, so this\nis potentially a starting point that can generalize to more complicated real\nprojective varieties. This subject has some suprising connections to algebraic\ntopology and category theory, which we exploit heavily in our work.\n  These questions are also valuable in applied math, because they directly\ntranslate to questions about positive semidefinite (PSD) matrices. In\nparticular, this relates to a long line of work concerning the extent to which\nit is possible to approximately check that a matrix is PSD by checking that\nsome principle submatrices are PSD, or to check if a partial matrix can be\napproximately completed to full PSD matrix.\n  We systematize both these practical and theoretical questions using a\nframework based on algebraic topology, category theory, and convex geometry. As\napplications of this framework we are able to classify the extreme nonnegative\nquadratics over many Stanley-Reisner varieties. We plan to follow these\nstructural results with a paper that is more focused on quantitative questions\nabout PSD matrix completion, which have applications in sparse semidefinite\nprogramming.\n"}
+{"abstract": "  Chiral antiferromagnets are currently considered for broad range of\napplications in spintronics, spin-orbitronics and magnonics. In contrast to the\nestablished approach relying on materials screening, the anisotropic and chiral\nresponses of low-dimensional antifferromagnets can be tailored relying on the\ngeometrical curvature. Here, we consider an achiral, anisotropic\nantiferromagnetic spin chain and demonstrate that these systems possess\ngeometry-driven effects stemming not only from the exchange interaction but\nalso from the anisotropy. Peculiarly, the anisotropy-driven effects are\ncomplementary to the curvature effects stemming from the exchange interaction\nand rather strong as they are linear in curvature. These effects are\nresponsible for the tilt of the equilibrium direction of vector order\nparameters and the appearance of the homogeneous Dzyaloshinskii-Moriya\ninteraction. The latter is a source of the geometry-driven weak ferromagnetism\nemerging in curvilinear antiferromagnetic spin chains. Our findings provide a\ndeeper fundamental insight into the physics of curvilinear antiferromagnets\nbeyond the $\\sigma$-model and offer an additional degree of freedom in the\ndesign of spintronic and magnonic devices.\n"}
+{"abstract": "  In a recent paper we showed that the collapse to a black hole in\none-parameter families of initial data for massless, minimally coupled scalar\nfields in spherically symmetric semi-classical loop quantum gravity exhibited a\nuniversal mass scaling similar to the one in classical general relativity. In\nparticular, no evidence of a mass gap appeared as had been suggested by\nprevious studies. The lack of a mass gap indicated the possible existence of a\nself-similar critical solution as in general relativity. Here we provide\nfurther evidence for its existence. Using an adaptive mesh refinement code, we\nshow that \"echoes\" arise as a result of the discrete self-similarity in\nspace-time. We also show the existence of \"wiggles\" in the mass scaling\nrelation, as in the classical theory. The results from the semi-classical\ntheory agree well with those of classical general relativity unless one takes\nunrealistically large values for the polymerization parameter.\n"}
+{"abstract": "  Nuclear-powered X-ray millisecond pulsars are the third type of millisecond\npulsars, which are powered by thermonuclear fusion processes. The corresponding\nbrightness oscillations, known as burst oscillations, are observed during some\nthermonuclear X-ray bursts, when the burning and cooling accreted matter gives\nrise to an azimuthally asymmetric brightness pattern on the surface of the\nspinning neutron star. Apart from providing neutron star spin rates, this X-ray\ntiming feature can be a useful tool to probe the fundamental physics of neutron\nstar interior and surface. This chapter presents an overview of the relatively\nnew field of nuclear-powered X-ray millisecond pulsars.\n"}
+{"abstract": "  It was recently shown that wavepackets with skewed momentum distribution\nexhibit a boomerang-like dynamics in the Anderson model due to Anderson\nlocalization: after an initial ballistic motion, they make a U-turn and\neventually come back to their starting point. In this paper, we study the\nrobustness of the quantum boomerang effect in various kinds of disordered and\ndynamical systems: tight-binding models with pseudo-random potentials, systems\nwith band random Hamiltonians, and the kicked rotor. Our results show that the\nboomerang effect persists in models with pseudo-random potentials. It is also\npresent in the kicked rotor, although in this case with a specific dependency\non the initial state. On the other hand, we find that random hopping processes\ninhibit any drift motion of the wavepacket, and consequently the boomerang\neffect. In particular, if the random nearest-neighbor hopping amplitudes have\nzero average, the wavepacket remains in its initial position.\n"}
+{"abstract": "  This article is a response to the continued assumption, cited even in reports\nand reviews of recent experimental breakthroughs and advances in theoretical\nmethods, that the antiJaynes-Cummings (AJC) interaction is an intractable\nenergy non-conserving component of the quantum Rabi model (QRM). We present\nthree key features of QRM dynamics : (a) the AJC interaction component has a\nconserved excitation number operator and is exactly solvable (b) QRM dynamical\nspace consists of a rotating frame (RF) dominated by an exactly solved\nJaynes-Cummings (JC) interaction specified by a conserved JC excitation number\noperator which generates the U(1) symmetry of RF and a correlated\ncounterrotating frame (CRF) dominated by an exactly solved antiJaynes-Cummings\n(AJC) interaction specified by a conserved AJC excitation number operator which\ngenerates the U(1) symmetry of CRF.\n"}
+{"abstract": "  Smart homes are one of the most promising applications of the emerging\nInternet of Things (IoT) technology. With the growing number of IoT related\ndevices such as smart thermostats, smart fridges, smart speaker, smart light\nbulbs and smart locks, smart homes promise to make our lives easier and more\ncomfortable. However, the increased deployment of such smart devices brings an\nincrease in potential security risks and home privacy breaches. In order to\novercome such risks, Intrusion Detection Systems are presented as pertinent\ntools that can provide network-level protection for smart devices deployed in\nhome environments. These systems monitor the network activities of the smart\nhome-connected de-vices and focus on alerting suspicious or malicious activity.\nThey also can deal with detected abnormal activities by hindering the impostors\nin accessing the victim devices. However, the employment of such systems in the\ncontext of a smart home can be challenging due to the devices hardware\nlimitations, which may restrict their ability to counter the existing and\nemerging attack vectors. Therefore, this paper proposes an experimental\ncomparison between the widely used open-source NIDSs namely Snort, Suricata and\nBro IDS to find the most appropriate one for smart homes in term of detection\naccuracy and resources consumption including CP and memory utilization.\nExperimental Results show that Suricata is the best performing NIDS for smart\nhomes\n"}
+{"abstract": "  Time-harmonic electromagnetic waves in vacuum are described by the Helmholtz\nequation $\\Delta u+\\omega ^{2}u=0 $ for $ (x,y,z) \\in \\mathbb{R}^3 $. For the\nevolution of such waves along the $z$-axis a Schr\\\"odinger equation can be\nderived through a multiple scaling ansatz. It is the purpose of this paper to\njustify this formal approximation by proving bounds between this formal\napproximation and true solutions of the original system. The challenge of the\npresented validity analysis is the fact that the Helmholtz equation is\nill-posed as an evolutionary system along the $z$-axis.\n"}
+{"abstract": "  Using detailed synchrotron diffraction, magnetization, thermodynamic and\ntransport measurements, we investigate the relationship between the mixed\nvalence of Ir, lattice strain and the resultant structural and magnetic ground\nstates in the geometrically frustrated triple perovskite iridate\nBa$_{3}$NaIr$_{2}$O$_{9}$. We observe a complex interplay between lattice\nstrain and structural phase co-existence, which is in sharp contrast to what is\ntypically observed in this family of compounds. The low temperature magnetic\nground state is characterized by the absence of long range order, and points\ntowards the condensation of a cluster glass state from an extended regime of\nshort range magnetic correlations.\n"}
+{"abstract": "  Modulo-wrapping receivers have attracted interest in several areas of digital\ncommunications, including precoding and lattice coding. The asymptotic capacity\nand error performance of the modulo AWGN channel have been well established.\nHowever, due to underlying assumptions of the asymptotic analyses, these\nfindings might not always be realistic in physical world applications, which\nare often dimension- or delay-limited. In this work, the optimum ways to\nachieve minimum probability of error for binary signaling through a scalar\nmodulo AWGN channel is examined under different scenarios where the receiver\nhas access to full or partial information. In case of partial information at\nthe receiver, an iterative estimation rule is proposed to reduce the error\nrate, and the performance of different estimators are demonstrated in simulated\nexperiments.\n"}
+{"abstract": "  We estimate the chirality of the cosmological medium due to parity violating\ndecays of standard model particles, focusing on the example of tau leptons. The\nnon-trivial chirality is however too small to make a significant contribution\nto the cosmological magnetic field via the chiral-magnetic effect.\n"}
+{"abstract": "  For a bipartite graph $G$ with parts $X$ and $Y$, an $X$-interval coloring is\na proper edge coloring of $G$ by integers such that the colors on the edges\nincident to any vertex in $X$ form an interval. Denote by $\\chi'_{int}(G,X)$\nthe minimum $k$ such that $G$ has an $X$-interval coloring with $k$ colors. The\nauthor and Toft conjectured [Discrete Mathematics 339 (2016), 2628--2639] that\nthere is a polynomial $P(x)$ such that if $G$ has maximum degree at most\n$\\Delta$, then $\\chi'_{int}(G,X) \\leq P(\\Delta)$. In this short note, we prove\nthis conjecture; in fact, we prove that a cubic polynomial suffices. We also\ndeduce some improved upper bounds on $\\chi'_{int}(G,X)$ for bipartite graphs\nwith small maximum degree.\n"}
+{"abstract": "  In pursuit of explainability, we develop generative models for sequential\ndata. The proposed models provide state-of-the-art classification results and\nrobust performance for speech phone classification. We combine modern neural\nnetworks (normalizing flows) and traditional generative models (hidden Markov\nmodels - HMMs). Normalizing flow-based mixture models (NMMs) are used to model\nthe conditional probability distribution given the hidden state in the HMMs.\nModel parameters are learned through judicious combinations of time-tested\nBayesian learning methods and contemporary neural network learning methods. We\nmainly combine expectation-maximization (EM) and mini-batch gradient descent.\nThe proposed generative models can compute likelihood of a data and hence\ndirectly suitable for maximum-likelihood (ML) classification approach. Due to\nstructural flexibility of HMMs, we can use different normalizing flow models.\nThis leads to different types of HMMs providing diversity in data modeling\ncapacity. The diversity provides an opportunity for easy decision fusion from\ndifferent models. For a standard speech phone classification setup involving 39\nphones (classes) and the TIMIT dataset, we show that the use of standard\nfeatures called mel-frequency-cepstral-coeffcients (MFCCs), the proposed\ngenerative models, and the decision fusion together can achieve $86.6\\%$\naccuracy by generative training only. This result is close to state-of-the-art\nresults, for examples, $86.2\\%$ accuracy of PyTorch-Kaldi toolkit [1], and\n$85.1\\%$ accuracy using light gated recurrent units [2]. We do not use any\ndiscriminative learning approach and related sophisticated features in this\narticle.\n"}
+{"abstract": "  Explaining the predictions of opaque machine learning algorithms is an\nimportant and challenging task, especially as complex models are increasingly\nused to assist in high-stakes decisions such as those arising in healthcare and\nfinance. Most popular tools for post-hoc explainable artificial intelligence\n(XAI) are either insensitive to context (e.g., feature attributions) or\ndifficult to summarize (e.g., counterfactuals). In this paper, I introduce\n\\emph{rational Shapley values}, a novel XAI method that synthesizes and extends\nthese seemingly incompatible approaches in a rigorous, flexible manner. I\nleverage tools from decision theory and causal modeling to formalize and\nimplement a pragmatic approach that resolves a number of known challenges in\nXAI. By pairing the distribution of random variables with the appropriate\nreference class for a given explanation task, I illustrate through theory and\nexperiments how user goals and knowledge can inform and constrain the solution\nset in an iterative fashion. The method compares favorably to state of the art\nXAI tools in a range of quantitative and qualitative comparisons.\n"}
+{"abstract": "  We show that there are 4 infinite families of lattice equable kites, given by\ncorresponding Pell or Pell-like equations, but up to Euclidean motions, there\nare exactly 5 lattice equable trapezoids (2 isosceles, 2 right, 1 singular) and\n4 lattice equable cyclic quadrilaterals. We also show that, with one exception,\nthe interior diagonals of lattice equable quadrilaterals are irrational.\n"}
+{"abstract": "  A major challenge in the study of cryptography is characterizing the\nnecessary and sufficient assumptions required to carry out a given\ncryptographic task. The focus of this work is the necessity of a broadcast\nchannel for securely computing symmetric functionalities (where all the parties\nreceive the same output) when one third of the parties, or more, might be\ncorrupted. Assuming all parties are connected via a peer-to-peer network, but\nno broadcast channel (nor a secure setup phase) is available, we prove the\nfollowing characterization:\n  1) A symmetric $n$-party functionality can be securely computed facing\n$n/3\\le t<n/2$ corruptions (\\ie honest majority), if and only if it is\n\\emph{$(n-2t)$-dominated}; a functionality is $k$-dominated, if \\emph{any}\n$k$-size subset of its input variables can be set to \\emph{determine} its\noutput.\n  2) Assuming the existence of one-way functions, a symmetric $n$-party\nfunctionality can be securely computed facing $t\\ge n/2$ corruptions (\\ie no\nhonest majority), if and only if it is $1$-dominated and can be securely\ncomputed with broadcast.\n  It follows that, in case a third of the parties might be corrupted, broadcast\nis necessary for securely computing non-dominated functionalities (in which\n\"small\" subsets of the inputs cannot determine the output), including, as\ninteresting special cases, the Boolean XOR and coin-flipping functionalities.\n"}
+{"abstract": "  We consider Shimura varieties for orthogonal or spin groups acting on\nhermitian symmetric domains of type IV. We give regular p-adic integral models\nfor these varieties over odd primes p at which the level subgroup is the\nconnected stabilizer of a vertex lattice in the orthogonal space. Our\nconstruction is obtained by combining results of Kisin and the first author\nwith an explicit presentation and resolution of a corresponding local model.\n"}
+{"abstract": "  To indirectly study the internal structure of giant clumps in main sequence\ngalaxies at $z \\sim 1-3$, we target very turbulent and gas-rich local analogues\nfrom the DYNAMO sample with the Hubble Space Telescope, over a wavelength range\nof $\\sim 200-480$ nm. We present a catalog of 58 clumps identified in six\nDYNAMO galaxies, including the WFC3/UVIS F225W, F336W, and F467M photometry\nwhere the ($225-336$) and ($336-467$) colours are sensitive to extinction and\nstellar population age respectively. We measure the internal colour gradients\nof clumps themselves to study their age and extinction properties. We find a\nmarked colour trend within individual clumps, where the resolved colour\ndistributions show that clumps generally have bluer ($336-467$) colours\n(denoting very young ages) in their centers than at their edges, with little\nvariation in the ($225-336$) colour associated with extinction. Furthermore, we\nfind that clumps whose colours suggest they are older, are preferentially\nlocated closer toward the centers of their galaxies, and we find no young\nclumps at small galactocentric distances. Both results are consistent with\nsimulations of high-redshift star forming systems that show clumps form via\nviolent disk instability, and through dynamic processes migrate to the centers\nof their galaxies to contribute to bulge growth on timescales of a few 100 Myr,\nwhile continually forming stars in their centers. When we compare the DYNAMO\nclumps to those in these simulations, we find the best agreement with the\nlong-lived clumps.\n"}
+{"abstract": "  In this paper, we analyse the causal aspects of evolving marginally trapped\nsurfaces in a D-dimensional spherically symmetric spacetime, sourced by perfect\nfluid with a cosmological constant. The norm of the normal to the marginally\ntrapped tube is shown to be the product of lie derivatives of the expansion\nparameter of future outgoing null rays along the incoming and outgoing null\ndirections. We obtain a closed form expression for this norm in terms of\nprincipal density, pressure, areal radius and cosmological constant. For the\ncase of a homogeneous fluid distribution, we obtain a simple formula for\ndetermining the causal nature of the evolving horizons. We obtain the causal\nphase portraits and highlight the critical radius. We identify many solutions\nwhere the causal signature of the marginally trapped tube or marginally\nanti-trapped tube is always null despite having an evolving area. These\nsolutions don't comply with the standard inner and outer horizon classification\nfor degenerate horizons. we propose an alternate prescription for this\nclassification of these degenerate horizons.\n"}
+{"abstract": "  We examine the feasibility of the Bell test (i.e., detecting a violation of\nthe Bell inequality) with the ATLAS detector in Large Hadron Collider (LHC) at\nCERN through the flavor entanglement between the B mesons. After addressing the\npossible issues that arise associated with the experiment and how they may be\ntreated based on an analogy with conventional Bell tests, we show in our\nsimulation study that under realistic conditions (expected from the LHC Run 3\noperation) the Bell test is feasible under mild assumptions. The definitive\nfactor for this promising result lies primarily in the fact that the ATLAS\ndetector is capable of measuring the decay times of the B mesons independently,\nwhich was not available in the previous experiment with the Belle detector at\nKEK. This result suggests the possibility of the Bell test in much higher\nenergy domains and may open up a new arena for experimental studies of quantum\nfoundations.\n"}
+{"abstract": "  Our contributions with this paper are twofold. First, we elucidate the\nmethodological requirements for a risk framework of custodial operations and\nargue for the value of this type of risk model as complementary with\ncryptographic and blockchain security models. Second, we present a risk model\nin the form of a library of attack-trees for Revault -- an open-source custody\nprotocol. The model can be used by organisations as a risk quantification\nframework for a thorough security analysis in their specific deployment\ncontext. Our work exemplifies an approach that can be used independent of which\ncustody protocol is being considered, including complex protocols with multiple\nstakeholders and active defence infrastructure.\n"}
+{"abstract": "  Obtaining high-quality parallel corpora is of paramount importance for\ntraining NMT systems. However, as many language pairs lack adequate\ngold-standard training data, a popular approach has been to mine so-called\n\"pseudo-parallel\" sentences from paired documents in two languages. In this\npaper, we outline some problems with current methods, propose computationally\neconomical solutions to those problems, and demonstrate success with novel\nmethods on the Tatoeba similarity search benchmark and on a downstream task,\nnamely NMT. We uncover the effect of resource-related factors (i.e. how much\nmonolingual/bilingual data is available for a given language) on the optimal\nchoice of bitext mining approach, and echo problems with the oft-used BUCC\ndataset that have been observed by others. We make the code and data used for\nour experiments publicly available.\n"}
+{"abstract": "  In this paper we consider the massive scalar perturbation on the top of a\nsmall spinning-like black hole in context of Einstein-bumblebee modified\ngravity in order to probe the role of spontaneous Lorentz symmetry breaking on\nthe superradiance scattering and corresponding instability. We show that at the\nlow-frequency limit of the scalar wave the superradiance scattering will be\nenhanced with the Lorentz-violating parameter $\\alpha<0$ and will be weakened\nwith $\\alpha>0$. Moreover, by addressing the black hole bomb issue, we extract\nan improved bound in the instability regime indicating that $\\alpha<0$\nincreases the parameter space of the scalar field instability, while $\\alpha>0$\ndecreases it.\n"}
+{"abstract": "  We highlight new results on the localization number of a graph, a parameter\nderived from the localization graph searching game. After introducing the game\nand providing an overview of existing results, we describe recent results on\nthe localization number. We describe bounds or exact values of the localization\nnumber of incidence graphs of designs, polarity graphs, and Kneser graphs.\n"}
+{"abstract": "  One of the most critical tasks for startups is to validate their business\nmodel. Therefore, entrepreneurs try to collect information such as feedback\nfrom other actors to assess the validity of their assumptions and make\ndecisions. However, previous work on decisional guidance for business model\nvalidation provides no solution for the highly uncertain and complex context of\nearlystage startups. The purpose of this paper is, thus, to develop design\nprinciples for a Hybrid Intelligence decision support system (HI-DSS) that\ncombines the complementary capabilities of human and machine intelligence. We\nfollow a design science research approach to design a prototype artifact and a\nset of design principles. Our study provides prescriptive knowledge for HI-DSS\nand contributes to previous work on decision support for business models, the\napplications of complementary strengths of humans and machines for making\ndecisions, and support systems for extremely uncertain decision-making\nproblems.\n"}
+{"abstract": "  We compute the Chow groups of smooth Gushel-Mukai varieties of dimension $5$.\n"}
+{"abstract": "  We study the distribution of the Frobenius traces on $K3$ surfaces. We\ncompare experimental data with the predictions made by the Sato--Tate\nconjecture, i.e. with the theoretical distributions derived from the theory of\nLie groups assuming equidistribution. Our sample consists of generic $K3$\nsurfaces, as well as of such having real and complex multiplication. We report\nevidence for the Sato--Tate conjecture for the surfaces considered.\n"}
+{"abstract": "  In this work, the anisotropic variant of the quantum Rabi model with\ndifferent coupling strengths of the rotating and counter-rotating wave terms is\nstudied by the Bogoliubov operator approach. The anisotropy preserves the\nparity symmetry of the original model. We derive the corresponding\n$G$-function, which yields both the regular and exceptional eigenvalues. The\nexceptional eigenvalues correspond to the crossing points of two energy levels\nwith different parities and are doubly degenerate. We find analytically that\nthe ground-state and the first excited state can cross several times,\nindicating multiple first-order phase transitions as function of the coupling\nstrength. These crossing points are related to manifest parity symmetry of the\nHamiltonian, in contrast to the level crossings in the asymmetric quantum Rabi\nmodel which are caused by a hidden symmetry.\n"}
+{"abstract": "  We study the connection between risk aversion, number of consumers and\nuniqueness of equilibrium. We consider an economy with two goods and $c$\nimpatience types, where each type has additive separable preferences with HARA\nBernoulli utility function,\n$u_H(x):=\\frac{\\gamma}{1-\\gamma}\\left(b+\\frac{a}{\\gamma}x\\right)^{1-\\gamma}$.\nWe show that if $\\gamma\\in \\left(1, \\frac{c}{c-1}\\right]$, the equilibrium is\nunique. Moreover, the methods used, involving Newton's symmetric polynomials\nand Descartes' rule of signs, enable us to offer new sufficient conditions for\nuniqueness in a closed-form expression highlighting the role played by\nendowments, patience and specific HARA parameters. Finally, new necessary and\nsufficient conditions in ensuring uniqueness are derived for the particular\ncase of CRRA Bernoulli utility functions with $\\gamma =3$.\n"}
+{"abstract": "  Saliency maps have shown to be both useful and misleading for explaining\nmodel predictions especially in the context of images. In this paper, we\nperform sanity checks for text modality and show that the conclusions made for\nimage do not directly transfer to text. We also analyze the effects of the\ninput multiplier in certain saliency maps using similarity scores,\nmax-sensitivity and infidelity evaluation metrics. Our observations reveal that\nthe input multiplier carries input's structural patterns in explanation maps,\nthus leading to similar results regardless of the choice of model parameters.\nWe also show that the smoothness of a Neural Network (NN) function can affect\nthe quality of saliency-based explanations. Our investigations reveal that\nreplacing ReLUs with Softplus and MaxPool with smoother variants such as\nLogSumExp (LSE) can lead to explanations that are more reliable based on the\ninfidelity evaluation metric.\n"}
+{"abstract": "  The main result provide a common generalization for Ramsey-type theorems\nconcerning finite colorings of edge sets of complete graphs with vertices in\ninfinite semigroups. We capture the essence of theorems proved in different\nfields: for natural numbers due to Milliken--Tylor, Deuber--Hindman,\nBergelson--Hindman, for combinatorial covering properties due to Scheepers and\nTsaban, and local properties in function spaces due to Scheepers. To this end,\nwe use idempotent ultrafilters in the \\v{C}ech--Stone compactifications of\ndiscrete infinite semigroups and topological games. The research is motivated\nby the recent breakthrough work of Tsaban about colorings and the Menger\ncovering property.\n"}
+{"abstract": "  Quantum effects fundamentally engender exotic physical phenomena in\nmacroscopic systems, which advance next-generation technological applications.\nRotational tunneling that represents the quantum phenomenon of the librational\nmotion of molecules is ubiquitous in hydrogen-contained materials. However, its\ndirect manifestation in realizing macroscopic physical properties is elusive.\nHere we report an observation of reentrant ferroelectricity under low pressure\nthat is mediated by the rotational tunneling of ammonium ions in molecule-based\n(NH$_4$)$_2$FeCl$_5 \\cdot$H$_2$O. Applying a small pressure leads to a\ntransition from spin-driven ferroelectricity to paraelectricity coinciding with\nthe stabilization of a collinear magnetic phase. Such a transition is\nattributed to the hydrogen bond fluctuations via the rotational tunneling of\nammonium groups as supported by theoretical calculations. Higher pressure lifts\nthe quantum fluctuations and leads to a reentrant ferroelectric phase\nconcomitant with another incommensurate magnetic phase. These results\ndemonstrate that the rotational tunneling emerges as a new route to control\nmagnetic-related properties in soft magnets, opening avenues for designing\nmulti-functional materials and realizing potential quantum control.\n"}
+{"abstract": "  How to obtain good value estimation is one of the key problems in\nReinforcement Learning (RL). Current value estimation methods, such as DDPG and\nTD3, suffer from unnecessary over- or underestimation bias. In this paper, we\nexplore the potential of double actors, which has been neglected for a long\ntime, for better value function estimation in continuous setting. First, we\nuncover and demonstrate the bias alleviation property of double actors by\nbuilding double actors upon single critic and double critics to handle\noverestimation bias in DDPG and underestimation bias in TD3 respectively. Next,\nwe interestingly find that double actors help improve the exploration ability\nof the agent. Finally, to mitigate the uncertainty of value estimate from\ndouble critics, we further propose to regularize the critic networks under\ndouble actors architecture, which gives rise to Double Actors Regularized\nCritics (DARC) algorithm. Extensive experimental results on challenging\ncontinuous control tasks show that DARC significantly outperforms\nstate-of-the-art methods with higher sample efficiency.\n"}
+{"abstract": "  How often is a quintic polynomial solvable by radicals? We establish that the\nnumber of such polynomials, monic and irreducible with integer coefficients in\n$[-H,H]$, is $O(H^{3.91})$. More generally, we show that if $n \\ge 3$ and $n\n\\notin \\{ 7, 8, 10 \\}$ then there are $O(H^{n-1.017})$ monic, irreducible\npolynomials of degree $n$ with integer coefficients in $[-H,H]$ and Galois\ngroup not containing $A_n$. Save for the alternating group and degrees\n$7,8,10$, this establishes a 1936 conjecture of van der Waerden.\n"}
+{"abstract": "  Epitaxial growth on a surface vicinal to a high-symmetry crystallographic\nplane occurs through the propagation of atomic steps, a process called\nstep-flow growth. In some instances, the steps tend to form close groups (or\nbunches), a phenomenon termed step bunching, which corresponds to an\ninstability of the equal-spacing step propagation. Over the last fifty years,\nvarious mechanisms have been proposed to explain step bunching, the most\nprominent of which are the inverse Ehrlich-Schwoebel effect (i.e., the\nasymmetry which favors the attachment of adatoms from the upper terrace),\nelastically mediated interactions between steps (in heteroepitaxy), step\npermeability (in electromigration-controlled growth), and the chemical effect\n(which couples the diffusion fields on all terraces). Beyond the discussion of\nthe influence of each of these mechanisms taken independently on the propensity\nto bunching, we propose a unified treatment of the effect of these mechanisms\non the onset of the bunching instability, which also accounts for their\ninterplay. This is done in the setting of the so-called quasistatic\napproximation, which by permitting mostly analytical treatment, offers a clear\nview of the influence on stability of the combined mechanisms. In particular,\nwe find that the Ehrlich-Schwoebel effect, elastic step-interactions and the\nchemical effect combine in a quasi-additive fashion, whereas step permeability\nis neither stabilizing nor destabilizing per se but changes the relative\ninfluence of the three aforementioned mechanisms. In a companion paper, we\ndemonstrate and discuss the importance of another mechanism, which we call the\ndynamics effect, that emerges when relaxing the simplifying but questionable\nquasistatic approximation.\n"}
+{"abstract": "  Deep reinforcement learning (DRL) has recently shown its success in tackling\ncomplex combinatorial optimization problems. When these problems are extended\nto multiobjective ones, it becomes difficult for the existing DRL approaches to\nflexibly and efficiently deal with multiple subproblems determined by weight\ndecomposition of objectives. This paper proposes a concise meta-learning-based\nDRL approach. It first trains a meta-model by meta-learning. The meta-model is\nfine-tuned with a few update steps to derive submodels for the corresponding\nsubproblems. The Pareto front is built accordingly. The computational\nexperiments on multiobjective traveling salesman problems demonstrate the\nsuperiority of our method over most of learning-based and iteration-based\napproaches.\n"}
+{"abstract": "  Let $E^*$ be a finite complex of locally free sheaves on a complex manifold\n$X$. We prove that to every connection of type $(1,0)$ on $E^*$ it is\ncanonically associated an $L_{\\infty}$ morphism $g\\colon A^{0,\n*}_X(\\mathcal{H}om^*_{O_X}(E^*,E^*))\\to \\dfrac{A^{*,*}_X}{A^{\\ge 2,*}_X}[2]$\nthat lifts the 1-component of Buchweitz-Flenner semiregularity map. An\napplication to deformations of coherent sheaves on projective manifolds is\ngiven.\n"}
+{"abstract": "  Single image generative models perform synthesis and manipulation tasks by\ncapturing the distribution of patches within a single image. The classical (pre\nDeep Learning) prevailing approaches for these tasks are based on an\noptimization process that maximizes patch similarity between the input and\ngenerated output. Recently, however, Single Image GANs were introduced both as\na superior solution for such manipulation tasks, but also for remarkable novel\ngenerative tasks. Despite their impressiveness, single image GANs require long\ntraining time (usually hours) for each image and each task. They often suffer\nfrom artifacts and are prone to optimization issues such as mode collapse. In\nthis paper, we show that all of these tasks can be performed without any\ntraining, within several seconds, in a unified, surprisingly simple framework.\nWe revisit and cast the \"good-old\" patch-based methods into a novel\noptimization-free framework. We start with an initial coarse guess, and then\nsimply refine the details coarse-to-fine using patch-nearest-neighbor search.\nThis allows generating random novel images better and much faster than GANs. We\nfurther demonstrate a wide range of applications, such as image editing and\nreshuffling, retargeting to different sizes, structural analogies, image\ncollage and a newly introduced task of conditional inpainting. Not only is our\nmethod faster ($\\times 10^3$-$\\times 10^4$ than a GAN), it produces superior\nresults (confirmed by quantitative and qualitative evaluation), less artifacts\nand more realistic global structure than any of the previous approaches\n(whether GAN-based or classical patch-based).\n"}
+{"abstract": "  We provide a semigroup approach to the viscous Hamilton-Jacobi equation. It\nturns out that exponential Orlicz hearts are suitable spaces to handle the\n(quadratic) non-linearity of the Hamiltonian. Based on an abstract extension\nresult for nonlinear semigroups on spaces of continuous functions, we represent\nthe solution of the viscous Hamilton-Jacobi equation as a strongly continuous\nconvex semigroup on an exponential Orlicz heart. As a result, the solution\ndepends continuously on the initial data. We further determine the symmetric\nLipschitz set which is invariant under the semigroup. This automatically yields\na priori estimates and regularity in Sobolev spaces. In particular, on the\ndomain restricted to the symmetric Lipschitz set, the generator can be\nexplicitly determined and linked with the viscous Hamilton-Jacobi equation.\n"}
+{"abstract": "  Searching for a more compact network width recently serves as an effective\nway of channel pruning for the deployment of convolutional neural networks\n(CNNs) under hardware constraints. To fulfill the searching, a one-shot\nsupernet is usually leveraged to efficiently evaluate the performance\n\\wrt~different network widths. However, current methods mainly follow a\n\\textit{unilaterally augmented} (UA) principle for the evaluation of each\nwidth, which induces the training unfairness of channels in supernet. In this\npaper, we introduce a new supernet called Bilaterally Coupled Network (BCNet)\nto address this issue. In BCNet, each channel is fairly trained and responsible\nfor the same amount of network widths, thus each network width can be evaluated\nmore accurately. Besides, we leverage a stochastic complementary strategy for\ntraining the BCNet, and propose a prior initial population sampling method to\nboost the performance of the evolutionary search. Extensive experiments on\nbenchmark CIFAR-10 and ImageNet datasets indicate that our method can achieve\nstate-of-the-art or competing performance over other baseline methods.\nMoreover, our method turns out to further boost the performance of NAS models\nby refining their network widths. For example, with the same FLOPs budget, our\nobtained EfficientNet-B0 achieves 77.36\\% Top-1 accuracy on ImageNet dataset,\nsurpassing the performance of original setting by 0.48\\%.\n"}
+{"abstract": "  We propose here - for the first time in the literature, to our best knowledge\n- an electroweak unification based on the $SU(5)_{L}\\times U(1)_{Y}$ gauge\ngroup. The spontaneous symmetry breaking takes place in the manner\n$SU(5)_{L}\\times U(1)_{Y} \\rightarrow U(1)_{em}$, due to a particular Higgs\nsector consisting of five scalar quintuplets. Each scalar quintuplet acquires\nits own vacuum expectation value, by means of a proper parametrization which is\nworked out once the overall vacuum expectation value in the model is\nestablished. The decoupling of the low energy regime (corresponding to the\nStandard Model) from the high scale (required by out model here) is\nstraightforwardly achieved in order to preserve the consistency with the\npresent experimental data. Finally, a promising phenomenological outcome is\nderived by simply tuning a single free parameter. Our results include, besides\na viable one-parameter mass spectrum, also the prediction of precisely three\ngenerations in the fermion sector and the electric charge quantization.\n"}
+{"abstract": "  We study strategic interactions between firms with heterogeneous beliefs\nabout future climate impacts. To that end, we propose a Cournot-type\nequilibrium model where firms choose mitigation efforts and production\nquantities such as to maximize the expected profits under their subjective\nbeliefs. It is shown that optimal mitigation efforts are increased by the\npresence of uncertainty and act as substitutes; i.e., one firm's lack of\nmitigation incentivizes others to act more decidedly, and vice versa.\n"}
+{"abstract": "  Several of the latest GAN-based vocoders show remarkable achievements,\noutperforming autoregressive and flow-based competitors in both qualitative and\nquantitative measures while synthesizing orders of magnitude faster. In this\nwork, we hypothesize that the common factor underlying their success is the\nmulti-resolution discriminating framework, not the minute details in\narchitecture, loss function, or training strategy. We experimentally test the\nhypothesis by evaluating six different generators paired with one shared\nmulti-resolution discriminating framework. For all evaluative measures with\nrespect to text-to-speech syntheses and for all perceptual metrics, their\nperformances are not distinguishable from one another, which supports our\nhypothesis.\n"}
+{"abstract": "  In this perspective piece, I benchmark gallium arsenide, silicon, and\ngermanium as material platforms for gate-defined quantum dot spin qubits. I\nfocus on materials stacks, quantum dot architectures, bandstructure properties\nand qualifiers for disorder from electrical transport. This brief note is far\nfrom being exhaustive and should be considered a first introduction to the\nmaterials challenges and opportunities towards a larger spin qubit quantum\nprocessor.\n"}
+{"abstract": "  Clustering using deep neural network models have been extensively studied in\nrecent years. Among the most popular frameworks are the VAE and GAN frameworks,\nwhich learns latent feature representations of data through encoder / decoder\nneural net structures. This is a suitable base for clustering tasks, as the\nlatent space often seems to effectively capture the inherent essence of data,\nsimplifying its manifold and reducing noise. In this article, the VAE framework\nis used to investigate how probability function gradient ascent over data\npoints can be used to process data in order to achieve better clustering.\nImprovements in classification is observed comparing with unprocessed data,\nalthough state of the art results are not obtained. Processing data with\ngradient descent however results in more distinct cluster separation, making it\nsimpler to investigate suitable hyper parameter settings such as the number of\nclusters. We propose a simple yet effective method for investigating suitable\nnumber of clusters for data, based on the DBSCAN clustering algorithm, and\ndemonstrate that cluster number determination is facilitated with gradient\nprocessing. As an additional curiosity, we find that our baseline model used\nfor comparison; a GMM on a t-SNE latent space for a VAE structure with weight\none on reconstruction during training (autoencoder), yield state of the art\nresults on the MNIST data, to our knowledge not beaten by any other existing\nmodel.\n"}
+{"abstract": "  We studied the impact of field heterogeneity on entrainment in a system of\nuniformly interacting phase oscillators. Field heterogeneity is shown to induce\ndynamical heterogeneity in the system. In effect, the heterogeneous field\npartitions the system into interacting groups of oscillators that feel the same\nlocal field strength and phase. Based on numerical and analytical analysis of\nthe explicit dynamical equations derived from the periodically forced Kuramoto\nmodel, we found that the heterogeneous field can disrupt entrainment at\ndifferent field frequencies when compared to the homogeneous field. This\ntransition occurs when the phase- and frequency-locked synchronization between\ngroups of oscillators is broken at a critical field frequency, causing each\ngroup to enter a new dynamical state (disrupted state). Strikingly, it is shown\nthat disrupted dynamics can differ between groups.\n"}
+{"abstract": "  Large-scale machine learning and data mining methods routinely distribute\ncomputations across multiple agents to parallelize processing. The time\nrequired for the computations at the agents is affected by the availability of\nlocal resources and/or poor channel conditions giving rise to the \"straggler\nproblem\". As a remedy to this problem, we employ Unequal Error Protection (UEP)\ncodes to obtain an approximation of the matrix product in the distributed\ncomputation setting to provide higher protection for the blocks with higher\neffect on the final result. We characterize the performance of the proposed\napproach from a theoretical perspective by bounding the expected reconstruction\nerror for matrices with uncorrelated entries. We also apply the proposed coding\nstrategy to the computation of the back-propagation step in the training of a\nDeep Neural Network (DNN) for an image classification task in the evaluation of\nthe gradients. Our numerical experiments show that it is indeed possible to\nobtain significant improvements in the overall time required to achieve the DNN\ntraining convergence by producing approximation of matrix products using UEP\ncodes in the presence of stragglers.\n"}
+{"abstract": "  Classes of branched surfaces extend the classes of surfaces or 2-dimensional\nmanifolds satisfying suitable properties and defined in various manners. Reeb\nspaces of smooth maps of suitable classes into surfaces whose codimensions are\nnegative are regarded as branched surfaces. They are the spaces of all\nconnected components of preimages and natural quotient spaces of the manifolds\nof the domains. They are defined for general smooth maps and important\ntopological objects in differential topology. They also play important roles in\napplied or applications of mathematics such as projections in data analysis and\nvisualizations.\n  The present paper concerns global topologies of branched surfaces and\nexplicit construction of canonically obtained maps from the branched surfaces\ninto surfaces of the targets via fundamental operations. The class of these\ninduced maps extends the class of smooth immersions of compact surfaces into\nsurfaces with no boundaries. It is also regarded as a variant of the class of\nso-called generic smooth maps between these surfaces. We study so-called\n\"geography\" of such maps as a natural, important and new study and also study\nglobal topological properties of the branched surfaces such as embeddability\ninto 3-dimensional closed and connected manifolds as a well-known variant of\nembeddability of graphs into surfaces.\n  The author has also believed that understanding global topologies of Reeb\nspaces of suitable smooth maps are important and obtained several Reeb spaces\nwith information on global algebraic topological or differential topological\nproperties. The present study can be also regarded as a study on these objects\nin 2-dimensional cases for {\\it simple} fold maps, generalized versions of\nso-called Morse functions and can be seen a new work motivated by various\ntheory of Morse functions and higher dimensional variants.\n"}
+{"abstract": "  A thorough investigation of local structure, influencing macroscopic\nproperties of the solid is of potential interest. We investigated the local\nstructure of GaN nanowires (NWs) with different native defect concentration\nsynthesized by the chemical vapor deposition technique. Extended X-ray\nabsorption fine structure (EXAFS) analysis and semi-empirical and the density\nfunctional theory (DFT) calculations were used to address the effect of dopant\nincorporation along with other defects on the co-ordination number and bond\nlength values. The decrease of the bond length values along preferential\ncrystal axes in the local tetrahedral structure of GaN emphasizes the preferred\nlattice site for oxygen doping. The preferential bond length contraction is\ncorroborated by the simulations. We have also studied the impact on the local\natomic configuration of GaN NWs with Al incorporation. AlxGa1-xN NWs are\nsynthesized via novel ion beam techniques of ion beam mixing and\npost-irradiation diffusion process. The change in the local tetrahedral\nstructure of GaN with Al incorporation is investigated by EXAFS analysis. The\nanalysis provides a clear understanding of choosing a suitable process for\nternary III-nitride random alloy formation. The local structure study with the\nEXAFS analysis is corroborated with the observed macroscopic properties studied\nusing Raman spectroscopy.\n"}
+{"abstract": "  Let $V$ be a vector space with countable dimension over a field, and let $u$\nbe an endomorphism of it which is locally finite, i.e. $(u^k(x))_{k \\geq 0}$ is\nlinearly dependent for all $x$ in $V$. We give several necessary and sufficient\nconditions for the decomposability of $u$ into the sum of two square-zero\nendomorphisms. Moreover, if $u$ is invertible, we give necessary and sufficient\nconditions for the decomposability of $u$ into the product of two involutions,\nas well as for the decomposability of $u$ into the product of two unipotent\nendomorphisms of index $2$. Our results essentially extend the ones that are\nknown in the finite-dimensional setting.\n  In particular, we obtain that every strictly upper-triangular infinite matrix\nwith entries in a field is the sum of two square-zero infinite matrices\n(potentially non-triangular, though), and that every upper-triangular infinite\nmatrix (with entries in a field) with only $\\pm 1$ on the diagonal is the\nproduct of two involutory infinite matrices.\n"}
+{"abstract": "  In this paper, we explore the dynamics of a Hamiltonian system after a double\nvan der Waals potential energy surface degenerates into a single well. The\nenergy of the system is increased from the bottom of the potential well up to\nthe dissociation energy, which occurs when the system becomes open. In\nparticular, we study the bifurcations of the basic families of periodic orbits\nof this system as the energy increases using Lagrangian descriptors and\nPoincar\\'e maps. We investigate the capability of Lagrangian descriptors to\nfind periodic orbits of bifurcating families for the case of resonant,\nsaddle-node and pitchfork bifurcations.\n"}
+{"abstract": "  In this paper, we propose a novel approach for the transcription of speech\nconversations with natural speaker overlap, from single channel recordings. We\npropose a combination of a speaker diarization system and a hybrid automatic\nspeech recognition (ASR) system with speaker activity assisted acoustic model\n(AM). An end-to-end neural network system is used for speaker diarization. Two\narchitectures, (i) input conditioned AM, and (ii) gated features AM, are\nexplored to incorporate the speaker activity information. The models output\nspeaker specific senones. The experiments on Switchboard telephone\nconversations show the advantage of incorporating speaker activity information\nin the ASR system for recordings with overlapped speech. In particular, an\nabsolute improvement of $11\\%$ in word error rate (WER) is seen for the\nproposed approach on natural conversation speech with automatic diarization.\n"}
+{"abstract": "  Recent studies have provided both empirical and theoretical evidence\nillustrating that heavy tails can emerge in stochastic gradient descent (SGD)\nin various scenarios. Such heavy tails potentially result in iterates with\ndiverging variance, which hinders the use of conventional convergence analysis\ntechniques that rely on the existence of the second-order moments. In this\npaper, we provide convergence guarantees for SGD under a state-dependent and\nheavy-tailed noise with a potentially infinite variance, for a class of\nstrongly convex objectives. In the case where the $p$-th moment of the noise\nexists for some $p\\in [1,2)$, we first identify a condition on the Hessian,\ncoined '$p$-positive (semi-)definiteness', that leads to an interesting\ninterpolation between positive semi-definite matrices ($p=2$) and diagonally\ndominant matrices with non-negative diagonal entries ($p=1$). Under this\ncondition, we then provide a convergence rate for the distance to the global\noptimum in $L^p$. Furthermore, we provide a generalized central limit theorem,\nwhich shows that the properly scaled Polyak-Ruppert averaging converges weakly\nto a multivariate $\\alpha$-stable random vector. Our results indicate that even\nunder heavy-tailed noise with infinite variance, SGD can converge to the global\noptimum without necessitating any modification neither to the loss function or\nto the algorithm itself, as typically required in robust statistics. We\ndemonstrate the implications of our results to applications such as linear\nregression and generalized linear models subject to heavy-tailed data.\n"}
+{"abstract": "  During the research cruise AL547 with RV ALKOR (October 20-31, 2020), a\ncollaborative underwater network of ocean observation systems was deployed in\nBoknis Eck (SW Baltic Sea, German exclusive economic zone (EEZ)) in the context\nof the project ARCHES (Autonomous Robotic Networks to Help Modern Societies).\nThis network was realized via a Digital Twin Prototype approach. During that\nperiod different scenarios were executed to demonstrate the feasibility of\nDigital Twins in an extreme environment such as underwater. One of the\nscenarios showed the collaboration of stage IV Digital Twins with their\nphysical counterparts on the seafloor. This way, we address the research\nquestion, whether Digital Twins represent a feasible approach to operate mobile\nad hoc networks for ocean and coastal observation.\n"}
+{"abstract": "  This paper introduces an adaptive model-free deep reinforcement approach that\ncan recognize and adapt to the diurnal patterns in the ride-sharing environment\nwith car-pooling. Deep Reinforcement Learning (RL) suffers from catastrophic\nforgetting due to being agnostic to the timescale of changes in the\ndistribution of experiences. Although RL algorithms are guaranteed to converge\nto optimal policies in Markov decision processes (MDPs), this only holds in the\npresence of static environments. However, this assumption is very restrictive.\nIn many real-world problems like ride-sharing, traffic control, etc., we are\ndealing with highly dynamic environments, where RL methods yield only\nsub-optimal decisions. To mitigate this problem in highly dynamic environments,\nwe (1) adopt an online Dirichlet change point detection (ODCP) algorithm to\ndetect the changes in the distribution of experiences, (2) develop a Deep Q\nNetwork (DQN) agent that is capable of recognizing diurnal patterns and making\ninformed dispatching decisions according to the changes in the underlying\nenvironment. Rather than fixing patterns by time of week, the proposed approach\nautomatically detects that the MDP has changed, and uses the results of the new\nmodel. In addition to the adaptation logic in dispatching, this paper also\nproposes a dynamic, demand-aware vehicle-passenger matching and route planning\nframework that dynamically generates optimal routes for each vehicle based on\nonline demand, vehicle capacities, and locations. Evaluation on New York City\nTaxi public dataset shows the effectiveness of our approach in improving the\nfleet utilization, where less than 50% of the fleet are utilized to serve the\ndemand of up to 90% of the requests, while maximizing profits and minimizing\nidle times.\n"}
+{"abstract": "  There is a growing concern that e-commerce platforms are amplifying\nvaccine-misinformation. To investigate, we conduct two-sets of algorithmic\naudits for vaccine misinformation on the search and recommendation algorithms\nof Amazon -- world's leading e-retailer. First, we systematically audit\nsearch-results belonging to vaccine-related search-queries without logging into\nthe platform -- unpersonalized audits. We find 10.47% of search-results promote\nmisinformative health products. We also observe ranking-bias, with Amazon\nranking misinformative search-results higher than debunking search-results.\nNext, we analyze the effects of personalization due to account-history, where\nhistory is built progressively by performing various real-world user-actions,\nsuch as clicking a product. We find evidence of filter-bubble effect in\nAmazon's recommendations; accounts performing actions on misinformative\nproducts are presented with more misinformation compared to accounts performing\nactions on neutral and debunking products. Interestingly, once user clicks on a\nmisinformative product, homepage recommendations become more contaminated\ncompared to when user shows an intention to buy that product.\n"}
+{"abstract": "  With the advent of deep learning, the number of works proposing new methods\nor improving existent ones has grown exponentially in the last years. In this\nscenario, \"very deep\" models were emerging, once they were expected to extract\nmore intrinsic and abstract features while supporting a better performance.\nHowever, such models suffer from the gradient vanishing problem, i.e.,\nbackpropagation values become too close to zero in their shallower layers,\nultimately causing learning to stagnate. Such an issue was overcome in the\ncontext of convolution neural networks by creating \"shortcut connections\"\nbetween layers, in a so-called deep residual learning framework. Nonetheless, a\nvery popular deep learning technique called Deep Belief Network still suffers\nfrom gradient vanishing when dealing with discriminative tasks. Therefore, this\npaper proposes the Residual Deep Belief Network, which considers the\ninformation reinforcement layer-by-layer to improve the feature extraction and\nknowledge retaining, that support better discriminative performance.\nExperiments conducted over three public datasets demonstrate its robustness\nconcerning the task of binary image classification.\n"}
+{"abstract": "  Nanowires (NWs) with their quasi-one-dimensionality often present different\nstructural and opto-electronic properties than their thin-film counterparts.\nThe thinner they are the larger these differences are, in particular in the\ncarrier-phonon scattering and thermal conductivity. In this work, we present\nfemtosecond transient absorbance measurements on GaAs0.8P0.2 NWs of two\ndifferent diameters, 36 and 51 nm. The results show that thinner NWs sustain\nthe hot-carriers at a higher temperature for longer times than thicker NWs. We\nexplain the observation suggesting that in thinner NWs, the build-up of a\nhot-phonon bottleneck is easier than in thicker NWs because of the increased\nphonon scattering at the NW sidewalls which facilitates the build-up of a large\nphonon density. The large number of optical phonons emitted during the carrier\nrelaxation processes generate a non-equilibrium population of acoustic phonons\nthat propagates less efficiently in thin NWs. This makes the possible\nacoustic-to-optical phonon up-conversion process easier, which prolongs the LO\nphonon lifetime resulting in the slowdown of the carrier cooling. The important\nobservation that the carrier temperature in thin NWs is higher than in thick\nNWs already at the beginning of the hot carrier regime suggests that the\nphonon-mediated scattering processes in the non-thermal regime play a major\nrole at least for the carrier densities investigated here (8x1018-4x1019 cm-3).\nOur results also suggest that the boundary scattering of phonons at crystal\ndefects is negligible compared to the surface scattering at the NW sidewalls.\n"}
+{"abstract": "  The uncertainty principle states that a measurement inevitably disturbs the\nsystem, while it is often supposed that a quantum system is not disturbed\nwithout state change. Korzekwa, Jennings, and Rudolph [Phys. Rev. A 89, 052108\n(2014)] pointed out a conflict between those two views, and concluded that\nstate-dependent formulations of error-disturbance relations are untenable.\nHere, we reconcile the conflict by showing that a quantum system is disturbed\nwithout state change, in favor of the recently obtained universally valid\nstate-dependent error-disturbance relations.\n"}
+{"abstract": "  This work focuses on comparing different solutions for machine translation on\nlow resource language pairs, namely, with zero-shot transfer learning and\nunsupervised machine translation. We discuss how the data size affects the\nperformance of both unsupervised MT and transfer learning. Additionally we also\nlook at how the domain of the data affects the result of unsupervised MT. The\ncode to all the experiments performed in this project are accessible on Github.\n"}
+{"abstract": "  This paper introduces properties and relations for proximal homotopy as well\nas descriptive proximal homotopy. A number of results are given for the\nhomotopy between Lodato-proximally continuous maps and for the homotopy between\ndescriptive Lodato proximally continuous maps. This paper also introduces\nhomotopic cycles in conjunction with paths in either proximity spaces in\ngeneral or in descriptive proximity spaces. Three main results in this paper\nare (1) that the product of descriptive Lodata proximity (dlp) spaces is also a\ndlp space, (2) every descriptive dlp relation is an equivalence relation and\n(3) every homotopic cycle has a free group representation.\n"}
+{"abstract": "  The analytical theory of our earlier study (Mortensen et al. (2021),\nMathematical Medicine and Biology, 38(1), pp. 106-131) is extended to address\nthe outstanding cases of fibroblast barrier distribution and myocyte strait\ndistribution. In particular, closed-form approximations to the resting membrane\npotential and to the critical parameter values for propagation are derived for\nthese two non-uniform fibroblast distributions and are in good agreement with\nnumerical estimates.\n"}
+{"abstract": "  Additive robotic construction of building-scale discrete bar structures, such\nas trusses and space frames, is increasingly attractive due to the potential\nimprovements in efficiency, safety, and design possibilities. However,\nprogramming complex robots, such as manipulators with seven degrees of freedom,\nto successfully complete construction tasks can be tedious, challenging, or\nimpossible for a human to do manually. Namely, the structure must be\nconstructed in a sequence that preserves structural properties, such as\nstiffness, at each step. At the same time, this sequence must allow for the\nrobot to precisely manipulate elements within the in-progress structure while\nrespecting geometric constraints that, for example, ensure the robot does not\ncollide with what it has built. In this work, we present an automated and newly\ngeneralized planning approach for jointly finding a construction sequence and\nrobot motion plan for additive construction that satisfies these requirements.\nOur approach can be applied in a variety of additive construction processes,\nand we demonstrate it specifically on spatial extrusion and discrete bar\nassembly in this paper. We demonstrate the effectiveness of our approach on\nseveral simulated and real-world extrusion and assembly tasks, including a\nhuman-scale physical prototype, for which our algorithm is deployed for the\nfirst time to plan the assembly of a complicated double tangent bar system\ndesign.\n"}
+{"abstract": "  Although previous research on Aspect-based Sentiment Analysis (ABSA) for\nIndonesian reviews in hotel domain has been conducted using CNN and XGBoost,\nits model did not generalize well in test data and high number of OOV words\ncontributed to misclassification cases. Nowadays, most state-of-the-art results\nfor wide array of NLP tasks are achieved by utilizing pretrained language\nrepresentation. In this paper, we intend to incorporate one of the foremost\nlanguage representation model, BERT, to perform ABSA in Indonesian reviews\ndataset. By combining multilingual BERT (m-BERT) with task transformation\nmethod, we manage to achieve significant improvement by 8% on the F1-score\ncompared to the result from our previous study.\n"}
+{"abstract": "  We report a Karl G. Jansky Very Large Array (JVLA) search for redshifted\nCO(1-0) or CO(2-1) emission, and a Hubble Space Telescope Wide Field Camera~3\n(HST-WFC3) search for rest-frame near-ultraviolet (NUV) stellar emission, from\nseven HI-selected galaxies associated with high-metallicity ([M/H]~$\\geq -1.3$)\ndamped Ly$\\alpha$ absorbers (DLAs) at $z\\approx 4$. The galaxies were earlier\nidentified by ALMA imaging of their [CII]~158$\\mu$m emission. We also used the\nJVLA to search for CO(2-1) emission from the field of a low-metallicity\n([M/H]~$=-2.47$) DLA at $z\\approx 4.8$. No statistically significant CO\nemission is detected from any of the galaxies, yielding upper limits of\n$M_{mol}<(7.4 - 17.9)\\times 10^{10}\\times (\\alpha_{CO}/4.36) M_\\odot$ on their\nmolecular gas mass. We detect rest-frame NUV emission from four of the seven\n[CII]~158$\\mu$m-emitting galaxies, the first detections of the stellar\ncontinuum from HI-selected galaxies at $z\\gtrsim 4$. The HST-WFC3 images yield\ntypical sizes of the stellar continua of $\\approx 2-4$~kpc and inferred\ndust-unobscured star-formation rates (SFRs) of $\\approx 5.0-17.5 M_\\odot$/yr,\nconsistent with, or slightly lower than, the total SFRs estimated from the\nfar-infrared (FIR) luminosity. We further stacked the CO(2-1) emission signals\nof six [CII]~158$\\mu$m-emitting galaxies in the image plane. Our non-detection\nof CO(2-1) emission in the stacked image yields the limit $M_{mol}<4.1 \\times\n10^{10}\\times (\\alpha_{CO}/4.36) M_\\odot$ on the average molecular gas mass of\nthe six galaxies. Our molecular gas mass estimates and NUV SFR estimates in\nHI-selected galaxies at $z\\approx 4$ are consistent with those of main-sequence\ngalaxies with similar [CII]~158$\\mu$m and FIR luminosities at similar\nredshifts. However, the NUV emission in the HI-selected galaxies appears more\nextended than that in main-sequence galaxies at similar redshifts.\n"}
+{"abstract": "  In a recent paper by Jafarov, Nagiyev, Oste and Van der Jeugt (2020 {\\sl J.\\\nPhys.\\ A} {\\bf 53} 485301), a confined model of the non-relativistic quantum\nharmonic oscillator, where the effective mass and the angular frequency are\ndependent on the position, was constructed and it was shown that the\nconfinement parameter gets quantized. By using a point canonical transformation\nstarting from the constant-mass Schr\\\"odinger equation for the Rosen-Morse II\npotential, it is shown here that similar results can be easily obtained without\nquantizing the confinement parameter. In addition, an extension to a confined\nshifted harmonic oscillator directly follows from the same point canonical\ntransformation.\n"}
+{"abstract": "  We describe the design of a soldered sapphire optical viewport, useful for\nspectroscopic applications of samples at high temperatures and high pressures.\nThe sapphire window is bonded via active soldering to a metal flange with a\nstructure of two c-shaped rings made of different metallic materials in\nbetween, as to mitigate thermally induced stress. A spectroscopic cell equipped\nwith two of the optical viewports has been successfully operated with alkali\nmetals in a noble gas environment at temperatures in the range $20\\,${\\deg}C to\n$450\\,${\\deg}C at noble gas pressures from $10^{-6}\\,$mbar to $330\\,$bar. At\nthe upper pressure range, we observe a leakage rate smaller than our readout\naccuracy of $30\\,$mbar per day.\n"}
+{"abstract": "  We propose new width-based planning and learning algorithms inspired from a\ncareful analysis of the design decisions made by previous width-based planners.\nThe algorithms are applied over the Atari-2600 games and our best performing\nalgorithm, Novelty guided Critical Path Learning (N-CPL), outperforms the\npreviously introduced width-based planning and learning algorithms $\\pi$-IW(1),\n$\\pi$-IW(1)+ and $\\pi$-HIW(n, 1). Furthermore, we present a taxonomy of the\nAtari-2600 games according to some of their defining characteristics. This\nanalysis of the games provides further insight into the behaviour and\nperformance of the algorithms introduced. Namely, for games with large\nbranching factors, and games with sparse meaningful rewards, N-CPL outperforms\n$\\pi$-IW, $\\pi$-IW(1)+ and $\\pi$-HIW(n, 1).\n"}
+{"abstract": "  Human social behavior plays a crucial role in how pathogens like SARS-CoV-2\nor fake news spread in a population. Social interactions determine the contact\nnetwork among individuals, while spreading, requiring individual-to-individual\ntransmission, takes place on top of the network. Studying the topological\naspects of a contact network, therefore, not only has the potential of leading\nto valuable insights into how the behavior of individuals impacts spreading\nphenomena, but it may also open up possibilities for devising effective\nbehavioral interventions. Because of the temporal nature of interactions -\nsince the topology of the network, containing who is in contact with whom,\nwhen, for how long, and in which precise sequence, varies (rapidly) in time -\nanalyzing them requires developing network methods and metrics that respect\ntemporal variability, in contrast to those developed for static (i.e.,\ntime-invariant) networks. Here, by means of event mapping, we propose a method\nto quantify how quickly agents mingle by transforming temporal network data of\nagent contacts. We define a novel measure called 'contact sequence centrality',\nwhich quantifies the impact of an individual on the contact sequences,\nreflecting the individual's behavioral potential for spreading. Comparing\ncontact sequence centrality across agents allows for ranking the impact of\nagents and identifying potential 'behavioral super-spreaders'. The method is\napplied to social interaction data collected at an art fair in Amsterdam. We\nrelate the measure to the existing network metrics, both temporal and static,\nand find that (mostly at longer time scales) traditional metrics lose their\nresemblance to contact sequence centrality. Our work highlights the importance\nof accounting for the sequential nature of contacts when analyzing social\ninteractions.\n"}
+{"abstract": "  We propose deep neural network algorithms to calculate efficient frontier in\nsome Mean-Variance and Mean-CVaR portfolio optimization problems. We show that\nwe are able to deal with such problems when both the dimension of the state and\nthe dimension of the control are high. Adding some additional constraints, we\ncompare different formulations and show that a new projected feedforward\nnetwork is able to deal with some global constraints on the weights of the\nportfolio while outperforming classical penalization methods. All developed\nformulations are compared in between. Depending on the problem and its\ndimension, some formulations may be preferred.\n"}
+{"abstract": "  We show that the assumption of a weak form of the Hardy-Littlewood conjecture\non the Goldbach problem suffices to disprove the possible existence of\nexceptional zeros of Dirichlet L-functions. This strengthens a result of the\nauthors named in the title.\n"}
+{"abstract": "  Imaging in clinical routine is subject to changing scanner protocols,\nhardware, or policies in a typically heterogeneous set of acquisition hardware.\nAccuracy and reliability of deep learning models suffer from those changes as\ndata and targets become inconsistent with their initial static training set.\nContinual learning can adapt to a continuous data stream of a changing imaging\nenvironment. Here, we propose a method for continual active learning on a data\nstream of medical images. It recognizes shifts or additions of new imaging\nsources - domains -, adapts training accordingly, and selects optimal examples\nfor labelling. Model training has to cope with a limited labelling budget,\nresembling typical real world scenarios. We demonstrate our method on\nT1-weighted magnetic resonance images from three different scanners with the\ntask of brain age estimation. Results demonstrate that the proposed method\noutperforms naive active learning while requiring less manual labelling.\n"}
+{"abstract": "  We investigate the large-scale clustering of the final spectroscopic sample\nof quasars from the recently completed extended Baryon Oscillation\nSpectroscopic Survey (eBOSS). The sample contains $343708$ objects in the\nredshift range $0.8<z<2.2$ and $72667$ objects with redshifts $2.2<z<3.5$,\ncovering an effective area of $4699~{\\rm deg}^{2}$. We develop a neural\nnetwork-based approach to mitigate spurious fluctuations in the density field\ncaused by spatial variations in the quality of the imaging data used to select\ntargets for follow-up spectroscopy. Simulations are used with the same angular\nand radial distributions as the real data to estimate covariance matrices,\nperform error analyses, and assess residual systematic uncertainties. We\nmeasure the mean density contrast and cross-correlations of the eBOSS quasars\nagainst maps of potential sources of imaging systematics to address algorithm\neffectiveness, finding that the neural network-based approach outperforms\nstandard linear regression. Stellar density is one of the most important\nsources of spurious fluctuations, and a new template constructed using data\nfrom the Gaia spacecraft provides the best match to the observed quasar\nclustering. The end-product from this work is a new value-added quasar\ncatalogue with the improved weights to correct for nonlinear imaging systematic\neffects, which will be made public. Our quasar catalogue is used to measure the\nlocal-type primordial non-Gaussianity in our companion paper, Mueller et al. in\npreparation.\n"}
+{"abstract": "  Advancements in heterogeneous computing technologies enable the significant\npotential of virtual reality (VR) applications. To offer the best user\nexperience (UX), a system should adopt an untethered, wireless-network-based\narchitecture to transfer VR content between the user and the content generator.\nHowever, modern wireless network technologies make implementing such an\narchitecture challenging, as VR applications require superior video quality --\nwith high resolution, high frame rates, and very low latency.\n  This paper presents OpenUVR, an open-source framework that uses commodity\nhardware components to satisfy the demands of interactive, real-time VR\napplications. OpenUVR significantly improves UX through a redesign of the\nsystem stack and addresses the most time-sensitive issues associated with\nredundant memory copying in modern computing systems. OpenUVR presents a\ncross-layered VR datapath to avoid redundant data operations and computation\namong system components, OpenUVR customizes the network stack to eliminate\nunnecessary memory operations incurred by mismatching data formats in each\nlayer, and OpenUVR uses feedback from mobile devices to remove memory buffers.\n  Together, these modifications allow OpenUVR to reduce VR application delays\nto 14.32 ms, meeting the 20 ms minimum latency in avoiding motion sickness. As\nan open-source system that is fully compatible with commodity hardware, OpenUVR\noffers the research community an opportunity to develop, investigate, and\noptimize applications for untethered, high-performance VR architectures.\n"}
+{"abstract": "  Magnetic field amplification by relativistic streaming plasma instabilities\nis central to a wide variety of high-energy astrophysical environments as well\nas to laboratory scenarios associated with intense lasers and electron beams.\nWe report on a new secondary nonlinear instability which arises for\nrelativistic dilute electron beams after the saturation of the linear Weibel\ninstability. This instability grows due to the transverse magnetic pressure\nassociated with the beam current filaments, which cannot be quickly neutralized\ndue to the inertia of background ions. We show that it can amplify the magnetic\nfield strength and spatial scale by orders of magnitude, leading to large-scale\nplasma cavities with strong magnetic field and to very efficient conversion of\nthe beam kinetic energy into magnetic energy. The instability growth rate,\nsaturation level, and scale length are derived analytically and shown to be in\ngood agreement with fully-kinetic simulations.\n"}
+{"abstract": "  We introduce a novel data-driven framework for the design of targeted gene\npanels for estimating exome-wide biomarkers in cancer immunotherapy. Our first\ngoal is to develop a generative model for the profile of mutation across the\nexome, which allows for gene- and variant type-dependent mutation rates. Based\non this model, we then propose a new procedure for estimating biomarkers such\nas Tumour Mutation Burden and Tumour Indel Burden. Our approach allows the\npractitioner to select a targeted gene panel of a prespecified size, and then\nconstruct an estimator that only depends on the selected genes. Alternatively,\nthe practitioner may apply our method to make predictions based on an existing\ngene panel, or to augment a gene panel to a given size. We demonstrate the\nexcellent performance of our proposal using an annotated mutation dataset from\n1144 Non-Small Cell Lung Cancer patients.\n"}
+{"abstract": "  Mechanically interlocked molecules have marked a breakthrough in the field of\ntopological chemistry and boosted the vigorous development of molecular\nmachinery. As an archetypal example of the interlocked molecules, catenanes\ncomprise macrocycles that are threaded through one another like links in a\nchain. Inspired by the transition metal-templated approach of catenanes\nsynthesis, the hierarchical assembly of DNA origami catenanes templated by gold\nnanoparticles is demonstrated in this work. DNA origami catenanes, which\ncontain two, three or four interlocked rings are successfully created. In\nparticular, the origami rings within the individual catenanes can be set free\nwith respect to one another by releasing the interconnecting gold\nnanoparticles. This work will set the basis for rich progress toward DNA-based\nmolecular architectures with unique structural programmability and well-defined\ntopology.\n"}
+{"abstract": "  Speech enhancement algorithms based on deep learning have greatly surpassed\ntheir traditional counterparts and are now being considered for the task of\nremoving acoustic echo from hands-free communication systems. This is a\nchallenging problem due to both real-world constraints like loudspeaker\nnon-linearities, and to limited compute capabilities in some communication\nsystems. In this work, we propose a system combining a traditional acoustic\necho canceller, and a low-complexity joint residual echo and noise suppressor\nbased on a hybrid signal processing/deep neural network (DSP/DNN) approach. We\nshow that the proposed system outperforms both traditional and other neural\napproaches, while requiring only 5.5% CPU for real-time operation. We further\nshow that the system can scale to even lower complexity levels.\n"}
+{"abstract": "  Modern software systems rely on Deep Neural Networks (DNN) when processing\ncomplex, unstructured inputs, such as images, videos, natural language texts or\naudio signals. Provided the intractably large size of such input spaces, the\nintrinsic limitations of learning algorithms, and the ambiguity about the\nexpected predictions for some of the inputs, not only there is no guarantee\nthat DNN's predictions are always correct, but rather developers must safely\nassume a low, though not negligible, error probability. A fail-safe Deep\nLearning based System (DLS) is one equipped to handle DNN faults by means of a\nsupervisor, capable of recognizing predictions that should not be trusted and\nthat should activate a healing procedure bringing the DLS to a safe state. In\nthis paper, we propose an approach to use DNN uncertainty estimators to\nimplement such a supervisor. We first discuss the advantages and disadvantages\nof existing approaches to measure uncertainty for DNNs and propose novel\nmetrics for the empirical assessment of the supervisor that rely on such\napproaches. We then describe our publicly available tool UNCERTAINTY-WIZARD,\nwhich allows transparent estimation of uncertainty for regular tf.keras DNNs.\nLastly, we discuss a large-scale study conducted on four different subjects to\nempirically validate the approach, reporting the lessons-learned as guidance\nfor software engineers who intend to monitor uncertainty for fail-safe\nexecution of DLS.\n"}
+{"abstract": "  The leading difficulty in achieving the contrast necessary to directly image\nexoplanets and associated structures (eg. protoplanetary disks) at wavelengths\nranging from the visible to the infrared are quasi-static speckles, and they\nare hard to distinguish from planets at the necessary level of precision. The\nsource of the quasi-static speckles is hardware aberrations that are not\ncompensated by the adaptive optics system. These aberrations are called\nnon-common path aberrations (NCPA). In 2013, Frazin showed how, in principle,\nsimultaneous millisecond (ms) telemetry from the wavefront sensor (WFS) and the\nscience camera behind a stellar coronagraph can be used as input into a\nregression scheme that simultaneously and self-consistently estimates the NCPA\nand the sought-after image of the planetary system (the exoplanet image). The\nphysical principle underlying the regression method is rather simple: the\nwavefronts, which are measured by the WFS, modulate the speckles caused by the\nNCPA and therefore can be used as probes of the optical system. The most\nimportant departure from realism in the author's 2013 article was the\nassumption that the WFS made error-free measurements. The simulations in Part I\nprovide results on the joint regression on the NCPA and the exoplanet image\nfrom three different methods, called the ideal, the naive, and the\nbias-corrected estimators. The ideal estimator is not physically realizable but\nis a useful as a benchmark for simulation studies, but the other two are, at\nleast in principle. This article provides the regression equations for all\nthree of these estimators as well as a supporting technical discussion.\nBriefly, the naive estimator simply uses the noisy WFS measurements without any\nattempt to account for the errors, and the bias-corrected estimator uses\nstatistical knowledge of the wavefronts to treat errors in the WFS\nmeasurements.\n"}
+{"abstract": "  As a result of the Hund's coupling, the band structure of the conducting\nelectrons in the skyrmion crystal (SkX) shares similar topological properties\nwith that of graphene, such as its cone-like shape, nonzero band Chern number,\nedge states, and etc. In this work, we rigorously demonstrate that the Klein\ntunneling phenomena is also shared by these two. We use the Green's function\ntechnique and calculated the transmission probability of the electrons\ntunneling through an electrostatic barrier in the SkX expressed by the double\nexchange model. Numerical results of the SkX reproduced the Dirac model\nobtained by linear fitting the two-dimensional band structure of the SkX.\n"}
+{"abstract": "  Goods trade is a supply chain transaction that involves shippers buying goods\nfrom suppliers and carriers providing goods transportation. Shippers are issued\ninvoices from suppliers and carriers. Shippers carry out goods receiving and\ninvoice processing before payment processing of bills for suppliers and\ncarriers, where invoice processing includes tasks like processing claims and\nadjusting the bill payments. Goods receiving involves verification of received\ngoods by the Shipper's receiving team. Invoice processing is carried out by the\nShipper's accounts payable team, which in turn is verified by the accounts\nreceivable teams of suppliers and carriers. This paper presents a\nblockchain-based accounts payable system that generates claims for the\ndeficiency in the goods received and accordingly adjusts the payment in the\nbills for suppliers and carriers. Primary motivations for these supply chain\norganizations to adopt blockchain-based accounts payable systems are to\neliminate the process redundancies (accounts payable vs. accounts receivable),\nto reduce the number of disputes among the transacting participants, and to\naccelerate the accounts payable processes via optimizations in the claims\ngeneration and blockchain-based dispute reconciliation.\n"}
+{"abstract": "  This study demonstrates the implementation of the stochastic ruler discrete\nsimulation optimization method for calibrating an agent-based model (ABM)\ndeveloped to simulate hepatitis C virus (HCV) transmission. The ABM simulates\nHCV transmission between agents interacting in multiple environments relevant\nfor HCV transmission in the Indian context. Key outcomes of the ABM are HCV and\ninjecting drug user (IDU) prevalences among the simulated cohort. Certain input\nparameters of the ABM need to be calibrated so that simulation outcomes attain\nvalues as close as possible to real-world HCV and IDU prevalences. We\nconceptualize the calibration process as a discrete simulation optimization\nproblem by discretizing the calibration parameter ranges, defining an\nappropriate objective function, and then applying the stochastic ruler random\nsearch method to solve this problem. We also present a method that exploits the\nmonotonic relationship between the simulation outcomes and calibration\nparameters to yield improved calibration solutions with lesser computational\neffort.\n"}
+{"abstract": "  We study the propagation of a specific class of instrumental systematics to\nthe reconstruction of the B-mode power spectrum of the cosmic microwave\nbackground (CMB). We focus on the non-idealities of the half-wave plate (HWP),\na polarization modulator that is to be deployed by future CMB experiments, such\nas the phase-A satellite mission LiteBIRD. We study the effects of non-ideal\nHWP properties, such as transmittance, phase shift, and cross-polarization. To\nthis end, we developed a simple, yet stand-alone end-to-end simulation pipeline\nadapted to LiteBIRD. We analyzed the effects of a possible mismatch between the\nmeasured frequency profiles of HWP properties (used in the mapmaking stage of\nthe pipeline) and the actual profiles (used in the sky-scanning step). We\nsimulated single-frequency, CMB-only observations to emphasize the effects of\nnon-idealities on the BB power spectrum. We also considered multi-frequency\nobservations to account for the frequency dependence of HWP properties and the\ncontribution of foreground emission. We quantified the systematic effects in\nterms of a bias $\\Delta r$ on the tensor-to-scalar ratio, $r$, with respect to\nthe ideal case without systematic effects. We derived the accuracy requirements\non the measurements of HWP properties by requiring $\\Delta r < 10^{-5}$ (1% of\nthe expected LiteBIRD sensitivity on $r$). Our analysis is introduced by a\ndetailed presentation of the mathematical formalism employed in this work,\nincluding the use of the Jones and Mueller matrix representations.\n"}
+{"abstract": "  We describe the class of graphs for which all metric spaces with diametrical\ngraphs belonging to this class are ultrametric. It is shown that a metric space\n$(X, d)$ is ultrametric iff the diametrical graph of the metric\n$d_{\\varepsilon}(x, y) = \\max\\{d(x, y), \\varepsilon\\}$ is either empty or\ncomplete multipartite for every $\\varepsilon > 0$. A refinement of the last\nresult is obtained for totally bounded spaces. Moreover, using complete\nmultipartite graphs we characterize the compact ultrametrizable topological\nspaces. The bounded ultrametric spaces, which are weakly similar to unbounded\nones, are also characterized via complete multipartite graphs.\n"}
+{"abstract": "  Non-maximum Suppression (NMS) is an essential postprocessing step in modern\nconvolutional neural networks for object detection. Unlike convolutions which\nare inherently parallel, the de-facto standard for NMS, namely GreedyNMS,\ncannot be easily parallelized and thus could be the performance bottleneck in\nconvolutional object detection pipelines. MaxpoolNMS is introduced as a\nparallelizable alternative to GreedyNMS, which in turn enables faster speed\nthan GreedyNMS at comparable accuracy. However, MaxpoolNMS is only capable of\nreplacing the GreedyNMS at the first stage of two-stage detectors like\nFaster-RCNN. There is a significant drop in accuracy when applying MaxpoolNMS\nat the final detection stage, due to the fact that MaxpoolNMS fails to\napproximate GreedyNMS precisely in terms of bounding box selection. In this\npaper, we propose a general, parallelizable and configurable approach\nPSRR-MaxpoolNMS, to completely replace GreedyNMS at all stages in all\ndetectors. By introducing a simple Relationship Recovery module and a Pyramid\nShifted MaxpoolNMS module, our PSRR-MaxpoolNMS is able to approximate GreedyNMS\nmore precisely than MaxpoolNMS. Comprehensive experiments show that our\napproach outperforms MaxpoolNMS by a large margin, and it is proven faster than\nGreedyNMS with comparable accuracy. For the first time, PSRR-MaxpoolNMS\nprovides a fully parallelizable solution for customized hardware design, which\ncan be reused for accelerating NMS everywhere.\n"}
+{"abstract": "  We investigate two inequalities of Bugeaud and Laurent, each involving\ntriples of classical exponents of Diophantine approximation associated to\n$\\ux\\in\\mathbb{R}^n$. We provide a complete description of parameter triples\nthat admit equality for suitable $\\ux$, which turns out rather surprising. For\n$n=2$ our results agree with work of Laurent. Moreover, we establish lower\nbounds for the Hausdorff and packing dimensions of the involved $\\ux$, and in\nspecial cases we can show they are sharp. Proofs are based on the variational\nprinciple in parametric geometry of numbers, we enclose sketches of associated\ncombined graphs (templates) where equality is feasible. A twist of our\nconstruction provides refined information on the joint spectrum of the\nrespective exponent triples.\n"}
+{"abstract": "  Let ${\\mathcal A}$ be the class of functions that are analytic in the unit\ndisc ${\\mathbb D}$, normalized such that $f(z)=z+\\sum_{n=2}^\\infty a_nz^n$, and\nlet class ${\\mathcal U}(\\lambda)$, $0<\\lambda\\le1$, consists of functions\n$f\\in{\\mathcal A}$, such that \\[ \\left |\\left (\\frac{z}{f(z)} \\right\n)^{2}f'(z)-1\\right | < \\lambda\\quad (z\\in {\\mathbb D}). \\] In this paper we\ndetermine the sharp upper bounds for the Hankel determinants of second and\nthird order for the inverse functions of functions from the class ${\\mathcal\nU}(\\lambda)$.\n"}
+{"abstract": "  Laminar flow velocity profiles depend heavily on fluid rheology. Developing\nmethods of laminar flow characterization, based on low-field magnetic resonance\n(MR), contributes to the widespread industrial application of the MR technique\nin rheology. In this paper, we designed a low-cost, palm-sized permanent magnet\nwith a 1H resonance frequency of 20.48 MHz to measure laminar flow. The magnet\nconsists of two disk magnets, which were each tilted at an angle of 1{\\deg}\nfrom a starting separation of 1.4 cm to generate a constant gradient, 65\ngauss/cm, in the direction of flow. Subsequently, a series of process methods,\nfor MR measurements, were proposed to characterize Newtonian and non-Newtonian\nfluid flows in a pipe, including phase-based method, magnitude-based method,\nand velocity spectrum method. The accuracies of the proposed methods were\nvalidated by simulations, and experiments of Poiseuille flow and shear-thinning\nflow on the designed magnet. The new velocity profile methods proposed are\nadvantageous because the MR instrumentation and measurement methods are simple\nand portable. The sophistication is found in the analysis although the physical\nprinciples are straight forward.\n"}
+{"abstract": "  Modern intelligent urban mobility applications are underpinned by\nlarge-scale, multivariate, spatiotemporal data streams. Working with this data\npresents unique challenges of data management, processing and presentation that\nis often overlooked by researchers. Therefore, in this work we present an\nintegrated data management and processing framework for intelligent urban\nmobility systems currently in use by our partner transit agencies. We discuss\nthe available data sources and outline our cloud-centric data management and\nstream processing architecture built upon open-source publish-subscribe and\nNoSQL data stores. We then describe our data-integrity monitoring methods. We\nthen present a set of visualization dashboards designed for our transit agency\npartners. Lastly, we discuss how these tools are currently being used for\nAI-driven urban mobility applications that use these tools.\n"}
+{"abstract": "  Most existing graph neural networks (GNNs) learn node embeddings using the\nframework of message passing and aggregation. Such GNNs are incapable of\nlearning relative positions between graph nodes within a graph. To empower GNNs\nwith the awareness of node positions, some nodes are set as anchors. Then,\nusing the distances from a node to the anchors, GNNs can infer relative\npositions between nodes. However, P-GNNs arbitrarily select anchors, leading to\ncompromising position-awareness and feature extraction. To eliminate this\ncompromise, we demonstrate that selecting evenly distributed and asymmetric\nanchors is essential. On the other hand, we show that choosing anchors that can\naggregate embeddings of all the nodes within a graph is NP-hard. Therefore,\ndevising efficient optimal algorithms in a deterministic approach is\npractically not feasible. To ensure position-awareness and bypass\nNP-completeness, we propose Position-Sensing Graph Neural Networks (PSGNNs),\nlearning how to choose anchors in a back-propagatable fashion. Experiments\nverify the effectiveness of PSGNNs against state-of-the-art GNNs, substantially\nimproving performance on various synthetic and real-world graph datasets while\nenjoying stable scalability. Specifically, PSGNNs on average boost AUC more\nthan 14% for pairwise node classification and 18% for link prediction over the\nexisting state-of-the-art position-aware methods. Our source code is publicly\navailable at: https://github.com/ZhenyueQin/PSGNN\n"}
+{"abstract": "  Given that symbolic and ordinary powers of an ideal do not always coincide,\nwe look for conditions on the ideal such that equality holds for every natural\nnumber. This paper focuses on studying the equality for Derksen ideals defined\nby finite groups acting linearly on a polynomial ring.\n"}
+{"abstract": "  Recently many algorithms were devised for reinforcement learning (RL) with\nfunction approximation. While they have clear algorithmic distinctions, they\nalso have many implementation differences that are algorithm-independent and\nsometimes under-emphasized. Such mixing of algorithmic novelty and\nimplementation craftsmanship makes rigorous analyses of the sources of\nperformance improvements across algorithms difficult. In this work, we focus on\na series of off-policy inference-based actor-critic algorithms -- MPO, AWR, and\nSAC -- to decouple their algorithmic innovations and implementation decisions.\nWe present unified derivations through a single control-as-inference objective,\nwhere we can categorize each algorithm as based on either\nExpectation-Maximization (EM) or direct Kullback-Leibler (KL) divergence\nminimization and treat the rest of specifications as implementation details. We\nperformed extensive ablation studies, and identified substantial performance\ndrops whenever implementation details are mismatched for algorithmic choices.\nThese results show which implementation or code details are co-adapted and\nco-evolved with algorithms, and which are transferable across algorithms: as\nexamples, we identified that tanh Gaussian policy and network sizes are highly\nadapted to algorithmic types, while layer normalization and ELU are critical\nfor MPO's performances but also transfer to noticeable gains in SAC. We hope\nour work can inspire future work to further demystify sources of performance\nimprovements across multiple algorithms and allow researchers to build on one\nanother's both algorithmic and implementational innovations.\n"}
+{"abstract": "  Understanding how attitudes towards the Climate Emergency vary can hold the\nkey to driving policy changes for effective action to mitigate climate related\nrisk. The Oil and Gas industry account for a significant proportion of global\nemissions and so it could be speculated that there is a relationship between\nCrude Oil Futures and sentiment towards the Climate Emergency. Using Latent\nDirichlet Allocation for Topic Modelling on a bespoke Twitter dataset, this\nstudy shows that it is possible to split the conversation surrounding the\nClimate Emergency into 3 distinct topics. Forecasting Crude Oil Futures using\nSeasonal AutoRegressive Integrated Moving Average Modelling gives promising\nresults with a root mean squared error of 0.196 and 0.209 on the training and\ntesting data respectively. Understanding variation in attitudes towards climate\nemergency provides inconclusive results which could be improved using\nspatial-temporal analysis methods such as Density Based Clustering (DBSCAN).\n"}
+{"abstract": "  We explore the possibility of probing flavor violations in the charged-lepton\nsector by means of high-luminosity lepton-photon and electron-muon collisions,\nby inverting initial and final states in a variety of decay channels presently\nused to bound such violations. In particular, we analyse the resonant lepton,\n$\\gamma\\, \\ell \\to \\ell^{\\prime}$, and neutral-meson, $e^- \\mu ^+ \\to\n\\phi,\\eta,\\pi^0\\!$, scattering channels, whose cross sections are critically\ndependent on the colliding-beams energy spread, being particularly demanding in\nthe case of leptonic processes. For these processes, we compute upper bounds to\nthe cross-section corresponding to present limits on the inverse decay channel\nrates. In order to circumvent the beam energy spread limitations we extend the\nanalysis to processes in which a photon accompanies the resonance in the final\nstate, compensating the off-shellness effects by radiative return. These\nprocesses might be studied at future facilities with moderate energies, in case\nlepton-photon and electron-muon collisions with sufficiently high luminosity\nwill be available.\n"}
+{"abstract": "  Deep generative models can synthesize photorealistic images of human faces\nwith novel identities. However, a key challenge to the wide applicability of\nsuch techniques is to provide independent control over semantically meaningful\nparameters: appearance, head pose, face shape, and facial expressions. In this\npaper, we propose VariTex - to the best of our knowledge the first method that\nlearns a variational latent feature space of neural face textures, which allows\nsampling of novel identities. We combine this generative model with a\nparametric face model and gain explicit control over head pose and facial\nexpressions. To generate complete images of human heads, we propose an additive\ndecoder that adds plausible details such as hair. A novel training scheme\nenforces a pose-independent latent space and in consequence, allows learning a\none-to-many mapping between latent codes and pose-conditioned exterior regions.\nThe resulting method can generate geometrically consistent images of novel\nidentities under fine-grained control over head pose, face shape, and facial\nexpressions. This facilitates a broad range of downstream tasks, like sampling\nnovel identities, changing the head pose, expression transfer, and more. Code\nand models are available for research on https://mcbuehler.github.io/VariTex.\n"}
+{"abstract": "  Quadratic Unconstrained Binary Optimization (QUBO) is a general-purpose\nmodeling framework for combinatorial optimization problems and is a requirement\nfor quantum annealers. This paper utilizes the eigenvalue decomposition of the\nunderlying Q matrix to alter and improve the search process by extracting the\ninformation from dominant eigenvalues and eigenvectors to implicitly guide the\nsearch towards promising areas of the solution landscape. Computational results\non benchmark datasets illustrate the efficacy of our routine demonstrating\nsignificant performance improvements on problems with dominant eigenvalues.\n"}
+{"abstract": "  We present a synthesis of fast radio burst (FRB) morphology (the change in\nflux as a function of time and frequency) as detected in the 400-800 MHz octave\nby the FRB project on the Canadian Hydrogen Intensity Mapping Experiment\n(CHIME/FRB), using events from the first CHIME/FRB catalog. The catalog\nconsists of 61 bursts from 18 repeating sources, plus 474 one-off FRBs,\ndetected between 2018 July 25 and 2019 July 2. We identify four observed\narchetypes of burst morphology (\"simple broadband,\" \"simple narrowband,\"\n\"temporally complex\" and \"downward drifting\") and describe relevant\ninstrumental biases that are essential for interpreting the observed\nmorphologies. Using the catalog properties of the FRBs, we confirm that bursts\nfrom repeating sources, on average, have larger widths and we show, for the\nfirst time, that bursts from repeating sources, on average, are narrower in\nbandwidth. This difference could be due to a beaming or propagation effects, or\nit could be intrinsic to the populations. We discuss potential implications of\nthese morphological differences for using FRBs as astrophysical tools.\n"}
+{"abstract": "  Influenced by the great success of deep learning in computer vision and\nlanguage understanding, research in recommendation has shifted to inventing new\nrecommender models based on neural networks. In recent years, we have witnessed\nsignificant progress in developing neural recommender models, which generalize\nand surpass traditional recommender models owing to the strong representation\npower of neural networks. In this survey paper, we conduct a systematic review\non neural recommender models from the perspective of recommendation modeling\nwith the accuracy goal, aiming to summarize this field to facilitate\nresearchers and practitioners working on recommender systems. Specifically,\nbased on the data usage during recommendation modeling, we divide the work into\ncollaborative filtering and information-rich recommendation: 1) collaborative\nfiltering, which leverages the key source of user-item interaction data; 2)\ncontent enriched recommendation, which additionally utilizes the side\ninformation associated with users and items, like user profile and item\nknowledge graph; and 3) temporal/sequential recommendation, which accounts for\nthe contextual information associated with an interaction, such as time,\nlocation, and the past interactions. After reviewing representative work for\neach type, we finally discuss some promising directions in this field.\n"}
+{"abstract": "  Given a graph $G = (V,E)$ with vertex weights $w(v)$ and a desired number of\nparts $k$, the goal in graph partitioning problems is to partition the vertex\nset V into parts $V_1,\\ldots,V_k$. Metrics for compactness, contiguity, and\nbalance of the parts $V_i$ are frequent objectives, with much existing\nliterature focusing on compactness and balance. Revisiting an old method known\nas striping, we give the first polynomial-time algorithms with guaranteed\ncontiguity and provable bicriteria approximations for compactness and balance\nfor planar grid graphs. We consider several types of graph partitioning,\nincluding when vertex weights vary smoothly or are stochastic, reflecting\nconcerns in various real-world instances. We show significant improvements in\nexperiments for balancing workloads for the fire department and reducing\nover-policing using 911 call data from South Fulton, GA.\n"}
+{"abstract": "  We study topological defect lines in two character rational conformal field\ntheories. Among them one set of two character theories are commutant pairs in\n$E_{8,1}$ conformal field theory. Using these defect lines we construct defect\npartition function in the $E_8$ theory. We find that the defects preserve only\na part of the $E_8$ current algebra symmetry. We also determine the defect\npartition function in $c=24$ CFT using these defects lines of 2 character\ntheories and we find that these defects preserve all current algebra symmetries\nof $c=24$ CFT.\n"}
+{"abstract": "  A common way to evaluate electronic integrals for polyatomic molecules is to\nuse Becke's partitioning scheme [J. Chem. Phys.88, 2547 (1988)] in conjunction\nwith overlapping grids centered at each atomic site. The Becke scheme was\ndesigned for integrands that fall off rapidly at large distances, such as those\napproximating bound electronic states. When applied to states in the electronic\ncontinuum, however, Becke scheme exhibits slow convergence and it is highly\nredundant. Here, we present a modified version of Becke scheme that is\napplicable to functions of the electronic continuum, such as those involved in\nmolecular photoionization and electron-molecule scattering, and which ensures\nconvergence and efficiency comparable to those realized in the calculation of\nbound states. In this modified scheme, the atomic weights already present in\nBecke's partition are smoothly switched off within a range of few bond lengths\nfrom their respective nuclei, and complemented by an asymptotically unitary\nweight. The atomic integrals are evaluated on small spherical grids, centered\non each atom, with size commensurate to the support of the corresponding atomic\nweight. The residual integral of the interstitial and long-range region is\nevaluated with a central master grid. The accuracy of the method is\ndemonstrated by evaluating integrals involving integrands containing Gaussian\nType Orbitals and Yukawa potentials, on the atomic sites, as well as spherical\nBessel functions centered on the master grid. These functions are\nrepresentative of those encountered in realistic electron-scattering and\nphotoionization calculations in polyatomic molecules.\n"}
+{"abstract": "  Typical high quality text-to-speech (TTS) systems today use a two-stage\narchitecture, with a spectrum model stage that generates spectral frames and a\nvocoder stage that generates the actual audio. High-quality spectrum models\nusually incorporate the encoder-decoder architecture with self-attention or\nbi-directional long short-term (BLSTM) units. While these models can produce\nhigh quality speech, they often incur O($L$) increase in both latency and\nreal-time factor (RTF) with respect to input length $L$. In other words, longer\ninputs leads to longer delay and slower synthesis speed, limiting its use in\nreal-time applications. In this paper, we propose a multi-rate attention\narchitecture that breaks the latency and RTF bottlenecks by computing a compact\nrepresentation during encoding and recurrently generating the attention vector\nin a streaming manner during decoding. The proposed architecture achieves high\naudio quality (MOS of 4.31 compared to groundtruth 4.48), low latency, and low\nRTF at the same time. Meanwhile, both latency and RTF of the proposed system\nstay constant regardless of input lengths, making it ideal for real-time\napplications.\n"}
+{"abstract": "  Pre-trained text-to-text transformers such as BART have achieved impressive\nperformance across a range of NLP tasks. Recent study further shows that they\ncan learn to generalize to novel tasks, by including task descriptions as part\nof the source sequence and training the model with (source, target) examples.\nAt test time, these fine-tuned models can make inferences on new tasks using\nthe new task descriptions as part of the input. However, this approach has\npotential limitations, as the model learns to solve individual (source, target)\nexamples (i.e., at the instance level), instead of learning to solve tasks by\ntaking all examples within a task as a whole (i.e., at the task level). To this\nend, we introduce Hypter, a framework that improves text-to-text transformer's\ngeneralization ability to unseen tasks by training a hypernetwork to generate\ntask-specific, light-weight adapters from task descriptions. Experiments on\nZEST dataset and a synthetic SQuAD dataset demonstrate that Hypter improves\nupon fine-tuning baselines. Notably, when using BART-Large as the main network,\nHypter brings 11.3% comparative improvement on ZEST dataset.\n"}
+{"abstract": "  In this letter, we propose a magnet-less non-reciprocal isolating system\nbased on time-varying metasurfaces. Two parallel time-varying metasurfaces, one\nfor frequency up-conversion and one for down-conversion by the same amount, are\nused for realizing a region of space where incident waves from opposite\ndirections experience an opposite Doppler frequency shift. As a result, any\ndevice within this region becomes sensitive to the illumination direction,\nexhibiting a different scattering response from opposite directions and thus\nbreaking reciprocity. Very importantly, thanks to the opposite frequency shift\nof the metasurfaces, the frequency of the transmitted electromagnetic field is\nthe same as for the incident one. Here, we demonstrate this general approach by\nusing a Bragg grating as the device between the time-varying metasurfaces. The\ncombined structure of the metasurfaces and the grating exhibits different\ntransmission and reflection properties for opposite illumination direction,\nthereby realizing an isolator. More broadly, this letter presents a strategy\nfor converting any conventional electromagnetic device to a non-reciprocal one\nby placing it between two time-varying metasurfaces. This approach opens the\ndoor to several new non-reciprocal components based on thin and lightweight\nmetasurfaces, which are simpler to realize compared to their volumetric\ncounterparts.\n"}
+{"abstract": "  Following the Hu-Kriz method of computing the $C_2$ genuine dual Steenrod\nalgebra $(H\\mathbf F_2)_{\\bigstar}(H\\mathbf F_2)$, we calculate the $C_4$\nequivariant Bredon cohomology of the classifying space $\\mathbf R P^{\\infty\n\\rho}=B_{C_4}\\Sigma_{2}$ as an $RO(C_4)$ graded Green-functor. We prove that as\na module over the homology of a point (which we also compute), this cohomology\nis not flat. As a result, it can't be used as a test module for obtaining\ngenerators in $(H\\mathbf F_2)_{\\bigstar}(H\\mathbf F_2)$ as Hu-Kriz do in the\n$C_2$ case.\n"}
+{"abstract": "  The paper introduces cycles cross ratio, which extends the classic cross\nratio of four points to various settings: conformal geometry, Lie spheres\ngeometry, etc. Just like its classic counterpart cycles cross ratio is a\nmeasure of anharmonicity between spheres with respect to inversion. It also\nprovides a M\\\"obius invariant distance between spheres. Many further properties\nof cycles cross ratio awaiting their exploration. In abstract framework the new\ninvariant can be considered in any projective space with a bilinear pairing.\n"}
+{"abstract": "  Self-testing results allow us to infer the underlying quantum mechanical\ndescription of states and measurements from classical outputs produced by\nnon-communicating parties. The standard definition of self-testing does not\napply in situations when there are two or more inequivalent optimal strategies.\nTo address this, we introduce the notion of self-testing convex combinations of\nreference strategies, which is a generalisation of self-testing to multiple\nstrategies. We show that the Glued Magic Square game [Quantum 4 (2020), p. 346]\nself-tests a convex combination of two inequivalent strategies. As a corollary,\nwe obtain that the Glued Magic square game self-tests two EPR pairs thus\nanswering an open question from [Quantum 4 (2020), p. 346]. Our self-test is\nrobust and extends to natural generalisations of the Glued Magic Square game.\n"}
+{"abstract": "  Nowadays, live-stream and short video shopping in E-commerce have grown\nexponentially. However, the sellers are required to manually match images of\nthe selling products to the timestamp of exhibition in the untrimmed video,\nresulting in a complicated process. To solve the problem, we present an\ninnovative demonstration of multi-modal retrieval system called \"Fashion\nFocus\", which enables to exactly localize the product images in the online\nvideo as the focuses. Different modality contributes to the community\nlocalization, including visual content, linguistic features and interaction\ncontext are jointly investigated via presented multi-modal learning. Our system\nemploys two procedures for analysis, including video content structuring and\nmulti-modal retrieval, to automatically achieve accurate video-to-shop\nmatching. Fashion Focus presents a unified framework that can orientate the\nconsumers towards relevant product exhibitions during watching videos and help\nthe sellers to effectively deliver the products over search and recommendation.\n"}
+{"abstract": "  Human-computer interaction (HCI) is significantly impacted by delayed\nresponses from a spoken dialogue system. Hence, end-to-end (e2e) spoken\nlanguage understanding (SLU) solutions have recently been proposed to decrease\nlatency. Such approaches allow for the extraction of semantic information\ndirectly from the speech signal, thus bypassing the need for a transcript from\nan automatic speech recognition (ASR) system. In this paper, we propose a\ncompact e2e SLU architecture for streaming scenarios, where chunks of the\nspeech signal are processed continuously to predict intent and slot values. Our\nmodel is based on a 3D convolutional neural network (3D-CNN) and a\nunidirectional long short-term memory (LSTM). We compare the performance of two\nalignment-free losses: the connectionist temporal classification (CTC) method\nand its adapted version, namely connectionist temporal localization (CTL). The\nlatter performs not only the classification but also localization of sequential\naudio events. The proposed solution is evaluated on the Fluent Speech Command\ndataset and results show our model ability to process incoming speech signal,\nreaching accuracy as high as 98.97 % for CTC and 98.78 % for CTL on\nsingle-label classification, and as high as 95.69 % for CTC and 95.28 % for CTL\non two-label prediction.\n"}
+{"abstract": "  The so-called block-term decomposition (BTD) tensor model, especially in its\nrank-$(L_r,L_r,1)$ version, has been recently receiving increasing attention\ndue to its enhanced ability of representing systems and signals that are\ncomposed of \\emph{block} components of rank higher than one, a scenario\nencountered in numerous and diverse applications. Its uniqueness and\napproximation have thus been thoroughly studied. The challenging problem of\nestimating the BTD model structure, namely the number of block terms (rank) and\ntheir individual (block) ranks, is of crucial importance in practice and has\nonly recently started to attract significant attention. In data-streaming\nscenarios and/or big data applications, where the tensor dimension in one of\nits modes grows in time or can only be processed incrementally, it is essential\nto be able to perform model selection and computation in a recursive\n(incremental/online) manner. To date there is only one such work in the\nliterature concerning the (general rank-$(L,M,N)$) BTD model, which proposes an\nincremental method, however with the BTD rank and block ranks assumed to be\na-priori known and time invariant. In this preprint, a novel approach to\nrank-$(L_r,L_r,1)$ BTD model selection and tracking is proposed, based on the\nidea of imposing column sparsity jointly on the factors and estimating the\nranks as the numbers of factor columns of nonnegligible magnitude. An online\nmethod of the alternating iteratively reweighted least squares (IRLS) type is\ndeveloped and shown to be computationally efficient and fast converging, also\nallowing the model ranks to change in time. Its time and memory efficiency are\nevaluated and favorably compared with those of the batch approach. Simulation\nresults are reported that demonstrate the effectiveness of the proposed scheme\nin both selecting and tracking the correct BTD model.\n"}
+{"abstract": "  Phase I clinical trials are designed to test the safety (non-toxicity) of\ndrugs and find the maximum tolerated dose (MTD). This task becomes\nsignificantly more challenging when multiple-drug dose-combinations (DC) are\ninvolved, due to the inherent conflict between the exponentially increasing DC\ncandidates and the limited patient budget. This paper proposes a novel Bayesian\ndesign, SDF-Bayes, for finding the MTD for drug combinations in the presence of\nsafety constraints. Rather than the conventional principle of escalating or\nde-escalating the current dose of one drug (perhaps alternating between drugs),\nSDF-Bayes proceeds by cautious optimism: it chooses the next DC that, on the\nbasis of current information, is most likely to be the MTD (optimism), subject\nto the constraint that it only chooses DCs that have a high probability of\nbeing safe (caution). We also propose an extension, SDF-Bayes-AR, that accounts\nfor patient heterogeneity and enables heterogeneous patient recruitment.\nExtensive experiments based on both synthetic and real-world datasets\ndemonstrate the advantages of SDF-Bayes over state of the art DC trial designs\nin terms of accuracy and safety.\n"}
+{"abstract": "  Let $R$ be a finitely generated positively graded algebra over a Noetherian\nlocal ring $B$, and $\\mathfrak{m} = [R]_+$ be the graded irrelevant ideal of\n$R$. We provide a local criterion characterizing the $B$-freeness of all the\nlocal cohomology modules $\\text{H}_\\mathfrak{m}^i(M)$ of a finitely generated\ngraded $R$-module $M$. We show that fiber-full modules are exactly the ones\nthat satisfy this criterion. When we change $B$ by an arbitrary Noetherian ring\n$A$, we study the fiber-full locus of a module in $\\text{Spec}(A)$: we show\nthat the fiber-full locus is always an open subset of $\\text{Spec}(A)$ and that\nit is dense when $A$ is generically reduced.\n"}
+{"abstract": "  I extend the calculations represented in \\cite{konav} regarding the\nresistivity in Kondo lattice materials from $3d$ syatem to $2d$ systems. In the\npresent work I consider a 2d system, and memory function is computed. However,\nresults found in 2d case are different from 3d system . I find that in $2d$ in\nlow temperature regime($ k_{B}T\\ll \\mu_d$) resistivity shows power\nlaw($\\frac{1}{T}$) behaviour and in the high temeprature regime($\nk_{B}T\\gg\\mu_d$) resistivity varies linearly with temperature. In $3d$ these\nbehaviours are as $\\frac{1}{T}$ and as $T^{\\frac{3}{2}}$ respectively.\n"}
+{"abstract": "  In online domain-specific customer service applications, many companies\nstruggle to deploy advanced NLP models successfully, due to the limited\navailability of and noise in their datasets. While prior research demonstrated\nthe potential of migrating large open-domain pretrained models for\ndomain-specific tasks, the appropriate (pre)training strategies have not yet\nbeen rigorously evaluated in such social media customer service settings,\nespecially under multilingual conditions. We address this gap by collecting a\nmultilingual social media corpus containing customer service conversations\n(865k tweets), comparing various pipelines of pretraining and finetuning\napproaches, applying them on 5 different end tasks. We show that pretraining a\ngeneric multilingual transformer model on our in-domain dataset, before\nfinetuning on specific end tasks, consistently boosts performance, especially\nin non-English settings.\n"}
+{"abstract": "  Robust visualization of complex data is critical for the effective use of NLP\nfor event classification, as the volume of data is large and the\nhigh-dimensional structure of text makes data challenging to summarize\nsuccinctly. In event extraction tasks in particular, visualization can aid in\nunderstanding and illustrating the textual relationships from which machine\nlearning tools produce insights. Through our case study which seeks to identify\npotential triggers of state-led mass killings from news articles using NLP, we\ndemonstrate how visualizations can aid in each stage, from exploratory analysis\nof raw data, to machine learning training analysis, and finally post-inference\nvalidation.\n"}
+{"abstract": "  We identified 8 additional stars as members of the Helmi stream (HStr) in the\ncombined GALAH+ DR3 and $Gaia$ EDR3 catalog. By consistently reevaluating\nclaimed members from the literature, we consolidate a sample of 22 HStr stars\nwith parameters determined from high-resolution spectroscopy and spanning a\nconsiderably wider (by $\\sim$0.5 dex) metallicity interval ($-2.5 \\lesssim\n\\rm[Fe/H] < -1.0$) than previously reported. Our study focuses on $\\alpha$ (Mg\nand Ca) and neutron-capture (Ba and Eu) elements. We find that the chemistry of\nHStr is typical of dwarf spheroidal (dSph) galaxies, in good agreement with\nprevious $N$-body simulations of this merging event. Stars of HStr constitute a\nclear declining sequence in $\\rm[\\alpha/Fe]$ for increasing metallicity up to\n$\\rm[Fe/H] \\sim -1.0$. Moreover, stars of HStr show a median value of $+$0.5\ndex for $\\rm[Eu/Fe]$ with a small dispersion ($\\pm$0.1 dex). Every star\nanalyzed with $\\rm[Fe/H] < -1.2$ belong to the $r$-process enhanced\n($\\rm[Eu/Fe] > +0.3$ and $\\rm[Ba/Eu] < 0.0$) metal-poor category, providing\nremarkable evidence that, at such low-metallicity regime, stars of HStr\nexperienced enrichment in neutron-capture elements predominantly via\n$r$-process nucleosynthesis. Finally, the extended metallicity range also\nsuggests an increase in $\\rm[Ba/Eu]$ for higher $\\rm[Fe/H]$, in conformity with\nother surviving dwarf satellite galaxies of the Milky Way.\n"}
+{"abstract": "  This paper develops a Closed-Loop Error Learning Control (CLELC) algorithm\nfor feedback linearizable systems with experimental validation on a mobile\nrobot. Traditional feedback and feedforward controllers are designed based on\nthe nominal model by using Feedback Linearization Control (FLC) method. Then,\nan intelligent controller is designed based on sliding mode learning algorithm\nthat utilizes closed-loop error dynamics to learn the system behavior. The\ncontrollers are working in parallel, and the intelligent controller can\ngradually replace the feedback controller from the control of the system. In\naddition to the stability of the sliding mode learning algorithm, the\nclosed-loop stability of an $n$th order feedback linearizable system is proven.\nThe simulation results demonstrate that CLELC algorithm can improve control\nperformance (e.g., smaller rise time, settling time and overshoot) in the\nabsence of uncertainties, and also provides robust control performance in the\npresence of uncertainties as compared to traditional FLC method. To test the\nefficiency and efficacy of CLELC algorithm, the trajectory tracking problem of\na tracked mobile robot is studied in real-time. The experimental results\ndemonstrate that CLELC algorithm ensures high-accurate trajectory tracking\nperformance than traditional FLC method.\n"}
+{"abstract": "  Tyshkovskiy and Panchin have recently published a commentary on our paper in\nwhich they outline several \"points of disagreement with the Segreto/Deigin\nhypothesis\". As our paper is titled \"The genetic structure of SARS-CoV-2 does\nnot rule out a laboratory origin\", points of disagreement should provide\nevidence that rules out a laboratory origin. However, Tyshkovskiy and Panchin\nprovide no such evidence and instead attempt to criticize our arguments that\nhighlight aspects of SARS-CoV-2 that could be consistent with the lab leak\nhypothesis. Strikingly, Tyshkovskiy and Panchin's main point of criticism is\nbased on a false premise that we have claimed RaTG13 to be a direct progenitor\nof SARS-CoV-2, and their other points of criticism are either incorrect or\nirrelevant to our hypotheses. Thus, the genetic structure of SARS-CoV-2 remains\nconsistent with both natural or laboratory origin, which means that both the\nzoonotic and the lab leak hypothesis need to be investigated equally\nthoroughly.\n"}
+{"abstract": "  Scanning transmission electron microscopy (STEM) is the most widespread\nadopted tool for atomic scale characterization of two-dimensional (2D)\nmaterials. Many 2D materials remain susceptible to electron beam damage,\ndespite the standardized practice to reduce the beam energy from 200 keV to 80\nor 60 keV. Although, all elements present can be detected by atomic\nelectrostatic potential imaging using integrated differential phase contrast\n(iDPC) STEM or electron ptychography, capturing dynamics with atomic resolution\nand enhanced sensitivity has remained a challenge. Here, by using iDPC-STEM, we\ncapture defect dynamics in 2D WS$_2$ by atomic electrostatic potential imaging\nwith a beam energy of only 30 keV. The direct imaging of atomic electrostatic\npotentials with high framerate reveals the presence and motion of single atoms\nnear defects and edges in WS$_2$ that are otherwise invisible with conventional\nannular dark-field STEM or cannot be captured sufficiently fast by electron\nptychography.\n"}
+{"abstract": "  Low dimensional ferroelectrics are highly desired for applications and full\nof exotic physics. Here a functionalized MXene Hf$_2$CF$_2$ monolayer is\ntheoretically studied, which manifests a nonpolar to polar transition upon\nmoderate biaxial compressive strain. Accompanying this structural transition, a\nmetal-semiconductor transition occurs. The in-plane shift of unilateral\nfluorine layer leads to a polarization pointing out-of-plane. Such\nferroelectricity is unconventional, similar to the recently-proposed\ninterlayer-sliding ferroelectricity but not identical. Due to its specific\nhexapetalous potential energy profile, the possible ferroelectric switching\npaths and domain walls are nontrivial, which are mediated via the metallic\nparaelectric state. In this sense, the metallic walls can be manipulated by\nreshaping the ferroelectric domains.\n"}
+{"abstract": "  In this article we study a particular class of compact connected orientable\nPL $4$-manifolds with empty or connected boundary which have infinite cyclic\nfundamental group. We show that the manifold in the class admits a handle\ndecomposition in which number of $2$-handles depends upon its second Betti\nnumber and other $h$-handles ($h \\leq 4$) are at most $2$. In particular, our\nmain result is that if $M$ is a closed connected orientable PL $4$-manifold\nwith fundamental group as $\\mathbb{{Z}}$, then $M$ admits either of the\nfollowing handle decompositions:\n  (1) one $0$-handle, two $1$-handles, $1+\\beta_2(M)$ $2$-handles, one\n$3$-handle and one $4$-handle,\n  (2) one $0$-handle, one $1$-handle, $\\beta_2(M)$ $2$-handles, one $3$-handle\nand one $4$-handle, where $\\beta_2(M)$ denotes the second Betti number of\nmanifold $M$ with $\\mathbb{Z}$ coefficients. Further, we extend this result to\nany compact connected orientable $4$-manifold $M$ with boundary and give three\npossible representations of $M$ in terms of handles.\n"}
+{"abstract": "  Measurements of Higgs boson production cross sections and couplings in events\nwhere the Higgs boson decays into a pair of photons are reported. Events are\nselected from a sample of proton-proton collisions at $\\sqrt{s} =$ 13 TeV\ncollected by the CMS detector at the LHC from 2016 to 2018, corresponding to an\nintegrated luminosity of 137 fb$^{-1}$. Analysis categories enriched in Higgs\nboson events produced via gluon fusion, vector boson fusion, vector boson\nassociated production, and production associated with top quarks are\nconstructed. The total Higgs boson signal strength, relative to the standard\nmodel (SM) prediction, is measured to be 1.12 $\\pm$ 0.09. Other properties of\nthe Higgs boson are measured, including SM signal strength modifiers,\nproduction cross sections, and its couplings to other particles. These include\nthe most precise measurements of gluon fusion and vector boson fusion Higgs\nboson production in several different kinematic regions, the first measurement\nof Higgs boson production in association with a top quark pair in five regions\nof the Higgs boson transverse momentum, and an upper limit on the rate of Higgs\nboson production in association with a single top quark. All results are found\nto be in agreement with the SM expectations.\n"}
+{"abstract": "  Recent progress in pretraining language models on large corpora has resulted\nin large performance gains on many NLP tasks. These large models acquire\nlinguistic knowledge during pretraining, which helps to improve performance on\ndownstream tasks via fine-tuning. To assess what kind of knowledge is acquired,\nlanguage models are commonly probed by querying them with `fill in the blank'\nstyle cloze questions. Existing probing datasets mainly focus on knowledge\nabout relations between words and entities. We introduce WDLMPro (Word\nDefinition Language Model Probing) to evaluate word understanding directly\nusing dictionary definitions of words. In our experiments, three popular\npretrained language models struggle to match words and their definitions. This\nindicates that they understand many words poorly and that our new probing task\nis a difficult challenge that could help guide research on LMs in the future.\n"}
+{"abstract": "  In this paper, we study a new operation named pushforward on diffeological\nvector pseudo-bundles, which is left adjoint to the pullback. We show how to\npushforward projective diffeological vector pseudo-bundles to get projective\ndiffeological vector spaces, producing many concrete new examples. This brings\nnew objects to diffeology from classical vector bundle theory.\n"}
+{"abstract": "  Semi-supervised learning (SSL) is an effective means to leverage unlabeled\ndata to improve a model's performance. Typical SSL methods like FixMatch assume\nthat labeled and unlabeled data share the same label space. However, in\npractice, unlabeled data can contain categories unseen in the labeled set,\ni.e., outliers, which can significantly harm the performance of SSL algorithms.\nTo address this problem, we propose a novel Open-set Semi-Supervised Learning\n(OSSL) approach called OpenMatch. Learning representations of inliers while\nrejecting outliers is essential for the success of OSSL. To this end, OpenMatch\nunifies FixMatch with novelty detection based on one-vs-all (OVA) classifiers.\nThe OVA-classifier outputs the confidence score of a sample being an inlier,\nproviding a threshold to detect outliers. Another key contribution is an\nopen-set soft-consistency regularization loss, which enhances the smoothness of\nthe OVA-classifier with respect to input transformations and greatly improves\noutlier detection. OpenMatch achieves state-of-the-art performance on three\ndatasets, and even outperforms a fully supervised model in detecting outliers\nunseen in unlabeled data on CIFAR10.\n"}
+{"abstract": "  The competition between thermal fluctuations and potential forces is the\nfoundation of our understanding of phase transitions and matter in equilibrium.\nDriving matter out of equilibrium allows for a new class of interactions which\nare neither attractive nor repulsive but transverse. The existence of such\ntransverse forces immediately raises the question of how they interfere with\nbasic principles of material self-organization. Despite a recent surge of\ninterest, this question remains open. Here, we show that activating transverse\nforces by homogeneous rotation of colloidal units generically turns otherwise\nquiescent solids into a crystal whorl state dynamically shaped by\nself-propelled dislocations. Simulations of both a minimal model and a full\nhydrodynamics model establish the generic nature of the chaotic dynamics of\nthese self-kneading polycrystals. Using a continuum theory, we explain how odd\nand Hall stresses conspire to destabilize chiral crystals from within. This\nchiral instability produces dislocations that are unbound by their\nself-propulsion. Their proliferation eventually leads to a crystalline whorl\nstate out of reach of equilibrium matter.\n"}
+{"abstract": "  We study stability of the sharp Poincar{\\'e} constant of the invariant\nprobability measure of a reversible diffusion process satisfying some natural\nconditions. The proof is based on the spectral interpretation of Poincar{\\'e}\ninequalities and Stein's method. In particular, these results are applied to\nthe gamma distributions and to strictly log-concave measures in dimension one,\ngiving stability for Brascamp-Lieb inequalities.\n"}
+{"abstract": "  In order to apply Optical Character Recognition (OCR) to historical printings\nof Latin script fully automatically, we report on our efforts to construct a\nwidely-applicable polyfont recognition model yielding text with a Character\nError Rate (CER) around 2% when applied out-of-the-box. Moreover, we show how\nthis model can be further finetuned to specific classes of printings with\nlittle manual and computational effort. The mixed or polyfont model is trained\non a wide variety of materials, in terms of age (from the 15th to the 19th\ncentury), typography (various types of Fraktur and Antiqua), and languages\n(among others, German, Latin, and French). To optimize the results we combined\nestablished techniques of OCR training like pretraining, data augmentation, and\nvoting. In addition, we used various preprocessing methods to enrich the\ntraining data and obtain more robust models. We also implemented a two-stage\napproach which first trains on all available, considerably unbalanced data and\nthen refines the output by training on a selected more balanced subset.\nEvaluations on 29 previously unseen books resulted in a CER of 1.73%,\noutperforming a widely used standard model with a CER of 2.84% by almost 40%.\nTraining a more specialized model for some unseen Early Modern Latin books\nstarting from our mixed model led to a CER of 1.47%, an improvement of up to\n50% compared to training from scratch and up to 30% compared to training from\nthe aforementioned standard model. Our new mixed model is made openly available\nto the community.\n"}
+{"abstract": "  This paper presents a novel mechatronic setup intended for providing\nrespiratory support to patients suffering from pulmonary failure. The setup\nrelies upon the circulation of an oxygenated perfluorocarbon (PFC) through the\nabdominal cavity. Such circulation provides a potential pathway for the\ntransport of oxygen to the bloodstream. However, the viability of this\ntechnology for $CO_2$ clearance has not been established. Moreover, there is a\nlack of experimental data enabling the modeling and identification of the\nunderlying dynamics of this technology. To address these gaps, we develop a\nflexible experimental perfusion setup capable of monitoring and controlling key\nvariables such as perfusate flowrate, temperature, pressure, and oxygenation.\nThe paper (i) briefly summarizes the design of this setup; (ii) highlights the\ndegree to which its data acquisition system enables the collection and\ncross-correlation of both perfusion-related and physiological variables; and\n(iii) discusses the development of flow, pressure, and temperature control\nalgorithms for the setup. Experiments with large animals (swine) show that the\nsetup is capable of successfully controlling the perfusion process, as well as\ngathering extensive data to support subsequent modeling and identification\nstudies.\n"}
+{"abstract": "  Yb-substituted Ni-Zn ferrites have been synthesized using sol-gel auto\ncombustion method. The structural characterization of the compositions has been\nperformed by X-ray diffraction analysis, field emission scanning electron\nmicroscopy (FESEM), quantum design physical properties measurement system\n(PPMS). That ensured the formation of single phase cubic spinel structure.\nCrystallite and average grain size are calculated and found to decrease with\nincreasing Yb3+ contents. Saturation magnetization and Bohr magnetic moment\ndecrease while the coercivity increases with the increase in Yb3+ contents\nsuccessfully explained by the Neels collinear two sub-lattice model and\ncritical size effect, respectively. Critical particle size has been estimated\nat 6.4 nm, the transition point between single domain regime (below the\ncritical size) and multi-domain regime (beyond the critical size). Curie\ntemperature reduces due to the weakening of A-O-B super exchange interaction\nand redistribution of cations, confirmed by the M-T graph. The compositions\nretain ferromagnetic ordered structured below Curie temperature and above Curie\ntemperature, it becomes paramagnetic, making them plausible candidates for high\ntemperature magnetic device applications. The relative quality factor peak is\nobtained at a very high frequency, indicating the compositions could also be\napplicable for high frequency magnetic device applications.\n"}
+{"abstract": "  Quasi-periodic plasmoid formation at the tip of magnetic streamer structures\nis observed to occur in experiments on the Big Red Ball as well as in\nsimulations of these experiments performed with the extended-MHD code, NIMROD.\nThis plasmoid formation is found to occur on a characteristic timescale\ndependent on pressure gradients and magnetic curvature in both experiment and\nsimulation. Single mode, or laminar, plasmoids exist when the pressure gradient\nis modest, but give way to turbulent plasmoid ejection when the system drive is\nhigher, producing plasmoids of many sizes. However, a critical pressure\ngradient is also observed, below which plasmoids are never formed. A simple\nheuristic model of this plasmoid formation process is presented and suggested\nto be a consequence of a dynamic loss of equilibrium in the high-$\\beta$ region\nof the helmet streamer. This model is capable of explaining the periodicity of\nplasmoids observed in the experiment and simulations and produces plasmoid\nperiods of 90 minutes when applied to 2D models of solar streamers with a\nheight of $3R_\\odot$. This is consistent with the location and frequency at\nwhich periodic plasma blobs have been observed to form by LASCO and SECCHI\ninstruments.\n"}
+{"abstract": "  Maximum Entropy (MaxEnt) reinforcement learning is a powerful learning\nparadigm which seeks to maximize return under entropy regularization. However,\naction entropy does not necessarily coincide with state entropy, e.g., when\nmultiple actions produce the same transition. Instead, we propose to maximize\nthe transition entropy, i.e., the entropy of next states. We show that\ntransition entropy can be described by two terms; namely, model-dependent\ntransition entropy and action redundancy. Particularly, we explore the latter\nin both deterministic and stochastic settings and develop tractable\napproximation methods in a near model-free setup. We construct algorithms to\nminimize action redundancy and demonstrate their effectiveness on a synthetic\nenvironment with multiple redundant actions as well as contemporary benchmarks\nin Atari and Mujoco. Our results suggest that action redundancy is a\nfundamental problem in reinforcement learning.\n"}
+{"abstract": "  Enhanced room-temperature electromechanical coupling in the lead-free\nferroelectric system $(1-x)$BaZr$_{0.2}$Ti$_{0.8}$O$_{3}$ -\n$x$Ba$_{0.7}$Ca$_{0.3}$TiO$_{3}$ (abbreviated as BZCT) at $x=0.5$ is attributed\nto the existence of a morphotropic phase region (MPR) containing an\nintermediate orthorhombic ($O$) phase between terminal rhombohedral ($R$) BZT\nand tetragonal ($T$) BCT phases. However, there is ambiguity regarding the\nmorphotropic phase transition in BZCT at room temperature - while some\nexperiments suggest a single $O$ phase within the MPR, others indicate\ncoexistence of three polar phases ($T+R+O$). Therefore, to understand the\nthermodynamic stability of polar phases and its relation to electromechanical\nswitching during morphotropic phase transition in BZCT, we develop a Landau\npotential based on the theory of polar anisotropy. Since intrinsic\nelectrostrictive anisotropy changes as a function of electromechanical\nprocessing, we establish a correlation between the parameters of our potential\nand the coefficients of electrostriction. We also conducted phase-field\nsimulations based on this potential to demonstrate changes in domain\nconfiguration from single-phase $O$ to three-phase $T+R+O$ at the equimolar\ncomposition with the increase in electrostrictive anisotropy. Diffusionless\nphase diagrams and the corresponding piezoelectric coefficients obtained from\nour model compare well with the experimental findings. Increase in\nelectrostrictive anisotropy increases the degeneracy of the free energy at\nambient temperature and pressure leading to decreasing polar anisotropy,\nalthough there is an accompanying increase in the electromechanical anisotropy\nmanifested by an increase in the difference between effective longitudinal and\ntransverse piezo-coefficients, $d_{33}$ and $d_{31}$.\n"}
+{"abstract": "  Clinical event sequences consist of thousands of clinical events that\nrepresent records of patient care in time. Developing accurate prediction\nmodels for such sequences is of a great importance for defining representations\nof a patient state and for improving patient care. One important challenge of\nlearning a good predictive model of clinical sequences is patient-specific\nvariability. Based on underlying clinical complications, each patient's\nsequence may consist of different sets of clinical events. However,\npopulation-based models learned from such sequences may not accurately predict\npatient-specific dynamics of event sequences. To address the problem, we\ndevelop a new adaptive event sequence prediction framework that learns to\nadjust its prediction for individual patients through an online model update.\n"}
+{"abstract": "  Fitting network models to neural activity is an important tool in\nneuroscience. A popular approach is to model a brain area with a probabilistic\nrecurrent spiking network whose parameters maximize the likelihood of the\nrecorded activity. Although this is widely used, we show that the resulting\nmodel does not produce realistic neural activity. To correct for this, we\nsuggest to augment the log-likelihood with terms that measure the dissimilarity\nbetween simulated and recorded activity. This dissimilarity is defined via\nsummary statistics commonly used in neuroscience and the optimization is\nefficient because it relies on back-propagation through the stochastically\nsimulated spike trains. We analyze this method theoretically and show\nempirically that it generates more realistic activity statistics. We find that\nit improves upon other fitting algorithms for spiking network models like GLMs\n(Generalized Linear Models) which do not usually rely on back-propagation. This\nnew fitting algorithm also enables the consideration of hidden neurons which is\notherwise notoriously hard, and we show that it can be crucial when trying to\ninfer the network connectivity from spike recordings.\n"}
+{"abstract": "  It is known that phonons have angular momentum, and when the time-reversal\nsymmetry (TRS) is broken, the total phonon angular momentum in the whole system\nbecomes nonzero. In this paper, we propose that as an angular momentum of\nphonons for a crystal without TRS, we need to consider the canonical angular\nmomentum, as opposed to the kinetic angular momentum in previous works. Next,\nwe show that the angular momentum of phonons without TRS exhibits universal\nbehaviors near the $\\Gamma$ point. We focus on in-plane oscillations in\ntwo-dimensional crystals as an example. By breaking the TRS, one of the\nacoustic phonon branches at the $\\Gamma$ point acquires a gap. We show that the\nangular momentum of its acoustic phonon with a gap has a peak with the height\n$\\pm \\hbar$ regardless of the details of the system. From this, we find that\nthis peak height changes discontinuously by changing the sign of the\nTRS-breaking parameter.\n"}
+{"abstract": "  This paper describes a novel lossless compression method for point cloud\ngeometry, building on a recent lossy compression method that aimed at\nreconstructing only the bounding volume of a point cloud. The proposed scheme\nstarts by partially reconstructing the geometry from the two depthmaps\nassociated to a single projection direction. The partial reconstruction\nobtained from the depthmaps is completed to a full reconstruction of the point\ncloud by sweeping section by section along one direction and encoding the\npoints which were not contained in the two depthmaps. The main ingredient is a\nlist-based encoding of the inner points (situated inside the feasible regions)\nby a novel arithmetic three dimensional context coding procedure that\nefficiently utilizes rotational invariances present in the input data.\nState-of-the-art bits-per-voxel results are obtained on benchmark datasets.\n"}
+{"abstract": "  If Z is an open subscheme of Spec ZZ, X is a sufficiently nice Z-model of a\nsmooth curve over QQ, and p is a closed point of Z, the Chabauty-Kim method\nleads to the construction of locally analytic functions on X(ZZ_p) which vanish\non X(Z); we call such functions \"Kim functions\". At least in broad outline, the\nmethod generalizes readily to higher dimensions. In fact, in some sense, the\nsurface M_{0,5} should be easier than the previously studied curve M_{0,4}\nsince its points are closely related to those of M_{0,4}, yet they face a\nfurther condition to integrality. This is mirrored by a certain \"weight\nadvantage\" we encounter, because of which, M_{0,5} possesses new Kim functions\nnot coming from M_{0,4}. Here we focus on the case \"ZZ[1/6] in half-weight 4\",\nwhere we provide a first nontrivial example of a Kim function on a surface.\n  Central to our approach to Chabauty-Kim theory (as developed in works by S.\nWewers, D. Corwin, and the first author) is the possibility of separating the\ngeometric part of the computation from its arithmetic context. However, we find\nthat in this case the geometric step grows beyond the bounds of standard\nalgorithms running on current computers. Therefore, some ingenuity is needed to\nsolve this seemingly straightforward problem, and our new Kim function is huge.\n"}
+{"abstract": "  X-ray pulse profile modeling of PSR J0740+6620, the most massive known\npulsar, with data from the NICER and XMM-Newton observatories recently led to a\nmeasurement of its radius. We investigate this measurement's implications for\nthe neutron star equation of state (EoS), employing a nonparametric EoS model\nbased on Gaussian processes and combining information from other x-ray, radio\nand gravitational-wave observations of neutron stars. Our analysis mildly\ndisfavors EoSs that support a disconnected hybrid star branch in the\nmass-radius relation, a proxy for strong phase transitions, with a Bayes factor\nof $6.9$. For such EoSs, the transition mass from the hadronic to the hybrid\nbranch is constrained to lie outside ($1,2$) $M_{\\odot}$. We also find that the\nconformal sound-speed bound is violated inside neutron star cores, which\nimplies that the core matter is strongly interacting. The squared sound speed\nreaches a maximum of $0.75^{+0.25}_{-0.24}\\, c^2$ at $3.60^{+2.25}_{-1.89}$\ntimes nuclear saturation density at 90% credibility. Since all but the\ngravitational-wave observations prefer a relatively stiff EoS, PSR J0740+6620's\ncentral density is only $3.57^{+1.3}_{-1.3}$ times nuclear saturation, limiting\nthe density range probed by observations of cold, nonrotating neutron stars in\n$\\beta$-equilibrium.\n"}
+{"abstract": "  Matched filters are routinely used in cosmology in order to detect galaxy\nclusters from mm observations through their thermal Sunyaev-Zeldovich (tSZ)\nsignature. In addition, they naturally provide an observable, the detection\nsignal-to-noise or significance, which can be used as a mass proxy in number\ncounts analyses of tSZ-selected cluster samples. In this work, we show that\nthis observable is, in general, non-Gaussian, and that it suffers from a\npositive bias, which we refer to as optimisation bias. Both aspects arise from\nthe fact that the signal-to-noise is constructed through an optimisation\noperation on noisy data, and hold even if the cluster signal is modelled\nperfectly well, no foregrounds are present, and the noise is Gaussian. After\nreviewing the general mathematical formalism underlying matched filters, we\nstudy the statistics of the signal-to-noise with a set Monte Carlo mock\nobservations, finding it to be well-described by a unit-variance Gaussian for\nsignal-to-noise values of 6 and above, and quantify the magnitude of the\noptimisation bias, for which we give an approximate expression that may be used\nin practice. We also consider the impact of the bias on the cluster number\ncounts of Planck and the Simons Observatory (SO), finding it to be negligible\nfor the former and potentially significant for the latter.\n"}
+{"abstract": "  Self-propelled droplets are composed of droplets driven by the waves they\nemit when bouncing on a vertically vibrated bath. Their dynamics is based on an\ninterplay between the waves and their source. The existence of self-spinning\nmodes is still controversial. Here, we show experimentally that these modes are\nstable for a class of droplets and emerge spontaneously from noise\nfluctuations. We perform a discrete stability analysis to confirm experimental\nobservations. In addition, we show that these self-spinning modes provide a\nunique opportunity for a direct experimental measurement of parameters used in\nthe wave-driven droplet models found in the literature to enable comparison and\ncalibration.\n"}
+{"abstract": "  Artificial Neural Networks (ANNs) became popular due to their successful\napplication difficult problems such image and speech recognition. However, when\npractitioners want to design an ANN they need to undergo laborious process of\nselecting a set of parameters and topology. Currently, there are several\nstate-of-the art methods that allow for the automatic selection of some of\nthese aspects. Learning Rate optimizers are a set of such techniques that\nsearch for good values of learning rates. Whilst these techniques are effective\nand have yielded good results over the years, they are general solution i.e.\nthey do not consider the characteristics of a specific network.\n  We propose a framework called AutoLR to automatically design Learning Rate\nOptimizers. Two versions of the system are detailed. The first one, Dynamic\nAutoLR, evolves static and dynamic learning rate optimizers based on the\ncurrent epoch and the previous learning rate. The second version, Adaptive\nAutoLR, evolves adaptive optimizers that can fine tune the learning rate for\neach network eeight which makes them generally more effective. The results are\ncompetitive with the best state of the art methods, even outperforming them in\nsome scenarios. Furthermore, the system evolved a classifier, ADES, that\nappears to be novel and innovative since, to the best of our knowledge, it has\na structure that differs from state of the art methods.\n"}
+{"abstract": "  We apply the Ionization Region Model (IRM) and the Orsay Boltzmann equation\nfor ELectrons coupled with Ionization and eXcited states kinetics (OBELIX)\nmodel to study the electron kinetics of a high power impulse magnetron\nsputtering (HiPIMS) discharge. In the IRM the bulk (cold) electrons are assumed\nto exhibit a Maxwellian energy distribution and the secondary (hot) electrons,\nemitted from the target surface upon ion bombardment, are treated as a high\nenergy tail, while in the OBELIX the electron energy distribution is calculated\nself-consistently using an isotropic Boltzmann equation. The two models are\nmerged in the sense that the output from the IRM is used as an input for\nOBELIX. The temporal evolutions of the particle densities are found to agree\nvery well between the two models. Furthermore, a very good agreement is\ndemonstrated between the bi-Maxwellian electron energy distribution assumed by\nthe IRM and the electron energy distribution calculated by the OBELIX model. It\ncan therefore be concluded that assuming a bi-Maxwellian electron energy\ndistribution, constituting a cold bulk electron group and a hot secondary\nelectron group, is a good approximation for modeling the HiPIMS discharge.\n"}
+{"abstract": "  We study the outflow dynamics and clogging phenomena of mixtures of soft,\nelastic low-friction spherical grains and hard frictional spheres of similar\nsize in a quasi-two-dimensional (2D) silo with narrow orifice at the bottom.\nPrevious work has demonstrated the crucial influence of elasticity and friction\non silo discharge. We show that the addition of small amounts, even as low as\n5\\%, of hard grains to an ensemble of soft, low-friction grains already has\nsignificant consequences. The mixtures allow a direct comparison of the\nprobabilities of the different types of particles to clog the orifice. We\nanalyze these probabilities for the hard, frictional and the soft, slippery\ngrains on the basis of their participation in the blocking arches, and compare\noutflow velocities and durations of non-permanent clogs for different\ncompositions of the mixtures. Experimental results are compared with numerical\nsimulations. The latter strongly suggest a significant influence of the\ninter-species particle friction.\n"}
+{"abstract": "  Isogeometric approach applied to Boundary Element Methods is an emerging\nresearch area. In this context, the aim of the present contribution is that of\ninvestigating, from a numerical point of view, the Symmetric Galerkin Boundary\nElement Method (SGBEM) devoted to the solution of 2D boundary value problems\nfor the Laplace equation, where the boundary and the unknowns on it are both\nrepresented by B-splines. We mainly compare this approach, which we call\nIGA-SGBEM, with a curvilinear SGBEM, which operates on any boundary given by\nexplicit parametric representation and where the approximate solution is\nobtained using Lagrangian basis. Both techniques are further compared with a\nstandard (conventional) SGBEM approach, where the boundary of the assigned\nproblem is approximated by linear elements and the numerical solution is\nexpressed in terms of Lagrangian basis. Several examples will be presented and\ndiscussed, underlying benefits and drawbacks of all the above-mentioned\napproaches.\n"}
+{"abstract": "  The modular decomposition of a symmetric map $\\delta\\colon X\\times X \\to\n\\Upsilon$ (or, equivalently, a set of symmetric binary relations, a\n2-structure, or an edge-colored undirected graph) is a natural construction to\ncapture key features of $\\delta$ in labeled trees. A map $\\delta$ is explained\nby a vertex-labeled rooted tree $(T,t)$ if the label $\\delta(x,y)$ coincides\nwith the label of the last common ancestor of $x$ and $y$ in $T$, i.e., if\n$\\delta(x,y)=t(\\mathrm{lca}(x,y))$. Only maps whose modular decomposition does\nnot contain prime nodes, i.e., the symbolic ultrametrics, can be exaplained in\nthis manner. Here we consider rooted median graphs as a generalization to\n(modular decomposition) trees to explain symmetric maps. We first show that\nevery symmetric map can be explained by \"extended\" hypercubes and half-grids.\nWe then derive a a linear-time algorithm that stepwisely resolves prime\nvertices in the modular decomposition tree to obtain a rooted and labeled\nmedian graph that explains a given symmetric map $\\delta$. We argue that the\nresulting \"tree-like\" median graphs may be of use in phylogenetics as a model\nof evolutionary relationships.\n"}
+{"abstract": "  Deep Metric Learning (DML) learns a non-linear semantic embedding from input\ndata that brings similar pairs together while keeps dissimilar data away from\neach other. To this end, many different methods are proposed in the last decade\nwith promising results in various applications. The success of a DML algorithm\ngreatly depends on its loss function. However, no loss function is perfect, and\nit deals only with some aspects of an optimal similarity embedding. Besides,\nthe generalizability of the DML on unseen categories during the test stage is\nan important matter that is not considered by existing loss functions. To\naddress these challenges, we propose novel approaches to combine different\nlosses built on top of a shared deep feature extractor. The proposed ensemble\nof losses enforces the deep model to extract features that are consistent with\nall losses. Since the selected losses are diverse and each emphasizes different\naspects of an optimal semantic embedding, our effective combining methods yield\na considerable improvement over any individual loss and generalize well on\nunseen categories. Here, there is no limitation in choosing loss functions, and\nour methods can work with any set of existing ones. Besides, they can optimize\neach loss function as well as its weight in an end-to-end paradigm with no need\nto adjust any hyper-parameter. We evaluate our methods on some popular datasets\nfrom the machine vision domain in conventional Zero-Shot-Learning (ZSL)\nsettings. The results are very encouraging and show that our methods outperform\nall baseline losses by a large margin in all datasets.\n"}
+{"abstract": "  We use FIRE-2 simulations to examine 3-D variations of gas-phase elemental\nabundances of [O/H], [Fe/H], and [N/H] in 11 Milky Way (MW) and M31-mass\ngalaxies across their formation histories at $z \\leq 1.5$ ($t_{\\rm lookback}\n\\leq 9.4$ Gyr), motivated by characterizing the initial conditions of stars for\nchemical tagging. Gas within $1$ kpc of the disk midplane is vertically\nhomogeneous to $\\lesssim 0.008$ dex at all $z \\leq 1.5$. We find negative\nradial gradients (metallicity decreases with galactocentric radius) at all\ntimes, which steepen over time from $\\approx -0.01$ dex kpc$^{-1}$ at $z = 1$\n($t_{\\rm lookback} = 7.8$ Gyr) to $\\approx -0.03$ dex kpc$^{-1}$ at $z = 0$,\nand which broadly agree with observations of the MW, M31, and nearby\nMW/M31-mass galaxies. Azimuthal variations at fixed radius are typically $0.14$\ndex at $z = 1$, reducing to $0.05$ dex at $z = 0$. Thus, over time radial\ngradients become steeper while azimuthal variations become weaker (more\nhomogeneous). As a result, azimuthal variations were larger than radial\nvariations at $z \\gtrsim 0.8$ ($t_{\\rm lookback} \\gtrsim 6.9$ Gyr).\nFurthermore, elemental abundances are measurably homogeneous (to $\\lesssim\n0.05$ dex) across a radial range of $\\Delta R \\approx 3.5$ kpc at $z \\gtrsim 1$\nand $\\Delta R \\approx 1.7$ kpc at $z = 0$. We also measure full distributions\nof elemental abundances, finding typically negatively skewed normal\ndistributions at $z \\gtrsim 1$ that evolve to typically Gaussian distributions\nby $z = 0$. Our results on gas abundances inform the initial conditions for\nstars, including the spatial and temporal scales for applying chemical tagging\nto understand stellar birth in the MW.\n"}
+{"abstract": "  We characterize mass, momentum, energy and metal outflow rates of multi-phase\ngalactic winds in a suite of FIRE-2 cosmological \"zoom-in\" simulations from the\nFeedback in Realistic Environments (FIRE) project. We analyze simulations of\nlow-mass dwarfs, intermediate-mass dwarfs, Milky Way-mass halos, and\nhigh-redshift massive halos. Consistent with previous work, we find that dwarfs\neject about 100 times more gas from their interstellar medium (ISM) than they\nform in stars, while this mass \"loading factor\" drops below one in massive\ngalaxies. Most of the mass is carried by the hot phase ($>10^5$ K) in massive\nhalos and the warm phase ($10^3-10^5$ K) in dwarfs; cold outflows ($<10^3$ K)\nare negligible except in high-redshift dwarfs. Energy, momentum and metal\nloading factors from the ISM are of order unity in dwarfs and significantly\nlower in more massive halos. Hot outflows have $2-5\\times$ higher specific\nenergy than needed to escape from the gravitational potential of dwarf halos;\nindeed, in dwarfs, the mass, momentum, and metal outflow rates increase with\nradius whereas energy is roughly conserved, indicating swept up halo gas.\nBurst-averaged mass loading factors tend to be larger during more powerful star\nformation episodes and when the inner halo is not virialized, but we see\neffectively no trend with the dense ISM gas fraction. We discuss how our\nresults can guide future controlled numerical experiments that aim to elucidate\nthe key parameters governing galactic winds and the resulting associated\npreventative feedback.\n"}
+{"abstract": "  Controlling bias in training datasets is vital for ensuring equal treatment,\nor parity, between different groups in downstream applications. A naive\nsolution is to transform the data so that it is statistically independent of\ngroup membership, but this may throw away too much information when a\nreasonable compromise between fairness and accuracy is desired. Another common\napproach is to limit the ability of a particular adversary who seeks to\nmaximize parity. Unfortunately, representations produced by adversarial\napproaches may still retain biases as their efficacy is tied to the complexity\nof the adversary used during training. To this end, we theoretically establish\nthat by limiting the mutual information between representations and protected\nattributes, we can assuredly control the parity of any downstream classifier.\nWe demonstrate an effective method for controlling parity through mutual\ninformation based on contrastive information estimators and show that they\noutperform approaches that rely on variational bounds based on complex\ngenerative models. We test our approach on UCI Adult and Heritage Health\ndatasets and demonstrate that our approach provides more informative\nrepresentations across a range of desired parity thresholds while providing\nstrong theoretical guarantees on the parity of any downstream algorithm.\n"}
+{"abstract": "  In programming education, it makes a difference whether you are dealing with\nbeginners or advanced students. As our future students will become even more\ntech-savvy, it is necessary to assess programming skills appropriately and\nquickly to protect them from boredom and optimally support the learning\nprocess. In this work, we advocate for the use of slice-based cohesion metrics\nto assess the process of program construction in a learning analytics setting.\nWe argue that semantically related parts during program construction are an\nessential part of programming skills. Therefore, we propose using cohesion\nmetrics on the level of variables to identify programmers' trains of thought\nbased on the cohesion of semantically related parts during program\nconstruction.\n"}
+{"abstract": "  A logic is said to admit an equational completeness theorem when it can be\ninterpreted into the equational consequence relative to some class of algebras.\nWe characterize logics admitting an equational completeness theorem that are\neither locally tabular or have some tautology. In particular, it is shown that\na protoalgebraic logic admits an equational completeness theorem precisely when\nits has two distinct logically equivalent formulas. While the problem of\ndetermining whether a logic admits an equational completeness theorem is shown\nto be decidable both for logics presented by a finite set of finite matrices\nand for locally tabular logics presented by a finite Hilbert calculus, it\nbecomes undecidable for arbitrary logics presented by finite Hilbert calculi.\n"}
+{"abstract": "  Efficient link configuration in millimeter wave (mmWave) communication\nsystems is a crucial yet challenging task due to the overhead imposed by beam\nselection. For vehicle-to-infrastructure (V2I) networks, side information from\nLIDAR sensors mounted on the vehicles has been leveraged to reduce the beam\nsearch overhead. In this letter, we propose a federated LIDAR aided beam\nselection method for V2I mmWave communication systems. In the proposed scheme,\nconnected vehicles collaborate to train a shared neural network (NN) on their\nlocally available LIDAR data during normal operation of the system. We also\npropose a reduced-complexity convolutional NN (CNN) classifier architecture and\nLIDAR preprocessing, which significantly outperforms previous works in terms of\nboth the performance and the complexity.\n"}
+{"abstract": "  Large-scale multi-agent cooperative control problems have materially enjoyed\nthe scalability, adaptivity, and flexibility of decentralized optimization.\nHowever, due to the mandatory iterative communications between the agents and\nthe system operator, the decentralized architecture is vulnerable to malicious\nattacks and privacy breach. Current research on addressing privacy preservation\nof both agents and the system operator in cooperative decentralized\noptimization with strongly coupled objective functions and constraints is still\nprimitive. To fill in the gaps, this paper proposes a novel privacy-preserving\ndecentralized optimization paradigm based on Paillier cryptosystem. The\nproposed paradigm achieves ideal correctness and security, as well as resists\nattacks from a range of adversaries. The efficacy and efficiency of the\nproposed approach are verified via numerical simulations and a real-world\nphysical platform.\n"}
+{"abstract": "  A heterodimensional cycle is an invariant set of a dynamical system\nconsisting of two hyperbolic periodic orbits with different dimensions of their\nunstable manifolds and a pair of orbits that connect them. For systems which\nare at least $C^2$, we show that bifurcations of a coindex-1 heterodimensional\ncycle within a generic 2-parameter family always create robust\nheterodimensional dynamics, i.e., chain-transitive sets which contain\ncoexisting orbits with different numbers of positive Lyapunov exponents and\npersist for an open set of parameter values. In particular, we solve the\nso-called $C^r$-stabilization problem for the coindex-1 heterodimensional\ncycles in any regularity class $r=2,\\ldots,\\infty,\\omega$. The results are\nbased on the observation that arithmetic properties of moduli of topological\nconjugacy of systems with heterodimensional cycles determine the emergence of\nBonatti-Diaz blenders.\n"}
+{"abstract": "  In this paper we introduce a discrete fractional resolvent family\n$\\{S_{\\alpha,\\beta}^n\\}_{n\\in\\mathbb{N}_0}$ generated by a closed linear\noperator in a Banach space $X$ for a given $\\alpha,\\beta>0.$ Moreover, we study\nits main properties and, as a consequence, we obtain a method to study the\nexistence and uniqueness of the solutions to discrete fractional difference\nequations in a Banach space.\n"}
+{"abstract": "  Virtual meetings are critical for remote work because of the need for\nsynchronous collaboration in the absence of in-person interactions. In-meeting\nmultitasking is closely linked to people's productivity and wellbeing. However,\nwe currently have limited understanding of multitasking in remote meetings and\nits potential impact. In this paper, we present what we believe is the most\ncomprehensive study of remote meeting multitasking behavior through an analysis\nof a large-scale telemetry dataset collected from February to May 2020 of U.S.\nMicrosoft employees and a 715-person diary study. Our results demonstrate that\nintrinsic meeting characteristics such as size, length, time, and type,\nsignificantly correlate with the extent to which people multitask, and\nmultitasking can lead to both positive and negative outcomes. Our findings\nsuggest important best-practice guidelines for remote meetings (e.g., avoid\nimportant meetings in the morning) and design implications for productivity\ntools (e.g., support positive remote multitasking).\n"}
+{"abstract": "  We present a comprehensive analysis of the potential sensitivity of the\nElectron-Ion Collider (EIC) to charged lepton flavor violation (CLFV) in the\nchannel $ep\\to \\tau X$, within the model-independent framework of the Standard\nModel Effective Field Theory (SMEFT). We compute the relevant cross sections to\nleading order in QCD and electroweak corrections and perform simulations of\nsignal and SM background events in various $\\tau$ decay channels, suggesting\nsimple cuts to enhance the associated estimated efficiencies. To assess the\ndiscovery potential of the EIC in $\\tau$-$e$ transitions, we study the\nsensitivity of other probes of this physics across a broad range of energy\nscales, from $pp \\to e \\tau X$ at the Large Hadron Collider to decays of $B$\nmesons and $\\tau$ leptons, such as $\\tau \\to e \\gamma$, $\\tau \\to e \\ell^+\n\\ell^-$, and crucially the hadronic modes $\\tau \\to e Y$ with $Y \\in \\{ \\pi, K,\n\\pi \\pi, K \\pi, ...\\}$. We find that electroweak dipole and four-fermion\nsemi-leptonic operators involving light quarks are already strongly constrained\nby $\\tau$ decays, while operators involving the $c$ and $b$ quarks present more\npromising discovery potential for the EIC. An analysis of three models of\nleptoquarks confirms the expectations based on the SMEFT results. We also\nidentify future directions needed to maximize the reach of the EIC in CLFV\nsearches: these include an optimization of the $\\tau$ tagger in hadronic\nchannels, an exploration of background suppression through tagging $b$ and $c$\njets in the final state, and a global fit by turning on all SMEFT couplings,\nwhich will likely reveal new discovery windows for the EIC.\n"}
+{"abstract": "  Word vector representations enable machines to encode human language for\nspoken language understanding and processing. Confusion2vec, motivated from\nhuman speech production and perception, is a word vector representation which\nencodes ambiguities present in human spoken language in addition to semantics\nand syntactic information. Confusion2vec provides a robust spoken language\nrepresentation by considering inherent human language ambiguities. In this\npaper, we propose a novel word vector space estimation by unsupervised learning\non lattices output by an automatic speech recognition (ASR) system. We encode\neach word in confusion2vec vector space by its constituent subword character\nn-grams. We show the subword encoding helps better represent the acoustic\nperceptual ambiguities in human spoken language via information modeled on\nlattice structured ASR output. The usefulness of the proposed Confusion2vec\nrepresentation is evaluated using semantic, syntactic and acoustic analogy and\nword similarity tasks. We also show the benefits of subword modeling for\nacoustic ambiguity representation on the task of spoken language intent\ndetection. The results significantly outperform existing word vector\nrepresentations when evaluated on erroneous ASR outputs. We demonstrate that\nConfusion2vec subword modeling eliminates the need for retraining/adapting the\nnatural language understanding models on ASR transcripts.\n"}
+{"abstract": "  This work proposes to use evolutionary computation as a pathway to allow a\nnew perspective on the modeling of energy expenditure and recovery of an\nindividual athlete during exercise. We revisit a theoretical concept called the\n\"three component hydraulic model\" which is designed to simulate metabolic\nsystems during exercise and which is able to address recently highlighted\nshortcomings of currently applied performance models. This hydraulic model has\nnot been entirely validated on individual athletes because it depends on\nphysiological measures that cannot be acquired in the required precision or\nquantity. This paper introduces a generalized interpretation and formalization\nof the three component hydraulic model that removes its ties to concrete\nmetabolic measures and allows to use evolutionary computation to fit its\nparameters to an athlete.\n"}
+{"abstract": "  The Epoch of Reionisation (EoR) is the period within which the neutral\nuniverse transitioned to an ionised one. This period remains unobserved using\nlow-frequency radio interferometers which target the 21 cm signal of neutral\nhydrogen emitted in this era. The Murchison Widefield Array (MWA) radio\ntelescope was built with the detection of this signal as one of its major\nscience goals. One of the most significant challenges towards a successful\ndetection is that of calibration, especially in the presence of the Earth's\nionosphere. By introducing refractive source shifts, distorting source shapes\nand scintillating flux densities, the ionosphere is a major nuisance in\nlow-frequency radio astronomy. We introduce SIVIO, a software tool developed\nfor simulating observations of the MWA through different ionospheric conditions\nestimated using thin screen approximation models and propagated into the\nvisibilities. This enables us to directly assess the impact of the ionosphere\non observed EoR data and the resulting power spectra. We show that the\nsimulated data captures the dispersive behaviour of ionospheric effects. We\nshow that the spatial structure of the simulated ionospheric media is\naccurately reconstructed either from the resultant source positional offsets or\nfrom parameters evaluated during the data calibration procedure. In turn, this\nwill inform on the best strategies of identifying and efficiently eliminating\nionospheric contamination in EoR data moving into the Square Kilometre Array\nera.\n"}
+{"abstract": "  This article presents a new hand architecture with three under-actuated\nfingers. Each finger performs spatial movements to achieve more complex and\nvaried grasping than the existing planar-movement fingers. The purpose of this\nhand is to grasp complex-shaped workpieces as they leave the machining centres.\nAmong the taxonomy of grips, cylindrical and spherical grips are often used to\ngrasp heavy objects. A combination of these two modes makes it possible to\ncapture most of the workpieces machined with 5-axis machines. However, the\nchange in grasping mode requires the fingers to reconfigure themselves to\nperform spatial movements. This solution requires the addition of two or three\nactuators to change the position of the fingers and requires sensors to\nrecognize the shape of the workpiece and determine the type of grasp to be\nused. This article proposes to extend the notion of under-actuated fingers to\nspatial movements. After a presentation of the kinematics of the fingers, the\nproblem of stability is discussed as well as the transmission of forces in this\nmechanism. The complete approach for calculating the stability conditions is\npresented from the study of Jacobian force transmission matrices. CAD\nrepresentations of the hand and its behavior in spherical and cylindrical grips\nare presented.\n"}
+{"abstract": "  The main contribution of this manuscript is a local normal form for\nHamiltonian actions of Poisson-Lie groups $K$ on a symplectic manifold equipped\nwith an $AN$-valued moment map, where $AN$ is the dual Poisson-Lie group of\n$K$. Our proof uses the delinearization theorem of Alekseev which relates a\nclassical Hamiltonian action of $K$ with $\\mathfrak{k}^*$-valued moment map to\na Hamiltonian action with an $AN$-valued moment map, via a deformation of\nsymplectic structures. We obtain our main result by proving a ``delinearization\ncommutes with symplectic quotients'' theorem which is also of independent\ninterest, and then putting this together with the local normal form theorem for\nclassical Hamiltonian actions wtih $\\mathfrak{k}^*$-valued moment maps. A key\ningredient for our main result is the delinearization\n$\\mathcal{D}(\\omega_{can})$ of the canonical symplectic structure on $T^*K$, so\nwe additionally take some steps toward explicit computations of\n$\\mathcal{D}(\\omega_{can})$. In particular, in the case $K=SU(2)$, we obtain\nexplicit formulas for the matrix coefficients of $\\mathcal{D}(\\omega_{can})$\nwith respect to a natural choice of coordinates on $T^*SU(2)$.\n"}
+{"abstract": "  We propose a three-dimensional (3D) multimodal medical imaging system that\ncombines freehand ultrasound and structured light 3D reconstruction in a single\ncoordinate system without requiring registration. To the best of our knowledge,\nthese techniques have not been combined before as a multimodal imaging\ntechnique. The system complements the internal 3D information acquired with\nultrasound, with the external surface measured with the structure light\ntechnique. Moreover, the ultrasound probe's optical tracking for pose\nestimation was implemented based on a convolutional neural network.\nExperimental results show the system's high accuracy and reproducibility, as\nwell as its potential for preoperative and intraoperative applications. The\nexperimental multimodal error, or the distance from two surfaces obtained with\ndifferent modalities, was 0.12 mm. The code is available as a Github\nrepository.\n"}
+{"abstract": "  Solving planning and scheduling problems for multiple tasks with highly\ncoupled state and temporal constraints is notoriously challenging. An appealing\napproach to effectively decouple the problem is to judiciously order the events\nsuch that decisions can be made over sequences of tasks. As many problems\nencountered in practice are over-constrained, we must instead find relaxed\nsolutions in which certain requirements are dropped. This motivates a\nformulation of optimality with respect to the costs of relaxing constraints and\nthe problem of finding an optimal ordering under which this relaxing cost is\nminimum. In this paper, we present Generalized Conflict-directed Ordering\n(GCDO), a branch-and-bound ordering method that generates an optimal total\norder of events by leveraging the generalized conflicts of both inconsistency\nand suboptimality from sub-solvers for cost estimation and solution space\npruning. Due to its ability to reason over generalized conflicts, GCDO is much\nmore efficient in finding high-quality total orders than the previous\nconflict-directed approach CDITO. We demonstrate this by benchmarking on\ntemporal network configuration problems, which involves managing networks over\ntime and makes necessary tradeoffs between network flows against CDITO and\nMixed Integer-Linear Programing (MILP). Our algorithm is able to solve two\norders of magnitude more benchmark problems to optimality and twice the\nproblems compared to CDITO and MILP within a runtime limit, respectively.\n"}
+{"abstract": "  Cryo-electron microscopy (cryo-EM) has become a major experimental technique\nto determine the structures of large protein complexes and molecular\nassemblies, as evidenced by the 2017 Nobel Prize. Although cryo-EM has been\ndrastically improved to generate high-resolution three-dimensional (3D) maps\nthat contain detailed structural information about macromolecules, the\ncomputational methods for using the data to automatically build structure\nmodels are lagging far behind. The traditional cryo-EM model building approach\nis template-based homology modeling. Manual de novo modeling is very\ntime-consuming when no template model is found in the database. In recent\nyears, de novo cryo-EM modeling using machine learning (ML) and deep learning\n(DL) has ranked among the top-performing methods in macromolecular structure\nmodeling. Deep-learning-based de novo cryo-EM modeling is an important\napplication of artificial intelligence, with impressive results and great\npotential for the next generation of molecular biomedicine. Accordingly, we\nsystematically review the representative ML/DL-based de novo cryo-EM modeling\nmethods. And their significances are discussed from both practical and\nmethodological viewpoints. We also briefly describe the background of cryo-EM\ndata processing workflow. Overall, this review provides an introductory guide\nto modern research on artificial intelligence (AI) for de novo molecular\nstructure modeling and future directions in this emerging field.\n"}
+{"abstract": "  2020 has been a year marked by the COVID-19 pandemic. This event has caused\ndisruptions to many aspects of normal life. An important aspect in reducing the\nimpact of the pandemic is to control its spread. Studies have shown that one\neffective method in reducing the transmission of COVID-19 is to wear masks.\nStrict mask-wearing policies have been met with not only public sensation but\nalso practical difficulty. We cannot hope to manually check if everyone on a\nstreet is wearing a mask properly. Existing technology to help automate mask\nchecking uses deep learning models on real-time surveillance camera footages.\nThe current dominant method to perform real-time mask detection uses Mask-RCNN\nwith ResNet as the backbone. While giving good detection results, this method\nis computationally intensive and its efficiency in real-time face mask\ndetection is not ideal. Our research proposes a new approach to mask detection\nby replacing Mask-R-CNN with a more efficient model \"YOLO\" to increase the\nprocessing speed of real-time mask detection and not compromise on accuracy.\nBesides, given the small volume as well as extreme imbalance of the mask\ndetection datasets, we adopt a latest progress made in few-shot visual\nclassification, simple CNAPs, to improve the classification performance.\n"}
+{"abstract": "  In computed tomography, data consist of measurements of the attenuation of\nX-rays passing through an object. The goal is to reconstruct the linear\nattenuation coefficient of the object's interior. For each position of the\nX-ray source, characterized by its angle with respect to a fixed coordinate\nsystem, one measures a set of data referred to as a view. A common assumption\nis that these view angles are known, but in some applications they are known\nwith imprecision. We propose a framework to solve a Bayesian inverse problem\nthat jointly estimates the view angles and an image of the object's attenuation\ncoefficient. We also include a few hyperparameters that characterize the\nlikelihood and the priors. Our approach is based on a Gibbs sampler where the\nassociated conditional densities are simulated using different sampling schemes\n- hence the term hybrid. In particular, the conditional distribution associated\nwith the reconstruction is nonlinear in the image pixels, non-Gaussian and\nhigh-dimensional. We approach this distribution by constructing a Laplace\napproximation that represents the target conditional locally at each Gibbs\niteration. This enables sampling of the attenuation coefficients in an\nefficient manner using iterative reconstruction algorithms. The numerical\nresults show that our algorithm is able to jointly identify the image and the\nview angles, while also providing uncertainty estimates of both. We demonstrate\nour method with 2D X-ray computed tomography problems using fan beam\nconfigurations.\n"}
+{"abstract": "  FPGAs are now used in public clouds to accelerate a wide range of\napplications, including many that operate on sensitive data such as financial\nand medical records. We present ShEF, a trusted execution environment (TEE) for\ncloud-based reconfigurable accelerators. ShEF is independent from CPU-based\nTEEs and allows secure execution under a threat model where the adversary can\ncontrol all software running on the CPU connected to the FPGA, has physical\naccess to the FPGA, and can compromise the FPGA interface logic of the cloud\nprovider. ShEF provides a secure boot and remote attestation process that\nrelies solely on existing FPGA mechanisms for root of trust. It also includes a\nShield component that provides secure access to data while the accelerator is\nin use. The Shield is highly customizable and extensible, allowing users to\ncraft a bespoke security solution that fits their accelerator's memory access\npatterns, bandwidth, and security requirements at minimum performance and area\noverheads. We describe a prototype implementation of ShEF for existing cloud\nFPGAs and measure the performance benefits of customizable security using five\naccelerator designs.\n"}
+{"abstract": "  The dual-path RNN (DPRNN) was proposed to more effectively model extremely\nlong sequences for speech separation in the time domain. By splitting long\nsequences to smaller chunks and applying intra-chunk and inter-chunk RNNs, the\nDPRNN reached promising performance in speech separation with a limited model\nsize. In this paper, we combine the DPRNN module with Convolution Recurrent\nNetwork (CRN) and design a model called Dual-Path Convolution Recurrent Network\n(DPCRN) for speech enhancement in the time-frequency domain. We replace the\nRNNs in the CRN with DPRNN modules, where the intra-chunk RNNs are used to\nmodel the spectrum pattern in a single frame and the inter-chunk RNNs are used\nto model the dependence between consecutive frames. With only 0.8M parameters,\nthe submitted DPCRN model achieves an overall mean opinion score (MOS) of 3.57\nin the wide band scenario track of the Interspeech 2021 Deep Noise Suppression\n(DNS) challenge. Evaluations on some other test sets also show the efficacy of\nour model.\n"}
+{"abstract": "  Quantitative lung measures derived from computed tomography (CT) have been\ndemonstrated to improve prognostication in coronavirus disease (COVID-19)\npatients, but are not part of the clinical routine since required manual\nsegmentation of lung lesions is prohibitively time-consuming. We propose a new\nfully automated deep learning framework for rapid quantification and\ndifferentiation between lung lesions in COVID-19 pneumonia from both contrast\nand non-contrast CT images using convolutional Long Short-Term Memory\n(ConvLSTM) networks. Utilizing the expert annotations, model training was\nperformed 5 times with separate hold-out sets using 5-fold cross-validation to\nsegment ground-glass opacity and high opacity (including consolidation and\npleural effusion). The performance of the method was evaluated on CT data sets\nfrom 197 patients with positive reverse transcription polymerase chain reaction\ntest result for SARS-CoV-2. Strong agreement between expert manual and\nautomatic segmentation was obtained for lung lesions with a Dice score\ncoefficient of 0.876 $\\pm$ 0.005; excellent correlations of 0.978 and 0.981 for\nground-glass opacity and high opacity volumes. In the external validation set\nof 67 patients, there was dice score coefficient of 0.767 $\\pm$ 0.009 as well\nas excellent correlations of 0.989 and 0.996 for ground-glass opacity and high\nopacity volumes. Computations for a CT scan comprising 120 slices were\nperformed under 2 seconds on a personal computer equipped with NVIDIA Titan RTX\ngraphics processing unit. Therefore, our deep learning-based method allows\nrapid fully-automated quantitative measurement of pneumonia burden from CT and\nmay generate results with an accuracy similar to the expert readers.\n"}
+{"abstract": "  DNA methylation is a well-studied genetic modification that regulates gene\ntranscription of Eukaryotes. Its alternations have been recognized as a\nsignificant component of cancer development. In this study, we use the DNA\nmethylation 450k data from The Cancer Genome Atlas to evaluate the efficacy of\nDNA methylation data on cancer classification for 30 cancer types. We propose a\nnew method for gene selection in high dimensional data(over 450 thousand).\nVariance filtering is first introduced for dimension reduction and Recursive\nfeature elimination (RFE) is then used for feature selection. We address the\nproblem of selecting a small subsets of genes from large number of methylated\nsites, and our parsimonious model is demonstrated to be efficient, achieving an\naccuracy over 91%, outperforming other studies which use DNA micro-arrays and\nRNA-seq Data . The performance of 20 models, which are based on 4 estimators\n(Random Forest, Decision Tree, Extra Tree and Support Vector Machine) and 5\nclassifiers (k-Nearest Neighbours, Support Vector Machine, XGboost, Light GBM\nand Multi-Layer Perceptron), is compared and robustness of the RFE algorithm is\nexamined. Results suggest that the combined model of extra tree plus catboost\nclassifier offers the best performance in cancer identification, with an\noverall validation accuracy of 91% , 92.3%, 93.3% and 93.5% for 20, 30, 40 and\n50 features respectively. The biological functions in cancer development of 50\nselected genes is also explored through enrichment analysis and the results\nshow that 12 out of 16 of our top features have already been identified to be\nspecific with cancer and we also propose some more genes to be tested for\nfuture studies. Therefore, our method may be utilzed as an auxiliary diagnostic\nmethod to determine the actual clinicopathological status of a specific cancer.\n"}
+{"abstract": "  We investigate a one-dimensional quantum emitter chain where transport of\nexcitations and correlations takes place via nearest neighbor, dipole-dipole\ninteractions. In the presence of collective radiative emission, we show that a\nphase imprinting wavepacket initialization procedure can lead to subradiant\ntransport and can preserve quantum correlations. In the context of cavity\nmediated transport, where emitters are coupled to a common delocalized optical\nmode, we analyze the effect of frequency disorder and nonidentical\nphoton-emitter couplings on excitation transport.\n"}
+{"abstract": "  We associate all small subgraph counting problems with a systematic graph\nencoding/representation system which makes a coherent use of graphlet\nstructures. The system can serve as a unified foundation for studying and\nconnecting many important graph problems in theory and practice. We describe\ntopological relations among graphlets (graph elements) in rigorous mathematics\nlanguage and from the perspective of graph encoding. We uncover, characterize\nand utilize algebraic and numerical relations in graphlet counts/frequencies.\nWe present a novel algorithm for efficiently counting small subgraphs as a\npractical product of our theoretical findings.\n"}
+{"abstract": "  We propose a framework to mine API usage scenarios from Stack Overflow. Each\ntask consists of a code example, the task description, and the reactions of\ndevelopers towards the code example. First, we present an algorithm to\nautomatically link a code example in a forum post to an API mentioned in the\ntextual contents of the forum post. Second, we generate a natural language\ndescription of the task by summarizing the discussions around the code example.\nThird, we automatically associate developers reactions (i.e., positive and\nnegative opinions) towards the code example to offer information about code\nquality. We evaluate the algorithms using three benchmarks.\n"}
+{"abstract": "  We study the problem of user-scheduling and resource allocation in\ndistributed multi-user, multiple-input multiple-output (MIMO) networks\nimplementing user-centric clustering and non-coherent transmission. We\nformulate a weighted sum-rate maximization problem which can provide user\nproportional fairness. As in this setup, users can be served by many\ntransmitters, user scheduling is particularly difficult. To solve this issue,\nwe use block coordinate descent, fractional programming, and compressive\nsensing to construct an algorithm that performs user-scheduling and\nbeamforming. Our results show that the proposed framework provides an 8- to\n10-fold gain in the long-term user spectral efficiency compared to benchmark\nschemes such as round-robin scheduling. Furthermore, we quantify the\nperformance loss due to imperfect channel state information and pilot training\noverhead using a defined area-based pilot-reuse factor.\n"}
+{"abstract": "  In this paper, we study the graph of homothety classes of stable free\nlattices in a two-dimensional representation over a local UFD. This generalizes\na classical result of the case where the base ring is a discrete valuation ring\ndue to Serre. As applications, we consider the case when the representation\ncomes from a residually reducible Hida family and we study the control theorem\nof Selmer groups. These results enable us to know the precise statement of the\nmain conjecture in residually reducible case as we will remark in section 4.\n"}
+{"abstract": "  We show that assuming the standard conjectures, for any smooth projective\nvariety $X$ of dimension $n$ over an algebraically closed field, there is a\nconstant $C>0$ such that for any positive rational number $r$ and for any\npolarized endomorphism $f$ of $X$, we have \\[ \\| G_r \\circ f \\| \\le C \\,\n\\mathrm{deg}(G_r \\circ f), \\] where $G_r$ is a correspondence of $X$ so that\nfor each $0\\le i\\le 2n$ its pullback action on the $i$-th Weil cohomology group\nis the multiplication-by-$r^i$ map. This inequality has been conjectured by the\nauthors to hold in a more general setting, which - in the special case of\npolarized endomorphisms - confirms the validity of the analog of a well known\nresult by Serre in the K\\\"ahler setting.\n"}
+{"abstract": "  As we gain access to a greater depth and range of health-related information\nabout individuals, three questions arise: (1) Can we build better models to\npredict individual-level risk of ill health? (2) How much data do we need to\neffectively predict ill health? (3) Are new methods required to process the\nadded complexity that new forms of data bring? The aim of the study is to apply\na machine learning approach to identify the relative contribution of personal,\nsocial, health-related, biomarker and genetic data as predictors of future\nhealth in individuals. Using longitudinal data from 6830 individuals in the UK\nfrom Understanding Society (2010-12 to 2015-17), the study compares the\npredictive performance of five types of measures: personal (e.g. age, sex),\nsocial (e.g. occupation, education), health-related (e.g. body weight, grip\nstrength), biomarker (e.g. cholesterol, hormones) and genetic single nucleotide\npolymorphisms (SNPs). The predicted outcome variable was limiting long-term\nillness one and five years from baseline. Two machine learning approaches were\nused to build predictive models: deep learning via neural networks and XGBoost\n(gradient boosting decision trees). Model fit was compared to traditional\nlogistic regression models. Results found that health-related measures had the\nstrongest prediction of future health status, with genetic data performing\npoorly. Machine learning models only offered marginal improvements in model\naccuracy when compared to logistic regression models, but also performed well\non other metrics e.g. neural networks were best on AUC and XGBoost on\nprecision. The study suggests that increasing complexity of data and methods\ndoes not necessarily translate to improved understanding of the determinants of\nhealth or performance of predictive models of ill health.\n"}
+{"abstract": "  Despite extensive research efforts in the recent years, computational\nmodeling of argumentation remains one of the most challenging areas of natural\nlanguage processing (NLP). This is primarily due to inherent complexity of the\ncognitive processes behind human argumentation, which commonly combine and\nintegrate plethora of different types of knowledge, requiring from\ncomputational models capabilities that are far beyond what is needed for most\nother (i.e., simpler) natural language understanding tasks. The existing large\nbody of work on mining, assessing, generating, and reasoning over arguments\nlargely acknowledges that much more common sense and world knowledge needs to\nbe integrated into computational models that would accurately model\nargumentation. A systematic overview and organization of the types of knowledge\nintroduced in existing models of computational argumentation (CA) is, however,\nmissing and this hinders targeted progress in the field. In this survey paper,\nwe fill this gap by (1) proposing a pyramid of types of knowledge required in\nCA tasks, (2) analysing the state of the art with respect to the reliance and\nexploitation of these types of knowledge, for each of the for main research\nareas in CA, and (3) outlining and discussing directions for future research\nefforts in CA.\n"}
+{"abstract": "  Point clouds, being the simple and compact representation of surface geometry\nof 3D objects, have gained increasing popularity with the evolution of deep\nlearning networks for classification and segmentation tasks. Unlike human,\nteaching the machine to analyze the segments of an object is a challenging task\nand quite essential in various machine vision applications. In this paper, we\naddress the problem of segmentation and labelling of the 3D point clouds by\nproposing a inception based deep network architecture called PIG-Net, that\neffectively characterizes the local and global geometric details of the point\nclouds. In PIG-Net, the local features are extracted from the transformed input\npoints using the proposed inception layers and then aligned by feature\ntransform. These local features are aggregated using the global average pooling\nlayer to obtain the global features. Finally, feed the concatenated local and\nglobal features to the convolution layers for segmenting the 3D point clouds.\nWe perform an exhaustive experimental analysis of the PIG-Net architecture on\ntwo state-of-the-art datasets, namely, ShapeNet [1] and PartNet [2]. We\nevaluate the effectiveness of our network by performing ablation study.\n"}
+{"abstract": "  A formula of the $D$-$D$ correlation function is derived. The deuterons are\ntreated either as elementary particles or as neutron-proton bound states. In\nthe first case the deuterons are directly emitted from a source and in the\nsecond one the deuteron formation is a final-state process simultaneous with a\ngeneration of $D$-$D$ correlation. The source radius of deuterons formed due to\nfinal-state interactions is bigger by the factor of $\\sqrt{2}$ than that of\ndirectly emitted deuterons. To check how sizable is the effect we compute the\n$D$-$D$ correlation function taking into the Bose-Einstein statistics of\ndeuterons, the $s$-wave scattering due to strong interaction and the Coulomb\nrepulsion. The correlation function is shown to be sensitive to the source\nradius for sources which are sufficiently small with RMS radii smaller than 3.5\nfm. Otherwise the correlation function is dominated by the Coulomb repulsion\nand weakly depends on the source radius. Measurements which can make use of our\nfinding are discussed.\n"}
+{"abstract": "  Recently developed deep neural networks achieved state-of-the-art results in\nthe subject of 6D object pose estimation for robot manipulation. However, those\nsupervised deep learning methods require expensive annotated training data.\nCurrent methods for reducing those costs frequently use synthetic data from\nsimulations, but rely on expert knowledge and suffer from the \"domain gap\" when\nshifting to the real world. Here, we present a proof of concept for a novel\napproach of autonomously generating annotated training data for 6D object pose\nestimation. This approach is designed for learning new objects in operational\nenvironments while requiring little interaction and no expertise on the part of\nthe user. We evaluate our autonomous data generation approach in two grasping\nexperiments, where we archive a similar grasping success rate as related work\non a non autonomously generated data set.\n"}
+{"abstract": "  We describe the implementation of a three-dimensional Paul ion trap\nfabricated from a stack of precision-machined silica glass wafers, which\nincorporates a pair of junctions for 2-dimensional ion transport. The trap has\n142 dedicated electrodes which can be used to define multiple potential wells\nin which strings of ions can be held. By supplying time-varying potentials,\nthis also allows for transport and re-configuration of ion strings. We describe\nthe design, simulation, fabrication and packaging of the trap, including\nexplorations of different parameter regimes and possible optimizations and\ndesign choices. We give results of initial testing of the trap, including\nmeasurements of heating rates and junction transport.\n"}
+{"abstract": "  Laterally large (~3 micrometers), atomically-thin two-dimensional (2D)\nBi2O2CO3 nanosheets (2D bismuth oxycarbonate, 2D bismutite) are fabricated via\nsonochemically-assisted template-free synthesis. Key to the synthesis of the\nfreestanding, laterally large 2D Bi2O2CO3 nanosheets from bulk Bi powder is\nchoice of suspension medium, controlled reaction temperatures and several hours\nprocessing time. Lateral sizes of 2D Bi2O2CO3 can be controlled between\nmicrometer-sized nanosheets and tens of nm sized nanoflakes solely based on the\nchoice of suspension medium. The here introduced 2D Bi2O2CO3 nanosheets/-flakes\nare then hybridized by a simple mix-and-match approach with TiO2 nanoparticles\nfor testing in suspension-type photocatalytic hydrogen production via water\nsplitting. This introduces the 2D Bi2O2CO3 with TiO2 as a promising\nnoble-metal-free co-catalyst for photocatalytic hydrogen evolution. Our results\nenrich the fabrication toolbox of emerging 2D pnictogen oxycarbonates towards\nlarge 2D nanosheets and demonstrate the promising potential of 2D Bi2O2CO3 as\nan advantageous (co-)catalyst for hydrogen evolution in photocatalytic water\nsplitting.\n"}
+{"abstract": "  A blocking set in a graph $G$ is a subset of vertices that intersects every\nmaximum independent set of $G$. Let ${\\sf mmbs}(G)$ be the size of a maximum\n(inclusion-wise) minimal blocking set of $G$. This parameter has recently\nplayed an important role in the kernelization of Vertex Cover parameterized by\nthe distance to a graph class ${\\cal F}$. Indeed, it turns out that the\nexistence of a polynomial kernel for this problem is closely related to the\nproperty that ${\\sf mmbs}({\\cal F})=\\sup_{G \\in {\\cal F}}{\\sf mmbs}(G)$ is\nbounded by a constant, and thus several recent results focused on determining\n${\\sf mmbs}({\\cal F})$ for different classes ${\\cal F}$. We consider the\nparameterized complexity of computing ${\\sf mmbs}$ under various\nparameterizations, such as the size of a maximum independent set of the input\ngraph and the natural parameter. We provide a panorama of the complexity of\ncomputing both ${\\sf mmbs}$ and ${\\sf mmhs}$, which is the size of a maximum\nminimal hitting set of a hypergraph, a closely related parameter. Finally, we\nconsider the problem of computing ${\\sf mmbs}$ parameterized by treewidth,\nespecially relevant in the context of kernelization. Given the \"counting\"\nnature of ${\\sf mmbs}$, it does not seem to be expressible in monadic\nsecond-order logic, hence its tractability does not follow from Courcelle's\ntheorem. Our main technical contribution is a fixed-parameter tractable\nalgorithm for this problem.\n"}
+{"abstract": "  In this work we propose a batch Bayesian optimization method for\ncombinatorial problems on permutations, which is well suited for expensive cost\nfunctions on permutations. We introduce LAW, a new efficient batch acquisition\nmethod based on the determinantal point process, using an acquisition weighted\nkernel. Relying on multiple parallel evaluations, LAW accelerates the search\nfor the optimal permutation. We provide a regret analysis for our method to\ngain insight in its theoretical properties. We then apply the framework to\npermutation problems, which have so far received little attention in the\nBayesian Optimization literature, despite their practical importance. We call\nthis method LAW2ORDER. We evaluate the method on several standard combinatorial\nproblems involving permutations such as quadratic assignment, flowshop\nscheduling and the traveling salesman, as well as on a structure learning task.\n"}
+{"abstract": "  We investigate the symplectic geometric and differential geometric aspects of\nthe moduli space of connections on a compact Riemann surface $X$. Fix a theta\ncharacteristic $K^{1/2}_X$ on $X$; it defines a theta divisor on the moduli\nspace ${\\mathcal M}$ of stable vector bundles on $X$ of rank $r$ degree zero.\nGiven a vector bundle $E \\in {\\mathcal M}$ lying outside the theta divisor, we\nconstruct a natural holomorphic connection on $E$ that depends holomorphically\non $E$. Using this holomorphic connection, we construct a canonical holomorphic\nisomorphism between the following two: \\begin{enumerate} \\item the moduli space\n$\\mathcal C$ of pairs $(E, D)$, where $E\\in {\\mathcal M}$ and $D$ is a\nholomorphic connection on $E$, and\n  \\item the space ${\\rm Conn}(\\Theta)$ given by the sheaf of holomorphic\nconnections on the line bundle on $\\mathcal M$ associated to the theta divisor.\n\\end{enumerate} The above isomorphism between $\\mathcal C$ and ${\\rm\nConn}(\\Theta)$ is symplectic structure preserving, and it moves holomorphically\nas $X$ runs over a holomorphic family of Riemann surfaces.\n"}
+{"abstract": "  Despite inextricable ties between race and language, little work has\nconsidered race in NLP research and development. In this work, we survey 79\npapers from the ACL anthology that mention race. These papers reveal various\ntypes of race-related bias in all stages of NLP model development, highlighting\nthe need for proactive consideration of how NLP systems can uphold racial\nhierarchies. However, persistent gaps in research on race and NLP remain: race\nhas been siloed as a niche topic and remains ignored in many NLP tasks; most\nwork operationalizes race as a fixed single-dimensional variable with a\nground-truth label, which risks reinforcing differences produced by historical\nracism; and the voices of historically marginalized people are nearly absent in\nNLP literature. By identifying where and how NLP literature has and has not\nconsidered race, especially in comparison to related fields, our work calls for\ninclusion and racial justice in NLP research practices.\n"}
+{"abstract": "  The Collatz dynamic is known to generate a complex quiver of sequences over\nnatural numbers which inflation propensity remains so unpredictable it could be\nused to generate reliable proof of work algorithms for the cryptocurrency\nindustry. Here we establish an ad hoc equivalent of modular arithmetic for\nCollatz sequences to automatically demonstrate the convergence of infinite\nquivers of numbers, based on five arithmetic rules we prove apply on the entire\nCollatz dynamic and which we further simulate to gain insight on their graph\ngeometry and computational properties. We then formally demonstrate these rules\ndefine an automaton that is playing a Hydra game on the graph of undecided\nnumbers we also prove is embedded in 24N-7, proving that in ZFC the Collatz\nconjecture is true, before giving a promising direction to also prove it in\nPeano arithmetic.\n"}
+{"abstract": "  We analyze the problem of active covering, where the learner is given an\nunlabeled dataset and can sequentially label query examples. The objective is\nto label query all of the positive examples in the fewest number of total label\nqueries. We show under standard non-parametric assumptions that a classical\nsupport estimator can be repurposed as an offline algorithm attaining an excess\nquery cost of $\\widetilde{\\Theta}(n^{D/(D+1)})$ compared to the optimal\nlearner, where $n$ is the number of datapoints and $D$ is the dimension. We\nthen provide a simple active learning method that attains an improved excess\nquery cost of $\\widetilde{O}(n^{(D-1)/D})$. Furthermore, the proposed\nalgorithms only require access to the positive labeled examples, which in\ncertain settings provides additional computational and privacy benefits.\nFinally, we show that the active learning method consistently outperforms\noffline methods as well as a variety of baselines on a wide range of benchmark\nimage-based datasets.\n"}
+{"abstract": "  We consider a network of autonomous agents whose outputs are actions in a\ngame with coupled constraints. In such network scenarios, agents seeking to\nminimize coupled cost functions using distributed information while satisfying\nthe coupled constraints. Current methods consider the small class of\nmulti-integrator agents using primal-dual methods. These methods can only\nensure constraint satisfaction in steady-state. In contrast, we propose an\ninexact penalty method using a barrier function for nonlinear agents with\nequilibrium-independent passive dynamics. We show that these dynamics converge\nto an epsilon-GNE while satisfying the constraints for all time, not only in\nsteady-state. We develop these dynamics in both the full-information and\npartial-information settings. In the partial-information setting, dynamic\nestimates of the others' actions are used to make decisions and are updated\nthrough local communication. Applications to optical networks and velocity\nsynchronization of flexible robots are provided.\n"}
+{"abstract": "  Two dynamical systems are topologically equivalent when their phase-portraits\ncan be morphed into each other by a homeomorphic coordinate transformation on\nthe state space. The induced equivalence classes capture qualitative properties\nsuch as stability or the oscillatory nature of the state trajectories, for\nexample. In this paper we develop a method to learn the topological class of an\nunknown stable system from a single trajectory of finitely many state\nobservations. Using a moderate deviations principle for the least squares\nestimator of the unknown system matrix $\\theta$, we prove that the probability\nof misclassification decays exponentially with the number of observations at a\nrate that is proportional to the square of the smallest singular value of\n$\\theta$.\n"}
+{"abstract": "  A recent case study from AWS by Chong et al. proposes an effective\nmethodology for Bounded Model Checking in industry. In this paper, we report on\na follow up case study that explores the methodology from the perspective of\nthree research questions: (a) can proof artifacts be used across verification\ntools; (b) are there bugs in verified code; and (c) can specifications be\nimproved. To study these questions, we port the verification tasks for\n$\\texttt{aws-c-common}$ library to SEAHORN and KLEE. We show the benefits of\nusing compiler semantics and cross-checking specifications with different\nverification techniques, and call for standardizing proof library extensions to\nincrease specification reuse. The verification tasks discussed are publicly\navailable online.\n"}
+{"abstract": "  Despite the significant advances in deep learning over the past decade, a\nmajor challenge that limits the wide-spread adoption of deep learning has been\ntheir fragility to adversarial attacks. This sensitivity to making erroneous\npredictions in the presence of adversarially perturbed data makes deep neural\nnetworks difficult to adopt for certain real-world, mission-critical\napplications. While much of the research focus has revolved around adversarial\nexample creation and adversarial hardening, the area of performance measures\nfor assessing adversarial robustness is not well explored. Motivated by this,\nthis study presents the concept of residual error, a new performance measure\nfor not only assessing the adversarial robustness of a deep neural network at\nthe individual sample level, but also can be used to differentiate between\nadversarial and non-adversarial examples to facilitate for adversarial example\ndetection. Furthermore, we introduce a hybrid model for approximating the\nresidual error in a tractable manner. Experimental results using the case of\nimage classification demonstrates the effectiveness and efficacy of the\nproposed residual error metric for assessing several well-known deep neural\nnetwork architectures. These results thus illustrate that the proposed measure\ncould be a useful tool for not only assessing the robustness of deep neural\nnetworks used in mission-critical scenarios, but also in the design of\nadversarially robust models.\n"}
+{"abstract": "  We present QuantumSync, the first quantum algorithm for solving a\nsynchronization problem in the context of computer vision. In particular, we\nfocus on permutation synchronization which involves solving a non-convex\noptimization problem in discrete variables. We start by formulating\nsynchronization into a quadratic unconstrained binary optimization problem\n(QUBO). While such formulation respects the binary nature of the problem,\nensuring that the result is a set of permutations requires extra care. Hence,\nwe: (I) show how to insert permutation constraints into a QUBO problem and (ii)\nsolve the constrained QUBO problem on the current generation of the adiabatic\nquantum computers D-Wave. Thanks to the quantum annealing, we guarantee global\noptimality with high probability while sampling the energy landscape to yield\nconfidence estimates. Our proof-of-concepts realization on the adiabatic D-Wave\ncomputer demonstrates that quantum machines offer a promising way to solve the\nprevalent yet difficult synchronization problems.\n"}
+{"abstract": "  The present paper gives new elements for the light variations of the EA\nvariable star AY Peg, on the basis of new times of minimum performed visually\nand with ccd by members of GEOS between 1985 and 2018, and the ASAS-SN set of\ndata available. On one hand, we can establish a new ephemeris with a possible\nquadratic term, and on the other hand, the amplitude of the primary minimum\nappears much deeper than the one given in GCVS. AY Peg varies between 13.1 and\n15.6 magnitude at its primary eclipse.\n"}
+{"abstract": "  Diagnostic classification models (DCMs) offer statistical tools to inspect\nthe fined-grained attribute of respondents' strengths and weaknesses. However,\nthe diagnosis accuracy deteriorates when misspecification occurs in the\npredefined item-attribute relationship, which is encoded into a Q-matrix. To\nprevent such misspecification, methodologists have recently developed several\nBayesian Q-matrix estimation methods for greater estimation flexibility.\nHowever, these methods become infeasible in the case of large-scale assessments\nwith a large number of attributes and items. In this study, we focused on the\ndeterministic inputs, noisy ``and'' gate (DINA) model and proposed a new\nframework for the Q-matrix estimation to find the Q-matrix with the maximum\nmarginal likelihood. Based on this framework, we developed a scalable\nestimation algorithm for the DINA Q-matrix by constructing an iteration\nalgorithm that utilizes stochastic optimization and variational inference. The\nsimulation and empirical studies reveal that the proposed method achieves\nhigh-speed computation, good accuracy, and robustness to potential\nmisspecifications, such as initial value's choices and hyperparameter settings.\nThus, the proposed method can be a useful tool for estimating a Q-matrix in\nlarge-scale settings.\n"}
+{"abstract": "  Novae are some of the most commonly detected optical transients and have the\npotential to provide valuable information about binary evolution. Binary\npopulation synthesis codes have emerged as the most effective tool for\nmodelling populations of binary systems, but such codes have traditionally\nemployed greatly simplified nova physics, precluding detailed study. In this\nwork, we implement a model treating H and He novae as individual events into\nthe binary population synthesis code \\binaryc. This treatment of novae\nrepresents a significant improvement on the `averaging' treatment currently\nemployed in modern population synthesis codes. We discuss the evolutionary\npathways leading to these phenomena and present nova event rates and\ndistributions of several important physical parameters. Most novae are produced\non massive white dwarfs, with approximately 70 and 55 per cent of nova events\noccurring on O/Ne white dwarfs for H and He novae respectively. Only 15 per\ncent of H-nova systems undergo a common-envelope phase, but these systems are\nresponsible for the majority of H nova events. All He-accreting He-nova systems\nare considered post-common-envelope systems, and almost all will merge with\ntheir donor star in a gravitational-wave driven inspiral. We estimate the\ncurrent annual rate of novae in M31 (Andromeda) to be approximately $41 \\pm 4$\nfor H novae, underpredicting the current observational estimate of\n$65^{+15}_{-16}$, and $0.14\\pm0.015$ for He novae. When varying common-envelope\nparameters, the H nova rate varies between 20 and 80 events per year.\n"}
+{"abstract": "  Here we introduce a new reconstruction technique for two-dimensional Bragg\nScattering Tomography (BST), based on the Radon transform models of [arXiv\npreprint, arXiv:2004.10961 (2020)]. Our method uses a combination of ideas from\nmultibang control and microlocal analysis to construct an objective function\nwhich can regularize the BST artifacts; specifically the boundary artifacts due\nto sharp cutoff in sinogram space (as observed in [arXiv preprint,\narXiv:2007.00208 (2020)]), and artifacts arising from approximations made in\nconstructing the model used for inversion. We then test our algorithm in a\nvariety of Monte Carlo (MC) simulated examples of practical interest in airport\nbaggage screening and threat detection. The data used in our studies is\ngenerated with a novel Monte-Carlo code presented here. The model, which is\navailable from the authors upon request, captures both the Bragg scatter\neffects described by BST as well as beam attenuation and Compton scatter.\n"}
+{"abstract": "  The automated analysis of medical images is currently limited by technical\nand biological noise and bias. The same source tissue can be represented by\nvastly different images if the image acquisition or processing protocols vary.\nFor an image analysis pipeline, it is crucial to compensate such biases to\navoid misinterpretations. Here, we evaluate, compare, and improve existing\ngenerative model architectures to overcome domain shifts for immunofluorescence\n(IF) and Hematoxylin and Eosin (H&E) stained microscopy images. To determine\nthe performance of the generative models, the original and transformed images\nwere segmented or classified by deep neural networks that were trained only on\nimages of the target bias. In the scope of our analysis, U-Net cycleGANs\ntrained with an additional identity and an MS-SSIM-based loss and Fixed-Point\nGANs trained with an additional structure loss led to the best results for the\nIF and H&E stained samples, respectively. Adapting the bias of the samples\nsignificantly improved the pixel-level segmentation for human kidney glomeruli\nand podocytes and improved the classification accuracy for human prostate\nbiopsies by up to 14%.\n"}
+{"abstract": "  We prove a quantitative equidistribution theorem for the eigenfunctions of a\nSchr\\\"odinger operator -\\Delta+V on a rectangular torus T for V\\in L^2(T). A\nkey application of our theorem is a quantitative equidistribution theorem for\nthe eigenfunctions of a Schr\\\"odinger operator whose potential models\ndisordered systems with N obstacles. We prove the validity of this\nequidistribution theorem in the thermodynamic limit, as N \\to\\infty, under the\nassumption that a weak disorder hypothesis is satisfied.\n  In particular, we show that this scale-invariant equidistribution theorem\nholds for the eigenfunctions of random displacement models almost surely with\nrespect to the joint density of the random positions of the potentials. In the\ncase of a general random Schr\\\"odinger operator, where disorder may be strong,\nwe deduce an equidistribution theorem on certain length scales, which\nestablishes a lower bound for the Anderson localization length as a function of\nthe energy, coupling parameter, density of scatterers and the L^2 norm of the\npotential.\n"}
+{"abstract": "  The heavy fermion superconductor URu$_2$Si$_2$ is a candidate for chiral,\ntime-reversal symmetry-breaking superconductivity with a nodal gap structure.\nHere, we microscopically visualized superconductivity and spatially\ninhomogeneous ferromagnetism in URu$_2$Si$_2$. We observed linear-$T$\nsuperfluid density, consistent with d-wave pairing symmetries including chiral\nd-wave, but did not observe the spontaneous magnetization expected for chiral\nd-wave. Local vortex pinning potentials had either four- or two-fold rotational\nsymmetries with various orientations at different locations. Taken together,\nthese data support a nodal gap structure in URu$_2$Si$_2$ and suggest that\nchirality either is not present or does not lead to detectable spontaneous\nmagnetization.\n"}
+{"abstract": "  The realization of practical intelligent reflecting surface (IRS)-assisted\nmulti-user communication (IRS-MUC) systems critically depends on the proper\nbeamforming design exploiting accurate channel state information (CSI).\nHowever, channel estimation (CE) in IRS-MUC systems requires a significantly\nlarge training overhead due to the numerous reflection elements involved in\nIRS. In this paper, we adopt a deep learning approach to implicitly learn the\nhistorical channel features and directly predict the IRS phase shifts for the\nnext time slot to maximize the average achievable sum-rate of an IRS-MUC system\ntaking into account the user mobility. By doing this, only a low-dimension\nmultiple-input single-output (MISO) CE is needed for transmit beamforming\ndesign, thus significantly reducing the CE overhead. To this end, a\nlocation-aware convolutional long short-term memory network (LA-CLNet) is first\ndeveloped to facilitate predictive beamforming at IRS, where the convolutional\nand recurrent units are jointly adopted to exploit both the spatial and\ntemporal features of channels simultaneously. Given the predictive IRS phase\nshift beamforming, an instantaneous CSI (ICSI)-aware fully-connected neural\nnetwork (IA-FNN) is then proposed to optimize the transmit beamforming matrix\nat the access point. Simulation results demonstrate that the sum-rate\nperformance achieved by the proposed method approaches that of the genie-aided\nscheme with the full perfect ICSI.\n"}
+{"abstract": "  We consider linear network error correction (LNEC) coding when errors may\noccur on edges of a communication network of which the topology is known. In\nthis paper, we first revisit and explore the framework of LNEC coding, and then\nunify two well-known LNEC coding approaches. Furthermore, by developing a\ngraph-theoretic approach to the framework of LNEC coding, we obtain a\nsignificantly enhanced characterization of the error correction capability of\nLNEC codes in terms of the minimum distances at the sink nodes. In LNEC coding,\nthe minimum required field size for the existence of LNEC codes, in particular\nLNEC maximum distance separable (MDS) codes which are a type of most important\noptimal codes, is an open problem not only of theoretical interest but also of\npractical importance, because it is closely related to the implementation of\nthe coding scheme in terms of computational complexity and storage requirement.\nBy applying the graph-theoretic approach, we obtain an improved upper bound on\nthe minimum required field size. The improvement over the existing results is\nin general significant. The improved upper bound, which is graph-theoretic,\ndepends only on the network topology and requirement of the error correction\ncapability but not on a specific code construction. However, this bound is not\ngiven in an explicit form. We thus develop an efficient algorithm that can\ncompute the bound in linear time. In developing the upper bound and the\nefficient algorithm for computing this bound, various graph-theoretic concepts\nare introduced. These concepts appear to be of fundamental interest in graph\ntheory and they may have further applications in graph theory and beyond.\n"}
+{"abstract": "  In ground-based astronomy, starlight distorted by the atmosphere couples\npoorly into single-mode waveguides but a correction by adaptive optics, even if\nonly partial, can boost coupling into the few-mode regime allowing the use of\nphotonic lanterns to convert into multiple single-mode beams. Corrected\nwavefronts result in focal patterns that couple mostly with the circularly\nsymmetric waveguide modes. A mode-selective photonic lantern is hence proposed\nto convert the multimode light into a subset of the single-mode waveguides of\nthe standard photonic lantern, thereby reducing the required number of outputs.\nWe ran simulations to show that only two out of the six waveguides of a 1x6\nphotonic lantern carry >95% of the coupled light to the outputs at $D/r_0 < 10$\nif the wavefront is partially corrected and the photonic lantern is made\nmode-selective.\n"}
+{"abstract": "  In Statistics, log-concave density estimation is a central problem within the\nfield of nonparametric inference under shape constraints. Despite great\nprogress in recent years on the statistical theory of the canonical estimator,\nnamely the log-concave maximum likelihood estimator, adoption of this method\nhas been hampered by the complexities of the non-smooth convex optimization\nproblem that underpins its computation. We provide enhanced understanding of\nthe structural properties of this optimization problem, which motivates the\nproposal of new algorithms, based on both randomized and Nesterov smoothing,\ncombined with an appropriate integral discretization of increasing accuracy. We\nprove that these methods enjoy, both with high probability and in expectation,\na convergence rate of order $1/T$ up to logarithmic factors on the objective\nfunction scale, where $T$ denotes the number of iterations. The benefits of our\nnew computational framework are demonstrated on both synthetic and real data,\nand our implementation is available in a github repository \\texttt{LogConcComp}\n(Log-Concave Computation).\n"}
+{"abstract": "  We establish existence, uniqueness as well as quantitative estimates for\nsolutions to the fractional nonlinear diffusion equation, $\\partial_t u\n+{\\mathcal L}_{s,p} (u)=0$, where ${\\mathcal L}_{s,p}=(-\\Delta)_p^s$ is the\nstandard fractional $p$-Laplacian operator. We work in the range of exponents\n$0<s<1$ and $1<p<2$, and in some sections $sp<1$. The equation is posed in the\nwhole space $x\\in {\\mathbb R}^N$. We first obtain weighted global integral\nestimates that allow establishing the existence of solutions for a class of\nlarge data that is proved to be roughly optimal. We study the class of\nself-similar solutions of forward type, that we describe in detail when they\nexist. We also explain what happens when possible self-similar solutions do not\nexist. We establish the dichotomy positivity versus extinction for nonnegative\nsolutions at any given time. We analyze the conditions for extinction in finite\ntime.\n"}
+{"abstract": "  An important problem in deep learning is the privacy and security of neural\nnetworks (NNs). Both aspects have long been considered separately. To date, it\nis still poorly understood how privacy enhancing training affects the\nrobustness of NNs. This paper experimentally evaluates the impact of training\nwith Differential Privacy (DP), a standard method for privacy preservation, on\nmodel vulnerability against a broad range of adversarial attacks. The results\nsuggest that private models are less robust than their non-private\ncounterparts, and that adversarial examples transfer better among DP models\nthan between non-private and private ones. Furthermore, detailed analyses of DP\nand non-DP models suggest significant differences between their gradients.\nAdditionally, this work is the first to observe that an unfavorable choice of\nparameters in DP training can lead to gradient masking, and, thereby, results\nin a wrong sense of security.\n"}
+{"abstract": "  Biological, physical, medical, and numerical applications involving membrane\nproblems on different scales are numerous. We propose an extension of the\nstandard Turing theory to the case of two domains separated by a permeable\nmembrane. To this aim, we study a reaction-diffusion system with zero-flux\nboundary conditions on the external boundary and Kedem-Katchalsky membrane\nconditions on the inner membrane. We use the same approach as in the classical\nTuring analysis but applied to membrane operators. The introduction of a\ndiagonalization theory for compact and self-adjoint membrane operators is\nneeded. Here, Turing instability is proven with the addition of new\nconstraints, due to the presence of membrane permeability coefficients. We\nperform an explicit one-dimensional analysis of the eigenvalue problem,\ncombined with numerical simulations, to validate the theoretical results.\nFinally, we observe the formation of discontinuous patterns in a system which\ncombines diffusion and dissipative membrane conditions, varying both diffusion\nand membrane permeability coefficients. The case of a fast reaction-diffusion\nsystem is also considered.\n"}
+{"abstract": "  We use analytic calculations and time-dependent spherically-symmetric\nsimulations to study the properties of isothermal galactic winds driven by\ncosmic-rays (CRs) streaming at the Alfv\\'en velocity. The simulations produce\ntime-dependent flows permeated by strong shocks; we identify a new linear\ninstability of sound waves that sources these shocks. The shocks substantially\nmodify the wind dynamics, invalidating previous steady state models: the CR\npressure $p_c$ has a staircase-like structure with $dp_c/dr \\simeq 0$ in most\nof the volume, and the time-averaged CR energetics are in many cases better\napproximated by $p_c \\propto \\rho^{1/2}$, rather than the canonical $p_c\n\\propto \\rho^{2/3}$. Accounting for this change in CR energetics, we\nanalytically derive new expressions for the mass-loss rate, momentum flux, wind\nspeed, and wind kinetic power in galactic winds driven by CR streaming. We show\nthat streaming CRs are ineffective at directly driving cold gas out of\ngalaxies, though CR-driven winds in hotter ISM phases may entrain cool gas. For\nthe same physical conditions, diffusive CR transport (Paper I) yields mass-loss\nrates that are a few-100 times larger than streaming transport, and asymptotic\nwind powers that are a factor of $\\simeq 4$ larger. We discuss the implications\nof our results for galactic wind theory and observations; strong shocks driven\nby CR-streaming-induced instabilities produce gas with a wide range of\ndensities and temperatures, consistent with the multiphase nature of observed\nwinds. We also quantify the applicability of the isothermal gas approximation\nfor modeling streaming CRs and highlight the need for calculations with more\nrealistic thermodynamics.\n"}
+{"abstract": "  The past year has seen numerous publications underlining the importance of a\nspace mission to the ice giants in the upcoming decade. Proposed mission plans\ninvolve a $\\sim$10 year cruise time to the ice giants. This cruise time can be\nutilized to search for low-frequency gravitational waves (GWs) by observing the\nDoppler shift caused by them in the Earth-spacecraft radio link. We calculate\nthe sensitivity of prospective ice giant missions to GWs. Then, adopting a\nsteady-state black hole binary population, we derive a conservative estimate\nfor the detection rate of extreme mass ratio inspirals (EMRIs), supermassive-\n(SMBH) and stellar mass binary black hole (sBBH) mergers. We link the SMBH\npopulation to the fraction of quasars $f_\\rm{bin}$ resulting from galaxy\nmergers that pair SMBHs to a binary. For a total of ten 40-day observations\nduring the cruise of a single spacecraft, $\\mathcal{O}(f_\\rm{bin})\\sim0.5$\ndetections of SMBH mergers are likely, if Allan deviation of Cassini-era noise\nis improved by $\\sim 10^2$ in the $10^{-5}-10^{-3}$ Hz range. For EMRIs the\nnumber of detections lies between $\\mathcal{O}(0.1) - \\mathcal{O}(100)$.\nFurthermore, ice giant missions combined with the Laser Interferometer Space\nAntenna (LISA) would improve the localisation by an order of magnitude compared\nto LISA by itself.\n"}
+{"abstract": "  We report the occurrence of a self-emerging frequency chimera state in\nspatially extended systems of coupled oscillators, where the coherence and\nincoherence are defined with respect to the emergent frequency of the\noscillations. This is generated by the local coupling among nonlinear\noscillators evolving under differing dynamical timescales starting from random\ninitial conditions. We show how they self-organize to structured patterns with\nspatial domains of coherence that are in frequency synchronization, coexisting\nwith domains that are incoherent in frequencies. Our study has relevance in\nunderstanding such patterns observed in real-world systems like neuronal\nsystems, power grids, social and ecological networks, where differing dynamical\ntimescales is natural and realistic amongthe interacting systems.\n"}
+{"abstract": "  We prove that perfect $3$-hash linear codes in $\\mathbb{F}_{3}^{n}$ must have\ndimension at most $ \\left(\\frac{1}{4}-\\epsilon\\right)n$ for some absolute\nconstant $\\epsilon > 0$.\n"}
+{"abstract": "  Meta-learning is a branch of machine learning which aims to quickly adapt\nmodels, such as neural networks, to perform new tasks by learning an underlying\nstructure across related tasks. In essence, models are being trained to learn\nnew tasks effectively rather than master a single task. Meta-learning is\nappealing for process control applications because the perturbations to a\nprocess required to train an AI controller can be costly and unsafe.\nAdditionally, the dynamics and control objectives are similar across many\ndifferent processes, so it is feasible to create a generalizable controller\nthrough meta-learning capable of quickly adapting to different systems. In this\nwork, we construct a deep reinforcement learning (DRL) based controller and\nmeta-train the controller using a latent context variable through a separate\nembedding neural network. We test our meta-algorithm on its ability to adapt to\nnew process dynamics as well as different control objectives on the same\nprocess. In both cases, our meta-learning algorithm adapts very quickly to new\ntasks, outperforming a regular DRL controller trained from scratch.\nMeta-learning appears to be a promising approach for constructing more\nintelligent and sample-efficient controllers.\n"}
+{"abstract": "  Electricity production currently generates approximately 25% of greenhouse\ngas emissions in the USA. Thus, increasing the amount of renewable energy is a\nkey step to carbon neutrality. However, integrating a large amount of\nfluctuating renewable generation is a significant challenge for power grid\noperating and planning. Grid reliability, i.e., an ability to meet operational\nconstraints under power fluctuations, is probably the most important of them.\nIn this paper, we propose computationally efficient and accurate methods to\nestimate the probability of failure, i.e. reliability constraints violation,\nunder a known distribution of renewable energy generation. To this end, we\ninvestigate an importance sampling approach, a flexible extension of\nMonte-Carlo methods, which adaptively changes the sampling distribution to\ngenerate more samples near the reliability boundary. The approach allows to\nestimate failure probability in real-time based only on a few dozens of random\nsamples, compared to thousands required by the plain Monte-Carlo. Our study\nfocuses on high voltage direct current power transmission grids with linear\nreliability constraints on power injections and line currents. We propose a\nnovel theoretically justified physics-informed adaptive importance sampling\nalgorithm and compare its performance to state-of-the-art methods on multiple\nIEEE power grid test cases.\n"}
+{"abstract": "  Expectation-maximization (EM) is a popular and well-established method for\nimage reconstruction in positron emission tomography (PET) but it often suffers\nfrom slow convergence. Ordered subset EM (OSEM) is an effective reconstruction\nalgorithm that provides significant acceleration during initial iterations, but\nit has been observed to enter a limit cycle. In this work, we investigate two\nclasses of algorithms for accelerating OSEM based on variance reduction for\npenalised PET reconstructions. The first is a stochastic variance reduced EM\nalgorithm, termed as SVREM, an extension of the classical EM to the stochastic\ncontext, by combining classical OSEM with insights from variance reduction\ntechniques for gradient descent. The second views OSEM as a preconditioned\nstochastic gradient ascent, and applies variance reduction techniques, i.e.,\nSAGA and SVRG, to estimate the update direction. We present several numerical\nexperiments to illustrate the efficiency and accuracy of the approaches. The\nnumerical results show that these approaches significantly outperform existing\nOSEM type methods for penalised PET reconstructions, and hold great potential.\n"}
+{"abstract": "  Topological phenomena are commonly studied in phases of matter which are\nseparated from a trivial phase by an unavoidable quantum phase transition. This\ncan be overly restrictive, leaving out scenarios of practical relevance --\nsimilar to the distinction between liquid water and vapor. Indeed, we show that\ntopological phenomena can be stable over a large part of parameter space even\nwhen the bulk is strictly speaking in a trivial phase of matter. In particular,\nwe focus on symmetry-protected topological phases which can be trivialized by\nextending the symmetry group. The topological Haldane phase in spin chains\nserves as a paradigmatic example where the $SO(3)$ symmetry is extended to\n$SU(2)$ by tuning away from the Mott limit. Although the Haldane phase is then\nadiabatically connected to a product state, we show that characteristic\nphenomena -- edge modes, entanglement degeneracies and bulk phase transitions\n-- remain parametrically stable. This stability is due to a separation of\nenergy scales, characterized by quantized invariants which are well-defined\nwhen a subgroup of the symmetry only acts on high-energy degrees of freedom.\nThe low-energy symmetry group is a quotient group whose emergent anomalies\nstabilize edge modes and unnecessary criticality, which can occur in any\ndimension.\n"}
+{"abstract": "  A Thickened Flame (TF) modeling approach is combined with a Large Eddy\nSimulation (LES) methodology to model premixed combustion and the accuracy of\nthese model predictions is evaluated by comparing with the piloted premixed\nstoichiometric methane-air flame data of Chen et al. [Combust. Flame 107 (1996)\n233-244] at a Reynolds number Re = 24,000. In the TF model, the flame front is\nartificially thickened to resolve it on the computational LES grid and the\nreaction rates are specified using reduced chemistry. The response of the\nthickened flame to turbulence is taken care of by incorporating an efficiency\nfunction in the governing equations. The efficiency function depends on the\ncharacteristics of the local turbulence and on the characteristics of the\npremixed flame such as laminar flame speed and thickness. Three variants of the\nTF model are examined: the original Thickened Flame model, the Power-law flame\nwrinkling model, and the dynamically modified TF model. Reasonable agreement is\nfound when comparing predictions with the experimental data and with\ncomputations reported using a probability distribution function (PDF) modeling\napproach. The results of the TF model are in better agreement with data when\ncompared with the predictions of the G-equation approach\n"}
+{"abstract": "  Surrender poses one of the major risks to life insurance and a sound modeling\nof its true probability has direct implication on the risk capital demanded by\nthe Solvency II directive. We add to the existing literature by performing\nextensive experiments that present highly practical results for various\nmodeling approaches, including XGBoost, random forest, GLM and neural networks.\nFurther, we detect shortcomings of prevalent model assessments, which are in\nessence based on a confusion matrix. Our results indicate that accurate label\npredictions and a sound modeling of the true probability can be opposing\nobjectives. We illustrate this with the example of resampling. While resampling\nis capable of improving label prediction in rare event settings, such as\nsurrender, and thus is commonly applied, we show theoretically and numerically\nthat models trained on resampled data predict significantly biased event\nprobabilities. Following a probabilistic perspective on surrender, we further\npropose time-dependent confidence bands on predicted mean surrender rates as a\ncomplementary assessment and demonstrate its benefit. This evaluation takes a\nvery practical, going concern perspective, which respects that the composition\nof a portfolio, as well as the nature of underlying risk drivers might change\nover time.\n"}
+{"abstract": "  We address a challenging problem of identifying main sources of hate speech\non Twitter. On one hand, we carefully annotate a large set of tweets for hate\nspeech, and deploy advanced deep learning to produce high quality hate speech\nclassification models. On the other hand, we create retweet networks, detect\ncommunities and monitor their evolution through time. This combined approach is\napplied to three years of Slovenian Twitter data. We report a number of\ninteresting results. Hate speech is dominated by offensive tweets, related to\npolitical and ideological issues. The share of unacceptable tweets is\nmoderately increasing with time, from the initial 20% to 30% by the end of\n2020. Unacceptable tweets are retweeted significantly more often than\nacceptable tweets. About 60% of unacceptable tweets are produced by a single\nright-wing community of only moderate size. Institutional Twitter accounts and\nmedia accounts post significantly less unacceptable tweets than individual\naccounts. However, the main sources of unacceptable tweets are anonymous\naccounts, and accounts that were suspended or closed during the last three\nyears.\n"}
+{"abstract": "  Successful quantitative investment usually relies on precise predictions of\nthe future movement of the stock price. Recently, machine learning based\nsolutions have shown their capacity to give more accurate stock prediction and\nbecome indispensable components in modern quantitative investment systems.\nHowever, the i.i.d. assumption behind existing methods is inconsistent with the\nexistence of diverse trading patterns in the stock market, which inevitably\nlimits their ability to achieve better stock prediction performance. In this\npaper, we propose a novel architecture, Temporal Routing Adaptor (TRA), to\nempower existing stock prediction models with the ability to model multiple\nstock trading patterns. Essentially, TRA is a lightweight module that consists\nof a set of independent predictors for learning multiple patterns as well as a\nrouter to dispatch samples to different predictors. Nevertheless, the lack of\nexplicit pattern identifiers makes it quite challenging to train an effective\nTRA-based model. To tackle this challenge, we further design a learning\nalgorithm based on Optimal Transport (OT) to obtain the optimal sample to\npredictor assignment and effectively optimize the router with such assignment\nthrough an auxiliary loss term. Experiments on the real-world stock ranking\ntask show that compared to the state-of-the-art baselines, e.g., Attention LSTM\nand Transformer, the proposed method can improve information coefficient (IC)\nfrom 0.053 to 0.059 and 0.051 to 0.056 respectively. Our dataset and code used\nin this work are publicly available:\nhttps://github.com/microsoft/qlib/tree/main/examples/benchmarks/TRA.\n"}
+{"abstract": "  We continue studying $6D, {\\cal N}=(1,1)$ supersymmetric Yang-Mills (SYM)\ntheory in the ${\\cal N}=(1,0)$ harmonic superspace formulation. Using the\nsuperfield background field method we explore the two-loop divergencies of the\neffective action in the gauge multiplet sector. It is explicitly demonstrated\nthat among four two-loop background-field dependent supergraphs contributing to\nthe effective action, only one diverges off shell. It is also shown that the\ndivergences are proportional to the superfield classical equations of motion\nand hence vanish on shell. Besides, we have analyzed a possible structure of\nthe two-loop divergences on general gauge and hypermultiplet background.\n"}
+{"abstract": "  This article reviews a class of adaptive group testing procedures that\noperate under a probabilistic model assumption as follows. Consider a set of\n$N$ items, where item $i$ has the probability $p$ ($p_i$ in the generalized\ngroup testing) to be defective, and the probability $1-p$ to be non-defective\nindependent from the other items. A group test applied to any subset of size\n$n$ is a binary test with two possible outcomes, positive or negative. The\noutcome is negative if all $n$ items are non-defective, whereas the outcome is\npositive if at least one item among the $n$ items is defective. The goal is\ncomplete identification of all $N$ items with the minimum expected number of\ntests.\n"}
+{"abstract": "  Liquid-liquid phase separation (LLPS) is important to control a wide range of\nreactions from gene expression to protein degradation in a cell-sized space. To\nbring a better understanding of the compatibility of such phase-separated\nstructures with protein synthesis, we study emergent LLPS in a cell-free\ntranscription-translation (TXTL) reaction. When the TXTL reaction composed of\nmany proteins is concentrated, the uniformly mixed state becomes unstable, and\nmembrane-less phases form spontaneously. This LLPS droplet formation is induced\nwhen the TXTL reaction is enclosed in water-in-oil emulsion droplets, in which\nwater evaporates from the surface. As the emulsion droplets shrink, smaller\nLLPS droplets appear inside the emulsion droplets and coalesce into large\nphase-separated domains that partition the localization of synthesized reporter\nproteins. The presence of PEG in the TXTL reaction is important not only for\nversatile cell-free protein synthesis but also for the formation of two large\ndomains capable of protein partitioning. Our results may shed light on the\ndynamic interplay of LLPS formation and cell-free protein synthesis toward the\nconstruction of synthetic organelles.\n"}
+{"abstract": "  Mobile and embedded platforms are increasingly required to efficiently\nexecute computationally demanding DNNs across heterogeneous processing\nelements. At runtime, the available hardware resources to DNNs can vary\nconsiderably due to other concurrently running applications. The performance\nrequirements of the applications could also change under different scenarios.\nTo achieve the desired performance, dynamic DNNs have been proposed in which\nthe number of channels/layers can be scaled in real time to meet different\nrequirements under varying resource constraints. However, the training process\nof such dynamic DNNs can be costly, since platform-aware models of different\ndeployment scenarios must be retrained to become dynamic. This paper proposes\nDynamic-OFA, a novel dynamic DNN approach for state-of-the-art platform-aware\nNAS models (i.e. Once-for-all network (OFA)). Dynamic-OFA pre-samples a family\nof sub-networks from a static OFA backbone model, and contains a runtime\nmanager to choose different sub-networks under different runtime environments.\nAs such, Dynamic-OFA does not need the traditional dynamic DNN training\npipeline. Compared to the state-of-the-art, our experimental results using\nImageNet on a Jetson Xavier NX show that the approach is up to 3.5x (CPU), 2.4x\n(GPU) faster for similar ImageNet Top-1 accuracy, or 3.8% (CPU), 5.1% (GPU)\nhigher accuracy at similar latency.\n"}
+{"abstract": "  NP (search) problems allow easy correctness tests for solutions. Climbing\nalgorithms allow also easy assessment of how close to yielding the correct\nanswer is the configuration at any stage of their run. This offers a great\nflexibility, as how sensible is any deviation from the standard procedures can\nbe instantly assessed.\n  An example is the Dual Matrix Algorithm (DMA) for linear programming,\nvariations of which were considered by A.Y. Levin in 1965 and by Yamnitsky and\nmyself in 1982. It has little sensitivity to numerical errors and to the number\nof inequalities. It offers substantial flexibility and, thus, potential for\nfurther developments.\n"}
+{"abstract": "  In high-energy leptonic collisions, such as at a multi-TeV muon collider, the\ncollinear splittings of the electroweak (EW) gauge bosons and leptons are the\ndominant phenomena, and the scattering processes should thus be formulated in\nterms of the EW parton distribution functions (EW PDFs). We complete this\nformalism in the Standard Model to include the QCD sector and evaluate the\nquark and gluon PDFs inside a lepton at the double-log accuracy. The splittings\nof the photon and subsequently the quarks and gluons control the quark/gluon\nPDFs below the EW scale. The massive gauge bosons lead to substantial\ncontributions at high scales. The jet production cross section can reach the\norder of a few nb (50 pb) in $e^+e^-$ ($\\mu^+\\mu^-$) collisions, at the TeV\nc.m. energies with a moderate acceptance cut, that governs the overall event\nshape up to about $p_T^j \\sim 60$ GeV.\n"}
+{"abstract": "  This article investigates the energy efficiency issue in non-orthogonal\nmultiple access (NOMA)-enhanced Internet-of-Things (IoT) networks, where a\nmobile unmanned aerial vehicle (UAV) is exploited as a flying base station to\ncollect data from ground devices via the NOMA protocol. With the aim of\nmaximizing network energy efficiency, we formulate a joint problem of UAV\ndeployment, device scheduling and resource allocation. First, we formulate the\njoint device scheduling and spectrum allocation problem as a three-sided\nmatching problem, and propose a novel low-complexity near-optimal algorithm. We\nalso introduce the novel concept of `exploration' into the matching game for\nfurther performance improvement. By algorithm analysis, we prove the\nconvergence and stability of the final matching state. Second, in an effort to\nallocate proper transmit power to IoT devices, we adopt the Dinkelbach's\nalgorithm to obtain the optimal power allocation solution. Furthermore, we\nprovide a simple but effective approach based on disk covering problem to\ndetermine the optimal number and locations of UAV's stop points to ensure that\nall IoT devices can be fully covered by the UAV via line-of-sight (LoS) links\nfor the sake of better channel condition. Numerical results unveil that: i) the\nproposed joint UAV deployment, device scheduling and resource allocation scheme\nachieves much higher EE compared to predefined stationary UAV deployment case\nand fixed power allocation scheme, with acceptable complexity; and ii) the\nUAV-aided IoT networks with NOMA greatly outperforms the OMA case in terms of\nnumber of accessed devices.\n"}
+{"abstract": "  Deformable convolution networks (DCNs) proposed to address the image\nrecognition with geometric or photometric variations typically involve\ndeformable convolution that convolves on arbitrary locations of input features.\nThe locations change with different inputs and induce considerable dynamic and\nirregular memory accesses which cannot be handled by classic neural network\naccelerators (NNAs). Moreover, bilinear interpolation (BLI) operation that is\nrequired to obtain deformed features in DCNs also cannot be deployed on\nexisting NNAs directly. Although a general purposed processor (GPP) seated\nalong with classic NNAs can process the deformable convolution, the processing\non GPP can be extremely slow due to the lack of parallel computing capability.\n  To address the problem, we develop a DCN accelerator on existing NNAs to\nsupport both the standard convolution and deformable convolution. Specifically,\nfor the dynamic and irregular accesses in DCNs, we have both the input and\noutput features divided into tiles and build a tile dependency table (TDT) to\ntrack the irregular tile dependency at runtime. With the TDT, we further\ndevelop an on-chip tile scheduler to handle the dynamic and irregular accesses\nefficiently. In addition, we propose a novel mapping strategy to enable\nparallel BLI processing on NNAs and apply layer fusion techniques for more\nenergy-efficient DCN processing. According to our experiments, the proposed\naccelerator achieves orders of magnitude higher performance and energy\nefficiency compared to the typical computing architectures including ARM,\nARM+TPU, and GPU with 6.6\\% chip area penalty to a classic NNA.\n"}
+{"abstract": "  Cloud providers offer end-users various pricing schemes to allow them to\ntailor VMs to their needs, e.g., a pay-as-you-go billing scheme, called\n\\textit{on-demand}, and a discounted contract scheme, called \\textit{reserved\ninstances}. This paper presents a cloud broker which offers users both the\nflexibility of on-demand instances and some level of discounts found in\nreserved instances. The broker employs a buy-low-and-sell-high strategy that\nplaces user requests into a resource pool of pre-purchased discounted cloud\nresources. By analysing user request time-series data, the broker takes a\nrisk-oriented approach to dynamically adjust the resource pool.\n  This approach does not require a training process which is useful at\nprocessing the large data stream. The broker is evaluated with high-frequency\nreal cloud datasets from Alibaba. The results show that the overall profit of\nthe broker is close to the theoretical optimal scenario where user requests can\nbe perfectly predicted.\n"}
+{"abstract": "  Thermal states of light are widely used in quantum optics for various quantum\nphenomena testing. Particularly, they can be utilized for characterization of\nphoton creation and photon annihilation operations. During the last decade the\nproblem of photon subtraction from multimode quantum states become of much\nsignificance. Therefore, in this work we present a technique for statistical\nparameter estimation of multimode multiphoton subtracted thermal states of\nlight, which can be used for multimode photon annihilation test.\n"}
+{"abstract": "  The recent experimental data of anomalous magnetic moments strongly indicate\nthe existence of new physics beyond the standard model. An energetic $\\mu^+$\nbeam is a potential option to the expected neutrino factories, the future muon\ncolliders and the $\\mu$SR(the spin rotation, resonance and relaxation)\ntechnology. It is proposed a prompt acceleration scheme of the $\\mu^+$ beam in\na donut wakefield driven by a shaped Laguerre-Gaussian (LG) laser pulse. The\nforward part of the donut wakefield can accelerate and also focus positive\nparticle beams effectively. The LG laser is shaped by a near-critical-density\nplasma. The shaped LG laser has the shorter rise time and can enlarge the\nacceleration field. The acceleration field driven by a shaped LG laser pulse is\nsix times higher than that driven by a normal LG laser pulse. The simulation\nresults show that the $ \\mu^+$ bunch can be accelerated from $200\\mathrm{MeV}$\nto 2GeV and the transversal size of the $\\mu^+$ bunch is also focused from\ninitial $\\omega_0=5\\mu m$ to $\\omega=1\\mu m$ within several picoseconds.\n"}
+{"abstract": "  HO Puppis (HO Pup) was considered as a Be-star candidate based on its\ngamma-Cassiopeiae-type light curve, but lacked spectroscopic confirmation.\nUsing distance measured from Gaia Data Release 2 and the\nspectral-energy-distribution (SED) fit on broadband photometry, the Be-star\nnature of HO Pup is ruled out. Furthermore, based on the 28,700 photometric\ndata points collected from various time-domain surveys and dedicated\nintensive-monitoring observations, the light curves of HO Pup closely resemble\nIW And-type stars (as pointed out in Kimura et al. 2020a), exhibiting\ncharacteristics such as quasi-standstill phase, brightening, and dips. The\nlight curve of HO Pup displays various variability timescales, including\nbrightening cycles ranging from 23 to 61 days, variations with periods between\n3.9 days and 50 minutes during the quasi-standstill phase, and a semi-regular\n~14-day period for the dip events. We have also collected time-series spectra\n(with various spectral resolutions), in which Balmer emission lines and other\nexpected spectral lines for an IW And-type star were detected (even though some\nof these lines were also expected to be present for Be stars). We detect Bowen\nfluorescence near the brightening phase, and that can be used to discriminate\nbetween IW And-type stars and Be stars. Finally, despite only observing for\nfour nights, the polarization variation was detected, indicating that HO Pup\nhas significant intrinsic polarization.\n"}
+{"abstract": "  We study an effective one-dimensional quantum model that includes friction\nand spin-orbit coupling (SOC), and show that the model exhibits spin\npolarization when both terms are finite. Most important, strong spin\npolarization can be observed even for moderate SOC, provided that friction is\nstrong. Our findings might help to explain the pronounced effect of chirality\non spin distribution and transport in chiral molecules. In particular, our\nmodel implies static magnetic properties of a chiral molecule, which lead to\nShiba-like states when a molecule is placed on a superconductor, in accordance\nwith recent experimental data.\n"}
+{"abstract": "  A recent paper by Mucino, Okon and Sudarsky attempts an assessment of the\nRelational Interpretation of quantum mechanics. The paper presupposes\nassumptions that are precisely those questioned in the Relational\nInterpretation, thus undermining the value of the assessment.\n"}
+{"abstract": "  Any large-scale spiking neuromorphic system striving for complexity at the\nlevel of the human brain and beyond will need to be co-optimized for\ncommunication and computation. Such reasoning leads to the proposal for\noptoelectronic neuromorphic platforms that leverage the complementary\nproperties of optics and electronics. Starting from the conjecture that future\nlarge-scale neuromorphic systems will utilize integrated photonics and fiber\noptics for communication in conjunction with analog electronics for\ncomputation, we consider two possible paths towards achieving this vision. The\nfirst is a semiconductor platform based on analog CMOS circuits and\nwaveguide-integrated photodiodes. The second is a superconducting approach that\nutilizes Josephson junctions and waveguide-integrated superconducting\nsingle-photon detectors. We discuss available devices, assess scaling\npotential, and provide a list of key metrics and demonstrations for each\nplatform. Both platforms hold potential, but their development will diverge in\nimportant respects. Semiconductor systems benefit from a robust fabrication\necosystem and can build on extensive progress made in purely electronic\nneuromorphic computing but will require III-V light source integration with\nelectronics at an unprecedented scale, further advances in ultra-low\ncapacitance photodiodes, and success from emerging memory technologies.\nSuperconducting systems place near theoretically minimum burdens on light\nsources (a tremendous boon to one of the most speculative aspects of either\nplatform) and provide new opportunities for integrated, high-endurance synaptic\nmemory. However, superconducting optoelectronic systems will also contend with\ninterfacing low-voltage electronic circuits to semiconductor light sources, the\nserial biasing of superconducting devices on an unprecedented scale, a less\nmature fabrication ecosystem, and cryogenic infrastructure.\n"}
+{"abstract": "  Motivation: Manual curation of genome-scale reconstructions is laborious, yet\nexisting automated curation tools typically do not take species-specific\nexperimental data and manually refined genome annotations into account.\nResults: We developed DEMETER, a COBRA Toolbox extension that enables the\nefficient simultaneous refinement of thousands of draft genome-scale\nreconstructions while ensuring adherence to the quality standards in the field,\nagreement with available experimental data, and refinement of pathways based on\nmanually refined genome annotations. Availability: DEMETER and tutorials are\navailable at https://github.com/opencobra/cobratoolbox.\n"}
+{"abstract": "  This work proposes to learn fair low-rank tensor decompositions by\nregularizing the Canonical Polyadic Decomposition factorization with the kernel\nHilbert-Schmidt independence criterion (KHSIC). It is shown, theoretically and\nempirically, that a small KHSIC between a latent factor and the sensitive\nfeatures guarantees approximate statistical parity. The proposed algorithm\nsurpasses the state-of-the-art algorithm, FATR (Zhu et al., 2018), in\ncontrolling the trade-off between fairness and residual fit on synthetic and\nreal data sets.\n"}
+{"abstract": "  Chinese character recognition has attracted much research interest due to its\nwide applications. Although it has been studied for many years, some issues in\nthis field have not been completely resolved yet, e.g. the zero-shot problem.\nPrevious character-based and radical-based methods have not fundamentally\naddressed the zero-shot problem since some characters or radicals in test sets\nmay not appear in training sets under a data-hungry condition. Inspired by the\nfact that humans can generalize to know how to write characters unseen before\nif they have learned stroke orders of some characters, we propose a\nstroke-based method by decomposing each character into a sequence of strokes,\nwhich are the most basic units of Chinese characters. However, we observe that\nthere is a one-to-many relationship between stroke sequences and Chinese\ncharacters. To tackle this challenge, we employ a matching-based strategy to\ntransform the predicted stroke sequence to a specific character. We evaluate\nthe proposed method on handwritten characters, printed artistic characters, and\nscene characters. The experimental results validate that the proposed method\noutperforms existing methods on both character zero-shot and radical zero-shot\ntasks. Moreover, the proposed method can be easily generalized to other\nlanguages whose characters can be decomposed into strokes.\n"}
+{"abstract": "  Tracking objects of interest in a video is one of the most popular and widely\napplicable problems in computer vision. However, with the years, a Cambrian\nexplosion of use cases and benchmarks has fragmented the problem in a multitude\nof different experimental setups. As a consequence, the literature has\nfragmented too, and now novel approaches proposed by the community are usually\nspecialised to fit only one specific setup. To understand to what extent this\nspecialisation is necessary, in this work we present UniTrack, a solution to\naddress five different tasks within the same framework. UniTrack consists of a\nsingle and task-agnostic appearance model, which can be learned in a supervised\nor self-supervised fashion, and multiple ``heads'' that address individual\ntasks and do not require training. We show how most tracking tasks can be\nsolved within this framework, and that the same appearance model can be\nsuccessfully used to obtain results that are competitive against specialised\nmethods for most of the tasks considered. The framework also allows us to\nanalyse appearance models obtained with the most recent self-supervised\nmethods, thus extending their evaluation and comparison to a larger variety of\nimportant problems.\n"}
+{"abstract": "  We consider the motion of a gyroscope on a closed timelike curve (CTC). A\ngyroscope is identified with a unit-length spacelike vector - a spin-vector -\northogonal to the tangent to the CTC, and satisfying the equations of\nFermi-Walker transport along the curve. We investigate the consequences of the\nperiodicity of the coefficients of the transport equations, which arise from\nthe periodicty of the CTC, which is assumed to be piecewise $C^2$. We show that\nevery CTC with period $T$ admits at least one $T-$periodic spin-vector.\nFurther, either every other spin-vector is $T-$periodic, or no others are. It\nfollows that gyroscopes carried by CTCs are either all $T-$periodic, or are\ngenerically not $T-$periodic. We consider examples of spacetimes admitting\nCTCs, and address the question of whether $T-$periodicity of gyroscopic motion\noccurs generically or only on a negligible set for these CTCs. We discuss these\nresults from the perspective of principles of consistency in spacetimes\nadmitting CTCs.\n"}
+{"abstract": "  In this paper I formulate Minimal Requirements for Candidate Predictions in\nquantum field theories, inspired by viewing the standard model as an effective\nfield theory. I then survey standard effective field theory regularization\nprocedures, to see if the vacuum expectation value of energy density\n($\\langle\\rho\\rangle$) is a quantity that meets these requirements. The verdict\nis negative, leading to the conclusion that $\\langle\\rho\\rangle$ is not a\nphysically significant quantity in the standard model. Rigorous extensions of\nflat space quantum field theory eliminate $\\langle\\rho\\rangle$ from their\nconceptual framework, indicating that it lacks physical significance in the\nframework of quantum field theory more broadly. This result has consequences\nfor problems in cosmology and quantum gravity, as it suggests that the correct\nsolution to the cosmological constant problem involves a revision of the vacuum\nconcept within quantum field theory.\n"}
+{"abstract": "  In this work, we obtain the Schr\\\"odinger equation solutions for the Varshni\npotential using the Nikiforov-Uvarov method. The energy eigenvalues are\nobtained in non-relativistic regime. The corresponding eigenfunction is\nobtained in terms of Laguerre polynomials. We applied the present results to\ncalculate heavy-meson masses of charmonium and bottomonium .The mass spectra\nfor charmonium and bottomonium multiplets have predicted numerically. The\nresults are in good agreement with experimental data and the work of other\nresearchers.\n"}
+{"abstract": "  We determine explicitly and discuss in detail the effects of the joint\npresence of a longitudinal and a transversal (random) magnetic field on the\nphases of the Random Energy Model (REM) and its hierarchical generalization,\nthe GREM. Our results extent known results both in the classical case of\nvanishing transversal field and in the quantum case for vanishing longitudinal\nfield. Following Derrida and Gardner, we argue that the longitudinal field has\nto be implemented hierarchically also in the Quantum GREM. We show that this\nensures the shrinking of the spin glass phase in the presence of the magnetic\nfields as also expected for the Quantum Sherrington-Kirkpatrick model.\n"}
+{"abstract": "  Starting from a discrete $C^*$-dynamical system $(\\mathfrak{A}, \\theta,\n\\omega_o)$, we define and study most of the main ergodic properties of the\ncrossed product $C^*$-dynamical system $(\\mathfrak{A}\\rtimes_\\alpha\\mathbb{Z},\n\\Phi_{\\theta, u},\\om_o\\circ E)$,\n$E:\\mathfrak{A}\\rtimes_\\alpha\\mathbb{Z}\\rightarrow\\ga$ being the canonical\nconditional expectation of $\\mathfrak{A}\\rtimes_\\alpha\\mathbb{Z}$ onto\n$\\mathfrak{A}$, provided $\\a\\in\\aut(\\ga)$ commute with the $*$-automorphism\n$\\th$ up tu a unitary $u\\in\\ga$. Here, $\\Phi_{\\theta,\nu}\\in\\aut(\\mathfrak{A}\\rtimes_\\alpha\\mathbb{Z})$ can be considered as the fully\nnoncommutative generalisation of the celebrated skew-product defined by H.\nAnzai for the product of two tori in the classical case.\n"}
+{"abstract": "  Extended scalar sectors are a common feature of almost all beyond Standard\nModel (SM) scenarios which, in fact, can address many of the SM shortcomings\nsolely on their own. While many beyond SM scenarios have lost their appeal due\nto the non-observation of their predicted particles or are experimentally\ninaccessible, scalar extensions are well within the reach of many current and\nupcoming experiments. Here, we discuss the novel phenomenon of dark\nCP-violation which was introduced for the first time in the context of\nnon-minimal Higgs frameworks with an extended dark sector and point out its\nexperimental probes.\n"}
+{"abstract": "  Electrically addressing spin systems is predicted to be a key component in\ndeveloping scalable semiconductor-based quantum processing architectures, to\nenable fast spin qubit manipulation and long-distance entanglement via\nmicrowave photons. However, single spins have no electric dipole, and therefore\na spin-orbit mechanism must be integrated in the qubit design. Here, we propose\nto couple microwave photons to atomically precise donor spin qubit devices in\nsilicon using the hyperfine interaction intrinsic to donor systems and an\nelectrically-induced spin-orbit coupling. We characterise a one-electron system\nbound to a tunnel-coupled donor pair (1P-1P) using the tight-binding method,\nand then estimate the spin-photon coupling achievable under realistic\nassumptions. We address the recent experiments on double quantum dots (DQDs) in\nsilicon and indicate the differences between DQD and 1P-1P systems. Our\nanalysis shows that it is possible to achieve strong spin-photon coupling in\n1P-1P systems in realistic device conditions without the need for an external\nmagnetic field gradient.\n"}
+{"abstract": "  In the past years, EUV lithography scanner systems have entered High-Volume\nManufacturing for state-of-the-art Integrated Circuits (IC), with critical\ndimensions down to 10 nm. This technology uses 13.5 nm EUV radiation, which is\nshaped and transmitted through a near-vacuum H2 background gas. This gas is\nexcited into a low-density H2 plasma by the EUV radiation, as generated in\npulsed mode operation by the Laser-Produced Plasma (LPP) in the EUV Source.\nThus, in the confinement created by the walls and mirrors within the scanner\nsystem, a reductive plasma environment is created that must be understood in\ndetail to maximize mirror transmission over lifetime and to minimize molecular\nand particle contamination in the scanner. Besides the irradiated mirrors,\nreticle and wafer, also the plasma and radical load to the surrounding\nconstruction materials must be considered. This paper will provide an overview\nof the EUV-induced plasma in scanner context. Special attention will be given\nto the plasma parameters in a confined geometry, such as may be found in the\nscanner area near the reticle. Also, the translation of these specific plasma\nparameters to off-line setups will be discussed.\n"}
+{"abstract": "  There has been much interest in novel models of dark matter that exhibit\ninteresting behavior on galactic scales. A primary motivation is the observed\nBaryonic Tully-Fisher Relation in which the mass of galaxies increases as the\nquartic power of rotation speed. This scaling is not obviously accounted for by\nstandard cold dark matter. This has prompted the development of dark matter\nmodels that exhibit some form of so-called MONDian phenomenology to account for\nthis galactic scaling, while also recovering the success of cold dark matter on\nlarge scales. A beautiful example of this are the so-called superfluid dark\nmatter models, in which a complex bosonic field undergoes spontaneous symmetry\nbreaking on galactic scales, entering a superfluid phase with a 3/2 kinetic\nscaling in the low energy effective theory, that mediates a long-ranged MONDian\nforce. In this work we examine the causality and locality properties of these\nand other related models. We show that the Lorentz invariant completions of the\nsuperfluid models exhibit high energy perturbations that violate global\nhyperbolicity of the equations of motion in the MOND regime and can be\nsuperluminal in other parts of phase space. We also examine a range of\nalternate models, finding that they also exhibit forms of non-locality.\n"}
+{"abstract": "  In this paper, we propose a class of monitoring statistics for a mean shift\nin a sequence of high-dimensional observations. Inspired by the recent\nU-statistic based retrospective tests developed by Wang et al.(2019) and Zhang\net al.(2020), we advance the U-statistic based approach to the sequential\nmonitoring problem by developing a new adaptive monitoring procedure that can\ndetect both dense and sparse changes in real-time. Unlike Wang et al.(2019) and\nZhang et al.(2020), where self-normalization was used in their tests, we\ninstead introduce a class of estimators for $q$-norm of the covariance matrix\nand prove their ratio consistency. To facilitate fast computation, we further\ndevelop recursive algorithms to improve the computational efficiency of the\nmonitoring procedure. The advantage of the proposed methodology is demonstrated\nvia simulation studies and real data illustrations.\n"}
+{"abstract": "  We examine the elements of the balance equation of entropy in open quantum\nevolutions, and their response as we go over from a Markovian to a\nnon-Markovian situation. In particular, we look at the heat current and entropy\nproduction rate in the non-Markovian reduced evolution, and a Markovian limit\nof the same, experienced by one of two interacting systems immersed in a\nMarkovian bath. The analysis naturally leads us to define a heat current\ndeficit and an entropy production rate deficit, being differences between the\nglobal and local versions of the corresponding quantities. The investigation\nbrings us, in certain cases, to a complementarity of the timeintegrated heat\ncurrent deficit with the relative entropy of entanglement between the two\nsystems.\n"}
+{"abstract": "  In this thesis we discuss various classical problems in enumerative geometry.\nWe are focused on ideas and methods which can be used explicitly for practical\ncomputations. Our approach is based on studying the limits of elliptic stable\nenvelopes with shifted equivariant or Kahler variables from elliptic cohomology\nto K-theory.\n  We prove that for a variety X we can obtain K-theoretic stable envelopes for\nthe variety X^G of the G-fixed points of X, where G is a cyclic group acting on\nX preserving the symplectic form.\n  We formalize the notion of symplectic duality, also known as 3-dimensional\nmirror symmetry. We obtain a factorization theorem about the limit of elliptic\nstable envelopes to a wall, which generalizes the result of M.Aganagic and\nA.Okounkov. This approach allows us to extend the action of quantum groups,\nquantum Weyl groups, R-matrices etc., to actions on the K-theory of the\nsymplectic dual variety. In the case of the Hilbert scheme of points in plane,\nour results imply the conjectures of E.Gorsky and A.Negut.\n  We propose a new approach to K-theoretic quantum difference equations.\n"}
+{"abstract": "  This paper deals with the output regulation problem of a linear\ntime-invariant system in the presence of sporadically available measurement\nstreams. A regulator with a continuous intersample injection term is proposed,\nwhere the intersample injection is provided by a linear dynamical system and\nthe state of which is reset with the arrival of every new measurement updates.\nThe resulting system is augmented with a timer triggering an instantaneous\nupdate of the new measurement and the overall system is then analyzed in a\nhybrid system framework. With the Lyapunov based stability analysis, we offer\nsufficient conditions to ensure the objectives of the output regulation problem\nare achieved under intermittency of the measurement streams. Then, from the\nsolution to linear matrix inequalities, a numerically tractable regulator\ndesign procedure is presented. Finally, with the help of an illustrative\nexample, the effectiveness of the theoretical results are validated.\n"}
+{"abstract": "  Group-10 transition metal dichalcogenides (TMDs) are rising in prominence\nwithin the highly innovative field of 2D materials. While PtS2 has been\ninvestigated for potential electronic applications, due to its high\ncharge-carrier mobility and strong layer-dependent bandgap, it has proven to be\none of the more difficult TMDs to synthesise. In contrast to most TMDs, Pt has\na significantly more stable monosulfide, the non-layered PtS. The existence of\ntwo stable platinum sulfides, sometimes within the same sample, has resulted in\nmuch confusion between the materials in the literature. Neither of these Pt\nsulfides have been thoroughly characterised as-of-yet. Here we utilise\ntime-efficient, scalable methods to synthesise high-quality thin films of both\nPt sulfides on a variety of substrates. The competing nature of the sulfides\nand limited thermal stability of these materials is demonstrated. We report\npeak-fitted X-ray photoelectron spectra, and Raman spectra using a variety of\nlaser wavelengths, for both materials. This systematic characterisation\nprovides a guide to differentiate between the sulfides using relatively simple\nmethods which is essential to enable future work on these interesting\nmaterials.\n"}
+{"abstract": "  Let $f(\\mathbb{z},\\bar{\\mathbb{z}})$ be a convenient Newton non-degenerate\nmixed polynomial with strongly polar non-negative mixed weighted homogeneous\nface functions. We consider a convenient regular simplicial cone subdivision\n$\\Sigma^*$ which is admissible for $f$ and take the toric modification\n$\\hat{\\pi} : X \\to \\mathbb{C}^n$ associated with $\\Sigma^*$. We show that the\ntoric modification resolves topologically the singularity of the mixed\nhypersurface germ defined by $f(\\mathbb{z},\\bar{\\mathbb{z}})$ under the\nAssumption (*) (Theorem 32). This result is an extension of the first part of\nTheorem 11 ([4]) by Mutsuo Oka. We also consider some typical examples (\\S 9).\n"}
+{"abstract": "  The spectrally resolved differential cross section of Compton scattering, $d\n\\sigma / d \\omega' \\vert_{\\omega' = const}$, rises from small towards larger\nlaser intensity parameter $\\xi$, reaches a maximum, and falls towards the\nasymptotic strong-field region. Expressed by invariant quantities: $d \\sigma\n/du \\vert_{u = const}$ rises from small towards larger values of $\\xi$, reaches\na maximum at $\\xi_{max} = \\frac49 {\\cal K} u m^2 / k \\cdot p$, ${\\cal K} =\n{\\cal O} (1)$, and falls at $\\xi > \\xi_{max}$ like $\\propto \\xi^{-3/2} \\exp\n\\left (- \\frac{2 u m^2}{3 \\xi \\, k \\cdot p} \\right )$ at $u \\ge 1$. [The\nquantity $u$ is the Ritus variable related to the light-front momentum-fraction\n$s = (1 + u)/u = k \\cdot k' / k \\cdot p$ of the emitted photon (four-momentum\n$k'$, frequency $\\omega'$), and $k \\cdot p/m^2$ quantifies the invariant energy\nin the entrance channel of electron (four-momentum $p$, mass $m$) and laser\n(four-wave vector $k$).] Such a behavior of a differential observable is to be\ncontrasted with the laser intensity dependence of the total probability,\n$\\lim_{\\chi = \\xi k \\cdot p/m^2, \\xi \\to \\infty} \\mathbb{P} \\propto \\alpha\n\\chi^{2/3} m^2 / k \\cdot p$, which is governed by the soft spectral part.\n  We combine the hard-photon yield from Compton with the seeded Breit-Wheeler\npair production in a folding model and obtain a rapidly increasing $e^+ e^-$\npair number at $\\xi \\lesssim 4$. Laser bandwidth effects are quantified in the\nweak-field limit of the related trident pair production.\n"}
+{"abstract": "  Semantic web technologies have shown their effectiveness, especially when it\ncomes to knowledge representation, reasoning, and data integration. However,\nthe original semantic web vision, whereby machine readable web data could be\nautomatically actioned upon by intelligent software web agents, has yet to be\nrealised. In order to better understand the existing technological\nopportunities and challenges, in this paper we examine the status quo in terms\nof intelligent software web agents, guided by research with respect to\nrequirements and architectural components, coming from the agents community. We\nuse the identified requirements to both further elaborate on the semantic web\nagent motivating use case scenario, and to summarise different perspectives on\nthe requirements from the semantic web agent literature. We subsequently\npropose a hybrid semantic web agent architecture, and use the various\ncomponents and subcomponents in order to provide a focused discussion in\nrelation to existing semantic web standards and community activities. Finally,\nwe highlight open research opportunities and challenges and take a broader\nperspective of the research by discussing the potential for intelligent\nsoftware web agents as an enabling technology for emerging domains, such as\ndigital assistants, cloud computing, and the internet of things.\n"}
+{"abstract": "  In the type IIB maximally supersymmetric pp-wave background, stringy excited\nmodes are described by BMN (Berenstein-Madalcena-Nastase) operators in the dual\n$\\mathcal{N}=4$ super-Yang-Mills theory. In this paper, we continue the studies\nof higher genus free BMN correlators with more stringy modes, mostly focusing\non the case of genus one and four stringy modes in different transverse\ndirections. Surprisingly, we find that the non negativity of torus two-point\nfunctions, which is a consequence of a previously proposed probability\ninterpretation and has been verified in the cases with two and three stringy\nmodes, is no longer true for the case of four or more stringy modes.\nNevertheless, the factorization formula, which is also a proposed holographic\ndictionary relating the torus two-point function to a string diagram\ncalculation, is still valid. We also check the correspondence of planar\nthree-point functions with Green-Schwarz string vertex with many string modes.\nWe discuss some issues in the case of multiple stringy modes in the same\ntransverse direction. Our calculations provide some new perspectives on pp-wave\nholography.\n"}
+{"abstract": "  Famously mathematical finance was started by Bachelier in his 1900 PhD thesis\nwhere - among many other achievements - he also provides a formal derivation of\nthe Kolmogorov forward equation. This forms also the basis for Dupire's (again\nformal) solution to the problem of finding an arbitrage free model calibrated\nto the volatility surface. The later result has rigorous counterparts in the\ntheorems of Kellerer and Lowther. In this survey article we revisit these\nhallmarks of stochastic finance, highlighting the role played by some optimal\ntransport results in this context.\n"}
+{"abstract": "  In this paper, a Gauss-Seidel method with oblique direction (GSO) is proposed\nfor finding the least-squares solution to a system of linear equations, where\nthe coefficient matrix may be full rank or rank deficient and the system is\noverdetermined or underdetermined. Through this method, the number of iteration\nsteps and running time can be reduced to a greater extent to find the\nleast-squares solution, especially when the columns of matrix A are close to\nlinear correlation. It is theoretically proved that GSO method converges to the\nleast-squares solution. At the same time, a randomized version--randomized\nGauss-Seidel method with oblique direction (RGSO) is established, and its\nconvergence is proved. Theoretical proof and numerical results show that the\nGSO method and the RGSO method are more efficient than the coordinate descent\n(CD) method and the randomized coordinate descent (RCD) method.\n"}
+{"abstract": "  The Reconfigurable Intelligent Surface (RIS) constitutes one of the prominent\ntechnologies for the next 6-th Generation (6G) of wireless communications. It\nis envisioned to enhance signal coverage in cases where obstacles block the\ndirect communication from Base Stations (BSs), and when high carrier\nfrequencies are used that are sensitive to attenuation losses. In the\nliterature, the exploitation of RISs is exclusively based on traditional\ncoherent demodulation, which necessitates the availability of Channel State\nInformation (CSI). Given the CSI, a multi-antenna BS or a dedicated controller\ncomputes the pre/post spatial coders and the RIS configuration. The latter\ntasks require significant amount of time and resources, which may not be\naffordable when the channel is time-varying or the CSI is not accurate enough.\nIn this paper, we consider the uplink between a single-antenna user and a\nmulti-antenna BS and present a novel RIS-empowered Orthogonal Frequency\nDivision Multiplexing (OFDM) communication system based on the differential\nphase shift keying, which is suitable for high noise and/or mobility scenarios.\nConsidering both an idealistic and a realistic channel model, analytical\nexpressions for the Signal-to-Interference and Noise Ratio (SINR) and the\nSymbol Error Probability (SEP) of the proposed non-coherent RIS-empowered\nsystem are presented. Our extensive computer simulation results verify the\naccuracy of the presented analysis and showcase the proposed system's\nperformance and superiority over coherent demodulation in different mobility\nand spatial correlation scenarios.\n"}
+{"abstract": "  Semantic diversity in Genetic Programming has proved to be highly beneficial\nin evolutionary search. We have witnessed a surge in the number of scientific\nworks in the area, starting first in discrete spaces and moving then to\ncontinuous spaces. The vast majority of these works, however, have focused\ntheir attention on single-objective genetic programming paradigms, with a few\nexceptions focusing on Evolutionary Multi-objective Optimization (EMO). The\nlatter works have used well-known robust algorithms, including the\nNon-dominated Sorting Genetic Algorithm II and the Strength Pareto Evolutionary\nAlgorithm, both heavily influenced by the notion of Pareto dominance. These\ninspiring works led us to make a step forward in EMO by considering\nMulti-objective Evolutionary Algorithms Based on Decomposition (MOEA/D). We\nshow, for the first time, how we can promote semantic diversity in MOEA/D in\nGenetic Programming.\n"}
+{"abstract": "  In this paper, we define a notion of containment and avoidance for subsets of\n$\\mathbb{R}^2$. Then we introduce a new, continuous and super-additive extremal\nfunction for subsets $P \\subseteq \\mathbb{R}^2$ called $px(n, P)$, which is the\nsupremum of $\\mu_2(S)$ over all open $P$-free subsets $S \\subseteq [0, n]^2$,\nwhere $\\mu_2(S)$ denotes the Lebesgue measure of $S$ in $\\mathbb{R}^2$. We show\nthat $px(n, P)$ fully encompasses the Zarankiewicz problem and more generally\nthe 0-1 matrix extremal function $ex(n, M)$ up to a constant factor. More\nspecifically, we define a natural correspondence between finite subsets $P\n\\subseteq \\mathbb{R}^2$ and 0-1 matrices $M_P$, and we prove that $px(n, P) =\n\\Theta(ex(n, M_P))$ for all finite subsets $P \\subseteq \\mathbb{R}^2$, where\nthe constants in the bounds depend only on the distances between the points in\n$P$.\n  We also discuss bounded infinite subsets $P$ for which $px(n, P)$ grows\nfaster than $ex(n, M)$ for all fixed 0-1 matrices $M$. In particular, we show\nthat $px(n, P) = \\Theta(n^{2})$ for any open subset $P \\subseteq \\mathbb{R}^2$.\nWe prove an even stronger result, that if $Q_P$ is the set of points with\nrational coordinates in any open subset $P \\subseteq \\mathbb{R}^2$, then $px(n,\nQ_P) = \\Theta(n^2)$. Finally, we obtain a strengthening of the\nK\\H{o}vari-S\\'{o}s-Tur\\'{a}n theorem that applies to infinite subsets of\n$\\mathbb{R}^2$. Specifically, for subsets $P_{s, t, c} \\subseteq \\mathbb{R}^2$\nconsisting of $t$ horizontal line segments of length $s$ with left endpoints on\nthe same vertical line with consecutive segments a distance of $c$ apart, we\nprove that $px(n, P_{s, t,c}) = O(s^{\\frac{1}{t}}n^{2-\\frac{1}{t}})$, where the\nconstant in the bound depends on $t$ and $c$. When $t = 2$, we show that this\nbound is sharp up to a constant factor that depends on $c$.\n"}
+{"abstract": "  Nonreciprocal signal operation is highly desired for various acoustic\napplications, where protection from unwanted backscattering can be realized so\nthat transmitting and receiving signals are processed in a full-duplex mode.\nHere we present the realization of a class of nonreciprocal circulators based\non simply structured acoustic metagratings, which consist only of a few solid\ncylinders and a steady fluid flow with low velocity. These innovative\nmetagratings are intelligently designed via a diffraction analysis of the\nlinearized potential flow equation and a genetic-algorithm-based optimization\nprocess. Unitary reflection efficiency between desired ports of the circulators\nare demonstrated through full-wave numerical simulations, confirming\nnonreciprocal and robust circulation of the acoustic signal over a broad range\nof flow velocity magnitude and profile. Our design provides a feasible degree\nof tunability, including switching from reciprocal to nonreciprocal operation\nand reversing the handedness of the circulator, presenting a convenient but\nefficient approach for the realization of nonreciprocal acoustic devices from\nwavelength-thick metagratings. It may find applications in various scenarios\nincluding underwater communication, energy harvesting, and acoustic sensing.\n"}
+{"abstract": "  This work presents a self-heating study of a 40-nm bulk-CMOS technology in\nthe ambient temperature range from 300 K down to 4.2 K. A custom test chip was\ndesigned and fabricated for measuring both the temperature rise in the MOSFET\nchannel and in the surrounding silicon substrate, using the gate resistance and\nsilicon diodes as sensors, respectively. Since self-heating depends on factors\nsuch as device geometry and power density, the test structure characterized in\nthis work was specifically designed to resemble actual devices used in\ncryogenic qubit control ICs. Severe self-heating was observed at deep-cryogenic\nambient temperatures, resulting in a channel temperature rise exceeding 50 K\nand having an impact detectable at a distance of up to 30 um from the device.\nBy extracting the thermal resistance from measured data at different\ntemperatures, it was shown that a simple model is able to accurately predict\nchannel temperatures over the full ambient temperature range from\ndeep-cryogenic to room temperature. The results and modeling presented in this\nwork contribute towards the full self-heating-aware IC design-flow required for\nthe reliable design and operation of cryo-CMOS circuits.\n"}
+{"abstract": "  In this digital era, almost in every discipline people are using automated\nsystems that generate information represented in document format in different\nnatural languages. As a result, there is a growing interest towards better\nsolutions for finding, organizing and analyzing these documents. In this paper,\nwe propose a system that clusters Amharic text documents using Encyclopedic\nKnowledge (EK) with neural word embedding. EK enables the representation of\nrelated concepts and neural word embedding allows us to handle the contexts of\nthe relatedness. During the clustering process, all the text documents pass\nthrough preprocessing stages. Enriched text document features are extracted\nfrom each document by mapping with EK and word embedding model. TF-IDF weighted\nvector of enriched feature was generated. Finally, text documents are clustered\nusing popular spherical K-means algorithm. The proposed system is tested with\nAmharic text corpus and Amharic Wikipedia data. Test results show that the use\nof EK with word embedding for document clustering improves the average accuracy\nover the use of only EK. Furthermore, changing the size of the class has a\nsignificant effect on accuracy.\n"}
+{"abstract": "  We study the scheduling problem of makespan minimization while taking machine\nconflicts into account. Machine conflicts arise in various settings, e.g.,\nshared resources for pre- and post-processing of tasks or spatial restrictions.\nIn this context, each job has a blocking time before and after its processing\ntime, i.e., three parameters. We seek for conflict-free schedules in which the\nblocking times of no two jobs intersect on conflicting machines. Given a set of\njobs, a set of machines, and a graph representing machine conflicts, the\nproblem SchedulingWithMachineConflicts (SMC), asks for a conflict-free schedule\nof minimum makespan.\n  We show that, unless $\\textrm{P}=\\textrm{NP}$, SMC on $m$ machines does not\nallow for a $\\mathcal{O}(m^{1-\\varepsilon})$-approximation algorithm for any\n$\\varepsilon>0$, even in the case of identical jobs and every choice of fixed\npositive parameters, including the unit case. Complementary, we provide\napproximation algorithms when a suitable collection of independent sets is\ngiven. Finally, we present polynomial time algorithms to solve the problem for\nthe case of unit jobs on special graph classes. Most prominently, we solve it\nfor bipartite graphs by using structural insights for conflict graphs of star\nforests.\n"}
+{"abstract": "  This paper presents a novel approach to characterize the dynamics of the\nlimit spectrum of large random matrices. This approach is based upon the notion\nwe call \"spectral dominance\". In particular, we show that the limit spectral\nmeasure can be determined as the derivative of the unique viscosity solution of\na partial integro-differential equation. This also allows to make general and\n\"short\" proofs for the convergence problem. We treat the cases of Dyson\nBrownian motions, Wishart processes and present a general class of models for\nwhich this characterization holds.\n"}
+{"abstract": "  We give a new construction of Lascoux-Sch\\\"utzenberger's charge statistic in\ntype A which is motivated by the geometric Satake equivalence\n"}
+{"abstract": "  Effective Capacity defines the maximum communication rate subject to a\nspecific delay constraint, while effective energy efficiency (EEE) indicates\nthe ratio between effective capacity and power consumption. We analyze the EEE\nof ultra-reliable networks operating in the finite blocklength regime. We\nobtain a closed form approximation for the EEE in quasi-static Nakagami-$m$\n(and Rayleigh as sub-case) fading channels as a function of power, error\nprobability, and latency. Furthermore, we characterize the QoS constrained EEE\nmaximization problem for different power consumption models, which shows a\nsignificant difference between finite and infinite blocklength coding with\nrespect to EEE and optimal power allocation strategy. As asserted in the\nliterature, achieving ultra-reliability using one transmission consumes huge\namount of power, which is not applicable for energy limited IoT devices. In\nthis context, accounting for empty buffer probability in machine type\ncommunication (MTC) and extending the maximum delay tolerance jointly enhances\nthe EEE and allows for adaptive retransmission of faulty packets. Our analysis\nreveals that obtaining the optimum error probability for each transmission by\nminimizing the non-empty buffer probability approaches EEE optimality, while\nbeing analytically tractable via Dinkelbach's algorithm. Furthermore, the\nresults illustrate the power saving and the significant EEE gain attained by\napplying adaptive retransmission protocols, while sacrificing a limited\nincrease in latency.\n"}
+{"abstract": "  In this paper, we introduce a non-abelian exterior product of Hom-Leibniz\nalgebras and investigate its relative to the Hopf's formula. We also construct\nan eight-term exact sequence in the homology of Hom-Leibniz algebras. Finally,\nwe relate the notion of capability of a Hom-Leibniz algebra to its exterior\nproduct.\n"}
+{"abstract": "  This paper proposes a nonlinear control architecture for flexible aircraft\nsimultaneous trajectory tracking and load alleviation. By exploiting the\ncontrol redundancy, the gust and maneuver loads are alleviated without\ndegrading the rigid-body command tracking performance. The proposed control\narchitecture contains four cascaded control loops: position control, flight\npath control, attitude control, and optimal multi-objective wing control. Since\nthe position kinematics are not influenced by model uncertainties, the\nnonlinear dynamic inversion control is applied. On the contrary, the flight\npath dynamics are perturbed by both model uncertainties and atmospheric\ndisturbances; thus the incremental sliding mode control is adopted.\nLyapunov-based analyses show that this method can simultaneously reduce the\nmodel dependency and the minimum possible gains of conventional sliding mode\ncontrol methods. Moreover, the attitude dynamics are in the strict-feedback\nform; thus the incremental backstepping sliding mode control is applied.\nFurthermore, a novel load reference generator is designed to distinguish the\nnecessary loads for performing maneuvers from the excessive loads. The load\nreferences are realized by the inner-loop optimal wing controller, while the\nexcessive loads are naturalized by flaps without influencing the outer-loop\ntracking performance. The merits of the proposed control architecture are\nverified by trajectory tracking tasks and gust load alleviation tasks in\nspatial von Karman turbulence fields.\n"}
+{"abstract": "  This paper presents a simple Fourier-matching method to rigorously study\nresonance frequencies of a sound-hard slab with a finite number of arbitrarily\nshaped cylindrical holes of diameter ${\\cal O}(h)$ for $h\\ll1$. Outside the\nholes, a sound field can be expressed in terms of its normal derivatives on the\napertures of holes. Inside each hole, since the vertical variable can be\nseparated, the field can be expressed in terms of a countable set of Fourier\nbasis functions. Matching the field on each aperture yields a linear system of\ncountable equations in terms of a countable set of unknown Fourier\ncoefficients. The linear system can be reduced to a finite-dimensional linear\nsystem based on the invertibility of its principal submatrix, which is proved\nby the well-posedness of a closely related boundary value problem for each hole\nin the limiting case $h\\to 0$, so that only the leading Fourier coefficient of\neach hole is preserved in the finite-dimensional system. The resonance\nfrequencies are those making the resulting finite-dimensional linear system\nrank deficient. By regular asymptotic analysis for $h \\ll 1$, we get a\nsystematic asymptotic formula for characterizing the resonance frequencies by\nthe 3D subwavelength structure. The formula reveals an important fact that when\nall holes are of the same shape, the Q-factor for any resonance frequency\nasymptotically behaves as ${\\cal O}(h^{-2})$ for $h\\ll1$ with its prefactor\nindependent of shapes of holes.\n"}
+{"abstract": "  In this paper we analyze the propositional extensions of the minimal\nclassical modal logic system E, which form a lattice denoted as CExtE. Our\nmethod of analysis uses algebraic calculations with canonical forms, which are\na generalization of the normal forms applicable to normal modal logics. As an\napplication, we identify a group of automorphisms of CExtE that is isomorphic\nto the symmetric group S4.\n"}
+{"abstract": "  We show that the quantum geometry of the Fermi surface can be numerically\ndescribed by a 3-dimensional discrete quantum manifold. This approach not only\navoids singularities in the Fermi sea, but it also enables the precise\ncomputation of the intrinsic Hall conductivity resolved in spin, as well as any\nother local properties of the Fermi surface. The method assures numerical\naccuracy when the Fermi level is arbitrarily close to singularities, and it\nremains robust when Kramers degeneracy is protected by symmetry. The approach\nis demonstrated by calculating the anomalous Hall and spin Hall conductivities\nof a 2-band lattice model of a Weyl semimetal and a full-band ab-initio model\nof zincblende GaAs.\n"}
+{"abstract": "  Doping can profoundly affect the electronic- and optical-structure of\nsemiconductors. Here we address the effect of surplus charges on non-radiative\n(NR) exciton and trion decay in doped semiconducting single-wall carbon\nnanotubes. The dependence of exciton photoluminescence quantum yields and\nexciton decay on the doping level, with its characteristically\nstretched-exponential kinetics, is attributed to diffusion-limited NR decay at\ncharged impurity sites. By contrast, trion decay is unimolecular with a rate\nconstant of $2.0\\,\\rm ps^{-1}$. Our experiments thus show that charged\nimpurities not only trap trions and scavenge mobile excitons but that they also\nfacilitate efficient NR energy dissipation for both.\n"}
+{"abstract": "  The nearby face-on spiral galaxy NGC 2617 underwent an unambiguous\n'inside-out' multi-wavelength outburst in Spring 2013, and a dramatic Seyfert\ntype change probably between 2010 and 2012, with the emergence of broad optical\nemission lines. To search for the jet activity associated with this variable\naccretion activity, we carried out multi-resolution and multi-wavelength radio\nobservations. Using the very long baseline interferometric (VLBI) observations\nwith the European VLBI Network (EVN) at 1.7 and 5.0 GHz, we find that NGC 2617\nshows a partially synchrotron self-absorbed compact radio core with a\nsignificant core shift, and an optically thin steep-spectrum jet extending\ntowards the north up to about two parsecs in projection. We also observed NGC\n2617 with the electronic Multi-Element Remotely Linked Interferometer Network\n(e-MERLIN) at 1.5 and 5.5 GHz, and revisited the archival data of the Very\nLarge Array (VLA) and the Very Long Baseline Array (VLBA). The radio core had a\nstable flux density of about 1.4 mJy at 5.0 GHz between 2013 June and 2014\nJanuary, in agreement with the expectation of a supermassive black hole in the\nlow accretion rate state. The northern jet component is unlikely to be\nassociated with the 'inside-out' outburst of 2013. Moreover, we report that\nmost optically selected changing-look AGN at z<0.83 are sub-mJy radio sources\nin the existing VLA surveys at 1.4 GHz, and it is unlikely that they are more\nactive than normal AGN at radio frequencies.\n"}
+{"abstract": "  With the development of the 5G and Internet of Things, amounts of wireless\ndevices need to share the limited spectrum resources. Dynamic spectrum access\n(DSA) is a promising paradigm to remedy the problem of inefficient spectrum\nutilization brought upon by the historical command-and-control approach to\nspectrum allocation. In this paper, we investigate the distributed DSA problem\nfor multi-user in a typical multi-channel cognitive radio network. The problem\nis formulated as a decentralized partially observable Markov decision process\n(Dec-POMDP), and we proposed a centralized off-line training and distributed\non-line execution framework based on cooperative multi-agent reinforcement\nlearning (MARL). We employ the deep recurrent Q-network (DRQN) to address the\npartial observability of the state for each cognitive user. The ultimate goal\nis to learn a cooperative strategy which maximizes the sum throughput of\ncognitive radio network in distributed fashion without coordination information\nexchange between cognitive users. Finally, we validate the proposed algorithm\nin various settings through extensive experiments. From the simulation results,\nwe can observe that the proposed algorithm can converge fast and achieve almost\nthe optimal performance.\n"}
+{"abstract": "  We introduce the notion of eigenstate of an operator in an abstract\nC*-algebra, and prove several properties. Most significantly, if the operator\nis self-adjoint, then every element of its spectrum has a corresponding\neigenstate.\n"}
+{"abstract": "  Blockchain applications that rely on the Proof-of-Work (PoW) have\nincreasingly become energy inefficient with a staggering carbon footprint. In\ncontrast, energy-efficient alternative consensus protocols such as\nProof-of-Stake (PoS) may cause centralization and unfairness in the blockchain\nsystem. To address these challenges, we propose a modular version of PoS-based\nblockchain systems called epos that resists the centralization of network\nresources by extending mining opportunities to a wider set of stakeholders.\nMoreover, epos leverages the in-built system operations to promote fair mining\npractices by penalizing malicious entities. We validate epos's achievable\nobjectives through theoretical analysis and simulations. Our results show that\nepos ensures fairness and decentralization, and can be applied to existing\nblockchain applications.\n"}
+{"abstract": "  We consider nonnegative time series forecasting framework. Based on recent\nadvances in Nonnegative Matrix Factorization (NMF) and Archetypal Analysis, we\nintroduce two procedures referred to as Sliding Mask Method (SMM) and Latent\nClustered Forecast (LCF). SMM is a simple and powerful method based on time\nwindow prediction using Completion of Nonnegative Matrices. This new procedure\ncombines low nonnegative rank decomposition and matrix completion where the\nhidden values are to be forecasted. LCF is two stage: it leverages archetypal\nanalysis for dimension reduction and clustering of time series, then it uses\nany black-box supervised forecast solver on the clustered latent\nrepresentation. Theoretical guarantees on uniqueness and robustness of the\nsolution of NMF Completion-type problems are also provided for the first time.\nFinally, numerical experiments on real-world and synthetic data-set confirms\nforecasting accuracy for both the methodologies.\n"}
+{"abstract": "  In this study, we extend upon the model by Haring et al. (2001) by\nintroducing retrial phenomenon in multi-server queueing system. When at most g\nnumber of guard channels are available, it allows new calls to join the retrial\ngroup. This retrial group is called orbit and can hold a maximum of m retrial\ncalls. The impact of retrial over certain performance measures is numerically\ninvestigated. The focus of this work is to construct optimization problems to\ndetermine the optimal number of channels, the optimal number of guard channels\nand the optimal orbit size. Further, it has been emphasized that the proposed\nmodel with retrial phenomenon reduces the blocking probability of new calls in\nthe system.\n"}
+{"abstract": "  With the rapidly evolving next-generation systems-of-systems, we face new\nsecurity, resilience, and operational assurance challenges. In the face of the\nincreasing attack landscape, it is necessary to cater to efficient mechanisms\nto verify software and device integrity to detect run-time modifications.\nTowards this direction, remote attestation is a promising defense mechanism\nthat allows a third party, the verifier, to ensure a remote device's (the\nprover's) integrity. However, many of the existing families of attestation\nsolutions have strong assumptions on the verifying entity's trustworthiness,\nthus not allowing for privacy preserving integrity correctness. Furthermore,\nthey suffer from scalability and efficiency issues. This paper presents a\nlightweight dynamic configuration integrity verification that enables inter and\nintra-device attestation without disclosing any configuration information and\ncan be applied on both resource-constrained edge devices and cloud services.\nOur goal is to enhance run-time software integrity and trustworthiness with a\nscalable solution eliminating the need for federated infrastructure trust.\n"}
+{"abstract": "  Understanding ventilation strategy of a supercavity is important for\ndesigning high-speed underwater vehicles wherein an artificial gas pocket is\ncreated behind a flow separation device for drag reduction. Our study\ninvestigates the effect of flow unsteadiness on the ventilation requirements to\nform (CQf) and collapse (CQc) a supercavity. Imposing flow unsteadiness on the\nincoming flow has shown an increment in higher CQf at low free stream velocity\nand lower CQf at high free stream velocity. High-speed imaging reveals\ndistinctly different behaviors in the recirculation region for low and high\nfreestream velocity under unsteady flows. At low free stream velocities, the\nrecirculation region formed downstream of a cavitator shifted vertically with\nflow unsteadiness, resulting in lower bubble collision and coalescence\nprobability, which is critical for the supercavity formation process. The\nrecirculation region negligibly changed with flow unsteadiness at high free\nstream velocity and less ventilation is required to form a supercavity compared\nto that of the steady incoming flow. Such a difference is attributed to the\nincreased transverse Reynolds stress that aids bubble collision in a confined\nspace of the recirculation region. CQc is found to heavily rely on the vertical\ncomponent of the flow unsteadiness and the free stream velocity. Interfacial\ninstability located upper rear of the supercavity develops noticeably with flow\nunsteadiness and additional bubbles formed by the distorted interface shed from\nthe supercavity, resulting in an increased CQc. Further analysis on the\nquantification of such additional bubble leakage rate indicates that the\ndevelopment and amplitude of the interfacial instability accounts for the\nvariation of CQc under a wide range of flow unsteadiness. Our study provides\nsome insights on the design of a ventilation strategy for supercavitating\nvehicles in practice.\n"}
+{"abstract": "  Emotion recognition and understanding is a vital component in human-machine\ninteraction. Dimensional models of affect such as those using valence and\narousal have advantages over traditional categorical ones due to the complexity\nof emotional states in humans. However, dimensional emotion annotations are\ndifficult and expensive to collect, therefore they are not as prevalent in the\naffective computing community. To address these issues, we propose a method to\ngenerate synthetic images from existing categorical emotion datasets using face\nmorphing as well as dimensional labels in the circumplex space with full\ncontrol over the resulting sample distribution, while achieving augmentation\nfactors of at least 20x or more.\n"}
+{"abstract": "  The paper proposes a new technique that substantially improves blind digital\nmodulation identification (DMI) algorithms that are based on higher-order\nstatistics (HOS). The proposed technique takes advantage of noise power\nestimation to make an offset on higher-order moments (HOM), thus getting an\nestimate of noise-free HOM. When tested for multiple-antenna systems, the\nproposed method outperforms other DMI algorithms, in terms of identification\naccuracy, that are based only on cumulants or do not consider HOM denoising,\neven for a receiver with impairments. The improvement is achieved with the same\norder of complexity of the common HOS-based DMI algorithms in the same context.\n"}
+{"abstract": "  The Internet-of-Things (IoT) is an emerging and cognitive technology which\nconnects a massive number of smart physical devices with virtual objects\noperating in diverse platforms through the internet. IoT is increasingly being\nimplemented in distributed settings, making footprints in almost every sector\nof our life. Unfortunately, for healthcare systems, the entities connected to\nthe IoT networks are exposed to an unprecedented level of security threats.\nRelying on a huge volume of sensitive and personal data, IoT healthcare systems\nare facing unique challenges in protecting data security and privacy. Although\nblockchain has posed to be the solution in this scenario thanks to its inherent\ndistributed ledger technology (DLT), it suffers from major setbacks of\nincreasing storage and computation requirements with the network size. This\npaper proposes a holochain-based security and privacy-preserving framework for\nIoT healthcare systems that overcomes these challenges and is particularly\nsuited for resource constrained IoT scenarios. The performance and thorough\nsecurity analyses demonstrate that a holochain-based IoT healthcare system is\nsignificantly better compared to blockchain and other existing systems.\n"}
+{"abstract": "  Context. Luminous Blue Variables (LBVs) are thought to be in a transitory\nphase between O stars on the main-sequence and the Wolf-Rayet stage. Recent\nstudies suggest that they might be formed through binary interaction. Only a\nfew are known in binary systems but their multiplicity fraction is uncertain.\nAims. This study aims at deriving the binary fraction among the Galactic\n(confirmed and candidate) LBV population. We combine multi-epoch spectroscopy\nand long-baseline interferometry. Methods. We use cross-correlation to measure\ntheir radial velocities. We identify spectroscopic binaries through significant\nRV variability (larger than 35 km/s). We investigate the observational biases\nto establish the intrinsic binary fraction. We use CANDID to detect\ninterferometric companions, derive their parameters and positions. Results. We\nderive an observed spectroscopic binary fraction of 26 %. Considering period\nand mass ratio ranges from Porb=1 to 1000 days, and q = 0.1-1.0, and a\nrepresentative set of orbital parameter distributions, we find a bias-corrected\nbinary fraction of 62%. From interferometry, we detect 14 companions out of 18\nobjects, providing a binary fraction of 78% at projected separations between 1\nand 120 mas. From the derived primary diameters, and the distances of these\nobjects, we measure for the first time the exact radii of Galactic LBVs to be\nbetween 100 and 650 Rsun, making unlikely to have short-period systems.\nConclusions. This analysis shows that the binary fraction among the Galactic\nLBV population is large. If they form through single-star evolution, their\norbit must be initially large. If they form through binary channel that implies\nthat either massive stars in short binary systems must undergo a phase of fully\nnon-conservative mass transfer to be able to sufficiently widen the orbit or\nthat LBVs form through merging in initially binary or triple systems.\n"}
+{"abstract": "  Anantharaman and Le Masson proved that any family of eigenbases of the\nadjacency operators of a family of graphs is quantum ergodic (a form of\ndelocalization) assuming the graphs satisfy conditions of expansion and high\ngirth. In this paper, we show that neither of these two conditions is\nsufficient by itself to necessitate quantum ergodicity. We also show that\nhaving conditions of expansion and a specific relaxation of the high girth\nconstraint present in later papers on quantum ergodicity is not sufficient. We\ndo so by proving new properties of the Cartesian product of two graphs where\none is infinite.\n"}
+{"abstract": "  We present the development and characterization of a generic, reconfigurable,\nlow-cost ($<$ 350 USD) software-defined digital receiver system (DRS) for\ntemporal correlation measurements in atomic spin ensembles. We demonstrate the\nuse of the DRS as a component of a high resolution magnetometer. Digital\nreceiver based fast Fourier transform spectrometers (FFTS) are generally\nsuperior in performance in terms of signal-to-noise ratio (SNR) compared to\ntraditional swept-frequency spectrum analyzers (SFSA). In applications where\nthe signals being analyzed are very narrow band in frequency domain, recording\nthem at high speeds over a reduced bandwidth provides flexibility to study them\nfor longer periods. We have built the DRS on the STEMLab 125-14 FPGA platform\nand it has two different modes of operation: FFT Spectrometer and real time raw\nvoltage recording mode. We evaluate its performance by using it in atomic spin\nnoise spectroscopy (SNS). We demonstrate that the SNR is improved by more than\none order of magnitude with the FFTS as compared to that of the commercial\nSFSA. We also highlight that with this DRS operating in the triggered data\nacquisition mode one can achieve spin noise (SN) signal with high SNR in a\nrecording time window as low as 100 msec. We make use of this feature to\nperform time resolved high-resolution magnetometry. While the receiver was\ninitially developed for SNS experiments, it can be easily used for other\natomic, molecular and optical (AMO) physics experiments as well.\n"}
+{"abstract": "  We introduce novel methods for encoding acyclicity and s-t-reachability\nconstraints for propositional formulas with underlying directed graphs. They\nare based on vertex elimination graphs, which makes them suitable for cases\nwhere the underlying graph is sparse. In contrast to solvers with ad hoc\nconstraint propagators for acyclicity and reachability constraints such as\nGraphSAT, our methods encode these constraints as standard propositional\nclauses, making them directly applicable with any SAT solver. An empirical\nstudy demonstrates that our methods together with an efficient SAT solver can\noutperform both earlier encodings of these constraints as well as GraphSAT,\nparticularly when underlying graphs are sparse.\n"}
+{"abstract": "  It is known that generalized deformation in the sense of Hitchin-Gaultieri is\na geometric realization of the degree-2 component of Kontsevich-Barannikov's\nhomological approach to extended deformation. Through extended deformation, one\nassociates a Frobenius structure to the extended moduli space. In this notes,\nwe prove that on primary Kodaira manifolds the restriction of the Frobenius\nstructure on the degree-2 component of the extended moduli space is trivial. It\ngeneralizes the author's past observation on Kodaira surface.\n"}
+{"abstract": "  Video activity recognition by deep neural networks is impressive for many\nclasses. However, it falls short of human performance, especially for\nchallenging to discriminate activities. Humans differentiate these complex\nactivities by recognising critical spatio-temporal relations among explicitly\nrecognised objects and parts, for example, an object entering the aperture of a\ncontainer. Deep neural networks can struggle to learn such critical\nrelationships effectively. Therefore we propose a more human-like approach to\nactivity recognition, which interprets a video in sequential temporal phases\nand extracts specific relationships among objects and hands in those phases.\nRandom forest classifiers are learnt from these extracted relationships. We\napply the method to a challenging subset of the something-something dataset and\nachieve a more robust performance against neural network baselines on\nchallenging activities.\n"}
+{"abstract": "  We study properties of twisted unions of metric spaces introduced by Johnson,\nLindenstrauss, and Schechtman, and by Naor and Rabani. In particular, we prove\nthat under certain natural mild assumptions twisted unions of $L_1$-embeddable\nmetric spaces also embed in $L_1$ with distortions bounded above by constants\nthat do not depend on the metric spaces themselves, or on their size, but only\non certain general parameters. This answers a question stated by Naor and by\nNaor and Rabani.\n  In the second part of the paper we give new simple examples of metric spaces\nsuch their every embedding into $L_p$, $1\\le p<\\infty$, has distortion at least\n$3$, but which are a union of two subsets, each isometrically embeddable in\n$L_p$. This extends an analogous result of K.~Makarychev and Y.~Makarychev from\nHilbert spaces to $L_p$-spaces, $1\\le p<\\infty$.\n"}
+{"abstract": "  Multi-channel speech enhancement aims to extract clean speech from a noisy\nmixture using signals captured from multiple microphones. Recently proposed\nmethods tackle this problem by incorporating deep neural network models with\nspatial filtering techniques such as the minimum variance distortionless\nresponse (MVDR) beamformer. In this paper, we introduce a different research\ndirection by viewing each audio channel as a node lying in a non-Euclidean\nspace and, specifically, a graph. This formulation allows us to apply graph\nneural networks (GNN) to find spatial correlations among the different channels\n(nodes). We utilize graph convolution networks (GCN) by incorporating them in\nthe embedding space of a U-Net architecture. We use LibriSpeech dataset and\nsimulate room acoustics data to extensively experiment with our approach using\ndifferent array types, and number of microphones. Results indicate the\nsuperiority of our approach when compared to prior state-of-the-art method.\n"}
+{"abstract": "  Quantum tomography is an important tool for the characterisation of quantum\noperations. In this paper, we present a framework of quantum tomography in\nfermionic systems. Compared with qubit systems, fermions obey the\nsuperselection rule, which sets constraints on states, processes and\nmeasurements in a fermionic system. As a result, we can only partly reconstruct\nan operation that acts on a subset of fermion modes, and the full\nreconstruction always requires at least one ancillary fermion mode in addition\nto the subset. We also report a protocol for the full reconstruction based on\ngates in Majorana fermion quantum computer, including a set of circuits for\nrealising the informationally-complete state preparation and measurement.\n"}
+{"abstract": "  Distant boundaries in linear non-Hermitian lattices can dramatically change\nenergy eigenvalues and corresponding eigenstates in a nonlocal way. This effect\nis known as non-Hermitian skin effect (NHSE). Combining non-Hermitian skin\neffect with nonlinear effects can give rise to a host of novel phenomenas,\nwhich may be used for nonlinear structure designs. Here we study nonlinear\nnon-Hermitian skin effect and explore nonlocal and substantial effects of edges\non stationary nonlinear solutions. We show that fractal and continuum bands\narise in a long lattice governed by a nonreciprocal discrete nonlinear\nSchrodinger equation. We show that stationary solutions are localized at the\nedge in the continuum band. We consider a non-Hermitian Ablowitz-Ladik model\nand show that nonlinear exceptional point disappears if the lattice is\ninfinitely long.\n"}
+{"abstract": "  While the family of layered pnictides $ABX_2$ ($A$ : rare or alkaline earth\nmetals, $B$ : transition metals, $X$ : Sb/Bi) can host Dirac dispersions based\non Sb/Bi square nets, nearly half of them has not been synthesized yet for\npossible combinations of the $A$ and $B$ cations. Here we report the\nfabrication of EuCdSb$_{\\mathrm{2}}$ with the largest $B$-site ionic radius,\nwhich is stabilized for the first time in thin film form by molecular beam\ndeposition. EuCdSb$_{\\mathrm{2}}$ crystallizes in an orthorhombic $Pnma$\nstructure and exhibits antiferromagnetic ordering of the Eu magnetic moments at\n$T_\\mathrm{N}=15$K. Our successful growth will be an important step for further\nexploring novel Dirac materials using film techniques.\n"}
+{"abstract": "  The \"eternal war in cache\" has reached browsers, with multiple cache-based\nside-channel attacks and countermeasures being suggested. A common approach for\ncountermeasures is to disable or restrict JavaScript features deemed essential\nfor carrying out attacks. To assess the effectiveness of this approach, in this\nwork we seek to identify those JavaScript features which are essential for\ncarrying out a cache-based attack. We develop a sequence of attacks with\nprogressively decreasing dependency on JavaScript features, culminating in the\nfirst browser-based side-channel attack which is constructed entirely from\nCascading Style Sheets (CSS) and HTML, and works even when script execution is\ncompletely blocked. We then show that avoiding JavaScript features makes our\ntechniques architecturally agnostic, resulting in microarchitectural website\nfingerprinting attacks that work across hardware platforms including Intel\nCore, AMD Ryzen, Samsung Exynos, and Apple M1 architectures. As a final\ncontribution, we evaluate our techniques in hardened browser environments\nincluding the Tor browser, Deter-Fox (Cao el al., CCS 2017), and Chrome Zero\n(Schwartz et al., NDSS 2018). We confirm that none of these approaches\ncompletely defend against our attacks. We further argue that the protections of\nChrome Zero need to be more comprehensively applied, and that the performance\nand user experience of Chrome Zero will be severely degraded if this approach\nis taken.\n"}
+{"abstract": "  Current open-domain question answering systems often follow a\nRetriever-Reader architecture, where the retriever first retrieves relevant\npassages and the reader then reads the retrieved passages to form an answer. In\nthis paper, we propose a simple and effective passage reranking method, named\nReader-guIDEd Reranker (RIDER), which does not involve training and reranks the\nretrieved passages solely based on the top predictions of the reader before\nreranking. We show that RIDER, despite its simplicity, achieves 10 to 20\nabsolute gains in top-1 retrieval accuracy and 1 to 4 Exact Match (EM) gains\nwithout refining the retriever or reader. In addition, RIDER, without any\ntraining, outperforms state-of-the-art transformer-based supervised rerankers.\nRemarkably, RIDER achieves 48.3 EM on the Natural Questions dataset and 66.4 EM\non the TriviaQA dataset when only 1,024 tokens (7.8 passages on average) are\nused as the reader input after passage reranking.\n"}
+{"abstract": "  A metal can be driven to an insulating phase through distinct mechanisms. A\npossible way is via the Coulomb interaction, which then defines the Mott\nmetal-insulator transition (MIT). Another possibility is the MIT driven by\ndisorder, the so-called Anderson MIT. Here we analyze interacting particles in\ndisordered Hubbard chains $-$ thus comprising the Mott-Anderson physics $-$ by\ninvestigating the ground-state entanglement with density functional theory. The\nlocalization signature on entanglement is found to be a local minimum at a\ncertain critical density. Individually, the Mott (Anderson) MIT has a single\ncritical density whose minimum entanglement decreases as the interaction\n(disorder) enhances. While in the Mott MIT entanglement saturates at finite\nvalues, characterizing partial localization, in the Anderson MIT the system\nreaches full localization, with zero entanglement, for sufficiently strong\ndisorder. In the combined Mott-Anderson MIT, we find three critical densities\nreferring to local minima on entanglement. One of them is the same as for the\nAnderson MIT, but now the presence of interaction requires a stronger disorder\npotential to induce localization. A second critical density is related to the\nMott MIT, but due to disorder it is displaced by a factor proportional to the\nconcentration of impurities. The third local minimum on entanglement is unique\nto the concomitant presence of disorder and interaction, found to be related to\nan effective density phenomenon, thus referred to as a Mott-like MIT. Since\nentanglement has been intrinsically connected to the magnetic susceptibility\n$-$ a quantity promptly available in cold atoms experiments $-$ our detailed\nnumerical description might be useful for the experimental investigation of\nMott-Anderson MIT.\n"}
+{"abstract": "  Today's world is a globalized and connected one, where people are\nincreasingly moving around and interacting with a greater number of services\nand devices of all kinds, including those that allow them to monitor their\nhealth. However, each company, institution or health system usually store its\npatients' data in an isolated way. Although this approach can have some\nbenefits related with privacy, security, etc., it also implies that each one of\nthem generates different, incomplete and possibly contradictory views of a\npatient's health data, losing part of the value that this information could\nbring to the patient. That is the reason why researchers from all over the\nworld are determined to replace the current institution-centered health systems\nwith new patient-centered ones. In these new systems, all the health\ninformation of a patient is integrated into a unique global vision. However,\nsome questions are still unanswered. Specifically, who should store and\nmaintain the information of a given patient and how should this information be\nmade available for other systems. To address this situation, this work proposes\na new solution towards making the Personal Health Trajectory of patients\navailable for both, the patients themselves and health institutions. By using\nthe concept of blockchains' federation and web services access to the global\nvision of a person health can be granted to existing and new solutions. To\ndemonstrate the viability of the proposal, an implementation is provided\nalongside the obtained results in a potential scenario.\n"}
+{"abstract": "  This paper proposes a novel framework for lung sound event detection,\nsegmenting continuous lung sound recordings into discrete events and performing\nrecognition on each event. Exploiting the lightweight nature of Temporal\nConvolution Networks (TCNs) and their superior results compared to their\nrecurrent counterparts, we propose a lightweight, yet robust, and completely\ninterpretable framework for lung sound event detection. We propose the use of a\nmulti-branch TCN architecture and exploit a novel fusion strategy to combine\nthe resultant features from these branches. This not only allows the network to\nretain the most salient information across different temporal granularities and\ndisregards irrelevant information, but also allows our network to process\nrecordings of arbitrary length. Results: The proposed method is evaluated on\nmultiple public and in-house benchmarks of irregular and noisy recordings of\nthe respiratory auscultation process for the identification of numerous\nauscultation events including inhalation, exhalation, crackles, wheeze,\nstridor, and rhonchi. We exceed the state-of-the-art results in all\nevaluations. Furthermore, we empirically analyse the effect of the proposed\nmulti-branch TCN architecture and the feature fusion strategy and provide\nquantitative and qualitative evaluations to illustrate their efficiency.\nMoreover, we provide an end-to-end model interpretation pipeline that\ninterprets the operations of all the components of the proposed framework. Our\nanalysis of different feature fusion strategies shows that the proposed feature\nconcatenation method leads to better suppression of non-informative features,\nwhich drastically reduces the classifier overhead resulting in a robust\nlightweight network.The lightweight nature of our model allows it to be\ndeployed in end-user devices such as smartphones, and it has the ability to\ngenerate predictions in real-time.\n"}
+{"abstract": "  In the last decade, deep neural networks have proven to be very powerful in\ncomputer vision tasks, starting a revolution in the computer vision and machine\nlearning fields. However, deep neural networks, usually, are not robust to\nperturbations of the input data. In fact, several studies showed that slightly\nchanging the content of the images can cause a dramatic decrease in the\naccuracy of the attacked neural network. Several methods able to generate\nadversarial samples make use of gradients, which usually are not available to\nan attacker in real-world scenarios. As opposed to this class of attacks,\nanother class of adversarial attacks, called black-box adversarial attacks,\nemerged, which does not make use of information on the gradients, being more\nsuitable for real-world attack scenarios. In this work, we compare three\nwell-known evolution strategies on the generation of black-box adversarial\nattacks for image classification tasks. While our results show that the\nattacked neural networks can be, in most cases, easily fooled by all the\nalgorithms under comparison, they also show that some black-box optimization\nalgorithms may be better in \"harder\" setups, both in terms of attack success\nrate and efficiency (i.e., number of queries).\n"}
+{"abstract": "  The rapid spread of the novel corona virus, SARS-CoV-2, has prompted an\nunprecedented response from governments across the world. A third of the world\npopulation have been placed in varying degrees of lockdown, and the Internet\nhas become the primary medium for conducting most businesses and schooling\nactivities. This paper aims to provide a multi-prospective account of Internet\nperformance during the first wave of the pandemic. We investigate the\nperformance of the Internet control plane and data plane from a number of\nglobally spread vantage points. We also look closer at two case studies. First,\nwe look at growth in video traffic during the pandemic, using traffic logs from\na global video conferencing provider. Second, we leverage a country-wide\ndeployment of measurement probes to assess the performance of mobile networks\nduring the outbreak. We find that the lockdown has visibly impacted almost all\naspects of Internet performance. Access networks have experienced an increase\nin peak and off-peak end to end latency. Mobile networks exhibit significant\nchanges in download speed, while certain types of video traffic has increased\nby an order of magnitude. Despite these changes, the Internet seems to have\ncoped reasonably well with the lockdown traffic.\n"}
+{"abstract": "  For an integer $k\\ge 3$, a $k$-path vertex cover of a graph $G=(V,E)$ is a\nset $T\\subseteq V$ that shares a vertex with every path subgraph of order $k$\nin $G$. The minimum cardinality of a $k$-path vertex cover is denoted by\n$\\psi_k(G)$. We give estimates -- mostly upper bounds -- on $\\psi_k(G)$ in\nterms of various parameters, including vertex degrees and the number of\nvertices and edges. The problem is also considered on chordal graphs and planar\ngraphs.\n"}
+{"abstract": "  High-dimensional limit theorems have been shown to be useful to derive tuning\nrules for finding the optimal scaling in random walk Metropolis algorithms. The\nassumptions under which weak convergence results are proved are however\nrestrictive; the target density is typically assumed to be of a product form.\nUsers may thus doubt the validity of such tuning rules in practical\napplications. In this paper, we shed some light on optimal scaling problems\nfrom a different perspective, namely a large-sample one. This allows to prove\nweak convergence results under realistic assumptions and to propose novel\nparameter-dimension-dependent tuning guidelines. The proposed guidelines are\nconsistent with previous ones when the target density is close to having a\nproduct form, but significantly different otherwise.\n"}
+{"abstract": "  The drone-based last-mile delivery is an emerging technology to deliver\nparcels using drones loaded on a truck. As more and more autonomous vehicles\n(AVs) will be available for delivery services, an opportunity is arising to\nfully automate drone-based last-mile delivery. In this paper, we integrate AVs\nwith drone-based last-mile delivery aiming to fully automate the last-mile\ndelivery process. We define a new problem called the autonomous vehicle routing\nproblem with drones (A-VRPD). A-VRPD is to select AVs from a pool of available\nAVs and to schedule them to serve customers with an objective of minimizing the\ntotal operational cost. We formulate A-VRPD as an Integer Linear Programming\n(ILP) and propose a greedy algorithm to solve the problem based on real-world\noperational costs for different types of AVs, traveling distances calculated\nconsidering the current traffic conditions, and varying load capacities of AVs.\nExtensive simulations performed under various random delivery scenarios\ndemonstrate that the proposed algorithm effectively increases profits for both\nthe delivery company and AV owners compared with traditional VRP-D (and TSP-D)\nalgorithm-based approaches.\n"}
+{"abstract": "  This paper presents two modifications for Loidreau's code-based cryptosystem.\nLoidreau's cryptosystem is a rank metric code-based cryptosystem constructed by\nusing Gabidulin codes in the McEliece setting. Recently a polynomial-time key\nrecovery attack was proposed to break Loidreau's cryptosystem in some cases. To\nprevent this attack, we propose the use of subcodes to disguise the secret\ncodes in Modification \\Rmnum{1}. In Modification \\Rmnum{2}, we choose a random\nmatrix of low column rank over $\\mathbb{F}_q$ to mix with the secret matrix.\nAccording to our analysis, these two modifications can both resist the existing\nstructural attacks. Additionally, we adopt the systematic generator matrix of\nthe public code to make a reduction in the public-key size. In additon to\nstronger resistance against structural attacks and more compact representation\nof public keys, our modifications also have larger information transmission\nrates.\n"}
+{"abstract": "  Built upon a sample of 134 quasars that was dedicated to a systematic study\nof \\mgii-BAL variability from Yi et al. (2019a), we investigate these quasars\nshowing \\mgii-BAL disappearance or emergence with the aid of at least three\nepoch optical spectra sampled more than 15 yr in the observed frame. We\nidentified 3/3 quasars undergoing pristine/tentative BAL transformations. The\nincidence of pristine BAL transformations in the sample is therefore derived to\nbe 2.2$_{-1.2}^{+2.2}$\\%, consistent with that of high-ionization BAL\ntransformations from the literature. Adopting an average \\mgii-BAL\ndisappearance timescale of rest-frame 6.89 yr among the six quasars, the\naverage characteristic lifetime of \\mgii\\ BALs in the sample is constrained to\nbe $>$160 yr along our line of sight. There is a diversity of BAL-profile\nvariability observed in the six quasars, probably reflecting a variety of\nmechanisms at work. Our investigations of \\mgii-BAL transitions, combined with\nobservational studies of BAL transitions from the literature, imply an overall\nFeLoBAL/LoBAL$\\rightarrow$HiBAL/non-BAL transformation sequence along with a\ndecrease in reddening. This sequence is consistent with the evacuation models\nfor the origin of commonly seen blue quasars, in which LoBAL quasars are in a\nshorted-lived, blowout phase.\n"}
+{"abstract": "  Trust is essential for sustaining cooperation among humans. The same\nprinciple applies during interaction with computers and robots: if we do not\ntrust them, we will not accept help from them. Extensive evidence has shown\nthat our trust in other agents depends on their performance. However, in\nuncertain environments, humans may not be able to estimate correctly other\nagents' performance, potentially leading to distrust or over-trust in peers and\nmachines. In the current study, we investigate whether humans' trust towards\npeers, computers and robots is biased by prior beliefs in uncertain interactive\nsettings. Participants made perceptual judgments and observed the simulated\nestimates of either a human participant, a computer or a social robot.\nParticipants could modify their judgments based on this feedback. Results show\nthat participants' belief about the nature of the interacting partner biased\ntheir compliance with the partners' judgments, although the partners' judgments\nwere identical. Surprisingly, the social robot was trusted more than the\ncomputer and the human partner. Trust in the alleged human partner was not\nfully predicted by its perceived performance, suggesting the emergence of\nnormative processes in peer interaction. Our findings offer novel insights in\nthe understanding of the mechanisms underlying trust towards peers and\nautonomous agents.\n"}
+{"abstract": "  Entropic causal inference is a framework for inferring the causal direction\nbetween two categorical variables from observational data. The central\nassumption is that the amount of unobserved randomness in the system is not too\nlarge. This unobserved randomness is measured by the entropy of the exogenous\nvariable in the underlying structural causal model, which governs the causal\nrelation between the observed variables. Kocaoglu et al. conjectured that the\ncausal direction is identifiable when the entropy of the exogenous variable is\nnot too large. In this paper, we prove a variant of their conjecture. Namely,\nwe show that for almost all causal models where the exogenous variable has\nentropy that does not scale with the number of states of the observed\nvariables, the causal direction is identifiable from observational data. We\nalso consider the minimum entropy coupling-based algorithmic approach presented\nby Kocaoglu et al., and for the first time demonstrate algorithmic\nidentifiability guarantees using a finite number of samples. We conduct\nextensive experiments to evaluate the robustness of the method to relaxing some\nof the assumptions in our theory and demonstrate that both the constant-entropy\nexogenous variable and the no latent confounder assumptions can be relaxed in\npractice. We also empirically characterize the number of observational samples\nneeded for causal identification. Finally, we apply the algorithm on Tuebingen\ncause-effect pairs dataset.\n"}
+{"abstract": "  The purpose of this paper is to provide answers to some questions raised in a\npaper by Kaneko and Koike about the modularity of the solutions of a\ndifferential equations of hypergeometric type. In particular, we provide a\nnumber-theoretic explanation of why the modularity of the solutions occurs in\nsome cases and does not occur in other cases. This also proves their conjecture\non the completeness of the list of modular solutions after adding some missing\ncases.\n"}
+{"abstract": "  The possibility to detect circumbinary planets and to study stellar magnetic\nfields through binary stars has sparked an increase in the research activity in\nthis area. In this paper we revisit the connection between stellar magnetic\nfields and the gravitational quadrupole moment $Q_{xx}$. We present three\nmagnetohydrodynamical simulations of solar mass stars with rotation periods of\n8.3, 1.2, and 0.8 days and perform a detailed analysis of the magnetic and\ndensity fields using a spherical harmonic decomposition. The extrema of\n$Q_{xx}$ are associated with changes of the magnetic field structure. This is\nevident in the simulation with a rotation period of 1.2 days. Its magnetic\nfield has a much more complex behaviour than other models as the large-scale\nnon-axisymmetric field dominates throughout the simulation and the axisymmetric\ncomponent is predominantly hemispheric. This triggers variations in the density\nfield that follow the magnetic field asymmetry with respect to the equator,\nchanging the $zz$ component of the inertia tensor, and thus modulating\n$Q_{xx}$. The magnetic fields of the other two runs are less variable in time\nand more symmetric with respect to the equator such that there are no large\nvariations in the density, therefore only small variations in $Q_{xx}$ are\nseen. If interpreted via the classical Applegate mechanism (tidal locking), the\nquadrupole moment variations obtained in the simulations are about two orders\nof magnitude below the observed values. However, if no tidal locking is\nassumed, our results are compatible with the observed eclipsing time\nvariations.\n"}
+{"abstract": "  Food image segmentation is a critical and indispensible task for developing\nhealth-related applications such as estimating food calories and nutrients.\nExisting food image segmentation models are underperforming due to two reasons:\n(1) there is a lack of high quality food image datasets with fine-grained\ningredient labels and pixel-wise location masks -- the existing datasets either\ncarry coarse ingredient labels or are small in size; and (2) the complex\nappearance of food makes it difficult to localize and recognize ingredients in\nfood images, e.g., the ingredients may overlap one another in the same image,\nand the identical ingredient may appear distinctly in different food images. In\nthis work, we build a new food image dataset FoodSeg103 (and its extension\nFoodSeg154) containing 9,490 images. We annotate these images with 154\ningredient classes and each image has an average of 6 ingredient labels and\npixel-wise masks. In addition, we propose a multi-modality pre-training\napproach called ReLeM that explicitly equips a segmentation model with rich and\nsemantic food knowledge. In experiments, we use three popular semantic\nsegmentation methods (i.e., Dilated Convolution based, Feature Pyramid based,\nand Vision Transformer based) as baselines, and evaluate them as well as ReLeM\non our new datasets. We believe that the FoodSeg103 (and its extension\nFoodSeg154) and the pre-trained models using ReLeM can serve as a benchmark to\nfacilitate future works on fine-grained food image understanding. We make all\nthese datasets and methods public at\n\\url{https://xiongweiwu.github.io/foodseg103.html}.\n"}
+{"abstract": "  Tumours behave as moving targets that can evade chemotherapeutic treatments\nby rapidly acquiring resistance via various mechanisms. In Balaz et al. (2021,\nBiosystems; 199:104290) we initiated the development of the agent-based\nopen-ended evolutionary simulator of novel drug delivery systems (DDS). It is\nan agent-based simulator where evolvable agents can change their perception of\nthe environment and thus adapt to tumour mutations. Here we mapped the\nparameters of evolvable agent properties to the realistic biochemical\nboundaries and test their efficacy by simulating their behaviour at the cell\nscale using the stochastic simulator, STEPS. We show that the shape of the\nparameter space evolved in our simulator is comparable to those obtained by the\nrational design.\n"}
+{"abstract": "  Real-time estimation of actual environment depth is an essential module for\nvarious autonomous system tasks such as localization, obstacle detection and\npose estimation. During the last decade of machine learning, extensive\ndeployment of deep learning methods to computer vision tasks yielded successful\napproaches for realistic depth synthesis out of a simple RGB modality. While\nmost of these models rest on paired depth data or availability of video\nsequences and stereo images, there is a lack of methods facing single-image\ndepth synthesis in an unsupervised manner. Therefore, in this study, latest\nadvancements in the field of generative neural networks are leveraged to fully\nunsupervised single-image depth synthesis. To be more exact, two\ncycle-consistent generators for RGB-to-depth and depth-to-RGB transfer are\nimplemented and simultaneously optimized using the Wasserstein-1 distance. To\nensure plausibility of the proposed method, we apply the models to a self\nacquised industrial data set as well as to the renown NYU Depth v2 data set,\nwhich allows comparison with existing approaches. The observed success in this\nstudy suggests high potential for unpaired single-image depth estimation in\nreal world applications.\n"}
+{"abstract": "  Adverse drug events (ADEs) are unexpected incidents caused by the\nadministration of a drug or medication. To identify and extract these events,\nwe require information about not just the drug itself but attributes describing\nthe drug (e.g., strength, dosage), the reason why the drug was initially\nprescribed, and any adverse reaction to the drug. This paper explores the\nrelationship between a drug and its associated attributes using relation\nextraction techniques. We explore three approaches: a rule-based approach, a\ndeep learning-based approach, and a contextualized language model-based\napproach. We evaluate our system on the n2c2-2018 ADE extraction dataset. Our\nexperimental results demonstrate that the contextualized language model-based\napproach outperformed other models overall and obtain the state-of-the-art\nperformance in ADE extraction with a Precision of 0.93, Recall of 0.96, and an\n$F_1$ score of 0.94; however, for certain relation types, the rule-based\napproach obtained a higher Precision and Recall than either learning approach.\n"}
+{"abstract": "  Homomorphic Encryption (HE), allowing computations on encrypted data\n(ciphertext) without decrypting it first, enables secure but prohibitively slow\nNeural Network (HENN) inference for privacy-preserving applications in clouds.\nTo reduce HENN inference latency, one approach is to pack multiple messages\ninto a single ciphertext in order to reduce the number of ciphertexts and\nsupport massive parallelism of Homomorphic Multiply-Add (HMA) operations\nbetween ciphertexts. However, different ciphertext packing schemes have to be\ndesigned for different convolution layers and each of them introduces overheads\nthat are far more expensive than HMA operations. In this paper, we propose a\nlow-rank factorization method called FFConv to unify convolution and ciphertext\npacking. To our knowledge, FFConv is the first work that is capable of\naccelerating the overheads induced by different ciphertext packing schemes\nsimultaneously, without incurring a significant increase in noise budget.\nCompared to prior art LoLa and Falcon, our method reduces the inference latency\nby up to 87% and 12%, respectively, with comparable accuracy on MNIST and\nCIFAR-10.\n"}
+{"abstract": "  This paper adds to the fundamental body of work on benchmarking the\nrobustness of deep learning (DL) classifiers. We innovate a new benchmarking\nmethodology to evaluate robustness of DL classifiers. Also, we introduce a new\nfour-quadrant statistical visualization tool, including minimum accuracy,\nmaximum accuracy, mean accuracy, and coefficient of variation, for benchmarking\nrobustness of DL classifiers. To measure robust DL classifiers, we created a\ncomprehensive 69 benchmarking image set, including a clean set, sets with\nsingle factor perturbations, and sets with two-factor perturbation conditions.\nAfter collecting experimental results, we first report that using two-factor\nperturbed images improves both robustness and accuracy of DL classifiers. The\ntwo-factor perturbation includes (1) two digital perturbations (salt & pepper\nnoise and Gaussian noise) applied in both sequences, and (2) one digital\nperturbation (salt & pepper noise) and a geometric perturbation (rotation)\napplied in both sequences. All source codes, related image sets, and\npreliminary data, figures are shared on a GitHub website to support future\nacademic research and industry projects. The web resources locate at\nhttps://github.com/caperock/robustai\n"}
+{"abstract": "  This paper theoretically analyzes cable network disconnection due to randomly\noccurring natural disasters, where the disaster-endurance (DE) levels of the\nnetwork are determined by a network entity such as the type of shielding method\nused for a duct containing cables. The network operator can determine which\nparts have a high DE level. When a part of a network can be protected, the\nplacement of that part can be specified to decrease the probability of\ndisconnecting two given nodes.\n  The maximum lower bound of the probability of connecting two given nodes is\nexplicitly derived. Conditions decreasing (not decreasing) the probability of\nconnecting two given nodes with a partially protected network are provided.\n"}
+{"abstract": "  Helium implantation in surfaces is of interest for plasma facing materials\nand other nuclear applications. Vanadium as both a representative bcc material\nand a material relevant for fusion applications is implanted using a Helium ion\nbeam microscope, and the resulting swelling and nanomechanical properties are\nquantified. These values are put in correlation to data obtained from micro\nresidual stress measurements using a focused ion beam based ring-core\ntechnique. We found that the swelling measured is similar to literature values.\nFurther we are able to measure the surface stress caused by the implantation\nand find it approaches the yield strength of the material at blistering doses.\nThe simple calculations performed in the present work, along with several\ngeometrical considerations deduced from experimental results confirm the\ndriving force for blister formation comes from bulging resulting mainly from\ngas pressure buildup, rather than solely stress induced buckling.\n"}
+{"abstract": "  This paper addresses finite-time horizon optimal control of single-loop\nnetworked control systems with stochastically modeled communication channel and\ndisturbances. To cope with the uncertainties, an optimization-based control\nscheme is proposed which uses a disturbance feedback and the age of information\nas central aspects. The disturbance feedback is an extension of the control law\nused for balanced stochastic optimal control previously proposed for control\nsystems without network. Balanced optimality is understood as a compromise\nbetween minimizing of expected deviations from the reference and minimization\nof the uncertainty of future states. Time-varying state constraints as well as\ntime-invariant input constraints are considered, and the controllers are\nsynthesized by semi-definite programs.\n"}
+{"abstract": "  Aims: LS 5039 is an enigmatic high-mass gamma-ray binary which hosts a\npowerful O6.5V companion, but the nature of the compact object is still to be\nestablished using multi-wavelength observations. Methods: We analyzed\nphase-resolved multi-instrument spectra of nonthermal emission from LS 5039 in\norder to produce reliable spectral models, which can be further employed to\nselect between various scenarios and theoretical models of the binary. Results:\nThe combined phase-resolved hard X-ray and MeV-range gamma-ray spectra obtained\nwith XMM-Newton, Suzaku, NuSTAR, INTEGRAL, and COMPTEL indicate a meaningful\nspectral hardening above 50~keV. The spectral break observed in both major\nphases of the binary may indicate the presence of a hardening in the spectrum\nof accelerated leptons which could originate from the interaction of wind from\nthe O6.5V companion star with the relativistic outflow from a yet unidentified\ncompact object.\n"}
+{"abstract": "  We consider the standard first passage percolation model in the rescaled\nlattice $\\mathbb Z^d/n$ for $d\\geq 2$ and a bounded domain $\\Omega$ in $\\mathbb\nR^d$. We denote by $\\Gamma^1$ and $\\Gamma^2$ two disjoint subsets of $\\partial\n\\Omega$ representing respectively the sources and the sinks, \\textit{i.e.},\nwhere the water can enter in $\\Omega$ and escape from $\\Omega$. A cutset is a\nset of edges that separates $\\Gamma ^1$ from $\\Gamma^2$ in $\\Omega$, it has a\ncapacity given by the sum of the capacities of its edges. Under some\nassumptions on $\\Omega$ and the distribution of the capacities of the edges, we\nalready know a law of large numbers for the sequence of minimal cutsets\n$(\\mathcal E_n^{min})_{n\\geq 1}$: the sequence $(\\mathcal E_n^{min})_{n\\geq 1}$\nconverges almost surely to the set of solutions of a continuous deterministic\nproblem of minimal cutset in an anisotropic network. We aim here to derive a\nlarge deviation principle for cutsets and deduce by contraction principle a\nlower large deviation principle for the maximal flow in $\\Omega$.\n"}
+{"abstract": "  We consider vacuum metrics admitting conformal compactification which is\nsmooth up to the scri $\\mathscr{I^+}$. We write metric in the Bondi-Sachs form\nand expand it into power series in the inverse affine distance $1/r$. Like in\nthe case of the luminosity distance, given the news tensor and initial data for\na part of metric the Einstein equations define coefficients of the series in a\nrecursive way. This is also true in the stationary case however now the news\ntensor vanishes and the role of initial data is taken by multipole moments. We\nfind an approximate form of metric and show that for nonvanishing mass it tends\nto the Kerr metric as it is the case at spacelike inifinity.\n"}
+{"abstract": "  This paper presents an experimental adaptation of a non-collaborative robot\narm to collaborate with the environment, as one step towards adapting legacy\nrobotic machinery to fit in industry 4.0 requirements. A cloud-based internet\nof things (CIoT) service is employed to connect, supervise and control a\nrobotic arm's motion using the added wireless sensing devices to the\nenvironment. A programmable automation controller (PAC) unit, connected to the\nrobot arm receives the most recent changes and updates the motion of the robot\narm. The experimental results show that the proposed non-expensive service is\ntractable and adaptable to higher level for machine to machine collaboration.\nThe proposed approach in this paper has industrial and educational\napplications. In the proposed approach, the CIoT technology is added as a\ntechnology interface between the sensors to the environment and the robotic\narm. The proposed approach is versatile and fits to variety of applications to\nmeet the flexible requirements of industry 4.0. The proposed approach has been\nimplemented in an experiment using MECA 500 robot arm and AMAX 5580\nprogrammable automation controller and ultrasonic proximity wireless sensor.\n"}
+{"abstract": "  Software bots are used to streamline tasks in Open Source Software (OSS)\nprojects' pull requests, saving development cost, time, and effort. However,\ntheir presence can be disruptive to the community. We identified several\nchallenges caused by bots in pull request interactions by interviewing 21\npractitioners, including project maintainers, contributors, and bot developers.\nIn particular, our findings indicate noise as a recurrent and central problem.\nNoise affects both human communication and development workflow by overwhelming\nand distracting developers. Our main contribution is a theory of how human\ndevelopers perceive annoying bot behaviors as noise on social coding platforms.\nThis contribution may help practitioners understand the effects of adopting a\nbot, and researchers and tool designers may leverage our results to better\nsupport human-bot interaction on social coding platforms.\n"}
+{"abstract": "  In this paper we consider the Lagrangian Averaged Navier-Stokes Equations,\nalso known as, LANS-$\\alpha$ Navier-Stokes model on the two dimensional torus.\nWe assume that the noise is a cylindrical Wiener process and its coefficient is\nmultiplied by $\\sqrt{\\alpha}$. We then study through the lenses of the large\nand moderate deviations principle the behaviour of the trajectories of the\nsolutions of the stochastic system as $\\alpha$ goes to 0. Instead of giving two\nseparate proofs of the two deviations principles we present a unifying approach\nto the proof of the LDP and MDP and express the rate function in term of the\nunique solution of the Navier-Stokes equations. Our proof is based on the weak\nconvergence approach to large deviations principle. As a by-product of our\nanalysis we also prove that the solutions of the stochastic LANS-$\\alpha$ model\nconverge in probability to the solutions of the deterministic Navier-Stokes\nequations.\n"}
+{"abstract": "  Reinforcement learning (RL) has been demonstrated suitable to develop agents\nthat play complex games with human-level performance. However, it is not\nunderstood how to effectively use RL to perform cybersecurity tasks. To develop\nsuch understanding, it is necessary to develop RL agents using simulation and\nemulation systems allowing researchers to model a broad class of realistic\nthreats and network conditions. Demonstrating that a specific RL algorithm can\nbe effective for defending a network under certain conditions may not\nnecessarily give insight about the performance of the algorithm when the\nthreats, network conditions, and security goals change. This paper introduces a\nnovel approach for network environment design and a software framework to\naddress the fundamental problem that network defense cannot be defined as a\nsingle game with a simple set of fixed rules. We show how our approach is\nnecessary to facilitate the development of RL network defenders that are robust\nagainst attacks aimed at the agent's learning. Our framework enables the\ndevelopment and simulation of adversaries with sophisticated behavior that\nincludes poisoning and evasion attacks on RL network defenders.\n"}
+{"abstract": "  The influence of high-enthalpy effects on hypersonic turbulent boundary\nlayers is investigated by means of direct numerical simulations (DNS). A\nquasi-adiabatic flat-plate air flow at free-stream Mach number equal to 10 is\nsimulated up to fully-developed turbulent conditions using a five-species,\nchemically-reacting model. A companion DNS based on a frozen-chemistry\nassumption is also carried out, in order to isolate the effect of finite-rate\nchemical reactions and assess their influence on turbulent quantities. In order\nto reduce uncertainties associated with turbulence generation at the inlet of\nthe computational domain, both simulations are initiated in the laminar flow\nregion and the flow is let to evolve up to the fully turbulent regime. Modal\nforcing by means of localized suction and blowing is used to trigger\nlaminar-to-turbulent transition. The high temperatures reached in the near wall\nregion including the viscous and buffer sublayers activate significant\ndissociation of both oxygen and nitrogen. This modifies in turn the\nthermodynamic and transport properties of the reacting mixture, affecting the\nfirst-order statistics of thermodynamic quantities. Due to the endothermic\nnature of the chemical reactions in the forward direction, temperature and\ndensity fluctuations in the reacting layer are smaller than in the\nfrozen-chemistry flow. However, the first- and second-order statistics of the\nvelocity field are found to be little affected by the chemical reactions under\na scaling that accounts for the modified fluid properties. We also observed\nthat the Strong Reynolds Analogy (SRA) remains well respected despite the\nsevere hypersonic conditions and that the computed skin friction coefficient\ndistributions match well the results of the Renard-Deck decomposition extended\nto compressible flows.\n"}
+{"abstract": "  The International Virtual Observatory Alliance (IVOA) has developed and\nbuilt, in the last two decades, an ecosystem of distributed resources,\ninteroperable and based upon open shared technological standards. In doing so\nthe IVOA has anticipated, putting into practice for the astrophysical domain,\nthe ideas of FAIR-ness of data and service resources and the Open-ness of\nsharing scientific results, leveraging on the underlying open standards\nrequired to fill the above. In Europe, efforts in supporting and developing the\necosystem proposed by the IVOA specifications has been provided by a continuous\nset of EU funded projects up to current H2020 ESCAPE ESFRI cluster. In the\nmeantime, in the last years, Europe has realised the importance of promoting\nthe Open Science approach for the research communities and started the European\nOpen Science Cloud (EOSC) project to create a distributed environment for\nresearch data, services and communities. In this framework the European VO\ncommunity, had to face the move from the interoperability scenario in the\nastrophysics domain into a larger audience perspective that includes a\ncross-domain FAIR approach. Within the ESCAPE project the CEVO Work Package\n(Connecting ESFRI to EOSC through the VO) has one task to deal with this\nintegration challenge: a challenge where an existing, mature, distributed\ne-infrastructure has to be matched to a forming, more general architecture.\nCEVO started its works in the first months of 2019 and has already worked on\nthe integration of the VO Registry into the EOSC e-infrastructure. This\ncontribution reports on the first year and a half of integration activities,\nthat involve applications, services and resources being aware of the VO\nscenario and compatible with the EOSC architecture.\n"}
+{"abstract": "  Since the introduction of the CDC 6600 in 1965 and its `scoreboarding'\ntechnique processors have not (necessarily) executed instructions in program\norder. Programmers of high-level code may sequence independent instructions in\narbitrary order, and it is a matter of significant programming abstraction and\ncomputational efficiency that the processor can be relied upon to make sensible\nparallelizations/reorderings of low-level instructions to take advantage of,\neg., multiple ALUs. At the architectural level such reordering is typically\nimplemented via a per-processor pipeline, into which instructions are fetched\nin order, but possibly committed out of order depending on local\nconsiderations, provided any reordering preserves sequential semantics from\nthat processor's perspective. However multicore architectures, where several\npipelines run in parallel, can expose these processor-level reorderings as\nunexpected, or `weak', behaviours. Such weak behaviours are hard to reason\nabout, and (via speculative execution) underlie at least one class of\nwidespread security vulnerability.\n  In this paper we introduce a novel program operator, \\emph{parallelized\nsequential composition}, which can be instantiated with a function that\ncontrols the reordering of atomic instructions. It generalises both sequential\nand parallel composition, and when appropriately instantiated exhibits many of\nthe weak behaviours of well-known hardware weak memory models. Our framework\nadmits the application of established compositional techniques (eg.\nOwicki-Gries) for reasoning about weak behaviours, and is convenient for\nabstractly expressing properties from the literature. The semantics and theory\nis encoded and verified in a theorem prover, and we give an implementation of\nthe pipeline semantics which we use to empirically show conformance against\nestablished models of ARM and RISC-V.\n"}
+{"abstract": "  Disorder in Weyl semimetals and superconductors is surprisingly subtle,\nattracting attention and competing theories in recent years. In this brief\nreview, we discuss the current theoretical understanding of the effects of\nshort-ranged, quenched disorder on the low energy-properties of\nthree-dimensional, topological Weyl semimetals and superconductors. We focus on\nthe role of non-perturbative rare region effects on destabilizing the semimetal\nphase and rounding the expected semimetal-to-diffusive metal transition into a\ncross over. Furthermore, the consequences of disorder on the resulting nature\nof excitations, transport, and topology are reviewed. New results on a\nbipartite random hopping model are presented that confirm previous results in a\n$p+ip$ Weyl superconductor, demonstrating that particle-hole symmetry is\ninsufficient to help stabilize the Weyl semimetal phase in the presence of\ndisorder. The nature of the avoided transition in a model for a single Weyl\ncone in the continuum is discussed. We close with a discussion of open\nquestions and future directions.\n"}
+{"abstract": "  This paper is devoted to the theoretical and numerical investigation of an\naugmented Lagrangian method for the solution of optimization problems with\ngeometric constraints. Specifically, we study situations where parts of the\nconstraints are nonconvex and possibly complicated, but allow for a fast\ncomputation of projections onto this nonconvex set. Typical problem classes\nwhich satisfy this requirement are optimization problems with disjunctive\nconstraints (like complementarity or cardinality constraints) as well as\noptimization problems over sets of matrices which have to satisfy additional\nrank constraints. The key idea behind our method is to keep these complicated\nconstraints explicitly in the constraints and to penalize only the remaining\nconstraints by an augmented Lagrangian function. The resulting subproblems are\nthen solved with the aid of a problem-tailored nonmonotone projected gradient\nmethod. The corresponding convergence theory allows for an inexact solution of\nthese subproblems. Nevertheless, the overall algorithm computes so-called\nMordukhovich-stationary points of the original problem under a mild asymptotic\nregularity condition, which is generally weaker than most of the respective\navailable problem-tailored constraint qualifications. Extensive numerical\nexperiments addressing complementarity- and cardinality-constrained\noptimization problems as well as a semidefinite reformulation of MAXCUT\nproblems visualize the power of our approach.\n"}
+{"abstract": "  Wet-chemical syntheses for quasi two-dimensional (2D) transition metal\ndichalcogenides (TMDs) have emerged as promising methods for straightforward\nsolution-processing of these materials. However, photoluminescence properties\nof colloidal TMDs are virtually unexplored due to the typically non-emitting\nsynthesis products. In this work, we demonstrate room temperature\nmicro-photoluminescence of delicate ultrathin colloidal WS2 nanosheets\nsynthesized from WCl6 and elemental sulfur in oleic acid and oleylamine at 320\n{\\deg}C for the first time. Both, mono- and multilayer photoluminescence are\nobserved, revealing comparable characteristics to exfoliated TMD monolayers and\nunderpinning the high quality of colloidal WS2 nanosheets. In addition, a\npromising long-term air-stability of colloidal WS2 nanosheets is found and the\ncontrol of photodegradation of the structures under laser excitation is\nidentified as a challenge for further advancing nanosheet monolayers. Our\nresults render colloidal TMDs as easily synthesized and highly promising 2D\nsemiconductors with optical properties fully competitive with conventionally\nfabricated ultrathin TMDs.\n"}
+{"abstract": "  Visual localization is one of the most important components for robotics and\nautonomous driving. Recently, inspiring results have been shown with CNN-based\nmethods which provide a direct formulation to end-to-end regress 6-DoF absolute\npose. Additional information like geometric or semantic constraints is\ngenerally introduced to improve performance. Especially, the latter can\naggregate high-level semantic information into localization task, but it\nusually requires enormous manual annotations. To this end, we propose a novel\nauxiliary learning strategy for camera localization by introducing\nscene-specific high-level semantics from self-supervised representation\nlearning task. Viewed as a powerful proxy task, image colorization task is\nchosen as complementary task that outputs pixel-wise color version of grayscale\nphotograph without extra annotations. In our work, feature representations from\ncolorization network are embedded into localization network by design to\nproduce discriminative features for pose regression. Meanwhile an attention\nmechanism is introduced for the benefit of localization performance. Extensive\nexperiments show that our model significantly improve localization accuracy\nover state-of-the-arts on both indoor and outdoor datasets.\n"}
+{"abstract": "  The Marked Binary Branching Tree (MBBT) is the family tree of a rate one\nbinary branching process, on which points have been generated according to a\nrate one Poisson point process, with i.i.d. uniformly distributed activation\ntimes assigned to the points. In frozen percolation on the MBBT, initially, all\npoints are closed, but as time progresses points can become either frozen or\nopen. Points become open at their activation times provided they have not\nbecome frozen before. Open points connect the parts of the tree below and above\nit and one says that a point percolates if the tree above it is infinite. We\nconsider a version of frozen percolation on the MBBT in which at times of the\nform $\\theta^n$, all points that percolate are frozen. The limiting model for\n$\\theta \\to 1$, in which points freeze as soon as they percolate, has been\nstudied before by R\\'ath, Swart, and Terpai. We extend their results by showing\nthat there exists a $0<\\theta^\\ast<1$ such that the model is endogenous for\n$\\theta \\leq\\theta^\\ast$ but not for $\\theta>\\theta^\\ast$. This means that for\n$\\theta \\leq \\theta^\\ast$, frozen percolation is a.s. determined by the MBBT\nbut for $\\theta>\\theta^\\ast$ one needs additional randomness to describe it.\n"}
+{"abstract": "  Due to increasing railway use, the capacity at railway yards and maintenance\nlocations is becoming limiting. Therefore, the scheduling of rolling stock\nmaintenance and the choice regarding optimal locations to perform maintenance\nis increasingly complicated. This research introduces a Maintenance Scheduling\nand Location Choice Problem (MSLCP). It simultaneously determines maintenance\nlocations and maintenance schedules of rolling stock, while it also considers\nthe available capacity of maintenance locations, measured in the number of\navailable teams. To solve the MSLCP, an optimization framework based on\nLogic-Based Benders' Decomposition (LBBD) is proposed by combining two models,\nthe Maintenance Location Choice Problem (MLCP) and the Activity Planning\nProblem (APP), to assess the capacity of a MLCP solution. Within the LBBD, four\ncut generation procedures are introduced to improve the computational\nperformance: a naive procedure, two heuristic procedures and the so-called\nmin-cut procedure that aims to exploit the specific characteristics of the\nproblem at hand. The framework is demonstrated on a realistic scenarios from\nthe Dutch railways. It is shown that the best choice for cut generation\nprocedure depends on the objective: when aiming to find a good but not\nnecessarily optimal solution, the min-cut procedure performs best, whereas when\naiming for the optimal solution, one of the heuristic procedures is the\npreferred option. The techniques used in the current research are new to the\ncurrent field and offer interesting next research opportunities.\n"}
+{"abstract": "  The angular momentum of an electron is characterized well by pseudospin with\n$J=3/2$ in the presence of strong spin-orbit interactions. We study\ntheoretically the Josephson effect of superconductors in which such two $J=3/2$\nelectrons form a Cooper pair. Within even-parity symmetry class,\npseudospin-quintet pairing states with $J=2$ can exist as well as\npseudospin-singlet state with $J=0$. We focus especially on the Josephson\nselection rule among these even-parity superconductors. We find that the\nselection rule between quintet states is severer than that between spin-triplet\nstates formed by two $S=1/2$ electrons. The effects of a pseudospin-active\ninterface on the selection rule are discussed as well as those of odd-frequency\nCooper pairs generated by pseudospin dependent band structures.\n"}
+{"abstract": "  The $q$-Onsager algebra $O_q$ is defined by two generators and two relations,\ncalled the $q$-Dolan/Grady relations. We investigate the alternating central\nextension $\\mathcal O_q$ of $O_q$. The algebra $\\mathcal O_q$ was introduced by\nBaseilhac and Koizumi, who called it the current algebra of $O_q$. Recently\nBaseilhac and Shigechi gave a presentation of $\\mathcal O_q$ by generators and\nrelations. The presentation is attractive, but the multitude of generators and\nrelations makes the presentation unwieldy. In this paper we obtain a\npresentation of $\\mathcal O_q$ that involves a subset of the original set of\ngenerators and a very manageable set of relations. We call this presentation\nthe compact presentation of $\\mathcal O_q$. This presentation resembles the\ncompact presentation of the alternating central extension for the positive part\nof $U_q(\\widehat{\\mathfrak{sl}}_2)$.\n"}
+{"abstract": "  For a convex body $K$ in $\\mathbb{R}^n$, we introduce and study the extremal\ngeneral affine surface areas, defined by \\[ {\\rm\nIS}_{\\varphi}(K):=\\sup_{K^\\prime\\subset K}{\\rm as}_{\\varphi}(K),\\quad {\\rm\nos}_{\\psi}(K):=\\inf_{K^\\prime\\supset K}{\\rm as}_{\\psi}(K) \\] where ${\\rm\nas}_{\\varphi}(K)$ and ${\\rm as}_{\\psi}(K)$ are the $L_\\varphi$ and $L_\\psi$\naffine surface area of $K$, respectively. We prove that there exist extremal\nconvex bodies that achieve the supremum and infimum, and that the functionals\n${\\rm IS}_{\\varphi}$ and ${\\rm os}_{\\psi}$ are continuous. In our main results,\nwe prove Blaschke-Santal\\'o type inequalities and inverse Santal\\'o type\ninequalities for the extremal general affine surface areas. This article may be\nregarded as an Orlicz extension of the recent work of Giladi, Huang, Sch\\\"utt\nand Werner (2020), who introduced and studied the extremal $L_p$ affine surface\nareas.\n"}
+{"abstract": "  Current studies in extractive question answering (EQA) have modeled\nsingle-span extraction setting, where a single answer span is a label to\npredict for a given question-passage pair. This setting is natural for general\ndomain EQA as the majority of the questions in the general domain can be\nanswered with a single span. Following general domain EQA models, current\nbiomedical EQA (BioEQA) models utilize single-span extraction setting with\npost-processing steps. In this paper, we investigate the difference of the\nquestion distribution across the general and biomedical domains and discover\nbiomedical questions are more likely to require list-type answers (multiple\nanswers) than factoid-type answers (single answer). In real-world use cases,\nthis emphasizes the need for Biomedical EQA models able to handle multiple\nquestion types. Based on this preliminary study, we propose a multi-span\nextraction setting, namely sequence tagging approach for BioEQA, which directly\ntackles questions with a variable number of phrases as their answer. Our\napproach can learn to decide the number of answers for a question from training\ndata. Our experimental result on the BioASQ 7b and 8b list-type questions\noutperformed the best-performing existing models without requiring\npost-processing steps.\n"}
+{"abstract": "  Quasi-linear diffusion (QLD), driven by the cyclotron instability, is\nproposed as a mechanism for the possible generation of synchrotron emission in\nthe nearby zone of SgrA$^*$. For physically reasonable parameters, the QLD, by\ncausing non-zero pitch angle scattering lets electrons with the relativistic\nfactors of the order of $10^8$ emit synchrotron radiation in the hard $X$-ray\nspectral band $\\sim120$ keV.\n"}
+{"abstract": "  The spatio-temporal structure of the bulk wakefields excited by a\nrelativistic electron bunch in plasma of semiconductors and semimetals is\nstudied. It is shown that these wakefield consists of the field of longitudinal\nplasmons and Cherenkov electromagnetic radiation, which is a set of eigen\nelectromagnetic waves of the semiconductor or semimetal waveguide. A branch of\nthe surface plasmons appears in a waveguide with an axial vacuum channel. The\nprocess of wake excitation of the surface plasmons by the relativistic electron\nbunch is also investigated. The intensity of the excited wake surface wave is\ndetermined.\n"}
+{"abstract": "  An aerial vehicle powered by flapping feathered wings was designed, developed\nand fabricated. Different from legacy flapping-wing aerial vehicles with\nmembrane wings, the new design uses authentic bird feathers to fabricate wings.\nIn field tests, a radio-controlled electric-powered aerial vehicle with\nflapping feathered wings successfully took off, flew up to 63.88 s and landed\nsafely. It was found that flapping feathered wings can generate sufficient\nthrust and lift to make a man-made aerial vehicle accomplish takeoff,\nsustainable flight and a safe landing.\n"}
+{"abstract": "  Chatbots are intelligent software built to be used as a replacement for human\ninteraction. Existing studies typically do not provide enough support for\nlow-resource languages like Bangla. Due to the increasing popularity of social\nmedia, we can also see the rise of interactions in Bangla transliteration\n(mostly in English) among the native Bangla speakers. In this paper, we propose\na novel approach to build a Bangla chatbot aimed to be used as a business\nassistant which can communicate in low-resource languages like Bangla and\nBangla Transliteration in English with high confidence consistently. Since\nannotated data was not available for this purpose, we had to work on the whole\nmachine learning life cycle (data preparation, machine learning modeling, and\nmodel deployment) using Rasa Open Source Framework, fastText embeddings,\nPolyglot embeddings, Flask, and other systems as building blocks. While working\nwith the skewed annotated dataset, we try out different components and\npipelines to evaluate which works best and provide possible reasoning behind\nthe observed results. Finally, we present a pipeline for intent classification\nand entity extraction which achieves reasonable performance (accuracy: 83.02%,\nprecision: 80.82%, recall: 83.02%, F1-score: 80%).\n"}
+{"abstract": "  As a new generation of Public Bicycle-sharing Systems (PBS), the dockless PBS\n(DL-PBS) is an important application of cyber-physical systems and intelligent\ntransportation. How to use AI to provide efficient bicycle dispatching\nsolutions based on dynamic bicycle rental demand is an essential issue for\nDL-PBS. In this paper, we propose a dynamic bicycle dispatching algorithm based\non multi-objective reinforcement learning (MORL-BD) to provide the optimal\nbicycle dispatching solution for DL-PBS. We model the DL-PBS system from the\nperspective of CPS and use deep learning to predict the layout of bicycle\nparking spots and the dynamic demand of bicycle dispatching. We define the\nmulti-route bicycle dispatching problem as a multi-objective optimization\nproblem by considering the optimization objectives of dispatching costs,\ndispatch truck's initial load, workload balance among the trucks, and the\ndynamic balance of bicycle supply and demand. On this basis, the collaborative\nmulti-route bicycle dispatching problem among multiple dispatch trucks is\nmodeled as a multi-agent MORL model. All dispatch paths between parking spots\nare defined as state spaces, and the reciprocal of dispatching costs is defined\nas a reward. Each dispatch truck is equipped with an agent to learn the optimal\ndispatch path in the dynamic DL-PBS network. We create an elite list to store\nthe Pareto optimal solutions of bicycle dispatch paths found in each action,\nand finally, get the Pareto frontier. Experimental results on the actual DL-PBS\nsystems show that compared with existing methods, MORL-BD can find a higher\nquality Pareto frontier with less execution time.\n"}
+{"abstract": "  There are many possible definitions of derivatives, here we present some and\npresent one that we have called generalized that allows us to put some of the\nothers as a particular case of this but, what interests us is to determine that\nthere is an infinite number of possible definitions of fractional derivatives,\nall are correct as differential operators each of which must be properly\ndefined in its algebra.\n  We introduce a generalized version of the fractional derivative that extends\nthe existing ones in the literature. To those extensions, it is associated with\na differentiable operator and a differential ring and applications that show\nthe advantages of the generalization.\n  We also review the different definitions of fractional derivatives proposed\nby Michele Caputo in \\cite{GJI:GJI529}, Khalil, Al Horani, Yousef, Sababheh in\n\\cite{khalil2014new}, Anderson and Ulness in \\cite{anderson2015newly}, Guebbai\nand Ghiat in \\cite{guebbai2016new}, Udita N. Katugampola in\n\\cite{katugampola2014new}, Camrud in \\cite{camrud2016conformable} and it is\nshown how the generalized version contains the previous ones as a particular\ncase.\n"}
+{"abstract": "  A determinantal facet ideal (DFI) is an ideal $J_\\Delta$ generated by maximal\nminors of a generic matrix parametrized by an associated simplicial complex\n$\\Delta$. In this paper, we construct an explicit linear strand for the initial\nideal with respect to any diagonal term order $<$ of an arbitrary DFI. In\nparticular, we show that if $\\Delta$ has no \\emph{1-nonfaces}, then the Betti\nnumbers of the linear strand of $J_\\Delta$ and its initial ideal coincide. We\napply this result to prove a conjecture of Ene, Herzog, and Hibi on Betti\nnumbers of closed binomial edge ideals in the case that the associated graph\nhas at most $2$ maximal cliques. More generally, we show that the linear strand\nof the initial ideal (with respect to $<$) of \\emph{any} DFI is supported on a\npolyhedral cell complex obtained as an induced subcomplex of the \\emph{complex\nof boxes}, introduced by Nagel and Reiner.\n"}
+{"abstract": "  An image-based deep learning framework is developed in this paper to predict\ndamage and failure in microstructure-dependent composite materials. The work is\nmotivated by the complexity and computational cost of high-fidelity simulations\nof such materials. The proposed deep learning framework predicts the\npost-failure full-field stress distribution and crack pattern in\ntwo-dimensional representations of the composites based on the geometry of\nmicrostructures. The material of interest is selected to be a high-performance\nunidirectional carbon fiber-reinforced polymer composite. The deep learning\nframework contains two stacked fully-convolutional networks, namely, Generator\n1 and Generator 2, trained sequentially. First, Generator 1 learns to translate\nthe microstructural geometry to the full-field post-failure stress\ndistribution. Then, Generator 2 learns to translate the output of Generator 1\nto the failure pattern. A physics-informed loss function is also designed and\nincorporated to further improve the performance of the proposed framework and\nfacilitate the validation process. In order to provide a sufficiently large\ndata set for training and validating the deep learning framework, 4500\nmicrostructural representations are synthetically generated and simulated in an\nefficient finite element framework. It is shown that the proposed deep learning\napproach can effectively predict the composites' post-failure full-field stress\ndistribution and failure pattern, two of the most complex phenomena to simulate\nin computational solid mechanics.\n"}
+{"abstract": "  We propose a new model to assess the mastery level of a given skill\nefficiently. The model, called Bayesian Adaptive Mastery Assessment (BAMA),\nuses information on the accuracy and the response time of the answers given and\ninfers the mastery at every step of the assessment. BAMA balances the length of\nthe assessment and the certainty of the mastery inference by employing a\nBayesian decision-theoretic framework adapted to each student. All these\nproperties contribute to a novel approach in assessment models for intelligent\nlearning systems. The purpose of this research is to explore the properties of\nBAMA and evaluate its performance concerning the number of questions\nadministered and the accuracy of the final mastery estimates across different\nstudents. We simulate student performances and establish that the model\nconverges with low variance and high efficiency leading to shorter assessment\nduration for all students. Considering the experimental results, we expect our\napproach to avoid the issue of over-practicing and under-practicing and\nfacilitate the development of Learning Analytics tools to support the tutors in\nthe evaluation of learning effects and instructional decision making.\n"}
+{"abstract": "  To cope with the growing demand for transportation on the railway system,\naccurate, robust, and high-frequency positioning is required to enable a safe\nand efficient utilization of the existing railway infrastructure. As a basis\nfor a localization system we propose a complete on-board mapping pipeline able\nto map robust meaningful landmarks, such as poles from power lines, in the\nvicinity of the vehicle. Such poles are good candidates for reliable and long\nterm landmarks even through difficult weather conditions or seasonal changes.\nTo address the challenges of motion blur and illumination changes in railway\nscenarios we employ a Dynamic Vision Sensor, a novel event-based camera. Using\na sideways oriented on-board camera, poles appear as vertical lines. To map\nsuch lines in a real-time event stream, we introduce Hough2Map, a novel\nconsecutive iterative event-based Hough transform framework capable of\ndetecting, tracking, and triangulating close-by structures. We demonstrate the\nmapping reliability and accuracy of Hough2Map on real-world data in typical\nusage scenarios and evaluate using surveyed infrastructure ground truth maps.\nHough2Map achieves a detection reliability of up to 92% and a mapping root mean\nsquare error accuracy of 1.1518m.\n"}
+{"abstract": "  Across a large range of scales, accreting sources show remarkably similar\npatterns of variability, most notably the log-normality of the luminosity\ndistribution and the linear root-mean square (rms)-flux relationship. These\nresults are often explained using the theory of propagating fluctuations in\nwhich fluctuations in the viscosity create perturbations in the accretion rate\nat all radii, propagate inwards and combine multiplicatively. While this idea\nhas been extensively studied analytically in a linear regime, there has been\nrelatively little numerical work investigating the non-linear behaviour. In\nthis paper, we present a suite of stochastically driven 1-d $\\alpha$-disc\nsimulations, exploring the behaviour of these discs. We find that the eponymous\npropagating fluctuations are present in all simulations across a wide range of\nmodel parameters, in contradiction to previous work. Of the model parameters,\nwe find by far the most important to be the timescale on which the viscosity\nfluctuations occur. Physically, this timescale will depend on the underlying\nphysical mechanism, thought to be the magnetorotational instability (MRI). We\nfind a close relationship between this fluctuation timescale and the break\nfrequency in the power spectral density (PSD) of the luminosity, a fact which\ncould allow observational probes of the behaviour of the MRI dynamo. We report\na fitting formula for the break frequency as a function of the fluctuation\ntimescale, the disc thickness and the mass of the central object.\n"}
+{"abstract": "  We present a regime where an ultra-intense laser pulse interacting with a\nfoil target results in high $\\gamma$-photon conversion efficiency, obtained via\nthree-dimensional quantum-electrodynamics particle-in-cell simulations. A\nsingle-cycle laser pulse is used under the tight-focusing condition for\nobtaining the $\\mathrm{\\lambda}^3$ regime. The simulations employ a radially\npolarized laser as it results in higher $\\gamma$-photon conversion efficiency\ncompared to both azimuthal and linear polarizations. A significant fraction of\nthe laser energy is transferred to positrons, while a part of the\nelectromagnetic wave escapes the target as attosecond single-cycle pulses.\n"}
+{"abstract": "  Nonconvex optimal-control problems governed by evolution problems in\ninfinite-dimensional spaces (as e.g. parabolic boundary-value problems) needs a\ncontinuous (and possibly also smooth) extension on some (preferably convex)\ncompactification, called relaxation, to guarantee existence of their solutions\nand to facilitate analysis by relatively conventional tools. When the control\nis valued in some subsets of Lebesgue spaces, the usual extensions are either\ntoo coarse (allowing in fact only very restricted nonlinearities) or too fine\n(being nonmetrizable). To overcome these drawbacks, a compromising convex\ncompactification is here devised, combining classical techniques for Young\nmeasures with Choquet theory. This is applied to parabolic optimal control\nproblems as far as existence and optimality conditions concerns.\n"}
+{"abstract": "  This paper establishes the local-in-time well-posedness of solutions to an\napproximating system constructed by mildly regularizing the dynamical sea ice\nmodel of {\\it W.D. Hibler, Journal of Physical Oceanography, 1979}. Our choice\nof regularization has been carefully designed, prompted by physical\nconsiderations, to retain the original coupled hyperbolic-parabolic character\nof Hibler's model. Various regularized versions of this model have been used\nwidely for the numerical simulation of the circulation and thickness of the\nArctic ice cover. However, due to the singularity in the ice rheology, the\nnotion of solutions to the original model is unclear. Instead, an approximating\nsystem, which captures current numerical study, is proposed. The well-posedness\ntheory of such a system provides a first-step groundwork in both numerical\nstudy and future analytical study.\n"}
+{"abstract": "  The Maximum Entropy Spectral Analysis (MESA) method, developed by Burg,\nprovides a powerful tool to perform spectral estimation of a time-series. The\nmethod relies on a Jaynes' maximum entropy principle and provides the means of\ninferring the spectrum of a stochastic process in terms of the coefficients of\nsome autoregressive process AR($p$) of order $p$. A closed form recursive\nsolution provides an estimate of the autoregressive coefficients as well as of\nthe order $p$ of the process. We provide a ready-to-use implementation of the\nalgorithm in the form of a python package \\texttt{memspectrum}. We characterize\nour implementation by performing a power spectral density analysis on synthetic\ndata (with known power spectral density) and we compare different criteria for\nstopping the recursion. Furthermore, we compare the performance of our code\nwith the ubiquitous Welch algorithm, using synthetic data generated from the\nreleased spectrum by the LIGO-Virgo collaboration. We find that, when compared\nto Welch's method, Burg's method provides a power spectral density (PSD)\nestimation with a systematically lower variance and bias. This is particularly\nmanifest in the case of a little number of data points, making Burg's method\nmost suitable to work in this regime.\n"}
+{"abstract": "  There is a class of binary post-AGB stars with a remarkable near-infrared\nexcess that are surrounded by Keplerian or quasi-Keplerian disks and extended\noutflows composed of gas escaping from the disk. The Keplerian dynamics had\nbeen well identified in four cases, namely the Red Rectangle, AC Her, IW Car,\nand IRAS 08544-4431. In these objects, the mass of the outflow represents ~ 10\n% of the nebular mass, the disk being the dominant component of the nebula. We\npresent interferometric NOEMA maps of 12CO and 13CO J=2-1 in 89 Her and 12CO\nJ=2-1 in AC Her, IRAS 19125+0343, and R Sct. Several properties of the nebula\nare obtained from the data and model fitting, including the structure, density,\nand temperature distributions, as well as the dynamics. We also discuss the\nuncertainties on the derived values. The presence of an expanding component in\nAC Her is doubtful, but thanks to new maps and models, we estimate an upper\nlimit to the mass of this outflow of < 3 10^-5 Mo, that is, the mass of the\noutflow is < 5 % of the total nebular mass. For 89 Her, we find a total nebular\nmass of 1.4 10^-2 Mo, of which ~ 50 % comes from an hourglass-shaped extended\noutflow. In the case of IRAS 19125+0343, the nebular mass is 1.1 10^-2 Mo,\nwhere the outflow contributes ~ 70 % of the total mass. The nebular mass of R\nSct is 3.2 10^-2 Mo, of which ~ 75 % corresponds to a very extended outflow\nthat surrounds the disk. Our results for IRAS 19125+0343 and R Sct lead us to\nintroduce a new subclass of binary post-AGB stars, for which the outflow is the\ndominant component of the nebula. Moreover, the outflow mass fraction found in\nAC Her is smaller than those found in other disk-dominated binary post-AGB\nstars. 89 Her would represent an intermediate case between both subclasses.\n"}
+{"abstract": "  In the (fully) dynamic set cover problem, we have a collection of $m$ sets\nfrom a universe of size $n$ that undergo element insertions and deletions; the\ngoal is to maintain an approximate set cover of the universe after each update.\nWe give an $O(f^2)$ update time algorithm for this problem that achieves an\n$f$-approximation, where $f$ is the maximum number of sets that an element\nbelongs to; under the unique games conjecture, this approximation is best\npossible for any fixed $f$. This is the first algorithm for dynamic set cover\nwith approximation ratio that {exactly} matches $f$ (as opposed to {almost} $f$\nin prior work), as well as the first one with runtime \\emph{independent of\n$n,m$} (for any approximation factor of $o(f^3)$).\n  Prior to our work, the state-of-the-art algorithms for this problem were\n$O(f^2)$ update time algorithms of Gupta et al. [STOC'17] and Bhattacharya et\nal. [IPCO'17] with $O(f^3)$ approximation, and the recent algorithm of\nBhattacharya et al. [FOCS'19] with $O(f \\cdot \\log{n}/\\epsilon^2)$ update time\nand $(1+\\epsilon) \\cdot f$ approximation, improving the $O(f^2 \\cdot\n\\log{n}/\\epsilon^5)$ bound of Abboud et al. [STOC'19].\n  The key technical ingredient of our work is an algorithm for maintaining a\n{maximal} matching in a dynamic hypergraph of rank $r$, where each hyperedge\nhas at most $r$ vertices, which undergoes hyperedge insertions and deletions in\n$O(r^2)$ amortized update time; our algorithm is randomized, and the bound on\nthe update time holds in expectation and with high probability. This result\ngeneralizes the maximal matching algorithm of Solomon [FOCS'16] with constant\nupdate time in ordinary graphs to hypergraphs, and is of independent merit; the\nprevious state-of-the-art algorithms for set cover do not translate to\n(integral) matchings for hypergraphs, let alone a maximal one. Our quantitative\nresult for the set cover problem is [...]\n"}
+{"abstract": "  Domain Adaptation (DA) techniques are important for overcoming the domain\nshift between the source domain used for training and the target domain where\ntesting takes place. However, current DA methods assume that the entire target\ndomain is available during adaptation, which may not hold in practice. This\npaper considers a more realistic scenario, where target data become available\nin smaller batches and adaptation on the entire target domain is not feasible.\nIn our work, we introduce a new, data-constrained DA paradigm where unlabeled\ntarget samples are received in batches and adaptation is performed continually.\nWe propose a novel source-free method for continual unsupervised domain\nadaptation that utilizes a buffer for selective replay of previously seen\nsamples. In our continual DA framework, we selectively mix samples from\nincoming batches with data stored in a buffer using buffer management\nstrategies and use the combination to incrementally update our model. We\nevaluate the classification performance of the continual DA approach with\nstate-of-the-art DA methods based on the entire target domain. Our results on\nthree popular DA datasets demonstrate that our method outperforms many existing\nstate-of-the-art DA methods with access to the entire target domain during\nadaptation.\n"}
+{"abstract": "  We report photometric estimates of effective temperature, $T_{\\rm eff}$,\nmetallicity, [Fe/H], carbonicity, [C/Fe], and absolute carbon abundances,\n$A{\\rm (C)}$, for over 700,000 stars from the Southern Photometric Local\nUniverse Survey (S-PLUS) Data Release 2, covering a substantial fraction of the\nequatorial Sloan Digital Sky Survey Stripe 82. We present an analysis for two\nstellar populations: 1) halo main-sequence turnoff stars and 2) K-dwarf stars\nof mass $0.58 < M/M_{\\odot} <0.75$ in the Solar Neighborhood. Application of\nthe Stellar Photometric Index Network Explorer (SPHINX) to the mixed-bandwidth\n(narrow- plus wide-band) filter photometry from S-PLUS produces robust\nestimates of the metallicities and carbon abundances in stellar atmospheres\nover a wide range of temperature, $4250 < T_{\\rm eff} \\textrm{(K)} < 7000$. The\nuse of multiple narrow-band S-PLUS filters enables SPHINX to achieve\nsubstantially lower levels of \"catastrophic failures\" (large offsets in\nmetallicity estimates relative to spectroscopic determinations) than previous\nefforts using a single metallicity-sensitive narrow-band filter. We constrain\nthe exponential slope of the Milky Way's K-dwarf halo metallicity distribution\nfunction (MDF), $\\lambda_{10, \\textrm{[Fe/H]}} = 0.85 \\pm 0.21$, over the\nmetallicity range $-2.5 < \\textrm{[Fe/H]} < -1.0$; the MDF of our local-volume\nK-dwarf sample is well-represented by a gamma distribution with parameters\n$\\alpha=2.8$ and $\\beta=4.2$. S-PLUS photometry obtains absolute carbon\nabundances with a precision of $\\sim 0.35$dex for stars with $T_{\\rm eff} <\n6500$K. We identify 364 candidate carbon-enhanced metal-poor stars, obtain\nassignments of these stars into the Yoon-Beers morphological groups in the\n$A$(C)-[Fe/H] space, and derive the CEMP frequencies.\n"}
+{"abstract": "  We present results for higher-order corrections to exclusive\n$\\mathrm{J}/\\psi$ production. This includes the first relativistic correction\nof order $v^2$ in quark velocity, and next-to-leading order corrections in\n$\\alpha_s$ for longitudinally polarized production. The relativistic\ncorrections are found to be important for a good description of the HERA data,\nespecially at small values of the photon virtuality. The next-to-leading order\nresults for longitudinal production are evaluated numerically. We also\ndemonstrate how the vector meson production provides complementary information\nto the structure functions for extracting the initial condition for the\nsmall-$x$ evolution of the dipole-proton scattering amplitude.\n"}
+{"abstract": "  We study the ensemble average of the thermal expectation value of an energy\nmomentum tensor in the presence of a random external metric. In a holographic\nsetup this quantity can be read off of the near boundary behavior of the metric\nin a stochastic theory of gravity. By numerically solving the associated\nEinstein equations and mapping the result to the dual boundary theory, we find\nthat the non relativistic energy power spectrum exhibits a power law behavior\nas expected by the theory of Kolmogorov.\n"}
+{"abstract": "  This study demonstrates that web-search traffic information, in particular,\nGoogle Trends data, is a credible novel source of high-quality and\neasy-to-access data for analyzing technology-based new ventures (TBNVs) growth\ntrajectories. Utilizing the diverse sample of 241 US-based TBNVs, we\ncomparatively analyze the relationship between companies' evolution curves\nrepresented by search activity on the one hand and by valuations achieved\nthrough rounds of venture investments on another. The results suggest that\nTBNV's growth dynamics are positively and strongly correlated with its web\nsearch traffic across the sample. This correlation is more robust when a\ncompany is a) more successful (in terms of valuation achieved) - especially if\nit is a \"unicorn\"; b) consumer-oriented (i.e., b2c); and 3) develops products\nin the form of a digital platform. Further analysis based on fuzzy-set\nQualitative Comparative Analysis (fsQCA) shows that for the most successful\ncompanies (\"unicorns\") and consumer-oriented digital platforms (i.e., b2c\ndigital platform companies) proposed approach may be extremely reliable, while\nfor other high-growth TBNVs it is useful for analyzing their growth dynamics,\nalbeit to a more limited degree. The proposed methodological approach opens a\nwide range of possibilities for analyzing, researching and predicting the\ngrowth of recently formed growth-oriented companies, in practice and academia.\n"}
+{"abstract": "  We prove that $\\mathbb{F}_p$ sketch, a well-celebrated streaming algorithm\nfor frequency moments estimation, is differentially private as is when $p\\in(0,\n1]$. $\\mathbb{F}_p$ sketch uses only polylogarithmic space, exponentially\nbetter than existing DP baselines and only worse than the optimal non-private\nbaseline by a logarithmic factor. The evaluation shows that $\\mathbb{F}_p$\nsketch can achieve reasonable accuracy with strong privacy guarantees.\n"}
+{"abstract": "  We study the formation of magnon-polaron excitations and the consequences of\ndifferent time scales between the magnon and lattice dynamics. The spin-spin\ninteractions along the 1D lattice are ruled by a Heisenberg Hamiltonian in the\nanisotropic form XXZ, in which each spin exhibits a vibrational degree of\nfreedom around its equilibrium position. By considering a magnetoelastic\ncoupling as a linear function of the relative displacement between\nnearest-neighbor spins, results provide an original framework for achieving a\nhybridized state of magnon-polaron. Such state is characterized by high\ncooperation between the underlying excitations, where the traveling or\nstationary formation of magnon-polaron depends on the effective magnetoelastic\ncoupling. A systematic investigation reveals the critical amount of the\nmagnon-lattice interaction ($\\chi_c$) necessary to emergence of the stationary\nmagnon-polaron quasi-particle. Different characteristic time scales of the\nmagnon and the vibrational dynamics unveiled the threshold between the two\nregimes, as well as a limiting value of critical magnetoelastic interaction,\nabove which the magnon velocity no longer interferes at the critical\nmagnetoelastic coupling capable of inducing the stationary regime.\n"}
+{"abstract": "  We prove the existence of Bayesian Nash Equilibrium (BNE) of general-sum\nBayesian games with continuous types and finite actions under the conditions\nthat the utility functions and the prior type distributions are continuous\nconcerning the players' types. Moreover, there exists a sequence of discretized\nBayesian games whose BNE strategies converge weakly to a BNE strategy of the\ninfinite Bayesian game. Our proof establishes a connection between the\nequilibria of the infinite Bayesian game and those of finite approximations,\nwhich leads to an algorithm to construct $\\varepsilon$-BNE of infinite Bayesian\ngames by discretizing players' type spaces.\n"}
+{"abstract": "  Applications in materials and biological imaging are limited by the ability\nto collect high-resolution data over large areas in practical amounts of time.\nOne solution to this problem is to collect low-resolution data and interpolate\nto produce a high-resolution image. However, most existing super-resolution\nalgorithms are designed for natural images, often require aligned pairing of\nhigh and low-resolution training data, and may not directly incorporate a model\nof the imaging sensor.\n  In this paper, we present a Multi-resolution Data Fusion (MDF) algorithm for\naccurate interpolation of low-resolution data at multiple resolutions up to 8x.\nOur approach uses small quantities of unpaired high-resolution data to train a\nneural network prior model denoiser and then uses the Multi-Agent Consensus\nEquilibrium (MACE) problem formulation to balance this denoiser with a forward\nmodel agent that promotes fidelity to measured data.\n  A key theoretical novelty is the analysis of mismatched back-projectors,\nwhich modify typical forward model updates for computational efficiency or\nimproved image quality. We use MACE to prove that using a mismatched\nback-projector is equivalent to using a standard back-projector and an\nappropriately modified prior model.\n  We present electron microscopy results at 4x and 8x interpolation factors\nthat exhibit reduced artifacts relative to existing methods while maintaining\nfidelity to acquired data and accurately resolving sub-pixel-scale features.\n"}
+{"abstract": "  We study episodic reinforcement learning under unknown adversarial\ncorruptions in both the rewards and the transition probabilities of the\nunderlying system. We propose new algorithms which, compared to the existing\nresults in (Lykouris et al., 2020), achieve strictly better regret bounds in\nterms of total corruptions for the tabular setting. To be specific, firstly,\nour regret bounds depend on more precise numerical values of total rewards\ncorruptions and transition corruptions, instead of only on the total number of\ncorrupted episodes. Secondly, our regret bounds are the first of their kind in\nthe reinforcement learning setting to have the number of corruptions show up\nadditively with respect to $\\min\\{\\sqrt{T}, \\text{PolicyGapComplexity}\\}$\nrather than multiplicatively. Our results follow from a general algorithmic\nframework that combines corruption-robust policy elimination meta-algorithms,\nand plug-in reward-free exploration sub-algorithms. Replacing the\nmeta-algorithm or sub-algorithm may extend the framework to address other\ncorrupted settings with potentially more structure.\n"}
+{"abstract": "  In this work we study the asymmetric heat flow, i.e., thermal rectification,\nof a one-dimensional, mass-graded system consisting of acoupled harmonic\noscillator lattice (ballistic spacer) and two diffusive leads attached to the\nboundaries of the former with both nearest-neighbor and next-nearest-neighbor\n(NNN) interactions. The latter enhance the rectification properties of the\nsystem and specially its independence on system size. The system presents a\nmaximum rectification efficiency for a very precise value of the parameter that\ncontrols the coupling strength of the NNN interactions that depend on the\ntemperature range wherein the device operates. The origin of this maximum value\nis the asymmetric local heat flow response corresponding to the NNN\ncontribution at both sides of the lighter mass-loaded diffusive lead as\nquantified by the spectral properties. Upon variation of the system's\nparameters the performance of the device is always enhanced in the presence of\nNNN interactions.\n"}
+{"abstract": "  Women are underrepresented in Computer Science disciplines at all levels,\nfrom undergraduate and graduate studies to participation and leadership in\nacademia and industry. Increasing female representation in the field is a grand\nchallenge for academics, policymakers, and society. Although the problem has\nbeen addressed for many years, progress has been difficult to be measured and\ncompared across countries and institutions, and has been invariably slow,\ndespite all the momentum and impulse for change taking place across several\ncountries. Therefore, it is important to reflect on knowledge, experiences,\nsuccesses, and challenges of existing policies, initiatives and interventions.\nThe main goal of this paper is to provide an overview of several initiatives,\nstudies, projects, and their outcomes. It contributes to building a body of\nknowledge about gender aspects in several areas: research, education, projects,\nnetworks and resources. This paper is mainly based on discussions in working\ngroups and the material collected for and during a series of talks on the topic\nheld by the first author and by feedback received by the community. This paper\nprovides the academic community, policymakers, industry and other stakeholders\nwith numerous examples of best practices, as well as studies and\nrecommendations on how to address key challenges about attracting, retaining,\nencouraging, and inspiring women to pursue a career in Computer Science. Future\nwork should address the issue in a systematic and research based way.\n"}
+{"abstract": "  Two dimensional (2D) ferromagnetic materials have attracted much attention in\nthe fields of condensed matter physics and materials science, but their\nsynthesis is still a challenge given their limitations on structural stability\nand susceptibility to oxidization. MAX phases nanolaminated ternary carbides or\nnitrides possess a unique crystal structure in which single-atom-thick A\nsublayers are interleaved by two dimensional MX slabs, providing nanostructured\ntemplates for designing 2D ferromagnetic materials if the non-magnetic A\nsublayers can be substituted replaced by magnetic elements. Here, we report\nthree new ternary magnetic MAX phases (Ta2FeC, Ti2FeN and Nb2FeC) with A\nsublayers of single-atom-thick 2D iron through an isomorphous replacement\nreaction of MAX precursors (Ta2AlC, Ti2AlN and Nb2AlC) with a Lewis acid salts\n(FeCl2). All these MAX phases exhibit ferromagnetic (FM) behavior. The Curie\ntemperature (Tc) of Ta2FeC and Nb2FeC MAX phase are 281 K and 291 K,\nrespectively, i.e. close to room temperature. The saturation magnetization of\nthese ternary magnetic MAX phases is almost two orders of magnitude higher than\nthat of V2(Sn,Fe)C MAX phase whose A-site is partial substituted by Fe.\nTheoretical calculations on magnetic orderings of spin moments of Fe atoms in\nthese nanolaminated magnetic MAX phases reveal that the magnetism can be mainly\nascribed to intralayer exchange interaction of the 2D Fe atomic layers. Owning\nto the richness in composition of MAX phases, there is a large compositional\nspace for constructing functional single-atom-thick 2D layers in materials\nusing these nanolaminated templates.\n"}
+{"abstract": "  Oxygen vacancies have been identified to play an important role in\naccelerating grain growth in polycrystalline perovskite-oxide ceramics. In\norder to advance the fundamental understanding of growth mechanisms at the\natomic scale, classical atomistic simulations were carried out to investigate\nthe atomistic structures and oxygen vacancy formation energies at grain\nboundaries in the prototypical perovskite-oxide material SrTiO$_3$. In this\nwork, we focus on two symmetric tilt grain boundaries, namely\n$\\Sigma$5(310)[001] and $\\Sigma$5(210)[001]. A one-dimensional continuum model\nis adapted to determine the electrostatic potential induced by charged lattice\nplanes in atomistic structure models containing grain boundaries and point\ndefects. By means of this model, electrostatic artifacts, which are inherent to\nsupercell models with periodic or open boundary conditions, can be taken into\naccount and corrected properly. We report calculated formation energies of\noxygen vacancies on all the oxygen sites across boundaries between two\nmisoriented grains, and we analyze and discuss the formation-energy values with\nrespect to local charge densities at the vacant sites.\n"}
+{"abstract": "  Domain adaptation has been widely explored by transferring the knowledge from\na label-rich source domain to a related but unlabeled target domain. Most\nexisting domain adaptation algorithms attend to adapting feature\nrepresentations across two domains with the guidance of a shared\nsource-supervised classifier. However, such classifier limits the\ngeneralization ability towards unlabeled target recognition. To remedy this, we\npropose a Transferable Semantic Augmentation (TSA) approach to enhance the\nclassifier adaptation ability through implicitly generating source features\ntowards target semantics. Specifically, TSA is inspired by the fact that deep\nfeature transformation towards a certain direction can be represented as\nmeaningful semantic altering in the original input space. Thus, source features\ncan be augmented to effectively equip with target semantics to train a more\ntransferable classifier. To achieve this, for each class, we first use the\ninter-domain feature mean difference and target intra-class feature covariance\nto construct a multivariate normal distribution. Then we augment source\nfeatures with random directions sampled from the distribution class-wisely.\nInterestingly, such source augmentation is implicitly implemented through an\nexpected transferable cross-entropy loss over the augmented source\ndistribution, where an upper bound of the expected loss is derived and\nminimized, introducing negligible computational overhead. As a light-weight and\ngeneral technique, TSA can be easily plugged into various domain adaptation\nmethods, bringing remarkable improvements. Comprehensive experiments on\ncross-domain benchmarks validate the efficacy of TSA.\n"}
+{"abstract": "  The statistical characterization of the distribution of visible matter in the\nuniverse is a central problem in modern cosmology. In this respect, a crucial\nquestion still lacking a definitive answer concerns how large are the greatest\nstructures in the universe. This point is closely related to whether or not\nsuch a distribution can be approximated as being homogeneous on large enough\nscales. Here we assess this problem by considering the size distribution of\nsuperclusters of galaxies and by leveraging on the properties of\nZipf-Mandelbrot law, providing a novel approach which complements standard\nanalysis based on the correlation functions. We find that galaxy superclusters\nare well described by a pure Zipf's law with no deviations and this implies\nthat all the catalogs currently available are not sufficiently large to spot a\ntruncation in the power-law behavior. This finding provides evidence that\nstructures larger than the greatest superclusters already observed are expected\nto be found when deeper redshift surveys will be completed. As a consequence\nthe scale beyond which galaxy distribution crossovers toward homogeneity, if\nany, should increase accordingly\n"}
+{"abstract": "  We introduce Ivy, a templated Deep Learning (DL) framework which abstracts\nexisting DL frameworks. Ivy unifies the core functions of these frameworks to\nexhibit consistent call signatures, syntax and input-output behaviour. New\nhigh-level framework-agnostic functions and classes, which are usable alongside\nframework-specific code, can then be implemented as compositions of the unified\nlow-level Ivy functions. Ivy currently supports TensorFlow, PyTorch, MXNet, Jax\nand NumPy. We also release four pure-Ivy libraries for mechanics, 3D vision,\nrobotics, and differentiable environments. Through our evaluations, we show\nthat Ivy can significantly reduce lines of code with a runtime overhead of less\nthan 1% in most cases. We welcome developers to join the Ivy community by\nwriting their own functions, layers and libraries in Ivy, maximizing their\naudience and helping to accelerate DL research through inter-framework\ncodebases. More information can be found at https://ivy-dl.org.\n"}
+{"abstract": "  The use of datasets is getting more relevance in surgical robotics since they\ncan be used to recognise and automate tasks. Also, this allows to use common\ndatasets to compare different algorithms and methods. The objective of this\nwork is to provide a complete dataset of three common training surgical tasks\nthat surgeons perform to improve their skills. For this purpose, 12 subjects\nteleoperated the da Vinci Research Kit to perform these tasks. The obtained\ndataset includes all the kinematics and dynamics information provided by the da\nVinci robot (both master and slave side) together with the associated video\nfrom the camera. All the information has been carefully timestamped and\nprovided in a readable csv format. A MATLAB interface integrated with ROS for\nusing and replicating the data is also provided.\n"}
+{"abstract": "  Dirac-Frenkel variational method with Davydov D2 trial wavefunction is\nextended by introducing a thermalization algorithm and applied to simulate\ndynamics of a general open quantum system. The algorithm allows to control\ntemperature variations of a harmonic finite size bath, when in contact with the\nquantum system. Thermalization of the bath vibrational modes is realised via\nstochastic scatterings, implemented as a discrete-time Bernoulli process with\nPoisson statistics. It controls bath temperature by steering vibrational modes'\nevolution towards their canonical thermal equilibrium. Numerical analysis of\nthe exciton relaxation dynamics in a small molecular cluster reveals that\nthermalization additionally provides significant calculation speed up due to\nreduced number of vibrational modes needed to obtain the convergence.\n"}
+{"abstract": "  In this paper, we consider the non-symmetric positive semidefinite Procrustes\n(NSPSDP) problem: Given two matrices $X,Y \\in \\mathbb{R}^{n,m}$, find the\nmatrix $A \\in \\mathbb{R}^{n,n}$ that minimizes the Frobenius norm of $AX-Y$ and\nwhich is such that $A+A^T$ is positive semidefinite. We generalize the\nsemi-analytical approach for the symmetric positive semidefinite Procrustes\nproblem, where $A$ is required to be positive semidefinite, that was proposed\nby Gillis and Sharma (A semi-analytical approach for the positive semidefinite\nProcrustes problem, Linear Algebra Appl. 540, 112-137, 2018). As for the\nsymmetric case, we first show that the NSPSDP problem can be reduced to a\nsmaller NSPSDP problem that always has a unique solution and where the matrix\n$X$ is diagonal and has full rank. Then, an efficient semi-analytical algorithm\nto solve the NSPSDP problem is proposed, solving the smaller and well-posed\nproblem with a fast gradient method which guarantees a linear rate of\nconvergence. This algorithm is also applicable to solve the complex NSPSDP\nproblem, where $X,Y \\in \\mathbb{C}^{n,m}$, as we show the complex NSPSDP\nproblem can be written as an overparametrized real NSPSDP problem. The\nefficiency of the proposed algorithm is illustrated on several numerical\nexamples.\n"}
+{"abstract": "  Random Forests (RF) are at the cutting edge of supervised machine learning in\nterms of prediction performance, especially in genomics. Iterative Random\nForests (iRF) use a tree ensemble from iteratively modified RF to obtain\npredictive and stable non-linear high-order Boolean interactions of features.\nThey have shown great promise for high-order biological interaction discovery\nthat is central to advancing functional genomics and precision medicine.\nHowever, theoretical studies into how tree-based methods discover high-order\nfeature interactions are missing. In this paper, to enable such theoretical\nstudies, we first introduce a novel discontinuous nonlinear regression model,\ncalled Locally Spiky Sparse (LSS) model, which is inspired by the thresholding\nbehavior in many biological processes. Specifically, LSS model assumes that the\nregression function is a linear combination of piece-wise constant Boolean\ninteraction terms. We define a quantity called depth-weighted prevalence (DWP)\nfor a set of signed features S and a given RF tree ensemble. We prove that,\nwith high probability under the LSS model, DWP of S attains a universal upper\nbound that does not involve any model coefficients, if and only if S\ncorresponds to a union of Boolean interactions in the LSS model. As a\nconsequence, we show that RF yields consistent interaction discovery under the\nLSS model. Simulation results show that DWP can recover the interactions under\nthe LSS model even when some assumptions such as the uniformity assumption are\nviolated.\n"}
+{"abstract": "  In this article, we study the forward dynamical behavior of nonautonomous\nlattice systems. We first construct a family of sets\n$\\{\\mathcal{A}_\\varepsilon(\\sigma)\\}_{\\sigma\\in \\Sigma}$ in arbitrary small\nneighborhood of a global attractor of the skew-product flow generated by a\ngeneral nonautonomous lattice system, which is forward invariant and uniformly\nforward attracts any bounded subset of the phase space. Moreover, under some\nsuitable conditions, we further construct a family of sets\n$\\{\\mathcal{B}_\\varepsilon(\\sigma)\\}_{\\sigma\\in \\Sigma}$ such that it uniformly\nforward exponentially attracts bounded subsets of the phase space. As an\napplication, we study the discrete Gray-Scott model in detail and illustrate\nhow to apply our abstract results to some concrete lattice system.\n"}
+{"abstract": "  We study dynamics of multi-soliton solutions of anti-self-dual Yang-Mills\nequations for G=GL(2,C) in four-dimensional spaces. The one-soliton solution\ncan be interpreted as a codimension-one soliton in four-dimensional spaces\nbecause the principal peak of action density localizes on a three-dimensional\nhyperplane. We call it the soliton wall. We prove that in the asymptotic\nregion, the n-soliton solution possesses n isolated localized lumps of action\ndensity, and interpret it as n intersecting soliton walls. More precisely, each\naction density lump is essentially the same as a soliton wall because it\npreserves its shape and \"velocity\" except for a position shift of principal\npeak in the scattering process. The position shift results from the nonlinear\ninteractions of the multi-solitons and is called the phase shift. We calculate\nthe phase shift factors explicitly and find that the action densities can be\nreal-valued in three kind of signatures. Finally, we show that the gauge group\ncan be G=U(2) in the Ultrahyperbolic space (the split signature (+, +, -, -)).\nThis implies that the intersecting soliton walls could be realized in all\nregion in N=2 string theories. It is remarkable that quasideterminants\ndramatically simplify the calculations and proofs.\n"}
+{"abstract": "  During the initial years of its inception, the Internet was widely used for\ntransferring data packets between users and respective data sources by using IP\naddresses. With the advancements in technology, the Internet has been used to\nshare data within several small and resource-constrained devices connected in\nbillions to create the framework for the so-called Internet of Things (IoT).\nThese systems were known for the presentation of a large quantum of data\nemerging within these devices. On the flip side, these devices are known to\nimpose huge overheads on the IoT network. Therefore, it was essential to\ndevelop solutions concerning different network-related problems as a part of\nIoT networking. In this paper, we review these challenges emerge in routing,\ncongestion, energy conservation, scalability, heterogeneity, reliability,\nsecurity, and quality of service (QoS). This can be leverage to use the\navailable network optimally. As part of this research work, a detailed survey\nis to be conducted on the network optimization process within IoT, as presented\nin another research. Owing to the advances in wireless networking, relevant\nInternet-of-Things (IoT) devices were equipped with several elements, including\nmultiple network access interfaces. The adoption of multipath TCP (MPTCP)\ntechnology would improve the total throughput of data transmission. On the\nother hand, leveraging traditional MPTCP path management algorithms lead to\nother problems in data transport areas along with even buffer blockage. This\nshall lead to massive issues in areas of reduction of transmission performance\nacross the entire IoT network. To this end, we develop a novel multipath\nalgorithm that would efficiently manage the data transport in an intelligently\nscheduled and seamless manner using multiple wireless/wireline paths.\n"}
+{"abstract": "  Decision-making under uncertainty is hugely important for any decisions\nsensitive to perturbations in observed data. One method of incorporating\nuncertainty into making optimal decisions is through robust optimization, which\nminimizes the worst-case scenario over some uncertainty set. We connect\nconformal prediction regions to robust optimization, providing finite sample\nvalid and conservative ellipsoidal uncertainty sets, aptly named conformal\nuncertainty sets. In pursuit of this connection we explicitly define\nMahalanobis distance as a potential conformity score in full conformal\nprediction. We also compare the coverage and optimization performance of\nconformal uncertainty sets, specifically generated with Mahalanobis distance,\nto traditional ellipsoidal uncertainty sets on a collection of simulated robust\noptimization examples.\n"}
+{"abstract": "  Auto encoding models have been extensively studied in recent years. They\nprovide an efficient framework for sample generation, as well as for analysing\nfeature learning. Furthermore, they are efficient in performing interpolations\nbetween data-points in semantically meaningful ways. In this paper, we build\nfurther on a previously introduced method for generating canonical, dimension\nindependent, stochastic interpolations. Here, the distribution of interpolation\npaths is represented as the distribution of a bridge process constructed from\nan artificial random data generating process in the latent space, having the\nprior distribution as its invariant distribution. As a result the stochastic\ninterpolation paths tend to reside in regions of the latent space where the\nprior has high mass. This is a desirable feature since, generally, such areas\nproduce semantically meaningful samples. In this paper, we extend the bridge\nprocess method by introducing a discriminator network that accurately\nidentifies areas of high latent representation density. The discriminator\nnetwork is incorporated as a change of measure of the underlying bridge process\nand sampling of interpolation paths is implemented using sequential Monte\nCarlo. The resulting sampling procedure allows for greater variability in\ninterpolation paths and stronger drift towards areas of high data density.\n"}
+{"abstract": "  Strange stars (SSs) are compact objects made of deconfined quarks. It is hard\nto distinguish SSs from neutron stars as a thin crust composed of normal\nhadronic matter may exist and obscure the whole surface of the SS. Here we\nsuggest that the intriguing repeating fast radio bursts (FRBs) are produced by\nthe intermittent fractional collapses of the crust of an SS induced by\nrefilling of accretion materials from its low-mass companion. The\nperiodic/sporadic/clustered temporal behaviors of FRBs could be well understood\nin our scenario. Especially, the periodicity is attributed to the modulation of\naccretion rate through the disk instabilities. To account for a $\\sim 16$-day\nperiodicity of the repeating FRB source 180916.J0158+65, a Shakura-Sunyaev disk\nwith a viscosity parameter of $\\alpha \\simeq 0.004$ and an accretion rate of\n$\\simeq 3 \\times 10^{16}$~g~s$^{-1}$ in the low state is invoked. Our scenario,\nif favored by future observations, will serve as indirect evidence for the\nstrange quark matter hypothesis.\n"}
+{"abstract": "  This paper is concerned with the sharp interface limit for the Allen-Cahn\nequation with a nonlinear Robin boundary condition in a bounded smooth domain\n$\\Omega\\subset\\mathbb{R}^2$. We assume that a diffuse interface already has\ndeveloped and that it is in contact with the boundary $\\partial\\Omega$. The\nboundary condition is designed in such a way that the limit problem is given by\nthe mean curvature flow with constant $\\alpha$-contact angle. For $\\alpha$\nclose to $90${\\deg} we prove a local in time convergence result for\nwell-prepared initial data for times when a smooth solution to the limit\nproblem exists. Based on the latter we construct a suitable curvilinear\ncoordinate system and carry out a rigorous asymptotic expansion for the\nAllen-Cahn equation with the nonlinear Robin boundary condition. Moreover, we\nshow a spectral estimate for the corresponding linearized Allen-Cahn operator\nand with its aid we derive strong norm estimates for the difference of the\nexact and approximate solutions using a Gronwall-type argument.\n"}
+{"abstract": "  These notes concern aspects of various graphs whose vertex set is a group $G$\nand whose edges reflect group structure in some way (so that they are invariant\nunder the action of the automorphism group of $G$). The graphs I will discuss\nare the power graph, enhanced power graph, deep commuting graph, commuting\ngraph, and non-generating graph, though I give a briefer discussion of the\nnilpotence and solvability graphs, and make some remarks on more general\ngraphs. Aspects to be discussed include induced subgraphs, forbidden subgraphs,\nconnectedness, and automorphism groups. We can also ask about the graphs formed\nby the edges in one graph but not in an earlier graph in the hierarchy. I have\nincluded some results on intersection graphs of subgroups of various types,\nwhich are often in a dual relation to one of the other graphs considered.\nAnother actor is the Gruenberg--Kegel graph, or prime graph, of a group: this\nvery small graph influences various graphs defined on the group. I say little\nabout Cayley graphs, since (except in special cases) these are not invariant\nunder the automorphism group of $G$.\n  The graphs all have the property that they contain \\emph{twins}, pairs of\nvertices with the same neighbours (save possibly one another). Being equal or\ntwins is an equivalence relation, and the automorphism group of the graph has a\nnormal subgroup inducing the symmetric group on each twin class. For some\npurposes, we can merge twin vertices and get a smaller graph. Continuing until\nno further twins occur, the result is independent of the reduction, and is the\n$1$-vertex graph if and only if the original graph is a \\emph{cograph}. So I\ndevote a section to cographs and twin reduction, and another to consequences\nfor automorphism groups.\n  There are briefer discussions of related matters.\n"}
+{"abstract": "  A common approach to the automatic detection of mispronunciation in language\nlearning is to recognize the phonemes produced by a student and compare it to\nthe expected pronunciation of a native speaker. This approach makes two\nsimplifying assumptions: a) phonemes can be recognized from speech with high\naccuracy, b) there is a single correct way for a sentence to be pronounced.\nThese assumptions do not always hold, which can result in a significant amount\nof false mispronunciation alarms. We propose a novel approach to overcome this\nproblem based on two principles: a) taking into account uncertainty in the\nautomatic phoneme recognition step, b) accounting for the fact that there may\nbe multiple valid pronunciations. We evaluate the model on non-native (L2)\nEnglish speech of German, Italian and Polish speakers, where it is shown to\nincrease the precision of detecting mispronunciations by up to 18% (relative)\ncompared to the common approach.\n"}
+{"abstract": "  We present a new multi-stream 3D mesh reconstruction network (MSMR-Net) for\nhand pose estimation from a single RGB image. Our model consists of an image\nencoder followed by a mesh-convolution decoder composed of connected graph\nconvolution layers. In contrast to previous models that form a single mesh\ndecoding path, our decoder network incorporates multiple cross-resolution\ntrajectories that are executed in parallel. Thus, global and local information\nare shared to form rich decoding representations at minor additional parameter\ncost compared to the single trajectory network. We demonstrate the\neffectiveness of our method in hand-hand and hand-object interaction scenarios\nat various levels of interaction. To evaluate the former scenario, we propose a\nmethod to generate RGB images of closely interacting hands. Moreoever, we\nsuggest a metric to quantify the degree of interaction and show that close hand\ninteractions are particularly challenging. Experimental results show that the\nMSMR-Net outperforms existing algorithms on the hand-object FreiHAND dataset as\nwell as on our own hand-hand dataset.\n"}
+{"abstract": "  Topological string theory near the conifold point of a Calabi-Yau threefold\ngives rise to factorially divergent power series which encode the all-genus\nenumerative information. These series lead to infinite towers of singularities\nin their Borel plane (also known as \"peacock patterns\"), and we conjecture that\nthe corresponding Stokes constants are integer invariants of the Calabi-Yau\nthreefold. We calculate these Stokes constants in some toric examples,\nconfirming our conjecture and providing in some cases explicit generating\nfunctions for the new integer invariants, in the form of q-series. Our\ncalculations in the toric case rely on the TS/ST correspondence, which promotes\nthe asymptotic series near the conifold point to spectral traces of operators,\nand makes it easier to identify the Stokes data. The resulting mathematical\nstructure turns out to be very similar to the one of complex Chern-Simons\ntheory. In particular, spectral traces correspond to state integral invariants\nand factorize in holomorphic/anti-holomorphic blocks.\n"}
+{"abstract": "  A fundamental aspect of racing is overtaking other race cars. Whereas\nprevious research on autonomous racing has majorly focused on lap-time\noptimization, here, we propose a method to plan overtaking maneuvers in\nautonomous racing. A Gaussian process is used to learn the behavior of the\nleading vehicle. Based on the outputs of the Gaussian process, a stochastic\nModel Predictive Control algorithm plans optimistic trajectories, such that the\ncontrolled autonomous race car is able to overtake the leading vehicle. The\nproposed method is tested in a simple simulation scenario.\n"}
+{"abstract": "  In this position paper, we argue for the need to investigate if and how\ngender stereotypes manifest in search and recommender systems.As a starting\npoint, we particularly focus on how these systems may propagate and reinforce\ngender stereotypes through their results in learning environments, a context\nwhere teachers and children in their formative stage regularly interact with\nthese systems. We provide motivating examples supporting our concerns and\noutline an agenda to support future research addressing the phenomena.\n"}
+{"abstract": "  The Tibet ASgamma experiment just reported their measurement of sub-PeV\ndiffuse gamma ray emission from the Galactic disk, with the highest energy up\nto 957 TeV. These gamma-rays are most likely the hadronic origin by cosmic ray\ninteraction with interstellar gas in the Galaxy. This measurement provides\ndirect evidence to the hypothesis that the Galactic cosmic rays can be\naccelerated beyond PeV energies. In this work, we try to explain the sub-PeV\ndiffuse gamma-ray spectrum within cosmic rays diffusive propagation model. We\nfind there is a tension between the sub-PeV diffuse gamma rays and the local\ncosmic ray spectrum. To describe the sub-PeV diffuse gamma-ray flux, it\ngenerally requires larger local cosmic-ray flux than measurement in the knee\nregion. We further calculate the PeV neutrino flux from the cosmic ray\npropagation model. Even all of these sub-PeV diffuse gamma rays originate from\nthe propagation, the Galactic neutrinos only account for less than ~15% of\nobserved flux, most of which are still from extragalactic sources.\n"}
+{"abstract": "  Recently, the end-to-end training approach for neural beamformer-supported\nmulti-channel ASR has shown its effectiveness in multi-channel speech\nrecognition. However, the integration of multiple modules makes it more\ndifficult to perform end-to-end training, particularly given that the\nmulti-channel speech corpus recorded in real environments with a sizeable data\nscale is relatively limited. This paper explores the usage of single-channel\ndata to improve the multi-channel end-to-end speech recognition system.\nSpecifically, we design three schemes to exploit the single-channel data,\nnamely pre-training, data scheduling, and data simulation. Extensive\nexperiments on CHiME4 and AISHELL-4 datasets demonstrate that all three methods\nimprove the multi-channel end-to-end training stability and speech recognition\nperformance, while the data scheduling approach keeps a much simpler pipeline\n(vs. pre-training) and less computation cost (vs. data simulation). Moreover,\nwe give a thorough analysis of our systems, including how the performance is\naffected by the choice of front-end, the data augmentation, training strategy,\nand single-channel data size.\n"}
+{"abstract": "  We present a web-based software tool, the Virtual Quantum Optics Laboratory\n(VQOL), that may be used for designing and executing realistic simulations of\nquantum optics experiments. A graphical user interface allows one to rapidly\nbuild and configure a variety of different optical experiments, while the\nruntime environment provides unique capabilities for visualization and\nanalysis. All standard linear optical components are available as well as\nsources of thermal, coherent, and entangled Gaussian states. A unique aspect of\nVQOL is the introduction of non-Gaussian measurements using detectors modeled\nas deterministic devices that \"click\" when the amplitude of the light falls\nabove a given threshold. We describe the underlying theoretical models and\nprovide several illustrative examples. We find that VQOL provides a a faithful\nrepresentation of many experimental quantum optics phenomena and may serve as\nboth a useful instructional tool for students as well as a valuable research\ntool for practitioners.\n"}
+{"abstract": "  Low-frequency time-dependent noise is one of the main obstacles on the road\ntowards a fully scalable quantum computer. The majority of solid-state qubit\nplatforms, from superconducting circuits to spins in semiconductors, are\ngreatly affected by $1/f$ noise. Among the different control techniques used to\ncounteract noise effects on the system, dynamical decoupling sequences are one\nof the most effective. However, most dynamical decoupling sequences require\nunbounded and instantaneous pulses, which are unphysical and can only implement\nidentity operations. Among methods that do restrict to bounded control fields,\nthere remains a need for protocols that implement arbitrary gates with\nlab-ready control fields. In this work, we introduce a protocol to design\nbounded and continuous control fields that implement arbitrary single-axis\nrotations while shielding the system from low-frequency time-dependent noise\nperpendicular to the control axis. We show the versatility of our method by\npresenting a set of non-negative-only control pulses that are immediately\napplicable to quantum systems with constrained control, such as singlet-triplet\nspin qubits. Finally, we demonstrate the robustness of our control pulses\nagainst classical $1/f$ noise and noise modeled with a random quantum bath,\nshowing that our pulses can even outperform ideal dynamical decoupling\nsequences.\n"}
+{"abstract": "  In probabilistic nonadaptive group testing (PGT), we aim to characterize the\nnumber of pooled tests necessary to identify a random $k$-sparse vector of\ndefectives with high probability. Recent work has shown that $n$ tests are\nnecessary when $k =\\omega(n/\\log n)$. It is also known that $O(k \\log n)$ tests\nare necessary and sufficient in other regimes. This leaves open the important\nsparsity regime where the probability of a defective item is $\\sim 1/\\log n$\n(or $k = \\Theta(n/\\log n)$) where the number of tests required is linear in\n$n$. In this work we aim to exactly characterize the number of tests in this\nsparsity regime. In particular, we seek to determine the number of defectives\n$\\lambda(\\alpha)n / \\log n$ that can be identified if the number of tests is\n$\\alpha n$. In the process, we give upper and lower bounds on the exact point\nat which individual testing becomes suboptimal, and the use of a carefully\nconstructed pooled test design is beneficial.\n"}
+{"abstract": "  Intermediate-mass black holes (IMBHs) by definition have masses of $M_{\\rm\nIMBH} \\sim 10^{2-5}~M_\\odot$, a range with few observational constraints.\nFinding IMBHs in globular star clusters (GCs) would validate a formation\nchannel for massive black-hole seeds in the early universe. Here, we simulate a\n60-hour observation with the next-generation Very Large Array (ngVLA) of 728 GC\ncandidates in the Virgo Cluster galaxy NGC\\,4472. Interpreting the radio\ndetection thresholds as signatures of accretion onto IMBHs, we benchmark IMBH\nmass thresholds in three scenarios and find the following: (1) Radio analogs of\nESO\\,243-49 HLX-1, a strong IMBH candidate with $M_{\\rm IMBH}^{\\rm HLX} \\sim\n10^{4-5}~M_\\odot$ in a star cluster, are easy to access in all 728 GC\ncandidates. (2) For the 30 GC candidates with extant X-ray detections, the\nempirical fundamental-plane relation involving black hole mass plus X-ray and\nradio luminosities suggests access to $M_{\\rm IMBH}^{\\rm FP} \\sim\n10^{1.7-3.6}~M_\\odot$, with an uncertainty of 0.44 dex. (3) A fiducial Bondi\naccretion model was applied to all 728 GC candidates and to radio stacks of GC\ncandidates. This model suggests access to IMBH masses, with a statistical\nuncertainty of 0.39 dex, of $M_{\\rm IMBH}^{\\rm B} \\sim 10^{4.9-5.1}~M_\\odot$\nfor individual GC candidates and $M_{\\rm IMBH}^{\\rm B,stack} \\sim\n10^{4.5}~M_\\odot$ for radio stacks of about 100-200 GC candidates. The fiducial\nBondi model offers initial guidance, but is subject to additional systematic\nuncertainties and should be superseded by hydrodynamical simulations of gas\nflows in GCs.\n"}
+{"abstract": "  Research in sociology and linguistics shows that people use language not only\nto express their own identity but to understand the identity of others. Recent\nwork established a connection between expression of identity and emoji usage on\nsocial media, through use of emoji skin tone modifiers. Motivated by that\nfinding, this work asks if, as with language, readers are sensitive to such\nacts of self-expression and use them to understand the identity of authors. In\nbehavioral experiments (n=488), where text and emoji content of social media\nposts were carefully controlled before being presented to participants, we find\nin the affirmative -- emoji are a salient signal of author identity. That\nsignal is distinct from, and complementary to, the one encoded in language.\nParticipant groups (based on self-identified ethnicity) showed no differences\nin how they perceive this signal, except in the case of the default yellow\nemoji. While both groups associate this with a White identity, the effect was\nstronger in White participants. Our finding that emoji can index social\nvariables will have experimental applications for researchers but also\nimplications for designers: supposedly ``neutral`` defaults may be more\nrepresentative of some users than others.\n"}
+{"abstract": "  The current state of the art for analytical and computational modelling of\ndeformation in nonlinear electroelastic and magnetoelastic membranes is\nreviewed. A general framework and a list of methods to model large deformation\nand associated instabilities (wrinkling, limit point, global bifurcation) due\nto coupled electromechanical or magnetoemechanical loading is presented.\n"}
+{"abstract": "  Cyber-physical systems are becoming core of the most modern systems\nconsisting control, data sharing and real-time monitoring. While centralized\ncontrol technique has been implemented in the past, recent innovation in\ndistributed control schemes makes it attractive due to various reasons. One of\nthem is the use of state-of-the-art communication protocols that makes the\nsystem more robust toward extreme conditions and ensures observability. Thus,\nas an application of cyber-physical systems, distributed control architectures\nare prone to various cyber-vulnerability which makes cybersecurity research\ncritical in this application domain. This paper reviews recent researches of\ndistributed control architectures, their cyber-vulnerabilities, and reported\nmitigation schemes. Finally, some research needs are addressed.\n"}
+{"abstract": "  We present a spatially resolved analysis of ionized gas at the nuclear region\nof the nearby galaxy NGC 1068. While NGC 1068 has been known to have gas\noutflows driven by its active galactic nucleus (AGN), more complex kinematical\nsignatures were recently reported, which were inconsistent with a rotation or\nsimple biconical outflows. To account for the nature of gas kinematics, we\nperformed a spatially resolved kinematical study, finding a morphologically\nsymmetric pair of approaching and receding gas blobs in the northeast region.\nThe midpoint of the two blobs is located at a distance of 180 pc from the\nnucleus in the projected plane. The ionized gas at the midpoint shows zero\nvelocity and high velocity dispersion, which are characteristics of an\noutflow-launching position, as the two sides of a bicone, i.e., approaching and\nreceding outflows are superposed on the line of sight, leading to no velocity\nshift but high velocity dispersion. We investigate the potential scenario of an\nadditional AGN based on a multiwavelength data set. While there are other\npossibilities, i.e., X-ray binary or supernova shock, the results from optical\nspectropolarimetry analysis are consistent with the presence of an additional\nAGN, which likely originates from a minor merger.\n"}
+{"abstract": "  We show that the category of pastures has arbitrary limits and colimits of\ndiagrams indexed by a small category.\n"}
+{"abstract": "  The Explorer-Director game, first introduced by Nedev and Muthukrishnan, can\nbe described as a game where two players -- Explorer and Director -- determine\nthe movement of a token on the vertices of a graph. At each time step, the\nExplorer specifies a distance that the token must move hoping to maximize the\namount of vertices ultimately visited, and the Director adversarially chooses\nwhere to move token in an effort to minimize this number. Given a graph and a\nstarting vertex, the number of vertices that are visited under optimal play is\ndenoted by $f_d(G,v)$.\n  In this paper, we first reduce the study of $f_d (G,v)$ to the determination\nof the minimal sets of vertices that are \\textit{closed} in a certain\ncombinatorial sense, thus providing a structural understanding of each player's\noptimal strategies. As an application, we address the problem on lattices and\ntrees. In the case of trees, we also provide a complete solution even in the\nmore restrictive setting where the strategy used by the Explorer is not allowed\nto depend on their opponent's responses. In addition to this paper, a\nsupplementary companion note will be posted to arXiv providing additional\nresults about the game in a variety of specific graph families.\n"}
+{"abstract": "  Binary black hole may form near a supermassive black hole. The background\nblack hole (BH) will affect the gravitational wave (GW) generated by the binary\nblack hole. It is well known that the Penrose process may provide extra energy\ndue to the ergosphere. In the present paper we investigate the energy\namplification of the gravitational wave by a Kerr black hole background. In\nparticular and different from the earlier studies, we compare the energies of\nthe waves in the cases with and without a nearby Kerr BH. We find that only\nwhen the binary black hole is moving relative to the Kerr background can the GW\nenergy be amplified. Otherwise, the energy will be suppressed by the background\nKerr black hole. This finding is consistent with the inequality found by Wald\nfor Penrose process. Taking into account realistic astrophysical scenarios, we\nfind that the Kerr black hole background can amplify the GW energy by at most 5\ntimes.\n"}
+{"abstract": "  We study local biholomorphisms with finite orbits in some neighborhood of the\norigin since they are intimately related to holomorphic foliations with closed\nleaves. We describe the structure of the set of periodic points in dimension 2.\nAs a consequence we show that given a local biholomorphism $F$, in dimension 2\nwith finite orbits, there exists an analytic curve passing through the origin\nand contained in the fixed point set of some non-trivial iterate of $F.$ As an\napplication we obtain that at least one eigenvalue of the linear part of $F$ at\nthe origin is a root of unity. Moreover, we show that such a result is sharp by\nexhibiting examples of local biholomorphisms, with finite orbits, such that\nexactly one of the eigenvalues is a root of unity. These examples are subtle\nsince we show they can not be embedded in one parameter groups.\n"}
+{"abstract": "  We present a joint model for entity-level relation extraction from documents.\nIn contrast to other approaches - which focus on local intra-sentence mention\npairs and thus require annotations on mention level - our model operates on\nentity level. To do so, a multi-task approach is followed that builds upon\ncoreference resolution and gathers relevant signals via multi-instance learning\nwith multi-level representations combining global entity and local mention\ninformation. We achieve state-of-the-art relation extraction results on the\nDocRED dataset and report the first entity-level end-to-end relation extraction\nresults for future reference. Finally, our experimental results suggest that a\njoint approach is on par with task-specific learning, though more efficient due\nto shared parameters and training steps.\n"}
+{"abstract": "  Descriptive and empirical sciences, such as History, are the sciences that\ncollect, observe and describe phenomena in order to explain them and draw\ninterpretative conclusions about influences, driving forces and impacts under\ngiven circumstances. Spreadsheet software and relational database management\nsystems are still the dominant tools for quantitative analysis and overall data\nmanagement in these these sciences, allowing researchers to directly analyse\nthe gathered data and perform scholarly interpretation. However, this current\npractice has a set of limitations, including the high dependency of the\ncollected data on the initial research hypothesis, usually useless for other\nresearch, the lack of representation of the details from which the registered\nrelations are inferred, and the difficulty to revisit the original data sources\nfor verification, corrections or improvements. To cope with these problems, in\nthis paper we present FAST CAT, a collaborative system for assistive data entry\nand curation in Digital Humanities and similar forms of empirical research. We\ndescribe the related challenges, the overall methodology we follow for\nsupporting semantic interoperability, and discuss the use of FAST CAT in the\ncontext of a European (ERC) project of Maritime History, called SeaLiT, which\nexamines economic, social and demographic impacts of the introduction of\nsteamboats in the Mediterranean area between the 1850s and the 1920s.\n"}
+{"abstract": "  The $L$-space conjecture asserts the equivalence, for prime 3-manifolds, of\nthree properties: not being an L-space, having a left-orderable fundamental\ngroup, and admitting a co-oriented taut foliation. We investigate these\nproperties for toroidal $3$-manifolds using various notions of slope detection.\nThis leads to a proof that toroidal $3$-manifolds with small order first\nhomology have left-orderable fundamental groups and, under certain fibring\nconditions, admit co-oriented taut foliations. It also allows us to show that\ncyclic branched covers of prime satellite knots are not $L$-spaces, have\nleft-orderable fundamental groups and, when they have fibred companion knots,\nadmit co-oriented taut foliations. A partial extension to prime toroidal links\nleads to a proof that prime quasi-alternating links are either hyperbolic or\n$(2, m)$-torus links. Our main technical result gives sufficient conditions for\ncertain slopes on the boundaries of rational homology solid tori to be detected\nby left-orders, foliations, and Heegaard Floer homology.\n"}
+{"abstract": "  We examine possible environmental sources of the enhanced star formation and\nactive galactic nucleus (AGN) activity in the $z = 3.09$ SSA22 protocluster\nusing Hubble WFC3 F160W ($\\sim1.6\\ \\rm \\mu m$) observations of the SSA22 field,\nincluding new observations centered on eight X-ray selected protocluster AGN.\nTo investigate the role of mergers in the observed AGN and star formation\nenhancement, we apply both quantitative (S\\'ersic-fit and Gini-$M_{20}$) and\nvisual morphological classifications to F160W images of protocluster Lyman\nbreak galaxies (LBGs) in the fields of the X-ray AGN and $z \\sim 3$ field LBGs\nin SSA22 and GOODS-N. We find no statistically significant differences between\nthe morphologies and merger fractions of protocluster and field LBGs, though we\nare limited by small number statistics in the protocluster. We also fit the\nUV-to-near-IR spectral energy distributions (SED) of F160W-detected\nprotocluster and field LBGs to characterize their stellar masses and star\nformation histories (SFH). We find that the mean protocluster LBG is by a\nfactor of $\\sim2$ times more massive and more attenuated than the mean $z \\sim\n3$ field LBG. We take our results to suggest that ongoing mergers are not more\ncommon among protocluster LBGs than field LBGs, though protocluster LBGs appear\nto be more massive. We speculate that the larger mass of the protocluster LBGs\ncontributes to the enhancement of SMBH mass and accretion rate in the\nprotocluster, which in turn drives the observed protocluster AGN enhancement.\n"}
+{"abstract": "  We report on a theoretical study on the rise of radiation-induced\nmagnetoresistance oscillations in two-dimensional systems of massive Dirac\nfermions. We study the bilayer system of monolayer graphene and hexagonal boron\nnitride (h-BN/graphene) and the trilayer system of hexagonal boron nitride\nencapsulated graphene (h-BN/graphene/h-BN). We extend the radiation-driven\nelectron orbit model that was previously devised to study the same oscillations\nin two-dimensional systems of Schr\\\"odinger electrons (GaAs/AlGaAS\nheterostructure) to the case of massive Dirac fermions. In the simulations we\nobtain clear oscillations for radiation frequencies in the terahertz and\nfar-infrared bands. %which contrasts with the two-dimensional Schrodinger\nelectrons case, %that are mainly sensitive to microwave frequencies. We\ninvestigate also the power and temperatures dependence. For the former we\nobtain similar results as for Schr\\\"odinger electrons and predict the rise of\nzero resistance states. For the latter we obtain a similar qualitatively\ndependence but quantitatively different when increasing temperature. While in\nGaAs the oscillations are wiped out in a few degrees, interestingly enough, for\nmassive Dirac fermions, we obtain observable oscillations for temperatures\nabove $100$ K and even at room temperature for the higher frequencies used in\nthe simulations.\n"}
+{"abstract": "  We propose a metrological strategy reaching Heisenberg scaling precision in\nthe estimation of functions of any number $l$ of arbitrary parameters encoded\nin a generic $M$-channel linear network. This scheme is experimentally feasible\nsince it only employs a single-mode squeezed vacuum and homodyne detection on a\nsingle output channel. Two auxiliary linear network are required and their role\nis twofold: to refocus the signal into a single channel after the interaction\nwith the interferometer, and to fix the function of the parameters to be\nestimated according to the linear network analysed. Although the refocusing\nrequires some knowledge on the parameters, we show that the required precision\non the prior measurement is shot-noise, and thus achievable with a classic\nmeasurement. We conclude by discussing two paradigmatic schemes in which the\nchoice of the auxiliary stages allows to change the function of the unknown\nparameter to estimate.\n"}
+{"abstract": "  The family of edge-sharing tri-coordinated iridates and ruthenates has\nemerged in recent years as a major platform for Kitaev spin liquid physics,\nwhere spins fractionalize into emergent magnetic fluxes and Majorana fermions\nwith Dirac-like dispersions. While such exotic states are usually pre-empted by\nlong-range magnetic order at low temperatures, signatures of Majorana fermions\nwith long coherent times have been predicted to manifest at intermediate and\nhigher energy scales, similar to the observation of spinons in quasi-1D spin\nchains. Here we present a Resonant Inelastic X-ray Scattering study of the\nmagnetic excitations of the hyperhoneycomb iridate $\\beta$-Li$_2$IrO$_3$ under\na magnetic field with a record-high-resolution spectrometer. At\nlow-temperatures, dispersing spin waves can be resolved around the predicted\nintertwined incommensurate spiral and field-induced zigzag orders, whose\nexcitation energy reaches a maximum of 16meV. A 2T magnetic field softens the\ndispersion around ${\\bf Q}=0$. The behavior of the spin waves under magnetic\nfield is consistent with our semiclassical calculations for the ground state\nand the dynamical spin structure factor, which further predicts that the ensued\nintertwined uniform states remain robust up to very high fields (100 T). Most\nsaliently, the low-energy magnon-like mode is superimposed by a broad continuum\nof excitations, centered around 35meV and extending up to 100meV. This\nhigh-energy continuum survives up to at least 300K -- well above the ordering\ntemperature of 38K -- and gives evidence for pairs of long-lived Majorana\nfermions of the proximate Kitaev spin liquid.\n"}
+{"abstract": "  Receiver sensitivity is a particularly important metric in optical\ncommunication links operating at low signal-to-noise ratios (SNRs), for example\nin deep-space communication, since it directly limits the maximum achievable\nreach and data rate. Pulse position modulation (PPM) with direct detection\nphoton-counting detectors are the most power-efficient solution known, however,\nthe sensitivity gain comes at the expense of reduced spectral efficiency. We\nshow that quadrature phase-shift keying (QPSK) modulation with a\nphase-sensitive ultralow noise pre-amplified coherent receiver outperforms\nother well-known power-efficient multi-dimensional coherent modulation formats,\nwhile simultaneously having higher spectral efficiency. It also results in\nbetter sensitivity than PPM for orders up to 64 with ideal direct detection\nusing photon-counting receivers. This is because of the bit error rate\ncharacteristics favoring the QPSK format when forward error correction with a\nlarge overhead is considered.\n"}
+{"abstract": "  The ability to model the evolution of compact binaries from the inspiral to\ncoalescence is central to gravitational wave astronomy. Current waveform\ncatalogues are built from vacuum binary black hole models, by evolving Einstein\nequations numerically and complementing them with knowledge from slow-motion\nexpansions. Much less is known about the coalescence process in the presence of\nmatter, or in theories other than General Relativity. Here, we explore the\nClose Limit Approximation as a powerful tool to understand the coalescence\nprocess in general setups. In particular, we study the head-on collision of two\nequal-mass, compact but horizonless objects. Our results show the appearance of\n\"echoes\" and indicate that a significant fraction of the merger energy goes\ninto these late-time repetitions. We also apply the Close Limit Approximation\nto investigate the effect of colliding black holes on surrounding scalar\nfields. Notably, our results indicate that observables obtained through\nperturbation theory may be extended to a significant segment of the merger\nphase, where in principle only a numerical approach is appropriate.\n"}
+{"abstract": "  We study entanglement-assisted quantum error-correcting codes (EAQECCs)\narising from classical one-point algebraic geometry codes from the Hermitian\ncurve with respect to the Hermitian inner product. Their only unknown parameter\nis $c$, the number of required maximally entangled quantum states since the\nHermitian dual of an AG code is unknown. In this article, we present an\nefficient algorithmic approach for computing $c$ for this family of EAQECCs. As\na result, this algorithm allows us to provide EAQECCs with excellent parameters\nover any field size.\n"}
+{"abstract": "  Coherence and entanglement are fundamental concepts in resource theory. The\ncoherence (entanglement) of assistance is the coherence (entanglement) that can\nbe extracted assisted by another party with local measurement and classical\ncommunication. We introduce and study the general coherence of assistance.\nFirst, in terms of real symmetric concave functions on the probability simplex,\nthe coherence of assistance and the entanglement of assistance are shown to be\nin one-to-one correspondence. We then introduce two classes of quantum states:\nthe assisted maximally coherent states and the assisted maximally entangled\nstates. They can be transformed into maximally coherent or entangled pure\nstates with the help of another party using local measurement and classical\ncommunication. We give necessary conditions for states to be assisted maximally\ncoherent or assisted maximally entangled. Based on these, a unified framework\nbetween coherence and entanglement including coherence (entanglement) measures,\ncoherence (entanglement) of assistance, coherence (entanglement) resources is\nproposed. Then we show that the coherence of assistance as well as entanglement\nof assistance are strictly larger than the coherence of convex roof and\nentanglement of convex roof for all full rank density matrices. So all full\nrank quantum states are distillable in the assisted coherence distillation.\n"}
+{"abstract": "  The benzene...ethene and parallel-displaced (PD) benzene...benzene dimers are\nthe most fundamental systems involving p-p stacking interactions. Several\nhigh-level ab initio investigations calculated the binding energies of these\ndimers at the CCSD(T)/CBS level of theory using various approaches such as\nreduced virtual orbital spaces and/or MP2-based basis set corrections. Here we\nobtain CCSDT(Q) binding energies using a Weizmann-3-type approach. In\nparticular, we extrapolate the SCF, CCSD, and (T) components using large\nheavy-atom augmented Gaussian basis sets (namely, SCF/jul-cc-pV{5,6}Z,\nCCSD/jul-cc-pV{Q,5}Z, and (T)/jul-cc-pV{T,Q}Z). We consider post-CCSD(T)\ncontributions up to CCSDT(Q), inner-shell, scalar-relativistic, and\nBorn-Oppenheimer corrections. Overall, our best relativistic, all-electron\nCCSDT(Q) binding energies are Delta Ee,all,rel = 1.234 (benzene...ethene) and\n2.550 (benzene...benzene PD), Delta H0 = 0.949 (benzene...ethene) and 2.310\n(benzene...benzene PD), and Delta H298 = 0.130 (benzene...ethene) and 1.461\n(benzene...benzene PD) kcal/mol. Important conclusions are reached regarding\nthe basis set convergence of the SCF, CCSD, (T), and post-CCSD(T) components.\nExplicitly correlated calculations are used as a sanity check on the\nconventional binding energies. Overall, post-CCSD(T) contributions are\ndestabilizing by 0.028 (benzene...ethene) and 0.058(benzene...benzene)\nkcal/mol, thus they cannot be neglected if 0.1 kcal/mol accuracy is sought.\n"}
+{"abstract": "  We obtain the superfluid weight and Berezinskii-Kosterlitz-Thouless (BKT)\ntransition temperature for highly unconventional superconducting states with\nthe coexistence of chiral d-wave superconductivity, charge density waves and\npair density waves in the strained graphene. Our results show that the\nstrain-induced flat bands can promote the superconducting transition\ntemperature approximately $50\\%$ compared to that of the original doped\ngraphene, which suggests that the flat-band superconductivity is a potential\nroute to get superconductivity with higher critical temperatures. In\nparticular, we obtain the superfluid weight for the pure superconducting\npair-density-wave states from which the deduced superconducting transition\ntemperature is shown to be much lower than the gap-opening temperature of the\npair density wave, which is helpful to understand the phenomenon of the\npseudogap state in high-$T_c$ cuprate superconductors. Finally, we show that\nthe BKT transition temperature versus doping for strained graphene exhibits a\ndome-like shape and it depends linearly on the spin-spin interaction strength.\n"}
+{"abstract": "  We consider an electron in a localized potential submitted to a weak\nexternal, timedependent field. In the linear response regime, the response\nfunction can be computed using Kubo's formula. In this paper, we consider the\nnumerical approximation of the response function by means of a truncation to a\nfinite region of space. This is necessarily a singular approximation because of\nthe discreteness of the spectrum of the truncated Hamiltonian, and in practice\na regularization (smoothing) has to be used. Our results provide error\nestimates for the response function past the ionization threshold with respect\nto both the smoothing parameter and the size of the computational domain.\n"}
+{"abstract": "  We investigate the formal semantics of a simple imperative language that has\nboth classical and quantum constructs. More specifically, we provide an\noperational semantics, a denotational semantics and two Hoare-style proof\nsystems: an abstract one and a concrete one. The two proof systems are\nsatisfaction-based, as inspired by the program logics of Barthe et al for\nprobabilistic programs. The abstract proof system turns out to be sound and\nrelatively complete, while the concrete one is sound only.\n"}
+{"abstract": "  We propose a Standard Model extension by a $U(1)_{R}$ gauge symmetry where\nonly right-handed chiral fermions can carry a non-trivial charge. Here we show\nthat the simplest anomaly-free solution to accommodate the proton charge radius\ndiscrepancy takes right-handed muons $\\mu_R$ and first generation quarks, $u_R$\nand $d_R$. Consistency with the latest muon's $(g-2)$ measurements is achieved\nthrough an extra light scalar, which itself must lie in the tens of MeV mass\nrange to be viable.\n"}
+{"abstract": "  The spectral gap of the Neumann and Dirichlet Laplacians are each known to\nhave a sharp positive lower bound among convex domains of a given diameter.\nBetween these cases, for each positive value of the Robin parameter an\nanalogous sharp lower bound on the spectral gap is conjectured. In this paper\nwe show the extension of this conjecture to negative Robin parameters fails\ncompletely, by proving the spectral gap of an explicit family of domains can be\nexponentially small.\n"}
+{"abstract": "  We explore some new off-shell and on-shell conserved quantities for a scalar\nfield in Minkowski space, using integrability condition. The off-shell\nconserved tensors are related to the kinematics of the field, while a linear\ncombination of the off-shell and the on-shell conserved tensors ends up with\nthe energy-momentum tensor for the scalar field. In the curved background,\nusing Ricci and Bianchi identities, Brans-Dicke type field equations emerge,\nwithout requiring the principle of equivalence. Further, starting from the\ncurvature scalar and using these identities, the field equations for modified\ngravity (Einstein-Hilbert action in the presence of higher-order terms)\nfollows.\n"}
+{"abstract": "  We report the results of a Monte Carlo global QCD analysis of unpolarized\nparton distribution functions (PDFs), including for the first time constraints\nfrom ratios of $^3$He to $^3$H structure functions recently obtained by the\nMARATHON experiment at Jefferson Lab. Our simultaneous analysis of nucleon PDFs\nand nuclear effects in $A=2$ and $A=3$ nuclei reveals the first indication for\nan isovector nuclear EMC effect in light nuclei. We find that while the\nMARATHON data yield relatively weak constraints on the $F_2^n/F_2^p$ neutron to\nproton structure function ratio and on the $d/u$ PDF ratio, they suggest an\nenhanced nuclear effect on the $d$-quark PDF in the bound proton, questioning\nthe assumptions commonly made in nuclear PDF analyses.\n"}
+{"abstract": "  The limit order book (LOB) depicts the fine-grained demand and supply\nrelationship for financial assets and is widely used in market microstructure\nstudies. Nevertheless, the availability and high cost of LOB data restrict its\nwider application. The LOB recreation model (LOBRM) was recently proposed to\nbridge this gap by synthesizing the LOB from trades and quotes (TAQ) data.\nHowever, in the original LOBRM study, there were two limitations: (1)\nexperiments were conducted on a relatively small dataset containing only one\nday of LOB data; and (2) the training and testing were performed in a\nnon-chronological fashion, which essentially re-frames the task as\ninterpolation and potentially introduces lookahead bias. In this study, we\nextend the research on LOBRM and further validate its use in real-world\napplication scenarios. We first advance the workflow of LOBRM by (1) adding a\ntime-weighted z-score standardization for the LOB and (2) substituting the\nordinary differential equation kernel with an exponential decay kernel to lower\ncomputation complexity. Experiments are conducted on the extended LOBSTER\ndataset in a chronological fashion, as it would be used in a real-world\napplication. We find that (1) LOBRM with decay kernel is superior to\ntraditional non-linear models, and module ensembling is effective; (2)\nprediction accuracy is negatively related to the volatility of order volumes\nresting in the LOB; (3) the proposed sparse encoding method for TAQ exhibits\ngood generalization ability and can facilitate manifold tasks; and (4) the\ninfluence of stochastic drift on prediction accuracy can be alleviated by\nincreasing historical samples.\n"}
+{"abstract": "  We construct finite dimensional families of non-steady solutions to the Euler\nequations, existing for all time, and exhibiting all kinds of qualitative\ndynamics in the phase space, for example: strange attractors and chaos,\ninvariant manifolds of arbitrary topology, and quasiperiodic invariant tori of\nany dimension.\n  The main theorem of the paper, from which these families of solutions are\nobtained, states that for any given vector field $X$ on a closed manifold $N$,\nthere is a Riemannian manifold $M$ on which the following holds: $N$ is\ndiffeomorphic to a finite dimensional manifold in the phase space of fluid\nvelocities (the space of divergence-free vector fields on $M$) that is\ninvariant under the Euler evolution, and on which the Euler equation reduces to\na finite dimensional ODE that is given by an arbitrarily small perturbation of\nthe vector field $X$ on $N$.\n"}
+{"abstract": "  The placement of a magnetic monopole into an electrically-neutral chiral\nplasma with a non-zero axial density results in an electric polarization of the\nmatter. The electric current produced by the chiral magnetic effect is balanced\nby charge diffusion and Ohmic dissipation, which generates a non-trivial charge\ndistribution. In turn, the latter induces a separation of chiralities along the\nmagnetic field of the monopole due to the chiral separation effect. We find the\nstationary states of such a system, with vanishing total electric current and\nstationary axial current balanced by the chiral anomaly. In this solution, the\nmonopole becomes \"dressed\" with an electric charge that is proportional to the\naveraged chiral density of the matter -- forming a chiral dyon. The interplay\nbetween the chiral effects on the one hand, and presence of magnetic field of\nthe monopole on the other, may affect the evolution of the monopole density in\nthe early Universe, contribute to the process of baryogenesis, and can also be\ninstrumental for detection of relic monopoles using chiral materials.\n"}
+{"abstract": "  Explaining the decision of a multi-modal decision-maker requires to determine\nthe evidence from both modalities. Recent advances in XAI provide explanations\nfor models trained on still images. However, when it comes to modeling multiple\nsensory modalities in a dynamic world, it remains underexplored how to\ndemystify the mysterious dynamics of a complex multi-modal model. In this work,\nwe take a crucial step forward and explore learnable explanations for\naudio-visual recognition. Specifically, we propose a novel space-time attention\nnetwork that uncovers the synergistic dynamics of audio and visual data over\nboth space and time. Our model is capable of predicting the audio-visual video\nevents, while justifying its decision by localizing where the relevant visual\ncues appear, and when the predicted sounds occur in videos. We benchmark our\nmodel on three audio-visual video event datasets, comparing extensively to\nmultiple recent multi-modal representation learners and intrinsic explanation\nmodels. Experimental results demonstrate the clear superior performance of our\nmodel over the existing methods on audio-visual video event recognition.\nMoreover, we conduct an in-depth study to analyze the explainability of our\nmodel based on robustness analysis via perturbation tests and pointing games\nusing human annotations.\n"}
+{"abstract": "  We consider the following model of degenerate and singular oscillatory\nintegral operators: \\begin{equation*} Tf(x)=\\int_{\\mathbb{R}} e^{i\\lambda\nS(x,y)}K(x,y)\\psi(x,y)f(y)dy, \\end{equation*} where the phase functions are\nhomogeneous polynomials of degree $n$ and the singular kernel $K(x,y)$\nsatisfies suitable conditions related to a real parameter $\\mu$. We show that\nthe sharp decay estimates on $L^2$ spaces, obtained in \\cite{liu1999model}, can\nbe preserved on more general $L^p$ spaces with an additional condition imposed\non the singular kernel. In fact, we obtain that \\begin{equation*}\n\\|Tf\\|_{L^p}\\leq C_{E,S,\\psi,\\mu,n,p}\\lambda^{-\\frac{1-\\mu}{n}}\\|f\\|_{L^p},\\ \\\n\\frac{n-2\\mu}{n-1-\\mu}\\leq p \\leq\\frac{n-2\\mu}{1-\\mu}. \\end{equation*} The case\nwithout the additional condition is also discussed.\n"}
+{"abstract": "  From a geometric point of view, Pauli's exclusion principle defines a\nhypersimplex. This convex polytope describes the compatibility of $1$-fermion\nand $N$-fermion density matrices, therefore it coincides with the convex hull\nof the pure $N$-representable $1$-fermion density matrices. Consequently, the\ndescription of ground state physics through $1$-fermion density matrices may\nnot necessitate the intricate pure state generalized Pauli constraints. In this\narticle, we study the generalization of the $1$-body $N$-representability\nproblem to ensemble states with fixed spectrum $\\mathbf{w}$, in order to\ndescribe finite-temperature states and distinctive mixtures of excited states.\nBy employing ideas from convex analysis and combinatorics, we present a\ncomprehensive solution to the corresponding convex relaxation, thus\ncircumventing the complexity of generalized Pauli constraints. In particular,\nwe adapt and further develop tools such as symmetric polytopes, sweep\npolytopes, and Gale order. For both fermions and bosons, generalized exclusion\nprinciples are discovered, which we determine for any number of particles and\ndimension of the $1$-particle Hilbert space. These exclusion principles are\nexpressed as linear inequalities satisfying hierarchies determined by the\nnon-zero entries of $\\mathbf{w}$. The two families of polytopes resulting from\nthese inequalities are part of the new class of so-called lineup polytopes.\n"}
+{"abstract": "  We investigate the effect of the Biermann battery during the Epoch of\nReionization (EoR) using cosmological Adaptive Mesh Refinement simulations\nwithin the framework of the SPHINX project. We develop a novel numerical\ntechnique to solve for the Biermann battery term in the Constrained Transport\nmethod, preserving both the zero divergence of the magnetic field and the\nabsence of Biermann battery for isothermal flows. The structure-preserving\nnature of our numerical method turns out to be very important to minimise\nnumerical errors during validation tests of the propagation of a Str\\\"omgren\nsphere and of a Sedov blast wave. We then use this new method to model the\nevolution of a 2.5 and 5 co-moving Mpc cosmological box with a state-of-the-art\ngalaxy formation model within the RAMSES code. Contrary to previous findings,\nwe show that three different Biermann battery channels emerge: the first one is\nassociated with linear perturbations before the EoR, the second one is the\nclassical Biermann battery associated with reionization fronts during the EoR,\nand the third one is associated with strong, supernova-driven outflows. While\nthe two former channels generate spontaneously volume-filling magnetic fields\nwith a strength on the order or below $10^{-20}$ G, the latter, owing to the\nhigher plasma temperature and a marginally-resolved turbulent dynamo, reaches a\nfield strength as high as $10^{-18}$ G in the intergalactic medium around\nmassive haloes.\n"}
+{"abstract": "  Several astrophysical scenarios have been proposed to explain the origin of\nthe population of binary black hole (BBH) mergers detected in gravitational\nwaves (GWs) by the LIGO/Virgo Collaboration. Among them, BBH mergers assembled\ndynamically in young massive and open clusters have been shown to produce\nmerger rate densities consistent with LIGO/Virgo estimated rates. We use the\nresults of a suite of direct, high-precision $N$-body evolutionary models of\nyoung massive and open clusters and build the population of BBH mergers, by\naccounting for both a cosmologically-motivated model for the formation of young\nmassive and open clusters and the detection probability of LIGO/Virgo. We show\nthat our models produce dynamically-paired BBH mergers that are well consistent\nwith the observed masses, mass ratios, effective spin parameters, and final\nspins of the second Gravitational Wave Transient Catalog (GWTC-2).\n"}
+{"abstract": "  We establish a sharp upper-bound for the first non-zero even eigenvalue\n(corresponding to an even eigenfunction) of the Hilbert-Brunn-Minkowski\noperator associated to a strongly convex $C^2$-smooth origin-symmetric convex\nbody $K$ in $\\mathbb{R}^n$. Our isospectral inequality is centro-affine\ninvariant, attaining equality if and only if $K$ is a (centered) ellipsoid;\nthis is reminiscent of the (non affine invariant) classical\nSzeg\\\"{o}--Weinberger isospectral inequality for the Neumann Laplacian. The new\nupper-bound complements the conjectural lower-bound, which has been shown to be\nequivalent to the log-Brunn-Minkowski inequality and is intimately related to\nthe uniqueness question in the even log-Minkowski problem. As applications, we\nobtain new strong non-uniqueness results in the even $L^p$-Minkowski problem in\nthe subcritical range $-n < p < 0$, as well as new rigidity results for the\ncritical exponent $p=-n$ and supercritical regime $p < -n$. In particular, we\nshow that any $K$ as above which is not an ellipsoid is a witness to\nnon-uniqueness in the even $L^p$-Minkowski problem for all $p \\in (-n,p_K)$ and\nsome $p_K \\in (-n,0)$, and that $K$ can be chosen so that $p_K$ is arbitrarily\nclose to $0$.\n"}
+{"abstract": "  We identify points of difference between Invariant Set Theory and standard\nquantum theory, and evaluate if these would lead to noticeable differences in\npredictions between the two theories. From this evaluation, we design a number\nof experiments, which, if undertaken, would allow us to investigate whether\nstandard quantum theory or invariant set theory best describes reality.\n"}
+{"abstract": "  We use numerical simulations and linear stability analysis to study an active\nnematic layer where the director is allowed to point out of the plane. Our\nresults highlight the difference between extensile and contractile systems.\nContractile stress suppresses the flows perpendicular to the layer and favours\nin-plane orientations of the director. By contrast extensile stress promotes\ninstabilities that can turn the director out of the plane, leaving behind a\npopulation of distinct, in-plane regions that continually elongate and divide.\nOur results suggest a mechanism for the initial stages of layer formation in\nliving systems, and explain the propensity of dislocation lines in\nthree-dimensional active nematics to be of twist-type in extensile or\nwedge-type in contractile materials.\n"}
+{"abstract": "  The scoring function, which measures the plausibility of triplets in\nknowledge graphs (KGs), is the key to ensure the excellent performance of KG\nembedding, and its design is also an important problem in the literature.\nAutomated machine learning (AutoML) techniques have recently been introduced\ninto KG to design task-aware scoring functions, which achieve state-of-the-art\nperformance in KG embedding. However, the effectiveness of searched scoring\nfunctions is still not as good as desired. In this paper, observing that\nexisting scoring functions can exhibit distinct performance on different\nsemantic patterns, we are motivated to explore such semantics by searching\nrelation-aware scoring functions. But the relation-aware search requires a much\nlarger search space than the previous one. Hence, we propose to encode the\nspace as a supernet and propose an efficient alternative minimization algorithm\nto search through the supernet in a one-shot manner. Finally, experimental\nresults on benchmark datasets demonstrate that the proposed method can\nefficiently search relation-aware scoring functions, and achieve better\nembedding performance than state-of-the-art methods.\n"}
+{"abstract": "  We show that\n  1. for every $A\\subseteq \\{0, 1\\}^n$, there exists a polytope $P\\subseteq\n\\mathbb{R}^n$ with $P \\cap \\{0, 1\\}^n = A$ and extension complexity\n$O(2^{n/2})$,\n  2. there exists an $A\\subseteq \\{0, 1\\}^n$ such that the extension complexity\nof any $P$ with $P\\cap \\{0, 1\\}^n = A$ must be at least\n$2^{\\frac{n}{3}(1-o(1))}$.\n  We also remark that the extension complexity of any 0/1-polytope in\n$\\mathbb{R}^n$ is at most $O(2^n/n)$ and pose the problem whether the upper\nbound can be improved to $O(2^{cn})$, for $c<1$.\n"}
+{"abstract": "  This work exploits commodity, ultra-low cost, commercial radio frequency\nidentification tags (RFID) as the elements of a reconfigurable surface. Such\nbatteryless tags are powered and controlled by a software-defined (SDR) reader,\nwith properly modified software, so that a source-destination link is assisted,\noperating at a different carrier frequency. In terms of theory, the optimal\ngain and corresponding best element configuration is offered, with tractable\npolynomial complexity (instead of exponential) in number of elements. In terms\nof practice, a concrete way to design and prototype a wireless, batteryless,\nRF-powered, reconfigurable surface is offered and a proof-of-concept is\nexperimentally demonstrated. It is also found that even with perfect channel\nestimation, the weak nature of backscattered links limits the performance\ngains, even for large number of surface elements. Impact of channel estimation\nerrors is also studied. Future extensions at various carrier frequencies could\nbe directly accommodated, through simple modifications in the antenna and\nmatching network of each RFID tag/surface element.\n"}
+{"abstract": "  Particle tracks and differential energy loss measured in high pressure\ngaseous detectors can be exploited for event identification in neutrinoless\ndouble beta decay~($0\\nu \\beta \\beta$) searches. We develop a new method based\non Kalman Filter in a Bayesian formalism (KFB) to reconstruct meandering tracks\nof MeV-scale electrons. With simulation data, we compare the signal and\nbackground discrimination power of the KFB method assuming different detector\ngranularities and energy resolutions. Typical background from $^{232}$Th and\n$^{238}$U decay chains can be suppressed by another order of magnitude than\nthat in published literatures, approaching the background-free regime. For the\nproposed PandaX-III experiment, the $0\\nu \\beta \\beta$ search half-life\nsensitivity at the 90\\% confidence level would reach $2.7 \\times 10^{26}$~yr\nwith 5-year live time, a factor of 2.7 improvement over the initial design\ntarget.\n"}
+{"abstract": "  With the evolution of quantum computing, researchers now-a-days tend to\nincline to find solutions to NP-complete problems by using quantum algorithms\nin order to gain asymptotic advantage. In this paper, we solve $k$-coloring\nproblem (NP-complete problem) using Grover's algorithm in any dimensional\nquantum system or any $d$-ary quantum system for the first time to the best of\nour knowledge, where $d \\ge 2$. A newly proposed comparator-based approach\nhelps to generalize the implementation of the $k$-coloring problem in any\ndimensional quantum system. Till date, $k$-coloring problem has been\nimplemented only in binary and ternary quantum system, hence, we abide to $d=2$\nor $d=3$, that is for binary and ternary quantum system for comparing our\nproposed work with the state-of-the-art techniques. This proposed approach\nmakes the reduction of the qubit cost possible, compared to the\nstate-of-the-art binary quantum systems. Further, with the help of newly\nproposed ternary comparator, a substantial reduction in quantum gate count for\nthe ternary oracle circuit of the $k$-coloring problem than the previous\napproaches has been obtained. An end-to-end automated framework has been put\nforward for implementing the $k$-coloring problem for any undirected and\nunweighted graph on any available Near-term quantum devices or Noisy\nIntermediate-Scale Quantum (NISQ) devices or multi-valued quantum simulator,\nwhich helps in generalizing our approach.\n"}
+{"abstract": "  The combination of ferromagnetism and semiconducting behavior offers an\navenue for realizing novel spintronics and spin-enhanced thermoelectrics. Here\nwe demonstrate the synthesis of doped and nanocomposite half Heusler\nFe$_{1+x}$VSb films by molecular beam epitaxy. For dilute excess Fe ($x <\n0.1$), we observe a decrease in the Hall electron concentration and no\nsecondary phases in X-ray diffraction, consistent with Fe doping into FeVSb.\nMagnetotransport measurements suggest weak ferromagnetism that onsets at a\ntemperature of $T_{c} \\approx$ 5K. For higher Fe content ($x > 0.1$),\nferromagnetic Fe nanostructures precipitate from the semiconducting FeVSb\nmatrix. The Fe/FeVSb interfaces are epitaxial, as observed by transmission\nelectron microscopy and X-ray diffraction. Magnetotransport measurements\nsuggest proximity-induced magnetism in the FeVSb, from the Fe/FeVSb interfaces,\nat an onset temperature of $T_{c} \\approx$ 20K.\n"}
+{"abstract": "  We extend the scheme of quantum teleportation by quantum walks introduced by\nWang et al. (2017). First, we introduce the mathematical definition of the\naccomplishment of quantum teleportation by this extended scheme. Secondly, we\nshow a useful necessary and sufficient condition that the quantum teleportation\nis accomplished rigorously. Our result classifies the parameters of the setting\nfor the accomplishment of the quantum teleportation.\n"}
+{"abstract": "  We describe the application of convolutional neural network style transfer to\nthe problem of improved visualization of underdrawings and ghost-paintings in\nfine art oil paintings. Such underdrawings and hidden paintings are typically\nrevealed by x-ray or infrared techniques which yield images that are grayscale,\nand thus devoid of color and full style information. Past methods for inferring\ncolor in underdrawings have been based on physical x-ray fluorescence spectral\nimaging of pigments in ghost-paintings and are thus expensive, time consuming,\nand require equipment not available in most conservation studios. Our\nalgorithmic methods do not need such expensive physical imaging devices. Our\nproof-of-concept system, applied to works by Pablo Picasso and Leonardo, reveal\ncolors and designs that respect the natural segmentation in the ghost-painting.\nWe believe the computed images provide insight into the artist and associated\noeuvre not available by other means. Our results strongly suggest that future\napplications based on larger corpora of paintings for training will display\ncolor schemes and designs that even more closely resemble works of the artist.\nFor these reasons refinements to our methods should find wide use in art\nconservation, connoisseurship, and art analysis.\n"}
+{"abstract": "  The growth of a pebble accreting planetary core is stopped when reaching its\n\\textit{isolation mass} that is due to a pressure maximum emerging at the outer\nedge of the gap opened in gas. This pressure maximum traps the inward drifting\npebbles stopping the accretion of solids onto the core. On the other hand, a\nlarge amount of pebbles ($\\sim 100M_\\oplus$) should flow through the orbit of\nthe core until reaching its isolation mass. The efficiency of pebble accretion\nincreases if the core grows in a dust trap of the protoplanetary disc. Dust\ntraps are observed as ring-like structures by ALMA suggesting the existence of\nglobal pressure maxima in discs that can also act as planet migration traps.\nThis work aims to reveal how large a planetary core can grow in such a pressure\nmaximum by pebble accretion. In our hydrodynamic simulations, pebbles are\ntreated as a pressureless fluid mutually coupled to the gas via drag force. Our\nresults show that in a global pressure maximum the pebble isolation mass for a\nplanetary core is significantly larger than in discs with power-law surface\ndensity profile. An increased isolation mass shortens the formation time of\ngiant planets.\n"}
+{"abstract": "  Parallelization is an algebraic operation that lifts problems to sequences in\na natural way. Given a sequence as an instance of the parallelized problem,\nanother sequence is a solution of this problem if every component is\ninstance-wise a solution of the original problem. In the Weihrauch lattice\nparallelization is a closure operator. Here we introduce a dual operation that\nwe call stashing and that also lifts problems to sequences, but such that only\nsome component has to be an instance-wise solution. In this case the solution\nis stashed away in the sequence. This operation, if properly defined, induces\nan interior operator in the Weihrauch lattice. We also study the action of the\nmonoid induced by stashing and parallelization on the Weihrauch lattice, and we\nprove that it leads to at most five distinct degrees, which (in the maximal\ncase) are always organized in pentagons. We also introduce another closely\nrelated interior operator in the Weihrauch lattice that replaces solutions of\nproblems by upper Turing cones that are strong enough to compute solutions. It\nturns out that on parallelizable degrees this interior operator corresponds to\nstashing. This implies that, somewhat surprisingly, all problems which are\nsimultaneously parallelizable and stashable have computability-theoretic\ncharacterizations. Finally, we apply all these results in order to study the\nrecently introduced discontinuity problem, which appears as the bottom of a\nnumber of natural stashing-parallelization pentagons. The discontinuity problem\nis not only the stashing of several variants of the lesser limited principle of\nomniscience, but it also parallelizes to the non-computability problem. This\nsupports the slogan that \"non-computability is the parallelization of\ndiscontinuity\".\n"}
+{"abstract": "  We consider close-packed tiling models of geometric objects -- a mixture of\nhardcore dimers and plaquettes -- as a generalisation of the familiar dimer\nmodels. Specifically, on an anisotropic cubic lattice, we demand that each site\nbe covered by either a dimer on a z-link or a plaquette in the x-y plane. The\nspace of such fully packed tilings has an extensive degeneracy. This maps onto\na fracton-type `higher-rank electrostatics', which can exhibit a\nplaquette-dimer liquid and an ordered phase. We analyse this theory in detail,\nusing height representations and T-duality to demonstrate that the concomitant\nphase transition occurs due to the proliferation of dipoles formed by defect\npairs. The resultant critical theory can be considered as a fracton version of\nthe Kosterlitz-Thouless transition. A significant new element is its UV-IR\nmixing, where the low energy behavior of the liquid phase and the transition\nout of it is dominated by local (short-wavelength) fluctuations, rendering the\ncritical phenomenon beyond the renormalization group paradigm.\n"}
+{"abstract": "  A complete overview of the surrounding vehicle environment is important for\ndriver assistance systems and highly autonomous driving. Fusing results of\nmultiple sensor types like camera, radar and lidar is crucial for increasing\nthe robustness. The detection and classification of objects like cars, bicycles\nor pedestrians has been analyzed in the past for many sensor types. Beyond\nthat, it is also helpful to refine these classes and distinguish for example\nbetween different pedestrian types or activities. This task is usually\nperformed on camera data, though recent developments are based on radar\nspectrograms. However, for most automotive radar systems, it is only possible\nto obtain radar targets instead of the original spectrograms. This work\ndemonstrates that it is possible to estimate the body height of walking\npedestrians using 2D radar targets. Furthermore, different pedestrian motion\ntypes are classified.\n"}
+{"abstract": "  We explore the potential of twisted light as a tool to unveil many-body\neffects in parabolically confined systems. According to the Generalized Kohn\nTheorem, the dipole response of such a multi-particle system to a spatially\nhomogeneous probe is indistinguishable from the response of a system of\nnon-interacting particles. Twisted light however can excite internal degrees of\nfreedom, resulting in the appearance of new peaks in the even multipole\nspectrum which are not present when the probe is a plane wave. We also\ndemonstrate the ability of the proposed twisted light probe to capture the\ntransition of interacting fermions into a strongly correlated regime in a\none-dimensional harmonic trap. We report that by suitable choice of the probe's\nparameters, the transition into a strongly correlated phase manifests itself as\nan approach and ultimate superposition of peaks in the second order quadrupole\nresponse. These features, observed in exact calculations for two electrons, are\nreproduced in adiabatic Time Dependent Density Functional Theory simulations.\n"}
+{"abstract": "  In this paper, we develop an adaptive high-order surface finite element\nmethod (FEM) incorporating the spectral deferred correction method for chain\ncontour discretization to solve polymeric self-consistent field equations on\ngeneral curved surfaces. The high-order surface FEM is obtained by the\nhigh-order surface geometrical approximation and the high-order function space\napproximation. Numerical results demonstrate that the precision order of these\nmethods is consistent with the theoretical prediction. In order to describe the\nsharp interface in the strongly segregated system more accurately, an adaptive\nFEM equipped with a new Log marking strategy is proposed. Compared with the\ntraditional strategy, the Log marking strategy can not only label the elements\nthat need to be refined or coarsened, but also give the refined or coarsened\ntimes, which can make full use of the information of a posterior error\nestimator and improve the ecciency of the adaptive algorithm. To demonstrate\nthe power of our approach, we investigate the self-assembled patterns of\ndiblock copolymers on several distinct curved surfaces. Numerical results\nillustrate the ecciency of the proposed method, especially for strongly\nsegregated systems with economical discretization nodes.\n"}
+{"abstract": "  It is well known that performance of a thermophotovoltaic (TPV) device can be\nenhanced if the vacuum gap between the thermal emitter and the TPV cell becomes\nnanoscale due to the photon tunneling of evanescent waves. Having multiple\nbandgaps, multi-junction TPV cells have received attention as an alternative\nway to improve its performance by selectively absorbing the spectral radiation\nin each subcell. In this work, we comprehensively analyze the optimized\nnear-field tandem TPV system consisting of the thin-ITO-covered tungsten\nemitter (at 1500 K) and GaInAsSb/InAs monolithic interconnected tandem TPV cell\n(at 300 K). We develop a simulation model by coupling the near-field radiation\nsolved by fluctuational electrodynamics and the diffusion-recombination-based\ncharge transport equations. The optimal configuration of the near-field tandem\nTPV system obtained by the genetic algorithm achieves the electrical power\noutput of 8.41 W/cm$^2$ and the conversion efficiency of 35.6\\% at the vacuum\ngap of 100 nm. We show that two resonance modes (i.e., surface plasmon\npolaritons supported by the ITO-vacuum interface and the confined waveguide\nmode in the tandem TPV cell) greatly contribute to the enhanced performance of\nthe optimized system. We also show that the near-field tandem TPV system is\nsuperior to the single-cell-based near-field TPV system in both power output\nand conversion efficiency through loss analysis. Interestingly, the\noptimization performed with the objective function of the conversion efficiency\nleads to the current matching condition for the tandem TPV system regardless of\nthe vacuum gap distances.\n"}
+{"abstract": "  We identify and describe unique early time behavior of a quantum system\ninitially in a superposition, interacting with its environment. This behavior\n-- the copycat process -- occurs after the system begins to decohere, but\nbefore complete einselection. To illustrate this behavior analytic solutions\nfor the system density matrix, its eigenvalues, and eigenstates a short time\nafter system-environment interactions begin are provided. Features of the\nsolutions and their connection to observables are discussed, including\npredictions for the continued evolution of the eigenstates towards\neinselection, time dependence of spin expectation values, and an estimate of\nthe system's decoherence time. In particular we explore which aspects of the\nearly stages of decoherence exhibit quadratic evolution to leading order, and\nwhich aspects exhibit more rapid linear behavior. Many features of our early\ntime perturbative solutions are agnostic of the spectrum of the environment. We\nalso extend our work beyond short time perturbation theory to compare with\nnumerical work from a companion paper.\n"}
+{"abstract": "  A source sequence is to be guessed with some fidelity based on a rate-limited\ndescription of an observed sequence with which it is correlated. The trade-off\nbetween the description rate and the exponential growth rate of the least power\nmean of the number of guesses is characterized.\n"}
+{"abstract": "  We present an end-to-end, model-based deep reinforcement learning agent which\ndynamically attends to relevant parts of its state during planning. The agent\nuses a bottleneck mechanism over a set-based representation to force the number\nof entities to which the agent attends at each planning step to be small. In\nexperiments, we investigate the bottleneck mechanism with several sets of\ncustomized environments featuring different challenges. We consistently observe\nthat the design allows the planning agents to generalize their learned\ntask-solving abilities in compatible unseen environments by attending to the\nrelevant objects, leading to better out-of-distribution generalization\nperformance.\n"}
+{"abstract": "  Quantum state estimation for continuously monitored dynamical systems\ninvolves assigning a quantum state to an individual system at some time,\nconditioned on the results of continuous observations. The quality of the\nestimation depends on how much observed information is used and on how\noptimality is defined for the estimate. In this work, we consider problems of\nquantum state estimation where some of the measurement records are not\navailable, but where the available records come from both before (past) and\nafter (future) the estimation time, enabling better estimates than is possible\nusing the past information alone. Past-future information for quantum systems\nhas been used in various ways in the literature, in particular, the quantum\nstate smoothing, the most-likely path, and the two-state vector and related\nformalisms. To unify these seemingly unrelated approaches, we propose a\nframework for partially-observed quantum system with continuous monitoring,\nwherein the first two existing formalisms can be accommodated, with some\ngeneralization. The unifying framework is based on state estimation with\nexpected cost minimization, where the cost can be defined either in the space\nof the unknown record or in the space of the unknown true state. Moreover, we\nconnect all three existing approaches conceptually by defining five new cost\nfunctions, and thus new types of estimators, which bridge the gaps between\nthem. We illustrate the applicability of our method by calculating all seven\nestimators we consider for the example of a driven two-level system\ndissipatively coupled to bosonic baths. Our theory also allows connections to\nclassical state estimation, which create further conceptual links between our\nquantum state estimators.\n"}
+{"abstract": "  Presence of haze in images obscures underlying information, which is\nundesirable in applications requiring accurate environment information. To\nrecover such an image, a dehazing algorithm should localize and recover\naffected regions while ensuring consistency between recovered and its\nneighboring regions. However owing to fixed receptive field of convolutional\nkernels and non uniform haze distribution, assuring consistency between regions\nis difficult. In this paper, we utilize an encoder-decoder based network\narchitecture to perform the task of dehazing and integrate an spatially aware\nchannel attention mechanism to enhance features of interest beyond the\nreceptive field of traditional conventional kernels. To ensure performance\nconsistency across diverse range of haze densities, we utilize greedy localized\ndata augmentation mechanism. Synthetic datasets are typically used to ensure a\nlarge amount of paired training samples, however the methodology to generate\nsuch samples introduces a gap between them and real images while accounting for\nonly uniform haze distribution and overlooking more realistic scenario of\nnon-uniform haze distribution resulting in inferior dehazing performance when\nevaluated on real datasets. Despite this, the abundance of paired samples\nwithin synthetic datasets cannot be ignored. Thus to ensure performance\nconsistency across diverse datasets, we train the proposed network within an\nadversarial prior-guided framework that relies on a generated image along with\nits low and high frequency components to determine if properties of dehazed\nimages matches those of ground truth. We preform extensive experiments to\nvalidate the dehazing and domain invariance performance of proposed framework\nacross diverse domains and report state-of-the-art (SoTA) results.\n"}
+{"abstract": "  In this paper, we extend constructions and results for the Taylor complex to\nthe generalized Taylor complex constructed by Herzog. We construct an explicit\nDG-algebra structure on the generalized Taylor complex and extend a result of\nKatth\\\"an on quotients of the Taylor complex by DG-ideals. We introduce a\ngeneralization of the Scarf complex for families of monomial ideals, and show\nthat this complex is always a direct summand of the minimal free resolution of\nthe sum of these ideals. We also give an example of an ideal where the\ngeneralized Scarf complex strictly contains the standard Scarf complex.\nMoreover, we introduce the notion of quasitransverse monomial ideals, and prove\na list of results relating to Golodness, Koszul homology, and other homological\nproperties for such ideals.\n"}
+{"abstract": "  Using the atomic carbon [CI](1$-$0) and [CI](2$-$1) emission maps observed\nwith the $Herschel\\ Space\\ Observatory$, and CO(1$-$0), HI, infrared and submm\nmaps from literatures, we estimate the [CI]-to-H$_2$ and CO-to-H$_2$ conversion\nfactors of $\\alpha_\\mathrm{[CI](1-0)}$, $\\alpha_\\mathrm{[CI](2-1)}$, and\n$\\alpha_\\mathrm{CO}$ at a linear resolution $\\sim1\\,$kpc scale for six nearby\ngalaxies of M 51, M 83, NGC 3627, NGC 4736, NGC 5055, and NGC 6946. This is\nperhaps the first effort, to our knowledge, in calibrating both [CI]-to-H$_2$\nconversion factors across the spiral disks at spatially resolved $\\sim1\\,$kpc\nscale though such studies have been discussed globally in galaxies near and\nfar. In order to derive the conversion factors and achieve these calibrations,\nwe adopt three different dust-to-gas ratio (DGR) assumptions which scale\napproximately with metallicity taken from precursory results. We find that for\nall DGR assumptions, the $\\alpha_\\mathrm{[CI](1-0)}$,\n$\\alpha_\\mathrm{[CI](2-1)}$, and $\\alpha_\\mathrm{CO}$ are mostly flat with\ngalactocentric radii, whereas both $\\alpha_\\mathrm{[CI](2-1)}$ and\n$\\alpha_\\mathrm{CO}$ show decrease in the inner regions of galaxies. And the\ncentral $\\alpha_\\mathrm{CO}$ and $\\alpha_\\mathrm{[CI](2-1)}$ values are on\naverage $\\sim 2.2$ and $1.8$ times lower than its galaxy averages. The obtained\ncarbon abundances from different DGR assumptions show flat profiles with\ngalactocentric radii, and the average carbon abundance of the galaxies is\ncomparable to the usually adopted value of $3 \\times 10^{-5}$. We find that\nboth metallicity and infrared luminosity correlate moderately with the\n$\\alpha_\\mathrm{CO}$ whereas only weakly with either the\n$\\alpha_\\mathrm{[CI](1-0)}$ or carbon abundance, and not at all with the\n$\\alpha_\\mathrm{[CI](2-1)}$.\n"}
+{"abstract": "  The robustness of an ecological network quantifies the resilience of the\necosystem it represents to species loss. It corresponds to the proportion of\nspecies that are disconnected from the rest of the network when extinctions\noccur sequentially. Classically, the robustness is calculated for a given\nnetwork, from the simulation of a large number of extinction sequences. The\nlink between network structure and robustness remains an open question. Setting\na joint probabilistic model on the network and the extinction sequences allows\nanalysis of this relation.\n  Bipartite stochastic block models have proven their ability to model\nbipartite networks e.g. plant-pollinator networks: species are divided into\nblocks and interaction probabilities are determined by the blocks of\nmembership. Analytical expressions of the expectation and variance of\nrobustness are obtained under this model, for different distributions of\nprimary extinction sequences. The impact of the network structure on the\nrobustness is analyzed through a set of properties and numerical illustrations.\nThe analysis of a collection of bipartite ecological networks allows us to\ncompare the empirical approach to our probabilistic approach, and illustrates\nthe relevance of the latter when it comes to computing the robustness of a\npartially observed or incompletely sampled network.\n"}
+{"abstract": "  The understanding of turbulent flows is one of the biggest current challenges\nin physics, as no first-principles theory exists to explain their observed\nspatio-temporal intermittency. Turbulent flows may be regarded as an intricate\ncollection of mutually-interacting vortices. This picture becomes accurate in\nquantum turbulence, which is built on tangles of discrete vortex filaments.\nHere, we study the statistics of velocity circulation in quantum and classical\nturbulence. We show that, in quantum flows, Kolmogorov turbulence emerges from\nthe correlation of vortex orientations, while deviations -- associated with\nintermittency -- originate from their non-trivial spatial arrangement. We then\nlink the spatial distribution of vortices in quantum turbulence to the\ncoarse-grained energy dissipation in classical turbulence, enabling the\napplication of existent models of classical turbulence intermittency to the\nquantum case. Our results provide a connection between the intermittency of\nquantum and classical turbulence and initiate a promising path to a better\nunderstanding of the latter.\n"}
+{"abstract": "  This paper presents the development of vision-based robotic arm manipulator\ncontrol by applying Proportional Derivative-Pseudoinverse Jacobian (PD-PIJ)\nkinematics and Denavit Hartenberg forward kinematics. The task of sorting\nobjects based on color is carried out to observe error propagation in the\nimplementation of manipulator on real system. The objects image captured by the\ndigital camera were processed based on HSV-color model and the centroid\ncoordinate of each object detected were calculated. These coordinates are end\neffector position target to pick each object and were placed to the right\nposition based on its color. Based on the end effector position target, PD-PIJ\ninverse kinematics method was used to determine the right angle of each joint\nof manipulator links. The angles found by PD-PIJ is the input of DH forward\nkinematics. The process was repeated until the square end effector reached the\ntarget. The experiment of model and implementation to actual manipulator were\nanalyzed using Probability Density Function (PDF) and Weibull Probability\nDistribution. The result shows that the manipulator navigation system had a\ngood performance. The real implementation of color sorting task on manipulator\nshows the probability of success rate cm is 94.46% for euclidian distance error\nless than 1.2 cm.\n"}
+{"abstract": "  We show that the massless integrable sector of the AdS_3 \\times S^3 \\times\nT^4 superstring theory, which admits a non-trivial relativistic limit, provides\na setting where it is possible to determine exact minimal solutions to the form\nfactor axioms, in integral form, based on analiticity considerations, along the\nsame lines of ordinary relativistic integrable models. We construct in full\ndetail the formulas for the two- and three-particle case, and show the\nsimilarities as well as the differences with respect to the off-shell Bethe\nansatz procedure of Babujian et al. We show that our expressions pass a series\nof non-trivial consistency checks which are substantially more involved than in\nthe traditional case. We speculate on the problems concerned in a possible\ngeneralisation to an arbitrary number of particles, and on a possible\nconnection with the hexagon programme.\n"}
+{"abstract": "  Video dimensions are continuously increasing to provide more realistic and\nimmersive experiences to global streaming and social media viewers. However,\nincrements in video parameters such as spatial resolution and frame rate are\ninevitably associated with larger data volumes. Transmitting increasingly\nvoluminous videos through limited bandwidth networks in a perceptually optimal\nway is a current challenge affecting billions of viewers. One recent practice\nadopted by video service providers is space-time resolution adaptation in\nconjunction with video compression. Consequently, it is important to understand\nhow different levels of space-time subsampling and compression affect the\nperceptual quality of videos. Towards making progress in this direction, we\nconstructed a large new resource, called the ETRI-LIVE Space-Time Subsampled\nVideo Quality (ETRI-LIVE STSVQ) database, containing 437 videos generated by\napplying various levels of combined space-time subsampling and video\ncompression on 15 diverse video contents. We also conducted a large-scale human\nstudy on the new dataset, collecting about 15,000 subjective judgments of video\nquality. We provide a rate-distortion analysis of the collected subjective\nscores, enabling us to investigate the perceptual impact of space-time\nsubsampling at different bit rates. We also evaluated and compared the\nperformance of leading video quality models on the new database.\n"}
+{"abstract": "  Topological protection of quantum correlations opens new horizons and\nopportunities in quantum technologies. A variety of topological effects has\nrecently been observed in qubit networks. However, the experimental\nidentification of the topological phase still remains challenging, especially\nin the entangled many-body case. Here, we propose an approach to independently\nprobe single- and two-photon topological invariants from the time evolution of\nthe two-photon state in a one-dimensional array of qubits. Extending the\nbulk-boundary correspondence to the two-photon scenario, we show that an\nappropriate choice of the initial state enables the retrieval of the\ntopological invariant for the different types of the two-photon states in the\ninteracting Su-Schrieffer-Heeger model. Our analysis of the Zak phase reveals\nadditional facets of topological protection in the case of collapse of bound\nphoton pairs.\n"}
+{"abstract": "  This paper presents conditions for constructing permutation-invariant quantum\ncodes for deletion errors and provides a method for constructing them. Our\ncodes give the first example of quantum codes that can correct two or more\ndeletion errors. Also, our codes give the first example of quantum codes that\ncan correct both multiple-qubit errors and multiple-deletion errors. We also\ndiscuss a generalization of the construction of our codes at the end.\n"}
+{"abstract": "  We derive from the subleading contributions to the chiral three-nucleon\ninteraction [published in Phys.~Rev.~C77, 064004 (2008) and Phys.~Rev.~C84,\n054001 (2011)] their first-order contributions to the energy per particle of\nisospin-symmetric nuclear matter and pure neutron matter in an analytical way.\nFor the variety of short-range and long-range terms that constitute the\nsubleading chiral 3N-force the pertinent closed 3-ring, 2-ring, and 1-ring\ndiagrams are evaluated. While 3-ring diagrams vanish by a spin-trace and the\nresults for 2-ring diagrams can be given in terms of elementary functions of\nthe ratio Fermi-momentum over pion mass, one ends up in most cases for the\nclosed 1-ring diagrams with one-parameter integrals. The same treatment is\napplied to the subsubleading chiral three-nucleon interactions as far as these\nhave been constructed up to now.\n"}
+{"abstract": "  Anaphora and ellipses are two common phenomena in dialogues. Without\nresolving referring expressions and information omission, dialogue systems may\nfail to generate consistent and coherent responses. Traditionally, anaphora is\nresolved by coreference resolution and ellipses by query rewrite. In this work,\nwe propose a novel joint learning framework of modeling coreference resolution\nand query rewriting for complex, multi-turn dialogue understanding. Given an\nongoing dialogue between a user and a dialogue assistant, for the user query,\nour joint learning model first predicts coreference links between the query and\nthe dialogue context, and then generates a self-contained rewritten user query.\nTo evaluate our model, we annotate a dialogue based coreference resolution\ndataset, MuDoCo, with rewritten queries. Results show that the performance of\nquery rewrite can be substantially boosted (+2.3% F1) with the aid of\ncoreference modeling. Furthermore, our joint model outperforms the\nstate-of-the-art coreference resolution model (+2% F1) on this dataset.\n"}
+{"abstract": "  In this work we initiate the study of Position Based Quantum Cryptography\n(PBQC) from the perspective of geometric functional analysis and its\nconnections with quantum games. The main question we are interested in asks for\nthe optimal amount of entanglement that a coalition of attackers have to share\nin order to compromise the security of any PBQC protocol. Known upper bounds\nfor that quantity are exponential in the size of the quantum systems\nmanipulated in the honest implementation of the protocol. However, known lower\nbounds are only linear.\n  In order to deepen the understanding of this question, here we propose a\nPosition Verification (PV) protocol and find lower bounds on the resources\nneeded to break it. The main idea behind the proof of these bounds is the\nunderstanding of cheating strategies as vector valued assignments on the\nBoolean hypercube. Then, the bounds follow from the understanding of some\ngeometric properties of particular Banach spaces, their type constants. Under\nsome regularity assumptions on the former assignment, these bounds lead to\nexponential lower bounds on the quantum resources employed, clarifying the\nquestion in this restricted case. Known attacks indeed satisfy the assumption\nwe make, although we do not know how universal this feature is. Furthermore, we\nshow that the understanding of the type properties of some more involved Banach\nspaces would allow to drop out the assumptions and lead to unconditional lower\nbounds on the resources used to attack our protocol. Unfortunately, we were not\nable to estimate the relevant type constant. Despite that, we conjecture an\nupper bound for this quantity and show some evidence supporting it. A positive\nsolution of the conjecture would lead to stronger security guarantees for the\nproposed PV protocol providing a better understanding of the question asked\nabove.\n"}
+{"abstract": "  The aura of mystery surrounding quantum physics makes it difficult to advance\nquantum technologies. Demystification requires methodological techniques that\nexplain the basics of quantum technologies without metaphors and abstract\nmathematics. The article provides an example of such an explanation for the\nBB84 quantum key distribution protocol based on phase coding. This allows you\nto seamlessly get acquainted with the real cryptographic installation QRate,\nused at the WorldSkills competition in the competence of \"Quantum\nTechnologies\".\n"}
+{"abstract": "  We study the equilibrium and nonequilibrium electronic transport properties\nof multiprobe topological systems using a combination of the\nLandauer-B\\\"uttiker approach and nonequilibrium Green's functions techniques.\nWe obtain general expressions for both nonequilibrium and equilibrium local\nelectronic currents that, by suitable projections, allow one to compute charge,\nspin, valley, and orbital currents. We show that external magnetic fields give\nrise to equilibrium charge currents in mesoscopic system and study the latter\nin the quantum Hall regime. Likewise, a spin-orbit interaction leads to local\nequilibrium spin currents, that we analyze in the quantum spin Hall regime.\n"}
+{"abstract": "  $TESS$ photometric data of LS~Cam from sectors 19, 20 and 26 are analysed.\nThe obtained power spectra from sectors 19 and 20 show multiple periodicities -\norbital variations ($P_{orb} = 0.14237$ days), slightly fluctuating\nsuperorbital variation ($ P_{so} \\approx 4.03$ days) and permanent negative\nsuperhump ($P_{-sh} = 0.1375$ days). In sector 26 an additional positive\nsuperhump ($P_{+sh} = 0.155$ days) is present. Using relations from literature,\nthe mass ratio and the masses of the two components are estimated to be $q\n=0.24$, $M_1 = 1.26M_\\odot$, and $M_2 = 0.30 M_\\odot$ respectively.\n"}
+{"abstract": "  We investigate the attenuation law in $z\\sim 6$ quasars by combining\ncosmological zoom-in hydrodynamical simulations of quasar host galaxies, with\nmulti-frequency radiative transfer calculations. We consider several dust\nmodels differing in terms of grain size distributions, dust mass and chemical\ncomposition, and compare the resulting synthetic Spectral Energy Distributions\n(SEDs) with data from bright, early quasars. We show that only dust models with\ngrain size distributions in which small grains ($a < 0.1~\\mu$m, corresponding\nto $\\approx 60\\%$ of the total dust mass) are selectively removed from the\ndusty medium provide a good fit to the data. Removal can occur if small grains\nare efficiently destroyed in quasar environments and/or early dust production\npreferentially results in large grains. Attenuation curves for these models are\nclose to flat, and consistent with recent data; they correspond to an effective\ndust-to-metal ratio $f_d \\simeq 0.38$, i.e. close to the Milky Way value.\n"}
+{"abstract": "  A wheel is a graph consisting of an induced cycle of length at least four and\na single additional vertex with at least three neighbours on the cycle. We\nprove that no Burling graph contains an induced wheel. Burling graphs are\ntriangle-free and have arbitrarily large chromatic number, so this answers a\nquestion of Trotignon and disproves a conjecture of Scott and Seymour.\n"}
+{"abstract": "  We study the diffusion properties of the strongly interacting quark-gluon\nplasma (sQGP) and evaluate the diffusion coefficient matrix for the baryon\n($B$), strange ($S$) and electric ($Q$) charges - $\\kappa_{qq'}$ ($q,q' = B, S,\nQ$) and show their dependence on temperature $T$ and baryon chemical potential\n$\\mu_B$. The non-perturbative nature of the sQGP is evaluated within the\nDynamical Quasi-Particle Model (DQPM) which is matched to reproduce the\nequation of state of the partonic matter above the deconfinement temperature\n$T_c$ from lattice QCD. The calculation of diffusion coefficients is based on\ntwo methods: i) the Chapman-Enskog method for the linearized Boltzmann\nequation, which allows to explore non-equilibrium corrections for the\nphase-space distribution function in leading order of the Knudsen numbers as\nwell as ii) the relaxation time approximation (RTA). In this work we explore\nthe differences between the two methods. We find a good agreement with the\navailable lattice QCD data in case of the electric charge diffusion coefficient\n(or electric conductivity) at vanishing baryon chemical potential as well as a\nqualitative agreement with the recent predictions from the holographic approach\nfor all diagonal components of the diffusion coefficient matrix. The knowledge\nof the diffusion coefficient matrix is also of special interest for more\naccurate hydrodynamic simulations.\n"}
+{"abstract": "  Training images with data transformations have been suggested as contrastive\nexamples to complement the testing set for generalization performance\nevaluation of deep neural networks (DNNs). In this work, we propose a practical\nframework ContRE (The word \"contre\" means \"against\" or \"versus\" in French.)\nthat uses Contrastive examples for DNN geneRalization performance Estimation.\nSpecifically, ContRE follows the assumption in contrastive learning that robust\nDNN models with good generalization performance are capable of extracting a\nconsistent set of features and making consistent predictions from the same\nimage under varying data transformations. Incorporating with a set of\nrandomized strategies for well-designed data transformations over the training\nset, ContRE adopts classification errors and Fisher ratios on the generated\ncontrastive examples to assess and analyze the generalization performance of\ndeep models in complement with a testing set. To show the effectiveness and the\nefficiency of ContRE, extensive experiments have been done using various DNN\nmodels on three open source benchmark datasets with thorough ablation studies\nand applicability analyses. Our experiment results confirm that (1) behaviors\nof deep models on contrastive examples are strongly correlated to what on the\ntesting set, and (2) ContRE is a robust measure of generalization performance\ncomplementing to the testing set in various settings.\n"}
+{"abstract": "  Computational micromagnetics has become an essential tool in academia and\nindustry to support fundamental research and the design and development of\ndevices. Consequently, computational micromagnetics is widely used in the\ncommunity, and the fraction of time researchers spend performing computational\nstudies is growing. We focus on reducing this time by improving the interface\nbetween the numerical simulation and the researcher. We have designed and\ndeveloped a human-centred research environment called Ubermag. With Ubermag,\nscientists can control an existing micromagnetic simulation package, such as\nOOMMF, from Jupyter notebooks. The complete simulation workflow, including\ndefinition, execution, and data analysis of simulation runs, can be performed\nwithin the same notebook environment. Numerical libraries, co-developed by the\ncomputational and data science community, can immediately be used for\nmicromagnetic data analysis within this Python-based environment. By design, it\nis possible to extend Ubermag to drive other micromagnetic packages from the\nsame environment.\n"}
+{"abstract": "  The recent wave of detections of interstellar aromatic molecules has sparked\ninterest in the chemical behavior of aromatic molecules under astrophysical\nconditions. In most cases, these detections have been made through chemically\nrelated molecules, called proxies, that implicitly indicate the presence of a\nparent molecule. In this study, we present the results of the theoretical\nevaluation of the hydrogenation reactions of different aromatic molecules\n(benzene, pyridine, pyrrole, furan, thiophene, silabenzene, and phosphorine).\nThe viability of these reactions allows us to evaluate the resilience of these\nmolecules to the most important reducing agent in the interstellar medium, the\nhydrogen atom (H). All significant reactions are exothermic and most of them\npresent activation barriers, which are, in several cases, overcome by quantum\ntunneling. Instanton reaction rate constants are provided between 50 K and 500\nK. For the most efficiently formed radicals, a second hydrogenation step has\nbeen studied. We propose that hydrogenated derivatives of furan, pyrrole, and\nspecially 2,3-dihydropyrrole, 2,5-dihydropyrrole, 2,3-dihydrofuran, and\n2,5-dihydrofuran are promising candidates for future interstellar detections.\n"}
+{"abstract": "  Colorectal cancer is a leading cause of cancer death for both men and women.\nFor this reason, histopathological characterization of colorectal polyps is the\nmajor instrument for the pathologist in order to infer the actual risk for\ncancer and to guide further follow-up. Colorectal polyps diagnosis includes the\nevaluation of the polyp type, and more importantly, the grade of dysplasia.\nThis latter evaluation represents a critical step for the clinical follow-up.\nThe proposed deep learning-based classification pipeline is based on\nstate-of-the-art convolutional neural network, trained using proper\ncountermeasures to tackle WSI high resolution and very imbalanced dataset. The\nexperimental results show that one can successfully classify adenomas dysplasia\ngrade with 70% accuracy, which is in line with the pathologists' concordance.\n"}
+{"abstract": "  Coherent configurations are a generalization of association schemes. In this\npaper, we introduce the concept of $Q$-polynomial coherent configurations and\nstudy the relationship among intersection numbers, Krein numbers, and\neigenmatrices. The examples of $Q$-polynomial coherent configurations are\nprovided from Delsarte designs in $Q$-polynomial schemes and spherical designs.\n"}
+{"abstract": "  Robots assisting us in factories or homes must learn to make use of objects\nas tools to perform tasks, e.g., a tray for carrying objects. We consider the\nproblem of learning commonsense knowledge of when a tool may be useful and how\nits use may be composed with other tools to accomplish a high-level task\ninstructed by a human. We introduce a novel neural model, termed TANGO, for\npredicting task-specific tool interactions, trained using demonstrations from\nhuman teachers instructing a virtual robot. TANGO encodes the world state,\ncomprising objects and symbolic relationships between them, using a graph\nneural network. The model learns to attend over the scene using knowledge of\nthe goal and the action history, finally decoding the symbolic action to\nexecute. Crucially, we address generalization to unseen environments where some\nknown tools are missing, but alternative unseen tools are present. We show that\nby augmenting the representation of the environment with pre-trained embeddings\nderived from a knowledge-base, the model can generalize effectively to novel\nenvironments. Experimental results show a 60.5-78.9% absolute improvement over\nthe baseline in predicting successful symbolic plans in unseen settings for a\nsimulated mobile manipulator.\n"}
+{"abstract": "  For the comparison of inequality and welfare in multiple attributes the use\nof generalized Gini indices is proposed. Spectral social evaluation functions\nare used in the multivariate setting, and Gini dominance orderings are\nintroduced that are uniform in attribute weights. Classes of spectral\nevaluators are considered that are ordered by their aversion to inequality.\nThen a set-valued representative endowment is defined that characterizes\n$d$-dimensioned welfare. It consists of all points above the lower border of a\nconvex compact in $R^d$, while the pointwise ordering of such endowments\ncorresponds to uniform Gini dominance. An application is given to the welfare\nof 28 European countries. Properties of uniform Gini dominance are derived,\nincluding relations to other orderings of $d$-variate distributions such as\nconvex and dependence orderings. The multi-dimensioned representative endowment\ncan be efficiently calculated from data; in a sampling context, it consistently\nestimates its population version.\n"}
+{"abstract": "  The principle of entropy increase is not only the basis of statistical\nmechanics, but also closely related to the irreversibility of time, the origin\nof life, chaos and turbulence. In this paper, we first discuss the dynamic\nsystem definition of entropy from the perspective of symbol and partition of\ninformation, and propose the entropy transfer characteristics based on the set\npartition. By introducing the hypothesis of limited accuracy of measurement\ninto the continuous dynamical system, two necessary mechanisms for the\nformation of chaos are obtained: the transfer of entropy from small scale to\nmacro scale (i.e. the increase of local entropy) and the dissipation of macro\ninformation. The relationship between the local entropy increase and Lyapunov\nexponent of dynamical system is established. And then the entropy increase and\nabnormal dissipation mechanism in physical system are analyzed and discussed.\n"}
+{"abstract": "  Adaptive mirrors based on voice-coil technology have force actuators with an\ninternal metrology to close a local loop for controlling its shape in position.\nWhen actuators are requested to be disabled or slaved, control matrices have to\nbe re-computed. The report describes the algorithms to re-compute the relevant\nmatrixes for controlling of the mirror without the need of recalibration. This\nis related in particular to MMT, LBT, Magellan, VLT, ELT and GMT adaptive\nmirrors that use the voice-coil technology. The technique is successfully used\nin practice with LBT and VLT-UT4 adaptive secondary mirror units.\n"}
+{"abstract": "  Data quantity and quality are crucial factors for data-driven learning\nmethods. In some target problem domains, there are not many data samples\navailable, which could significantly hinder the learning process. While data\nfrom similar domains may be leveraged to help through domain adaptation,\nobtaining high-quality labeled data for those source domains themselves could\nbe difficult or costly. To address such challenges on data insufficiency for\nclassification problem in a target domain, we propose a weak adaptation\nlearning (WAL) approach that leverages unlabeled data from a similar source\ndomain, a low-cost weak annotator that produces labels based on task-specific\nheuristics, labeling rules, or other methods (albeit with inaccuracy), and a\nsmall amount of labeled data in the target domain. Our approach first conducts\na theoretical analysis on the error bound of the trained classifier with\nrespect to the data quantity and the performance of the weak annotator, and\nthen introduces a multi-stage weak adaptation learning method to learn an\naccurate classifier by lowering the error bound. Our experiments demonstrate\nthe effectiveness of our approach in learning an accurate classifier with\nlimited labeled data in the target domain and unlabeled data in the source\ndomain.\n"}
+{"abstract": "  Physics-Informed Neural Networks (PINN) are neural networks encoding the\nproblem governing equations, such as Partial Differential Equations (PDE), as a\npart of the neural network. PINNs have emerged as a new essential tool to solve\nvarious challenging problems, including computing linear systems arising from\nPDEs, a task for which several traditional methods exist. In this work, we\nfocus first on evaluating the potential of PINNs as linear solvers in the case\nof the Poisson equation, an omnipresent equation in scientific computing. We\ncharacterize PINN linear solvers in terms of accuracy and performance under\ndifferent network configurations (depth, activation functions, input data set\ndistribution). We highlight the critical role of transfer learning. Our results\nshow that low-frequency components of the solution converge quickly as an\neffect of the F-principle. In contrast, an accurate solution of the high\nfrequencies requires an exceedingly long time. To address this limitation, we\npropose integrating PINNs into traditional linear solvers. We show that this\nintegration leads to the development of new solvers whose performance is on par\nwith other high-performance solvers, such as PETSc conjugate gradient linear\nsolvers, in terms of performance and accuracy. Overall, while the accuracy and\ncomputational performance are still a limiting factor for the direct use of\nPINN linear solvers, hybrid strategies combining old traditional linear solver\napproaches with new emerging deep-learning techniques are among the most\npromising methods for developing a new class of linear solvers.\n"}
+{"abstract": "  In an article published in 1987 in Combinatorica \\cite{MR918397}, Frieze and\nJackson established a lower bound on the length of the longest induced path\n(and cycle) in a sparse random graph. Their bound is obtained through a rough\nanalysis of a greedy algorithm. In the present work, we provide a sharp\nasymptotic for the length of the induced path constructed by their algorithm.\nTo this end, we introduce an alternative algorithm that builds the same induced\npath and whose analysis falls into the framework of a previous work by the\nauthors on depth-first exploration of a configuration model \\cite{EFMN}. We\nalso analyze an extension of our algorithm that mixes depth-first and\nbreadth-first explorations and generates $m$-induced paths.\n"}
+{"abstract": "  Extended depth of focus (EDOF) optics can enable lower complexity optical\nimaging systems when compared to active focusing solutions. With existing EDOF\noptics, however, it is difficult to achieve high resolution and high collection\nefficiency simultaneously. The subwavelength pitch of meta-optics enables\nengineering very steep phase gradients, and thus meta-optics can achieve both a\nlarge physical aperture and high numerical aperture. Here, we demonstrate a\nfast (f/1.75) EDOF meta-optic operating at visible wavelengths, with an\naperture of 2 mm and focal range from 3.5 mm to 14.5 mm (286 diopters to 69\ndiopters), which is a 250 elongation of the depth of focus relative to a\nstandard lens. Depth-independent performance is shown by imaging at a range of\nfinite conjugates, with a minimum spatial resolution of ~9.84{\\mu}m (50.8\ncycles/mm). We also demonstrate operation of a directly integrated EDOF\nmeta-optic camera module to evaluate imaging at multiple object distances, a\nfunctionality which would otherwise require a varifocal lens.\n"}
+{"abstract": "  Non-unitary neutrino mixing in the light neutrino sector is a direct\nconsequence of type-I seesaw neutrino mass models. In these models, light\nneutrino mixing is described by a sub-matrix of the full lepton mixing matrix\nand, then, it is not unitary in general. In consequence, neutrino oscillations\nare characterized by additional parameters, including new sources of CP\nviolation. Here we perform a combined analysis of short and long-baseline\nneutrino oscillation data in this extended mixing scenario. We did not find a\nsignificant deviation from unitary mixing, and the complementary data sets have\nbeen used to constrain the non-unitarity parameters. We have also found that\nthe T2K and NOvA tension in the determination of the Dirac CP-phase is not\nalleviated in the context of non-unitary neutrino mixing.\n"}
+{"abstract": "  Reshaping accurate and realistic 3D human bodies from anthropometric\nparameters (e.g., height, chest size, etc.) poses a fundamental challenge for\nperson identification, online shopping and virtual reality. Existing approaches\nfor creating such 3D shapes often suffer from complex measurement by range\ncameras or high-end scanners, which either involve heavy expense cost or result\nin low quality. However, these high-quality equipments limit existing\napproaches in real applications, because the equipments are not easily\naccessible for common users. In this paper, we have designed a 3D human body\nreshaping system by proposing a novel feature-selection-based local mapping\ntechnique, which enables automatic anthropometric parameter modeling for each\nbody facet. Note that the proposed approach can leverage limited anthropometric\nparameters (i.e., 3-5 measurements) as input, which avoids complex measurement,\nand thus better user-friendly experience can be achieved in real scenarios.\nSpecifically, the proposed reshaping model consists of three steps. First, we\ncalculate full-body anthropometric parameters from limited user inputs by\nimputation technique, and thus essential anthropometric parameters for 3D body\nreshaping can be obtained. Second, we select the most relevant anthropometric\nparameters for each facet by adopting relevance masks, which are learned\noffline by the proposed local mapping technique. Third, we generate the 3D body\nmeshes by mapping matrices, which are learned by linear regression from the\nselected parameters to mesh-based body representation. We conduct experiments\nby anthropomorphic evaluation and a user study from 68 volunteers. Experiments\nshow the superior results of the proposed system in terms of mean\nreconstruction error against the state-of-the-art approaches.\n"}
+{"abstract": "  Recent work in fair machine learning has proposed dozens of technical\ndefinitions of algorithmic fairness and methods for enforcing these\ndefinitions. However, we still lack an understanding of how to develop machine\nlearning systems with fairness criteria that reflect relevant stakeholders'\nnuanced viewpoints in real-world contexts. To address this gap, we propose a\nframework for eliciting stakeholders' subjective fairness notions. Combining a\nuser interface that allows stakeholders to examine the data and the algorithm's\npredictions with an interview protocol to probe stakeholders' thoughts while\nthey are interacting with the interface, we can identify stakeholders' fairness\nbeliefs and principles. We conduct a user study to evaluate our framework in\nthe setting of a child maltreatment predictive system. Our evaluations show\nthat the framework allows stakeholders to comprehensively convey their fairness\nviewpoints. We also discuss how our results can inform the design of predictive\nsystems.\n"}
+{"abstract": "  We study the identifiability of the interaction kernels in mean-field\nequations for intreacting particle systems. The key is to identify function\nspaces on which a probabilistic loss functional has a unique minimizer. We\nprove that identifiability holds on any subspace of two reproducing kernel\nHilbert spaces (RKHS), whose reproducing kernels are intrinsic to the system\nand are data-adaptive. Furthermore, identifiability holds on two ambient L2\nspaces if and only if the integral operators associated with the reproducing\nkernels are strictly positive. Thus, the inverse problem is ill-posed in\ngeneral. We also discuss the implications of identifiability in computational\npractice.\n"}
+{"abstract": "  This paper describes the AISpeech-SJTU system for the accent identification\ntrack of the Interspeech-2020 Accented English Speech Recognition Challenge. In\nthis challenge track, only 160-hour accented English data collected from 8\ncountries and the auxiliary Librispeech dataset are provided for training. To\nbuild an accurate and robust accent identification system, we explore the whole\nsystem pipeline in detail. First, we introduce the ASR based phone\nposteriorgram (PPG) feature to accent identification and verify its efficacy.\nThen, a novel TTS based approach is carefully designed to augment the very\nlimited accent training data for the first time. Finally, we propose the test\ntime augmentation and embedding fusion schemes to further improve the system\nperformance. Our final system is ranked first in the challenge and outperforms\nall the other participants by a large margin. The submitted system achieves\n83.63\\% average accuracy on the challenge evaluation data, ahead of the others\nby more than 10\\% in absolute terms.\n"}
+{"abstract": "  Signal predictions for galactic dark matter (DM) searches often rely on\nassumptions on the DM phase-space distribution function (DF) in halos. This\napplies to both particle (e.g. $p$-wave suppressed or Sommerfeld-enhanced\nannihilation, scattering off atoms, etc.) and macroscopic DM candidates (e.g.\nmicrolensing of primordial black holes). As experiments and observations\nimprove in precision, better assessing theoretical uncertainties becomes\npressing in the prospect of deriving reliable constraints on DM candidates or\ntrustworthy hints for detection. Most reliable predictions of DFs in halos are\nbased on solving the steady-state collisionless Boltzmann equation (e.g.\nEddington-like inversions, action-angle methods, etc.) consistently with\nobservational constraints. One can do so starting from maximal symmetries and a\nminimal set of degrees of freedom, and then increasing complexity. Key issues\nare then whether adding complexity, which is computationally costy, improves\npredictions, and if so where to stop. Clues can be obtained by making\npredictions for zoomed-in hydrodynamical cosmological simulations in which one\ncan access the true (coarse-grained) phase-space information. Here, we test an\naxisymmetric extension of the Eddington inversion to predict the full DM DF\nfrom its density profile and the total gravitational potential of the system.\nThis permits to go beyond spherical symmetry, and is a priori well suited for\nspiral galaxies. We show that axisymmetry does not necessarily improve over\nspherical symmetry because the (observationally unconstrained) angular momentum\nof the DM halo is not generically aligned with the baryonic one. Theoretical\nerrors are similar to those of the Eddington inversion though, at the 10-20%\nlevel for velocity-dependent predictions related to particle DM searches in\nspiral galaxies. We extensively describe the approach and comment on the\nresults.\n"}
+{"abstract": "  A system of synchronized radio telescopes is utilized to search for\nhypothetical wide bandwidth interstellar communication signals. Transmitted\nsignals are hypothesized to have characteristics that enable high channel\ncapacity and minimally low energy per information bit, while containing\nenergy-efficient signal elements that are readily discoverable, distinct from\nrandom noise. A hypothesized transmitter signal is described. Signal reception\nand discovery processes are detailed. Observations using individual and\nmultiple synchronized radio telescopes, during 2017 - 2021, are described.\nConclusions and further work are suggested.\n"}
+{"abstract": "  We compare the solutions of two one-dimensional Poisson problems on an\ninterval with Robin boundary conditions, one with given data, and one where the\ndata has been symmetrized. When the Robin parameter is positive and the\nsymmetrization is symmetric decreasing rearrangement, we prove that the\nsolution to the symmetrized problem has larger increasing convex means. When\nthe Robin parameter equals zero (so that we have Neumann boundary conditions)\nand the symmetrization is decreasing rearrangement, we similarly show that the\nsolution to the symmetrized problem has larger convex means.\n"}
+{"abstract": "  In this paper, we connect two types of representations of a permutation\n$\\sigma$ of the finite field $\\F_q$. One type is algebraic, in which the\npermutation is represented as the composition of degree-one polynomials and $k$\ncopies of $x^{q-2}$, for some prescribed value of $k$. The other type is\ncombinatorial, in which the permutation is represented as the composition of a\ndegree-one rational function followed by the product of $k$ $2$-cycles on\n$\\bP^1(\\F_q):=\\F_q\\cup\\{\\infty\\}$, where each $2$-cycle moves $\\infty$. We show\nthat, after modding out by obvious equivalences amongst the algebraic\nrepresentations, then for each $k$ there is a bijection between the algebraic\nrepresentations of $\\sigma$ and the combinatorial representations of $\\sigma$.\nWe also prove analogous results for permutations of $\\bP^1(\\F_q)$. One\nconsequence is a new characterization of the notion of Carlitz rank of a\npermutation on $\\F_q$, which we use elsewhere to provide an explicit formula\nfor the Carlitz rank. Another consequence involves a classical theorem of\nCarlitz, which says that if $q>2$ then the group of permutations of $\\F_q$ is\ngenerated by the permutations induced by degree-one polynomials and $x^{q-2}$.\nOur bijection provides a new perspective from which the two proofs of this\nresult in the literature can be seen to arise naturally, without requiring the\nclever tricks that previously appeared to be needed in order to discover those\nproofs.\n"}
+{"abstract": "  In a recent paper, Jean Gaudart and colleagues studied the factors associated\nwith the spatial heterogeneity of the first wave of COVID-19 in France. We make\nsome critical comments on their work which may be useful for future, similar\nstudies.\n"}
+{"abstract": "  Online community moderators are on the front lines of combating problems like\nhate speech and harassment, but new modes of interaction can introduce\nunexpected challenges. In this paper, we consider moderation practices and\nchallenges in the context of real-time, voice-based communication through 25\nin-depth interviews with moderators on Discord. Our findings suggest that the\naffordances of voice-based online communities change what it means to moderate\ncontent and interactions. Not only are there new ways to break rules that\nmoderators of text-based communities find unfamiliar, such as disruptive noise\nand voice raiding, but acquiring evidence of rule-breaking behaviors is also\nmore difficult due to the ephemerality of real-time voice. While moderators\nhave developed new moderation strategies, these strategies are limited and\noften based on hearsay and first impressions, resulting in problems ranging\nfrom unsuccessful moderation to false accusations. Based on these findings, we\ndiscuss how voice communication complicates current understandings and\nassumptions about moderation, and outline ways that platform designers and\nadministrators can design technology to facilitate moderation.\n"}
+{"abstract": "  We detect Lyman $\\alpha$ absorption from the escaping atmosphere of HD\n63433c, a $R=2.67 R_\\oplus$, $P=20.5$ d mini Neptune orbiting a young (440 Myr)\nsolar analogue in the Ursa Major Moving Group. Using HST/STIS, we measure a\ntransit depth of $11.1 \\pm 1.5$% in the blue wing and $8 \\pm 3$% in the red.\nThis signal is unlikely to be due to stellar variability, but should be\nconfirmed by an upcoming second visit with HST. We do not detect Lyman $\\alpha$\nabsorption from the inner planet, a smaller $R=2.15 R_\\oplus$ mini Neptune on a\n7.1 d orbit. We use Keck/NIRSPEC to place an upper limit of 0.5% on helium\nabsorption for both planets. We measure the host star's X-ray spectrum and FUV\nflux with XMM-Newton, and model the outflow from both planets using a 3D\nhydrodynamic code. This model provides a reasonable match to the light curve in\nthe blue wing of the Lyman $\\alpha$ line and the helium non-detection for\nplanet c, although it does not explain the tentative red wing absorption or\nreproduce the excess absorption spectrum in detail. Its predictions of strong\nLyman $\\alpha$ and helium absorption from b are ruled out by the observations.\nThis model predicts a much shorter mass loss timescale for planet b, suggesting\nthat b and c are fundamentally different: while the latter still retains its\nhydrogen/helium envelope, the former has likely lost its primordial atmosphere.\n"}
+{"abstract": "  For a group $G$ that is a limit group over Droms RAAGs such that $G$ has\ntrivial center, we show that $\\Sigma^1(G) = \\emptyset = \\Sigma^1(G,\n\\mathbb{Q})$. For a group $H$ that is a finitely presented residually Droms\nRAAG we calculate $\\Sigma^1(H)$ and $\\Sigma^2(H)_{dis}$. In addition, we obtain\na necessary condition for $[\\chi]$ to belong to $\\Sigma^n(H)$.\n"}
+{"abstract": "  In this paper, we study a general class of causal processes with exogenous\ncovariates, including many classical processes such as the ARMA-GARCH, APARCH,\nARMAX, GARCH-X and APARCH-X processes.\n  Under some Lipschitz-type conditions, the existence of a $\\tau$-weakly\ndependent strictly stationary and ergodic solution is established.\n  We provide conditions for the strong consistency and derive the asymptotic\ndistribution of the quasi-maximum likelihood estimator (QMLE), both when the\ntrue parameter is an interior point of the parameter's space and when it\nbelongs to the boundary.\n  A significance Wald-type test of parameter is developed. This test is quite\nextensive and includes the test of nullity of the parameter's components, which\nin particular, allows us to assess the relevance of the exogenous covariates.\n  Relying on the QMLE of the model, we also propose a penalized criterion to\naddress the problem of the model selection for this class. The weak and the\nstrong consistency of the procedure are established.\n  Finally, Monte Carlo simulations are conducted to numerically illustrate the\nmain results.\n"}
+{"abstract": "  Dissipation of electromagnetic energy through absorption is a fundamental\nprocess that underpins phenomena ranging from photovoltaics to photography,\nanalytical spectroscopy, photosynthesis, and human vision. Absorption is also a\ndynamic process that depends on the duration of the optical illumination. Here\nwe report on the resonant plasmonic absorption of a nanostructured metamaterial\nand the non-resonant absorption of an unstructured gold film at different\noptical pulse durations. By examining the absorption in travelling and standing\nwaves, we observe a plasmonic relaxation time of 11 fs as the characteristic\ntransition time. The metamaterial acts as a beam-splitter with low absorption\nfor shorter pulses, while as a good absorber for longer pulses. The transient\nnature of the absorption puts a frequency limit of ~90 THz on the bandwidth of\ncoherently-controlled, all-optical switching devices, which is still a thousand\ntimes faster than other leading switching technologies.\n"}
+{"abstract": "  The paper describes our proposed methodology for the seven basic expression\nclassification track of Affective Behavior Analysis in-the-wild (ABAW)\nCompetition 2021. In this task, facial expression recognition (FER) methods aim\nto classify the correct expression category from a diverse background, but\nthere are several challenges. First, to adapt the model to in-the-wild\nscenarios, we use the knowledge from pre-trained large-scale face recognition\ndata. Second, we propose an ensemble model with a convolution neural network\n(CNN), a CNN-recurrent neural network (CNN-RNN), and a CNN-Transformer\n(CNN-Transformer), to incorporate both spatial and temporal information. Our\nensemble model achieved F1 as 0.4133, accuracy as 0.6216 and final metric as\n0.4821 on the validation set.\n"}
+{"abstract": "  We introduce the problem of \\emph{timely} private information retrieval (PIR)\nfrom $N$ non-colluding and replicated servers. In this problem, a user desires\nto retrieve a message out of $M$ messages from the servers, whose contents are\ncontinuously updating. The retrieval process should be executed in a timely\nmanner such that no information is leaked about the identity of the message. To\nassess the timeliness, we use the \\emph{age of information} (AoI) metric.\nInterestingly, the timely PIR problem reduces to an AoI minimization subject to\nPIR constraints under \\emph{asymmetric traffic}. We explicitly characterize the\noptimal tradeoff between the PIR rate and the AoI metric (peak AoI or average\nAoI) for the case of $N=2$, $M=3$. Further, we provide some structural insights\non the general problem with arbitrary $N$, $M$.\n"}
+{"abstract": "  Conflict-avoiding codes (CACs) have been used in multiple-access collision\nchannel without feedback. The size of a CAC is the number of potential users\nthat can be supported in the system. A code with maximum size is called\noptimal. The use of an optimal CAC enables the largest possible number of\nasynchronous users to transmit information efficiently and reliably. In this\npaper, a new upper bound on the maximum size of arbitrary equi-difference CAC\nis presented. Furthermore, three optimal constructions of equi-difference CACs\nare also given. One is a generalized construction for prime length $L=p$ and\nthe other two are for two-prime length $L=pq$.\n"}
+{"abstract": "  We compute the electromagnetic fields generated in relativistic heavy-ion\ncollisions using the iEBE-VISHNU framework. We calculated the incremental drift\nvelocity from the possible four sources of the electric force (coulomb,\nLorentz, Faraday, and Plasma-based) on the particles created. The effect of\nthis external electromagnetic field on the flow harmonics of particles was\ninvestigated, and we found out that the flow harmonics values get suppressed\nand rouse in a non-uniform fashion throughout the evolution. More precisely, a\nmaximum of close to three percent increase in elliptic flow was observed. We\nalso found mass more dominant factor than charges for the change in flow\nharmonics due to the created electromagnetic field. On the top of that, the\nmagnetic field perpendicular to the reaction plane is found to be sizable while\nthe different radial electric forces were found to cancel out each other.\nFinally, we found out that the inclusion of an electromagnetic field affects\nthe flow of particles by suppressing or rising it in a non-uniform fashion\nthroughout the evolution.\n"}
+{"abstract": "  Large-scale deep neural networks (DNNs) such as convolutional neural networks\n(CNNs) have achieved impressive performance in audio classification for their\npowerful capacity and strong generalization ability. However, when training a\nDNN model on low-resource tasks, it is usually prone to overfitting the small\ndata and learning too much redundant information. To address this issue, we\npropose to use variational information bottleneck (VIB) to mitigate overfitting\nand suppress irrelevant information. In this work, we conduct experiments ona\n4-layer CNN. However, the VIB framework is ready-to-use and could be easily\nutilized with many other state-of-the-art network architectures. Evaluation on\na few audio datasets shows that our approach significantly outperforms baseline\nmethods, yielding more than 5.0% improvement in terms of classification\naccuracy in some low-source settings.\n"}
+{"abstract": "  Outlier detection has gained increasing interest in recent years, due to\nnewly emerging technologies and the huge amount of high-dimensional data that\nare now available. Outlier detection can help practitioners to identify\nunwanted noise and/or locate interesting abnormal observations. To address\nthis, we developed a novel method for outlier detection for use in, possibly\nhigh-dimensional, datasets with both discrete and continuous variables. We\nexploit the family of decomposable graphical models in order to model the\nrelationship between the variables and use this to form an exact likelihood\nratio test for an observation that is considered an outlier. We show that our\nmethod outperforms the state-of-the-art Isolation Forest algorithm on a real\ndata example.\n"}
+{"abstract": "  Effective caching is crucial for the performance of modern-day computing\nsystems. A key optimization problem arising in caching -- which item to evict\nto make room for a new item -- cannot be optimally solved without knowing the\nfuture. There are many classical approximation algorithms for this problem, but\nmore recently researchers started to successfully apply machine learning to\ndecide what to evict by discovering implicit input patterns and predicting the\nfuture. While machine learning typically does not provide any worst-case\nguarantees, the new field of learning-augmented algorithms proposes solutions\nthat leverage classical online caching algorithms to make the machine-learned\npredictors robust. We are the first to comprehensively evaluate these\nlearning-augmented algorithms on real-world caching datasets and\nstate-of-the-art machine-learned predictors. We show that a straightforward\nmethod -- blindly following either a predictor or a classical robust algorithm,\nand switching whenever one becomes worse than the other -- has only a low\noverhead over a well-performing predictor, while competing with classical\nmethods when the coupled predictor fails, thus providing a cheap worst-case\ninsurance.\n"}
+{"abstract": "  We give an elementary topological obstruction for a $(2q{+}1)$-manifold $M$\nto admit a contact open book with flexible Weinstein pages: if the torsion\nsubgroup of the $q$-th integral homology group is non-zero, then no such\ncontact open book exists. We achieve this by proving that a symplectomorphism\nof a flexible Weinstein manifold acts trivially on cohomology. We also produce\nexamples of non-trivial loops of flexible contact structures using related\nideas.\n"}
+{"abstract": "  Out-of-order speculation, a technique ubiquitous since the early 1990s,\nremains a fundamental security flaw. Via attacks such as Spectre and Meltdown,\nan attacker can trick a victim, in an otherwise entirely correct program, into\nleaking its secrets through the effects of misspeculated execution, in a way\nthat is entirely invisible to the programmer's model. This has serious\nimplications for application sandboxing and inter-process communication.\n  Designing efficient mitigations, that preserve the performance of\nout-of-order execution, has been a challenge. The speculation-hiding techniques\nin the literature have been shown to not close such channels comprehensively,\nallowing adversaries to redesign attacks. Strong, precise guarantees are\nnecessary, but at the same time mitigations must achieve high performance to be\nadopted. We present Strictness Ordering, a new constraint system that shows how\nwe can comprehensively eliminate transient side channel attacks, while still\nallowing complex speculation and data forwarding between speculative\ninstructions. We then present GhostMinion, a cache modification built using a\nvariety of new techniques designed to provide Strictness Order at only 2.5%\noverhead.\n"}
+{"abstract": "  Feedback-based control techniques are useful tools in precision measurements\nas they allow to actively shape the mechanical response of high quality factor\noscillators used in force detection measurements. In this paper we implement a\nfeedback technique on a high-stress low-loss SiN membrane resonator, exploiting\nthe charges trapped on the dielectric membrane. A properly delayed feedback\nforce (dissipative feedback) enables the narrowing of the thermomechanical\ndisplacement variance in a similar manner to the cooling of the normal\nmechanical mode down to an effective temperature Te f f . In the experiment\nhere reported we started from room temperature and gradually increasing the\nfeedback gain we were able to cool down the first normal mode of the resonator\nto a minimum temperature of about 124mK. This limit is imposed by our\nexperimental set-up and in particular by the the injection of the read-out\nnoise into the feedback. We discuss the implementation details and possible\nimprovements to the technique\n"}
+{"abstract": "  The asymmetric skew divergence smooths one of the distributions by mixing it,\nto a degree determined by the parameter $\\lambda$, with the other distribution.\nSuch divergence is an approximation of the KL divergence that does not require\nthe target distribution to be absolutely continuous with respect to the source\ndistribution. In this paper, an information geometric generalization of the\nskew divergence called the $\\alpha$-geodesical skew divergence is proposed, and\nits properties are studied.\n"}
+{"abstract": "  \"Necks\" are features of lipid membranes characterized by an uniquley large\ncurvature, functioning as bridges between different compartments. These\nfeatures are ubiquitous in the life-cycle of the cell and instrumental in\nprocesses such as division, extracellular vesicles uptake and cargo transport\nbetween organelles, but also in life-threatening conditions, as in the\nendocytosis of viruses and phages. Yet, the very existence of lipid necks\nchallenges our understanding of membranes biophysics: their curvature, often\norders of magnitude larger than elsewhere, is energetically prohibitive, even\nwith the arsenal of molecular machineries and signalling pathways that cells\nhave at their disposal. Using a geometric triality, namely a correspondence\nbetween three different classes of geometric objects, here we demonstrate that\nlipid necks are in fact metastable, thus can exist for finite, but potentially\nlong times even in the absence of stabilizing mechanisms. This framework allows\nus to explicitly calculate the forces a corpuscle must overcome in order to\npenetrate cellular membranes, thus paving the way for a predictive theory of\nendo/exo-cytic processes.\n"}
+{"abstract": "  Second-order continuous-time dissipative dynamical systems with viscous and\nHessian driven damping have inspired effective first-order algorithms for\nsolving convex optimization problems. While preserving the fast convergence\nproperties of the Nesterov-type acceleration, the Hessian driven damping makes\nit possible to significantly attenuate the oscillations. To study the stability\nof these algorithms with respect to perturbations, errors, we analyze the\nbehavior of the corresponding continuous systems when the gradient computation\nis subject to errors. {We provide a quantitative analysis of the asymptotic\nbehavior of two types of systems, those with implicit and explicit Hessian\ndriven damping}. We consider convex, strongly convex, and non-smooth objective\nfunctions defined on a real Hilbert space and show that, depending on the\nformulation, different integrability conditions on the perturbations are\nsufficient to maintain the convergence rates of the systems. We highlight the\ndifferences between the implicit and explicit Hessian damping, and in\nparticular point out that the assumptions on the objective and perturbations\nneeded in the implicit case are more stringent than in the explicit case.\n"}
+{"abstract": "  Most of the ongoing projects aimed at the development of specific therapies\nand vaccines against COVID-19 use the SARS-CoV-2 spike (S) protein as the main\ntarget [1-3]. The binding of the spike protein with the ACE2 receptor (ACE2) of\nthe host cell constitutes the first and key step for virus entry. During this\nprocess, the receptor binding domain (RBD) of the S protein plays an essential\nrole, since it contains the receptor binding motif (RBM), responsible for the\ndocking to the receptor. So far, mostly biochemical methods are being tested in\norder to prevent binding of the virus to ACE2 [4]. Here we show, with the help\nof atomistic simulations, that external electric fields of easily achievable\nand moderate strengths can dramatically destabilise the S protein, inducing\nlong-lasting structural damage. One striking field-induced conformational\nchange occurs at the level of the recognition loop L3 of the RBD where two\nparallel beta sheets, believed to be responsible for a high affinity to ACE2\n[5], undergo a change into an unstructured coil, which exhibits almost no\nbinding possibilities to the ACE2 receptor (Figure 1a). Remarkably, while the\nstructural flexibility of S allows the virus to improve its probability of\nentering the cell, it is also the origin of the surprising vulnerability of S\nupon application of electric fields of strengths at least two orders of\nmagnitude smaller than those required for damaging most proteins. Our findings\nsuggest the existence of a clean physical method to weaken the SARS-CoV-2 virus\nwithout further biochemical processing. Moreover, the effect could be used for\ninfection prevention purposes and also to develop technologies for in-vitro\nstructural manipulation of S. Since the method is largely unspecific, it can be\nsuitable for application to mutations in S, to other proteins of SARS-CoV-2 and\nin general to membrane proteins of other virus types.\n"}
+{"abstract": "  X-ray emission from the gravitational wave transient GW170817 is well\ndescribed as non-thermal afterglow radiation produced by a structured\nrelativistic jet viewed off-axis. We show that the X-ray counterpart continues\nto be detected at 3.3 years after the merger. Such long-lasting signal is not a\nprediction of the earlier jet models characterized by a narrow jet core and a\nviewing angle of about 20 deg, and is spurring a renewed interest in the origin\nof the X-ray emission. We present a comprehensive analysis of the X-ray dataset\naimed at clarifying existing discrepancies in the literature, and in particular\nthe presence of an X-ray rebrightening at late times. Our analysis does not\nfind evidence for an increase in the X-ray flux, but confirms a growing tension\nbetween the observations and the jet model. Further observations at radio and\nX-ray wavelengths would be critical to break the degeneracy between models.\n"}
+{"abstract": "  Unmanned aerial vehicle (UAV) based visual tracking has been confronted with\nnumerous challenges, e.g., object motion and occlusion. These challenges\ngenerally introduce unexpected mutations of target appearance and result in\ntracking failure. However, prevalent discriminative correlation filter (DCF)\nbased trackers are insensitive to target mutations due to a predefined label,\nwhich concentrates on merely the centre of the training region. Meanwhile,\nappearance mutations caused by occlusion or similar objects usually lead to the\ninevitable learning of wrong information. To cope with appearance mutations,\nthis paper proposes a novel DCF-based method to enhance the sensitivity and\nresistance to mutations with an adaptive hybrid label, i.e., MSCF. The ideal\nlabel is optimized jointly with the correlation filter and remains temporal\nconsistency. Besides, a novel measurement of mutations called mutation threat\nfactor (MTF) is applied to correct the label dynamically. Considerable\nexperiments are conducted on widely used UAV benchmarks. The results indicate\nthat the performance of MSCF tracker surpasses other 26 state-of-the-art\nDCF-based and deep-based trackers. With a real-time speed of _38 frames/s, the\nproposed approach is sufficient for UAV tracking commissions.\n"}
+{"abstract": "  We present the first full description of Media Cloud, an open source platform\nbased on crawling hyperlink structure in operation for over 10 years, that for\nmany uses will be the best way to collect data for studying the media ecosystem\non the open web. We document the key choices behind what data Media Cloud\ncollects and stores, how it processes and organizes these data, and its open\nAPI access as well as user-facing tools. We also highlight the strengths and\nlimitations of the Media Cloud collection strategy compared to relevant\nalternatives. We give an overview two sample datasets generated using Media\nCloud and discuss how researchers can use the platform to create their own\ndatasets.\n"}
+{"abstract": "  We study three graph complexes related to the higher genus\nGrothendieck-Teichm\\\"uller Lie algebra and diffeomorphism groups of manifolds.\nWe show how the cohomology of these graph complexes is related, and we compute\nthe cohomology as the genus $g$ tends to $\\infty$. As a byproduct, we find that\nthe Malcev completion of the genus $g$ mapping class group relative to the\nsymplectic group is Koszul in the stable limit (partially answering a question\nof Hain). Moreover, we obtain that any elliptic associator gives a solution to\nthe elliptic Kashiwara-Vergne problem.\n"}
+{"abstract": "  The $n$-queens puzzle is to place $n$ mutually non-attacking queens on an $n\n\\times n$ chessboard. We present a simple two stage randomized algorithm to\nconstruct such configurations. In the first stage, a random greedy algorithm\nconstructs an approximate \\textit{toroidal} $n$-queens configuration. In this\nwell-known variant the diagonals wrap around the board from left to right and\nfrom top to bottom. We show that with high probability this algorithm succeeds\nin placing $(1-o(1))n$ queens on the board. In the second stage, the method of\nabsorbers is used to obtain a complete solution to the non-toroidal problem. By\ncounting the number of choices available at each step of the random greedy\nalgorithm we conclude that there are more than $\\left( \\left( 1 - o(1) \\right)\nn e^{-3} \\right)^n$ solutions to the $n$-queens problem. This proves a\nconjecture of Rivin, Vardi, and Zimmerman in a strong form.\n"}
+{"abstract": "  Experiments overall suggested that dilute solid solution of manganese in\nbody-centered cubic iron transforms from antiferromagnetic coupling into a\nferromagnetic coupling at about 2 at.% Mn. Despite long-term theoretical\nstudies, this phase transition is poorly understood, and the transition\nmechanism is still open. Based on DFT calculations with dense k-point meshes,\nwe reveal that this \"iso-structural\" phase transition (IPT) occurs at 1.85 at.%\nMn, originating from the shifting of 3d eg level of Mn across the Fermi level\nand consequent intra-atomic electron transfer within 3d states of Mn. The IPT\ninvolves a sudden change of the bulk modulus accompanied by a small yet\ndetectable change of the lattice constant, an inversion of magnetic coupling\nbetween solute Mn and Fe matrix, and a change in bonding strength between Mn\nand the first-nearest neighboring Fe atoms. Our interpretation of this IPT\nplays an enlightening role in understanding similar IPTs in other materials.\n"}
+{"abstract": "  The interaction of localised solitary waves with large-scale, time-varying\ndispersive mean flows subject to nonconvex flux is studied in the framework of\nthe modified Korteweg-de Vries (mKdV) equation, a canonical model for nonlinear\ninternal gravity wave propagation in stratified fluids. The principal feature\nof the studied interaction is that both the solitary wave and the large-scale\nmean flow -- a rarefaction wave or a dispersive shock wave (undular bore) --\nare described by the same dispersive hydrodynamic equation. A recent\ntheoretical and experimental study of this new type of dynamic soliton-mean\nflow interaction has revealed two main scenarios when the solitary wave either\ntunnels through the varying mean flow that connects two constant asymptotic\nstates, or remains trapped inside it. While the previous work considered convex\nsystems, in this paper it is demonstrated that the presence of a nonconvex\nhydrodynamic flux introduces significant modifications to the scenarios for\ntransmission and trapping. A reduced set of Whitham modulation equations,\ntermed the solitonic modulation system, is used to formulate a general,\napproximate mathematical framework for solitary wave-mean flow interaction with\nnonconvex flux. Solitary wave trapping is conveniently stated in terms of\ncrossing characteristics for the solitonic system. Numerical simulations of the\nmKdV equation agree with the predictions of modulation theory. The developed\ntheory draws upon general properties of dispersive hydrodynamic partial\ndifferential equations, not on the complete integrability of the mKdV equation.\nAs such, the mathematical framework developed here enables application to other\nfluid dynamic contexts subject to nonconvex flux.\n"}
+{"abstract": "  We compute the cosmological constant of a spherical space in the limit of\nweak gravity. To this end we use a duality developed by the present authors in\na previous work. This duality allows one to treat the Newtonian cosmological\nfluid as the probability fluid of a single particle in nonrelativistic quantum\nmechanics. We apply this duality to the case when the spacetime manifold on\nwhich this quantum mechanics is defined is given by\n$\\mathbb{R}\\times\\mathbb{S}^3$. Here $\\mathbb{R}$ stands for the time axis and\n$\\mathbb{S}^3$ is a 3-dimensional sphere endowed with the standard round\nmetric. A quantum operator $\\Lambda$ satisfying all the requirements of a\ncosmological constant is identified, and the matrix representing $\\Lambda$\nwithin the Hilbert space $L^2\\left(\\mathbb{S}^3\\right)$ of quantum states is\nobtained. Numerical values for the expectation value of the operator $\\Lambda$\nin certain quantum states are obtained, that are in good agreement with the\nexperimentally measured cosmological constant.\n"}
+{"abstract": "  Given a closed, orientable surface of constant negative curvature and genus\n$g \\ge 2$, we study a family of generalized Bowen-Series boundary maps and\nprove the following rigidity result: in this family the topological entropy is\nconstant and depends only on the genus of the surface. We give an explicit\nformula for this entropy and show that the value of the topological entropy\nalso stays constant in the Teichm\\\"uller space of the surface. The proofs use\nconjugation to maps of constant slope.\n"}
+{"abstract": "  Objective: Magnetic Resonance Spectroscopy (MRS) is a noninvasive tool to\nreveal metabolic information. One challenge of MRS is the relatively low\nSignal-Noise Ratio (SNR) due to low concentrations of metabolites. To improve\nthe SNR, the most common approach is to average signals that are acquired in\nmultiple times. The data acquisition time, however, is increased by multiple\ntimes accordingly, resulting in the scanned objects uncomfortable or even\nunbearable. Methods: By exploring the multiple sampled data, a deep learning\ndenoising approach is proposed to learn a mapping from the low SNR signal to\nthe high SNR one. Results: Results on simulated and in vivo data show that the\nproposed method significantly reduces the data acquisition time with slightly\ncompromised metabolic accuracy. Conclusion: A deep learning denoising method\nwas proposed to significantly shorten the time of data acquisition, while\nmaintaining signal accuracy and reliability. Significance: Provide a solution\nof the fundamental low SNR problem in MRS with artificial intelligence.\n"}
+{"abstract": "  We present an approach for encoding visual task relationships to improve\nmodel performance in an Unsupervised Domain Adaptation (UDA) setting. Semantic\nsegmentation and monocular depth estimation are shown to be complementary\ntasks; in a multi-task learning setting, a proper encoding of their\nrelationships can further improve performance on both tasks. Motivated by this\nobservation, we propose a novel Cross-Task Relation Layer (CTRL), which encodes\ntask dependencies between the semantic and depth predictions. To capture the\ncross-task relationships, we propose a neural network architecture that\ncontains task-specific and cross-task refinement heads. Furthermore, we propose\nan Iterative Self-Learning (ISL) training scheme, which exploits semantic\npseudo-labels to provide extra supervision on the target domain. We\nexperimentally observe improvements in both tasks' performance because the\ncomplementary information present in these tasks is better captured.\nSpecifically, we show that: (1) our approach improves performance on all tasks\nwhen they are complementary and mutually dependent; (2) the CTRL helps to\nimprove both semantic segmentation and depth estimation tasks performance in\nthe challenging UDA setting; (3) the proposed ISL training scheme further\nimproves the semantic segmentation performance. The implementation is available\nat https://github.com/susaha/ctrl-uda.\n"}
+{"abstract": "  We propose a bootstrap program for CFTs near intersecting boundaries which\nform a co-dimension 2 edge. We describe the kinematical setup and show that\nbulk 1-pt functions and bulk-edge 2-pt functions depend on a non-trivial\ncross-ratio and on the angle between the boundaries. Using the boundary OPE\n(BOE) with respect to each boundary, we derive two independent conformal block\nexpansions for these correlators. The matching of the two BOE expansions leads\nto a crossing equation. We analytically solve this equation in several simple\ncases, notably for a free bulk field, where we recover Feynman-diagrammatic\nresults by Cardy.\n"}
+{"abstract": "  With the development of modern radio interferometers, wide-field continuum\nsurveys have been planned and undertaken, for which accurate wide-field imaging\nmethods are essential. Based on the widely-used W-stacking method, we propose a\nnew wide-field imaging algorithm that can synthesize visibility data from a\nmodel of the sky brightness via degridding, able to construct dirty maps from\nmeasured visibility data via gridding. Results carry the smallest approximation\nerror yet achieved relative to the exact calculation involving the direct\nFourier transform. In contrast to the original W-stacking method, the new\nalgorithm performs least-misfit optimal gridding (and degridding) in all three\ndirections, and is capable of achieving much higher accuracy than is feasible\nwith the original algorithm. In particular, accuracy at the level of single\nprecision arithmetic is readily achieved by choosing a least-misfit convolution\nfunction of width W=7 and an image cropping parameter of x0=0.25. If the\naccuracy required is only that attained by the original W-stacking method, the\ncomputational cost for both the gridding and FFT steps can be substantially\nreduced using the proposed method by making an appropriate choice of the width\nand image cropping parameters.\n"}
+{"abstract": "  We give lower bounds on the performance of two of the most popular sampling\nmethods in practice, the Metropolis-adjusted Langevin algorithm (MALA) and\nmulti-step Hamiltonian Monte Carlo (HMC) with a leapfrog integrator, when\napplied to well-conditioned distributions. Our main result is a nearly-tight\nlower bound of $\\widetilde{\\Omega}(\\kappa d)$ on the mixing time of MALA from\nan exponentially warm start, matching a line of algorithmic results up to\nlogarithmic factors and answering an open question of Chewi et. al. We also\nshow that a polynomial dependence on dimension is necessary for the relaxation\ntime of HMC under any number of leapfrog steps, and bound the gains achievable\nby changing the step count. Our HMC analysis draws upon a novel connection\nbetween leapfrog integration and Chebyshev polynomials, which may be of\nindependent interest.\n"}
+{"abstract": "  Generalized numbers, arithmetic operators and derivative operators, grouped\nin four classes based on symmetry features, are introduced. Their building\nelement is the pair of $q$-logarithm/$q$-exponential inverse functions. Some of\nthe objects were previously described in the literature, while others are newly\ndefined. Commutativity, associativity and distributivity, and also a pair of\nlinear/nonlinear derivatives are observed within each class. Two entropic\nfunctionals emerge from the formalism, one of them is the nonadditive Tsallis\nentropy.\n"}
+{"abstract": "  Stellar activity due to different processes (magnetic activity, photospheric\nflows) affects the measurement of radial velocities (RV). Radial velocities\nhave been widely used to detect exoplanets, although the stellar signal\nsignificantly impacts the detection and characterisation performance,\nespecially for low mass planets. On the other hand, RV time series are also\nvery rich in information on stellar processes. In this lecture, I review the\ncontext of RV observations, describe how radial velocities are measured, and\nthe properties of typical observations. I present the challenges represented by\nstellar activity for exoplanet studies, and describe the processes at play.\nFinally, I review the approaches which have been developed, including\nobservations and simulations, as well as solar and stellar comparisons.\n"}
+{"abstract": "  In this paper, a unified batch-online learning approach is introduced to\nlearn a linear representation of nonlinear system dynamics using the Koopman\noperator. The presented system modeling approach leverages a novel incremental\nKoopman-based update law that retrieves a mini-batch of samples stored in a\nmemory to not only minimizes the instantaneous Koopman operator's\nidentification errors but also the identification errors for the batch of\nretrieved samples. Discontinuous modifications of gradient flows are presented\nfor the online update law to assure finite-time convergence under\neasy-to-verify conditions defined on the batch of data. Therefore, this unified\nonline-batch framework allows performing joint sample- and time-domain analysis\nfor converging the Koopman operator's parameters. More specifically, it is\nshown that if the collected mini-batch of samples guarantees a rank condition,\nthen finite-time guarantee in the time domain can be certified and the settling\ntime depends on the quality of collected samples being reused in the update\nlaw. Moreover, the efficiency of the proposed Koopman-based update law is\nfurther analyzed by showing that the identification regret in continuous time\ngrows sub-linearly with time. Furthermore, to avoid learning corrupted dynamics\ndue to the selection of an inappropriate set of Koopman observables, a\nhigher-layer meta learner employs a discrete Bayesian optimization algorithm to\nobtain the best library of observable functions for the operator. Since\nfinite-time convergence of the Koopman model for each set of observable is\nguaranteed under a rank condition on stored data, the fitness of each set of\nobservables can be obtained based on the identification error on the stored\nsamples in the proposed framework and even without implementing any controller\nbased on the learned system.\n"}
+{"abstract": "  Recently, Ni and Pan proved a $q$-congruence on certain sums involving\ncentral $q$-binomial coefficients, which was conjectured by Guo. In this paper,\nwe give a generalization of this $q$-congruence and confirm another\n$q$-congruence, also conjectured by Guo. Our proof uses Ni and Pan's technique\nand a simple $q$-congruence observed by Guo and Schlosser.\n"}
+{"abstract": "  The current COVID-19 pandemic has shown us that we are still facing\nunpredictable challenges in our society. The necessary constrain on social\ninteractions affected heavily how we envision and prepare the future of social\nrobots and artificial agents in general. Adapting current affective perception\nmodels towards constrained perception based on the hard separation between\nfacial perception and affective understanding would help us to provide robust\nsystems. In this paper, we perform an in-depth analysis of how recognizing\naffect from persons with masks differs from general facial expression\nperception. We evaluate how the recently proposed FaceChannel adapts towards\nrecognizing facial expressions from persons with masks. In Our analysis, we\nevaluate different training and fine-tuning schemes to understand better the\nimpact of masked facial expressions. We also perform specific feature-level\nvisualization to demonstrate how the inherent capabilities of the FaceChannel\nto learn and combine facial features change when in a constrained social\ninteraction scenario.\n"}
+{"abstract": "  The segmentation of coronary arteries by convolutional neural network is\npromising yet requires a large amount of labor-intensive manual annotations.\nTransferring knowledge from retinal vessels in widely-available public labeled\nfundus images (FIs) has a potential to reduce the annotation requirement for\ncoronary artery segmentation in X-ray angiograms (XAs) due to their common\ntubular structures. However, it is challenged by the cross-anatomy domain shift\ndue to the intrinsically different vesselness characteristics in different\nanatomical regions under even different imaging protocols. To solve this\nproblem, we propose a Semi-Supervised Cross-Anatomy Domain Adaptation (SS-CADA)\nwhich requires only limited annotations for coronary arteries in XAs. With the\nsupervision from a small number of labeled XAs and publicly available labeled\nFIs, we propose a vesselness-specific batch normalization (VSBN) to\nindividually normalize feature maps for them considering their different\ncross-anatomic vesselness characteristics. In addition, to further facilitate\nthe annotation efficiency, we employ a self-ensembling mean-teacher (SEMT) to\nexploit abundant unlabeled XAs by imposing a prediction consistency constraint.\nExtensive experiments show that our SS-CADA is able to solve the challenging\ncross-anatomy domain shift, achieving accurate segmentation for coronary\narteries given only a small number of labeled XAs.\n"}
+{"abstract": "  Semantic segmentation using convolutional neural networks (CNN) is a crucial\ncomponent in image analysis. Training a CNN to perform semantic segmentation\nrequires a large amount of labeled data, where the production of such labeled\ndata is both costly and labor intensive. Semi-supervised learning algorithms\naddress this issue by utilizing unlabeled data and so reduce the amount of\nlabeled data needed for training. In particular, data augmentation techniques\nsuch as CutMix and ClassMix generate additional training data from existing\nlabeled data. In this paper we propose a new approach for data augmentation,\ntermed ComplexMix, which incorporates aspects of CutMix and ClassMix with\nimproved performance. The proposed approach has the ability to control the\ncomplexity of the augmented data while attempting to be semantically-correct\nand address the tradeoff between complexity and correctness. The proposed\nComplexMix approach is evaluated on a standard dataset for semantic\nsegmentation and compared to other state-of-the-art techniques. Experimental\nresults show that our method yields improvement over state-of-the-art methods\non standard datasets for semantic image segmentation.\n"}
+{"abstract": "  We study reinforcement learning (RL) with linear function approximation under\nthe adaptivity constraint. We consider two popular limited adaptivity models:\nthe batch learning model and the rare policy switch model, and propose two\nefficient online RL algorithms for episodic linear Markov decision processes,\nwhere the transition probability and the reward function can be represented as\na linear function of some known feature mapping. In specific, for the batch\nlearning model, our proposed LSVI-UCB-Batch algorithm achieves an $\\tilde\nO(\\sqrt{d^3H^3T} + dHT/B)$ regret, where $d$ is the dimension of the feature\nmapping, $H$ is the episode length, $T$ is the number of interactions and $B$\nis the number of batches. Our result suggests that it suffices to use only\n$\\sqrt{T/dH}$ batches to obtain $\\tilde O(\\sqrt{d^3H^3T})$ regret. For the rare\npolicy switch model, our proposed LSVI-UCB-RareSwitch algorithm enjoys an\n$\\tilde O(\\sqrt{d^3H^3T[1+T/(dH)]^{dH/B}})$ regret, which implies that $dH\\log\nT$ policy switches suffice to obtain the $\\tilde O(\\sqrt{d^3H^3T})$ regret. Our\nalgorithms achieve the same regret as the LSVI-UCB algorithm (Jin et al.,\n2019), yet with a substantially smaller amount of adaptivity. We also establish\na lower bound for the batch learning model, which suggests that the dependency\non $B$ in our regret bound is tight.\n"}
+{"abstract": "  The consortium of the European project 16NRM05 designed a novel ionisation\nvacuum gauge in which the electrons take a straight path from the emitting\ncathode through the ionisation space into a Faraday cup. Compared to existing\nionisation vacuum gauges, this has the advantage that the electron path length\nis well defined. It is independent of the point and angle of emission and is\nnot affected by space charge around the collector. In addition, the electrons\ndo not hit the anode where they can be reflected, generate secondary electrons\nor cause desorption of neutrals or ions. This design was chosen in order to\ndevelop a more stable ionisation vacuum gauge suitable as reference standard in\nthe range of 10-6 Pa to 10-2 Pa for calibration purposes of other vacuum gauges\nand quadrupole mass spectrometers. Prototype gauges were produced by two\ndifferent manufacturers and showed predictable sensitivities with a very small\nspread (< 1.5%), very good short-term repeatability (< 0.05%) and\nreproducibility (< 1%), even after changing the emission cathode and drop-down\ntests. These characteristics make the gauge also attractive for industrial\napplications, because a gauge exchange does not require calibration or\nre-adjustment of a process.\n"}
+{"abstract": "  In this work we present a detailed analysis of variational quantum phase\nestimation (VQPE), a method based on real-time evolution for ground and excited\nstate estimation on near-term hardware. We derive the theoretical ground on\nwhich the approach stands, and demonstrate that it provides one of the most\ncompact variational expansions to date for solving strongly correlated\nHamiltonians. At the center of VQPE lies a set of equations, with a simple\ngeometrical interpretation, which provides conditions for the time evolution\ngrid in order to decouple eigenstates out of the set of time evolved expansion\nstates, and connects the method to the classical filter diagonalization\nalgorithm. Further, we introduce what we call the unitary formulation of VQPE,\nin which the number of matrix elements that need to be measured scales linearly\nwith the number of expansion states, and we provide an analysis of the effects\nof noise which substantially improves previous considerations. The unitary\nformulation allows for a direct comparison to iterative phase estimation. Our\nresults mark VQPE as both a natural and highly efficient quantum algorithm for\nground and excited state calculations of general many-body systems. We\ndemonstrate a hardware implementation of VQPE for the transverse field Ising\nmodel. Further, we illustrate its power on a paradigmatic example of strong\ncorrelation (Cr2 in the SVP basis set), and show that it is possible to reach\nchemical accuracy with as few as ~50 timesteps.\n"}
+{"abstract": "  Polynomial factorization in conventional sense is an ill-posed problem due to\nits discontinuity with respect to coefficient perturbations, making it a\nchallenge for numerical computation using empirical data. As a regularization,\nthis paper formulates the notion of numerical factorization based on the\ngeometry of polynomial spaces and the stratification of factorization\nmanifolds. Furthermore, this paper establishes the existence, uniqueness,\nLipschitz continuity, condition number, and convergence of the numerical\nfactorization to the underlying exact factorization, leading to a robust and\nefficient algorithm with a MATLAB implementation capable of accurate polynomial\nfactorizations using floating point arithmetic even if the coefficients are\nperturbed.\n"}
+{"abstract": "  In this paper we propose a novel approach to realize forecast verification.\nSpecifically, we introduce a strategy for assessing the severity of forecast\nerrors based on the evidence that, on the one hand, a false alarm just\nanticipating an occurring event is better than one in the middle of consecutive\nnon-occurring events, and that, on the other hand, a miss of an isolated event\nhas a worse impact than a miss of a single event, which is part of several\nconsecutive occurrences. Relying on this idea, we introduce a novel definition\nof confusion matrix and skill scores giving greater importance to the value of\nthe prediction rather than to its quality. Then, we introduce a deep ensemble\nlearning procedure for binary classification, in which the probabilistic\noutcomes of a neural network are clustered via optimization of these\nvalue-weighted skill scores. We finally show the performances of this approach\nin the case of three applications concerned with pollution, space weather and\nstock prize forecasting.\n"}
+{"abstract": "  This article aims to numerically investigate the combustion phenomenon of\ncoaxial gaseous CH4 LOx at supercritical pressures. The choice of turbulence\nmodel, real gas model, and chemical kinetics model are the critical parameters\nin numerical simulations of cryogenic combustion at high pressure. At this\nsupercritical operating pressure, the ideal gas law does not remain valid for\nsuch cases. Therefore, we have systematically carried out a comparative study\nto analyze the importance of real gas models, turbulence parameters, and\nchemical kinetics at such conditions. The comparison of real gas models with\nthe NIST database reveals better conformity of SRK (Soave Redlich Kwong\nEquation of State (EoS)) model predictions with the database. Further, the\ncomputed results indicate that the Standard k-e turbulence model with modified\nconstant captures the better flame shape and temperature peak position compared\nto other RANS based turbulence models while invoking the non-premixed steady\nb-PDF flamelet model for simulating the combustion process. Furthermore, a\ncomparative study comparing two different chemical kinetics models indicates\nthat the reduced Jones Lindstedt mechanism can accurately predict the flame\ncharacteristics with the least computational cost. Finally, we have studied the\neffect of chamber pressure and LOx inlet temperature on the flame\ncharacteristics. The flame characteristics exhibit a strong sensitivity towards\nthe chamber pressure due to the weakening of the pseudo-boiling effect with an\nincrease in pressure. As a consequence of lower turbulent rates of energy and\nmass transfer through the transcritical mixing layer, the flame spreading\nbecomes narrower at elevated pressure and temperature, thereby yielding an\nincreased flame length at transcritical conditions.\n"}
+{"abstract": "  We study the effect of changes in the parameters of a two-dimensional\npotential energy surface on the phase space structures relevant for chemical\nreaction dynamics. The changes in the potential energy are representative of\nchemical reactions such as isomerization between two structural conformations\nor dissociation of a molecule with an intermediate. We present a two degrees of\nfreedom quartic Hamiltonian that shows pitchfork bifurcation when the\nparameters are varied and we derive the bifurcation criteria relating the\nparameters. Next, we describe the phase space structures - unstable periodic\norbits and their associated invariant manifolds, and phase space dividing\nsurfaces - for the systems that can show trajectories undergo reaction defined\nas crossing of a potential energy barrier. Finally, we quantify the reaction\ndynamics for these systems by obtaining the directional flux and gap time\ndistribution to illustrate the dependence on total energy and the coupling\nstrength between the two degrees of freedom.\n"}
+{"abstract": "  Services on the public Internet are frequently scanned, then subject to\nbrute-force and denial-of-service attacks. We would like to run such services\nstealthily, available to friends but hidden from adversaries. In this work, we\npropose a moving target defense named \"Chhoyhopper\" that utilizes the vast IPv6\naddress space to conceal publicly available services. The client and server to\nhop to different IPv6 addresses in a pattern based on a shared, pre-distributed\nsecret and the time-of-day. By hopping over a /64 prefix, services cannot be\nfound by active scanners, and passively observed information is useless after\ntwo minutes. We demonstrate our system with SSH, and show that it can be\nextended to other applications.\n"}
+{"abstract": "  Most video super-resolution methods focus on restoring high-resolution video\nframes from low-resolution videos without taking into account compression.\nHowever, most videos on the web or mobile devices are compressed, and the\ncompression can be severe when the bandwidth is limited. In this paper, we\npropose a new compression-informed video super-resolution model to restore\nhigh-resolution content without introducing artifacts caused by compression.\nThe proposed model consists of three modules for video super-resolution:\nbi-directional recurrent warping, detail-preserving flow estimation, and\nLaplacian enhancement. All these three modules are used to deal with\ncompression properties such as the location of the intra-frames in the input\nand smoothness in the output frames. For thorough performance evaluation, we\nconducted extensive experiments on standard datasets with a wide range of\ncompression rates, covering many real video use cases. We showed that our\nmethod not only recovers high-resolution content on uncompressed frames from\nthe widely-used benchmark datasets, but also achieves state-of-the-art\nperformance in super-resolving compressed videos based on numerous quantitative\nmetrics. We also evaluated the proposed method by simulating streaming from\nYouTube to demonstrate its effectiveness and robustness. The source codes and\ntrained models are available at\nhttps://github.com/google-research/google-research/tree/master/comisr.\n"}
+{"abstract": "  Number of zeros seen by a particle around small clusters of other particles\nis encoded in the root partition, and partly characterizes the correlations in\nfractional quantum Hall trial wavefunctions. We explore a generalization\nwherein we consider the counting of zeros seen by a cluster of particles on\nanother cluster. Numbers of such zeros between clusters in the Laughlin\nwavefunctions are fully determined by the root partition. However, such a\ncounting is unclear for general Jain states where a polynomial expansion is\ndifficult. Here we consider the simplest state beyond the Laughlin\nwavefunction, namely a state containing a single quasiparticle of the Laughlin\nstate. We show numerically and analytically that in the trial wavefunction for\nthe quasiparticle of the Laughlin state, counting of zeros seen by a cluster on\nanother cluster depends on the relative dimensions of the two clusters. We\nfurther ask if the patterns in the counting of zeros extend, in at least an\napproximate sense, to wavefunctions beyond the trial states. Using numerical\ncomputations in systems up to $N=9$, we present results for the statistical\ndistribution of zeros around particle clusters at the center of an FQH droplet\nin the ground state of a Hamiltonian that is perturbed away from the $V_1$\ninteraction (short-range repulsion). Evolution of this distribution with the\nstrength of the perturbation shows that the counting of zeros is altered by\neven a weak perturbation away from the parent Hamiltonian, though the\nperturbations do not change the phase of the system.\n"}
+{"abstract": "  Stochastic high dimensional bandit problems with low dimensional structures\nare useful in different applications such as online advertising and drug\ndiscovery. In this work, we propose a simple unified algorithm for such\nproblems and present a general analysis framework for the regret upper bound of\nour algorithm. We show that under some mild unified assumptions, our algorithm\ncan be applied to different high dimensional bandit problems. Our framework\nutilizes the low dimensional structure to guide the parameter estimation in the\nproblem, therefore our algorithm achieves the best regret bounds in the LASSO\nbandit, as well as novel bounds in the low-rank matrix bandit, the group sparse\nmatrix bandit, and in a new problem: the multi-agent LASSO bandit.\n"}
+{"abstract": "  Agriculture is the foundation of human civilization. However, the rapid\nincrease and aging of the global population pose challenges on this cornerstone\nby demanding more healthy and fresh food. Internet of Things (IoT) technology\nmakes modern autonomous greenhouse a viable and reliable engine of food\nproduction. However, the educated and skilled labor capable of overseeing\nhigh-tech greenhouses is scarce. Artificial intelligence (AI) and cloud\ncomputing technologies are promising solutions for precision control and\nhigh-efficiency production in such controlled environments. In this paper, we\npropose a smart agriculture solution, namely iGrow: (1) we use IoT and cloud\ncomputing technologies to measure, collect, and manage growing data, to support\niteration of our decision-making AI module, which consists of an incremental\nmodel and an optimization algorithm; (2) we propose a three-stage incremental\nmodel based on accumulating data, enabling growers/central computers to\nschedule control strategies conveniently and at low cost; (3) we propose a\nmodel-based iterative optimization algorithm, which can dynamically optimize\nthe greenhouse control strategy in real-time production. In the simulated\nexperiment, evaluation results show the accuracy of our incremental model is\ncomparable to an advanced tomato simulator, while our optimization algorithms\ncan beat the champion of the 2nd Autonomous Greenhouse Challenge. Compelling\nresults from the A/B test in real greenhouses demonstrate that our solution\nsignificantly increases production (commercially sellable fruits) (+ 10.15%)\nand net profit (+ 87.07%) with statistical significance compared to planting\nexperts.\n"}
+{"abstract": "  Recent urbanization has coincided with the enrichment of geotagged data, such\nas street view and point-of-interest (POI). Region embedding enhanced by the\nricher data modalities has enabled researchers and city administrators to\nunderstand the built environment, socioeconomics, and the dynamics of cities\nbetter. While some efforts have been made to simultaneously use multi-modal\ninputs, existing methods can be improved by incorporating different measures of\n'proximity' in the same embedding space - leveraging not only the data that\ncharacterizes the regions (e.g., street view, local businesses pattern) but\nalso those that depict the relationship between regions (e.g., trips, road\nnetwork). To this end, we propose a novel approach to integrate multi-modal\ngeotagged inputs as either node or edge features of a multi-graph based on\ntheir relations with the neighborhood region (e.g., tiles, census block, ZIP\ncode region, etc.). We then learn the neighborhood representation based on a\ncontrastive-sampling scheme from the multi-graph. Specifically, we use street\nview images and POI features to characterize neighborhoods (nodes) and use\nhuman mobility to characterize the relationship between neighborhoods (directed\nedges). We show the effectiveness of the proposed methods with quantitative\ndownstream tasks as well as qualitative analysis of the embedding space: The\nembedding we trained outperforms the ones using only unimodal data as regional\ninputs.\n"}
+{"abstract": "  Context. The recent discovery of much greater magnetic flux cancellation\ntaking place at the photosphere than previously realised has led us in our\nprevious works to suggest magnetic reconnection driven by flux cancellation as\nthe cause of a wide range of dynamic phenomena, including jets of various kinds\nand solar atmospheric heating. Aims. Previously, the theory considered energy\nrelease at a two-dimensional current sheet. Here we develop the theory further\nby extending it to an axisymmetric current sheet in three dimensions without\nresorting to complex variable theory. Methods. We analytically study\nreconnection and treat the current sheet as a three-dimensional structure. We\napply the theory to the cancellation of two fragments of equal but opposite\nflux that approach each another and are located in an overlying horizontal\nmagnetic field. Results. The energy release occurs in two phases. During Phase\n1, a separator is formed and reconnection is driven at it as it rises to a\nmaximum height and then moves back down to the photosphere, heating the plasma\nand accelerating a plasma jet as it does so. During Phase 2 the fluxes cancel\nin the photosphere and accelerate a mixture of cool and hot plasma upwards.\n"}
+{"abstract": "  Topological spin textures can be found in both two-dimensional and\nthree-dimensional nanostructures, which are of great importance to advanced\nspintronic applications. Here we report the current-induced skyrmion tube\ndynamics in three-dimensional synthetic antiferromagnetic (SyAF) bilayer and\nmultilayer nanostructures. It is found that the SyAF skyrmion tube made of\nthinner sublayer skyrmions is more stable during its motion, which ensures that\na higher speed of the skyrmion tube can be reached effectively at larger\ndriving current. In the SyAF multilayer with a given total thickness, the\ncurrent-induced deformation of the SyAF skyrmion tube decreases with an\nincreasing number of interfaces; namely, the rigidity of the SyAF skyrmion tube\nwith a given thickness increases with the number of ferromagnetic (FM) layers.\nFor the SyAF multilayer with an even number of FM layers, the skyrmion Hall\neffect can be eliminated when the thicknesses of all FM layers are identical.\nLarger damping parameter leads to smaller deformation and slower speed of the\nSyAF skyrmion tube. Larger fieldlike torque leads to larger deformation and a\nhigher speed of the SyAF skyrmion tube. Our results are useful for\nunderstanding the dynamic behaviors of three-dimensional topological spin\ntextures and may provide guidelines for building SyAF spintronic devices.\n"}
+{"abstract": "  The liquid-solid diffusion couple technique, supported by phenomenological\nanalysis and nano-indentation tests, is proposed on account of the relatively\nlow melting points of Mg to explore the diffusion mobility and creep\ndeformation. The potential of this strategy is demonstrated in Mg-Ga hcp alloys\nwhere Ga solute (i.e. impurity) and Mg solvent diffusions in hcp Mg-Ga alloys\nwere both unveiled. It was followed by mapping the compressive creep behavior\nvia nanoindentation along the composition arrays within the same Mg-Ga couple\nsample. The compressive creep resistance of Mg-Ga hcp alloys increased with the\nGa content, and this enhancement was similar to the one found in Mg-Zn alloys\nand superior to the one reported in Mg-Al alloys though Al is a slower impurity\ndiffuser in hcp-Mg than Zn and Ga. Thereby, the solvent diffusion and its\nvariation with the composition, rather than the solute diffusion, was suggested\nto govern the creep properties at high temperatures and low stresses.\n"}
+{"abstract": "  Irrigation decision systems and water need models have been important\nresearch topics in agriculture since 90s. They improve the efficiency of crop\nyields, provide an appropriate use of water on the earth and so, prevent the\nwater scarcity in some regions. In this paper, a comprehensive survey on water\nneed models depending on crop growth and irrigation decision systems has been\nconducted based on mathematical maodelling. The following outcomes and\nsolutions are the main contributions. Crop growth models and correspondingly\nwater need models are suffer from un-modeled dynamics of the environment and\nlack of sensory devices. Literature review with the latest developments on\nwater need models, irrigation decision systems, applied control methods and\ndiscussions are expected to be useful for the future strategies.\n"}
+{"abstract": "  The bulk of computational approaches for modeling physical systems in\nmaterials science derive from either analytical (i.e. physics based) or\ndata-driven (i.e. machine-learning based) origins. In order to combine the\nstrengths of these two approaches, we advance a novel machine learning approach\nfor solving equations of the generalized Lippmann-Schwinger (L-S) type. In this\nparadigm, a given problem is converted into an equivalent L-S equation and\nsolved as an optimization problem, where the optimization procedure is\ncalibrated to the problem at hand. As part of a learning-based loop unrolling,\nwe use a recurrent convolutional neural network to iteratively solve the\ngoverning equations for a field of interest. This architecture leverages the\ngeneralizability and computational efficiency of machine learning approaches,\nbut also permits a physics-based interpretation. We demonstrate our learning\napproach on the two-phase elastic localization problem, where it achieves\nexcellent accuracy on the predictions of the local (i.e., voxel-level) elastic\nstrains. Since numerous governing equations can be converted into an equivalent\nL-S form, the proposed architecture has potential applications across a range\nof multiscale materials phenomena.\n"}
+{"abstract": "  We discuss the possibility of unifying in a simple and economical manner the\nYukawa couplings of third generation fermions in a non-supersymmetric SO(10)\nmodel with an intermediate symmetry breaking, focusing on two possible patterns\nwith intermediate Pati-Salam and minimal left-right groups. For this purpose,\nwe start with a two Higgs doublet model at the electroweak scale and assume a\nminimal Yukawa sector at the high energy scales. We first enforce gauge\ncoupling unification at the two-loop level by including the threshold\ncorrections in the renormalisation group running which are generated by the\nheavy fields that appear at the intermediate symmetry breaking scale. We then\nstudy the running of the Yukawa couplings of the top quark, bottom quark and\ntau lepton at two-loops in these two breaking schemes, when the appropriate\nmatching conditions are imposed. We find that the unification of the third\nfamily Yukawa couplings can be achieved while retaining a viable spectrum,\nprovided that the ratio of the vacuum expectation values of the two Higgs\ndoublet fields is large, $\\tan\\beta \\approx 60$.\n"}
+{"abstract": "  Time series forecasting is a growing domain with diverse applications.\nHowever, changes of the system behavior over time due to internal or external\ninfluences are challenging. Therefore, predictions of a previously learned\nfore-casting model might not be useful anymore. In this paper, we present\nEVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal\nData (EVARS-GPR), a novel online algorithm that is able to handle sudden shifts\nin the target variable scale of seasonal data. For this purpose, EVARS-GPR\ncom-bines online change point detection with a refitting of the prediction\nmodel using data augmentation for samples prior to a change point. Our\nexperiments on sim-ulated data show that EVARS-GPR is applicable for a wide\nrange of output scale changes. EVARS-GPR has on average a 20.8 % lower RMSE on\ndifferent real-world datasets compared to methods with a similar computational\nresource con-sumption. Furthermore, we show that our algorithm leads to a\nsix-fold reduction of the averaged runtime in relation to all comparison\npartners with a periodical refitting strategy. In summary, we present a\ncomputationally efficient online fore-casting algorithm for seasonal time\nseries with changes of the target variable scale and demonstrate its\nfunctionality on simulated as well as real-world data. All code is publicly\navailable on GitHub: https://github.com/grimmlab/evars-gpr.\n"}
+{"abstract": "  We derive gain-tuning rules for the positive and negative spatial-feedback\nloops of a spatially-distributed filter to change the resolution of its spatial\nband-pass characteristic accordingly to a wavelet zoom, while preserving\ntemporal stability. The filter design is inspired by the canonical spatial\nfeedback structure of the primary visual cortex and is motivated by\nunderstanding attentional control of visual resolution. Besides biology, our\ncontrol-theoretical design strategy is relevant for the development of\nneuromorphic multiresolution distributed sensors through the feedback\ninterconnection of elementary spatial transfer functions and gain tuning.\n"}
+{"abstract": "  Neural network based Artificial Intelligence (AI) has reported increasing\nscales in experiments. However, this paper raises a rarely reported stage in\nsuch experiments called Post-Selection alter the reader to several possible\nprotocol flaws that may result in misleading results. All AI methods fall into\ntwo broad schools, connectionist and symbolic. The Post-Selection fall into two\nkinds, Post-Selection Using Validation Sets (PSUVS) and Post-Selection Using\nTest Sets (PSUTS). Each kind has two types of post-selectors, machines and\nhumans. The connectionist school received criticisms for its \"black box\" and\nnow the Post-Selection; but the seemingly \"clean\" symbolic school seems more\nbrittle because of its human PSUTS. This paper first presents a controversial\nview: all static \"big data\" are non-scalable. We then analyze why\nerror-backprop from randomly initialized weights suffers from severe local\nminima, why PSUVS lacks cross-validation, why PSUTS violates well-established\nprotocols, and why every paper involved should transparently report the\nPost-Selection stage. To avoid future pitfalls in AI competitions, this paper\nproposes a new AI metrics, called developmental errors for all networks\ntrained, under Three Learning Conditions: (1) an incremental learning\narchitecture (due to a \"big data\" flaw), (2) a training experience and (3) a\nlimited amount of computational resources. Developmental Networks avoid\nPost-Selections because they automatically discover context-rules on the fly by\ngenerating emergent Turing machines (not black boxes) that are optimal in the\nsense of maximum-likelihood across lifetime, conditioned on the Three Learning\nConditions.\n"}
+{"abstract": "  We study quantum transport in disordered systems with particle-hole symmetric\nHamiltonians. The particle-hole symmetry is spontaneously broken after\naveraging with respect to disorder, and the resulting massless mode is treated\nin a random-phase representation of the invariant measure of the\nsymmetry-group. We compute the resulting fermionic functional integral of the\naverage two-particle Green's function in a perturbation theory around the\ndiffusive limit. The results up to two-loop order show that the corrections\nvanish, indicating that the diffusive quantum transport is robust. On the other\nhand, the diffusion coefficient depends strongly on the particle-hole symmetric\nHamiltonian we choose to study. This reveals a connection between the\nunderlying microscopic theory and the classical long-scale metallic behaviour\nof these systems.\n"}
+{"abstract": "  We prove a Darboux-Jouanolou type theorem on the algebraic integrability of\npolynomial differential $r$-forms over arbitrary fields ($r\\geq 1$). We also\ninvestigate the Darboux's method for producing integrating factors.\n"}
+{"abstract": "  The photoelectric conversion efficiency of a solar cell is dependent on its\ntemperature. When the solar radiation is incident on the photovoltaics (PV)\npanel, a large portion of it is absorbed by the underlying material which\nincreases its internal energy leading to the generation of heat. An overheated\nPV panel results in a decline in its performance which calls for an efficient\ncooling mechanism that can offer an optimum output of the electrical power. In\nthe present numerical work, thermal management with a porous nanochannels\ndevice capable to dissipate high heat flux is employed to regulate the\ntemperature of a commercial PV panel by integrating the device on the back face\nof the panel. The spatial and temporal variation of the PV surface temperature\nis obtained by solving the energy balance equation numerically. By evaluating\nthe steady-state PV surface temperature with and without thermal management,\nthe extent of cooling and the resulting enhancement in the electrical power\noutput is studied in detail. The nanochannels device is found to reduce the PV\nsurface temperature significantly with an average cooling of 31.5 oC.\nAdditionally, the enhancement in the electrical power output by ~33% and the\nreduction in the response time to 1/8th highlight the potential of using porous\nnanochannels as a thermal management device. Furthermore, the numerical method\nis used to develop a universal curve which can predict the extent of PV cooling\nfor any generic thermal management device.\n"}
+{"abstract": "  In the recent period of time with a lot of social platforms emerging, the\nrelationships among various units can be framed with respect to either\npositive, negative or no relation. These units can be individuals, countries or\nothers that form the basic structural component of a signed network. These\nsigned networks picture a dynamic characteristic of the graph so formed\nallowing only few combinations of signs that brings the structural balance\ntheorem in picture. Structural balance theory affirms that signed social\nnetworks tend to be organized so as to avoid conflictual situations,\ncorresponding to cycles of unstable relations. The aim of structural balance in\nnetworks is to find proper partitions of nodes that guarantee equilibrium in\nthe system allowing only few combination triangles with signed edges to be\npermitted in graph. Most of the works in this field of networking have either\nexplained the importance of signed graph or have applied the balance theorem\nand tried to solve problems. Following the recent time trends with each nation\nemerging to be superior and competing to be the best, the probable doubt of\nhappening of WW-III(World War-III) comes into every individuals mind.\nNevertheless, our paper aims at answering some of the interesting questions on\nWorld War-III. In this project we have worked with the creation of a signed\ngraph picturing the World War-III participating countries as nodes and have\npredicted the best possible coalition of countries that will be formed during\nwar. Also, we have visually depicted the number of communities that will be\nformed in this war and the participating countries in each communities.\n"}
+{"abstract": "  A mixed multigraph is a multigraph which may contain both undirected and\ndirected edges. An orientation of a mixed multigraph $G$ is an assignment of\nexactly one direction to each undirected edge of $G$. A mixed multigraph $G$\ncan be oriented to a strongly connected digraph if and only if $G$ is\nbridgeless and strongly connected [Boesch and Tindell, Am. Math. Mon., 1980].\nFor each $r \\in \\mathbb{N}$, let $f(r)$ denote the smallest number such that\nany strongly connected bridgeless mixed multigraph with radius $r$ can be\noriented to a digraph of radius at most $f(r)$. We improve the current best\nupper bound of $4r^2+4r$ on $f(r)$ [Chung, Garey and Tarjan, Networks, 1985] to\n$1.5 r^2 + r + 1$. Our upper bound is tight upto a multiplicative factor of\n$1.5$ since, $\\forall r \\in \\mathbb{N}$, there exists an undirected bridgeless\ngraph of radius $r$ such that every orientation of it has radius at least $r^2\n+ r$ [Chv\\'atal and Thomassen, J. Comb. Theory. Ser. B., 1978]. We prove a\nmarginally better lower bound, $f(r) \\geq r^2 + 3r + 1$, for mixed multigraphs.\nWhile this marginal improvement does not help with asymptotic estimates, it\nclears a natural suspicion that, like undirected graphs, $f(r)$ may be equal to\n$r^2 + r$ even for mixed multigraphs. En route, we show that if each edge of\n$G$ lies in a cycle of length at most $\\eta$, then the oriented radius of $G$\nis at most $1.5 r \\eta$. All our proofs are constructive and lend themselves to\npolynomial time algorithms.\n"}
+{"abstract": "  Recovery of a 3D head model including the complete face and hair regions is\nstill a challenging problem in computer vision and graphics. In this paper, we\nconsider this problem using only a few multi-view portrait images as input.\nPrevious multi-view stereo methods that have been based, either on optimization\nstrategies or deep learning techniques, suffer from low-frequency geometric\nstructures such as unclear head structures and inaccurate reconstruction in\nhair regions. To tackle this problem, we propose a prior-guided implicit neural\nrendering network. Specifically, we model the head geometry with a learnable\nsigned distance field (SDF) and optimize it via an implicit differentiable\nrenderer with the guidance of some human head priors, including the facial\nprior knowledge, head semantic segmentation information and 2D hair orientation\nmaps. The utilization of these priors can improve the reconstruction accuracy\nand robustness, leading to a high-quality integrated 3D head model. Extensive\nablation studies and comparisons with state-of-the-art methods demonstrate that\nour method can generate high-fidelity 3D head geometries with the guidance of\nthese priors.\n"}
+{"abstract": "  During the first wave of Covid-19 information decoupling could be observed in\nthe flow of news media content. The corollary of the content alignment within\nand between news sources experienced by readers (i.e., all news transformed\ninto Corona-news), was that the novelty of news content went down as media\nfocused monotonically on the pandemic event. This all-important Covid-19 news\ntheme turned out to be quite persistent as the pandemic continued, resulting in\nthe, from a news media's perspective, paradoxical situation where the same news\nwas repeated over and over. This information phenomenon, where novelty\ndecreases and persistence increases, has previously been used to track change\nin news media, but in this study we specifically test the claim that new\ninformation decoupling behavior of media can be used to reliably detect change\nin news media content originating in a negative event, using a Bayesian\napproach to change point detection.\n"}
+{"abstract": "  Retrieval is a crucial stage in web search that identifies a small set of\nquery-relevant candidates from a billion-scale corpus. Discovering more\nsemantically-related candidates in the retrieval stage is very promising to\nexpose more high-quality results to the end users. However, it still remains\nnon-trivial challenges of building and deploying effective retrieval models for\nsemantic matching in real search engine. In this paper, we describe the\nretrieval system that we developed and deployed in Baidu Search. The system\nexploits the recent state-of-the-art Chinese pretrained language model, namely\nEnhanced Representation through kNowledge IntEgration (ERNIE), which\nfacilitates the system with expressive semantic matching. In particular, we\ndeveloped an ERNIE-based retrieval model, which is equipped with 1) expressive\nTransformer-based semantic encoders, and 2) a comprehensive multi-stage\ntraining paradigm. More importantly, we present a practical system workflow for\ndeploying the model in web-scale retrieval. Eventually, the system is fully\ndeployed into production, where rigorous offline and online experiments were\nconducted. The results show that the system can perform high-quality candidate\nretrieval, especially for those tail queries with uncommon demands. Overall,\nthe new retrieval system facilitated by pretrained language model (i.e., ERNIE)\ncan largely improve the usability and applicability of our search engine.\n"}
+{"abstract": "  How risks are managed implicitly and explicitly at multiple levels of agile\nprojects has not been extensively studied and there is a need to investigate\nhow risk management can be used in large agile projects. This is the objective\nof this exploratory study which investigates the following research question:\nHow does a large software/hardware development project using agile practices\nmanage uncertainty at project/subproject and work package levels?\n"}
+{"abstract": "  In Chen-Cramer Crypto 2006 paper \\cite{cc} algebraic geometric secret sharing\nschemes were proposed such that the \"Fundamental Theorem in\nInformation-Theoretically Secure Multiparty Computation\" by Ben-Or, Goldwasser\nand Wigderson \\cite{BGW88} and Chaum, Cr\\'{e}peau and Damg{\\aa}rd \\cite{CCD88}\ncan be established over constant-size base finite fields. These algebraic\ngeometric secret sharing schemes defined by a curve of genus $g$ over a\nconstant size finite field ${\\bf F}_q$ is quasi-threshold in the following\nsense, any subset of $u \\leq T-1$ players (non qualified) has no information of\nthe secret and any subset of $u \\geq T+2g$ players (qualified) can reconstruct\nthe secret. It is natural to ask that how far from the threshold these\nquasi-threshold secret sharing schemes are? How many subsets of $u \\in [T,\nT+2g-1]$ players can recover the secret or have no information of the secret?\n  In this paper it is proved that almost all subsets of $u \\in [T,T+g-1]$\nplayers have no information of the secret and almost all subsets of $u \\in\n[T+g,T+2g-1]$ players can reconstruct the secret when the size $q$ goes to the\ninfinity and the genus satisfies $\\lim \\frac{g}{\\sqrt{q}}=0$. Then algebraic\ngeometric secret sharing schemes over large finite fields are asymptotically\nthreshold in this case. We also analyze the case when the size $q$ of the base\nfield is fixed and the genus goes to the infinity.\n"}
+{"abstract": "  In this work, we calibrate the relationship between Halpha emission and M\ndwarf ages. We compile a sample of 892 M dwarfs with Halpha equivalent width\n(HaEW) measurements from the literature that are either co-moving with a white\ndwarf of known age (21 stars) or in a known young association (871 stars). In\nthis sample we identify 7 M dwarfs that are new candidate members of known\nassociations. By dividing the stars into active and inactive categories\naccording to their HaEW and spectral type (SpT), we find that the fraction of\nactive dwarfs decreases with increasing age, and the form of the decline\ndepends on SpT. Using the compiled sample of age-calibrators we find that HaEW\nand fractional Halpha luminosity (LHaLbol) decrease with increasing age. HaEW\nfor SpT<M7 decreases gradually up until ~1Gyr. For older ages, we found only\ntwo early M dwarfs which are both inactive and seem to continue the gradual\ndecrease. We also found 14 mid-type out of which 11 are inactive and present a\nsignificant decrease of HaEW, suggesting that the magnetic activity decreases\nrapidly after ~1Gyr. We fit LHaLbol versus age with a broken power-law and find\nan index of -0.11+0.02-0.01 for ages <~776Myr. The index becomes much steeper\nat older ages however a lack of field age-calibrators leaves this part of the\nrelation far less constrained. Finally, from repeated independent measurements\nfor the same stars we find that 94% of these has a level of HaEW variability\n<=5A at young ages (<1Gyr).\n"}
+{"abstract": "  Atomic layer deposition (ALD) provides uniform and conformal thin films that\nare of interest for a range of applications. To better understand the\nproperties of amorphous ALD films, we need improved understanding of their\nlocal atomic structure. Previous work demonstrated measurement of how the local\natomic structure of ALD-grown aluminum oxide (AlOx) evolves in operando during\ngrowth by employing synchrotron high energy X-ray diffraction (HE-XRD). In this\nwork, we report on efforts to employ electron diffraction pair distribution\nfunction (ePDF) measurements using more broadly available transmission electron\nmicroscope (TEM) instrumentation to study the atomic structure of amorphous\nALD-AlOx. We observe electron beam damage in the ALD-coated samples during ePDF\nat ambient temperature and successfully mitigate this beam damage using ePDF at\ncryogenic temperatures (cryo-ePDF). We employ cryo-ePDF and Reverse Monte Carlo\n(RMC) modeling to obtain structural models of ALD-AlOx coatings formed at a\nrange of deposition temperatures from 150-332{\\deg}C. From these model\nstructures, we derive structural metrics including stoichiometry, pair\ndistances, and coordination environments in the ALD-AlOx films as a function of\ndeposition temperature. The structural variations we observe with growth\ntemperature are consistent with temperature-dependent changes in the surface\nhydroxyl density on the growth surface. The sample preparation and cryo-ePDF\nprocedures we report here can be used for routine measurement of ALD-grown\namorphous thin films to improve our understanding of the atomic structure of\nthese materials, establish structure-property relationships, and help\naccelerate the timescale for the application of ALD to address technological\nneeds.\n"}
+{"abstract": "  Hermite reciprocity refers to a series of natural isomorphisms involving\ncompositions of symmetric, exterior, and divided powers of the standard\n$SL_2$-representation. We survey several equivalent constructions of these\nisomorphisms, as well as their recent applications to Green's Conjecture on\nsyzygies of canonical curves. The most geometric approach to Hermite\nreciprocity is based on an idea of Voisin to realize certain multilinear\nconstructions cohomologically by working on a Hilbert scheme of points. We\nexplain how in the case of ${\\bf P}^1$ this can be reformulated in terms of\ncohomological properties of Schwarzenberger bundles. We then proceed to study\nthese bundles from several perspectives:\n  We show that their exterior powers have supernatural cohomology, arising as\nspecial cases of a construction of Eisenbud and Schreyer.\n  We recover basic properties of secant varieties $\\Sigma$ of rational normal\ncurves (normality, Cohen-Macaulayness, rational singularities) by considering\ntheir desingularizations via Schwarzenberger bundles, and applying the\nKempf-Weyman geometric technique.\n  We show that Hermite reciprocity is equivalent to the self-duality of the\nunique rank one Ulrich module on the affine cone $\\widehat{\\Sigma}$ of some\nsecant variety, and we explain how for a Schwarzenberger bundle of rank $k$ and\ndegree $d\\ge k$, Hermite reciprocity can be viewed as the unique (up to\nscaling) non-zero section of $(Sym^k\\mathcal{E})(-d+k-1)$.\n"}
+{"abstract": "  The design of 6th Generation (6G) wireless networks points towards flexible\nconnect-and-compute technologies capable to support innovative services and use\ncases. Targeting the 2030 horizon, 6G networks are poised to pave the way for\nsustainable human-centered smart societies and vertical industries, such that\nwireless networks will be transformed into a distributed smart connectivity\ninfrastructure, where new terminal types are embedded in the daily environment.\nIn this context, the RISE-6G project aims at investigating innovative solutions\nthat capitalize on the latest advances in the emerging technology of\nReconfigurable Intelligent Surfaces (RISs), which offers dynamic and\ngoal-oriented radio wave propagation control, enabling the concept of the\nwireless environment as a service. The project will focus on: i) the realistic\nmodeling of RIS-assisted signal propagation, ii) the investigation of the\nfundamental limits of RIS-empowered wireless communications and sensing, and\niii) the design of efficient algorithms for orchestrating networking RISs, in\norder to implement intelligent, sustainable, and dynamically programmable\nwireless environments enabling diverse services that go well beyond the 5G\ncapabilities. RISE-6G will offer two unprecedented proof-of-concepts for\nrealizing controlled wireless environments in near-future use cases.\n"}
+{"abstract": "  The quantum effective action yields equations of motion and correlation\nfunctions including all quantum corrections. We discuss here how it encodes\nalso Noether currents at the full quantum level. This holds both for\ncovariantly conserved currents associated to real symmetries that leave the\naction invariant as well as for non-conserved Noether currents associated to\nextended symmetry transformations which change the action, but in a specific\nway. We discuss then in particular symmetries and extended symmetries\nassociated to space-time geometry for relativistic quantum field theories.\nThese encompass local dilatations or Weyl gauge transformation, local Lorentz\ntransformations and local shear transformations. Together they constitute the\nsymmetry group of the frame bundle GL$(d)$. The corresponding non-conserved\nNoether currents are the dilatation or Weyl current, the spin current and the\nshear current for which divergence-type equations of motion are obtained from\nthe quantum effective action.\n"}
+{"abstract": "  Questions about a text or an image that cannot be answered raise distinctive\nissues for an AI. This note discusses the problem of unanswerable questions in\nVQA (visual question answering), in QA (visual question answering), and in AI\ngenerally.\n"}
+{"abstract": "  We show the existence of a compact K\\\"ahler manifold which does not fit in a\nproper flat family over an irreducible base with one projective (possibly\nsingular) fiber. We also give a topological version of this statement. This\nstrengthens our earlier counterexamples to the Kodaira algebraic approximation\nproblem.\n"}
+{"abstract": "  Multiple teams participate in a random competition. In each round the winner\nreceives one point. We study the times until ties occur among teams. We\nconstruct martingales and supermartingales that enable us to prove the results\nregarding these stopping times. The problems studied in this paper are\nmotivated by their applications to databases and their storage engines that are\nbased on augmented balanced search trees. The ties in the competitions are\nrelated to the necessary rebalancing operations that have to be executed on the\ndatabase.\n"}
+{"abstract": "  This Letter reports results from the first long-baseline search for sterile\nantineutrinos mixing in an accelerator-based antineutrino-dominated beam. The\nrate of neutral-current interactions in the two NOvA detectors, at distances of\n1 km and 810 km from the beam source, is analyzed using an exposure of\n$12.51\\times10^{20}$ protons-on-target from the NuMI beam at Fermilab running\nin antineutrino mode. A total of $121$ of neutral-current candidates are\nobserved at the Far Detector, compared to a prediction of\n$122\\pm11$(stat.)$\\pm15$(syst.) assuming mixing between three active flavors.\nNo evidence for $\\bar{\\nu}_{\\mu}\\rightarrow\\bar{\\nu}_{s}$ oscillation is\nobserved. Interpreting this result within a 3+1 model, constraints are placed\non the mixing angles ${\\theta}_{24} < 25^{\\circ}$ and ${\\theta}_{34} <\n32^{\\circ}$ at the 90% C.L. for $0.05$eV$^{2} \\leq \\Delta m^{2}_{41} \\leq\n0.5$eV$^{2}$, the range of mass splittings that produces no significant\noscillations at the Near Detector. These are the first 3+1 confidence limits\nset using long-baseline accelerator antineutrinos.\n"}
+{"abstract": "  The recovery of freshly fallen meteorites from tracked and triangulated\nmeteors is critical to determining their source asteroid families. However,\nlocating meteorite fragments in strewn fields remains a challenge with very few\nmeteorites being recovered from the meteors triangulated in past and ongoing\nmeteor camera networks. We examined if locating meteorites can be automated\nusing machine learning and an autonomous drone. Drones can be programmed to fly\na grid search pattern and take systematic pictures of the ground over a large\nsurvey area. Those images can be analyzed using a machine learning classifier\nto identify meteorites in the field among many other features. Here, we\ndescribe a proof-of-concept meteorite classifier that deploys off-line a\ncombination of different convolution neural networks to recognize meteorites\nfrom images taken by drones in the field. The system was implemented in a\nconceptual drone setup and tested in the suspected strewn field of a recent\nmeteorite fall near Walker Lake, Nevada.\n"}
+{"abstract": "  Detecting and understanding rotation in stellar interiors is nowadays one of\nthe unsolved problems in stellar physics. Asteroseismology has been able to\nprovide insights on rotation for the Sun, solar-like stars, and compact objects\nlike white dwarfs. However, this is still very difficult for intermediate-mass\nstars. These stars are moderate-to-rapid rotators. Rotation splits and shifts\nthe oscillation modes, which makes the oscillation spectrum more complex and\nharder to interpret. Here we study the oscillation patterns of a sample of\nbenchmark $\\delta$~Sct stars belonging to eclipsing binary systems with the\nobjective to find the frequency spacing related to the rotational splitting\n($\\delta r$). For this task, we combine three techniques: the Fourier\ntransform, the autocorrelation function, and the histogram of frequency\ndifferences. The last two showed a similar behaviour. For most of the stars, it\nwas necessary to determine the large separation ($\\Delta\\nu$) prior to spot\n$\\delta r$. This is the first time we may clearly state that one of the\nperiodicities present in the p~modes oscillation spectra of $\\delta$~Sct stars\ncorresponds to the rotational splitting. This is true independently of the\nstellar rotation rate. These promising results pave the way to find a robust\nmethodology to determine rotational splittings from the oscillation spectra of\n$\\delta$~Sct stars and, thus, understanding the rotational profile of\nintermediate-mass pulsating stars.\n"}
+{"abstract": "  We introduce a general reduction strategy that enables one to search for\nsolutions of parameterized linear difference equations in difference rings.\nHere we assume that the ring itself can be decomposed by a direct sum of\nintegral domains (using idempotent elements) that enjoys certain technical\nfeatures and that the coefficients of the difference equation are not\ndegenerated. Using this mechanism we can reduce the problem to find solutions\nin a ring (with zero-divisors) to search solutions in several copies of\nintegral domains. Utilizing existing solvers in this integral domain setting,\nwe obtain a general solver where the components of the linear difference\nequations and the solutions can be taken from difference rings that are built\ne.g., by $R\\Pi\\Sigma$-extensions over $\\Pi\\Sigma$-fields. This class of\ndifference rings contains, e.g., nested sums and products, products over roots\nof unity and nested sums defined over such objects.\n"}
+{"abstract": "  We study counting propositional logic as an extension of propositional logic\nwith counting quantifiers. We prove that the complexity of the underlying\ndecision problem perfectly matches the appropriate level of Wagner's counting\nhierarchy, but also that the resulting logic admits a satisfactory\nproof-theoretical treatment. From the latter, a type system for a probabilistic\nlambda-calculus is derived in the spirit of the Curry-Howard correspondence,\nshowing the potential of counting propositional logic as a useful tool in\nseveral fields of theoretical computer science.\n"}
+{"abstract": "  Multi-Party Quantum Computation (MPQC) has attracted a lot of attention as a\npotential killer-app for quantum networks through it's ability to preserve\nprivacy and integrity of the highly valuable computations they would enable.\nContributing to the latest challenges in this field, we present a composable\nprotocol achieving blindness and verifiability even in the case of a single\nhonest client. The security of our protocol is reduced, in an\ninformation-theoretically secure way, to that of a classical composable Secure\nMulti-Party Computation (SMPC) used to coordinate the various parties. Our\nscheme thus provides a statistically secure upgrade of such classical scheme to\na quantum one with the same level of security.\n  In addition, (i) the clients can delegate their computation to a powerful\nfully fault-tolerant server and only need to perform single qubit operations to\nunlock the full potential of multi-party quantum computation; (ii) the amount\nof quantum communication with the server is reduced to sending quantum states\nat the beginning of the computation and receiving the output states at the end,\nwhich is optimal and removes the need for interactive quantum communication;\nand (iii) it has a low constant multiplicative qubit overhead compared to the\nsingle-client delegated protocol it is built upon.\n  The main technical ingredient of our paper is the bootstraping of the MPQC\nconstruction by Double Blind Quantum Computation, a new composable resource for\nblind multiparty quantum computation, that demonstrates the surprising fact\nthat the full protocol does not require verifiability of all components to\nachieve security.\n"}
+{"abstract": "  CeRh$_2$As$_2$, a non-symmorphic heavy fermion material, was recently\nreported to host a remarkable phase diagram with two superconducting phases. In\nthis material, the two inequivalent Ce sites per unit cell, related by\ninversion symmetry, introduce a sublattice structure corresponding to an extra\ninternal degree of freedom. Here we propose a classification of the possible\nsuperconducting states in CeRh$_2$As$_2$ from the two Ce-sites perspective.\nBased on the superconducting fitness analysis and the quasiclassical\nEilenberger equations, we discuss two limits: Rashba spin-orbit coupling and\ninter-layer hopping dominated normal state. In both limits, we are able find\ntwo scenarios that generate phase diagrams in qualitative agreement with\nexperiments: i) intra-sublattice pairing with an even-odd transition under\nmagnetic field, and ii) inter-sublattice pairing with an odd-odd transition\nunder magnetic field.\n"}
+{"abstract": "  We present a differentiable soft-body physics simulator that can be composed\nwith neural networks as a differentiable layer. In contrast to other\ndifferentiable physics approaches that use explicit forward models to define\nstate transitions, we focus on implicit state transitions defined via function\nminimization. Implicit state transitions appear in implicit numerical\nintegration methods, which offer the benefits of large time steps and excellent\nnumerical stability, but require a special treatment to achieve\ndifferentiability due to the absence of an explicit differentiable forward\npass. In contrast to other implicit differentiation approaches that require\nexplicit formulas for the force function and the force Jacobian matrix, we\npresent an energy-based approach that allows us to compute these derivatives\nautomatically and in a matrix-free fashion via reverse-mode automatic\ndifferentiation. This allows for more flexibility and productivity when\ndefining physical models and is particularly important in the context of neural\nnetwork training, which often relies on reverse-mode automatic differentiation\n(backpropagation). We demonstrate the effectiveness of our differentiable\nsimulator in policy optimization for locomotion tasks and show that it achieves\nbetter sample efficiency than model-free reinforcement learning.\n"}
+{"abstract": "  Background: The early stage of defect prediction in the software development\nlife cycle can reduce testing effort and ensure the quality of software. Due to\nthe lack of historical data within the same project, Cross-Project Defect\nPrediction (CPDP) has become a popular research topic among researchers. CPDP\ntrained classifiers based on labeled data sets of one project to predict fault\nin another project. Goals: Software Defect Prediction (SDP) data sets consist\nof manually designed static features, which are software metrics. In CPDP,\nsource and target project data divergence is the major challenge in achieving\nhigh performance. In this paper, we propose a Generative Adversarial Network\n(GAN)-based data transformation to reduce data divergence between source and\ntarget projects. Method: We apply the Generative Adversarial Method where label\ndata sets are choosing as real data, while target data sets are choosing as\nfake data. The Discriminator tries to measure the perfection of domain\nadaptation through loss function. Through the generator, target data sets try\nto adapt the source project domain and, finally, apply machine learning\nclassifier (i.e., Naive Bayes) to classify faulty modules. Results: Our result\nshows that it is possible to predict defects based on the Generative\nAdversarial Method. Our model performs quite well in a cross-project\nenvironment when we choose JDT as a target data sets. However, all chosen data\nsets are facing a large class imbalance problem which affects the performance\nof our model.\n"}
+{"abstract": "  We present a number of low-resource approaches to the tasks of the Zero\nResource Speech Challenge 2021. We build on the unsupervised representations of\nspeech proposed by the organizers as a baseline, derived from CPC and clustered\nwith the k-means algorithm. We demonstrate that simple methods of refining\nthose representations can narrow the gap, or even improve upon the solutions\nwhich use a high computational budget. The results lead to the conclusion that\nthe CPC-derived representations are still too noisy for training language\nmodels, but stable enough for simpler forms of pattern matching and retrieval.\n"}
+{"abstract": "  Convolutional neural networks (CNNs), inspired by biological visual cortex\nsystems, are a powerful category of artificial neural networks that can extract\nthe hierarchical features of raw data to greatly reduce the network parametric\ncomplexity and enhance the predicting accuracy. They are of significant\ninterest for machine learning tasks such as computer vision, speech\nrecognition, playing board games and medical diagnosis. Optical neural networks\noffer the promise of dramatically accelerating computing speed to overcome the\ninherent bandwidth bottleneck of electronics. Here, we demonstrate a universal\noptical vector convolutional accelerator operating beyond 10 TeraOPS (TOPS:\noperations per second), generating convolutions of images of 250,000 pixels\nwith 8 bit resolution for 10 kernels simultaneously, enough for facial image\nrecognition. We then use the same hardware to sequentially form a deep optical\nCNN with ten output neurons, achieving successful recognition of full 10 digits\nwith 900 pixel handwritten digit images with 88% accuracy. Our results are\nbased on simultaneously interleaving temporal, wavelength and spatial\ndimensions enabled by an integrated microcomb source. This approach is scalable\nand trainable to much more complex networks for demanding applications such as\nunmanned vehicle and real time video recognition.\n"}
+{"abstract": "  This study investigates the structure of Arf rings. From the perspective of\nring extensions, a decomposition of integrally closed ideals is given. Using\nthis, we present a kind of their prime ideal decomposition in Arf rings, and\ndetermine their structure in the case where both R and the integral closure of\nR are local rings.\n"}
+{"abstract": "  Energy-efficiency is a key concern for neural network applications. To\nalleviate this issue, hardware acceleration using FPGAs or GPUs can provide\nbetter energy-efficiency than general-purpose processors. However, further\nimprovement of the energy-efficiency of such accelerators will be extremely\nbeneficial specially to deploy neural network in power-constrained edge\ncomputing environments. In this paper, we experimentally explore the potential\nof device-level energy-efficiency techniques (e.g.,supply voltage underscaling,\nfrequency scaling, and data quantization) for representative off-the-shelf\nFPGAs compared to GPUs. Frequency scaling in both platforms can improve the\npower and energy consumption but with performance overhead, e.g.,in GPUs it\nimproves the power consumption and GOPs/J by up to 34% and 28%, respectively.\nHowever, leveraging reduced-precision instructions improves power (up to 13%),\nenergy (up to 20%), and performance (up to 7%) simultaneously, with negligible\nreduction in accuracy of neural network accuracy.\n"}
+{"abstract": "  Quantum Hall states - the progenitors of the growing family of topological\ninsulators -- are rich source of exotic quantum phases. The nature of these\nstates is reflected in the gapless edge modes, which in turn can be classified\nas integer - carrying electrons, fractional - carrying fractional charges; and\nneutral - carrying excitations with zero net charge but a well-defined amount\nof heat. The latter two may obey anyonic statistics, which can be abelian or\nnon-abelian. The most-studied putative non-abelian state is the spin-polarized\nfilling factor {\\nu}=5/2, whose charge e/4 quasiparticles are accompanied by\nneutral modes. This filling, however, permits different possible topological\norders, which can be abelian or non-abelian. While numerical calculations favor\nthe non-abelian anti-Pfaffian (A-Pf) order to have the lowest energy, recent\nthermal conductance measurements suggested the experimentally realized order to\nbe the particle-hole Pfaffian (PH-Pf) order. It has been suggested that lack of\nthermal equilibration among the different edge modes of the A-Pf order can\naccount for this discrepancy. The identification of the topological order is\ncrucial for the interpretation of braiding (interference) operations, better\nunderstanding of the thermal equilibration process, and the reliability of the\nnumerical studies. We developed a new method that helps identifying the\ntopological order of the {\\nu}=5/2 state. By creating an interface between the\ntwo 2D half-planes, one hosting the {\\nu}=5/2 state and the other an integer\n{\\nu}=3 state, the interface supported a fractional {\\nu}=1/2 charge mode with\n1/2 quantum conductance and a neutral Majorana mode. The presence of the\nMajorana mode, probed by measuring noise, propagating in the opposite direction\nto the charge mode, asserted the presence of the PH-Pf order but not that of\nthe A-Pf order.\n"}
+{"abstract": "  We present a novel method for graph partitioning, based on reinforcement\nlearning and graph convolutional neural networks. Our approach is to\nrecursively partition coarser representations of a given graph. The neural\nnetwork is implemented using SAGE graph convolution layers, and trained using\nan advantage actor critic (A2C) agent. We present two variants, one for finding\nan edge separator that minimizes the normalized cut or quotient cut, and one\nthat finds a small vertex separator. The vertex separators are then used to\nconstruct a nested dissection ordering to permute a sparse matrix so that its\ntriangular factorization will incur less fill-in. The partitioning quality is\ncompared with partitions obtained using METIS and SCOTCH, and the nested\ndissection ordering is evaluated in the sparse solver SuperLU. Our results show\nthat the proposed method achieves similar partitioning quality as METIS and\nSCOTCH. Furthermore, the method generalizes across different classes of graphs,\nand works well on a variety of graphs from the SuiteSparse sparse matrix\ncollection.\n"}
+{"abstract": "  Given a transitive DG-Lie algebroid $(\\mathcal{A}, \\rho)$ over a smooth\nseparated scheme $X$ of finite type over a field $\\mathbb{K}$ of characteristic\n$0$ we define a notion of connection $\\nabla \\colon\n\\mathbf{R}\\Gamma(X,\\mathrm{Ker} \\rho) \\to \\mathbf{R}\\Gamma\n(X,\\Omega_X^1[-1]\\otimes \\mathrm{Ker} \\rho)$ and construct an $L_\\infty$\nmorphism between DG-Lie algebras $f \\colon \\mathbf{R}\\Gamma(X, \\mathrm{Ker}\n\\rho) \\rightsquigarrow\\mathbf{R}\\Gamma(X, \\Omega_X^{\\leq 1} [2])$ associated to\na connection and to a cyclic form on the DG-Lie algebroid. In this way, we\nobtain a lifting of the first component of the modified Buchweitz-Flenner\nsemiregularity map in the algebraic context, which has an application to the\ndeformation theory of coherent sheaves on $X$ admitting a finite locally free\nresolution. Another application is to the deformations of (Zariski) principal\nbundles on $X$.\n"}
+{"abstract": "  During the global spread of COVID-19, Japan has been among the top countries\nto maintain a relatively low number of infections, despite implementing limited\ninstitutional interventions. Using a Tokyo Metropolitan dataset, this study\ninvestigated how these limited intervention policies have affected public\nhealth and economic conditions in the COVID-19 context. A causal loop analysis\nsuggested that there were risks to prematurely terminating such interventions.\nOn the basis of this result and subsequent quantitative modelling, we found\nthat the short-term effectiveness of a short-term pre-emptive stay-at-home\nrequest caused a resurgence in the number of positive cases, whereas an\nadditional request provided a limited negative add-on effect for economic\nmeasures (e.g. the number of electronic word-of-mouth (eWOM) communications and\nrestaurant visits). These findings suggest the superiority of a mild and\ncontinuous intervention as a long-term countermeasure under epidemic pressures\nwhen compared to strong intermittent interventions.\n"}
+{"abstract": "  We characterize the membership of Hankel operators with general symbols in\nthe Schatten Classes $S^p,\\, p\\in(0,1),$ of the large Bergman spaces\n$A^2_{\\omega}$. The case $p\\geq 1$ was proved by Lin and Rochberg.\n"}
+{"abstract": "  A model for ripple formation on liquid surfaces exposed to an external laser\nor particle beam and a variable ground is developed. The external incident beam\nis hereby mechanically coupled to the liquid surface due to surface roughness.\nStarting from the Navier Stokes equation the coupled equations for the velocity\npotential and the surface height are derived in a shallow-water approximation\nwith special attention to viscosity. The resulting equations obey conservation\nlaws for volume and momentum where characteristic potentials for gravitation\nand surface tension are identified analogously to conservative forces. The\napproximate solutions are discussed in the context of ripple formation in laser\nmaterials processing involving melting of a surface by a laser beam. Linear\nstability analysis provides the formation of a damped wave modified by an\ninterplay between the external beam, the viscosity, and the surface tension.\nThe limit of small viscosity leads to damped gravitational and the limit of\nhigh viscosity to capillary waves. The resulting wavelengths are in the order\nof the ripples occurring in laser welding experiments hinting to the\ninvolvement of hydrodynamic processes in their origin. By discussing the\nresponse of the system to external periodic excitations with the help of\nFloquet multipliers, we show that the ripple formation could be triggered by a\na periodically modulated external beam, e.g. appropriate repetition rates of an\nincident laser beam. The weak nonlinear stability analysis provides ranges\nwhere hexagonal or stripe structures can appear. The orientation of stripe\nstructures and ripples are shown to be dependent on the incident angle of the\nlaser or particle beam where a minimal angle is reported. Numerical simulations\nconfirm the findings and allow to describe the influence of variable grounds.\n"}
+{"abstract": "  Physically-motivated and mathematically robust atom-centred representations\nof molecular structures are key to the success of modern atomistic machine\nlearning (ML) methods. They lie at the foundation of a wide range of methods to\npredict the properties of both materials and molecules as well as to explore\nand visualize the chemical compound and configuration space. Recently, it has\nbecome clear that many of the most effective representations share a\nfundamental formal connection: that they can all be expressed as a\ndiscretization of N-body correlation functions of the local atom density,\nsuggesting the opportunity of standardizing and, more importantly, optimizing\nthe calculation of such representations. We present an implementation, named\nlibrascal, whose modular design lends itself both to developing refinements to\nthe density-based formalism and to rapid prototyping for new developments of\nrotationally equivariant atomistic representations. As an example, we discuss\nSOAP features, perhaps the most widely used member of this family of\nrepresentations, to show how the expansion of the local density can be\noptimized for any choice of radial basis set. We discuss the representation in\nthe context of a kernel ridge regression model, commonly used with SOAP\nfeatures, and analyze how the computational effort scales for each of the\nindividual steps of the calculation. By applying data reduction techniques in\nfeature space, we show how to further reduce the total computational cost by at\nup to a factor of 4 or 5 without affecting the model's symmetry properties and\nwithout significantly impacting its accuracy.\n"}
+{"abstract": "  Modeling of work systems occurs for all sorts of reasons. Requirements need\nto be expressed. A pre-existing situation may need to be charted and analyzed.\nEarly design decisions may be captured using architecture principles. Detailed\ndesign may be worked out. We all regard these activities as essentially being\nforms of modeling. In the work systems modeling library, we consider work\nsystem engineering from a modeling perspective. In the field of work system\nengineering, a whole plethora of modeling methods is available to system\nengineers and architects. Each of these methods can be used to model some\n(aspects) of a domain related to an existing and/or a planned work system. The\naspects may refer to requirements, architecture, design, processing, data, etc,\netc. In other words, these methodes are essentially all intended to model\ndifferent aspects of work systems and/or their context. The aim of the work\nsystems modeling library (WSML) is to bring together methodical knowledge\nconcerning the modeling of work systems.\n"}
+{"abstract": "  We present a deep radio-polarimetric observation of the stellar bow shock\nEB27 associated to the massive star BD+43 3654. This is the only stellar bow\nshock confirmed to have non-thermal radio emission. We used the Jansky Very\nLarge Array in S band (2 - 4GHz) to test whether this synchrotron emission is\npolarised. The unprecedented sensitivity achieved allowed us to map even the\nfainter regions of the bow shock, revealing that the more diffuse emission is\nsteeper and the bow shock brighter than previously reported. No linear\npolarisation is detected in the bow shock above 0.5%, although we detected\npolarised emission from two southern sources, probably extragalactic in nature.\nWe modeled the intensity and morphology of the radio emission to better\nconstrain the magnetic field and injected power in relativistic electrons.\nFinally, we derived a set of more precise parameters for the system EB27-BD+43\n3654 using Gaia Early Data Release 3, including the spatial velocity. The new\ntrajectory, back in time, intersects the core of the Cyg OB2 association.\n"}
+{"abstract": "  A debate is emerging regarding the recent inconsistent results of different\nstudies for the Cosmic Star Formation Rate Density (CSFRD) at high-z. We employ\nUV and IR datasets to investigate the star formation rate function (SFRF) at\n${\\rm z \\sim 0-9}$. We find that the SFRFs derived from the dust corrected\n${\\rm UV}$ (${\\rm UV_{corr}}$) data contradict those from IR on some key issues\nsince they are described by different distributions (Schechter vs double-power\nlaw), imply different physics for galaxy formation (${\\rm UV_{corr}}$ data\nsuggest a SFR limit/strong mechanism that diminish the number density of high\nstar forming systems with respect IR) and compare differently with the stellar\nmass density evolution obtained from SED fitting (${\\rm UV_{corr}}$ is in\nagreement, while IR in tension up to 0.5 dex). However, both tracers agree on a\nconstant CSFRD evolution at ${\\rm z \\sim 1-4}$ and point to a plateau instead\nof a peak. In addition, using both indicators we demonstrate that the evolution\nof the {\\it observed} CSFRD can be described by only {\\bf 2} parameters and a\nfunction that has the form of a Gamma distribution (${\\bf \\Gamma(a,bt)}$). In\ncontrast to previous parameterizations used in the literature our framework\nconnects the parameters to physical properties like the star formation rate\ndepletion time and cosmic baryonic gas density. The build up of stellar mass\noccurs in $\\Gamma(a,bt)$ distributed steps and is the result of gas consumption\nup to the limit that there is no eligible gas for SF at t = ${\\rm \\infty}$,\nresulting to a final cosmic stellar mass density of $\\sim 0.5 \\times 10^9 \\,\n{\\rm \\frac{M_{\\odot}}{Mpc^3}}$.\n"}
+{"abstract": "  We investigate the collective decay dynamics of atoms with a generic\nmultilevel structure (angular momenta $F\\leftrightarrow F'$) coupled to two\nlight modes of different polarization inside a cavity. In contrast to two-level\natoms, we find that multilevel atoms can harbour eigenstates that are perfectly\ndark to cavity decay even within the subspace of permutationally symmetric\nstates (collective Dicke manifold). The dark states arise from destructive\ninterference between different internal transitions and are shown to be\nentangled. Remarkably, the superradiant decay of multilevel atoms can end up\nstuck in one of these dark states, where a macroscopic fraction of the atoms\nremains excited. This opens the door to the preparation of entangled dark\nstates of matter through collective dissipation useful for quantum sensing and\nquantum simulation. Our predictions should be readily observable in current\noptical cavity experiments with alkaline-earth atoms or Raman-dressed\ntransitions.\n"}
+{"abstract": "  A logical function can be used to characterizing a property of a state of\nBoolean network (BN), which is considered as an aggregation of states. To\nillustrate the dynamics of a set of logical functions, which characterize our\nconcerned properties of a BN, the invariant subspace containing the set of\nlogical functions is proposed, and its properties are investigated. Then the\ninvariant subspace of Boolean control network (BCN) is also proposed. The\ndynamics of invariant subspace of BCN is also invariant. Finally, using outputs\nas the set of logical functions, the minimum realization of BCN is proposed,\nwhich provides a possible solution to overcome the computational complexity of\nlarge scale BNs/BCNs.\n"}
+{"abstract": "  We show that the BFV quantization scheme can be implemented in the\nnonprojectable 2+1 Horava theory. This opens the possibility of imposing more\ngeneral gauge conditions in the quantization of this theory. The BFV\nquantization is based on the canonical formalism, which is suitable to\nincorporate the measure associated to the second-class constraints that the\ntheory has. Special features of the Hamiltonian density and the matrix of\nsecond-class constraints allow that the system be involutive in terms of Dirac\nbrackets, which is a nontrivial requisite for implementing the BFV formalism.\nWe present the BRST symmetry transformations in the canonical variables. The\ntheory is of rank one, in the classification introduced by Fradkin and\nFradkina. The originally called relativistic gauge-fixing conditions of the BFV\nformalism can be implemented in the nonprojectable Horava theory, extended to\nnonrelativistic forms. We show that the nonlocal gauge condition introduced in\nthe projectable theory can be included among these gauges.\n"}
+{"abstract": "  Cell instance segmentation in fluorescence microscopy images is becoming\nessential for cancer dynamics and prognosis. Data extracted from cancer\ndynamics allows to understand and accurately model different metabolic\nprocesses such as proliferation. This enables customized and more precise\ncancer treatments. However, accurate cell instance segmentation, necessary for\nfurther cell tracking and behavior analysis, is still challenging in scenarios\nwith high cell concentration and overlapping edges. Within this framework, we\npropose a novel cell instance segmentation approach based on the well-known\nU-Net architecture. To enforce the learning of morphological information per\npixel, a deep distance transformer (DDT) acts as a back-bone model. The DDT\noutput is subsequently used to train a top-model. The following top-models are\nconsidered: a three-class (\\emph{e.g.,} foreground, background and cell border)\nU-net, and a watershed transform. The obtained results suggest a performance\nboost over traditional U-Net architectures. This opens an interesting research\nline around the idea of injecting morphological information into a fully\nconvolutional model.\n"}
+{"abstract": "  This paper reports on the second \"Throughput\" phase of the Tracking Machine\nLearning (TrackML) challenge on the Codalab platform. As in the first\n\"Accuracy\" phase, the participants had to solve a difficult experimental\nproblem linked to tracking accurately the trajectory of particles as e.g.\ncreated at the Large Hadron Collider (LHC): given O($10^5$) points, the\nparticipants had to connect them into O($10^4$) individual groups that\nrepresent the particle trajectories which are approximated helical. While in\nthe first phase only the accuracy mattered, the goal of this second phase was a\ncompromise between the accuracy and the speed of inference. Both were measured\non the Codalab platform where the participants had to upload their software.\nThe best three participants had solutions with good accuracy and speed an order\nof magnitude faster than the state of the art when the challenge was designed.\nAlthough the core algorithms were less diverse than in the first phase, a\ndiversity of techniques have been used and are described in this paper. The\nperformance of the algorithms are analysed in depth and lessons derived.\n"}
+{"abstract": "  The quantum levels population behavior of the two coupled flux qubits\ndepending on the external driving field characteristics is studied. The\nexplicit expressions for the multiphoton transition probabilities at an\narbitrary control field amplitude is obtained for the case of small tunnel\nsplitting energies. We describe the controllable features of their formation\nand thereby creating or destroying entanglement by system bias tuning on the\ndirect inter-level transition and during the transition through intermediate\nstates. We found a feature of the qubits population inverting that ends in the\nindependence of the resonances positions from the qubits coupling strength.\nUsing Floquet--Markov equation we numerically demonstrate, that the positions\nof multiphoton resonances are stable to dissipative processes.\n"}
+{"abstract": "  Investigating the problem of setting control limits in the case of parameter\nuncertainty is more accessible when monitoring the variance because only one\nparameter has to be estimated. Simply ignoring the induced uncertainty\nfrequently leads to control charts with poor false alarm performances.\nAdjusting the unconditional in-control (IC) average run length (ARL) makes the\nsituation even worse. Guaranteeing a minimum conditional IC ARL with some given\nprobability is another very popular approach to solving these difficulties.\nHowever, it is very conservative as well as more complex and more difficult to\ncommunicate. We utilize the probability of a false alarm within the planned\nnumber of points to be plotted on the control chart. It turns out that\nadjusting this probability produces notably different limit adjustments\ncompared to controlling the unconditional IC ARL. We then develop numerical\nalgorithms to determine the respective modifications of the upper and two-sided\nexponentially weighted moving average (EWMA) charts based on the sample\nvariance for normally distributed data. These algorithms are made available\nwithin an R package. Finally, the impacts of the EWMA smoothing constant and\nthe size of the preliminary sample on the control chart design and its\nperformance are studied.\n"}
+{"abstract": "  Four stars pulsating simultaneously with a dominant period\n$P_D$$\\in$(0.28,0.39) d and an {\\it additional} period $P_A$$\\in$(0.20,0.27) d\nhave been identified from among the more than 3000 RR Lyrae stars observed by\nthe Kepler space telescope during NASA's K2 Mission. All four stars are located\nin the direction of the Galactic Bulge and have period ratios, $P_A$/$P_D$,\nsignificantly smaller than those of most double-mode RR Lyrae (RRd) stars:\n$P_A$/$P_D$$\\in$(0.694,0.710) vs. $P_1$/$P_0$$\\in$(0.726,0.748). Three of the\nstars are faint ($<$$V$$>$=18--20 mag) and distant and are among the `peculiar'\nRRd (pRRd) stars discovered by Prudil et al. (2017); the fourth star, EPIC\n216764000 (=V1125 Sgr), is a newly discovered pRRd star several magnitudes\nbrighter than the other three stars. In this paper the high-precision\nlong-cadence K2 photometry is analyzed in detail and used to study the\ncycle-to-cycle light variations. The pulsational characteristics of pRRd stars\nare compared with those of `classical' and `anomalous' RRd (cRRd, aRRd) stars.\nThe conclusion by Prudil et al. that pRRd stars form a separate group of\ndouble-mode pulsators and are not simply very-short-period cRRd stars is\nconfirmed. V1127 Aql and AH Cam are identified as other probable members of the\nclass of pRRd stars.\n"}
+{"abstract": "  Recent investigations into the inner-workings of state-of-the-art large-scale\npre-trained Transformer-based Natural Language Understanding (NLU) models\nindicate that they appear to know humanlike syntax, at least to some extent. We\nprovide novel evidence that complicates this claim: we find that\nstate-of-the-art Natural Language Inference (NLI) models assign the same labels\nto permuted examples as they do to the original, i.e. they are largely\ninvariant to random word-order permutations. This behavior notably differs from\nthat of humans; we struggle with ungrammatical sentences. To measure the\nseverity of this issue, we propose a suite of metrics and investigate which\nproperties of particular permutations lead models to be word-order invariant.\nIn the MNLI dataset, for example, we find almost all (98.7%) examples contain\nat least one permutation which elicits the gold label. Models are sometimes\neven able to assign gold labels to permutations that they originally failed to\npredict correctly. We provide a comprehensive empirical evaluation of this\nphenomenon, and further show that this issue exists for both Transformers and\npre-Transformer RNN / ConvNet based encoders, as well as across multiple\nlanguages (English and Mandarin Chinese). Our code and data are available at\nhttps://github.com/facebookresearch/unlu.\n"}
+{"abstract": "  The coexistence of ferroelectric and topological orders in two-dimensional\n(2D) atomic crystals allows non-volatile and switchable quantum spin Hall\nstates. Here we offer a general design principle for 2D bilayer\nheterostructures that can host ferroelectricity and nontrivial band topology\nsimultaneously using only topologically trivial building blocks. The built-in\nelectric field arising from the out-of-plane polarization across the\nheterostrucuture enables a robust control of the band gap size and band\ninversion strength, which can be utilized to manipulate topological phase\ntransitions. Using first-principles calculations, we demonstrate a series of\nbilayer heterostructures are 2D ferroelectric topological insulators (2DFETIs)\ncharacterized with a direct coupling between band topology and polarization\nstate. We propose a few 2DFETI-based quantum electronics including domain-wall\nquantum circuits and topological memristor.\n"}
+{"abstract": "  Motivated by their success in the single-objective domain, we propose a very\nsimple linear programming-based matheuristic for tri-objective binary integer\nprogramming. To tackle the problem, we obtain lower bound sets by means of the\nvector linear programming solver Bensolve. Then, simple heuristic approaches,\nsuch as rounding and path relinking, are applied to this lower bound set to\nobtain high-quality approximations of the optimal set of trade-off solutions.\nThe proposed algorithm is compared to a recently suggested algorithm which is,\nto the best of our knowledge, the only existing matheuristic method for\ntri-objective integer programming. Computational experiments show that our\nmethod produces a better approximation of the true Pareto front using\nsignificantly less time than the benchmark method on standard benchmark\ninstances for the three-objective knapsack problem.\n"}
+{"abstract": "  The incompressible Navier-Stokes equations are solved in a channel, using a\nDiscontinuous Galerkin method over staggered grids. The resulting linear\nsystems are studied both in terms of the structure and in terms of the spectral\nfeatures of the related coefficient matrices. In fact, the resulting matrices\nare of block type, each block showing Toeplitz-like, band, and tensor structure\nat the same time. Using this rich matrix-theoretic information and the\nToeplitz, Generalized Locally Toeplitz technology, a quite complete spectral\nanalysis is presented, with the target of designing and analyzing fast\niterative solvers for the associated large linear systems. Quite promising\nnumerical results are presented, commented, and critically discussed for\nelongated two- and three-dimensional geometries.\n"}
+{"abstract": "  The study examined the participation of female students of South Eastern\nNigerian tertiary institutions in Information and Communication Technologies\n(ICTs). The study discussed the attendant gender divide in ICTs participation,\nreasons for low female participation in ICT, consequences of not bridging the\ndivide and ways of encouraging female participation in ICT. A structured\nquestionnaire was used to elicit information from respondents. A multi stage\nrandom sampling technique was used in the selection of respondents. One hundred\nand thirty six (136) undergraduate female students of tertiary institutions in\nSouth Eastern Nigeria constituted the study sample. Data collected was analysed\nusing descriptive statistics. Findings suggest that high cost of ICT and high\nlevel of male dominance, which made females think that ICT is for males were\nthe major reasons for low female participation in ICT. Reducing the cost of\nInformation Technology, and parental involvement in their children selection\nchoice of study were suggested to encourage female participation in Information\nand Communication Technologies.\n"}
+{"abstract": "  This paper introduces RyanSpeech, a new speech corpus for research on\nautomated text-to-speech (TTS) systems. Publicly available TTS corpora are\noften noisy, recorded with multiple speakers, or lack quality male speech data.\nIn order to meet the need for a high quality, publicly available male speech\ncorpus within the field of speech recognition, we have designed and created\nRyanSpeech which contains textual materials from real-world conversational\nsettings. These materials contain over 10 hours of a professional male voice\nactor's speech recorded at 44.1 kHz. This corpus's design and pipeline make\nRyanSpeech ideal for developing TTS systems in real-world applications. To\nprovide a baseline for future research, protocols, and benchmarks, we trained 4\nstate-of-the-art speech models and a vocoder on RyanSpeech. The results show\n3.36 in mean opinion scores (MOS) in our best model. We have made both the\ncorpus and trained models for public use.\n"}
+{"abstract": "  In this paper the growth of a 16mm diameter Ce-doped Tl$_2$NaYCl$_6$ and its\ncharacterization as a gamma-ray detector are reported. With a 16 mm dia. x 8 mm\ncylindrical sample, energy resolution of 4.1% (FWHM) at 662 keV and light yield\nof 27,800ph/MeV are measured. Decay times of 91 ns (34%), 462 ns (52%), and 2.1\nmicroseconds (15%) are calculated. The x-ray excited emission spectrum exhibits\nbands that are similar to other Tl-based elpasolite scintillators like\nTl$_2$NaYCl$_6$:Ce.\n"}
+{"abstract": "  Intelligent reflecting surface (IRS) is a novel burgeoning concept, which\npossesses advantages in enhancing wireless communication and user localization,\nwhile maintaining low hardware cost and energy consumption. Herein, we\nestablish an IRS-aided mmWave-MIMO based joint localization and communication\nsystem (IMM-JLCS), and probe into its performance evaluation and optimization\ndesign. Specifically, first, we provide the signal, channel and estimation\nerror models, and contrive the working process of the IMM-JLCS in detail. Then,\nby configuring appropriate IRS phase shifts, we derive the closed-form\nexpressions of the Cramer-Rao Lower Bound (CRLB) of the position/orientation\nestimation errors and the effective achievable data rate (EADR), with respect\nto the time allocation ratio of the beam alignment and localization stage\n(BALS). Subsequently, we investigate the trade-off between the two performance\nmetrics, for which we propose a joint optimization algorithm. Finally, we carry\nout simulations and comparisons to view the trade-off and validate the\neffectiveness of the proposed algorithm, in the presence of distinct levels of\nestimation uncertainty and user mobility. Our results demonstrate that the\nproposed algorithm can find the joint optimal solution for the\nposition/orientation estimation accuracy and EADR, with its optimization\nperformance being robust to slight localization or channel estimation errors\nand user mobility.\n"}
+{"abstract": "  In this paper we present an extended variant of low rank parity check matrix\n(LRPC) codes that have received significant interests in recent years. It is\nshown that the extension indeed yields a superfamily of LRPC codes, which are\ntermed low row rank parity check codes. The decoding method of the proposed\ncodes is also investigated.\n"}
+{"abstract": "  Many facts come with an expiration date, from the name of the President to\nthe basketball team Lebron James plays for. But language models (LMs) are\ntrained on snapshots of data collected at a specific moment in time, and this\ncan limit their utility, especially in the closed-book setting where the\npretraining corpus must contain the facts the model should memorize. We\nintroduce a diagnostic dataset aimed at probing LMs for factual knowledge that\nchanges over time and highlight problems with LMs at either end of the spectrum\n-- those trained on specific slices of temporal data, as well as those trained\non a wide range of temporal data. To mitigate these problems, we propose a\nsimple technique for jointly modeling text with its timestamp. This improves\nmemorization of seen facts from the training time period, as well as\ncalibration on predictions about unseen facts from future time periods. We also\nshow that models trained with temporal context can be efficiently ``refreshed''\nas new data arrives, without the need for retraining from scratch.\n"}
+{"abstract": "  Next-generation space observatories will conduct the first systematic surveys\nof terrestrial exoplanet atmospheres and search for evidence of life beyond\nEarth. While in-depth observations of the nearest habitable worlds may yield\nenticing results, there are fundamental questions about planetary habitability\nand evolution which can only be answered through population-level studies of\ndozens to hundreds of terrestrial planets. To determine the requirements for\nnext-generation observatories to address these questions, we have developed\nBioverse. Bioverse combines existing knowledge of exoplanet statistics with a\nsurvey simulation and hypothesis testing framework to determine whether\nproposed space-based direct imaging and transit spectroscopy surveys will be\ncapable of detecting various hypothetical statistical relationships between the\nproperties of terrestrial exoplanets. Following a description of the code, we\napply Bioverse to determine whether an ambitious direct imaging or transit\nsurvey would be able to determine the extent of the circumstellar habitable\nzone and study the evolution of Earth-like planets. Given recent evidence that\nEarth-sized habitable zone planets are likely much rarer than previously\nbelieved (Pascucci et al. 2019), we find that space missions with large search\nvolumes will be necessary to study the population of terrestrial and habitable\nworlds. Moving forward, Bioverse provides a methodology for performing trade\nstudies of future observatory concepts to maximize their ability to address\npopulation-level questions, including and beyond the specific examples explored\nhere.\n"}
+{"abstract": "  We study a model of interacting neurons. The structure of this neural system\nis composed of two layers of neurons such that the neurons of the first layer\nsend their spikes to the neurons of the second one: if $N$ is the number of\nneurons of the first layer, at each spiking time of the first layer, every\nneuron of both layers receives an amount of potential of the form $U/\\sqrt{N},$\nwhere $U$ is a centered random variable. This kind of structure of neurons can\nmodel a part of the structure of the visual cortex: the first layer represents\nthe primary visual cortex V1 and the second one the visual area V2. In the\nmodel, we study the \"averaged effect\" of the neurons of the first layer on a\nsingle neuron of the second layer. The theoretical model consists in two\nstochastic processes, one modelling the membrane potential of the neurons of\nthe first layer, and the other the membrane potential of the particular neuron\nof the second layer. We prove the convergence of these processes as the number\nof neurons~$N$ goes to infinity and obtain a convergence speed. The proofs rely\non similar arguments as those used in [Erny, L\\\"ocherbach, Loukianova (2022)]:\nthe convergence speed of the semigroups of the processes is obtained from the\nconvergence speed of their infinitesimal generators using a Trotter-Kato\nformula, and from the regularity of the limit semigroup. Contrarily to the\nsituation in [Erny, L\\\"ocherbach, Loukianova (2022)], the stochastic flow of\nthe limit process is not continuous, and we need to use Girsanov's theorem for\njump processes result to recover the regularity of the limit semigroup from the\nregularity of the stochastic flow of an auxiliary process.\n"}
+{"abstract": "  The total atomization energy of a molecule is the thermochemical cognate of\nthe heat of formation in the gas phase, its most fundamental thermochemical\nproperty. We decompose it into different components and provide a survey of\nthem. It emerges that the connected triple excitations contribution is the\nthird most important one, about an order of magnitude less important than the\n\"big two\" contributions (mean-field Hartree-Fock and valence CCSD correlation),\nbut 1-2 orders of magnitude more important than the remainder. For the 200\ntotal atomization energies of small molecules in the W4-17 benchmark, we have\ninvestigated the basis set convergence of the connected triple excitations\ncontribution (T). Achieving basis set convergence for the valence triple\nexcitations energy is much easier than for the valence singles and doubles\ncorrelation energy. Using reference data obtained from spdfghi and spdfghik\nbasis sets, we show that extrapolation from quintuple-zeta and sextuple-zeta\nyields values within about 0.004 kcal/mol RMS. Convergence to within about 0.01\nkcal/mol is achievable with quadruple- and quintuple-zeta basis sets, and to\nwithin about 0.05 kcal/mol with triple- and quadruple-zeta basis sets. It\nappears that radial flexibility in the basis set is more important here than\nadding angular momenta L: apparently, replacing nZaPa basis sets with\ntruncations of 7ZaPa at L=n gains about one angular momentum for small values\nof n. We end the article with a brief outlook for the future of accurate\nelectronic structure calculations.\n"}
+{"abstract": "  Verifying integrity of software execution in low-end micro-controller units\n(MCUs) is a well-known open problem. The central challenge is how to securely\ndetect software exploits with minimal overhead, since these MCUs are designed\nfor low cost, low energy and small size. Some recent work yielded inexpensive\nhardware/software co-designs for remotely verifying code and execution\nintegrity. In particular, a means of detecting unauthorized code modifications\nand control-flow attacks were proposed, referred to as Remote Attestation (RA)\nand Control-Flow Attestation (CFA), respectively. Despite this progress,\ndetection of data-only attacks remains elusive. Such attacks exploit software\nvulnerabilities to corrupt intermediate computation results stored in data\nmemory, changing neither the program code nor its control flow. Motivated by\nlack of any current techniques (for low-end MCUs) that detect these attacks, in\nthis paper we propose, implement and evaluate DIALED, the first Data-Flow\nAttestation (DFA) technique applicable to the most resource-constrained\nembedded devices (e.g., TI MSP430). DIALED works in tandem with a companion CFA\nscheme to detect all (currently known) types of runtime software exploits at\nfairly low cost.\n"}
+{"abstract": "  To understand and infer meaning in language, neural models have to learn\ncomplicated nuances. Discovering distinctive linguistic phenomena from data is\nnot an easy task. For instance, lexical ambiguity is a fundamental feature of\nlanguage which is challenging to learn. Even more prominently, inferring the\nmeaning of rare and unseen lexical units is difficult with neural networks.\nMeaning is often determined from context. With context, languages allow meaning\nto be conveyed even when the specific words used are not known by the reader.\nTo model this learning process, a system has to learn from a few instances in\ncontext and be able to generalize well to unseen cases. The learning process is\nhindered when training data is scarce for a task. Even with sufficient data,\nlearning patterns for the long tail of the lexical distribution is challenging.\nIn this thesis, we focus on understanding certain potentials of contexts in\nneural models and design augmentation models to benefit from them. We focus on\nmachine translation as an important instance of the more general language\nunderstanding problem. To translate from a source language to a target\nlanguage, a neural model has to understand the meaning of constituents in the\nprovided context and generate constituents with the same meanings in the target\nlanguage. This task accentuates the value of capturing nuances of language and\nthe necessity of generalization from few observations. The main problem we\nstudy in this thesis is what neural machine translation models learn from data\nand how we can devise more focused contexts to enhance this learning. Looking\nmore in-depth into the role of context and the impact of data on learning\nmodels is essential to advance the NLP field. Moreover, it helps highlight the\nvulnerabilities of current neural networks and provides insights into designing\nmore robust models.\n"}
+{"abstract": "  In this conference paper I present the first full global fit of a dark matter\neffective field theory with the global fitting framework GAMBIT. I show the\nresults of exhaustive parameter space explorations of the effective dark matter\nmodel, including a general set of operators up to dimension 7, and using the\nmost up-to-date constraints from direct and indirect detection of dark matter,\nrelic abundance requirements and collider searches for dark matter candidates.\n"}
+{"abstract": "  In this paper, we examine linear conditions on finite sets of points in\nprojective space implied by the Cayley-Bacharach condition. In particular, by\nbounding the number of points satisfying the Cayley-Bacharach condition, we\nforce them to lie on unions of low-dimensional linear spaces. These results are\nmotivated by investigations into degrees of irrationality of complete\nintersections, which are controlled by minimum-degree rational maps to\nprojective space. As an application of our main theorem, we describe the fibers\nof such maps for certain complete intersections of codimension two.\n"}
+{"abstract": "  In this paper we introduce a realistic and challenging, multi-source and\nmulti-room acoustic environment and an improved algorithm for the estimation of\nsource-dominated microphone clusters in acoustic sensor networks. Our proposed\nclustering method is based on a single microphone per node and on unsupervised\nclustered federated learning which employs a light-weight autoencoder model. We\npresent an improved clustering control strategy that takes into account the\nvariability of the acoustic scene and allows the estimation of a dynamic range\nof clusters using reduced amounts of training data. The proposed approach is\noptimized using clustering-based measures and validated via a network-wide\nclassification task.\n"}
+{"abstract": "  In previous work, we constructed for a smooth complex variety $X$ and for a\nlinear algebraic group $G$ a mixed Hodge structure on the complete local ring\n$\\widehat{\\mathcal{O}}_\\rho$ to the moduli space of representations of the\nfundamental group $\\pi_1(X,x)$ into $G$ at a representation $\\rho$ underlying a\nvariation of mixed Hodge structure. We now show that the jump ideals $J_k^i\n\\subset \\widehat{\\mathcal{O}}_\\rho$, defining the locus of representations such\nthe the dimension of the cohomology of $X$ in degree $i$ of the associated\nlocal system is greater than $k$, are sub-mixed Hodge structures; this is in\naccordance with various known motivicity results for these loci. In rank one we\nalso recover, and find new cases, where these loci are translated sub-tori of\nthe moduli of representations. Our methods are first transcendental, relying on\nHodge theory, and then combined with tools of homotopy and algebra.\n"}
+{"abstract": "  We show that the concept of the ADM mass in general relativity can be\nunderstood as the limit of the total mean curvature plus the total defect of\ndihedral angle of the boundary of large Riemannian polyhedra. We also express\nthe $n$-dimensional mass as a suitable integral of geometric quantities that\ndetermine the $(n-1)$-dimensional mass.\n"}
+{"abstract": "  Treatment planning in low-dose-rate prostate brachytherapy (LDR-PB) aims to\nproduce arrangement of implantable radioactive seeds that deliver a minimum\nprescribed dose to the prostate whilst minimizing toxicity to healthy tissues.\nThere can be multiple seed arrangements that satisfy this dosimetric criterion,\nnot all deemed 'acceptable' for implant from a physician's perspective. This\nleads to plans that are subjective to the physician's/centre's preference,\nplanning style, and expertise. We propose a method that aims to reduce this\nvariability by training a model to learn from a large pool of successful\nretrospective LDR-PB data (961 patients) and create consistent plans that mimic\nthe high-quality manual plans. Our model is based on conditional generative\nadversarial networks that use a novel loss function for penalizing the model on\nspatial constraints of the seeds. An optional optimizer based on a simulated\nannealing (SA) algorithm can be used to further fine-tune the plans if\nnecessary (determined by the treating physician). Performance analysis was\nconducted on 150 test cases demonstrating comparable results to that of the\nmanual prehistorical plans. On average, the clinical target volume covering\n100% of the prescribed dose was 98.9% for our method compared to 99.4% for\nmanual plans. Moreover, using our model, the planning time was significantly\nreduced to an average of 2.5 mins/plan with SA, and less than 3 seconds without\nSA. Compared to this, manual planning at our centre takes around 20 mins/plan.\n"}
+{"abstract": "  We call a real algebraic hypersurface in $(\\mathbb{C}^*)^n$ simplicial if it\nis given by a real Laurent polynomial in $n$-variables that has exactly $n+1$\nmonomials with non-zero coefficients and such that the convex hull in\n$\\mathbb{R}^n$ of the $n+1$ points of $\\mathbb{Z} ^n$ corresponding to the\nexponents is a non-degenerate $n$-dimensional simplex. Such hypersurfaces are\nnatural building blocks from which more complicated objects can be constructed,\nfor example using O. Viro's Patchworking method. Drawing inspiration from\nrelated work by G. Kerr and I. Zharkov, we describe the action of the complex\nconjugation on the homology of the coamoebas of simplicial real algebraic\nhypersurfaces, hoping it might prove useful in a variety of problems related to\ntopology of real algebraic varieties. In particular, assuming a reasonable\nconjecture, we identify the conditions under which such a hypersurface is\nGalois maximal.\n"}
+{"abstract": "  We consider a stationary Markov process that models certain queues with a\nbulk service of fixed number m of admitted customers. We find an integral\nexpression of its transition probability function in terms of certain\nmulti-orthogonal polynomials. We study the convergence of the appropriate\nscheme of simultaneous quadrature rules to design an algorithm for computing\nthis integral expression.\n"}
+{"abstract": "  There is a growing interest in the area of machine learning and creativity.\nThis survey presents an overview of the history and the state of the art of\ncomputational creativity theories, machine learning techniques, including\ngenerative deep learning, and corresponding automatic evaluation methods. After\npresenting a critical discussion of the key contributions in this area, we\noutline the current research challenges and emerging opportunities in this\nfield.\n"}
+{"abstract": "  For the first time the scientific community in Latin America working at the\nforefront of research in high energy, cosmology and astroparticle physics\n(HECAP) have come together to discuss and provide scientific input towards the\ndevelopment of a regional strategy.\n  The present document, the Latin American HECAP Physics Briefing Book, is the\nresult of this ambitious bottom-up effort. This report contains the work\nperformed by the Preparatory Group to synthesize the main contributions and\ndiscussions for each of the topical working groups. This briefing book\ndiscusses the relevant emerging projects developing in the region and considers\npotentially impactful future initiatives and participation of the Latin\nAmerican HECAP community in international flagship projects to provide the\nessential input for the creation of a long-term HECAP strategy in the region.\n"}
+{"abstract": "  We propose a collocation and quasi-collocation method for solving second\norder boundary value problems $L_2 y=f$, in which the differential operator\n$L_2$ can be represented in the product formulation, aiming mostly on singular\nand singularly perturbed boundary value problems. Seeking an approximating\nCanonical Complete Chebyshev spline $s$ by a collocation method leads to demand\nthat $L_2s$ interpolates the function $f$. On the other hand, in\nquasi-collocation method we require that $L_2 s$ is equal to an approximation\nof $f$ by the Schoenberg operator. We offer the calculation of both methods\nbased on the Green's function, and give their error bounds.\n"}
+{"abstract": "  We study theoretically the phase diagram of strongly-coupled two-dimensional\nBose-Fermi mixtures interacting with attractive short-range potentials as a\nfunction of the particle densities. We focus on the limit where the size of the\nbound state between a boson and a fermion is small compared to the average\ninter-boson separation and develop a functional renormalization group approach\nthat accounts for the bound-state physics arising from the extended Fr\\\"ohlich\nHamiltonian. By including three-body correlations we are able to reproduce the\npolaron-to-molecule transition in two-dimensional Fermi gases in the extreme\nlimit of vanishing boson density. We predict frequency- and momentum-resolved\nspectral functions and study the impact of three-body correlations on\nquasiparticle properties. At finite boson density, we find that when the bound\nstate energy exceeds the Fermi energy by a critical value, the fermions and\nbosons can form a fermionic composite with a well-defined Fermi surface. These\ncomposites constitute a Fermi sea of dressed Feshbach molecules in the case of\nultracold atoms while in the case of atomically thin semiconductors a trion\nliquid emerges. As the boson density is increased further, the effective energy\ngap of the composites decreases, leading to a transition into a\nstrongly-correlated phase where polarons are hybridized with molecular degrees\nof freedom. We highlight the universal connection between two-dimensional\nsemiconductors and ultracold atoms and we discuss perspectives for further\nexploring the rich structure of strongly-coupled Bose-Fermi mixtures in these\ncomplementary systems.\n"}
+{"abstract": "  This paper concerns a convex, stochastic zeroth-order optimization (S-ZOO)\nproblem, where the objective is to minimize the expectation of a cost function\nand its gradient is not accessible directly. To solve this problem, traditional\noptimization techniques mostly yield query complexities that grow polynomially\nwith dimensionality, i.e., the number of function evaluations is a polynomial\nfunction of the number of decision variables. Consequently, these methods may\nnot perform well in solving massive-dimensional problems arising in many modern\napplications. Although more recent methods can be provably\ndimension-insensitive, almost all of them work with arguably more stringent\nconditions such as everywhere sparse or compressible gradient. Thus, prior to\nthis research, it was unknown whether dimension-insensitive S-ZOO is possible\nwithout such conditions. In this paper, we give an affirmative answer to this\nquestion by proposing a sparsity-inducing stochastic gradient-free (SI-SGF)\nalgorithm. It is proved to achieve dimension-insensitive query complexity in\nboth convex and strongly convex cases when neither gradient sparsity nor\ngradient compressibility is satisfied. Our numerical results demonstrate the\nstrong potential of the proposed SI-SGF compared with existing alternatives.\n"}
+{"abstract": "  Using brain imaging quantitative traits (QTs) to identify the genetic risk\nfactors is an important research topic in imaging genetics. Many efforts have\nbeen made via building linear models, e.g. linear regression (LR), to extract\nthe association between imaging QTs and genetic factors such as single\nnucleotide polymorphisms (SNPs). However, to the best of our knowledge, these\nlinear models could not fully uncover the complicated relationship due to the\nloci's elusive and diverse impacts on imaging QTs. Though deep learning models\ncan extract the nonlinear relationship, they could not select relevant genetic\nfactors. In this paper, we proposed a novel multi-task deep feature selection\n(MTDFS) method for brain imaging genetics. MTDFS first adds a multi-task\none-to-one layer and imposes a hybrid sparsity-inducing penalty to select\nrelevant SNPs making significant contributions to abnormal imaging QTs. It then\nbuilds a multi-task deep neural network to model the complicated associations\nbetween imaging QTs and SNPs. MTDFS can not only extract the nonlinear\nrelationship but also arms the deep neural network with the feature selection\ncapability. We compared MTDFS to both LR and single-task DFS (DFS) methods on\nthe real neuroimaging genetic data. The experimental results showed that MTDFS\nperformed better than both LR and DFS in terms of the QT-SNP relationship\nidentification and feature selection. In a word, MTDFS is powerful for\nidentifying risk loci and could be a great supplement to the method library for\nbrain imaging genetics.\n"}
+{"abstract": "  A rectangular dual of a graph $G$ is a contact representation of $G$ by\naxis-aligned rectangles such that (i)~no four rectangles share a point and\n(ii)~the union of all rectangles is a rectangle. The partial representation\nextension problem for rectangular duals asks whether a given partial\nrectangular dual can be extended to a rectangular dual, that is, whether there\nexists a rectangular dual where some vertices are represented by prescribed\nrectangles. Combinatorially, a rectangular dual can be described by a regular\nedge labeling (REL), which determines the orientations of the rectangle\ncontacts.\n  We describe two approaches to solve the partial representation extension\nproblem for rectangular duals with given REL. On the one hand, we characterise\nthe RELs that admit an extension, which leads to a linear-time testing\nalgorithm. In the affirmative, we can construct an extension in linear time.\nThis partial representation extension problem can also be formulated as a\nlinear program (LP). We use this LP to solve the simultaneous representation\nproblem for the case of rectangular duals when each input graph is given\ntogether with a REL.\n"}
+{"abstract": "  Suitable composable data center networks (DCNs) are essential to support the\ndisaggregation of compute components in highly efficient next generation data\ncenters (DCs). However, designing such composable DCNs can be challenging. A\ncomposable DCN that adopts a full mesh backplane between disaggregated compute\ncomponents within a rack and employs dedicated interfaces on each\npoint-to-point link is wasteful and expensive. In this paper, we propose and\ndescribe two (i.e., electrical, and electrical-optical) variants of a network\nfor composable DC (NetCoD). NetCoD adopts a targeted design to reduce the\nnumber of transceivers required when a mesh physical backplane is deployed\nbetween disaggregated compute components in the same rack. The targeted design\nleverages optical communication techniques and components to achieve this with\nminimal or no network performance degradation. We formulate a MILP model to\nevaluate the performance of both variants of NetCoD in rack-scale composable\nDCs that implement different forms of disaggregation. The electrical-optical\nvariant of NetCoD achieves similar performance as a reference network while\nutilizing fewer transceivers per compute node. The targeted adoption of optical\ntechnologies by both variants of NetCoD achieves greater (4 - 5 times greater)\nutilization of available network throughput than the reference network which\nimplements a generic design. Under the various forms of disaggregation\nconsidered, both variant of NetCoD achieve near-optimal compute energy\nefficiency in the composable DC while satisfying both compute and network\nconstraints. This is because marginal concession of optimal compute energy\nefficiency is often required to achieve overall optimal energy efficiency in\ncomposable DCs.\n"}
+{"abstract": "  The present study reports the effect of different source terms on the near\nand far-field acoustic characteristics of compressible flow over a rectangular\ncavity using hybrid computational aeroacoustics methodology. We use a low\ndispersive and dissipative compressible fluid flow solver in conjunction with\nan acoustic perturbation equation solver based on the spectral/hp element\nmethod. The hybrid approach involves calculating the base fields and the\nacoustic sources from a fluid simulation in the first step. In the next step,\nthe acoustic solver utilizes the variables to predict the acoustic propagation\ndue to the given sources. The validation of the methodology against benchmark\ncases provides quite accurate results while compared against the existing\nliterature. The study is then extended to assess the importance of the entropy\nsource term for the flow over a rectangular cavity. The predictions of hybrid\nsimulations with vortex and entropy source terms reproduce the perturbation\npressure values very close to the existing direct numerical simulation results.\nMoreover, the results suggest that the use of just the vortex source terms\nover-predicts the perturbation pressure near the source region. Finally, we\nhave carried out detailed simulations with all the source terms to investigate\nthe noise sources for compressible flow over the cavity for different Mach\nnumber ranges (M = 0.4; 0.5; 0.6; 0.7; 1.5). The obtained acoustic spectra and\nthe sound directivity are in close agreement with the reference experiment.\n"}
+{"abstract": "  Rare extragalactic objects can carry substantial information about the past,\npresent, and future universe. Given the size of astronomical databases in the\ninformation era it can be assumed that very many outlier galaxies are included\nin existing and future astronomical databases. However, manual search for these\nobjects is impractical due to the required labor, and therefore the ability to\ndetect such objects largely depends on computer algorithms. This paper\ndescribes an unsupervised machine learning algorithm for automatic detection of\noutlier galaxy images, and its application to several Hubble Space Telescope\nfields. The algorithm does not require training, and therefore is not dependent\non the preparation of clean training sets. The application of the algorithm to\na large collection of galaxies detected a variety of outlier galaxy images. The\nalgorithm is not perfect in the sense that not all objects detected by the\nalgorithm are indeed considered outliers, but it reduces the dataset by two\norders of magnitude to allow practical manual identification. The catalogue\ncontains 147 objects that would be very difficult to identify without using\nautomation.\n"}
+{"abstract": "  We study a perturbation family of N=2 3d gauge theories and its relation to\nquantum K-theory. A 3d version of the Intriligator-Vafa formula is given for\nthe quantum K-theory ring of Grassmannians. The 3d BPS half-index of the gauge\ntheory is connected to the theory of bilateral hypergeometric q-series, and to\nmodular q-characters of a class of conformal field theories in a certain\nmassless limit. Turning on 3d Wilson lines at torsion points leads to mock\nmodular behavior. Perturbed correlators in the IR regime are computed by\ndetermining the UV-IR map in the presence of deformations.\n"}
+{"abstract": "  In this paper, we investigate the classical and Bayesian estimation of\nunknown parameters of the Gumbel type-II distribution based on adaptive type-II\nprogressive hybrid censored sample (AT-II PHCS). The maximum likelihood\nestimates (MLEs) and maximum product spacing estimates (MPSEs) are developed\nand computed numerically using Newton-Raphson method. Bayesian approaches are\nemployed to estimate parameters under symmetric and asymmetric loss functions.\nBayesian estimates are not in explicit forms. Thus, Bayesian estimates are\nobtained by using Markov chain Monte Carlo (MCMC) method along with the\nMetropolis-Hastings (MH) algorithm. Based on the normality property of MLEs the\nasymptotic confidence intervals are constructed. Also, bootstrap intervals and\nhighest posterior density (HPD) credible intervals are constructed. Further a\nMonte Carlo simulation study is carried out. Finally, the data set based on the\ndeath rate due to Covid-19 in India is analyzed for illustration of the\npurpose.\n"}
+{"abstract": "  We give a new and conceptually straightforward proof of the well-known\npresentation for the Temperley-Lieb algebra, via an alternative new\npresentation. Our method involves twisted semigroup algebras, and we make use\nof two apparently new submonoids of the Temperley-Lieb monoid.\n"}
+{"abstract": "  We present a null-stream-based Bayesian unmodeled framework to probe generic\ngravitational-wave polarizations. Generic metric theories allow six\ngravitational-wave polarization states, but general relativity only permits the\nexistence of two of them namely the tensorial polarizations. The strain signal\nmeasured by an interferometer is a linear combination of the polarization modes\nand such a linear combination depends on the geometry of the detector and the\nsource location. The detector network of Advanced LIGO and Advanced Virgo\nallows us to measure different linear combinations of the polarization modes\nand therefore we can constrain the polarization content by analyzing how the\npolarization modes are linearly combined. We propose the basis formulation to\nconstruct a null stream along the polarization basis modes without requiring\nmodeling the basis explicitly. We conduct a mock data study and we show that\nthe framework is capable of probing pure and mixed polarizations in the\nAdvanced LIGO-Advanced Virgo 3-detector network without knowing the sky\nlocation of the source from electromagnetic counterparts. We also discuss the\neffect of the presence of the uncaptured orthogonal polarization component in\nthe framework, and we propose using the plug-in method to test the existence of\nthe orthogonal polarizations.\n"}
+{"abstract": "  The Kitaev model realizes a quantum spin liquid where the spin excitations\nare fractionalized into itinerant Majorana fermions and localized\n$\\mathbb{Z}_2$ vortices. Quantum entanglement between the fractional\nexcitations can be utilized for decoherence-free topological quantum\ncomputation. Of particular interest is the anyonic statistics realized by\nbraiding the vortex excitations under a magnetic field. Despite the promising\npotential, the practical methodology for creation and control of the vortex\nexcitations remains elusive thus far. Here we theoretically propose how one can\ncreate and move the vortices in the Kitaev spin liquid. We find that the\nvortices are induced by a local modulation of the exchange interaction;\nespecially, the local Dzyaloshinskii-Moriya (symmetric off-diagonal)\ninteraction can create vortices most efficiently in the (anti)ferromagnetic\nKitaev model, as it effectively flips the sign of the Kitaev interaction. We\ntest this idea by performing the {\\it ab initio} calculation for a candidate\nmaterial $\\alpha$-RuCl$_3$ through the manipulation of the ligand positions\nthat breaks the inversion symmetry and induces the local Dzyaloshinskii-Moriya\ninteraction. We also demonstrate a braiding of vortices by adiabatically and\nsuccessively changing the local bond modulations.\n"}
+{"abstract": "  In this paper, a low-cost, simple and reliable bi-static Radar Cross Section\n(RCS) measurement method making use a historic Marconi set-up is presented. It\nuses a transmitting (Tx) antenna (located at a constant position, at a\nreference angle of {\\theta} = 0o) and a receiver (Rx) antenna (mounted on a\nmoveable arm calibrated in the azimuthal direction with an accuracy of 0.1o). A\ntime gating method is used to extract the information from the reflection in\nthe time domain; applying time filter allows removing the antenna side lobe\neffects and other ambient noise. In this method, the Rx antenna (on the movable\narm) is used to measure the reflected field in the angular range from 1o to 90o\nof reflection from the structure (printed PCB) and from the reference\nconfiguration represented by a ground (GND) plane of the same dimension. The\ntime gating method is then applied to each pair of PCB / GND measurements to\nextract the bi-static RCS pattern of the structure at a given frequency. Here\ncomparison of measurement results carried out at 18 GHz and 32 GHz with\nsimulation indicates the successful performance of the proposed method. It can\nbe used as a low-cost, reliable and available option in future measurement and\nscientific research.\n"}
+{"abstract": "  Rare events arising in nonlinear atmospheric dynamics remain hard to predict\nand attribute. We address the problem of forecasting rare events in a\nprototypical example, Sudden Stratospheric Warmings (SSWs). Approximately once\nevery other winter, the boreal stratospheric polar vortex rapidly breaks down,\nshifting midlatitude surface weather patterns for months. We focus on two key\nquantities of interest: the probability of an SSW occurring, and the expected\nlead time if it does occur, as functions of initial condition. These\n\\emph{optimal forecasts} concretely measure the event's progress. Direct\nnumerical simulation can estimate them in principle, but is prohibitively\nexpensive in practice: each rare event requires a long integration to observe,\nand the cost of each integration grows with model complexity. We describe an\nalternative approach using integrations that are \\emph{short} compared to the\ntimescale of the warming event. We compute the probability and lead time\nefficiently by solving equations involving the transition operator, which\nencodes all information about the dynamics. We relate these optimal forecasts\nto a small number of interpretable physical variables, suggesting optimal\nmeasurements for forecasting. We illustrate the methodology on a prototype SSW\nmodel developed by Holton and Mass (1976) and modified by stochastic forcing.\nWhile highly idealized, this model captures the essential nonlinear dynamics of\nSSWs and exhibits the key forecasting challenge: the dramatic separation in\ntimescales between a single event and the return time between successive\nevents. Our methodology is designed to fully exploit high-dimensional data from\nmodels and observations, and has the potential to identify detailed predictors\nof many complex rare events in meteorology.\n"}
+{"abstract": "  We introduce a multilabel probing task to assess the morphosyntactic\nrepresentations of word embeddings from multilingual language models. We\ndemonstrate this task with multilingual BERT (Devlin et al., 2018), training\nprobes for seven typologically diverse languages of varying morphological\ncomplexity: Afrikaans, Croatian, Finnish, Hebrew, Korean, Spanish, and Turkish.\nThrough this simple but robust paradigm, we show that multilingual BERT renders\nmany morphosyntactic features easily and simultaneously extractable (e.g.,\ngender, grammatical case, pronominal type). We further evaluate the probes on\nsix \"held-out\" languages in a zero-shot transfer setting: Arabic, Chinese,\nMarathi, Slovenian, Tagalog, and Yoruba. This style of probing has the added\nbenefit of revealing the linguistic properties that language models recognize\nas being shared across languages. For instance, the probes performed well on\nrecognizing nouns in the held-out languages, suggesting that multilingual BERT\nhas a conception of noun-hood that transcends individual languages; yet, the\nsame was not true of adjectives.\n"}
+{"abstract": "  Online tracking of multiple objects in videos requires strong capacity of\nmodeling and matching object appearances. Previous methods for learning\nappearance embedding mostly rely on instance-level matching without considering\nthe temporal continuity provided by videos. We design a new instance-to-track\nmatching objective to learn appearance embedding that compares a candidate\ndetection to the embedding of the tracks persisted in the tracker. It enables\nus to learn not only from videos labeled with complete tracks, but also\nunlabeled or partially labeled videos. We implement this learning objective in\na unified form following the spirit of constrastive loss. Experiments on\nmultiple object tracking datasets demonstrate that our method can effectively\nlearning discriminative appearance embeddings in a semi-supervised fashion and\noutperform state of the art methods on representative benchmarks.\n"}
+{"abstract": "  Unobserved confounding is one of the greatest challenges for causal\ndiscovery. The case in which unobserved variables have a widespread effect on\nmany of the observed ones is particularly difficult because most pairs of\nvariables are conditionally dependent given any other subset, rendering the\ncausal effect unidentifiable. In this paper we show that beyond conditional\nindependencies, under the principle of independent mechanisms, unobserved\nconfounding in this setting leaves a statistical footprint in the observed data\ndistribution that allows for disentangling spurious and causal effects. Using\nthis insight, we demonstrate that a sparse linear Gaussian directed acyclic\ngraph among observed variables may be recovered approximately and propose an\nadjusted score-based causal discovery algorithm that may be implemented with\ngeneral purpose solvers and scales to high-dimensional problems. We find, in\naddition, that despite the conditions we pose to guarantee causal recovery,\nperformance in practice is robust to large deviations in model assumptions.\n"}
+{"abstract": "  Recently Brakerski, Christiano, Mahadev, Vazirani and Vidick (FOCS 2018) have\nshown how to construct a test of quantumness based on the learning with errors\n(LWE) assumption: a test that can be solved efficiently by a quantum computer\nbut cannot be solved by a classical polynomial-time computer under the LWE\nassumption. This test has lead to several cryptographic applications. In\nparticular, it has been applied to producing certifiable randomness from a\nsingle untrusted quantum device, self-testing a single quantum device and\ndevice-independent quantum key distribution.\n  In this paper, we show that this test of quantumness, and essentially all the\nabove applications, can actually be implemented by a very weak class of quantum\ncircuits: constant-depth quantum circuits combined with logarithmic-depth\nclassical computation. This reveals novel complexity-theoretic properties of\nthis fundamental test of quantumness and gives new concrete evidence of the\nsuperiority of small-depth quantum circuits over classical computation.\n"}
+{"abstract": "  Pion electroproduction off the proton is analyzed in a new framework based on\na general parametrization of transition amplitudes, including constraints from\ngauge invariance and threshold behavior. Data with energies $1.13~{\\rm\nGeV}<W<1.6~{\\rm GeV}$ and $Q^2$ below $6~{\\rm GeV}^2$ are included. The model\nis an extension of the latest J\\\"ulich-Bonn solution incorporating constraints\nfrom pion-induced and photoproduction data. Performing large scale fits\n($\\sim10^5$ data) we find a set of solutions with $\\chi^2_{\\rm dof}=1.69-1.81$\nwhich allows us to assess the systematic uncertainty of the approach.\n"}
+{"abstract": "  A good distortion representation is crucial for the success of deep blind\nimage quality assessment (BIQA). However, most previous methods do not\neffectively model the relationship between distortions or the distribution of\nsamples with the same distortion type but different distortion levels. In this\nwork, we start from the analysis of the relationship between perceptual image\nquality and distortion-related factors, such as distortion types and levels.\nThen, we propose a Distortion Graph Representation (DGR) learning framework for\nIQA, named GraphIQA, in which each distortion is represented as a graph, \\ieno,\nDGR. One can distinguish distortion types by learning the contrast relationship\nbetween these different DGRs, and infer the ranking distribution of samples\nfrom different levels in a DGR. Specifically, we develop two sub-networks to\nlearn the DGRs: a) Type Discrimination Network (TDN) that aims to embed DGR\ninto a compact code for better discriminating distortion types and learning the\nrelationship between types; b) Fuzzy Prediction Network (FPN) that aims to\nextract the distributional characteristics of the samples in a DGR and predicts\nfuzzy degrees based on a Gaussian prior. Experiments show that our GraphIQA\nachieves the state-of-the-art performance on many benchmark datasets of both\nsynthetic and authentic distortions.\n"}
+{"abstract": "  The OSIRIS detector is a subsystem of the liquid scintillator fillling chain\nof the JUNO reactor neutrino experiment. Its purpose is to validate the\nradiopurity of the scintillator to assure that all components of the JUNO\nscintillator system work to specifications and only neutrino-grade scintillator\nis filled into the JUNO Central Detector. The aspired sensitivity level of\n$10^{-16}$ g/g of $^{238}$U and $^{232}$Th requires a large ($\\sim$20 m$^3$)\ndetection volume and ultralow background levels. The present paper reports on\nthe design and major components of the OSIRIS detector, the detector simulation\nas well as the measuring strategies foreseen and the sensitivity levels to U/Th\nthat can be reached in this setup.\n"}
+{"abstract": "  Let $\\mathbb{F}_{q}$ be the finite field with $q$ elements. This paper mainly\nresearches the polynomial representation of double cyclic codes over\n$\\mathbb{F}_{q}+v\\mathbb{F}_{q}+v^2\\mathbb{F}_{q}$ with $v^3=v$. Firstly, we\ngive the generating polynomials of these double cyclic codes. Secondly, we show\nthe generating matrices of them. Meanwhile, we get quantitative information\nrelated to them by the matrix forms. Finally, we investigate the relationship\nbetween the generators of double cyclic codes and their duals.\n"}
+{"abstract": "  Nano quadcopters are ideal for gas source localization (GSL) as they are\nsafe, agile and inexpensive. However, their extremely restricted sensors and\ncomputational resources make GSL a daunting challenge. In this work, we propose\na novel bug algorithm named `Sniffy Bug', which allows a fully autonomous swarm\nof gas-seeking nano quadcopters to localize a gas source in an unknown,\ncluttered and GPS-denied environments. The computationally efficient, mapless\nalgorithm foresees in the avoidance of obstacles and other swarm members, while\npursuing desired waypoints. The waypoints are first set for exploration, and,\nwhen a single swarm member has sensed the gas, by a particle swarm\noptimization-based procedure. We evolve all the parameters of the bug (and PSO)\nalgorithm, using our novel simulation pipeline, `AutoGDM'. It builds on and\nexpands open source tools in order to enable fully automated end-to-end\nenvironment generation and gas dispersion modeling, allowing for learning in\nsimulation. Flight tests show that Sniffy Bug with evolved parameters\noutperforms manually selected parameters in cluttered, real-world environments.\n"}
+{"abstract": "  The textbook Newton's iteration is practically inapplicable on solutions of\nnonlinear systems with singular Jacobians. By a simple modification, a novel\nextension of Newton's iteration regains its local quadratic convergence toward\nnonisolated solutions that are semiregular as properly defined regardless of\nwhether the system is square, underdetermined or overdetermined while Jacobians\ncan be rank-deficient. Furthermore, the iteration serves as a regularization\nmechanism for computing singular solutions from empirical data. When a system\nis perturbed, its nonisolated solutions can be altered substantially or even\ndisappear. The iteration still locally converges to a stationary point that\napproximates a singular solution of the underlying system with an error bound\nin the same order of the data accuracy. Geometrically, the iteration\napproximately approaches the nearest point on the solution manifold. The method\nsimplifies the modeling of nonlinear systems by permitting nonisolated\nsolutions and enables a wide range of applications in algebraic computation.\n"}
+{"abstract": "  Noether's theorem identifies fundamental conserved quantities, called Noether\ncharges, from a Hamiltonian. To-date Noether charges remain largely elusive\nwithin theories of gravity: We do not know how to directly measure them, and\ntheir physical interpretation remains unsettled in general spacetimes. Here we\nshow that the surface gravity as naturally defined for a family of observers in\narbitrarily dynamical spacetimes is a directly measurable Noether charge. This\nNoether charge reduces to the accepted value on stationary horizons, and, when\nintegrated over a closed surface, yields an energy with the characteristics of\ngravitating mass. Stokes' theorem then identifies the gravitating energy\ndensity as the time-component of a locally conserved Noether current in general\nspacetimes. Our conclusion, that this Noether charge is extractable from\nastronomical observations, holds the potential for determining the detailed\ndistribution of the gravitating mass in galaxies, galaxy clusters and beyond.\n"}
+{"abstract": "  The strong excitonic effect in monolayer transition metal dichalcogenide\n(TMD) semiconductors has enabled many fascinating light-matter interaction\nphenomena. Examples include strongly coupled exciton-polaritons and nearly\nperfect atomic monolayer mirrors. The strong light-matter interaction also\nopens the door for dynamical control of mechanical motion through the exciton\nresonance of monolayer TMDs. Here we report the observation of\nexciton-optomechanical coupling in a suspended monolayer MoSe2 mechanical\nresonator. By moderate optical pumping near the MoSe2 exciton resonance, we\nhave observed optical damping and anti-damping of mechanical vibrations as well\nas the optical spring effect. The exciton-optomechanical coupling strength is\nalso gate-tunable. Our observations can be understood in a model based on\nphotothermal backaction and gate-induced mirror symmetry breaking in the device\nstructure. The observation of gate-tunable exciton-optomechanical coupling in a\nmonolayer semiconductor may find applications in nanoelectromechanical systems\n(NEMS) and in exciton-optomechanics.\n"}
+{"abstract": "  We study efficient quantum certification algorithms for quantum state set and\nunitary quantum channel. We present an algorithm that uses\n$O(\\varepsilon^{-4}\\ln |\\mathcal{P}|)$ copies of an unknown state to\ndistinguish whether the unknown state is contained in or $\\varepsilon$-far from\na finite set $\\mathcal{P}$ of known states with respect to the trace distance.\nThis algorithm is more sample-efficient in some settings. Previous study showed\nthat one can distinguish whether an unknown unitary $U$ is equal to or\n$\\varepsilon$-far from a known or unknown unitary $V$ in fixed dimension with\n$O(\\varepsilon^{-2})$ uses of the unitary, in which the Choi state is used and\nthus an ancilla system is needed. We give an algorithm that distinguishes the\ntwo cases with $O(\\varepsilon^{-1})$ uses of the unitary, using much fewer or\nno ancilla compared with previous results.\n"}
+{"abstract": "  We consider the recent privacy preserving methods that train the models not\non original images, but on mixed images that look like noise and hard to trace\nback to the original images. We explain that those mixed images will be samples\non the decision boundaries of the trained model, and although such methods\nsuccessfully hide the contents of images from the entity in charge of federated\nlearning, they provide crucial information to that entity about the decision\nboundaries of the trained model. Once the entity has exact samples on the\ndecision boundaries of the model, they may use it for effective adversarial\nattacks on the model during training and/or afterwards. If we have to hide our\nimages from that entity, how can we trust them with the decision boundaries of\nour model? As a remedy, we propose a method to encrypt the images, and have a\ndecryption module hidden inside the model. The entity in charge of federated\nlearning will only have access to a set of complex-valued coefficients, but the\nmodel will first decrypt the images and then put them through the convolutional\nlayers. This way, the entity will not see the training images and they will not\nknow the location of the decision boundaries of the model.\n"}
+{"abstract": "  The ability to explain decisions to its end-users is a necessity to deploy AI\nas critical decision support. Yet making AI explainable to end-users is a\nrelatively ignored and challenging problem. To bridge the gap, we first\nidentified twelve end-user-friendly explanatory forms that do not require\ntechnical knowledge to comprehend, including feature-, example-, and rule-based\nexplanations. We then instantiated the explanatory forms as prototyping cards\nin four AI-assisted critical decision-making tasks, and conducted a user study\nto co-design low-fidelity prototypes with 32 layperson participants. The\nresults verified the relevance of using the explanatory forms as building\nblocks of explanations, and identified their proprieties (pros, cons,\napplicable explainability needs, and design implications). The explanatory\nforms, their proprieties, and prototyping support constitute the\nEnd-User-Centered explainable AI framework EUCA. It serves as a practical\nprototyping toolkit for HCI/AI practitioners and researchers to build\nend-user-centered explainable AI.\n  The EUCA framework is available at http://weina.me/end-user-xai\n"}
+{"abstract": "  Motivated by Stanley's conjecture on the multiplication of Jack symmetric\nfunctions, we prove a couple of identities showing that skew Jack symmetric\nfunctions are semi-invariant up to translation and rotation of a $\\pi$ angle of\nthe skew diagram. It follows that, in some special cases, the coefficients of\nthe skew Jack symmetric functions with respect to the basis of the monomial\nsymmetric functions are polynomials with nonnegative integer coefficients.\n"}
+{"abstract": "  eQuilibrator (equilibrator.weizmann.ac.il) is a calculator for biochemical\nequilibrium constants and Gibbs free energies, originally designed as a\nweb-based interface. While the website now counts ${\\sim}1000$ distinct monthly\nusers, its design could not accommodate larger compound databases and it lacked\nan application programming interface (API) for integration in other tools\ndeveloped by the systems biology community. Here, we report a new python-based\npackage for eQuilibrator, that comes with many new features such as a 50-fold\nlarger compound database, the ability to add novel compound structures,\nimprovements in speed and memory use, and correction for Mg2+ ion\nconcentrations. Moreover, it adds the ability to compute the covariance matrix\nof the uncertainty between estimates, for which we show the advantages and\ndescribe the application in metabolic modeling. We foresee that these\nimprovements will make thermodynamic modeling more accessible and facilitate\nthe integration of eQuilibrator into other software platforms.\n"}
+{"abstract": "  Mixtures of Hidden Markov Models (MHMMs) are frequently used for clustering\nof sequential data. An important aspect of MHMMs, as of any clustering\napproach, is that they can be interpretable, allowing for novel insights to be\ngained from the data. However, without a proper way of measuring\ninterpretability, the evaluation of novel contributions is difficult and it\nbecomes practically impossible to devise techniques that directly optimize this\nproperty. In this work, an information-theoretic measure (entropy) is proposed\nfor interpretability of MHMMs, and based on that, a novel approach to improve\nmodel interpretability is proposed, i.e., an entropy-regularized Expectation\nMaximization (EM) algorithm. The new approach aims for reducing the entropy of\nthe Markov chains (involving state transition matrices) within an MHMM, i.e.,\nassigning higher weights to common state transitions during clustering. It is\nargued that this entropy reduction, in general, leads to improved\ninterpretability since the most influential and important state transitions of\nthe clusters can be more easily identified. An empirical investigation shows\nthat it is possible to improve the interpretability of MHMMs, as measured by\nentropy, without sacrificing (but rather improving) clustering performance and\ncomputational costs, as measured by the v-measure and number of EM iterations,\nrespectively.\n"}
+{"abstract": "  The penalized Cox proportional hazard model is a popular analytical approach\nfor survival data with a large number of covariates. Such problems are\nespecially challenging when covariates vary over follow-up time (i.e., the\ncovariates are time-dependent). The standard R packages for fully penalized Cox\nmodels cannot currently incorporate time-dependent covariates. To address this\ngap, we implement a variant of gradient descent algorithm (proximal gradient\ndescent) for fitting penalized Cox models. We apply our implementation to real\nand simulated data sets.\n"}
+{"abstract": "  In this paper, a new learning algorithm for Federated Learning (FL) is\nintroduced. The proposed scheme is based on a weighted gradient aggregation\nusing two-step optimization to offer a flexible training pipeline. Herein, two\ndifferent flavors of the aggregation method are presented, leading to an order\nof magnitude improvement in convergence speed compared to other distributed or\nFL training algorithms like BMUF and FedAvg. Further, the aggregation algorithm\nacts as a regularizer of the gradient quality. We investigate the effect of our\nFL algorithm in supervised and unsupervised Speech Recognition (SR) scenarios.\nThe experimental validation is performed based on three tasks: first, the\nLibriSpeech task showing a speed-up of 7x and 6% word error rate reduction\n(WERR) compared to the baseline results. The second task is based on session\nadaptation providing 20% WERR over a powerful LAS model. Finally, our\nunsupervised pipeline is applied to the conversational SR task. The proposed FL\nsystem outperforms the baseline systems in both convergence speed and overall\nmodel performance.\n"}
+{"abstract": "  Starting from the Bonn potential, relativistic Brueckner-Hartree-Fock (RBHF)\nequations are solved for nuclear matter in the full Dirac space, which provides\na unique way to determine the single-particle potentials and avoids the\napproximations applied in the RBHF calculations in the Dirac space with\npositive-energy states (PESs) only. The uncertainties of the RBHF calculations\nin the Dirac space with PESs only are investigated, and the importance of the\nRBHF calculations in the full Dirac space is demonstrated. In the RBHF\ncalculations in the full Dirac space, the empirical saturation properties of\nsymmetric nuclear matter are reproduced, and the obtained equation of state\nagrees with the results based on the relativistic Green's function approach up\nto the saturation density.\n"}
+{"abstract": "  Deep neural networks (DNNs) are prominent due to their superior performance\nin many fields. The deep-learning-as-a-service (DLaaS) paradigm enables\nindividuals and organizations (clients) to outsource their DNN learning tasks\nto the cloud-based platforms. However, the DLaaS server may return incorrect\nDNN models due to various reasons (e.g., Byzantine failures). This raises the\nserious concern of how to verify if the DNN models trained by potentially\nuntrusted DLaaS servers are indeed correct. To address this concern, in this\npaper, we design VeriDL, a framework that supports efficient correctness\nverification of DNN models in the DLaaS paradigm. The key idea of VeriDL is the\ndesign of a small-size cryptographic proof of the training process of the DNN\nmodel, which is associated with the model and returned to the client. Through\nthe proof, VeriDL can verify the correctness of the DNN model returned by the\nDLaaS server with a deterministic guarantee and cheap overhead. Our experiments\non four real-world datasets demonstrate the efficiency and effectiveness of\nVeriDL.\n"}
+{"abstract": "  An important task at future colliders is the investigation of the Higgs-boson\nsector. Here the measurement of the triple Higgs coupling(s) plays a special\nrole. Based on previous analyses, within the framework of Two Higgs Doublet\nModels (2HDM) type~I and~II, we define and analyze several two-dimensional\nbenchmark planes, that are over large parts in agreement with all theoretical\nand experimental constraints. For these planes we evaluate di-Higgs production\ncross sections at future high-energy $e^+e^-$ colliders, such as ILC or CLIC.\nWe consider two different channels for the neutral di-Higgs pairs $h_i\nh_j=hh,hH,HH,AA$: $e^+e^- \\to h_i h_j Z$ and $e^+e^- \\to h_i h_j \\nu \\bar \\nu$.\nIn both channels the various triple Higgs-boson couplings contribute\nsubstantially. We find regions with a strong enhancement of the production\nchannel of two SM-like light Higgs bosons and/or with very large production\ncross sections involving one light and one heavy or two heavy 2HDM Higgs\nbosons, offering interesting prospects for the ILC or CLIC. The mechanisms\nleading to these enhanced production cross sections are analyzed in detail. We\npropose the use of cross section distributions with the invariant mass of the\ntwo final Higgs bosons where the contributions from intermediate resonant and\nnon-resonant BSM Higgs bosons play a crucial role. We outline which process at\nwhich center-of-mass energy would be best suited to probe the corresponding\ntriple Higgs-boson couplings.\n"}
+{"abstract": "  We propose Nester, a method for injecting neural networks into constrained\nstructured predictors. The job of the neural network(s) is to compute an\ninitial, raw prediction that is compatible with the input data but does not\nnecessarily satisfy the constraints. The structured predictor then builds a\nstructure using a constraint solver that assembles and corrects the raw\npredictions in accordance with hard and soft constraints. In doing so, Nester\ntakes advantage of the features of its two components: the neural network\nlearns complex representations from low-level data while the constraint\nprogramming component reasons about the high-level properties of the prediction\ntask. The entire architecture can be trained in an end-to-end fashion. An\nempirical evaluation on handwritten equation recognition shows that Nester\nachieves better performance than both the neural network and the constrained\nstructured predictor on their own, especially when training examples are\nscarce, while scaling to more complex problems than other neuro-programming\napproaches. Nester proves especially useful to reduce errors at the semantic\nlevel of the problem, which is particularly challenging for neural network\narchitectures.Sub\n"}
+{"abstract": "  Given an $(r + 1)$-chromatic graph $H$, the fundamental edge stability result\nof Erd\\H{o}s and Simonovits says that all $n$-vertex $H$-free graphs have at\nmost $(1 - 1/r + o(1)) \\binom{n}{2}$ edges, and any $H$-free graph with that\nmany edges can be made $r$-partite by deleting $o(n^{2})$ edges.\n  Here we consider a natural variant of this -- the minimum degree stability of\n$H$-free graphs. In particular, what is the least $c$ such that any $n$-vertex\n$H$-free graph with minimum degree greater than $cn$ can be made $r$-partite by\ndeleting $o(n^{2})$ edges? We determine this least value for all 3-chromatic\n$H$ and for very many non-3-colourable $H$ (all those in which one is commonly\ninterested) as well as bounding it for the remainder. This extends the\nAndr\\'{a}sfai-Erd\\H{o}s-S\\'{o}s theorem and work of Alon and Sudakov.\n"}
+{"abstract": "  We present a detailed study of the decoherence correction to surface-hopping\nthat was recently derived from the exact factorization approach. Ab initio\nmultiple spawning calculations that use the same initial conditions and same\nelectronic structure method are used as a reference for three molecules:\nethylene, methaniminium cation, and fulvene, for which non-adiabatic dynamics\nfollows a photo-excitation. A comparison with the Granucci-Persico energy-based\ndecoherence correction, and the augmented fewest-switches surface-hopping\nscheme shows that the three decoherence-corrected methods operate on individual\ntrajectories in a qualitatively different way, but results averaged over\ntrajectories are similar for these systems.\n"}
+{"abstract": "  HOMFLY polynomials are one of the major knot invariants being actively\nstudied. They are difficult to compute in the general case but can be far more\neasily expressed in certain specific cases. In this paper, we examine two\nparticular knots, as well as one more general infinite class of knots. From our\ncalculations, we see some apparent patterns in the polynomials for the knots\n$9_{35}$ and $9_{46}$, and in particular their $F$-factors. These properties\nare of a form that seems conducive to finding a general formula for them, which\nwould yield a general formula for the HOMFLY polynomials of the two knots.\nMotivated by these observations, we demonstrate and conjecture some properties\nboth of the $F$-factors and HOMFLY polynomials of these knots and of the more\ngeneral class that contains them, namely pretzel knots with 3 odd parameters.\nWe make the first steps toward a matrix-less general formula for the HOMFLY\npolynomials of these knots.\n"}
+{"abstract": "  We have applied relativistic coupled-cluster (RCC) theory to determine the\nisotope shift (IS) constants of the first eight low-lying states of the Li,\nBe$^+$ and Ar$^{15+}$ isoelectronic systems. Though the RCC theory with\nsingles, doubles and triples approximation (RCCSDT method) is an exact method\nfor these systems for a given set of basis functions, we notice large\ndifferences in the results from this method when various procedures in the RCC\ntheory framework are adopted to estimate the IS constants. This has been\ndemonstrated by presenting the IS constants of the aforementioned states from\nthe finite-field, expectation value and analytical response (AR) approaches of\nthe RCCSDT method. Contributions from valence triple excitations, Breit\ninteraction and lower-order QED effects to the evaluation of these IS constants\nare also highlighted. Our results are compared with high-precision calculations\nreported using few-body methods wherever possible. We find that results from\nthe AR procedure are more reliable than the other two approaches. This analysis\nis crucial for understanding the roles of electron correlation effects in the\naccurate determination of IS constants in the heavier atomic systems, where\nfew-body methods cannot be applied.\n"}
+{"abstract": "  Modern neuroscience employs in silico experimentation on ever-increasing and\nmore detailed neural networks. The high modelling detail goes hand in hand with\nthe need for high model reproducibility, reusability and transparency. Besides,\nthe size of the models and the long timescales under study mandate the use of a\nsimulation system with high computational performance, so as to provide an\nacceptable time to result. In this work, we present EDEN (Extensible Dynamics\nEngine for Networks), a new general-purpose, NeuroML-based neural simulator\nthat achieves both high model flexibility and high computational performance,\nthrough an innovative model-analysis and code-generation technique. The\nsimulator runs NeuroML v2 models directly, eliminating the need for users to\nlearn yet another simulator-specific, model-specification language. EDEN's\nfunctional correctness and computational performance were assessed through\nNeuroML models available on the NeuroML-DB and Open Source Brain model\nrepositories. In qualitative experiments, the results produced by EDEN were\nverified against the established NEURON simulator, for a wide range of models.\nAt the same time, computational-performance benchmarks reveal that EDEN runs up\nto 2 orders-of-magnitude faster than NEURON on a typical desktop computer, and\ndoes so without additional effort from the user. Finally, and without added\nuser effort, EDEN has been built from scratch to scale seamlessly over multiple\nCPUs and across computer clusters, when available.\n"}
+{"abstract": "  Critical role of Internet of Things (IoT) in various domains like smart city,\nhealthcare, supply chain and transportation has made them the target of\nmalicious attacks. Past works in this area focused on centralized Intrusion\nDetection System (IDS), assuming the existence of a central entity to perform\ndata analysis and identify threats. However, such IDS may not always be\nfeasible, mainly due to spread of data across multiple sources and gathering at\ncentral node can be costly. Also, the earlier works primarily focused on\nimproving True Positive Rate (TPR) and ignored the False Positive Rate (FPR),\nwhich is also essential to avoid unnecessary downtime of the systems. In this\npaper, we first present an architecture for IDS based on hybrid ensemble model,\nnamed PHEC, which gives improved performance compared to state-of-the-art\narchitectures. We then adapt this model to a federated learning framework that\nperforms local training and aggregates only the model parameters. Next, we\npropose Noise-Tolerant PHEC in centralized and federated settings to address\nthe label-noise problem. The proposed idea uses classifiers using weighted\nconvex surrogate loss functions. Natural robustness of KNN classifier towards\nnoisy data is also used in the proposed architecture. Experimental results on\nfour benchmark datasets drawn from various security attacks show that our model\nachieves high TPR while keeping FPR low on noisy and clean data. Further, they\nalso demonstrate that the hybrid ensemble models achieve performance in\nfederated settings close to that of the centralized settings.\n"}
+{"abstract": "  A deep Transformer model with good evaluation score does not mean each\nsubnetwork (a.k.a transformer block) learns reasonable representation.\nDiagnosing abnormal representation and avoiding it can contribute to achieving\na better evaluation score. We propose an innovative perspective for analyzing\nattention patterns: summarize block-level patterns and assume abnormal patterns\ncontribute negative influence. We leverage Wav2Vec 2.0 as a research target and\nanalyze a pre-trained model's pattern. All experiments leverage\nLibrispeech-100-clean as training data. Through avoiding diagnosed abnormal\nones, our custom Wav2Vec 2.0 outperforms the original version about 4.8%\nabsolute word error rate (WER) on test-clean with viterbi decoding. Our version\nis still 0.9% better when decoding with a 4-gram language model. Moreover, we\nidentify that avoiding abnormal patterns is the main contributor for\nperformance boosting.\n"}
+{"abstract": "  For each $p\\geq 1$, the star automaton group $\\mathcal{G}_{S_p}$ is an\nautomaton group which can be defined starting from a star graph on $p+1$\nvertices. We study Schreier graphs associated with the action of the group\n$\\mathcal{G}_{S_p}$ on the regular rooted tree $T_{p+1}$ of degree $p+1$ and on\nits boundary $\\partial T_{p+1}$. With the transitive action on the $n$-th level\nof $T_{p+1}$ is associated a finite Schreier graph $\\Gamma^p_n$, whereas there\nexist uncountably many orbits of the action on the boundary, represented by\ninfinite Schreier graphs which are obtained as limits of the sequence\n$\\{\\Gamma_n^p\\}_{n\\geq 1}$ in the Gromov-Hausdorff topology. We obtain an\nexplicit description of the spectrum of the graphs $\\{\\Gamma_n^p\\}_{n\\geq 1}$.\nThen, by using amenability of $\\mathcal{G}_{S_p}$, we prove that the spectrum\nof each infinite Schreier graph is the union of a Cantor set of zero Lebesgue\nmeasure, which is the Julia set of the quadratic map $f_p(z) = z^2-2(p-1)z\n-2p$, and a countable collection of isolated points supporting the KNS spectral\nmeasure. We also give a complete classification of the infinite Schreier graphs\nup to isomorphism of unrooted graphs, showing that they may have $1$, $2$ or\n$2p$ ends, and that the case of $1$ end is generic with respect to the uniform\nmeasure on $\\partial T_{p+1}$.\n"}
+{"abstract": "  Policy optimization methods remain a powerful workhorse in empirical\nReinforcement Learning (RL), with a focus on neural policies that can easily\nreason over complex and continuous state and/or action spaces. Theoretical\nunderstanding of strategic exploration in policy-based methods with non-linear\nfunction approximation, however, is largely missing. In this paper, we address\nthis question by designing ENIAC, an actor-critic method that allows non-linear\nfunction approximation in the critic. We show that under certain assumptions,\ne.g., a bounded eluder dimension $d$ for the critic class, the learner finds a\nnear-optimal policy in $O(\\poly(d))$ exploration rounds. The method is robust\nto model misspecification and strictly extends existing works on linear\nfunction approximation. We also develop some computational optimizations of our\napproach with slightly worse statistical guarantees and an empirical adaptation\nbuilding on existing deep RL tools. We empirically evaluate this adaptation and\nshow that it outperforms prior heuristics inspired by linear methods,\nestablishing the value via correctly reasoning about the agent's uncertainty\nunder non-linear function approximation.\n"}
+{"abstract": "  A new model maps a quantum random walk described by a Hadamard operator to a\nparticular case of a birth and death process. The model is represented by a 2D\nMarkov chain with a stochastic matrix, i.e., all the transition rates are\npositive, although the Hadamard operator contains negative entries (this is\npossible by increasing the dimensionality of the system). The probability\ndistribution of the walker population is preserved using the Markovian\nproperty. By applying a proper transformation to the population distribution of\nthe random walk, the probability distributions of the quantum states |0>, 1>\nare revealed. Thus, the new model has two unique properties: it reveals the\nprobability distribution of the quantum states as a unitary system and\npreserves the population distribution of the random walker as a Markovian\nsystem.\n"}
+{"abstract": "  Population size estimation based on the capture-recapture experiment is an\ninteresting problem in various fields including epidemiology, criminology,\ndemography, etc. In many real-life scenarios, there exists inherent\nheterogeneity among the individuals and dependency between capture and\nrecapture attempts. A novel trivariate Bernoulli model is considered to\nincorporate these features, and the Bayesian estimation of the model parameters\nis suggested using data augmentation. Simulation results show robustness under\nmodel misspecification and the superiority of the performance of the proposed\nmethod over existing competitors. The method is applied to analyse real case\nstudies on epidemiological surveillance. The results provide interesting\ninsight on the heterogeneity and dependence involved in the capture-recapture\nmechanism. The methodology proposed can assist in effective decision-making and\npolicy formulation.\n"}
+{"abstract": "  The period in which hydrogen in the intergalactic medium (IGM) is ionized,\nknown as the Epoch of Reionization (EoR) is still poorly understood. The timing\nand duration of the EoR is expected to be governed by the underlying\nastrophysics. Furthermore, most models of reionization predict a correlation\nbetween the density and ionization field. Here we consider using the mean\ndispersion measure (DM) of high redshift Fast Radio Bursts (FRBs) as a probe of\nthe underlying astrophysics and morphology of the EoR. To do this, we forecast\nobservational scenarios by building mock data sets of non-repeating FRBs\nbetween redshifts $8\\leq z \\leq 10$. It is assumed that all FRBs have\naccompanying spectroscopic redshift measurements. We find that samples of 100\nhigh redshift FRBs, in the above mentioned narrow redshift range, can rule out\nuncorrelated reionization at $68\\%$ credibility, while larger samples, $\\geq\n10^4$ FRBs, can rule out uncorrelated reionization at $95\\%$ credibility. We\nalso find 100 high redshift FRBs can rule out scenarios where the Universe is\nentirely neutral at $z = 10$ with $68\\%$ credibility. Further with $\\geq 10^5$\nFRBs, we can constrain the duration $\\Delta z$ of reionization (duration\nbetween mean ionized fraction 0.25 to 0.75) to $\\Delta z = 2.0^{+0.5}_{-0.4}$,\nand the midpoint of reionization to $z = 7.8^{+0.4}_{-0.2}$ at $95\\%$\ncredibility.\n"}
+{"abstract": "  First-order methods for solving convex optimization problems have been at the\nforefront of mathematical optimization in the last 20 years. The rapid\ndevelopment of this important class of algorithms is motivated by the success\nstories reported in various applications, including most importantly machine\nlearning, signal processing, imaging and control theory. First-order methods\nhave the potential to provide low accuracy solutions at low computational\ncomplexity which makes them an attractive set of tools in large-scale\noptimization problems. In this survey we cover a number of key developments in\ngradient-based optimization methods. This includes non-Euclidean extensions of\nthe classical proximal gradient method, and its accelerated versions.\nAdditionally we survey recent developments within the class of projection-free\nmethods, and proximal versions of primal-dual schemes. We give complete proofs\nfor various key results, and highlight the unifying aspects of several\noptimization algorithms.\n"}
+{"abstract": "  Currently, due to the COVID-19 pandemic the public life in most European\ncountries stopped almost completely due to measures against the spread of the\nvirus. Efforts to limit the number of new infections are threatened by the\nadvent of new variants of the SARS-COV-2 virus, most prominent the B.1.1.7\nstrain with higher infectivity. In this article we consider a basic two-strain\nSIR model to explain the spread of those variants in Germany on small time\nscales. For a linearized version of the model we calculate relevant variables\nlike the time of minimal infections or the dynamics of the share of variants\nanalytically. These analytical approximations and numerical simulations are in\na good agreement to data reported by the Robert Koch Institute (RKI) in\nGermany.\n"}
+{"abstract": "  This paper generalizes the concept of index and co-index and some related\nresults for free actions of G = S0 on a paracompact Hausdorff space which were\nintroduced by Conner and Floyd. We define the index and co-index of a\nfinitistic free G-space X, where G = Sd , d = 1 or 3 and prove that the index\nof X is not more than the mod 2 cohomology index of X. We observe that the\nindex and co-index of a (2n + 1)-sphere (resp. (4n+3)-sphere) for the action of\ncomponentwise multiplication of G = S1 (resp. S3) is n.\n  We also determine the orbit spaces of free actions of G = S3 on a finitistic\nspace X with the mod 2 cohomology and the rational cohomology product of\nspheres. The orbit spaces of circle actions on the mod 2 cohomology X is also\ndiscussed. Using these calculation, we obtain an upper bound of the index of X\nand the Borsuk-Ulam type results.\n"}
+{"abstract": "  There has been considerable interest in properties of condensed matter at\nfinite temperature, including non-equilibrium behavior and extreme conditions\nup to the warm dense matter regime. Such behavior is encountered, e.g., in\nexperimental time resolved x-ray absorption spectroscopy (XAS) in the presence\nof intense laser fields. In an effort to simulate such behavior, we present an\napproach for calculations of finite-temperature x-ray absorption spectra in\narbitrary materials, using a generalization of the real-space Green's function\nformalism. The method is incorporated as an option in the core-level x-ray\nspectroscopy code FEFF10. To illustrate the approach, we present calculations\nfor several materials together with comparisons to experiment and with other\nmethods.\n"}
+{"abstract": "  Let $G$ be a 4-chromatic maximal planar graph (MPG) with the minimum degree\nof at least 4 and let $C$ be an even-length cycle of $G$.If $|f(C)|=2$ for\nevery $f$ in some Kempe equivalence class of $G$, then we call $C$ an unchanged\nbichromatic cycle (UBC) of $G$, and correspondingly $G$ an unchanged\nbichromatic cycle maximal planar graph (UBCMPG) with respect to $C$, where\n$f(C)=\\{f(v)| v\\in V(C)\\}$. For an UBCMPG $G$ with respect to an UBC $C$, the\nsubgraph of $G$ induced by the set of edges belonging to $C$ and its interior\n(or exterior), denoted by $G^C$, is called a base-module of $G$; in particular,\nwhen the length of $C$ is equal to four, we use $C_4$ instead of $C$ and call\n$G^{C_4}$ a 4-base-module. In this paper, we first study the properties of\nUBCMPGs and show that every 4-base-module $G^{C_4}$ contains a 4-coloring under\nwhich $C_4$ is bichromatic and there are at least two bichromatic paths with\ndifferent colors between one pair of diagonal vertices of $C_4$ (these paths\nare called module-paths). We further prove that every 4-base-module $G^{C_4}$\ncontains a 4-coloring (called decycle coloring) for which the ends of a\nmodule-path are colored by distinct colors. Finally, based on the technique of\nthe contracting and extending operations of MPGs, we prove that\n55-configurations and 56-configurations are reducible by converting the\nreducibility problem of these two classes of configurations into the decycle\ncoloring problem of 4-base-modules.\n"}
+{"abstract": "  Graph neural networks (GNN) have been successful in many fields, and derived\nvarious researches and applications in real industries. However, in some\nprivacy sensitive scenarios (like finance, healthcare), training a GNN model\ncentrally faces challenges due to the distributed data silos. Federated\nlearning (FL) is a an emerging technique that can collaboratively train a\nshared model while keeping the data decentralized, which is a rational solution\nfor distributed GNN training. We term it as federated graph learning (FGL).\nAlthough FGL has received increasing attention recently, the definition and\nchallenges of FGL is still up in the air. In this position paper, we present a\ncategorization to clarify it. Considering how graph data are distributed among\nclients, we propose four types of FGL: inter-graph FL, intra-graph FL and\ngraph-structured FL, where intra-graph is further divided into horizontal and\nvertical FGL. For each type of FGL, we make a detailed discussion about the\nformulation and applications, and propose some potential challenges.\n"}
+{"abstract": "  We investigate a multiphase Cahn-Hilliard model for tumor growth with general\nsource terms. The multiphase approach allows us to consider multiple cell types\nand multiple chemical species (oxygen and/or nutrients) that are consumed by\nthe tumor. Compared to classical two-phase tumor growth models, the multiphase\nmodel can be used to describe a stratified tumor exhibiting several layers of\ntissue (e.g., proliferating, quiescent and necrotic tissue) more precisely. Our\nmodel consists of a convective Cahn-Hilliard type equation to describe the\ntumor evolution, a velocity equation for the associated volume-averaged\nvelocity field, and a convective reaction-diffusion type equation to describe\nthe density of the chemical species. The velocity equation is either\nrepresented by Darcy's law or by the Brinkman equation. We first construct a\nglobal weak solution of the multiphase Cahn-Hilliard-Brinkman model. After\nthat, we show that such weak solutions of the system converge to a weak\nsolution of the multiphase Cahn-Hilliard-Darcy system as the viscosities tend\nto zero in some suitable sense. This means that the existence of a global weak\nsolution to the Cahn-Hilliard-Darcy system is also established.\n"}
+{"abstract": "  Nested networks or slimmable networks are neural networks whose architectures\ncan be adjusted instantly during testing time, e.g., based on computational\nconstraints. Recent studies have focused on a \"nested dropout\" layer, which is\nable to order the nodes of a layer by importance during training, thus\ngenerating a nested set of sub-networks that are optimal for different\nconfigurations of resources. However, the dropout rate is fixed as a\nhyper-parameter over different layers during the whole training process.\nTherefore, when nodes are removed, the performance decays in a human-specified\ntrajectory rather than in a trajectory learned from data. Another drawback is\nthe generated sub-networks are deterministic networks without well-calibrated\nuncertainty. To address these two problems, we develop a Bayesian approach to\nnested neural networks. We propose a variational ordering unit that draws\nsamples for nested dropout at a low cost, from a proposed Downhill\ndistribution, which provides useful gradients to the parameters of nested\ndropout. Based on this approach, we design a Bayesian nested neural network\nthat learns the order knowledge of the node distributions. In experiments, we\nshow that the proposed approach outperforms the nested network in terms of\naccuracy, calibration, and out-of-domain detection in classification tasks. It\nalso outperforms the related approach on uncertainty-critical tasks in computer\nvision.\n"}
+{"abstract": "  On 6 January 2021, a mob of right-wing conservatives stormed the USA Capitol\nHill interrupting the session of congress certifying 2020 Presidential election\nresults. Immediately after the start of the event, posts related to the riots\nstarted to trend on social media. A social media platform which stood out was a\nfree speech endorsing social media platform Parler; it is being claimed as the\nplatform on which the riots were planned and talked about. Our report presents\na contrast between the trending content on Parler and Twitter around the time\nof riots. We collected data from both platforms based on the trending hashtags\nand draw comparisons based on what are the topics being talked about, who are\nthe people active on the platforms and how organic is the content generated on\nthe two platforms. While the content trending on Twitter had strong resentments\ntowards the event and called for action against rioters and inciters, Parler\ncontent had a strong conservative narrative echoing the ideas of voter fraud\nsimilar to the attacking mob. We also find a disproportionately high\nmanipulation of traffic on Parler when compared to Twitter.\n"}
+{"abstract": "  We present a number of complexity results concerning the problem of counting\nvertices of an integral polytope defined by a system of linear inequalities.\nThe focus is on polytopes with small integer vertices, particularly 0/1\npolytopes and half-integral polytopes.\n"}
+{"abstract": "  We denote by $\\pi_k(R_n)$ the $k$-th homotopy group of the $n$-th rotation\ngroup $R_n$ and $\\pi_k(R_n:2)$ the 2-primary components of it. We determine the\ngroup structures of $\\pi_k(R_n:2)$ for $k = 23$ and $24$ by use of the\nfibration $R_{n+1}\\overset{R_n}{\\longrightarrow}S^n$. The method is based on\nToda's composition methods.\n"}
+{"abstract": "  Let $M_1$ and $M_2$ be functions on $[0,1]$ such that $M_1(t^{1/p})$ and\n$M_2(t^{1/p})$ are Orlicz functions for some $p \\in (0,1].$ Assume that\n$M_2^{-1} (1/t)/M_1^{-1} (1/t)$ is non-decreasing for $t \\geq 1.$ Let\n$(\\alpha_i)_{i=1}^\\infty$ be a non-increasing sequence of non-negative real\nnumbers. Under some conditions on $(\\alpha_i)_{i=1}^\\infty,$ sharp two-sided\nestimates for entropy numbers of diagonal operators $T_\\alpha :\\ell_{M_1}\n\\rightarrow \\ell_{M_2}$ generated by $(\\alpha_i)_{i=1}^\\infty,$ where\n$\\ell_{M_1}$ and $\\ell_{M_2}$ are Orlicz sequence spaces, are proved. The\nresults generalise some works of Edmunds and Netrusov and hence a result of\nCobos, K\\\"{u}hn and Schonbek.\n"}
+{"abstract": "  We show that if $N$ is a closed manifold of dimension $n=4$ (resp. $n=5$)\nwith $\\pi_2(N) = 0$ (resp. $\\pi_2(N)=\\pi_3(N)=0$) that admits a metric of\npositive scalar curvature, then a finite cover $\\hat N$ of $N$ is homotopy\nequivalent to $S^n$ or connected sums of $S^{n-1}\\times S^1$. Our approach\ncombines recent advances in the study of positive scalar curvature with a novel\nargument of Alpert--Balitskiy--Guth.\n  Additionally, we prove a more general mapping version of this result. In\nparticular, this implies that if $N$ is a closed manifold of dimensions $4$ or\n$5$, and $N$ admits a map of nonzero degree to a closed aspherical manifold,\nthen $N$ does not admit any Riemannian metric with positive scalar curvature.\n"}
+{"abstract": "  Offline reinforcement learning (RL) tries to learn the near-optimal policy\nwith recorded offline experience without online exploration. Current offline RL\nresearch includes: 1) generative modeling, i.e., approximating a policy using\nfixed data; and 2) learning the state-action value function. While most\nresearch focuses on the state-action function part through reducing the\nbootstrapping error in value function approximation induced by the distribution\nshift of training data, the effects of error propagation in generative modeling\nhave been neglected. In this paper, we analyze the error in generative\nmodeling. We propose AQL (action-conditioned Q-learning), a residual generative\nmodel to reduce policy approximation error for offline RL. We show that our\nmethod can learn more accurate policy approximations in different benchmark\ndatasets. In addition, we show that the proposed offline RL method can learn\nmore competitive AI agents in complex control tasks under the multiplayer\nonline battle arena (MOBA) game Honor of Kings.\n"}
+{"abstract": "  In this paper, we focus on studying duplicate logging statements, which are\nlogging statements that have the same static text message. We manually studied\nover 4K duplicate logging statements and their surrounding code in five\nlarge-scale open source systems. We uncovered five patterns of duplicate\nlogging code smells. For each instance of the duplicate logging code smell, we\nfurther manually identify the potentially problematic and justifiable cases.\nThen, we contact developers to verify our manual study result. We integrated\nour manual study result and the feedback of developers into our automated\nstatic analysis tool, DLFinder, which automatically detects problematic\nduplicate logging code smells. We evaluated DLFinder on the five manually\nstudied systems and three additional systems. In total, combining the results\nof DLFinder and our manual analysis, we reported 91 problematic duplicate\nlogging code smell instances to developers and all of them have been fixed. We\nfurther study the relationship between duplicate logging statements, including\nthe problematic instances of duplicate logging code smells, and code clones. We\nfind that 83% of the duplicate logging code smell instances reside in cloned\ncode, but 17% of them reside in micro-clones that are difficult to detect using\nautomated clone detection tools. We also find that more than half of the\nduplicate logging statements reside in cloned code snippets, and a large\nportion of them reside in very short code blocks which may not be effectively\ndetected by existing code clone detection tools. Our study shows that, in\naddition to general source code that implements the business logic, code clones\nmay also result in bad logging practices that could increase maintenance\ndifficulties.\n"}
+{"abstract": "  The real world is awash with multi-agent problems that require collective\naction by self-interested agents, from the routing of packets across a computer\nnetwork to the management of irrigation systems. Such systems have local\nincentives for individuals, whose behavior has an impact on the global outcome\nfor the group. Given appropriate mechanisms describing agent interaction,\ngroups may achieve socially beneficial outcomes, even in the face of short-term\nselfish incentives. In many cases, collective action problems possess an\nunderlying graph structure, whose topology crucially determines the\nrelationship between local decisions and emergent global effects. Such\nscenarios have received great attention through the lens of network games.\nHowever, this abstraction typically collapses important dimensions, such as\ngeometry and time, relevant to the design of mechanisms promoting cooperation.\nIn parallel work, multi-agent deep reinforcement learning has shown great\npromise in modelling the emergence of self-organized cooperation in complex\ngridworld domains. Here we apply this paradigm in graph-structured collective\naction problems. Using multi-agent deep reinforcement learning, we simulate an\nagent society for a variety of plausible mechanisms, finding clear transitions\nbetween different equilibria over time. We define analytic tools inspired by\nrelated literatures to measure the social outcomes, and use these to draw\nconclusions about the efficacy of different environmental interventions. Our\nmethods have implications for mechanism design in both human and artificial\nagent systems.\n"}
+{"abstract": "  In this paper, we tackle the problem of human-robot coordination in sequences\nof manipulation tasks. Our approach integrates hierarchical human motion\nprediction with Task and Motion Planning (TAMP). We first devise a hierarchical\nmotion prediction approach by combining Inverse Reinforcement Learning and\nshort-term motion prediction using a Recurrent Neural Network. In a second\nstep, we propose a dynamic version of the TAMP algorithm Logic-Geometric\nProgramming (LGP). Our version of Dynamic LGP, replans periodically to handle\nthe mismatch between the human motion prediction and the actual human behavior.\nWe assess the efficacy of the approach by training the prediction algorithms\nand testing the framework on the publicly available MoGaze dataset.\n"}
+{"abstract": "  We constructed involutions for a Halphen pencil of index 2, and proved that\nthe birational mapping corresponding to the autonomous reduction of the\nelliptic Painlev\\'e equation for the same pencil can be obtained as the\ncomposition of two such involutions.\n"}
+{"abstract": "  Let $(M, g, f)$ be a $4$-dimensional complete noncompact gradient shrinking\nRicci soliton with the equation $Ric+\\nabla^2f=\\lambda g$, where $\\lambda$ is a\npositive real number. We prove that if $M$ has constant scalar curvature\n$S=2\\lambda$, it must be a quotient of $\\mathbb{S}^2\\times \\mathbb{R}^2$.\nTogether with the known results, this implies that a $4$-dimensional complete\ngradient shrinking Ricci soliton has constant scalar curvature if and only if\nit is rigid, that is, it is either Einstein, or a finite quotient of Gaussian\nshrinking soliton $\\Bbb{R}^4$, $\\Bbb{S}^{2}\\times\\Bbb{R}^{2}$ or\n$\\Bbb{S}^{3}\\times\\Bbb{R}$.\n"}
+{"abstract": "  Minimization of suitable statistical distances~(between the data and model\ndensities) has proved to be a very useful technique in the field of robust\ninference. Apart from the class of $\\phi$-divergences of \\cite{a} and \\cite{b},\nthe Bregman divergence (\\cite{c}) has been extensively used for this purpose.\nHowever, since the data density must have a linear presence in the cross\nproduct term of the Bregman divergence involving both the data and model\ndensities, several useful divergences cannot be captured by the usual Bregman\nform. In this respect, we provide an extension of the ordinary Bregman\ndivergence by considering an exponent of the density function as the argument\nrather than the density function itself. We demonstrate that many useful\ndivergence families, which are not ordinarily Bregman divergences, can be\naccommodated within this extended description. Using this formulation, one can\ndevelop many new families of divergences which may be useful in robust\ninference. In particular, through an application of this extension, we propose\nthe new class of the GSB divergence family. We explore the applicability of the\nminimum GSB divergence estimator in discrete parametric models. Simulation\nstudies as well as conforming real data examples are given to demonstrate the\nperformance of the estimator and to substantiate the theory developed.\n"}
+{"abstract": "  Let $G$ be a graph with vertex set $V(G)$ and edge set $E(G)$, and let\n$d(u,w)$ denote the length of a $u-w$ geodesic in $G$. For any $v\\in V(G)$ and\n$e=xy\\in E(G)$, let $d(e,v)=\\min\\{d(x,v),d(y,v)\\}$. For distinct $e_1, e_2\\in\nE(G)$, let $R\\{e_1,e_2\\}=\\{z\\in V(G):d(z,e_1)\\neq d(z,e_2)\\}$. Kelenc et al.\n[Discrete Appl. Math. 251 (2018) 204-220] introduced the edge dimension of a\ngraph: A vertex subset $S\\subseteq V(G)$ is an edge resolving set of $G$ if\n$|S\\cap R\\{e_1,e_2\\}|\\ge 1$ for any distinct $e_1, e_2\\in E(G)$, and the edge\ndimension $edim(G)$ of $G$ is the minimum cardinality among all edge resolving\nsets of $G$.\n  For a real-valued function $g$ defined on $V(G)$ and for $U\\subseteq V(G)$,\nlet $g(U)=\\sum_{s\\in U}g(s)$. Then $g:V(G)\\rightarrow[0,1]$ is an edge\nresolving function of $G$ if $g(R\\{e_1,e_2\\})\\ge1$ for any distinct $e_1,e_2\\in\nE(G)$. The fractional edge dimension $edim_f(G)$ of $G$ is\n$\\min\\{g(V(G)):g\\mbox{ is an edge resolving function of }G\\}$. Note that\n$edim_f(G)$ reduces to $edim(G)$ if the codomain of edge resolving functions is\nrestricted to $\\{0,1\\}$.\n  We introduce and study fractional edge dimension and obtain some general\nresults on the edge dimension of graphs. We show that there exist two\nnon-isomorphic graphs on the same vertex set with the same edge metric\ncoordinates. We construct two graphs $G$ and $H$ such that $H \\subset G$ and\nboth $edim(H)-edim(G)$ and $edim_f(H)-edim_f(G)$ can be arbitrarily large. We\nshow that a graph $G$ with $edim(G)=2$ cannot have $K_5$ or $K_{3,3}$ as a\nsubgraph, and we construct a non-planar graph $H$ satisfying $edim(H)=2$. It is\neasy to see that, for any connected graph $G$ of order $n\\ge3$, $1\\le edim_f(G)\n\\le \\frac{n}{2}$; we characterize graphs $G$ satisfying $edim_f(G)=1$ and\nexamine some graph classes satisfying $edim_f(G)=\\frac{n}{2}$. We also\ndetermine the fractional edge dimension for some classes of graphs.\n"}
+{"abstract": "  Mathematical modeling is an essential step, for example, to analyze the\ntransient behavior of a dynamical process and to perform engineering studies\nsuch as optimization and control. With the help of first-principles and expert\nknowledge, a dynamic model can be built, but for complex dynamic processes,\nappearing, e.g., in biology, chemical plants, neuroscience, financial markets,\nthis often remains an onerous task. Hence, data-driven modeling of the dynamics\nprocess becomes an attractive choice and is supported by the rapid advancement\nin sensor and measurement technology. A data-driven approach, namely operator\ninference framework, models a dynamic process, where a particular structure of\nthe nonlinear term is assumed. In this work, we suggest combining the operator\ninference with certain deep neural network approaches to infer the unknown\nnonlinear dynamics of the system. The approach uses recent advancements in deep\nlearning and possible prior knowledge of the process if possible. We also\nbriefly discuss several extensions and advantages of the proposed methodology.\nWe demonstrate that the proposed methodology accomplishes the desired tasks for\ndynamics processes encountered in neural dynamics and the glycolytic\noscillator.\n"}
+{"abstract": "  We present the first lattice-QCD determination of the form factors describing\nthe semileptonic decays $\\Lambda_b \\to \\Lambda_c^*(2595)\\ell^-\\bar{\\nu}$ and\n$\\Lambda_b \\to \\Lambda_c^*(2625)\\ell^-\\bar{\\nu}$, where the $\\Lambda_c^*(2595)$\nand $\\Lambda_c^*(2625)$ are the lightest charm baryons with $J^P=\\frac12^-$ and\n$J^P=\\frac32^-$, respectively. These decay modes provide new opportunities to\ntest lepton flavor universality and also play an important role in global\nanalyses of the strong interactions in $b\\to c$ semileptonic decays. We\ndetermine the full set of vector, axial vector, and tensor form factors for\nboth decays, but only in a small kinematic region near the zero-recoil point.\nThe lattice calculation uses three different ensembles of gauge-field\nconfigurations with $2+1$ flavors of domain-wall fermions, and we perform\nextrapolations of the form factors to the continuum limit and physical pion\nmass. We present Standard-Model predictions for the differential decay rates\nand angular observables. In the kinematic region considered, the differential\ndecay rate for the $\\frac12^-$ final state is found to be approximately 2.5\ntimes larger than the rate for the $\\frac32^-$ final state. We also test the\ncompatibility of our form-factor results with zero-recoil sum rules.\n"}
+{"abstract": "  With the two flavor Nambu-Jona-Lasinio (NJL) model we carry out a\nphenomenological study on the chiral phase structure, mesonic properties and\ntransport properties in a momentum anisotropic quark matter. To calculate\ntransport coefficients we have utilized the kinetic theory in the relaxation\ntime approximation, where the momentum anisotropy is embedded in the estimation\nof both distribution function and the relaxation time by introducing an\nanisotropy parameter $\\xi$ . It is shown that an increase of the anisotropy\nparameter $\\xi$ may results in a catalysis of chiral symmetry breaking. The\ncritical endpoint (CEP) is shifted to smaller temperatures and larger quark\nchemical potentials as $\\xi $ increases, the impact of momentum anisotropy on\ntemperature of CEP is almost the same as that on the quark chemical potential\nof CEP. The meson masses and the associated decay widths also exhibit a\nsignificant $\\xi$ dependence. It is observed that the temperature behavior of\nscaled shear viscosity $\\eta/T^3$ and scaled electrical conductivity\n$\\sigma_{el}/T$ exhibit a similar dip structure, with the minima of both\n$\\eta/T^3$ and $\\sigma_{el}/T$ shifting toward higher temperatures with\nincreasing $\\xi$. Furthermore, we demonstrate that the Seebeck coefficient $S$\ndecreases when temperature goes up and its sign is positive, indicating the\ndominant carriers for converting the temperature gradient to the electric field\nare up-quarks. The Seebeck coefficient $S$ is significantly enhanced with a\nlarge $\\xi$ for the temperature below the critical temperature.\n"}
+{"abstract": "  We propose an analytical framework to model the effect of single and multiple\nmechanical surface oscillators on the dynamics of vertically polarized elastic\nwaves propagating in a semi-infinite medium. The formulation extends the\ncanonical Lamb's problem, originally developed to obtain the wavefield induced\nby a harmonic line source in an elastic half-space, to the scenario where a\nfinite cluster of vertical oscillators is attached to the medium surface. In\nshort, our approach utilizes the solution of the classical Lamb's problem as\nGreen's function to formulate the multiple scattered fields generated by the\nresonators. For an arbitrary number of resonators, arranged atop the elastic\nhalf-space in an arbitrary configuration, the displacement fields are obtained\nin closed-form and validated with numerics developed in a two-dimensional\nfinite element environment.\n"}
+{"abstract": "  We study principal-agent problems in which a principal commits to an\noutcome-dependent payment scheme (a.k.a. contract) so as to induce an agent to\ntake a costly, unobservable action. We relax the assumption that the principal\nperfectly knows the agent by considering a Bayesian setting where the agent's\ntype is unknown and randomly selected according to a given probability\ndistribution, which is known to the principal. Each agent's type is\ncharacterized by her own action costs and action-outcome distributions. In the\nliterature on non-Bayesian principal-agent problems, considerable attention has\nbeen devoted to linear contracts, which are simple, pure-commission payment\nschemes that still provide nice approximation guarantees with respect to\nprincipal-optimal (possibly non-linear) contracts. While in non-Bayesian\nsettings an optimal contract can be computed efficiently, this is no longer the\ncase for our Bayesian principal-agent problems. This further motivates our\nfocus on linear contracts, which can be optimized efficiently given their\nsingle-parameter nature. Our goal is to analyze the properties of linear\ncontracts in Bayesian settings, in terms of approximation guarantees with\nrespect to optimal contracts and general tractable contracts (i.e.,\nefficiently-computable ones). First, we study the approximation guarantees of\nlinear contracts with respect to optimal ones, showing that the former suffer\nfrom a multiplicative loss linear in the number of agent's types. Nevertheless,\nwe prove that linear contracts can still provide a constant multiplicative\napproximation $\\rho$ of the optimal principal's expected utility, though at the\nexpense of an exponentially-small additive loss $2^{-\\Omega(\\rho)}$. Then, we\nswitch to tractable contracts, showing that, surprisingly, linear contracts\nperform well among them.\n"}
+{"abstract": "  In this paper, we make a first attempt to incorporate both commuting demand\nand transit network connectivity in bus route planning (CT-Bus), and formulate\nit as a constrained optimization problem: planning a new bus route with k edges\nover an existing transit network without building new bus stops to maximize a\nlinear aggregation of commuting demand and connectivity of the transit network.\nWe prove the NP-hardness of CT-Bus and propose an expansion-based greedy\nalgorithm that iteratively scans potential candidate paths in the network. To\nboost the efficiency of computing the connectivity of new networks with\ncandidate paths, we convert it to a matrix trace estimation problem and employ\na Lanczos method to estimate the natural connectivity of the transit network\nwith a guaranteed error bound. Furthermore, we derive upper bounds on the\nobjective values and use them to greedily select candidates for expansion. Our\nexperiments conducted on real-world transit networks in New York City and\nChicago verify the efficiency, effectiveness, and scalability of our\nalgorithms.\n"}
+{"abstract": "  We consider a problem wherein jobs arrive at random times and assume random\nvalues. Upon each job arrival, the decision-maker must decide immediately\nwhether or not to accept the job and gain the value on offer as a reward, with\nthe constraint that they may only accept at most $n$ jobs over some reference\ntime period. The decision-maker only has access to $M$ independent realisations\nof the job arrival process. We propose an algorithm, Non-Parametric Sequential\nAllocation (NPSA), for solving this problem. Moreover, we prove that the\nexpected reward returned by the NPSA algorithm converges in probability to\noptimality as $M$ grows large. We demonstrate the effectiveness of the\nalgorithm empirically on synthetic data and on public fraud-detection datasets,\nfrom where the motivation for this work is derived.\n"}
+{"abstract": "  Deep generative models have been shown powerful in generating novel molecules\nwith desired chemical properties via their representations such as strings,\ntrees or graphs. However, these models are limited in recommending synthetic\nroutes for the generated molecules in practice. We propose a generative model\nto generate molecules via multi-step chemical reaction trees. Specifically, our\nmodel first propose a chemical reaction tree with predicted reaction templates\nand commercially available molecules (starting molecules), and then perform\nforward synthetic steps to obtain product molecules. Experiments show that our\nmodel can generate chemical reactions whose product molecules are with desired\nchemical properties. Also, the complete synthetic routes for these product\nmolecules are provided.\n"}
+{"abstract": "  Field-induced reorientation of colloidal particles is especially relevant to\nmanipulate the optical properties of a nanomaterial for target applications. We\nhave recently shown that surprisingly feeble external stimuli are able to\ntransform uniaxial nematic liquid crystals (LCs) of cuboidal particles into\nbiaxial nematic LCs. In the light of these results, here we apply an external\nfield that forces the reorientation of colloidal cuboids in nematic LCs and\nsparks a uniaxial-to-biaxial texture switching. By Dynamic Monte Carlo\nsimulation, we investigate the unsteady-state reorientation dynamics at the\nparticle scale when the field is applied (uniaxial-to-biaxial switching) and\nthen removed (biaxial-to-uniaxial switching). We detect a strong correlation\nbetween the response time, being the time taken for the system to reorient, and\nparticle anisotropy, which spans from rod-like to plate-like geometries.\nInterestingly, self-dual shaped cuboids, theoretically considered as the most\nsuitable to promote phase biaxiality for being exactly in between prolate and\noblate particles, exhibit surprisingly slow response times, especially if\ncompared to prolate cuboids.\n"}
+{"abstract": "  In orthogonal time frequency space (OTFS) modulation, information-carrying\nsymbols reside in the delay-Doppler (DD) domain. By operating in the DD domain,\nan appealing property for communication arises: time-frequency (TF) dispersive\nchannels encountered in high mobility environments become time-invariant. The\ntime-invariance of the channel in the DD domain enables efficient equalizers\nfor time-frequency dispersive channels. In this paper, we propose an OTFS\nsystem based on the discrete Zak transform. The presented formulation not only\nallows an efficient implementation of OTFS but also simplifies the derivation\nand analysis of the input-output relation of TF dispersive channel in the DD\ndomain.\n"}
+{"abstract": "  The open-world deployment of Machine Learning (ML) algorithms in\nsafety-critical applications such as autonomous vehicles needs to address a\nvariety of ML vulnerabilities such as interpretability, verifiability, and\nperformance limitations. Research explores different approaches to improve ML\ndependability by proposing new models and training techniques to reduce\ngeneralization error, achieve domain adaptation, and detect outlier examples\nand adversarial attacks. In this paper, we review and organize practical ML\ntechniques that can improve the safety and dependability of ML algorithms and\ntherefore ML-based software. Our organization maps state-of-the-art ML\ntechniques to safety strategies in order to enhance the dependability of the ML\nalgorithm from different aspects, and discuss research gaps as well as\npromising solutions.\n"}
+{"abstract": "  Method: We develop CNN-based methods for automatic ICD coding based on\nclinical text from intensive care unit (ICU) stays. We come up with the Shallow\nand Wide Attention convolutional Mechanism (SWAM), which allows our model to\nlearn local and low-level features for each label. The key idea behind our\nmodel design is to look for the presence of informative snippets in the\nclinical text that correlated with each code, and we infer that there exists a\ncorrespondence between \"informative snippet\" and convolution filter. Results:\nWe evaluate our approach on MIMIC-III, an open-access dataset of ICU medical\nrecords. Our approach substantially outperforms previous results on top-50\nmedical code prediction on MIMIC-III dataset. We attribute this improvement to\nSWAM, by which the wide architecture gives the model ability to more\nextensively learn the unique features of different codes, and we prove it by\nablation experiment. Besides, we perform manual analysis of the performance\nimbalance between different codes, and preliminary conclude the characteristics\nthat determine the difficulty of learning specific codes. Conclusions: We\npresent SWAM, an explainable CNN approach for multi-label document\nclassification, which employs a wide convolution layer to learn local and\nlow-level features for each label, yields strong improvements over previous\nmetrics on the ICD-9 code prediction task, while providing satisfactory\nexplanations for its internal mechanics.\n"}
+{"abstract": "  We report a new measurement of the beam-spin asymmetry $\\boldsymbol{\\Sigma}$\nfor the $\\vec{\\gamma} n \\rightarrow K^+\\Sigma^-$ reaction using quasi-free\nneutrons in a liquid-deuterium target. The new dataset includes data at\npreviously unmeasured photon energy and angular ranges, thereby providing new\nconstraints on partial wave analyses used to extract properties of the excited\nnucleon states. The experimental data were obtained using the CEBAF Large\nAcceptance Spectrometer (CLAS), housed in Hall B of the Thomas Jefferson\nNational Accelerator Facility (JLab). The CLAS detector measured reaction\nproducts from a liquid-deuterium target produced by an energy-tagged, linearly\npolarised photon beam with energies in the range 1.1 to 2.3 GeV. Predictions\nfrom an isobar model indicate strong sensitivity to $N(1720)3/2^+$,\n$\\Delta(1900)1/2^-$, and $N(1895)1/2^-$, with the latter being a state not\nconsidered in previous photoproduction analyses. When our data are incorporated\nin the fits of partial-wave analyses, one observes significant changes in\n$\\gamma$-$n$ couplings of the resonances which have small branching ratios to\nthe $\\pi N$ channel.\n"}
+{"abstract": "  Voice Conversion (VC) is a technique that aims to transform the\nnon-linguistic information of a source utterance to change the perceived\nidentity of the speaker. While there is a rich literature on VC, most proposed\nmethods are trained and evaluated on clean speech recordings. However, many\nacoustic environments are noisy and reverberant, severely restricting the\napplicability of popular VC methods to such scenarios. To address this\nlimitation, we propose Voicy, a new VC framework particularly tailored for\nnoisy speech. Our method, which is inspired by the de-noising auto-encoders\nframework, is comprised of four encoders (speaker, content, phonetic and\nacoustic-ASR) and one decoder. Importantly, Voicy is capable of performing\nnon-parallel zero-shot VC, an important requirement for any VC system that\nneeds to work on speakers not seen during training. We have validated our\napproach using a noisy reverberant version of the LibriSpeech dataset.\nExperimental results show that Voicy outperforms other tested VC techniques in\nterms of naturalness and target speaker similarity in noisy reverberant\nenvironments.\n"}
+{"abstract": "  Fundus photography has routinely been used to document the presence and\nseverity of retinal degenerative diseases such as age-related macular\ndegeneration (AMD), glaucoma, and diabetic retinopathy (DR) in clinical\npractice, for which the fovea and optic disc (OD) are important retinal\nlandmarks. However, the occurrence of lesions, drusen, and other retinal\nabnormalities during retinal degeneration severely complicates automatic\nlandmark detection and segmentation. Here we propose HBA-U-Net: a U-Net\nbackbone enriched with hierarchical bottleneck attention. The network consists\nof a novel bottleneck attention block that combines and refines self-attention,\nchannel attention, and relative-position attention to highlight retinal\nabnormalities that may be important for fovea and OD segmentation in the\ndegenerated retina. HBA-U-Net achieved state-of-the-art results on fovea\ndetection across datasets and eye conditions (ADAM: Euclidean Distance (ED) of\n25.4 pixels, REFUGE: 32.5 pixels, IDRiD: 32.1 pixels), on OD segmentation for\nAMD (ADAM: Dice Coefficient (DC) of 0.947), and on OD detection for DR (IDRiD:\nED of 20.5 pixels). Our results suggest that HBA-U-Net may be well suited for\nlandmark detection in the presence of a variety of retinal degenerative\ndiseases.\n"}
+{"abstract": "  We propose HOI Transformer to tackle human object interaction (HOI) detection\nin an end-to-end manner. Current approaches either decouple HOI task into\nseparated stages of object detection and interaction classification or\nintroduce surrogate interaction problem. In contrast, our method, named HOI\nTransformer, streamlines the HOI pipeline by eliminating the need for many\nhand-designed components. HOI Transformer reasons about the relations of\nobjects and humans from global image context and directly predicts HOI\ninstances in parallel. A quintuple matching loss is introduced to force HOI\npredictions in a unified way. Our method is conceptually much simpler and\ndemonstrates improved accuracy. Without bells and whistles, HOI Transformer\nachieves $26.61\\% $ $ AP $ on HICO-DET and $52.9\\%$ $AP_{role}$ on V-COCO,\nsurpassing previous methods with the advantage of being much simpler. We hope\nour approach will serve as a simple and effective alternative for HOI tasks.\nCode is available at https://github.com/bbepoch/HoiTransformer .\n"}
+{"abstract": "  We report the dependence of the magnetization dynamics in a square artificial\nspin-ice lattice on the in-plane magnetic field angle. Using two complementary\nmeasurement techniques - broadband ferromagnetic resonance and micro-focused\nBrillouin light scattering spectroscopy - we systematically study the evolution\nof the lattice dynamics, both for a coherent radiofrequency excitation and an\nincoherent thermal excitation of spin dynamics. We observe a splitting of modes\nfacilitated by inter-element interactions that can be controlled by the\nexternal field angle and magnitude. Detailed time-dependent micromagnetic\nsimulations reveal that the split modes are localized in different regions of\nthe square network. This observation suggests that it is possible to\ndisentangle modes with different spatial profiles by tuning the external field\nconfiguration.\n"}
+{"abstract": "  This paper presents TULIP, a new architecture for a binary neural network\n(BNN) that uses an optimal schedule for executing the operations of an\narbitrary BNN. It was constructed with the goal of maximizing energy efficiency\nper classification. At the top-level, TULIP consists of a collection of unique\nprocessing elements (TULIP-PEs) that are organized in a SIMD fashion. Each\nTULIP-PE consists of a small network of binary neurons, and a small amount of\nlocal memory per neuron. The unique aspect of the binary neuron is that it is\nimplemented as a mixed-signal circuit that natively performs the inner-product\nand thresholding operation of an artificial binary neuron. Moreover, the binary\nneuron, which is implemented as a single CMOS standard cell, is reconfigurable,\nand with a change in a single parameter, can implement all standard operations\ninvolved in a BNN. We present novel algorithms for mapping arbitrary nodes of a\nBNN onto the TULIP-PEs. TULIP was implemented as an ASIC in TSMC 40nm-LP\ntechnology. To provide a fair comparison, a recently reported BNN that employs\na conventional MAC-based arithmetic processor was also implemented in the same\ntechnology. The results show that TULIP is consistently 3X more\nenergy-efficient than the conventional design, without any penalty in\nperformance, area, or accuracy.\n"}
+{"abstract": "  Convolution has been the core ingredient of modern neural networks,\ntriggering the surge of deep learning in vision. In this work, we rethink the\ninherent principles of standard convolution for vision tasks, specifically\nspatial-agnostic and channel-specific. Instead, we present a novel atomic\noperation for deep neural networks by inverting the aforementioned design\nprinciples of convolution, coined as involution. We additionally demystify the\nrecent popular self-attention operator and subsume it into our involution\nfamily as an over-complicated instantiation. The proposed involution operator\ncould be leveraged as fundamental bricks to build the new generation of neural\nnetworks for visual recognition, powering different deep learning models on\nseveral prevalent benchmarks, including ImageNet classification, COCO detection\nand segmentation, together with Cityscapes segmentation. Our involution-based\nmodels improve the performance of convolutional baselines using ResNet-50 by up\nto 1.6% top-1 accuracy, 2.5% and 2.4% bounding box AP, and 4.7% mean IoU\nabsolutely while compressing the computational cost to 66%, 65%, 72%, and 57%\non the above benchmarks, respectively. Code and pre-trained models for all the\ntasks are available at https://github.com/d-li14/involution.\n"}
+{"abstract": "  Let $P\\in \\Bbb Q_p[x,y]$, $s\\in \\Bbb C$ with sufficiently large real part,\nand consider the integral operator $\n(A_{P,s}f)(y):=\\frac{1}{1-p^{-1}}\\int_{\\Bbb Z_p}|P(x,y)|^sf(x) |dx| $ on\n$L^2(\\Bbb Z_p)$. We show that if $P$ is homogeneous then for each character\n$\\chi$ of $\\Bbb Z_p^\\times$ the characteristic function $\\det(1-uA_{P,s,\\chi})$\nof the restriction $A_{P,s,\\chi}$ of $A_{P,s}$ to the eigenspace $L^2(\\Bbb\nZ_p)_\\chi$ is the $q$-Wronskian of a set of solutions of a (possibly confluent)\n$q$-hypergeometric equation. In particular, the nonzero eigenvalues of\n$A_{P,s,\\chi}$ are the reciprocals of the zeros of such $q$-Wronskian.\n"}
+{"abstract": "  This report is part of the DataflowOpt project on optimization of modern\ndataflows and aims to introduce a data quality-aware cost model that covers the\nfollowing aspects in combination: (1) heterogeneity in compute nodes, (2)\ngeo-distribution, (3) massive parallelism, (4) complex DAGs and (5) streaming\napplications. Such a cost model can be then leveraged to devise cost-based\noptimization solutions that deal with task placement and operator\nconfiguration.\n"}
+{"abstract": "  This work evaluates the applicability of super-resolution generative\nadversarial networks (SRGANs) as a methodology for the reconstruction of\nturbulent-flow quantities from coarse wall measurements. The method is applied\nboth for the resolution enhancement of wall fields and the estimation of\nwall-parallel velocity fields from coarse wall measurements of shear stress and\npressure. The analysis has been carried out with a database of a turbulent\nopen-channel flow with friction Reynolds number $Re_{\\tau}=180$ generated\nthrough direct numerical simulation. Coarse wall measurements have been\ngenerated with three different downsampling factors $f_d=[4,8,16]$ from the\nhigh-resolution fields, and wall-parallel velocity fields have been\nreconstructed at four inner-scaled wall-normal distances $y^+=[15,30,50,100]$.\nWe first show that SRGAN can be used to enhance the resolution of coarse wall\nmeasurements. If compared with direct reconstruction from the sole coarse wall\nmeasurements, SRGAN provides better instantaneous reconstructions, both in\nterms of mean-squared error and spectral-fractional error. Even though lower\nresolutions in the input wall data make it more challenging to achieve highly\naccurate predictions, the proposed SRGAN-based network yields very good\nreconstruction results. Furthermore, it is shown that even for the most\nchallenging cases the SRGAN is capable of capturing the large-scale structures\nthat populate the flow. The proposed novel methodology has great potential for\nclosed-loop control applications relying on non-intrusive sensing.\n"}
+{"abstract": "  We present a novel deep neural model for text detection in document images.\nFor robust text detection in noisy scanned documents, the advantages of\nmulti-task learning are adopted by adding an auxiliary task of text\nenhancement. Namely, our proposed model is designed to perform noise reduction\nand text region enhancement as well as text detection. Moreover, we enrich the\ntraining data for the model with synthesized document images that are fully\nlabeled for text detection and enhancement, thus overcome the insufficiency of\nlabeled document image data. For the effective exploitation of the synthetic\nand real data, the training process is separated in two phases. The first phase\nis training only synthetic data in a fully-supervised manner. Then real data\nwith only detection labels are added in the second phase. The enhancement task\nfor the real data is weakly-supervised with information from their detection\nlabels. Our methods are demonstrated in a real document dataset with\nperformances exceeding those of other text detection methods. Moreover,\nablations are conducted and the results confirm the effectiveness of the\nsynthetic data, auxiliary task, and weak-supervision. Whereas the existing text\ndetection studies mostly focus on the text in scenes, our proposed method is\noptimized to the applications for the text in scanned documents.\n"}
+{"abstract": "  The outbreak of the COVID-19 pandemic triggers infodemic over online social\nnetworks. It is thus important for governments to ensure their official\nmessages outpace misinformation and efficiently reach the public. Some\ncountries and regions that are currently worst affected by the virus including\nEurope, South America and India, encounter an additional difficulty:\nmultilingualism. Understanding the specific role of multilingual users in the\nprocess of information diffusion is critical to adjust their publishing\nstrategies for the governments of such countries and regions. In this paper, we\ninvestigate the role of multilingual users in diffusing information during the\nCOVID-19 pandemic on popular social networks. We collect a large-scale dataset\nof Twitter from a populated multilingual region from the beginning of the\npandemic. With this dataset, we successfully show that multilingual users act\nas bridges in diffusing COVID-19 related information. We further study the\nmental health of multilingual users and show that being the bridges,\nmultilingual users tend to be more negative. This is confirmed by a recent\npsychological study stating that excessive exposure to social media may result\nin a negative mood.\n"}
+{"abstract": "  Cell-free (CF) massive multiple-input multiple-output (MIMO) is a promising\nsolution to provide uniform good performance for unmanned aerial vehicle (UAV)\ncommunications. In this paper, we propose the UAV communication with wireless\npower transfer (WPT) aided CF massive MIMO systems, where the harvested energy\n(HE) from the downlink WPT is used to support both uplink data and pilot\ntransmission. We derive novel closed-form downlink HE and uplink spectral\nefficiency (SE) expressions that take hardware impairments of UAV into account.\nUAV communications with current small cell (SC) and cellular massive MIMO\nenabled WPT systems are also considered for comparison. It is significant to\nshow that CF massive MIMO achieves two and five times higher 95\\%-likely uplink\nSE than the ones of SC and cellular massive MIMO, respectively. Besides, the\nlarge-scale fading decoding receiver cooperation can reduce the interference of\nthe terrestrial user. Moreover, the maximum SE can be achieved by changing the\ntime-splitting fraction. We prove that the optimal time-splitting fraction for\nmaximum SE is determined by the number of antennas, altitude and hardware\nquality factor of UAVs. Furthermore, we propose three UAV trajectory design\nschemes to improve the SE. It is interesting that the angle search scheme\nperforms best than both AP search and line path schemes. Finally, simulation\nresults are presented to validate the accuracy of our expressions.\n"}
+{"abstract": "  This paper is concerned with the Richards equation in a heterogeneous domain,\neach subdomain of which is homogeneous and represents a rocktype. Our first\ncontribution is to rigorously prove convergence toward a weak solution of\ncell-centered finite-volume schemes with upstream mobility and without\nKirchhoff's transform. Our second contribution is to numerically demonstrate\nthe relevance of locally refining the grid at the interface between subregions,\nwhere discontinuities occur, in order to preserve an acceptable accuracy for\nthe results computed with the schemes under consideration.\n"}
+{"abstract": "  Following the pandemic outbreak, several works have proposed to diagnose\nCOVID-19 with deep learning in computed tomography (CT); reporting performance\non-par with experts. However, models trained/tested on the same in-distribution\ndata may rely on the inherent data biases for successful prediction, failing to\ngeneralize on out-of-distribution samples or CT with different scanning\nprotocols. Early attempts have partly addressed bias-mitigation and\ngeneralization through augmentation or re-sampling, but are still limited by\ncollection costs and the difficulty of quantifying bias in medical images. In\nthis work, we propose Mixing-AdaSIN; a bias mitigation method that uses a\ngenerative model to generate de-biased images by mixing texture information\nbetween different labeled CT scans with semantically similar features. Here, we\nuse Adaptive Structural Instance Normalization (AdaSIN) to enhance de-biasing\ngeneration quality and guarantee structural consistency. Following, a\nclassifier trained with the generated images learns to correctly predict the\nlabel without bias and generalizes better. To demonstrate the efficacy of our\nmethod, we construct a biased COVID-19 vs. bacterial pneumonia dataset based on\nCT protocols and compare with existing state-of-the-art de-biasing methods. Our\nexperiments show that classifiers trained with de-biased generated images\nreport improved in-distribution performance and generalization on an external\nCOVID-19 dataset.\n"}
+{"abstract": "  We propose a solution to the longstanding permalloy problem$-$why the\nparticular composition of permalloy, Fe$_{21.5}$Ni$_{78.5}$, achieves a\ndramatic drop in hysteresis, while its material constants show no obvious\nsignal of this behavior. We use our recently developed coercivity tool to show\nthat a delicate balance between local instabilities and magnetic material\nconstants are necessary to explain the dramatic drop of hysteresis at 78.5% Ni.\nOur findings are in agreement with the permalloy experiments and, more broadly,\nprovide theoretical guidance for the discovery of novel low hysteresis magnetic\nalloys.\n"}
+{"abstract": "  In this letter, we present a novel method for automatic extrinsic calibration\nof high-resolution LiDARs and RGB cameras in targetless environments. Our\napproach does not require checkerboards but can achieve pixel-level accuracy by\naligning natural edge features in the two sensors. On the theory level, we\nanalyze the constraints imposed by edge features and the sensitivity of\ncalibration accuracy with respect to edge distribution in the scene. On the\nimplementation level, we carefully investigate the physical measuring\nprinciples of LiDARs and propose an efficient and accurate LiDAR edge\nextraction method based on point cloud voxel cutting and plane fitting. Due to\nthe edges' richness in natural scenes, we have carried out experiments in many\nindoor and outdoor scenes. The results show that this method has high\nrobustness, accuracy, and consistency. It can promote the research and\napplication of the fusion between LiDAR and camera. We have open-sourced our\ncode on GitHub to benefit the community.\n"}
+{"abstract": "  We systematically investigated the heating of coronal loops on metal-free\nstars with various stellar masses and magnetic fields by magnetohydrodynamic\nsimulations. It is found that the coronal property is dependent on the coronal\nmagnetic field strength $B_{\\rm c}$ because it affects the difference of the\nnonlinearity of the Alfv\\'{e}nic waves. Weaker $B_{\\rm c}$ leads to cooler and\nless dense coronae because most of the input waves dissipate in the lower\natmosphere on account of the larger nonlinearity. Accordingly EUV and X-ray\nluminosities also correlate with $B_{\\rm c}$, while they are emitted in a wide\nrange of the field strength. Finally we extend our results to evaluating the\ncontribution from low-mass Population III coronae to the cosmic reionization.\nWithin the limited range of our parameters on magnetic fields and loop lengths,\nthe EUV and X-ray radiations give a weak impact on the ionization and heating\nof the gas at high redshifts. However, there still remains a possibility of the\ncontribution to the reionization from energetic flares involving long magnetic\nloops.\n"}
+{"abstract": "  This paper introduces a new benchmark for large-scale image similarity\ndetection. This benchmark is used for the Image Similarity Challenge at\nNeurIPS'21 (ISC2021). The goal is to determine whether a query image is a\nmodified copy of any image in a reference corpus of size 1~million. The\nbenchmark features a variety of image transformations such as automated\ntransformations, hand-crafted image edits and machine-learning based\nmanipulations. This mimics real-life cases appearing in social media, for\nexample for integrity-related problems dealing with misinformation and\nobjectionable content. The strength of the image manipulations, and therefore\nthe difficulty of the benchmark, is calibrated according to the performance of\na set of baseline approaches. Both the query and reference set contain a\nmajority of \"distractor\" images that do not match, which corresponds to a\nreal-life needle-in-haystack setting, and the evaluation metric reflects that.\nWe expect the DISC21 benchmark to promote image copy detection as an important\nand challenging computer vision task and refresh the state of the art.\n"}
+{"abstract": "  We address the problem of learning of continuous exponential family\ndistributions with unbounded support. While a lot of progress has been made on\nlearning of Gaussian graphical models, we are still lacking scalable algorithms\nfor reconstructing general continuous exponential families modeling\nhigher-order moments of the data beyond the mean and the covariance. Here, we\nintroduce a computationally efficient method for learning continuous graphical\nmodels based on the Interaction Screening approach. Through a series of\nnumerical experiments, we show that our estimator maintains similar\nrequirements in terms of accuracy and sample complexity compared to alternative\napproaches such as maximization of conditional likelihood, while considerably\nimproving upon the algorithm's run-time.\n"}
+{"abstract": "  We examine rotational transitions of HCl in collisions with H$_2$ by carrying\nout quantum mechanical close-coupling and quasi-classical trajectory\ncalculations on a recently developed globally accurate full-dimensional ab\ninitio potential energy surface for the H$_3$Cl system. Signatures of rainbow\nscattering in rotationally inelastic collisions are found in the state resolved\nintegral and differential cross sections as functions of the impact parameter\n(initial orbital angular momentum) and final rotational quantum number. We show\nthe coexistence of distinct dynamical regimes for the HCl rotational transition\ndriven by the short-range repulsive and long-range attractive forces whose\nrelative importance depends on the collision energy and final rotational state\nsuggesting that classification of rainbow scattering into rotational and\n$l$-type rainbows is effective for H$_2$+HCl collisions. While the\nquasi-classical trajectory method satisfactorily predicts the overall behavior\nof the rotationally inelastic cross sections, its capability to accurately\ndescribe signatures of rainbow scattering appears to be limited for the present\nsystem.\n"}
+{"abstract": "  The ongoing shift of cloud services from monolithic designs to microservices\ncreates high demand for efficient and high performance datacenter networking\nstacks, optimized for fine-grained workloads. Commodity networking systems\nbased on software stacks and peripheral NICs introduce high overheads when it\ncomes to delivering small messages.\n  We present Dagger, a hardware acceleration fabric for cloud RPCs based on\nFPGAs, where the accelerator is closely-coupled with the host processor over a\nconfigurable memory interconnect. The three key design principle of Dagger are:\n(1) offloading the entire RPC stack to an FPGA-based NIC, (2) leveraging memory\ninterconnects instead of PCIe buses as the interface with the host CPU, and (3)\nmaking the acceleration fabric reconfigurable, so it can accommodate the\ndiverse needs of microservices. We show that the combination of these\nprinciples significantly improves the efficiency and performance of cloud RPC\nsystems while preserving their generality. Dagger achieves 1.3-3.8x higher\nper-core RPC throughput compared to both highly-optimized software stacks, and\nsystems using specialized RDMA adapters. It also scales up to 84 Mrps with 8\nthreads on 4 CPU cores, while maintaining state-of-the-art us-scale tail\nlatency. We also demonstrate that large third-party applications, like\nmemcached and MICA KVS, can be easily ported on Dagger with minimal changes to\ntheir codebase, bringing their median and tail KVS access latency down to 2.8 -\n3.5us and 5.4 - 7.8us, respectively. Finally, we show that Dagger is beneficial\nfor multi-tier end-to-end microservices with different threading models by\nevaluating it using an 8-tier application implementing a flight check-in\nservice.\n"}
+{"abstract": "  Numerous electronic cash schemes have been proposed over the years - however\nnone have been embraced by financial institutions as an alternative to fiat\ncurrency. David Chaum's ecash scheme was the closest to something that mimicked\na modern day currency system, with the important property that it provided\nanonymity for users when purchasing coins from a bank, and subsequently\nspending them at a merchant premises. However it lacked a crucial element\npresent in current fiat-based systems - the ability to continuously spend or\ntransfer coins. Bitcoin reignited the interest in cryptocurrencies in the last\ndecade but is now seen as more of an asset store as opposed to a financial\ninstrument. One interesting thing that has come out of the Bitcoin system is\nblockchains and the associated distributed consensus protocols. In this paper\nwe propose a transferable electronic cash scheme using blockchain technology\nwhich allows users to continuously reuse coins within the system.\n"}
+{"abstract": "  We present a bottom-up differentiable relaxation of the process of drawing\npoints, lines and curves into a pixel raster. Our approach arises from the\nobservation that rasterising a pixel in an image given parameters of a\nprimitive can be reformulated in terms of the primitive's distance transform,\nand then relaxed to allow the primitive's parameters to be learned. This\nrelaxation allows end-to-end differentiable programs and deep networks to be\nlearned and optimised and provides several building blocks that allow control\nover how a compositional drawing process is modelled. We emphasise the\nbottom-up nature of our proposed approach, which allows for drawing operations\nto be composed in ways that can mimic the physical reality of drawing rather\nthan being tied to, for example, approaches in modern computer graphics. With\nthe proposed approach we demonstrate how sketches can be generated by directly\noptimising against photographs and how auto-encoders can be built to transform\nrasterised handwritten digits into vectors without supervision. Extensive\nexperimental results highlight the power of this approach under different\nmodelling assumptions for drawing tasks.\n"}
+{"abstract": "  When the density of the fluid surrounding suspended Brownian particles is\nappreciable, in addition to the forces appearing in the traditional Ornstein\nand Uhlenbeck theory of Brownian motion, additional forces emerge as the\ndisplaced fluid in the vicinity of the randomly moving Brownian particle acts\nback on the particle giving rise to long-range force correlations which\nmanifest as a ``long-time tail'' in the decay of the velocity autocorrelation\nfunction known as hydrodynamic memory. In this paper, after recognizing that\nfor Brownian particles immersed in a Newtonian, viscous fluid, the hydrodynamic\nmemory term in the generalized Langevin equation is essentially the 1/2\nfractional derivative of the velocity of the Brownian particle, we present a\nrheological analogue for Brownian motion with hydrodynamic memory which\nconsists of a linear dashpot of a fractional Scott-Blair element and an\ninerter. The synthesis of the proposed mechanical network that is suggested\nfrom the structure of the generalized Langevin equation simplifies appreciably\nthe calculations of the mean-square displacement and its time-derivatives which\ncan also be expressed in terms of the two-parameter Mittag--Leffler function.\n"}
+{"abstract": "  The discrimination of quantum processes, including quantum states, channels,\nand superchannels, is a fundamental topic in quantum information theory. It is\noften of interest to analyze the optimal performance that can be achieved when\ndiscrimination strategies are restricted to a given subset of all strategies\nallowed by quantum mechanics. In this paper, we present a general formulation\nof the task of finding the maximum success probability for discriminating\nquantum processes as a convex optimization problem whose Lagrange dual problem\nexhibits zero duality gap. The proposed formulation can be applied to any\nrestricted strategy. We also derive necessary and sufficient conditions for an\noptimal restricted strategy to be optimal within the set of all strategies. We\nprovide a simple example in which the dual problem given by our formulation can\nbe much easier to solve than the original problem. We also show that the\noptimal performance of each restricted process discrimination problem can be\nwritten in terms of a certain robustness measure. This finding has the\npotential to provide a deeper insight into the discrimination performance of\nvarious restricted strategies.\n"}
+{"abstract": "  The spinel-structure CuIr$_{2}$S$_{4}$ compound displays a rather unusual\norbitally-driven three-dimensional Peierls-like insulator-metal transition. The\nlow-T symmetry-broken insulating state is especially interesting due to the\nexistence of a metastable irradiation-induced disordered weakly conducting\nstate. Here we study intense femtosecond optical pulse irradiation effects by\nmeans of the all-optical ultrafast multi-pulse time-resolved spectroscopy. We\nshow that the structural coherence of the low-T broken symmetry state is\nstrongly suppressed on a sub-picosecond timescale above a threshold excitation\nfluence resulting in a structurally inhomogeneous transient state which\npersists for several-tens of picoseconds before reverting to the low-T\ndisordered weakly conducting state. The electronic order shows a transient gap\nfilling at a significantly lower fluence threshold. The data suggest that the\nphotoinduced-transition dynamics to the high-T metallic phase is governed by\nfirst-order-transition nucleation kinetics that prevents the complete ultrafast\nstructural transition even when the absorbed energy significantly exceeds the\nequilibrium enthalpy difference to the high-T metallic phase. In contrast, the\ndynamically-decoupled electronic order is transiently suppressed on a\nsub-picosecond timescale rather independently due to a photoinduced Mott\ntransition.\n"}
+{"abstract": "  The Compressed Baryonic Matter~(CBM) experiment in the upcoming Facility for\nAntiproton and Ion Research~(FAIR), designed to take data in nuclear collisions\nat very high interaction rates of up to 10 MHz, will employ a free-streaming\ndata acquisition with self-triggered readout electronics, without any hardware\ntrigger. A simulation framework with a realistic digitization of the detectors\nin the muon chamber (MuCh) subsystem in CBM has been developed to provide a\nrealistic simulation of the time-stamped data stream. In this article, we\ndescribe the implementation of the free-streaming detector simulation and the\nbasic data related effects on the detector with respect to the interaction\nrate.\n"}
+{"abstract": "  Under the last-in, first-out (LIFO) discipline, jobs arriving later at a\nclass always receive priority of service over earlier arrivals at any class\nbelonging to the same station. Subcritical LIFO queueing networks with Poisson\nexternal arrivals are known to be stable, but an open problem has been whether\nthis is also the case when external arrivals are given by renewal processes.\nHere, we show that this weaker assumption is not sufficient for stability by\nconstructing a family of examples where the number of jobs in the network\nincreases to infinity over time.\n  This behavior contrasts with that for the other classical disciplines:\nprocessor sharing (PS), infinite server (IS), and first-in, first-out (FIFO),\nwhich are stable under general conditions on the renewals of external arrivals.\nTogether with LIFO, PS and IS constitute the classical symmetric disciplines;\nwith the inclusion of FIFO, these disciplines constitute the classical\nhomogeneous disciplines. Our examples show that a general theory for stability\nof either family is doubtful.\n"}
+{"abstract": "  Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly\ncontagious virus responsible for coronavirus disease 2019 (CoViD-19). The\nsymptoms of CoViD-19 are essentially reflected in the respiratory system,\nalthough other organs are also affected. More than 2.4 million people have died\nworldwide due to this disease. Despite CoViD-19 vaccines are already being\nadministered, alternative treatments that address the immunopathology of the\ninfection are still needed. For this end, a deeper knowledge on how our immune\nsystem responds to SARS-CoV-2 is required. In this study, we propose a\nnon-integer order model to understand the dynamics of cytotoxic T lymphocytes\nin the presence of SARS-CoV-2. We calculated the basic reproduction number and\nanalysed the values of the model parameters in order to understand which ones\ninhibit or enhance the progression of the infection. Numerical simulations were\nperformed for different values of the order of the fractional derivative and\nfor different proliferation functions of cytotoxic T lymphocytes.\n"}
+{"abstract": "  Recently, nearly complete intersection ideals were defined by Boocher and\nSeiner to establish lower bounds on Betti numbers for monomial ideals\n(arXiv:1706.09866). Stone and Miller then characterized nearly complete\nintersections using the theory of edge ideals (arXiv:2101.07901). We extend\ntheir work to fully characterize nearly complete intersections of arbitrary\ngenerating degrees and use this characterization to compute minimal free\nresolutions of nearly complete intersections from their degree 2 part.\n"}
+{"abstract": "  We propose to reinterpret Einstein's field equations as a nonlinear\neigenvalue problem, where the cosmological constant $\\Lambda$ plays the role of\nthe (smallest) eigenvalue. This interpretation is fully worked out for a simple\nmodel of scalar gravity. The essential ingredient for the feasibility of this\napproach is that the classical field equations be nonlinear, i.e., that the\ngravitational field is itself a source of gravity. The cosmological\nconsequences and implications of this approach are developed and discussed.\n"}
+{"abstract": "  Given a real function $f$, the rate function for the large deviations of the\ndiffusion process of drift $\\nabla f$ given by the Freidlin-Wentzell theorem\ncoincides with the time integral of the energy dissipation for the gradient\nflow associated with $f$. This paper is concerned with the stability in the\nhilbertian framework of this common action functional when $f$ varies. More\nprecisely, we show that if $(f_h)_h$ is uniformly $\\lambda$-convex for some\n$\\lambda \\in \\mathbb{R}$ and converges towards $f$ in the sense of Mosco\nconvergence, then the related functionals $\\Gamma$-converge in the strong\ntopology of curves.\n"}
+{"abstract": "  Adding noises to artificial neural network(ANN) has been shown to be able to\nimprove robustness in previous work. In this work, we propose a new technique\nto compute the pathwise stochastic gradient estimate with respect to the\nstandard deviation of the Gaussian noise added to each neuron of the ANN. By\nour proposed technique, the gradient estimate with respect to noise levels is a\nbyproduct of the backpropagation algorithm for estimating gradient with respect\nto synaptic weights in ANN. Thus, the noise level for each neuron can be\noptimized simultaneously in the processing of training the synaptic weights at\nnearly no extra computational cost. In numerical experiments, our proposed\nmethod can achieve significant performance improvement on robustness of several\npopular ANN structures under both black box and white box attacks tested in\nvarious computer vision datasets.\n"}
+{"abstract": "  Mobile edge computing (MEC) is regarded as a promising wireless access\narchitecture to alleviate the intensive computation burden at resource limited\nmobile terminals (MTs). Allowing the MTs to offload partial tasks to MEC\nservers could significantly decrease task processing delay. In this study, to\nminimize the processing delay for a multi-user MEC system, we jointly optimize\nthe local content splitting ratio, the transmission/computation power\nallocation, and the MEC server selection under a dynamic environment with\ntime-varying task arrivals and wireless channels. The reinforcement learning\n(RL) technique is utilized to deal with the considered problem. Two deep RL\nstrategies, that is, deep Q-learning network (DQN) and deep deterministic\npolicy gradient (DDPG), are proposed to efficiently learn the offloading\npolicies adaptively. The proposed DQN strategy takes the MEC selection as a\nunique action while using convex optimization approach to obtain the remaining\nvariables. And the DDPG strategy takes all dynamic variables as actions.\nNumerical results demonstrates that both proposed strategies perform better\nthan existing schemes. And the DDPG strategy is superior to the DQN strategy as\nit can learn all variables online although it requires relatively large\ncomplexity.\n"}
+{"abstract": "  We present counterfactual planning as a design approach for creating a range\nof safety mechanisms that can be applied in hypothetical future AI systems\nwhich have Artificial General Intelligence.\n  The key step in counterfactual planning is to use an AGI machine learning\nsystem to construct a counterfactual world model, designed to be different from\nthe real world the system is in. A counterfactual planning agent determines the\naction that best maximizes expected utility in this counterfactual planning\nworld, and then performs the same action in the real world.\n  We use counterfactual planning to construct an AGI agent emergency stop\nbutton, and a safety interlock that will automatically stop the agent before it\nundergoes an intelligence explosion. We also construct an agent with an input\nterminal that can be used by humans to iteratively improve the agent's reward\nfunction, where the incentive for the agent to manipulate this improvement\nprocess is suppressed. As an example of counterfactual planning in a non-agent\nAGI system, we construct a counterfactual oracle.\n  As a design approach, counterfactual planning is built around the use of a\ngraphical notation for defining mathematical counterfactuals. This two-diagram\nnotation also provides a compact and readable language for reasoning about the\ncomplex types of self-referencing and indirect representation which are\ntypically present inside machine learning agents.\n"}
+{"abstract": "  We reassess an alternative CPT-odd electrodynamics obtained from a\nPalatini-like procedure. Starting from a more general situation, we analyze the\nphysical consistency of the model for different values of the parameter\nintroduced in the mass tensor. We show that there is a residual gaugeinvariance\nin the model if the local transformation is taken to vary only in the direction\nof the Lorentz-breaking vector.\n"}
+{"abstract": "  The atomic-level vdW heterostructures have been one of the most interesting\nquantum material systems, due to their exotic physical properties. The\ninterlayer coupling in these systems plays a critical role to realize novel\nphysical observation and enrich interface functionality. However, there is\nstill lack of investigation on the tuning of interlayer coupling in a\nquantitative way. A prospective strategy to tune the interlayer coupling is to\nchange the electronic structure and interlayer distance by high pressure, which\nis a well-established method to tune the physical properties. Here, we\nconstruct a high-quality WS2/MoSe2 heterostructure in a DAC and successfully\ntuned the interlayer coupling through hydrostatic pressure. Typical\nphotoluminescence spectra of the monolayer MoSe2 (ML-MoSe2), monolayer WS2\n(ML-WS2) and WS2/MoSe2 heterostructure have been observed and it's intriguing\nthat their photoluminescence peaks shift with respect to applied pressure in a\nquite different way. The intralayer exciton of ML-MoSe2 and ML-WS2 show blue\nshift under high pressure with a coefficient of 19.8 meV/GPa and 9.3 meV/GPa,\nrespectively, while their interlayer exciton shows relative weak pressure\ndependence with a coefficient of 3.4 meV/GPa. Meanwhile, external pressure\nhelps to drive stronger interlayer interaction and results in a higher ratio of\ninterlayer/intralayer exciton intensity, indicating the enhanced interlayer\nexciton behavior. The first-principles calculation reveals the stronger\ninterlayer interaction which leads to enhanced interlayer exciton behavior in\nWS2/MoSe2 heterostructure under external pressure and reveals the robust peak\nof interlayer exciton. This work provides an effective strategy to study the\ninterlayer interaction in vdW heterostructures, which could be of great\nimportance for the material and device design in various similar quantum\nsystems.\n"}
+{"abstract": "  It is well-known that each statistic in the family of power divergence\nstatistics, across $n$ trials and $r$ classifications with index parameter\n$\\lambda\\in\\mathbb{R}$ (the Pearson, likelihood ratio and Freeman-Tukey\nstatistics correspond to $\\lambda=1,0,-1/2$, respectively) is asymptotically\nchi-square distributed as the sample size tends to infinity. In this paper, we\nobtain explicit bounds on this distributional approximation, measured using\nsmooth test functions, that hold for a given finite sample $n$, and all index\nparameters ($\\lambda>-1$) for which such finite sample bounds are meaningful.\nWe obtain bounds that are of the optimal order $n^{-1}$. The dependence of our\nbounds on the index parameter $\\lambda$ and the cell classification\nprobabilities is also optimal, and the dependence on the number of cells is\nalso respectable. Our bounds generalise, complement and improve on recent\nresults from the literature.\n"}
+{"abstract": "  We present the first results of the Fermilab Muon g-2 Experiment for the\npositive muon magnetic anomaly $a_\\mu \\equiv (g_\\mu-2)/2$. The anomaly is\ndetermined from the precision measurements of two angular frequencies.\nIntensity variation of high-energy positrons from muon decays directly encodes\nthe difference frequency $\\omega_a$ between the spin-precession and cyclotron\nfrequencies for polarized muons in a magnetic storage ring. The storage ring\nmagnetic field is measured using nuclear magnetic resonance probes calibrated\nin terms of the equivalent proton spin precession frequency\n${\\tilde{\\omega}'^{}_p}$ in a spherical water sample at 34.7$^{\\circ}$C. The\nratio $\\omega_a / {\\tilde{\\omega}'^{}_p}$, together with known fundamental\nconstants, determines $a_\\mu({\\rm FNAL}) = 116\\,592\\,040(54)\\times 10^{-11}$\n(0.46\\,ppm). The result is 3.3 standard deviations greater than the standard\nmodel prediction and is in excellent agreement with the previous Brookhaven\nNational Laboratory (BNL) E821 measurement. After combination with previous\nmeasurements of both $\\mu^+$ and $\\mu^-$, the new experimental average of\n$a_\\mu({\\rm Exp}) = 116\\,592\\,061(41)\\times 10^{-11}$ (0.35\\,ppm) increases the\ntension between experiment and theory to 4.2 standard deviations\n"}
+{"abstract": "  Recently, antiferromagnets have received revived interest due to their\nsignificant potential for developing next-generation ultrafast magnetic\nstorage. Here we report dc spin pumping by the acoustic resonant mode in a\ncanted easy-plane antiferromagnet {\\alpha}-Fe2O3 enabled by the\nDzyaloshinskii-Moriya interaction. Systematic angle and frequency dependent\nmeasurements demonstrate that the observed spin pumping signals arise from\nresonance-induced spin injection and inverse spin Hall effect in\n{\\alpha}-Fe2O3/metal heterostructures, mimicking the behavior of spin pumping\nin conventional ferromagnet/nonmagnet systems. The pure spin current nature is\nfurther corroborated by reversal of the polarity of spin pumping signals when\nthe spin detector is switched from platinum to tungsten which has an opposite\nsign of the spin Hall angle. Our results highlight the potential opportunities\noffered by the low-frequency acoustic resonant mode in canted easy-plane\nantiferromagnets for developing next-generation, functional spintronic devices.\n"}
+{"abstract": "  The worldsheet string theory dual to free 4d ${\\cal N}=4$ super Yang-Mills\ntheory was recently proposed in arXiv:2104.08263. It is described by a free\nfield sigma model on the twistor space of ${\\rm AdS}_5\\times {\\rm S}^5$, and is\na direct generalisation of the corresponding model for tensionless string\ntheory on ${\\rm AdS}_3\\times {\\rm S}^3$. As in the case of ${\\rm AdS}_3$, the\nworldsheet theory contains spectrally flowed representations. We proposed in\narXiv:2104.08263 that in each such sector only a finite set of generalised zero\nmodes (`wedge modes') are physical. Here we show that after imposing the\nappropriate residual gauge conditions, this worldsheet description reproduces\nprecisely the spectrum of the planar gauge theory. More specifically, the\nstates in the sector with $w$ units of spectral flow match with single trace\noperators built out of $w$ super Yang-Mills fields (`letters'). The resulting\nphysical picture is a covariant version of the BMN light-cone string, now with\na finite number of twistorial string bit constituents of an essentially\ntopological worldsheet.\n"}
+{"abstract": "  Questions concerning quantitative and asymptotic properties of the elliptic\nmeasure corresponding to a uniformly elliptic divergence form operator have\nbeen the focus of recent studies. In this setting we show that the elliptic\nmeasure of an operator with coefficients satisfying a vanishing Carleson\ncondition in the upper half space is an asymptotically optimal $A_\\infty$\nweight. In particular, for such operators the logarithm of the elliptic kernel\nis in the space of (locally) vanishing mean oscillation.\n  To achieve this, we prove local, quantitative estimates on a quantity\n(introduced by Fefferman, Kenig and Pipher) that controls the $A_\\infty$\nconstant. Our work uses recent results obtained by David, Li and Mayboroda.\nThese quantitative estimates may offer a new framework to approach similar\nproblems.\n"}
+{"abstract": "  Previous gesture elicitation studies have found that user proposals are\ninfluenced by legacy bias which may inhibit users from proposing gestures that\nare most appropriate for an interaction. Increasing production during\nelicitation studies has shown promise moving users beyond legacy gestures.\nHowever, variety decreases as more symbols are produced. While several studies\nhave used increased production since its introduction, little research has\nfocused on understanding the effect on the proposed gesture quality, on why\nvariety decreases, and on whether increased production should be limited. In\nthis paper, we present a gesture elicitation study aimed at understanding the\nimpact of increased production. We show that users refine the most promising\ngestures and that how long it takes to find promising gestures varies by\nparticipant. We also show that gestural refinements provide insight into the\ngestural features that matter for users to assign semantic meaning and discuss\nimplications for training gesture classifiers.\n"}
+{"abstract": "  This paper proposes a learning and scheduling algorithm to minimize the\nexpected cumulative holding cost incurred by jobs, where statistical parameters\ndefining their individual holding costs are unknown a priori. In each time\nslot, the server can process a job while receiving the realized random holding\ncosts of the jobs remaining in the system. Our algorithm is a learning-based\nvariant of the $c\\mu$ rule for scheduling: it starts with a preemption period\nof fixed length which serves as a learning phase, and after accumulating enough\ndata about individual jobs, it switches to nonpreemptive scheduling mode. The\nalgorithm is designed to handle instances with large or small gaps in jobs'\nparameters and achieves near-optimal performance guarantees. The performance of\nour algorithm is captured by its regret, where the benchmark is the minimum\npossible cost attained when the statistical parameters of jobs are fully known.\nWe prove upper bounds on the regret of our algorithm, and we derive a regret\nlower bound that is almost matching the proposed upper bounds. Our numerical\nresults demonstrate the effectiveness of our algorithm and show that our\ntheoretical regret analysis is nearly tight.\n"}
+{"abstract": "  In this paper we show how to compute port behaviour of multiports which have\nport equations of the general form $B v_P - Q i_P = s,$ which cannot even be\nput into the hybrid form, indeed may have number of equations ranging from $0$\nto $2n,$ where $n$ is the number of ports.\n  We do this through repeatedly solving with different source inputs, a larger\nnetwork obtained by terminating the multiport by its adjoint through a gyrator.\nThe method works for linear multiports which are consistent for arbitrary\ninternal source values and further have the property that the port conditions\nuniquely determine internal conditions.\n  We also present the most general version of maximum power transfer theorem\npossible. This version of the theorem states that `stationarity' (derivative\nzero condition) of power transfer occurs when the multiport is terminated by\nits adjoint, provided the resulting network has a solution.\n  If this network does not have a solution there is no port condition for which\nstationarity holds. This theorem does not require that the multiport has a\nhybrid immittance matrix.\n"}
+{"abstract": "  We characterise the sensitivity of several additive tensor decompositions\nwith respect to perturbations of the original tensor. These decompositions\ninclude canonical polyadic decompositions, block term decompositions, and sums\nof tree tensor networks. Our main result shows that the condition number of all\nthese decompositions is invariant under Tucker compression. This result can\ndramatically speed up the computation of the condition number in practical\napplications. We give the example of an $265\\times 371\\times 7$ tensor of rank\n$3$ from a food science application whose condition number was computed in\n$6.9$ milliseconds by exploiting our new theorem, representing a speedup of\nfour orders of magnitude over the previous state of the art.\n"}
+{"abstract": "  Eventually after Dieudonn\\'e-Grothendieck, we give intrinsic definitions of\n\\'etale, lisse and non-ramifi\\'e morphisms for general adic rings and general\nlocally convex rings. And we investigate the corresponding \\'etale-like,\nlisse-like and non-ramifi\\'e-like morphisms for general $\\infty$-Banach,\n$\\infty$-Born\\'e and $\\infty$-ind-Fr\\'echet $\\infty$-rings and\n$\\infty$-functors into $\\infty$-groupoid (as in the work of\nBambozzi-Ben-Bassat-Kremnizer) in some intrinsic way by using the corresponding\ninfinitesimal stacks and crystalline stacks. The two directions of\ngeneralization will intersect at Huber's book in the strongly noetherian\nsituation.\n"}
+{"abstract": "  In this paper, we propose a novel graph learning framework for phrase\ngrounding in the image. Developing from the sequential to the dense graph\nmodel, existing works capture coarse-grained context but fail to distinguish\nthe diversity of context among phrases and image regions. In contrast, we pay\nspecial attention to different motifs implied in the context of the scene graph\nand devise the disentangled graph network to integrate the motif-aware\ncontextual information into representations. Besides, we adopt interventional\nstrategies at the feature and the structure levels to consolidate and\ngeneralize representations. Finally, the cross-modal attention network is\nutilized to fuse intra-modal features, where each phrase can be computed\nsimilarity with regions to select the best-grounded one. We validate the\nefficiency of disentangled and interventional graph network (DIGN) through a\nseries of ablation studies, and our model achieves state-of-the-art performance\non Flickr30K Entities and ReferIt Game benchmarks.\n"}
+{"abstract": "  I report a tentative ($\\sim4\\sigma$) emission line at $\\nu=100.84\\,$GHz from\n\"COS-3mm-1'\", a 3mm-selected galaxy reported by Williams et al. 2019 that is\nundetected at optical and near infrared wavelengths. The line was found in the\nALMA Science Archive after re-processing ALMA band 3 observations targeting a\ndifferent source. Assuming the line corresponds to the $\\rm CO(6\\to5)$\ntransition, this tentative detection implies a spectroscopic redshift of\n$z=5.857$, in agreement with the galaxy's redshift constraints from\nmulti-wavelength photometry. This would make this object the highest redshift\n3mm-selected galaxy and one of the highest redshift dusty star-forming galaxies\nknown to-date. Here, I report the characteristics of this tentative detection\nand the physical properties that can be inferred assuming the line is real.\nFinally, I advocate for follow-up observations to corroborate this\nidentification and to confirm the high-redshift nature of this optically-dark\ndusty star-forming galaxy.\n"}
+{"abstract": "  We here report a monitor of the BL Lac object 1ES 1218+304 in both B- and\nR-bands by the GWAC-F60A telescope in eight nights, when it was triggerd to be\nat its highest X-ray flux in history by the VERITAS Observatory and Swift\nfollow-ups. Both ANOVA and $\\chi^2$-test enable us to clearly reveal an\nintra-day variability in optical wavelengths in seven out of the eight nights.\nA bluer-when-brighter chromatic relationship has been clearly identified in\nfive out of the eight nights, which can be well explained by the shock-in-jet\nmodel. In addtion, a quasi-periodic oscilation phenomenon in both bands could\nbe tentatively identified in the first night. A positive delay between the two\nbands has been revealed in three out of the eight nights, and a negative one in\nthe other nights. The identfied minimum time delay enables us to estimate the\n$M_{\\mathrm{BH}}=2.8\\times10^7 \\rm M_{\\odot}$that is invalid.\n"}
+{"abstract": "  Motivated by challenges in Earth mantle convection, we present a massively\nparallel implementation of an Eulerian-Lagrangian method for the\nadvection-diffusion equation in the advection-dominated regime. The advection\nterm is treated by a particle-based, characteristics method coupled to a\nblock-structured finite-element framework. Its numerical and computational\nperformance is evaluated in multiple, two- and three-dimensional benchmarks,\nincluding curved geometries, discontinuous solutions, pure advection, and it is\napplied to a coupled non-linear system modeling buoyancy-driven convection in\nStokes flow. We demonstrate the parallel performance in a strong and weak\nscaling experiment, with scalability to up to $147,456$ parallel processes,\nsolving for more than $5.2 \\times 10^{10}$ (52 billion) degrees of freedom per\ntime-step.\n"}
+{"abstract": "  We extend the new approach introduced in arXiv:1912.02064v2 [math.PR] and\narXiv:2102.10119v1 [math.PR] for dealing with stochastic Volterra equations\nusing the ideas of Rough Path theory and prove global existence and uniqueness\nresults. The main idea of this approach is simple: Instead of the iterated\nintegrals of a path comprising the data necessary to solve any equation driven\nby that path, now iterated integral convolutions with the Volterra kernel\ncomprise said data. This leads to the corresponding abstract objects called\nVolterra-type Rough Paths, as well as the notion of the convolution product, an\nextension of the natural tensor product used in Rough Path Theory.\n"}
+{"abstract": "  Handwritten document image binarization is challenging due to high\nvariability in the written content and complex background attributes such as\npage style, paper quality, stains, shadow gradients, and non-uniform\nillumination. While the traditional thresholding methods do not effectively\ngeneralize on such challenging real-world scenarios, deep learning-based\nmethods have performed relatively well when provided with sufficient training\ndata. However, the existing datasets are limited in size and diversity. This\nwork proposes LS-HDIB - a large-scale handwritten document image binarization\ndataset containing over a million document images that span numerous real-world\nscenarios. Additionally, we introduce a novel technique that uses a combination\nof adaptive thresholding and seamless cloning methods to create the dataset\nwith accurate ground truths. Through an extensive quantitative and qualitative\nevaluation over eight different deep learning based models, we demonstrate the\nenhancement in the performance of these models when trained on the LS-HDIB\ndataset and tested on unseen images.\n"}
+{"abstract": "  In the developing countries, most of the Manual Material Handling (MMH)\nrelated tasks are labor-intensive. It is not possible for these countries to\nassess injuries during lifting heavy weight, as multi-camera motion capture,\nforce plate and electromyography (EMG) systems are very expensive. In this\nstudy, we proposed an easy to use, portable and low cost system, which will\nhelp the developing countries to evaluate injuries for their workers. The\nsystem consists of two hardware and three software. The joint angle profiles\nare collected using smartphone camera and Kinovea software. The vertical Ground\nReaction Force (GRF) is collected using Wii balance board and Brainblox\nsoftware. Finally, the musculoskeletal analysis is performed using OpenSim\nStatic Optimization tool to find the muscle force. The system will give us\naccess to a comprehensive biomechanical analysis, from collecting joint angle\nprofiles to generating muscle force profiles. This proposed framework has the\npotential to assess and prevent MMH related injuries in developing countries.\n"}
+{"abstract": "  When fitting statistical models, some predictors are often found to be\ncorrelated with each other, and functioning together. Many group variable\nselection methods are developed to select the groups of predictors that are\nclosely related to the continuous or categorical response. These existing\nmethods usually assume the group structures are well known. For example,\nvariables with similar practical meaning, or dummy variables created by\ncategorical data. However, in practice, it is impractical to know the exact\ngroup structure, especially when the variable dimensional is large. As a\nresult, the group variable selection results may be selected. To solve the\nchallenge, we propose a two-stage approach that combines a variable clustering\nstage and a group variable stage for the group variable selection problem. The\nvariable clustering stage uses information from the data to find a group\nstructure, which improves the performance of the existing group variable\nselection methods. For ultrahigh dimensional data, where the predictors are\nmuch larger than observations, we incorporated a variable screening method in\nthe first stage and shows the advantages of such an approach. In this article,\nwe compared and discussed the performance of four existing group variable\nselection methods under different simulation models, with and without the\nvariable clustering stage. The two-stage method shows a better performance, in\nterms of the prediction accuracy, as well as in the accuracy to select active\npredictors. An athlete's data is also used to show the advantages of the\nproposed method.\n"}
+{"abstract": "  Click-Through Rate (CTR) prediction plays an important role in many\nindustrial applications, and recently a lot of attention is paid to the deep\ninterest models which use attention mechanism to capture user interests from\nhistorical behaviors. However, most current models are based on sequential\nmodels which truncate the behavior sequences by a fixed length, thus have\ndifficulties in handling very long behavior sequences. Another big problem is\nthat sequences with the same length can be quite different in terms of time,\ncarrying completely different meanings. In this paper, we propose a\nnon-sequential approach to tackle the above problems. Specifically, we first\nrepresent the behavior data in a sparse key-vector format, where the vector\ncontains rich behavior info such as time, count and category. Next, we enhance\nthe Deep Interest Network to take such rich information into account by a novel\nattention network. The sparse representation makes it practical to handle large\nscale long behavior sequences. Finally, we introduce a multidimensional\npartition framework to mine behavior interactions. The framework can partition\ndata into custom designed time buckets to capture the interactions among\ninformation aggregated in different time buckets. Similarly, it can also\npartition the data into different categories and capture the interactions among\nthem. Experiments are conducted on two public datasets: one is an advertising\ndataset and the other is a production recommender dataset. Our models\noutperform other state-of-the-art models on both datasets.\n"}
+{"abstract": "  We present an idealized study of the baroclinic structure of a rotating,\nstratified throughflow across a finite amplitude ridge in the f-plane that is\nforced by a steady, uniform inflow and outflow of equal magnitude. The\nresulting equilibrated circulation is characterized by unstable,\nwestern-intensified boundary currents and flow along f/H contours atop the\nridge near the crest that closes the forced circulation. We find that bottom\nEkman dynamics localized to lateral boundary currents amplify the\nstratification expected by simple stretching/squashing: a strongly stratified\nbottom front (high PV anomaly) along the anticyclonic boundary current\nassociated with net upslope transport, and a bottom mixed layer front\n(vanishing PV anomaly) localized to the cyclonic boundary current where there\nis net downslope transport. PV anomalies associated with the two fronts are\nadvected by both the mean flow and eddies that result from baroclinic growth,\nresulting in a spatial distribution where high PV is concentrated along the\nridge, and low PV is advected into the interior ocean downstream from the\nridge, at mid-depth. Using a framework of volume integrated PV conservation\nwhich incorporates the net fluxes associated with bottom topography, we conform\nan approximate integral balance between the PV injection from the bottom\nboundary layer on the ridge and net advection across the ridge. Implications of\nthese findings for understanding the interplay between large scale and bottom\nboundary dynamics are discussed.\n"}
+{"abstract": "  We elucidate the problem of estimating large-dimensional covariance matrices\nin the presence of correlations between samples. To this end, we generalize the\nMarcenko-Pastur equation and the Ledoit-Peche shrinkage estimator using methods\nof random matrix theory and free probability. We develop an efficient algorithm\nthat implements the corresponding analytic formulas, based on the Ledoit-Wolf\nkernel estimation technique. We also provide an associated open-source Python\nlibrary, called \"shrinkage\", with a user-friendly API to assist in practical\ntasks of estimation of large covariance matrices. We present an example of its\nusage for synthetic data generated according to exponentially-decaying\nauto-correlations.\n"}
+{"abstract": "  The chemical composition of the Sun is a fundamental yardstick in astronomy,\nrelative to which essentially all cosmic objects are referenced. We reassess\nthe solar abundances of all 83 long-lived elements, using highly realistic\nsolar modelling and state-of-the-art spectroscopic analysis techniques coupled\nwith the best available atomic data and observations. Our new improved analysis\nconfirms the relatively low solar abundances of C, N, and O obtained in our\nprevious 3D-based studies: $\\log\\epsilon_{\\text{C}}=8.46\\pm0.04$,\n$\\log\\epsilon_{\\text{N}}=7.83\\pm0.07$, and\n$\\log\\epsilon_{\\text{O}}=8.69\\pm0.04$. The revised solar abundances for the\nother elements also typically agree well with our previously recommended values\nwith just Li, F, Ne, Mg, Cl, Kr, Rb, Rh, Ba, W, Ir, and Pb differing by more\nthan $0.05$ dex. The here advocated present-day photospheric metal mass\nfraction is only slightly higher than our previous value, mainly due to the\nrevised Ne abundance from Genesis solar wind measurements: $X_{\\rm\nsurface}=0.7438\\pm0.0054$, $Y_{\\rm surface}=0.2423\\pm 0.0054$, $Z_{\\rm\nsurface}=0.0139\\pm 0.0006$, and $Z_{\\rm surface}/X_{\\rm surface}=0.0187\\pm\n0.0009$. Overall the solar abundances agree well with those of CI chondritic\nmeteorites but we identify a correlation with condensation temperature such\nthat moderately volatile elements are enhanced by $\\approx 0.04$ dex in the CI\nchondrites and refractory elements possibly depleted by $\\approx 0.02$ dex,\nconflicting with conventional wisdom of the past half-century. Instead the\nsolar chemical composition resembles more closely that of the fine-grained\nmatrix of CM chondrites. The so-called solar modelling problem remains intact\nwith our revised solar abundances, suggesting shortcomings with the computed\nopacities and/or treatment of mixing below the convection zone in existing\nstandard solar models.\n"}
+{"abstract": "  Causal inference methods are widely applied in various decision-making\ndomains such as precision medicine, optimal policy and economics. Central to\nthese applications is the treatment effect estimation of intervention\nstrategies. Current estimation methods are mostly restricted to the\ndeterministic treatment, which however, is unable to address the stochastic\nspace treatment policies. Moreover, previous methods can only make binary\nyes-or-no decisions based on the treatment effect, lacking the capability of\nproviding fine-grained effect estimation degree to explain the process of\ndecision making. In our study, we therefore advance the causal inference\nresearch to estimate stochastic intervention effect by devising a new\nstochastic propensity score and stochastic intervention effect estimator (SIE).\nMeanwhile, we design a customized genetic algorithm specific to stochastic\nintervention effect (Ge-SIO) with the aim of providing causal evidence for\ndecision making. We provide the theoretical analysis and conduct an empirical\nstudy to justify that our proposed measures and algorithms can achieve a\nsignificant performance lift in comparison with state-of-the-art baselines.\n"}
+{"abstract": "  Let $p\\geq 5$ be a prime. We construct modular Galois representations for\nwhich the $\\mathbb{Z}_p$-corank of the $p$-primary Selmer group over the\ncyclotomic $\\mathbb{Z}_p$-extension is large. The method is based on a purely\nGalois theoretic lifting construction.\n"}
+{"abstract": "  In this work we prove the non-degeneracy of the critical points of the Robin\nfunction for the Fractional Laplacian under symmetry and convexity assumptions\non the domain $\\Omega$. This work extends to the fractional setting the results\nof M. Grossi concerning the classical Laplace operator.\n"}
+{"abstract": "  Despite their remarkable expressibility, convolution neural networks (CNNs)\nstill fall short of delivering satisfactory results on single image dehazing,\nespecially in terms of faithful recovery of fine texture details. In this\npaper, we argue that the inadequacy of conventional CNN-based dehazing methods\ncan be attributed to the fact that the domain of hazy images is too far away\nfrom that of clear images, rendering it difficult to train a CNN for learning\ndirect domain shift through an end-to-end manner and recovering texture details\nsimultaneously. To address this issue, we propose to add explicit constraints\ninside a deep CNN model to guide the restoration process. In contrast to direct\nlearning, the proposed mechanism shifts and narrows the candidate region for\nthe estimation output via multiple confident neighborhoods. Therefore, it is\ncapable of consolidating the expressibility of different architectures,\nresulting in a more accurate indirect domain shift (IDS) from the hazy images\nto that of clear images. We also propose two different training schemes,\nincluding hard IDS and soft IDS, which further reveal the effectiveness of the\nproposed method. Our extensive experimental results indicate that the dehazing\nmethod based on this mechanism outperforms the state-of-the-arts.\n"}
+{"abstract": "  Noncoplanar radiation therapy treatment planning has the potential to improve\ndosimetric quality as compared to traditional coplanar techniques. Likewise,\nautomated treatment planning algorithms can reduce a planner's active treatment\nplanning time and remove inter-planner variability. To address the limitations\nof traditional treatment planning, we have been developing a suite of\nalgorithms called station parameter optimized radiation therapy (SPORT). Within\nthe SPORT suite of algorithms, we propose a method called NC-POPS to produce\nnoncoplanar (NC) plans using the fully automated Pareto Optimal Projection\nSearch (POPS) algorithm. Our NC-POPS algorithm extends the original POPS\nalgorithm to the noncoplanar setting with potential applications to both IMRT\nand VMAT. The proposed algorithm consists of two main parts: 1) noncoplanar\nbeam angle optimization (BAO) and 2) fully automated inverse planning using the\nPOPS algorithm. We evaluate the performance of NC-POPS by comparing between\nvarious noncoplanar and coplanar configurations. To evaluate plan quality, we\ncompute the homogeneity index (HI), conformity index (CI), and dose-volume\nhistogram (DVH) statistics for various organs-at-risk (OARs). As compared to\nthe evaluated coplanar baseline methods, the proposed NC-POPS method achieves\nsignificantly better OAR sparing, comparable or better dose conformity, and\nsimilar dose homogeneity. Our proposed NC-POPS algorithm provides a modular\napproach for fully automated treatment planning of noncoplanar IMRT cases with\nthe potential to substantially improve treatment planning workflow and plan\nquality.\n"}
+{"abstract": "  Recently, there has been a significant interest in performing convolution\nover irregularly sampled point clouds. Since point clouds are very different\nfrom regular raster images, it is imperative to study the generalization of the\nconvolution networks more closely, especially their robustness under variations\nin scale and rotations of the input data. This paper investigates different\nvariants of PointConv, a convolution network on point clouds, to examine their\nrobustness to input scale and rotation changes. Of the variants we explored,\ntwo are novel and generated significant improvements. The first is replacing\nthe multilayer perceptron based weight function with much simpler third degree\npolynomials, together with a Sobolev norm regularization. Secondly, for 3D\ndatasets, we derive a novel viewpoint-invariant descriptor by utilizing 3D\ngeometric properties as the input to PointConv, in addition to the regular 3D\ncoordinates. We have also explored choices of activation functions,\nneighborhood, and subsampling methods. Experiments are conducted on the 2D\nMNIST & CIFAR-10 datasets as well as the 3D SemanticKITTI & ScanNet datasets.\nResults reveal that on 2D, using third degree polynomials greatly improves\nPointConv's robustness to scale changes and rotations, even surpassing\ntraditional 2D CNNs for the MNIST dataset. On 3D datasets, the novel\nviewpoint-invariant descriptor significantly improves the performance as well\nas robustness of PointConv. We achieve the state-of-the-art semantic\nsegmentation performance on the SemanticKITTI dataset, as well as comparable\nperformance with the current highest framework on the ScanNet dataset among\npoint-based approaches.\n"}
+{"abstract": "  We study the fluctuations of eigenstate expectation values in a\nmicrocanonical ensemble. Assuming the eigenstate thermalization hypothesis, an\nanalytical formula for the finite-size scaling of the fluctuations is derived.\nThe same problem was studied by Beugeling et al. [Phys. Rev. E 89, 042112\n(2014)]. We compare our results with theirs.\n"}
+{"abstract": "  We derive upper and lower bounds on the sum of distances of a spherical code\nof size $N$ in $n$ dimensions when $N\\sim n^\\alpha, 0<\\alpha\\le 2.$ The bounds\nare derived by specializing recent general, universal bounds on energy of\nspherical sets. We discuss asymptotic behavior of our bounds along with several\nexamples of codes whose sum of distances closely follows the upper bound.\n"}
+{"abstract": "  This article describes our approach to quantifying the characteristics of\nmeteors such as temperature, chemical composition, and others. We are using a\nnew approach based on colourimetry. We analyze an image of Leonid meteor-6230\nobtained by Mike Hankey in 2012. Analysis of the temporal features of the\nmeteoroid trail is performed. For determining the meteor characteristics we use\nthe \"tuning technique\" in combination with a simulation model of intrusion. The\nprogenitor of the meteor was found as an object weighing 900 kg at a speed of\n36.5 km/s. The meteoroid reached a critical value of the pressure at an\naltitude of about 29 km in a time of about 4.6 sec with a residual mass of\nabout 20 kg, and a residual speed of about 28 km/s. At this moment, a meteoroid\nexploded and destroyed. We use the meteor multicolour light curves revealed\nfrom a DSLR image in the RGB colour standard. We switch from the RGB colour\nsystem to Johnson's RVB colour system introducing colour corrections. This\nallows one to determine the colour characteristics of the meteor radiation. We\nare using a new approach based on colourimetry. Colourimetry of BGR three-beam\nlight curves allows the identification of the brightest spectral lines. Our\napproach based on colourimetry allows direct measurements of temperature in the\nmeteor trail. We find a part of the trajectory where the meteoroid radiates as\nan absolutely black body. The R/G and B/G light curves ratio allow one to\nidentify the wavelengths of the emission lines using the transmission curves of\nthe RGB filters. At the end of the trajectory, the meteoroid radiates in the\nlines Ca II H, K 393, 397 nm, Fe I 382, 405 nm, Mg I 517 nm, Na I 589 nm, as\nwell as atmospheric O I 779 nm.\n"}
+{"abstract": "  In this paper, we have proposed a model of accelerating Universe with binary\nmixture of bulk viscous fluid and dark energy. and probed the model parameters:\npresent values of Hubble's constant $H_{0}$, Equation of state paper of dark\nenergy $\\omega_{de}$ and density parameter of dark energy $(\\Omega_{de})_{0}$\nwith recent OHD as well as joint Pantheon compilation of SN Ia data and OHD.\nUsing cosmic chronometric technique, we obtain $H_{0} = 69.80 \\pm\n1.64~km~s^{-1}Mpc^{-1}$ and $70.0258 \\pm 1.72~km~s^{-1}Mpc^{-1}$ by restricting\nour derived model with recent OHD and joint Pantheon compilation SN Ia data and\nOHD respectively. The age of the Universe in derived model is estimated as\n$t_{0} = 13.82 \\pm 0.33\\; Gyrs$. Also, we observe that derived model represents\na model of transitioning Universe with transition redshift $z_{t} = 0.7286$. We\nhave constrained the present value of jerk parameter as $j_{0} = 0.969 \\pm\n0.0075$ with joint OHD and Pantheon data. From this analysis, we observed that\nthe model of the Universe, presented in this paper shows a marginal departure\nfrom $\\Lambda$CDM model.\n"}
+{"abstract": "  In 1848 Ch.~Hermite asked if there exists some way to write cubic\nirrationalities periodically. A little later in order to approach the problem\nC.G.J.~Jacobi and O.~Perron generalized the classical continued fraction\nalgorithm to the three-dimensional case, this algorithm is called now the\nJacobi-Perron algorithm. This algorithm is known to provide periodicity only\nfor some cubic irrationalities.\n  In this paper we introduce two new algorithms in the spirit of Jacobi-Perron\nalgorithm: the heuristic algebraic periodicity detecting algorithm and the\n$\\sin^2$-algorithm. The heuristic algebraic periodicity detecting algorithm is\na very fast and efficient algorithm, its output is periodic for numerous\nexamples of cubic irrationalities, however its periodicity for cubic\nirrationalities is not proven. The $\\sin^2$-algorithm is limited to the\ntotally-real cubic case (all the roots of cubic polynomials are real numbers).\nIn the recent paper~\\cite{Karpenkov2021} we proved the periodicity of the\n$\\sin^2$-algorithm for all cubic totally-real irrationalities. To our best\nknowledge this is the first Jacobi-Perron type algorithm for which the cubic\nperiodicity is proven. The $\\sin^2$-algorithm provides the answer to Hermite's\nproblem for the totally real case (let us mention that the case of cubic\nalgebraic numbers with complex conjugate roots remains open).\n  We conclude this paper with one important application of Jacobi-Perron type\nalgorithms to computation of independent elements in the maximal groups of\ncommuting matrices of algebraic irrationalities.\n"}
+{"abstract": "  We show how the Shannon entropy function H(p,q)is expressible as a linear\ncombination of other Shannon entropy functions involving quotients of\npolynomials in p,q of degree n for any given positive integer n. An application\nto cryptographic keys is presented.\n"}
+{"abstract": "  Idioms are unlike other phrases in two important ways. First, the words in an\nidiom have unconventional meanings. Second, the unconventional meaning of words\nin an idiom are contingent on the presence of the other words in the idiom.\nLinguistic theories disagree about whether these two properties depend on one\nanother, as well as whether special theoretical machinery is needed to\naccommodate idioms. We define two measures that correspond to these two\nproperties, and we show that idioms fall at the expected intersection of the\ntwo dimensions, but that the dimensions themselves are not correlated. Our\nresults suggest that idioms are no more anomalous than other types of phrases,\nand that introducing special machinery to handle idioms may not be warranted.\n"}
+{"abstract": "  We study anticommutative algebras with the property that commutator of any\ntwo multiplications is a derivation.\n"}
+{"abstract": "  Hidden Markov chain, or Markov field, models, with observations in a\nEuclidean space, play a major role across signal and image processing. The\npresent work provides a statistical framework which can be used to extend these\nmodels, along with related, popular algorithms (such as the Baum-Welch\nalgorithm), to the case where the observations lie in a Riemannian manifold. It\nis motivated by the potential use of hidden Markov chains and fields, with\nobservations in Riemannian manifolds, as models for complex signals and images.\n"}
+{"abstract": "  Video annotation and analysis is an important activity for teaching with and\nabout audiovisual media artifacts because it helps students to learn how to\nidentify textual and formal connections in media products. But school teachers\nlack adequate tools for video annotation and analysis in media education that\nare easy-to-use, integrate into established teaching organization, and support\nquick collaborative work. To address these challenges, we followed a\ndesign-based research approach and conducted qualitative interviews with\nteachers to develop TRAVIS GO, a web application for simple and collaborative\nvideo annotation. TRAVIS GO allows for quick and easy use within established\nteaching settings. The web application provides basic analytical features in an\nadaptable work space. Key didactic features include tagging and commenting on\nposts, sharing and exporting projects, and working in live collaboration.\nTeachers can create assignments according to grade level, learning subject, and\nclass size. Our work contributes further insights for the CSCW community about\nhow to implement user demands into developing educational tools.\n"}
+{"abstract": "  Using a nominally symmetric annular combustor, we present experimental\nevidence of a predicted spontaneous symmetry breaking and an unexpected\nexplicit symmetry breaking in the neighborhood of the Hopf bifurcation, which\nseparates linearly-stable azimuthal thermoacoustic modes from self-oscillating\nmodes. We derive and solve a multidimensional Fokker-Planck equation to unravel\na unified picture of the phase space topology. We demonstrate that symmetric\nprobability density functions of the thermoacoustic state vector are elusive,\nbecause the effect of asymmetries, even imperceptible ones, is magnified close\nto the bifurcation.\n"}
+{"abstract": "  We propose the operation of \\textbf{LEvEL}, the Low-Energy Neutrino\nExperiment at the LHC, a neutrino detector near the Large Hadron Collider Beam\nDump. Such a detector is capable of exploring an intense, low-energy neutrino\nflux and can measure neutrino cross sections that have previously never been\nobserved. These cross sections can inform other future neutrino experiments,\nsuch as those aiming to observe neutrinos from supernovae, allowing such\nmeasurements to accomplish their fundamental physics goals. We perform detailed\nsimulations to determine neutrino production at the LHC beam dump, as well as\nneutron and muon backgrounds. Measurements at a few to ten percent precision of\nneutrino-argon charged current and neutrino-nucleus coherent scattering cross\nsections are attainable with 100~ton-year and 1~ton-year exposures at LEvEL,\nrespectively, concurrent with the operation of the High Luminosity LHC. We also\nestimate signal and backgrounds for an experiment exploiting the forward\ndirection of the LHC beam dump, which could measure neutrinos above 100 GeV.\n"}
+{"abstract": "  Deflection of light due to massive objects was predicted by Einstein in his\nGeneral Theory of Relativity. This deflection of light has been calculated by\nmany researchers in past, for spherically symmetric objects. But, in reality,\nmost of these gravitating objects are not spherical instead they are\nellipsoidal ( oblate) in shape. The objective of the present work is to study\ntheoretically the effect of this ellipticity on the trajectory of a light ray.\nHere, we obtain a converging series expression for the deflection of a light\nray due to an ellipsoidal gravitating object, characterised by an ellipticity\nparameter. As a boundary condition, by setting the ellipticity parameter to be\nequal to zero, we get back the same expression for deflection as due to\nSchwarzschild object. It is also found that the additional contribution in\ndeflection angle due to this ellipticity though small, but could be typically\nhigher than the similar contribution caused by the rotation of a celestial\nobject. Therefore for a precise estimate of the deflection due to a celestial\nobject, the calculations presented here would be useful.\n"}
+{"abstract": "  A new method is proposed to search for the double beauty tetraquark\n$bb\\bar{u}\\bar{d}$ with a mass below the $BB$ threshold. The\n$b\\bar{d}-\\bar{b}d$ mixing can result in evolution of the tetraquark content to\n$b\\bar{b}\\bar{u}d$ with the following strong decay into the\n$\\Upsilon$(1S)$\\pi^-$ and $\\Upsilon$(2S)$\\pi^-$ final states in a secondary\nvertex displaced from the primary $pp$ collision vertex. This experimental\nsignature is clean, has a high selection efficiency and can be well separated\nfrom backgrounds. Experimental signatures for searches for other double heavy\ntetraquarks are also discussed, suggesting the possibility of the $b\\bar{d}$ or\n$b\\bar{s}$ mixing.\n"}
+{"abstract": "  We show that splitting forcing does not have the weak Sacks property below\nany condition, answering a question of Laguzzi, Mildenberger and\nStuber-Rousselle. We also show how some partition results for splitting trees\nhold or fail and we determine the value of cardinal invariants after an\n$\\omega_2$-length countable support iteration of splitting forcing.\n"}
+{"abstract": "  The recently discovered layered kagome metals AV$_3$Sb$_5$ (A = K, Rb, and\nCs) with vanadium kagome networks provide a novel platform to explore\ncorrelated quantum states intertwined with topological band structures. Here we\nreport the prominent effect of hole doping on both superconductivity and charge\ndensity wave (CDW) order, achieved by selective oxidation of exfoliated thin\nflakes. A superconducting dome is revealed as a function of the effective\ndoping content. The superconducting transition temperature ($T_{\\mathrm{c}}$)\nand upper critical field in thin flakes are significantly enhanced compared\nwith the bulk, which is accompanied by the suppression of CDW. Our detailed\nanalyses establish the pivotal role of van Hove singularities (VHSs) in\npromoting correlated quantum orders in these kagome metals. Our experiment not\nonly demonstrates the intriguing nature of superconducting and CDW orders, but\nalso provides a novel route to tune the carrier concentration through both\nselective oxidation and electric gating. This establishes AV$_3$Sb$_5$ as a\ntunable 2D platform for the further exploration of topology and correlation\namong 3$d$ electrons in kagome lattices.\n"}
+{"abstract": "  This article provides a self-contained pedagogical introduction to the\nrelativistic kinetic theory of a dilute gas propagating on a curved spacetime\nmanifold (M,g) of arbitrary dimension. Special emphasis is made on geometric\naspects of the theory in order to achieve a formulation which is manifestly\ncovariant on the relativistic phase space. Whereas most previous work has\nfocussed on the tangent bundle formulation, here we work on the cotangent\nbundle associated with (M,g) which is more naturally adapted to the Hamiltonian\nframework of the theory. In the first part of this work we discuss the relevant\ngeometric structures of the cotangent bundle T*M, starting with the natural\nsymplectic form on T*M, the one-particle Hamiltonian and the Liouville vector\nfield, defined as the corresponding Hamiltonian vector field. Next, we discuss\nthe Sasaki metric on T*M and its most important properties, including the role\nit plays for the physical interpretation of the one-particle distribution\nfunction. In the second part of this work we describe the general relativistic\ntheory of a collisionless gas, starting with the derivation of the\ncollisionless Boltzmann equation for a neutral simple gas. Subsequently, the\ndescription is generalized to a charged gas consisting of several species of\nparticles and the general relativistic Vlasov-Maxwell equations are derived for\nthis system. The last part of this work is devoted to a transparent derivation\nof the collision term, leading to the general relativistic Boltzmann equation\non (M,g). The meaning of global and local equilibrium and the strident\nrestrictions for the existence of the former on a curved spacetime are\ndiscussed. We close this article with an application of our formalism to the\nexpansion of a homogeneous and isotropic universe filled with a collisional\nsimple gas and its behavior in the early and late epochs. [abbreviated version]\n"}
+{"abstract": "  We investigate asymptotic behavior of polynomials $p^{\\omega}_n(z)$\nsatisfying varying non-Hermitian orthogonality relations $$ \\int_{-1}^{1}\nx^kp^{\\omega}_n(x)h(x) e^{\\mathrm{i} \\omega x}\\mathrm{d} x =0, \\quad\nk\\in\\{0,\\ldots,n-1\\}, $$ where $h(x) = h^*(x) (1 - x)^{\\alpha} (1 + x)^{\\beta},\n\\ \\omega = \\lambda n, \\ \\lambda \\geq 0 $ and $h(x)$ is holomorphic and\nnon-vanishing in a certain neighborhood in the plane. These polynomials are an\nextension of so-called kissing polynomials ($\\alpha = \\beta = 0$) introduced in\nconnection with complex Gaussian quadrature rules with uniform good properties\nin $\\omega$.\n"}
+{"abstract": "  COVID-19 has resulted in over 100 million infections and caused worldwide\nlock downs due to its high transmission rate and limited testing options.\nCurrent diagnostic tests can be expensive, limited in availability,\ntime-intensive and require risky in-person appointments. It has been\nestablished that symptomatic COVID-19 seriously impairs normal functioning of\nthe respiratory system, thus affecting the coughing acoustics. The 2021 DiCOVA\nChallenge @ INTERSPEECH was designed to find scientific and engineering\ninsights to the question by enabling participants to analyze an acoustic\ndataset gathered from COVID-19 positive and non-COVID-19 individuals. In this\nreport we describe our participation in the Challenge (Track 1). We achieved\n82.37% AUC ROC on the blind test outperforming the Challenge's baseline of\n69.85%.\n"}
+{"abstract": "  We establish unconditional sharp upper bounds of the $k$-th moments of the\nfamily of quadratic Dirichlet $L$-functions at the central point for $0 \\leq k\n\\leq 2$.\n"}
+{"abstract": "  Vehicle trajectory optimization is essential to ensure vehicles travel\nefficiently and safely. This paper presents an infrastructure assisted\nconstrained connected automated vehicles (CAVs) trajectory optimization method\non curved roads. This paper systematically formulates the problem based on a\ncurvilinear coordinate which is flexible to model complex road geometries.\nFurther, to deal with the spatial varying road obstacles, traffic regulations,\nand geometric characteristics, two-dimensional vehicle kinematics is given in a\nspatial formulation with exact road information provided by the infrastructure.\nConsequently, we applied a multi-objective model predictive control (MPC)\napproach to optimize the trajectories in a rolling horizon while satisfying the\ncollision avoidances and vehicle kinematics constraints. To verify the\nefficiency of our method, a numerical simulation is conducted. As the results\nsuggest, the proposed method can provide smooth vehicular trajectories, avoid\nroad obstacles, and simultaneously follow traffic regulations, which is robust\nto road geometries and disturbances.\n"}
+{"abstract": "  WarpX is a general purpose electromagnetic particle-in-cell code that was\noriginally designed to run on many-core CPU architectures. We describe the\nstrategy followed to allow WarpX to use the GPU-accelerated nodes on OLCF's\nSummit supercomputer, a strategy we believe will extend to the upcoming\nmachines Frontier and Aurora. We summarize the challenges encountered, lessons\nlearned, and give current performance results on a series of relevant benchmark\nproblems.\n"}
+{"abstract": "  Quiver representations arise naturally in many areas across mathematics. Here\nwe describe an algorithm for calculating the vector space of sections, or\ncompatible assignments of vectors to vertices, of any finite-dimensional\nrepresentation of a finite quiver. Consequently, we are able to define and\ncompute principal components with respect to quiver representations. These\nprincipal components are solutions to constrained optimisation problems defined\nover the space of sections, and are eigenvectors of an associated matrix\npencil.\n"}
+{"abstract": "  We demonstrate that global observations of high-energy cosmic rays contribute\nto understanding unique characteristics of a large-scale magnetic flux rope\ncausing a magnetic storm in August 2018. Following a weak interplanetary shock\non 25 August 2018, a magnetic flux rope caused an unexpectedly large\ngeomagnetic storm. It is likely that this event became geoeffective because the\nflux rope was accompanied by a corotating interaction region and compressed by\nhigh-speed solar wind following the flux rope. In fact, a Forbush decrease was\nobserved in cosmic-ray data inside the flux rope as expected, and a significant\ncosmic-ray density increase exceeding the unmodulated level before the shock\nwas also observed near the trailing edge of the flux rope. The cosmic-ray\ndensity increase can be interpreted in terms of the adiabatic heating of cosmic\nrays near the trailing edge of the flux rope, as the corotating interaction\nregion prevents free expansion of the flux rope and results in the compression\nnear the trailing edge. A northeast-directed spatial gradient in the cosmic-ray\ndensity was also derived during the cosmic-ray density increase, suggesting\nthat the center of the heating near the trailing edge is located northeast of\nEarth. This is one of the best examples demonstrating that the observation of\nhigh-energy cosmic rays provides us with information that can only be derived\nfrom the cosmic ray measurements to observationally constrain the\nthree-dimensional macroscopic picture of the interaction between coronal mass\nejections and the ambient solar wind, which is essential for prediction of\nlarge magnetic storms.\n"}
+{"abstract": "  The generalization of representations learned via contrastive learning\ndepends crucially on what features of the data are extracted. However, we\nobserve that the contrastive loss does not always sufficiently guide which\nfeatures are extracted, a behavior that can negatively impact the performance\non downstream tasks via \"shortcuts\", i.e., by inadvertently suppressing\nimportant predictive features. We find that feature extraction is influenced by\nthe difficulty of the so-called instance discrimination task (i.e., the task of\ndiscriminating pairs of similar points from pairs of dissimilar ones). Although\nharder pairs improve the representation of some features, the improvement comes\nat the cost of suppressing previously well represented features. In response,\nwe propose implicit feature modification (IFM), a method for altering positive\nand negative samples in order to guide contrastive models towards capturing a\nwider variety of predictive features. Empirically, we observe that IFM reduces\nfeature suppression, and as a result improves performance on vision and medical\nimaging tasks. The code is available at: \\url{https://github.com/joshr17/IFM}.\n"}
+{"abstract": "  The generalised extreme value (GEV) distribution is a three parameter family\nthat describes the asymptotic behaviour of properly renormalised maxima of a\nsequence of independent and identically distributed random variables. If the\nshape parameter $\\xi$ is zero, the GEV distribution has unbounded support,\nwhereas if $\\xi$ is positive, the limiting distribution is heavy-tailed with\ninfinite upper endpoint but finite lower endpoint. In practical applications,\nwe assume that the GEV family is a reasonable approximation for the\ndistribution of maxima over blocks, and we fit it accordingly. This implies\nthat GEV properties, such as finite lower endpoint in the case $\\xi>0$, are\ninherited by the finite-sample maxima, which might not have bounded support.\nThis is particularly problematic when predicting extreme observations based on\nmultiple and interacting covariates. To tackle this usually overlooked issue,\nwe propose a blended GEV distribution, which smoothly combines the left tail of\na Gumbel distribution (GEV with $\\xi=0$) with the right tail of a Fr\\'echet\ndistribution (GEV with $\\xi>0$) and, therefore, has unbounded support. Using a\nBayesian framework, we reparametrise the GEV distribution to offer a more\nnatural interpretation of the (possibly covariate-dependent) model parameters.\nIndependent priors over the new location and spread parameters induce a joint\nprior distribution for the original location and scale parameters. We introduce\nthe concept of property-preserving penalised complexity (P$^3$C) priors and\napply it to the shape parameter to preserve first and second moments. We\nillustrate our methods with an application to NO$_2$ pollution levels in\nCalifornia, which reveals the robustness of the bGEV distribution, as well as\nthe suitability of the new parametrisation and the P$^3$C prior framework.\n"}
+{"abstract": "  Long linear carbon-chains have been attracting intense interest arising from\nthe remarkable properties predicted and their potential applications in future\nnanotechnology. Here we comprehensively interrogate the excitonic transitions\nand the associated relaxation dynamics of nanotube confined long linear\ncarbon-chains by using steady state and time-resolved Raman spectroscopies. The\nexciton relaxation dynamics on the confined carbon-chains occurs on a hundreds\nof picoseconds timescale, in strong contrast to the host dynamics that occurs\non a few picosecond timescale. A prominent time-resolved Raman response is\nobserved over a broad energy range extending from 1.2 to 2.8 eV, which includes\nthe strong Raman resonance region around 2.2 eV. Evidence for a strong coupling\nbetween the chain and the nanotube host is found from the dynamics at high\nexcitation energies which provides a clear evidence for an efficient energy\ntransfer from the host carbon nanotube to the chain. Our experimental study\npresents the first unique characterization of the long linear carbon-chain\nexciton dynamics, providing indispensable knowledge for the understanding of\nthe interactions between different carbon allotropes.\n"}
+{"abstract": "  Recent work has shown that every 3D root system allows the construction of a\ncorreponding 4D root system via an `induction theorem'. In this paper, we look\nat the icosahedral case of $H_3\\rightarrow H_4$ in detail and perform the\ncalculations explicitly. Clifford algebra is used to perform group theoretic\ncalculations based on the versor theorem and the Cartan-Dieudonn\\'e theorem,\ngiving a simple construction of the Pin and Spin covers. Using this connection\nwith $H_3$ via the induction theorem sheds light on geometric aspects of the\n$H_4$ root system (the $600$-cell) as well as other related polytopes and their\nsymmetries, such as the famous Grand Antiprism and the snub 24-cell. The\nuniform construction of root systems from 3D and the uniform procedure of\nsplitting root systems with respect to subrootsystems into separate invariant\nsets allows further systematic insight into the underlying geometry. All\ncalculations are performed in the even subalgebra of Cl(3), including the\nconstruction of the Coxeter plane, which is used for visualising the\ncomplementary pairs of invariant polytopes, and are shared as supplementary\ncomputational work sheets. This approach therefore constitutes a more\nsystematic and general way of performing calculations concerning groups, in\nparticular reflection groups and root systems, in a Clifford algebraic\nframework.\n"}
+{"abstract": "  The notion of hypothetical bias (HB) constitutes, arguably, the most\nfundamental issue in relation to the use of hypothetical survey methods.\nWhether or to what extent choices of survey participants and subsequent\ninferred estimates translate to real-world settings continues to be debated.\nWhile HB has been extensively studied in the broader context of contingent\nvaluation, it is much less understood in relation to choice experiments (CE).\nThis paper reviews the empirical evidence for HB in CE in various fields of\napplied economics and presents an integrative framework for how HB relates to\nexternal validity. Results suggest mixed evidence on the prevalence, extent and\ndirection of HB as well as considerable context and measurement dependency.\nWhile HB is found to be an undeniable issue when conducting CEs, the empirical\nevidence on HB does not render CEs unable to represent real-world preferences.\nWhile health-related choice experiments often find negligible degrees of HB,\nexperiments in consumer behaviour and transport domains suggest that\nsignificant degrees of HB are ubiquitous. Assessments of bias in environmental\nvaluation studies provide mixed evidence. Also, across these disciplines many\nstudies display HB in their total willingness to pay estimates and opt-in rates\nbut not in their hypothetical marginal rates of substitution (subject to scale\ncorrection). Further, recent findings in psychology and brain imaging studies\nsuggest neurocognitive mechanisms underlying HB that may explain some of the\ndiscrepancies and unexpected findings in the mainstream CE literature. The\nreview also observes how the variety of operational definitions of HB prohibits\nconsistent measurement of HB in CE. The paper further identifies major sources\nof HB and possible moderating factors. Finally, it explains how HB represents\none component of the wider concept of external validity.\n"}
+{"abstract": "  Federated learning (FL) has become a prevalent distributed machine learning\nparadigm with improved privacy. After learning, the resulting federated model\nshould be further personalized to each different client. While several methods\nhave been proposed to achieve personalization, they are typically limited to a\nsingle local device, which may incur bias or overfitting since data in a single\ndevice is extremely limited. In this paper, we attempt to realize\npersonalization beyond a single client. The motivation is that during FL, there\nmay exist many clients with similar data distribution, and thus the\npersonalization performance could be significantly boosted if these similar\nclients can cooperate with each other. Inspired by this, this paper introduces\na new concept called federated adaptation, targeting at adapting the trained\nmodel in a federated manner to achieve better personalization results. However,\nthe key challenge for federated adaptation is that we could not outsource any\nraw data from the client during adaptation, due to privacy concerns. In this\npaper, we propose PFA, a framework to accomplish Privacy-preserving Federated\nAdaptation. PFA leverages the sparsity property of neural networks to generate\nprivacy-preserving representations and uses them to efficiently identify\nclients with similar data distributions. Based on the grouping results, PFA\nconducts an FL process in a group-wise way on the federated model to accomplish\nthe adaptation. For evaluation, we manually construct several practical FL\ndatasets based on public datasets in order to simulate both the class-imbalance\nand background-difference conditions. Extensive experiments on these datasets\nand popular model architectures demonstrate the effectiveness of PFA,\noutperforming other state-of-the-art methods by a large margin while ensuring\nuser privacy. We will release our code at: https://github.com/lebyni/PFA.\n"}
+{"abstract": "  Micro-expression can reflect people's real emotions. Recognizing\nmicro-expressions is difficult because they are small motions and have a short\nduration. As the research is deepening into micro-expression recognition, many\neffective features and methods have been proposed. To determine which direction\nof movement feature is easier for distinguishing micro-expressions, this paper\nselects 18 directions (including three types of horizontal, vertical and\noblique movements) and proposes a new low-dimensional feature called the\nHistogram of Single Direction Gradient (HSDG) to study this topic. In this\npaper, HSDG in every direction is concatenated with LBP-TOP to obtain the LBP\nwith Single Direction Gradient (LBP-SDG) and analyze which direction of\nmovement feature is more discriminative for micro-expression recognition. As\nwith some existing work, Euler Video Magnification (EVM) is employed as a\npreprocessing step. The experiments on the CASME II and SMIC-HS databases\nsummarize the effective and optimal directions and demonstrate that HSDG in an\noptimal direction is discriminative, and the corresponding LBP-SDG achieves\nstate-of-the-art performance using EVM.\n"}
+{"abstract": "  This paper presents the advantages of alternative data from Super-Apps to\nenhance user' s income estimation models. It compares the performance of these\nalternative data sources with the performance of industry-accepted bureau\nincome estimators that takes into account only financial system information;\nsuccessfully showing that the alternative data manage to capture information\nthat bureau income estimators do not. By implementing the TreeSHAP method for\nStochastic Gradient Boosting Interpretation, this paper highlights which of the\ncustomer' s behavioral and transactional patterns within a Super-App have a\nstronger predictive power when estimating user' s income. Ultimately, this\npaper shows the incentive for financial institutions to seek to incorporate\nalternative data into constructing their risk profiles.\n"}
+{"abstract": "  State-of-the-art computer vision systems are trained to predict a fixed set\nof predetermined object categories. This restricted form of supervision limits\ntheir generality and usability since additional labeled data is needed to\nspecify any other visual concept. Learning directly from raw text about images\nis a promising alternative which leverages a much broader source of\nsupervision. We demonstrate that the simple pre-training task of predicting\nwhich caption goes with which image is an efficient and scalable way to learn\nSOTA image representations from scratch on a dataset of 400 million (image,\ntext) pairs collected from the internet. After pre-training, natural language\nis used to reference learned visual concepts (or describe new ones) enabling\nzero-shot transfer of the model to downstream tasks. We study the performance\nof this approach by benchmarking on over 30 different existing computer vision\ndatasets, spanning tasks such as OCR, action recognition in videos,\ngeo-localization, and many types of fine-grained object classification. The\nmodel transfers non-trivially to most tasks and is often competitive with a\nfully supervised baseline without the need for any dataset specific training.\nFor instance, we match the accuracy of the original ResNet-50 on ImageNet\nzero-shot without needing to use any of the 1.28 million training examples it\nwas trained on. We release our code and pre-trained model weights at\nhttps://github.com/OpenAI/CLIP.\n"}
+{"abstract": "  We recast elliptic surfaces over the projective line in terms of the\nnon-commutative tori with real multiplication. The correspondence is used to\nstudy the Picard numbers, the ranks and the minimal models of such surfaces. As\nan example, we calculate the Picard numbers of elliptic surfaces with complex\nmultiplication.\n"}
+{"abstract": "  In this paper, we perform a detailed analysis of the phase shift phenomenon\nof the classical soliton cellular automaton known as the box-ball system,\nultimately resulting in a statement and proof of a formula describing this\nphase shift. This phenomenon has been observed since the nineties, when the\nsystem was first introduced by Takahashi and Satsuma, but no explicit global\ndescription was made beyond its observation. By using the\nGessel-Viennot-Lindstr\\\"{o}m lemma and path-counting arguments, we present here\na novel proof of the classical phase shift formula for the continuous-time Toda\nlattice, as discovered by Moser, and use this proof to derive a discrete-time\nToda lattice analogue of the phase shift phenomenon. By carefully analysing the\nconnection between the box-ball system and the discrete-time Toda lattice,\nthrough the mechanism of tropicalisation/dequantisation, we translate this\ndiscrete-time Toda lattice phase shift formula into our new formula for the\nbox-ball system phase shift.\n"}
+{"abstract": "  Existing computer vision research in categorization struggles with\nfine-grained attributes recognition due to the inherently high intra-class\nvariances and low inter-class variances. SOTA methods tackle this challenge by\nlocating the most informative image regions and rely on them to classify the\ncomplete image. The most recent work, Vision Transformer (ViT), shows its\nstrong performance in both traditional and fine-grained classification tasks.\nIn this work, we propose a multi-stage ViT framework for fine-grained image\nclassification tasks, which localizes the informative image regions without\nrequiring architectural changes using the inherent multi-head self-attention\nmechanism. We also introduce attention-guided augmentations for improving the\nmodel's capabilities. We demonstrate the value of our approach by experimenting\nwith four popular fine-grained benchmarks: CUB-200-2011, Stanford Cars,\nStanford Dogs, and FGVC7 Plant Pathology. We also prove our model's\ninterpretability via qualitative results.\n"}
+{"abstract": "  Monitoring electronic properties of 2D materials is an essential step to open\na way for applications such as electronic devices and sensors. From this\nperspective, Bernal bilayer graphene (BLG) is a fairly simple system that\noffers great possibilities for tuning electronic gap and charge carriers'\nmobility by selective functionalization (adsorptions of atoms or molecules).\nHere, we present a detailed numerical study of BLG electronic properties when\ntwo types of adsorption site are present simultaneously. We focus on realistic\ncases that could be realized experimentally with adsorbate concentration c\nvarying from 0.25% to 5%. For a given value of c, when the electronic doping is\nlower than c we show that quantum effects, which are ignored in usual\nsemi-classical calculations, strongly affect the electronic structure and the\ntransport properties. A wide range of behaviors is indeed found, such as gap\nopening, metallic behavior or abnormal conductivity, which depend on the\nadsorbate positions, the c value, the doping, and eventually the coupling\nbetween midgap states which can create a midgap band. These behaviors are\nunderstood by simple arguments based on the fact that BLG lattice is bipartite.\nWe also analyze the conductivity at low temperature, where multiple scattering\neffects cannot be ignored. Moreover, when the Fermi energy lies in the band of\nmidgap states, the average velocity of charge carriers cancels but conduction\nis still possible thanks to quantum fluctuations of the velocity.\n"}
+{"abstract": "  Spear Phishing is a harmful cyber-attack facing business and individuals\nworldwide. Considerable research has been conducted recently into the use of\nMachine Learning (ML) techniques to detect spear-phishing emails. ML-based\nsolutions may suffer from zero-day attacks; unseen attacks unaccounted for in\nthe training data. As new attacks emerge, classifiers trained on older data are\nunable to detect these new varieties of attacks resulting in increasingly\ninaccurate predictions. Spear Phishing detection also faces scalability\nchallenges due to the growth of the required features which is proportional to\nthe number of the senders within a receiver mailbox. This differs from\ntraditional phishing attacks which typically perform only a binary\nclassification between phishing and benign emails. Therefore, we devise a\npossible solution to these problems, named RAIDER: Reinforcement AIded Spear\nPhishing DEtectoR. A reinforcement-learning based feature evaluation system\nthat can automatically find the optimum features for detecting different types\nof attacks. By leveraging a reward and penalty system, RAIDER allows for\nautonomous features selection. RAIDER also keeps the number of features to a\nminimum by selecting only the significant features to represent phishing emails\nand detect spear-phishing attacks. After extensive evaluation of RAIDER over\n11,000 emails and across 3 attack scenarios, our results suggest that using\nreinforcement learning to automatically identify the significant features could\nreduce the dimensions of the required features by 55% in comparison to existing\nML-based systems. It also improves the accuracy of detecting spoofing attacks\nby 4% from 90% to 94%. In addition, RAIDER demonstrates reasonable detection\naccuracy even against a sophisticated attack named Known Sender in which\nspear-phishing emails greatly resemble those of the impersonated sender.\n"}
+{"abstract": "  Radiative seesaw models have the attractive property of providing dark matter\ncandidates in addition to generation of neutrino masses. Here we present a\nstudy of neutrino signals from the annihilation of dark matter particles which\nhave been gravitationally captured in the Sun, in the framework of the\nscotogenic model. We compute expected event rates in the IceCube detector in\nits 86-string configuration. As fermionic dark matter does not accumulate in\nthe Sun, we study the case of scalar dark matter, with a scan over the\nparameter space. Due to a naturally small mass splitting between the two\nneutral scalar components, inelastic scattering processes with nucleons can\noccur. We find that for small mass splittings, the model yields very high event\nrates. If a detailed analysis at IceCube can exclude these parameter points,\nour findings can be translated into a lower limit on one of the scalar\ncouplings in the model. For larger mass splittings only the elastic case needs\nto be considered. We find that in this scenario the XENON1T limits exclude all\npoints with sufficiently large event rates.\n"}
+{"abstract": "  We consider a unitary circuit where the underlying gates are chosen to be\nR-matrices satisfying the Yang-Baxter equation and correlation functions can be\nexpressed through a transfer matrix formalism. These transfer matrices are no\nlonger Hermitian and differ from the ones guaranteeing local conservation laws,\nbut remain mutually commuting at different values of the spectral parameter\ndefining the circuit. Exact eigenstates can still be constructed as a Bethe\nansatz, but while these transfer matrices are diagonalizable in the\ninhomogeneous case, the homogeneous limit corresponds to an exceptional point\nwhere multiple eigenstates coalesce and Jordan blocks appear. Remarkably, the\ncomplete set of (generalized) eigenstates is only obtained when taking into\naccount a combinatorial number of nontrivial vacuum states. In all cases, the\nBethe equations reduce to those of the integrable spin-1 chain and exhibit a\nglobal SU(2) symmetry, significantly reducing the total number of eigenstates\nrequired in the calculation of correlation functions. A similar construction is\nshown to hold for the calculation of out-of-time-order correlations.\n"}
+{"abstract": "  This paper focuses on understanding how the generalization error scales with\nthe amount of the training data for deep neural networks (DNNs). Existing\ntechniques in statistical learning require computation of capacity measures,\nsuch as VC dimension, to provably bound this error. It is however unclear how\nto extend these measures to DNNs and therefore the existing analyses are\napplicable to simple neural networks, which are not used in practice, e.g.,\nlinear or shallow ones or otherwise multi-layer perceptrons. Moreover, many\ntheoretical error bounds are not empirically verifiable. We derive estimates of\nthe generalization error that hold for deep networks and do not rely on\nunattainable capacity measures. The enabling technique in our approach hinges\non two major assumptions: i) the network achieves zero training error, ii) the\nprobability of making an error on a test point is proportional to the distance\nbetween this point and its nearest training point in the feature space and at a\ncertain maximal distance (that we call radius) it saturates. Based on these\nassumptions we estimate the generalization error of DNNs. The obtained estimate\nscales as O(1/(\\delta N^{1/d})), where N is the size of the training data and\nis parameterized by two quantities, the effective dimensionality of the data as\nperceived by the network (d) and the aforementioned radius (\\delta), both of\nwhich we find empirically. We show that our estimates match with the\nexperimentally obtained behavior of the error on multiple learning tasks using\nbenchmark data-sets and realistic models. Estimating training data requirements\nis essential for deployment of safety critical applications such as autonomous\ndriving etc. Furthermore, collecting and annotating training data requires a\nhuge amount of financial, computational and human resources. Our empirical\nestimates will help to efficiently allocate resources.\n"}
+{"abstract": "  We study a class of elliptic and parabolic equations in non-divergence form\nwith singular coefficients in an upper half space with the homogeneous\nDirichlet boundary condition. Intrinsic weighted Sobolev spaces are found in\nwhich the existence and uniqueness of strong solutions are proved when the\npartial oscillations of coefficients in small parabolic cylinders are small.\nOur results are new even when the coefficients are constants\n"}
+{"abstract": "  The influence of forward speed on stochastic free-surface crossing, in a\nGaussian wave field, is investigated. The case of a material point moving with\na constant forward speed is considered; the wave field is assumed stationary in\ntime, and homogeneous in space. The focus is on up-crossing events, which are\ndefined as the material point crossing the free surface, into the water domain.\nThe effect of the Doppler shift (induced by the forward speed) on the\nup-crossing frequency, and the related conditional joint distribution of wave\nkinematic variables is analytically investigated. Some general trends are\nillustrated through different examples, where three kinds of wave direction\ndistribution are considered: unidirectional, short-crested anisotropic, and\nisotropic. The way the developed approach may be used in the context of\nslamming on marine structures is briefly discussed.\n"}
+{"abstract": "  In this paper, we consider the use of cross-layer network coding (CLNC),\ncaching, and device-to-device (D2D) communications to jointly optimize the\ndelivery of a set of popular contents to a set of user devices (UDs). In the\nconsidered D2D network, a group of near-by UDs cooperate with each other and\nuse NC to combine their cached files, so as the completion time required for\ndelivering all requested contents to all UDs is minimized. Unlike the previous\nwork that considers only one transmitting UD at a time, our work allows\nmultiple UDs to transmit simultaneously given the interference among the active\nlinks is small. Such configuration brings a new trade-off among scheduling UDs\nto transmitting UDs, selecting the coding decisions and the transmission\nrate/power. Therefore, we consider the completion time minimization problem\nthat involves scheduling multiple transmitting UDs, determining their\ntransmission rates/powers and file combinations. The problem is shown to be\nintractable because it involves all future coding decisions. To tackle the\nproblem at each transmission slot, we first design a graph called herein the\nD2D Rate-Aware IDNC graph where its vertices have weights that judiciously\nbalance between the rates/powers of the transmitting UDs and the number of\ntheir scheduled UDs. Then, we propose an innovative and efficient CLNC solution\nthat iteratively selects a set of transmitting UDs only if the interference\ncaused by the transmissions of the newly selected UDs does not significantly\nimpact the overall completion time. Simulation results show that the proposed\nsolution offers significant completion time reduction compared with the\nexisting algorithms.\n"}
+{"abstract": "  Massive machine type communications (mMTC) is one of the cornerstone services\nthat have to be supported by 5G systems. 3GPP has already introduced LTE-M and\nNB-IoT, often referred to as cellular IoT, in 3GPP Releases 13, 14, and 15 and\nsubmitted these technologies as part of 3GPP IMT-2020 (i.e., 5G) technology\nsubmission to ITU-R. Even though NB-IoT and LTE-M have shown to satisfy 5G mMTC\nrequirements defined by ITU-R, it is expected that these cellular IoT solutions\nwill not address all aspects of IoT and ongoing digitalization, including the\nsupport for direct communication between \"things\" with flexible deployments,\ndifferent business models, as well as support for even higher node densities\nand enhanced coverage. In this paper, we introduce the DECT-2020 standard\nrecently published by ETSI for mMTC communications. We evaluate its performance\nand compare it to the existing LPWAN solutions showing that it outperforms\nthose in terms of supported density of nodes while still keeping delay and loss\nguarantees at the required level.\n"}
+{"abstract": "  The paths leading to future networks are pointing towards a data-driven\nparadigm to better cater to the explosive growth of mobile services as well as\nthe increasing heterogeneity of mobile devices, many of which generate and\nconsume large volumes and variety of data. These paths are also hampered by\nsignificant challenges in terms of security, privacy, services provisioning,\nand network management. Blockchain, which is a technology for building\ndistributed ledgers that provide an immutable log of transactions recorded in a\ndistributed network, has become prominent recently as the underlying technology\nof cryptocurrencies and is revolutionizing data storage and processing in\ncomputer network systems. For future data-driven networks (DDNs), blockchain is\nconsidered as a promising solution to enable the secure storage, sharing, and\nanalytics of data, privacy protection for users, robust, trustworthy network\ncontrol, and decentralized routing and resource managements. However, many\nimportant challenges and open issues remain to be addressed before blockchain\ncan be deployed widely to enable future DDNs. In this article, we present a\nsurvey on the existing research works on the application of blockchain\ntechnologies in computer networks, and identify challenges and potential\nsolutions in the applications of blockchains in future DDNs. We identify\napplication scenarios in which future blockchain-empowered DDNs could improve\nthe efficiency and security, and generally the effectiveness of network\nservices.\n"}
+{"abstract": "  Cross-language authorship attribution problems rely on either translation to\nenable the use of single-language features, or language-independent feature\nextraction methods. Until recently, the lack of datasets for this problem\nhindered the development of the latter, and single-language solutions were\nperformed on machine-translated corpora. In this paper, we present a novel\nlanguage-independent feature for authorship analysis based on dependency graphs\nand universal part of speech tags, called DT-grams (dependency tree grams),\nwhich are constructed by selecting specific sub-parts of the dependency graph\nof sentences. We evaluate DT-grams by performing cross-language authorship\nattribution on untranslated datasets of bilingual authors, showing that, on\naverage, they achieve a macro-averaged F1 score of 0.081 higher than previous\nmethods across five different language pairs. Additionally, by providing\nresults for a diverse set of features for comparison, we provide a baseline on\nthe previously undocumented task of untranslated cross-language authorship\nattribution.\n"}
+{"abstract": "  As the 5G standards mature and awareness of the capabilities of the\ntechnology increases, industry verticals are becoming more eager to test new\nservices and develop them to the level of maturity required for market\nadoption. Network slicing, i.e. multiple virtual networks running on a common\ninfrastructure, is considered a key mechanism to serve the multitude of tenants\n(e.g. vertical industries) targeted by forthcoming fifth generation (5G)\nsystems, in a flexible and cost-efficient manner. It is predicted that one of\nthe most popular models for customers will be the Network Slice as a Service\n(NSaaS) model. This model allows a Network Service Customer to order and\nconfigure a Network Slice and offered it as a service. This work presents\nOpenlice a Service based, opens-ource OSS for delivering NSaaS following\nemerging standards from SDOs. We strongly believe that such open source\nsolutions make it easier for organizations to enable complex scenarios\nespecially in the are of Non-Public Networks.\n"}
+{"abstract": "  We have measured trigonometric parallaxes for four water masers associated\nwith distant massive young stars in the inner regions of the Galaxy using the\nVLBA as part of the BeSSeL Survey. G026.50$+$0.28. is located at the near end\nof the Galactic bar, perhaps at the origin of the Norma spiral arm.\nG020.77$-$0.05 is in the Galactic Center region and is likely associated with a\nfar-side extension of the Scutum arm. G019.60$-$0.23 and G020.08$-$0.13 are\nlikely associated and lie well past the Galactic Center. These sources appear\nto be in the Sagittarius spiral arm, but an association with the Perseus arm\ncannot be ruled out.\n"}
+{"abstract": "  The goal of this paper is to adapt speaker embeddings for solving the problem\nof speaker diarisation. The quality of speaker embeddings is paramount to the\nperformance of speaker diarisation systems. Despite this, prior works in the\nfield have directly used embeddings designed only to be effective on the\nspeaker verification task. In this paper, we propose three techniques that can\nbe used to better adapt the speaker embeddings for diarisation: dimensionality\nreduction, attention-based embedding aggregation, and non-speech clustering. A\nwide range of experiments is performed on various challenging datasets. The\nresults demonstrate that all three techniques contribute positively to the\nperformance of the diarisation system achieving an average relative improvement\nof 25.07% in terms of diarisation error rate over the baseline.\n"}
+{"abstract": "  Prevention and early diagnosis of breast cancer (BC) is an essential\nprerequisite for the selection of proper treatment. The substantial pressure\ndue to the increase of demand for faster and more precise diagnostic results\ndrives for automatic solutions. In the past decade, deep learning techniques\nhave demonstrated their power over several domains, and Computer-Aided (CAD)\ndiagnostic became one of them. However, when it comes to the analysis of Whole\nSlide Images (WSI), most of the existing works compute predictions from levels\nindependently. This is, however, in contrast to the histopathologist expert\napproach who requires to see a global architecture of tissue structures\nimportant in BC classification.\n  We present a deep learning-based solution and framework for processing WSI\nbased on a novel approach utilizing the advantages of image levels. We apply\nthe weighing of information extracted from several levels into the final\nclassification of the malignancy. Our results demonstrate the profitability of\nglobal information with an increase of accuracy from 72.2% to 84.8%.\n"}
+{"abstract": "  In the present paper we address double parton scattering (DPS) in quasi-real\nphoton-proton interactions. By using electromagnetic and hadronic models of the\nphoton light cone wave functions, we compute the so-called effective\ncross-section, $\\sigma_{eff}^{\\gamma p}$ which allows us to calculate the DPS\ncontribution to these processes under dedicated assumptions. In particular, for\nthe four-jet photoproduction in HERA kinematics we found a sizeable DPS\ncontribution. We show that if the photon virtuality $Q^2$ could be measured and\nthus the dependence of $\\sigma_{eff}^{\\gamma p}$ on such a parameter exposed,\ninformation on the transverse distance between partons active in proton could\nbe extracted. To this aim, we set lower limits on the integrated luminosity\nneeded to observe such an effect which would allow the extraction of novel\ninformation on the proton structure.\n"}
+{"abstract": "  This paper studies the dissipative generalized surface quasi-geostrophic\nequations in a supercritical regime where the order of the dissipation is small\nrelative to order of the velocity, and the velocities are less regular than the\nadvected scalar by up to one order of derivative. We also consider a\nnon-degenerate modification of the endpoint case in which the velocity is less\nsmooth than the advected scalar by slightly more than one order. The existence\nand uniqueness theory of these equations in the borderline Sobolev spaces is\naddressed, as well as the instantaneous smoothing effect of their corresponding\nsolutions. In particular, it is shown that solutions emanating from initial\ndata belonging to these Sobolev classes immediately enter a Gevrey class. Such\nresults appear to be the first of its kind for a quasilinear parabolic equation\nwhose coefficients are of higher order than its linear term; they rely on an\napproximation scheme which modifies the flux in such a way that preserves the\nunderlying commutator structure lost by having to work in the critical space\nsetting, as well as delicate adaptations of well-known commutator estimates to\nGevrey classes.\n"}
+{"abstract": "  A nonnoetherian spacetime is a Lorentzian manifold that contains a set of\ncausal curves with no distinct interior points. We show that on such a\nspacetime, hidden within the free Dirac Lagrangian is the entire standard model\n(with four massive neutral scalar bosons), with the correct spin, electric\ncharge, color charge, and relative mass orderings of each particle.\nFurthermore, using two 'fusion rules', we are able to reproduce almost all of\nthe standard model trivalent vertices, as well as electroweak parity violation.\nFinally, we find that on a nonnoetherian spacetime, C, P, and T each sit in a\ndifferent connected component of the full Lorentz group, and their product is\nthe identity.\n"}
+{"abstract": "  We use theory and numerical computation to determine the shape of an\naxisymmetric fluid membrane with a resistance to bending and constant area. The\nmembrane connects two rings in the classic geometry that produces a catenoidal\nshape in a soap film. In our problem, we find infinitely many branches of\nsolutions for the shape and external force as functions of the separation of\nthe rings, analogous to the infinite family of eigenmodes for the Euler\nbuckling of a slender rod. Special attention is paid to the catenoid, which\nemerges as the shape of maximal allowable separation when the area is less than\na critical area equal to the planar area enclosed by the two rings. A\nperturbation theory argument directly relates the tension of catenoidal\nmembranes to the stability of catenoidal soap films in this regime. When the\nmembrane area is larger than the critical area, we find additional cylindrical\ntether solutions to the shape equations at large ring separation, and that\narbitrarily large ring separations are possible. These results apply for the\ncase of vanishing Gaussian curvature modulus; when the Gaussian curvature\nmodulus is nonzero and the area is below the critical area, the force and the\nmembrane tension diverge as the ring separation approaches its maximum value.\nWe also examine the stability of our shapes and analytically show that\ncatenoidal membranes have markedly different stability properties than their\nsoap film counterparts.\n"}
+{"abstract": "  Since the mapping relationship between definitized intra-interventional X-ray\nand undefined pre-interventional Computed Tomography(CT) is uncertain,\nauxiliary positioning devices or body markers, such as medical implants, are\ncommonly used to determine this relationship. However, such approaches can not\nbe widely used in clinical due to the complex realities. To determine the\nmapping relationship, and achieve a initializtion post estimation of human body\nwithout auxiliary equipment or markers, proposed method applies image\nsegmentation and deep feature matching to directly match the X-ray and CT\nimages. As a result, the well-trained network can directly predict the spatial\ncorrespondence between arbitrary X-ray and CT. The experimental results show\nthat when combining our approach with the conventional approach, the achieved\naccuracy and speed can meet the basic clinical intervention needs, and it\nprovides a new direction for intra-interventional registration.\n"}
+{"abstract": "  Billions of photos are uploaded to the web daily through various types of\nsocial networks. Some of these images receive millions of views and become\npopular, whereas others remain completely unnoticed. This raises the problem of\npredicting image popularity on social media. The popularity of an image can be\naffected by several factors, such as visual content, aesthetic quality, user,\npost metadata, and time. Thus, considering all these factors is essential for\naccurately predicting image popularity. In addition, the efficiency of the\npredictive model also plays a crucial role. In this study, motivated by\nmultimodal learning, which uses information from various modalities, and the\ncurrent success of convolutional neural networks (CNNs) in various fields, we\npropose a deep learning model, called visual-social convolutional neural\nnetwork (VSCNN), which predicts the popularity of a posted image by\nincorporating various types of visual and social features into a unified\nnetwork model. VSCNN first learns to extract high-level representations from\nthe input visual and social features by utilizing two individual CNNs. The\noutputs of these two networks are then fused into a joint network to estimate\nthe popularity score in the output layer. We assess the performance of the\nproposed method by conducting extensive experiments on a dataset of\napproximately 432K images posted on Flickr. The simulation results demonstrate\nthat the proposed VSCNN model significantly outperforms state-of-the-art\nmodels, with a relative improvement of greater than 2.33%, 7.59%, and 14.16% in\nterms of Spearman's Rho, mean absolute error, and mean squared error,\nrespectively.\n"}
+{"abstract": "  We consider some energy integrals under slow growth and we prove that the\nlocal minimizers are locally Lipschitz continuous. Many examples are given,\neither with subquadratic $p,q-$growth and/or anisotropic growth.\n"}
+{"abstract": "  Making use of the gauge/string duality, it is possible to study some aspects\nof the string breaking phenomenon in the three quark system. Our results point\nout that the string breaking distance is not universal and depends on quark\ngeometry. The estimates of the ratio of the string breaking distance in the\nthree quark system to that in the quark-antiquark system would range\napproximately from $\\frac{2}{3}$ to $1$. In addition, it is shown that there\nare special geometries which allow more than one breaking distance.\n"}
+{"abstract": "  We present a measurement of the Hubble constant $H_0$ from surface brightness\nfluctuation (SBF) distances for 63 bright, mainly early-type galaxies out to\n100 Mpc observed with the Wide Field Camera 3 Infrared Channel (WFC3/IR) on the\nHubble Space Telescope (HST). The sample is drawn from several independent HST\nimaging programs using the F110W bandpass of WFC3/IR. The majority of galaxies\nare in the 50 to 80 Mpc range and come from the MASSIVE galaxy survey. The\nmedian statistical uncertainty on individual distance measurements is 4%. We\nconstruct the Hubble diagram with these IR SBF distances and constrain $H_0$\nusing {four} different treatments of the galaxy velocities. For the SBF zero\npoint calibration, we use both the existing tie to Cepheid variables, updated\nfor consistency with the latest determination of the distance to the Large\nMagellanic Cloud from detached eclipsing binaries, and a new tie to the tip of\nthe red giant branch (TRGB) calibrated from the maser distance to NGC4258.\nThese two SBF calibrations are consistent with each other and with theoretical\npredictions from stellar population models. From a weighted average of the\nCepheid and TRGB calibrations, we derive $H_0=73.3{\\,\\pm\\,}0.7{\\,\\pm\\,}2.4$\nkm/s/Mpc, where the error bars reflect the statistical and systematic\nuncertainties. This result accords well with recent measurements of $H_0$ from\nType~Ia supernovae, time delays in multiply lensed quasars, and water masers.\nThe systematic uncertainty could be reduced to below 2% by calibrating the SBF\nmethod with precision TRGB distances for a statistical sample of massive\nearly-type galaxies out to the Virgo cluster measured with the James Webb Space\nTelescope.\n"}
+{"abstract": "  Despite significant advancements in the field of multi-agent navigation,\nagents still lack the sophistication and intelligence that humans exhibit in\nmulti-agent settings. In this paper, we propose a framework for learning a\nhuman-like general collision avoidance policy for agent-agent interactions in\nfully decentralized, multi-agent environments. Our approach uses knowledge\ndistillation with reinforcement learning to shape the reward function based on\nexpert policies extracted from human trajectory demonstrations through behavior\ncloning. We show that agents trained with our approach can take human-like\ntrajectories in collision avoidance and goal-directed steering tasks not\nprovided by the demonstrations, outperforming the experts as well as\nlearning-based agents trained without knowledge distillation.\n"}
+{"abstract": "  In this note we show that the Linet-Tian family of solutions of the vacuum\nEinstein equations with a cosmological constant are a restricted set of the\nsolutions of the Einstein field equations for a rotating perfect fluid\npreviously found by A. Krasi\\'nski.\n"}
+{"abstract": "  This paper provides an overview of the current state of the stripping model\nfor short gamma-ray bursts. After the historical joint detection of the\ngravitational wave event GW170817 and the accompanying gamma-ray burst\nGRB170817A, the relation between short gamma-ray bursts and neutron star\nmergers has been reliably confirmed. We show that many properties of\nGRB170817A, which turned out to be peculiar in comparison with other short\ngamma-ray bursts, are naturally explained in the context of the stripping\nmodel, specifically, the time (1.7 s) between the peak of the gravitational\nwave signal and the detection of the gamma-ray burst, its total isotropic\nenergy, and the parameters of the red and blue components of the accompanying\nkilonova.\n"}
+{"abstract": "  Increasingly important photomechanical materials produce stress and\nmechanical work when illuminated. We propose experimentally accessible\nperformance metrics for photostress and photowork, enabling comparison of\nmaterials performance. We relate these metrics to material properties,\nproviding a framework for the design and optimization of photomechanical\nmaterials.\n"}
+{"abstract": "  Real-world data is usually segmented by attributes and distributed across\ndifferent parties. Federated learning empowers collaborative training without\nexposing local data or models. As we demonstrate through designed attacks, even\nwith a small proportion of corrupted data, an adversary can accurately infer\nthe input attributes. We introduce an adversarial learning based procedure\nwhich tunes a local model to release privacy-preserving intermediate\nrepresentations. To alleviate the accuracy decline, we propose a defense method\nbased on the forward-backward splitting algorithm, which respectively deals\nwith the accuracy loss and privacy loss in the forward and backward gradient\ndescent steps, achieving the two objectives simultaneously. Extensive\nexperiments on a variety of datasets have shown that our defense significantly\nmitigates privacy leakage with negligible impact on the federated learning\ntask.\n"}
+{"abstract": "  Humans tend to build environments with structure, which consists of mainly\nplanar surfaces. From the intersection of planar surfaces arise straight lines.\nLines have more degrees-of-freedom than points. Thus, line-based\nStructure-from-Motion (SfM) provides more information about the environment. In\nthis paper, we present solutions for SfM using lines, namely, incremental SfM.\nThese approaches consist of designing state observers for a camera's dynamical\nvisual system looking at a 3D line. We start by presenting a model that uses\nspherical coordinates for representing the line's moment vector. We show that\nthis parameterization has singularities, and therefore we introduce a more\nsuitable model that considers the line's moment and shortest viewing ray.\nConcerning the observers, we present two different methodologies. The first\nuses a memory-less state-of-the-art framework for dynamic visual systems. Since\nthe previous states of the robotic agent are accessible -- while performing the\n3D mapping of the environment -- the second approach aims at exploiting the use\nof memory to improve the estimation accuracy and convergence speed. The two\nmodels and the two observers are evaluated in simulation and real data, where\nmobile and manipulator robots are used.\n"}
+{"abstract": "  We show that, by utilising temporal quantum correlations as expressed by\npseudo-density operators (PDOs), it is possible to recover formally the\nstandard quantum dynamical evolution as a sequence of teleportations in time.\nWe demonstrate that any completely positive evolution can be formally\nreconstructed by teleportation with different temporally correlated states.\nThis provides a different interpretation of maximally correlated PDOs, as\nresources to induce quantum time-evolution. Furthermore, we note that the\npossibility of this protocol stems from the strict formal correspondence\nbetween spatial and temporal entanglement in quantum theory. We proceed to\ndemonstrate experimentally this correspondence, by showing a multipartite\nviolation of generalised temporal and spatial Bell inequalities and verifying\nagreement with theoretical predictions to a high degree of accuracy, in\nhigh-quality photon qubits.\n"}
+{"abstract": "  Shear bands originating from in situ tensile tests of\nAl$_{88}$Y$_{7}$Fe$_{5}$ melt-spun ribbons conducted in a transmission electron\nmicroscope are compared with ones which had formed ex situ during cold rolling.\nDuring in situ straining, the observations of a spearhead-like shear front, a\nmeniscus-like foil thickness reduction and no apparent shear steps to\naccommodate strain suggest shear band initiation by a rejuvenating shear front\nfollowed by shearing along the already softened paths. This leads to necking\nand subsequent failure under the reduced constraint of a 2D geometry in the\nthin foil and thus explains the observed lack of ductility under tension. In\ncontrast, shear bands formed during cold rolling display distinct alternating\ndensity changes and shear off-sets. An explanation for this difference may be\nthat in situ shear bands rip before such features could develop. Moreover, both\nin and ex situ experiments suggest that initiation, propagation and arrest of\nshear bands occur during different stages.\n"}
+{"abstract": "  We consider the problem of sensor selection for designing observer and filter\nfor continuous linear time invariant systems such that the sensor precisions\nare minimized, and the estimation errors are bounded by the prescribed\n$\\mathcal{H}_2/\\mathcal{H}_{\\infty}$ performance criteria. The proposed\nintegrated framework formulates the precision minimization as a convex\noptimization problem subject to linear matrix inequalities, and it is solved\nusing an algorithm based on the alternating direction method of multipliers\n(ADMM). We also present a greedy approach for sensor selection and demonstrate\nthe performance of the proposed algorithms using numerical simulations.\n"}
+{"abstract": "  We study the quantization of the corner symmetry algebra of 3d gravity, that\nis the algebra of observables associated with 1d spatial boundaries. In the\ncontinuum field theory, at the classical level, this symmetry algebra is given\nby the central extension of the Poincar\\'e loop algebra. At the quantum level,\nwe construct a discrete current algebra based on a quantum symmetry group given\nby the Drinfeld double $\\mathcal{D}\\mathrm{SU}(2)$. Those discrete currents\ndepend on an integer $N$, a discreteness parameter, understood as the number of\nquanta of geometry on the 1d boundary: low $N$ is the deep quantum regime,\nwhile large $N$ should lead back to a continuum picture. We show that this\nalgebra satisfies two fundamental properties. First, it is compatible with the\nquantum space-time picture given by the Ponzano-Regge state-sum model, which\nprovides discrete path integral amplitudes for 3d quantum gravity. The integer\n$N$ then counts the flux lines attached to the boundary. Second, we analyse the\nrefinement, coarse-graining and fusion processes as $N$ changes, and we show\nthat the $N\\rightarrow\\infty$ limit is a classical limit where we recover the\nPoincar\\'e current algebra. Identifying such a discrete current algebra on\nquantum boundaries is an important step towards understanding how conformal\nfield theories arise on spatial boundaries in quantized space-times such as in\nloop quantum gravity.\n"}
+{"abstract": "  Gate-all-around nanowire transistor, due to its extremely tight electrostatic\ncontrol and vertical integration capability, is a highly promising candidate\nfor sub-5 nm technology node. In particular, the junctionless nanowire\ntransistors are highly scalable with reduced variability due to avoidance of\nsteep source/drain junction formation by ion implantation. Here we demonstrate\na dual-gated junctionless nanowire \\emph{p}-type field effect transistor using\ntellurium nanowire as the channel. The dangling-bond-free surface due to the\nunique helical crystal structure of the nanowire, coupled with an integration\nof dangling-bond-free, high quality hBN gate dielectric, allows us to achieve a\nphonon-limited field effect hole mobility of $570\\,\\mathrm{cm^{2}/V\\cdot s}$ at\n270 K, which is well above state-of-the-art strained Si hole mobility. By\nlowering the temperature, the mobility increases to\n$1390\\,\\mathrm{cm^{2}/V\\cdot s}$ and becomes primarily limited by Coulomb\nscattering. \\txc{The combination of an electron affinity of $\\sim$4 eV and a\nsmall bandgap of tellurium provides zero Schottky barrier height for hole\ninjection at the metal-contact interface}, which is remarkable for reduction of\ncontact resistance in a highly scaled transistor. Exploiting these properties,\ncoupled with the dual-gated operation, we achieve a high drive current of\n$216\\,\\mathrm{\\mu A/\\mu m}$ while maintaining an on-off ratio in excess of\n$2\\times10^4$. The findings have intriguing prospects for alternate channel\nmaterial based next-generation electronics.\n"}
+{"abstract": "  In [I.R. Khairulin et al., submitted to Phys. Rev. Lett.] we propose a method\nfor amplifying a train of sub-femtosecond pulses of circularly or elliptically\npolarized extreme ultraviolet (XUV) radiation, constituted by high-order\nharmonics of an infrared (IR) laser field, in a neon-like active medium of a\nplasma-based X-ray laser, additionally irradiated with a replica of a\nfundamental frequency laser field used to generate harmonics, and show the\npossibility of maintaining or enhancing the ellipticity of high-harmonic\nradiation during its amplification. In the present paper we describe this\nprocess in detail both for a single harmonic component and a sub-femtosecond\npulse train formed by a set of harmonics. We derive the analytical theory and\ndescribe both analytically and numerically the evolution of the high-harmonic\nfield during its propagation through the medium. We discuss also the\npossibility of an experimental implementation of the suggested technique in an\nactive medium of an X-ray laser based on neon-like Ti^{12+} ions irradiated by\nan IR laser field with a wavelength of 3.9 microns.\n"}
+{"abstract": "  Bottom-up approaches for image-based multi-person pose estimation consist of\ntwo stages: (1) keypoint detection and (2) grouping of the detected keypoints\nto form person instances. Current grouping approaches rely on learned embedding\nfrom only visual features that completely ignore the spatial configuration of\nhuman poses. In this work, we formulate the grouping task as a graph\npartitioning problem, where we learn the affinity matrix with a Graph Neural\nNetwork (GNN). More specifically, we design a Geometry-aware Association GNN\nthat utilizes spatial information of the keypoints and learns local affinity\nfrom the global context. The learned geometry-based affinity is further fused\nwith appearance-based affinity to achieve robust keypoint association. Spectral\nclustering is used to partition the graph for the formation of the pose\ninstances. Experimental results on two benchmark datasets show that our\nproposed method outperforms existing appearance-only grouping frameworks, which\nshows the effectiveness of utilizing spatial context for robust grouping.\nSource code is available at: https://github.com/jiahaoLjh/PoseGrouping.\n"}
+{"abstract": "  Existing works for aspect-based sentiment analysis (ABSA) have adopted a\nunified approach, which allows the interactive relations among subtasks.\nHowever, we observe that these methods tend to predict polarities based on the\nliteral meaning of aspect and opinion terms and mainly consider relations\nimplicitly among subtasks at the word level. In addition, identifying multiple\naspect-opinion pairs with their polarities is much more challenging. Therefore,\na comprehensive understanding of contextual information w.r.t. the aspect and\nopinion are further required in ABSA. In this paper, we propose Deep\nContextualized Relation-Aware Network (DCRAN), which allows interactive\nrelations among subtasks with deep contextual information based on two modules\n(i.e., Aspect and Opinion Propagation and Explicit Self-Supervised Strategies).\nEspecially, we design novel self-supervised strategies for ABSA, which have\nstrengths in dealing with multiple aspects. Experimental results show that\nDCRAN significantly outperforms previous state-of-the-art methods by large\nmargins on three widely used benchmarks.\n"}
+{"abstract": "  A common feature of electromagnetic emission from solar flares is the\npresence of intensity pulsations that vary as a function of time. Known as\nquasi-periodic pulsations (QPPs), these variations in flux appear to include\nperiodic components and characteristic time-scales. Here, we analyse a GOES\nM3.7 class flare exhibiting pronounced QPPs across a broad band of wavelengths\nusing imaging and time-series analysis. We identify QPPs in the timeseries of\nX-ray, low frequency radio and EUV wavelengths using wavelet analysis, and\nlocalise the region of the flare site from which the QPPs originate via X-ray\nand EUV imaging. It was found that the pulsations within the 171 \\.A, 1600 \\.A,\nsoft X-ray (SXR), and hard X-ray (HXR) light curves yielded similar periods of\n$\\sim$122 s, $\\sim$131s, $\\sim$123 s, and $\\sim$137 s, respectively, indicating\na common progenitor. The low frequency radio emission at 2.5 MHz contained a\nlonger period of $\\sim$231 s. Imaging analysis indicates that the location of\nthe X-ray and EUV pulsations originates from a HXR footpoint linked to a system\nof nearby open magnetic field lines. Our results suggest that intermittent\nparticle acceleration, likely due to 'bursty' magnetic reconnection, is\nresponsible for the QPPs. The precipitating electrons accelerated towards the\nchromosphere produce the X-ray and EUV pulsations, while the escaping electrons\nresult in low frequency radio pulses in the form of type III radio bursts. The\nmodulation of the reconnection process, resulting in episodic particle\nacceleration, explains the presence of these QPPs across the entire spatial\nrange of flaring emission.\n"}
+{"abstract": "  Data is the engine of modern computer vision, which necessitates collecting\nlarge-scale datasets. This is expensive, and guaranteeing the quality of the\nlabels is a major challenge. In this paper, we investigate efficient annotation\nstrategies for collecting multi-class classification labels for a large\ncollection of images. While methods that exploit learnt models for labeling\nexist, a surprisingly prevalent approach is to query humans for a fixed number\nof labels per datum and aggregate them, which is expensive. Building on prior\nwork on online joint probabilistic modeling of human annotations and\nmachine-generated beliefs, we propose modifications and best practices aimed at\nminimizing human labeling effort. Specifically, we make use of advances in\nself-supervised learning, view annotation as a semi-supervised learning\nproblem, identify and mitigate pitfalls and ablate several key design choices\nto propose effective guidelines for labeling. Our analysis is done in a more\nrealistic simulation that involves querying human labelers, which uncovers\nissues with evaluation using existing worker simulation methods. Simulated\nexperiments on a 125k image subset of the ImageNet100 show that it can be\nannotated to 80% top-1 accuracy with 0.35 annotations per image on average, a\n2.7x and 6.7x improvement over prior work and manual annotation, respectively.\nProject page: https://fidler-lab.github.io/efficient-annotation-cookbook\n"}
+{"abstract": "  There is currently a gap between the natural language expression of scholarly\npublications and their structured semantic content modeling to enable\nintelligent content search. With the volume of research growing exponentially\nevery year, a search feature operating over semantically structured content is\ncompelling. The SemEval-2021 Shared Task NLPContributionGraph (a.k.a. 'the NCG\ntask') tasks participants to develop automated systems that structure\ncontributions from NLP scholarly articles in the English language. Being the\nfirst-of-its-kind in the SemEval series, the task released structured data from\nNLP scholarly articles at three levels of information granularity, i.e. at\nsentence-level, phrase-level, and phrases organized as triples toward Knowledge\nGraph (KG) building. The sentence-level annotations comprised the few sentences\nabout the article's contribution. The phrase-level annotations were scientific\nterm and predicate phrases from the contribution sentences. Finally, the\ntriples constituted the research overview KG. For the Shared Task,\nparticipating systems were then expected to automatically classify contribution\nsentences, extract scientific terms and relations from the sentences, and\norganize them as KG triples.\n  Overall, the task drew a strong participation demographic of seven teams and\n27 participants. The best end-to-end task system classified contribution\nsentences at 57.27% F1, phrases at 46.41% F1, and triples at 22.28% F1. While\nthe absolute performance to generate triples remains low, in the conclusion of\nthis article, the difficulty of producing such data and as a consequence of\nmodeling it is highlighted.\n"}
+{"abstract": "  The paper utilizes H\\\"older graphical derivatives for characterizing H\\\"older\nstrong subregularity, isolated calmness and sharp minimum. As applications, we\ncharacterize H\\\"older isolated calmness in linear semi-infinite optimization\nand H\\\"older sharp minimizers of some penalty functions for constrained\noptimization.\n"}
+{"abstract": "  Given the prominence of current 3D sensors, a fine-grained analysis on the\nbasic point cloud data is worthy of further investigation. Particularly, real\npoint cloud scenes can intuitively capture complex surroundings in the real\nworld, but due to 3D data's raw nature, it is very challenging for machine\nperception. In this work, we concentrate on the essential visual task, semantic\nsegmentation, for large-scale point cloud data collected in reality. On the one\nhand, to reduce the ambiguity in nearby points, we augment their local context\nby fully utilizing both geometric and semantic features in a bilateral\nstructure. On the other hand, we comprehensively interpret the distinctness of\nthe points from multiple resolutions and represent the feature map following an\nadaptive fusion method at point-level for accurate semantic segmentation.\nFurther, we provide specific ablation studies and intuitive visualizations to\nvalidate our key modules. By comparing with state-of-the-art networks on three\ndifferent benchmarks, we demonstrate the effectiveness of our network.\n"}
+{"abstract": "  Vehicle safety systems have substantially decreased motor vehicle\ncrash-related injuries and fatalities, but injuries to the lumbar spine still\nhave been reported. Experimental and computational analyses of upright and,\nparticularly, reclined occupants in frontal crashes have shown that the lumbar\nspine can be subjected to axial compression followed by combined\ncompression-flexion loading. Lumbar spine failure tolerance in combined\ncompression-flexion has not been widely explored in the literature. Therefore,\nthe goal of this study was to measure the failure tolerance of the lumbar spine\nin combined compression and flexion. Forty 3-vertebra lumbar spine segments\nwere pre-loaded with axial compression and then subjected to dynamic flexion\nbending until failure. Clinically relevant middle vertebra fractures were\nobserved in twenty-one of the specimens, including compression and burst\nfractures. The remaining nineteen specimens experienced failure at the potting\ngrip interface. Since specimen characteristics and pre-test axial load varied\nwidely within the sample, failure forces (mean 3.4 kN, range 1.6-5.1 kN) and\nmoments (mean 73 Nm, range 0-181 Nm) also varied widely. Tobit univariate\nregressions were performed to determine the relationship between censored\nfailure tolerance and specimen sex, segment type (upper/lower), age, and\ncross-sectional area. Age, sex, and cross-sectional area significantly affected\nfailure force and moment individually (p<0.0024). These data can be used to\ndevelop injury prediction tools for lumbar spine fractures and further research\nin future safety systems.\n"}
+{"abstract": "  As more and more robots are envisioned to cooperate with humans sharing the\nsame space, it is desired for robots to be able to predict others' trajectories\nto navigate in a safe and self-explanatory way. We propose a Convolutional\nNeural Network-based approach to learn, detect, and extract patterns in\nsequential trajectory data, known here as Social Pattern Extraction Convolution\n(Social-PEC). A set of experiments carried out on the human trajectory\nprediction problem shows that our model performs comparably to the state of the\nart and outperforms in some cases. More importantly, the proposed approach\nunveils the obscurity in the previous use of a pooling layer, presenting a way\nto intuitively explain the decision-making process.\n"}
+{"abstract": "  It is widely believed that natural image data exhibits low-dimensional\nstructure despite the high dimensionality of conventional pixel\nrepresentations. This idea underlies a common intuition for the remarkable\nsuccess of deep learning in computer vision. In this work, we apply dimension\nestimation tools to popular datasets and investigate the role of\nlow-dimensional structure in deep learning. We find that common natural image\ndatasets indeed have very low intrinsic dimension relative to the high number\nof pixels in the images. Additionally, we find that low dimensional datasets\nare easier for neural networks to learn, and models solving these tasks\ngeneralize better from training to test data. Along the way, we develop a\ntechnique for validating our dimension estimation tools on synthetic data\ngenerated by GANs allowing us to actively manipulate the intrinsic dimension by\ncontrolling the image generation process. Code for our experiments may be found\nhere https://github.com/ppope/dimensions.\n"}
+{"abstract": "  We derive explicitly the structural properties of the $p$-adic special\northogonal groups in dimension three, for all primes $p$, and, along the way,\nthe two-dimensional case. In particular, starting from the unique definite\nquadratic form in three dimensions (up to linear equivalence and rescaling), we\nshow that every element of $SO(3)_p$ is a rotation around an axis. An important\npart of the analyis is the classification of all definite forms in two\ndimensions, yielding a description of the rotation subgroups around any fixed\naxis, which all turn out to be abelian and parametrised naturally by the\nprojective line.\n  Furthermore, we find that for odd primes $p$, the entire group $SO(3)_p$\nadmits a representation in terms of Cardano angles of rotations around the\nreference axes, in close analogy to the real orthogonal case. However, this\nworks only for certain orderings of the product of rotations around the\ncoordinate axes, depending on the prime; furthermore, there is no general Euler\nangle decomposition. For $p=2$, no Euler or Cardano decomposition exists.\n"}
+{"abstract": "  Direct-to-satellite (DtS) communication has gained importance recently to\nsupport globally connected Internet of things (IoT) networks. However,\nrelatively long distances of densely deployed satellite networks around the\nEarth cause a high path loss. In addition, since high complexity operations\nsuch as beamforming, tracking and equalization have to be performed in IoT\ndevices partially, both the hardware complexity and the need for high-capacity\nbatteries of IoT devices increase. The reconfigurable intelligent surfaces\n(RISs) have the potential to increase the energy-efficiency and to perform\ncomplex signal processing over the transmission environment instead of IoT\ndevices. But, RISs need the information of the cascaded channel in order to\nchange the phase of the incident signal. This study proposes graph attention\nnetworks (GATs) for the challenging channel estimation problem and examines the\nperformance of DtS IoT networks for different RIS configurations under GAT\nchannel estimation.\n"}
+{"abstract": "  Mechatronic systems are commonly used in the industry, where fast and\naccurate motion performance is always required to guarantee manufacturing\nprecision and efficiency. Nevertheless, the system model and parameters are\ndifficult to be obtained accurately. Moreover, the high-order modes, strong\ncoupling in the multi-axis systems, or unmodeled frictions will bring uncertain\ndynamics to the system. To overcome the above-mentioned issues and enhance the\nmotion performance, this paper introduces a novel intelligent and totally\nmodel-free control method for mechatronic systems with unknown dynamics. In\ndetail, a 2-degree-of-freedom (DOF) architecture is designed, which organically\nmerges a generalized super-twisting algorithm with a unique iterative learning\nlaw. The controller solely utilizes the input-output data collected in\niterations such that it works without any knowledge of the system parameters.\nThe rigorous proof of convergence ability is given and a case study on\nflexture-joint dual-drive H-gantry stage is shown to validate the effectiveness\nof the proposed method.\n"}
+{"abstract": "  We construct a new class of efficient Monte Carlo methods based on\ncontinuous-time piecewise deterministic Markov processes (PDMP) suitable for\ninference in high dimensional sparse models, i.e. models for which there is\nprior knowledge that many coordinates are likely to be exactly $0$. This is\nachieved with the fairly simple idea of endowing existing PDMP samplers with\nsticky coordinate axes, coordinate planes etc. Upon hitting those subspaces, an\nevent is triggered, during which the process sticks to the subspace, this way\nspending some time in a sub-model. That introduces non-reversible jumps between\ndifferent (sub-)models. The approach can also be combined with local\nimplementations of PDMP samplers to target measures that additionally exhibit a\nsparse dependency structure. We illustrate the new method for a number of\nstatistical models where both the sample size $N$ and the dimensionality $d$ of\nthe parameter space are large.\n"}
+{"abstract": "  The direct imaging of rocky exoplanets is one of the major science goals for\nupcoming large telescopes. The contrast requirement for imaging such planets is\nchallenging. However, the mid-IR (InfraRed) regime provides the optimum\ncontrast to directly detect the thermal signatures of exoplanets in our solar\nneighbourhood. We aim to exploit novel fast chopping techniques newly developed\nfor astronomy with the aid of adaptive optics to look for thermal signatures of\nexoplanets around bright stars in the solar neighbourhood. We use the upgraded\nVISIR (Very Large Telescope Imager and Spectrometer for the mid-InfraRed)\ninstrument with high contrast imaging (HCI) capability optimized for\nobservations at 10~$\\mu$m to look for exoplanets around five nearby ($d$ < 4\npc) stars. The instrument provides an improved signal-to-noise (S/N) by a\nfactor of $\\sim$4 in the N-band compared to standard VISIR for a given S/N and\ntime. In this work we achieve a detection sensitivity of sub-mJy, which is\nsufficient to detect few Jupiter mass planets in nearby systems. Although no\ndetections are made we achieve most sensitive limits within $<2''$ for all the\nobserved targets compared to previous campaigns. For $\\epsilon$ Indi A and\n$\\epsilon$ Eri we achieve detection limits very close to the giant planets\ndiscovered by RV, with the limits on $\\epsilon$ Indi A being the most sensitive\nto date. Our non-detection therefore supports an older age for $\\epsilon$ Indi\nA. The results presented here show the promise for high contrast imaging and\nexoplanet detections in the mid-IR regime.\n"}
+{"abstract": "  Effectively recognising and applying emotions to interactions is a highly\ndesirable trait for social robots. Implicitly understanding how subjects\nexperience different kinds of actions and objects in the world is crucial for\nnatural HRI interactions, with the possibility to perform positive actions and\navoid negative actions. In this paper, we utilize the NICO robot's appearance\nand capabilities to give the NICO the ability to model a coherent affective\nassociation between a perceived auditory stimulus and a temporally asynchronous\nemotion expression. This is done by combining evaluations of emotional valence\nfrom vision and language. NICO uses this information to make decisions about\nwhen to extend conversations in order to accrue more affective information if\nthe representation of the association is not coherent. Our primary contribution\nis providing a NICO robot with the ability to learn the affective associations\nbetween a perceived auditory stimulus and an emotional expression. NICO is able\nto do this for both individual subjects and specific stimuli, with the aid of\nan emotion-driven dialogue system that rectifies emotional expression\nincoherences. The robot is then able to use this information to determine a\nsubject's enjoyment of perceived auditory stimuli in a real HRI scenario.\n"}
+{"abstract": "  Core Damage Frequency (CDF) is a risk metric often employed by nuclear\nregulatory bodies worldwide. Numerical values for this metric are required by\nU.S. regulators, prior to reactor licensing, and reported values can trigger\nregulatory inspections. CDF is reported as a constant, sometimes accompanied by\na confidence interval. It is well understood that CDF characterizes the arrival\nrate of a stochastic point process modeling core damage events. However,\nconsequences of the assumptions imposed on this stochastic process as a\ncomputational necessity are often overlooked. Herein, we revisit CDF in the\ncontext of modern point process theory. We argue that the assumptions required\nto yield a constant CDF are typically unrealistic. We further argue that\ntreating CDF as an informative approximation is suspect, because of the\ninherent difficulties in quantifying its quality as an approximation.\n"}
+{"abstract": "  Weakly-supervised semantic segmentation (WSSS) is introduced to narrow the\ngap for semantic segmentation performance from pixel-level supervision to\nimage-level supervision. Most advanced approaches are based on class activation\nmaps (CAMs) to generate pseudo-labels to train the segmentation network. The\nmain limitation of WSSS is that the process of generating pseudo-labels from\nCAMs that use an image classifier is mainly focused on the most discriminative\nparts of the objects. To address this issue, we propose Puzzle-CAM, a process\nthat minimizes differences between the features from separate patches and the\nwhole image. Our method consists of a puzzle module and two regularization\nterms to discover the most integrated region in an object. Puzzle-CAM can\nactivate the overall region of an object using image-level supervision without\nrequiring extra parameters. % In experiments, Puzzle-CAM outperformed previous\nstate-of-the-art methods using the same labels for supervision on the PASCAL\nVOC 2012 test dataset. In experiments, Puzzle-CAM outperformed previous\nstate-of-the-art methods using the same labels for supervision on the PASCAL\nVOC 2012 dataset. Code associated with our experiments is available at\nhttps://github.com/OFRIN/PuzzleCAM.\n"}
+{"abstract": "  Recent work has revealed two classes of Globular Clusters (GCs), dubbed\nType-I and Type-II. Type-II GCs are characterized by a blue- and a red- red\ngiant branch composed of stars with different metallicities, often coupled with\ndistinct abundances in the slow-neutron capture elements (s-elements). Here we\ncontinue the chemical tagging of Type-II GCs by adding the two least-massive\nclusters of this class, NGC1261 and NGC6934. Based on both spectroscopy and\nphotometry, we find that red stars in NGC1261 are slightly enhanced in [Fe/H]\nby ~0.1 dex and confirm that red stars of NGC 6934 are enhanced in iron by ~0.2\ndex. Neither NGC1261 nor NGC6934 show internal variations in the s-elements,\nwhich suggests a GC mass threshold for the occurrence of s-process enrichment.\nWe found a significant correlation between the additional Fe locked in the red\nstars of Type-II GCs and the present-day mass of the cluster. Nevertheless,\nmost Type II GCs retained a small fraction of Fe produced by SNe II, lower than\nthe 2%; NGC6273, M54 and omega Centauri are remarkable exceptions. In the\nappendix, we infer for the first time chemical abundances of Lanthanum, assumed\nas representative of the s-elements, in M54, the GC located in the nucleus of\nthe Sagittarius dwarf galaxy. Red-sequence stars are marginally enhanced in\n[La/Fe] by 0.10\\pm0.06 dex, in contrast with the large [La/Fe] spread of most\nType II GCs. We suggest that different processes are responsible for the\nenrichment in iron and s-elements in Type-II GCs.\n"}
+{"abstract": "  A set of quantum states is said to be absolutely entangled, when at least one\nstate in the set remains entangled for any definition of subsystems, i.e. for\nany choice of the global reference frame. In this work we investigate the\nproperties of absolutey entangled sets (AES) of pure quantum states. For the\ncase of a two-qubit system, we present a sufficient condition to detect an AES,\nand use it to construct families of $N$ states such that $N-3$ (the maximal\npossible number) remain entangled for any definition of subsystems. For a\ngeneral bipartition $d=d_1d_2$, we prove that sets of\n$N>\\left\\lfloor{(d_{1}+1)(d_{2}+1)/2}\\right \\rfloor$ states are AES with Haar\nmeasure 1. Then, we define AES for multipartitions. We derive a general lower\nbound on the number of states in an AES for a given multipartition, and also\nconstruct explicit examples. In particular, we exhibit an AES with respect to\nany possible multi-partitioning of the total system.\n"}
+{"abstract": "  We study the numerical approximation of stochastic evolution equations with a\nmonotone drift driven by an infinite-dimensional Wiener process. To discretize\nthe equation, we combine a drift-implicit two-step BDF method for the temporal\ndiscretization with an abstract Galerkin method for the spatial discretization.\nAfter proving well-posedness of the BDF2-Maruyama scheme, we establish a\nconvergence rate of the strong error for equations under suitable Lipschitz\nconditions. We illustrate our theoretical results through various numerical\nexperiments and compare the performance of the BDF2-Maruyama scheme to the\nbackward Euler--Maruyama scheme.\n"}
+{"abstract": "  Many long-term goals, such as learning a language, require people to\nregularly practice every day to achieve mastery. At the same time, people\nregularly surf the web and read social news feeds in their spare time. We have\nbuilt a browser extension that teaches vocabulary to users in the context of\nFacebook feeds and arbitrary websites, by showing users interactive quizzes\nthey can answer without leaving the website. On Facebook, the quizzes show up\nas part of the news feed, while on other sites, the quizzes appear where\nadvertisements normally would. In our user study, we examined the effectiveness\nof inserting microlearning tasks into social news feeds. We compared vocabulary\nlearning rates when we inserted interactive quizzes into feeds, versus\ninserting links that lead them to a website where they could do the quizzes.\nOur results suggest that users engage with and learn from our embedded quizzes,\nand engagement increases when the quizzes can be done directly within their\nfeeds.\n"}
+{"abstract": "  The $s,p-d$ exchange coupling between the spins of band carriers and of\ntransition metal (TM) dopants ranging from Ti to Cu in ZnO is studied within\nthe density functional theory. The $+U$ corrections are included to reproduce\nthe experimental ZnO band gap and the dopant levels. The $p-d$ coupling reveals\nunexpectedly complex features. In particular, (i) the $p-d$ coupling constants\n$N_0\\beta$ vary about 10 times when going from V to Cu, (ii) not only the value\nbut also the sign of $N_0\\beta$ depends on the charge state of the dopant,\n(iii) the $p-d$ coupling with the heavy holes and the light holes is not the\nsame; in the case of Fe, Co and Ni, $N_0\\beta$s for the two subbands can differ\ntwice, and for Cu the opposite sign of the coupling is found for light and\nheavy holes. The main features of the $p-d$ coupling are determined by the\n$p-d$ hybridization between the $d$(TM) and $p$(O) orbitals. In contrast, the\n$s-d$ coupling constant $N_0\\alpha$ is almost the same for all TM ions, and\ndoes not depend on the charge state of the dopant. The TM-induced spin\npolarization of the $p$(O) orbitals contributes to the $s-d$ coupling,\nenhancing $N_0\\alpha$.\n"}
+{"abstract": "  We present results on electron transport in quasi-one dimensional (1D)\nquantum wires in GaAs/AlGaAs heterostructures obtained using an asymmetric\nconfinement potential. The variation of the energy levels of the spatially\nquantized states is followed from strong confinement through weak confinement\nto the onset of two-dimensionality. An anticrossing of the initial ground and\nfirst excited states is found as the asymmetry of the potential is varied\ngiving rise to two anticrossing events which occur on either side of symmetric\nconfinement. We present results analysing this behaviour and showing how it can\nbe affected by the inhomogeneity in background potential. The use of an\nenhanced source-drain voltage to alter the energy levels is shown to be a\nsignificant validation of the analysis by showing the formation of double rows\nof electrons which correlate with the anticrossing.\n"}
+{"abstract": "  For any given positive integer $l$, we prove that every plane deformation of\na circle which preserves the $1/2$ and $1/(2l+1)$-rational caustics is trivial\ni.e. the deformation consists only of similarities (rescalings plus\nisometries).\n"}
+{"abstract": "  The progress in neuromorphic computing is fueled by the development of novel\nnonvolatile memories capable of storing analog information and implementing\nneural computation efficiently. However, like most other analog circuits, these\ndevices and circuits are prone to imperfections, such as temperature\ndependency, noise, tuning error, etc., often leading to considerable\nperformance degradation in neural network implementations. Indeed,\nimperfections are major obstacles in the path of further progress and ultimate\ncommercialization of these technologies. Hence, a practically viable approach\nshould be developed to deal with these nonidealities and unleash the full\npotential of nonvolatile memories in neuromorphic systems. Here, for the first\ntime, we report a comprehensive characterization of critical imperfections in\ntwo analog-grade memories, namely passively-integrated memristors and\nredesigned eFlash memories, which both feature long-term retention, high\nendurance, analog storage, low-power operation, and compact nano-scale\nfootprint. Then, we propose a holistic approach that includes modifications in\nthe training, tuning algorithm, memory state optimization, and circuit design\nto mitigate these imperfections. Our proposed methodology is corroborated on a\nhybrid software/experimental framework using two benchmarks: a moderate-size\nconvolutional neural network and ResNet-18 trained on CIFAR-10 and ImageNet\ndatasets, respectively. Our proposed approaches allow 2.5x to 9x improvements\nin the energy consumption of memory arrays during inference and sub-percent\naccuracy drop across 25-100 C temperature range. The defect tolerance is\nimproved by >100x, and a sub-percent accuracy drop is demonstrated in deep\nneural networks built with 64x64 passive memristive crossbars featuring 25%\nnormalized switching threshold variations.\n"}
+{"abstract": "  We study the behavior of R\\'enyi entropies for pure states from standard\nassumptions about chaos in the high-energy spectrum of the Hamiltonian of a\nmany-body quantum system. We compute the exact long-time averages of R\\'enyi\nentropies and show that the quantum noise around these values is exponentially\nsuppressed in the microcanonical entropy. For delocalized states over the\nmicrocanonical band, the long-time average approximately reproduces the\nequilibration proposal of H. Liu and S. Vardhan, with extra structure arising\nat the order of non-planar permutations. We analyze the equilibrium\napproximation for AdS/CFT systems describing black holes in equilibrium in a\nbox. We extend our analysis to the situation of an evaporating black hole, and\ncomment on the possible gravitational description of the new terms in our\napproximation.\n"}
+{"abstract": "  Beam breakup instability is a potential issue for all particle accelerators\nand is often the limiting factor for the maximum beam current that can be\nachieved. This is particularly relevant for Energy Recovery Linacs with\nmultiple passes where a relatively small amount of charge can result in a large\nbeam current. Recent studies have shown that the choice of filling pattern and\nrecirculation scheme for a multi-pass energy recovery linac can drastically\naffect the interactions between the beam and RF system. In this paper we\nfurther explore this topic to study how filling patterns affect the beam\nbreakup instability and how this can allow us to optimise the design in order\nto minimise this effect. We present a theoretical model of the beam-RF\ninteraction as well as numerical modeling and show that the threshold current\ncan vary by factors of 2-4, and potentially even more depending on the machine\ndesign parameters. Therefore a judicious choice of filling pattern can greatly\nincrease the onset of BBU, expanding the utility of future ERLs.\n"}
+{"abstract": "  This paper aims to devise a generalized maximum likelihood (ML) estimator to\nrobustly detect signals with unknown noise statistics in multiple-input\nmultiple-output (MIMO) systems. In practice, there is little or even no\nstatistical knowledge on the system noise, which in many cases is non-Gaussian,\nimpulsive and not analyzable. Existing detection methods have mainly focused on\nspecific noise models, which are not robust enough with unknown noise\nstatistics. To tackle this issue, we propose a novel ML detection framework to\neffectively recover the desired signal. Our framework is a fully probabilistic\none that can efficiently approximate the unknown noise distribution through a\nnormalizing flow. Importantly, this framework is driven by an unsupervised\nlearning approach, where only the noise samples are required. To reduce the\ncomputational complexity, we further present a low-complexity version of the\nframework, by utilizing an initial estimation to reduce the search space.\nSimulation results show that our framework outperforms other existing\nalgorithms in terms of bit error rate (BER) in non-analytical noise\nenvironments, while it can reach the ML performance bound in analytical noise\nenvironments. The code of this paper is available at\nhttps://github.com/skypitcher/manfe.\n"}
+{"abstract": "  The applicability of process mining techniques hinges on the availability of\nevent logs capturing the execution of a business process. In some use cases,\nparticularly those involving customer-facing processes, these event logs may\ncontain private information. Data protection regulations restrict the use of\nsuch event logs for analysis purposes. One way of circumventing these\nrestrictions is to anonymize the event log to the extent that no individual can\nbe singled out using the anonymized log. This paper addresses the problem of\nanonymizing an event log in order to guarantee that, upon disclosure of the\nanonymized log, the probability that an attacker may single out any individual\nrepresented in the original log, does not increase by more than a threshold.\nThe paper proposes a differentially private disclosure mechanism, which\noversamples the cases in the log and adds noise to the timestamps to the extent\nrequired to achieve the above privacy guarantee. The paper reports on an\nempirical evaluation of the proposed approach using 14 real-life event logs in\nterms of data utility loss and computational efficiency.\n"}
+{"abstract": "  Recently, Rendle has warned that the use of sampling-based top-$k$ metrics\nmight not suffice. This throws a number of recent studies on deep\nlearning-based recommendation algorithms, and classic non-deep-learning\nalgorithms using such a metric, into jeopardy. In this work, we thoroughly\ninvestigate the relationship between the sampling and global top-$K$ Hit-Ratio\n(HR, or Recall), originally proposed by Koren[2] and extensively used by\nothers. By formulating the problem of aligning sampling top-$k$ ($SHR@k$) and\nglobal top-$K$ ($HR@K$) Hit-Ratios through a mapping function $f$, so that\n$SHR@k\\approx HR@f(k)$, we demonstrate both theoretically and experimentally\nthat the sampling top-$k$ Hit-Ratio provides an accurate approximation of its\nglobal (exact) counterpart, and can consistently predict the correct winners\n(the same as indicate by their corresponding global Hit-Ratios).\n"}
+{"abstract": "  We look for minimal conditions on a two-dimensional metric surface $X$ of\nlocally finite Hausdorff $2$-measure under which $X$ admits an (almost)\nparametrization with good geometric and analytic properties. Only assuming that\n$X$ is locally geodesic, we show that Jordan domains in $X$ of finite boundary\nlength admit a quasiconformal almost parametrization. If $X$ satisfies some\nfurther conditions then such an almost parametrization can be upgraded to a\ngeometrically quasiconformal homeomorphism or a quasisymmetric homeomorphism.\nIn particular, we recover Rajala's recent quasiconformal uniformization theorem\nin the special case that $X$ is locally geodesic as well as Bonk-Kleiner's\nquasisymmetric uniformization theorem. On the way we establish the existence of\nSobolev discs spanning a given Jordan curve in $X$ under nearly minimal\nassumptions on $X$ and prove the continuity of energy minimizers.\n"}
+{"abstract": "  In Japan, teacher and student is randomly matched in the first year of\nelementary school. Under the quasi-natural experimental setting, we examine how\nlearning in female teacher homeroom class in the elementary school influence\npupils' smoking behavior after they become adult. We found that pupils are\nunlikely to smoke later in life if they belonged to female teacher homeroom\nclass in pupil's first year of school.\n"}
+{"abstract": "  Colloidal gels formed by strongly attractive particles at low particle volume\nfractions are composed of space-spanning networks of uniformly sized clusters.\nWe study the thermal fluctuations of the clusters using differential dynamic\nmicroscopy by decomposing them into two modes of dynamics, and link them to the\nmacroscopic viscoelasticity via rheometry. The first mode, dominant at early\ntimes, represents the localized, elastic fluctuations of individual clusters.\nThe second mode, pronounced at late times, reflects the collective,\nviscoelastic dynamics facilitated by the connectivity of the clusters. By\nmixing two types of particles of distinct attraction strengths in different\nproportions, we control the transition time at which the collective mode starts\nto dominate, and hence tune the frequency dependence of the linear viscoelastic\nmoduli of the binary gels.\n"}
+{"abstract": "  In the paper we continue to study Special Bohr-Sommerfeld geometry of compact\nsymplectic manifolds. Using natural deformation parameters we avoid the\ndifficulties appeared in the definition of the moduli space of Special\nBohr-Sommerfeld cycles for compact simply connected algebraic varieties. As a\nbyproduct we present certain remarks on the Weinstein structures and Eliashberg\nconjectures.\n"}
+{"abstract": "  Because all stars contribute to its gravitational potential, stellar clusters\namplify perturbations collectively. In the limit of small fluctuations, this is\ndescribed through linear response theory, via the so-called response matrix.\nWhile the evaluation of this matrix is somewhat straightforward for unstable\nmodes (i.e. with a positive growth rate), it requires a careful analytic\ncontinuation for damped modes (i.e. with a negative growth rate). We present a\ngeneric method to perform such a calculation in spherically symmetric stellar\nclusters. When applied to an isotropic isochrone cluster, we recover the\npresence of a low-frequency weakly damped $\\ell = 1$ mode. We finally use a set\nof direct $N$-body simulations to test explicitly this prediction through the\nstatistics of the correlated random walk undergone by a cluster's density\ncentre.\n"}
+{"abstract": "  Convergence of order $O(1/\\sqrt{n})$ is obtained for the distance in total\nvariation between the Poisson distribution and the distribution of the number\nof fixed size cycles in generalized random graphs with random vertex weights.\nThe weights are assumed to be independent identically distributed random\nvariables which have a power-law distribution. The proof is based on the\nChen--Stein approach and on the derived properties of the ratio of the sum of\nsquares of random variables and the sum of these variables. These properties\ncan be applied to other asymptotic problems related to generalized random\ngraphs.\n"}
+{"abstract": "  Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) is\nthe task of predicting the entailment relation between a pair of sentences\n(premise and hypothesis). This task has been described as a valuable testing\nground for the development of semantic representations, and is a key component\nin natural language understanding evaluation benchmarks. Models that understand\nentailment should encode both, the premise and the hypothesis. However,\nexperiments by Poliak et al. revealed a strong preference of these models\ntowards patterns observed only in the hypothesis, based on a 10 dataset\ncomparison. Their results indicated the existence of statistical irregularities\npresent in the hypothesis that bias the model into performing competitively\nwith the state of the art. While recast datasets provide large scale generation\nof NLI instances due to minimal human intervention, the papers that generate\nthem do not provide fine-grained analysis of the potential statistical patterns\nthat can bias NLI models. In this work, we analyze hypothesis-only models\ntrained on one of the recast datasets provided in Poliak et al. for word-level\npatterns. Our results indicate the existence of potential lexical biases that\ncould contribute to inflating the model performance.\n"}
+{"abstract": "  This paper considers the equilibrium positions of $n$ particles in one\ndimension. Two forces act on the particles; a nonlocal repulsive\nparticle-interaction force and an external force which pushes them to an\nimpenetrable barrier. While the continuum limit as $n \\to \\infty$ is known for\na certain class of potentials, numerical simulations show that a discrete\nboundary layer appears at the impenetrable barrier, i.e. the positions of\n$o(n)$ particles do not fit to the particle density predicted by the continuum\nlimit. In this paper we establish a first-order $\\Gamma$-convergence result\nwhich guarantees that these $o(n)$ particles converge to a specific continuum\nboundary-layer profile.\n"}
+{"abstract": "  Priors allow us to robustify inference and to incorporate expert knowledge in\nBayesian hierarchical models. This is particularly important when there are\nrandom effects that are hard to identify based on observed data. The challenge\nlies in understanding and controlling the joint influence of the priors for the\nvariance parameters, and makemyprior is an R package that guides the\nformulation of joint prior distributions for variance parameters. A joint prior\ndistribution is constructed based on a hierarchical decomposition of the total\nvariance in the model along a tree, and takes the entire model structure into\naccount. Users input their prior beliefs or express ignorance at each level of\nthe tree. Prior beliefs can be general ideas about reasonable ranges of\nvariance values and need not be detailed expert knowledge. The constructed\npriors lead to robust inference and guarantee proper posteriors. A graphical\nuser interface facilitates construction and assessment of different choices of\npriors through visualization of the tree and joint prior. The package aims to\nexpand the toolbox of applied researchers and make priors an active component\nin their Bayesian workflow.\n"}
+{"abstract": "  Consider a nonlinear wave equation for a massless scalar field with\nself-interaction in the spatially flat Friedmann-Lema\\^{i}tre-Robertson-Walker\nspacetimes. For the case of accelerated expansion, we show that blow-up in a\nfinite time occurs for the equation with arbitrary power nonlinearity as well\nas upper bounds of the lifespan of blow-up solutions. Comparing to the case of\nthe Minkowski spacetime, we discuss how the scale factor affects the lifespan\nof blow-up solutions of the equation.\n"}
+{"abstract": "  In a geographically distributed population, assortative clustering plays an\nimportant role in evolution by modifying local environments. To examine its\neffects in a linear habitat, we consider a one-dimensional grid of cells, where\neach cell is either empty or occupied by an organism whose replication strategy\nis genetically inherited to offspring. The strategy determines whether to have\noffspring in surrounding cells, as a function of the neighborhood\nconfiguration. If more than one offspring compete for a cell, then they can be\nall exterminated due to the cost of conflict depending on environmental\nconditions. We find that the system is more densely populated in an unfavorable\nenvironment than in a favorable one because only the latter has to pay the cost\nof conflict. This observation agrees reasonably well with a mean-field analysis\nwhich takes assortative clustering of strategies into consideration. Our\nfinding suggests a possibility of intrinsic nonlinearity between environmental\nconditions and population density when an evolutionary process is involved.\n"}
+{"abstract": "  Ultrahigh repetition rate lasers will become vital light sources for many\nfuture technologies; however, their realization is challenging because the\ncavity size must be minimized. Whispering-gallery-mode (WGM) microresonators\nare attractive for this purpose since they allow the strong light-matter\ninteraction usually needed to enable mode-locking. However, the optimum\nparameter ranges are entirely unknown since no experiments have yet been\nconducted. Here, we numerically investigate pulsed operation in a toroidal WGM\nmicroresonator with gain and saturable absorption (SA) to study the\nexperimental feasibility. We show that dispersion is the key parameter for\nachieving passive mode-locking in this system. Moreover, the design guideline\nprovided in this work can apply to any small resonators with gain and SA and is\nnot limited to a specific cavity system.\n"}
+{"abstract": "  Density functional theory (DFT) based modeling of electronic excited states\nis of importance for investigation of the photophysical/photochemical\nproperties and spectroscopic characterization of large systems. The widely used\nlinear response time-dependent DFT (TDDFT) approach is however not effective at\nmodeling many types of excited states, including (but not limited to)\ncharge-transfer states, doubly excited states and core-level excitations. In\nthis perspective, we discuss state-specific orbital optimized (OO) DFT\napproaches as an alterative to TDDFT for electronic excited states. We motivate\nthe use of OO-DFT methods and discuss reasons behind their relatively\nrestricted historical usage (vs TDDFT). We subsequently highlight modern\ndevelopments that address these factors and allow efficient and reliable OO-DFT\ncomputations. Several successful applications of OO-DFT for challenging\nelectronic excitations are also presented, indicating their practical efficacy.\nOO-DFT approaches are thus increasingly becoming a useful route for computing\nexcited states of large chemical systems. We conclude by discussing the\nlimitations and challenges still facing OO-DFT methods, as well as some\npotential avenues for addressing them.\n"}
+{"abstract": "  Executing scientific workflows with heterogeneous tasks on HPC platforms\nposes several challenges which will be further exacerbated by the upcoming\nexascale platforms. At that scale, bespoke solutions will not enable effective\nand efficient workflow executions. In preparation, we need to look at ways to\nmanage engineering effort and capability duplication across software systems by\nintegrating independently developed, production-grade software solutions. In\nthis paper, we integrate RADICAL-Pilot (RP) and Parsl and develop an MPI\nexecutor to enable the execution of workflows with heterogeneous (non)MPI\nPython functions at scale. We characterize the strong and weak scaling of the\nintegrated RP-Parsl system when executing two use cases from polar science, and\nof the function executor on both SDSC Comet and TACC Frontera. We gain\nengineering insight about how to analyze and integrate workflow and runtime\nsystems, minimizing changes in their code bases and overall development effort.\nOur experiments show that the overheads of the integrated system are invariant\nof resource and workflow scale, and measure the impact of diverse MPI\noverheads. Together, those results define a blueprint towards an ecosystem\npopulated by specialized, efficient, effective and independently-maintained\nsoftware systems to face the upcoming scaling challenges.\n"}
+{"abstract": "  This paper proposes novel end-to-end framework for detecting suspicious\npulmonary nodules in chest CT scans. The method core idea is a new nodule\nsegmentation architecture with a model-based feature projection block on\nthree-dimensional convolutions. This block acts as a preliminary feature\nextractor for a two-dimensional U-Net-like convolutional network. Using the\nproposed approach along with an axial, coronal, and sagittal projection\nanalysis makes it possible to abandon the widely used false positives reduction\nstep. The proposed method achieves SOTA on LUNA2016 with 0.959 average\nsensitivity, and 0.936 sensitivity if the false-positive level per scan is\n0.25. The paper describes the proposed approach and represents the experimental\nresults on LUNA2016 as well as ablation studies.\n"}
+{"abstract": "  Pruning the weights of randomly initialized neural networks plays an\nimportant role in the context of lottery ticket hypothesis. Ramanujan et al.\n(2020) empirically showed that only pruning the weights can achieve remarkable\nperformance instead of optimizing the weight values. However, to achieve the\nsame level of performance as the weight optimization, the pruning approach\nrequires more parameters in the networks before pruning and thus more memory\nspace. To overcome this parameter inefficiency, we introduce a novel framework\nto prune randomly initialized neural networks with iteratively randomizing\nweight values (IteRand). Theoretically, we prove an approximation theorem in\nour framework, which indicates that the randomizing operations are provably\neffective to reduce the required number of the parameters. We also empirically\ndemonstrate the parameter efficiency in multiple experiments on CIFAR-10 and\nImageNet.\n"}
+{"abstract": "  In this paper, we use the Carnot-Caratheodory distance from sub-Riemanian\ngeometry to prove entropy decay estimates for all finite dimensional symmetric\nquantum Markov semigroups. This estimate is independent of the environment size\nand hence stable under tensorization. Our approach relies on the transference\nprinciple, the existence of $t$-designs, and the sub-Riemannian diameter of\ncompact Lie groups and implies estimates for the spectral gap.\n"}
+{"abstract": "  We study online Multi-Agent Path Finding (MAPF), where new agents are\nconstantly revealed over time and all agents must find collision-free paths to\ntheir given goal locations. We generalize existing complexity results of\n(offline) MAPF to online MAPF. We classify online MAPF algorithms into\ndifferent categories based on (1) controllability (the set of agents that they\ncan plan paths for at each time) and (2) rationality (the quality of paths they\nplan) and study the relationships between them. We perform a competitive\nanalysis for each category of online MAPF algorithms with respect to\ncommonly-used objective functions. We show that a naive algorithm that routes\nnewly-revealed agents one at a time in sequence achieves a competitive ratio\nthat is asymptotically bounded from both below and above by the number of\nagents with respect to flowtime and makespan. We then show a counter-intuitive\nresult that, if rerouting of previously-revealed agents is not allowed, any\nrational online MAPF algorithms, including ones that plan optimal paths for all\nnewly-revealed agents, have the same asymptotic competitive ratio as the naive\nalgorithm, even on 2D 4-neighbor grids. We also derive constant lower bounds on\nthe competitive ratio of any rational online MAPF algorithms that allow\nrerouting. The results thus provide theoretical insights into the effectiveness\nof using MAPF algorithms in an online setting for the first time.\n"}
+{"abstract": "  In this paper, we use the first-principles calculations based on the density\nfunctional theory to investigate structural, electronic and magnetic properties\nof Fe$_{2}$YSn with (Y = Mn, Ti and V). The generalized gradient approximation\n(GGA) method is used for calculations. The Cu$_{2}$MnAl type structure is\nenergetically more stable than the Hg$_{2}$CuTi type structure. The negative\nformation energy is shown as the evidence of thermodynamic stability of the\nalloy. The calculated total spin moment is found as 3$\\mu_\\text{B}$ and\n0$\\mu_\\text{B}$ at the equilibrium lattice constant for Fe$_{2}$MnSn and\nFe$_{2}$TiSn respectively, which agrees with the Slater-Pauling rule of $M_t=\nZ_t-24$. The study of electronic and magnetic properties proves that\nFe$_{2}$MnSn and Fe$_{2}$TiSn full-Heusler alloys are complete half-metallic\nferromagnetic materials.\n"}
+{"abstract": "  The different thermo-elastic properties of glass fibers and polymer matrices\ncan generate residual thermal stresses in injection-molded fiber-reinforced\nplastic (FRP) objects. During cooling from mold to room temperature, these\nstresses can be relaxed by large deformations resulting from an instability of\nthe unwarped configuration (i.e., buckling). This article investigates the\nthermal buckling of thin FRP disks via an analytical formulation based on the\nFoppl-von Karman theory. Expanding on our previous work, cylindrical orthotropy\nwith material parameters varying over the disk thickness is assumed in order to\naccount for thickness dependency of the glass fiber orientation distribution. A\ndisk parameter generalizing the thermal anisotropy ratio for homogeneous\northotropic disks is introduced and its relation with the occurrence and\nperiodicity of buckling is discussed. This is done for a skin-coreskin model,\nfor which the core-to-total thickness ratio is defined. For fiber orientation\ndistributions typical of injection-molded disks, it is found that there exists\na value of the thickness ratio for which no buckling occurs. It is also\ndemonstrated that the periodicity of the first buckling mode is described by\nthe generalized thermal anisotropy ratio, thus extending the results obtained\nfor a homogeneous fiber orientation distribution. Improvements in the accuracy\nof the predictions for experimental data available in the literature when using\nthe skin-core-skin model are shown. Finally, we study the relation between\nbuckling temperature and disk thickness and propose an expression for the\ndependence of the normalized buckling temperature on the thermal anisotropy\nratio. Results of FEM simulations are used to validate the proposed expression,\nproving its applicability and accuracy.\n"}
+{"abstract": "  Neural networks serve as effective controllers in a variety of complex\nsettings due to their ability to represent expressive policies. The complex\nnature of neural networks, however, makes their output difficult to verify and\npredict, which limits their use in safety-critical applications. While\nsimulations provide insight into the performance of neural network controllers,\nthey are not enough to guarantee that the controller will perform safely in all\nscenarios. To address this problem, recent work has focused on formal methods\nto verify properties of neural network outputs. For neural network controllers,\nwe can use a dynamics model to determine the output properties that must hold\nfor the controller to operate safely. In this work, we develop a method to use\nthe results from neural network verification tools to provide probabilistic\nsafety guarantees on a neural network controller. We develop an adaptive\nverification approach to efficiently generate an overapproximation of the\nneural network policy. Next, we modify the traditional formulation of Markov\ndecision process (MDP) model checking to provide guarantees on the\noverapproximated policy given a stochastic dynamics model. Finally, we\nincorporate techniques in state abstraction to reduce overapproximation error\nduring the model checking process. We show that our method is able to generate\nmeaningful probabilistic safety guarantees for aircraft collision avoidance\nneural networks that are loosely inspired by Airborne Collision Avoidance\nSystem X (ACAS X), a family of collision avoidance systems that formulates the\nproblem as a partially observable Markov decision process (POMDP).\n"}
+{"abstract": "  We study four-point functions of scalars, conserved currents, and stress\ntensors in a conformal field theory, generated by a local contact term in the\nbulk dual description, in two different causal configurations. The first of\nthese is the standard Regge configuration in which the chaos bound applies. The\nsecond is the `causally scattering configuration' in which the correlator\ndevelops a bulk point singularity. We find an expression for the coefficient of\nthe bulk point singularity in terms of the bulk S matrix of the bulk dual\nmetric, gauge fields and scalars, and use it to determine the Regge scaling of\nthe correlator on the causally scattering sheet in terms of the Regge growth of\nthis S matrix. We then demonstrate that the Regge scaling on this sheet is\ngoverned by the same power as in the standard Regge configuration, and so is\nconstrained by the chaos bound, which turns out to be violated unless the bulk\nflat space S matrix grows no faster than $s^2$ in the Regge limit. It follows\nthat in the context of the AdS/CFT correspondence, the chaos bound applied to\nthe boundary field theory implies that the S matrices of the dual bulk scalars,\ngauge fields, and gravitons obey the Classical Regge Growth (CRG) conjecture.\n"}
+{"abstract": "  This paper deals with data-driven output synchronization for heterogeneous\nleader-follower linear multi-agent systems. Given a multi-agent system that\nconsists of one autonomous leader and a number of heterogeneous followers with\nexternal disturbances, we provide necessary and sufficient data-based\nconditions for output synchronization. We also provide a design method for\nobtaining such output synchronizing protocols directly from data. The results\nare then extended to the special case that the followers are disturbance-free.\nFinally, a simulation example is provided to illustrate our results.\n"}
+{"abstract": "  In machine translation field, in both academia and industry, there is a\ngrowing interest in increasingly powerful systems, using corpora of several\nhundred million to several billion examples. These systems represent the\nstate-of-the-art. Here we defend the idea of developing in parallel <<frugal>>\nbilingual translation systems, trained with relatively small corpora. Based on\nthe observation of a standard human professional translator, we estimate that\nthe corpora should be composed at maximum of a monolingual sub-corpus of 75\nmillion examples for the source language, a second monolingual sub-corpus of 6\nmillion examples for the target language, and an aligned bilingual sub-corpus\nof 6 million bi-examples. A less desirable alternative would be an aligned\nbilingual corpus of 47.5 million bi-examples.\n"}
+{"abstract": "  We survey the area of strongly regular graphs satisfying the 4-vertex\ncondition and find several new families. We describe a switching operation on\ncollinearity graphs of polar spaces that produces cospectral graphs. The\nobtained graphs satisfy the 4-vertex condition if the original graph belongs to\na symplectic polar space.\n"}
+{"abstract": "  It is well known that two-sided markets are unfair in a number of ways. For\ninstance, female workers at Uber earn less than their male colleagues per mile\ndriven. Similar observations have been made for other minority subgroups in\nother two-sided markets. Here, we suggest a novel market-clearing mechanism for\ntwo-sided markets, which promotes equalisation of the pay per hour worked\nacross multiple subgroups, as well as within each subgroup. In the process, we\nintroduce a novel notion of subgroup fairness (which we call Inter-fairness),\nwhich can be combined with other notions of fairness within each subgroup\n(called Intra-fairness), and the utility for the customers (Customer-Care) in\nthe objective of the market-clearing problem. While the novel non-linear terms\nin the objective complicate market clearing by making the problem non-convex,\nwe show that a certain non-convex augmented Lagrangian relaxation can be\napproximated to any precision in time polynomial in the number of market\nparticipants using semi-definite programming. This makes it possible to\nimplement the market-clearing mechanism efficiently. On the example of\ndriver-ride assignment in an Uber-like system, we demonstrate the efficacy and\nscalability of the approach, and trade-offs between Inter- and Intra-fairness.\n"}
+{"abstract": "  We report the discovery of a bright, compact ultraviolet source at a\nprojected separation of 1.1~kpc from the known active galactic nucleus (AGN) in\nMrk~766 based on Astrosat/UVIT observations. We perform radial profile analysis\nand derive the UV flux almost free from the nearby contaminating sources. The\nnew source is about 2.5 and 5.6 times fainter than the AGN in the far and near\nUV bands. The two sources appear as a pair of nuclei in Mrk~766. We investigate\nthe nature of the new source based on the UV flux ratio, X-ray and optical\nemission. The new source is highly unlikely to be another accreting\nsupermassive black hole in Mrk~766 as it lacks X-ray emission. We find that the\nUV/Optical flux of the new source measured at four different bands closely\nfollow the shape of the template spectrum of starburst galaxies. This strongly\nsuggests that the new source is a compact star-forming region.\n"}
+{"abstract": "  Tate-Hochschild cohomology of an algebra is a generalization of ordinary\nHochschild cohomology, which is defined on positive and negative degrees and\nhas a ring structure. Our purpose of this paper is to study the eventual\nperiodicity of an algebra by using the Tate-Hochschild cohomology ring. First,\nwe deal with eventually periodic algebras and show that they are not\nnecessarily Gorenstein algebras. Secondly, we characterize the eventual\nperiodicity of a Gorenstein algebra as the existence of an invertible\nhomogeneous element of the Tate-Hochschild cohomology ring of the algebra,\nwhich is our main result. Finally, we use tensor algebras to establish a way of\nconstructing eventually periodic Gorenstein algebras.\n"}
+{"abstract": "  The chiral anomaly is a fundamental quantum mechanical phenomenon which is of\ngreat importance to both particle physics and condensed matter physics alike.\nIn the context of QED it manifests as the breaking of chiral symmetry in the\npresence of electromagnetic fields. It is also known that anomalous chiral\nsymmetry breaking can occur through interactions alone, as is the case for\ninteracting one dimensional systems. In this paper we investigate the interplay\nbetween these two modes of anomalous chiral symmetry breaking in the context of\ninteracting Weyl semimetals. Using Fujikawa's path integral method we show that\nthe chiral charge continuity equation is modified by the presence of\ninteractions which can be viewed as including the effect of the electric and\nmagnetic fields generated by the interacting quantum matter. This can be\nunderstood further using dimensional reduction and a Luttinger liquid\ndescription of the lowest Landau level. These effects manifest themselves in\nthe non-linear response of the system. In particular we find an interaction\ndependent density response due to a change in the magnetic field as well as a\ncontribution to the non-equilibrium and inhomogeneous anomalous Hall response\nwhile preserving its equilibrium value.\n"}
+{"abstract": "  We study a class of perturbative scalar quantum field theories where dynamics\nis characterized by Lorentz-invariant or Lorentz-breaking non-local operators\nof fractional order and the underlying spacetime has a varying spectral\ndimension. These theories are either ghost free or power-counting\nrenormalizable but they cannot be both at the same time. However, some of them\nare one-loop unitary and finite, and possibly unitary and finite at all orders.\n"}
+{"abstract": "  In this note we prove a sharp lower bound on the necessary number of nestings\nof nested absolute-value functions of generalized hinging hyperplanes (GHH) to\nrepresent arbitrary CPWL functions. Previous upper bound states that $n+1$\nnestings is sufficient for GHH to achieve universal representation power, but\nthe corresponding lower bound was unknown. We prove that $n$ nestings is\nnecessary for universal representation power, which provides an almost tight\nlower bound. We also show that one-hidden-layer neural networks don't have\nuniversal approximation power over the whole domain. The analysis is based on a\nkey lemma showing that any finite sum of periodic functions is either\nnon-integrable or the zero function, which might be of independent interest.\n"}
+{"abstract": "  Purpose: To develop a knowledge-based voxel-wise dose prediction system using\na convolution neural network for high-dose-rate brachytherapy cervical cancer\ntreatments with a tandem-and-ovoid (T&O) applicator. Methods: A 3D U-NET was\nutilized to output dose predictions using organ-at-risk (OAR), high-risk\nclinical target volume (HRCTV), and possible source locations. A sample of\nprevious T&O treatments comprising 397 cases (273 training:62 validation:62\ntest), HRCTV and OARs (bladder/rectum/sigmoid) was used. Structures and dose\nwere interpolated to 1x1x2.5mm3 dose planes with two input channels (source\npositions, voxel identification) and one output channel for dose. We evaluated\ndose difference (\\Delta D)(xyz)=D_(actual)(x,y,z)-D_(predicted)(x,y,z) and dice\nsimilarity coefficients in all cohorts across the clinically-relevant dose\nrange (20-130% of prescription), mean and standard deviation. We also examined\ndiscrete DVH metrics used for T&O plan quality assessment: HRCTV D_90%(dose to\nhottest 90% volume) and OAR D_2cc, with \\Delta\nD_x=D_(x,actual)-D_(x,predicted). Pearson correlation coefficient, standard\ndeviation, and mean quantified model performance on the clinical metrics.\nResults: Voxel-wise dose difference accuracy for 20-130% dose range for\ntraining(test) ranges for mean (\\Delta D) and standard deviation for all voxels\nwas [-0.3%+/-2.0% to +1.0%+/-12.0%] ([-0.1%+/-4% to +4.0%+/-26%]). Voxel-wise\ndice similarity coefficients for 20-130% dose ranged from [0.96, 0.91]([0.94,\n0.87]). DVH metric prediction in the training (test) set were HRCTV(\\Delta\nD_90)=-0.19+/-0.55 Gy (-0.09+/-0.67 Gy), bladder(\\Delta D_2cc)=-0.06+/-0.54 Gy\n(-0.17+/-0.67 Gy), rectum(\\Delta D)_2cc=-0.03+/-0.36 Gy (-0.04+/-0.46 Gy), and\nsigmoid(\\Delta D_2cc)=-0.01+/-0.34 Gy (0.00+/-0.44 Gy). Conclusion: 3D\nknowledge-based dose predictions for T&O brachytherapy provide accurate\nvoxel-level and DVH metric estimates.\n"}
+{"abstract": "  Using data samples collected with the BESIII detector operating at the BEPCII\nstorage ring at center-of-mass energies from 4.178 to 4.600 GeV, we study the\nprocess $e^+e^-\\rightarrow\\pi^{0}X(3872)\\gamma$ and search for\n$Z_c(4020)^{0}\\rightarrow X(3872)\\gamma$. We find no significant signal and set\nupper limits on\n$\\sigma(e^+e^-\\rightarrow\\pi^{0}X(3872)\\gamma)\\cdot\\mathcal{B}(X(3872)\\rightarrow\\pi^{+}\\pi^{-}J/\\psi)$\nand\n$\\sigma(e^+e^-\\rightarrow\\pi^{0}Z_c(4020)^{0})\\cdot\\mathcal{B}(Z_c(4020)^{0}\\rightarrow\nX(3872)\\gamma)\\cdot\\mathcal{B}(X(3872)\\rightarrow\\pi^{+}\\pi^{-}J/\\psi)$ for\neach energy point at $90\\%$ confidence level, which is of the order of several\ntenths pb.\n"}
+{"abstract": "  We study some sum-product problems over matrix rings. Firstly, for $A, B,\nC\\subseteq M_n(\\mathbb{F}_q)$, we have $$ |A+BC|\\gtrsim q^{n^2}, $$ whenever\n$|A||B||C|\\gtrsim q^{3n^2-\\frac{n+1}{2}}$. Secondly, if a set $A$ in\n$M_n(\\mathbb{F}_q)$ satisfies $|A|\\geq C(n)q^{n^2-1}$ for some sufficiently\nlarge $C(n)$, then we have $$ \\max\\{|A+A|, |AA|\\}\\gtrsim\n\\min\\left\\{\\frac{|A|^2}{q^{n^2-\\frac{n+1}{4}}}, q^{n^2/3}|A|^{2/3}\\right\\}. $$\nThese improve the results due to The and Vinh (2020), and generalize the\nresults due to Mohammadi, Pham, and Wang (2021). We also give a new proof for a\nrecent result due to The and Vinh (2020). Our method is based on spectral graph\ntheory and linear algebra.\n"}
+{"abstract": "  Supernova remnants (SNRs) are observable for about 6-15x10^4 years before\nthey fade into the Galactic interstellar medium. With a Galactic supernova rate\nof approximately two per century, we can expect to have of the order of 1200\nSNRs in our Galaxy. However, only about 300 of them are known to date, with the\nmajority having been discovered in Galactic plane radio surveys. Given that\nthese SNRs represent the brightest tail of the distribution and are mostly\nlocated close to the plane, they are not representative of the complete sample.\nHere we report findings from the search for new SNRs in the eROSITA all-sky\nsurvey data which led to the detection of one of the largest SNRs discovered at\nwavelengths other than the radio: G249.5+24.5. This source is located at a\nrelatively high Galactic latitude, where SNRs are not usually expected to be\nfound. The remnant, 'Hoinga', has a diameter of about 4.4 degrees and shows a\ncircular shaped morphology with diffuse X-ray emission filling almost the\nentire remnant. Spectral analysis of the remnant emission reveals that an APEC\nspectrum from collisionally ionised diffuse gas and a plane-parallel shock\nplasma model with non-equilibrium ionisation are both able to provide an\nadequate description of the data, suggesting a gas temperature of the order of\nkT = 0.1 keV and an absorbing column density of N_H=3.6 x 10^20 cm^-2.\nSubsequent searches for a radio counterpart of the Hoinga remnant identified\nits radio emission in archival data from the Continuum HI Parkes All-Sky Survey\n(CHIPASS) and the 408-MHz `Haslam' all-sky survey. The radio spectral index\nalpha=-0.69 +- 0.08 obtained from these data definitely confirms the SNR nature\nof Hoinga. From its size and X-ray and radio spectral properties we conclude\nthat Hoinga is a middle-aged Vela-like SNR located at a distance of about twice\nthat of the Vela SNR, i.e. at ~500 pc.\n"}
+{"abstract": "  Cross-resolution image alignment is a key problem in multiscale gigapixel\nphotography, which requires to estimate homography matrix using images with\nlarge resolution gap. Existing deep homography methods concatenate the input\nimages or features, neglecting the explicit formulation of correspondences\nbetween them, which leads to degraded accuracy in cross-resolution challenges.\nIn this paper, we consider the cross-resolution homography estimation as a\nmultimodal problem, and propose a local transformer network embedded within a\nmultiscale structure to explicitly learn correspondences between the multimodal\ninputs, namely, input images with different resolutions. The proposed local\ntransformer adopts a local attention map specifically for each position in the\nfeature. By combining the local transformer with the multiscale structure, the\nnetwork is able to capture long-short range correspondences efficiently and\naccurately. Experiments on both the MS-COCO dataset and the real-captured\ncross-resolution dataset show that the proposed network outperforms existing\nstate-of-the-art feature-based and deep-learning-based homography estimation\nmethods, and is able to accurately align images under $10\\times$ resolution\ngap.\n"}
+{"abstract": "  In this note, we are concerned with dark modes in a class of non-Markovian\nopen quantum systems. Based on a microscopic model, a time-convoluted linear\nquantum stochastic differential equation and an output equation are derived to\ndescribe the system dynamics. The definition of dark modes is given building on\nthe input-output structure of the system. Then, we present a necessary and\nsufficient condition for the existence of dark modes. Also, the problem of dark\nmode synthesis via Hamiltonian engineering is constructively solved and an\nexample is presented to illustrate our results.\n"}
+{"abstract": "  The data available in Electronic Health Records (EHRs) provides the\nopportunity to transform care, and the best way to provide better care for one\npatient is through learning from the data available on all other patients.\nTemporal modelling of a patient's medical history, which takes into account the\nsequence of past events, can be used to predict future events such as a\ndiagnosis of a new disorder or complication of a previous or existing disorder.\nWhile most prediction approaches use mostly the structured data in EHRs or a\nsubset of single-domain predictions and outcomes, we present MedGPT a novel\ntransformer-based pipeline that uses Named Entity Recognition and Linking tools\n(i.e. MedCAT) to structure and organize the free text portion of EHRs and\nanticipate a range of future medical events (initially disorders). Since a\nlarge portion of EHR data is in text form, such an approach benefits from a\ngranular and detailed view of a patient while introducing modest additional\nnoise. MedGPT effectively deals with the noise and the added granularity, and\nachieves a precision of 0.344, 0.552 and 0.640 (vs LSTM 0.329, 0.538 and 0.633)\nwhen predicting the top 1, 3 and 5 candidate future disorders on real world\nhospital data from King's College Hospital, London, UK (\\textasciitilde600k\npatients). We also show that our model captures medical knowledge by testing it\non an experimental medical multiple choice question answering task, and by\nexamining the attentional focus of the model using gradient-based saliency\nmethods.\n"}
+{"abstract": "  We present the clustering analysis of photometric luminous red galaxies\n(LRGs) at a redshift range of $0.1\\leq z \\leq 1.05$ using $615,317$ photometric\nLRGs selected from the Hyper Suprime-Cam Subaru Strategic Program covering\n$\\sim124$ deg$^{2}$. Our sample covers a broad range of stellar masses and\nphotometric redshifts and enables a halo occupation distribution analysis to\nstudy the redshift and stellar-mass dependence of dark halo properties of LRGs.\nWe find a tight correlation between the characteristic dark halo mass to host\ncentral LRGs, $M_{\\min}$, and the number density of LRGs independently of\nredshifts, indicating that the formation of LRGs is associated with the global\nenvironment. The $M_{\\min}$ of LRGs depends only weakly on the stellar mass\n$M_{\\star}$ at $M_{\\star} \\lesssim 10^{10.75}h^{-2} M_{\\odot}$ at $0.3<z<1.05$,\nin contrast to the case for all photometrically selected galaxies for which\n$M_{\\min}$ shows significant dependence on $M_{\\star}$ even at low $M_{\\star}$.\nThe weak stellar mass dependence is indicative of the dark halo mass being the\nkey parameter for the formation of LRGs rather than the stellar mass. Our\nresult suggests that the halo mass of $\\sim 10^{12.5 \\pm 0.2}h^{-1} M_{\\odot}$\nis the critical mass for an efficient halo quenching due to the halo\nenvironment. We compare our result with the result of the hydrodynamical\nsimulation to find that low-mass LRGs at $z \\sim 1$ will increase their stellar\nmasses by an order magnitude from $z=1$ to $0$ through mergers and satellite\naccretions, and a large fraction of massive LRGs at $z<0.9$ consist of LRGs\nthat are recently migrated from massive green valley galaxies or those evolved\nfrom less massive LRGs through mergers and satellite accretions.\n"}
+{"abstract": "  In this work, we consider the nonlocal obstacle problem with a given obstacle\n$\\psi$ in a bounded Lipschitz domain $\\Omega$ in $\\mathbb{R}^{d}$, such that\n$\\mathbb{K}_\\psi^s=\\{v\\in H^s_0(\\Omega):v\\geq\\psi \\text{ a.e. in\n}\\Omega\\}\\neq\\emptyset$, given by\n\\[u\\in\\mathbb{K}_\\psi^s:\\langle\\mathcal{L}_au,v-u\\rangle\\geq\\langle\nF,v-u\\rangle\\quad\\forall v\\in\\mathbb{K}^s_\\psi,\\] for $F\\in H^{-s}(\\Omega)$,\nthe dual space of $H^s_0(\\Omega)$, $0<s<1$. The nonlocal operator\n$\\mathcal{L}_a:H^s_0(\\Omega)\\to H^{-s}(\\Omega)$ is defined with a measurable,\nbounded, strictly positive singular kernel $a(x,y)$, possibly not symmetric, by\n\\[\\langle\\mathcal{L}_au,v\\rangle=P.V.\\int_{\\mathbb{R}^d}\\int_{\\mathbb{R}^d}v(x)(u(x)-u(y))a(x,y)dydx=\\mathcal{E}_a(u,v),\\]\nwith $\\mathcal{E}_a$ being a Dirichlet form. Also, the fractional operator\n$\\tilde{\\mathcal{L}}_A=-D^s\\cdot AD^s$ defined with the distributional Riesz\n$s$-fractional derivative and a bounded matrix $A(x)$ gives a well defined\nintegral singular kernel. The corresponding $s$-fractional obstacle problem\nconverges as $s\\nearrow1$ to the obstacle problem in $H^1_0(\\Omega)$ with the\noperator $-D\\cdot AD$ given with the gradient $D$.\n  We mainly consider problems involving the bilinear form $\\mathcal{E}_a$ with\none or two obstacles, and the N-membranes problem, deriving a weak maximum\nprinciple, comparison properties, approximation by bounded penalization, and\nthe Lewy-Stampacchia inequalities. This provides regularity of the solutions,\nincluding a global estimate in $L^\\infty(\\Omega)$, local H\\\"older regularity\nwhen $a$ is symmetric, and local regularity in $W^{2s,p}_{loc}(\\Omega)$ and\n$C^1(\\Omega)$ for fractional $s$-Laplacian obstacle-type problems. These novel\nresults are complemented with the extension of the Lewy-Stampacchia\ninequalities to the order dual of $H^s_0(\\Omega)$ and some remarks on the\nassociated $s$-capacity for general $\\mathcal{L}_a$.\n"}
+{"abstract": "  The application of reliable structural health monitoring (SHM) technologies\nto operational wind turbine blades is a challenging task, due to the uncertain\nnature of the environments they operate in. In this paper, a novel SHM\nmethodology, which uses Gaussian Processes (GPs) is proposed. The methodology\ntakes advantage of the fact that the blades on a turbine are nominally\nidentical in structural properties and encounter the same environmental and\noperational variables (EOVs). The properties of interest are the first edgewise\nfrequencies of the blades. The GPs are used to predict the edge frequencies of\none blade given that of another, after these relationships between the pairs of\nblades have been learned when the blades are in a healthy state. In using this\napproach, the proposed SHM methodology is able to identify when the blades\nstart behaving differently from one another over time. To validate the concept,\nthe proposed SHM system is applied to real onshore wind turbine blade data,\nwhere some form of damage was known to have taken place. X-bar control chart\nanalysis of the residual errors between the GP predictions and actual\nfrequencies show that the system successfully identified early onset of damage\nas early as six months before it was identified and remedied.\n"}
+{"abstract": "  Multi-robot systems are an efficient method to explore and map an unknown\nenvironment. The simulataneous localization and mapping (SLAM) algorithm is\ncommon for single robot systems, however multiple robots can share respective\nmap data in order to merge a larger global map. This thesis contributes to the\nmulti-robot mapping problem by considering cases in which robots have\ncommunication range limitations. The architecture coordinates a team of robots\nand the central server to explore an unknown environment by exploiting a\nhierarchical choice structure. The coordination algorithms ensure that the\nhierarchy of robots choose frontier points that provide maximum information\ngain, while maintaining viable communication amongst themselves and the central\ncomputer through an ad-hoc relay network. In addition, the robots employ a\nbackup choice algorithm in cases when no valid frontier points remain by\narranging the communication relay network as a fireline back to the source.\n  This work contributes a scalable, efficient, and robust architecture towards\nhybrid multi-robot mapping systems that take into account communication range\nlimitations. The architecture is tested in a simulation environment using\nvarious maps.\n"}
+{"abstract": "  We study pluripotential complex Monge-Amp\\`ere flows in big cohomology\nclasses on compact K{\\\"a}hler manifolds. We use the Perron method, considering\npluripotential subsolutions to the Cauchy problem. We prove that, under natural\nassumptions on the data, the upper envelope of all subsolutions is continuous\nin space and semi-concave in time, and provides a unique pluripotential\nsolution with such regularity. We apply this theory to study pluripotential\nK{\\\"a}hler-Ricci flows on compact K{\\\"a}hler manifolds of general type as well\nas on stable varieties.\n"}
+{"abstract": "  We present the abundance analyses of 7 Carbon enhanced metal-poor (CEMP)\nstars to understand the origin of carbon in them. We used high-resolution\noptical spectra to derive abundances of various elements. We also used\nlow-resolution Near-Infrared (NIR) spectra to derive the abundance of O and\n12C/13C from the CO molecular band and compared their values with those derived\nfrom high-resolution optical spectra. We identified a good agreement between\nthe values. Thus, in cool CEMP stars, the NIR observations complement the\nhigh-resolution optical observations to derive the oxygen abundance and the\n12C/13C ratio. This enables us to probe fainter cool CEMP stars using NIR\nspectroscopy. C, N, O abundances of all the program stars in this study show\nabundances that are consistent with binary mass transfer from a low-mass\nlow-metallicity Asymptotic Giant Branch (AGB) companion which is further\nsupported by the presence of enhancement in neutron-capture elements and\ndetection of radial velocity variation. One of the stars show abundance\npatterns similar to a CEMP-s star whereas the abundance pattern of the rest of\nthe stars satisfy the criteria required to classify them as CEMP-r/s stars. The\nsub-classification of some of the stars studied here is revisited. The\nabundance of neutron capture elements in these CEMP-r/s stars resembles to that\nof i-process models where proton ingestion episodes in the companion low-mass\nlow-metallicity AGB stars produce the necessary neutron density required for\nthe onset of i-process.\n"}
+{"abstract": "  We study the feasibility of reaching the ultrastrong (USC) and deep-strong\ncoupling (DSC) regimes of light-matter interaction, in particular at resonance\ncondition, with a superconducting charge qubit, also known as Cooper-Pair box\n(CPB). We show that by shunting the charge qubit with a high-impedance\nLC-circuit, one can maximally reach both USC and DSC regimes exceeding the\nclassical upper bound $|g|\\leq \\sqrt{\\omega_q\\omega_r}/2$ between two harmonic\nsystems with frequencies $\\omega_q$ and $\\omega_r$. In our case, the\nfundamental model corresponds to an enhanced quantum Rabi model, which contains\na displacement field operator that breaks its internal parity symmetry.\nFurthermore, we consider a multipartite device consisting of two CPBs\nultrastrongly coupled to an oscillator as a mediator and study a quantum state\ntransfer protocol between a pair of transmon qubits, all of them subjected to\nlocal incoherent noise channels with realistic parameters. This work opens the\ndoor for studying light-matter interactions beyond the quantum Rabi model at\nextreme coupling strengths, providing a new building block for applications\nwithin quantum computation and quantum information processing.\n"}
+{"abstract": "  The observation of beam spin asymmetries in two-pion production in\nsemi-inclusive deep inelastic scattering off an unpolarized proton target is\nreported. The data presented here were taken in the fall of 2018 with the\nCLAS12 spectrometer using a 10.6 GeV longitudinally spin-polarized electron\nbeam delivered by CEBAF at JLab. The measured asymmetries provide the first\nopportunity to extract the parton distribution function $e(x)$, which provides\ninformation about the interaction between gluons and quarks, in a collinear\nframework that offers cleaner access than previous measurements. The\nasymmetries also constitute the first ever signal sensitive to the\nhelicity-dependent two-pion fragmentation function $G_1^\\perp$. A clear sign\nchange is observed around the $\\rho$ mass that appears in model calculations\nand is indicative of the dependence of the produced pions on the helicity of\nthe fragmenting quark.\n"}
+{"abstract": "  There is a growing interest in the community in making an embodied AI agent\nperform a complicated task while interacting with an environment following\nnatural language directives. Recent studies have tackled the problem using\nALFRED, a well-designed dataset for the task, but achieved only very low\naccuracy. This paper proposes a new method, which outperforms the previous\nmethods by a large margin. It is based on a combination of several new ideas.\nOne is a two-stage interpretation of the provided instructions. The method\nfirst selects and interprets an instruction without using visual information,\nyielding a tentative action sequence prediction. It then integrates the\nprediction with the visual information etc., yielding the final prediction of\nan action and an object. As the object's class to interact is identified in the\nfirst stage, it can accurately select the correct object from the input image.\nMoreover, our method considers multiple egocentric views of the environment and\nextracts essential information by applying hierarchical attention conditioned\non the current instruction. This contributes to the accurate prediction of\nactions for navigation. A preliminary version of the method won the ALFRED\nChallenge 2020. The current version achieves the unseen environment's success\nrate of 4.45% with a single view, which is further improved to 8.37% with\nmultiple views.\n"}
+{"abstract": "  The behaviour of the generalised Riesz function defined by\n\\[S_{m,p}(x)=\\sum_{k=0}^\\infty \\frac{(-)^{k-1}x^k}{k! \\zeta(mk+p)}\\qquad (m\\geq\n1,\\ p\\geq 1)\\] is considered for large positive values of $x$. A numerical\nscheme is given to compute this function which enables the visualisation of its\nasymptotic form. The two cases $m=2$, $p=1$ and $m=p=2$ (introduced\nrespectively by Hardy and Littlewood in 1918 and Riesz in 1915) are examined in\ndetail. It is found on numerical evidence that these functions appear to\nexhibit the $x^{-1/4}$ and $x^{-3/4}$ decay, superimposed on an oscillatory\nstructure, required for the truth of the Riemann hypothesis.\n"}
+{"abstract": "  We find stationary thin-brane geometries that are dual to\nfar-from-equilibrium steady states of two-dimensional holographic interfaces.\nThe flow of heat at the boundary agrees with the result of CFT and the known\nenergy-transport coefficients of the thin-brane model. We argue that by\nentangling outgoing excitations the interface produces coarse-grained entropy\nat a maximal rate, and point out similarities and differences with double-sided\nblack funnels. The non-compact, non-Killing and far from-equilibrium event\nhorizon of our solutions coincides with the local (apparent) horizon on the\ncolder side, but lies behind it on the hotter side of the interface. We also\nshow that the thermal conductivity of a pair of interfaces jumps at the\nHawking-Page phase transition from a regime described by classical scatterers\nto a quantum regime in which heat flows unobstructed.\n"}
+{"abstract": "  Existing work on automated hate speech classification assumes that the\ndataset is fixed and the classes are pre-defined. However, the amount of data\nin social media increases every day, and the hot topics changes rapidly,\nrequiring the classifiers to be able to continuously adapt to new data without\nforgetting the previously learned knowledge. This ability, referred to as\nlifelong learning, is crucial for the real-word application of hate speech\nclassifiers in social media. In this work, we propose lifelong learning of hate\nspeech classification on social media. To alleviate catastrophic forgetting, we\npropose to use Variational Representation Learning (VRL) along with a memory\nmodule based on LB-SOINN (Load-Balancing Self-Organizing Incremental Neural\nNetwork). Experimentally, we show that combining variational representation\nlearning and the LB-SOINN memory module achieves better performance than the\ncommonly-used lifelong learning techniques.\n"}
+{"abstract": "  Q-learning, which seeks to learn the optimal Q-function of a Markov decision\nprocess (MDP) in a model-free fashion, lies at the heart of reinforcement\nlearning. When it comes to the synchronous setting (such that independent\nsamples for all state-action pairs are drawn from a generative model in each\niteration), substantial progress has been made towards understanding the sample\nefficiency of Q-learning. Consider a $\\gamma$-discounted infinite-horizon MDP\nwith state space $\\mathcal{S}$ and action space $\\mathcal{A}$: to yield an\nentrywise $\\varepsilon$-approximation of the optimal Q-function,\nstate-of-the-art theory for Q-learning requires a sample size exceeding the\norder of $\\frac{|\\mathcal{S}||\\mathcal{A}|}{(1-\\gamma)^5\\varepsilon^{2}}$,\nwhich fails to match existing minimax lower bounds. This gives rise to natural\nquestions: what is the sharp sample complexity of Q-learning? Is Q-learning\nprovably sub-optimal? This paper addresses these questions for the synchronous\nsetting: (1) when $|\\mathcal{A}|=1$ (so that Q-learning reduces to TD\nlearning), we prove that the sample complexity of TD learning is minimax\noptimal and scales as $\\frac{|\\mathcal{S}|}{(1-\\gamma)^3\\varepsilon^2}$ (up to\nlog factor); (2) when $|\\mathcal{A}|\\geq 2$, we settle the sample complexity of\nQ-learning to be on the order of\n$\\frac{|\\mathcal{S}||\\mathcal{A}|}{(1-\\gamma)^4\\varepsilon^2}$ (up to log\nfactor). Our theory unveils the strict sub-optimality of Q-learning when\n$|\\mathcal{A}|\\geq 2$, and rigorizes the negative impact of over-estimation in\nQ-learning. Finally, we extend our analysis to accommodate asynchronous\nQ-learning (i.e., the case with Markovian samples), sharpening the horizon\ndependency of its sample complexity to be $\\frac{1}{(1-\\gamma)^4}$.\n"}
+{"abstract": "  We examine the bound state solutions of the Dirac equation under the spin and\npseudospin symmetries for a new suggested combined potential, Hulten plus a\nclass of Yukawa potential including a Coulomb-like tensor interaction. An\nimproved scheme is employed to deal with the centrifugal (pseudo-centrifugal)\nterm. Using the Nikiforov-Uvarov and SUSYQM methods, we analytically develop\nthe relativistic energy eigenvalues and associated Dirac spinor components of\nwave functions. We find that both methods give entirely the same results.\nModifiable of our results into some particular potential cases, useful for\nother physical systems, are also discussed. We obtain complete agreement with\nthe findings of previous works. The spin and pseudospin bound state energy\nspectra for various levels are presented in the absence as well as the presence\nof tensor coupling. Both energy spectrums are sensitive with regards to the\nquantum numbers $\\kappa$ and $n$, as well as the parameter $\\delta$. We also\nnotice that the degeneracies between Dirac spin and pseudospin doublet\neigenstate partners are completely removed by the tensor interaction. Finally,\nwe present the parameter space of allowable bound state regions of potential\nstrength $V_0$ with constants for both considered symmetry limits $C_S$ and\n$C_{PS}$.\n"}
+{"abstract": "  Heterogeneous graph is a kind of data structure widely existing in real life.\nNowadays, the research of graph neural network on heterogeneous graph has\nbecome more and more popular. The existing heterogeneous graph neural network\nalgorithms mainly have two ideas, one is based on meta-path and the other is\nnot. The idea based on meta-path often requires a lot of manual preprocessing,\nat the same time it is difficult to extend to large scale graphs. In this\npaper, we proposed the general heterogeneous message passing paradigm and\ndesigned R-GSN that does not need meta-path, which is much improved compared to\nthe baseline R-GCN. Experiments have shown that our R-GSN algorithm achieves\nthe state-of-the-art performance on the ogbn-mag large scale heterogeneous\ngraph dataset.\n"}
+{"abstract": "  Since model quantization helps to reduce the model size and computation\nlatency, it has been successfully applied in many applications of mobile\nphones, embedded devices and smart chips. The mixed-precision quantization\nmodel can match different quantization bit-precisions according to the\nsensitivity of different layers to achieve great performance. However, it is a\ndifficult problem to quickly determine the quantization bit-precision of each\nlayer in deep neural networks according to some constraints (e.g., hardware\nresources, energy consumption, model size and computation latency). To address\nthis issue, we propose a novel sequential single path search (SSPS) method for\nmixed-precision quantization,in which the given constraints are introduced into\nits loss function to guide searching process. A single path search cell is used\nto combine a fully differentiable supernet, which can be optimized by\ngradient-based algorithms. Moreover, we sequentially determine the candidate\nprecisions according to the selection certainties to exponentially reduce the\nsearch space and speed up the convergence of searching process. Experiments\nshow that our method can efficiently search the mixed-precision models for\ndifferent architectures (e.g., ResNet-20, 18, 34, 50 and MobileNet-V2) and\ndatasets (e.g., CIFAR-10, ImageNet and COCO) under given constraints, and our\nexperimental results verify that SSPS significantly outperforms their uniform\ncounterparts.\n"}
+{"abstract": "  This paper studies distributed resource allocation problem in multi-agent\nsystems, where all the agents cooperatively minimize the sum of their cost\nfunctions with global resource constraints over stochastic communication\nnetworks. This problem arises from many practical domains such as economic\ndispatch in smart grid, task assignment, and power allocation in robotic\ncontrol. Most of existing works cannot converge to the optimal solution if\nstates deviate from feasible region due to disturbance caused by environmental\nnoise, misoperation, malicious attack, etc. To solve this problem, we propose a\ndistributed deviation-tracking resource allocation algorithm and prove that it\nlinearly converges to the optimal solution with constant stepsizes. We further\nexplore its resilience properties of the proposed algorithm. Most importantly,\nthe algorithm still converges to the optimal solution under the disturbance\ninjection and random communication failure. In order to improve the convergence\nrate, the optimal stepsizes for the fastest convergence rate are established.\nWe also prove the algorithm converges linearly to the optimal solution in mean\nsquare even with uncoordinated stepsizes, i.e., agents are allowed to employ\ndifferent stepsizes. Simulations are provided to verify the theoretical\nresults.\n"}
+{"abstract": "  Methods based on convolutional neural networks have improved the performance\nof biomedical image segmentation. However, most of these methods cannot\nefficiently segment objects of variable sizes and train on small and biased\ndatasets, which are common in biomedical use cases. While methods exist that\nincorporate multi-scale fusion approaches to address the challenges arising\nwith variable sizes, they usually use complex models that are more suitable for\ngeneral semantic segmentation computer vision problems. In this paper, we\npropose a novel architecture called MSRF-Net, which is specially designed for\nmedical image segmentation tasks. The proposed MSRF-Net is able to exchange\nmulti-scale features of varying receptive fields using a dual-scale dense\nfusion block (DSDF). Our DSDF block can exchange information rigorously across\ntwo different resolution scales, and our MSRF sub-network uses multiple DSDF\nblocks in sequence to perform multi-scale fusion. This allows the preservation\nof resolution, improved information flow, and propagation of both high- and\nlow-level features to obtain accurate segmentation maps. The proposed MSRF-Net\nallows to capture object variabilities and provides improved results on\ndifferent biomedical datasets. Extensive experiments on MSRF-Net demonstrate\nthat the proposed method outperforms most of the cutting-edge medical image\nsegmentation state-of-the-art methods. MSRF-Net advances the performance on\nfour publicly available datasets, and also, MSRF-Net is more generalizable as\ncompared to state-of-the-art methods.\n"}
+{"abstract": "  Course estimation is a key component for the development of autonomous\nnavigation systems for robots. While state-of-the-art methods widely use\nvisual-based algorithms, it is worth noting that they all fail to deal with the\ncomplexity of the real world by being computationally greedy and sometimes too\nslow. They often require obstacles to be highly textured to improve the overall\nperformance, particularly when the obstacle is located within the focus of\nexpansion (FOE) where the optic flow (OF) is almost null. This study proposes\nthe FAst ITerative Half-plane (FAITH) method to determine the course of a micro\nair vehicle (MAV). This is achieved by means of an event-based camera, along\nwith a fast RANSAC-based algorithm that uses event-based OF to determine the\nFOE. The performance is validated by means of a benchmark on a simulated\nenvironment and then tested on a dataset collected for indoor obstacle\navoidance. Our results show that the computational efficiency of our solution\noutperforms state-of-the-art methods while keeping a high level of accuracy.\nThis has been further demonstrated onboard an MAV equipped with an event-based\ncamera, showing that our event-based FOE estimation can be achieved online\nonboard tiny drones, thus opening the path towards fully neuromorphic solutions\nfor autonomous obstacle avoidance and navigation onboard MAVs.\n"}
+{"abstract": "  We investigate the asymptotic fluctuation of three interacting particle\nsystems: the geometric q-TASEP, the geometric q-PushTASEP and the q-PushASEP.\nWe prove that the rescaled particle position converges to the GUE Tracy-Widom\ndistribution in the homogeneous case. If the jump rates of the first finitely\nmany particles are perturbed in the first two models, we obtain Baik-Ben\nArous-Peche and Gaussian limiting fluctuations.\n"}
+{"abstract": "  The names of variables and functions serve as implicit documentation and are\ninstrumental for program comprehension. But choosing good meaningful names is\nhard. We perform a sequence of experiments in which a total of 334 subjects are\nrequired to choose names in given programming scenarios. The first experiment\nshows that the probability that two developers would select the same name is\nlow: in the 47 instances in our experiments the median probability was only\n6.9%. At the same time, given that a specific name is chosen, it is usually\nunderstood by the majority of developers. Analysis of the names given in the\nexperiment suggests a model where naming is a (not necessarily cognizant or\nserial) three-step process: (1) selecting the concepts to include in the name,\n(2) choosing the words to represent each concept, and (3) constructing a name\nusing these words. A followup experiment, using the same experimental setup,\nthen checked whether using this model explicitly can improve the quality of\nnames. The results were that names selected by subjects using the model were\njudged by two independent judges to be superior to names chosen in the original\nexperiment by a ratio of two-to-one. Using the model appears to encourage the\nuse of more concepts and longer names.\n"}
+{"abstract": "  We consider the Navier-Stokes system in three dimensions perturbed by a\ntransport noise which is sufficiently smooth in space and rough in time. The\nexistence of a weak solution was proved recently, however, as in the\ndeterministic setting the question of uniqueness remains a major open problem.\nAn important feature of systems with uniqueness is the semigroup property\nsatisfied by their solutions. Without uniqueness, this property cannot hold\ngenerally. We select a system of solutions satisfying the semigroup property\nwith appropriately shifted rough path. In addition, the selected solutions\nrespect the well accepted admissibility criterium for physical solutions,\nnamely, maximization of the energy dissipation. Finally, under suitable\nassumptions on the driving rough path, we show that the Navier-Stokes system\ngenerates a measurable random dynamical system. To the best of our knowledge,\nthis is the first construction of a measurable single-valued random dynamical\nsystem in the state space for an SPDE without uniqueness.\n"}
+{"abstract": "  This paper empirically examines how the opening of K-12 schools and colleges\nis associated with the spread of COVID-19 using county-level panel data in the\nUnited States. Using data on foot traffic and K-12 school opening plans, we\nanalyze how an increase in visits to schools and opening schools with different\nteaching methods (in-person, hybrid, and remote) is related to the 2-weeks\nforward growth rate of confirmed COVID-19 cases. Our debiased panel data\nregression analysis with a set of county dummies, interactions of state and\nweek dummies, and other controls shows that an increase in visits to both K-12\nschools and colleges is associated with a subsequent increase in case growth\nrates. The estimates indicate that fully opening K-12 schools with in-person\nlearning is associated with a 5 (SE = 2) percentage points increase in the\ngrowth rate of cases. We also find that the positive association of K-12 school\nvisits or in-person school openings with case growth is stronger for counties\nthat do not require staff to wear masks at schools. These results have a causal\ninterpretation in a structural model with unobserved county and time\nconfounders. Sensitivity analysis shows that the baseline results are robust to\ntiming assumptions and alternative specifications.\n"}
+{"abstract": "  In cell-free massive multiple-input multiple-output (MIMO) the fluctuations\nof the channel gain from the access points to a user are large due to the\ndistributed topology of the system. Because of these fluctuations, data\ndecoding schemes that treat the channel as deterministic perform inefficiently.\nA way to reduce the channel fluctuations is to design a precoding scheme that\nequalizes the effective channel gain seen by the users. Conjugate beamforming\n(CB) poorly contributes to harden the effective channel at the users. In this\nwork, we propose a variant of CB dubbed enhanced normalized CB (ECB), in that\nthe precoding vector consists of the conjugate of the channel estimate\nnormalized by its squared norm. For this scheme, we derive an exact closed-form\nexpression for an achievable downlink spectral efficiency (SE), accounting for\nchannel estimation errors, pilot reuse and user's lack of channel state\ninformation (CSI), assuming independent Rayleigh fading channels. We also\ndevise an optimal max-min fairness power allocation based only on large-scale\nfading quantities. ECB greatly boosts the channel hardening enabling the users\nto reliably decode data relying only on statistical CSI. As the provided\neffective channel is nearly deterministic, acquiring CSI at the users does not\nyield a significant gain.\n"}
+{"abstract": "  The healthcare industry has witnessed significant transformations in e-health\nservices where Electronic Health Records (EHRs) are transferred to mobile edge\nclouds to facilitate healthcare. Many edge cloud-based system designs have been\nproposed, but some technical challenges still remain, such as low quality of\nservices (QoS), data privacy and system security due to centralized healthcare\narchitectures. In this paper, we propose a novel hybrid approach of data\noffloading and data sharing for healthcare using edge cloud and blockchain.\nFirst, an efficient data offloading scheme is proposed where IoT health data\ncan be offloaded to nearby edge servers for data processing with privacy\nawareness. Then, a data sharing scheme is integrated to enable data exchange\namong healthcare users via blockchain. Particularly, a trustworthy access\ncontrol mechanism is developed using smart contracts for access authentication\nto achieve secure EHRs sharing. Implementation results from extensive\nreal-world experiments show the superior advantages of the proposal over the\nexisting schemes in terms of improved QoS, enhanced data privacy and security,\nand low smart contract costs.\n"}
+{"abstract": "  Skew completable unimodular rows of odd length are completable over\npolynomial extension of a local ring if dimension of local ring and length of\nunimodular rows are same.\n"}
+{"abstract": "  In this paper, we design receivers for filter bank multicarrier-based\n(FBMC-based) massive MIMO considering practical aspects such as channel\nestimation and equalization. In particular, we propose a spectrally efficient\npilot structure and a channel estimation technique in the uplink to jointly\nestimate all the users' channel impulse responses. We mathematically analyze\nour proposed channel estimator and find the statistics of the channel\nestimation errors. These statistics are incorporated into our proposed\nequalizers to deal with the imperfect channel state information (CSI) effect.\nWe revisit the channel equalization problem for FBMC-based massive MIMO,\naddress the shortcomings of the existing equalizers in the literature, and make\nthem more applicable to practical scenarios. The proposed receiver in this\npaper consists of two stages. In the first stage, a linear combining of the\nreceived signals at the base station (BS) antennas provides a coarse channel\nequalization and removes any multiuser interference. In the second stage, a per\nsubcarrier fractionally spaced equalizer (FSE) takes care of any residual\ndistortion of the channel for the user of interest. We propose an FSE design\nbased on the equivalent channel at the linear combiner output. This enables the\napplicability of our proposed technique to small and/or distributed antenna\nsetups such as cell-free massive MIMO. Finally, the efficacy of the proposed\ntechniques is corroborated through numerical analysis.\n"}
+{"abstract": "  We study the band structure of self-adjoint elliptic operators $\\mathbb{A}_g=\n-\\nabla \\cdot \\sigma_{g} \\nabla$, where $\\sigma_g$ has the symmetries of a\nhoneycomb tiling of $\\mathbb{R}^2$. We focus on the case where $\\sigma_{g}$ is\na real-valued scalar: $\\sigma_{g}=1$ within identical, disjoint \"inclusions\",\ncentered at vertices of a honeycomb lattice, and $\\sigma_{g}=g \\gg1 $ (high\ncontrast) in the complement of the inclusion set (bulk). Such operators govern,\ne.g. transverse electric (TE) modes in photonic crystal media consisting of\nhigh dielectric constant inclusions (semi-conductor pillars) within a\nhomogeneous lower contrast bulk (air), a configuration used in many physical\nstudies. Our approach, which is based on monotonicity properties of the\nassociated energy form, extends to a class of high contrast elliptic operators\nthat model heterogeneous and anisotropic honeycomb media.\n  Our results concern the global behavior of dispersion surfaces, and the\nexistence of conical crossings (Dirac points) occurring in the lowest two\nenergy bands as well as in bands arbitrarily high in the spectrum. Dirac points\nare the source of important phenomena in fundamental and applied physics, e.g.\ngraphene and its artificial analogues, and topological insulators. The key\nhypotheses are the non-vanishing of the Dirac (Fermi) velocity $v_D(g)$,\nverified numerically, and a spectral isolation condition, verified analytically\nin many configurations. Asymptotic expansions, to any order in $g^{-1}$, of\nDirac point eigenpairs and $v_D(g)$ are derived with error bounds.\n  Our study illuminates differences between the high contrast behavior of\n$\\mathbb{A}_g$ and the corresponding strong binding regime for Schroedinger\noperators.\n"}
+{"abstract": "  Effective theories describing black hole exteriors contain many open-system\nfeatures due to the large number of gapless degrees of freedom that lie beyond\nreach across the horizon. A simple solvable Caldeira-Leggett type model of a\nquantum field interacting within a small area with many unmeasured thermal\ndegrees of freedom was recently proposed in arXiv:2106.09854 to provide a toy\nmodel of this kind of dynamics against which more complete black hole\ncalculations might be compared. We here compute the response of a simple\nUnruh-DeWitt detector (or qubit) interacting with a massless quantum field\n$\\phi$ coupled to such a hotspot. Our treatment differs from traditional\ntreatments of Unruh-DeWitt detectors by using Open-EFT tools to reliably\ncalculate the qubit's late-time behaviour. We use these tools to determine the\nefficiency with which the qubit thermalizes as a function of its proximity to\nthe hotspot. We identify a Markovian regime in which thermalization does occur,\nthough only for qubits closer to the hotspot than a characteristic distance\nscale set by the $\\phi$-hotspot coupling. We compute the thermalization time,\nand find that it varies inversely with the $\\phi$-qubit coupling strength in\nthe standard way.\n"}
+{"abstract": "  The realization of an efficient quantum optical interface for multi-qubit\nsystems is an outstanding challenge in science and engineering. Using two atoms\nin individually-controlled optical tweezers coupled to a nanofabricated\nphotonic crystal cavity, we demonstrate entanglement generation, fast\nnon-destructive readout, and full quantum control of atomic qubits. The\nentangled state is verified in free space after being transported away from the\ncavity by encoding the qubits into long-lived states and using dynamical\ndecoupling. Our approach bridges quantum operations at an optical link and in\nfree space with a coherent one-way transport, potentially enabling an\nintegrated optical interface for atomic quantum processors.\n"}
+{"abstract": "  This paper provides a general overview of different perspectives and studies\non trust, offers a definition of trust, and provides factors that play a\nsubstantial role in developing social trust, and shows from which perspectives\nit can be fostered. The results showed that trust is playing an important role\nin success for organizations involved in cross-national strategic partnerships.\nTrust can reduce transaction costs, promotes inter-organizational\nrelationships, and improve subordinate relationships between managers.\n"}
+{"abstract": "  Artificial Intelligence (AI) technologies have long been positioned as a tool\nto provide crucial data-driven decision support to people. In this survey\npaper, we look at how AI in general, and collaboration assistants (CAs or\nchatbots for short) in particular, have been used during a true global exigency\n- the COVID-19 pandemic. The key observation is that chatbots missed their\n\"Apollo moment\" when they could have really provided contextual, personalized,\nreliable decision support at scale that the state-of-the-art makes possible. We\nreview the existing capabilities that are feasible and methods, identify the\npotential that chatbots could have met, the use-cases they were deployed on,\nthe challenges they faced and gaps that persisted, and draw lessons that, if\nimplemented, would make them more relevant in future health emergencies.\n"}
+{"abstract": "  Knowledge distillation~(KD) is an effective learning paradigm for improving\nthe performance of lightweight student networks by utilizing additional\nsupervision knowledge distilled from teacher networks. Most pioneering studies\neither learn from only a single teacher in their distillation learning methods,\nneglecting the potential that a student can learn from multiple teachers\nsimultaneously, or simply treat each teacher to be equally important, unable to\nreveal the different importance of teachers for specific examples. To bridge\nthis gap, we propose a novel adaptive multi-teacher multi-level knowledge\ndistillation learning framework~(AMTML-KD), which consists two novel insights:\n(i) associating each teacher with a latent representation to adaptively learn\ninstance-level teacher importance weights which are leveraged for acquiring\nintegrated soft-targets~(high-level knowledge) and (ii) enabling the\nintermediate-level hints~(intermediate-level knowledge) to be gathered from\nmultiple teachers by the proposed multi-group hint strategy. As such, a student\nmodel can learn multi-level knowledge from multiple teachers through AMTML-KD.\nExtensive results on publicly available datasets demonstrate the proposed\nlearning framework ensures student to achieve improved performance than strong\ncompetitors.\n"}
+{"abstract": "  The vibrational quenching cross sections and corresponding low-temperature\nrate constants for the v = 1 and v = 2 states of CN- colliding with He and Ar\natoms have been computed ab initio using new three dimensional potential energy\nsurfaces. Little work has so far been carried out on low-energy vibrationally\ninelastic collisions for anions with neutral atoms. The cross sections and\nrates calculated at energies and temperatures relevant for both ion traps and\nastrochemical modelling, are found by the present calculations to be even\nsmaller than those of the similar C2- /He and C2-/Ar systems which are in turn\nof the order of those existing for the collisions involving neutral diatom-atom\nsystems. The implications of our finding in the present case rather small\ncomputed rate constants are discussed for their possible role in the dynamics\nof molecular cooling and in the evolution of astrochemical modelling networks.\n"}
+{"abstract": "  Non-convex optimization is ubiquitous in modern machine learning. Researchers\ndevise non-convex objective functions and optimize them using off-the-shelf\noptimizers such as stochastic gradient descent and its variants, which leverage\nthe local geometry and update iteratively. Even though solving non-convex\nfunctions is NP-hard in the worst case, the optimization quality in practice is\noften not an issue -- optimizers are largely believed to find approximate\nglobal minima. Researchers hypothesize a unified explanation for this\nintriguing phenomenon: most of the local minima of the practically-used\nobjectives are approximately global minima. We rigorously formalize it for\nconcrete instances of machine learning problems.\n"}
+{"abstract": "  Nowadays, analysis of Transparent Environmental Microorganism Images (T-EM\nimages) in the field of computer vision has gradually become a new and\ninteresting spot. This paper compares different deep learning classification\nperformance for the problem that T-EM images are challenging to analyze. We\ncrop the T-EM images into 8 * 8 and 224 * 224 pixel patches in the same\nproportion and then divide the two different pixel patches into foreground and\nbackground according to ground truth. We also use four convolutional neural\nnetworks and a novel ViT network model to compare the foreground and background\nclassification experiments. We conclude that ViT performs the worst in\nclassifying 8 * 8 pixel patches, but it outperforms most convolutional neural\nnetworks in classifying 224 * 224 pixel patches.\n"}
+{"abstract": "  Given finite configurations $P_1, \\dots, P_n \\subset \\mathbb{R}^d$, let us\ndenote by $\\mathbf{m}_{\\mathbb{R}^d}(P_1, \\dots, P_n)$ the maximum density a\nset $A \\subseteq \\mathbb{R}^d$ can have without containing congruent copies of\nany $P_i$. We will initiate the study of this geometrical parameter, called the\nindependence density of the considered configurations, and give several results\nwe believe are interesting. For instance we show that, under suitable size and\nnon-degeneracy conditions, $\\mathbf{m}_{\\mathbb{R}^d}(t_1 P_1, t_2 P_2, \\dots,\nt_n P_n)$ progressively `untangles' and tends to $\\prod_{i=1}^n\n\\mathbf{m}_{\\mathbb{R}^d}(P_i)$ as the ratios $t_{i+1}/t_i$ between consecutive\ndilation parameters grow large; this shows an exponential decay on the density\nwhen forbidding multiple dilates of a given configuration, and gives a common\ngeneralization of theorems by Bourgain and by Bukh in geometric Ramsey theory.\nWe also consider the analogous parameter $\\mathbf{m}_{S^d}(P_1, \\dots, P_n)$ on\nthe more complicated framework of sets on the unit sphere $S^d$, obtaining the\ncorresponding results in this setting.\n"}
+{"abstract": "  In this paper, we present a distributed resource allocation mechanism in\ncognitive radio networks, based on a new coopeti-tion methodology, which\ncombines advantages of nodes competition and cooperation. We postulate that\nthis new method allows for fully distributed resource management between\ncognitive radio devices. The presented framework is generic, however, we\nconsider it for the application in OFDMA networks. Coopetition takes the best\nfrom cooperative and competitive problem formulation and provides the\nopportunity to control the balance between fairness and spectral efficiency\n(SE) of resource allocation. Simulation results confirm that coopetition allows\nfor efficient resource utilization, and may be used practically in wireless\ncognitive networks.\n"}
+{"abstract": "  We advocate profunctors between posets compared to order preserving maps. We\nintroduce the graph and ascent of such profunctors. We apply this in\ncommutative algebra where these give classes of Alexander dual square-free\nmonomial ideals giving the full and natural generalized setting of isotonian\nideals and letterplace ideals for posets. We study the poset of profunctors\nfrom ${\\mathbb N}$ to ${\\mathbb N}$. Such profunctors identify as order\npresering maps $f : {\\mathbb N} \\rightarrow {\\mathbb N} \\cup \\{\\infty \\}$. For\nour applications to infinite posets we also introduce a topology on the set of\nprofunctors between two posets and study its properties.\n"}
+{"abstract": "  The quasi-one-dimensional organic conductors (TMTTF)$_2X$ with\nnon-centrosymmetric anions commonly undergo charge- and anion-order transitions\nupon cooling. While for compounds with tetrahedral anions ($X$ = BF$_4^-$,\nReO$_4^-$, and ClO$_4^-$) the charge-ordered phase is rather well understood,\nthe situation is less clear in the case of planar triangular anions, such as\n(TMTTF)$_2$NO$_3$. Here we explore the electronic and structural transitions by\ntransport experiments, optical and magnetic spectroscopy. This way we analyze\nthe temperature dependence of the charge imbalance 2$\\delta$ and an activated\nbehavior of $\\rho(T)$ with $\\Delta_{\\rm CO}\\approx 530$~K below $T_{\\rm CO} =\n250$~K. Since (TMTTF)$_2$NO$_3$ follows the universal relation between charge\nimbalance 2$\\delta$ and size of the gap $\\Delta_{\\rm CO}$, our findings suggest\nthat charge order is determined by TMTTF stacks with little influence of the\nanions. Clear signatures of anion ordering are detected at $T_{\\rm AO}=50$~K.\nThe tetramerization affects the dc transport, the vibrational features of\ndonors and acceptors, and leads to formation of spin singlets.\n"}
+{"abstract": "  Improving our knowledge of global Milky Way (MW) properties is critical for\nconnecting the detailed measurements only possible from within our Galaxy to\nour understanding of the broader galaxy population. We train Gaussian Process\nRegression (GPR) models on SDSS galaxies to map from galaxy properties (stellar\nmass, apparent axis ratio, star formation rate, bulge-to-total ratio, disk\nscale length, and bar vote fraction) to UV (GALEX $FUV/NUV$), optical (SDSS\n$ugriz$) and IR (2MASS $JHKs$ and WISE $W1/W2/W3/W4$) fluxes and uncertainties.\nWith these models we estimate the photometric properties of the MW, resulting\nin a full UV-to-IR spectral energy distribution (SED) as it would be measured\nexternally, viewed face-on. We confirm that the Milky Way lies in the green\nvalley in optical diagnostic diagrams, but show for the first time that the MW\nis in the star-forming region in standard UV and IR diagnostics --\ncharacteristic of the population of red spiral galaxies. Although our GPR\nmethod predicts one band at a time, the resulting MW UV--IR SED is consistent\nwith SEDs of local spirals with characteristics broadly similar to the MW,\nsuggesting that these independent predictions can be combined reliably. Our\nUV--IR SED will be invaluable for reconstructing the MW's star formation\nhistory using the same tools employed for external galaxies, allowing\ncomparisons of results from \\textit{in situ} measurements to those from the\nmethods used for extra-galactic objects.\n"}
+{"abstract": "  Preserving privacy is a growing concern in our society where sensors and\ncameras are ubiquitous. In this work, for the first time, we propose a\ntrainable image acquisition method that removes the sensitive identity\nrevealing information in the optical domain before it reaches the image sensor.\nThe method benefits from a trainable optical convolution kernel which transmits\nthe desired information while filters out the sensitive content. As the\nsensitive content is suppressed before it reaches the image sensor, it does not\nenter the digital domain therefore is unretrievable by any sort of privacy\nattack. This is in contrast with the current digital privacy-preserving methods\nthat are all vulnerable to direct access attack. Also, in contrast with the\nprevious optical privacy-preserving methods that cannot be trained, our method\nis data-driven and optimized for the specific application at hand. Moreover,\nthere is no additional computation, memory, or power burden on the acquisition\nsystem since this processing happens passively in the optical domain and can\neven be used together and on top of the fully digital privacy-preserving\nsystems. The proposed approach is adaptable to different digital neural\nnetworks and content. We demonstrate it for several scenarios such as smile\ndetection as the desired attribute while the gender is filtered out as the\nsensitive content. We trained the optical kernel in conjunction with two\nadversarial neural networks where the analysis network tries to detect the\ndesired attribute and the adversarial network tries to detect the sensitive\ncontent. We show that this method can reduce 65.1% of sensitive content when it\nis selected to be the gender and it only loses 7.3% of the desired content.\nMoreover, we reconstruct the original faces using the deep reconstruction\nmethod that confirms the ineffectiveness of reconstruction attacks to obtain\nthe sensitive content.\n"}
+{"abstract": "  Sixth-generation wireless communication (6G) will be an integrated\narchitecture of \"space, air, ground and sea\". One of the most difficult part of\nthis architecture is the underwater information acquisition which need to\ntransmitt information cross the interface between water and air.In this\nsenario, ocean of things (OoT) will play an important role, because it can\nserve as a hub connecting Internet of things (IoT) and Internet of underwater\nthings (IoUT). OoT device not only can collect data through underwater methods,\nbut also can utilize radio frequence over the air. For underwater\ncommunications, underwater acoustic communications (UWA COMMs) is the most\neffective way for OoT devices to exchange information, but it is always\ntormented by doppler shift and synchronization errors. In this paper, in order\nto overcome UWA tough conditions, a deep neural networks based receiver for\nunderwater acoustic chirp communication, called C-DNN, is proposed. Moreover,\nto improve the performance of DL-model and solve the problem of model\ngeneralization, we also proposed a novel federated meta learning (FML) enhanced\nacoustic radio cooperative (ARC) framework, dubbed ARC/FML, to do transfer.\nParticularly, tractable expressions are derived for the convergence rate of FML\nin a wireless setting, accounting for effects from both scheduling ratio, local\nepoch and the data amount on a single node.From our analysis and simulation\nresults, it is shown that, the proposed C-DNN can provide a better BER\nperformance and lower complexity than classical matched filter (MF) in\nunderwater acoustic communications scenario. The ARC/FML framework has good\nconvergence under a variety of channels than federated learning (FL). In\nsummary, the proposed ARC/FML for OoT is a promising scheme for information\nexchange across water and air.\n"}
+{"abstract": "  The U.S. Food & Drug Administration (FDA) requires that e-cigarette\nadvertisements include a prominent warning label that reminds consumers that\nnicotine is addictive. However, the high volume of vaping-related posts on\nsocial media makes compliance auditing expensive and time-consuming, suggesting\nthat an automated, scalable method is needed. We sought to develop and evaluate\na deep learning system designed to automatically determine if an Instagram post\npromotes vaping, and if so, if an FDA-compliant warning label was included or\nif a non-compliant warning label was visible in the image. We compiled and\nlabeled a dataset of 4,363 Instagram images, of which 44% were vaping-related,\n3% contained FDA-compliant warning labels, and 4% contained non-compliant\nlabels. Using a 20% test set for evaluation, we tested multiple neural network\nvariations: image processing backbone model (Inceptionv3, ResNet50,\nEfficientNet), data augmentation, progressive layer unfreezing, output bias\ninitialization designed for class imbalance, and multitask learning. Our final\nmodel achieved an area under the curve (AUC) and [accuracy] of 0.97 [92%] on\nvaping classification, 0.99 [99%] on FDA-compliant warning labels, and 0.94\n[97%] on non-compliant warning labels. We conclude that deep learning models\ncan effectively identify vaping posts on Instagram and track compliance with\nFDA warning label requirements.\n"}
+{"abstract": "  We describe the deployment and first tests on Sky of CONCERTO, a large\nfield-of-view (18.6arc-min) spectral-imaging instrument. The instrument\noperates in the range 130-310GHz from the APEX 12-meters telescope located at\n5100m a.s.l. on the Chajnantor plateau. Spectra with R=1-300 are obtained using\na fast (2.5Hz mechanical frequency) Fourier Transform Spectrometer (FTS),\ncoupled to a continuous dilution cryostat with a base temperature of 60mK. Two\n2152-pixels arrays of Lumped Element Kinetic Inductance Detectors (LEKID) are\ninstalled in the cryostat that also contains the cold optics and the front-end\nelectronics. CONCERTO, installed in April 2021, generates more than 20k spectra\nper second during observations. We describe the final development phases, the\ninstallation and the first results obtained on Sky.\n"}
+{"abstract": "  Discovery of quantized electric conductance by the group of van Wees in 1988\nwas a major breakthrough in physics. Later, the group of Schwab has proven the\nexistence of quantized thermal conductance. Advancing one step further, we\npresent that quantized entropy current can be interpreted and it ease the\ndescription of a transferred quantized energy package. This might yield a\nuniversal transport behavior of the microscopic world. During the transfer of a\nsingle energy quantum, $h \\nu$ between two neighboring domains the minimum\nentropy increment is calculated. Furthermore, the possible existence of the\nminimum entropy production can be formulated.\n"}
+{"abstract": "  For the one-dimensional mass-critical/supercritical pseudo-relativistic\nnonlinear Schrodinger equation, a stationary solution can be constructed as an\nenergy minimizer under an additional kinetic energy constraint and the set of\nenergy minimizers is orbitally stable in \\cite{BGV}. In this study, we proved\nthe local uniqueness and established the orbital stability of the solitary wave\nby improving that of the energy minimizer set. A key aspect thereof is the\nreformulation of the variational problem in the non-relativistic regime, which\nwe consider to be more natural because the proof extensively relies on the\nsubcritical nature of the limiting model. Thus, the role of the additional\nconstraint is clarified, a more suitable Gagliardo-Nirenberg inequality is\nintroduced, and the non-relativistic limit is proved. Subsequently, this limit\nis employed to derive the local uniqueness and orbital stability.\n"}
+{"abstract": "  This paper proposes an ecological adaptive cruise control (EACC) concept with\nthe primary goal to minimize the fuel consumption in a city bus with an\ninternal combustion engine (ICE). A hybrid model predictive control (HMPC) is\nimplemented in this work to control both continuous and discrete-time\nvariables. Moreover, a multi-objective optimization problem for EACC is\nformulated in time-domain as a mixed-integer quadratically constrained\nquadratic programming (MIQCQP) problem. The proposed HMPC-EACC performs robust\nvehicle-following while tracking a leading vehicle and plans fuel-efficient\nacceleration and deceleration maneuvers for the host vehicle. Additionally, it\nuses the signal phase and timing (SPaT) information to compute a green wave\nreference speed for the host vehicle to cross the signalized intersections at a\ngreen phase. Moreover, the proposed controller performs pulse and glide (PnG)\nto optimally control the engine ON and OFF states and save additional fuel.\nFurthermore, the performance of the proposed strategy is evaluated on a\nreal-world driving profile and compared against a baseline controller from the\nliterature. Finally, the influence of different prediction horizons on the fuel\nsavings and computation times are studied. The results reveal significant\nreduction in fuel consumption with HMPC-EACC and demonstrate that the proposed\ncontroller is real-time capable.\n"}
+{"abstract": "  Learning to model how the world changes as time elapses has proven a\nchallenging problem for the computer vision community. We propose a\nself-supervised solution to this problem using temporal cycle consistency\njointly in vision and language, training on narrated video. Our model learns\nmodality-agnostic functions to predict forward and backward in time, which must\nundo each other when composed. This constraint leads to the discovery of\nhigh-level transitions between moments in time, since such transitions are\neasily inverted and shared across modalities. We justify the design of our\nmodel with an ablation study on different configurations of the cycle\nconsistency problem. We then show qualitatively and quantitatively that our\napproach yields a meaningful, high-level model of the future and past. We apply\nthe learned dynamics model without further training to various tasks, such as\npredicting future action and temporally ordering sets of images. Project page:\nhttps://dave.ml/mmcc\n"}
+{"abstract": "  We present two Dialectica-like constructions for models of intensional\nMartin-L\\\"of type theory based on G\\\"odel's original Dialectica interpretation\nand the Diller-Nahm variant, bringing dependent types to categorical proof\ntheory. We set both constructions within a logical predicates style theory for\ndisplay map categories where we show that 'quasifibred' versions of dependent\nproducts and universes suffice to construct their standard counterparts. To\nsupport the logic required for dependent products in the first construction, we\npropose a new semantic notion of finite sum for dependent types, generalizing\nfinitely-complete extensive categories. The second avoids extensivity\nassumptions using biproducts in a Kleisli category for a fibred additive monad.\n"}
+{"abstract": "  In this paper, we propose a novel multi-color balance adjustment for color\nconstancy. The proposed method, called \"n-color balancing,\" allows us not only\nto perfectly correct n target colors on the basis of corresponding ground truth\ncolors but also to correct colors other than the n colors. In contrast,\nalthough white-balancing can perfectly adjust white, colors other than white\nare not considered in the framework of white-balancing in general. In an\nexperiment, the proposed multi-color balancing is demonstrated to outperform\nboth conventional white and multi-color balance adjustments including\nBradford's model.\n"}
+{"abstract": "  The Peer-to-Peer systems (P2P) were led these last years as the major\ntechnology of access upon various resources on Internet. These systems build a\ncluster witch contains a very large number of peers. As the result the\nselection of peers who can answer for a given query is a very difficult\nproblem. The efficiency of the selection algorithms can be improved by\nintroducing of semantics into the process of queries routing. We present in\nthis paper a novel improved version of our semantic routing algorithm\nLearningPeerSelection (LPS) presented in CORIA 2009, an incremental strategy of\nupdating knowledge bases and an advanced experimental study. To test the\nproposed algorithm, we defined a layer of routing on the PeerSim simulator.\n"}
+{"abstract": "  Transit surveys have revealed a significant population of compact\nmulti-planet systems, containing several sub-Neptune-mass planets on close-in,\ntightly-packed orbits. These systems are thought to have formed through a final\nphase of giant impacts, which would tend to leave systems close to the edge of\nstability. Here, we assess this hypothesis, comparing observed eccentricities\nin systems exhibiting transit-timing variations (TTVs), with the maximum\neccentricities compatible with long-term stability. We use the machine-learning\nclassifier SPOCK (Tamayo et al. 2020) to rapidly classify the stability of\nnumerous initial configurations and hence determine these stability limits.\nWhile previous studies have argued that multi-planet systems are often\nmaximally packed, in the sense that they could not host any additional planets,\nwe find that the existing planets in these systems have measured eccentricities\nbelow the limits allowed by stability by a factor of 2--10. We compare these\nresults against predictions from the giant impact theory of planet formation,\nderived from both $N$-body integrations and theoretical expectations that in\nthe absence of dissipation, the orbits of such planets should be distributed\nuniformly throughout the phase space volume allowed by stability. We find that\nthe observed systems have systematically lower eccentricities than this\nscenario predicts, with a median eccentricity about 4 times lower than\npredicted. These findings suggest that if such systems formed through giant\nimpacts, then some dissipation must occur to damp their eccentricities. This\nmay take place during formation, perhaps through interactions with the natal\ngas disk or a leftover population of planetesimals, or over longer timescales\nthrough the coupling of tidal and secular processes.\n"}
+{"abstract": "  Let $\\mathcal{O}$ be a discrete valuation ring with unique maximal ideal\n$\\mathfrak{p}$ and with finite residue field $\\mathbb{F}_{q}$, the field with\n$q$ elements where $q$ is a power of a prime $p$. For $r \\ge 1$, we write\n$\\mathcal{O}_r$ for the reduction of $\\mathcal{O}$ modulo the ideal\n$\\mathfrak{p}^r$. An irreducible representation of the finite group\n$G_r=\\mathrm{GL}_{N}(\\mathcal{O}_{r})$ is called stable if its restriction to\nthe principal congruence kernel\n$K^l=1+\\mathfrak{p}^{l}\\mathrm{M}_{N}(\\mathcal{O}_r)$, where\n$l=[\\frac{r+1}{2}]$, consists of representations whose stabilisers modulo\n$K^{l'}$ are centralisers of Jordan canonical matrix in\n$\\mathfrak{g}_{l'}=\\mathrm{M}_{N}(\\mathcal{{O}}_{l'})$, where $l'=r-l$.\n  Their study is motivated by constructions of strongly semisimple\nrepresentations, introduced by the work of Hill, which is a special case of\nstable representations. In this paper, we explore the construction of stable\n(ordinary) irreducible representations of the finite group\n$G_r=\\mathrm{GL}_{N}(\\mathcal{O}_{r})$ for $N \\ge 2$.\n"}
+{"abstract": "  Current voice conversion (VC) methods can successfully convert timbre of the\naudio. As modeling source audio's prosody effectively is a challenging task,\nthere are still limitations of transferring source style to the converted\nspeech. This study proposes a source style transfer method based on\nrecognition-synthesis framework. Previously in speech generation task, prosody\ncan be modeled explicitly with prosodic features or implicitly with a latent\nprosody extractor. In this paper, taking advantages of both, we model the\nprosody in a hybrid manner, which effectively combines explicit and implicit\nmethods in a proposed prosody module. Specifically, prosodic features are used\nto explicit model prosody, while VAE and reference encoder are used to\nimplicitly model prosody, which take Mel spectrum and bottleneck feature as\ninput respectively. Furthermore, adversarial training is introduced to remove\nspeaker-related information from the VAE outputs, avoiding leaking source\nspeaker information while transferring style. Finally, we use a modified\nself-attention based encoder to extract sentential context from bottleneck\nfeatures, which also implicitly aggregates the prosodic aspects of source\nspeech from the layered representations. Experiments show that our approach is\nsuperior to the baseline and a competitive system in terms of style transfer;\nmeanwhile, the speech quality and speaker similarity are well maintained.\n"}
+{"abstract": "  Safety-critical software systems are in many cases designed and implemented\nas families of products, usually referred to as Software Product Lines (SPLs).\nProducts within an SPL vary from each other in terms of which features they\ninclude. Applying existing analysis techniques to SPLs and their safety cases\nis usually challenging because of the potentially exponential number of\nproducts with respect to the number of supported features. In this paper, we\npresent a methodology and infrastructure for certified \\emph{lifting} of\nexisting single-product safety analyses to product lines. To ensure certified\nsafety of our infrastructure, we implement it in an interactive theorem prover,\nincluding formal definitions, lemmas, correctness criteria theorems, and\nproofs. We apply this infrastructure to formalize and lift a Change Impact\nAssessment (CIA) algorithm. We present a formal definition of the lifted\nalgorithm, outline its correctness proof (with the full machine-checked proof\navailable online), and discuss its implementation within a model management\nframework.\n"}
+{"abstract": "  The functional form of Coulomb interactions in the transition metal\ndichalcogenides and other van der Waals solids is critical to many of their\nunique properties, e.g. strongly-correlated electron states, superconductivity\nand emergent ferromagnetism. This paper presents measurements of key excitonic\nenergy levels in MoSe2/WSe2 heterostructures. These measurements are obtained\nfrom resonance Raman experiments on specific Raman peaks only observed at\nexcited states of the excitons. This data is used to validate a model of the\nCoulomb potential in these structures which predicts the exciton energies to\nwithin ~5 meV / 2.5%. This model is used to determine the effect of\nheterostructure formation on the single-particle band gaps of the layers and\nwill have a wide applicability in designing the next generation of more complex\ntransition metal dichalcogenide structures.\n"}
+{"abstract": "  This paper deals with the fully parabolic chemotaxis system of local sensing\nin higher dimensions. Despite the striking similarity between this system and\nthe Keller--Segel system, we prove the absence of finite-time blow-up\nphenomenon in this system even in the supercritical case. It means that for any\nregular initial data, independently of the magnitude of mass, the classical\nsolution exists globally in time in the higher dimensional setting. Moreover,\nfor the exponential decaying motility case, it is established that solutions\nmay blow up at infinite time for any magnitude of mass. In order to prove our\ntheorem, we deal with some auxiliary identity as an evolution equation with a\ntime dependent operator. In view of this new perspective, the direct\nconsequence of the abstract theory is rich enough to establish global existence\nof the system.\n"}
+{"abstract": "  The Internet of Medical Things (IoMT) paradigm is becoming mainstream in\nmultiple clinical trials and healthcare procedures. Cardiovascular diseases\nmonitoring, usually involving electrocardiogram (ECG) traces analysis, is one\nof the most promising and high-impact applications. Nevertheless, to fully\nexploit the potential of IoMT in this domain, some steps forward are needed.\nFirst, the edge-computing paradigm must be added to the picture. A certain\nlevel of near-sensor processing has to be enabled, to improve the scalability,\nportability, reliability, responsiveness of the IoMT nodes. Second, novel,\nincreasingly accurate, data analysis algorithms, such as those based on\nartificial intelligence and Deep Learning, must be exploited. To reach these\nobjectives, designers and programmers of IoMT nodes, have to face challenging\noptimization tasks, in order to execute fairly complex computing tasks on\nlow-power wearable and portable processing systems, with tight power and\nbattery lifetime budgets. In this work, we explore the implementation of a\ncognitive data analysis algorithm, based on a convolutional neural network\ntrained to classify ECG waveforms, on a resource-constrained\nmicrocontroller-based computing platform. To minimize power consumption, we add\nan adaptivity layer that dynamically manages the hardware and software\nconfiguration of the device to adapt it at runtime to the required operating\nmode. Our experimental results show that adapting the node setup to the\nworkload at runtime can save up to 50% power consumption. Our optimized and\nquantized neural network reaches an accuracy value higher than 97% for\narrhythmia disorders detection on MIT-BIH Arrhythmia dataset.\n"}
+{"abstract": "  We present the first BVR photometry, period variation, and photometric\nlight-curve analysis of two poorly studied eclipsing binaries V1321 Cyg and CR\nTau. Observations were carried out from November 2017 to January 2020 at the\nobservatory of Uzhhorod National University. Period variations were studied\nusing all available early published as well as our minima times. We have used\nnewly developed ELISa code for the light curve analysis and determination of\nphotometric parameters of both systems. We found that V1321 Cyg is a close\ndetached eclipsing system with a low photometric mass ratio of $q=0.28$ which\nsuggests that the binary is a post mass transfer system. No significant period\nchanges in this system are detected. CR Tau is, on the other hand, a\nsemi-detached system where the secondary component almost fills its Roche lobe.\nWe detected a long-term period increase at a rate of $1.49 \\times 10^{-7} d/y$,\nwhich support mass transfer from lower mass secondary component to the more\nmassive primary.\n"}
+{"abstract": "  Environmental Sound Classification (ESC) is a challenging field of research\nin non-speech audio processing. Most of current research in ESC focuses on\ndesigning deep models with special architectures tailored for specific audio\ndatasets, which usually cannot exploit the intrinsic patterns in the data.\nHowever recent studies have surprisingly shown that transfer learning from\nmodels trained on ImageNet is a very effective technique in ESC. Herein, we\npropose SoundCLR, a supervised contrastive learning method for effective\nenvironment sound classification with state-of-the-art performance, which works\nby learning representations that disentangle the samples of each class from\nthose of other classes. Our deep network models are trained by combining a\ncontrastive loss that contributes to a better probability output by the\nclassification layer with a cross-entropy loss on the output of the classifier\nlayer to map the samples to their respective 1-hot encoded labels. Due to the\ncomparatively small sizes of the available environmental sound datasets, we\npropose and exploit a transfer learning and strong data augmentation pipeline\nand apply the augmentations on both the sound signals and their log-mel\nspectrograms before inputting them to the model. Our experiments show that our\nmasking based augmentation technique on the log-mel spectrograms can\nsignificantly improve the recognition performance. Our extensive benchmark\nexperiments show that our hybrid deep network models trained with combined\ncontrastive and cross-entropy loss achieved the state-of-the-art performance on\nthree benchmark datasets ESC-10, ESC-50, and US8K with validation accuracies of\n99.75\\%, 93.4\\%, and 86.49\\% respectively. The ensemble version of our models\nalso outperforms other top ensemble methods. The code is available at\nhttps://github.com/alireza-nasiri/SoundCLR.\n"}
+{"abstract": "  We study the Morse index of minimal surfaces with free boundary in a\nhalf-space. We improve previous estimates relating the Neumann index to the\nDirichlet index and use this to answer a question of Ambrozio, Buzano,\nCarlotto, and Sharp concerning the non-existence of index two embedded minimal\nsurfaces with free boundary in a half-space. We also give a simplified proof of\na result of Chodosh and Maximo concerning lower bounds for the index of the\nCosta deformation family.\n"}
+{"abstract": "  Deep Neural Networks (DNN) are increasingly commonly used in software\nengineering and code intelligence tasks. These are powerful tools that are\ncapable of learning highly generalizable patterns from large datasets through\nmillions of parameters. At the same time, training DNNs means walking a knife's\nedges, because their large capacity also renders them prone to memorizing data\npoints. While traditionally thought of as an aspect of over-training, recent\nwork suggests that the memorization risk manifests especially strongly when the\ntraining datasets are noisy and memorization is the only recourse.\nUnfortunately, most code intelligence tasks rely on rather noise-prone and\nrepetitive data sources, such as GitHub, which, due to their sheer size, cannot\nbe manually inspected and evaluated. We evaluate the memorization and\ngeneralization tendencies in neural code intelligence models through a case\nstudy across several benchmarks and model families by leveraging established\napproaches from other fields that use DNNs, such as introducing targeted noise\ninto the training dataset. In addition to reinforcing prior general findings\nabout the extent of memorization in DNNs, our results shed light on the impact\nof noisy dataset in training.\n"}
+{"abstract": "  We study the question which Boolean algebras have the property that for every\ngenerating set there is an ultrafilter selecting maximal number of its\nelements. We call it the ultrafilter selection property. For cardinality\naleph-one the property is equivalent to the fact that the space of ultrafilters\nis not Corson compact. We also consider the pointwise topology on a Boolean\nalgebra, proving a result on the Lindel\\\"of number in the context of the\nultrafilter selection property. Finally, we discuss poset Boolean algebras,\ninterval algebras, and semilattices in the context of ultrafilter selection\nproperties.\n"}
+{"abstract": "  Non-intrusive load monitoring (NILM) helps disaggregate the household's main\nelectricity consumption to energy usages of individual appliances, thus greatly\ncutting down the cost in fine-grained household load monitoring. To address the\narisen privacy concern in NILM applications, federated learning (FL) could be\nleveraged for NILM model training and sharing. When applying the FL paradigm in\nreal-world NILM applications, however, we are faced with the challenges of edge\nresource restriction, edge model personalization and edge training data\nscarcity.\n  In this paper we present FedNILM, a practical FL paradigm for NILM\napplications at the edge client. Specifically, FedNILM is designed to deliver\nprivacy-preserving and personalized NILM services to large-scale edge clients,\nby leveraging i) secure data aggregation through federated learning, ii)\nefficient cloud model compression via filter pruning and multi-task learning,\nand iii) personalized edge model building with unsupervised transfer learning.\nOur experiments on real-world energy data show that, FedNILM is able to achieve\npersonalized energy disaggregation with the state-of-the-art accuracy, while\nensuring privacy preserving at the edge client.\n"}
+{"abstract": "  Let $q$ be a power of the prime number $p$, let $K={\\mathbb F}_q(t)$, and let\n$r\\ge 2$ be an integer. For points ${\\mathbf a}, {\\mathbf b}\\in K$ which are\n$\\mathbb{F}_q$-linearly independent, we show that there exist positive\nconstants $N_0$ and $c_0$ such that for each integer $\\ell\\ge N_0$ and for each\ngenerator $\\tau$ of ${\\mathbb F}_{q^\\ell}/{\\mathbb F}_q$, we have that for all\nexcept $N_0$ values $\\lambda\\in{\\overline{\\mathbb{F}_q}}$, the corresponding\nspecializations ${\\mathbf a}, {\\mathbf b}(\\tau)$ and ${\\mathbf b}(\\tau)$ cannot\nhave orders of degrees less than $c_0\\log\\log\\ell$ as torsion points for the\nDrinfeld module $\\Phi^{(\\tau,\\lambda)}:\\mathbb{F}_q[T] {\\longrightarrow}\n{\\mathrm{End}}_{\\overline{\\mathbb{F}_q}}({\\mathbb G}_a)$ (where ${\\mathbb G}_a$\nis the additive group scheme), given by $\\Phi^{(\\tau,\\lambda)}_T(x)=\\tau\nx+\\lambda x^q + x^{q^r}$.\n"}
+{"abstract": "  We consider a system of static spin qubits embedded in a one-dimensional spin\ncoherent channel and develop a scheme to readout the state of one and two\nqubits separately. We use unpolarized flying qubits for this purpose that\nscatter off from the static qubits due to the Heisenberg exchange interaction.\nAnalysing the transmission coefficient as a function of density matrix elements\nalong with additional unitary gates we reconstruct the state of static qubits.\n"}
+{"abstract": "  We propose a novel and effective purification based adversarial defense\nmethod against pre-processor blind white- and black-box attacks. Our method is\ncomputationally efficient and trained only with self-supervised learning on\ngeneral images, without requiring any adversarial training or retraining of the\nclassification model. We first show an empirical analysis on the adversarial\nnoise, defined to be the residual between an original image and its adversarial\nexample, has almost zero mean, symmetric distribution. Based on this\nobservation, we propose a very simple iterative Gaussian Smoothing (GS) which\ncan effectively smooth out adversarial noise and achieve substantially high\nrobust accuracy. To further improve it, we propose Neural Contextual Iterative\nSmoothing (NCIS), which trains a blind-spot network (BSN) in a self-supervised\nmanner to reconstruct the discriminative features of the original image that is\nalso smoothed out by GS. From our extensive experiments on the large-scale\nImageNet using four classification models, we show that our method achieves\nboth competitive standard accuracy and state-of-the-art robust accuracy against\nmost strong purifier-blind white- and black-box attacks. Also, we propose a new\nbenchmark for evaluating a purification method based on commercial image\nclassification APIs, such as AWS, Azure, Clarifai and Google. We generate\nadversarial examples by ensemble transfer-based black-box attack, which can\ninduce complete misclassification of APIs, and demonstrate that our method can\nbe used to increase adversarial robustness of APIs.\n"}
+{"abstract": "  We present a unified approach for constructing Slepian functions - also known\nas prolate spheroidal wave functions - on the sphere for arbitrary tensor ranks\nincluding scalar, vectorial, and rank 2 tensorial Slepian functions, using\nspin-weighted spherical harmonics. For the special case of spherical cap\nregions, we derived commuting operators, allowing for a numerically stable and\ncomputationally efficient construction of the spin-weighted\nspherical-harmonic-based Slepian functions. Linear relationships between the\nspin-weighted and the classical scalar, vectorial, tensorial, and higher-rank\nspherical harmonics allow the construction of classical\nspherical-harmonic-based Slepian functions from their spin-weighted\ncounterparts, effectively rendering the construction of spherical-cap Slepian\nfunctions for any tensorial rank a computationally fast and numerically stable\ntask.\n"}
+{"abstract": "  A metastable cosmic-string network is a generic consequence of many grand\nunified theories (GUTs) when combined with cosmic inflation. Metastable cosmic\nstrings are not topologically stable, but decay on cosmic time scales due to\npair production of GUT monopoles. This leads to a network consisting of\nmetastable long strings on superhorizon scales as well as of string loops and\nsegments on subhorizon scales. We compute for the first time the complete\nstochastic gravitational-wave background (SGWB) arising from all these network\nconstituents, including several technical improvements to both the derivation\nof the loop and segment contributions. We find that the gravitational waves\nemitted by string loops provide the main contribution to the gravitational-wave\nspectrum in the relevant parameter space. The resulting spectrum is consistent\nwith the tentative signal observed by the NANOGrav and Parkes pulsar timing\ncollaborations for a string tension of G\\mu ~ 10^-11...-7 and has ample\ndiscovery space for ground- and space-based detectors. For GUT-scale string\ntensions, G\\mu ~ 10^-8...-7, metastable strings predict a SGWB in the\nLIGO-Virgo-KAGRA band that could be discovered in the near future.\n"}
+{"abstract": "  We propose a novel transformer-based styled handwritten text image generation\napproach, HWT, that strives to learn both style-content entanglement as well as\nglobal and local writing style patterns. The proposed HWT captures the long and\nshort range relationships within the style examples through a self-attention\nmechanism, thereby encoding both global and local style patterns. Further, the\nproposed transformer-based HWT comprises an encoder-decoder attention that\nenables style-content entanglement by gathering the style representation of\neach query character. To the best of our knowledge, we are the first to\nintroduce a transformer-based generative network for styled handwritten text\ngeneration. Our proposed HWT generates realistic styled handwritten text images\nand significantly outperforms the state-of-the-art demonstrated through\nextensive qualitative, quantitative and human-based evaluations. The proposed\nHWT can handle arbitrary length of text and any desired writing style in a\nfew-shot setting. Further, our HWT generalizes well to the challenging scenario\nwhere both words and writing style are unseen during training, generating\nrealistic styled handwritten text images.\n"}
+{"abstract": "  This paper extends Bayesian mortality projection models for multiple\npopulations considering the stochastic structure and the effect of spatial\nautocorrelation among the observations. We explain high levels of\noverdispersion according to adjacent locations based on the conditional\nautoregressive model. In an empirical study, we compare different hierarchical\nprojection models for the analysis of geographical diversity in mortality\nbetween the Japanese counties in multiple years, according to age. By a Markov\nchain Monte Carlo (MCMC) computation, results have demonstrated the flexibility\nand predictive performance of our proposed model.\n"}
+{"abstract": "  The knowledge on attacks contained in Cyber Threat Intelligence (CTI) reports\nis very important to effectively identify and quickly respond to cyber threats.\nHowever, this knowledge is often embedded in large amounts of text, and\ntherefore difficult to use effectively. To address this challenge, we propose a\nnovel approach and tool called EXTRACTOR that allows precise automatic\nextraction of concise attack behaviors from CTI reports. EXTRACTOR makes no\nstrong assumptions about the text and is capable of extracting attack behaviors\nas provenance graphs from unstructured text. We evaluate EXTRACTOR using\nreal-world incident reports from various sources as well as reports of DARPA\nadversarial engagements that involve several attack campaigns on various OS\nplatforms of Windows, Linux, and FreeBSD. Our evaluation results show that\nEXTRACTOR can extract concise provenance graphs from CTI reports and show that\nthese graphs can successfully be used by cyber-analytics tools in\nthreat-hunting.\n"}
+{"abstract": "  The current tension between the direct and the early Universe measurements of\nthe Hubble Constant, $H_0$, requires detailed scrutiny of all the data and\nmethods used in the studies on both sides of the debate. The Cepheids in the\ntype Ia supernova (SNIa) host galaxy NGC 5584 played a key role in the local\nmeasurement of $H_0$. The SH0ES project used the observations of this galaxy to\nderive a relation between Cepheids' periods and ratios of their amplitudes in\ndifferent optical bands of the Hubble Space Telescope (HST), and used these\nrelations to analyse the light curves of the Cepheids in around half of the\ncurrent sample of local SNIa host galaxies. In this work, we present an\nindependent detailed analysis of the Cepheids in NGC 5584. We employ different\ntools for our photometric analysis and a completely different method for our\nlight curve analysis, and we do not find a systematic difference between our\nperiod and mean magnitude measurements compared to those reported by SH0ES. By\nadopting a period-luminosity relation calibrated by the Cepheids in the Milky\nWay, we measure a distance modulus $\\mu=31.810\\pm0.047$ (mag) which is in\nagreement with $\\mu=31.786\\pm0.046$ (mag) measured by SH0ES. In addition, the\nrelations we find between periods and amplitude ratios of the Cepheids in NGC\n5584 are significantly tighter than those of SH0ES and their potential impact\non the direct $H_0$ measurement will be investigated in future studies.\n"}
+{"abstract": "  A commonly cited inefficiency of neural network training using\nback-propagation is the update locking problem: each layer must wait for the\nsignal to propagate through the full network before updating. Several\nalternatives that can alleviate this issue have been proposed. In this context,\nwe consider a simple alternative based on minimal feedback, which we call\nDecoupled Greedy Learning (DGL). It is based on a classic greedy relaxation of\nthe joint training objective, recently shown to be effective in the context of\nConvolutional Neural Networks (CNNs) on large-scale image classification. We\nconsider an optimization of this objective that permits us to decouple the\nlayer training, allowing for layers or modules in networks to be trained with a\npotentially linear parallelization. With the use of a replay buffer we show\nthat this approach can be extended to asynchronous settings, where modules can\noperate and continue to update with possibly large communication delays. To\naddress bandwidth and memory issues we propose an approach based on online\nvector quantization. This allows to drastically reduce the communication\nbandwidth between modules and required memory for replay buffers. We show\ntheoretically and empirically that this approach converges and compare it to\nthe sequential solvers. We demonstrate the effectiveness of DGL against\nalternative approaches on the CIFAR-10 dataset and on the large-scale ImageNet\ndataset.\n"}
+{"abstract": "  Precise control over the electronic and optical properties of defect centers\nin solid-state materials is necessary for their applications as quantum\nsensors, transducers, memories, and emitters. In this study, we demonstrate,\nfrom first principles, how to tune these properties via the formation of defect\npolaritons. Specifically, we investigate three defect types -- CHB, CB-CB, and\nCB-VN -- in monolayer hexagonal boron nitride (hBN). The lowest-lying\nelectronic excitation of these systems is coupled to an optical cavity where we\nexplore the strong light-matter coupling regime. For all defect systems, we\nshow that the polaritonic splitting that shifts the absorption energy of the\nlower polariton is much higher than can be expected from a Jaynes-Cummings\ninteraction. In addition, we find that the absorption intensity of the lower\npolariton increases by several orders of magnitude, suggesting a possible route\ntoward overcoming phonon-limited single photon emission from defect centers.\nFinally, we find that initially localized electronic transition densities can\nbecome delocalized across the entire material under strong light-matter\ncoupling. These findings are a result of an effective continuum of electronic\ntransitions near the lowest-lying electronic transition for both pristine hBN\nand hBN with defect centers that dramatically enhances the strength of the\nlight-matter interaction. We expect our findings to spur experimental\ninvestigations of strong light-matter coupling between defect centers and\ncavity photons for applications in quantum technologies.\n"}
+{"abstract": "  We propose a method for the blind separation of sounds of musical instruments\nin audio signals. We describe the individual tones via a parametric model,\ntraining a dictionary to capture the relative amplitudes of the harmonics. The\nmodel parameters are predicted via a U-Net, which is a type of deep neural\nnetwork. The network is trained without ground truth information, based on the\ndifference between the model prediction and the individual time frames of the\nshort-time Fourier transform. Since some of the model parameters do not yield a\nuseful backpropagation gradient, we model them stochastically and employ the\npolicy gradient instead. To provide phase information and account for\ninaccuracies in the dictionary-based representation, we also let the network\noutput a direct prediction, which we then use to resynthesize the audio signals\nfor the individual instruments. Due to the flexibility of the neural network,\ninharmonicity can be incorporated seamlessly and no preprocessing of the input\nspectra is required. Our algorithm yields high-quality separation results with\nparticularly low interference on a variety of different audio samples, both\nacoustic and synthetic, provided that the sample contains enough data for the\ntraining and that the spectral characteristics of the musical instruments are\nsufficiently stable to be approximated by the dictionary.\n"}
+{"abstract": "  We propose a novel approach to dimensionality reduction combining techniques\nof metric geometry and distributed persistent homology, in the form of a\ngradient-descent based method called DIPOLE. DIPOLE is a\ndimensionality-reduction post-processing step that corrects an initial\nembedding by minimizing a loss functional with both a local, metric term and a\nglobal, topological term. By fixing an initial embedding method (we use\nIsomap), DIPOLE can also be viewed as a full dimensionality-reduction pipeline.\nThis framework is based on the strong theoretical and computational properties\nof distributed persistent homology and comes with the guarantee of almost sure\nconvergence. We observe that DIPOLE outperforms popular methods like UMAP,\nt-SNE, and Isomap on a number of popular datasets, both visually and in terms\nof precise quantitative metrics.\n"}
+{"abstract": "  Continual relation extraction is an important task that focuses on extracting\nnew facts incrementally from unstructured text. Given the sequential arrival\norder of the relations, this task is prone to two serious challenges, namely\ncatastrophic forgetting and order-sensitivity. We propose a novel\ncurriculum-meta learning method to tackle the above two challenges in continual\nrelation extraction. We combine meta learning and curriculum learning to\nquickly adapt model parameters to a new task and to reduce interference of\npreviously seen tasks on the current task. We design a novel relation\nrepresentation learning method through the distribution of domain and range\ntypes of relations. Such representations are utilized to quantify the\ndifficulty of tasks for the construction of curricula. Moreover, we also\npresent novel difficulty-based metrics to quantitatively measure the extent of\norder-sensitivity of a given model, suggesting new ways to evaluate model\nrobustness. Our comprehensive experiments on three benchmark datasets show that\nour proposed method outperforms the state-of-the-art techniques. The code is\navailable at the anonymous GitHub repository:\nhttps://github.com/wutong8023/AAAI_CML.\n"}
+{"abstract": "  A methodology to generate sparse Galerkin models of chaotic/unsteady fluid\nflows containing a minimal number of active triadic interactions is proposed.\nThe key idea is to find an appropriate set of basis functions for the\nprojection representing elementary flow structures that interact minimally one\nwith the other and thus result in a triadic interaction coefficient tensor with\nsparse structure. Interpretable and computationally efficient Galerkin models\ncan be thus obtained, since a reduced number of triadic interactions needs to\nbe computed to evaluate the right hand side of the model. To find the basis\nfunctions, a subspace rotation technique is used, whereby a set of Proper\nOrthogonal Decomposition (POD) modes is rotated into a POD subspace of larger\ndimension using coordinates associated to low-energy dissipative scales to\nalter energy paths and the structure of the triadic interaction coefficient\ntensor. This rotation is obtained as the solution of a non-convex optimisation\nproblem that maximises the energy captured by the new basis, promotes sparsity\nand ensures long-term temporal stability of the sparse Galerkin system. We\ndemonstrate the approach on two-dimensional lid-driven cavity flow at $Re = 2\n\\times 10^4$ where the motion is chaotic. We show that the procedure generates\nGalerkin models with a reduced set of active triadic interactions, distributed\nin modal space according to established knowledge of scale interactions in\ntwo-dimensional flows. This property, however, is only observed if long-term\ntemporal stability is explicitly included in the formulation, indicating that a\ndynamical constraint is necessary to obtain a physically consistent\nsparsification.\n"}
+{"abstract": "  This memo describes NTR-TSU submission for SIGTYP 2021 Shared Task on\npredicting language IDs from speech.\n  Spoken Language Identification (LID) is an important step in a multilingual\nAutomated Speech Recognition (ASR) system pipeline. For many low-resource and\nendangered languages, only single-speaker recordings may be available,\ndemanding a need for domain and speaker-invariant language ID systems. In this\nmemo, we show that a convolutional neural network with a Self-Attentive Pooling\nlayer shows promising results for the language identification task.\n"}
+{"abstract": "  Industrial cyber-physical systems (ICPSs) manage critical infrastructures by\ncontrolling the processes based on the \"physics\" data gathered by edge sensor\nnetworks. Recent innovations in ubiquitous computing and communication\ntechnologies have prompted the rapid integration of highly interconnected\nsystems to ICPSs. Hence, the \"security by obscurity\" principle provided by\nair-gapping is no longer followed. As the interconnectivity in ICPSs increases,\nso does the attack surface. Industrial vulnerability assessment reports have\nshown that a variety of new vulnerabilities have occurred due to this\ntransition while the most common ones are related to weak boundary protection.\nAlthough there are existing surveys in this context, very little is mentioned\nregarding these reports. This paper bridges this gap by defining and reviewing\nICPSs from a cybersecurity perspective. In particular, multi-dimensional\nadaptive attack taxonomy is presented and utilized for evaluating real-life\nICPS cyber incidents. We also identify the general shortcomings and highlight\nthe points that cause a gap in existing literature while defining future\nresearch directions.\n"}
+{"abstract": "  Policy-based reinforcement learning methods suffer from the policy collapse\nproblem. We find valued-based reinforcement learning methods with\n{\\epsilon}-greedy mechanism are capable of enjoying three characteristics,\nClosed-form Diversity, Objective-invariant Exploration and Adaptive Trade-off,\nwhich help value-based methods avoid the policy collapse problem. However,\nthere does not exist a parallel mechanism for policy-based methods that\nachieves all three characteristics. In this paper, we propose an entropy\nregularization free mechanism that is designed for policy-based methods, which\nachieves Closed-form Diversity, Objective-invariant Exploration and Adaptive\nTrade-off. Our experiments show that our mechanism is super sample-efficient\nfor policy-based methods and boosts a policy-based baseline to a new\nState-Of-The-Art on Arcade Learning Environment.\n"}
+{"abstract": "  Despite the recent successes of reinforcement learning in games and robotics,\nit is yet to become broadly practical. Sample efficiency and unreliable\nperformance in rare but challenging scenarios are two of the major obstacles.\nDrawing inspiration from the effectiveness of deliberate practice for achieving\nexpert-level human performance, we propose a new adversarial sampling approach\nguided by a failure predictor named \"CoachNet\". CoachNet is trained online\nalong with the agent to predict the probability of failure. This probability is\nthen used in a stochastic sampling process to guide the agent to more\nchallenging episodes. This way, instead of wasting time on scenarios that the\nagent has already mastered, training is focused on the agent's \"weak spots\". We\npresent the design of CoachNet, explain its underlying principles, and\nempirically demonstrate its effectiveness in improving sample efficiency and\ntest-time robustness in common continuous control tasks.\n"}
+{"abstract": "  We propose the study of the inclusive hadroproduction of a heavy-flavored jet\nin association with a light jet, as a probe channel of strong interactions at\nhigh energies. We build up a hybrid factorization that encodes genuine\nhigh-energy effects, provided by a partial next-to-leading BFKL resummation,\ninside the standard collinear structure of the cross section. We present a\ndetailed analysis of different distributions, shaped on kinematic ranges\ntypical of experimental analyses at the Large Hadron Collider, and differential\nin rapidity, azimuthal angle and transverse momentum. The fair stability that\nthese distributions exhibit under higher-order corrections motivates our\ninterest toward future studies. Here, the hybrid factorization could help to\ndeepen our understanding of heavy-flavor physics in wider kinematic ranges,\nlike the ones accessible at the Electron-Ion Collider.\n"}
+{"abstract": "  Random samples of quantum states are an important resource for various tasks\nin quantum information science, and samples in accordance with a\nproblem-specific distribution can be indispensable ingredients. Some algorithms\ngenerate random samples by a lottery that follows certain rules and yield\nsamples from the set of distributions that the lottery can access. Other\nalgorithms, which use random walks in the state space, can be tailored to any\ndistribution, at the price of autocorrelations in the sample and with\nrestrictions to low-dimensional systems in practical implementations. In this\npaper, we present a two-step algorithm for sampling from the quantum state\nspace that overcomes some of these limitations.\n  We first produce a CPU-cheap large proposal sample, of uncorrelated entries,\nby drawing from the family of complex Wishart distributions, and then reject or\naccept the entries in the proposal sample such that the accepted sample is\nstrictly in accordance with the target distribution. We establish the explicit\nform of the induced Wishart distribution for quantum states. This enables us to\ngenerate a proposal sample that mimics the target distribution and, therefore,\nthe efficiency of the algorithm, measured by the acceptance rate, can be many\norders of magnitude larger than that for a uniform sample as the proposal.\n  We demonstrate that this sampling algorithm is very efficient for one-qubit\nand two-qubit states, and reasonably efficient for three-qubit states, while it\nsuffers from the \"curse of dimensionality\" when sampling from structured\ndistributions of four-qubit states.\n"}
+{"abstract": "  Recently, in topological insulators (TIs) the phenomenon of planar Hall\neffect (PHE) wherein a current driven in presence an in-plane magnetic field\ngenerates a transverse voltage has been experimentally witnessed. There have\nbeen a couple of theoretical explanations of this phenomenon. We investigate\nthis phenomenon based on scattering theory on a normal metal-TI-normal metal\nhybrid structure and calculate the conductances in longitudinal and transverse\ndirections to the applied bias. The transverse conductance depends on the\nspatial location between the two NM-TI junctions where it is calculated. It is\nzero in the drain electrode when the chemical potentials of the top and the\nbottom TI surfaces ($\\mu_t$ and $\\mu_b$ respectively) are equal. The\nlongitudinal conductance is $\\pi$-periodic in $\\phi$-the angle between the bias\ndirection and the direction of the in-plane magnetic field. The transverse\nconductance is $\\pi$-periodic in $\\phi$ when $\\mu_t=\\mu_b$ whereas it is\n$2\\pi$-periodic in $\\phi$ when $\\mu_t\\neq\\mu_b$. As a function of the magnetic\nfield, the magnitude of transverse conductance increases initially and peaks.\nAt higher magnetic fields, it decays for angles $\\phi$ closer to $0,\\pi$\nwhereas oscillates for angles $\\phi$ close to $\\pi/2$. The conductances\noscillate with the length of the TI region. A finite width of the system makes\nthe transport separate into finitely many channels. The features of the\nconductances are similar to those in the limit of infinitely wide system except\nwhen the width is so small that only one channel participates in the transport.\nWhen only one channel participates in transport, the transverse conductance in\nthe region $0<x<L$ is zero for $\\mu_t=\\mu_b$ and the transverse conductance in\nthe region $x>L$ is zero even for the case $\\mu_t\\neq\\mu_b$. We understand the\nfeatures in the obtained results.\n"}
+{"abstract": "  In this paper we establish the generalized Beukers integral\n$I_{m}(a_{1},...,a_{n})$ with some methods of partial fraction decomposition.\nThus one obtains an explicit expression of the generalized Beukers integral.\nFurther, we estimate the rational denominator of $I$ and. In the second section\nof this paper, we provide some estimates of the upper and lower bound of the\nvalue $J_{3}$, which involves the generalized Beukers integral and is related\nto $\\zeta(5)$.\n"}
+{"abstract": "  Discoveries of ordered quantum states of matter are of great fundamental\ninterest, and often lead to unique applications. The most well known example --\nsuperconductivity -- is caused by the formation and condensation of pairs of\nelectrons. A key property of superconductors is diamagnetism: magnetic fields\nare screened by dissipationless currents. Fundamentally, what distinguishes\nsuperconducting states from normal states is a spontaneously broken symmetry\ncorresponding to long-range coherence of fermion pairs. Here we report a set of\nexperimental observations in hole doped Ba$_{1-x}$K$_x$Fe$_2$As$_2$ which are\nnot consistent with conventional superconducting behavior. Our specific-heat\nmeasurements indicate the formation of fermionic bound states when the\ntemperature is lowered from the normal state. However, for $x \\sim 0.8$,\ninstead of the standard for superconductors, zero resistance and diamagnetic\nscreening, for a range of temperatures, we observe the opposite effect: the\ngeneration of self-induced magnetic fields measured by spontaneous Nernst\neffect and muon spin rotation experiments. The finite resistance and the lack\nof any detectable diamagnetic screening in this state exclude the spontaneously\nbroken symmetry associated with superconducting two-fermion correlations.\nInstead, combined evidence from transport and thermodynamic measurements\nindicates that the formation of fermionic bound states leads to spontaneous\nbreaking of time-reversal symmetry above the superconducting transition\ntemperature. These results demonstrate the existence of a\nbroken-time-reversal-symmetry bosonic metal state. In the framework of a\nmultiband theory, such a state is characterized by quartic correlations: the\nlong-range order exists only for {\\it pairs} of fermion pairs.\n"}
+{"abstract": "  Deep Neural Networks (NNs) have been widely utilized in contact-rich\nmanipulation tasks to model the complicated contact dynamics. However, NN-based\nmodels are often difficult to decipher which can lead to seemingly inexplicable\nbehaviors and unidentifiable failure cases. In this work, we address the\ninterpretability of NN-based models by introducing the kinodynamic images. We\npropose a methodology that creates images from the kinematic and dynamic data\nof a contact-rich manipulation task. Our formulation visually reflects the\ntask's state by encoding its kinodynamic variations and temporal evolution. By\nusing images as the state representation, we enable the application of\ninterpretability modules that were previously limited to vision-based tasks. We\nuse this representation to train Convolution-based Networks and we extract\ninterpretations of the model's decisions with Grad-CAM, a technique that\nproduces visual explanations. Our method is versatile and can be applied to any\nclassification problem using synchronous features in manipulation to visually\ninterpret which parts of the input drive the model's decisions and distinguish\nits failure modes. We evaluate this approach on two examples of real-world\ncontact-rich manipulation: pushing and cutting, with known and unknown objects.\nFinally, we demonstrate that our method enables both detailed visual\ninspections of sequences in a task, as well as high-level evaluations of a\nmodel's behavior and tendencies. Data and code for this work are available at\nhttps://github.com/imitsioni/interpretable_manipulation.\n"}
+{"abstract": "  The goal of Question Answering over Knowledge Graphs (KGQA) is to find\nanswers for natural language questions over a knowledge graph. Recent KGQA\napproaches adopt a neural machine translation (NMT) approach, where the natural\nlanguage question is translated into a structured query language. However, NMT\nsuffers from the out-of-vocabulary problem, where terms in a question may not\nhave been seen during training, impeding their translation. This issue is\nparticularly problematic for the millions of entities that large knowledge\ngraphs describe. We rather propose a KGQA approach that delegates the\nprocessing of entities to entity linking (EL) systems. NMT is then used to\ncreate a query template with placeholders that are filled by entities\nidentified in an EL phase. Slot filling is used to decide which entity fills\nwhich placeholder. Experiments for QA over Wikidata show that our approach\noutperforms pure NMT: while there remains a strong dependence on having seen\nsimilar query templates during training, errors relating to entities are\ngreatly reduced.\n"}
+{"abstract": "  Real world applications such as economics and policy making often involve\nsolving multi-agent games with two unique features: (1) The agents are\ninherently asymmetric and partitioned into leaders and followers; (2) The\nagents have different reward functions, thus the game is general-sum. The\nmajority of existing results in this field focuses on either symmetric solution\nconcepts (e.g. Nash equilibrium) or zero-sum games. It remains open how to\nlearn the Stackelberg equilibrium -- an asymmetric analog of the Nash\nequilibrium -- in general-sum games efficiently from noisy samples.\n  This paper initiates the theoretical study of sample-efficient learning of\nthe Stackelberg equilibrium, in the bandit feedback setting where we only\nobserve noisy samples of the reward. We consider three representative\ntwo-player general-sum games: bandit games, bandit-reinforcement learning\n(bandit-RL) games, and linear bandit games. In all these games, we identify a\nfundamental gap between the exact value of the Stackelberg equilibrium and its\nestimated version using finitely many noisy samples, which can not be closed\ninformation-theoretically regardless of the algorithm. We then establish sharp\npositive results on sample-efficient learning of Stackelberg equilibrium with\nvalue optimal up to the gap identified above, with matching lower bounds in the\ndependency on the gap, error tolerance, and the size of the action spaces.\nOverall, our results unveil unique challenges in learning Stackelberg\nequilibria under noisy bandit feedback, which we hope could shed light on\nfuture research on this topic.\n"}
+{"abstract": "  With increasing data and model complexities, the time required to train\nneural networks has become prohibitively large. To address the exponential rise\nin training time, users are turning to data parallel neural networks (DPNN) to\nutilize large-scale distributed resources on computer clusters. Current DPNN\napproaches implement the network parameter updates by synchronizing and\naveraging gradients across all processes with blocking communication\noperations. This synchronization is the central algorithmic bottleneck. To\ncombat this, we introduce the Distributed Asynchronous and Selective\nOptimization (DASO) method which leverages multi-GPU compute node architectures\nto accelerate network training. DASO uses a hierarchical and asynchronous\ncommunication scheme comprised of node-local and global networks while\nadjusting the global synchronization rate during the learning process. We show\nthat DASO yields a reduction in training time of up to 34% on classical and\nstate-of-the-art networks, as compared to other existing data parallel training\nmethods.\n"}
+{"abstract": "  We introduce GEM, a living benchmark for natural language Generation (NLG),\nits Evaluation, and Metrics. Measuring progress in NLG relies on a constantly\nevolving ecosystem of automated metrics, datasets, and human evaluation\nstandards. Due to this moving target, new models often still evaluate on\ndivergent anglo-centric corpora with well-established, but flawed, metrics.\nThis disconnect makes it challenging to identify the limitations of current\nmodels and opportunities for progress. Addressing this limitation, GEM provides\nan environment in which models can easily be applied to a wide set of tasks and\nin which evaluation strategies can be tested. Regular updates to the benchmark\nwill help NLG research become more multilingual and evolve the challenge\nalongside models. This paper serves as the description of the data for which we\nare organizing a shared task at our ACL 2021 Workshop and to which we invite\nthe entire NLG community to participate.\n"}
+{"abstract": "  We present an improved scheme for absorption imaging of alkali atoms at\nmoderate magnetic fields, where the excited state is well in the Paschen-Back\nregime but the ground state hyperfine manifold is not. It utilizes four atomic\nlevels to obtain an approximately closed optical cycle. With the resulting\nabsorption of the corresponding two laser frequencies we extract the atomic\ncolumn density of a $^{39}$K Bose-Einstein condensate. The scheme can be\nreadily applied to all other alkali-like species.\n"}
+{"abstract": "  Aims: We present the first measurements of the solar-wind angular-momentum\n(AM) flux recorded by the Solar Orbiter spacecraft. Our aim is the validation\nof these measurements to support future studies of the Sun's AM loss. Methods:\nWe combine 60-minute averages of the proton bulk moments and the magnetic field\nmeasured by the Solar Wind Analyser (SWA) and the magnetometer (MAG) onboard\nSolar Orbiter. We calculate the AM flux per solid-angle element using data from\nthe first orbit of the mission's cruise phase during 2020. We separate the\ncontributions from protons and from magnetic stresses to the total AM flux.\nResults: The AM flux varies significantly over time. The particle contribution\ntypically dominates over the magnetic-field contribution during our measurement\ninterval. The total AM flux shows the largest variation and is typically\nanti-correlated with the radial solar-wind speed. We identify a compression\nregion, potentially associated with a co-rotating interaction region or a\ncoronal mass ejection, that leads to a significant localised increase in the AM\nflux, yet without a significant increase in the AM per unit mass. We repeat our\nanalysis using the density estimate from the Radio and Plasma Waves (RPW)\ninstrument. Using this independent method, we find a decrease in the peaks of\npositive AM flux but otherwise consistent results. Conclusions: Our results\nlargely agree with previous measurements of the solar-wind AM flux in terms of\namplitude, variability, and dependence on radial solar-wind bulk speed. Our\nanalysis highlights the potential for future, more detailed, studies of the\nsolar wind's AM and its other large-scale properties with data from Solar\nOrbiter. We emphasise the need to study the radial evolution and latitudinal\ndependence of the AM flux in combination with data from Parker Solar Probe and\nassets at heliocentric distances of 1 au and beyond.\n"}
+{"abstract": "  In this paper, we present a semi-supervised training technique using\npseudo-labeling for end-to-end neural diarization (EEND). The EEND system has\nshown promising performance compared with traditional clustering-based methods,\nespecially in the case of overlapping speech. However, to get a well-tuned\nmodel, EEND requires labeled data for all the joint speech activities of every\nspeaker at each time frame in a recording. In this paper, we explore a\npseudo-labeling approach that employs unlabeled data. First, we propose an\niterative pseudo-label method for EEND, which trains the model using unlabeled\ndata of a target condition. Then, we also propose a committee-based training\nmethod to improve the performance of EEND. To evaluate our proposed method, we\nconduct the experiments of model adaptation using labeled and unlabeled data.\nExperimental results on the CALLHOME dataset show that our proposed\npseudo-label achieved a 37.4% relative diarization error rate reduction\ncompared to a seed model. Moreover, we analyzed the results of semi-supervised\nadaptation with pseudo-labeling. We also show the effectiveness of our approach\non the third DIHARD dataset.\n"}
+{"abstract": "  We investigate Nagaoka ferromagnetism in the two-dimensional Hubbard model\nwith one hole using the spin-adapted ($SU(2)$ conserving) full configuration\ninteraction quantum Monte Carlo method. This methodology gives us access to the\nground state energies of all possible spin states $S$ of finite Hubbard\nlattices, here obtained for lattices up to 24 sites, for various interaction\nstrengths ($U$). The critical interaction strength, $U_c$, at which the Nagaoka\ntransition occurs is determined for each lattice and is found to be\nproportional to the lattice size for the larger lattices. Below $U_c$ the\noverall ground states are found to favour the minimal total spin ($S=\\frac 1\n2$), and no intermediate spin state is found to be the overall ground state on\nlattices larger than 16 sites. However, at $U_c$, the energies of all the spin\nstates are found to be nearly degenerate, implying that large fluctuations in\ntotal spin can be expected in the vicinity of the Nagaoka transition.\n"}
+{"abstract": "  Lepton flavor violated process are strongly suppressed in the Standard Model\ndue to very small neutrino mass, but can be sizable in some extended models.\nThe current experimental bounds on decay modes $\\ell^{'\\pm} \\to a \\ell^{\\pm} $\nare much weaker than other flavor violated processes because of the huge\nirreducible backgrounds $\\ell' \\to \\ell \\bar{\\nu}_{\\ell} \\nu_{\\ell'}$. In this\npaper, we give the full helicity density matrix of both the signal and\nbackgrounds, and then study polarization effects. Particularly, we treat\ninclusively the two missing neutrinos in the background, and we find that both\nlongitudinal and transverse polarization effects survives even the relative\nkinematical degrees of freedom of the two neutrinos are integrated out.\nFurthermore, we have show that signal and backgrounds have distinctive\npolarization effects which can be measured by using energy fractions of the\ncharged decay products. This is particularly useful because kinematical\nreconstruction is not required. In case of that the decaying lepton is not\npolarized, we show that correlations between polarizations of lepton pair\ngenerated at colliders are still useful to search for the signals. More\ninterestingly, polarization correlations depends on product of scalar and\npseudo-scalar ALP couplings, and hence are sensitive to their relative sign. We\ndemonstrate that how the polarization correlation effects can be used to\ninvestigate flavor violating decays $\\tau^{\\pm} \\to a \\ell^{\\pm} $ at the\nBelleII experiment.\n"}
+{"abstract": "  We show that the resource theory of contextuality does not admit catalysts,\ni.e., there are no correlations that can enable an otherwise impossible\nresource conversion and still be recovered afterward. As a corollary, we\nobserve that the same holds for nonlocality. As entanglement allows for\ncatalysts, this adds a further example to the list of \"anomalies of\nentanglement,\" showing that nonlocality and entanglement behave differently as\nresources. We also show that catalysis remains impossible even if, instead of\nclassical randomness, we allow some more powerful behaviors to be used freely\nin the free transformations of the resource theory.\n"}
+{"abstract": "  Off-policy sampling and experience replay are key for improving sample\nefficiency and scaling model-free temporal difference learning methods. When\ncombined with function approximation, such as neural networks, this combination\nis known as the deadly triad and is potentially unstable. Recently, it has been\nshown that stability and good performance at scale can be achieved by combining\nemphatic weightings and multi-step updates. This approach, however, is\ngenerally limited to sampling complete trajectories in order, to compute the\nrequired emphatic weighting. In this paper we investigate how to combine\nemphatic weightings with non-sequential, off-line data sampled from a replay\nbuffer. We develop a multi-step emphatic weighting that can be combined with\nreplay, and a time-reversed $n$-step TD learning algorithm to learn the\nrequired emphatic weighting. We show that these state weightings reduce\nvariance compared with prior approaches, while providing convergence\nguarantees. We tested the approach at scale on Atari 2600 video games, and\nobserved that the new X-ETD($n$) agent improved over baseline agents,\nhighlighting both the scalability and broad applicability of our approach.\n"}
+{"abstract": "  We establish a platform to transfer $L_p$-completely bounded maps on tensor\nproducts of von Neumann algebras to $L_p$-completely bounded maps on the\ncorresponding amalgamated free products. As a consequence, we obtain a\nH\\\"ormander-Mikhlin multiplier theory for free products of groups. Let\n$\\mathbb{F}_\\infty$ be a free group on infinite generators $\\{g_1,\ng_2,\\cdots\\}$. Given $d\\ge1$ and a bounded symbol $m$ on $\\mathbb{Z}^d$\nsatisfying the classical H\\\"ormander-Mikhlin condition, the linear map\n$M_m:\\mathbb{C}[\\mathbb{F}_\\infty]\\to \\mathbb{C}[\\mathbb{F}_\\infty]$ defined by\n$\\lambda(g)\\mapsto m(k_1,\\cdots, k_d)\\lambda(g)$ for $g=g_{i_1}^{k_1}\\cdots\ng_{i_n}^{k_n}\\in\\mathbb{F}_\\infty$ in reduced form (with $k_l=0$ in\n$m(k_1,\\cdots, k_d)$ for $l>n$), extends to a complete bounded map on\n$L_p(\\widehat{\\mathbb{F}}_\\infty)$ for all $1<p<\\infty$, where\n$\\widehat{\\mathbb{F}}_\\infty$ is the group von Neumann algebra of\n$\\mathbb{F}_\\infty$. A similar result holds for any free product of discrete\ngroups.\n"}
+{"abstract": "  [Context & motivation] Driven by the need for faster time-to-market and\nreduced development lead-time, large-scale systems engineering companies are\nadopting agile methods in their organizations. This agile transformation is\nchallenging and it is common that adoption starts bottom-up with agile software\nteams within the context of traditional company structures.\n  [Question/Problem] This creates the challenge of agile teams working within a\ndocument-centric and plan-driven (or waterfall) environment. While it may be\ndesirable to take the best of both worlds, it is not clear how that can be\nachieved especially with respect to managing requirements in large-scale\nsystems.\n  [Principal ideas/Results] This paper presents an exploratory case study at an\nautomotive company, focusing on two departments of a large-scale systems\ncompany that is in the process of company-wide agile adoption.\n  [Contribution] We present challenges related to requirements engineering that\nagile teams face while working within a larger plan-driven context and propose\npotential strategies to mitigate the challenges. Challenges relate to, e.g.,\ndevelopment teams not being aware of the high-level requirement and dealing\nwith flexibility of writing user stories. We found that strategies for\novercoming most of these challenges are still lacking and thus call for more\nresearch.\n"}
+{"abstract": "  One of the critical challenges facing imaging studies of the 21-cm signal at\nthe Epoch of Reionization (EoR) is the separation of astrophysical foreground\ncontamination. These foregrounds are known to lie in a wedge-shaped region of\n$(k_{\\perp},k_{\\parallel})$ Fourier space. Removing these Fourier modes excises\nthe foregrounds at grave expense to image fidelity, since the cosmological\ninformation at these modes is also removed by the wedge filter. However, the\n21-cm EoR signal is non-Gaussian, meaning that the lost wedge modes are\ncorrelated to the surviving modes by some covariance matrix. We have developed\na machine learning-based method which exploits this information to identify\nionized regions within a wedge-filtered image. Our method reliably identifies\nthe largest ionized regions and can reconstruct their shape, size, and location\nwithin an image. We further demonstrate that our method remains viable when\ninstrumental effects are accounted for, using the Hydrogen Epoch of\nReionization Array and the Square Kilometre Array as fiducial instruments. The\nability to recover spatial information from wedge-filtered images unlocks the\npotential for imaging studies using current- and next-generation instruments\nwithout relying on detailed models of the astrophysical foregrounds themselves.\n"}
+{"abstract": "  We consider a cognitive radio based Internet of Things (CR-IoT) system where\nthe secondary IoT device (SD) accesses the licensed channel during the\ntransmission vacancies of the primary IoT device (PD). We focus on the impact\nof the IoT devices' heterogeneous traffic pattern on the energy efficiency and\non the age of information (AoI) performance of the SD. We first derive\nclosed-form expressions of the energy efficiency and the average AoI, and\nsubsequently explore their convexity and monotonicity to the transmit power.\nFollowing these characterizations, an optimal transmit power optimization\nalgorithm (TPOA) is proposed for the SD to maximize the energy efficiency while\nmaintaining the average AoI under a predefined threshold. Numerical results\nverify the different preferences of the SD toward different PD traffic\npatterns, and provides insights into the tradeoff between the energy efficiency\nand the average AoI.\n"}
+{"abstract": "  We consider the decoupling theory of a broad class of $C^5$ surfaces\n$\\mathbb{M} \\subset \\mathbb{R}^3$ lacking planar points. In particular, our\napproach also applies to surfaces which are not graphed by mixed homogeneous\npolynomials. The study of $\\mathbb{M}$ furnishes opportunity to recast\niterative linear decoupling in a more general form. Here, Taylor-based analysis\nis combined with efforts to build a library of canonical surfaces\n(non-cylindrical in general) by which $\\mathbb{M}$ may be approximated for\ndecoupling purposes. The work presented may be generalized to the consideration\nof other surfaces not addressed.\n"}
+{"abstract": "  Reconstructing interactions from observational data is a critical need for\ninvestigating natural biological networks, wherein network dimensionality (i.e.\nnumber of interacting components) is usually high and interactions are\ntime-varying. These pose a challenge to existing methods that can quantify only\nsmall interaction networks or assume static interactions under steady state.\nHere, we proposed a novel approach to reconstruct high-dimensional,\ntime-varying interaction networks using empirical time series. This method,\nnamed \"multiview distance regularized S-map\", generalized the state space\nreconstruction to accommodate high dimensionality and overcome difficulties in\nquantifying massive interactions with limited data. When we evaluated this\nmethod using the time series generated from a large theoretical model involving\nhundreds of interacting species, estimated interaction strengths were in good\nagreement with theoretical expectations. As a result, reconstructed networks\npreserved important topological properties, such as centrality, strength\ndistribution and derived stability measures. Moreover, our method effectively\nforecasted the dynamic behavior of network nodes. Applying this method to a\nnatural bacterial community helped identify keystone species from the\ninteraction network and revealed the mechanisms governing the dynamical\nstability of bacterial community. Our method overcame the challenge of high\ndimensionality and disentangled complex time-varying interactions in large\nnatural dynamical systems.\n"}
+{"abstract": "  A central goal of synthetic biology is the design of molecular controllers\nthat can manipulate the dynamics of intracellular networks in a stable and\naccurate manner. To address the fact that detailed knowledge about\nintracellular networks is unavailable, integral-feedback controllers (IFCs)\nhave been put forward for controlling molecular abundances. These controllers\ncan maintain accuracy in spite of the uncertainties in the controlled networks.\nHowever, this desirable feature is achieved only if stability is also\nmaintained. In this paper, we show that molecular IFCs can suffer from a\nhazardous instability called negative-equilibrium catastrophe (NEC), whereby\nall nonnegative equilibria vanish under the action of the controllers, and some\nof the molecular abundances blow up. We show that unimolecular IFCs do not\nexist due to a NEC. We then derive a family of bimolecular IFCs that are\nsafeguarded against NECs when uncertain unimolecular networks, with any number\nof molecular species, are controlled. However, when IFCs are applied on\nuncertain bimolecular (and hence most intracellular) networks, we show that\npreventing NECs generally becomes an intractable problem as the number of\ninteracting molecular species increases.\n"}
+{"abstract": "  The Dirac Equation is solved approximately for relativistic generalized\nWoods-Saxon potential including Coulomb-like tensor potential in exact\npseudospin and spin symmetry limits. The bound states energy eigenvalues are\nfound by using wavefunction boundary conditions, and corresponding radial\nwavefunctions are obtained in terms of hypergeometric function. Some numerical\nexamples are given for the dependence of bound states energy eigenvalues on\nquantum numbers and potential parameters.\n"}
+{"abstract": "  We introduce a new framework for solving an important class of computational\nproblems involving finite permutation groups, which includes calculating set\nstabilisers, intersections of subgroups, and isomorphisms of combinatorial\nstructures. Our techniques are inspired by and generalise 'partition\nbacktrack', which is the current state-of-the-art algorithm introduced by\nJeffrey Leon in 1991. But, instead of ordered partitions, we use labelled\ndirected graphs to organise our backtrack search algorithms, which allows for a\nricher representation of many problems while often resulting in smaller search\nspaces. In this article we present the theory underpinning our framework, we\ndescribe our algorithms, and we show the results of some experiments. An\nimplementation of our algorithms is available as free software in the\nGraphBacktracking package for GAP.\n"}
+{"abstract": "  The early development of a zygote can be mathematically described by a\ndevelopmental tree. To compare developmental trees of different species, we\nneed to define distances on trees. If children cells after a division are not\ndistinguishable, developmental trees are represented by the space of rooted\ntrees with possibly repeated labels, where all vertices are unordered. On this\nspace, we define two metrics: the best-match metric and the left-regular\nmetric, which show some advantages over existing methods. If children cells\nafter a division are partially distinguishable, developmental trees are\nrepresented by the space of rooted trees with possibly repeated labels, where\nvertices can be ordered or unordered. This space cannot have a metric. Instead,\nwe define a semimetric, which is a variant of the best-match metric. To compute\nthe best-match distance between two trees, the expected time complexity and\nworst-case time complexity are both $\\mathcal{O}(n^2)$, where $n$ is the tree\nsize. To compute the left-regular distance between two trees, the expected time\ncomplexity is $\\mathcal{O}(n)$, and the worst-case time complexity is\n$\\mathcal{O}(n\\log n)$.\n"}
+{"abstract": "  It has been recognized for some time that even for perfect conductors, the\ninteraction Casimir entropy, due to quantum/thermal fluctuations, can be\nnegative. This result was not considered problematic because it was thought\nthat the self-entropies of the bodies would cancel this negative interaction\nentropy, yielding a total entropy that was positive. In fact, this cancellation\nseems not to occur. The positive self-entropy of a perfectly conducting sphere\ndoes indeed just cancel the negative interaction entropy of a system consisting\nof a perfectly conducting sphere and plate, but a model with weaker coupling in\ngeneral possesses a regime where negative self-entropy appears. The physical\nmeaning of this surprising result remains obscure. In this paper we re-examine\nthese issues, using improved physical and mathematical techniques, partly based\non the Abel-Plana formula, and present numerical results for arbitrary\ntemperatures and couplings, which exhibit the same remarkable features.\n"}
+{"abstract": "  A software architect uses quality requirements to design the architecture of\na system. However, it is essential to ensure that the system's final\narchitectural design achieves the standard quality requirements. The existing\narchitectural evaluation frameworks require basic skills and experience for\npractical usage, which novice software architects lack.\n  We propose a framework that enables novice software architects to infer the\nsystem's quality requirements and tactics using the software architectural\nblock-line diagram. The framework takes an image as input, extracts various\ncomponents and connections, and maps them to viable architectural patterns,\nfollowed by identifying the system's corresponding quality attributes (QAs) and\ntactics. The framework includes a specifically trained machine learning model\nbased on image processing and semantic similarity methods to assist software\narchitects in evaluating a given design by a) evaluating an input architectural\ndesign based on the architectural patterns present in it, b) lists out the\nstrengths and weaknesses of the design in terms of QAs, c) recommends the\nnecessary architectural tactics that can be embedded in the design to achieve\nthe lacking QAs.\n  To train our framework, we developed a dataset of 2,035 architectural images\nfrom fourteen architectural patterns such as Client-Server, Microservices, and\nModel View Controller, available at\nhttps://www.doi.org/10.6084/m9.figshare.14156408. The framework achieves a\nCorrect Recognition Rate of 98.71% in identifying the architectural patterns.\nWe evaluated the proposed framework's effectiveness and usefulness by using\ncontrolled and experimental groups, in which the experimental group performed\napproximately 150% better than the controlled group. The experiments were\nperformed as a part of the Masters of Computer Science course in an Engineering\nInstitution.\n"}
+{"abstract": "  Hyperproperties are system properties that require quantification over\nmultiple execution traces of a system. Hyperproperties can express several\nspecifications of interest for cyber-physical systems--such as opacity,\nrobustness, and noninterference--which cannot be expressed using linear-time\nproperties. This paper presents for the first time a discretization-free\napproach for the formal verification of discrete-time uncertain dynamical\nsystems against hyperproperties. The proposed approach involves decomposition\nof complex hyperproperties into several verification conditions by exploiting\nthe automata-based structures corresponding to the complements of the original\nspecifications. These verification conditions are then discharged by\nsynthesizing so-called augmented barrier certificates, which provide certain\nsafety guarantees for the underlying system. For systems with polynomial-type\ndynamics, we present a sound procedure to synthesize polynomial-type augmented\nbarrier certificates by reducing the problem to sum-of-squares optimizations.\nWe demonstrate the effectiveness of our proposed approaches on two physical\ncase studies against two important hyperproperties: initial-state opacity and\ninitial-state robustness.\n"}
+{"abstract": "  Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO)\nsystems have been considered as one of the primary candidates for the fifth\ngeneration (5G) and beyond 5G wireless communication networks to satisfy the\never-increasing capacity demands. Full-duplex technology can further enhance\nthe advantages of mmWave massive MIMO systems. However, strong\nself-interference (SI) is the major limiting factor in full-duplex technology.\nHence, this paper proposes a novel angular-based joint hybrid\nprecoding/combining (AB-JHPC) technique for the full-duplex mmWave massive-MIMO\nsystems. Our primary goals are listed as: (i) improving the self-interference\ncancellation (SIC), (ii) increasing the intended signal power, (iii) decreasing\nthe channel estimation overhead, (iv) designing the massive MIMO systems with a\nlow number of RF chains. First, the RF-stage of AB-JHPC is developed via slow\ntime-varying angle-of-departure (AoD) and angle-of-arrival (AoA) information. A\njoint transmit/receive RF beamformer design is proposed for covering\n(excluding) the AoD/AoA support of intended (SI) channel. Second, the BB-stage\nof AB-JHPC is constructed via the reduced-size effective intended channel.\nAfter using the well-known singular value decomposition(SVD) approach at the\nBB-stage, we also propose a new semi-blind minimum mean square error (S-MMSE)\ntechnique to further suppress the residual SI power by using AoD/AoA\nparameters. The numerical results demonstrate that the SI signal is remarkably\ncanceled via the proposed AB-JHPC technique. It is shown that AB-JHPC achieves\n85.7 dB SIC and the total amount of SIC almost linearly increases via antenna\nisolation techniques. We observe that the proposed full-duplex mmWave massive\nMIMO systems double the achievable rate capacity compared to its half-duplex\ncounterpart as the antenna array size increases and the transmit/receive\nantenna isolation improves.\n"}
+{"abstract": "  We discuss the spectral decomposition of the hypergeometric differential\noperators on the line $\\mathrm{Re}\\, z=1/2$. Such operators arise in the\nproblem of decomposition of tensor products of unitary representations of the\nuniversal covering of the group $SL(2\\,{\\mathbb R}$. Our main purpose is a\nsearch of natural bases in generalized eigenspaces and variants of the\ninversion formula.\n"}
+{"abstract": "  In this article we prove the existence of a new family of periodic solutions\nfor discrete, nonlinear Schrodinger equations subject to spatially localized\ndriving and damping and we show numerically that they provide a more accurate\napproximation to metastable states in these systems than previous proposals. We\nalso study the stability properties of these solutions and show that they fit\nwell with a previously proposed mechanism for the emergence and persistence of\nmetastable behavior.\n"}
+{"abstract": "  Serverless computing has emerged as a new paradigm for running short-lived\ncomputations in the cloud. Due to its ability to handle IoT workloads, there\nhas been considerable interest in running serverless functions at the edge.\nHowever, the constrained nature of the edge and the latency sensitive nature of\nworkloads result in many challenges for serverless platforms. In this paper, we\npresent LaSS, a platform that uses model-driven approaches for running\nlatency-sensitive serverless computations on edge resources. LaSS uses\nprincipled queuing-based methods to determine an appropriate allocation for\neach hosted function and auto-scales the allocated resources in response to\nworkload dynamics. LaSS uses a fair-share allocation approach to guarantee a\nminimum of allocated resources to each function in the presence of overload. In\naddition, it utilizes resource reclamation methods based on container deflation\nand termination to reassign resources from over-provisioned functions to\nunder-provisioned ones. We implement a prototype of our approach on an\nOpenWhisk serverless edge cluster and conduct a detailed experimental\nevaluation. Our results show that LaSS can accurately predict the resources\nneeded for serverless functions in the presence of highly dynamic workloads,\nand reprovision container capacity within hundreds of milliseconds while\nmaintaining fair share allocation guarantees.\n"}
+{"abstract": "  In this paper, we propose a simple enhancement for the passkey entry protocol\nin the authentication stage 1 of Secure Simple Pairing using preexisting\ncryptographic hash functions and random integer generation present in the\nprotocol. The new protocol is more secure and efficient than previous known\nprotocols. Our research mainly focuses on strengthening the passkey entry\nprotocol and protecting the devices against passive eavesdropping and active\nMan-in-the-middle (MITM) attacks in both Bluetooth Basic Rate/Enhanced Data\nRate (BR/EDR) and Bluetooth Low Energy (Bluetooth LE). This method can be used\nfor any device which uses the passkey entry protocol.\n"}
+{"abstract": "  Optimal mechanical impact absorbers are reusable and exhibit high specific\nenergy absorption. The forced intrusion of liquid water in hydrophobic\nnanoporous materials, such as zeolitic imidazolate frameworks (ZIFs), presents\nan attractive pathway to engineer such systems. However, to harness their full\npotential, it is crucial to understand the underlying water intrusion and\nex-trusion mechanisms under realistic, high-rate deformation conditions.\nHerein, we report a critical increase of the energy absorption capacity of\nconfined water-ZIF systems at elevated strain rates. Starting from ZIF-8 as\nproof-of-concept, we demonstrate that this attractive rate depend-ence is\ngenerally applicable to cage-type ZIFs but disappears for channel-containing\nzeolites. Molecular simulations reveal that this phenomenon originates from the\nintrinsic nanosecond timescale needed for critical-sized water clusters to\nnucleate inside the nanocages, expediting water transport through the\nframework. Harnessing this fundamental understanding, design rules are\nformulated to construct effective, tailorable, and reusable impact energy\nabsorbers for challenging new applications.\n"}
+{"abstract": "  Monitoring and controlling the state of polarization of electromagnetic waves\nis of significant interest for various basic and practical applications such as\nlinear position sensing and medical imaging. Here, we propose the first\nconformal digital metamaterial absorber to detect the polarization state of THz\nincident waves. The proposed polarimeter is capable of characterizing four\nindependent polarization states of (TE, TM, $\\pm {45^\\circ}$ linear, and\nRCP/LCP) by observing the reflectivity of the structure with respect to the x-\nand y-direction. Besides, the proposed structure displays a strong absorptivity\nabove 90\\% up to the incidence angle of $50^{\\circ}$ for oblique incident waves\nwith different polarizations. By mere changing the bias voltage of two\northogonal VO2 microwires via two independent computer-programmed multichannel\nDC network, distinct conditions for reflected waves occurs under excitations of\ndifferent polarizations, whereby the polarization state of the incident wave\nmay readily be estimated. We believe that the proposed metasurface-based\npolarimeter can pave the way for polarization detection applications on curved\nsurfaces.\n"}
+{"abstract": "  For reductive groups $G$ over a number field we discuss automorphic liftings\nfrom cuspidal irreducible automorphic representations $\\pi$ of $G(\\mathbb{A})$\nto cuspidal irreducible automorphic representations on $H(\\mathbb{A})$ for the\nquasi-split inner form $H$ of $G$. We show the existence of cohomological\nnontrivial weak global liftings in many cases. A priori these weak liftings do\nnot give a description of the precise nature of the corresponding local\nliftings at the ramified places and in particular do not characterize the image\nof the lift. For inner forms of the group $H=\\mathrm{GSp}(4)$ however we\naddress these finer details. Especially, we prove the recent conjectures of\nIbukiyama and Kitayama on paramodular newforms of squarefree level.\n"}
+{"abstract": "  In this work, we investigate gravitational baryogenesis in the framework of\n$f(P)$ gravity to understand the applicability of this class of modified\ngravity in addressing the baryon asymmetry of the Universe. For the analysis,\nwe set $f(P) = \\alpha P$ where $\\alpha$ is the model parameter. We found that\nin $f(P)$ gravity, the CP-violating interaction acquires a modification through\nthe addition of the nontopological cubic term $P$ in addition to the Ricci\nscalar $R$ and the mathematical expression of the baryon-to-entropy ratio\ndepends not only on the time derivative of $R$ but also the time derivative of\n$P$. Additionally, we also investigate the consequences of a more complete and\ngeneralized CP-violating interaction proportional to $f(P)$ instead of $P$ in\naddressing the baryon asymmetry of the Universe. For this type of interaction,\nwe report that the baryon-to-entropy ratio is proportional to $\\dot{R}$,\n$\\dot{P}$ and $f^{'}(P)$. We report that for both of these cases, rational\nvalues of $\\alpha$ and $\\chi$ generate acceptable baryon-to-entropy ratios\ncompatible with observations.\n"}
+{"abstract": "  We study the dark matter phenomenology of Standard Model extensions\naddressing the reported anomaly in the $R_K$ observable at one-loop. The\narticle covers the case of fermionic singlet DM coupling leptophilically,\nquarkphilically or amphiphilically to the SM. The setup utilizes a large\ncoupling of the new particle content to the second lepton generation to explain\nthe $R_K$ anomaly, which in return tends to diminish the dark matter relic\ndensity. Further, dark matter direct detection experiments provide stringent\nbounds even in cases where the dark matter candidate only contributes a small\nfraction of the observed dark matter energy density. In fact, direct detection\nrules out all considered models as an explanation for the $R_K$ anomaly in the\ncase of Dirac dark matter. Conversely, for Majorana dark matter, the $R_K$\nanomaly can be addressed in agreement with direct detection in coannihilation\nscenarios. For leptophilic dark matter this region only exists for $M_\\text{DM}\n\\lesssim 1000 \\, \\mathrm{GeV}$ and dark matter is underabundant. Quarkphilic\nand amphiphilic scenarios even provide narrow regions of parameter space where\nthe observed relic density can be reproduced while offering an explanation to\n$R_K$ in agreement with direct detection experiments.\n"}
+{"abstract": "  This paper introduces new algorithm for line extraction from laser range data\nincluding methodology for efficient computation. The task is cast to series of\none dimensional problems in various spaces. A fast and simple specialization of\nDBSCAN algorithm is proposed to solve one dimensional subproblems. Experiments\nsuggest that the method is suitable for real-time applications, handles noise\nwell and may be useful in practice.\n"}
+{"abstract": "  We introduce a new sparse sliced inverse regression estimator called Cholesky\nmatrix penalization and its adaptive version for achieving sparsity in\nestimating the dimensions of the central subspace. The new estimators use the\nCholesky decomposition of the covariance matrix of the covariates and include a\nregularization term in the objective function to achieve sparsity in a\ncomputationally efficient manner. We establish the theoretical values of the\ntuning parameters that achieve estimation and variable selection consistency\nfor the central subspace. Furthermore, we propose a new projection information\ncriterion to select the tuning parameter for our proposed estimators and prove\nthat the new criterion facilitates selection consistency. The Cholesky matrix\npenalization estimator inherits the strength of the Matrix Lasso and the Lasso\nsliced inverse regression estimator; it has superior performance in numerical\nstudies and can be adapted to other sufficient dimension methods in the\nliterature.\n"}
+{"abstract": "  Let $1<p<\\infty$ and let $n\\geq 1$. It is proved that a function $f:{\\mathbb\nR}\\to {\\mathbb C}$ is $n$-times Fr\\'echet differentiable on ${\\mathcal S}^p$ at\nevery self-adjoint operator if and only if $f$ is $n$-times differentiable,\n$f',f'',\\ldots,f^{(n)}$ are bounded and $f^{(n)}$ is uniformly continuous.\n"}
+{"abstract": "  The knowledge of distribution grid models, including topologies and line\nimpedances, is essential to grid monitoring, control and protection. However,\nthis information is often unavailable, incomplete or outdated. The increasing\ndeployment of smart meters (SMs) provides a unique opportunity to address this\nissue. This paper proposes a two-stage data-driven framework for distribution\ngrid modeling using SM data. In the first stage, we propose to identify the\ntopology via reconstructing a weighted Laplacian matrix of distribution\nnetworks, which is mathematically proven to be robust against moderately\nheterogeneous R/X profiles. In the second stage, we develop nonlinear least\nabsolute deviations (LAD) and least squares (LS) regression models to estimate\nline impedances of single branches based on a nonlinear inverse power flow,\nwhich is then embedded within a bottom-up sweep algorithm to achieve the\nidentification across the network in a branch-wise manner. Because the\nestimation models are inherently non-convex programs and NP-hard, we specially\naddress their tractable convex relaxations and verify the exactness. In\naddition, we design a conductor library to significantly narrow down the\nsolution space. Numerical results on the modified IEEE 13-bus, 37-bus and\n69-bus test feeders validate the effectiveness of the proposed methods.\n"}
+{"abstract": "  The early and robust detection of anomalies occurring in discrete\nmanufacturing processes allows operators to prevent harm, e.g. defects in\nproduction machinery or products. While current approaches for data-driven\nanomaly detection provide good results on the exact processes they were trained\non, they often lack the ability to flexibly adapt to changes, e.g. in products.\nContinual learning promises such flexibility, allowing for an automatic\nadaption of previously learnt knowledge to new tasks. Therefore, this article\ndiscusses different continual learning approaches from the group of\nregularization strategies, which are implemented, evaluated and compared based\non a real industrial metal forming dataset.\n"}
+{"abstract": "  The possible symmetries of the superconducting pair amplitude is a\nconsequence of the fermionic nature of the Cooper pairs. For spin-$1/2$ systems\nthis leads to the $\\mathcal{SPOT}=-1$ classification of superconductivity,\nwhere $\\mathcal{S}$, $\\mathcal{P}$, $\\mathcal{O}$, and $\\mathcal{T}$ refer to\nthe exchange operators for spin, parity, orbital, and time between the paired\nelectrons. However, this classification no longer holds for higher spin\nfermions, where each electron also possesses a finite orbital angular momentum\nstrongly coupled with the spin degree of freedom, giving instead a conserved\ntotal angular moment. For such systems, we here instead introduce the\n$\\mathcal{JPT}=-1$ classification, where $\\mathcal{J}$ is the exchange operator\nfor the $z$-component of the total angular momentum quantum numbers. We then\nspecifically focus on spin-$3/2$ fermion systems and several superconducting\ncubic half-Heusler compounds that have recently been proposed to be spin-$3/2$\nsuperconductors. By using a generic Hamiltonian suitable for these compounds we\ncalculate the superconducting pair amplitudes and find finite pair amplitudes\nfor all possible symmetries obeying the $\\mathcal{JPT}=-1$ classification,\nincluding all possible odd-frequency (odd-$\\omega$) combinations. Moreover, one\nof the very interesting properties of spin-$3/2$ superconductors is the\npossibility of them hosting a Bogoliubov Fermi surface (BFS), where the\nsuperconducting energy gap is closed across a finite area. We show that a\nspin-$3/2$ superconductor with a pair potential satisfying an odd-gap\ntime-reversal product and being non-commuting with the normal-state Hamiltonian\nhosts both a BFS and has finite odd-$\\omega$ pair amplitudes. We then reduce\nthe full spin-$3/2$ Hamiltonian to an effective two-band model and show that\nodd-$\\omega$ pairing is inevitably present in superconductors with a BFS and\nvice versa.\n"}
+{"abstract": "  Utilization of Machine Learning (ML) algorithms, especially Deep Neural\nNetwork (DNN) models, becomes a widely accepted standard in many domains more\nparticularly IoT-based systems. DNN models reach impressive performances in\nseveral sensitive fields such as medical diagnosis, smart transport or security\nthreat detection, and represent a valuable piece of Intellectual Property. Over\nthe last few years, a major trend is the large-scale deployment of models in a\nwide variety of devices. However, this migration to embedded systems is slowed\ndown because of the broad spectrum of attacks threatening the integrity,\nconfidentiality and availability of embedded models. In this review, we cover\nthe landscape of attacks targeting the confidentiality of embedded DNN models\nthat may have a major impact on critical IoT systems, with a particular focus\non model extraction and data leakage. We highlight the fact that Side-Channel\nAnalysis (SCA) is a relatively unexplored bias by which model's confidentiality\ncan be compromised. Input data, architecture or parameters of a model can be\nextracted from power or electromagnetic observations, testifying a real need\nfrom a security point of view.\n"}
+{"abstract": "  This paper studies the nature of fractional linear transformations in a\ngeneral relativity context as well as in a quantum theoretical framework. Two\nfeatures are found to deserve special attention: the first is the possibility\nof separating the limit-point condition at infinity into loxodromic,\nhyperbolic, parabolic and elliptic cases. This is useful in a context in which\none wants to look for a correspondence between essentially self-adjoint\nspherically symmetric Hamiltonians of quantum physics and the theory of\nBondi-Metzner-Sachs transformations in general relativity. The analogy\ntherefore arising, suggests that further investigations might be performed for\na theory in which the role of fractional linear maps is viewed as a bridge\nbetween the quantum theory and general relativity. The second aspect to point\nout is the possibility of interpreting the limit-point condition at both ends\nof the positive real line, for a second-order singular differential operator,\nwhich occurs frequently in applied quantum mechanics, as the limiting procedure\narising from a very particular Kleinian group which is the hyperbolic cyclic\ngroup. In this framework, this work finds that a consistent system of equations\ncan be derived and studied. Hence one is led to consider the entire\ntranscendental functions, from which it is possible to construct a fundamental\nsystem of solutions of a second-order differential equation with singular\nbehavior at both ends of the positive real line, which in turn satisfy the\nlimit-point conditions.\n"}
+{"abstract": "  We examine how introduction of Shared Connected and Automated vehicles\n(SCAVs) as a new mobility mode could affect travel demand, welfare, as well as\ntraffic congestion in the network. To do so, we adapt an agent-based day-to-day\nadjustment process and develop a central dispatching system, which is\nimplemented on an in-house traffic microsimulator. We consider a two-sided\nmarket in which demand and SCAV fleet size change endogenously. For dispatching\nSCAV fleet size, we take changing traffic conditions into account. There are\ntwo available transport modes: private Connected Automated Vehicles (CAVs) and\nSCAVs. The designed system is applied on downtown Toronto network using real\ndata. The results show that demand of SCAVs goes up by 43 per cent over seven\nstudy days from 670 trips on the first day to 959 trips on the seventh day.\nWhereas, there is a 10 per cent reduction in private CAV demand from 2807 trips\nto 2518 trips during the same duration. Moreover, total travel time of the\nnetwork goes down by seven per cent indicating that traffic congestion was\nreduced in the network.\n"}
+{"abstract": "  The following paper proposes a new approach to determine whether a logical\n(CNF) formula is satisfiable or not using probability theory methods.\nFurthermore, we will introduce an algorithm that speeds up the standard\nsolution for (CNF-SAT) in some cases. It is known that any (CNF) formula is\nsolved with a time complexity of $2^n$ where n is the number of different\nliterals in the (CNF) formula. In our approach, we will follow an enhanced\nmethod from a probabilistic point of view that does not always increase\nexponentially with the number of different literals. This will enhance the\nchance of determining whether a large formula is satisfiable or not in many\ncases. Additionally, we will point out at some promising properties that follow\nfrom applying probability theory concepts and axioms to logic, which might\noriginate more insights about the satisfiability of logical formulas.\n"}
+{"abstract": "  We show that $\\Theta$-positive Anosov representations\n$\\rho:\\Gamma\\to\\mathsf{PO}(p,q)$ of a surface group $\\Gamma$ satisfy root vs\nweight collar lemmas for all the Anosov roots, and are positively ratioed with\nrespect to all such roots. From this we deduce that $\\Theta$-positive Anosov\nrepresentations $\\rho:\\Gamma\\to\\mathsf{PO}(p,q)$ form connected components of\ncharacter varieties.\n"}
+{"abstract": "  In recent years, biometric authentication technology for smartphones has\nbecome widespread, with the mainstream methods being fingerprint authentication\nand face recognition. However, fingerprint authentication cannot be used when\nhands are wet, and face recognition cannot be used when a person is wearing a\nmask. Therefore, we examine a personal authentication system using the pinna as\na new approach for biometric authentication on smartphones. Authentication\nsystems based on the acoustic transfer function of the pinna (PRTF: Pinna\nRelated Transfer Function) have been investigated. However, the authentication\naccuracy decreases due to the positional fluctuation across each measurement.\nIn this paper, we propose multimodal personal authentication on smartphones\nusing PRTF. The pinna image and positional sensor information are used with the\nPRTF, and the effectiveness of the authentication method is examined. We\ndemonstrate that the proposed authentication system can compensate for the\npositional changes in each measurement and improve robustness.\n"}
+{"abstract": "  We propose a Concentrated Document Topic Model(CDTM) for unsupervised text\nclassification, which is able to produce a concentrated and sparse document\ntopic distribution. In particular, an exponential entropy penalty is imposed on\nthe document topic distribution. Documents that have diverse topic\ndistributions are penalized more, while those having concentrated topics are\npenalized less. We apply the model to the benchmark NIPS dataset and observe\nmore coherent topics and more concentrated and sparse document-topic\ndistributions than Latent Dirichlet Allocation(LDA).\n"}
+{"abstract": "  We show that neural networks with absolute value activation function and with\nthe path norm, the depth, the width and the network weights having logarithmic\ndependence on $1/\\varepsilon$ can $\\varepsilon$-approximate functions that are\nanalytic on certain regions of $\\mathbb{C}^d$.\n"}
+{"abstract": "  We consider a bilevel attacker-defender problem to find the worst-case attack\non the relays that control the transmission grid. The attacker maximizes load\nshed by infiltrating a number of relays and rendering the components connected\nto them inoperable. The defender responds by minimizing the load shed,\nre-dispatching using a DC optimal power flow (DCOPF) problem on the remaining\nnetwork. Though worst-case interdiction problems on the transmission grid are\nwell-studied, there remains a need for exact and scalable methods. Methods\nbased on using duality on the inner problem rely on the bounds of the dual\nvariables of the defender problem in order to reformulate the bilevel problem\nas a mixed integer linear problem. Valid dual bounds tend to be large,\nresulting in weak linear programming relaxations and making the problem\ndifficult to solve at scale. Often smaller heuristic bounds are used, resulting\nin a lower bound. In this work we also consider a lower bound, where instead of\nbounding the dual variables, we drop the constraints corresponding to Ohm's\nlaw, relaxing DCOPF to capacitated network flow. We present theoretical results\nshowing that, for uncongested networks, approximating DCOPF with network flow\nyields the same set of injections, which suggests that this restriction likely\ngives a high-quality lower bound in the uncongested case. Furthermore, we show\nthat in the network flow relaxation of the defender problem, the duals are\nbounded by 1, so we can solve our restriction exactly. Last, we see empirically\nthat this formulation scales well computationally. Through experiments on 16\nnetworks with up to 6468 buses, we find that this bound is almost always as\ntight as we can get from guessing the dual bounds, even for congested networks.\nIn addition, calculating the bound is approximately 150 times faster than\nachieving the same bound with the reformulation guessing the dual bounds.\n"}
+{"abstract": "  A variety is a class of algebraic structures axiomatized by a set of\nequations. An equation is linear if there is at most one occurrence of an\noperation symbol on each side. We show that a variety axiomatized by linear\nequations has the strong amalgamation property.\n  Suppose further that the language has no constant symbol and, for each\nequation, either one side is operation-free, or exactly the same variables\nappear on both sides. Then also the joint embedding property holds.\n  Examples include most varieties defining classical Maltsev conditions. In a\nfew special cases, the above properties are preserved when further unary\noperations appear in the equations.\n"}
+{"abstract": "  Let $\\mathcal{L}=(L,[\\cdot\\,,\\cdot],\\delta)$ be an algebraic Lie algebroid\nover a smooth projective curve of genus $g\\geq 2$ such that $L$ is a line\nbundle whose degree is less than $2-2g$. Let $r$ and $d$ be coprime numbers. We\nprove that the motivic class (in the Grothendieck ring of varieties) of the\nmoduli space of $\\mathcal{L}$-connections of rank $r$ and degree $d$ over $X$\ndoes not depend on the Lie algebroid structure $[\\cdot\\,,\\cdot]$ and $\\delta$\nof $\\mathcal{L}$ and neither on the line bundle $L$ itself, but only the degree\nof $L$ (and of course on $r,d,g$ and $X$). In particular it is equal to the\nmotivic class of the moduli space of $K_X(D)$-twisted Higgs bundles of rank $r$\nand degree $d$, for $D$ any divisor of positive degree. As a consequence,\nsimilar results (actually a little stronger) are obtained for the corresponding\n$E$-polynomials. Some applications of these results are then deduced.\n"}
+{"abstract": "  The main two families of real hypersurfaces in complex space forms are Hopf\nand ruled. However, very little is known about real hypersurfaces in the\nindefinite complex projective space $\\mathbb{C}P^n_p$. In a previous work,\nKimura and the second author introduced Hopf real hypersurfaces in\n$\\mathbb{C}P^n_p$. In this paper, ruled real hypersurfaces in the indefinite\ncomplex projective space are introduced, as those whose maximal holomorphic\ndistribution is integrable, and such that the leaves are totally geodesic\nholomorphic hyperplanes. A detailed description of the shape operator is\ncomputed, obtaining two main different families. A method of construction is\nexhibited, by gluing in a suitable way totally geodesic holomorphic hyperplanes\nalong a non-null curve. Next, the classification of all minimal ruled real\nhypersurfaces is obtained, in terms of three main families of curves, namely\ngeodesics, totally real circles and a third case which is not a Frenet curve,\nbut can be explicitly computed. Four examples are described.\n"}
+{"abstract": "  A remarkable result at the intersection of number theory and group theory\nstates that the order of a finite group $G$ (denoted $|G|$) is divisible by the\ndimension $d_R$ of any irreducible complex representation of $G$. We show that\nthe integer ratios ${ |G|^2 / d_R^2 } $ are combinatorially constructible using\nfinite algorithms which take as input the amplitudes of combinatoric\ntopological strings ($G$-CTST) of finite groups based on 2D Dijkgraaf-Witten\ntopological field theories ($G$-TQFT2). The ratios are also shown to be\neigenvalues of handle creation operators in $G$-TQFT2/$G$-CTST. These strings\nhave recently been discussed as toy models of wormholes and baby universes by\nMarolf and Maxfield, and Gardiner and Megas. Boundary amplitudes of the\n$G$-TQFT2/$G$-CTST provide algorithms for combinatoric constructions of\nnormalized characters. Stringy S-duality for closed $G$-CTST gives a dual\nexpansion generated by disconnected entangled surfaces. There are universal\nrelations between $G$-TQFT2 amplitudes due to the finiteness of the number $K $\nof conjugacy classes. These relations can be labelled by Young diagrams and are\ncaptured by null states in an inner product constructed by coupling the\n$G$-TQFT2 to a universal TQFT2 based on symmetric group algebras. We discuss\nthe scenario of a 3D holographic dual for this coupled theory and the\nimplications of the scenario for the factorization puzzle of 2D/3D holography\nraised by wormholes in 3D.\n"}
+{"abstract": "  Exploration of new superconductors has always been one of the research\ndirections in condensed matter physics. We report here a new layered\nheterostructure of [(Fe,Al)(OH)2][FeSe]1.2, which is synthesized by the\nhydrothermal ion-exchange technique. The structure is suggested by a\ncombination of X-ray powder diffraction and the electron diffraction (ED).\n[(Fe,Al)(OH)2][FeSe]1.2 is composed of the alternating stacking of tetragonal\nFeSe layer and hexagonal (Fe,Al)(OH)2 layer. In [(Fe,Al)(OH)2][FeSe]1.2, there\nexists mismatch between the FeSe sub-layer and (Fe,Al)(OH)2 sub-layer, and the\nlattice of the layered heterostructure is quasi-commensurate. The\nas-synthesized [(Fe,Al)(OH)2][FeSe]1.2 is non-superconducting due to the Fe\nvacancies in the FeSe layer. The superconductivity with a Tc of 40 K can be\nachieved after a lithiation process, which is due to the elimination of the Fe\nvacancies in the FeSe layer. The Tc is nearly the same as that of (Li,Fe)OHFeSe\nalthough the structure of [(Fe,Al)(OH)2][FeSe]1.2 is quite different from that\nof (Li,Fe)OHFeSe. The new layered heterostructure of [(Fe,Al)(OH)2][FeSe]1.2\ncontains an iron selenium tetragonal lattice interleaved with a hexagonal metal\nhydroxide lattice. These results indicate that the superconductivity is very\nrobust for FeSe-based superconductors. It opens a path for exploring\nsuper-conductivity in iron-base superconductors.\n"}
+{"abstract": "  We present Graph Neural Diffusion (GRAND) that approaches deep learning on\ngraphs as a continuous diffusion process and treats Graph Neural Networks\n(GNNs) as discretisations of an underlying PDE. In our model, the layer\nstructure and topology correspond to the discretisation choices of temporal and\nspatial operators. Our approach allows a principled development of a broad new\nclass of GNNs that are able to address the common plights of graph learning\nmodels such as depth, oversmoothing, and bottlenecks. Key to the success of our\nmodels are stability with respect to perturbations in the data and this is\naddressed for both implicit and explicit discretisation schemes. We develop\nlinear and nonlinear versions of GRAND, which achieve competitive results on\nmany standard graph benchmarks.\n"}
+{"abstract": "  We introduce the notion of soficity for locally compact groups and list a\nnumber of open problems.\n"}
+{"abstract": "  In this study, an order by disorder mechanism has been proposed in a\ntwo-dimensional PXP model, where the extensive degeneracy of the classical\nground-state manifold is due to strict occupation constraints instead of\ngeometrical frustrations. By performing an unbias large-scale quantum monte\ncarlos simulation, we find that local quantum fluctuations, which usually work\nagainst long-range ordering, lift the macroscopic classical degeneracy and give\nrise to a compressible ground state with charge-density-wave long-range order.\nA simple trial wavefunction has been proposed to capture the essence of the\nground-state of the two-dimensional PXP model. The finite temperature\nproperties of this model have also been studied, and we find a thermal phase\ntransition with an universality class of two-dimensional Ising model.\n"}
+{"abstract": "  Theoretical models of galaxy-AGN co-evolution ascribe an important role for\nthe feedback process to a short, luminous, obscured, and dust-enshrouded phase\nduring which the accretion rate of the SMBH is expected to be at its maximum\nand the associated AGN-driven winds are also predicted to be maximally\ndeveloped. To test this scenario, we have isolated a text-book candidate from\nthe eROSITA Final Equatorial-Depth Survey (eFEDS) obtained within the\nPerformance and Verification program of the eROSITA telescope on board Spectrum\nRoentgen Gamma. From an initial catalog of 246 hard X-ray selected sources\nmatched with the photometric and spectroscopic information available within the\neROSITA and Hyper Suprime-Cam consortia, three candidates Quasars in the\nfeedback phase have been isolated applying the diagnostic proposed in Brusa et\nal. (2015). Only one source (eFEDSU J091157.5+014327) has a spectrum already\navailable (from SDSS-DR16, z=0.603) and it unambiguously shows the presence of\na broad component (FWHM~1650 km/s) in the [OIII]5007 line. The associated\nobserved L_[OIII] is ~2.6x10^{42} erg/s, one to two orders of magnitude larger\nthan that observed in local Seyferts and comparable to those observed in a\nsample of z~0.5 Type 1 Quasars. From the multiwavelength data available we\nderive an Eddington Ratio (L_bol/L_Edd) of ~0.25, and a bolometric correction\nin the hard X-ray of k_bol~10, lower than those observed for objects at similar\nbolometric luminosity. The presence of an outflow, the high X-ray luminosity\nand moderate X-ray obscuration (L_X~10^44.8 erg/s, N_H~2.7x10^22 cm^-2) and the\nred optical color, all match the prediction of quasars in the feedback phase\nfrom merger driven models. Forecasting to the full eROSITA all-sky survey with\nits spectroscopic follow-up, we predict that by the end of 2024 we will have a\nsample of few hundreds such objects at z=0.5-2.\n"}
+{"abstract": "  The simultaneous rise of machine learning as a service and concerns over user\nprivacy have increasingly motivated the need for private inference (PI). While\nrecent work demonstrates PI is possible using cryptographic primitives, the\ncomputational overheads render it impractical. The community is largely\nunprepared to address these overheads, as the source of slowdown in PI stems\nfrom the ReLU operator whereas optimizations for plaintext inference focus on\noptimizing FLOPs. In this paper we re-think the ReLU computation and propose\noptimizations for PI tailored to properties of neural networks. Specifically,\nwe reformulate ReLU as an approximate sign test and introduce a novel\ntruncation method for the sign test that significantly reduces the cost per\nReLU. These optimizations result in a specific type of stochastic ReLU. The key\nobservation is that the stochastic fault behavior is well suited for the\nfault-tolerant properties of neural network inference. Thus, we provide\nsignificant savings without impacting accuracy. We collectively call the\noptimizations Circa and demonstrate improvements of up to 4.7x storage and 3x\nruntime over baseline implementations; we further show that Circa can be used\non top of recent PI optimizations to obtain 1.8x additional speedup.\n"}
+{"abstract": "  Let $\\zeta^*(s)=\\sum_{n=1}^{+\\infty}(-1)^n/n^s$ and $\\tau$ the operator\ndefined on the Frechet space of holomorphic functions in $\\{s\\in \\mathbb C\n:1/2< Re \\, s<1\\}$ by $\\tau f(s)= f(s-2i\\pi/\\log 2)$. We show that the Riemann\nHypothesis is equivalent to the strong recurrence of $\\zeta^*(s)$ for $\\tau$.\nIt follows that a sufficient condition for $RH$ would be that every sum of a\nseries of eigenvectors with unimodular eigenvalues for an operator $u$ is\nstrongly recurrent for $u$. But we give a counterexample showing that it is not\nthe case.\n"}
+{"abstract": "  We present the results of a weekly monitoring of the new black hole candidate\nX-ray binary MAXI J1631-472 carried out with the MeerKAT radio interferometer,\nthe Neil Gehrels Swift Observatory, and the Monitor of All-sky X-ray Image\n(MAXI) instrument, during its 2018-2019 outburst. The source exhibits a number\nof X-ray states, in particular both high- and low-luminosity hard states\nbracketed by extended soft states. Radio flaring is observed shortly after a\ntransition from hard/intermediate states to the soft state. This is broadly in\nagreement with existing empirical models, but its extended duration hints at\nmultiple unresolved flares and/or jet-ISM interactions. In the hard state\nradio:X-ray plane, the source is revealed to be 'radio quiet' at high\nluminosities, but to rejoin the `standard' track at lower luminosities, an\nincreasingly commonly-observed pattern of behaviour.\n"}
+{"abstract": "  Automated surgical gesture recognition is of great importance in\nrobot-assisted minimally invasive surgery. However, existing methods assume\nthat training and testing data are from the same domain, which suffers from\nsevere performance degradation when a domain gap exists, such as the simulator\nand real robot. In this paper, we propose a novel unsupervised domain\nadaptation framework which can simultaneously transfer multi-modality\nknowledge, i.e., both kinematic and visual data, from simulator to real robot.\nIt remedies the domain gap with enhanced transferable features by using\ntemporal cues in videos, and inherent correlations in multi-modal towards\nrecognizing gesture. Specifically, we first propose an MDO-K to align\nkinematics, which exploits temporal continuity to transfer motion directions\nwith smaller gap rather than position values, relieving the adaptation burden.\nMoreover, we propose a KV-Relation-ATT to transfer the co-occurrence signals of\nkinematics and vision. Such features attended by correlation similarity are\nmore informative for enhancing domain-invariance of the model. Two feature\nalignment strategies benefit the model mutually during the end-to-end learning\nprocess. We extensively evaluate our method for gesture recognition using DESK\ndataset with peg transfer procedure. Results show that our approach recovers\nthe performance with great improvement gains, up to 12.91% in ACC and 20.16% in\nF1score without using any annotations in real robot.\n"}
+{"abstract": "  Personalized recommendation system has become pervasive in various video\nplatform. Many effective methods have been proposed, but most of them didn't\ncapture the user's multi-level interest trait and dependencies between their\nviewed micro-videos well. To solve these problems, we propose a Self-over-Co\nAttention module to enhance user's interest representation. In particular, we\nfirst use co-attention to model correlation patterns across different levels\nand then use self-attention to model correlation patterns within a specific\nlevel. Experimental results on filtered public datasets verify that our\npresented module is useful.\n"}
+{"abstract": "  Deep neural networks (DNNs) are known to be vulnerable to adversarial\nexamples/attacks, raising concerns about their reliability in safety-critical\napplications. A number of defense methods have been proposed to train robust\nDNNs resistant to adversarial attacks, among which adversarial training has so\nfar demonstrated the most promising results. However, recent studies have shown\nthat there exists an inherent tradeoff between accuracy and robustness in\nadversarially-trained DNNs. In this paper, we propose a novel technique Dual\nHead Adversarial Training (DH-AT) to further improve the robustness of existing\nadversarial training methods. Different from existing improved variants of\nadversarial training, DH-AT modifies both the architecture of the network and\nthe training strategy to seek more robustness. Specifically, DH-AT first\nattaches a second network head (or branch) to one intermediate layer of the\nnetwork, then uses a lightweight convolutional neural network (CNN) to\naggregate the outputs of the two heads. The training strategy is also adapted\nto reflect the relative importance of the two heads. We empirically show, on\nmultiple benchmark datasets, that DH-AT can bring notable robustness\nimprovements to existing adversarial training methods. Compared with TRADES,\none state-of-the-art adversarial training method, our DH-AT can improve the\nrobustness by 3.4% against PGD40 and 2.3% against AutoAttack, and also improve\nthe clean accuracy by 1.8%.\n"}
+{"abstract": "  Context. The Sagittarius (Sgr) dwarf galaxy is merging with the Milky Way,\nand the study of its globular clusters (GCs) is important to understand the\nhistory and outcome of this ongoing process. Aims. Our main goal is to\ncharacterize the GC system of the Sgr dwarf galaxy. This task is hampered by\nhigh foreground stellar contamination, mostly from the Galactic bulge. Methods.\nWe performed a GC search specifically tailored to find new GC members within\nthe main body of this dwarf galaxy using the combined data of the VISTA\nVariables in the Via Lactea Extended Survey (VVVX) near-infrared survey and the\nGaia Early Data Release 3 (EDR3) optical database. Results. We applied proper\nmotion (PM) cuts to discard foreground bulge and disk stars, and we found a\nnumber of GC candidates in the main body of the Sgr dwarf galaxy. We selected\nthe best GCs as those objects that have significant overdensities above the\nstellar background of the Sgr galaxy and that possess color-magnitude diagrams\n(CMDs) with well-defined red giant branches (RGBs) consistent with the distance\nand reddening of this galaxy. Conclusions. We discover eight new GC members of\nthe Sgr galaxy, which adds up to 29 total GCs known in this dwarf galaxy. This\ntotal number of GCs shows that the Sgr dwarf galaxy hosts a rather rich GC\nsystem. Most of the new GCs appear to be predominantly metal-rich and have low\nluminosity. In addition, we identify ten other GC candidates that are more\nuncertain and need more data for proper confirmation.\n"}
+{"abstract": "  Nano-optic imagers that modulate light at sub-wavelength scales could unlock\nunprecedented applications in diverse domains ranging from robotics to\nmedicine. Although metasurface optics offer a path to such ultra-small imagers,\nexisting methods have achieved image quality far worse than bulky refractive\nalternatives, fundamentally limited by aberrations at large apertures and low\nf-numbers. In this work, we close this performance gap by presenting the first\nneural nano-optics. We devise a fully differentiable learning method that\nlearns a metasurface physical structure in conjunction with a novel, neural\nfeature-based image reconstruction algorithm. Experimentally validating the\nproposed method, we achieve an order of magnitude lower reconstruction error.\nAs such, we present the first high-quality, nano-optic imager that combines the\nwidest field of view for full-color metasurface operation while simultaneously\nachieving the largest demonstrated 0.5 mm, f/2 aperture.\n"}
+{"abstract": "  In this paper, we address Novel Class Discovery (NCD), the task of unveiling\nnew classes in a set of unlabeled samples given a labeled dataset with known\nclasses. We exploit the peculiarities of NCD to build a new framework, named\nNeighborhood Contrastive Learning (NCL), to learn discriminative\nrepresentations that are important to clustering performance. Our contribution\nis twofold. First, we find that a feature extractor trained on the labeled set\ngenerates representations in which a generic query sample and its neighbors are\nlikely to share the same class. We exploit this observation to retrieve and\naggregate pseudo-positive pairs with contrastive learning, thus encouraging the\nmodel to learn more discriminative representations. Second, we notice that most\nof the instances are easily discriminated by the network, contributing less to\nthe contrastive loss. To overcome this issue, we propose to generate hard\nnegatives by mixing labeled and unlabeled samples in the feature space. We\nexperimentally demonstrate that these two ingredients significantly contribute\nto clustering performance and lead our model to outperform state-of-the-art\nmethods by a large margin (e.g., clustering accuracy +13% on CIFAR-100 and +8%\non ImageNet).\n"}
+{"abstract": "  Reinforcement learning is a powerful approach to learn behaviour through\ninteractions with an environment. However, behaviours are usually learned in a\npurely reactive fashion, where an appropriate action is selected based on an\nobservation. In this form, it is challenging to learn when it is necessary to\nexecute new decisions. This makes learning inefficient, especially in\nenvironments that need various degrees of fine and coarse control. To address\nthis, we propose a proactive setting in which the agent not only selects an\naction in a state but also for how long to commit to that action. Our TempoRL\napproach introduces skip connections between states and learns a skip-policy\nfor repeating the same action along these skips. We demonstrate the\neffectiveness of TempoRL on a variety of traditional and deep RL environments,\nshowing that our approach is capable of learning successful policies up to an\norder of magnitude faster than vanilla Q-learning.\n"}
+{"abstract": "  We study a two-player Stackelberg game with incomplete information such that\nthe follower's strategy belongs to a known family of parameterized functions\nwith an unknown parameter vector. We design an adaptive learning approach to\nsimultaneously estimate the unknown parameter and minimize the leader's cost,\nbased on adaptive control techniques and hysteresis switching. Our approach\nguarantees that the leader's cost predicted using the parameter estimate\nbecomes indistinguishable from its actual cost in finite time, up to a\npreselected, arbitrarily small error threshold. Also, the first-order necessary\ncondition for optimality holds asymptotically for the predicted cost.\nAdditionally, if a persistent excitation condition holds, then the parameter\nestimation error becomes bounded by a preselected, arbitrarily small threshold\nin finite time as well. For the case where there is a mismatch between the\nfollower's strategy and the parameterized function that is known to the leader,\nour approach is able to guarantee the same convergence results for error\nthresholds larger than the size of the mismatch. The algorithms and the\nconvergence results are illustrated via a simulation example in the domain of\nnetwork security.\n"}
+{"abstract": "  A marked Petri net is lucent if there are no two different reachable markings\nenabling the same set of transitions, i.e., states are fully characterized by\nthe transitions they enable. Characterizing the class of systems that are\nlucent is a foundational and also challenging question. However, little\nresearch has been done on the topic. In this paper, it is shown that all\nfree-choice nets having a home cluster are lucent. These nets have a so-called\nhome marking such that it is always possible to reach this marking again. Such\na home marking can serve as a regeneration point or as an end-point. The result\nis highly relevant because in many applications, we want the system to be\nlucent and many well-behaved process models fall into the class identified in\nthis paper. Unlike previous work, we do not require the marked Petri net to be\nlive and strongly connected. Most of the analysis techniques for free-choice\nnets are tailored towards well-formed nets. The approach presented in this\npaper provides a novel perspective enabling new analysis techniques for\nfree-choice nets that do not need to be well-formed. Therefore, we can also\nmodel systems and processes that are terminating and/or have an initialization\nphase.\n"}
+{"abstract": "  It is becoming increasingly popular for distributed systems to exploit\noffload to reduce load on the CPU. Remote Direct Memory Access (RDMA) offload,\nin particular, has become popular. However, RDMA still requires CPU\nintervention for complex offloads that go beyond simple remote memory access.\nAs such, the offload potential is limited and RDMA-based systems usually have\nto work around such limitations.\n  We present RedN, a principled, practical approach to implementing complex\nRDMA offloads, without requiring any hardware modifications. Using\nself-modifying RDMA chains, we lift the existing RDMA verbs interface to a\nTuring complete set of programming abstractions. We explore what is possible in\nterms of offload complexity and performance with a commodity RDMA NIC. We show\nhow to integrate these RDMA chains into applications, such as the Memcached\nkey-value store, allowing us to offload complex tasks such as key lookups. RedN\ncan reduce the latency of key-value get operations by up to 2.6x compared to\nstate-of-the-art KV designs that use one-sided RDMA primitives (e.g., FaRM-KV),\nas well as traditional RPC-over-RDMA approaches. Moreover, compared to these\nbaselines, RedN provides performance isolation and, in the presence of\ncontention, can reduce latency by up to 35x while providing applications with\nfailure resiliency to OS and process crashes.\n"}
+{"abstract": "  Let $\\mathcal{E}$ be a $\\mathbb{Q}$-isogeny class of elliptic curves defined\nover $\\mathbb{Q}$. The isogeny graph associated to $\\mathcal{E}$ is a graph\nwhich has a vertex for each element of $\\mathcal{E}$ and an edge for each\n$\\mathbb{Q}$-isogeny of prime degree that maps one element of $\\mathcal{E}$ to\nanother element of $\\mathcal{E}$, with the degree recorded as a label of the\nedge. The isogeny-torsion graph associated to $\\mathcal{E}$ is the isogeny\ngraph associated to $\\mathcal{E}$ where, in addition, we label each vertex with\nthe abstract group structure of the torsion subgroup over $\\mathbb{Q}$ of the\ncorresponding elliptic curve. The main result of the article is a determination\nof which isogeny-torsion graphs associated to $\\mathbb{Q}$-isogeny classes of\nelliptic curves defined over $\\mathbb{Q}$ correspond to infinitely many\n$\\textit{j}$-invariants.\n"}
+{"abstract": "  The mechanism of thermal driving for launching mass outflows is\ninterconnected with classical thermal instability (TI). In a recent paper, we\ndemonstrated that as a result of this interconnectedness, radial wind solutions\nof X-ray heated flows are prone to becoming clumpy. In this paper, we first\nshow that the Bernoulli function determines whether or not the entropy mode can\ngrow due to TI in dynamical flows. Based on this finding, we identify a\ncritical `unbound' radius beyond which TI should accompany thermal driving. Our\nnumerical disk wind simulations support this result and reveal that clumpiness\nis a consequence of buoyancy disrupting the stratified structure of steady\nstate solutions. Namely, instead of a smooth transition layer separating the\nhighly ionized disk wind from the cold phase atmosphere below, hot bubbles\nformed from TI rise up and fragment the atmosphere. These bubbles first appear\nwithin large scale vortices that form below the transition layer, and they\nresult in the episodic production of distinctive cold phase structures referred\nto as irradiated atmospheric fragments (IAFs). Upon interacting with the wind,\nIAFs advect outward and develop extended crests. The subsequent disintegration\nof the IAFs takes place within a turbulent wake that reaches high elevations\nabove the disk. We show that this dynamics has the following observational\nimplications: dips in the absorption measure distribution are no longer\nexpected within TI zones and there can be a less sudden desaturation of X-ray\nabsorption lines such as \\OVIII as well as multiple absorption troughs in\n\\FeXXVK.\n"}
+{"abstract": "  Advances in high-precision dielectric spectroscopy has enabled access to\nnon-linear susceptibilities of polar molecular liquids. The observed\nnon-monotonic behavior has been claimed to provide strong support for theories\nof dynamic arrest based on thermodynamic amorphous order. Here we approach this\nquestion from the perspective of dynamic facilitation, an alternative view\nfocusing on emergent kinetic constraints underlying the dynamic arrest of a\nliquid approaching its glass transition. We derive explicit expressions for the\nfrequency-dependent higher-order dielectric susceptibilities exhibiting a\nnon-monotonic shape, the height of which increases as temperature is lowered.\nWe demonstrate excellent agreement with the experimental data for glycerol,\nchallenging the idea that non-linear response functions reveal correlated\nrelaxation in supercooled liquids.\n"}
+{"abstract": "  We give concrete, \"infinitesimal\" conditions for a proper geodesically\ncomplete CAT(0) space to have semistable fundamental group at infinity.\n"}
+{"abstract": "  We investigate the potential of type II supernovae (SNe) to constrain\naxion-like particles (ALPs) coupled simultaneously to nucleons and electrons.\nALPs coupled to nucleons can be efficiently produced in the SN core via\nnucleon-nucleon bremsstrahlung and, for a wide range of parameters, leave the\nSN unhindered, producing a large ALP flux. For masses exceeding 1 MeV, these\nALPs would decay into electron-positron pairs, generating a positron flux. In\nthe case of Galactic SNe, the annihilation of the created positrons with the\nelectrons present in the Galaxy would contribute to the 511 keV annihilation\nline. Using the SPI (SPectrometer on INTEGRAL) observation of this line, allows\nus to exclude a wide range of the axion-electron coupling, $10^{-19} \\lesssim\ng_{ae} \\lesssim 10^{-11}$, for $g_{ap}\\sim 10^{-9}$. Additionally, ALPs from\nextra-galactic SNe decaying into electron-positron pairs would yield a\ncontribution to the cosmic X-ray background. In this case, we constrain the\nALP-electron coupling down to $g_{ae} \\sim 10^{-20}$.\n"}
+{"abstract": "  The point-to-set principle \\cite{LutLut17} characterizes the Hausdorff\ndimension of a subset $E\\subseteq\\R^n$ by the \\textit{effective} (or\nalgorithmic) dimension of its individual points. This characterization has been\nused to prove several results in classical, i.e., without any computability\nrequirements, analysis. Recent work has shown that algorithmic techniques can\nbe fruitfully applied to Marstrand's projection theorem, a fundamental result\nin fractal geometry.\n  In this paper, we introduce an extension of point-to-set principle - the\nnotion of \\textit{optimal oracles} for subsets $E\\subseteq\\R^n$. One of the\nprimary motivations of this definition is that, if $E$ has optimal oracles,\nthen the conclusion of Marstrand's projection theorem holds for $E$. We show\nthat every analytic set has optimal oracles. We also prove that if the\nHausdorff and packing dimensions of $E$ agree, then $E$ has optimal oracles.\nMoreover, we show that the existence of sufficiently nice outer measures on $E$\nimplies the existence of optimal Hausdorff oracles. In particular, the\nexistence of exact gauge functions for a set $E$ is sufficient for the\nexistence of optimal Hausdorff oracles, and is therefore sufficient for\nMarstrand's theorem. Thus, the existence of optimal oracles extends the\ncurrently known sufficient conditions for Marstrand's theorem to hold.\n  Under certain assumptions, every set has optimal oracles. However, assuming\nthe axiom of choice and the continuum hypothesis, we construct sets which do\nnot have optimal oracles. This construction naturally leads to a generalization\nof Davies theorem on projections.\n"}
+{"abstract": "  In this study, we tried to see and characterize potential threats to digital\nactivism in the internet-active nation of Indonesia by doing network analysis\non a recent digital activism event on Twitter, which protested against a recent\nlaw related to alcoholic beverage investment. We hoped insights from the study\ncan help the nation moving forward as public discourses are likely to stay\nonline post-COVID. From this study, we found that threats in form of hashtag\nhijackings happen often in digital activism, and there were traces of a\nsystematic information campaign in our observed case. We also found that the\nusage of bots is prevalent in and they showed significant activity, although\nthe extent to which they influenced the conversation needs to be followed\nthrough more. These threats are something to think about as activism goes\nincreasingly digital after COVID-19 as it can imbue unwanted messages, sow\npolarization, and distract the conversation from the real issue.\n"}
+{"abstract": "  Diffusion tensor imaging (DTI) is a prevalent neuroimaging tool in analyzing\nthe anatomical structure. The distinguishing feature of DTI is that the\nvoxel-wise variable is a 3x3 positive definite matrix other than a scalar,\ndescribing the diffusion process at the voxel. Recently, several statistical\nmethods have been proposed to analyze the DTI data. This paper focuses on the\nstatistical inference of eigenvalues of DTI because it provides more\ntransparent clinical interpretations. However, the statistical inference of\neigenvalues is statistically challenging because few treat these responses as\nrandom eigenvalues. In our paper, we rely on the distribution of the Wishart\nmatrix's eigenvalues to model the random eigenvalues. A hierarchical model\nwhich captures the eigenvalues' randomness and spatial auto-correlation is\nproposed to infer the local covariate effects. The Monte-Carlo\nExpectation-Maximization algorithm is implemented for parameter estimation.\nBoth simulation studies and application to IXI data-set are used to demonstrate\nour proposal. The results show that our proposal is more proper in analyzing\nauto-correlated random eigenvalues compared to alternatives.\n"}
+{"abstract": "  Structured stochastic multi-armed bandits provide accelerated regret rates\nover the standard unstructured bandit problems. Most structured bandits,\nhowever, assume the knowledge of the structural parameter such as Lipschitz\ncontinuity, which is often not available. To cope with the latent structural\nparameter, we consider a transfer learning setting in which an agent must learn\nto transfer the structural information from the prior tasks to the next task,\nwhich is inspired by practical problems such as rate adaptation in wireless\nlink. We propose a novel framework to provably and accurately estimate the\nLipschitz constant based on previous tasks and fully exploit it for the new\ntask at hand. We analyze the efficiency of the proposed framework in two folds:\n(i) the sample complexity of our estimator matches with the\ninformation-theoretic fundamental limit; and (ii) our regret bound on the new\ntask is close to that of the oracle algorithm with the full knowledge of the\nLipschitz constant under mild assumptions. Our analysis reveals a set of useful\ninsights on transfer learning for latent Lipschitzconstants such as the\nfundamental challenge a learner faces. Our numerical evaluations confirm our\ntheoretical findings and show the superiority of the proposed framework\ncompared to baselines.\n"}
+{"abstract": "  Aiming at the traditional grasping method for manipulators based on 2D\ncamera, when faced with the scene of gathering or covering, it can hardly\nperform well in unstructured scenes that appear as gathering and covering, for\nthe reason that can't recognize objects accurately in cluster scenes from a\nsingle perspective and the manipulators can't make the environment better for\ngrasping. In this case, a novel method of pushing-grasping collaborative based\non the deep Q-network in dual perspectives is proposed in this paper. This\nmethod adopts an improved deep Q network algorithm, with an RGB-D camera to\nobtain the information of objects' RGB images and point clouds from two\nperspectives, and combines the pushing and grasping actions so that the trained\nmanipulator can make the scenes better for grasping so that it can perform well\nin more complicated grasping scenes. What's more, we improved the reward\nfunction of the deep Q-network and propose the piecewise reward function to\nspeed up the convergence of the deep Q-network. We trained different models and\ntried different methods in the V-REP simulation environment, and it concluded\nthat the method proposed in this paper converges quickly and the success rate\nof grasping objects in unstructured scenes raises up to 83.5%. Besides, it\nshows the generalization ability and well performance when novel objects appear\nin the scenes that the manipulator has never grasped before.\n"}
+{"abstract": "  Several calibration techniques have been proposed in the literature for the\ncalibration of two-component two-dimensional (2C-2D) particle image velocimetry\n(PIV) and three-component two-dimensional (3C-2D) stereoscopic PIV (SPIV)\nsystems. These techniques generally involve the use of a calibration target\nthat is assumed to be at the exact centre of the laser sheet within the field\nof view (FOV), which in practice is very difficult to achieve. In 3C-2D SPIV,\nseveral methods offer different correction schemes based on the computation of\na disparity map, which are aimed at correcting errors produced due to this\nmisalignment. These techniques adjust the calibration of individual cameras to\nreduce the disparity error, but in doing so can create unintended errors in the\nmeasurement position and/or the velocity measurements, such as introducing a\nbias in the measured three-component (3-C) displacements. This paper introduces\na novel method to ensure accurate alignment of the laser sheet with the\ncalibration target so that the uncertainty in displacement measurements is less\nthan or equal to the uncertainty inherent to the PIV and hence, no correction\nscheme is required. The proposed method has been validated with a simple\nexperiment in which true displacements are given to a particle container\n(illuminated by an aligned laser sheet) and the measured 3C displacements are\ncompared with the given true displacements. An uncertainty of less than 7.6\nmicrometres (equivalent to 0.114 pixels) in the measured 3C displacements\ndemonstrates the effectiveness of the new alignment method and eliminates the\nneed for any ad hoc post-correction scheme.\n"}
+{"abstract": "  This note is a short description of TeCoMiner, an interactive tool for\nexploring the topic content of text collections. Unlike other topic modeling\ntools, TeCoMiner is not based on some generative probabilistic model but on\ntopological considerations about co-occurrence networks of terms. We outline\nthe methods used for identifying topics, describe the features of the tool, and\nsketch an application, using a corpus of policy related scientific news on\nenvironmental issues published by the European Commission over the last decade.\n"}
+{"abstract": "  Light fidelity (LiFi), which is based on visible light communications (VLC),\nis celebrated as a cutting-edge technological paradigm that is envisioned to be\nan indispensable part of 6G systems. Nonetheless, LiFi performance is subject\nto efficiently overcoming the line-of-sight blockage, whose adverse effect on\nwireless reception reliability becomes even more pronounced in highly dynamic\nenvironments, such as vehicular application scenarios. Meanwhile,\nreconfigurable intelligent surfaces (RIS) emerged recently as a revolutionary\nconcept that transfers the physical propagation environment into a fully\ncontrollable and customisable space in a low-cost low-power fashion. We\nanticipate that the integration of RIS in LiFi-enabled networks will not only\nsupport blockage mitigation but will also provision complex interactions among\nnetwork entities, and is hence manifested as a promising platform that enables\na plethora of technological trends and new applications. In this article, for\nthe first time in the open literature, we set the scene for a holistic overview\nof RIS-assisted LiFi systems. Specifically, we explore the underlying RIS\narchitecture from the perspective of physics and present a forward-looking\nvision that outlines potential operational elements supported by RIS-enabled\ntransceivers and RIS-enabled environments. Finally, we highlight major\nassociated challenges and offer a look ahead toward promising future\ndirections.\n"}
+{"abstract": "  We consider a system of charged one-dimensional spin-$\\frac{1}{2}$ fermions\nat low temperature. We study how the energy of a highly-excited quasiparticle\n(or hole) relaxes toward the chemical potential in the regime of weak\ninteractions. The dominant relaxation processes involve collisions with two\nother fermions. We find a dramatic enhancement of the relaxation rate at low\nenergies, with the rate scaling as the inverse sixth power of the excitation\nenergy. This behavior is caused by the long-range nature of the Coulomb\ninteraction.\n"}
+{"abstract": "  In order to objectively assess new medical imaging technologies via\ncomputer-simulations, it is important to account for all sources of variability\nthat contribute to image data. One important source of variability that can\nsignificantly limit observer performance is associated with the variability in\nthe ensemble of objects to-be-imaged. This source of variability can be\ndescribed by stochastic object models (SOMs), which are generative models that\ncan be employed to sample from a distribution of to-be-virtually-imaged\nobjects. It is generally desirable to establish SOMs from experimental imaging\nmeasurements acquired by use of a well-characterized imaging system, but this\ntask has remained challenging. Deep generative neural networks, such as\ngenerative adversarial networks (GANs) hold potential for such tasks. To\nestablish SOMs from imaging measurements, an AmbientGAN has been proposed that\naugments a GAN with a measurement operator. However, the original AmbientGAN\ncould not immediately benefit from modern training procedures and GAN\narchitectures, which limited its ability to be applied to realistically sized\nmedical image data. To circumvent this, in this work, a modified AmbientGAN\ntraining strategy is proposed that is suitable for modern progressive or\nmulti-resolution training approaches such as employed in the Progressive\nGrowing of GANs and Style-based GANs. AmbientGANs established by use of the\nproposed training procedure are systematically validated in a controlled way by\nuse of computer-simulated measurement data corresponding to a stylized imaging\nsystem. Finally, emulated single-coil experimental magnetic resonance imaging\ndata are employed to demonstrate the methods under less stylized conditions.\n"}
+{"abstract": "  In this paper, we study the evolution of opinions over social networks with\nbounded confidence in social cliques. Node initial opinions are independently\nand identically distributed; at each time step, nodes review the average\nopinions of a randomly selected local clique. The clique averages may represent\nlocal group pressures on peers. Then nodes update their opinions under bounded\nconfidence: only when the difference between an agent individual opinion and\nthe corresponding local clique pressure is below a threshold, this agent\nopinion is updated according to the DeGroot rule as a weighted average of the\ntwo values. As a result, this opinion dynamics is a generalization of the\nclassical Deffuant-Weisbuch model in which only pairwise interactions take\nplace. First of all, we prove conditions under which all node opinions converge\nto finite limits. We show that in the limits the event that all nodes achieve a\nconsensus, and the event that all nodes achieve pairwise distinct limits, i.e.,\nsocial disagreements, are both nontrivial events. Next, we show that opinion\nfluctuations may take place in the sense that at least one agent in the network\nfails to hold a converging opinion trajectory. In fact, we prove that this\nfluctuation event happens with a strictly positive probability, and also\nconstructively present an initial value event under which the fluctuation event\narises with probability one. These results add to the understanding of the role\nof bounded confidence in social opinion dynamics, and the possibility of\nfluctuation reveals that bringing in cliques in Deffuant-Weisbuch models have\nfundamentally changed the behavior of such opinion dynamical processes.\n"}
+{"abstract": "  Modeling complex systems and data using the language of graphs and networks\nhas become an essential topic across a range of different disciplines.\nArguably, this network-based perspective derives is success from the relative\nsimplicity of graphs: A graph consists of nothing more than a set of vertices\nand a set of edges, describing relationships between pairs of such vertices.\nThis simple combinatorial structure makes graphs interpretable and flexible\nmodeling tools. The simplicity of graphs as system models, however, has been\nscrutinized in the literature recently. Specifically, it has been argued from a\nvariety of different angles that there is a need for higher-order networks,\nwhich go beyond the paradigm of modeling pairwise relationships, as\nencapsulated by graphs. In this survey article we take stock of these recent\ndevelopments. Our goals are to clarify (i) what higher-order networks are, (ii)\nwhy these are interesting objects of study, and (iii) how they can be used in\napplications.\n"}
+{"abstract": "  At sufficiently low temperatures magnetic materials often enter a correlated\nphase hosting collective, coherent magnetic excitations such as magnons or\ntriplons. Drawing on the enormous progress on topological materials of the last\nfew years, recent research has led to new insights into the geometry and\ntopology of these magnetic excitations. Berry phases associated to magnetic\ndynamics can lead to observable consequences in heat and spin transport while\nanalogues of topological insulators and semimetals can arise within magnon band\nstructures from natural magnetic couplings. Magnetic excitations offer a\nplatform to explore the interplay of magnetic symmetries and topology, to drive\ntopological transitions using magnetic fields. examine the effects of\ninteractions on topological bands and to generate topologically protected spin\ncurrents at interfaces. In this review, we survey progress on all these topics,\nhighlighting aspects of topological matter that are unique to magnon systems\nand the avenues yet to be fully investigated.\n"}
+{"abstract": "  Parameter estimation procedures provide valuable guidance in the\nunderstanding and improvement of organic solar cells and other devices. They\noften rely on one-dimensional models, but in the case of bulk-heterojunction\n(BHJ) designs, it is not straightforward that these models' parameters have a\nconsistent physical interpretation. Indeed, contrarily to two- or\nthree-dimensional models, the BHJ morphology is not explicitly described in\none-dimensional models and must be implicitly expressed through effective\nparameters. In order to inform experimental decisions, a helpful parameter\nestimation method must establish that one can correctly interpret the provided\nparameters. However, only a few works have been undertaken to reach that\nobjective in the context of BHJ organic solar cells. In this work, a realistic\ntwo-dimensional model of BHJ solar cells is used to investigate the behavior of\nstate-of-the-art parameter estimation procedures in situations that emulate\nexperimental conditions. We demonstrate that fitting solely current-voltage\ncharacteristics by an effective medium one-dimensional model can yield\nnonsensical results, which may lead to counter-productive decisions about\nfuture design choices. In agreement with previously published literature, we\nexplicitly demonstrate that fitting several characterization results together\ncan drastically improve the robustness of the parameter estimation. Based on a\ndetailed analysis of parameter estimation results, a set of recommendations is\nformulated to avoid the most problematic pitfalls and increase awareness about\nthe limitations that cannot be circumvented.\n"}
+{"abstract": "  In this work, we investigate dynamic oversampling techniques for large-scale\nmultiple-antenna systems equipped with low-cost and low-power 1-bit\nanalog-to-digital converters at the base stations. To compensate for the\nperformance loss caused by the coarse quantization, oversampling is applied at\nthe receiver. Unlike existing works that use uniform oversampling, which\nsamples the signal at a constant rate, a novel dynamic oversampling scheme is\nproposed. The basic idea is to perform time-varying nonuniform oversampling,\nwhich selects samples with nonuniform patterns that vary over time. We consider\ntwo system design criteria: a design that maximizes the achievable sum rate and\nanother design that minimizes the mean square error of detected symbols.\nDynamic oversampling is carried out using a dimension reduction matrix\n$\\mathbf{\\Delta}$, which can be computed by the generalized eigenvalue\ndecomposition or by novel submatrix-level feature selection algorithms.\nMoreover, the proposed scheme is analyzed in terms of convergence,\ncomputational complexity and power consumption at the receiver. Simulations\nshow that systems with the proposed dynamic oversampling outperform those with\nuniform oversampling in terms of computational cost, achievable sum rate and\nsymbol error rate performance.\n"}
+{"abstract": "  We propose a novel codimension-n holography, called cone holography, between\na gravitational theory in $(d+1)$-dimensional conical spacetime and a CFT on\nthe $(d+1-n)$-dimensional defects. Similar to wedge holography, the cone\nholography can be obtained by taking the zero-volume limit of holographic\ndefect CFT. Remarkably, it can be regarded as a holographic dual of the edge\nmodes on the defects. For one class of solutions, we prove that the cone\nholography is equivalent to AdS/CFT, by showing that the classical\ngravitational action and thus the CFT partition function in large N limit are\nthe same for the two theories. In general, cone holography and AdS/CFT are\ndifferent due to the infinite towers of massive Kaluza-Klein modes on the\nbranes. We test cone holography by studying Weyl anomaly, Entanglement/R\\'enyi\nentropy and correlation functions, and find good agreements between the\nholographic and the CFT results. In particular, the c-theorem is obeyed by cone\nholography. These are strong supports for our proposal. We discuss two kinds of\nboundary conditions, the mixed boundary condition and Neumann boundary\ncondition, and find that they both define a consistent theory of cone\nholography. We also analyze the mass spectrum on the brane and find that the\nlarger the tension is, the more continuous the mass spectrum is. The cone\nholography can be regarded as a generalization of the wedge holography, and it\nis closely related to the defect CFT, entanglement/R\\'enyi entropy and\nAdS/BCFT(dCFT). Thus it is expected to have a wide range of applications.\n"}
+{"abstract": "  We consider the monotone inclusion problems in real Hilbert spaces. Proximal\nsplitting algorithms are very popular technique to solve it and generally\nachieve weak convergence under mild assumptions. Researchers assume strong\nconditions like strong convexity or strong monotonicity on the considered\noperators to prove strong convergence of the algorithms. Mann iteration method\nand normal S-iteration method are popular methods to solve fixed point\nproblems. We propose a new common fixed point algorithm based on normal\nS-iteration method {using Tikhonov regularization }to find common fixed point\nof nonexpansive operators and prove strong convergence of the generated\nsequence to the set of common fixed points without assuming strong convexity\nand strong monotonicity. Based on the proposed fixed point algorithm, we\npropose a forward-backward-type algorithm and a Douglas-Rachford algorithm in\nconnection with Tikhonov regularization to find the solution of monotone\ninclusion problems. Further, we consider the complexly structured monotone\ninclusion problems which are very popular these days. We also propose a\nstrongly convergent forward-backward-type primal-dual algorithm and a\nDouglas-Rachford-type primal-dual algorithm to solve the monotone inclusion\nproblems. Finally, we conduct a numerical experiment to solve image deblurring\nproblems.\n"}
+{"abstract": "  Evolved low- to intermediate-mass stars are known to shed their gaseous\nenvelope into a large, dusty, molecule-rich circumstellar nebula which\ntypically develops a high degree of structural complexity. Most of the\nlarge-scale, spatially correlated structures in the nebula are thought to\noriginate from the interaction of the stellar wind with a companion. As part of\nthe Atomium large programme, we observed the M-type asymptotic giant branch\n(AGB) star R Hydrae with ALMA. The morphology of the inner wind of R Hya, which\nhas a known companion at ~3500 au, was determined from maps of CO and SiO\nobtained at high angular resolution. A map of the CO emission reveals a\nmulti-layered structure consisting of a large elliptical feature at an angular\nscale of ~10'' that is oriented along the north-south axis. The wind morphology\nwithin the elliptical feature is dominated by two hollow bubbles. The bubbles\nare on opposite sides of the AGB star and lie along an axis with a position\nangle of ~115 deg. Both bubbles are offset from the central star, and their\nappearance in the SiO channel maps indicates that they might be shock waves\ntravelling through the AGB wind. An estimate of the dynamical age of the\nbubbles yields an age of the order of 100 yr, which is in agreement with the\npreviously proposed elapsed time since the star last underwent a thermal pulse.\nWhen the CO and SiO emission is examined on subarcsecond angular scales, there\nis evidence for an inclined, differentially rotating equatorial density\nenhancement, strongly suggesting the presence of a second nearby companion. The\nposition angle of the major axis of this disc is ~70 deg in the plane of the\nsky. We tentatively estimate that a lower limit on the mass of the nearby\ncompanion is ~0.65 Msol on the basis of the highest measured speeds in the disc\nand the location of its inner rim at ~6 au from the AGB star.\n"}
+{"abstract": "  Synthesized speech from articulatory movements can have real-world use for\npatients with vocal cord disorders, situations requiring silent speech, or in\nhigh-noise environments. In this work, we present EMA2S, an end-to-end\nmultimodal articulatory-to-speech system that directly converts articulatory\nmovements to speech signals. We use a neural-network-based vocoder combined\nwith multimodal joint-training, incorporating spectrogram, mel-spectrogram, and\ndeep features. The experimental results confirm that the multimodal approach of\nEMA2S outperforms the baseline system in terms of both objective evaluation and\nsubjective evaluation metrics. Moreover, results demonstrate that joint\nmel-spectrogram and deep feature loss training can effectively improve system\nperformance.\n"}
+{"abstract": "  Neural Ordinary Differential Equations (NODEs) use a neural network to model\nthe instantaneous rate of change in the state of a system. However, despite\ntheir apparent suitability for dynamics-governed time-series, NODEs present a\nfew disadvantages. First, they are unable to adapt to incoming data points, a\nfundamental requirement for real-time applications imposed by the natural\ndirection of time. Second, time series are often composed of a sparse set of\nmeasurements that could be explained by many possible underlying dynamics.\nNODEs do not capture this uncertainty. In contrast, Neural Processes (NPs) are\na family of models providing uncertainty estimation and fast data adaptation\nbut lack an explicit treatment of the flow of time. To address these problems,\nwe introduce Neural ODE Processes (NDPs), a new class of stochastic processes\ndetermined by a distribution over Neural ODEs. By maintaining an adaptive\ndata-dependent distribution over the underlying ODE, we show that our model can\nsuccessfully capture the dynamics of low-dimensional systems from just a few\ndata points. At the same time, we demonstrate that NDPs scale up to challenging\nhigh-dimensional time-series with unknown latent dynamics such as rotating\nMNIST digits.\n"}
+{"abstract": "  We show that the permanent of a matrix can be written as the expectation\nvalue of a function of random variables each with zero mean and unit variance.\nThis result is used to show that Glynn's theorem and a simplified MacMahon\ntheorem extend from a common probabilistic interpretation of the permanent.\nCombining the methods in these two proofs, we prove a new result that relates\nthe permanent of a matrix to the expectation value of a product of hyperbolic\ntrigonometric functions, or, equivalently, the partition function of a spin\nsystem. We conclude by discussing how the main theorem can be generalized and\nhow the techniques used to prove it can be applied to more general problems in\ncombinatorics.\n"}
+{"abstract": "  To improve the detection accuracy and generalization of steganalysis, this\npaper proposes the Steganalysis Contrastive Framework (SCF) based on\ncontrastive learning. The SCF improves the feature representation of\nsteganalysis by maximizing the distance between features of samples of\ndifferent categories and minimizing the distance between features of samples of\nthe same category. To decrease the computing complexity of the contrastive loss\nin supervised learning, we design a novel Steganalysis Contrastive Loss\n(StegCL) based on the equivalence and transitivity of similarity. The StegCL\neliminates the redundant computing in the existing contrastive loss. The\nexperimental results show that the SCF improves the generalization and\ndetection accuracy of existing steganalysis DNNs, and the maximum promotion is\n2% and 3% respectively. Without decreasing the detection accuracy, the training\ntime of using the StegCL is 10% of that of using the contrastive loss in\nsupervised learning.\n"}
+{"abstract": "  We prove the existence and the Besov regularity of the density of the\nsolution to a general parabolic SPDE which includes the stochastic Burgers\nequation on an unbounded domain. We use an elementary approach based on the\nfractional integration by parts.\n"}
+{"abstract": "  In the new era of very large telescopes, where data is crucial to expand\nscientific knowledge, we have witnessed many deep learning applications for the\nautomatic classification of lightcurves. Recurrent neural networks (RNNs) are\none of the models used for these applications, and the LSTM unit stands out for\nbeing an excellent choice for the representation of long time series. In\ngeneral, RNNs assume observations at discrete times, which may not suit the\nirregular sampling of lightcurves. A traditional technique to address irregular\nsequences consists of adding the sampling time to the network's input, but this\nis not guaranteed to capture sampling irregularities during training.\nAlternatively, the Phased LSTM unit has been created to address this problem by\nupdating its state using the sampling times explicitly. In this work, we study\nthe effectiveness of the LSTM and Phased LSTM based architectures for the\nclassification of astronomical lightcurves. We use seven catalogs containing\nperiodic and nonperiodic astronomical objects. Our findings show that LSTM\noutperformed PLSTM on 6/7 datasets. However, the combination of both units\nenhances the results in all datasets.\n"}
+{"abstract": "  Recurrent event analyses have found a wide range of applications in\nbiomedicine, public health, and engineering, among others, where study subjects\nmay experience a sequence of event of interest during follow-up. The R package\nreReg (Chiou and Huang 2021) offers a comprehensive collection of practical and\neasy-to-use tools for regression analysis of recurrent events, possibly with\nthe presence of an informative terminal event. The regression framework is a\ngeneral scale-change model which encompasses the popular Cox-type model, the\naccelerated rate model, and the accelerated mean model as special cases.\nInformative censoring is accommodated through a subject-specific frailty\nwithout no need for parametric specification. Different regression models are\nallowed for the recurrent event process and the terminal event. Also included\nare visualization and simulation tools.\n"}
+{"abstract": "  Segmentation of additive manufacturing (AM) defects in X-ray Computed\nTomography (XCT) images is challenging, due to the poor contrast, small sizes\nand variation in appearance of defects. Automatic segmentation can, however,\nprovide quality control for additive manufacturing. Over recent years,\nthree-dimensional convolutional neural networks (3D CNNs) have performed well\nin the volumetric segmentation of medical images. In this work, we leverage\ntechniques from the medical imaging domain and propose training a 3D U-Net\nmodel to automatically segment defects in XCT images of AM samples. This work\nnot only contributes to the use of machine learning for AM defect detection but\nalso demonstrates for the first time 3D volumetric segmentation in AM. We train\nand test with three variants of the 3D U-Net on an AM dataset, achieving a mean\nintersection of union (IOU) value of 88.4%.\n"}
+{"abstract": "  Retinal artery/vein (A/V) classification is a critical technique for\ndiagnosing diabetes and cardiovascular diseases. Although deep learning based\nmethods achieve impressive results in A/V classification, their performances\nusually degrade severely when being directly applied to another database, due\nto the domain shift, e.g., caused by the variations in imaging protocols. In\nthis paper, we propose a novel vessel-mixing based consistency regularization\nframework, for cross-domain learning in retinal A/V classification. Specially,\nto alleviate the severe bias to source domain, based on the label smooth prior,\nthe model is regularized to give consistent predictions for unlabeled\ntarget-domain inputs that are under perturbation. This consistency\nregularization implicitly introduces a mechanism where the model and the\nperturbation is opponent to each other, where the model is pushed to be robust\nenough to cope with the perturbation. Thus, we investigate a more difficult\nopponent to further inspire the robustness of model, in the scenario of retinal\nA/V, called vessel-mixing perturbation. Specially, it effectively disturbs the\nfundus images especially the vessel structures by mixing two images regionally.\nWe conduct extensive experiments on cross-domain A/V classification using four\npublic datasets, which are collected by diverse institutions and imaging\ndevices. The results demonstrate that our method achieves the state-of-the-art\ncross-domain performance, which is also close to the upper bound obtained by\nfully supervised learning on target domain.\n"}
+{"abstract": "  This paper studies equilibrium quality of semi-separable position auctions\n(known as the Ad Types setting) with greedy or optimal allocation combined with\ngeneralized second-price (GSP) or Vickrey-Clarke-Groves (VCG) pricing. We make\nthree contributions: first, we give upper and lower bounds on the Price of\nAnarchy (PoA) for auctions which use greedy allocation with GSP pricing, greedy\nallocations with VCG pricing, and optimal allocation with GSP pricing. Second,\nwe give Bayes-Nash equilibrium characterizations for two-player, two-slot\ninstances (for all auction formats) and show that there exists both a revenue\nhierarchy and revenue equivalence across some formats. Finally, we use\nno-regret learning algorithms and bidding data from a large online advertising\nplatform and no-regret learning algorithms to evaluate the performance of the\nmechanisms under semi-realistic conditions. For welfare, we find that the\noptimal-to-realized welfare ratio (an empirical PoA analogue) is broadly better\nthan our upper bounds on PoA; For revenue, we find that the hierarchy in\npractice may sometimes agree with simple theory, but generally appears\nsensitive to the underlying distribution of bidder valuations.\n"}
+{"abstract": "  This paper introduces the notion of an Input Constrained Control Barrier\nFunction (ICCBF), as a method to synthesize safety-critical controllers for\nnon-linear control affine systems with input constraints. The method identifies\na subset of the safe set of states, and constructs a controller to render the\nsubset forward invariant. The feedback controller is represented as the\nsolution to a quadratic program, which can be solved efficiently for real-time\nimplementation. Furthermore, we show that ICCBFs are a generalization of Higher\nOrder Control Barrier Functions, and thus are applicable to systems of\nnon-uniform relative degree. Simulation results are presented for the adaptive\ncruise control problem, and a spacecraft rendezvous problem.\n"}
+{"abstract": "  Electron-hole asymmetry is a fundamental property in solids that can\ndetermine the nature of quantum phase transitions and the regime of operation\nfor devices. The observation of electron-hole asymmetry in graphene and\nrecently in the phase diagram of bilayer graphene has spurred interest into\nwhether it stems from disorder or from fundamental interactions such as\ncorrelations. Here, we report an effective new way to access electron-hole\nasymmetry in 2D materials by directly measuring the quasiparticle self-energy\nin graphene/Boron Nitride field effect devices. As the chemical potential moves\nfrom the hole to the electron doped side, we see an increased strength of\nelectronic correlations manifested by an increase in the band velocity and\ninverse quasiparticle lifetime. These results suggest that electronic\ncorrelations play an intrinsic role in driving electron hole asymmetry in\ngraphene and provide a new insight for asymmetries in more strongly correlated\nmaterials.\n"}
+{"abstract": "  Finding shortest paths in a given network (e.g., a computer network or a road\nnetwork) is a well-studied task with many applications. We consider this task\nunder the presence of an adversary, who can manipulate the network by\nperturbing its edge weights to gain an advantage over others. Specifically, we\nintroduce the Force Path Problem as follows. Given a network, the adversary's\ngoal is to make a specific path the shortest by adding weights to edges in the\nnetwork. The version of this problem in which the adversary can cut edges is\nNP-complete. However, we show that Force Path can be solved to within arbitrary\nnumerical precision in polynomial time. We propose the PATHPERTURB algorithm,\nwhich uses constraint generation to build a set of constraints that require\npaths other than the adversary's target to be sufficiently long. Across a\nhighly varied set of synthetic and real networks, we show that the optimal\nsolution often reduces the required perturbation budget by about half when\ncompared to a greedy baseline method.\n"}
+{"abstract": "  We explore a two-qubit system defined on valley isospins of two electrons\nconfined in a gate-defined double quantum dot created within a MoS$_2$\nmonolayer flake. We show how to initialize, control, interact and read out such\nvalley qubits only by electrical means using voltages applied to the local\nplanar gates, which are layered on the top of the flake. By demonstrating the\ntwo-qubit exchange or readout via the Pauli blockade, we prove that valley\nqubits in transition-metal-dichalcogenide semiconductors family fulfill the\nuniversality criteria and represent a scalable quantum computing platform. Our\nnumerical experiments are based on the tight-binding model for a MoS$_2$\nmonolayer, which gives single-electron eigenstates that are then used to\nconstruct a basis of Slater-determinants for the two-electron configuration\nspace. We express screened electron-electron interactions in this basis by\ncalculating the Coulomb matrix elements using localized Slater-type orbitals.\nThen we solve the time-dependent Schr\\\"odinger equation and obtain an exact\ntime-evolution of the two-electron system. During the evolution we\nsimultaneously solve the Poison equation, finding the confinement potential\ncontrolled via voltages applied to the gates.\n"}
+{"abstract": "  This paper is concerned with the problem of representing and learning the\noptimal control law for the linear quadratic Gaussian (LQG) optimal control\nproblem. In recent years, there is a growing interest in re-visiting this\nclassical problem, in part due to the successes of reinforcement learning (RL).\nThe main question of this body of research (and also of our paper) is to\napproximate the optimal control law {\\em without} explicitly solving the\nRiccati equation. For this purpose, a novel simulation-based algorithm, namely\nan ensemble Kalman filter (EnKF), is introduced in this paper. The algorithm is\nused to obtain formulae for optimal control, expressed entirely in terms of the\nEnKF particles. For the general partially observed LQG problem, the proposed\nEnKF is combined with a standard EnKF (for the estimation problem) to obtain\nthe optimal control input based on the use of the separation principle. A\nnonlinear extension of the algorithm is also discussed which clarifies the\nduality roots of the proposed EnKF. The theoretical results and algorithms are\nillustrated with numerical experiments.\n"}
+{"abstract": "  Computational couplings of Markov chains provide a practical route to\nunbiased Monte Carlo estimation that can utilize parallel computation. However,\nthese approaches depend crucially on chains meeting after a small number of\ntransitions. For models that assign data into groups, e.g. mixture models, the\nobvious approaches to couple Gibbs samplers fail to meet quickly. This failure\nowes to the so-called \"label-switching\" problem; semantically equivalent\nrelabelings of the groups contribute well-separated posterior modes that impede\nfast mixing and cause large meeting times. We here demonstrate how to avoid\nlabel switching by considering chains as exploring the space of partitions\nrather than labelings. Using a metric on this space, we employ an optimal\ntransport coupling of the Gibbs conditionals. This coupling outperforms\nalternative couplings that rely on labelings and, on a real dataset, provides\nestimates more precise than usual ergodic averages in the limited time regime.\nCode is available at github.com/tinnguyen96/coupling-Gibbs-partition.\n"}
+{"abstract": "  Standard machine learning approaches require centralizing the users' data in\none computer or a shared database, which raises data privacy and\nconfidentiality concerns. Therefore, limiting central access is important,\nespecially in healthcare settings, where data regulations are strict. A\npotential approach to tackling this is Federated Learning (FL), which enables\nmultiple parties to collaboratively learn a shared prediction model by using\nparameters of locally trained models while keeping raw training data locally.\nIn the context of AI-assisted pain-monitoring, we wish to enable\nconfidentiality-preserving and unobtrusive pain estimation for long-term\npain-monitoring and reduce the burden on the nursing staff who perform frequent\nroutine check-ups. To this end, we propose a novel Personalized Federated Deep\nLearning (PFDL) approach for pain estimation from face images. PFDL performs\ncollaborative training of a deep model, implemented using a lightweight CNN\narchitecture, across different clients (i.e., subjects) without sharing their\nface images. Instead of sharing all parameters of the model, as in standard FL,\nPFDL retains the last layer locally (used to personalize the pain estimates).\nThis (i) adds another layer of data confidentiality, making it difficult for an\nadversary to infer pain levels of the target subject, while (ii) personalizing\nthe pain estimation to each subject through local parameter tuning. We show\nusing a publicly available dataset of face videos of pain (UNBC-McMaster\nShoulder Pain Database), that PFDL performs comparably or better than the\nstandard centralized and FL algorithms, while further enhancing data privacy.\nThis, has the potential to improve traditional pain monitoring by making it\nmore secure, computationally efficient, and scalable to a large number of\nindividuals (e.g., for in-home pain monitoring), providing timely and\nunobtrusive pain measurement.\n"}
+{"abstract": "  We study the emptiness and $\\lambda$-reachability problems for unary and\nbinary Probabilistic Finite Automata (PFA) and characterise the complexity of\nthese problems in terms of the degree of ambiguity of the automaton and the\nsize of its alphabet. Our main result is that emptiness and\n$\\lambda$-reachability are solvable in EXPTIME for polynomially ambiguous unary\nPFA and if, in addition, the transition matrix is over $\\{0, 1\\}$, we show they\nare in NP. In contrast to the Skolem-hardness of the $\\lambda$-reachability and\nemptiness problems for exponentially ambiguous unary PFA, we show that these\nproblems are NP-hard even for finitely ambiguous unary PFA. For binary\npolynomially ambiguous PFA with fixed and commuting transition matrices, we\nprove NP-hardness of the $\\lambda$-reachability (dimension $9$), nonstrict\nemptiness (dimension $37$) and strict emptiness (dimension $40$) problems.\n"}
+{"abstract": "  Federated learning (FL) has emerged with increasing popularity to collaborate\ndistributed medical institutions for training deep networks. However, despite\nexisting FL algorithms only allow the supervised training setting, most\nhospitals in realistic usually cannot afford the intricate data labeling due to\nabsence of budget or expertise. This paper studies a practical yet challenging\nFL problem, named \\textit{Federated Semi-supervised Learning} (FSSL), which\naims to learn a federated model by jointly utilizing the data from both labeled\nand unlabeled clients (i.e., hospitals). We present a novel approach for this\nproblem, which improves over traditional consistency regularization mechanism\nwith a new inter-client relation matching scheme. The proposed learning scheme\nexplicitly connects the learning across labeled and unlabeled clients by\naligning their extracted disease relationships, thereby mitigating the\ndeficiency of task knowledge at unlabeled clients and promoting discriminative\ninformation from unlabeled samples. We validate our method on two large-scale\nmedical image classification datasets. The effectiveness of our method has been\ndemonstrated with the clear improvements over state-of-the-arts as well as the\nthorough ablation analysis on both tasks\\footnote{Code will be made available\nat \\url{https://github.com/liuquande/FedIRM}}.\n"}
+{"abstract": "  The quasi-one-dimensional spin ladder compounds, BaFe$_2$S$_3$ and\nBaFe$_2$Se$_3$, are investigated by infrared spectroscopy and density\nfunctional theory (DFT) calculations. We observe strong anisotropic electronic\nproperties and an optical gap in the leg direction that is gradually filled\nabove the antiferromagnetic (afm) ordering temperature, turning the systems\ninto a metallic phase. Combining the optical data with the DFT calculations we\nassociate the optical gap feature with the $p$-$d$ transition that appears only\nin the afm ordered state. Hence, the insulating ground state along the leg\ndirection is attributed to Slater physics rather than Mott-type correlations.\n"}
+{"abstract": "  The on-going COVID-19 pandemic highlights the severe health risks posed by\ndeep submicron sized airborne viruses and particulates in the spread of\ninfectious diseases. There is an urgent need for the development of efficient,\ndurable and reusable filters for this size range. Here we report the\nrealization of efficient particulate filters using nanowire-based low-density\nmetal foams which combine extremely large surface areas with excellent\nmechanical properties. The metal foams exhibit outstanding filtration\nefficiencies (>96.6%) in the PM_{0.3} regime, with potentials for further\nimprovement. Their mechanical stability and light weight, chemical and\nradiation resistance, ease of cleaning and reuse, and recyclability further\nmake such metal foams promising filters for combating COVID-19 and other types\nof airborne particulates.\n"}
+{"abstract": "  Mendelian randomization (MR) is a statistical method exploiting genetic\nvariants as instrumental variables to estimate the causal effect of modifiable\nrisk factors on an outcome of interest. Despite wide uses of various popular\ntwo-sample MR methods based on genome-wide association study summary level\ndata, however, those methods could suffer from potential power loss or/and\nbiased inference when the chosen genetic variants are in linkage disequilibrium\n(LD), and also have relatively large direct effects on the outcome whose\ndistribution might be heavy-tailed which is commonly referred to as the\nidiosyncratic pleiotropy phenomenon. To resolve those two issues, we propose a\nnovel Robust Bayesian Mendelian Randomization (RBMR) model that uses the more\nrobust multivariate generalized t-distribution to model such direct effects in\na probabilistic model framework which can also incorporate the LD structure\nexplicitly. The generalized t-distribution can be represented as a Gaussian\nscaled mixture so that our model parameters can be estimated by the EM-type\nalgorithms. We compute the standard errors by calibrating the evidence lower\nbound using the likelihood ratio test. Through extensive simulation studies, we\nshow that our RBMR has robust performance compared to other competing methods.\nWe also apply our RBMR method to two benchmark data sets and find that RBMR has\nsmaller bias and standard errors. Using our proposed RBMR method, we find that\ncoronary artery disease is associated with increased risk of critically ill\ncoronavirus disease 2019 (COVID-19). We also develop a user-friendly R package\nRBMR for public use.\n"}
+{"abstract": "  On the space of $\\pm 1$ spin configurations on the 3d-square lattice, we\nconsider the \\emph{shaken dynamics}, a parallel Markovian dynamics that can be\ninterpreted in terms of Probabilistic Cellular Automata, whose transition\nprobabilities are defined in terms of a pair ferromagnetic Ising-type\nHamiltonian with nearest neighbor interaction $J$, depending on an additional\nparameter $q$, measuring the tendency of the system to remain locally in the\nsame state. We compute the stationary measure of the shaken dynamics and we\ninvestigate its relation with the Gibbs measure for the Ising model. It turns\nout that the two parameters $J$ and $q$ tune the geometry of the underlying\nlattice. By a judicious use of perturbative methods we show rigorously that our\nmodel exhibits a line of critical points in $J-q$ plane that separates the\nordered phase from the disordered one, and we perform numerical simulation to\ndetermine the phase transition curve. Our method allows us to find in a unified\nway the critical values of $J$ for Ising model with first neighbors\ninteraction, defined on a whole class of lattice, intermediate between the\ntwo-dimensional hexagonal and the three-dimensional cubic one, such as, for\nexample, the tetrahedral lattice. Finally we estimate the critical exponents of\nthe magnetic susceptibility and show that our model captures a phase transition\nin the geometry of the system at $q = 0$.\n"}
+{"abstract": "  Current supervised sketch-based image retrieval (SBIR) methods achieve\nexcellent performance. However, the cost of data collection and labeling\nimposes an intractable barrier to practical deployment of real applications. In\nthis paper, we present the first attempt at unsupervised SBIR to remove the\nlabeling cost (category annotations and sketch-photo pairings) that is\nconventionally needed for training. Existing single-domain unsupervised\nrepresentation learning methods perform poorly in this application, due to the\nunique cross-domain (sketch and photo) nature of the problem. We therefore\nintroduce a novel framework that simultaneously performs unsupervised\nrepresentation learning and sketch-photo domain alignment. Technically this is\nunderpinned by exploiting joint distribution optimal transport (JDOT) to align\ndata from different domains during representation learning, which we extend\nwith trainable cluster prototypes and feature memory banks to further improve\nscalability and efficacy. Extensive experiments show that our framework\nachieves excellent performance in the new unsupervised setting, and performs\ncomparably or better than state-of-the-art in the zero-shot setting.\n"}
+{"abstract": "  We use first principles molecular dynamics simulations coupled to the\nthermodynamic integration method to study the hcp-bcc transition and melting of\nberyllium up to a pressure of 1600~GPa. We derive the melting line by equating\nsolid and liquid Gibbs free energies, and represent it by a Simon Glatzel fit\n$T_m= 1564~\\text{K} (1 + P/(15.6032 ~\\text{GPa}))^{0.383}$, which is in good\nagreement with previous two-phase simulations below 6000~K. We also derive the\nhcp-bcc solid-solid phase boundary and show the quasiharmonic approximation\nunderestimates the stability of the hcp structure, predicting lower transition\npressures between hcp and bcc phases. However, our results are consistent with\nthe stability regime predicted by the phonon quasiparticle method. We also\npredict that hcp-bcc-liquid triple point is located at 164.7~GPa and 4314~K. In\naddition, we compute the shock Hugoniot curve, and show that it is in good\nagreement with experiments, intersecting our derived melting curve at\n$\\sim$235~GPa at 4900~K. Finally, we show that an isentropic compression path\nthat intersects the melting curve at both low and high temperature in the\nliquid regime, can reappear in the solid after a gap as large as 7000~K.\nTherefore, we predict that a large section of the melting curve could be\nsampled, in principle, by a ramp compression experiment, where solid and liquid\nBe would coexist as the sample is compressed.\n"}
+{"abstract": "  After a few microseconds of the creation of our Universe through the Big\nBang, the primordial matter was believed to be a soup of the fundamental\nconstituents of matter -- quarks and gluons. This is expected to be created in\nthe laboratory by colliding heavy nuclei at ultra-relativistic speeds. A plasma\nof quarks and gluons, called Quark-Gluon Plasma (QGP) can be created at the\nenergy and luminosity frontiers in the Relativistic Heavy Ion Collider (RHIC),\nat Brookhaven National Laboratory, New York, USA, and the Large Hadron Collider\n(LHC) at CERN, Geneva, Switzerland. Heavy quarks, namely the charm and bottom\nquarks, are considered as novel probes to characterize QGP, and hence the\nproduced Quantum Chromodynamics (QCD) matter. Heavy quark transport\ncoefficients play a significant role in understanding the properties of QGP.\nExperimental measurements of nuclear suppression factor and elliptic flow can\nconstrain the heavy quark transport coefficients, which are key ingredients for\nphenomenological studies, and they help to disentangle different energy loss\nmechanisms. We give a general perspective of the heavy quark drag and diffusion\ncoefficients in QGP and discuss their potentials as probes to disentangle\ndifferent hadronization mechanisms, as well as to probe the initial\nelectromagnetic fields produced in non-central heavy-ion collisions.\nExperimental perspectives on future measurements are discussed with special\nemphasis on heavy-flavors as next-generation probes in view of new\ntechnological developments.\n"}
+{"abstract": "  We present simulations which predict significantly higher laser to X-ray\nefficiencies than those previously found in high intensity (1e20-1e22 W/cm2)\nlaser-solid simulations. The bremsstrahlung emission is shown to last for\n10-100 ps, which is difficult to model with conventional particle-in-cell (PIC)\ncodes. The importance of collective effects is also demonstrated, showing the\nlimitations of Monte Carlo modelling in these systems. A new, open-source\nhybrid-PIC code with bremsstrahlung routines has been developed to model this\nX-ray production in 3D. Special boundary conditions are used to emulate complex\nelectron refluxing behaviour, which has been characterised in 2D full-PIC\nsimulations. The peak X-ray efficiency was recorded in thick gold targets, with\n7.4% conversion of laser energy into X-rays of energy 1 MeV or higher. The\ntarget size is shown to play a role in the conversion efficiency and angular\ndistribution of emitted X-rays, and a simple analytic model is presented for\nestimating these efficiencies.\n"}
+{"abstract": "  Studying the diffusion and kinetic equilibration of heavy quarks within a hot\nQCD medium profits from the knowledge of a coloured Lorentz force that acts on\nthem. Starting from the spatial components of the vector current, and carrying\nout two matching computations, one for the heavy quark mass scale ($M$) and\nanother for thermal scales ($\\sqrt{MT}$, $T$), we determine 1-loop matching\ncoefficients for the electric and magnetic parts of a Lorentz force. The\nmagnetic part has a non-zero anomalous dimension, which agrees with that\nextracted from two other considerations, one thermal and the other in vacuum.\nThe matching coefficient could enable a lattice study of a colour-magnetic\n2-point correlator.\n"}
+{"abstract": "  In this paper, we provide a general framework for the construction of the\nEinstein frame within non-linear extensions of the teleparallel equivalents of\nGeneral Relativity. These include the metric teleparallel and the symmetric\nteleparallel, but also the general teleparallel theories. We write the actions\nin a form where we separate the Einstein--Hilbert term, the conformal mode due\nto the non-linear nature of the theories (which is analogous to the extra\ndegree of freedom in $f(R)$ theories), and the sector that manifestly shows the\ndynamics arising from the breaking of local symmetries. This frame is then used\nto study the theories around the Minkowski background, and we show how all the\nnon-linear extensions share the same quadratic action around Minkowski. As a\nmatter of fact, we find that the gauge symmetries that are lost by going to the\nnon-linear generalisations of the teleparallel General Relativity equivalents\narise as accidental symmetries in the linear theory around Minkowski.\nRemarkably, we also find that the conformal mode can be absorbed into a Weyl\nrescaling of the metric at this order and, consequently, it disappears from the\nlinear spectrum so only the usual massless spin 2 perturbation propagates.\nThese findings unify in a common framework the known fact that no additional\nmodes propagate on Minkowski backgrounds, and we can trace it back to the\nexistence of accidental gauge symmetries of such a background.\n"}
+{"abstract": "  For a caching system with multiple users, we aim to characterize the\nmemory-rate tradeoff for caching with uncoded cache placement, under nonuniform\nfile popularity. Focusing on the modified coded caching scheme (MCCS) recently\nproposed by Yu, etal., we formulate the cache placement optimization problem\nfor the MCCS to minimize the average delivery rate under nonuniform file\npopularity, restricting to a class of popularity-first placements. We then\npresent two information-theoretic lower bounds on the average rate for caching\nwith uncoded placement, one for general cache placements and the other\nrestricted to the popularity-first placements. By comparing the average rate of\nthe optimized MCCS with the lower bounds, we prove that the optimized MCCS\nattains the general lower bound for the two-user case, providing the exact\nmemory-rate tradeoff. Furthermore, it attains the popularity-first-based lower\nbound for the case of general K users with distinct file requests. In these two\ncases, our results also reveal that the popularity-first placement is optimal\nfor the MCCS, and zero-padding used in coded delivery incurs no loss of\noptimality. For the case of K users with redundant file requests, our analysis\nshows that there may exist a gap between the optimized MCCS and the lower\nbounds due to zero-padding. We next fully characterize the optimal\npopularity-first cache placement for the MCCS, which is shown to possess a\nsimple file-grouping structure and can be computed via an efficient algorithm\nusing closed-form expressions. Finally, we extend our study to accommodate both\nnonuniform file popularity and sizes, where we show that the optimized MCCS\nattains the lower bound for the two-user case, providing the exact memory-rate\ntradeoff. Numerical results show that, for general settings, the gap between\nthe optimized MCCS and the lower bound only exists in limited cases and is very\nsmall.\n"}
+{"abstract": "  Novelty detection using deep generative models such as autoencoder,\ngenerative adversarial networks mostly takes image reconstruction error as\nnovelty score function. However, image data, high dimensional as it is,\ncontains a lot of different features other than class information which makes\nmodels hard to detect novelty data. The problem gets harder in multi-modal\nnormality case. To address this challenge, we propose a new way of measuring\nnovelty score in multi-modal normality cases using orthogonalized latent space.\nSpecifically, we employ orthogonal low-rank embedding in the latent space to\ndisentangle the features in the latent space using mutual class information.\nWith the orthogonalized latent space, novelty score is defined by the change of\neach latent vector. Proposed algorithm was compared to state-of-the-art novelty\ndetection algorithms using GAN such as RaPP and OCGAN, and experimental results\nshow that ours outperforms those algorithms.\n"}
+{"abstract": "  The concept of k-spectrum for genomes is here investigated as a basic tool to\nanalyze genomes. Related spectral notions based on k-mers are introduced with\nsome related mathematical properties which are relevant for informational\nanalysis of genomes. Procedures to generate spectral segmentations of genomes\nare provided and are tested (under several values of length k for k-mers) on\ncases of real genomes, such as some human chromosomes and Saccharomyces\ncerevisiae.\n"}
+{"abstract": "  In this paper we consider the strategic asset allocation of an insurance\ncompany. This task can be seen as a special case of portfolio optimization. In\nthe 1950s, Markowitz proposed to formulate portfolio optimization as a\nbicriteria optimization problem considering risk and return as objectives.\nHowever, recent developments in the field of insurance require four and more\nobjectives to be considered, among them the so-called solvency ratio that stems\nfrom the Solvency II directive of the European Union issued in 2009. Moreover,\nthe distance to the current portfolio plays an important role. While literature\non portfolio optimization with three objectives is already scarce, applications\nwith four and more objectives have not yet been solved so far by\nmulti-objective approaches based on scalarizations. However, recent algorithmic\nimprovements in the field of exact multi-objective methods allow the\nincorporation of many objectives and the generation of well-spread\nrepresentations within few iterations. We describe the implementation of such\nan algorithm for a strategic asset allocation with four objective functions and\ndemonstrate its usefulness for the practitioner. Our approach is in operative\nuse in a German insurance company. Our partners report a significant\nimprovement in their decision making process since, due to the proper\nintegration of the new objectives, the software proposes portfolios of much\nbetter quality than before within short running time.\n"}
+{"abstract": "  Programming languages with algebraic effects often track the computations'\neffects using type-and-effect systems. In this paper, we propose to view an\nalgebraic effect theory of a computation as a variable context; consequently,\nwe propose to track algebraic effects of a computation with \\emph{contextual\nmodal types}. We develop ECMTT, a novel calculus which tracks algebraic effects\nby a contextualized variant of the modal $\\Box$ (necessity) operator, that it\ninherits from Contextual Modal Type Theory (CMTT).\n  Whereas type-and-effect systems add effect annotations on top of a prior\nprogramming language, the effect annotations in ECMTT are inherent to the\nlanguage, as they are managed by programming constructs corresponding to the\nlogical introduction and elimination forms for the $\\Box$ modality. Thus, the\ntype-and-effect system of ECMTT is actually just a type system.\n  Our design obtains the properties of local soundness and completeness, and\ndetermines the operational semantics solely by $\\beta$-reduction, as customary\nin other logic-based calculi. In this view, effect handlers arise naturally as\na witness that one context (i.e., algebraic theory) can be reached from\nanother, generalizing explicit substitutions from CMTT.\n  To the best of our knowledge, ECMTT is the first system to relate algebraic\neffects to modal types. We also see it as a step towards providing a\ncorrespondence in the style of Curry and Howard that may transfer a number of\nresults from the fields of modal logic and modal type theory to that of\nalgebraic effects.\n"}
+{"abstract": "  We extend a classical test of subsphericity, based on the first two moments\nof the eigenvalues of the sample covariance matrix, to the high-dimensional\nregime where the signal eigenvalues of the covariance matrix diverge to\ninfinity and either $p/n \\rightarrow 0$ or $p/n \\rightarrow \\infty$. In the\nlatter case we further require that the divergence of the eigenvalues is\nsuitably fast in a specific sense. Our work can be seen to complement that of\nSchott (2006) who established equivalent results in the case $p/n \\rightarrow\n\\gamma \\in (0, \\infty)$. As our second main contribution, we use the test to\nderive a consistent estimator for the latent dimension of the model.\nSimulations and a real data example are used to demonstrate the results,\nproviding also evidence that the test might be further extendable to a wider\nasymptotic regime.\n"}
+{"abstract": "  Recent works on ride-sharing order dispatching have highlighted the\nimportance of taking into account both the spatial and temporal dynamics in the\ndispatching process for improving the transportation system efficiency. At the\nsame time, deep reinforcement learning has advanced to the point where it\nachieves superhuman performance in a number of fields. In this work, we propose\na deep reinforcement learning based solution for order dispatching and we\nconduct large scale online A/B tests on DiDi's ride-dispatching platform to\nshow that the proposed method achieves significant improvement on both total\ndriver income and user experience related metrics. In particular, we model the\nride dispatching problem as a Semi Markov Decision Process to account for the\ntemporal aspect of the dispatching actions. To improve the stability of the\nvalue iteration with nonlinear function approximators like neural networks, we\npropose Cerebellar Value Networks (CVNet) with a novel distributed state\nrepresentation layer. We further derive a regularized policy evaluation scheme\nfor CVNet that penalizes large Lipschitz constant of the value network for\nadditional robustness against adversarial perturbation and noises. Finally, we\nadapt various transfer learning methods to CVNet for increased learning\nadaptability and efficiency across multiple cities. We conduct extensive\noffline simulations based on real dispatching data as well as online AB tests\nthrough the DiDi's platform. Results show that CVNet consistently outperforms\nother recently proposed dispatching methods. We finally show that the\nperformance can be further improved through the efficient use of transfer\nlearning.\n"}
+{"abstract": "  Let $G$ be a graph on $n$ nodes. In the stochastic population protocol model,\na collection of $n$ indistinguishable, resource-limited nodes collectively\nsolve tasks via pairwise interactions. In each interaction, two randomly chosen\nneighbors first read each other's states, and then update their local states. A\nrich line of research has established tight upper and lower bounds on the\ncomplexity of fundamental tasks, such as majority and leader election, in this\nmodel, when $G$ is a clique. Specifically, in the clique, these tasks can be\nsolved fast, i.e., in $n \\operatorname{polylog} n$ pairwise interactions, with\nhigh probability, using at most $\\operatorname{polylog} n$ states per node.\n  In this work, we consider the more general setting where $G$ is an arbitrary\ngraph, and present a technique for simulating protocols designed for\nfully-connected networks in any connected regular graph. Our main result is a\nsimulation that is efficient on many interesting graph families: roughly, the\nsimulation overhead is polylogarithmic in the number of nodes, and quadratic in\nthe conductance of the graph. As a sample application, we show that, in any\nregular graph with conductance $\\phi$, both leader election and exact majority\ncan be solved in $\\phi^{-2} \\cdot n \\operatorname{polylog} n$ pairwise\ninteractions, with high probability, using at most $\\phi^{-2} \\cdot\n\\operatorname{polylog} n$ states per node. This shows that there are fast and\nspace-efficient population protocols for leader election and exact majority on\ngraphs with good expansion properties. We believe our results will prove\ngenerally useful, as they allow efficient technology transfer between the\nwell-mixed (clique) case, and the under-explored spatial setting.\n"}
+{"abstract": "  We present a technique for a complete 3D reconstruction of small objects\nmoving in front of a textured background. It is a particular variation of\nmultibody structure from motion, which specializes to two objects only. The\nscene is captured in several static configurations between which the relative\npose of the two objects may change. We reconstruct every static configuration\nindividually and segment the points locally by finding multiple poses of\ncameras that capture the scene's other configurations. Then, the local\nsegmentation results are combined, and the reconstructions are merged into the\nresulting model of the scene. In experiments with real artifacts, we show that\nour approach has practical advantages when reconstructing 3D objects from all\nsides. In this setting, our method outperforms the state-of-the-art. We\nintegrate our method into the state of the art 3D reconstruction pipeline\nCOLMAP.\n"}
+{"abstract": "  We propose a probe for the analysis of deep learning architectures that is\nbased on machine learning and approximation theoretical principles. Given a\ndeep learning architecture and a training set, during or after training, the\nSparsity Probe allows to analyze the performance of intermediate layers by\nquantifying the geometrical features of representations of the training set. We\nshow how the Sparsity Probe enables measuring the contribution of adding depth\nto a given architecture, to detect under-performing layers, etc., all this\nwithout any auxiliary test data set.\n"}
+{"abstract": "  We propose a stochastic SIR model, specified as a system of stochastic\ndifferential equations, to analyse the data of the Italian COVID-19 epidemic,\ntaking also into account the under-detection of infected and recovered\nindividuals in the population. We find that a correct assessment of the amount\nof under-detection is important to obtain reliable estimates of the critical\nmodel parameters. Moreover, a single SIR model over the whole epidemic period\nis unable to correctly describe the behaviour of the pandemic. Then, the\nadaptation of the model in every time-interval between relevant government\ndecrees that implement contagion mitigation measures, provides short-term\npredictions and a continuously updated assessment of the basic reproduction\nnumber.\n"}
+{"abstract": "  The article is devoted to questions concerning the problems of compactness of\nsolutions of the Dirichlet problem for the Beltrami equation in some simply\nconnected domain. In terms of prime ends, we have proved results of a detailed\nform for the case when the maximal dilations of these solutions satisfy certain\nintegral constraints. In addition, in this article we have proved theorems on\nthe local and global behavior of plane and spatial mappings with direct and\ninverse modulus conditions.\n"}
+{"abstract": "  Process digitization and integration is an increasing need for enterprises,\nwhile cyber-attacks denote a growing threat. Using the Business Process\nManagement Notation (BPMN) is common to handle the digital and integration\nfocus within and across organizations. In other parts of the same companies,\nthreat modeling and attack graphs are used for analyzing the security posture\nand resilience.\n  In this paper, we propose a novel approach to use attack graph simulations on\nprocesses represented in BPMN. Our contributions are the identification of\nBPMN's attack surface, a mapping of BPMN elements to concepts in a Meta Attack\nLanguage (MAL)-based Domain-Specific Language (DSL), called coreLang, and a\nprototype to demonstrate our approach in a case study using a real-world\ninvoice integration process. The study shows that non-invasively enriching BPMN\ninstances with cybersecurity analysis through attack graphs is possible without\nmuch human expert input. The resulting insights into potential vulnerabilities\ncould be beneficial for the process modelers.\n"}
+{"abstract": "  We identify infinite classes of potentials for which the Coleman instantons\ndo not exist. For these potentials, the decay of a false vacuum must be\ndescribed by the new instantons introduced in [7,8].\n"}
+{"abstract": "  Systematic differences in the the proton's charge radius, as determined by\nordinary atoms and muonic atoms, have caused a resurgence of interest in\nelastic lepton scattering measurements. The proton's charge radius, defined as\nthe slope of the charge form factor at Q$^2$=0, does not depend on the probe.\nAny difference in the apparent size of the proton, when determined from\nordinary versus muonic hydrogen, could point to new physics or need for the\nhigher order corrections. While recent measurements seem to now be in\nagreement, there is to date no high precision elastic scattering data with both\nelectrons and positrons. A high precision proton radius measurement could be\nperformed in Hall B at Jefferson Lab with a positron beam and the calorimeter\nbased setup of the PRad experiment. This measurement could also be extended to\ndeuterons where a similar discrepancy has been observed between the muonic and\nelectronic determination of deuteron charge radius. A new, high precision\nmeasurement with positrons, when viewed alongside electron scattering\nmeasurements and the forthcoming MUSE muon scattering measurement, could help\nprovide new insights into the origins of the proton radius puzzle, and also\nprovide new experimental constraints on radiative correction calculations.\n"}
+{"abstract": "  Lithium iron phosphate (LixFePO4), a cathode material used in rechargeable\nLi-ion batteries, phase separates upon de/lithiation under equilibrium. The\ninterfacial structure and chemistry within these cathode materials affects\nLi-ion transport, and therefore battery performance. Correlative imaging of\nLixFePO4 was performed using four-dimensional scanning transmission electron\nmicroscopy (4D-STEM), scanning transmission X-ray microscopy (STXM), and X-ray\nptychography in order to analyze the local structure and chemistry of the same\nparticle set. Over 50,000 diffraction patterns from 10 particles provided\nmeasurements of both structure and chemistry at a nanoscale spatial resolution\n(16.6-49.5 nm) over wide (several micron) fields-of-view with statistical\nrobustness.LixFePO4 particles at varying stages of delithiation were measured\nto examine the evolution of structure and chemistry as a function of\ndelithiation. In lithiated and delithiated particles, local variations were\nobserved in the degree of lithiation even while local lattice structures\nremained comparatively constant, and calculation of linear coefficients of\nchemical expansion suggest pinning of the lattice structures in these\npopulations. Partially delithiated particles displayed broadly core-shell-like\nstructures, however, with highly variable behavior both locally and per\nindividual particle that exhibited distinctive intermediate regions at the\ninterface between phases, and pockets within the lithiated core that correspond\nto FePO4 in structure and chemistry.The results provide insight into the\nLixFePO4 system, subtleties in the scope and applicability of Vegards law\n(linear lattice parameter-composition behavior) under local versus global\nmeasurements, and demonstrate a powerful new combination of experimental and\nanalytical modalities for bridging the crucial gap between local and\nstatistical characterization.\n"}
+{"abstract": "  Large Transformers pretrained over clinical notes from Electronic Health\nRecords (EHR) have afforded substantial gains in performance on predictive\nclinical tasks. The cost of training such models (and the necessity of data\naccess to do so) coupled with their utility motivates parameter sharing, i.e.,\nthe release of pretrained models such as ClinicalBERT. While most efforts have\nused deidentified EHR, many researchers have access to large sets of sensitive,\nnon-deidentified EHR with which they might train a BERT model (or similar).\nWould it be safe to release the weights of such a model if they did? In this\nwork, we design a battery of approaches intended to recover Personal Health\nInformation (PHI) from a trained BERT. Specifically, we attempt to recover\npatient names and conditions with which they are associated. We find that\nsimple probing methods are not able to meaningfully extract sensitive\ninformation from BERT trained over the MIMIC-III corpus of EHR. However, more\nsophisticated \"attacks\" may succeed in doing so: To facilitate such research,\nwe make our experimental setup and baseline probing models available at\nhttps://github.com/elehman16/exposing_patient_data_release\n"}
+{"abstract": "  In ultrasound tomography, the speed of sound inside an object is estimated\nbased on acoustic measurements carried out by sensors surrounding the object.\nAn accurate forward model is a prominent factor for high-quality image\nreconstruction, but it can make computations far too time-consuming in many\napplications. Using approximate forward models, it is possible to speed up the\ncomputations, but the quality of the reconstruction may have to be compromised.\nIn this paper, a neural network -based approach is proposed, that can\ncompensate for modeling errors caused by the approximate forward models. The\napproach is tested with various different imaging scenarios in a simulated\ntwo-dimensional domain. The results show that with fairly small training\ndatasets, the proposed approach can be utilized to approximate the modelling\nerrors, and to significantly improve the image reconstruction quality in\nultrasound tomography, compared to commonly used inversion algorithms.\n"}
+{"abstract": "  This paper deals with the topological entropy for hom Markov shifts\n$\\mathcal{T}_M$ on $d$-tree. If $M$ is a reducible adjacency matrix with $q$\nirreducible components $M_1, \\cdots, M_q$, we show that\n$h(\\mathcal{T}_{M})=\\max_{1\\leq i\\leq q}h(\\mathcal{T}_{M_{i}})$ fails\ngenerally, and present a case study with full characterization in terms of the\nequality. Though that it is likely the sets $\\{h(\\mathcal{T}_{M}):M\\text{ is\nbinary and irreducible}\\}$ and $\\{h(\\mathcal{T}_{X}):X\\text{ is a one-sided\nshift}\\}$ are not coincident, we show the two sets share the common closure.\nDespite the fact that such closure is proved to contain the interval $[d \\log\n2, \\infty)$, numerical experiments suggest its complement contain open\nintervals.\n"}
+{"abstract": "  In this note, we study the holographic CFT in the de Sitter static patch at\nfinite temperature $T$ and chemical potential. We find that butterfly velocity\n$v_B$ in such field theory degenerates for all values of the Hubble parameter\n$H$ and $T$. We interpret this as a chaos disruption caused by the interplay\nbetween the expansion of chaotic correlations constrained by $v_B$ and effects\ncaused by de Sitter curvature. The chemical potential restores healthy\nbutterfly velocity for some range of temperatures. Also, we provide some\nanalogy of this chaos suppression with the Schwinger effect in de Sitter and\nblack hole formation from shock wave collision.\n"}
+{"abstract": "  A rotation curve inequality that holds for spherically symmetric mass\ndistributions is derived, and tested against the SPARC galaxy rotation curves\ndataset. We identify several Galaxies, eg NGC7793 and UGC05253, which are\ncandidates for hosting non-spherical dark matter structures that could be\ndetected by more precise measurements.\n"}
+{"abstract": "  Wide-area synchrophasor ambient measurements provide a valuable data source\nfor real-time oscillation mode monitoring and analysis. This paper introduces a\nnovel method for identifying inter-area oscillation modes using wide-area\nambient measurements. Based on multivariate empirical mode decomposition\n(MEMD), which can analyze multi-channel non-stationary and nonlinear signals,\nthe proposed method is capable of detecting the common oscillation mode that\nexists in multiple synchrophasor measurements at low amplitudes. Test results\nbased on two real-world datasets validate the effectiveness of the proposed\nmethod.\n"}
+{"abstract": "  We demonstrate an individual single-walled carbon nanotube light emitter\nintegrated onto a microcavity and a waveguide operating in the telecom\nwavelength regime. Light emission from the carbon nanotube is enhanced at the\ncavity resonance and is efficiently extracted from the waveguide facet. We have\ntransferred carbon nanotubes to a nanobeam cavity with a dry process, ensuring\nthat an individual carbon nanotube is used. The guided light emission from a\nchirality-identified single carbon nanotube has a narrow linewidth of less than\n1.3 nm and an off-resonance rejection of $\\sim$17 dB. The waveguide-coupled\ndevice configuration is compatible with fully integrated on-chip designs and is\npromising for carbon-nanotube-based photonics.\n"}
+{"abstract": "  This paper suggests the use of multiple distributed intelligent reflecting\nsurfaces (IRSs) towards a smarter control of the propagation environment.\nNotably, we also take into account the inevitable correlated Rayleigh fading in\nIRS-assisted systems. In particular, in a single-input and single-output (SISO)\nsystem, we consider and compare two insightful scenarios, namely, a finite\nnumber of large IRSs and a large number of finite size IRSs to show which\nimplementation method is more advantageous. In this direction, we derive the\ncoverage probability in closed-form for both cases contingent on statistical\nchannel state information (CSI) by using the deterministic equivalent (DE)\nanalysis. Next, we obtain the optimal coverage probability. Among others,\nnumerical results reveal that the addition of more surfaces outperforms the\ndesign scheme of adding more elements per surface. Moreover, in the case of\nuncorrelated Rayleigh fading, statistical CSI-based IRS systems do not allow\nthe optimization of the coverage probability.\n"}
+{"abstract": "  This paper presents a sparse solver based on the alternating direction method\nof multipliers algorithm for a linear model predictive control (MPC)\nformulation in which the terminal state is constrained to a given ellipsoid.\nThe motivation behind this solver is to substitute the typical polyhedral\ninvariant set used as a terminal constraint in many nominal and robust linear\nMPC formulations with an invariant set in the form of an ellipsoid, which is\n(typically) much easier to compute and results in an optimization problem with\nsignificantly fewer constraints, even for average-sized systems. However, this\noptimization problem is no longer the quadratic programming problem found in\nmost linear MPC approaches, thus meriting the development of a tailored solver.\nThe proposed solver is suitable for its use in embedded systems, since it is\nsparse, has a small memory footprint and requires no external libraries. We\nshow the results of its implementation in an embedded system to control a\nsimulated multivariable plant, comparing it against other alternatives.\n"}
+{"abstract": "  We show, using the same Lagrangian for the $K_1(1270) \\to \\pi K^*_0(1430)$\nand $K^*_0(1430) \\to K_1(1270) \\pi$ decays, that the present PDG data on the\npartial decay width of $K_1(1270) \\to \\pi K^*_0(1430)$ implies a width for\n$K^*_0(1430) \\to K_1(1270) \\pi$ decay which is about ten times larger than the\ntotal $K^*_0(1430)$ width. A discussion on this inconsistency is done,\nstressing its relationship to the existence of two $K_1(1270)$ states obtained\nwith the chiral unitary theory, which are not considered in the experimental\nanalyses of $K\\pi\\pi$ data.\n"}
+{"abstract": "  This article considers average marginal effects (AME) in a panel data fixed\neffects logit model. Relating the identified set of the AME to an extremal\nmoment problem, we first show how to obtain sharp bounds on the AME\nstraightforwardly, without any optimization. Then, we consider two strategies\nto build confidence intervals on the AME. In the first, we estimate the sharp\nbounds with a semiparametric two-step estimator. The second, very simple\nstrategy estimates instead a quantity known to be at a bounded distance from\nthe AME. It does not require any nonparametric estimation but may result in\nlarger confidence intervals. Monte Carlo simulations suggest that both\napproaches work well in practice, the second being often very competitive.\nFinally, we show that our results also apply to average treatment effects, the\naverage structural functions and ordered, fixed effects logit models.\n"}
+{"abstract": "  Ram Pressure Stripping can remove gas from satellite galaxies in clusters via\na direct interaction between the intracluster medium (ICM) and the interstellar\nmedium. This interaction is generally thought of as a contact force per area,\nhowever we point out that these gases must interact in a hydrodynamic fashion,\nand argue that this will lead to mixing of the galactic gas with the ICM wind.\nWe develop an analytic framework for how mixing is related to the acceleration\nof stripped gas from a satellite galaxy. We then test this model using three\n\"wind-tunnel\" simulations of Milky Way-like galaxies interacting with a moving\nICM, and find excellent agreement with predictions using the analytic\nframework. Focusing on the dense clumps in the stripped tails, we find that\nthey are nearly uniformly mixed with the ICM, indicating that all gas in the\ntail mixes with the surroundings, and dense clumps are not separate entities to\nbe modeled differently than diffuse gas. We find that while mixing drives\nacceleration of stripped gas, the density and velocity of the surrounding wind\nwill determine whether the mixing results in the heating of stripped gas into\nthe ICM, or the cooling of the ICM into dense clouds.\n"}
+{"abstract": "  Domain shift is a major challenge for object detectors to generalize well to\nreal world applications. Emerging techniques of domain adaptation for two-stage\ndetectors help to tackle this problem. However, two-stage detectors are not the\nfirst choice for industrial applications due to its long time consumption. In\nthis paper, a novel Domain Adaptive YOLO (DA-YOLO) is proposed to improve\ncross-domain performance for one-stage detectors. Image level features\nalignment is used to strictly match for local features like texture, and\nloosely match for global features like illumination. Multi-scale instance level\nfeatures alignment is presented to reduce instance domain shift effectively ,\nsuch as variations in object appearance and viewpoint. A consensus\nregularization to these domain classifiers is employed to help the network\ngenerate domain-invariant detections. We evaluate our proposed method on\npopular datasets like Cityscapes, KITTI, SIM10K and etc.. The results\ndemonstrate significant improvement when tested under different cross-domain\nscenarios.\n"}
+{"abstract": "  Most of the recent work on terminology integration in machine translation has\nassumed that terminology translations are given already inflected in forms that\nare suitable for the target language sentence. In day-to-day work of\nprofessional translators, however, it is seldom the case as translators work\nwith bilingual glossaries where terms are given in their dictionary forms;\nfinding the right target language form is part of the translation process. We\nargue that the requirement for apriori specified target language forms is\nunrealistic and impedes the practical applicability of previous work. In this\nwork, we propose to train machine translation systems using a source-side data\naugmentation method that annotates randomly selected source language words with\ntheir target language lemmas. We show that systems trained on such augmented\ndata are readily usable for terminology integration in real-life translation\nscenarios. Our experiments on terminology translation into the morphologically\ncomplex Baltic and Uralic languages show an improvement of up to 7 BLEU points\nover baseline systems with no means for terminology integration and an average\nimprovement of 4 BLEU points over the previous work. Results of the human\nevaluation indicate a 47.7% absolute improvement over the previous work in term\ntranslation accuracy when translating into Latvian.\n"}
+{"abstract": "  The adaptive stochastic gradient descent (SGD) with momentum has been widely\nadopted in deep learning as well as convex optimization. In practice, the last\niterate is commonly used as the final solution to make decisions. However, the\navailable regret analysis and the setting of constant momentum parameters only\nguarantee the optimal convergence of the averaged solution. In this paper, we\nfill this theory-practice gap by investigating the convergence of the last\niterate (referred to as individual convergence), which is a more difficult task\nthan convergence analysis of the averaged solution. Specifically, in the\nconstrained convex cases, we prove that the adaptive Polyak's Heavy-ball (HB)\nmethod, in which only the step size is updated using the exponential moving\naverage strategy, attains an optimal individual convergence rate of\n$O(\\frac{1}{\\sqrt{t}})$, as opposed to the optimality of $O(\\frac{\\log t}{\\sqrt\n{t}})$ of SGD, where $t$ is the number of iterations. Our new analysis not only\nshows how the HB momentum and its time-varying weight help us to achieve the\nacceleration in convex optimization but also gives valuable hints how the\nmomentum parameters should be scheduled in deep learning. Empirical results on\noptimizing convex functions and training deep networks validate the correctness\nof our convergence analysis and demonstrate the improved performance of the\nadaptive HB methods.\n"}
+{"abstract": "  NASA's Transiting Exoplanet Survey Satellite (TESS) mission is expected to\ndiscover hundreds of planets via single transits first identified in their\nlight curves. Determining the orbital period of these single transit candidates\ntypically requires a significant amount of follow-up work to observe a second\ntransit or measure a radial velocity orbit. In Yao et al. (2019), we developed\nsimulations that demonstrated the ability to use archival photometric data in\ncombination with TESS to \"precover\" the orbital period for these candidates\nwith a precision of several minutes, assuming circular orbits. In this work, we\nincorporate updated models for TESS single transits, allowing for eccentric\norbits, along with an updated methodology to improve the reliability of the\nresults. Additionally, we explore how radial velocity (RV) observations can be\nused to follow up single transit events, using strategies distinct from those\nemployed when the orbital period is known. We find that the use of an estimated\nperiod based on a circular orbit to schedule reconnaissance RV observations can\nefficiently distinguish eclipsing binaries from planets. For candidates that\npass reconnaissance RV observations, we simulate RV monitoring campaigns that\nenable one to obtain an approximate orbital solution. We find this method can\nregularly determine the orbital periods for planets more massive than 0.5 M_J\nwith orbital periods as long as 100 days.\n"}
+{"abstract": "  Current speech agent interactions are typically user-initiated, limiting the\ninteractions they can deliver. Future functionality will require agents to be\nproactive, sometimes interrupting users. Little is known about how these spoken\ninterruptions should be designed, especially in urgent interruption contexts.\nWe look to inform design of proactive agent interruptions through investigating\nhow people interrupt others engaged in complex tasks. We therefore developed a\nnew technique to elicit human spoken interruptions of people engaged in other\ntasks. We found that people interrupted sooner when interruptions were urgent.\nSome participants used access rituals to forewarn interruptions, but most\nrarely used them. People balanced speed and accuracy in timing interruptions,\noften using cues from the task they interrupted. People also varied phrasing\nand delivery of interruptions to reflect urgency. We discuss how our findings\ncan inform speech agent design and how our paradigm can help gain insight into\nhuman interruptions in new contexts.\n"}
+{"abstract": "  A rank-adaptive integrator for the dynamical low-rank approximation of matrix\nand tensor differential equations is presented. The fixed-rank integrator\nrecently proposed by two of the authors is extended to allow for an adaptive\nchoice of the rank, using subspaces that are generated by the integrator\nitself. The integrator first updates the evolving bases and then does a\nGalerkin step in the subspace generated by both the new and old bases, which is\nfollowed by rank truncation to a given tolerance. It is shown that the adaptive\nlow-rank integrator retains the exactness, robustness and symmetry-preserving\nproperties of the previously proposed fixed-rank integrator. Beyond that, up to\nthe truncation tolerance, the rank-adaptive integrator preserves the norm when\nthe differential equation does, it preserves the energy for Schr\\\"odinger\nequations and Hamiltonian systems, and it preserves the monotonic decrease of\nthe functional in gradient flows. Numerical experiments illustrate the\nbehaviour of the rank-adaptive integrator.\n"}
+{"abstract": "  SARS-CoV-2 is the third betacoronavirus to enter the human population in the\npast 20 years, revealing a concerning pattern. Clearly, preventing a future\npandemic from such viruses is a critical priority. Previous studies have shown\nthat shRNAs can be powerful suppressors of RNA viruses in transgenic animals\nand substantially reduce transmission. Thus, we propose the introduction of\nanti-betacoronavirus shRNAs using CRISPR/CAS9 gene drive into the horseshoe bat\npopulation, the natural reservoir of those viruses, to combat this pandemic\nthreat at its source. Importantly, our approach is not expected to create any\nharm to bats and can benefit other animals in the ecosystem that contract\nbetacoronaviruses from bats. We map the ethical and the technical aspects and\nsuggest guidelines for moving forward with this proposal.\n"}
+{"abstract": "  Lattice-skin structures composed of a thin-shell skin and a lattice infill\nare widespread in nature and large-scale engineering due to their efficiency\nand exceptional mechanical properties. Recent advances in additive\nmanufacturing, or 3D printing, make it possible to create lattice-skin\nstructures of almost any size with arbitrary shape and geometric complexity. We\npropose a novel gradient-based approach to optimising both the shape and infill\nof lattice-skin structures to improve their efficiency further. The respective\ngradients are computed by fully considering the lattice-skin coupling while the\nlattice topology and shape optimisation problems are solved in a sequential\nmanner. The shell is modelled as a Kirchhoff-Love shell and analysed using\nisogeometric subdivision surfaces, whereas the lattice is modelled as a\npin-jointed truss. The lattice consists of many cells, possibly of different\nsizes, with each containing a small number of struts. We propose a penalisation\napproach akin to the SIMP (solid isotropic material with penalisation) method\nfor topology optimisation of the lattice. Furthermore, a corresponding\nsensitivity filter and a lattice extraction technique are introduced to ensure\nthe stability of the optimisation process and to eliminate scattered struts of\nsmall cross-sectional areas. The developed topology optimisation technique is\nsuitable for non-periodic, non-uniform lattices. For shape optimisation of both\nthe shell and the lattice, the geometry of the lattice-skin structure is\nparameterised using the free-form deformation technique. The topology and shape\noptimisation problems are solved in an iterative, sequential manner. The\neffectiveness of the proposed approach and the influence of different\nalgorithmic parameters are demonstrated with several numerical examples.\n"}
+{"abstract": "  The elastic energy of mixing for multi-component solid solutions is derived\nby generalizing Eshelby's sphere-in-hole model for binary alloys. By surveying\nthe dependence of the elastic energy on chemical composition and lattice\nmisfit, we propose a lattice strain coefficient {\\lambda}*. Applying to several\nhigh-entropy alloys and superalloys, we found that most solid solution alloys\nare stable when {\\lambda}*<0.16, analogous to the Hume-Rothery atomic-size rule\nfor binary alloys. We also reveal that the polydispersity index {\\delta},\nfrequently used for describing strain in multi-component alloys, is directly\nrelated to the elastic energy (e) with e=q{\\delta}^2, q being an elastic\nconstant. Furthermore, the effects of (i) the number and (ii) the atomic-size\ndistribution of constituting elements on the phase stability of high-entropy\nalloys were quantified. The present derivations open for richer considerations\nof elastic effects in high-entropy alloys, offering immediate support for\nquantitative assessments of their thermodynamic properties and studying related\nstrengthening mechanisms.\n"}
+{"abstract": "  The formal semantics of an interpreted first-order logic (FOL) statement can\nbe given in Tarskian Semantics or a basically equivalent Game Semantics. The\nlatter maps the statement and the interpretation into a two-player semantic\ngame. Many combinatorial problems can be described using interpreted FOL\nstatements and can be mapped into a semantic game. Therefore, learning to play\na semantic game perfectly leads to the solution of a specific instance of a\ncombinatorial problem. We adapt the AlphaZero algorithm so that it becomes\nbetter at learning to play semantic games that have different characteristics\nthan Go and Chess. We propose a general framework, Persephone, to map the FOL\ndescription of a combinatorial problem to a semantic game so that it can be\nsolved through a neural MCTS based reinforcement learning algorithm. Our goal\nfor Persephone is to make it tabula-rasa, mapping a problem stated in\ninterpreted FOL to a solution without human intervention.\n"}
+{"abstract": "  The diamond is the graph obtained by removing an edge from the complete graph\non 4 vertices. A graph is ($P_6$, diamond)-free if it contains no induced\nsubgraph isomorphic to a six-vertex path or a diamond. In this paper we show\nthat the chromatic number of a ($P_6$, diamond)-free graph $G$ is no larger\nthan the maximum of 6 and the clique number of $G$. We do this by reducing the\nproblem to imperfect ($P_6$, diamond)-free graphs via the Strong Perfect Graph\nTheorem, dividing the imperfect graphs into several cases, and giving a proper\ncolouring for each case. We also show that there is exactly one\n6-vertex-critical ($P_6$, diamond, $K_6$)-free graph. Together with the\nLov\\'asz theta function, this gives a polynomial time algorithm to compute the\nchromatic number of ($P_6$, diamond)-free graphs.\n"}
+{"abstract": "  Distinguishability and predictability are part of complementarity relations\nwhich apply to two different kinds of interference experiments, with and\nwithout a path-detector, respectively. In [Opt. Comm. 179, 337 (2000)], Englert\nand Bergou pointed out the possible connection between distinguishability,\npredictability, and entanglement. They even conjectured that an entanglement\nmeasure was hidden between the measures of distinguishability and\npredictability. Here, we push forward this conjecture. We start defining a new\nentropic distinguishability measure and suggesting an entanglement measure as\nthe difference between this entropic distinguishability and an entropic\npredictability measure already defined in the literature. Besides, we prove\nthat it is possible to define an entanglement monotone from the largest value\nof the distinguishability and the corresponding predictability, provided that\nthe predictability satisfy the criteria already established in the literature.\nThus, this result formally connects an entanglement monotone with\ndistinguishability and the corresponding predictability, without appealing to\nspecific measures.\n"}
+{"abstract": "  A matching is compatible to two or more labeled point sets of size $n$ with\nlabels $\\{1,\\dots,n\\}$ if its straight-line drawing on each of these point sets\nis crossing-free. We study the maximum number of edges in a matching compatible\nto two or more labeled point sets in general position in the plane. We show\nthat for any two labeled convex sets of $n$ points there exists a compatible\nmatching with $\\lfloor \\sqrt {2n}\\rfloor$ edges. More generally, for any $\\ell$\nlabeled point sets we construct compatible matchings of size\n$\\Omega(n^{1/\\ell})$. As a corresponding upper bound, we use probabilistic\narguments to show that for any $\\ell$ given sets of $n$ points there exists a\nlabeling of each set such that the largest compatible matching has\n${\\mathcal{O}}(n^{2/({\\ell}+1)})$ edges. Finally, we show that $\\Theta(\\log n)$\ncopies of any set of $n$ points are necessary and sufficient for the existence\nof a labeling such that any compatible matching consists only of a single edge.\n"}
+{"abstract": "  Consider a Hamiltonian diffeomorphism $g$ on a surface. We describe several\nscenarios where a curve $L$ and its image $g(L)$ provide a simple evidence that\n$g$ is not autonomous.\n"}
+{"abstract": "  Balancing the needs of data privacy and predictive utility is a central\nchallenge for machine learning in healthcare. In particular, privacy concerns\nhave led to a dearth of public datasets, complicated the construction of\nmulti-hospital cohorts and limited the utilization of external machine learning\nresources. To remedy this, new methods are required to enable data owners, such\nas hospitals, to share their datasets publicly, while preserving both patient\nprivacy and modeling utility. We propose NeuraCrypt, a private encoding scheme\nbased on random deep neural networks. NeuraCrypt encodes raw patient data using\na randomly constructed neural network known only to the data-owner, and\npublishes both the encoded data and associated labels publicly. From a\ntheoretical perspective, we demonstrate that sampling from a sufficiently rich\nfamily of encoding functions offers a well-defined and meaningful notion of\nprivacy against a computationally unbounded adversary with full knowledge of\nthe underlying data-distribution. We propose to approximate this family of\nencoding functions through random deep neural networks. Empirically, we\ndemonstrate the robustness of our encoding to a suite of adversarial attacks\nand show that NeuraCrypt achieves competitive accuracy to non-private baselines\non a variety of x-ray tasks. Moreover, we demonstrate that multiple hospitals,\nusing independent private encoders, can collaborate to train improved x-ray\nmodels. Finally, we release a challenge dataset to encourage the development of\nnew attacks on NeuraCrypt.\n"}
+{"abstract": "  The paper addresses an improved inner current reference calculation to be\nemployed in the control of modular multilevel converters operating during\neither balanced or unbalanced conditions. The suggested reference calculation\nis derived based on the AC and DC additive and differential voltage components\napplied to the upper and lower arms of the converter. In addition, the impacts\ncaused not only by the AC network's impedances but also by the MMC's arm\nimpedances are also considered during the derivation of the AC additive current\nreference expressions. Another issue discussed in this article regards that\nsingular voltage conditions, where the positive-sequence component is equal to\nthe negative one, may occur not only in the AC network but also internally\n(within the converter's applied voltages). Several different inner current\nreference calculation methods are compared and their applicability during the\nformer fault conditions is analyzed. The paper presents a detailed formulation\nof the inner current reference calculation and applies it to different\nunbalanced AC grid faults where it is shown that the presented approach can be\npotentially used to maintain the internal energy of the converter balanced\nduring normal and fault conditions.\n"}
+{"abstract": "  The purpose of this technical report is to review the main properties of an\naccelerated composite gradient (ACG) method commonly referred to as the Fast\nIterative Shrinkage-Thresholding Algorithm (FISTA). In addition, we state a\nversion of FISTA for solving both convex and strongly convex composite\nminimization problems and derive its iteration complexities to generate\niterates satisfying various stopping criteria, including one which arises in\nthe course of solving other composite optimization problems via inexact\nproximal point schemes. This report also discusses different reformulations of\nthe convex version of FISTA and how they relate to other formulations in the\nliterature.\n"}
+{"abstract": "  We present a Convolutional Neural Network (CNN) architecture for inverse\nRaman amplifier design. This model aims at finding the pump powers and\nwavelengths required for a target signal power evolution, both in distance\nalong the fiber and in frequency. Using the proposed framework, the prediction\nof the pump configuration required to achieve a target power profile is\ndemonstrated numerically with high accuracy in C-band considering both\ncounter-propagating and bidirectional pumping schemes. For a distributed Raman\namplifier based on a 100 km single-mode fiber, a low mean set (0.51, 0.54 and\n0.64 dB) and standard deviation set (0.62, 0.43 and 0.38 dB) of the maximum\ntest error are obtained numerically employing 2 and 3 counter, and 4\nbidirectional propagating pumps, respectively.\n"}
+{"abstract": "  The intrinsic orbital magnetization of a TMD monolayer is usually calculated\nfor a plane unbounded system without mentioning the geometrical shape of\nsamples and boundary conditions (BCs) for electron wave functions. The method\nof calculations includes allowing for the Berry curvature contribution also in\nthe case when the system is described by the two-band minimal model [9]. In the\npresent paper, we show that the geometrical and topological properties of the\nspecimen, as well as the BCs, play an important role in the problem of\nmagnetization even for a macroscopic specimen.\n"}
+{"abstract": "  A conventional approach to train neural ordinary differential equations\n(ODEs) is to fix an ODE solver and then learn the neural network's weights to\noptimize a target loss function. However, such an approach is tailored for a\nspecific discretization method and its properties, which may not be optimal for\nthe selected application and yield the overfitting to the given solver. In our\npaper, we investigate how the variability in solvers' space can improve neural\nODEs performance. We consider a family of Runge-Kutta methods that are\nparameterized by no more than two scalar variables. Based on the solvers'\nproperties, we propose an approach to decrease neural ODEs overfitting to the\npre-defined solver, along with a criterion to evaluate such behaviour.\nMoreover, we show that the right choice of solver parameterization can\nsignificantly affect neural ODEs models in terms of robustness to adversarial\nattacks. Recently it was shown that neural ODEs demonstrate superiority over\nconventional CNNs in terms of robustness. Our work demonstrates that the model\nrobustness can be further improved by optimizing solver choice for a given\ntask. The source code to reproduce our experiments is available at\nhttps://github.com/juliagusak/neural-ode-metasolver.\n"}
+{"abstract": "  In this article, we are presenting the relationship between environmental\npollution and the income level of the selected twenty-four countries. We\nimplemented a data-based research analysis where, for each country, we analyzed\nthe related data for fifty-six years, from 1960 to 2016, to assess the\nrelationship between the carbon emission and income level. After performing the\nrelated data analysis for each country, we concluded whether the results for\nthat country were in line with the Environmental Kuznets Curve (EKC)\nhypothesis. The EKC hypothesis suggests that the carbon emission per capita\nstarts a declining trend when the country-specific high level of income is\nreached. The results of our data analyses show that the EKC hypothesis is valid\nfor high-income countries and the declining trends of carbon emission are\nclearly observed when the income level reaches a specific high enough level. On\nthe other hand, for the non-high income countries, our analysis results show\nthat it is too early to make an assessment at this growth stage of their\neconomies because they have not reached their related high-enough income per\ncapita levels yet. Furthermore, we performed two more additional analyses on\nhigh-income countries. First, we analyzed the related starting years of their\ncarbon emission declining trends. The big variance in the starting years of the\ncarbon emission declining trends shows that the international policies are\nclearly ineffective in initiating the declining trend in carbon emission. In\naddition, for the high-income countries, we explained the differences in their\ncarbon emission per capita levels in 2014 with their SGI indices and their\ndependence on high-carbon emission energy production.\n"}
+{"abstract": "  Momentum strategies are an important part of alternative investments and are\nat the heart of commodity trading advisors (CTAs). These strategies have,\nhowever, been found to have difficulties adjusting to rapid changes in market\nconditions, such as during the 2020 market crash. In particular, immediately\nafter momentum turning points, where a trend reverses from an uptrend\n(downtrend) to a downtrend (uptrend), time-series momentum (TSMOM) strategies\nare prone to making bad bets. To improve the response to regime change, we\nintroduce a novel approach, where we insert an online changepoint detection\n(CPD) module into a Deep Momentum Network (DMN) [1904.04912] pipeline, which\nuses an LSTM deep-learning architecture to simultaneously learn both trend\nestimation and position sizing. Furthermore, our model is able to optimise the\nway in which it balances 1) a slow momentum strategy which exploits persisting\ntrends, but does not overreact to localised price moves, and 2) a fast\nmean-reversion strategy regime by quickly flipping its position, then swapping\nit back again to exploit localised price moves. Our CPD module outputs a\nchangepoint location and severity score, allowing our model to learn to respond\nto varying degrees of disequilibrium, or smaller and more localised\nchangepoints, in a data driven manner. Back-testing our model over the period\n1995-2020, the addition of the CPD module leads to an improvement in Sharpe\nratio of one-third. The module is especially beneficial in periods of\nsignificant nonstationarity, and in particular, over the most recent years\ntested (2015-2020) the performance boost is approximately two-thirds. This is\ninteresting as traditional momentum strategies have been underperforming in\nthis period.\n"}
+{"abstract": "  The forthcoming generation of multi-petawatt lasers opens the way to abundant\npair production by the nonlinear Breit-Wheeler process, i.e., the decay of a\nphoton into an electron-positron pair inside an intense laser field. In this\npaper we explore the optimal conditions for Breit-Wheeler pair production in\nthe head-on collision of a laser pulse with gamma photons. The role of the\nlaser peak intensity versus the focal spot size and shape is examined keeping a\nconstant laser energy to match experimental constraints. A simple model for the\nsoft-shower case, where most pairs originate from the decay of the initial\ngamma photons, is derived. This approach provides us with a semi-analytical\nmodel for more complex situations involving either Gaussian or Laguerre-Gauss\n(LG) laser beams. We then explore the influence of the order of the LG beams on\npair creation. Finally we obtain the result that, above a given threshold, a\nlarger spot size (or a higher order in the case of LG laser beams) is more\nfavorable than a higher peak intensity. Our results match very well with\nthree-dimensional particle-in-cell simulations and can be used to guide\nupcoming experimental campaigns.\n"}
+{"abstract": "  Novel view synthesis is a long-standing problem in machine learning and\ncomputer vision. Significant progress has recently been made in developing\nneural scene representations and rendering techniques that synthesize\nphotorealistic images from arbitrary views. These representations, however, are\nextremely slow to train and often also slow to render. Inspired by neural\nvariants of image-based rendering, we develop a new neural rendering approach\nwith the goal of quickly learning a high-quality representation which can also\nbe rendered in real-time. Our approach, MetaNLR++, accomplishes this by using a\nunique combination of a neural shape representation and 2D CNN-based image\nfeature extraction, aggregation, and re-projection. To push representation\nconvergence times down to minutes, we leverage meta learning to learn neural\nshape and image feature priors which accelerate training. The optimized shape\nand image features can then be extracted using traditional graphics techniques\nand rendered in real time. We show that MetaNLR++ achieves similar or better\nnovel view synthesis results in a fraction of the time that competing methods\nrequire.\n"}
+{"abstract": "  The integral model of a GU(n-1,1) Shimura variety carries a universal abelian\nscheme over it, and the dual top exterior power of its Lie algebra carries a\nnatural hermitian metric. We express the arithmetic volume of this metrized\nline bundle, defined as an iterated self-intersection in the Gillet-Soule\narithmetic Chow ring, in terms of logarithmic derivatives of Dirichlet\nL-functions.\n"}
+{"abstract": "  A recent line of work has studied the relationship between the Wishart matrix\n$X^\\top X$, where $X\\in \\mathbb{R}^{d\\times n}$ has i.i.d. standard Gaussian\nentries, and the corresponding Gaussian matrix with independent entries above\nthe diagonal. Jiang and Li (2015) and Bubeck et al. (2016) showed that these\ntwo matrix ensembles converge in total variation whenever $d/n^3\\to \\infty$,\nand Bubeck et al. (2016) showed this to be sharp. In this paper we aim to\nidentify the precise threshold for $d$ in terms of $n$ for subsets of Wishart\nmatrices to converge in total variation to independent Gaussians. It turns out\nthat the combinatorial structure of the revealed entries, viewed as the\nadjacency matrix of a graph $G$, characterizes the distance from fully\nindependent. Specifically, we show that the threshold for $d$ depends on the\nnumber of various small subgraphs in $G$. So, even when the number of revealed\nentries is fixed, the threshold can vary wildly depending on their\nconfiguration. Convergence of masked Wishart to independent Gaussians thus\ninherently involves an interplay between both probabilistic and combinatorial\nphenomena. Our results determine the sharp threshold for a large family of $G$,\nincluding Erd\\H{o}s-R\\'enyi $G\\sim \\mathcal{G}(n,p)$ at all values $p\\gtrsim\nn^{-2}\\mathrm{polylog}(n)$. Our proof techniques are both combinatorial and\ninformation theoretic, which together allow us to carefully unravel the\ndependencies in the masked Wishart ensemble.\n"}
+{"abstract": "  The realization of multifunctional two-dimensional (2D) materials is\nfundamentally intriguing, such as combination of piezoelectricity with\ntopological insulating phase or ferromagnetism. In this work, a Janus monolayer\n$\\mathrm{SrAlGaSe_4}$ is built from 2D $\\mathrm{MA_2Z_4}$ family with dynamic,\nmechanical and thermal stabilities, which is piezoelectric due to lacking\ninversion symmetry. The unstrained $\\mathrm{SrAlGaSe_4}$ monolayer is a narrow\ngap normal insulator (NI) with spin orbital coupling (SOC). However, the NI to\ntopological insulator (TI) phase transition can be induced by the biaxial\nstrain, and a piezoelectric quantum spin Hall insulator (PQSHI) can be\nachieved. More excitingly, the phase transformation point is only about 1.01\ntensile strain, and nontrivial band topology can hold until considered 1.16\ntensile strain. Moreover, a Rashba spin splitting in the conduction bands can\nexit in PQSHI due to the absence of a horizontal mirror symmetry and the\npresence of SOC. For monolayer $\\mathrm{SrAlGaSe_4}$, both in-plane and much\nweak out-of-plane piezoelectric polarizations can be induced with a uniaxial\nstrain applied. The calculated piezoelectric strain coefficients $d_{11}$ and\n$d_{31}$ of monolayer $\\mathrm{SrAlGaSe_4}$ are -1.865 pm/V and -0.068 pm/V at\n1.06 tensile strain as a representative TI. In fact, many PQSHIs can be\nrealized from 2D $\\mathrm{MA_2Z_4}$ family. To confirm that, similar to\n$\\mathrm{SrAlGaSe_4}$, the coexistence of piezoelectricity and topological\norders can be realized by strain (about 1.04 tensile strain) in the\n$\\mathrm{CaAlGaSe_4}$ monolayer. Our works suggest that Janus monolayer\n$\\mathrm{SrAlGaSe_4}$ is a pure 2D system for PQSHI, enabling future studies\nexploring the interplay between piezoelectricity and topological orders, which\ncan lead to novel applications in electronics and spintronics.\n"}
+{"abstract": "  Hyperfine-structure constants of odd Ra$^{+}$ due to the interactions of\nnuclear magnetic dipole, electric quadrupole, and magnetic octupole moments\nwith the electrons are investigated in the framework of relativistic\ncoupled-cluster method within single- and double-excitation approximation. The\ncalculated energies and magnetic dipole hyperfine-structure constants $A$\nexhibit a good agreement with available experimental values. Combining with the\nexperimental electric quadrupole hyperfine-structure constant, we also\nextracted the electric quadrupole moments $Q$ of $^{209,211,221,223}$Ra. Our\n$Q$($^{221}$Ra) and $Q$($^{223}$Ra) are consistent with the referenced values\nfrom a semi-empirical analysis (Z. Phys. D: At., Mol. Clusters 11, 105 (1988)),\nbut $Q(^{211}$Ra)=$0.33(2)$ is smaller than the referenced value $0.48(4)$ by\nabout 30\\%. Furthermore, we also performed a procedure for assessing the\ncontributions of magnetic octupole moment to the hyperfine splitting. The\nsensitivity of hyperfine-structure interval measurements in $^{223}$Ra$^{+}$\nthat can reveal the effect caused by the nuclear octupole moment are found to\nbe on the order of kHz.\n"}
+{"abstract": "  Person search has recently emerged as a challenging task that jointly\naddresses pedestrian detection and person re-identification. Existing\napproaches follow a fully supervised setting where both bounding box and\nidentity annotations are available. However, annotating identities is\nlabor-intensive, limiting the practicability and scalability of current\nframeworks. This paper inventively considers weakly supervised person search\nwith only bounding box annotations. We proposed to address this novel task by\ninvestigating three levels of context clues (i.e., detection, memory and scene)\nin unconstrained natural images. The first two are employed to promote local\nand global discriminative capabilities, while the latter enhances clustering\naccuracy. Despite its simple design, our CGPS achieves 80.0% in mAP on\nCUHK-SYSU, boosting the baseline model by 8.8%. Surprisingly, it even achieves\ncomparable performance with several supervised person search models. Our code\nis available at https://github.com/ljpadam/CGPS\n"}
+{"abstract": "  In the setting of Carnot groups, we prove the $q-$Logarithmic Sobolev\ninequality for probability measures as a function of the Carnot-Carath\\'eodory\ndistance. As an application, we use the Hamilton-Jacobi equation in the setting\nof Carnot groups to prove the $p-$Talagrand inequality and hypercontractivity.\n"}
+{"abstract": "  The exploration of germanium (Ge) detectors with amorphous Ge (a-Ge) contacts\nhas drawn attention to the searches for rare-event physics such as dark matter\nand neutrinoless double-beta decay. The charge barrier height (CBH) of the a-Ge\ncontacts deposited on the detector surface is crucial to suppress the leakage\ncurrent of the detector in order to achieve la ow-energy detection threshold\nand high-energy resolution. The temperature-dependent CBH of a-Ge contacts for\nthree Ge detectors is analyzed to study the bulk leakage current (BLC)\ncharacteristics. The detectors were fabricated at the University of South\nDakota using homegrown crystals. The CBH is determined from the BLC when the\ndetectors are operated in the reverse bias mode with a guard-ring structure,\nwhich separates the BLC from the surface leakage current (SLC). The results\nshow that CBH is temperature dependent. The direct relation of the CBH\nvariation to temperature is related to the barrier inhomogeneities created on\nthe interface of a-Ge and crystalline Ge. The inhomogeneities that occur at the\ninterface were analyzed using the Gaussian distribution model for three\ndetectors. The CBH of a-Ge contact is projected to zero temperature. The\nimplication of the CBH at zero temperature is discussed for Ge detectors with\na-Ge contacts in searching for rare-event physics.\n"}
+{"abstract": "  Automated cooking machine is a goal for the future. The main aim is to make\nthe cooking process easier, safer, and create human welfare. To allow robots to\naccurately perform the cooking activities, it is important for them to\nunderstand the cooking environment and recognize the objects, especially\ncorrectly identifying the state of the cooking objects. This will significantly\nimprove the correctness of the following cooking recipes. In this project,\nseveral parts of the experiment were conducted to design a robust deep\nconvolutional neural network for classifying the state of the cooking objects\nfrom scratch. The model is evaluated by using various techniques, such as\nadjusting architecture layers, tuning key hyperparameters, and using different\noptimization techniques to maximize the accuracy of state classification.\n"}
+{"abstract": "  Research in Cognitive Science suggests that humans understand and represent\nknowledge of the world through causal relationships. In addition to\nobservations, they can rely on experimenting and counterfactual reasoning --\ni.e. referring to an alternative course of events -- to identify causal\nrelations and explain atypical situations. Different instances of control\nsystems, such as smart homes, would benefit from having a similar causal model,\nas it would help the user understand the logic of the system and better react\nwhen needed. However, while data-driven methods achieve high levels of\ncorrelation detection, they mainly fall short of finding causal relations,\nnotably being limited to observations only. Notably, they struggle to identify\nthe cause from the effect when detecting a correlation between two variables.\nThis paper introduces a new way to learn causal models from a mixture of\nexperiments on the environment and observational data. The core of our method\nis the use of selected interventions, especially our learning takes into\naccount the variables where it is impossible to intervene, unlike other\napproaches. The causal model we obtain is then used to generate Causal Bayesian\nNetworks, which can be later used to perform diagnostic and predictive\ninference. We use our method on a smart home simulation, a use case where\nknowing causal relations pave the way towards explainable systems. Our\nalgorithm succeeds in generating a Causal Bayesian Network close to the\nsimulation's ground truth causal interactions, showing encouraging prospects\nfor application in real-life systems.\n"}
+{"abstract": "  Studies of neutron stars are at their peak after the multi-messenger\nobservation of the binary merger event GW170817, which strongly constraints the\nstellar parameters like tidal deformability, masses and radii. Although current\nand future observations will provide stronger limits on the neutron stars\nparameters, knowledge of explicit interior solutions to Einstein's equations,\nwhich connect observed parameters with the internal structure, are crucial to\nhave a satisfactory description of the interior of these compact objects. A\nwell known exact solution, which has shown a relatively good approximation to a\nneutron star, is the Tolman VII solution. In order to provide a better fitting\nfor the energy density profile, with the realistic equations of state for\nneutron stars, recently Jiang and Yagi proposed a modified version of this\nmodel which introduces an additional parameter $\\alpha$ reflecting the\ninterplay of the quadratic and the newly added quartic term in the energy\ndensity profile. Here we study the dynamical stability of this modified Tolman\nVII solution using the theory of infinitesimal and adiabatic radial\noscillations developed by Chandrasekhar. For this purpose, we determine values\nof the critical adiabatic index, for the onset of instability, considering\nconfigurations with varying compactness and $\\alpha$. We found that the new\nmodels are stable against radial oscillations for a considerable range of\nvalues of compactness and the new parameter $\\alpha$, thus supporting their\napplicability as a physically plausible approximation of realistic neutron\nstars.\n"}
+{"abstract": "  We present single-mode nanowire (NW) lasers with ultralow threshold in the\nnear-infrared spectral range. To ensure the single-mode operation, the NW\ndiameter and length are reduced specifically to minimize the longitudinal and\ntransverse modes of the NW cavity. Increased optical losses and reduced gain\nvolume by the dimension reduction are compensated by excellent NW morphology\nand InGaAs/GaAs multi-quantum disks. At 5 K, a threshold low as 1.6 {\\mu}J/cm2\nper pulse is achieved with a resulting quality factor exceeding 6400. By\nfurther passivating the NW with an AlGaAs shell to suppress surface\nnon-radiative recombination, single-mode lasing operation is obtained with a\nthreshold of only 48 {\\mu}J/cm2 per pulse at room temperature with a high\ncharacteristic temperature of 223 K and power output of ~ 0.9 {\\mu}W. These\nsingle-mode, ultralow threshold, high power output NW lasers are promising for\nthe development of near-infrared nanoscale coherent light sources for\nintegrated photonic circuits, sensing, and spectroscopy.\n"}
+{"abstract": "  In reconfigurable intelligent surfaces (RISs) aided communications, the\nexisting passive beamforming (PB) design involves polynomial complexity in the\nnumber of reflecting elements, and thus is difficult to implement due to a\nmassive number of reflecting elements. To overcome this difficulty, we propose\na reflection-angle-based cascaded channel model by adopting the generalized\nSnell's law, in which the dimension of the variable space involved in\noptimization is significantly reduced, resulting in a simplified hierarchical\npassive beamforming (HPB) design. We develop an efficient two-stage HPB\nalgorithm, which exploits the angular domain property of the channel, to\nmaximize the achievable rate of the target user. Simulation results demonstrate\nthe appealing performance and low complexity of the proposed HPB design.\n"}
+{"abstract": "  In this paper a methodology is described to estimate multigroup neutron\nsource distributions which must be added into a subcritical system to drive it\nto a steady state prescribed power distribution. This work has been motivated\nby the principle of operation of the ADS (Accelerator Driven System) reactors,\nwhich have subcritical cores stabilized by the action of external sources. We\nuse the energy multigroup two-dimensional neutron transport equation in the\ndiscrete ordinates formulation (SN) and the equation which is adjoint to it,\nwhose solution is interpreted here as a distribution measuring the importance\nof the angular flux of neutrons to a linear functional. These equations are\ncorrelated through a reciprocity relation, leading to a relationship between\nthe interior sources of neutrons and the power produced by unit length of\nheight of the domain. A coarse-mesh numerical method of the spectral nodal\nclass, referred to as adjoint response matrix constant-nodal method, is applied\nto numerically solve the adjoint SN equations. Numerical experiments are\nperformed to analyze the accuracy of the present methodology so as to\nillustrate its potential practical applications.\n"}
+{"abstract": "  We show that in three-dimensional (3D) topological metals, a subset of the\nvan Hove singularities of the density of states sits exactly at the transitions\nbetween topological and trivial gapless phases. We may refer to these as\ntopological van Hove singularities. By investigating two minimal models, we\nshow that they originate from energy saddle points located between Weyl points\nwith opposite chiralities, and we illustrate their topological nature through\ntheir magnetotransport properties in the ballistic regime. We exemplify the\nrelation between van Hove singularities and topological phase transitions in\nWeyl systems by analyzing the 3D Hofstadter model, which offers a simple and\ninteresting playground to consider different kinds of Weyl metals and to\nunderstand the features of their density of states. In this model, as a\nfunction of the magnetic flux, the occurrence of topological van Hove\nsingularities can be explicitly checked.\n"}
+{"abstract": "  In this paper we present a methodology for data accesses when solving batches\nof Tridiagonal and Pentadiagonal matrices that all share the same\nleft-hand-side (LHS) matrix. The intended application is to the numerical\nsolution of Partial Differential Equations via the finite-difference method,\nalthough the methodology is applicable more broadly. By only storing one copy\nof this matrix, a significant reduction in storage overheads is obtained,\ntogether with a corresponding decrease in compute time. Taken together, these\ntwo performance enhancements lead to an overall more efficient implementation\nover the current state of the art algorithms cuThomasBatch and cuPentBatch,\nallowing for a greater number of systems to be solved on a single GPU. We\ndemonstrate the methodology in the case of the Diffusion Equation,\nHyperdiffusion Equation, and the Cahn--Hilliard Equation, all in one spatial\ndimension. In this last example, we demonstrate how the method can be used to\nperform $2^{20}$ independent simulations of phase separation in one dimension.\nIn this way, we build up a robust statistical description of the coarsening\nphenomenon which is the defining behavior of phase separation. We anticipate\nthat the method will be of further use in other similar contexts requiring\nstatistical simulation of physical systems.\n"}
+{"abstract": "  Neural approaches have achieved state-of-the-art accuracy on machine\ntranslation but suffer from the high cost of collecting large scale parallel\ndata. Thus, a lot of research has been conducted for neural machine translation\n(NMT) with very limited parallel data, i.e., the low-resource setting. In this\npaper, we provide a survey for low-resource NMT and classify related works into\nthree categories according to the auxiliary data they used: (1) exploiting\nmonolingual data of source and/or target languages, (2) exploiting data from\nauxiliary languages, and (3) exploiting multi-modal data. We hope that our\nsurvey can help researchers to better understand this field and inspire them to\ndesign better algorithms, and help industry practitioners to choose appropriate\nalgorithms for their applications.\n"}
+{"abstract": "  Graphite is a ubiquitous electrode material with particular promise for use\nin e.g., energy storage and desalination devices, but very little is known\nabout the properties of the graphite-electrolyte double layer at\ntechnologically relevant concentrations. Here, the (electrified)\ngraphite-NaCl(aq) interface was examined using constant chemical potential\nmolecular dynamics simulations; this approach avoids ion depletion (due to\nsurface adsorption) and maintains a constant concentration of ions beyond the\nsurface. Specific Na+ adsorption at the graphite basal surface causes charging\nof the interface in the absence of an applied potential. At moderate bulk\nconcentrations, this leads to accumulation of counter-ions in a diffuse layer\nto balance the effective surface charge, consistent with established models of\nthe electrical double layer (DL). Beyond 0.6 M, however, a combination of\nover-screening and ion crowding in the DL results in alternating compact layers\nof ion density perpendicular to the interface. The transition to this regime is\nmarked by an increasing DL size and anomalous negative shifts to the potential\nof zero charge with incremental changes to the bulk concentration. Our\nobservations are supported by changes to the position of the differential\ncapacitance minimum measured by electrochemical impedance spectroscopy.\nFurthermore, a striking level of agreement between the differential capacitance\nfrom simulations and experiments allows us to critically assess the accepted\nnorm that electrochemical capacitance measurements report simply on the density\nof states of the graphite material. Finally, ion crowding at the highest\nconcentrations (beyond 5 M) leads to the formation of liquid-like NaCl clusters\nconfined to highly non-ideal regions of the double layer, where ion diffusion\nis up to five times slower than in the bulk.\n"}
+{"abstract": "  The distance matrix $\\mathcal{D}$ of a connected graph $G$ is the matrix\nindexed by the vertices of $G$ which entry $\\mathcal{D}_{i,j}$ equals the\ndistance between the vertices $v_i$ and $v_j$. The distance signless Laplacian\nmatrix $\\mathcal{Q}(G)$ of graph $G$ is defined as\n$\\mathcal{Q}(G)=Diag(Tr)+\\mathcal{D}(G)$, where $Diag(Tr)$ is the diagonal\nmatrix of the vertex transmissions in $G$. The largest eigenvalue of\n$\\mathcal{Q}(G)$ is called the distance signless Laplacian spectral radius of\n$G$, written as $\\eta_1(G)$. And a perfect matching in a graph is a set of\ndisadjacent edges covering every vertex of $G$. In this paper, we present two\nsuffcient conditions in terms of the distance signless Laplacian sepectral\nradius for the exsitence of perfect matchings in graphs and bipatite graphs.\n"}
+{"abstract": "  A continuum, post-deposition mesoscopic model of a Moir\\'e-regulated\nself-assembly of metal nanoclusters on a twisted bilayer graphene is presented.\nQuasi-two-dimensional nanocluster-like steady states at a low adsorbate\ncoverage are analytically determined for Pt, Ni, and Pb adsorbates, pointing\nthat nanoclusters self-assemble at the Moir\\'e cells centers. This is followed\nby the computations of nanoclusters self-assembly dynamics. Differences in the\nself-assembly efficiency for three chosen metals are highlighted across three\ntypical values of an initial submonolayer coverage and for three temperature\nregimes. Accounting for the adsorption potential of metal atoms onto graphene\nleads to a significantly faster nanoclusters self-assembly and has a transient\nimpact on the nanoclusters morphologies. A model extensions to the cases of\nnanoclusters self-assembly on a Moir\\'e formed by a monolayer graphene over a\nmetal substrate, and the electromigration-guided self-assembly on such Moir\\'e\nare proposed.\n"}
+{"abstract": "  The inviscid limit for the two-dimensional compressible viscoelastic\nequations on the half plane is considered under the no-slip boundary condition.\nWhen the initial deformation tensor is a perturbation of the identity matrix\nand the initial density is near a positive constant, we establish the uniform\nestimates of solutions to the compressible viscoelastic flows in the conormal\nSobolev spaces. It is well-known that for the corresponding inviscid limit of\nthe compressible Navier-Stokes equations with the no-slip boundary condition,\none does not expect the uniform energy estimates of solutions due to the\nappearance of strong boundary layers. However, when the deformation tensor\neffect is taken into account, our results show that the deformation tensor\nplays an important role in the vanishing viscosity process and can prevent the\nformation of strong boundary layers. As a result we are able to justify the\ninviscid limit of solutions for the compressible viscous flows under the\nno-slip boundary condition governed by the viscoelastic equations, based on the\nuniform conormal regularity estimates achieved in this paper.\n"}
+{"abstract": "  This paper develops an efficient procedure for designing low-complexity\ncodebooks for precoding in a full-dimension (FD) multiple-input multiple-output\n(MIMO) system with a uniform planar array (UPA) antenna at the transmitter (Tx)\nusing tensor learning. In particular, instead of using statistical channel\nmodels, we utilize a model-free data-driven approach with foundations in\nmachine learning to generate codebooks that adapt to the surrounding\npropagation conditions. We use a tensor representation of the FD-MIMO channel\nand exploit its properties to design quantized version of the channel\nprecoders. We find the best representation of the optimal precoder as a\nfunction of Kronecker Product (KP) of two low-dimensional precoders,\nrespectively corresponding to the horizontal and vertical dimensions of the\nUPA, obtained from the tensor decomposition of the channel. We then quantize\nthis precoder to design product codebooks such that an average loss in mutual\ninformation due to quantization of channel state information (CSI) is\nminimized. The key technical contribution lies in exploiting the constraints on\nthe precoders to reduce the product codebook design problem to an unsupervised\nclustering problem on a Cartesian Product Grassmann manifold (CPM), where the\ncluster centroids form a finite-sized precoder codebook. This codebook can be\nfound efficiently by running a $K$-means clustering on the CPM. With a suitable\ninduced distance metric on the CPM, we show that the construction of product\ncodebooks is equivalent to finding the optimal set of centroids on the factor\nmanifolds corresponding to the horizontal and vertical dimensions. Simulation\nresults are presented to demonstrate the capability of the proposed design\ncriterion in learning the codebooks and the attractive performance of the\ndesigned codebooks.\n"}
+{"abstract": "  We establish a framework for doing second order conformal perturbation theory\nfor the symmetric orbifold Sym$^N(T^4)$ to all orders in $N$. This allows us to\ncompute how 1/4-BPS states of the D1-D5 system on $AdS_3\\times S^3\\times T^4$\nare lifted as we move away from the orbifold point. As an application we\nconfirm a previous observation that in the large $N$ limit not all 1/4-BPS\nstates that can be lifted do get lifted. This provides evidence that the\nsupersymmetric index actually undercounts the number of 1/4-BPS states at a\ngeneric point in the moduli space.\n"}
+{"abstract": "  Well ordered covers of square-free monomial ideals are subsets of the minimal\ngenerating set ordered in a certain way that give rise to a Lyubeznik\nresolution for the ideal, and have guaranteed nonvanishing Betti numbers in\ncertain degrees. This paper is about square-free monomial ideals which have a\nwell ordered cover. We consider the question of subadditivity of syzygies of\nsquare-free monomial ideals via complements in the lcm lattice of the ideal,\nand examine how lattice complementation breaks well ordered covers of the ideal\ninto (well ordered) covers of subideals. We also introduce a family of well\nordered covers called strongly disjoint sets of simplicial bouquets\n(generalizing work of Kimura on graphs), which are relatively easy to identify\nin simplicial complexes. We examine the subadditivity property via numerical\ncharacteristics of these bouquets.\n"}
+{"abstract": "  Topological phases of matter is an exotic phenomena in modern condense matter\nphysics, which has attracted much attention due to the unique boundary states\nand transport properties. Recently, this topological concept in electronic\nmaterials has been exploited in many other fields of physics. Motivated by\ndesigning and controlling the behavior of electromagnetic waves, in optical,\nmicrowave, and sound frequencies, topological photonics emerges as a rapid\ngrowing research field. Due to the flexibility and diversity of superconducting\nquantum circuits system, it is an promising platform to realize exotic\ntopological phases of matter and to probe and explore topologically-protected\neffects in new ways. Here, we review theoretical and experimental advances of\ntopological photonics on superconducting quantum circuits via the\nexperimentally demonstrated parametric tunable coupling techniques, including\nusing of the superconducting transmission line resonator, superconducting\nqubits, and the coupled system of them. On superconducting circuits, the\nflexible interactions and intrinsic nonlinearity making topological photonics\nin this system not only a simple photonic analog of topological effects for\nnovel devices, but also a realm of exotic but less-explored fundamental\nphysics.\n"}
+{"abstract": "  Given a query patch from a novel class, one-shot object detection aims to\ndetect all instances of that class in a target image through the semantic\nsimilarity comparison. However, due to the extremely limited guidance in the\nnovel class as well as the unseen appearance difference between query and\ntarget instances, it is difficult to appropriately exploit their semantic\nsimilarity and generalize well. To mitigate this problem, we present a\nuniversal Cross-Attention Transformer (CAT) module for accurate and efficient\nsemantic similarity comparison in one-shot object detection. The proposed CAT\nutilizes transformer mechanism to comprehensively capture bi-directional\ncorrespondence between any paired pixels from the query and the target image,\nwhich empowers us to sufficiently exploit their semantic characteristics for\naccurate similarity comparison. In addition, the proposed CAT enables feature\ndimensionality compression for inference speedup without performance loss.\nExtensive experiments on COCO, VOC, and FSOD under one-shot settings\ndemonstrate the effectiveness and efficiency of our method, e.g., it surpasses\nCoAE, a major baseline in this task by 1.0% in AP on COCO and runs nearly 2.5\ntimes faster. Code will be available in the future.\n"}
+{"abstract": "  The scientific impact of current and upcoming photometric galaxy surveys is\ncontingent on our ability to obtain redshift estimates for large numbers of\nfaint galaxies. In the absence of spectroscopically confirmed redshifts,\nbroad-band photometric redshift point estimates (photo-$z$s) have been\nsuperseded by photo-$z$ probability density functions (PDFs) that encapsulate\ntheir nontrivial uncertainties. Initial applications of photo-$z$ PDFs in weak\ngravitational lensing studies of cosmology have obtained the redshift\ndistribution function $\\mathcal{N}(z)$ by employing computationally\nstraightforward stacking methodologies that violate the laws of probability. In\nresponse, mathematically self-consistent models of varying complexity have been\nproposed in an effort to answer the question, \"What is the right way to obtain\nthe redshift distribution function $\\mathcal{N}(z)$ from a catalog of photo-$z$\nPDFs?\" This letter aims to motivate adoption of such principled methods by\naddressing the contrapositive of the more common presentation of such models,\nanswering the question, \"Under what conditions do traditional stacking methods\nsuccessfully recover the true redshift distribution function $\\mathcal{N}(z)$?\"\nBy placing stacking in a rigorous mathematical environment, we identify two\nsuch conditions: those of perfectly informative data and perfectly informative\nprior information. Stacking has maintained its foothold in the astronomical\ncommunity for so long because the conditions in question were only weakly\nviolated in the past. These conditions, however, will be strongly violated by\nfuture galaxy surveys. We therefore conclude that stacking must be abandoned in\nfavor of mathematically supported methods in order to advance observational\ncosmology.\n"}
+{"abstract": "  The non-autoregressive models have boosted the efficiency of neural machine\ntranslation through parallelized decoding at the cost of effectiveness when\ncomparing with the autoregressive counterparts. In this paper, we claim that\nthe syntactic and semantic structures among natural language are critical for\nnon-autoregressive machine translation and can further improve the performance.\nHowever, these structures are rarely considered in the existing\nnon-autoregressive models. Inspired by this intuition, we propose to\nincorporate the explicit syntactic and semantic structures of languages into a\nnon-autoregressive Transformer, for the task of neural machine translation.\nMoreover, we also consider the intermediate latent alignment within target\nsentences to better learn the long-term token dependencies. Experimental\nresults on two real-world datasets (i.e., WMT14 En-De and WMT16 En-Ro) show\nthat our model achieves a significantly faster speed, as well as keeps the\ntranslation quality when compared with several state-of-the-art\nnon-autoregressive models.\n"}
+{"abstract": "  We present Wi-Lo, which allows to convert an ordinary 802.11 (WiFi) access\npoint into an internet of things (IoT) gateway supporting the low-power wide\narea network (LPWAN) technology LoRa in the downlink. Our Wi-Lo system only\nrequires a software update and no additional hardware. It uses signal emulation\ntechnique based on complementary code keying modulation from 802.11b in order\nto emulate a downlink LoRa (long range) transmission. The Wi-Lo gateway can be\nused by a normal WiFi-enabled smartphone to send packets to LoRa compliant IoT\ndevices like smart sensors. We implemented a prototype using commodity WiFi\nhardware. Experimental results show that Wi-Lo enables a normal WiFi node to\ncommunication to LoRa devices even over long distances, which is comparable to\nthe configurations using pure LoRa transmitter and receivers.\n"}
+{"abstract": "  Real-world imagery is often characterized by a significant imbalance of the\nnumber of images per class, leading to long-tailed distributions. An effective\nand simple approach to long-tailed visual recognition is to learn feature\nrepresentations and a classifier separately, with instance and class-balanced\nsampling, respectively. In this work, we introduce a new framework, by making\nthe key observation that a feature representation learned with instance\nsampling is far from optimal in a long-tailed setting. Our main contribution is\na new training method, referred to as Class-Balanced Distillation (CBD), that\nleverages knowledge distillation to enhance feature representations. CBD allows\nthe feature representation to evolve in the second training stage, guided by\nthe teacher learned in the first stage. The second stage uses class-balanced\nsampling, in order to focus on under-represented classes. This framework can\nnaturally accommodate the usage of multiple teachers, unlocking the information\nfrom an ensemble of models to enhance recognition capabilities. Our experiments\nshow that the proposed technique consistently outperforms the state of the art\non long-tailed recognition benchmarks such as ImageNet-LT, iNaturalist17 and\niNaturalist18. The experiments also show that our method does not sacrifice the\naccuracy of head classes to improve the performance of tail classes, unlike\nmost existing work.\n"}
+{"abstract": "  Learning representations of nodes in a low dimensional space is a crucial\ntask with numerous interesting applications in network analysis, including link\nprediction, node classification, and visualization. Two popular approaches for\nthis problem are matrix factorization and random walk-based models. In this\npaper, we aim to bring together the best of both worlds, towards learning node\nrepresentations. In particular, we propose a weighted matrix factorization\nmodel that encodes random walk-based information about nodes of the network.\nThe benefit of this novel formulation is that it enables us to utilize kernel\nfunctions without realizing the exact proximity matrix so that it enhances the\nexpressiveness of existing matrix decomposition methods with kernels and\nalleviates their computational complexities. We extend the approach with a\nmultiple kernel learning formulation that provides the flexibility of learning\nthe kernel as the linear combination of a dictionary of kernels in data-driven\nfashion. We perform an empirical evaluation on real-world networks, showing\nthat the proposed model outperforms baseline node embedding algorithms in\ndownstream machine learning tasks.\n"}
+{"abstract": "  Supply voltage scaling is one of the most effective techniques to reduce the\npower consumption of microprocessors. However, technology limitations such as\naging and process variability enforce microprocessor designers to apply\npessimistic voltage guardbands to guarantee correct operation in the field for\nany foreseeable workload. This worst-case design practice makes energy\nefficiency hard to scale with technology evolution. Improving energy-efficiency\nrequires the identification of the chip design margins through time-consuming\nand comprehensive characterization of its operational limits. Such a\ncharacterization of state-of-the-art multi-core CPUs fabricated in aggressive\ntechnologies is a multi-parameter process, which requires statistically\nsignificant information. In this paper, we present an automated framework to\nsupport system-level voltage and frequency scaling characterization of Applied\nMicro's state-of-the-art ARMv8-based multicore CPUs used in the X-Gene 2\nmicro-server family. The fully automated framework can provide fine-grained\ninformation of the system's state by monitoring any abnormal behavior that may\noccur during reduced supply voltage conditions. We also propose a new metric to\nquantify the behavior of a microprocessor when it operates beyond nominal\nconditions. Our experimental results demonstrate potential uses of the\ncharacterization framework to identify the limits of operation for improved\nenergy efficiency.\n"}
+{"abstract": "  In this technical report, we present our solution of KDD Cup 2021 OGB\nLarge-Scale Challenge - PCQM4M-LSC Track. We adopt Graphormer and ExpC as our\nbasic models. We train each model by 8-fold cross-validation, and additionally\ntrain two Graphormer models on the union of training and validation sets with\ndifferent random seeds. For final submission, we use a naive ensemble for these\n18 models by taking average of their outputs. Using our method, our team\nMachineLearning achieved 0.1200 MAE on test set, which won the first place in\nKDD Cup graph prediction track.\n"}
+{"abstract": "  Touch data, and in particular text-entry data, has been mostly collected in\nthe laboratory, under controlled conditions. While touch and text-entry data\nhave consistently shown its potential for monitoring and detecting a variety of\nconditions and impairments, its deployment in-the-wild remains a challenge. In\nthis paper, we present WildKey, an Android keyboard toolkit that allows for the\nusable deployment of in-the-wild user studies. WildKey is able to analyze\ntext-entry behaviors through implicit and explicit text-entry data collection\nwhile ensuring user privacy. We detail each of the WildKey's components and\nfeatures, all of the metrics collected, and discuss the steps taken to ensure\nuser privacy and promote compliance.\n"}
+{"abstract": "  We propose nonabelian higher-rank gauge theories in 2+1D and 3+1D. The gauge\ngroup is constructed from the volume-preserving diffeomorphisms of space. We\nshow that the intriguing physics of the lowest Landau level (LLL) limit can be\ninterpreted as the consequences of the symmetry. We derive the renowned\nGirvin-MacDonald-Platzman (GMP) algebra as well as the topological Wen-Zee term\nwithin our formalism. Using the gauge symmetry in 2+1D, we derive the LLL\neffective action of vortex crystal in rotating Bose gas as well as Wigner\ncrystal of electron in an applied magnetic field. We show that the nonlinear\nsigma models of ferromagnets in 2+1D and 3+1D exhibit the higher-rank gauge\nsymmetries that we introduce in this paper. We interpret the fractonic behavior\nof the excitations on the lowest Landau level and of skyrmions in ferromagnets\nas the consequence of the higher-rank gauge symmetry.\n"}
+{"abstract": "  For $G=G_{n, 1/2}$, the Erd\\H{o}s--Renyi random graph, let $X_n$ be the\nrandom variable representing the number of distinct partitions of $V(G)$ into\nsets $A_1, \\ldots, A_q$ so that the degree of each vertex in $G[A_i]$ is\ndivisible by $q$ for all $i\\in[q]$. We prove that if $q\\geq 3$ is odd then\n$X_n\\xrightarrow{d}{\\mathrm{Po}(1/q!)}$, and if $q \\geq 4$ is even then\n$X_n\\xrightarrow{d}{\\mathrm{Po}(2^q/q!)}$. More generally, we show that the\ndistribution is still asymptotically Poisson when we require all degrees in\n$G[A_i]$ to be congruent to $x_i$ modulo $q$ for each $i\\in[q]$, where the\nresidues $x_i$ may be chosen freely. For $q=2$, the distribution is not\nasymptotically Poisson, but it can be determined explicitly.\n"}
+{"abstract": "  We report a theoretical study of the coherence dynamics of spin qubits in\ntwo-dimensional materials (2DMs) and van-der-Waals heterostructures, as a\nfunction of the host thickness and the composition of the surrounding\nenvironment. We focus on MoS$_2$ and WS$_2$, two promising systems for quantum\ntechnology applications, and we consider the decoherence arising from the\ninteraction of the spin qubit with nuclear spins. We show that the Hahn-echo\ncoherence time is determined by a complex interplay between the source of\ndecoherence in the qubit host and in the environment, which in turn determines\nwhether the noise evolution is in a classical or quantum mechanical regime. We\nsuggest that the composition and thickness of van-der-Waals heterostructures\nencapsulating a qubit host can be engineered to maximize coherence times.\nFinally, we discuss how quantum sensors may be able to probe the dynamics of\nthe nuclear bath in 2DMs.\n"}
+{"abstract": "  The use of few-femtosecond, extreme ultraviolet (XUV) pulses, produced by\nhigh-order harmonic generation, in combination with few-femtosecond infrared\n(IR) pulses in pump-probe experiments has great potential to disclose ultrafast\ndynamics in molecules, nanostructures and solids. A crucial prerequisite is a\nreliable characterization of the temporal properties of the XUV and IR pulses.\nSeveral techniques have been developed. The majority of them applies phase\nreconstruction algorithms to a photoelectron spectrogram obtained by ionizing\nan atomic target in a pump-probe fashion. If the ionizing radiation is a single\nharmonic, all the information is encoded in a two-color two-photon signal\ncalled sideband (SB). In this work, we present a simplified model to interpret\nthe time-frequency mapping of the SB signal and we show that the temporal\ndispersion of the pulses directly maps onto the shape of its spectrogram.\nFinally, we derive an analytical solution, which allows us to propose a novel\nprocedure to estimate the second-order dispersion of the XUV and IR pulses in\nreal time and with no need for iterative algorithms.\n"}
+{"abstract": "  We prove a slab theorem for convex ancient solutions to mean curvature flow\nwithout any additional hypotheses (such as concavity of the arrival time,\nbounded curvature on compact time intervals, or noncollapsing in the sense of\ninscribed radii). By carefully exploiting this, we are able to obtain a\nHarnack-type inequality and a corresponding compactness theorem for the entire\nsolutions.\n  Using the compactness theorem, we are then able to give a short proof of the\nequivalence of noncollapsing in the sense of inscribed radii and entire arrival\ntimes for convex ancient mean curvature flows.\n  Finally, we provide a useful characterization of the non-entire solutions. In\nparticular, we prove that they are necessarily asymptotic to at least one Grim\nhyperplane.\n  As a consequence, we rule out collapsing singularity models in (not\nnecessarily embedded) compact $(n-1)$-convex mean curvature flow. We also\nremove the noncollapsing hypothesis from certain recent classification results\nfor ancient solutions.\n"}
+{"abstract": "  *The following abbreviates the abstract. Please refer to the thesis for the\nfull abstract.*\n  After a disaster, locating and extracting victims quickly is critical because\nmortality rises rapidly after the first two days. To assist search and rescue\nteams and improve response times, teams of camera-equipped aerial robots can\nengage in tasks such as mapping buildings and locating victims.\n  These sensing tasks encapsulate difficult (NP-Hard) problems. One way to\nsimplify planning for these tasks is to focus on maximizing sensing performance\nover a short time horizon. Specifically, consider the problem of how to select\nmotions for a team of robots to maximize a notion of sensing quality (the\nsensing objective) over the near future, say by maximizing the amount of\nunknown space in a map that robots will observe over the next several seconds.\nBy repeating this process regularly, the team can react quickly to new\nobservations as they work to complete the sensing task. In technical terms,\nthis planning and control process forms an example of receding-horizon control.\nFortunately, common sensing objectives benefit from well-known monotonicity\nproperties (e.g. submodularity), and greedy algorithms can exploit these\nmonotonicity properties to solve the receding-horizon optimization problems\nthat we study near-optimally.\n  However, greedy algorithms typically force robots to make decisions\nsequentially so that planning time grows with the number of robots. Further,\nrecent works that investigate sequential greedy planning, have demonstrated\nthat reducing the number of sequential steps while retaining suboptimality\nguarantees can be hard or impossible.\n  We demonstrate that halting growth in planning time is sometimes possible. To\ndo so, we introduce novel greedy algorithms involving fixed numbers of\nsequential steps.\n"}
+{"abstract": "  Backdoor attacks are a kind of insidious security threat against machine\nlearning models. After being injected with a backdoor in training, the victim\nmodel will produce adversary-specified outputs on the inputs embedded with\npredesigned triggers but behave properly on normal inputs during inference. As\na sort of emergent attack, backdoor attacks in natural language processing\n(NLP) are investigated insufficiently. As far as we know, almost all existing\ntextual backdoor attack methods insert additional contents into normal samples\nas triggers, which causes the trigger-embedded samples to be detected and the\nbackdoor attacks to be blocked without much effort. In this paper, we propose\nto use the syntactic structure as the trigger in textual backdoor attacks. We\nconduct extensive experiments to demonstrate that the syntactic trigger-based\nattack method can achieve comparable attack performance (almost 100% success\nrate) to the insertion-based methods but possesses much higher invisibility and\nstronger resistance to defenses. These results also reveal the significant\ninsidiousness and harmfulness of textual backdoor attacks. All the code and\ndata of this paper can be obtained at https://github.com/thunlp/HiddenKiller.\n"}
+{"abstract": "  Symmetries have proven to be important ingredients in the analysis of neural\nnetworks. So far their use has mostly been implicit or seemingly coincidental.\n  We undertake a systematic study of the role that symmetry plays. In\nparticular, we clarify how symmetry interacts with the learning algorithm. The\nkey ingredient in our study is played by Noether's celebrated theorem which,\ninformally speaking, states that symmetry leads to conserved quantities (e.g.,\nconservation of energy or conservation of momentum). In the realm of neural\nnetworks under gradient descent, model symmetries imply restrictions on the\ngradient path. E.g., we show that symmetry of activation functions leads to\nboundedness of weight matrices, for the specific case of linear activations it\nleads to balance equations of consecutive layers, data augmentation leads to\ngradient paths that have \"momentum\"-type restrictions, and time symmetry leads\nto a version of the Neural Tangent Kernel.\n  Symmetry alone does not specify the optimization path, but the more\nsymmetries are contained in the model the more restrictions are imposed on the\npath. Since symmetry also implies over-parametrization, this in effect implies\nthat some part of this over-parametrization is cancelled out by the existence\nof the conserved quantities.\n  Symmetry can therefore be thought of as one further important tool in\nunderstanding the performance of neural networks under gradient descent.\n"}
+{"abstract": "  Sensors are being extensively deployed and are expected to expand at\nsignificant rates in the coming years. They typically generate a large volume\nof data on the internet of things (IoT) application areas like smart cities,\nintelligent traffic systems, smart grid, and e-health. Cloud, edge and fog\ncomputing are potential and competitive strategies for collecting, processing,\nand distributing IoT data. However, cloud, edge, and fog-based solutions need\nto tackle the distribution of a high volume of IoT data efficiently through\nconstrained and limited resource network infrastructures. This paper addresses\nthe issue of conveying a massive volume of IoT data through a network with\nlimited communications resources (bandwidth) using a cognitive communications\nresource allocation based on Reinforcement Learning (RL) with SARSA algorithm.\nThe proposed network infrastructure (PSIoTRL) uses a Publish/ Subscribe\narchitecture to access massive and highly distributed IoT data. It is\ndemonstrated that the PSIoTRL bandwidth allocation for buffer flushing based on\nSARSA enhances the IoT aggregator buffer occupation and network link\nutilization. The PSIoTRL dynamically adapts the IoT aggregator traffic flushing\naccording to the Pub/Sub topic's priority and network constraint requirements.\n"}
+{"abstract": "  We study odd parity perturbations of spherically symmetric black holes with\ntime-dependent scalar hair in shift-symmetric higher-order scalar-tensor\ntheories. The analysis is performed in a general way without assuming the\ndegeneracy conditions. Nevertheless, we end up with second-order equations for\na single master variable, similarly to cosmological tensor modes. We thus\nidentify the general form of the quadratic Lagrangian for the odd parity\nperturbations, leading to a generalization of the Regge-Wheeler equation. We\nalso investigate the structure of the effective metric for the master variable\nand refine the stability conditions. As an application of our generalized\nRegge-Wheeler equation, we compute the quasi-normal modes of a certain\nnontrivial black hole solution. Finally, our result is extended to include the\nmatter energy-momentum tensor as a source term.\n"}
+{"abstract": "  Superconducting qubits are a promising platform for building a larger-scale\nquantum processor capable of solving otherwise intractable problems. In order\nfor the processor to reach practical viability, the gate errors need to be\nfurther suppressed and remain stable for extended periods of time. With recent\nadvances in qubit control, both single- and two-qubit gate fidelities are now\nin many cases limited by the coherence times of the qubits. Here we\nexperimentally employ closed-loop feedback to stabilize the frequency\nfluctuations of a superconducting transmon qubit, thereby increasing its\ncoherence time by 26\\% and reducing the single-qubit error rate from $(8.5 \\pm\n2.1)\\times 10^{-4}$ to $(5.9 \\pm 0.7)\\times 10^{-4}$. Importantly, the\nresulting high-fidelity operation remains effective even away from the qubit\nflux-noise insensitive point, significantly increasing the frequency bandwidth\nover which the qubit can be operated with high fidelity. This approach is\nhelpful in large qubit grids, where frequency crowding and parasitic\ninteractions between the qubits limit their performance.\n"}
+{"abstract": "  In this paper, we develop a method to assess the sensitivity of local average\ntreatment effect estimates to potential violations of the monotonicity\nassumption of Imbens and Angrist (1994). We parameterize the degree to which\nmonotonicity is violated using two sensitivity parameters: the first one\ndetermines the share of defiers in the population, and the second one measures\ndifferences in the distributions of outcomes between compliers and defiers. For\neach pair of values of these sensitivity parameters, we derive sharp bounds on\nthe outcome distributions of compliers in the first-order stochastic dominance\nsense. We identify the robust region that is the set of all values of\nsensitivity parameters for which a given empirical conclusion, e.g. that the\nlocal average treatment effect is positive, is valid. Researchers can assess\nthe credibility of their conclusion by evaluating whether all the plausible\nsensitivity parameters lie in the robust region. We obtain confidence sets for\nthe robust region through a bootstrap procedure and illustrate the sensitivity\nanalysis in an empirical application. We also extend this framework to analyze\ntreatment effects of the entire population.\n"}
+{"abstract": "  We report the discovery of an extended very-high-energy (VHE) gamma-ray\nsource around the location of the middle-aged (207.8 kyr) pulsar PSR J0622+3749\nwith the Large High Altitude Air Shower Observatory (LHAASO). The source is\ndetected with a significance of $8.2\\sigma$ for $E>25$~TeV assuming a Gaussian\ntemplate. The best-fit location is (R.A.,\nDec.)$=(95^{\\circ}\\!.47\\pm0^{\\circ}\\!.11,\\,37^{\\circ}\\!.92 \\pm0^{\\circ}\\!.09)$,\nand the extension is $0^{\\circ}\\!.40\\pm0^{\\circ}\\!.07$. The energy spectrum can\nbe described by a power-law spectrum with an index of ${-2.92 \\pm 0.17_{\\rm\nstat} \\pm 0.02_{\\rm sys} }$. No clear extended multi-wavelength counterpart of\nthe LHAASO source has been found from the radio to sub-TeV bands. The LHAASO\nobservations are consistent with the scenario that VHE electrons escaped from\nthe pulsar, diffused in the interstellar medium, and scattered the interstellar\nradiation field. If interpreted as the pulsar halo scenario, the diffusion\ncoefficient, inferred for electrons with median energies of $\\sim160$~TeV, is\nconsistent with those obtained from the extended halos around Geminga and\nMonogem and much smaller than that derived from cosmic ray secondaries. The\nLHAASO discovery of this source thus likely enriches the class of so-called\npulsar halos and confirms that high-energy particles generally diffuse very\nslowly in the disturbed medium around pulsars.\n"}
+{"abstract": "  This paper develops probabilistic PV forecasters by taking advantage of\nrecent breakthroughs in deep learning. It tailored forecasting tool, named\nencoder-decoder, is implemented to compute intraday multi-output PV quantiles\nforecasts to efficiently capture the time correlation. The models are trained\nusing quantile regression, a non-parametric approach that assumes no prior\nknowledge of the probabilistic forecasting distribution. The case study is\ncomposed of PV production monitored on-site at the University of Li\\`ege\n(ULi\\`ege), Belgium. The weather forecasts from the regional climate model\nprovided by the Laboratory of Climatology are used as inputs of the deep\nlearning models. The forecast quality is quantitatively assessed by the\ncontinuous ranked probability and interval scores. The results indicate this\narchitecture improves the forecast quality and is computationally efficient to\nbe incorporated in an intraday decision-making tool for robust optimization.\n"}
+{"abstract": "  Nuclear Magnetic Resonance (NMR) shielding constants of transition metals in\nsolvated complexes are computed at the relativistic density functional theory\n(DFT) level. The solvent effects evaluated with subsystem-DFT approaches are\ncompared with the reference solvent shifts predicted from supermolecular\ncalculations. Two subsystem-DFT approaches are analyzed -- in the standard\nfrozen density embedding (FDE) scheme the transition metal complexes are\nembedded in an environment of solvent molecules whose density is kept frozen,\nin the second approach the densities of the complex and of its environment are\nrelaxed in the \"freeze-and-thaw\" procedure. The latter approach improves the\ndescription of the solvent effects in most cases, nevertheless the FDE\ndeficiencies are rather large in some cases.\n"}
+{"abstract": "  Static Application Security Testing (SAST) is a popular quality assurance\ntechnique in software engineering. However, integrating SAST tools into\nindustry-level product development and security assessment poses various\ntechnical and managerial challenges. In this work, we reported a longitudinal\ncase study of adopting SAST as a part of a human-driven security assessment for\nan open-source e-government project. We described how SASTs are selected,\nevaluated, and combined into a novel approach for software security assessment.\nThe approach was preliminarily evaluated using semi-structured interviews. Our\nresult shows that (1) while some SAST tools out-perform others, it is possible\nto achieve better performance by combining more than one SAST tools and (2)\nSAST tools should be used towards a practical performance and in the\ncombination with triangulated approaches for human-driven vulnerability\nassessment in real-world projects.\n"}
+{"abstract": "  This is the opening article of the abstract book of conference \"Set-Theoretic\nTopology and Topological Algebra\" in honor of professor Alexander Arhangelskii\non the occasion of his 80th birthday held in 2018 at Moscow State University.\n"}
+{"abstract": "  For a non-negative separable random field $Z(t), t\\in \\mathbb{R}^d$\nsatisfying some mild assumptions we show that \\begin{eqnarray*}\n  H_Z^\\delta =\n  \\lim_{T\\to\\infty} \\frac{1}{T^d} E \\{\\sup_{ t\\in [0,T]^d \\cap \\delta\n\\mathbb{Z}^d } Z(t) \\} <\\infty \\end{eqnarray*} for $\\delta \\ge 0$ where $0\n\\mathbb{Z}^d := \\mathbb{R}^d$ and prove that $H_Z^0$ can be approximated by\n$H_Z^\\delta$ if $\\delta$ tends to 0. These results extend the classical\nfindings for the Pickands constants $H_{Z}^\\delta$, defined for $Z(t)=\n\\exp\\left( \\sqrt{ 2} B_\\alpha (t)- |t|^{2\\alpha }\\right), t\\in \\mathbb{R}$ with\n$B_\\alpha$ a standard fractional Brownian motion with Hurst parameter $\\alpha\n\\in (0,1]$.\n  The continuity of $H_{Z}^\\delta$ at $\\delta=0$ is additionally shown for two\nparticular extensions of Pickands constants.\n"}
+{"abstract": "  Characterizing plasticity mechanisms below the ductile-to-brittle transition\ntemperature is traditionally difficult to accomplish in asystematic fashion.\nHere, we use a new experimental setup to perform in situ cryogenic mechanical\ntesting of pure Sn micropillars at room temperature and at 142{\\deg}C.\nSubsequent electron microscopy characterization of the micropillars shows a\nclear difference in the deformation mechanisms at room temperature and at\ncryogenic temperatures. At room temperature, the Sn micropillars deformed\nthrough dislocation plasticity, while at142{\\deg}C they exhibited both higher\nstrength and deformation twinning. Two different orientations were tested, a\nsymmetric (100) orientation and a non-symmetric (451) orientation. The\ndeformation mechanisms were found to be the same for both orientations\n"}
+{"abstract": "  Semiconductor emitter can possibly achieve sharp cutoff wavelength due to its\nintrinsic bandgap absorption and almost zero sub-bandgap emission without\ndoping. A germanium wafer based selective emitter with front-side\nantireflection and backside metal coating is studied here for\nthermophotovoltaic (TPV) energy conversion. Optical simulation predicts the\nspectral emittance above 0.9 in the wavelengths from 1 to 1.85 um and below 0.2\nin the sub-bandgap range with sharp cutoff around the bandgap, indicating\nsuperior spectral selectivity behavior. This is confirmed by excellent\nagreement with indirectly measured spectral emittance of the fabricated\nGe-based selective emitter sample. Furthermore, the TPV efficiency by paring\nthe Ge-based selective emitter with a GaSb cell is theoretically analyzed at\ndifferent temperatures. This work will facilitate the development of the\nsemiconductor-based selective emitters for enhancing TPV performance.\n"}
+{"abstract": "  We prove the equivalence in the covariant phase space of the metric and\nconnection formulations for Palatini gravity, with nonmetricity and torsion, on\na spacetime manifold with boundary. To this end, we will rely on the\ncohomological approach provided by the relative bicomplex framework. Finally,\nwe discuss some of the physical implications derived from this equivalence in\nthe context of singularity identification through curvature invariants.\n"}
+{"abstract": "  A common observation in data-driven applications is that high dimensional\ndata has a low intrinsic dimension, at least locally. In this work, we consider\nthe problem of estimating a $d$ dimensional sub-manifold of $\\mathbb{R}^D$ from\na finite set of noisy samples. Assuming that the data was sampled uniformly\nfrom a tubular neighborhood of $\\mathcal{M}\\in \\mathcal{C}^k$, a compact\nmanifold without boundary, we present an algorithm that takes a point $r$ from\nthe tubular neighborhood and outputs $\\hat p_n\\in \\mathbb{R}^D$, and\n$\\widehat{T_{\\hat p_n}\\mathcal{M}}$ an element in the Grassmanian $Gr(d, D)$.\nWe prove that as the number of samples $n\\to\\infty$ the point $\\hat p_n$\nconverges to $p\\in \\mathcal{M}$ and $\\widehat{T_{\\hat p_n}\\mathcal{M}}$\nconverges to $T_p\\mathcal{M}$ (the tangent space at that point) with high\nprobability. Furthermore, we show that the estimation yields asymptotic rates\nof convergence of $n^{-\\frac{k}{2k + d}}$ for the point estimation and\n$n^{-\\frac{k-1}{2k + d}}$ for the estimation of the tangent space. These rates\nare known to be optimal for the case of function estimation.\n"}
+{"abstract": "  Understanding the limits of list-decoding and list-recovery of Reed-Solomon\n(RS) codes is of prime interest in coding theory and has attracted a lot of\nattention in recent decades. However, the best possible parameters for these\nproblems are still unknown, and in this paper, we take a step in this\ndirection. We show the existence of RS codes that are list-decodable or\nlist-recoverable beyond the Johnson radius for \\emph{any} rate, with a\npolynomial field size in the block length. In particular, we show that for any\n$\\epsilon\\in (0,1)$ there exist RS codes that are list-decodable from radius\n$1-\\epsilon$ and rate less than $\\frac{\\epsilon}{2-\\epsilon}$, with constant\nlist size. We deduce our results by extending and strengthening a recent result\nof Ferber, Kwan, and Sauermann on puncturing codes with large minimum distance\nand by utilizing the underlying code's linearity.\n"}
+{"abstract": "  In this research, we propose a new low-precision framework, TENT, to leverage\nthe benefits of a tapered fixed-point numerical format in TinyML models. We\nintroduce a tapered fixed-point quantization algorithm that matches the\nnumerical format's dynamic range and distribution to that of the deep neural\nnetwork model's parameter distribution at each layer. An accelerator\narchitecture for the tapered fixed-point with TENT framework is proposed.\nResults show that the accuracy on classification tasks improves up to ~31 %\nwith an energy overhead of ~17-30 % as compared to fixed-point, for ConvNet and\nResNet-18 models.\n"}
+{"abstract": "  Determining the clumpiness of matter around galaxies is pivotal to a full\nunderstanding of the spatially inhomogeneous, multi-phase gas in the\ncircumgalactic medium (CGM). We combine high spatially resolved 3D observations\nwith hydrodynamical cosmological simulations to measure the cold circumgalactic\ngas clumpiness. We present new adaptive-optics-assisted VLT/MUSE observations\nof a quadruply lensed quasar, targeting the CGM of 2 foreground $z\\sim$1\ngalaxies observed in absorption. We additionally use zoom-in FOGGIE simulations\nwith exquisite resolution ($\\sim$0.1 kpc scales) in the CGM of galaxies to\ncompute the physical properties of cold gas traced by Mg\\,II absorbers. By\ncontrasting these mock-observables with the VLT/MUSE observations, we find a\nlarge spread of fractional variations of Mg\\,II equivalent widths with physical\nseparation, both in observations and simulations. The simulations indicate a\ndependence of the Mg\\,II coherence length on the underlying gas morphology\n(filaments vs clumps). The $z_{\\rm abs}$=1.168 Mg\\,II system shows coherence\nover $\\gtrsim$ 6 kpc and is associated with an [O\\,II] emitting galaxy situated\n89 kpc away, with SFR $\\geq$ 4.6 $\\pm$ {1.5} $\\rm M_{\\odot}$/yr and\n$M_{*}=10^{9.6\\pm0.2} M_{\\odot}$. Based on this combined analysis, we determine\nthat the absorber is consistent with being an inflowing filament. The $z_{\\rm\nabs}$=1.393 Mg\\,II system traces dense CGM gas clumps varying in strength over\n$\\lesssim$ 2 kpc physical scales. Our findings suggest that this absorber is\nlikely related to an outflowing clump. Our joint approach combining\n3D-spectroscopy observations of lensed systems and simulations with extreme\nresolution in the CGM put new constraints on the clumpiness of cold CGM gas, a\nkey diagnostic of the baryon cycle.\n"}
+{"abstract": "  We investigate how the addition of quantum resources changes the statistical\ncomplexity of quantum circuits by utilizing the framework of quantum resource\ntheories. Measures of statistical complexity that we consider include the\nRademacher complexity and the Gaussian complexity, which are well-known\nmeasures in computational learning theory that quantify the richness of classes\nof real-valued functions. We derive bounds for the statistical complexities of\nquantum circuits that have limited access to certain resources and apply our\nresults to two special cases: (1) stabilizer circuits that are supplemented\nwith a limited number of T gates and (2) instantaneous quantum polynomial-time\nClifford circuits that are supplemented with a limited number of CCZ gates. We\nshow that the increase in the statistical complexity of a quantum circuit when\nan additional quantum channel is added to it is upper bounded by the free\nrobustness of the added channel. Finally, we derive bounds for the\ngeneralization error associated with learning from training data arising from\nquantum circuits.\n"}
+{"abstract": "  We analyze the interaction with uniform external fields of nematic liquid\ncrystals within a recent generalized free-energy posited by Virga and falling\nin the class of quartic functionals in the spatial gradients of the nematic\ndirector. We review some known interesting solutions, i. e., uniform\nheliconical structures, which correspond to the so-called twist-bend nematic\nphase and we also study the transition between this phase and the standard\nuniform nematic one. Moreover, we find liquid crystal configurations, which\nclosely resemble some novel, experimentally detected, structures called\nSkyrmion Tubes. Skyrmion Tubes are characterized by a localized\ncylindrically-symmetric pattern surrounded by either twist-bend or uniform\nnematic phase. We study the equilibrium differential equations and find\nnumerical solutions and analytical approximations.\n"}
+{"abstract": "  In the pursuit of natural language understanding, there has been a long\nstanding interest in tracking state changes throughout narratives. Impressive\nprogress has been made in modeling the state of transaction-centric dialogues\nand procedural texts. However, this problem has been less intensively studied\nin the realm of general discourse where ground truth descriptions of states may\nbe loosely defined and state changes are less densely distributed over\nutterances. This paper proposes to turn to simplified, fully observable systems\nthat show some of these properties: Sports events. We curated 2,263 soccer\nmatches including time-stamped natural language commentary accompanied by\ndiscrete events such as a team scoring goals, switching players or being\npenalized with cards. We propose a new task formulation where, given paragraphs\nof commentary of a game at different timestamps, the system is asked to\nrecognize the occurrence of in-game events. This domain allows for rich\ndescriptions of state while avoiding the complexities of many other real-world\nsettings. As an initial point of performance measurement, we include two\nbaseline methods from the perspectives of sentence classification with temporal\ndependence and current state-of-the-art generative model, respectively, and\ndemonstrate that even sophisticated existing methods struggle on the state\ntracking task when the definition of state broadens or non-event chatter\nbecomes prevalent.\n"}
+{"abstract": "  We consider Sobolev mappings $f\\in W^{1,q}(\\Omega,\\IC)$, $1<q<\\infty$,\nbetween planar domains $\\Omega\\subset \\IC$. We analyse the Radon-Riesz property\nfor convex functionals of the form \\[f\\mapsto \\int_\\Omega \\Phi(|Df(z)|,J(z,f))\n\\; dz \\]\n  and show that under certain criteria, which hold in important cases, weak\nconvergence in $W_{loc}^{1,q}(\\Omega)$ of (for instance) a minimising sequence\ncan be improved to strong convergence. This finds important applications in the\nminimisation problems for mappings of finite distortion and the $L^p$ and\n$Exp$\\,-Teichm\\\"uller theories.\n"}
+{"abstract": "  Athreya, Bufetov, Eskin and Mirzakhani have shown the number of mapping class\ngroup lattice points intersecting a closed ball of radius $R$ in\nTeichm\\\"{u}ller space is asymptotic to $e^{hR}$, where $h$ is the dimension of\nthe Teichm\\\"{u}ller space. We show for any pseudo-Anosov mapping class $f$,\nthere exists a power $n$, such that the number of lattice points of the $f^n$\nconjugacy class intersecting a closed ball of radius $R$ is coarsely asymptotic\nto $e^{\\frac{h}{2}R}$.\n"}
+{"abstract": "  Clinical diagnostic and treatment decisions rely upon the integration of\npatient-specific data with clinical reasoning. Cancer presents a unique context\nthat influence treatment decisions, given its diverse forms of disease\nevolution. Biomedical imaging allows noninvasive assessment of disease based on\nvisual evaluations leading to better clinical outcome prediction and\ntherapeutic planning. Early methods of brain cancer characterization\npredominantly relied upon statistical modeling of neuroimaging data. Driven by\nthe breakthroughs in computer vision, deep learning became the de facto\nstandard in the domain of medical imaging. Integrated statistical and deep\nlearning methods have recently emerged as a new direction in the automation of\nthe medical practice unifying multi-disciplinary knowledge in medicine,\nstatistics, and artificial intelligence. In this study, we critically review\nmajor statistical and deep learning models and their applications in brain\nimaging research with a focus on MRI-based brain tumor segmentation. The\nresults do highlight that model-driven classical statistics and data-driven\ndeep learning is a potent combination for developing automated systems in\nclinical oncology.\n"}
+{"abstract": "  Multi-Schur functions are symmetric functions that generalize the\nsupersymmetric Schur functions, the flagged Schur functions, and the refined\ndual Grothendieck functions, which have been intensively studied by Lascoux. In\nthis paper, we give a new free-fermionic presentation of them. The multi-Schur\nfunctions are indexed by a partition and two ``tuples of tuples'' of\nindeterminates. We construct a family of linear bases of the fermionic Fock\nspace that are indexed by such data and prove that they correspond to the\nmulti-Schur functions through the boson-fermion correspondence. By focusing on\nsome special bases, which we call refined bases, we give a straightforward\nmethod of expanding a multi-Schur function in the refined dual Grothendieck\npolynomials. We also present a sufficient condition for a multi-Schur function\nto have its Hall-dual function in the completed ring of symmetric functions.\n"}
+{"abstract": "  Real-time sensing of ultra-wideband radio-frequency signal with high\nfrequency resolution is challenging, which is confined by the sampling rate of\nelectronic analog-to-digital converter and the capability of digital signal\nprocessing. By combining quantum mechanics with compressed sensing, quantum\ncompressed sensing is proposed for wideband radio-frequency signal frequency\nmeasurement. By using an electro-optical crystal as a sensor which modulates\nthe wave function of the coherent photons with the signal to be measured. The\nfrequency spectrum could be recovered by detecting the modulated sparse photons\nwith a low time-jitter single-photon detector and a time-to-digital converter.\nMore than 50 GHz real-time analysis bandwidth is demonstrated with the Fourier\ntransform limit resolution. The further simulation shows it can be extended to\nmore than 300 GHz with the present technologies.\n"}
+{"abstract": "  The COVID-19 pandemic has inspired unprecedented data collection and computer\nvision modelling efforts worldwide, focusing on diagnosis and stratification of\nCOVID-19 from medical images. Despite this large-scale research effort, these\nmodels have found limited practical application due in part to unproven\ngeneralization of these models beyond their source study. This study\ninvestigates the generalizability of key published models using the publicly\navailable COVID-19 Computed Tomography data through cross dataset validation.\nWe then assess the predictive ability of these models for COVID-19 severity\nusing an independent new dataset that is stratified for COVID-19 lung\ninvolvement. Each inter-dataset study is performed using histogram\nequalization, and contrast limited adaptive histogram equalization with and\nwithout a learning Gabor filter. The study shows high variability in the\ngeneralization of models trained on these datasets due to varied sample image\nprovenances and acquisition processes amongst other factors. We show that under\ncertain conditions, an internally consistent dataset can generalize well to an\nexternal dataset despite structural differences between these datasets with f1\nscores up to 86%. Our best performing model shows high predictive accuracy for\nlung involvement score for an independent dataset for which expertly labelled\nlung involvement stratification is available. Creating an ensemble of our best\nmodel for disease positive prediction with our best model for disease negative\nprediction using a min-max function resulted in a superior model for lung\ninvolvement prediction with average predictive accuracy of 75% for zero lung\ninvolvement and 96% for 75-100% lung involvement with almost linear\nrelationship between these stratifications.\n"}
+{"abstract": "  It is still nontrivial to develop a new fast COVID-19 screening method with\nthe easier access and lower cost, due to the technical and cost limitations of\nthe current testing methods in the medical resource-poor districts. On the\nother hand, there are more and more ocular manifestations that have been\nreported in the COVID-19 patients as growing clinical evidence[1]. This\ninspired this project. We have conducted the joint clinical research since\nJanuary 2021 at the ShiJiaZhuang City, Heibei province, China, which approved\nby the ethics committee of The fifth hospital of ShiJiaZhuang of Hebei Medical\nUniversity. We undertake several blind tests of COVID-19 patients by Union\nHospital, Tongji Medical College, Huazhong University of Science and\nTechnology, Wuhan, China. Meantime as an important part of the ongoing globally\nCOVID-19 eye test program by AIMOMICS since February 2020, we propose a new\nfast screening method of analyzing the eye-region images, captured by common\nCCD and CMOS cameras. This could reliably make a rapid risk screening of\nCOVID-19 with the sustainable stable high performance in different countries\nand races. Our model for COVID-19 rapid prescreening have the merits of the\nlower cost, fully self-performed, non-invasive, importantly real-time, and thus\nenables the continuous health surveillance. We further implement it as the open\naccessible APIs, and provide public service to the world. Our pilot experiments\nshow that our model is ready to be usable to all kinds of surveillance\nscenarios, such as infrared temperature measurement device at airports and\nstations, or directly pushing to the target people groups smartphones as a\npackaged application.\n"}
+{"abstract": "  Approximately 75% of energy used in petrochemical and refining industries is\nconsumed by furnaces. Operating furnaces at optimal conditions results in huge\namounts of savings. In this paper, we model the furnace efficiency optimization\nas a multi-objective problem involving multiple interactions among the\ncontrolled variables and propose a cooperative game based formulation for the\nfactory of future. The controlled variables are Absorbed Duty and Coil Outlet\nTemperature. We propose a comprehensive solution to select the best combination\nof manipulated variables (fired duty, throughput and coil inlet temperature)\nsatisfying multiple criteria using a cooperative game theory approach. We\ncompare this approach with the standard multi-objective optimization using\nNSGA-II and RNSGA-II algorithms.\n"}
+{"abstract": "  One of the long-standing goals of quantum transport is to use the noise,\nrather than the average current, for information processing. However, achieving\nthis requires on-demand control of quantum fluctuations in the electric\ncurrent. In this paper, we demonstrate theoretically that transport through a\nmolecular spin-valve provides access to many different statistics of electron\ntunneling events. Simply by changing highly tunable parameters, such as\nelectrode spin-polarization, magnetization angle, and voltage, one is able to\nswitch between Poisson behavior, bunching and anti-bunching of electron\ntunnelings, and positive and negative temporal correlations. The molecular\nspin-valve is modeled by a single spin-degenerate molecular orbital with local\nelectronic repulsion coupled to two ferromagnetic leads with magnetization\norientations allowed to rotate relative to each other. The electron transport\nis described via Born-Markov master equation and fluctuations are studied with\nhigher-order waiting time distributions. For highly magnetized parallel-aligned\nelectrodes, we find that strong positive temporal correlations emerge in the\nvoltage range where the second transport channel is partially open. These are\ncaused by a spin-induced electron-bunching, which does not manifest in the\nstationary current alone.\n"}
+{"abstract": "  Recent technological advancements have proliferated the use of small embedded\ndevices for collecting, processing, and transferring the security-critical\ninformation. The Internet of Things (IoT) has enabled remote access and control\nof these network-connected devices. Consequently, an attacker can exploit\nsecurity vulnerabilities and compromise these devices. In this context, the\nsecure boot becomes a useful security mechanism to verify the integrity and\nauthenticity of the software state of the devices. However, the current secure\nboot schemes focus on detecting the presence of potential malware on the device\nbut not on disinfecting and restoring the soft-ware to a benign state. This\nmanuscript presents CARE- the first secure boot framework that provides\ndetection, resilience, and onboard recovery mechanism for the com-promised\ndevices. The framework uses a prototype hybrid CARE: Code Authentication and\nResilience Engine to verify the software state and restore it to a benign\nstate. It uses Physical Memory Protection (PMP) and other security enchaining\ntechniques of RISC-V processor to pro-vide resilience from modern attacks. The\nstate-of-the-art comparison and performance analysis results indicate that the\nproposed secure boot framework provides a promising resilience and recovery\nmechanism with very little 8 % performance and resource overhead\n"}
+{"abstract": "  Rigid electron rotation of a fully penetrated Rotamak-FRC produces a pressure\nflux function that is more peaked than the Solov'ev flux function. This paper\nexplores the implications of this peaked pressure flux function, including the\nisothermal case, which appear when the temperature profile is broader than the\ndensity profile, creating both benefits and challenges to a Rotamak-FRC based\nfusion reactor. In this regime, the density distribution becomes very peaked,\nenhancing the fusion power. The separatrix has a tendency to become oblate,\nwhich can be mitigated by flux conserving current loops. Plasma extends outside\nthe separatrix, notably in the open field line region. This model does not\napply to very kinetic FRCs or FRCs in which there are significant ion flows,\nbut it may have some applicability to their outer layers.\n"}
+{"abstract": "  We apply unsupervised learning techniques to classify the different phases of\nthe $J_1-J_2$ antiferromagnetic Ising model on the honeycomb lattice. We\nconstruct the phase diagram of the system using convolutional autoencoders.\nThese neural networks can detect phase transitions in the system via `anomaly\ndetection', without the need for any label or a priori knowledge of the phases.\nWe present different ways of training these autoencoders and we evaluate them\nto discriminate between distinct magnetic phases. In this process, we highlight\nthe case of high temperature or even random training data. Finally, we analyze\nthe capability of the autoencoder to detect the ground state degeneracy through\nthe reconstruction error.\n"}
+{"abstract": "  A key assumption in the theory of nonlinear adaptive control is that the\nuncertainty of the system can be expressed in the linear span of a set of known\nbasis functions. While this assumption leads to efficient algorithms, it limits\napplications to very specific classes of systems. We introduce a novel\nnonparametric adaptive algorithm that learns an infinite-dimensional density\nover parameters to cancel an unknown disturbance in a reproducing kernel\nHilbert space. Surprisingly, the resulting control input admits an analytical\nexpression that enables its implementation despite its underlying\ninfinite-dimensional structure. While this adaptive input is rich and\nexpressive -- subsuming, for example, traditional linear parameterizations --\nits computational complexity grows linearly with time, making it comparatively\nmore expensive than its parametric counterparts. Leveraging the theory of\nrandom Fourier features, we provide an efficient randomized implementation that\nrecovers the complexity of classical parametric methods while provably\nretaining the expressivity of the nonparametric input. In particular, our\nexplicit bounds only depend polynomially on the underlying parameters of the\nsystem, allowing our proposed algorithms to efficiently scale to\nhigh-dimensional systems. As an illustration of the method, we demonstrate the\nability of the randomized approximation algorithm to learn a predictive model\nof a 60-dimensional system consisting of ten point masses interacting through\nNewtonian gravitation.\n"}
+{"abstract": "  Framing involves the positive or negative presentation of an argument or\nissue depending on the audience and goal of the speaker (Entman 1983).\nDifferences in lexical framing, the focus of our work, can have large effects\non peoples' opinions and beliefs. To make progress towards reframing arguments\nfor positive effects, we create a dataset and method for this task. We use a\nlexical resource for \"connotations\" to create a parallel corpus and propose a\nmethod for argument reframing that combines controllable text generation\n(positive connotation) with a post-decoding entailment component (same\ndenotation). Our results show that our method is effective compared to strong\nbaselines along the dimensions of fluency, meaning, and\ntrustworthiness/reduction of fear.\n"}
+{"abstract": "  Recent observational missions have uncovered a significant number of compact\nmulti-exoplanet systems. The tight orbital spacing of these systems has led to\nmuch effort being applied to the understanding of their stability; however, a\nkey limitation of the majority of these studies is the termination of\nsimulations as soon as the orbits of two planets cross. In this work we explore\nthe stability of compact, three-planet systems and continue our simulations all\nthe way to the first collision of planets to yield a better understanding of\nthe lifetime of these systems. We perform over $25,000$ integrations of a\nSun-like star orbited by three Earth-like secondaries for up to a billion\norbits to explore a wide parameter space of initial conditions in both the\nco-planar and inclined cases, with a focus on the initial orbital spacing. We\ncalculate the probability of collision over time and determine the probability\nof collision between specific pairs of planets. We find systems that persist\nfor over $10^8$ orbits after an orbital crossing and show how the\npost-instability survival time of systems depends upon the initial orbital\nseparation, mutual inclination, planetary radius, and the closest encounter\nexperienced. Additionally, we examine the effects of very small changes in the\ninitial positions of the planets upon the time to collision and show the effect\nthat the choice of integrator can have upon simulation results. We generalise\nour results throughout to show both the behaviour of systems with an inner\nplanet initially located at $1$ AU and $0.25$ AU.\n"}
+{"abstract": "  Visual information extraction (VIE) has attracted considerable attention\nrecently owing to its various advanced applications such as document\nunderstanding, automatic marking and intelligent education. Most existing works\ndecoupled this problem into several independent sub-tasks of text spotting\n(text detection and recognition) and information extraction, which completely\nignored the high correlation among them during optimization. In this paper, we\npropose a robust visual information extraction system (VIES) towards real-world\nscenarios, which is a unified end-to-end trainable framework for simultaneous\ntext detection, recognition and information extraction by taking a single\ndocument image as input and outputting the structured information.\nSpecifically, the information extraction branch collects abundant visual and\nsemantic representations from text spotting for multimodal feature fusion and\nconversely, provides higher-level semantic clues to contribute to the\noptimization of text spotting. Moreover, regarding the shortage of public\nbenchmarks, we construct a fully-annotated dataset called EPHOIE\n(https://github.com/HCIILAB/EPHOIE), which is the first Chinese benchmark for\nboth text spotting and visual information extraction. EPHOIE consists of 1,494\nimages of examination paper head with complex layouts and background, including\na total of 15,771 Chinese handwritten or printed text instances. Compared with\nthe state-of-the-art methods, our VIES shows significant superior performance\non the EPHOIE dataset and achieves a 9.01% F-score gain on the widely used\nSROIE dataset under the end-to-end scenario.\n"}
+{"abstract": "  We prove a Liouville type theorem for the linearly perturbed Paneitz\nequation: For $\\epsilon>0$ small enough, if $u_\\epsilon$ is a positive smooth\nsolution of\n  $$P_{S^3} u_\\epsilon+\\epsilon u_\\epsilon=-u_\\epsilon^{-7} \\qquad\n\\mathrm{~~on~~}S^3,$$ where $P_{S^3}$ is the Paneitz operator of the round\nmetric $g_{S^3}$, then $u_\\epsilon$ is constant. This confirms a conjecture\nproposed by Fengbo Hang and Paul Yang in [ Int. Math. Res. Not. IMRN, 2020 (11)\n].\n"}
+{"abstract": "  This paper proposes an $SE_2(3)$ based extended Kalman filtering (EKF)\nframework for the inertial-integrated state estimation problem. The error\nrepresentation using the straight difference of two vectors in the inertial\nnavigation system may not be reasonable as it does not take the direction\ndifference into consideration.\n  Therefore, we choose to use the $SE_2(3)$ matrix Lie group to represent the\nstate of the inertial-integrated navigation system which consequently leads to\nthe common frame error representation.\n  With the new velocity and position error definition, we leverage the group\naffine dynamics with the autonomous error properties and derive the error state\ndifferential equation for the inertial-integrated navigation on the\nnorth-east-down (NED) navigation frame and the earth-centered earth-fixed\n(ECEF) frame, respectively, the corresponding EKF, terms as $SE_2(3)$ based EKF\nhas also been derived. It provides a new perspective on the geometric EKF with\na more sophisticated formula for the inertial-integrated navigation system.\nFurthermore, we design two new modified error dynamics on the NED frame and the\nECEF frame respectively by introducing new auxiliary vectors. Finally the\nequivalence of the left-invariant EKF and left $SE_2(3)$ based EKF have been\nshown in navigation frame and ECEF frame.\n"}
+{"abstract": "  Fine-tuning pre-trained cross-lingual language models can transfer\ntask-specific supervision from one language to the others. In this work, we\npropose to improve cross-lingual fine-tuning with consistency regularization.\nSpecifically, we use example consistency regularization to penalize the\nprediction sensitivity to four types of data augmentations, i.e., subword\nsampling, Gaussian noise, code-switch substitution, and machine translation. In\naddition, we employ model consistency to regularize the models trained with two\naugmented versions of the same training set. Experimental results on the XTREME\nbenchmark show that our method significantly improves cross-lingual fine-tuning\nacross various tasks, including text classification, question answering, and\nsequence labeling.\n"}
+{"abstract": "  Image denoising is of great importance for medical imaging system, since it\ncan improve image quality for disease diagnosis and downstream image analyses.\nIn a variety of applications, dynamic imaging techniques are utilized to\ncapture the time-varying features of the subject, where multiple images are\nacquired for the same subject at different time points. Although\nsignal-to-noise ratio of each time frame is usually limited by the short\nacquisition time, the correlation among different time frames can be exploited\nto improve denoising results with shared information across time frames. With\nthe success of neural networks in computer vision, supervised deep learning\nmethods show prominent performance in single-image denoising, which rely on\nlarge datasets with clean-vs-noisy image pairs. Recently, several\nself-supervised deep denoising models have been proposed, achieving promising\nresults without needing the pairwise ground truth of clean images. In the field\nof multi-image denoising, however, very few works have been done on extracting\ncorrelated information from multiple slices for denoising using self-supervised\ndeep learning methods. In this work, we propose Deformed2Self, an end-to-end\nself-supervised deep learning framework for dynamic imaging denoising. It\ncombines single-image and multi-image denoising to improve image quality and\nuse a spatial transformer network to model motion between different slices.\nFurther, it only requires a single noisy image with a few auxiliary\nobservations at different time frames for training and inference. Evaluations\non phantom and in vivo data with different noise statistics show that our\nmethod has comparable performance to other state-of-the-art unsupervised or\nself-supervised denoising methods and outperforms under high noise levels.\n"}
+{"abstract": "  In an intelligent transportation system, the key problem of traffic\nforecasting is how to extract the periodic temporal dependencies and complex\nspatial correlation. Current state-of-the-art methods for traffic flow\nforecasting are based on graph architectures and sequence learning models, but\nthey do not fully exploit spatial-temporal dynamic information in the traffic\nsystem. Specifically, the temporal dependence of the short-range is diluted by\nrecurrent neural networks, and the existing sequence model ignores local\nspatial information because the convolution operation uses global average\npooling. Besides, there will be some traffic accidents during the transitions\nof objects causing congestion in the real world that trigger increased\nprediction deviation. To overcome these challenges, we propose the\nSpatial-Temporal Conv-sequence Learning (STCL), in which a focused temporal\nblock uses unidirectional convolution to effectively capture short-term\nperiodic temporal dependence, and a spatial-temporal fusion module is able to\nextract the dependencies of both interactions and decrease the feature\ndimensions. Moreover, the accidents features impact on local traffic\ncongestion, and position encoding is employed to detect anomalies in complex\ntraffic situations. We conduct a large number of experiments on real-world\ntasks and verify the effectiveness of our proposed method.\n"}
+{"abstract": "  In the segmentation of fine-scale structures from natural and biomedical\nimages, per-pixel accuracy is not the only metric of concern. Topological\ncorrectness, such as vessel connectivity and membrane closure, is crucial for\ndownstream analysis tasks. In this paper, we propose a new approach to train\ndeep image segmentation networks for better topological accuracy. In\nparticular, leveraging the power of discrete Morse theory (DMT), we identify\nglobal structures, including 1D skeletons and 2D patches, which are important\nfor topological accuracy. Trained with a novel loss based on these global\nstructures, the network performance is significantly improved especially near\ntopologically challenging locations (such as weak spots of connections and\nmembranes). On diverse datasets, our method achieves superior performance on\nboth the DICE score and topological metrics.\n"}
+{"abstract": "  This paper presents a one-sided immersed boundary (IB) method using kernel\nfunctions constructed via a moving least squares (MLS) method. The resulting\nkernels effectively couple structural degrees of freedom to fluid variables on\nonly one side of the fluid-structure interface. This reduces spurious feedback\nforcing and internal flows that are typically observed in IB models that use\nisotropic kernel functions to couple the structure to fluid degrees of freedom\non both sides of the interface. The method developed here extends the original\nMLS methodology introduced by Vanella and Balaras (J Comput Phys, 2009). Prior\nIB/MLS methods have used isotropic kernel functions that coupled fluid\nvariables on both sides of the boundary to the interfacial degrees of freedom.\nThe original IB/MLS approach converts the cubic spline weights typically\nemployed in MLS reconstruction into an IB kernel function that satisfies\nparticular discrete moment conditions. This paper shows that the same approach\ncan be used to construct one-sided kernel functions (kernel functions are\nreferred to as generating functions in the MLS literature). We also examine the\nperformance of the new approach for a family of kernel functions introduced by\nPeskin. It is demonstrated that the one-sided MLS construction tends to\ngenerate non-monotone interpolation kernels with large over- and undershoots.\nWe present two simple weight shifting strategies to construct generating\nfunctions that are positive and monotone, which enhances the stability of the\nresulting IB methodology. Benchmark cases are used to test the order of\naccuracy and verify the one-sided IB/MLS simulations in both two and three\nspatial dimensions. This new IB/MLS method is also used to simulate flow over\nthe Ahmed car model, which highlights the applicability of this methodology for\nmodeling complex engineering flows.\n"}
+{"abstract": "  Most of the existing spoken language understanding systems can perform only\nsemantic frame parsing based on a single-round user query. They cannot take\nusers' feedback to update/add/remove slot values through multiround\ninteractions with users. In this paper, we introduce a novel multi-step spoken\nlanguage understanding system based on adversarial learning that can leverage\nthe multiround user's feedback to update slot values. We perform two\nexperiments on the benchmark ATIS dataset and demonstrate that the new system\ncan improve parsing performance by at least $2.5\\%$ in terms of F1, with only\none round of feedback. The improvement becomes even larger when the number of\nfeedback rounds increases. Furthermore, we also compare the new system with\nstate-of-the-art dialogue state tracking systems and demonstrate that the new\ninteractive system can perform better on multiround spoken language\nunderstanding tasks in terms of slot- and sentence-level accuracy.\n"}
+{"abstract": "  While compressing a colloidal state by optical means alone has been\npreviously achieved through a specific time-dependence of the trap stiffness,\nrealizing quickly the reverse transformation stumbles upon the necessity of a\ntransiently expulsive trap. To circumvent this difficulty, we propose to drive\nthe colloids by a combination of optical trapping and diffusiophoretic forces,\nboth time-dependent. Forcing via diffusiophoresis is enforced by controlling\nthe salt concentration at the boundary of the domain where the colloids are\nconfined. The method takes advantage of the separation of time scales between\nsalt and colloidal dynamics, and realizes a fast decompression in an optical\ntrap that remains confining at all times. We thereby obtain a so-called\nshortcut to adiabaticity protocol where colloidal dynamics, enslaved to salt\ndynamics, can nevertheless be controlled as desired.\n"}
+{"abstract": "  Classical scaling relationships for rheological quantities such as the\n$\\mu(J)$-rheology have become increasingly popular for closures of two-phase\nflow modeling. However, these frameworks have been derived for monodisperse\nparticles. We aim to extend these considerations to sediment transport modeling\nby using a more realistic sediment composition. We investigate the rheological\nbehavior of sheared sediment beds composed of polydisperse spherical particles\nin a laminar Couette-type shear flow. The sediment beds consist of particles\nwith a diameter size ratio of up to ten, which corresponds to grains ranging\nfrom fine to coarse sand. The data was generated using fully coupled, grain\nresolved direct numerical simulations using a combined lattice Boltzmann -\ndiscrete element method. These highly-resolved data yield detailed\ndepth-resolved profiles of the relevant physical quantities that determine the\nrheology, i.e., the local shear rate of the fluid, particle volume fraction,\ntotal shear, and granular pressure. A comparison against experimental data\nshows excellent agreement for the monodisperse case. We improve upon the\nparameterization of the $\\mu(J)$-rheology by expressing its empirically derived\nparameters as a function of the maximum particle volume fraction. Furthermore,\nwe extend these considerations by exploring the creeping regime for viscous\nnumbers much lower than used by previous studies to calibrate these\ncorrelations. Considering the low viscous numbers of our data, we found that\nthe friction coefficient governing the quasi-static state in the creeping\nregime tends to a finite value for vanishing shear, which decreases the\ncritical friction coefficient by a factor of three for all cases investigated.\n"}
+{"abstract": "  There has been increasing attention to semi-supervised learning (SSL)\napproaches in machine learning to forming a classifier in situations where the\ntraining data for a classifier consists of a limited number of classified\nobservations but a much larger number of unclassified observations. This is\nbecause the procurement of classified data can be quite costly due to high\nacquisition costs and subsequent financial, time, and ethical issues that can\narise in attempts to provide the true class labels for the unclassified data\nthat have been acquired. We provide here a review of statistical SSL approaches\nto this problem, focussing on the recent result that a classifier formed from a\npartially classified sample can actually have smaller expected error rate than\nthat if the sample were completely classified.\n"}
+{"abstract": "  We study the effects caused by Rashba and Dresselhaus spin-orbit coupling\nover the thermoelectric transport properties of a single-electron transistor,\nviz., a quantum dot connected to one-dimensional leads. Using linear response\ntheory and employing the numerical renormalization group method, we calculate\nthe thermopower, electrical and thermal conductances, dimensionless\nthermoelectric figure of merit, and study the Wiedemann-Franz law, showing\ntheir temperature maps. Our results for all those properties indicate that\nspin-orbit coupling drives the system into the Kondo regime. We show that the\nthermoelectric transport properties, in the presence of spin-orbit coupling,\nobey the expected universality of the Kondo strong coupling fixed point. In\naddition, our results show a notable increase in the thermoelectric figure of\nmerit, caused by the spin-orbit coupling in the one-dimensional quantum dot\nleads.\n"}
+{"abstract": "  In the class of reduced Abelian torsion-free groups $G$ of finite rank, we\ndescribe TI-groups, this means that every associative ring on $G$ is filial. If\nevery associative multiplication on $G$ is the zero multiplication, then $G$ is\ncalled a $nil_a$-group. It is proved that a reduced Abelian torsion-free group\n$G$ of finite rank is a $TI$-group if and only if $G$ is a homogeneous Murley\ngroup or $G$ is a $nil_a$-group. We also study the interrelations between the\nclass of homogeneous Murley groups and the class of $nil_a$-groups. For any\ntype $t\\ne (\\infty,\\infty,\\ldots)$ and every integer $n>1$, there exist\n$2^{\\aleph_0}$ pairwise non-quasi-isomorphic homogeneous Murley groups of type\n$t$ and rank $n$ which are $nil_a$-groups. We describe types $t$ such that\nthere exists a homogeneous Murley group of type $t$ which is not a\n$nil_a$-group. This paper will be published in Beitr\\\"{a}ge zur Algebra und\nGeometrie / Contributions to Algebra and Geometry.\n"}
+{"abstract": "  The present paper is devoted to an algebraic treatment of the joint spectral\ntheory within the framework of Noetherian modules over an algebra finite\nextension of an algebraically closed field. We prove the spectral mapping\ntheorem and analyze the index of tuples in purely algebraic case. The index\nfunction over tuples from the coordinate ring of a variety is naturally\nextended up to a numerical Tor-polynomial. Based on Serre's multiplicity\nformula, we deduce that Tor-polynomial is just the Samuel polynomial of the\nlocal algebra.\n"}
+{"abstract": "  We address aspects of coarse-graining in classical Statistical Physics from\nthe viewpoint of the symplectic non-squeezing theorem. We make some comments\nregarding the implications of the symplectic non-squeezing theorem for the\nBBGKY hierarchy. We also see the cubic cells appearing in coarse-graining as a\ndirect consequence of the uniqueness of Hofer's metric on the group of\nHamiltonian diffeomorphisms of the phase space.\n"}
+{"abstract": "  Next generation wireless networks are expected to be extremely complex due to\ntheir massive heterogeneity in terms of the types of network architectures they\nincorporate, the types and numbers of smart IoT devices they serve, and the\ntypes of emerging applications they support. In such large-scale and\nheterogeneous networks (HetNets), radio resource allocation and management\n(RRAM) becomes one of the major challenges encountered during system design and\ndeployment. In this context, emerging Deep Reinforcement Learning (DRL)\ntechniques are expected to be one of the main enabling technologies to address\nthe RRAM in future wireless HetNets. In this paper, we conduct a systematic\nin-depth, and comprehensive survey of the applications of DRL techniques in\nRRAM for next generation wireless networks. Towards this, we first overview the\nexisting traditional RRAM methods and identify their limitations that motivate\nthe use of DRL techniques in RRAM. Then, we provide a comprehensive review of\nthe most widely used DRL algorithms to address RRAM problems, including the\nvalue- and policy-based algorithms. The advantages, limitations, and use-cases\nfor each algorithm are provided. We then conduct a comprehensive and in-depth\nliterature review and classify existing related works based on both the radio\nresources they are addressing and the type of wireless networks they are\ninvestigating. To this end, we carefully identify the types of DRL algorithms\nutilized in each related work, the elements of these algorithms, and the main\nfindings of each related work. Finally, we highlight important open challenges\nand provide insights into several future research directions in the context of\nDRL-based RRAM. This survey is intentionally designed to guide and stimulate\nmore research endeavors towards building efficient and fine-grained DRL-based\nRRAM schemes for future wireless networks.\n"}
+{"abstract": "  Quantum Computing has been evolving in the last years. Although nowadays\nquantum algorithms performance has shown superior to their classical\ncounterparts, quantum decoherence and additional auxiliary qubits needed for\nerror tolerance routines have been huge barriers for quantum algorithms\nefficient use. These restrictions lead us to search for ways to minimize\nalgorithms costs, i.e the number of quantum logical gates and the depth of the\ncircuit. For this, quantum circuit synthesis and quantum circuit optimization\ntechniques are explored. We studied the viability of using Projective\nSimulation, a reinforcement learning technique, to tackle the problem of\nquantum circuit synthesis for noise quantum computers with limited number of\nqubits. The agent had the task of creating quantum circuits up to 5 qubits to\ngenerate GHZ states in the IBM Tenerife (IBM QX4) quantum processor. Our\nsimulations demonstrated that the agent had a good performance but its capacity\nfor learning new circuits decreased as the number of qubits increased.\n"}
+{"abstract": "  In this era of Gaia and ALMA, dynamical stellar mass measurements provide\nbenchmarks that are independent of observations of stellar characteristics and\ntheir uncertainties. These benchmarks can then be used to validate and improve\nstellar evolutionary models, which can lead to both imprecise and inaccurate\nmass predictions for pre-main-sequence, low-mass stars. We present the\ndynamical stellar masses derived from disks around three M-stars (FP Tau,\nJ0432+1827, and J1100-7619) using ALMA observations of $^{12}$CO (J=2--1) and\n$^{13}$CO (J=2--1) emission. These are the first dynamical stellar mass\nmeasurements for J0432+1827 and J1100-7619 and the most precise measurement for\nFP Tau. Fiducial stellar evolutionary model tracks, which do not include any\ntreatment of magnetic activity, agree with the dynamical measurement of\nJ0432+1827 but underpredict the mass by $\\sim$60\\% for FP Tau and $\\sim$80\\%\nfor J1100-7619. Possible explanations for the underpredictions include\ninaccurate assumptions of stellar effective temperature, undetected binarity\nfor J1100-7619, and that fiducial stellar evolutionary models are not complex\nenough to represent these stars. In the former case, the stellar effective\ntemperatures would need to be increased by $\\sim$40K to $\\sim$340K to reconcile\nthe fiducial model predictions with the dynamically-measured masses. In the\nlatter case, we show that the dynamical masses can be reproduced using results\nfrom stellar evolutionary models with starspots, which incorporate fractional\nstarspot coverage to represent the manifestation of magnetic activity. Folding\nin low-mass M-stars from the literature and assuming that the stellar effective\ntemperatures are imprecise but accurate, we find tentative evidence of a\nrelationship between fractional starspot coverage and observed effective\ntemperature for these young, cool stars.\n"}
+{"abstract": "  Understanding the physical processes involved in interfacial heat transfer is\ncritical for the interpretation of thermometric measurements and the\noptimization of heat dissipation in nanoelectronic devices that are based on\ntransition metal dichalcogenide (TMD) semiconductors. We model the phononic and\nelectronic contributions to the thermal boundary conductance (TBC) variability\nfor the MoS$_{2}$-SiO$_{2}$ and WS$_{2}$-SiO$_{2}$ interface. A\nphenomenological theory to model diffuse phonon transport at disordered\ninterfaces is introduced and yields $G$=13.5 and 12.4 MW/K/m$^{2}$ at 300 K for\nthe MoS$_{2}$-SiO$_{2}$ and WS$_{2}$-SiO$_{2} $ interface, respectively. We\ncompare its predictions to those of the coherent phonon model and find that the\nformer fits the MoS$_{2}$-SiO$_{2}$ data from experiments and simulations\nsignificantly better. Our analysis suggests that heat dissipation at the\nTMD-SiO$_{2}$ interface is dominated by phonons scattered diffusely by the\nrough interface although the electronic TBC contribution can be significant\neven at low electron densities ($n\\leq10^{12}$ cm$^{-2}$) and may explain some\nof the variation in the experimental TBC data from the literature. The physical\ninsights from our study can be useful for the development of thermally aware\ndesigns in TMD-based nanoelectronics.\n"}
+{"abstract": "  Large-scale finite element simulations of complex physical systems governed\nby partial differential equations crucially depend on adaptive mesh refinement\n(AMR) to allocate computational budget to regions where higher resolution is\nrequired. Existing scalable AMR methods make heuristic refinement decisions\nbased on instantaneous error estimation and thus do not aim for long-term\noptimality over an entire simulation. We propose a novel formulation of AMR as\na Markov decision process and apply deep reinforcement learning (RL) to train\nrefinement policies directly from simulation. AMR poses a new problem for RL in\nthat both the state dimension and available action set changes at every step,\nwhich we solve by proposing new policy architectures with differing generality\nand inductive bias. The model sizes of these policy architectures are\nindependent of the mesh size and hence scale to arbitrarily large and complex\nsimulations. We demonstrate in comprehensive experiments on static function\nestimation and the advection of different fields that RL policies can be\ncompetitive with a widely-used error estimator and generalize to larger, more\ncomplex, and unseen test problems.\n"}
+{"abstract": "  We investigate wave-vortex interaction emerging from an expanding compact\nvortex cluster in a two-dimensional Bose-Einstein condensate. We adapt\ntechniques developed for compact gravitational objects to derive the\ncharacteristic modes of the wave-vortex interaction perturbatively around an\neffective vortex flow field. We demonstrate the existence of orbits or\nsound-rings, in analogy to gravitational light-rings, and compute the\ncharacteristic spectrum for the out-of-equilibrium vortex cluster. The spectrum\nobtained from numerical simulations of a stochastic Gross-Pitaevskii equation\nexhibiting an expanding vortex cluster is in excellent agreement with\nanalytical predictions. Our findings are relevant for 2d-quantum turbulence,\nthe semi-classical limit around fluid flows, and rotating compact objects\nexhibiting discrete circulation.\n"}
+{"abstract": "  A $C^*$-algebra satisfies the Universal Coefficient Theorem (UCT) of\nRosenberg and Schochet if it is equivalent in Kasparov's $KK$-theory to a\ncommutative $C^*$-algebra. This paper is motivated by the problem of\nestablishing the range of validity of the UCT, and in particular, whether the\nUCT holds for all nuclear $C^*$-algebras.\n  We introduce the idea of a $C^*$-algebra that \"decomposes\" over a class\n$\\mathcal{C}$ of $C^*$-algebras. Roughly, this means that locally, there are\napproximately central elements that approximately cut the $C^*$-algebra into\ntwo $C^*$-subalgebras from $\\mathcal{C}$ that have well-behaved intersection.\nWe show that if a $C^*$-algebra decomposes over the class of nuclear, UCT\n$C^*$-algebras, then it satisfies the UCT. The argument is based on controlled\n$KK$-theory, as introduced by the authors in earlier work. Nuclearity is used\nvia Kasparov's Hilbert module version of Voiculescu's theorem, and Haagerup's\ntheorem that nuclear $C^*$-algebras are amenable\n  We say that a $C^*$-algebra has finite complexity if it is in the smallest\nclass of $C^*$-algebras containing the finite-dimensional $C^*$-algebras, and\nclosed under decomposability; our main result implies that all $C^*$-algebras\nin this class satisfy the UCT. The class of $C^*$-algebras with finite\ncomplexity is large, and comes with an ordinal-number invariant measuring the\ncomplexity level. We conjecture that a $C^*$-algebra of finite nuclear\ndimension and real rank zero has finite complexity; this (and several other\nrelated conjectures) would imply the UCT for all separable nuclear\n$C^*$-algebras. We also give new local formulations of the UCT, and some other\nnecessary and sufficient conditions for the UCT to hold for all nuclear\n$C^*$-algebras.\n"}
+{"abstract": "  How to effectively remove the noise while preserving the image structure\nfeatures is a challenging issue in the field of image denoising. In recent\nyears, fractional PDE based methods have attracted more and more research\nefforts due to the ability to balance the noise removal and the preservation of\nimage edges and textures. Among the existing fractional PDE algorithms, there\nare only a few using spatial fractional order derivatives, and all the\nfractional derivatives involved are one-sided derivatives. In this paper, an\nefficient feature-preserving fractional PDE algorithm is proposed for image\ndenoising based on a nonlinear spatial-fractional anisotropic diffusion\nequation. Two-sided Grumwald-Letnikov fractional derivatives were used in the\nPDE model which are suitable to depict the local self-similarity of images. The\nShort Memory Principle is employed to simplify the approximation scheme.\nExperimental results show that the proposed method is of a satisfactory\nperformance, i.e. it keeps a remarkable balance between noise removal and\nfeature preserving, and has an extremely high structural retention property.\n"}
+{"abstract": "  The electronic and superconducting properties of Fe1-xSe single-crystal\nflakes grown hydrothermally are studied by the transport measurements under\nzero and high magnetic fields up to 38.5 T. The results contrast sharply with\nthose previously reported for nematically ordered FeSe by\nchemical-vapor-transport (CVT) growth. No signature of the electronic\nnematicity, but an evident metal-to-nonmetal crossover with increasing\ntemperature, is detected in the normal state of the present hydrothermal\nsamples. Interestingly, a higher superconducting critical temperature Tc of\n13.2 K is observed compared to a suppressed Tc of 9 K in the presence of the\nnematicity in the CVT FeSe. Moreover, the upper critical field in the\nzero-temperature limit is found to be isotropic with respect to the field\ndirection and to reach a higher value of ~42 T, which breaks the Pauli limit by\na factor of 1.8.\n"}
+{"abstract": "  We establish limit laws for the distribution in small intervals of the roots\nof the quadratic congruence $\\mu^2 \\equiv D \\bmod m$, with $D > 0$ square-free\nand $D\\not\\equiv 1 \\bmod 4$. This is achieved by translating the problem to\nconvergence of certain geodesic random line processes in the hyperbolic plane.\nThis geometric interpretation allows us in particular to derive an explicit\nexpression for the pair correlation density of the roots.\n"}
+{"abstract": "  Cathodes are critical components of rechargeable batteries. Conventionally,\nthe search for cathode materials relies on experimental trial-and-error and a\ntraversing of existing computational/experimental databases. While these\nmethods have led to the discovery of several commercially-viable cathode\nmaterials, the chemical space explored so far is limited and many phases will\nhave been overlooked, in particular those that are metastable. We describe a\ncomputational framework for battery cathode exploration, based on ab initio\nrandom structure searching (AIRSS), an approach that samples local minima on\nthe potential energy surface to identify new crystal structures. We show that,\nby delimiting the search space using a number of constraints, including\nchemically aware minimum interatomic separations, cell volumes, and space group\nsymmetries, AIRSS can efficiently predict both thermodynamically stable and\nmetastable cathode materials. Specifically, we investigate LiCoO2, LiFePO4, and\nLixCuyFz to demonstrate the efficiency of the method by rediscovering the known\ncrystal structures of these cathode materials. The effect of parameters, such\nas minimum separations and symmetries, on the efficiency of the sampling is\ndiscussed in detail. The adaptation of the minimum interatomic distances, on a\nspecies-pair basis, from low-energy optimized structures to efficiently capture\nthe local coordination environment of atoms, is explored. A family of novel\ncathode materials based, on the transition-metal oxalates,is proposed. They\ndemonstrate superb energy density, oxygen-redox stability, and lithium\ndiffusion properties. This article serves both as an introduction to the\ncomputational framework, and as a guide to battery cathode material discovery\nusing AIRSS.\n"}
+{"abstract": "  For a given Finsler-Minkowski norm $\\mathcal{F}$ in $\\mathbb{R}^N$ and a\nbounded smooth domain $\\Omega\\subset\\mathbb{R}^N$ $\\big(N\\geq 2\\big)$, we\nestablish the following weighted anisotropic Sobolev inequality $$\nS\\left(\\int_{\\Omega}|u|^q\nf\\,dx\\right)^\\frac{1}{q}\\leq\\left(\\int_{\\Omega}\\mathcal{F}(\\nabla u)^p\nw\\,dx\\right)^\\frac{1}{p},\\quad\\forall\\,u\\in\nW_0^{1,p}(\\Omega,w)\\leqno{\\mathcal{(P)}} $$ where $W_0^{1,p}(\\Omega,w)$ is the\nweighted Sobolev space under a class of $p$-admissible weights $w$, where $f$\nis some nonnegative integrable function in $\\Omega$. We discuss the case\n$0<q<1$ and observe that $$ \\mu(\\Omega):=\\inf_{u\\in\nW_{0}^{1,p}(\\Omega,w)}\\Bigg\\{\\int_{\\Omega}\\mathcal{F}(\\nabla u)^p\nw\\,dx:\\int_{\\Omega}|u|^{q}f\\,dx=1\\Bigg\\}\\leqno{\\mathcal{(Q)}} $$ is associated\nwith singular weighted anisotropic $p$-Laplace equations. To this end, we also\nstudy existence and regularity properties of solutions for weighted anisotropic\n$p$-Laplace equations under the mixed and exponential singularities.\n"}
+{"abstract": "  Neural network (NN) training and generalization in the infinite-width limit\nare well-characterized by kernel methods with a neural tangent kernel (NTK)\nthat is stationary in time. However, finite-width NNs consistently outperform\ncorresponding kernel methods, suggesting the importance of feature learning,\nwhich manifests as the time evolution of NTKs. Here, we analyze the phenomenon\nof kernel alignment of the NTK with the target functions during gradient\ndescent. We first provide a mechanistic explanation for why alignment between\ntask and kernel occurs in deep linear networks. We then show that this behavior\noccurs more generally if one optimizes the feature map over time to accelerate\nlearning while constraining how quickly the features evolve. Empirically,\ngradient descent undergoes a feature learning phase, during which top\neigenfunctions of the NTK quickly align with the target function and the loss\ndecreases faster than power law in time; it then enters a kernel gradient\ndescent (KGD) phase where the alignment does not improve significantly and the\ntraining loss decreases in power law. We show that feature evolution is faster\nand more dramatic in deeper networks. We also found that networks with multiple\noutput nodes develop separate, specialized kernels for each output channel, a\nphenomenon we termed kernel specialization. We show that this class-specific\nalignment is does not occur in linear networks.\n"}
+{"abstract": "  Let $\\mathcal{A}$ be a generalized q-Weyl algebra, it is generated by\n$u,v,Z,Z^{-1}$ with relations $ZuZ^{-1}=q^2u$, $ZvZ^{-1}=q^{-2}v$,\n$uv=P(q^{-1}Z)$, $vu=P(qZ)$, where $P$ is a Laurent polynomial. A Hermitian\nform $(\\cdot,\\cdot)$ on $\\mathcal{A}$ is called invariant if\n$(Za,b)=(a,bZ^{-1})$, $(ua,b)=(a,sbv)$, $(va,b)=(a,s^{-1}bu)$ for some $s\\in\n\\mathbb{C}$ with $|s|=1$ and all $a,b\\in \\mathcal{A}$. In this paper we\nclassify positive definite invariant Hermitian forms on generalized q-Weyl\nalgebras.\n"}
+{"abstract": "  Forest roads in Romania are unique natural wildlife sites used for recreation\nby countless tourists. In order to protect and maintain these roads, we propose\nRovisLab AMTU (Autonomous Mobile Test Unit), which is a robotic system designed\nto autonomously navigate off-road terrain and inspect if any deforestation or\ndamage occurred along tracked route. AMTU's core component is its embedded\nvision module, optimized for real-time environment perception. For achieving a\nhigh computation speed, we use a learning system to train a multi-task Deep\nNeural Network (DNN) for scene and instance segmentation of objects, while the\nkeypoints required for simultaneous localization and mapping are calculated\nusing a handcrafted FAST feature detector and the Lucas-Kanade tracking\nalgorithm. Both the DNN and the handcrafted backbone are run in parallel on the\nGPU of an NVIDIA AGX Xavier board. We show experimental results on the test\ntrack of our research facility.\n"}
+{"abstract": "  Tokenization is fundamental to pretrained language models (PLMs). Existing\ntokenization methods for Chinese PLMs typically treat each character as an\nindivisible token. However, they ignore the unique feature of the Chinese\nwriting system where additional linguistic information exists below the\ncharacter level, i.e., at the sub-character level. To utilize such information,\nwe propose sub-character (SubChar for short) tokenization. Specifically, we\nfirst encode the input text by converting each Chinese character into a short\nsequence based on its glyph or pronunciation, and then construct the vocabulary\nbased on the encoded text with sub-word tokenization. Experimental results show\nthat SubChar tokenizers have two main advantages over existing tokenizers: 1)\nThey can tokenize inputs into much shorter sequences, thus improving the\ncomputational efficiency. 2) Pronunciation-based SubChar tokenizers can encode\nChinese homophones into the same transliteration sequences and produce the same\ntokenization output, hence being robust to all homophone typos. At the same\ntime, models trained with SubChar tokenizers perform competitively on\ndownstream tasks. We release our code at\nhttps://github.com/thunlp/SubCharTokenization to facilitate future work.\n"}
+{"abstract": "  Space weather phenomena such as solar flares, have massive destructive power\nwhen reaches certain amount of magnitude. Such high magnitude solar flare event\ncan interfere space-earth radio communications and neutralize space-earth\nelectronics equipment. In the current study, we explorer the deep learning\napproach to build a solar flare forecasting model and examine its limitations\nalong with the ability of features extraction, based on the available\ntime-series data. For that purpose, we present a multi-layer 1D Convolutional\nNeural Network (CNN) to forecast solar flare events probability occurrence of M\nand X classes at 1,3,6,12,24,48,72,96 hours time frame. In order to train and\nevaluate the performance of the model, we utilised the available Geostationary\nOperational Environmental Satellite (GOES) X-ray time series data, ranged\nbetween July 1998 and January 2019, covering almost entirely the solar cycles\n23 and 24. The forecasting model were trained and evaluated in two different\nscenarios (1) random selection and (2) chronological selection, which were\ncompare afterward. Moreover we compare our results to those considered as\nstate-of-the-art flare forecasting models, both with similar approaches and\ndifferent ones.The majority of the results indicates that (1) chronological\nselection obtain a degradation factor of 3\\% versus the random selection for\nthe M class model and elevation factor of 2\\% for the X class model. (2) When\nconsider utilizing only X-ray time-series data, the suggested model achieve\nhigh score results compare to other studies. (3) The suggested model combined\nwith solely X-ray time-series fails to distinguish between M class magnitude\nand X class magnitude solar flare events. All source code are available at\nhttps://github.com/vladlanda/Low-Dimensional-Convolutional-Neural-Network-For-Solar-Flares-GOES-Time-Series-Classification\n"}
+{"abstract": "  Powerful new, high resolution, high sensitivity, multi-frequency, wide-field\nradio surveys such as the Australian Square Kilometre Array Pathfinder (ASKAP)\nEvolutionary Map of the Universe (EMU) are emerging. They will offer fresh\nopportunities to undertake new determinations of useful parameters for various\nkinds of extended astrophysical phenomena. Here, we consider specific\napplication to angular size determinations of Planetary Nebulae (PNe) via a new\nradio continuum Spectral Energy Distribution (SED) fitting technique. We show\nthat robust determinations of angular size can be obtained, comparable to the\nbest optical and radio observations but with the potential for consistent\napplication across the population. This includes unresolved and/or heavily\nobscured PNe that are extremely faint or even non-detectable in the optical.\n"}
+{"abstract": "  Plant reflectance spectra - the profile of light reflected by leaves across\ndifferent wavelengths - supply the spectral signature for a species at a\nspatial location to enable estimation of functional and taxonomic diversity for\nplants. We consider leaf spectra as \"responses\" to be explained spatially.\nThese spectra/reflectances are functions over a wavelength band that respond to\nthe environment.\n  Our motivating data are gathered for several families from the Cape Floristic\nRegion (CFR) in South Africa and lead us to develop rich novel spatial models\nthat can explain spectra for genera within families. Wavelength responses for\nan individual leaf are viewed as a function of wavelength, leading to\nfunctional data modeling. Local environmental features become covariates. We\nintroduce wavelength - covariate interaction since the response to\nenvironmental regressors may vary with wavelength, so may variance. Formal\nspatial modeling enables prediction of reflectances for genera at unobserved\nlocations with known environmental features. We incorporate spatial dependence,\nwavelength dependence, and space-wavelength interaction (in the spirit of\nspace-time interaction). We implement out-of-sample validation to select a best\nmodel, discovering that the model features listed above are all informative for\nthe functional data analysis. We then supply interpretation of the results\nunder the selected model.\n"}
+{"abstract": "  Using the Yebes 40m and IRAM 30m radiotelescopes, we detected two series of\nharmonically related lines in space that can be fitted to a symmetric rotor.\nThe lines have been seen towards the cold dense cores TMC-1, L483, L1527, and\nL1544. High level of theory ab initio calculations indicate that the best\npossible candidate is the acetyl cation, CH3CO+, which is the most stable\nproduct resulting from the protonation of ketene. We have produced this species\nin the laboratory and observed its rotational transitions Ju = 10 up to Ju =\n27. Hence, we report the discovery of CH3CO+ in space based on our\nobservations, theoretical calculations, and laboratory experiments. The derived\nrotational and distortion constants allow us to predict the spectrum of CH3CO+\nwith high accuracy up to 500 GHz. We derive an abundance ratio\nN(H2CCO)/N(CH3CO+) = 44. The high abundance of the protonated form of H2CCO is\ndue to the high proton affinity of the neutral species. The other isomer,\nH2CCOH+, is found to be 178.9 kJ/mol above CH3CO+. The observed intensity ratio\nbetween the K=0 and K=1 lines, 2.2, strongly suggests that the A and E symmetry\nstates have suffered interconversion processes due to collisions with H and/or\nH2, or during their formation through the reaction of H3+ with H2CCO.\n"}
+{"abstract": "  Modular symmetry offers the possibility to provide an origin of discrete\nflavour symmetry and to break it along particular symmetry preserving\ndirections without introducing flavons or driving fields. It is also possible\nto use a weighton field to account for charged fermion mass hierarchies rather\nthan a Froggatt-Nielsen mechanism. Such an approach can be applied to flavoured\nGrand Unified Theories (GUTs) which can be greatly simplified using modular\nforms. As an example, we consider a modular version of a previously proposed\n$S_4\\times SU(5)$ GUT, with Gatto-Sartori-Tonin and Georgi-Jarlskog relations,\nin which all flavons and driving fields are removed, with their effect replaced\nby modular forms with moduli assumed to be at various fixed points, rendering\nthe theory much simpler. In the neutrino sector there are two right-handed\nneutrinos constituting a Littlest Seesaw model satisfying Constrained\nSequential Dominance (CSD) where the two columns of the Dirac neutrino mass\nmatrix are proportional to $(0,1, -1)$ and $(1, n, 2-n)$ respectively, and\n$n=1+\\sqrt{6}\\approx 3.45$ is prescribed by the modular symmetry, with\npredictions subject to charged lepton mixing corrections. We perform a\nnumerical analysis, showing quark and lepton mass and mixing correlations\naround the best fit points.\n"}
+{"abstract": "  The security of code-based cryptography usually relies on the hardness of the\nsyndrome decoding (SD) problem for the Hamming weight. The best generic\nalgorithms are all improvements of an old algorithm by Prange, and they are\nknown under the name of Information Set Decoding (ISD) algorithms. This work\naims to extend ISD algorithms' scope by changing the underlying weight function\nand alphabet size of SD. More precisely, we show how to use Wagner's algorithm\nin the ISD framework to solve SD for a wide range of weight functions. We also\ncalculate the asymptotic complexities of ISD algorithms both in the classical\nand quantum case. We then apply our results to the Lee metric, which currently\nreceives a significant amount of attention. By providing the parameters of SD\nfor which decoding in the Lee weight seems to be the hardest, our study could\nhave several applications for designing code-based cryptosystems and their\nsecurity analysis, especially against quantum adversaries.\n"}
+{"abstract": "  Identifying universal properties of non-equilibrium quantum states is a major\nchallenge in modern physics. A fascinating prediction is that classical\nhydrodynamics emerges universally in the evolution of any interacting quantum\nsystem. Here, we experimentally probe the quantum dynamics of 51 individually\ncontrolled ions, realizing a long-range interacting spin chain. By measuring\nspace-time resolved correlation functions in an infinite temperature state, we\nobserve a whole family of hydrodynamic universality classes, ranging from\nnormal diffusion to anomalous superdiffusion, that are described by L\\'evy\nflights. We extract the transport coefficients of the hydrodynamic theory,\nreflecting the microscopic properties of the system. Our observations\ndemonstrate the potential for engineered quantum systems to provide key\ninsights into universal properties of non-equilibrium states of quantum matter.\n"}
+{"abstract": "  We report results from searches for anisotropic stochastic gravitational-wave\nbackgrounds using data from the first three observing runs of the Advanced LIGO\nand Advanced Virgo detectors. For the first time, we include Virgo data in our\nanalysis and run our search with a new efficient pipeline called {\\tt PyStoch}\non data folded over one sidereal day. We use gravitational-wave radiometry\n(broadband and narrow band) to produce sky maps of stochastic\ngravitational-wave backgrounds and to search for gravitational waves from point\nsources. A spherical harmonic decomposition method is employed to look for\ngravitational-wave emission from spatially-extended sources. Neither technique\nfound evidence of gravitational-wave signals. Hence we derive 95\\%\nconfidence-level upper limit sky maps on the gravitational-wave energy flux\nfrom broadband point sources, ranging from $F_{\\alpha, \\Theta} < {\\rm (0.013 -\n7.6)} \\times 10^{-8} {\\rm erg \\, cm^{-2} \\, s^{-1} \\, Hz^{-1}},$ and on the\n(normalized) gravitational-wave energy density spectrum from extended sources,\nranging from $\\Omega_{\\alpha, \\Theta} < {\\rm (0.57 - 9.3)} \\times 10^{-9} \\,\n{\\rm sr^{-1}}$, depending on direction ($\\Theta$) and spectral index\n($\\alpha$). These limits improve upon previous limits by factors of $2.9 -\n3.5$. We also set 95\\% confidence level upper limits on the frequency-dependent\nstrain amplitudes of quasimonochromatic gravitational waves coming from three\ninteresting targets, Scorpius X-1, SN 1987A and the Galactic Center, with best\nupper limits range from $h_0 < {\\rm (1.7-2.1)} \\times 10^{-25},$ a factor of\n$\\geq 2.0$ improvement compared to previous stochastic radiometer searches.\n"}
+{"abstract": "  Advances in differentiable numerical integrators have enabled the use of\ngradient descent techniques to learn ordinary differential equations (ODEs). In\nthe context of machine learning, differentiable solvers are central for Neural\nODEs (NODEs), a class of deep learning models with continuous depth, rather\nthan discrete layers. However, these integrators can be unsatisfactorily slow\nand inaccurate when learning systems of ODEs from long sequences, or when\nsolutions of the system vary at widely different timescales in each dimension.\nIn this paper we propose an alternative approach to learning ODEs from data: we\nrepresent the underlying ODE as a vector field that is related to another base\nvector field by a differentiable bijection, modelled by an invertible neural\nnetwork. By restricting the base ODE to be amenable to integration, we can\ndrastically speed up and improve the robustness of integration. We demonstrate\nthe efficacy of our method in training and evaluating continuous neural\nnetworks models, as well as in learning benchmark ODE systems. We observe\nimprovements of up to two orders of magnitude when integrating learned ODEs\nwith GPUs computation.\n"}
+{"abstract": "  We examine the structure of global conformal multiplets in 2D celestial CFT.\nFor a 4D bulk theory containing massless particles of spin\n$s=\\{0,\\frac{1}{2},1,\\frac{3}{2},2\\}$ we classify and construct all\nSL(2,$\\mathbb{C}$) primary descendants which are organized into 'celestial\ndiamonds'. This explicit construction is achieved using a wavefunction-based\napproach that allows us to map 4D scattering amplitudes to celestial CFT\ncorrelators of operators with SL(2,$\\mathbb{C}$) conformal dimension $\\Delta$\nand spin $J$. Radiative conformal primary wavefunctions have $J=\\pm s$ and give\nrise to conformally soft theorems for special values of $\\Delta \\in\n\\frac{1}{2}\\mathbb{Z}$. They are located either at the top of celestial\ndiamonds, where they descend to trivial null primaries, or at the left and\nright corners, where they descend both to and from generalized conformal\nprimary wavefunctions which have $|J|\\leq s$. Celestial diamonds naturally\nincorporate degeneracies of opposite helicity particles via the 2D shadow\ntransform relating radiative primaries and account for the global and\nasymptotic symmetries in gauge theory and gravity.\n"}
+{"abstract": "  Many studies on machine learning (ML) for computer-aided diagnosis have so\nfar been mostly restricted to high-quality research data. Clinical data\nwarehouses, gathering routine examinations from hospitals, offer great promises\nfor training and validation of ML models in a realistic setting. However, the\nuse of such clinical data warehouses requires quality control (QC) tools.\nVisual QC by experts is time-consuming and does not scale to large datasets. In\nthis paper, we propose a convolutional neural network (CNN) for the automatic\nQC of 3D T1-weighted brain MRI for a large heterogeneous clinical data\nwarehouse. To that purpose, we used the data warehouse of the hospitals of the\nGreater Paris area (Assistance Publique-H\\^opitaux de Paris [AP-HP]).\nSpecifically, the objectives were: 1) to identify images which are not proper\nT1-weighted brain MRIs; 2) to identify acquisitions for which gadolinium was\ninjected; 3) to rate the overall image quality. We used 5000 images for\ntraining and validation and a separate set of 500 images for testing. In order\nto train/validate the CNN, the data were annotated by two trained raters\naccording to a visual QC protocol that we specifically designed for application\nin the setting of a data warehouse. For objectives 1 and 2, our approach\nachieved excellent accuracy (balanced accuracy and F1-score \\textgreater 90\\%),\nsimilar to the human raters. For objective 3, the performance was good but\nsubstantially lower than that of human raters. Nevertheless, the automatic\napproach accurately identified (balanced accuracy and F1-score \\textgreater\n80\\%) low quality images, which would typically need to be excluded. Overall,\nour approach shall be useful for exploiting hospital data warehouses in medical\nimage computing.\n"}
+{"abstract": "  Although depth extraction with passive sensors has seen remarkable\nimprovement with deep learning, these approaches may fail to obtain correct\ndepth if they are exposed to environments not observed during training. Online\nadaptation, where the neural network trains while deployed, with unsupervised\nlearning provides a convenient solution. However, online adaptation causes a\nneural network to forget the past. Thus, past training is wasted and the\nnetwork is not able to provide good results if it observes past scenes. This\nwork deals with practical online-adaptation where the input is online and\ntemporally-correlated, and training is completely unsupervised. Regularization\nand replay-based methods without task boundaries are proposed to avoid\ncatastrophic forgetting while adapting to online data. Experiments are\nperformed on different datasets with both structure-from-motion and stereo.\nResults of forgetting as well as adaptation are provided, which are superior to\nrecent methods. The proposed approach is more inline with the artificial\ngeneral intelligence paradigm as the neural network learns the scene where it\nis deployed without any supervision (target labels and tasks) and without\nforgetting about the past. Code is available at github.com/umarKarim/cou_stereo\nand github.com/umarKarim/cou_sfm.\n"}
+{"abstract": "  Consider the setting where there are B>1 candidate statistical models, and\none is interested in model selection. Two common approaches to solve this\nproblem are to select a single model or to combine the candidate models through\nmodel averaging. Instead, we select a subset of the combined parameter space\nassociated with the models. Specifically, a model averaging perspective is used\nto increase the parameter space, and a model selection criterion is used to\nselect a subset of this expanded parameter space. We account for the\nvariability of the criterion by adapting Yekutieli (2012)'s method to Bayesian\nmodel averaging (BMA). Yekutieli (2012)'s method treats model selection as a\ntruncation problem. We truncate the joint support of the data and the parameter\nspace to only include small values of the covariance penalized error (CPE)\ncriterion. The CPE is a general expression that contains several information\ncriteria as special cases. Simulation results show that as long as the\ntruncated set does not have near zero probability, we tend to obtain lower mean\nsquared error than BMA. Additional theoretical results are provided that\nprovide the foundation for these observations. We apply our approach to a\ndataset consisting of American Community Survey (ACS) period estimates to\nillustrate that this perspective can lead to improvements of a single model.\n"}
+{"abstract": "  Deep learning classifiers are now known to have flaws in the representations\nof their class. Adversarial attacks can find a human-imperceptible perturbation\nfor a given image that will mislead a trained model. The most effective methods\nto defend against such attacks trains on generated adversarial examples to\nlearn their distribution. Previous work aimed to align original and adversarial\nimage representations in the same way as domain adaptation to improve\nrobustness. Yet, they partially align the representations using approaches that\ndo not reflect the geometry of space and distribution. In addition, it is\ndifficult to accurately compare robustness between defended models. Until now,\nthey have been evaluated using a fixed perturbation size. However, defended\nmodels may react differently to variations of this perturbation size. In this\npaper, the analogy of domain adaptation is taken a step further by exploiting\noptimal transport theory. We propose to use a loss between distributions that\nfaithfully reflect the ground distance. This leads to SAT (Sinkhorn Adversarial\nTraining), a more robust defense against adversarial attacks. Then, we propose\nto quantify more precisely the robustness of a model to adversarial attacks\nover a wide range of perturbation sizes using a different metric, the Area\nUnder the Accuracy Curve (AUAC). We perform extensive experiments on both\nCIFAR-10 and CIFAR-100 datasets and show that our defense is globally more\nrobust than the state-of-the-art.\n"}
+{"abstract": "  As governments around the world decide to deploy digital health passports as\na tool to curb the spread of Covid-19, it becomes increasingly important to\nconsider how these can be constructed with privacy-by-design.\n  In this paper we discuss the privacy and security issues of common approaches\nfor constructing digital health passports. We then show how to construct, and\ndeploy, secure and private digital health passports, in a simple and efficient\nmanner. We do so by using a protocol for distributed password-based token\nissuance, secret sharing and by leveraging modern smart phones' secure\nhardware.\n  Our solution only requires a constant amount of asymmetric cryptographic\noperations and a single round of communication between the user and the party\nverifying the user's digital health passport, and only two rounds between the\nuser and the server issuing the digital health passport.\n"}
+{"abstract": "  According to Freud \"words were originally magic and to this day words have\nretained much of their ancient magical power\". By words, behaviors are\ntransformed and problems are solved. The way we use words reveals our\nintentions, goals and values. Novel tools for text analysis help understand the\nmagical power of words. This power is multiplied, if it is combined with the\nstudy of social networks, i.e. with the analysis of relationships among social\nunits. This special issue of the International Journal of Information\nManagement, entitled \"Combining Social Network Analysis and Text Mining: from\nTheory to Practice\", includes heterogeneous and innovative research at the\nnexus of text mining and social network analysis. It aims to enrich work at the\nintersection of these fields, which still lags behind in theoretical,\nempirical, and methodological foundations. The nine articles accepted for\ninclusion in this special issue all present methods and tools that have\nbusiness applications. They are summarized in this editorial introduction.\n"}
+{"abstract": "  Neural networks for stock price prediction(NNSPP) have been popular for\ndecades. However, most of its study results remain in the research paper and\ncannot truly play a role in the securities market. One of the main reasons\nleading to this situation is that the prediction error(PE) based evaluation\nresults have statistical flaws. Its prediction results cannot represent the\nmost critical financial direction attributes. So it cannot provide investors\nwith convincing, interpretable, and consistent model performance evaluation\nresults for practical applications in the securities market. To illustrate, we\nhave used data selected from 20 stock datasets over six years from the Shanghai\nand Shenzhen stock market in China, and 20 stock datasets from NASDAQ and NYSE\nin the USA. We implement six shallow and deep neural networks to predict stock\nprices and use four prediction error measures for evaluation. The results show\nthat the prediction error value only partially reflects the model accuracy of\nthe stock price prediction, and cannot reflect the change in the direction of\nthe model predicted stock price. This characteristic determines that PE is not\nsuitable as an evaluation indicator of NNSPP. Otherwise, it will bring huge\npotential risks to investors. Therefore, this paper establishes an experiment\nplatform to confirm that the PE method is not suitable for the NNSPP\nevaluation, and provides a theoretical basis for the necessity of creating a\nnew NNSPP evaluation method in the future.\n"}
+{"abstract": "  The Radar Echo Telescope for Cosmic Rays (RET-CR) is a recently initiated\nexperiment designed to detect the englacial cascade of a cosmic-ray initiated\nair shower via in-ice radar, toward the goal of a full-scale, next-generation\nexperiment to detect ultra high energy neutrinos in polar ice. For cosmic rays\nwith a primary energy greater than 10 PeV, roughly 10% of an air-shower's\nenergy reaches the surface of a high elevation ice-sheet ($\\gtrsim$2 km)\nconcentrated into a radius of roughly 10 cm. This penetrating shower core\ncreates an in-ice cascade many orders of magnitude more dense than the\npreceding in-air cascade. This dense cascade can be detected via the radar echo\ntechnique, where transmitted radio is reflected from the ionization deposit\nleft in the wake of the cascade. RET-CR will test the radar echo method in\nnature, with the in-ice cascade of a cosmic-ray initiated air-shower serving as\na test beam. We present the projected event rate and sensitivity based upon a\nthree part simulation using CORSIKA, GEANT4, and RadioScatter. RET-CR expects\n$\\sim$1 radar echo event per day.\n"}
+{"abstract": "  The near-infinite chemical diversity of natural and artificial macromolecules\narises from the vast range of possible component monomers, linkages, and\npolymers topologies. This enormous variety contributes to the ubiquity and\nindispensability of macromolecules but hinders the development of general\nmachine learning methods with macromolecules as input. To address this, we\ndeveloped GLAMOUR, a framework for chemistry-informed graph representation of\nmacromolecules that enables quantifying structural similarity, and\ninterpretable supervised learning for macromolecules.\n"}
+{"abstract": "  Understanding the formation of lead halide (LH) perovskite solution\nprecursors is crucial to gain insight into the evolution of these materials to\nthin films for solar cells. Using density-functional theory in conjunction with\nthe polarizable continuum model, we investigate 18 complexes with chemical\nformula PbX$_2$M$_4$, where X = Cl, Br, I and M are common solvent molecules.\nThrough the analysis of structural properties, binding energies, and charge\ndistributions, we clarify the role of halogen species and solvent molecules in\nthe formation of LH perovskite precursors. We find that interatomic distances\nare critically affected by the halogen species, while the energetic stability\nis driven by the solvent coordination to the backbones. Regardless of the\nsolvent, lead iodide complexes are more strongly bound than the others. Based\non the charge distribution analysis, we find that all solvent molecules bind\ncovalently with the LH backbones and that Pb-I and Pb-Br bonds lose ionicity in\nsolution. Our results contribute to clarify the physical properties of LH\nperovskite solution precursors and offer a valuable starting point for further\ninvestigations on their crystalline intermediates.\n"}
+{"abstract": "  We investigate the effect of the population III (Pop III) stars supernova\nexplosion~(SN) on the high redshifts reionization history using the latest\nPlanck data. It is predicted that massive Pop~III stars~($130M_\\odot\\leq M\\leq\n270M_\\odot$) explode energetically at the end of their stellar life as\npair-instability supernovae (PISNe). In the explosion, supernova remnants grow\nas hot ionized bubbles and enhance the ionization fraction in the early stage\nof the reionization history. This enhancement affects the optical depth of the\ncosmic microwave background~(CMB) and generates the additional anisotropy of\nthe CMB polarization on large scales. Therefore, analyzing the Planck\npolarization data allows us to examine the Pop III star SNe and the abundance\nof their progenitors, massive Pop III stars. In order to model the SN\ncontribution to reionization, we introduce a new parameter $\\zeta$, which\nrelates to the abundance of the SNe to the collapse fraction of the Universe.\nUsing the Markov chain Monte Carlo method with the latest Planck polarization\ndata, we obtain the constraint on our model parameter, $\\zeta$. Our constraint\ntells us that observed CMB polarization is consistent with the abundance of\nPISNe predicted from the star formation rate and initial mass function of Pop\nIII stars in recent cosmological simulations. We also suggest that combining\nfurther observations on the late reionization history such as high redshift\nquasi-stellar object~(QSO) observations can provide tighter constraints and\nimportant information on the nature of Pop III stars.\n"}
+{"abstract": "  Neural Networks (NNs) can be used to solve Ordinary and Partial Differential\nEquations (ODEs and PDEs) by redefining the question as an optimization\nproblem. The objective function to be optimized is the sum of the squares of\nthe PDE to be solved and of the initial/boundary conditions. A feed forward NN\nis trained to minimise this loss function evaluated on a set of collocation\npoints sampled from the domain where the problem is defined. A compact and\nsmooth solution, that only depends on the weights of the trained NN, is then\nobtained. This approach is often referred to as PINN, from Physics Informed\nNeural Network~\\cite{raissi2017physics_1, raissi2017physics_2}. Despite the\nsuccess of the PINN approach in solving various classes of PDEs, an\nimplementation of this idea that is capable of solving a large class of ODEs\nand PDEs with good accuracy and without the need to finely tune the\nhyperparameters of the network, is not available yet. In this paper, we\nintroduce a new implementation of this concept - called dNNsolve - that makes\nuse of dual Neural Networks to solve ODEs/PDEs. These include: i) sine and\nsigmoidal activation functions, that provide a more efficient basis to capture\nboth secular and periodic patterns in the solutions; ii) a newly designed\narchitecture, that makes it easy for the the NN to approximate the solution\nusing the basis functions mentioned above. We show that dNNsolve is capable of\nsolving a broad range of ODEs/PDEs in 1, 2 and 3 spacetime dimensions, without\nthe need of hyperparameter fine-tuning.\n"}
+{"abstract": "  In the status quo, dementia is yet to be cured. Precise diagnosis prior to\nthe onset of the symptoms can prevent the rapid progression of the emerging\ncognitive impairment. Recent progress has shown that Electroencephalography\n(EEG) is the promising and cost-effective test to facilitate the detection of\nneurocognitive disorders. However, most of the existing works have been using\nonly resting-state EEG. The efficiencies of EEG signals from various cognitive\ntasks, for dementia classification, have yet to be thoroughly investigated. In\nthis study, we designed four cognitive tasks that engage different cognitive\nperformances: attention, working memory, and executive function. We\ninvestigated these tasks by using statistical analysis on both time and\nfrequency domains of EEG signals from three classes of human subjects: Dementia\n(DEM), Mild Cognitive Impairment (MCI), and Normal Control (NC). We also\nfurther evaluated the classification performances of two features extraction\nmethods: Principal Component Analysis (PCA) and Filter Bank Common Spatial\nPattern (FBCSP). We found that the working memory related tasks yielded good\nperformances for dementia recognition in both cases using PCA and FBCSP.\nMoreover, FBCSP with features combination from four tasks revealed the best\nsensitivity of 0.87 and the specificity of 0.80. To our best knowledge, this is\nthe first work that concurrently investigated several cognitive tasks for\ndementia recognition using both statistical analysis and classification scores.\nOur results yielded essential information to design and aid in conducting\nfurther experimental tasks to early diagnose dementia patients.\n"}
+{"abstract": "  Frailty models are survival analysis models which account for heterogeneity\nand random effects in the data. In these models, the random effect (the\nfrailty) is assumed to have a multiplicative effect on the hazard. In this\npaper, we present frailty models using phase-type distributions as the\nfrailties. We explore the properties of the proposed frailty models and derive\nexpectation-maximization algorithms for maximum-likelihood estimation. The\nalgorithms' performance is illustrated in several numerical examples of\npractical significance.\n"}
+{"abstract": "  Is it possible to use convolutional neural networks pre-trained without any\nnatural images to assist natural image understanding? The paper proposes a\nnovel concept, Formula-driven Supervised Learning. We automatically generate\nimage patterns and their category labels by assigning fractals, which are based\non a natural law existing in the background knowledge of the real world.\nTheoretically, the use of automatically generated images instead of natural\nimages in the pre-training phase allows us to generate an infinite scale\ndataset of labeled images. Although the models pre-trained with the proposed\nFractal DataBase (FractalDB), a database without natural images, does not\nnecessarily outperform models pre-trained with human annotated datasets at all\nsettings, we are able to partially surpass the accuracy of ImageNet/Places\npre-trained models. The image representation with the proposed FractalDB\ncaptures a unique feature in the visualization of convolutional layers and\nattentions.\n"}
+{"abstract": "  Node classification and link prediction are widely studied in graph\nrepresentation learning. While both transductive node classification and link\nprediction operate over a single input graph, they have so far been studied\nseparately. Node classification models take an input graph with node features\nand incomplete node labels, and implicitly assume that the graph is\nrelationally complete, i.e., no edges are missing. By contrast, link prediction\nmodels are solely motivated by relational incompleteness of the input graphs,\nand do not typically leverage node features or classes. We propose a unifying\nperspective and study the problems of (i) transductive node classification over\nincomplete graphs and (ii) link prediction over graphs with node features,\nintroduce a new dataset for this setting, WikiAlumni, and conduct an extensive\nbenchmarking study.\n"}
+{"abstract": "  Fourier phase retrieval is the problem of reconstructing a signal given only\nthe magnitude of its Fourier transformation. Optimization-based approaches,\nlike the well-established Gerchberg-Saxton or the hybrid input output\nalgorithm, struggle at reconstructing images from magnitudes that are not\noversampled. This motivates the application of learned methods, which allow\nreconstruction from non-oversampled magnitude measurements after a learning\nphase. In this paper, we want to push the limits of these learned methods by\nmeans of a deep neural network cascade that reconstructs the image successively\non different resolutions from its non-oversampled Fourier magnitude. We\nevaluate our method on four different datasets (MNIST, EMNIST, Fashion-MNIST,\nand KMNIST) and demonstrate that it yields improved performance over other\nnon-iterative methods and optimization-based methods.\n"}
+{"abstract": "  Let $(R,\\frak m)$ be a Noetherian local ring of prime characteristic $p>0$,\nand $t$ an integer such that $H_{\\frak m}^j(R)/0^F_{H^j_{\\frak m}(R)}$ has\nfinite length for all $j<t$. The aim of this paper is to show that there exists\nan uniform bound for Frobenius test exponents of ideals generated by filter\nregular sequences of length at most $t$.\n"}
+{"abstract": "  Dzyaloshinskii-Moriya interaction, DMI in short, represents an antisymmetric\ntype of magnetic interactions that favour orthogonal orientation of spins and\ncompetes with Heisenberg exchange. Being introduced to explain weak\nferromagnetism in antiferromagnets without an inversion center between magnetic\natoms such an anisotropic interaction can be used to analyze other non-trivial\nmagnetic structures of technological importance including spin spirals and\nskyrmions. Despite the fact that the corresponding DMI contribution to the\nmagnetic energy of the system has a very compact form of the vector product of\nspins, the determination of DMI from first-principles electronic structure is a\nvery challenging methodological and technical problem whose solution opens a\ndoor into the fascinating microscopic world of complex magnetic materials. In\nthis paper we review a few such methods developed by us for calculating DMI and\ntheir applications to study the properties of real materials.\n"}
+{"abstract": "  Proportional-integral-derivative (PID) control is the most widely used in\nindustrial control, robot control and other fields. However, traditional PID\ncontrol is not competent when the system cannot be accurately modeled and the\noperating environment is variable in real time. To tackle these problems, we\npropose a self-adaptive model-free SAC-PID control approach based on\nreinforcement learning for automatic control of mobile robots. A new\nhierarchical structure is developed, which includes the upper controller based\non soft actor-critic (SAC), one of the most competitive continuous control\nalgorithms, and the lower controller based on incremental PID controller. Soft\nactor-critic receives the dynamic information of the mobile robot as input, and\nsimultaneously outputs the optimal parameters of incremental PID controllers to\ncompensate for the error between the path and the mobile robot in real time. In\naddition, the combination of 24-neighborhood method and polynomial fitting is\ndeveloped to improve the adaptability of SAC-PID control method to complex\nenvironments. The effectiveness of the SAC-PID control method is verified with\nseveral different difficulty paths both on Gazebo and real mecanum mobile\nrobot. Futhermore, compared with fuzzy PID control, the SAC-PID method has\nmerits of strong robustness, generalization and real-time performance.\n"}
+{"abstract": "  People's opinions evolve over time as they interact with their friends,\nfamily, colleagues, and others. In the study of opinion dynamics on networks,\none often encodes interactions between people in the form of dyadic\nrelationships, but many social interactions in real life are polyadic (i.e.,\nthey involve three or more people). In this paper, we extend an asynchronous\nbounded-confidence model (BCM) on graphs, in which nodes are connected pairwise\nby edges, to an asynchronous BCM on hypergraphs, in which arbitrarily many\nnodes can be connected by a single hyperedge. We show that our hypergraph BCM\nconverges to consensus under a wide range of initial conditions for the\nopinions of the nodes, including for non-uniform and asymmetric initial opinion\ndistributions. We also show that, under suitable conditions, echo chambers can\nform on hypergraphs with community structure. We demonstrate that the opinions\nof individuals can sometimes jump from one opinion cluster to another in a\nsingle time step, a phenomenon (which we call ``opinion jumping'') that is not\npossible in standard dyadic BCMs. Additionally, we observe that there is a\nphase transition in the convergence time on {a complete hypergraph} when the\nvariance $\\sigma^2$ of the initial opinion distribution equals the confidence\nbound $c$. We prove that the convergence time grows at least exponentially fast\nwith the number of nodes when $\\sigma^2 > c$ and the initial opinions are\nnormally distributed. Therefore, to determine the convergence properties of our\nhypergraph BCM when the variance and the number of hyperedges are both large,\nit is necessary to use analytical methods instead of relying only on Monte\nCarlo simulations.\n"}
+{"abstract": "  Limited expert time is a key bottleneck in medical imaging. Due to advances\nin image classification, AI can now serve as decision-support for medical\nexperts, with the potential for great gains in radiologist productivity and, by\nextension, public health. However, these gains are contingent on building and\nmaintaining experts' trust in the AI agents. Explainable AI may build such\ntrust by helping medical experts to understand the AI decision processes behind\ndiagnostic judgements. Here we introduce and evaluate explanations based on\nBayesian Teaching, a formal account of explanation rooted in the cognitive\nscience of human learning. We find that medical experts exposed to explanations\ngenerated by Bayesian Teaching successfully predict the AI's diagnostic\ndecisions and are more likely to certify the AI for cases when the AI is\ncorrect than when it is wrong, indicating appropriate trust. These results show\nthat Explainable AI can be used to support human-AI collaboration in medical\nimaging.\n"}
+{"abstract": "  The goal of this paper is to review and critically assess different methods\nto monitor key process variables for ethanol production from lignocellulosic\nbiomass. Because cellulose-based biofuels cannot yet compete with\nnon-cellulosic biofuels, process control and optimization are of importance to\nlower the production costs. This study reviews different monitoring schemes, to\nindicate what the added value of real-time monitoring is for process control.\nFurthermore, a comparison is made on different monitoring techniques to measure\nthe off-gas, the concentrations of dissolved components in the inlet to the\nprocess, the concentrations of dissolved components in the reactor, and the\nbiomass concentration. Finally, soft sensor techniques and available models are\ndiscussed, to give an overview of modeling techniques that analyze data, with\nthe aim of coupling the soft sensor predictions to the control and optimization\nof cellulose to ethanol fermentation. The paper ends with a discussion of\nfuture needs and developments.\n"}
+{"abstract": "  The value semigroup of a $k$-semiroot $C_k$ of a plane branch $C$ allow us to\nrecover part of the value semigroup $\\Gamma =\\langle v_0,\\ldots ,v_g\\rangle$ of\n$C$, that is, it is related to topological invariants of $C$. In this paper we\nconsider the set of values of differentials $\\Lambda_k$ of $C_k$, that is an\nanalytical invariant, and we show how it determine part of the set of values of\ndifferentials $\\Lambda$ of $C$. As a consequence, in a fixed topological class,\nwe relate the Tjurina number $\\tau$ of $C$ with the Tjurina number of $C_k$. In\nparticular, we show that $\\tau\\leq \\mu-\\frac{3n_g-2}{4}\\mu_{g-1}$ where\n$n_g=gcd(v_0,\\ldots ,v_{g-1})$, $\\mu$ and $\\mu_{g-1}$ denote the Milnor number\nof $C$ and $C_{g-1}$ respectively. If $n_g=2$, we have that\n$\\tau=\\mu-\\mu_{g-1}$ for any curve in the topological class determined by\n$\\Gamma$ that is a generalization of a result obtained by Luengo and Pfister.\n"}
+{"abstract": "  A point of a metric space is called a geodesic star with $m$ arms if it is\nthe endpoint of $m$ disjoint geodesics. For every $m\\in\\{1,2,3,4\\}$, we prove\nthat the set of all geodesic stars with $m$ arms in the Brownian sphere has\ndimension $5-m$. This complements recent results of Miller and Qian, who proved\nthat this dimension is smaller than or equal to $5-m$.\n"}
+{"abstract": "  In this PhD thesis, we explore and apply methods inspired by the free energy\nprinciple to two important areas in machine learning and neuroscience. The free\nenergy principle is a general mathematical theory of the necessary\ninformation-theoretic behaviours of systems that maintain a separation from\ntheir environment. A core postulate of the theory is that complex systems can\nbe seen as performing variational Bayesian inference and minimizing an\ninformation-theoretic quantity called the variational free energy. The thesis\nis structured into three independent sections. Firstly, we focus on predictive\ncoding, a neurobiologically plausible process theory derived from the free\nenergy principle which argues that the primary function of the brain is to\nminimize prediction errors, showing how predictive coding can be scaled up and\nextended to be more biologically plausible, and elucidating its close links\nwith other methods such as Kalman Filtering. Secondly, we study active\ninference, a neurobiologically grounded account of action through variational\nmessage passing, and investigate how these methods can be scaled up to match\nthe performance of deep reinforcement learning methods. We additionally provide\na detailed mathematical understanding of the nature and origin of the\ninformation-theoretic objectives that underlie exploratory behaviour. Finally,\nwe investigate biologically plausible methods of credit assignment in the\nbrain. We first demonstrate a close link between predictive coding and the\nbackpropagation of error algorithm. We go on to propose novel and simpler\nalgorithms which allow for backprop to be implemented in purely local,\nbiologically plausible computations.\n"}
+{"abstract": "  This paper considers the problem of retrieving an object from many tightly\npacked objects using a combination of robotic pushing and grasping actions.\nObject retrieval in dense clutter is an important skill for robots to operate\nin households and everyday environments effectively. The proposed solution,\nVisual Foresight Tree (VFT), intelligently rearranges the clutter surrounding a\ntarget object so that it can be grasped easily. Rearrangement with nested\nnonprehensile actions is challenging as it requires predicting complex object\ninteractions in a combinatorially large configuration space of multiple\nobjects. We first show that a deep neural network can be trained to accurately\npredict the poses of the packed objects when the robot pushes one of them. The\npredictive network provides visual foresight and is used in a tree search as a\nstate transition function in the space of scene images. The tree search returns\na sequence of consecutive push actions yielding the best arrangement of the\nclutter for grasping the target object. Experiments in simulation and using a\nreal robot and objects show that the proposed approach outperforms model-free\ntechniques as well as model-based myopic methods both in terms of success rates\nand the number of executed actions, on several challenging tasks. A video\nintroducing VFT, with robot experiments, is accessible at\nhttps://youtu.be/7cL-hmgvyec. The full source code is available at\nhttps://github.com/arc-l/vft.\n"}
+{"abstract": "  We derive a method to enhance the evaluation for a text-based Emotion Aware\nRecommender that we have developed. However, we did not implement a suitable\nway to assess the top-N recommendations subjectively. In this study, we\nintroduce an emotion-aware Pseudo Association Method to interconnect disjointed\nusers across different datasets so data files can be combined to form a more\nextensive data file. Users with the same user IDs found in separate data files\nin the same dataset are often the same users. However, users with the same user\nID may not be the same user across different datasets. We advocate an emotion\naware Pseudo Association Method to associate users across different datasets.\nThe approach interconnects users with different user IDs across different\ndatasets through the most similar users' emotion vectors (UVECs). We found the\nmethod improved the evaluation process of assessing the top-N recommendations\nobjectively.\n"}
+{"abstract": "  We solve the two-dimensional hydrodynamic equations of hot accretion flow in\nthe presence of the thermal conduction. The flow is assumed to be in\nsteady-state and axisymmetric, and self-similar approximation is adopted in the\nradial direction. In this hydrodynamic study, we consider the viscous stress\ntensor to mimic the effects of the magnetorotational instability for driving\nangular momentum. We impose the physical boundary conditions at both the\nrotation axis and the equatorial plane and obtain the solutions in the full $\nr-\\theta $ space. We have found that thermal conduction is indispensable term\nfor investigating the inflow-wind structure of the hot accretion flows with\nvery low mass accretion rates. One of the most interesting results here is that\nthe disc is convectively stable in hot accretion mode and in the presence of\nthe thermal conduction. Furthermore, the properties of wind and also its\ndriving mechanisms are studied. Our analytical results are consistent with\nprevious numerical simulations of hot accretion flow.\n"}
+{"abstract": "  We propose network benchmarking: a procedure to efficiently benchmark the\nquality of a quantum network link connecting quantum processors in a quantum\nnetwork. This procedure is based on the standard randomized benchmarking\nprotocol and provides an estimate for the fidelity of a quantum network link.\nWe provide statistical analysis of the protocol as well as a simulated\nimplementation inspired by NV-center systems using Netsquid, a special purpose\nsimulator for noisy quantum networks.\n"}
+{"abstract": "  In virtual-state spectroscopy, information about the energy-level structure\nof an arbitrary sample is retrieved by Fourier transforming sets of measured\ntwo-photon absorption probabilities of entangled photon pairs where the degree\nof entanglement and the delay time between the photons have been varied. This\nworks well for simple systems but quickly becomes rather difficult when many\nintermediate states are involved. We propose and discuss an extension of\nentangled two-photon absorption spectroscopy that solves this problem by means\nof repeated measurements at different pump wavelengths. Specifically, we\ndemonstrate that our extension works well for a variety of realistic\nexperimental setups.\n"}
+{"abstract": "  Owing to the hybridization of cerium's localised 4$f$ electron and conduction\nband composed of $d$-electrons, cerium based intermetallics exhibit various\nkinds of magnetic interactions. In crystals, these can result in exotic types\nof magnetic ordering. In this study, we report a detailed single-crystal\nneutron diffraction study on CePdAl$_3$ and CePtAl$_3$. We have synthesized a\nlarge crystal of CePdAl$_3$, which crystallizes in a non-centrosymmetric,\northorhombic structure with space group $Cmc2_1$, a new, distorted variant of\nthe tetragonal BaNiSn$_3$ structure observed in other Ce$T$Al$_3$ compounds,\nsuch as CePtAl$_3$. Low-temperature diffraction measurements showed that\nCePdAl$_3$ orders in a collinear antiferromagnetic structure below T$_N$=5.3\n(1) K, with magnetic moments pointing along the $a$-axis direction and an\nordered magnetic moment $\\mu$=1.64(3) $\\mu_B$/Ce$^{3+}$. Tetragonal CePtAl$_3$\nshows a modulated, cycloidal type of ordering with\n$\\vec{k}=(\\frac{2}{3}\\,0\\,0)$, and a transition temperature T$_N$=3.2 K.\nSymmetry analysis allows two types of ordering, which show modulation of both\namplitude and direction of magnetic moments. These results allow to conclude\nthat in Ce$T$Al$_3$ system the orthorhombic distortion ($T$=Pd, Ag) releases\nsome underlying magnetic frustration that results in modulated types of\nmagnetic ordering in tetragonal compounds ($T$=Cu,Au,Pt).\n"}
+{"abstract": "  Link prediction and node classification are two important downstream tasks of\nnetwork representation learning. Existing methods have achieved acceptable\nresults but they perform these two tasks separately, which requires a lot of\nduplication of work and ignores the correlations between tasks. Besides,\nconventional models suffer from the identical treatment of information of\nmultiple views, thus they fail to learn robust representation for downstream\ntasks. To this end, we tackle link prediction and node classification problems\nsimultaneously via multi-task multi-view learning in this paper. We first\nexplain the feasibility and advantages of multi-task multi-view learning for\nthese two tasks. Then we propose a novel model named as MT-MVGCN to perform\nlink prediction and node classification tasks simultaneously. More\nspecifically, we design a multi-view graph convolutional network to extract\nabundant information of multiple views in a network, which is shared by\ndifferent tasks. We further apply two attention mechanisms: view attention\nmechanism and task attention mechanism to make views and tasks adjust the view\nfusion process. Moreover, view reconstruction can be introduced as an auxiliary\ntask to boost the performance of the proposed model. Experiments on real-world\nnetwork datasets demonstrate that our model is efficient yet effective, and\noutperforms advanced baselines in these two tasks.\n"}
+{"abstract": "  It is well-known that typability, type inhabitation and type inference are\nundecidable in the Girard-Reynolds polymorphic system F. It has recently been\nproven that type inhabitation remains undecidable even in the predicative\nfragment of system F in which all universal instantiations have an atomic\nwitness (system Fat). In this paper we analyze typability and type inference in\nCurry style variants of system Fat and show that typability is decidable and\nthat there is an algorithm for type inference which is capable of dealing with\nnon-redundancy constraints.\n"}
+{"abstract": "  In this paper, we propose two simple yet efficient computational algorithms\nto obtain approximate optimal designs for multi-dimensional linear regression\non a large variety of design spaces. We focus on the two commonly used optimal\ncriteria, $D$- and $A$-optimal criteria. For $D$-optimality, we provide an\nalternative proof for the monotonic convergence for $D$-optimal criterion and\npropose an efficient computational algorithm to obtain the approximate\n$D$-optimal design. We further show that the proposed algorithm converges to\nthe $D$-optimal design, and then prove that the approximate $D$-optimal design\nconverges to the continuous $D$-optimal design under certain conditions. For\n$A$-optimality, we provide an efficient algorithm to obtain approximate\n$A$-optimal design and conjecture the monotonicity of the proposed algorithm.\nNumerical comparisons suggest that the proposed algorithms perform well and\nthey are comparable or superior to some existing algorithms.\n"}
+{"abstract": "  Using multiple monitors is commonly thought to improve productivity, but this\nis hard to check experimentally. We use a survey, taken by 101 practitioners of\nwhich 80% have coded professionally for at least 2 years, to assess subjective\nperspectives based on experience. To improve validity, we compare situations in\nwhich developers naturally use different setups -- the difference between\nworking at home or at the office, and how things changed when developers were\nforced to work from home due to the Covid-19 pandemic. The results indicate\nthat using multiple monitors is indeed perceived as beneficial and desirable.\n19% of the respondents reported adding a monitor to their home setup in\nresponse to the Covid-19 situation. At the same time, the single most\ninfluential factor cited as affecting productivity was not the physical setup\nbut interactions with co-workers -- both reduced productivity due to lack of\nconnections available at work, and improved productivity due to reduced\ninterruptions from co-workers. A central implication of our work is that\nempirical research on software development should be conducted in settings\nsimilar to those actually used by practitioners, and in particular using\nworkstations configured with multiple monitors.\n"}
+{"abstract": "  This research proposes a new integrated framework for identifying safe\nlanding locations and planning in-flight divert maneuvers. The state-of-the-art\nalgorithms for landing zone selection utilize local terrain features such as\nslopes and roughness to judge the safety and priority of the landing point.\nHowever, when there are additional chances of observation and diverting in the\nfuture, these algorithms are not able to evaluate the safety of the decision\nitself to target the selected landing point considering the overall descent\ntrajectory. In response to this challenge, we propose a reinforcement learning\nframework that optimizes a landing site selection strategy concurrently with a\nguidance and control strategy to the target landing site. The trained agent\ncould evaluate and select landing sites with explicit consideration of the\nterrain features, quality of future observations, and control to achieve a safe\nand efficient landing trajectory at a system-level. The proposed framework was\nable to achieve 94.8 $\\%$ of successful landing in highly challenging landing\nsites where over 80$\\%$ of the area around the initial target lading point is\nhazardous, by effectively updating the target landing site and feedback control\ngain during descent.\n"}
+{"abstract": "  We use rank correlations as distance functions to establish the\ninterconnectivity between stock returns, building weighted signed networks for\nthe stocks of seven European countries, the US and Japan. We establish the\ntheoretical relationship between the level of balance in a network and stock\npredictability, studying its evolution from 2005 to the third quarter of 2020.\nWe find a clear balance-unbalance transition for six of the nine countries,\nfollowing the August 2011 Black Monday in the US, when the Economic Policy\nUncertainty index for this country reached its highest monthly level before the\nCOVID-19 crisis. This sudden loss of balance is mainly caused by a\nreorganization of the market networks triggered by a group of low\ncapitalization stocks belonging to the non-financial sector. After the\ntransition, the stocks of companies in these groups become all negatively\ncorrelated between them and with most of the rest of the stocks in the market.\nThe implied change in the network topology is directly related to a decrease in\nstocks predictability, a finding with novel important implications for asset\nallocation and portfolio hedging strategies.\n"}
+{"abstract": "  In this article we fully describe the domain of the infinitesimal generator\nof the optimal state semigroup which arises in the theory of the\nlinear-quadratic problem for a specific class of boundary control systems. This\nrepresents an improvement over earlier work of the authors, joint with\nLasiecka, where a set inclusion was established, but not an equality. The novel\npart of the proof of this result developes through appropriate asymptotic\nestimates that take advantage of the regularity analysis carried out in the\nstudy of the optimization problem, while the powers of positive operators and\ninterpolation are still key tools. We also attest to the validity of an assumed\nrelation between two significant parameters in the case of distinct systems of\ncoupled hyperbolic-parabolic partial differential equations which are pertinent\nto the underlying framework.\n"}
+{"abstract": "  Physics-Informed Neural Networks promise to revolutionize science and\nengineering practice, by introducing domain-aware deep machine learning models\ninto scientific computation. Several software suites have emerged to make the\nimplementation and usage of these architectures available to the research and\nindustry communities. Here we introduce\\linebreak TensorDiffEq, built on\nTensorflow 2.x, which presents an intuitive Keras-like interface for problem\ndomain definition, model definition, and solution of forward and inverse\nproblems using physics-aware deep learning methods. TensorDiffEq takes full\nadvantage of Tensorflow 2.x infrastructure for deployment on multiple GPUs,\nallowing the implementation of large high-dimensional and complex models.\nSimultaneously, TensorDiffEq supports the Keras API for custom neural network\narchitecture definitions. In the case of smaller or simpler models, the package\nallows for rapid deployment on smaller-scale CPU platforms with negligible\nchanges to the implementation scripts. We demonstrate the basic usage and\ncapabilities of TensorDiffEq in solving forward, inverse, and data assimilation\nproblems of varying sizes and levels of complexity. The source code is\navailable at https://github.com/tensordiffeq.\n"}
+{"abstract": "  We introduce a formal language for specifying dynamic updates for Software\nDefined Networks. Our language builds upon Network Kleene Algebra with Tests\n(NetKAT) and adds constructs for synchronisations and multi-packet behaviour to\ncapture the interaction between the control- and data-plane in dynamic updates.\nWe provide a sound and ground-complete axiomatisation of our language. We\nexploit the equational theory to provide an efficient reasoning method about\nsafety properties for dynamic networks. We implement our equational theory in\nDyNetiKAT -- a tool prototype, based on the Maude Rewriting Logic and the\nNetKAT tool, and apply it to a case study. We show that we can analyse the case\nstudy for networks with hundreds of switches using our initial tool prototype.\n"}
+{"abstract": "  The ability of photonic crystal waveguides (PCWs) to confine and slow down\nlight makes them an ideal component to enhance the performance of various\nphotonic devices, such as optical modulators or sensors. However, the\nintegration of PCWs in photonic applications poses design challenges, most\nnotably, engineering the PCW mode dispersion and creating efficient coupling\ndevices. Here, we solve these challenges with photonic inverse design, and\nexperimentally demonstrate a slow-light PCW optical phased array (OPA) with a\nwide steering range. Even and odd mode PCWs are engineered for a group index of\n25, over a bandwidth of 20nm and 12nm, respectively. Additionally, for both PCW\ndesigns, we create strip waveguide couplers and free-space vertical couplers.\nFinally, also relying on inverse design, the radiative losses of the PCW are\nengineered, allowing us to construct OPAs with a 20{\\deg} steering range in a\n20nm bandwidth.\n"}
+{"abstract": "  Weighted model counting (WMC) is a popular framework to perform probabilistic\ninference with discrete random variables. Recently, WMC has been extended to\nweighted model integration (WMI) in order to additionally handle continuous\nvariables. At their core, WMI problems consist of computing integrals and sums\nover weighted logical formulas. From a theoretical standpoint, WMI has been\nformulated by patching the sum over weighted formulas, which is already present\nin WMC, with Riemann integration. A more principled approach to integration,\nwhich is rooted in measure theory, is Lebesgue integration. Lebesgue\nintegration allows one to treat discrete and continuous variables on equal\nfooting in a principled fashion. We propose a theoretically sound measure\ntheoretic formulation of weighted model integration, which naturally reduces to\nweighted model counting in the absence of continuous variables. Instead of\nregarding weighted model integration as an extension of weighted model\ncounting, WMC emerges as a special case of WMI in our formulation.\n"}
+{"abstract": "  Inspired by the analysis of variance (ANOVA) decomposition of functions we\npropose a Gaussian-Uniform mixture model on the high-dimensional torus which\nrelies on the assumption that the function we wish to approximate can be well\nexplained by limited variable interactions. We consider three approaches,\nnamely wrapped Gaussians, diagonal wrapped Gaussians and products of von Mises\ndistributions. The sparsity of the mixture model is ensured by the fact that\nits summands are products of Gaussian-like density functions acting on low\ndimensional spaces and uniform probability densities defined on the remaining\ndirections. To learn such a sparse mixture model from given samples, we propose\nan objective function consisting of the negative log-likelihood function of the\nmixture model and a regularizer that penalizes the number of its summands. For\nminimizing this functional we combine the Expectation Maximization algorithm\nwith a proximal step that takes the regularizer into account. To decide which\nsummands of the mixture model are important, we apply a Kolmogorov-Smirnov\ntest. Numerical examples demonstrate the performance of our approach.\n"}
+{"abstract": "  In this work, three heuristic derivations of the Hawking temperature are\npresented. The main characteristic of these derivations is their extreme\nsimplicity, which makes them easily accessible to a wide and diverse audience.\n"}
+{"abstract": "  Lagrangian cobordism induces a preorder on the set of Legendrian links in any\ncontact 3-manifold. We show that any finite collection of null-homologous\nLegendrian links in a tight contact 3-manifold with a common rotation number\nhas an upper bound with respect to the preorder. In particular, we construct an\nexact Lagrangian cobordism from each element of the collection to a common\nLegendrian link. This construction allows us to define a notion of minimal\nLagrangian genus between any two null-homologous Legendrian links with a common\nrotation number.\n"}
+{"abstract": "  Let $G$ be a simple graph with maximum degree $\\Delta(G)$. A subgraph $H$ of\n$G$ is overfull if $|E(H)|>\\Delta(G)\\lfloor |V(H)|/2 \\rfloor$. Chetwynd and\nHilton in 1985 conjectured that a graph $G$ with $\\Delta(G)>|V(G)|/3$ has\nchromatic index $\\Delta(G)$ if and only if $G$ contains no overfull subgraph.\nThe 1-factorization conjecture is a special case of this overfull conjecture,\nwhich states that for even $n$, every regular $n$-vertex graph with degree at\nleast about $n/2$ has a 1-factorization and was confirmed for large graphs in\n2014. Supporting the overfull conjecture as well as generalizing the\n1-factorization conjecture in an asymptotic way, in this paper, we show that\nfor any given $0<\\varepsilon <1$, there exists a positive integer $n_0$ such\nthat the following statement holds: if $G$ is a graph on $2n\\ge n_0$ vertices\nwith minimum degree at least $(1+\\varepsilon)n$, then $G$ has chromatic index\n$\\Delta(G)$ if and only if $G$ contains no overfull subgraph.\n"}
+{"abstract": "  Grammar compression is, next to Lempel-Ziv (LZ77) and run-length\nBurrows-Wheeler transform (RLBWT), one of the most flexible approaches to\nrepresenting and processing highly compressible strings. The main idea is to\nrepresent a text as a context-free grammar whose language is precisely the\ninput string. This is called a straight-line grammar (SLG). An AVL grammar,\nproposed by Rytter [Theor. Comput. Sci., 2003] is a type of SLG that\nadditionally satisfies the AVL-property: the heights of parse-trees for\nchildren of every nonterminal differ by at most one. In contrast to other SLG\nconstructions, AVL grammars can be constructed from the LZ77 parsing in\ncompressed time: $\\mathcal{O}(z \\log n)$ where $z$ is the size of the LZ77\nparsing and $n$ is the length of the input text. Despite these advantages, AVL\ngrammars are thought to be too large to be practical.\n  We present a new technique for rapidly constructing a small AVL grammar from\nan LZ77 or LZ77-like parse. Our algorithm produces grammars that are always at\nleast five times smaller than those produced by the original algorithm, and\nnever more than double the size of grammars produced by the practical Re-Pair\ncompressor [Larsson and Moffat, Proc. IEEE, 2000]. Our algorithm also achieves\nlow peak RAM usage. By combining this algorithm with recent advances in\napproximating the LZ77 parsing, we show that our method has the potential to\nconstruct a run-length BWT from an LZ77 parse in about one third of the time\nand peak RAM required by other approaches. Overall, we show that AVL grammars\nare surprisingly practical, opening the door to much faster construction of key\ncompressed data structures.\n"}
+{"abstract": "  There is a well-known correspondence between coherent theories (and their\ninterpretations) and coherent categories (resp. functors), hence the\n(2,1)-category $\\mathbf{Coh_{\\sim}}$ (of small coherent categories, coherent\nfunctors and all natural isomorphisms) is of logical interest. We prove that\nthis category admits all small 2-limits and 2-colimits (in the ($\\infty\n$,1)-categorical sense), and prove a 2-categorical small object argument to\nprovide weak factorisation systems for coherent functors.\n"}
+{"abstract": "  Since early 2020 the COVID-19 pandemic has had a considerable impact on many\naspects of daily life. A range of different measures have been implemented\nworldwide to reduce the rate of new infections and to manage the pressure on\nnational health services. A primary strategy has been to reduce gatherings and\nthe potential for transmission through the prioritisation of remote working and\neducation. Enhanced hand hygiene and the use of facial masks have decreased the\nspread of pathogens when gatherings are unavoidable. These particular measures\npresent challenges for reliable biometric recognition, e.g. for facial-, voice-\nand hand-based biometrics. At the same time, new challenges create new\nopportunities and research directions, e.g. renewed interest in non-constrained\niris or periocular recognition, touch-less fingerprint- and vein-based\nauthentication and the use of biometric characteristics for disease detection.\nThis article presents an overview of the research carried out to address those\nchallenges and emerging opportunities.\n"}
+{"abstract": "  We derive a large-scale hydrodynamic equation, including diffusive and\ndissipative effects, for systems with generic static position-dependent driving\nforces coupling to local conserved quantities. We show that this equation\npredicts entropy increase and thermal states as the only stationary states. The\nequation applies to any hydrodynamic system with any number of local,\nPT-symmetric conserved quantities, in arbitrary dimension. It is fully\nexpressed in terms of elements of an extended Onsager matrix. In integrable\nsystems, this matrix admits an expansion in the density of excitations. We\nevaluate exactly its 2-particle-hole contribution, which dominates at low\ndensity, in terms of the scattering phase and dispersion of the quasiparticles,\ngiving a lower bound for the extended Onsager matrix and entropy production. We\nconclude with a molecular dynamics simulation, demonstrating thermalisation\nover diffusive time scales in the Toda interacting particle model with an\ninhomogeneous energy field.\n"}
+{"abstract": "  The electric charge renormalization constant, as defined in the Thomson\nlimit, is expressed in terms of self-energies of the photon-Z-boson system in\nan arbitrary R_\\xi-gauge to all perturbative orders. The derivation as carried\nout in the Standard Model holds in all spontaneously broken gauge theories with\nthe SU(2)_w \\times U(1)_Y gauge group in the electroweak sector and is based on\nthe application of charge universality to a fake fermion with infinitesimal\nweak hypercharge and vanishing weak isospin, which effectively decouples from\nall other particles. Charge universality, for instance, follows from the known\nuniversal form of the charge renormalization constant as derived within the\nbackground-field formalism. Finally, we have generalized the described\nprocedure to gauge theories with gauge group U(1)_Y \\times G with any Lie group\nG, only assuming that electromagnetic gauge symmetry is unbroken and mixes with\nU(1)_Y transformations in a non-trivial way.\n"}
+{"abstract": "  Given an action of a groupoid by isomorphisms on a Fell bundle (over another\ngroupoid), we form a semidirect-product Fell bundle, and prove that its\n$C^{*}$-algebra is isomorphic to a crossed product.\n"}
+{"abstract": "  We present an effective field theory describing the relevant interactions of\nthe Standard Model with an electrically neutral particle that can account for\nthe dark matter in the Universe. The possible mediators of these interactions\nare assumed to be heavy. The dark matter candidates that we consider have spin\n0, 1/2 or 1, belong to an electroweak multiplet with arbitrary isospin and\nhypercharge and their stability at cosmological scales is guaranteed by\nimposing a $\\mathbb{Z}_2$ symmetry. We present the most general framework for\ndescribing the interaction of the dark matter with standard particles, and\nconstruct a general non-redundant basis of the gauge-invariant operators up to\ndimension six. The basis includes multiplets with non-vanishing hypercharge,\nwhich can also be viable DM candidates. We give two examples illustrating the\nphenomenological use of such a general effective framework. First, we consider\nthe case of a scalar singlet, provide convenient semi-analytical expressions\nfor the relevant dark matter observables, use present experimental data to set\nconstraints on the Wilson coefficients of the operators, and show how the\ninterplay of different operators can open new allowed windows in the parameter\nspace of the model. Then we study the case of a lepton isodoublet, which\ninvolves co-annihilation processes, and we discuss the impact of the operators\non the particle mass splitting and direct detection cross sections. These\nexamples highlight the importance of the contribution of the various\nnon-renormalizable operators, which can even dominate over the gauge\ninteractions in certain cases.\n"}
+{"abstract": "  In this paper, we investigate the roles that social robots can take in\nphysical exercise with human partners. In related work, robots or virtual\nintelligent agents take the role of a coach or instructor whereas in other\napproaches they are used as motivational aids. These are two \"paradigms\", so to\nspeak, within the small but growing area of robots for social exercise. We\ndesigned an online questionnaire to test whether the preferred role in which\npeople want to see robots would be the companion or the coach. The\nquestionnaire asks people to imagine working out with a robot with the help of\nthree utilized questionnaires: (1) CART-Q which is used for judging\ncoach-athlete relationships, (2) the mind perception questionnaire and (3) the\nSystem Usability Scale (SUS). We present the methodology, some preliminary\nresults as well as our intended future work on personal robots for coaching.\n"}
+{"abstract": "  Aims: To study the heating of solar chromospheric magnetic and nonmagnetic\nregions by acoustic and magnetoacoustic waves, the deposited acoustic-energy\nflux derived from observations of strong chromospheric lines is compared with\nthe total integrated radiative losses. Methods: A set of 23 quiet-Sun and\nweak-plage regions were observed in the Mg II k and h lines with the Interface\nRegion Imaging Spectrograph (IRIS). The deposited acoustic-energy flux was\nderived from Doppler velocities observed at two different geometrical heights\ncorresponding to the middle and upper chromosphere. A set of scaled nonlocal\nthermodynamic equilibrium 1D hydrostatic semi-empirical models (obtained by\nfitting synthetic to observed line profiles) was applied to compute the\nradiative losses. The characteristics of observed waves were studied by means\nof a wavelet analysis. Results: Observed waves propagate upward at supersonic\nspeed. In the quiet chromosphere, the deposited acoustic flux is sufficient to\nbalance the radiative losses and maintain the semi-empirical temperatures in\nthe layers under study. In the active-region chromosphere, the comparison shows\nthat the contribution of acoustic-energy flux to the radiative losses is only\n10 - 30 %. Conclusions: Acoustic and magnetoacoustic waves play an important\nrole in the chromospheric heating, depositing a main part of their energy in\nthe chromosphere. Acoustic waves compensate for a substantial fraction of the\nchromospheric radiative losses in quiet regions. In active regions, their\ncontribution is too small to balance the radiative losses and the chromosphere\nhas to be heated by other mechanisms.\n"}
+{"abstract": "  In this work we explore the effects that a possible primordial magnetic field\ncan have on the inflaton effective potential, taking as the underlying model a\nwarm inflation scenario, based on global supersymmetry with a\nnew-inflation-type potential. The decay scheme for the inflaton field is a\ntwo-step process of radiation production, where the inflaton couples to heavy\nintermediate superfields, which in turn interact with light particles. In this\ncontext, we consider that both sectors, heavy and light, are charged and work\nin the strong magnetic field approximation for the light fields. We find an\nanalytical expression for the one-loop effective potential, for an arbitrary\nmagnetic field strength, and show that the trend of the magnetic contribution\nis to make the potential flatter, preserving the conditions for a successful\ninflationary process.\n"}
+{"abstract": "  Embedding-based methods for reasoning in knowledge hypergraphs learn a\nrepresentation for each entity and relation. Current methods do not capture the\nprocedural rules underlying the relations in the graph. We propose a simple\nembedding-based model called ReAlE that performs link prediction in knowledge\nhypergraphs (generalized knowledge graphs) and can represent high-level\nabstractions in terms of relational algebra operations. We show theoretically\nthat ReAlE is fully expressive and provide proofs and empirical evidence that\nit can represent a large subset of the primitive relational algebra operations,\nnamely renaming, projection, set union, selection, and set difference. We also\nverify experimentally that ReAlE outperforms state-of-the-art models in\nknowledge hypergraph completion, and in representing each of these primitive\nrelational algebra operations. For the latter experiment, we generate a\nsynthetic knowledge hypergraph, for which we design an algorithm based on the\nErdos-R'enyi model for generating random graphs.\n"}
+{"abstract": "  We study the effects of electronic correlations on fragile topology using\ndynamical mean-field theory. Fragile topological insulators (FTIs) offer\nobstruction to the formation of exponentially localized Wannier functions, but\nthey can be trivialized by adding certain trivial degrees of freedom. For the\nsame reason, FTIs do not host symmetry-protected flow of edge states between\nbulk bands in cylindrical boundary conditions but are expected to have a\nspectral flow between the fragile bands and other bands under certain twisted\nboundary conditions. We here analyze commonly observed effects of strong\ncorrelations, such as the Mott-insulator transition and magnetism, on a known\nmodel hosting fragile topology. We show that in the nonmagnetic case, fragile\ntopology, along with the twisted boundary states, is stable with interactions\nbelow a critical interaction strength. Above this interaction strength, a\ntransition to the Mott insulating phase occurs, and the twisted boundary states\ndisappear. Furthermore, by applying a homogeneous magnetic field, the fragile\ntopology is destroyed. However, we show that a magnetic field can induce a\ntopological phase transition which converts a fragile topological insulator to\na Chern insulator. Finally, we study ferromagnetic solutions of the fragile\ntopological model.\n"}
+{"abstract": "  We formulate non-relativistic string theory on the Newton-Cartan space time\nusing the vielbein approach mooted in the Galilean gauge theory. Geometric\nimplication has been discussed at length. The outcome is the establishment of\nsome points of non-relativistic diffeomorphism which has to some extent\nmystified in the literature.\n"}
+{"abstract": "  Modelling the end point of binary black hole mergers is a cornerstone of\nmodern gravitational-wave astronomy. Extracting multiple quasinormal mode\nfrequencies from the ringdown signal allows the remnant black hole to be\nstudied in unprecedented detail. Previous studies on numerical relativity\nsimulations of aligned-spin binaries have found that it is possible to start\nthe ringdown analysis much earlier than previously thought if overtones (and\npossibly mirror modes) are included. This increases the signal-to-noise ratio\nin the ringdown making identification of subdominant modes easier. In this\npaper we study, for the first time, black hole binaries with misaligned spins\nand find a much greater variation in the performance of ringdown fits than in\nthe aligned-spin case. The inclusion of mirror modes and higher harmonics,\nalong with overtones, improves the reliability of ringdown fits with an early\nstart time; however, there remain cases with poor performing fits. While using\novertones in conjunction with an early ringdown start time is an enticing\npossibility, it is necessary to proceed with caution. We also consider for the\nfirst time the use of numerical relativity surrogate models in this type of\nquasinormal mode study and address important questions of accuracy in the\nunderlying numerical waveforms used for the fit.\n"}
+{"abstract": "  In this paper, we generalize the compact subcell weighted essentially non\noscillatory (CSWENO) limiting strategy for Runge-Kutta discontinuous Galerkin\nmethod developed recently by us in 2021 for structured meshes to unstructured\ntriangular meshes. The main idea of the limiting strategy is to divide the\nimmediate neighbors of a given cell into the required stencil and to use a WENO\nreconstruction for limiting. This strategy can be applied for any type of WENO\nreconstruction. We have used the WENO reconstruction proposed by Zhu and Shu in\n2019 and provided accuracy tests and results for two-dimensional Burgers'\nequation and two dimensional Euler equations to illustrate the performance of\nthis limiting strategy.\n"}
+{"abstract": "  Atmospheric heavy elements have been observed in more than a quarter of white\ndwarfs (WDs) at different cooling ages, indicating ongoing accretion of\nasteroidal material, whilst only a few per cent of the WDs possess a dust disk,\nand all these WDs are accreting metals. Here, assuming that a rubble-pile\nasteroid is scattered inside a WD's Roche lobe by a planet, we study its tidal\ndisruption and the long-term evolution of the resulting fragments. We find that\nafter a few pericentric passages, the asteroid is shredded into its constituent\nparticles, forming a flat, thin ring. On a timescale of Myr, tens of per cent\nof the particles are scattered onto the WD, and are therefore directly accreted\nwithout first passing through a circularised close-in disk. Fragment mutual\ncollisions are most effective for coplanar fragments, and are thus only\nimportant in $10^3-10^4$ yr before the orbital coplanarity is broken by the\nplanet. We show that for a rubble pile asteroid with a size frequency\ndistribution of the component particles following that of the near earth\nobjects, it has to be roughly at least 10 km in radius such that enough\nfragments are generated and $\\ge10\\%$ of its mass is lost to mutual collisions.\nAt relative velocities of tens of km/s, such collisions grind down the tidal\nfragments into smaller and smaller dust grains. The WD radiation forces may\nshrink those grains' orbits, forming a dust disk. Tidal disruption of a\nmonolithic asteroid creates large km-size fragments, and only parent bodies\n$\\ge100$ km are able to generate enough fragments for mutual collisions to be\nsignificant. Hence, those large asteroids experience a disk phase before being\naccreted.\n"}
+{"abstract": "  In the fusion community, the use of high performance computing (HPC) has been\nmostly dominated by heavy-duty plasma simulations, such as those based on\nparticle-in-cell and gyrokinetic codes. However, there has been a growing\ninterest in applying machine learning for knowledge discovery on top of large\namounts of experimental data collected from fusion devices. In particular, deep\nlearning models are especially hungry for accelerated hardware, such as\ngraphics processing units (GPUs), and it is becoming more common to find those\nmodels competing for the same resources that are used by simulation codes,\nwhich can be either CPU- or GPU-bound. In this paper, we give examples of deep\nlearning models -- such as convolutional neural networks, recurrent neural\nnetworks, and variational autoencoders -- that can be used for a variety of\ntasks, including image processing, disruption prediction, and anomaly detection\non diagnostics data. In this context, we discuss how deep learning can go from\nusing a single GPU on a single node to using multiple GPUs across multiple\nnodes in a large-scale HPC infrastructure.\n"}
+{"abstract": "  In this paper, we study the generalization performance of min $\\ell_2$-norm\noverfitting solutions for the neural tangent kernel (NTK) model of a two-layer\nneural network with ReLU activation that has no bias term. We show that,\ndepending on the ground-truth function, the test error of overfitted NTK models\nexhibits characteristics that are different from the \"double-descent\" of other\noverparameterized linear models with simple Fourier or Gaussian features.\nSpecifically, for a class of learnable functions, we provide a new upper bound\nof the generalization error that approaches a small limiting value, even when\nthe number of neurons $p$ approaches infinity. This limiting value further\ndecreases with the number of training samples $n$. For functions outside of\nthis class, we provide a lower bound on the generalization error that does not\ndiminish to zero even when $n$ and $p$ are both large.\n"}
+{"abstract": "  We investigate the kinematic properties of a large (N=998) sample of COSMOS\nspectroscopic galaxy members distributed among 79 groups. We identify the\nBrightest Group Galaxies (BGGs) and cross-match our data with the VLA-COSMOS\nDeep survey at 1.4 GHz, classifying our parent sample into radio/non-radio BGGs\nand radio/non-radio satellites. The radio luminosity distribution spans from\n$L_R\\sim2\\times10^{21}$ W Hz$^{-1}$ to $L_R\\sim3\\times$10$^{25}$ W Hz$^{-1}$. A\nphase-space analysis, performed by comparing the velocity ratio (line-of-sight\nvelocity divided by the group velocity dispersion) with the galaxy-group centre\noffset, reveals that BGGs (radio and non-radio) are mostly ($\\sim$80\\%) ancient\ninfallers. Furthermore, the strongest ($L_R>10^{23}$ W Hz$^{-1}$) radio\ngalaxies are always found within 0.2$R_{\\rm vir}$ from the group centre.\nComparing our samples with HORIZON-AGN, we find that the velocities and offsets\nof simulated galaxies are more similar to radio BGGs than to non-radio BGGs,\nalbeit statistical tests still highlight significant differences between\nsimulated and real objects. We find that radio BGGs are more likely to be\nhosted in high-mass groups. Finally, we observe correlations between the powers\nof BGG radio galaxies and the X-ray temperatures, $T_{\\rm x}$, and X-ray\nluminosities, $L_{\\rm x}$, of the host groups. This supports the existence of a\nlink between the intragroup medium and the central radio source. The occurrence\nof powerful radio galaxies at group centres can be explained by Chaotic Cold\nAccretion, as the AGN can feed from both the galactic and intragroup\ncondensation, leading to the observed positive $L_{\\rm R}-T_{\\rm x}$\ncorrelation.\n"}
+{"abstract": "  Spectral-based subspace clustering methods have proved successful in many\nchallenging applications such as gene sequencing, image recognition, and motion\nsegmentation. In this work, we first propose a novel spectral-based subspace\nclustering algorithm that seeks to represent each point as a sparse convex\ncombination of a few nearby points. We then extend the algorithm to constrained\nclustering and active learning settings. Our motivation for developing such a\nframework stems from the fact that typically either a small amount of labelled\ndata is available in advance; or it is possible to label some points at a cost.\nThe latter scenario is typically encountered in the process of validating a\ncluster assignment. Extensive experiments on simulated and real data sets show\nthat the proposed approach is effective and competitive with state-of-the-art\nmethods.\n"}
+{"abstract": "  We present the first determination of transverse momentum dependent (TMD)\nphoton densities with the Parton Branching method. The photon distribution is\ngenerated perturbatively without intrinsic photon component. The input\nparameters for quarks and gluons are determined from fits to precision\nmeasurements of deep inelastic scattering cross sections at HERA. The TMD\ndensities are used to predict the mass and transverse momentum spectra of very\nhigh mass lepton pairs from both Drell-Yan production and Photon-Initiated\nlepton processes at the LHC.\n"}
+{"abstract": "  The increase of generation capacity in the area of responsibility of the\ndistribution system operator (DSO) requires strengthening of coordination\nbetween transmission system operator (TSO) and DSO in order to prevent\nconflicting or counteracting use of flexibility options. For this purpose,\nmethods for the standardized description and identification of the aggregated\nflexibility potential of distribution grids (DGs) are developed. Approaches for\nidentifying the feasible operation region (FOR) of DGs can be categorized into\ntwo main classes: Data-driven/stochastic approaches and optimization based\napproaches. While the latter have the advantage of working in real-world\nscenarios where no full grid models exist, when relying on naive sampling\nstrategies, they suffer from poor coverage of the edges of the FOR. To underpin\nthe need for improved sampling strategies for data-driven approaches, in this\npaper we point out and analyse the shortcomings of naive sampling strategies\nwith focus on the problem of leptocurtic distribution of resulting\ninterconnection power flows (IPFs). We refer to this problem as convolution\nproblem, as it can be traced back to the fact that the probability density\nfunction (PDF) of the sum of two or more independent random variables is the\nconvolution of their respective PDFs. To demonstrate the convolution problem,\nwe construct a series of synthetic 0.4 kV feeders, which are characterized by\nan increasing number of nodes and apply a sampling strategy to them that draws\nset-values for the controllable distributed energy resources (DERs) from\nindependent uniform distributions. By calculating the power flow for each\nsample in each feeder, we end up with a collapsing IPF point cloud clearly\nindicating the convolution problem.\n"}
+{"abstract": "  Experiments with pretrained models such as BERT are often based on a single\ncheckpoint. While the conclusions drawn apply to the artifact (i.e., the\nparticular instance of the model), it is not always clear whether they hold for\nthe more general procedure (which includes the model architecture, training\ndata, initialization scheme, and loss function). Recent work has shown that\nre-running pretraining can lead to substantially different conclusions about\nperformance, suggesting that alternative evaluations are needed to make\nprincipled statements about procedures. To address this question, we introduce\nMultiBERTs: a set of 25 BERT-base checkpoints, trained with similar\nhyper-parameters as the original BERT model but differing in random\ninitialization and data shuffling. The aim is to enable researchers to draw\nrobust and statistically justified conclusions about pretraining procedures.\nThe full release includes 25 fully trained checkpoints, as well as statistical\nguidelines and a code library implementing our recommended hypothesis testing\nmethods. Finally, for five of these models we release a set of 28 intermediate\ncheckpoints in order to support research on learning dynamics.\n"}
+{"abstract": "  Functional Magnetic Resonance Imaging (fMRI) maps cerebral activation in\nresponse to stimuli but this activation is often difficult to detect,\nespecially in low-signal contexts and single-subject studies. Accurate\nactivation detection can be guided by the fact that very few voxels are, in\nreality, truly activated and that activated voxels are spatially localized, but\nit is challenging to incorporate both these facts. We provide a computationally\nfeasible and methodologically sound model-based approach, implemented in the R\npackage MixfMRI, that bounds the a priori expected proportion of activated\nvoxels while also incorporating spatial context. Results on simulation\nexperiments for different levels of activation detection difficulty are\nuniformly encouraging. The value of the methodology in low-signal and\nsingle-subject fMRI studies is illustrated on a sports imagination experiment.\nConcurrently, we also extend the potential use of fMRI as a clinical tool to,\nfor example, detect awareness and improve treatment in individual patients in\npersistent vegetative state, such as traumatic brain injury survivors.\n"}
+{"abstract": "  A generalised notion of Kac-Moody algebra is defined using smooth maps from a\ncompact real manifold $\\mathcal{M}$ to a finite-dimensional Lie group, by means\nof complete orthonormal bases for a Hermitian inner product on the manifold and\na Fourier expansion. The Peter--Weyl theorem for the case of manifolds related\nto compact Lie groups and coset spaces is discussed, and appropriate Hilbert\nbases for the space $L^{2}(\\mathcal{M})$ of square-integrable functions are\nconstructed. It is shown that such bases are characterised by the\nrepresentation theory of the compact Lie group, from which a complete set of\nlabelling operator is obtained. The existence of central extensions of\ngeneralised Kac-Moody algebras is analysed using a duality property of\nHermitian operators on the manifold, and the corresponding root systems are\nconstructed. Several applications of physically relevant compact groups and\ncoset spaces are discussed.\n"}
+{"abstract": "  We present two fast algorithms which apply inclusion-exclusion principle to\nsum over the bosonic diagrams in bare diagrammatic quantum Monte Carlo (dQMC)\nand inchworm Monte Carlo method, respectively. In the case of inchworm Monte\nCarlo, the proposed fast algorithm gives an extension to the work\n[\"Inclusion-exclusion principle for many-body diagrammatics\", Phys. Rev. B,\n98:115152, 2018] from fermionic to bosonic systems. We prove that the proposed\nfast algorithms reduce the computational complexity from double factorial to\nexponential. Numerical experiments are carried out to verify the theoretical\nresults and to compare the efficiency of the methods.\n"}
+{"abstract": "  Regret-based algorithms are highly efficient at finding approximate Nash\nequilibria in sequential games such as poker games. However, most regret-based\nalgorithms, including counterfactual regret minimization (CFR) and its\nvariants, rely on iterate averaging to achieve convergence. Inspired by recent\nadvances on last-iterate convergence of optimistic algorithms in zero-sum\nnormal-form games, we study this phenomenon in sequential games, and provide a\ncomprehensive study of last-iterate convergence for zero-sum extensive-form\ngames with perfect recall (EFGs), using various optimistic regret-minimization\nalgorithms over treeplexes. This includes algorithms using the vanilla entropy\nor squared Euclidean norm regularizers, as well as their dilated versions which\nadmit more efficient implementation. In contrast to CFR, we show that all of\nthese algorithms enjoy last-iterate convergence, with some of them even\nconverging exponentially fast. We also provide experiments to further support\nour theoretical results.\n"}
+{"abstract": "  Pre-trained language models such as ClinicalBERT have achieved impressive\nresults on tasks such as medical Natural Language Inference. At first glance,\nthis may suggest that these models are able to perform medical reasoning tasks,\nsuch as mapping symptoms to diseases. However, we find that standard benchmarks\nsuch as MedNLI contain relatively few examples that require such forms of\nreasoning. To better understand the medical reasoning capabilities of existing\nlanguage models, in this paper we introduce DisKnE, a new benchmark for Disease\nKnowledge Evaluation. To construct this benchmark, we annotated each positive\nMedNLI example with the types of medical reasoning that are needed. We then\ncreated negative examples by corrupting these positive examples in an\nadversarial way. Furthermore, we define training-test splits per disease,\nensuring that no knowledge about test diseases can be learned from the training\ndata, and we canonicalize the formulation of the hypotheses to avoid the\npresence of artefacts. This leads to a number of binary classification\nproblems, one for each type of reasoning and each disease. When analysing\npre-trained models for the clinical/biomedical domain on the proposed\nbenchmark, we find that their performance drops considerably.\n"}
+{"abstract": "  Pre-trained language models achieve outstanding performance in NLP tasks.\nVarious knowledge distillation methods have been proposed to reduce the heavy\ncomputation and storage requirements of pre-trained language models. However,\nfrom our observations, student models acquired by knowledge distillation suffer\nfrom adversarial attacks, which limits their usage in security sensitive\nscenarios. In order to overcome these security problems, RoSearch is proposed\nas a comprehensive framework to search the student models with better\nadversarial robustness when performing knowledge distillation. A directed\nacyclic graph based search space is built and an evolutionary search strategy\nis utilized to guide the searching approach. Each searched architecture is\ntrained by knowledge distillation on pre-trained language model and then\nevaluated under a robustness-, accuracy- and efficiency-aware metric as\nenvironmental fitness. Experimental results show that RoSearch can improve\nrobustness of student models from 7%~18% up to 45.8%~47.8% on different\ndatasets with comparable weight compression ratio to existing distillation\nmethods (4.6$\\times$~6.5$\\times$ improvement from teacher model BERT_BASE) and\nlow accuracy drop. In addition, we summarize the relationship between student\narchitecture and robustness through statistics of searched models.\n"}
+{"abstract": "  We investigate the near horizon geometry of the simplest representative of\nthe class of axisymmetric space-times: the Kerr Vaidya metrics. Kerr Vaidya\nmetrics can be derived from the Vaidya metric by the complex coordinate\ntransformation suggested by Newman and Janis. We show that the energy momentum\ntensor belongs to type 3 in the Segre Hawking Ellis classification but has a\nspecial form with all Lorentz invariant eigenvalues belonging to zero. We find\na location of the apparent horizon for quasi-stationary Kerr Vaidya black\nholes. The energy-momentum tensor of the Kerr Vaidya geometries violates the\nnull energy condition. We show that energy density, pressure, and flux for an\ninfalling observer are diverging in the outgoing Kerr Vaidya metric. This\nfirewall leads to the violation of a specific quantum energy inequality.\n"}
+{"abstract": "  We consider a massless higher spin field theory within the BRST approach and\nconstruct a general off-shell cubic vertex corresponding to irreducible higher\nspin fields of helicities $s_1, s_2, s_3$. Unlike the previous works on cubic\nvertices, which do not take into account of the trace constraints, we use the\ncomplete BRST operator, including the trace constraints that describe an\nirreducible representation with definite integer helicity. As a result, we\ngeneralize the cubic vertex found in [arXiv:1205.3131 [hep-th]] and calculate\nthe new contributions to the vertex, which contain additional terms with a\nsmaller number space-time derivatives of the fields as well as the terms\nwithout derivatives.\n"}
+{"abstract": "  Fluid, heat and species transport and oxygen reduction in the cathode of a\nPEM fuel cell are simulated using multi relaxation lattice Boltzmann method.\nHeat generation due to oxygen reduction and its effects on transport and\nreaction are considered. Simulations for various cell operating voltages,\ntemperatures and flow rates with various values of porous media properties,\nnamely, permeability, porosity, and effective porous media diffusion\ncoefficient, are performed to study transport and operating characteristics in\nthe electrode. It is seen that maximum output power density achievable is\nlimited by the mass transport rate. A small increase in current density is\nobtained by increasing the operating temperature. However, this results in an\nincrease in the rate of heat generation. Permeability and porosity of the gas\ndiffusion layer do not show a significant impact on the performance in the\nrange of values presently simulated. Higher permeability, in turn, resulted in\nenhancement of thermal gradients in the porous layer. A significant increase in\nthe maximum current density obtainable is observed with increase in flow rates.\nThe higher convection associated with high flow rate facilitates better\ntransport of species and heat at the catalyst layer resulting in larger current\ndensity with a lesser chance of hotspot formation. Increased species diffusion\ncoefficient also resulted in increasing the power output substantially. In\naddition, the fuel utilization is also improved at high diffusion rates in the\nporous media. The study analyses and shows the impact of various operating and\nmaterial parameters affecting the performance of a PEM fuel cell with special\nattention on enhancing the maximum power density attainable.\n"}
+{"abstract": "  In d-dimensional CFTs with a large number of degrees of freedom an important\nset of operators consists of the stress tensor and its products, multi stress\ntensors. Thermalization of such operators, the equality between their\nexpectation values in heavy states and at finite temperature, is equivalent to\na universal behavior of their OPE coefficients with a pair of identical heavy\noperators. We verify this behavior in a number of examples which include\nholographic and free CFTs and provide a bootstrap argument for the general\ncase. In a free CFT we check the thermalization of multi stress tensor\noperators directly and also confirm the equality between the contributions of\nmulti stress tensors to heavy-heavy-light-light correlators and to the\ncorresponding thermal light-light two-point functions by disentangling the\ncontributions of other light operators. Unlike multi stress tensors, these\nlight operators violate the Eigenstate Thermalization Hypothesis and do not\nthermalize.\n"}
+{"abstract": "  Following ideas of Gromov we prove scalar and mean curvature comparison\nresults for Riemannian bands with lower scalar curvature bounds in dimension\n$n\\leq7$. The model spaces we use are warped products over scalar-flat\nmanifolds with $\\log$-concave warping functions.\n"}
+{"abstract": "  Video watermarking embeds a message into a cover video in an imperceptible\nmanner, which can be retrieved even if the video undergoes certain\nmodifications or distortions. Traditional watermarking methods are often\nmanually designed for particular types of distortions and thus cannot\nsimultaneously handle a broad spectrum of distortions. To this end, we propose\na robust deep learning-based solution for video watermarking that is end-to-end\ntrainable. Our model consists of a novel multiscale design where the watermarks\nare distributed across multiple spatial-temporal scales. It gains robustness\nagainst various distortions through a differentiable distortion layer, whereas\nnon-differentiable distortions, such as popular video compression standards,\nare modeled by a differentiable proxy. Extensive evaluations on a wide variety\nof distortions show that our method outperforms traditional video watermarking\nmethods as well as deep image watermarking models by a large margin. We further\ndemonstrate the practicality of our method on a realistic video-editing\napplication.\n"}
+{"abstract": "  Since the beginning of the COVID-19 pandemic, many dashboards have emerged as\nuseful tools to monitor the evolution of the pandemic, inform the public, and\nassist governments in decision making. Our goal is to develop a globally\napplicable method, integrated in a twice daily updated dashboard that provides\nan estimate of the trend in the evolution of the number of cases and deaths\nfrom reported data of more than 200 countries and territories, as well as a\nseven-day forecast. One of the significant difficulties to manage a quickly\npropagating epidemic is that the details of the dynamic needed to forecast its\nevolution are obscured by the delays in the identification of cases and deaths\nand by irregular reporting. Our forecasting methodology substantially relies on\nestimating the underlying trend in the observed time series using robust\nseasonal trend decomposition techniques. This allows us to obtain forecasts\nwith simple, yet effective extrapolation methods in linear or log scale. We\npresent the results of an assessment of our forecasting methodology and discuss\nits application to the production of global and regional risk maps.\n"}
+{"abstract": "  We study the impact of non-local modifications of General Relativity on\nstellar structure. In particular, assuming an analytic distortion function, we\nmade use of remnant stars to put qualitative constraints on a parameter not\ndirectly restricted by solar system tests. Using current data sets available\nfor white dwarfs and strange quark stars candidates, we find that the most\nstringent bounds come from the objects displaying the highest core densities,\nsuch as strange quark stars and neutron stars. Specifically, the constraints\nobtained from this class of stars are three to four orders of magnitude tighter\nthan those obtained using white dwarfs.\n"}
+{"abstract": "  Let p be prime. We describe explicitly the resolution of singularities of\nseveral families of wild Z/pZ-quotient singularities in dimension two,\nincluding families that generalize the quotient singularities of type E_6, E_7,\nand E_8 from p=2 to arbitrary characteristics. We prove that for odd primes,\nany power of p can appear as the determinant of the intersection matrix of a\nwild Z/pZ-quotient singularity. We also provide evidence towards the conjecture\nthat in this situation one may choose the wild action to be ramified precisely\nat the origin.\n"}
+{"abstract": "  We perform a zero-$\\beta$ magnetohydrodynamic simulation for the C7.7 class\nflare initiated at 01:18 UT on 2011 June 21 using the Message Passing Interface\nAdaptive Mesh Refinement Versatile Advection Code (MPI-AMRVAC). The initial\ncondition for the simulation involves a flux rope which we realize through the\nregularized Biot-Savart laws, whose parameters are constrained by observations\nfrom the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory\n(SDO) and the Extreme Ultraviolet Imager (EUVI) on the twin Solar Terrestrial\nRelations Observatory (STEREO). This data-constrained initial state is then\nrelaxed to a force-free state by the magneto-frictional module in MPI-AMRVAC.\nThe further time-evolving simulation results reproduce the eruption\ncharacteristics obtained by SDO/AIA 94 A, 304 A, and STEREO/EUVI 304 A\nobservations fairly well. The simulated flux rope possesses similar eruption\ndirection, height range, and velocities to the observations. Especially, the\ntwo phases of slow evolution and fast eruption are reproduced by varying the\ndensity distribution in light of the filament material draining process. Our\ndata-constrained simulations also show other advantages, such as a large field\nof view (about 0.76 solar radii). We study the twist of the magnetic flux rope\nand the decay index of the overlying field, and find that in this event, both\nthe magnetic strapping force and the magnetic tension force are sufficiently\nweaker than the magnetic hoop force, thus allowing the successful eruption of\nthe flux rope. We also find that the anomalous resistivity is necessary in\nkeeping the correct morphology of the erupting flux rope.\n"}
+{"abstract": "  Our data challenge Chen et al.'s interpretation of smFRET results, i.e. the\nrepetitive unfolding of G4 with one-base translocations. We believe that the\nobserved oscillatory curve represents the alternate binding of DHX36 to the 3'\nand 5'G-tetrad of G4s, rather than the repetitive unfolding between the\ncanonical and the transformed non-canonical G4s. Noteworthily, our results\nreported here also call into question the previously published smFRET data of\nhelicase-mediated G4 unfolding. Therefore,discriminating the smFRET signal of\nrepetitive binding from that of unfolding is very important to avoid\nmisinterpreting smFRET results.\n"}
+{"abstract": "  To improve the convergence property of approximate message-passing (AMP),\nconvolutional AMP (CAMP) has been proposed. CAMP replaces the Onsager\ncorrection in AMP with a convolution of messages in all preceding iterations\nwhile it uses the same low-complexity matched filter (MF) as AMP. This paper\nderives state evolution (SE) equations to design the Bayes-optimal denoiser in\nCAMP. Numerical results imply that CAMP with the Bayes-optimal denoiser--called\nBayes-optimal CAMP--can achieve the Bayes-optimal performance for\nright-orthogonally invariant sensing matrices with low-to-moderate condition\nnumbers.\n"}
+{"abstract": "  We compute the $\\text{SO}(n+1)$-equivariant mod $2$ Borel cohomology of the\nfree iterated loop space $Z^{S^n}$ when $n$ is odd and $Z$ is a product of mod\n$2$ Eilenberg Mac Lane spaces. When $n=1$, this recovers Ottosen and\nB\\\"okstedt's computation for the free loop space. The highlight of our\ncomputation is a construction of cohomology classes using an\n$\\text{O}(n)$-equivariant evaluation map and a pushforward map. We then\nreinterpret our computation as giving a presentation of the zeroth derived\nfunctor of the Borel cohomology of $Z^{S^n}$ for arbitrary $Z$. We also include\nan appendix where we give formulas for computing the zeroth derived functor of\nthe cohomology of mapping spaces, and study the dependence of such derived\nfunctors on the Steenrod operations.\n"}
+{"abstract": "  Anomaly detection techniques are growing in importance at the Large Hadron\nCollider (LHC), motivated by the increasing need to search for new physics in a\nmodel-agnostic way. In this work, we provide a detailed comparative study\nbetween a well-studied unsupervised method called the autoencoder (AE) and a\nweakly-supervised approach based on the Classification Without Labels (CWoLa)\ntechnique. We examine the ability of the two methods to identify a new physics\nsignal at different cross sections in a fully hadronic resonance search. By\nconstruction, the AE classification performance is independent of the amount of\ninjected signal. In contrast, the CWoLa performance improves with increasing\nsignal abundance. When integrating these approaches with a complete background\nestimate, we find that the two methods have complementary sensitivity. In\nparticular, CWoLa is effective at finding diverse and moderately rare signals\nwhile the AE can provide sensitivity to very rare signals, but only with\ncertain topologies. We therefore demonstrate that both techniques are\ncomplementary and can be used together for anomaly detection at the LHC.\n"}
+{"abstract": "  Prescription Drug Monitoring Programs (PDMPs) seek to potentially reduce\nopioid misuse by restricting the sale of opioids in a state. We examine\ndiscontinuities along state borders, where one side may have a PDMP and the\nother side may not. We find that electronic PDMP implementation, whereby\ndoctors and pharmacists can observe a patient's opioid purchase history,\nreduces a state's opioid sales but increases opioid sales in neighboring\ncounties on the other side of the state border. We also find systematic\ndifferences in opioid sales and mortality between border counties and interior\ncounties. These differences decrease when neighboring states both have ePDMPs,\nwhich is consistent with the hypothesis that individuals cross state lines to\npurchase opioids. Our work highlights the importance of understanding the\nopioid market as connected across counties or states, as we show that states\nare affected by the opioid policies of their neighbors.\n"}
+{"abstract": "  This article deals with the uniqueness in identifying multiple parameters\nsimultaneously in the one-dimensional time-fractional diffusion-wave equation\nof fractional time-derivative order $\\in (0,2)$ with the zero Robin boundary\ncondition. Using the Laplace transform and a transformation formula, we prove\nthe uniqueness in determining an order of the fractional derivative, a\nspatially varying potential, initial values and Robin coefficients\nsimultaneously by boundary measurement data, provided that all the eigenmodes\nof an initial value do not vanish. Furthermore, for another formulation of\ninverse problem with input source term in place of initial value, by the\nuniqueness in the case of non-zero initial value and a Duhamel principle, we\nprove the simultaneous uniqueness in determining multiple parameters for a\ntime-fractional diffusion-wave equation.\n"}
+{"abstract": "  We develop a new nonparametric method to reconstruct the Equation of State\n(EoS) of Neutron Star with multimessenger data. As an universal function\napproximator, the Feed-Forward Neural Network (FFNN) with one hidden layer and\na sigmoidal activation function can approximately fit any continuous function.\nThus we are able to implement the nonparametric FFNN representation of the\nEoSs. This new representation is validated by its capabilities of fitting the\ntheoretical EoSs and recovering the injected parameters. Then we adopt this\nnonparametric method to analyze the real data, including mass-tidal\ndeformability measurement from the Binary Neutron Star (BNS) merger\nGravitational Wave (GW) event GW170817 and mass-radius measurement of PSR\nJ0030+0451 by {\\it NICER}. We take the publicly available samples to construct\nthe likelihood and use the nested sampling to obtain the posteriors of the\nparameters of FFNN according to the Bayesian theorem, which in turn can be\ntranslated to the posteriors of EoS parameters. Combining all these data, for a\ncanonical 1.4 $M_\\odot$ neutron star, we get the radius\n$R_{1.4}=11.83^{+1.25}_{-1.08}$ km and the tidal deformability $\\Lambda_{1.4} =\n323^{+334}_{-165}$ (90\\% confidence interval).Furthermore, we find that in the\nhigh density region ($\\geq 3\\rho_{\\rm sat}$), the 90\\% lower limits of the\n$c_{\\rm s}^2/c^2$ ($c_{\\rm s}$ is the sound speed and $c$ is the velocity of\nlight in the vacuum) are above $1/3$, which means that the so-called conformal\nlimit (i.e., $c_{\\rm s}^2/c^2<1/3$) is not always valid in the neutron stars.\n"}
+{"abstract": "  This paper considers a massive MIMO system under the double scattering\nchannels. We derive a closed-form expression of the uplink ergodic spectral\nefficiency (SE) by exploiting the maximum-ratio combining technique with\nimperfect channel state information. We then formulate and solve a total uplink\ndata power optimization problem that aims at simultaneously satisfying the\nrequired SEs from all the users with limited power resources. We further\npropose algorithms to cope with the congestion issue appearing when at least\none user is served by lower SE than requested. Numerical results illustrate the\neffectiveness of our proposed power optimization. More importantly, our\nproposed congestion-handling algorithms can guarantee the required SEs to many\nusers under congestion, even when the SE requirement is high.\n"}
+{"abstract": "  The processing of low-frequency interaural time differences is found to be\nproblematic among hearing-impaired people. The current generation of\nbeamformers does not consider this deficiency. In an attempt to tackle this\nissue, we propose to replace the inaudible interaural time differences in the\nlow-frequency region with the interaural level differences. In addition, a\nbeamformer is introduced and analyzed, which enhances the low-frequency\ninteraural level differences of the sound sources using a near-field\ntransformation. The proposed beamforming problem is relaxed to a convex problem\nusing semi-definite relaxation. The instrumental analysis suggests that the\nlow-frequency interaural level differences are enhanced without hindering the\nprovided intelligibility. A psychoacoustic localization test is done using a\nlistening experiment, which suggests that the replacement of time differences\ninto level differences improves the localization performance of normal-hearing\nlisteners for an anechoic scene but not for a reverberant scene.\n"}
+{"abstract": "  We propose a method for verifying that a given feasible point for a\npolynomial optimization problem is globally optimal. The approach relies on the\nLasserre hierarchy and the result of Lasserre regarding the importance of the\nconvexity of the feasible set as opposed to that of the individual constraints.\nBy focusing solely on certifying global optimality and relaxing the Lasserre\nhierarchy using necessary conditions for positive semidefiniteness based on\nmatrix determinants, the proposed method is implementable as a computationally\ntractable linear program. We demonstrate this method via application to several\ninstances of polynomial optimization, including the optimal power flow problem\nused to operate electric power systems.\n"}
+{"abstract": "  Adversarial learning can learn fairer and less biased models of language than\nstandard methods. However, current adversarial techniques only partially\nmitigate model bias, added to which their training procedures are often\nunstable. In this paper, we propose a novel approach to adversarial learning\nbased on the use of multiple diverse discriminators, whereby discriminators are\nencouraged to learn orthogonal hidden representations from one another.\nExperimental results show that our method substantially improves over standard\nadversarial removal methods, in terms of reducing bias and the stability of\ntraining.\n"}
+{"abstract": "  In recent years, machine learning and AI have been introduced in many\nindustrial fields. In fields such as finance, medicine, and autonomous driving,\nwhere the inference results of a model may have serious consequences, high\ninterpretability as well as prediction accuracy is required. In this study, we\npropose CGA2M+, which is based on the Generalized Additive 2 Model (GA2M) and\ndiffers from it in two major ways. The first is the introduction of\nmonotonicity. Imposing monotonicity on some functions based on an analyst's\nknowledge is expected to improve not only interpretability but also\ngeneralization performance. The second is the introduction of a higher-order\nterm: given that GA2M considers only second-order interactions, we aim to\nbalance interpretability and prediction accuracy by introducing a higher-order\nterm that can capture higher-order interactions. In this way, we can improve\nprediction performance without compromising interpretability by applying\nlearning innovation. Numerical experiments showed that the proposed model has\nhigh predictive performance and interpretability. Furthermore, we confirmed\nthat generalization performance is improved by introducing monotonicity.\n"}
+{"abstract": "  We study automated intrusion prevention using reinforcement learning. In a\nnovel approach, we formulate the problem of intrusion prevention as an optimal\nstopping problem. This formulation allows us insight into the structure of the\noptimal policies, which turn out to be threshold based. Since the computation\nof the optimal defender policy using dynamic programming is not feasible for\npractical cases, we approximate the optimal policy through reinforcement\nlearning in a simulation environment. To define the dynamics of the simulation,\nwe emulate the target infrastructure and collect measurements. Our evaluations\nshow that the learned policies are close to optimal and that they indeed can be\nexpressed using thresholds.\n"}
+{"abstract": "  We describe the models we built for hospital admissions and occupancy of\nCOVID-19 patients in the Netherlands. These models were used to make short-term\ndecisions about transfers of patients between regions and for long-term policy\nmaking. We motivate and describe the model we used for predicting admissions\nand how we use this to make predictions on occupancy.\n"}
+{"abstract": "  With very few exceptions, recent research in fair division has mostly focused\non deterministic allocations. Deviating from this trend, we study the fairness\nnotion of interim envy-freeness (iEF) for lotteries over allocations, which\nserves as a sweet spot between the too stringent notion of ex-post\nenvy-freeness and the very weak notion of ex-ante envy-freeness. iEF is a\nnatural generalization of envy-freeness to random allocations in the sense that\na deterministic envy-free allocation is iEF (when viewed as a degenerate\nlottery). It is also certainly meaningful as it allows for a richer solution\nspace, which includes solutions that are provably better than envy-freeness\naccording to several criteria. Our analysis relates iEF to other fairness\nnotions as well, and reveals tradeoffs between iEF and efficiency. Even though\nseveral of our results apply to general fair division problems, we are\nparticularly interested in instances with equal numbers of agents and items\nwhere allocations are perfect matchings of the items to the agents.\nEnvy-freeness can be trivially decided and (when it can be achieved, it)\nimplies full efficiency in this setting. Although computing iEF allocations in\nmatching allocation instances is considerably more challenging, we show how to\ncompute them in polynomial time, while also maximizing several efficiency\nobjectives. Our algorithms use the ellipsoid method for linear programming and\nefficient solutions to a novel variant of the bipartite matching problem as a\nseparation oracle. We also study the extension of interim envy-freeness notion\nwhen payments to or from the agents are allowed. We present a series of results\non two optimization problems, including a generalization of the classical rent\ndivision problem to random allocations using interim envy-freeness as the\nsolution concept.\n"}
+{"abstract": "  The Geroch/Stephani transformation is a solution-generating transformation,\nand may generate spiky solutions. The spikes in solutions generated so far are\neither early-time permanent spikes or transient spikes. We want to generate a\nsolution with a late-time permanent spike. We achieve this by applying the\nStephani transformation with the rotational Killing vector field of the locally\nrotationally symmetric Jacobs solution. The late-time permanent spike occurs\nalong the cylindrical axis. The generated solution also features a rich variety\nof transient structures. We introduce a new technique to analyse these\nstructures. Our findings lead us to discover a transient behaviour, which we\ncall the overshoot transition.\n"}
+{"abstract": "  Many biological systems can be described by finite Markov models. A general\nmethod for simplifying master equations is presented that is based on merging\nadjacent states. The approach preserves the steady-state probability\ndistribution and all steady-state fluxes except the one between the merged\nstates. Different levels of coarse graining of the underlying microscopic\ndynamics can be obtained by iteration, with the result being independent of the\norder in which states are merged. A criterion for the optimal level of coarse\ngraining or resolution of the process is proposed via a tradeoff between the\nsimplicity of the coarse-grained model and the information loss relative to the\noriginal model. As a case study, the method is applied to the cycle kinetics of\nthe molecular motor kinesin.\n"}
+{"abstract": "  We reconcile for the first time the strict mathematical formalism of\nmultivariate cumulants with the usage of cumulants in anisotropic flow analyses\nin high-energy nuclear collisions. This reconciliation yields to the next\ngeneration of observables to be used in flow analyses. We review all\nfundamental properties of multivariate cumulants and use them as a foundation\nto establish two simple necessary conditions to determine whether some\nmultivariate observable is a multivariate cumulant in the basis they are\nexpressed in. We argue that properties of cumulants are preserved only for the\nstochastic observables on which the cumulant expansion has been performed\ndirectly, and if there are no underlying symmetries due to which some terms in\nthe cumulant expansion are identically zero. We illustrate one possibility of\nhow new multivariate cumulants of azimuthal angles can be defined which do\nsatisfy all fundamental properties of multivariate cumulants, by defining them\nevent-by-event and by keeping all non-isotropic terms in the cumulant\nexpansion. We introduce new cumulants of flow amplitudes named Asymmetric\nCumulants, which generalize recently introduced Symmetric Cumulants for the\ncase when flow amplitudes are raised to different powers. Finally, we present\nthe new concept of Cumulants of Symmetry Plane Correlations and provide the\nfirst realisation for the lowest orders. All the presented results are\nsupported by Monte Carlo studies using state-of-the-art models.\n"}
+{"abstract": "  A widely recognized difficulty in federated learning arises from the\nstatistical heterogeneity among clients: local datasets often come from\ndifferent but not entirely unrelated distributions, and personalization is,\ntherefore, necessary to achieve optimal results from each individual's\nperspective. In this paper, we show how the excess risks of personalized\nfederated learning with a smooth, strongly convex loss depend on data\nheterogeneity from a minimax point of view. Our analysis reveals a surprising\ntheorem of the alternative for personalized federated learning: there exists a\nthreshold such that (a) if a certain measure of data heterogeneity is below\nthis threshold, the FedAvg algorithm [McMahan et al., 2017] is minimax optimal;\n(b) when the measure of heterogeneity is above this threshold, then doing pure\nlocal training (i.e., clients solve empirical risk minimization problems on\ntheir local datasets without any communication) is minimax optimal. As an\nimplication, our results show that the presumably difficult\n(infinite-dimensional) problem of adapting to client-wise heterogeneity can be\nreduced to a simple binary decision problem of choosing between the two\nbaseline algorithms. Our analysis relies on a new notion of algorithmic\nstability that takes into account the nature of federated learning.\n"}
+{"abstract": "  A century ago, Srinivasa Ramanujan -- the great self-taught Indian genius of\nmathematics -- died, shortly after returning from Cambridge, UK, where he had\ncollaborated with Godfrey Hardy. Ramanujan contributed numerous outstanding\nresults to different branches of mathematics, like analysis and number theory,\nwith a focus on special functions and series. Here we refer to apparently weird\nvalues which he assigned to two simple divergent series, $\\sum_{n \\geq 1} n$\nand $\\sum_{n \\geq 1} n^{3}$. These values are sensible, however, as analytic\ncontinuations, which correspond to Riemann's $\\zeta$-function. Moreover, they\nhave applications in physics: we discuss the vacuum energy of the photon field,\nfrom which one can derive the Casimir force, which has been experimentally\nmeasured. We further discuss its interpretation, which remains controversial.\nThis is a simple way to illustrate the concept of renormalization, which is\nvital in quantum field theory.\n"}
+{"abstract": "  Nowadays, formal methods are used in various areas for the verification of\nprograms or for code generation from models in order to increase the quality of\nsoftware and to reduce costs. However, there are still fields in which formal\nmethods have not been widely adopted, despite the large set of possible\nbenefits offered. This is the case for the area of programmable logic\ncontrollers (PLC). This article aims to evaluate the potential of formal\nmethods in the context of PLC development. For this purpose, the general\nconcepts of formal methods are first introduced and then transferred to the PLC\narea, resulting in an engineering-oriented description of the technology that\nis based on common concepts from PLC development. Based on this description,\nPLC professionals with varying degrees of experience were interviewed for their\nperspective on the topic and to identify possible use cases within the PLC\ndomain. The survey results indicate the technology's high potential in the PLC\narea, either as a tool to directly support the developer or as a key element\nwithin a model-based systems engineering toolchain. The evaluation of the\nsurvey results is performed with the aid of a demo application that\ncommunicates with the Totally Integrated Automation Portal from Siemens and\ngenerates programs via Fastsynth, a model-based open source code generator.\nBenchmarks based on an industry-related PLC project show satisfactory synthesis\ntimes and a successful integration into the workflow of a PLC developer.\n"}
+{"abstract": "  Inspired by the recent discovery of the $X(6900)$ meson at {\\tt LHCb}\nexperiment, we investigate the inclusive production rate of the $C$-odd\nfully-charmed tetraquarks associated with light hadrons at the $B$ factory\nwithin the nonrelativistic QCD (NRQCD) factorization framework. The\nshort-distance coefficient is computed at lowest order in velocity and\n$\\alpha_s$. Employing the diquark-antidiquark model to roughly estimate the\nlong-distance NRQCD matrix elements, we predict the rate for inclusive\nproduction of the $1^{+-}$ $T_{4c}$ state and discuss the observation prospects\nat {\\tt Belle 2} experiment.\n"}
+{"abstract": "  Modeling distributions on Riemannian manifolds is a crucial component in\nunderstanding non-Euclidean data that arises, e.g., in physics and geology. The\nbudding approaches in this space are limited by representational and\ncomputational tradeoffs. We propose and study a class of flows that uses convex\npotentials from Riemannian optimal transport. These are universal and can model\ndistributions on any compact Riemannian manifold without requiring domain\nknowledge of the manifold to be integrated into the architecture. We\ndemonstrate that these flows can model standard distributions on spheres, and\ntori, on synthetic and geological data. Our source code is freely available\nonline at http://github.com/facebookresearch/rcpm\n"}
+{"abstract": "  Pretrained language models (PLMs) such as BERT adopt a training paradigm\nwhich first pretrain the model in general data and then finetune the model on\ntask-specific data, and have recently achieved great success. However, PLMs are\nnotorious for their enormous parameters and hard to be deployed on real-life\napplications. Knowledge distillation has been prevailing to address this\nproblem by transferring knowledge from a large teacher to a much smaller\nstudent over a set of data. We argue that the selection of thee three key\ncomponents, namely teacher, training data, and learning objective, is crucial\nto the effectiveness of distillation. We, therefore, propose a four-stage\nprogressive distillation framework ERNIE-Tiny to compress PLM, which varies the\nthree components gradually from general level to task-specific level.\nSpecifically, the first stage, General Distillation, performs distillation with\nguidance from pretrained teacher, gerenal data and latent distillation loss.\nThen, General-Enhanced Distillation changes teacher model from pretrained\nteacher to finetuned teacher. After that, Task-Adaptive Distillation shifts\ntraining data from general data to task-specific data. In the end,\nTask-Specific Distillation, adds two additional losses, namely Soft-Label and\nHard-Label loss onto the last stage. Empirical results demonstrate the\neffectiveness of our framework and generalization gain brought by ERNIE-Tiny.In\nparticular, experiments show that a 4-layer ERNIE-Tiny maintains over\n98.0%performance of its 12-layer teacher BERT base on GLUE benchmark,\nsurpassing state-of-the-art (SOTA) by 1.0% GLUE score with the same amount of\nparameters. Moreover, ERNIE-Tiny achieves a new compression SOTA on five\nChinese NLP tasks, outperforming BERT base by 0.4% accuracy with 7.5x fewer\nparameters and9.4x faster inference speed.\n"}
+{"abstract": "  Plant species identification in the wild is a difficult problem in part due\nto the high variability of the input data, but also because of complications\ninduced by the long-tail effects of the datasets distribution. Inspired by the\nmost recent fine-grained visual classification approaches which are based on\nattention to mitigate the effects of data variability, we explore the idea of\nusing object detection as a form of attention. We introduce a bottom-up\napproach based on detecting plant organs and fusing the predictions of a\nvariable number of organ-based species classifiers. We also curate a new\ndataset with a long-tail distribution for evaluating plant organ detection and\norgan-based species identification, which is publicly available.\n"}
+{"abstract": "  Using an effective field theory approach for higher-spin fields, we derive\nthe interactions of colour singlet and electrically neutral particles with a\nspin higher than unity, concentrating on the spin-3/2, spin-2, spin-5/2 and\nspin-3 cases. We compute the decay rates and production cross sections in the\nmain channels for spin-3/2 and spin-2 states at both electron-positron and\nhadron colliders, and identify the most promising novel experimental signatures\nfor discovering such particles at the LHC. The discussion is qualitatively\nextended to the spin-5/2 and spin-3 cases. Higher-spin particles exhibit a rich\nphenomenology and have signatures that often resemble the ones of\nsupersymmetric and extra-dimensional theories. To enable further studies of\nhigher-spin particles at collider and beyond, we collect the relevant Feynman\nrules and other technical details.\n"}
+{"abstract": "  Length Rate Quotient (LRQ) is the first algorithm of interleaved shaping -- a\nnovel concept proposed to provide per-flow shaping for a flow aggregate without\nper-flow queuing. This concept has been adopted by Time-Sensitive Networking\n(TSN) and Deterministic Networking (DetNet). An appealing property of\ninterleaved shaping is that, when an interleaved shaper is appended to a FIFO\nsystem, it does not increase the worst-case delay of the system. Based on this\n\"shaping-for-free\" property, an approach has been introduced to deliver bounded\nend-to-end latency. Specifically, at each output link of a node, class-based\naggregate scheduling is used together with one interleaved shaper per-input\nlink and per-class, and the interleaved shaper re-shapes every flow to its\ninitial traffic constraint. In this paper, we investigate other properties of\ninterleaved LRQ shapers, particularly as stand-alone elements. In addition,\nunder per-flow setting, we also investigate per-flow LRQ based flow aggregation\nand derive its properties. The analysis focuses directly on the timing of\noperations, such as shaping and scheduling, in the network. This timing based\nmethod can be found in the Guaranteed Rate (GR) server model and more generally\nthe max-plus branch of network calculus. With the derived properties, we not\nonly show that an improved end-to-end latency bound can be obtained for the\ncurrent approach, but also demonstrate with two examples that new approaches\nmay be devised. End-to-end delay bounds for the three approaches are derived\nand compared. As a highlight, the two new approaches do not require different\nnode architectures in allocating (shaping / scheduling) queues, which implies\nthat they can be readily adapted for use in TSN and DetNet. This together with\nthe derived properties of LRQ shed new insights on providing the TSN / DetNet\nqualities of service.\n"}
+{"abstract": "  We characterize for the Poisson Hail problem up to the boundary the\ncollection of critical moments which guarantee stability in full generality. In\nparticular, we treat the case of infinite speed of propagation.\n"}
+{"abstract": "  Human activity recognition in videos has been widely studied and has recently\ngained significant advances with deep learning approaches; however, it remains\na challenging task. In this paper, we propose a novel framework that\nsimultaneously considers both implicit and explicit representations of human\ninteractions by fusing information of local image where the interaction\nactively occurred, primitive motion with the posture of individual subject's\nbody parts, and the co-occurrence of overall appearance change. Human\ninteractions change, depending on how the body parts of each human interact\nwith the other. The proposed method captures the subtle difference between\ndifferent interactions using interacting body part attention. Semantically\nimportant body parts that interact with other objects are given more weight\nduring feature representation. The combined feature of interacting body part\nattention-based individual representation and the co-occurrence descriptor of\nthe full-body appearance change is fed into long short-term memory to model the\ntemporal dynamics over time in a single framework. We validate the\neffectiveness of the proposed method using four widely used public datasets by\noutperforming the competing state-of-the-art method.\n"}
+{"abstract": "  The modulation of the electronic structure by an external magnetic field,\nwhich could further control the electronic transport behaviour of a system, is\nhighly desired. Herein, an unconventional anomalous Hall effect (UAHE) was\nobserved during magnetization process in the magnetic Weyl semimetal EuB6,\nresulting in an unconventional anomalous Hall-conductivity as high as ~1000\n{\\Omega}-1 cm-1 and a Hall-angle up to ~10%. The system even only shows the\nUAHE, meaning that the anomalous Hall signal completely comes from the UAHE,\nwith UAHE accounting for 100% and 87.5% of the AHE and the total Hall response,\nrespectively. Theoretical calculations revealed that a largely enhanced Berry\ncurvature was induced by the dynamic folding of the topological bands due to\nthe spin-canting effect under external magnetic fields, which further produced\nthe prominent UAHE even in a low-field magnetization process. These findings\nelucidate the connection between the non-collinear magnetism and the\ntopological electronic state as well as reveal a novel manner to manipulate the\ntransport behaviour of topological electrons.\n"}
+{"abstract": "  We present a detailed theoretical analysis for the spectral properties of\nAndreev bound states in the multiterminal Josephson junctions by employing a\nsymmetry-constrained scattering matrix approach. We find that in the synthetic\nfive-dimensional space of superconducting phases, crossings of Andreev bands\nmay support the non-Abelian $SU(2)$ monopoles with a topological charge\ncharacterized by the second class Chern number. We propose that these\ntopological defects can be detected via nonlinear response measurement of the\ncurrent autocorrelations. In addition, multiterminal Josephson junction devices\ncan be tested as a hardware platform for realizing holonomic quantum\ncomputation.\n"}
+{"abstract": "  Most evolutionary robotics studies focus on evolving some targeted behavior\nwithout taking the energy usage into account. This limits the practical value\nof such systems because energy efficiency is an important property for\nreal-world autonomous robots. In this paper, we mitigate this problem by\nextending our simulator with a battery model and taking energy consumption into\naccount during fitness evaluations. Using this system we investigate how energy\nawareness affects the evolution of robots. Since our system is to evolve\nmorphologies as well as controllers, the main research question is twofold: (i)\nwhat is the impact on the morphologies of the evolved robots, and (ii) what is\nthe impact on the behavior of the evolved robots if energy consumption is\nincluded in the fitness evaluation? The results show that including the energy\nconsumption in the fitness in a multi-objective fashion (by NSGA-II) reduces\nthe average size of robot bodies while at the same time reducing their speed.\nHowever, robots generated without size reduction can achieve speeds comparable\nto robots from the baseline set.\n"}
+{"abstract": "  3D mask face presentation attack detection (PAD) plays a vital role in\nsecuring face recognition systems from emergent 3D mask attacks. Recently,\nremote photoplethysmography (rPPG) has been developed as an intrinsic liveness\nclue for 3D mask PAD without relying on the mask appearance. However, the rPPG\nfeatures for 3D mask PAD are still needed expert knowledge to design manually,\nwhich limits its further progress in the deep learning and big data era. In\nthis letter, we propose a pure rPPG transformer (TransRPPG) framework for\nlearning intrinsic liveness representation efficiently. At first, rPPG-based\nmulti-scale spatial-temporal maps (MSTmap) are constructed from facial skin and\nbackground regions. Then the transformer fully mines the global relationship\nwithin MSTmaps for liveness representation, and gives a binary prediction for\n3D mask detection. Comprehensive experiments are conducted on two benchmark\ndatasets to demonstrate the efficacy of the TransRPPG on both intra- and\ncross-dataset testings. Our TransRPPG is lightweight and efficient (with only\n547K parameters and 763M FLOPs), which is promising for mobile-level\napplications.\n"}
+{"abstract": "  We present an analytic model for the splashback mass function of dark matter\nhalos, which is parameterized by a single coefficient and constructed in the\nframework of the generalized excursion set theory and the self-similar\nspherical infall model. The value of the single coefficient that quantifies the\ndiffusive nature of the splashback boundary is determined at various redshifts\nby comparing the model with the numerical results from the Erebos N-body\nsimulations for the Planck and the WMAP7 cosmologies. Showing that the analytic\nmodel with the best-fit coefficient provides excellent matches to the numerical\nresults in the mass range of $5\\le M/(10^{12}h^{-1}\\,M_{\\odot})< 10^{3}$, we\nemploy the Bayesian and Akaike Information Criterion tests to confirm that our\nmodel is most preferred by the numerical results to the previous models at\nredshifts of $0.3\\le z\\le 3$ for both of the cosmologies. It is also found that\nthe diffusion coefficient decreases almost linearly with redshifts, converging\nto zero at a certain threshold redshift, $z_{c}$, whose value significantly\ndiffers between the Planck and WMAP7 cosmologies. Our result implies that the\nsplashback mass function of dark matter halos at $z\\ge z_{c}$ is well described\nby a parameter-free analytic formula and that $z_{c}$ may have a potential to\nindependently constrain the initial conditions of the universe.\n"}
+{"abstract": "  Spiking Neural Networks (SNNs) have the potential for achieving low energy\nconsumption due to their biologically sparse computation. Several studies have\nshown that the off-chip memory (DRAM) accesses are the most energy-consuming\noperations in SNN processing. However, state-of-the-art in SNN systems do not\noptimize the DRAM energy-per-access, thereby hindering achieving high\nenergy-efficiency. To substantially minimize the DRAM energy-per-access, a key\nknob is to reduce the DRAM supply voltage but this may lead to DRAM errors\n(i.e., the so-called approximate DRAM). Towards this, we propose SparkXD, a\nnovel framework that provides a comprehensive conjoint solution for resilient\nand energy-efficient SNN inference using low-power DRAMs subjected to\nvoltage-induced errors. The key mechanisms of SparkXD are: (1) improving the\nSNN error tolerance through fault-aware training that considers bit errors from\napproximate DRAM, (2) analyzing the error tolerance of the improved SNN model\nto find the maximum tolerable bit error rate (BER) that meets the targeted\naccuracy constraint, and (3) energy-efficient DRAM data mapping for the\nresilient SNN model that maps the weights in the appropriate DRAM location to\nminimize the DRAM access energy. Through these mechanisms, SparkXD mitigates\nthe negative impact of DRAM (approximation) errors, and provides the required\naccuracy. The experimental results show that, for a target accuracy within 1%\nof the baseline design (i.e., SNN without DRAM errors), SparkXD reduces the\nDRAM energy by ca. 40% on average across different network sizes.\n"}
+{"abstract": "  Franson-type nonlocal quantum correlation based on the particle nature of\nquantum mechanics has been intensively studied for both fundamental physics and\npotential applications of quantum key distribution between remotely separated\nparties over the last several decades. Recently, a coherence theory of\ndeterministic quantum features has been applied for Franson-type nonlocal\ncorrelation [arXiv:2102.06463] to understand its quantumness in a purely\nclassical manner, where the resulting features are deterministic and\nmacroscopic. Here, nearly sub-Poisson distributed coherent photon pairs\nobtained from an attenuated laser are used for the experimental demonstrations\nof the coherence Franson-type nonlocal correlation. As an essential requirement\nof quantum mechanics, quantum superposition is macroscopically provided using\npolarization basis-randomness via a half-wave plate, satisfying fairness\ncompared with the original scheme based on phase bases. The observed coherence\nquantum feature of the modified Franson correlation successfully demonstrates\nthe proposed wave nature of quantum mechanics, where the unveiled nonlocal\ncorrelation is relied on a definite phase relation between the paired coherent\nphotons.\n"}
+{"abstract": "  For given integers $n$ and $d$, both at least 2, we consider a homogeneous\nmultivariate polynomial $f_d$ of degree $d$ in variables indexed by the edges\nof the complete graph on $n$ vertices and coefficients depending on\ncardinalities of certain unions of edges. Cardinaels, Borst and Van Leeuwaarden\n(arXiv:2111.05777, 2021) asked whether $f_d$, which arises in a model of\njob-occupancy in redundancy scheduling, attains its minimum over the standard\nsimplex at the uniform probability vector. Brosch, Laurent and Steenkamp [SIAM\nJ. Optim. 31 (2021), 2227--2254] proved that $f_d$ is convex over the standard\nsimplex if $d=2$ and $d=3$, implying the desired result for these $d$. We give\na symmetry reduction to show that for fixed $d$, the polynomial is convex over\nthe standard simplex (for all $n\\geq 2$) if a constant number of constant\nmatrices (with size and coefficients independent of $n$) are positive\nsemidefinite. This result is then used in combination with a computer-assisted\nverification to show that the polynomial $f_d$ is convex for $d\\leq 9$.\n"}
+{"abstract": "  Mitral valve repair is a surgery to restore the function of the mitral valve.\nTo achieve this, a prosthetic ring is sewed onto the mitral annulus. Analyzing\nthe sutures, which are punctured through the annulus for ring implantation, can\nbe useful in surgical skill assessment, for quantitative surgery and for\npositioning a virtual prosthetic ring model in the scene via augmented reality.\nThis work presents a neural network approach which detects the sutures in\nendoscopic images of mitral valve repair and therefore solves a landmark\ndetection problem with varying amount of landmarks, as opposed to most other\nexisting deep learning-based landmark detection approaches. The neural network\nis trained separately on two data collections from different domains with the\nsame architecture and hyperparameter settings. The datasets consist of more\nthan 1,300 stereo frame pairs each, with a total over 60,000 annotated\nlandmarks. The proposed heatmap-based neural network achieves a mean positive\npredictive value (PPV) of 66.68$\\pm$4.67% and a mean true positive rate (TPR)\nof 24.45$\\pm$5.06% on the intraoperative test dataset and a mean PPV of\n81.50\\pm5.77\\% and a mean TPR of 61.60$\\pm$6.11% on a dataset recorded during\nsurgical simulation. The best detection results are achieved when the camera is\npositioned above the mitral valve with good illumination. A detection from a\nsideward view is also possible if the mitral valve is well perceptible.\n"}
+{"abstract": "  It has long been believed that core-hole lifetime (CHL) of an atom is an\nintrinsic physical property, and controlling it is significant yet is very\nhard. Here, CHL of the 2p state of W atom is manipulated experimentally through\nadjusting the emission rate of a resonant fluorescence channel with the\nassistance of an x-ray thin-film planar cavity. The emission rate is\naccelerated by a factor linearly proportional to the cavity field amplitude,\nthat can be directly controlled by choosing different cavity modes or changing\nthe angle offset in experiment. This experimental observation is in good\nagreement with theoretical predictions. It is found that the manipulated\nresonant fluorescence channel even can dominate the CHL. The controllable CHL\nrealized here will facilitate the nonlinear investigations and modern x-ray\nscattering techniques in hard x-ray region.\n"}
+{"abstract": "  Hadronic cross sections are important ingredients in many of the ongoing\nresearch methods in high-energy nuclear physics, and it is always important to\nmeasure and/or calculate the probabilities of different types of reactions. In\nheavy-ion transport simulations at a few GeV energies, these hadronic cross\nsections are essential and so far mostly the exclusive processes are used,\nhowever, if one interested in total production rates the inclusive cross\nsections are also necessary to know. In this paper, we introduce a\nstatistical-based method, which is able to give good estimates to exclusive and\ninclusive cross sections as well in the energy range of a few GeV. The method\nand its estimates for not well-known cross sections, will be used in a\nBoltzmann-Uehling-Uhlenbeck (BUU) type off-shell transport code to explain\ncharmonium and bottomonium mass shifts in heavy-ion collisions.\n"}
+{"abstract": "  In machine learning (ML), it is in general challenging to provide a detailed\nexplanation on how a trained model arrives at its prediction. Thus, usually we\nare left with a black-box, which from a scientific standpoint is not\nsatisfactory. Even though numerous methods have been recently proposed to\ninterpret ML models, somewhat surprisingly, interpretability in ML is far from\nbeing a consensual concept, with diverse and sometimes contrasting motivations\nfor it. Reasonable candidate properties of interpretable models could be model\ntransparency (i.e. how does the model work?) and post hoc explanations (i.e.,\nwhat else can the model tell me?). Here, I review the current debate on ML\ninterpretability and identify key challenges that are specific to ML applied to\nmaterials science.\n"}
+{"abstract": "  We propose a reliable scheme to recover the photon blockade effect in the\ndispersive-Jaynes-Cummings model, which describes a two-level atom coupled to a\nsingle-mode cavity field in the large-detuning regime. This is achieved by\nintroducing a transversal driving to the atom and then photonic nonlinearity is\nobtained. The eigen-energy spectrum of the system is obtained analytically, and\nthe photon blockade effect is confirmed by numerically calculating the\nphoton-number distributions and the equal-time second-order correlation\nfunction of the cavity field in the presence of system dissipations. We find\nthat the photon blockade effect can be recovered at proper atomic and\ncavity-field drivings. This work will provide a new method to generate photon\nblockade in the dispersively coupled quantum optical systems.\n"}
+{"abstract": "  The reconstruction of spectral function from correlation function in\nEuclidean space is a challenging task. In this paper, we employ the Machine\nLearning techniques in terms of the radial basis functions networks to\nreconstruct the spectral function from a finite number of correlation data. To\ntest our method, we first generate one type of correlation data using a mock\nspectral function by mixing several Breit-Wigner propagators. We found that\ncompared with other traditional methods, TSVD, Tikhonov, and MEM, our approach\ngives a continuous and unified reconstruction for both positive definite and\nnegative spectral function, which is especially useful for studying the QCD\nphase transition. Moreover, our approach has considerably better performance in\nthe low frequency region. This has advantages for the extraction of transport\ncoefficients which are related to the zero frequency limit of the spectral\nfunction. With the mock data generated through a model spectral function of\nstress energy tensor, we find our method gives a precise and stable extraction\nof the transport coefficients.\n"}
+{"abstract": "  Non-singular weighted surface algebras satisfy the necessary condition found\nin [6] for existence of cluster tilting modules. We show that any such algebra\nwhose Gabriel quiver is bipartite, has a module satisfying the necessary ext\nvanishing condition. We show that it is 3-cluster tilting precisely for the\nnon-singular triangular or spherical algebras but not for any other weighted\nsurface algebra with bipartite Gabriel quiver.\n"}
+{"abstract": "  Field emission tips with apex radius of curvature below 100nm are not\nadequately described by the standard theoretical models based on the\nFowler-Nordheim and Murphy-Good formalisms. This is due to the breakdown of the\n`constant electric field' assumption within the tunneling region leading to\nsubstantial errors in current predictions. A uniformly applicable\ncurvature-corrected field emission theory requires that the tunneling potential\nbe approximately universal irrespective of the emitter shape. Using the line\ncharge model, it is established analytically that smooth generic emitter tips\napproximately follow this universal trend when the anode is far away. This is\nverified using COMSOL for various emitter shapes including the locally\nnon-parabolic `hemisphere on a cylindrical post'. It is also found numerically\nthat the curvature-corrected tunneling potential provides an adequate\napproximation when the anode is in close proximity as well as in the presence\nof other emitters.\n"}
+{"abstract": "  The direct shooting method is a classic approach for the solution of Optimal\nControl Problems (OCPs). It parameterizes the control variables and transforms\nthe OCP to the Nonlinear Programming (NLP) problem to solve. This method is\neasy to use and it often introduces less parameters compared with all-variable\nparameterization method like the Pseudo-spectral (PS) method. However, it is\nlong believed that its solution is not guaranteed to satisfy the optimality\nconditions of the OCP and the costates are not available in using this method.\nIn this paper, we show that the direct shooting method may also provide the\ncostate information, and it is proved that both the state and the costate\nsolutions converge to the optimal as long as the control variable tends to the\noptimal, while the parameterized control may approach the optimal control with\nreasonable parameterization. This gives us the credit for the optimal control\ncomputation when employing the direct shooting method.\n"}
+{"abstract": "  The breakthrough of achieving fully homomorphic encryption sparked enormous\nstudies on where and how to apply homomorphic encryption schemes so that\noperations can be performed on encrypted data without the secret key while\nstill obtaining the correct outputs. Due to the computational cost, inflated\nciphertext size and limited arithmetic operations that are allowed in most\nencryption schemes, feasible applications of homomorphic encryption are few.\nWhile theorists are working on the mathematical and cryptographical foundations\nof homomorphic encryption in order to overcome the current limitations,\npractitioners are also re-designing queries and algorithms to adapt the\nfunctionalities of the current encryption schemes. As an initial study on\nworking with homomorphically encrypted graphs, this paper provides an\neasy-to-implement interactive algorithm to check whether or not a\nhomomorphically encrypted graph is chordal. This algorithm is simply a\nrefactoring of a current method to run on the encrypted adjacency matrices.\n"}
+{"abstract": "  The paper adopts parallel computing systems for predictive analysis in both\nCPU and GPU leveraging Spark Big Data platform. The traffic dataset is adopted\nto predict the traffic jams in Los Angeles County. It is collected from a\npopular platform in the USA for tracking information on the road using the\ndevice information and reports shared by the users. Large-scale traffic data\nset can be stored and processed using both GPU and CPU in this Scalable Big\nData systems. The major contribution of this paper is to improve the\nperformance of machine learning in distributed parallel computing systems with\nGPU to predict the traffic congestion. We show that the parallel computing can\nbe achieve using both GPU and CPU with the existing Apache Spark platform. Our\nmethod can be applicable to other large scale datasets in different domains.\nThe process modeling, as well as results, are interpreted using computing time\nand metrics: AUC, Precision and Recall. It should help the traffic management\nin Smart City.\n"}
+{"abstract": "  The Cauchy problem for the Hardy-H\\'enon parabolic equation is studied in the\ncritical and subcritical regime in weighted Lebesgue spaces on the Euclidean\nspace $\\mathbb{R}^d$. Well-posedness for singular initial data and existence of\nnon-radial forward self-similar solution of the problem are previously shown\nonly for the Hardy and Fujita cases ($\\gamma\\le 0$) in earlier works. The\nweighted spaces enable us to treat the potential $|x|^{\\gamma}$ as an increase\nor decrease of the weight, thereby we can prove well-posedness to the problem\nfor all $\\gamma$ with $-\\min\\{2,d\\}<\\gamma$ including the H\\'enon case\n($\\gamma>0$). As a byproduct of the well-posedness, the self-similar solutions\nto the problem are also constructed for all $\\gamma$ without restrictions. A\nnon-existence result of local solution for supercritical data is also shown.\nTherefore our critical exponent $s_c$ turns out to be optimal in regards to the\nsolvability.\n"}
+{"abstract": "  We present inverted spin-valves fabricated from CVD-grown bilayer graphene\n(BLG) that show more than a doubling in device performance at room temperature\ncompared to state-of-the art bilayer graphene spin-valves. This is made\npossible by a PDMS droplet-assisted full-dry transfer technique that\ncompensates for previous process drawbacks in device fabrication.\nGate-dependent Hanle measurements show spin lifetimes of up to 5.8 ns and a\nspin diffusion length of up to 26 $\\mu$m at room temperature combined with a\ncharge carrier mobility of $\\approx$ 24 000 cm$^{2}$(Vs)$^{-1}$ for the best\ndevice. Our results demonstrate that CVD-grown BLG shows equally good room\ntemperature spin transport properties as both CVD-graphene and even exfoliated\nsingle-layer graphene.\n"}
+{"abstract": "  The intelligent reflecting surface (IRS) is a promising new paradigm in\nwireless communications for meeting the growing connectivity demands in\nnext-generation mobile networks. IRS, also known as software-controlled\nmetasurfaces, consist of an array of adjustable radio wave reflectors, enabling\nsmart radio environments, e.g., for enhancing the signal-to-noise ratio (SNR)\nand spatial diversity of wireless channels. Research on IRS to date has been\nlargely focused on constructive applications. In this work, we demonstrate for\nthe first time that the IRS provides a practical low-cost toolkit for attackers\nto easily perform complex signal manipulation attacks on the physical layer in\nreal time. We introduce the environment reconfiguration attack (ERA) as a novel\nclass of jamming attacks in wireless radio networks. Here, an adversary\nleverages the IRS to rapidly vary the electromagnetic propagation environment\nto disturb legitimate receivers. The IRS gives the adversary a key advantage\nover traditional jamming: It no longer has to actively emit jamming signals,\ninstead the IRS reflects existing legitimate signals. In addition, the\nadversary doesn't need any knowledge about the legitimate channel. We\nthoroughly investigate the ERA in wireless systems based on the widely employed\northogonal frequency division multiplexing (OFDM) modulation. We present\ninsights into the attack through analytical analysis, simulations, as well as\nexperiments. Our results show that the ERA allows to severely degrade the\navailable data rates even with reasonably small IRS sizes. Finally, we\nimplement an attacker setup and demonstrate a practical ERA to slow down an\nentire Wi-Fi network.\n"}
+{"abstract": "  Epsilon-near-zero and epsilon near-pole materials enable reflective systems\nsupporting a class of symmetry-protected and accidental embedded eigenstates\n(EE) characterized by a diverging phase-resonance. Here we show that pairs of\ntopologically protected scattering singularities necessarily emerge from EEs\nwhen a non-Hermitian parameter is introduced, lifting the degeneracy between\noppositely charged singularities. The underlying topological charges are\ncharacterized by an integer winding number and appear as phase vortices of the\ncomplex reflection coefficient. By creating and annihilating them, we show that\nthese singularities obey charge conservation, and provide versatile control of\namplitude, phase and polarization in reflection, with potential applications\nfor polarization control and sensing.\n"}
+{"abstract": "  This paper develops a novel control-theoretic framework to analyze the\nnon-asymptotic convergence of Q-learning. We show that the dynamics of\nasynchronous Q-learning with a constant step-size can be naturally formulated\nas a discrete-time stochastic affine switching system. Moreover, the evolution\nof the Q-learning estimation error is over- and underestimated by trajectories\nof two simpler dynamical systems. Based on these two systems, we derive a new\nfinite-time error bound of asynchronous Q-learning when a constant stepsize is\nused. Our analysis also sheds light on the overestimation phenomenon of\nQ-learning. We further illustrate and validate the analysis through numerical\nsimulations.\n"}
+{"abstract": "  We study the shape reconstruction of an inclusion from the {faraway}\nmeasurement of the associated electric field. This is an inverse problem of\npractical importance in biomedical imaging and is known to be notoriously\nill-posed. By incorporating Drude's model of the permittivity parameter, we\npropose a novel reconstruction scheme by using the plasmon resonance with a\nsignificantly enhanced resonant field. We conduct a delicate sensitivity\nanalysis to establish a sharp relationship between the sensitivity of the\nreconstruction and the plasmon resonance. It is shown that when plasmon\nresonance occurs, the sensitivity functional blows up and hence ensures a more\nrobust and effective construction. Then we combine the Tikhonov regularization\nwith the Laplace approximation to solve the inverse problem, which is an\norganic hybridization of the deterministic and stochastic methods and can\nquickly calculate the minimizer while capture the uncertainty of the solution.\nWe conduct extensive numerical experiments to illustrate the promising features\nof the proposed reconstruction scheme.\n"}
+{"abstract": "  Image deblurring is relevant in many fields of science and engineering. To\nsolve this problem, many different approaches have been proposed and among the\nvarious methods, variational ones are extremely popular. These approaches are\ncharacterized by substituting the original problem with a minimization one\nwhere the functional is composed of two terms, a data fidelity term and a\nregularization term. In this paper we propose, in the classical $\\ell^2-\\ell^1$\nminimization with the non-negativity constraint of the solution, the use of the\ngraph Laplacian as regularization operator. Firstly, we describe how to\nconstruct the graph Laplacian from the observed noisy and blurred image. Once\nthe graph Laplacian has been built, we solve efficiently the proposed\nminimization problem splitting the convolution operator and the graph Laplacian\nby the alternating direction method of multipliers (ADMM). Some selected\nnumerical examples show the good performances of the proposed algorithm.\n"}
+{"abstract": "  A toy statistical model which mimics localized-to-itinerant electron\ntransitions is introduced. We consider a system of two types of charge\ncarriers: (1) localized ones, and (2) itinerant ones. There is chemical\nequilibrium between these two. The solution of the model shows that there is a\ncrossover, at a specific value of a local interaction energy parameter $J$, at\nwhich carriers transfer from being itinerant to a localized/mixed valence\nstate. The model has a very crude analogy to the observed $\\gamma\\rta\\alpha$\ntransition in metal cerium.\n"}
+{"abstract": "  We study isogenies between K3 surfaces in positive characteristic. Our main\nresult is a characterization of K3 surfaces isogenous to a given K3 surface $X$\nin terms of certain integral sublattices of the second rational $\\ell$-adic and\ncrystalline cohomology groups of $X$. This is a positive characteristic analog\nof a result of Huybrechts, and extends results of the second author. We give\napplications to the reduction types of K3 surfaces and to the surjectivity of\nthe period morphism. To prove these results we describe a theory of B-fields\nand Mukai lattices in positive characteristic, which may be of independent\ninterest. We also prove some results on lifting twisted Fourier--Mukai\nequivalences to characteristic 0, generalizing results of Lieblich and Olsson.\n"}
+{"abstract": "  Unknowingly, identifiers in the source code of a software system play a vital\nrole in determining the quality of the system. Ambiguous and confusing\nidentifier names lead developers to not only misunderstand the behavior of the\ncode but also increases comprehension time and thereby causes a loss in\nproductivity. Even though correcting poor names through rename operations is a\nviable option for solving this problem, renaming itself is an act of rework and\nis not immune to defect injection.\n  In this study, we aim to understand the motivations that drive developers to\nname and rename identifiers and the decisions they make in determining the\nname. Using our results, we propose the development of a linguistic model that\ndetermines identifier names based on the behavior of the identifier. As a\nprerequisite to constructing the model, we conduct multiple studies to\ndetermine the features that should feed into the model. In this paper, we\ndiscuss findings from our completed studies and justify the continuation of\nresearch on this topic through further studies.\n"}
+{"abstract": "  Chirped dynamically assisted pair production in spatial inhomogeneous\nelectric fields is studied by the Dirac-Heisenberg-Wigner formalism. The\neffects of the chirp parameter on the reduced momentum spectrum, the reduced\ntotal yield of the created pairs for either low or high frequency one-color\nfield and two-color dynamically assisted combinational fields are investigated\nin detail. Also, the enhancement factor is obtained in the later two-color\nfield case. It is found that for the low frequency field, no matter whether it\nis accompanied by the other high frequency field, its chirping has a little\neffect on the pair production. For the one-color high frequency field or/and\ntwo-color fields, the momentum spectrum exhibits incomplete interference and\nthe interference effect becomes more and more remarkable as chirp increases. We\nalso find that in the chirped dynamically assisted field, the reduced total\nyield is enhanced significantly when the chirps are acting on the two fields,\ncompared with that the chirp is acting only for the low frequency strong field.\nSpecifically, by the chirping, it is found the reduced pair number is increased\nby more than one order of magnitude in the field with a relative narrow spatial\nscale, while it is enhanced at least two times in other case of field with\nlarger spatial scales or even in the quasi-homogeneous region. We also obtain\nsome optimal chirp parameters and spatial scales for the total yield and\nenhancement factor in different scenarios of the studied external field. These\nresults may provide a theoretical basis for possible experiments in the future.\n"}
+{"abstract": "  Cytoarchitecture describes the spatial organization of neuronal cells in the\nbrain, including their arrangement into layers and columns with respect to cell\ndensity, orientation, or presence of certain cell types. It allows to segregate\nthe brain into cortical areas and subcortical nuclei, links structure with\nconnectivity and function, and provides a microstructural reference for human\nbrain atlases. Mapping boundaries between areas requires to scan histological\nsections at microscopic resolution. While recent high-throughput scanners allow\nto scan a complete human brain in the order of a year, it is practically\nimpossible to delineate regions at the same pace using the established gold\nstandard method. Researchers have recently addressed cytoarchitectonic mapping\nof cortical regions with deep neural networks, relying on image patches from\nindividual 2D sections for classification. However, the 3D context, which is\nneeded to disambiguate complex or obliquely cut brain regions, is not taken\ninto account. In this work, we combine 2D histology with 3D topology by\nreformulating the mapping task as a node classification problem on an\napproximate 3D midsurface mesh through the isocortex. We extract deep features\nfrom cortical patches in 2D histological sections which are descriptive of\ncytoarchitecture, and assign them to the corresponding nodes on the 3D mesh to\nconstruct a large attributed graph. By solving the brain mapping problem on\nthis graph using graph neural networks, we obtain significantly improved\nclassification results. The proposed framework lends itself nicely to\nintegration of additional neuroanatomical priors for mapping.\n"}
+{"abstract": "  In sharp contrast to its classical counterpart, quantum measurement plays a\nfundamental role in quantum mechanics and blurs the essential distinction\nbetween the measurement apparatus and the objects under investigation. An\nappealing phenomenon in quantum measurements, termed as quantum Zeno effect,\ncan be observed in particular subspaces selected by measurement Hamiltonian.\nHere we apply the top-down Zeno mechanism to the particle physics. We indeed\ndevelop an alternative insight into the properties of fundamental particles,\nbut not intend to challenge the Standard Model (SM). In a unified and simple\nmanner, our effective model allows to merge the origin of neutrino's small mass\nand oscillations, the hierarchy pattern for masses of electric charged\nfermions, the color confinement, and the discretization of quantum numbers,\nusing a perturbative theory for the dynamical quantum Zeno effect. Under\nvarious conditions for vanishing transition amplitudes among particle\neigenstates in the effective model, it is remarkable to probe results that are\nsomewhat reminiscent of SM, including: (i) neutrino oscillations with big-angle\nmixing and small masses emerge from the energy-momentum conservation, (ii)\nelectrically-charged fermions hold masses in a hierarchy pattern due to the\nelectric-charge conservation, (iii) color confinement and the associated\nasymptotic freedom can be deduced from the color-charge conservation. We make\nseveral anticipations about the basic properties for fundamental particles: (i)\nthe total mass of neutrinos and the existence of a nearly massless neutrino (of\nany generation), (ii) the discretization in quantum numbers for the\nnew-discovered electrically-charged fermions, (iii) the confinement and the\nassociated asymptotic freedom for any particle containing more than two\nconserved charges.\n"}
+{"abstract": "  Purpose: Ureteroscopy is an efficient endoscopic minimally invasive technique\nfor the diagnosis and treatment of upper tract urothelial carcinoma (UTUC).\nDuring ureteroscopy, the automatic segmentation of the hollow lumen is of\nprimary importance, since it indicates the path that the endoscope should\nfollow. In order to obtain an accurate segmentation of the hollow lumen, this\npaper presents an automatic method based on Convolutional Neural Networks\n(CNNs).\n  Methods: The proposed method is based on an ensemble of 4 parallel CNNs to\nsimultaneously process single and multi-frame information. Of these, two\narchitectures are taken as core-models, namely U-Net based in residual\nblocks($m_1$) and Mask-RCNN($m_2$), which are fed with single still-frames\n$I(t)$. The other two models ($M_1$, $M_2$) are modifications of the former\nones consisting on the addition of a stage which makes use of 3D Convolutions\nto process temporal information. $M_1$, $M_2$ are fed with triplets of frames\n($I(t-1)$, $I(t)$, $I(t+1)$) to produce the segmentation for $I(t)$.\n  Results: The proposed method was evaluated using a custom dataset of 11\nvideos (2,673 frames) which were collected and manually annotated from 6\npatients. We obtain a Dice similarity coefficient of 0.80, outperforming\nprevious state-of-the-art methods.\n  Conclusion: The obtained results show that spatial-temporal information can\nbe effectively exploited by the ensemble model to improve hollow lumen\nsegmentation in ureteroscopic images. The method is effective also in presence\nof poor visibility, occasional bleeding, or specular reflections.\n"}
+{"abstract": "  The nearest stars provide a fundamental constraint for our understanding of\nstellar physics and the Galaxy. The nearby sample serves as an anchor where all\nobjects can be seen and understood with precise data. This work is triggered by\nthe most recent data release of the astrometric space mission Gaia and uses its\nunprecedented high precision parallax measurements to review the census of\nobjects within 10 pc. The first aim of this work was to compile all stars and\nbrown dwarfs within 10 pc observable by Gaia, and compare it with the Gaia\nCatalogue of Nearby Stars as a quality assurance test. We complement the list\nto get a full 10 pc census, including bright stars, brown dwarfs, and\nexoplanets. We started our compilation from a query on all objects with a\nparallax larger than 100 mas using SIMBAD. We completed the census by adding\ncompanions, brown dwarfs with recent parallax measurements not in SIMBAD yet,\nand vetted exoplanets. The compilation combines astrometry and photometry from\nthe recent Gaia Early Data Release 3 with literature magnitudes, spectral types\nand line-of-sight velocities. We give a description of the astrophysical\ncontent of the 10 pc sample. We find a multiplicity frequency of around 28%.\nAmong the stars and brown dwarfs, we estimate that around 61% are M stars and\nmore than half of the M stars are within the range M3.0 V to M5.0 V. We give an\noverview of the brown dwarfs and exoplanets that should be detected in the next\nGaia data releases along with future developments. We provide a catalogue of\n540 stars, brown dwarfs, and exoplanets in 339 systems, within 10 pc from the\nSun. This list is as volume-complete as possible from current knowledge and\nprovides benchmark stars that can be used, for instance, to define calibration\nsamples and to test the quality of the forthcoming Gaia releases. It also has a\nstrong outreach potential.\n"}
+{"abstract": "  Relative weight analysis is a classic tool for detecting whether one variable\nor interaction in a model is relevant. In this study, we focus on the\nconstruction of relative weights for non-linear interactions using restricted\ncubic splines. Our aim is to provide an accessible method to analyze a\nmultivariate model and identify one subset with the most representative set of\nvariables. Furthermore, we developed a procedure for treating control, fixed,\nfree and interaction terms simultaneously in the residual weight analysis. The\ninteractions are residualized properly against their main effects to maintain\ntheir true effects in the model. We tested this method using two simulated\nexamples.\n"}
+{"abstract": "  Thermal monopoles, identified after Abelian projection as magnetic currents\nwrapping non-trivially around the thermal circle, are studied in $N_f = 2+1$\nQCD at the physical point. The distribution in the number of wrappings, which\nin pure gauge theories points to a condensation temperature coinciding with\ndeconfinement, points in this case to around 275 MeV, almost twice the QCD\ncrossover temperature $T_c$; similar indications emerge looking for the\nformation of a percolating current cluster. The possible relation with other\nnon-perturbative phenomena observed above $T_c$ is discussed.\n"}
+{"abstract": "  The one-phase and two-phase Muskat problems with arbitrary viscosity contrast\nare studied in all dimensions. They are quasilinear parabolic equations for the\ngraph free boundary. We prove that small data in the scaling invariant\nhomogeneous Besov space $\\dot B^1_{\\infty, 1}$ lead to unique global solutions.\nThe proof exploits a new structure of the Dirichlet-Neumann operator which\nallows us to implement a robust fixed-point argument. As a consequence of this\nmethod, the initial data is only assumed to be in $\\dot B^1_{\\infty, 1}$ and\nthe solution map is Lipschitz continuous in the same topology. For the general\nMuskat problem, the only known scaling invariant result was obtained in the\nWiener algebra (plus an $L^2$ assumption) which is strictly contained in $\\dot\nB^1_{\\infty, 1}$.\n"}
+{"abstract": "  Given a hypergraph with uncertain node weights following known probability\ndistributions, we study the problem of querying as few nodes as possible until\nthe identity of a node with minimum weight can be determined for each\nhyperedge. Querying a node has a cost and reveals the precise weight of the\nnode, drawn from the given probability distribution. Using competitive\nanalysis, we compare the expected query cost of an algorithm with the expected\ncost of an optimal query set for the given instance. For the general case, we\ngive a polynomial-time $f(\\alpha)$-competitive algorithm, where $f(\\alpha)\\in\n[1.618+\\epsilon,2]$ depends on the approximation ratio $\\alpha$ for an\nunderlying vertex cover problem. We also show that no algorithm using a similar\napproach can be better than $1.5$-competitive. Furthermore, we give\npolynomial-time $4/3$-competitive algorithms for bipartite graphs with\narbitrary query costs and for hypergraphs with a single hyperedge and uniform\nquery costs, with matching lower bounds.\n"}
+{"abstract": "  During Multi-Agent Path Finding (MAPF) problems, agents can be delayed by\nunexpected events. To address such situations recent work describes k-Robust\nConflict-BasedSearch (k-CBS): an algorithm that produces coordinated and\ncollision-free plan that is robust for up to k delays. In this work we\nintroducing a variety of pairwise symmetry breaking constraints, specific to\nk-robust planning, that can efficiently find compatible and optimal paths for\npairs of conflicting agents. We give a thorough description of the new\nconstraints and report large improvements to success rate ina range of domains\nincluding: (i) classic MAPF benchmarks;(ii) automated warehouse domains and;\n(iii) on maps from the 2019 Flatland Challenge, a recently introduced railway\ndomain where k-robust planning can be fruitfully applied to schedule trains.\n"}
+{"abstract": "  In the present work we demonstrate that C-doped Zr$_{5}$Pt$_{3}$ is an\nelectron-phonon superconductor (with critical temperature T$_\\mathrm{C}$ =\n3.7\\,K) with a nonsymmorphic topological Dirac nodal-line semimetal state,\nwhich we report here for the first time. The superconducting properties of\nZr$_{5}$Pt$_{3}$C$_{0.5}$ have been investigated by means of magnetization and\nmuon spin rotation and relaxation ($\\mu$SR) measurements. We find that at low\ntemperatures the depolarization rate is almost constant and can be well\ndescribed by a single-band $s-$wave model with a superconducting gap of\n$2\\Delta(0)/k_\\mathrm{B}T_\\mathrm{C}$ = 3.84, close to the value of BCS theory.\nFrom transverse field $\\mu$SR analysis we estimate the London penetration depth\n$\\lambda_{L}$ = 469 nm, superconducting carrier density $n_{s}$ =\n2$\\times$10$^{26}$ $m^{-3}$, and effective mass m$^{*}$ = 1.584 $m_{e}$. Zero\nfield $\\mu$SR confirms the absence of any spontaneous magnetic moment in the\nsuperconducting ground state. To gain additional insights into the electronic\nground state of C-doped Zr$_5$Pt$_3$, we have also performed first-principles\ncalculations within the framework of density functional theory (DFT). The\nobserved homogenous electronic character of the Fermi surface as well as the\nmutual decrease of $T_\\mathrm{C}$ and density of states at the Fermi level are\nconsistent with the experimental findings. However, the band structure reveals\nthe presence of robust, gapless fourfold-degenarate nodal lines protected by\n$6_{3}$ screw rotations and glide mirror planes. Therefore, Zr$_5$Pt$_3$\nrepresents a novel, unprecedented condensed matter system to investigate the\nintricate interplay between superconductivity and topology.\n"}
+{"abstract": "  After inflation the Universe presumably undergoes a phase of reheating which\nin effect starts the thermal big bang cosmology. However, so far we have very\nlittle direct experimental or observational evidence of this important phase of\nthe Universe. In this letter, we argue that measuring the spectrum of freely\npropagating relativistic particles, i.e. dark radiation, produced during\nreheating may provide us with powerful information on the reheating phase. To\ndemonstrate this possibility we consider a situation where the dark radiation\nis produced in the decays of heavy, non-relativistic particles. We show that\nthe spectrum crucially depends on whether the heavy particle once dominated the\nUniverse or not. Characteristic features caused by the dependence on the number\nof the relativistic degrees of freedom may even allow to infer the temperature\nwhen the decay of the heavy particle occurred.\n"}
+{"abstract": "  We prove some new results on reflected BSDEs and doubly reflected BSDEs\ndriven by a multi-dimensional RCLL martingale. The goal is to develop a general\nmulti-asset framework encompassing a wide spectrum of nonlinear financial\nmodels, including as particular cases the setups studied by Peng and Xu\n\\cite{PX2009} and Dumitrescu et al. \\cite{DGQS2018} who dealt with BSDEs driven\nby a one-dimensional Brownian motion and a purely discontinuous martingale with\na single jump. Our results are not covered by existing literature on reflected\nand doubly reflected BSDEs driven by a Brownian motion and a Poisson random\nmeasure.\n"}
+{"abstract": "  A quantum random access memory (qRAM) is considered an essential computing\nunit to enable polynomial speedups in quantum information processing. Proposed\nimplementations include using neutral atoms and superconducting circuits to\nconstruct a binary tree, but these systems still require demonstrations of the\nelementary components. Here, we propose a photonic integrated circuit (PIC)\narchitecture integrated with solid-state memories as a viable platform for\nconstructing a qRAM. We also present an alternative scheme based on quantum\nteleportation and extend it to the context of quantum networks. Both\nimplementations rely on already demonstrated components: electro-optic\nmodulators, a Mach-Zehnder interferometer (MZI) network, and nanocavities\ncoupled to artificial atoms for spin-based memory writing and retrieval. Our\napproaches furthermore benefit from built-in error-detection based on photon\nheralding. Detailed theoretical analysis of the qRAM efficiency and query\nfidelity shows that our proposal presents viable near-term designs for a\ngeneral qRAM.\n"}
+{"abstract": "  We derive new constraints on the spectrum of two-dimensional conformal field\ntheories with central charge $c>1.$ Employing the pillow representation of the\nfour point correlator of identical scalars with dimension\n$\\Delta_{\\mathcal{O}}$ and positivity of the coefficients of its expansion in\nthe elliptic nome we place central charge dependent bounds on the dimension of\nthe first excited Virasoro primary the scalar couples to, in the form\n$\\Delta_1<f(c,\\Delta_{\\mathcal{O}}).$ We give an analytic expression for\n$f(c,\\Delta_{\\mathcal{O}})$ and write down transcendental equations that\nsignificantly improve the analytic bound. We numerically evaluate the stronger\nbounds for arbitrary fixed values of $c$ and $\\Delta_{\\mathcal{O}}.$\n"}
+{"abstract": "  A binary neutron star (BNS) merger can lead to various outcomes, from\nindefinitely stable neutron stars, through supramassive (SMNS) or hypermassive\n(HMNS) neutron stars supported only temporarily against gravity, to black holes\nformed promptly after the merger. Up-to-date constraints on the BNS total mass\nand the neutron star equation of state suggest that a long-lived SMNS may form\nin $\\sim 0.45-0.9$ of BNS mergers. A maximally rotating SMNS needs to lose\n$\\sim 3-6\\times 10^{52}$ erg of it's rotational energy before it collapses, on\na fraction of the spin-down timescale. A SMNS formation imprints on the\nelectromagnetic counterparts to the BNS merger. However, a comparison with\nobservations reveals tensions. First, the distribution of collapse times is too\nwide and that of released energies too narrow (and the energy itself too large)\nto explain the observed distributions of internal X-ray plateaus, invoked as\nevidence for SMNS-powered energy injection. Secondly, the immense energy\ninjection into the blastwave should lead to extremely bright radio transients\nwhich previous studies found to be inconsistent with deep radio observations of\nshort gamma-ray bursts. Furthermore, we show that upcoming all-sky radio\nsurveys will constrain the extracted energy distribution, independently of a\nGRB jet formation. Our results can be self-consistently understood, provided\nthat most BNS merger remnants collapse shortly after formation (even if their\nmasses are low enough to allow for SMNS formation). This naturally occurs if\nthe remnant retains half or less of its initial energy by the time it enters\nsolid body rotation.\n"}
+{"abstract": "  The derivation of nonlocal strong forms for many physical problems remains\ncumbersome in traditional methods. In this paper, we apply the variational\nprinciple/weighted residual method based on nonlocal operator method for the\nderivation of nonlocal forms for elasticity, thin plate, gradient elasticity,\nelectro-magneto-elasticity and phase field fracture method. The nonlocal\ngoverning equations are expressed as integral form on support and dual-support.\nThe first example shows that the nonlocal elasticity has the same form as\ndual-horizon non-ordinary state-based peridynamics. The derivation is simple\nand general and it can convert efficiently many local physical models into\ntheir corresponding nonlocal forms. In addition, a criterion based on the\ninstability of the nonlocal gradient is proposed for the fracture modelling in\nlinear elasticity. Several numerical examples are presented to validate\nnonlocal elasticity and the nonlocal thin plate .\n"}
+{"abstract": "  Let $K$ be an algebraically closed, complete, non-Archimedean valued field of\ncharacteristic zero, and let $\\mathscr{X}$ be a $K$-analytic space (in the\nsense of Huber). In this work, we pursue a non-Archimedean characterization of\nCampana's notion of specialness. We say $\\mathscr{X}$ is $K$-analytically\nspecial if there exists a connected, finite type algebraic group $G/K$, a dense\nopen subset $\\mathscr{U}\\subset G^{\\text{an}}$ with\n$\\text{codim}(G^{\\text{an}}\\setminus \\mathscr{U}) \\geq 2$, and an analytic\nmorphism $\\mathscr{U} \\to \\mathscr{X}$ which is Zariski dense.\n  With this definition, we prove several results which illustrate that this\ndefinition correctly captures Campana's notion of specialness in the\nnon-Archimedean setting. These results inspire us to make non-Archimedean\ncounterparts to conjectures of Campana. As preparation for our proofs, we prove\nauxiliary results concerning the indeterminacy locus of a meromorphic mapping\nbetween $K$-analytic spaces, the notion of pseudo-$K$-analytically Brody\nhyperbolic, and extensions of meromorphic maps from smooth, irreducible\n$K$-analytic spaces to the analytification of a semi-abelian variety.\n"}
+{"abstract": "  Herein, we have compared the performance of SVM and MLP in emotion\nrecognition using speech and song channels of the RAVDESS dataset. We have\nundertaken a journey to extract various audio features, identify optimal\nscaling strategy and hyperparameter for our models. To increase sample size, we\nhave performed audio data augmentation and addressed data imbalance using\nSMOTE. Our data indicate that optimised SVM outperforms MLP with an accuracy of\n82 compared to 75%. Following data augmentation, the performance of both\nalgorithms was identical at ~79%, however, overfitting was evident for the SVM.\nOur final exploration indicated that the performance of both SVM and MLP were\nsimilar in which both resulted in lower accuracy for the speech channel\ncompared to the song channel. Our findings suggest that both SVM and MLP are\npowerful classifiers for emotion recognition in a vocal-dependent manner.\n"}
+{"abstract": "  Recent research on model interpretability in natural language processing\nextensively uses feature scoring methods for identifying which parts of the\ninput are the most important for a model to make a prediction (i.e. explanation\nor rationale). However, previous research has shown that there is no clear best\nscoring method across various text classification tasks while practitioners\ntypically have to make several other ad-hoc choices regarding the length and\nthe type of the rationale (e.g. short or long, contiguous or not). Inspired by\nthis, we propose a simple yet effective and flexible method that allows\nselecting optimally for each data instance: (1) a feature scoring method; (2)\nthe length; and (3) the type of the rationale. Our method is inspired by input\nerasure approaches to interpretability which assume that the most faithful\nrationale for a prediction should be the one with the highest difference\nbetween the model's output distribution using the full text and the text after\nremoving the rationale as input respectively. Evaluation on four standard text\nclassification datasets shows that our proposed method provides more faithful,\ncomprehensive and highly sufficient explanations compared to using a fixed\nfeature scoring method, rationale length and type. More importantly, we\ndemonstrate that a practitioner is not required to make any ad-hoc choices in\norder to extract faithful rationales using our approach.\n"}
+{"abstract": "  Metamaterials and photonic/phononic crystals have been successfully developed\nin recent years to achieve advanced wave manipulation and control, both in\nelectromagnetism and mechanics. However, the underlying concepts are yet to be\nfully applied to the field of fluid dynamics and water waves. Here, we present\nan example of the interaction of surface gravity waves with a mechanical\nmetamaterial, i.e. periodic underwater oscillating resonators. In particular,\nwe study a device composed by an array of periodic submerged harmonic\noscillators whose objective is to absorb wave energy and dissipate it inside\nthe fluid in the form of heat. The study is performed using a state of the art\ndirect numerical simulation of the Navier-Stokes equation in its\ntwo-dimensional form with free boundary and moving bodies. We use a Volume of\nFluid interface technique for tracking the surface and an Immersed Boundary\nmethod for the fluid-structure interaction. We first study the interaction of a\nmonochromatic wave with a single oscillator and then add up to four resonators\ncoupled only fluid-mechanically. We study the efficiency of the device in terms\nof the total energy dissipation and find that by adding resonators, the\ndissipation increases in a non trivial way. As expected, a large energy\nattenuation is achieved when the wave and resonators are characterised by\nsimilar frequencies. As the number of resonators is increased, the range of\nattenuated frequencies also increases. The concept and results presented herein\nare of relevance for applications in coastal protection.\n"}
+{"abstract": "  Regional data analysis is concerned with the analysis and modeling of\nmeasurements that are spatially separated by specifically accounting for\ntypical features of such data. Namely, measurements in close proximity tend to\nbe more similar than the ones further separated. This might hold also true for\ncross-dependencies when multivariate spatial data is considered. Often,\nscientists are interested in linear transformations of such data which are easy\nto interpret and might be used as dimension reduction. Recently, for that\npurpose spatial blind source separation (SBSS) was introduced which assumes\nthat the observed data are formed by a linear mixture of uncorrelated, weakly\nstationary random fields. However, in practical applications, it is well-known\nthat when the spatial domain increases in size the weak stationarity\nassumptions can be violated in the sense that the second order dependency is\nvarying over the domain which leads to non-stationary analysis. In our work we\nextend the SBSS model to adjust for these stationarity violations, present\nthree novel estimators and establish the identifiability and affine\nequivariance property of the unmixing matrix functionals defining these\nestimators. In an extensive simulation study, we investigate the performance of\nour estimators and also show their use in the analysis of a geochemical dataset\nwhich is derived from the GEMAS geochemical mapping project.\n"}
+{"abstract": "  We analyze the electromagnetic field of a small bunch that uniformly moves in\na circular waveguide and transverses a boundary between an area filled up with\ncold magnetized electron plasma and a vacuum area. The magnetic field is\nsupposed to be strong but finite so the perturbation technique can be applied.\nTwo cases are studied in detail: the bunch is flying out of the plasma into a\nvacuum, and, inversely, the bunch is flying into the plasma out of the vacuum\narea of waveguide. The investigation of the waveguide mode components is\nperformed analytically with methods of the complex variable function theory.\nThe main peculiarities of the bunch radiation in such situations are revealed.\n"}
+{"abstract": "  This paper proposes a control method for allowing aggregates of\nthermostatically controlled loads to provide synthetic inertia and primary\nfrequency regulation services to the grid. The proposed control framework is\nfully distributed and basically consists in the modification of the thermostat\nlogic as a function of the grid frequency. Three strategies are considered: in\nthe first one, the load aggregate provides synthetic inertia by varying its\nactive power demand proportionally to the frequency rate of change; in the\nsecond one, the load aggregate provides primary frequency regulation by varying\nits power demand proportionally to frequency; in the third one, the two\nservices are combined. The performances of the proposed control solutions are\nanalyzed in the forecasted scenario of the electric power system of Sardinia in\n2030, characterized by a huge installation of wind and photovoltaic generation\nand no coil and combustible oil power plants. The considered load aggregate is\ncomposed by domestic refrigerators and water heaters. Results prove the\neffectiveness of the proposed approach and show that, in the particular case of\nrefrigerators and water heaters, the contribution to the frequency regulation\nis more significant in the case of positive frequency variations. Finally, the\ncorrelation between the regulation performances and the level of penetration of\nthe load aggregate with respect to the system total load is evaluated.\n"}
+{"abstract": "  In this paper we give some sufficient conditions for the nonnegativity of\nimmanants of square submatrices of Catalan-Stieltjes matrices and their\ncorresponding Hankel matrices. To obtain these sufficient conditions, we\nconstruct new planar networks with a recursive nature for Catalan-Stieltjes\nmatrices. As applications, we provide a unified way to produce inequalities for\nmany combinatorial polynomials, such as the Eulerian polynomials, Schr\\\"{o}der\npolynomials and Narayana polynomials.\n"}
+{"abstract": "  In this paper, we propose a first-order distributed optimization algorithm\nthat is provably robust to Byzantine failures-arbitrary and potentially\nadversarial behavior, where all the participating agents are prone to failure.\nWe model each agent's state over time as a two-state Markov chain that\nindicates Byzantine or trustworthy behaviors at different time instants. We set\nno restrictions on the maximum number of Byzantine agents at any given time. We\ndesign our method based on three layers of defense: 1) Temporal gradient\naveraging, 2) robust aggregation, and 3) gradient normalization. We study two\nsettings for stochastic optimization, namely Sample Average Approximation and\nStochastic Approximation, and prove that for strongly convex and smooth\nnon-convex cost functions, our algorithm achieves order-optimal statistical\nerror and convergence rates.\n"}
+{"abstract": "  Robotic systems for retail have gained a lot of attention due to the\nlabor-intensive nature of such business environments. Many tasks have the\npotential to be automated via intelligent robotic systems that have\nmanipulation capabilities. For example, empty shelves can be replenished, stray\nproducts can be picked up or new items can be delivered. However, many\nchallenges make the realization of this vision a challenge. In particular,\nrobots are still too expensive and do not work out of the box. In this paper,\nwe discuss a work-in-progress approach for enabling power-on-and-go robots in\nretail environments through a combination of active, physical sensors and\npassive, artificial sensors. In particular, we use low-cost hardware sensors in\nconjunction with machine learning techniques in order to generate high-quality\nenvironmental information. More specifically, we present a setup in which a\nstandard monocular camera and Bluetooth low-energy yield a reliable robot\nsystem that can immediately be used after placing a couple of sensors in the\nenvironment. The camera information is used to synthesize accurate 3D point\nclouds, whereas the BLE data is used to integrate the data into a complex map\nof the environment. The combination of active and passive sensing enables\nhigh-quality sensing capabilities at a fraction of the costs traditionally\nassociated with such tasks.\n"}
+{"abstract": "  Financial speculators often seek to increase their potential gains with\nleverage. Debt is a popular form of leverage, and with over 39.88B USD of total\nvalue locked (TVL), the Decentralized Finance (DeFi) lending markets are\nthriving. Debts, however, entail the risks of liquidation, the process of\nselling the debt collateral at a discount to liquidators. Nevertheless, few\nquantitative insights are known about the existing liquidation mechanisms.\n  In this paper, to the best of our knowledge, we are the first to study the\nbreadth of the borrowing and lending markets of the Ethereum DeFi ecosystem. We\nfocus on Aave, Compound, MakerDAO, and dYdX, which collectively represent over\n85% of the lending market on Ethereum. Given extensive liquidation data\nmeasurements and insights, we systematize the prevalent liquidation mechanisms\nand are the first to provide a methodology to compare them objectively. We find\nthat the existing liquidation designs well incentivize liquidators but sell\nexcessive amounts of discounted collateral at the borrowers' expenses. We\nmeasure various risks that liquidation participants are exposed to and quantify\nthe instabilities of existing lending protocols. Moreover, we propose an\noptimal strategy that allows liquidators to increase their liquidation profit,\nwhich may aggravate the loss of borrowers.\n"}
+{"abstract": "  In this paper, we investigate a key problem of Narrowband-Internet of Things\n(NB-IoT) in the context of 5G with Mobile Edge Computing (MEC). We address the\nchallenge that IoT devices may have different priorities when demanding\nbandwidth for data transmission in specific applications and services. Due to\nthe scarcity of bandwidth in an MEC enabled IoT network, our objective is to\noptimize bandwidth allocation for a group of NB-IoT devices in a way that the\ngroup can work collaboratively to maximize their overall utility. To this end,\nwe design an optimal distributed algorithm and use simulations to demonstrate\nits efficacy to effectively manage various IoT data streams in a fully\ndistributed framework.\n"}
+{"abstract": "  Federated Learning is a distributed machine learning approach which enables\nmodel training without data sharing. In this paper, we propose a new federated\nlearning algorithm, Federated Averaging with Client-level Momentum (FedCM), to\ntackle problems of partial participation and client heterogeneity in real-world\nfederated learning applications. FedCM aggregates global gradient information\nin previous communication rounds and modifies client gradient descent with a\nmomentum-like term, which can effectively correct the bias and improve the\nstability of local SGD. We provide theoretical analysis to highlight the\nbenefits of FedCM. We also perform extensive empirical studies and demonstrate\nthat FedCM achieves superior performance in various tasks and is robust to\ndifferent levels of client numbers, participation rate and client\nheterogeneity.\n"}
+{"abstract": "  We derive braided $C^*$-tensor categories from gapped ground states on\ntwo-dimensional quantum spin systems satisfying some additional condition which\nwe call the approximate Haag duality.\n"}
+{"abstract": "  The active motion of phoretic colloids leads them to accumulate at boundaries\nand interfaces. Such an excess accumulation, with respect to their passive\ncounterparts, makes the dynamics of phoretic colloids particularly sensitive to\nthe presence of boundaries and pave new routes to externally control their\nsingle particle as well as collective behavior. Here we review some recent\ntheoretical results about the dynamics of phoretic colloids close to and\nadsorbed at fluid interfaces in particular highlighting similarities and\ndifferences with respect to solid-fluid interfaces.\n"}
+{"abstract": "  We propose an effective two-stage approach to tackle the problem of\nlanguage-based Human-centric Spatio-Temporal Video Grounding (HC-STVG) task. In\nthe first stage, we propose an Augmented 2D Temporal Adjacent Network\n(Augmented 2D-TAN) to temporally ground the target moment corresponding to the\ngiven description. Primarily, we improve the original 2D-TAN from two aspects:\nFirst, a temporal context-aware Bi-LSTM Aggregation Module is developed to\naggregate clip-level representations, replacing the original max-pooling.\nSecond, we propose to employ Random Concatenation Augmentation (RCA) mechanism\nduring the training phase. In the second stage, we use pretrained MDETR model\nto generate per-frame bounding boxes via language query, and design a set of\nhand-crafted rules to select the best matching bounding box outputted by MDETR\nfor each frame within the grounded moment.\n"}
+{"abstract": "  In model selection, several types of cross-validation are commonly used and\nmany variants have been introduced. While consistency of some of these methods\nhas been proven, their rate of convergence to the oracle is generally still\nunknown. Until now, an asymptotic analysis of crossvalidation able to answer\nthis question has been lacking. Existing results focus on the ''pointwise''\nestimation of the risk of a single estimator, whereas analysing model selection\nrequires understanding how the CV risk varies with the model. In this article,\nwe investigate the asymptotics of the CV risk in the neighbourhood of the\noptimal model, for trigonometric series estimators in density estimation.\nAsymptotically, simple validation and ''incomplete'' V --fold CV behave like\nthe sum of a convex function fn and a symmetrized Brownian changed in time W\ngn/V. We argue that this is the right asymptotic framework for studying model\nselection.\n"}
+{"abstract": "  First and foremost, we show that a 4-dimensional conformally flat generalized\nRicci recurrent spacetime $(GR)_4$ is an Einstein manifold. We examine such a\nspacetime as a solution of $f(R, G)$-gravity theory and it is shown that the\nadditional terms from the modification of the gravitational sector can be\nexpressed as a perfect fluid. Several energy conditions are investigated with\n$f(R, G) = R +\\sqrt{G}$ and $f(R, G) = R^2+GlnG$. For both the models, weak,\nnull and dominant energy conditions are satisfied while strong energy condition\nis violated, which is a good agreement with the recent observational studies\nwhich reveals that the current universe is in accelerating phase.\n"}
+{"abstract": "  Ice growth from liquid phase has been extensively investigated in various\nconditions, especially for ice freely grown in undercooled water and aqueous\nsolutions. Although unidirectional ice growth plays a significant role in sea\nice and freeze casting, the detailed pattern formation of unidirectionally\ngrown ice in an aqueous solution remains elusive. For the first time, we in\nsitu proved a crossover from lamellar to spongy ice morphologies of a single\nice crystal via unidirectional freezing of an aqueous solution. The spongy ice\nmorphology originates from the intersect of tilted lamellar ice and is observed\nin a single ice crystal, which is intrinsically different from the competitive\ngrowth of bi-crystal composed of two differently orientated grains in\ndirectional solidification. These results provide a complete physical picture\nof unidirectionally grown ice from aqueous solution and are believed to promote\nour understanding of various pattern of ice in many relevant domains where\npattern formation of ice crystal is vital.\n"}
+{"abstract": "  The letter provides a geometrical interpretation of frequency in electric\ncircuits. According to this interpretation, the frequency is defined as a\nmultivector with symmetric and antisymmetric components. The conventional\ndefinition of frequency is shown to be a special case of the proposed\ntheoretical framework. Several examples serve to show the features, generality\nas well as practical aspects of the proposed approach.\n"}
+{"abstract": "  Fix a Calabi-Yau 3-fold $X$ satisfying the Bogomolov-Gieseker conjecture of\nBayer-Macr\\`i-Toda, such as the quintic 3-fold. We express Joyce's generalised\nDT invariants counting Gieseker semistable sheaves of any rank $r\\ge1$ on $X$\nin terms of those counting sheaves of rank 0 and pure dimension 2.\n  The basic technique is to reduce the ranks of sheaves by replacing them by\nthe cokernels of their Mochizuki/Joyce-Song pairs and then use wall crossing to\nhandle their stability.\n"}
+{"abstract": "  Analyzing performance within asynchronous many-task-based runtime systems is\nchallenging because millions of tasks are launched concurrently. Especially for\nlong-term runs the amount of data collected becomes overwhelming. We study HPX\nand its performance-counter framework and APEX to collect performance data and\nenergy consumption. We added HPX application-specific performance counters to\nthe Octo-Tiger full 3D AMR astrophysics application. This enables the combined\nvisualization of physical and performance data to highlight bottlenecks with\nrespect to different solvers. We examine the overhead introduced by these\nmeasurements, which is around 1%, with respect to the overall application\nruntime. We perform a convergence study for four different levels of refinement\nand analyze the application's performance with respect to adaptive grid\nrefinement. The measurements' overheads are small, enabling the combined use of\nperformance data and physical properties with the goal of improving the code's\nperformance. All of these measurements were obtained on NERSC's Cori, Louisiana\nOptical Network Infrastructure's QueenBee2, and Indiana University's Big Red 3.\n"}
+{"abstract": "  A parallelized three-dimensional (3D) boundary element method is used to\nsimulate the interaction between an incoming solitary wave and a 3D submerged\nhorizontal plate under the assumption of potential flow. The numerical setup\nfollows closely the setup of laboratory experiments recently performed at\nShanghai Jiao Tong University. The numerical results are compared with the\nexperimental results. An overall good agreement is found for the\ntwo-dimensional wave elevation, the horizontal force and the vertical force\nexerted on the plate, and the pitching moment. Even though there are some\ndiscrepancies, the comparison shows that a model solving the fully nonlinear\npotential flow equations with a free surface using a 3D boundary element method\ncan satisfactorily capture the main features of the interaction between\nnonlinear waves and a submerged horizontal plate.\n"}
+{"abstract": "  It is an important yet challenging setting to continually learn new tasks\nfrom a few examples. Although numerous efforts have been devoted to either\ncontinual learning or few-shot learning, little work has considered this new\nsetting of few-shot continual learning (FSCL), which needs to minimize the\ncatastrophic forgetting to the old tasks and gradually improve the ability of\nfew-shot generalization. In this paper, we provide a first systematic study on\nFSCL and present an effective solution with deep neural networks. Our solution\nis based on the observation that continual learning of a task sequence\ninevitably interferes few-shot generalization, which makes it highly nontrivial\nto extend few-shot learning strategies to continual learning scenarios. We draw\ninspirations from the robust brain system and develop a method that (1)\ninterdependently updates a pair of fast / slow weights for continual learning\nand few-shot learning to disentangle their divergent objectives, inspired by\nthe biological model of meta-plasticity and fast / slow synapse; and (2)\napplies a brain-inspired two-step consolidation strategy to learn a task\nsequence without forgetting in the fast weights while improve generalization\nwithout overfitting in the slow weights. Extensive results on various\nbenchmarks show that our method achieves a better performance than joint\ntraining of all the tasks ever seen. The ability of few-shot generalization is\nalso substantially improved from incoming tasks and examples.\n"}
+{"abstract": "  Computer voice is experiencing a renaissance through the growing popularity\nof voice-based interfaces, agents, and environments. Yet, how to measure the\nuser experience (UX) of voice-based systems remains an open and urgent\nquestion, especially given that their form factors and interaction styles tend\nto be non-visual, intangible, and often considered disembodied or \"body-less.\"\nAs a first step, we surveyed the ACM and IEEE literatures to determine which\nquantitative measures and measurements have been deemed important for voice UX.\nOur findings show that there is little consensus, even with similar situations\nand systems, as well as an overreliance on lab work and unvalidated scales. In\nresponse, we offer two high-level descriptive frameworks for guiding future\nresearch, developing standardized instruments, and informing ongoing review\nwork. Our work highlights the current strengths and weaknesses of voice UX\nresearch and charts a path towards measuring voice UX in a more comprehensive\nway.\n"}
+{"abstract": "  Experimental measurements on commercial adaptive cruise control (ACC)\nvehicles \\RoundTwo{are} becoming increasingly available from around the world,\nproviding an unprecedented opportunity to study the traffic flow\ncharacteristics that arise from this technology. This paper adds new\nexperimental evidence to this knowledge base and presents a comprehensive\nempirical study on the ACC equilibrium behaviors via the resulting fundamental\ndiagrams. We find that like human-driven vehicles, ACC systems display a linear\nequilibrium spacing-speed relationship (within the range of available data) but\nthe key parameters of these relationships can differ significantly from\nhuman-driven traffic depending on input settings: At the minimum headway\nsetting, equilibrium capacities in excess of 3500 vehicles per hour are\nobserved, together with an extremely fast equilibrium wave speed of 100\nkilometers per hour on average. These fast waves are unfamiliar to human\ndrivers, and may pose a safety risk. The results also suggest that ACC jam\nspacing can be much larger than in human traffic, which reduces the network\nstorage capacity.\n"}
+{"abstract": "  In this paper we derived in QCD the BFKL linear, inhomogeneous equation for\nthe factorial moments of multiplicity distribution($M_k$) from LMM equation. In\nparticular, the equation for the average multiplicity of the color-singlet\ndipoles($N$) turns out to be the homogeneous BFKL while $M_k \\propto N^k$ at\nsmall $x$. Second, using the diffusion approximation for the BFKL kernel we\nshow that the factorial moments are equal to: $M_k=k!N( N-1)^{k-1}$ which leads\nto the multiplicity distribution:$ \\frac{\\sigma_n}{\\sigma_{in}}=\\frac{1}{N} (\n\\frac{N\\,-\\,1}{N})^{n - 1}$. We also suggest a procedure for finding\ncorrections to this multiplicity distribution which will be useful for\ndescriptions of the experimental data.\n"}
+{"abstract": "  In this work we formalize the (pure observational) task of predicting node\nattribute evolution in temporal graphs. We show that node representations of\ntemporal graphs can be cast into two distinct frameworks: (a) The de-facto\nstandard approach, which we denote {\\em time-and-graph}, where equivariant\ngraph (e.g., GNN) and sequence (e.g., RNN) representations are intertwined to\nrepresent the temporal evolution of the graph; and (b) an approach that we\ndenote {\\em time-then-graph}, where the sequences describing the node and edge\ndynamics are represented first (e.g., RNN), then fed as node and edge\nattributes into a (static) equivariant graph representation that comes after\n(e.g., GNN). In real-world datasets, we show that our {\\em time-then-graph}\nframework achieves the same prediction performance as state-of-the-art {\\em\ntime-and-graph} methods. Interestingly, {\\em time-then-graph} representations\nhave an expressiveness advantage over {\\em time-and-graph} representations when\nboth use component GNNs that are not most-expressive (e.g., 1-Weisfeiler-Lehman\nGNNs). We introduce a task where this expressiveness advantage allows {\\em\ntime-then-graph} methods to succeed while state-of-the-art {\\em time-and-graph}\nmethods fail.\n"}
+{"abstract": "  We study a family of Siegel modular forms that are constructed using Jacobi\nforms that arise in Umbral moonshine. All but one of them arise as the\nWeyl-Kac-Borcherds denominator formula of some Borcherds-Kac-Moody (BKM) Lie\nsuperalgebras. These Lie superalgebras have a $\\widehat{sl(2)}$ subalgebra\nwhich we use to study the Siegel modular forms. We show that the expansion of\nthe Umbral Jacobi forms in terms of $\\widehat{sl(2)}$ characters leads to\nvector-valued modular forms. We obtain closed formulae for these vector-valued\nmodular forms. In the Lie algebraic context, the Fourier coefficients of these\nvector-valued modular forms are related to multiplicities of roots appearing on\nthe sum side of the Weyl-Kac-Borcherds denominator formulae.\n"}
+{"abstract": "  The internet is filled with fake face images and videos synthesized by deep\ngenerative models. These realistic DeepFakes pose a challenge to determine the\nauthenticity of multimedia content. As countermeasures, artifact-based\ndetection methods suffer from insufficiently fine-grained features that lead to\nlimited detection performance. DNN-based detection methods are not efficient\nenough, given that a DeepFake can be created easily by mobile apps and\nDNN-based models require high computational resources. For the first time, we\nshow that DeepFake faces have fewer feature points than real ones, especially\nin certain facial regions. Inspired by feature point detector-descriptors to\nextract discriminative features at the pixel level, we propose the Fused Facial\nRegion_Feature Descriptor (FFR_FD) for effective and fast DeepFake detection.\nFFR_FD is only a vector extracted from the face, and it can be constructed from\nany feature point detector-descriptors. We train a random forest classifier\nwith FFR_FD and conduct extensive experiments on six large-scale DeepFake\ndatasets, whose results demonstrate that our method is superior to most state\nof the art DNN-based models.\n"}
+{"abstract": "  With the standard model working well in describing the collider data, the\nfocus is now on determining the standard model parameters as well as for any\nhint of deviation. In particular, the determination of the couplings of the\nHiggs boson with itself and with other particles of the model is important to\nbetter understand the electroweak symmetry breaking sector of the model. In\nthis letter, we look at the process $pp \\to WWH$, in particular through the\nfusion of bottom quarks. Due to the non-negligible coupling of the Higgs boson\nwith the bottom quarks, there is a dependence on the $WWHH$ coupling in this\nprocess. This sub-process receives largest contribution when the $W$ bosons are\nlongitudinally polarized. We compute one-loop QCD corrections to various final\nstates with polarized $W$ bosons. We find that the corrections to the final\nstate with the longitudinally polarized $W$ bosons are large. It is shown that\nthe measurement of the polarization of the $W$ bosons can be used as a tool to\nprobe the $WWHH$ coupling in this process. We also examine the effect of\nvarying $WWHH$ coupling in the $\\kappa$-framework.\n"}
+{"abstract": "  When a solution to an abstract inverse linear problem on Hilbert space is\napproximable by finite linear combinations of vectors from the cyclic subspace\nassociated with the datum and with the linear operator of the problem, the\nsolution is said to be a Krylov solution, i.e., it belongs to the Krylov\nsubspace of the problem. Krylov solvability of the inverse problem allows for\nsolution approximations that, in applications, correspond to the very efficient\nand popular Krylov subspace methods. We study here the possible behaviours of\npersistence, gain, or loss of Krylov solvability under suitable small\nperturbations of the inverse problem -- the underlying motivations being the\nstability or instability of Krylov methods under small noise or uncertainties,\nas well as the possibility to decide a priori whether an inverse problem is\nKrylov solvable by investigating a potentially easier, perturbed problem. We\npresent a whole scenario of occurrences in the first part of the work. In the\nsecond, we exploit the weak gap metric induced, in the sense of Hausdorff\ndistance, by the Hilbert weak topology, in order to conveniently monitor the\ndistance between perturbed and unperturbed Krylov subspaces.\n"}
+{"abstract": "  In several domains of physics, including first principle simulations and\nclassical models for polarizable systems, the minimization of an energy\nfunction with respect to a set of auxiliary variables must be performed to\ndefine the dynamics of physical degrees of freedom. In this paper, we discuss a\nrecent algorithm proposed to efficiently and rigorously simulate this type of\nsystems: the Mass-Zero (MaZe) Constrained Dynamics. In MaZe the minimum\ncondition is imposed as a constraint on the auxiliary variables treated as\ndegrees of freedom of zero inertia driven by the physical system. The method is\nformulated in the Lagrangian framework, enabling the properties of the approach\nto emerge naturally from a fully consistent dynamical and statistical\nviewpoint. We begin by presenting MaZe for typical minimization problems where\nthe imposed constraints are holonomic and summarizing its key formal\nproperties, notably the exact Born-Oppenheimer dynamics followed by the\nphysical variables and the exact sampling of the corresponding physical\nprobability density. We then generalize the approach to the case of conditions\non the auxiliary variables that linearly involve their velocities. Such\nconditions occur, for example, when describing systems in external magnetic\nfield and they require to adapt MaZe to integrate semiholonomic constraints.\nThe new development is presented in the second part of this paper and\nillustrated via a proof-of-principle calculation of the charge transport\nproperties of a simple classical polarizable model of NaCl.\n"}
+{"abstract": "  A quadratic dynamical system with practical applications is taken into\nconsidered. This system is transformed into a new bilinear system with Hadamard\nproducts by means of the implicit matrix structure. The corresponding quadratic\nbilinear equation is subsequently established via the Volterra series. Under\nproper conditions the existence of the solution to the equation is proved by\nusing a fixed-point iteration.\n"}
+{"abstract": "  This paper studies the informativity problem for reachability and\nnull-controllability of constrained systems. To be precise, we will focus on an\nunknown linear systems with convex conic constraints from which we measure data\nconsisting of exact state trajectories of finite length. We are interested in\nperforming system analysis of such an unknown system on the basis of the\nmeasured data. However, from such measurements it is only possible to obtain a\nunique system explaining the data in very restrictive cases. This means that we\ncan not approach this problem using system identification combined with model\nbased analysis. As such, we will formulate conditions on the data under which\nany such system consistent with the measurements is guaranteed to be reachable\nor null-controllable. These conditions are stated in terms of spectral\nconditions and subspace inclusions, and therefore they are easy to verify.\n"}
+{"abstract": "  Generative adversarial networks (GANs) have been being widely used in various\napplications. Arguably, GANs are really complex, and little has been known\nabout their generalization. In this paper, we make a comprehensive analysis\nabout generalization of GANs. We decompose the generalization error into an\nexplicit composition: generator error + discriminator error + optimization\nerror. The first two errors show the capacity of the player's families, are\nirreducible and optimizer-independent. We then provide both uniform and\nnon-uniform generalization bounds in different scenarios, thanks to our new\nbridge between Lipschitz continuity and generalization. Our bounds overcome\nsome major limitations of existing ones. In particular, our bounds show that\npenalizing the zero- and first-order informations of the GAN loss will improve\ngeneralization, answering the long mystery of why imposing a Lipschitz\nconstraint can help GANs perform better in practice. Finally, we show why data\naugmentation penalizes the zero- and first-order informations of the loss,\nhelping the players generalize better, and hence explaining the highly\nsuccessful use of data augmentation for GANs.\n"}
+{"abstract": "  Nigam et al. reported a genetic algorithm (GA) utilizing the SELFIES\nrepresentation and also propose an adaptive, neural network-based penalty that\nis supposed to improve the diversity of the generated molecules. The main\nclaims of the paper are that this GA outperforms other generative techniques\n(as measured by the penalized logP) and that a neural network-based adaptive\npenalty increases the diversity of the generated molecules. In this work, we\ninvestigated the reproducibility of their claims. Overall, we were able to\nreproduce comparable results using the SELFIES-based GA, but mostly by\nexploiting deficiencies of the (easily optimizable) fitness function (i.e.,\ngenerating long, sulfur containing chains). In addition, we reproduce results\nshowing that the discriminator can be used to bias the generation of molecules\nto ones that are similar to the reference set. Lastly, we attempted to quantify\nthe evolution of the diversity, understand the influence of some\nhyperparameters, and propose improvements to the adaptive penalty.\n"}
+{"abstract": "  Most of the state-of-the-art indirect visual SLAM methods are based on the\nsparse point features. However, it is hard to find enough reliable point\nfeatures for state estimation in the case of low-textured scenes. Line features\nare abundant in urban and indoor scenes. Recent studies have shown that the\ncombination of point and line features can provide better accuracy despite the\ndecrease in computational efficiency. In this paper, measurements of point and\nline features are extracted from RGB-D data to create map features, and points\non a line are treated as keypoints. We propose an extended approach to make\nmore use of line observation information. And we prove that, in the local\nbundle adjustment, the estimation uncertainty of keyframe poses can be reduced\nwhen considering more landmarks with independent measurements in the\noptimization process. Experimental results on two public RGB-D datasets\ndemonstrate that the proposed method has better robustness and accuracy in\nchallenging environments.\n"}
+{"abstract": "  Background. Digital pathology has aroused widespread interest in modern\npathology. The key of digitalization is to scan the whole slide image (WSI) at\nhigh magnification. The lager the magnification is, the richer details WSI will\nprovide, but the scanning time is longer and the file size of obtained is\nlarger. Methods. We design a strategy to scan slides with low resolution (5X)\nand a super-resolution method is proposed to restore the image details when in\ndiagnosis. The method is based on a multi-scale generative adversarial network,\nwhich sequentially generates three high-resolution images such as 10X, 20X and\n40X. Results. The peak-signal-to-noise-ratio of 10X to 40X generated images are\n24.16, 22.27 and 20.44, and the structural-similarity-index are 0.845, 0.680\nand 0.512, which are better than other super-resolution networks. Visual\nscoring average and standard deviation from three pathologists is 3.63\nplus-minus 0.52, 3.70 plus-minus 0.57 and 3.74 plus-minus 0.56 and the p value\nof analysis of variance is 0.37, indicating that generated images include\nsufficient information for diagnosis. The average value of Kappa test is 0.99,\nmeaning the diagnosis of generated images is highly consistent with that of the\nreal images. Conclusion. This proposed method can generate high-quality 10X,\n20X, 40X images from 5X images at the same time, in which the time and storage\ncosts of digitalization can be effectively reduced up to 1/64 of the previous\ncosts. The proposed method provides a better alternative for low-cost storage,\nfaster image share of digital pathology. Keywords. Digital pathology;\nSuper-resolution; Low resolution scanning; Low cost\n"}
+{"abstract": "  In this work, we are interested in the transient dynamics of a fluid\nconfiguration consisting of three fixed cylinders whose axes distribute over an\nequilateral triangle in transverse flow << fluidic pinball >>. As the Reynolds\nnumber is increased on the route to chaos, its transient dynamics tell us about\nthe contribution of the elementary degrees of freedom of the system to the lift\nand drag coefficients.\n"}
+{"abstract": "  We consider the stochastically forced Burgers equation with an emphasis on\nspatially rough driving noise. We show that the law of the process at a fixed\ntime $t$, conditioned on no explosions, is absolutely continuous with respect\nto the stochastic heat equation obtained by removing the nonlinearity from the\nequation. This establishes a form of ellipticity in this infinite dimensional\nsetting. The results follow from a recasting of the Girsanov Theorem to handle\nless spatially regular solutions while only proving absolute continuity at a\nfixed time and not on path-space. The results are proven by decomposing the\nsolution into the sum of auxiliary processes which are then shown to be\nabsolutely continuous in law to a stochastic heat equation. The number of\nlevels in this decomposition diverges to infinite as we move to the\nstochastically forced Burgers equation associated to the KPZ equation, which we\nconjecture is just beyond the validity of our results (and certainly the\ncurrent proof). The analysis provides insights into the structure of the\nsolution as we approach the regularity of KPZ. A number of techniques from\nsingular SPDEs are employed as we are beyond the regime of classical solutions\nfor much of the paper.\n"}
+{"abstract": "  A CFD-driven deterministic symbolic identification algorithm for learning\nexplicit algebraic Reynolds-stress models (EARSM) from high-fidelity data is\ndeveloped building on\n  the frozen-training SpaRTA algorithm of [1].\n  Corrections for the Reynolds stress tensor and the production of transported\nturbulent quantities of a baseline linear eddy viscosity model (LEVM) are\nexpressed as functions of tensor polynomials selected from a library of\ncandidate functions. The CFD-driven training consists in solving a blackbox\noptimization problem in which the fitness of candidate EARSM models is\nevaluated by running RANS simulations. Unlike the frozen-training approach, the\nproposed methodology is not restricted to data sets for which full fields of\nhigh-fidelity data are available. However, the solution of a high-dimensional\nexpensive blackbox function optimization problem is required. Several steps are\nthen undertaken to reduce the associated computational burden. First, a\nsensitivity analysis is used to identify the most influential terms and to\nreduce the dimensionality of the search space. Afterwards, the Constrained\nOptimization using Response Surface (CORS) algorithm, which approximates the\nblack-box cost function using a response surface constructed from a limited\nnumber of CFD solves, is used to find the optimal model parameters. Model\ndiscovery and cross-validation is performed for three configurations of 2D\nturbulent separated flows in channels of variable section using different sets\nof training data to show the flexibility of the method. The discovered models\nare then applied to the prediction of an unseen 2D separated flow with higher\nReynolds number and different geometry. The predictions for the new case are\nshown to be not only more accurate than the baseline LEVM, but also of a\nmulti-purpose EARSM model derived from purely physical arguments.\n"}
+{"abstract": "  We provide a rigorous derivation of the precise late-time asymptotics for\nsolutions to the scalar wave equation on subextremal Kerr backgrounds,\nincluding the asymptotics for projections to angular frequencies $\\ell\\geq 1$\nand $\\ell\\geq 2$. The $\\ell$-dependent asymptotics on Kerr spacetimes differ\nsignificantly from the non-rotating Schwarzschild setting (\"Price's law\"). The\nmain differences with Schwarzschild are slower decay rates for higher angular\nfrequencies and oscillations along the null generators of the event horizon. We\nintroduce a physical space-based method that resolves the following two main\ndifficulties for establishing $\\ell$-dependent asymptotics in the Kerr setting:\n1) the coupling of angular modes and 2) a loss of ellipticity in the\nergoregion. Our mechanism identifies and exploits the existence of conserved\ncharges along null infinity via a time invertibility theory, which in turn\nrelies on new elliptic estimates in the full black hole exterior. This\nframework is suitable for resolving the conflicting numerology in Kerr\nlate-time asymptotics that appears in the numerics literature.\n"}
+{"abstract": "  We provide a universal characterization of the construction taking a scheme\n$X$ to its stable $\\infty$-category $\\text{Mot}(X)$ of noncommutative motives,\npatterned after the universal characterization of algebraic K-theory due to\nBlumberg--Gepner--Tabuada. As a consequence, we obtain a corepresentability\ntheorem for secondary K-theory. We envision this as a fundamental tool for the\nconstruction of trace maps from secondary K-theory.\n  Towards these main goals, we introduce a preliminary formalism of \"stable\n$(\\infty, 2)$-categories\"; notable examples of these include (quasicoherent or\nconstructible) sheaves of stable $\\infty$-categories. We also develop the\nrudiments of a theory of presentable enriched $\\infty$-categories -- and in\nparticular, a theory of presentable $(\\infty, n)$-categories -- which may be of\nintependent interest.\n"}
+{"abstract": "  In this paper, we scrutinize the effectiveness of various clustering\ntechniques, investigating their applicability in Cultural Heritage monitoring\napplications. In the context of this paper, we detect the level of\ndecomposition and corrosion on the walls of Saint Nicholas fort in Rhodes\nutilizing hyperspectral images. A total of 6 different clustering approaches\nhave been evaluated over a set of 14 different orthorectified hyperspectral\nimages. Experimental setup in this study involves K-means, Spectral, Meanshift,\nDBSCAN, Birch and Optics algorithms. For each of these techniques we evaluate\nits performance by the use of performance metrics such as Calinski-Harabasz,\nDavies-Bouldin indexes and Silhouette value. In this approach, we evaluate the\noutcomes of the clustering methods by comparing them with a set of annotated\nimages which denotes the ground truth regarding the decomposition and/or\ncorrosion area of the original images. The results depict that a few clustering\ntechniques applied on the given dataset succeeded decent accuracy, precision,\nrecall and f1 scores. Eventually, it was observed that the deterioration was\ndetected quite accurately.\n"}
+{"abstract": "  Hierarchical least-squares programs with linear constraints (HLSP) are a type\nof optimization problem very common in robotics. Each priority level contains\nan objective in least-squares form which is subject to the linear constraints\nof the higher priority hierarchy levels. Active-set methods (ASM) are a popular\nchoice for solving them. However, they can perform poorly in terms of\ncomputational time if there are large changes of the active set. We therefore\npropose a computationally efficient primal-dual interior-point method (IPM) for\nHLSP's which is able to maintain constant numbers of solver iterations in these\nsituations. We base our IPM on the null-space method which requires only a\nsingle decomposition per Newton iteration instead of two as it is the case for\nother IPM solvers. After a priority level has converged we compose a set of\nactive constraints judging upon the dual and project lower priority levels into\ntheir null-space. We show that the IPM-HLSP can be expressed in least-squares\nform which avoids the formation of the quadratic Karush-Kuhn-Tucker (KKT)\nHessian. Due to our choice of the null-space basis the IPM-HLSP is as fast as\nthe state-of-the-art ASM-HLSP solver for equality only problems.\n"}
+{"abstract": "  Aggregation of heating, ventilation, and air conditioning (HVAC) loads can\nprovide reserves to absorb volatile renewable energy, especially solar\nphoto-voltaic (PV) generation. However, the time-varying PV generation is not\nperfectly known when the system operator decides the HVAC control schedules. To\nconsider the unknown uncertain PV generation, in this paper, we formulate a\ndistributionally robust chance-constrained (DRCC) building load control problem\nunder two typical ambiguity sets: the moment-based and Wasserstein ambiguity\nsets. We derive mixed-integer linear programming (MILP) reformulations for DRCC\nproblems under both sets. Especially for the DRCC problem under the Wasserstein\nambiguity set, we utilize the right-hand side (RHS) uncertainty to derive a\nmore compact MILP reformulation than the commonly known MILP reformulations\nwith big-M constants. All the results also apply to general individual chance\nconstraints with RHS uncertainty. Furthermore, we propose an adjustable\nchance-constrained variant to achieve a trade-off between the operational risk\nand costs. We derive MILP reformulations under the Wasserstein ambiguity set\nand second-order conic programming (SOCP) reformulations under the moment-based\nset. Using real-world data, we conduct computational studies to demonstrate the\nefficiency of the solution approaches and the effectiveness of the solutions.\n"}
+{"abstract": "  The thermal conductivity and shear viscosity of dense nuclear matter, along\nwith the corresponding shear viscosity timescale of canonical neutron stars\n(NSs), are investigated, where the effect of Fermi surface depletion (i.e., the\n$Z$-factor effect) induced by the nucleon-nucleon correlation are taken into\naccount. The factors which are responsible for the transport coefficients,\nincluding the equation of state for building the stellar structure, nucleon\neffective masses, in-medium cross sections, and the $Z$-factor at Fermi\nsurfaces, are all calculated in the framework of the Brueckner theory. The\nFermi surface depletion is found to enhance the transport coefficients by\nseveral times at high densities, which is more favorable to damping the\ngravitational-wave-driven $r$-mode instability of NSs. Yet, the onset of the\n$Z$-factor-quenched neutron triplet superfluidity provides the opposite\neffects, which can be much more significant than the above mentioned $Z$-factor\neffect itself. Therefore, different from the previous understanding, the\nnucleon shear viscosity is still smaller than the lepton one in the superfluid\nNS matter at low temperatures. Accordingly, the shear viscosity cannot stablize\ncanonical NSs against $r$-mode oscillations even at quite low core temperatures\n$10^6$ K.\n"}
+{"abstract": "  In robotics, ergodic control extends the tracking principle by specifying a\nprobability distribution over an area to cover instead of a trajectory to\ntrack. The original problem is formulated as a spectral multiscale coverage\nproblem, typically requiring the spatial distribution to be decomposed as\nFourier series. This approach does not scale well to control problems requiring\nexploration in search space of more than 2 dimensions. To address this issue,\nwe propose the use of tensor trains, a recent low-rank tensor decomposition\ntechnique from the field of multilinear algebra. The proposed solution is\nefficient, both computationally and storage-wise, hence making it suitable for\nits online implementation in robotic systems. The approach is applied to a\npeg-in-hole insertion task requiring full 6D end-effector poses, implemented\nwith a 7-axis Franka Emika Panda robot. In this experiment, ergodic exploration\nallows the task to be achieved without requiring the use of force/torque\nsensors.\n"}
+{"abstract": "  Gait recognition is a promising video-based biometric for identifying\nindividual walking patterns from a long distance. At present, most gait\nrecognition methods use silhouette images to represent a person in each frame.\nHowever, silhouette images can lose fine-grained spatial information, and most\npapers do not regard how to obtain these silhouettes in complex scenes.\nFurthermore, silhouette images contain not only gait features but also other\nvisual clues that can be recognized. Hence these approaches can not be\nconsidered as strict gait recognition.\n  We leverage recent advances in human pose estimation to estimate robust\nskeleton poses directly from RGB images to bring back model-based gait\nrecognition with a cleaner representation of gait. Thus, we propose GaitGraph\nthat combines skeleton poses with Graph Convolutional Network (GCN) to obtain a\nmodern model-based approach for gait recognition. The main advantages are a\ncleaner, more elegant extraction of the gait features and the ability to\nincorporate powerful spatio-temporal modeling using GCN. Experiments on the\npopular CASIA-B gait dataset show that our method archives state-of-the-art\nperformance in model-based gait recognition.\n  The code and models are publicly available.\n"}
+{"abstract": "  We propose a method for constructing confidence intervals that account for\nmany forms of spatial correlation. The interval has the familiar `estimator\nplus and minus a standard error times a critical value' form, but we propose\nnew methods for constructing the standard error and the critical value. The\nstandard error is constructed using population principal components from a\ngiven `worst-case' spatial covariance model. The critical value is chosen to\nensure coverage in a benchmark parametric model for the spatial correlations.\nThe method is shown to control coverage in large samples whenever the spatial\ncorrelation is weak, i.e., with average pairwise correlations that vanish as\nthe sample size gets large. We also provide results on correct coverage in a\nrestricted but nonparametric class of strong spatial correlations, as well as\non the efficiency of the method. In a design calibrated to match economic\nactivity in U.S. states the method outperforms previous suggestions for\nspatially robust inference about the population mean.\n"}
+{"abstract": "  In light of the increasing coupling between electricity and gas networks,\nthis paper introduces two novel iterative methods for efficiently solving the\nmultiperiod optimal electricity and gas flow (MOEGF) problem. The first is an\niterative MILP-based method and the second is an iterative LP-based method with\nan elaborate procedure for ensuring an integral solution. The convergence of\nthe two approaches is founded on two key features. The first is a penalty term\nwith a single, automatically tuned, parameter for controlling the step size of\nthe gas network iterates. The second is a sequence of supporting hyperplanes\ntogether with an increasing number of carefully constructed halfspaces for\ncontrolling the convergence of the electricity network iterates. Moreover, the\ntwo proposed algorithms use as a warm start the solution from a novel\npolyhedral relaxation of the MOEGF problem, for a noticeable improvement in\ncomputation time as compared to a cold start. Unlike the first method, which\ninvokes a branch-and-bound algorithm to find an integral solution, the second\nmethod implements an elaborate steering procedure that guides the continuous\nvariables to take integral values at the solution. Numerical evaluation\ndemonstrates that the two proposed methods can converge to high-quality\nfeasible solutions in computation times at least two orders of magnitude faster\nthan both a state-of-the-art nonlinear branch-and-bound (NLBB) MINLP solver and\na mixed-integer convex programming (MICP) relaxation of the MOEGF problem. The\nexperimental setup consists of five test cases, three of which involve the real\nelectricity and gas transmission networks of the state of Victoria with actual\nlinepack and demand profiles.\n"}
+{"abstract": "  Attention Mechanism is a widely used method for improving the performance of\nconvolutional neural networks (CNNs) on computer vision tasks. Despite its\npervasiveness, we have a poor understanding of what its effectiveness stems\nfrom. It is popularly believed that its effectiveness stems from the visual\nattention explanation, advocating focusing on the important part of input data\nrather than ingesting the entire input. In this paper, we find that there is\nonly a weak consistency between the attention weights of features and their\nimportance. Instead, we verify the crucial role of feature map multiplication\nin attention mechanism and uncover a fundamental impact of feature map\nmultiplication on the learned landscapes of CNNs: with the high order\nnon-linearity brought by the feature map multiplication, it played a\nregularization role on CNNs, which made them learn smoother and more stable\nlandscapes near real samples compared to vanilla CNNs. This smoothness and\nstability induce a more predictive and stable behavior in-between real samples,\nand make CNNs generate better. Moreover, motivated by the proposed\neffectiveness of feature map multiplication, we design feature map\nmultiplication network (FMMNet) by simply replacing the feature map addition in\nResNet with feature map multiplication. FMMNet outperforms ResNet on various\ndatasets, and this indicates that feature map multiplication plays a vital role\nin improving the performance even without finely designed attention mechanism\nin existing methods.\n"}
+{"abstract": "  From crying to babbling and then to speech, infant's vocal tract goes through\nanatomic restructuring. In this paper, we propose a non-invasive fast method of\nusing infant cry signals with convolutional neural network (CNN) based age\nclassification to diagnose the abnormality of the vocal tract development as\nearly as 4-month age. We study F0, F1, F2, and spectrograms and relate them to\nthe postnatal development of infant vocalization. A novel CNN based age\nclassification is performed with binary age pairs to discover the pattern and\ntendency of the vocal tract changes. The effectiveness of this approach is\nevaluated on Baby2020 with healthy infant cries and Baby Chillanto database\nwith pathological infant cries. The results show that our approach yields\n79.20% accuracy for healthy cries, 84.80% for asphyxiated cries, and 91.20% for\ndeaf cries. Our method first reveals that infants' vocal tract develops to a\ncertain level at 4-month age and infants can start controlling the vocal folds\nto produce discontinuous cry sounds leading to babbling. Early diagnosis of\ngrowth abnormality of the vocal tract can help parents keep vigilant and adopt\nmedical treatment or training therapy for their infants as early as possible.\n"}
+{"abstract": "  Second-order nonlinear optics is the base for a large variety of devices\naimed at the active manipulation of light. However, physical principles\nrestrict its occurrence to non-centrosymmetric, anisotropic matter. This\nsignificantly limits the number of base materials exhibiting nonlinear optics.\nHere, we show that embedding chromophores in an array of conical channels 13 nm\nacross in monolithic silica results in mesoscopic anisotropic matter and thus\nin a hybrid material showing second-harmonic generation (SHG). This non-linear\noptics is compared to the one achieved in corona-poled polymer films containing\nthe identical chromophores. It originates in confinement-induced orientational\norder of the elongated guest molecules in the nanochannels. This leads to a\nnon-centrosymmetric dipolar order and hence to a non-linear light-matter\ninteraction on the sub-wavelength, single-pore scale. Our study demonstrates\nthat the advent of large-scale, self-organised nanoporosity in monolithic\nsolids along with confinement-controllable orientational order of chromophores\nat the single-pore scale provides a reliable and accessible tool to design\nmaterials with a nonlinear meta-optics.\n"}
+{"abstract": "  Structured light harnessing multiple degrees of freedom has become a powerful\napproach to use complex states of light in fundamental studies and\napplications. Here, we investigate the light field of an ultrafast laser beam\nwith a wavelength-depended polarization state, a beam we term spectral vector\nbeam. We demonstrate a simple technique to generate and tune such structured\nbeams and demonstrate their spectroscopic capabilities. By only measuring the\npolarization state using fast photodetectors, it is possible to track\npulse-to-pulse changes in the frequency spectrum caused by, e.g. narrowband\ntransmission or absorption. In our experiments, we reach read-out rates of\naround 6 MHz, which is limited by our technical ability to modulate the\nspectrum and can in principle reach GHz read-out rates. In simulations we\nextend the spectral range to more than 1000 nm by using a supercontinuum light\nsource, thereby paving the way to various applications requiring high-speed\nspectroscopic measurements.\n"}
+{"abstract": "  The complementarity and substitutability between products are essential\nconcepts in retail and marketing. Qualitatively, two products are said to be\nsubstitutable if a customer can replace one product by the other, while they\nare complementary if they tend to be bought together. In this article, we take\na network perspective to help automatically identify complements and\nsubstitutes from sales transaction data. Starting from a bipartite\nproduct-purchase network representation, with both transaction nodes and\nproduct nodes, we develop appropriate null models to infer significant\nrelations, either complements or substitutes, between products, and design\nmeasures based on random walks to quantify their importance. The resulting\nunipartite networks between products are then analysed with community detection\nmethods, in order to find groups of similar products for the different types of\nrelationships. The results are validated by combining observations from a\nreal-world basket dataset with the existing product hierarchy, as well as a\nlarge-scale flavour compound and recipe dataset.\n"}
+{"abstract": "  Let $V$ be an elementary abelian $2$-group and $X$ be a finite\n$V$-CW-complex. In this memoir we study two cochain complexes of modules over\nthe mod2 Steenrod algebra $\\mathrm{A}$, equipped with an action of\n$\\mathrm{H}^{*}V$, the mod2 cohomology of $V$, both associated with $X$. The\nfirst, which we call the \"topological complex\", is defined using the orbit\nfiltration of $X$. The second, which we call the \"algebraic complex\", is\ndefined just in terms of the unstable $\\mathrm{A}$-module $\\mathrm{H}^*_V X$,\nthe mod2 equivariant cohomology of $X$. Our study makes intensive use of the\ntheory of unstable $\\mathrm{H}^{*}V$-$\\mathrm{A}$-modules which is a by-product\nof the researches on Sullivan conjecture. There is a noteworthy overlap between\nthe topological part of our memoir and the paper \"Syzygies in equivariant\ncohomology in positive characteristic\", by Allday, Franz and Puppe, which has\njust appeared; however our techniques are quite different from theirs (the name\n\"Steenrod\" does not show up in their article).\n"}
+{"abstract": "  Sun-like stars form from the contraction of cold and dense interstellar\nclouds. How the collapse proceeds and what are the main physical processes\ndriving it, however, is still under debate and a final consensus on the\ntimescale of the process has not been reached. Does this contraction proceed\nslowly, sustained by strong magnetic fields and ambipolar diffusion, or is it\ndriven by fast collapse with gravity dominating the entire process? One way to\nanswer this question is to measure the age of prestellar cores through\nstatistical methods based on observations or via reliable chemical\nchronometers, which should better reflect the physical conditions of the cores.\nHere we report APEX observations of ortho-H$_2$D$^+$ and para-D$_2$H$^+$ for\nsix cores in the Ophiuchus complex and combine them with detailed\nthree-dimensional magneto-hydrodynamical simulations including chemistry,\nproviding a range of ages for the observed cores up to 200 kyr. The outcome of\nour simulations and subsequent analysis provides a good match with the\nobservational results in terms of physical (core masses and volume densities)\nand dynamical parameters such as the Mach number and the virial parameter. We\nshow that models of fast collapse successfully reproduce the observed range of\nchemical abundance ratios as the timescales to reach the observed stages is\ncomparable to the dynamical time of the cores (i.e. the free-fall time) and\nmuch shorter than the ambipolar diffusion time, measured from the electron\nfraction in the simulations. To confirm that this ratio can be used to\ndistinguish between different star-formation scenarios a larger (statistically\nrelevant) sample of star-forming cores should be explored.\n"}
+{"abstract": "  The experiments involving a slider moving on top of granular media consisting\nof photoelastic particles in two dimensions have uncovered elaborate dynamics\nthat may vary from continuous motion to crackling, periodic motion, and\nstick-slip type of behavior. We establish that there is a clear correlation\nbetween the slider dynamics and the response of the force network that\nspontaneously develop in the granular system. This correlation is established\nby application of the persistence homology that allows for formulation of\nobjective measures for quantification of time-dependent force networks. We find\nthat correlation between the slider dynamics and the force network properties\nis particularly strong in the dynamical regime characterized by well-defined\nstick-slip type of dynamics.\n"}
+{"abstract": "  We investigate resource allocation scheme to reduce the energy consumption of\nfederated learning (FL) in the integrated fog-cloud computing enabled\nInternet-of-things (IoT) networks. In the envisioned system, IoT devices are\nconnected with the centralized cloud server (CS) via multiple fog access points\n(F-APs). We consider two different scenarios for training the local models. In\nthe first scenario, local models are trained at the IoT devices and the F-APs\nupload the local model parameters to the CS. In the second scenario, local\nmodels are trained at the F-APs based on the collected data from the IoT\ndevices and the F-APs collaborate with the CS for updating the model\nparameters. Our objective is to minimize the overall energy-consumption of both\nscenarios subject to FL time constraint. Towards this goal, we devise a joint\noptimization of scheduling of IoT devices with the F-APs, transmit power\nallocation, computation frequency allocation at the devices and F-APs and\ndecouple it into two subproblems. In the first subproblem, we optimize the IoT\ndevice scheduling and power allocation, while in the second subproblem, we\noptimize the computation frequency allocation. For each scenario, we develop a\nconflict graph based solution to iteratively solve the two subproblems.\nSimulation results show that the proposed two schemes achieve a considerable\nperformance gain in terms of the energy consumption minimization. The presented\nsimulation results interestingly reveal that for a large number of IoT devices\nand large data sizes, it is more energy efficient to train the local models at\nthe IoT devices instead of the F-APs.\n"}
+{"abstract": "  In Si quantum dots, valley degree of freedom, in particular the generally\nsmall valley splitting and the dot-dependent valley-orbit phase, adds\ncomplexities to the low-energy electron dynamics and the associated spin qubit\nmanipulation. Here we propose a four-level model to extract tunnel coupling\ninformation for a Si double quantum dot (DQD). This scheme is based on a charge\nsensing measurement on the ground state as proposed in the widely used protocol\nfor a GaAs double dot [DiCarlo et. al., PRL 92. 226801]. Our theory can help\ndetermine both intra- and inter-valley tunnel coupling with high accuracy, and\nis robust against system parameters such as valley splittings in the individual\nquantum dots.\n"}
+{"abstract": "  Diffusion is the movement of molecules from a region of higher concentration\nto a region of lower concentration. It can be used to describe the interactions\namong data points. In many machine learning problems including transductive\nsemi-supervised learning and few-shot learning, the relationship between\nlabeled and unlabeled data points is a critical component for high\nclassification accuracy. In this paper, inspired by the convection-diffusion\nODE, we propose a novel diffusion residual network (Diff-ResNet) to introduce\ndiffusion mechanism into neural networks internally. Under the structured data\nassumption, it is proved that the diffusion mechanism can increase the\ndistance-diameter ratio that improves the separability of inter-class points\nand reduces the distance among local intra-class points. This property can be\neasily adopted by the residual networks for constructing the separable\nhyperplanes. Extensive experiments of synthetic binary classification,\nsemi-supervised graph node classification and few-shot image classification in\nvarious datasets validate the effectiveness of the proposed diffusion\nmechanism.\n"}
+{"abstract": "  Stochastic dynamic models have been extensively used for the description of\nprocesses with uncertainties arising in the operations research, behavioral\nsciences, and many other application areas. A large class of the problems from\nthese domains is characterized by the necessity to deal with several distinct\ngroups of populations, which are usually labeled as \"active\" and \"passive\".\nMotivated by important applications of queueing networks and neuroscience, the\nmain focus of the present work is on the analysis of reflecting stochastic\ndynamics of such mixed populations. We develop a general mathematical modeling\nframework to describe the reflecting stochastic dynamics for active-passive\npopulations. The analysis of this model is carried out via a combination of\nlow- and high-delity results obtained from the solution of the underlying\ncoupled system of SDEs and from the simulations with a\nstatistical-mechanics-based lattice gas model, where we employ a kinetic Monte\nCarlo procedure. We provide details of the queueing theory and neuronal models\nand discuss a relationship between reflecting SDEs and a model of queueing\ntheory via a limit theorem. Furthermore, we present several representative\nnumerical examples, and discuss an intrinsic interconnection between active and\npassive particles in the underlying stochastic process. Finally, possible\nextensions of the proposed methodology have been highlighted.\n"}
+{"abstract": "  A 1D mechanical model for nanogranular films, based on a structural\ninterface, is here presented. The analytical dispersion relation for the\nfrequency and lifetimes of the acoustics breathing modes is obtained in terms\nof the interface layer thickness and porosity. The model is successfully\nbenchmarked both against 3D Finite Element Method simulations and experimental\nphotoacoustic data on a paradigmatic system available from the literature. A\nsimpler 1D model, based on an homogenized interface, is also presented and its\nlimitations and pitfalls discussed at the light of the more sophisticated\npillar model. The pillar model captures the relevant physics responsible for\nacoustic dissipation at a disordered interface. Furthermore, the present\nfindings furnish to the experimentalist an easy-to-adopt, benchmarked\nanalytical tool to extract the interface layer physical parameters upon fitting\nof the acoustic data. The model is scale invariant and may be deployed, other\nthan the case of granular materials, where a patched interface is involved.\n"}
+{"abstract": "  The theory of unified product and extending structures for alternative and\npre-alternative algebras are developed. It is proved that the extending\nstructures of these algebras can be classified by using some non-abelian\ncohomology and deformation map theory.\n"}
+{"abstract": "  We study a problem concerning parabolic induction in certain $p$-adic unitary\ngroups. More precisely, for $E/F$ a quadratic extension of $p$-adic fields the\nassociated unitary group $G=\\mathrm{U}(n,n+1)$ contains a parabolic subgroup\n$P$ with Levi component $L$ isomorphic to $\\mathrm{GL}_n(E) \\times\n\\mathrm{U}_1(E)$. Let $\\pi$ be an irreducible supercuspidal representation of\n$L$ of depth zero. We use Hecke algebra methods to determine when the\nparabolically induced representation $\\iota_P^G \\pi$ is reducible.\n"}
+{"abstract": "  One aim shared by multiple settings, such as continual learning or transfer\nlearning, is to leverage previously acquired knowledge to converge faster on\nthe current task. Usually this is done through fine-tuning, where an implicit\nassumption is that the network maintains its plasticity, meaning that the\nperformance it can reach on any given task is not affected negatively by\npreviously seen tasks. It has been observed recently that a pretrained model on\ndata from the same distribution as the one it is fine-tuned on might not reach\nthe same generalisation as a freshly initialised one. We build and extend this\nobservation, providing a hypothesis for the mechanics behind it. We discuss the\nimplication of losing plasticity for continual learning which heavily relies on\noptimising pretrained models.\n"}
+{"abstract": "  We analyze a diffuse interface model that couples a viscous Cahn-Hilliard\nequation for the phase variable with a diffusion-reaction equation for the\nnutrient concentration. The system under consideration also takes into account\nsome important mechanisms like chemotaxis, active transport as well as nonlocal\ninteraction of Oono's type. When the spatial dimension is three, we prove the\nexistence and uniqueness of global weak solutions to the model with singular\npotentials including the physically relevant logarithmic potential. Then we\nobtain some regularity properties of the weak solutions when $t>0$. In\nparticular, with the aid of the viscous term, we prove the so-called\ninstantaneous separation property of the phase variable such that it stays away\nfrom the pure states $\\pm1$ as long as $t>0$. Furthermore, we study long-time\nbehavior of the system, by proving the existence of a global attractor and\ncharacterizing its $\\omega$-limit set.\n"}
+{"abstract": "  The action of qubit channels on projective measurements on a qubit state is\nused to establish an equivalence between channels and properties of generalized\nmeasurements characterized by bias and sharpness parameters. This can be\ninterpreted as shifting the description of measurement dynamics from the\nSchrodinger to the Heisenberg picture. In particular, unital quantum channels\nare shown to induce unbiased measurements. The Markovian channels are found to\nbe equivalent to measurements for which sharpness is a monotonically decreasing\nfunction of time. These results are illustrated by considering various noise\nchannels. Further, the effect of bias and sharpness parameters on the energy\ncost of a measurement and its interplay with non-Markovianity of dynamics is\nalso discussed\n"}
+{"abstract": "  The graduates careers are the most spectacular and visible outcome of\nexcellent university education. This is also important for the university\nperformance assessment when its graduates can easily find jobs in the labor\nmarket. The information about graduates matching their qualifications and\nfields of studies versus undertaken employment, creates an important set of\ndata for future students and employers to be analyzed. Additionally, there is\nbusiness environment pressure to transform workplaces and whole organizations\ntowards a more green and sustainable form. Green Jobs (GJ) are the elements of\nthe whole economic transformation. This change is based on the green\nqualifications and green careers which translate theoretical assumptions into\nbusiness language. Therefore, the choice of future career path is based on\nspecified criteria, which were examined by surveys performed among graduates by\nthe career office at Wroclaw University of Technology (WUT) in Poland. The aim\nof this article was to address the question about the most significant criteria\nof green career paths among graduates of WUT in 2019. Special attention was\npaid to the GJ understood as green careers. In this article, the multi-criteria\nBellinger method was explained, presented, and then used to analyze chosen\nfactors of choice graduates career paths and then compared with Gale-Shapley\nalgorithm results in a comparative analysis. Future research can develop a\ngraduate profile willing to be employed in GJ.\n"}
+{"abstract": "  Soft cellular systems, such as foams or biological tissues, exhibit highly\ncomplex rheological properties, even in the quasistatic regime, that numerical\nmodeling can help to apprehend. We present a numerical implementation of\nquasistatic strain within the widely used cellular Potts model. The accuracy of\nthe method is tested by simulating the quasistatic strain 2D dry foams, both\nordered and disordered. The implementation of quasistatic strain in CPM allows\nthe investigation of sophisticated interplays between stress-strain\nrelationship and structural changes that take place in cellular systems.\n"}
+{"abstract": "  In this paper, we apply the angle-resolved Optically Detected Magnetic\nResonance (ODMR) technique to study series of strained (Cd, Mn)Te/(Cd, Mg)Te\nquantum wells (QWs) produced by molecular beam epitaxy. By analyzing\ncharacteristic features of ODMR angular scans, we determine strain-induced\naxial-symmetry spin Hamiltonian parameter D with neV precision. Furthermore, we\nuse low-temperature optical reflectivity measurements and X-ray diffraction\nscans to evaluate the local strain present in QW material. In our analysis, we\ntake into account different thermal expansion coefficients of GaAs substrate\nand CdTe buffer. The additional deformation due to the thermal expansion\neffects has the same magnitude as deformation origination from the different\ncompositions of the samples. Based on the evaluated deformations and values of\nstrain-induced axial-symmetry spin Hamiltonian parameter D, we find strain\nspin-lattice coefficient G11 = (72.2 +- 1.9) neV for Mn2+ in CdTe and shear\ndeformation potential b = (-0.94 +- 0.11) eV for CdTe.\n"}
+{"abstract": "  Neural networks have enabled applications in artificial intelligence through\nmachine learning, and neuromorphic computing. Software implementations of\nneural networks on conventional computers that have separate memory and\nprocessor (and that operate sequentially) are limited in speed and energy\nefficiency. Neuromorphic engineering aims to build processors in which hardware\nmimics neurons and synapses in the brain for distributed and parallel\nprocessing. Neuromorphic engineering enabled by photonics (optical physics) can\noffer sub-nanosecond latencies and high bandwidth with low energies to extend\nthe domain of artificial intelligence and neuromorphic computing applications\nto machine learning acceleration, nonlinear programming, intelligent signal\nprocessing, etc. Photonic neural networks have been demonstrated on integrated\nplatforms and free-space optics depending on the class of applications being\ntargeted. Here, we discuss the prospects and demonstrated applications of these\nphotonic neural networks.\n"}
+{"abstract": "  The general linear model (GLM) is a widely popular and convenient tool for\nestimating the functional brain response and identifying areas of significant\nactivation during a task or stimulus. However, the classical GLM is based on a\nmassive univariate approach that does not explicitly leverage the similarity of\nactivation patterns among neighboring brain locations. As a result, it tends to\nproduce noisy estimates and be underpowered to detect significant activations,\nparticularly in individual subjects and small groups. A recent alternative, a\ncortical surface-based spatial Bayesian GLM, leverages spatial dependencies\namong neighboring cortical vertices to produce more accurate estimates and\nareas of functional activation. The spatial Bayesian GLM can be applied to\nindividual and group-level analysis. In this study, we assess the reliability\nand power of individual and group-average measures of task activation produced\nvia the surface-based spatial Bayesian GLM. We analyze motor task data from 45\nsubjects in the Human Connectome Project (HCP) and HCP Retest datasets. We also\nextend the model to multi-run analysis and employ subject-specific cortical\nsurfaces rather than surfaces inflated to a sphere for more accurate\ndistance-based modeling. Results show that the surface-based spatial Bayesian\nGLM produces highly reliable activations in individual subjects and is powerful\nenough to detect trait-like functional topologies. Additionally, spatial\nBayesian modeling enhances reliability of group-level analysis even in\nmoderately sized samples (n=45). The power of the spatial Bayesian GLM to\ndetect activations above a scientifically meaningful effect size is nearly\ninvariant to sample size, exhibiting high power even in small samples (n=10).\nThe spatial Bayesian GLM is computationally efficient in individuals and groups\nand is convenient to implement with the open-source BayesfMRI R package.\n"}
+{"abstract": "  We prove new $L^p(\\mathbb{R}^3)$ bounds on Stein's square function for\n$p\\geq3.25$. As an application, it improves the maximal Bochner-Riesz\nconjecture to the same range of $p$.\n"}
+{"abstract": "  In large-scale systems there are fundamental challenges when centralised\ntechniques are used for task allocation. The number of interactions is limited\nby resource constraints such as on computation, storage, and network\ncommunication. We can increase scalability by implementing the system as a\ndistributed task-allocation system, sharing tasks across many agents. However,\nthis also increases the resource cost of communications and synchronisation,\nand is difficult to scale.\n  In this paper we present four algorithms to solve these problems. The\ncombination of these algorithms enable each agent to improve their task\nallocation strategy through reinforcement learning, while changing how much\nthey explore the system in response to how optimal they believe their current\nstrategy is, given their past experience. We focus on distributed agent systems\nwhere the agents' behaviours are constrained by resource usage limits, limiting\nagents to local rather than system-wide knowledge. We evaluate these algorithms\nin a simulated environment where agents are given a task composed of multiple\nsubtasks that must be allocated to other agents with differing capabilities, to\nthen carry out those tasks. We also simulate real-life system effects such as\nnetworking instability. Our solution is shown to solve the task allocation\nproblem to 6.7% of the theoretical optimal within the system configurations\nconsidered. It provides 5x better performance recovery over no-knowledge\nretention approaches when system connectivity is impacted, and is tested\nagainst systems up to 100 agents with less than a 9% impact on the algorithms'\nperformance.\n"}
+{"abstract": "  Topic models such as the Structural Topic Model (STM) estimate latent topical\nclusters within text. An important step in many topic modeling applications is\nto explore relationships between the discovered topical structure and metadata\nassociated with the text documents. Methods used to estimate such relationships\nmust take into account that the topical structure is not directly observed, but\ninstead being estimated itself. The authors of the STM, for instance, perform\nrepeated OLS regressions of sampled topic proportions on metadata covariates by\nusing a Monte Carlo sampling technique known as the method of composition. In\nthis paper, we propose two improvements: first, we replace OLS with more\nappropriate Beta regression. Second, we suggest a fully Bayesian approach\ninstead of the current blending of frequentist and Bayesian methods. We\ndemonstrate our improved methodology by exploring relationships between Twitter\nposts by German members of parliament (MPs) and different metadata covariates.\n"}
+{"abstract": "  Using miniscope recordings of calcium fluorescence signals in the CA1 region\nof the hippocampus of mice, we monitor the neural activity of hippocampal\nregions while the animals are freely moving in an open chamber. Using a\ndata-driven statistical modeling approach, the statistical properties of the\nrecorded data are mapped to spin-glass models with pairwise interactions.\nConsidering the parameter space of the model, the observed system is generally\nnear a critical state between two distinct phases. The close proximity to the\ncriticality is found to be robust against different ways of sampling and\nsegmentation of the measured data. By independently altering the coupling\ndistribution and the network structure of the statistical model, the network\nstructures are found to be vital to maintain the proximity to the critical\nstate. We further find the observed assignment of the coupling strengths makes\nthe net coupling at each site more balanced with slight variation, which likely\nhelps the maintenance of the critical state. Network analysis on the\nconnectivity obtained by thresholding the coupling strengths find the\nconnectivity of the networks to be well described by a random network model.\nThese results are consistent across different experiments, sampling and\nsegmentation choices in our analysis.\n"}
+{"abstract": "  The aim of this note is to explore the interplay between the eikonal\nresummation in impact-parameter space and the exponentiation of infrared\ndivergences in momentum space for gravity amplitudes describing collisions of\nmassive objects. The eikonal governs the classical dynamics relevant to the\ntwo-body problem, and its infrared properties are directly linked to the\nzero-frequency limit of the gravitational wave emission spectrum and to\nradiation-reaction effects. Combining eikonal and infrared exponentiations it\nis possible to derive these properties at a given loop order starting from\nlower-loop data. This is illustrated explicitly in $\\mathcal{N}=8$ supergravity\nand in general relativity by deriving the divergent part of the two-loop\neikonal from tree-level and one-loop elastic amplitudes.\n"}
+{"abstract": "  In the past decade, advances in electronics technology have made larger\nimaging sensors available to the experimental fluid mechanics community. These\nadvancements have enabled the measurement of 2-component 2-dimensional (2C-2D)\nvelocity fields using particle image velocimetry (PIV) with much higher spatial\nresolution than previously possible. However, due to the large size of the\nsensor, the lens distortion needs to be taken into account as it will now have\na more significant effect on the measurement quality that must be corrected to\nensure accurate high-fidelity 2C-2D velocity field measurements. In this paper,\ntwo dewarping models, a second-order rational function (R2) and a bicubic\npolynomial (P3) are investigated with regards to 2C-2D PIV measurements of a\nturbulent boundary layer (TBL) using a large imaging sensor. Two approaches are\nconsidered and compared: (i) dewarping the images prior to the PIV\ncross-correlation analysis and (ii) undertaking the PIV cross-correlation\nanalysis using the original recorded distorted images then followed by using\nthe mapping functions derived for image dewarping to provide the correct\nspatial location of the velocity measurement point. The results demonstrate\nthat the use of P3 dewarping model to correct lens distortion yields better\nresults than the R2 dewarping model. Furthermore, both approaches for the P3\ndewarping model yield results which are statistically indistinguishable.\n"}
+{"abstract": "  Bayesian models of axisymmetric plasmas using Gaussian processes and force\nbalance equations have been developed. These models give the full joint\nposterior probability distributions over plasma current distributions and\npressure profiles given the magnetic field and pressure measurements\nsimultaneously. The toroidal currents such as plasma and magnetic field coil\ncurrents are modelled as a grid of toroidal current carrying solid beams. The\nplasma pressure and poloidal current flux profiles are given as a function of\nthe poloidal magnetic flux surface, determined by the toroidal currents.\nInference of all these physics parameters is a tomographic problem, thus, in\norder to exclude unreasonable solutions, two different prior distributions have\nbeen exploited: a Gaussian process prior and an equilibrium prior. The Gaussian\nprocess prior constrains the plasma current distributions by their covariance\n(smoothness) function whose hyperparameters have been optimally selected by\nBayesian Occam's razor. On the other hand, the equilibrium prior imposes the\nmagnetohydrodynamic force balance by introducing observations that the\ndifferences between the magnetic force and the plasma pressure gradient are\nalmost zero at every plasma current beam. These \\textit{virtual} observations\nemphasise equilibrium solutions \\textit{a~priori} as a part of the prior\nknowledge. These models with the two different priors employ predictive models\nof magnetic sensors and other plasma diagnostics in order to find all possible\nsolutions consistent with all the measurements. The complex, high dimensional\nposterior distributions are explored by a new method based on the Gibbs\nsampling scheme.\n"}
+{"abstract": "  The nucleon-pion-state contribution to QCD two-point and three-point\nfunctions relevant for lattice calculations of the nucleon electromagnetic form\nfactors are studied in chiral perturbation theory. To leading order the results\ndepend on a few experimentally known low-energy constants only, and the\nnucleon-pion-state contribution to the form factors can be estimated. The\nnucleon-pion-state contribution to the electric form factor $G_{\\rm E}(Q^2)$ is\nat the +5 percent level for a source-sink separation of 2 fm, and it increases\nwith increasing momentum transfer $Q^2$. For the magnetic form factor the\nnucleon-pion-state contribution leads to an underestimation of $G_{\\rm M}(Q^2)$\nby about 5 percent that decreases with increasing $Q^2$. For smaller\nsource-sink separations that are accessible in present-day lattice simulations\nthe impact is larger, although the ChPT results may not be applicable for such\nsmall time separations. Still, a comparison with lattice data at $t\\approx 1.6$\nfm works reasonably well.\n"}
+{"abstract": "  Primordial black holes (PBHs) has been considered to form a part of dark\nmatter for a long time but the possibility has been poorly constrained over a\nwide mass range, including the stellar mass range ($1-100~M_{\\odot}$). However,\ndue to the discovery of merger events of black hole binaries by LIGO-Virgo\ngravitational wave observatories, the interest for PBHs in the stellar mass\nwindow has been aroused again. Fast radio bursts (FRBs) are bright radio\ntransients with millisecond duration and very high all-sky occurrence rate.\nLensing effect of these bursts has been proposed as one of the optimal probes\nfor constraining the abundance of PBHs in the stellar mass range. In this\npaper, we first investigate constraints on the abundance of PBHs from the\nlatest $593$ FRB observations for both the monochromatic mass distribution and\nthree other popular extended mass distributions related to different formation\nmechanisms of PBHs. It is found that constraints from currently public FRB\nobservations are relatively weaker than those from existing gravitational wave\ndetections. Furthermore, we forecast constraining power of future FRB\nobservations on the abundance of PBHs with different mass distributions of PBHs\nand different redshift distributions of FRBs taken into account. Finally, We\nfind that constraints of parameter space on extended mass distributions from\n$\\sim10^5$ FRBs with $\\overline{\\Delta t}\\leq1 ~\\rm ms$ would be comparable\nwith what can be constrained from gravitational wave events. It is foreseen\nthat upcoming complementary multi-messenger observations will yield\nconsiderable constraints on the possibilities of PBHs in this intriguing mass\nwindow.\n"}
+{"abstract": "  Generative modelling has been a topic at the forefront of machine learning\nresearch for a substantial amount of time. With the recent success in the field\nof machine learning, especially in deep learning, there has been an increased\ninterest in explainable and interpretable machine learning. The ability to\nmodel distributions and provide insight in the density estimation and exact\ndata likelihood is an example of such a feature. Normalizing Flows (NFs), a\nrelatively new research field of generative modelling, has received substantial\nattention since it is able to do exactly this at a relatively low cost whilst\nenabling competitive generative results. While the generative abilities of NFs\nare typically explored, we focus on exploring the data distribution modelling\nfor Out-of-Distribution (OOD) detection. Using one of the state-of-the-art NF\nmodels, GLOW, we attempt to detect OOD examples in the ISIC dataset. We notice\nthat this model under performs in conform related research. To improve the OOD\ndetection, we explore the masking methods to inhibit co-adaptation of the\ncoupling layers however find no substantial improvement. Furthermore, we\nutilize Wavelet Flow which uses wavelets that can filter particular frequency\ncomponents, thus simplifying the modeling process to data-driven conditional\nwavelet coefficients instead of complete images. This enables us to efficiently\nmodel larger resolution images in the hopes that it would capture more relevant\nfeatures for OOD. The paper that introduced Wavelet Flow mainly focuses on its\nability of sampling high resolution images and did not treat OOD detection. We\npresent the results and propose several ideas for improvement such as\ncontrolling frequency components, using different wavelets and using other\nstate-of-the-art NF architectures.\n"}
+{"abstract": "  Due to high annotation costs making the best use of existing human-created\ntraining data is an important research direction. We, therefore, carry out a\nsystematic evaluation of transferability of BERT-based neural ranking models\nacross five English datasets. Previous studies focused primarily on zero-shot\nand few-shot transfer from a large dataset to a dataset with a small number of\nqueries. In contrast, each of our collections has a substantial number of\nqueries, which enables a full-shot evaluation mode and improves reliability of\nour results. Furthermore, since source datasets licences often prohibit\ncommercial use, we compare transfer learning to training on pseudo-labels\ngenerated by a BM25 scorer. We find that training on pseudo-labels -- possibly\nwith subsequent fine-tuning using a modest number of annotated queries -- can\nproduce a competitive or better model compared to transfer learning. Yet, it is\nnecessary to improve the stability and/or effectiveness of the few-shot\ntraining, which, sometimes, can degrade performance of a pretrained model.\n"}
+{"abstract": "  Let $\\varepsilon>0$ be a small parameter. We consider the domain\n$\\Omega_\\varepsilon:=\\Omega\\setminus D_\\varepsilon$, where $\\Omega$ is an open\ndomain in $\\mathbb{R}^n$, and $D_\\varepsilon$ is a family of small balls of the\nradius $d_\\varepsilon=o(\\varepsilon)$ distributed periodically with period\n$\\varepsilon$. Let $\\Delta_\\varepsilon$ be the Laplace operator in\n$\\Omega_\\varepsilon$ subject to the Robin condition ${\\partial u\\over \\partial\nn}+\\gamma_\\varepsilon u = 0$ with $\\gamma_\\varepsilon\\ge 0$ on the boundary of\nthe holes and the Dirichlet condition on the exterior boundary. Kaizu (1985,\n1989) and Brillard (1988) have shown that, under appropriate assumptions on\n$d_\\varepsilon$ and $\\gamma_\\varepsilon$, the operator $\\Delta_\\varepsilon$\nconverges in the strong resolvent sense to the sum of the Dirichlet Laplacian\nin $\\Omega$ and a constant potential. We improve this result deriving estimates\non the rate of convergence in terms of $L^2\\to L^2$ and $L^2\\to H^1$ operator\nnorms. As a byproduct we establish the estimate on the distance between the\nspectra of the associated operators.\n"}
+{"abstract": "  Modern large-scale knowledge graphs, such as DBpedia, are datasets which\nrequire large computational resources to serve and process. Moreover, they\noften have longer release cycles, which leads to outdated information in those\ngraphs. In this paper, we present DBpedia on Demand -- a system which serves\nDBpedia resources on demand without the need to materialize and store the\nentire graph, and which even provides limited querying functionality.\n"}
+{"abstract": "  We study the $a_1a_1$ and $Za_1$ decay channels of the next-to-lightest\nCP-even Higgs boson $h_2$ of the NMSSM at the LHC, where the $h_2$ is produced\nin gluon fusion. It is found that while the $h_2$ discovery is impossible\nthrough the latter channel, the former one in the $ 4\\tau$ final state is a\npromising channel to discover the $h_2$ with masses up to around 250 GeV at the\nLHC. Such a discovery of the $h_2$ is mostly accompanied with a light $a_1$,\nwhich is a clear evidence for distinguishing the NMSSM from the MSSM since such\na light $a_1$ is impossible in the MSSM.\n"}
+{"abstract": "  It is easier to hear birds than see them, however, they still play an\nessential role in nature and they are excellent indicators of deteriorating\nenvironmental quality and pollution. Recent advances in Machine Learning and\nConvolutional Neural Networks allow us to detect and classify bird sounds, by\ndoing this, we can assist researchers in monitoring the status and trends of\nbird populations and biodiversity in ecosystems. We propose a sound detection\nand classification pipeline for analyzing complex soundscape recordings and\nidentify birdcalls in the background. Our pipeline learns from weak labels,\nclassifies fine-grained bird vocalizations in the wild, and is robust against\nbackground sounds (e.g., airplanes, rain, etc). Our solution achieved 10th\nplace of 816 teams at the BirdCLEF 2021 Challenge hosted on Kaggle.\n"}
+{"abstract": "  Metal-Organic Frameworks (MOFs) have gained much interest due to their\nintrinsic tunable nature. In this work, we study how linker functionalization\nmodifies the electronic structure of the host MOF, more specifically the\nMIL-47(V)-R (R=-F, -Cl, -Br, -OH, -CH$_3$, -CF$_3$, and -OCH$_3$). It is shown\nthat the presence of a functional group leads to a splitting of the\n$\\pi$-orbital on the linker. Moreover, the upward shift of the split-off\n$\\pi$-band correlates well with the electron withdrawing/donating nature of the\nfunctional groups. For halide functional groups the presence of lone-pair back\ndonation is corroborated by calculated Hirshfeld-I charges. In case of the\nferromagnetic configuration of the host MIL-47(V$^{\\mathrm{+IV}}$) material a\nhalf-metal to insulator transition is noted for the -Br, -OCH$_3$, and -OH\nfunctional groups, while for the anti-ferromagnetic configuration only the\nhydroxy-group results in an effective reduction of the band gap.\n"}
+{"abstract": "  We present a simulation showing that super-Nyquist frequencies may have\nperiodic amplitude and frequency modulations, even if actually stable, in time\nseries sampled like the Kepler data. These modulations are caused by the\nbarycentric time correction, which destroys the evenly spaced time\nmeasurements, making the Nyquist frequency variable over the spacecraft orbit\naround the Sun. These modulations can easily be identified in pulsating stars\nfrom Kepler's photometric data.\n"}
+{"abstract": "  PNIPAm microgels synthesized via free radical polymerization (FRP) are often\nconsidered as neutral colloids in aqueous media, although it is well known,\nsince the pioneering work of Pelton and coworkers [Langmuir 1989, 5, 816-818],\nthat the vanishing electrophoretic mobility characterizing swollen microgels\nlargely increases above the lower critical solution temperature (LCST) of\nPNIPAm, at which microgels partially collapse. The presence of an electric\ncharge has been attributed to the ionic initiators that are employed when FRP\nis performed in water and that stay anchored to microgel particles. Combining\ndynamic light scattering (DLS), electrophoresis, transmission electron\nmicroscopy (TEM) and atomic force microscopy (AFM) experiments, we show that\ncollapsed ionic PNIPAm microgels undergo large mobility reversal and reentrant\ncondensation when they are co-suspended with oppositely charged\npolyelectrolytes (PE) or nanoparticles (NP), while their stability remains\nunaffected by PE or NP addition at lower temperatures, where microgels are\nswollen and their charge density is low. Our results highlight a somehow\ndouble-faced electrostatic behavior of PNIPAm microgels due to their tunable\ncharge density: they behave as quasi-neutral colloids at temperature below\nLCST, while they strongly interact with oppositely charged species when they\nare in their collapsed state. The very similar phenomenology encountered when\nmicrogels are surrounded by polylysine chains and silica nanoparticles points\nto the general character of this twofold behavior of PNIPAm-based colloids in\nwater.\n"}
+{"abstract": "  We use semi-derived Ringel-Hall algebras of quivers with loops to realize the\nwhole quantum Borcherds-Bozec algebras and quantum generalized Kac-Moody\nalgebras.\n"}
+{"abstract": "  Relevant information about physical properties of the surface of airless\nbodies such as porosity, particle size, or roughness can be inferred knowing\nthe dependence of the brightness with illumination and observing geometry.\nAdditionally, this knowledge is necessary to standardize or photometrically\ncorrect data acquired under different illumination conditions. In this work we\ndevelop a robust, automatic, and efficient photometric modeling methodology\nwhich is tested and validated using Bennu images acquired by the camera MapCam\nfrom the OSIRIS-REx spacecraft. It consists of a supervised machine learning\nalgorithm through an artificial neural network. Our system provides a more\nprecise modeling for all color filters than the previous procedures which are\nalready published, offering an improvement over this classic approach of up to\n14.30%, as well as a considerable reduction in computing time.\n"}
+{"abstract": "  Testing is recommended for all close contacts of confirmed COVID-19 patients.\nHowever, existing group testing methods are oblivious to the circumstances of\ncontagion provided by contact tracing. Here, we build upon a well-known\nsemi-adaptive pool testing method, Dorfman's method with imperfect tests, and\nderive a simple group testing method based on dynamic programming that is\nspecifically designed to use the information provided by contact tracing.\nExperiments using a variety of reproduction numbers and dispersion levels,\nincluding those estimated in the context of the COVID-19 pandemic, show that\nthe pools found using our method result in a significantly lower number of\ntests than those found using standard Dorfman's method, especially when the\nnumber of contacts of an infected individual is small. Moreover, our results\nshow that our method can be more beneficial when the secondary infections are\nhighly overdispersed.\n"}
+{"abstract": "  The online recruitment matching system has been the core technology and\nservice platform in CareerBuilder. One of the major challenges in an online\nrecruitment scenario is to provide good matches between job posts and\ncandidates using a recommender system on the scale. In this paper, we discussed\nthe techniques for applying an embedding-based recommender system for the large\nscale of job to candidates matching. To learn the comprehensive and effective\nembedding for job posts and candidates, we have constructed a fused-embedding\nvia different levels of representation learning from raw text, semantic\nentities and location information. The clusters of fused-embedding of job and\ncandidates are then used to build and train the Faiss index that supports\nruntime approximate nearest neighbor search for candidate retrieval. After the\nfirst stage of candidate retrieval, a second stage reranking model that\nutilizes other contextual information was used to generate the final matching\nresult. Both offline and online evaluation results indicate a significant\nimprovement of our proposed two-staged embedding-based system in terms of\nclick-through rate (CTR), quality and normalized discounted accumulated gain\n(nDCG), compared to those obtained from our baseline system. We further\ndescribed the deployment of the system that supports the million-scale job and\ncandidate matching process at CareerBuilder. The overall improvement of our job\nto candidate matching system has demonstrated its feasibility and scalability\nat a major online recruitment site.\n"}
+{"abstract": "  This paper reports a CPU-level real-time stereo matching method for surgical\nimages (10 Hz on 640 * 480 image with a single core of i5-9400). The proposed\nmethod is built on the fast ''dense inverse searching'' algorithm, which\nestimates the disparity of the stereo images. The overlapping image patches\n(arbitrary squared image segment) from the images at different scales are\naligned based on the photometric consistency presumption. We propose a Bayesian\nframework to evaluate the probability of the optimized patch disparity at\ndifferent scales. Moreover, we introduce a spatial Gaussian mixed probability\ndistribution to address the pixel-wise probability within the patch. In-vivo\nand synthetic experiments show that our method can handle ambiguities resulted\nfrom the textureless surfaces and the photometric inconsistency caused by the\nLambertian reflectance. Our Bayesian method correctly balances the probability\nof the patch for stereo images at different scales. Experiments indicate that\nthe estimated depth has higher accuracy and fewer outliers than the baseline\nmethods in the surgical scenario.\n"}
+{"abstract": "  We propose a machine learning based approach for automatic 3D building\nreconstruction and vectorization. Taking a single-channel photogrammetric\ndigital surface model (DSM) and panchromatic (PAN) image as input, we first\nfilter out non-building objects and refine the building shapes of input DSM\nwith a conditional generative adversarial network (cGAN). The refined DSM and\nthe input PAN image are then used through a semantic segmentation network to\ndetect edges and corners of building roofs. Later, a set of vectorization\nalgorithms are proposed to build roof polygons. Finally, the height information\nfrom the refined DSM is added to the polygons to obtain a fully vectorized\nlevel of detail (LoD)-2 building model. We verify the effectiveness of our\nmethod on large-scale satellite images, where we obtain state-of-the-art\nperformance.\n"}
+{"abstract": "  We outline the Great Misalignment Problem in natural language processing\nresearch, this means simply that the problem definition is not in line with the\nmethod proposed and the human evaluation is not in line with the definition nor\nthe method. We study this misalignment problem by surveying 10 randomly sampled\npapers published in ACL 2020 that report results with human evaluation. Our\nresults show that only one paper was fully in line in terms of problem\ndefinition, method and evaluation. Only two papers presented a human evaluation\nthat was in line with what was modeled in the method. These results highlight\nthat the Great Misalignment Problem is a major one and it affects the validity\nand reproducibility of results obtained by a human evaluation.\n"}
+{"abstract": "  Let $f(z,w)=(p(z),q(z,w))$ be a holomorphic skew product with a\nsuperattracting fixed point at the origin. In the previous paper we have\nsucceeded to specify a dominant term of $q$ by the order of $p$ and the Newton\npolygon of $q$, and to construct a B\\\"{o}ttcher coordinate on an invariant\nwedge. By using the same idea and terminologies, we give inequalities of\nattraction rates for the vertical dynamics of $f$ in this paper. The results\nhold not only for the superattracting case, but for all the other cases.\n"}
+{"abstract": "  We consider a problem of superconductivity coexistence with the\nspin-density-wave order in disordered multiband metals. It is assumed that\nrandom variations of the disorder potential on short length scales render the\ninteractions between electrons to develop spatial correlations. As a\nconsequence, both superconducting and magnetic order parameters become\nspatially inhomogeneous and are described by the universal phenomenological\nquantities, whereas all the microscopic details are encoded in the correlation\nfunction of the coupling strength fluctuations. We consider a minimal model\nwith two nested two-dimensional Fermi surfaces and disorder potentials which\ninclude both intra- and inter-band scattering. The model is analyzed using the\nquasiclassical approach to show that short-scale pairing-potential disorder\nleads to a broadening of the coexistence region.\n"}
+{"abstract": "  We propose a new, exactly solvable Schr\\\"{o}dinger equation. The potential\npartner is given by \\[{\nV=}-Bp\\operatorname{csch}[px]^{2}-9p(B+p)\\operatorname*{sech}[3px]^{2}+(B\\coth[px]-3(B+p)\\tanh[3px])^{2}.\\]\nobtained using supersymmetric method with shape invariance property having a\nsuperpotential $W(x,A,B)=A\\tanh 3px-B\\coth px.$ We derive entirely the exact\nsolutions of this family of Schr\\\"{o}dinger equations with the eigenvalue given\nby $E_{n}^{\\left( -\\right) }=(A-B)^{2}-(A-B-4np)^{2}% $ and the corresponding\neigenfunctions are determined exactly and in closed form. Schr\\\"{o}dinger\nequations, and Sturm-Liouville equations in general, are challenging to solve\nin closed form, and only a few of them are known. Therefore, in a strict\nmathematical sense, discovering new solvable equations is essential in\nunderstanding the eluded solutions' underpinnings. This result has potential\napplications in nuclear physics and chemistry, and other fields of science.\n"}
+{"abstract": "  The rapid advancement of deep learning models that can generate and synthesis\nhyper-realistic videos known as Deepfakes and their ease of access to the\ngeneral public have raised concern from all concerned bodies to their possible\nmalicious intent use. Deep learning techniques can now generate faces, swap\nfaces between two subjects in a video, alter facial expressions, change gender,\nand alter facial features, to list a few. These powerful video manipulation\nmethods have potential use in many fields. However, they also pose a looming\nthreat to everyone if used for harmful purposes such as identity theft,\nphishing, and scam. In this work, we propose a Convolutional Vision Transformer\nfor the detection of Deepfakes. The Convolutional Vision Transformer has two\ncomponents: Convolutional Neural Network (CNN) and Vision Transformer (ViT).\nThe CNN extracts learnable features while the ViT takes in the learned features\nas input and categorizes them using an attention mechanism. We trained our\nmodel on the DeepFake Detection Challenge Dataset (DFDC) and have achieved 91.5\npercent accuracy, an AUC value of 0.91, and a loss value of 0.32. Our\ncontribution is that we have added a CNN module to the ViT architecture and\nhave achieved a competitive result on the DFDC dataset.\n"}
+{"abstract": "  We perform, in a manifestly $SO(n-1,1)$ [$SO(n)$] covariant fashion, the\nHamiltonian analysis of general relativity in $n$ dimensions written as a\nconstrained $BF$ theory. We solve the constraint on the $B$ field in a way\nnaturally adapted to the foliation of the spacetime that avoids explicitly the\nintroduction of the vielbein. This leads to a form of the action involving a\npresymplectic structure, which is reduced by doing a suitable parametrization\nof the connection and then, after integrating out some auxiliary fields, the\nHamiltonian form involving only first-class constraints is obtained.\n"}
+{"abstract": "  Recently, Kumar and Ghosh have derived Kerr-like rotating black hole\nsolutions in the framework of four-dimensional Einstein-Gauss-Bonnet theory of\ngravity and investigated the black hole shadow. Using the steady-state\nNovikov-Thorne model, we study thin accretion disk processes for such rotating\nblack holes including the energy flux, temperature distribution, emission\nspectrum, energy conversion efficiency as well as the radius of the innermost\nstable circular orbit. We also study the effects of the Gauss-Bonnet coupling\nparameter $\\alpha$ on these quantities. The results are compared to slowly\nrotating relativistic Kerr black holes which show that for a positive\nGauss-Bonnet coupling, thin accretion disks around rotating black holes in\nfour-dimensional Einstein-Gauss-Bonnet gravity are hotter and more efficient\nthan that for Kerr black holes with the same rotation parameter $a$, while for\na negative coupling they are cooler and less efficient. Thus the accretion disk\nprocesses may be considered as tools for testing Einstein-Gauss-Bonnet gravity\nusing astrophysical observations.\n"}
+{"abstract": "  We prove the hard Lefschetz property for pseudomanifolds and cycles in any\ncharacteristic with respect to an appropriate Artinian reduction. The proof is\na combination of Adiprasito's biased pairing theory and a generalization of a\nformula of Papadakis-Petrotou to arbitrary characteristic. In particular, we\nprove the Lefschetz theorem for doubly Cohen Macaulay complexes, solving a\ngeneralization of the g-conjecture due to Stanley. We also provide a simplified\npresentation of the characteristic 2 case, and generalize it to pseudomanifolds\nand cycles.\n"}
+{"abstract": "  In this paper we provide a formal framework for comparing the expressive\npower of Behavior Trees (BTs) to other action selection architectures. Taking\ninspiration from the analogous comparisons of structural programming\nmethodologies, we formalise the concept of `expressiveness'. This leads us to\nan expressiveness hierarchy of control architectures, which includes BTs,\nDecision Trees (DTs), Teleo-reactive Programs (TRs) and Finite State Machines\n(FSMs). By distinguishing between BTs with auxiliary variables and those\nwithout, we demonstrate the existence of a trade-off in BT design between\nreadability and expressiveness. We discuss what this means for BTs in practice.\n"}
+{"abstract": "  Data-intensive science is increasingly reliant on real-time processing\ncapabilities and machine learning workflows, in order to filter and analyze the\nextreme volumes of data being collected. This is especially true at the energy\nand intensity frontiers of particle physics where bandwidths of raw data can\nexceed 100 Tb/s of heterogeneous, high-dimensional data sourced from hundreds\nof millions of individual sensors. In this paper, we introduce a new\ndata-driven approach for designing and optimizing high-throughput data\nfiltering and trigger systems such as those in use at physics facilities like\nthe Large Hadron Collider (LHC). Concretely, our goal is to design a\ndata-driven filtering system with a minimal run-time cost for determining which\ndata event to keep, while preserving (and potentially improving upon) the\ndistribution of the output as generated by the hand-designed trigger system. We\nintroduce key insights from interpretable predictive modeling and\ncost-sensitive learning in order to account for non-local inefficiencies in the\ncurrent paradigm and construct a cost-effective data filtering and trigger\nmodel that does not compromise physics coverage.\n"}
+{"abstract": "  Extremely high data rates at modern synchrotron and X-ray free-electron laser\nlight source beamlines motivate the use of machine learning methods for data\nreduction, feature detection, and other purposes. Regardless of the\napplication, the basic concept is the same: data collected in early stages of\nan experiment, data from past similar experiments, and/or data simulated for\nthe upcoming experiment are used to train machine learning models that, in\neffect, learn specific characteristics of those data; these models are then\nused to process subsequent data more efficiently than would general-purpose\nmodels that lack knowledge of the specific dataset or data class. Thus, a key\nchallenge is to be able to train models with sufficient rapidity that they can\nbe deployed and used within useful timescales. We describe here how specialized\ndata center AI (DCAI) systems can be used for this purpose through a\ngeographically distributed workflow. Experiments show that although there are\ndata movement cost and service overhead to use remote DCAI systems for DNN\ntraining, the turnaround time is still less than 1/30 of using a locally\ndeploy-able GPU.\n"}
+{"abstract": "  In this paper we aim at presenting a concise but also comprehensive study of\ntime-dependent (tdependent) Hamiltonian dynamics on a locally conformal\nsymplectic (lcs) manifold. We present a generalized geometric theory of\ncanonical transformations and formulate a time-dependent geometric\nHamilton-Jacobi theory on lcs manifolds. In contrast to previous papers\nconcerning locally conformal symplectic manifolds, here the introduction of the\ntime dependency brings out interesting geometric properties, as it is the\nintroduction of contact geometry in locally symplectic patches. To conclude, we\nshow examples of the applications of our formalism, in particular, we present\nsystems of differential equations with time-dependent parameters, which admit\ndifferent physical interpretations as we shall point out.\n"}
+{"abstract": "  We investigate upper bounds on the length of cost optimal plans that are\nvalid for problems with 0-cost actions. We employ these upper bounds as\nhorizons for a SAT-based encoding of planning with costs. Given an initial\nupper bound on the cost of the optimal plan, we experimentally show that this\nSAT-based approach is able to compute plans with better costs, and in many\ncases it can match the optimal cost. Also, in multiple instances, the approach\nis successful in proving that a certain cost is the optimal plan cost.\n"}
+{"abstract": "  We report on the first resolved HI observations of two blue ultra-diffuse\ngalaxies (UDGs)using the Giant Metrewave Radio Telescope (GMRT). These\nobservations add to the sofar limited number of UDGs with resolved HI data. The\ntargets are from contrasting non-cluster environments: UDG-B1 is projected in\nthe outskirts of Hickson Compact Group 25 and Secco-dI-2 (SdI-2) is an isolated\nUDG. These UDGs also have contrasting effective radii with Re of 3.7 kpc\n(similar to the Milky Way) and 1.3 kpc respectively. SdI-2 has an unusually\nlarge MHI/M* ratio =28.9, confirming a previous single dish HI observation.\nBoth galaxies display HI morphological and kinematic signatures consistent with\na recent tidal interaction, which is also supported by observations from other\nwavelengths, including optical spectroscopy. Within the limits of the\nobservations' resolution, our analysis indicates that SdI-2 is dark\nmatter-dominated within its HI radius and this is also likely to be the case\nfor UDG-B1. Our study highlights the importance of high spatial and spectral\nresolution HI observations for the study of the dark matter properties of UDGs.\n"}
+{"abstract": "  The H-frame (also known as H-Bot) architecture is a simple and elegant\ntwo-axis parallel positioning system used to construct the XY stage of 3D\nprinters. It holds potential for high speed and excellent dynamic performance\ndue to the use of frame-mounted motors that reduce the moving mass of the\nprinter while allowing for the use of (heavy) higher torque motors. However,\nthe H-frame's dynamic accuracy is limited during high-acceleration and\nhigh-speed motion due to racking -- i.e., parasitic torsional motions of the\nprinter's gantry due to a force couple. Mechanical solutions to the racking\nproblem are either costly or detract from the simplicity of the H-frame. In\nthis paper, we introduce a feedforward software compensation algorithm, based\non the filtered B-splines (FBS) method, that rectifies errors due to racking.\nThe FBS approach expresses the motion command to the machine as a linear\ncombination of B-splines. The B-splines are filtered through an identified\nmodel of the machine dynamics and the control points of the B-spline based\nmotion command are optimized such that the tracking error is minimized. To\ncompensate racking using the FBS algorithm, an accurate frequency response\nfunction of the racking motion is obtained and coupled to the H-frame's x- and\ny-axis dynamics with a kinematic model. The result is a coupled linear\nparameter varying model of the H-frame that is utilized in the FBS framework to\ncompensate racking. An approximation of the proposed racking compensation\nalgorithm, that decouples the x- and y-axis compensation, is developed to\nsignificantly improve its computational efficiency with almost no loss of\ncompensation accuracy. Experiments on an H-frame 3D printer demonstrate a 43\npercent improvement in the shape accuracy of a printed part using the proposed\nalgorithm compared to the standard FBS approach without racking compensation.\n"}
+{"abstract": "  We study the nodal set, that is the zero set, of \"flat\" Laplace\neigenfunctions on the standard two dimensional flat torus\n$\\mathbb{T}^2=\\mathbb{R}^2/\\mathbb{Z}^2$. We find the asymptotic law for the\nnodal length of these eigenfunctions in any ball of radius larger than the\nPlanck-scale. In particular, we prove that the nodal set equidistributes above\nPlanck-scale. At Planck-scale, we show that the nodal set equidistributes\naround almost every point for some eigenfunctions, but we also construct\neigenfunctions whose nodal set fails to equidistribute. The proof is based on\nBourgain's de-randomisation and the main new ingredient, which might be of\nindependent interest, is the integrability of arbitrarily large powers of the\nlogarithm of \\textquotedblleft generic \\textquotedblright eigenfunctions, based\non the work of Nazarov \\cite{N93,Nun}, and some arithmetic ingredients called\nsemi-correlations.\n"}
+{"abstract": "  We study the quasi-periodic oscillations from the accretion disk around\nrotating traversable wormholes by means of the resonance models. We investigate\nthe linear stability of the circular geodesic orbits in the equatorial plane\nfor a general class of wormhole geometries deriving analytical expressions for\nthe epicyclic frequencies. Since wormholes can often mimic black holes in the\nastrophysical observations, we analyze the properties of the quasi-circular\noscillatory motion in comparison with the Kerr black hole. We demonstrate that\nwormholes possess distinctive features, which can be observationally\nsignificant. It is characteristic for the Kerr black hole that the orbital and\nthe epicyclic frequencies obey a constant ordering in the whole range of the\nspin parameter. In contrast, for wormhole spacetimes we can have various types\nof orderings between the frequencies in the different regions of the parametric\nspace. This enables the excitation of much more diverse types of resonances\nincluding parametric and forced resonances of lower order, which could lead to\nstronger observable signals. In addition, for co-rotating orbits the resonances\ncan be excited in a very close neighbourhood of the wormhole throat for a wide\nrange of values of the angular momentum, making wormholes a valuable laboratory\nfor testing strong gravity.\n"}
+{"abstract": "  Commonsense inference to understand and explain human language is a\nfundamental research problem in natural language processing. Explaining human\nconversations poses a great challenge as it requires contextual understanding,\nplanning, inference, and several aspects of reasoning including causal,\ntemporal, and commonsense reasoning. In this work, we introduce CIDER -- a\nmanually curated dataset that contains dyadic dialogue explanations in the form\nof implicit and explicit knowledge triplets inferred using contextual\ncommonsense inference. Extracting such rich explanations from conversations can\nbe conducive to improving several downstream applications. The annotated\ntriplets are categorized by the type of commonsense knowledge present (e.g.,\ncausal, conditional, temporal). We set up three different tasks conditioned on\nthe annotated dataset: Dialogue-level Natural Language Inference, Span\nExtraction, and Multi-choice Span Selection. Baseline results obtained with\ntransformer-based models reveal that the tasks are difficult, paving the way\nfor promising future research. The dataset and the baseline implementations are\npublicly available at https://cider-task.github.io/cider/.\n"}
+{"abstract": "  The coronavirus continues to disrupt our everyday lives as it spreads at an\nexponential rate. It needs to be detected quickly in order to quarantine\npositive patients so as to avoid further spread. This work proposes a new\nconvolutional neural network (CNN) architecture called 'slow Encoding CNN. The\nproposed model's best performance wrt Sensitivity, Positive Predictive Value\n(PPV) found to be SP=0.67, PP=0.98, SN=0.96, and PN=0.52 on AI AGAINST COVID19\n- Screening X-ray images for COVID-19 Infections competition's test data\nsamples. SP and PP stand for the Sensitivity and PPV of the COVID-19 positive\nclass, while PN and SN stand for the Sensitivity and PPV of the COVID-19\nnegative class.\n"}
+{"abstract": "  In an effort to increase spatial and temporal resolution of ultrafast\nelectron diffraction and microscopy, ultra-high brightness photocathodes are\nactively sought to improve electron beam quality. Beam dynamics codes often\napproximate the Coulomb interaction with mean-field space charge, which is a\ngood approximation in traditional beams. However, point-to-point Coulomb\neffects, such as disorder induced heating and the Boersch effect, can not be\nneglected in cold, dense beams produced by such photocathodes. In this paper,\nwe introduce two new numerical methods to calculate the important effects of\nthe photocathode image charge when using a point-to-point interaction model.\nEquipped with an accurate model of the image charge, we calculate the effects\nof point-to-point interactions on two high brightness photoemission beamlines\nfor ultrafast diffraction. The first beamline uses a 200 keV gun, whereas the\nsecond uses a 5 MeV gun, each operating in the single-shot diffraction regime\nwith $10^5$ electrons/pulse. Assuming a zero photoemission temperature, it is\nshown that including stochastic Coulomb effects increases the final emittance\nof these beamlines by over a factor of 2 and decreases the peak transverse\nphase space density by over a factor of 3 as compared to mean-field\nsimulations. We then introduce a method to compute the energy released by\ndisorder induced heating using the pair correlation function. This disorder\ninduced heating energy was found to scale very near the theoretical result for\nstationary ultracold plasmas, and it accounts for over half of the emittance\ngrowth above mean-field simulations.\n"}
+{"abstract": "  We present upper bounds on the quantum and private capacity of single-mode,\nphase-insentitive Bosonic Gaussian Channels based on degradable extensions. Our\nfindings are state-of-the-art in the following parameter regions: low\ntemperature and high transmissivity for the thermal attenuator, low temperature\nfor additive Gaussian noise, high temperature and intermediate amplification\nfor the thermal amplifier.\n"}
+{"abstract": "  The single lap shear test is widely used to measure the strength of\ndissimilar welds even though such a test brings limited understanding of the\nintrinsic weld toughness. The present study proposes a numerical finite element\n(FE) analysis and experimental characterization of dissimilar joints presenting\nvarious microstructures (thickness of the intermetallic layer (IML) and\nhardness profile). For this purpose, Friction Melt Bonding (FMB) and Friction\nStir Welding (FSW) were used to join aluminum AA6061 and Dual Phase steel\n(DP980). The FE simulations allowed calculating the evolution of the J-integral\nnear this notch tip. It shows that crack initiation depends significantly on\nthe plastic properties of the welded metallic alloys around the notch tip and\nthe width of the welded zone, which both are significantly different for FSW\nand FMB processes. Nevertheless, a similar weld fracture toughness J_C of\napproximately 1 kJ.m-2 is estimated from the analysis for both FMB and FSW.\nThis is three orders of magnitude higher than the fracture toughness of the\nintermetallic layer, revealing that the plastic dissipation in the Al and steel\nplates around the crack tip has a major effect on the weld toughness.\n"}
+{"abstract": "  Recent progress in constraining the massive accretions (>1:10) experienced by\nthe Milky Way (MW) and the Andromeda galaxy (M31) offers an opportunity to\nunderstand the dwarf galaxy population of the Local Group. Using zoom-in dark\nmatter-only simulations of MW-mass haloes and concentrating on subhaloes that\nare thought to be capable of hosting dwarf galaxies, we demonstrate that the\ninfall of a massive progenitor is accompanied with the accretion and\ndestruction of a large number of subhaloes. Massive accreted progenitors do not\nincrease the total number of infalling subhaloes onto a MW-mass host, but\ninstead focus surrounding subhaloes onto the host causing a clustering in the\ninfall time of subhaloes. This leads to a temporary elevation in the number of\nsubhaloes as well as changes in their cumulative radial profile within the\nvirial radius of the host. Surviving associated subhaloes with a massive\nprogenitor have a large diversity in their orbits. We find that the star\nformation quenching times of Local Group dwarf spheroidal galaxies ($10^{5}\n\\mathrm{M_{\\odot}} \\lesssim \\mathrm{M}_{*} \\lesssim 10^{7} \\mathrm{M_{\\odot}}$)\nare clustered around the times of the most massive accretions suffered by the\nMW and M31. Our results imply that a) the quenching time of dwarf spheroidals\nis a good proxy of their infall time and b) the absence of recently quenched\nsatellites around M31 suggests that M33 is not on its first infall and was\naccreted much earlier.\n"}
+{"abstract": "  We study the possibility of transferring fermions from a trivial system as\nparticle source to an empty system but at topological phase as a mold for\ncasting a stable topological insulator dynamically. We show that this can be\nrealized by a non-Hermitian unidirectional hopping, which connects a central\nsystem at topological phase and a trivial flat-band system with a periodic\ndriving chemical potential, which scans over the valence band of the central\nsystem. The near exceptional-point dynamics allows a unidirectional dynamical\nprocess: the time evolution from an initial state with full-filled source\nsystem to a stable topological insulating state approximately. The result is\ndemonstrated numerically by a source-assistant QWZ model and SSH chain in the\npresence of random perturbation. Our finding reveals a classical analogy of\nquench dynamics in quantum matter and provides a way for topological quantum\nstate engineering.\n"}
+{"abstract": "  In this work, we propose a privacy-preservation framework, TradeChain, which\ndecouples the trade events of participants using decentralised identities.\nTradeChain adopts the Self-Sovereign Identity (SSI) principles and makes the\nfollowing novel contributions: a) it incorporates two separate ledgers: a\npublic permissioned blockchain for maintaining identities and the permissioned\nblockchain for recording trade flows, b) it uses Zero Knowledge Proofs (ZKPs)\non traders' private credentials to prove multiple identities on trade ledger\nand c) allows data owners to define dynamic access rules for verifying\ntraceability information from the trade ledger using access tokens and\nCiphertext Policy Attribute-Based Encryption (CP-ABE). A proof of concept\nimplementation of TradeChain is presented on Hyperledger Indy and Fabric and an\nextensive evaluation of execution time, latency and throughput reveals minimal\noverheads.\n"}
+{"abstract": "  We conduct a comparative analysis of desktop web search behaviour of users\nfrom Germany (n=558) and Switzerland (n=563) based on a combination of web\ntracking and survey data. We find that web search accounts for 13% of all\ndesktop browsing, with the share being higher in Switzerland than in Germany.\nWe find that in over 50% of cases users clicked on the first search result,\nwith over 97% of all clicks being made on the first page of search outputs.\nMost users rely on Google when conducting searches, and users preferences for\nother engines are related to their demographics. We also test relationships\nbetween user demographics and daily number of searches, average share of search\nactivities among tracked events by user as well as the tendency to click on\nhigher- or lower-ranked results. We find differences in such relationships\nbetween the two countries that highlights the importance of comparative\nresearch in this domain. Further, we observe differences in the temporal\npatterns of web search use between women and men, marking the necessity of\ndisaggregating data by gender in observational studies regarding online\ninformation behaviour.\n"}
+{"abstract": "  Cell detection in histopathology images is of great value in clinical\npractice. \\textit{Convolutional neural networks} (CNNs) have been applied to\ncell detection to improve the detection accuracy, where cell annotations are\nrequired for network training. However, due to the variety and large number of\ncells, complete annotations that include every cell of interest in the training\nimages can be challenging. Usually, incomplete annotations can be achieved,\nwhere positive labeling results are carefully examined to ensure their\nreliability but there can be other positive instances, i.e., cells of interest,\nthat are not included in the annotations. This annotation strategy leads to a\nlack of knowledge about true negative samples. Most existing methods simply\ntreat instances that are not labeled as positive as truly negative during\nnetwork training, which can adversely affect the network performance. In this\nwork, to address the problem of incomplete annotations, we formulate the\ntraining of detection networks as a positive-unlabeled learning problem.\nSpecifically, the classification loss in network training is revised to take\ninto account incomplete annotations, where the terms corresponding to negative\nsamples are approximated with the true positive samples and the other samples\nof which the labels are unknown. To evaluate the proposed method, experiments\nwere performed on a publicly available dataset for mitosis detection in breast\ncancer cells, and the experimental results show that our method improves the\nperformance of cell detection given incomplete annotations for training.\n"}
+{"abstract": "  The Australian Square Kilometre Array Pathfinder (ASKAP) collects images of\nthe sky at radio wavelengths with an unprecedented field of view, combined with\na high angular resolution and sub-millijansky sensitivities. The large quantity\nof data produced is used by the ASKAP Variables and Slow Transients (VAST)\nsurvey science project to study the dynamic radio sky. Efficient pipelines are\nvital in such research, where searches often form a `needle in a haystack' type\nof problem to solve. However, the existing pipelines developed among the\nradio-transient community are not suitable for the scale of ASKAP datasets.\n  In this paper we provide a technical overview of the new \"VAST Pipeline\": a\nmodern and scalable Python-based data pipeline for transient searches, using\nup-to-date dependencies and methods. The pipeline allows source association to\nbe performed at scale using the Pandas DataFrame interface and the well-known\nAstropy crossmatch functions. The Dask Python framework is used to parallelise\noperations as well as scale them both vertically and horizontally, by means of\na cluster of workers. A modern web interface for data exploration and querying\nhas also been developed using the latest Django web framework combined with\nBootstrap.\n"}
+{"abstract": "  We present the Multilingual TEDx corpus, built to support speech recognition\n(ASR) and speech translation (ST) research across many non-English source\nlanguages. The corpus is a collection of audio recordings from TEDx talks in 8\nsource languages. We segment transcripts into sentences and align them to the\nsource-language audio and target-language translations. The corpus is released\nalong with open-sourced code enabling extension to new talks and languages as\nthey become available. Our corpus creation methodology can be applied to more\nlanguages than previous work, and creates multi-way parallel evaluation sets.\nWe provide baselines in multiple ASR and ST settings, including multilingual\nmodels to improve translation performance for low-resource language pairs.\n"}
+{"abstract": "  This study presents a novel pressure-based methodology for the efficient\nnumerical solution of a four-equation two-phase diffuse interface model. The\nproposed methodology has the potential to simulate low-Mach flows with mass\ntransfer. In contrast to the classical conservative four-equation model\nformulation, the adopted set of equations features volume fraction,\ntemperature, velocity and pressure as the primary variables. The model includes\nthe effects of viscosity, surface tension, thermal conductivity and gravity,\nand has the ability to incorporate complex equations of state. Additionally, a\nGibbs free energy relaxation procedure is used to model mass transfer. A key\ncharacteristic of the proposed methodology is the use of high performance and\nscalable solvers for the solution of the Helmholtz equation for the pressure,\nwhich drastically reduces the computational cost compared to analogous\ndensity-based approaches. We demonstrate the capabilities of the methodology to\nsimulate flows with large density and viscosity ratios through extended\nverification against a range of different test cases. Finally, the potential of\nthe methodology to tackle challenging phase change flows is demonstrated with\nthe simulation of three-dimensional nucleate boiling.\n"}
+{"abstract": "  In this work, we propose the world's first closed-loop ML-based planning\nbenchmark for autonomous driving. While there is a growing body of ML-based\nmotion planners, the lack of established datasets and metrics has limited the\nprogress in this area. Existing benchmarks for autonomous vehicle motion\nprediction have focused on short-term motion forecasting, rather than long-term\nplanning. This has led previous works to use open-loop evaluation with L2-based\nmetrics, which are not suitable for fairly evaluating long-term planning. Our\nbenchmark overcomes these limitations by introducing a large-scale driving\ndataset, lightweight closed-loop simulator, and motion-planning-specific\nmetrics. We provide a high-quality dataset with 1500h of human driving data\nfrom 4 cities across the US and Asia with widely varying traffic patterns\n(Boston, Pittsburgh, Las Vegas and Singapore). We will provide a closed-loop\nsimulation framework with reactive agents and provide a large set of both\ngeneral and scenario-specific planning metrics. We plan to release the dataset\nat NeurIPS 2021 and organize benchmark challenges starting in early 2022.\n"}
+{"abstract": "  Structure from Motion (SfM) often fails to estimate accurate poses in\nenvironments that lack suitable visual features. In such cases, the quality of\nthe final 3D mesh, which is contingent on the accuracy of those estimates, is\nreduced. One way to overcome this problem is to combine data from a monocular\ncamera with that of a LIDAR. This allows fine details and texture to be\ncaptured while still accurately representing featureless subjects. However,\nfusing these two sensor modalities is challenging due to their fundamentally\ndifferent characteristics. Rather than directly fusing image features and LIDAR\npoints, we propose to leverage common geometric features that are detected in\nboth the LIDAR scans and image data, allowing data from the two sensors to be\nprocessed in a higher-level space. In particular, we propose to find\ncorrespondences between 3D lines extracted from LIDAR scans and 2D lines\ndetected in images before performing a bundle adjustment to refine poses. We\nalso exploit the detected and optimized line segments to improve the quality of\nthe final mesh. We test our approach on the recently published dataset, Newer\nCollege Dataset. We compare the accuracy and the completeness of the 3D mesh to\na ground truth obtained with a survey-grade 3D scanner. We show that our method\ndelivers results that are comparable to a state-of-the-art LIDAR survey while\nnot requiring highly accurate ground truth pose estimates.\n"}
+{"abstract": "  Let $X$ be a separable Banach space and let $Q:X^*\\rightarrow X$ be a linear,\nbounded, non-negative and symmetric operator and let $A:D(A)\\subseteq\nX\\rightarrow X$ be the infinitesimal generator of a strongly continuous\nsemigroup of contractions on $X$. We consider the abstract Wiener space\n$(X,\\mu_\\infty,H_\\infty)$ where $\\mu_\\infty$ is a centred non-degenerate\nGaussian measure on $X$ with covariance operator defined, at least formally, as\n\\begin{align*} Q_\\infty=\\int_0^{+\\infty} e^{sA}Qe^{sA^*}ds, \\end{align*} and\n$H_\\infty$ is the Cameron--Martin space associated to $\\mu_\\infty$.\n  Let $H$ be the reproducing kernel Hilbert space associated with $Q$ with\ninner product $[\\cdot,\\cdot]_H$. We assume that the operator $Q_\\infty\nA^*:D(A^*)\\subseteq X^*\\rightarrow X$ extends to a bounded linear operator\n$B\\in \\mathcal L(H)$ which satisfies $B+B^*=-{\\rm Id}_H$, where ${\\rm Id}_H$\ndenotes the identity operator on $H$. Let $D$ and $D^2$ be the first and second\norder Fr\\'echet derivative operators, we denote by $D_H$ and $D^2_H$ the\nclosure in $L^2(X,\\mu_\\infty)$ of the operators $QD$ and $QD^2$ and by\n$W^{1,2}_H(X,\\mu_\\infty)$ and and $W^{2,2}_H(X,\\mu_\\infty)$ their domains in\n$L^2(X,\\mu_\\infty)$, respectively,. Furthermore, we denote by $D_{A_\\infty}$\nthe closure of the operator $Q_\\infty A^*D$ and by\n$W^{1,2}_{A_\\infty}(X,\\mu_\\infty)$ its domain in $L^2(X,\\mu_\\infty)$. We\ncharacterize the domain of the operator $L$, associated to the bilinear form\n\\begin{align*} (u,v)\\mapsto-\\int_{X}[BD_Hu,D_Hv]_Hd\\mu_\\infty, \\qquad u,v\\in\nW^{1,2}_H(X,\\mu_\\infty), \\end{align*} in $L^2(X,\\mu_\\infty)$. More precisely,\nwe prove that $D(L)$ coincides, up to an equivalent remorming, with a subspace\nof $W^{2,2}_H(X,\\mu_\\infty)\\cap W^{1,2}_{A_\\infty}(X,\\mu_\\infty)$. We stress\nthat we are able to treat the case when $L$ is degenerate and non-symmetric.\n"}
+{"abstract": "  We present DEGARI (Dynamic Emotion Generator And ReclassIfier), an\nexplainable system for emotion attribution and recommendation. This system\nrelies on a recently introduced commonsense reasoning framework, the TCL logic,\nwhich is based on a human-like procedure for the automatic generation of novel\nconcepts in a Description Logics knowledge base. Starting from an ontological\nformalization of emotions based on the Plutchik model, known as ArsEmotica, the\nsystem exploits the logic TCL to automatically generate novel commonsense\nsemantic representations of compound emotions (e.g. Love as derived from the\ncombination of Joy and Trust according to Plutchik). The generated emotions\ncorrespond to prototypes, i.e. commonsense representations of given concepts,\nand have been used to reclassify emotion-related contents in a variety of\nartistic domains, ranging from art datasets to the editorial contents available\nin RaiPlay, the online platform of RAI Radiotelevisione Italiana (the Italian\npublic broadcasting company). We show how the reported results (evaluated in\nthe light of the obtained reclassifications, the user ratings assigned to such\nreclassifications, and their explainability) are encouraging, and pave the way\nto many further research directions.\n"}
+{"abstract": "  High dimensional data reduction techniques are provided by using partial\nleast squares within deep learning. Our framework provides a nonlinear\nextension of PLS together with a disciplined approach to feature selection and\narchitecture design in deep learning. This leads to a statistical\ninterpretation of deep learning that is tailor made for predictive problems. We\ncan use the tools of PLS, such as scree-plot, bi-plot to provide model\ndiagnostics. Posterior predictive uncertainty is available using MCMC methods\nat the last layer. Thus we achieve the best of both worlds: scalability and\nfast predictive rule construction together with uncertainty quantification. Our\nkey construct is to employ deep learning within PLS by predicting the output\nscores as a deep learner of the input scores. As with PLS our X-scores are\nconstructed using SVD and applied to both regression and classification\nproblems and are fast and scalable. Following Frank and Friedman 1993, we\nprovide a Bayesian shrinkage interpretation of our nonlinear predictor. We\nintroduce a variety of new partial least squares models: PLS-ReLU,\nPLS-Autoencoder, PLS-Trees and PLS-GP. To illustrate our methodology, we use\nsimulated examples and the analysis of preferences of orange juice and\npredicting wine quality as a function of input characteristics. We also\nillustrate Brillinger's estimation procedure to provide the feature selection\nand data dimension reduction. Finally, we conclude with directions for future\nresearch.\n"}
+{"abstract": "  We revisit the two-player planar target-defense game initially posed by\nIsaacs where a pursuer (or defender) attempts to guard a target set from an\nattack by an evader (or attacker). This paper builds on existing analytical\nsolutions to games of defending a simple shape of target area to develop a\ngeneralized and extended solution to the same game with a compact convex target\nset with smooth boundary. Isaacs' method is applied to address the game of kind\nand games of degree. A geometric solution approach is used to find the barrier\nsurface that demarcates the winning sets of the players. A value function\ncoupled with a set of optimal state feedback strategies in each winning set is\nderived and proven to correspond to the saddle point solution of the game. The\nproposed solutions are illustrated by means of numerical simulations.\n"}
+{"abstract": "  Due to their increasing spread, confidence in neural network predictions\nbecame more and more important. However, basic neural networks do not deliver\ncertainty estimates or suffer from over or under confidence. Many researchers\nhave been working on understanding and quantifying uncertainty in a neural\nnetwork's prediction. As a result, different types and sources of uncertainty\nhave been identified and a variety of approaches to measure and quantify\nuncertainty in neural networks have been proposed. This work gives a\ncomprehensive overview of uncertainty estimation in neural networks, reviews\nrecent advances in the field, highlights current challenges, and identifies\npotential research opportunities. It is intended to give anyone interested in\nuncertainty estimation in neural networks a broad overview and introduction,\nwithout presupposing prior knowledge in this field. A comprehensive\nintroduction to the most crucial sources of uncertainty is given and their\nseparation into reducible model uncertainty and not reducible data uncertainty\nis presented. The modeling of these uncertainties based on deterministic neural\nnetworks, Bayesian neural networks, ensemble of neural networks, and test-time\ndata augmentation approaches is introduced and different branches of these\nfields as well as the latest developments are discussed. For a practical\napplication, we discuss different measures of uncertainty, approaches for the\ncalibration of neural networks and give an overview of existing baselines and\nimplementations. Different examples from the wide spectrum of challenges in\ndifferent fields give an idea of the needs and challenges regarding\nuncertainties in practical applications. Additionally, the practical\nlimitations of current methods for mission- and safety-critical real world\napplications are discussed and an outlook on the next steps towards a broader\nusage of such methods is given.\n"}
+{"abstract": "  Nowadays voice search for points of interest (POI) is becoming increasingly\npopular. However, speech recognition for local POI has remained to be a\nchallenge due to multi-dialect and massive POI. This paper improves speech\nrecognition accuracy for local POI from two aspects. Firstly, a geographic\nacoustic model (Geo-AM) is proposed. The Geo-AM deals with multi-dialect\nproblem using dialect-specific input feature and dialect-specific top layer.\nSecondly, a group of geo-specific language models (Geo-LMs) are integrated into\nour speech recognition system to improve recognition accuracy of long tail and\nhomophone POI. During decoding, specific language models are selected on demand\naccording to users' geographic location. Experiments show that the proposed\nGeo-AM achieves 6.5%$\\sim$10.1% relative character error rate (CER) reduction\non an accent testset and the proposed Geo-AM and Geo-LM totally achieve over\n18.7% relative CER reduction on Tencent Map task.\n"}
+{"abstract": "  Time series are often complex and rich in information but sparsely labeled\nand therefore challenging to model. In this paper, we propose a self-supervised\nframework for learning generalizable representations for non-stationary time\nseries. Our approach, called Temporal Neighborhood Coding (TNC), takes\nadvantage of the local smoothness of a signal's generative process to define\nneighborhoods in time with stationary properties. Using a debiased contrastive\nobjective, our framework learns time series representations by ensuring that in\nthe encoding space, the distribution of signals from within a neighborhood is\ndistinguishable from the distribution of non-neighboring signals. Our\nmotivation stems from the medical field, where the ability to model the dynamic\nnature of time series data is especially valuable for identifying, tracking,\nand predicting the underlying patients' latent states in settings where\nlabeling data is practically impossible. We compare our method to recently\ndeveloped unsupervised representation learning approaches and demonstrate\nsuperior performance on clustering and classification tasks for multiple\ndatasets.\n"}
+{"abstract": "  Joint probability mass function (PMF) estimation is a fundamental machine\nlearning problem. The number of free parameters scales exponentially with\nrespect to the number of random variables. Hence, most work on nonparametric\nPMF estimation is based on some structural assumptions such as clique\nfactorization adopted by probabilistic graphical models, imposition of low rank\non the joint probability tensor and reconstruction from 3-way or 2-way\nmarginals, etc. In the present work, we link random projections of data to the\nproblem of PMF estimation using ideas from tomography. We integrate this idea\nwith the idea of low-rank tensor decomposition to show that we can estimate the\njoint density from just one-way marginals in a transformed space. We provide a\nnovel algorithm for recovering factors of the tensor from one-way marginals,\ntest it across a variety of synthetic and real-world datasets, and also perform\nMAP inference on the estimated model for classification.\n"}
+{"abstract": "  Understanding and predicting the duration or \"return-to-normal\" time of\ntraffic incidents is important for system-level management and optimisation of\nroad transportation networks. Increasing real-time availability of multiple\ndata sources characterising the state of urban traffic networks, together with\nadvances in machine learning offer the opportunity for new and improved\napproaches to this problem that go beyond static statistical analyses of\nincident duration. In this paper we consider two such improvements: dynamic\nupdate of incident duration predictions as new information about incidents\nbecomes available and automated interpretation of the factors responsible for\nthese predictions. For our use case, we take one year of incident data and\ntraffic state time-series from the M25 motorway in London. We use it to train\nmodels that predict the probability distribution of incident durations,\nutilising both time-invariant and time-varying features of the data. The latter\nallow predictions to be updated as an incident progresses, and more information\nbecomes available. For dynamic predictions, time-series features are fed into\nthe Match-Net algorithm, a temporal convolutional hitting-time network,\nrecently developed for dynamical survival analysis in clinical applications.\nThe predictions are benchmarked against static regression models for survival\nanalysis and against an established dynamic technique known as landmarking and\nfound to perform favourably by several standard comparison measures. To provide\ninterpretability, we utilise the concept of Shapley values from the domain of\ninterpretable artificial intelligence to rank the features most relevant to the\nmodel predictions at different time horizons. For example, the time of day is\nalways a significantly influential time-invariant feature, whereas the\ntime-series features strongly influence predictions at 5 and 60-minute\nhorizons.\n"}
+{"abstract": "  A constant increase in the need of clean energy demands more innovation from\nthe research community. Active and stable materials that can make use of the\nsolar radiation to promote different reactions are the cornerstone of emerging\ntechnologies. Polymeric carbon nitrides that harvest solar radiation to drive\nelectrochemical reactions are considered solid candidates. In this respect,\npolymeric carbon nitrides were prepared by the thermal polycondensation of\nmelamine. As-obtained materials were characterized by synchrotron radiation and\nlab-based techniques. The electron band structure was fully characterized by a\ncombined electrochemical optoelectronic study. Electron microscopy studies\nbefore and after the photoelectrochemical experiments showed morphological and\nstructure degradation. The work at-hand concludes that polymeric carbon\nnitrides are prone to photoelectrochemical degradation at high overpotentials.\n"}
+{"abstract": "  Several counterexample models to the Nelson-Seiberg theorem have been\ndiscovered in previous literature, with generic superpotentials respecting the\nR-symmetry and non-generic R-charge assignments for chiral fields. This work\npresent a sufficient condition for such counterexample models: The number of\nR-charge 2 fields, which is greater than the number of R-charge 0 fields, must\nbe less than or equal to the number of R-charge 0 fields plus the number of\nindependent field pairs with opposite R-charges and satisfying some extra\nrequirements. We give a correct count of such field pairs when there are\nmultiple field pairs with degenerated R-charges. These models give\nsupersymmetric vacua with spontaneous R-symmetry breaking, thus are\ncounterexamples to both the Nelson-Seiberg theorem and its extensions.\n"}
+{"abstract": "  Kriging is a widely recognized method for making spatial predictions. On the\nsphere, popular methods such as ordinary kriging assume that the spatial\nprocess is intrinsically homogeneous. However, intrinsic homogeneity is too\nstrict in many cases. This research uses intrinsic random function (IRF) theory\nto relax the homogeneity assumption. A key component of modeling IRF processes\nis estimating the degree of non-homogeneity. A graphical approach is proposed\nto accomplish this estimation. With the ability to estimate non-homogeneity, an\nIRF universal kriging procedure can be developed. Results from simulation\nstudies are provided to demonstrate the advantage of using IRF universal\nkriging as opposed to ordinary kriging when the underlying process is not\nintrinsically homogeneous.\n"}
+{"abstract": "  Infographics are documents designed to effectively communicate information\nusing a combination of textual, graphical and visual elements. In this work, we\nexplore the automatic understanding of infographic images by using Visual\nQuestion Answering technique.To this end, we present InfographicVQA, a new\ndataset that comprises a diverse collection of infographics along with natural\nlanguage questions and answers annotations. The collected questions require\nmethods to jointly reason over the document layout, textual content, graphical\nelements, and data visualizations. We curate the dataset with emphasis on\nquestions that require elementary reasoning and basic arithmetic skills.\nFinally, we evaluate two strong baselines based on state of the art multi-modal\nVQA models, and establish baseline performance for the new task. The dataset,\ncode and leaderboard will be made available at http://docvqa.org\n"}
+{"abstract": "  In this chapter, we will give comprehensive examples of applying RL in\noptimizing the physical layer of wireless communications by defining different\nclass of problems and the possible solutions to handle them. In Section 9.2, we\npresent all the basic theory needed to address a RL problem, i.e. Markov\ndecision process (MDP), Partially observable Markov decision process (POMDP),\nbut also two very important and widely used algorithms for RL, i.e. the\nQ-learning and SARSA algorithms. We also introduce the deep reinforcement\nlearning (DRL) paradigm and the section ends with an introduction to the\nmulti-armed bandits (MAB) framework. Section 9.3 focuses on some toy examples\nto illustrate how the basic concepts of RL are employed in communication\nsystems. We present applications extracted from literature with simplified\nsystem models using similar notation as in Section 9.2 of this Chapter. In\nSection 9.3, we also focus on modeling RL problems, i.e. how action and state\nspaces and rewards are chosen. The Chapter is concluded in Section 9.4 with a\nprospective thought on RL trends and it ends with a review of a broader state\nof the art in Section 9.5.\n"}
+{"abstract": "  Multiple populations in globular clusters are usually explained by the\nformation of stars out of material with a chemical composition that is polluted\nto different degrees by the ejecta of short-lived, massive stars of various\ntype. Among other things, these polluters differ by the amount of helium they\nspread in the surrounding medium. In this study we investigate whether the\npresent-day photometric method used to infer the helium content of multiple\npopulations indeed gives the true value or underestimates it by missing very\nHe-rich, but rare stars. We focus on the specific case of NGC6752. We compute\natmosphere models and synthetic spectra along isochrones produced for this\ncluster for a very broad range of He abundances covering the predictions of\ndifferent pollution scenarios, including the extreme case of the fast-rotating\nmassive star (FRMS) scenario. We calculate synthetic photometry in HST filters\nbest suited to study the helium content. We subsequently build synthetic\nclusters with various distributions of stars. We finally determine the maximum\nhelium mass fraction of these synthetic clusters using a method similar to that\napplied to observational data. We build toy models of clusters with various\ndistributions of multiple populations and ensure that we are able to recover\nthe input maximum Y. We then build synthetic clusters with the populations\npredicted by the FRMS scenario and find that while we slightly underestimate\nthe maximum Y value, we are still able to detect stars much more He-rich than\nthe current observed maximum Y. It is easier to determine the maximum Y on main\nsequence stars than on red giant branch stars, but qualitatively the results\nare unaffected by the sample choice. We show that in NGC6752 it is unlikely\nthat stars more He-rich than the current observational limit of about 0.3 are\npresent.\n"}
+{"abstract": "  We propose a data-driven online convex optimization algorithm for controlling\ndynamical systems. In particular, the control scheme makes use of an initially\nmeasured input-output trajectory and behavioral systems theory which enable it\nto handle unknown discrete-time linear time-invariant systems as well as a\npriori unknown time-varying cost functions. Further, only output feedback\ninstead of full state measurements is required for the proposed approach.\nAnalysis of the closed loop's performance reveals that the algorithm achieves\nsublinear regret if the variation of the cost functions is sublinear. The\neffectiveness of the proposed algorithm, even in the case of noisy\nmeasurements, is illustrated by a simulation example.\n"}
+{"abstract": "  The paper studies categories of definable subassignments with some category\nequivalences to semi-algebraic and constructible subsets of arc spaces of\nalgebraic varieties. These materials allow us to compare the motivic measure of\nCluckers-Loeser and the one of Denef-Loeser in certain classes of definable\nsubassignments.\n"}
+{"abstract": "  The FCAL collaboration is preparing large-scale prototypes of special\ncalorimeters to be used in the very forward region at future electron-positron\ncolliders for a precise measurement of integrated luminosity and for instant\nluminosity measurement and assisting beam-tuning. LumiCal is designed as a\nsilicon-tungsten sandwich calorimeter with very thin sensor planes to keep the\nMoli\\`ere radius small, facilitating such the measurement of electron showers\nin the presence of background. Dedicated front-end electronics has been\ndeveloped to match the timing and dynamic range requirements. A partially\ninstrumented prototype was investigated in a 1 to 5 GeV electron beam at the\nDESY II synchrotron. In the recent beam tests, a multi-plane compact prototype\nwas equipped with thin detector planes fully assembled with readout electronics\nand installed in 1 mm gaps between tungsten plates of one radiation length\nthickness. High statistics data were used to perform sensor alignment, and to\nmeasure the longitudinal and transversal shower development in the sandwich.\nThis talk covers the latest status of the calorimeter prototype development and\nselected performance results, obtained in test beam measurements, the prospects\nfor the upcoming DESY test beam, as well as the expected simulation\nperformance.\n"}
+{"abstract": "  Since the end of 2019, COVID-19 has significantly affected the lives of\npeople around the world. Towards the end of 2020, several COVID-19 vaccine\ncandidates with relatively high efficacy have been reported in the final phase\nof clinical trials. Vaccines have been considered as critical tools for opening\nup social and economic activities, thereby lessening the impact of this disease\non the society. This paper presents a simulation of COVID-19 spread using\nmodified Susceptible-Infected-Removed (SIR) model under vaccine intervention in\nseveral localities of Malaysia, i.e. those cities or states with high\nrelatively COVID-19 cases such as Kuala Lumpur, Penang, Sabah, and Sarawak. The\nresults show that at different vaccine efficacy levels (0.75, 0.85, and 0.95),\nthe curves of active infection vary slightly, indicating that vaccines with\nefficacy above 0.75 would produce the herd immunity required to level the\ncurves. In addition, disparity is significant between implementing or not\nimplementing a vaccination program. Simulation results also show that lowering\nthe reproduction number, R0 is necessary to keep the infection curve flat\ndespite vaccination. This is due to the assumption that vaccination is mostly\ncarried out gradually at the assumed fixed rate. The statement is based on our\nsimulation results with two values of R0: 1.1 and 1.2, indicative of reduction\nof R0 by social distancing. The lower R0 shows a smaller peak amplitude about\nhalf the value simulated with R0=1.2. In conclusion, the simulation model\nsuggests a two-pronged strategy to combat the COVID-19 pandemic in Malaysia:\nvaccination and compliance with standard operating procedure issued by the\nWorld Health Organization (e.g. social distancing).\n"}
+{"abstract": "  Intracranial recordings in epilepsy patients are increasingly utilized to\ngain insight into the electrophysiological mechanisms of human cognition. There\nare currently several practical limitations to conducting research with these\npatients, including patient and researcher availability and the cognitive\nabilities of patients, which limit the amount of task-related data that can be\ncollected. Prior studies have synchronized clinical audio, video, and neural\nrecordings to understand naturalistic behaviors, but these recordings are\ncentered on the patient to understand their seizure semiology and thus do not\ncapture and synchronize audiovisual stimuli from tasks. Here, we describe a\nplatform for cognitive monitoring of neurosurgical patients during their\nhospitalization that benefits both patients and researchers alike. We provide\nthe full specifications for this system and describe some example use cases in\nperception, memory, and sleep research. Our system opens up new avenues to\ncollect more data per patient using real-world tasks, affording new\npossibilities to conduct longitudinal studies of the electrophysiological basis\nof human cognition under naturalistic conditions.\n"}
+{"abstract": "  This book is organized into eight chapters. The first three gently introduce\nthe basic principles of hybrid high-order methods on a linear diffusion\nproblem, the key ideas underlying the mathematical analysis, and some useful\nvariants of the method as well as links to other methods from the literature.\nThe following four present various challenging applications to solid mechanics,\nincluding linear elasticity and hyperelasticity, elastodynamics,\ncontact/friction, and plasticity. The last chapter reviews implementation\naspects. This book is primarily intended for graduate students, researchers (in\napplied mathematics, numerical analysis, and computational mechanics), and\nengineers working in related fields of application. Basic knowledge of the\ndevising and analysis of finite element methods is assumed. Special effort was\nmade to streamline the presentation so as to pinpoint the essential ideas,\naddress key mathematical aspects, present examples, and provide bibliographic\npointers.\n"}
+{"abstract": "  In this paper, we construct a bialgebra theory for associative conformal\nalgebras, namely antisymmetric infinitesimal conformal bialgebras. On the one\nhand, it is an attempt to give conformal structures for antisymmetric\ninfinitesimal bialgebras. On the other hand, under certain conditions, such\nstructures are equivalent to double constructions of Frobenius conformal\nalgebras, which are associative conformal algebras that are decomposed into the\ndirect sum of another associative conformal algebra and its conformal dual as\n$\\mathbb{C}[\\partial]$-modules such that both of them are subalgebras and the\nnatural conformal bilinear form is invariant. The coboundary case leads to the\nintroduction of associative conformal Yang-Baxter equation whose antisymmetric\nsolutions give antisymmetric infinitesimal conformal bialgebras. Moreover, the\nconstruction of antisymmetric solutions of associative conformal Yang-Baxter\nequation is given from $\\mathcal{O}$-operators of associative conformal\nalgebras as well as dendriform conformal algebras.\n"}
+{"abstract": "  In recent years a huge interdisciplinary field has emerged which is devoted\nto the complex dynamics of anomalous transport with long-time memory and\nnon-markovian features. It was found that the framework of fractional calculus\nand its generalizations are able to capture these phenomena. Many of the\nclassical models are based on continuous-time renewal processes and use the\nMontroll Weiss continuous time random walk (CTRW) approach. On the other hand\ntheir discrete time counterparts are rarely considered in the literature\ndespite their importance in various applications. The goal of the present paper\nis to give a brief sketch of our recently introduced discrete-time Prabhakar\ngeneralization of the fractional Poisson process and the related discrete-time\nrandom walk (DTRW) model. We show that this counting process is connected with\nthe continuous time Prabhakar renewal process by a (well scaled)\ncontinuous-time limit. We deduce the state probabilities and discrete time\ngeneralized fractional Kolmogorov-Feller equations governing the Prabhakar DTRW\nand discuss effects such as long time memory (nonmarkovianity) as a hallmark of\nthe complexity of the process.\n"}
+{"abstract": "  Stochastic processes with random reinforced relocations have been introduced\nin the physics literature to model animal foraging behaviour. Such a process\nevolves as a Markov process, except at random relocation times, when it chooses\na time at random in its whole past according to some \"memory kernel\", and jumps\nto its value at that random time.\n  We prove a quenched large deviations principle for the value of the process\nat large times. The difficulty in proving this result comes from the fact that\nthe process is not Markov because of the relocations. Furthermore, the random\ninter-relocation times act as a random environment.\n  We also prove a partial large deviations principle for the occupation measure\nof the process in the case when the memory kernel is decreasing. We use a\nsimilar approach to prove large deviations principle for the profile of the\nweighted random recursive tree (WRRT) in the case when older nodes are more\nlikely to attract new links than more recent nodes.\n"}
+{"abstract": "  When users on social media share content without considering its veracity,\nthey may unwittingly be spreading misinformation. In this work, we investigate\nthe design of lightweight interventions that nudge users to assess the accuracy\nof information as they share it. Such assessment may deter users from posting\nmisinformation in the first place, and their assessments may also provide\nuseful guidance to friends aiming to assess those posts themselves. In support\nof lightweight assessment, we first develop a taxonomy of the reasons why\npeople believe a news claim is or is not true; this taxonomy yields a checklist\nthat can be used at posting time. We conduct evaluations to demonstrate that\nthe checklist is an accurate and comprehensive encapsulation of people's\nfree-response rationales. In a second experiment, we study the effects of three\nbehavioral nudges -- 1) checkboxes indicating whether headings are accurate, 2)\ntagging reasons (from our taxonomy) that a post is accurate via a checklist and\n3) providing free-text rationales for why a headline is or is not accurate --\non people's intention of sharing the headline on social media. From an\nexperiment with 1668 participants, we find that both providing accuracy\nassessment and rationale reduce the sharing of false content. They also reduce\nthe sharing of true content, but to a lesser degree that yields an overall\ndecrease in the fraction of shared content that is false. Our findings have\nimplications for designing social media and news sharing platforms that draw\nfrom richer signals of content credibility contributed by users. In addition,\nour validated taxonomy can be used by platforms and researchers as a way to\ngather rationales in an easier fashion than free-response.\n"}
+{"abstract": "  This paper extends the Radon transform, a classical image processing tool for\nfast tomography and denoising, to the quantum computing platform. A new kind of\nperiodic discrete Radon transform (PDRT), called quantum Radon transform (QRT),\nis proposed. The QRT has a quantum implementation that is exponentially faster\nthan the classical Radon transform. Based on the QRT, we design an efficient\nquantum image denoising algorithm. The simulation results show that QRT\npreserves the good denoising capability as in the classical PDRT. Also, a\nquantum algorithm for interpolation-based discrete Radon transform (IDRT) is\nproposed, which can be used for fast line detection. Both the quantum extension\nof IDRT and the line detection algorithm can provide polynomial speedups over\nthe classical counterparts.\n"}
+{"abstract": "  Fluctuations of the glacier calving front have an important influence over\nthe ice flow of whole glacier systems. It is therefore important to precisely\nmonitor the position of the calving front. However, the manual delineation of\nSAR images is a difficult, laborious and subjective task. Convolutional neural\nnetworks have previously shown promising results in automating the glacier\nsegmentation in SAR images, making them desirable for further exploration of\ntheir possibilities. In this work, we propose to compute uncertainty and use it\nin an Uncertainty Optimization regime as a novel two-stage process. By using\ndropout as a random sampling layer in a U-Net architecture, we create a\nprobabilistic Bayesian Neural Network. With several forward passes, we create a\nsampling distribution, which can estimate the model uncertainty for each pixel\nin the segmentation mask. The additional uncertainty map information can serve\nas a guideline for the experts in the manual annotation of the data.\nFurthermore, feeding the uncertainty map to the network leads to 95.24% Dice\nsimilarity, which is an overall improvement in the segmentation performance\ncompared to the state-of-the-art deterministic U-Net-based glacier segmentation\npipelines.\n"}
+{"abstract": "  We study nested variational inequalities, which are variational inequalities\nwhose feasible set is the solution set of another variational inequality. We\npresent a projected averaging Tikhonov algorithm requiring the weakest\nconditions in the literature to guarantee the convergence to solutions of the\nnested variational inequality. Specifically, we only need monotonicity of the\nupper- and the lower-level variational inequalities. Also, we provide the first\ncomplexity analysis for nested variational inequalities considering optimality\nof both the upper- and lower-level.\n"}
+{"abstract": "  Evolving trees arise in many real-life scenarios from computer file systems\nand dynamic call graphs, to fake news propagation and disease spread. Most\nlayout algorithms for static trees, however, do not work well in an evolving\nsetting (e.g., they are not designed to be stable between time steps). Dynamic\ngraph layout algorithms are better suited to this task, although they often\nintroduce unnecessary edge crossings. With this in mind we propose two methods\nfor visualizing evolving trees that guarantee no edge crossings, while\noptimizing (1) desired edge length realization, (2) layout compactness, and (3)\nstability. We evaluate the two new methods, along with four prior approaches\n(two static and two dynamic), on real-world datasets using quantitative\nmetrics: stress, desired edge length realization, layout compactness,\nstability, and running time. The new methods are fully functional and available\non github.\n"}
+{"abstract": "  We discuss a remarkable correspondence between the description of Black Holes\nas highly occupied condensates of $N$ weakly interacting gravitons and that of\nColor Glass Condensates (CGCs) as highly occupied gluon states. In both cases,\nthe dynamics of \"wee partons\" in Regge asymptotics is controlled by emergent\nsemi-hard scales that lead to perturbative unitarization and classicalization\nof $2\\rightarrow N$ particle amplitudes at weak coupling. In particular, they\nattain a maximal entropy permitted by unitarity, bounded by the inverse\ncoupling $\\alpha$ of the respective constituents. Strikingly, this entropy is\nequal to the area measured in units of the Goldstone constant corresponding to\nthe spontaneous breaking of Poincar{\\'{e}} symmetry by the corresponding\ngraviton or gluon condensate. In gravity, the Goldstone constant is the Planck\nscale, and gives rise to the Bekenstein-Hawking entropy. Likewise, in the CGC,\nthe corresponding Goldstone scale is determined by the onset of gluon\nscreening. We point to further similarities in Black Hole formation,\nthermalization and decay, to that of the Glasma matter formed from colliding\nCGCs in ultrarelativistic nuclear collisions, which decays into a Quark-Gluon\nPlasma.\n"}
+{"abstract": "  In this study, we present the ro-vibrationally resolved gas-phase spectrum of\nthe diatomic molecule TiO around 1000\\,cm$^{-1}$. Molecules were produced in a\nlaser ablation source by vaporizing a pure titanium sample in the atmosphere of\ngaseous nitrous oxide. Adiabatically expanded gas, containing TiO, formed a\nsupersonic jet and was probed perpendicularly to its propagation by infrared\nradiation from quantum cascade lasers. Fundamental bands of $^{46-50}$TiO and\nvibrational hotbands of $^{48}$TiO are identified and analyzed. In a\nmass-independent fitting procedure combining the new infrared data with pure\nrotational and electronic transitions from the literature, a Dunham-like\nparameterization is obtained. From the present data set, the multi-isotopic\nanalysis allows to determine the spin-rotation coupling constant $\\gamma$ and\nthe Born-Oppenheimer correction coefficient $\\Delta_{\\rm U_{10}}^{\\mathrm{Ti}}$\nfor the first time. The parameter set enables to calculate the Born-Oppenheimer\ncorrection coefficients $\\Delta_{\\rm U_{02}}^{\\mathrm{Ti}}$ and $\\Delta_{\\rm\nU_{02}}^{\\mathrm{O}}$. In addition, the vibrational transition moments for the\nobserved vibrational transitions are reported.\n"}
+{"abstract": "  Most real world applications require dealing with stochasticity like sensor\nnoise or predictive uncertainty, where formal specifications of desired\nbehavior are inherently probabilistic. Despite the promise of formal\nverification in ensuring the reliability of neural networks, progress in the\ndirection of probabilistic specifications has been limited. In this direction,\nwe first introduce a general formulation of probabilistic specifications for\nneural networks, which captures both probabilistic networks (e.g., Bayesian\nneural networks, MC-Dropout networks) and uncertain inputs (distributions over\ninputs arising from sensor noise or other perturbations). We then propose a\ngeneral technique to verify such specifications by generalizing the notion of\nLagrangian duality, replacing standard Lagrangian multipliers with \"functional\nmultipliers\" that can be arbitrary functions of the activations at a given\nlayer. We show that an optimal choice of functional multipliers leads to exact\nverification (i.e., sound and complete verification), and for specific forms of\nmultipliers, we develop tractable practical verification algorithms.\n  We empirically validate our algorithms by applying them to Bayesian Neural\nNetworks (BNNs) and MC Dropout Networks, and certifying properties such as\nadversarial robustness and robust detection of out-of-distribution (OOD) data.\nOn these tasks we are able to provide significantly stronger guarantees when\ncompared to prior work -- for instance, for a VGG-64 MC-Dropout CNN trained on\nCIFAR-10, we improve the certified AUC (a verified lower bound on the true AUC)\nfor robust OOD detection (on CIFAR-100) from $0\\% \\rightarrow 29\\%$. Similarly,\nfor a BNN trained on MNIST, we improve on the robust accuracy from $60.2\\%\n\\rightarrow 74.6\\%$. Further, on a novel specification -- distributionally\nrobust OOD detection -- we improve the certified AUC from $5\\% \\rightarrow\n23\\%$.\n"}
+{"abstract": "  In-situ X-ray diffraction was used to investigate the structural\nrearrangements during annealing from 77 K up to the crystallization temperature\nof $\\mathit{Cu_{44}Zr_{44}Al_8Hf_2Co_2}$ bulk metallic glass rejuvenated by\nhigh pressure torsion performed at cryogenic temperatures and at room\ntemperature. The structural evolution was evaluated by dynamic mechanical\nanalysis as well as by differential scanning calorimetry to determine\nrelaxation dynamics and crystallization behaviour. Using a measure of the\nconfigurational entropy calculated from the X-ray pair correlation function the\nstructural footprint of the deformation-induced rejuvenation in bulk metallic\nglass is revealed. With synchrotron radiation temperature and time resolutions\ncomparable to calorimetric experiments are possible. This opens new\nexperimental possibilities allowing to unambiguously correlate changes in\natomic configuration and structure to calorimetrically observed signals and can\nattribute those to changes of the dynamic and vibrational relaxations in glassy\nmaterials. The results confirm that the structural footprint of the\n$\\mathit{\\beta}$-transition is related to entropic relaxation with\ncharacteristics of a first-order transition. The DMA data shows that in the\nrange of the $\\mathit{\\beta}$-transition non-reversible structural\nrearrangements are preferentially activated. The low temperature\n$\\mathit{\\gamma}$-transition is mostly triggering reversible deformations and\nshows a change of slope in the entropic footprint with second order\ncharacteristics.\n"}
+{"abstract": "  Consider the geometric range space $(X, \\mathcal{H}_d)$ where $X \\subset\n\\mathbb{R}^d$ and $\\mathcal{H}_d$ is the set of ranges defined by\n$d$-dimensional halfspaces. In this setting we consider that $X$ is the\ndisjoint union of a red and blue set. For each halfspace $h \\in \\mathcal{H}_d$\ndefine a function $\\Phi(h)$ that measures the \"difference\" between the fraction\nof red and fraction of blue points which fall in the range $h$. In this context\nthe maximum discrepancy problem is to find the $h^* = \\arg \\max_{h \\in (X,\n\\mathcal{H}_d)} \\Phi(h)$. We aim to instead find an $\\hat{h}$ such that\n$\\Phi(h^*) - \\Phi(\\hat{h}) \\le \\varepsilon$. This is the central problem in\nlinear classification for machine learning, in spatial scan statistics for\nspatial anomaly detection, and shows up in many other areas. We provide a\nsolution for this problem in $O(|X| + (1/\\varepsilon^d) \\log^4\n(1/\\varepsilon))$ time, which improves polynomially over the previous best\nsolutions. For $d=2$ we show that this is nearly tight through conditional\nlower bounds. For different classes of $\\Phi$ we can either provide a\n$\\Omega(|X|^{3/2 - o(1)})$ time lower bound for the exact solution with a\nreduction to APSP, or an $\\Omega(|X| + 1/\\varepsilon^{2-o(1)})$ lower bound for\nthe approximate solution with a reduction to 3SUM.\n  A key technical result is a $\\varepsilon$-approximate halfspace range\ncounting data structure of size $O(1/\\varepsilon^d)$ with $O(\\log\n(1/\\varepsilon))$ query time, which we can build in $O(|X| + (1/\\varepsilon^d)\n\\log^4 (1/\\varepsilon))$ time.\n"}
+{"abstract": "  In this paper we study the possibility of having a wormhole (WH) as a\ncandidate for the Sgr A$^\\star$ central object and test this idea by\nconstraining their geometry using the motion of S2 star and the reconstructed\nshadow images. In particular, we consider three WH models, including WHs in\nEinstein theory, brane-world gravity, and Einstein-Dirac-Maxwell theory. To\nthis end, we have constrained the WH throat using the motion of S2 star and\nshown that the flare out condition is satisfied. We also consider the accretion\nof infalling gas model and study the accretion rate and the intensity of the\nelectromagnetic radiation as well as the shadow images.\n"}
+{"abstract": "  Motivated by experiments on colloidal membranes composed of chiral rod-like\nviruses, we use Monte Carlo methods to determine the phase diagram for the\nliquid crystalline order of the rods and the membrane shape. We generalize the\nLebwohl-Lasher model for a nematic with a chiral coupling to a curved surface\nwith edge tension and a resistance to bending, and include an energy cost for\ntilting of the rods relative to the local membrane normal. The membrane is\nrepresented by a triangular mesh of hard beads joined by bonds, where each bead\nis decorated by a director. The beads can move, the bonds can reconnect and the\ndirectors can rotate at each Monte Carlo step. When the cost of tilt is small,\nthe membrane tends to be flat, with the rods only twisting near the edge for\nlow chiral coupling, and remaining parallel to the normal in the interior of\nthe membrane. At high chiral coupling, the rods twist everywhere, forming a\ncholesteric state. When the cost of tilt is large, the emergence of the\ncholesteric state at high values of the chiral coupling is accompanied by the\nbending of the membrane into a saddle shape. Increasing the edge tension tends\nto flatten the membrane. These results illustrate the geometric frustration\narising from the inability of a surface normal to have twist.\n"}
+{"abstract": "  Spectral gaps in the vibrational modes of disordered solids are key design\nelements in the synthesis and control of phononic metamaterials that exhibit a\nplethora of novel elastic and mechanical properties. However, reliably\nproducing these gaps often require a high degree of network specificity through\ncomplex control optimization procedures. In this work, we present as an\nadditional tool to the existing repertoire, a numerical scheme that rapidly\ngenerates sizeable spectral gaps in absence of any fine tuning of the network\nstructure or elastic parameters. These gaps occur even in disordered\npolydisperse systems consisting of relatively few particles ($N \\sim\n10^2-10^3$). Our proposed procedure exploits sticky potentials that have\nrecently been shown to suppress the formation of soft modes, thus effectively\nrecovering the linear elastic regime where band structures appear, at much\nshorter length scales than in conventional models of disordered solids. Our\napproach is relevant to design and realization of gapped spectra in a variety\nof physical setups ranging from colloidal suspensions to 3D-printed elastic\nnetworks.\n"}
+{"abstract": "  Quantum many-body systems are characterized by their correlations. While\nequal-time correlators and unequal-time commutators between operators are\nstandard observables, the direct access to unequal-time anti-commutators poses\na formidable experimental challenge. Here, we propose a general technique for\nmeasuring unequal-time anti-commutators using the linear response of a system\nto a non-Hermitian perturbation. We illustrate the protocol at the example of a\nBose-Hubbard model, where the approach to thermal equilibrium in a closed\nquantum system can be tracked by measuring both sides of the\nfluctuation-dissipation relation. We relate the scheme to the quantum Zeno\neffect and weak measurements, and illustrate possible implementations at the\nexample of a cold-atom system. Our proposal provides a way of characterizing\ndynamical correlations in quantum many-body systems with potential applications\nin understanding strongly correlated matter as well as for novel quantum\ntechnologies.\n"}
+{"abstract": "  This thesis is dedicated to study the thermodynamic properties of a\nmagnetized neutral vector boson gas at any temperature, with the aim to provide\nequations of state that allow more general and precise descriptions of\nastrophysical phenomena. The all temperature analytical expressions for the\nthermodynamic magnitudes, as well as their non relativistic limits, are\nobtained starting from the energy spectrum given by Proca's theory. With these\nexpressions, and considering the system under astrophysical conditions\n(particle densities, temperatures and magnetic fields in the order of the\nestimated for Neutron Stars), we investigate the Bose Einstein condensation,\nthe magnetic properties and the equations of state of the gas, making a special\nemphasis on the influence of antiparticles and magnetic field. In all cases,\nthe results are compared with their analogues in the low temperature and the\nnon relativistic limits. This allows us to establish the ranges of validity of\nthese approximations and to achieve a better understanding of their effects on\nthe studied system.\n"}
+{"abstract": "  In this letter, we reanalyze the multi-component strongly interacting massive\nparticle (mSIMP) scenario using an effective operator approach. As in the\nsingle-component SIMP case, the total relic abundance of mSIMP dark matter (DM)\nis determined by the coupling strengths of $3 \\to 2$ processes achieved by a\nfive-point effective operator. Intriguingly, we find that there is an\nunavoidable $2 \\to 2$ process induced by the corresponding five-point\ninteraction in the dark sector, which would reshuffle the mass densities of\nSIMP DM after the chemical freeze-out. We dub this DM scenario as reshuffled\nSIMP (rSIMP). Given this observation, we then numerically solve the coupled\nBoltzmann equations including the $3 \\to 2$ and $2 \\to 2$ processes to get the\ncorrect yields of rSIMP DM. It turns out that the masses of rSIMP DM must be\nnearly degenerate for them to contribute sizable abundances. On the other hand,\nwe also introduce effective operators to bridge the dark sector and visible\nsector via a vector portal coupling. Since the signal strength of detecting DM\nis proportional to the individual densities, thereby, obtaining the right\namount of DM particles is crucial in the rSIMP scenario. The cosmological and\ntheoretical constraints for rSIMP models are discussed as well.\n"}
+{"abstract": "  We have derived the stellar atmospheric parameters, the effective temperature\nT$_{eff}$, the microturbulent velocity $\\zeta$, the surface gravity log g, and\nthe metallicity [Fe/H] for HE 0017+0055, HE 2144-1832, HE 2339-0837, HD 145777,\nand CD-27 14351 from local thermodynamic equilibrium analyses using model\natmospheres. Elemental abundances of C, N, $\\alpha$-elements, iron-peak\nelements, and several neutron-capture elements are estimated using the\nequivalent width measurement technique as well as spectrum synthesis\ncalculations in some cases. In the context of the double enhancement observed\nin four of the programme stars, we have critically examined whether the\nliterature i-process model yields ([X/Fe]) of heavy elements can explain the\nobserved abundance distribution. The estimated metallicity [Fe/H] of the\nprogramme stars ranges from -1.63 to -2.74. All five stars show enhanced\nabundance for Ba, and four of them exhibit enhanced abundance for Eu. Based on\nour analysis, HE 0017+0055, HE 2144-1832, and HE 2339-0837 are found to be\nCEMP-r/s stars, whereas HD 145777 and CD-27 14351 show characteristic\nproperties of CEMP-s stars. From a detailed analysis of different classifiers\nof CEMP stars, using a large sample of similar stars from the literature, we\nhave identified the one which best describes the CEMP-s and CEMP-r/s stars. We\nhave also examined if [hs/ls] alone can be used as a classifier, and if there\nare any limiting values for [hs/ls] ratio that can be used to distinguish\nCEMP-s and CEMP-r/s stars. In spite of peaking at different values of [hs/ls],\nCEMP-s and CEMP-r/s stars show an overlap in the range 0.0 < [hs/ls] < 1.5 and\nhence this ratio alone can not be used to distinguish CEMP-s and CEMP-r/s\nstars.\n"}
+{"abstract": "  We present near-infrared [Fe II] images of four Class 0/I jets (HH 1/2, HH\n34, HH 111, HH 46/47) observed with the Hubble Space Telescope Wide Field\nCamera 3. The unprecedented angular resolution allows us to measure proper\nmotions, jet widths and trajectories, and extinction along the jets. In all\ncases, we detect the counter-jet which was barely visible or invisible at\nshorter wavelengths. We measure tangential velocities of a few hundred km/s,\nconsistent with previous HST measurements over 10 years ago. We measure the jet\nwidth as close as a few tens of au from the star, revealing high collimations\nof about 2 degrees for HH 1, HH 34, HH 111 and about 8 degrees for HH 46, all\nof which are preserved up to large distances. For HH 34, we find evidence of a\nlarger initial opening angle of about 7 degrees. Measurement of knot positions\nreveals deviations in trajectory of both the jet and counter-jet of all\nsources. Analysis of asymmetries in the inner knot positions for HH 111\nsuggests the presence of a low mass stellar companion at separation 20-30 au.\nFinally, we find extinction values of 15-20 mag near the source which gradually\ndecreases moving downstream along the jet. These observations have allowed us\nto study the counter-jet at unprecedented high angular resolution, and will be\na valuable reference for planning future JWST mid-infrared observations which\nwill peer even closer into the jet engine.\n"}
+{"abstract": "  Hereditary hemolytic anemias are genetic disorders that affect the shape and\ndensity of red blood cells. Genetic tests currently used to diagnose such\nanemias are expensive and unavailable in the majority of clinical labs. Here,\nwe propose a method for identifying hereditary hemolytic anemias based on a\nstandard biochemistry method, called Percoll gradient, obtained by centrifuging\na patient's blood. Our hybrid approach consists on using spatial data-driven\nfeatures, extracted with a convolutional neural network and spectral\nhandcrafted features obtained from fast Fourier transform. We compare late and\nearly feature fusion with AlexNet and VGG16 architectures. AlexNet with late\nfusion of spectral features performs better compared to other approaches. We\nachieved an average F1-score of 88% on different classes suggesting the\npossibility of diagnosing of hereditary hemolytic anemias from Percoll\ngradients. Finally, we utilize Grad-CAM to explore the spatial features used\nfor classification.\n"}
+{"abstract": "  We prove existence and uniqueness of solutions of a class of abstract fully\nnonlinear mean field game systems. We justify that such problems are related to\ncontrolled local or nonlocal diffusions, or more specifically, controlled time\nchange rates of stochastic (L\\'evy) processes. Both the system of equations and\nthe precise control interpretation seem to be new. We show that the results\napply for degenerate equations of low fractional order, and some nondegenerate\nequations, local and nonlocal.\n"}
+{"abstract": "  We obtain a sufficient condition for benign overfitting of linear regression\nproblem. Our result does not rely on concentration argument but on small-ball\nassumption and thus can holds in heavy-tailed case. The basic idea is to\nestablish a coordinate small-ball estimate in terms of effective rank so that\nwe can calibrate the balance of epsilon-Net and exponential probability. Our\nresult indicates that benign overfitting is not depending on concentration\nproperty of the input vector. Finally, we discuss potential difficulties for\nbenign overfitting beyond linear model and a benign overfitting result without\ntruncated effective rank.\n"}
+{"abstract": "  We present a simple short proof of the Fundamental Theorem of Algebra,\nwithout complex analysis and with a minimal use of topology. It can be taught\nin a first year calculus class.\n"}
+{"abstract": "  Shaped by human movement, place connectivity is quantified by the strength of\nspatial interactions among locations. For decades, spatial scientists have\nresearched place connectivity, applications, and metrics. The growing\npopularity of social media provides a new data stream where spatial social\ninteraction measures are largely devoid of privacy issues, easily assessable,\nand harmonized. In this study, we introduced a global multi-scale place\nconnectivity index (PCI) based on spatial interactions among places revealed by\ngeotagged tweets as a spatiotemporal-continuous and easy-to-implement\nmeasurement. The multi-scale PCI, demonstrated at the US county level, exhibits\na strong positive association with SafeGraph population movement records (10\npercent penetration in the US population) and Facebook's social connectedness\nindex (SCI), a popular connectivity index based on social networks. We found\nthat PCI has a strong boundary effect and that it generally follows the\ndistance decay, although this force is weaker in more urbanized counties with a\ndenser population. Our investigation further suggests that PCI has great\npotential in addressing real-world problems that require place connectivity\nknowledge, exemplified with two applications: 1) modeling the spatial spread of\nCOVID-19 during the early stage of the pandemic and 2) modeling hurricane\nevacuation destination choice. The methodological and contextual knowledge of\nPCI, together with the launched visualization platform and open-sourced PCI\ndatasets at various geographic levels, are expected to support research fields\nrequiring knowledge in human spatial interactions.\n"}
+{"abstract": "  The well known phenomenon of exponential contraction for solutions to the\nviscous Hamilton-Jacobi equation in the space-periodic setting is based on the\nMarkov mechanism. However, the corresponding Lyapunov exponent $\\lambda(\\nu)$\ncharacterizing the exponential rate of contraction depends on the viscosity\n$\\nu$. The Markov mechanism provides only a lower bound for $\\lambda(\\nu)$\nwhich vanishes in the limit $\\nu \\to 0$. At the same time, in the inviscid case\n$\\nu=0$ one also has exponential contraction based on a completely different\ndynamical mechanism. This mechanism is based on hyperbolicity of\naction-minimizing orbits for the related Lagrangian variational problem.\n  In this paper we consider the discrete time case (kicked forcing), and\nestablish a uniform lower bound for $\\lambda(\\nu)$ which is valid for all\n$\\nu\\geq 0$. The proof is based on a nontrivial interplay between the dynamical\nand Markov mechanisms for exponential contraction. We combine PDE methods with\nthe ideas from the Weak KAM theory.\n"}
+{"abstract": "  Let $U\\not\\equiv \\pm\\infty$ be a $\\delta$-subharmonic function on a closed\ndisc of radius $R$ centered at zero. In the previous two parts of our paper, we\nobtained general and explicit estimates of the integral of the positive part of\nthe radial maximum growth characteristic ${\\mathsf M}_U(t):=\n\\sup\\bigl\\{U(z)\\bigm| |z|=r\\bigr\\}$ over the increasing integration function\n$m$ on the segment $[0, r]\\subset [0,R)$ through the difference characteristic\nof Nevanlinna and the quantities associated with the integration function $m$.\nThe third part of our paper contains estimates of these quantities in terms of\nthe Hausdorff $h$-measure and $h$-content of compact subset $S\\subset [0, r]$\nsuch that the integration function $m$ is constant on each open component of\nthe connectivity of the complement $[0, r]\\setminus S$. The case of the\nd-dimensional Hausdorff measure is highlighted separately.\n"}
+{"abstract": "  Many software engineering tasks, such as testing, and anomaly detection can\nbenefit from the ability to infer a behavioral model of the software.Most\nexisting inference approaches assume access to code to collect execution\nsequences. In this paper, we investigate a black-box scenario, where the system\nunder analysis cannot be instrumented, in this granular fashion.This scenario\nis particularly prevalent with control systems' log analysis in the form of\ncontinuous signals. In this situation, an execution trace amounts to a\nmultivariate time-series of input and output signals, where different states of\nthe system correspond to different `phases` in the time-series. The main\nchallenge is to detect when these phase changes take place. Unfortunately, most\nexisting solutions are either univariate, make assumptions on the data\ndistribution, or have limited learning power.Therefore, we propose a hybrid\ndeep neural network that accepts as input a multivariate time series and\napplies a set of convolutional and recurrent layers to learn the non-linear\ncorrelations between signals and the patterns over time.We show how this\napproach can be used to accurately detect state changes, and how the inferred\nmodels can be successfully applied to transfer-learning scenarios, to\naccurately process traces from different products with similar execution\ncharacteristics. Our experimental results on two UAV autopilot case studies\nindicate that our approach is highly accurate (over 90% F1 score for state\nclassification) and significantly improves baselines (by up to 102% for change\npoint detection).Using transfer learning we also show that up to 90% of the\nmaximum achievable F1 scores in the open-source case study can be achieved by\nreusing the trained models from the industrial case and only fine tuning them\nusing as low as 5 labeled samples, which reduces the manual labeling effort by\n98%.\n"}
+{"abstract": "  The simultaneous advancement of high resolution integral field unit\nspectroscopy and robust full-spectral fitting codes now make it possible to\nexamine spatially-resolved kinematic, chemical composition, and star-formation\nhistory from nearby galaxies. We take new MUSE data from the Snapshot Optical\nSpectroscopic Imaging of Mergers and Pairs for Legacy Exploration (SOSIMPLE)\nsurvey to examine NGC 7135. With counter-rotation of gas, disrupted kinematics\nand asymmetric chemical distribution, NGC 7135 is consistent with an ongoing\nmerger. Though well hidden by the current merger, we are able to distinguish\nstars originating from an older merger, occurring 6-10 Gyr ago. We further find\na gradient in ex-situ material with galactocentric radius, with the accreted\nfraction rising from 0% in the galaxy centre, to ~7% within 0.6 effective\nradii.\n"}
+{"abstract": "  Ultra Diffuse Galaxies (UDGs), a type of large Low Surface Brightness (LSB)\ngalaxies with particularly large effective radii (r_eff > 1.5 kpc), are now\nroutinely studied in the local (z<0.1) universe. While they are found to be\nabundant in clusters, groups, and in the field, their formation mechanisms\nremain elusive and an active topic of debate. New insights may be found by\nstudying their counterparts at higher redshifts (z>1.0), even though\ncosmological surface brightness dimming makes them particularly diffcult to\ndetect and study there. This work uses the deepest Hubble Space Telescope (HST)\nimaging stacks of z > 1 clusters, namely: SPT-CL J2106-5844 and MOO J1014+0038.\nThese two clusters, at z=1.13 and z=1.23, were monitored as part of the HST\nSee-Change program. Compared to the Hubble Extreme Deep Field (XDF) as\nreference field, we find statistical over-densities of large LSB galaxies in\nboth clusters. Based on stellar population modelling and assuming no size\nevolution, we find that the faintest sources we can detect are about as bright\nas expected for the progenitors of the brightest local UDGs. We find that the\nLSBs we detect in SPT-CL J2106-5844 and MOO J1014-5844 already have old stellar\npopulations that place them on the red sequence. Correcting for incompleteness,\nand based on an extrapolation of local scaling relations, we estimate that\ndistant UDGs are relatively under-abundant compared to local UDGs by a factor ~\n3. A plausible explanation for the implied increase with time would be a\nsignificant size growth of these galaxies in the last ~ 8 Gyr, as also\nsuggested by hydrodynamical simulations.\n"}
+{"abstract": "  In this work, 1272 superflares on 311 stars are collected from 22,539\nsolar-type stars from the second-year observation of Transiting Exoplanet\nSurvey Satellite (TESS), which almost covered the northern hemisphere of the\nsky. Three superflare stars contain hot Jupiter candidates or ultrashort-period\nplanet candidates. We obtain $\\gamma = -1.76\\pm 0.11$ of the correlation\nbetween flare frequency and flare energy ($dN/dE\\propto E^{-\\gamma}$) for all\nsuperflares and get $\\beta=0.42\\pm0.01$ of the correlation between superflare\nduration and energy ($T_{\\text {duration }} \\propto E^{\\beta}$), which supports\nthat a similar mechanism is shared by stellar superflares and solar flares.\nStellar photometric variability ($R_{\\rm var}$) is estimated for all solar-type\nstars, and the relation of $E\\propto {R_{\\rm var}}^{3/2}$ is included. An\nindicator of chromospheric activity ($S$-index) is obtained by using data from\nthe Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) for 7454\nsolar-type stars. Distributions of these two properties indicate that the Sun\nis generally less active than superflare stars. We find that saturation-like\nfeature of $R_{\\rm var}\\sim 0.1$ may be the reason for superflare energy\nsaturating around $10^{36}$ erg. Object TIC 93277807 was captured by the TESS\nfirst-year mission and generated the most energetic superflare. This superflare\nis valuable and unique that can be treated as an extreme event, which may be\ngenerated by different mechanisms rather than other superflares.\n"}
+{"abstract": "  A number of challenges of the standard $\\Lambda$CDM model has been emerging\nduring the past few years as the accuracy of cosmological observations\nimproves. In this review we discuss in a unified manner many existing signals\nin cosmological and astrophysical data that appear to be in some tension\n($2\\sigma$ or larger) with the standard $\\Lambda$CDM model as defined by the\nPlanck18 parameter values. In addition to the major well studied $5\\sigma$\nchallenge of $\\Lambda$CDM (the Hubble $H_0$ crisis) and other well known\ntensions (the growth tension and the lensing amplitude $A_L$ anomaly), we\ndiscuss a wide range of other less discussed less-standard signals which appear\nat a lower statistical significance level than the $H_0$ tension (also known as\n'curiosities' in the data) which may also constitute hints towards new physics.\nFor example such signals include cosmic dipoles (the fine structure constant\n$\\alpha$, velocity and quasar dipoles), CMB asymmetries, BAO Ly$\\alpha$\ntension, age of the Universe issues, the Lithium problem, small scale\ncuriosities like the core-cusp and missing satellite problems, quasars Hubble\ndiagram, oscillating short range gravity signals etc. The goal of this\npedagogical review is to collectively present the current status of these\nsignals and their level of significance, with emphasis to the Hubble crisis and\nrefer to recent resources where more details can be found for each signal. We\nalso briefly discuss possible theoretical approaches that can potentially\nexplain the non-standard nature of some of these signals.\n"}
+{"abstract": "  We test the theoretical free energy surface (FES) for two-step nucleation\n(TSN) proposed by Iwamatsu [J. Chem. Phys. 134, 164508 (2011)] by comparing the\npredictions of the theory to numerical results for the FES recently reported\nfrom Monte Carlo simulations of TSN in a simple lattice system [James, et al.,\nJ. Chem. Phys. 150, 074501 (2019)]. No adjustable parameters are used to make\nthis comparison. That is, all the parameters of the theory are evaluated\ndirectly for the model system, yielding a predicted FES which we then compare\nto the FES obtained from simulations. We find that the theoretical FES\nsuccessfully predicts the numerically-evaluated FES over a range of\nthermodynamic conditions that spans distinct regimes of behavior associated\nwith TSN. All the qualitative features of the FES are captured by the theory\nand the quantitative comparison is also very good. Our results demonstrate that\nIwamatsu's extension of classical nucleation theory provides an excellent\nframework for understanding the thermodynamics of TSN.\n"}
+{"abstract": "  For solving large-scale consistent linear system, we combine two efficient\nrow index selection strategies with Kaczmarz-type method with oblique\nprojection, and propose a greedy randomized Kaczmarz method with oblique\nprojection (GRKO) and the maximal weighted residual Kaczmarz method with\noblique projection (MWRKO) . Through those method, the number of iteration\nsteps and running time can be reduced to a greater extent to find the\nleast-norm solution, especially when the rows of matrix A are close to linear\ncorrelation. Theoretical proof and numerical results show that GRKO method and\nMWRKO method are more effective than greedy randomized Kaczmarz method and\nmaximal weighted residual Kaczmarz method respectively.\n"}
+{"abstract": "  We present the new software OpDiLib, a universal add-on for classical\noperator overloading AD tools that enables the automatic differentiation (AD)\nof OpenMP parallelized code. With it, we establish support for OpenMP features\nin a reverse mode operator overloading AD tool to an extent that was previously\nonly reported on in source transformation tools. We achieve this with an\nevent-based implementation ansatz that is unprecedented in AD. Combined with\nmodern OpenMP features around OMPT, we demonstrate how it can be used to\nachieve differentiation without any additional modifications of the source\ncode; neither do we impose a priori restrictions on the data access patterns,\nwhich makes OpDiLib highly applicable. For further performance optimizations,\nrestrictions like atomic updates on the adjoint variables can be lifted in a\nfine-grained manner for any parts of the code. OpDiLib can also be applied in a\nsemi-automatic fashion via a macro interface, which supports compilers that do\nnot implement OMPT. In a detailed performance study, we demonstrate the\napplicability of OpDiLib for a pure operator overloading approach in a hybrid\nparallel environment. We quantify the cost of atomic updates on the adjoint\nvector and showcase the speedup and scaling that can be achieved with the\ndifferent configurations of OpDiLib in both the forward and the reverse pass.\n"}
+{"abstract": "  We propose a new mechanism to communicate between fermion dark matter and the\nStandard Model (SM) only through the four-form flux. The four-form couplings\nare responsible for the relaxation of the Higgs mass to the correct value and\nthe initial displacement of the reheating pseudo-scalar field from the minimum.\nWe show that the simultaneous presence of the pseudo-scalar coupling to fermion\ndark matter and the flux-induced Higgs mixing gives rise to unsuppressed\nannihilations of dark matter into the SM particles at present, whereas the\ndirect detection bounds from XENON1T can be avoided. We suggest exploring the\ninteresting bulk parameter space of the model for which dark matter annihilates\ndominantly into a pair of singlet-like scalars with similar mass as for dark\nmatter.\n"}
+{"abstract": "  As it is said by Van Gogh, great things are done by a series of small things\nbrought together. Aesthetic experience arises from the aggregation of\nunderlying visual components. However, most existing deep image aesthetic\nassessment (IAA) methods over-simplify the IAA process by failing to model\nimage aesthetics with clearly-defined visual components as building blocks. As\na result, the connection between resulting aesthetic predictions and underlying\nvisual components is mostly invisible and hard to be explicitly controlled,\nwhich limits the model in both performance and interpretability. This work aims\nto model image aesthetics from the level of visual components. Specifically,\nobject-level regions detected by a generic object detector are defined as\nvisual components, namely object-level visual components (OVCs). Then generic\nfeatures representing OVCs are aggregated for the aesthetic prediction based\nupon proposed object-level and graph attention mechanisms, which dynamically\ndetermines the importance of individual OVCs and relevance between OVC pairs,\nrespectively. Experimental results confirm the superiority of our framework\nover previous relevant methods in terms of SRCC and PLCC on the aesthetic\nrating distribution prediction. Besides, quantitative analysis is done towards\nmodel interpretation by observing how OVCs contribute to aesthetic predictions,\nwhose results are found to be supported by psychology on aesthetics and\nphotography rules. To the best of our knowledge, this is the first attempt at\nthe interpretation of a deep IAA model.\n"}
+{"abstract": "  A recent line of work has shown that end-to-end optimization of Bayesian\nfilters can be used to learn state estimators for systems whose underlying\nmodels are difficult to hand-design or tune, while retaining the core\nadvantages of probabilistic state estimation. As an alternative approach for\nstate estimation in these settings, we present an end-to-end approach for\nlearning state estimators modeled as factor graph-based smoothers. By unrolling\nthe optimizer we use for maximum a posteriori inference in these probabilistic\ngraphical models, we can learn probabilistic system models in the full context\nof an overall state estimator, while also taking advantage of the distinct\naccuracy and runtime advantages that smoothers offer over recursive filters. We\nstudy this approach using two fundamental state estimation problems, object\ntracking and visual odometry, where we demonstrate a significant improvement\nover existing baselines. Our work comes with an extensive code release, which\nincludes training and evaluation scripts, as well as Python libraries for Lie\ntheory and factor graph optimization:\nhttps://sites.google.com/view/diffsmoothing/\n"}
+{"abstract": "  Spatial relations between objects in an image have proved useful for\nstructural object recognition. Structural constraints can act as regularization\nin neural network training, improving generalization capability with small\ndatasets. Several relations can be modeled as a morphological dilation of a\nreference object with a structuring element representing the semantics of the\nrelation, from which the degree of satisfaction of the relation between another\nobject and the reference object can be derived. However, dilation is not\ndifferentiable, requiring an approximation to be used in the context of\ngradient-descent training of a network. We propose to approximate dilations\nusing convolutions based on a kernel equal to the structuring element. We show\nthat the proposed approximation, even if slightly less accurate than previous\napproximations, is definitely faster to compute and therefore more suitable for\ncomputationally intensive neural network applications.\n"}
+{"abstract": "  The present study examines to what extent cultural background determines\nsensorimotor synchronization in humans\n"}
+{"abstract": "  Understanding how the magnetic activity of low-mass stars depends on their\nfundamental parameters is an important goal of stellar astrophysics. Previous\nstudies show that activity levels are largely determined by the stellar Rossby\nnumber which is defined as the rotation period divided by the convective\nturnover time. However, we currently have little information on the role that\nchemical composition plays. In this work, we investigate how metallicity\naffects magnetic activity using photometric variability as an activity proxy.\nSimilarly to other proxies, we demonstrate that the amplitude of photometric\nvariability is well parameterised by the Rossby number, although in a more\ncomplex way. We also show that variability amplitude and metallicity are\ngenerally positively correlated. This trend can be understood in terms of the\neffect that metallicity has on stellar structure and, hence, the convective\nturnover time (or, equivalently, the Rossby number). Lastly, we demonstrate\nthat the metallicity dependence of photometric variability results in a\nrotation period detection bias whereby the periods of metal-rich stars are more\neasily recovered for stars of a given mass.\n"}
+{"abstract": "  Hadamard matrices in $\\{0,1\\}$ presentation are square $m\\times m$ matrices\nwhose entries are zeros and ones and whose rows considered as vectors in $\\Bbb\nR^m$ produce the Gram matrix of a special form with respect to the standard\nscalar product in $\\Bbb R^m$. The concept of Hadamard matrices is extended in\nthe present paper. As a result pseudo-Hadamard matrices of the first generation\nare defined and investigated. An algorithm for generating these pseudo-Hadamard\nmatrices is designed and is used for testing some conjectures.\n"}
+{"abstract": "  In this paper we investigate a novel set of polarizing agents --\nmixed-valence compounds -- by theoretical and experimental methods and\ndemonstrate their performance in high-field Dynamic Nuclear Polarization (DNP)\nexperiments in the solid state. Mixed-valence compounds constitute a group of\nmolecules, in which molecular mobility persists even in solids. Consequently,\nsuch polarizing agents can be used to perform Overhauser-DNP experiments in\nsolid-state, with favorable conditions for dynamic nuclear polarization\nformation at ultra-high magnetic fields.\n"}
+{"abstract": "  Parafermions are a natural generalization of Majorana fermions. We consider a\nbreathing Kagome lattice with complex hoppings by imposing $\\mathbb{Z}_{3}$\nclock symmetry in the complex energy plane. It is a non-Hermitian\ngeneralization of the second-order topological insulator characterized by the\nemergence of topological corner states. We demonstrate that the topological\ncorner states are parafermions in the present $\\mathbb{Z}_{3}$ clock-symmetric\nmodel. It is also shown that the model is realized in electric circuits\nproperly designed, where the parafermion corner states are observed by\nimpedance resonance. We also construct $\\mathbb{Z}_{4}$ and $\\mathbb{Z}_{6}$\nparafermions on breathing square and honeycomb lattices, respectively.\n"}
+{"abstract": "  Code review is a widely-used practice in software development companies to\nidentify defects. Hence, code review has been included in many software\nengineering curricula at universities worldwide. However, teaching code review\nis still a challenging task because the code review effectiveness depends on\nthe code reading and analytical skills of a reviewer. While several studies\nhave investigated the code reading techniques that students should use to find\ndefects during code review, little has focused on a learning activity that\ninvolves analytical skills. Indeed, developing a code review checklist should\nstimulate students to develop their analytical skills to anticipate potential\nissues (i.e., software defects). Yet, it is unclear whether students can\nanticipate potential issues given their limited experience in software\ndevelopment (programming, testing, etc.). We perform a qualitative analysis to\ninvestigate whether students are capable of creating code review checklists,\nand if the checklists can be used to guide reviewers to find defects. In\naddition, we identify common mistakes that students make when developing a code\nreview checklist. Our results show that while there are some misconceptions\namong students about the purpose of code review, students are able to\nanticipate potential defects and create a relatively good code review\nchecklist. Hence, our results lead us to conclude that developing a code review\nchecklist can be a part of the learning activities for code review in order to\nscaffold students' skills.\n"}
+{"abstract": "  Level 5 autonomy for self-driving cars requires a robust visual perception\nsystem that can parse input images under any visual condition. However,\nexisting semantic segmentation datasets are either dominated by images captured\nunder normal conditions or are small in scale. To address this, we introduce\nACDC, the Adverse Conditions Dataset with Correspondences for training and\ntesting semantic segmentation methods on adverse visual conditions. ACDC\nconsists of a large set of 4006 images which are equally distributed between\nfour common adverse conditions: fog, nighttime, rain, and snow. Each\nadverse-condition image comes with a high-quality fine pixel-level semantic\nannotation, a corresponding image of the same scene taken under normal\nconditions, and a binary mask that distinguishes between intra-image regions of\nclear and uncertain semantic content. Thus, ACDC supports both standard\nsemantic segmentation and the newly introduced uncertainty-aware semantic\nsegmentation. A detailed empirical study demonstrates the challenges that the\nadverse domains of ACDC pose to state-of-the-art supervised and unsupervised\napproaches and indicates the value of our dataset in steering future progress\nin the field. Our dataset and benchmark are publicly available.\n"}
+{"abstract": "  For the validation of safety-critical systems regarding safety and comfort,\ne.g., in the context of automated driving, engineers often have to cope with\nlarge (parametric) test spaces for which it is infeasible to test through all\npossible parameter configurations. At the same time, critical behavior of a\nwell-engineered system with respect to prescribed safety and comfort\nrequirements tends to be extremely rare, speaking of probabilities of order\n$10^{-6}$ or less, but clearly has to be examined carefully for valid\nargumentation. Hence, common approaches such as boundary value analysis are\ninsufficient while methods based on random sampling from the parameter space\n(simple Monte Carlo) lack the ability to detect these rare critical events\nefficiently, i.e., with appropriate simulation budget. For this reason, a more\nsophisticated simulation-based approach is proposed which employs optimistic\noptimization on an objective function called \"criticality\" in order to identify\neffectively the set of critical parameter configurations. Within the scope of\nthe ITEA 3 TESTOMAT project (http://www.testomatproject.eu/) the collaboration\npartners OFFIS e.V. and AKKA Germany GmbH conducted a case study on applying\ncriticality-based rare event simulation to the charging process of an\nautomotive battery management system given as a model. The present technical\nreport documents the industrial use case, the approach, application and\nexperimental results, as well as lessons learned from the case study.\n"}
+{"abstract": "  Homonym identification is important for WSD that require coarse-grained\npartitions of senses. The goal of this project is to determine whether\ncontextual information is sufficient for identifying a homonymous word. To\ncapture the context, BERT embeddings are used as opposed to Word2Vec, which\nconflates senses into one vector. SemCor is leveraged to retrieve the\nembeddings. Various clustering algorithms are applied to the embeddings.\nFinally, the embeddings are visualized in a lower-dimensional space to\nunderstand the feasibility of the clustering process.\n"}
+{"abstract": "  Quantum computers are a leading platform for the simulation of many-body\nphysics. This task has been recently facilitated by the possibility to program\ndirectly the time-dependent pulses sent to the computer. Here, we use this\nfeature to simulate quantum lattice models with long-range hopping. Our\napproach is based on an exact mapping between periodically driven quantum\nsystems and one-dimensional lattices in the synthetic Floquet direction. By\nengineering a periodic drive with a power-law spectrum, we simulate a lattice\nwith long-range hopping, whose decay exponent is freely tunable. We propose and\nrealize experimentally two protocols to probe the long tails of the Floquet\neigenfunctions and to identify a scaling transition between weak and strong\nlong-range couplings. Our work offers a useful benchmark of pulse engineering\nand opens the route towards quantum simulations of rich nonequilibrium effects.\n"}
+{"abstract": "  A technique is presented, which creates MCB junctions that can be pivoted to\nany desirable angle. The MCB junction equipped with a specific glass liquid\ncell can be used to produce a MCB junction, of which the electrodes are covered\nwith a microscopic layer of fluid, thus producing a partially wet phase MCB\njunction.\n"}
+{"abstract": "  We report the results of the first search for the decay $B_s^0 \\rightarrow\n\\eta^\\prime \\eta$ using $121.4~\\textrm{fb}^{-1}$ of data collected at the\n$\\Upsilon(5S)$ resonance with the Belle detector at the KEKB asymmetric-energy\n$e^+e^-$ collider. We observe no significant signal and set a 90\\%\nconfidence-level upper limit of %$7.1 \\times 10^{-5}$ $6.5 \\times 10^{-5}$ on\nthe branching fraction of this decay.\n"}
+{"abstract": "  Path planning is a key component in mobile robotics. A wide range of path\nplanning algorithms exist, but few attempts have been made to benchmark the\nalgorithms holistically or unify their interface. Moreover, with the recent\nadvances in deep neural networks, there is an urgent need to facilitate the\ndevelopment and benchmarking of such learning-based planning algorithms. This\npaper presents PathBench, a platform for developing, visualizing, training,\ntesting, and benchmarking of existing and future, classical and learned 2D and\n3D path planning algorithms, while offering support for Robot Oper-ating System\n(ROS). Many existing path planning algorithms are supported; e.g. A*,\nwavefront, rapidly-exploring random tree, value iteration networks, gated path\nplanning networks; and integrating new algorithms is easy and clearly\nspecified. We demonstrate the benchmarking capability of PathBench by comparing\nimplemented classical and learned algorithms for metrics, such as path length,\nsuccess rate, computational time and path deviation. These evaluations are done\non built-in PathBench maps and external path planning environments from video\ngames and real world databases. PathBench is open source.\n"}
+{"abstract": "  This paper studies a dynamical system, which contains two contours. There is\na cluster on each contour. The cluster contains particles, located in adjacent\ncells. The clusters move under prescribed rules. The delays of clusters are due\nto that the clusters cannot pass through the node simultaneously. The dynamical\nsystem belongs to the class of contour networks introduced by A. P. Buslaev.\n"}
+{"abstract": "  We adapt and extend Yosida's parametrix method, originally introduced for the\nconstruction of the fundamental solution to a parabolic operator on a\nRiemannian manifold, to derive Varadhan-type asymptotic estimates for the\ntransition density of a degenerate diffusion under the weak H\\\"ormander\ncondition. This diffusion process, widely studied by Yor in a series of papers,\nfinds direct application in the study of a class of path-dependent financial\nderivatives known as Asian options. We obtain the Varadhan formula\n\\begin{equation} \\frac{-2 \\log p(t,x;T,y) } { \\Psi(t,x;T,y) } \\to 1, \\qquad\n\\text{as } \\quad T-t \\to 0^+, \\end{equation} where $p$ denotes the transition\ndensity and $\\Psi$ denotes the optimal cost function of a deterministic control\nproblem associated to the diffusion. We provide a partial proof of this\nformula, and present numerical evidence to support the validity of an\nintermediate inequality that is required to complete the proof. We also derive\nan asymptotic expansion of the cost function $\\Psi$, expressed in terms of\nelementary functions, which is useful in order to design efficient\napproximation formulas for the transition density.\n"}
+{"abstract": "  Investigating how the cutoff energy $E_{\\rm cut}$ varies with X-ray flux and\nphoton index $\\Gamma$ in individual AGNs opens a new window to probe the yet\nunclear coronal physics. So far $E_{\\rm cut}$ variations have only been\ndetected in several AGNs but different patterns have been reported. Here we\nreport new detections of $E_{\\rm cut}$ variations in two Seyfert galaxies with\nmultiple NuSTAR exposures. While in NGC 3227 $E_{\\rm cut}$ monotonically\nincreases with $\\Gamma$, the $E_{\\rm cut}$-$\\Gamma$ relation exhibits a\n$\\Lambda$ shape in SWIFT J2127.4+5654 ($E_{\\rm cut}$ increasing with $\\Gamma$\nat $\\Gamma$ $\\lesssim$ 2.05, but reversely decreasing at $\\Gamma$ $\\gtrsim$\n2.05), indicating more than a single underlying mechanism is involved.\nMeanwhile both galaxies show softer spectra while they brighten in X-ray, a\ncommon phenomenon in Seyfert galaxies. Plotting all 7 AGNs with $E_{\\rm cut}$\nvariations ever reported with NuSTAR observations in the $E_{\\rm cut}$-$\\Gamma$\ndiagram, we find they could be unified with the $\\Lambda$ pattern. Although the\nsample is small and SWIFT J2127.4+5654 is the only source with $\\Gamma$ varying\nacross the break point thus the only one exhibiting the complete $\\Lambda$\npattern in a single source, the discoveries shed new light on the coronal\nphysics in AGNs. Possible underlying physical mechanisms are discussed.\n"}
+{"abstract": "  We consider exact and averaged control problem for a system of quasi-linear\nODEs and SDEs with a non-negative definite symmetric matrix of the system. The\nstrategy of the proof is the standard linearization of the system by fixing the\nfunction appearing in the nonlinear part of the system, and then applying the\nLeray-Schauder fixed point theorem. We shall also need the continuous induction\narguments to prolong the control to the final state which is a novel approach\nin the field. This enables us to obtain controllability for arbitrarily large\ninitial data (so called global controllability).\n"}
+{"abstract": "  A common challenge across all areas of machine learning is that training data\nis not distributed like test data, due to natural shifts, \"blind spots,\" or\nadversarial examples; such test examples are referred to as out-of-distribution\n(OOD) test examples. We consider a model where one may abstain from predicting,\nat a fixed cost. In particular, our transductive abstention algorithm takes\nlabeled training examples and unlabeled test examples as input, and provides\npredictions with optimal prediction loss guarantees. The loss bounds match\nstandard generalization bounds when test examples are i.i.d. from the training\ndistribution, but add an additional term that is the cost of abstaining times\nthe statistical distance between the train and test distribution (or the\nfraction of adversarial examples). For linear regression, we give a\npolynomial-time algorithm based on Celis-Dennis-Tapia optimization algorithms.\nFor binary classification, we show how to efficiently implement it using a\nproper agnostic learner (i.e., an Empirical Risk Minimizer) for the class of\ninterest. Our work builds on a recent abstention algorithm of Goldwasser,\nKalais, and Montasser (2020) for transductive binary classification.\n"}
+{"abstract": "  Helical symmetry of massive Dirac fermions is broken explicitly in the\npresence of electric and magnetic fields. Here we present two equations for the\ndivergence of helical and axial-vector currents following the Jackiw-Johnson\napproach to the anomaly of the neutral axial vector current. We discover the\ncontribution from the helical symmetry breaking is attributed to the occupancy\nof the two states at the top of the valence band and the bottom of the\nconduction band. The explicit symmetry breaking fully cancels the anomalous\ncorrection from the quantum fluctuation in the band gap. The chiral anomaly can\nbe derived from the helical symmetry breaking. It provides an alternative route\nto understand the chiral anomaly from the point of view of the helical symmetry\nbreaking. The pertinent physical consequences in condensed matter are the\nhelical magnetic effect which means a charge current circulating at the\ndirection of the magnetic field, and the mass-dependent positive longitudinal\nmagnetoconductivity as a transport signature. The discovery not only reflects\nanomalous magneto-transport properties of massive Dirac materials but also\nreveals the close relation between the helical symmetry breaking and the\nphysics of chiral anomaly in quantum field theory and high energy physics.\n"}
+{"abstract": "  The study deals with the methods and means of checking the reliability of\nusernames of online communities on the basis of computer-linguistic analysis of\nthe results of their communicative interaction. The methodological basis of the\nstudy is a combination of general scientific methods and special approaches to\nthe study of the data verification of online communities in the Ukrainian\nsegment of the global information environment. The algorithm of functioning of\nthe utility Verifier of online community username is developed. The\ninformational model of the automated means of checking the usernames of online\ncommunity is designed. The utility Verifier of online community username data\nvalidation system approbation is realized in the online community. The\nindicator of the data verification system effectiveness is determined.\n"}
+{"abstract": "  Deep learning is a powerful approach with good performance on many different\ntasks. However, these models often require massive computational resources. It\nis a worrying trend that we increasingly need models that work well on more\ncomplex problems. In this paper, we propose and verify the effectiveness and\nefficiency of SCNN, an innovative neural network inspired by the swarm concept.\nIn addition to introducing the relevant theories, our detailed experiments\nsuggest that fewer parameters may perform better than models with more\nparameters. Besides, our experiments show that SCNN needs less data than\ntraditional models. That could be an essential hint for problems where there is\nnot much data.\n"}
+{"abstract": "  FO transductions, aperiodic deterministic two-way transducers, as well as\naperiodic streaming string transducers are all equivalent models for first\norder definable functions. In this paper, we solve the long standing open\nproblem of expressions capturing first order definable functions, thereby\ngeneralizing the seminal SF=AP (star free expressions = aperiodic languages)\nresult of Sch\\\"utzenberger. Our result also generalizes a lesser known\ncharacterization by Sch\\\"utzenberger of aperiodic languages by SD-regular\nexpressions (SD=AP). We show that every first order definable function over\nfinite words captured by an aperiodic deterministic two-way transducer can be\ndescribed with an SD-regular transducer expression (SDRTE). An SDRTE is a\nregular expression where Kleene stars are used in a restricted way: they can\nappear only on aperiodic languages which are prefix codes of bounded\nsynchronization delay. SDRTEs are constructed from simple functions using the\ncombinators unambiguous sum (deterministic choice), Hadamard product, and\nunambiguous versions of the Cauchy product and the k-chained Kleene-star, where\nthe star is restricted as mentioned. In order to construct an SDRTE associated\nwith an aperiodic deterministic two-way transducer, (i) we concretize\nSch\\\"utzenberger's SD=AP result, by proving that aperiodic languages are\ncaptured by SD-regular expressions which are unambiguous and stabilising; (ii)\nby structural induction on the unambiguous, stabilising SD-regular expressions\ndescribing the domain of the transducer, we construct SDRTEs. Finally, we also\nlook at various formalisms equivalent to SDRTEs which use the function\ncomposition, allowing to trade the k-chained star for a 1-star.\n"}
+{"abstract": "  Cassegrain designs can be used to build thin lenses. We analyze the\nrelationships between system thickness and aperture sizes of the two mirrors as\nwell as FoV size. Our analysis shows that decrease in lens thickness imposes\ntight constraint on the aperture and FoV size. To mitigate this limitation, we\npropose to fill the gaps between the primary and the secondary with high index\nmaterial. The Gassegrain optics cuts the track length into half and high index\nmaterial reduces ray angle and height, consequently the incident ray angle can\nbe increased, i.e., the FoV angle is extended. Defining telephoto ratio as the\nratio of lens thickness to focal length, we achieve telephoto ratios as small\nas 0.43 for a visible Cassegrain thin lens and 1.20 for an infrared Cassegrain\nthin lens. To achieve an arbitrary FoV coverage, we present an strategy by\nintegrating multiple thin lenses on one plane with each unit covering a\ndifferent FoV region. To avoid physically tilting each unit, we propose beam\nsteering with metasurface. By image stitching, we obtain wide FoV images.\n"}
+{"abstract": "  Deep neural networks have been widely studied in autonomous driving\napplications such as semantic segmentation or depth estimation. However,\ntraining a neural network in a supervised manner requires a large amount of\nannotated labels which are expensive and time-consuming to collect. Recent\nstudies leverage synthetic data collected from a virtual environment which are\nmuch easier to acquire and more accurate compared to data from the real world,\nbut they usually suffer from poor generalization due to the inherent domain\nshift problem. In this paper, we propose a Domain-Agnostic Contrastive Learning\n(DACL) which is a two-stage unsupervised domain adaptation framework with\ncyclic adversarial training and contrastive loss. DACL leads the neural network\nto learn domain-agnostic representation to overcome performance degradation\nwhen there exists a difference between training and test data distribution. Our\nproposed approach achieves better performance in the monocular depth estimation\ntask compared to previous state-of-the-art methods and also shows effectiveness\nin the semantic segmentation task.\n"}
+{"abstract": "  Class imbalance is an inherent problem in many machine learning\nclassification tasks. This often leads to trained models that are unusable for\nany practical purpose. In this study we explore an unsupervised approach to\naddress these imbalances by leveraging transfer learning from pre-trained image\nclassification models to encoder-based Generative Adversarial Network (eGAN).\nTo the best of our knowledge, this is the first work to tackle this problem\nusing GAN without needing to augment with synthesized fake images.\n  In the proposed approach we use the discriminator network to output a\nnegative or positive score. We classify as minority, test samples with negative\nscores and as majority those with positive scores. Our approach eliminates\nepistemic uncertainty in model predictions, as the P(minority) + P(majority)\nneed not sum up to 1. The impact of transfer learning and combinations of\ndifferent pre-trained image classification models at the generator and\ndiscriminator is also explored. Best result of 0.69 F1-score was obtained on\nCIFAR-10 classification task with imbalance ratio of 1:2500.\n  Our approach also provides a mechanism of thresholding the specificity or\nsensitivity of our machine learning system. Keywords: Class imbalance, Transfer\nLearning, GAN, nash equilibrium\n"}
+{"abstract": "  Continuous-depth neural models, where the derivative of the model's hidden\nstate is defined by a neural network, have enabled strong sequential data\nprocessing capabilities. However, these models rely on advanced numerical\ndifferential equation (DE) solvers resulting in a significant overhead both in\nterms of computational cost and model complexity. In this paper, we present a\nnew family of models, termed Closed-form Continuous-depth (CfC) networks, that\nare simple to describe and at least one order of magnitude faster while\nexhibiting equally strong modeling abilities compared to their ODE-based\ncounterparts. The models are hereby derived from the analytical closed-form\nsolution of an expressive subset of time-continuous models, thus alleviating\nthe need for complex DE solvers all together. In our experimental evaluations,\nwe demonstrate that CfC networks outperform advanced, recurrent models over a\ndiverse set of time-series prediction tasks, including those with long-term\ndependencies and irregularly sampled data. We believe our findings open new\nopportunities to train and deploy rich, continuous neural models in\nresource-constrained settings, which demand both performance and efficiency.\n"}
+{"abstract": "  We examine the dynamics of electron beams that, in free space, are\nself-accelerating, in the presence of an additional magnetic field. We focus\nour attention in the case of Airy beams that follow parabolic trajectories and\nin generalized classes of beams associated with power-law trajectories. We\nstudy the interplay between beam self-acceleration and the circular motion\ncaused by the magnetic field. In the case of Airy beams, using an integral\nrepresentation, we find closed-form solutions for the electron wavefunction. We\nalso derive asymptotic formulas for the beam trajectories both for Airy beams\nand for self-accelerating power-law beams. A ray optics description is rather\nuseful for the interpretation of the beam dynamics. Our results are in\nexcellent comparison with direct numerical simulations.\n"}
+{"abstract": "  We report on a Python-toolbox for unbiased statistical analysis of\nfluorescence intermittency properties of single emitters. Intermittency, i.e.,\nstep-wise temporal variations in the instantaneous emission intensity and\nfluorescence decay rate properties are common to organic fluorophores, II-VI\nquantum dots and perovskite quantum dots alike. Unbiased statistical analysis\nof intermittency switching time distributions, involved levels and lifetimes is\nimportant to avoid interpretation artefacts. This work provides an\nimplementation of Bayesian changepoint analysis and level clustering applicable\nto time-tagged single-photon detection data of single emitters that can be\napplied to real experimental data and as tool to verify the ramifications of\nhypothesized mechanistic intermittency models. We provide a detailed Monte\nCarlo analysis to illustrate these statistics tools, and to benchmark the\nextent to which conclusions can be drawn on the photophysics of highly complex\nsystems, such as perovskite quantum dots that switch between a plethora of\nstates instead of just two.\n"}
+{"abstract": "  Self-adaptive software systems continuously adapt in response to internal and\nexternal changes in their execution environment, captured as contexts. The COP\nparadigm posits a technique for the development of self-adaptive systems,\ncapturing their main characteristics with specialized programming language\nconstructs. COP adaptations are specified as independent modules composed in\nand out of the base system as contexts are activated and deactivated in\nresponse to sensed circumstances from the surrounding environment. However, the\ndefinition of adaptations, their contexts and associated specialized behavior,\nneed to be specified at design time. In complex CPS this is intractable due to\nnew unpredicted operating conditions. We propose Auto-COP, a new technique to\nenable generation of adaptations at run time. Auto-COP uses RL options to build\naction sequences, based on the previous instances of the system execution.\nOptions are explored in interaction with the environment, and the most suitable\noptions for each context are used to generate adaptations exploiting COP. To\nvalidate Auto-COP, we present two case studies exhibiting different system\ncharacteristics and application domains: a driving assistant and a robot\ndelivery system. We present examples of Auto-COP code generated at run time, to\nillustrate the types of circumstances (contexts) requiring adaptation, and the\ncorresponding generated adaptations for each context. We confirm that the\ngenerated adaptations exhibit correct system behavior measured by\ndomain-specific performance metrics, while reducing the number of required\nexecution/actuation steps by a factor of two showing that the adaptations are\nregularly selected by the running system as adaptive behavior is more\nappropriate than the execution of primitive actions.\n"}
+{"abstract": "  We consider second order differential equations with real coefficients that\nare in the limit circle case at infinity. Using the semiclassical Ansatz, we\nconstruct solutions (the Jost solutions) of such equations with a prescribed\nasymptotic behavior for $x\\to\\infty$. It turns out that in the limit circle\ncase, this Ansatz can be chosen common for all values of the spectral parameter\n$z$. This leads to asymptotic formulas for all solutions of considered\ndifferential equations, both homogeneous and non-homogeneous. We also\nefficiently describe all self-adjoint realizations of the corresponding\ndifferential operators in terms of boundary conditions at infinity and\n  find a representation for their resolvents.\n"}
+{"abstract": "  In order to detect unknown intrusions and runtime errors of computer\nprograms, the cyber-security community has developed various detection\ntechniques. Anomaly detection is an approach that is designed to profile the\nnormal runtime behavior of computer programs in order to detect intrusions and\nerrors as anomalous deviations from the observed normal. However, normal but\nunobserved behavior can trigger false positives. This limitation has\nsignificantly decreased the practical viability of anomaly detection\ntechniques. Reported approaches to this limitation span a simple alert\nthreshold definition to distribution models for approximating all normal\nbehavior based on the limited observation. However, each assumption or\napproximation poses the potential for even greater false positive rates. This\npaper presents our study on how to explain the presence of anomalies using a\nneural network, particularly Long Short-Term Memory, independent of actual data\ndistributions. We present and compare three anomaly detection models, and\nreport on our experience running different types of attacks on an Apache\nHypertext Transfer Protocol server. We performed a comparative study, focusing\non each model's ability to detect the onset of each attack while avoiding false\npositives resulting from unknown normal behavior. Our best-performing model\ndetected the true onset of every attack with zero false positives.\n"}
+{"abstract": "  Weakly coupled ferroelectric/dielectric superlattice thin film\nheterostructures exhibit complex nanoscale polarization configurations that\narise from a balance of competing electrostatic, elastic, and domain-wall\ncontributions to the free energy. A key feature of these configurations is that\nthe polarization can locally have a significant component that is not along the\nthin-film surface normal direction, while maintaining zero net in-plane\npolarization. PbTiO3/SrTiO3 thin-film superlattice heterostructures on a\nconducting SrRuO3 bottom electrode on SrTiO3 have a room-temperature stripe\nnanodomain pattern with nanometer-scale lateral period. Ultrafast time-resolved\nx-ray free electron laser diffraction and scattering experiments reveal that\nabove-bandgap optical pulses induce rapidly propagating acoustic pulses and a\nperturbation of the domain diffuse scattering intensity arising from the\nnanoscale stripe domain configuration. With 400 nm optical excitation, two\nseparate acoustic pulses are observed: a high-amplitude pulse resulting from\nstrong optical absorption in the bottom electrode and a weaker pulse arising\nfrom the depolarization field screening effect due to absorption directly\nwithin the superlattice. The picosecond scale variation of the nanodomain\ndiffuse scattering intensity is consistent with a larger polarization change\nthan would be expected due to the polarization-tetragonality coupling of\nuniformly polarized ferroelectrics. The polarization change is consistent\ninstead with polarization rotation facilitated by the reorientation of the\nin-plane component of the polarization at the domain boundaries of the striped\npolarization structure. The complex steady-state configuration within these\nferroelectric heterostructures leads to polarization rotation phenomena that\nhave been previously available only through the selection of bulk crystal\ncomposition.\n"}
+{"abstract": "  We introduce a novel method for the implementation of shape optimziation in\nfluid dynamics applications, where we propose to use the shape derivative to\ndetermine deformation fields with the help of the $p-$ Laplacian for $p > 2$.\nThis approach is closely related to the computation of steepest descent\ndirections of the shape functional in the $W^{1,\\infty}-$ topology. Our\napproach is demonstrated for shape optimization related to drag-minimal free\nfloating bodies. The method is validated against existing approaches with\nrespect to convergence of the optimization algorithm, the obtained shape, and\nregarding the quality of the computational grid after large deformations. Our\nnumerical results strongly indicate that shape optimization related to the\n$W^{1,\\infty}$-topology -- though numerically more demanding -- seems to be\nsuperior over the classical approaches invoking Hilbert space methods,\nconcerning the convergence, the obtained shapes and the mesh quality after\nlarge deformations, in particular when the optimal shape features sharp\ncorners.\n"}
+{"abstract": "  We consider a Nicholson's equation with multiple pairs of time-varying delays\nand nonlinear terms given by mixed monotone functions. Sufficient conditions\nfor the permanence, local stability and global attractivity of its positive\nequilibrium are established. Our criteria depend on the size of some delays,\nimprove results in recent literature and provide answers to some open problems.\n"}
+{"abstract": "  Underactuated systems like sea vessels have degrees of motion that are\ninsufficiently matched by a set of independent actuation forces. In addition,\nthe underlying trajectory-tracking control problems grow in complexity in order\nto decide the optimal rudder and thrust control signals. This enforces several\ndifficult-to-solve constraints that are associated with the error dynamical\nequations using classical optimal tracking and adaptive control approaches. An\nonline machine learning mechanism based on integral reinforcement learning is\nproposed to find a solution for a class of nonlinear tracking problems with\npartial prior knowledge of the system dynamics. The actuation forces are\ndecided using innovative forms of temporal difference equations relevant to the\nvessel's surge and angular velocities. The solution is implemented using an\nonline value iteration process which is realized by employing means of the\nadaptive critics and gradient descent approaches. The adaptive learning\nmechanism exhibited well-functioning and interactive features in react to\ndifferent desired reference-tracking scenarios.\n"}
+{"abstract": "  The inverse problem of finding the optimal network structure for a specific\ntype of dynamical process stands out as one of the most challenging problems in\nnetwork science. Focusing on the susceptible-infected-susceptible type of\ndynamics on annealed networks whose structures are fully characterized by the\ndegree distribution, we develop an analytic framework to solve the inverse\nproblem. We find that, for relatively low or high infection rates, the optimal\ndegree distribution is unique, which consists of no more than two distinct\nnodal degrees. For intermediate infection rates, the optimal degree\ndistribution is multitudinous and can have a broader support. We also find\nthat, in general, the heterogeneity of the optimal networks decreases with the\ninfection rate. A surprising phenomenon is the existence of a specific value of\nthe infection rate for which any degree distribution would be optimal in\ngenerating maximum spreading prevalence. The analytic framework and the\nfindings provide insights into the interplay between network structure and\ndynamical processes with practical implications.\n"}
+{"abstract": "  Quantum entanglement between two or more bipartite entities is a core concept\nin quantum information areas limited to microscopic regimes directly governed\nby Heisenberg uncertainty principle via quantum superposition, resulting in\nnondeterministic and probabilistic quantum features. Such quantum features\ncannot be generated by classical means. Here, a pure classical method of\non-demand entangled light-pair generation is presented in a macroscopic regime\nvia basis randomness. This conflicting idea of conventional quantum mechanics\ninvokes a fundamental question about both classicality and quantumness, where\nsuperposition is key to its resolution.\n"}
+{"abstract": "  In this paper we consider the problem of transmission power allocation for\nremote estimation of a dynamical system in the case where the estimator is able\nto simultaneously receive packets from multiple interfering sensors, as it is\npossible e.g. with the latest wireless technologies such as 5G and WiFi. To\nthis end we introduce a general model where packet arrival probabilities are\ndetermined based on the received Signal-to-Interference-and-Noise Ratio and\nwith two different receivers design schemes, one implementing standard\nmulti-packet reception technique and one implementing Successive Interference\nCancellation decoding algorithm in addition. Then we cast the power allocation\nproblem as an optimization task where the mean error covariance at the remote\nestimator is minimized, while penalizing the mean transmission power\nconsumption. For the infinite-horizon problem we show the existence of a\nstationary optimal policy, while for the finite-horizon case we derive some\nstructural properties under the special scenario where the overall system to be\nestimated can be seen as a set of independent subsystems. Numerical simulations\nillustrate the improvement given by the proposed receivers over orthogonal\nschemes that schedules only one sensor transmission at a time in order to avoid\ninterference.\n"}
+{"abstract": "  Cloud-Radio Access Networks (Cloud-RANs) are separating the mobile networks\nbase station functions into three units, the connection between the two of them\nis referred to as the fronthaul network. This work demonstrates the\ntransmission of user data transport blocks between the distributed Medium\nAccess Control (MAC) layer and local Physical (PHY) layer in the radiounit over\na Passive Optical Network (PON). PON networks provide benefits in terms of\neconomy and flexibility when used for Cloud-RAN fronthaul transport. However,\nthe PON upstream scheduling can introduce additional latency that might not\nsatisfy the requirements imposed by Cloud-RAN functional split. In this work we\ndemonstrate how our virtual Dynamic Bandwidth Allocation(DBA) concept can be\nused to effectively communicate with the mobile Long Term Evolution (LTE)\nscheduler, adopting the well known cooperative DBA mechanism, to reduce the PON\nlatency to satisfactory values. Thus, our results show the feasibility ofusing\nPON technology as transport medium of the fronthaul for the MAC/PHY functional\nsplit, in a fully virtualised environment.Further background traffic is added,\nso that measurements show a more realistic scenario. The obtained round trip\ntimes indicates that using PON at fronthaul might be limited to the distance of\n11km for a synchronised scenario, or no compliance for a non-synchronised\nscenario.\n"}
+{"abstract": "  This paper studied the faint, diffuse extended X-ray emission associated with\nthe radio lobes and the hot gas in the intracluster medium (ICM) environment\nfor a sample of radio galaxies. We used shallow ($\\sim 10$ ks) archival Chandra\nobservations for 60 radio galaxies (7 FR I and 53 FR II) with $0.0222 \\le z \\le\n1.785$ selected from the 298 extragalactic radio sources identified in the 3CR\ncatalog. We used Bayesian statistics to look for any asymmetry in the extended\nX-ray emission between regions that contain the radio lobes and regions that\ncontain the hot gas in the ICM. In the Chandra broadband ($0.5 - 7.0$ keV),\nwhich has the highest detected X-ray flux and the highest signal-to-noise\nratio, we found that the non-thermal X-ray emission from the radio lobes\ndominates the thermal X-ray emission from the environment for $\\sim 77\\%$ of\nthe sources in our sample. We also found that the relative amount of on-jet\naxis non-thermal emission from the radio lobes tends to increase with redshift\ncompared to the off-jet axis thermal emission from the environment. This\nsuggests that the dominant X-ray mechanism for the non-thermal X-ray emission\nin the radio lobes is due to the inverse Compton upscattering of cosmic\nmicrowave background (CMB) seed photons by relativistic electrons in the radio\nlobes, a process for which the observed flux is roughly redshift independent\ndue to the increasing CMB energy density with increasing redshift.\n"}
+{"abstract": "  We investigate what can be learned about a population of distant KBOs by\nstudying the statistical properties of their light curves. Whereas others have\nsuccessfully inferred the properties of individual, highly variable KBOs, we\nshow that the fraction of KBOs with low amplitudes also provides fundamental\ninformation about a population. Each light curve is primarily the result of two\nfactors: shape and orientation. We consider contact binaries and ellipsoidal\nshapes, with and without flattening. After developing the mathematical\nframework, we apply it to the existing body of KBO light curve data. Principal\nconclusions are as follows. (1) When using absolute magnitude H as a proxy for\nsize, it is more accurate to use the maximum of the light curve rather than the\nmean. (2) Previous investigators have noted that smaller KBOs have\nhigher-amplitude light curves, and have interpreted this as evidence that they\nare systematically more irregular in shape than larger KBOs; we show that a\npopulation of flattened bodies with uniform proportions could also explain this\nresult. (3) Our analysis indicates that prior assessments of the fraction of\ncontact binaries in the Kuiper Belt may be artificially low. (4) The pole\norientations of some KBOs can be inferred from observed changes in their light\ncurves; however, these KBOs constitute a biased sample, whose pole orientations\nare not representative of the population overall. (5) Although surface\ntopography, albedo patterns, limb darkening, and other surface properties can\naffect individual light curves, they do not have a strong influence on the\nstatistics overall. (6) Photometry from the OSSOS survey is incompatible with\nprevious results and its statistical properties defy easy interpretation. We\nalso discuss the promise of this approach for the analysis of future, much\nlarger data sets such as the one anticipated from the Rubin Observatory.\n"}
+{"abstract": "  We study the relation (and differences) between stability and Property (S) in\nthe simple and stably finite framework. This leads us to characterize stable\nelements in terms of its support, and study these concepts from different sides\n: hereditary subalgebras, projections in the multiplier algebra and order\nproperties in the Cuntz semigroup. We use these approaches to show both that\ncancellation at infinity on the Cuntz semigroup just holds when its Cuntz\nequivalence is given by isomorphism at the level of Hilbert right-modules, and\nthat different notions as Regularity, $\\omega$-comparison, Corona Factorization\nProperty, property R, etc.. are equivalent under mild assumptions.\n"}
+{"abstract": "  Engineered dynamical maps that combine not only coherent, but also unital and\ndissipative transformations of quantum states, have demonstrated a number of\ntechnological applications, and promise to be a beneficial tool also in quantum\nthermodynamic processes. Here, we exploit control of a spin qutrit to\ninvestigate energy exchange fluctuations of an open quantum system. The qutrit\nengineer dynamics can be understood as an autonomous feedback process, where\nrandom measurement events condition the subsequent dissipative evolution. To\nanalyze this dynamical process, we introduce a generalization of the\nSagawa-Ueda-Tasaki relation for dissipative dynamics and verify it\nexperimentally. Not only we characterize the efficacy of the autonomous\nfeedback protocol, but also find that the characteristic function of energy\nvariations $G(\\eta)$ becomes insensitive to the process details at a single\nspecific value of its argument. This allows us to demonstrate that a\nfluctuation theorem of the Jarzynski type holds for this general dissipative\nfeedback dynamics, while previous relations were limited to unital dynamics.\nMoreover, in addition to the feedback efficacy, we find a witness of unitality\nassociated with the fixed point of the dynamics.\n"}
+{"abstract": "  We present high-pressure electrical transport measurements on the newly\ndiscovered V-based superconductors $A$V$_3$Sb$_5$ ($A$ = Rb and K), which have\nan ideal Kagome lattice of vanadium. Two superconducting domes under pressure\nare observed in both compounds, as previously observed in their sister compound\nCsV$_3$Sb$_5$. For RbV$_3$Sb$_5$, the $T_c$ increases from 0.93 K at ambient\npressure to the maximum of 4.15 K at 0.38 GPa in the first dome. The second\nsuperconducting dome has the highest $T_c$ of 1.57 K at 28.8 GPa. KV$_3$Sb$_5$\ndisplays a similar double-dome phase diagram, however, its two maximum $T_c$s\nare lower, and the $T_c$ drops faster in the second dome than RbV$_3$Sb$_5$. An\nintegrated temperature-pressure phase diagram of $A$V$_3$Sb$_5$ ($A$ = Cs, Rb\nand K) is constructed, showing that the ionic radius of the intercalated\nalkali-metal atoms has a significant effect. Our work demonstrates that\ndouble-dome superconductivity under pressure is a common feature of these\nV-based Kagome metals.\n"}
+{"abstract": "  We present a new algorithm for efficiently computing the $N$-point\ncorrelation functions (NPCFs) of a 3D density field for arbitrary $N$. This can\nbe applied both to a discrete spectroscopic galaxy survey and a continuous\nfield. By expanding the statistics in a separable basis of isotropic functions\nbuilt from spherical harmonics, the NPCFs can be estimated by counting pairs of\nparticles in space, leading to an algorithm with complexity $\\mathcal{O}(N_{\\rm\ng}^2)$ for $N_{\\rm g}$ particles, or $\\mathcal{O}\\left(N_\\mathrm{FFT}\\log\nN_\\mathrm{FFT}\\right)$ when using a Fast Fourier Transform with\n$N_\\mathrm{FFT}$ grid-points. In practice, the rate-limiting step for $N>3$\nwill often be the summation of the histogrammed spherical harmonic\ncoefficients, particularly if the number of radial and angular bins is large.\nIn this case, the algorithm scales linearly with $N_{\\rm g}$. The approach is\nimplemented in the ENCORE code, which can compute the 3PCF, 4PCF, 5PCF, and\n6PCF of a BOSS-like galaxy survey in $\\sim$ $100$ CPU-hours, including the\ncorrections necessary for non-uniform survey geometries. We discuss the\nimplementation in depth, along with its GPU acceleration, and provide practical\ndemonstration on realistic galaxy catalogs. Our approach can be\nstraightforwardly applied to current and future datasets to unlock the\npotential of constraining cosmology from the higher-point functions.\n"}
+{"abstract": "  We unveil the microscopic origin of the topologically ordered counterpart of\nthe s-wave superconductor in this work. For this, we employ the recently\ndeveloped unitary renormalisation group (URG) method on a generalised model of\n2D electrons attractive interactions. The effective Hamiltonian obtained at the\nstable low-energy fixed point of the RG flow corresponds to a gapped,\ninsulating state of quantum matter we call the Cooper pair insulator (CPI). We\nshow that the CPI ground state manifold displays several signatures of\ntopological order, including a four-fold degeneracy when placed on the torus.\nSpectral flow arguments reveal the emergent gauge-theoretic structure of the\neffective Hamiltonian, as it can be written entirely in terms of non-local\nWilson loops. It also contains a topological $\\theta$-term whose coefficient is\nquantised, in keeping with the requirement of invariance of the ground state\nunder large gauge transformations. We find that the long-ranged many-particle\nentanglement content of the CPI ground state is driven by inter-helicity\ntwo-particle scattering processes. Analysis of the passage from CPI to BCS\nsuperconducting ground state shows the RG flow promotes fluctuations in the\nnumber of condensed Cooper pairs and lowers those in the conjugate global\nphase. Consequently, the distinct signatures of long-ranged entanglement in the\nCPI are replaced by the well-known short-ranged entanglement of the BCS state.\nFinally, we study the renormalisation of the entanglement in $k$-space for both\nthe CPI and BCS ground states. The topologically ordered CPI state is shown to\npossess an emergent hierarchy of scales of entanglement, and that this\nhierarchy collapses in the BCS state. Our work offers clear evidence for the\nmicroscopic origins of topological order in this prototypical system, and lays\nthe foundation for similar investigations in other systems of correlated\nelectrons.\n"}
+{"abstract": "  In this paper, we develop a compositional vector-based semantics of positive\ntransitive sentences in quantum natural language processing for a non-English\nlanguage, i.e. Persian, to compare the parametrized quantum circuits of two\nsynonymous sentences in two languages, English and Persian. By considering\ngrammar+meaning of a transitive sentence, we translate DisCoCat diagram via\nZX-calculus into quantum circuit form. Also, we use a bigraph method to rewrite\nDisCoCat diagram and turn into quantum circuit in the semantic side.\n"}
+{"abstract": "  This paper details the FPGA implementation methodology for Convolutional\nSpiking Neural Networks (CSNN) and applies this methodology to low-power\nradioisotope identification using high-resolution data. Power consumption of 75\nmW has been achieved on an FPGA implementation of a CSNN, with an inference\naccuracy of 90.62% on a synthetic dataset. The chip validation method is\npresented. Prototyping was accelerated by evaluating SNN parameters using\nSpiNNaker neuromorphic platform.\n"}
+{"abstract": "  We present a comprehensive review of the structural chemistry of hybrid lead\nhalides of stoichiometry APbX4, A2PbX4 or AAPbX4, where A and A are organic\nammonium cations and X = Cl, Br or I. These compounds may be considered as\nlayered perovskites, containing isolated, infinite layers of corner-sharing\nPbX4 octahedra separated by the organic species. We first extract over 250\ncrystal structures from the CCDC and classify them in terms of unit cell\nmetrics and crystal symmetry. Symmetry mode analysis is then used to identify\nthe nature of key structural distortions of the [PbX4] layers. Two generic\ntypes of distortion are prevalent in this family: tilting of the octahedral\nunits and shifts of the inorganic layers relative to each other. Although the\noctahedral tilting modes are well-known in the crystallography of purely\ninorganic perovskites, the additional layer shift modes are shown to enrich\nenormously the structural options available in layered hybrid perovskites. Some\nexamples and trends are discussed in more detail in order to show how the\nnature of the interlayer organic species can influence the overall structural\narchitecture, although the main aim of the paper is to encourage workers in the\nfield to make use of the systematic crystallographic methods used here to\nfurther understand and rationalise their own compounds, and perhaps to be able\nto design-in particular structural features in future work.\n"}
+{"abstract": "  Many different types of fractional calculus have been proposed, which can be\norganised into some general classes of operators. For a unified mathematical\ntheory, results should be proved in the most general possible setting. Two\nimportant classes of fractional-calculus operators are the fractional integrals\nand derivatives with respect to functions (dating back to the 1970s) and those\nwith general analytic kernels (introduced in 2019). To cover both of these\nsettings in a single study, we can consider fractional integrals and\nderivatives with analytic kernels with respect to functions, which have never\nbeen studied in detail before. Here we establish the basic properties of these\ngeneral operators, including series formulae, composition relations, function\nspaces, and Laplace transforms. The tools of convergent series, from fractional\ncalculus with analytic kernels, and of operational calculus, from fractional\ncalculus with respect to functions, are essential ingredients in the analysis\nof the general class that covers both.\n"}
+{"abstract": "  The twin group $TW_n$ on $n$ strands is the group generated by $t_1, \\dots,\nt_{n-1}$ with defining relations $t_i^2=1$, $t_it_j = t_jt_i$ if $|i-j|>1$. We\nfind a new instance of semisimple Schur--Weyl duality for tensor powers of a\nnatural $n$-dimensional reflection representation of $TW_n$, depending on a\nparameter $q$. At $q=1$ the representation coincides with the natural\npermutation representation of the symmetric group, so the new Schur--Weyl\nduality may be regarded as a $q$-analogue of the one motivating the definition\nof the partition algebra.\n"}
+{"abstract": "  Developing theoretical models for nonequilibrium quantum systems poses\nsignificant challenges. Here we develop and study a multimode model of a\ndriven-dissipative Josephson junction chain of atomic Bose-Einstein\ncondensates, as realised in the experiment of Labouvie et al. [Phys. Rev. Lett.\n116, 235302 (2016)]. The model is based on c-field theory, a beyond-mean-field\napproach to Bose-Einstein condensates that incorporates fluctuations due to\nfinite temperature and dissipation. We find the c-field model is capable of\ncapturing all key features of the nonequilibrium phase diagram, including\nbistability and a critical slowing down in the lower branch of the bistable\nregion. Our model is closely related to the so-called Lugiato-Lefever equation,\nand thus establishes new connections between nonequilibrium dynamics of\nultracold atoms with nonlinear optics, exciton-polariton superfluids, and\ndriven damped sine-Gordon systems.\n"}
+{"abstract": "  Motivated by applications in unsourced random access, this paper develops a\nnovel scheme for the problem of compressed sensing of binary signals. In this\nproblem, the goal is to design a sensing matrix $A$ and a recovery algorithm,\nsuch that the sparse binary vector $\\mathbf{x}$ can be recovered reliably from\nthe measurements $\\mathbf{y}=A\\mathbf{x}+\\sigma\\mathbf{z}$, where $\\mathbf{z}$\nis additive white Gaussian noise. We propose to design $A$ as a parity check\nmatrix of a low-density parity-check code (LDPC), and to recover $\\mathbf{x}$\nfrom the measurements $\\mathbf{y}$ using a Markov chain Monte Carlo algorithm,\nwhich runs relatively fast due to the sparse structure of $A$. The performance\nof our scheme is comparable to state-of-the-art schemes, which use dense\nsensing matrices, while enjoying the advantages of using a sparse sensing\nmatrix.\n"}
+{"abstract": "  Few-shot class-incremental learning (FSCIL) aims to design machine learning\nalgorithms that can continually learn new concepts from a few data points,\nwithout forgetting knowledge of old classes. The difficulty lies in that\nlimited data from new classes not only lead to significant overfitting issues\nbut also exacerbate the notorious catastrophic forgetting problems. Moreover,\nas training data come in sequence in FSCIL, the learned classifier can only\nprovide discriminative information in individual sessions, while FSCIL requires\nall classes to be involved for evaluation. In this paper, we address the FSCIL\nproblem from two aspects. First, we adopt a simple but effective decoupled\nlearning strategy of representations and classifiers that only the classifiers\nare updated in each incremental session, which avoids knowledge forgetting in\nthe representations. By doing so, we demonstrate that a pre-trained backbone\nplus a non-parametric class mean classifier can beat state-of-the-art methods.\nSecond, to make the classifiers learned on individual sessions applicable to\nall classes, we propose a Continually Evolved Classifier (CEC) that employs a\ngraph model to propagate context information between classifiers for\nadaptation. To enable the learning of CEC, we design a pseudo incremental\nlearning paradigm that episodically constructs a pseudo incremental learning\ntask to optimize the graph parameters by sampling data from the base dataset.\nExperiments on three popular benchmark datasets, including CIFAR100,\nminiImageNet, and Caltech-USCD Birds-200-2011 (CUB200), show that our method\nsignificantly outperforms the baselines and sets new state-of-the-art results\nwith remarkable advantages.\n"}
+{"abstract": "  Instance contrast for unsupervised representation learning has achieved great\nsuccess in recent years. In this work, we explore the idea of instance\ncontrastive learning in unsupervised domain adaptation (UDA) and propose a\nnovel Category Contrast technique (CaCo) that introduces semantic priors on top\nof instance discrimination for visual UDA tasks. By considering instance\ncontrastive learning as a dictionary look-up operation, we construct a\nsemantics-aware dictionary with samples from both source and target domains\nwhere each target sample is assigned a (pseudo) category label based on the\ncategory priors of source samples. This allows category contrastive learning\n(between target queries and the category-level dictionary) for\ncategory-discriminative yet domain-invariant feature representations: samples\nof the same category (from either source or target domain) are pulled closer\nwhile those of different categories are pushed apart simultaneously. Extensive\nUDA experiments in multiple visual tasks ($e.g.$, segmentation, classification\nand detection) show that the simple implementation of CaCo achieves superior\nperformance as compared with the highly-optimized state-of-the-art methods.\nAnalytically and empirically, the experiments also demonstrate that CaCo is\ncomplementary to existing UDA methods and generalizable to other learning\nsetups such as semi-supervised learning, unsupervised model adaptation, etc.\n"}
+{"abstract": "  This paper presents the first wireless and programmable neural stimulator\nleveraging magnetoelectric (ME) effects for power and data transfer. Thanks to\nlow tissue absorption, low misalignment sensitivity and high power transfer\nefficiency, the ME effect enables safe delivery of high power levels (a few\nmilliwatts) at low resonant frequencies (~250 kHz) to mm-sized implants deep\ninside the body (30-mm depth). The presented MagNI (Magnetoelectric Neural\nImplant) consists of a 1.5-mm$^2$ 180-nm CMOS chip, an in-house built 4x2 mm ME\nfilm, an energy storage capacitor, and on-board electrodes on a flexible\npolyimide substrate with a total volume of 8.2 mm$^3$ . The chip with a power\nconsumption of 23.7 $\\mu$W includes robust system control and data recovery\nmechanisms under source amplitude variations (1-V variation tolerance). The\nsystem delivers fully-programmable bi-phasic current-controlled stimulation\nwith patterns covering 0.05-to-1.5-mA amplitude, 64-to-512-$\\mu$s pulse width,\nand 0-to-200Hz repetition frequency for neurostimulation.\n"}
+{"abstract": "  Robot Assisted Therapy is a new paradigm in many therapies such as the\ntherapy of children with autism spectrum disorder. In this paper we present the\nuse of a parrot-like robot as an assistive tool in turn taking therapy. The\ntherapy is designed in the form of a card game between a child with autism and\na therapist or the robot. The intervention was implemented in a single subject\nstudy format and the effect sizes for different turn taking variables are\ncalculated. The results show that the child robot interaction had larger effect\nsize than the child trainer effect size in most of the turn taking variables.\nFurthermore the therapist point of view on the proposed Robot Assisted Therapy\nis evaluated using a questionnaire. The therapist believes that the robot is\nappealing to children which may ease the therapy process. The therapist\nsuggested to add other functionalities and games to let children with autism to\nlearn more turn taking tasks and better generalize the learned tasks\n"}
+{"abstract": "  We present a new suite of atmosphere models with flexible cloud parameters to\ninvestigate the effects of clouds on brown dwarfs across the L/T transition. We\nfit these models to a sample of 13 objects with well-known masses, distances,\nand spectral types spanning L3-T5. Our modelling is guided by\nspatially-resolved photometry from the Hubble Space Telescope and the W. M.\nKeck Telescopes covering visible to near-infrared wavelengths. We find that,\nwith appropriate cloud parameters, the data can be fit well by atmospheric\nmodels with temperature and surface gravity in agreement with the predictions\nof evolutionary models. We see a clear trend in the cloud parameters with\nspectral type, with earlier-type objects exhibiting higher-altitude clouds with\nsmaller grains (0.25-0.50 micron) and later-type objects being better fit with\ndeeper clouds and larger grains ($\\geq$1 micron). Our results confirm previous\nwork that suggests L dwarfs are dominated by submicron particles, whereas T\ndwarfs have larger particle sizes.\n"}
+{"abstract": "  Herein, design of false data injection attack on a distributed cyber-physical\nsystem is considered. A stochastic process with linear dynamics and Gaussian\nnoise is measured by multiple agent nodes, each equipped with multiple sensors.\nThe agent nodes form a multi-hop network among themselves. Each agent node\ncomputes an estimate of the process by using its sensor observation and\nmessages obtained from neighboring nodes, via Kalman-consensus filtering. An\nexternal attacker, capable of arbitrarily manipulating the sensor observations\nof some or all agent nodes, injects errors into those sensor observations. The\ngoal of the attacker is to steer the estimates at the agent nodes as close as\npossible to a pre-specified value, while respecting a constraint on the attack\ndetection probability. To this end, a constrained optimization problem is\nformulated to find the optimal parameter values of a certain class of linear\nattacks. The parameters of linear attack are learnt on-line via a combination\nof stochastic approximation based update of a Lagrange multiplier, and an\noptimization technique involving either the Karush-Kuhn-Tucker (KKT) conditions\nor online stochastic gradient descent. The problem turns out to be convex for\nsome special cases. Desired convergence of the proposed algorithms are proved\nby exploiting the convexity and properties of stochastic approximation\nalgorithms. Finally, numerical results demonstrate the efficacy of the attack.\n"}
+{"abstract": "  Population protocols are a fundamental model in distributed computing, where\nmany nodes with bounded memory and computational power have random pairwise\ninteractions over time. This model has been studied in a rich body of\nliterature aiming to understand the tradeoffs between the memory and time\nneeded to perform computational tasks.\n  We study the population protocol model focusing on the communication\ncomplexity needed to achieve consensus with high probability. When the number\nof memory states is $s = O(\\log \\log{n})$, the best upper bound known was given\nby a protocol with $O(n \\log{n})$ communication, while the best lower bound was\n$\\Omega(n \\log(n)/s)$ communication.\n  We design a protocol that shows the lower bound is sharp. When each agent has\n$s=O(\\log{n}^{\\theta})$ states of memory, with $\\theta \\in (0,1/2)$, consensus\ncan be reached in time $O(\\log(n))$ with $O(n \\log{(n)}/s)$ communications with\nhigh probability.\n"}
+{"abstract": "  We show that nonparaxial polarized light beams propagating in a bulk\nnonlinear Kerr medium naturally exhibit a coupling between the motional and the\npolarization degrees of freedom, realizing a spin-orbit-coupled mixture of\nfluids of light. We investigate the impact of this mechanism on the Bogoliubov\nmodes of the fluid, using a suitable density-phase formalism built upon a\nlinearization of the exact Helmholtz equation. The Bogoliubov spectrum is found\nto be anisotropic, and features both low-frequency gapless branches and\nhigh-frequency gapped ones. We compute the amplitudes of these modes and\npropose a couple of experimental protocols to study their excitation\nmechanisms. This allows us to highlight a phenomenon of hybridization between\ndensity and spin modes, which is absent in the paraxial description and\nrepresents a typical fingerprint of spin-orbit coupling.\n"}
+{"abstract": "  Model-based reinforcement learning (RL) is more sample efficient than\nmodel-free RL by using imaginary trajectories generated by the learned dynamics\nmodel. When the model is inaccurate or biased, imaginary trajectories may be\ndeleterious for training the action-value and policy functions. To alleviate\nsuch problem, this paper proposes to adaptively reweight the imaginary\ntransitions, so as to reduce the negative effects of poorly generated\ntrajectories. More specifically, we evaluate the effect of an imaginary\ntransition by calculating the change of the loss computed on the real samples\nwhen we use the transition to train the action-value and policy functions.\nBased on this evaluation criterion, we construct the idea of reweighting each\nimaginary transition by a well-designed meta-gradient algorithm. Extensive\nexperimental results demonstrate that our method outperforms state-of-the-art\nmodel-based and model-free RL algorithms on multiple tasks. Visualization of\nour changing weights further validates the necessity of utilizing reweight\nscheme.\n"}
+{"abstract": "  We present a learning-based planner that aims to robustly drive a vehicle by\nmimicking human drivers' driving behavior. We leverage a mid-to-mid approach\nthat allows us to manipulate the input to our imitation learning network\nfreely. With that in mind, we propose a novel feedback synthesizer for data\naugmentation. It allows our agent to gain more driving experience in various\npreviously unseen environments that are likely to encounter, thus improving\noverall performance. This is in contrast to prior works that rely purely on\nrandom synthesizers. Furthermore, rather than completely commit to imitating,\nwe introduce task losses that penalize undesirable behaviors, such as\ncollision, off-road, and so on. Unlike prior works, this is done by introducing\na differentiable vehicle rasterizer that directly converts the waypoints output\nby the network into images. This effectively avoids the usage of heavyweight\nConvLSTM networks, therefore, yields a faster model inference time. About the\nnetwork architecture, we exploit an attention mechanism that allows the network\nto reason critical objects in the scene and produce better interpretable\nattention heatmaps. To further enhance the safety and robustness of the\nnetwork, we add an optional optimization-based post-processing planner\nimproving the driving comfort. We comprehensively validate our method's\neffectiveness in different scenarios that are specifically created for\nevaluating self-driving vehicles. Results demonstrate that our learning-based\nplanner achieves high intelligence and can handle complex situations. Detailed\nablation and visualization analysis are included to further demonstrate each of\nour proposed modules' effectiveness in our method.\n"}
+{"abstract": "  In numerous practical applications, especially in medical image\nreconstruction, it is often infeasible to obtain a large ensemble of\nground-truth/measurement pairs for supervised learning. Therefore, it is\nimperative to develop unsupervised learning protocols that are competitive with\nsupervised approaches in performance. Motivated by the maximum-likelihood\nprinciple, we propose an unsupervised learning framework for solving ill-posed\ninverse problems. Instead of seeking pixel-wise proximity between the\nreconstructed and the ground-truth images, the proposed approach learns an\niterative reconstruction network whose output matches the ground-truth in\ndistribution. Considering tomographic reconstruction as an application, we\ndemonstrate that the proposed unsupervised approach not only performs on par\nwith its supervised variant in terms of objective quality measures but also\nsuccessfully circumvents the issue of over-smoothing that supervised approaches\ntend to suffer from. The improvement in reconstruction quality comes at the\nexpense of higher training complexity, but, once trained, the reconstruction\ntime remains the same as its supervised counterpart.\n"}
+{"abstract": "  Transmon qubits fabricated with tantalum metal have been shown to possess\nenergy relaxation times greater than 300 $\\mu$s and, as such, present an\nattractive platform for high precision, correlated noise studies across\nmultiple higher energy transitions. Tracking the multi-level fluctuating qudit\nfrequencies with a precision enabled by the high coherence of the device allows\nus to extract the charge offset and quasi-particle dynamics. We observe\nqualitatively different charge offset behavior in the tantalum device than\nthose measured in previous low frequency charge noise studies. In particular,\nwe find the charge offset dynamics are dominated by rare, discrete jumps\nbetween a finite number of quasi-stationary charge configurations, a previously\nunobserved charge noise process in superconducting qubits.\n"}
+{"abstract": "  We consider a family of character sums as multiplicative analogues of\nKloosterman sums. Using Gauss sums, Jacobi sums and Deligne's bound for\nhyper-Kloosterman sums, we establish asymptotic formulae for any real\n(positive) moments of the above character sum as the character runs over all\nnon-trivial multiplicative characters mod $p.$ These also allow us to obtain\nasymptotic formulae for moments of such character sums weighted by special\n$L$-values (at $1/2$ and $1$).\n"}
+{"abstract": "  One of the challenges in optimization of high dimensional problems is finding\nappropriate solutions in a way that are as close as possible to the global\noptima. In this regard, one of the most common phenomena that occurs is the\ncurse of dimensionality in which a large scale feature space generates more\nparameters that need to be estimated. Heuristic algorithms, such as\nGravitational Search Algorithm, are among the tools proposed for optimizing\nlarge-scale problems, but in this case, they cannot solve the problem on their\nown. This paper proposes a novel method for optimizing large scale problems by\nimproving the gravitational search algorithm's performance. In order to\nincrease the efficiency of the gravitational search algorithm in solving large\nscale problems, the proposed method combines this algorithm with the\ncooperative-coevolution methods. For the evaluation of the performance of the\nproposed algorithm, we consider two approaches. In the first approach, the\nproposed algorithm is compared with the gravitational search algorithm, and in\nthe second approach, it is compared with some of the most significant research\nin this field. In the first approach, our method was able to improve the\nperformance of the original gravitational algorithm to solve large scale\nproblems, and in the second one, the results indicate more favorable\nperformance, in some benchmark functions, compared with other cooperative\nmethods.\n"}
+{"abstract": "  The proportional odds cumulative logit model (POCLM) is a standard regression\nmodel for an ordinal response. Ordinality of predictors can be incorporated by\nmonotonicity constraints for the corresponding parameters. It is shown that\nestimators defined by optimization, such as maximum likelihood estimators, for\nan unconstrained model and for parameters in the interior set of the parameter\nspace of a constrained model are asymptotically equivalent. This is used in\norder to derive asymptotic confidence regions and tests for the constrained\nmodel, involving simple modifications for finite samples. The finite sample\ncoverage probability of the confidence regions is investigated by simulation.\nTests concern the effect of individual variables, monotonicity, and a specified\nmonotonicity direction. The methodology is applied on real data related to the\nassessment of school performance.\n"}
+{"abstract": "  The objective of this work was to know the amount and frequency with which\nthe people of Arandas in the Altos de Jalisco region use disposable cups and\nthen know how willing they are to use edible cups made with natural gelatin. In\nthis regard, it is worth commenting that these can not only be nutritious for\nthose who consume them (since gelatin is a fortifying nutrient created from the\nskin and bone of pigs and cows), but they could also be degraded in a few days\nor be ingested by animals. To collect the information, a survey consisting of\nsix questions was used, which was applied to 31 people by telephone and another\n345 personally (in both cases they were applied to young people and adults).\nThe results show that the residents of that town considerably use plastic cups\nin the different events that take place each week, which are more numerous\nduring the patron saint festivities or at the end of the year. Even so, these\npeople would be willing to change these habits, although for this, measures\nmust be taken that do not affect the companies in that area, which work mainly\nwith plastics and generate a high percentage of jobs.\n"}
+{"abstract": "  Many mathematical optimization algorithms fail to sufficiently explore the\nsolution space of high-dimensional nonlinear optimization problems due to the\ncurse of dimensionality. This paper proposes generative models as a complement\nto optimization algorithms to improve performance in problems with high\ndimensionality. To demonstrate this method, a conditional generative\nadversarial network (C-GAN) is used to augment the solutions produced by a\ngenetic algorithm (GA) for a 311-dimensional nonconvex multi-objective\nmixed-integer nonlinear optimization. The C-GAN, composed of two networks with\nthree fully connected hidden layers, is trained on solutions generated by GA,\nand then given sets of desired labels (i.e., objective function values),\ngenerates complementary solutions corresponding to those labels. Six\nexperiments are conducted to evaluate the capabilities of the proposed method.\nThe generated complementary solutions are compared to the original solutions in\nterms of optimality and diversity. The generative model generates solutions\nwith objective functions up to 100% better, and with hypervolumes up to 100%\nhigher, than the original solutions. These findings show that a C-GAN with even\na simple training approach and architecture can, with a much shorter runtime,\nhighly improve the diversity and optimality of solutions found by an\noptimization algorithm for a high-dimensional nonlinear optimization problem.\n[Link to GitHub repository: https://github.com/PouyaREZ/GAN_GA]\n"}
+{"abstract": "  Quantum many-body dynamics generically results in increasing entanglement\nthat eventually leads to thermalization of local observables. This makes the\nexact description of the dynamics complex despite the apparent simplicity of\n(high-temperature) thermal states. For accurate but approximate simulations one\nneeds a way to keep track of essential (quantum) information while discarding\ninessential one. To this end, we first introduce the concept of the information\nlattice, which supplements the physical spatial lattice with an additional\ndimension and where a local Hamiltonian gives rise to well defined locally\nconserved von Neumann information current. This provides a convenient and\ninsightful way of capturing the flow, through time and space, of information\nduring quantum time evolution, and gives a distinct signature of when local\ndegrees of freedom decouple from long-range entanglement. As an example, we\ndescribe such decoupling of local degrees of freedom for the mixed field\ntransverse Ising model. Building on this, we secondly construct algorithms to\ntime-evolve sets of local density matrices without any reference to a global\nstate. With the notion of information currents, we can motivate algorithms\nbased on the intuition that information for statistical reasons flow from small\nto large scales. Using this guiding principle, we construct an algorithm that,\nat worst, shows two-digit convergence in time-evolutions up to very late times\nfor diffusion process governed by the mixed field transverse Ising Hamiltonian.\nWhile we focus on dynamics in 1D with nearest-neighbor Hamiltonians, the\nalgorithms do not essentially rely on these assumptions and can in principle be\ngeneralized to higher dimensions and more complicated Hamiltonians.\n"}
+{"abstract": "  There is a growing need for ranking universities, departments, research\ngroups, and individual scholars. Usually, the scientific community measures the\nscientific merits of the researchers by using a variety of indicators that take\ninto account both the productivity of scholars and the impact of their\npublications. We propose the t-index, the new indicator to measure the\nscientific merits of the individual researchers. The proposed t-index takes\ninto account the number of citations, number of coauthors on every published\npaper, and career duration. The t-index makes the possible comparison of\nresearchers at various stages of their careers. We also use in this paper the\nData Envelopment Analysis (DEA) to measure the scientific merits of the\nindividual researchers within the observed group of researchers. We chose 15\nscholars in the scientific area of transportation engineering and measured\ntheir t-index values, as well as DEA scores.\n"}
+{"abstract": "  We generalize Bonahon-Wong's $\\mathrm{SL}_2$-quantum trace map to the setting\nof $\\mathrm{SL}_3$. More precisely, given a non-zero complex parameter $q=e^{2\n\\pi i \\hbar}$, we associate to each isotopy class of framed oriented links $K$\nin a thickened punctured surface $\\mathfrak{S} \\times (0, 1)$ a Laurent\npolynomial $\\mathrm{Tr}_\\lambda^q(K) = \\mathrm{Tr}_\\lambda^q(K)(X_i^q)$ in\n$q$-deformations $X_i^q$ of the Fock-Goncharov $\\mathcal{X}$-coordinates for\nhigher Teichm\\\"{u}ller space. This construction depends on a choice $\\lambda$\nof ideal triangulation of the surface $\\mathfrak{S}$. Along the way, we propose\na definition for a $\\mathrm{SL}_n$-version of this invariant.\n"}
+{"abstract": "  Face recognition systems are extremely vulnerable to morphing attacks, in\nwhich a morphed facial reference image can be successfully verified as two or\nmore distinct identities. In this paper, we propose a morph attack detection\nalgorithm that leverages an undecimated 2D Discrete Wavelet Transform (DWT) for\nidentifying morphed face images. The core of our framework is that artifacts\nresulting from the morphing process that are not discernible in the image\ndomain can be more easily identified in the spatial frequency domain. A\ndiscriminative wavelet sub-band can accentuate the disparity between a real and\na morphed image. To this end, multi-level DWT is applied to all images,\nyielding 48 mid and high-frequency sub-bands each. The entropy distributions\nfor each sub-band are calculated separately for both bona fide and morph\nimages. For some of the sub-bands, there is a marked difference between the\nentropy of the sub-band in a bona fide image and the identical sub-band's\nentropy in a morphed image. Consequently, we employ Kullback-Liebler Divergence\n(KLD) to exploit these differences and isolate the sub-bands that are the most\ndiscriminative. We measure how discriminative a sub-band is by its KLD value\nand the 22 sub-bands with the highest KLD values are chosen for network\ntraining. Then, we train a deep Siamese neural network using these 22 selected\nsub-bands for differential morph attack detection. We examine the efficacy of\ndiscriminative wavelet sub-bands for morph attack detection and show that a\ndeep neural network trained on these sub-bands can accurately identify morph\nimagery.\n"}
+{"abstract": "  People ``understand'' the world via vision, hearing, tactile, and also the\npast experience. Human experience can be learned through normal learning (we\ncall it explicit knowledge), or subconsciously (we call it implicit knowledge).\nThese experiences learned through normal learning or subconsciously will be\nencoded and stored in the brain. Using these abundant experience as a huge\ndatabase, human beings can effectively process data, even they were unseen\nbeforehand. In this paper, we propose a unified network to encode implicit\nknowledge and explicit knowledge together, just like the human brain can learn\nknowledge from normal learning as well as subconsciousness learning. The\nunified network can generate a unified representation to simultaneously serve\nvarious tasks. We can perform kernel space alignment, prediction refinement,\nand multi-task learning in a convolutional neural network. The results\ndemonstrate that when implicit knowledge is introduced into the neural network,\nit benefits the performance of all tasks. We further analyze the implicit\nrepresentation learnt from the proposed unified network, and it shows great\ncapability on catching the physical meaning of different tasks. The source code\nof this work is at : https://github.com/WongKinYiu/yolor.\n"}
+{"abstract": "  Water shapes and defines the properties of biological systems. Therefore,\nunderstanding the nature of the mutual interaction between water and biological\nsystems is of primary importance for a proper assessment of biological activity\nand the development of new drugs and vaccines. A handy way to characterize the\ninteractions between biological systems and water is to analyze their impact on\nwater density and dynamics in the proximity of the interfaces. It is well\nestablished that water bulk density and dynamical properties are recovered at\ndistances in the order of $\\sim1$~nm from the surface of biological systems.\nSuch evidence led to the definition of \\emph{hydration} water as the thin layer\nof water covering the surface of biological systems and affecting-defining\ntheir properties and functionality. Here, we review some of our latest\ncontributions showing that phospholipid membranes affect the structural\nproperties and the hydrogen bond network of water at greater distances than the\ncommonly evoked $\\sim1$~nm from the membrane surface. Our results imply that\nthe concept of hydration water should be revised or extended, and pave the way\nto a deeper understanding of the mutual interactions between water and\nbiological systems.\n"}
+{"abstract": "  Whitney partition is a very important concept in modern analysis. We discuss\nhere a quasiconformal version of the Whitney partition that can be usefull for\nSobolev spaces.\n"}
+{"abstract": "  The possibility of carrying out a meaningful forensics analysis on printed\nand scanned images plays a major role in many applications. First of all,\nprinted documents are often associated with criminal activities, such as\nterrorist plans, child pornography pictures, and even fake packages.\nAdditionally, printing and scanning can be used to hide the traces of image\nmanipulation or the synthetic nature of images, since the artifacts commonly\nfound in manipulated and synthetic images are gone after the images are printed\nand scanned. A problem hindering research in this area is the lack of large\nscale reference datasets to be used for algorithm development and benchmarking.\nMotivated by this issue, we present a new dataset composed of a large number of\nsynthetic and natural printed face images. To highlight the difficulties\nassociated with the analysis of the images of the dataset, we carried out an\nextensive set of experiments comparing several printer attribution methods. We\nalso verified that state-of-the-art methods to distinguish natural and\nsynthetic face images fail when applied to print and scanned images. We\nenvision that the availability of the new dataset and the preliminary\nexperiments we carried out will motivate and facilitate further research in\nthis area.\n"}
+{"abstract": "  There has recently been much activity within the Kardar-Parisi-Zhang\nuniversality class spurred by the construction of the canonical limiting\nobject, the parabolic Airy sheet $\\mathcal{S}:\\mathbb{R}^2\\to\\mathbb{R}$\n[arXiv:1812.00309]. The parabolic Airy sheet provides a coupling of parabolic\nAiry$_2$ processes -- a universal limiting geodesic weight profile in planar\nlast passage percolation models -- and a natural goal is to understand this\ncoupling. Geodesic geometry suggests that the difference of two parabolic\nAiry$_2$ processes, i.e., a difference profile, encodes important structural\ninformation. This difference profile $\\mathcal{D}$, given by\n$\\mathbb{R}\\to\\mathbb{R}:x\\mapsto \\mathcal{S}(1,x)-\\mathcal{S}(-1,x)$, was\nfirst studied by Basu, Ganguly, and Hammond [arXiv:1904.01717], who showed that\nit is monotone and almost everywhere constant, with its points of non-constancy\nforming a set of Hausdorff dimension $1/2$. Noticing that this is also the\nHausdorff dimension of the zero set of Brownian motion, we adopt a different\napproach. Establishing previously inaccessible fractal structure of\n$\\mathcal{D}$, we prove, on a global scale, that $\\mathcal{D}$ is absolutely\ncontinuous on compact sets to Brownian local time (of rate four) in the sense\nof increments, which also yields the main result of [arXiv:1904.01717] as a\nsimple corollary. Further, on a local scale, we explicitly obtain Brownian\nlocal time of rate four as a local limit of $\\mathcal{D}$ at a point of\nincrease, picked by a number of methods, including at a typical point sampled\naccording to the distribution function $\\mathcal{D}$. Our arguments rely on the\nrepresentation of $\\mathcal{S}$ in terms of a last passage problem through the\nparabolic Airy line ensemble and an understanding of geodesic geometry at\ndeterministic and random times.\n"}
+{"abstract": "  This paper is about solving polynomial systems. It first recalls how to do\nthat efficiently with a very high probability of correctness by reconstructing\na rational univariate representation (rur) using Groebner revlex computation,\nBerlekamp-Massey algorithm and Hankel linear system solving modulo several\nprimes in parallel. Then it introduces a new method (theorem \\ref{prop:check})\nfor rur certification that is effective for most polynomial systems.These\nalgorithms are implemented in\nhttps://www-fourier.univ-grenoble-alpes.fr/~parisse/giac.html since version\n1.7.0-13 or 1.7.0-17 for certification, it has (July 2021) leading performances\non multiple CPU, at least for an open-source software.\n"}
+{"abstract": "  The Burling sequence is a sequence of triangle-free graphs of increasing\nchromatic number. Any graph which is an induced subgraph of a graph in this\nsequence is called a Burling graph. These graphs have attracted some attention\nbecause they have geometric representations and because they provide\ncounter-examples to several conjectures about bounding the chromatic number in\nclasses of graphs.\n  We recall an equivalent definition of Burling graphs from the first part of\nthis work: the graphs derived from a tree. We then give several structural\nproperties of derived graphs.\n"}
+{"abstract": "  In this paper, we try to create an effective mathematical model for the well\nknown slayer exciter transformer circuit. We aim to analyze various aspects of\nthe slayer exciter circuit, by using physical and computational methods. We use\na computer simulation for data collection of various parameters pertaining to\nthe circuit. Using this data, we generate plots for various components and\nparameters. We also derive an approximate equation to maximize the secondary\noutput voltage generated by the circuit. We also discuss a possible method to\nconstruct such a circuit using low cost materials.\n"}
+{"abstract": "  The main challenge of dynamic texture synthesis lies in how to maintain\nspatial and temporal consistency in synthesized videos. The major drawback of\nexisting dynamic texture synthesis models comes from poor treatment of the\nlong-range texture correlation and motion information. To address this problem,\nwe incorporate a new loss term, called the Shifted Gram loss, to capture the\nstructural and long-range correlation of the reference texture video.\nFurthermore, we introduce a frame sampling strategy to exploit long-period\nmotion across multiple frames. With these two new techniques, the application\nscope of existing texture synthesis models can be extended. That is, they can\nsynthesize not only homogeneous but also structured dynamic texture patterns.\nThorough experimental results are provided to demonstrate that our proposed\ndynamic texture synthesis model offers state-of-the-art visual performance.\n"}
+{"abstract": "  The QAnon conspiracy posits that Satan-worshiping Democrats operate a covert\nchild sex-trafficking operation, which Donald Trump is destined to expose and\nannihilate. Emblematic of the ease with which political misconceptions can\nspread through social media, QAnon originated in late 2017 and rapidly grew to\nshape the political beliefs of millions. To illuminate the process by which a\nconspiracy theory spreads, we report two computational studies examining the\nsocial network structure and semantic content of tweets produced by users\ncentral to the early QAnon network on Twitter. Using data mined in the summer\nof 2018, we examined over 800,000 tweets about QAnon made by about 100,000\nusers. The majority of users disseminated rather than produced information,\nserving to create an online echochamber. Users appeared to hold a simplistic\nmental model in which political events are viewed as a struggle between\nantithetical forces-both observed and unobserved-of Good and Evil.\n"}
+{"abstract": "  This paper is concerned with a model for the dynamics of a single species in\na one-dimensional heterogeneous environment. The environment consists of two\nkinds of patches, which are periodically alternately arranged along the spatial\naxis. We first establish the well-posedness for the Cauchy problem. Next, we\ngive existence and uniqueness results for the positive steady state and we\nanalyze the long-time behavior of the solutions to the evolution problem.\nAfterwards, based on dynamical systems methods, we investigate the spreading\nproperties and the existence of pulsating traveling waves in the positive and\nnegative directions. It is shown that the asymptotic spreading speed, c * ,\nexists and coincides with the minimal wave speed of pulsating traveling waves\nin positive and negative directions. In particular, we give a variational\nformula for c * by using the principal eigenvalues of certain linear periodic\neigenvalue problems.\n"}
+{"abstract": "  Toward the goal of automatic production for sports broadcasts, a paramount\ntask consists in understanding the high-level semantic information of the game\nin play. For instance, recognizing and localizing the main actions of the game\nwould allow producers to adapt and automatize the broadcast production,\nfocusing on the important details of the game and maximizing the spectator\nengagement. In this paper, we focus our analysis on action spotting in soccer\nbroadcast, which consists in temporally localizing the main actions in a soccer\ngame. To that end, we propose a novel feature pooling method based on NetVLAD,\ndubbed NetVLAD++, that embeds temporally-aware knowledge. Different from\nprevious pooling methods that consider the temporal context as a single set to\npool from, we split the context before and after an action occurs. We argue\nthat considering the contextual information around the action spot as a single\nentity leads to a sub-optimal learning for the pooling module. With NetVLAD++,\nwe disentangle the context from the past and future frames and learn specific\nvocabularies of semantics for each subsets, avoiding to blend and blur such\nvocabulary in time. Injecting such prior knowledge creates more informative\npooling modules and more discriminative pooled features, leading into a better\nunderstanding of the actions. We train and evaluate our methodology on the\nrecent large-scale dataset SoccerNet-v2, reaching 53.4% Average-mAP for action\nspotting, a +12.7% improvement w.r.t the current state-of-the-art.\n"}
+{"abstract": "  We analyze the Lanczos method for matrix function approximation (Lanczos-FA),\nan iterative algorithm for computing $f(\\mathbf{A}) \\mathbf{b}$ when\n$\\mathbf{A}$ is a Hermitian matrix and $\\mathbf{b}$ is a given mathbftor.\nAssuming that $f : \\mathbb{C} \\rightarrow \\mathbb{C}$ is piecewise analytic, we\ngive a framework, based on the Cauchy integral formula, which can be used to\nderive {\\em a priori} and \\emph{a posteriori} error bounds for Lanczos-FA in\nterms of the error of Lanczos used to solve linear systems. Unlike many error\nbounds for Lanczos-FA, these bounds account for fine-grained properties of the\nspectrum of $\\mathbf{A}$, such as clustered or isolated eigenvalues. Our\nresults are derived assuming exact arithmetic, but we show that they are easily\nextended to finite precision computations using existing theory about the\nLanczos algorithm in finite precision. We also provide generalized bounds for\nthe Lanczos method used to approximate quadratic forms $\\mathbf{b}^\\textsf{H}\nf(\\mathbf{A}) \\mathbf{b}$, and demonstrate the effectiveness of our bounds with\nnumerical experiments.\n"}
+{"abstract": "  We use \\textit{ab initio} molecular dynamics simulations to investigate the\nproperties of the dry surface of pure silica and sodium silicate glasses. The\nsurface layers are defined based on the atomic distributions along the\ndirection ($z-$direction) perpendicular to the surfaces. We show that these\nsurfaces have a higher concentration of dangling bonds as well as two-membered\n(2M) rings than the bulk samples. Increasing concentration of Na$_2$O reduces\nthe proportion of structural defects.\n  From the vibrational density of states, one concludes that 2M rings have a\nunique vibrational signature at a frequency $\\approx850$~cm$^{-1}$, compatible\nwith experimental findings.\n  We also find that, due to the presence of surfaces, the atomic vibration in\nthe $z-$direction is softer than for the two other directions. The electronic\ndensity of states shows clear the differences between the surface and interior\nand we can attribute these to specific structural units. Finally, the analysis\nof the electron localization function allows to get insight on the influence of\nlocal structure and the presence of Na on the nature of chemical bonding in the\nglasses.\n"}
+{"abstract": "  In recent years, graph neural networks (GNNs) have gained increasing\nattention, as they possess the excellent capability of processing graph-related\nproblems. In practice, hyperparameter optimisation (HPO) is critical for GNNs\nto achieve satisfactory results, but this process is costly because the\nevaluations of different hyperparameter settings require excessively training\nmany GNNs. Many approaches have been proposed for HPO, which aims to identify\npromising hyperparameters efficiently. In particular, the genetic algorithm\n(GA) for HPO has been explored, which treats GNNs as a black-box model, of\nwhich only the outputs can be observed given a set of hyperparameters. However,\nbecause GNN models are sophisticated and the evaluations of hyperparameters on\nGNNs are expensive, GA requires advanced techniques to balance the exploration\nand exploitation of the search and make the optimisation more effective given\nlimited computational resources. Therefore, we proposed a tree-structured\nmutation strategy for GA to alleviate this issue. Meanwhile, we reviewed the\nrecent HPO works, which gives room for the idea of tree-structure to develop,\nand we hope our approach can further improve these HPO methods in the future.\n"}
+{"abstract": "  We study the cross sections for the inclusive production of $\\psi(2S)$ and\n$X(3872)$ hadrons in $pp$ collisions at the LHC at two different center-of-mass\nenergies and compare with experimental data obtained by the ATLAS, CMS, and\nLHCb collaborations.\n"}
+{"abstract": "  We present sufficient conditions to have global hypoellipticity for a class\nof Vekua-type operators defined on a compact Lie group. When the group has the\nproperty that every non-trivial representation is not self-dual we show that\nthese sufficient conditions are also necessary. We also present results about\nthe global solvability for this class of operators.\n"}
+{"abstract": "  This paper unapologetically reflects on the critical role that Black feminism\ncan and should play in abolishing algorithmic oppression. Positioning\nalgorithmic oppression in the broader field of feminist science and technology\nstudies, I draw upon feminist philosophical critiques of science and technology\nand discuss histories and continuities of scientific oppression against\nhistorically marginalized people. Moreover, I examine the concepts of\ninvisibility and hypervisibility in oppressive technologies a l\\'a the\ncanonical double bind. Furthermore, I discuss what it means to call for\ndiversity as a solution to algorithmic violence, and I critique dialectics of\nthe fairness, accountability, and transparency community. I end by inviting you\nto envision and imagine the struggle to abolish algorithmic oppression by\nabolishing oppressive systems and shifting algorithmic development practices,\nincluding engaging our communities in scientific processes, centering\nmarginalized communities in design, and consensual data and algorithmic\npractices.\n"}
+{"abstract": "  Recent trends and advancement in including more diverse and heterogeneous\nhardware in High-Performance Computing is challenging software developers in\ntheir pursuit for good performance and numerical stability. The well-known\nmaxim \"software outlives hardware\" may no longer necessarily hold true, and\ndevelopers are today forced to re-factor their codebases to leverage these\npowerful new systems. CFD is one of the many application domains affected. In\nthis paper, we present Neko, a portable framework for high-order spectral\nelement flow simulations. Unlike prior works, Neko adopts a modern\nobject-oriented approach, allowing multi-tier abstractions of the solver stack\nand facilitating hardware backends ranging from general-purpose processors down\nto exotic vector processors and FPGAs. We show that Neko's performance and\naccuracy are comparable to NekRS, and thus on-par with Nek5000's successor on\nmodern CPU machines. Furthermore, we develop a performance model, which we use\nto discuss challenges and opportunities for high-order solvers on emerging\nhardware.\n"}
+{"abstract": "  We introduce a formulation of optimal transport problem for distributions on\nfunction spaces, where the stochastic map between functional domains can be\npartially represented in terms of an (infinite-dimensional) Hilbert-Schmidt\noperator mapping a Hilbert space of functions to another. For numerous machine\nlearning tasks, data can be naturally viewed as samples drawn from spaces of\nfunctions, such as curves and surfaces, in high dimensions. Optimal transport\nfor functional data analysis provides a useful framework of treatment for such\ndomains. In this work, we develop an efficient algorithm for finding the\nstochastic transport map between functional domains and provide theoretical\nguarantees on the existence, uniqueness, and consistency of our estimate for\nthe Hilbert-Schmidt operator. We validate our method on synthetic datasets and\nstudy the geometric properties of the transport map. Experiments on real-world\ndatasets of robot arm trajectories further demonstrate the effectiveness of our\nmethod on applications in domain adaptation.\n"}
+{"abstract": "  We derive a factor graph EM (FGEM) algorithm, a technique that permits\ncombined parameter estimation and statistical inference, to determine hidden\nkinetic microstates from patch clamp measurements. Using the cystic fibrosis\ntransmembrane conductance regulator (CFTR) and nicotinic acetylcholine receptor\n(nAChR) as examples, we perform {\\em Monte Carlo} simulations to demonstrate\nthe performance of the algorithm. We show that the performance, measured in\nterms of the probability of estimation error, approaches the theoretical\nperformance limit of maximum {\\em a posteriori} estimation. Moreover, the\nalgorithm provides a reliability score for its estimates, and we demonstrate\nthat the score can be used to further improve the performance of estimation. We\nuse the algorithm to estimate hidden kinetic states in lab-obtained CFTR single\nchannel patch clamp traces.\n"}
+{"abstract": "  Both coronal holes and active regions are source regions of the solar wind.\nThe distribution of these coronal structures across both space and time is well\nknown, but it is unclear how much each source contributes to the solar wind. In\nthis study we use photospheric magnetic field maps observed over the past four\nsolar cycles to estimate what fraction of magnetic open solar flux is rooted in\nactive regions, a proxy for the fraction of all solar wind originating in\nactive regions. We find that the fractional contribution of active regions to\nthe solar wind varies between 30% to 80% at any one time during solar maximum\nand is negligible at solar minimum, showing a strong correlation with sunspot\nnumber. While active regions are typically confined to latitudes\n$\\pm$30$^{\\circ}$ in the corona, the solar wind they produce can reach\nlatitudes up to $\\pm$60$^{\\circ}$. Their fractional contribution to the solar\nwind also correlates with coronal mass ejection rate, and is highly variable,\nchanging by $\\pm$20% on monthly timescales within individual solar maxima. We\nspeculate that these variations could be driven by coronal mass ejections\ncausing reconfigurations of the coronal magnetic field on sub-monthly\ntimescales.\n"}
+{"abstract": "  At large virtuality $Q^2$, the coupling to the $\\rho$ meson production\nchannels provides us with a natural explanation of the surprisingly large cross\nsection of the $\\omega$, as well as the $\\pi^+$, meson electroproduction\nrecently measured at backward angles, without destroying the good agreement\nbetween the Regge pole model and the data at the real photon point. Together\nwith elastic re-scattering of the outgoing meson it also provides us with a way\nto explain why the node, that appears at $u\\sim -0.15$ GeV$^2$ at the real\nphoton point, disappears at moderate virtuality $Q^2$. Predictions are given\nfor the electroproduction of the $\\pi^0$ meson.\n"}
+{"abstract": "  In this paper, we use a New Radio (NR) Vehicular-to-everything (V2X) standard\ncompliant simulator based on ns-3, to study the impact of NR numerologies on\nthe end-to-end performance. In particular, we focus on NR V2X Mode 2, used for\nautonomous resource selection in out-of-coverage communications, and consider\nthe two key procedures defined in 3GPP: sensing and non-sensing based resource\nselection. We pay particular attention to the interplay between the operational\nnumerology and the resource selection window length, a key parameter of NR V2X\nMode 2. The results in a standard-compliant, end-to-end simulation platform\nshow that in all cases, for basic service messages, a higher numerology is\nbeneficial because of different reasons, depending on the way the resource\nselection window length is established.\n"}
+{"abstract": "  This is an evolving document describing the meta-theory, the implementation,\nand the instantiations of Gillian, a multi-language symbolic analysis platform.\n"}
+{"abstract": "  Quantum geometry has emerged as a central and ubiquitous concept in quantum\nsciences, with direct consequences on quantum metrology and many-body quantum\nphysics. In this context, two fundamental geometric quantities are known to\nplay complementary roles: the Fubini-Study metric, which introduces a notion of\ndistance between quantum states defined over a parameter space, and the Berry\ncurvature associated with Berry-phase effects and topological band structures.\nIn fact, recent studies have revealed direct relations between these two\nimportant quantities, suggesting that topological properties can, in special\ncases, be deduced from the quantum metric. In this work, we establish general\nand exact relations between the quantum metric and the topological invariants\nof generic Dirac Hamiltonians. In particular, we demonstrate that topological\nindices (Chern numbers or winding numbers) are bounded by the quantum volume\ndetermined by the quantum metric. Our theoretical framework, which builds on\nthe Clifford algebra of Dirac matrices, is applicable to topological insulators\nand semimetals of arbitrary spatial dimensions, with or without chiral\nsymmetry. This work clarifies the role of the Fubini-Study metric in\ntopological states of matter, suggesting unexplored topological responses and\nmetrological applications in a broad class of quantum-engineered systems.\n"}
+{"abstract": "  We introduce (dual) strongly relative CS-Rickart objects in abelian\ncategories, as common generalizations of (dual) strongly relative Rickart\nobjects and strongly extending (lifting) objects. We give general properties,\nand we study direct summands, (co)products of (dual) strongly relative\nCS-Rickart objects and classes all of whose objects are (dual) strongly\nself-CS-Rickart.\n"}
+{"abstract": "  Graphene supports surface plasmon polaritons with comparatively slow\npropagation velocities in the THz region, which becomes increasingly\ninteresting for future communication technologies. This ability can be used to\nrealize compact antennas, which are up to two orders of magnitude smaller than\ntheir metallic counterparts. For a proper functionality of these antennas some\nminimum material requirements have to be fulfilled, which are presently\ndifficult to achieve, since the fabrication and transfer technologies for\ngraphene are still evolving. In this work we analyze available graphene\nmaterials experimentally and extract intrinsic characteristics at THz\nfrequencies, in order to predict the dependency of the THz signal emission\nthreshold as a function of the graphene relaxation time tau_r and the chemical\npotential mu_c.\n"}
+{"abstract": "  We give an unconditional proof that self-dual Artin representations of\n$\\mathbb{Q}$ of dimension $3$ have density $0$ among all Artin representations\nof $\\mathbb{Q}$ of dimension $3$. Previously this was known under the\nassumption of Malle's Conjecture.\n"}
+{"abstract": "  We report transport measurements on Josephson junctions consisting of Bi2Te3\ntopological insulator (TI) thin films contacted by superconducting Nb\nelectrodes. For a device with junction length L = 134 nm, the critical\nsupercurrent Ic can be modulated by an electrical gate which tunes the carrier\ntype and density of the TI film. Ic can reach a minimum when the TI is near the\ncharge neutrality regime with the Fermi energy lying close to the Dirac point\nof the surface state. In the p-type regime the Josephson current can be well\ndescribed by a short ballistic junction model. In the n-type regime the\njunction is ballistic at 0.7 K < T < 3.8 K while for T < 0.7 K the diffusive\nbulk modes emerge and contribute a larger Ic than the ballistic model. We\nattribute the lack of diffusive bulk modes in the p-type regime to the\nformation of p-n junctions. Our work provides new clues for search of Majorana\nzero mode in TI-based superconducting devices.\n"}
+{"abstract": "  Let $0<p,q\\leq \\infty$ and denote by $\\mathcal S_p^N$ and $\\mathcal S_q^N$\nthe corresponding Schatten classes of real $N\\times N$ matrices. We study\napproximation quantities of natural identities $\\mathcal S_p^N\\hookrightarrow\n\\mathcal S_q^N$ between Schatten classes and prove asymptotically sharp bounds\nup to constants only depending on $p$ and $q$, showing how approximation\nnumbers are intimately related to the Gelfand numbers and their duals, the\nKolmogorov numbers. In particular, we obtain new bounds for those sequences of\n$s$-numbers. Our results improve and complement bounds previously obtained by\nB. Carl and A. Defant [J. Approx. Theory, 88(2):228--256, 1997], Y. Gordon, H.\nK\\\"onig, and C. Sch\\\"utt [J. Approx. Theory, 49(3):219--239, 1987], A. Hinrichs\nand C. Michels [Rend. Circ. Mat. Palermo (2) Suppl., (76):395--411, 2005], and\nA. Hinrichs, J. Prochno, and J. Vyb\\'iral [preprint, 2020]. We also treat the\ncase of quasi-Schatten norms, which is relevant in applications such as\nlow-rank matrix recovery.\n"}
+{"abstract": "  We propose a polarization-independent optical isolator that does not use\nwalk-off between the two orthogonal polarizations. The design is based on two\nFaraday rotators in combination with two half-wave plates in a closed,\nSagnac-interferometer-like, configuration. An experimental prototype is tested\nsuccessfully under variation of the input light polarization state with\nisolation level between 43 dB and 50 dB for all input polarizations (linear,\ncircular, or elliptical).\n"}
+{"abstract": "  Known quantum and classical perturbative long-distance corrections to the\nNewton potential are extended into the short-distance regime using evolution\nequations for a `running' gravitational coupling, which is used to construct\nexamples non-perturbative potentials for the gravitational binding of two\nparticles. Model-I is based on the complete set of the relevant Feynman\ndiagrams. Its potential has a singularity at a distance below which it becomes\ncomplex and the system gets black hole-like features. Model-II is based on a\nreduced set of diagrams and its coupling approaches a non-Gaussian fixed point\nas the distance is reduced. Energies and eigenfunctions are obtained and used\nin a study of time-dependent collapse (model-I) and bouncing (both models) of a\nspherical wave packet. The motivation for such non-perturbative `toy' models\nstems from a desire to elucidate the mass dependence of binding energies found\n25 years ago in an explorative numerical simulation within the dynamical\ntriangulation approach to quantum gravity. Models I \\& II suggest indeed an\nexplanation of this mass dependence, in which the Schwarzschild scale plays a\nrole. An estimate of the renormalized Newton coupling is made by matching with\nthe small-mass region. Comparison of the dynamical triangulation results for\nmass renormalization with `renormalized perturbation theory' in the continuum\nleads to an independent estimate of this coupling, which is used in an improved\nanalysis of the binding energy data.\n"}
+{"abstract": "  Contrastive learning has nearly closed the gap between supervised and\nself-supervised learning of image representations, and has also been explored\nfor videos. However, prior work on contrastive learning for video data has not\nexplored the effect of explicitly encouraging the features to be distinct\nacross the temporal dimension. We develop a new temporal contrastive learning\nframework consisting of two novel losses to improve upon existing contrastive\nself-supervised video representation learning methods. The local-local temporal\ncontrastive loss adds the task of discriminating between non-overlapping clips\nfrom the same video, whereas the global-local temporal contrastive aims to\ndiscriminate between timesteps of the feature map of an input clip in order to\nincrease the temporal diversity of the learned features. Our proposed temporal\ncontrastive learning framework achieves significant improvement over the\nstate-of-the-art results in various downstream video understanding tasks such\nas action recognition, limited-label action classification, and\nnearest-neighbor video retrieval on multiple video datasets and backbones. We\nalso demonstrate significant improvement in fine-grained action classification\nfor visually similar classes. With the commonly used 3D ResNet-18 architecture,\nwe achieve 82.4% (+5.1% increase over the previous best) top-1 accuracy on\nUCF101 and 52.9% (+5.4% increase) on HMDB51 action classification, and 56.2%\n(+11.7% increase) Top-1 Recall on UCF101 nearest neighbor video retrieval.\n"}
+{"abstract": "  Let $X$ be a complex irreducible smooth projective curve, and let ${\\mathbb\nL}$ be an algebraic line bundle on $X$ with a nonzero section $\\sigma_0$. Let\n$\\mathcal{M}$ denote the moduli space of stable Hitchin pairs $(E,\\, \\theta)$,\nwhere $E$ is an algebraic vector bundle on $X$ of fixed rank $r$ and degree\n$\\delta$, and $\\theta\\, \\in\\, H^0(X,\\, End(E)\\otimes K_X\\otimes{\\mathbb L})$.\nAssociating to every stable Hitchin pair its spectral data, an isomorphism of\n$\\mathcal{M}$ with a moduli space $\\mathcal{P}$ of stable sheaves of pure\ndimension one on the total space of $K_X\\otimes{\\mathbb L}$ is obtained. Both\nthe moduli spaces $\\mathcal{P}$ and $\\mathcal{M}$ are equipped with algebraic\nPoisson structures, which are constructed using $\\sigma_0$. Here we prove that\nthe above isomorphism between $\\mathcal{P}$ and $\\mathcal{M}$ preserves the\nPoisson structures.\n"}
+{"abstract": "  We study the {\\em Budgeted Dominating Set} (BDS) problem on uncertain graphs,\nnamely, graphs with a probability distribution $p$ associated with the edges,\nsuch that an edge $e$ exists in the graph with probability $p(e)$. The input to\nthe problem consists of a vertex-weighted uncertain graph $\\G=(V, E, p,\n\\omega)$ and an integer {\\em budget} (or {\\em solution size}) $k$, and the\nobjective is to compute a vertex set $S$ of size $k$ that maximizes the\nexpected total domination (or total weight) of vertices in the closed\nneighborhood of $S$. We refer to the problem as the {\\em Probabilistic Budgeted\nDominating Set}~(PBDS) problem and present the following results.\n\\begin{enumerate} \\dnsitem We show that the PBDS problem is NP-complete even\nwhen restricted to uncertain {\\em trees} of diameter at most four. This is in\nsharp contrast with the well-known fact that the BDS problem is solvable in\npolynomial time in trees. We further show that PBDS is \\wone-hard for the\nbudget parameter $k$, and under the {\\em Exponential time hypothesis} it cannot\nbe solved in $n^{o(k)}$ time.\n  \\item We show that if one is willing to settle for $(1-\\epsilon)$\napproximation, then there exists a PTAS for PBDS on trees. Moreover, for the\nscenario of uniform edge-probabilities, the problem can be solved optimally in\npolynomial time.\n  \\item We consider the parameterized complexity of the PBDS problem, and show\nthat Uni-PBDS (where all edge probabilities are identical) is \\wone-hard for\nthe parameter pathwidth. On the other hand, we show that it is FPT in the\ncombined parameters of the budget $k$ and the treewidth.\n  \\item Finally, we extend some of our parameterized results to planar and\napex-minor-free graphs. \\end{enumerate}\n"}
+{"abstract": "  Many states of linear real scalar quantum fields (in particular\nReeh-Schlieder states) on flat as well as curved spacetime are entangled on\nspacelike separated local algebras of observables. It has been argued that this\nentanglement can be \"harvested\" by a pair of so-called particle detectors, for\nexample singularly or non-locally coupled quantum mechanical harmonic\noscillator Unruh detectors. In an attempt to avoid such imperfect coupling, we\nanalyse a model-independent local and covariant entanglement harvesting\nprotocol based on the local probes of a recently proposed measurement theory of\nquantum fields. We then introduce the notion of a local particle detector\nconcretely given by a local mode of a linear real scalar probe field on\npossibly curved spacetime and possibly under the influence of external fields.\nIn a non-perturbative analysis we find that local particle detectors cannot\nharvest entanglement below a critical coupling strength when the corresponding\nprobe fields are initially prepared in quasi-free Reeh-Schlieder states and are\ncoupled to a system field prepared in a quasi-free state. This is a consequence\nof the fact that Reeh-Schlieder states restrict to truly mixed states on any\nlocal mode.\n"}
+{"abstract": "  Previously, statistical textbook wisdom has held that interpolating noisy\ndata will generalize poorly, but recent work has shown that data interpolation\nschemes can generalize well. This could explain why overparameterized deep nets\ndo not necessarily overfit. Optimal data interpolation schemes have been\nexhibited that achieve theoretical lower bounds for excess risk in any\ndimension for large data (Statistically Consistent Interpolation). These are\nnon-parametric Nadaraya-Watson estimators with singular kernels. The recently\nproposed weighted interpolating nearest neighbors method (wiNN) is in this\nclass, as is the previously studied Hilbert kernel interpolation scheme, in\nwhich the estimator has the form $\\hat{f}(x)=\\sum_i y_i w_i(x)$, where $w_i(x)=\n\\|x-x_i\\|^{-d}/\\sum_j \\|x-x_j\\|^{-d}$. This estimator is unique in being\ncompletely parameter-free. While statistical consistency was previously proven,\nconvergence rates were not established. Here, we comprehensively study the\nfinite sample properties of Hilbert kernel regression. We prove that the excess\nrisk is asymptotically equivalent pointwise to $\\sigma^2(x)/\\ln(n)$ where\n$\\sigma^2(x)$ is the noise variance. We show that the excess risk of the plugin\nclassifier is less than $2|f(x)-1/2|^{1-\\alpha}\\,(1+\\varepsilon)^\\alpha\n\\sigma^\\alpha(x)(\\ln(n))^{-\\frac{\\alpha}{2}}$, for any $0<\\alpha<1$, where $f$\nis the regression function $x\\mapsto\\mathbb{E}[y|x]$. We derive asymptotic\nequivalents of the moments of the weight functions $w_i(x)$ for large $n$, for\ninstance for $\\beta>1$, $\\mathbb{E}[w_i^{\\beta}(x)]\\sim_{n\\rightarrow\n\\infty}((\\beta-1)n\\ln(n))^{-1}$. We derive an asymptotic equivalent for the\nLagrange function and exhibit the nontrivial extrapolation properties of this\nestimator. We present heuristic arguments for a universal $w^{-2}$ power-law\nbehavior of the probability density of the weights in the large $n$ limit.\n"}
+{"abstract": "  Estimating the density of a continuous random variable X has been studied\nextensively in statistics, in the setting where n independent observations of X\nare given a priori and one wishes to estimate the density from that. Popular\nmethods include histograms and kernel density estimators. In this review paper,\nwe are interested instead in the situation where the observations are generated\nby Monte Carlo simulation from a model. Then, one can take advantage of\nvariance reduction methods such as stratification, conditional Monte Carlo, and\nrandomized quasi-Monte Carlo (RQMC), and obtain a more accurate density\nestimator than with standard Monte Carlo for a given computing budget. We\ndiscuss several ways of doing this, proposed in recent papers, with a focus on\nmethods that exploit RQMC. A first idea is to directly combine RQMC with a\nstandard kernel density estimator. Another one is to adapt a simulation-based\nderivative estimation method such as smoothed perturbation analysis or the\nlikelihood ratio method to obtain a continuous estimator of the cdf, whose\nderivative is an unbiased estimator of the density. This can then be combined\nwith RQMC. We summarize recent theoretical results with these approaches and\ngive numerical illustrations of how they improve the convergence of the mean\nsquare integrated error.\n"}
+{"abstract": "  We present an original, generic, and efficient approach for computing the\nfirst and second partial derivatives of ray velocities along ray paths in\ngeneral anisotropic elastic media. These derivatives are used in solving\nkinematic problems, like two-point ray bending methods and seismic tomography,\nand they are essential for evaluating the dynamic properties along the rays\n(amplitudes and phases). The traveltime is delivered through an integral over a\ngiven Lagrangian defined at each point along the ray. Although the Lagrangian\ncannot be explicitly expressed in terms of the medium properties and the ray\ndirection components, its derivatives can still be formulated analytically\nusing the corresponding arclength-related Hamiltonian that can be explicitly\nexpressed in terms of the medium properties and the slowness vector components.\nThis requires first to invert for the slowness vector components, given the ray\ndirection components. Computation of the slowness vector and the ray velocity\nderivatives is considerably simplified by using an auxiliary\nscaled-time-related Hamiltonian obtained directly from the Christoffel equation\nand connected to the arclength-related Hamiltonian by a simple scale factor.\nThis study consists of two parts. In Part I, we consider general anisotropic\n(triclinic) media, and provide the derivatives (gradients and Hessians) of the\nray velocity, with respect to (1) the spatial/directional vectors and (2) the\nelastic model parameters. In Part II, we apply the theory of Part I explicitly\nto polar anisotropic media (transverse isotropy with tilted axis of symmetry,\nTTI), and obtain the explicit ray velocity derivatives for the coupled qP and\nqSV waves and for SH waves.\n"}
+{"abstract": "  With the purpose of defending against lateral movement in today's borderless\nnetworks, Zero Trust Architecture (ZTA) adoption is gaining momentum. With a\nfull scale ZTA implementation, it is unlikely that adversaries will be able to\nspread through the network starting from a compromised endpoint. However, the\nalready authenticated and authorised session of a compromised endpoint can be\nleveraged to perform limited, though malicious, activities ultimately rendering\nthe endpoints the Achilles heel of ZTA. To effectively detect such attacks,\ndistributed collaborative intrusion detection systems with an attack\nscenario-based approach have been developed. Nonetheless, Advanced Persistent\nThreats (APTs) have demonstrated their ability to bypass this approach with a\nhigh success ratio. As a result, adversaries can pass undetected or potentially\nalter the detection logging mechanisms to achieve a stealthy presence.\nRecently, blockchain technology has demonstrated solid use cases in the cyber\nsecurity domain. In this paper, motivated by the convergence of ZTA and\nblockchain-based intrusion detection and prevention, we examine how ZTA can be\naugmented onto endpoints. Namely, we perform a state-of-the-art review of ZTA\nmodels, real-world architectures with a focus on endpoints, and\nblockchain-based intrusion detection systems. We discuss the potential of\nblockchain's immutability fortifying the detection process and identify open\nchallenges as well as potential solutions and future directions.\n"}
+{"abstract": "  We investigate the low-temperature charge-density-wave (CDW) state of bulk\nTaS$_2$ with a fully self-consistent DFT+U approach, over which the controversy\nhas remained unresolved regarding the out-of-plane metallic band. By examining\nthe innate structure of the Hubbard U potential, we reveal that the\nconventional use of atomic-orbital basis could seriously misevaluate the\nelectron correlation in the CDW state. By adopting a generalized basis,\ncovering the whole David star, we successfully reproduce the Mott insulating\nnature with the layer-by-layer antiferromagnetic order. Similar consideration\nshould be applied for description of the electron correlation in molecular\nsolid.\n"}
+{"abstract": "  A stable cut of a graph is a cut whose weight cannot be increased by changing\nthe side of a single vertex. Equivalently, a cut is stable if all vertices have\nthe (weighted) majority of their neighbors on the other side. In this paper we\nstudy Min Stable Cut, the problem of finding a stable cut of minimum weight,\nwhich is closely related to the Price of Anarchy of the Max Cut game. Since\nthis problem is NP-hard, we study its complexity on graphs of low treewidth,\nlow degree, or both. We show that the problem is weakly NP-hard on severely\nrestricted trees, so bounding treewidth alone cannot make it tractable. We\nmatch this with a pseudo-polynomial DP algorithm running in time $(\\Delta\\cdot\nW)^{O(tw)}n^{O(1)}$, where $tw$ is the treewidth, $\\Delta$ the maximum degree,\nand $W$ the maximum weight. On the other hand, bounding $\\Delta$ is also not\nenough, as the problem is NP-hard for unweighted graphs of bounded degree. We\ntherefore parameterize Min Stable Cut by both $tw+\\Delta$ and obtain an FPT\nalgorithm running in time $2^{O(\\Delta tw)}(n+\\log W)^{O(1)}$. Our main result\nis to provide a reduction showing that both aforementioned algorithms are\nessentially optimal, even if we replace treewidth by pathwidth: if there exists\nan algorithm running in $(nW)^{o(pw)}$ or $2^{o(\\Delta pw)}(n+\\log W)^{O(1)}$,\nthen the ETH is false. Complementing this, we show that we can obtain an FPT\napproximation scheme parameterized by treewidth, if we consider almost-stable\nsolutions.\n  Motivated by these mostly negative results, we consider Unweighted Min Stable\nCut. Here our results already imply a much faster exact algorithm running in\ntime $\\Delta^{O(tw)}n^{O(1)}$. We show that this is also probably essentially\noptimal: an algorithm running in $n^{o(pw)}$ would contradict the ETH.\n"}
+{"abstract": "  These are lecture notes based on the first part of a course on 'Mathematical\nData Science' that I taught to final year BSc students in the UK between 2019\nand 2020. Topics include: Concentration of measure in high dimensions; Gaussian\nrandom vectors in high dimensions; Random projections; Separation/disentangling\nof Gaussian data.\n"}
+{"abstract": "  Long-range correlated errors can severely impact the performance of NISQ\n(noisy intermediate-scale quantum) devices, and fault-tolerant quantum\ncomputation. Characterizing these errors is important for improving the\nperformance of these devices, via calibration and error correction, and to\nensure correct interpretation of the results. We propose a compressed sensing\nmethod for detecting two-qubit correlated dephasing errors, assuming only that\nthe correlations are sparse (i.e., at most s pairs of qubits have correlated\nerrors, where s << n(n-1)/2, and n is the total number of qubits). In\nparticular, our method can detect long-range correlations between any two\nqubits in the system (i.e., the correlations are not restricted to be\ngeometrically local).\n  Our method is highly scalable: it requires as few as m = O(s log n)\nmeasurement settings, and efficient classical postprocessing based on convex\noptimization. In addition, when m = O(s log^4(n)), our method is highly robust\nto noise, and has sample complexity O(max(n,s)^2 log^4(n)), which can be\ncompared to conventional methods that have sample complexity O(n^3). Thus, our\nmethod is advantageous when the correlations are sufficiently sparse, that is,\nwhen s < O(n^(3/2) / log^2(n)). Our method also performs well in numerical\nsimulations on small system sizes, and has some resistance to\nstate-preparation-and-measurement (SPAM) errors. The key ingredient in our\nmethod is a new type of compressed sensing measurement, which works by\npreparing entangled Greenberger-Horne-Zeilinger states (GHZ states) on random\nsubsets of qubits, and measuring their decay rates with high precision.\n"}
+{"abstract": "  In this paper, we study the role of the symmetry energy on the neutron-drip\ntransition in both nonaccreting and accreting neutron stars, allowing for the\npresence of a strong magnetic field as in magnetars. The density, pressure, and\ncomposition at the neutron-drip threshold are determined using the recent set\nof the Brussels-Montreal microscopic nuclear mass models, which mainly differ\nin their predictions for the value of the symmetry energy $J$ and its slope $L$\nin infinite homogeneous nuclear matter at saturation. Although some\ncorrelations between on the one hand the neutron-drip density, the pressure,\nthe proton fraction and on the other hand $J$ (or equivalently $L$) are found,\nthese correlations are radically different in nonaccreting and accreting\nneutron stars. In particular, the neutron-drip density is found to increase\nwith $L$ in the former case, but decreases in the latter case depending on the\ncomposition of ashes from x-ray bursts and superbursts. We have qualitatively\nexplained these different behaviors using a simple mass formula. We have also\nshown that the details of the nuclear structure may play a more important role\nthan the symmetry energy in accreting neutron-star crusts.\n"}
+{"abstract": "  Aminopropyl modified mesoporous SiO2 nanoparticles, MCM-41 type, have been\nsynthesized by the co-condensation method from tetraethylorthosilicate (TEOS)\nand aminopropyltriethoxysilane (APTES). By means of modifying TEOS/APTES ratio\nwe have carried out an in-depth characterization of the nanoparticles as a\nfunction of APTES content. Surface charge and nanoparticles morphology were\nstrongly influenced by the amount of APTES and particles changed from hexagonal\nto bean-like morphology insofar APTES increased. Besides, the porous structure\nwas also affected, showing a contraction of the lattice parameter and pore\nsize, while increasing the wall thickness. These results bring about new\ninsights about the nanoparticles formation during the co-condensation process.\nThe model proposed herein considers that different interactions stablished\nbetween TEOS and APTES with the structure directing agent have consequences on\npore size, wall thickness and particle morphology. Finally, APTES is an\nexcellent linker to covalently attach active targeting agents such as folate\ngroups. We have hypothesized that APTES could also play a role in the\nbiological behavior of the nanoparticles. So, the internalization efficiency of\nthe nanoparticles has been tested with cancerous LNCaP and non-cancerous\npreosteoblast-like MC3T3-E1 cells. The results indicate a cooperative effect\nbetween aminopropylsilane presence and folic acid, only for the cancerous LNCaP\ncell line.\n"}
+{"abstract": "  Spoken communication occurs in a \"noisy channel\" characterized by high levels\nof environmental noise, variability within and between speakers, and lexical\nand syntactic ambiguity. Given these properties of the received linguistic\ninput, robust spoken word recognition -- and language processing more generally\n-- relies heavily on listeners' prior knowledge to evaluate whether candidate\ninterpretations of that input are more or less likely. Here we compare several\nbroad-coverage probabilistic generative language models in their ability to\ncapture human linguistic expectations. Serial reproduction, an experimental\nparadigm where spoken utterances are reproduced by successive participants\nsimilar to the children's game of \"Telephone,\" is used to elicit a sample that\nreflects the linguistic expectations of English-speaking adults. When we\nevaluate a suite of probabilistic generative language models against the\nyielded chains of utterances, we find that those models that make use of\nabstract representations of preceding linguistic context (i.e., phrase\nstructure) best predict the changes made by people in the course of serial\nreproduction. A logistic regression model predicting which words in an\nutterance are most likely to be lost or changed in the course of spoken\ntransmission corroborates this result. We interpret these findings in light of\nresearch highlighting the interaction of memory-based constraints and\nrepresentations in language processing.\n"}
+{"abstract": "  This article summarizes the talk given at the LCWS 2021 conference on the\nstatus and news of the WHIZARD Monte Carlo event generator. We presented its\nfeatures relevant for the physics program of future lepton and especially\nlinear colliders as well as recent developments towards including NLO\nperturbative corrections and a UFO interface to study models beyond the\nStandard Model. It takes as reference the version 3.0.0$\\beta$ released in\nAugust 2020 and additionally discusses the developments that will be included\nin the next major version 3.0.0 to be released in April 2021.\n"}
+{"abstract": "  It is suggested that many-body quantum chaos appears as spontaneous symmetry\nbreaking of unitarity in interacting quantum many-body systems. It has been\nshown that many-body level statistics, probed by the spectral form factor (SFF)\ndefined as $K(\\beta,t)=\\langle|{\\rm Tr}\\, \\exp(-\\beta H + itH)|^2\\rangle$, is\ndominated by a diffusion-type mode in a field theory analysis. The key finding\nof this paper is that the \"unitary\" $\\beta=0$ case is different from the $\\beta\n\\to 0^+$ limit, with the latter leading to a finite mass of these modes due to\ninteractions. This mass suppresses a rapid exponential ramp in the SFF, which\nis responsible for the fast emergence of Poisson statistics in the\nnon-interacting case, and gives rise to a non-trivial random matrix structure\nof many-body levels. The interaction-induced mass in the SFF shares\nsimilarities with the dephasing rate in the theory of weak localization and the\nLyapunov exponent of the out-of-time-ordered correlators.\n"}
+{"abstract": "  The current literature on intelligent reflecting surface (IRS) focuses on\noptimizing the IRS phase shifts to yield coherent beamforming gains, under the\nassumption of perfect channel state information (CSI) of individual\nIRS-assisted links, which is highly impractical. This work, instead, considers\nthe random rotations scheme at the IRS in which the reflecting elements only\nemploy random phase rotations without requiring any CSI. The only CSI then\nneeded is at the base station (BS) of the overall channel to implement the\nbeamforming transmission scheme. Under this framework, we derive the sum-rate\nscaling laws in the large number of users regime for the IRS-assisted\nmultiple-input single-output (MISO) broadcast channel, with optimal dirty paper\ncoding (DPC) scheme and the lower-complexity random beamforming (RBF) and\ndeterministic beamforming (DBF) schemes at the BS. The random rotations scheme\nincreases the sum-rate by exploiting multi-user diversity, but also compromises\nthe gain to some extent due to correlation. Finally, energy efficiency\nmaximization problems in terms of the number of BS antennas, IRS elements and\ntransmit power are solved using the derived scaling laws. Simulation results\nshow the proposed scheme to improve the sum-rate, with performance becoming\nclose to that under coherent beamforming for a large number of users.\n"}
+{"abstract": "  This paper studies numerically the Weeks-Chandler-Andersen (WCA) system,\nwhich is shown to obey hidden scale invariance with a density-scaling exponent\nthat varies from below 5 to above 500. This unprecedented variation makes it\nadvantageous to use the fourth-order Runge-Kutta algorithm for tracing out\nisomorphs. Good isomorph invariance of structure and dynamics is observed over\nmore than three orders of magnitude temperature variation. For all state points\nstudied, the virial potential-energy correlation coefficient and the\ndensity-scaling exponent are controlled mainly by the temperature. Based on the\nassumption of statistically independent pair interactions, a mean-field theory\nis developed that rationalizes this finding and provides an excellent fit to\ndata at low temperatures and densities.\n"}
+{"abstract": "  A time-reversal invariant topological insulator occupying a Euclidean\nhalf-space determines a 'Quaternionic' self-adjoint Fredholm family. We show\nthat the discrete spectrum data for such a family is geometrically encoded in a\nnon-trivial 'Real' gerbe. The gerbe invariant, rather than a na\\\"ive counting\nof Dirac points, precisely captures how edge states completely fill up the bulk\nspectral gap in a topologically protected manner.\n"}
+{"abstract": "  We study the impact of strong magnetic fields on the pasta phases that are\nexpected to exist in the inner crust of neutron stars. We employ the\nrelativistic mean field model to describe the nucleon interaction and use the\nself-consistent Thomas-Fermi approximation to calculate the nonuniform matter\nin neutron star crust. The properties of pasta phases and crust-core transition\nare examined. It is found that as the magnetic field strength $B$ is less than\n$10^{17}$ G, the effects of magnetic field are not evident comparing with the\nresults without magnetic field. As $B$ is stronger than $10^{18}$ G, the onset\ndensities of pasta phases and crust-core transition density decrease\nsignificantly, and the density distributions of nucleons and electrons are also\nchanged obviously.\n"}
+{"abstract": "  Long-term availability of minerals and industrial materials is a necessary\ncondition for sustainable development as they are the constituents of any\nmanufacturing product. To enhance the efficiency of material management, we\ndefine a computer-vision-enabled material measurement system and provide a\nsurvey of works relevant to its development with particular emphasis on the\nfoundations. A network of such systems for wide-area material stock monitoring\nis also covered. Finally, challenges and future research directions are\ndiscussed. As the first article bridging industrial ecology and advanced\ncomputer vision, this survey is intended to support both research communities\ntowards more sustainable manufacturing.\n"}
+{"abstract": "  We study the regular surface defect in the Omega-deformed four dimensional\nsupersymmetric gauge theory with gauge group SU(N) with 2N hypermultiplets in\nfundamental representation. We prove its vacuum expectation value obeys the\nKnizhnik-Zamolodchikov equation for the 4-point conformal block of current\nalgebra of a two dimensional conformal field theory. The level and the vertex\noperators are determined by the parameters of the Omega-background and the\nmasses of the hypermultiplets, the 4-point cross-ratio is determined by the\ncomplexified gauge coupling. We clarify that in a somewhat subtle way the\nbranching rule is parametrized by the Coulomb moduli. This is an example of the\nBPS/CFT relation.\n"}
+{"abstract": "  In our previous article with Yukio Kametani, we investigated the geometric\nstructure underlying a large scale interacting system on infinite graphs, via\nconstructing a suitable cohomology theory called uniformly local cohomology,\nwhich reflects the geometric property of the microscopic model, using a class\nof functions called the uniformly local functions. In this article, we\nintroduce the co-local functions on the geometric structure associated to a\nlarge scale interacting system. We may similarly define the notion of uniformly\nlocal functions for co-local functions. However, contrary to the functions\nappearing in our previous article, the co-local functions reflect the\nstochastic property of the model, namely the probability measure on the\nconfiguration space. We then prove a decomposition theorem of Varadhan type for\nclosed co-local forms. The space of co-local functions and forms contain the\nspace of $L^2$-functions and forms. In the last section, we state a conjecture\nconcerning the decomposition theorem for the $L^2$-case.\n"}
+{"abstract": "  Data scientists often develop machine learning models to solve a variety of\nproblems in the industry and academy but not without facing several challenges\nin terms of Model Development. The problems regarding Machine Learning\nDevelopment involves the fact that such professionals do not realize that they\nusually perform ad-hoc practices that could be improved by the adoption of\nactivities presented in the Software Engineering Development Lifecycle. Of\ncourse, since machine learning systems are different from traditional Software\nsystems, some differences in their respective development processes are to be\nexpected. In this context, this paper is an effort to investigate the\nchallenges and practices that emerge during the development of ML models from\nthe software engineering perspective by focusing on understanding how software\ndevelopers could benefit from applying or adapting the traditional software\nengineering process to the Machine Learning workflow.\n"}
+{"abstract": "  For robust GPS-vision navigation in urban areas, we propose an\nIntegrity-driven Landmark Attention (ILA) technique via stochastic\nreachability. Inspired by cognitive attention in humans, we perform convex\noptimization to select a subset of landmarks from GPS and vision measurements\nthat maximizes integrity-driven performance. Given known measurement error\nbounds in non-faulty conditions, our ILA follows a unified approach to address\nboth GPS and vision faults and is compatible with any off-the-shelf estimator.\nWe analyze measurement deviation to estimate the stochastic reachable set of\nexpected position for each landmark, which is parameterized via probabilistic\nzonotope (p-Zonotope). We apply set union to formulate a p-Zonotopic cost that\nrepresents the size of position bounds based on landmark inclusion/exclusion.\nWe jointly minimize the p-Zonotopic cost and maximize the number of landmarks\nvia convex relaxation. For an urban dataset, we demonstrate improved\nlocalization accuracy and robust predicted availability for a pre-defined alert\nlimit.\n"}
+{"abstract": "  A quantum magnetic state due to magnetic charges is never observed, even\nthough they are treated as quantum mechanical variable in theoretical\ncalculations. Here, we demonstrate the occurrence of a novel quantum disordered\nstate of magnetic charges in nanoengineered magnetic honeycomb lattice of\nultra-small connecting elements. The experimental research, performed using\nspin resolved neutron scattering, reveals a massively degenerate ground state,\ncomprised of low integer and energetically forbidden high integer magnetic\ncharges, that manifests cooperative paramagnetism at low temperature. The\nsystem tends to preserve the degenerate configuration even under large magnetic\nfield application. It exemplifies the robustness of disordered correlation of\nmagnetic charges in 2D honeycomb lattice. The realization of quantum disordered\nground state elucidates the dominance of exchange energy, which is enabled due\nto the nanoscopic magnetic element size in nanoengineered honeycomb.\nConsequently, an archetypal platform is envisaged to study quantum mechanical\nphenomena due to emergent magnetic charges.\n"}
+{"abstract": "  One of the most important parts of Artificial Neural Networks is minimizing\nthe loss functions which tells us how good or bad our model is. To minimize\nthese losses we need to tune the weights and biases. Also to calculate the\nminimum value of a function we need gradient. And to update our weights we need\ngradient descent. But there are some problems with regular gradient descent ie.\nit is quite slow and not that accurate. This article aims to give an\nintroduction to optimization strategies to gradient descent. In addition, we\nshall also discuss the architecture of these algorithms and further\noptimization of Neural Networks in general\n"}
+{"abstract": "  In this paper, we develop efficient and accurate algorithms for evaluating\n$\\varphi(A)$ and $\\varphi(A)b$, where $A$ is an $N\\times N$ matrix, $b$ is an\n$N$ dimensional vector and $\\varphi$ is the function defined by\n$\\varphi(x)\\equiv\\sum\\limits^{\\infty}_{k=0}\\frac{z^k}{(1+k)!}$. Such matrix\nfunction (the so-called $\\varphi$-function) plays a key role in a class of\nnumerical methods well-known as exponential integrators. The algorithms use the\nscaling and modified squaring procedure combined with truncated Taylor series.\nThe backward error analysis is presented to find the optimal value of the\nscaling and the degree of the Taylor approximation. Some useful techniques are\nemployed for reducing the computational cost. Numerical comparisons with\nstate-of-the-art algorithms show that the algorithms perform well in both\naccuracy and efficiency.\n"}
+{"abstract": "  Multidisciplinary cooperation is now common in research since social issues\ninevitably involve multiple disciplines. In research articles, reference\ninformation, especially citation content, is an important representation of\ncommunication among different disciplines. Analyzing the distribution\ncharacteristics of references from different disciplines in research articles\nis basic to detecting the sources of referred information and identifying\ncontributions of different disciplines. This work takes articles in PLoS as the\ndata and characterizes the references from different disciplines based on\nCitation Content Analysis (CCA). First, we download 210,334 full-text articles\nfrom PLoS and collect the information of the in-text citations. Then, we\nidentify the discipline of each reference in these academic articles. To\ncharacterize the distribution of these references, we analyze three\ncharacteristics, namely, the number of citations, the average cited intensity\nand the average citation length. Finally, we conclude that the distributions of\nreferences from different disciplines are significantly different. Although\nmost references come from Natural Science, Humanities and Social Sciences play\nimportant roles in the Introduction and Background sections of the articles.\nBasic disciplines, such as Mathematics, mainly provide research methods in the\narticles in PLoS. Citations mentioned in the Results and Discussion sections of\narticles are mainly in-discipline citations, such as citations from Nursing and\nMedicine in PLoS.\n"}
+{"abstract": "  This paper looks into the modeling of hallucination in the human's brain.\nHallucinations are known to be causally associated with some malfunctions\nwithin the interaction of different areas of the brain involved in perception.\nFocusing on visual hallucination and its underlying causes, we identify an\nadversarial mechanism between different parts of the brain which are\nresponsible in the process of visual perception. We then show how the\ncharacterized adversarial interactions in the brain can be modeled by a\ngenerative adversarial network.\n"}
+{"abstract": "  The Radiance Enhancement (RE) method was introduced for efficient detection\nof clouds from the space. Recently we have also reported that due to high\nreflectance of combustion-originated smokes this approach can also be\ngeneralized for detection of the forest fires by retrieving and analyzing\ndatasets collected from a space orbiting micro-spectrometer operating in the\nnear infrared spectral range. In our previous publication we have performed a\ncomparison of observed and synthetic radiance spectra by developing a method\nfor computation of surface reflectance consisting of different canopies by\nweighted sum based on their areal coverage. However, this approach should be\njustified by a method based on corresponding proportions of the upwelling\nradiance. The results of computations we performed in this study reveals a good\nmatch between areal coverage of canopies and the corresponding proportions of\nthe upwelling radiance due to effect of the instrument slit function.\n"}
+{"abstract": "  Security issues in shipped code can lead to unforeseen device malfunction,\nsystem crashes or malicious exploitation by crackers, post-deployment. These\nvulnerabilities incur a cost of repair and foremost risk the credibility of the\ncompany. It is rewarding when these issues are detected and fixed well ahead of\ntime, before release. Common Weakness Estimation (CWE) is a nomenclature\ndescribing general vulnerability patterns observed in C code. In this work, we\npropose a deep learning model that learns to detect some of the common\ncategories of security vulnerabilities in source code efficiently. The AI\narchitecture is an Attention Fusion model, that combines the effectiveness of\nrecurrent, convolutional and self-attention networks towards decoding the\nvulnerability hotspots in code. Utilizing the code AST structure, our model\nbuilds an accurate understanding of code semantics with a lot less learnable\nparameters. Besides a novel way of efficiently detecting code vulnerability, an\nadditional novelty in this model is to exactly point to the code sections,\nwhich were deemed vulnerable by the model. Thus helping a developer to quickly\nfocus on the vulnerable code sections; and this becomes the \"explainable\" part\nof the vulnerability detection. The proposed AI achieves 98.40% F1-score on\nspecific CWEs from the benchmarked NIST SARD dataset and compares well with\nstate of the art.\n"}
+{"abstract": "  Off-policy multi-step reinforcement learning algorithms consist of\nconservative and non-conservative algorithms: the former actively cut traces,\nwhereas the latter do not. Recently, Munos et al. (2016) proved the convergence\nof conservative algorithms to an optimal Q-function. In contrast,\nnon-conservative algorithms are thought to be unsafe and have a limited or no\ntheoretical guarantee. Nonetheless, recent studies have shown that\nnon-conservative algorithms empirically outperform conservative ones. Motivated\nby the empirical results and the lack of theory, we carry out theoretical\nanalyses of Peng's Q($\\lambda$), a representative example of non-conservative\nalgorithms. We prove that it also converges to an optimal policy provided that\nthe behavior policy slowly tracks a greedy policy in a way similar to\nconservative policy iteration. Such a result has been conjectured to be true\nbut has not been proven. We also experiment with Peng's Q($\\lambda$) in complex\ncontinuous control tasks, confirming that Peng's Q($\\lambda$) often outperforms\nconservative algorithms despite its simplicity. These results indicate that\nPeng's Q($\\lambda$), which was thought to be unsafe, is a theoretically-sound\nand practically effective algorithm.\n"}
+{"abstract": "  As the use of algorithmic systems in high-stakes decision-making increases,\nthe ability to contest algorithmic decisions is being recognised as an\nimportant safeguard for individuals. Yet, there is little guidance on what\n`contestability'--the ability to contest decisions--in relation to algorithmic\ndecision-making requires. Recent research presents different conceptualisations\nof contestability in algorithmic decision-making. We contribute to this growing\nbody of work by describing and analysing the perspectives of people and\norganisations who made submissions in response to Australia's proposed `AI\nEthics Framework', the first framework of its kind to include `contestability'\nas a core ethical principle. Our findings reveal that while the nature of\ncontestability is disputed, it is seen as a way to protect individuals, and it\nresembles contestability in relation to human decision-making. We reflect on\nand discuss the implications of these findings.\n"}
+{"abstract": "  It is well known that entropy production is a proxy to the detection of\nnon-equilibrium, i.e. of the absence of detailed balance; however, due to the\nglobal character of this quantity, its knowledge does not allow to identify\nspatial currents or fluxes of information among specific elements of the system\nunder study. In this respect, much more insight can be gained by studying\ntransfer entropy and response, which allow quantifying the relative influence\nof parts of the system and the asymmetry of the fluxes. In order to understand\nthe relation between the above-mentioned quantities, we investigate spatially\nasymmetric extended systems. First, we consider a simplified linear stochastic\nmodel, which can be studied analytically; then, we include nonlinear terms in\nthe dynamics. Extensive numerical investigation shows the relation between\nentropy production and the above-introduced degrees of asymmetry. Finally, we\napply our approach to the highly nontrivial dynamics generated by the Lorenz\n'96 model for Earth oceanic circulation.\n"}
+{"abstract": "  Recurrent neural networks are a powerful means in diverse applications. We\nshow that, together with so-called conceptors, they also allow fast learning,\nin contrast to other deep learning methods. In addition, a relatively small\nnumber of examples suffices to train neural networks with high accuracy. We\ndemonstrate this with two applications, namely speech recognition and detecting\ncar driving maneuvers. We improve the state of the art by application-specific\npreparation techniques: For speech recognition, we use mel frequency cepstral\ncoefficients leading to a compact representation of the frequency spectra, and\ndetecting car driving maneuvers can be done without the commonly used\npolynomial interpolation, as our evaluation suggests.\n"}
+{"abstract": "  Network filtering is an important form of dimension reduction to isolate the\ncore constituents of large and interconnected complex systems. We introduce a\nnew technique to filter large dimensional networks arising out of dynamical\nbehavior of the constituent nodes, exploiting their spectral properties. As\nopposed to the well known network filters that rely on preserving key\ntopological properties of the realized network, our method treats the spectrum\nas the fundamental object and preserves spectral properties. Applying\nasymptotic theory for high dimensional data for the filter, we show that it can\nbe tuned to interpolate between zero filtering to maximal filtering that\ninduces sparsity and consistency while having the least spectral distance from\na linear shrinkage estimator. We apply our proposed filter to covariance\nnetworks constructed from financial data, to extract the key subnetwork\nembedded in the full sample network.\n"}
+{"abstract": "  Intent Classification (IC) and Slot Labeling (SL) models, which form the\nbasis of dialogue systems, often encounter noisy data in real-word\nenvironments. In this work, we investigate how robust IC/SL models are to noisy\ndata. We collect and publicly release a test-suite for seven common noise types\nfound in production human-to-bot conversations (abbreviations, casing,\nmisspellings, morphological variants, paraphrases, punctuation and synonyms).\nOn this test-suite, we show that common noise types substantially degrade the\nIC accuracy and SL F1 performance of state-of-the-art BERT-based IC/SL models.\nBy leveraging cross-noise robustness transfer -- training on one noise type to\nimprove robustness on another noise type -- we design aggregate\ndata-augmentation approaches that increase the model performance across all\nseven noise types by +10.8% for IC accuracy and +15 points for SL F1 on\naverage. To the best of our knowledge, this is the first work to present a\nsingle IC/SL model that is robust to a wide range of noise phenomena.\n"}
+{"abstract": "  The Malmquist-Takenaka system is a perturbation of the classical\ntrigonometric system, where powers of $z$ are replaced by products of other\nM\\\"obius transforms of the disc. The system is also inherently connected to the\nso-called nonlinear phase unwinding decomposition which has been in the center\nof some recent activity. We prove $L^p$ bounds for the maximal partial sum\noperator of the Malmquist-Takenaka series under additional assumptions on the\nzeros of the M\\\"obius transforms. We locate the problem in the time-frequency\nsetting and, in particular, we connect it to the polynomial Carleson theorem.\n"}
+{"abstract": "  We study the properties of the Nakajima-Zwanzig memory kernel for a qubit\nimmersed in a many-body localized (i.e.\\ disordered and interacting) bath. We\nargue that the memory kernel decays as a power law in both the localized and\nergodic regimes, and show how this can be leveraged to extract $t\\to\\infty$\npopulations for the qubit from finite time ($J t \\leq 10^2$) data in the\nthermalizing phase. This allows us to quantify how the long-time values of the\npopulations approach the expected thermalized state as the bath approaches the\nthermodynamic limit. This approach should provide a good complement to\nstate-of-the-art numerical methods, for which the long-time dynamics with large\nbaths are impossible to simulate in this phase. Additionally, our numerics on\nfinite baths reveal the possibility for unbounded exponential growth in the\nmemory kernel, a phenomenon rooted in the appearance of exceptional points in\nthe projected Liouvillian governing the reduced dynamics. In small systems\namenable to exact numerics, we find that these pathologies may have some\ncorrelation with delocalization.\n"}
+{"abstract": "  Human evaluation of modern high-quality machine translation systems is a\ndifficult problem, and there is increasing evidence that inadequate evaluation\nprocedures can lead to erroneous conclusions. While there has been considerable\nresearch on human evaluation, the field still lacks a commonly-accepted\nstandard procedure. As a step toward this goal, we propose an evaluation\nmethodology grounded in explicit error analysis, based on the Multidimensional\nQuality Metrics (MQM) framework. We carry out the largest MQM research study to\ndate, scoring the outputs of top systems from the WMT 2020 shared task in two\nlanguage pairs using annotations provided by professional translators with\naccess to full document context. We analyze the resulting data extensively,\nfinding among other results a substantially different ranking of evaluated\nsystems from the one established by the WMT crowd workers, exhibiting a clear\npreference for human over machine output. Surprisingly, we also find that\nautomatic metrics based on pre-trained embeddings can outperform human crowd\nworkers. We make our corpus publicly available for further research.\n"}
+{"abstract": "  We define a multi-group version of the mean-field or Curie-Weiss spin model.\nFor this model, we show how, analogously to the classical (single-group) model,\nthe three temperature regimes are defined. Then we use the method of moments to\ndetermine for each regime how the vector of the group magnetisations behaves\nasymptotically. Some possible applications to social or political sciences are\ndiscussed.\n"}
+{"abstract": "  Short pulse lasers are used to characterize the nonlinear response of\namplified photodetectors. Two widely used balanced detectors are characterized\nin terms of amplitude, area, broadening, and balancing the mismatch of their\nimpulse response. The dynamic impact of pulses on the detector is also\ndiscussed. It is demonstrated that using photodetectors with short pulses\ntriggers nonlinearities even when the source average power is well below the\ndetector continuous power saturation threshold.\n"}
+{"abstract": "  To enable a deep learning-based system to be used in the medical domain as a\ncomputer-aided diagnosis system, it is essential to not only classify diseases\nbut also present the locations of the diseases. However, collecting\ninstance-level annotations for various thoracic diseases is expensive.\nTherefore, weakly supervised localization methods have been proposed that use\nonly image-level annotation. While the previous methods presented the disease\nlocation as the most discriminative part for classification, this causes a deep\nnetwork to localize wrong areas for indistinguishable X-ray images. To solve\nthis issue, we propose a spatial attention method using disease masks that\ndescribe the areas where diseases mainly occur. We then apply the spatial\nattention to find the precise disease area by highlighting the highest\nprobability of disease occurrence. Meanwhile, the various sizes, rotations and\nnoise in chest X-ray images make generating the disease masks challenging. To\nreduce the variation among images, we employ an alignment module to transform\nan input X-ray image into a generalized image. Through extensive experiments on\nthe NIH-Chest X-ray dataset with eight kinds of diseases, we show that the\nproposed method results in superior localization performances compared to\nstate-of-the-art methods.\n"}
+{"abstract": "  Probability is an important question in the ontological interpretation of\nquantum mechanics. It has been discussed in some trajectory interpretations\nsuch as Bohmian mechanics and stochastic mechanics. New questions arise when\nthe probability domain extends to the complex space, including the generation\nof complex trajectory, the definition of the complex probability, and the\nrelation of the complex probability to the quantum probability. The complex\ntreatment proposed in this article applies the optimal quantum guidance law to\nderive the stochastic differential equation governing a particle random motion\nin the complex plane. The probability distribution of the particle position\nover the complex plane is formed by an ensemble of the complex quantum random\ntrajectories, which are solved from the complex stochastic differential\nequation. Meanwhile, this probability distribution is verified by the solution\nof the complex Fokker Planck equation. It is shown that quantum probability and\nclassical probability can be integrated under the framework of complex\nprobability, such that they can both be derived from the same probability\ndistribution by different statistical ways of collecting spatial points.\n"}
+{"abstract": "  We consider a family of Schr\\\"odinger equations with unbounded Hamiltonian\nquadratic nonlinearities on a generic tori of dimension $d\\geq1$. We study the\nbehaviour of high Sobolev norms $H^{s}$, $s\\gg1$, of solutions with initial\nconditions in $H^{s}$ whose\n  $H^{\\rho}$-Sobolev norm, $1\\ll\\rho\\ll s$, is smaller than $\\e\\ll1$. We\nprovide a control of the $H^{s}$-norm over a time interval of order\n$O(\\e^{-2})$. %where $\\e\\ll1$ is the size of the initial condition in\n$H^{\\rho}$.\n  Due to the lack of conserved quantities controlling high Sobolev norms, the\nkey ingredient of the proof is the construction of a modified energy equivalent\nto the \"low norm\" $H^{\\rho}$ (when $\\rho$ is sufficiently high) over a\nnontrivial time interval $O(\\e^{-2})$. This is achieved by means of normal form\ntechniques for quasi-linear equations involving para-differential calculus. The\nmain difficulty is to control the possible loss of derivatives due to the small\ndivisors arising form three waves interactions. By performing \"tame\" energy\nestimates we obtain upper bounds for higher Sobolev norms $H^{s}$.\n"}
+{"abstract": "  Deepfakes are the result of digital manipulation to obtain credible videos in\norder to deceive the viewer. This is done through deep learning techniques\nbased on autoencoders or GANs that become more accessible and accurate year\nafter year, resulting in fake videos that are very difficult to distinguish\nfrom real ones. Traditionally, CNN networks have been used to perform deepfake\ndetection, with the best results obtained using methods based on EfficientNet\nB7. In this study, we combine various types of Vision Transformers with a\nconvolutional EfficientNet B0 used as a feature extractor, obtaining comparable\nresults with some very recent methods that use Vision Transformers. Differently\nfrom the state-of-the-art approaches, we use neither distillation nor ensemble\nmethods. The best model achieved an AUC of 0.951 and an F1 score of 88.0%, very\nclose to the state-of-the-art on the DeepFake Detection Challenge (DFDC).\n"}
+{"abstract": "  The focus of this research is sensor applications including radar and sonar.\nOptimal sensing means achieving the best signal quality with the least time and\nenergy cost, which allows processing more data. This paper presents novel work\nby using an integer linear programming \"algorithm\" to achieve optimal sensing\nby selecting the best possible number of signals of a type or a combination of\nmultiple types of signals to ensure the best sensing quality considering all\ngiven constraints. A solution based on a heuristic algorithm is implemented to\nimprove the computing time performance. What is novel in this solution is\nsynthesis of an optimized signal mix using information such as but not limited\nto signal quality, energy and computing time.\n"}
+{"abstract": "  Mahi-mahi (Coryphaena hippurus) are a highly migratory pelagic fish, but\nlittle is known about what environmental factors drive their broad\ndistribution. This study examined how temperature influences aerobic scope and\nswimming performance in mahi. Mahi were acclimated to four temperatures\nspanning their natural range (20, 24, 28, and 32{\\deg}C; 5-27 days) and\ncritical swimming speed (Ucrit), metabolic rates, aerobic scope, and optimal\nswim speed were measured. Aerobic scope and Ucrit were highest in\n28{\\deg}C-acclimated fish. 20{\\deg}C-acclimated mahi experienced significantly\ndecreased aerobic scope and Ucrit relative to 28{\\deg}C-acclimated fish (57 and\n28% declines, respectively). 32{\\deg}C-acclimated mahi experienced increased\nmortality and a significant 23% decline in Ucrit, and a trend for a 26% decline\nin factorial aerobic scope relative to 28{\\deg}C-acclimated fish. Absolute\naerobic scope showed a similar pattern to factorial aerobic scope. Our results\nare generally in agreement with previously observed distribution patterns for\nwild fish. Although thermal performance can vary across life stages, the\nhighest tested swim performance and aerobic scope found in the present study\n(28{\\deg}C), aligns with recently observed habitat utilization patterns for\nwild mahi and could be relevant for climate change predictions.\n"}
+{"abstract": "  The mapped bases or Fake Nodes Approach (FNA), introduced in [10], allows to\nchange the set of nodes without the need of resampling the function. Such\nscheme has been successfully applied in preventing the appearance of the Gibbs\nphenomenon when interpolating discontinuous functions. However, the originally\nproposed S-Gibbs map suffers of a subtle instability when the interpolant is\nconstructed at equidistant nodes, due to the Runge's phenomenon. Here, we\npropose a novel approach, termed Gibbs-Runge-Avoiding Stable Polynomial\nApproximation (GRASPA), where both Runge's and Gibbs phenomena are mitigated.\nAfter providing a theoretical analysis of the Lebesgue constant associated to\nthe mapped nodes, we test the new approach by performing different numerical\nexperiments which confirm the theoretical findings.\n"}
+{"abstract": "  We prove that the torsion points of an abelian variety are equidistributed\nover the corresponding berkovich space with respect to the canonical measure.\n"}
+{"abstract": "  We give explicit examples of quaternion-K\\\"ahler and hypercomplex structures\non bundles over hyperK\\\"ahler manifolds. We study the infinitesimal symmetries\nof these examples and the associated Galicki-Lawson quaternion-K\\\"ahler moment\nmap. In particular, this leads us to a new proof a\nhyperK\\\"ahler/quaternion-K\\\"ahler type correspondence. We also give examples of\nother Einstein metrics and balanced Hermitian structures on these bundles.\n"}
+{"abstract": "  Code review plays an important role in software quality control. A typical\nreview process would involve a careful check of a piece of code in an attempt\nto find defects and other quality issues/violations. One type of issues that\nmay impact the quality of the software is code smells - i.e., bad programming\npractices that may lead to defects or maintenance issues. Yet, little is known\nabout the extent to which code smells are identified during code reviews. To\ninvestigate the concept behind code smells identified in code reviews and what\nactions reviewers suggest and developers take in response to the identified\nsmells, we conducted an empirical study of code smells in code reviews using\nthe two most active OpenStack projects (Nova and Neutron). We manually checked\n19,146 review comments obtained by keywords search and random selection, and\ngot 1,190 smell-related reviews to study the causes of code smells and actions\ntaken against the identified smells. Our analysis found that 1) code smells\nwere not commonly identified in code reviews, 2) smells were usually caused by\nviolation of coding conventions, 3) reviewers usually provided constructive\nfeedback, including fixing (refactoring) recommendations to help developers\nremove smells, and 4) developers generally followed those recommendations and\nactioned the changes. Our results suggest that 1) developers should closely\nfollow coding conventions in their projects to avoid introducing code smells,\nand 2) review-based detection of code smells is perceived to be a trustworthy\napproach by developers, mainly because reviews are context-sensitive (as\nreviewers are more aware of the context of the code given that they are part of\nthe project's development team).\n"}
+{"abstract": "  With the popularity of Machine Learning (ML) solutions, algorithms and data\nhave been released faster than the capacity of processing them. In this\ncontext, the problem of Algorithm Recommendation (AR) is receiving a\nsignificant deal of attention recently. This problem has been addressed in the\nliterature as a learning task, often as a Meta-Learning problem where the aim\nis to recommend the best alternative for a specific dataset. For such, datasets\nencoded by meta-features are explored by ML algorithms that try to learn the\nmapping between meta-representations and the best technique to be used. One of\nthe challenges for the successful use of ML is to define which features are the\nmost valuable for a specific dataset since several meta-features can be used,\nwhich increases the meta-feature dimension. This paper presents an empirical\nanalysis of Feature Selection and Feature Extraction in the meta-level for the\nAR problem. The present study was focused on three criteria: predictive\nperformance, dimensionality reduction, and pipeline runtime. As we verified,\napplying Dimensionality Reduction (DR) methods did not improve predictive\nperformances in general. However, DR solutions reduced about 80% of the\nmeta-features, obtaining pretty much the same performance as the original setup\nbut with lower runtimes. The only exception was PCA, which presented about the\nsame runtime as the original meta-features. Experimental results also showed\nthat various datasets have many non-informative meta-features and that it is\npossible to obtain high predictive performance using around 20% of the original\nmeta-features. Therefore, due to their natural trend for high dimensionality,\nDR methods should be used for Meta-Feature Selection and Meta-Feature\nExtraction.\n"}
+{"abstract": "  Whereas electron-phonon scattering typically relaxes the electron's momentum\nin metals, a perpetual exchange of momentum between phonons and electrons\nconserves total momentum and can lead to a coupled electron-phonon liquid with\nunique transport properties. This theoretical idea was proposed decades ago and\nhas been revisited recently, but the experimental signatures of an\nelectron-phonon liquid have been rarely reported. We present evidence of such a\nbehavior in a transition metal ditetrelide, NbGe$_2$, from three different\nexperiments. First, quantum oscillations reveal an enhanced quasiparticle mass,\nwhich is unexpected in NbGe$_2$ due to weak electron-electron correlations,\nhence pointing at electron-phonon interactions. Second, resistivity\nmeasurements exhibit a discrepancy between the experimental data and calculated\ncurves within a standard Fermi liquid theory. Third, Raman scattering shows\nanomalous temperature dependence of the phonon linewidths which fits an\nempirical model based on phonon-electron coupling. We discuss structural\nfactors, such as chiral symmetry, short metallic bonds, and a low-symmetry\ncoordination environment as potential sources of coupled electron-phonon\nliquids.\n"}
+{"abstract": "  A variety of techniques have been developed for the approximation of\nnon-periodic functions. In particular, there are approximation techniques based\non rank-$1$ lattices and transformed rank-$1$ lattices, including methods that\nuse sampling sets consisting of Chebyshev- and tent-transformed nodes. We\ncompare these methods with a parameterized transformed Fourier system that\nyields similar $\\ell_2$-approximation errors.\n"}
+{"abstract": "  We show that the cobordism class of a polarization of Hodge module defines a\nnatural transformation from the Grothendieck group of Hodge modules to the\ncobordism group of self-dual bounded complexes with real coefficients and\nconstructible cohomology sheaves in a compatible way with pushforward by proper\nmorphisms. This implies a new proof of the well-definedness of the natural\ntransformation from the Grothendieck group of varieties over a given variety to\nthe above cobordism group (with real coefficients). As a corollary, we get a\nslight extension of a conjecture of Brasselet, Sch\\\"urmann and Yokura, showing\nthat in the $\\bf Q$-homologically isolated singularity case, the homology\n$L$-class which is the specialization of the Hirzebruch class coincides with\nthe intersection complex $L$-class defined by Goresky, MacPherson, and others\nif and only if the sum of the reduced Euler-Hodge signatures of the stalks of\nthe shifted intersection complex vanishes. Here Hodge signature uses a\npolarization of Hodge structure, and it does not seem easy to define it by a\npurely topological method.\n"}
+{"abstract": "  Stellar flares are an important aspect of magnetic activity -- both for\nstellar evolution and circumstellar habitability viewpoints - but automatically\nand accurately finding them is still a challenge to researchers in the Big Data\nera of astronomy. We present an experiment to detect flares in space-borne\nphotometric data using deep neural networks. Using a set of artificial data and\nreal photometric data we trained a set of neural networks, and found that the\nbest performing architectures were the recurrent neural networks (RNNs) using\nLong Short-Term Memory (LSTM) layers. The best trained network detected flares\nover {$5\\sigma$ with $\\gtrsim80$\\% recall and precision} and was also capable\nto distinguish typical false signals (e.g. maxima of RR Lyr stars) from real\nflares. Testing the network trained on Kepler data on TESS light curves showed\nthat the neural net is able to generalize and find flares - with similar\neffectiveness - in completely new data having previously unseen sampling and\ncharacteristics.\n"}
+{"abstract": "  This paper studies Imitation Learning from Observations alone (ILFO) where\nthe learner is presented with expert demonstrations that consist only of states\nvisited by an expert (without access to actions taken by the expert). We\npresent a provably efficient model-based framework MobILE to solve the ILFO\nproblem. MobILE involves carefully trading off strategic exploration against\nimitation - this is achieved by integrating the idea of optimism in the face of\nuncertainty into the distribution matching imitation learning (IL) framework.\nWe provide a unified analysis for MobILE, and demonstrate that MobILE enjoys\nstrong performance guarantees for classes of MDP dynamics that satisfy certain\nwell studied notions of structural complexity. We also show that the ILFO\nproblem is strictly harder than the standard IL problem by presenting an\nexponential sample complexity separation between IL and ILFO. We complement\nthese theoretical results with experimental simulations on benchmark OpenAI Gym\ntasks that indicate the efficacy of MobILE.\n"}
+{"abstract": "  The seven basic facial expression classifications are a basic way to express\ncomplex human emotions and are an important part of artificial intelligence\nresearch. Based on the traditional Bayesian neural network framework, the\nResNet18_BNN network constructed in this paper has been improved in the\nfollowing three aspects: (1) A new objective function is proposed, which is\ncomposed of the KL loss of uncertain parameters and the intersection of\nspecific parameters. Entropy loss composition. (2) Aiming at a special\nobjective function, a training scheme for alternately updating these two\nparameters is proposed. (3) Only model the parameters of the last convolution\ngroup. Through testing on the FER2013 test set, we achieved 71.5% and 73.1%\naccuracy in PublicTestSet and PrivateTestSet, respectively. Compared with\ntraditional Bayesian neural networks, our method brings the highest\nclassification accuracy gain.\n"}
+{"abstract": "  Motivated by emerging applications such as live-streaming e-commerce,\npromotions and recommendations, we introduce and solve a general class of\nnon-stationary multi-armed bandit problems that have the following two\nfeatures: (i) the decision maker can pull and collect rewards from up to\n$K\\,(\\ge 1)$ out of $N$ different arms in each time period; (ii) the expected\nreward of an arm immediately drops after it is pulled, and then\nnon-parametrically recovers as the arm's idle time increases. With the\nobjective of maximizing the expected cumulative reward over $T$ time periods,\nwe design a class of ``Purely Periodic Policies'' that jointly set a period to\npull each arm. For the proposed policies, we prove performance guarantees for\nboth the offline problem and the online problems. For the offline problem when\nall model parameters are known, the proposed periodic policy obtains an\napproximation ratio that is at the order of $1-\\mathcal O(1/\\sqrt{K})$, which\nis asymptotically optimal when $K$ grows to infinity. For the online problem\nwhen the model parameters are unknown and need to be dynamically learned, we\nintegrate the offline periodic policy with the upper confidence bound procedure\nto construct on online policy. The proposed online policy is proved to\napproximately have $\\widetilde{\\mathcal O}(N\\sqrt{T})$ regret against the\noffline benchmark. Our framework and policy design may shed light on broader\noffline planning and online learning applications with non-stationary and\nrecovering rewards.\n"}
+{"abstract": "  We associate to a sufficiently generic oriented matroid program and choice of\nlinear system of parameters a finite dimensional algebra, whose representation\ntheory is analogous to blocks of Bernstein--Gelfand--Gelfand category\n$\\mathcal{O}$. When the data above comes from a generic linear program for a\nhyperplane arrangement, we recover the algebra defined by\nBraden--Licata--Proudfoot--Webster.\n"}
+{"abstract": "  This paper presents a framework to enable a team of heterogeneous mobile\nrobots to model and sense a multiscale system. We propose a coupled strategy,\nwhere robots of one type collect high-fidelity measurements at a slow time\nscale and robots of another type collect low-fidelity measurements at a fast\ntime scale, for the purpose of fusing measurements together. The multiscale\nmeasurements are fused to create a model of a complex, nonlinear spatiotemporal\nprocess. The model helps determine optimal sensing locations and predict the\nevolution of the process. Key contributions are: i) consolidation of multiple\ntypes of data into one cohesive model, ii) fast determination of optimal\nsensing locations for mobile robots, and iii) adaptation of models online for\nvarious monitoring scenarios. We illustrate the proposed framework by modeling\nand predicting the evolution of an artificial plasma cloud. We test our\napproach using physical marine robots adaptively sampling a process in a water\ntank.\n"}
+{"abstract": "  We develop a distributed framework for the physics-informed neural networks\n(PINNs) based on two recent extensions, namely conservative PINNs (cPINNs) and\nextended PINNs (XPINNs), which employ domain decomposition in space and in\ntime-space, respectively. This domain decomposition endows cPINNs and XPINNs\nwith several advantages over the vanilla PINNs, such as parallelization\ncapacity, large representation capacity, efficient hyperparameter tuning, and\nis particularly effective for multi-scale and multi-physics problems. Here, we\npresent a parallel algorithm for cPINNs and XPINNs constructed with a hybrid\nprogramming model described by MPI $+$ X, where X $\\in\n\\{\\text{CPUs},~\\text{GPUs}\\}$. The main advantage of cPINN and XPINN over the\nmore classical data and model parallel approaches is the flexibility of\noptimizing all hyperparameters of each neural network separately in each\nsubdomain. We compare the performance of distributed cPINNs and XPINNs for\nvarious forward problems, using both weak and strong scalings. Our results\nindicate that for space domain decomposition, cPINNs are more efficient in\nterms of communication cost but XPINNs provide greater flexibility as they can\nalso handle time-domain decomposition for any differential equations, and can\ndeal with any arbitrarily shaped complex subdomains. To this end, we also\npresent an application of the parallel XPINN method for solving an inverse\ndiffusion problem with variable conductivity on the United States map, using\nten regions as subdomains.\n"}
+{"abstract": "  We analyse the impact of the selective pressure for the global optimisation\ncapabilities of steady-state EAs. For the standard bimodal benchmark function\n\\twomax we rigorously prove that using uniform parent selection leads to\nexponential runtimes with high probability to locate both optima for the\nstandard ($\\mu$+1)~EA and ($\\mu$+1)~RLS with any polynomial population sizes.\nOn the other hand, we prove that selecting the worst individual as parent leads\nto efficient global optimisation with overwhelming probability for reasonable\npopulation sizes. Since always selecting the worst individual may have\ndetrimental effects for escaping from local optima, we consider the performance\nof stochastic parent selection operators with low selective pressure for a\nfunction class called \\textsc{TruncatedTwoMax} where one slope is shorter than\nthe other. An experimental analysis shows that the EAs equipped with inverse\ntournament selection, where the loser is selected for reproduction and small\ntournament sizes, globally optimise \\textsc{TwoMax} efficiently and effectively\nescape from local optima of \\textsc{TruncatedTwoMax} with high probability.\nThus they identify both optima efficiently while uniform (or stronger)\nselection fails in theory and in practice. We then show the power of inverse\nselection on function classes from the literature where populations are\nessential by providing rigorous proofs or experimental evidence that it\noutperforms uniform selection equipped with or without a restart strategy. We\nconclude the paper by confirming our theoretical insights with an empirical\nanalysis of the different selective pressures on standard benchmarks of the\nclassical MaxSat and Multidimensional Knapsack Problems.\n"}
+{"abstract": "  We study the spectral dimensions and spectral asymptotics of Krein-Feller\noperators for arbitrary finite Borel measures on $\\left(0,1\\right).$\nConnections between the spectral dimension, the $L^{q}$-spectrum, the partition\nentropy and the optimised coarse multifractal dimension are established. In\nparticular, we show that the upper spectral dimension always corresponds to the\nfixed point of the $L^{q}$-spectrum of the corresponding measure. Natural\nbounds reveal intrinsic connections to the Minkowski dimension of the support\nof the associated Borel measure. Further, we give a sufficient condition on the\n$L^{q}$-spectrum to guarantee the existence of the spectral dimension. As an\napplication, we confirm the existence of the spectral dimension of\nself-conformal measures with and without overlap as well as of certain measures\nof pure point type. We construct a simple example for which the spectral\ndimension does not exist and determine explicitly its upper and lower spectral\ndimension.\n"}
+{"abstract": "  We propose a novel approach to the statistical analysis of simulation models\nand, especially, agent-based models (ABMs). Our main goal is to provide a fully\nautomated and model-independent tool-kit to inspect simulations and perform\ncounterfactual analysis. Our approach: (i) is easy-to-use by the modeller, (ii)\nimproves reproducibility of results, (iii) optimizes running time given the\nmodeller's machine, (iv) automatically chooses the number of required\nsimulations and simulation steps to reach user-specified statistical\nconfidence, and (v) automatically performs a variety of statistical tests. In\nparticular, our framework is designed to distinguish the transient dynamics of\nthe model from its steady-state behaviour (if any), estimate properties of the\nmodel in both \"phases\", and provide indications on the ergodic (or non-ergodic)\nnature of the simulated processes -- which, in turns allows one to gauge the\nreliability of a steady-state analysis. Estimates are equipped with statistical\nguarantees, allowing for robust comparisons across computational experiments.\nTo demonstrate the effectiveness of our approach, we apply it to two models\nfrom the literature: a large scale macro-financial ABM and a small scale\nprediction market model. Compared to prior analyses of these models, we obtain\nnew insights and we are able to identify and fix some erroneous conclusions.\n"}
+{"abstract": "  We investigate a Maclaurin inequality for vectors and its connection to an\nAleksandrov-type inequality for parallelepipeds.\n"}
+{"abstract": "  A quantum effect at ambient conditions is presented. A benzene dithiol (BDT)\nmolecule or a tetrahydrofuran (THF) molecule is used as a barrier molecule\nbridging the gold electrodes from a Mechanically Controllable Break junction.\nIt has been known for a long time that the environment of a junction couples to\nthe conduction. With a microscopic layer of fluid it is actually possible to\ninfluence this coupling. The electrode-molecule-electrode configuration is\nlined with a microscopic layer of THF fluid, also known as the partially wet\nphase. This is effectively decoupling the electrode-molecule-electrode system\nfrom environmental modes outside of this system. The effects on coherence and\nquantum interference are exceptional.\n"}
+{"abstract": "  Photodynamically active fibres (PAFs) are a novel class of stimulus-sensitive\nsystems capable of triggering antibiotic-free antibacterial effect on-demand\nwhen exposed to light. Despite their relevance in infection control, however,\nthe broad clinical applicability of PAFs has not yet been fully realised due to\nthe limited control in fibrous microstructure, cell tolerance and antibacterial\nactivity in the physiologic environment. We addressed this challenge by\ncreating semicrystalline electrospun fibres with varying content of\npoly[(L-lactide)-co-(glycolide)] (PLGA), poly(epsilon-caprolactone) (PCL) and\nmethylene blue (MB), whereby the effect of polymer morphology, fibre\ncomposition and photosensitiser (PS) uptake on wet state fibre behaviour and\nfunctions was studied. The presence of crystalline domains and PS-polymer\nsecondary interactions proved key to accomplishing long-lasting fibrous\nmicrostructure, controlled mass loss and controlled MB release profiles (37\ndegC, pH 7.4, 8 weeks). PAFs with equivalent PLGA:PCL weight ratio successfully\npromoted attachment and proliferation of L929 cells over a 7-day culture with\nand without light activation, while triggering up to 2.5 and 4 log reduction in\nE. coli and S. mutans viability, respectively. These results support the\ntherapeutic applicability of PAFs for frequently encountered bacterial\ninfections, opening up new opportunities in photodynamic fibrous systems with\nintegrated wound healing and infection control capabilities.\n"}
+{"abstract": "  In this work, we investigate the recovery of a parameter in a diffusion\nprocess given by the order of derivation in time for a class of diffusion type\nequations, including both classical and time-fractional diffusion equations,\nfrom the flux measurement observed at one point on the boundary. The\nmathematical model for time-fractional diffusion equations involves a\nDjrbashian-Caputo fractional derivative in time. We prove a uniqueness result\nin an unknown medium (e.g., diffusion coefficients, obstacle, initial condition\nand source), i.e., the recovery of the order of derivation in a diffusion\nprocess having several pieces of unknown information. The proof relies on the\nanalyticity of the solution at large time, asymptotic decay behavior, strong\nmaximum principle of the elliptic problem and suitable application of the Hopf\nlemma. Further we provide an easy-to-implement reconstruction algorithm based\non a nonlinear least-squares formulation, and several numerical experiments are\npresented to complement the theoretical analysis.\n"}
+{"abstract": "  The system of stability equations for galactic halos is under-determined in\nmost of the models of dark matter (DM). Conventionally, the issue is resolved\nby taking the temperature as a constant, and the chemical potential and the\nmass density as position-dependent variables. In this paper, to close the\nunder-determined set of equations, we remove the mass density using\nobservations and leave the temperature and the chemical potential as\nposition-dependent variables. We analyze observations of the mass profiles of\n175 late-type galaxies in the Spitzer Photometry \\& Accurate Rotation Curves\n(SPARC) database as well as 26 late-type dwarfs in the Little Things database,\nto construct the temperature profile of their DM halos by assuming that (1) DM\nin the halos obeys either the Fermi-Dirac or the Maxwell-Boltzmann\ndistribution, and (2) the halos are in the virial state. We derive the\ndispersion velocity of DM at the center of the halos and show that its\ncorrelation with the halo's total mass is consistent with the direct\nobservations of visible matter. Taking the latter agreement as a validation of\nour analysis, we derive the mass to the temperature of DM at the edge of the\nhalos and show that it is galaxy independent and is equal to\n$m/T_{R_{200}}\\simeq 10^{10}$ in natural units. In the thermal models of DM,\nsuch universal temperature is inherently assumed. In this paper, we derive the\nuniversal temperature from observations without imposing it by assumptions.\nTherefore, $T_{R_{200}}$ in the above ratio can be expressed in terms of the\ntemperature of the cosmic microwave background (CMB) at the time of DM\ndecoupling. This result is used to study possible cosmological scenarios. We\nshow that observations are at odds with (1) non-thermal DM, (2) hot DM, and (3)\ncollision-less cold DM. If DM is warm, we estimate its mass to be in the range\nof keV--MeV.\n"}
+{"abstract": "  Phonon polaritons (PhPs),light coupled to lattice vibrations,with in-plane\nhyperbolic dispersion exhibit ray-like propagation with large wavevectors and\nenhanced density of optical states along certain directions on a surface. As\nsuch, they have raised a surge of interest as they promise unprecedented\npossibilities for the manipulation of infrared light with planar circuitry and\nat the nanoscale. Here, we demonstrate, for the first time, the focusing of\nin-plane hyperbolic PhPs propagating along thin slabs of MoO3. To that end, we\ndeveloped metallic nanoantennas of convex geometries for both the efficient\nlaunching and focusing of the polaritons. Remarkably, the foci obtained exhibit\nenhanced near-field confinement and absorption compared to foci produced by\nin-plane isotropic PhPs. More intriguingly, foci sizes as small as lamdap/5\n=lamda0/50 were achieved (lamdap is the polariton wavelength and lamda0 the\nphoton wavelength). Focusing of in-plane hyperbolic polaritons introduces a\nfirst and most basic building block developing planar polariton optics\nutilizing in-plane anisotropic van der Waals materials and metasurfaces.\n"}
+{"abstract": "  Sequential matching using hand-crafted heuristics has been standard practice\nin route-based place recognition for enhancing pairwise similarity results for\nnearly a decade. However, precision-recall performance of these algorithms\ndramatically degrades when searching on short temporal window (TW) lengths,\nwhile demanding high compute and storage costs on large robotic datasets for\nautonomous navigation research. Here, influenced by biological systems that\nrobustly navigate spacetime scales even without vision, we develop a joint\nvisual and positional representation learning technique, via a sequential\nprocess, and design a learning-based CNN+LSTM architecture, trainable via\nbackpropagation through time, for viewpoint- and appearance-invariant place\nrecognition. Our approach, Sequential Place Learning (SPL), is based on a CNN\nfunction that visually encodes an environment from a single traversal, thus\nreducing storage capacity, while an LSTM temporally fuses each visual embedding\nwith corresponding positional data -- obtained from any source of motion\nestimation -- for direct sequential inference. Contrary to classical two-stage\npipelines, e.g., match-then-temporally-filter, our network directly eliminates\nfalse-positive rates while jointly learning sequence matching from a single\nmonocular image sequence, even using short TWs. Hence, we demonstrate that our\nmodel outperforms 15 classical methods while setting new state-of-the-art\nperformance standards on 4 challenging benchmark datasets, where one of them\ncan be considered solved with recall rates of 100% at 100% precision, correctly\nmatching all places under extreme sunlight-darkness changes. In addition, we\nshow that SPL can be up to 70x faster to deploy than classical methods on a 729\nkm route comprising 35,768 consecutive frames. Extensive experiments\ndemonstrate the... Baseline code available at\nhttps://github.com/mchancan/deepseqslam\n"}
+{"abstract": "  Policy gradient methods are an attractive approach to multi-agent\nreinforcement learning problems due to their convergence properties and\nrobustness in partially observable scenarios. However, there is a significant\nperformance gap between state-of-the-art policy gradient and value-based\nmethods on the popular StarCraft Multi-Agent Challenge (SMAC) benchmark. In\nthis paper, we introduce semi-on-policy (SOP) training as an effective and\ncomputationally efficient way to address the sample inefficiency of on-policy\npolicy gradient methods. We enhance two state-of-the-art policy gradient\nalgorithms with SOP training, demonstrating significant performance\nimprovements. Furthermore, we show that our methods perform as well or better\nthan state-of-the-art value-based methods on a variety of SMAC tasks.\n"}
+{"abstract": "  Deep neural networks are powerful machines for visual pattern recognition,\nbut reasoning tasks that are easy for humans may still be difficult for neural\nmodels. Humans possess the ability to extrapolate reasoning strategies learned\non simple problems to solve harder examples, often by thinking for longer. For\nexample, a person who has learned to solve small mazes can easily extend the\nvery same search techniques to solve much larger mazes by spending more time.\nIn computers, this behavior is often achieved through the use of algorithms,\nwhich scale to arbitrarily hard problem instances at the cost of more\ncomputation. In contrast, the sequential computing budget of feed-forward\nneural networks is limited by their depth, and networks trained on simple\nproblems have no way of extending their reasoning to accommodate harder\nproblems. In this work, we show that recurrent networks trained to solve simple\nproblems with few recurrent steps can indeed solve much more complex problems\nsimply by performing additional recurrences during inference. We demonstrate\nthis algorithmic behavior of recurrent networks on prefix sum computation,\nmazes, and chess. In all three domains, networks trained on simple problem\ninstances are able to extend their reasoning abilities at test time simply by\n\"thinking for longer.\"\n"}
+{"abstract": "  We propose a new end-to-end neural diarization (EEND) system that is based on\nConformer, a recently proposed neural architecture that combines convolutional\nmappings and Transformer to model both local and global dependencies in speech.\nWe first show that data augmentation and convolutional subsampling layers\nenhance the original self-attentive EEND in the Transformer-based EEND, and\nthen Conformer gives an additional gain over the Transformer-based EEND.\nHowever, we notice that the Conformer-based EEND does not generalize as well\nfrom simulated to real conversation data as the Transformer-based model. This\nleads us to quantify the mismatch between simulated data and real speaker\nbehavior in terms of temporal statistics reflecting turn-taking between\nspeakers, and investigate its correlation with diarization error. By mixing\nsimulated and real data in EEND training, we mitigate the mismatch further,\nwith Conformer-based EEND achieving 24% error reduction over the baseline\nSA-EEND system, and 10% improvement over the best augmented Transformer-based\nsystem, on two-speaker CALLHOME data.\n"}
+{"abstract": "  Low-Rank Representation (LRR) highly suffers from discarding the locality\ninformation of data points in subspace clustering, as it may not incorporate\nthe data structure nonlinearity and the non-uniform distribution of\nobservations over the ambient space. Thus, the information of the observational\ndensity is lost by the state-of-art LRR models, as they take a constant number\nof adjacent neighbors into account. This, as a result, degrades the subspace\nclustering accuracy in such situations. To cope with deficiency, in this paper,\nwe propose to consider a hypergraph model to facilitate having a variable\nnumber of adjacent nodes and incorporating the locality information of the\ndata. The sparsity of the number of subspaces is also taken into account. To do\nso, an optimization problem is defined based on a set of regularization terms\nand is solved by developing a tensor Laplacian-based algorithm. Extensive\nexperiments on artificial and real datasets demonstrate the higher accuracy and\nprecision of the proposed method in subspace clustering compared to the\nstate-of-the-art methods. The outperformance of this method is more revealed in\npresence of inherent structure of the data such as nonlinearity, geometrical\noverlapping, and outliers.\n"}
+{"abstract": "  In light of increasing recent attention to political polarization,\nunderstanding how polarization can arise poses an important theoretical\nquestion. While more classical models of opinion dynamics seem poorly equipped\nto study this phenomenon, a recent novel approach by H\\k{a}z{\\l}a, Jin, Mossel,\nand Ramnarayan (HJMR) proposes a simple geometric model of opinion evolution\nthat provably exhibits strong polarization in specialized cases. Moreover,\npolarization arises quite organically in their model: in each time step, each\nagent updates opinions according to their correlation/response with an issue\ndrawn at random. However, their techniques do not seem to extend beyond a set\nof special cases they identify, which benefit from fragile symmetry or\ncontractiveness assumptions, leaving open how general this phenomenon really\nis.\n  In this paper, we further the study of polarization in related geometric\nmodels. We show that the exact form of polarization in such models is quite\nnuanced: even when strong polarization does not hold, it is possible for weaker\nnotions of polarization to nonetheless attain. We provide a concrete example\nwhere weak polarization holds, but strong polarization provably fails. However,\nwe show that strong polarization provably holds in many variants of the HJMR\nmodel, which are also robust to a wider array of distributions of random issues\n-- this indicates that the form of polarization introduced by HJMR is more\nuniversal than suggested by their special cases. We also show that the weaker\nnotions connect more readily to the theory of Markov chains on general state\nspaces.\n"}
+{"abstract": "  Finite mixtures of regressions with fixed covariates are a commonly used\nmodel-based clustering methodology to deal with regression data. However, they\nassume assignment independence, i.e. the allocation of data points to the\nclusters is made independently of the distribution of the covariates. In order\nto take into account the latter aspect, finite mixtures of regressions with\nrandom covariates, also known as cluster-weighted models (CWMs), have been\nproposed in the univariate and multivariate literature. In this paper, the CWM\nis extended to matrix data, e.g. those data where a set of variables are\nsimultaneously observed at different time points or locations. Specifically,\nthe cluster-specific marginal distribution of the covariates, and the\ncluster-specific conditional distribution of the responses given the\ncovariates, are assumed to be matrix normal. Maximum likelihood parameter\nestimates are derived using an ECM algorithm. Parameter recovery,\nclassification assessment and the capability of the BIC to detect the\nunderlying groups are analyzed on simulated data. Finally, two real data\napplications concerning educational indicators and the Italian non-life\ninsurance market are presented.\n"}
+{"abstract": "  We present a novel direction-aware feature-level frequency decomposition\nnetwork for single image deraining. Compared with existing solutions, the\nproposed network has three compelling characteristics. First, unlike previous\nalgorithms, we propose to perform frequency decomposition at feature-level\ninstead of image-level, allowing both low-frequency maps containing structures\nand high-frequency maps containing details to be continuously refined during\nthe training procedure. Second, we further establish communication channels\nbetween low-frequency maps and high-frequency maps to interactively capture\nstructures from high-frequency maps and add them back to low-frequency maps\nand, simultaneously, extract details from low-frequency maps and send them back\nto high-frequency maps, thereby removing rain streaks while preserving more\ndelicate features in the input image. Third, different from existing algorithms\nusing convolutional filters consistent in all directions, we propose a\ndirection-aware filter to capture the direction of rain streaks in order to\nmore effectively and thoroughly purge the input images of rain streaks. We\nextensively evaluate the proposed approach in three representative datasets and\nexperimental results corroborate our approach consistently outperforms\nstate-of-the-art deraining algorithms.\n"}
+{"abstract": "  Energy landscapes are high-dimensional surfaces representing the dependence\nof system energy on variable configurations, which determine crucially the\nsystem's emergent behavior but are difficult to be analyzed due to their\nhigh-dimensional nature. In this article, we introduce an approach to reveal\nthe complete energy landscapes of small spin glasses and Boolean satisfiability\nproblems, which also unravels their non-equilibrium dynamics at an arbitrary\ntemperature for an arbitrarily long time. In contrary to our common belief, our\nresults show that it can be less likely to identify the ground states when\ntemperature decreases, due to trapping in individual local minima, which ceases\nat different time, leading to multiple abrupt jumps with time in the\nground-state probability. Simulations agree well with theoretical predictions\non these remarkable phenomena. Finally, for large systems, we introduce a\nvariant approach to extract partially the energy landscapes and observe both\nanalytically and in simulations similar phenomena. This work introduces new\nmethodology to unravel the non-equilibrium dynamics of glassy systems, and\nprovides us with a clear, complete and new physical picture on their long-time\nbehaviors inaccessible by modern numerics.\n"}
+{"abstract": "  From a theoretical view, this paper addresses the general problem of\ndesigning ordinal classification methods based on comparing actions with subset\nof actions, which are representative of their classes (categories). The basic\ndemand of the proposal consists in setting a relational system (D, S), where S\nis a reflexive relation compatible with the preferential order of the set of\nclasses, and D is a transitive relation such that D is a subset of S. Different\nordinal classification methods can be derived from diverse model of preferences\nfulfilling the basic conditions on S and D. Two complementary assignment\nprocedures compose each method, which correspond through the transposition\noperation and should be used complementarily. The methods work under relatively\nslight conditions on the representative actions and satisfy several fundamental\nproperties. ELECTRE TRI-nC, INTERCLASS-nC, and the hierarchical ELECTRE TRI-nC\nwith interacting criteria, can be considered as particular cases of this\ngeneral framework.\n"}
+{"abstract": "  NuSTAR observed the black hole candidate XTE J1908$+$094 during its 2013 and\n2019 outbursts. We use relativistic reflection to measure the spin of the black\nhole through 19 different assumptions of relxill flavors and parameter\ncombinations. The most favored model in terms of Deviance Information Criterion\n(DIC) measures the spin of the black hole to be $a = 0.55^{+0.29}_{-0.45}$, and\nan inclination of $\\theta=27^{+2}_{-3}$ degrees ($1\\sigma$ statistical errors).\nWe look at the effects of coronal geometry assumptions and density of the\naccretion disk on the spin prediction. All 19 tested models provide consistent\nspin estimates. We discuss the evolution of spin measurement techniques using\nrelativistic reflection in X-ray binaries and discuss the implications of this\nspin measurement in reconciling the distributions of stellar mass black hole\nspin measurements made through X-ray and gravitational wave observations.\n"}
+{"abstract": "  The search for exoplanets is pushing adaptive optics systems on ground-based\ntelescopes to their limits. One of the major limitations at small angular\nseparations, exactly where exoplanets are predicted to be, is the servo-lag of\nthe adaptive optics systems. The servo-lag error can be reduced with predictive\ncontrol where the control is based on the future state of the atmospheric\ndisturbance. We propose to use a linear data-driven integral predictive\ncontroller based on subspace methods that is updated in real time. The new\ncontroller only uses the measured wavefront errors and the changes in the\ndeformable mirror commands, which allows for closed-loop operation without\nrequiring pseudo-open loop reconstruction. This enables operation with\nnon-linear wavefront sensors such as the pyramid wavefront sensor. We show that\nthe proposed controller performs near-optimal control in simulations for both\nstationary and non-stationary disturbances and that we are able to gain several\norders of magnitude in raw contrast. The algorithm has been demonstrated in the\nlab with MagAO-X, where we gain more than two orders of magnitude in contrast.\n"}
+{"abstract": "  The behavior of Bayesian model averaging (BMA) for the normal linear\nregression model in the presence of influential observations that contribute to\nmodel misfit is investigated. Remedies to attenuate the potential negative\nimpacts of such observations on inference and prediction are proposed. The\nmethodology is motivated by the view that well-behaved residuals and good\npredictive performance often go hand-in-hand. Focus is placed on regression\nmodels that use variants on Zellner's g prior. Studying the impact of various\nforms of model misfit on BMA predictions in simple situations points to\nprescriptive guidelines for \"tuning\" Zellner's g prior to obtain optimal\npredictions. The tuning of the prior distribution is obtained by considering\ntheoretical properties that should be enjoyed by the optimal fits of the\nvarious models in the BMA ensemble. The methodology can be thought of as an\n\"empirical Bayes\" approach to modeling, as the data help to inform the\nspecification of the prior in an attempt to attenuate the negative impact of\ninfluential cases.\n"}
+{"abstract": "  This paper considers the information bottleneck (IB) problem of a Rayleigh\nfading multiple-input multiple-out (MIMO) channel with an oblivious relay. The\nrelay is constrained to operate without knowledge of the codebooks, i.e., it\nperforms oblivious processing. Moreover, due to the bottleneck constraint, it\nis impossible for the relay to inform the destination node of the perfect\nchannel state information (CSI) in each channel realization. To evaluate the\nbottleneck rate, we first provide an upper bound by assuming that the\ndestination node can get the perfect CSI at no cost. Then, we provide four\nachievable schemes where each scheme satisfies the bottleneck constraint and\ngives a lower bound to the bottleneck rate. In the first and second schemes,\nthe relay splits the capacity of the relay-destination link into two parts, and\nconveys both the CSI and its observation to the destination node. Due to CSI\ntransmission, the performance of these two schemes is sensitive to the MIMO\nchannel dimension, especially the channel input dimension. To ensure that it\nstill performs well when the channel dimension grows large, in the third and\nfourth achievable schemes, the relay only transmits compressed observation to\nthe destination node. Numerical results show that with simple symbol-by-symbol\noblivious relay processing and compression, the proposed achievable schemes\nwork well and can demonstrate lower bounds coming quite close to the upper\nbound on a wide range of relevant system parameters.\n"}
+{"abstract": "  In this paper, we present a system for remote health-monitoring of first\nresponders over TETRA radio links. The system features a smart garment that\nperiodically records and sends physiological parameters of first responders to\na remote agent, which processes the recordings and feeds back the health-status\nnotifications and warnings in the form of electrotactile stimuli. The choice of\nTETRA as the connectivity solution is driven by its routine use by first\nresponders all over the world, thus representing a convenient and\neconomically-effective connectivity basis. Although the support for data\ncommunications in TETRA is rather limited and in practice reduced to the Short\nData Service, we show that TETRA can serve the intended purpose in the\nconsidered scenario, achieving tolerable delay and message-loss performance.\nMoreover, when the system is examined and optimized in terms of the peak\nAge-of-Information, a metric suitable to characterize the quasi-periodic nature\nof the monitoring process, its performance becomes rather favorable, enabling\ntimely monitoring of the first responders' health status.\n"}
+{"abstract": "  Every integral current in a locally compact metric space $X$ can be\napproximated by a Lipschitz chain with respect to the normal mass, provided\nthat Lipschitz maps into $X$ can be extended slightly.\n"}
+{"abstract": "  Graphics Processing Units (GPUs) are currently the dominating programmable\narchitecture for Deep Learning (DL) accelerators. The adoption of Field\nProgrammable Gate Arrays (FPGAs) in DL accelerators is however getting\nmomentum. In this paper, we demonstrate that Direct Hardware Mapping (DHM) of a\nConvolutional Neural Network (CNN) on an embedded FPGA substantially\noutperforms a GPU implementation in terms of energy efficiency and execution\ntime. However, DHM is highly resource intensive and cannot fully substitute the\nGPU when implementing a state-of-the-art CNN. We thus propose a hybrid FPGA-GPU\nDL acceleration method and demonstrate that heterogeneous acceleration\noutperforms GPU acceleration even including communication overheads.\n  Experimental results are conducted on a heterogeneous multi-platform setup\nembedding an Nvidia(R) Jetson TX2 CPU-GPU board and an Intel(R) Cyclone10GX\nFPGA board. The SqueezeNet, MobileNetv2, and ShuffleNetv2 mobile-oriented CNNs\nare experimented. We show that heterogeneous FPG-AGPU acceleration outperforms\nGPU acceleration for classification inference task over MobileNetv2 (12%-30%\nenergy reduction, 4% to 26% latency reduction), SqueezeNet (21%-28% energy\nreduction, same latency), and ShuffleNetv2 (25% energy reduction, 21% latency\nreduction).\n"}
+{"abstract": "  We address fundamental limitations of quantum teleportation -- the process of\ntransferring quantum information using classical communication and preshared\nentanglement. We develop a new teleportation protocol based upon the idea of\nusing ancillary entanglement catalytically, i.e. without depleting it. This\nprotocol is then used to show that catalytic entanglement allows for a\nnoiseless quantum channel to be simulated with a quality that could never be\nachieved using only entanglement from the shared state, even for catalysts with\na small dimension. On the one hand, this allows for a more faithful\ntransmission of quantum information using generic states and a fixed amount of\nconsumed entanglement. On the other hand, this shows, for the first time, that\nentanglement catalysis provides a genuine advantage in a generic\nquantum-information processing task. Finally, we show that similar ideas can be\ndirectly applied to study quantum catalysis for more general problems in\nquantum mechanics. As an application, we show that catalysts can activate\nso-called passive states, a concept that finds widespread application e.g. in\nquantum thermodynamics.\n"}
+{"abstract": "  Cavity optomechanical systems enable fine manipulation of nanomechanical\ndegrees of freedom with light, adding operational functionality and impacting\ntheir appeal in photonic technologies. We show that distinct mechanical modes\ncan be exploited with a temporally modulated laser drive to steer between\nbistable steady states induced by changes of cavity radiation pressure. We\ninvestigate the influence of thermo-optic nonlinearity on these Floquet\ndynamics and find that it can inhibit or enhance the performance of the\ncoupling mechanism in contrast to their often performance limiting character.\nOur results provide new techniques for the characterization of thermal\nproperties and the control of optomechanical systems in sensing and\ncomputational applications\n"}
+{"abstract": "  We present a new particle filtering algorithm for nonlinear systems in the\ndiscrete-time setting. Our algorithm is based on the Stein variational gradient\ndescent (SVGD) framework, which is a general approach to sample from a target\ndistribution. We merge the standard two-step paradigm in particle filtering\ninto one step so that SVGD can be used. A distinguishing feature of the\nproposed algorithm is that, unlike most particle filtering methods, all the\nparticles at any time step are equally weighted and thus no update on the\nweights is needed. We further extended our algorithm to allow for updating\nprevious particles within a sliding window. This strategy may improve the\nreliability of the algorithm with respect to unexpected disturbance in the\ndynamics or outlier-measurements. The efficacy of the proposed algorithms is\nillustrated through several numerical examples in comparison with a standard\nparticle filtering method.\n"}
+{"abstract": "  Fomalhaut C (LP 876-10) is a low mass M4V star in the intriguing Fomalhaut\ntriple system and, like Fomalhaut A, possesses a debris disc. It is one of very\nfew nearby M-dwarfs known to host a debris disc and of these has by far the\nlowest stellar mass. We present new resolved observations of the debris disc\naround Fomalhaut C with the Atacama Large Millimetre Array which allow us to\nmodel its properties and investigate the system's unique history. The ring has\na radius of 26 au and a narrow full width at half maximum of at most 4.2 au. We\nfind a 3$\\sigma$ upper limit on the eccentricity of 0.14, neither confirming\nnor ruling out previous dynamic interactions with Fomalhaut A that could have\naffected Fomalhaut C's disc. We detect no $^{12}$CO J=3-2 emission in the\nsystem and do not detect the disc in scattered light with HST/STIS or\nVLT/SPHERE. We find the original Herschel detection to be consistent with our\nALMA model's radial size. We place the disc in the context of the wider debris\ndisc population and find that its radius is as expected from previous disc\nradius-host luminosity trends. Higher signal-to-noise observations of the\nsystem would be required to further constrain the disc properties and provide\nfurther insight to the history of the Fomalhaut triple system as a whole.\n"}
+{"abstract": "  The phenomenon of charge pumping and the modern theory of electric\npolarization are reconsidered by analytically taking into account emergent\nnon-Hermitian contributions. These are accounted for through the use of an\nextended definition of the velocity operator and are determined by means of a\ndynamic Hellmann-Feynman theorem (DHFT) that we derive here for the first time.\nThe DHFT introduces generalized Berry curvatures and it is valid for\ncalculating observables nonperturbatively, hence with results valid to all\norders of the external fields. By using the extended velocity operator we\nrigorously show how the charge pumping is linked up with the boundaries of the\nmaterial (with the non-Hermiticity being essential for this connection), and by\nmeans of the DHFT we show that the well-known topological quantization of the\npumped charge breaks down due to a nonintegrable Aharonov-Anandan phase in\ndriven non-equilibrium processes whenever the periodic gauge cannot be applied\nto the Floquet-Bloch states. Likewise, we show that the electronic polarization\nchange has an additional non-Hermitian contribution, which is overlooked in the\nmodern theory of electric polarization. The non-Hermitian contribution is by\ndefinition a bulk quantity that may equally be evaluated as a boundary quantity\ndue to a symmetric structure that allows the bulk integration to be transformed\ninto a boundary one. This non-Hermitian contribution is very sensitive to the\nrealistic boundary conditions imposed on the wavefunctions and it is therefore\nexpected to be significant in biased insulators where charge accumulation over\ntheir boundaries is present during the process that causes the polarization\nchange. Finally, we show how a well-defined surface-charge theorem can be\nformulated in terms of the boundary non-Hermitian contribution.\n"}
+{"abstract": "  In this paper, we propose a Bayesian framework to create two dimensional\nionospheric images of high spatio-temporal resolution to monitor ionospheric\nirregularities as measured by the S4 index. Here, we recast the standard\nBayesian recursive filtering for a linear Gaussian state-space model, also\nreferred to as the Kalman filter, first by augmenting the (pierce point)\nobservation model with connectivity information stemming from the insight and\nassumptions/standard modeling about the spatial distribution of the\nscintillation activity on the ionospheric shell at 350 km altitude. Thus, we\nachieve to handle the limited spatio-temporal observations. Then, by\nintroducing a set of Kalman filters running in parallel, we mitigate the\nuncertainty related to a tuning parameter of the proposed augmented model. The\noutput images are a weighted average of the state estimates of the individual\nfilters. We demonstrate our approach by rendering two dimensional real-time\nionospheric images of S4 amplitude scintillation at 350 km over South America\nwith temporal resolution of one minute. Furthermore, we employ extra S4 data\nthat was not used in producing these ionospheric images, to check and verify\nthe ability of our images to predict this extra data in particular ionospheric\npierce points. Our results show that in areas with a network of ground\nreceivers with a relatively good coverage (e.g. within a couple of kilometers\ndistance) the produced images can provide reliable real-time results. Our\nproposed algorithmic framework can be readily used to visualize real-time\nionospheric images taking as inputs the available scintillation data provided\nfrom freely available web-servers.\n"}
+{"abstract": "  The Sunyaev-Zel'dovich (SZ) effect introduces a specific distortion of the\nblackbody spectrum of the cosmic microwave background (CMB) radiation when it\nscatters off hot gas in clusters of galaxies. The frequency dependence of the\ndistortion is only independent of the cluster redshift when the evolution of\nthe CMB radiation is adiabatic. Using 370 clusters within the redshift range\n$0.07\\lesssim z\\lesssim1.4$ from the largest SZ-selected cluster sample to date\nfrom the Atacama Cosmology Telescope, we provide new constraints on the\ndeviation of CMB temperature evolution from the standard model\n$\\alpha=0.017^{+0.029}_{-0.032}$, where $T(z)=T_0(1+z)^{1-\\alpha}$. This result\nis consistent with no deviation from the standard adiabatic model. Combining it\nwith previous, independent datasets we obtain a joint constraint of\n$\\alpha=-0.001\\pm0.012$. Attributing deviation from adiabaticity to the decay\nof dark energy, this result constrains its effective equation of state\n$w_\\mathrm{eff}=-0.998^{+0.008}_{-0.010}$.\n"}
+{"abstract": "  We perform on-the-fly non-adiabatic molecular dynamics simulations using the\nsymmetrical quasi-classical (SQC) approach with the recently suggested\nmolecular Tully models: ethylene and fulvene. We attempt to provide benchmarks\nof the SQC methods using both the square and the triangle windowing schemes as\nwell as the recently proposed electronic zero-point-energy correction scheme\n(so-called the gamma correction). We use the quasi-diabatic propagation scheme\nto directly interface the diabatic SQC methods with adiabatic electronic\nstructure calculations. Our results showcase the drastic improvement of the\naccuracy by using the trajectory-adjusted gamma-corrections, which outperform\nthe widely used trajectory surface hopping method with decoherence corrections.\nThese calculations provide useful and non-trivial tests to systematically\ninvestigate the numerical performance of various diabatic quantum dynamics\napproaches, going beyond simple diabatic model systems that have been used as\nthe major workhorse in the quantum dynamics field. At the same time, these\navailable benchmark studies will also likely foster the development of new\nquantum dynamics approaches based on these techniques.\n"}
+{"abstract": "  Minimal algebraic surfaces of general type $X$ such that\n$K^2_X=2\\chi(\\mathcal{O}_X)-6$ or $K^2_X=2\\chi(\\mathcal{O}_X)-5$ are called\nHorikawa surfaces. In this note we study $\\mathbb{Z}^2_2$-actions on Horikawa\nsurfaces. The main result is that all the connected components of Gieseker's\nmoduli space of canonical models of surfaces of general type with invariants\nsatisfying these relations contain surfaces with $\\mathbb{Z}^2_2$-actions.\n"}
+{"abstract": "  We investigate the electromagnetic form factor $F(q^2)$ of the meson by using\nthe solvable $\\phi^{3}$ scalar field model in $(1+1)$ dimensions. As the\ntransverse rotations are absent in $(1+1)$ dimensions, the advantage of the\nlight-front dynamics (LFD) with the light-front time $x^+ = x^0 + x^3$ as the\nevolution parameter is maximized in contrast to the usual instant form dynamics\n(IFD) with the ordinary time $x^0$ as the evolution parameter. In LFD, the\nindividual $x^+$-ordered amplitudes contributing to $F(q^2)$ are invariant\nunder the boost, i.e., frame-independent, while the individual $x^0$-ordered\namplitudes in IFD are not invariant under the boost but dependent on the\nreference frame. The LFD allows to get the analytic result for the one-loop\ntriangle diagram which covers not only the spacelike ($q^{2}<0$) but also\ntimelike region ($q^{2}>0$). Using the analytic results, we verify that the\nreal and imaginary parts of the form factor satisfy the dispersion relations in\nthe entire $q^{2}$ space. Comparing with the results in $(3+1)$ dimensions, we\ndiscuss the transverse momentum effects on $F(q^2)$ . We also discuss the\nlongitudinal charge density in terms of the boost invariant variable $\\tilde z\n= p^+ x^-$ in LFD.\n"}
+{"abstract": "  We report a comprehensive de Haas--van Alphen (dHvA) study of the\nheavy-fermion material CeRhIn$_5$ in magnetic fields up to 70~T. Several dHvA\nfrequencies gradually emerge at high fields as a result of magnetic breakdown.\nAmong them is the thermodynamically important $\\beta_1$ branch, which has not\nbeen observed so far. Comparison of our angule-dependent dHvA spectra with\nthose of the non-$4f$ compound LaRhIn$_5$ and with band-structure calculations\nevidences that the Ce $4f$ electrons in CeRhIn$_5$ remain localized over the\nwhole field range. This rules out any significant Fermi-surface reconstruction,\neither at the suggested nematic phase transition at $B^{*}\\approx$ 30~T or at\nthe putative quantum critical point at $B_c \\simeq$ 50~T. Our results rather\ndemonstrate the robustness of the Fermi surface and the localized nature of the\n4$f$ electrons inside and outside of the antiferromagnetic phase.\n"}
+{"abstract": "  We study turbulent flows in a smooth straight pipe of circular cross--section\nup to $Re_{\\tau} \\approx 6000$ using direct--numerical-simulation (DNS) of the\nNavier--Stokes equations. The DNS results highlight systematic deviations from\nPrandtl friction law, amounting to about $2\\%$, which would extrapolate to\nabout $4\\%$ at extreme Reynolds numbers. Data fitting of the DNS friction\ncoefficient yields an estimated von K\\'arm\\'an constant $k \\approx 0.387$,\nwhich nicely fits the mean velocity profile, and which supports universality of\ncanonical wall-bounded flows. The same constant also applies to the pipe\ncenterline velocity, thus providing support for the claim that the asymptotic\nstate of pipe flow at extreme Reynolds numbers should be plug flow. At the\nReynolds numbers under scrutiny, no evidence for saturation of the logarithmic\ngrowth of the inner peak of the axial velocity variance is found. Although no\nouter peak of the velocity variance directly emerges in our DNS, we provide\nstrong evidence that it should appear at $Re_{\\tau} \\gtrsim 10^4$, as a result\nof turbulence production exceeding dissipation over a large part of the outer\nwall layer, thus invalidating the classical equilibrium hypothesis.\n"}
+{"abstract": "  The growth of online consumer health questions has led to the necessity for\nreliable and accurate question answering systems. A recent study showed that\nmanual summarization of consumer health questions brings significant\nimprovement in retrieving relevant answers. However, the automatic\nsummarization of long questions is a challenging task due to the lack of\ntraining data and the complexity of the related subtasks, such as the question\nfocus and type recognition. In this paper, we introduce a reinforcement\nlearning-based framework for abstractive question summarization. We propose two\nnovel rewards obtained from the downstream tasks of (i) question-type\nidentification and (ii) question-focus recognition to regularize the question\ngeneration model. These rewards ensure the generation of semantically valid\nquestions and encourage the inclusion of key medical entities/foci in the\nquestion summary. We evaluated our proposed method on two benchmark datasets\nand achieved higher performance over state-of-the-art models. The manual\nevaluation of the summaries reveals that the generated questions are more\ndiverse and have fewer factual inconsistencies than the baseline summaries\n"}
+{"abstract": "  How to estimate heterogeneity, e.g. the effect of some variable differing\nacross observations, is a key question in political science. Methods for doing\nso make simplifying assumptions about the underlying nature of the\nheterogeneity to draw reliable inferences. This paper allows a common way of\nsimplifying complex phenomenon (placing observations with similar effects into\ndiscrete groups) to be integrated into regression analysis. The framework\nallows researchers to (i) use their prior knowledge to guide which groups are\npermissible and (ii) appropriately quantify uncertainty. The paper does this by\nextending work on \"structured sparsity\" from a traditional penalized likelihood\napproach to a Bayesian one by deriving new theoretical results and inferential\ntechniques. It shows that this method outperforms state-of-the-art methods for\nestimating heterogeneous effects when the underlying heterogeneity is grouped\nand more effectively identifies groups of observations with different effects\nin observational data.\n"}
+{"abstract": "  We study the Minkowski formula of conformal Killing-Yano two-forms in a\nspacetime of constant curvature. We establish the spacetime Alexandrov theorem\nwith a free boundary.\n"}
+{"abstract": "  State-of-the-art transformer models use pairwise dot-product based\nself-attention, which comes at a computational cost quadratic in the input\nsequence length. In this paper, we investigate the global structure of\nattention scores computed using this dot product mechanism on a typical\ndistribution of inputs, and study the principal components of their variation.\nThrough eigen analysis of full attention score matrices, as well as of their\nindividual rows, we find that most of the variation among attention scores lie\nin a low-dimensional eigenspace. Moreover, we find significant overlap between\nthese eigenspaces for different layers and even different transformer models.\nBased on this, we propose to compute scores only for a partial subset of token\npairs, and use them to estimate scores for the remaining pairs. Beyond\ninvestigating the accuracy of reconstructing attention scores themselves, we\ninvestigate training transformer models that employ these approximations, and\nanalyze the effect on overall accuracy. Our analysis and the proposed method\nprovide insights into how to balance the benefits of exact pair-wise attention\nand its significant computational expense.\n"}
+{"abstract": "  It is necessary to improve the performance of some special classes or to\nparticularly protect them from attacks in adversarial learning. This paper\nproposes a framework combining cost-sensitive classification and adversarial\nlearning together to train a model that can distinguish between protected and\nunprotected classes, such that the protected classes are less vulnerable to\nadversarial examples. We find in this framework an interesting phenomenon\nduring the training of deep neural networks, called Min-Max property, that is,\nthe absolute values of most parameters in the convolutional layer approach zero\nwhile the absolute values of a few parameters are significantly larger becoming\nbigger. Based on this Min-Max property which is formulated and analyzed in a\nview of random distribution, we further build a new defense model against\nadversarial examples for adversarial robustness improvement. An advantage of\nthe built model is that it does no longer need adversarial training, and thus,\nhas a higher computational efficiency than most existing models of needing\nadversarial training. It is experimentally confirmed that, regarding the\naverage accuracy of all classes, our model is almost as same as the existing\nmodels when an attack does not occur and is better than the existing models\nwhen an attack occurs. Specifically, regarding the accuracy of protected\nclasses, the proposed model is much better than the existing models when an\nattack occurs.\n"}
+{"abstract": "  We continue the study of Confined Vortex Surfaces (\\CVS{}) that we introduced\nin the previous paper.\n  We classify the solutions of the \\CVS{} equation and find the analytical\nformula for the velocity field for arbitrary background strain eigenvalues in\nthe stable region.\n  The vortex surface cross-section has the form of four symmetric hyperbolic\nsheets with a simple equation $|y| |x|^\\mu =1$ in each quadrant of the tube\ncross-section ($x y $ plane).\n  We use the dilute gas approximation for the vorticity structures in a\nturbulent flow, assuming their size is much smaller than the mean distance\nbetween them.\n  We introduce the Gaussian random background strain for each vortex surface as\nan accumulation of a large number of small random contributions coming from\nother surfaces far away. We compute this self-consistent background strain,\nrelating the variance of the strain to the energy dissipation rate.\n  We find a universal asymmetric distribution for energy dissipation.\n  A new phenomenon is a probability distribution of the shape of the profile of\nthe vortex tube in the $x y$ plane.\n  This phenomenon naturally leads to imitation of the \"multi-fractal\" scaling\nof the moments of velocity difference $v(\\vec r_1) - \\vec v(\\vec r_2)$.\n  These moments have a nontrivial dependence of $n, \\log |r_1 - r_2|$,\napproximating power laws with nonlinear index $\\zeta(n)$. The rough estimate we\nprovide here is not matching the observed DNS data, which may indicate\nnecessity of the full 3D solution of the \\CVS{} equations.\n  We argue that the approximate relations for these moments suggested in a\nrecent paper by Sreenivasan and Yakhot are consistent with the \\CVS{} theory.\nWe reinterpret their renormalization parameter $\\alpha\\approx 0.95$ in the\nBernoulli law $ p = - \\frac{1}{2}\\alpha \\vec v^2$ as a probability to find no\nvortex surface at a random point in space.\n"}
+{"abstract": "  Cooperation in multi-agent and multi-robot systems can help agents build\nvarious formations, shapes, and patterns presenting corresponding functions and\npurposes adapting to different situations. Relationships between agents such as\ntheir spatial proximity and functional similarities could play a crucial role\nin cooperation between agents. Trust level between agents is an essential\nfactor in evaluating their relationships' reliability and stability, much as\npeople do. This paper proposes a new model called Relative Needs Entropy (RNE)\nto assess trust between robotic agents. RNE measures the distance of needs\ndistribution between individual agents or groups of agents. To exemplify its\nutility, we implement and demonstrate our trust model through experiments\nsimulating a heterogeneous multi-robot grouping task in a persistent urban\nsearch and rescue mission consisting of tasks at two levels of difficulty. The\nresults suggest that RNE trust-Based grouping of robots can achieve better\nperformance and adaptability for diverse task execution compared to the\nstate-of-the-art energy-based or distance-based grouping models.\n"}
+{"abstract": "  Electropolymerization is a bottom-up materials engineering process of micro\nand nano-scale that utilizes electrical signals to deposit conducting\ndendrites' morphologies by a redox reaction in the liquid phase. It resembles\nsynaptogenesis in the brain, in which electrical stimulation in the brain\ncauses the formation of synapses from the cellular neural composites. The\nstrategy has been recently explored for neuromorphic engineering by\nestablishing link between the electrical signals and the dendrites' shapes.\nSince the geometry of these structures determines their electrochemical\nproperties, understanding the mechanisms that regulate the polymer assembly\nunder electrically programmed conditions is an important aspect. In this\nmanuscript, we simulate this phenomenon using mesoscale simulations, taking\ninto account the important features of spatial-temporal potential mapping based\non the time-varying signal, the motion of charged particles in the liquid due\nto the electric field, and the attachment of particles on the electrode. The\nstudy helps in visualizing the motion of particles in different electrical\nconditions, which is not possible to probe experimentally. Consistent with the\nexperiments, the higher AC frequency of electrical activities favors linear\nwire-like growth, while lower frequency leads to more dense and fractal\ndendrites growth, and voltage offset leads to asymmetrical growth. We find that\ndendrites' shape and growth process systematically depend on particle\nconcentration and random scattering. We discover that the different dendrites'\narchitectures are associated with different Laplace and diffusion fields, which\ngovern the monomers trajectory and subsequent dendrites' growth. Such\nunconventional engineering routes could have a variety of applications from\nneuromorphic engineering to bottom-up computing strategies.\n"}
+{"abstract": "  In Meta-Reinforcement Learning (meta-RL) an agent is trained on a set of\ntasks to prepare for and learn faster in new, unseen, but related tasks. The\ntraining tasks are usually hand-crafted to be representative of the expected\ndistribution of test tasks and hence all used in training. We show that given a\nset of training tasks, learning can be both faster and more effective (leading\nto better performance in the test tasks), if the training tasks are\nappropriately selected. We propose a task selection algorithm,\nInformation-Theoretic Task Selection (ITTS), based on information theory, which\noptimizes the set of tasks used for training in meta-RL, irrespectively of how\nthey are generated. The algorithm establishes which training tasks are both\nsufficiently relevant for the test tasks, and different enough from one\nanother. We reproduce different meta-RL experiments from the literature and\nshow that ITTS improves the final performance in all of them.\n"}
+{"abstract": "  We propose the convergent graph solver (CGS), a deep learning method that\nlearns iterative mappings to predict the properties of a graph system at its\nstationary state (fixed point) with guaranteed convergence. CGS systematically\ncomputes the fixed points of a target graph system and decodes them to estimate\nthe stationary properties of the system without the prior knowledge of existing\nsolvers or intermediate solutions. The forward propagation of CGS proceeds in\nthree steps: (1) constructing the input dependent linear contracting iterative\nmaps, (2) computing the fixed-points of the linear maps, and (3) decoding the\nfixed-points to estimate the properties. The contractivity of the constructed\nlinear maps guarantees the existence and uniqueness of the fixed points\nfollowing the Banach fixed point theorem. To train CGS efficiently, we also\nderive a tractable analytical expression for its gradient by leveraging the\nimplicit function theorem. We evaluate the performance of CGS by applying it to\nvarious network-analytic and graph benchmark problems. The results indicate\nthat CGS has competitive capabilities for predicting the stationary properties\nof graph systems, irrespective of whether the target systems are linear or\nnon-linear. CGS also shows high performance for graph classification problems\nwhere the existence or the meaning of a fixed point is hard to be clearly\ndefined, which highlights the potential of CGS as a general graph neural\nnetwork architecture.\n"}
+{"abstract": "  Partial subtle mirroring of nonverbal behaviors during conversations (also\nknown as mimicking or parallel empathy), is essential for rapport building,\nwhich in turn is essential for optimal human-human communication outcomes.\nMirroring has been studied in interactions between robots and humans, and in\ninteractions between Embodied Conversational Agents (ECAs) and humans. However,\nvery few studies examine interactions between humans and ECAs that are\nintegrated with robots, and none of them examine the effect of mirroring\nnonverbal behaviors in such interactions. Our research question is whether\nintegrating an ECA able to mirror its interlocutor's facial expressions and\nhead movements (continuously or intermittently) with a human-service robot will\nimprove the user's experience with the support robot that is able to perform\nuseful mobile manipulative tasks (e.g. at home). Our contribution is the\ncomplex integration of an expressive ECA, able to track its interlocutor's\nface, and to mirror his/her facial expressions and head movements in real time,\nintegrated with a human support robot such that the robot and the agent are\nfully aware of each others', and of the users', nonverbals cues. We also\ndescribe a pilot study we conducted towards answering our research question,\nwhich shows promising results for our forthcoming larger user study.\n"}
+{"abstract": "  We perform the shadow calculation of the loop quantum gravity motivated\nregular black hole recently proposed by Ashtekar, Olmedo and Singh (will be\ntermed AOS black hole hereafter). In the process, we also construct the\nrotating loop quantum gravity inspired solution of the originally proposed\nstatic spherically symmetric AOS black hole by applying the modified\nNewman-Janis algorithm. We study the quantum effects on the shadows of both the\nnon-rotating and rotating loop quantum black hole solutions. It is observed\nthat the general shape of the shadow for non-rotating AOS black hole is\ncircular in shape as is expected for its classical counter part too, but the\npresence of loop quantum gravity inspired modification contracts the shadow\nradius and the effect reduces with the increase in the mass of the black hole.\nOn a similar note, in the rotating situation, we find contraction in shadow\nradius due to quantum effects and the tapered nature of the shadow as expected\nfrom the classical Kerr case. However, instead of the symmetrical contraction,\nlike non-rotating one, we found more contraction on one side relative to the\nother when we compare our result with the shadow of the Kerr black hole. We\nfinally studied super-radiance in rotating AOS background and observed that the\nsuper-radiance condition for massless scalar field is identical to that of the\nKerr case with the rotation of the BH being more compared to Kerr in the low\nmass regime. With an increase in mass of the rotating AOS black hole, the\ndifference from Kerr starts to become insignificant.\n"}
+{"abstract": "  We report the discovery of a transiting, temperate, Neptune-sized exoplanet\norbiting the nearby ($d$ = 27.5 pc), M3V star TOI-1231 (NLTT 24399, L 248-27,\n2MASS J10265947-5228099). The planet was detected using photometric data from\nthe Transiting Exoplanet Survey Satellite and followed up with observations\nfrom the Las Cumbres Observatory and the Antarctica Search for Transiting\nExoPlanets program. Combining the photometric data sets, we find that the newly\ndiscovered planet has a radius of 3.65$^{+0.16}_{-0.15}$ R$_{\\oplus}$, and an\norbital period of 24.246 days. Radial velocity measurements obtained with the\nPlanet Finder Spectrograph on the Magellan Clay telescope confirm the existence\nof the planet and lead to a mass measurement of 15.5$\\pm$3.3 M$_{\\oplus}$. With\nan equilibrium temperature of just 330K TOI-1231 b is one of the coolest small\nplanets accessible for atmospheric studies thus far, and its host star's bright\nNIR brightness (J=8.88, K$_{s}$=8.07) make it an exciting target for HST and\nJWST. Future atmospheric observations would enable the first comparative\nplanetology efforts in the 250-350 K temperature regime via comparisons with\nK2-18 b. Furthermore, TOI-1231's high systemic radial velocity (70.5 k\\ms) may\nallow for the detection of low-velocity hydrogen atoms escaping the planet by\nDoppler shifting the H I Ly-alpha stellar emission away from the geocoronal and\nISM absorption features.\n"}
+{"abstract": "  Massive bosonic fields in the background of a Kerr black hole can either\ntrigger superradiant instabilities (black-hole bombs) or form equilibrium\nconfigurations corresponding to pure bound states, known as stationary scalar\nclouds. Here, similar phenomena are shown to emerge in the fluctuation dynamics\nof a rotating photon-fluid model. In the presence of suitable vortex flows, the\ndensity fluctuations are governed by the massive Klein-Gordon equation on a\n(2+1) curved spacetime, possessing an ergoregion and an event horizon. We\nreport on superradiant instabilities originating from quasi-bound phonon states\ntrapped by the vortex background and, remarkably, on the existence of\nstationary modes in synchronous rotation with the horizon. These represent the\nacoustic counterpart of astrophysical scalar clouds. Our system offers a\npromising platform for analogue gravity experiments on superradiant\ninstabilities of massive bosons and black-hole-field equilibrium\nconfigurations.\n"}
+{"abstract": "  The Fermi National Accelerator Laboratory has measured the anomalous\nprecession frequency $a^{}_\\mu = (g^{}_\\mu-2)/2$ of the muon to a combined\nprecision of 0.46 parts per million with data collected during its first\nphysics run in 2018. This paper documents the measurement of the magnetic field\nin the muon storage ring. The magnetic field is monitored by nuclear magnetic\nresonance systems and calibrated in terms of the equivalent proton spin\nprecession frequency in a spherical water sample at 34.7$^\\circ$C. The measured\nfield is weighted by the muon distribution resulting in $\\tilde{\\omega}'^{}_p$,\nthe denominator in the ratio $\\omega^{}_a$/$\\tilde{\\omega}'^{}_p$ that together\nwith known fundamental constants yields $a^{}_\\mu$. The reported uncertainty on\n$\\tilde{\\omega}'^{}_p$ for the Run-1 data set is 114 ppb consisting of\nuncertainty contributions from frequency extraction, calibration, mapping,\ntracking, and averaging of 56 ppb, and contributions from fast transient fields\nof 99 ppb.\n"}
+{"abstract": "  Targeted syntactic evaluations have demonstrated the ability of language\nmodels to perform subject-verb agreement given difficult contexts. To elucidate\nthe mechanisms by which the models accomplish this behavior, this study applies\ncausal mediation analysis to pre-trained neural language models. We investigate\nthe magnitude of models' preferences for grammatical inflections, as well as\nwhether neurons process subject-verb agreement similarly across sentences with\ndifferent syntactic structures. We uncover similarities and differences across\narchitectures and model sizes -- notably, that larger models do not necessarily\nlearn stronger preferences. We also observe two distinct mechanisms for\nproducing subject-verb agreement depending on the syntactic structure of the\ninput sentence. Finally, we find that language models rely on similar sets of\nneurons when given sentences with similar syntactic structure.\n"}
+{"abstract": "  Accurate quantification of model uncertainty has long been recognized as a\nfundamental requirement for trusted AI. In regression tasks, uncertainty is\ntypically quantified using prediction intervals calibrated to a specific\noperating point, making evaluation and comparison across different studies\ndifficult. Our work leverages: (1) the concept of operating characteristics\ncurves and (2) the notion of a gain over a simple reference, to derive a novel\noperating point agnostic assessment methodology for prediction intervals. The\npaper describes the corresponding algorithm, provides a theoretical analysis,\nand demonstrates its utility in multiple scenarios. We argue that the proposed\nmethod addresses the current need for comprehensive assessment of prediction\nintervals and thus represents a valuable addition to the uncertainty\nquantification toolbox.\n"}
+{"abstract": "  Contrastive learning has recently seen tremendous success in self-supervised\nlearning. So far, however, it is largely unclear why the learned\nrepresentations generalize so effectively to a large variety of downstream\ntasks. We here prove that feedforward models trained with objectives belonging\nto the commonly used InfoNCE family learn to implicitly invert the underlying\ngenerative model of the observed data. While the proofs make certain\nstatistical assumptions about the generative model, we observe empirically that\nour findings hold even if these assumptions are severely violated. Our theory\nhighlights a fundamental connection between contrastive learning, generative\nmodeling, and nonlinear independent component analysis, thereby furthering our\nunderstanding of the learned representations as well as providing a theoretical\nfoundation to derive more effective contrastive losses.\n"}
+{"abstract": "  We have calculated coupling constants of a neutral pion with the lowest two\nnucleon resonances. This includes both the diagonal as well as non-diagonal\ncoupling constant involving a nucleon resonance and a nucleon. For this, we\nfirst calculate vacuum-to-pion correlation function of the interpolating fields\nof two nucleons and then take its matrix elements with respect to nucleon\nand/or nucleon resonance spinors. Using different QCD sum rules obtained from\ndifferent matrix elements we eliminate unwanted coupling constants and solve\nfor the desired ones.16\n"}
+{"abstract": "  We propose GraphMineSuite (GMS): the first benchmarking suite for graph\nmining that facilitates evaluating and constructing high-performance graph\nmining algorithms. First, GMS comes with a benchmark specification based on\nextensive literature review, prescribing representative problems, algorithms,\nand datasets. Second, GMS offers a carefully designed software platform for\nseamless testing of different fine-grained elements of graph mining algorithms,\nsuch as graph representations or algorithm subroutines. The platform includes\nparallel implementations of more than 40 considered baselines, and it\nfacilitates developing complex and fast mining algorithms. High modularity is\npossible by harnessing set algebra operations such as set intersection and\ndifference, which enables breaking complex graph mining algorithms into simple\nbuilding blocks that can be separately experimented with. GMS is supported with\na broad concurrency analysis for portability in performance insights, and a\nnovel performance metric to assess the throughput of graph mining algorithms,\nenabling more insightful evaluation. As use cases, we harness GMS to rapidly\nredesign and accelerate state-of-the-art baselines of core graph mining\nproblems: degeneracy reordering (by up to >2x), maximal clique listing (by up\nto >9x), k-clique listing (by 1.1x), and subgraph isomorphism (by up to 2.5x),\nalso obtaining better theoretical performance bounds.\n"}
+{"abstract": "  A novel multiple-input multiple-output (MIMO) dual-function radar\ncommunication (DFRC) system is proposed. The system transmits wideband,\northogonal frequency division multiplexing (OFDM) waveforms using a small\nsubset of the available antennas in each channel use. The proposed system\nassigns most carriers to antennas in a shared fashion, thus efficiently\nexploiting the available communication bandwidth, and a small set of\nsubcarriers to active antennas in an exclusive fashion (private subcarriers). A\nnovel target estimation approach is proposed to overcome the coupling of target\nparameters introduced by subcarrier sharing. The obtained parameters are\nfurther refined via an iterative approach, which formulates a sparse signal\nrecovery problem based on the data of the private subcarriers. The system is\nendowed with beamforming capability, via waveform precoding and antenna\nselection. The precoding and antenna selection matrices are optimally\nco-designed to meet a joint sensing-communication system performance. The\nsparsity of the transmit array is exploited at the communication receiver to\nrecover the transmitted information. The use of shared subcarriers enables high\ncommunication rate, while the sparse transmit array maintains low system\nhardware cost. The sensing problem is formulated by taking into account\nfrequency selective fading, and a method is proposed to estimate the channel\ncoefficients during the sensing process. The functionality of the proposed\nsystem is demonstrated via simulations.\n"}
+{"abstract": "  Cyber Threat hunting is a proactive search for known attack behaviors in the\norganizational information system. It is an important component to mitigate\nadvanced persistent threats (APTs). However, the attack behaviors recorded in\nprovenance data may not be completely consistent with the known attack\nbehaviors. In this paper, we propose DeepHunter, a graph neural network (GNN)\nbased graph pattern matching approach that can match provenance data against\nknown attack behaviors in a robust way. Specifically, we design a graph neural\nnetwork architecture with two novel networks: attribute embedding networks that\ncould incorporate Indicators of Compromise (IOCs) information, and graph\nembedding networks that could capture the relationships between IOCs. To\nevaluate DeepHunter, we choose five real and synthetic APT attack scenarios.\nResults show that DeepHunter can hunt all attack behaviors, and the accuracy\nand robustness of DeepHunter outperform the state-of-the-art method, Poirot.\n"}
+{"abstract": "  Let $G$ be a locally compact group and $p,q\\in \\mathbb{R}$ with $p>1$\n$p\\not=2$ and $q$ between $2$ and $p$ (if $p<2$ then $p<q<2$, if $p>2$ then\n$2<q<p.$) The main result of the paper is that $A_q(G)$ multiplies $A_p(G)$,\nmore precisely we show that the Banach algebra $A_p(G)$is a Banach module on\n$A_q(G).$\n"}
+{"abstract": "  In condensed matter systems, zero-dimensional or one-dimensional Majorana\nmodes can be realized respectively as the end and edge states of\none-dimensional and two-dimensional topological superconductors. In this\n$\\textit{top-down}$ approach, $(d-1)$-dimensional Majorana modes are obtained\nas the boundary states of a topologically nontrivial $d$-dimensional bulk. In a\n$\\textit{bottom-up}$ approach instead, $d$-dimensional Majorana modes in a\n$d$-dimensional system can be realized as the continuous limit of a periodic\nlattice of coupled $(d-1)$-dimensional Majorana modes. We illustrate this idea\nby considering one-dimensional proximitized superconductors with\nspatially-modulated potential or magnetic fields. The ensuing inhomogenous\ntopological state exhibits one-dimensional counterpropagating Majorana modes\nwith finite dispersion, and with a Majorana gap which can be controlled by\nexternal fields. In the massless case, the Majorana modes have opposite\nMajorana polarizations and pseudospins, are conformally invariant, and realize\ncentrally extended quantum mechanical supersymmetry. The supersymmetry exhibits\nspontaneous partial breaking. Consequently, the massless Majorana fermion can\nbe identified as a Goldstino, i.e., the Nambu-Goldstone fermion associated with\nthe spontaneously broken supersymmetry.\n"}
+{"abstract": "  We study rotating global AdS solutions in five-dimensional Einstein gravity\ncoupled to a multiplet complex scalar within a cohomogeneity-1 ansatz. The\nonset of the gravitational and scalar field superradiant instabilities of the\nMyers-Perry-AdS black hole mark bifurcation points to black resonators and\nhairy Myers-Perry-AdS black holes, respectively. These solutions are subject to\nthe other (gravitational or scalar) instability, and result in hairy black\nresonators which contain both gravitational and scalar hair. The hairy black\nresonators have smooth zero-horizon limits that we call graviboson stars. In\nthe hairy black resonator and graviboson solutions, multiple scalar components\nwith different frequencies are excited, and hence these are multioscillating\nsolutions. The phase structure of the solutions are examined in the\nmicrocanonical ensemble, i.e. at fixed energy and angular momenta. It is found\nthat the entropy of the hairy black resonator is never the largest among them.\nWe also find that hairy black holes with higher scalar wavenumbers are\nentropically dominant and occupy more of phase space than those of lower\nwavenumbers.\n"}
+{"abstract": "  The scattering of Dirac fermions in the background fields of topological\nsolitons of the $(2+1)$-dimensional nonlinear $O(3)$ $\\sigma$-model is studied\nusing both analytical and numerical methods. General formulae describing\nfermion scattering are obtained and the symmetry properties of the partial\nscattering amplitudes and elements of the $S$-matrix are determined. Within the\nframework of the Born approximation, the scattering amplitudes, differential\ncross-sections, and total cross-sections of fermion-soliton scattering are\nobtained in analytical forms, and their symmetry properties and asymptotic\nbehavior are investigated. The dependences of the first several partial\nelements of the $S$-matrix on the momentum of the fermion are obtained using\nnumerical methods, and some properties of these dependences are ascertained and\ndiscussed.\n"}
+{"abstract": "  In this paper, we calculate four geometric constants for discrete Morrey\nspaces. The constants are generalized von Neumann-Jordan constant, modified von\nNeumann-Jordan constant, von Neumann-Jordan type constant, and Zb\\\"{a}ganu\nconstant. The four constants measure uniformly nonsquareness of the above\nspaces. We obtain that the value of each of the four constants for the above\nspaces is two, which means that the spaces are NOT uniformly nonsquare.\n"}
+{"abstract": "  This work presents a new fine-grained transparent object segmentation\ndataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale\ntransparent object segmentation dataset. Unlike Trans10K-v1 that only has two\nlimited categories, our new dataset has several appealing benefits. (1) It has\n11 fine-grained categories of transparent objects, commonly occurring in the\nhuman domestic environment, making it more practical for real-world\napplication. (2) Trans10K-v2 brings more challenges for the current advanced\nsegmentation methods than its former version. Furthermore, a novel\ntransformer-based segmentation pipeline termed Trans2Seg is proposed. Firstly,\nthe transformer encoder of Trans2Seg provides the global receptive field in\ncontrast to CNN's local receptive field, which shows excellent advantages over\npure CNN architectures. Secondly, by formulating semantic segmentation as a\nproblem of dictionary look-up, we design a set of learnable prototypes as the\nquery of Trans2Seg's transformer decoder, where each prototype learns the\nstatistics of one category in the whole dataset. We benchmark more than 20\nrecent semantic segmentation methods, demonstrating that Trans2Seg\nsignificantly outperforms all the CNN-based methods, showing the proposed\nalgorithm's potential ability to solve transparent object segmentation.\n"}
+{"abstract": "  \"Changing-look\" quasars are a new class of highly variable active galactic\nnuclei that have changed their spectral type over surprisingly short timescales\nof just a few years. The origin of this phenomenon is debated, but is likely to\nreflect some change in the accretion flow. To investigate the disk-corona\nsystems in these objects, we measure optical/UV-X-ray spectral indices\n($\\alpha_{\\rm OX}$) and Eddington ratios ($\\lambda_{\\rm Edd}$) of ten\npreviously-discovered changing-look quasars at two or more epochs. By comparing\nthese data with simulated results based on the behavior of X-ray binaries, we\nfind possible similarities in spectral indices below 1% Eddington ratio. We\nfurther investigate the Eddington ratios of changing-look quasars before and\nafter their spectral type changes, and find that changing-look quasars cross\nthe 1% Eddington ratio boundary when their broad emission lines\ndisappear/emerge. This is consistent with the disk-wind model as the origin of\nbroad emission lines.\n"}
+{"abstract": "  Top-performing computer vision models are powered by convolutional neural\nnetworks (CNNs). Training an accurate CNN highly depends on both the raw sensor\ndata and their associated ground truth (GT). Collecting such GT is usually done\nthrough human labeling, which is time-consuming and does not scale as we wish.\nThis data labeling bottleneck may be intensified due to domain shifts among\nimage sensors, which could force per-sensor data labeling. In this paper, we\nfocus on the use of co-training, a semi-supervised learning (SSL) method, for\nobtaining self-labeled object bounding boxes (BBs), i.e., the GT to train deep\nobject detectors. In particular, we assess the goodness of multi-modal\nco-training by relying on two different views of an image, namely, appearance\n(RGB) and estimated depth (D). Moreover, we compare appearance-based\nsingle-modal co-training with multi-modal. Our results suggest that in a\nstandard SSL setting (no domain shift, a few human-labeled data) and under\nvirtual-to-real domain shift (many virtual-world labeled data, no human-labeled\ndata) multi-modal co-training outperforms single-modal. In the latter case, by\nperforming GAN-based domain translation both co-training modalities are on\npair; at least, when using an off-the-shelf depth estimation model not\nspecifically trained on the translated images.\n"}
+{"abstract": "  High entropy alloys offer a huge search space for new electrocatalysts.\nSearching for a global property maximum in one quinary system could require,\ndepending on compositional resolution, the synthesis of up to 10E6 samples\nwhich is impossible using conventional approaches. Co-sputtered materials\nlibraries address this challenge by synthesis of controlled composition\ngradients of each element. However, even such a materials library covers less\nthan 1% of the composition space of a quinary system. We present a new strategy\nusing deposition source permutations optimized for highest improvement of the\ncovered new compositions. Using this approach, the composition space can be\nsampled in different subsections allowing identification of the contribution of\nindividual elements and their combinations on electrochemical activity.\nUnsupervised machine learning reveals that electrochemical activity is governed\nby the complex interplay of chemical and structural factors. Out of 2394\nmeasured compositions, a new highly active composition for the oxygen reduction\nreaction around Ru17Rh5Pd19Ir29Pt30 was identified.\n"}
+{"abstract": "  In this work, we introduce a novel method for solving the set inversion\nproblem by formulating it as a binary classification problem. Aiming to develop\na fast algorithm that can work effectively with high-dimensional and\ncomputationally expensive nonlinear models, we focus on active learning, a\nfamily of new and powerful techniques which can achieve the same level of\naccuracy with fewer data points compared to traditional learning methods.\nSpecifically, we propose OASIS, an active learning framework using Support\nVector Machine algorithms for solving the problem of set inversion. Our method\nworks well in high dimensions and its computational cost is relatively robust\nto the increase of dimension. We illustrate the performance of OASIS by several\nsimulation studies and show that our algorithm outperforms VISIA, the\nstate-of-the-art method.\n"}
+{"abstract": "  Comprehensive calculations were performed to predict the phase behaviour of\nlarge spherical colloids mixed with small spherical colloids that act as\ndepletant. To this end, the free volume theory (FVT) of Lekkerkerker et al.\n[Europhys. Lett. 20 (1992) 559] is used as a basis and is extended to\nexplicitly include the hard-sphere character of colloidal depletants into the\nexpression for the free volume fraction. Taking the excluded volume of the\ndepletants into account in both the system and the reservoir provides a\nrelation between the depletant concentration in the reservoir and in the system\nthat accurately matches with computer simulation results of Dijkstra et al.\n[Phys. Rev. E 59 (1999) 5744]. Moreover, the phase diagrams for highly\nasymmetric mixtures with size ratios q . 0:2 obtained by using this new\napproach corroborates simulation results significantly better than earlier FVT\napplications to binary hard-sphere mixtures. The phase diagram of a binary\nhard-sphere mixture with a size ratio of q = 0:4, where a binary interstitial\nsolid solution is formed at high densities, is investigated using a numerical\nfree volume approach. At this size ratio, the obtained phase diagram is\nqualitatively different from previous FVT approaches for hard-sphere and\npenetrable depletants, but again compares well with simulation predictions.\n"}
+{"abstract": "  We show that hermitian K-theory is universal among generalized motivic\ncohomology theories with transfers along finite Gorenstein morphisms with\ntrivialized dualizing sheaf. As an application, we obtain a Hilbert scheme\nmodel for hermitian K-theory as a motivic space. We also give an application to\ncomputational complexity: we prove that 1-generic minimal border rank tensors\ndegenerate to the big Coppersmith-Winograd tensor.\n"}
+{"abstract": "  Event detection on social media has attracted a number of researches, given\nthe recent availability of large volumes of social media discussions. Previous\nworks on social media event detection either assume a specific type of event,\nor assume certain behavior of observed variables. In this paper, we propose a\ngeneral method for event detection on social media that makes few assumptions.\nThe main assumption we make is that when an event occurs, affected semantic\naspects will behave differently from its usual behavior. We generalize the\nrepresentation of time units based on word embeddings of social media text, and\npropose an algorithm to detect events in time series in a general sense. In the\nexperimental evaluation, we use a novel setting to test if our method and\nbaseline methods can exhaustively catch all real-world news in the test period.\nThe evaluation results show that when the event is quite unusual with regard to\nthe base social media discussion, it can be captured more effectively with our\nmethod. Our method can be easily implemented and can be treated as a starting\npoint for more specific applications.\n"}
+{"abstract": "  We prove that in a $C^1$-open and $C^k$-dense set of some classes of $C^k$\nAnosov flows all Lyapunov exponents have multiplicity 1 with respect to\nappropriate measures. The classes are geodesic flows with equilibrium states of\nH\\\"older-continuous potentials, volume-preserving flows, and all fiber-bunched\nAnosov flows with equilibrium states of H\\\"older-continuous potentials. In the\nproof, we use and prove perturbative results for jets of flows to modify\neigenvalues of certain Poincar\\'e maps and, using a Markov partition, apply the\nsimplicity criterion of Avila and Viana.\n"}
+{"abstract": "  Pyserini is an easy-to-use Python toolkit that supports replicable IR\nresearch by providing effective first-stage retrieval in a multi-stage ranking\narchitecture. Our toolkit is self-contained as a standard Python package and\ncomes with queries, relevance judgments, pre-built indexes, and evaluation\nscripts for many commonly used IR test collections. We aim to support, out of\nthe box, the entire research lifecycle of efforts aimed at improving ranking\nwith modern neural approaches. In particular, Pyserini supports sparse\nretrieval (e.g., BM25 scoring using bag-of-words representations), dense\nretrieval (e.g., nearest-neighbor search on transformer-encoded\nrepresentations), as well as hybrid retrieval that integrates both approaches.\nThis paper provides an overview of toolkit features and presents empirical\nresults that illustrate its effectiveness on two popular ranking tasks. We also\ndescribe how our group has built a culture of replicability through shared\nnorms and tools that enable rigorous automated testing.\n"}
+{"abstract": "  Keyword extraction is the task of identifying words (or multi-word\nexpressions) that best describe a given document and serve in news portals to\nlink articles of similar topics. In this work we develop and evaluate our\nmethods on four novel data sets covering less represented, morphologically-rich\nlanguages in European news media industry (Croatian, Estonian, Latvian and\nRussian). First, we perform evaluation of two supervised neural\ntransformer-based methods (TNT-KID and BERT+BiLSTM CRF) and compare them to a\nbaseline TF-IDF based unsupervised approach. Next, we show that by combining\nthe keywords retrieved by both neural transformer based methods and extending\nthe final set of keywords with an unsupervised TF-IDF based technique, we can\ndrastically improve the recall of the system, making it appropriate to be used\nas a recommendation system in the media house environment.\n"}
+{"abstract": "  An expander code is a binary linear code whose parity-check matrix is the\nbi-adjacency matrix of a bipartite expander graph. We provide a new formula for\nthe minimum distance of such codes. We also provide a new proof of the result\nthat $2(1-\\varepsilon) \\gamma n$ is a lower bound of the minimum distance of\nthe expander code given by a $(m,n,d,\\gamma,1-\\varepsilon)$ expander bipartite\ngraph.\n"}
+{"abstract": "  When we deploy machine learning models in high-stakes medical settings, we\nmust ensure these models make accurate predictions that are consistent with\nknown medical science. Inherently interpretable networks address this need by\nexplaining the rationale behind each decision while maintaining equal or higher\naccuracy compared to black-box models. In this work, we present a novel\ninterpretable neural network algorithm that uses case-based reasoning for\nmammography. Designed to aid a radiologist in their decisions, our network\npresents both a prediction of malignancy and an explanation of that prediction\nusing known medical features. In order to yield helpful explanations, the\nnetwork is designed to mimic the reasoning processes of a radiologist: our\nnetwork first detects the clinically relevant semantic features of each image\nby comparing each new image with a learned set of prototypical image parts from\nthe training images, then uses those clinical features to predict malignancy.\nCompared to other methods, our model detects clinical features (mass margins)\nwith equal or higher accuracy, provides a more detailed explanation of its\nprediction, and is better able to differentiate the classification-relevant\nparts of the image.\n"}
+{"abstract": "  With recent advances in the development of more powerful quantum computers,\nthe research area of quantum software engineering is emerging, having the goal\nto provide concepts, principles, and guidelines to develop high-quality quantum\napplications. In classical software engineering, lifecycles are used to\ndocument the process of designing, implementing, maintaining, analyzing, and\nadapting software. Such lifecycles provide a common understanding of how to\ndevelop and operate an application, which is especially important due to the\ninterdisciplinary nature of quantum computing. Since today`s quantum\napplications are, in most cases, hybrid, consisting of quantum and classical\nprograms, the lifecycle for quantum applications must involve the development\nof both kinds of programs. However, the existing lifecycles only target the\ndevelopment of quantum or classical programs in isolation. Additionally, the\nvarious programs must be orchestrated, e.g., using workflows. Thus, the\ndevelopment of quantum applications also incorporates the workflow lifecycle.\nIn this chapter, we analyze the software artifacts usually comprising a quantum\napplication and present their corresponding lifecycles. Furthermore, we\nidentify the points of connection between the various lifecycles and integrate\nthem into the overall quantum software development lifecycle. Therefore, the\nintegrated lifecycle serves as a basis for the development and execution of\nhybrid quantum applications.\n"}
+{"abstract": "  Deciding what constitutes a single module, what classes belong to which\nmodule or the right set of modules for a specific software system has always\nbeen a challenging task. The problem is even harder in large-scale software\nsystems composed of thousands of classes and hundreds of modules. Over the\nyears, researchers have been proposing different techniques to support\ndevelopers in re-modularizing their software systems. In particular, the\nsearch-based software re-modularization is an active research topic within the\nsoftware engineering community for more than 20 years.\n  This paper describes our efforts in applying search-based software\nre-modularization approaches at Adyen, a large-scale payment company. Adyen's\ncode base has 5.5M+ lines of code, split into around hundreds of modules. We\nleveraged the existing body of knowledge in the field to devise our own search\nalgorithm and applied it to our code base. Our results show that search-based\napproaches scale to large code bases as ours. Our algorithm can find solutions\nthat improve the code base according to the metrics we optimize for, and\ndevelopers see value in the recommendations. Based on our experiences, we then\nlist a set of challenges and opportunities for future researchers, aiming at\nmaking search-based software re-modularization more efficient for large-scale\nsoftware companies.\n"}
+{"abstract": "  In constrained reinforcement learning (RL), a learning agent seeks to not\nonly optimize the overall reward but also satisfy the additional safety,\ndiversity, or budget constraints. Consequently, existing constrained RL\nsolutions require several new algorithmic ingredients that are notably\ndifferent from standard RL. On the other hand, reward-free RL is independently\ndeveloped in the unconstrained literature, which learns the transition dynamics\nwithout using the reward information, and thus naturally capable of addressing\nRL with multiple objectives under the common dynamics. This paper bridges\nreward-free RL and constrained RL. Particularly, we propose a simple\nmeta-algorithm such that given any reward-free RL oracle, the approachability\nand constrained RL problems can be directly solved with negligible overheads in\nsample complexity. Utilizing the existing reward-free RL solvers, our framework\nprovides sharp sample complexity results for constrained RL in the tabular MDP\nsetting, matching the best existing results up to a factor of horizon\ndependence; our framework directly extends to a setting of tabular two-player\nMarkov games, and gives a new result for constrained RL with linear function\napproximation.\n"}
+{"abstract": "  Knowledge distillation can lead to deploy-friendly networks against the\nplagued computational complexity problem, but previous methods neglect the\nfeature hierarchy in detectors. Motivated by this, we propose a general\nframework for detection distillation. Our method, called Hands-on Guidance\nDistillation, distills the latent knowledge of all stage features for imposing\nmore comprehensive supervision, and focuses on the essence simultaneously for\npromoting more intense knowledge absorption. Specifically, a series of novel\nmechanisms are designed elaborately, including correspondence establishment for\nconsistency, hands-on imitation loss measure and re-weighted optimization from\nboth micro and macro perspectives. We conduct extensive evaluations with\ndifferent distillation configurations over VOC and COCO datasets, which show\nbetter performance on accuracy and speed trade-offs. Meanwhile, feasibility\nexperiments on different structural networks further prove the robustness of\nour HGD.\n"}
+{"abstract": "  The biomechanical energy harvester is expected to harvest the electric\nenergies from human motions. A tradeoff between harvesting energy and keeping\nthe user's natural movements should be balanced via optimization techniques. In\nprevious studies, the hardware itself has been specialized in advance for a\nsingle task like walking with constant speed on a flat. A key ingredient is\nContinuous Variable Transmission (CVT) to extend it applicable for multiple\ntasks. CVT could continuously adjust its gear ratio to balance the tradeoff for\neach task; however, such gear-ratio optimization problem remains open yet since\nits optimal solution may depend on the user, motion, and environment.\nTherefore, this paper focuses on a framework for data-driven optimization of a\ngear ratio in a CVT-equipped biomechanical energy harvester. Since the data\ncollection requires a heavy burden on the user, we have to optimize the gear\nratio for each task in the shortest possible time. To this end, our framework\nis designed sample-efficiently based on the fact that the user encounters\nmultiple tasks, which are with similarities with each other. Specifically, our\nframework employs multi-task Bayesian optimization to reuse the optimization\nresults of the similar tasks previously optimized by finding their\nsimilarities. Through experiments, we confirmed that, for each task, the\nproposed framework could achieve the optimal gear ratio of around 50~\\% faster\nthan one by random search, and that takes only around 20~minutes. Experimental\nresults also suggested that the optimization can be accelerated by actively\nexploiting similarities with previously optimized tasks.\n"}
+{"abstract": "  Self-attention models have been successfully applied in end-to-end speech\nrecognition systems, which greatly improve the performance of recognition\naccuracy. However, such attention-based models cannot be used in online speech\nrecognition, because these models usually have to utilize a whole acoustic\nsequences as inputs. A common method is restricting the field of attention\nsights by a fixed left and right window, which makes the computation costs\nmanageable yet also introduces performance degradation. In this paper, we\npropose Memory-Self-Attention (MSA), which adds history information into the\nRestricted-Self-Attention unit. MSA only needs localtime features as inputs,\nand efficiently models long temporal contexts by attending memory states.\nMeanwhile, recurrent neural network transducer (RNN-T) has proved to be a great\napproach for online ASR tasks, because the alignments of RNN-T are local and\nmonotonic. We propose a novel network structure, called Memory-Self-Attention\n(MSA) Transducer. Both encoder and decoder of the MSA Transducer contain the\nproposed MSA unit. The experiments demonstrate that our proposed models improve\nWER results than Restricted-Self-Attention models by $13.5 on WSJ and $7.1 on\nSWBD datasets relatively, and without much computation costs increase.\n"}
+{"abstract": "  Online gaming is growing faster than ever before, with increasing challenges\nof providing better user experience. Recommender systems (RS) for online games\nface unique challenges since they must fulfill players' distinct desires, at\ndifferent user levels, based on their action sequences of various action types.\nAlthough many sequential RS already exist, they are mainly single-sequence,\nsingle-task, and single-user-level. In this paper, we introduce a new\nsequential recommendation model for multiple sequences, multiple tasks, and\nmultiple user levels (abbreviated as M$^3$Rec) in Tencent Games platform, which\ncan fully utilize complex data in online games. We leverage Graph Neural\nNetwork and multi-task learning to design M$^3$Rec in order to model the\ncomplex information in the heterogeneous sequential recommendation scenario of\nTencent Games. We verify the effectiveness of M$^3$Rec on three online games of\nTencent Games platform, in both offline and online evaluations. The results\nshow that M$^3$Rec successfully addresses the challenges of recommendation in\nonline games, and it generates superior recommendations compared with\nstate-of-the-art sequential recommendation approaches.\n"}
+{"abstract": "  We show that robustly transitive endomorphisms of a closed manifolds must\nhave a non-trivial dominated splitting or be a local diffeomorphism. This\nallows to get some topological obstructions for the existence of robustly\ntransitive endomorphisms. To obtain the result we must understand the structure\nof the kernel of the differential and the recurrence to the critical set of the\nendomorphism after perturbation.\n"}
+{"abstract": "  Message authentication guarantees the integrity of messages exchanged over\nuntrusted channels. Yet, the required per-message authentication tags\nconsiderably expand packet sizes, which is especially problematic in\nconstrained environments. To address this issue, progressive message\nauthentication aggregates and distributes integrity protection over multiple\nmessages, promising to reduce overheads while upholding strong security of\ntraditional integrity protection. However, as we show in this paper, existing\nprogressive message authentication schemes are susceptible to packet drops: By\ninterfering with just two selected packets, an attacker can remove integrity\nprotection from a complete sequence of messages.\n  Revisiting the security of progressive message authentication, we consider it\nimperative to thwart such attacks by rethinking how authentication tags depend\non the successful reception of packets. To this end, we propose R2-D2, which\nrelies on randomized dependencies with parameterized security guarantees to\nmitigate network-level attacks inherent to current progressive authentication\nschemes. To deploy our approach to resource-constrained devices, we introduce\nSP-MAC, which implements R2-D2 using efficient XOR operations. Our evaluation\nshows that SP-MAC protects against sophisticated network-layer attacks, and\nstill operates as resources-conscious and fast as existing insecure schemes.\n"}
+{"abstract": "  Anomaly detection from a single image is challenging since anomaly data is\nalways rare and can be with highly unpredictable types. With only anomaly-free\ndata available, most existing methods train an AutoEncoder to reconstruct the\ninput image and find the difference between the input and output to identify\nthe anomalous region. However, such methods face a potential problem - a coarse\nreconstruction generates extra image differences while a high-fidelity one may\ndraw in the anomaly. In this paper, we solve this contradiction by proposing a\ntwo-stage approach, which generates high-fidelity yet anomaly-free\nreconstructions. Our Unsupervised Two-stage Anomaly Detection (UTAD) relies on\ntwo technical components, namely the Impression Extractor (IE-Net) and the\nExpert-Net. The IE-Net and Expert-Net accomplish the two-stage anomaly-free\nimage reconstruction task while they also generate intuitive intermediate\nresults, making the whole UTAD interpretable. Extensive experiments show that\nour method outperforms state-of-the-arts on four anomaly detection datasets\nwith different types of real-world objects and textures.\n"}
+{"abstract": "  The prediction of material properties through electronic-structure\nsimulations based on density-functional theory has become routinely common,\nthanks, in part, to the steady increase in the number and robustness of\navailable simulation packages. This plurality of codes and methods aiming to\nsolve similar problems is both a boon and a burden. While providing great\nopportunities for cross-verification, these packages adopt different methods,\nalgorithms, and paradigms, making it challenging to choose, master, and\nefficiently use any one for a given task. Leveraging recent advances in\nmanaging reproducible scientific workflows, we demonstrate how developing\ncommon interfaces for workflows that automatically compute material properties\ncan tackle the challenge mentioned above, greatly simplifying interoperability\nand cross-verification. We introduce design rules for reproducible and reusable\ncode-agnostic workflow interfaces to compute well-defined material properties,\nwhich we implement for eleven different quantum engines and use to compute\nthree different material properties. Each implementation encodes carefully\nselected simulation parameters and workflow logic, making the implementer's\nexpertise of the quantum engine directly available to non-experts. Full\nprovenance and reproducibility of the workflows is guaranteed through the use\nof the AiiDA infrastructure. All workflows are made available as open-source\nand come pre-installed with the Quantum Mobile virtual machine, making their\nuse straightforward.\n"}
+{"abstract": "  We investigate population and individual firing behaviors in sparsely\nsynchronized rhythms (SSRs) in a spiking neural network of the hippocampal\ndentate gyrus (DG). The main encoding granule cells (GCs) are grouped into\nlamellar clusters. In each GC cluster, there is one inhibitory (I) basket cell\n(BC) along with excitatory (E) GCs, and they form the E-I loop. Winner-take-all\ncompetition, leading to sparse activation of the GCs, occurs in each GC\ncluster. Such sparsity has been thought to enhance pattern separation performed\nin the DG. During the winner-take-all competition, SSRs are found to appear in\neach population of the GCs and the BCs through interaction of excitation of the\nGCs with inhibition of the BCs. Sparsely synchronized spiking stripes appear\nsuccessively with the population frequency $f_p~ (= 13$ Hz) in the raster plots\nof spikes. We also note that excitatory hilar mossy cells (MCs) control the\nfiring activity of the GC-BC loop by providing excitation to both the GCs and\nthe BCs. SSR also appears in the population of MCs via interaction with the GCs\n(i.e., GC-MC loop). Population behaviors in the SSRs are quantitatively\ncharacterized in terms of the synchronization measures. In addition, we\ninvestigate individual firing activity of GCs, BCs, and MCs in the SSRs.\nIndividual GCs exhibit random spike skipping, leading to a multi-peaked\ninter-spike-interval histogram, which is well characterized in terms of the\nrandom phase-locking degree. On the other hand, both BCs and MCs show\n\"intrastripe\" burstings within stripes, together with \"interstripe\" random\nspike skipping. MC loss may occur during epileptogenesis. With decreasing the\nfraction of the MCs, changes in the population and individual firings in the\nSSRs are also studied. Finally, quantitative association between the\npopulation/individual firing behaviors in the SSRs and the winner-take-all\ncompetition is discussed.\n"}
+{"abstract": "  It is desirable to include more controllable attributes to enhance the\ndiversity of generated responses in open-domain dialogue systems. However,\nexisting methods can generate responses with only one controllable attribute or\nlack a flexible way to generate them with multiple controllable attributes. In\nthis paper, we propose a Progressively trained Hierarchical Encoder-Decoder\n(PHED) to tackle this task. More specifically, PHED deploys Conditional\nVariational AutoEncoder (CVAE) on Transformer to include one aspect of\nattributes at one stage. A vital characteristic of the CVAE is to separate the\nlatent variables at each stage into two types: a global variable capturing the\ncommon semantic features and a specific variable absorbing the attribute\ninformation at that stage. PHED then couples the CVAE latent variables with the\nTransformer encoder and is trained by minimizing a newly derived ELBO and\ncontrolled losses to produce the next stage's input and produce responses as\nrequired. Finally, we conduct extensive evaluations to show that PHED\nsignificantly outperforms the state-of-the-art neural generation models and\nproduces more diverse responses as expected.\n"}
+{"abstract": "  In this paper, we give some observation of applying modern optimization\nmethods for functionals describing digital predistortion (DPD) of signals with\northogonal frequency division multiplexing (OFDM) modulation. The considered\nfamily of model functionals is determined by the class of cascade\nWiener--Hammerstein models, which can be represented as a computational graph\nconsisting of various nonlinear blocks. To assess optimization methods with the\nbest convergence depth and rate as a properties of this models family we\nmultilaterally consider modern techniques used in optimizing neural networks\nand numerous numerical methods used to optimize non-convex multimodal\nfunctions. The research emphasizes the most effective of the considered\ntechniques and describes several useful observations about the model properties\nand optimization methods behavior.\n"}
+{"abstract": "  We present a general, efficient technique for providing contextual\npredictions that are \"multivalid\" in various senses, against an online sequence\nof adversarially chosen examples $(x,y)$. This means that the resulting\nestimates correctly predict various statistics of the labels $y$ not just\nmarginally -- as averaged over the sequence of examples -- but also\nconditionally on $x \\in G$ for any $G$ belonging to an arbitrary intersecting\ncollection of groups $\\mathcal{G}$.\n  We provide three instantiations of this framework. The first is mean\nprediction, which corresponds to an online algorithm satisfying the notion of\nmulticalibration from Hebert-Johnson et al. The second is variance and higher\nmoment prediction, which corresponds to an online algorithm satisfying the\nnotion of mean-conditioned moment multicalibration from Jung et al. Finally, we\ndefine a new notion of prediction interval multivalidity, and give an algorithm\nfor finding prediction intervals which satisfy it. Because our algorithms\nhandle adversarially chosen examples, they can equally well be used to predict\nstatistics of the residuals of arbitrary point prediction methods, giving rise\nto very general techniques for quantifying the uncertainty of predictions of\nblack box algorithms, even in an online adversarial setting. When instantiated\nfor prediction intervals, this solves a similar problem as conformal\nprediction, but in an adversarial environment and with multivalidity guarantees\nstronger than simple marginal coverage guarantees.\n"}
+{"abstract": "  This work introduces ParAMS -- a versatile Python package that aims to make\nparameterization workflows in computational chemistry and physics more\naccessible, transparent and reproducible. We demonstrate how ParAMS facilitates\nthe parameter optimization for potential energy surface (PES) models, which can\notherwise be a tedious specialist task. Because of the package's modular\nstructure, various functionality can be easily combined to implement a\ndiversity of parameter optimization protocols. For example, the choice of PES\nmodel and the parameter optimization algorithm can be selected independently.\nAn illustration of ParAMS' strengths is provided in two case studies: i) a\ndensity functional-based tight binding (DFTB) repulsive potential for the\ninorganic ionic crystal ZnO, and ii) a ReaxFF force field for the simulation of\norganic disulfides.\n"}
+{"abstract": "  Even though Afaan Oromo is the most widely spoken language in the Cushitic\nfamily by more than fifty million people in the Horn and East Africa, it is\nsurprisingly resource-scarce from a technological point of view. The increasing\namount of various useful documents written in English language brings to\ninvestigate the machine that can translate those documents and make it easily\naccessible for local language. The paper deals with implementing a translation\nof English to Afaan Oromo and vice versa using Neural Machine Translation. But\nthe implementation is not very well explored due to the limited amount and\ndiversity of the corpus. However, using a bilingual corpus of just over 40k\nsentence pairs we have collected, this study showed a promising result. About a\nquarter of this corpus is collected via Community Engagement Platform (CEP)\nthat was implemented to enrich the parallel corpus through crowdsourcing\ntranslations.\n"}
+{"abstract": "  We introduce the notion of a bicocycle double cross product (resp. sum) Lie\ngroup (resp. Lie algebra), and a bicocycle double cross product bialgebra,\ngeneralizing the unified products. On the level of Lie groups the construction\nyields a Lie group on the product space of two pointed manifolds, none of which\nbeing necessarily a subgroup. On the level of Lie algebras, similarly, a Lie\nalgebra is obtained on the direct sum of two vector spaces, none of which is\nrequired to be a subalgebra. Finally, on the quantum level the theory presents\na bialgebra, on the tensor product of two (co)algebras that are not necessarily\nsub-bialgebras, the semidual of which being a cocycle bicrossproduct bialgebra.\n"}
+{"abstract": "  The present study deals a scientometric analysis of 8486 bibliometric\npublications retrieved from the Web of Science database during the period 2008\nto 2017. Data is collected and analyzed using Bibexcel software. The study\nfocuses on various aspect of the quantitative research such as growth of papers\n(year wise), Collaborative Index (CI), Degree of Collaboration (DC),\nCo-authorship Index (CAI), Collaborative Co-efficient (CC), Modified\nCollaborative Co-Efficient (MCC), Lotka's Exponent value, Kolmogorov-Smirnov\ntest (K-S Test).\n"}
+{"abstract": "  The spontaneous breaking of parity-time ($\\mathcal{PT}$) symmetry, which\nyields rich critical behavior in non-Hermitian systems, has stimulated much\ninterest. Whereas most previous studies were performed within the\nsingle-particle or mean-field framework, exploring the interplay between\n$\\mathcal{PT}$ symmetry and quantum fluctuations in a many-body setting is a\nburgeoning frontier. Here, by studying the collective excitations of a Fermi\nsuperfluid under an imaginary spin-orbit coupling, we uncover an emergent\n$\\mathcal{PT}$-symmetry breaking in the Anderson-Bogoliubov (AB) modes, whose\nquasiparticle spectra undergo a transition from being completely real to\ncompletely imaginary, even though the superfluid ground state retains an\nunbroken $\\mathcal{PT}$ symmetry. The critical point of the transition is\nmarked by a non-analytic kink in the speed of sound, as the latter completely\nvanishes at the critical point where the system is immune to low-frequency\nperturbations.These critical phenomena derive from the presence of a spectral\npoint gap in the complex quasiparticle dispersion, and are therefore\ntopological in origin.\n"}
+{"abstract": "  The application machine learning (ML) algorithms to turbulence modeling has\nshown promise over the last few years, but their application has been\nrestricted to eddy viscosity based closure approaches. In this article we\ndiscuss rationale for the application of machine learning with high-fidelity\nturbulence data to develop models at the level of Reynolds stress transport\nmodeling. Based on these rationale we compare different machine learning\nalgorithms to determine their efficacy and robustness at modeling the different\ntransport processes in the Reynolds Stress Transport Equations. Those data\ndriven algorithms include Random forests, gradient boosted trees and neural\nnetworks. The direct numerical simulation (DNS) data for flow in channels is\nused both as training and testing of the ML models. The optimal\nhyper-parameters of the ML algorithms are determined using Bayesian\noptimization. The efficacy of above mentioned algorithms is assessed in the\nmodeling and prediction of the terms in the Reynolds stress transport\nequations. It was observed that all the three algorithms predict the turbulence\nparameters with acceptable level of accuracy. These ML models are then applied\nfor prediction of the pressure strain correlation of flow cases that are\ndifferent from the flows used for training, to assess their robustness and\ngeneralizability. This explores the assertion that ML based data driven\nturbulence models can overcome the modeling limitations associated with the\ntraditional turbulence models and ML models trained with large amounts data\nwith different classes of flows can predict flow field with reasonable accuracy\nfor unknown flows with similar flow physics. In addition to this verification\nwe carry out validation for the final ML models by assessing the importance of\ndifferent input features for prediction.\n"}
+{"abstract": "  We investigate the effects of field temperature $T^{(f)}$ on the entanglement\nharvesting between two uniformly accelerated detectors. For their parallel\nmotion, the thermal nature of fields does not produce any entanglement, and\ntherefore, the outcome is the same as the non-thermal situation. On the\ncontrary, $T^{(f)}$ affects entanglement harvesting when the detectors are in\nanti-parallel motion, i.e., when detectors $A$ and $B$ are in the right and\nleft Rindler wedges, respectively. While for $T^{(f)}=0$ entanglement\nharvesting is possible for all values of $A$'s acceleration $a_A$, in the\npresence of temperature, it is possible only within a narrow range of $a_A$. In\n$(1+1)$ dimensions, the range starts from specific values and extends to\ninfinity, and as we increase $T^{(f)}$, the minimum required value of $a_A$ for\nentanglement harvesting increases. Moreover, above a critical value $a_A=a_c$\nharvesting increases as we increase $T^{(f)}$, which is just opposite to the\naccelerations below it. There are several critical values in $(1+3)$ dimensions\nwhen they are in different accelerations. Contrary to the single range in\n$(1+1)$ dimensions, here harvesting is possible within several discrete ranges\nof $a_A$. Interestingly, for equal accelerations, one has a single critical\npoint, with nature quite similar to $(1+1)$ dimensional results. We also\ndiscuss the dependence of mutual information among these detectors on $a_A$ and\n$T^{(f)}$.\n"}
+{"abstract": "  We study thermodynamics and critical behaviors of higher-dimensional Lovelock\nblack holes with non-maximally symmetric horizons in the canonical ensemble of\nextended phase space. The effects from non-constancy of the horizon of the\nblack hole via appearing two chargelike parameters in thermodynamic quantities\nof third-order Lovelock black holes are investigated. We find that Ricci flat\nblack holes with nonconstant curvature horizon show critical behavior. This is\nan interesting feature that is not seen for any kind of black hole in Einstein\nor Lovelock gravity in the literature. We examine how various interesting\nthermodynamic phenomena such as standard first-order small-large black hole\nphase transition, a reentrant phase transition, or zeroth order phase\ntransition happens for Ricci flat, spherical, or hyperbolic black holes with\nnonconstant curvature horizon depending on the values of Lovelock coefficient\nand chargelike parameters. While for a spherical black hole of third order\nLovelock gravity with constant curvature horizon phase transition is observed\nonly for $7\\leq d \\leq11$, for our solution criticality and phase transition\nexist in every dimension. With a proper choice of the free parameters, a\nlarge-small-large black hole phase transition occurs. This process is\naccompanied by a finite jump of the Gibbs free energy referred to as a\nzeroth-order phase transition. For the case $\\kappa=-1$ a novel behavior is\nfound for which three critical points could exist.\n"}
+{"abstract": "  We study from the proof complexity perspective the (informal) proof search\nproblem:\n  Is there an optimal way to search for propositional proofs?\n  We note that for any fixed proof system there exists a time-optimal proof\nsearch algorithm. Using classical proof complexity results about reflection\nprinciples we prove that a time-optimal proof search algorithm exists without\nrestricting proof systems iff a p-optimal proof system exists.\n  To characterize precisely the time proof search algorithms need for\nindividual formulas we introduce a new proof complexity measure based on\nalgorithmic information concepts. In particular, to a proof system $P$ we\nattach {\\bf information-efficiency function} $i_P(\\tau)$ assigning to a\ntautology a natural number, and we show that:\n  - $i_P(\\tau)$ characterizes time any $P$-proof search algorithm has to use on\n$\\tau$ and that for a fixed $P$ there is such an information-optimal algorithm,\n  - a proof system is information-efficiency optimal iff it is p-optimal,\n  - for non-automatizable systems $P$ there are formulas $\\tau$ with short\nproofs but having large information measure $i_P(\\tau)$.\n  We isolate and motivate the problem to establish unconditional\nsuper-logarithmic lower bounds for $i_P(\\tau)$ where no super-polynomial size\nlower bounds are known. We also point out connections of the new measure with\nsome topics in proof complexity other than proof search.\n"}
+{"abstract": "  Finely tuning MPI applications and understanding the influence of\nkeyparameters (number of processes, granularity, collective\noperationalgorithms, virtual topology, and process placement) is critical\ntoobtain good performance on supercomputers. With the high consumptionof\nrunning applications at scale, doing so solely to optimize theirperformance is\nparticularly costly. Havinginexpensive but faithful predictions of expected\nperformance could bea great help for researchers and system administrators.\nThemethodology we propose decouples the complexity of the platform, whichis\ncaptured through statistical models of the performance of its maincomponents\n(MPI communications, BLAS operations), from the complexityof adaptive\napplications by emulating the application and skippingregular non-MPI parts of\nthe code. We demonstrate the capability of our method with\nHigh-PerformanceLinpack (HPL), the benchmark used to rank supercomputers in\ntheTOP500, which requires careful tuning. We briefly present (1) how\ntheopen-source version of HPL can be slightly modified to allow a fastemulation\non a single commodity server at the scale of asupercomputer. Then we present\n(2) an extensive (in)validation studythat compares simulation with real\nexperiments and demonstrates our ability to predict theperformance of HPL\nwithin a few percent consistently. This study allows us toidentify the main\nmodeling pitfalls (e.g., spatial and temporal nodevariability or network\nheterogeneity and irregular behavior) that needto be considered. Last, we show\n(3) how our ``surrogate'' allowsstudying several subtle HPL parameter\noptimization problems whileaccounting for uncertainty on the platform.\n"}
+{"abstract": "  In 2010, the unified gas kinetic scheme (UGKS) was proposed by Xu et al . (A\nunified gas-kinetic scheme for continuum and rarefied flows, Journal of\nComputational Physics, 2010). In the past decade, many numerical techniques\nhave been developed to improve the capability of the UGKS in the aspects of\nefficiency increment, memory reduction, and physical modeling. The methodology\nof the direct modeling of the UGKS on discretization scale provides a general\nframework for construction of multiscale method for multiscale transport\nprocesses. This paper reviews the development and extension of the UGKS in its\nfirst decade.\n"}
+{"abstract": "  An accurate description of electron correlation is one of the most\nchallenging problems in quantum chemistry. The exact electron correlation can\nbe obtained by means of full configuration interaction (FCI). A simple strategy\nfor approximating FCI at a reduced computational cost is selected CI (SCI),\nwhich diagonalizes the Hamiltonian within only the chosen configuration space.\nRecovery of the contributions of the remaining configurations is possible with\nsecond-order perturbation theory. Here, we apply adaptive sampling\nconfiguration interaction (ASCI) combined with molecular orbital optimizations\n(ASCI-SCF) corrected with second-order perturbation theory (ASCI-SCF-PT2) for\ngeometry optimization by implementing the analytical nuclear gradient algorithm\nfor ASCI-PT2 with the Z-vector (Lagrangian) formalism. We demonstrate that for\nphenalenyl radicals and anthracene, optimized geometries and the number of\nunpaired electrons can be obtained at nearly the CASSCF accuracy by\nincorporating PT2 corrections and extrapolating them. We demonstrate the\ncurrent algorithm's utility for optimizing the equilibrium geometries and\nelectronic structures of 6-ring-fused polycyclic aromatic hydrocarbons and\n4-periacene.\n"}
+{"abstract": "  Deep neural networks (DNNs) are widely used in pattern-recognition tasks for\nwhich a human comprehensible, quantitative description of the data-generating\nprocess, e.g., in the form of equations, cannot be achieved. While doing so,\nDNNs often produce an abstract (entangled and non-interpretable) representation\nof the data-generating process. This is one of the reasons why DNNs are not\nextensively used in physics-signal processing: physicists generally require\ntheir analyses to yield quantitative information about the studied systems. In\nthis article we use DNNs to disentangle components of oscillating time series,\nand recover meaningful information. We show that, because DNNs can find useful\nabstract feature representations, they can be used when prior knowledge about\nthe signal-generating process exists, but is not complete, as it is\nparticularly the case in \"new-physics\" searches. To this aim, we train our DNN\non synthetic oscillating time series to perform two tasks: a regression of the\nsignal latent parameters and signal denoising by an Autoencoder-like\narchitecture. We show that the regression and denoising performance is similar\nto those of least-square curve fittings (LS-fit) with true latent parameters'\ninitial guesses, in spite of the DNN needing no initial guesses at all. We then\nexplore applications in which we believe our architecture could prove useful\nfor time-series processing in physics, when prior knowledge is incomplete. As\nan example, we employ DNNs as a tool to inform LS-fits when initial guesses are\nunknown. We show that the regression can be performed on some latent\nparameters, while ignoring the existence of others. Because the Autoencoder\nneeds no prior information about the physical model, the remaining unknown\nlatent parameters can still be captured, thus making use of partial prior\nknowledge, while leaving space for data exploration and discoveries.\n"}
+{"abstract": "  The reliability of cardiovascular computational models depends on the\naccurate solution of the hemodynamics, the realistic characterization of the\nhyperelastic and electric properties of the tissues along with the correct\ndescription of their interaction. The resulting\nfluid-structure-electrophysiology interaction (FSEI) thus requires an immense\ncomputational power, usually available in large supercomputing centers, and\nrequires long time to obtain results even if multi-CPU processors are used (MPI\nacceleration). In recent years, graphics processing units (GPUs) have emerged\nas a convenient platform for high performance computing, as they allow for\nconsiderable reductions of the time-to-solution. This approach is particularly\nappealing if the tool has to support medical decisions that require solutions\nwithin reduced times and possibly obtained by local computational resources.\nAccordingly, our multi-physics solver has been ported to GPU architectures\nusing CUDA Fortran to tackle fast and accurate hemodynamics simulations of the\nhuman heart without resorting to large-scale supercomputers. This work\ndescribes the use of CUDA to accelerate the FSEI on heterogeneous clusters,\nwhere both the CPUs and GPUs are used in synergistically with minor\nmodifications of the original source code. The resulting GPU accelerated code\nsolves a single heartbeat within a few hours (from three to ten depending on\nthe grid resolution) running on premises computing facility made of few GPU\ncards, which can be easily installed in a medical laboratory or in a hospital,\nthus opening towards a systematic computational fluid dynamics (CFD) aided\ndiagnostic.\n"}
+{"abstract": "  The density matrix formalism is a fundamental tool in studying various\nproblems in quantum information processing. In the space of density matrices,\nthe most well-known and physically relevant measures are the Hilbert-Schmidt\nensemble and the Bures-Hall ensemble. In this work, we propose a generalized\nensemble of density matrices, termed quantum interpolating ensemble, which is\nable to interpolate between these two seemingly unrelated ensembles. As a first\nstep to understand the proposed ensemble, we derive the exact mean formulas of\nentanglement entropies over such an ensemble generalizing several recent\nresults in the literature. We also derive some key properties of the\ncorresponding orthogonal polynomials relevant to obtaining other statistical\ninformation of the entropies. Numerical results demonstrate the usefulness of\nthe proposed ensemble in estimating the degree of entanglement of quantum\nstates.\n"}
+{"abstract": "  We study tight projective 2-designs in three different settings. In the\ncomplex setting, Zauner's conjecture predicts the existence of a tight\nprojective 2-design in every dimension. Pandey, Paulsen, Prakash, and Rahaman\nrecently proposed an approach to make quantitative progress on this conjecture\nin terms of the entanglement breaking rank of a certain quantum channel. We\nshow that this quantity is equal to the size of the smallest weighted\nprojective 2-design. Next, in the finite field setting, we introduce a notion\nof projective 2-designs, we characterize when such projective 2-designs are\ntight, and we provide a construction of such objects. Finally, in the\nquaternionic setting, we show that every tight projective 2-design for H^d\ndetermines an equi-isoclinic tight fusion frame of d(2d-1) subspaces of\nR^d(2d+1) of dimension 3.\n"}
+{"abstract": "  Quantum key distribution (QKD) provides information theoretically secures key\nexchange requiring authentication of the classic data processing channel via\npre-sharing of symmetric private keys. In previous studies, the lattice-based\npost-quantum digital signature algorithm Aigis-Sig, combined with public-key\ninfrastructure (PKI) was used to achieve high-efficiency quantum security\nauthentication of QKD, and its advantages in simplifying the MAN network\nstructure and new user entry were demonstrated. This experiment further\nintegrates the PQC algorithm into the commercial QKD system, the Jinan field\nmetropolitan QKD network comprised of 14 user nodes and 5 optical switching\nnodes. The feasibility, effectiveness and stability of the post-quantum\ncryptography (PQC) algorithm and advantages of replacing trusted relays with\noptical switching brought by PQC authentication large-scale metropolitan area\nQKD network were verified. QKD with PQC authentication has potential in\nquantum-secure communications, specifically in metropolitan QKD networks.\n"}
+{"abstract": "  Neural network training and validation rely on the availability of large\nhigh-quality datasets. However, in many cases only incomplete datasets are\navailable, particularly in health care applications, where each patient\ntypically undergoes different clinical procedures or can drop out of a study.\nSince the data to train the neural networks need to be complete, most studies\ndiscard the incomplete datapoints, which reduces the size of the training data,\nor impute the missing features, which can lead to artefacts. Alas, both\napproaches are inadequate when a large portion of the data is missing. Here, we\nintroduce GapNet, an alternative deep-learning training approach that can use\nhighly incomplete datasets. First, the dataset is split into subsets of samples\ncontaining all values for a certain cluster of features. Then, these subsets\nare used to train individual neural networks. Finally, this ensemble of neural\nnetworks is combined into a single neural network whose training is fine-tuned\nusing all complete datapoints. Using two highly incomplete real-world medical\ndatasets, we show that GapNet improves the identification of patients with\nunderlying Alzheimer's disease pathology and of patients at risk of\nhospitalization due to Covid-19. By distilling the information available in\nincomplete datasets without having to reduce their size or to impute missing\nvalues, GapNet will permit to extract valuable information from a wide range of\ndatasets, benefiting diverse fields from medicine to engineering.\n"}
+{"abstract": "  Any-to-any voice conversion (VC) aims to convert the timbre of utterances\nfrom and to any speakers seen or unseen during training. Various any-to-any VC\napproaches have been proposed like AUTOVC, AdaINVC, and FragmentVC. AUTOVC, and\nAdaINVC utilize source and target encoders to disentangle the content and\nspeaker information of the features. FragmentVC utilizes two encoders to encode\nsource and target information and adopts cross attention to align the source\nand target features with similar phonetic content. Moreover, pre-trained\nfeatures are adopted. AUTOVC used dvector to extract speaker information, and\nself-supervised learning (SSL) features like wav2vec 2.0 is used in FragmentVC\nto extract the phonetic content information. Different from previous works, we\nproposed S2VC that utilizes Self-Supervised features as both source and target\nfeatures for VC model. Supervised phoneme posteriororgram (PPG), which is\nbelieved to be speaker-independent and widely used in VC to extract content\ninformation, is chosen as a strong baseline for SSL features. The objective\nevaluation and subjective evaluation both show models taking SSL feature CPC as\nboth source and target features outperforms that taking PPG as source feature,\nsuggesting that SSL features have great potential in improving VC.\n"}
+{"abstract": "  We consider a toy model for emergence of chaos in a quantum many-body\nshort-range-interacting system: two one-dimensional hard-core particles in a\nbox, with a small mass defect as a perturbation over an integrable system, the\nlatter represented by two equal mass particles. To that system, we apply a\nquantum generalization of Chirikov's criterion for the onset of chaos, i.e. the\ncriterion of overlapping resonances. There, classical nonlinear resonances\ntranslate almost verbatim to the quantum language. Quantum mechanics intervenes\nat a later stage: the resonances occupying less than one Hamiltonian eigenstate\nare excluded from the chaos criterion. Resonances appear as contiguous patches\nof low purity unperturbed eigenstates, separated by the groups of undestroyed\nstates -- the quantum analogues of the classical KAM tori.\n"}
+{"abstract": "  We review recent numerical studies of two-dimensional (2D) Dirac fermion\ntheories that exhibit an unusual mechanism of topological protection against\nAnderson localization. These describe surface-state quasiparticles of\ntime-reversal invariant, three-dimensional (3D) topological superconductors\n(TSCs), subject to the effects of quenched disorder. Numerics reveal a\nsurprising connection between 3D TSCs in classes AIII, CI, and DIII, and 2D\nquantum Hall effects in classes A, C, and D. Conventional arguments derived\nfrom the non-linear $\\sigma$-model picture imply that most TSC surface states\nshould Anderson localize for arbitrarily weak disorder (CI, AIII), or exhibit\nweak antilocalizing behavior (DIII). The numerical studies reviewed here\ninstead indicate spectrum-wide surface quantum criticality, characterized by\nrobust eigenstate multifractality throughout the surface-state energy spectrum.\nIn other words, there is an \"energy stack\" of critical wave functions. For\nclass AIII, multifractal eigenstate and conductance analysis reveals identical\nstatistics for states throughout the stack, consistent with the class A integer\nquantum-Hall plateau transition (QHPT). Class CI TSCs exhibit surface stacks of\nclass C spin QHPT states. Critical stacking of a third kind, possibly\nassociated to the class D thermal QHPT, is identified for nematic velocity\ndisorder of a single Majorana cone in class DIII. The Dirac theories studied\nhere can be represented as perturbed 2D Wess-Zumino-Novikov-Witten sigma\nmodels; the numerical results link these to Pruisken models with the\ntopological angle $\\vartheta = \\pi$. Beyond applications to TSCs, all three\nstacked Dirac theories (CI, AIII, DIII) naturally arise in the effective\ndescription of dirty $d$-wave quasiparticles, relevant to the high-$T_c$\ncuprates.\n"}
+{"abstract": "  We consider the effects of the heat balance on the structural stability of a\npreflare current layer. The problem of small perturbations is solved in the\npiecewise homogeneous MHD approximation taking into account the viscosity, the\nelectrical and thermal conductivity, and the radiative cooling. Solution of the\nproblem allows the formation of an instability of a thermal nature. There is no\nexternal magnetic field inside the current layer in equilibrium state, but it\ncan penetrate inside when the current layer is disturbed. Formation of a\nmagnetic field perturbation inside the layer creates a dedicated frequency in a\nbroadband disturbance subject to thermal instability. In the linear phase, the\ngrowth time of the instability is proportional to the characteristic time of\nradiative cooling of plasma and depends on the logarithmic derivatives of the\nradiative cooling function with respect to the plasma parameters. The\ninstability results in transverse fragmentation of the current layer with a\nspatial period of 1-10 Mm along the layer in a wide range of coronal plasma\nparameters. The role of that instability in the triggering for the primary\nenergy release in solar flares is discussed.\n"}
+{"abstract": "  In this article, we consider a class of functions on $\\mathbb{R}^d$, called\npositive homogeneous functions, which interact well with certain continuous\none-parameter groups of (generally anisotropic) dilations. Generalizing the\nEuclidean norm, positive homogeneous functions appear naturally in the study of\nconvolution powers of complex-valued functions on $\\mathbb{Z}^d$. As the\nspherical measure is a Radon measure on the unit sphere which is invariant\nunder the symmetry group of the Euclidean norm, to each positive homogeneous\nfunction $P$, we construct a Radon measure $\\sigma_P$ on $S=\\{\\eta \\in\n\\mathbb{R}^d:P(\\eta)=1\\}$ which is invariant under the symmetry group of $P$.\nWith this measure, we prove a generalization of the classical polar-coordinate\nintegration formula and deduce a number of corollaries in this setting. We then\nturn to the study of convolution powers of complex functions on $\\mathbb{Z}^d$\nand certain oscillatory integrals which arise naturally in that context. Armed\nwith our integration formula and the Van der Corput lemma, we establish sup\nnorm-type estimates for convolution powers; this result is new and partially\nextends results of [20] and [21].\n"}
+{"abstract": "  Cyber attacks pose crucial threats to computer system security, and put\ndigital treasuries at excessive risks. This leads to an urgent call for an\neffective intrusion detection system that can identify the intrusion attacks\nwith high accuracy. It is challenging to classify the intrusion events due to\nthe wide variety of attacks. Furthermore, in a normal network environment, a\nmajority of the connections are initiated by benign behaviors. The class\nimbalance issue in intrusion detection forces the classifier to be biased\ntoward the majority/benign class, thus leave many attack incidents undetected.\nSpurred by the success of deep neural networks in computer vision and natural\nlanguage processing, in this paper, we design a new system named DeepIDEA that\ntakes full advantage of deep learning to enable intrusion detection and\nclassification. To achieve high detection accuracy on imbalanced data, we\ndesign a novel attack-sharing loss function that can effectively move the\ndecision boundary towards the attack classes and eliminates the bias towards\nthe majority/benign class. By using this loss function, DeepIDEA respects the\nfact that the intrusion mis-classification should receive higher penalty than\nthe attack mis-classification. Extensive experimental results on three\nbenchmark datasets demonstrate the high detection accuracy of DeepIDEA. In\nparticular, compared with eight state-of-the-art approaches, DeepIDEA always\nprovides the best class-balanced accuracy.\n"}
+{"abstract": "  We establish a convergence theorem for a certain type of stochastic gradient\ndescent, which leads to a convergent variant of the back-propagation algorithm\n"}
+{"abstract": "  Many applications require the robustness, or ideally the invariance, of a\nneural network to certain transformations of input data. Most commonly, this\nrequirement is addressed by either augmenting the training data, using\nadversarial training, or defining network architectures that include the\ndesired invariance automatically. Unfortunately, the latter often relies on the\nability to enlist all possible transformations, which make such approaches\nlargely infeasible for infinite sets of transformations, such as arbitrary\nrotations or scaling. In this work, we propose a method for provably invariant\nnetwork architectures with respect to group actions by choosing one element\nfrom a (possibly continuous) orbit based on a fixed criterion. In a nutshell,\nwe intend to 'undo' any possible transformation before feeding the data into\nthe actual network. We analyze properties of such approaches, extend them to\nequivariant networks, and demonstrate their advantages in terms of robustness\nas well as computational efficiency in several numerical examples. In\nparticular, we investigate the robustness with respect to rotations of images\n(which can possibly hold up to discretization artifacts only) as well as the\nprovable rotational and scaling invariance of 3D point cloud classification.\n"}
+{"abstract": "  Reranking is attracting incremental attention in the recommender systems,\nwhich rearranges the input ranking list into the final rank-ing list to better\nmeet user demands. Most existing methods greedily rerank candidates through the\nrating scores from point-wise or list-wise models. Despite effectiveness,\nneglecting the mutual influence between each item and its contexts in the final\nranking list often makes the greedy strategy based reranking methods\nsub-optimal. In this work, we propose a new context-wise reranking framework\nnamed Generative Rerank Network (GRN). Specifically, we first design the\nevaluator, which applies Bi-LSTM and self-attention mechanism to model the\ncontextual information in the labeled final ranking list and predict the\ninteraction probability of each item more precisely. Afterwards, we elaborate\non the generator, equipped with GRU, attention mechanism and pointer network to\nselect the item from the input ranking list step by step. Finally, we apply\ncross-entropy loss to train the evaluator and, subsequently, policy gradient to\noptimize the generator under the guidance of the evaluator. Empirical results\nshow that GRN consistently and significantly outperforms state-of-the-art\npoint-wise and list-wise methods. Moreover, GRN has achieved a performance\nimprovement of 5.2% on PV and 6.1% on IPV metric after the successful\ndeployment in one popular recommendation scenario of Taobao application.\n"}
+{"abstract": "  The formation of the solar system's giant planets predated the ultimate epoch\nof massive impacts that concluded the process of terrestrial planet formation.\nFollowing their formation, the giant planets' orbits evolved through an episode\nof dynamical instability. Several qualities of the solar system have recently\nbeen interpreted as evidence of this event transpiring within the first ~100\nMyr after the Sun's birth; around the same time as the final assembly of the\ninner planets. In a series of recent papers we argued that such an early\ninstability could resolve several problems revealed in classic numerical\nstudies of terrestrial planet formation; namely the small masses of Mars and\nthe asteroid belt. In this paper, we revisit the early instability scenario\nwith a large suite of simulations specifically designed to understand the\ndegree to which Earth and Mars' formation are sensitive to the specific\nevolution of Jupiter and Saturn's orbits. By deriving our initial terrestrial\ndisks directly from recent high-resolution simulations of planetesimal\naccretion, our results largely confirm our previous findings regarding the\ninstability's efficiency of truncating the terrestrial disk outside of the\nEarth-forming region in simulations that best replicate the outer solar system.\nMoreover, our work validates the primordial 2:1 Jupiter-Saturn resonance within\nthe early instability framework as a viable evolutionary path for the solar\nsystem. While our simulations elucidate the fragility of the terrestrial system\nduring the epoch of giant planet migration, many realizations yield outstanding\nsolar system analogs when scrutinized against a number of observational\nconstraints. Finally, we highlight the inability of models to form adequate\nMercury-analogs and the low eccentricities of Earth and Venus as the most\nsignificant outstanding problems for future numerical studies to resolve.\n"}
+{"abstract": "  5G cellular networks are being deployed all over the world and this\narchitecture supports ultra-dense network (UDN) deployment. Small cells have a\nvery important role in providing 5G connectivity to the end users. Exponential\nincreases in devices, data and network demands make it mandatory for the\nservice providers to manage handovers better, to cater to the services that a\nuser desire. In contrast to any traditional handover improvement scheme, we\ndevelop a 'Deep-Mobility' model by implementing a deep learning neural network\n(DLNN) to manage network mobility, utilizing in-network deep learning and\nprediction. We use network key performance indicators (KPIs) to train our model\nto analyze network traffic and handover requirements. In this method, RF signal\nconditions are continuously observed and tracked using deep learning neural\nnetworks such as the Recurrent neural network (RNN) or Long Short-Term Memory\nnetwork (LSTM) and system level inputs are also considered in conjunction, to\ntake a collective decision for a handover. We can study multiple parameters and\ninteractions between system events along with the user mobility, which would\nthen trigger a handoff in any given scenario. Here, we show the fundamental\nmodeling approach and demonstrate usefulness of our model while investigating\nimpacts and sensitivities of certain KPIs from the user equipment (UE) and\nnetwork side.\n"}
+{"abstract": "  As machine learning systems become more powerful they also become\nincreasingly unpredictable and opaque. Yet, finding human-understandable\nexplanations of how they work is essential for their safe deployment. This\ntechnical report illustrates a methodology for investigating the causal\nmechanisms that drive the behaviour of artificial agents. Six use cases are\ncovered, each addressing a typical question an analyst might ask about an\nagent. In particular, we show that each question cannot be addressed by pure\nobservation alone, but instead requires conducting experiments with\nsystematically chosen manipulations so as to generate the correct causal\nevidence.\n"}
+{"abstract": "  In this paper, we investigate the model reference adaptive control approach\nfor uncertain piecewise affine systems with performance guarantees. The\nproposed approach ensures the error metric, defined as the weighted Euclidean\nnorm of the state tracking error, to be confined within a user-defined\ntime-varying performance bound. We introduce an auxiliary performance function\nto construct a barrier Lyapunov function. This auxiliary performance signal is\nreset at each switching instant, which prevents the transgression of the\nbarriers caused by the jumps of the error metric at switching instants. The\ndwell time constraints are derived based on the parameters of the user-defined\nperformance bound and the auxiliary performance function. We also prove that\nthe Lyapunov function is non-increasing even at the switching instants and thus\ndoes not impose extra dwell time constraints. Furthermore, we propose the\nrobust modification of the adaptive controller for the uncertain piecewise\naffine systems subject to unmatched disturbances. A Numerical example validates\nthe correctness of the proposed approach.\n"}
+{"abstract": "  For a commutative Noetherian ring $R$ and a module-finite $R$-algebra\n$\\Lambda$, we study the set $\\mathsf{tors} \\Lambda$ (respectively,\n$\\mathsf{torf}\\Lambda$) of torsion (respectively, torsionfree) classes of the\ncategory of finitely generated $\\Lambda$-modules. We construct a bijection from\n$\\mathsf{torf}\\Lambda$ to $\\prod_{\\mathfrak{p}} \\mathsf{torf}(\\Lambda\\otimes_R\n\\kappa(\\mathfrak{p}))$, and an embedding $\\Phi_{\\rm t}$ from $\\mathsf{tors}\n\\Lambda$ to $\\mathbb{T}_R(\\Lambda):=\\prod_{\\mathfrak{p}}\n\\mathsf{tors}(\\Lambda\\otimes_R \\kappa(\\mathfrak{p}))$, where $\\mathfrak{p}$\nruns all prime ideals of $R$. When $\\Lambda=R$, these give classifications of\ntorsionfree classes, torsion classes and Serre subcategories of $\\mathsf{mod}\nR$ due to Takahashi, Stanley-Wang and Gabriel. To give a description of\n$\\mathrm{Im} \\Phi_{\\rm t}$, we introduce the notion of compatible elements in\n$\\mathbb{T}_R(\\Lambda)$, and prove that all elements in $\\mathrm{Im} \\Phi_{\\rm\nt}$ are compatible. We give a sufficient condition on $(R, \\Lambda)$ such that\nall compatible elements belong to $\\mathrm{Im} \\Phi_{\\rm t}$ (we call $(R,\n\\Lambda)$ compatible in this case). For example, if $R$ is semi-local and $\\dim\nR \\leq 1$, then $(R, \\Lambda)$ is compatible. We also give a sufficient\ncondition in terms of silting $\\Lambda$-modules. As an application, for a\nDynkin quiver $Q$, $(R, RQ)$ is compatible and we have a poset isomorphism\n$\\mathsf{tors} RQ \\simeq \\mathsf{Hom}_{\\rm poset}(\\mathsf{Spec} R,\n\\mathfrak{C}_Q)$ for the Cambrian lattice $\\mathfrak{C}_Q$ of $Q$.\n"}
+{"abstract": "  Outflows driven by active galactic nuclei (AGN) are an important channel for\naccreting supermassive black holes (SMBHs) to interact with their host galaxies\nand clusters. Properties of the outflows are however poorly constrained due to\nthe lack of kinetically resolved data of the hot plasma that permeates the\ncircumgalactic and intracluster space. In this work, we use a single parameter,\noutflow-to-accretion mass-loading factor $m=\\dot{M}_{\\rm out}/\\dot{M}_{\\rm\nBH}$, to characterize the outflows that mediate the interaction between SMBHs\nand their hosts. By modeling both M87 and Perseus, and comparing the simulated\nthermal profiles with the X-ray observations of these two systems, we\ndemonstrate that $m$ can be constrained between $200-500$. This parameter\ncorresponds to a bulk flow speed between $4,000-7,000\\,{\\rm km\\,s}^{-1}$ at\naround 1 kpc, and a thermalized outflow temperature between\n$10^{8.7}-10^{9}\\,{\\rm K}$. Our results indicate that the dominant outflow\nspeeds in giant elliptical galaxies and clusters are much lower than in the\nclose vicinity of the SMBH, signaling an efficient coupling with and\ndeceleration by the surrounding medium on length scales below 1 kpc.\nConsequently, AGNs may be efficient at launching outflows $\\sim10$ times more\nmassive than previously uncovered by measurements of cold, obscuring material.\nWe also examine the mass and velocity distribution of the cold gas, which\nultimately forms a rotationally supported disk in simulated clusters. The\nrarity of such disks in observations indicates that further investigations are\nneeded to understand the evolution of the cold gas after it forms.\n"}
+{"abstract": "  Adversarial attacks have threatened the application of deep neural networks\nin security-sensitive scenarios. Most existing black-box attacks fool the\ntarget model by interacting with it many times and producing global\nperturbations. However, global perturbations change the smooth and\ninsignificant background, which not only makes the perturbation more easily be\nperceived but also increases the query overhead. In this paper, we propose a\nnovel framework to perturb the discriminative areas of clean examples only\nwithin limited queries in black-box attacks. Our framework is constructed based\non two types of transferability. The first one is the transferability of model\ninterpretations. Based on this property, we identify the discriminative areas\nof a given clean example easily for local perturbations. The second is the\ntransferability of adversarial examples. It helps us to produce a local\npre-perturbation for improving query efficiency. After identifying the\ndiscriminative areas and pre-perturbing, we generate the final adversarial\nexamples from the pre-perturbed example by querying the targeted model with two\nkinds of black-box attack techniques, i.e., gradient estimation and random\nsearch. We conduct extensive experiments to show that our framework can\nsignificantly improve the query efficiency during black-box perturbing with a\nhigh attack success rate. Experimental results show that our attacks outperform\nstate-of-the-art black-box attacks under various system settings.\n"}
+{"abstract": "  Detection of visual anomalies refers to the problem of finding patterns in\ndifferent imaging data that do not conform to the expected visual appearance\nand is a widely studied problem in different domains. Due to the nature of\nanomaly occurrences and underlying generating processes, it is hard to\ncharacterize them and obtain labeled data. Obtaining labeled data is especially\ndifficult in biomedical applications, where only trained domain experts can\nprovide labels, which often come in large diversity and complexity. Recently\npresented approaches for unsupervised detection of visual anomalies approaches\nomit the need for labeled data and demonstrate promising results in domains,\nwhere anomalous samples significantly deviate from the normal appearance.\nDespite promising results, the performance of such approaches still lags behind\nsupervised approaches and does not provide a one-fits-all solution. In this\nwork, we present an image-to-image translation-based framework that\nsignificantly surpasses the performance of existing unsupervised methods and\napproaches the performance of supervised methods in a challenging domain of\ncancerous region detection in histology imagery.\n"}
+{"abstract": "  In this paper, we present two new families of spatially homogeneous black\nhole solution for $z=4$ Ho\\v{r}ava-Lifshitz Gravity equations in $(4+1)$\ndimensions with general coupling constant $\\lambda$ and the especial case\n$\\lambda=1$, considering $\\beta=-1/3$. The three-dimensional horizons are\nconsidered to have Bianchi types $II$ and $III$ symmetries, and hence the\nhorizons are modeled on two types of Thurston $3$-geometries, namely the Nil\ngeometry and $H^2\\times R$. Being foliated by compact 3-manifolds, the horizons\nare neither spherical, hyperbolic, nor toroidal, and therefore are not of the\npreviously studied topological black hole solutions in Ho\\v{r}ava-Lifshitz\ngravity. Using the Hamiltonian formalism, we establish the conventional\nthermodynamics of the solutions defining the mass and entropy of the black hole\nsolutions for several classes of solutions. It turned out that for both horizon\ngeometries the area term in the entropy receives two non-logarithmic negative\ncorrections proportional to Ho\\v{r}ava-Lifshitz parameters. Also, we show that\nchoosing some proper set of parameters the solutions can exhibit locally stable\nor unstable behavior.\n"}
+{"abstract": "  In this paper, we obtain the $H^{p_1}\\times H^{p_2}\\times H^{p_3}\\to H^p$\nboundedness for trilinear Fourier multiplier operators, which is a trilinear\nanalogue of the multiplier theorem of Calder\\'on and Torchinsky (Adv. Math. 24\n: 101-171, 1977). Our result improves the trilinear estimate in the very recent\nwork of the authors, Lee, Heo, Hong, Park, and Yang (Math. Ann., to appear ) by\nadditionally assuming an appropriate vanishing moment condition, which is\nnatural in the boundedness into the Hardy space $H^p$ for $0<p\\le 1$.\n"}
+{"abstract": "  In this article, we develop an arithmetic analogue of Fourier--Jacobi period\nintegrals for a pair of unitary groups of equal rank. We construct the\nso-called Fourier--Jacobi cycles, which are algebraic cycles on the product of\nunitary Shimura varieties and abelian varieties. We propose the arithmetic\nGan--Gross--Prasad conjecture for these cycles, which is related to central\nderivatives of certain Rankin--Selberg $L$-functions, and develop a relative\ntrace formula approach toward this conjecture. As a necessary ingredient, we\npropose the conjecture of the corresponding arithmetic fundamental lemma, and\nconfirm it for unitary groups of rank at most two and for the minuscule case.\n"}
+{"abstract": "  We present two related Stata modules, r_ml_stata and c_ml_stata, for fitting\npopular Machine Learning (ML) methods both in regression and classification\nsettings. Using the recent Stata/Python integration platform (sfi) of Stata 16,\nthese commands provide hyper-parameters' optimal tuning via K-fold\ncross-validation using greed search. More specifically, they make use of the\nPython Scikit-learn API to carry out both cross-validation and outcome/label\nprediction.\n"}
+{"abstract": "  As wearable devices move toward the face (i.e. smart earbuds, glasses), there\nis an increasing need to facilitate intuitive interactions with these devices.\nCurrent sensing techniques can already detect many mouth-based gestures;\nhowever, users' preferences of these gestures are not fully understood. In this\npaper, we investigate the design space and usability of mouth-based\nmicrogestures. We first conducted brainstorming sessions (N=16) and compiled an\nextensive set of 86 user-defined gestures. Then, with an online survey (N=50),\nwe assessed the physical and mental demand of our gesture set and identified a\nsubset of 14 gestures that can be performed easily and naturally. Finally, we\nconducted a remote Wizard-of-Oz usability study (N=11) mapping gestures to\nvarious daily smartphone operations under a sitting and walking context. From\nthese studies, we develop a taxonomy for mouth gestures, finalize a practical\ngesture set for common applications, and provide design guidelines for future\nmouth-based gesture interactions.\n"}
+{"abstract": "  We study the Hawking radiation from the five-dimensional charged static\nsquashed Kaluza-Klein black hole by the tunneling of charged fermions and\ncharged scalar particles, including the phenomenological quantum gravity\neffects predicted by the generalized uncertainty principle with the minimal\nmeasurable length. We derive corrections of the Hawking temperature to general\nrelativity, which are related to the energy of the emitted particle, the size\nof the compact extra dimension, the charge of the black hole and the existence\nof the minimal length in the squashed Kaluza-Klein geometry. We show that the\nquantum gravity effect may slow down the increase of the Hawking temperature\ndue to the radiation, which may lead to the thermodynamic stable remnant of the\norder of the Planck mass after the evaporation of the squashed Kaluza-Klein\nblack hole. We also find that the sparsity of the Hawking radiation may become\ninfinite when the mass of the squashed Kaluza-Klein black hole approaches its\nremnant mass.\n"}
+{"abstract": "  We study the existence of nontrivial solutions for a nonlinear fractional\nelliptic equation in presence of logarithmic and critical exponential\nnonlinearities. This problem extends [5] to fractional $N/s$-Laplacian\nequations with logarithmic nonlinearity. We overcome the lack of compactness\ndue to the critical exponential nonlinearity by using the fractional\nTrudinger-Moser inequality. The existence result is established via critical\npoint theory.\n"}
+{"abstract": "  The non-equilibrium dynamics of stochastic light in a coherently-driven\nnonlinear cavity resembles the equilibrium dynamics of a Brownian particle in a\nscalar potential. This resemblance has been known for decades, but the\ncorrespondence between the two systems has never been properly assessed. Here\nwe demonstrate that this correspondence can be exact, approximate, or break\ndown, depending on the cavity nonlinear response and driving frequency. For\nweak on-resonance driving, the nonlinearity vanishes and the correspondence is\nexact: The cavity dissipation and driving amplitude define a scalar potential,\nthe noise variance defines an effective temperature, and the intra-cavity field\nsatisfies Boltzmann statistics. For moderately strong non-resonant driving, the\ncorrespondence is approximate: We introduce a potential that approximately\ncaptures the nonlinear dynamics of the intra-cavity field, and we quantify the\naccuracy of this approximation via deviations from Boltzmann statistics. For\nvery strong non-resonant driving, the correspondence breaks down: The\nintra-cavity field dynamics is governed by non-conservative forces which\npreclude a description based on a scalar potential only. We furthermore show\nthat this breakdown is accompanied by a phase transition for the intra-cavity\nfield fluctuations, reminiscent of a non-Hermitian phase transition. Our work\nestablishes clear connections between optical and stochastic thermodynamic\nsystems, and suggests that many fundamental results for overdamped Langevin\noscillators may be used to understand and improve resonant optical\ntechnologies.\n"}
+{"abstract": "  We consider the problem of consistently estimating the conditional\ndistribution $P(Y \\in A |X)$ of a functional data object $Y=(Y(t): t\\in[0,1])$\ngiven covariates $X$ in a general space, assuming that $Y$ and $X$ are related\nby a functional linear regression model. Two natural estimation methods are\nproposed, based on either bootstrapping the estimated model residuals, or\nfitting functional parametric models to the model residuals and estimating $P(Y\n\\in A |X)$ via simulation. Whether either of these methods lead to consistent\nestimation depends on the consistency properties of the regression operator\nestimator, and the space within which $Y$ is viewed. We show that under general\nconsistency conditions on the regression operator estimator, which hold for\ncertain functional principal component based estimators, consistent estimation\nof the conditional distribution can be achieved, both when $Y$ is an element of\na separable Hilbert space, and when $Y$ is an element of the Banach space of\ncontinuous functions. The latter results imply that sets $A$ that specify path\nproperties of $Y$, which are of interest in applications, can be considered.\nThe proposed methods are studied in several simulation experiments, and data\nanalyses of electricity price and pollution curves.\n"}
+{"abstract": "  The performance of face recognition system degrades when the variability of\nthe acquired faces increases. Prior work alleviates this issue by either\nmonitoring the face quality in pre-processing or predicting the data\nuncertainty along with the face feature. This paper proposes MagFace, a\ncategory of losses that learn a universal feature embedding whose magnitude can\nmeasure the quality of the given face. Under the new loss, it can be proven\nthat the magnitude of the feature embedding monotonically increases if the\nsubject is more likely to be recognized. In addition, MagFace introduces an\nadaptive mechanism to learn a wellstructured within-class feature distributions\nby pulling easy samples to class centers while pushing hard samples away. This\nprevents models from overfitting on noisy low-quality samples and improves face\nrecognition in the wild. Extensive experiments conducted on face recognition,\nquality assessments as well as clustering demonstrate its superiority over\nstate-of-the-arts. The code is available at\nhttps://github.com/IrvingMeng/MagFace.\n"}
+{"abstract": "  Using a fast and accurate neural network potential we are able to\nsystematically explore the energy landscape of large unit cells of bulk\nmagnesium oxide with the minima hopping method. The potential is trained with a\nfocus on the near-stoichiometric compositions, in particular on suboxides,\ni.e., Mg$_x$O$_{1-x}$ with $0.50<x<0.60$. Our extensive exploration\ndemonstrates that for bulk stoichiometric compounds, there are several new\nlow-energy rocksalt-like structures in which Mg atoms are octahedrally\nsix--coordinated and form trigonal prismatic motifs with different stacking\nsequences. Furthermore, we find a dense spectrum of novel non-stoichiometric\ncrystal phases of Mg$_x$O$_{1-x}$ for each composition of $x$. These structures\nare mostly similar to the rock salt structure with octahedral coordination and\nfive--coordinated Mg atoms. Due to the removal of one oxygen atom, the energy\nlandscape becomes more glass-like with oxygen-vacancy type structures that all\nlie very close to each other energetically. For the same number of magnesium\nand oxygen atoms our oxygen-deficient structures are lower in energy if the\nvacancies are aligned along lines or planes than rock salt structures with\nrandomly distributed oxygen vacancies. We also found the putative global minima\nconfigurations for each composition of the non-stoichiometric suboxide\nstructures. These structures are predominantly composed of (111) slabs of the\nrock salt structure which are terminated with Mg atoms at the top and bottom,\nand are stacked in different sequences along the $z$-direction. Like other\nMagn\\'eli-type phases, these structures have properties that differ\nconsiderably from their stoichiometric counterparts such as low lattice thermal\nconductivity and high electrical conductivity.\n"}
+{"abstract": "  The macroscopic dynamics of a droplet impacting a solid is crucially\ndetermined by the intricate air dynamics occurring at the vanishingly small\nlength scale between droplet and substrate prior to direct contact. Here we\ninvestigate the inverse problem, namely the role of air for the impact of a\nhorizontal flat disk onto a liquid surface, and find an equally significant\neffect. Using an in-house experimental technique, we measure the free surface\ndeflections just before impact, with a precision of a few micrometers. Whereas\nstagnation pressure pushes down the surface in the center, we observe a lift-up\nunder the edge of the disk, which sets in at a later stage, and which we show\nto be consistent with a Kelvin-Helmholtz instability of the water-air\ninterface.\n"}
+{"abstract": "  Reinforcement learning (RL) agents in human-computer interactions\napplications require repeated user interactions before they can perform well.\nTo address this \"cold start\" problem, we propose a novel approach of using\ncognitive models to pre-train RL agents before they are applied to real users.\nAfter briefly reviewing relevant cognitive models, we present our general\nmethodological approach, followed by two case studies from our previous and\nongoing projects. We hope this position paper stimulates conversations between\nRL, HCI, and cognitive science researchers in order to explore the full\npotential of the approach.\n"}
+{"abstract": "  In this paper we investigate how gradient-based algorithms such as gradient\ndescent, (multi-pass) stochastic gradient descent, its persistent variant, and\nthe Langevin algorithm navigate non-convex loss-landscapes and which of them is\nable to reach the best generalization error at limited sample complexity. We\nconsider the loss landscape of the high-dimensional phase retrieval problem as\na prototypical highly non-convex example. We observe that for phase retrieval\nthe stochastic variants of gradient descent are able to reach perfect\ngeneralization for regions of control parameters where the gradient descent\nalgorithm is not. We apply dynamical mean-field theory from statistical physics\nto characterize analytically the full trajectories of these algorithms in their\ncontinuous-time limit, with a warm start, and for large system sizes. We\nfurther unveil several intriguing properties of the landscape and the\nalgorithms such as that the gradient descent can obtain better generalization\nproperties from less informed initializations.\n"}
+{"abstract": "  Magnetic field source localization and imaging happen at different scales.\nThe sensing baseline ranges from meter scale such as magnetic anomaly\ndetection, centimeter scale such as brain field imaging to nanometer scale such\nas the imaging of magnetic skyrmion and single cell. Here we show how atomic\nvapor cell can be used to realize a baseline of 109.6 {\\mu}m with a magnetic\nsensitivity of 10pT/sqrt(Hz)@0.6-100Hz and a dynamic range of 2062-4124nT.We\nuse free induction decay (FID) scheme to suppress low-frequency noise and avoid\nscale factor variation for different domains due to light non-uniformity. The\nmeasurement domains are scanned by digital micro-mirror device (DMD). The\ncurrents of 22mA, 30mA, 38mA and 44mA are applied in the coils to generate\ndifferent fields along the pumping axis which are measured respectively by\nfitting the FID signals of the probe light. The residual fields of every domain\nare obtained from the intercept of linearly-fitting of the measurement data\ncorresponding to these four currents. The coil-generated fields are calculated\nby deducting the residual fields from the total fields. The results demonstrate\nthat the hole of shield affects both the residual and the coil-generated field\ndistribution. The potential impact of field distribution measurement with an\noutstanding comprehensive properties of spatial resolution, sensitivity and\ndynamic range is far-reaching. It could lead to capability of 3D magnetography\nfor small stuffs and/or organs in millimeter or even smaller scale.\n"}
+{"abstract": "  This paper presents libtxsize, a library to estimate the size requirements of\narbitrary Bitcoin transactions. To account for different use cases, the library\nprovides estimates in bytes, virtual bytes, and weight units. In addition to\nall currently existing input, output, and witness types, the library also\nsupports estimates for the anticipated Pay-to-Taproot transaction type, so that\nestimates can be used as input for models attempting to quantify the impact of\nTaproot on Bitcoin's scalability. libtxsize is based on analytic models, whose\ncredibility is established through first-principle analysis of transaction\ntypes as well as exhaustive empirical validation. Consequently, the paper can\nalso serve as reference for different Bitcoin data and transaction types, their\nsemantics, and their size requirements (both from an analytic and empirical\npoint of view).\n"}
+{"abstract": "  We present a new approach for fast calculation of gravitational lensing\nproperties, including the lens potential, deflection angles, convergence, and\nshear, of elliptical Navarro-Frenk-White (NFW) and Hernquist density profiles,\nby approximating them by superpositions of elliptical density profiles for\nwhich simple analytic expressions of gravitational lensing properties are\navailable. This model achieves high fractional accuracy better than $10^{-4}$\nin the range of the radius normalized by the scale radius of $10^{-4}-10^3$.\nThese new approximations are $\\sim 300$ times faster in solving the lens\nequation for a point source compared with the traditional approach resorting to\nexpensive numerical integrations, and are implemented in {\\tt glafic} software.\n"}
+{"abstract": "  There are situations where data relevant to a machine learning problem are\ndistributed among multiple locations that cannot share the data due to\nregulatory, competitiveness, or privacy reasons. For example, data present in\nusers' cellphones, manufacturing data of companies in a given industrial\nsector, or medical records located at different hospitals. Federated Learning\n(FL) provides an approach to learn a joint model over all the available data\nacross silos. In many cases, participating sites have different data\ndistributions and computational capabilities. In these heterogeneous\nenvironments previous approaches exhibit poor performance: synchronous FL\nprotocols are communication efficient, but have slow learning convergence;\nconversely, asynchronous FL protocols have faster convergence, but at a higher\ncommunication cost. Here we introduce a novel Semi-Synchronous Federated\nLearning protocol that mixes local models periodically with minimal idle time\nand fast convergence. We show through extensive experiments that our approach\nsignificantly outperforms previous work in data and computationally\nheterogeneous environments.\n"}
+{"abstract": "  Measures of algorithmic fairness often do not account for human perceptions\nof fairness that can substantially vary between different sociodemographics and\nstakeholders. The FairCeptron framework is an approach for studying perceptions\nof fairness in algorithmic decision making such as in ranking or\nclassification. It supports (i) studying human perceptions of fairness and (ii)\ncomparing these human perceptions with measures of algorithmic fairness. The\nframework includes fairness scenario generation, fairness perception\nelicitation and fairness perception analysis. We demonstrate the FairCeptron\nframework by applying it to a hypothetical university admission context where\nwe collect human perceptions of fairness in the presence of minorities. An\nimplementation of the FairCeptron framework is openly available, and it can\neasily be adapted to study perceptions of algorithmic fairness in other\napplication contexts. We hope our work paves the way towards elevating the role\nof studies of human fairness perceptions in the process of designing\nalgorithmic decision making systems.\n"}
+{"abstract": "  For a fixed graph $H$ and for arbitrarily large host graphs $G$, the number\nof homomorphisms from $H$ to $G$ and the number of subgraphs isomorphic to $H$\ncontained in $G$ have been extensively studied in extremal graph theory and\ngraph limits theory when the host graphs are allowed to be dense. This paper\naddresses the case when the host graphs are robustly sparse and proves a\ngeneral theorem that solves a number of open questions proposed since 1990s and\nstrengthens a number of results in the literature.\n  We prove that for any graph $H$ and any set ${\\mathcal H}$ of homomorphisms\nfrom $H$ to members of a hereditary class ${\\mathcal G}$ of graphs, if\n${\\mathcal H}$ satisfies a natural and mild condition, and contracting disjoint\nsubgraphs of radius $O(\\lvert V(H) \\rvert)$ in members of ${\\mathcal G}$ cannot\ncreate a graph with large edge-density, then an obvious lower bound for the\nsize of ${\\mathcal H}$ gives a good estimation for the size of ${\\mathcal H}$.\nThis result determines the maximum number of $H$-homomorphisms, the maximum\nnumber of $H$-subgraphs, and the maximum number $H$-induced subgraphs in graphs\nin any hereditary class with bounded expansion up to a constant factor; it also\ndetermines the exact value of the asymptotic logarithmic density for\n$H$-homomorphisms, $H$-subgraphs and $H$-induced subgraphs in graphs in any\nhereditary nowhere dense class. Hereditary classes with bounded expansion\ninclude (topological) minor-closed families and many classes of graphs with\ncertain geometric properties; nowhere dense classes are the most general sparse\nclasses in sparsity theory. Our machinery also allows us to determine the\nmaximum number of $H$-subgraphs in the class of all $d$-degenerate graphs with\nany fixed $d$.\n"}
+{"abstract": "  The multiplicative and additive compounds of a matrix play an important role\nin several fields of mathematics including geometry, multi-linear algebra,\ncombinatorics, and the analysis of nonlinear time-varying dynamical systems.\nThere is a growing interest in applications of these compounds, and their\ngeneralizations, in systems and control theory. This tutorial paper provides a\ngentle introduction to these topics with an emphasis on the geometric\ninterpretation of the compounds, and surveys some of their recent applications.\n"}
+{"abstract": "  Gaussian distributions can be generalized from Euclidean space to a wide\nclass of Riemannian manifolds. Gaussian distributions on manifolds are harder\nto make use of in applications since the normalisation factors, which we will\nrefer to as partition functions, are complicated, intractable integrals in\ngeneral that depend in a highly non-linear way on the mean of the given\ndistribution. Nonetheless, on Riemannian symmetric spaces, the partition\nfunctions are independent of the mean and reduce to integrals over finite\ndimensional vector spaces. These are generally still hard to compute\nnumerically when the dimension (more precisely the rank $N$) of the underlying\nsymmetric space gets large. On the space of positive definite Hermitian\nmatrices, it is possible to compute these integrals exactly using methods from\nrandom matrix theory and the so-called Stieltjes-Wigert polynomials. In other\ncases of interest to applications, such as the space of symmetric positive\ndefinite (SPD) matrices or the Siegel domain (related to block-Toeplitz\ncovariance matrices), these methods seem not to work quite as well.\nNonetheless, it remains possible to compute leading order terms in a large $N$\nlimit, which provide increasingly accurate approximations as $N$ grows. This\nlimit is inspired by realizing a given partition function as the partition\nfunction of a zero-dimensional quantum field theory or even Chern-Simons\ntheory. From this point of view the large $N$ limit arises naturally and\nsaddle-point methods, Feynman diagrams, and certain universalities that relate\ndifferent spaces emerge.\n"}
+{"abstract": "  A neoclassically optimized compact stellarator with simple coils has been\ndesigned. The magnetic field of the new stellarator is generated by only four\nplanar coils including two interlocking coils of elliptical shape and two\ncircular poloidal field coils. The interlocking coil topology is the same as\nthat of the Columbia Non-neutral Torus (CNT). The new configuration was\nobtained by minimizing the effective helical ripple directly via the shape of\nthe two interlocking coils. The optimized compact stellarator has very low\neffective ripple in the plasma core implying excellent neoclassical\nconfinement. This is confirmed by the results of the drift-kinetic code SFINCS\nshowing that the particle diffusion coefficient of the new configuration is one\norder of magnitude lower than CNT's.\n"}
+{"abstract": "  Given a compact Riemann surface $\\Sigma$ of genus $g_\\Sigma\\, \\geq\\, 2$, and\nan effective divisor $D\\, =\\, \\sum_i n_i x_i$ on $\\Sigma$ with\n$\\text{degree}(D)\\, <\\, 2(g_\\Sigma -1)$, there is a unique cone metric on\n$\\Sigma$ of constant negative curvature $-4$ such that the cone angle at each\n$x_i$ is $2\\pi n_i$ (see McOwen and Troyanov [McO,Tr]). We describe the Higgs\nbundle corresponding to this uniformization associated to the above conical\nmetric. We also give a family of Higgs bundles on $\\Sigma$ parametrized by a\nnonempty open subset of $H^0(\\Sigma,\\,K_\\Sigma^{\\otimes 2}\\otimes{\\mathcal\nO}_\\Sigma(-2D))$ that correspond to conical metrics of the above type on moving\nRiemann surfaces. These are inspired by Hitchin's results in [Hi1], for the\ncase $D\\,=\\, 0$.\n"}
+{"abstract": "  Package-to-group recommender systems recommend a set of unified items to a\ngroup of people. Different from conventional settings, it is not easy to\nmeasure the utility of group recommendations because it involves more than one\nuser. In particular, fairness is crucial in group recommendations. Even if some\nmembers in a group are substantially satisfied with a recommendation, it is\nundesirable if other members are ignored to increase the total utility. Many\nmethods for evaluating and applying the fairness of group recommendations have\nbeen proposed in the literature. However, all these methods maximize the score\nand output only one package. This is in contrast to conventional recommender\nsystems, which output several (e.g., top-$K$) candidates. This can be\nproblematic because a group can be dissatisfied with the recommended package\nowing to some unobserved reasons, even if the score is high. To address this\nissue, we propose a method to enumerate fair packages efficiently. Our method\nfurthermore supports filtering queries, such as top-$K$ and intersection, to\nselect favorite packages when the list is long. We confirm that our algorithm\nscales to large datasets and can balance several aspects of the utility of the\npackages.\n"}
+{"abstract": "  Milwaukee's 53206 ZIP code, located on the city's near North Side, has drawn\nconsiderable attention for its poverty and incarceration rates, as well as for\nits large proportion of vacant properties. As a result, it has benefited from\ntargeted policies at the city level. Keeping in mind that ZIP codes are often\nnot the most effective unit of geographic analysis, this study investigates\nMilwaukee's socioeconomic conditions at the block group level. These smaller\nareas' statistics are then compared with those of their corresponding ZIP\ncodes. The 53206 ZIP code is compared against others in Milwaukee for eight\nsocioeconomic variables and is found to be near the extreme end of most\nrankings. This ZIP code would also be among Chicago's most extreme areas, but\nwould lie near the middle of the rankings if located in Detroit. Parts of other\nZIP codes, which are often adjacent, are statistically similar to 53206,\nhowever--suggesting that a focus solely on ZIP codes, while a convenient\nshorthand, might overlook neighborhoods that have similar need for investment.\nA multivariate index created for this study performs similarly to a standard\nmultivariate index of economic deprivation if spatial correlation is taken into\naccount, confirming that poverty and other socioeconomic stresses are\nclustered, both in the 53206 ZIP code and across Milwaukee.\n"}
+{"abstract": "  Coulomb fission mechanism may take place if the maximum Coulomb-excitation\nenergy transfer in a reaction exceeds the fission barrier of either the\nprojectile or target. This condition is satisfied by all the reactions used for\nthe earlier blocking measurements except one reaction 208 Pb + Natural Ge\ncrystal, where the measured timescale was below the measuring limit of the\nblocking measurements < 1 as. Hence, inclusion of the Coulomb fission in the\ndata analysis of the blocking experiments leads us to interpret that the\nmeasured time longer than a few attoseconds (about 2-2.5 as) is nothing but\nbelonging to the Coulomb fission timescale and shorter than 1 as are due to the\nquasifission. Consequently, this finding resolves the critical discrepancies\nbetween the fission timescale measurements using the nuclear and blocking\ntechniques. This, in turn, validates the fact that the quasifission timescale\nis indeed of the order of zeptoseconds in accordance with the nuclear\nexperiments and theories. It thus provides a radical input in understanding the\nreaction mechanism for heavy element formation via fusion evaporation processes\n"}
+{"abstract": "  Consider the family of bounded degree graphs in any minor-closed family (such\nas planar graphs). Let d be the degree bound and n be the number of vertices of\nsuch a graph. Graphs in these classes have hyperfinite decompositions, where,\nfor a sufficiently small \\e > 0, one removes \\edn edges to get connected\ncomponents of size independent of n. An important tool for sublinear algorithms\nand property testing for such classes is the partition oracle, introduced by\nthe seminal work of Hassidim-Kelner-Nguyen-Onak (FOCS 2009). A partition oracle\nis a local procedure that gives consistent access to a hyperfinite\ndecomposition, without any preprocessing. Given a query vertex v, the partition\noracle outputs the component containing v in time independent of n. All the\nanswers are consistent with a single hyperfinite decomposition. The partition\noracle of Hassidim et al. runs in time d^poly(d/\\e) per query. They pose the\nopen problem of whether poly(d/\\e)-time partition oracles exist. Levi-Ron\n(ICALP 2013) give a refinement of the previous approach, to get a partition\noracle that runs in time d^{\\log(d/\\e)-per query. In this paper, we resolve\nthis open problem and give \\poly(d/\\e)-time partition oracles for bounded\ndegree graphs in any minor-closed family. Unlike the previous line of work\nbased on combinatorial methods, we employ techniques from spectral graph\ntheory. We build on a recent spectral graph theoretical toolkit for\nminor-closed graph families, introduced by the authors to develop efficient\nproperty testers. A consequence of our result is a poly(d/\\e)-query tester for\nany monotone and additive property of minor-closed families (such as bipartite\nplanar graphs). Our result also gives poly(d/\\e)-query algorithms for additive\n{\\e}n-approximations for problems such as maximum matching, minimum vertex\ncover, maximum independent set, and minimum dominating set for these graph\nfamilies.\n"}
+{"abstract": "  This arXiv report provides a short introduction to the information-theoretic\nmeasure proposed by Chen and Golan in 2016 for analyzing machine- and\nhuman-centric processes in data intelligence workflows. This introduction was\ncompiled based on several appendices written to accompany a few research papers\non topics of data visualization and visual analytics. Although the original\n2016 paper and the follow-on papers were mostly published in the field of\nvisualization and visual analytics, the cost-benefit measure can help explain\nthe informative trade-off in a wide range of data intelligence phenomena\nincluding machine learning, human cognition, language development, and so on.\nMeanwhile, there is an ongoing effort to improve its mathematical properties in\norder to make it more intuitive and usable in practical applications as a\nmeasurement tool.\n"}
+{"abstract": "  Face recognition (FR) using deep convolutional neural networks (DCNNs) has\nseen remarkable success in recent years. One key ingredient of DCNN-based FR is\nthe appropriate design of a loss function that ensures discrimination between\nvarious identities. The state-of-the-art (SOTA) solutions utilise normalised\nSoftmax loss with additive and/or multiplicative margins. Despite being\npopular, these Softmax+margin based losses are not theoretically motivated and\nthe effectiveness of a margin is justified only intuitively. In this work, we\nutilise an alternative framework that offers a more direct mechanism of\nachieving discrimination among the features of various identities. We propose a\nnovel loss that is equivalent to a triplet loss with proxies and an implicit\nmechanism of hard-negative mining. We give theoretical justification that\nminimising the proposed loss ensures a minimum separability between all\nidentities. The proposed loss is simple to implement and does not require heavy\nhyper-parameter tuning as in the SOTA solutions. We give empirical evidence\nthat despite its simplicity, the proposed loss consistently achieves SOTA\nperformance in various benchmarks for both high-resolution and low-resolution\nFR tasks.\n"}
+{"abstract": "  Bimetallic nanoparticles (BNPs) exhibit diverse morphologies such as\ncore-shell, Janus, onion-like, quasi-Janus, and homogeneous structures.\nAlthough extensive effort has been directed towards understanding the\nequilibrium configurations of BNPs, kinetic mechanisms involved in their\ndevelopment have not been explored systematically. Since these systems often\ncontain a miscibility gap, experimental studies have alluded to spinodal\ndecomposition (SD) as a likely mechanism for the formation of such structures.\nWe present a novel phase-field model for confined (embedded)systems to study\nSD-induced morphological evolution within a BNP. It initiates with the\nformation of compositionally modulated rings as a result of surface-directed SD\nand eventually develops into core-shell or Janus structures due to\ncoarsening/breakdown of the rings. The final configuration depends crucially on\ncontact angle and particle size -Janus is favored at smaller sizes and higher\ncontact angles. Our simulations also illustrate the formation of metastable,\nkinetically trapped structures as a result of competition between capillarity\nand diffusion.\n"}
+{"abstract": "  We consider the vector space $E_{\\rho,p}$ of entire functions of finite\norder, whose types are not more than $p>0$, endowed with Frechet topology,\nwhich is generated by a sequence of weighted norms. We call a function $f\\in\nE_{\\rho,p}$ {\\it typical} if it is surjective and has an infinite number\ncritical points such that each of them is non-degenerate and all the values of\n$f$ at these points are pairwise different. We prove that the set of all\ntypical functions contains a set which is $G_\\delta$ and dense in $E_{\\rho,p}$.\nFurthermore, we show that inverse to any typical function has Riemann surface\nwhose monodromy group coincides with finitary symmetric group of permutations\nof naturals, which is unsolvable in the following strong sense: it does not\nhave a normal tower of subgroups, whose factor groups are or abelian or finite.\nAs a consequence from these facts and Topological Galois Theory, we obtain that\ngenerically (in the above sense) for $f\\in E_{\\rho,p}$ the solution of equation\n$f(w)=z$ cannot be represented via $z$ and complex constants by a finite number\nof the following actions: algebraic operations (i.e., rational ones and\nsolutions of polynomial equations) and quadratures (in particular,\nsuperpositions with elementary functions).\n"}
+{"abstract": "  The standard diffusive spreading, characterized by a Gaussian distribution\nwith mean square displacement that grows linearly with time, can break down,\nfor instance, under the presence of correlations and heterogeneity. In this\nwork, we consider the spread of a population of fractional (long-time\ncorrelated) Brownian walkers, with time-dependent and heterogeneous\ndiffusivity. We aim to obtain the possible scenarios related to these\nindividual-level features from the observation of the temporal evolution of the\npopulation spatial distribution. We develop and discuss the possibility and\nlimitations of this connection for the broad class of self-similar diffusion\nprocesses. Our results are presented in terms of a general framework, which is\nthen used to address well-known processes, such as Laplace diffusion, nonlinear\ndiffusion, and their extensions.\n"}
+{"abstract": "  The existence of massive compact stars $(M\\gtrsim 2.1 M_{\\odot})$ implies\nthat the conformal limit of the speed of sound $c_s^2=1/3$ is violated if those\nstars have a crust of ordinary nuclear matter. Here we show that, if the most\nmassive objects are strange quark stars, i.e. stars entirely composed of\nquarks, the conformal limit can be respected while observational limits on\nthose objects are also satisfied. By using astrophysical data associated with\nthose massive stars, derived from electromagnetic and gravitational wave\nsignals, we show, within a Bayesian analysis framework and by adopting a\nconstant speed of sound equation of state, that the posterior distribution of\n$c_s^2$ is peaked around 0.3, and the maximum mass of the most probable\nequation of state is $\\sim 2.13 M_{\\odot}$. We discuss which new data would\nrequire a violation of the conformal limit even when considering strange quark\nstars, in particular we analyze the possibility that the maximum mass of\ncompact stars is larger than $2.5M_{\\odot}$, as it would be if the secondary\ncomponent of GW190814 is a compact star and not a black hole. Finally, we\ndiscuss how the new data for PSR J0740+6620 obtained by the NICER collaboration\ncompare with our analysis (not based on them) and with other possible\ninterpretations.\n"}
+{"abstract": "  From Swift monitoring of a sample of active galactic nuclei (AGN) we found a\ntransient X-ray obscuration event in Seyfert-1 galaxy NGC 3227, and thus\ntriggered our joint XMM-Newton, NuSTAR, and Hubble Space Telescope (HST)\nobservations to study this event. Here in the first paper of our series we\npresent the broadband continuum modelling of the spectral energy distribution\n(SED) for NGC 3227, extending from near infrared (NIR) to hard X-rays. We use\nour new spectra taken with XMM-Newton, NuSTAR, and HST/COS in 2019, together\nwith archival unobscured XMM-Newton, NuSTAR, and HST/STIS data, in order to\ndisentangle various spectral components of NGC 3227 and recover the underlying\ncontinuum. We find the observed NIR-optical-UV continuum is explained well by\nan accretion disk blackbody component (Tmax = 10 eV), which is internally\nreddened by E(B-V) = 0.45 with a Small Magellanic Cloud (SMC) extinction law.\nWe derive the inner radius (12 Rg) and the accretion rate (0.1 solar mass per\nyear) of the disk by modelling the thermal disk emission. The internal\nreddening in NGC 3227 is most likely associated with outflows from the dusty\nAGN torus. In addition, an unreddened continuum component is also evident,\nwhich likely arises from scattered radiation, associated with the extended\nnarrow-line region (NLR) of NGC 3227. The extreme ultraviolet (EUV) continuum,\nand the 'soft X-ray excess', can be explained with a 'warm Comptonisation'\ncomponent. The hard X-rays are consistent with a power-law and a neutral\nreflection component. The intrinsic bolometric luminosity of the AGN in NGC\n3227 is about 2.2e+43 erg/s in 2019, corresponding to 3% Eddington luminosity.\nOur continuum modelling of the new triggered data of NGC 3227 requires the\npresence of a new obscuring gas with column density NH = 5e+22 cm^-2, partially\ncovering the X-ray source (Cf = 0.6).\n"}
+{"abstract": "  No quantum circuit can turn a completely unknown unitary gate into its\ncoherently controlled version. Yet, coherent control of unknown gates has been\nrealised in experiments, making use of a different type of initial resources.\nHere, we formalise the task achieved by these experiments, extending it to the\ncontrol of arbitrary noisy channels, and to more general types of control\ninvolving higher dimensional control systems. For the standard notion of\ncoherent control, we identify the information-theoretic resource for\ncontrolling an arbitrary quantum channel on a $d$-dimensional system:\nspecifically, the resource is an extended quantum channel acting as the\noriginal channel on a $d$-dimensional sector of a $(d+1)$-dimensional system.\nUsing this resource, arbitrary controlled channels can be built with a\nuniversal circuit architecture. We then extend the standard notion of control\nto more general notions, including control of multiple channels with possibly\ndifferent input and output systems. Finally, we develop a theoretical\nframework, called supermaps on routed channels, which provides a compact\nrepresentation of coherent control as an operation performed on the extended\nchannels, and highlights the way the operation acts on different sectors.\n"}
+{"abstract": "  This paper presents a taxonomy that allows defining the fault tolerance\nregimes fail-operational, fail-degraded, and fail-safe in the context of\nautomotive systems. Fault tolerance regimes such as these are widely used in\nrecent publications related to automated driving, yet without definitions. This\nlargely holds true for automotive safety standards, too. We show that fault\ntolerance regimes defined in scientific publications related to the automotive\ndomain are partially ambiguous as well as taxonomically unrelated. The\npresented taxonomy is based on terminology stemming from ISO 26262 as well as\nfrom systems engineering. It uses four criteria to distinguish fault tolerance\nregimes. In addition to fail-operational, fail-degraded, and fail-safe, the\ncore terminology consists of operational and fail-unsafe. These terms are\nsupported by definitions of available performance, nominal performance,\nfunctionality, and a concise definition of the safe state. For verification, we\nshow by means of two examples from the automotive domain that the taxonomy can\nbe applied to hierarchical systems of different complexity.\n"}
+{"abstract": "  We propose Preferential MoE, a novel human-ML mixture-of-experts model that\naugments human expertise in decision making with a data-based classifier only\nwhen necessary for predictive performance. Our model exhibits an interpretable\ngating function that provides information on when human rules should be\nfollowed or avoided. The gating function is maximized for using human-based\nrules, and classification errors are minimized. We propose solving a coupled\nmulti-objective problem with convex subproblems. We develop approximate\nalgorithms and study their performance and convergence. Finally, we demonstrate\nthe utility of Preferential MoE on two clinical applications for the treatment\nof Human Immunodeficiency Virus (HIV) and management of Major Depressive\nDisorder (MDD).\n"}
+{"abstract": "  The rapid expansion of distributed energy resources (DERs) is one of the most\nsignificant changes to electricity systems around the world. Examples of DERs\ninclude solar panels, small natural gas-fueled generators, combined heat and\npower plants, etc. Due to the small supply capacities of these DERs, it is\nimpractical for them to participate directly in the wholesale electricity\nmarket. We study in this paper an efficient aggregation model where a\nprofit-maximizing aggregator procures electricity from DERs, and sells them in\nthe wholesale market. The interaction between the aggregator and the DER owners\nis modeled as a Stackelberg game: the aggregator adopts two-part pricing by\nannouncing a participation fee and a per-unit price of procurement for each DER\nowner, and the DER owner responds by choosing her payoff-maximizing energy\nsupplies. We show that our proposed model preserves full market efficiency,\ni.e., the social welfare achieved by the aggregation model is the same as that\nwhen DERs participate directly in the wholesale market. We also note that\ntwo-part pricing is critical for market efficiency, and illustrate via an\nexample that with one-part pricing, there will be an efficiency loss from DER\naggregation, due to the profit-seeking behavior of the aggregator.\n"}
+{"abstract": "  Learning and reasoning over graphs is increasingly done by means of\nprobabilistic models, e.g. exponential random graph models, graph embedding\nmodels, and graph neural networks. When graphs are modeling relations between\npeople, however, they will inevitably reflect biases, prejudices, and other\nforms of inequity and inequality. An important challenge is thus to design\naccurate graph modeling approaches while guaranteeing fairness according to the\nspecific notion of fairness that the problem requires. Yet, past work on the\ntopic remains scarce, is limited to debiasing specific graph modeling methods,\nand often aims to ensure fairness in an indirect manner.\n  We propose a generic approach applicable to most probabilistic graph modeling\napproaches. Specifically, we first define the class of fair graph models\ncorresponding to a chosen set of fairness criteria. Given this, we propose a\nfairness regularizer defined as the KL-divergence between the graph model and\nits I-projection onto the set of fair models. We demonstrate that using this\nfairness regularizer in combination with existing graph modeling approaches\nefficiently trades-off fairness with accuracy, whereas the state-of-the-art\nmodels can only make this trade-off for the fairness criterion that they were\nspecifically designed for.\n"}
+{"abstract": "  This paper reports a comprehensive study on the applicability of ultra-scaled\nferroelectric FinFETs with 6 nm thick hafnium zirconium oxide layer for\nneuromorphic computing in the presence of process variation, flicker noise, and\ndevice aging. An intricate study has been conducted about the impact of such\nvariations on the inference accuracy of pre-trained neural networks consisting\nof analog, quaternary (2-bit/cell) and binary synapse. A pre-trained neural\nnetwork with 97.5% inference accuracy on the MNIST dataset has been adopted as\nthe baseline. Process variation, flicker noise, and device aging\ncharacterization have been performed and a statistical model has been developed\nto capture all these effects during neural network simulation. Extrapolated\nretention above 10 years have been achieved for binary read-out procedure. We\nhave demonstrated that the impact of (1) retention degradation due to the oxide\nthickness scaling, (2) process variation, and (3) flicker noise can be abated\nin ferroelectric FinFET based binary neural networks, which exhibits superior\nperformance over quaternary and analog neural network, amidst all variations.\nThe performance of a neural network is the result of coalesced performance of\ndevice, architecture and algorithm. This research corroborates the\napplicability of deeply scaled ferroelectric FinFETs for non-von Neumann\ncomputing with proper combination of architecture and algorithm.\n"}
+{"abstract": "  The holographic light-front QCD framework provides a unified nonperturbative\ndescription of the hadron mass spectrum, form factors and quark distributions.\nIn this article we extend holographic QCD in order to describe the gluonic\ndistribution in both the proton and pion from the coupling of the metric\nfluctuations induced by the spin-two Pomeron with the energy momentum tensor in\nanti--de Sitter space, together with constraints imposed by the Veneziano\nmodel{\\color{blue},} without additional free parameters. The gluonic and quark\ndistributions are shown to have significantly different effective QCD scales.\n"}
+{"abstract": "  Filinski constructed a symmetric lambda-calculus consisting of expressions\nand continuations which are symmetric, and functions which have duality. In his\ncalculus, functions can be encoded to expressions and continuations using\nprimitive operators. That is, the duality of functions is not derived in the\ncalculus but adopted as a principle of the calculus. In this paper, we propose\na simple symmetric lambda-calculus corresponding to the negation-free natural\ndeduction based bilateralism in proof-theoretic semantics. In our calculus,\ncontinuation types are represented as not negations of formulae but formulae\nwith negative polarity. Function types are represented as the implication and\nbut-not connectives in intuitionistic and paraconsistent logics, respectively.\nOur calculus is not only simple but also powerful as it includes a call-value\ncalculus corresponding to the call-by-value dual calculus invented by Wadler.\nWe show that mutual transformations between expressions and continuations are\ndefinable in our calculus to justify the duality of functions. We also show\nthat every typable function has dual types. Thus, the duality of function is\nderived from bilateralism.\n"}
+{"abstract": "  For a pair of polynomials with real or complex coefficients, given in any\nparticular basis, the problem of finding their GCD is known to be ill-posed. An\nanswer is still desired for many applications, however. Hence, looking for a\nGCD of so-called approximate polynomials where this term explicitly denotes\nsmall uncertainties in the coefficients has received significant attention in\nthe field of hybrid symbolic-numeric computation. In this paper we give an\nalgorithm, based on one of Victor Ya. Pan, to find an approximate GCD for a\npair of approximate polynomials given in a Lagrange basis. More precisely, we\nsuppose that these polynomials are given by their approximate values at\ndistinct known points. We first find each of their roots by using a Lagrange\nbasis companion matrix for each polynomial, cluster the roots of each\npolynomial to identify multiple roots, and then \"marry\" the two polynomials to\nfind their GCD. At no point do we change to the monomial basis, thus preserving\nthe good conditioning properties of the original Lagrange basis. We discuss\nadvantages and drawbacks of this method. The computational cost is dominated by\nthe rootfinding step; unless special-purpose eigenvalue algorithms are used,\nthe cost is cubic in the degrees of the polynomials. In principle, this cost\ncould be reduced but we do not do so here.\n"}
+{"abstract": "  The diffusive behaviour of simple random-walk proposals of many Markov Chain\nMonte Carlo (MCMC) algorithms results in slow exploration of the state space\nmaking inefficient the convergence to a target distribution. Hamiltonian/Hybrid\nMonte Carlo (HMC), by introducing fictious momentum variables, adopts\nHamiltonian dynamics, rather than a probability distribution, to propose future\nstates in the Markov chain. Splitting schemes are numerical integrators for\nHamiltonian problems that may advantageously replace the St\\\"ormer-Verlet\nmethod within HMC methodology. In this paper a family of stable methods for\nunivariate and multivariate Gaussian distributions, taken as guide-problems for\nmore realistic situations, is proposed. Differently from similar methods\nproposed in the recent literature, the considered schemes are featured by null\nexpectation of the random variable representing the energy error. The\neffectiveness of the novel procedures is shown for bivariate and multivariate\ntest cases taken from the literature.\n"}
+{"abstract": "  This article is an introduction to machine learning for financial\nforecasting, planning and analysis (FP\\&A). Machine learning appears well\nsuited to support FP\\&A with the highly automated extraction of information\nfrom large amounts of data. However, because most traditional machine learning\ntechniques focus on forecasting (prediction), we discuss the particular care\nthat must be taken to avoid the pitfalls of using them for planning and\nresource allocation (causal inference). While the naive application of machine\nlearning usually fails in this context, the recently developed double machine\nlearning framework can address causal questions of interest. We review the\ncurrent literature on machine learning in FP\\&A and illustrate in a simulation\nstudy how machine learning can be used for both forecasting and planning. We\nalso investigate how forecasting and planning improve as the number of data\npoints increases.\n"}
+{"abstract": "  We show that the Weil representation associated with any discriminant form\nadmits a basis in which the action of the representation involves algebraic\nintegers. The action of a general element of\n$\\operatorname{SL}_{2}(\\mathbb{Z})$ on many parts of these bases is simple an\nexplicit, a fact that we use for determining the dimension of the space of\ninvariants for some families of discriminant forms.\n"}
+{"abstract": "  We give an overview of the work done during the past ten years on the Casimir\ninteraction in electronic topological materials, our focus being solids which\npossess surface or bulk electronic band structures with nontrivial topologies,\nwhich can be evinced through optical properties that are characterizable in\nterms of nonzero topological invariants. The examples we review are\nthree-dimensional magnetic topological insulators, two-dimensional Chern\ninsulators, graphene monolayers exhibiting the relativistic quantum Hall\neffect, and time reversal symmetry-broken Weyl semimetals, which are\nfascinating systems in the context of Casimir physics, firstly for the reason\nthat they possess electromagnetic properties characterizable by axial vectors\n(because of time reversal symmetry breaking), and depending on the mutual\norientation of a pair of such axial vectors, two systems can experience a\nrepulsive Casimir-Lifshitz force even though they may be dielectrically\nidentical. Secondly, the repulsion thus generated is potentially robust against\nweak disorder, as such repulsion is associated with a Hall conductivity which\nis topologically protected in the zero-frequency limit. Finally, the far-field\nlow-temperature behavior of the Casimir force of such systems can provide\nsignatures of topological quantization.\n"}
+{"abstract": "  In this paper we study dynamical systems generated by a gonosomal evolution\noperator of a bisexual population. We find explicitly all (uncountable set) of\nfixed points of the operator. It is shown that each fixed point has eigenvalues\nless or equal to 1. Moreover, we show that each trajectory converges to a fixed\npoint, i.e. the operator is reqular. There are uncountable family of invariant\nsets each of which consisting unique fixed point. Thus there is one-to-one\ncorrespondence between such invariant sets and the set of fixed points. Any\ntrajectory started at a point of the invariant set converges to the\ncorresponding fixed point.\n"}
+{"abstract": "  Although routinely utilized in literature, orthogonal waveforms may lose\northogonality in distributed multi-input multi-output (MIMO) radar with\nspatially separated transmit (TX) and receive (RX) antennas, as the waveforms\nmay experience distinct delays and Doppler frequency offsets unique to\ndifferent TX-RX propagation paths. In such cases, the output of each\nwaveform-specific matched filter (MF), employed to unravel the waveforms at the\nRXs, contains both an \\auto term and multiple cross terms, i.e., the filtered\nresponse of the desired and, respectively, undesired waveforms. We consider the\nimpact of non-orthogonal waveforms and their cross terms on target detection\nwith or without timing, frequency, and phase errors. To this end, we present a\ngeneral signal model for distributed MIMO radar, examine target detection using\nexisting coherent/non-coherent detectors and two new detectors, including a\nhybrid detector that requires phase coherence locally but not across\ndistributed antennas, and provide a statistical analysis leading to closed-form\nexpressions of false alarm and detection probabilities for all detectors. Our\nresults show that cross terms can behave like foes or allies, respectively, if\nthey and the auto term add destructively or constructively, depending on the\npropagation delay, frequency, and phase offsets. Regarding sync errors, we show\nthat phase errors affect only coherent detectors, frequency errors degrade all\nbut the non-coherent detector, while all are impacted by timing errors, which\nresult in a loss in the signal-to-noise ratio (SNR).\n"}
+{"abstract": "  We establish well-posedness conclusions for the Cauchy problem associated to\nthe dispersion generalized Zakharov-Kutnesov equation in bi-periodic Sobolev\nspaces $H^{s}\\left(\\mathbb{T}^{2}\\right)$,\n$s>(\\frac{3}{2}-\\frac{1}{2^{\\alpha+2}})(\\frac{3}{2}-\\frac{\\beta}{4})$.\n"}
+{"abstract": "  In this comment we show untenability of key points of the recent article of\nN. Biancacci, E. Metral and M. Migliorati [Phys. Rev. Accel. Beams 23, 124402\n(2020)], hereafter the Article and the Authors. Specifically, the main Eqs.\n(23), suggested to describe mode coupling, are shown to be unacceptable even as\nan approximation. The Article claims the solution of this pair of equations to\nbe in \"excellent agreement\" with the pyHEADTAIL simulations for CERN PS, which\nis purportedly demonstrated by Fig. 6. Were it really so, it would be a signal\nof a mistake in the code. However, the key part of the simulation results is\nnot actually shown, and the demonstrated agreement has all the features of an\nillusion.\n"}
+{"abstract": "  Let $\\mathcal{F}$ and $\\mathcal{K}$ be commuting $C^\\infty$ diffeomorphisms\nof the cylinder $\\mathbb{T}\\times\\mathbb{R}$ that are, respectively, close to\n$\\mathcal{F}_0 (x, y)=(x+\\omega(y), y)$ and $T_\\alpha (x, y)=(x+\\alpha, y)$,\nwhere $\\omega(y)$ is non-degenerate and $\\alpha$ is Diophantine. Using the KAM\niterative scheme for the group action we show that $\\mathcal{F}$ and\n$\\mathcal{K}$ are simultaneously $C^\\infty$-linearizable if $\\mathcal{F}$ has\nthe intersection property (including the exact symplectic maps) and\n$\\mathcal{K}$ satisfies a semi-conjugacy condition. We also provide examples\nshowing necessity of these conditions. As a consequence, we get local rigidity\nof certain elliptic $\\mathbb{Z}^2$-actions on the cylinder.\n"}
+{"abstract": "  Recently, Haynes, Hedetniemi and Henning published the book Topics in\nDomination in Graphs, which comprises 16 contributions that present advanced\ntopics in graph domination, featuring open problems, modern techniques, and\nrecent results. One of these contributions is the chapter Multiple Domination,\nby Hansberg and Volkmann, where they put into context all relevant research\nresults on multiple domination that have been found up to 2020. In this note,\nwe show how to improve some results on double domination that are included in\nthe book.\n"}
+{"abstract": "  The recent discovery of a Galactic fast radio burst (FRB) occurring\nsimultaneously with an X-ray burst (XRB) from the Galactic magnetar SGR\nJ1935+2154 implies that at least some FRBs arise from magnetar activities. We\npropose that FRBs are triggered by crust fracturing of magnetars, with the\nburst event rate depending on the magnetic field strength in the crust. Since\nthe crust fracturing rate is relatively higher in polar regions, FRBs are\npreferred to be triggered near the directions of multipolar magnetic poles.\nCrust fracturing produces Alfv\\'en waves, forming a charge starved region in\nthe magnetosphere and leading to non-stationary pair plasma discharges. An FRB\nis produced by coherent plasma radiation due to nonuniform pair production\nacross magnetic field lines. Meanwhile, the FRB-associated XRB is produced by\nthe rapid relaxation of the external magnetic field lines. In this picture, the\nsharp-peak hard X-ray component in association with FRB 200428 is from a region\nbetween adjacent trapped fireballs, and its spectrum with a high cutoff energy\nis attributed to resonant Compton scattering. The persistent X-ray emission is\nfrom a hot spot heated by the magnetospheric activities, and its temperature\nevolution is dominated by magnetar surface cooling. Within this picture,\nmagnetars with stronger fields tend to produce brighter and more frequent\nrepeated bursts.\n"}
+{"abstract": "  In the Priority $k$-Center problem, the input consists of a metric space\n$(X,d)$, an integer $k$ and for each point $v \\in X$ a priority radius $r(v)$.\nThe goal is to choose $k$-centers $S \\subseteq X$ to minimize $\\max_{v \\in X}\n\\frac{1}{r(v)} d(v,S)$. If all $r(v)$'s were uniform, one obtains the classical\n$k$-center problem. Plesn\\'ik [Plesn\\'ik, Disc. Appl. Math. 1987] introduced\nthis problem and gave a $2$-approximation algorithm matching the best possible\nalgorithm for vanilla $k$-center. We show how the problem is related to two\ndifferent notions of fair clustering [Harris et al., NeurIPS 2018; Jung et al.,\nFORC 2020]. Motivated by these developments we revisit the problem and, in our\nmain technical contribution, develop a framework that yields constant factor\napproximation algorithms for Priority $k$-Center with outliers. Our framework\nextends to generalizations of Priority $k$-Center to matroid and knapsack\nconstraints, and as a corollary, also yields algorithms with fairness\nguarantees in the lottery model of Harris et al.\n"}
+{"abstract": "  In this study, a geometric version of an NP-hard problem (\"Almost $2-SAT$\"\nproblem) is introduced which has potential applications in clustering,\nseparation axis, binary sensor networks, shape separation, image processing,\netc. Furthermore, it has been illustrated that the new problem known as \"Two\nDisjoint Convex Hulls\" can be solved in polynomial time due to some\ncombinatorial aspects and geometric properties. For this purpose, an $O(n^2)$\nalgorithm has also been presented which employs the Separating Axis Theorem\n(SAT) and the duality of points/lines.\n"}
+{"abstract": "  How does the chromatic number of a graph chosen uniformly at random from all\ngraphs on $n$ vertices behave? This quantity is a random variable, so one can\nask (i) for upper and lower bounds on its typical values, and (ii) for bounds\non how much it varies: what is the width (e.g., standard deviation) of its\ndistribution?\n  On (i) there has been considerable progress over the last 45 years; on (ii),\nwhich is our focus here, remarkably little. One would like both upper and lower\nbounds on the width of the distribution, and ideally a description of the\n(appropriately scaled) limiting distribution. There is a well known upper bound\nof Shamir and Spencer of order $\\sqrt{n}$, improved slightly by Alon to\n$\\sqrt{n}/\\log n$, but no non-trivial lower bound was known until 2019, when\nthe first author proved that the width is at least $n^{1/4-o(1)}$ for\ninfinitely many $n$, answering a longstanding question of Bollob\\'as.\n  In this paper we have two main aims: first, we shall prove a much stronger\nlower bound on the width. We shall show unconditionally that, for some values\nof $n$, the width is at least $n^{1/2-o(1)}$, matching the upper bounds up to\nthe error term. Moreover, conditional on a recently announced sharper explicit\nestimate for the chromatic number, we improve the lower bound to order\n$\\sqrt{n} \\log \\log n /\\log^3 n$, within a logarithmic factor of the upper\nbound.\n  Secondly, we will describe a number of conjectures as to what the true\nbehaviour of the variation in $\\chi(G_{n,1/2})$ is, and why. The first form of\nthis conjecture arises from recent work of Bollob\\'as, Heckel, Morris,\nPanagiotou, Riordan and Smith. We will also give much more detailed\nconjectures, suggesting that the true width, for the worst case $n$, matches\nour lower bound up to a constant factor. These conjectures also predict a\nGaussian limiting distribution.\n"}
+{"abstract": "  Over the last few years, ReS2 has generated a myriad of unattended queries\nregarding its structure, the concomitant thickness dependent electronic\nproperties and apparently contrasting experimental optical response. In this\nwork, with elaborate first-principles investigations, using density functional\ntheory (DFT) and time-dependent DFT (TDDFT), we identify the structure of ReS2,\nwhich is capable of reproducing and analyzing the layer-dependent optical\nresponse. The theoretical results are further validated by an in-depth\nstructural, chemical, optical and optoelectronic analysis of the large-area\nReS2 thin films, grown by chemical vapor deposition (CVD) process. Micro-Raman\n(MR), X-ray photoelectron spectroscopy (XPS), cross-sectional transmission\nelectron microscopy (TEM) and energy-dispersive X-ray analysis (EDAX) have\nenabled the optimization of the uniform growth of the CVD films. The\ncorrelation between the layer-dependent optical and electronic properties of\nthe excited states was established by static photoluminescence (PL) and\ntransient absorption (TA) measurements. Sulfur vacancy-induced localized\nmid-gap states render a significantly long life-time of the excitons in these\nfilms. The ionic gel top-gated photo-detectors, fabricated from the as-prepared\nCVD films, exhibit a large photo-response of ~ 5 A/W and a remarkable\ndetectivity of ~ 1011 Jones. The outcome of the present work will be useful to\npromote the application of vertically grown large-area films in the field of\noptics and opto-electronics.\n"}
+{"abstract": "  The interest in offensive content identification in social media has grown\nsubstantially in recent years. Previous work has dealt mostly with post level\nannotations. However, identifying offensive spans is useful in many ways. To\nhelp coping with this important challenge, we present MUDES, a multilingual\nsystem to detect offensive spans in texts. MUDES features pre-trained models, a\nPython API for developers, and a user-friendly web-based interface. A detailed\ndescription of MUDES' components is presented in this paper.\n"}
+{"abstract": "  Context: Backsourcing is the process of insourcing previously outsourced\nactivities. When companies experience environmental or strategic changes, or\nchallenges with outsourcing, backsourcing can be a viable alternative. While\noutsourcing and related processes have been extensively studied in software\nengineering, few studies report experiences with backsourcing. Objectives: We\nintend to summarize the results of the research literature on the backsourcing\nof IT, with a focus on software development. By identifying practical relevance\nexperience, we aim to present findings that may help companies considering\nbacksourcing. In addition, we aim to identify gaps in the current research\nliterature and point out areas for future work. Method: Our systematic\nliterature review (SLR) started with a search for empirical studies on the\nbacksourcing of software development. From each study we identified the\ncontexts in which backsourcing occurs, the factors leading to the decision to\nbacksource, the backsourcing process itself, and the outcomes of backsourcing.\nWe employed inductive coding to extract textual data from the papers identified\nand qualitative cross-case analysis to synthesize the evidence from\nbacksourcing experiences. Results: We identified 17 papers that reported 26\ncases of backsourcing, six of which were related to software development. The\ncases came from a variety of contexts. The most common reasons for backsourcing\nwere improving quality, reducing costs, and regaining control of outsourced\nactivities. The backsourcing process can be described as containing five\nsub-processes: change management, vendor relationship management, competence\nbuilding, organizational build-up, and transfer of ownership. Furthermore, ...\n"}
+{"abstract": "  The unbound proton-rich nuclei $^{16}$F and $^{15}$F are investigated\nexperimentally and theoretically. Several experiments using the resonant\nelastic scattering method were performed at GANIL with radioactive beams to\ndetermine the properties of the low lying states of these nuclei. Strong\nasymmetry between $^{16}$F-$^{16}$N and $^{15}$F-$^{15}$C mirror nuclei is\nobserved. The strength of the $nucleon-nucleon$ effective interaction involving\nthe loosely bound proton in the $s_{1/2}$ orbit is significantly modified with\nrespect to their mirror nuclei $^{16}$N and $^{15}$C. The reduction of the\neffective interaction is estimated by calculating the interaction energies with\na schematic zero-range force. It is found that, after correcting for the\neffects due to changes in the radial distribution of the single-particle wave\nfunctions, the mirror symmetry of the $n-p$ interaction is preserved between\n$^{16}$F and $^{16}$N, while a difference of 63\\% is measured between the $p-p$\nversus $n-n$ interactions in the second excited state of $^{15}$F and $^{15}$C\nnuclei. Several explanations are proposed.\n"}
+{"abstract": "  We extend to Segal-Piatetski-Shapiro sequences previous results on the\nLuca-Schinzel question over integral valued polynomial sequences. Namely, we\nprove that for any real $c$ larger than $1$ the sequence $(\\sum_{m\\le n}\n\\varphi(\\lfloor m^c \\rfloor) /\\lfloor m^c \\rfloor)_n$ is dense modulo $1$,\nwhere $\\varphi$ denotes Euler's totient function. The main part of the proof\nconsists in showing that when $R$ is a large integer, the sequence of the\nresidues of $\\lfloor m^c \\rfloor$ modulo $R$ contains blocks of consecutive\nvalues which are in an arithmetic progression.\n"}
+{"abstract": "  A decentralized feedback controller for multi-agent systems, inspired by\nvehicle platooning, is proposed. The closed-loop resulting from the\ndecentralized control action has three distinctive features: the generation of\ncollision-free trajectories, flocking of the system towards a consensus state\nin velocity, and asymptotic convergence to a prescribed pattern of distances\nbetween agents. For each feature, a rigorous dynamical analysis is provided,\nyielding a characterization of the set of parameters and initial configurations\nwhere collision avoidance, flocking, and pattern formation is guaranteed.\nNumerical tests assess the theoretical results presented.\n"}
+{"abstract": "  The extended state observer (ESO) is an inherent element of robust\nobserver-based control systems that allows estimating the impact of disturbance\non system dynamics. Proper tuning of ESO parameters is necessary to ensure a\ngood quality of estimated quantities and impacts the overall performance of the\nrobust control structure. In this paper, we propose a neural network (NN) based\ntuning procedure that allows the user to prioritize between selected quality\ncriteria such as the control and observation errors and the specified features\nof the control signal. The designed NN provides an accurate assessment of the\ncontrol system performance and returns a set of ESO parameters that delivers a\nnear-optimal solution to the user-defined cost function. The proposed tuning\nprocedure, using an estimated state from the single closed-loop experiment\nproduces near-optimal ESO gains within seconds.\n"}
+{"abstract": "  We explicitly construct the Dirichlet series\n$$L_{\\mathrm{Tam}}(s):=\\sum_{m=1}^{\\infty}\\frac{P_{\\mathrm{Tam}}(m)}{m^s},$$\nwhere $P_{\\mathrm{Tam}}(m)$ is the proportion of elliptic curves $E/\\mathbb{Q}$\nin short Weierstrass form with Tamagawa product $m.$ Although there are no\n$E/\\mathbb{Q}$ with everywhere good reduction, we prove that the proportion\nwith trivial Tamagawa product is $P_{\\mathrm{Tam}}(1)=0.5053\\dots.$ As a\ncorollary, we find that $L_{\\mathrm{Tam}}(-1)=1.8193\\dots$ is the average\nTamagawa product for elliptic curves over $\\mathbb{Q}.$ We give an application\nof these results to canonical and Weil heights.\n"}
+{"abstract": "  This article focuses on a preaveraging description of polymer nonequilibrium\nstretching, where a single polymer undergoes a transient process from\nequilibrium to nonequilibrium steady state by pulling one chain end. The\npreaveraging method combined with mode analysis reduces the original Langevin\nequation to a simplified form for both a stretched steady state and an\nequilibrium state, even in the presence of self-avoiding repulsive interactions\nspanning a long range. However, the transient stretching process exhibits\nevolution of a hierarchal regime structure, which means a qualitative temporal\nchange in probabilistic distributions assumed in preaveraging. We investigate\nthe preaveraging method for evolution of the regime structure with\nconsideration of the nonequilibrium work relations and deviations from the\nfluctuation-dissipation relation.\n"}
+{"abstract": "  We consider an extension of the classical capital accumulation model, and\npresent an example in which the Hamilton-Jacobi-Bellman (HJB) equation is\nneither necessary nor sufficient for a function to be the value function. Next,\nwe present assumptions under which the HJB equation becomes a necessary and\nsufficient condition for a function to be the value function, and using this\nresult, we propose a new method for solving the original problem using the\nsolution of the HJB equation. Our assumptions are so mild that many\nmacroeconomic growth models satisfy them. Therefore, our results ensure that\nthe solution of the HJB equation is rigorously the value function in many\nmacroeconomic models, and present a new solving method for these models.\n"}
+{"abstract": "  We present, here, advanced DFT-NEGF techniques that we have implemented in\nour ATOmistic MOdelling Solver, ATOMOS, to explore transport in novel materials\nand devices and in particular in van-der-Waals heterojunction transistors. We\ndescribe our methodologies using plane-wave DFT, followed by a Wannierization\nstep, and linear combination of atomic orbital DFT, that leads to an orthogonal\nand non-orthogonal NEGF model, respectively. We then describe in detail our\nnon-orthogonal NEGF implementation including the Sancho-Rubio and\nelectron-phonon scattering within a non-orthogonal framework. We also present\nour methodology to extract electron-phonon coupling from first principle and\ninclude them in our transport simulations. Finally, we apply our methods\ntowards the exploration of novel 2D materials and devices. This includes 2D\nmaterial selection and the Dynamically-Doped FET for ultimately scaled MOSFETS,\nthe exploration of vdW TFETs, in particular the HfS2/WSe2 TFET that could\nachieve high on-current levels, and the study of Schottky-barrier height and\ntransport through a metal-semiconducting WTe2/WS2 VDW junction transistor.\n"}
+{"abstract": "  Typicality arguments attempt to use the Copernican Principle to draw\nconclusions about the cosmos and presently unknown conscious beings within it.\nThe most notorious is the Doomsday Argument, which purports to constrain\nhumanity's future from its current lifespan alone. These arguments rest on a\nlikelihood calculation that penalizes models in proportion to the number of\ndistinguishable observers. I argue that such reasoning leads to solipsism, the\nbelief that one is the only being in the world, and is therefore unacceptable.\nUsing variants of the \"Sleeping Beauty\" thought experiment as a guide, I\npresent a framework for evaluating observations in a large cosmos: Fine\nGraining with Auxiliary Indexicals (FGAI). FGAI requires the construction of\nspecific models of physical outcomes and observations. Valid typicality\narguments then emerge from the combinatorial properties of third-person\nphysical microhypotheses. Indexical (observer-relative) facts do not directly\nconstrain physical theories. Instead they serve to weight different provisional\nevaluations of credence. These weights define a probabilistic reference class\nof locations. As indexical knowledge changes, the weights shift. I show that\nthe self-applied Doomsday Argument fails in FGAI, even though it can work for\nan external observer. I also discuss how FGAI could handle observations in\nlarge universes with Boltzmann brains.\n"}
+{"abstract": "  We propose a novel keypoint voting scheme based on intersecting spheres, that\nis more accurate than existing schemes and allows for a smaller set of more\ndisperse keypoints. The scheme is based upon the distance between points, which\nas a 1D quantity can be regressed more accurately than the 2D and 3D vector and\noffset quantities regressed in previous work, yielding more accurate keypoint\nlocalization. The scheme forms the basis of the proposed RCVPose method for 6\nDoF pose estimation of 3D objects in RGB-D data, which is particularly\neffective at handling occlusions. A CNN is trained to estimate the distance\nbetween the 3D point corresponding to the depth mode of each RGB pixel, and a\nset of 3 disperse keypoints defined in the object frame. At inference, a sphere\ncentered at each 3D point is generated, of radius equal to this estimated\ndistance. The surfaces of these spheres vote to increment a 3D accumulator\nspace, the peaks of which indicate keypoint locations. The proposed radial\nvoting scheme is more accurate than previous vector or offset schemes, and is\nrobust to disperse keypoints. Experiments demonstrate RCVPose to be highly\naccurate and competitive, achieving state-of-the-art results on the LINEMOD\n99.7% and YCB-Video 97.2% datasets, notably scoring +7.9% higher (71.1%) than\nprevious methods on the challenging Occlusion LINEMOD dataset.\n"}
+{"abstract": "  The ability to label and track physical objects that are assets in digital\nrepresentations of the world is foundational to many complex systems. Simple,\nyet powerful methods such as bar- and QR-codes have been highly successful,\ne.g. in the retail space, but the lack of security, limited information content\nand impossibility of seamless integration with the environment have prevented a\nlarge-scale linking of physical objects to their digital twins. This paper\nproposes to link digital assets created through BIM with their physical\ncounterparts using fiducial markers with patterns defined by Cholesteric\nSpherical Reflectors (CSRs), selective retroreflectors produced using liquid\ncrystal self-assembly. The markers leverage the ability of CSRs to encode\ninformation that is easily detected and read with computer vision while\nremaining practically invisible to the human eye. We analyze the potential of a\nCSR-based infrastructure from the perspective of BIM, critically reviewing the\noutstanding challenges in applying this new class of functional materials, and\nwe discuss extended opportunities arising in assisting autonomous mobile robots\nto reliably navigate human-populated environments, as well as in augmented\nreality.\n"}
+{"abstract": "  During the preparatory phase of the International Linear Collider (ILC)\nproject, all technical development and engineering design needed for the start\nof ILC construction must be completed, in parallel with intergovernmental\ndiscussion of governance and sharing of responsibilities and cost. The ILC\nPreparatory Laboratory (Pre-lab) is conceived to execute the technical and\nengineering work and to assist the intergovernmental discussion by providing\nrelevant information upon request. It will be based on a worldwide partnership\namong laboratories with a headquarters hosted in Japan. This proposal, prepared\nby the ILC International Development Team and endorsed by the International\nCommittee for Future Accelerators, describes an organisational framework and\nwork plan for the Pre-lab. Elaboration, modification and adjustment should be\nintroduced for its implementation, in order to incorporate requirements arising\nfrom the physics community, laboratories, and governmental authorities\ninterested in the ILC.\n"}
+{"abstract": "  Music source separation (MSS) is the task of separating a music piece into\nindividual sources, such as vocals and accompaniment. Recently, neural network\nbased methods have been applied to address the MSS problem, and can be\ncategorized into spectrogram and time-domain based methods. However, there is a\nlack of research of using complementary information of spectrogram and\ntime-domain inputs for MSS. In this article, we propose a CatNet framework that\nconcatenates a UNet separation branch using spectrogram as input and a WavUNet\nseparation branch using time-domain waveform as input for MSS. We propose an\nend-to-end and fully differentiable system that incorporate spectrogram\ncalculation into CatNet. In addition, we propose a novel mix-audio data\naugmentation method that randomly mix audio segments from the same source as\naugmented audio segments for training. Our proposed CatNet MSS system achieves\na state-of-the-art vocals separation source distortion ratio (SDR) of 7.54 dB,\noutperforming MMDenseNet of 6.57 dB evaluated on the MUSDB18 dataset.\n"}
+{"abstract": "  Laser Doppler holography was introduced as a full-field imaging technique to\nmeasure blood flow in the retina and choroid with an as yet unrivaled temporal\nresolution. We here investigate separating the different contributions to the\npower Doppler signal in order to isolate the flow waveforms of vessels in the\nposterior pole of the human eye. Distinct flow behaviors are found in retinal\narteries and veins with seemingly interrelated waveforms. We demonstrate a full\nfield mapping of the local resistivity index, and the possibility to perform\nunambiguous identification of retinal arteries and veins on the basis of their\nsystolodiastolic variations. Finally we investigate the arterial flow waveforms\nin the retina and choroid and find synchronous and similar waveforms, although\nwith a lower pulsatility in choroidal vessels. This work demonstrates the\npotential held by laser Doppler holography to study ocular hemodynamics in\nhealthy and diseased eyes.\n"}
+{"abstract": "  Recent worldwide events shed light on the need of human-centered systems\nengineering in the healthcare domain. These systems must be prepared to evolve\nquickly but safely, according to unpredicted environments and ever-changing\npathogens that spread ruthlessly. Such scenarios suffocate hospitals'\ninfrastructure and disable healthcare systems that are not prepared to deal\nwith unpredicted environments without costly re-engineering. In the face of\nthese challenges, we offer the SA-BSN -- Self-Adaptive Body Sensor Network --\nprototype to explore the rather dynamic patient's health status monitoring. The\nexemplar is focused on self-adaptation and comes with scenarios that hinder an\ninterplay between system reliability and battery consumption that is available\nafter each execution. Also, we provide: (i) a noise injection mechanism, (ii)\nfile-based patient profiles' configuration, (iii) six healthcare sensor\nsimulations, and (iv) an extensible/reusable controller implementation for\nself-adaptation. The artifact is implemented in ROS (Robot Operating System),\nwhich embraces principles such as ease of use and relies on an active open\nsource community support.\n"}
+{"abstract": "  This paper investigates the theory of robustness against adversarial attacks.\nWe focus on randomized classifiers (\\emph{i.e.} classifiers that output random\nvariables) and provide a thorough analysis of their behavior through the lens\nof statistical learning theory and information theory. To this aim, we\nintroduce a new notion of robustness for randomized classifiers, enforcing\nlocal Lipschitzness using probability metrics. Equipped with this definition,\nwe make two new contributions. The first one consists in devising a new upper\nbound on the adversarial generalization gap of randomized classifiers. More\nprecisely, we devise bounds on the generalization gap and the adversarial gap\n(\\emph{i.e.} the gap between the risk and the worst-case risk under attack) of\nrandomized classifiers. The second contribution presents a yet simple but\nefficient noise injection method to design robust randomized classifiers. We\nshow that our results are applicable to a wide range of machine learning models\nunder mild hypotheses. We further corroborate our findings with experimental\nresults using deep neural networks on standard image datasets, namely CIFAR-10\nand CIFAR-100. All robust models we trained models can simultaneously achieve\nstate-of-the-art accuracy (over $0.82$ clean accuracy on CIFAR-10) and enjoy\n\\emph{guaranteed} robust accuracy bounds ($0.45$ against $\\ell_2$ adversaries\nwith magnitude $0.5$ on CIFAR-10).\n"}
+{"abstract": "  We study the performance of quantum annealing for two sets of problems,\nnamely, 2-satisfiability (2-SAT) problems represented by Ising-type\nHamiltonians, and nonstoquastic problems which are obtained by adding extra\ncouplings to the 2-SAT problem Hamiltonians. In addition, we add to the\ntransverse Ising-type Hamiltonian used for quantum annealing a third term, the\ntrigger Hamiltonian with ferromagnetic or antiferromagnetic couplings, which\nvanishes at the beginning and end of the annealing process. We also analyze\nsome problem instances using the energy spectrum, average energy or overlap of\nthe state during the evolution with the instantaneous low lying eigenstates of\nthe Hamiltonian, and identify some non-adiabatic mechanisms which can enhance\nthe performance of quantum annealing.\n"}
+{"abstract": "  We analyze the citation time-series of manuscripts in three different fields\nof science; physics, social science and technology. The evolution of the\ntime-series of the yearly number of citations, namely the citation\ntrajectories, diffuse anomalously, their variance scales with time $\\propto\nt^{2H}$, where $H\\neq 1/2$. We provide detailed analysis of the various factors\nthat lead to the anomalous behavior: non-stationarity, long-ranged correlations\nand a fat-tailed increment distribution. The papers exhibit high degree of\nheterogeneity, across the various fields, as the statistics of the highest\ncited papers is fundamentally different from that of the lower ones. The\ncitation data is shown to be highly correlated and non-stationary; as all the\npapers except the small percentage of them with high number of citations, die\nout in time.\n"}
+{"abstract": "  The Ensemble Kalman inversion (EKI), proposed by Iglesias et al. for the\nsolution of Bayesian inverse problems of type $y=A u^\\dagger +\\varepsilon$,\nwith $u^\\dagger$ being an unknown parameter and $y$ a given datum, is a\npowerful tool usually derived from a sequential Monte Carlo point of view. It\ndescribes the dynamics of an ensemble of particles $\\{u^j(t)\\}_{j=1}^J$, whose\ninitial empirical measure is sampled from the prior, evolving over an\nartificial time $t$ towards an approximate solution of the inverse problem.\nUsing spectral techniques, we provide a complete description of the\ndeterministic dynamics of EKI and their asymptotic behavior in parameter space.\nIn particular, we analyze the dynamics of deterministic EKI and mean-field EKI.\nWe demonstrate that the Bayesian posterior can only be recovered with the\nmean-field limit and not with finite sample sizes or deterministic EKI.\nFurthermore, we show that -- even in the deterministic case -- residuals in\nparameter space do not decrease monotonously in the Euclidean norm and suggest\na problem-adapted norm, where monotonicity can be proved. Finally, we derive a\nsystem of ordinary differential equations governing the spectrum and\neigenvectors of the covariance matrix.\n"}
+{"abstract": "  The LIGO and Virgo gravitational-wave detectors carried out the first half of\ntheir third observing run from April through October 2019. During this period,\nthey detected 39 new signals from the coalescence of black holes or neutron\nstars, more than quadrupling the total number of detected events. These\ndetections included some unprecedented sources, like a pair of black holes with\nunequal masses (GW190412), a massive pair of neutron stars (GW190425), a black\nhole potentially in the supernova pair-instability mass gap (GW190521), and\neither the lightest black hole or the heaviest neutron star known to date\n(GW190814). Collectively, the full set of signals provided astrophysically\nvaluable information about the distributions of compact objects and their\nevolution throughout cosmic history. It also enabled more constraining and\ndiverse tests of general relativity, including new probes of the fundamental\nnature of black holes. This review summarizes the highlights of these results\nand their implications.\n"}
+{"abstract": "  Spin sum rules depend on the choice of a pivot, i.e. the point about which\nthe angular momentun is defined, usually identified with the center of the\nnucleon. The latter is however not unique in a relativistic theory and has led\nto apparently contradictory results in the literature. Using the recently\ndeveloped phase-space approach, we compute for the first time the contribution\nassociated with the motion of the center of the nucleon, and we derive a\ngeneral spin sum rule which reduces to established results after appropriate\nchoices for the pivot and the spin component.\n"}
+{"abstract": "  Recently developed large pre-trained language models, e.g., BERT, have\nachieved remarkable performance in many downstream natural language processing\napplications. These pre-trained language models often contain hundreds of\nmillions of parameters and suffer from high computation and latency in\nreal-world applications. It is desirable to reduce the computation overhead of\nthe models for fast training and inference while keeping the model performance\nin downstream applications. Several lines of work utilize knowledge\ndistillation to compress the teacher model to a smaller student model. However,\nthey usually discard the teacher's knowledge when in inference. Differently, in\nthis paper, we propose RefBERT to leverage the knowledge learned from the\nteacher, i.e., facilitating the pre-computed BERT representation on the\nreference sample and compressing BERT into a smaller student model. To\nguarantee our proposal, we provide theoretical justification on the loss\nfunction and the usage of reference samples. Significantly, the theoretical\nresult shows that including the pre-computed teacher's representations on the\nreference samples indeed increases the mutual information in learning the\nstudent model. Finally, we conduct the empirical evaluation and show that our\nRefBERT can beat the vanilla TinyBERT over 8.1\\% and achieves more than 94\\% of\nthe performance of $\\BERTBASE$ on the GLUE benchmark. Meanwhile, RefBERT is\n7.4x smaller and 9.5x faster on inference than BERT$_{\\rm BASE}$.\n"}
+{"abstract": "  As machine learning (ML) models become more widely deployed in high-stakes\napplications, counterfactual explanations have emerged as key tools for\nproviding actionable model explanations in practice. Despite the growing\npopularity of counterfactual explanations, a deeper understanding of these\nexplanations is still lacking. In this work, we systematically analyze\ncounterfactual explanations through the lens of adversarial examples. We do so\nby formalizing the similarities between popular counterfactual explanation and\nadversarial example generation methods identifying conditions when they are\nequivalent. We then derive the upper bounds on the distances between the\nsolutions output by counterfactual explanation and adversarial example\ngeneration methods, which we validate on several real-world data sets. By\nestablishing these theoretical and empirical similarities between\ncounterfactual explanations and adversarial examples, our work raises\nfundamental questions about the design and development of existing\ncounterfactual explanation algorithms.\n"}
+{"abstract": "  Mahalanobis distance between treatment group and control group covariate\nmeans is often adopted as a balance criterion when implementing a\nrerandomization strategy. However, this criterion may not work well for\nhigh-dimensional cases because it balances all orthogonalized covariates\nequally. Here, we propose leveraging principal component analysis (PCA) to\nidentify proper subspaces in which Mahalanobis distance should be calculated.\nNot only can PCA effectively reduce the dimensionality for high-dimensional\ncases while capturing most of the information in the covariates, but it also\nprovides computational simplicity by focusing on the top orthogonal components.\nWe show that our PCA rerandomization scheme has desirable theoretical\nproperties on balancing covariates and thereby on improving the estimation of\naverage treatment effects. We also show that this conclusion is supported by\nnumerical studies using both simulated and real examples.\n"}
+{"abstract": "  We prove that if a family of compact connected sets in the plane has the\nproperty that every three members of it are intersected by a line, then there\nare three lines intersecting all the sets in the family. This answers a\nquestion of Eckhoff from 1993, who proved that, under the same condition, there\nare four lines intersecting all the sets. In fact, we prove a colorful version\nof this result, under weakened conditions on the sets.\n  A triple of sets $A,B,C$ in the plane is said to be a {\\em tight} if\n$\\textrm{conv}(A\\cup B)\\cap \\textrm{conv}(A\\cup C)\\cap \\textrm{conv}(B\\cap\nC)\\neq \\emptyset.$ This notion was first introduced by Holmsen, where he showed\nthat if $\\mathcal{F}$ is a family of compact convex sets in the plane in which\nevery three sets form a tight triple, then there is a line intersecting at\nleast $\\frac{1}{8}|\\mathcal{F}|$ members of $\\mathcal{F}$. Here we prove that\nif $\\mathcal{F}_1,\\dots,\\mathcal{F}_6$ are families of compact connected sets\nin the plane such that every three sets, chosen from three distinct families\n$\\mathcal{F}_i$, form a tight triple, then there exists $1\\le j\\le 6$ and three\nlines intersecting every member of $\\mathcal{F}_j$. In particular, this\nimproves $\\frac{1}{8}$ to $\\frac{1}{3}$ in Holmsen's result.\n"}
+{"abstract": "  This paper describes a machine learning approach for annotating and analyzing\ndata curation work logs at ICPSR, a large social sciences data archive. The\nsystems we studied track curation work and coordinate team decision-making at\nICPSR. Repository staff use these systems to organize, prioritize, and document\ncuration work done on datasets, making them promising resources for studying\ncuration work and its impact on data reuse, especially in combination with data\nusage analytics. A key challenge, however, is classifying similar activities so\nthat they can be measured and associated with impact metrics. This paper\ncontributes: 1) a schema of data curation activities; 2) a computational model\nfor identifying curation actions in work log descriptions; and 3) an analysis\nof frequent data curation activities at ICPSR over time. We first propose a\nschema of data curation actions to help us analyze the impact of curation work.\nWe then use this schema to annotate a set of data curation logs, which contain\nrecords of data transformations and project management decisions completed by\nrepository staff. Finally, we train a text classifier to detect the frequency\nof curation actions in a large set of work logs. Our approach supports the\nanalysis of curation work documented in work log systems as an important step\ntoward studying the relationship between research data curation and data reuse.\n"}
+{"abstract": "  In a recent article we presented a model for hadronic rescattering, and some\nresults were shown for pp collisions at LHC energies. In order to extend the\nstudies to pA and AA collisions, the Angantyr model for heavy-ion collisions is\ntaken as the starting point. Both these models are implemented within the\ngeneral-purpose Monte Carlo event generator Pythia, which makes the matching\nreasonably straightforward, and allows for detailed studies of the full\nspace--time evolution. The rescattering rate is significantly higher than in\npp, especially for central AA collisions, where the typical primary hadron\nrescatters several times. We study the impact of rescattering on a number of\ndistributions, such as pT and eta spectra, and the space--time evolution of the\nwhole collision process. Notably rescattering is shown to give a significant\ncontribution to elliptic flow in XeXe and PbPb, and to give a nontrivial impact\non charm production.\n"}
+{"abstract": "  In passive linear systems, complete combining of powers carried by waves from\nseveral input channels into a single output channel is forbidden by the energy\nconservation law. Here, we demonstrate that complete combination of both\ncoherent and incoherent plane waves can be achieved using metasurfaces with\nproperties varying in space and time. The proposed structure reflects waves of\nthe same frequency but incident at different angles towards a single direction.\nThe frequencies of the output waves are shifted by the metasurface, ensuring\nperfect incoherent power combining. The proposed concept of power combining is\ngeneral and can be applied for electromagnetic waves from the microwave to\noptical domains, as well as for waves of other physical nature.\n"}
+{"abstract": "  In recent years, there has been significant growth of distributed energy\nresources (DERs) penetration in the power grid. The stochastic and intermittent\nfeatures of variable DERs such as roof top photovoltaic (PV) bring substantial\nuncertainties to the grid on the consumer end and weaken the grid reliability.\nIn addition, the fact that numerous DERs are widespread in the grid makes it\nhard to monitor and manage DERs. To address this challenge, this paper proposes\na novel real-time grid-supporting energy management (GSEM) strategy for\ngrid-supporting microgrid (MG). This strategy can not only properly manage DERs\nin a MG but also enable DERs to provide grid services, which enables a MG to be\ngrid-supporting via flexible trading power. The proposed GSEM strategy is based\non a 2-step optimization which includes a routine economic dispatch (ED) step\nand an acceptable trading power range determination step. Numerical simulations\ndemonstrate the performance of the proposed GSEM strategy which enables the\ngrid operator to have a dispatch choice of trading power with MG and enhance\nthe reliability and resilience of the main grid.\n"}
+{"abstract": "  The approximation of solutions to second order Hamilton--Jacobi--Bellman\n(HJB) equations by deep neural networks is investigated. It is shown that for\nHJB equations that arise in the context of the optimal control of certain\nMarkov processes the solution can be approximated by deep neural networks\nwithout incurring the curse of dimension. The dynamics is assumed to depend\naffinely on the controls and the cost depends quadratically on the controls.\nThe admissible controls take values in a bounded set.\n"}
+{"abstract": "  Creating and destroying threads on modern Linux systems incurs high latency,\nabsent concurrency, and fails to scale as we increase concurrency. To address\nthis concern we introduce a process-local cache of idle threads. Specifically,\ninstead of destroying a thread when it terminates, we cache and then recycle\nthat thread in the context of subsequent thread creation requests. This\napproach shows significant promise in various applications and benchmarks that\ncreate and destroy threads rapidly and illustrates the need for and potential\nbenefits of improved concurrency infrastructure. With caching, the cost of\ncreating a new thread drops by almost an order of magnitude. As our experiments\ndemonstrate, this results in significant performance improvements for multiple\napplications that aggressively create and destroy numerous threads.\n"}
+{"abstract": "  An intense activity is nowadays devoted to the definition of models capturing\nthe properties of complex networks. Among the most promising approaches, it has\nbeen proposed to model these graphs via their clique incidence bipartite\ngraphs. However, this approach has, until now, severe limitations resulting\nfrom its incapacity to reproduce a key property of this object: the overlapping\nnature of cliques in complex networks. In order to get rid of these limitations\nwe propose to encode the structure of clique overlaps in a network thanks to a\nprocess consisting in iteratively factorising the maximal bicliques between the\nupper level and the other levels of a multipartite graph. We show that the most\nnatural definition of this factorising process leads to infinite series for\nsome instances. Our main result is to design a restriction of this process that\nterminates for any arbitrary graph. Moreover, we show that the resulting\nmultipartite graph has remarkable combinatorial properties and is closely\nrelated to another fundamental combinatorial object. Finally, we show that, in\npractice, this multipartite graph is computationally tractable and has a size\nthat makes it suitable for complex network modelling.\n"}
+{"abstract": "  In a conventional domain adaptation person Re-identification (Re-ID) task,\nboth the training and test images in target domain are collected under the\nsunny weather. However, in reality, the pedestrians to be retrieved may be\nobtained under severe weather conditions such as hazy, dusty and snowing, etc.\nThis paper proposes a novel Interference Suppression Model (ISM) to deal with\nthe interference caused by the hazy weather in domain adaptation person Re-ID.\nA teacherstudent model is used in the ISM to distill the interference\ninformation at the feature level by reducing the discrepancy between the clear\nand the hazy intrinsic similarity matrix. Furthermore, in the distribution\nlevel, the extra discriminator is introduced to assist the student model make\nthe interference feature distribution more clear. The experimental results show\nthat the proposed method achieves the superior performance on two synthetic\ndatasets than the stateof-the-art methods. The related code will be released\nonline https://github.com/pangjian123/ISM-ReID.\n"}
+{"abstract": "  The mobile data traffic has been exponentially growing during the last\ndecades, which has been enabled by the densification of the network\ninfrastructure in terms of increased cell density (i.e., ultra-dense network\n(UDN)) and/or increased number of active antennas per access point (AP) (i.e.,\nmassive multiple-input multiple-output (mMIMO)). However, neither UDN nor mMIMO\nwill meet the increasing data rate demands of the sixth generation (6G)\nwireless communications due to the inter-cell interference and large\nquality-of-service variations, respectively. Cell-free (CF) mMIMO, which\ncombines the best aspects of UDN and mMIMO, is viewed as a key solution to this\nissue. In such systems, each user equipment (UE) is served by a preferred set\nof surrounding APs cooperatively. In this paper, we provide a survey of the\nstate-of-the-art literature on CF mMIMO. As a starting point, the significance\nand the basic properties of CF mMIMO are highlighted. We then present the\ncanonical framework, where the essential details (i.e., transmission procedure\nand mathematical system model) are discussed. Next, we provide a deep look at\nthe resource allocation and signal processing problems related to CF mMIMO and\nsurvey the up-to-date schemes and algorithms. After that, we discuss the\npractical issues when implementing CF mMIMO. Potential future directions are\nthen pointed out. Finally, we conclude this paper with a summary of the key\nlessons learned in this field. This paper aims to provide a starting point for\nanyone who wants to conduct research on CF mMIMO for future wireless networks.\n"}
+{"abstract": "  Autonomous exploration is an application of growing importance in robotics. A\npromising strategy is ergodic trajectory planning, whereby an agent spends in\neach area a fraction of time which is proportional to its probability\ninformation density function. In this paper, a decentralized ergodic\nmulti-agent trajectory planning algorithm featuring limited communication\nconstraints is proposed. The agents' trajectories are designed by optimizing a\nweighted cost encompassing ergodicity, control energy and close-distance\noperation objectives. To solve the underlying optimal control problem, a\nsecond-order descent iterative method coupled with a projection operator in the\nform of an optimal feedback controller is used. Exhaustive numerical analyses\nshow that the multi-agent solution allows a much more efficient exploration in\nterms of completion task time and control energy distribution by leveraging\ncollaboration among agents.\n"}
+{"abstract": "  Federated averaging (FedAvg) is a communication efficient algorithm for the\ndistributed training with an enormous number of clients. In FedAvg, clients\nkeep their data locally for privacy protection; a central parameter server is\nused to communicate between clients. This central server distributes the\nparameters to each client and collects the updated parameters from clients.\nFedAvg is mostly studied in centralized fashions, which requires massive\ncommunication between server and clients in each communication. Moreover,\nattacking the central server can break the whole system's privacy. In this\npaper, we study the decentralized FedAvg with momentum (DFedAvgM), which is\nimplemented on clients that are connected by an undirected graph. In DFedAvgM,\nall clients perform stochastic gradient descent with momentum and communicate\nwith their neighbors only. To further reduce the communication cost, we also\nconsider the quantized DFedAvgM. We prove convergence of the (quantized)\nDFedAvgM under trivial assumptions; the convergence rate can be improved when\nthe loss function satisfies the P{\\L} property. Finally, we numerically verify\nthe efficacy of DFedAvgM.\n"}
+{"abstract": "  Since the evolution of digital computers, the storage of data has always been\nin terms of discrete bits that can store values of either 1 or 0. Hence, all\ncomputer programs (such as MATLAB), convert any input continuous signal into a\ndiscrete dataset. Applying this to oscillating signals, such as audio, opens a\ndomain for processing as well as editing. The Fourier transform, which is an\nintegral over infinite limits, for the use of signal processing is discrete.\nThe essential feature of the Fourier transform is to decompose any signal into\na combination of multiple sinusoidal waves that are easy to deal with. The\ndiscrete Fourier transform (DFT) can be represented as a matrix, with each data\npoint acting as an orthogonal point, allowing one to perform complicated\ntransformations on individual frequencies. Due to this formulation, all the\nconcepts of linear algebra and linear transforms prove to be extremely useful\nhere. In this paper, we first explain the theoretical basis of audio processing\nusing linear algebra, and then focus on a simulation coded in MATLAB, to\nprocess and edit various audio samples. The code is open ended and easily\nexpandable by just defining newer matrices which can transform over the\noriginal audio signal. Finally, this paper attempts to highlight and briefly\nexplain the results that emerge from the simulation\n"}
+{"abstract": "  Resistive Random-Access-Memory (ReRAM) crossbar is a promising technique for\ndeep neural network (DNN) accelerators, thanks to its in-memory and in-situ\nanalog computing abilities for Vector-Matrix Multiplication-and-Accumulations\n(VMMs). However, it is challenging for crossbar architecture to exploit the\nsparsity in the DNN. It inevitably causes complex and costly control to exploit\nfine-grained sparsity due to the limitation of tightly-coupled crossbar\nstructure. As the countermeasure, we developed a novel ReRAM-based DNN\naccelerator, named Sparse-Multiplication-Engine (SME), based on a hardware and\nsoftware co-design framework. First, we orchestrate the bit-sparse pattern to\nincrease the density of bit-sparsity based on existing quantization methods.\nSecond, we propose a novel weigh mapping mechanism to slice the bits of a\nweight across the crossbars and splice the activation results in peripheral\ncircuits. This mechanism can decouple the tightly-coupled crossbar structure\nand cumulate the sparsity in the crossbar. Finally, a superior squeeze-out\nscheme empties the crossbars mapped with highly-sparse non-zeros from the\nprevious two steps. We design the SME architecture and discuss its use for\nother quantization methods and different ReRAM cell technologies. Compared with\nprior state-of-the-art designs, the SME shrinks the use of crossbars up to 8.7x\nand 2.1x using Resent-50 and MobileNet-v2, respectively, with less than 0.3%\naccuracy drop on ImageNet.\n"}
+{"abstract": "  The current world challenges include issues such as infectious disease\npandemics, environmental health risks, food safety, and crime prevention.\nThrough this article, a special emphasis is given to one of the main challenges\nin the healthcare sector during the COVID-19 pandemic, the cyber risk. Since\nthe beginning of the Covid-19 pandemic, the World Health Organization has\ndetected a dramatic increase in the number of cyber-attacks. For instance, in\nItaly the COVID-19 emergency has heavily affected cybersecurity; from January\nto April 2020, the total of attacks, accidents, and violations of privacy to\nthe detriment of companies and individuals has doubled. Using a systematic and\nrigorous approach, this paper aims to analyze the literature on the cyber risk\nin the healthcare sector to understand the real knowledge on this topic. The\nfindings highlight the poor attention of the scientific community on this\ntopic, except in the United States. The literature lacks research contributions\nto support cyber risk management in subject areas such as Business, Management\nand Accounting; Social Science; and Mathematics. This research outlines the\nneed to empirically investigate the cyber risk, giving a practical solution to\nhealth facilities. Keywords: cyber risk; cyber-attack; cybersecurity; computer\nsecurity; COVID-19; coronavirus;information technology risk; risk management;\nrisk assessment; health facilities; healthcare sector;systematic literature\nreview; insurance\n"}
+{"abstract": "  The paper presents a solution for the problem of choosing a method for\nanalytical determining of weight factors for a genetic algorithm additive\nfitness function. This algorithm is the basis for an evolutionary process,\nwhich forms a stable and effective query population in a search engine to\nobtain highly relevant results. The paper gives a formal description of an\nalgorithm fitness function, which is a weighted sum of three heterogeneous\ncriteria. The selected methods for analytical determining of weight factors are\ndescribed in detail. It is noted that expert assessment methods are impossible\nto use. The authors present a research methodology using the experimental\nresults from earlier in the discussed project \"Data Warehouse Support on the\nBase Intellectual Web Crawler and Evolutionary Model for Target Information\nSelection\". There is a description of an initial dataset with data ranges for\ncalculating weights. The calculation order is illustrated by examples. The\nresearch results in graphical form demonstrate the fitness function behavior\nduring the genetic algorithm operation using various weighting options.\n"}
+{"abstract": "  Saccadic eye movements allow animals to bring different parts of an image\ninto high-resolution. During free viewing, inhibition of return incentivizes\nexploration by discouraging previously visited locations. Despite this\ninhibition, here we show that subjects make frequent return fixations. We\nsystematically studied a total of 44,328 return fixations out of 217,440\nfixations across different tasks, in monkeys and humans, and in static images\nor egocentric videos. The ubiquitous return fixations were consistent across\nsubjects, tended to occur within short offsets, and were characterized by\nlonger duration than non-return fixations. The locations of return fixations\ncorresponded to image areas of higher saliency and higher similarity to the\nsought target during visual search tasks. We propose a biologically-inspired\ncomputational model that capitalizes on a deep convolutional neural network for\nobject recognition to predict a sequence of fixations. Given an input image,\nthe model computes four maps that constrain the location of the next saccade: a\nsaliency map, a target similarity map, a saccade size map, and a memory map.\nThe model exhibits frequent return fixations and approximates the properties of\nreturn fixations across tasks and species. The model provides initial steps\ntowards capturing the trade-off between exploitation of informative image\nlocations combined with exploration of novel image locations during scene\nviewing.\n"}
+{"abstract": "  This note relies mainly on a refined version of the main results of the paper\nby F. Catrina and D. Costa (J. Differential Equations 2009). We provide very\nshort and self-contained proofs. Our results are sharp and minimizers are\nobtained in suitable functional spaces. As main tools we use the so-called\n\\textit{expand of squares} method to establish sharp weighted\n$L^{2}$-Caffarelli-Kohn-Nirenberg (CKN) inequalities and density arguments.\n"}
+{"abstract": "  In this paper we study quantum group deformations of the infinite dimensional\nsymmetry algebra of asymptotically AdS spacetimes in three dimensions. Building\non previous results in the finite dimensional subalgebras we classify all\npossible Lie bialgebra structures and for selected examples, we explicitly\nconstruct the related Hopf algebras. Using cohomological arguments we show that\nthis construction can always be performed by a so-called twist deformation. The\nresulting structures can be compared to the well-known $\\kappa$-Poincar\\'e Hopf\nalgebras constructed on the finite dimensional Poincar\\'e or (anti) de Sitter\nalgebra. The dual $\\kappa$ Minkowski spacetime is supposed to describe a\nspecific non-commutative geometry. Importantly, we find that some incarnations\nof the $\\kappa$-Poincar\\'e can not be extended consistently to the infinite\ndimensional algebras. Furthermore, certain deformations can have potential\nphysical applications if subalgebras are considered. Since the conserved\ncharges associated with asymptotic symmetries in 3-dimensional form a centrally\nextended algebra we also discuss briefly deformations of such algebras. The\npresence of the full symmetry algebra might have observable consequences that\ncould be used to rule out these deformations. }\n"}
+{"abstract": "  The properties of modified Hayward black hole space-time can be investigated\nthrough analyzing the particle geodesics. By means of a detailed analysis of\nthe corresponding effective potentials for a massive particle, we find all\npossible orbits which are allowed by the energy levels. The trajectories of\norbits are plotted by solving the equation of orbital motion numerically. We\nconclude that whether there is an escape orbit is associated with $b$ (angular\nmomentum). The properties of orbital motion are related to $b$, $\\alpha$\n($\\alpha$ is associated with the time delay) and $\\beta$ ($\\beta$ is related to\n1-loop quantum corrections). There are no escape orbits when $b$ $<$ $4.016M$,\n$\\alpha$ = 0.50 and $\\beta$ = 1.00. For fixed $\\alpha$ = 0.50 and $\\beta$ =\n1.00, if $b$ $<$ $3.493M$, there only exist unstable orbits. Comparing with the\nregular Hayward black hole, we go for a reasonable speculation by mean of the\nexisting calculating results that the introduction of the modified term makes\nthe radius of the innermost circular orbit (ISCO) and the corresponding angular\nmomentum larger.\n"}
+{"abstract": "  We present Hubble Space Telescope imaging of a pre-explosion counterpart to\nSN 2019yvr obtained 2.6 years before its explosion as a type Ib supernova (SN\nIb). Aligning to a post-explosion Gemini-S/GSAOI image, we demonstrate that\nthere is a single source consistent with being the SN 2019yvr progenitor\nsystem, the second SN Ib progenitor candidate after iPTF13bvn. We also analyzed\npre-explosion Spitzer/IRAC imaging, but we do not detect any counterparts at\nthe SN location. SN 2019yvr was highly reddened, and comparing its spectra and\nphotometry to those of other, less extinguished SNe Ib we derive\n$E(B-V)=0.51\\substack{+0.27\\\\-0.16}$ mag for SN 2019yvr. Correcting photometry\nof the pre-explosion source for dust reddening, we determine that this source\nis consistent with a $\\log(L/L_{\\odot}) = 5.3 \\pm 0.2$ and $T_{\\mathrm{eff}} =\n6800\\substack{+400\\\\-200}$ K star. This relatively cool photospheric\ntemperature implies a radius of 320$\\substack{+30\\\\-50} R_{\\odot}$, much larger\nthan expectations for SN Ib progenitor stars with trace amounts of hydrogen but\nin agreement with previously identified SN IIb progenitor systems. The\nphotometry of the system is also consistent with binary star models that\nundergo common envelope evolution, leading to a primary star hydrogen envelope\nmass that is mostly depleted but seemingly in conflict with the SN Ib\nclassification of SN 2019yvr. SN 2019yvr had signatures of strong circumstellar\ninteraction in late-time ($>$150 day) spectra and imaging, and so we consider\neruptive mass loss and common envelope evolution scenarios that explain the SN\nIb spectroscopic class, pre-explosion counterpart, and dense circumstellar\nmaterial. We also hypothesize that the apparent inflation could be caused by a\nquasi-photosphere formed in an extended, low-density envelope or circumstellar\nmatter around the primary star.\n"}
+{"abstract": "  Precise localization of polyp is crucial for early cancer screening in\ngastrointestinal endoscopy. Videos given by endoscopy bring both richer\ncontextual information as well as more challenges than still images. The\ncamera-moving situation, instead of the common camera-fixed-object-moving one,\nleads to significant background variation between frames. Severe internal\nartifacts (e.g. water flow in the human body, specular reflection by tissues)\ncan make the quality of adjacent frames vary considerately. These factors\nhinder a video-based model to effectively aggregate features from neighborhood\nframes and give better predictions. In this paper, we present Spatial-Temporal\nFeature Transformation (STFT), a multi-frame collaborative framework to address\nthese issues. Spatially, STFT mitigates inter-frame variations in the\ncamera-moving situation with feature alignment by proposal-guided deformable\nconvolutions. Temporally, STFT proposes a channel-aware attention module to\nsimultaneously estimate the quality and correlation of adjacent frames for\nadaptive feature aggregation. Empirical studies and superior results\ndemonstrate the effectiveness and stability of our method. For example, STFT\nimproves the still image baseline FCOS by 10.6% and 20.6% on the comprehensive\nF1-score of the polyp localization task in CVC-Clinic and ASUMayo datasets,\nrespectively, and outperforms the state-of-the-art video-based method by 3.6%\nand 8.0%, respectively. Code is available at\n\\url{https://github.com/lingyunwu14/STFT}.\n"}
+{"abstract": "  Recently, a careful canonical quantisation of the theory of closed bosonic\ntensionless strings has resulted in the discovery of three separate vacua and\nhence three different quantum theories that emerge from this single classical\ntensionless theory. In this note, we perform lightcone quantisation with the\naim of determination of the critical dimension of these three inequivalent\nquantum theories. The satisfying conclusion of a rather long and tedious\ncalculation is that one of vacua does not lead to any constraint on the number\nof dimensions, while the other two give $D=26$. This implies that all three\nquantum tensionless theories can be thought of as consistent sub-sectors of\nquantum tensile bosonic closed string theory.\n"}
+{"abstract": "  The Poisson gauge algebra is a semi-classical limit of complete\nnon-commutative gauge algebra. In the present work we formulate the Poisson\ngauge theory which is a dynamical field theoretical model having the Poisson\ngauge algebra as a corresponding algebra of gauge symmetries. The proposed\nmodel is designed to investigate the semi-classical features of the full\nnon-commutative gauge theory with coordinate dependent non-commutativity\n$\\Theta^{ab}(x)$, especially whose with a non-constant rank. We derive the\nexpression for the covariant derivative of matter field. The commutator\nrelation for the covariant derivatives defines the Poisson field strength which\nis covariant under the Poisson gauge transformations and reproduces the\nstandard $U(1)$ field strength in the commutative limit. We derive the\ncorresponding Bianchi identities. The field equations for the gauge and the\nmatter fields are obtained from the gauge invariant action. We consider\ndifferent examples of linear in coordinates Poisson structures\n$\\Theta^{ab}(x)$, as well as non-linear ones, and obtain explicit expressions\nfor all proposed constructions. Our model is unique up to invertible field\nredefinitions and coordinate transformations.\n"}
+{"abstract": "  In stochastic dynamic environments, team stochastic games have emerged as a\nversatile paradigm for studying sequential decision-making problems of fully\ncooperative multi-agent systems. However, the optimality of the derived\npolicies is usually sensitive to the model parameters, which are typically\nunknown and required to be estimated from noisy data in practice. To mitigate\nthe sensitivity of the optimal policy to these uncertain parameters, in this\npaper, we propose a model of \"robust\" team stochastic games, where players\nutilize a robust optimization approach to make decisions. This model extends\nteam stochastic games to the scenario of incomplete information and meanwhile\nprovides an alternative solution concept of robust team optimality. To seek\nsuch a solution, we develop a learning algorithm in the form of a Gauss-Seidel\nmodified policy iteration and prove its convergence. This algorithm, compared\nwith robust dynamic programming, not only possesses a faster convergence rate,\nbut also allows for using approximation calculations to alleviate the curse of\ndimensionality. Moreover, some numerical simulations are presented to\ndemonstrate the effectiveness of the algorithm by generalizing the game model\nof social dilemmas to sequential robust scenarios.\n"}
+{"abstract": "  Traditional toxicity detection models have focused on the single utterance\nlevel without deeper understanding of context. We introduce CONDA, a new\ndataset for in-game toxic language detection enabling joint intent\nclassification and slot filling analysis, which is the core task of Natural\nLanguage Understanding (NLU). The dataset consists of 45K utterances from 12K\nconversations from the chat logs of 1.9K completed Dota 2 matches. We propose a\nrobust dual semantic-level toxicity framework, which handles utterance and\ntoken-level patterns, and rich contextual chatting history. Accompanying the\ndataset is a thorough in-game toxicity analysis, which provides comprehensive\nunderstanding of context at utterance, token, and dual levels. Inspired by NLU,\nwe also apply its metrics to the toxicity detection tasks for assessing\ntoxicity and game-specific aspects. We evaluate strong NLU models on CONDA,\nproviding fine-grained results for different intent classes and slot classes.\nFurthermore, we examine the coverage of toxicity nature in our dataset by\ncomparing it with other toxicity datasets.\n"}
+{"abstract": "  A novel formulation of the hyperspectral broadband phase retrieval is\ndeveloped for the scenario where both object and modulation phase masks are\nspectrally varying. The proposed algorithm is based on a complex domain version\nof the alternating direction method of multipliers (ADMM) and Spectral\nProximity Operators (SPO) derived for Gaussian and Poissonian observations.\nComputations for these operators are reduced to the solution of sets of cubic\n(for Gaussian) and quadratic (for Poissonian) algebraic equations. These\nproximity operators resolve two problems. Firstly, the complex domain spectral\ncomponents of signals are extracted from the total intensity observations\ncalculated as sums of the signal spectral intensities. In this way, the\nspectral analysis of the total intensities is achieved. Secondly, the noisy\nobservations are filtered, compromising noisy intensity observations and their\npredicted counterparts. The ability to resolve the hyperspectral broadband\nphase retrieval problem and to find the spectrum varying object are essentially\ndefined by the spectral properties of object and image formation operators. The\nsimulation tests demonstrate that the phase retrieval in this formulation can\nbe successfully resolved.\n"}
+{"abstract": "  We investigate the interaction between compactness principles and guessing\nprinciples in the Radin forcing extensions \\cite{MR670992}. In particular, we\nshow that in any Radin forcing extension with respect to a measure sequence on\n$\\kappa$, if $\\kappa$ is weakly compact, then $\\diamondsuit(\\kappa)$ holds,\nanswering a question raised in \\cite{MR3960897}. This provides contrast with a\nwell-known theorem of Woodin \\cite{CummingsWoodin}, who showed in a certain\nRadin extension over a suitably prepared ground model relative to the existence\nof large cardinals, the diamond principle fails at a strongly inaccessible\nMahlo cardinal. Refining the analysis of the Radin extensions, we consistently\ndemonstrate a scenario where a compactness principle, stronger than the\ndiagonal stationary reflection principle, holds yet the diamond principle fails\nat a strongly inaccessible cardinal, improving a result from \\cite{MR3960897}.\n"}
+{"abstract": "  In general-purpose particle detectors, the particle-flow algorithm may be\nused to reconstruct a comprehensive particle-level view of the event by\ncombining information from the calorimeters and the trackers, significantly\nimproving the detector resolution for jets and the missing transverse momentum.\nIn view of the planned high-luminosity upgrade of the CERN Large Hadron\nCollider (LHC), it is necessary to revisit existing reconstruction algorithms\nand ensure that both the physics and computational performance are sufficient\nin an environment with many simultaneous proton-proton interactions (pileup).\nMachine learning may offer a prospect for computationally efficient event\nreconstruction that is well-suited to heterogeneous computing platforms, while\nsignificantly improving the reconstruction quality over rule-based algorithms\nfor granular detectors. We introduce MLPF, a novel, end-to-end trainable,\nmachine-learned particle-flow algorithm based on parallelizable,\ncomputationally efficient, and scalable graph neural networks optimized using a\nmulti-task objective on simulated events. We report the physics and\ncomputational performance of the MLPF algorithm on a Monte Carlo dataset of top\nquark-antiquark pairs produced in proton-proton collisions in conditions\nsimilar to those expected for the high-luminosity LHC. The MLPF algorithm\nimproves the physics response with respect to a rule-based benchmark algorithm\nand demonstrates computationally scalable particle-flow reconstruction in a\nhigh-pileup environment.\n"}
+{"abstract": "  A multilayer network depicts different types of interactions among the same\nset of nodes. For example, protease networks consist of five to seven layers,\nwhere different layers represent distinct types of experimentally confirmed\nmolecule interactions among proteins. In a multilayer protease network, the\nco-expression layer is obtained through the meta-analysis of transcriptomic\ndata from various sources and platforms. While in some researches the\nco-expression layer is in turn represented as a multilayered network, a\nfundamental problem is how to obtain a single-layer network from the\ncorresponding multilayered network. This process is called multilayer network\naggregation. In this work, we propose a maximum a posteriori estimation-based\nalgorithm for multilayer network aggregation. The method allows to aggregate a\nweighted multilayer network while conserving the core information of the\nlayers. We evaluate the method through an unweighted friendship network and a\nmultilayer gene co-expression network. We compare the aggregated gene\nco-expression network with a network obtained from conflated datasets and a\nnetwork obtained from averaged weights. The Von Neumann entropy is adopted to\ncompare the mixedness of the three networks, and, together with other network\nmeasurements, shows the effectiveness of the proposes method.\n"}
+{"abstract": "  Over the past two decades machine learning has permeated almost every realm\nof technology. At the same time, many researchers have begun using category\ntheory as a unifying language, facilitating communication between different\nscientific disciplines. It is therefore unsurprising that there is a burgeoning\ninterest in applying category theory to machine learning. We aim to document\nthe motivations, goals and common themes across these applications. We touch on\ngradient-based learning, probability, and equivariant learning.\n"}
+{"abstract": "  In this paper, we propose CHOLAN, a modular approach to target end-to-end\nentity linking (EL) over knowledge bases. CHOLAN consists of a pipeline of two\ntransformer-based models integrated sequentially to accomplish the EL task. The\nfirst transformer model identifies surface forms (entity mentions) in a given\ntext. For each mention, a second transformer model is employed to classify the\ntarget entity among a predefined candidates list. The latter transformer is fed\nby an enriched context captured from the sentence (i.e. local context), and\nentity description gained from Wikipedia. Such external contexts have not been\nused in the state of the art EL approaches. Our empirical study was conducted\non two well-known knowledge bases (i.e., Wikidata and Wikipedia). The empirical\nresults suggest that CHOLAN outperforms state-of-the-art approaches on standard\ndatasets such as CoNLL-AIDA, MSNBC, AQUAINT, ACE2004, and T-REx.\n"}
+{"abstract": "  In this paper we prove that the Cauchy problem of the Muskat equation is\nwellposed locally in time for any initial data in $\\dot C^1(\\mathbb{R}^d)\\cap\nL^2(\\mathbb{R}^d)$.\n"}
+{"abstract": "  For the need of measurements focused in condensed matter physics and\nespecially Bernoulli effect in superconductors we have developed an active\nresonator with dual operational amplifiers. A tunable high-Q resonator is\nperformed in the schematics of the the General Impedance Converter (GIC). In\nthe framework of frequency dependent open-loop gain of operational amplifiers,\na general formula of the frequency dependence of the impedance of GIC is\nderived. The explicit formulas for the resonance frequency and Q-factor include\nas immanent parameter the crossover frequency of the operational amplifier.\nVoltage measurements of GIC with a lock-in amplifier perfectly agree with the\nderived formulas. A table reveals that electrometer operational amplifiers are\nthe best choice to build the described resonator.\n"}
+{"abstract": "  Clinical SPECT-MPI images of 345 patients acquired from a dedicated cardiac\nSPECT in list-mode format were retrospectively employed to predict normal-dose\nimages from low-dose data at the half, quarter, and one-eighth-dose levels. A\ngenerative adversarial network was implemented to predict non-gated normal-dose\nimages in the projection space at the different reduced dose levels.\nEstablished metrics including the peak signal-to-noise ratio (PSNR), root mean\nsquared error (RMSE), and structural similarity index metrics (SSIM) in\naddition to Pearson correlation coefficient analysis and derived parameters\nfrom Cedars-Sinai software were used to quantitatively assess the quality of\nthe predicted normal-dose images. For clinical evaluation, the quality of the\npredicted normal-dose images was evaluated by a nuclear medicine specialist\nusing a seven-point (-3 to +3) grading scheme. By considering PSNR, SSIM, and\nRMSE quantitative parameters among the different reduced dose levels, the\nhighest PSNR (42.49) and SSIM (0.99), and the lowest RMSE (1.99) were obtained\nat the half-dose level in the reconstructed images. Pearson correlation\ncoefficients were measured 0.997, 0.994, and 0.987 for the predicted\nnormal-dose images at the half, quarter, and one-eighth-dose levels,\nrespectively. Regarding the normal-dose images as the reference, the\nBland-Altman plots sketched for the Cedars-Sinai selected parameters exhibited\nremarkably less bias and variance in the predicted normal-dose images compared\nwith the low-dose data at the entire reduced dose levels. Overall, considering\nthe clinical assessment performed by a nuclear medicine specialist, 100%, 80%,\nand 11% of the predicted normal-dose images were clinically acceptable at the\nhalf, quarter, and one-eighth-dose levels, respectively.\n"}
+{"abstract": "  Classically transmission conditions between subdomains are optimized for a\nsimplified two subdomain decomposition to obtain optimized Schwarz methods for\nmany subdomains. We investigate here if such a simplified optimization suffices\nfor the magnetotelluric approximation of Maxwell's equation which leads to a\ncomplex diffusion problem. We start with a direct analysis for 2 and 3\nsubdomains, and present asymptotically optimized transmission conditions in\neach case. We then optimize transmission conditions numerically for 4, 5 and 6\nsubdomains and observe the same asymptotic behavior of optimized transmission\nconditions. We finally use the technique of limiting spectra to optimize for a\nvery large number of subdomains in a strip decomposition. Our analysis shows\nthat the asymptotically best choice of transmission conditions is the same in\nall these situations, only the constants differ slightly. It is therefore\nenough for such diffusive type approximations of Maxwell's equations, which\ninclude the special case of the Laplace and screened Laplace equation, to\noptimize transmission parameters in the simplified two subdomain decomposition\nsetting to obtain good transmission conditions for optimized Schwarz methods\nfor more general decompositions.\n"}
+{"abstract": "  Given a prediction task, understanding when one can and cannot design a\nconsistent convex surrogate loss, particularly a low-dimensional one, is an\nimportant and active area of machine learning research. The prediction task may\nbe given as a target loss, as in classification and structured prediction, or\nsimply as a (conditional) statistic of the data, as in risk measure estimation.\nThese two scenarios typically involve different techniques for designing and\nanalyzing surrogate losses. We unify these settings using tools from property\nelicitation, and give a general lower bound on prediction dimension. Our lower\nbound tightens existing results in the case of discrete predictions, showing\nthat previous calibration-based bounds can largely be recovered via property\nelicitation. For continuous estimation, our lower bound resolves on open\nproblem on estimating measures of risk and uncertainty.\n"}
+{"abstract": "  Serverless computing is the latest paradigm in cloud computing, offering a\nframework for the development of event driven, pay-as-you-go functions in a\nhighly scalable environment. While these traits offer a powerful new\ndevelopment paradigm, they have also given rise to a new form of cyber-attack\nknown as Denial of Wallet (forced financial exhaustion). In this work, we\ndefine and identify the threat of Denial of Wallet and its potential attack\npatterns. Also, we demonstrate how this new form of attack can potentially\ncircumvent existing mitigation systems developed for a similar style of attack,\nDenial of Service. Our goal is twofold. Firstly, we will provide a concise and\ninformative overview of this emerging attack paradigm. Secondly, we propose\nthis paper as a starting point to enable researchers and service providers to\ncreate effective mitigation strategies. We include some simulated experiments\nto highlight the potential financial damage that such attacks can cause and the\ncreation of an isolated test bed for continued safe research on these attacks.\n"}
+{"abstract": "  Motivated by the immutable nature of Ethereum smart contracts and of their\ntransactions, quite many approaches have been proposed to detect defects and\nsecurity problems before smart contracts become persistent in the blockchain\nand they are granted control on substantial financial value.\n  Because smart contracts source code might not be available, static analysis\napproaches mostly face the challenge of analysing compiled Ethereum bytecode,\nthat is available directly from the official blockchain. However, due to the\nintrinsic complexity of Ethereum bytecode (especially in jump resolution),\nstatic analysis encounters significant obstacles that reduce the accuracy of\nexiting automated tools.\n  This paper presents a novel static analysis algorithm based on the symbolic\nexecution of the Ethereum operand stack that allows us to resolve jumps in\nEthereum bytecode and to construct an accurate control-flow graph (CFG) of the\ncompiled smart contracts. EtherSolve is a prototype implementation of our\napproach. Experimental results on a significant set of real world Ethereum\nsmart contracts show that EtherSolve improves the accuracy of the execrated\nCFGs with respect to the state of the art available approaches.\n  Many static analysis techniques are based on the CFG representation of the\ncode and would therefore benefit from the accurate extraction of the CFG. For\nexample, we implemented a simple extension of EtherSolve that allows to detect\ninstances of the re-entrancy vulnerability.\n"}
+{"abstract": "  Significant galaxy mergers throughout cosmic time play a fundamental role in\ntheories of galaxy evolution. The widespread usage of human classifiers to\nvisually assess whether galaxies are in merging systems remains a fundamental\ncomponent of many morphology studies. Studies that employ human classifiers\nusually construct a control sample, and rely on the assumption that the bias\nintroduced by using humans will be evenly applied to all samples. In this work,\nwe test this assumption and develop methods to correct for it. Using the\nstandard binomial statistical methods employed in many morphology studies, we\nfind that the merger fraction, error, and the significance of the difference\nbetween two samples are dependent on the intrinsic merger fraction of any given\nsample. We propose a method of quantifying merger biases of individual human\nclassifiers and incorporate these biases into a full probabilistic model to\ndetermine the merger fraction and the probability of an individual galaxy being\nin a merger. Using 14 simulated human responses and accuracies, we are able to\ncorrectly label a galaxy as ''merger'' or ''isolated'' to within 1\\% of the\ntruth. Using 14 real human responses on a set of realistic mock galaxy\nsimulation snapshots our model is able to recover the pre-coalesced merger\nfraction to within 10\\%. Our method can not only increase the accuracy of\nstudies probing the merger state of galaxies at cosmic noon, but also can be\nused to construct more accurate training sets in machine learning studies that\nuse human classified data-sets.\n"}
+{"abstract": "  We present predictions for the gluon-fusion Higgs $p_T$ spectrum at third\nresummed and fixed order (N$^3$LL$'+$N$^3$LO) including fiducial cuts as\nrequired by experimental measurements at the Large Hadron Collider. Integrating\nthe spectrum, we predict for the first time the total fiducial cross section to\nthird order (N$^3$LO) and improved by resummation. The N$^3$LO correction is\nenhanced by cut-induced logarithmic effects and is not reproduced by the\ninclusive N$^3$LO correction times a lower-order acceptance. These are the\nhighest-order predictions of their kind achieved so far at a hadron collider.\n"}
+{"abstract": "  We introduce a random differential operator, that we call the\n$\\mathtt{CS}_\\tau$ operator, whose spectrum is given by the $\\mbox{Sch}_\\tau$\npoint process introduced by Kritchevski, Valk\\'o and Vir\\'ag (2012) and whose\neigenvectors match with the description provided by Rifkind and Vir\\'ag (2018).\nThis operator acts on $\\mathbf{R}^2$-valued functions from the interval $[0,1]$\nand takes the form: $$ 2 \\begin{pmatrix} 0 & -\\partial_t \\\\ \\partial_t & 0\n\\end{pmatrix} + \\sqrt{\\tau} \\begin{pmatrix} d\\mathcal{B} + \\frac1{\\sqrt 2}\nd\\mathcal{W}_1 & \\frac1{\\sqrt 2} d\\mathcal{W}_2\\\\ \\frac1{\\sqrt 2}\nd\\mathcal{W}_2 & d\\mathcal{B} - \\frac1{\\sqrt 2} d\\mathcal{W}_1\\end{pmatrix}\\,,\n$$ where $d\\mathcal{B}$, $d\\mathcal{W}_1$ and $d\\mathcal{W}_2$ are independent\nwhite noises. Then, we investigate the high part of the spectrum of the\nAnderson Hamiltonian $\\mathcal{H}_L := -\\partial_t^2 + dB$ on the segment\n$[0,L]$ with white noise potential $dB$, when $L\\to\\infty$. We show that the\noperator $\\mathcal{H}_L$, recentred around energy levels $E \\sim L/\\tau$ and\nunitarily transformed, converges in law as $L\\to\\infty$ to $\\mathtt{CS}_\\tau$\nin an appropriate sense. This allows to answer a conjecture of Rifkind and\nVir\\'ag (2018) on the behavior of the eigenvectors of $\\mathcal{H}_L$. Our\napproach also explains how such an operator arises in the limit of\n$\\mathcal{H}_L$. Finally we show that at higher energy levels, the Anderson\nHamiltonian matches (asymptotically in $L$) with the unperturbed Laplacian\n$-\\partial_t^2$. In a companion paper, it is shown that at energy levels much\nsmaller than $L$, the spectrum is localized with Poisson statistics: the\npresent paper therefore identifies the delocalized phase of the Anderson\nHamiltonian.\n"}
+{"abstract": "  We study the problem of controlling the free surface, by fluid jets on the\nboundary, for a two dimensional solid container in the context of the gravity\nwaves and the sloshing problem. By using conformal maps and the\nDirichlet--Neumann operator, the problem is formulated as a second order\nevolutionary equation on the free surface involving a self-adjoint operator. We\npresent then the appropriate Sobolev spaces where having solutions for the\nsystem and study the exact controllability through an observability inequality\nfor the adjoint problem.\n"}
+{"abstract": "  While state-of-the-art NLP models have been achieving the excellent\nperformance of a wide range of tasks in recent years, important questions are\nbeing raised about their robustness and their underlying sensitivity to\nsystematic biases that may exist in their training and test data. Such issues\ncome to be manifest in performance problems when faced with out-of-distribution\ndata in the field. One recent solution has been to use counterfactually\naugmented datasets in order to reduce any reliance on spurious patterns that\nmay exist in the original data. Producing high-quality augmented data can be\ncostly and time-consuming as it usually needs to involve human feedback and\ncrowdsourcing efforts. In this work, we propose an alternative by describing\nand evaluating an approach to automatically generating counterfactual data for\ndata augmentation and explanation. A comprehensive evaluation on several\ndifferent datasets and using a variety of state-of-the-art benchmarks\ndemonstrate how our approach can achieve significant improvements in model\nperformance when compared to models training on the original data and even when\ncompared to models trained with the benefit of human-generated augmented data.\n"}
+{"abstract": "  The family of graphynes, novel two-dimensional semiconductors with various\nand fascinating chemical and physical properties, has attracted great interest\nfrom both science and industry. Currently, the focus of graphynes is on\ngraphdiyne, or graphyne-2. In this work, we systematically study the effect of\nacetylene, i.e., carbon-carbon triple bond, links on the electronic and optical\nproperties of a series of graphynes (graphyne-n, where n = 1-5, the number of\nacetylene bonds) using the ab initio calculations. We find an even-odd pattern,\ni.e., n = 1, 3, 5 and n = 2, 4 having different features, which has not be\ndiscovered in studying graphyne or graphdyine only. It is found that as the\nnumber of acetylene bonds increases, the electron effective mass increases\ncontinuously in the low energy range because of the flatter conduction band\ninduced by the longer acetylene links. Meanwhile, longer acetylene links result\nin larger redshift of the imaginary part of the dielectric function, loss\nfunction, and extinction coefficient. In this work, we propose an effective\nmethod to tune and manipulate both the electronic and optical properties of\ngraphynes for the applications in optoelectronic devices and photo-chemical\ncatalysis.\n"}
+{"abstract": "  Most deep learning models are data-driven and the excellent performance is\nhighly dependent on the abundant and diverse datasets. However, it is very hard\nto obtain and label the datasets of some specific scenes or applications. If we\ntrain the detector using the data from one domain, it cannot perform well on\nthe data from another domain due to domain shift, which is one of the big\nchallenges of most object detection models. To address this issue, some\nimage-to-image translation techniques have been employed to generate some fake\ndata of some specific scenes to train the models. With the advent of Generative\nAdversarial Networks (GANs), we could realize unsupervised image-to-image\ntranslation in both directions from a source to a target domain and from the\ntarget to the source domain. In this study, we report a new approach to making\nuse of the generated images. We propose to concatenate the original 3-channel\nimages and their corresponding GAN-generated fake images to form 6-channel\nrepresentations of the dataset, hoping to address the domain shift problem\nwhile exploiting the success of available detection models. The idea of\naugmented data representation may inspire further study on object detection and\nother applications.\n"}
+{"abstract": "  Probing optical excitations with high resolution is important for\nunderstanding their dynamics and controlling their interaction with other\nphotonic elements. This can be done using state-of-the-art electron\nmicroscopes, which provide the means to sample optical excitations with\ncombined meV--sub-nm energy--space resolution. For reciprocal photonic systems,\nelectrons traveling in opposite directions produce identical signals, while\nthis symmetry is broken in nonreciprocal structures. Here, we theoretically\ninvestigate this phenomenon by analyzing electron energy-loss spectroscopy\n(EELS) and cathodoluminescence (CL) in structures consisting of magnetically\nbiased InAs as an instance of gyrotropic nonreciprocal material. We find that\nthe spectral features associated with excitations of InAs films depend on the\nelectron propagation direction in both EELS and CL, and can be tuned by varying\nthe applied magnetic field within a relatively modest sub-tesla regime. The\nmagnetic field modifies the optical field distribution of the sampled\nresonances, and this in turn produces a direction-dependent coupling to the\nelectron. The present results pave the way to the use of electron microscope\nspectroscopies to explore the near-field characteristics of nonreciprocal\nsystems with high spatial resolution.\n"}
+{"abstract": "  In this work, we consider the target detection problem in a sensing\narchitecture where the radar is aided by a reconfigurable intelligent surface\n(RIS), that can be modeled as an array of sub-wavelength small reflective\nelements capable of imposing a tunable phase shift to the impinging waves and,\nultimately, of providing the radar with an additional echo of the target. A\ntheoretical analysis is carried out for closely- and widely-spaced (with\nrespect to the target) radar and RIS and for different beampattern\nconfigurations, and some examples are provided to show that large gains can be\nachieved by the considered detection architecture.\n"}
+{"abstract": "  In this note we study analytically and numerically the existence and\nstability of standing waves for one dimensional nonlinear Schr\\\"odinger\nequations whose nonlinearities are the sum of three powers. Special attention\nis paid to the curves of non-existence and curves of stability change on the\nparameter planes.\n"}
+{"abstract": "  Quantum reservoir computing (QRC) and quantum extreme learning machines\n(QELM) are two emerging approaches that have demonstrated their potential both\nin classical and quantum machine learning tasks. They exploit the quantumness\nof physical systems combined with an easy training strategy, achieving an\nexcellent performance. The increasing interest in these unconventional\ncomputing approaches is fueled by the availability of diverse quantum platforms\nsuitable for implementation and the theoretical progresses in the study of\ncomplex quantum systems. In this review article, recent proposals and first\nexperiments displaying a broad range of possibilities are reviewed when quantum\ninputs, quantum physical substrates and quantum tasks are considered. The main\nfocus is the performance of these approaches, on the advantages with respect to\nclassical counterparts and opportunities.\n"}
+{"abstract": "  We consider the problem of scheduling maintenance for a collection of\nmachines under partial observations when the state of each machine deteriorates\nstochastically in a Markovian manner. We consider two observational models:\nfirst, the state of each machine is not observable at all, and second, the\nstate of each machine is observable only if a service-person visits them. The\nagent takes a maintenance action, e.g., machine replacement, if he is chosen\nfor the task. We model both problems as restless multi-armed bandit problem and\npropose the Whittle index policy for scheduling the visits. We show that both\nmodels are indexable. For the first model, we derive a closed-form expression\nfor the Whittle index. For the second model, we propose an efficient algorithm\nto compute the Whittle index by exploiting the qualitative properties of the\noptimal policy. We present detailed numerical experiments which show that for\nmultiple instances of the model, the Whittle index policy outperforms myopic\npolicy and can be close-to-optimal in different setups.\n"}
+{"abstract": "  By using a novel technique that establishes a correspondence between general\nrelativity and metric-affine theories based on the Ricci tensor, we are able to\nset stringent constraints on the free parameter of Born-Infeld gravity from the\nones recently obtained for Born-Infeld electrodynamics by using light-by-light\nscattering data from ATLAS. We also discuss how these gravity theories plus\nmatter fit within an effective field theory framework.\n"}
+{"abstract": "  We apply a quantum teleportation protocol based on the Hayden-Preskill\nthought experiment to quantify how scrambling a given quantum evolution is. It\nhas an advantage over the direct measurement of out-of-time ordered correlators\nwhen used to diagnose the information scrambling in the presence of decoherence\neffects stemming from a noisy quantum device. We demonstrate the protocol by\napplying it to two physical systems: Ising spin chain and SU(2) lattice\nYang-Mills theory. To this end, we numerically simulate the time evolution of\nthe two theories in the Hamiltonian formalism. The lattice Yang-Mills theory is\nimplemented with a suitable truncation of Hilbert space on the basis of the\nKogut-Susskind formalism. On a two-leg ladder geometry and with the lowest\nnontrivial spin representations, it can be mapped to a spin chain, which we\ncall it Yang-Mills-Ising model and is also directly applicable to future\ndigital quantum simulations. We find that the Yang-Mills-Ising model shows the\nsignal of information scrambling at late times.\n"}
+{"abstract": "  Zernike polynomials are one of the most widely used mathematical descriptors\nof optical aberrations in the fields of imaging and adaptive optics. Their\nmathematical orthogonality as well as isomorphisms with experimentally\nobservable aberrations make them a very powerful tool in solving numerous\nproblems in beam optics. However, Zernike aberrations show cross-coupling\nbetween individual modes when used in combination with Gaussian beams, an\neffect that has not been extensively studied. Here we propose a novel framework\nthat is capable of explaining the fundamental cross-compensation of Zernike\ntype aberrations, both in low-aberration and high-aberration regimes. Our\napproach is based on analysing the coupling between Zernike modes and different\nclasses of Laguerre-Gauss modes which allows investigating aberrated beams not\nonly on a single plane but also during their 3D propagation.\n"}
+{"abstract": "  Earth's modern atmosphere is highly oxygenated and is a remotely detectable\nsignal of its surface biosphere. However, the lifespan of oxygen-based\nbiosignatures in Earth's atmosphere remains uncertain, particularly for the\ndistant future. Here we use a combined biogeochemistry and climate model to\nexamine the likely timescale of oxygen-rich atmospheric conditions on Earth.\nUsing a stochastic approach, we find that the mean future lifespan of Earth's\natmosphere with oxygen levels more than 1% of the present atmospheric level is\n1.08+-0.14 billion years. The model projects that a deoxygenation of the\natmosphere, with atmospheric oxygen dropping sharply to levels reminiscnet of\nthe Archaean Earth, will most probably be triggered before the inception of\nmoist greenhouse conditions in Earth's climate system and before the extensive\nloss of surface water from the atmosphere. We find that future deoxygenation is\nan inevitable consequence of increasing solar fluxes, whereas its precise\ntiming is modulated by the exchange flux of reducing power between the mantle\nand the ocean-atmosphere-crust system. Our results suggest that the planetary\ncarbonate-silicate cycle will tend to lead to terminally CO2-limited biospheres\nand rapid atmospheric deoxygenation, emphasizing the need for robust\natmospheric biosignatures applicable to weakly oxygenated and anoxic exoplanet\natmospheres and highlighting the potential importance of atmospheric organic\nhaze during the terminal stages of planetary habitability.\n"}
+{"abstract": "  We prove two theorems about the Malcev Lie algebra associated to the Torelli\ngroup of a surface of genus $g$: stably, it is Koszul and the kernel of the\nJohnson homomorphism consists only of trivial $Sp_{2g}(Z)$-representations\nlying in the centre.\n"}
+{"abstract": "  A wide variety of use case templates supports different variants to link a\nuse case with its associated requirements. Regardless of the linking, a reader\nmust process the related information simultaneously to understand them. Linking\nvariants are intended to cause a specific reading behavior in which a reader\ninterrelates a use case and its associated requirements. Due to the effort to\ncreate and maintain links, we investigated the impact of different linking\nvariants on the reading behavior in terms of visual effort and the intended way\nof interrelating both artifacts. We designed an eye tracking study about\nreading a use case and requirements. We conducted the study twice each with 15\nsubjects as a baseline experiment and as a repetition. The results of the\nbaseline experiment, its repetition, and their joint analysis are consistent.\nAll investigated linking variants cause comparable visual effort. In all cases,\nreading the single artifacts one after the other is the most frequently\noccurring behavior. Only links embedded in the fields of a use case description\nsignificantly increase the readers' efforts to interrelate both artifacts. None\nof the investigated linking variants impedes reading a use case and\nrequirements. However, only the most detailed linking variant causes readers to\nprocess related information simultaneously.\n"}
+{"abstract": "  Nowadays new technologies, and especially artificial intelligence, are more\nand more established in our society. Big data analysis and machine learning,\ntwo sub-fields of artificial intelligence, are at the core of many recent\nbreakthroughs in many application fields (e.g., medicine, communication,\nfinance, ...), including some that are strongly related to our day-to-day life\n(e.g., social networks, computers, smartphones, ...). In machine learning,\nsignificant improvements are usually achieved at the price of an increasing\ncomputational complexity and thanks to bigger datasets. Currently, cutting-edge\nmodels built by the most advanced machine learning algorithms typically became\nsimultaneously very efficient and profitable but also extremely complex. Their\ncomplexity is to such an extent that these models are commonly seen as\nblack-boxes providing a prediction or a decision which can not be interpreted\nor justified. Nevertheless, whether these models are used autonomously or as a\nsimple decision-making support tool, they are already being used in machine\nlearning applications where health and human life are at stake. Therefore, it\nappears to be an obvious necessity not to blindly believe everything coming out\nof those models without a detailed understanding of their predictions or\ndecisions. Accordingly, this thesis aims at improving the interpretability of\nmodels built by a specific family of machine learning algorithms, the so-called\ntree-based methods. Several mechanisms have been proposed to interpret these\nmodels and we aim along this thesis to improve their understanding, study their\nproperties, and define their limitations.\n"}
+{"abstract": "  Upon investigating whether the variation of the antineutron-nucleus\nannihilation cross-sections at very low energies satisfy Bethe-Landau's power\nlaw of $\\sigma_{\\rm ann} (p) \\propto 1/p^{\\alpha}$ behavior as a function of\nthe antineutron momentum $p$, we uncover unexpected regular oscillatory\nstructures in the low antineutron energy region from 0.001 to 10 MeV, with\nsmall amplitudes and narrow periodicity in the logarithm of the antineutron\nenergies, for large-$A$ nuclei such as Pb and Ag. Subsequent semiclassical\nanalyses of the $S$ matrices reveal that these oscillations are pocket\nresonances that arise from quasi-bound states inside the pocket and the\ninterference between the waves reflecting inside the optical potential pockets\nwith those from beyond the potential barriers, implicit in the nuclear Ramsauer\neffect. They are the continuation of bound states in the continuum.\nExperimental observations of these pocket resonances will provide vital\ninformation on the properties of the optical model potentials and the nature of\nthe antineutron annihilation process.\n"}
+{"abstract": "  We revisit Allendoerfer-Weil's formula for the Euler characteristic of\nembedded hypersurfaces in constant sectional curvature manifolds, first taking\nsome time to re-prove it while demonstrating techniques of [2] and then\napplying it to gain new understanding of isoparametric hypersurfaces.\n"}
+{"abstract": "  Neural data compression has been shown to outperform classical methods in\nterms of $RD$ performance, with results still improving rapidly. At a high\nlevel, neural compression is based on an autoencoder that tries to reconstruct\nthe input instance from a (quantized) latent representation, coupled with a\nprior that is used to losslessly compress these latents. Due to limitations on\nmodel capacity and imperfect optimization and generalization, such models will\nsuboptimally compress test data in general. However, one of the great strengths\nof learned compression is that if the test-time data distribution is known and\nrelatively low-entropy (e.g. a camera watching a static scene, a dash cam in an\nautonomous car, etc.), the model can easily be finetuned or adapted to this\ndistribution, leading to improved $RD$ performance. In this paper we take this\nconcept to the extreme, adapting the full model to a single video, and sending\nmodel updates (quantized and compressed using a parameter-space prior) along\nwith the latent representation. Unlike previous work, we finetune not only the\nencoder/latents but the entire model, and - during finetuning - take into\naccount both the effect of model quantization and the additional costs incurred\nby sending the model updates. We evaluate an image compression model on\nI-frames (sampled at 2 fps) from videos of the Xiph dataset, and demonstrate\nthat full-model adaptation improves $RD$ performance by ~1 dB, with respect to\nencoder-only finetuning.\n"}
+{"abstract": "  We give the characterization of the embeddings between weighted Tandori and\nCes\\`{a}ro function spaces using the combination of duality arguments for\nweighted Lebesgue spaces and weighted Tandori spaces with estimates for the\niterated integral operators.\n"}
+{"abstract": "  In the dynamical models of gamma-ray burst (GRB) afterglows, the uniform\nassumption of the shocked region is known as provoking total energy\nconservation problem. In this work we consider shocks originating from\nmagnetized ejecta, extend the energy-conserving hydrodynamical model of Yan et\nal. (2007) to the MHD limit by applying the magnetized jump conditions from\nZhang & Kobayashi (2005). Compared with the non-conservative models, our\nLorentz factor of the whole shocked region is larger by a factor\n$\\lesssim\\sqrt{2}$. The total pressure of the forward shocked region is higher\nthan the reversed shocked region, in the relativistic regime with a factor of\nabout 3 in our interstellar medium (ISM) cases while ejecta magnetization\ndegree $\\sigma<1$, and a factor of about 2.4 in the wind cases. For $\\sigma\\le\n1$, the non-conservative model loses $32-42$% of its total energy for ISM\ncases, and for wind cases $25-38$%, which happens specifically in the forward\nshocked region, making the shock synchrotron emission from the forward shock\nless luminous than expected. Once the energy conservation problem is fixed, the\nlate time light curves from the forward shock become nearly independent of the\nejecta magnetization. The reverse shocked region doesn't suffer from the energy\nconservation problem since the changes of the Lorentz factor are recompensed by\nthe changes of the shocked particle number density. The early light curves from\nthe reverse shock are sensitive to the magnetization of the ejecta, thus are an\nimportant probe of the magnetization degree.\n"}
+{"abstract": "  We associate a deformation of Heisenberg algebra to the suitably normalized\nYang $R$-matrix and we investigate its properties. Moreover, we construct new\nexamples of quantum vertex algebras which possess the same representation\ntheory as the aforementioned deformed Heisenberg algebra.\n"}
+{"abstract": "  Recently discovered intrinsic antiferromagnetic topological insulator\nMnBi$_2$Te$_4$ presents an exciting platform for realization of the quantum\nanomalous Hall effect and a number of related phenomena at elevated\ntemperatures. An important characteristic making this material attractive for\napplications is its predicted large magnetic gap at the Dirac point (DP).\nHowever, while the early experimental measurements reported on large DP gaps, a\nnumber of recent studies claimed to observe a gapless dispersion of the\nMnBi$_2$Te$_4$ Dirac cone. Here, using micro($\\mu$)-laser angle-resolved\nphotoemission spectroscopy, we study the electronic structure of 15 different\nMnBi$_2$Te$_4$ samples, grown by two different chemists groups. Based on the\ncareful energy distribution curves analysis, the DP gaps between 15 and 65 meV\nare observed, as measured below the N\\'eel temperature at about 10-16 K. At\nthat, roughly half of the studied samples show the DP gap of about 30 meV,\nwhile for a quarter of the samples the gaps are in the 50 to 60 meV range.\nSummarizing the results of both our and other groups, in the currently\navailable MnBi$_2$Te$_4$ samples the DP gap can acquire an arbitrary value\nbetween a few and several tens of meV. Further, based on the density functional\ntheory, we discuss a possible factor that might contribute to the reduction of\nthe DP gap size, which is the excess surface charge that can appear due to\nvarious defects in surface region. We demonstrate that the DP gap is influenced\nby the applied surface charge and even can be closed, which can be taken\nadvantage of to tune the MnBi$_2$Te$_4$ DP gap size.\n"}
+{"abstract": "  Society is changing, has always changed, and will keep changing. However,\nchanges are becoming faster and what used to happen between generations, now\nhappens in the same generation. Computing Science is one of the reasons for\nthis speed and permeates, basically, every other knowledge area. This paper\n(written in Portugu\\^es) describes, briefly, the worldwide initiatives to\nintroduce Computing Science teaching in schools. As the paper's main\nconclusion, it is essential to introduce Computing Science and Computational\nThinking for kids before they enter into a university.\n"}
+{"abstract": "  Innovations in neural architectures have fostered significant breakthroughs\nin language modeling and computer vision. Unfortunately, novel architectures\noften result in challenging hyper-parameter choices and training instability if\nthe network parameters are not properly initialized. A number of\narchitecture-specific initialization schemes have been proposed, but these\nschemes are not always portable to new architectures. This paper presents\nGradInit, an automated and architecture agnostic method for initializing neural\nnetworks. GradInit is based on a simple heuristic; the norm of each network\nlayer is adjusted so that a single step of SGD or Adam with prescribed\nhyperparameters results in the smallest possible loss value. This adjustment is\ndone by introducing a scalar multiplier variable in front of each parameter\nblock, and then optimizing these variables using a simple numerical scheme.\nGradInit accelerates the convergence and test performance of many convolutional\narchitectures, both with or without skip connections, and even without\nnormalization layers. It also improves the stability of the original\nTransformer architecture for machine translation, enabling training it without\nlearning rate warmup using either Adam or SGD under a wide range of learning\nrates and momentum coefficients. Code is available at\nhttps://github.com/zhuchen03/gradinit.\n"}
+{"abstract": "  High-level understanding of stories in video such as movies and TV shows from\nraw data is extremely challenging. Modern video question answering (VideoQA)\nsystems often use additional human-made sources like plot synopses, scripts,\nvideo descriptions or knowledge bases. In this work, we present a new approach\nto understand the whole story without such external sources. The secret lies in\nthe dialog: unlike any prior work, we treat dialog as a noisy source to be\nconverted into text description via dialog summarization, much like recent\nmethods treat video. The input of each modality is encoded by transformers\nindependently, and a simple fusion method combines all modalities, using soft\ntemporal attention for localization over long inputs. Our model outperforms the\nstate of the art on the KnowIT VQA dataset by a large margin, without using\nquestion-specific human annotation or human-made plot summaries. It even\noutperforms human evaluators who have never watched any whole episode before.\nCode is available at https://engindeniz.github.io/dialogsummary-videoqa\n"}
+{"abstract": "  The effect of radiative heat transfer on the entropy generation in a\ntwo-phase non-isothermal fluid flow between two infinite horizontal parallel\nplates under the influence of a constant pressure gradient and transverse\nnon-invasive magnetic field have been explored. Both the fluids are considered\nto be viscous, incompressible, immiscible, Newtonian, and electrically\nconducting. The governing equations in Cartesian coordinate are solved\nanalytically with the help of appropriate boundary conditions to obtain the\nvelocity and temperature profile inside the channel. Application of transverse\nmagnetic field is found to reduce the throughput and the temperature\ndistribution of the fluids in a pressure-driven flow. The temperature and fluid\nflow inside the channel can also be non-invasively altered by tuning the\nmagnetic field intensity, the temperature difference between the channel walls\nand the fluids, and several intrinsic fluid properties. The entropy generation\ndue to the heat transfer, magnetic field, and fluid flow irreversibilities can\nbe controlled by altering the Hartmann number, radiation parameter, Brinkmann\nnumber, filling ratio, and the ratios of fluid viscosities, thermal and\nelectrical conductivities. The surfaces of the channel wall are found to act as\na strong source of entropy generation and heat transfer irreversibility. The\nrate of heat transfer at the channel walls can also be tweaked by the magnetic\nfield intensity, temperature differences, and fluid properties. The proposed\nstrategies in the present study can be of significance in the design and\ndevelopment of gen-next microscale reactors, micro heat exchangers, and energy\nharvesting devices.\n"}
+{"abstract": "  For any regular Courant algebroid $E$ over a smooth manifold $M$ with\ncharacteristic distribution $F$ and ample Lie algebroid $A_E$, we prove that\nthere exists a canonical homological vector field on the graded manifold\n$A_E[1] \\oplus (TM/F)^\\ast[2]$ such that the associated dg manifold\n$\\mathcal{M}_E$, which we call the minimal model of the Courant algebroid $E$,\nencodes all cohomological data of $E$. Thereby, the standard cohomology\n$H^\\bullet_{\\operatorname{st}}(E)$ of $E$ can be identified with the cohomology\n$H^\\bullet(\\mathcal{M}_E)$ of the function space on $\\mathcal{M}_E$. To compute\nit, we find a natural transgression map $[d_T] \\colon\nH^{\\bullet}_{\\operatorname{CE}}\\big(A_E; S^{\\diamond}(TM/F[-2])\\big) \\to\nH^{\\bullet+3}_{\\CE}\\big(A_E; S^{\\diamond-1}(TM/F[-2])\\big)$ from which we\nconstruct a spectral sequence which converges to\n$H^\\bullet_{\\operatorname{st}}(E)$. Moreover, we give applications to\ngeneralized exact Courant algebroids and those arising from regular Lie\nalgebroids .\n"}
+{"abstract": "  For optimal power flow problems with chance constraints, a particularly\neffective method is based on a fixed point iteration applied to a sequence of\ndeterministic power flow problems. However, a priori, the convergence of such\nan approach is not necessarily guaranteed. This article analyses the\nconvergence conditions for this fixed point approach, and reports numerical\nexperiments including for large IEEE networks.\n"}
+{"abstract": "  We consider QCD radiative corrections to the associated production of a\nheavy-quark pair ($Q{\\bar Q}$) with a generic colourless system $F$ at hadron\ncolliders. We discuss the resummation formalism for the production of the\n$Q{\\bar Q}F$ system at small values of its total transverse momentum $q_T$. The\nperturbative expansion of the resummation formula leads to the explicit\ningredients that can be used to apply the $q_T$ subtraction formalism to\nfixed-order calculations for this class of processes. We use the $q_T$\nsubtraction formalism to perform a fully differential perturbative computation\nfor the production of a top-antitop quark pair and a Higgs boson. At\nnext-to-leading order we compare our results with those obtained with\nestablished subtraction methods and we find complete agreement. We present, for\nthe first time, the results for the flavour off-diagonal partonic channels at\nthe next-to-next-to-leading order.\n"}
+{"abstract": "  A strong edge coloring of a graph $G$ is a proper edge coloring of $G$ such\nthat every color class is an induced matching. The minimum number of colors\nrequired is termed the strong chromatic index. In this paper, we determine the\nexact value of the strong chromatic index of all unitary Cayley graphs. Our\ninvestigations reveal an underlying product structure from which the unitary\nCayley graphs emerge. We then go on to give tight bounds for the strong\nchromatic index of the Cartesian product of two trees, including an exact\nformula for the product in the case of stars. Further, we give bounds for the\nstrong chromatic index of the product of a tree with a cycle. For any tree,\nthose bounds may differ from the actual value only by not more than a small\nadditive constant (at most 2 for even cycles and at most 5 for odd cycles),\nmoreover they yield the exact value when the length of the cycle is divisible\nby $4$.\n"}
+{"abstract": "  The complex matrix representation for a quaternion matrix is used in this\npaper to find necessary and sufficient conditions for the existence of an\n$H$-selfadjoint $m$th root of a given $H$-selfadjoint quaternion matrix. In the\nprocess, when such an $H$-selfadjoint $m$th root exists, its construction is\nalso given.\n"}
+{"abstract": "  Decentralized network theories focus on achieving consensus and in speeding\nup the rate of convergence to consensus. However, network cohesion (i.e.,\nmaintaining consensus) during transitions between consensus values is also\nimportant when transporting flexible structures. Deviations in the robot\npositions due to loss of cohesion when moving flexible structures from one\nposition to another, such as uncuredcomposite aircraft wings, can cause large\ndeformations, which in turn, can result in potential damage. The major\ncontribution of this work is to develop a decentralized approach to transport\nflexible objects in a cohesive manner using local force measurements, without\nthe need for additional communication between the robots. Additionally,\nstability conditions are developed for discrete-time implementation of the\nproposed cohesive transition approach, and experimental results are presented,\nwhich show that the proposed cohesive transportation approach can reduce the\nrelative deformations by 85% when compared to the case without it.\n"}
+{"abstract": "  In this paper, we present a new mathematical model for pandemics called\nSUTRA. The acronym stands for Susceptible, Undetected, Tested (positive), and\nRemoved Approach. A novel feature of our model is that it allows estimation of\nparameters from reported infection data, unlike most other models that estimate\nparameter values from other considerations. This gives the model the ability to\npredict the future trajectory well, as long as parameters do not change. In\naddition, it is possible to quantify how the model parameter values were\naffected by various interventions to control the pandemic, and/or the arrival\nof new mutants. We have applied our model to analyze and predict the\nprogression of the COVID-19 pandemic in several countries. We present our\npredictions for two countries: India and US. In both cases, the model-computed\ntrajectory closely matches actual one. Moreover, our predictions were used by\nentities such as the Reserve Bank of India to formulate policy.\n"}
+{"abstract": "  The classical Jordan curve theorem for digital curves asserts that the Jordan\ncurve theorem remains valid in the Khalimsky plane. Since the Khalimsky plane\nis a quotient space of $\\mathbb R^2$ induced by a tiling of squares, it is\nnatural to ask for which other tilings of the plane it is possible to obtain a\nsimilar result. In this paper we prove a Jordan curve theorem which is valid\nfor every locally finite tiling of $\\mathbb R^2$. As a corollary of our result,\nwe generalize some classical Jordan curve theorems for grids of points,\nincluding Rosenfeld's theorem.\n"}
+{"abstract": "  The use of statistical methods in sport analytics has gained a rapidly\ngrowing interest over the last decade, and nowadays is common practice. In\nparticular, the interest in understanding and predicting an athlete's\nperformance throughout his/her career is motivated by the need to evaluate the\nefficacy of training programs, anticipate fatigue to prevent injuries and\ndetect unexpected of disproportionate increases in performance that might be\nindicative of doping. Moreover, fast evolving data gathering technologies\nrequire up to date modelling techniques that adapt to the distinctive features\nof sports data. In this work, we propose a hierarchical Bayesian model for\ndescribing and predicting the evolution of performance over time for shot put\nathletes. To account for seasonality and heterogeneity in recorded results, we\nrely both on a smooth functional contribution and on a linear mixed effect\nmodel with heteroskedastic errors to represent the athlete-specific\ntrajectories. The resulting model provides an accurate description of the\nperformance trajectories and helps specifying both the intra- and\ninter-seasonal variability of measurements. Further, the model allows for the\nprediction of athletes' performance in future seasons. We apply our model to an\nextensive real world data set on performance data of professional shot put\nathletes recorded at elite competitions.\n"}
+{"abstract": "  We analyze dispersion relations of magnons in ferromagnetic nanostructures\nwith uniaxial anisotropy taking into account inertial terms, i.e. magnetic\nnutation. Inertial effects are parametrized by damping-independent parameter\n$\\beta$, which allows for an unambiguous discrimination of inertial effects\nfrom Gilbert damping parameter $\\alpha$. The analysis of magnon dispersion\nrelation shows its two branches are modified by the inertial effect, albeit in\ndifferent ways. The upper nutation branch starts at $\\omega=1/ \\beta$, the\nlower branch coincides with FMR in the long-wavelength limit and deviates from\nthe zero-inertia parabolic dependence $\\simeq\\omega_{FMR}+Dk^2$ of the exchange\nmagnon. Taking a realistic experimental geometry of magnetic thin films,\nnanowires and nanodiscs, magnon eigenfrequencies, eigenvectors and $Q$-factors\nare found to depend on the shape anisotropy. The possibility of phase-matched\nmagneto-elastic excitation of nutation magnons is discussed and the condition\nwas found to depend on $\\beta$, exchange stiffness $D$ and the acoustic\nvelocity.\n"}
+{"abstract": "  In this paper, we present a neat yet effective transformer-based framework\nfor visual grounding, namely TransVG, to address the task of grounding a\nlanguage query to the corresponding region onto an image. The state-of-the-art\nmethods, including two-stage or one-stage ones, rely on a complex module with\nmanually-designed mechanisms to perform the query reasoning and multi-modal\nfusion. However, the involvement of certain mechanisms in fusion module design,\nsuch as query decomposition and image scene graph, makes the models easily\noverfit to datasets with specific scenarios, and limits the plenitudinous\ninteraction between the visual-linguistic context. To avoid this caveat, we\npropose to establish the multi-modal correspondence by leveraging transformers,\nand empirically show that the complex fusion modules (\\eg, modular attention\nnetwork, dynamic graph, and multi-modal tree) can be replaced by a simple stack\nof transformer encoder layers with higher performance. Moreover, we\nre-formulate the visual grounding as a direct coordinates regression problem\nand avoid making predictions out of a set of candidates (\\emph{i.e.}, region\nproposals or anchor boxes). Extensive experiments are conducted on five widely\nused datasets, and a series of state-of-the-art records are set by our TransVG.\nWe build the benchmark of transformer-based visual grounding framework and make\nthe code available at \\url{https://github.com/djiajunustc/TransVG}.\n"}
+{"abstract": "  Modular transformations of string theory are shown to play a crucial role in\nthe discussion of discrete flavor symmetries in the Standard Model. They\ninclude CP transformations and provide a unification of CP with traditional\nflavor symmetries within the framework of the \"eclectic flavor\" scheme. The\nunified flavor group is non-universal in moduli space and exhibits the\nphenomenon of \"Local Flavor Unification\", where different sectors of the theory\n(like quarks and leptons) can be subject to different flavor structures.\n"}
+{"abstract": "  The cost of using a blockchain infrastructure as well as the time required to\nsearch and retrieve information from it must be considered when designing a\ndecentralized application. In this work, we examine a comprehensive set of data\nmanagement approaches for Ethereum applications and assess the associated cost\nin gas as well as the retrieval performance. More precisely, we analyze the\nstorage and retrieval of various-sized data, utilizing smart contract storage.\nIn addition, we study hybrid approaches by using IPFS and Swarm as storage\nplatforms along with Ethereum as a timestamping proof mechanism. Such schemes\nare especially effective when large chunks of data have to be managed.\nMoreover, we present methods for low-cost data handling in Ethereum, namely the\nevent-logs, the transaction payload, and the almost surprising exploitation of\nunused function arguments. Finally, we evaluate these methods on a\ncomprehensive set of experiments.\n"}
+{"abstract": "  Air pollutants, such as particulate matter, negatively impact human health.\nMost existing pollution monitoring techniques use stationary sensors, which are\ntypically sparsely deployed. However, real-world pollution distributions vary\nrapidly with position and the visual effects of air pollution can be used to\nestimate concentration, potentially at high spatial resolution. Accurate\npollution monitoring requires either densely deployed conventional point\nsensors, at-a-distance vision-based pollution monitoring, or a combination of\nboth.\n  The main contribution of this paper is that to the best of our knowledge, it\nis the first publicly available, high temporal and spatial resolution air\nquality dataset containing simultaneous point sensor measurements and\ncorresponding images. The dataset enables, for the first time, high spatial\nresolution evaluation of image-based air pollution estimation algorithms. It\ncontains PM2.5, PM10, temperature, and humidity data. We evaluate several\nstate-of-art vision-based PM concentration estimation algorithms on our dataset\nand quantify the increase in accuracy resulting from higher point sensor\ndensity and the use of images. It is our intent and belief that this dataset\ncan enable advances by other research teams working on air quality estimation.\nOur dataset is available at\nhttps://github.com/implicitDeclaration/HVAQ-dataset/tree/master.\n"}
+{"abstract": "  In this paper, important concepts from finite group theory are translated to\nlocalities, in particular to linking localities. Here localities are group-like\nstructures associated to fusion systems which were introduced by Chermak.\nLinking localities (by Chermak also called proper localities) are special kinds\nof localities which correspond to linking systems. Thus they contain the\nalgebraic information that is needed to study $p$-completed classifying spaces\nof fusion systems as generalizations of $p$-completed classifying spaces of\nfinite groups.\n  Because of the group-like nature of localities, there is a natural notion of\npartial normal subgroups. Given a locality $\\mathcal{L}$ and a partial normal\nsubgroup $\\mathcal{N}$ of $\\mathcal{L}$, we show that there is a largest\npartial normal subgroup $\\mathcal{N}^\\perp$ of $\\mathcal{L}$ which, in a\ncertain sense, commutes elementwise with $\\mathcal{N}$ and thus morally plays\nthe role of a \"centralizer\" of $\\mathcal{N}$ in $\\mathcal{L}$. This leads to a\nnice notion of the generalized Fitting subgroup $F^*(\\mathcal{L})$ of a linking\nlocality $\\mathcal{L}$. Building on these results we define and study special\nkinds of linking localities called regular localities. It turns out that there\nis a theory of components of regular localities akin to the theory of\ncomponents of finite groups. The main concepts we introduce and work with in\nthe present paper (in particular $\\mathcal{N}^\\perp$ in the special case of\nlinking localities, $F^*(\\mathcal{L})$, regular localities and components of\nregular localities) were already introduced and studied in a preprint by\nChermak. However, we give a different and self-contained approach to the\nsubject where we reprove Chermak's theorems and also show several new results.\n"}
+{"abstract": "  Double-descent curves in neural networks describe the phenomenon that the\ngeneralisation error initially descends with increasing parameters, then grows\nafter reaching an optimal number of parameters which is less than the number of\ndata points, but then descends again in the overparameterised regime. Here we\nuse a neural network Gaussian process (NNGP) which maps exactly to a fully\nconnected network (FCN) in the infinite width limit, combined with techniques\nfrom random matrix theory, to calculate this generalisation behaviour, with a\nparticular focus on the overparameterised regime. An advantage of our NNGP\napproach is that the analytical calculations are easier to interpret. We argue\nthat neural network generalization performance improves in the\noverparameterised regime precisely because that is where they converge to their\nequivalent Gaussian process.\n"}
+{"abstract": "  Quantum computing has been attracting tremendous efforts in recent years. One\nprominent application is to perform quantum simulations of electron\ncorrelations in large molecules and solid-state materials, where orbital\ndegrees of freedom are crucial to quantitatively model electronic properties.\nElectron orbitals unlike quantum spins obey crystal symmetries, making the\natomic orbital in optical lattices a natural candidate to emulate electron\norbitals. Here, we construct atom-orbital qubits by manipulating $s$- and\n$d$-orbitals of atomic Bose-Einstein condensation in an optical lattice.\nNoise-resilient quantum gate operations are achieved by performing holonomic\nquantum control, which admits geometrical protection. We find it is critical to\neliminate the orbital leakage error in the system. The gate robustness is\ntested by varying the intensity of the laser forming the lattice. Our work\nopens up wide opportunities for atom-orbital based quantum information\nprocessing, of vital importance to programmable quantum simulations of\nmulti-orbital physics in molecules and quantum materials.\n"}
+{"abstract": "  The x-vector architecture has recently achieved state-of-the-art results on\nthe speaker verification task. This architecture incorporates a central layer,\nreferred to as temporal pooling, which stacks statistical parameters of the\nacoustic frame distribution. This work proposes to highlight the significant\neffect of the temporal pooling content on the training dynamics and task\nperformance. An evaluation with different pooling layers is conducted, that is,\nincluding different statistical measures of central tendency. Notably, 3rd and\n4th moment-based statistics (skewness and kurtosis) are also tested to complete\nthe usual mean and standard-deviation parameters. Our experiments show the\ninfluence of the pooling layer content in terms of speaker verification\nperformance, but also for several classification tasks (speaker, channel or\ntext related), and allow to better reveal the presence of external information\nto the speaker identity depending on the layer content.\n"}
+{"abstract": "  We give some Korovkin-type theorems on convergence and estimates of rates of\napproximations of nets of functions, satisfying suitable axioms, whose\nparticular cases are filter/ideal convergence, almost convergence and\ntriangular A-statistical convergence, where A is a non-negative summability\nmethod. Furthermore, we give some applications to Mellin-type convolution and\nbivariate Kantorovich-type discrete operators.\n"}
+{"abstract": "  We construct an agent-based SEIR model to simulate COVID-19 spread at a\n16000-student mostly non-residential urban university during the Fall 2021\nSemester. We find that mRNA vaccine coverage above 80% makes it possible to\nsafely reopen to in-person instruction. If vaccine coverage is 100%, then our\nmodel indicates that facemask use is not necessary. Our simulations with\nvaccine coverage below 70% exhibit a right-skew for total infections over the\nsemester, which suggests that high levels of infection are not exceedingly rare\nwith campus social connections the main transmission route. Less effective\nvaccines or incidence of new variants may require additional intervention such\nas screening testing to reopen safely.\n"}
+{"abstract": "  A big challenge in current biology is to understand the exact\nself-organization mechanism underlying complex multi-physics coupling\ndevelopmental processes. With multiscale computations of from subcellular gene\nexpressions to cell population dynamics that is based on first principles, we\nshow that cell cycles can self-organize into periodic stripes in the\ndevelopment of E. coli populations from one single cell, relying on the moving\ngraded nutrient concentration profile, which provides directing positional\ninformation for cells to keep their cycle phases in place. Resultantly, the\nstatistical cell cycle distribution within the population is observed to\ncollapse to a universal function and shows a scale invariance. Depending on the\nradial distribution mode of genetic oscillations in cell populations, a\ntransition between gene patterns is achieved. When an inhibitor-inhibitor gene\nnetwork is subsequently activated by a gene-oscillatory network, cell\npopulations with zebra stripes can be established, with the positioning\nprecision of cell-fate-specific domains influenced by cells' speed of free\nmotions. Such information may provide important implications for understanding\nrelevant dynamic processes of multicellular systems, such as biological\ndevelopment.\n"}
+{"abstract": "  The Perdew-Zunger self-interaction correction(PZ-SIC) improves the\nperformance of density functional approximations(DFAs) for the properties that\ninvolve significant self-interaction error(SIE), as in stretched bond\nsituations, but overcorrects for equilibrium properties where SIE is\ninsignificant. This overcorrection is often reduced by LSIC, local scaling of\nthe PZ-SIC to the local spin density approximation(LSDA). Here we propose a new\nscaling factor to use in an LSIC-like approach that satisfies an additional\nimportant constraint: the correct coefficient of atomic number Z in the\nasymptotic expansion of the exchange-correlation(xc) energy for atoms. LSIC and\nLSIC+ are scaled by functions of the iso-orbital indicator z{\\sigma}, which\ndistinguishes one-electron regions from many-electron regions. LSIC+ applied to\nLSDA works better for many equilibrium properties than LSDA-LSIC and the\nPerdew, Burke, and Ernzerhof(PBE) generalized gradient approximation(GGA), and\nalmost as well as the strongly constrained and appropriately normed(SCAN)\nmeta-GGA. LSDA-LSIC and LSDA-LSIC+, however, both fail to predict interaction\nenergies involving weaker bonds, in sharp contrast to their earlier successes.\nIt is found that more than one set of localized SIC orbitals can yield a nearly\ndegenerate energetic description of the same multiple covalent bond, suggesting\nthat a consistent chemical interpretation of the localized orbitals requires a\nnew way to choose their Fermi orbital descriptors. To make a locally\nscaled-down SIC to functionals beyond LSDA requires a gauge transformation of\nthe functional's energy density. The resulting SCAN-sdSIC, evaluated on\nSCAN-SIC total and localized orbital densities, leads to an acceptable\ndescription of many equilibrium properties including the dissociation energies\nof weak bonds.\n"}
+{"abstract": "  A sizable $\\cos 4\\phi$ azimuthal asymmetry in exclusive di-pion production\nnear $\\rho^0$ resonance peak in ultraperipheral heavy ion collisions recently\nhas been reported by STAR collaboration. We show that both elliptic gluon\nWigner distribution and final state soft photon radiation can give rise to this\nazimuthal asymmetry. The fact that the QED effect alone severely underestimates\nthe observed asymmetry might signal the existence of the nontrivial correlation\nin quantum phase distribution of gluons.\n"}
+{"abstract": "  We study random compact subsets of R^3 which can be described as \"random\nMenger sponges\". We use those random sets to construct a pair of compact sets A\nand B in R^3 which are of the same positive measure, such that A can be covered\nby finitely many translates of B, B can be covered by finitely many translates\nof A, and yet A and B are not equidecomposable. Furthermore, we construct the\nfirst example of a compact subset of R^3 of positive measure which is not a\ndomain of expansion. This answers a question of Adrian Ioana.\n"}
+{"abstract": "  Workflow decision making is critical to performing many practical workflow\napplications. Scheduling in edge-cloud environments can address the high\ncomplexity of workflow applications, while decreasing the data transmission\ndelay between the cloud and end devices. However, due to the heterogeneous\nresources in edge-cloud environments and the complicated data dependencies\nbetween the tasks in a workflow, significant challenges for workflow scheduling\nremain, including the selection of an optimal tasks-servers solution from the\npossible numerous combinations. Existing studies are mainly done subject to\nrigorous conditions without fluctuations, ignoring the fact that workflow\nscheduling is typically present in uncertain environments. In this study, we\nfocus on reducing the execution cost of workflow applications mainly caused by\ntask computation and data transmission, while satisfying the workflow deadline\nin uncertain edge-cloud environments. The Triangular Fuzzy Numbers (TFNs) are\nadopted to represent the task processing time and data transferring time. A\ncost-driven fuzzy scheduling strategy based on an Adaptive Discrete Particle\nSwarm Optimization (ADPSO) algorithm is proposed, which employs the operators\nof Genetic Algorithm (GA). This strategy introduces the randomly two-point\ncrossover operator, neighborhood mutation operator, and adaptive multipoint\nmutation operator of GA to effectively avoid converging on local optima. The\nexperimental results show that our strategy can effectively reduce the workflow\nexecution cost in uncertain edge-cloud environments, compared with other\nbenchmark solutions.\n"}
+{"abstract": "  While classical spin systems in random networks have been intensively\nstudied, much less is known about quantum magnets in random graphs. Here, we\ninvestigate interacting quantum spins on small-world networks, building on\nmean-field theory and extensive quantum Monte Carlo simulations. Starting from\none-dimensional (1D) rings, we consider two situations: all-to-all interacting\nand long-range interactions randomly added. The effective infinite dimension of\nthe lattice leads to a magnetic ordering at finite temperature $T_\\mathrm{c}$\nwith mean-field criticality. Nevertheless, in contrast to the classical case,\nwe find two distinct power-law behaviors for $T_\\mathrm{c}$ versus the average\nstrength of the extra couplings. This is controlled by a competition between a\ncharacteristic length scale of the random graph and the thermal correlation\nlength of the underlying 1D system, thus challenging mean-field theories. We\nalso investigate the fate of a gapped 1D spin chain against the small-world\neffect.\n"}
+{"abstract": "  Bitcoin is built on a blockchain, an immutable decentralised ledger that\nallows entities (users) to exchange Bitcoins in a pseudonymous manner. Bitcoins\nare associated with alpha-numeric addresses and are transferred via\ntransactions. Each transaction is composed of a set of input addresses\n(associated with unspent outputs received from previous transactions) and a set\nof output addresses (to which Bitcoins are transferred). Despite Bitcoin was\ndesigned with anonymity in mind, different heuristic approaches exist to detect\nwhich addresses in a specific transaction belong to the same entity. By\napplying these heuristics, we build an Address Correspondence Network: in this\nrepresentation, addresses are nodes are connected with edges if at least one\nheuristic detects them as belonging to the same entity. %addresses are nodes\nand edges are drawn between addresses detected as belonging to the same entity\nby at least one heuristic. %nodes represent addresses and edges model the\nlikelihood that two nodes belong to the same entity %In this network, connected\ncomponents represent sets of addresses controlled by the same entity. In this\npaper, we analyse for the first time the Address Correspondence Network and\nshow it is characterised by a complex topology, signalled by a broad, skewed\ndegree distribution and a power-law component size distribution. Using a\nlarge-scale dataset of addresses for which the controlling entities are known,\nwe show that a combination of external data coupled with standard community\ndetection algorithms can reliably identify entities. The complex nature of the\nAddress Correspondence Network reveals that usage patterns of individual\nentities create statistical regularities; and that these regularities can be\nleveraged to more accurately identify entities and gain a deeper understanding\nof the Bitcoin economy as a whole.\n"}
+{"abstract": "  Federated learning plays an important role in the process of smart cities.\nWith the development of big data and artificial intelligence, there is a\nproblem of data privacy protection in this process. Federated learning is\ncapable of solving this problem. This paper starts with the current\ndevelopments of federated learning and its applications in various fields. We\nconduct a comprehensive investigation. This paper summarize the latest research\non the application of federated learning in various fields of smart cities.\nIn-depth understanding of the current development of federated learning from\nthe Internet of Things, transportation, communications, finance, medical and\nother fields. Before that, we introduce the background, definition and key\ntechnologies of federated learning. Further more, we review the key\ntechnologies and the latest results. Finally, we discuss the future\napplications and research directions of federated learning in smart cities.\n"}
+{"abstract": "  We report a combined experimental and theoretical study of the PdSe2-xTex\nsystem. With increasing Te fraction, structural evolutions, first from an\northorhombic phase (space group Pbca) to a monoclinic phase (space group C2/c)\nand then to a trigonal phase (space group P-3m1), are observed accompanied with\nclearly distinct electrical transport behavior. The monoclinic phase (C2/c) is\na completely new polymorphic phase and is discovered within a narrow range of\nTe composition (0.3 \\leq x \\leq 0.8). This phase has a different packing\nsequence from all known transition metal dichalcogenides to date. Electronic\ncalculations and detailed transport analysis of the new polymorphic\nPdSe1.3Te0.7 phase are presented. In the trigonal phase region,\nsuperconductivity with enhanced critical temperature is also observed within a\nnarrow range of Te content (1.0 \\leq x \\leq 1.2). The rich phase diagram, new\npolymorphic structure as well as abnormally enhanced superconductivity could\nfurther stimulate more interest to explore new types of polymorphs and\ninvestigate their transport and electronic properties in the transition metal\ndichalcogenides family that are of significant interest.\n"}
+{"abstract": "  Although the frequency-division duplex (FDD) massive multiple-input\nmultiple-output (MIMO) system can offer high spectral and energy efficiency, it\nrequires to feedback the downlink channel state information (CSI) from users to\nthe base station (BS), in order to fulfill the precoding design at the BS.\nHowever, the large dimension of CSI matrices in the massive MIMO system makes\nthe CSI feedback very challenging, and it is urgent to compress the feedback\nCSI. To this end, this paper proposes a novel dilated convolution based CSI\nfeedback network, namely DCRNet. Specifically, the dilated convolutions are\nused to enhance the receptive field (RF) of the proposed DCRNet without\nincreasing the convolution size. Moreover, advanced encoder and decoder blocks\nare designed to improve the reconstruction performance and reduce computational\ncomplexity as well. Numerical results are presented to show the superiority of\nthe proposed DCRNet over the conventional networks. In particular, the proposed\nDCRNet can achieve almost the state-of-the-arts (SOTA) performance with much\nlower floating point operations (FLOPs). The open source code and checkpoint of\nthis work are available at https://github.com/recusant7/DCRNet.\n"}
+{"abstract": "  This paper considers the approximation of a monomial $x^n$ over the interval\n$[-1,1]$ by a lower-degree polynomial. This polynomial approximation can be\neasily computed analytically and is obtained by truncating the analytical\nChebyshev series expansion of $x^n$. The error in the polynomial approximation\nin the supremum norm has an exact expression with an interesting probabilistic\ninterpretation. We use this interpretation along with concentration\ninequalities to develop a useful upper bound for the error.\n"}
+{"abstract": "  In this letter, we present experimental data demonstrating spin wave\ninterference detection using spin Hall effect (ISHE). Two coherent spin waves\nare excited in a yttrium-iron garnet (YIG) waveguide by continuous microwave\nsignals. The initial phase difference between the spin waves is controlled by\nthe external phase shifter. The ISHE voltage is detected at a distance of 2 mm\nand 4 mm away from the spin wave generating antennae by an attached Pt layer.\nExperimental data show ISHE voltage oscillation as a function of the phase\ndifference between the two interfering spin waves. This experiment demonstrates\nan intriguing possibility of using ISHE in spin wave logic circuit converting\nspin wave phase into an electric signal\n"}
+{"abstract": "  Initial powder mixtures of Cu, Fe and Co are exposed to severe plastic\ndeformation by high-pressure torsion to prepare solid solutions. A broad range\nof compositions is investigated, whereas this study aims at the synthesis of\nsoft magnetic materials and therefore at the formation of a homogeneous and\nnanocrystalline microstructure. For intermediate ferromagnetic contents,\nhigh-pressure torsion at room temperature yields single-phase supersaturated\nsolid solutions. For higher ferromagnetic contents, two consecutive steps of\nhigh-pressure torsion deformation at different temperatures yield the desired\nnanocrystalline microstructure. Depending on the Co-to-Fe-ratio, either a\nsingle-phase supersaturated solid solution or a nanocomposite forms. The\ncomposite exhibits an enhanced magnetic moment, indicating the formation of a\n(Fe,Co)-alloy upon severe plastic deformation. Soft magnetic properties are\nverified for large Co-to-Fe-ratios and this microstructure is found to remain\nstable up to 400 {\\deg}C.\n"}
+{"abstract": "  Let n be a positive integer and t a non-zero integer. We consider the\nelliptic curve over Q given by E : y 2 = x 3 + tx 2 -- n 2 (t + 3n 2)x + n 6.\nIt is a special case of an elliptic surface studied recently by Bettin, David\nand Delaunay [2] and it generalizes Washington's family. The point (0, n 3)\nbelongs to E(Q) and we obtain some results about its nondivisibility in E(Q).\nOur work extends to this two-parameter family of elliptic curves a previous\nstudy of Duquesne (mainly stated for n = 1 and t > 0).\n"}
+{"abstract": "  The dataset presented provides high-resolution images of real, filled out\nbank checks containing various complex backgrounds, and handwritten text and\nsignatures in the respective fields, along with both pixel-level and\npatch-level segmentation masks for the signatures on the checks. The images of\nbank checks were obtained from different sources, including other publicly\navailable check datasets, publicly available images on the internet, as well as\nscans and images of real checks. Using the GIMP graphics software, pixel-level\nsegmentation masks for signatures on these checks were manually generated as\nbinary images. An automated script was then used to generate patch-level masks.\nThe dataset was created to train and test networks for extracting signatures\nfrom bank checks and other similar documents with very complex backgrounds.\n"}
+{"abstract": "  Automated driving is now possible in diverse road and traffic conditions.\nHowever, there are still situations that automated vehicles cannot handle\nsafely and efficiently. In this case, a Transition of Control (ToC) is\nnecessary so that the driver takes control of the driving. Executing a ToC\nrequires the driver to get full situation awareness of the driving environment.\nIf the driver fails to get back the control in a limited time, a Minimum Risk\nManeuver (MRM) is executed to bring the vehicle into a safe state (e.g.,\ndecelerating to full stop). The execution of ToCs requires some time and can\ncause traffic disruption and safety risks that increase if several vehicles\nexecute ToCs/MRMs at similar times and in the same area. This study proposes to\nuse novel C-ITS traffic management measures where the infrastructure exploits\nV2X communications to assist Connected and Automated Vehicles (CAVs) in the\nexecution of ToCs. The infrastructure can suggest a spatial distribution of\nToCs, and inform vehicles of the locations where they could execute a safe stop\nin case of MRM. This paper reports the first field operational tests that\nvalidate the feasibility and quantify the benefits of the proposed\ninfrastructure-assisted ToC and MRM management. The paper also presents the CAV\nand roadside infrastructure prototypes implemented and used in the trials. The\nconducted field trials demonstrate that infrastructure-assisted traffic\nmanagement solutions can reduce safety risks and traffic disruptions.\n"}
+{"abstract": "  Commonly, machine learning models minimize an empirical expectation. As a\nresult, the trained models typically perform well for the majority of the data\nbut the performance may deteriorate in less dense regions of the dataset. This\nissue also arises in generative modeling. A generative model may overlook\nunderrepresented modes that are less frequent in the empirical data\ndistribution. This problem is known as complete mode coverage. We propose a\nsampling procedure based on ridge leverage scores which significantly improves\nmode coverage when compared to standard methods and can easily be combined with\nany GAN. Ridge leverage scores are computed by using an explicit feature map,\nassociated with the next-to-last layer of a GAN discriminator or of a\npre-trained network, or by using an implicit feature map corresponding to a\nGaussian kernel. Multiple evaluations against recent approaches of complete\nmode coverage show a clear improvement when using the proposed sampling\nstrategy.\n"}
+{"abstract": "  Recently, deep generative models for molecular graphs are gaining more and\nmore attention in the field of de novo drug design. A variety of models have\nbeen developed to generate topological structures of drug-like molecules, but\nexplorations in generating three-dimensional structures are still limited.\nExisting methods have either focused on low molecular weight compounds without\nconsidering drug-likeness or generate 3D structures indirectly using atom\ndensity maps. In this work, we introduce Ligand Neural Network (L-Net), a novel\ngraph generative model for designing drug-like molecules with high-quality 3D\nstructures. L-Net directly outputs the topological and 3D structure of\nmolecules (including hydrogen atoms), without the need for additional atom\nplacement or bond order inference algorithm. The architecture of L-Net is\nspecifically optimized for drug-like molecules, and a set of metrics is\nassembled to comprehensively evaluate its performance. The results show that\nL-Net is capable of generating chemically correct, conformationally valid, and\nhighly druglike molecules. Finally, to demonstrate its potential in\nstructure-based molecular design, we combine L-Net with MCTS and test its\nability to generate potential inhibitors targeting ABL1 kinase.\n"}
+{"abstract": "  Information management has enter a completely new era, quantum era. However,\nthere exists a lack of sufficient theory to extract truly useful quantum\ninformation and transfer it to a form which is intuitive and straightforward\nfor decision making. Therefore, based on the quantum model of mass function, a\nfortified dual check system is proposed to ensure the judgment generated\nretains enough high accuracy. Moreover, considering the situations in real\nlife, everything takes place in an observable time interval, then the concept\nof time interval is introduced into the frame of the check system. The proposed\nmodel is very helpful in disposing uncertain quantum information in this paper.\nAnd some applications are provided to verify the rationality and correctness of\nthe proposed method.\n"}
+{"abstract": "  In this paper we prove upper and lower bounds on the minimal spherical\ndispersion. In particular, we see that the inverse $N(\\varepsilon,d)$ of the\nminimal spherical dispersion is, for fixed $\\varepsilon>0$, up to logarithmic\nterms linear in the dimension $d$. We also derive upper and lower bounds on the\nexpected dispersion for points chosen independently and uniformly at random\nfrom the Euclidean unit sphere.\n"}
+{"abstract": "  We prove global well-posedness for a coupled Dirac--Klein-Gordon (DKG) system\nin $1+2$ dimensions under the assumption of small, compact, high-regularity\ndata. We reveal hidden structure within the Klein-Gordon part of the system,\nwhich allows us to treat the nonlinearities which are below-critical in two\nspatial dimensions. Furthermore, we provide the first asymptotic decay results\nfor the DKG system in $1+2$ dimensions.\n"}
+{"abstract": "  The autonomous automotive industry is one of the largest and most\nconventional projects worldwide, with many technology companies effectively\ndesigning and orienting their products towards automobile safety and accuracy.\nThese products are performing very well over the roads in developed countries.\nBut can fail in the first minute in an underdeveloped country because there is\nmuch difference between a developed country environment and an underdeveloped\ncountry environment. The following study proposed to train these Artificial\nintelligence models in environment space in an underdeveloped country like\nPakistan. The proposed approach on image classification uses convolutional\nneural networks for image classification for the model. For model pre-training\nGerman traffic signs data set was selected then fine-tuned on Pakistan's\ndataset. The experimental setup showed the best results and accuracy from the\npreviously conducted experiments. In this work to increase the accuracy, more\ndataset was collected to increase the size of images in every class in the data\nset. In the future, a low number of classes are required to be further\nincreased where more images for traffic signs are required to be collected to\nget more accuracy on the training of the model over traffic signs of Pakistan's\nmost used and popular roads motorway and national highway, whose traffic signs\ncolor, size, and shapes are different from common traffic signs.\n"}
+{"abstract": "  Internet of Things Driven Data Analytics (IoT-DA) has the potential to excel\ndata-driven operationalisation of smart environments. However, limited research\nexists on how IoT-DA applications are designed, implemented, operationalised,\nand evolved in the context of software and system engineering life-cycle. This\narticle empirically derives a framework that could be used to systematically\ninvestigate the role of software engineering (SE) processes and their\nunderlying practices to engineer IoT-DA applications. First, using existing\nframeworks and taxonomies, we develop an evaluation framework to evaluate\nsoftware processes, methods, and other artefacts of SE for IoT-DA. Secondly, we\nperform a systematic mapping study to qualitatively select 16 processes (from\nacademic research and industrial solutions) of SE for IoT-DA. Thirdly, we apply\nour developed evaluation framework based on 17 distinct criterion (a.k.a.\nprocess activities) for fine-grained investigation of each of the 16 SE\nprocesses. Fourthly, we apply our proposed framework on a case study to\ndemonstrate development of an IoT-DA healthcare application. Finally, we\nhighlight key challenges, recommended practices, and the lessons learnt based\non framework's support for process-centric software engineering of IoT-DA. The\nresults of this research can facilitate researchers and practitioners to\nengineer emerging and next-generation of IoT-DA software applications.\n"}
+{"abstract": "  The IoT consists of a lot of devices such as embedded systems, wireless\nsensor nodes (WSNs), control systems, etc. It is essential for some of these\ndevices to protect information that they process and transmit. The issue is\nthat an adversary may steal these devices to gain a physical access to the\ndevice. There is a variety of ways that allows to reveal cryptographic keys.\nOne of them are optical Fault Injection attacks. We performed successful\noptical Fault Injections into different type of gates, in particular INV, NAND,\nNOR, FF. In our work we concentrate on the selection of the parameters\nconfigured by an attacker and their influence on the success of the Fault\nInjections.\n"}
+{"abstract": "  Liquid-liquid phase separation (LLPS) is currently of great interest in cell\nbiology. LLPS is an example of what is called an emergent phenomenon -- an idea\nthat comes from condensed-matter physics. Emergent phenomena have the\ncharacteristic feature of having a switch-like response. I show that the Hill\nequation of biochemistry can be used as a simple model of strongly cooperative,\nswitch-like, behaviour. One result is that a switch-like response requires\nrelatively few molecules, even ten gives a strongly switch-like response. Thus\nif a biological function enabled by LLPS relies on LLPS to provide a\nswitch-like response to a stimulus, then condensates large enough to be visible\nin optical microscopy are not needed.\n"}
+{"abstract": "  Ensemble methods are generally regarded to be better than a single model if\nthe base learners are deemed to be \"accurate\" and \"diverse.\" Here we\ninvestigate a semi-supervised ensemble learning strategy to produce\ngeneralizable blind image quality assessment models. We train a multi-head\nconvolutional network for quality prediction by maximizing the accuracy of the\nensemble (as well as the base learners) on labeled data, and the disagreement\n(i.e., diversity) among them on unlabeled data, both implemented by the\nfidelity loss. We conduct extensive experiments to demonstrate the advantages\nof employing unlabeled data for BIQA, especially in model generalization and\nfailure identification.\n"}
+{"abstract": "  The surge in the internet of things (IoT) devices seriously threatens the\ncurrent IoT security landscape, which requires a robust network intrusion\ndetection system (NIDS). Despite superior detection accuracy, existing machine\nlearning or deep learning based NIDS are vulnerable to adversarial examples.\nRecently, generative adversarial networks (GANs) have become a prevailing\nmethod in adversarial examples crafting. However, the nature of discrete\nnetwork traffic at the packet level makes it hard for GAN to craft adversarial\ntraffic as GAN is efficient in generating continuous data like image synthesis.\nUnlike previous methods that convert discrete network traffic into a grayscale\nimage, this paper gains inspiration from SeqGAN in sequence generation with\npolicy gradient. Based on the structure of SeqGAN, we propose Attack-GAN to\ngenerate adversarial network traffic at packet level that complies with domain\nconstraints. Specifically, the adversarial packet generation is formulated into\na sequential decision making process. In this case, each byte in a packet is\nregarded as a token in a sequence. The objective of the generator is to select\na token to maximize its expected end reward. To bypass the detection of NIDS,\nthe generated network traffic and benign traffic are classified by a black-box\nNIDS. The prediction results returned by the NIDS are fed into the\ndiscriminator to guide the update of the generator. We generate malicious\nadversarial traffic based on a real public available dataset with attack\nfunctionality unchanged. The experimental results validate that the generated\nadversarial samples are able to deceive many existing black-box NIDS.\n"}
+{"abstract": "  In sponsored search, retrieving synonymous keywords for exact match type is\nimportant for accurately targeted advertising. Data-driven deep learning-based\nmethod has been proposed to tackle this problem. An apparent disadvantage of\nthis method is its poor generalization performance on entity-level long-tail\ninstances, even though they might share similar concept-level patterns with\nfrequent instances. With the help of a large knowledge base, we find that most\ncommercial synonymous query-keyword pairs can be abstracted into meaningful\nconceptual patterns through concept tagging. Based on this fact, we propose a\nnovel knowledge-driven conceptual retrieval framework to mitigate this problem,\nwhich consists of three parts: data conceptualization, matching via conceptual\npatterns and concept-augmented discrimination. Both offline and online\nexperiments show that our method is very effective. This framework has been\nsuccessfully applied to Baidu's sponsored search system, which yields a\nsignificant improvement in revenue.\n"}
+{"abstract": "  In this paper, a unified gas-kinetic wave-particle scheme (UGKWP) for the\ndisperse dilute gas-particle multiphase flow is proposed. The gas phase is\nalways in the hydrodynamic regime. However, the particle phase covers different\nflow regimes from particle trajectory crossing to the hydrodynamic wave\ninteraction with the variation of local particle phase Knudsen number. The\nUGKWP is an appropriate method for the capturing of the multiscale transport\nmechanism in the particle phase through its coupled wave-particle formulation.\nIn the regime with intensive particle collision, the evolution of solid\nparticle will be followed by the analytic wave with quasi-equilibrium\ndistribution; while in the rarefied regime the non-equilibrium particle phase\nwill be captured through particle tracking and collision, which plays a\ndecisive role in recovering particle trajectory crossing behavior. The\ngas-kinetic scheme (GKS) is employed for the simulation of gas flow. In the\nhighly collision regime for the particles, no particles will be sampled in\nUGKWP and the wave formulation for solid particle with the hydrodynamic gas\nphase will reduce the system to the two-fluid Eulerian model. On the other\nhand, in the collisionless regime for the solid particle, the free transport of\nsolid particle will be followed in UGKWP, and coupled system will return to the\nEulerian-Lagrangian formulation for the gas and particle. The scheme will be\ntested for in all flow regimes, which include the non-equilibrium particle\ntrajectory crossing, the particle concentration under different Knudsen number,\nand the dispersion of particle flow with the variation of Stokes number. A\nexperiment of shock-induced particle bed fluidization is simulated and the\nresults are compared with experimental measurements. These numerical solutions\nvalidate suitability of the proposed scheme for the simulation of gas-particle\nmultiphase flow.\n"}
+{"abstract": "  Manic episodes of bipolar disorder can lead to uncritical behaviour and\ndelusional psychosis, often with destructive consequences for those affected\nand their surroundings. Early detection and intervention of a manic episode are\ncrucial to prevent escalation, hospital admission and premature death. However,\npeople with bipolar disorder may not recognize that they are experiencing a\nmanic episode and symptoms such as euphoria and increased productivity can also\ndeter affected individuals from seeking help. This work proposes to perform\nuser-independent, automatic mood-state detection based on actigraphy and\nelectrodermal activity acquired from a wrist-worn device during mania and after\nrecovery (euthymia). This paper proposes a new deep learning-based ensemble\nmethod leveraging long (20h) and short (5 minutes) time-intervals to\ndiscriminate between the mood-states. When tested on 47 bipolar patients, the\nproposed classification scheme achieves an average accuracy of 91.59% in\neuthymic/manic mood-state recognition.\n"}
+{"abstract": "  Biological infants are naturally curious and try to comprehend their physical\nsurroundings by interacting, in myriad multisensory ways, with different\nobjects - primarily macroscopic solid objects - around them. Through their\nvarious interactions, they build hypotheses and predictions, and eventually\nlearn, infer and understand the nature of the physical characteristics and\nbehavior of these objects. Inspired thus, we propose a model for\ncuriosity-driven learning and inference for real-world AI agents. This model is\nbased on the arousal of curiosity, deriving from observations along\ndiscontinuities in the fundamental macroscopic solid-body physics parameters,\ni.e., shape constancy, spatial-temporal continuity, and object permanence. We\nuse the term body-budget to represent the perceived fundamental properties of\nsolid objects. The model aims to support the emulation of learning from scratch\nfollowed by substantiation through experience, irrespective of domain, in\nreal-world AI agents.\n"}
+{"abstract": "  In previous works, questioning the mathematical nature of the connection in\nthe translations gauge theory formulation of Teleparallel Equivalent to General\nRelativity (TEGR) Theory led us to propose a new formulation using a Cartan\nconnection. In this review, we summarize the presentation of that proposal and\ndiscuss it from a gauge theoretic perspective.\n"}
+{"abstract": "  Emotion dynamics modeling is a significant task in emotion recognition in\nconversation. It aims to predict conversational emotions when building\nempathetic dialogue systems. Existing studies mainly develop models based on\nRecurrent Neural Networks (RNNs). They cannot benefit from the power of the\nrecently-developed pre-training strategies for better token representation\nlearning in conversations. More seriously, it is hard to distinguish the\ndependency of interlocutors and the emotional influence among interlocutors by\nsimply assembling the features on top of RNNs. In this paper, we develop a\nseries of BERT-based models to specifically capture the inter-interlocutor and\nintra-interlocutor dependencies of the conversational emotion dynamics.\nConcretely, we first substitute BERT for RNNs to enrich the token\nrepresentations. Then, a Flat-structured BERT (F-BERT) is applied to link up\nutterances in a conversation directly, and a Hierarchically-structured BERT\n(H-BERT) is employed to distinguish the interlocutors when linking up\nutterances. More importantly, a Spatial-Temporal-structured BERT, namely\nST-BERT, is proposed to further determine the emotional influence among\ninterlocutors. Finally, we conduct extensive experiments on two popular emotion\nrecognition in conversation benchmark datasets and demonstrate that our\nproposed models can attain around 5\\% and 10\\% improvement over the\nstate-of-the-art baselines, respectively.\n"}
+{"abstract": "  We introduce a symmetric fractional-order reduction (SFOR) method to\nconstruct numerical algorithms on general nonuniform temporal meshes for\nsemilinear fractional diffusion-wave equations. By using the novel order\nreduction method, the governing problem is transformed to an equivalent coupled\nsystem, where the explicit orders of time-fractional derivatives involved are\nall $\\alpha/2$ $(1<\\alpha<2)$. The linearized L1 scheme and Alikhanov scheme\nare then proposed on general time meshes. Under some reasonable regularity\nassumptions and weak restrictions on meshes, the optimal convergence is derived\nfor the two kinds of difference schemes by $H^2$ energy method. An adaptive\ntime stepping strategy which based on the (fast linearized) L1 and Alikhanov\nalgorithms is designed for the semilinear diffusion-wave equations. Numerical\nexamples are provided to confirm the accuracy and efficiency of proposed\nalgorithms.\n"}
+{"abstract": "  The use of neural networks and reinforcement learning has become increasingly\npopular in autonomous vehicle control. However, the opaqueness of the resulting\ncontrol policies presents a significant barrier to deploying neural\nnetwork-based control in autonomous vehicles. In this paper, we present a\nreinforcement learning based approach to autonomous vehicle longitudinal\ncontrol, where the rule-based safety cages provide enhanced safety for the\nvehicle as well as weak supervision to the reinforcement learning agent. By\nguiding the agent to meaningful states and actions, this weak supervision\nimproves the convergence during training and enhances the safety of the final\ntrained policy. This rule-based supervisory controller has the further\nadvantage of being fully interpretable, thereby enabling traditional validation\nand verification approaches to ensure the safety of the vehicle. We compare\nmodels with and without safety cages, as well as models with optimal and\nconstrained model parameters, and show that the weak supervision consistently\nimproves the safety of exploration, speed of convergence, and model\nperformance. Additionally, we show that when the model parameters are\nconstrained or sub-optimal, the safety cages can enable a model to learn a safe\ndriving policy even when the model could not be trained to drive through\nreinforcement learning alone.\n"}
+{"abstract": "  In this article, we consider the class of 2-Calabi-Yau tilted algebras that\nare defined by a quiver with potential whose dual graph is a tree. We call\nthese algebras \\emph{dimer tree algebras} because they can also be realized as\nquotients of dimer algebras on a disc. These algebras are wild in general. For\nevery such algebra $B$, we construct a polygon $\\mathcal{S}$ with a\ncheckerboard pattern in its interior that gives rise to a category\n$\\text{Diag}(\\mathcal{S})$. The indecomposable objects of\n$\\text{Diag}(\\mathcal{S})$ are the 2-diagonals in $\\mathcal{S}$, and its\nmorphisms are given by certain pivoting moves between the 2-diagonals. We\nconjecture that the category $\\text{Diag}(\\mathcal{S})$ is equivalent to the\nstable syzygy category over the algebra $B$, such that the rotation of the\npolygon corresponds to the shift functor on the syzygies. In particular, the\nnumber of indecomposable syzygies is finite and the projective resolutions are\nperiodic. We prove the conjecture in the special case where every chordless\ncycle in the quiver is of length three.\n  As a consequence, we obtain an explicit description of the projective\nresolutions. Moreover, we show that the syzygy category is equivalent to the\n2-cluster category of type $\\mathbb{A}$, and we introduce a new derived\ninvariant for the algebra $B$ that can be read off easily from the quiver.\n"}
+{"abstract": "  Transformer language models have shown remarkable ability in detecting when a\nword is anomalous in context, but likelihood scores offer no information about\nthe cause of the anomaly. In this work, we use Gaussian models for density\nestimation at intermediate layers of three language models (BERT, RoBERTa, and\nXLNet), and evaluate our method on BLiMP, a grammaticality judgement benchmark.\nIn lower layers, surprisal is highly correlated to low token frequency, but\nthis correlation diminishes in upper layers. Next, we gather datasets of\nmorphosyntactic, semantic, and commonsense anomalies from psycholinguistic\nstudies; we find that the best performing model RoBERTa exhibits surprisal in\nearlier layers when the anomaly is morphosyntactic than when it is semantic,\nwhile commonsense anomalies do not exhibit surprisal at any intermediate layer.\nThese results suggest that language models employ separate mechanisms to detect\ndifferent types of linguistic anomalies.\n"}
+{"abstract": "  We search for a first-order phase transition gravitational wave signal in 45\npulsars from the NANOGrav 12.5 year dataset. We find that the data can be\nexplained in terms of a strong first order phase transition taking place at\ntemperatures below the electroweak scale. In our search, we find that the\nsignal from a first order phase transition is degenerate with that generated by\nSupermassive Black Hole Binary mergers. An interesting open question is how\nwell gravitational wave observatories could separate such signals.\n"}
+{"abstract": "  The paper is a continuation of arXiv:2012.10364, where the approach was\ndeveloped to constructing the exact matrix model for any generalized Ising\nsystem, and such model was constructed for certain 2d system. In this paper,\nthe properties of the model are specified for light block diagonalization. A\ncorresponding example is considered. For the example, general exact partition\nfunction is obtained and analysed. The analysis shows that the free energy does\nnot depend on the amount of rows with a large amount of cells. For the example\nwith light boundary conditions, the partition function is obtained and the\nspecific free energy per spin is plotted.\n"}
+{"abstract": "  We consider the problem of interpretable network representation learning for\nsamples of network-valued data. We propose the Principal Component Analysis for\nNetworks (PCAN) algorithm to identify statistically meaningful low-dimensional\nrepresentations of a network sample via subgraph count statistics. The PCAN\nprocedure provides an interpretable framework for which one can readily\nvisualize, explore, and formulate predictive models for network samples. We\nfurthermore introduce a fast sampling-based algorithm, sPCAN, which is\nsignificantly more computationally efficient than its counterpart, but still\nenjoys advantages of interpretability. We investigate the relationship between\nthese two methods and analyze their large-sample properties under the common\nregime where the sample of networks is a collection of kernel-based random\ngraphs. We show that under this regime, the embeddings of the sPCAN method\nenjoy a central limit theorem and moreover that the population level embeddings\nof PCAN and sPCAN are equivalent. We assess PCAN's ability to visualize,\ncluster, and classify observations in network samples arising in nature,\nincluding functional connectivity network samples and dynamic networks\ndescribing the political co-voting habits of the U.S. Senate. Our analyses\nreveal that our proposed algorithm provides informative and discriminatory\nfeatures describing the networks in each sample. The PCAN and sPCAN methods\nbuild on the current literature of network representation learning and set the\nstage for a new line of research in interpretable learning on network-valued\ndata. Publicly available software for the PCAN and sPCAN methods are available\nat https://www.github.com/jihuilee/.\n"}
+{"abstract": "  Real-time visual localization of needles is necessary for various surgical\napplications, including surgical automation and visual feedback. In this study\nwe investigate localization and autonomous robotic control of needles in the\ncontext of our magneto-suturing system. Our system holds the potential for\nsurgical manipulation with the benefit of minimal invasiveness and reduced\npatient side effects. However, the non-linear magnetic fields produce\nunintuitive forces and demand delicate position-based control that exceeds the\ncapabilities of direct human manipulation. This makes automatic needle\nlocalization a necessity. Our localization method combines neural network-based\nsegmentation and classical techniques, and we are able to consistently locate\nour needle with 0.73 mm RMS error in clean environments and 2.72 mm RMS error\nin challenging environments with blood and occlusion. The average localization\nRMS error is 2.16 mm for all environments we used in the experiments. We\ncombine this localization method with our closed-loop feedback control system\nto demonstrate the further applicability of localization to autonomous control.\nOur needle is able to follow a running suture path in (1) no blood, no tissue;\n(2) heavy blood, no tissue; (3) no blood, with tissue; and (4) heavy blood,\nwith tissue environments. The tip position tracking error ranges from 2.6 mm to\n3.7 mm RMS, opening the door towards autonomous suturing tasks.\n"}
+{"abstract": "  We consider the propagation of acoustic waves in a 2D waveguide unbounded in\none direction and containing a compact obstacle. The wavenumber is fixed so\nthat only one mode can propagate. The goal of this work is to propose a method\nto cloak the obstacle. More precisely, we add to the geometry thin outer\nresonators of width $\\varepsilon$ and we explain how to choose their positions\nas well as their lengths to get a transmission coefficient approximately equal\nto one as if there were no obstacle. In the process we also investigate several\nrelated problems. In particular, we explain how to get zero transmission and\nhow to design phase shifters. The approach is based on asymptotic analysis in\npresence of thin resonators. An essential point is that we work around\nresonance lengths of the resonators. This allows us to obtain effects of order\none with geometrical perturbations of width $\\varepsilon$. Various numerical\nexperiments illustrate the theory.\n"}
+{"abstract": "  Starting from an anti-symplectic involution on a K3 surface, one can consider\na natural Lagrangian subvariety inside the moduli space of sheaves over the K3.\nOne can also construct a Prymian integrable system following a construction of\nMarkushevich--Tikhomirov, extended by Arbarello--Sacc\\`a--Ferretti, Matteini\nand Sawon--Chen. In this article we address a question of Sawon, showing that\nthese integrable systems and their associated natural Lagrangians degenerate,\nrespectively, into fix loci of involutions considered by Heller--Schaposnik,\nGarcia-Prada--Wilkins and Basu--Garcia-Prada.\n  Along the way we find interesting results such as the proof that the\nDonagi--Ein--Lazarsfeled degeneration is a degeneration of symplectic\nvarieties, a generalization of this degeneration, originally described for K3\nsurfaces, to the case of an arbitrary smooth projective surface, and a\ndescription of the behaviour of certain involutions under this degeneration.\n"}
+{"abstract": "  Earthquakes are lethal and costly. This study aims at avoiding these\ncatastrophic events by the application of injection policies retrieved through\nreinforcement learning. With the rapid growth of artificial intelligence,\nprediction-control problems are all the more tackled by function approximation\nmodels that learn how to control a specific task, even for systems with\nunmodeled/unknown dynamics and important uncertainties. Here, we show for the\nfirst time the possibility of controlling earthquake-like instabilities using\nstate-of-the-art deep reinforcement learning techniques. The controller is\ntrained using a reduced model of the physical system, i.e, the spring-slider\nmodel, which embodies the main dynamics of the physical problem for a given\nearthquake magnitude. Its robustness to unmodeled dynamics is explored through\na parametric study. Our study is a first step towards minimizing seismicity in\nindustrial projects (geothermal energy, hydrocarbons production, CO2\nsequestration) while, in a second step for inspiring techniques for natural\nearthquakes control and prevention.\n"}
+{"abstract": "  In this work we study the decidability of the global modal logic arising from\nKripke frames evaluated on certain residuated lattices (including all BL\nalgebras), known in the literature as crisp modal many-valued logics. We\nexhibit a large family of these modal logics that are undecidable, in\nopposition to classical modal logic and to the propositional logics defined\nover the same classes of algebras. These include the global modal logics\narising from the standard Lukasiewicz and Product algebras. Furthermore, it is\nshown that global modal Lukasiewicz and Product logics are not recursively\naxiomatizable. We conclude the paper by solving negatively the open question of\nwhether a global modal logic coincides with the local modal logic closed under\nthe unrestricted necessitation rule.\n"}
+{"abstract": "  We study a variant of online convex optimization where the player is\npermitted to switch decisions at most $S$ times in expectation throughout $T$\nrounds. Similar problems have been addressed in prior work for the discrete\ndecision set setting, and more recently in the continuous setting but only with\nan adaptive adversary. In this work, we aim to fill the gap and present\ncomputationally efficient algorithms in the more prevalent oblivious setting,\nestablishing a regret bound of $O(T/S)$ for general convex losses and\n$\\widetilde O(T/S^2)$ for strongly convex losses. In addition, for stochastic\ni.i.d.~losses, we present a simple algorithm that performs $\\log T$ switches\nwith only a multiplicative $\\log T$ factor overhead in its regret in both the\ngeneral and strongly convex settings. Finally, we complement our algorithms\nwith lower bounds that match our upper bounds in some of the cases we consider.\n"}
+{"abstract": "  Forecasting financial time series is considered to be a difficult task due to\nthe chaotic feature of the series. Statistical approaches have shown solid\nresults in some specific problems such as predicting market direction and\nsingle-price of stocks; however, with the recent advances in deep learning and\nbig data techniques, new promising options have arises to tackle financial time\nseries forecasting. Moreover, recent literature has shown that employing a\ncombination of statistics and machine learning may improve accuracy in the\nforecasts in comparison to single solutions. Taking into consideration the\nmentioned aspects, in this work, we proposed the MegazordNet, a framework that\nexplores statistical features within a financial series combined with a\nstructured deep learning model for time series forecasting. We evaluated our\napproach predicting the closing price of stocks in the S&P 500 using different\nmetrics, and we were able to beat single statistical and machine learning\nmethods.\n"}
+{"abstract": "  We describe an algorithm for computing a $\\Q$-rational model for the quotient\nof a modular curve by an automorphism group, under mild assumptions on the\ncurve and the automorphisms, by determining $q$-expansions for a basis of the\ncorresponding space of cusp forms. We also give a moduli interpretation for\ngeneral morphisms between modular curves.\n"}
+{"abstract": "  We show that, given an almost-source algebra $A$ of a $p$-block of a finite\ngroup $G$, then the unit group of $A$ contains a basis stabilized by the left\nand right multiplicative action of the defect group if and only if, in a sense\nto be made precise, certain relative multiplicities of local pointed groups are\ninvariant with respect to the fusion system. We also show that, when $G$ is\n$p$-solvable, those two equivalent conditions hold for some almost-source\nalgebra of the given $p$-block. One motive lies in the fact that, by a theorem\nof Linckelmann, if the two equivalent conditions hold for $A$, then any stable\nbasis for $A$ is semicharacteristic for the fusion system.\n"}
+{"abstract": "  Sound Source Localization (SSL) are used to estimate the position of sound\nsources. Various methods have been used for detecting sound and its\nlocalization. This paper presents a system for stationary sound source\nlocalization by cubical microphone array consisting of eight microphones placed\non four vertical adjacent faces which is mounted on three wheel\nomni-directional drive for the inspection and monitoring of the disaster\nvictims in disaster areas. The proposed method localizes sound source on a 3D\nspace by grid search method using Generalized Cross Correlation Phase Transform\n(GCC-PHAT) which is robust when operating in real life scenario where there is\nlack of visibility. The computed azimuth and elevation angle of victimized\nhuman voice are fed to embedded omni-directional drive system which navigates\nthe vehicle automatically towards the stationary sound source.\n"}
+{"abstract": "  To any $k$-dimensional subspace of $\\mathbb Q^n$ one can naturally associate\na point in the Grassmannian ${\\rm Gr}_{n,k}(\\mathbb R)$ and two shapes of\nlattices of rank $k$ and $n-k$ respectively. These lattices originate by\nintersecting the $k$-dimensional subspace with the lattice $\\mathbb Z^n$. Using\nunipotent dynamics we prove simultaneous equidistribution of all of these\nobjects under a congruence conditions when $(k,n) \\neq (2,4)$.\n"}
+{"abstract": "  We find an explicit presentation of relative linear Steinberg groups\n$\\mathrm{St}(n, R, I)$ for any ring $R$ and $n \\geq 4$ by generators and\nrelations as abstract groups. We also prove a similar result for relative\nsimply laced Steinberg groups $\\mathrm{St}(\\Phi; R, I)$ for commutative $R$ and\n$\\Phi \\in \\{\\mathsf A_\\ell, \\mathsf D_\\ell, \\mathsf E_\\ell \\mid \\ell \\geq 3\\}$.\n"}
+{"abstract": "  Soft electronics are a promising and revolutionary alternative for\ntraditional electronics when safe physical interaction between machines and the\nhuman body is required. Among various materials architectures developed for\nproducing soft and stretchable electronics, Liquid-Metal Embedded Elastomers\n(LMEEs), which contain Ga-based inclusions as a conductive phase, has drawn\nconsiderable attention in various emerging fields such as wearable computing\nand bio-inspired robotics. This is because LMEEs exhibit a unique combination\nof desirable mechanical, electrical, and thermal properties. For instance,\nthese so-called multifunctional materials can undergo large deformations as\nhigh as 600% strain without losing their electrical conductivity. Moreover, the\ndesperation of conductive liquid-metal inclusions within the entire medium of\nan elastomer makes it possible to fabricate autonomously self-healing circuits\nthat maintain their electrical functionality after extreme mechanical damage\ninduction. The electrically self-healing property is of great importance for\nfurther progress in autonomous soft robotics, where materials are subjected to\nvarious modes of mechanical damage such as tearing. In this short review, we\nreview the fundamental characteristics of LMEEs, their advantages over other\nconductive composites, materials used in LMMEs, their preparation and\nactivation process, and the fabrication process of self-healing circuits.\nAdditionally, we will review the soft-lithography-enabled techniques for\nliquid-metal pattering.\n"}
+{"abstract": "  The research in anomaly detection lacks a unified definition of what\nrepresents an anomalous instance. Discrepancies in the nature itself of an\nanomaly lead to multiple paradigms of algorithms design and experimentation.\nPredictive maintenance is a special case, where the anomaly represents a\nfailure that must be prevented. Related time-series research as outlier and\nnovelty detection or time-series classification does not apply to the concept\nof an anomaly in this field, because they are not single points which have not\nbeen seen previously and may not be precisely annotated. Moreover, due to the\nlack of annotated anomalous data, many benchmarks are adapted from supervised\nscenarios.\n  To address these issues, we generalise the concept of positive and negative\ninstances to intervals to be able to evaluate unsupervised anomaly detection\nalgorithms. We also preserve the imbalance scheme for evaluation through the\nproposal of the Preceding Window ROC, a generalisation for the calculation of\nROC curves for time-series scenarios. We also adapt the mechanism from a\nestablished time-series anomaly detection benchmark to the proposed\ngeneralisations to reward early detection. Therefore, the proposal represents a\nflexible evaluation framework for the different scenarios. To show the\nusefulness of this definition, we include a case study of Big Data algorithms\nwith a real-world time-series problem provided by the company ArcelorMittal,\nand compare the proposal with an evaluation method.\n"}
+{"abstract": "  For any graph $G$ of order $p$, a bijection $f: V(G)\\to [1,p]$ is called a\nnumbering of the graph $G$ of order $p$. The strength $str_f(G)$ of a numbering\n$f: V(G)\\to [1,p]$ of $G$ is defined by $str_f(G) = \\max\\{f(u)+f(v)\\; |\\; uv\\in\nE(G)\\},$ and the strength $str(G)$ of a graph $G$ itself is $str(G) =\n\\min\\{str_f(G)\\;|\\; f \\mbox{ is a numbering of } G\\}.$ A numbering $f$ is\ncalled a strength labeling of $G$ if $str_f(G)=str(G)$. In this paper, we\nobtained a sufficient condition for a graph to have $str(G)=|V(G)|+\\d(G)$.\nConsequently, many questions raised in [Bounds for the strength of graphs, {\\it\nAust. J. Combin.} {\\bf72(3)}, (2018) 492--508] and [On the strength of some\ntrees, {\\it AKCE Int. J. Graphs Comb.} (Online 2019)\ndoi.org/10.1016/j.akcej.2019.06.002] are solved. Moreover, we showed that every\ngraph $G$ either has $str(G)=|V(G)|+\\d(G)$ or is a proper subgraph of a graph\n$H$ that has $str(H) = |V(H)| + \\d(H)$ with $\\d(H)=\\d(G)$. Further, new good\nlower bounds of $str(G)$ are also obtained. Using these, we determined the\nstrength of 2-regular graphs and obtained new lower bounds of $str(Q_n)$ for\nvarious $n$, where $Q_n$ is the $n$-regular hypercube.\n"}
+{"abstract": "  Today's intelligent applications can achieve high performance accuracy using\nmachine learning (ML) techniques, such as deep neural networks (DNNs).\nTraditionally, in a remote DNN inference problem, an edge device transmits raw\ndata to a remote node that performs the inference task. However, this may incur\nhigh transmission energy costs and puts data privacy at risk. In this paper, we\npropose a technique to reduce the total energy bill at the edge device by\nutilizing model compression and time-varying model split between the edge and\nremote nodes. The time-varying representation accounts for time-varying\nchannels and can significantly reduce the total energy at the edge device while\nmaintaining high accuracy (low loss). We implement our approach in an image\nclassification task using the MNIST dataset, and the system environment is\nsimulated as a trajectory navigation scenario to emulate different channel\nconditions. Numerical simulations show that our proposed solution results in\nminimal energy consumption and $CO_2$ emission compared to the considered\nbaselines while exhibiting robust performance across different channel\nconditions and bandwidth regime choices.\n"}
+{"abstract": "  Neural networks-based learning of the distribution of non-dispatchable\nrenewable electricity generation from sources such as photovoltaics (PV) and\nwind as well as load demands has recently gained attention. Normalizing flow\ndensity models are particularly well suited for this task due to the training\nthrough direct log-likelihood maximization. However, research from the field of\nimage generation has shown that standard normalizing flows can only learn\nsmeared-out versions of manifold distributions. Previous works on normalizing\nflow-based scenario generation do not address this issue, and the smeared-out\ndistributions result in the sampling of noisy time series. In this paper, we\npropose reducing the dimensionality through principal component analysis (PCA),\nwhich sets up the normalizing flow in a lower-dimensional space while\nmaintaining the direct and computationally efficient likelihood maximization.\nWe train the resulting principal component flow (PCF) on data of PV and wind\npower generation as well as load demand in Germany in the years 2013 to 2015.\nThe results of this investigation show that the PCF preserves critical features\nof the original distributions, such as the probability density and frequency\nbehavior of the time series. The application of the PCF is, however, not\nlimited to renewable power generation but rather extends to any data set, time\nseries, or otherwise, which can be efficiently reduced using PCA.\n"}
+{"abstract": "  Cosmic rays interacting with the atmosphere result in a flux of secondary\nparticles including muons and electrons. Atmospheric ray tomography (ART) uses\nthe muons and electrons for detecting objects and their composition. This paper\npresents new methods and a proof-of-concept tomography system developed for the\nART of low-Z materials. We introduce the Particle Track Filtering (PTF) and\nMulti-Modality Tomographic Reconstruction (MMTR) methods. Based on Geant4\nmodels we optimized the tomography system, the parameters of PTF and MMTR.\nBased on plastic scintillating fiber arrays we achieved the spatial resolution\n120 $\\mu$m and 1 mrad angular resolution in the track reconstruction. We\ndeveloped a novel edge detection method to separate the logical volumes of\nscanned object. We show its effectiveness on single (e.g. water, aluminum) and\ndouble material (e.g. explosive RDX in flesh) objects. The tabletop tomograph\nwe built showed excellent agreement between simulations and measurements. We\nare able to increase the discriminating power of ART on low-Z materials\nsignificantly. This work opens up new routes for the commercialization of ART\ntomography.\n"}
+{"abstract": "  Stochastic gradient descent (SGD) has become the most attractive optimization\nmethod in training large-scale deep neural networks due to its simplicity, low\ncomputational cost in each updating step, and good performance. Standard excess\nrisk bounds show that SGD only needs to take one pass over the training data\nand more passes could not help to improve the performance. Empirically, it has\nbeen observed that SGD taking more than one pass over the training data\n(multi-pass SGD) has much better excess risk bound performance than the SGD\nonly taking one pass over the training data (one-pass SGD). However, it is not\nvery clear that how to explain this phenomenon in theory. In this paper, we\nprovide some theoretical evidences for explaining why multiple passes over the\ntraining data can help improve performance under certain circumstance.\nSpecifically, we consider smooth risk minimization problems whose objective\nfunction is non-convex least squared loss. Under Polyak-Lojasiewicz (PL)\ncondition, we establish faster convergence rate of excess risk bound for\nmulti-pass SGD than that for one-pass SGD.\n"}
+{"abstract": "  Two-dimensional magnetic skyrmions are particle-like magnetic domains in\nmagnetic thin films. The kinetic property of the magnetic skyrmions at finite\ntemperature is well described by the Thiele equation, including a stochastic\nfield and a finite mass. In this paper, the validity of the constant-mass\napproximation is examined by comparing the Fourier spectrum of Brownian motions\ndescribed by the Thiele equation and the Landau-Lifshitz-Gilbert equation.\nThen, the 4-dimensional Fokker-Planck equation is derived from the Thiele\nequation with a mass-term. Consequently, an expression of the diffusion flow\nand diffusion constant in a tensor form is derived, extending Chandrasekhar's\nmethod for Thiele dynamics.\n"}
+{"abstract": "  Nano Electro Mechanical (NEM) contact switches have been widely studied as\none of the alternative for classical field effect transistor (FET). An ideal\nNEM contact switch with hysteresis free switching slope (SS) of 0 mV/dec is\ndesired to achieve the ultimate scaling of the complementary metal oxide\nsemiconductor (CMOS) integrated circuits (IC) but never realized. Here we show,\nlow pull-in voltage, hysteresis free graphene based NEM contact switch with hBN\nas a contact larger. The hysteresis voltage is greatly reduced by exploiting\nthe weak adhesion energy between the graphene and hexagonal boron nitride\n(hBN). The graphene NEM contact switch with hBN as contact exhibits low pull-in\nvoltage of < 2 V, high contact life time of more than 6x10^4 switching cycles,\nON/OFF ratio of 10^4 orders of magnitude and hysteresis voltage of as small as\n< 0.1 V. Our G-hBN NEM contact switch can be potentially used in ultra-low\npower energy efficient CMOS IC's.\n"}
+{"abstract": "  In recent work [arXiv:2003.06939v2] a novel fermion to qubit mapping --\ncalled the compact encoding -- was introduced which outperforms all previous\nlocal mappings in both the qubit to mode ratio, and the locality of mapped\noperators. There the encoding was demonstrated for square and hexagonal\nlattices. Here we present an extension of that work by illustrating how to\napply the compact encoding to other regular lattices. We give constructions for\nvariants of the compact encoding on all regular tilings with maximum degree 4.\nThese constructions yield edge operators with Pauli weight at most 3 and use\nfewer than 1.67 qubits per fermionic mode. Additionally we demonstrate how the\ncompact encoding may be applied to a cubic lattice, yielding edge operators\nwith Pauli weight no greater than 4 and using approximately 2.5 qubits per\nmode. In order to properly analyse the compact encoding on these lattices a\nmore general group theoretic framework is required, which we elaborate upon in\nthis work. We expect this framework to find use in the design of fermion to\nqubit mappings more generally.\n"}
+{"abstract": "  We propose and demonstrate a method to characterize a gated InGaAs\nsingle-photon detector (SPD). Ultrashort weak coherent pulses, from a\nmode-locked sub-picosecond pulsed laser, were used to measure photon counts, at\nvarying arrival times relative to the start of the SPD gate voltage. The uneven\ndetection probabilities within the gate window were used to estimate the\nafterpulse probability with respect to various detector parameters: excess\nbias, width of gate window and hold-off time. We estimated a lifetime of 2.1\nmicroseconds for the half-life of trapped carriers, using a power-law fit to\nthe decay in afterpulse probability. Finally, we quantify the timing jitter of\nthe SPD using a time to digital converter with a resolution of 55 ps.\n"}
+{"abstract": "  A review is made of the field of contextuality in quantum mechanics. We study\nthe historical emergence of the concept from philosophical and logical issues.\nWe present and compare the main theoretical frameworks that have been derived.\nFinally, we focus on the complex task of establishing experimental tests of\ncontextuality. Throughout this work, we try to show that the conceptualisation\nof contextuality has progressed through different complementary perspectives,\nbefore summoning them together to analyse the signification of contextuality\nexperiments. Doing so, we argue that contextuality emerged as a discrete\nlogical problem and developed into a quantifiable quantum resource.\n"}
+{"abstract": "  High-performance hybrid automatic speech recognition (ASR) systems are often\ntrained with clustered triphone outputs, and thus require a complex training\npipeline to generate the clustering. The same complex pipeline is often\nutilized in order to generate an alignment for use in frame-wise cross-entropy\ntraining. In this work, we propose a flat-start factored hybrid model trained\nby modeling the full set of triphone states explicitly without relying on\nclustering methods. This greatly simplifies the training of new models.\nFurthermore, we study the effect of different alignments used for Viterbi\ntraining. Our proposed models achieve competitive performance on the\nSwitchboard task compared to systems using clustered triphones and other\nflat-start models in the literature.\n"}
+{"abstract": "  Motivated by M-theory, we study rank n K-theoretic Donaldson-Thomas theory on\na toric threefold X. In the presence of compact four-cycles, we discuss how to\ninclude the contribution of D4-branes wrapping them. Combining this with a\nsimple assumption on the (in)dependence on Coulomb moduli in the 7d theory, we\nshow that the partition function factorizes and, when X is Calabi-Yau and it\nadmits an ADE ruling, it reproduces the 5d master formula for the geometrically\nengineered theory on A(n-1) ALE space, thus extending the usual geometric\nengineering dictionary to n>1. We finally speculate about implications for\ninstanton counting on Taub-NUT.\n"}
+{"abstract": "  We consider a finite abelian group $M$ of odd exponent $n$ with a symplectic\nform $\\omega: M\\times M\\to \\mu_n$ and the Heisenberg extension $1\\to \\mu_n\\to\nH\\to M\\to 1$ with the commutator $\\omega$. According to the Stone - von Neumann\ntheorem, $H$ admits an irreducible representation with the tautological central\ncharacter (defined up to a non-unique isomorphism). We construct such\nirreducible representation of $H$ defined up to a unique isomorphism, so\ncanonical in this sense.\n"}
+{"abstract": "  In this paper, we study communication-efficient distributed stochastic\ngradient descent (SGD) with data sets of users distributed over a certain area\nand communicating through wireless channels. Since the time for one iteration\nin the proposed approach is independent of the number of users, it is\nwell-suited to scalable distributed SGD. Furthermore, since the proposed\napproach is based on preamble-based random access, which is widely adopted for\nmachine-type communication (MTC), it can be easily employed for training models\nwith a large number of devices in various Internet-of-Things (IoT) applications\nwhere MTC is used for their connectivity. For fading channel, we show that\nnoncoherent combining can be used. As a result, no channel state information\n(CSI) estimation is required. From analysis and simulation results, we can\nconfirm that the proposed approach is not only scalable, but also provides\nimproved performance as the number of devices increases.\n"}
+{"abstract": "  Using a systematic, symmetry-preserving continuum approach to the Standard\nModel strong-interaction bound-state problem, we deliver parameter-free\npredictions for all semileptonic $B_c \\to \\eta_c, J/\\psi$ transition form\nfactors on the complete domains of empirically accessible momentum transfers.\nWorking with branching fractions calculated therefrom, the following values of\nthe ratios for $\\tau$ over $\\mu$ final states are obtained:\n$R_{\\eta_c}=0.313(22)$ and $R_{J/\\psi}=0.242(47)$. Combined with other recent\nresults, our analysis confirms a $2\\sigma$ discrepancy between the Standard\nModel prediction for $R_{J/\\psi}$ and the single available experimental result.\n"}
+{"abstract": "  Session-based recommendation aims to predict user the next action based on\nhistorical behaviors in an anonymous session. For better recommendations, it is\nvital to capture user preferences as well as their dynamics. Besides, user\npreferences evolve over time dynamically and each preference has its own\nevolving track. However, most previous works neglect the evolving trend of\npreferences and can be easily disturbed by the effect of preference drifting.\nIn this paper, we propose a novel Preference Evolution Networks for\nsession-based Recommendation (PEN4Rec) to model preference evolving process by\na two-stage retrieval from historical contexts. Specifically, the first-stage\nprocess integrates relevant behaviors according to recent items. Then, the\nsecond-stage process models the preference evolving trajectory over time\ndynamically and infer rich preferences. The process can strengthen the effect\nof relevant sequential behaviors during the preference evolution and weaken the\ndisturbance from preference drifting. Extensive experiments on three public\ndatasets demonstrate the effectiveness and superiority of the proposed model.\n"}
+{"abstract": "  The objective of this work is to localize sound sources that are visible in a\nvideo without using manual annotations. Our key technical contribution is to\nshow that, by training the network to explicitly discriminate challenging image\nfragments, even for images that do contain the object emitting the sound, we\ncan significantly boost the localization performance. We do so elegantly by\nintroducing a mechanism to mine hard samples and add them to a contrastive\nlearning formulation automatically. We show that our algorithm achieves\nstate-of-the-art performance on the popular Flickr SoundNet dataset.\nFurthermore, we introduce the VGG-Sound Source (VGG-SS) benchmark, a new set of\nannotations for the recently-introduced VGG-Sound dataset, where the sound\nsources visible in each video clip are explicitly marked with bounding box\nannotations. This dataset is 20 times larger than analogous existing ones,\ncontains 5K videos spanning over 200 categories, and, differently from Flickr\nSoundNet, is video-based. On VGG-SS, we also show that our algorithm achieves\nstate-of-the-art performance against several baselines.\n"}
+{"abstract": "  In this paper, several weighted summation formulas of $q$-hyperharmonic\nnumbers are derived. As special cases, several formulas of hyperharmonic\nnumbers of type $\\sum_{\\ell=1}^{n} {\\ell}^{p} H_{\\ell}^{(r)}$ and\n$\\sum_{\\ell=0}^{n} {\\ell}^{p} H_{n-\\ell}^{(r)}$ are obtained.\n"}
+{"abstract": "  Estimating 3D bounding boxes from monocular images is an essential component\nin autonomous driving, while accurate 3D object detection from this kind of\ndata is very challenging. In this work, by intensive diagnosis experiments, we\nquantify the impact introduced by each sub-task and found the `localization\nerror' is the vital factor in restricting monocular 3D detection. Besides, we\nalso investigate the underlying reasons behind localization errors, analyze the\nissues they might bring, and propose three strategies. First, we revisit the\nmisalignment between the center of the 2D bounding box and the projected center\nof the 3D object, which is a vital factor leading to low localization accuracy.\nSecond, we observe that accurately localizing distant objects with existing\ntechnologies is almost impossible, while those samples will mislead the learned\nnetwork. To this end, we propose to remove such samples from the training set\nfor improving the overall performance of the detector. Lastly, we also propose\na novel 3D IoU oriented loss for the size estimation of the object, which is\nnot affected by `localization error'. We conduct extensive experiments on the\nKITTI dataset, where the proposed method achieves real-time detection and\noutperforms previous methods by a large margin. The code will be made available\nat: https://github.com/xinzhuma/monodle.\n"}
+{"abstract": "  We introduce and analyze a space-time hybridized discontinuous Galerkin\nmethod for the evolutionary Navier--Stokes equations. Key features of the\nnumerical scheme include point-wise mass conservation, energy stability, and\npressure robustness. We prove that there exists a solution to the resulting\nnonlinear algebraic system in two and three spatial dimensions, and that this\nsolution is unique in two spatial dimensions under a small data assumption. A\npriori error estimates are derived for the velocity in a mesh-dependent energy\nnorm.\n"}
+{"abstract": "  Modeling a crystal as a periodic point set, we present a fingerprint\nconsisting of density functions that facilitates the efficient search for new\nmaterials and material properties. We prove invariance under isometries,\ncontinuity, and completeness in the generic case, which are necessary features\nfor the reliable comparison of crystals. The proof of continuity integrates\nmethods from discrete geometry and lattice theory, while the proof of generic\ncompleteness combines techniques from geometry with analysis. The fingerprint\nhas a fast algorithm based on Brillouin zones and related inclusion-exclusion\nformulae. We have implemented the algorithm and describe its application to\ncrystal structure prediction.\n"}
+{"abstract": "  The decomposition of the overall effect of a treatment into direct and\nindirect effects is here investigated with reference to a recursive system of\nbinary random variables. We show how, for the single mediator context, the\nmarginal effect measured on the log odds scale can be written as the sum of the\nindirect and direct effects plus a residual term that vanishes under some\nspecific conditions. We then extend our definitions to situations involving\nmultiple mediators and address research questions concerning the decomposition\nof the total effect when some mediators on the pathway from the treatment to\nthe outcome are marginalized over. Connections to the counterfactual\ndefinitions of the effects are also made. Data coming from an encouragement\ndesign on students' attitude to visit museums in Florence, Italy, are\nreanalyzed. The estimates of the defined quantities are reported together with\ntheir standard errors to compute p-values and form confidence intervals.\n"}
+{"abstract": "  Room temperature two-dimensional (2D) ferromagnetism is highly desired in\npractical spintronics applications. Recently, 1T phase CrTe2 (1T-CrTe2)\nnanosheets with five and thicker layers have been successfully synthesized,\nwhich all exhibit the properties of ferromagnetic (FM) metals with Curie\ntemperatures around 305 K. However, whether the ferromagnetism therein can be\nmaintained when continuously reducing the nanosheet's thickness to monolayer\nlimit remains unknown. Here, through first-principles calculations, we explore\nthe evolution of magnetic properties of 1 to 6 layers CrTe2 nanosheets and\nseveral interesting points are found: First, unexpectedly, monolayer CrTe2\nprefers a zigzag antiferromagnetic (AFM) state with its energy much lower than\nthat of FM state. Second, in 2 to 4 layers CrTe2, both the intralayer and\ninterlayer magnetic coupling are AFM. Last, when the number of layers is equal\nto or greater than five, the intralayer and interlayer magnetic coupling become\nFM. Theoretical analysis reveals that the in-plane lattice contraction of few\nlayer CrTe2 compared to bulk is the main factor producing intralayer AFM-FM\nmagnetic transition. At the same time, as long as the intralayer coupling gets\nFM, the interlayer coupling will concomitantly switch from AFM to FM. Such\nhighly thickness dependent magnetism provides a new perspective to control the\nmagnetic properties of 2D materials.\n"}
+{"abstract": "  Whereas the Si photonic platform is highly attractive for scalable optical\nquantum information processing, it lacks practical solutions for efficient\nphoton generation. Self-assembled semiconductor quantum dots (QDs) efficiently\nemitting photons in the telecom bands ($1460-1625$ nm) allow for heterogeneous\nintegration with Si. In this work, we report on a novel, robust, and\nindustry-compatible approach for achieving single-photon emission from InAs/InP\nQDs heterogeneously integrated with a Si substrate. As a proof of concept, we\ndemonstrate a simple vertical emitting device, employing a metallic mirror\nbeneath the QD emitter, and experimentally obtained photon extraction\nefficiencies of $\\sim10\\%$. Nevertheless, the figures of merit of our\nstructures are comparable with values previously only achieved for QDs emitting\nat shorter wavelength or by applying technically demanding fabrication\nprocesses. Our architecture and the simple fabrication procedure allows for the\ndemonstration of a single-photon generation with purity $\\mathcal{P}>98\\%$ at\nthe liquid helium temperature and $\\mathcal{P}=75\\%$ at $80$ K.\n"}
+{"abstract": "  Crowd-sourced traffic data offer great promise in environmental modeling.\nHowever, archives of such traffic data are typically not made available for\nresearch; instead, the data must be acquired in real time. The objective of\nthis paper is to present methods we developed for acquiring and analyzing time\nseries of real-time crowd-sourced traffic data. We present scripts, which can\nbe run in Unix/Linux like computational environments, to automatically download\ntiles of crowd-sourced Google traffic congestion maps for a user-specifiable\nregion of interest. Broad and international applicability of our method is\ndemonstrated for Manhattan in New York City and Mexico City. We also\ndemonstrate that Google traffic data can be used to quantify decreases in\ntraffic congestion due to social distancing policies implemented to curb the\nCOVID-19 pandemic in the South Bronx in New York City.\n"}
+{"abstract": "  Research has shown that Educational Robotics (ER) enhances student\nperformance, interest, engagement and collaboration. However, until now, the\nadoption of robotics in formal education has remained relatively scarce. Among\nother causes, this is due to the difficulty of determining the alignment of\neducational robotic learning activities with the learning outcomes envisioned\nby the curriculum, as well as their integration with traditional, non-robotics\nlearning activities that are well established in teachers' practices. This work\ninvestigates the integration of ER into formal mathematics education, through a\nquasi-experimental study employing the Thymio robot and Scratch programming to\nteach geometry to two classes of 15-year-old students, for a total of 26\nparticipants. Three research questions were addressed: (1) Should an ER-based\ntheoretical lecture precede, succeed or replace a traditional theoretical\nlecture? (2) What is the students' perception of and engagement in the ER-based\nlecture and exercises? (3) Do the findings differ according to students' prior\nappreciation of mathematics? The results suggest that ER activities are as\nvalid as traditional ones in helping students grasp the relevant theoretical\nconcepts. Robotics activities seem particularly beneficial during exercise\nsessions: students freely chose to do exercises that included the robot, rated\nthem as significantly more interesting and useful than their traditional\ncounterparts, and expressed their interest in introducing ER in other\nmathematics lectures. Finally, results were generally consistent between the\nstudents that like and did not like mathematics, suggesting the use of robotics\nas a means to broaden the number of students engaged in the discipline.\n"}
+{"abstract": "  Recent studies on the analysis of the multilingual representations focus on\nidentifying whether there is an emergence of language-independent\nrepresentations, or whether a multilingual model partitions its weights among\ndifferent languages. While most of such work has been conducted in a\n\"black-box\" manner, this paper aims to analyze individual components of a\nmultilingual neural translation (NMT) model. In particular, we look at the\nencoder self-attention and encoder-decoder attention heads (in a many-to-one\nNMT model) that are more specific to the translation of a certain language pair\nthan others by (1) employing metrics that quantify some aspects of the\nattention weights such as \"variance\" or \"confidence\", and (2) systematically\nranking the importance of attention heads with respect to translation quality.\nExperimental results show that surprisingly, the set of most important\nattention heads are very similar across the language pairs and that it is\npossible to remove nearly one-third of the less important heads without hurting\nthe translation quality greatly.\n"}
+{"abstract": "  The CMS experiment at the LHC has measured the differential cross sections of\nZ bosons decaying to pairs of leptons, as functions of transverse momentum and\nrapidity, in lead-lead collisions at a nucleon-nucleon center-of-mass energy of\n5.02 TeV. The measured Z boson elliptic azimuthal anisotropy coefficient is\ncompatible with zero, showing that Z bosons do not experience significant\nfinal-state interactions in the medium produced in the collision. Yields of Z\nbosons are compared to Glauber model predictions and are found to deviate from\nthese expectations in peripheral collisions, indicating the presence of initial\ncollision geometry and centrality selection effects. The precision of the\nmeasurement allows, for the first time, for a data-driven determination of the\nnucleon-nucleon integrated luminosity as a function of lead-lead centrality,\nthereby eliminating the need for its estimation based on a Glauber model.\n"}
+{"abstract": "  We study the statistical theory of offline reinforcement learning (RL) with\ndeep ReLU network function approximation. We analyze a variant of fitted-Q\niteration (FQI) algorithm under a new dynamic condition that we call Besov\ndynamic closure, which encompasses the conditions from prior analyses for deep\nneural network function approximation. Under Besov dynamic closure, we prove\nthat the FQI-type algorithm enjoys the sample complexity of\n$\\tilde{\\mathcal{O}}\\left( \\kappa^{1 + d/\\alpha} \\cdot \\epsilon^{-2 -\n2d/\\alpha} \\right)$ where $\\kappa$ is a distribution shift measure, $d$ is the\ndimensionality of the state-action space, $\\alpha$ is the (possibly fractional)\nsmoothness parameter of the underlying MDP, and $\\epsilon$ is a user-specified\nprecision. This is an improvement over the sample complexity of\n$\\tilde{\\mathcal{O}}\\left( K \\cdot \\kappa^{2 + d/\\alpha} \\cdot \\epsilon^{-2 -\nd/\\alpha} \\right)$ in the prior result [Yang et al., 2019] where $K$ is an\nalgorithmic iteration number which is arbitrarily large in practice.\nImportantly, our sample complexity is obtained under the new general dynamic\ncondition and a data-dependent structure where the latter is either ignored in\nprior algorithms or improperly handled by prior analyses. This is the first\ncomprehensive analysis for offline RL with deep ReLU network function\napproximation under a general setting.\n"}
+{"abstract": "  As a means for testing whether a group of agents jointly maximize random\nutility, we introduce the correlated random utility model. The correlated\nrandom utility model asks that agents face correlated random draws of\npreferences which govern their decisions. We study joint random utility\nmaximization through the lens of joint stochastic choice data (correlated\nchoice rule), a novel type of data to the stochastic choice framework. Key is\nthe property of marginality, which demands the independence of any given\nagent's marginal choices from the budgets faced by the remaining agents.\nMarginality permits the construction of well-defined marginal stochastic choice\nfunctions. Marginality and non-negativity of an analogue of the Block-Marschak\npolynomials characterize joint random utility maximization for small\nenvironments. For larger environments, we offer an example of a correlated\nchoice rule establishing that each of the marginal stochastic choice rule may\nbe stochastically rational while the correlated choice rule is not.\n"}
+{"abstract": "  (abridged) Context. The origin of hot exozodiacal dust and its connection\nwith outer dust reservoirs remains unclear. Aims. We aim to explore the\npossible connection between hot exozodiacal dust and warm dust reservoirs (>\n100 K) in asteroid belts. Methods. We use precision near-infrared\ninterferometry with VLTI/PIONIER to search for resolved emission at H band\naround a selected sample of nearby stars. Results. Our observations reveal the\npresence of resolved near-infrared emission around 17 out of 52 stars, four of\nwhich are shown to be due to a previously unknown stellar companion. The 13\nother H-band excesses are thought to originate from the thermal emission of hot\ndust grains. Taking into account earlier PIONIER observations, and after\nreevaluating the warm dust content of all our PIONIER targets through spectral\nenergy distribution modeling, we find a detection rate of 17.1(+8.1)(-4.6)% for\nH-band excess around main sequence stars hosting warm dust belts, which is\nstatistically compatible with the occurrence rate of 14.6(+4.3)(-2.8)% found\naround stars showing no signs of warm dust. After correcting for the\nsensitivity loss due to partly unresolved hot disks, under the assumption that\nthey are arranged in a thin ring around their sublimation radius, we however\nfind tentative evidence at the 3{\\sigma} level that H-band excesses around\nstars with outer dust reservoirs (warm or cold) could be statistically larger\nthan H-band excesses around stars with no detectable outer dust. Conclusions.\nOur observations do not suggest a direct connection between warm and hot dust\npopulations, at the sensitivity level of the considered instruments, although\nthey bring to light a possible correlation between the level of H-band excesses\nand the presence of outer dust reservoirs in general.\n"}
+{"abstract": "  We perform Brownian dynamics simulations of active stiff polymers undergoing\nrun-reverse dynamics, and so mimic bacterial swimming, in porous media. In\naccord with recent experiments of \\emph{Escherichia coli}, the polymer dynamics\nare characterized by trapping phases interrupted by directed hopping motion\nthrough the pores. We find that the effective translational diffusivities of\nrun-reverse agents can be enhanced up to two orders in magnitude, compared to\ntheir non-reversing counterparts, and exhibit a non-monotonic behavior as a\nfunction of the reversal rate, which we rationalize using a coarse-grained\nmodel. Furthermore, we discover a geometric criterion for the optimal\nspreading, which emerges when their run lengths are comparable to the longest\nstraight path available in the porous medium. More significantly, our criterion\nunifies results for porous media with disparate pore sizes and shapes and thus\nprovides a fundamental principle for optimal transport of microorganisms and\ncargo-carriers in densely-packed biological and environmental settings.\n"}
+{"abstract": "  In this paper, we study normal magnetic curves in $C$-manifolds. We prove\nthat magnetic trajectories with respect to the contact magnetic fields are\nindeed $\\theta_{\\alpha }$-slant curves with certain curvature functions. Then,\nwe give the parametrizations of normal magnetic curves in $\\mathbb{R}^{2n+s}$\nwith its structures as a $C$-manifold.\n"}
+{"abstract": "  The population protocol model describes a network of $n$ anonymous agents who\ncannot control with whom they interact. The agents collectively solve some\ncomputational problem through random pairwise interactions, each agent updating\nits own state in response to seeing the state of the other agent. They are\nequivalent to the model of chemical reaction networks, describing abstract\nchemical reactions such as $A+B \\rightarrow C+D$, when the latter is subject to\nthe restriction that all reactions have two reactants and two products, and all\nrate constants are 1. The counting problem is that of designing a protocol so\nthat $n$ agents, all starting in the same state, eventually converge to states\nwhere each agent encodes in its state an exact or approximate description of\npopulation size $n$. In this survey paper, we describe recent algorithmic\nadvances on the counting problem.\n"}
+{"abstract": "  For mission-critical sensing and control applications such as those to be\nenabled by 5G Ultra-Reliable, Low-Latency Communications (URLLC), it is\ncritical to ensure the communication quality of individual packets.\n  Prior studies have considered Probabilistic Per-packet Real-time\nCommunications (PPRC) guarantees for single-cell, single-channel networks with\nimplicit deadline constraints, but they have not considered real-world\ncomplexities such as inter-cell interference and multiple communication\nchannels.\n  Towards ensuring PPRC in multi-cell, multi-channel wireless networks, we\npropose a real-time scheduling algorithm based on\n\\emph{local-deadline-partition (LDP)}. The LDP algorithm is suitable for\ndistributed implementation, and it ensures probabilistic per-packet real-time\nguarantee for multi-cell, multi-channel networks with general deadline\nconstraints. We also address the associated challenge of the schedulability\ntest of PPRC traffic. In particular, we propose the concept of \\emph{feasible\nset} and identify a closed-form sufficient condition for the schedulability of\nPPRC traffic.\n  We propose a distributed algorithm for the schedulability test, and the\nalgorithm includes a procedure for finding the minimum sum work density of\nfeasible sets which is of interest by itself. We also identify a necessary\ncondition for the schedulability of PPRC traffic, and use numerical studies to\nunderstand a lower bound on the approximation ratio of the LDP algorithm.\n  We experimentally study the properties of the LDP algorithm and observe that\nthe PPRC traffic supportable by the LDP algorithm is significantly higher than\nthat of a state-of-the-art algorithm.\n"}
+{"abstract": "  Reinforcement learning (RL)-based neural architecture search (NAS) generally\nguarantees better convergence yet suffers from the requirement of huge\ncomputational resources compared with gradient-based approaches, due to the\nrollout bottleneck -- exhaustive training for each sampled generation on proxy\ntasks. In this paper, we propose a general pipeline to accelerate the\nconvergence of the rollout process as well as the RL process in NAS. It is\nmotivated by the interesting observation that both the architecture and the\nparameter knowledge can be transferred between different experiments and even\ndifferent tasks. We first introduce an uncertainty-aware critic (value\nfunction) in Proximal Policy Optimization (PPO) to utilize the architecture\nknowledge in previous experiments, which stabilizes the training process and\nreduces the searching time by 4 times. Further, an architecture knowledge pool\ntogether with a block similarity function is proposed to utilize parameter\nknowledge and reduces the searching time by 2 times. It is the first to\nintroduce block-level weight sharing in RLbased NAS. The block similarity\nfunction guarantees a 100% hitting ratio with strict fairness. Besides, we show\nthat a simply designed off-policy correction factor used in \"replay buffer\" in\nRL optimization can further reduce half of the searching time. Experiments on\nthe Mobile Neural Architecture Search (MNAS) search space show the proposed\nFast Neural Architecture Search (FNAS) accelerates standard RL-based NAS\nprocess by ~10x (e.g. ~256 2x2 TPUv2 x days / 20,000 GPU x hour -> 2,000 GPU x\nhour for MNAS), and guarantees better performance on various vision tasks.\n"}
+{"abstract": "  This study creates a physiologically realistic virtual patient database\n(VPD), representing the human arterial system, for the primary purpose of\nstudying the affects of arterial disease on haemodynamics. A low dimensional\nrepresentation of an anatomically detailed arterial network is outlined, and a\nphysiologically realistic posterior distribution for its parameters is\nconstructed through a Bayesian approach. This approach combines both\nphysiological/geometrical constraints and the available measurements reported\nin the literature. A key contribution of this work is to present a framework\nfor including all such available information for the creation of virtual\npatients (VPs). The Markov Chain Monte Carlo (MCMC) method is used to sample\nrandom VPs from this posterior distribution, and the pressure and flow-rate\nprofiles associated with the VPs are computed through a model of pulse wave\npropagation. This combination of the arterial network parameters (representing\nthe VPs) and the haemodynamics waveforms of pressure and flow-rates at various\nlocations (representing functional response of the VPs) makes up the VPD. While\n75,000 VPs are sampled from the posterior distribution, 10,000 are discarded as\nthe initial burn-in period. A further 12,857 VPs are subsequently removed due\nto the presence of negative average flow-rate. Due to an undesirable behaviour\nobserved in some VPs -- asymmetric under- and over-damped pressure and\nflow-rate profiles in the left and right sides of the arterial system -- a\nfilter is proposed for their removal. The final VPD has 28,868 subjects. It is\nshown that the methodology is appropriate by comparing the VPD statistics to\nthose reported in literature across real populations. A good agreement between\nthe two is found while respecting physiological/geometrical constraints. The\npre-filter database is made available at\nhttps://doi.org/10.5281/zenodo.4549764.\n"}
+{"abstract": "  Stuttering is a complex speech disorder identified by repeti-tions,\nprolongations of sounds, syllables or words and blockswhile speaking. Specific\nstuttering behaviour differs strongly,thus needing personalized therapy.\nTherapy sessions requirea high level of concentration by the therapist. We\nintroduceSTAN, a system to aid speech therapists in stuttering therapysessions.\nSuch an automated feedback system can lower thecognitive load on the therapist\nand thereby enable a more con-sistent therapy as well as allowing analysis of\nstuttering overthe span of multiple therapy sessions.\n"}
+{"abstract": "  We present a study of far and near-ultraviolet emission from the accretion\ndisk in a powerful Seyfert 1 galaxy IC4329A using observations performed with\nthe Ultraviolet Imaging Telescope (UVIT) onboard AstroSat. These data provide\nthe highest spatial resolution and deepest images of IC4329A in the far and\nnear UV bands acquired to date. The excellent spatial resolution of the UVIT\ndata has allowed us to accurately separate the extended emission from the host\ngalaxy and the AGN emission in the far and near UV bands. We derive the\nintrinsic AGN flux after correcting for the Galactic and internal reddening, as\nwell as for the contribution of emission lines from the broad and narrow-line\nregions. The intrinsic UV continuum emission shows a marked deficit compared to\nthat expected from the \"standard\" models of the accretion disk around an\nestimated black hole mass of 1-2x10^8Msun when the disk extends to the\ninnermost stable circular orbit. We find that the intrinsic UV continuum is\nfully consistent with the standard disk models, but only if the disk emits from\ndistances larger than 80-150 gravitational radii.\n"}
+{"abstract": "  Modern programming follows the continuous integration (CI) and continuous\ndeployment (CD) approach rather than the traditional waterfall model. Even the\ndevelopment of modern programming languages uses the CI/CD approach to swiftly\nprovide new language features and to adapt to new development environments.\nUnlike in the conventional approach, in the modern CI/CD approach, a language\nspecification is no more the oracle of the language semantics because both the\nspecification and its implementations can co-evolve. In this setting, both the\nspecification and implementations may have bugs, and guaranteeing their\ncorrectness is non-trivial.\n  In this paper, we propose a novel N+1-version differential testing to resolve\nthe problem. Unlike the traditional differential testing, our approach consists\nof three steps: 1) to automatically synthesize programs guided by the syntax\nand semantics from a given language specification, 2) to generate conformance\ntests by injecting assertions to the synthesized programs to check their final\nprogram states, 3) to detect bugs in the specification and implementations via\nexecuting the conformance tests on multiple implementations, and 4) to localize\nbugs on the specification using statistical information. We actualize our\napproach for the JavaScript programming language via JEST, which performs\nN+1-version differential testing for modern JavaScript engines and ECMAScript,\nthe language specification describing the syntax and semantics of JavaScript in\na natural language. We evaluated JEST with four JavaScript engines that support\nall modern JavaScript language features and the latest version of ECMAScript\n(ES11, 2020). JEST automatically synthesized 1,700 programs that covered 97.78%\nof syntax and 87.70% of semantics from ES11. Using the assertion-injection, it\ndetected 44 engine bugs in four engines and 27 specification bugs in ES11.\n"}
+{"abstract": "  Wilson's theorem for the factorial got generalized to the moduli $p^2$ in\n1900 and $p^3$ in 2000 by J.W.L. Glaisher and Z-H. Sun respectively. This paper\nwhich studies more generally the multiple harmonic sums $\\mathcal{H}_{\\lbrace\ns\\rbrace^{2l}=1;p-1},2\\leq 2l\\leq p-1$ modulo $p^4$ in association with the\nStirling numbers $\\left[\\begin{array}{l}\\;\\;\\;p\\\\2s-1\\end{array}\\right], 2\\leq\n2s\\leq p-1$ modulo $p^4$ is concerned with establishing a generalization of\nWilson, Glaisher and Sun's results to the modulus $p^4$. We also break\np-residues of convolutions of three divided Bernoulli numbers of respective\norders $p-1$, $p-3$ and $p-5$ into smaller pieces and generalize some results\nof Sun for some of the generalized harmonic numbers of order $p-1$ modulo\n$p^4$.\n"}
+{"abstract": "  We address the two issues raised by Bayle, Vallisneri, Babak, and Petiteau\n(in their gr-qc document arXiv:2106.03976) about our matrix formulation of\nTime-Delay Interferometry (TDI) (arXiv:2105.02054) \\cite{TDJ21}. In so doing we\nexplain and quantify our concerns about the results derived by Vallisneri,\nBayle, Babak and Petiteau \\cite{Vallisneri2020} by applying their data\nprocessing technique (named TDI-$\\infty$) to the two heterodyne measurements\nmade by a two-arm space-based GW interferometer. First we show that the\nsolutions identified by the TDI-$\\infty$ algorithm derived by Vallisneri,\nBayle, Babak and Petiteau \\cite{Vallisneri2020} {\\underbar {do}} depend on the\nboundary-conditions selected for the two-way Doppler data. We prove this by\nadopting the (non-physical) boundary conditions used by Vallisneri {\\it et al.}\nand deriving the corresponding analytic expression for a laser-noise-canceling\ncombination. We show it to be characterized by a number of Doppler measurement\nterms that grows with the observation time and works for any time-dependent\ntime delays. We then prove that, for a constant-arm-length interferometer whose\ntwo-way light times are equal to twice and three-times the sampling time, the\nsolutions identified by TDI-$\\infty$ are linear combinations of the TDI\nvariable $X$. In the second part of this document we address the concern\nexpressed by Bayle {\\it et al.} regarding our matrix formulation of TDI when\nthe two-way light-times are constant but not equal to integer multiples of the\nsampling time. We mathematically prove the homomorphism between the delay\noperators and their matrix representation \\cite{TDJ21} holds in general. By\nsequentially applying two order-$m$ Fractional-Delay (FD) Lagrange filters of\ndelays $l_1$, $l_2$ we find its result to be equal to applying an order-$m$ FD\nLagrange filter of delay $l_1 + l_2$.\n"}
+{"abstract": "  We propose a semantic similarity metric for image registration. Existing\nmetrics like Euclidean Distance or Normalized Cross-Correlation focus on\naligning intensity values, giving difficulties with low intensity contrast or\nnoise. Our approach learns dataset-specific features that drive the\noptimization of a learning-based registration model. We train both an\nunsupervised approach using an auto-encoder, and a semi-supervised approach\nusing supplemental segmentation data to extract semantic features for image\nregistration. Comparing to existing methods across multiple image modalities\nand applications, we achieve consistently high registration accuracy. A learned\ninvariance to noise gives smoother transformations on low-quality images.\n"}
+{"abstract": "  This paper surveys several recent abstract summarization methods: T5,\nPegasus, and ProphetNet. We implement the systems in two languages: English and\nIndonesian languages. We investigate the impact of pre-training models (one T5,\nthree Pegasuses, three ProphetNets) on several Wikipedia datasets in English\nand Indonesian language and compare the results to the Wikipedia systems'\nsummaries. The T5-Large, the Pegasus-XSum, and the ProphetNet-CNNDM provide the\nbest summarization. The most significant factors that influence ROUGE\nperformance are coverage, density, and compression. The higher the scores, the\nbetter the summary. Other factors that influence the ROUGE scores are the\npre-training goal, the dataset's characteristics, the dataset used for testing\nthe pre-trained model, and the cross-lingual function. Several suggestions to\nimprove this paper's limitation are: 1) assure that the dataset used for the\npre-training model must sufficiently large, contains adequate instances for\nhandling cross-lingual purpose; 2) Advanced process (finetuning) shall be\nreasonable. We recommend using the large dataset consists of comprehensive\ncoverage of topics from many languages before implementing advanced processes\nsuch as the train-infer-train procedure to the zero-shot translation in the\ntraining stage of the pre-training model.\n"}
+{"abstract": "  We discuss $C^1$ regularity and developability of isometric immersions of\nflat domains into $\\mathbb R^3$ enjoying a local fractional Sobolev $W^{1+s,\n\\frac2s}$ regularity for $2/3 \\le s< 1 $, generalizing the known results on\nSobolev and H\\\"older regimes. Ingredients of the proof include analysis of the\nweak Codazzi-Mainardi equations of the isometric immersions and study of\n$W^{2,\\frac2s}$ planar deformations with symmetric Jacobian derivative and\nvanishing distributional Jacobian determinant. On the way, we also show that\nthe distributional Jacobian determinant, conceived as an operator defined on\nthe Jacobian matrix, behaves like determinant of gradient matrices under\nproducts by scalar functions.\n"}
+{"abstract": "  SPT-3G is the third survey receiver operating on the South Pole Telescope\ndedicated to high-resolution observations of the cosmic microwave background\n(CMB). Sensitive measurements of the temperature and polarization anisotropies\nof the CMB provide a powerful dataset for constraining cosmology. Additionally,\nCMB surveys with arcminute-scale resolution are capable of detecting galaxy\nclusters, millimeter-wave bright galaxies, and a variety of transient\nphenomena. The SPT-3G instrument provides a significant improvement in mapping\nspeed over its predecessors, SPT-SZ and SPTpol. The broadband optics design of\nthe instrument achieves a 430 mm diameter image plane across observing bands of\n95 GHz, 150 GHz, and 220 GHz, with 1.2 arcmin FWHM beam response at 150 GHz. In\nthe receiver, this image plane is populated with 2690 dual-polarization,\ntri-chroic pixels (~16000 detectors) read out using a 68X digital\nfrequency-domain multiplexing readout system. In 2018, SPT-3G began a multiyear\nsurvey of 1500 deg$^{2}$ of the southern sky. We summarize the unique optical,\ncryogenic, detector, and readout technologies employed in SPT-3G, and we report\non the integrated performance of the instrument.\n"}
+{"abstract": "  We clarify and generalize the ant on a rubber rope paradox, which is a\nmathematical puzzle with a solution that appears counterintuitive. In this\npaper, we show that the ant can still reach the end of the rope even if we\nconsider the step length of the ant and stretching length of the rubber rope as\nrandom variables.\n"}
+{"abstract": "  The young T Tauri star WW Cha was recently proposed to be a close binary\nobject with strong infrared and submillimeter excess associated with\ncircum-system emission. This makes WW Cha a very interesting source for\nstudying the influence of dynamical effects on circumstellar as well as\ncircumbinary material. We derive the relative astrometric positions and flux\nratios of the stellar companion in WW Cha from the interferometric model\nfitting of observations made with the VLTI instruments AMBER, PIONIER, and\nGRAVITY in the near-infrared from 2011 to 2020. For two epochs, the resulting\nuv-coverage in spatial frequencies permits us to perform the first image\nreconstruction of the system in the K band. The positions of nine epochs are\nused to determine the orbital elements and the total mass of the system. We\nfind the secondary star orbiting the primary with a period of T=206.55 days, a\nsemimajor axis of a=1.01 au, and a relatively high eccentricity of e=0.45.\nCombining the orbital solution with distance measurements from Gaia DR2 and the\nanalysis of evolutionary tracks, the dynamical mass of Mtot=3.20 Msol can be\nexplained by a mass ratio between ~0.5 and 1. The orbital angular momentum\nvector is in close alignment with the angular momentum vector of the outer disk\nas measured by ALMA and SPHERE. The analysis of the relative photometry\nsuggests the presence of infrared excess surviving in the system and likely\noriginating from truncated circumstellar disks. The flux ratio between the two\ncomponents appears variable, in particular in the K band, and may hint at\nperiods of triggered higher and lower accretion or changes in the disks'\nstructures. The knowledge of the orbital parameters, combined with a relatively\nshort period, makes WW Cha an ideal target for studying the interaction of a\nclose young T Tauri binary with its surrounding material, such as\ntime-dependent accretion phenomena.\n"}
+{"abstract": "  This paper describes a system by which Unmanned Aerial Vehicles (UAVs) can\ngather high-quality face images that can be used in biometric identification\ntasks. Success in face-based identification depends in large part on the image\nquality, and a major factor is how frontal the view is. Face recognition\nsoftware pipelines can improve identification rates by synthesizing frontal\nviews from non-frontal views by a process call {\\em frontalization}. Here we\nexploit the high mobility of UAVs to actively gather frontal images using\ncomponents of a synthetic frontalization pipeline. We define a frontalization\nerror and show that it can be used to guide an UAVs to capture frontal views.\nFurther, we show that the resulting image stream improves matching quality of a\ntypical face recognition similarity metric. The system is implemented using an\noff-the-shelf hardware and software components and can be easily transfered to\nany ROS enabled UAVs.\n"}
+{"abstract": "  The article presents a matrix differential operator and a pseudoinverse\nmatrix differential operator for finding a particular solution to\nnonhomogeneous linear ordinary differential equations (ODE) with constant\ncoefficients with special types of the right-hand side. Calculation requires\nthe determination of an inverse or pseudoinverse matrix. If the matrix is\nsingular, the Moore-Penrose pseudoinverse matrix is used for the calculation,\nwhich is simply calculated as the inverse submatrix of the considered matrix.\nIt is shown that block matrices are effectively used to calculate a particular\nsolution.\n"}
+{"abstract": "  The role of bipolar jets in the formation of stars, and in particular how\nthey are launched, is still not well understood. We probe the protostellar jet\nlaunching mechanism, via high resolution observations of the near-IR [FeII]\n1.53,1.64 micron lines. We consider the bipolar jet from the Classical T Tauri\nstar, DO Tau, & investigate jet morphology & kinematics close to the star,\nusing AO-assisted IFU observations from GEMINI/NIFS. The brighter, blue-shifted\njet is collimated quickly after launch. This early collimation requires the\npresence of magnetic fields. We confirm velocity asymmetries between the two\njet lobes, & confirm no time variability in the asymmetry over a 20 year\ninterval. This sustained asymmetry is in accordance with recent simulations of\nmagnetised disk-winds. We examine the data for jet rotation. We report an upper\nlimit on differences in radial velocity of 6.3 & 8.7 km/s for the blue &\nred-shifted jets, respectively. Interpreting this as an upper limit on jet\nrotation implies that any steady, axisymmetric magneto-centrifugal model of jet\nlaunching is constrained to a launch radius in the disk-plane of 0.5 & 0.3 au\nfor the blue & red-shifted jets, respectively. This supports an X-wind or\nnarrow disk-wind model. This pertains only to the observed high velocity [FeII]\nemission, & does not rule out a wider flow launched from a wider radius. We\nreport detection of small amplitude jet axis wiggling in both lobes. We rule\nout orbital motion of the jet source as the cause. Precession can better\naccount for the observations but requires double the precession angle, & a\ndifferent phase for the counter-jet. Such non-solid body precession could arise\nfrom an inclined massive Jupiter companion, or a warping instability induced by\nlaunching a magnetic disk-wind. Overall, our observations are consistent with\nan origin of the DO Tau jets from the inner regions of the disk.\n"}
+{"abstract": "  Infrared dark clouds (IRDCs) are potential hosts of the elusive early phases\nof high-mass star formation (HMSF). Here we conduct an in-depth analysis of the\nfragmentation properties of a sample of 10 IRDCs, which have been highlighted\nas some of the best candidates to study HMSF within the Milky Way. To do so, we\nhave obtained a set of large mosaics covering these IRDCs with ALMA at band 3\n(or 3mm). These observations have a high angular resolution (~3arcsec or\n~0.05pc), and high continuum and spectral line sensitivity (~0.15mJy/beam and\n~0.2K per 0.1km/s channel at the N2H+(1-0) transition). From the dust continuum\nemission, we identify 96 cores ranging from low- to high-mass (M = 3.4 to\n50.9Msun) that are gravitationally bound (alpha_vir = 0.3 to 1.3) and which\nwould require magnetic field strengths of B = 0.3 to 1.0mG to be in virial\nequilibrium. We combine these results with a homogenised catalogue of\nliterature cores to recover the hierarchical structure within these clouds over\nfour orders of magnitude in spatial scale (0.01pc to 10pc). Using supplementary\nobservations at an even higher angular resolution, we find that the smallest\nfragments (<0.02pc) within this hierarchy do not currently have the mass and/or\nthe density required to form high-mass stars. Nonetheless, the new ALMA\nobservations presented in this paper have facilitated the identification of 19\n(6 quiescent and 13 star-forming) cores that retain >16Msun without further\nfragmentation. These high-mass cores contain trans-sonic non-thermal motions,\nare kinematically sub-virial, and require moderate magnetic field strengths for\nsupport against collapse. The identification of these potential sites of\nhigh-mass star formation represents a key step in allowing us to test the\npredictions from high-mass star and cluster formation theories.\n"}
+{"abstract": "  Recent observations of the HDO/H$_2$O ratio toward protostars in isolated and\nclustered environments show an apparent dichotomy, where isolated sources show\nhigher D/H ratios than clustered counterparts. Establishing which physical and\nchemical processes create this differentiation can provide insights into the\nchemical evolution of water during star formation and the chemical diversity\nduring the star formation process and in young planetary systems. Methods: The\nevolution of water is modeled using 3D physicochemical models of a dynamic\nstar-forming environment. The physical evolution during the protostellar\ncollapse is described by tracer particles from a 3D MHD simulation of a\nmolecular cloud region. Each particle trajectory is post-processed using\nRADMC-3D to calculate the temperature and radiation field. The chemical\nevolution is simulated using a three-phase grain-surface chemistry model and\nthe results are compared with interferometric observations of H$_2$O, HDO, and\nD$_2$O in hot corinos toward low-mass protostars. Results: The physicochemical\nmodel reproduces the observed HDO/H$_2$O and D$_2$O/HDO ratios in hot corinos,\nbut shows no correlation with cloud environment for similar identical\nconditions. The observed dichotomy in water D/H ratios requires variation in\nthe initial conditions (e.g., the duration and temperature of the prestellar\nphase). Reproducing the observed D/H ratios in hot corinos requires a\nprestellar phase duration $t\\sim$1-3 Myr and temperatures in the range $T \\sim$\n10-20 K prior to collapse. This work demonstrates that the observed\ndifferentiation between clustered and isolated protostars stems from\ndifferences in the molecular cloud or prestellar core conditions and does not\narise during the protostellar collapse itself.\n"}
+{"abstract": "  In this paper we report on detailed temperature and magnetic field dependence\nof m agnetization of IV-VI semiconductor PbTe doped with mixed valence\ntransition metal Cr$^{2+/3+}$. The material is studied solely by an integral\nsuperconducting quantum interference device magnetometer in order to\nquantitatively determine the contribution of single substitutional Cr$^{3+}$ as\nwell as of various Cr-Te magnetic nanocrystals, including their identification.\nThe applied experimental procedure reveals the presence of about\n$10^{19}$~cm$^{-3}$ paramagnetic Cr$^{3+}$ ions formed via self-ionization of\nCr$^{2+}$ resonant donors. These are known to improve the thermoelectric figure\nof merit parameter zT of this semiconductor. The magnetic finding excellently\nagrees with previous Hall effect studies thus providing a new experimental\nsupport for the proposed electronic structure model of PbTe:Cr system with\nresonant Cr$^{2+/3+}$ state located (at low temperatures) about 100 meV above\nthe bottom of the conduction band. Below room temperature a ferromagnetic-like\nsignal points to the presence of Cr-rich nanocrystalline precipitates. Two most\nlikely candidates, namely: Cr$_2$Te$_3$ and Cr$_5$Te$_8$ are identified upon\ndedicated temperature cycling of the sample at the remnant state. As an\nensemble, the nanocrystals exhibits (blocked) superparamagnetic properties. The\nmagnetic susceptibility of both n- and p-type PbTe in the temperature range\n$100 < T < 400$~K has been established. These magnitudes are essential in\nproper accounting for the high temperature magnetic susceptibility of PbTe:Cr.\n"}
+{"abstract": "  The largest experiments in machine learning now require resources far beyond\nthe budget of all but a few institutions. Fortunately, it has recently been\nshown that the results of these huge experiments can often be extrapolated from\nthe results of a sequence of far smaller, cheaper experiments. In this work, we\nshow that not only can the extrapolation be done based on the size of the\nmodel, but on the size of the problem as well. By conducting a sequence of\nexperiments using AlphaZero and Hex, we show that the performance achievable\nwith a fixed amount of compute degrades predictably as the game gets larger and\nharder. Along with our main result, we further show that the test-time and\ntrain-time compute available to an agent can be traded off while maintaining\nperformance.\n"}
+{"abstract": "  Deep Reinforcement Learning (DRL) has shown outstanding performance on\ninducing effective action policies that maximize expected long-term return on\nmany complex tasks. Much of DRL work has been focused on sequences of events\nwith discrete time steps and ignores the irregular time intervals between\nconsecutive events. Given that in many real-world domains, data often consists\nof temporal sequences with irregular time intervals, and it is important to\nconsider the time intervals between temporal events to capture latent\nprogressive patterns of states. In this work, we present a general Time-Aware\nRL framework: Time-aware Q-Networks (TQN), which takes into account physical\ntime intervals within a deep RL framework. TQN deals with time irregularity\nfrom two aspects: 1) elapsed time in the past and an expected next observation\ntime for time-aware state approximation, and 2) action time window for the\nfuture for time-aware discounting of rewards. Experimental results show that by\ncapturing the underlying structures in the sequences with time irregularities\nfrom both aspects, TQNs significantly outperform DQN in four types of contexts\nwith irregular time intervals. More specifically, our results show that in\nclassic RL tasks such as CartPole and MountainCar and Atari benchmark with\nrandomly segmented time intervals, time-aware discounting alone is more\nimportant while in the real-world tasks such as nuclear reactor operation and\nseptic patient treatment with intrinsic time intervals, both time-aware state\nand time-aware discounting are crucial. Moreover, to improve the agent's\nlearning capacity, we explored three boosting methods: Double networks, Dueling\nnetworks, and Prioritized Experience Replay, and our results show that for the\ntwo real-world tasks, combining all three boosting methods with TQN is\nespecially effective.\n"}
+{"abstract": "  Anti-unification in logic programming refers to the process of capturing\ncommon syntactic structure among given goals, computing a single new goal that\nis more general called a generalization of the given goals. Finding an\narbitrary common generalization for two goals is trivial, but looking for those\ncommon generalizations that are either as large as possible (called largest\ncommon generalizations) or as specific as possible (called most specific\ngeneralizations) is a non-trivial optimization problem, in particular when\ngoals are considered to be \\textit{unordered} sets of atoms. In this work we\nprovide an in-depth study of the problem by defining two different\ngeneralization relations. We formulate a characterization of what constitutes a\nmost specific generalization in both settings. While these generalizations can\nbe computed in polynomial time, we show that when the number of variables in\nthe generalization needs to be minimized, the problem becomes NP-hard. We\nsubsequently revisit an abstraction of the largest common generalization when\nanti-unification is based on injective variable renamings, and prove that it\ncan be computed in polynomially bounded time.\n"}
+{"abstract": "  Wavelet scattering networks, which are convolutional neural networks (CNNs)\nwith fixed filters and weights, are promising tools for image analysis.\nImposing symmetry on image statistics can improve human interpretability, aid\nin generalization, and provide dimension reduction. In this work, we introduce\na fast-to-compute, translationally invariant and rotationally equivariant\nwavelet scattering network (EqWS) and filter bank of wavelets (triglets). We\ndemonstrate the interpretability and quantify the invariance/equivariance of\nthe coefficients, briefly commenting on difficulties with implementing scale\nequivariance. On MNIST, we show that training on a rotationally invariant\nreduction of the coefficients maintains rotational invariance when generalized\nto test data and visualize residual symmetry breaking terms. Rotation\nequivariance is leveraged to estimate the rotation angle of digits and\nreconstruct the full rotation dependence of each coefficient from a single\nangle. We benchmark EqWS with linear classifiers on EMNIST and CIFAR-10/100,\nintroducing a new second-order, cross-color channel coupling for the color\nimages. We conclude by comparing the performance of an isotropic reduction of\nthe scattering coefficients and RWST, a previous coefficient reduction, on an\nisotropic classification of magnetohydrodynamic simulations with astrophysical\nrelevance.\n"}
+{"abstract": "  From spiking activity in neuronal networks to force chains in granular\nmaterials, the behavior of many real-world systems depends on a network of both\nstrong and weak interactions. These interactions give rise to complex and\nhigher-order system behaviors, and are encoded using data as the network's\nedges. However, distinguishing between true weak edges and low-weight edges\ncaused by noise remains a challenge. We address this problem by examining the\nhigher-order structure of noisy, weak edges added to model networks. We find\nthat the structure of low-weight, noisy edges varies according to the topology\nof the model network to which it is added. By investigating this variation more\nclosely, we see that at least three qualitative classes of noise structure\nemerge. Furthermore, we observe that the structure of noisy edges contains\nenough model-specific information to classify the model networks with moderate\naccuracy. Finally, we offer network generation rules that can drive different\ntypes of structure in added noisy edges. Our results demonstrate that noise\ndoes not present as a monolithic nuisance, but rather as a nuanced,\ntopology-dependent, and even useful entity in characterizing higher-order\nnetwork interactions. Hence, we provide an alternate approach to noise\nmanagement by embracing its role in such interactions.\n"}
+{"abstract": "  We argue that long optical storage times are required to establish\nentanglement at high rates over large distances using memory-based quantum\nrepeaters. Triggered by this conclusion, we investigate the $^3$H$_6$\n$\\leftrightarrow$ $^3$H$_4$ transition at 795.325 nm of Tm:Y$_3$Ga$_5$O$_{12}$\n(Tm:YGG). Most importantly, we show that the optical coherence time can reach\n1.1 ms, and, using laser pulses, we demonstrate optical storage based on the\natomic frequency comb protocol up to 100 $\\mu$s as well as a memory decay time\nT$_M$ of 13.1 $\\mu$s. Possibilities of how to narrow the gap between the\nmeasured value of T$_m$ and its maximum of 275 $\\mu$s are discussed. In\naddition, we demonstrate quantum state storage using members of non-classical\nphoton pairs. Our results show the potential of Tm:YGG for creating quantum\nmemories with long optical storage times, and open the path to building\nextended quantum networks.\n"}
+{"abstract": "  The asteroid exploration project \"Hayabusa2\" has successfully returned\nsamples from the asteroid (162173) Ryugu. In this study, we measured the linear\npolarization degrees of Ryugu using four ground-based telescopes from 2020\nSeptember 27 to December 25, covering a wide-phase angle (Sun-target-observer's\nangle) range from 28$^\\circ$ to 104$^\\circ$. We found that the polarization\ndegree of Ryugu reached 53$\\%$ around a phase angle of 100$^\\circ$, the highest\nvalue among all asteroids and comets thus far reported. The high polarization\ndegree of Ryugu can be attributed to the scattering properties of its surface\nlayers, in particular the relatively small contribution of multiply-scattered\nlight. Our polarimetric results indicate that Ryugu's surface is covered with\nlarge grains. On the basis of a comparison with polarimetric measurements of\npulverized meteorites, we can infer the presence of submillimeter-sized grains\non the surface layer of Ryugu. We also conjecture that this size boundary\nrepresents the grains that compose the aggregate. It is likely that a very\nbrittle structure has been lost in the recovered samples, although they may\nhold a record of its evolution. Our data will be invaluable for future\nexperiments aimed at reproducing the surface structure of Ryugu.\n"}
+{"abstract": "  Pencil x-ray beam imaging provides superior spatial resolution than other\nimaging geometries like sheet beam and cone beam geometries due to the\nillumination of a line instead of an area or volume. However, the pencil beam\ngeometry suffers from long scan times and concerns over dose discourage\nlaboratory use of pencil beam x-ray sources. Molecular imaging techniques like\nXLCT imaging benefit most from pencil beam imaging to accurately localize the\ndistribution of contrast agents embedded in a small animal object. To\ninvestigate the dose deposited by pencil beam x-ray imaging in XLCT, dose\nestimations from one angular projection scan by three different x-ray source\nenergies were performed on a small animal object composed of water, bone, and\nblood with a Monte Carlo simulation platform, GATE (Geant4 Application for\nTomographic Emission). Our results indicate that, with an adequate x-ray\nbenchtop source with high brilliance and quasi-monochromatic properties like\nthe Sigray source, the dose concerns can be reduced. With the Sigray source,\nthe bone marrow was estimated to have a radiation dose of 30 mGy for a typical\nXLCT imaging, in which we have 6 angular projections, 100 micrometer scan step\nsize, and 10^6 x-ray photons per linear scan.\n"}
+{"abstract": "  As of today, the fifth generation (5G) mobile communication system has been\nrolled out in many countries and the number of 5G subscribers already reaches a\nvery large scale. It is time for academia and industry to shift their attention\ntowards the next generation. At this crossroad, an overview of the current\nstate of the art and a vision of future communications are definitely of\ninterest. This article thus aims to provide a comprehensive survey to draw a\npicture of the sixth generation (6G) system in terms of drivers, use cases,\nusage scenarios, requirements, key performance indicators (KPIs), architecture,\nand enabling technologies. First, we attempt to answer the question of \"Is\nthere any need for 6G?\" by shedding light on its key driving factors, in which\nwe predict the explosive growth of mobile traffic until 2030, and envision\npotential use cases and usage scenarios. Second, the technical requirements of\n6G are discussed and compared with those of 5G with respect to a set of KPIs in\na quantitative manner. Third, the state-of-the-art 6G research efforts and\nactivities from representative institutions and countries are summarized, and a\ntentative roadmap of definition, specification, standardization, and regulation\nis projected. Then, we identify a dozen of potential technologies and introduce\ntheir principles, advantages, challenges, and open research issues. Finally,\nthe conclusions are drawn to paint a picture of \"What 6G may look like?\". This\nsurvey is intended to serve as an enlightening guideline to spur interests and\nfurther investigations for subsequent research and development of 6G\ncommunications systems.\n"}
+{"abstract": "  Let $A$ and $B$ be $C^*$-algebras, $A$ separable and $I$ an ideal in $B$. We\nshow that for any completely positive contractive linear map $\\psi\\colon A\\to\nB/I$ there is a continuous family $\\Theta_t\\colon A\\to B$, for $t\\in\n[1,\\infty)$, of lifts of $\\psi$ that are asymptotically linear, asymptotically\ncompletely positive and asymptotically contractive. If $\\psi$ is orthogonality\npreserving, then $\\Theta_t$ can be chosen to have this property asymptotically.\nIf $A$ and $B$ carry continuous actions of a second countable locally compact\ngroup $G$ such that $I$ is $G$-invariant and $\\psi$ is equivariant, we show\nthat the family $\\Theta_t$ can be chosen to be asymptotically equivariant. If a\nlinear completely positive lift for $\\psi$ exists, we can arrange that\n$\\Theta_t$ is linear and completely positive for all $t\\in [1,\\infty)$. In the\nequivariant setting, if $A$, $B$ and $\\psi$ are unital, we show that\nasymptotically linear unital lifts are only guaranteed to exist if $G$ is\namenable. This leads to a new characterization of amenability in terms of the\nexistence of asymptotically equivariant unital sections for quotient maps.\n"}
+{"abstract": "  Batch normalization is a key component of most image classification models,\nbut it has many undesirable properties stemming from its dependence on the\nbatch size and interactions between examples. Although recent work has\nsucceeded in training deep ResNets without normalization layers, these models\ndo not match the test accuracies of the best batch-normalized networks, and are\noften unstable for large learning rates or strong data augmentations. In this\nwork, we develop an adaptive gradient clipping technique which overcomes these\ninstabilities, and design a significantly improved class of Normalizer-Free\nResNets. Our smaller models match the test accuracy of an EfficientNet-B7 on\nImageNet while being up to 8.7x faster to train, and our largest models attain\na new state-of-the-art top-1 accuracy of 86.5%. In addition, Normalizer-Free\nmodels attain significantly better performance than their batch-normalized\ncounterparts when finetuning on ImageNet after large-scale pre-training on a\ndataset of 300 million labeled images, with our best models obtaining an\naccuracy of 89.2%. Our code is available at https://github.com/deepmind/\ndeepmind-research/tree/master/nfnets\n"}
+{"abstract": "  In this paper, we present a general numerical platform for designing\naccurate, efficient, and stable numerical algorithms for incompressible\nhydrodynamic models that obeys the thermodynamical laws. The obtained numerical\nschemes are automatically linear in time. It decouples the hydrodynamic\nvariable and other state variables such that only small-size linear problems\nneed to be solved at each time marching step. Furthermore, if the classical\nvelocity projection method is utilized, the velocity field and pressure field\ncan be decoupled. In the end, only a few elliptic-type equations shall be\nsolved in each time step. This strategy is made possible through a sequence of\nmodel reformulations by fully exploring the models' thermodynamic structures.\nThe generalized Onsager principle directly guides these reformulation\nprocedures. In the reformulated but equivalent models, the reversible and\nirreversible components can be identified, guiding the numerical platform to\ndecouple the reversible and irreversible dynamics. This eventually leads to\ndecoupled numerical algorithms, given that the coupling terms only involve\nirreversible dynamics. To further demonstrate the numerical platform's power,\nwe apply it to several specific incompressible hydrodynamic models. The energy\nstability of the proposed numerical schemes is shown in detail. The\nsecond-order accuracy in time is verified numerically through time step\nrefinement tests. Several benchmark numerical examples are presented to further\nillustrate the proposed numerical framework's accuracy, stability, and\nefficiency.\n"}
+{"abstract": "  We place observational constraints on two models within a class of scenarios\nfeaturing an elastic interaction between dark energy and dark matter that only\nproduces momentum exchange up to first order in cosmological perturbations. The\nfirst one corresponds to a perfect-fluid model of the dark components with an\nexplicit interacting Lagrangian, where dark energy acts as a dark radiation at\nearly times and behaves as a cosmological constant at late times. The second\none is a dynamical dark energy model with a dark radiation component, where the\nmomentum exchange covariantly modifies the conservation equations in the dark\nsector. Using Cosmic Microwave Background (CMB), Baryon Acoustic Oscillations\n(BAO), and Supernovae type Ia (SnIa) data, we show that the Hubble tension can\nbe alleviated due to the additional radiation, while the $\\sigma_8$ tension\npresent in the $\\Lambda$-Cold-Dark-Matter model can be eased by the weaker\ngalaxy clustering that occurs in these interacting models. Furthermore, we show\nthat, while CMB+BAO+SnIa data put only upper bounds on the coupling strength,\nadding low-redshift data in the form of a constraint on the parameter $S_8$\nstrongly favours nonvanishing values of the interaction parameters. Our\nfindings are in line with other results in the literature that could signal a\nuniversal trend of the momentum exchange among the dark sector.\n"}
+{"abstract": "  We study analytically how noninteracting weakly active particles, for which\npassive Brownian diffusion cannot be neglected and activity can be treated\nperturbatively, distribute and behave near boundaries in various geometries. In\nparticular, we develop a perturbative approach for the model of active\nparticles driven by an exponentially correlated random force (active\nOrnstein-Uhlenbeck particles). This approach involves a relatively simple\nexpansion of the distribution in powers of the P\\'{e}clet number and in terms\nof Hermite polynomials. We use this approach to cleanly formulate boundary\nconditions, which allows us to study weakly active particles in several\ngeometries: confinement by a single wall or between two walls in 1D,\nconfinement in a circular or wedge-shaped region in 2D, motion near a\ncorrugated boundary, and finally absorption onto a sphere. We consider how\nquantities such as the density, pressure, and flow of the active particles\nchange as we gradually increase the activity away from a purely passive system.\nThese results for the limit of weak activity help us gain insight into how\nactive particles behave in the presence of various types of boundaries.\n"}
+{"abstract": "  Quantum Darwinism proposes that the proliferation of redundant information\nplays a major role in the emergence of objectivity out of the quantum world. Is\nthis kind of objectivity necessarily classical? We show that if one takes\nSpekkens' notion of noncontextuality as the notion of classicality and the\napproach of Brand\\~{a}o, Piani and Horodecki to quantum Darwinism, the answer\nto the above question is `yes', if the environment encodes sufficiently well\nthe proliferated information. Moreover, we propose a threshold on this\nencoding, above which one can unambiguously say that classical objectivity has\nemerged under quantum Darwinism.\n"}
+{"abstract": "  Some audio declipping methods produce waveforms that do not fully respect the\nphysical process of clipping, which is why we refer to them as inconsistent.\nThis letter reports what effect on perception it has if the solution by\ninconsistent methods is forced consistent by postprocessing. We first propose a\nsimple sample replacement method, then we identify its main weaknesses and\npropose an improved variant. The experiments show that the vast majority of\ninconsistent declipping methods significantly benefit from the proposed\napproach in terms of objective perceptual metrics. In particular, we show that\nthe SS PEW method based on social sparsity combined with the proposed method\nperforms comparable to top methods from the consistent class, but at a\ncomputational cost of one order of magnitude lower.\n"}
+{"abstract": "  The strong constraints from the Fermi-LAT data on the isotropic gamma-ray\nbackground suggest that the neutrinos observed by IceCube might possibly come\nfrom sources that are hidden to gamma-ray observations. A possibility recently\ndiscussed in the literature is that neutrinos may come from jets of collapsing\nmassive stars which fail to break out of the stellar envelope, and for this\nreason they are known as choked jets, or choked Gamma-Ray Bursts (GRBs). In\nthis paper, we estimate the neutrino flux and spectrum expected from these\nsources, focusing on Type II SNe. We perform detailed calculations of pg\ninteractions, accounting for all the neutrino production channels and\nscattering angles. We provide predictions of expected event rates for operating\nneutrino telescopes, such as ANTARES and IceCube, as well as for the future\ngeneration telescope KM3NeT. We find that for GRB energies channeled into\nprotons spanning between 10^51 - 10^53 erg, choked GRBs may substantially\ncontribute to the observed astrophysical neutrino flux, if their local rate is\n80 - 1 Gpc^-3 yr^-1 respectively.\n"}
+{"abstract": "  It is becoming increasingly common to study complex associations between\nmultiple phenotypes and high-dimensional genomic features in biomedicine.\nHowever, it requires flexible and efficient joint statistical models if there\nare correlations between multiple response variables and between\nhigh-dimensional predictors. We propose a structured multivariate Bayesian\nvariable selection model to identify sparse predictors associated with multiple\ncorrelated response variables. The approach makes use of known structure\ninformation between the multiple response variables and high-dimensional\npredictors via a Markov random field (MRF) prior for the latent indicator\nvariables of the coefficient matrix of a sparse seemingly unrelated regressions\n(SSUR). The structure information included in the MRF prior can improve the\nmodel performance (i.e., variable selection and response prediction) compared\nto other common priors. In addition, we employ random effects to capture\nheterogeneity of grouped samples. The proposed approach is validated by\nsimulation studies and applied to a pharmacogenomic study which includes\npharmacological profiling and multi-omics data (i.e., gene expression, copy\nnumber variation and mutation) from in vitro anti-cancer drug sensitivity\nscreening.\n"}
+{"abstract": "  This paper proposes a framework to investigate the value of sharing\nprivacy-protected smart meter data between domestic consumers and load serving\nentities. The framework consists of a discounted differential privacy model to\nensure individuals cannot be identified from aggregated data, a ANN-based\nshort-term load forecasting to quantify the impact of data availability and\nprivacy protection on the forecasting error and an optimal procurement problem\nin day-ahead and balancing markets to assess the market value of the\nprivacy-utility trade-off. The framework demonstrates that when the load\nprofile of a consumer group differs from the system average, which is\nquantified using the Kullback-Leibler divergence, there is significant value in\nsharing smart meter data while retaining individual consumer privacy.\n"}
+{"abstract": "  Rare-earth (R) nickelates (such as perovskite RNiO3, trilayer R4Ni3O10, and\ninfinite layer RNiO2) have attracted tremendous interest very recently.\nHowever, unlike widely studied RNiO3 and RNiO2 films, the synthesis of trilayer\nnickelate R4Ni3O10 films is rarely reported. Here, single-crystalline\n(Nd0.8Sr0.2)4Ni3O10 epitaxial films were coherently grown on SrTiO3 substrates\nby high-pressure magnetron sputtering. The crystal and electronic structures of\n(Nd0.8Sr0.2)4Ni3O10 films were characterized by high-resolution X-ray\ndiffraction and X-ray photoemission spectroscopy, respectively. The electrical\ntransport measurements reveal a metal-insulator transition near 82 K and\nnegative magnetoresistance in (Nd0.8Sr0.2)4Ni3O10 films. Our work provides a\nnovel route to synthesize high-quality trilayer nickelate R4Ni3O10 films.\n"}
+{"abstract": "  This paper presents a new mathematical signal transform that is especially\nsuitable for decoding information related to non-rigid signal displacements. We\nprovide a measure theoretic framework to extend the existing Cumulative\nDistribution Transform [ACHA 45 (2018), no. 3, 616-641] to arbitrary (signed)\nsignals on $\\overline{\\mathbb{R}}$. We present both forward (analysis) and\ninverse (synthesis) formulas for the transform, and describe several of its\nproperties including translation, scaling, convexity, linear separability and\nothers. Finally, we describe a metric in transform space, and demonstrate the\napplication of the transform in classifying (detecting) signals under random\ndisplacements.\n"}
+{"abstract": "  This study is devoted to the implications of scale-dependent gravity in\nCosmology. Redshift-space distortion data indicate that there is a tension\nbetween $\\Lambda$CDM and available observations as far as the value of the rms\ndensity fluctuation, $\\sigma_8$, is concerned. It has been pointed out that\nthis tension may be alleviated in alternative theories in which gravity is\nweaker at red-shift $z \\sim 1$. We study the evolution of density perturbations\nfor non-relativistic matter on top of a spatially flat FLRW Universe, and we\ncompute the combination $A=f \\sigma_8$ in the framework of scale-dependent\ngravity, where both Newton's constant and the cosmological constant are allowed\nto vary with time. Upon comparison between available observational data\n(supernovae data as well as redshift-space distortion data) and theoretical\npredictions of the model, we determine the numerical value of $\\sigma_8$ that\nbest fits the data.\n"}
+{"abstract": "  Active learning aims to achieve greater accuracy with less training data by\nselecting the most useful data samples from which it learns. Single-criterion\nbased methods (i.e., informativeness and representativeness based methods) are\nsimple and efficient; however, they lack adaptability to different real-world\nscenarios. In this paper, we introduce a multiple-criteria based active\nlearning algorithm, which incorporates three complementary criteria, i.e.,\ninformativeness, representativeness and diversity, to make appropriate\nselections in the active learning rounds under different data types. We\nconsider the selection process as a Determinantal Point Process, which good\nbalance among these criteria. We refine the query selection strategy by both\nselecting the hardest unlabeled data sample and biasing towards the classifiers\nthat are more suitable for the current data distribution. In addition, we also\nconsider the dependencies and relationships between these data points in data\nselection by means of centroidbased clustering approaches. Through evaluations\non synthetic and real-world datasets, we show that our method performs\nsignificantly better and is more stable than other multiple-criteria based AL\nalgorithms.\n"}
+{"abstract": "  Studies of the production of heavy-flavour baryons are of prominent\nimportance to investigate hadronization mechanisms at the LHC, in particular\nthrough the study of the evolution of the baryon-over-meson production ratio.\nMeasurements performed in pp and p--Pb collisions at the LHC have revealed\nunexpected features, qualitatively similar to what was observed in heavy-ion\ncollisions and, in the charm sector, not in line with the expectations based on\nprevious measurements from $\\rm e^+e^-$ colliders and in ep collisions. These\nresults suggest that charmed baryon formation might not be universal and that\nthe baryon-over-meson ratio depends on the collision system or multiplicity.\n  A review of ALICE measurements of charmed baryons, including $\\rm\n\\Lambda_c^+/D^0$ as a function of charged-particle multiplicity in pp, p--Pb\nand Pb--Pb collisions, $\\rm \\Sigma_c^{0, +, ++}/D^0$ and $\\rm \\Xi_c^{0, +}/D^0$\nas a function of $p_{\\rm T}$ in pp collisions and $\\rm\n\\Gamma(\\Xi_c^0\\rightarrow\\Xi^-e^+\\nu_e)/\\Gamma(\\Xi_c^0\\rightarrow\\Xi^-\\pi^+)$,\nwill be presented. Comparison to phenomenological models will be also\ndiscussed. Emphasis will be given to the discussion of the impact of these\nstudies on the understanding of hadronization processes.\n"}
+{"abstract": "  In this paper, the interaction between two immiscible fluids with a finite\nmobility ratio is investigated numerically within a Hele-Shaw cell. Fingering\ninstabilities initiated at the interface between a low viscosity fluid and a\nhigh viscosity fluid are analysed at varying capillary numbers and mobility\nratios using a finite mobility ratio model. The present work is motivated by\nthe possible development of interfacial instabilities that can occur in porous\nmedia during the process of CO$_2$ sequestration, but does not pretend to\nanalyse this complex problem. Instead, we present a detailed study of the\nanalogous problem occurring in a Hele-Shaw cell, giving indications of possible\nplume patterns that can develop during the CO$_2$ injection. The numerical\nscheme utilises a boundary element method in which the normal velocity at the\ninterface of the two fluids is directly computed through the evaluation of a\nhypersingular integral. The boundary integral equation is solved using a\nNeumann convergent series with cubic B-Spline boundary discretisation,\nexhibiting 6th order spatial convergence. The convergent series allows the long\nterm non-linear dynamics of growing viscous fingers to be explored accurately\nand efficiently. Simulations in low mobility ratio regimes reveal large\ndifferences in fingering patterns compared to those predicted by previous high\nmobility ratio models. Most significantly, classical finger shielding between\ncompeting fingers is inhibited. Secondary fingers can possess significant\nvelocity, allowing greater interaction with primary fingers compared to high\nmobility ratio flows. Eventually, this interaction can lead to base thinning\nand the breaking of fingers into separate bubbles.\n"}
+{"abstract": "  HTCondor is a major workload management system used in distributed high\nthroughput computing (dHTC) environments, e.g., the Open Science Grid. One of\nthe distinguishing features of HTCondor is the native support for data\nmovement, allowing it to operate without a shared filesystem. Coupling data\nhandling and compute scheduling is both convenient for users and allows for\nsignificant infrastructure flexibility but does introduce some limitations. The\ndefault HTCondor data transfer mechanism routes both the input and output data\nthrough the submission node, making it a potential bottleneck. In this document\nwe show that by using a node equipped with a 100 Gbps network interface (NIC)\nHTCondor can serve data at up to 90 Gbps, which is sufficient for most current\nuse cases, as it would saturate the border network links of most research\nuniversities at the time of writing.\n"}
+{"abstract": "  A stochastic process with movement, return, and rest phases is considered in\nthis paper. For the movement phase, the particles move following the dynamics\nof Gaussian process or ballistic type of L\\'evy walk, and the time of each\nmovement is random. For the return phase, the particles will move back to the\norigin with a constant velocity or acceleration or under the action of a\nharmonic force after each movement, so that this phase can also be treated as a\nnon-instantaneous resetting. After each return, a rest with a random time at\nthe origin follows. The asymptotic behaviors of the mean squared displacements\nwith different kinds of movement dynamics, random resting time, and returning\nare discussed. The stationary distributions are also considered when the\nprocess is localized. Besides, the mean first passage time is considered when\nthe dynamic of movement phase is Brownian motion.\n"}
+{"abstract": "  We reconsider the widely held view that the Mannheim--Kazanas (MK) vacuum\nsolution for a static, spherically-symmetric system in conformal gravity (CG)\npredicts flat rotation curves, such as those observed in galaxies, without the\nneed for dark matter. This prediction assumes that test particles have fixed\nrest mass and follow timelike geodesics in the MK metric in the vacuum region\nexterior to a spherically-symmetric representation of the galactic mass\ndistribution. Such geodesics are not conformally invariant, however, which\nleads to an apparent discrepancy with the analogous calculation performed in\nthe conformally-equivalent Schwarzschild-de-Sitter (SdS) metric, where the\nlatter does not predict flat rotation curves. This difference arises since the\nmass of particles in CG must instead be generated dynamically through\ninteraction with a scalar field. The energy-momentum of this required scalar\nfield means that, in a general conformal frame from the equivalence class of CG\nsolutions outside a static, spherically-symmetric matter distribution, the\nspacetime is not given by the MK vacuum solution. A unique frame does exist,\nhowever, for which the metric retains the MK form, since the scalar field\nenergy-momentum vanishes despite the field being non-zero and radially\ndependent. Nonetheless, we show that in both this MK frame and the Einstein\nframe, in which the scalar field is constant, massive particles follow timelike\ngeodesics of the SdS metric, thereby resolving the apparent frame dependence of\nphysical predictions and unambiguously yielding rotation curves with no flat\nregion. We also comment on how our analysis resolves the long-standing\nuncertainty regarding gravitational lensing in the MK metric. (Abridged)\n"}
+{"abstract": "  Traumatic Brain Injury (TBI) is a common cause of death and disability.\nHowever, existing tools for TBI diagnosis are either subjective or require\nextensive clinical setup and expertise. The increasing affordability and\nreduction in size of relatively high-performance computing systems combined\nwith promising results from TBI related machine learning research make it\npossible to create compact and portable systems for early detection of TBI.\nThis work describes a Raspberry Pi based portable, real-time data acquisition,\nand automated processing system that uses machine learning to efficiently\nidentify TBI and automatically score sleep stages from a single-channel\nElectroen-cephalogram (EEG) signal. We discuss the design, implementation, and\nverification of the system that can digitize EEG signal using an Analog to\nDigital Converter (ADC) and perform real-time signal classification to detect\nthe presence of mild TBI (mTBI). We utilize Convolutional Neural Networks (CNN)\nand XGBoost based predictive models to evaluate the performance and demonstrate\nthe versatility of the system to operate with multiple types of predictive\nmodels. We achieve a peak classification accuracy of more than 90% with a\nclassification time of less than 1 s across 16 s - 64 s epochs for TBI vs\ncontrol conditions. This work can enable development of systems suitable for\nfield use without requiring specialized medical equipment for early TBI\ndetection applications and TBI research. Further, this work opens avenues to\nimplement connected, real-time TBI related health and wellness monitoring\nsystems.\n"}
+{"abstract": "  Active metric learning is the problem of incrementally selecting high-utility\nbatches of training data (typically, ordered triplets) to annotate, in order to\nprogressively improve a learned model of a metric over some input domain as\nrapidly as possible. Standard approaches, which independently assess the\ninformativeness of each triplet in a batch, are susceptible to highly\ncorrelated batches with many redundant triplets and hence low overall utility.\nWhile a recent work \\cite{kumari2020batch} proposes batch-decorrelation\nstrategies for metric learning, they rely on ad hoc heuristics to estimate the\ncorrelation between two triplets at a time. We present a novel batch active\nmetric learning method that leverages the Maximum Entropy Principle to learn\nthe least biased estimate of triplet distribution for a given set of prior\nconstraints. To avoid redundancy between triplets, our method collectively\nselects batches with maximum joint entropy, which simultaneously captures both\ninformativeness and diversity. We take advantage of the submodularity of the\njoint entropy function to construct a tractable solution using an efficient\ngreedy algorithm based on Gram-Schmidt orthogonalization that is provably\n$\\left( 1 - \\frac{1}{e} \\right)$-optimal. Our approach is the first batch\nactive metric learning method to define a unified score that balances\ninformativeness and diversity for an entire batch of triplets. Experiments with\nseveral real-world datasets demonstrate that our algorithm is robust,\ngeneralizes well to different applications and input modalities, and\nconsistently outperforms the state-of-the-art.\n"}
+{"abstract": "  The quality of the semiconductor-barrier interface plays a pivotal role in\nthe demonstration of high quality reproducible quantum dots for quantum\ninformation processing. In this work, we have measured SiMOSFET Hall bars on\nundoped Si substrates in order to investigate the quality of the devices\nfabricated in a full CMOS process. We report a record mobility of 17'500 cm2/Vs\nwith a sub-10 nm oxide thickness indicating a high quality interface, suitable\nfor future qubit applications. We also study the influence of gate materials on\nthe mobilities and discuss the underlying mechanisms, giving insight into\nfurther material optimization for large scale quantum processors.\n"}
+{"abstract": "  Recently, the authors have proposed and analyzed isogeometric tearing and\ninterconnecting (IETI-DP) solvers for multi-patch discretizations in\nIsogeometric Analysis. Conforming and discontinuous Galerkin settings have been\nconsidered. In both cases, we have assumed that the interfaces between the\npatches consist of whole edges. In this paper, we present a generalization that\nallows us to drop this requirement. This means that the patches can meet in\nT-junctions, which increases the flexibility of the geometric model\nsignificantly. We use vertex-based primal degrees of freedom. For the\nT-junctions, we propose to follow the idea of \"fat vertices\".\n"}
+{"abstract": "  In a series of publications, Kocherginsky and Gruebele presented a systematic\nframework for chemical transport and thermodiffusion to predict the Soret\ncoefficients from thermodynamics. A macroscopic derivation of the Onsager\nreciprocal relations without recourse to microscopic fluctuations or equations\nof motion was also discussed. Their important contributions and some confusions\nare discussed.\n"}
+{"abstract": "  A planetary system consists of a host star and one or more planets, arranged\ninto a particular configuration. Here, we consider what information belongs to\nthe configuration, or ordering, of 4286 Kepler planets in their 3277 planetary\nsystems. First, we train a neural network model to predict the radius and\nperiod of a planet based on the properties of its host star and the radii and\nperiod of its neighbors. The mean absolute error of the predictions of the\ntrained model is a factor of 2.1 better than the MAE of the predictions of a\nnaive model which draws randomly from dynamically allowable periods and radii.\nSecond, we adapt a model used for unsupervised part-of-speech tagging in\ncomputational linguistics to investigate whether planets or planetary systems\nfall into natural categories with physically interpretable \"grammatical rules.\"\nThe model identifies two robust groups of planetary systems: (1) compact\nmulti-planet systems and (2) systems around giant stars ($\\log{g} \\lesssim\n4.0$), although the latter group is strongly sculpted by the selection bias of\nthe transit method. These results reinforce the idea that planetary systems are\nnot random sequences -- instead, as a population, they contain predictable\npatterns that can provide insight into the formation and evolution of planetary\nsystems.\n"}
+{"abstract": "  The unprecedented wide bandgap tunability (~1 eV) of\nAl$_x$In$_{1-x}$As$_y$Sb$_{1-y}$ latticed-matched to GaSb enables the\nfabrication of photodetectors over a wide range from near-infrared to\nmid-infrared. In this paper, the valence band-offsets in AlxIn1-xAsySb1-y with\ndifferent Al compositions are analyzed by tight-binding calculations and X-ray\nphotoelectron spectroscopy (XPS) measurements. The observed weak variation in\nvalence band offsets is consistent with the lack of any minigaps in the valence\nband, compared to the conduction band.\n"}
+{"abstract": "  In this paper, we give upper and lower bounds for the spectral norms of\nr-circulant matrices with the generalized bi-periodic Fibonacci numbers.\nMoreover, we investigate the eigenvalues and determinants of these matrices.\n"}
+{"abstract": "  The looping pendulum is a simple physical system consisting of two masses\nconnected by a string that passes over a rod. We derive equations of motion for\nthe looping pendulum using Newtonian mechanics, and show that these equations\ncan be solved numerically to give a good description of the system's dynamics.\nThe numerical solution captures complex aspects of the looping pendulum's\nbehavior, and is in good agreement with the experimental results.\n"}
+{"abstract": "  We consider Markov Decision Processes (MDPs) with deterministic transitions\nand study the problem of regret minimization, which is central to the analysis\nand design of optimal learning algorithms. We present logarithmic\nproblem-specific regret lower bounds that explicitly depend on the system\nparameter (in contrast to previous minimax approaches) and thus, truly quantify\nthe fundamental limit of performance achievable by any learning algorithm.\nDeterministic MDPs can be interpreted as graphs and analyzed in terms of their\ncycles, a fact which we leverage in order to identify a class of deterministic\nMDPs whose regret lower bound can be determined numerically. We further\nexemplify this result on a deterministic line search problem, and a\ndeterministic MDP with state-dependent rewards, whose regret lower bounds we\ncan state explicitly. These bounds share similarities with the known\nproblem-specific bound of the multi-armed bandit problem and suggest that\nnavigation on a deterministic MDP need not have an effect on the performance of\na learning algorithm.\n"}
+{"abstract": "  All the laws of particle physics are time-reversible. A time arrow emerges\nonly when ensembles of classical particles are treated probabilistically,\noutside of physics laws, and the second law of thermodynamics is introduced. In\nquantum physics, despite its intrinsically probabilistic nature, no mechanism\nfor a time arrow has been proposed. We propose an entropy for quantum physics,\nwhich may conduce to the emergence of a time arrow. The proposed entropy is a\nmeasure of randomness over the degrees of freedom of a quantum state. It is\ndimensionless, it is a relativistic scalar, it is invariant under coordinate\ntransformation of position and momentum that maintain conjugate properties, and\nunder CPT transformations; and its minimum is positive due to the uncertainty\nprinciple.\n"}
+{"abstract": "  In many real-world games, such as traders repeatedly bargaining with\ncustomers, it is very hard for a single AI trader to make good deals with\nvarious customers in a few turns, since customers may adopt different\nstrategies even the strategies they choose are quite simple. In this paper, we\nmodel this problem as fast adaptive learning in the finitely repeated games. We\nbelieve that past game history plays a vital role in such a learning procedure,\nand therefore we propose a novel framework (named, F3) to fuse the past and\ncurrent game history with an Opponent Action Estimator (OAE) module that uses\npast game history to estimate the opponent's future behaviors. The experiments\nshow that the agent trained by F3 can quickly defeat opponents who adopt\nunknown new strategies. The F3 trained agent obtains more rewards in a fixed\nnumber of turns than the agents that are trained by deep reinforcement\nlearning. Further studies show that the OAE module in F3 contains\nmeta-knowledge that can even be transferred across different games.\n"}
+{"abstract": "  Recent result by Adachi-Iyama-Reiten has shown a bijective correspondence\nbetween support $\\tau$-tilting modules and functorially finite torsion classes.\nOn the other hand, the techniques of gluing torsion classes along a recollement\nwere investigated by Liu-Vit\\'oria-Yang and Ma-Huang. In this article, we show\nthat gluing torsion classes can be restricted to functorially finiteness\ncondition in the symmetric ladders of height 2. Then using the above\ncorrespondence with support $\\tau$-tilting modules, we present explicit\nconstructions of gluing of support $\\tau$-tilting modules via symmetric ladders\nof height 2. Finally, we apply the results to triangular matrix algebras to\ngive a more detailed version of the known Jasso's reduction and study maximal\ngreen sequences.\n"}
+{"abstract": "  Let $(\\Sigma, g)$ be a closed, oriented, negatively curved surface, and fix\npairwise disjoint simple closed geodesics $\\gamma_{\\star,1}, \\dots\n\\gamma_{\\star, r}$. We give an asymptotic growth as $L \\to +\\infty$ of the\nnumber of primitive closed geodesic of length less than $L$ intersecting\n$\\gamma_{\\star,j}$ exactly $n_j$ times, where $n_1, \\dots, n_r$ are fixed\nnonnegative integers. This is done by introducing a dynamical scattering\noperator associated to the surface with boundary obtained by cutting $\\Sigma$\nalong $\\gamma_{\\star,1}, \\dots, \\gamma_{\\star, r}$ and by using the theory of\nPollicott-Ruelle resonances for open systems.\n"}
+{"abstract": "  We present exact black hole solutions endowed with magnetic charge coming\nfrom exponential and logarithmic nonlinear electrodynamics (NLED). Classically,\nwe analyze the null and timelike geodesics, all of which contain both the bound\nand the scattering orbits. Using the effective geometry formalism, we found\nthat photon can have nontrivial stable (both circular and non-circular) bound\norbits. The noncircular bound orbits for the one-horizon case mostly take the\nform of precessed ellipse. For the extremal and three-horizon cases we find\nmany-world orbits where photon crosses the outer horizon but bounces back\nwithout hitting the true (or second, respectively) horizon, producing the\nepicycloid and epitrochoid paths. Semiclassically, we investigate their Hawking\ntemperature, stability, and phase transition. The nonlinearity enables black\nhole stability with smaller radius than its RN counterpart. However, for\nvery-strong nonlinear regime, the thermodynamic behavior tends to be\nSchwarzschild-like.\n"}
+{"abstract": "  Autoencoders are widely used in machine learning applications, in particular\nfor anomaly detection. Hence, they have been introduced in high energy physics\nas a promising tool for model-independent new physics searches. We scrutinize\nthe usage of autoencoders for unsupervised anomaly detection based on\nreconstruction loss to show their capabilities, but also their limitations. As\na particle physics benchmark scenario, we study the tagging of top jet images\nin a background of QCD jet images. Although we reproduce the positive results\nfrom the literature, we show that the standard autoencoder setup cannot be\nconsidered as a model-independent anomaly tagger by inverting the task: due to\nthe sparsity and the specific structure of the jet images, the autoencoder\nfails to tag QCD jets if it is trained on top jets even in a semi-supervised\nsetup. Since the same autoencoder architecture can be a good tagger for a\nspecific example of an anomaly and a bad tagger for a different example, we\nsuggest improved performance measures for the task of model-independent anomaly\ndetection. We also improve the capability of the autoencoder to learn\nnon-trivial features of the jet images, such that it is able to achieve both\ntop jet tagging and the inverse task of QCD jet tagging with the same setup.\nHowever, we want to stress that a truly model-independent and powerful\nautoencoder-based unsupervised jet tagger still needs to be developed.\n"}
+{"abstract": "  We introduce a family of Markov Chain Monte Carlo (MCMC) methods designed to\nsample from target distributions with irregular geometry using an adaptive\nscheme. In cases where targets exhibit non-Gaussian behaviour, we propose that\nadaption should be regional in nature as opposed to global. Our algorithms\nminimize the information projection side of the Kullback-Leibler (KL)\ndivergence between the proposal distribution class and the target to encourage\nproposals distributed similarly to the regional geometry of the target. Unlike\ntraditional adaptive MCMC, this procedure rapidly adapts to the geometry of the\ncurrent position as it explores the space without the need for a large batch of\nsamples. We extend this approach to multimodal targets by introducing a heavily\ntempered chain to enable faster mixing between regions of interest. The\ndivergence minimization algorithms are tested on target distributions with\nmultiple irregularly shaped modes and we provide results demonstrating the\neffectiveness of our methods.\n"}
+{"abstract": "  Within a structural health monitoring (SHM) framework, we propose a\nsimulation-based classification strategy to move towards online damage\nlocalization. The procedure combines parametric Model Order Reduction (MOR)\ntechniques and Fully Convolutional Networks (FCNs) to analyze raw vibration\nmeasurements recorded on the monitored structure. First, a dataset of possible\nstructural responses under varying operational conditions is built through a\nphysics-based model, allowing for a finite set of predefined damage scenarios.\nThen, the dataset is used for the offline training of the FCN. Because of the\nextremely large number of model evaluations required by the dataset\nconstruction, MOR techniques are employed to reduce the computational burden.\nThe trained classifier is shown to be able to map unseen vibrational\nrecordings, e.g. collected on-the-fly from sensors placed on the structure, to\nthe actual damage state, thus providing information concerning the presence and\nalso the location of damage. The proposed strategy has been validated by means\nof two case studies, concerning a 2D portal frame and a 3D portal frame railway\nbridge; MOR techniques have allowed us to respectively speed up the analyses\nabout 30 and 420 times. For both the case studies, after training the\nclassifier has attained an accuracy greater than 85%.\n"}
+{"abstract": "  Polymer blends consisting of two or more polymers are important for a wide\nvariety of industries and processes, but, the precise mechanism of their\nthermomechanical behaviour is incompletely understood. In order to understand\nclearly, it is essential to determine the miscibility and interactions between\nthe components in polymer blend and its macroscopic thermomechanical\nproperties. In this study, we performed experiments on SEBS and isotactic PP\nblends (SP) as well as molecular dynamics simulations, aiming to know the role\nplayed by molecular interactions on the thermomechanical properties. To\ninvestigate the glass transition temperature (Tg) of SEBS, PP and their blends\nat different ratio, the unit cell of the polymer molecular structure of each\nwas established. The LAMMPS molecular dynamics method was used to predict the\ndensity, specific volume, free volume, enthalpy, kinetic energy, potential\nenergy and bond energy. The (Tg) s of the SEBS, PP and SP blends were predicted\nby analysing these properties. Interestingly, the simulated values of the Tg of\nSEBS, PP and their blends showed good agreement with our experimental results\nobtained from dynamic mechanical analysis (DMA). This technique used in this\nwork can be used in studying glass transition of other complex polymer blends.\n"}
+{"abstract": "  This article aims to present a unified framework for grading-based voting\nprocesses. The idea is to represent the grades of each voter on d candidates as\na point in R^d and to define the winner of the vote using the deepest point of\nthe scatter plot. The deepest point is obtained by the maximization of a depth\nfunction. Universality, unanimity, and neutrality properties are proved to be\nsatisfied. Monotonicity and Independence to Irrelevant Alternatives are also\nstudied. It is shown that usual voting processes correspond to specific choices\nof depth functions. Finally, some basic paradoxes are explored for these voting\nprocesses.\n"}
+{"abstract": "  Zero-shot learning (ZSL) aims to discriminate images from unseen classes by\nexploiting relations to seen classes via their attribute-based descriptions.\nSince attributes are often related to specific parts of objects, many recent\nworks focus on discovering discriminative regions. However, these methods\nusually require additional complex part detection modules or attention\nmechanisms. In this paper, 1) we show that common ZSL backbones (without\nexplicit attention nor part detection) can implicitly localize attributes, yet\nthis property is not exploited. 2) Exploiting it, we then propose SELAR, a\nsimple method that further encourages attribute localization, surprisingly\nachieving very competitive generalized ZSL (GZSL) performance when compared\nwith more complex state-of-the-art methods. Our findings provide useful insight\nfor designing future GZSL methods, and SELAR provides an easy to implement yet\nstrong baseline.\n"}
+{"abstract": "  We have studied the charged BTZ black holes in noncommutative spaces arising\nfrom two independent approaches. First, by using the Seiberg-Witten map\nfollowed by a dynamic choice of gauge in the Chern-Simons gauge theory. Second,\nby inducing the fuzziness in the mass and charge by a Lorentzian distribution\nfunction with the width being the same as the minimal length of the associated\nnoncommutativity. In the first approach, we have found the existence of\nnon-static and non-stationary BTZ black holes in noncommutative spaces for the\nfirst time in the literature, while the second approach facilitates us to\nintroduce a proper bound on the noncommutative parameter so that the\ncorresponding black hole becomes stable and physical. We have used a\ncontemporary tunneling formalism to study the thermodynamics of the black holes\narising from both of the approaches and analyze their behavior within the\ncontext.\n"}
+{"abstract": "  The genericity of Arnold diffusion in the analytic category is an open\nproblem. In this paper, we study this problem in the following a priori\nunstable Hamiltonian system with a time-periodic perturbation\n\\[\\mathcal{H}_\\varepsilon(p,q,I,\\varphi,t)=h(I)+\\sum_{i=1}^n\\pm\n\\left(\\frac{1}{2}p_i^2+V_i(q_i)\\right)+\\varepsilon H_1(p,q,I,\\varphi, t), \\]\nwhere $(p,q)\\in \\mathbb{R}^n\\times\\mathbb{T}^n$,\n$(I,\\varphi)\\in\\mathbb{R}^d\\times\\mathbb{T}^d$ with $n, d\\geq 1$, $V_i$ are\nMorse potentials, and $\\varepsilon$ is a small non-zero parameter. The\nunperturbed Hamiltonian is not necessarily convex, and the induced inner\ndynamics does not need to satisfy a twist condition. Using geometric methods we\nprove that Arnold diffusion occurs for generic analytic perturbations $H_1$.\nIndeed, the set of admissible $H_1$ is $C^\\omega$ dense and $C^3$ open (a\nfortiori, $C^\\omega$ open). Our perturbative technique for the genericity is\nvalid in the $C^k$ topology for all $k\\in [3,\\infty)\\cup\\{\\infty, \\omega\\}$.\n"}
+{"abstract": "  We address the fluid-structure interaction of flexible fin models oscillated\nin a water flow. Here, we investigate in particular the dependence of\nhydrodynamic force distributions on fin geometry and flapping frequency. For\nthis purpose, we employ state-of-the-art techniques in pressure evaluation to\ndescribe fluid force maps with high temporal and spatial resolution on the\ndeforming surfaces of the hydrofoils. Particle tracking velocimetry (PTV) is\nused to measure the 3D fluid velocity field, and the hydrodynamic stress tensor\nis subsequently calculated based on the Navier-Stokes equation. The shape and\nkinematics of the fin-like foils are linked to their ability to generate\npropulsive thrust efficiently, as well as the accumulation of external contact\nforces and the resulting internal tension throughout a flapping cycle.\n"}
+{"abstract": "  In this paper, we shall consider spherically symmetric spacetime solutions\ndescribing the interior of stellar compact objects, in the context of\nhigher-order curvature theory of the f(R) type. We shall derive the non--vacuum\nfield equations of the higher-order curvature theory, without assuming any\nspecific form of the $\\mathrm{f(R)}$ theory, specifying the analysis for a\nspherically symmetric spacetime with two unknown functions. We obtain a system\nof highly non-linear differential equations, which consists of four\ndifferential equations with six unknown functions. To solve such a system, we\nassume a specific form of metric potentials, using the Krori-Barua ansatz. We\nsuccessfully solve the system of differential equations, and we derive all the\ncomponents of the energy-momentum tensor. Moreover, we derive the non-trivial\ngeneral form of $\\mathrm{f(R)}$ that may generate such solutions and calculate\nthe dynamic Ricci scalar of the anisotropic star. Accordingly, we calculate the\nasymptotic form of the function $\\mathrm{f(R)}$, which is a polynomial\nfunction. We match the derived interior solution with the exterior one, which\nwas derived in \\cite{Nashed:2019tuk}, with the latter also resulting in a\nnon-trivial form of the Ricci scalar. Notably but rather expected, the exterior\nsolution differs from the Schwarzschild one in the context of general\nrelativity. The matching procedure will eventually relate two constants with\nthe mass and radius of the compact stellar object. We list the necessary\nconditions that any compact anisotropic star must satisfy and explain in detail\nthat our model bypasses all of these conditions for a special compact star\n$\\textit {Her X--1 }$, which has an estimated mass and radius \\textit {(mass =\n0.85 $\\pm 0.15M_{\\circledcirc}$\\,\\, and\\, \\,radius $= 8.1 \\pm 0.41$km)}.\n"}
+{"abstract": "  The deformation theory of curves is studied by using the canonical ideal. The\nproblem of lifting curves with automorphisms is reduced to a lifting problem of\nlinear representations. As an application we prove that the dihedral group\n$D_{p^h}$ of order $2p^h$ is a local Oort group.\n"}
+{"abstract": "  RISC-V is a relatively new, open instruction set architecture with a mature\necosystem and an official formal machine-readable specification. It is\ntherefore a promising playground for formal-methods research.\n  However, we observe that different formal-methods research projects are\ninterested in different aspects of RISC-V and want to simplify, abstract,\napproximate, or ignore the other aspects. Often, they also require different\nencoding styles, resulting in each project starting a new formalization\nfrom-scratch. We set out to identify the commonalities between projects and to\nrepresent the RISC-V specification as a program with holes that can be\ninstantiated differently by different projects.\n  Our formalization of the RISC-V specification is written in Haskell and\nleverages existing tools rather than requiring new domain-specific tools,\ncontrary to other approaches. To our knowledge, it is the first RISC-V\nspecification able to serve as the interface between a processor-correctness\nproof and a compiler-correctness proof, while supporting several other projects\nwith diverging requirements as well.\n"}
+{"abstract": "  Facial Expression Recognition (FER) in the wild is an extremely challenging\ntask in computer vision due to variant backgrounds, low-quality facial images,\nand the subjectiveness of annotators. These uncertainties make it difficult for\nneural networks to learn robust features on limited-scale datasets. Moreover,\nthe networks can be easily distributed by the above factors and perform\nincorrect decisions. Recently, vision transformer (ViT) and data-efficient\nimage transformers (DeiT) present their significant performance in traditional\nclassification tasks. The self-attention mechanism makes transformers obtain a\nglobal receptive field in the first layer which dramatically enhances the\nfeature extraction capability. In this work, we first propose a novel pure\ntransformer-based mask vision transformer (MVT) for FER in the wild, which\nconsists of two modules: a transformer-based mask generation network (MGN) to\ngenerate a mask that can filter out complex backgrounds and occlusion of face\nimages, and a dynamic relabeling module to rectify incorrect labels in FER\ndatasets in the wild. Extensive experimental results demonstrate that our MVT\noutperforms state-of-the-art methods on RAF-DB with 88.62%, FERPlus with\n89.22%, and AffectNet-7 with 64.57%, respectively, and achieves a comparable\nresult on AffectNet-8 with 61.40%.\n"}
+{"abstract": "  We study the emergence of chaotic behavior of Follow-the-Regularized Leader\n(FoReL) dynamics in games. We focus on the effects of increasing the population\nsize or the scale of costs in congestion games, and generalize recent results\non unstable, chaotic behaviors in the Multiplicative Weights Update dynamics to\na much larger class of FoReL dynamics. We establish that, even in simple linear\nnon-atomic congestion games with two parallel links and any fixed learning\nrate, unless the game is fully symmetric, increasing the population size or the\nscale of costs causes learning dynamics to become unstable and eventually\nchaotic, in the sense of Li-Yorke and positive topological entropy.\nFurthermore, we show the existence of novel non-standard phenomena such as the\ncoexistence of stable Nash equilibria and chaos in the same game. We also\nobserve the simultaneous creation of a chaotic attractor as another chaotic\nattractor gets destroyed. Lastly, although FoReL dynamics can be strange and\nnon-equilibrating, we prove that the time average still converges to an exact\nequilibrium for any choice of learning rate and any scale of costs.\n"}
+{"abstract": "  We derive Legendre polynomials using Cauchy determinants with a\ngeneralization to power functions with real exponents greater than -1/2. We\nalso provide a geometric formulation of Gram-Schmidt orthogonalization using\nthe Hodge star operator.\n"}
+{"abstract": "  We establish the validity of the Euler$+$Prandtl approximation for solutions\nof the Navier-Stokes equations in the half plane with Dirichlet boundary\nconditions, in the vanishing viscosity limit, for initial data which are\nanalytic only near the boundary, and Sobolev smooth away from the boundary. Our\nproof does not require higher order correctors, and works directly by\nestimating an $L^{1}$-type norm for the vorticity of the error term in the\nexpansion Navier-Stokes$-($Euler$+$Prandtl$)$. An important ingredient in the\nproof is the propagation of local analyticity for the Euler equation, a result\nof independent interest.\n"}
+{"abstract": "  Thermal machines exploit interactions with multiple heat baths to perform\nuseful tasks, such as work production and refrigeration. In the quantum regime,\ntasks with no classical counterpart become possible. Here, we consider the\nminimal setting for quantum thermal machines, namely two-qubit autonomous\nthermal machines that use only incoherent interactions with their environment,\nand investigate the fundamental resources needed to generate entanglement. Our\ninvestigation is systematic, covering different types of interactions, bosonic\nand fermionic environments, and different resources that can be supplied to the\nmachine. We adopt an operational perspective in which we assess the\nnonclassicality of the generated entanglement through its ability to perform\nuseful tasks such as Einstein-Podolsky-Rosen steering, quantum teleportation\nand Bell nonlocality. We provide both constructive examples of nonclassical\neffects and general no-go results that demarcate the fundamental limits in\nautonomous entanglement generation. Our results open up a path toward\nunderstanding nonclassical phenomena in thermal processes.\n"}
+{"abstract": "  Electricity supply must be matched with demand at all times. This helps\nreduce the chances of issues such as load frequency control and the chances of\nelectricity blackouts. To gain a better understanding of the load that is\nlikely to be required over the next 24h, estimations under uncertainty are\nneeded. This is especially difficult in a decentralized electricity market with\nmany micro-producers which are not under central control.\n  In this paper, we investigate the impact of eleven offline learning and five\nonline learning algorithms to predict the electricity demand profile over the\nnext 24h. We achieve this through integration within the long-term agent-based\nmodel, ElecSim. Through the prediction of electricity demand profile over the\nnext 24h, we can simulate the predictions made for a day-ahead market. Once we\nhave made these predictions, we sample from the residual distributions and\nperturb the electricity market demand using the simulation, ElecSim. This\nenables us to understand the impact of errors on the long-term dynamics of a\ndecentralized electricity market.\n  We show we can reduce the mean absolute error by 30% using an online\nalgorithm when compared to the best offline algorithm, whilst reducing the\nrequired tendered national grid reserve required. This reduction in national\ngrid reserves leads to savings in costs and emissions. We also show that large\nerrors in prediction accuracy have a disproportionate error on investments made\nover a 17-year time frame, as well as electricity mix.\n"}
+{"abstract": "  Two-dimensional (2D) Dirac states with linear band dispersion have attracted\nenormous interest since the discovery of graphene. However, to date, 2D Dirac\nsemimetals are still very rare due to the fact that 2D Dirac states are\ngenerally fragile against perturbations such as spin-orbit couplings.\nNonsymmorphic crystal symmetries can enforce the formation of Dirac nodes,\nproviding a new route to establishing symmetry-protected Dirac states in 2D\nmaterials. Here we report the symmetry-protected Dirac states in nonsymmorphic\nalpha-antimonene. The antimonene was synthesized by the method of molecular\nbeam epitaxy. Two Dirac cones with large anisotropy were observed by\nangle-resolved photoemission spectroscopy. The Dirac state in alpha-antimonene\nis of spin-orbit type in contrast to the spinless Dirac states in graphene. The\nresult extends the 'graphene' physics into a new family of 2D materials where\nspin-orbit coupling is present.\n"}
+{"abstract": "  Neutron diffraction and X-ray pair distribution function (XPDF) experiments\nwere performed in order to investigate the magnetic and local crystal\nstructures of Ba2FeSbSe5 and to compare them to the average (i.e. long-range)\nstructural model, previously obtained by single crystal X-ray diffraction.\nChanges in the local crystal structure (i.e. in the second coordination sphere)\nare observed upon cooling from 295 K to 95 K resulting in deviations from the\naverage (i.e. long-range) crystal structure. This work demonstrates, that these\nobservations cannot be explained by local or long-range magnetoelastic effects\ninvolving Fe-Fe correlations. Instead, we found, that the observed differences\nbetween local and average crystal structure can be explained by Sb-5s lone pair\ndynamics. We also find, that below the N\\'eel temperature (TN = 58 K), the two\ndistinct magnetic Fe3+ sites order collinearly, such that a combination of\nantiparallel and parallel spin arrangements along the b-axis results. The\nnearest-neighbor arrangement (J1 = 6 {\\AA}) is fully antiferromagnetic, while\nnext-nearest-neighbor interactions are ferromagnetic in nature.\n"}
+{"abstract": "  A growing number of applications require the reconstructionof 3D objects from\na very small number of views. In this research, we consider the problem of\nreconstructing a 3D object from only 4 Flash X-ray CT views taken during the\nimpact of a Kolsky bar. For such ultra-sparse view datasets, even model-based\niterative reconstruction (MBIR) methods produce poor quality results.\n  In this paper, we present a framework based on a generalization of\nPlug-and-Play, known as Multi-Agent Consensus Equilibrium (MACE), for\nincorporating complex and nonlinear prior information into ultra-sparse CT\nreconstruction. The MACE method allows any number of agents to simultaneously\nenforce their own prior constraints on the solution. We apply our method on\nsimulated and real data and demonstrate that MACE reduces artifacts, improves\nreconstructed image quality, and uncovers image features which were otherwise\nindiscernible.\n"}
+{"abstract": "  Ultrasound tongue imaging is widely used for speech production research, and\nit has attracted increasing attention as its potential applications seem to be\nevident in many different fields, such as the visual biofeedback tool for\nsecond language acquisition and silent speech interface. Unlike previous\nstudies, here we explore the feasibility of age estimation using the ultrasound\ntongue image of the speakers. Motivated by the success of deep learning, this\npaper leverages deep learning on this task. We train a deep convolutional\nneural network model on the UltraSuite dataset. The deep model achieves mean\nabsolute error (MAE) of 2.03 for the data from typically developing children,\nwhile MAE is 4.87 for the data from the children with speech sound disorders,\nwhich suggest that age estimation using ultrasound is more challenging for the\nchildren with speech sound disorder. The developed method can be used a tool to\nevaluate the performance of speech therapy sessions. It is also worthwhile to\nnotice that, although we leverage the ultrasound tongue imaging for our study,\nthe proposed methods may also be extended to other imaging modalities (e.g.\nMRI) to assist the studies on speech production.\n"}
+{"abstract": "  This paper presents U-LanD, a framework for joint detection of key frames and\nlandmarks in videos. We tackle a specifically challenging problem, where\ntraining labels are noisy and highly sparse. U-LanD builds upon a pivotal\nobservation: a deep Bayesian landmark detector solely trained on key video\nframes, has significantly lower predictive uncertainty on those frames vs.\nother frames in videos. We use this observation as an unsupervised signal to\nautomatically recognize key frames on which we detect landmarks. As a test-bed\nfor our framework, we use ultrasound imaging videos of the heart, where sparse\nand noisy clinical labels are only available for a single frame in each video.\nUsing data from 4,493 patients, we demonstrate that U-LanD can exceedingly\noutperform the state-of-the-art non-Bayesian counterpart by a noticeable\nabsolute margin of 42% in R2 score, with almost no overhead imposed on the\nmodel size. Our approach is generic and can be potentially applied to other\nchallenging data with noisy and sparse training labels.\n"}
+{"abstract": "  In this paper we prove a Faber-Krahn type inequality for the first eigenvalue\nof the Hermite operator with Robin boundary condition. We prove that the\noptimal set is an half-space and we also address the equality case in such\ninequality.\n"}
+{"abstract": "  Motivated by kidney exchange, we study the following mechanism-design\nproblem: On a directed graph (of transplant compatibilities among patient-donor\npairs), the mechanism must select a simple path (a chain of transplantations)\nstarting at a distinguished vertex (an altruistic donor) such that the total\nlength of this path is as large as possible (a maximum number of patients\nreceive a kidney). However, the mechanism does not have direct access to the\ngraph. Instead, the vertices are partitioned over multiple players (hospitals),\nand each player reports a subset of her vertices to the mechanism. In\nparticular, a player may strategically omit vertices to increase how many of\nher vertices lie on the path returned by the mechanism.\n  Our objective is to find mechanisms that limit incentives for such\nmanipulation while producing long paths. Unfortunately, in worst-case\ninstances, competing with the overall longest path is impossible while\nincentivizing (approximate) truthfulness, i.e., requiring that hiding nodes\ncannot increase a player's utility by more than a factor of $1 + o(1)$. We\ntherefore adopt a semi-random model where a small ($o(n)$) number of random\nedges are added to worst-case instances. While it remains impossible for\ntruthful mechanisms to compete with the overall longest path, we give a\ntruthful mechanism that competes with a weaker but non-trivial benchmark: the\nlength of any path whose subpaths within each player have a minimum average\nlength. In fact, our mechanism satisfies even a stronger notion of\ntruthfulness, which we call matching-time incentive compatibility. This notion\nof truthfulness requires that each player not only reports her nodes truthfully\nbut also does not stop the returned path at any of her nodes in order to divert\nit to a continuation inside her own subgraph.\n"}
+{"abstract": "  Iterative Green's function, based on cyclic reduction of block tridiagonal\nmatrices, has been the ideal algorithm, through tight-binding models, to\ncompute the surface density-of-states of semi-infinite topological electronic\nmaterials. In this paper, we apply this method to photonic and acoustic\ncrystals, using finite-element discretizations and a generalized eigenvalue\nformulation, to calculate the local density-of-states on a single surface of\nsemi-infinite lattices. The three-dimensional (3D) examples of gapless\nhelicoidal surface states in Weyl and Dirac crystals are shown and the\ncomputational cost, convergence and accuracy are analyzed.\n"}
+{"abstract": "  Electroweak probes are potential tool to study the properties of the hot and\ndense strongly interacting matter produced in relativistic nuclear collisions\ndue to their unique nature. A selection of the new experimental analysis and\nresults from theory calculations on electromagnetic and weak probes presented\nat the Hard Probes 2020 are discussed in this contribution.\n"}
+{"abstract": "  The goal of the paper is to provide a detailed explanation on how the\n(continuous) cosine transform and the discrete(-time) cosine transform arise\nnaturally as certain manifestations of the celebrated Gelfand transform. We\nbegin with the introduction of the cosine convolution $\\star_c$, which can be\nviewed as an \"arithmetic mean\" of the classical convolution and its \"twin\nbrother\", the anticonvolution. The d'Alambert property of $\\star_c$ plays a\npivotal role in establishing the bijection between $\\Delta(L^1(G),\\star_c)$ and\nthe cosine class $\\mathcal{COS}(G),$ which turns out to be an open map if\n$\\mathcal{COS}(G)$ is equipped with the topology of uniform convergence on\ncompacta $\\tau_{ucc}$. Subsequently, if $G = \\mathbb{R},\\mathbb{Z}, S^1$ or\n$\\mathbb{Z}_n$ we find a relatively simple topological space which is\nhomeomorphic to $\\Delta(L^1(G),\\star_c).$ Finally, we witness the \"reduction\"\nof the Gelfand transform to the aforementioned cosine transforms.\n"}
+{"abstract": "  Magnetic braking (MB) likely plays a vital role in the evolution of low-mass\nX-ray binaries (LMXBs). However, it is still uncertain about the physics of MB,\nand there are various proposed scenarios for MB in the literature. To examine\nand discriminate the efficiency of MB, we investigate the LMXB evolution with\nfive proposed MB laws. Combining detailed binary evolution calculation with\nbinary population synthesis, we obtain the expected properties of LMXBs and\ntheir descendants binary millisecond pulsars. We then discuss the strength and\nweakness of each MB law by comparing the calculated results with observations.\nWe conclude that the $\\tau$-boosted MB law seems to best match the\nobservational characteristics.\n"}
+{"abstract": "  Atomtronics experiments with ultracold atomic gases allow us to explore\nquantum transport phenomena of a weakly-interacting Bose-Einstein condensate\n(BEC). Here, we focus on two-terminal transport of such a BEC in the vicinity\nof zero temperature. By using the tunnel Hamiltonian and Bogoliubov theory, we\nobtain a DC Josephson current expression in the BEC and apply it to\nexperimentally relevant situations such as quantum point contact and planar\njunction. Due to the absence of Andreev bound states but the presence of\ncouplings associated with condensation elements, a current-phase relation in\nthe BEC is found to be different from one in an s-wave superconductor. In\naddition, it turns out that the DC Josephson current in the BEC depends on the\nsign of tunneling elements, which allows to realize the so-called $\\pi$\njunction by using techniques of artificial gauge fields.\n"}
+{"abstract": "  The Galactic B[e] supergiant MWC 137 is surrounded by a large-scale optical\nnebula. To shed light on the physical conditions and kinematics of the nebula,\nwe analyze the optical forbidden emission lines [NII] 6548,6583 and [SII]\n6716,6731 in long-slit spectra taken with ALFOSC at the Nordic Optical\nTelescope. The radial velocities display a complex behavior but, in general,\nthe northern nebular features are predominantly approaching while the southern\nones are mostly receding. The electron density shows strong variations across\nthe nebula with values spreading from about zero to ~800 cm$^{-3}$. Higher\ndensities are found closer to MWC~137 and in regions of intense emission,\nwhereas in regions with high radial velocities the density decreases\nsignificantly. We also observe the entire nebula in the two [SII] lines with\nthe scanning Fabry-Perot interferometer attached to the 6-m telescope of the\nSpecial Astrophysical Observatory. These data reveal a new bow-shaped feature\nat PA = 225-245 and a distance 80\" from MWC 137. A new H$\\alpha$ image has been\ntaken with the Danish 1.54-m telescope on La Silla. No expansion or changes in\nthe nebular morphology appear within 18.1 years. We derive a mass of 37 (+9/-5)\nsolar masses and an age of $4.7\\pm0.8$ Myr for MWC 137. Furthermore, we detect\na period of 1.93 d in the time series photometry collected with the TESS\nsatellite, which could suggest stellar pulsations. Other, low-frequency\nvariability is seen as well. Whether these signals are caused by internal\ngravity waves in the early-type star or by variability in the wind and\ncircumstellar matter currently cannot be distinguished.\n"}
+{"abstract": "  This article proposes an artificial data generating algorithm that is simple\nand easy to customize. The fundamental concept is to perform random permutation\nof Monte Carlo generated random numbers which conform to the unconditional\nprobability distribution of the original real time series. Similar to\nconstraint surrogate methods, random permutations are only accepted if a given\nobjective function is minimized. The objective function is selected in order to\ndescribe the most important features of the stochastic process. The algorithm\nis demonstrated by producing simulated log-returns of the S\\&P 500 stock index.\n"}
+{"abstract": "  Creating programs with block-based programming languages like Scratch is easy\nand fun. Block-based programs can nevertheless contain bugs, in particular when\nlearners have misconceptions about programming. Even when they do not, Scratch\ncode is often of low quality and contains code smells, further inhibiting\nunderstanding, reuse, and fun. To address this problem, in this paper we\nintroduce LitterBox, a linter for Scratch programs. Given a program or its\npublic project ID, LitterBox checks the program against patterns of known bugs\nand code smells. For each issue identified, LitterBox provides not only the\nlocation in the code, but also a helpful explanation of the underlying reason\nand possible misconceptions. Learners can access LitterBox through an easy to\nuse web interface with visual information about the errors in the block-code,\nwhile for researchers LitterBox provides a general, open source, and extensible\nframework for static analysis of Scratch programs.\n"}
+{"abstract": "  In the first paper of this series (Rhea et al. 2020), we demonstrated that\nneural networks can robustly and efficiently estimate kinematic parameters for\noptical emission-line spectra taken by SITELLE at the Canada-France-Hawaii\nTelescope. This paper expands upon this notion by developing an artificial\nneural network to estimate the line ratios of strong emission-lines present in\nthe SN1, SN2, and SN3 filters of SITELLE. We construct a set of 50,000\nsynthetic spectra using line ratios taken from the Mexican Million Model\ndatabase replicating Hii regions. Residual analysis of the network on the test\nset reveals the network's ability to apply tight constraints to the line\nratios. We verified the network's efficacy by constructing an activation map,\nchecking the [N ii] doublet fixed ratio, and applying a standard k-fold\ncross-correlation. Additionally, we apply the network to SITELLE observation of\nM33; the residuals between the algorithm's estimates and values calculated\nusing standard fitting methods show general agreement. Moreover, the neural\nnetwork reduces the computational costs by two orders of magnitude. Although\nstandard fitting routines do consistently well depending on the signal-to-noise\nratio of the spectral features, the neural network can also excel at\npredictions in the low signal-to-noise regime within the controlled environment\nof the training set as well as on observed data when the source spectral\nproperties are well constrained by models. These results reinforce the power of\nmachine learning in spectral analysis.\n"}
+{"abstract": "  We present a polarization-resolved, high-resolution Raman scattering study of\nthe three consecutive charge density wave (CDW) regimes in $1T$-TaS$_2$ single\ncrystals, supported by \\textit{ab initio} calculations. Our analysis of the\nspectra within the low-temperature commensurate (C-CDW) regime shows\n$\\mathrm{P3}$ symmetry of the system, thus excluding the previously proposed\ntriclinic stacking of the \"star-of-David\" structure, and promoting trigonal or\nhexagonal stacking instead. The spectra of the high-temperature incommensurate\n(IC-CDW) phase directly project the phonon density of states due to the\nbreaking of the translational invariance, supplemented by sizeable\nelectron-phonon coupling. Between 200 and 352\\,K, our Raman spectra show\ncontributions from both the IC-CDW and the C-CDW phase, indicating their\ncoexistence in the so-called nearly-commensurate (NC-CDW) phase. The\ntemperature-dependence of the symmetry-resolved Raman conductivity indicates\nthe stepwise reduction of the density of states in the CDW phases, followed by\na Mott transition within the C-CDW phase. We determine the size of the Mott gap\nto be $\\Omega_{\\rm gap}\\approx 170-190$ meV, and track its temperature\ndependence.\n"}
+{"abstract": "  Often neglected in traditional education, spatial thinking has played a\ncritical role in science, technology, engineering, and mathematics (STEM)\neducation. Spatial thinking skills can be enhanced by training, life\nexperience, and practice. One approach to train these skills is through 3D\nmodeling (also known as Computer-Aided Design or CAD). Although 3D modeling\ntools have shown promising results in training and enhancing spatial thinking\nskills in undergraduate engineering students when it comes to novices,\nespecially middle and high-school students, they are not sufficient to provide\nrich 3D experience since the 3D models created in CAD are isolated the actual\n3D physical world. Resulting in novice students finding it difficult to create\nerror-free 3D models that would 3D print successfully. This leads to student\nfrustration where students are not motivated to create 3D models themselves;\ninstead, they prefer to download them from online repositories. To address this\nproblem, researchers are focusing on integrating 3D models and displays into\nthe physical world with the help of technologies like Augmented Reality (AR).\nIn this demo, we present an AR application, 3DARVisualizer, that helps us\nexplore the role of AR as a 3D model debugger, including enhancing 3D modeling\nabilities and spatial thinking skills of middle- and high-school students.\n"}
+{"abstract": "  The stochastic block model (SBM) and degree-corrected block model (DCBM) are\nnetwork models often selected as the fundamental setting in which to analyze\nthe theoretical properties of community detection methods. We consider the\nproblem of spectral clustering of SBM and DCBM networks under a local form of\nedge differential privacy. Using a randomized response privacy mechanism called\nthe edge-flip mechanism, we develop theoretical guarantees for differentially\nprivate community detection, demonstrating conditions under which this strong\nprivacy guarantee can be upheld while achieving spectral clustering convergence\nrates that match the known rates without privacy. We prove the strongest\ntheoretical results are achievable for dense networks (those with node degree\nlinear in the number of nodes), while weak consistency is achievable under mild\nsparsity (node degree greater than $\\sqrt{n}$). We empirically demonstrate our\nresults on a number of network examples.\n"}
+{"abstract": "  A beautifully simple free generating set for the commutator subgroup of a\nfree group was constructed by Tomaszewski. We give a new geometric proof of his\ntheorem, and show how to give a similar free generating set for the commutator\nsubgroup of a surface group. We also give a simple representation-theoretic\ndescription of the structure of the abelianizations of these commutator\nsubgroups and calculate their homology.\n"}
+{"abstract": "  Accurate and comprehensive diatomic molecular spectroscopic data have long\nbeen vital in a wide variety of applications for measuring and monitoring\nastrophysical, industrial and other gaseous environments. These data are also\nused extensively for benchmarking quantum chemistry and applications from\nquantum computers, ultracold chemistry and the search for physics beyond the\nstandard model. Useful data can be highly detailed like line lists or summative\nlike molecular constants, and obtained from theory, experiment or a\ncombination. There are plentiful (though not yet sufficient) data available,\nbut these data are often scattered. For example, molecular constants have not\nbeen compiled since 1979 despite the existing compilation still being cited\nmore than 200 times annually. Further, the data are interconnected but updates\nin one type of data are not yet routinely applied to update interconnected\ndata: in particular, new experimental and ab-initio data are not routinely\nunified with other data on the molecule. This paper provide information and\nstrategies to strengthen the connection between data producers (e.g. ab-initio\nelectronic structure theorists and experimental spectroscopists), data\nmodellers (e.g. line list creators and others who connect data on one aspect of\nthe molecule to the full energetic and spectroscopic description) and data\nusers (astronomers, chemical physicists etc). All major data types are\ndescribed including their source, use, compilation and interconnectivity.\nExplicit advice is provided for theoretical and experimental data producers,\ndata modellers and data users to facilitate optimal use of new data with\nappropriate attribution.\n"}
+{"abstract": "  Data markets have the potential to foster new data-driven applications and\nhelp growing data-driven businesses. When building and deploying such markets\nin practice, regulations such as the European Union's General Data Protection\nRegulation (GDPR) impose constraints and restrictions on these markets\nespecially when dealing with personal or privacy-sensitive data. In this paper,\nwe present a candidate architecture for a privacy-preserving personal data\nmarket, relying on cryptographic primitives such as multi-party computation\n(MPC) capable of performing privacy-preserving computations on the data.\nBesides specifying the architecture of such a data market, we also present a\nprivacy-risk analysis of the market following the LINDDUN methodology.\n"}
+{"abstract": "  The Einstein-Podolsky-Rosen (EPR) steering, which is regarded as a category\nof quantum nonlocal correlations, owns the asymmetric property in contrast with\nthe entanglement and the Bell nonlocality. For the multipartite EPR steering,\nmonogamy, which limits the two observers to steer the third one simultaneously,\nemerges as an essential property. However, more configurations of shareability\nrelations in the reduced subsystem which are beyond the monogamy could be\nobserved by increasing the numbers of measurement setting, in which the\nexperimental verification is still absent. Here, in an optical experiment, we\nprovide a proof-of-principle demonstration of shareability of the EPR steering\nwithout constraint of monogamy in the three-qubit system, in which Alice could\nbe steered by Bob and Charlie simultaneously. Moreover, based on the reduced\nbipartite EPR steering detection, we verify the genuine three-qubit\nentanglement. This work provides a basis for an improved understanding of the\nmultipartite EPR steering and has potential applications in many quantum\ninformation protocols, such as multipartite entanglement detection and quantum\ncryptography.\n"}
+{"abstract": "  HD 163296 is a Herbig Ae star that underwent a dramatic $\\sim$0.8 magnitude\ndrop in brightness in the V photometric band in 2001 and a brightening in the\nnear-IR in 2002. Because the star possesses Herbig-Haro objects travelling in\noutflowing bipolar jets, it was suggested that the drop in brightness was due\nto a clump of dust entrained in a disk wind, blocking the line-on-sight toward\nthe star. In order to quantify this hypothesis, we investigated the brightness\ndrop at visible wavelengths and the brightening at near-IR wavelengths of HD\n163296 using the Monte Carlo Radiative Transfer Code, HOCHUNK3D. We created\nthree models to understand the events. Model 1 describes the quiescent state of\nthe system. Model 2 describes the change in structure that led to the drop in\nbrightness in 2001. Model 3 describes the structure needed to produce the\nobserved 2002 brightening of the near-IR wavelengths. Models 2 and 3 utilize a\ncombination of a disk wind and central bipolar flow. By introducing a filled\nbipolar cavity in Models 2 and 3, we were able to successfully simulate a\njet-like structure for the star with a disk wind and created the drop and\nsubsequent increase in brightness of the system. On the other hand, when the\nbipolar cavity is not filled, Model 1 replicates the quiescent state of the\nsystem.\n"}
+{"abstract": "  Finding nearest neighbors in high-dimensional spaces is a fundamental\noperation in many diverse application domains. Locality Sensitive Hashing (LSH)\nis one of the most popular techniques for finding approximate nearest neighbor\nsearches in high-dimensional spaces. The main benefits of LSH are its\nsub-linear query performance and theoretical guarantees on the query accuracy.\nIn this survey paper, we provide a review of state-of-the-art LSH and\nDistributed LSH techniques. Most importantly, unlike any other prior survey, we\npresent how Locality Sensitive Hashing is utilized in different application\ndomains.\n"}
+{"abstract": "  We introduce a design of electrically isolated floating bilayer GaAs quantum\nwells (QW) in which application of a large gating voltage controllably and\nhighly reproducibly induces charges that remain trapped in the bilayer after\nremoval of the gating voltage. At smaller gate voltages, the bilayer is fully\nelectrically isolated from external electrodes by thick insulating barriers.\nThis design permits full control of the total and differential densities of two\ncoupled 2D electron systems. The floating bilayer design provides a unique\napproach for studying systems inaccessible by simple transport measurements. It\nalso provides the ability to measure the charge transfer between the layers,\neven when the in-plane resistivities of the 2D systems diverge. We measure the\ncapacitance and inter-layer tunneling spectra of the QW bilayer with\nindependent control of the top and bottom layer electron densities. Our\nmeasurements display strongly enhanced inter-layer tunneling current at the\ntotal filling factor of 1, a signature of exciton condensation of a strongly\ninterlayer-correlated bilayer system. With fully tunable densities of\nindividual layers, the floating bilayer QW system provides a versatile platform\nto access previously unavailable information on the quantum phases in electron\nbilayer systems.\n"}
+{"abstract": "  There is an extensive literature on the asymptotic order of Sudler's\ntrigonometric product $P_N (\\alpha) = \\prod_{n=1}^N |2 \\sin (\\pi n \\alpha)|$\nfor fixed or for \"typical\" values of $\\alpha$. In the present paper we\nestablish a structural result, which for a given $\\alpha$ characterizes those\n$N$ for which $P_N(\\alpha)$ attains particularly large values. This\ncharacterization relies on the coefficients of $N$ in its Ostrowski expansion\nwith respect to $\\alpha$, and allows us to obtain very precise estimates for\n$\\max_{1 \\le N \\leq M} P_N(\\alpha)$ and for $\\sum_{N=1}^M P_N(\\alpha)^c$ in\nterms of $M$, for any $c>0$. Furthermore, our arguments give a natural\nexplanation of the fact that the value of the hyperbolic volume of the\ncomplement of the figure-eight knot appears generically in results on the\nasymptotic order of the Sudler product and of the Kashaev invariant.\n"}
+{"abstract": "  We present a detailed analysis of a cool-core galaxy cluster Abell 3017, at a\nredshift of z=0.219, which has been identified to be merging with its companion\ncluster Abell 3016. This study has made use of X-ray Chandra, UV (GALEX),\noptical (ESO/VLT), mid-infrared (WISE), and radio uGMRT observations of this\ncluster. Using various image processing techniques, such as unsharp masking,\n2-d fits using Beta models, contour binning and the use of surface brightness\nprofiles, we show the existence of a pair of X-ray cavities, at a projected\ndistance of $\\sim$20'' (70 kpc) and $\\sim$16'' (57 kpc), respectively from the\ncore of Abell~3017. We also detect an excess of X-ray emission located at 25''\n$\\sim$(88 kpc) south of the centre of Abell 3017, is likely due to the bulk\nmotions in the ICM either by gas sloshing or ram-pressure striping due to a\nmerger. We find that the radio lobes are responsible for the observed X-ray\ncavities detected in this system. The lower values of the mid-IR WISE colour\n[W1-W2] and [W2-W3] imply that the central BCG of Abell~3017 is a star-forming\ngalaxy. The current star formation rate of the central BCG, estimated from the\n${\\rm H\\alpha}$ and GALEX FUV luminosities, are equal to be $\\sim 5.06\\pm 0.78$\nMsun yr$^{-1}$ and $\\sim 9.20\\pm 0.81$ Msun yr$^{-1}$, respectively. We detect,\nfor the first time, a radio phoenix $\\sim$150 kpc away from the radio core,\nwith a spectral index of ($\\alpha \\!\\leq\\! -1.8$). We also report the detection\nof $\\rm~Pa_\\alpha$ emission in this cluster using ESO VLT SINFONI imaging data.\n"}
+{"abstract": "  An asymptotic formula is given for the number of y-smooth numbers up to x in\na Beatty sequence corresponding to an irrational number of finite type.\n"}
+{"abstract": "  The extreme fragility of deep neural networks when presented with tiny\nperturbations in their inputs was independently discovered by several research\ngroups in 2013, but in spite of enormous effort these adversarial examples\nremained a baffling phenomenon with no clear explanation. In this paper we\nintroduce a new conceptual framework (which we call the Dimpled Manifold Model)\nwhich provides a simple explanation for why adversarial examples exist, why\ntheir perturbations have such tiny norms, why these perturbations look like\nrandom noise, and why a network which was adversarially trained with\nincorrectly labeled images can still correctly classify test images. In the\nlast part of the paper we describe the results of numerous experiments which\nstrongly support this new model, and in particular our assertion that\nadversarial perturbations are roughly perpendicular to the low dimensional\nmanifold which contains all the training examples.\n"}
+{"abstract": "  The numerical solution of dynamical systems with memory requires the\nefficient evaluation of Volterra integral operators in an evolutionary manner.\nAfter appropriate discretisation, the basic problem can be represented as a\nmatrix-vector product with a lower diagonal but densely populated matrix. For\ntypical applications, like fractional diffusion or large scale dynamical\nsystems with delay, the memory cost for storing the matrix approximations and\ncomplete history of the data then would become prohibitive for an accurate\nnumerical approximation. For Volterra-integral operators of convolution type,\nthe \\emph{fast and oblivious convolution quadrature} method of Sch\\\"adle,\nLopez-Fernandez, and Lubich allows to compute the discretized valuation with\n$N$ time steps in $O(N \\log N)$ complexity and only requiring $O(\\log N)$\nactive memory to store a compressed version of the complete history of the\ndata. We will show that this algorithm can be interpreted as an\n$\\mathcal{H}$-matrix approximation of the underlying integral operator and,\nconsequently, a further improvement can be achieved, in principle, by resorting\nto $\\mathcal{H}^2$-matrix compression techniques. We formulate a variant of the\n$\\mathcal{H}^2$-matrix vector product for discretized Volterra integral\noperators that can be performed in an evolutionary and oblivious manner and\nrequires only $O(N)$ operations and $O(\\log N)$ active memory. In addition to\nthe acceleration, more general asymptotically smooth kernels can be treated and\nthe algorithm does not require a-priori knowledge of the number of time steps.\nThe efficiency of the proposed method is demonstrated by application to some\ntypical test problems.\n"}
+{"abstract": "  Abridged for arXiv: In this work, we apply a powerful new technique in order\nto observationally derive accurate assembly histories through a self-consistent\ncombined stellar dynamical and population galaxy model. We present this\napproach for three edge-on lenticular galaxies from the Fornax3D project -- FCC\n153, FCC 170, and FCC 177 -- in order to infer their mass assembly histories\nindividually and in the context of the Fornax cluster. The method was tested on\nmock data from simulations to quantify its reliability. We find that the\ngalaxies studied here have all been able to form dynamically-cold (intrinsic\nvertical velocity dispersion $\\sigma_z \\lesssim 50\\ {\\rm km}\\ {\\rm s}^{-1}$)\nstellar disks after cluster infall. Moreover, the pre-existing (old) high\nangular momentum components have retained their angular momentum (orbital\ncircularity $\\lambda_z > 0.8$) through to the present day. Comparing the\nderived assembly histories with a comparable galaxy in a low-density\nenvironment -- NGC 3115 -- we find evidence for cluster-driven suppression of\nstellar accretion and merging. We measured the intrinsic stellar\nage--velocity-dispersion relation and find that the shape of the relation is\nconsistent with galaxies in the literature across redshift. There is tentative\nevidence for enhancement in the luminosity-weighted intrinsic vertical velocity\ndispersion due to the cluster environment. But importantly, there is an\nindication that metallicity may be a key driver of this relation. We finally\nspeculate that the cluster environment is responsible for the S0 morphology of\nthese galaxies via the gradual external perturbations, or `harassment',\ngenerated within the cluster.\n"}
+{"abstract": "  Knowledge distillation (KD) has been actively studied for image\nclassification tasks in deep learning, aiming to improve the performance of a\nstudent model based on the knowledge from a teacher model. However, there have\nbeen very few efforts for applying KD in image regression with a scalar\nresponse, and there is no KD method applicable to both tasks. Moreover,\nexisting KD methods often require a practitioner to carefully choose or adjust\nthe teacher and student architectures, making these methods less scalable in\npractice. Furthermore, although KD is usually conducted in scenarios with\nlimited labeled data, very few techniques are developed to alleviate such data\ninsufficiency. To solve the above problems in an all-in-one manner, we propose\nin this paper a unified KD framework based on conditional generative\nadversarial networks (cGANs), termed cGAN-KD. Fundamentally different from\nexisting KD methods, cGAN-KD distills and transfers knowledge from a teacher\nmodel to a student model via cGAN-generated samples. This unique mechanism\nmakes cGAN-KD suitable for both classification and regression tasks, compatible\nwith other KD methods, and insensitive to the teacher and student\narchitectures. Also, benefiting from the recent advances in cGAN methodology\nand our specially designed subsampling and filtering procedures, cGAN-KD also\nperforms well when labeled data are scarce. An error bound of a student model\ntrained in the cGAN-KD framework is derived in this work, which theoretically\nexplains why cGAN-KD takes effect and guides the implementation of cGAN-KD in\npractice. Extensive experiments on CIFAR-10 and Tiny-ImageNet show that we can\nincorporate state-of-the-art KD methods into the cGAN-KD framework to reach a\nnew state of the art. Also, experiments on RC-49 and UTKFace demonstrate the\neffectiveness of cGAN-KD in image regression tasks, where existing KD methods\nare inapplicable.\n"}
+{"abstract": "  For a complex Lie group G and a prime number p, Totaro had conjectured that\nthe dimension of the singular cohomology with Z/p-coefficients of classifying\nspace of G is bounded above by that of the de Rham cohomology of the\nclassifying stack of (the split form of) G in characteristic p. This conjecture\nwas recently proven by Kubrak--Prikhodko. In this note, we give a shorter\nproof.\n"}
+{"abstract": "  In this paper, we propose some variational formulations with the use of high\norder impedance boundary condition (HOIBC) to solve the scattering problem. We\nstudy the existence and uniqueness of the solution. Then, a discretization of\nthese formulations is done. We give validations of the HOIBC obtained with a\nMoM code that show the improvement in accuracy over the standard impedance\nboundary condition (SIBC) computations.\n"}
+{"abstract": "  Bayesian experimental design (BED) aims at designing an experiment to\nmaximize the information gathering from the collected data. The optimal design\nis usually achieved by maximizing the mutual information (MI) between the data\nand the model parameters. When the analytical expression of the MI is\nunavailable, e.g., having implicit models with intractable data distributions,\na neural network-based lower bound of the MI was recently proposed and a\ngradient ascent method was used to maximize the lower bound. However, the\napproach in Kleinegesse et al., 2020 requires a pathwise sampling path to\ncompute the gradient of the MI lower bound with respect to the design\nvariables, and such a pathwise sampling path is usually inaccessible for\nimplicit models. In this work, we propose a hybrid gradient approach that\nleverages recent advances in variational MI estimator and evolution strategies\n(ES) combined with black-box stochastic gradient ascent (SGA) to maximize the\nMI lower bound. This allows the design process to be achieved through a unified\nscalable procedure for implicit models without sampling path gradients. Several\nexperiments demonstrate that our approach significantly improves the\nscalability of BED for implicit models in high-dimensional design space.\n"}
+{"abstract": "  Medical Visual Question Answering (VQA) is a multi-modal challenging task\nwidely considered by research communities of the computer vision and natural\nlanguage processing. Since most current medical VQA models focus on visual\ncontent, ignoring the importance of text, this paper proposes a multi-view\nattention-based model(MuVAM) for medical visual question answering which\nintegrates the high-level semantics of medical images on the basis of text\ndescription. Firstly, different methods are utilized to extract the features of\nthe image and the question for the two modalities of vision and text. Secondly,\nthis paper proposes a multi-view attention mechanism that include\nImage-to-Question (I2Q) attention and Word-to-Text (W2T) attention. Multi-view\nattention can correlate the question with image and word in order to better\nanalyze the question and get an accurate answer. Thirdly, a composite loss is\npresented to predict the answer accurately after multi-modal feature fusion and\nimprove the similarity between visual and textual cross-modal features. It\nconsists of classification loss and image-question complementary (IQC) loss.\nFinally, for data errors and missing labels in the VQA-RAD dataset, we\ncollaborate with medical experts to correct and complete this dataset and then\nconstruct an enhanced dataset, VQA-RADPh. The experiments on these two datasets\nshow that the effectiveness of MuVAM surpasses the state-of-the-art method.\n"}
+{"abstract": "  We explore hierarchical black hole (BH) mergers in nuclear star clusters\n(NSCs), globular clusters (GCs) and young star clusters (YSCs), accounting for\nboth original and dynamically assembled binary BHs (BBHs). We find that the\nmedian mass of both first- and nth-generation dynamical mergers is larger in\nGCs and YSCs with respect to NSCs, because the lighter BHs are ejected by\nsupernova kicks from the lower-mass clusters. Also, first- and nth-generation\nBH masses are strongly affected by the metallicity of the progenitor stars: the\nmedian mass of the primary BH of a nth-generation merger is $\\sim{}24-38$\nM$_\\odot$ ($\\sim{}9-15$ M$_\\odot$) in metal-poor (metal-rich) NSCs. The maximum\nBH mass mainly depends on the escape velocity: BHs with mass up to several\nthousand M$_\\odot$ form in NSCs, while YSCs and GCs host BHs with mass up to\nseveral hundred M$_\\odot$. Furthermore, we calculate the fraction of mergers\nwith at least one component in the pair-instability mass gap ($f_{\\rm PI}$) and\nin the intermediate-mass BH regime ($f_{\\rm IMBH}$). In the fiducial model for\ndynamical BBHs with metallicity $Z=0.002$, we find $f_{\\rm PI}\\approx{}0.05$,\n$0.02$ and $0.007$ ($f_{\\rm IMBH}\\approx{}0.01$, $0.002$ and $0.001$) in NSCs,\nGCs and YSCs, respectively. Both $f_{\\rm PI}$ and $f_{\\rm IMBH}$ drop by at\nleast one order of magnitude at solar metallicity. Finally, we investigate the\nformation of GW190521 by assuming that it is either a nearly equal-mass BBH or\nan intermediate-mass ratio inspiral.\n"}
+{"abstract": "  We consider an interacting particle system with two species under strong\ncompetition dynamics between the two species. Then, through the hydrodynamic\nlimit procedure for the microscopic model, we derive a one-phase Stefan type\nfree boundary problem with non-linear diffusion, letting the competition rate\ndivergent. Non-linearity of diffusion comes from a zero-range dynamics for one\nspecies while we impose the other species to weakly diffuse according to the\nKawasaki dynamics for technical reasons, which macroscopically corresponds to\nthe vanishing viscosity method.\n"}
+{"abstract": "  Visual place recognition is a challenging task in computer vision and a key\ncomponent of camera-based localization and navigation systems. Recently,\nConvolutional Neural Networks (CNNs) achieved high results and good\ngeneralization capabilities. They are usually trained using pairs or triplets\nof images labeled as either similar or dissimilar, in a binary fashion. In\npractice, the similarity between two images is not binary, but rather\ncontinuous. Furthermore, training these CNNs is computationally complex and\ninvolves costly pair and triplet mining strategies.\n  We propose a Generalized Contrastive loss (GCL) function that relies on image\nsimilarity as a continuous measure, and use it to train a siamese CNN.\nFurthermore, we propose three techniques for automatic annotation of image\npairs with labels indicating their degree of similarity, and deploy them to\nre-annotate the MSLS, TB-Places, and 7Scenes datasets.\n  We demonstrate that siamese CNNs trained using the GCL function and the\nimproved annotations consistently outperform their binary counterparts. Our\nmodels trained on MSLS outperform the state-of-the-art methods, including\nNetVLAD, and generalize well on the Pittsburgh, TokyoTM and Tokyo 24/7\ndatasets. Furthermore, training a siamese network using the GCL function does\nnot require complex pair mining. We release the source code at\nhttps://github.com/marialeyvallina/generalized_contrastive_loss.\n"}
+{"abstract": "  We prove formulas for the rational Chow motives of moduli spaces of\nsemistable vector bundles and Higgs bundles of rank 3 and coprime degree on a\nsmooth projective curve. Our approach involves identifying criteria to lift\nidentities in (a completion of) the Grothendieck group of effective Chow\nmotives to isomorphisms in the category of Chow motives. For the Higgs moduli\nspace, we use motivic Bialynicki-Birula decompositions associated to a scaling\naction with variation of stability and wall-crossing for moduli spaces of rank\n2 pairs, which occur in the fixed locus of this action.\n"}
+{"abstract": "  The NLP community has seen substantial recent interest in grounding to\nfacilitate interaction between language technologies and the world. However, as\na community, we use the term broadly to reference any linking of text to data\nor non-textual modality. In contrast, Cognitive Science more formally defines\n\"grounding\" as the process of establishing what mutual information is required\nfor successful communication between two interlocutors -- a definition which\nmight implicitly capture the NLP usage but differs in intent and scope. We\ninvestigate the gap between these definitions and seek answers to the following\nquestions: (1) What aspects of grounding are missing from NLP tasks? Here we\npresent the dimensions of coordination, purviews and constraints. (2) How is\nthe term \"grounding\" used in the current research? We study the trends in\ndatasets, domains, and tasks introduced in recent NLP conferences. And finally,\n(3) How to advance our current definition to bridge the gap with Cognitive\nScience? We present ways to both create new tasks or repurpose existing ones to\nmake advancements towards achieving a more complete sense of grounding.\n"}
+{"abstract": "  We develop a Bayesian spatio-temporal model to study pre-industrial grain\nmarket integration during the Finnish famine of the 1860s. Our model takes into\naccount several problematic features often present when analysing multiple\nspatially interdependent time series. For example, compared with the error\ncorrection methodology commonly applied in econometrics, our approach allows\nsimultaneous modeling of multiple interdependent time series avoiding\ncumbersome statistical testing needed to predetermine the market leader as a\npoint of reference. Furthermore, introducing a flexible spatio-temporal\nstructure enables analysing detailed regional and temporal dynamics of the\nmarket mechanisms. Applying the proposed method, we detected spatially\nasymmetric \"price ripples\" that spread out from the shock origin. We\ncorroborated the existing literature on the speedier adjustment to emerging\nprice differentials during the famine, but we observed this principally in\nurban markets. This hastened return to long-run equilibrium means faster and\nlonger travel of price shocks, implying prolonged out-of-equilibrium dynamics,\nproliferated influence of market shocks, and, importantly, a wider spread of\nfamine conditions.\n"}
+{"abstract": "  We present the analysis of the diffuse, low column density HI environment of\n18 MHONGOOSE galaxies. We obtained deep observations with the Robert C. Byrd\nGreen Bank Telescope, and reached down to a 3sigma column density detection\nlimit of NHI=6.3x10^{17} cm^{-2} over a 20 km/s linewidth. We analyze the\nenvironment around these galaxies, with a focus on HI gas that reaches column\ndensities below NHI=10^{19} cm^{-2}. We calculate the total amount of HI gas in\nand around the galaxies revealing that nearly all of these galaxies contained\nexcess HI outside of their disks. We quantify the amount of diffuse gas in the\nmaps of each galaxy, defined by HI gas with column densities below 10^{19}\ncm^{-2}, and find a large spread in percentages of diffuse gas. However, by\nbinning the percentage of diffuse HI into quarters, we find that the bin with\nthe largest number of galaxies is the lowest quartile (0-25\\% diffuse HI). We\nidentified several galaxies which may be undergoing gas accretion onto the\ngalaxy disk using multiple methods of analysis, including azimuthally averaging\ncolumn densities beyond the disk, and identifying structure within our\nintegrated intensity (Moment 0) maps. We measured HI mass outside the disks of\nmost of our galaxies, with rising cumulative flux even at large radii. We also\nfind a strong correlation between the fraction of diffuse gas in a galaxy and\nits baryonic mass, and test this correlation using both Spearman and Pearson\ncorrelation coefficients. We see evidence of a dark matter halo mass threshold\nof M_{halo}~10^{11.1} \\msun{} in which galaxies with high fractions of diffuse\nHI all reside below. It is in this regime in which cold-mode accretion should\ndominate. Finally, we suggest a rotation velocity of v_{rot}~80 km\\s as an\nupper threshold to find diffuse gas-dominated galaxies.\n"}
+{"abstract": "  Certain applications require the use of signals that combine both the\ncapability to operate with low signal-to-noise ratios and the ability to\nsupport multiple users without interference. In the case where many users have\nvery different signal-to-noise ratios, it is necessary to consider coding\nschemes that can be used in a multi-user environment but with different noise\nimmunity levels. Traditional detection systems based on the correlation\nfunction and coding sequences have significant limitations in satisfying both\nobjectives, since the cross-correlation between coded signals corresponding\nwith different users is linked to the use of the same coded sequences length.\nThe research topic of binary sequences that have null cross-correlation and\ndifferent length has not been studied in depth, but it has potential\napplications in multi-user environments. In this work an algorithm to generate\nbinary sequences completely uncorrelated with certain sets of complementary\nsequences is presented. The proposed algorithm is based on nested Barker\nsequences, and it is compared with a previous proposal based on an iterative\nalgorithm. This approach allows to generate more diversity of sequences of\ndifferent length than the iterative approach, which it makes useful for\napplications based on binary sequences detection and expand the horizon of many\napplications.\n"}
+{"abstract": "  In many control problems that include vision, optimal controls can be\ninferred from the location of the objects in the scene. This information can be\nrepresented using feature points, which is a list of spatial locations in\nlearned feature maps of an input image. Previous works show that feature points\nlearned using unsupervised pre-training or human supervision can provide good\nfeatures for control tasks. In this paper, we show that it is possible to learn\nefficient feature point representations end-to-end, without the need for\nunsupervised pre-training, decoders, or additional losses. Our proposed\narchitecture consists of a differentiable feature point extractor that feeds\nthe coordinates of the estimated feature points directly to a soft actor-critic\nagent. The proposed algorithm yields performance competitive to the\nstate-of-the art on DeepMind Control Suite tasks.\n"}
+{"abstract": "  In this thesis, we focus on the proposal of distributed workflow systems\ndedicated to the automation of administrative business processes. We propose an\napproach to build such systems by relying on the concepts of multiagent\nsystems, Peer to Peer (P2P) architecture, Service-Oriented Architecture (SOA)\nand structured documents (artifacts) cooperative edition. Indeed, we develop\nmathematical tools that allow any workflow systems designer, to express each\nadministrative process in the form of an attributed grammar whose symbols\nrepresent tasks to be executed, productions specify a scheduling of these\ntasks, and instances (the derivation trees that conform to it) represent the\ndifferent execution scenarios leading to business goal states. The obtained\ngrammatical model is then introduced into a proposed P2P system which is in\ncharge of carrying out the completely decentralised execution of the underlying\nprocess's instances. The said system orchestrates a process's instance\nexecution as a choreography during which, various software agents driven by\nhuman agents (actors), coordinate themselves through artifacts that they\ncollectively edit. The exchanged artifacts represent the system's memory: they\nprovide information on already executed tasks, on those ready to be executed\nand on their executors. The software agents are autonomous and identical: they\nexecute the same unique protocol each time they receive an artifact. This\nprotocol allows them to identify the tasks they must immediately execute, to\nexecute them, to update the artifact and to disseminate it if necessary, for\nthe continuation of the execution. Moreover, actors potentially have only a\npartial perception of processes in which they are involved. In practice, this\nmeans that certain tasks can be carried out confidentially.\n"}
+{"abstract": "  Functor lifting along a fibration is used for several different purposes in\ncomputer science. In the theory of coalgebras, it is used to define coinductive\npredicates, such as simulation preorder and bisimilarity. Codensity lifting is\na scheme to obtain a functor lifting along a fibration. It generalizes a few\nprevious lifting schemes including the Kantorovich lifting. In this paper, we\nseek a property of functor lifting called fiberedness. Hinted by a known result\nfor Kantorovich lifting, we identify a sufficient condition for a codensity\nlifting to be fibered. We see that this condition applies to many examples that\nhave been studied. As an application, we derive some results on\nbisimilarity-like notions.\n"}
+{"abstract": "  Particle beam eigen-emittances comprise the lowest set of rms-emittances that\ncan be imposed to a beam through symplectic optical elements. For cases of\npractical relevance this paper introduces an approximation providing a very\nsimple and powerful relation between transverse eigen-emittance variation and\nthe beam phase integral. This relation enormously facilitates modeling\neigen-emittance tailoring scenarios. It reveals that difference of\neigen-emittances is given by the beam phase integral or vorticity rather than\nby angular momentum. Within the approximation any beam is equivalent to two\nobjects rotating at angular velocities of same strength and different sign. A\ndescription through circular beam modes has been done already in [A. Burov, S.\nNagaitsev, and Y. Derbenev, Circular modes, beam adapters, and their\napplications in beam optics, Phys. Rev. E 66, 016503 (2002)]. The new relation\npresented here is a complementary and vivid approach to provide a physical\npicture of the nature of eigen-emittances for cases of practical interest.\n"}
+{"abstract": "  In this paper, we propose an image compression algorithm called Microshift.\nWe employ an algorithm hardware co-design methodology, yielding a\nhardware-friendly compression approach with low power consumption. In our\nmethod, the image is first micro-shifted, then the sub-quantized values are\nfurther compressed. Two methods, the FAST and MRF model, are proposed to\nrecover the bit-depth by exploiting the spatial correlation of natural images.\nBoth methods can decompress images progressively. Our compression algorithm\ncompresses images to 1.25 bits per pixel on average with PSNR of 33.16 dB,\noutperforming other on-chip compression algorithms. Then, we propose a hardware\narchitecture and implement the algorithm on an FPGA and ASIC. The results on\nthe VLSI design further validate the low hardware complexity and high power\nefficiency, showing our method is promising, particularly for low-power\nwireless vision sensor networks.\n"}
+{"abstract": "  Automated cyber threat detection in computer networks is a major challenge in\ncybersecurity. The cyber domain has inherent challenges that make traditional\nmachine learning techniques problematic, specifically the need to learn\ncontinually evolving attacks through global collaboration while maintaining\ndata privacy, and the varying resources available to network owners. We present\na scheme to mitigate these difficulties through an architectural approach using\ncommunity model sharing with a streaming analytic pipeline. Our streaming\napproach trains models incrementally as each log record is processed, thereby\nadjusting to concept drift resulting from changing attacks. Further, we\ndesigned a community sharing approach which federates learning through merging\nmodels without the need to share sensitive cyber-log data. Finally, by\nstandardizing data and Machine Learning processes in a modular way, we provide\nnetwork security operators the ability to manage cyber threat events and model\nsensitivity through community member and analytic method weighting in ways that\nare best suited for their available resources and data.\n"}
+{"abstract": "  We initiate the homotopical study of racks and quandles, two algebraic\nstructures that govern knot theory and related braided structures in algebra\nand geometry. We prove analogs of Milnor's theorem on free groups for these\ntheories and their pointed variants, identifying the homotopy types of the free\nracks and free quandles on spaces of generators. These results allow us to\ncomplete the stable classification of racks and quandles by identifying the\nring spectra that model their stable homotopy theories. As an application, we\nshow that the stable homotopy of a knot quandle is, in general, more\ncomplicated than what any Wirtinger presentation coming from a diagram\npredicts.\n"}
+{"abstract": "  The existence, uniqueness and stability of periodic traveling waves for the\nfractional Benjamin-Bona-Mahony equation is considered. In our approach, we\ngive sufficient conditions to prove a uniqueness result for the single-lobe\nsolution obtained by a constrained minimization problem. The spectral stability\nis then shown by determining that the associated linearized operator around the\nwave restricted to the orthogonal of the tangent space related to the momentum\nand mass at the periodic wave has no negative eigenvalues. We propose the\nPetviashvili's method to investigate the spectral stability of the periodic\nwaves for the fractional Benjamin-Bona-Mahony equation, numerically. Some\nremarks concerning the orbital stability of periodic traveling waves are also\npresented.\n"}
+{"abstract": "  Object detection is an important computer vision task with plenty of\nreal-world applications; therefore, how to enhance its robustness against\nadversarial attacks has emerged as a crucial issue. However, most of the\nprevious defense methods focused on the classification task and had few\nanalysis in the context of the object detection task. In this work, to address\nthe issue, we present a novel class-aware robust adversarial training paradigm\nfor the object detection task. For a given image, the proposed approach\ngenerates an universal adversarial perturbation to simultaneously attack all\nthe occurred objects in the image through jointly maximizing the respective\nloss for each object. Meanwhile, instead of normalizing the total loss with the\nnumber of objects, the proposed approach decomposes the total loss into\nclass-wise losses and normalizes each class loss using the number of objects\nfor the class. The adversarial training based on the class weighted loss can\nnot only balances the influence of each class but also effectively and evenly\nimproves the adversarial robustness of trained models for all the object\nclasses as compared with the previous defense methods. Furthermore, with the\nrecent development of fast adversarial training, we provide a fast version of\nthe proposed algorithm which can be trained faster than the traditional\nadversarial training while keeping comparable performance. With extensive\nexperiments on the challenging PASCAL-VOC and MS-COCO datasets, the evaluation\nresults demonstrate that the proposed defense methods can effectively enhance\nthe robustness of the object detection models.\n"}
+{"abstract": "  Some generalizations of the relation between high-energy astrophysical\nneutrino and cosmic ray fluxes are obtained, taking into account present\nresults on the cosmic ray spectrum and composition as well as a more realistic\nmodeling of the Galactic and extragalactic cosmic ray components down to PeV\nenergies. It is found that the level of neutrino fluxes measured by IceCube can\nbe consistent with sources that are thin to escaping protons. This could also\nmake it easier for heavier nuclei to be emitted from the sources without\nsuffering excessive disintegration processes.\n"}
+{"abstract": "  We investigate the problem of synthesizing T-depth-optimal quantum circuits\nover the universal fault-tolerant Clifford+T gate set, where the implementation\nof the non-Clifford T-gate is the most expensive.\n  We use nested meet-in-the-middle (MITM) technique to develop algorithms for\nsynthesizing provably \\emph{depth-optimal} and \\emph{T-depth-optimal} circuits\nfor exactly implementable unitaries. These algorithms improve space complexity.\nSpecifically, for synthesizing T-depth-optimal circuits we define a special\nsubset of T-depth-1 unitaries, which can generate the T-depth-optimal\ndecomposition (up to a Clifford). This plays a crucial role in having better\ntime complexity as well. We get an algorithm with space and time complexity\n$O\\left(\\left(n\\cdot 2^{5.6n}\\right)^{\\lceil d/c\\rceil}\\right)$ and\n$O\\left(\\left(n\\cdot 2^{5.6n}\\right)^{(c-1)\\lceil d/c\\rceil}\\right)$\nrespectively, where $d$ is the minimum T-depth and $c\\geq 2$ is a constant.\nThis is much better than the complexity of the algorithm by Amy~et~al.(2013),\nthe previous best with a complexity much more than\n$O\\left(\\left(2^{kn^2}\\right)^{\\lceil d/2\\rceil}\\right)$, where $k$ is a\nconstant. For example, our new methods took 2 seconds for a task that would\nhave taken more than 4 days using the methods in Amy~et~al.(2013).\n  We design an even more efficient algorithm for synthesizing T-depth-optimal\ncircuits. The claimed efficiency and optimality depends on some conjectures,\nwhich have been inspired from the work of Mosca and Mukhopadhyay (2020). To the\nbest of our knowledge, the conjectures are not related to the previous work.\nOur algorithm has space and time complexity $\\poly(n,2^{5.6n},d)$ (or\n$\\poly(n^{\\log n},2^{5.6n},d)$ under some weaker assumptions).\n"}
+{"abstract": "  Radiative-transfer (RT) is a fundamental part of modelling exoplanet\natmospheres with general circulation models (GCMs). An accurate RT scheme is\nrequired for estimates of the atmospheric energy transport and for gaining\nphysical insight from model spectra. We implement three RT schemes for Exo-FMS:\nsemi-grey, non-grey `picket fence', and real gas with correlated-k. We\nbenchmark the Exo-FMS GCM using these RT schemes to hot Jupiter simulation\nresults from the literature. We perform a HD 209458b-like simulation with the\nthree schemes and compare their results. These simulations are then\npost-processed to compare their observable differences. The semi-grey scheme\nresults show qualitative agreement with previous studies in line with\nvariations seen between GCM models. The real gas model reproduces well the\ntemperature and dynamical structures from other studies. After post-processing\nour non-grey picket fence scheme compares very favourably with the real gas\nmodel, producing similar transmission spectra, emission spectra and phase curve\nbehaviours. Exo-FMS is able to reliably reproduce the essential features of\ncontemporary GCM models in the hot gas giant regime. Our results suggest the\npicket fence approach offers a simple way to improve upon RT realism beyond\nsemi-grey schemes.\n"}
+{"abstract": "  We propose a straightforward vocabulary adaptation scheme to extend the\nlanguage capacity of multilingual machine translation models, paving the way\ntowards efficient continual learning for multilingual machine translation. Our\napproach is suitable for large-scale datasets, applies to distant languages\nwith unseen scripts, incurs only minor degradation on the translation\nperformance for the original language pairs and provides competitive\nperformance even in the case where we only possess monolingual data for the new\nlanguages.\n"}
+{"abstract": "  Three-dimensional topological insulators (TIs) attract much attention due to\nits topologically protected Dirac surface states. Doping into TIs or their\nproximity with normal superconductors can promote the realization of\ntopological superconductivity(SC) and Majorana fermions with potential\napplications in quantum computations. Here, an emergent superconductivity was\nobserved in local mesoscopic point-contacts on the topological insulator Bi2Se3\nby applying a voltage pulse through the contacts, evidenced by the Andreev\nreflection peak in the point-contact spectra and a visible resistance drop in\nthe four-probe electrical resistance measurements. More intriguingly, the\nsuperconductivity can be erased with thermal cycles by warming up to high\ntemperatures (300 K) and induced again by the voltage pulse at the base\ntemperature (1.9 K), suggesting a significance for designing new types of\nquantum devices. Nematic behaviour is also observed in the superconducting\nstate, similar to the case of CuxBi2Se3 as topological superconductor\ncandidates.\n"}
+{"abstract": "  To provide high data rate aerial links for 5G and beyond wireless networks,\nthe integration of free-space optical (FSO) communications and aerial platforms\nhas been recently suggested as a practical solution. To fully reap the benefit\nof aerial-based FSO systems, in this paper, an analytical channel model for a\nlong-range ground-to-air FSO link under the assumption of plane wave optical\nbeam profile at the receiver is derived. Particularly, the model includes the\ncombined effects of transmitter divergence angle, random wobbling of the\nreceiver, jitter due to beam wander, attenuation loss, and atmospheric\nturbulence. Furthermore, a closed-form expression for the outage probability of\nthe considered link is derived which makes it possible to evaluate the\nperformance of such systems. Numerical results are then provided to corroborate\nthe accuracy of the proposed analytical expressions and to prove the\nsuperiority of the proposed channel model over the previous models in\nlong-range aerial FSO links.\n"}
+{"abstract": "  The origin of high-Tc superconductivity remains an enigma even though\ntremendous research effort and progress have been made on cuprate and iron\npnictide superconductors. Aiming to mimic the cuprate-like electronic\nconfiguration of transition metal, superconductivity has been recently found in\nnickelates. This discovery hallmarks a new era in the search and understanding\nof the high-Tc superconductivity. However, unlike the cuprate and iron\npnictide, in which the superconductivity was initially found in a compound\ncontaining La, the superconductivity in the nickelate has only been observed in\nNd- and Pr-based compounds. This raises a central question of whether the f\nelectron of the rare-earth element is critical for superconductivity in the\nnickelates. Here, we report the observation of superconductivity in\ninfinite-layer Ca-doped LaNiO2 (La1-xCaxNiO2) thin films and construct their\nphase diagram. Unlike the metal-insulator transition in Nd- and Pr-based\nnickelates, the undoped and underdoped La1-xCaxNiO2 thin films are entirely\ninsulating from 300 down to 2 K. A superconducting dome is observed from\n0.15<x<0.3 with weakly insulating behavior at the overdoped regime. Moreover,\nthe sign of the Hall coefficient RH changes at low temperature for samples with\na higher doping level. However, distinct from the Nd- and Pr-based nickelates,\nthe RH-sign-change temperature remains around 35 K as the doping increases,\nsuggesting a different multiband structure in the La1-xCaxNiO2. These results\nalso emphasize the significant role of lattice correlation on the multiband\nstructures of the infinite-layer nickelates.\n"}
+{"abstract": "  The main goal of the present paper is to evaluate the perturbed locations and\ninvestigate the linear stability of the triangular points. We studied the\nproblem in the elliptic restricted three body problem frame of work. The\nproblem is generalized in the sense that the two primaries are considered as\ntriaxial bodies. It is found that the locations of these points are affected by\nthe triaxiality coefficients of the primaries and the eccentricity of orbits.\nAlso it is observed that the stability regions depend on the involved\nperturbations. In addition to this we studied the periodic orbits in the\nvicinity of the triangular points.\n"}
+{"abstract": "  The research direction of identifying acoustic bio-markers of respiratory\ndiseases has received renewed interest following the onset of COVID-19\npandemic. In this paper, we design an approach to COVID-19 diagnostic using\ncrowd-sourced multi-modal data. The data resource, consisting of acoustic\nsignals like cough, breathing, and speech signals, along with the data of\nsymptoms, are recorded using a web-application over a period of ten months. We\ninvestigate the use of statistical descriptors of simple time-frequency\nfeatures for acoustic signals and binary features for the presence of symptoms.\nUnlike previous works, we primarily focus on the application of simple linear\nclassifiers like logistic regression and support vector machines for acoustic\ndata while decision tree models are employed on the symptoms data. We show that\na multi-modal integration of acoustics and symptoms classifiers achieves an\narea-under-curve (AUC) of 92.40, a significant improvement over any individual\nmodality. Several ablation experiments are also provided which highlight the\nacoustic and symptom dimensions that are important for the task of COVID-19\ndiagnostics.\n"}
+{"abstract": "  Tensors are widely used to represent multiway arrays of data. The recovery of\nmissing entries in a tensor has been extensively studied, generally under the\nassumption that entries are missing completely at random (MCAR). However, in\nmost practical settings, observations are missing not at random (MNAR): the\nprobability that a given entry is observed (also called the propensity) may\ndepend on other entries in the tensor or even on the value of the missing\nentry. In this paper, we study the problem of completing a partially observed\ntensor with MNAR observations, without prior information about the\npropensities. To complete the tensor, we assume that both the original tensor\nand the tensor of propensities have low multilinear rank. The algorithm first\nestimates the propensities using a convex relaxation and then predicts missing\nvalues using a higher-order SVD approach, reweighting the observed tensor by\nthe inverse propensities. We provide finite-sample error bounds on the\nresulting complete tensor. Numerical experiments demonstrate the effectiveness\nof our approach.\n"}
+{"abstract": "  Electroweak radiative corrections to the cross section of the process $e^+\ne^- \\to Z H$ are considered. The complete one-loop electroweak radiative\ncorrections are evaluated with the help of the SANC system. Higher-order\ncontributions of the initial state radiation are computed in the QED structure\nfunction formalism. Numerical results are produced by a new version of the\nReneSANCe event generator and MCSANCee integrator for the conditions of future\nelectron-positron colliders. The resulting theoretical uncertainty in the\ndescription of this process is estimated.\n"}
+{"abstract": "  This paper addresses the persistent monitoring problem defined on a network\nwhere a set of nodes (targets) needs to be monitored by a team of dynamic\nenergy-aware agents. The objective is to control the agents' motion to jointly\noptimize the overall agent energy consumption and a measure of overall node\nstate uncertainty, evaluated over a finite period of interest. To achieve these\nobjectives, we extend an established event-driven Receding Horizon Control\n(RHC) solution by adding an optimal controller to account for agent motion\ndynamics and associated energy consumption. The resulting RHC solution is\ncomputationally efficient, distributed and on-line. Finally, numerical results\nare provided highlighting improvements compared to an existing RHC solution\nthat uses energy-agnostic first-order agents.\n"}
+{"abstract": "  Financial trading has been widely analyzed for decades with market\nparticipants and academics always looking for advanced methods to improve\ntrading performance. Deep reinforcement learning (DRL), a recently\nreinvigorated method with significant success in multiple domains, still has to\nshow its benefit in the financial markets. We use a deep Q-network (DQN) to\ndesign long-short trading strategies for futures contracts. The state space\nconsists of volatility-normalized daily returns, with buying or selling being\nthe reinforcement learning action and the total reward defined as the\ncumulative profits from our actions. Our trading strategy is trained and tested\nboth on real and simulated price series and we compare the results with an\nindex benchmark. We analyze how training based on a combination of artificial\ndata and actual price series can be successfully deployed in real markets. The\ntrained reinforcement learning agent is applied to trading the E-mini S&P 500\ncontinuous futures contract. Our results in this study are preliminary and need\nfurther improvement.\n"}
+{"abstract": "  In this paper, we study the effects of rainbow gravity on relativistic\nBose-Einstein condensation and thermodynamics parameters. We initially\ndiscussed some formal aspects of the model to only then compute the corrections\nto the Bose-Einstein condensation. The calculations were carried out by\ncomputing the generating functional, from which we extract the thermodynamics\nparameters. The corrected critical temperature $T_c$ that sets the\nBose-Einstein Condensation was also computed for the three mostly adopted cases\nfor the rainbow functions. We have also obtained a phenomenological upper bound\nfor a combination of the quantities involved in the model, besides showing the\npossibility of occurrence of the Bose-Einstein condensation in two spatial\ndimensions under appropriate conditions on those functions. Finally, we have\ndiscussed how harder is for the particles at an arbitrary temperature $T<T_c$\nto enter the condensed state when compared with the usual scenario.\n"}
+{"abstract": "  Compound chondrules, i.e. chondrules fused together, make a powerful probe of\nthe density and compositional diversity in chondrule-forming environments, but\ntheir abundance among the dominating porphyritic textures may have been\ndrastically underestimated. I report herein microscopic observations and\nLA-ICP-MS analyses of lobate chondrules in the CO3 chondrites Miller Range\n07193 and 07342. Lobes in a given chondrule show correlated volatile and\nmoderately volatile element abundances but refractory element concentrations\nare essentially independent. This indicates that they formed by the collision\nof preexisting droplets whose refractory elements behaved in closed system,\nwhile their more volatile elements were buffered by the same gaseous medium.\nThe presence of lobes would otherwise be difficult to explain, as surface\ntension should have rapidly imposed a spherical shape at the temperature peak.\nIn fact, since most chondrules across chondrite groups are nonspherical, a\nmajority are probably compounds variously relaxed toward sphericity. The lack\nof correlation of refractory elements between conjoined compound chondrule\ncomponents is inconsistent with derivation of chondrules from the disruption of\nhomogenized melt bodies as in impact scenarios and evokes rather the melting of\nindependent mm-size nebular aggregates. Yet a \"nebular\" setting for chondrule\nformation would need to involve not only increased solid concentration, e.g. by\nsettling to the midplane, but also a boost in relative velocities between\ndroplets during chondrule-forming events to account for observed compound\nchondrule frequencies .\n"}
+{"abstract": "  Learning and analyzing rap lyrics is a significant basis for many web\napplications, such as music recommendation, automatic music categorization, and\nmusic information retrieval, due to the abundant source of digital music in the\nWorld Wide Web. Although numerous studies have explored the topic, knowledge in\nthis field is far from satisfactory, because critical issues, such as prosodic\ninformation and its effective representation, as well as appropriate\nintegration of various features, are usually ignored. In this paper, we propose\na hierarchical attention variational autoencoder framework (HAVAE), which\nsimultaneously consider semantic and prosodic features for rap lyrics\nrepresentation learning. Specifically, the representation of the prosodic\nfeatures is encoded by phonetic transcriptions with a novel and effective\nstrategy~(i.e., rhyme2vec). Moreover, a feature aggregation strategy is\nproposed to appropriately integrate various features and generate\nprosodic-enhanced representation. A comprehensive empirical evaluation\ndemonstrates that the proposed framework outperforms the state-of-the-art\napproaches under various metrics in different rap lyrics learning tasks.\n"}
+{"abstract": "  In addition to spectacular signatures such as black hole superradiance and\nthe rotation of CMB polarization, the plenitude of axions appearing in the\nstring axiverse may have potentially dangerous implications. An example is the\ncosmological overproduction of relic axions and moduli by the misalignment\nmechanism, more pronounced in regions where the signals mentioned above may be\nobservable, that is for large axion decay constant. In this work, we study the\nminimal requirements to soften this problem and show that the fundamental\nrequirement is a long period of low-scale inflation. However, in this case, if\nthe inflationary Hubble scale is lower than around $O(100)$ eV, no relic DM\naxion is produced in the early Universe. Cosmological production of some axions\nmay be activated, via the misalignment mechanism, if their potential minimum\nchanges between inflation and today. As a particular example, we study in\ndetail how the maximal-misalignment mechanism dilutes the effect of dangerous\naxions and allows the production of axion DM in a controlled way. In this case,\nthe potential of the axion that realises the mechanism shifts by a factor\n$\\Delta\\theta=\\pi$ between the inflationary epoch and today, and the axion\nstarts to oscillate from the top of its potential. We also show that axions\nwith masses $m_a\\sim O(1-100)\\, H_0$ realising the maximal-misalignment\nmechanism generically behave as dark energy with a decay constant that can take\nvalues well below the Planck scale, avoiding problems associated to\nsuper-Planckian scales. Finally, we briefly study the basic phenomenological\nimplications of the mechanism and comment on the compatibility of this type of\nmaximally-misaligned quintessence with the swampland criteria.\n"}
+{"abstract": "  In this work, we present the first linear time deterministic algorithm\ncomputing the 4-edge-connected components of an undirected graph. First, we\nshow an algorithm listing all 3-edge-cuts in a given 3-edge-connected graph,\nand then we use the output of this algorithm in order to determine the\n4-edge-connected components of the graph.\n"}
+{"abstract": "  (abridged) Within the Orion A molecular cloud, the integral-shaped filament\n(ISF) is a prominent, degree-long structure of dense gas and dust, with clear\nsigns of recent and on-going high-mass star formation. We used the ArTeMiS\nbolometer camera at APEX to map a 0.6x0.2 deg^2 region covering OMC-1, OMC-2,\nOMC-3 at 350 and 450 micron. We combined these data with Herschel-SPIRE maps to\nrecover extended emission. The combined Herschel-ArTeMiS maps provide details\non the distribution of dense, cold material, with a high spatial dynamic range,\nfrom our 8'' resolution (0.016 pc) up to the size of the map ~10-15 deg. By\ncombining Herschel and ArTeMiS data at 160, 250, 350 and 450 micron, we\nconstructed high-resolution temperature and H2 column density maps. We\nextracted radial profiles from the column density map in several,\nrepresentative portions of the ISF, that we fitted with Gaussian and Plummer\nmodels to derive their intrinsic widths. We also compared the distribution of\nmaterial traced by ArTeMiS with that seen in the higher density tracer\nN2H+(1-0) recently observed with the ALMA interferometer. All the radial\nprofiles that we extracted show clear deviation from a Gaussian, with evidence\nfor an inner plateau, previously not seen using Herschel-only data. We measure\nintrinsic half-power widths in the range 0.06 to 0.11 pc. This is significantly\nlarger than the Gaussian widths measured for fibers seen in N2H+, which\nprobably traces only the dense innermost regions of the large-scale filament.\nThese half-power widths are within a factor of two of the value of 0.1 pc found\nfor a large sample of nearby filaments in various low-mass star-forming\nregions, which tends to indicate that the physical conditions governing the\nfragmentation of prestellar cores within transcritical or supercritical\nfilaments are the same over a large range of masses per unit length.\n"}
+{"abstract": "  In this work, we study the following problem, that we refer to as Low Rank\ncolumn-wise Compressive Sensing (LRcCS): how to recover an $n \\times q$\nrank-$r$ matrix, $X^* =[x^*_1 , x^*_2 ,...x^*_q]$ from $m$ independent linear\nprojections of each of its $q$ columns, i.e., from $y_k := A_k x^*_k , k \\in\n[q]$, when $y_k$ is an $m$-length vector. The matrices $A_k$ are known and\nmutually independent for different $k$. The regime of interest is low-rank,\ni.e., $r \\ll \\min(n,q)$, and undersampled measurements, i.e., $m < n$. Even\nthough many LR recovery problems have been extensively studied in the last\ndecade, this particular problem has received little attention so far in terms\nof methods with provable guarantees. We introduce a novel gradient descent (GD)\nbased solution called altGDmin. We show that, if all entries of all $A_k$s are\ni.i.d. Gaussian, and if the right singular vectors of $X^*$ satisfy the\nincoherence assumption, then $\\epsilon$-accurate recovery of $X^*$ is possible\nwith $mq > C (n+q) r^2 \\log(1/\\epsilon)$ total samples and $O( mq nr \\log\n(1/\\epsilon))$ time. Compared to existing work, to our best knowledge, this is\nthe fastest solution and, for $\\epsilon < 1/\\sqrt{r}$, it also has the best\nsample complexity. Moreover, we show that a simple extension of our approach\nalso solves LR Phase Retrieval (LRPR), which is the magnitude-only\ngeneralization of LRcCS. It involves recovering $X^*$ from the magnitudes of\nentries of $y_k$. We show that altGDmin-LRPR has matching sample complexity and\nbetter time complexity when compared with the (best) existing solution for\nLRPR.\n"}
+{"abstract": "  In this paper we propose a traffic surveillance camera calibration method\nbased on detection of pairs of vanishing points associated with vehicles in the\ntraffic surveillance footage. To detect the vanishing points we propose a CNN\nwhich outputs heatmaps in which the positions of vanishing points are\nrepresented using the diamond space parametrization which enables us to detect\nvanishing points from the whole infinite projective space. From the detected\npairs of vanishing points for multiple vehicles in a scene we establish the\nscene geometry by estimating the focal length of the camera and the orientation\nof the road plane. We show that our method achieves competitive results on the\nBrnoCarPark dataset while having fewer requirements than the current state of\nthe art approach.\n"}
+{"abstract": "  We study the flow of elongated grains (wooden pegs of length $L$=20 mm with\ncircular cross section of diameter $d_c$=6 and 8 mm) from a silo with a\nrotating bottom and a circular orifice of diameter $D$. In the small orifice\nrange ($D/d<5$) clogs are mostly broken by the rotating base, and the flow is\nintermittent with avalanches and temporary clogs. Here\n$d\\equiv(\\frac{3}{2}d_c^2L)^{1/3}$ is the effective grain diameter. Unlike for\nspherical grains, for rods the flow rate $W$ clearly deviates from the power\nlaw dependence $W\\propto (D-kd)^{2.5}$ at lower orifice sizes in the\nintermittent regime, where $W$ is measured in between temporary clogs only.\nInstead, below about $D/d<3$ an exponential dependence $W\\propto e^{\\kappa D}$\nis detected. Here $k$ and $\\kappa$ are constants of order unity. Even more\nimportantly, rotating the silo base leads to a strong -- more than 50% --\ndecrease of the flow rate, which otherwise does not depend significantly on the\nvalue of $\\omega$ in the continuous flow regime. In the intermittent regime,\n$W(\\omega)$ appears to follow a non-monotonic trend, although with considerable\nnoise. A simple picture, in terms of the switching from funnel flow to mass\nflow and the alignment of the pegs due to rotation, is proposed to explain the\nobserved difference between spherical and elongated grains. We also observe\nshear induced orientational ordering of the pegs at the bottom such that their\nlong axes in average are oriented at a small angle $\\langle\\theta\\rangle\n\\approx 15^\\circ$ to the motion of the bottom.\n"}
+{"abstract": "  Numerical computation of the Karhunen--Lo\\`eve expansion is computationally\nchallenging in terms of both memory requirements and computing time. We compare\ntwo state-of-the-art methods that claim to efficiently solve for the K--L\nexpansion: (1) the matrix-free isogeometric Galerkin method using interpolation\nbased quadrature proposed by the authors in [1] and (2) our new matrix-free\nimplementation of the isogeometric collocation method proposed in [2]. Two\nthree-dimensional benchmark problems indicate that the Galerkin method performs\nsignificantly better for smooth covariance kernels, while the collocation\nmethod performs slightly better for rough covariance kernels.\n"}
+{"abstract": "  Learning concepts that are consistent with human perception is important for\nDeep Neural Networks to win end-user trust. Post-hoc interpretation methods\nlack transparency in the feature representations learned by the models. This\nwork proposes a guided learning approach with an additional concept layer in a\nCNN- based architecture to learn the associations between visual features and\nword phrases. We design an objective function that optimizes both prediction\naccuracy and semantics of the learned feature representations. Experiment\nresults demonstrate that the proposed model can learn concepts that are\nconsistent with human perception and their corresponding contributions to the\nmodel decision without compromising accuracy. Further, these learned concepts\nare transferable to new classes of objects that have similar concepts.\n"}
+{"abstract": "  Federated edge learning (FEEL) is a widely adopted framework for training an\nartificial intelligence (AI) model distributively at edge devices to leverage\ntheir data while preserving their data privacy. The execution of a power-hungry\nlearning task at energy-constrained devices is a key challenge confronting the\nimplementation of FEEL. To tackle the challenge, we propose the solution of\npowering devices using wireless power transfer (WPT). To derive guidelines on\ndeploying the resultant wirelessly powered FEEL (WP-FEEL) system, this work\naims at the derivation of the tradeoff between the model convergence and the\nsettings of power sources in two scenarios: 1) the transmission power and\ndensity of power-beacons (dedicated charging stations) if they are deployed, or\notherwise 2) the transmission power of a server (access-point). The development\nof the proposed analytical framework relates the accuracy of distributed\nstochastic gradient estimation to the WPT settings, the randomness in both\ncommunication and WPT links, and devices' computation capacities. Furthermore,\nthe local-computation at devices (i.e., mini-batch size and processor clock\nfrequency) is optimized to efficiently use the harvested energy for gradient\nestimation. The resultant learning-WPT tradeoffs reveal the simple scaling laws\nof the model-convergence rate with respect to the transferred energy as well as\nthe devices' computational energy efficiencies. The results provide useful\nguidelines on WPT provisioning to provide a guaranteer on learning performance.\nThey are corroborated by experimental results using a real dataset.\n"}
+{"abstract": "  The dynamic response of power grids to small disturbances influences their\noverall stability. This paper examines the effect of network topology on the\nlinearized time-invariant dynamics of electric power systems. The proposed\nframework utilizes ${\\cal H}_2$-norm based stability metrics to study the\noptimal placement of lines on existing networks as well as the topology design\nof new networks. The design task is first posed as an NP-hard mixed-integer\nnonlinear program (MINLP) that is exactly reformulated as a mixed-integer\nlinear program (MILP) using McCormick linearization. To improve computation\ntime, graph-theoretic properties are exploited to derive valid inequalities\n(cuts) and tighten bounds on the continuous optimization variables. Moreover, a\ncutting plane generation procedure is put forth that is able to interject the\nMILP solver and augment additional constraints to the problem on-the-fly. The\nefficacy of our approach in designing optimal grid topologies is demonstrated\nthrough numerical tests on the IEEE 39-bus network.\n"}
+{"abstract": "  We study the connection of matter density and its tracers from the PDF\nperspective. One aspect of this connection is the conditional expectation value\n$\\langle \\delta_{\\mathrm{tracer}}|\\delta_m\\rangle$ when averaging both tracer\nand matter density over some scale. We present a new way to incorporate a\nLagrangian bias expansion of this expectation value into standard frameworks\nfor modelling the PDF of density fluctuations and counts-in-cells statistics.\nUsing N-body simulations and mock galaxy catalogs we confirm the accuracy of\nthis expansion and compare it to the more commonly used Eulerian\nparametrization. For halos hosting typical luminous red galaxies, the\nLagrangian model provides a significantly better description of $\\langle\n\\delta_{\\mathrm{tracer}}|\\delta_m\\rangle$ at second order in perturbations. A\nsecond aspect of the matter-tracer connection is shot-noise, \\ie the scatter of\ntracer density around $\\langle \\delta_{\\mathrm{tracer}}|\\delta_m\\rangle$. It is\nwell known that this noise can be significantly non-Poissonian and we validate\nthe performance of a more general, two-parameter shot-noise model for different\ntracers and simulations. Both parts of our analysis are meant to pave the way\nfor forthcoming applications to survey data.\n"}
+{"abstract": "  Face masks have long been used in many areas of everyday life to protect\nagainst the inhalation of hazardous fumes and particles. They also offer an\neffective solution in healthcare for bi-directional protection against\nair-borne diseases. Wearing and positioning the mask correctly is essential for\nits function. Convolutional neural networks (CNNs) offer an excellent solution\nfor face recognition and classification of correct mask wearing and\npositioning. In the context of the ongoing COVID-19 pandemic, such algorithms\ncan be used at entrances to corporate buildings, airports, shopping areas, and\nother indoor locations, to mitigate the spread of the virus. These application\nscenarios impose major challenges to the underlying compute platform. The\ninference hardware must be cheap, small and energy efficient, while providing\nsufficient memory and compute power to execute accurate CNNs at a reasonably\nlow latency. To maintain data privacy of the public, all processing must remain\non the edge-device, without any communication with cloud servers. To address\nthese challenges, we present a low-power binary neural network classifier for\ncorrect facial-mask wear and positioning. The classification task is\nimplemented on an embedded FPGA, performing high-throughput binary operations.\nClassification can take place at up to ~6400 frames-per-second, easily enabling\nmulti-camera, speed-gate settings or statistics collection in crowd settings.\nWhen deployed on a single entrance or gate, the idle power consumption is\nreduced to 1.6W, improving the battery-life of the device. We achieve an\naccuracy of up to 98% for four wearing positions of the MaskedFace-Net dataset.\nTo maintain equivalent classification accuracy for all face structures,\nskin-tones, hair types, and mask types, the algorithms are tested for their\nability to generalize the relevant features over all subjects using the\nGrad-CAM approach.\n"}
+{"abstract": "  We study and model the properties of galaxy clusters in the normal-branch\nDvali-Gabadadze-Porrati (nDGP) model of gravity, which is representative of a\nwide class of theories which exhibit the Vainshtein screening mechanism. Using\nthe first cosmological simulations which incorporate both full baryonic physics\nand nDGP, we find that, despite being efficiently screened within clusters, the\nfifth force can raise the temperature of the intra-cluster gas, affecting the\nscaling relations between the cluster mass and three observable mass proxies:\nthe gas temperature, the Compton $Y$-parameter of the Sunyaev-Zel'dovich effect\nand the X-ray analogue of the $Y$-parameter. Therefore, unless properly\naccounted for, this could lead to biased measurements of the cluster mass in\ntests that make use of cluster observations, such as cluster number counts, to\nprobe gravity. Using a suite of dark-matter-only simulations, which span a wide\nrange of box sizes and resolutions, and which feature very different strengths\nof the fifth force, we also calibrate general fitting formulae which can\nreproduce the nDGP halo concentration at percent accuracy for $0\\leq z\\leq1$,\nand halo mass function with $\\lesssim3\\%$ accuracy at $0\\leq z\\leq1$\n(increasing to $\\lesssim5\\%$ for $1\\leq z\\leq 2$), over a halo mass range\nspanning four orders of magnitude. Our model for the concentration can be used\nfor converting between halo mass overdensities and predicting statistics such\nas the nonlinear matter power spectrum. The results of this work will form part\nof a framework for unbiased constraints of gravity using the data from ongoing\nand upcoming cluster surveys.\n"}
+{"abstract": "  Sentiment analysis on software engineering (SE) texts has been widely used in\nthe SE research, such as evaluating app reviews or analyzing developers\nsentiments in commit messages. To better support the use of automated sentiment\nanalysis for SE tasks, researchers built an SE-domain-specified sentiment\ndictionary to further improve the accuracy of the results. Unfortunately,\nrecent work reported that current mainstream tools for sentiment analysis still\ncannot provide reliable results when analyzing the sentiments in SE texts. We\nsuggest that the reason for this situation is because the way of expressing\nsentiments in SE texts is largely different from the way in social network or\nmovie comments. In this paper, we propose to improve sentiment analysis in SE\ntexts by using sentence structures, a different perspective from building a\ndomain dictionary. Specifically, we use sentence structures to first identify\nwhether the author is expressing her sentiment in a given clause of an SE text,\nand to further adjust the calculation of sentiments which are confirmed in the\nclause. An empirical evaluation based on four different datasets shows that our\napproach can outperform two dictionary-based baseline approaches, and is more\ngeneralizable compared to a learning-based baseline approach.\n"}
+{"abstract": "  This paper is dedicated to the spectral optimization problem $$\n\\mathrm{min}\\left\\{\\lambda_1^s(\\Omega)+\\cdots+\\lambda_m^s(\\Omega) + \\Lambda\n\\mathcal{L}_n(\\Omega)\\colon \\Omega\\subset D \\mbox{ s-quasi-open}\\right\\} $$\nwhere $\\Lambda>0, D\\subset \\mathbb{R}^n$ is a bounded open set and\n$\\lambda_i^s(\\Omega)$ is the $i$-th eigenvalues of the fractional Laplacian on\n$\\Omega$ with Dirichlet boundary condition on $\\mathbb{R}^n\\setminus \\Omega$.\nWe first prove that the first $m$ eigenfunctions on an optimal set are locally\nH\\\"{o}lder continuous in the class $C^{0,s}$ and, as a consequence, that the\noptimal sets are open sets. Then, via a blow-up analysis based on a Weiss type\nmonotonicity formula, we prove that the topological boundary of a minimizer\n$\\Omega$ is composed of a relatively open regular part and a closed singular\npart of Hausdorff dimension at most $n-n^*$, for some $n^*\\geq 3$. Finally we\nuse a viscosity approach to prove $C^{1,\\alpha}$-regularity of the regular part\nof the boundary.\n"}
+{"abstract": "  Deriving quantum error correction and quantum control from the Schrodinger\nequation for a unified qubit-environment Hamiltonian will give insights into\nhow microscopic degrees of freedom affect the capability to control and correct\nquantum information beyond that of phenomenological theory. Here, we\ninvestigate the asymptotic reduced state of two qubits coupled to each other\nsolely via a common heat bath of linear harmonic oscillators and search for\nevidence of fault-tolerant excited qubit states. We vary the Hamiltonian\nparameters, including the qubit-qubit and qubit-bath detuning, the bath\nspectral density, and whether or not we use the Markov approximation in the\ncalculation of our dynamics. In proximity to special values of these\nparameters, we identify these states as asymptotic reduced states that are\narbitrarily pure, excited, unique, and have high singlet fidelity. We emphasize\nthe central role of the Lamb-shift as an agent responsible for fault tolerant\nexcitations. To learn how these parameters relate to performance, we discuss\nnumerical studies on fidelity and error recovery time.\n"}
+{"abstract": "  In this work, we begin the study of a new class of dynamical systems\ndetermined by interval maps generated by the symbolic action of erasing\nsubstitution rules. We do this by discussing in some detail the geometric,\nanalytical, dynamical and arithmetic properties of a particular example, which\nhas the virtue of being arguably the simplest and that at the same time\nproduces interesting properties and new challenging problems.\n"}
+{"abstract": "  During the performance verification phase of the SRG/eROSITA telescope, the\neROSITA Final Equatorial-Depth Survey (eFEDS) has been carried out. It covers a\n140 deg$^2$ field located at 126$^\\circ <$ R.A. $< 146^\\circ$ and -3$^\\circ <$\nDec. $< +6^\\circ$ with a nominal exposure over the field of 2.2 ks. 542\ncandidate clusters were detected in this field, down to a flux limit $F_X \\sim\n10^{-14}$ erg s$^{-1}$ cm$^{-2}$ in the 0.5-2 keV band. In order to understand\nradio-mode feedback in galaxy clusters, we study the radio emission of\nbrightest cluster galaxies of eFEDS clusters, and we relate it to the X-ray\nproperties of the host cluster. Using LOFAR we identify 227 radio galaxies\nhosted in the BCGs of the 542 galaxy clusters and groups detected in eFEDS. We\ntreat non-detections as radio upper limits. We analyse the properties of radio\ngalaxies, such as redshift and luminosity distribution, offset from the cluster\ncentre, largest linear size and radio power. We study their relation to the\nintracluster medium of the host cluster. We perform statistical tests to deal\nwith upper limits on the radio luminosities. BCGs with radio-loud AGN are more\nlikely to lie close to the cluster centre than radio-quiet BCGs. There is a\nclear relation between the cluster's X-ray luminosity and the radio power of\nthe BCG. Statistical tests indicate that this correlation is not produced by\nselection effects in the radio band. We see no apparent link between largest\nlinear size of the radio galaxy and central density of the host cluster.\nConverting the radio luminosity to kinetic luminosity, we find that radiative\nlosses of the intracluster medium are in an overall balance with the heating\nprovided by the central AGN. Finally, we tentatively classify our objects into\ndisturbed and relaxed, and we show that the link between the AGN and the ICM\napparently holds regardless of the dynamical state of the cluster.\n"}
+{"abstract": "  Many current neural networks for medical imaging generalise poorly to data\nunseen during training. Such behaviour can be caused by networks overfitting\neasy-to-learn, or statistically dominant, features while disregarding other\npotentially informative features. For example, indistinguishable differences in\nthe sharpness of the images from two different scanners can degrade the\nperformance of the network significantly. All neural networks intended for\nclinical practice need to be robust to variation in data caused by differences\nin imaging equipment, sample preparation and patient populations.\n  To address these challenges, we evaluate the utility of spectral decoupling\nas an implicit bias mitigation method. Spectral decoupling encourages the\nneural network to learn more features by simply regularising the networks'\nunnormalised prediction scores with an L2 penalty, thus having no added\ncomputational costs.\n  We show that spectral decoupling allows training neural networks on datasets\nwith strong spurious correlations and increases networks' robustness for data\ndistribution shifts. To validate our findings, we train networks with and\nwithout spectral decoupling to detect prostate cancer tissue slides and\nCOVID-19 in chest radiographs. Networks trained with spectral decoupling\nachieve up to 9.5 percent point higher performance on external datasets.\n  Our results show that spectral decoupling helps with generalisation issues\nassociated with neural networks, and can be used to complement or replace\ncomputationally expensive explicit bias mitigation methods, such as stain\nnormalization in histological images. We recommend using spectral decoupling as\nan implicit bias mitigation method in any neural network intended for clinical\nuse.\n"}
+{"abstract": "  Data-driven reduced order models (ROMs) are combined with the\nLippmann-Schwinger integral equation to produce a direct nonlinear inversion\nmethod. The ROM is viewed as a Galerkin projection and is sparse due to Lanczos\northogonalization. Embedding into the continuous problem, a data-driven\ninternal solution is produced. This internal solution is then used in the\nLippmann-Schwinger equation, thus making further iterative updates unnecessary.\nWe show numerical experiments for spectral domain domain data for which our\ninversion is far superior to the Born inversion and works as well as when the\ntrue internal solution is known.\n"}
+{"abstract": "  This work deals with mixing and dissipation ehancement for the solution of\nadvection-diffusion equation driven by a Ornstein-Uhlenbeck velocity field. We\nare able to prove a quantitative mixing result, uniform in the diffusion\nparameter, and enhancement of dissipation over a finite time horizon.\n"}
+{"abstract": "  In the past, several works have investigated ways for combining quantitative\nand qualitative methods in research assessment exercises. In this work, we aim\nat introducing a methodology to explore whether citation-based metrics,\ncalculated only considering open bibliographic and citation data, can yield\ninsights on how human peer-review of research assessment exercises is\nconducted. To understand if and what metrics provide relevant information, we\npropose to use a series of machine learning models to replicate the decisions\nof the committees of the research assessment exercises.\n"}
+{"abstract": "  Downlink beamforming is an essential technology for wireless cellular\nnetworks; however, the design of beamforming vectors that maximize the weighted\nsum rate (WSR) is an NP-hard problem and iterative algorithms are typically\napplied to solve it. The weighted minimum mean square error (WMMSE) algorithm\nis the most widely used one, which iteratively minimizes the WSR and converges\nto a local optimal. Motivated by the recent developments in meta-learning\ntechniques to solve non-convex optimization problems, we propose a\nmeta-learning based iterative algorithm for WSR maximization in a MISO downlink\nchannel. A long-short-term-memory (LSTM) network-based meta-learning model is\nbuilt to learn a dynamic optimization strategy to update the variables\niteratively. The learned strategy aims to optimize each variable in a less\ngreedy manner compared to WMMSE, which updates variables by computing their\nfirst-order stationary points at each iteration step. The proposed algorithm\noutperforms WMMSE significantly in the high signal to noise ratio(SNR) regime\nand shows the comparable performance when the SNR is low.\n"}
+{"abstract": "  The representations of a $k$-graph $C^*$-algebra $C^*(\\Lambda)$ which arise\nfrom $\\Lambda$-semibranching function systems are closely linked to the\ndynamics of the $k$-graph $\\Lambda$. In this paper, we undertake a systematic\nanalysis of the question of irreducibility for these representations. We\nprovide a variety of necessary and sufficient conditions for irreducibility, as\nwell as a number of examples indicating the optimality of our results. We also\nexplore the relationship between irreducible $\\Lambda$-semibranching\nrepresentations and purely atomic representations of $C^*(\\Lambda)$. Throughout\nthe paper, we work in the setting of row-finite source-free $k$-graphs; this\npaper constitutes the first analysis of $\\Lambda$-semibranching representations\nat this level of generality.\n"}
+{"abstract": "  Parton distributions can be defined in terms of the entropy of entanglement\nbetween the spatial region probed by deep inelastic scattering (DIS) and the\nrest of the proton. For very small $x$, the proton becomes a maximally\nentangled state. This approach leads to a simple relation $S = \\ln N $ between\nthe average number $N$ of color-singlet dipoles in the proton wave function and\nthe entropy of the produced hadronic state $S$. At small $x$, the multiplicity\nof dipoles is given by the gluon structure function, $N = x G(x,Q^2)$.\nRecently, the H1 Collaboration analyzed the entropy of the produced hadronic\nstate in DIS, and studied its relation to the gluon structure function; poor\nagreement with the predicted relation was found. In this letter we argue that a\nmore accurate account of the number of color-singlet dipoles in the kinematics\nof H1 experiment (where hadrons are detected in the current fragmentation\nregion) is given not by $xG(x,Q^2)$ but by the sea quark structure function\n$x\\Sigma(x,Q^2)$. Sea quarks originate from the splitting of gluons, so at\nsmall $x$ $x\\Sigma(x,Q^2)\\,\\sim\\, xG(x,Q^2)$, but in the current fragmentation\nregion this proportionality is distorted by the contribution of the\nquark-antiquark pair produced by the virtual photon splitting. In addition, the\nmultiplicity of color-singlet dipoles in the current fragmentation region is\nquite small, and one needs to include $\\sim 1/N$ corrections to $S= \\ln N$\nasymptotic formula. Taking both of these modifications into account, we find\nthat the data from the H1 Collaboration in fact agree well with the prediction\nbased on entanglement.\n"}
+{"abstract": "  Semantic parsing is challenging due to the structure gap and the semantic gap\nbetween utterances and logical forms. In this paper, we propose an unsupervised\nsemantic parsing method - Synchronous Semantic Decoding (SSD), which can\nsimultaneously resolve the semantic gap and the structure gap by jointly\nleveraging paraphrasing and grammar constrained decoding. Specifically, we\nreformulate semantic parsing as a constrained paraphrasing problem: given an\nutterance, our model synchronously generates its canonical utterance and\nmeaning representation. During synchronous decoding: the utterance paraphrasing\nis constrained by the structure of the logical form, therefore the canonical\nutterance can be paraphrased controlledly; the semantic decoding is guided by\nthe semantics of the canonical utterance, therefore its logical form can be\ngenerated unsupervisedly. Experimental results show that SSD is a promising\napproach and can achieve competitive unsupervised semantic parsing performance\non multiple datasets.\n"}
+{"abstract": "  The integration of small-scale Unmanned Aerial Vehicles (UAVs) into\nIntelligent Transportation Systems (ITSs) will empower novel smart-city\napplications and services. After the unforeseen outbreak of the COVID-19\npandemic, the public demand for delivery services has multiplied. Mobile\nrobotic systems inherently offer the potential for minimizing the amount of\ndirect human-to-human interactions with the parcel delivery process. The\nproposed system-of-systems consists of various complex aspects such as\nassigning and distributing delivery jobs, establishing and maintaining reliable\ncommunication links between the vehicles, as well as path planning and mobility\ncontrol. In this paper, we apply a system-level perspective for identifying key\nchallenges and promising solution approaches for modeling, analysis, and\noptimization of UAV-aided parcel delivery. We present a system-of-systems model\nfor UAV-assisted parcel delivery to cope with higher capacity requirements\ninduced by the COVID-19. To demonstrate the benefits of hybrid vehicular\ndelivery, we present a case study focusing on the prioritization of\ntime-critical deliveries such as medical goods. The results further confirm\nthat the capacity of traditional delivery fleets can be upgraded with drone\nusage. Furthermore, we observe that the delay incurred by prioritizing\ntime-critical deliveries can be compensated with drone deployment. Finally,\ncentralized and decentralized communication approaches for data transmission\ninside hybrid delivery fleets are compared.\n"}
+{"abstract": "  Stepped wedge cluster randomized trials (SW-CRTs) with binary outcomes are\nincreasingly used in prevention and implementation studies. Marginal models\nrepresent a flexible tool for analyzing SW-CRTs with population-averaged\ninterpretations, but the joint estimation of the mean and intraclass\ncorrelation coefficients (ICCs) can be computationally intensive due to large\ncluster-period sizes. Motivated by the need for marginal inference in SW-CRTs,\nwe propose a simple and efficient estimating equations approach to analyze\ncluster-period means. We show that the quasi-score for the marginal mean\ndefined from individual-level observations can be reformulated as the\nquasi-score for the same marginal mean defined from the cluster-period means.\nAn additional mapping of the individual-level ICCs into correlations for the\ncluster-period means further provides a rigorous justification for the\ncluster-period approach. The proposed approach addresses a long-recognized\ncomputational burden associated with estimating equations defined based on\nindividual-level observations, and enables fast point and interval estimation\nof the intervention effect and correlations. We further propose matrix-adjusted\nestimating equations to improve the finite-sample inference for ICCs. By\nproviding a valid approach to estimate ICCs within the class of generalized\nlinear models for correlated binary outcomes, this article operationalizes key\nrecommendations from the CONSORT extension to SW-CRTs, including the reporting\nof ICCs.\n"}
+{"abstract": "  We present the design and experimental demonstration of an open-endcap radio\nfrequency trap to confine ion crystals in the radial-two dimensional (2D)\nstructural phase. The central axis of the trap is kept free of obstructions to\nallow for site-resolved imaging of ions in the 2D crystal plane, and the\nconfining potentials are provided by four segmented blade electrodes. We\ndiscuss the design challenges, fabrication techniques, and voltage requirements\nfor implementing this open-endcap trap. Finally, we validate its operation by\nconfining up to 29 ions in a 2D triangular lattice, oriented such that both\nin-plane principal axes of the 2D crystal lie in the radial direction.\n"}
+{"abstract": "  Meta-analysis is a powerful tool for drug safety assessment by synthesizing\ntreatment-related toxicological findings from independent clinical trials.\nHowever, published clinical studies may or may not report all adverse events\n(AEs) if the observed number of AEs were fewer than a pre-specified\nstudy-dependent cutoff. Subsequently, with censored information ignored, the\nestimated incidence rate of AEs could be significantly biased. To address this\nnon-ignorable missing data problem in meta-analysis, we propose a Bayesian\nmultilevel regression model to accommodate the censored rare event data. The\nperformance of the proposed Bayesian model of censored data compared to other\nexisting methods is demonstrated through simulation studies under various\ncensoring scenarios. Finally, the proposed approach is illustrated using data\nfrom a recent meta-analysis of 125 clinical trials involving PD-1/PD-L1\ninhibitors with respect to their toxicity profiles.\n"}
+{"abstract": "  Ground Penetrating Radar (GPR) is an effective non-destructive evaluation\n(NDE) device for inspecting and surveying subsurface objects (i.e., rebars,\nutility pipes) in complex environments. However, the current practice for GPR\ndata collection requires a human inspector to move a GPR cart along pre-marked\ngrid lines and record the GPR data in both X and Y directions for\npost-processing by 3D GPR imaging software. It is time-consuming and tedious\nwork to survey a large area. Furthermore, identifying the subsurface targets\ndepends on the knowledge of an experienced engineer, who has to make manual and\nsubjective interpretation that limits the GPR applications, especially in\nlarge-scale scenarios. In addition, the current GPR imaging technology is not\nintuitive, and not for normal users to understand, and not friendly to\nvisualize. To address the above challenges, this paper presents a novel robotic\nsystem to collect GPR data, interpret GPR data, localize the underground\nutilities, reconstruct and visualize the underground objects' dense point cloud\nmodel in a user-friendly manner. This system is composed of three modules: 1) a\nvision-aided Omni-directional robotic data collection platform, which enables\nthe GPR antenna to scan the target area freely with an arbitrary trajectory\nwhile using a visual-inertial-based positioning module tags the GPR\nmeasurements with positioning information; 2) a deep neural network (DNN)\nmigration module to interpret the raw GPR B-scan image into a cross-section of\nobject model; 3) a DNN-based 3D reconstruction method, i.e., GPRNet, to\ngenerate underground utility model represented as fine 3D point cloud.\nComparative studies on synthetic and field GPR raw data with various\nincompleteness and noise are performed.\n"}
+{"abstract": "  This short paper describes an ongoing research project that requires the\nautomated self-play learning and evaluation of a large number of board games in\ndigital form. We describe the approach we are taking to determine relevant\nfeatures, for biasing MCTS playouts for arbitrary games played on arbitrary\ngeometries. Benefits of our approach include efficient implementation, the\npotential to transfer learnt knowledge to new contexts, and the potential to\nexplain strategic knowledge embedded in features in human-comprehensible terms.\n"}
+{"abstract": "  Interactive robots navigating photo-realistic environments face challenges\nunderlying vision-and-language navigation (VLN), but in addition, they need to\nbe trained to handle the dynamic nature of dialogue. However, research in\nCooperative Vision-and-Dialog Navigation (CVDN), where a navigator interacts\nwith a guide in natural language in order to reach a goal, treats the dialogue\nhistory as a VLN-style static instruction. In this paper, we present VISITRON,\na navigator better suited to the interactive regime inherent to CVDN by being\ntrained to: i) identify and associate object-level concepts and semantics\nbetween the environment and dialogue history, ii) identify when to interact vs.\nnavigate via imitation learning of a binary classification head. We perform\nextensive ablations with VISITRON to gain empirical insights and improve\nperformance on CVDN. VISITRON is competitive with models on the static CVDN\nleaderboard. We also propose a generalized interactive regime to fine-tune and\nevaluate VISITRON and future such models with pre-trained guides for\nadaptability.\n"}
+{"abstract": "  We investigate the structure of the minimal displacement set in $8$-located\ncomplexes with the SD'-property. We show that such set embeds isometrically\ninto the complex. Since $8$-location and simple connectivity imply Gromov\nhyperbolicity, the minimal displacement set in such complex is systolic. Using\nthese results, we construct a low-dimensional classifying space for the family\nof virtually cyclic subgroups of a group acting properly on an $8$-located\ncomplex with the SD'-property.\n"}
+{"abstract": "  The great performance of machine learning algorithms and deep neural networks\nin several perception and control tasks is pushing the industry to adopt such\ntechnologies in safety-critical applications, as autonomous robots and\nself-driving vehicles. At present, however, several issues need to be solved to\nmake deep learning methods more trustworthy, predictable, safe, and secure\nagainst adversarial attacks. Although several methods have been proposed to\nimprove the trustworthiness of deep neural networks, most of them are tailored\nfor specific classes of adversarial examples, hence failing to detect other\ncorner cases or unsafe inputs that heavily deviate from the training samples.\nThis paper presents a lightweight monitoring architecture based on coverage\nparadigms to enhance the model robustness against different unsafe inputs. In\nparticular, four coverage analysis methods are proposed and tested in the\narchitecture for evaluating multiple detection logics. Experimental results\nshow that the proposed approach is effective in detecting both powerful\nadversarial examples and out-of-distribution inputs, introducing limited\nextra-execution time and memory requirements.\n"}
+{"abstract": "  Out-of-time-order correlators (OTOCs) have become established as a tool to\ncharacterise quantum information dynamics and thermalisation in interacting\nquantum many-body systems. It was recently argued that the expected exponential\ngrowth of the OTOC is connected to the existence of correlations beyond those\nencoded in the standard Eigenstate Thermalisation Hypothesis (ETH). We show\nexplicitly, by an extensive numerical analysis of the statistics of operator\nmatrix elements in conjunction with a detailed study of OTOC dynamics, that the\nOTOC is indeed a precise tool to explore the fine details of the ETH. In\nparticular, while short-time dynamics is dominated by correlations, the\nlong-time saturation behaviour gives clear indications of an operator-dependent\nenergy scale $\\omega_{\\textrm{GOE}}$ associated to the emergence of an\neffective Gaussian random matrix theory. We provide an estimation of the\nfinite-size scaling of $\\omega_{\\textrm{GOE}}$ for the general class of\nobservables composed of sums of local operators in the infinite-temperature\nregime and found linear behaviour for the models considered.\n"}
+{"abstract": "  The ALICE collaboration at the large hadron collider (LHC) recently reported\nhigh-statistics $p_t$ spectrum data from 5 TeV and 13 TeV $p$-$p$ collisions.\nParticle data for each energy were partitioned into event classes based on the\ntotal yields within two disjoint pseudorapidity $\\eta$ intervals denoted by\nacronyms V0M and SPD. For each energy the spectra resulting from the two\nselection methods were then compared to a minimum-bias INEL $> 0$ average over\nthe entire event population. The nominal goal was determination of the role of\njets in high-multiplicity $p$-$p$ collisions and especially the jet\ncontribution to the low-$p_t$ parts of spectra. A related motivation was\nresponse to recent claims of \"collective\" behavior and other nominal indicators\nof quark-gluon plasma (QGP) formation in small collision systems. In the\npresent study a two-component (soft + hard) model (TCM) of hadron production in\n$p$-$p$ collisions is applied to the ALICE spectrum data. As in previous TCM\nstudies of a variety of A-B collision systems the jet and nonjet contributions\nto $p$-$p$ spectra are accurately separated over the entire $p_t$ acceptance.\nDistinction is maintained among spectrum normalizations, jet contributions to\nspectra and systematic biases resulting from V0M and SPD event selection. The\nstatistical significance of data-model differences is established. The effect\nof {\\em spherocity} (azimuthal asymmetry measure nominally sensitive to jet\nproduction) on ensemble-mean $p_t$ vs event multiplicity $n_{ch}$ is\ninvestigated and found to have little relation to jet production. The general\nresults of the TCM analysis are as expected from a conventional QCD description\nof jet production in $p$-$p$ collisions.\n"}
+{"abstract": "  We introduce the notion of Gelfand pairs and zonal spherical functions for\nIwahori-Hecke algebras.\n"}
+{"abstract": "  Despite the immense societal importance of ethically designing artificial\nintelligence (AI), little research on the public perceptions of ethical AI\nprinciples exists. This becomes even more striking when considering that\nethical AI development has the aim to be human-centric and of benefit for the\nwhole society. In this study, we investigate how ethical principles\n(explainability, fairness, security, accountability, accuracy, privacy, machine\nautonomy) are weighted in comparison to each other. This is especially\nimportant, since simultaneously considering ethical principles is not only\ncostly, but sometimes even impossible, as developers must make specific\ntrade-off decisions. In this paper, we give first answers on the relative\nimportance of ethical principles given a specific use case - the use of AI in\ntax fraud detection. The results of a large conjoint survey (n=1099) suggest\nthat, by and large, German respondents found the ethical principles equally\nimportant. However, subsequent cluster analysis shows that different preference\nmodels for ethically designed systems exist among the German population. These\nclusters substantially differ not only in the preferred attributes, but also in\nthe importance level of the attributes themselves. We further describe how\nthese groups are constituted in terms of sociodemographics as well as opinions\non AI. Societal implications as well as design challenges are discussed.\n"}
+{"abstract": "  The Eastin-Knill theorem states that no quantum error correcting code can\nhave a universal set of transversal gates. For CSS codes that can implement\nClifford gates transversally it suffices to provide one additional non-Clifford\ngate, such as the T-gate, to achieve universality. Common methods to implement\nfault-tolerant T-gates like magic state distillation generate a significant\nhardware overhead that will likely prevent their practical usage in the\nnear-term future. Recently methods have been developed to mitigate the effect\nof noise in shallow quantum circuits that are not protected by error\ncorrection. Error mitigation methods require no additional hardware resources\nbut suffer from a bad asymptotic scaling and apply only to a restricted class\nof quantum algorithms. In this work, we combine both approaches and show how to\nimplement encoded Clifford+T circuits where Clifford gates are protected from\nnoise by error correction while errors introduced by noisy encoded T-gates are\nmitigated using the quasi-probability method. As a result, Clifford+T circuits\nwith a number of T-gates inversely proportional to the physical noise rate can\nbe implemented on small error-corrected devices without magic state\ndistillation. We argue that such circuits can be out of reach for\nstate-of-the-art classical simulation algorithms.\n"}
+{"abstract": "  In this paper we introduce the long-range dependent completely correlated\nmixed fractional Brownian motion (ccmfBm). This is a process that is driven by\na mixture of Brownian motion (Bm) and a long-range dependent completely\ncorrelated fractional Brownian motion (fBm, ccfBm) that is constructed from the\nBrownian motion via the Molchan--Golosov representation. Thus, there is a\nsingle Bm driving the mixed process. In the short time-scales the ccmfBm\nbehaves like the Bm (it has Brownian H\\\"older index and quadratic variation).\nHowever, in the long time-scales it behaves like the fBm (it has long-range\ndependence governed by the fBm's Hurst index). We provide a transfer principle\nfor the ccmfBm and use it to construct the Cameron--Martin--Girsanov--Hitsuda\ntheorem and prediction formulas. Finally, we illustrate the ccmfBm by\nsimulations.\n"}
+{"abstract": "  We consider a time-delayed HIV/AIDS epidemic model with education\ndissemination and study the asymptotic dynamics of solutions as well as the\nasymptotic behavior of the endemic equilibrium with respect to the amount of\ninformation disseminated about the disease. Under appropriate assumptions on\nthe infection rates, we show that if the basic reproduction number is less than\nor equal to one, then the disease will be eradicated in the long run and any\nsolution to the Cauchy problem converges to the unique disease-free equilibrium\nof the model. On the other hand, when the basic reproduction number is greater\nthan one, we prove that the disease will be permanent but its impact on the\npopulation can be significantly minimized as the amount of education\ndissemination increases. In particular, under appropriate hypothesis on the\nmodel parameters, we establish that the size of the component of the infected\npopulation of the endemic equilibrium decreases linearly as a function of the\namount of information disseminated. We also fit our model to a set of data on\nHIV/AIDS in order to estimate the infection, effective response, and\ninformation rates of the disease. We then use these estimates to present\nnumerical simulations to illustrate our theoretical findings.\n"}
+{"abstract": "  The proposed space-borne laser interferometric gravitational wave (GW)\nobservatory TianQin adopts a geocentric orbit for its nearly equilateral\ntriangular constellation formed by three identical drag-free satellites. The\ngeocentric distance of each satellite is $\\approx 1.0 \\times 10^{5}\n~\\mathrm{km}$, which makes the armlengths of the interferometer be $\\approx\n1.73 \\times 10^{5} ~\\mathrm{km}$. It is aimed to detect the GWs in $0.1\n~\\mathrm{mHz}-1 ~\\mathrm{Hz}$. For space-borne detectors, the armlengths are\nunequal and change continuously which results in that the laser frequency noise\nis nearly $7-8$ orders of magnitude higher than the secondary noises (such as\nacceleration noise, optical path noise, etc.). The time delay interferometry\n(TDI) that synthesizes virtual interferometers from time-delayed one-way\nfrequency measurements has been proposed to suppress the laser frequency noise\nto the level that is comparable or below the secondary noises. In this work, we\nevaluate the performance of various data combinations for both first- and\nsecond-generation TDI based on the five-year numerically optimized orbits of\nthe TianQin's satellites which exhibit the actual rotating and flexing of the\nconstellation. We find that the time differences of symmetric interference\npaths of the data combinations are $\\sim 10^{-8}$ s for the first-generation\nTDI and $\\sim 10^{-12}$ s for the second-generation TDI, respectively. While\nthe second-generation TDI is guaranteed to be valid for TianQin, the\nfirst-generation TDI is possible to be competent for GW signal detection with\nimproved stabilization of the laser frequency noise in the concerned GW\nfrequencies.\n"}
+{"abstract": "  Medical imaging plays a pivotal role in diagnosis and treatment in clinical\npractice. Inspired by the significant progress in automatic image captioning,\nvarious deep learning (DL)-based architectures have been proposed for\ngenerating radiology reports for medical images. However, model uncertainty\n(i.e., model reliability/confidence on report generation) is still an\nunder-explored problem. In this paper, we propose a novel method to explicitly\nquantify both the visual uncertainty and the textual uncertainty for the task\nof radiology report generation. Such multi-modal uncertainties can sufficiently\ncapture the model confidence scores at both the report-level and the\nsentence-level, and thus they are further leveraged to weight the losses for\nachieving more comprehensive model optimization. Our experimental results have\ndemonstrated that our proposed method for model uncertainty characterization\nand estimation can provide more reliable confidence scores for radiology report\ngeneration, and our proposed uncertainty-weighted losses can achieve more\ncomprehensive model optimization and result in state-of-the-art performance on\na public radiology report dataset.\n"}
+{"abstract": "  In a recent work, we provided a standardized and exact analytical formalism\nfor computing in the semiclassical regime the radiation force experienced by a\ntwo-level atom interacting with any number of plane waves with arbitrary\nintensities, frequencies, phases, and propagation directions [J. Opt. Soc. Am.\nB \\textbf{35}, 127-132 (2018)]. Here, we extend this treatment to the\nmultilevel atom case, where degeneracy of the atomic levels is considered and\npolarization of light enters into play. A matrix formalism is developed to this\naim.\n"}
+{"abstract": "  With the rising demand of smart mobility, ride-hailing service is getting\npopular in the urban regions. These services maintain a system for serving the\nincoming trip requests by dispatching available vehicles to the pickup points.\nAs the process should be socially and economically profitable, the task of\nvehicle dispatching is highly challenging, specially due to the time-varying\ntravel demands and traffic conditions. Due to the uneven distribution of travel\ndemands, many idle vehicles could be generated during the operation in\ndifferent subareas. Most of the existing works on vehicle dispatching system,\ndesigned static relocation centers to relocate idle vehicles. However, as\ntraffic conditions and demand distribution dynamically change over time, the\nstatic solution can not fit the evolving situations. In this paper, we propose\na dynamic future demand aware vehicle dispatching system. It can dynamically\nsearch the relocation centers considering both travel demand and traffic\nconditions. We evaluate the system on real-world dataset, and compare with the\nexisting state-of-the-art methods in our experiments in terms of several\nstandard evaluation metrics and operation time. Through our experiments, we\ndemonstrate that the proposed system significantly improves the serving ratio\nand with a very small increase in operation cost.\n"}
+{"abstract": "  Nowadays, we are witnessing an increasing demand in both corporates and\nacademia for exploiting Deep Learning (DL) to solve complex real-world\nproblems. A DL program encodes the network structure of a desirable DL model\nand the process by which the model learns from the training dataset. Like any\nsoftware, a DL program can be faulty, which implies substantial challenges of\nsoftware quality assurance, especially in safety-critical domains. It is\ntherefore crucial to equip DL development teams with efficient fault detection\ntechniques and tools. In this paper, we propose NeuraLint, a model-based fault\ndetection approach for DL programs, using meta-modelling and graph\ntransformations. First, we design a meta-model for DL programs that includes\ntheir base skeleton and fundamental properties. Then, we construct a\ngraph-based verification process that covers 23 rules defined on top of the\nmeta-model and implemented as graph transformations to detect faults and design\ninefficiencies in the generated models (i.e., instances of the meta-model).\nFirst, the proposed approach is evaluated by finding faults and design\ninefficiencies in 28 synthesized examples built from common problems reported\nin the literature. Then NeuraLint successfully finds 64 faults and design\ninefficiencies in 34 real-world DL programs extracted from Stack Overflow posts\nand GitHub repositories. The results show that NeuraLint effectively detects\nfaults and design issues in both synthesized and real-world examples with a\nrecall of 70.5 % and a precision of 100 %. Although the proposed meta-model is\ndesigned for feedforward neural networks, it can be extended to support other\nneural network architectures such as recurrent neural networks. Researchers can\nalso expand our set of verification rules to cover more types of issues in DL\nprograms.\n"}
+{"abstract": "  According to the \"Hilbert Space Fundamentalism\" Thesis, all features of a\nphysical system, including the $3$D-space, a preferred basis, and factorization\ninto subsystems, uniquely emerge from the state vector and the Hamiltonian\nalone. I give a simplified account of the proof from arXiv:2102.08620 showing\nthat such emerging structures cannot be both unique and physically relevant.\n"}
+{"abstract": "  The ${}^{12}\\mathrm{C} + {}^{12}\\mathrm{C}$ fusion reaction plays a vital\nrole in the explosive phenomena of the universe. The resonances in the Gamow\nwindow rule its reaction rate and products. Hence, the determination of the\nresonance parameters by nuclear models is indispensable as the direct\nmeasurement is not feasible. Here, for the first time, we report the resonances\nin the ${}^{12}\\mathrm{C} + {}^{12}\\mathrm{C}$ fusion reaction described by a\nfull-microscopic nuclear model. The model plausibly reproduces the measured\nlow-energy astrophysical $S$-factors and predicts the resonances in the Gamow\nwindow. Contradictory to the hindrance model, we conclude that there is no\nlow-energy suppression of the $S$-factor.\n"}
+{"abstract": "  When we look at the world around us, we see complex physical systems and\nemergent phenomena. Emergence occurs when a system is observed to have\nproperties that its parts do not have on their own. These properties or\nbehaviors emerge only when the parts interact in a wider whole. Examples of\nemergence can vary from the synchronization of pendulum clocks hanging on the\nsame wall to the phenomenon of life as an emergent property of chemistry. One\nof the most complex systems that exist in nature is the human brain. It\ncontains on average 100 to 200 billion neurons and about 100 trillion synapses\nconnecting them. From this vast neuronal dynamics, the ability to learn and\nstore memory emerges as well as the ability to have complex cognitive skills,\nconscious experience and a sense of self. In this work, we investigated how\ncomplex systems like the human brain and chaotic systems create emergent\nproperties. In order to do so, we used network theory (paper 1), chaos and\nsynchronization theory (paper 2 and 3).\n"}
+{"abstract": "  Background: The critical view of safety (CVS) is poorly adopted in surgical\npractices although it is ubiquitously recommended to prevent major bile duct\ninjuries during laparoscopic cholecystectomy (LC). This study aims to determine\nwhether performing a short intraoperative time out can improve CVS\nimplementation. Methods: Surgeons performing LCs at an academic centre were\ninvited to perform a 5-second long time out to verify CVS before dividing the\ncystic duct (5-second rule). The primary endpoint was to compare the rate of\nCVS achievement between LCs performed in the year before and the year after the\n5-second rule. The CVS achievement rate was computed using the mediated\nvideo-based assessment of two independent reviewers. Clinical outcomes, LC\nworkflows, and postoperative reports were also compared. Results: Three hundred\nand forty-three (171 before and 172 after the 5-second rule) of the 381 LCs\nperformed over the 2-year study were analysed. After the implementation of the\n5-second rule, the rate of CVS achievement increased significantly (15.9 vs\n44.1 %; P<0.001) as well as the rate of bailout procedures (8.2 vs 15.7 %;\nP=0.045), the median time to clip the cystic duct or artery (17:26\n[interquartile range: 16:46] vs 23:12 [17:16] minutes; P=0.007), and the rate\nof postoperative CVS reporting (1.3 vs 28.8 %; P<0.001). Morbidity was\ncomparable (1.75 vs 2.33 % before and after the 5-second rule respectively;\nP=0.685). Conclusion: Performing a short intraoperative time out improves CVS\nimplementation during LC. Systematic intraoperative cognitive aids should be\nstudied to sustain the uptake of guidelines.\n"}
+{"abstract": "  We prove the support recovery for a general class of linear and nonlinear\nevolutionary partial differential equation (PDE) identification from a single\nnoisy trajectory using $\\ell_1$ regularized Pseudo-Least Squares\nmodel~($\\ell_1$-PsLS). In any associative $\\mathbb{R}$-algebra generated by\nfinitely many differentiation operators that contain the unknown PDE operator,\napplying $\\ell_1$-PsLS to a given data set yields a family of candidate models\nwith coefficients $\\mathbf{c}(\\lambda)$ parameterized by the regularization\nweight $\\lambda\\geq 0$. The trace of $\\{\\mathbf{c}(\\lambda)\\}_{\\lambda\\geq 0}$\nsuffers from high variance due to data noises and finite difference\napproximation errors. We provide a set of sufficient conditions which guarantee\nthat, from a single trajectory data denoised by a Local-Polynomial filter, the\nsupport of $\\mathbf{c}(\\lambda)$ asymptotically converges to the true\nsigned-support associated with the underlying PDE for sufficiently many data\nand a certain range of $\\lambda$. We also show various numerical experiments to\nvalidate our theory.\n"}
+{"abstract": "  The strain effect from a substrate is an important experimental route to\ncontrol electronic and magnetic properties in transition-metal oxide (TMO) thin\nfilms. Using hard x-ray photoemission spectroscopy, we investigate the strain\ndependence of the valence states in LaNiO$_{3}$ thin films, strongly correlated\nperovskite TMO, grown on four substrates: LaAlO$_{3}$,\n(LaAlO$_{3}$)$_{0.3}$(SrAl$_{0.5}$Ta$_{0.5}$O$_{3}$)$_{0.7}$, SrTiO$_{3}$, and\nDyScO$_{3}$. A Madelung potential analysis of core-level spectra suggests that\nthe point-charge description is valid for the La ions while it breaks down for\nNi and O ions due to a strong covalent bonding between the two. A clear x-ray\nphoton-energy dependence of the valence spectra is analyzed by the density\nfunctional theory, which points to a presence of the La 5$p$ state near the\nFermi level.\n"}
+{"abstract": "  Collaborative playlists (CP) enable listeners to curate music together,\ntranslating long-standing social practices around music consumption into the\nage of streaming. Yet despite their role in connecting people through music, we\nlack an understanding of factors that are critical to CPs and their enjoyment.\nTo understand what users consider important to CPs and their usage, we\ninvestigated aspects that are perceived to be most useful and lacking in\ntoday's CP implementations. We conducted a survey to collect open-ended text\nresponses from real-world CP users. Using thematic analysis, we derived the\nCodebook of Critical CP Factors, which comprises eight aspects. We gained\ninsights into which aspects are particularly useful, and which are absent and\ndesired by current CP users. From these findings we propose design implications\nto inform further design of CP functionalities and platforms, and highlight\npotential benefits and challenges related to their adoption in current music\nservices.\n"}
+{"abstract": "  In this paper, we consider a linear-quadratic-Gaussian defending assets\ndifferential game (DADG) where the attacker and the defender do not know each\nother's state information while they know the trajectory of a moving asset.\nBoth players can choose to observe the other player's state information by\npaying a cost. The defender and the attacker have to craft both control\nstrategies and observation strategies. We obtain a closed-form feedback\nsolution that characterizes the Nash control strategies. We show that the\ntrajectory of the asset does not affect both players' observation choices.\nMoreover, we show that the observation choices of the defender and the attacker\ncan be decoupled and the Nash observation strategies can be found by solving\ntwo independent optimization problems. A set of necessary conditions is\ndeveloped to characterize the optimal observation instances. Based on the\nnecessary conditions, an effective algorithm is proposed to numerically compute\nthe optimal observation instances. A case study is presented to demonstrate the\neffectiveness of the optimal observation instances.\n"}
+{"abstract": "  We establish a framework for the study of the effective theory of weak\nconvergence of measures. We define two effective notions of weak convergence of\nmeasures on $\\mathbb{R}$: one uniform and one non-uniform. We show that these\nnotions are equivalent. By means of this equivalence, we prove an effective\nversion of the Portmanteau Theorem, which consists of multiple equivalent\ndefinitions of weak convergence of measures.\n"}
+{"abstract": "  The exact distributed controllability of the semilinear heat equation\n$\\partial_{t}y-\\Delta y + g(y)=f \\,1_{\\omega}$ posed over multi-dimensional and\nbounded domains, assuming that $g\\in C^1(\\mathbb{R})$ satisfies the growth\ncondition $\\limsup_{r\\to \\infty} g(r)/(\\vert r\\vert \\ln^{3/2}\\vert r\\vert)=0$\nhas been obtained by Fern\\'andez-Cara and Zuazua in 2000. The proof based on a\nnon constructive fixed point arguments makes use of precise estimates of the\nobservability constant for a linearized heat equation. In the one dimensional\nsetting, assuming that $g^\\prime$ does not grow faster than $\\beta\n\\ln^{3/2}\\vert r\\vert$ at infinity for $\\beta>0$ small enough and that\n$g^\\prime$ is uniformly H\\\"older continuous on $\\mathbb{R}$ with exponent $p\\in\n[0,1]$, we design a constructive proof yielding an explicit sequence converging\nto a controlled solution for the semilinear equation, at least with order $1+p$\nafter a finite number of iterations.\n"}
+{"abstract": "  Excitonic insulators (EI) arise from the formation of bound electron-hole\npairs (excitons) in semiconductors and provide a solid-state platform for\nquantum many-boson physics. Strong exciton-exciton repulsion is expected to\nstabilize condensed superfluid and crystalline phases by suppressing both\ndensity and phase fluctuations. Although spectroscopic signatures of EIs have\nbeen reported, conclusive evidence for strongly correlated EI states has\nremained elusive. Here, we demonstrate a strongly correlated spatially indirect\ntwo-dimensional (2D) EI ground state formed in transition metal dichalcogenide\n(TMD) semiconductor double layers. An equilibrium interlayer exciton fluid is\nformed when the bias voltage applied between the two electrically isolated TMD\nlayers, is tuned to a range that populates bound electron-hole pairs, but not\nfree electrons or holes. Capacitance measurements show that the fluid is\nexciton-compressible but charge-incompressible - direct thermodynamic evidence\nof the EI. The fluid is also strongly correlated with a dimensionless exciton\ncoupling constant exceeding 10. We further construct an exciton phase diagram\nthat reveals both the Mott transition and interaction-stabilized\nquasi-condensation. Our experiment paves the path for realizing the exotic\nquantum phases of excitons, as well as multi-terminal exciton circuitry for\napplications.\n"}
+{"abstract": "  QNMnonreciprocal_resonators is an extension (posted in 2021) of the QNMEig\nsolver of the freeware package MAN. It provides a comprehensive presentation of\nthe computation and normalization of electromagnetic quasinormal modes (QNMs)\nof resonators composed of nonreciprocal materials. It features a theoretical\nbackground on the topic and a COMSOL model that illustrates how to put into\npractice the theory on the example of a Yttrium iron garnet wire in a\nhomogenous background. This document provides the necessary details on how the\nmodel is built so that the interested readers may easily modify it for\ncomputing QNMs of other nonreciprocal resonators.\n"}
+{"abstract": "  With the increasing importance of the Mittag-Leffler function in the physical\napplications, these days many researchers are studying various generalizations\nand extensions of the Mittag-Leffler function. In this paper efforts are made\nto define bicomplex extension of the Mittag-Leffler function and also its\nanalyticity and region of convergence are discussed. Various properties of the\nbicomplex Mittag-Leffler function including integral representation, recurrence\nrelations, duplication formula and differential relations are established.\n"}
+{"abstract": "  Understanding the scene around the ego-vehicle is key to assisted and\nautonomous driving. Nowadays, this is mostly conducted using cameras and laser\nscanners, despite their reduced performances in adverse weather conditions.\nAutomotive radars are low-cost active sensors that measure properties of\nsurrounding objects, including their relative speed, and have the key advantage\nof not being impacted by rain, snow or fog. However, they are seldom used for\nscene understanding due to the size and complexity of radar raw data and the\nlack of annotated datasets. Fortunately, recent open-sourced datasets have\nopened up research on classification, object detection and semantic\nsegmentation with raw radar signals using end-to-end trainable models. In this\nwork, we propose several novel architectures, and their associated losses,\nwhich analyse multiple \"views\" of the range-angle-Doppler radar tensor to\nsegment it semantically. Experiments conducted on the recent CARRADA dataset\ndemonstrate that our best model outperforms alternative models, derived either\nfrom the semantic segmentation of natural images or from radar scene\nunderstanding, while requiring significantly fewer parameters. Both our code\nand trained models are available at https://github.com/valeoai/MVRSS.\n"}
+{"abstract": "  Even-parity interorbital spin-triplet pairing emerges as an intriguing\ncandidate in multiorbital superconductors with significant Hund's and\nspin-orbit coupling. Within such a state, the pairing is dominated by the\nintraband pseudospin-singlet component via the spin-orbit coupling,\ndistinguishing it from a pure spin triplet and motivating the name, shadowed\ntriplet. With atomic spin-orbit coupling, it was shown that this pairing\nexhibits an anisotropic $s$-wave character, while higher angular momentum\npairing symmetries such as $d$- or $g$-wave have been proposed based on\nphenomenological analyses in candidate systems. A natural question is then\nwhether higher angular momentum pairings may arise with this form of\nspin-triplet pairing. Here, we examine the interplay between spin-orbit\ncoupling and the electronic dispersions in correlated metals and demonstrate\nhow they can be realized. We apply this idea to Sr$_{2}$RuO$_{4}$ and determine\nthe competition among different pairing states as multiple spin-orbit coupling\nparameters are tuned. The presence of both $d$- and $g$-wave pairings,\nincluding a $d+ig$ state, are found when momentum-dependent spin-orbit coupling\nwith $d$-wave character is increased. Implications of the theory and future\ndirections are also discussed.\n"}
+{"abstract": "  Personalization and active learning are key aspects to successful learning.\nThese aspects are important to address in intelligent educational applications,\nas they help systems to adapt and close the gap between students with varying\nabilities, which becomes increasingly important in the context of online and\ndistance learning. We run a comparative head-to-head study of learning outcomes\nfor two popular online learning platforms: Platform A, which follows a\ntraditional model delivering content over a series of lecture videos and\nmultiple-choice quizzes, and Platform B, which creates a personalized learning\nenvironment and provides problem-solving exercises and personalized feedback.\nWe report on the results of our study using pre- and post-assessment quizzes\nwith participants taking courses on an introductory data science topic on two\nplatforms. We observe a statistically significant increase in the learning\noutcomes on Platform B, highlighting the impact of well-designed and\nwell-engineered technology supporting active learning and problem-based\nlearning in online education. Moreover, the results of the self-assessment\nquestionnaire, where participants reported on perceived learning gains, suggest\nthat participants using Platform B improve their metacognition.\n"}
+{"abstract": "  Central Pattern Generators (CPGs) models have been long used to investigate\nboth the neural mechanisms that underlie animal locomotion as well as a tool\nfor robotic research. In this work we propose a spiking CPG neural network and\nits implementation on neuromorphic hardware as a means to control a simulated\nlamprey model. To construct our CPG model, we employ the naturally emerging\ndynamical systems that arise through the use of recurrent neural populations in\nthe Neural Engineering Framework (NEF). We define the mathematical formulation\nbehind our model, which consists of a system of coupled abstract oscillators\nmodulated by high-level signals, capable of producing a variety of output\ngaits. We show that with this mathematical formulation of the Central Pattern\nGenerator model, the model can be turned into a Spiking Neural Network (SNN)\nthat can be easily simulated with Nengo, an SNN simulator. The spiking CPG\nmodel is then used to produce the swimming gaits of a simulated lamprey robot\nmodel in various scenarios. We show that by modifying the input to the network,\nwhich can be provided by sensory information, the robot can be controlled\ndynamically in direction and pace. The proposed methodology can be generalized\nto other types of CPGs suitable for both engineering applications and\nscientific research. We test our system on two neuromorphic platforms,\nSpiNNaker and Loihi. Finally, we show that this category of spiking algorithms\nshows a promising potential to exploit the theoretical advantages of\nneuromorphic hardware in terms of energy efficiency and computational speed.\n"}
+{"abstract": "  In fruit tree growth, pruning is an important management practice for\npreventing overcrowding, improving canopy access to light and promoting\nregrowth. Due to the slow nature of agriculture, decisions in pruning are\ntypically made using tradition or rules of thumb rather than data-driven\nanalysis. Many existing algorithmic, simulation-based approaches rely on\nhigh-fidelity digital captures or purely computer-generated fruit trees, and\nare unable to provide specific results on an orchard scale. We present a\nframework for suggesting pruning strategies on LiDAR-scanned commercial fruit\ntrees using a scoring function with a focus on improving light distribution\nthroughout the canopy. A scoring function to assess the quality of the tree\nshape based on its light availability and size was developed for comparative\nanalysis between trees, and was validated against yield characteristics,\ndemonstrating a reasonable correlation against fruit count with an $R^2$ score\nof 0.615 for avocado and 0.506 for mango. A tool was implemented for simulating\npruning by algorithmically estimating which parts of a tree point cloud would\nbe removed given specific cut points using structural analysis of the tree,\nvalidated experimentally with an average F1 score of 0.78 across 144\nexperiments. Finally, new pruning locations were suggested and we used the\nprevious two stages to estimate the improvement of the tree given these\nsuggestions. The light distribution was improved by up to 25.15\\%,\ndemonstrating a 16\\% improvement over commercial pruning on a real tree, and\ncertain cut points were discovered which improved light distribution with a\nsmaller negative impact on tree volume. The final results suggest value in the\nframework as a decision making tool for commercial growers, or as a starting\npoint for automated pruning since the entire process can be performed with\nlittle human intervention.\n"}
+{"abstract": "  Convolutional neural networks (CNNs) have become a powerful technique to\ndecode EEG and have become the benchmark for motor imagery EEG\nBrain-Computer-Interface (BCI) decoding. However, it is still challenging to\ntrain CNNs on multiple subjects' EEG without decreasing individual performance.\nThis is known as the negative transfer problem, i.e. learning from dissimilar\ndistributions causes CNNs to misrepresent each of them instead of learning a\nricher representation. As a result, CNNs cannot directly use multiple subjects'\nEEG to enhance model performance directly. To address this problem, we extend\ndeep transfer learning techniques to the EEG multi-subject training case. We\npropose a multi-branch deep transfer network, the Separate-Common-Separate\nNetwork (SCSN) based on splitting the network's feature extractors for\nindividual subjects. We also explore the possibility of applying Maximum-mean\ndiscrepancy (MMD) to the SCSN (SCSN-MMD) to better align distributions of\nfeatures from individual feature extractors. The proposed network is evaluated\non the BCI Competition IV 2a dataset (BCICIV2a dataset) and our online recorded\ndataset. Results show that the proposed SCSN (81.8%, 53.2%) and SCSN-MMD\n(81.8%, 54.8%) outperformed the benchmark CNN (73.4%, 48.8%) on both datasets\nusing multiple subjects. Our proposed networks show the potential to utilise\nlarger multi-subject datasets to train an EEG decoder without being influenced\nby negative transfer.\n"}
+{"abstract": "  It is well known and has been shown that the gain performance of Gaseous\nElectron Multipliers (GEM) depends on the size of the holes. With an optical\nscanner it is possible to measure the dimensions of the holes, and to predict\nthe performance of GEMs. However, the gain prediction of GEMs that are\nmanufactured with a double mask etching technique is not straightforward. With\nthe hole size information alone, it is not possible to make precise prediction\nof the gain. We show that the alignment of the photo-masks between the two\nsides of the GEM foils plays a crucial role. A misalignment of a few microns\ncan lower the gain substantially. The study is performed by using the Helsinki\nhigh definition optical scanner for quality control of GEM foils, and this will\nshow its true potential.\n"}
+{"abstract": "  In the fields of co-simulation and component-based modelling, designers\nimport models as building blocks to create a composite model that provides more\ncomplex functionalities. Modelling tools perform instantaneous cycle detection\n(ICD) on the composite models having feedback loops to reject the models if the\nloops are mathematically unsound and to improve simulation performance. In this\ncase, the analysis relies heavily on the availability of dependency information\nfrom the imported models. However, the cycle detection problem becomes harder\nwhen the model's input to output dependencies are mode-dependent, i.e. changes\nfor certain events generated internally or externally as inputs. The number of\npossible modes created by composing such models increases significantly and\nunknown factors such as environmental inputs make the offline (statical) ICD a\ndifficult task. In this paper, an online ICD method is introduced to address\nthis issue for the models used in cyber-physical systems. The method utilises\nan oracle as a central source of information that can answer whether the\nindividual models can make mode transition without creating instantaneous\ncycles. The oracle utilises three types of data-structures created offline that\nare adaptively chosen during online (runtime) depending on the frequency as\nwell as the number of models that make mode transitions. During the analysis,\nthe models used online are stalled from running, resulting in the discrepancy\nwith the physical system. The objective is to detect an absence of the\ninstantaneous cycle while minimising the stall time of the model simulation\nthat is induced from the analysis. The benchmark results show that our method\nis an adequate alternative to the offline analysis methods and significantly\nreduces the analysis time.\n"}
+{"abstract": "  Spiking neural networks (SNNs) offer an inherent ability to process\nspatial-temporal data, or in other words, realworld sensory data, but suffer\nfrom the difficulty of training high accuracy models. A major thread of\nresearch on SNNs is on converting a pre-trained convolutional neural network\n(CNN) to an SNN of the same structure. State-of-the-art conversion methods are\napproaching the accuracy limit, i.e., the near-zero accuracy loss of SNN\nagainst the original CNN. However, we note that this is made possible only when\nsignificantly more energy is consumed to process an input. In this paper, we\nargue that this trend of \"energy for accuracy\" is not necessary -- a little\nenergy can go a long way to achieve the near-zero accuracy loss. Specifically,\nwe propose a novel CNN-to-SNN conversion method that is able to use a\nreasonably short spike train (e.g., 256 timesteps for CIFAR10 images) to\nachieve the near-zero accuracy loss. The new conversion method, named as\nexplicit current control (ECC), contains three techniques (current\nnormalisation, thresholding for residual elimination, and consistency\nmaintenance for batch-normalisation), in order to explicitly control the\ncurrents flowing through the SNN when processing inputs. We implement ECC into\na tool nicknamed SpKeras, which can conveniently import Keras CNN models and\nconvert them into SNNs. We conduct an extensive set of experiments with the\ntool -- working with VGG16 and various datasets such as CIFAR10 and CIFAR100 --\nand compare with state-of-the-art conversion methods. Results show that ECC is\na promising method that can optimise over energy consumption and accuracy loss\nsimultaneously.\n"}
+{"abstract": "  The Kepler-1647 is a binary system with two Sun-type stars (approximately\n1.22 and 0.97 Solar mass). It has the most massive circumbinary planet (1.52\nJupiter mass) with the longest orbital period (1,107.6 days) detected by the\nKepler probe and is located within the habitable zone (HZ) of the system. In\nthis work, we investigated the ability to form and house an Earth-sized planet\nwithin its HZ. First, we computed the limits of its HZ and performed numerical\nstability tests within that region. We found that HZ has three sub-regions that\nshow stability, one internal, one co-orbital, and external to the host planet\nKepler-1647b. Within the limits of these three regions, we performed numerical\nsimulations of planetary formation. In the regions inner and outer to the\nplanet, we used two different density profiles to explore different conditions\nof formation. In the co-orbital region, we used eight different values of total\ndisc mass. We showed that many resonances are located within regions causing\nmuch of the disc material to be ejected before a planet is formed. Thus, the\nsystem might have two asteroid belts with Kirkwood gaps, similar to the Solar\nSystem\\'s main belt of asteroids. The co-orbital region proved to be extremely\nsensitive, not allowing the planet formation, but showing that this binary\nsystem has the capacity to have Trojan bodies. Finally, we looked for regions\nof stability for an Earth-sized moon. We found that there is stability for a\nmoon with this mass up to 0.4 Hill\\'s radius from the host planet.\n"}
+{"abstract": "  Distribution matchers for finite alphabets are shown to have informational\ndivergences that grow logarithmically with the block length, generalizing a\nbasic result for binary strings.\n"}
+{"abstract": "  An important issue in the field of academic measurement is how to evaluate\nacademic influence scientifically and comprehensively, which can help\ngovernment and research organizations better allocate academic resources and\nrecruit researchers. It is generally accepted that using weighted citations to\nmeasure academic influence is more reasonable than treating all citations\nequally. Given the limitations of the existing c-index, the first index in\nbibliometric literature that measures output based on the quantity and quality\nof received citations, we propose the x-index, which assigns weight to each\ncitation according to its distance. By defining collaboration distance and\ncitation distance, we first analyze the properties of the collaboration network\nand citation distance, then perform theoretical and empirical analyses on\nc-index to reveal its shortcomings, finally, we suggest the x-index and conduct\nexperiments and analysis on the x-index. Experimental results demonstrate that\ncompared with the c-index, h-index, and g-index, the x-index shows a stronger\ndiscriminatory power.\n"}
+{"abstract": "  The resonant magnetic perturbation (RMP) field has been utilized to suppress\nthe edge localized mode (ELM) in tokamak plasmas. The pedestal top during the\nRMP ELM suppression is characterized by the increased broadband fluctuation,\ni.e. plasma turbulence. The origin of the increased fluctuation and its\ncharacteristics were not fully understood. Here, we show that the increased\nfluctuation involves the weak but significant nonlinear coupling in a narrow\nregion at the pedestal top and the spatial structure of turbulence becomes less\npredictable around that region as expected for the stochastic fields around\nmagnetic islands. We analyzed the bicoherence and the rescaled Jensen-Shannon\ncomplexity of the electron temperature fluctuation measured in KSTAR experiment\nto identify the nonlinear coupling and the change of statistical turbulence\ncharacteristics, respectively. Our analyses suggest that the nonlinear coupling\nthrough a partially stochastic island layer at the pedestal top contributes to\nthe fluctuation increase during the RMP ELM suppression.\n"}
+{"abstract": "  We review the most recent RANSAC-like hypothesize-and-verify robust\nestimators. The best performing ones are combined to create a state-of-the-art\nversion of the Universal Sample Consensus (USAC) algorithm. A recent objective\nis to implement a modular and optimized framework, making future RANSAC modules\neasy to be included. The proposed method, USACv20, is tested on eight publicly\navailable real-world datasets, estimating homographies, fundamental and\nessential matrices. On average, USACv20 leads to the most geometrically\naccurate models and it is the fastest in comparison to the state-of-the-art\nrobust estimators. All reported properties improved performance of original\nUSAC algorithm significantly. The pipeline will be made available after\npublication.\n"}
+{"abstract": "  We introduce a new definition of speculative bubbles in discrete-time models\nbased on the discounted stock price losing mass at some finite drop-down under\nan equivalent martingale measure. We provide equivalent probabilistic\ncharacterisations of this definition and give examples of discrete-time\nmartingales that are speculative bubbles and those that are not. In the\nMarkovian case, we provide sufficient analytic conditions for the presence of\nspeculative bubbles. We also show that the existence of speculative bubbles is\ndirectly linked to the existence of a non-trivial solution to a linear Volterra\nintegral equation of the second kind involving the Markov kernel. Finally, we\nshow that our definition of speculative bubbles in discrete time is consistent\nwith the strict local martingale definition of speculative bubbles in\ncontinuous time in the sense that a properly discretised strict local\nmartingale in continuous time is a speculative bubble in discrete time.\n"}
+{"abstract": "  In the era of big data, deep learning has become an increasingly popular\ntopic. It has outstanding achievements in the fields of image recognition,\nobject detection, and natural language processing et al. The first priority of\ndeep learning is exploiting valuable information from a large amount of data,\nwhich will inevitably induce privacy issues that are worthy of attention.\nPresently, several privacy-preserving deep learning methods have been proposed,\nbut most of them suffer from a non-negligible degradation of either efficiency\nor accuracy. Negative database (\\textit{NDB}) is a new type of data\nrepresentation which can protect data privacy by storing and utilizing the\ncomplementary form of original data. In this paper, we propose a\nprivacy-preserving deep learning method named NegDL based on \\textit{NDB}.\nSpecifically, private data are first converted to \\textit{NDB} as the input of\ndeep learning models by a generation algorithm called \\textit{QK}-hidden\nalgorithm, and then the sketches of \\textit{NDB} are extracted for training and\ninference. We demonstrate that the computational complexity of NegDL is the\nsame as the original deep learning model without privacy protection.\nExperimental results on Breast Cancer, MNIST, and CIFAR-10 benchmark datasets\ndemonstrate that the accuracy of NegDL could be comparable to the original deep\nlearning model in most cases, and it performs better than the method based on\ndifferential privacy.\n"}
+{"abstract": "  Nowadays ransomware has become a new profitable form of attack. This type of\nmalware acts as a form of extortion which encrypts the files in a victim's\ncomputer and forces the victim to pay the ransom to have the data recovered.\nEven companies and tech savvy people must use extensive resources to maintain\nbackups for recovery or else they will lose valuable data, not mentioning\naverage users. Unfortunately, not any recovery tool can effectively defend\nvarious types of ransomware. To address this challenge, we propose a novel\nransomware defense mechanism that can be easily deployed in modern Windows\nsystem to recover the data and mitigate a ransomware attack. The uniqueness of\nour approach is to fight the virus like a virus. We leverage Alternative Data\nStreams which are sometimes used by malicious applications, to develop a data\nprotection method that misleads the ransomware to attack only file 'shells'\ninstead of the actual file content. We evaluated different file encrypting\nransomware and demonstrate usability, efficiency and effectiveness of our\napproach.\n"}
+{"abstract": "  Spoken dialogue systems such as Siri and Alexa provide great convenience to\npeople's everyday life. However, current spoken language understanding (SLU)\npipelines largely depend on automatic speech recognition (ASR) modules, which\nrequire a large amount of language-specific training data. In this paper, we\npropose a Transformer-based SLU system that works directly on phones. This\nacoustic-based SLU system consists of only two blocks and does not require the\npresence of ASR module. The first block is a universal phone recognition\nsystem, and the second block is a Transformer-based language model for phones.\nWe verify the effectiveness of the system on an intent classification dataset\nin Mandarin Chinese.\n"}
+{"abstract": "  This paper presents a formulation of Snapshot Positioning as a mixed-integer\nleast-squares problem. In snapshot positioning one estimates a position from\ncode-phase and possibly Doppler observations of a Global Navigation Satellite\nSystems (GNSS) without knowing the time of departure (timestamp) of the codes.\nSolving the problem allows a receiver to determine a fix from short\nradio-frequency snapshots missing the time-stamp information embedded in the\nGNSS data stream. This is used to reduced the time to first fix in some\nreceivers, and it is used in certain wildlife trackers. This paper presents two\nnew formulations of the problem and an algorithm that solves the resulting\nmixed-integer least-squares problems. We also show that the new formulations\ncan produce fixes even with huge initial errors, much larger than permitted in\nVan Diggelen's widely-cited coarse-time navigation method.\n"}
+{"abstract": "  Intelligent and adaptive online education systems aim to make high-quality\neducation available for a diverse range of students. However, existing systems\nusually depend on a pool of hand-made questions, limiting how fine-grained and\nopen-ended they can be in adapting to individual students. We explore targeted\nquestion generation as a controllable sequence generation task. We first show\nhow to fine-tune pre-trained language models for deep knowledge tracing\n(LM-KT). This model accurately predicts the probability of a student answering\na question correctly, and generalizes to questions not seen in training. We\nthen use LM-KT to specify the objective and data for training a model to\ngenerate questions conditioned on the student and target difficulty. Our\nresults show we succeed at generating novel, well-calibrated language\ntranslation questions for second language learners from a real online education\nplatform.\n"}
+{"abstract": "  Exciton-polaritons are quasi-particles arising from the strong coupling\nregime between excitons and photons. In planar microcavitites, phenomena such\nas superfluidity or Bose-Einstein condensation can be observed. Those systems\nhave demonstrated to be very efficient in the hydrodynamic generation of\ntopological excitations, such as vortex-antivortex pairs or dark solitons.\nHowever, the lifetime and motion of those excitations were limited by the\ndriven dissipative nature of the system. In this thesis, we present a rich\nvariety of results about the generation and control of such topological\nexcitations. Taking advantage of the optical bistability present in our system,\nwe were able to greatly enhanced the propagation length of vortices and\nsolitons generated in the wake of a structural defect, revealing in the mean\ntime an unexpected binding mechanism of the solitons which propagate parallel.\nThis behaviour was recovered in a specifically designed experiment, where we\nartificially imprint dark soliton pairs on demand on a polariton superfluid.\nThe adaptability of our technique allowed for a detailed study of this\nphenomenon, that we directly connected to the driven-dissipative nature of our\nsystem. Finally, confined dark solitons were generated within guided intensity\nchannels on a static polariton fluid. The absence of flow lead to the\ndevelopment of transverse snake instabilities of which we studied the\ninteresting properties.\n"}
+{"abstract": "  In Machine Learning, the $\\mathsf{SHAP}$-score is a version of the Shapley\nvalue that is used to explain the result of a learned model on a specific\nentity by assigning a score to every feature. While in general computing\nShapley values is an intractable problem, we prove a strong positive result\nstating that the $\\mathsf{SHAP}$-score can be computed in polynomial time over\ndeterministic and decomposable Boolean circuits. Such circuits are studied in\nthe field of Knowledge Compilation and generalize a wide range of Boolean\ncircuits and binary decision diagrams classes, including binary decision trees\nand Ordered Binary Decision Diagrams (OBDDs).\n  We also establish the computational limits of the SHAP-score by observing\nthat computing it over a class of Boolean models is always polynomially as hard\nas the model counting problem for that class. This implies that both\ndeterminism and decomposability are essential properties for the circuits that\nwe consider. It also implies that computing $\\mathsf{SHAP}$-scores is\nintractable as well over the class of propositional formulas in DNF. Based on\nthis negative result, we look for the existence of fully-polynomial randomized\napproximation schemes (FPRAS) for computing $\\mathsf{SHAP}$-scores over such\nclass. In contrast to the model counting problem for DNF formulas, which admits\nan FPRAS, we prove that no such FPRAS exists for the computation of\n$\\mathsf{SHAP}$-scores. Surprisingly, this negative result holds even for the\nclass of monotone formulas in DNF. These techniques can be further extended to\nprove another strong negative result: Under widely believed complexity\nassumptions, there is no polynomial-time algorithm that checks, given a\nmonotone DNF formula $\\varphi$ and features $x,y$, whether the\n$\\mathsf{SHAP}$-score of $x$ in $\\varphi$ is smaller than the\n$\\mathsf{SHAP}$-score of $y$ in $\\varphi$.\n"}
+{"abstract": "  Reactions on the proton-rich nuclides drive the nucleosynthesis in\nCore-Collapse Supernovae (CCSNe) and in X-ray bursts (XRBs). CCSNe eject the\nnucleosynthesis products to the interstellar medium and hence are a potential\ninventory of p-nuclei, whereas in XRBs nucleosynthesis powers the light curves.\nIn both astrophysical sites the Ni-Cu cycle, which features a competition\nbetween $^{59}$Cu(p,$\\alpha$)$^{56}$Ni and $^{59}$Cu(p,$\\gamma$)$^{60}$Zn,\ncould potentially halt the production of heavier elements. Here, we report the\nfirst direct measurement of $^{59}$Cu(p,$\\alpha$)$^{56}$Ni using a\nre-accelerated $^{59}$Cu beam and cryogenic solid hydrogen target. Our results\nshow that the reaction proceeds predominantly to the ground state of $^{56}$Ni\nand the experimental rate has been found to be lower than Hauser-Feshbach-based\nstatistical predictions. New results hint that the $\\nu p$-process could\noperate at higher temperatures than previously inferred and therefore remains a\nviable site for synthesizing the heavier elements.\n"}
+{"abstract": "  Rendering objects invisible to impinging acoustic waves (cloaking) and\ncreating acoustic illusions (holography) has been attempted using active and\npassive approaches. While passive methods are applicable only to narrow\nfrequency bands, active approaches attempt to respond dynamically, interfering\nwith broadband incident or scattered wavefields by emitting secondary waves.\nWithout prior knowledge of the primary wavefield, the signals for the secondary\nsources need to be estimated and adapted in real-time. This has thus far\nimpeded active cloaking and holography for broadband wavefields. We present\nexperimental results of active acoustic cloaking and holography without prior\nknowledge of the wavefield so that objects remain invisible and illusions\nintact even for broadband moving sources. This opens novel research directions\nand facilitates practical applications including architectural acoustics, and\nstealth.\n"}
+{"abstract": "  Pretrained text encoders, such as BERT, have been applied increasingly in\nvarious natural language processing (NLP) tasks, and have recently demonstrated\nsignificant performance gains. However, recent studies have demonstrated the\nexistence of social bias in these pretrained NLP models. Although prior works\nhave made progress on word-level debiasing, improved sentence-level fairness of\npretrained encoders still lacks exploration. In this paper, we proposed the\nfirst neural debiasing method for a pretrained sentence encoder, which\ntransforms the pretrained encoder outputs into debiased representations via a\nfair filter (FairFil) network. To learn the FairFil, we introduce a contrastive\nlearning framework that not only minimizes the correlation between filtered\nembeddings and bias words but also preserves rich semantic information of the\noriginal sentences. On real-world datasets, our FairFil effectively reduces the\nbias degree of pretrained text encoders, while continuously showing desirable\nperformance on downstream tasks. Moreover, our post-hoc method does not require\nany retraining of the text encoders, further enlarging FairFil's application\nspace.\n"}
+{"abstract": "  Medical toxicology is the clinical specialty that treats the toxic effects of\nsubstances, be it an overdose, a medication error, or a scorpion sting. The\nvolume of toxicological knowledge and research has, as with other medical\nspecialties, outstripped the ability of the individual clinician to entirely\nmaster and stay current with it. The application of machine learning techniques\nto medical toxicology is challenging because initial treatment decisions are\noften based on a few pieces of textual data and rely heavily on prior\nknowledge. ML techniques often do not represent knowledge in a way that is\ntransparent for the physician, raising barriers to usability. Rule-based\nsystems and decision tree learning are more transparent approaches, but often\ngeneralize poorly and require expert curation to implement and maintain. Here,\nwe construct a probabilistic logic network to represent a portion of the\nknowledge base of a medical toxicologist. Our approach transparently mimics the\nknowledge representation and clinical decision-making of practicing clinicians.\nThe software, dubbed Tak, performs comparably to humans on straightforward\ncases and intermediate difficulty cases, but is outperformed by humans on\nchallenging clinical cases. Tak outperforms a decision tree classifier at all\nlevels of difficulty. Probabilistic logic provides one form of explainable\nartificial intelligence that may be more acceptable for use in healthcare, if\nit can achieve acceptable levels of performance.\n"}
+{"abstract": "  Dimensionality reduction (DR) techniques help analysts to understand patterns\nin high-dimensional spaces. These techniques, often represented by scatter\nplots, are employed in diverse science domains and facilitate similarity\nanalysis among clusters and data samples. For datasets containing many\ngranularities or when analysis follows the information visualization mantra,\nhierarchical DR techniques are the most suitable approach since they present\nmajor structures beforehand and details on demand. However, current\nhierarchical DR techniques are not fully capable of addressing literature\nproblems because they do not preserve the projection mental map across\nhierarchical levels or are not suitable for most data types. This work presents\nHUMAP, a novel hierarchical dimensionality reduction technique designed to be\nflexible on preserving local and global structures and preserve the mental map\nthroughout hierarchical exploration. We provide empirical evidence of our\ntechnique's superiority compared with current hierarchical approaches and show\ntwo case studies to demonstrate its strengths.\n"}
+{"abstract": "  In this article, we introduce the notion of nonlocal diffusion as a means to\ndescribe the change, and possible polarization, that occurs in some public\nsentiment.\n"}
+{"abstract": "  This Conceptual Design Report describes LUXE (Laser Und XFEL Experiment), an\nexperimental campaign that aims to combine the high-quality and high-energy\nelectron beam of the European XFEL with a powerful laser to explore the\nuncharted terrain of quantum electrodynamics characterised by both high energy\nand high intensity. We will reach this hitherto inaccessible regime of quantum\nphysics by analysing high-energy electron-photon and photon-photon interactions\nin the extreme environment provided by an intense laser focus. The physics\nbackground and its relevance are presented in the science case which in turn\nleads to, and justifies, the ensuing plan for all aspects of the experiment:\nOur choice of experimental parameters allows (i) effective field strengths to\nbe probed at and beyond the Schwinger limit and (ii) a precision to be achieved\nthat permits a detailed comparison of the measured data with calculations. In\naddition, the high photon flux predicted will enable a sensitive search for new\nphysics beyond the Standard Model. The initial phase of the experiment will\nemploy an existing 40 TW laser, whereas the second phase will utilise an\nupgraded laser power of 350 TW. All expectations regarding the performance of\nthe experimental set-up as well as the expected physics results are based on\ndetailed numerical simulations throughout.\n"}
+{"abstract": "  We combine Newton's variational method with ideas from eigenvector\ncontinuation to construct a fast & accurate emulator for two-body scattering\nobservables. The emulator will facilitate the application of rigorous\nstatistical methods for interactions that depend smoothly on a set of free\nparameters. Our approach begins with a trial $K$ or $T$ matrix constructed from\na small number of exact solutions to the Lippmann--Schwinger equation.\nSubsequent emulation only requires operations on small matrices. We provide\nseveral applications to short-range potentials with and without the Coulomb\ninteraction and partial-wave coupling. It is shown that the emulator can\naccurately extrapolate far from the support of the training data. When used to\nemulate the neutron-proton cross section with a modern chiral interaction as a\nfunction of 26 free parameters, it reproduces the exact calculation with\nnegligible error and provides an over 300x improvement in CPU time.\n"}
+{"abstract": "  We introduce the UPG package for highly efficient Bayesian inference in\nprobit, logit, multinomial logit and binomial logit models. UPG offers a\nconvenient estimation framework for balanced and imbalanced data settings where\nsampling efficiency is ensured through Markov chain Monte Carlo boosting\nmethods. All sampling algorithms are implemented in C++, allowing for rapid\nparameter estimation. In addition, UPG provides several methods for fast\nproduction of output tables and summary plots that are easily accessible to a\nbroad range of users.\n"}
+{"abstract": "  Sommerfeld-enhanced dark matter (DM) annihilation through $s$-wave has been\nwidely considered as a consistent explanation for both the observed cosmic-ray\n(CR) positron excess and the DM thermal relic density. However, as the $s$-wave\nSommerfeld-enhanced annihilation cross section increases monotonically with\ndecreasing DM velocity, severe constraints appear from the data of gamma rays\nfrom dwarf spheroidal satellite galaxies (dSphs) and the cosmic microwave\nbackground (CMB), as the relevant typical DM velocities are even lower than\nthat in the Galactic halo. In this work, we consider Sommerfeld-enhanced\n$p$-wave DM annihilation where the DM annihilation cross section can be\nenhanced at certain velocities but eventually be highly suppressed when the DM\nvelocities become extremely low. We calculate the velocity-dependent\nastrophysics factors ($J$-factors) for the Sommerfeld-enhanced $p$-wave DM\nannihilation for fifteen nearby dSphs. Taking the channel of DM annihilating\ninto $4\\mu$ through two light mediators as an example, we show that there are\nparameter regions where this mechanism can account for the CR positron excess\nand the DM relic density, while being compatible with the constraints from\ndSphs gamma rays measured by Fermi-LAT and that from the CMB measured by\nPLANCK.\n"}
+{"abstract": "  Conventional wireless techniques are becoming inadequate for beyond\nfifth-generation (5G) networks due to latency and bandwidth considerations. To\nimprove the error performance and throughput of wireless communication systems,\nwe propose physical layer network coding (PNC) in an intelligent reflecting\nsurface (IRS)-assisted environment. We consider an IRS-aided butterfly network,\nwhere we propose an algorithm for obtaining the optimal IRS phases. Also,\nanalytic expressions for the bit error rate (BER) are derived. The numerical\nresults demonstrate that the proposed scheme significantly improves the BER\nperformance. For instance, the BER at the relay in the presence of a 32-element\nIRS is three orders of magnitudes less than that without an IRS.\n"}
+{"abstract": "  The Quantum Fisher Information matrix (QFIM) is a central metric in promising\nalgorithms, such as Quantum Natural Gradient Descent and Variational Quantum\nImaginary Time Evolution. Computing the full QFIM for a model with $d$\nparameters, however, is computationally expensive and generally requires\n$\\mathcal{O}(d^2)$ function evaluations. To remedy these increasing costs in\nhigh-dimensional parameter spaces, we propose using simultaneous perturbation\nstochastic approximation techniques to approximate the QFIM at a constant cost.\nWe present the resulting algorithm and successfully apply it to prepare\nHamiltonian ground states and train Variational Quantum Boltzmann Machines.\n"}
+{"abstract": "  Low-temperature plasma (LTP) processing is a green and flexible food\nprocessing technology with only known limitation of low energy efficiency.\nThree established fundamental mechanisms of plasma food processing includes: i)\nphysical damage to membranes and internal cellular components due to\nbombardment of energetic ions: ii) radicals and charged species led chemical\nreactions: iii) and radiation-induced modifications. In this investigation, we\npresent a new mechanism of generation and extraction of energetic $N_2$\nneutrals from glow discharge plasmas for efficient food processing. For\nnitrogen dc glow discharge plasma we demonstrate by experiment selective\ngeneration and extraction of energetic $N_2$ neutrals and $N_2^+$ ions for\ntreatment of different food samples. Using numerical simulations, the plasma\nprocessing parameters are defined for selective extraction of $N_2$ or $N_2^+$\nspecies with specific energies. Treatment of native finger millet flour or ragi\nflour (Eleusine coracana) with selectively extracted energetic $N_2$ neutrals,\nwith an estimated average energy of 34.20 eV, resulted in improvement of\nshelf-life and functional properties. Additionally, this processing technique\nachieved high energy efficiency of ~ 46%. This method of food engineering has\nadvantages of control over active species generation, high power efficiency,\nreduced processing cost, and chemical-free. Therefore, it opens up wide\npossibilities in plasma food processing for global food security and food\nsafety.\n"}
+{"abstract": "  Fluid-filled soft visuotactile sensors such as the Soft-bubbles alleviate key\nchallenges for robust manipulation, as they enable reliable grasps along with\nthe ability to obtain high-resolution sensory feedback on contact geometry and\nforces. Although they are simple in construction, their utility has been\nlimited due to size constraints introduced by enclosed custom IR/depth imaging\nsensors to directly measure surface deformations. Towards mitigating this\nlimitation, we investigate the application of state-of-the-art monocular depth\nestimation to infer dense internal (tactile) depth maps directly from the\ninternal single small IR imaging sensor. Through real-world experiments, we\nshow that deep networks typically used for long-range depth estimation (1-100m)\ncan be effectively trained for precise predictions at a much shorter range\n(1-100mm) inside a mostly textureless deformable fluid-filled sensor. We\npropose a simple supervised learning process to train an object-agnostic\nnetwork requiring less than 10 random poses in contact for less than 10 seconds\nfor a small set of diverse objects (mug, wine glass, box, and fingers in our\nexperiments). We show that our approach is sample-efficient, accurate, and\ngeneralizes across different objects and sensor configurations unseen at\ntraining time. Finally, we discuss the implications of our approach for the\ndesign of soft visuotactile sensors and grippers.\n"}
+{"abstract": "  A natural notion of higher order rectifiability is introduced for subsets of\nHeisenberg groups $\\mathbb{H}^n$ in terms of covering a set almost everywhere\nby a countable union of $(\\mathbf{C}_H^{1,\\alpha},\\mathbb{H})$-regular\nsurfaces, for some $0 < \\alpha \\leq 1$. We prove that a sufficient condition\nfor $C^{1,\\alpha}$-rectifiability of low-codimensional subsets in Heisenberg\ngroups is the almost everywhere existence of suitable approximate tangent\nparaboloids.\n"}
+{"abstract": "  In complex oxides, the electrons under the influence of competing energetics\nare the cornerstone of coexistence (or phase-separation) of two or more\nelectronic/magnetic phases in same structural configuration. Probing of growth\nand evolution of such phase-coexistence state is crucial to determine the\ncorrect mechanism of related phase-transition. Here, we demonstrate the\ncombination of terahertz (THz) time-domain spectroscopy and DC transport as a\nnovel strategy to probe the electronic phase-coexistence. This is demonstrated\nin disorder controlled phase-separated rare-earth nickelate thin films which\nexhibit metal-insulator transition in dc conductivity at around 180 K but lack\nthis transition in terahertz (THz) dynamics conductivity down to low\ntemperature. Such pronounced disparity exploits two extreme attributes: i)\nenormous sensitivity of THz radiation to a spatial range of its\nwavelength-compatible electronic inhomogeneities and ii) insensitivity to a\nrange beyond the size of its wavelength. This feature is generic in nature\n(sans a photo-induced effect), depends solely on the size of\ninsulating/metallic clusters and formulates a methodology with unique\nsensitivity to investigate electronic phase-coexistence and phase transition of\nany material system.\n"}
+{"abstract": "  Deep learning has shown impressive performance on challenging perceptual\ntasks and has been widely used in software to provide intelligent services.\nHowever, researchers found deep neural networks vulnerable to adversarial\nexamples. Since then, many methods are proposed to defend against adversaries\nin inputs, but they are either attack-dependent or shown to be ineffective with\nnew attacks. And most of existing techniques have complicated structures or\nmechanisms that cause prohibitively high overhead or latency, impractical to\napply on real software.\n  We propose DAFAR, a feedback framework that allows deep learning models to\ndetect/purify adversarial examples in high effectiveness and universality, with\nlow area and time overhead. DAFAR has a simple structure, containing a victim\nmodel, a plug-in feedback network, and a detector. The key idea is to import\nthe high-level features from the victim model's feature extraction layers into\nthe feedback network to reconstruct the input. This data stream forms a\nfeedback autoencoder. For strong attacks, it transforms the imperceptible\nattack on the victim model into the obvious reconstruction-error attack on the\nfeedback autoencoder directly, which is much easier to detect; for weak\nattacks, the reformation process destroys the structure of adversarial\nexamples. Experiments are conducted on MNIST and CIFAR-10 data-sets, showing\nthat DAFAR is effective against popular and arguably most advanced attacks\nwithout losing performance on legitimate samples, with high effectiveness and\nuniversality across attack methods and parameters.\n"}
+{"abstract": "  Applying simultaneously the methodology of Non-Equilibrium Thermodynamics\nwith Internal Variables (NET-IV) and the framework of General Equation for the\nNon-Equilibrium Reversible-Irreversible Coupling (GENERIC), we demonstrate\nthat, in heat conduction theories, entropy current multipliers can be\ninterpreted as relaxed state variables. Fourier's law and its various\nextensions -- the Maxwell-Cattaneo-Vernotte, Guyer-Krumhansl, Jeffreys type,\nGinzburg-Landau (Allen-Cahn) type and ballistic-diffusive -- heat conduction\nequations are derived in both formulations. Along these lines, a comparison of\nNET-IV and GENERIC is also performed. Our results may pave the way for\nmicroscopic/multiscale understanding of beyond-Fourier heat conduction, and\nopen new ways for numerical simulations of heat-conduction problems.\n"}
+{"abstract": "  Indoor panorama typically consists of human-made structures parallel or\nperpendicular to gravity. We leverage this phenomenon to approximate the scene\nin a 360-degree image with (H)orizontal-planes and (V)ertical-planes. To this\nend, we propose an effective divide-and-conquer strategy that divides pixels\nbased on their plane orientation estimation; then, the succeeding instance\nsegmentation module conquers the task of planes clustering more easily in each\nplane orientation group. Besides, parameters of V-planes depend on camera yaw\nrotation, but translation-invariant CNNs are less aware of the yaw change. We\nthus propose a yaw-invariant V-planar reparameterization for CNNs to learn. We\ncreate a benchmark for indoor panorama planar reconstruction by extending\nexisting 360 depth datasets with ground truth H\\&V-planes (referred to as\nPanoH&V dataset) and adopt state-of-the-art planar reconstruction methods to\npredict H\\&V-planes as our baselines. Our method outperforms the baselines by a\nlarge margin on the proposed dataset.\n"}
+{"abstract": "  This work presents a novel technique that integrates the methodologies of\nmachine learning and system identification to solve multiclass problems. Such\nan approach allows to extract and select sets of representative features with\nreduced dimensionality, as well as predicts categorical outputs. The efficiency\nof the method was tested by running case studies investigated in machine\nlearning, obtaining better absolute results when compared with classical\nclassification algorithms.\n"}
+{"abstract": "  Cloud computing facilitates the access of applications and data from any\nlocation by a distributed storage system. Erasure codes offer better data\nreplication technique with reduced storage costs for more reliability. This\npaper considers the erasure-coded data center with multiple servers in a\nwireless network where each is equipped with a base-station. The cause of\nlatency in the file retrieval process is mainly due to queuing delays at each\nserver. This work puts forth a stochastic optimization framework for obtaining\nthe optimal scheduling policy that maximizes users' quality of service (QoS)\nwhile adhering to the latency requirements. We further show that the problem\nhas non-linear functions of expectations in objective and constraints and is\nimpossible to solve with traditional SGD like algorithms. We propose a new\nalgorithm that addresses compositional structure in the problem. Further, we\nshow that the proposed algorithm achieves a faster convergence rate than the\nbest-known results. Finally, we test the efficacy of the proposed method in a\nsimulated environment.\n"}
+{"abstract": "  A weighted simplicial complex is a simplicial complex with values (called\nweights) on the vertices. In this paper, we consider weighted simplicial\ncomplexes with $\\mathbb{R}^2$-valued weights. We study the weighted homology\nand the weighted analytic torsion for such weighted simplicial complexes.\n"}
+{"abstract": "  We study the Cauchy problem for the quasi-geostrophic equations with the\ncritical dissipation in the two dimensional half space under the homogeneous\nDirichlet boundary condition. We show the global existence, the uniqueness and\nthe analyticity of solutions, and the real analyticity up to the boundary is\nobtained. We will show one of natural ways to estimate the nonlinear term for\nfunctions satisfying the Dirichlet boundary condition.\n"}
+{"abstract": "  The main purpose of this paper is to give a combinatorial realization of\nKirillov-Reshetikhin (KR simply) crystals $B^{r, s}$ for type\n$\\text{E}_n^{(1)}$ with a minuscule node $r$ and $s \\ge 1$. To do this, we\ndescribe explicitly the crystal of the quantum nilpotent subalgebra associated\nwith the translation by the negative of the $r$-th fundamental weight. Then the\ncrystal can be extended as an affine crystal, in which a certain subcrystal\ncharacterized by the $\\varepsilon_r^*$-statistic is isomorphic to $B^{r,s}$ as\nan affine crystal, where $\\varepsilon_r^*$ is also realized precisely in terms\nof triple and quadruple paths.\n"}
+{"abstract": "  The well known constant rank constraint qualification [Math. Program. Study\n21:110--126, 1984] introduced by Janin for nonlinear programming has been\nrecently extended to a conic context by exploiting the eigenvector structure of\nthe problem. In this paper we propose a more general and geometric approach for\ndefining a new extension of this condition to the conic context. The main\nadvantage of our approach is that we are able to recast the strong second-order\nproperties of the constant rank condition in a conic context. In particular, we\nobtain a second-order necessary optimality condition that is stronger than the\nclassical one obtained under Robinson's constraint qualification, in the sense\nthat it holds for every Lagrange multiplier, even though our condition is\nindependent of Robinson's condition.\n"}
+{"abstract": "  We study the topological susceptibility and the fourth cumulant of the QCD\nvacuum in the presence of a uniform, background magnetic field in two-and-three\nflavor QCD finding novel, model-independent sum rules relating the shift in the\ntopological susceptibility due to the background field to the shift in the\nquark condensates, and the shift in the fourth cumulant to the shifts in the\nquark condensates and susceptibilities\n"}
+{"abstract": "  In this paper novel simulation methods are provided for the generalised\ninverse Gaussian (GIG) L\\'{e}vy process. Such processes are intractable for\nsimulation except in certain special edge cases, since the L\\'{e}vy density\nassociated with the GIG process is expressed as an integral involving certain\nBessel Functions, known as the Jaeger integral in diffusive transport\napplications. We here show for the first time how to solve the problem\nindirectly, using generalised shot-noise methods to simulate the underlying\npoint processes and constructing an auxiliary variables approach that avoids\nany direct calculation of the integrals involved. The resulting augmented\nbivariate process is still intractable and so we propose a novel thinning\nmethod based on upper bounds on the intractable integrand. Moreover our\napproach leads to lower and upper bounds on the Jaeger integral itself, which\nmay be compared with other approximation methods. The shot noise method\ninvolves a truncated infinite series of decreasing random variables, and as\nsuch is approximate, although the series are found to be rapidly convergent in\nmost cases. We note that the GIG process is the required Brownian motion\nsubordinator for the generalised hyperbolic (GH) L\\'{e}vy process and so our\nsimulation approach will straightforwardly extend also to the simulation of\nthese intractable proceses. Our new methods will find application in forward\nsimulation of processes of GIG and GH type, in financial and engineering data,\nfor example, as well as inference for states and parameters of stochastic\nprocesses driven by GIG and GH L\\'{e}vy processes.\n"}
+{"abstract": "  Neural networks training on edge terminals is essential for edge AI\ncomputing, which needs to be adaptive to evolving environment. Quantised models\ncan efficiently run on edge devices, but existing training methods for these\ncompact models are designed to run on powerful servers with abundant memory and\nenergy budget. For example, quantisation-aware training (QAT) method involves\ntwo copies of model parameters, which is usually beyond the capacity of on-chip\nmemory in edge devices. Data movement between off-chip and on-chip memory is\nenergy demanding as well. The resource requirements are trivial for powerful\nservers, but critical for edge devices. To mitigate these issues, We propose\nResource Constrained Training (RCT). RCT only keeps a quantised model\nthroughout the training, so that the memory requirements for model parameters\nin training is reduced. It adjusts per-layer bitwidth dynamically in order to\nsave energy when a model can learn effectively with lower precision. We carry\nout experiments with representative models and tasks in image application and\nnatural language processing. Experiments show that RCT saves more than 86\\%\nenergy for General Matrix Multiply (GEMM) and saves more than 46\\% memory for\nmodel parameters, with limited accuracy loss. Comparing with QAT-based method,\nRCT saves about half of energy on moving model parameters.\n"}
+{"abstract": "  We present the results of a temporal and spectral analysis of the transient\nsource ULX-4 in the galaxy M51. The data used were drawn from Chandra,\nXMM-Newton and Swift-XRT archives, spanning the years 2000-2019. The X-ray flux\nof the source is seen to vary by two orders of magnitudes within a month but a\nshort-term variability was not observed over the time intervals of 100-2000\nsecond in the 0.3-10 keV energy band. We find some evidence for the existence\nof bi-modality feature in the flux distribution of ULX-4. We identified two\noptical sources as possible counterparts within an error radius of 0.\"18 at 95%\nconfidence level for ULX-4 based on the archival HST/ACS and HST/WFC3 data.\nBlackbody fits of the spectral energy distributions (SEDs) indicate the\nspectral type to be B-type stars. One of these counterparts exhibits a\nlow-amplitude optical periodicity of 264 days in the F606W filter; if we assume\nthis apparent periodicity is associated with the orbital motion of the donor,\nthen it is more likely that the donor is a red supergiant satisfying the long\nperiodicity and accretion via Roche-lobe overflow. Consequently, the SED would\nthen have to be interpreted as a superposition of emissions from a cold donor\nand a hot flow component, most likely from an accretion disk. If, on the other\nhand, the periodicity is super orbital in nature i.e., due to possible\ninteractions of the compact object with a circumstellar disk, the donor could\nthen be a Be/X star hosting a neutron star.\n"}
+{"abstract": "  With current high precision collider data, the reliable estimation of\ntheoretical uncertainties due to missing higher orders (MHOs) in perturbation\ntheory has become a pressing issue for collider phenomenology. Traditionally,\nthe size of the MHOs is estimated through scale variation, a simple but ad hoc\nmethod without probabilistic interpretation. Bayesian approaches provide a\ncompelling alternative to estimate the size of the MHOs, but it is not clear\nhow to interpret the perturbative scales, like the factorisation and\nrenormalisation scales, in a Bayesian framework. Recently, it was proposed that\nthe scales can be incorporated as hidden parameters into a Bayesian model. In\nthis paper, we thoroughly scrutinise Bayesian approaches to MHO estimation and\nsystematically study the performance of different models on an extensive set of\nhigh-order calculations. We extend the framework in two significant ways.\nFirst, we define a new model that allows for asymmetric probability\ndistributions. Second, we introduce a prescription to incorporate information\non perturbative scales without interpreting them as hidden model parameters. We\nclarify how the two scale prescriptions bias the result towards specific scale\nchoice, and we discuss and compare different Bayesian MHO estimates among\nthemselves and to the traditional scale variation approach. Finally, we provide\na practical prescription of how existing perturbative results at the standard\nscale variation points can be converted to 68%/95% credibility intervals in the\nBayesian approach using the new public code MiHO.\n"}
+{"abstract": "  We consider the self-collision of portals in classical general relativity.\nPortals are wormholes supported by a single loop of negative mass cosmic\nstring, and being wormholes, portals have a nontrivial topology. Portals can be\nconstructed so that the curvature is zero everywhere outside the cosmic string,\nwith vanishing ADM mass. The conical singularities of these wormholes can be\nsmoothed, yielding a spatial topology of $S^2 \\times S^1$ with a point\ncorresponding to spatial infinity removed. If one attempts to collide the\nmouths of a smoothed portal to induce self-annihilation, one naively might\nthink that a Euclidean topology is recovered, which would violate the classical\nno topology change theorems. We consider a particular limit of smoothed portals\nsupported by an anisotropic fluid, and find that while the portal mouths do not\nexperience an acceleration as they are brought close together, a curvature\nsingularity forms in the limit that the separation distance vanishes. We find\nthat in general relativity, the interaction between portal mouths is not\nprimarily gravitational in nature, but depends critically on matter\ninteractions.\n"}
+{"abstract": "  To ensure accountability and mitigate harm, it is critical that diverse\nstakeholders can interrogate black-box automated systems and find information\nthat is understandable, relevant, and useful to them. In this paper, we eschew\nprior expertise- and role-based categorizations of interpretability\nstakeholders in favor of a more granular framework that decouples stakeholders'\nknowledge from their interpretability needs. We characterize stakeholders by\ntheir formal, instrumental, and personal knowledge and how it manifests in the\ncontexts of machine learning, the data domain, and the general milieu. We\nadditionally distill a hierarchical typology of stakeholder needs that\ndistinguishes higher-level domain goals from lower-level interpretability\ntasks. In assessing the descriptive, evaluative, and generative powers of our\nframework, we find our more nuanced treatment of stakeholders reveals gaps and\nopportunities in the interpretability literature, adds precision to the design\nand comparison of user studies, and facilitates a more reflexive approach to\nconducting this research.\n"}
+{"abstract": "  We present a new method to model the mass distribution of galaxy clusters\nthat combines a parametric and a free-form approach to reconstruct cluster\ncores with strong lensing constraints. It aims at combining the advantages of\nboth approaches, by keeping the robustness of the parametric component with an\nincreased flexibility thanks to a free-form surface of B-spline functions. We\ndemonstrate the capabilities of this new approach on the simulated cluster\nHera, which has been used to evaluate lensing codes for the analysis of the\nFrontier Fields clusters. The method leads to better reproduction of the\nconstraints, with an improvement by a factor $\\sim3-4$ on the root-mean-square\nerror on multiple-image positions, when compared to parametric-only approaches.\nThe resulting models show a better accuracy in the reconstruction of the\namplitude of the convergence field while conserving a high fidelity on other\nlensing observables already well reproduced. We make this method publicly\navailable through its implementation in the Lenstool software.\n"}
+{"abstract": "  Let $X$ be a compact, hyperbolic surface of genus $g\\geq 2$. In this paper,\nwe prove that the twisted Selberg and Ruelle zeta functions, associated with an\narbitrary, finite-dimensional, complex representation $\\chi$ of $\\pi_1(X)$\nadmit a meromorphic continuation to $\\C$. Moreover, we study the behaviour of\nthe twisted Ruelle zeta function at $s=0$ and prove that at this point, it has\na zero of order $\\dim(\\chi)(2g-2)$.\n"}
+{"abstract": "  By exploiting properties of boundaries associated with Coxeter groups we\nobtain a complete characterization of simple right-angled multi-parameter Hecke\nC$^{\\ast}$-algebras. This extends previous results by Caspers, Larsen and the\nauthor. Based on work by Raum and Skalski, we further describe the center and\nthe character space of right-angled Hecke C$^{\\ast}$-algebras.\n"}
+{"abstract": "  Galton's rank order statistic is one of the oldest statistical tools for\ntwo-sample comparisons. It is also a very natural index to measure departures\nfrom stochastic dominance. Yet, its asymptotic behaviour has been investigated\nonly partially, under restrictive assumptions. This work provides a\ncomprehensive {study} of this behaviour, based on the analysis of the so-called\ncontact set (a modification of the set in which the quantile functions\ncoincide). We show that a.s. convergence to the population counterpart holds if\nand only if {the} contact set has zero Lebesgue measure. When this set is\nfinite we show that the asymptotic behaviour is determined by the local\nbehaviour of a suitable reparameterization of the quantile functions in a\nneighbourhood of the contact points. Regular crossings result in standard rates\nand Gaussian limiting distributions, but higher order contacts (in the sense\nintroduced in this work) or contacts at the extremes of the supports may result\nin different rates and non-Gaussian limits.\n"}
+{"abstract": "  An Euler tour in a hypergraph $H$ is a closed walk that traverses each edge\nof $H$ exactly once, and an Euler family is a family of closed walks that\njointly traverse each edge of $H$ exactly once. An $\\ell$-covering\n$k$-hypergraph, for $2 \\le \\ell < k$, is a $k$-uniform hypergraph in which\nevery $\\ell$-subset of vertices lie together in at least one edge.\n  In this paper we prove that every $\\ell$-covering $k$-hypergraph, for $k \\ge\n3$, admits an Euler family.\n"}
+{"abstract": "  As the quality of mobile cameras starts to play a crucial role in modern\nsmartphones, more and more attention is now being paid to ISP algorithms used\nto improve various perceptual aspects of mobile photos. In this Mobile AI\nchallenge, the target was to develop an end-to-end deep learning-based image\nsignal processing (ISP) pipeline that can replace classical hand-crafted ISPs\nand achieve nearly real-time performance on smartphone NPUs. For this, the\nparticipants were provided with a novel learned ISP dataset consisting of\nRAW-RGB image pairs captured with the Sony IMX586 Quad Bayer mobile sensor and\na professional 102-megapixel medium format camera. The runtime of all models\nwas evaluated on the MediaTek Dimensity 1000+ platform with a dedicated AI\nprocessing unit capable of accelerating both floating-point and quantized\nneural networks. The proposed solutions are fully compatible with the above NPU\nand are capable of processing Full HD photos under 60-100 milliseconds while\nachieving high fidelity results. A detailed description of all models developed\nin this challenge is provided in this paper.\n"}
+{"abstract": "  The beyond-mean-field Lee-Huang-Yang (LHY) correction is ubiquitous in dilute\nultracold quantum gases. However, its effects are often elusive due to the\ntypically much larger influence of the mean-field energy. In this work, we\nstudy an ultracold mixture of $^{23}$Na and $^{87}$Rb with tunable attractive\ninterspecies interactions. The LHY effects manifest in the formation of\nself-bound quantum liquid droplets and the expansion dynamics of the gas-phase\nsample. A liquid-to-gas phase diagram is obtained by measuring the critical\natom numbers below which the self-bound behavior disappears. In stark contrast\nto trapped gas-phase condensates, the gas-phase mixture formed following the\nliquid-to-gas phase transition shows an anomalous expansion featuring a larger\nrelease energy for increasing mean-field attractions.\n"}
+{"abstract": "  Biclustering is a method for detecting homogeneous submatrices in a given\nobserved matrix, and it is an effective tool for relational data analysis.\nAlthough there are many studies that estimate the underlying bicluster\nstructure of a matrix, few have enabled us to determine the appropriate number\nof biclusters in an observed matrix. Recently, a statistical test on the number\nof biclusters has been proposed for a regular-grid bicluster structure, where\nwe assume that the latent bicluster structure can be represented by row-column\nclustering. However, when the latent bicluster structure does not satisfy such\nregular-grid assumption, the previous test requires a larger number of\nbiclusters than necessary (i.e., a finer bicluster structure than necessary)\nfor the null hypothesis to be accepted, which is not desirable in terms of\ninterpreting the accepted bicluster structure. In this study, we propose a new\nstatistical test on the number of biclusters that does not require the\nregular-grid assumption and derive the asymptotic behavior of the proposed test\nstatistic in both null and alternative cases. We illustrate the effectiveness\nof the proposed method by applying it to both synthetic and practical\nrelational data matrices.\n"}
+{"abstract": "  The onset of frictional motion is characterized by the transition from\nquasi-static stable slip growth to dynamic unstable crack propagation. Here, we\ninvestigate how microscopic heterogeneities influence the macroscopic stability\nof disordered interfaces. Building on the physics of depinning, we propose an\nanalytical framework to estimate the critical length $L_\\mathrm{c}$ of the\ninstability nucleus, and benchmark it against 2D numerical dynamic\ncalculations. The interplay between material properties and disorder gives\nbirth to three instability regimes (local, extremal, and homogenized), each\nrelated to different nucleation scenarios.\n"}
+{"abstract": "  Natural language processing (NLP) is becoming an important means for\nautomatic recognition of human traits and states, such as intoxication,\npresence of psychiatric disorders, presence of airway disorders and states of\nstress. Such applications have the potential to be an important pillar for\nonline help lines, and may gradually be introduced into eHealth modules.\nHowever, NLP is language specific and for languages such as Dutch, NLP models\nare scarce. As a result, recent Dutch NLP models have a low capture of long\nrange semantic dependencies over sentences. To overcome this, here we present\nbelabBERT, a new Dutch language model extending the RoBERTa architecture.\nbelabBERT is trained on a large Dutch corpus (+32 GB) of web crawled texts. We\napplied belabBERT to the classification of psychiatric illnesses. First, we\nevaluated the strength of text-based classification using belabBERT, and\ncompared the results to the existing RobBERT model. Then, we compared the\nperformance of belabBERT to audio classification for psychiatric disorders.\nFinally, a brief exploration was performed, extending the framework to a hybrid\ntext- and audio-based classification. Our results show that belabBERT\noutperformed the current best text classification network for Dutch, RobBERT.\nbelabBERT also outperformed classification based on audio alone.\n"}
+{"abstract": "  We propose an analytical parametrization of the comoving distance and Hubble\nparameter to study the cosmic expansion history beyond the vanilla $\\Lambda$CDM\nmodel. The parametrization is generalized enough to include the contribution of\nspatial curvature and to capture the higher redshift behaviors. With this\nparameterization, we study the late time cosmic behavior and put constraints on\nthe cosmological parameters like present values of Hubble parameter ($H_{0}$),\nmatter energy density parameter ($\\Omega_{m0}$), spatial curvature energy\ndensity parameter ($\\Omega_{k0}$) and baryonic matter energy density parameter\n($\\Omega_{b0}$) using different combinations data like CMB (Cosmic microwave\nbackground), BAO (baryon acoustic oscillation), and SN (Pantheon sample for\ntype Ia supernovae). We also rigorously study the Hubble tension in the\nframework of late time modification from the standard $\\Lambda$CDM model. We\nfind that the late time modification of the cosmic expansion can solve the\nHubble tension between CMB $\\&$ SHOES (local distance ladder observation for\n$H_{0}$), between CMB+BAO $\\&$ SHOES and between CMB+SN $\\&$ SHOES, but the\nlate time modification can not solve the Hubble tension between CMB+BAO+SN and\nSHOES. That means CMB, BAO, and SN data combined put strong enough constraints\non $H_{0}$ (even with varying $\\Omega_{k0}$) and on other background\ncosmological parameters so that the addition of $H_{0}$ prior from SHOES (or\nfrom similar other local distance observations) can not significantly pull the\n$H_{0}$ value towards the corresponding SHOES value.\n"}
+{"abstract": "  It is pointed out that the solutions of the Klein-Gordon and the Dirac\nequation derived in the paper addressed in this Comment (and many more\nsolutions) may be obtained from generating functions.\n"}
+{"abstract": "  Autonomous experiments are excellent tools to increase the efficiency of\nmaterial discovery. Indeed, AI and ML methods can help optimizing valuable\nexperimental resources as, for example, beam time in neutron scattering\nexperiments, in addition to scientists' knowledge and experience. Active\nlearning methods form a particular class of techniques that acquire knowledge\non a specific quantity of interest by autonomous decisions on what or where to\ninvestigate next based on previous measurements. For instance, Gaussian Process\nRegression (GPR) is a well-known technique that can be exploited to accomplish\nactive learning tasks for scattering experiments as was recently demonstrated.\nGaussian processes are not only capable to approximate functions by their\nposterior mean function, but can also quantify uncertainty about the\napproximation itself. Hence, if we perform function evaluations at locations of\nhighest uncertainty, the function can be \"optimally\" learned in an iterative\nmanner. We suggest the use of log-Gaussian processes, being a natural approach\nto successfully conduct autonomous neutron scattering experiments in general\nand TAS experiments with the instrument PANDA at MLZ in particular.\n"}
+{"abstract": "  This article is based on lecture notes for the Marie Curie Training school\n\"Initial Training on Numerical Methods for Active Matter\". It provides an\nintroductory overview of modeling approaches for active matter and is primarily\ntargeted at PhD students (or other readers) who encounter some of these\napproaches for the first time. The aim of the article is to help put the\ndescribed modeling approaches into perspective.\n"}
+{"abstract": "  Data labeling is a time intensive process. As such, many data scientists use\nvarious tools to aid in the data generation and labeling process. While these\ntools help automate labeling, many still require user interaction throughout\nthe process. Additionally, most target only a few network frameworks. Any\nresearchers exploring multiple frameworks must find additional tools orwrite\nconversion scripts. This paper presents an automated tool for generating\nsynthetic data in arbitrary network formats. It uses Robot Operating System\n(ROS) and Gazebo, which are common tools in the robotics community. Through ROS\nparadigms, it allows extensive user customization of the simulation environment\nand data generation process. Additionally, a plugin-like framework allows the\ndevelopment of arbitrary data format writers without the need to change the\nmain body of code. Using this tool, the authors were able to generate an\narbitrarily large image dataset for three unique training formats using\napproximately 15 min of user setup time and a variable amount of hands-off run\ntime, depending on the dataset size. The source code for this data generation\ntool is available at https://github.com/Navy-RISE-Lab/nn_data_collection\n"}
+{"abstract": "  This paper presents a simple decidable logic of functional dependence LFD,\nbased on an extension of classical propositional logic with dependence atoms\nplus dependence quantifiers treated as modalities, within the setting of\ngeneralized assignment semantics for first order logic. The expressive\nstrength, complete proof calculus and meta-properties of LFD are explored.\nVarious language extensions are presented as well, up to undecidable\nmodal-style logics for independence and dynamic logics of changing dependence\nmodels. Finally, more concrete settings for dependence are discussed:\ncontinuous dependence in topological models, linear dependence in vector\nspaces, and temporal dependence in dynamical systems and games.\n"}
+{"abstract": "  We study the asymptotic behaviour of the renormalised $s$-fractional Gaussian\nperimeter of a set $E$ inside a domain $\\Omega$ as $s\\to 0^+$. Contrary to the\nEuclidean case, as the Gaussian measure is finite, the shape of the set at\ninfinity does not matter, but, surprisingly, the limit set function is never\nadditive.\n"}
+{"abstract": "  Designing optimal control pulses that drive a noisy qubit to a target state\nis a challenging and crucial task for quantum engineering. In a situation where\nthe properties of the quantum noise affecting the system are dynamic, a\nperiodic characterization procedure is essential to ensure the models are\nupdated. As a result, the operation of the qubit is disrupted frequently. In\nthis paper, we propose a protocol that addresses this challenge by making use\nof a spectator qubit to monitor the noise in real-time. We develop a quantum\nmachine-learning-based quantum feature engineering approach for designing the\nprotocol. The complexity of the protocol is front-loaded in a characterization\nphase, which allow real-time execution during the quantum computations. We\npresent the results of numerical simulations that showcase the favorable\nperformance of the protocol.\n"}
+{"abstract": "  Recurrent Neural Networks (RNNs), more specifically their Long Short-Term\nMemory (LSTM) variants, have been widely used as a deep learning tool for\ntackling sequence-based learning tasks in text and speech. Training of such\nLSTM applications is computationally intensive due to the recurrent nature of\nhidden state computation that repeats for each time step. While sparsity in\nDeep Neural Nets has been widely seen as an opportunity for reducing\ncomputation time in both training and inference phases, the usage of non-ReLU\nactivation in LSTM RNNs renders the opportunities for such dynamic sparsity\nassociated with neuron activation and gradient values to be limited or\nnon-existent. In this work, we identify dropout induced sparsity for LSTMs as a\nsuitable mode of computation reduction. Dropout is a widely used regularization\nmechanism, which randomly drops computed neuron values during each iteration of\ntraining. We propose to structure dropout patterns, by dropping out the same\nset of physical neurons within a batch, resulting in column (row) level hidden\nstate sparsity, which are well amenable to computation reduction at run-time in\ngeneral-purpose SIMD hardware as well as systolic arrays. We conduct our\nexperiments for three representative NLP tasks: language modelling on the PTB\ndataset, OpenNMT based machine translation using the IWSLT De-En and En-Vi\ndatasets, and named entity recognition sequence labelling using the CoNLL-2003\nshared task. We demonstrate that our proposed approach can be used to translate\ndropout-based computation reduction into reduced training time, with\nimprovement ranging from 1.23x to 1.64x, without sacrificing the target metric.\n"}
+{"abstract": "  Few-shot learning aims to generalize unseen classes that appear during\ntesting but are unavailable during training. Prototypical networks incorporate\nfew-shot metric learning, by constructing a class prototype in the form of a\nmean vector of the embedded support points within a class. The performance of\nprototypical networks in extreme few-shot scenarios (like one-shot) degrades\ndrastically, mainly due to the desuetude of variations within the clusters\nwhile constructing prototypes. In this paper, we propose to replace the typical\nprototypical loss function with an Episodic Triplet Mining (ETM) technique. The\nconventional triplet selection leads to overfitting, because of all possible\ncombinations being used during training. We incorporate episodic training for\nmining the semi hard positive and the semi hard negative triplets to overcome\nthe overfitting. We also propose an adaptation to make use of unlabeled\ntraining samples for better modeling. Experimenting on two different audio\nprocessing tasks, namely speaker recognition and audio event detection; show\nimproved performances and hence the efficacy of ETM over the prototypical loss\nfunction and other meta-learning frameworks. Further, we show improved\nperformances when unlabeled training samples are used.\n"}
+{"abstract": "  Long waves bring many important challenges in the ocean and coastal\nengineering, including but are not limited to harbor resonance and run-up.\nTherefore, understanding and modeling their dynamics is crucially important.\nAlthough their dynamics over various types of geometries are well-studied in\nthe literature, the study of the geometries with power-law variations remains\nan open problem in this setting. With this motivation, in this paper, we derive\nthe exact analytical solutions of the long-wave equation over nonlinear depth\nand breadth profiles having power-law forms given by $h(x)=c_1 x^a$ and\n$b(x)=c_2 x^c$, where the parameters $c_1, c_2, a, c$ are some constants. We\nshow that for these types of power-law forms of depth and breadth profiles, the\nlong-wave equation admits solutions in terms of Bessel functions and\nCauchy-Euler series. We also derive the seiching periods and resonance\nconditions for these forms of depth and breadth variations. Our results can be\nused to investigate the long-wave dynamics and their envelope characteristics\nover equilibrium beach profiles, the effects of nonlinear harbor entrances and\nangled nonlinear seawall breadth variations in the power-law forms on these\ndynamics, and the effects of reconstruction, geomorphological changes,\nsedimentation, and dredging to harbor resonance, to the shift in resonance\nperiods and to the seiching characteristics in lakes and barrages.\n"}
+{"abstract": "  Analyzing the relation between intelligence and neural activity is of the\nutmost importance in understanding the working principles of the human brain in\nhealth and disease. In existing literature, functional brain connectomes have\nbeen used successfully to predict cognitive measures such as intelligence\nquotient (IQ) scores in both healthy and disordered cohorts using machine\nlearning models. However, existing methods resort to flattening the brain\nconnectome (i.e., graph) through vectorization which overlooks its topological\nproperties. To address this limitation and inspired from the emerging graph\nneural networks (GNNs), we design a novel regression GNN model (namely RegGNN)\nfor predicting IQ scores from brain connectivity. On top of that, we introduce\na novel, fully modular sample selection method to select the best samples to\nlearn from for our target prediction task. However, since such deep learning\narchitectures are computationally expensive to train, we further propose a\n\\emph{learning-based sample selection} method that learns how to choose the\ntraining samples with the highest expected predictive power on unseen samples.\nFor this, we capitalize on the fact that connectomes (i.e., their adjacency\nmatrices) lie in the symmetric positive definite (SPD) matrix cone. Our results\non full-scale and verbal IQ prediction outperforms comparison methods in autism\nspectrum disorder cohorts and achieves a competitive performance for\nneurotypical subjects using 3-fold cross-validation. Furthermore, we show that\nour sample selection approach generalizes to other learning-based methods,\nwhich shows its usefulness beyond our GNN architecture.\n"}
+{"abstract": "  The success of deep learning sparked interest in whether the brain learns by\nusing similar techniques for assigning credit to each synaptic weight for its\ncontribution to the network output. However, the majority of current attempts\nat biologically-plausible learning methods are either non-local in time,\nrequire highly specific connectivity motives, or have no clear link to any\nknown mathematical optimization method. Here, we introduce Deep Feedback\nControl (DFC), a new learning method that uses a feedback controller to drive a\ndeep neural network to match a desired output target and whose control signal\ncan be used for credit assignment. The resulting learning rule is fully local\nin space and time and approximates Gauss-Newton optimization for a wide range\nof feedback connectivity patterns. To further underline its biological\nplausibility, we relate DFC to a multi-compartment model of cortical pyramidal\nneurons with a local voltage-dependent synaptic plasticity rule, consistent\nwith recent theories of dendritic processing. By combining dynamical system\ntheory with mathematical optimization theory, we provide a strong theoretical\nfoundation for DFC that we corroborate with detailed results on toy experiments\nand standard computer-vision benchmarks.\n"}
+{"abstract": "  This paper offers a finite-state abstraction of traffic coordination and\ncongestion in a network of interconnected roads (NOIR). By applying mass\nconservation, we model traffic coordination as a Markov process. Model\nPredictive Control (MPC) is applied to control traffic congestion through the\nboundary of the traffic network. The optimal boundary inflow is assigned as the\nsolution of a constrained quadratic programming problem. Additionally, the\nmovement phases commanded by traffic signals are determined using receding\nhorizon optimization. In simulation, we show how traffic congestion can be\nsuccessfully controlled through optimizing boundary inflow and movement phases\nat traffic network junctions.\n"}
+{"abstract": "  In this paper, based on neural networks, we develop a data-driven model for\nextremely fast prediction of steady-state heat convection of a hot object with\narbitrary complex geometry in a two-dimensional space. According to the\ngoverning equations, the steady-state heat convection is dominated by\nconvection and thermal diffusion terms, thus the distribution of the physical\nfields would exhibit stronger correlations between adjacent points. Therefore,\nthe proposed neural network model uses Convolutional Neural Network (CNN)\nlayers as the encoder and Deconvolutional Neural Network (DCNN) layers as the\ndecoder. Compared with fully connected (FC) network model, the CNN based model\nis good for capturing and reconstructing the spatial relationships of low-rank\nfeature spaces, such as edge intersections, parallelism and symmetry.\nFurthermore, we applied the signed distance function (SDF) as the network input\nfor representing the problem geometry, which contains more information compared\nto a binary image. For displaying the strong learning and generalization\nability of the proposed network model, the training dataset only contains hot\nobjects with simple geometries: triangles, quadrilaterals, pentagons, hexagons\nand dodecagons; while the validating cases use arbitrary and complex\ngeometries. According to the study, the trained network model can accurately\npredict the velocity and temperature field of the problems with complex\ngeometries which has never been seen by the network model during the model\ntraining; and the prediction speed is four orders faster than the CFD. The\nability of the accurate and extremely faster prediction of the network model\nsuggests the potentials of applying such kind of models to the applications of\nreal-time control, optimization, and design in future.\n"}
+{"abstract": "  Let $f$ be a $C^{1+\\alpha}$ diffeomorphism of a compact manifold $M$\npreserving a smooth measure $\\mu$. We show that if $f:(M,\\mu)\\to (M,\\mu)$ is\nexponentially mixing then it is Bernoulli.\n"}
+{"abstract": "  Coronary artery disease leading up to stenosis, the partial or total blocking\nof coronary arteries, is a severe condition that affects millions of patients\neach year. Automated identification and classification of stenosis severity\nfrom minimally invasive procedures would be of great clinical value, but\nexisting methods do not match the accuracy of experienced cardiologists, due to\nthe complexity of the task. Although a number of computational approaches for\nquantitative assessment of stenosis have been proposed to date, the performance\nof these methods is still far from the required levels for clinical\napplications. In this paper, we propose a two-step deep-learning framework to\npartially automate the detection of stenosis from X-ray coronary angiography\nimages. In the two steps, we used two distinct convolutional neural network\narchitectures, one to automatically identify and classify the angle of view,\nand another to determine the bounding boxes of the regions of interest in\nframes where stenosis is visible. Transfer learning and data augmentation\ntechniques were used to boost the performance of the system in both tasks. We\nachieved a 0.97 accuracy on the task of classifying the Left/Right Coronary\nArtery (LCA/RCA) angle view and 0.68/0.73 recall on the determination of the\nregions of interest, for LCA and RCA, respectively. These results compare\nfavorably with previous results obtained using related approaches, and open the\nway to a fully automated method for the identification of stenosis severity\nfrom X-ray angiographies.\n"}
+{"abstract": "  Property inference attacks consider an adversary who has access to the\ntrained model and tries to extract some global statistics of the training data.\nIn this work, we study property inference in scenarios where the adversary can\nmaliciously control part of the training data (poisoning data) with the goal of\nincreasing the leakage.\n  Previous work on poisoning attacks focused on trying to decrease the accuracy\nof models either on the whole population or on specific sub-populations or\ninstances. Here, for the first time, we study poisoning attacks where the goal\nof the adversary is to increase the information leakage of the model. Our\nfindings suggest that poisoning attacks can boost the information leakage\nsignificantly and should be considered as a stronger threat model in sensitive\napplications where some of the data sources may be malicious.\n  We describe our \\emph{property inference poisoning attack} that allows the\nadversary to learn the prevalence in the training data of any property it\nchooses. We theoretically prove that our attack can always succeed as long as\nthe learning algorithm used has good generalization properties.\n  We then verify the effectiveness of our attack by experimentally evaluating\nit on two datasets: a Census dataset and the Enron email dataset. We were able\nto achieve above $90\\%$ attack accuracy with $9-10\\%$ poisoning in all of our\nexperiments.\n"}
+{"abstract": "  We study ribbons of vanishing Gaussian curvature, i.e., flat ribbons,\nconstructed along a curve in $\\mathbb{R}^{3}$. In particular, we first\ninvestigate to which extent the ruled structure determines a flat ribbon: in\nother words, we ask whether for a given curve $\\gamma$ and ruling angle (angle\nbetween the ruling line and the curve's tangent) there exists a well-defined\nflat ribbon. It turns out that the answer is positive only up to an initial\ncondition, expressed by a choice of normal vector at a point. We then study the\nset of infinitely narrow flat ribbons along a fixed curve $\\gamma$ in terms of\nenergy. By extending a well-known formula for the bending energy of the\nrectifying developable, introduced in the literature by Sadowsky in 1930, we\nobtain an upper bound for the ratio between the bending energies of two\nsolutions of the initial value problem. We finally draw further conclusions\nunder some additional assumptions on the ruling angle and the curve $\\gamma$.\n"}
+{"abstract": "  It is well-known that liquid and saturated vapor, separated by a flat\ninterface in an unbounded space, are in equilibrium. One would similarly expect\na liquid drop, sitting on a flat substrate, to be in equilibrium with the vapor\nsurrounding it. Yet, it is not: as shown in this work, the drop evaporates.\nMathematically, this conclusion is deduced using the diffuse-interface model,\nbut it can also be reformulated in terms of the maximum-entropy principle,\nsuggesting model independence. Physically, evaporation of drops is due to the\nso-called Kelvin effect, which gives rise to a liquid-to-vapor mass flux in all\ncases where the boundary of the liquid phase is convex.\n"}
+{"abstract": "  StarCraft II (SC2) is a real-time strategy game in which players produce and\ncontrol multiple units to fight against opponent's units. Due to its\ndifficulties, such as huge state space, various action space, a long time\nhorizon, and imperfect information, SC2 has been a research hotspot in\nreinforcement learning. Recently, an agent called AlphaStar (AS) has been\nproposed, which shows good performance, obtaining a high win rate of 99.8%\nagainst human players. We implemented a mini-scaled version of it called\nmini-AlphaStar (mAS) based on AS's paper and pseudocode. The difference between\nAS and mAS is that we substituted the hyper-parameters of AS with smaller ones\nfor mini-scale training. Codes of mAS are all open-sourced\n(https://github.com/liuruoze/mini-AlphaStar) for future research.\n"}
+{"abstract": "  Graph Neural Networks (GNNs) are the first choice methods for graph machine\nlearning problems thanks to their ability to learn state-of-the-art level\nrepresentations from graph-structured data. However, centralizing a massive\namount of real-world graph data for GNN training is prohibitive due to\nuser-side privacy concerns, regulation restrictions, and commercial\ncompetition. Federated Learning is the de-facto standard for collaborative\ntraining of machine learning models over many distributed edge devices without\nthe need for centralization. Nevertheless, training graph neural networks in a\nfederated setting is vaguely defined and brings statistical and systems\nchallenges. This work proposes SpreadGNN, a novel multi-task federated training\nframework capable of operating in the presence of partial labels and absence of\na central server for the first time in the literature. SpreadGNN extends\nfederated multi-task learning to realistic serverless settings for GNNs, and\nutilizes a novel optimization algorithm with a convergence guarantee,\nDecentralized Periodic Averaging SGD (DPA-SGD), to solve decentralized\nmulti-task learning problems. We empirically demonstrate the efficacy of our\nframework on a variety of non-I.I.D. distributed graph-level molecular property\nprediction datasets with partial labels. Our results show that SpreadGNN\noutperforms GNN models trained over a central server-dependent federated\nlearning system, even in constrained topologies. The source code is publicly\navailable at https://github.com/FedML-AI/SpreadGNN\n"}
+{"abstract": "  Imaging surveys of CO and other molecular transition lines are fundamental to\nmeasuring the large-scale distribution of molecular gas in the Milky Way. Due\nto finite angular resolution and sensitivity, however, observational effects\nare inevitable in the surveys, but few studies are available on the extent of\nuncertainties involved. The purpose of this work is to investigate the\ndependence of observations on angular resolution (beam sizes), sensitivity\n(noise levels), distances, and molecular tracers. To this end, we use\nhigh-quality CO images of a large-scale region (25.8 <l< 49.7 deg and |b|<5\ndeg) mapped by the Milky Way Imaging Scroll Painting (MWISP) survey as a\nbenchmark to simulate observations with larger beam sizes and higher noise\nlevels, deriving corresponding beam filling and sensitivity clip factors. The\nsensitivity clip factor is defined to be the completeness of observed flux.\nTaking the entire image as a whole object, we found that 12CO has the largest\nbeam filling and sensitivity clip factors and C18O has the lowest. For\nmolecular cloud samples extracted from images, the beam filling factor can be\ndescribed by a characteristic size, $l_{1/4}$=0.762 (in beam size), at which\nthe beam filling factor is approximately 1/4. The sensitivity clip factor shows\na similar relationship but is more correlated with the mean voxel\nsignal-to-noise ratio of molecular clouds. This result may serve as a practical\nreference on beam filling and sensitivity clip factors in further analyses of\nthe MWISP data and other observations.\n"}
+{"abstract": "  MAROON-X is a fiber-fed, red-optical, high precision radial velocity\nspectrograph recently commissioned at the Gemini North telescope on Mauna Kea,\nHawai'i. With a resolving power of 85,000 and a wavelength coverage of 500-920\nnm, it delivers radial velocity measurements for late K and M dwarfs with\nsub-50 cm s$^{-1}$ precision. MAROON-X is currently the only optical EPRV\nspectrograph on a 8m-class telescope in the northern hemisphere and the only\nEPRV instrument on a large telescope with full access by the entire US\ncommunity. We report here on the results of the commissioning campaign in\nDecember 2019 and early science results.\n"}
+{"abstract": "  We present POD, a smartphone that flies, as a new way to achieve hands-free,\neyes-up mobile computing. Unlike existing drone-carried user interfaces, POD\nfeatures a smartphone-sized display and the computing and sensing power of a\nmodern smartphone. We share our experience in building a prototype of POD,\ndiscuss the technical challenges facing it, and describe early results toward\naddressing them.\n"}
+{"abstract": "  The purpose of this manuscript is to prove the conjectures made in\n\\cite{Tosatti2011,Tosatti2012} for families of Calabi--Yau manifolds. In\nparticular, these results confirm the conjectural understanding of collapsed\nGromov--Hausdorff limits which arise in the study of the long-time behavior of\nthe K\\\"ahler--Ricci flow on compact K\\\"ahler manifolds with semi-ample\ncanonical bundle and intermediate Kodaira dimension.\n"}
+{"abstract": "  The recent advent of various forms of Federated Knowledge Distillation (FD)\npaves the way for a new generation of robust and communication-efficient\nFederated Learning (FL), where mere soft-labels are aggregated, rather than\nwhole gradients of Deep Neural Networks (DNN) as done in previous FL schemes.\nThis security-per-design approach in combination with increasingly performant\nInternet of Things (IoT) and mobile devices opens up a new realm of\npossibilities to utilize private data from industries as well as from\nindividuals as input for artificial intelligence model training. Yet in\nprevious FL systems, lack of trust due to the imbalance of power between\nworkers and a central authority, the assumption of altruistic worker\nparticipation and the inability to correctly measure and compare contributions\nof workers hinder this technology from scaling beyond small groups of already\nentrusted entities towards mass adoption. This work aims to mitigate the\naforementioned issues by introducing a novel decentralized federated learning\nframework where heavily compressed 1-bit soft-labels, resembling 1-hot label\npredictions, are aggregated on a smart contract. In a context where workers'\ncontributions are now easily comparable, we modify the Peer Truth Serum for\nCrowdsourcing mechanism (PTSC) for FD to reward honest participation based on\npeer consistency in an incentive compatible fashion. Due to heavy reductions of\nboth computational complexity and storage, our framework is a fully\non-blockchain FL system that is feasible on simple smart contracts and\ntherefore blockchain agnostic. We experimentally test our new framework and\nvalidate its theoretical properties.\n"}
+{"abstract": "  Waveplates having spatially varying fast-axis orientation and retardance\nprovide an elegant and easy way to locally manipulate different attributes of\nlight beams namely, polarization, amplitude and phase, leading to the\ngeneration of exotic structured light beams. The fabrication of such doubly\ninhomogeneous waveplates (d-plates) is more complex, compared to that of singly\ninhomogeneous waveplates (s-plates) having uniform retardance, which can be\neasily fabricated by different means such as photoalignment of liquid crystals,\nmetasurfaces etc. Here, exploiting the SU(2) formalism, we establish\nanalytically that any d-plate can be equivalently implemented using a pair of\nquarter-wave s-plates and a half-wave s-plate. An important advantage of this\nmethod is that it gives the flexibility to realize a whole family of distinct\nd-plates using the same triplet of s-plates. To underline the scope of this\nmethod, we propose novel d-plates for spatially tailoring the phase and complex\namplitudes of light beams. Towards complex amplitude shaping, we present a\ngeneric method for carving out higher-order eigenmodes of light using a d-plate\nin conjugation with a polarizer. A generalized q-plate-like gadget, for\nimparting a polarization-dependent phase profile to a scalar light beam, is\nproposed as a demonstration of phase-polarization interplay. For these two\nillustrations, the corresponding three-s-plate gadget is constructed, and its\nfunctioning is validated with extensive numerical simulations. The main result\nand its illustrations are generic and agnostic to the way the s-plates are\nfabricated and we believe they carry the potential to push the current state of\nthe art in interdisciplinary applications involving structured light beams.\n"}
+{"abstract": "  A key challenge in Imitation Learning (IL) is that optimal state actions\ndemonstrations are difficult for the teacher to provide. For example in\nrobotics, providing kinesthetic demonstrations on a robotic manipulator\nrequires the teacher to control multiple degrees of freedom at once. The\ndifficulty of requiring optimal state action demonstrations limits the space of\nproblems where the teacher can provide quality feedback. As an alternative to\nstate action demonstrations, the teacher can provide corrective feedback such\nas their preferences or rewards. Prior work has created algorithms designed to\nlearn from specific types of noisy feedback, but across teachers and tasks\ndifferent forms of feedback may be required. Instead we propose that in order\nto learn from a diversity of scenarios we need to learn from a variety of\nfeedback. To learn from a variety of feedback we make the following insight:\nthe teacher's cost function is latent and we can model a stream of feedback as\na stream of loss functions. We then use any online learning algorithm to\nminimize the sum of these losses. With this insight we can learn from a\ndiversity of feedback that is weakly correlated with the teacher's true cost\nfunction. We unify prior work into a general corrective feedback meta-algorithm\nand show that regardless of feedback we can obtain the same regret bounds. We\ndemonstrate our approach by learning to perform a household navigation task on\na robotic racecar platform. Our results show that our approach can learn\nquickly from a variety of noisy feedback.\n"}
+{"abstract": "  We report on a joint experimental and theoretical study of photoelectron\ncircular dichroism (PECD) in methyloxirane. By detecting O 1s-photoelectrons in\ncoincidence with fragment ions, we deduce the molecule's orientation and\nphotoelectron emission direction in the laboratory frame. Thereby, we retrieve\na fourfold differential PECD clearly beyond 50%. This strong chiral asymmetry\nis reproduced by ab initio electronic structure calculations. Providing such a\npronounced contrast makes PECD of fixed-in-space chiral molecules an even more\nsensitive tool for chiral recognition in the gas phase.\n"}
+{"abstract": "  Dark energy is the constituent with an enormous abundance of the present\nuniverse, responsible for the universe's accelerated expansion. Therefore, it\nis plausible that dark energy may interact within any compact astrophysical\nobjects. The author in Ref. [Phys. Rev. D 83, 127501 (2011)], constructs an\nexact star solution consisting of an ordinary matter and phantom field from a\nconstant density star (CDS) known as Schwarzschild interior solution. The star\ndenotes a dark energy star (DES). The author claims that the phantom field\nrepresents dark energy within the star. So far, the role of the phantom field\nas dark energy in DES is not systematically studied yet. Related to this issue,\nwe analyze the energy condition of DES. We expect that DES shall violate the\nstrong energy condition (SEC) for a particular condition. We discover that SEC\nis fully violated only when the compactness reaches the Buchdahl limit.\nFurthermore, we also investigate the causal conditions and stabilities due to\nthe convective motion and gravitational cracking. We also find that those\nconditions are violated. These results indicate that DES is not physically\nstable. However, we may consider DES as an ultra-compact object of which we can\ncalculate the gravitational wave echo time and echo frequency and compare them\nto those of CDS. We find that the contribution of the phantom field delays the\ngravitational wave echoes. The effective potential of the perturbed DES is also\nstudied. The potential also enjoys a potential well like CDS but with a deeper\nwell. We also investigate the possibility that DES could form a gravastar when\n$ C=1 $. It is found that gravastar produced from DES possesses no singularity\nwith a dS-like phase as the interior. These results could open more\nopportunities for the observational study of dark energy in the near future,\nmostly from the compact astrophysical objects.\n"}
+{"abstract": "  We review the trade-offs between speed, fluctuations, and thermodynamic cost\ninvolved with biological processes in nonequilibrium states, and discuss how\noptimal these processes are in light of the universal bound set by the\nthermodynamic uncertainty relation (TUR). The values of the uncertainty product\n$\\mathcal{Q}$ of TUR, which can be used as a measure of the precision of\nenzymatic processes realized for a given thermodynamic cost, are suboptimal\nwhen the substrate concentration $[S]$ is at the Michaelis constant\n($K_\\text{M}$), and some of the key biological processes are found to work\naround this condition. We illustrate the utility of $\\mathcal{Q}$ in assessing\nhow close the molecular motors and biomass producing machineries are to the TUR\nbound, and for the cases of biomass production (or biological copying\nprocesses) we discuss how their optimality quantified in terms of $\\mathcal{Q}$\nis balanced with the error rate in the information transfer process. We also\ntouch upon the trade-offs in other error-minimizing processes in biology, such\nas gene regulation and chaperone-assisted protein folding. A spectrum of\n$\\mathcal{Q}$ recapitulating the biological processes surveyed here provides\nglimpses into how biological systems are evolved to optimize and balance the\nconflicting functional requirements.\n"}
+{"abstract": "  We introduce a framework for Bayesian experimental design (BED) with implicit\nmodels, where the data-generating distribution is intractable but sampling from\nit is still possible. In order to find optimal experimental designs for such\nmodels, our approach maximises mutual information lower bounds that are\nparametrised by neural networks. By training a neural network on sampled data,\nwe simultaneously update network parameters and designs using stochastic\ngradient-ascent. The framework enables experimental design with a variety of\nprominent lower bounds and can be applied to a wide range of scientific tasks,\nsuch as parameter estimation, model discrimination and improving future\npredictions. Using a set of intractable toy models, we provide a comprehensive\nempirical comparison of prominent lower bounds applied to the aforementioned\ntasks. We further validate our framework on a challenging system of stochastic\ndifferential equations from epidemiology.\n"}
+{"abstract": "  Deploying convolutional neural networks (CNNs) for embedded applications\npresents many challenges in balancing resource-efficiency and task-related\naccuracy. These two aspects have been well-researched in the field of CNN\ncompression. In real-world applications, a third important aspect comes into\nplay, namely the robustness of the CNN. In this paper, we thoroughly study the\nrobustness of uncompressed, distilled, pruned and binarized neural networks\nagainst white-box and black-box adversarial attacks (FGSM, PGD, C&W, DeepFool,\nLocalSearch and GenAttack). These new insights facilitate defensive training\nschemes or reactive filtering methods, where the attack is detected and the\ninput is discarded and/or cleaned. Experimental results are shown for distilled\nCNNs, agent-based state-of-the-art pruned models, and binarized neural networks\n(BNNs) such as XNOR-Net and ABC-Net, trained on CIFAR-10 and ImageNet datasets.\nWe present evaluation methods to simplify the comparison between CNNs under\ndifferent attack schemes using loss/accuracy levels, stress-strain graphs,\nbox-plots and class activation mapping (CAM). Our analysis reveals susceptible\nbehavior of uncompressed and pruned CNNs against all kinds of attacks. The\ndistilled models exhibit their strength against all white box attacks with an\nexception of C&W. Furthermore, binary neural networks exhibit resilient\nbehavior compared to their baselines and other compressed variants.\n"}
+{"abstract": "  The fusion probability for the production of superheavy nuclei in cold fusion\nreactions was investigated and compared with recent experimental results for\n$^{48}$Ca, $^{50}$Ti, and $^{54}$Cr incident on a $^{208}$Pb target.\nCalculations were performed within the fusion-by-diffusion model (FbD) using\nnew nuclear data tables by Jachimowicz et al. It is shown that the experimental\ndata could be well explained within the framework of the FbD model. The\nsaturation of the fusion probability at bombarding energies above the\ninteraction barrier is reproduced. It emerges naturally from the physical\neffect of the suppression of contributions of higher partial waves in fusion\nreactions and is related to the critical angular momentum. The role of the\ndifference in values of the rotational energies in the fusion saddle point and\ncontact (sticking) configuration of the projectile-target system is discussed.\n"}
+{"abstract": "  In this paper we show that if $\\theta$ is a $T$-design of an association\nscheme $(\\Omega, \\mathcal{R})$, and the Krein parameters $q_{i,j}^h$ vanish for\nsome $h \\in T$ and all $i, j \\in T$, then $\\theta$ consists of precisely half\nof the vertices of $(\\Omega, \\mathcal{R})$ or it is a $T'$-design, where\n$|T'|>|T|$. We then apply this result to various problems in finite geometry.\nIn particular, we show for the first time that nontrivial $m$-ovoids of\ngeneralised octagons of order $(s, s^2)$ are hemisystems, and hence no\n$m$-ovoid of a Ree-Tits octagon can exist. We give short proofs of similar\nresults for (i) partial geometries with certain order conditions; (ii) thick\ngeneralised quadrangles of order $(s,s^2)$; (iii) the dual polar spaces\n$\\rm{DQ}(2d, q)$, $\\rm{DW}(2d-1,q)$ and $\\rm{DH}(2d-1,q^2)$, for $d \\ge 3$;\n(iv) the Penttila-Williford scheme. In the process of (iv), we also consider a\nnatural generalisation of the Penttila-Williford scheme in $\\rm{Q}^-(2n-1, q)$,\n$n\\geqslant 3$.\n"}
+{"abstract": "  An inequality is derived for the average $t$-energy of pinned distance\nmeasures, where $0 < t < 1$. This refines Mattila's theorem on distance sets to\npinned distance sets, and gives an analogue of Liu's theorem for pinned\ndistance sets of dimension smaller than 1.\n"}
+{"abstract": "  We prove that with high probability over the choice of a random graph $G$\nfrom the Erd\\H{o}s-R\\'enyi distribution $G(n,1/2)$, a natural\n$n^{O(\\varepsilon^2 \\log n)}$-time, degree $O(\\varepsilon^2 \\log n)$\nsum-of-squares semidefinite program cannot refute the existence of a valid\n$k$-coloring of $G$ for $k = n^{1/2 +\\varepsilon}$. Our result implies that the\nrefutation guarantee of the basic semidefinite program (a close variant of the\nLov\\'asz theta function) cannot be appreciably improved by a natural $o(\\log\nn)$-degree sum-of-squares strengthening, and this is tight up to a $n^{o(1)}$\nslack in $k$. To the best of our knowledge, this is the first lower bound for\ncoloring $G(n,1/2)$ for even a single round strengthening of the basic SDP in\nany SDP hierarchy.\n  Our proof relies on a new variant of instance-preserving non-pointwise\ncomplete reduction within SoS from coloring a graph to finding large\nindependent sets in it. Our proof is (perhaps surprisingly) short, simple and\ndoes not require complicated spectral norm bounds on random matrices with\ndependent entries that have been otherwise necessary in the proofs of many\nsimilar results [BHK+16, HKP+17, KB19, GJJ+20, MRX20].\n  Our result formally holds for a constraint system where vertices are allowed\nto belong to multiple color classes; we leave the extension to the formally\nstronger formulation of coloring, where vertices must belong to unique colors\nclasses, as an outstanding open problem.\n"}
+{"abstract": "  Quadrotors can achieve aggressive flight by tracking complex maneuvers and\nrapidly changing directions. Planning for aggressive flight with trajectory\noptimization could be incredibly fast, even in higher dimensions, and can\naccount for dynamics of the quadrotor, however, only provides a locally optimal\nsolution. On the other hand, planning with discrete graph search can handle\nnon-convex spaces to guarantee optimality but suffers from exponential\ncomplexity with the dimension of search. We introduce a framework for\naggressive quadrotor trajectory generation with global reasoning capabilities\nthat combines the best of trajectory optimization and discrete graph search.\nSpecifically, we develop a novel algorithmic framework that interleaves these\ntwo methods to complement each other and generate trajectories with provable\nguarantees on completeness up to discretization. We demonstrate and\nquantitatively analyze the performance of our algorithm in challenging\nsimulation environments with narrow gaps that create severe attitude\nconstraints and push the dynamic capabilities of the quadrotor. Experiments\nshow the benefits of the proposed algorithmic framework over standalone\ntrajectory optimization and graph search-based planning techniques for\naggressive quadrotor flight.\n"}
+{"abstract": "  In many real-world problems, complex dependencies are present both among\nsamples and among features. The Kronecker sum or the Cartesian product of two\ngraphs, each modeling dependencies across features and across samples, has been\nused as an inverse covariance matrix for a matrix-variate Gaussian\ndistribution, as an alternative to a Kronecker-product inverse covariance\nmatrix, due to its more intuitive sparse structure. However, the existing\nmethods for sparse Kronecker-sum inverse covariance estimation are limited in\nthat they do not scale to more than a few hundred features and samples and that\nthe unidentifiable parameters pose challenges in estimation. In this paper, we\nintroduce EiGLasso, a highly scalable method for sparse Kronecker-sum inverse\ncovariance estimation, based on Newton's method combined with\neigendecomposition of the two graphs for exploiting the structure of Kronecker\nsum. EiGLasso further reduces computation time by approximating the Hessian\nbased on the eigendecomposition of the sample and feature graphs. EiGLasso\nachieves quadratic convergence with the exact Hessian and linear convergence\nwith the approximate Hessian. We describe a simple new approach to estimating\nthe unidentifiable parameters that generalizes the existing methods. On\nsimulated and real-world data, we demonstrate that EiGLasso achieves two to\nthree orders-of-magnitude speed-up compared to the existing methods.\n"}
+{"abstract": "  We report the signatures of dynamic spin fluctuations in the layered\nhoneycomb Li$_3$Cu$_2$SbO$_6$ compound, with a 3$d$ S = 1/2 $d^9$ Cu$^{2+}$\nconfiguration, through muon spin rotation and relaxation ($\\mu$SR) and neutron\nscattering studies. Our zero-field (ZF) and longitudinal-field (LF)-$\\mu$SR\nresults demonstrate the slowing down of the Cu$^{2+}$ spin fluctuations below\n4.0 K. The saturation of the ZF relaxation rate at low temperature, together\nwith its weak dependence on the longitudinal field between 0 and 3.2 kG,\nindicates the presence of dynamic spin fluctuations persisting even at 80 mK\nwithout static order. Neutron scattering study reveals the gaped magnetic\nexcitations with three modes at 7.7, 13.5 and 33 meV. Our DFT calculations\nreveal that the next nearest neighbors (NNN) AFM exchange ($J_{AFM}$ = 31 meV)\nis stronger than the NN FM exchange ($J_{FM}$ = -21 meV) indicating the\nimportance of the orbital degrees of freedom. Our results suggest that the\nphysics of Li$_3$Cu$_2$SbO$_6$ can be explained by an alternating AFM chain\nrather than the honeycomb lattice.\n"}
+{"abstract": "  Conventional planar video streaming is the most popular application in mobile\nsystems and the rapid growth of 360 video content and virtual reality (VR)\ndevices are accelerating the adoption of VR video streaming. Unfortunately,\nvideo streaming consumes significant system energy due to the high power\nconsumption of the system components (e.g., DRAM, display interfaces, and\ndisplay panel) involved in this process.\n  We propose BurstLink, a novel system-level technique that improves the energy\nefficiency of planar and VR video streaming. BurstLink is based on two key\nideas. First, BurstLink directly transfers a decoded video frame from the host\nsystem to the display panel, bypassing the host DRAM. To this end, we extend\nthe display panel with a double remote frame buffer (DRFB), instead of the\nDRAM's double frame buffer, so that the system can directly update the DRFB\nwith a new frame while updating the panel's pixels with the current frame\nstored in the DRFB. Second, BurstLink transfers a complete decoded frame to the\ndisplay panel in a single burst, using the maximum bandwidth of modern display\ninterfaces. Unlike conventional systems where the frame transfer rate is\nlimited by the pixel-update throughput of the display panel, BurstLink can\nalways take full advantage of the high bandwidth of modern display interfaces\nby decoupling the frame transfer from the pixel update as enabled by the DRFB.\nThis direct and burst frame transfer of BurstLink significantly reduces energy\nconsumption in video display by reducing access to the host DRAM and increasing\nthe system's residency at idle power states.\n  We evaluate BurstLink using an analytical power model that we rigorously\nvalidate on a real modern mobile system. Our evaluation shows that BurstLink\nreduces system energy consumption for 4K planar and VR video streaming by 41%\nand 33%, respectively.\n"}
+{"abstract": "  Game-theoretic attribution techniques based on Shapley values are used\nextensively to interpret black-box machine learning models, but their exact\ncalculation is generally NP-hard, requiring approximation methods for\nnon-trivial models. As the computation of Shapley values can be expressed as a\nsummation over a set of permutations, a common approach is to sample a subset\nof these permutations for approximation. Unfortunately, standard Monte Carlo\nsampling methods can exhibit slow convergence, and more sophisticated quasi\nMonte Carlo methods are not well defined on the space of permutations. To\naddress this, we investigate new approaches based on two classes of\napproximation methods and compare them empirically. First, we demonstrate\nquadrature techniques in a RKHS containing functions of permutations, using the\nMallows kernel to obtain explicit convergence rates of $O(1/n)$, improving on\n$O(1/\\sqrt{n})$ for plain Monte Carlo. The RKHS perspective also leads to quasi\nMonte Carlo type error bounds, with a tractable discrepancy measure defined on\npermutations. Second, we exploit connections between the hypersphere\n$\\mathbb{S}^{d-2}$ and permutations to create practical algorithms for\ngenerating permutation samples with good properties. Experiments show the above\ntechniques provide significant improvements for Shapley value estimates over\nexisting methods, converging to a smaller RMSE in the same number of model\nevaluations.\n"}
+{"abstract": "  In this paper, an extension of the random field Ginzburg-Landau model on the\nhypercubic lattice is considered by adding $p$-spin ($p\\geqslant 2$)\ninteractions coupled to general disorders. This new model is called the random\nfield mixed-spin Ginzburg-Landau model. We proved that, in the infinite volume\nlimit of this model, the variance of spin overlap vanishes.\n"}
+{"abstract": "  Logarithmic potentials and many other potentials satisfy maximum principle.\nThe dyadic version of logarithmic potential can be easily introduced, it lives\non dyadic tree and also satisfies maximum principle. But its analog on bi-tree\ndoes not have this property. We prove here that \"on average\" we can still have\nsomething like maximum principle on bi-tree. We use the surrogate maximum\nprinciple to prove embedding theorems of Carleson type on bi-disc.\n"}
+{"abstract": "  Molecular structures of RNA molecules reconstructed from X-ray\ncrystallography frequently contain errors. Motivated by this problem we examine\nclustering on a torus since RNA shapes can be described by dihedral angles. A\npreviously developed clustering method for torus data involves two tuning\nparameters and we assess clustering results for different parameter values in\nrelation to the problem of so-called RNA clashes. This clustering problem is\npart of the dynamically evolving field of statistics on manifolds. Statistical\nproblems on the torus highlight general challenges for statistics on manifolds.\nTherefore, the torus PCA and clustering methods we propose make an important\ncontribution to directional statistics and statistics on manifolds in general.\n"}
+{"abstract": "  To unravel the structures of C12H12O7 isomers, identified as light-absorbing\nphotooxidation products of syringol in atmospheric chamber experiments, we\napply a graph-based molecule generator and machine learning workflow. To\naccomplish this in a bias-free manner, molecular graphs of the entire chemical\nsubspace of C12H12O7 were generated, assuming that the isomers contain two\nC6-rings; this led to 260 million molecular graphs and 120 million stable\nstructures. Using quantum chemistry excitation energies and oscillator\nstrengths as training data, we predicted these quantities using kernel ridge\nregression and simulated UV/Vis absorption spectra. Then we determined the\nprobability of the molecules to cause the experimental spectrum within the\nerrors of the different methods. Molecules whose spectra were likely to match\nthe experimental spectrum were clustered according to structural features,\nresulting in clusters of > 500,000 molecules. While we identified several\nfeatures that correlate with a high probability to cause the experimental\nspectrum, no clear composition of necessary features can be given. Thus, the\nabsorption spectrum is not sufficient to uniquely identify one specific isomer\nstructure. If more structural features were known from experimental data, the\nnumber of structures could be reduced to a few tens of thousands candidates. We\noffer a procedure to detect when sufficient fragmentation data has been\nincluded to reduce the number of possible molecules. The most efficient\nstrategy to obtain valid candidates is obtained if structural data is applied\nalready at the bias-free molecule generation stage. The systematic enumeration,\nhowever, is necessary to avoid mis-identification of molecules, while it\nguarantees that there are no other molecules that would also fit the spectrum\nin question.\n"}
+{"abstract": "  The semileptonic decays of $\\Lambda_{b}\\to\\Lambda_{c}^{(*)}\\ell\\nu_\\ell$ and\n$\\Lambda_{c}\\to\\Lambda^{(*)}\\ell\\nu_\\ell$ are studied in the light-front quark\nmodel in this work. Instead of the quark-diquark approximation, we use the\nthree-body wave functions obtained by baryon spectroscopy. The ground states\n$(1/2^{+})$, the $\\lambda$-mode orbital excited states $(1/2^{-})$, and the\nfirst radial excited states $(1/2^{+})$ of $\\Lambda_{(c)}^{(*)}$ are\nconsidered. The discussions are given for the form factors, partial widths,\nbranching fractions, leptonic forward-backward asymmetries, hadron\npolarizations, lepton polarizations, and the lepton flavor universalities. Our\nresults are useful for the inputs of heavy baryon decays and understanding the\nbaryon structures, and helpful for experimental measurements.\n"}
+{"abstract": "  We study the electronic properties of the heterobilayer of vanadium and iron\noxychlorides, VOCl and FeOCl, two layered air stable van der Waals insulating\noxides with different types of antiferromagnetic order in bulk: VOCl monolayers\nare ferromagnetic (FM) whereas the FeOCl monolayers are antiferromagnetic (AF).\nWe use density functional theory (DFT) calculations, with Hubbard correction\nthat is found to be needed to describe correctly the insulating nature of these\ncompounds. We compute the magnetic anisotropy and propose a spin model\nHamiltonian. Our calculations show that interlayer coupling in weak and\nferromagnetic so that magnetic order of the monolayers is preserved in the\nheterobilayers providing thereby a van der Waals heterostructure that combines\ntwo monolayers with different magnetic order. Interlayer exchange should lead\nboth to exchange bias and to the emergence of hybrid collective modes that that\ncombine FM and AF magnons. The energy band of the heterobilayer show a type II\nband alignment, and feature spin-splitting of the states of the AF layer due to\nthe breaking of the inversion symmetry.\n"}
+{"abstract": "  We design multi-horizon forecasting models for limit order book (LOB) data by\nusing deep learning techniques. Unlike standard structures where a single\nprediction is made, we adopt encoder-decoder models with sequence-to-sequence\nand Attention mechanisms to generate a forecasting path. Our methods achieve\ncomparable performance to state-of-art algorithms at short prediction horizons.\nImportantly, they outperform when generating predictions over long horizons by\nleveraging the multi-horizon setup. Given that encoder-decoder models rely on\nrecurrent neural layers, they generally suffer from slow training processes. To\nremedy this, we experiment with utilising novel hardware, so-called Intelligent\nProcessing Units (IPUs) produced by Graphcore. IPUs are specifically designed\nfor machine intelligence workload with the aim to speed up the computation\nprocess. We show that in our setup this leads to significantly faster training\ntimes when compared to training models with GPUs.\n"}
+{"abstract": "  According to mechanistic theories of working memory (WM), information is\nretained as persistent spiking activity of cortical neural networks. Yet, how\nthis activity is related to changes in the oscillatory profile observed during\nWM tasks remains an open issue. We explore joint effects of input gamma-band\noscillations and noise on the dynamics of several firing rate models of WM. The\nconsidered models have a metastable active regime, i.e. they demonstrate\nlong-lasting transient post-stimulus firing rate elevation. We start from a\nsingle excitatory-inhibitory circuit and demonstrate that either gamma-band or\nnoise input could stabilize the active regime, thus supporting WM retention. We\nthen consider a system of two circuits with excitatory intercoupling. We find\nthat fast coupling allows for better stabilization by common noise compared to\nindependent noise and stronger amplification of this effect by in-phase gamma\ninputs compared to anti-phase inputs. Finally, we consider a multi-circuit\nsystem comprised of two clusters, each containing a group of circuits receiving\na common noise input and a group of circuits receiving independent noise. Each\ncluster is associated with its own local gamma generator, so all its circuits\nreceive gamma-band input in the same phase. We find that gamma-band input\ndifferentially stabilizes the activity of the \"common-noise\" groups compared to\nthe \"independent-noise\" groups. If the inter-cluster connections are fast, this\neffect is more pronounced when the gamma-band input is delivered to the\nclusters in the same phase rather than in the anti-phase. Assuming that the\ncommon noise comes from a large-scale distributed WM representation, our\nresults demonstrate that local gamma oscillations can stabilize the activity of\nthe corresponding parts of this representation, with stronger effect for fast\nlong-range connections and synchronized gamma oscillations.\n"}
+{"abstract": "  We present and describe the GPFDA package for R. The package provides\nflexible functionalities for dealing with Gaussian process regression (GPR)\nmodels for functional data. Multivariate functional data, functional data with\nmultidimensional inputs, and nonseparable and/or nonstationary covariance\nstructures can be modeled. In addition, the package fits functional regression\nmodels where the mean function depends on scalar and/or functional covariates\nand the covariance structure is modeled by a GPR model. In this paper, we\npresent the versatility of GPFDA with respect to mean function and covariance\nfunction specifications and illustrate the implementation of estimation and\nprediction of some models through reproducible numerical examples.\n"}
+{"abstract": "  Risk modeling with EHR data is challenging due to a lack of direct\nobservations on the disease outcome, and the high dimensionality of the\ncandidate predictors. In this paper, we develop a surrogate assisted\nsemi-supervised-learning (SAS) approach to risk modeling with high dimensional\npredictors, leveraging a large unlabeled data on candidate predictors and\nsurrogates of outcome, as well as a small labeled data with annotated outcomes.\nThe SAS procedure borrows information from surrogates along with candidate\npredictors to impute the unobserved outcomes via a sparse working imputation\nmodel with moment conditions to achieve robustness against mis-specification in\nthe imputation model and a one-step bias correction to enable interval\nestimation for the predicted risk. We demonstrate that the SAS procedure\nprovides valid inference for the predicted risk derived from a high dimensional\nworking model, even when the underlying risk prediction model is dense and the\nrisk model is mis-specified. We present an extensive simulation study to\ndemonstrate the superiority of our SSL approach compared to existing supervised\nmethods. We apply the method to derive genetic risk prediction of type-2\ndiabetes mellitus using a EHR biobank cohort.\n"}
+{"abstract": "  Using the Global Magneto-Ionic Medium Survey (GMIMS) Low-Band South (LBS)\nsouthern sky polarization survey, covering 300 to 480 MHz at 81 arcmin\nresolution, we reveal the brightest region in the Southern polarized sky at\nthese frequencies. The region, G150-50, covers nearly 20deg$^2$, near\n(l,b)~(150 deg,-50 deg). Using GMIMS-LBS and complementary data at higher\nfrequencies (~0.6--30 GHz), we apply Faraday tomography and Stokes QU-fitting\ntechniques. We find that the magnetic field associated with G150-50 is both\ncoherent and primarily in the plane of the sky, and indications that the region\nis associated with Radio Loop II. The Faraday depth spectra across G150-50 are\nbroad and contain a large-scale spatial gradient. We model the magnetic field\nin the region as an expanding shell, and we can reproduce both the observed\nFaraday rotation and the synchrotron emission in the GMIMS-LBS band. Using\nQU-fitting, we find that the Faraday spectra are produced by several Faraday\ndispersive sources along the line-of-sight. Alternatively, polarization horizon\neffects that we cannot model are adding complexity to the high-frequency\npolarized spectra. The magnetic field structure of Loop II dominates a large\nfraction of the sky, and studies of the large-scale polarized sky will need to\naccount for this object. Studies of G150-50 with high angular resolution could\nmitigate polarization horizon effects, and clarify the nature of G150-50.\n"}
+{"abstract": "  It has been shown that deep learning models are vulnerable to adversarial\nattacks. We seek to further understand the consequence of such attacks on the\nintermediate activations of neural networks. We present an evaluation metric,\nPOP-N, which scores the effectiveness of projecting data to N dimensions under\nthe context of visualizing representations of adversarially perturbed inputs.\nWe conduct experiments on CIFAR-10 to compare the POP-2 score of several\ndimensionality reduction algorithms across various adversarial attacks.\nFinally, we utilize the 2D data corresponding to high POP-2 scores to generate\nexample visualizations.\n"}
+{"abstract": "  In this paper, we investigate two types of $U(1)$-gauge field theories on\n$G_2$-manifolds. One is the $U(1)$-Yang-Mills theory which admits the classical\ninstanton solutions, we show that $G_2$-manifolds emerge from the\nanti-self-dual $U(1)$ instantons, which is an analogy of Yang's result for\nCalabi-Yau manifolds. The other one is the higher-order $U(1)$-Chern-Simons\ntheory as a generalization of K\\\"{a}hler-Chern-Simons theory, by suitable\nchoice of gauge and regularization technique, we calculate the partition\nfunction under semiclassical approximation.\n"}
+{"abstract": "  In this paper we systematically consider the baryon ($B$) and lepton ($L$)\nnumber violating dinucleon to dilepton decays ($pp\\to \\ell^+\\ell^{\\prime+},\npn\\to \\ell^+\\bar\\nu^\\prime, nn\\to \\bar\\nu\\bar\\nu^\\prime$) with $\\Delta B=\\Delta\nL=-2$ in the framework of effective field theory. We start by constructing a\nbasis of dimension-12 (dim-12) operators mediating such processes in the low\nenergy effective field theory (LEFT) below the electroweak scale. Then we\nconsider their standard model effective field theory (SMEFT) completions\nupwards and their chiral realizations in baryon chiral perturbation theory\n(B$\\chi$PT) downwards. We work to the first nontrivial orders in each effective\nfield theory, collect along the way the matching conditions, and express the\ndecay rates in terms of the Wilson coefficients associated with the dim-12\noperators in SMEFT and the low energy constants pertinent to B$\\chi$PT. We find\nthe current experimental limits push the associated new physics scale larger\nthan $1-3$ TeV, which is still accessible to the future collider searches.\nThrough weak isospin symmetry, we find the current experimental limits on the\npartial lifetimes of transitions $pp\\to \\ell^+\\ell^{\\prime+}, pn\\to\n\\ell^+\\bar\\nu^\\prime$ imply stronger limits on $nn\\to\\bar\\nu\\bar\\nu^\\prime$\nthan their existing lower bounds, which are improved by $2-3$ orders of\nmagnitude. Furthermore, assuming charged mode transitions are also dominantly\ngenerated by the similar dim-12 SMEFT interactions, the experimental limits on\n$pp\\to e^+e^+,e^+\\mu^+,\\mu^+\\mu^+$ lead to stronger limits on $pn\\to\n\\ell^+_\\alpha\\bar\\nu_\\beta$ with $\\alpha,\\beta=e,\\mu$ than their existing\nbounds. Conversely, the same assumptions help us to set a lower bound on the\nlifetime of the experimentally unsearched mode $pp\\to e^+\\tau^+$ from that of\n$pn\\to e^+\\bar\\nu_\\tau$, i.e., $\\Gamma^{-1}_{pp\\to e^+\\tau^+}\\gtrsim 2\\times\n10^{34}~\\rm yrs$.\n"}
+{"abstract": "  Graph convolutional network (GCN) based approaches have achieved significant\nprogress for solving complex, graph-structured problems. GCNs incorporate the\ngraph structure information and the node (or edge) features through message\npassing and computes 'deep' node representations. Despite significant progress\nin the field, designing GCN architectures for heterogeneous graphs still\nremains an open challenge. Due to the schema of a heterogeneous graph, useful\ninformation may reside multiple hops away. A key question is how to perform\nmessage passing to incorporate information of neighbors multiple hops away\nwhile avoiding the well-known over-smoothing problem in GCNs. To address this\nquestion, we propose our GCN framework 'Deep Heterogeneous Graph Convolutional\nNetwork (DHGCN)', which takes advantage of the schema of a heterogeneous graph\nand uses a hierarchical approach to effectively utilize information many hops\naway. It first computes representations of the target nodes based on their\n'schema-derived ego-network' (SEN). It then links the nodes of the same type\nwith various pre-defined metapaths and performs message passing along these\nlinks to compute final node representations. Our design choices naturally\ncapture the way a heterogeneous graph is generated from the schema. The\nexperimental results on real and synthetic datasets corroborate the design\nchoice and illustrate the performance gains relative to competing alternatives.\n"}
+{"abstract": "  We extend Araki's well-known results on the equivalence of the KMS condition\nand the variational principle for equilibrium states of quantum lattice systems\nwith short-range interactions, to a large class of models possibly containing\nmean-field interactions (representing an extreme form of long-range\ninteractions). This result is reminiscent of van Hemmen's work on equilibrium\nstates for mean-field models. The extension was made possible by our recent\noutcomes on states minimizing the free energy density of mean-field models on\nthe lattice, as well as on the infinite volume dynamics for such models.\n"}
+{"abstract": "  Verification of AI is a challenge that has engineering, algorithmic and\nprogramming language components. For example, AI planners are deployed to model\nactions of autonomous agents. They comprise a number of searching algorithms\nthat, given a set of specified properties, find a sequence of actions that\nsatisfy these properties. Although AI planners are mature tools from the\nalgorithmic and engineering points of view, they have limitations as\nprogramming languages. Decidable and efficient automated search entails\nrestrictions on the syntax of the language, prohibiting use of higher-order\nproperties or recursion. This paper proposes a methodology for embedding plans\nproduced by AI planners into dependently-typed language Agda, which enables\nusers to reason about and verify more general and abstract properties of plans,\nand also provides a more holistic programming language infrastructure for\nmodelling plan execution.\n"}
+{"abstract": "  The prevalence of employing attention mechanisms has brought along concerns\non the interpretability of attention distributions. Although it provides\ninsights about how a model is operating, utilizing attention as the explanation\nof model predictions is still highly dubious. The community is still seeking\nmore interpretable strategies for better identifying local active regions that\ncontribute the most to the final decision. To improve the interpretability of\nexisting attention models, we propose a novel Bilinear Representative\nNon-Parametric Attention (BR-NPA) strategy that captures the task-relevant\nhuman-interpretable information. The target model is first distilled to have\nhigher-resolution intermediate feature maps. From which, representative\nfeatures are then grouped based on local pairwise feature similarity, to\nproduce finer-grained, more precise attention maps highlighting task-relevant\nparts of the input. The obtained attention maps are ranked according to the\n`active level' of the compound feature, which provides information regarding\nthe important level of the highlighted regions. The proposed model can be\neasily adapted in a wide variety of modern deep models, where classification is\ninvolved. It is also more accurate, faster, and with a smaller memory footprint\nthan usual neural attention modules. Extensive experiments showcase more\ncomprehensive visual explanations compared to the state-of-the-art\nvisualization model across multiple tasks including few-shot classification,\nperson re-identification, fine-grained image classification. The proposed\nvisualization model sheds imperative light on how neural networks `pay their\nattention' differently in different tasks.\n"}
+{"abstract": "  The radio-wavelength detection of extensive air showers (EAS) initiated by\ncosmic-ray interactions in the Earth's atmosphere is a promising technique for\ninvestigating the origin of these particles and the physics of their\ninteractions. The Low Frequency Array (LOFAR) and the Owens Valley Long\nWavelength Array (OVRO-LWA) have both demonstrated that the dense cores of low\nfrequency radio telescope arrays yield detailed information on the radiation\nground pattern, which can be used to reconstruct key EAS properties and infer\nthe primary cosmic-ray composition. Here, we demonstrate a new observation mode\nof the Murchison Widefield Array (MWA), tailored to the observation of the\nsub-microsecond coherent bursts of radiation produced by EAS. We first show how\nan aggregate 30.72 MHz bandwidth (3072x 10 kHz frequency channels) recorded at\n0.1 ms resolution with the MWA's voltage capture system (VCS) can be\nsynthesised back to the full bandwidth Nyquist resolution of 16.3 ns. This\nprocess, which involves `inverting' two sets of polyphase filterbanks, retains\n90.5% of the signal-to-noise of a cosmic ray signal. We then demonstrate the\ntiming and positional accuracy of this mode by resolving the location of a\ncalibrator pulse to within 5 m. Finally, preliminary observations show that the\nrate of nanosecond radio-frequency interference (RFI) events is 0.1 Hz, much\nlower than that found at the sites of other radio telescopes that study cosmic\nrays. We conclude that the identification of cosmic rays at the MWA, and hence\nwith the low-frequency component of the Square Kilometre Array, is feasible\nwith minimal loss of efficiency due to RFI.\n"}
+{"abstract": "  Ionic charges were related through bulk modulus through linear regression.\nThese two parameters yield a straight regression line, when plotted but fall on\ndifferent positions due the variation in bulk modulus values. The calculated\nvalues of bulk moduli reflecting elastic characteristics are in close agreement\nwith other available values. As, these values are only differ by average of 3%\nfrom values of literature. Moreover, the regression resulted in a good values\nof correlation coefficient (R=0.77) and Probability (P=0.001). These all show\nthe accuracy and reliability of the current work. The technique adopted in this\nwork will be helpful to material scientists for finding new materials with\nreferred elastic characteristics among structurally similar materials, also the\ncalculated data will act as reference for upcoming investigation of the studied\ncompounds.\n"}
+{"abstract": "  The paradigm of variational quantum classifiers (VQCs) encodes\n\\textit{classical information} as quantum states, followed by quantum\nprocessing and then measurements to generate classical predictions. VQCs are\npromising candidates for efficient utilization of a near-term quantum device:\nclassifiers involving $M$-dimensional datasets can be implemented with only\n$\\lceil \\log_2 M \\rceil$ qubits by using an amplitude encoding. A general\nframework for designing and training VQCs, however, has not been proposed, and\na fundamental understanding of its power and analytical relationships with\nclassical classifiers are not well understood. An encouraging specific\nembodiment of VQCs, quantum circuit learning (QCL), utilizes an ansatz: it\nexpresses the quantum evolution operator as a circuit with a predetermined\ntopology and parametrized gates; training involves learning the gate parameters\nthrough optimization. In this letter, we first address the open questions about\nVQCs and then show that they, including QCL, fit inside the well-known kernel\nmethod. Based on such correspondence, we devise a design framework of efficient\nansatz-independent VQCs, which we call the unitary kernel method (UKM): it\ndirectly optimizes the unitary evolution operator in a VQC. Thus, we show that\nthe performance of QCL is bounded from above by the UKM. Next, we propose a\nvariational circuit realization (VCR) for designing efficient quantum circuits\nfor a given unitary operator. By combining the UKM with the VCR, we establish\nan efficient framework for constructing high-performing circuits. We finally\nbenchmark the relatively superior performance of the UKM and the VCR via\nextensive numerical simulations on multiple datasets.\n"}
+{"abstract": "  LS 5039 is a high-mass gamma-ray binary hosting a compact object of unknown\ntype. NuSTAR observed LS 5039 during its entire 3.9 day binary period. We\nperformed a periodic signal search up to 1000 Hz which did not produce credible\nperiod candidates. We do see the 9.05 s period candidate, originally reported\nby Yoneda et al. 2020 using the same data, in the Fourier power spectrum, but\nwe find that the statistical significance of this feature is too low to claim\nit as a real detection. We also did not find significant bursts or\nquasi-periodic variability. The modulation with the orbital period is clearly\nseen and remains unchanged over a decade long timescale when compared to the\nearlier Suzaku light curve. The joint analysis of the NuSTAR and Suzaku XIS\ndata shows that the 0.7-70 keV spectrum can be satisfactory described by a\nsingle absorbed power-law model with no evidence of cutoff at higher energies.\nThe slope of the spectrum anti-correlates with the flux during the binary\norbit. Therefore, if LS 5039 hosts a young neutron star, its X-ray pulsations\nappear to be outshined by the intrabinary shock emission. The lack of spectral\nlines and/or an exponential cutoff at higher energies suggests that the\nputative neutron star is not actively accreting. Although a black hole scenario\nstill remains a possibility, the lack of variability or Fe K$\\alpha$ lines,\nwhich typically accompany accretion, makes it less likely.\n"}
+{"abstract": "  We extend the recently developed rough path theory for Volterra equations\nfrom (Harang and Tindel, 2019) to the case of more rough noise and/or more\nsingular Volterra kernels. It was already observed in (Harang and Tindel, 2019)\nthat the Volterra rough path introduced there did not satisfy any geometric\nrelation, similar to that observed in classical rough path theory. Thus, an\nextension of the theory to more irregular driving signals requires a deeper\nunderstanding of the specific algebraic structure arising in the Volterra rough\npath. Inspired by the elements of \"non-geometric rough paths\" developed in\n(Gubinelli, 2010) and (Hairer and Kelly, 2015) we provide a simple description\nof the Volterra rough path and the controlled Volterra process in terms of\nrooted trees, and with this description we are able to solve rough volterra\nequations in driven by more irregular signals.\n"}
+{"abstract": "  Single phonon excitations are sensitive probes of light dark matter in the\nkeV-GeV mass window. For anisotropic target materials, the signal depends on\nthe direction of the incoming dark matter wind and exhibits a daily modulation.\nWe discuss in detail the various sources of anisotropy, and carry out a\ncomparative study of 26 crystal targets, focused on sub-MeV dark matter\nbenchmarks. We compute the modulation reach for the most promising targets,\ncorresponding to the cross section where the daily modulation can be observed\nfor a given exposure, which allows us to combine the strength of DM-phonon\ncouplings and the amplitude of daily modulation. We highlight Al$_2$O$_3$\n(sapphire), CaWO$_4$ and h-BN (hexagonal boron nitride) as the best polar\nmaterials for recovering a daily modulation signal, which feature\n$\\mathcal{O}(1 - 100)\\%$ variations of detection rates throughout the day,\ndepending on the dark matter mass and interaction. The directional nature of\nsingle phonon excitations offers a useful handle to mitigate backgrounds, which\nis crucial for fully realizing the discovery potential of near future\nexperiments.\n"}
+{"abstract": "  The superspace ring $\\Omega_n$ is a rank $n$ polynomial ring tensor a rank\n$n$ exterior algebra. Using an extension of the Vandermonde determinant to\n$\\Omega_n$, the authors previously defined a family of doubly graded quotients\n$\\mathbb{W}_{n,k}$ of $\\Omega_n$ which carry an action of the symmetric group\n$\\mathfrak{S}_n$ and satisfy a bigraded version of Poincar\\'e Duality. In this\npaper, we examine the duality modules $\\mathbb{W}_{n,k}$ in greater detail. We\ndescribe a monomial basis of $\\mathbb{W}_{n,k}$ and give combinatorial formulas\nfor its bigraded Hilbert and Frobenius series. These formulas involve new\ncombinatorial objects called {\\em ordered superpartitions}. These are ordered\nset partitions $(B_1 \\mid \\cdots \\mid B_k)$ of $\\{1,\\dots,n\\}$ in which the\nnon-minimal elements of any block $B_i$ may be barred or unbarred.\n"}
+{"abstract": "  This paper studies the problem of finding an anomalous arm in a multi-armed\nbandit when (a) each arm is a finite-state Markov process, and (b) the arms are\nrestless. Here, anomaly means that the transition probability matrix (TPM) of\none of the arms (the odd arm) is different from the common TPM of each of the\nnon-odd arms. The TPMs are unknown to a decision entity that wishes to find the\nindex of the odd arm as quickly as possible, subject to an upper bound on the\nerror probability. We derive a problem instance-specific asymptotic lower bound\non the expected time required to find the odd arm index, where the asymptotics\nis as the error probability vanishes. Further, we devise a policy based on the\nprinciple of certainty equivalence, and demonstrate that under a continuous\nselection assumption and a certain regularity assumption on the TPMs, the\npolicy achieves the lower bound arbitrarily closely. Thus, while the lower\nbound is shown for all problem instances, the upper bound is shown only for\nthose problem instances satisfying the continuous selection and the regularity\nassumptions. Our achievability analysis is based on resolving the\nidentifiability problem in the context of a certain lifted countable-state\ncontrolled Markov process.\n"}
+{"abstract": "  We introduce a novel approach to unsupervised and semi-supervised domain\nadaptation for semantic segmentation. Unlike many earlier methods that rely on\nadversarial learning for feature alignment, we leverage contrastive learning to\nbridge the domain gap by aligning the features of structurally similar label\npatches across domains. As a result, the networks are easier to train and\ndeliver better performance. Our approach consistently outperforms\nstate-of-the-art unsupervised and semi-supervised methods on two challenging\ndomain adaptive segmentation tasks, particularly with a small number of target\ndomain annotations. It can also be naturally extended to weakly-supervised\ndomain adaptation, where only a minor drop in accuracy can save up to 75% of\nannotation cost.\n"}
+{"abstract": "  The Simultaneous Localization and Mapping (SLAM) problem addresses the\npossibility of a robot to localize itself in an unknown environment and\nsimultaneously build a consistent map of this environment. Recently, cameras\nhave been successfully used to get the environment's features to perform SLAM,\nwhich is referred to as visual SLAM (VSLAM). However, classical VSLAM\nalgorithms can be easily induced to fail when either the motion of the robot or\nthe environment is too challenging. Although new approaches based on Deep\nNeural Networks (DNNs) have achieved promising results in VSLAM, they still are\nunable to outperform traditional methods. To leverage the robustness of deep\nlearning to enhance traditional VSLAM systems, we propose to combine the\npotential of deep learning-based feature descriptors with the traditional\ngeometry-based VSLAM, building a new VSLAM system called LIFT-SLAM. Experiments\nconducted on KITTI and Euroc datasets show that deep learning can be used to\nimprove the performance of traditional VSLAM systems, as the proposed approach\nwas able to achieve results comparable to the state-of-the-art while being\nrobust to sensorial noise. We enhance the proposed VSLAM pipeline by avoiding\nparameter tuning for specific datasets with an adaptive approach while\nevaluating how transfer learning can affect the quality of the features\nextracted.\n"}
+{"abstract": "  The idea that, after their evaporation, Planck-mass black holes might tunnel\ninto metastable white holes has recently been intensively studied. Those relics\nhave been considered as a dark matter candidate. We show that the model is\nseverely constrained and underline some possible detection paths. We also\ninvestigate, in a more general setting, the way the initial black hole mass\nspectrum would be distorted by both the bouncing effect and the Hawking\nevaporation.\n"}
+{"abstract": "  Multiview embedding is a way to model strange attractors that takes advantage\nof the way measurements are often made in real chaotic systems, using\nmultidimensional measurements to make up for a lack of long timeseries.\nPredictive multiview embedding adapts this approach to the problem of\npredicting new values, and provides a natural framework for combining multiple\nsources of information such as natural measurements and computer model runs for\npotentially improved prediction. Here, using 18 month ahead prediction of\nmonthly averages, we show how predictive multiview embedding can be combined\nwith simple statistical approaches to explore predictability of four climate\nvariables by a GCM, build prediction bounds, explore the local manifold\nstructure of the attractor, and show that even though the GCM does not predict\na particular variable well, a hybrid model combining information from the GCM\nand empirical data predicts that variable significantly better than the purely\nempirical model.\n"}
+{"abstract": "  Influence maximization is the problem of finding a small subset of nodes in a\nnetwork that can maximize the diffusion of information. Recently, it has also\nfound application in HIV prevention, substance abuse prevention, micro-finance\nadoption, etc., where the goal is to identify the set of peer leaders in a\nreal-world physical social network who can disseminate information to a large\ngroup of people. Unlike online social networks, real-world networks are not\ncompletely known, and collecting information about the network is costly as it\ninvolves surveying multiple people. In this paper, we focus on this problem of\nnetwork discovery for influence maximization. The existing work in this\ndirection proposes a reinforcement learning framework. As the environment\ninteractions in real-world settings are costly, so it is important for the\nreinforcement learning algorithms to have minimum possible environment\ninteractions, i.e, to be sample efficient. In this work, we propose CLAIM -\nCurriculum LeArning Policy for Influence Maximization to improve the sample\nefficiency of RL methods. We conduct experiments on real-world datasets and\nshow that our approach can outperform the current best approach.\n"}
+{"abstract": "  Connected and Automated Vehicles (CAVs) have real-time information from the\nsurrounding environment by using local on-board sensors, V2X\n(Vehicle-to-Everything) communications, pre-loaded vehicle-specific lookup\ntables, and map database. CAVs are capable of improving energy efficiency by\nincorporating these information. In particular, Eco-Cruise and Eco-Lane\nSelection on highways and/or motorways have immense potential to save energy,\nbecause there are generally fewer traffic controllers and the vehicles keep\nmoving in general. In this paper, we present a cooperative and energy-efficient\nlane-selection strategy named MultiCruise, where each CAV selects one among\nmultiple candidate lanes that allows the most energy-efficient travel.\nMultiCruise incorporates an Eco-Cruise component to select the most\nenergy-efficient lane. The Eco-Cruise component calculates the driving\nparameters and prospective energy consumption of the ego vehicle for each\ncandidate lane, and the Eco-Lane Selection component uses these values. As a\nresult, MultiCruise can account for multiple data sources, such as the road\ncurvature and the surrounding vehicles' velocities and accelerations. The\neco-autonomous driving strategy, MultiCruise, is tested, designed and verified\nby using a co-simulation test platform that includes autonomous driving\nsoftware and realistic road networks to study the performance under realistic\ndriving conditions. Our experimental evaluations show that our eco-autonomous\nMultiCruise saves up to 8.5% fuel consumption.\n"}
+{"abstract": "  We apply the method of linear perturbations to the case of\nSpin(7)-structures, showing that the only nontrivial perturbations are those\ndetermined by a rank one nilpotent matrix.\n  We consider linear perturbations of the Bryant-Salamon metric on the spin\nbundle over $S^4$ that retain invariance under the action of Sp(2), showing\nthat the metrics obtained in this way are isometric.\n"}
+{"abstract": "  Given a homogeneous ideal $I \\subseteq k[x_0,\\dots,x_n]$, the Containment\nproblem studies the relation between symbolic and regular powers of $I$, that\nis, it asks for which pair $m, r \\in \\mathbb{N}$, $I^{(m)} \\subseteq I^r$\nholds. In the last years, several conjectures have been posed on this problem,\ncreating an active area of current interests and ongoing investigations. In\nthis paper, we investigated the Stable Harbourne Conjecture and the Stable\nHarbourne -- Huneke Conjecture and we show that they hold for the defining\nideal of a Complement of a Steiner configuration of points in\n$\\mathbb{P}^{n}_{k}$. We can also show that the ideal of a Complement of a\nSteiner Configuration of points has expected resurgence, that is, its\nresurgence is strictly less than its big height, and it also satisfies\nChudnovsky and Demailly's Conjectures. Moreover, given a hypergraph $H$, we\nalso study the relation between its colourability and the failure of the\ncontainment problem for the cover ideal associated to $H$. We apply these\nresults in the case that $H$ is a Steiner System.\n"}
+{"abstract": "  Electric vehicles can offer a low carbon emission solution to reverse rising\nemission trends. However, this requires that the energy used to meet the demand\nis green. To meet this requirement, accurate forecasting of the charging demand\nis vital. Short and long-term charging demand forecasting will allow for better\noptimisation of the power grid and future infrastructure expansions. In this\npaper, we propose to use publicly available data to forecast the electric\nvehicle charging demand. To model the complex spatial-temporal correlations\nbetween charging stations, we argue that Temporal Graph Convolution Models are\nthe most suitable to capture the correlations. The proposed Temporal Graph\nConvolutional Networks provide the most accurate forecasts for short and\nlong-term forecasting compared with other forecasting methods.\n"}
+{"abstract": "  We study the effects of addition of Chern-Simons (CS) term in the minimal\nYang Mills (YM) matrix model composed of two $2 \\times 2$ matrices with $SU(2)$\ngauge and $SO(2)$ global symmetry. We obtain the Hamiltonian of this system in\nappropriate coordinates and demonstrate that its dynamics is sensitive to the\nvalues of both the CS coupling, $\\kappa$, and the conserved conjugate momentum,\n$p_\\phi$, associated to the $SO(2)$ symmetry. We examine the behavior of the\nemerging chaotic dynamics by computing the Lyapunov exponents and plotting the\nPoincar\\'{e} sections as these two parameters are varied and, in particular,\nfind that the largest Lyapunov exponents evaluated within a range of values of\n$\\kappa$ are above that is computed at $\\kappa=0$, for $\\kappa p_\\phi < 0$. We\nalso give estimates of the critical exponents for the Lyapunov exponent as the\nsystem transits from the chatoic to non-chaotic phase with $p_\\phi$ approaching\nto a critical value.\n"}
+{"abstract": "  Single cell RNA sequencing (scRNA-seq) data makes studying the development of\ncells possible at unparalleled resolution. Given that many cellular\ndifferentiation processes are hierarchical, their scRNA-seq data is expected to\nbe approximately tree-shaped in gene expression space. Inference and\nrepresentation of this tree-structure in two dimensions is highly desirable for\nbiological interpretation and exploratory analysis. Our two contributions are\nan approach for identifying a meaningful tree structure from high-dimensional\nscRNA-seq data, and a visualization method respecting the tree-structure. We\nextract the tree structure by means of a density based minimum spanning tree on\na vector quantization of the data and show that it captures biological\ninformation well. We then introduce DTAE, a tree-biased autoencoder that\nemphasizes the tree structure of the data in low dimensional space. We compare\nto other dimension reduction methods and demonstrate the success of our method\nexperimentally. Our implementation relying on PyTorch and Higra is available at\ngithub.com/hci-unihd/DTAE.\n"}
+{"abstract": "  The imbalanced data classification remains a vital problem. The key is to\nfind such methods that classify both the minority and majority class correctly.\nThe paper presents the classifier ensemble for classifying binary,\nnon-stationary and imbalanced data streams where the Hellinger Distance is used\nto prune the ensemble. The paper includes an experimental evaluation of the\nmethod based on the conducted experiments. The first one checks the impact of\nthe base classifier type on the quality of the classification. In the second\nexperiment, the Hellinger Distance Weighted Ensemble (HDWE) method is compared\nto selected state-of-the-art methods using a statistical test with two base\nclassifiers. The method was profoundly tested based on many imbalanced data\nstreams and obtained results proved the HDWE method's usefulness.\n"}
+{"abstract": "  The celebrated Takens' embedding theorem concerns embedding an attractor of a\ndynamical system in a Euclidean space of appropriate dimension through a\ngeneric delay-observation map. The embedding also establishes a topological\nconjugacy. In this paper, we show how an arbitrary sequence can be mapped into\nanother space as an attractive solution of a nonautonomous dynamical system.\nSuch mapping also entails a topological conjugacy and an embedding between the\nsequence and the attractive solution spaces. This result is not a\ngeneralization of Takens embedding theorem but helps us understand what exactly\nis required by discrete-time state space models widely used in applications to\nembed an external stimulus onto its solution space. Our results settle another\nbasic problem concerning the perturbation of an autonomous dynamical system. We\ndescribe what exactly happens to the dynamics when exogenous noise perturbs\ncontinuously a local irreducible attracting set (such as a stable fixed point)\nof a discrete-time autonomous dynamical system.\n"}
+{"abstract": "  Invariant object recognition is one of the most fundamental cognitive tasks\nperformed by the brain. In the neural state space, different objects with\nstimulus variabilities are represented as different manifolds. In this\ngeometrical perspective, object recognition becomes the problem of linearly\nseparating different object manifolds. In feedforward visual hierarchy, it has\nbeen suggested that the object manifold representations are reformatted across\nthe layers, to become more linearly separable. Thus, a complete theory of\nperception requires characterizing the ability of linear readout networks to\nclassify object manifolds from variable neural responses.\n  A theory of the perceptron of isolated points was pioneered by E. Gardner who\nformulated it as a statistical mechanics problem and analyzed it using replica\ntheory. In this thesis, we generalize Gardner's analysis and establish a theory\nof linear classification of manifolds synthesizing statistical and geometric\nproperties of high dimensional signals. [..] Next, we generalize our theory\nfurther to linear classification of general perceptual manifolds, such as point\nclouds. We identify that the capacity of a manifold is determined that\neffective radius, R_M, and effective dimension, D_M. Finally, we show\nextensions relevant for applications to real data, incorporating correlated\nmanifolds, heterogenous manifold geometries, sparse labels and nonlinear\nclassifications. Then, we demonstrate how object-based manifolds transform in\nstandard deep networks.\n  This thesis lays the groundwork for a computational theory of neuronal\nprocessing of objects, providing quantitative measures for linear separability\nof object manifolds. We hope this theory will provide new insights into the\ncomputational principles underlying processing of sensory representations in\nbiological and artificial neural networks.\n"}
+{"abstract": "  We introduce a perturbation expansion for athermal systems that allows an\nexact determination of displacement fields away from the crystalline state as a\nresponse to disorder. We show that the displacement fields in energy minimized\nconfigurations of particles interacting through central potentials with\nmicroscopic disorder, can be obtained as a series expansion in the strength of\nthe disorder. We introduce a hierarchy of force balance equations that allows\nan order-by-order determination of the displacement fields, with the solutions\nat lower orders providing sources for the higher order solutions. This allows\nthe simultaneous force balance equations to be solved, within a hierarchical\nperturbation expansion to arbitrary accuracy. We present exact results for an\nisotropic defect introduced into the crystalline ground state at linear order\nand second order in our expansion. We show that the displacement fields\nproduced by the defect display interesting self-similar properties at every\norder. We derive a $|\\delta r| \\sim 1/r$ and $|\\delta f| \\sim 1/r^2$ decay for\nthe displacement fields and excess forces at large distances $r$ away from the\ndefect. Finally we derive non-linear corrections introduced by the interactions\nbetween defects at second order in our expansion. We verify our exact results\nwith displacement fields obtained from energy minimized configurations of soft\ndisks.\n"}
+{"abstract": "  We study the problem of simultaneous search for multiple targets over a\nmultidimensional unit cube and derive the fundamental resolution limit of\nnon-adaptive querying procedures using the 20 questions estimation framework.\nThe performance criterion that we consider is the achievable resolution, which\nis defined as the maximal $L_\\infty$ norm between the location vector and its\nestimated version where the maximization is over the possible location vectors\nof all targets. The fundamental resolution limit is then defined as the minimal\nachievable resolution of any non-adaptive query procedure. We drive\nnon-asymptotic and second-order asymptotic bounds on the minimal achievable\nresolution by relating the current problem to a data transmission problem over\na multiple access channel, using the information spectrum method by Han and\nborrowing results from finite blocklength information theory for random access\nchannel coding. Our results extend the purely first-order asymptotic analyses\nof Kaspi \\emph{et al.} (ISIT 2015) for the one-dimensional case. Specifically,\nwe consider more general channels, derive the non-asymptotic and second-order\nasymptotic results and establish a phase transition phenomenon.\n"}
+{"abstract": "  Within vehicles, the Controller Area Network (CAN) allows efficient\ncommunication between the electronic control units (ECUs) responsible for\ncontrolling the various subsystems. The CAN protocol was not designed to\ninclude much support for secure communication. The fact that so many critical\nsystems can be accessed through an insecure communication network presents a\nmajor security concern. Adding security features to CAN is difficult due to the\nlimited resources available to the individual ECUs and the costs that would be\nassociated with adding the necessary hardware to support any additional\nsecurity operations without overly degrading the performance of standard\ncommunication. Replacing the protocol is another option, but it is subject to\nmany of the same problems. The lack of security becomes even more concerning as\nvehicles continue to adopt smart features. Smart vehicles have a multitude of\ncommunication interfaces would an attacker could exploit to gain access to the\nnetworks. In this work we propose a security framework that is based on\nphysically unclonable functions (PUFs) and lightweight cryptography (LWC). The\nframework does not require any modification to the standard CAN protocol while\nalso minimizing the amount of additional message overhead required for its\noperation. The improvements in our proposed framework results in major\nreduction in the number of CAN frames that must be sent during operation. For a\nsystem with 20 ECUs for example, our proposed framework only requires 6.5% of\nthe number of CAN frames that is required by the existing approach to\nsuccessfully authenticate every ECU.\n"}
+{"abstract": "  Traditional statistics forbids use of test data (a.k.a. holdout data) during\ntraining. Dwork et al. 2015 pointed out that current practices in machine\nlearning, whereby researchers build upon each other's models, copying\nhyperparameters and even computer code -- amounts to implicitly training on the\ntest set. Thus error rate on test data may not reflect the true population\nerror. This observation initiated {\\em adaptive data analysis}, which provides\nevaluation mechanisms with guaranteed upper bounds on this difference. With\nstatistical query (i.e. test accuracy) feedbacks, the best upper bound is\nfairly pessimistic: the deviation can hit a practically vacuous value if the\nnumber of models tested is quadratic in the size of the test set.\n  In this work, we present a simple new estimate, {\\em Rip van Winkle's Razor}.\nIt relies upon a new notion of \\textquotedblleft information\ncontent\\textquotedblright\\ of a model: the amount of information that would\nhave to be provided to an expert referee who is intimately familiar with the\nfield and relevant science/math, and who has been just been woken up after\nfalling asleep at the moment of the creation of the test data (like\n\\textquotedblleft Rip van Winkle\\textquotedblright\\ of the famous fairy tale).\nThis notion of information content is used to provide an estimate of the above\ndeviation which is shown to be non-vacuous in many modern settings.\n"}
+{"abstract": "  We bring fresh insight into the ensemble properties of PbS colloidal quantum\ndots with a critical review of the literature on semiconductors followed by\nsystematic comparisons between steady-state photocurrent and photoluminescence\nmeasurements. Our experiments, performed with sufficiently low powers to\nneglect nonlinear effects, indicate that the photoluminescence spectra have no\nother noticeable contribution beside the radiative recombination of thermalized\nphotocarriers (i.e. photocarriers in thermodynamic quasi-equilibrium). A\nphenomenological model based on the local Kirchhoff law is proposed that makes\nit possible to identify the nature of the thermalized photocarriers and to\nextract their temperatures from the measurements. Two regimes are observed: for\nhighly compact assemblies of PbS quantum dots stripped from organic ligands,\nthe thermalization concerns photocarriers distributed over a wide energy range.\nWith PbS quantum dots cross-linked with 1,2-ethanedithiol or longer organic\nligand chains, the thermalization concerns solely the fundamental exciton and\ncan quantitatively explain all the observations, including the precise Stokes\nshift between the absorbance and luminescence maxima.\n"}
+{"abstract": "  NASA's Stardust mission utilized a sample collector composed of aerogel and\naluminum foil to return cometary and interstellar particles to Earth. Analysis\nof the aluminum foil begins with locating craters produced by hypervelocity\nimpacts of cometary and interstellar dust. Interstellar dust craters are\ntypically less than one micrometer in size and are sparsely distributed, making\nthem difficult to find. In this paper, we describe a convolutional neural\nnetwork based on the VGG16 architecture that achieves high specificity and\nsensitivity in locating impact craters in the Stardust interstellar collector\nfoils. We evaluate its implications for current and future analyses of Stardust\nsamples.\n"}
+{"abstract": "  We prove Veech's conjecture on the equivalence of Sarnak's conjecture on\nM\\\"obius orthogonality with a Kolmogorov type property of Furstenberg systems\nof the M\\''obius function. This yields a combinatorial condition on the\nM\\\"obius function itself which is equivalent to Sarnak's conjecture. As a\nmatter of fact, our arguments remain valid in a larger context: we characterize\nall bounded arithmetic functions orthogonal to all topological systems whose\nall ergodic measures yield systems from a fixed characteristic class (zero\nentropy class is an example of such a characteristic class) with the\ncharacterization persisting in the logarithmic setup. As a corollary, we obtain\nthat the logarithmic Sarnak's conjecture holds if and only if the logarithmic\nM\\''obius orthogonality is satisfied for all dynamical systems whose ergodic\nmeasures yield nilsystems.\n"}
+{"abstract": "  A new numerical approach is proposed for the simulation of coupled\nthree-dimensional and one-dimensional elliptic equations (3D-1D coupling)\narising from dimensionality reduction of 3D-3D problems with thin inclusions.\nThe method is based on a well posed mathematical formulation and results in a\nnumerical scheme with high robustness and flexibility in handling geometrical\ncomplexities. This is achieved by means of a three-field approach to split the\n1D problems from the bulk 3D problem, and then resorting to the minimization of\na properly designed functional to impose matching conditions at the interfaces.\nThanks to the structure of the functional, the method allows the use of\nindependent meshes for the various subdomains.\n"}
+{"abstract": "  For real-time semantic segmentation, how to increase the speed while\nmaintaining high resolution is a problem that has been discussed and solved.\nBackbone design and fusion design have always been two essential parts of\nreal-time semantic segmentation. We hope to design a light-weight network based\non previous design experience and reach the level of state-of-the-art real-time\nsemantic segmentation without any pre-training. To achieve this goal, a\nencoder-decoder architectures are proposed to solve this problem by applying a\ndecoder network onto a backbone model designed for real-time segmentation tasks\nand designed three different ways to fuse semantics and detailed information in\nthe aggregation phase. We have conducted extensive experiments on two semantic\nsegmentation benchmarks. Experiments on the Cityscapes and CamVid datasets show\nthat the proposed FRFNet strikes a balance between speed calculation and\naccuracy. It achieves 72% Mean Intersection over Union (mIoU%) on the\nCityscapes test dataset with the speed of 144 on a single RTX 1080Ti card. The\nCode is available at https://github.com/favoMJ/FRFNet.\n"}
+{"abstract": "  Recently discovered ferromagnetism of the layered van der Waals material\nVI$_3$ attracts much research attention. Despite substantial progress,in the\nfollowing important aspects no consensus has been reached: (i) a possible\ndeviation of the easy axis from the normal to the VI$_3$ layers, (ii) a\npossible inequivalence of the V atoms, (iii) the value of the V magnetic\nmoments. The theoretical works differ in the conclusions on the conduction\nnature of the system,the value and the role of the V orbital moments. To the\nbest of our knowledge there is no theoretical works addressing issues (i) and\n(ii) and only one work dealing with the reduced value of the V moment. By\ncombining the symmetry arguments with density functional theory (DFT) and\nDFT+$U$ calculations we have shown that the antidimerization distortion of the\ncrystal structure reported in Phys. Rev. B {\\bf 99}, 041402(R) (2019) must lead\nto the deviation of the easy axis from the normal to the VI$_3$ layers in close\ncorrelation with the experimental results. The antidimerization accompanied by\nthe breaking the inversion symmetry leads to the inequivalence of the V atoms.\nOur DFT+U calculations result in large value 0.8\\mu_B$ of the V orbital moments\nof the V atoms leading to reduced total V moment in agreement with a number of\nexperimental results and with the physical picture suggested in Phys. Rev. B bf\n101, 100402(R) (2020). We obtained large intraatomic noncollinearity of the V\nspin and orbital moments revealing strong competition between effects coursed\nby the on-site electron correlation, spin-orbit coupling, and interatomic\nhybridization since pure intraatomic effects lead to collinear spin and orbital\nmoments. Our calculations confirm the experimental results of strong\nmagnetoelastic coupling revealing itself in the strong dependence of the\nmagnetic properties on the distortion of the atomic structure.\n"}
+{"abstract": "  We consider the Nemytskii operators $u\\to |u|$ and $u\\to u^{\\pm}$ in a\nbounded domain $\\Omega$ with $C^2$ boundary. We give elementary proofs of the\nboundedness in $H^s(\\Omega)$ with $0\\le s<3/2$.\n"}
+{"abstract": "  The Central Sets Theorem was introduced by H. Furstenberg and then afterwards\nseveral mathematicians have provided various versions and extensions of this\ntheorem. All of these theorems deal with central sets, and its origin from the\nalgebra of Stone-Cech compactification of arbitrary semigroup, say $\\beta S$.\nIt can be proved that every closed subsemigroup of $\\beta S$ is generated by a\nfilter. We will show that, under some restrictions, one can derive the Central\nSets Theorem for any closed subsemigroup of $\\beta S$ . We will derive this\ntheorem using the corresponding filter and its algebra. Later we will also deal\nwith how the notions of largeness along filters are preserved under some well\nbehaved homomorphisms and give some consequences.\n"}
+{"abstract": "  In this paper we conjecture combinatorial Rogers-Ramanujan type colored\npartition identities related to standard representations of the affine Lie\nalgebra of type $C^{(1)}_\\ell$, $\\ell\\geq2$, and we conjecture similar colored\npartition identities with no obvious connection to representation theory of\naffine Lie algebras.\n"}
+{"abstract": "  Understanding the limits of phononic heat dissipation from a two-dimensional\nlayered material (2DLM) to its hexagonal boron nitride (h-BN) substrate and how\nit varies with the structure of the 2DLM is important for the design and\nthermal management of h-BN-supported nanoelectronic devices. We formulate an\nelasticity-based theory to model the phonon-mediated heat dissipation between a\n2DLM and its h-BN substrate. By treating the h-BN substrate as a semi-infinite\nstack of harmonically coupled thin plates, we obtain semi-analytical\nexpressions for the thermal boundary conductance (TBC) and interfacial phonon\ntransmission spectrum. We evaluate the temperature-dependent TBC of the\n$N$-layer 2DLM (graphene or MoS$_{2}$) on different common substrates (h-BN vs.\na-SiO$_{2}$) at different values of $N$. The results suggest that h-BN is\nsubstantially more effective for heat dissipation from MoS$_{2}$ than\na-SiO$_{2}$ especially at large $N$. To understand the limitations of the our\nstack model, we also compare its predictions in the $N=\\infty$ limit to those\nof the more exact Atomistic Green's Function model for the graphite-BN and\nmolybdenite-BN interfaces. Our stack model provides clear insights into the key\nrole of the flexural modes in the TBC and how the anisotropic elastic\nproperties of h-BN affect heat dissipation.\n"}
+{"abstract": "  Shimizu introduced a region crossing change unknotting operation for knot\ndiagrams. As extensions, two integral region choice problems were proposed and\nthe existences of solutions of the problems were shown for all non-trivial knot\ndiagrams by Ahara and Suzuki, and Harada. We relate both integral region choice\nproblems with an Alexander numbering for regions of a link diagram, and give\nalternative proofs of the existences of solutions for knot diagrams. We also\ndiscuss the problems on link diagrams. For each of the problems on the diagram\nof a two-component link, we give a necessary and sufficient condition that\nthere exists a solution.\n"}
+{"abstract": "  By means of identical cubic elements, we generate a partition of a volume in\nwhich a particle-based cosmological simulation is carried out. In each cubic\nelement, we determine the gas particles with a normalized density greater than\nan arbitrarily chosen density threshold. By using a proximity parameter, we\ncalculate the neighboring cubic elements and generate a list of neighbors. By\nimposing dynamic conditions on the gas particles, we identify gas clumps and\ntheir neighbors, so that we calculate and fit some properties of the groups so\nidentified, including the mass, size and velocity dispersion, in terms of their\nmultiplicity (here defined simply as the number of member galaxies). Finally,\nwe report the value of the ratio of kinetic energy to gravitational energy of\nsuch dense gas clumps, which will be useful as initial conditions in\nsimulations of gravitational collapse of gas clouds and clusters of gas clouds.\n"}
+{"abstract": "  Boundary representation (B-rep) models are the standard way 3D shapes are\ndescribed in Computer-Aided Design (CAD) applications. They combine lightweight\nparametric curves and surfaces with topological information which connects the\ngeometric entities to describe manifolds. In this paper we introduce BRepNet, a\nneural network architecture designed to operate directly on B-rep data\nstructures, avoiding the need to approximate the model as meshes or point\nclouds. BRepNet defines convolutional kernels with respect to oriented coedges\nin the data structure. In the neighborhood of each coedge, a small collection\nof faces, edges and coedges can be identified and patterns in the feature\nvectors from these entities detected by specific learnable parameters. In\naddition, to encourage further deep learning research with B-reps, we publish\nthe Fusion 360 Gallery segmentation dataset. A collection of over 35,000 B-rep\nmodels annotated with information about the modeling operations which created\neach face. We demonstrate that BRepNet can segment these models with higher\naccuracy than methods working on meshes, and point clouds.\n"}
+{"abstract": "  Low-mass compact galaxies (ultracompact dwarfs [UCDs] and compact ellipticals\n[cEs]) populate the stellar size-mass plane between globular clusters and\nearly-type galaxies. Known to be formed either in-situ with an intrinsically\nlow mass or resulting from the stripping of a more massive galaxy, the presence\nof a supermassive or an intermediate-mass black hole (BH) could help\ndiscriminate between these possible scenarios. With this aim, we have performed\na multiwavelength search of active BH activity, i.e. active galactic nuclei\n(AGN), in a sample of 937 low-mass compact galaxies (580 UCDs and 357 cEs).\nThis constitutes the largest study of AGN activity in these types of galaxies.\nBased on their X-ray luminosity, radio luminosity and morphology, and/or\noptical emission line diagnostic diagrams, we find a total of 11 cEs that host\nan AGN. We also study for the first time the location of both low-mass compact\ngalaxies (UCDs and cEs) and dwarf galaxies hosting AGN on the BH-galaxy scaling\nrelations, finding that low-mass compact galaxies tend to be overmassive in the\nBH mass-stellar mass plane but not as much in the BH mass-stellar velocity\ndispersion correlation. This, together with available BH mass measurements for\nsome of the low-mass compact galaxies, supports a stripping origin for the\nmajority of these objects that would contribute to the scatter seen at the\nlow-mass end of the BH-galaxy scaling relations. However, the differences are\ntoo large to be explained solely by this scatter, and thus our results suggest\nthat a flattening at such low-masses is also plausible, happening at a velocity\ndispersion of ~20-40 km/s.\n"}
+{"abstract": "  The first and second-order supersymmetry transformations can be used to\nmanipulate one or two energy levels of the initial spectrum when generating new\nexactly solvable Hamiltonians from a given initial potential. In this paper, we\nwill construct the first and second-order supersymmetric partners of the\ntrigonometric Rosen-Morse potential. Firstly, it is identified a set of\nsolutions of the initial stationary Schr\\\"odinger equation which are\nappropriate for implementing in a simple way non-singular transformations,\nwithout inducing new singularities in the built potential. Then, the way the\nspectral manipulation works is illustrated through several specific examples.\n"}
+{"abstract": "  We define a subclass of quasiregular curves, called signed quasiregular\ncurves, which contains holomorphic curves and quasiregular mappings. As our\nmain result, we prove a growth theorem of Bonk-Heinonen type for signed\nquasiregular curves. To obtain our main result, we prove that signed\nquasiregular curves satisfy a weak reverse H\\\"older inequality and that this\nweak reverse H\\\"older inequality implies the main result. We also obtain higher\nintegrability for signed quasiregular curves. Further, we prove a cohomological\nvalue distribution result for signed quasiregular curves by using our main\nresult and equidistribution.\n"}
+{"abstract": "  Visual object localization is the key step in a series of object detection\ntasks. In the literature, high localization accuracy is achieved with the\nmainstream strongly supervised frameworks. However, such methods require\nobject-level annotations and are unable to detect objects of unknown\ncategories. Weakly supervised methods face similar difficulties. In this paper,\na self-paced learning framework is proposed to achieve accurate object\nlocalization on the rank list returned by instance search. The proposed\nframework mines the target instance gradually from the queries and their\ncorresponding top-ranked search results. Since a common instance is shared\nbetween the query and the images in the rank list, the target visual instance\ncan be accurately localized even without knowing what the object category is.\nIn addition to performing localization on instance search, the issue of\nfew-shot object detection is also addressed under the same framework. Superior\nperformance over state-of-the-art methods is observed on both tasks.\n"}
+{"abstract": "  We investigate theoretically and numerically the light-matter interaction in\na two-level system (TLS) as a model system for excitation in a solid-state band\nstructure. We identify five clearly distinct excitation regimes, categorized\nwith well-known adiabaticity parameters: (1) the perturbative multiphoton\nabsorption regime for small driving field strengths, and four light\nfield-driven regimes, where intraband motion connects different TLS: (2) the\nimpulsive Landau-Zener (LZ) regime, (3) the non-impulsive LZ regime, (4) the\nadiabatic regime and (5) the adiabatic-impulsive regime for large electric\nfield strengths. This categorization is tremendously helpful to understand the\nhighly complex excitation dynamics in any TLS, in particular when the driving\nfield strength varies, and naturally connects Rabi physics with Landau-Zener\nphysics. In addition, we find an insightful analytical expression for the\nphoton orders connecting the perturbative multiphoton regime with the light\nfield-driven regimes. Moreover, in the adiabatic-impulsive regime, adiabatic\nmotion and impulsive LZ transitions are equally important, leading to an\ninversion symmetry breaking of the TLS when applying few-cycle laser pulses.\nThis categorization allows a deep understanding of driven TLS in a large\nvariety of settings ranging from cold atoms and molecules to solids and qubits,\nand will help to find optimal driving parameters for a given purpose.\n"}
+{"abstract": "  We construct examples of centrally harmonic spaces by generalizing work of\nCopson and Ruse. We show that these examples are generically not centrally\nharmonic at other points. We use this construction to exhibit manifolds which\nare not conformally flat but such that their density function agrees with\nEuclidean space.\n"}
+{"abstract": "  Understanding how sea quarks behave inside a nucleon is one of the most\nimportant physics goals of the proposed Electron-Ion Collider in China (EicC),\nwhich is designed to have 3.5 GeV polarized electron beam (80% polarization)\ncolliding with 20 GeV polarized proton beam (70% polarization) at instantaneous\nluminosity of $2 \\times 10^{33} {\\rm cm}^{-2} {\\rm s}^{-1}$. A specific topic\nat EicC is to understand the polarization of individual quarks inside a\nlongitudinally polarized nucleon. The potential of various future EicC data,\nincluding the inclusive and semi-inclusive deep inelastic scattering data from\nboth doubly polarized electron-proton and electron-$^3{\\rm He}$ collisions, to\nreduce the uncertainties of parton helicity distributions is explored at the\nnext-to-leading order in QCD, using the Error PDF Updating Method Package ({\\sc\nePump}) which is based on the Hessian profiling method. We show that the\nsemi-inclusive data are well able to provide good separation between flavour\ndistributions, and to constrain their uncertainties in the $x>0.005$ region,\nespecially when electron-$^3{\\rm He}$ collisions, acting as effective\nelectron-neutron collisions, are taken into account. To enable this study, we\nhave generated a Hessian representation of the DSSV14 set of PDF replicas,\nnamed DSSV14H PDFs.\n"}
+{"abstract": "  The $K$-hull of a compact set $A\\subset\\mathbb{R}^d$, where $K\\subset\n\\mathbb{R}^d$ is a fixed compact convex body, is the intersection of all\ntranslates of $K$ that contain $A$. A set is called $K$-strongly convex if it\ncoincides with its $K$-hull. We propose a general approach to the analysis of\nfacial structure of $K$-strongly convex sets, similar to the well developed\ntheory for polytopes, by introducing the notion of $k$-dimensional faces, for\nall $k=0,\\dots,d-1$. We then apply our theory in the case when $A=\\Xi_n$ is a\nsample of $n$ points picked uniformly at random from $K$. We show that in this\ncase the set of $x\\in\\mathbb{R}^d$ such that $x+K$ contains the sample $\\Xi_n$,\nupon multiplying by $n$, converges in distribution to the zero cell of a\ncertain Poisson hyperplane tessellation. From this results we deduce\nconvergence in distribution of the corresponding $f$-vector of the $K$-hull of\n$\\Xi_n$ to a certain limiting random vector, without any normalisation, and\nalso the convergence of all moments of the $f$-vector.\n"}
+{"abstract": "  The Auger Surface Detector consists of a large array of water Cherenkov\ndetector tanks each with a volume of 12,000 liters, for the detection of high\nenergy cosmic rays. The accuracy in the measurement of the integrated signal\namplitude of the detector unit has been studied using experimental air shower\ndata. It can be described as a Poisson-like term with a normalization constant\nthat depends on the zenith angle of the primary cosmic ray. This dependence\nreflects the increasing contribution to the signal of the muonic component of\nthe shower, both due to the increasing muon/electromagnetic (e+- and gamma)\nratio and muon track length with zenith angle.\n"}
+{"abstract": "  Muon beams of low emittance provide the basis for the intense,\nwell-characterised neutrino beams of a neutrino factory and for multi-TeV\nlepton-antilepton collisions at a muon collider. The international Muon\nIonization Cooling Experiment (MICE) has demonstrated the principle of\nionization cooling, the technique by which it is proposed to reduce the\nphase-space volume occupied by the muon beam at such facilities. This paper\ndocuments the performance of the detectors used in MICE to measure the\nmuon-beam parameters, and the physical properties of the liquid hydrogen energy\nabsorber during running.\n"}
+{"abstract": "  Is it possible to use natural language to intervene in a model's behavior and\nalter its prediction in a desired way? We investigate the effectiveness of\nnatural language interventions for reading-comprehension systems, studying this\nin the context of social stereotypes. Specifically, we propose a new language\nunderstanding task, Linguistic Ethical Interventions (LEI), where the goal is\nto amend a question-answering (QA) model's unethical behavior by communicating\ncontext-specific principles of ethics and equity to it. To this end, we build\nupon recent methods for quantifying a system's social stereotypes, augmenting\nthem with different kinds of ethical interventions and the desired model\nbehavior under such interventions. Our zero-shot evaluation finds that even\ntoday's powerful neural language models are extremely poor ethical-advice\ntakers, that is, they respond surprisingly little to ethical interventions even\nthough these interventions are stated as simple sentences. Few-shot learning\nimproves model behavior but remains far from the desired outcome, especially\nwhen evaluated for various types of generalization. Our new task thus poses a\nnovel language understanding challenge for the community.\n"}
+{"abstract": "  We study a class of general U$(1)^\\prime$ models to explain the observed dark\nmatter relic abundance and light neutrino masses. The model contains three\nright handed neutrinos and three gauge singlet Majorana fermions to generate\nthe light neutrino mass via the inverse seesaw mechanism. We assign one pair of\ndegenerate sterile neutrinos to be the dark matter candidate whose relic\ndensity is generated by the freeze-in mechanism. We consider different regimes\nof the masses of the dark matter particle and the ${Z^\\prime}$ gauge boson. The\nproduction of the dark matter can occur at different reheating temperatures in\nvarious scenarios depending on the masses of the ${Z^\\prime}$ boson and the\ndark matter candidate. We also note that if the mass of the sterile neutrino\ndark matter is $\\gtrsim 1 \\rm{MeV}$ and if the $Z^\\prime$ is heavier than the\ndark matter, the decay of the dark matter candidate into positrons can explain\nthe long standing puzzle of the galactic $511\\rm{keV}$ line in the Milky Way\ncenter observed by the INTEGRAL satellite. We constrain the model parameters\nfrom the dark matter analysis, vacuum stability and the collider searches of\nheavy ${Z^\\prime}$ at the LHC. For the case with light $Z^\\prime$, we also\ncompare how far the parameter space allowed from dark matter relic density can\nbe probed by the future lifetime frontier experiments SHiP and FASERs in the\nspecial case of $U(1)_{B-L}$ model.\n"}
+{"abstract": "  We prove that for any non-symmetric irreducible divisible convex set, the\nproximal limit set is the full projective boundary.\n"}
+{"abstract": "  For graphical user interface (UI) design, it is important to understand what\nattracts visual attention. While previous work on saliency has focused on\ndesktop and web-based UIs, mobile app UIs differ from these in several\nrespects. We present findings from a controlled study with 30 participants and\n193 mobile UIs. The results speak to a role of expectations in guiding where\nusers look at. Strong bias toward the top-left corner of the display, text, and\nimages was evident, while bottom-up features such as color or size affected\nsaliency less. Classic, parameter-free saliency models showed a weak fit with\nthe data, and data-driven models improved significantly when trained\nspecifically on this dataset (e.g., NSS rose from 0.66 to 0.84). We also\nrelease the first annotated dataset for investigating visual saliency in mobile\nUIs.\n"}
+{"abstract": "  The nonlocal Darboux transformation for the stationary axially symmetric\nSchr\\\"odinger and Helmholtz equations is considered. Formulae for the nonlocal\nDarboux transformation are obtained and its relation to the generalized Moutard\ntransformation is established. New examples of two - dimensional potencials and\nexact solutions for the stationary axially symmetric Schr\\\"odinger and\nHelmholtz equations are obtained as an application of the nonlocal Darboux\ntransformation.\n"}
+{"abstract": "  A high-order quadrature algorithm is presented for computing integrals over\ncurved surfaces and volumes whose geometry is implicitly defined by the level\nsets of (one or more) multivariate polynomials. The algorithm recasts the\nimplicitly defined geometry as the graph of an implicitly defined, multi-valued\nheight function, and applies a dimension reduction approach needing only\none-dimensional quadrature. In particular, we explore the use of Gauss-Legendre\nand tanh-sinh methods and demonstrate that the quadrature algorithm inherits\ntheir high-order convergence rates. Under the action of $h$-refinement with $q$\nfixed, the quadrature schemes yield an order of accuracy of $2q$, where $q$ is\nthe one-dimensional node count; numerical experiments demonstrate up to 22nd\norder. Under the action of $q$-refinement with the geometry fixed, the\nconvergence is approximately exponential, i.e., doubling $q$ approximately\ndoubles the number of accurate digits of the computed integral. Complex\ngeometry is automatically handled by the algorithm, including, e.g.,\nmulti-component domains, tunnels, and junctions arising from multiple\npolynomial level sets, as well as self-intersections, cusps, and other kinds of\nsingularities. A variety of numerical experiments demonstrates the quadrature\nalgorithm on two- and three-dimensional problems, including: randomly generated\ngeometry involving multiple high-curvature pieces; challenging examples\ninvolving high degree singularities such as cusps; adaptation to simplex\nconstraint cells in addition to hyperrectangular constraint cells; and boolean\noperations to compute integrals on overlapping domains.\n"}
+{"abstract": "  Automated three-dimensional (3D) object reconstruction is the task of\nbuilding a geometric representation of a physical object by means of sensing\nits surface. Even though new single view reconstruction techniques can predict\nthe surface, they lead to incomplete models, specially, for non commons objects\nsuch as antique objects or art sculptures. Therefore, to achieve the task's\ngoals, it is essential to automatically determine the locations where the\nsensor will be placed so that the surface will be completely observed. This\nproblem is known as the next-best-view problem. In this paper, we propose a\ndata-driven approach to address the problem. The proposed approach trains a 3D\nconvolutional neural network (3D CNN) with previous reconstructions in order to\nregress the \\btxt{position of the} next-best-view. To the best of our\nknowledge, this is one of the first works that directly infers the\nnext-best-view in a continuous space using a data-driven approach for the 3D\nobject reconstruction task. We have validated the proposed approach making use\nof two groups of experiments. In the first group, several variants of the\nproposed architecture are analyzed. Predicted next-best-views were observed to\nbe closely positioned to the ground truth. In the second group of experiments,\nthe proposed approach is requested to reconstruct several unseen objects,\nnamely, objects not considered by the 3D CNN during training nor validation.\nCoverage percentages of up to 90 \\% were observed. With respect to current\nstate-of-the-art methods, the proposed approach improves the performance of\nprevious next-best-view classification approaches and it is quite fast in\nrunning time (3 frames per second), given that it does not compute the\nexpensive ray tracing required by previous information metrics.\n"}
+{"abstract": "  Here we present detailed analysis of the distinct X-ray emission features\npresent within the Eastern radio lobe of the Pictor A galaxy, around the jet\ntermination region, utilising the data obtained from the Chandra X-ray\nObservatory. Various emission features have been selected for the study based\non their enhanced X-ray surface brightness, including five sources that appear\npoint-like, as well as three extended regions, one characterised by a\nfilamentary morphology. For those, we perform a basic spectral analysis within\nthe 0.5-7keV range. We also investigate various correlations between the X-ray\nemission features and the non-thermal radio emission, utilising the\nhigh-resolution radio maps from the Very Large Array at GHz frequencies. The\nmain novel findings following from our analysis, regard the newly recognized\nbright X-ray filament located upstream of the jet termination region, extending\nfor at least thirty kiloparsec (projected), and inclined with respect to the\njet axis. For this feature, we observe a clear anti-correlation between the\nX-ray surface brightness and the polarized radio intensity, as well as a\ndecrease in the radio rotation measure with respect to the surroundings. We\nspeculate on the nature of the filament, in particular addressing a possibility\nthat it is related to the presence of a hot X-ray emitting thermal gas, only\npartly mixed with the non-thermal radio/X-ray emitting electrons within the\nlobe, combined with the reversals in the lobe's net magnetic field.\n"}
+{"abstract": "  We prove bounds in the local $ L^2 $ range for exotic paraproducts motivated\nby bilinear multipliers associated with convex sets. One result assumes an\nexponential boundary curve. Another one assumes a higher order lacunarity\ncondition.\n"}
+{"abstract": "  Pressure calibration for most diamond-anvil cell (DAC) experiments is mainly\nbased on the ruby scale, which is key to implement this powerful tool for\nhigh-pressure study. However, the ruby scale can often hardly be used for\nprogrammably-controlled DAC devices, especially the piezoelectric-driving\ncells, where a continuous pressure calibration is required. In this work, we\npresent an effective pressure gauge for DACs made of manganin metal, based on\nthe four-probe resistivity measurements. Pressure dependence of its resistivity\nis well established and shows excellent linear relations in the 0 - 30 GPa\npressure range with a slope of 23.4 (9) GPa for the first-cycle compression, in\ncontrast to that of multiple-cycle compression and decompression having a\nnearly identical slope of 33.7 (4) GPa likely due to the strain effect. In\naddition, such-established manganin scale can be used for continuously\nmonitoring the cell pressure of piezoelectric-driving DACs, and the reliability\nof this method is also verified by the fixed-point method with a Bi pressure\nstandard. Realization of continuous pressure calibration for\nprogrammably-controlled DACs would offer many opportunities for study of\ndynamics, kinetics, and critical behaviors of pressure-induced phase\ntransitions.\n"}
+{"abstract": "  We give a protocol for Asynchronous Distributed Key Generation (A-DKG) that\nis optimally resilient (can withstand $f<\\frac{n}{3}$ faulty parties), has a\nconstant expected number of rounds, has $\\tilde{O}(n^3)$ expected communication\ncomplexity, and assumes only the existence of a PKI. Prior to our work, the\nbest A-DKG protocols required $\\Omega(n)$ expected number of rounds, and\n$\\Omega(n^4)$ expected communication.\n  Our A-DKG protocol relies on several building blocks that are of independent\ninterest. We define and design a Proposal Election (PE) protocol that allows\nparties to retrospectively agree on a valid proposal after enough proposals\nhave been sent from different parties. With constant probability the elected\nproposal was proposed by a non-faulty party. In building our PE protocol, we\ndesign a Verifiable Gather protocol which allows parties to communicate which\nproposals they have and have not seen in a verifiable manner. The final\nbuilding block to our A-DKG is a Validated Asynchronous Byzantine Agreement\n(VABA) protocol. We use our PE protocol to construct a VABA protocol that does\nnot require leaders or an asynchronous DKG setup. Our VABA protocol can be used\nmore generally when it is not possible to use threshold signatures.\n"}
+{"abstract": "  This paper develops an averaging technique based on the combination of the\neigenfunction expansion method and the collaboration method to investigate the\nmultiple scattering effect of the SH wave propagation in a porous medium. The\nsemi-analytical averaging technique is conducted using Monto Carlo method to\nunderstand the macroscopic dispersion and attenuation phenomena of the stress\nwave propagation in a porous solid caused by the multiple scattering effects.\nThe averaging technique is verified by finite element analysis. Finally, a\nsimple homogenized elastic model with damping is proposed to describe the\nmacroscopic dispersion and attenuation effects of SH waves in porous media.\n"}
+{"abstract": "  We analyze the spectral stability of small-amplitude, periodic,\ntraveling-wave solutions of a Boussinesq-Whitham system. These solutions are\nshown numerically to exhibit high-frequency instabilities when subject to\nbounded perturbations on the real line. We use a formal perturbation method to\nestimate the asymptotic behavior of these instabilities in the small-amplitude\nregime. We compare these asymptotic results with direct numerical computations.\nThis is the second paper in a series of three that investigates high-frequency\ninstabilities of Stokes waves.\n"}
+{"abstract": "  Crowdworker-constructed natural language inference (NLI) datasets have been\nfound to contain statistical artifacts associated with the annotation process\nthat allow hypothesis-only classifiers to achieve better-than-random\nperformance (Poliak et al., 2018; Gururanganet et al., 2018; Tsuchiya, 2018).\nWe investigate whether MedNLI, a physician-annotated dataset with premises\nextracted from clinical notes, contains such artifacts (Romanov and Shivade,\n2018). We find that entailed hypotheses contain generic versions of specific\nconcepts in the premise, as well as modifiers related to responsiveness,\nduration, and probability. Neutral hypotheses feature conditions and behaviors\nthat co-occur with, or cause, the condition(s) in the premise. Contradiction\nhypotheses feature explicit negation of the premise and implicit negation via\nassertion of good health. Adversarial filtering demonstrates that performance\ndegrades when evaluated on the difficult subset. We provide partition\ninformation and recommendations for alternative dataset construction strategies\nfor knowledge-intensive domains.\n"}
+{"abstract": "  Cyber-defense systems are being developed to automatically ingest Cyber\nThreat Intelligence (CTI) that contains semi-structured data and/or text to\npopulate knowledge graphs. A potential risk is that fake CTI can be generated\nand spread through Open-Source Intelligence (OSINT) communities or on the Web\nto effect a data poisoning attack on these systems. Adversaries can use fake\nCTI examples as training input to subvert cyber defense systems, forcing the\nmodel to learn incorrect inputs to serve their malicious needs.\n  In this paper, we automatically generate fake CTI text descriptions using\ntransformers. We show that given an initial prompt sentence, a public language\nmodel like GPT-2 with fine-tuning, can generate plausible CTI text with the\nability of corrupting cyber-defense systems. We utilize the generated fake CTI\ntext to perform a data poisoning attack on a Cybersecurity Knowledge Graph\n(CKG) and a cybersecurity corpus. The poisoning attack introduced adverse\nimpacts such as returning incorrect reasoning outputs, representation\npoisoning, and corruption of other dependent AI-based cyber defense systems. We\nevaluate with traditional approaches and conduct a human evaluation study with\ncybersecurity professionals and threat hunters. Based on the study,\nprofessional threat hunters were equally likely to consider our fake generated\nCTI as true.\n"}
+{"abstract": "  Recently, evidence has emerged for a field-induced even- to odd-parity\nsuperconducting phase transition in CeRh$_2$As$_2$ [S. Khim et al., Science 373\n1012 (2021)]. Here we argue that the P4/nmm non-symmorphic crystal structure of\nCeRh$_2$As$_2$ plays a key role in enabling this transition by ensuring large\nspin-orbit interactions near the Brillouin zone boundaries, which naturally\nleads to the required near-degeneracy of the even- and odd-parity channels. We\nfurther comment on the relevance of our theory to FeSe, which crystallizes in\nthe same structure.\n"}
+{"abstract": "  This article discusses self-organization in cold atoms via light-mediated\ninteractions induced by feedback from a single retro-reflecting mirror.\nDiffractive dephasing between the pump beam and the spontaneous sidebands\nselects the lattice period. Spontaneous breaking of the rotational and\ntranslational symmetry occur in the 2D plane transverse to the pump. We\nelucidate how diffractive ripples couple sites on the self-induced atomic\nlattice. The nonlinear phase shift of the atomic cloud imprinted onto the\noptical beam is the parameter determining coupling strength. The interaction\ncan be tailored to operate either on external degrees of freedom leading to\natomic crystallization for thermal atoms and supersolids for a quantum\ndegenerate gas, or on internal degrees of freedom like populations of the\nexcited state or Zeeman sublevels. Using the light polarization degrees of\nfreedom on the Poincar{\\'e} sphere (helicity and polarization direction),\nspecific irreducible tensor components of the atomic Zeeman states can be\ncoupled leading to spontaneous magnetic ordering of states of dipolar and\nquadrupolar nature. The requirements for critical interaction strength are\ncompared for the different situations. Connections and extensions to\nlongitudinally pumped cavities, counterpropagating beam schemes and the CARL\ninstability are discussed.\n"}
+{"abstract": "  We investigate transient nonlinear localization, namely the self-excitation\nof energy bursts in an atomic lattice at finite temperature. As a basic model\nwe consider the diatomic Lennard-Jones chain. Numerical simulations suggest\nthat the effect originates from two different mechanisms. One is the thermal\nexcitation of genuine discrete breathers with frequency in the phonon gap. The\nsecond is an effect of nonlinear coupling of fast, lighter particles with slow\nvibrations of the heavier ones. The quadratic term of the force generate an\neffective potential that can lead to transient grow of local energy on time\nscales the can be relatively long for small mass ratios. This heuristics is\nsupported by a multiple-scale approximation based on the natural time-scale\nseparation. For illustration, we consider a simplified single-particle model\nthat allows for some insight of the localization dynamics.\n"}
+{"abstract": "  The observation by BESIII and LHCb of states with hidden charm and open\nstrangeness ($c\\bar c q\\bar s$) presents new opportunities for the development\nof a global model of heavy-quark exotics. Here we extend the dynamical diquark\nmodel to encompass such states, using the same values of Hamiltonian parameters\npreviously obtained from the nonstrange and hidden-strange sectors. The large\nmass splitting between $Z_{cs}(4000)$ and $Z_{cs}(4220)$ suggests substantial\nSU(3)$_{\\rm flavor}$ mixing between all $J^P \\! = \\! 1^+$ states, while their\naverage mass compared to that of other sectors offers a direct probe of flavor\noctet-singlet mixing among exotics. We also explore the inclusion of\n$\\eta$-like exchanges within the states, and find their effects to be quite\nlimited. In addition, using the same diquark-mass parameters, we find\n$P_c(4312)$ and $P_{cs}(4459)$ to fit well as corresponding nonstrange and\nopen-strange pentaquarks.\n"}
+{"abstract": "  Appearance-based detectors achieve remarkable performance on common scenes,\nbut tend to fail for scenarios lack of training data. Geometric motion\nsegmentation algorithms, however, generalize to novel scenes, but have yet to\nachieve comparable performance to appearance-based ones, due to noisy motion\nestimations and degenerate motion configurations. To combine the best of both\nworlds, we propose a modular network, whose architecture is motivated by a\ngeometric analysis of what independent object motions can be recovered from an\negomotion field. It takes two consecutive frames as input and predicts\nsegmentation masks for the background and multiple rigidly moving objects,\nwhich are then parameterized by 3D rigid transformations. Our method achieves\nstate-of-the-art performance for rigid motion segmentation on KITTI and Sintel.\nThe inferred rigid motions lead to a significant improvement for depth and\nscene flow estimation. At the time of submission, our method ranked 1st on\nKITTI scene flow leaderboard, out-performing the best published method (scene\nflow error: 4.89% vs 6.31%).\n"}
+{"abstract": "  The planning of whole-body motion and step time for bipedal locomotion is\nconstructed as a model predictive control (MPC) problem, in which a sequence of\noptimization problems needs to be solved online. While directly solving these\nproblems is extremely time-consuming, we propose a predictive gait synthesizer\nto offer immediate solutions. Based on the full-dimensional model, a library of\ngaits with different speeds and periods is first constructed offline. Then the\nproposed gait synthesizer generates real-time gaits at 1kHz by synthesizing the\ngait library based on the online prediction of centroidal dynamics. We prove\nthat the constructed MPC problem can ensure the uniform ultimate boundedness\n(UUB) of the CoM states and show that our proposed gait synthesizer can provide\nfeasible solutions to the MPC optimization problems. Simulation and\nexperimental results on a bipedal robot with 8 degrees of freedom (DoF) are\nprovided to show the performance and robustness of this approach.\n"}
+{"abstract": "  Meshfree discretizations of state-based peridynamic models are attractive due\nto their ability to naturally describe fracture of general materials. However,\ntwo factors conspire to prevent meshfree discretizations of state-based\nperidynamics from converging to corresponding local solutions as resolution is\nincreased: quadrature error prevents an accurate prediction of bulk mechanics,\nand the lack of an explicit boundary representation presents challenges when\napplying traction loads. In this paper, we develop a reformulation of the\nlinear peridynamic solid (LPS) model to address these shortcomings, using\nimproved meshfree quadrature, a reformulation of the nonlocal dilitation, and a\nconsistent handling of the nonlocal traction condition to construct a model\nwith rigorous accuracy guarantees. In particular, these improvements are\ndesigned to enforce discrete consistency in the presence of evolving fractures,\nwhose {\\it a priori} unknown location render consistent treatment difficult. In\nthe absence of fracture, when a corresponding classical continuum mechanics\nmodel exists, our improvements provide asymptotically compatible convergence to\ncorresponding local solutions, eliminating surface effects and issues with\ntraction loading which have historically plagued peridynamic discretizations.\nWhen fracture occurs, our formulation automatically provides a sharp\nrepresentation of the fracture surface by breaking bonds, avoiding the loss of\nmass. We provide rigorous error analysis and demonstrate convergence for a\nnumber of benchmarks, including manufactured solutions, free-surface,\nnonhomogeneous traction loading, and composite material problems. Finally, we\nvalidate simulations of brittle fracture against a recent experiment of dynamic\ncrack branching in soda-lime glass, providing evidence that the scheme yields\naccurate predictions for practical engineering problems.\n"}
+{"abstract": "  Nowadays, target recognition technique plays an important role in many\nfields. However, the current target image information based methods suffer from\nthe influence of image quality and the time cost of image reconstruction. In\nthis paper, we propose a novel imaging-free target recognition method combining\nghost imaging (GI) and generative adversarial networks (GAN). Based on the\nmechanism of GI, a set of random speckles sequence is employed to illuminate\ntarget, and a bucket detector without resolution is utilized to receive echo\nsignal. The bucket signal sequence formed after continuous detections is\nconstructed into a bucket signal array, which is regarded as the sample of GAN.\nThen, conditional GAN is used to map bucket signal array and target category.\nIn practical application, the speckles sequence in training step is employed to\nilluminate target, and the bucket signal array is input GAN for recognition.\nThe proposed method can improve the problems caused by conventional recognition\nmethods that based on target image information, and provide a certain\nturbulence-free ability. Extensive experiments show that the proposed method\nachieves promising performance.\n"}
+{"abstract": "  The problem of discriminating between many quantum channels with certainty is\nanalyzed under the assumption of prior knowledge of algebraic relations among\npossible channels. It is shown, by explicit construction of a novel family of\nquantum algorithms, that when the set of possible channels faithfully\nrepresents a finite subgroup of SU(2) (e.g., $C_n, D_{2n}, A_4, S_4, A_5$) the\nrecently-developed techniques of quantum signal processing can be modified to\nconstitute subroutines for quantum hypothesis testing. These algorithms, for\ngroup quantum hypothesis testing (G-QHT), intuitively encode discrete\nproperties of the channel set in SU(2) and improve query complexity at least\nquadratically in $n$, the size of the channel set and group, compared to\nna\\\"ive repetition of binary hypothesis testing. Intriguingly, performance is\ncompletely defined by explicit group homomorphisms; these in turn inform simple\nconstraints on polynomials embedded in unitary matrices. These constructions\ndemonstrate a flexible technique for mapping questions in quantum inference to\nthe well-understood subfields of functional approximation and discrete algebra.\nExtensions to larger groups and noisy settings are discussed, as well as paths\nby which improved protocols for quantum hypothesis testing against structured\nchannel sets have application in the transmission of reference frames, proofs\nof security in quantum cryptography, and algorithms for property testing.\n"}
+{"abstract": "  The set of associative and commutative hypercomplex numbers, called the\nperfect hypercomplex algebra (PHA) is investigated. Necessary and sufficient\nconditions for an algebra to be a PHA via semi-tensor product(STP) of matrices\nare reviewed. The zero set is defined for non-invertible hypercomplex numbers\nin a given PHA, and a characteristic function is proposed for calculating zero\nset. Then PHA of different dimensions are considered. First, $2$-dimensional\nPHAs are considered as examples to calculate their zero sets etc. Second, all\nthe $3$-dimensional PHAs are obtained and the corresponding zero sets are\ninvestigated. Third, $4$-dimensional or even higher dimensional PHAs are also\nconsidered. Finally, matrices over pre-assigned PHA, called perfect\nhypercomplex matrices (PHMs) are considered. Their properties are also\ninvestigated.\n"}
+{"abstract": "  We prove a family of identities, expressing generating functions of powers of\ncharacteristic polynomials of permutations, as finite or infinite products.\nThese generalize formulae first obtained in a study of the geometry/topology of\nsymmetric products of real/algebraic tori. The proof uses formal power series\nexpansions of plethystic exponentials, and has been motivated by some recent\napplications of these combinatorial tools in supersymmetric gauge and string\ntheories. Since the methods are elementary, we tried to be self-contained, and\nrelate to other topics such as the q-binoomial theorem, and the cycle index and\nMolien series for the symmetric group.\n"}
+{"abstract": "  The Complete Calibration of the Color-Redshift Relation (C3R2) survey is\nobtaining spectroscopic redshifts in order to map the relation between galaxy\ncolor and redshift to a depth of i ~ 24.5 (AB). The primary goal is to enable\nsufficiently accurate photometric redshifts for Stage IV dark energy projects,\nparticularly Euclid and the Roman Space Telescope, which are designed to\nconstrain cosmological parameters through weak lensing. We present 676 new\nhigh-confidence spectroscopic redshifts obtained by the C3R2 survey in the\n2017B-2019B semesters using the DEIMOS, LRIS, and MOSFIRE multi-object\nspectrographs on the Keck telescopes. Combined with the 4454 redshifts\npreviously published by this project, the C3R2 survey has now obtained and\npublished 5130 high-quality galaxy spectra and redshifts. If we restrict\nconsideration to only the 0.2 < z(phot) < 2.6 range of interest for the Euclid\ncosmological goals, then with the current data release C3R2 has increased the\nspectroscopic redshift coverage of the Euclid color space from 51% (as reported\nby Masters et al. 2015) to the current 91%. Once completed and combined with\nextensive data collected by other spectroscopic surveys, C3R2 should provide\nthe spectroscopic calibration set needed to enable photometric redshifts to\nmeet the cosmology requirements for Euclid, and make significant headway toward\nsolving the problem for Roman.\n"}
+{"abstract": "  Connectivity maintenance is crucial for the real world deployment of\nmulti-robot systems, as it ultimately allows the robots to communicate,\ncoordinate and perform tasks in a collaborative way. A connectivity maintenance\ncontroller must keep the multi-robot system connected independently from the\nsystem's mission and in the presence of undesired real world effects such as\ncommunication delays, model errors, and computational time delays, among\nothers. In this paper we present the implementation, on a real robotic setup,\nof a connectivity maintenance control strategy based on Control Barrier\nFunctions. During experimentation, we found that the presence of communication\ndelays has a significant impact on the performance of the controlled system,\nwith respect to the ideal case. We propose a heuristic to counteract the\neffects of communication delays, and we verify its efficacy both in simulation\nand with physical robot experiments.\n"}
+{"abstract": "  Both children and adults have been shown to benefit from the integration of\nmultisensory and sensorimotor enrichment into pedagogy. For example,\nintegrating pictures or gestures into foreign language (L2) vocabulary learning\ncan improve learning outcomes relative to unisensory learning. However, whereas\nadults seem to benefit to a greater extent from sensorimotor enrichment such as\nthe performance of gestures in contrast to multisensory enrichment with\npictures, this is not the case in elementary school children. Here, we compared\nmultisensory- and sensorimotor-enriched learning in an intermediate age group\nthat falls between the age groups tested in previous studies (elementary school\nchildren and young adults), in an attempt to determine the developmental time\npoint at which children's responses to enrichment mature from a child-like\npattern into an adult-like pattern. Twelve-year-old and fourteen-year-old\nGerman children were trained over 5 consecutive days on auditorily-presented,\nconcrete and abstract, Spanish vocabulary. The vocabulary was learned under\npicture-enriched, gesture-enriched, and non-enriched (auditory-only)\nconditions. The children performed vocabulary recall and translation tests at 3\ndays, 2 months, and 6 months post-learning. Both picture and gesture enrichment\ninterventions were found to benefit children's L2 learning relative to\nnon-enriched learning up to 6 months post-training. Interestingly,\ngesture-enriched learning was even more beneficial than picture-enriched\nlearning for the fourteen-year-olds, while the twelve-year-olds benefitted\nequivalently from learning enriched with pictures and gestures. These findings\nprovide evidence for opting to integrate gestures rather than pictures into L2\npedagogy starting at fourteen years of age.\n"}
+{"abstract": "  A classical approach to the restricted three-body problem is to analyze the\ndynamics of the massless body in the synodic reference frame. A different\napproach is represented by the perturbative treatment: in particular the\naveraged problem of a mean-motion resonance allows to investigate the long-term\nbehavior of the solutions through a suitable approximation that focuses on a\nparticular region of the phase space. In this paper, we intend to bridge a gap\nbetween the two approaches in the specific case of mean-motion resonant\ndynamics, establish the limit of validity of the averaged problem, and take\nadvantage of its results in order to compute trajectories in the synodic\nreference frame. After the description of each approach, we develop a rigorous\ntreatment of the averaging process, estimate the size of the transformation and\nprove that the averaged problem is a suitable approximation of the restricted\nthree-body problem as long as the solutions are located outside the Hill's\nsphere of the secondary. In such a case, a rigorous theorem of stability over\nfinite but large timescales can be proven. We establish that a solution of the\naveraged problem provides an accurate approximation of the trajectories on the\nsynodic reference frame within a finite time that depend on the minimal\ndistance to the Hill's sphere of the secondary. The last part of this work is\ndevoted to the co-orbital motion (i.e., the dynamics in 1:1 mean-motion\nresonance) in the circular-planar case. In this case, an interpretation of the\nsolutions of the averaged problem in the synodic reference frame is detailed\nand a method that allows to compute co-orbital trajectories is displayed.\n"}
+{"abstract": "  In a graph G, the cardinality of the smallest ordered set of vertices that\ndistinguishes every element of V (G) (resp. E(G)) is called the vertex (resp.\nedge) metric dimension of G. In [16] it was shown that both vertex and edge\nmetric dimension of a unicyclic graph G always take values from just two\nexplicitly given consecutive integers that are derived from the structure of\nthe graph. A natural problem that arises is to determine under what conditions\nthese dimensions take each of the two possible values. In this paper for each\nof these two metric dimensions we characterize three graph configurations and\nprove that it takes the greater of the two possible values if and only if the\ngraph contains at least one of these configurations. One of these\nconfigurations is the same for both dimensions, while the other two are\nspecific for each of them. This enables us to establish the exact value of the\nmetric dimensions for a unicyclic graph and also to characterize when each of\nthese two dimensions is greater than the other one.\n"}
+{"abstract": "  In this work we consider the generalized zeta function method to obtain\ntemperature corrections to the vacuum (Casimir) energy density, at zero\ntemperature, associated with quantum vacuum fluctuations of a scalar field\nsubjected to a helix boundary condition and whose modes propagate in\n(3+1)-dimensional Euclidean spacetime. We find closed and analytical\nexpressions for both the two-point heat kernel function and free energy density\nin the massive and massless scalar field cases. In particular, for the massless\nscalar field case, we also calculate the thermodynamics quantities internal\nenergy density and entropy density, with their corresponding high- and\nlow-temperature limits. We show that the temperature correction term in the\nfree energy density must suffer a finite renormalization, by subtracting the\nscalar thermal blackbody radiation contribution, in order to provide the\ncorrect classical limit at high temperatures. We check that, at low\ntemperature, the entropy density vanishes as the temperature goes to zero, in\naccordance with the third law of thermodynamics. We also point out that, at low\ntemperatures, the dominant term in the free energy and internal energy\ndensities is the vacuum energy density at zero temperature. Finally, we also\nshow that the pressure obeys an equation of state.\n"}
+{"abstract": "  The COVID-19 pandemic has been damaging to the lives of people all around the\nworld. Accompanied by the pandemic is an infodemic, an abundant and\nuncontrolled spreading of potentially harmful misinformation. The infodemic may\nseverely change the pandemic's course by interfering with public health\ninterventions such as wearing masks, social distancing, and vaccination. In\nparticular, the impact of the infodemic on vaccination is critical because it\nholds the key to reverting to pre-pandemic normalcy. This paper presents\nfindings from a global survey on the extent of worldwide exposure to the\nCOVID-19 infodemic, assesses different populations' susceptibility to false\nclaims, and analyzes its association with vaccine acceptance. Based on\nresponses gathered from over 18,400 individuals from 40 countries, we find a\nstrong association between perceived believability of misinformation and\nvaccination hesitancy. Additionally, our study shows that only half of the\nonline users exposed to rumors might have seen the fact-checked information.\nMoreover, depending on the country, between 6% and 37% of individuals\nconsidered these rumors believable. Our survey also shows that poorer regions\nare more susceptible to encountering and believing COVID-19 misinformation. We\ndiscuss implications of our findings on public campaigns that proactively\nspread accurate information to countries that are more susceptible to the\ninfodemic. We also highlight fact-checking platforms' role in better\nidentifying and prioritizing claims that are perceived to be believable and\nhave wide exposure. Our findings give insights into better handling of risk\ncommunication during the initial phase of a future pandemic.\n"}
+{"abstract": "  We consider numerical solutions for the Allen-Cahn equation with standard\ndouble well potential and periodic boundary conditions. Surprisingly it is\nfound that using standard numerical discretizations with high precision\ncomputational solutions may converge to completely incorrect steady states.\nThis happens for very smooth initial data and state-of-the-art algorithms. We\nanalyze this phenomenon and showcase the resolution of this problem by a new\nsymmetry-preserving filter technique. We develop a new theoretical framework\nand rigorously prove the convergence to steady states for the filtered\nsolutions.\n"}
+{"abstract": "  Strong coupling between light and matter is the foundation of promising\nquantum photonic devices such as deterministic single photon sources, single\natom lasers and photonic quantum gates, which consist of an atom and a photonic\ncavity. Unlike atom-based systems, a strong coupling unit based on an\nemitter-plasmonic nanocavity system has the potential to bring these devices to\nthe microchip scale at ambient conditions. However, efficiently and precisely\npositioning a single or a few emitters into a plasmonic nanocavity is\nchallenging. In addition, placing a strong coupling unit on a designated\nsubstrate location is a demanding task. Here, fluorophore-modified DNA strands\nare utilized to drive the formation of particle-on-film plasmonic nanocavities\nand simultaneously integrate the fluorophores into the high field region of the\nnanocavities. High cavity yield and fluorophore coupling yield are\ndemonstrated. This method is then combined with e-beam lithography to position\nthe strong coupling units on designated locations of a substrate. Furthermore,\nthe high correlation between electronic transition of the fluorophore and the\ncavity resonance is observed, implying more vibrational modes may be involved.\nOur system makes strong coupling units more practical on the microchip scale\nand at ambient conditions and provides a stable platform for investigating\nfluorophore-plasmonic nanocavity interaction.\n"}
+{"abstract": "  Cloud-based services are surging into popularity in recent years. However,\noutages, i.e., severe incidents that always impact multiple services, can\ndramatically affect user experience and incur severe economic losses. Locating\nthe root-cause service, i.e., the service that contains the root cause of the\noutage, is a crucial step to mitigate the impact of the outage. In current\nindustrial practice, this is generally performed in a bootstrap manner and\nlargely depends on human efforts: the service that directly causes the outage\nis identified first, and the suspected root cause is traced back manually from\nservice to service during diagnosis until the actual root cause is found.\nUnfortunately, production cloud systems typically contain a large number of\ninterdependent services. Such a manual root cause analysis is often\ntime-consuming and labor-intensive. In this work, we propose COT, the first\noutage triage approach that considers the global view of service correlations.\nCOT mines the correlations among services from outage diagnosis data. After\nlearning from historical outages, COT can infer the root cause of emerging ones\naccurately. We implement COT and evaluate it on a real-world dataset containing\none year of data collected from Microsoft Azure, one of the representative\ncloud computing platforms in the world. Our experimental results show that COT\ncan reach a triage accuracy of 82.1%~83.5%, which outperforms the\nstate-of-the-art triage approach by 28.0%~29.7%.\n"}
+{"abstract": "  Voice Conversion (VC) emerged as a significant domain of research in the\nfield of speech synthesis in recent years due to its emerging application in\nvoice-assisting technology, automated movie dubbing, and speech-to-singing\nconversion to name a few. VC basically deals with the conversion of vocal style\nof one speaker to another speaker while keeping the linguistic contents\nunchanged. VC task is performed through a three-stage pipeline consisting of\nspeech analysis, speech feature mapping, and speech reconstruction. Nowadays\nthe Generative Adversarial Network (GAN) models are widely in use for speech\nfeature mapping from source to target speaker. In this paper, we propose an\nadaptive learning-based GAN model called ALGAN-VC for an efficient one-to-one\nVC of speakers. Our ALGAN-VC framework consists of some approaches to improve\nthe speech quality and voice similarity between source and target speakers. The\nmodel incorporates a Dense Residual Network (DRN) like architecture to the\ngenerator network for efficient speech feature learning, for source to target\nspeech feature conversion. We also integrate an adaptive learning mechanism to\ncompute the loss function for the proposed model. Moreover, we use a boosted\nlearning rate approach to enhance the learning capability of the proposed\nmodel. The model is trained by using both forward and inverse mapping\nsimultaneously for a one-to-one VC. The proposed model is tested on Voice\nConversion Challenge (VCC) 2016, 2018, and 2020 datasets as well as on our\nself-prepared speech dataset, which has been recorded in Indian regional\nlanguages and in English. A subjective and objective evaluation of the\ngenerated speech samples indicated that the proposed model elegantly performed\nthe voice conversion task by achieving high speaker similarity and adequate\nspeech quality.\n"}
+{"abstract": "  Software systems have been continuously evolved and delivered with high\nquality due to the widespread adoption of automated tests. A recurring issue\nhurting this scenario is the presence of flaky tests, a test case that may pass\nor fail non-deterministically. A promising, but yet lacking more empirical\nevidence, approach is to collect static data of automated tests and use them to\npredict their flakiness. In this paper, we conducted an empirical study to\nassess the use of code identifiers to predict test flakiness. To do so, we\nfirst replicate most parts of the previous study of Pinto~et~al.~(MSR~2020).\nThis replication was extended by using a different ML Python platform\n(Scikit-learn) and adding different learning algorithms in the analyses. Then,\nwe validated the performance of trained models using datasets with other flaky\ntests and from different projects. We successfully replicated the results of\nPinto~et~al.~(2020), with minor differences using Scikit-learn; different\nalgorithms had performance similar to the ones used previously. Concerning the\nvalidation, we noticed that the recall of the trained models was smaller, and\nclassifiers presented a varying range of decreases. This was observed in both\nintra-project and inter-projects test flakiness prediction.\n"}
+{"abstract": "  We demonstrate a method to double the collection efficiency in Laser Tweezers\nRaman Spectroscopy (LTRS) by collecting both the forward and back-scattered\nlight in a single-shot multitrack measurement. Our method can collect signals\nat different sample volumes, granting both the pinpoint spatial selectivity of\nconfocal Raman and the bulk sensitivity of non-confocal Raman simultaneously.\nFurther, we display that our approach allows for reduced detector integration\ntime and laser power. Thus, our method will enable the monitoring of biological\nsamples sensitive to high intensities for longer times. Additionally, we\ndemonstrate that by a simple modification, we can add polarization sensitivity\nand retrieve extra biochemical information.\n"}
+{"abstract": "  Although there are several proposals of relativistic spin in the literature,\nthe recognition of intrinsicality as a key characteristic for the definition of\nthis concept is responsible for selecting a single tensor operator that\nadequately describes such a quantity. This intrinsic definition does not\ncorrespond to Wigner's spin operator, which is the definition that is widely\nadopted in the relativistic quantum information theory literature. Here, the\ndifferences between the predictions obtained considering the intrinsic spin and\nWigner's spin are investigated. The measurements involving the intrinsic spin\nare modeled by means of the interaction with an electromagnetic field in a\nrelativistic Stern-Gerlach setup.\n"}
+{"abstract": "  This study uses an innovative measure, the Semantic Brand Score, to assess\nthe interest of stakeholders in different company core values. Among others, we\nfocus on corporate social responsibility (CSR) core value statements, and on\nthe attention they receive from five categories of stakeholders (customers,\ncompany communication teams, employees, associations and media). Combining big\ndata methods and tools of Social Network Analysis and Text Mining, we analyzed\nabout 58,000 Italian tweets and found that different stakeholders have\ndifferent prevailing interests. CSR gets much less attention than expected.\nCore values related to customers and employees are in the foreground.\n"}
+{"abstract": "  Given $E_0, E_1, F_0, F_1, E$ rearrangement invariant function spaces, $a_0$,\n$a_1$, $b_0$, $b_1$, $b$ slowly varying functions and $0< \\theta_0<\\theta_1<1$,\nwe characterize the interpolation spaces $$(\\overline{X}^{\\mathcal\nR}_{\\theta_0,b_0,E_0,a_0,F_0}, \\overline{X}^{\\mathcal R}_{\\theta_1,\nb_1,E_1,a_1,F_1})_{\\theta,b,E},\\quad (\\overline{X}^{\\mathcal L}_{\\theta_0,\nb_0,E_0,a_0,F_0}, \\overline{X}^{\\mathcal\nL}_{\\theta_1,b_1,E_1,a_1,F_1})_{\\theta,b,E}$$ and $$(\\overline{X}^{\\mathcal\nR}_{\\theta_0,b_0,E_0,a_0,F_0}, \\overline{X}^{\\mathcal L}_{\\theta_1,\nb_1,E_1,a_1,F_1})_{\\theta,b,E},\\quad (\\overline{X}^{\\mathcal L}_{\\theta_0,\nb_0,E_0,a_0,F_0}, \\overline{X}^{\\mathcal\nR}_{\\theta_1,b_1,E_1,a_1,F_1})_{\\theta,b,E},$$ for all possible values of\n$\\theta\\in[0,1]$. Applications to interpolation identities for grand and small\nLebesgue spaces, Gamma spaces and $A$ and $B$-type spaces are given.\n"}
+{"abstract": "  Given the impeding timeline of developing good quality quantum processing\nunits, it is the moment to rethink the approach to advance quantum computing\nresearch. Rather than waiting for quantum hardware technologies to mature, we\nneed to start assessing in tandem the impact of the occurrence of quantum\ncomputing in various scientific fields. However, to this purpose, we need to\nuse a complementary but quite different approach than proposed by the NISQ\nvision, which is heavily focused on and burdened by the engineering challenges.\nThat is why we propose and advocate the PISQ approach: Perfect Intermediate\nScale Quantum computing based on the already known concept of perfect qubits.\nThis will allow researchers to focus much more on the development of new\napplications by defining the algorithms in terms of perfect qubits and evaluate\nthem on quantum computing simulators that are executed on supercomputers. It is\nnot the long-term solution but will currently allow universities to research on\nquantum logic and algorithms and companies can already start developing their\ninternal know-how on quantum solutions.\n"}
+{"abstract": "  The main goal of this paper is to prove $L^1$-comparison and contraction\nprinciples for weak solutions (in the sense of distributions) of Hele-Shaw flow\nwith a linear Drift. The flow is considered with a general reaction term\nincluding the Lipschitz continuous case, and subject to mixed homogeneous\nboundary conditions : Dirichlet and Neumann. Our approach combines\nDiPerna-Lions renormalization type with Kruzhkov device of doubling and\nde-doubling variables. The $L^1$-contraction principle allows afterwards to\nhandle the problem in a general framework of nonlinear semigroup theory in\n$L^1,$ taking thus advantage of this strong theory to study existence,\nuniqueness, comparison of weak solutions, $L^1$-stability as well as many\nfurther questions.\n"}
+{"abstract": "  This paper presents a novel task together with a new benchmark for detecting\ngeneric, taxonomy-free event boundaries that segment a whole video into chunks.\nConventional work in temporal video segmentation and action detection focuses\non localizing pre-defined action categories and thus does not scale to generic\nvideos. Cognitive Science has known since last century that humans consistently\nsegment videos into meaningful temporal chunks. This segmentation happens\nnaturally, without pre-defined event categories and without being explicitly\nasked to do so. Here, we repeat these cognitive experiments on mainstream CV\ndatasets; with our novel annotation guideline which addresses the complexities\nof taxonomy-free event boundary annotation, we introduce the task of Generic\nEvent Boundary Detection (GEBD) and the new benchmark Kinetics-GEBD. Our\nKinetics-GEBD has the largest number of boundaries (e.g. 32 of ActivityNet, 8\nof EPIC-Kitchens-100) which are in-the-wild, taxonomy-free, cover generic event\nchange, and respect human perception diversity. We view GEBD as an important\nstepping stone towards understanding the video as a whole, and believe it has\nbeen previously neglected due to a lack of proper task definition and\nannotations. Through experiment and human study we demonstrate the value of the\nannotations. Further, we benchmark supervised and un-supervised GEBD approaches\non the TAPOS dataset and our Kinetics-GEBD. We release our annotations and\nbaseline codes at CVPR'21 LOVEU Challenge:\nhttps://sites.google.com/view/loveucvpr21.\n"}
+{"abstract": "  Results. We illustrate our profile-fitting technique and present the K\\,{\\sc\ni} velocity structure of the dense ISM along the paths to all targets. As a\nvalidation test of the dust map, we show comparisons between distances to\nseveral reconstructed clouds with recent distance assignments based on\ndifferent techniques. Target star extinctions estimated by integration in the\n3D map are compared with their K\\,{\\sc i} 7699 A absorptions and the degree of\ncorrelation is found comparable to the one between the same K\\,{\\sc i} line and\nthe total hydrogen column for stars distributed over the sky that are part of a\npublished high resolution survey. We show images of the updated dust\ndistribution in a series of vertical planes in the Galactic longitude interval\n150-182.5 deg and our estimated assignments of radial velocities to the opaque\nregions. Most clearly defined K\\,{\\sc i} absorptions may be assigned to a dense\ndust cloud between the Sun and the target star. It appeared relatively\nstraightforward to find a velocity pattern consistent will all absorptions and\nensuring coherence between adjacent lines of sight, at the exception of a few\nweak lines. We compare our results with recent determinations of velocities of\nseveral clouds and find good agreement. These results demonstrate that the\nextinction-K\\,{\\sc i} relationship is tight enough to allow linking the radial\nvelocity of the K\\,{\\sc i} lines to the dust clouds seen in 3D, and that their\ncombination may be a valuable tool in building a 3D kinetic structure of the\ndense ISM. We discuss limitations and perspectives for this technique.\n"}
+{"abstract": "  Aluminum scandium nitride alloy (Al1-xScxN) is regarded as a promising\nmaterial for high-performance acoustic devices used in wireless communication\nsystems. Phonon scattering and heat conduction processes govern the energy\ndissipation in acoustic resonators, ultimately determining their performance\nquality. This work reports, for the first time, on phonon scattering processes\nand thermal conductivity in Al1-xScxN alloys with the Sc content (x) up to\n0.26. The thermal conductivity measured presents a descending trend with\nincreasing x. Temperature-dependent measurements show an increase in thermal\nconductivity as the temperature increases at temperatures below 200K, followed\nby a plateau at higher temperatures (T> 200K). Application of a virtual crystal\nphonon conduction model allows us to elucidate the effects of boundary and\nalloy scattering on the observed thermal conductivity behaviors. We further\ndemonstrate that the alloy scattering is caused mainly by strain-field\ndifference, and less by the atomic mass difference between ScN and AlN, which\nis in contrast to the well-studied Al1-xGaxN and SixGe1-x alloy systems where\natomic mass difference dominates the alloy scattering. This work studies and\nprovides the quantitative knowledge for phonon scattering and the thermal\nconductivity in Al1-xScxN, paving the way for future investigation of materials\nand design of acoustic devices.\n"}
+{"abstract": "  We present two further classical novae, V906 Car and V5668 Sgr, that show\njets and accretion disc spectral signatures in their H-alpha complexes\nthroughout the first 1000 days following their eruptions. From extensive\ndensely time-sampled spectroscopy, we measure the appearance of the first\nhigh-velocity absorption component in V906 Car, and the duration of the\ncommencement of the main H-alpha emission. We constrain the time taken for\nV5668 Sgr to transition to the nebular phase using [N II] 6584\\r{A}. We find\nthese timings to be consistent with the jet and accretion disc model for\nexplaining optical spectral line profile changes in classical novae, and\ndiscuss the implications of this model for enrichment of the interstellar\nmedium.\n"}
+{"abstract": "  We utilize transverse ac susceptibility measurements to characterize magnetic\nanisotropy in archetypal exchange-bias bilayers of ferromagnet Permalloy (Py)\nand antiferromagnet CoO. Unidirectional anisotropy is observed for thin Py, but\nbecomes negligible at larger Py thicknesses, even though the directional\nasymmetry of the magnetic hysteresis loop remains significant. Additional\nmagnetoelectronic measurements, magneto-optical imaging, as well as\nmicromagnetic simulations show that these surprising behaviors are likely\nassociated with asymmetry of spin flop distribution created in CoO during Py\nmagnetization reversal, which facilitates the rotation of the latter back into\nits field-cooled direction. Our findings suggest new possibilities for\nefficient realization of multistable nanomagnetic systems for neuromorphic\napplications.\n"}
+{"abstract": "  High-resolution gamma-ray spectroscopy of 18N is performed with the Advanced\nGAmma Tracking Array AGATA, following deep-inelastic processes induced by an\n18O beam on a 181Ta target. Six states are newly identified, which together\nwith the three known excitations exhaust all negative-parity excited states\nexpected in 18N below the neutron threshold. Spin and parities are proposed for\nall located states on the basis of decay branchings and comparison with\nlarge-scale shell-model calculations performed in the p-sd space, with the YSOX\ninteraction. Of particular interest is the location of the 0^-_1 and 1^-_2\nexcitations, which provide strong constrains for cross-shell p-sd matrix\nelements based on realistic interactions, and help to simultaneously reproduce\nthe ground and first-excited states in 16N and 18N, for the first time.\nUnderstanding the 18N structure may also have significant impact on\nneutron-capture cross-section calculations in r-process modeling including\nlight neutron-rich nuclei.\n"}
+{"abstract": "  The Willmore Problem seeks the surface in $\\mathbb S^3\\subset\\mathbb R^4$ of\na given topological type minimizing the squared-mean-curvature energy $W = \\int\n|\\mathbf{H}_{\\mathbb{R}^4}|^2 = \\operatorname{area} + \\int H_{\\mathbb{S}^3}^2$.\nThe longstanding Willmore Conjecture that the Clifford torus minimizes $W$\namong genus-$1$ surfaces is now a theorem of Marques and Neves [19], but the\ngeneral conjecture [10] that Lawson's [16] minimal surface\n$\\xi_{g,1}\\subset\\mathbb S^3$ minimizes $W$ among surfaces of genus $g>1$\nremains open. Here we prove this conjecture under the additional assumption\nthat the competitor surfaces $M\\subset\\mathbb S^3$ share the ambient symmetries\nof $\\xi_{g,1}$. Specifcally, we show each Lawson surface $\\xi_{m,k}$ satisfies\nthe analogous $W$-minimizing property under a somewhat smaller symmetry group\n${G}_{m,k}<SO(4)$, using a local computation of the orbifold Euler number\n$\\chi_o(M/{G}_{m,k})$ to exclude certain intersection patterns of $M$ with the\ngreat circles fixed by generators of ${G}_{m,k}$. We also describe a genus 2\nexample where the Willmore Problem may not be solvable among surfaces with its\nsymmetry.\n"}
+{"abstract": "  Quantum error correcting codes (QECCs) are the means of choice whenever\nquantum systems suffer errors, e.g., due to imperfect devices, environments, or\nfaulty channels. By now, a plethora of families of codes is known, but there is\nno universal approach to finding new or optimal codes for a certain task and\nsubject to specific experimental constraints. In particular, once found, a QECC\nis typically used in very diverse contexts, while its resilience against errors\nis captured in a single figure of merit, the distance of the code. This does\nnot necessarily give rise to the most efficient protection possible given a\ncertain known error or a particular application for which the code is employed.\n  In this paper, we investigate the loss channel, which plays a key role in\nquantum communication, and in particular in quantum key distribution over long\ndistances. We develop a numerical set of tools that allows to optimize an\nencoding specifically for recovering lost particles without the need for\nbackwards communication, where some knowledge about what was lost is available,\nand demonstrate its capabilities. This allows us to arrive at new codes ideal\nfor the distribution of entangled states in this particular setting, and also\nto investigate if encoding in qudits or allowing for non-deterministic\ncorrection proves advantageous compared to known QECCs. While we here focus on\nthe case of losses, our methodology is applicable whenever the errors in a\nsystem can be characterized by a known linear map.\n"}
+{"abstract": "  The Facility for Antiproton and Ion Research (FAIR), an international\naccelerator centre, is under construction in Darmstadt, Germany. FAIR will\nprovide high-intensity primary beams of protons and heavy-ions, and intense\nsecondary beams of antiprotons and of rare short-lived isotopes. These beams,\ntogether with a variety of modern experimental setups, will allow to perform a\nunique research program on nuclear astrophysics, including the exploration of\nthe nucleosynthesis in the universe, and the exploration of QCD matter at high\nbaryon densities, in order to shed light on the properties of neutron stars,\nand the dynamics of neutron star mergers. The Compressed Baryonic Matter (CBM)\nexperiment at FAIR will investigate collisions between heavy nuclei, and\nmeasure various diagnostic probes, which are sensitive to the high-density\nequation-of-state (EOS), and to the microscopic degrees-of-freedom of\nhigh-density matter. The CBM physics program will be discussed.\n"}
+{"abstract": "  The phase diagram of cuprate high-temperature superconductors is investigated\non the basis of the three-band d-p model. We use the optimization variational\nMonte Carlo method, where improved many-body wave functions have been proposed\nto make the ground-state wave function more precise. We investigate the\nstability of antiferromagnetic state by changing the band parameters such as\nthe hole number, level difference $\\Delta_{dp}$ between $d$ and $p$ electrons\nand transfer integrals. We show that the antiferromagnetic correlation weakens\nwhen $\\Delta_{dp}$ decreases and the pure $d$-wave superconducting phase may\nexist in this region. We present phase diagrams including antiferromagnetic and\nsuperconducting regions by varying the band parameters. The phase diagram\nobtained by changing the doping rate $x$ contains antiferromagnetic,\nsuperconducting and also phase-separated phases. We propose that\nhigh-temperature superconductivity will occur near the antiferromagnetic\nboundary in the space of band parameters.\n"}
+{"abstract": "  Natural language contexts display logical regularities with respect to\nsubstitutions of related concepts: these are captured in a functional\norder-theoretic property called monotonicity. For a certain class of NLI\nproblems where the resulting entailment label depends only on the context\nmonotonicity and the relation between the substituted concepts, we build on\nprevious techniques that aim to improve the performance of NLI models for these\nproblems, as consistent performance across both upward and downward monotone\ncontexts still seems difficult to attain even for state-of-the-art models. To\nthis end, we reframe the problem of context monotonicity classification to make\nit compatible with transformer-based pre-trained NLI models and add this task\nto the training pipeline. Furthermore, we introduce a sound and complete\nsimplified monotonicity logic formalism which describes our treatment of\ncontexts as abstract units. Using the notions in our formalism, we adapt\ntargeted challenge sets to investigate whether an intermediate context\nmonotonicity classification task can aid NLI models' performance on examples\nexhibiting monotonicity reasoning.\n"}
+{"abstract": "  A renewal system divides the slotted timeline into back to back time periods\ncalled renewal frames. At the beginning of each frame, it chooses a policy from\na set of options for that frame. The policy determines the duration of the\nframe, the penalty incurred during the frame (such as energy expenditure), and\na vector of performance metrics (such as instantaneous number of jobs served).\nThe starting points of this line of research are Chapter 7 of the book\n[Nee10a], the seminal work [Nee13a], and Chapter 5 of the PhD thesis of\nChih-ping Li [Li11]. These works consider stochastic optimization over a single\nrenewal system. By way of contrast, this thesis considers optimization over\nmultiple parallel renewal systems, which is computationally more challenging\nand yields much more applications. The goal is to minimize the time average\noverall penalty subject to time average overall constraints on the\ncorresponding performance metrics. The main difficulty, which is not present in\nearlier works, is that these systems act asynchronously due to the fact that\nthe renewal frames of different renewal systems are not aligned. The goal of\nthe thesis is to resolve this difficulty head-on via a new asynchronous\nalgorithm and a novel supermartingale stopping time analysis which shows our\nalgorithms not only converge to the optimal solution but also enjoy fast\nconvergence rates. Based on this general theory, we further develop novel\nalgorithms for data center server provision problems with performance\nguarantees as well as new heuristics for the multi-user file downloading\nproblems.\n"}
+{"abstract": "  Selecting skilled mutual funds through the multiple testing framework has\nreceived increasing attention from finance researchers and statisticians. The\nintercept $\\alpha$ of Carhart four-factor model is commonly used to measure the\ntrue performance of mutual funds, and positive $\\alpha$'s are considered as\nskilled. We observe that the standardized OLS estimates of $\\alpha$'s across\nthe funds possess strong dependence and nonnormality structures, indicating\nthat the conventional multiple testing methods are inadequate for selecting the\nskilled funds. We start from a decision theoretic perspective, and propose an\noptimal testing procedure to minimize a combination of false discovery rate and\nfalse non-discovery rate. Our proposed testing procedure is constructed based\non the probability of each fund not being skilled conditional on the\ninformation across all of the funds in our study. To model the distribution of\nthe information used for the testing procedure, we consider a mixture model\nunder dependence and propose a new method called ``approximate empirical Bayes\"\nto fit the parameters. Empirical studies show that our selected skilled funds\nhave superior long-term and short-term performance, e.g., our selection\nstrongly outperforms the S\\&P 500 index during the same period.\n"}
+{"abstract": "  Due to the increase of Internet-of-Things (IoT) devices, IoT networks are\ngetting overcrowded. Networks can be extended with more gateways, increasing\nthe number of supported devices. However, as investigated in this work, massive\nMIMO has the potential to increase the number of simultaneous connections,\nwhile also lowering the energy expenditure of these devices. We present a study\nof the channel characteristics of massive MIMO in the unlicensed sub-GHz band.\nThe goal is to support IoT applications with strict requirements in terms of\nnumber of devices, power consumption, and reliability. The assessment is based\non experimental measurements using both a uniform linear and a rectangular\narray. Our study demonstrates and validates the advantages of deploying massive\nMIMO gateways to serve IoT nodes. While the results are general, here we\nspecifically focus on static nodes. The array gain and channel hardening effect\nyield opportunities to lower the transmit-power of IoT nodes while also\nincreasing reliability. The exploration confirms that exploiting large arrays\nbrings great opportunities to connect a massive number of IoT devices by\nseparating the nodes in the spatial domain. In addition, we give an outlook on\nhow static IoT nodes could be scheduled based on partial channel state\ninformation.\n"}
+{"abstract": "  Given the complexity of typical data science projects and the associated\ndemand for human expertise, automation has the potential to transform the data\nscience process.\n  Key insights:\n  * Automation in data science aims to facilitate and transform the work of\ndata scientists, not to replace them.\n  * Important parts of data science are already being automated, especially in\nthe modeling stages, where techniques such as automated machine learning\n(AutoML) are gaining traction.\n  * Other aspects are harder to automate, not only because of technological\nchallenges, but because open-ended and context-dependent tasks require human\ninteraction.\n"}
+{"abstract": "  We present PLONQ, a progressive neural image compression scheme which pushes\nthe boundary of variable bitrate compression by allowing quality scalable\ncoding with a single bitstream. In contrast to existing learned variable\nbitrate solutions which produce separate bitstreams for each quality, it\nenables easier rate-control and requires less storage. Leveraging the latent\nscaling based variable bitrate solution, we introduce nested quantization, a\nmethod that defines multiple quantization levels with nested quantization\ngrids, and progressively refines all latents from the coarsest to the finest\nquantization level. To achieve finer progressiveness in between any two\nquantization levels, latent elements are incrementally refined with an\nimportance ordering defined in the rate-distortion sense. To the best of our\nknowledge, PLONQ is the first learning-based progressive image coding scheme\nand it outperforms SPIHT, a well-known wavelet-based progressive image codec.\n"}
+{"abstract": "  A silicon quantum photonic circuit was proposed and demonstrated as an\nintegrated quantum light source for telecom band polarization entangled Bell\nstate generation and dynamical manipulation. Biphoton states were firstly\ngenerated in four silicon waveguides by spontaneous four wave mixing. They were\ntransformed to polarization entangled Bell states through on-chip quantum\ninterference and quantum superposition, and then coupled to optical fibers. The\nproperty of polarization entanglement in generated photon pairs was\ndemonstrated by two-photon interferences under two non-orthogonal polarization\nbases. The output state could be dynamically switched between two polarization\nentangled Bell states, which was demonstrated by the experiment of simplified\nBell state measurement. The experiment results indicate that its manipulation\nspeed supported a modulation rate of several tens kHz, showing its potential on\napplications of quantum communication and quantum information processing\nrequiring dynamical quantum entangled Bell state control.\n"}
+{"abstract": "  The independence equivalence class of a graph $G$ is the set of graphs that\nhave the same independence polynomial as $G$. Beaton, Brown and Cameron\n(arXiv:1810.05317) found the independence equivalence classes of even cycles,\nand raised the problem of finding the independence equivalence class of odd\ncycles. The problem is completely solved in this paper.\n"}
+{"abstract": "  With the aid of both a semi-analytical and a numerically exact method we\ninvestigate the charge dynamics in the vicinity of half-filling in the one- and\ntwo-dimensional $t$-$J$ model derived from a Fermi-Hubbard model in the limit\nof large interaction $U$ and hence small exchange coupling $J$. The spin\ndegrees of freedom are taken to be disordered. So we consider the limit $0 < J\n\\ll T \\ll W$ where $W$ is the band width. We focus on evaluating the spectral\ndensity of a single hole excitation and the charge gap which separates the\nupper and the lower Hubbard band. One of the key findings is the evidence for\nthe absence of sharp edges of the Hubbard band, instead Gaussian tails appear.\n"}
+{"abstract": "  Natural numbers satisfying a certain unusual property are defined by the\nauthor in a previous note. Later, the author called such numbers\n$v$-palindromic numbers and proved a periodic phenomenon pertaining to such\nnumbers and repeated concatenations of the digits of a number. It was left as a\nproblem of further investigation to find the smallest period. In this paper, we\nprovide a method to find the smallest period. Some theorems from signal\nprocessing are used, but we also supply our own proofs.\n"}
+{"abstract": "  In this thesis, the properties of mixtures of Bose-Einstein condensates at $T\n= 0$ have been investigated using quantum Monte Carlo (QMC) methods and Density\nFunctional Theory (DFT) with the aim of understanding physics beyond the\nmean-field theory in Bose-Bose mixtures.\n"}
+{"abstract": "  We consider the simple exclusion process on Z x {0, 1}, that is, an\n\"horizontal ladder\" composed of 2 lanes. Particles can jump according to a\nlane-dependent translation-invariant nearest neighbour jump kernel, i.e.\n\"horizontally\" along each lane, and \"vertically\" along the scales of the\nladder. We prove that generically, the set of extremal invariant measures\nconsists of (i) translation-invariant product Bernoulli measures; and, modulo\ntranslations along Z: (ii) at most two shock measures (i.e. asymptotic to\nBernoulli measures at $\\pm$$\\infty$) with asymptotic densities 0 and 2; (iii)\nat most three shock measures with a density jump of magnitude 1. We fully\ndetermine this set for certain parameter values. In fact, outside degenerate\ncases, there is at most one shock measure of type (iii). The result can be\npartially generalized to vertically cyclic ladders with arbitrarily many lanes.\nFor the latter, we answer an open question of [5] about rotational invariance\nof stationary measures.\n"}
+{"abstract": "  This paper revisits the multi-agent epistemic logic presented in [10], where\nagents and sets of agents are replaced by abstract, intensional \"names\". We\nmake three contributions. First, we study its model theory, providing adequate\nnotions of bisimulation and frame morphisms, and use them to study the logic's\nexpressive power and definability. Second, we show that the logic has a natural\nneighborhood semantics, which in turn allows to show that the axiomatization in\n[10] does not rely on possibly controversial introspective properties of\nknowledge. Finally, we extend the logic with common and distributed knowledge\noperators, and provide a sound and complete axiomatization for each of these\nextensions. These results together put the original epistemic logic with names\nin a more modern context and opens the door for a logical analysis of epistemic\nphenomena where group membership is uncertain or variable.\n"}
+{"abstract": "  We give a short introduction to the contact invariant in bordered Floer\nhomology defined by F\\\"oldv\\'ari, Hendricks, and the authors. The construction\nrelies on a special class of foliated open books. We discuss a procedure to\nobtain such a foliated open book and present a definition of the contact\ninvariant. We also provide a \"local proof\", through an explicit bordered\ncomputation, of the vanishing of the contact invariant for overtwisted\nstructures.\n"}
+{"abstract": "  The main purpose of our paper is a new approach to design of algorithms of\nKaczmarz type in the framework of operators in Hilbert space. Our applications\ninclude a diverse list of optimization problems, new Karhunen-Lo\\`eve\ntransforms, and Principal Component Analysis (PCA) for digital images. A key\nfeature of our algorithms is our use of recursive systems of projection\noperators. Specifically, we apply our recursive projection algorithms for new\ncomputations of PCA probabilities and of variance data. For this we also make\nuse of specific reproducing kernel Hilbert spaces, factorization for kernels,\nand finite-dimensional approximations. Our projection algorithms are designed\nwith view to maximum likelihood solutions, minimization of \"cost\" problems,\nidentification of principal components, and data-dimension reduction.\n"}
+{"abstract": "  The deformability of a compact object under the presence of a tidal\nperturbation is encoded in the tidal Love numbers (TLNs), which vanish for\nisolated black holes in vacuum. We show that the TLNs of black holes surrounded\nby matter fields do not vanish and can be used to probe the environment around\nbinary black holes. In particular, we compute the TLNs for the case of a black\nhole surrounded by a scalar condensate under the presence of scalar and vector\ntidal perturbations, finding a strong power-law behavior of the TLN in terms of\nthe mass of the scalar field. Using this result as a proxy for gravitational\ntidal perturbations, we show that future gravitational-wave detectors like the\nEinstein Telescope and LISA can impose stringent constraints on the mass of\nultralight bosons that condensate around black holes due to accretion or\nsuperradiance. Interestingly, LISA could measure the tidal deformability of\ndressed black holes across the range from stellar-mass ($\\approx 10^2 M_\\odot$)\nto supermassive ($\\approx 10^7 M_\\odot$) objects, providing a measurement of\nthe mass of ultralight bosons in the range $(10^{-17} - 10^{-13}) \\, {\\rm eV}$\nwith less than $10\\%$ accuracy, thus filling the gap between other\nsuperradiance-driven constraints coming from terrestrial and space\ninterferometers. Altogether, LISA and Einstein Telescope can probe tidal\neffects from dressed black holes in the combined mass range $(10^{-17} -\n10^{-11}) \\, {\\rm eV}$.\n"}
+{"abstract": "  In this paper, we propose a generalized natural inflation (GNI) model to\nstudy axion-like particle (ALP) inflation and dark matter (DM). GNI contains\ntwo additional parameters $(n_1, n_2)$ in comparison with the natural\ninflation, that make GNI more general. The $n_1$ build the connection between\nGNI and other ALP inflation model, $n_2$ controls the inflaton mass. After\nconsidering the cosmic microwave background and other cosmological observation\nlimits, the model can realize small-field inflation with a wide mass range, and\nthe ALP inflaton considering here can serve as the DM candidate for certain\nparameter spaces.\n"}
+{"abstract": "  In this work, an $r$-linearly converging adaptive solver is constructed for\nparabolic evolution equations in a simultaneous space-time variational\nformulation. Exploiting the product structure of the space-time cylinder, the\nfamily of trial spaces that we consider are given as the spans of\nwavelets-in-time and (locally refined) finite element spaces-in-space.\nNumerical results illustrate our theoretical findings.\n"}
+{"abstract": "  Dynamical variational auto-encoders (DVAEs) are a class of deep generative\nmodels with latent variables, dedicated to time series data modeling. DVAEs can\nbe considered as extensions of the variational autoencoder (VAE) that include\nthe modeling of temporal dependencies between successive observed and/or latent\nvectors in data sequences. Previous work has shown the interest of DVAEs and\ntheir better performance over the VAE for speech signals (spectrogram)\nmodeling. Independently, the VAE has been successfully applied to speech\nenhancement in noise, in an unsupervised noise-agnostic set-up that does not\nrequire the use of a parallel dataset of clean and noisy speech samples for\ntraining, but only requires clean speech signals. In this paper, we extend\nthose works to DVAE-based single-channel unsupervised speech enhancement, hence\nexploiting both speech signals unsupervised representation learning and\ndynamics modeling. We propose an unsupervised speech enhancement algorithm\nbased on the most general form of DVAEs, that we then adapt to three specific\nDVAE models to illustrate the versatility of the framework. More precisely, we\ncombine DVAE-based speech priors with a noise model based on nonnegative matrix\nfactorization, and we derive a variational expectation-maximization (VEM)\nalgorithm to perform speech enhancement. Experimental results show that the\nproposed approach based on DVAEs outperforms its VAE counterpart and a\nsupervised speech enhancement baseline.\n"}
+{"abstract": "  In this paper, we propose a new approach to pathological speech synthesis.\nInstead of using healthy speech as a source, we customise an existing\npathological speech sample to a new speaker's voice characteristics. This\napproach alleviates the evaluation problem one normally has when converting\ntypical speech to pathological speech, as in our approach, the voice conversion\n(VC) model does not need to be optimised for speech degradation but only for\nthe speaker change. This change in the optimisation ensures that any\ndegradation found in naturalness is due to the conversion process and not due\nto the model exaggerating characteristics of a speech pathology. To show a\nproof of concept of this method, we convert dysarthric speech using the\nUASpeech database and an autoencoder-based VC technique. Subjective evaluation\nresults show reasonable naturalness for high intelligibility dysarthric\nspeakers, though lower intelligibility seems to introduce a marginal\ndegradation in naturalness scores for mid and low intelligibility speakers\ncompared to ground truth. Conversion of speaker characteristics for low and\nhigh intelligibility speakers is successful, but not for mid. Whether the\ndifferences in the results for the different intelligibility levels is due to\nthe intelligibility levels or due to the speakers needs to be further\ninvestigated.\n"}
+{"abstract": "  We propose a novel deep neural network architecture to integrate imaging and\ngenetics data, as guided by diagnosis, that provides interpretable biomarkers.\nOur model consists of an encoder, a decoder and a classifier. The encoder\nlearns a non-linear subspace shared between the input data modalities. The\nclassifier and the decoder act as regularizers to ensure that the\nlow-dimensional encoding captures predictive differences between patients and\ncontrols. We use a learnable dropout layer to extract interpretable biomarkers\nfrom the data, and our unique training strategy can easily accommodate missing\ndata modalities across subjects. We have evaluated our model on a population\nstudy of schizophrenia that includes two functional MRI (fMRI) paradigms and\nSingle Nucleotide Polymorphism (SNP) data. Using 10-fold cross validation, we\ndemonstrate that our model achieves better classification accuracy than\nbaseline methods, and that this performance generalizes to a second dataset\ncollected at a different site. In an exploratory analysis we further show that\nthe biomarkers identified by our model are closely associated with the\nwell-documented deficits in schizophrenia.\n"}
+{"abstract": "  In this paper, we study covert communications between {a pair of} legitimate\ntransmitter-receiver against a watchful warden over slow fading channels. There\ncoexist multiple friendly helper nodes who are willing to protect the covert\ncommunication from being detected by the warden. We propose an uncoordinated\njammer selection scheme where those helpers whose instantaneous channel gains\nto the legitimate receiver fall below a pre-established selection threshold\nwill be chosen as jammers radiating jamming signals to defeat the warden. By\ndoing so, the detection accuracy of the warden is expected to be severely\ndegraded while the desired covert communication is rarely affected. We then\njointly design the optimal selection threshold and message transmission rate\nfor maximizing covert throughput under the premise that the detection error of\nthe warden exceeds a certain level. Numerical results are presented to validate\nour theoretical analyses. It is shown that the multi-jammer assisted covert\ncommunication outperforms the conventional single-jammer method in terms of\ncovert throughput, and the maximal covert throughput improves significantly as\nthe total number of helpers increases, which demonstrates the validity and\nsuperiority of our proposed scheme.\n"}
+{"abstract": "  Loop closure detection is an essential component of Simultaneous Localization\nand Mapping (SLAM) systems, which reduces the drift accumulated over time. Over\nthe years, several deep learning approaches have been proposed to address this\ntask, however their performance has been subpar compared to handcrafted\ntechniques, especially while dealing with reverse loops. In this paper, we\nintroduce the novel LCDNet that effectively detects loop closures in LiDAR\npoint clouds by simultaneously identifying previously visited places and\nestimating the 6-DoF relative transformation between the current scan and the\nmap. LCDNet is composed of a shared encoder, a place recognition head that\nextracts global descriptors, and a relative pose head that estimates the\ntransformation between two point clouds. We introduce a novel relative pose\nhead based on the unbalanced optimal transport theory that we implement in a\ndifferentiable manner to allow for end-to-end training. Extensive evaluations\nof LCDNet on multiple real-world autonomous driving datasets show that our\napproach outperforms state-of-the-art loop closure detection and point cloud\nregistration techniques by a large margin, especially while dealing with\nreverse loops. Moreover, we integrate our proposed loop closure detection\napproach into a LiDAR SLAM library to provide a complete mapping system and\ndemonstrate the generalization ability using different sensor setup in an\nunseen city.\n"}
+{"abstract": "  An issue documents discussions around required changes in issue-tracking\nsystems, while a commit contains the change itself in the version control\nsystems. Recovering links between issues and commits can facilitate many\nsoftware evolution tasks such as bug localization, and software documentation.\nA previous study on over half a million issues from GitHub reports only about\n42.2% of issues are manually linked by developers to their pertinent commits.\nAutomating the linking of commit-issue pairs can contribute to the improvement\nof the said tasks. By far, current state-of-the-art approaches for automated\ncommit-issue linking suffer from low precision, leading to unreliable results,\nsometimes to the point that imposes human supervision on the predicted links.\nThe low performance gets even more severe when there is a lack of textual\ninformation in either commits or issues. Current approaches are also proven\ncomputationally expensive.\n  We propose Hybrid-Linker to overcome such limitations by exploiting two\ninformation channels; (1) a non-textual-based component that operates on\nnon-textual, automatically recorded information of the commit-issue pairs to\npredict a link, and (2) a textual-based one which does the same using textual\ninformation of the commit-issue pairs. Then, combining the results from the two\nclassifiers, Hybrid-Linker makes the final prediction. Thus, every time one\ncomponent falls short in predicting a link, the other component fills the gap\nand improves the results. We evaluate Hybrid-Linker against competing\napproaches, namely FRLink and DeepLink on a dataset of 12 projects.\nHybrid-Linker achieves 90.1%, 87.8%, and 88.9% based on recall, precision, and\nF-measure, respectively. It also outperforms FRLink and DeepLink by 31.3%, and\n41.3%, regarding the F-measure. Moreover, Hybrid-Linker exhibits extensive\nimprovements in terms of performance as well.\n"}
+{"abstract": "  We derive the torque on a spheroid of an arbitrary aspect ratio $\\kappa$\nsedimenting in a linearly stratified ambient. The analysis demarcates regions\nin parameter space corresponding to broadside-on and edgewise (longside-on)\nsettling in the limit $Re, Ri_v \\ll 1$, where $Re = \\rho_0UL/\\mu$ and $Ri_v\n=\\gamma L^3g/\\mu U$, the Reynolds and viscous Richardson numbers, respectively,\nare dimensionless measures of the importance of inertial and buoyancy forces\nrelative to viscous ones. Here, $L$ is the spheroid semi-major axis, $U$ an\nappropriate settling velocity scale, $\\mu$ the fluid viscosity, and\n$\\gamma\\,(>0)$ the (constant)\\,density gradient characterizing the stably\nstratified ambient, with $\\rho_0$ being the fluid density taken to be a\nconstant within the Boussinesq framework. A reciprocal theorem formulation\nidentifies three contributions to the torque: (1) an $O(Re)$ inertial\ncontribution that already exists in a homogeneous ambient, and orients the\nspheroid broadside-on; (2) an $O(Ri_v)$ hydrostatic contribution due to the\nambient linear stratification that also orients the spheroid broadside-on; and\n(3) a hydrodynamic contribution arising from the perturbation of the ambient\nstratification by the spheroid whose nature depends on $Pe$; $Pe = UL/D$ being\nthe Peclet number with $D$ the diffusivity of the stratifying agent. For $Pe\n\\gg 1$, the hydrodynamic contribution is $O(Ri_v^{\\frac{2}{3}}$) in the Stokes\nstratification regime characterized by $Re \\ll Ri_v^{\\frac{1}{3}}$, and orients\nthe spheroid edgewise regardless of $\\kappa$. The differing orientation\ndependencies of the inertial and large-$Pe$ hydrodynamic stratification torques\nimply that the broadside-on and edgewise settling regimes are separated by two\ndistinct $\\kappa$-dependent critical curves in the\n$Ri_v/Re^{\\frac{3}{2}}-\\kappa$ plane. The predictions are consistent with\nrecent experimental observations.\n"}
+{"abstract": "  The Central Molecular Zone (CMZ; the central ~500 pc of the Milky Way) hosts\nmolecular clouds in an extreme environment of strong shear, high gas pressure\nand density, and complex chemistry. G0.253+0.016, also known as `the Brick', is\nthe densest, most compact and quiescent of these clouds. High-resolution\nobservations with the Atacama Large Millimeter/submillimeter Array (ALMA) have\nrevealed its complex, hierarchical structure. In this paper we compare the\nproperties of recent hydrodynamical simulations of the Brick to those of the\nALMA observations. To facilitate the comparison, we post-process the simulation\nand create synthetic ALMA maps of molecular line emission from eight molecules.\nWe correlate the line emission maps to each other and to the mass column\ndensity, and find that HNCO is the best mass tracer of the eight emission\nlines. Additionally, we characterise the spatial structure of the observed and\nsimulated cloud using the density probability distribution function (PDF),\nspatial power spectrum, fractal dimension, and moments of inertia. While we\nfind good agreement between the observed and simulated data in terms of power\nspectra and fractal dimensions, there are key differences in terms of the\ndensity PDFs and moments of inertia, which we attribute to the omission of\nmagnetic fields in the simulations. Models that include the external\ngravitational potential generated by the stars in the CMZ better reproduce the\nobserved structure, highlighting that cloud structure in the CMZ results from\nthe complex interplay between internal physics (turbulence, self-gravity,\nmagnetic fields) and the impact of the extreme environment.\n"}
+{"abstract": "  Deep Neural Networks (DNNs) could be easily fooled by Adversarial Examples\n(AEs) with the imperceptible difference to original samples in human eyes. To\nkeep the difference imperceptible, the existing attacking bound the adversarial\nperturbations by the $\\ell_\\infty$ norm, which is then served as the standard\nto align different attacks for a fair comparison. However, when investigating\nattack transferability, i.e., the capability of the AEs from attacking one\nsurrogate DNN to cheat other black-box DNN, we find that only using the\n$\\ell_\\infty$ norm is not sufficient to measure the attack strength, according\nto our comprehensive experiments concerning 7 transfer-based attacks, 4\nwhite-box surrogate models, and 9 black-box victim models. Specifically, we\nfind that the $\\ell_2$ norm greatly affects the transferability in\n$\\ell_\\infty$ attacks. Since larger-perturbed AEs naturally bring about better\ntransferability, we advocate that the strength of all attacks should be\nmeasured by both the widely used $\\ell_\\infty$ and also the $\\ell_2$ norm.\nDespite the intuitiveness of our conclusion and advocacy, they are very\nnecessary for the community, because common evaluations (bounding only the\n$\\ell_\\infty$ norm) allow tricky enhancements of the \"attack transferability\"\nby increasing the \"attack strength\" ($\\ell_2$ norm) as shown by our simple\ncounter-example method, and the good transferability of several existing\nmethods may be due to their large $\\ell_2$ distances.\n"}
+{"abstract": "  Traditional link adaptation (LA) schemes in cellular network must be revised\nfor networks beyond the fifth generation (b5G), to guarantee the strict latency\nand reliability requirements advocated by ultra reliable low latency\ncommunications (URLLC). In particular, a poor error rate prediction potentially\nincreases retransmissions, which in turn increase latency and reduce\nreliability. In this paper, we present an interference prediction method to\nenhance LA for URLLC. To develop our prediction method, we propose a kernel\nbased probability density estimation algorithm, and provide an in depth\nanalysis of its statistical performance. We also provide a low complxity\nversion, suitable for practical scenarios. The proposed scheme is compared with\nstate-of-the-art LA solutions over fully compliant 3rd generation partnership\nproject (3GPP) calibrated channels, showing the validity of our proposal.\n"}
+{"abstract": "  Low magnetic field scanning tunneling spectroscopy of individual Abrikosov\nvortices in heavily overdoped Bi$_2$Sr$_2$CaCu$_2$O$_{8+\\delta}$ unveils a\nclear d-wave electronic structure of the vortex core, with a zero-bias\nconductance peak at the vortex center that splits with increasing distance from\nthe core. We show that previously reported unconventional electronic\nstructures, including the low energy checkerboard charge order in the vortex\nhalo and the absence of a zero-bias conductance peak at the vortex center, are\ndirect consequences of short inter-vortex distance and consequent vortex-vortex\ninteractions prevailing in earlier experiments.\n"}
+{"abstract": "  For a tree $T$ and a positive integer $n$, let $B_nT$ denote the $n$-strand\nbraid group on $T$. We use discrete Morse theory techniques to show that\n$H^*(B_nT)$ is the exterior face ring determined by an explicit simplicial\ncomplex that measures $n$-local interactions among essential vertices of $T$.\nIn this first version of the paper we work out proof details in the case of a\nbinary tree.\n"}
+{"abstract": "  We investigate the monopole-antimonopole pair solution in the SU(2) x U(1)\nWeinberg-Salam theory with $\\phi$-winding number, $n=3$ for bifurcation\nphenomena. The magnetic monopole merges with antimonopole to form a vortex ring\nwith finite diameter at $n=3$. Other than the fundamental solution, two new\nbifurcating solution branches were found when Higgs coupling constant\n$\\lambda$, reaches a critical value $\\lambda_c$. The two new branches possess\nhigher energies than the fundamental solutions. These bifurcating solutions\nbehave differently from the vortex ring configuration in SU(2) Yang-Mills-Higgs\ntheory since thery are full vortex-ring. We investigate on the total energy\n$E$, vortex ring diameter $d_{\\rho}$, and magnetic dipole moment $\\mu_m$, for\n$0 \\leq \\lambda \\leq 49$.\n"}
+{"abstract": "  This paper surveys the recent attempts at leveraging machine learning to\nsolve constrained optimization problems. It focuses on surveying the work on\nintegrating combinatorial solvers and optimization methods with machine\nlearning architectures. These approaches hold the promise to develop new hybrid\nmachine learning and optimization methods to predict fast, approximate,\nsolutions to combinatorial problems and to enable structural logical inference.\nThis paper presents a conceptual review of the recent advancements in this\nemerging area.\n"}
+{"abstract": "  Every polynomial $P(X)\\in \\mathbb Z[X]$ satisfies the congruences\n$P(n+m)\\equiv P(n) \\mod m$ for all integers $n, m\\ge 0$. An integer valued\nsequence $(a_n)_{n\\ge 0}$ is called a pseudo-polynomial when it satisfies these\ncongruences. Hall characterized pseudo-polynomials and proved that they are not\nnecessarily polynomials. A long standing conjecture of Ruzsa says that a\npseudo-polynomial $a_n$ is a polynomial as soon as $\\limsup_n \\vert\na_n\\vert^{1/n}<e$. Under this growth assumption, Perelli and Zannier proved\nthat the generating series $\\sum_{n=0}^\\infty a_n z^n$ is a $G$-function. A\nprimary pseudo-polynomial is an integer valued sequence $(a_n)_{n\\ge 0}$ such\nthat $a_{n+p}\\equiv a_n \\mod p$ for all integers $n\\ge 0$ and all prime numbers\n$p$. The same conjecture has been formulated for them, which implies Ruzsa's,\nand this paper revolves around this conjecture. We obtain a Hall type\ncharacterization of primary pseudo-polynomials and draw various consequences\nfrom it. We give a new proof and generalize a result due to Zannier that any\nprimary pseudo-polynomial with an algebraic generating series is a polynomial.\nThis leads us to formulate a conjecture on diagonals of rational fractions and\nprimary pseudo-polynomials, which is related to classic conjectures of Christol\nand van der Poorten. We make the Perelli-Zannier Theorem effective. We prove a\nP\\'olya type result: if there exists a function $F$ analytic in a right-half\nplane with not too large exponential growth (in a precise sense) and such that\nfor all large $n$ the primary pseudo-polynomial $a_n=F(n)$, then $a_n$ is a\npolynomial. Finally, we show how to construct a non-polynomial primary\npseudo-polynomial starting from any primary pseudo-polynomial generated by a\n$G$-function different of $1/(1-x)$.\n"}
+{"abstract": "  A novel Bayesian approach to the problem of variable selection using Gaussian\nprocess regression is proposed. The selection of the most relevant variables\nfor a problem at hand often results in an increased interpretability and in\nmany cases is an essential step in terms of model regularization. In detail,\nthe proposed method relies on so-called nearest neighbor Gaussian processes,\nthat can be considered as highly scalable approximations of classical Gaussian\nprocesses. To perform a variable selection the mean and the covariance function\nof the process are conditioned on a random set $\\mathcal{A}$. This set holds\nthe indices of variables that contribute to the model. While the specification\nof a priori beliefs regarding $\\mathcal{A}$ allows to control the number of\nselected variables, so-called reference priors are assigned to the remaining\nmodel parameters. The application of the reference priors ensures that the\nprocess covariance matrix is (numerically) robust. For the model inference a\nMetropolis within Gibbs algorithm is proposed. Based on simulated data, an\napproximation problem from computer experiments and two real-world datasets,\nthe performance of the new approach is evaluated.\n"}
+{"abstract": "  The worldwide refugee crisis is a major current challenge, affecting the\nhealth and education of millions of families with children due to displacement.\nDespite the various challenges and risks of migration practices, numerous\nrefugee families have access to interactive technologies during these\nprocesses. The aim of this ongoing study is to explore the role of technologies\nin the transitions of refugee families in Scotland. Based on Tudge's\necocultural theory, a qualitative case-study approach has been adopted.\nSemi-structured interviews have been conducted with volunteers who work with\nrefugee families in a big city in Scotland, and proxy observations of young\nchildren were facilitated remotely by their refugee parents. A preliminary\noverview of the participants' insights of the use and role of technology for\ntransitioning into a new culture is provided here.\n"}
+{"abstract": "  New physics increasing the expansion rate just prior to recombination is\namong the least unlikely solutions to the Hubble tension, and would be expected\nto leave an important signature in the early Integrated Sachs-Wolfe (eISW)\neffect, a source of Cosmic Microwave Background (CMB) anisotropies arising from\nthe time-variation of gravitational potentials when the Universe was not\ncompletely matter dominated. Why, then, is there no clear evidence for new\nphysics from the CMB alone, and why does the $\\Lambda$CDM model fit CMB data so\nwell? These questions and the vastness of the Hubble tension theory model space\nmotivate general consistency tests of $\\Lambda$CDM. I perform an eISW-based\nconsistency test of $\\Lambda$CDM introducing the parameter $A_{\\rm eISW}$,\nwhich rescales the eISW contribution to the CMB power spectra. A fit to Planck\nCMB data yields $A_{\\rm eISW}=0.988 \\pm 0.027$, in perfect agreement with the\n$\\Lambda$CDM expectation $A_{\\rm eISW}=1$, and posing an important challenge\nfor early-time new physics, which I illustrate in a case study focused on early\ndark energy (EDE). I explicitly show that the increase in $\\omega_c$ needed for\nEDE to preserve the fit to the CMB, which has been argued to worsen the fit to\nweak lensing and galaxy clustering measurements, is specifically required to\nlower the amplitude of the eISW effect, which would otherwise exceed\n$\\Lambda$CDM's prediction by $\\approx 20\\%$: this is a generic problem beyond\nEDE and likely applying to most models enhancing the expansion rate around\nrecombination. Early-time new physics models invoked to address the Hubble\ntension are therefore faced with the significant challenge of making a similar\nprediction to $\\Lambda$CDM for the eISW effect, while not degrading the fit to\nother measurements in doing so.\n"}
+{"abstract": "  Portfolio optimization approaches inevitably rely on multivariate modeling of\nmarkets and the economy. In this paper, we address three sources of error\nrelated to the modeling of these complex systems: 1. oversimplifying\nhypothesis; 2. uncertainties resulting from parameters' sampling error; 3.\nintrinsic non-stationarity of these systems. For what concerns point 1. we\npropose a L0-norm sparse elliptical modeling and show that sparsification is\neffective. The effects of points 2. and 3. are quantifified by studying the\nmodels' likelihood in- and out-of-sample for parameters estimated over train\nsets of different lengths. We show that models with larger off-sample\nlikelihoods lead to better performing portfolios up to when two to three years\nof daily observations are included in the train set. For larger train sets, we\nfound that portfolio performances deteriorate and detach from the models'\nlikelihood, highlighting the role of non-stationarity. We further investigate\nthis phenomenon by studying the out-of-sample likelihood of individual\nobservations showing that the system changes significantly through time. Larger\nestimation windows lead to stable likelihood in the long run, but at the cost\nof lower likelihood in the short-term: the `optimal' fit in finance needs to be\ndefined in terms of the holding period. Lastly, we show that sparse models\noutperform full-models in that they deliver higher out of sample likelihood,\nlower realized portfolio volatility and improved portfolios' stability,\navoiding typical pitfalls of the Mean-Variance optimization.\n"}
+{"abstract": "  Berry's phase, which is associated with the slow cyclic motion with a finite\nperiod, looks like a Dirac monopole when seen from far away but smoothly\nchanges to a dipole near the level crossing point in the parameter space in an\nexactly solvable model. This topology change of Berry's phase is visualized as\na result of lensing effect; the monopole supposed to be located at the level\ncrossing point appears at the displaced point when the variables of the model\ndeviate from the precisely adiabatic movement. The effective magnetic field\ngenerated by Berry's phase is determined by a simple geometrical consideration\nof the magnetic flux coming from the displaced Dirac monopole.\n"}
+{"abstract": "  The study of high-energy gamma rays from passive Giant Molecular Clouds\n(GMCs) in our Galaxy is an indirect way to characterize and probe the paradigm\nof the \"sea\" of cosmic rays in distant parts of the Galaxy. By using data from\nthe High Altitude Water Cherenkov (HAWC) observatory, we measure the gamma-ray\nflux above 1 TeV of a set of these clouds to test the paradigm.\n  We selected high-galactic latitude clouds that are in HAWC's field-of-view\nand which are within 1~kpc distance from the Sun. We find no significant excess\nemission in the cloud regions, nor when we perform a stacked log-likelihood\nanalysis of GMCs. Using a Bayesian approach, we calculate 95\\% credible\nintervals upper limits of the gamma-ray flux and estimate limits on the\ncosmic-ray energy density of these regions. These are the first limits to\nconstrain gamma-ray emission in the multi-TeV energy range ($>$1 TeV) using\npassive high-galactic latitude GMCs. Assuming that the main gamma-ray\nproduction mechanism is due to proton-proton interaction, the upper limits are\nconsistent with a cosmic-ray flux and energy density similar to that measured\nat Earth.\n"}
+{"abstract": "  In Sun and sun-like stars, it is believed that the cycles of the large-scale\nmagnetic field are produced due to the existence of differential rotation and\nhelicity in the plasma flows in their convection zones (CZs). Hence, it is\nexpected that for each star, there is a critical dynamo number for the\noperation of a large-scale dynamo. As a star slows down, it is expected that\nthe large-scale dynamo ceases to operate above a critical rotation period. In\nour study, we explore the possibility of the operation of the dynamo in the\nsubcritical region using the Babcock--Leighton type kinematic dynamo model. In\nsome parameter regimes, we find that the dynamo shows hysteresis behavior,\ni.e., two dynamo solutions are possible depending on the initial parameters --\ndecaying solution if started with weak field and strong oscillatory solution\n(subcritical dynamo) when started with a strong field. However, under large\nfluctuations in the dynamo parameter, the subcritical dynamo mode is unstable\nin some parameter regimes. Therefore, our study supports the possible existence\nof subcritical dynamo in some stars which was previously shown in a mean-field\ndynamo model with distributed $\\alpha$ and MHD turbulent dynamo simulations.\n"}
+{"abstract": "  Familiar laws of physics are applied to study human relations, modelled by\ntheir world lines (worldlines, WLs) combined with social networks. We focus\nupon the simplest, basic element of any society: a married couple, stable due\nto the dynamic balance between attraction and repulsion. By building\nworldlines/worldsheets, we arrive at a two-level coordinate systems: one\ndescribing the behaviour of a string-like binary system (here, a married\ncouple), the other one, external, corresponding to the motion of this couple in\nthe medium, in which the worldline is embedded, sweeping there a string-like\nsheet or brane.\n  The approach is illustrated by simple examples (semi-quantitative toy models)\nof worldlines/sheets, open to further extension, perfections and\ngeneralization. World lines (WLs) are combined with social networks (SN). Our\ninnovation is in the application of basic physical laws, attraction and\nrepulsion to human behaviour. Simple illustrative examples with empirical\ninputs taken from intuition and/or observation are appended. This is an initial\nattempt, open to unlimited applications.\n"}
+{"abstract": "  Model errors are increasingly seen as a fundamental performance limiter in\nboth Numerical Weather Prediction and Climate Prediction simulations run with\nstate of the art Earth system digital twins.This has motivated recent efforts\naimed at estimating and correcting the systematic, predictable components of\nmodel error in a consistent data assimilation framework. While encouraging\nresults have been obtained with a careful examination of the spatial aspects of\nthe model error estimates, less attention has been devoted to the time\ncorrelation aspects of model errors and their impact on the assimilation cycle.\nIn this work we employ a Lagged Analysis Increment Covariance (LAIG) diagnostic\nto gain insight in the temporal evolution of systematic model errors in the\nECMWF operational data assimilation system, evaluate the effectiveness of the\ncurrent weak constraint 4DVar algorithm in reducing these types of errors and,\nbased on these findings,start exploring new ideas for the development of model\nerror estimation and correction strategies in data assimilation.\n"}
+{"abstract": "  Art and science are different ways of exploring the world, but together they\nhave the potential to be thought-provoking, facilitate a science-society\ndialogue, raise public awareness of science, and develop an understanding of\nart. For several years, we have been teaching an astro-animation class at the\nMaryland Institute College of Art as a collaboration between students and NASA\nscientists. Working in small groups, the students create short animations based\non the research of the scientists who are going to follow the projects as\nmentors. By creating these animations, students bring the power of their\nimagination to see the research of the scientists through a different lens.\nAstro-animation is an undergraduate-level course jointly taught by an\nastrophysicist and an animator. In this paper we present the motivation behind\nthe class, describe the details of how it is carried out, and discuss the\ninteractions between artists and scientists. We describe how such a program\noffers an effective way for art students, not only to learn about science but\nto have a glimpse of \"science in action\". The students have the opportunity to\nbecome involved in the process of science as artists, as observers first and\npotentially through their own art research. Every year, one or more internships\nat NASA Goddard Space Flight Center have been available for our students in the\nsummer. Two students describe their experiences undertaking these internships\nand how science affects their creation of animations for this program and in\ngeneral. We also explain the genesis of our astro-animation program, how it is\ntaught in our animation department, and we present the highlights of an\ninvestigation of the effectiveness of this program we carried out with the\nsupport of an NEA research grant. In conclusion we discuss how the program may\ngrow in new directions, such as contributing to informal STE(A)M learning.\n"}
+{"abstract": "  In this paper, we give a proof to a statement in Perelman's paper for finite\nextinction time of Ricci flow. Our proof draws on different techniques from the\none given in Morgan-Tian's exposition and is extrinsic in nature, which relies\non the co-area formula instead of the Gauss-Bonnet theorem, and is potentially\ngeneralizable to higher dimensions.\n"}
+{"abstract": "  Optical implementations of neural networks (ONNs) herald the next-generation\nhigh-speed and energy-efficient deep learning computing by harnessing the\ntechnical advantages of large bandwidth and high parallelism of optics.\nHowever, due to the problems of incomplete numerical domain, limited hardware\nscale, or inadequate numerical accuracy, the majority of existing ONNs were\nstudied for basic classification tasks. Given that regression is a fundamental\nform of deep learning and accounts for a large part of current artificial\nintelligence applications, it is necessary to master deep learning regression\nfor further development and deployment of ONNs. Here, we demonstrate a\nsilicon-based optical coherent dot-product chip (OCDC) capable of completing\ndeep learning regression tasks. The OCDC adopts optical fields to carry out\noperations in complete real-value domain instead of in only positive domain.\nVia reusing, a single chip conducts matrix multiplications and convolutions in\nneural networks of any complexity. Also, hardware deviations are compensated\nvia in-situ backpropagation control provided the simplicity of chip\narchitecture. Therefore, the OCDC meets the requirements for sophisticated\nregression tasks and we successfully demonstrate a representative neural\nnetwork, the AUTOMAP (a cutting-edge neural network model for image\nreconstruction). The quality of reconstructed images by the OCDC and a 32-bit\ndigital computer is comparable. To the best of our knowledge, there is no\nprecedent of performing such state-of-the-art regression tasks on ONN chip. It\nis anticipated that the OCDC can promote novel accomplishment of ONNs in modern\nAI applications including autonomous driving, natural language processing, and\nscientific study.\n"}
+{"abstract": "  The paper describes a number of simple but quite effective methods for\nconstructing exact solutions of PDEs, that involve a relatively small amount of\nintermediate calculations. The methods employ two main ideas: (i) simple exact\nsolutions can serve to construct more complex solutions of the equations under\nconsideration and (ii) exact solutions of some equations can serve to construct\nsolutions of other, more complex equations. In particular, we propose a method\nfor constructing complex solutions from simple solutions using translation and\nscaling. We show that in some cases, rather complex solutions can be obtained\nby adding one or more terms to simpler solutions. There are situations where\nnonlinear superposition allows us to construct a complex composite solution\nusing similar simple solutions. We also propose a few methods for constructing\ncomplex exact solutions to linear and nonlinear PDEs by introducing\ncomplex-valued parameters into simpler solutions. The effectiveness of the\nmethods is illustrated by a large number of specific examples (over 30 in\ntotal). These include nonlinear heat/diffusion equations, wave type equations,\nKlein--Gordon type equations, hydrodynamic boundary layer equations,\nNavier--Stokes equations, and some other PDEs. Apart from exact solutions to\n`ordinary' PDEs, we also describe some exact solutions to more complex\nnonlinear delay PDEs. Along with the unknown function at the current time,\n$u=u(x,t)$, these equations contain the same function at a past time,\n$w=u(x,t-\\tau)$, where $\\tau>0$ is the delay time. Furthermore, we look at\nnonlinear partial functional-differential equations of the pantograph type,\nwhich in addition to the unknown $u=u(x,t)$, also contain the same functions\nwith dilated or contracted arguments, $w=u(px,qt)$, where $p$ and $q$ are\nscaling parameters.\n"}
+{"abstract": "  Light's internal reflectivity near a critical angle is very sensitive to the\nangle of incidence and the optical properties of the external medium near the\ninterface. Novel applications in biology and medicine of subcritical internal\nreflection are being pursued. In many practical situations the refractive index\nof the external medium may vary with respect to its bulk value due to different\nphysical phenomena at surfaces. Thus, there is a pressing need to understand\nthe effects of a refractive-index gradient at a surface for near-critical-angle\nreflection. In this work we investigate theoretically the reflectivity near the\ncritical angle at an interface with glass assuming the external medium has a\ncontinuous depth-dependent refractive index. We present graphs of the internal\nreflectivity as a function of the angle of incidence, which exhibit the effects\nof a refractive-index gradient at the interface. We analyse the behaviour of\nthe reflectivity curves before total internal reflection is achieved. Our\nresults provide insight into how one can recognise the existence of a\nrefractive-index gradient at the interface and shed light on the viability of\ncharacterising it.\n"}
+{"abstract": "  In this paper, we present difference of convex algorithms for solving bilevel\nprograms in which the upper level objective functions are difference of convex\nfunctions, and the lower level programs are fully convex. This nontrivial class\nof bilevel programs provides a powerful modelling framework for dealing with\napplications arising from hyperparameter selection in machine learning. Thanks\nto the full convexity of the lower level program, the value function of the\nlower level program turns out to be convex and hence the bilevel program can be\nreformulated as a difference of convex bilevel program. We propose two\nalgorithms for solving the reformulated difference of convex program and show\ntheir convergence under very mild assumptions. Finally we conduct numerical\nexperiments to a bilevel model of support vector machine classification.\n"}
+{"abstract": "  We propose a new method for unsupervised continual knowledge consolidation in\ngenerative models that relies on the partitioning of Variational Autoencoder's\nlatent space. Acquiring knowledge about new data samples without forgetting\nprevious ones is a critical problem of continual learning. Currently proposed\nmethods achieve this goal by extending the existing model while constraining\nits behavior not to degrade on the past data, which does not exploit the full\npotential of relations within the entire training dataset. In this work, we\nidentify this limitation and posit the goal of continual learning as a\nknowledge accumulation task. We solve it by continuously re-aligning latent\nspace partitions that we call bands which are representations of samples seen\nin different tasks, driven by the similarity of the information they contain.\nIn addition, we introduce a simple yet effective method for controlled\nforgetting of past data that improves the quality of reconstructions encoded in\nlatent bands and a latent space disentanglement technique that improves\nknowledge consolidation. On top of the standard continual learning evaluation\nbenchmarks, we evaluate our method on a new knowledge consolidation scenario\nand show that the proposed approach outperforms state-of-the-art by up to\ntwofold across all testing scenarios.\n"}
+{"abstract": "  Registration is a transformation estimation problem between two point clouds,\nwhich has a unique and critical role in numerous computer vision applications.\nThe developments of optimization-based methods and deep learning methods have\nimproved registration robustness and efficiency. Recently, the combinations of\noptimization-based and deep learning methods have further improved performance.\nHowever, the connections between optimization-based and deep learning methods\nare still unclear. Moreover, with the recent development of 3D sensors and 3D\nreconstruction techniques, a new research direction emerges to align\ncross-source point clouds. This survey conducts a comprehensive survey,\nincluding both same-source and cross-source registration methods, and summarize\nthe connections between optimization-based and deep learning methods, to\nprovide further research insight. This survey also builds a new benchmark to\nevaluate the state-of-the-art registration algorithms in solving cross-source\nchallenges. Besides, this survey summarizes the benchmark data sets and\ndiscusses point cloud registration applications across various domains.\nFinally, this survey proposes potential research directions in this rapidly\ngrowing field.\n"}
+{"abstract": "  We perform asymptotic analysis for the Euler--Riesz system posed in either\n$\\mathbb{T}^d$ or $\\mathbb{R}^d$ in the high-force regime and establish a\nquantified relaxation limit result from the Euler--Riesz system to the\nfractional porous medium equation. We provide a unified approach for asymptotic\nanalysis regardless of the presence of pressure, based on the modulated energy\nestimates, the Wasserstein distance of order $2$, and the bounded Lipschitz\ndistance.\n"}
+{"abstract": "  Resistive random-access memory is one of the most promising candidates for\nthe next generation of non-volatile memory technology. However, its crossbar\nstructure causes severe \"sneak-path\" interference, which also leads to strong\ninter-cell correlation. Recent works have mainly focused on sub-optimal data\ndetection schemes by ignoring inter-cell correlation and treating sneak-path\ninterference as independent noise. We propose a near-optimal data detection\nscheme that can approach the performance bound of the optimal detection scheme.\nOur detection scheme leverages a joint data and sneak-path interference\nrecovery and can use all inter-cell correlations. The scheme is appropriate for\ndata detection of large memory arrays with only linear operation complexity.\n"}
+{"abstract": "  We present a robust version of the life-cycle optimal portfolio choice\nproblem in the presence of labor income, as introduced in Biffis, Gozzi and\nProsdocimi (\"Optimal portfolio choice with path dependent labor income: the\ninfinite horizon case\", SIAM Journal on Control and Optimization, 58(4),\n1906-1938.) and Dybvig and Liu (\"Lifetime consumption and investment:\nretirement and constrained borrowing\", Journal of Economic Theory, 145, pp.\n885-907). In particular, in Biffis, Gozzi and Prosdocimi the influence of past\nwages on the future ones is modelled linearly in the evolution equation of\nlabor income, through a given weight function. The optimization relies on the\nresolution of an infinite dimensional HJB equation. We improve the state of art\nin three ways. First, we allow the weight to be a Radon measure. This\naccommodates for more realistic weighting of the sticky wages, like e.g. on a\ndiscrete temporal grid according to some periodic income. Second, there is a\ngeneral correlation structure between labor income and stocks market. This\nnaturally affects the optimal hedging demand, which may increase or decrease\naccording to the correlation sign. Third, we allow the weight to change with\ntime, possibly lacking perfect identification. The uncertainty is specified by\na given set of Radon measures $K$, in which the weight process takes values.\nThis renders the inevitable uncertainty on how the past affects the future, and\nincludes the standard case of error bounds on a specific estimate for the\nweight. Under uncertainty averse preferences, the decision maker takes a maxmin\napproach to the problem. Our analysis confirms the intuition: in the infinite\ndimensional setting, the optimal policy remains the best investment strategy\nunder the worst case weight.\n"}
+{"abstract": "  Conventional multi-agent path planners typically determine a path that\noptimizes a single objective, such as path length. Many applications, however,\nmay require multiple objectives, say time-to-completion and fuel use, to be\nsimultaneously optimized in the planning process. Often, these criteria may not\nbe readily compared and sometimes lie in competition with each other. Simply\napplying standard multi-objective search algorithms to multi-agent path finding\nmay prove to be inefficient because the size of the space of possible\nsolutions, i.e., the Pareto-optimal set, can grow exponentially with the number\nof agents (the dimension of the search space). This paper presents an approach\nthat bypasses this so-called curse of dimensionality by leveraging our prior\nmulti-agent work with a framework called subdimensional expansion. One example\nof subdimensional expansion, when applied to A*, is called M* and M* was\nlimited to a single objective function. We combine principles of dominance and\nsubdimensional expansion to create a new algorithm named multi-objective M*\n(MOM*), which dynamically couples agents for planning only when those agents\nhave to \"interact\" with each other. MOM* computes the complete Pareto-optimal\nset for multiple agents efficiently and naturally trades off sub-optimal\napproximations of the Pareto-optimal set and computational efficiency. Our\napproach is able to find the complete Pareto-optimal set for problem instances\nwith hundreds of solutions which the standard multi-objective A* algorithms\ncould not find within a bounded time.\n"}
+{"abstract": "  The chromatic index of a cubic graph is either 3 or 4. Edge-Kempe switching,\nwhich can be used to transform edge-colorings, is here considered for\n3-edge-colorings of cubic graphs. Computational results for edge-Kempe\nswitching of cubic graphs up to order 30 and bipartite cubic graphs up to order\n36 are tabulated. Families of cubic graphs of orders $4n+2$ and $4n+4$ with\n$2^n$ edge-Kempe equivalence classes are presented; it is conjectured that\nthere are no cubic graphs with more edge-Kempe equivalence classes. New\nfamilies of nonplanar bipartite cubic graphs with exactly one edge-Kempe\nequivalence class are also obtained. Edge-Kempe switching is further connected\nto cycle switching of Steiner triple systems, for which an improvement of the\nestablished classification algorithm is presented.\n"}
+{"abstract": "  Type-B permutation tableaux are combinatorial objects introduced by Lam and\nWilliams that have an interesting connection with the partially asymmetric\nsimple exclusion process (PASEP). In this paper, we compute the expected value\nof several statistics on these tableaux. Some of these computations are\nmotivated by a similar paper on permutation tableaux. Others are motivated by\nthe PASEP. In particular, we compute the expected number of rows, unrestricted\nrows, diagonal ones, adjacent south steps, and adjacent west steps.\n"}
+{"abstract": "  Adversarial training (AT) is currently one of the most successful methods to\nobtain the adversarial robustness of deep neural networks. However, the\nphenomenon of robust overfitting, i.e., the robustness starts to decrease\nsignificantly during AT, has been problematic, not only making practitioners\nconsider a bag of tricks for a successful training, e.g., early stopping, but\nalso incurring a significant generalization gap in the robustness. In this\npaper, we propose an effective regularization technique that prevents robust\noverfitting by optimizing an auxiliary `consistency' regularization loss during\nAT. Specifically, we discover that data augmentation is a quite effective tool\nto mitigate the overfitting in AT, and develop a regularization that forces the\npredictive distributions after attacking from two different augmentations of\nthe same instance to be similar with each other. Our experimental results\ndemonstrate that such a simple regularization technique brings significant\nimprovements in the test robust accuracy of a wide range of AT methods. More\nremarkably, we also show that our method could significantly help the model to\ngeneralize its robustness against unseen adversaries, e.g., other types or\nlarger perturbations compared to those used during training. Code is available\nat https://github.com/alinlab/consistency-adversarial.\n"}
+{"abstract": "  Deep learning techniques are increasingly being adopted for classification\ntasks over the past decade, yet explaining how deep learning architectures can\nachieve state-of-the-art performance is still an elusive goal. While all the\ntraining information is embedded deeply in a trained model, we still do not\nunderstand much about its performance by only analyzing the model. This paper\nexamines the neuron activation patterns of deep learning-based classification\nmodels and explores whether the models' performances can be explained through\nneurons' activation behavior. We propose two approaches: one that models\nneurons' activation behavior as a graph and examines whether the neurons form\nmeaningful communities, and the other examines the predictability of neurons'\nbehavior using entropy. Our comprehensive experimental study reveals that both\nthe community quality and entropy can provide new insights into the deep\nlearning models' performances, thus paves a novel way of explaining deep\nlearning models directly from the neurons' activation pattern.\n"}
+{"abstract": "  Cavity ring-down spectroscopy is a ubiquitous optical method used to study\nlight-matter interactions with high resolution, sensitivity and accuracy.\nHowever, it has never been performed with the multiplexing advantages of direct\nfrequency comb spectroscopy without sacrificing orders of magnitude of\nresolution. We present dual-comb cavity ring-down spectroscopy (DC-CRDS) based\non the parallel heterodyne detection of ring-down signals with a local\noscillator comb to yield absorption and dispersion spectra. These spectra are\nobtained from widths and positions of cavity modes. We present two approaches\nwhich leverage the dynamic cavity response to coherently or randomly driven\nchanges in the amplitude or frequency of the probe field. Both techniques yield\naccurate spectra of methane - an important greenhouse gas and breath biomarker.\nThe high sensitivity and accuracy of broadband DC-CRDS, shows promise for\napplications like studies of the structure and dynamics of large molecules,\nmultispecies trace gas detection and isotopic composition.\n"}
+{"abstract": "  Using direct numerical simulations of rotating Rayleigh-B\\'enard convection,\nwe explore the transitions between turbulent states from a 3D flow state\ntowards a quasi-2D condensate known as the large-scale vortex (LSV). We vary\nthe Rayleigh number $Ra$ as control parameter and study the system response\n(strength of the LSV) in terms of order parameters assessing the energetic\ncontent in the flow and the upscale energy flux. By sensitively probing the\nboundaries of the domain of existence of the LSV, we find discontinuous\ntransitions and we identify the presence of a hysteresis loop as well as\nnucleation & growth type of dynamics, manifesting a remarkable correspondence\nwith first-order phase transitions in equilibrium statistical mechanics. We\nshow furthermore that the creation of the condensate state coincides with a\ndiscontinuous transition of the energy transport into the largest mode of the\nsystem.\n"}
+{"abstract": "  Radial imaging techniques, such as projection-reconstruction (PR), are used\nin magnetic resonance imaging (MRI) for dynamic imaging, angiography, and\nshort-imaging. They are robust to flow and motion, have diffuse aliasing\npatterns, and support short readouts and echo times. One drawback is that\nstandard implementations do not support anisotropic field-of-view (FOV) shapes,\nwhich are used to match the imaging parameters to the object or\nregion-of-interest. A set of fast, simple algorithms for 2-D and 3-D PR, and\n3-D cones acquisitions are introduced that match the sampling density in\nfrequency space to the desired FOV shape. Tailoring the acquisitions allows for\nreduction of aliasing artifacts in undersampled applications or scan time\nreductions without introducing aliasing in fully-sampled applications. It also\nmakes possible new radial imaging applications that were previously unsuitable,\nsuch as imaging elongated regions or thin slabs. 2-D PR longitudinal leg images\nand thin-slab, single breath-hold 3-D PR abdomen images, both with isotropic\nresolution, demonstrate these new possibilities. No scan time to volume\nefficiency is lost by using anisotropic FOVs. The acquisition trajectories can\nbe computed on a scan by scan basis.\n"}
+{"abstract": "  We develop a visual analytics system, NewsKaleidoscope, to investigate the\nhow news reporting of events varies. NewsKaleidoscope combines several backend\ntext language processing techniques with a coordinated visualization interface\ntailored for visualization non-expert users. To robustly evaluate\nNewsKaleidoscope, we conduct a trio of user studies. (1) A usability study with\nnews novices assesses the overall system and the specific insights promoted for\njournalism-agnostic users. (2) A follow-up study with news experts assesses the\ninsights promoted for journalism-savvy users. (3) Based on identified system\nlimitations in these two studies, we amend NewsKaleidoscope design and conduct\na third study to validate these improvements. Results indicate that, for both\nnews novice and experts, NewsKaleidoscope supports an effective, task-driven\nworkflow for analyzing the diversity of news coverage about events, though\njournalism expertise has a significant influence on the user insights and\ntakeaways. Our insights while developing and evaluating NewsKaleidoscope can\naid future interface designs that combine visualization with natural language\nprocessing to analyze coverage diversity in news event reporting.\n"}
+{"abstract": "  We investigate the possibility of simultaneously explaining inflation, the\nneutrino masses and the baryon asymmetry through extending the Standard Model\nby a triplet Higgs. The neutrino masses are generated by the vacuum expectation\nvalue of the triplet Higgs, while a combination of the triplet and doublet\nHiggs' plays the role of the inflaton. Additionally, the dynamics of the\ntriplet, and its inherent lepton number violating interactions, lead to the\ngeneration of a lepton asymmetry during inflation. The resultant baryon\nasymmetry, inflationary predictions and neutrino masses are consistent with\ncurrent observational and experimental results.\n"}
+{"abstract": "  Prosody plays an important role in characterizing the style of a speaker or\nan emotion, but most non-parallel voice or emotion style transfer algorithms do\nnot convert any prosody information. Two major components of prosody are pitch\nand rhythm. Disentangling the prosody information, particularly the rhythm\ncomponent, from the speech is challenging because it involves breaking the\nsynchrony between the input speech and the disentangled speech representation.\nAs a result, most existing prosody style transfer algorithms would need to rely\non some form of text transcriptions to identify the content information, which\nconfines their application to high-resource languages only. Recently,\nSpeechSplit has made sizeable progress towards unsupervised prosody style\ntransfer, but it is unable to extract high-level global prosody style in an\nunsupervised manner. In this paper, we propose AutoPST, which can disentangle\nglobal prosody style from speech without relying on any text transcriptions.\nAutoPST is an Autoencoder-based Prosody Style Transfer framework with a\nthorough rhythm removal module guided by the self-expressive representation\nlearning. Experiments on different style transfer tasks show that AutoPST can\neffectively convert prosody that correctly reflects the styles of the target\ndomains.\n"}
+{"abstract": "  Virtual Reality (VR) head-mounted displays (HMDs) have been studied widely as\ntools for the most diverse kinds of training activities. One special kind that\nis the basis for many real-world applications is spatial knowledge acquisition\nand navigation. For example, knowing well by heart escape routes can be an\nimportant factor for firefighters and soldiers. Prior research on how well\nknowledge acquired in virtual worlds translates to real, physical one has had\nmixed results, with some suggesting spatial learning in VR is akin to using a\nregular 2D display. However, VR HMDs have evolved drastically in the last\ndecade, and little is known about how spatial training skills in a simulated\nenvironment using up-to-date VR HMDs compares to training in the real world. In\nthis paper, we aim to investigate how people trained in a VR maze compare\nagainst those trained in a physical maze in terms of recall of the position of\nitems inside the environment. While our results did not find significant\ndifferences in time performance for people who experienced the physical and\nthose who trained in VR, other behavioural factors were different.\n"}
+{"abstract": "  We trace what happens with asymptotically free behavior of the running\ncoupling in $\\phi^{3}$ theory in six-dimensional space-time if to compactify\ntwo spatial dimensions on a 2D closed manifold. The result can be considered as\nan effective 4D theory of infinitely many KK-type scalar fields with triple\ninteractions. The effective \\emph{dimensional} coupling inherits running to\nzero at high mass scales in a modified form depending on the size of the\ncompact manifold. Some physical implications are discussed.\n"}
+{"abstract": "  Let $\\beta=\\frac{1+\\sqrt{5}}{2}$, $(a_n)_{n \\in \\mathbb{N}^+}$ be a\nnon-uniform morphic sequence involving the infinite Fibonacci word and\n$(\\delta(n))_{n \\in \\mathbb{N}^+}$ be a positive sequence such that for all\npositive integers $n$, $\\delta(n)=\\frac{1}{\\sqrt{5}}\\sum_{j \\geq\n0}\\epsilon_j\\beta^{j+2}$ if the unique Zeckendorf expansion of $n$ is\n$n=\\sum_{j \\geq 0}\\epsilon_jF_{j+2}$ with Fibonacci numbers $F_0,F_1,F_2...$.\nWe define and study some Dirichlet series in the form of $\\sum_{n\\geq\n1}\\frac{a_n}{(\\delta(n))^s}$ and relations between them. Moreover, we compute\nthe values of some infinite series involving the infinite Fibonacci word.\n"}
+{"abstract": "  Learning representations of stochastic processes is an emerging problem in\nmachine learning with applications from meta-learning to physical object models\nto time series. Typical methods rely on exact reconstruction of observations,\nbut this approach breaks down as observations become high-dimensional or noise\ndistributions become complex. To address this, we propose a unifying framework\nfor learning contrastive representations of stochastic processes (CReSP) that\ndoes away with exact reconstruction. We dissect potential use cases for\nstochastic process representations, and propose methods that accommodate each.\nEmpirically, we show that our methods are effective for learning\nrepresentations of periodic functions, 3D objects and dynamical processes. Our\nmethods tolerate noisy high-dimensional observations better than traditional\napproaches, and the learned representations transfer to a range of downstream\ntasks.\n"}
+{"abstract": "  Among numerous applications of muon tomography, deployment in civil\nstructures has caught attraction of many recently. In this work, the\nappropriateness of muon scattering tomography to detect defects in concrete\nstructures has been studied numerically. A few basic concrete structures that\nare frequently used in civil construction, have been considered as test cases.\nA simulation has been performed on Geant4 platform where an imaging setup built\nwith several gaseous ionization detectors, having a specific spatial resolution\nfor tracking the muons, have been modeled. The images of the test cases with\nand without the defect have been simulated for a month-long exposure of cosmic\nmuons on the basis of their scattering from the composite concrete structures.\nThe images have been compared using t-test to evaluate the performance of the\nimaging setup in identifying the defects. Further processing of the images has\nbeen done with a pattern recognition method proposed in our earlier work to\nimprove defect identification. The efficacy of the said method has been\nevaluated in terms of the PRM-score devised in this work. The limitation and\nadvantages of the present application of the muon scattering tomography\nencompassing the imaging and image processing technique in non-destructive\nevaluation of concrete structures have been discussed.\n"}
+{"abstract": "  This paper studies network resilience against structured additive\nperturbations to its topology. We consider dynamic networks modeled as linear\ntime-invariant systems subject to perturbations of bounded energy satisfying\nspecific sparsity and entry-wise constraints. Given an energy level, the\nstructured pseudospectral abscissa captures the worst-possible perturbation an\nadversary could employ to de-stabilize the network, and the structured\nstability radius is the maximum energy in the structured perturbation that the\nnetwork can withstand without becoming unstable. Building on a novel\ncharacterization of the worst-case structured perturbation, we propose\niterative algorithms that efficiently compute the structured pseudospectral\nabscissa and structured stability radius. We provide theoretical guarantees of\nthe local convergence of the algorithms and illustrate their efficacy and\naccuracy on several network examples.\n"}
+{"abstract": "  Due to the zero nontrivial aperiodic correlation of complete complementary\ncode (CCCs), it is used in asynchronous multi carrier code division multiple\naccess (MC-CDMA) communication to provide zero interference performance.\nHowever, there is no CCC for some lengths. In this case, if the system pays\nmore attention to the correlation, it can use mutually orthogonal complementary\nsequence sets (MOCSSs), and if the system pays more attention to the set size,\nit can use Z-complementary code sets (ZCCSs). In this paper, we propose a\ndirect construction of $q$-ary $(q^{v+1},q,q^m,q^{m-v})$-ZCCS. In addition,\nbased on the known CCCs, we propose a new construction of MOCSSs and CCCs\nrespectively.\n"}
+{"abstract": "  We show that, in almost every $n$-vertex random directed graph process, a\ncopy of every possible $n$-vertex oriented cycle will appear strictly before a\ndirected Hamilton cycle does, except of course for the directed cycle itself.\nFurthermore, given an arbitrary $n$-vertex oriented cycle, we determine the\nsharp threshold for its appearance in the binomial random directed graph. These\nresults confirm, in a strong form, a conjecture of Ferber and Long.\n"}
+{"abstract": "  The paper addresses a joint sequential changepoint detection and\nidentification/isolation problem for a general stochastic model, assuming that\nthe observed data may be dependent and non-identically distributed, the prior\ndistribution of the change point is arbitrary, and the post-change hypotheses\nare composite. The developed detection-identification theory generalizes the\nchangepoint detection theory developed by Tartakovsky (2019) to the case of\nmultiple composite post-change hypotheses when one has not only to detect a\nchange as quickly as possible but also to identify (or isolate) the true\npost-change distribution. We propose a multi-hypothesis change\ndetection-identification rule and show that it is nearly optimal, minimizing\nmoments of the delay to detection as the probability of a false alarm and the\nprobabilities of misidentification go to zero.\n"}
+{"abstract": "  Machine learning models are poised to make a transformative impact on\nchemical sciences by dramatically accelerating computational algorithms and\namplifying insights available from computational chemistry methods. However,\nachieving this requires a confluence and coaction of expertise in computer\nscience and physical sciences. This review is written for new and experienced\nresearchers working at the intersection of both fields. We first provide\nconcise tutorials of computational chemistry and machine learning methods,\nshowing how insights involving both can be achieved. We then follow with a\ncritical review of noteworthy applications that demonstrate how computational\nchemistry and machine learning can be used together to provide insightful (and\nuseful) predictions in molecular and materials modeling, retrosyntheses,\ncatalysis, and drug design.\n"}
+{"abstract": "  It is known that the numerical invariants Betti numbers and Bass numbers are\nworthwhile tools for decoding a large amount of information about modules over\ncommutative rings. We highlight this fact, further, by establishing some\ncriteria for certain semidualizing complexes via their Betti and Bass numbers.\nTwo distinguished types of semidualizing complexes are the shifts of the\nunderlying rings and dualizing complexes. Let $C$ be a semidualizing complex\nfor an analytically irreducible local ring $R$ and set $n:=\\sup C$ and\n$d:=\\dim_RC$. We show that $C$ is quasi-isomorphic to a shift of $R$ if and\nonly if the $n$th Betti number of $C$ is one. Also, we show that $C$ is a\ndualizing complex for $R$ if and only if the $d$th Bass number of $C$ is one.\n"}
+{"abstract": "  We construct families of fundamental, dipole, and tripole solitons in the\nfractional Schr\\\"{o}dinger equation (FSE)\\ incorporating self-focusing cubic\nand defocusing quintic terms modulated by factors $\\cos ^{2}x$ and $\\sin^{2}x$,\nrespectively. While the fundamental solitons are similar to those in the model\nwith the uniform nonlinearity, the multipole complexes exist only in the\npresence of the nonlinear lattice. The shapes and stability of all the solitons\nstrongly depend on the L\\'{e}vy index (LI)\\ that determines the FSE\nfractionality. Stability areas are identified in the plane of LI and\npropagation constant by means of numerical methods, and some results are\nexplained with the help of an analytical approximation. The stability areas are\nbroadest for the fundamental solitons and narrowest for the tripoles.\n"}
+{"abstract": "  Kerala, the south-west coastal state of India, was ravaged by a series of\nfloods during the South-West Monsoon of 2018. The season was marked by severely\nanomalous rainfall trends, with upto 150 mm of departures from the mean daily\nprecipitation in the northern districts of the State. Although, there were many\nstudies about the hydrogeological factors which aggravated the floods in\nKerala, no attempt was made to delve into the physics which actually resulted\nin anomalous precipitation during the year. This study intends to document the\ndynamical phenomenon which caused the Kerala Floods of 2018. The westward\npropagating convectively-coupled Mixed Rossby-Gravity (MRG) waves were excited\nby the synoptic disturbances of the tropical Pacific at the pressure level of\n700 hPa, during the Indian Monsoon of 2018. They travelled across the Indian\nOcean in two significant modes -- a predominant slow moving wave of 20-40 days\nperiod (as Madden-Julian Oscillation) and a secondary faster wave of 5-8 days\nperiod. They are characterised by vertical phase propagation to the upper\ntroposphere, a precursor to deep convection and intense precipitation. Further,\nthe propagation of these waves through a medium enhances its relative vorticity\nand the gyres or circulations thus formed are symmetrical about the equator.\nConsequently, the meanders in the wind field and widening of the Intertropical\nConvergence Zone were observed. The MRG waves, especially the slow mode induced\ndivergence in the wind field, which fueled convection in tropics and brought\nvery heavy rainfall to the State of Kerala in 2018.\n"}
+{"abstract": "  Many promising optoelectronic devices, such as broadband photodetectors,\nnonlinear frequency converters, and building blocks for data communication\nsystems, exploit photoexcited charge carriers in graphene. For these systems,\nit is essential to understand, and eventually control, the cooling dynamics of\nthe photoinduced hot-carrier distribution. There is, however, still an active\ndebate on the different mechanisms that contribute to hot-carrier cooling. In\nparticular, the intrinsic cooling mechanism that ultimately limits the cooling\ndynamics remains an open question. Here, we address this question by studying\ntwo technologically relevant systems, consisting of high-quality graphene with\na mobility >10,000 cm$^2$V$^{-1}$s$^{-1}$ and environments that do not\nefficiently take up electronic heat from graphene: WSe$_2$-encapsulated\ngraphene and suspended graphene. We study the cooling dynamics of these two\nhigh-quality graphene systems using ultrafast pump-probe spectroscopy at room\ntemperature. Cooling via disorder-assisted acoustic phonon scattering and\nout-of-plane heat transfer to the environment is relatively inefficient in\nthese systems, predicting a cooling time of tens of picoseconds. However, we\nobserve much faster cooling, on a timescale of a few picoseconds. We attribute\nthis to an intrinsic cooling mechanism, where carriers in the hot-carrier\ndistribution with enough kinetic energy emit optical phonons. During phonon\nemission, the electronic system continuously re-thermalizes, re-creating\ncarriers with enough energy to emit optical phonons. We develop an analytical\nmodel that explains the observed dynamics, where cooling is eventually limited\nby optical-to-acoustic phonon coupling. These fundamental insights into the\nintrinsic cooling mechanism of hot carriers in graphene will play a key role in\nguiding the development of graphene-based optoelectronic devices.\n"}
+{"abstract": "  We derive universal thermodynamic inequalities that bound from below the\nmoments of first-passage times of stochastic currents in nonequilibrium\nstationary states and in the limit where the thresholds that define the\nfirst-passage problem are large. These inequalities describe a tradeoff between\nspeed, uncertainty, and dissipation in nonequilibrium processes, which are\nquantified, respectively, with the moments of the first-passage times of\nstochastic currents, the splitting probability, and the mean entropy production\nrate. Near equilibrium, the inequalities imply that mean-first passage times\nare lower bounded by the Van't Hoff-Arrhenius law, whereas far from thermal\nequilibrium the bounds describe a universal speed limit for rate processes.\nWhen the current is the stochastic entropy production, then the bounds are\nequalities, a remarkable property that follows from the fact that the\nexponentiated negative entropy production is a martingale.\n"}
+{"abstract": "  We revisit the dissipative approach to producing and stabilizing\nspin-squeezed states of an ensemble of $N$ two-level systems, providing a\ndetailed analysis of two surprising yet generic features of such protocols. The\nfirst is a macroscopic sensitivity of the steady state to whether $N$ is even\nor odd. We discuss how this effect can be avoided (if the goal is\nparity-insensitive squeezing), or could be exploited as a new kind of sensing\nmodality with single-spin sensitivity. The second effect is an anomalous\nemergent long timescale and a \"prethermalized\" regime that occurs for even weak\nsingle-spin dephasing. We also discuss a general hybrid-systems approach for\nimplementing dissipative spin squeezing that does not require squeezed input\nlight or complex multi-level atoms, but instead makes use of bosonic\nreservoir-engineering ideas. Our protocol is compatible with a variety of\nplatforms, including trapped ions, NV defect spins coupled to diamond\noptomechanical crystals, and spin ensembles coupled to superconducting\nmicrowave circuits.\n"}
+{"abstract": "  We first show the realization of exceptional points in a non-Hermitian\nsuperconducting system based on a conventional superconductor and then\ndemonstrate that, surprisingly, the system hosts odd-frequency pairing, solely\ngenerated by the non-Hermiticity. While there is a coexistence of even- and\nodd-frequency pairs under general conditions, we find that the even-frequency\nterm vanishes at the exceptional degeneracies, leaving only odd-frequency\npairing. This exceptional odd-frequency pairing is directly given by the\nimaginary part of the eigenvalues at the exceptional points and can be measured\nfrom the spectral function. Our results thus put forward non-Hermitian systems\nas a powerful platform to realize odd-frequency superconducting pairing.\n"}
+{"abstract": "  The recent advances in power plants and energy resources have extended the\napplications of DC-DC converters in the power systems (especially in the\ncontext of DC micro-grids). Parameter identification can extract the parameters\nof the converters and generate accurate discrete simulation models. In this\npaper, we propose a measurement-based converter parameter calibration method by\nan adaptive Approximate Bayesian Computation with sequential Monte Carlo\nsampler (ABC SMC), which estimates the parameters related to passive and\nparasitic components. At first, we propose to find suitable prior distribution\nfor the parameter which we don't know the prior information about them. With\nhaving prior distributions, we can use the ABC SMC to find the exact values of\nthe parameters of the converter. We chose the distance function carefully and\nbased on the simulations we assigned the best method for the threshold\nsequencing. For improving the computationally of the algorithm, we propose an\nadaptive weight that helps the algorithm to find the optimal values with fewer\nsimulations. The effectiveness of the proposed method is validated for a DC-DC\nbuck converter. The results show that the proposed approach can accurately and\nefficiently estimate the posterior distributions of the buck parameters subject\nto gross errors in the prior distributions of the parameters. The proposed\nalgorithm can also be applied to other parameter identifications and\noptimization applications such as rectifiers, filters, or power supplies, among\nothers.\n"}
+{"abstract": "  We develop a new physical model for the rain effect and show that the\nwell-known atmosphere scattering model (ASM) for the haze effect naturally\nemerges as its homogeneous continuous limit. Via depth-aware fusion of\nmulti-layer rain streaks according to the camera imaging mechanism, the new\nmodel can better capture the sophisticated non-deterministic degradation\npatterns commonly seen in real rainy images. We also propose a Densely\nScale-Connected Attentive Network (DSCAN) that is suitable for both deraining\nand dehazing tasks. Our design alleviates the bottleneck issue existent in\nconventional multi-scale networks and enables more effective information\nexchange and aggregation. Extensive experimental results demonstrate that the\nproposed DSCAN is able to deliver superior derained/dehazed results on both\nsynthetic and real images as compared to the state-of-the-art. Moreover, it is\nshown that for our DSCAN, the synthetic dataset built using the new physical\nmodel yields better generalization performance on real images in comparison\nwith the existing datasets based on over-simplified models.\n"}
+{"abstract": "  The comparison of the Hamiltonians of the noncommutative isotropic harmonic\noscillator and Landau problem are analysed to study the specific conditions\nunder which these two models are indistinguishable. The energy eigenvalues and\neigenstates of Landau problem in symmetric and two Landau gauges are evaluated\nanalytically. The Hamiltonian of a noncommutative isotropic harmonic oscillator\nis found by using Bopp's shift in commutative coordinate space. The result\nshows that the two systems are isomorphic up to the similar values of $n_{r}$\nand $m_{l}$ and $qB = eB > 0$ for both gauge choices. However, there is an\nadditional requirement for Landau gauge where the noncommutative oscillator has\nto lose one spatial degree of freedom. It also needs to be parametrized by a\nfactor $\\zeta$ for their Hamiltonians to be consistent with each other. The\nwavefunctions and probability density functions are then plotted and the\nbehaviour that emerges is explained. Finally, the effects of noncommutativity\nor magnetic field on the eigenstates and their probability distribution of the\nisomorphic system are shown.\n"}
+{"abstract": "  For robot touch to converge with the human sense of touch, artificial\ntransduction should involve biologically-plausible population codes analogous\nto those of natural afferents. Using a biomimetic tactile sensor with\n3d-printed skin based on the dermal-epidermal boundary, we propose two novel\nfeature sets to mimic slowly-adapting and rapidly-adapting type-I tactile\nmechanoreceptor function. Their plausibility is tested with three classic\nexperiments from the study of natural touch: impingement on a flat plate to\nprobe adaptation and spatial modulation; stimulation by spatially-complex\nridged stimuli to probe single afferent responses; and perception of grating\norientation to probe the population response. Our results show a match between\nartificial and natural afferent responses in their sensitivity to edges and\ngaps; likewise, the human and robot psychometric functions match for grating\norientation. These findings could benefit robot manipulation, prosthetics and\nthe neurophysiology of touch.\n"}
+{"abstract": "  Gaussian mixture models are a popular tool for model-based clustering, and\nmixtures of factor analyzers are Gaussian mixture models having parsimonious\nfactor covariance structure for mixture components. There are several recent\nextensions of mixture of factor analyzers to deep mixtures, where the Gaussian\nmodel for the latent factors is replaced by a mixture of factor analyzers. This\nconstruction can be iterated to obtain a model with many layers. These deep\nmodels are challenging to fit, and we consider Bayesian inference using\nsparsity priors to further regularize the estimation. A scalable natural\ngradient variational inference algorithm is developed for fitting the model,\nand we suggest computationally efficient approaches to the architecture choice\nusing overfitted mixtures where unnecessary components drop out in the\nestimation. In a number of simulated and two real examples, we demonstrate the\nversatility of our approach for high-dimensional problems, and demonstrate that\nthe use of sparsity inducing priors can be helpful for obtaining improved\nclustering results.\n"}
+{"abstract": "  Despite the constant advances in computer vision, integrating modern\nsingle-image detectors in real-time handgun alarm systems in video-surveillance\nis still debatable. Using such detectors still implies a high number of false\nalarms and false negatives. In this context, most existent studies select one\nof the latest single-image detectors and train it on a better dataset or use\nsome pre-processing, post-processing or data-fusion approach to further reduce\nfalse alarms. However, none of these works tried to exploit the temporal\ninformation present in the videos to mitigate false detections. This paper\npresents a new system, called MULTI Confirmation-level Alarm SysTem based on\nConvolutional Neural Networks (CNN) and Long Short Term Memory networks (LSTM)\n(MULTICAST), that leverages not only the spacial information but also the\ntemporal information existent in the videos for a more reliable handgun\ndetection. MULTICAST consists of three stages, i) a handgun detection stage,\nii) a CNN-based spacial confirmation stage and iii) LSTM-based temporal\nconfirmation stage. The temporal confirmation stage uses the positions of the\ndetected handgun in previous instants to predict its trajectory in the next\nframe. Our experiments show that MULTICAST reduces by 80% the number of false\nalarms with respect to Faster R-CNN based-single-image detector, which makes it\nmore useful in providing more effective and rapid security responses.\n"}
+{"abstract": "  We present an analysis of the intracluster light in the Frontier Field\nCluster MACS J1149.5+2223 (z=0.544), which combines new and archival Hubble\nWFC3/IR imaging to provide continuous radial coverage out to 2.8 Mpc from the\nbrightest cluster galaxy. Employing careful treatment of potential systematic\nbiases and using data at the largest radii to determine the background sky\nlevel, we reconstruct the surface brightness profile out to a radius of 2 Mpc.\nThis radius is the largest to which the intracluster light (ICL) has been\nmeasured for an individual cluster. Within this radius, we measure a total\nluminosity of 1.5e13 Lsun for the brightest cluster galaxy plus ICL light. From\nthe profile and its logarithmic slope, we identify the transition from the\nbrightest cluster galaxy to ICL at r~70 kpc. Remarkably, we also detect an\ninflection in the profile centered in the 1.2-1.7 Mpc (0.37-0.52 r200m) radial\nbin, a signature of an infall caustic in the stellar distribution. Based upon\nthe shape and strength of the feature, we interpret it as potentially being at\nthe splashback radius, although the radius is smaller than theoretical\npredictions. If this is the splashback radius, then it is the first such\ndetection in the ICL and the first detection of the splashback radius for an\nindividual cluster. Similar analyses should be possible with the other Frontier\nField clusters, and eventually with clusters from the Euclid and Roman\nmissions.\n"}
+{"abstract": "  When manipulating a novel object with complex dynamics, a state\nrepresentation is not always available, for example for deformable objects.\nLearning both a representation and dynamics from observations requires large\namounts of data. We propose Learned Visual Similarity Predictive Control\n(LVSPC), a novel method for data-efficient learning to control systems with\ncomplex dynamics and high-dimensional state spaces from images. LVSPC leverages\na given simple model approximation from which image observations can be\ngenerated. We use these images to train a perception model that estimates the\nsimple model state from observations of the complex system online. We then use\ndata from the complex system to fit the parameters of the simple model and\nlearn where this model is inaccurate, also online. Finally, we use Model\nPredictive Control and bias the controller away from regions where the simple\nmodel is inaccurate and thus where the controller is less reliable. We evaluate\nLVSPC on two tasks; manipulating a tethered mass and a rope. We find that our\nmethod performs comparably to state-of-the-art reinforcement learning methods\nwith an order of magnitude less data. LVSPC also completes the rope\nmanipulation task on a real robot with 80% success rate after only 10 trials,\ndespite using a perception system trained only on images from simulation.\n"}
+{"abstract": "  In this note we confirm the guess of Calegari, Garoufalidis and Zagier in\nArxiv 1712.04887 that $R_\\zeta=c_\\zeta^2$, where $R_\\zeta$ is their map on\n$K_3$ defined using the cyclic quantum dilogarithm and $c_\\zeta$ is the Chern\nclass map on $K_3$.\n"}
+{"abstract": "  For a positive trace-class operator $C$ and a bounded operator $A$, we\nprovide an explicit description of the closure of the orbit-closed\n$C$-numerical range of $A$ in terms of those operators submajorized by $C$ and\nthe essential numerical range of $A$. This generalizes and subsumes recent work\nof Chan, Li and Poon for the $k$-numerical range, as well as some of our own\nprevious work on the orbit-closed $C$-numerical range.\n"}
+{"abstract": "  We study target space theory on a torus for the states with $N_L+N_R=2$\nthrough Double Field Theory. The spin-two Fierz-Pauli fields are not allowed\nwhen all spatial dimensions are non-compact. The massive states provide both\nnon-vanishing momentum and winding numbers in the target space theory. To\nderive the cubic action, we provide the unique constraint for $N_L\\neq N_R$\ncompatible with the integration by part. We first make a correspondence of\nmassive and massless fields. The quadratic action is gauge invariant by\nintroducing the mass term. We then proceed to the cubic order. The cubic action\nis also gauge invariant by introducing the coupling between the one-form field\nand other fields. The massive states do not follow the consistent truncation.\nOne should expect the self-consistent theory by summing over infinite modes.\nHence the naive expectation is wrong up to the cubic order. In the end, we show\nthat the momentum and winding modes cannot both appear for only one compact\ndoubled space.\n"}
+{"abstract": "  Pharmaceutical companies are relying more often on external sources of\ninnovation to boost their discovery research productivity. However, more\nin-depth knowledge about how external innovation may translate to successful\nproduct launches is still required in order to better understand how to best\nleverage the innovation ecosystem. We analyzed the pre-approval publication\nhistories for FDA-approved new molecular entities (NMEs) and new biologic\nentities (NBEs) launched by 13 top research pharma companies during the last\ndecade (2006-2016). We found that academic institutions contributed the\nmajority of pre-approval publications and that publication subject matter is\nclosely aligned with the strengths of the respective innovator. We found this\nto also be true for candidate drugs terminated in Phase 3, but the volume of\nliterature on these molecules is substantially less than for approved drugs.\nThis may suggest that approved drugs are often associated with a more robust\ndataset provided by a large number of institutes. Collectively, the results of\nour analysis support the hypothesis that a collaborative research innovation\nenvironment spanning across academia, industry and government is highly\nconducive to successful drug approvals.\n"}
+{"abstract": "  The mechanical responses of single crystalline Body-Centered Cubic (BCC)\nmetals, such as molybdenum (Mo), outperform other metals at high temperatures,\nso much so that they are considered as excellent candidates for applications\nunder extreme conditions, such as the divertor of fusion reactors. The\nexcellent thermomechanical stability of molybdenum at high temperatures\n(400-1000$^{\\rm o}$C) has also been detected through nanoindentation, pointing\ntowards connections to emergent local dislocation mechanisms related to defect\nnucleation. In this work, we carry out a computational study of the effects of\nhigh temperature on the mechanical deformation properties of single crystalline\nMo under nanoindentation. Molecular dynamics (MD) simulations of spherical\nnanoindentation are performed at two indenter tip diameters and crystalline\nsample orientations [100], [110], and [111], for the temperature range of\n10-1000K. We investigate how the increase of temperature influences the\nnanoindentation process, modifying dislocation densities, mechanisms, atomic\ndisplacements and also, hardness, in agreement with reported experimental\nmeasurements. Our results suggest that the characteristic formation and\nhigh-temperature stability of [001] dislocation junctions in Mo during\nnanoindentation, in contrast to other BCC metals, may be the cause of the\npersistent thermomechanical stability of Mo.\n"}
+{"abstract": "  Higher-order topological insulators in two dimensions have states that\nlocalize at their corners, called corner states. In this paper, we reveal\ncharacteristics of the penetration depth of their corner states by using the\nBenalcazar-Bernevig-Hughes model. First, we show that when we change the energy\nof the corner states toward the end of the edge gap by adding an on-site\npotential to the corner site, the penetration depth along the edge diverges\ntoward infinity while the penetration depth into the bulk remaining finite. We\nanalytically derive the corner-state wavefunction in a form of elliptic\nintegrals, which reproduces this anisotropic behavior of corner states. This\nmeans that corner states have two kinds of penetration depths, and they behave\ndifferently. At last, we show that hybridizations between corner states are\ngoverned by the penetration depth through interference between the corner\nstates. It is because the corner states almost do not interfere with edge\nstates or bulk states.\n"}
+{"abstract": "  Motivated by thermal conductivity experiments in spin chain compounds, we\npropose a phenomenological model to account for a ballistic magnetic transport\ncoupled to a diffusive phononic one, along the line of the seminal\ntwo-temperature diffusive transport Sanders-Walton model. Although the\nexpression for the effective thermal conductivity is identical to that of\nSanders-Walton, the interpretation is entirely different, as the \"magnetic\nconductivity\" is replaced by an \"effective transfer conductivity\" between the\nmagnetic and phononic component. This model also reveals the fascinating\npossibility of visualizing the ballistic character of magnetic transport, for\nappropriately chosen material parameters, as a two peak counter-propagating\nfeature in the phononic temperature. It is also appropriate for the analysis of\nany thermal transport experiment involving a diffusive component coupled to a\nballistic one.\n"}
+{"abstract": "  Given a Lipschitz map $f$ from a cube into a metric space, we find several\nequivalent conditions for $f$ to have a Lipschitz factorization through a\nmetric tree. As an application we prove a recent conjecture of David and Schul.\nThe techniques developed for the proof of the factorization result yield\nseveral other new and seemingly unrelated results. We prove that if $f$ is a\nLipschitz mapping from an open set in $\\mathbb{R}^n$ onto a metric space $X$,\nthen the topological dimension of $X$ equals $n$ if and only if $X$ has\npositive $n$-dimensional Hausdorff measure. We also prove an area formula for\nlength-preserving maps between metric spaces, which gives, in particular, a new\nformula for integration on countably rectifiable sets in the Heisenberg group.\n"}
+{"abstract": "  For a probability measure $\\mu$ on SL d (R), we consider the Furstenberg\nstationary measure on the space of flags. Under general non-degeneracy\nconditions, if $\\mu$ is discrete and if g log g d$\\mu$(g) < +$\\infty$, then the\nmeasure $\\nu$ is exact-dimensional.\n"}
+{"abstract": "  Tactical selection of experiments to estimate an underlying model is an\ninnate task across various fields. Since each experiment has costs associated\nwith it, selecting statistically significant experiments becomes necessary.\nClassic linear experimental design deals with experiment selection so as to\nminimize (functions of) variance in estimation of regression parameter.\nTypically, standard algorithms for solving this problem assume that data\nassociated with each experiment is fully known. This isn't often true since\nmissing data is a common problem. For instance, remote sensors often miss data\ndue to poor connection. Hence experiment selection under such scenarios is a\nwidespread but challenging task. Though decoupling the tasks and using standard\ndata imputation methods like matrix completion followed by experiment selection\nmight seem a way forward, they perform sub-optimally since the tasks are\nnaturally interdependent. Standard design of experiments is an NP hard problem,\nand the additional objective of imputing for missing data amplifies the\ncomputational complexity. In this paper, we propose a maximum-entropy-principle\nbased framework that simultaneously addresses the problem of design of\nexperiments as well as the imputation of missing data. Our algorithm exploits\nhomotopy from a suitably chosen convex function to the non-convex cost\nfunction; hence avoiding poor local minima. Further, our proposed framework is\nflexible to incorporate additional application specific constraints.\nSimulations on various datasets show improvement in the cost value by over 60%\nin comparison to benchmark algorithms applied sequentially to the imputation\nand experiment selection problems.\n"}
+{"abstract": "  In this paper, we study wave transmission in a rotating fluid with multiple\nalternating convectively stable and unstable layers. We have discussed wave\ntransmissions in two different circumstances: cases where the wave is\npropagative in each layer and cases where wave tunneling occurs. We find that\nefficient wave transmission can be achieved by `resonant propagation' or\n`resonant tunneling', even when stable layers are strongly stratified, and we\ncall this phenomenon `enhanced wave transmission'. Enhanced wave transmission\nonly occurs when the total number of layers is odd (embedding stable layers are\nalternatingly embedded within clamping convective layers, or vise versa). For\nwave propagation, the occurrence of enhanced wave transmission requires that\nclamping layers have similar properties, the thickness of each clamping layer\nis close to a multiple of the half wavelength of the corresponding propagative\nwave, and the total thickness of embedded layers is close to a multiple of the\nhalf wavelength of the corresponding propagating wave (resonant propagation).\nFor wave tunneling, we have considered two cases: tunneling of gravity waves\nand tunneling of inertial waves. In both cases, efficient tunneling requires\nthat clamping layers have similar properties, the thickness of each embedded\nlayer is much smaller than the corresponding e-folding decay distance, and the\nthickness of each clamping layer is close to a multiple-and-a-half of half\nwavelength (resonant tunneling).\n"}
+{"abstract": "  Digital contact tracing is a public health intervention. It should be\nintegrated with local health policy, provide rapid and accurate notifications\nto exposed individuals, and encourage high app uptake and adherence to\nquarantine. Real-time monitoring and evaluation of effectiveness of app-based\ncontact tracing is key for improvement and public trust.\n"}
+{"abstract": "  In the field of multimodal segmentation, the correlation between different\nmodalities can be considered for improving the segmentation results. In this\npaper, we propose a multi-modality segmentation network with a correlation\nconstraint. Our network includes N model-independent encoding paths with N\nimage sources, a correlation constraint block, a feature fusion block, and a\ndecoding path. The model independent encoding path can capture\nmodality-specific features from the N modalities. Since there exists a strong\ncorrelation between different modalities, we first propose a linear correlation\nblock to learn the correlation between modalities, then a loss function is used\nto guide the network to learn the correlated features based on the linear\ncorrelation block. This block forces the network to learn the latent correlated\nfeatures which are more relevant for segmentation. Considering that not all the\nfeatures extracted from the encoders are useful for segmentation, we propose to\nuse dual attention based fusion block to recalibrate the features along the\nmodality and spatial paths, which can suppress less informative features and\nemphasize the useful ones. The fused feature representation is finally\nprojected by the decoder to obtain the segmentation result. Our experiment\nresults tested on BraTS-2018 dataset for brain tumor segmentation demonstrate\nthe effectiveness of our proposed method.\n"}
+{"abstract": "  The operation of Terahertz (THz) communication can be significantly impacted\nby the interaction between the transmitted wave and the molecules in the\natmosphere. In particular, it has been observed experimentally that the signal\nundergoes not only molecular absorption, but also molecular re-radiation. Two\nextreme modeling assumptions are prevalent in the literature, where the\nre-radiated energy is modeled in the first as additive Gaussian noise and in\nthe second as a scattered component strongly correlated to the actual signal.\nSince the exact characterization is still an open problem, we provide in this\npaper the first comparative study of the performance of a reconfigurable\nintelligent surface (RIS) assisted THz system under these two extreme models of\nre-radiation. In particular, we employ an RIS to overcome the large pathloss by\ncreating a virtual line-of-sight (LOS) path. We then develop an optimization\nframework for this setup and utilize the block-coordinate descent (BCD) method\nto iteratively optimize both RIS configuration vector and receive beamforming\nweight resulting in significant throughput gains for the user of interest\ncompared to random RIS configurations. As expected, our results reveal that\nbetter throughput is achieved under the scattering assumption for the molecular\nre-radiation than the noise assumption.\n"}
+{"abstract": "  Contrastive learning (CL) has recently emerged as an effective approach to\nlearning representation in a range of downstream tasks. Central to this\napproach is the selection of positive (similar) and negative (dissimilar) sets\nto provide the model the opportunity to `contrast' between data and class\nrepresentation in the latent space. In this paper, we investigate CL for\nimproving model robustness using adversarial samples. We first designed and\nperformed a comprehensive study to understand how adversarial vulnerability\nbehaves in the latent space. Based on this empirical evidence, we propose an\neffective and efficient supervised contrastive learning to achieve model\nrobustness against adversarial attacks. Moreover, we propose a new sample\nselection strategy that optimizes the positive/negative sets by removing\nredundancy and improving correlation with the anchor. Extensive experiments\nshow that our Adversarial Supervised Contrastive Learning (ASCL) approach\nachieves comparable performance with the state-of-the-art defenses while\nsignificantly outperforms other CL-based defense methods by using only $42.8\\%$\npositives and $6.3\\%$ negatives.\n"}
+{"abstract": "  Air traffic management and specifically air-traffic control (ATC) rely mostly\non voice communications between Air Traffic Controllers (ATCos) and pilots. In\nmost cases, these voice communications follow a well-defined grammar that could\nbe leveraged in Automatic Speech Recognition (ASR) technologies. The callsign\nused to address an airplane is an essential part of all ATCo-pilot\ncommunications. We propose a two-steps approach to add contextual knowledge\nduring semi-supervised training to reduce the ASR system error rates at\nrecognizing the part of the utterance that contains the callsign. Initially, we\nrepresent in a WFST the contextual knowledge (i.e. air-surveillance data) of an\nATCo-pilot communication. Then, during Semi-Supervised Learning (SSL) the\ncontextual knowledge is added by second-pass decoding (i.e. lattice\nre-scoring). Results show that `unseen domains' (e.g. data from airports not\npresent in the supervised training data) are further aided by contextual SSL\nwhen compared to standalone SSL. For this task, we introduce the Callsign Word\nError Rate (CA-WER) as an evaluation metric, which only assesses ASR\nperformance of the spoken callsign in an utterance. We obtained a 32.1% CA-WER\nrelative improvement applying SSL with an additional 17.5% CA-WER improvement\nby adding contextual knowledge during SSL on a challenging ATC-based test set\ngathered from LiveATC.\n"}
+{"abstract": "  Communal pairing in superconductors introduces variational freedom for Cooper\npairs to share fermions. Temporal oscillations of the superconducting gap\nentropically drive communal pairing through the order by disorder\nphenomenology, stabilising a finite momentum space width of the superconducting\ngap that increases with interaction strength, creating a smooth evolution from\nthe weakly interacting BCS state to the strongly interacting BEC state.\n"}
+{"abstract": "  In this paper, we study various aspects of the ODE's flow $X$ solution to the\nequation $\\partial_t X(t,x)=b(X(t,x))$, $X(0,x)=x$ in the $d$-dimensional torus\n$Y_d$, where $b$ is a regular $\\mathbb{Z}^d$-periodic vector field from\n$\\mathbb{R}^d$ in $\\mathbb{R}^d$.We present an original and complete picture in\nany dimension of all logical connections between the following seven conditions\ninvolving the field $b$: (i) the everywhere asymptotics of the flow $X$, (ii)\nthe almost-everywhere asymptotics of the flow $X$, (iii) the global\nrectification of the vector field $b$ in $Y_d$, (iv) the ergodicity of the flow\nrelated to an invariant probability measure which is absolutely continuous with\nrespect to Lebesgue's measure, (v) the unit set condition for Herman's rotation\nset $C_b$ composed of the means of $b$ related to the invariant probability\nmeasures, (vi) the unit set condition for the subset $D_b$ of $C_b$ composed of\nthe means of $b$ related to the invariant probability measures which are\nabsolutely continuous with respect to Lebesgue's measure, (vii) the\nhomogenization of the linear transport equation with oscillating data and the\noscillating velocity $b(x/\\varepsilon)$ when $b$ is divergence free. The main\nand surprising result of the paper is that the almost-everywhere asymptotics of\nthe flow $X$ and the unit set condition for $D_b$ are equivalent when $D_b$ is\nassumed to be non empty, and that the two conditions turn to be equivalent to\nthe homogenization of the transport equation when $b$ is divergence free. In\ncontrast, using an elementary approach based on classical tools of PDE's\nanalysis, we extend the two-dimensional results of Oxtoby and Marchetto to any\n$d$-dimensional Stepanoff flow: this shows that the ergodicity of the flow may\nhold without satisfying the everywhere asymptotics of the flow.\n"}
+{"abstract": "  Online misogyny has become an increasing worry for Arab women who experience\ngender-based online abuse on a daily basis. Misogyny automatic detection\nsystems can assist in the prohibition of anti-women Arabic toxic content.\nDeveloping such systems is hindered by the lack of the Arabic misogyny\nbenchmark datasets. In this paper, we introduce an Arabic Levantine Twitter\ndataset for Misogynistic language (LeT-Mi) to be the first benchmark dataset\nfor Arabic misogyny. We further provide a detailed review of the dataset\ncreation and annotation phases. The consistency of the annotations for the\nproposed dataset was emphasized through inter-rater agreement evaluation\nmeasures. Moreover, Let-Mi was used as an evaluation dataset through\nbinary/multi-/target classification tasks conducted by several state-of-the-art\nmachine learning systems along with Multi-Task Learning (MTL) configuration.\nThe obtained results indicated that the performances achieved by the used\nsystems are consistent with state-of-the-art results for languages other than\nArabic, while employing MTL improved the performance of the misogyny/target\nclassification tasks.\n"}
+{"abstract": "  In critical infrastructures like airports, much care has to be devoted in\nprotecting radio communication networks from external electromagnetic\ninterference. Protection of such mission-critical radio communication networks\nis usually tackled by exploiting radiogoniometers: at least three suitably\ndeployed radiogoniometers, and a gateway gathering information from them,\npermit to monitor and localise sources of electromagnetic emissions that are\nnot supposed to be present in the monitored area. Typically, radiogoniometers\nare connected to the gateway through relay nodes. As a result, some degree of\nfault-tolerance for the network of relay nodes is essential in order to offer a\nreliable monitoring. On the other hand, deployment of relay nodes is typically\nquite expensive. As a result, we have two conflicting requirements: minimise\ncosts while guaranteeing a given fault-tolerance. In this paper, we address the\nproblem of computing a deployment for relay nodes that minimises the relay node\nnetwork cost while at the same time guaranteeing proper working of the network\neven when some of the relay nodes (up to a given maximum number) become faulty\n(fault-tolerance). We show that, by means of a computation-intensive\npre-processing on a HPC infrastructure, the above optimisation problem can be\nencoded as a 0/1 Linear Program, becoming suitable to be approached with\nstandard Artificial Intelligence reasoners like MILP, PB-SAT, and SMT/OMT\nsolvers. Our problem formulation enables us to present experimental results\ncomparing the performance of these three solving technologies on a real case\nstudy of a relay node network deployment in areas of the Leonardo da Vinci\nAirport in Rome, Italy.\n"}
+{"abstract": "  Single image dehazing is a challenging ill-posed problem that has drawn\nsignificant attention in the last few years. Recently, convolutional neural\nnetworks have achieved great success in image dehazing. However, it is still\ndifficult for these increasingly complex models to recover accurate details\nfrom the hazy image. In this paper, we pay attention to the feature extraction\nand utilization of the input image itself. To achieve this, we propose a\nMulti-scale Topological Network (MSTN) to fully explore the features at\ndifferent scales. Meanwhile, we design a Multi-scale Feature Fusion Module\n(MFFM) and an Adaptive Feature Selection Module (AFSM) to achieve the selection\nand fusion of features at different scales, so as to achieve progressive image\ndehazing. This topological network provides a large number of search paths that\nenable the network to extract abundant image features as well as strong fault\ntolerance and robustness. In addition, ASFM and MFFM can adaptively select\nimportant features and ignore interference information when fusing different\nscale representations. Extensive experiments are conducted to demonstrate the\nsuperiority of our method compared with state-of-the-art methods.\n"}
+{"abstract": "  In this paper we consider $m$ ($m \\geq 1$)conjunctions of Max-atoms that is\natoms of the form $\\max(z,y) + r \\geq x$, where the offset $r$ is a real\nconstant and $x,y,z$ are variables. We show that the Max-atom problem (MAP)\nbelongs to $\\textsf{P}$. Indeed, we provide an algorithm which solves the MAP\nin $O(n^{6} m^{2} + n^{4} m^{3} + n^{2} m^{4})$ operations, where $n$ is the\nnumber of variables which compose the max-atoms. As a by-product other problems\nalso known to be in $\\textsf{NP} \\cap \\textsf{co-NP}$ are in $\\textsf{P}$. P1:\nthe problem to know if a tropical cone is trivial or not. P2: problem of\ntropical rank of a tropical matrix. P3: parity game problem. P4: scheduling\nproblem with AND/OR precedence constraints. P5: problem on hypergraph (shortest\npath). P6: problem in model checking and $\\mu$-calculus.\n"}
+{"abstract": "  Galactic winds constitute a primary feedback process in the ecology and\nevolution of galaxies. They are ubiquitously observed and exhibit a rich\nphenomenology, whose origin is actively investigated both theoretically and\nobservationally. Cosmic rays have been widely recognized as a possible driving\nagent of galactic winds, especially in Milky-Way like galaxies. The formation\nof cosmic ray-driven winds is intimately connected with the microphysics of the\ncosmic ray transport in galaxies, making it an intrinsically non-linear and\nmultiscale phenomenon. In this complex interplay the cosmic ray distribution\naffects the wind launching and, in turns, is shaped by the presence of winds.\nIn this review we summarize the present knowledge of the physics of cosmic rays\ninvolved in the wind formation and of the wind hydrodynamics. We also discuss\nthe theoretical difficulties connected with the study of cosmic ray-driven\nwinds and possible future improvements and directions.\n"}
+{"abstract": "  The magnetic, electronic, and optical properties of rare earth-oxides are\ndirectly influenced by the valency of the metallic cation. With the development\nof next generation electron energy-loss spectrometers, high-energy lanthanide\nfine structure can be studied with improved signal-to-noise for quantitative\nanalysis. Unfortunately, the behavior of rare-earth $4f$ orbital electrons is\nnot well understood. To establish best practices for analysis of energy-loss\nspectra from lanthanide oxides, we have performed a comparative study of the\nfour traditional white line analysis methods extended to lanthanide $M_{4,5}$\nedges resulting from $3d \\rightarrow 4f$ orbital transitions using data from\nGatan's EELS Atlas. The ${M_4}/{M_5}$ spectral feature ratios were examined as\na function of $4f$ occupancy. The ${M_4}/{M_5}$ spectral feature ratio\ndecreases exponentially as $4f$ occupancy increases, except for a plateau\nbetween S$\\text{m}^{3+}$ and D$\\text{y}^{3+}$. The full-width at half the\nmaximum intensity of the $M_4$ edges shows increased broadening for\nS$\\text{m}^{3+}$ through D$\\text{y}^{3+}$. We suggest that the plateau results\nfrom $4f$ orbital half-filling and is explained through the relationship\nbetween electron transition probability and transition lifetime as expressed\nthrough Fermi's Golden Rule. Of the four spectral analysis methods described,\nonly the integrated area method can be ascribed a quantitative physical\ninterpretation.\n"}
+{"abstract": "  Synapses change on multiple timescales, ranging from milliseconds to minutes,\ndue to a combination of both short- and long-term plasticity. Here we develop\nan extension of the common Generalized Linear Model to infer both short- and\nlong-term changes in the coupling between a pre- and post-synaptic neuron based\non observed spiking activity. We model short-term synaptic plasticity using\nadditive effects that depend on the presynaptic spike timing, and we model\nlong-term changes in both synaptic weight and baseline firing rate using point\nprocess adaptive smoothing. Using simulations, we first show that this model\ncan accurately recover time-varying synaptic weights 1) for both depressing and\nfacilitating synapses, 2) with a variety of long-term changes (including\nrealistic changes, such as due to STDP), 3) with a range of pre- and\npost-synaptic firing rates, and 4) for both excitatory and inhibitory synapses.\nWe then apply our model to two experimentally recorded putative synaptic\nconnections. We find that simultaneously tracking fast changes in synaptic\nweights, slow changes in synaptic weights, and unexplained variations in\nbaseline firing is essential. Omitting any one of these factors can lead to\nspurious inferences for the others. Altogether, this model provides a flexible\nframework for tracking short- and long-term variation in spike transmission.\n"}
+{"abstract": "  Factorization models express a statistical object of interest in terms of a\ncollection of simpler objects. For example, a matrix or tensor can be expressed\nas a sum of rank-one components. However, in practice, it can be challenging to\ninfer the relative impact of the different components as well as the number of\ncomponents. A popular idea is to include infinitely many components having\nimpact decreasing with the component index. This article is motivated by two\nlimitations of existing methods: (1) lack of careful consideration of the\nwithin component sparsity structure; and (2) no accommodation for grouped\nvariables and other non-exchangeable structures. We propose a general class of\ninfinite factorization models that address these limitations. Theoretical\nsupport is provided, practical gains are shown in simulation studies, and an\necology application focusing on modelling bird species occurrence is discussed.\n"}
+{"abstract": "  Liquid polyamorphism is the intriguing possibility for a single component\nsubstance to exist in multiple liquid phases. We propose a minimal model for\nthis phenomenon. Starting with a binary lattice model with critical azeotropy\nand liquid-liquid demixing, we allow interconversion of the two species,\nturning the system into a single-component fluid with two states differing in\nenergy and entropy. Unveiling the phase diagram of the non-interconverting\nbinary mixture gives unprecedented insight on the phase behaviors accessible to\nthe interconverting fluid, such as a liquid-liquid transition with a critical\npoint, or a singularity-free scenario, exhibiting thermodynamic anomalies\nwithout polyamorphism. The model provides a unified theoretical framework to\ndescribe supercooled water and a variety of polyamorphic liquids with\nwater-like anomalies.\n"}
+{"abstract": "  Deep Neural Networks (DNN) are typically tested for accuracy relying on a set\nof unlabelled real world data (operational dataset), from which a subset is\nselected, manually labelled and used as test suite. This subset is required to\nbe small (due to manual labelling cost) yet to faithfully represent the\noperational context, with the resulting test suite containing roughly the same\nproportion of examples causing misprediction (i.e., failing test cases) as the\noperational dataset. However, while testing to estimate accuracy, it is\ndesirable to also learn as much as possible from the failing tests in the\noperational dataset, since they inform about possible bugs of the DNN. A smart\nsampling strategy may allow to intentionally include in the test suite many\nexamples causing misprediction, thus providing this way more valuable inputs\nfor DNN improvement while preserving the ability to get trustworthy unbiased\nestimates. This paper presents a test selection technique (DeepEST) that\nactively looks for failing test cases in the operational dataset of a DNN, with\nthe goal of assessing the DNN expected accuracy by a small and ''informative''\ntest suite (namely with a high number of mispredictions) for subsequent DNN\nimprovement. Experiments with five subjects, combining four DNN models and\nthree datasets, are described. The results show that DeepEST provides DNN\naccuracy estimates with precision close to (and often better than) those of\nexisting sampling-based DNN testing techniques, while detecting from 5 to 30\ntimes more mispredictions, with the same test suite size.\n"}
+{"abstract": "  We consider the sequential decision optimization on the periodic environment,\nthat occurs in a wide variety of real-world applications when the data involves\nseasonality, such as the daily demand of drivers in ride-sharing and dynamic\ntraffic patterns in transportation. In this work, we focus on learning the\nstochastic periodic world by leveraging this seasonal law. To deal with the\ngeneral action space, we use the bandit based on Gaussian process (GP) as the\nbase model due to its flexibility and generality, and propose the Periodic-GP\nmethod with a temporal periodic kernel based on the upper confidence bound.\nTheoretically, we provide a new regret bound of the proposed method, by\nexplicitly characterizing the periodic kernel in the periodic stationary model.\nEmpirically, the proposed algorithm significantly outperforms the existing\nmethods in both synthetic data experiments and a real data application on\nMadrid traffic pollution.\n"}
+{"abstract": "  Corner-shared ABX$_3$ perovskites have long featured prominently in\nsolid-state chemistry and condensed matter physics. Still, the joint\nunderstanding of their two main subgroups-halides and oxides-has not been fully\ndeveloped. Indeed, unlike the case that compounds having a single repeated\nmotif (\"monomorphous\"), certain cubic perovskites can manifest a non-thermal\ndistribution of local motifs (\"polymorphous networks\"). Such intrinsic\ndeformations can include positional degrees of freedom. Unlike thermal motion,\nsuch intrinsic distortions do not time-average to zero. The present study\ncompares electronic structure features of oxide and halide perovskites starting\nfrom the intrinsic polymorphous network described by DFT minimization of the\ninternal energy, continuing to finite temperature thermal disorder using AIMD.\nWe find that (i) different oxide vs. halide ABX$_3$ compounds adopt different\nenergy-lowering distortion modes. The DFT calculated pair distribution function\n(PDF) of SrTiO$_3$ agrees with the recently measured PDF. (ii) In both oxides\nand halides, such intrinsic distortions lead to bandgap blueshifts with respect\nto monomorphous structure. (iii) For oxide perovskites, high-temperature AIMD\nsimulations initiated from the polymorphous structures reveal that the\nthermally-induced distortions can lead to a bandgap redshift. (iv) In contrast,\nfor cubic CsPbI$_3$, both the intrinsic distortions and the thermal distortions\ncontribute in tandem to bandgap blueshift, the former, intrinsic effect being\ndominant. (v) In the oxide SrTiO$_3$ and CaTiO$_3$ (but not in halide),\noctahedral tilting leads to the emergence of a distinct $\\Gamma$-$\\Gamma$\ndirect bandgap component as a secondary valley minimum to the well-known\nindirect R-$\\Gamma$ gap. Understanding such intrinsic vs. thermal effects on\noxide vs. halide perovskites holds the potential for designing target\nelectronic properties.\n"}
+{"abstract": "  Recently, a novel relativistic polyatomic BGK model was suggested by Pennisi\nand Ruggeri [J. of Phys. Conf. Series, 1035, (2018)] to overcome drawbacks of\nthe Anderson-Witting model and Marle model.In this paper, we prove the unique\nexistence and asymptotic behavior of classical solutions to the relativistic\npolyatomic BGK model when the initial data is sufficiently close to a global\nequilibrium.\n"}
+{"abstract": "  We investigate the Glauber divergences in nonfactorizable annihilation\namplitudes for two-body hadronic heavy meson decays in the $k_T$ factorization\ntheorem at one-loop level. These divergences can be absorbed into the Glauber\nfactors in the dominant kinematic regions of small parton momenta, which modify\nthe interference between a nonfactorizable annihilation amplitude and other\namplitudes by rotating it with a phase. We postulate that only the Glauber\neffect associated with a pion is significant, due to its special role as a\n$q\\bar q$ bound state and as a pseudo Nambu-Goldstone boson simultaneously. It\nis elaborated that the data of the $D\\to\\pi\\pi$ and $\\pi K$ branching ratios\nhave revealed prominent Glauber effects. This work provides a solid theoretical\nground for the factorization-assisted topological-amplitude parametrization of\ntwo-body hadronic $D$ meson decays.\n"}
+{"abstract": "  The three pentaquark states, $P_{c}(4312)$, $P_{c}(4440)$, and $P_{c}(4457)$,\ndiscovered by the LHCb Collaboration in 2019, can be nicely arranged into a\nmultiplet of $\\bar{D}^{(\\ast)}\\Sigma_{c}^{(\\ast)}$ of seven molecules dictated\nby heavy quark spin symmetry. In this work we employ the effective Lagrangian\napproach to investigate the two decay modes of $P_{c}(4457)$, $P_{c}(4457) \\to\nP_{c}(4312) \\pi$ and $P_{c}(4457) \\to P_{c}(4312) \\gamma$, via the triangle\nmechanism, assuming that $P_{c}(4457)$ and $P_{c}(4312)$ are\n$\\bar{D}^{\\ast}\\Sigma_{c}$ and $\\bar{D}\\Sigma_{c}$ bound states but the spin of\n$P_{c}(4457)$ can be either 1/2 or 3/2. Our results show that the spin of\n$P_{c}(4457)$ can not be discriminated through these two decay modes. The decay\nwidths of $P_{c}(4457) \\to P_{c}(4312) \\pi$ and $P_{c}(4457) \\to P_{c}(4312)\n\\gamma$ are estimated to be of order of 100 keV and 1 keV, respectively. The\nratio of the partial decay widths of $P_{c}(4457) \\to P_{c}(4312) \\pi$ to\n$P_{c}(4457) \\to P_{c}(4312) \\gamma$ is similar to the ratio of $D^{\\ast}\\to\nD\\pi$ to $D^{\\ast}\\to D\\gamma$, which could be used to check the molecular\nnature of $P_{c}(4457)$ and $P_{c}(4312)$ if they can be observed in the\nfuture.\n"}
+{"abstract": "  We show that the Vietoris-Rips complex $\\mathcal R(n,r)$ built over $n$\npoints sampled at random from a uniformly positive probability measure on a\nconvex body $K\\subseteq \\mathbb R^d$ is a.a.s. contractible when $r \\geq c\n\\left(\\frac{\\ln n}{n}\\right)^{1/d}$ for a certain constant that depends on $K$\nand the probability measure used. This answers a question of Kahle [Discrete\nComput. Geom. 45 (2011), 553-573]. We also extend the proof to show that if $K$\nis a compact, smooth $d$-manifold with boundary - but not necessarily convex -\nthen $\\mathcal R(n,r)$ is a.a.s. homotopy equivalent to $K$ when $c_1\n\\left(\\frac{\\ln n}{n}\\right)^{1/d} \\leq r \\leq c_2$ for constants $c_1=c_1(K),\nc_2=c_2(K)$. Our proofs expose a connection with the game of cops and robbers.\n"}
+{"abstract": "  Deep learning based methods have been used successfully in recommender system\nproblems. Approaches using recurrent neural networks, transformers, and\nattention mechanisms are useful to model users' long- and short-term\npreferences in sequential interactions. To explore different session-based\nrecommendation solutions, Booking.com recently organized the WSDM WebTour 2021\nChallenge, which aims to benchmark models to recommend the final city in a\ntrip. This study presents our approach to this challenge. We conducted several\nexperiments to test different state-of-the-art deep learning architectures for\nrecommender systems. Further, we proposed some changes to Neural Attentive\nRecommendation Machine (NARM), adapted its architecture for the challenge\nobjective, and implemented training approaches that can be used in any\nsession-based model to improve accuracy. Our experimental result shows that the\nimproved NARM outperforms all other state-of-the-art benchmark methods.\n"}
+{"abstract": "  The availability of open-source projects facilitates developers to contribute\nand collaborate on a wide range of projects. As a result, the developer\ncommunity contributing to such open-source projects is also increasing. Many of\nthe projects involve frequent updates and extensive reuses. A well-updated\ndocumentation helps in a better understanding of the software project and also\nfacilitates efficient contribution and reuse. Though software documentation\nplays an important role in the development and maintenance of software, it also\nsuffers from various issues that include insufficiency, inconsistency,\nill-maintainability, and so on. Exploring the perception of developers towards\ndocumentation could help in understanding the reasons behind prevalent issues\nin software documentation. It could further aid in deciding on training that\ncould be given to the developer community towards building more sustainable\nprojects for society. Analyzing sentiments of contributors to a project could\nprovide insights on understanding developer perceptions. Hence, as the first\nstep towards this direction, we analyze sentiments of commit messages specific\nto the documentation of a software project. To this end, we considered the\ncommit history of 998 GitHub projects from the GHTorrent dataset and identified\n10,996 commits that correspond to the documentation of repositories. Further,\nwe apply sentiment analysis techniques to obtain insights on the type of\nsentiment being expressed in commit messages of the selected commits. We\nobserve that around 45% of the identified commit messages express trust\nemotion.\n"}
+{"abstract": "  In many modern applications, a dependent functional response is observed for\neach subject over repeated time, leading to longitudinal functional data. In\nthis paper, we propose a novel statistical procedure to test whether the mean\nfunction varies over time. Our approach relies on reducing the dimension of the\nresponse using data-driven orthogonal projections and it employs a\nlikelihood-based hypothesis testing. We investigate the methodology\ntheoretically and discuss a computationally efficient implementation. The\nproposed test maintains the type I error rate, and shows excellent power to\ndetect departures from the null hypothesis in finite sample simulation studies.\nWe apply our method to the longitudinal diffusion tensor imaging study of\nmultiple sclerosis (MS) patients to formally assess whether the brain's health\ntissue, as summarized by fractional anisotropy (FA) profile, degrades over time\nduring the study period.\n"}
+{"abstract": "  The helimagnets Cr$_{1/3}M$S$_2$ ($M$ = Nb or Ta) have attracted renewed\nattention due to the discovery of a chiral soltion lattice (CSL) stabilized in\nCr$_{1/3}$NbS$_2$ in an applied magnetic field, but reports of unusual\nlow-temperature transport and magnetic properties in this system lack a\nunifying explanation. Here we present electronic structure calculations\ndemonstrating that Cr$_{1/3}M$S$_2$ ($M$ = Nb or Ta) are half-metals whose\nlow-temperature electronic and magnetic behavior can be explained by the\npresence of a gap-like feature (width in range 40-100 meV) in the density of\nstates of one spin channel. Our magnetometry measurements confirm the existence\nof this gap. Dynamic spin fluctuations driven by excitations across this gap\nare seen over a wide range of frequencies (0.1 Hz to MHz) with AC\nsusceptibility and muon-spin relaxation ($\\mu^+$SR) measurements. We show\nfurther how effects due to the CSL in Cr$_{1/3}$NbS$_2$, as detected with\n$\\mu^+$SR, dominate over the gap-driven magnetism when the CSL is stabilized as\nthe majority phase.\n"}
+{"abstract": "  We investigate possible reasons for the absence of historical records of the\nsupernova of 1054 in Europe. At the same time, we search for the new evidences\nas well. We establish that the previously acclaimed 'Arabic' records from ibn\nButlan originate from Europe. As one of the most prominent scientists of the\nera, he was in Constantinople at the time of the supernova and actively\nparticipated in the medieval Church feud known as the Great Schism. Next, we\nreconstruct the European sky at the time of the event and find that the 'new\nstar' (SN 1054) was in the west while the planet Venus was on the opposite side\nof the sky (in the east) with the Sun sited directly between these two equally\nbright objects, as documented in East-Asian records.\n"}
+{"abstract": "  Recent advances in deep generative models have led to impressive results in a\nvariety of application domains. Motivated by the possibility that deep learning\nmodels might memorize part of the input data, there have been increased efforts\nto understand how memorization arises. In this work, we extend a recently\nproposed measure of memorization for supervised learning (Feldman, 2019) to the\nunsupervised density estimation problem and adapt it to be more computationally\nefficient. Next, we present a study that demonstrates how memorization can\noccur in probabilistic deep generative models such as variational autoencoders.\nThis reveals that the form of memorization to which these models are\nsusceptible differs fundamentally from mode collapse and overfitting.\nFurthermore, we show that the proposed memorization score measures a phenomenon\nthat is not captured by commonly-used nearest neighbor tests. Finally, we\ndiscuss several strategies that can be used to limit memorization in practice.\nOur work thus provides a framework for understanding problematic memorization\nin probabilistic generative models.\n"}
+{"abstract": "  Sound event localization aims at estimating the positions of sound sources in\nthe environment with respect to an acoustic receiver (e.g. a microphone array).\nRecent advances in this domain most prominently focused on utilizing deep\nrecurrent neural networks. Inspired by the success of transformer architectures\nas a suitable alternative to classical recurrent neural networks, this paper\nintroduces a novel transformer-based sound event localization framework, where\ntemporal dependencies in the received multi-channel audio signals are captured\nvia self-attention mechanisms. Additionally, the estimated sound event\npositions are represented as multivariate Gaussian variables, yielding an\nadditional notion of uncertainty, which many previously proposed deep\nlearning-based systems designed for this application do not provide. The\nframework is evaluated on three publicly available multi-source sound event\nlocalization datasets and compared against state-of-the-art methods in terms of\nlocalization error and event detection accuracy. It outperforms all competing\nsystems on all datasets with statistical significant differences in\nperformance.\n"}
+{"abstract": "  We formulate and prove an analogue of Beurling's theorem for the Fourier\ntransform on the Heisenberg group. As a consequence we deduce Hardy and\nCowling-Price theorems.\n"}
+{"abstract": "  Cheung and Piliouras (2020) recently showed that two variants of the\nMultiplicative Weights Update method - OMWU and MWU - display opposite\nconvergence properties depending on whether the game is zero-sum or\ncooperative. Inspired by this work and the recent literature on learning to\noptimize for single functions, we introduce a new framework for learning\nlast-iterate convergence to Nash Equilibria in games, where the update rule's\ncoefficients (learning rates) along a trajectory are learnt by a reinforcement\nlearning policy that is conditioned on the nature of the game: \\textit{the game\nsignature}. We construct the latter using a new decomposition of two-player\ngames into eight components corresponding to commutative projection operators,\ngeneralizing and unifying recent game concepts studied in the literature. We\ncompare the performance of various update rules when their coefficients are\nlearnt, and show that the RL policy is able to exploit the game signature\nacross a wide range of game types. In doing so, we introduce CMWU, a new\nalgorithm that extends consensus optimization to the constrained case, has\nlocal convergence guarantees for zero-sum bimatrix games, and show that it\nenjoys competitive performance on both zero-sum games with constant\ncoefficients and across a spectrum of games when its coefficients are learnt.\n"}
+{"abstract": "  How will the coronavirus disease 2019 (COVID-19) pandemic develop in the\ncoming months and years? Based on an expert survey, we examine key aspects that\nare likely to influence COVID-19 in Europe. The future challenges and\ndevelopments will strongly depend on the progress of national and global\nvaccination programs, the emergence and spread of variants of concern, and\npublic responses to nonpharmaceutical interventions (NPIs). In the short term,\nmany people are still unvaccinated, VOCs continue to emerge and spread, and\nmobility and population mixing is expected to increase over the summer.\nTherefore, policies that lift restrictions too much and too early risk another\ndamaging wave. This challenge remains despite the reduced opportunities for\ntransmission due to vaccination progress and reduced indoor mixing in the\nsummer. In autumn 2021, increased indoor activity might accelerate the spread\nagain, but a necessary reintroduction of NPIs might be too slow. The incidence\nmay strongly rise again, possibly filling intensive care units, if vaccination\nlevels are not high enough. A moderate, adaptive level of NPIs will thus remain\nnecessary. These epidemiological aspects are put into perspective with the\neconomic, social, and health-related consequences and thereby provide a\nholistic perspective on the future of COVID-19.\n"}
+{"abstract": "  Assisting end users to identify desired results from a large dataset is an\nimportant problem for multi-criteria decision making. To address this problem,\ntop-k and skyline queries have been widely adopted, but they both have inherent\ndrawbacks, i.e., the user either has to provide a specific utility function or\nfaces many results. The k-regret minimization query is proposed, which\nintegrates the merits of top-k and skyline queries. Due to the NP-hardness of\nthe problem, the k-regret minimization query is time consuming and the greedy\nframework is widely adopted. However, formal theoretical analysis of the greedy\napproaches for the quality of the returned results is still lacking. In this\npaper, we first fill this gap by conducting a nontrivial theoretical analysis\nof the approximation ratio of the returned results. To speed up query\nprocessing, a sampling-based method, StocPreGreed,, is developed to reduce the\nevaluation cost. In addition, a theoretical analysis of the required sample\nsize is conducted to bound the quality of the returned results. Finally,\ncomprehensive experiments are conducted on both real and synthetic datasets to\ndemonstrate the efficiency and effectiveness of the proposed methods.\n"}
+{"abstract": "  The Preconditioned Conjugate Gradient (PCG) method is widely used for solving\nlinear systems of equations with sparse matrices. A recent version of PCG,\nPipelined PCG, eliminates the dependencies in the computations of the PCG\nalgorithm so that the non-dependent computations can be overlapped with\ncommunication. In this paper, we propose three methods for efficient execution\nof the Pipelined PCG algorithm on GPU accelerated heterogeneous architectures.\n  The first two methods achieve task-parallelism using asynchronous executions\nof different tasks on CPU cores and GPU. The third method achieves data\nparallelism by decomposing the workload between CPU and GPU based on a\nperformance model. The performance model takes into account the relative\nperformance of CPU cores and GPU using some initial executions and performs 2D\ndata decomposition. We also implement optimization strategies like kernel\nfusion for GPU and merging vector operations for CPU. Our methods give up to 8x\nspeedup and on average 3x speedup over PCG CPU implementation of Paralution and\nPETSc libraries. They also give up to 5x speedup and on average 1.45x speedup\nover PCG GPU implementation of Paralution and PETSc libraries. The third method\nalso provides an efficient solution for solving problems that cannot be fit\ninto the GPU memory and gives up to 2.5x speedup for such problems.\n"}
+{"abstract": "  We develop Stratified Shortest Solution Imitation Learning (3SIL) to learn\nequational theorem proving in a deep reinforcement learning (RL) setting. The\nself-trained models achieve state-of-the-art performance in proving problems\ngenerated by one of the top open conjectures in quasigroup theory, the Abelian\nInner Mapping (AIM) Conjecture. To develop the methods, we first use two\nsimpler arithmetic rewriting tasks that share tree-structured proof states and\nsparse rewards with the AIM problems. On these tasks, 3SIL is shown to\nsignificantly outperform several established RL and imitation learning methods.\nThe final system is then evaluated in a standalone and cooperative mode on the\nAIM problems. The standalone 3SIL-trained system proves in 60 seconds more\ntheorems (70.2%) than the complex, hand-engineered Waldmeister system (65.5%).\nIn the cooperative mode, the final system is combined with the Prover9 system,\nproving in 2 seconds what standalone Prover9 proves in 60 seconds.\n"}
+{"abstract": "  Semi-iNat is a challenging dataset for semi-supervised classification with a\nlong-tailed distribution of classes, fine-grained categories, and domain shifts\nbetween labeled and unlabeled data. This dataset is behind the second iteration\nof the semi-supervised recognition challenge to be held at the FGVC8 workshop\nat CVPR 2021. Different from the previous one, this dataset (i) includes images\nof species from different kingdoms in the natural taxonomy, (ii) is at a larger\nscale -- with 810 in-class and 1629 out-of-class species for a total of 330k\nimages, and (iii) does not provide in/out-of-class labels, but provides coarse\ntaxonomic labels (kingdom and phylum) for the unlabeled images. This document\ndescribes baseline results and the details of the dataset which is available\nhere: \\url{https://github.com/cvl-umass/semi-inat-2021}.\n"}
+{"abstract": "  X-ray ptychography has revolutionised nanoscale phase contrast imaging at\nlarge-scale synchrotron sources in recent years. We present here the first\nsuccessful demonstration of the technique in a small-scale laboratory setting.\nWe conducted an experiment with a liquid metal-jet X-ray source and a single\nphoton-counting detector with a high spectral resolution. The experiment used a\nspot size of 5 microns to produce a ptychographic phase image of a Siemens star\ntest pattern with a sub-micron spatial resolution. The result and methodology\npresented show how high-resolution phase contrast imaging can now be performed\nat small-scale laboratory sources worldwide.\n"}
+{"abstract": "  We will prove that there are trajectories generated by the function at the\norigin of the 5x+1 problem which are divergent. The iterative application of\nthis function on the set of positive integers allows us to determine that more\nthan 17% of all these integers start divergent trajectories. Regarding the 3x+1\nproblem, this percentage tends towards zero, suggesting that all positive\nintegers are part of converging trajectories. Despite this appearance, we\ncannot conclude that all positive integers belong to convergent trajectories.\nNevertheless, the results obtained in this paper allow us to follow the\nevolution of the distribution of trajectories and to understand why the values\nof positive integers ending in the integer 1 are more and more large.\n"}
+{"abstract": "  In the last decade, companies adopted DevOps as a fast path to deliver\nsoftware products according to customer expectations, with well aligned teams\nand in continuous cycles. As a basic practice, DevOps relies on pipelines that\nsimulate factory swim-lanes. The more automation in the pipeline, the shorter a\nlead time is supposed to be. However, applying DevOps is challenging,\nparticularly for industrial control systems (ICS) that support critical\ninfrastructures and that must obey to rigorous requirements from security\nregulations and standards. Current research on security compliant DevOps\npresents open gaps for this particular domain and in general for systematic\napplication of security standards. In this paper, we present a systematic\napproach to integrate standard-based security activities into DevOps pipelines\nand highlight their automation potential. Our intention is to share our\nexperiences and help practitioners to overcome the trade-off between adding\nsecurity activities into the development process and keeping a short lead time.\nWe conducted an evaluation of our approach at a large industrial company\nconsidering the IEC 62443-4-1 security standard that regulates ICS. The results\nstrengthen our confidence in the usefulness of our approach and artefacts, and\nin that they can support practitioners to achieve security compliance while\npreserving agility including short lead times.\n"}
+{"abstract": "  Approximation fixpoint theory (AFT) provides an algebraic framework for the\nstudy of fixpoints of operators on bilattices and has found its applications in\ncharacterizing semantics for various classes of logic programs and nonmonotonic\nlanguages. In this paper, we show one more application of this kind: the\nalternating fixpoint operator by Knorr et al. for the study of the well-founded\nsemantics for hybrid MKNF knowledge bases is in fact an approximator of AFT in\ndisguise, which, thanks to the power of abstraction of AFT, characterizes not\nonly the well-founded semantics but also two-valued as well as three-valued\nsemantics for hybrid MKNF knowledge bases. Furthermore, we show an improved\napproximator for these knowledge bases, of which the least stable fixpoint is\ninformation richer than the one formulated from Knorr et al.'s construction.\nThis leads to an improved computation for the well-founded semantics. This work\nis built on an extension of AFT that supports consistent as well as\ninconsistent pairs in the induced product bilattice, to deal with\ninconsistencies that arise in the context of hybrid MKNF knowledge bases. This\npart of the work can be considered generalizing the original AFT from symmetric\napproximators to arbitrary approximators.\n"}
+{"abstract": "  Sparse inverse covariance estimation (i.e., edge de-tection) is an important\nresearch problem in recent years, wherethe goal is to discover the direct\nconnections between a set ofnodes in a networked system based upon the observed\nnodeactivities. Existing works mainly focus on unimodal distributions,where it\nis usually assumed that the observed activities aregenerated from\nasingleGaussian distribution (i.e., one graph).However, this assumption is too\nstrong for many real-worldapplications. In many real-world applications (e.g.,\nbrain net-works), the node activities usually exhibit much more complexpatterns\nthat are difficult to be captured by one single Gaussiandistribution. In this\nwork, we are inspired by Latent DirichletAllocation (LDA) [4] and consider\nmodeling the edge detectionproblem as estimating a mixture ofmultipleGaussian\ndistribu-tions, where each corresponds to a separate sub-network. Toaddress\nthis problem, we propose a novel model called GaussianMixture Graphical Lasso\n(MGL). It learns the proportionsof signals generated by each mixture component\nand theirparameters iteratively via an EM framework. To obtain\nmoreinterpretable networks, MGL imposes a special regularization,called Mutual\nExclusivity Regularization (MER), to minimize theoverlap between different\nsub-networks. MER also addresses thecommon issues in read-world data sets,i.e.,\nnoisy observationsand small sample size. Through the extensive experiments\nonsynthetic and real brain data sets, the results demonstrate thatMGL can\neffectively discover multiple connectivity structuresfrom the observed node\nactivities\n"}
+{"abstract": "  The branching factor of a game is the average number of new states reachable\nfrom a given state. It is a widely used metric in AI research on board games,\nbut less often computed or discussed for videogames. This paper provides\nestimates for the branching factors of 103 Atari 2600 games, as implemented in\nthe Arcade Learning Environment (ALE). Depending on the game, ALE exposes\nbetween 3 and 18 available actions per frame of gameplay, which is an upper\nbound on branching factor. This paper shows, based on an enumeration of the\nfirst 1 million distinct states reachable in each game, that the average\nbranching factor is usually much lower, in many games barely above 1. In\naddition to reporting the branching factors, this paper aims to clarify what\nconstitutes a distinct state in ALE.\n"}
+{"abstract": "  A pure frequency domain method for the computation of periodic solutions of\nnonlinear ordinary differential equations (ODEs) is proposed in this study. The\nmethod is particularly suitable for the analysis of systems that feature\ndistinct states, i.e. where the ODEs involve piecewise defined functions. An\nevent-driven scheme is used which is based on the direct calculation of the\nstate transition time instants between these states. An analytical formulation\nof the governing nonlinear algebraic system of equations is developed for the\ncase of piecewise polynomial systems. Moreover, it is shown that derivatives of\nthe solution of up to second order can be calculated analytically, making the\nmethod especially attractive for design studies. The methodology is applied to\nseveral structural dynamical systems with conservative and dissipative\nnonlinearities in externally excited and autonomous configurations. Great\nperformance and robustness of the proposed procedure was ascertained.\n"}
+{"abstract": "  We introduce a gravitational waveform inversion strategy that discovers\nmechanical models of binary black hole (BBH) systems. We show that only a\nsingle time series of (possibly noisy) waveform data is necessary to construct\nthe equations of motion for a BBH system. Starting with a class of universal\ndifferential equations parameterized by feed-forward neural networks, our\nstrategy involves the construction of a space of plausible mechanical models\nand a physics-informed constrained optimization within that space to minimize\nthe waveform error. We apply our method to various BBH systems including\nextreme and comparable mass ratio systems in eccentric and non-eccentric\norbits. We show the resulting differential equations apply to time durations\nlonger than the training interval, and relativistic effects, such as perihelion\nprecession, radiation reaction, and orbital plunge, are automatically accounted\nfor. The methods outlined here provide a new, data-driven approach to studying\nthe dynamics of binary black hole systems.\n"}
+{"abstract": "  Compressed sensing (CS) involves sampling signals at rates less than their\nNyquist rates and attempting to reconstruct them after sample acquisition. Most\nsuch algorithms have parameters, for example the regularization parameter in\nLASSO, which need to be chosen carefully for optimal performance. These\nparameters can be chosen based on assumptions on the noise level or signal\nsparsity, but this knowledge may often be unavailable. In such cases, cross\nvalidation (CV) can be used to choose these parameters in a purely data-driven\nfashion. Previous work analysing the use of CV in CS has been based on the\n$\\ell_2$ cross-validation error with Gaussian measurement noise. But it is well\nknown that the $\\ell_2$ error is not robust to impulse noise and provides a\npoor estimate of the recovery error, failing to choose the best parameter. Here\nwe propose using the $\\ell_1$ CV error which provides substantial performance\nbenefits given impulse measurement noise. Most importantly, we provide a\ndetailed theoretical analysis and error bounds for the use of $\\ell_1$ CV error\nin CS reconstruction. We show that with high probability, choosing the\nparameter that yields the minimum $\\ell_1$ CV error is equivalent to choosing\nthe minimum recovery error (which is not observable in practice). To our best\nknowledge, this is the first paper which theoretically analyzes $\\ell_1$-based\nCV in CS.\n"}
+{"abstract": "  In this work, we prove some trace theorems for function spaces with a\nnonlocal character that contain the classical $W^{s,p}$ space as a subspace.\nThe result we obtain generalizes well known trace theorems for\n$W^{s,p}(\\Omega)$ functions which has a well-defined $W^{1-s/p, p}$ trace on\nthe boundary of a domain with sufficient regularity. The new generalized spaces\nare associated with norms that are characterized by nonlocal interaction\nkernels defined heterogeneously with a special localization feature on the\nboundary. Intuitively speaking, the class contains functions that are as rough\nas an $L^p$ function inside the domain of definition but as smooth as a\n$W^{s,p}$ function near the boundary. Our result is that the $W^{1-s/p, p}$\nnorm of the trace on the boundary such functions is controlled by the nonlocal\nnorms that are weaker than the classical $W^{s, p}$ norm. These results are\nimprovement and refinement of the classical results since the boundary trace\ncan be attained without imposing regularity of the function in the interior of\nthe domain. They also extend earlier results in the case of $p=2$. In the\nmeantime, we prove Hardy-type inequalities for functions in the new generalized\nspaces that vanish on the boundary, showing them having the same decay rate to\nthe boundary as functions in the smaller space $W^{s,p}(\\Omega)$. A\nPoincar\\'e-type inequality is also derived. An application of the new theory\nleads to the well-posedness of a nonlinear variational problem that allows\npossible singular behavior in the interior with imposed smoother data on the\nboundary.\n"}
+{"abstract": "  We propose a systematic and topological study of limits $\\lim_{\\nu\\to\n0^+}G_\\mathbb{R}\\cdot(\\nu x)$ of continuous families of adjoint orbits for\nnon-compact simple Lie groups. This limit is always a finite union of nilpotent\norbits. We describe explicitly these nilpotent orbits in terms of Richardson\norbits in the case of hyperbolic semisimple elements. We also show that one can\napproximate minimal nilpotent orbits or even nilpotent orbits by elliptic\nsemisimple orbits. The special cases of $\\mathrm{SL}_n(\\mathbb{R})$ and\n$\\mathrm{SU}(p,q)$ are computed in detail.\n"}
+{"abstract": "  The recent success of graph neural networks has significantly boosted\nmolecular property prediction, advancing activities such as drug discovery. The\nexisting deep neural network methods usually require large training dataset for\neach property, impairing their performances in cases (especially for new\nmolecular properties) with a limited amount of experimental data, which are\ncommon in real situations. To this end, we propose Meta-MGNN, a novel model for\nfew-shot molecular property prediction. Meta-MGNN applies molecular graph\nneural network to learn molecular representation and builds a meta-learning\nframework for model optimization. To exploit unlabeled molecular information\nand address task heterogeneity of different molecular properties, Meta-MGNN\nfurther incorporates molecular structure, attribute based self-supervised\nmodules and self-attentive task weights into the former framework,\nstrengthening the whole learning model. Extensive experiments on two public\nmulti-property datasets demonstrate that Meta-MGNN outperforms a variety of\nstate-of-the-art methods.\n"}
+{"abstract": "  An important challenge in reinforcement learning is training agents that can\nsolve a wide variety of tasks. If tasks depend on each other (e.g. needing to\nlearn to walk before learning to run), curriculum learning can speed up\nlearning by focusing on the next best task to learn. We explore curriculum\nlearning in a complex, visual domain with many hard exploration challenges:\nMinecraft. We find that learning progress (defined as a change in success\nprobability of a task) is a reliable measure of learnability for automatically\nconstructing an effective curriculum. We introduce a learning-progress based\ncurriculum and test it on a complex reinforcement learning problem (called\n\"Simon Says\") where an agent is instructed to obtain a desired goal item. Many\nof the required skills depend on each other. Experiments demonstrate that: (1)\na within-episode exploration bonus for obtaining new items improves\nperformance, (2) dynamically adjusting this bonus across training such that it\nonly applies to items the agent cannot reliably obtain yet further increases\nperformance, (3) the learning-progress based curriculum elegantly follows the\nlearning curve of the agent, and (4) when the learning-progress based\ncurriculum is combined with the dynamic exploration bonus it learns much more\nefficiently and obtains far higher performance than uniform baselines. These\nresults suggest that combining intra-episode and across-training exploration\nbonuses with learning progress creates a promising method for automated\ncurriculum generation, which may substantially increase our ability to train\nmore capable, generally intelligent agents.\n"}
+{"abstract": "  We prove the unique existence of supersonic solutions of the Euler- Poisson\nsystem for potential flow in a three-dimensional rectangular cylinder when\nprescribing the velocity and the strength of electric field at the entrance.\nOverall, the main framework is similar to [1], but there are several technical\ndifferences to be taken care of vary carefully. And, it is our main goal to\ntreat all the technical differences occurring when one considers a three\ndimensional supersonic solution of the steady Euler-Poisson system.\n"}
+{"abstract": "  We try to obtain meromorphic solution of Time dependent Schroedinger equation\nwhich partially satisfies Painleve Integrable property. Our study and analysis\nexhibits meromorphic behavior of classical particle trajectory. In other words,\nparticle is confined in punctured complex domain in singular fundamental\ndomain. . We have explicitly developed solution from Jacobi Elliptic function\nand pole expansion approach in which solution remains meromorphic . Branch\npoint analysis also shows solution branches out near such singular point.\nMeromorphic behavior is significant for a classical particle within quantum\nlimit.\n"}
+{"abstract": "  We consider the spin boson model with external magnetic field. We prove a\npath integral formula for the heat kernel, known as Feynman-Kac-Nelson (FKN)\nformula. We use this path integral representation to express the ground state\nenergy as a stochastic integral. Using this connection, we determine the\nexpansion coefficients of the ground state energy with respect to the magnetic\nfield strength and express them in terms of correlation functions of a\ncontinuous Ising model. We then use a recently proven correlation inequality to\nprove that the second order derivative is finite. As an application, we show\nexistence of ground states in infrared-singular situations.\n"}
+{"abstract": "  Federated learning aims to learn a global model that performs well on client\ndevices with limited cross-client communication. Personalized federated\nlearning (PFL) further extends this setup to handle data heterogeneity between\nclients by learning personalized models. A key challenge in this setting is to\nlearn effectively across clients even though each client has unique data that\nis often limited in size. Here we present pFedGP, a solution to PFL that is\nbased on Gaussian processes (GPs) with deep kernel learning. GPs are highly\nexpressive models that work well in the low data regime due to their Bayesian\nnature. However, applying GPs to PFL raises multiple challenges. Mainly, GPs\nperformance depends heavily on access to a good kernel function, and learning a\nkernel requires a large training set. Therefore, we propose learning a shared\nkernel function across all clients, parameterized by a neural network, with a\npersonal GP classifier for each client. We further extend pFedGP to include\ninducing points using two novel methods, the first helps to improve\ngeneralization in the low data regime and the second reduces the computational\ncost. We derive a PAC-Bayes generalization bound on novel clients and\nempirically show that it gives non-vacuous guarantees. Extensive experiments on\nstandard PFL benchmarks with CIFAR-10, CIFAR-100, and CINIC-10, and on a new\nsetup of learning under input noise show that pFedGP achieves well-calibrated\npredictions while significantly outperforming baseline methods, reaching up to\n21% in accuracy gain.\n"}
+{"abstract": "  The nature of trions and their interaction with light has remained a puzzle.\nThe composition and dispersion of polaritons involving trions provide insights\ninto this puzzle. Trions and excitons in doped two-dimensional (2D) materials\nare not independent excitations but are strongly coupled as a result of Coulomb\ninteractions. When excitons in doped 2D materials are also strongly coupled\nwith light inside an optical waveguide, the resulting polariton states are\ncoherent superpositions of exciton, trion, and photon states. We realize these\nexciton-trion-polaritons by coupling an electron-doped monolayer of\ntwo-dimensional material MoSe2 to the optical mode in a photonic crystal\nwaveguide. Our theoretical model, based on a many-body description of these\npolaritons, reproduces the measured polariton energy band dispersion and Rabi\nsplittings with excellent accuracy. Our work sheds light on the structure of\ntrion states in 2D matrials and also on the indirect mechanism by which they\ninteract with light.\n"}
+{"abstract": "  The Time Dependent Boltzmann equation (TDBE) is a viable option to study\nstrongly out-of-equilibrium thermalization dynamics which are becoming\nincreasingly critical for many novel physical applications like Ultrafast\nthermalization, Terahertz radiation etc. However its applicability is greatly\nlimited by the impractical scaling of the solution to its scattering integral\nterm. In our previous work\\cite{Michael} we had proposed a numerical solver to\ncalculate the scattering integral term in the TDBE and then improved on\nit\\cite{1DPaper} to include second degree momentum discretisation and adaptive\ntime stepping. Our solver requires no close-to-equilibrium assumptions and can\nwork with realistic band structures and scattering amplitudes. Moreover, it is\nnumerically efficient and extremely robust against inherent numerical\ninstabilities. While in our previous work \\cite{1DPaper} we showcased the\napplication of our solver to 1D materials, here we showcase its applications to\na simple 2D system and analyse thermalisations of the introduced\nout-of-equilibrium excitations. The excitations added at higher energies were\nfound to thermalise faster than those introduced at relatively lower energies.\nAlso, we conclude that the thermalisation of the out-of-equilibrium population\nto equilibrium values is not a simple exponential decay but rather a\nnon-trivial function of time. Nonetheless, by fitting a double exponential\nfunction to the decay of the out-of-equilibrium population with time we were\nable to generate quantitative insights into the time scales involved in the\nthermalisations.\n"}
+{"abstract": "  Defined by Borel, a real number is normal to an integer base $b$, greater\nthan or equal to $2$, if in its base-$b$ expansion every block of digits occurs\nwith the same limiting frequency as every other block of the same length. We\nconsider the problem of insertion in constructed base-$b$ normal expansions to\nobtain normality to base $(b+1)$.\n"}
+{"abstract": "  We investigate the quality of space approximation of a class of stochastic\nintegral equations of convolution type with Gaussian noise. Such equations\narise, for example, when considering mild solutions of stochastic fractional\norder partial differential equations but also when considering mild solutions\nof classical stochastic partial differential equations. The key requirement for\nthe equations is a smoothing property of the deterministic evolution operator\nwhich is typical in parabolic type problems. We show that if one has access to\nnonsmooth data estimates for the deterministic error operator together with its\nderivative of a space discretization procedure, then one obtains error\nestimates in pathwise H\\\"older norms with rates that can be read off the\ndeterministic error rates. We illustrate the main result by considering a class\nof stochastic fractional order partial differential equations and space\napproximations performed by spectral Galerkin methods and finite elements. We\nalso improve an existing result on the stochastic heat equation.\n"}
+{"abstract": "  Consider the random graph~\\(G(n,p)\\) obtained by allowing each edge in the\ncomplete graph on~\\(n\\) vertices to be present with probability~\\(p\\)\nindependent of the other edges. In this paper, we study the minimum number of\nedge edit operations needed to convert~\\(G(n,p)\\) into an Eulerian graph. We\nobtain deviation estimates for three types Eulerian edit numbers based on\nwhether we perform only edge additions or only edge deletions or a combination\nof both and show that with high probability, roughly~\\(\\frac{n}{4}\\) operations\nsuffice in all three cases.\n"}
+{"abstract": "  We consider bond and site Bernoulli Percolation in both the oriented and the\nnon-oriented cases on $\\mathbb{Z}^d$ and obtain rigorous upper bounds for the\ncritical points in those models for every dimension $d \\geq 3$.\n"}
+{"abstract": "  This paper presents a novel multi-axis force-sensing approach in robotic\nminimally invasive surgery with no modification to the surgical instrument.\nThus, it is adaptable to different surgical instruments. A novel 6-axis optical\nforce sensor, with local signal conditioning and digital electronics, was\nmounted onto the proximal shaft of a da Vinci EndoWrist instrument. A new\ncannula design comprising an inner tube and an outer tube was proposed. The\ninner tube is attached to the cannula interface to the robot base through a\ncompliant leaf spring with adjustable stiffness. It allows bending of the\ninstrument shaft due to the tip forces. The outer tube mechanically filters out\nthe body forces from affecting the instrument bending behavior. A mathematical\nmodel of the sensing principle was developed and used for model-based\ncalibration. A data-driven calibration based on a shallow neural network\narchitecture comprising a single 5-nodes hidden layer and a 5x1 output layer is\ndiscussed. Extensive testing was conducted to validate that the sensor can\nsuccessfully measure the lateral forces and moments and the axial torque\napplied to the instruments distal end within the desired resolution, accuracy,\nand range requirements.\n"}
+{"abstract": "  We prove the conjectured first order expansion of the Levy-Lieb functional in\nthe semiclassical limit, arising from Density Functional Theory (DFT). This is\naccomplished by interpreting the problem as the singular perturbation of an\nOptimal Transport problem via a Dirichlet penalization.\n"}
+{"abstract": "  Domain generalization (DG) methods aim to achieve generalizability to an\nunseen target domain by using only training data from the source domains.\nAlthough a variety of DG methods have been proposed, a recent study shows that\nunder a fair evaluation protocol, called DomainBed, the simple empirical risk\nminimization (ERM) approach works comparable to or even outperforms previous\nmethods. Unfortunately, simply solving ERM on a complex, non-convex loss\nfunction can easily lead to sub-optimal generalizability by seeking sharp\nminima. In this paper, we theoretically show that finding flat minima results\nin a smaller domain generalization gap. We also propose a simple yet effective\nmethod, named Stochastic Weight Averaging Densely (SWAD), to find flat minima.\nSWAD finds flatter minima and suffers less from overfitting than does the\nvanilla SWA by a dense and overfit-aware stochastic weight sampling strategy.\nSWAD shows state-of-the-art performances on five DG benchmarks, namely PACS,\nVLCS, OfficeHome, TerraIncognita, and DomainNet, with consistent and large\nmargins of +1.6% averagely on out-of-domain accuracy. We also compare SWAD with\nconventional generalization methods, such as data augmentation and consistency\nregularization methods, to verify that the remarkable performance improvements\nare originated from by seeking flat minima, not from better in-domain\ngeneralizability. Last but not least, SWAD is readily adaptable to existing DG\nmethods without modification; the combination of SWAD and an existing DG method\nfurther improves DG performances. Source code is available at\nhttps://github.com/khanrc/swad.\n"}
+{"abstract": "  Text generation has become one of the most important yet challenging tasks in\nnatural language processing (NLP). The resurgence of deep learning has greatly\nadvanced this field by neural generation models, especially the paradigm of\npretrained language models (PLMs). In this paper, we present an overview of the\nmajor advances achieved in the topic of PLMs for text generation. As the\npreliminaries, we present the general task definition and briefly describe the\nmainstream architectures of PLMs for text generation. As the core content, we\ndiscuss how to adapt existing PLMs to model different input data and satisfy\nspecial properties in the generated text. We further summarize several\nimportant fine-tuning strategies for text generation. Finally, we present\nseveral future directions and conclude this paper. Our survey aims to provide\ntext generation researchers a synthesis and pointer to related research.\n"}
+{"abstract": "  While synthetic bilingual corpora have demonstrated their effectiveness in\nlow-resource neural machine translation (NMT), adding more synthetic data often\ndeteriorates translation performance. In this work, we propose alternated\ntraining with synthetic and authentic data for NMT. The basic idea is to\nalternate synthetic and authentic corpora iteratively during training. Compared\nwith previous work, we introduce authentic data as guidance to prevent the\ntraining of NMT models from being disturbed by noisy synthetic data.\nExperiments on Chinese-English and German-English translation tasks show that\nour approach improves the performance over several strong baselines. We\nvisualize the BLEU landscape to further investigate the role of authentic and\nsynthetic data during alternated training. From the visualization, we find that\nauthentic data helps to direct the NMT model parameters towards points with\nhigher BLEU scores and leads to consistent translation performance improvement.\n"}
+{"abstract": "  We give algorithms to compute decompositions of a given polynomial, or more\ngenerally mixed tensor, as sum of rank one tensors, and to establish whether\nsuch a decomposition is unique. In particular, we present methods to compute\nthe decomposition of a general plane quintic in seven powers, and of a general\nspace cubic in five powers; the two decompositions of a general plane sextic of\nrank nine, and the five decompositions of a general plane septic. Furthermore,\nwe give Magma implementations of all our algorithms.\n"}
+{"abstract": "  In order to treat the multiple time scales of ocean dynamics in an efficient\nmanner, the baroclinic-barotropic splitting technique has been widely used for\nsolving the primitive equations for ocean modeling. Based on the framework of\nstrong stability-preserving Runge-Kutta approach, we propose two high-order\nmultirate explicit time-stepping schemes (SSPRK2-SE and SSPRK3-SE) for the\nresulting split system in this paper. The proposed schemes allow for a large\ntime step to be used for the three-dimensional baroclinic (slow) mode and a\nsmall time step for the two-dimensional barotropic (fast) mode, in which each\nof the two mode solves just need to satisfy their respective CFL conditions for\nnumerical stability. Specifically, at each time step, the baroclinic velocity\nis first computed by advancing the baroclinic mode and fluid thickness of the\nsystem with the large time-step \\textcolor{black}{and the assistance of some\nintermediate approximations of the baroctropic mode obtained by substepping\nwith the small-time step}; then the barotropic velocity is corrected by using\nthe small time step to re-advance the barotropic mode under an improved\nbarotropic forcing produced by interpolation of the forcing terms from the\npreceding baroclinic mode solves; lastly, the fluid thickness is updated by\ncoupling the baroclinic and barotropic velocities. Additionally, numerical\ninconsistencies on the discretized sea surface height caused by the mode\nsplitting are relieved via a reconciliation process with carefully calculated\nflux deficits. Two benchmark tests from the \"MPAS-Ocean\" platform are carried\nout to numerically demonstrate the performance and parallel scalability of the\nproposed SSPRK-SE schemes.\n"}
+{"abstract": "  The performance of neural network models is often limited by the availability\nof big data sets. To treat this problem, we survey and develop novel synthetic\ndata generation and augmentation techniques for enhancing low/zero-sample\nlearning in satellite imagery. In addition to extending synthetic data\ngeneration approaches, we propose a hierarchical detection approach to improve\nthe utility of synthetic training samples. We consider existing techniques for\nproducing synthetic imagery--3D models and neural style transfer--as well as\nintroducing our own adversarially trained reskinning network, the\nGAN-Reskinner, to blend 3D models. Additionally, we test the value of synthetic\ndata in a two-stage, hierarchical detection/classification model of our own\nconstruction. To test the effectiveness of synthetic imagery, we employ it in\nthe training of detection models and our two stage model, and evaluate the\nresulting models on real satellite images. All modalities of synthetic data are\ntested extensively on practical, geospatial analysis problems. Our experiments\nshow that synthetic data developed using our approach can often enhance\ndetection performance, particularly when combined with some real training\nimages. When the only source of data is synthetic, our GAN-Reskinner often\nboosts performance over conventionally rendered 3D models and in all cases the\nhierarchical model outperforms the baseline end-to-end detection architecture.\n"}
+{"abstract": "  The introduction of transient learning degrees of freedom into a system can\nlead to novel material design and training protocols that guide a system into a\ndesired metastable state. In this approach, some degrees of freedom, which were\nnot initially included in the system dynamics, are first introduced and\nsubsequently removed from the energy minimization process once the desired\nstate is reached. Using this conceptual framework, we create stable jammed\npackings that exist in exceptionally deep energy minima marked by the absence\nof low-frequency quasilocalized modes; this added stability persists in the\nthermodynamic limit. The inclusion of particle radii as transient degrees of\nfreedom leads to deeper and much more stable minima than does the inclusion of\nparticle stiffnesses. This is because particle radii couple to the jamming\ntransition whereas stiffnesses do not. Thus different choices for the added\ndegrees of freedom can lead to very different training outcomes.\n"}
+{"abstract": "  A Non-Binary Snow Index for Multi-Component Surfaces (NBSI-MS) is proposed to\nmap snow/ice cover. The NBSI-MS is based on the spectral characteristics of\ndifferent Land Cover Types (LCTs) such as snow, water, vegetation, bare land,\nimpervious, and shadow surfaces. This index can increase the separability\nbetween NBSI-MS values corresponding to snow from other LCTs and accurately\ndelineate the snow/ice cover in non-binary maps. To test the robustness of the\nNBSI-MS, Greenland and France-Italy regions were examined where snow interacts\nwith highly diversified geographical ecosystem. Data recorded by Landsat 5 TM,\nLandsat 8 OLI, and Sentinel-2A MSI satellites have been used. The NBSI-MS\nperformance was also compared against the well-known NDSI, NDSII-1, S3, and SWI\nmethods and evaluated based on Ground Reference Test Pixels (GRTPs) over\nnon-binarized results. The results show that the NBSI-MS achieves overall\naccuracy (OA) ranging from 0.99 to 1 with kappa coefficient values in the same\nrange as OA. The precision assessment confirms the performance superiority of\nthe proposed NBSI-MS method for removing water and shadow surfaces over the\ncompared relevant indices.\n"}
+{"abstract": "  Xova is a software package that implements baseline-dependent time and\nchannel averaging on Measurement Set data. The uv-samples along a baseline\ntrack are aggregated into a bin until a specified decorrelation tolerance is\nexceeded. The degree of decorrelation in the bin correspondingly determines the\namount of channel and timeslot averaging that is suitable for samples in the\nbin. This necessarily implies that the number of channels and timeslots varies\nper bin and the output data loses the rectilinear input shape of the input\ndata.\n"}
+{"abstract": "  We consider the combination of uplink code-domain non-orthogonal multiple\naccess (NOMA) with massive multiple-input multiple-output (MIMO) and\nreconfigurable intelligent surfaces (RISs). We assume a setup in which the base\nstation (BS) is capable of forming beams towards the RISs under line-of-sight\nconditions, and where each RIS is covering a cluster of users. In order to\nsupport multi-user transmissions within a cluster, code-domain NOMA via\nspreading is utilized. We investigate the optimization of the RIS phase-shifts\nsuch that a large number of users is supported. As it turns out, it is a\ncoupled optimization problem that depends on the detection order under\ninterference cancellation and the applied filtering at the BS. We propose to\ndecouple those variables by using sum-rate optimized phase-shifts as the\ninitial solution, allowing us to obtain a decoupled estimate of those\nvariables. Then, in order to determine the final phase-shifts, the problem is\nrelaxed into a semidefinite program that can be solved efficiently via convex\noptimization algorithms. Simulation results show the effectiveness of our\napproach in improving the detectability of the users.\n"}
+{"abstract": "  The development of thermal energy storage materials is the most attractive\nstrategy to harvest the solar energy and increase the energy utilization\nefficiency. Phase change materials (PCMs) have received much attention in this\nresearch field for several decades. Herein, we reported a new kind of PCM micro\ntopological structure, design direction, and the ultra-flexible, form-stable\nand smart PCMs, polyrotaxane. The structure of polyrotaxane was fully confirmed\nby 1H nuclear magnetic resonance,attenuated total reflection-fourier transform\ninfrared and X-ray diffraction. Then the tensile properties,thermal stability\nin the air, phase change energy storage and shape memory properties of the\nfilms were systematically analyzed. The results showed that all the mechanical\nperformance, thermal stability in air and shape memory properties of\npolyrotaxanes were enhanced significantly compared to those of polyethylene\noxide (PEO). The form stability at temperatures above the melting point of PEO\nsignificantly increased with the {\\alpha}-CD addition. Further with the high\nphase transition enthalpy and excellent cycle performance, the polyrotaxane\nfilms are therefore promising sustainable and advanced form-stable phase change\nmaterials for thermal energy storage. Notably, its ultra-high flexibility,\nremolding ability and excellent shape memory properties provide a convenient\nway for the intelligent heat treatment packaging of complex and flexible\nelectronic devices. In addition, this is a totally novel insight for\npolyrotaxane application and new design method for form-stable PCMs.\n"}
+{"abstract": "  The scaleup of quantum computers operating in the microwave domain requires\nadvanced control electronics, and the use of integrated components that operate\nat the temperature of the quantum devices is potentially beneficial. However,\nsuch an approach requires ultra-low power dissipation and high signal quality\nin order to ensure quantum-coherent operations. Here, we report an on-chip\ndevice that is based on a Josephson junction coupled to a spiral resonator and\nis capable of coherent continuous-wave microwave emission. We show that\ncharacteristics of the device accurately follow a theory based on a\nperturbative treatment of the capacitively shunted Josephson junction as a gain\nelement. The infidelity of typical quantum gate operations due to the phase\nnoise of this cryogenic 25-pW microwave source is less than 0.1% up to 10-ms\nevolution times, which is below the infidelity caused by dephasing in\nstate-of-the-art superconducting qubits. Together with future cryogenic\namplitude and phase modulation techniques, our approach may lead to scalable\ncryogenic control systems for quantum processors.\n"}
+{"abstract": "  We study space-pass tradeoffs in graph streaming algorithms for parameter\nestimation and property testing problems such as estimating the size of maximum\nmatchings and maximum cuts, weight of minimum spanning trees, or testing if a\ngraph is connected or cycle-free versus being far from these properties. We\ndevelop a new lower bound technique that proves that for many problems of\ninterest, including all the above, obtaining a $(1+\\epsilon)$-approximation\nrequires either $n^{\\Omega(1)}$ space or $\\Omega(1/\\epsilon)$ passes, even on\nhighly restricted families of graphs such as bounded-degree planar graphs. For\nmultiple of these problems, this bound matches those of existing algorithms and\nis thus (asymptotically) optimal.\n  Our results considerably strengthen prior lower bounds even for arbitrary\ngraphs: starting from the influential work of [Verbin, Yu; SODA 2011], there\nhas been a plethora of lower bounds for single-pass algorithms for these\nproblems; however, the only multi-pass lower bounds proven very recently in\n[Assadi, Kol, Saxena, Yu; FOCS 2020] rules out sublinear-space algorithms with\nexponentially smaller $o(\\log{(1/\\epsilon)})$ passes for these problems.\n  One key ingredient of our proofs is a simple streaming XOR Lemma, a generic\nhardness amplification result, that we prove: informally speaking, if a\n$p$-pass $s$-space streaming algorithm can only solve a decision problem with\nadvantage $\\delta > 0$ over random guessing, then it cannot solve XOR of $\\ell$\nindependent copies of the problem with advantage much better than\n$\\delta^{\\ell}$. This result can be of independent interest and useful for\nother streaming lower bounds as well.\n"}
+{"abstract": "  We propose a novel algorithm for multi-player multi-armed bandits without\ncollision sensing information. Our algorithm circumvents two problems shared by\nall state-of-the-art algorithms: it does not need as an input a lower bound on\nthe minimal expected reward of an arm, and its performance does not scale\ninversely proportionally to the minimal expected reward. We prove a theoretical\nregret upper bound to justify these claims. We complement our theoretical\nresults with numerical experiments, showing that the proposed algorithm\noutperforms state-of-the-art in practice as well.\n"}
+{"abstract": "  Here it is proposed a three-dimensional plasmonic nonvolatile memory crossbar\narrays that can ensure a dual-mode operation in electrical and optical domains.\nThis can be realized through plasmonics that serves as a bridge between\nphotonics and electronics as the metal electrode is part of the waveguide. The\nproposed arrangement is based on low-loss long-range dielectric-loaded surface\nplasmon polariton waveguide where a metal stripe is placed between a buffer\nlayer and ridge. To achieve a dual-mode operation the materials were defined\nthat can provide both electrical and optical modulation functionality.\n"}
+{"abstract": "  Harnessing the potential wide-ranging quantum science applications of\nmolecules will require control of their interactions. Here, we use microwave\nradiation to directly engineer and tune the interaction potentials between\nultracold calcium monofluoride (CaF) molecules. By merging two optical\ntweezers, each containing a single molecule, we probe collisions in three\ndimensions. The correct combination of microwave frequency and power creates an\neffective repulsive shield, which suppresses the inelastic loss rate by a\nfactor of six, in agreement with theoretical calculations. The demonstrated\nmicrowave shielding shows a general route to the creation of long-lived, dense\nsamples of ultracold molecules and evaporative cooling.\n"}
+{"abstract": "  We provide an explicit characterization of the covariant isotropy group of\nany Grothendieck topos, i.e. the group of (extended) inner automorphisms of any\nsheaf over a small site. As a consequence, we obtain an explicit\ncharacterization of the centre of a Grothendieck topos, i.e. the automorphism\ngroup of its identity functor.\n"}
+{"abstract": "  One of the fundamental task in graph data mining is to find a planted\ncommunity(dense subgraph), which has wide application in biology, finance, spam\ndetection and so on. For a real network data, the existence of a dense subgraph\nis generally unknown. Statistical tests have been devised to testing the\nexistence of dense subgraph in a homogeneous random graph. However, many\nnetworks present extreme heterogeneity, that is, the degrees of nodes or\nvertexes don't concentrate on a typical value. The existing tests designed for\nhomogeneous random graph are not straightforwardly applicable to the\nheterogeneous case. Recently, scan test was proposed for detecting a dense\nsubgraph in heterogeneous(inhomogeneous) graph(\\cite{BCHV19}). However, the\ncomputational complexity of the scan test is generally not polynomial in the\ngraph size, which makes the test impractical for large or moderate networks. In\nthis paper, we propose a polynomial-time test that has the standard normal\ndistribution as the null limiting distribution. The power of the test is\ntheoretically investigated and we evaluate the performance of the test by\nsimulation and real data example.\n"}
+{"abstract": "  Domain experts often need to extract structured information from large\ncorpora. We advocate for a search paradigm called ``extractive search'', in\nwhich a search query is enriched with capture-slots, to allow for such rapid\nextraction. Such an extractive search system can be built around syntactic\nstructures, resulting in high-precision, low-recall results. We show how the\nrecall can be improved using neural retrieval and alignment. The goals of this\npaper are to concisely introduce the extractive-search paradigm; and to\ndemonstrate a prototype neural retrieval system for extractive search and its\nbenefits and potential. Our prototype is available at\n\\url{https://spike.neural-sim.apps.allenai.org/} and a video demonstration is\navailable at \\url{https://vimeo.com/559586687}.\n"}
+{"abstract": "  We study the Symmetric Rendezvous Search Problem for a multi-robot system.\nThere are $n>2$ robots arbitrarily located on a line. Their goal is to meet\nsomewhere on the line as quickly as possible. The robots do not know the\ninitial location of any of the other robots or their own positions on the line.\nThe symmetric version of the problem requires the robots to execute the same\nsearch strategy to achieve rendezvous. Therefore, we solve the problem in an\nonline fashion with a randomized strategy. In this paper, we present a\nsymmetric rendezvous algorithm which achieves a constant competitive ratio for\nthe total distance traveled by the robots. We validate our theoretical results\nthrough simulations.\n"}
+{"abstract": "  External knowledge (a.k.a. side information) plays a critical role in\nzero-shot learning (ZSL) which aims to predict with unseen classes that have\nnever appeared in training data. Several kinds of external knowledge, such as\ntext and attribute, have been widely investigated, but they alone are limited\nwith incomplete semantics. Some very recent studies thus propose to use\nKnowledge Graph (KG) due to its high expressivity and compatibility for\nrepresenting kinds of knowledge. However, the ZSL community is still in short\nof standard benchmarks for studying and comparing different external knowledge\nsettings and different KG-based ZSL methods. In this paper, we proposed six\nresources covering three tasks, i.e., zero-shot image classification (ZS-IMGC),\nzero-shot relation extraction (ZS-RE), and zero-shot KG completion (ZS-KGC).\nEach resource has a normal ZSL benchmark and a KG containing semantics ranging\nfrom text to attribute, from relational knowledge to logical expressions. We\nhave clearly presented these resources including their construction,\nstatistics, data formats and usage cases w.r.t. different ZSL methods. More\nimportantly, we have conducted a comprehensive benchmarking study, with two\ngeneral and state-of-the-art methods, two setting-specific methods and one\ninterpretable method. We discussed and compared different ZSL paradigms w.r.t.\ndifferent external knowledge settings, and found that our resources have great\npotential for developing more advanced ZSL methods and more solutions for\napplying KGs for augmenting machine learning. All the resources are available\nat https://github.com/China-UK-ZSL/Resources_for_KZSL.\n"}
+{"abstract": "  We define a model of interactive communication where two agents with private\ntypes can exchange information before a game is played. The model contains\nBayesian persuasion as a special case of a one-round communication protocol. We\ndefine message complexity corresponding to the minimum number of interactive\nrounds necessary to achieve the best possible outcome. Our main result is that\nfor bilateral trade, agents don't stop talking until they reach an efficient\noutcome: Either agents achieve an efficient allocation in finitely many rounds\nof communication; or the optimal communication protocol has infinite number of\nrounds. We show an important class of bilateral trade settings where efficient\nallocation is achievable with a small number of rounds of communication.\n"}
+{"abstract": "  The Earth's N2-dominated atmosphere is a very special feature. Firstly, N2 as\nmain gas is unique on the terrestrial planets in the inner solar system and\ngives a hint for tectonic activity. Studying the origins of atmospheric\nnitrogen and its stability provides insights into the uniqueness of the Earth's\nhabitat. Secondly, the coexistence of N2 and O2 within an atmosphere is\nunequaled in the entire solar system. Such a combination is strongly linked to\nthe existence of aerobic lifeforms. The availability of nitrogen on the\nsurface, in the ocean, and within the atmosphere can enable or prevent the\nhabitability of a terrestrial planet, since nitrogen is vitally required by all\nknown lifeforms. In the present work, the different origins of atmospheric\nnitrogen, the stability of nitrogen dominated atmospheres, and the development\nof early Earth's atmospheric N2 are discussed. We show why N2-O2-atmospheres\nconstitute a biomarker not only for any lifeforms but for aerobic lifeforms,\nwhich was the first major step that led to higher developed life on Earth.\n"}
+{"abstract": "  Risk capital allocations (RCAs) are an important tool in quantitative risk\nmanagement, where they are utilized to, e.g., gauge the profitability of\ndistinct business units, determine the price of a new product, and conduct the\nmarginal economic capital analysis. Nevertheless, the notion of RCA has been\nliving in the shadow of another, closely related notion, of risk measure (RM)\nin the sense that the latter notion often shapes the fashion in which the\nformer notion is implemented. In fact, as the majority of the RCAs known\nnowadays are induced by RMs, the popularity of the two are apparently very much\ncorrelated. As a result, it is the RCA that is induced by the Conditional Tail\nExpectation (CTE) RM that has arguably prevailed in scholarly literature and\napplications. Admittedly, the CTE RM is a sound mathematical object and an\nimportant regulatory RM, but its appropriateness is controversial in, e.g.,\nprofitability analysis and pricing. In this paper, we address the question as\nto whether or not the RCA induced by the CTE RM may concur with alternatives\nthat arise from the context of profit maximization. More specifically, we\nprovide exhaustive description of all those probabilistic model settings, in\nwhich the mathematical and regulatory CTE RM may also reflect the risk\nperception of a profit-maximizing insurer.\n"}
+{"abstract": "  According to Skolem's conjecture, if an exponential Diophantine equation is\nnot solvable, then it is not solvable modulo an appropriately chosen modulus.\nBesides several concrete equations, the conjecture has only been proved for\nrather special cases. In this paper we prove the conjecture for equations of\nthe form $x^n-by_1^{k_1}\\dots y_\\ell^{k_\\ell}=\\pm 1$, where\n$b,x,y_1,\\dots,y_\\ell$ are fixed integers and $n,k_1,\\dots,k_\\ell$ are\nnon-negative integral unknowns. This result extends a recent theorem of Hajdu\nand Tijdeman.\n"}
+{"abstract": "  The COVID-19 pandemic has impacted lives and economies across the globe,\nleading to many deaths. While vaccination is an important intervention, its\nroll-out is slow and unequal across the globe. Therefore, extensive testing\nstill remains one of the key methods to monitor and contain the virus. Testing\non a large scale is expensive and arduous. Hence, we need alternate methods to\nestimate the number of cases. Online surveys have been shown to be an effective\nmethod for data collection amidst the pandemic. In this work, we develop\nmachine learning models to estimate the prevalence of COVID-19 using\nself-reported symptoms. Our best model predicts the daily cases with a mean\nabsolute error (MAE) of 226.30 (normalized MAE of 27.09%) per state, which\ndemonstrates the possibility of predicting the actual number of confirmed cases\nby utilizing self-reported symptoms. The models are developed at two levels of\ndata granularity - local models, which are trained at the state level, and a\nsingle global model which is trained on the combined data aggregated across all\nstates. Our results indicate a lower error on the local models as opposed to\nthe global model. In addition, we also show that the most important symptoms\n(features) vary considerably from state to state. This work demonstrates that\nthe models developed on crowd-sourced data, curated via online platforms, can\ncomplement the existing epidemiological surveillance infrastructure in a\ncost-effective manner. The code is publicly available at\nhttps://github.com/parthpatwa/Can-Self-Reported-Symptoms-Predict-Daily-COVID-19-Cases.\n"}
+{"abstract": "  The current manuscript highlights the preparation of NiFe2O4 nanoparticles by\nadopting sol-gel auto combustion route. The prime focus of this study is to\ninvestigate the impact of gamma irradiation on the microstructural,\nmorphological, functional, optical and magnetic characteristics. The resulted\nNiFe2O4 products have been characterized employing numerous instrumental\nequipments such as FESEM, XRD, UV visible spectroscopy, FTIR and PPMS for a\nvariety of gamma ray doses (0 kGy, 25 kGy and 100 kGy). FESEM micrographs\nillustrate the aggregation of ferrite nanoparticles in pristine NiFe2O4 product\nhaving an average particle size of 168 nm and the surface morphology is altered\nafter exposure to gamma-irradiation. XRD spectra have been analyzed employing\nRietveld method and the results of the XRD investigation reveal the desired\nphases (cubic spinel phases) of NiFe2O4 with observing other transitional\nphases. Several microstructural parameters such as bond length, bond angle,\nhopping length etc. have been determined from the analysis of Rietveld method.\nThis study reports that the gamma irradiations demonstrate a great influence on\noptical bandgap energy and it varies from 1.80 and 1.89 eV evaluated via K M\nfunction. FTIR measurement depicts a proof for the persistence of Ni-O and Fe-O\nstretching vibrations within the respective products and thus indicating the\nsuccessful development of NiFe2O4. The saturation magnetization (MS) of\npristine Ni ferrite product is noticed to be 28.08 emug-1. A considerable\nincrease in MS is observed in case of low gamma-dose (25 kGy) and a decrement\nnature is disclosed after the result of high dose of gamma irradiation\n(100kGy).\n"}
+{"abstract": "  This paper extends the concept of informative selection, population\ndistribution and sample distribution to a spatial process context. These\nnotions were first defined in a context where the output of the random process\nof interest consists of independent and identically distributed realisations\nfor each individual of a population. It has been showed that informative\nselection was inducing a stochastic dependence among realisations on the\nselected units. In the context of spatial processes, the \"population\" is a\ncontinuous space and realisations for two different elements of the population\nare not independent. We show how informative selection may induce a different\ndependence among selected units and how the sample distribution differs from\nthe population distribution.\n"}
+{"abstract": "  Fueled by the call for formative assessments, diagnostic classification\nmodels (DCMs) have recently gained popularity in psychometrics. Despite their\npotential for providing diagnostic information that aids in classroom\ninstruction and students' learning, empirical applications of DCMs to classroom\nassessments have been highly limited. This is partly because how DCMs with\ndifferent estimation methods perform in small sample contexts is not yet\nwell-explored. Hence, this study aims to investigate the performance of\nrespondent classification and item parameter estimation with a comprehensive\nsimulation design that resembles classroom assessments using different\nestimation methods. The key findings are the following: (1) although the marked\ndifference in respondent classification accuracy was not observed among the\nmaximum likelihood (ML), Bayesian, and nonparametric methods, the Bayesian\nmethod provided slightly more accurate respondent classification in\nparsimonious DCMs than the ML method, and in complex DCMs, the ML method\nyielded the slightly better result than the Bayesian method; (2) while item\nparameter recovery was poor in both Bayesian and ML methods, the Bayesian\nmethod exhibited unstable slip values owing to the multimodality of their\nposteriors under complex DCMs, and the ML method produced irregular estimates\nthat appear to be well-estimated due to a boundary problem under parsimonious\nDCMs.\n"}
+{"abstract": "  We study the constant term functor for $\\mathbb{F}_p$-sheaves on the affine\nGrassmannian in characteristic $p$ with respect to a Levi subgroup. Our main\nresult is that the constant term functor induces a tensor functor between\ncategories of equivariant perverse $\\mathbb{F}_p$-sheaves. We apply this fact\nto get information about the Tannakian monoids of the corresponding categories\nof perverse sheaves. As a byproduct we also obtain geometric proofs of several\nresults due to Herzig on the mod $p$ Satake transform and the structure of the\nspace of mod $p$ Satake parameters.\n"}
+{"abstract": "  This paper studies the expressive power of artificial neural networks (NNs)\nwith rectified linear units. To study them as a model of real-valued\ncomputation, we introduce the concept of Max-Affine Arithmetic Programs and\nshow equivalence between them and NNs concerning natural complexity measures.\nWe then use this result to show that two fundamental combinatorial optimization\nproblems can be solved with polynomial-size NNs, which is equivalent to the\nexistence of very special strongly polynomial time algorithms. First, we show\nthat for any undirected graph with $n$ nodes, there is an NN of size\n$\\mathcal{O}(n^3)$ that takes the edge weights as input and computes the value\nof a minimum spanning tree of the graph. Second, we show that for any directed\ngraph with $n$ nodes and $m$ arcs, there is an NN of size $\\mathcal{O}(m^2n^2)$\nthat takes the arc capacities as input and computes a maximum flow. These\nresults imply in particular that the solutions of the corresponding parametric\noptimization problems where all edge weights or arc capacities are free\nparameters can be encoded in polynomial space and evaluated in polynomial time,\nand that such an encoding is provided by an NN.\n"}
+{"abstract": "  We present SMURF, a method for unsupervised learning of optical flow that\nimproves state of the art on all benchmarks by $36\\%$ to $40\\%$ (over the prior\nbest method UFlow) and even outperforms several supervised approaches such as\nPWC-Net and FlowNet2. Our method integrates architecture improvements from\nsupervised optical flow, i.e. the RAFT model, with new ideas for unsupervised\nlearning that include a sequence-aware self-supervision loss, a technique for\nhandling out-of-frame motion, and an approach for learning effectively from\nmulti-frame video data while still only requiring two frames for inference.\n"}
+{"abstract": "  This work describes and demonstrates the operation of a virtual X-ray\nalgorithm operating on finite-element post-processing results which allows for\nhigher polynomial orders in geometry representation as well as density\ndistribution. A nested hierarchy of oriented bounding boxes is used for\npreselecting candidate elements undergoing a ray-casting procedure. The exact\nintersection points of the ray with the finite element are not computed,\ninstead the ray is discretized by a sequence of points. The element-local\ncoordinates of each discretized point are determined using a local\nNewton-iteration and the resulting densities are accumulated. This procedure\nresults in highly accurate virtual X-ray images of finite element models.\n"}
+{"abstract": "  As the Data Science field continues to mature, and we collect more data, the\ndemand to store and analyze them will continue to increase. This increase in\ndata availability and demand for analytics will put a strain on data centers\nand compute clusters-with implications for both energy costs and emissions. As\nthe world battles a climate crisis, it is prudent for organizations with data\ncenters to have a framework for combating increasing energy costs and emissions\nto meet demand for analytics work. In this paper, I present a generalized\nframework for organizations to audit data centers energy efficiency to\nunderstand the resources required to operate a given data center and effective\nsteps organizations can take to improve data center efficiency and lower the\nenvironmental impact.\n"}
+{"abstract": "  We study the bar instability in collisionless, rotating, anisotropic, stellar\nsystems, using N-body simulations and also the matrix technique for calculation\nof modes with the perturbed collisionless Boltzmann equation. These methods are\napplied to spherical systems with an initial Plummer density distribution, but\nmodified kinematically in two ways: the velocity distribution is tangentially\nanisotropic, using results of Dejonghe, and the system is set in rotation by\nreversing the velocities of a fraction of stars in various regions of phase\nspace, a la Lynden-Bell. The aim of the N-body simulations is first to survey\nthe parameter space, and, using those results, to identify regions of phase\nspace (by radius and orbital inclination) which have the most important\ninfluence on the bar instability. The matrix method is then used to identify\nthe resonant interactions in the system which have the greatest effect on the\ngrowth rate of a bar. Complementary series of N-body simulations examine these\nprocesses in relation to the evolving frequency distribution and the pattern\nspeed. Finally, the results are synthesised with an existing theoretical\nframework, and used to consider the old question of constructing a stability\ncriterion.\n"}
+{"abstract": "  Polygonal billiards are an example of pseudo-chaotic dynamics, a combination\nof integrable evolution and sudden jumps due to conical singular points that\narise from the corners of the polygons. Such pseudo-chaotic behaviour, often\ncharacterised by an algebraic separation of nearby trajectories, is believed to\nbe linked to the wild dependence that particle transport has on the fine\ndetails of the billiard table. Here we address this relation through a detailed\nnumerical study of the statistics of displacement in a family of polygonal\nchannel billiards with parallel walls. We show that transport is characterised\nby strong anomalous diffusion, with a mean square displacement that scales in\ntime faster than linear, and with a probability density of the displacement\nexhibiting exponential tails and ballistic fronts. In channels of finite length\nthe distribution of first-passage times is characterised by fat tails, with a\nmean first-passage time that diverges when the aperture angle is rational.\nThese findings have non trivial consequences for a variety of experiments.\n"}
+{"abstract": "  A set of vertices $S\\subseteq V(G)$ is a basis or resolving set of a graph\n$G$ if for each $x,y\\in V(G)$ there is a vertex $u\\in S$ such that $d(x,u)\\neq\nd(y,u)$. A basis $S$ is a fault-tolerant basis if $S\\setminus \\{x\\}$ is a basis\nfor every $x \\in S$. The fault-tolerant metric dimension (FTMD) $\\beta'(G)$ of\n$G$ is the minimum cardinality of a fault-tolerant basis. It is shown that each\ntwin vertex of $G$ belongs to every fault-tolerant basis of $G$. As a\nconsequence, $\\beta'(G) = n(G)$ iff each vertex of $G$ is a twin vertex, which\ncorrects a wrong characterization of graphs $G$ with $\\beta'(G) = n(G)$ from\n[Mathematics 7(1) (2019) 78]. This FTMD problem is reinvestigated for Butterfly\nnetworks, Benes networks, and silicate networks. This extends partial results\nfrom [IEEE Access 8 (2020) 145435--145445], and at the same time, disproves\nrelated conjectures from the same paper.\n"}
+{"abstract": "  Within the framework of the flux formulation of Double Field Theory (DFT) we\nemploy a generalised Scherk-Schwarz ansatz and discuss the classification of\nthe twists that in the presence of the strong constraint give rise to constant\ngeneralised fluxes interpreted as gaugings. We analyse the various\npossibilities of turning on the fluxes $H_{ijk}, F_{ij}{}^k, Q_i{}^{jk}$ and\n$R^{ijk}$, and the solutions for the twists allowed in each case. While we do\nnot impose the DFT (or equivalently supergravity) equations of motion, our\nresults provide solution-generating techniques in supergravity when applied to\na background that does solve the DFT equations. At the same time, our results\ngive rise also to canonical transformations of 2-dimensional $\\sigma$-models, a\nfact which is interesting especially because these are integrability-preserving\ntransformations on the worldsheet. Both the solution-generating techniques of\nsupergravity and the canonical transformations of 2-dimensional $\\sigma$-models\narise as maps that leave the generalised fluxes of DFT and their flat\nderivatives invariant. These maps include the known\nabelian/non-abelian/Poisson-Lie T-duality transformations, Yang-Baxter\ndeformations, as well as novel generalisations of them.\n"}
+{"abstract": "  A didactical survey of the foundations of Algorithmic Information Theory.\nThese notes are short on motivation, history and background but introduce some\nof the main techniques and concepts of the field.\n  The \"manuscript\" has been evolving over the years. Please, look at \"Version\nhistory\" below to see what has changed when.\n"}
+{"abstract": "  In multicentric calculus one takes a polynomial $p$ with distinct roots as a\nnew variable and represents complex valued functions by $\\mathbb C^d$-valued\nfunctions, where $d$ is the degree of $p$. An application is e.g. the\npossibility to represent a piecewise constant holomorphic function as a\nconvergent power series, simultaneously in all components of $|p(z)| \\le \\rho$.\nIn this paper we study the necessary modifications needed, if we take a\nrational function $r=p/q$ as the new variable instead. This allows to consider\nfunctions defined in neighborhoods of any compact set as opposed to the\npolynomial case where the domains $|p(z)| \\le \\rho$ are always polynomially\nconvex. Two applications are formulated. One giving a convergent power series\nexpression for Sylvester equations $AX-XB =C$ in the general case of $A,B$\nbeing bounded operators in Banach spaces with distinct spectra. The other\napplication formulates a K-spectral result for bounded operators in Hilbert\nspaces.\n"}
+{"abstract": "  Image-only and pseudo-LiDAR representations are commonly used for monocular\n3D object detection. However, methods based on them have shortcomings of either\nnot well capturing the spatial relationships in neighbored image pixels or\nbeing hard to handle the noisy nature of the monocular pseudo-LiDAR point\ncloud. To overcome these issues, in this paper we propose a novel\nobject-centric voxel representation tailored for monocular 3D object detection.\nSpecifically, voxels are built on each object proposal, and their sizes are\nadaptively determined by the 3D spatial distribution of the points, allowing\nthe noisy point cloud to be organized effectively within a voxel grid. This\nrepresentation is proved to be able to locate the object in 3D space\naccurately. Furthermore, prior works would like to estimate the orientation via\ndeep features extracted from an entire image or a noisy point cloud. By\ncontrast, we argue that the local RoI information from the object image patch\nalone with a proper resizing scheme is a better input as it provides complete\nsemantic clues meanwhile excludes irrelevant interferences. Besides, we\ndecompose the confidence mechanism in monocular 3D object detection by\nconsidering the relationship between 3D objects and the associated 2D boxes.\nEvaluated on KITTI, our method outperforms state-of-the-art methods by a large\nmargin. The code will be made publicly available soon.\n"}
+{"abstract": "  Optical atomic clocks have already overcome the eighteenth decimal digit of\ninstability and uncertainty demonstrating incredible control over external\nperturbations of the clock transition frequency. At the same time there is an\nincreasing demand for atomic and ionic transitions with minimal sensitivity to\nexternal fields, with practical operational wavelengths and robust readout\nprotocols. One of the goals is to simplify clock's operation maintaining its\nrelative uncertainty at low 10-18 level. It is especially important for\ntransportable and envisioned space-based optical clocks. We proved earlier that\nthe 1.14um inner-shell magnetic dipole transition in neutral thulium possesses\nvery low blackbody radiation shift compared to other neutrals. Here we\ndemonstrate operation of a bi-colour thulium optical clock with extraordinary\nlow sensitivity to the Zeeman shift due to a simultaneous interrogation of two\nclock transitions and data processing. Our experiment shows suppression of the\nquadratic Zeeman shift by at least three orders of magnitude. The effect of\ntensor lattice Stark shift can be also reduced to below 10-18 in fractional\nfrequency units. All these features make thulium optical clock almost free from\nhard-to-control systematic shifts. Together with convenient cooling and\ntrapping laser wavelengths, it provides great perspectives for thulium lattice\nclock as a high-performance transportable system.\n"}
+{"abstract": "  In the past decades, great progress has been made in the field of optical and\nparticle-based measurement techniques for experimental analysis of fluid flows.\nParticle Image Velocimetry (PIV) technique is widely used to identify flow\nparameters from time-consecutive snapshots of particles injected into the\nfluid. The computation is performed as post-processing of the experimental data\nvia proximity measure between particles in frames of reference. However, the\npost-processing step becomes problematic as the motility and density of the\nparticles increases, since the data emerges in extreme rates and volumes.\nMoreover, existing algorithms for PIV either provide sparse estimations of the\nflow or require large computational time frame preventing from on-line use. The\ngoal of this manuscript is therefore to develop an accurate on-line algorithm\nfor estimation of the fine-grained velocity field from PIV data. As the data\nconstitutes a pair of images, we employ computer vision methods to solve the\nproblem. In this work, we introduce a convolutional neural network adapted to\nthe problem, namely Volumetric Correspondence Network (VCN) which was recently\nproposed for the end-to-end optical flow estimation in computer vision. The\nnetwork is thoroughly trained and tested on a dataset containing both synthetic\nand real flow data. Experimental results are analyzed and compared to that of\nconventional methods as well as other recently introduced methods based on\nneural networks. Our analysis indicates that the proposed approach provides\nimproved efficiency also keeping accuracy on par with other state-of-the-art\nmethods in the field. We also verify through a-posteriori tests that our newly\nconstructed VCN schemes are reproducing well physically relevant statistics of\nvelocity and velocity gradients.\n"}
+{"abstract": "  We study a predictive model for explaining the apparent deviation of the muon\nanomalous magnetic moment from the Standard Model expectation. There are no new\nscalars and hence no new hierarchy puzzles beyond those associated with the\nHiggs; the only new particles at the TeV scale are vector-like singlet and\ndoublet leptons. Interestingly, this simple model provides a calculable example\nviolating the Wilsonian notion of naturalness: despite the absence of any\nsymmetries prohibiting its generation, the coefficient of the naively leading\ndimension-six operator for $(g-2)$ vanishes at one-loop. While effective field\ntheorists interpret this either as a surprising UV cancellation of power\ndivergences, or as a delicate cancellation between matching UV and calculable\nIR corrections to $(g-2)$ from parametrically separated scales, there is a\nsimple explanation in the full theory: the loop integrand is a total derivative\nof a function vanishing in both the deep UV and IR. The leading contribution to\n$(g-2)$ arises from dimension-eight operators, and thus the required masses of\nnew fermions are lower than naively expected, with a sizeable portion of\nparameter space already covered by direct searches at the LHC. The viable\nparameter space free of fine-tuning for the muon mass will be fully covered by\nfuture direct LHC searches, and {\\it all} of the parameter space can be probed\nby precision measurements at planned future lepton colliders.\n"}
+{"abstract": "  We introduce the problem of optimal congestion control in cache networks,\nwhereby \\emph{both} rate allocations and content placements are optimized\n\\emph{jointly}. We formulate this as a maximization problem with non-convex\nconstraints, and propose solving this problem via (a) a Lagrangian barrier\nalgorithm and (b) a convex relaxation. We prove different optimality guarantees\nfor each of these two algorithms; our proofs exploit the fact that the\nnon-convex constraints of our problem involve DR-submodular functions.\n"}
+{"abstract": "  In context of the Wolfram Physics Project, a certain class of abstract\nrewrite systems known as \"multiway systems\" have played an important role in\ndiscrete models of spacetime and quantum mechanics. However, as abstract\nmathematical entities, these rewrite systems are interesting in their own\nright. This paper undertakes the effort to establish computational properties\nof multiway systems. Specifically, we investigate growth rates and growth\nclasses of string-based multiway systems. After introducing the concepts of\n\"growth functions\", \"growth rates\" and \"growth classes\" to quantify a system's\nstate-space growth over \"time\" (successive steps of evolution) on different\nlevels of precision, we use them to show that multiway systems can, in a\nspecific sense, grow slower than all computable functions while never exceeding\nthe growth rate of exponential functions. In addition, we start developing a\nclassification scheme for multiway systems based on their growth class.\nFurthermore, we find that multiway growth functions are not trivially regular\nbut instead \"computationally diverse\", meaning that they are capable of\ncomputing or approximating various commonly encountered mathematical functions.\nWe discuss several implications of these properties as well as their physical\nrelevance. Apart from that, we present and exemplify methods for explicitly\nconstructing multiway systems to yield desired growth functions.\n"}
+{"abstract": "  The metastable helium line at 1083 nm can be used to probe the extended upper\natmospheres of close-in exoplanets and thus provide insight into their\natmospheric mass loss, which is likely to be significant in sculpting their\npopulation. We used an ultranarrowband filter centered on this line to observe\ntwo transits of the low-density gas giant HAT-P-18b, using the 200\" Hale\nTelescope at Palomar Observatory, and report the detection of its extended\nupper atmosphere. We constrain the excess absorption to be $0.46\\pm0.12\\%$ in\nour 0.635 nm bandpass, exceeding the transit depth from the Transiting\nExoplanet Survey Satellite (TESS) by $3.9\\sigma$. If we fit this signal with a\n1D Parker wind model, we find that it corresponds to an atmospheric mass loss\nrate between $8.3^{+2.8}_{-1.9} \\times 10^{-5}$ $M_\\mathrm{J}$/Gyr and\n$2.63^{+0.46}_{-0.64} \\times 10^{-3}$ $M_\\mathrm{J}$/Gyr for thermosphere\ntemperatures ranging from 4000 K to 13000 K, respectively. With a J magnitude\nof 10.8, this is the faintest system for which such a measurement has been made\nto date, demonstrating the effectiveness of this approach for surveying mass\nloss on a diverse sample of close-in gas giant planets.\n"}
+{"abstract": "  In recent years, artificial neural networks (ANNs) have won numerous contests\nin pattern recognition and machine learning. ANNS have been applied to problems\nranging from speech recognition to prediction of protein secondary structure,\nclassification of cancers, and gene prediction. Here, we intend to maximize the\nchances of finding the Higgs boson decays to two $\\tau$ leptons in the pseudo\ndataset using a Machine Learning technique to classify the recorded events as\nsignal or background.\n"}
+{"abstract": "  We combine infectious disease transmission and the non-pharmaceutical\nintervention (NPI) response to disease incidence into one closed model\nconsisting of two coupled delay differential equations for the incidence rate\nand the time-dependent reproduction number. The model contains three free\nparameters, the initial reproduction number, the intervention strength, and the\nresponse delay relative to the time of infection. The NPI response is modeled\nby assuming that the rate of change of the reproduction number is proportional\nto the negative deviation of the incidence rate from an intervention threshold.\nThis delay dynamical system exhibits damped oscillations in one part of the\nparameter space, and growing oscillations in another, and these are separated\nby a surface where the solution is a strictly periodic nonlinear oscillation.\nFor parameters relevant for the COVID-19 pandemic, the tipping transition from\ndamped to growing oscillations occurs for response delays of the order of one\nweek, and suggests that effective control and mitigation of successive epidemic\nwaves cannot be achieved unless NPIs are implemented in a precautionary manner,\nrather than merely as a response to the present incidence rate.\n"}
+{"abstract": "  Goal Recognition is the task of discerning the correct intended goal that an\nagent aims to achieve, given a set of possible goals, a domain model, and a\nsequence of observations as a sample of the plan being executed in the\nenvironment. Existing approaches assume that the possible goals are formalized\nas a conjunction in deterministic settings. In this paper, we develop a novel\napproach that is capable of recognizing temporally extended goals in Fully\nObservable Non-Deterministic (FOND) planning domain models, focusing on goals\non finite traces expressed in Linear Temporal Logic (LTLf) and (Pure) Past\nLinear Temporal Logic (PLTLf). We empirically evaluate our goal recognition\napproach using different LTLf and PLTLf goals over six common FOND planning\ndomain models, and show that our approach is accurate to recognize temporally\nextended goals at several levels of observability.\n"}
+{"abstract": "  Additive Manufacturing (AM) processes intended for large scale components\ndeposit large volumes of material to shorten process duration. This reduces the\nresolution of the AM process, which is typically defined by the size of the\ndeposition nozzle. If the resolution limitation is not considered when\ndesigning for Large-Scale Additive Manufacturing (LSAM), difficulties can arise\nin the manufacturing process, which may require the adaptation of the\ndeposition parameters. This work incorporates the nozzle size constraint into\nTopology Optimization (TO) in order to generate optimized designs suitable to\nthe process resolution. This article proposes and compares two methods, which\nare based on existing TO techniques that enable control of minimum and maximum\nmember size, and of minimum cavity size. The first method requires the minimum\nand maximum member size to be equal to the deposition nozzle size, thus design\nfeatures of uniform width are obtained in the optimized design. The second\nmethod defines the size of the solid members sufficiently small for the\nresulting structure to resemble a structural skeleton, which can be interpreted\nas the deposition path. Through filtering and projection techniques, the thin\nstructures are thickened according to the chosen nozzle size. Thus, a topology\ntailored to the size of the deposition nozzle is obtained along with a\ndeposition proposal. The methods are demonstrated and assessed using 2D and 3D\nbenchmark problems.\n"}
+{"abstract": "  We reobserved in the $R_C$ and $i'_{Sloan}$ bands, during the years\n2020-2021, seven Mira variables in Cassiopeia, for which historical\n$i'_{Sloan}$ light curves were available from Asiago Observatory plates taken\nin the years 1967-84. The aim was to check if any of them had undergone a\nsubstantial change in the period or in the light curve shape. Very recent\npublic data form ZTF-DR5 were also used to expand our time base window. A\nmarked color change was detected for all the stars along their variability\ncycle. The star V890 Cas showed a significant period decrease of 12\\% from 483\nto 428 days, one of the largest known to date. All the stars, save AV Cas,\nshowed a smaller variation amplitude in the recent CCD data, possibly due to a\nphotometric accuracy higher than that of the photographic plates.\n"}
+{"abstract": "  Furstenberg--Zimmer structure theory refers to the extension of the dichotomy\nbetween the compact and weakly mixing parts of a measure preserving dynamical\nsystem and the algebraic and geometric descriptions of such parts to a\nconditional setting, where such dichotomy is established relative to a factor\nand conditional analogues of those algebraic and geometric descriptions are\nsought. Although the unconditional dichotomy and the characterizations are\nknown for arbitrary systems, the relative situation is understood under certain\ncountability and separability hypotheses on the underlying groups and spaces.\nThe aim of this article is to remove these restrictions in the relative\nsituation and establish a Furstenberg--Zimmer structure theory in full\ngenerality. To achieve this generalization we had to change our perspective\nfrom systems defined on concrete probability spaces to systems defined on\nabstract probability algebras, and we had to introduce novel tools to analyze\nsystems relative to a factor. However, the change of perspective and the new\ntools lead to some simplifications in the arguments and the presentation which\nwe arrange in a self-contained manner. As an independent byproduct, we\nestablish a connection between the relative analysis of systems in ergodic\ntheory and the internal logic in certain Boolean topoi.\n"}
+{"abstract": "  Very thin elastic sheets, even at zero temperature, exhibit nonlinear elastic\nresponse by virtue of their dominant bending modes. Their behavior is even\nricher at finite temperature. Here we use molecular dynamics (MD) to study the\nvibrations of a thermally fluctuating two-dimensional elastic sheet with one\nend clamped at its zero-temperature length. We uncover a tilt phase in which\nthe sheet fluctuates about a mean plane inclined with respect to the\nhorizontal, thus breaking reflection symmetry. We determine the phase behavior\nas a function of the aspect ratio of the sheet and the temperature. We show\nthat tilt may be viewed as a type of transverse buckling instability induced by\nclamping coupled to thermal fluctuations and develop an analytic model that\npredicts the tilted and untilted regions of the phase diagram. Qualitative\nagreement is found with the MD simulations. Unusual response driven by control\nof purely geometric quantities like the aspect ratio, as opposed to external\nfields, offers a very rich playground for two-dimensional mechanical\nmetamaterials.\n"}
+{"abstract": "  We constructs a new network by superposition of hexahedron , which are\nscale-free, highly sparse,disassortative ,and maximal planar graphs. The\nnetwork degree distribution, agglomeration coefficient and degree of\ncorrelation are computed separately using the iterative method, and these\ncharacteristics are found to be very rich. The method of network characteristic\nanalysis can be applied to some actual systems, so as to study the complexity\nof real network system under the framework of complex network theory.\n"}
+{"abstract": "  In this paper we explore conditions on variable symbols with respect to Haar\nsystems, defining Calder\\'on-Zygmund type operators with respect to the dyadic\nmetrics associated to the Haar bases.We show that Petermichl's dyadic kernel\ncan be seen as a variable kernel singular integral and we extend it to dyadic\nsystems built on spaces of homogeneous type.\n"}
+{"abstract": "  We consider the problem of statistical inference for a class of\npartially-observed diffusion processes, with discretely-observed data and\nfinite-dimensional parameters. We construct unbiased estimators of the score\nfunction, i.e. the gradient of the log-likelihood function with respect to\nparameters, with no time-discretization bias. These estimators can be\nstraightforwardly employed within stochastic gradient methods to perform\nmaximum likelihood estimation or Bayesian inference. As our proposed\nmethodology only requires access to a time-discretization scheme such as the\nEuler-Maruyama method, it is applicable to a wide class of diffusion processes\nand observation models. Our approach is based on a representation of the score\nas a smoothing expectation using Girsanov theorem, and a novel adaptation of\nthe randomization schemes developed in Mcleish [2011], Rhee and Glynn [2015],\nJacob et al. [2020a]. This allows one to remove the time-discretization bias\nand burn-in bias when computing smoothing expectations using the conditional\nparticle filter of Andrieu et al. [2010]. Central to our approach is the\ndevelopment of new couplings of multiple conditional particle filters. We prove\nunder assumptions that our estimators are unbiased and have finite variance.\nThe methodology is illustrated on several challenging applications from\npopulation ecology and neuroscience.\n"}
+{"abstract": "  Chronological age of healthy people is able to be predicted accurately using\ndeep neural networks from neuroimaging data, and the predicted brain age could\nserve as a biomarker for detecting aging-related diseases. In this paper, a\nnovel 3D convolutional network, called two-stage-age-network (TSAN), is\nproposed to estimate brain age from T1-weighted MRI data. Compared with\nexisting methods, TSAN has the following improvements. First, TSAN uses a\ntwo-stage cascade network architecture, where the first-stage network estimates\na rough brain age, then the second-stage network estimates the brain age more\naccurately from the discretized brain age by the first-stage network. Second,\nto our knowledge, TSAN is the first work to apply novel ranking losses in brain\nage estimation, together with the traditional mean square error (MSE) loss.\nThird, densely connected paths are used to combine feature maps with different\nscales. The experiments with $6586$ MRIs showed that TSAN could provide\naccurate brain age estimation, yielding mean absolute error (MAE) of $2.428$\nand Pearson's correlation coefficient (PCC) of $0.985$, between the estimated\nand chronological ages. Furthermore, using the brain age gap between brain age\nand chronological age as a biomarker, Alzheimer's disease (AD) and Mild\nCognitive Impairment (MCI) can be distinguished from healthy control (HC)\nsubjects by support vector machine (SVM). Classification AUC in AD/HC and\nMCI/HC was $0.904$ and $0.823$, respectively. It showed that brain age gap is\nan effective biomarker associated with risk of dementia, and has potential for\nearly-stage dementia risk screening. The codes and trained models have been\nreleased on GitHub: https://github.com/Milan-BUAA/TSAN-brain-age-estimation.\n"}
+{"abstract": "  This paper investigates task-oriented communication for edge inference, where\na low-end edge device transmits the extracted feature vector of a local data\nsample to a powerful edge server for processing. It is critical to encode the\ndata into an informative and compact representation for low-latency inference\ngiven the limited bandwidth. We propose a learning-based communication scheme\nthat jointly optimizes feature extraction, source coding, and channel coding in\na task-oriented manner, i.e., targeting the downstream inference task rather\nthan data reconstruction. Specifically, we leverage an information bottleneck\n(IB) framework to formalize a rate-distortion tradeoff between the\ninformativeness of the encoded feature and the inference performance. As the IB\noptimization is computationally prohibitive for the high-dimensional data, we\nadopt a variational approximation, namely the variational information\nbottleneck (VIB), to build a tractable upper bound. To reduce the communication\noverhead, we leverage a sparsity-inducing distribution as the variational prior\nfor the VIB framework to sparsify the encoded feature vector. Furthermore,\nconsidering dynamic channel conditions in practical communication systems, we\npropose a variable-length feature encoding scheme based on dynamic neural\nnetworks to adaptively adjust the activated dimensions of the encoded feature\nto different channel conditions. Extensive experiments evidence that the\nproposed task-oriented communication system achieves a better rate-distortion\ntradeoff than baseline methods and significantly reduces the feature\ntransmission latency in dynamic channel conditions.\n"}
+{"abstract": "  In this manuscript, we consider a scenario in which a spin-1/2 quanton goes\nthrough a superposition of co-rotating and counter-rotating geodetic circular\npaths, which play the role of the paths of a Mach-Zehnder interferometer in a\nstationary and axisymmetric spacetime. Since the spin of the particle plays the\nrole of a quantum clock, as the quanton moves in a superposed path it gets\nentangled with the momentum (or the path), and this will cause the\ninterferometric visibility (or the internal quantum coherence) to drop, since,\nin stationary axisymmetric spacetimes there is a difference in proper time\nelapsed along the two trajectories. However, as we show here, the proper time\nof each path will couple to the corresponding local Wigner rotation, and the\neffect in the spin of the superposed particle will be a combination of both.\nBesides, we discuss a general framework to study the local Wigner rotations of\nspin-1/2 particles in general stationary axisymmetric spacetimes for circular\norbits.\n"}
+{"abstract": "  The apparent clustering in longitude of perihelion $\\varpi$ and ascending\nnode $\\Omega$ of extreme trans-Neptunian objects (ETNOs) has been attributed to\nthe gravitational effects of an unseen 5-10 Earth-mass planet in the outer\nsolar system. To investigate how selection bias may contribute to this\nclustering, we consider 14 ETNOs discovered by the Dark Energy Survey, the\nOuter Solar System Origins Survey, and the survey of Sheppard and Trujillo.\nUsing each survey's published pointing history, depth, and TNO tracking\nselections, we calculate the joint probability that these objects are\nconsistent with an underlying parent population with uniform distributions in\n$\\varpi$ and $\\Omega$. We find that the mean scaled longitude of perihelion and\norbital poles of the detected ETNOs are consistent with a uniform population at\na level between $17\\%$ and $94\\%$, and thus conclude that this sample provides\nno evidence for angular clustering.\n"}
+{"abstract": "  This paper presents DLL, a fast direct map-based localization technique using\n3D LIDAR for its application to aerial robots. DLL implements a point cloud to\nmap registration based on non-linear optimization of the distance of the points\nand the map, thus not requiring features, neither point correspondences. Given\nan initial pose, the method is able to track the pose of the robot by refining\nthe predicted pose from odometry. Through benchmarks using real datasets and\nsimulations, we show how the method performs much better than Monte-Carlo\nlocalization methods and achieves comparable precision to other\noptimization-based approaches but running one order of magnitude faster. The\nmethod is also robust under odometric errors. The approach has been implemented\nunder the Robot Operating System (ROS), and it is publicly available.\n"}
+{"abstract": "  Considering an example of the long-range Kitaev model, we are looking for a\ncorrelation length in a model with long range interactions whose correlation\nfunctions away from a critical point have power-law tails instead of the usual\nexponential decay. It turns out that quasiparticle spectrum depends on a\ndistance from the critical point in a way that allows to identify the standard\ncorrelation length exponent, $\\nu$. The exponent implicitly defines a\ncorrelation length $\\xi$ that diverges when the critical point is approached.\nWe show that the correlation length manifests itself also in the correlation\nfunction but not in its exponential tail because there is none. Instead $\\xi$\nis a distance that marks a crossover between two different algebraic decays\nwith different exponents. At distances shorter than $\\xi$ the correlator decays\nwith the same power law as at the critical point while at distances longer than\n$\\xi$ it decays faster, with a steeper power law. For this correlator it is\npossible to formulate the usual scaling hypothesis with $\\xi$ playing the role\nof the scaling distance. The correlation length also leaves its mark on the\nsubleading anomalous fermionic correlator but, interestingly, there is a regime\nof long range interactions where its short distance critical power-law decay is\nsteeper than its long distance power law tail.\n"}
+{"abstract": "  We present a numerical study on a disordered artificial spin-ice system which\ninterpolates between the long-range ordered square ice and the fully degenerate\nshakti ice. Starting from the square-ice geometry, disorder is implemented by\nadding vertical/horizontal magnetic islands to the center of some randomly\nchosen square plaquettes of the array, at different densities. When no island\nis added we have ordered square ice. When all square plaquettes have been\nmodified we obtain shakti ice, which is disordered yet in a topological phase\ncorresponding to the Rys F-model. In between, geometrical frustration due to\nthese additional center spins disrupts the long-range Ising order of\nsquare-ice, giving rise to a spin-glass regime at low temperatures. The\nartificial spin system proposed in our work provides an experimental platform\nto study the interplay between quenched disorder and geometrical frustration.\n"}
+{"abstract": "  Low power spintronic devices based on the propagation of pure magnonic spin\ncurrents in antiferromagnetic insulator materials offer several distinct\nadvantages over ferromagnetic components including higher frequency magnons and\na stability against disturbing external magnetic fields. In this work, we make\nuse of the insulating antiferromagnetic phase of iron oxide, the mineral\nhematite $\\alpha$-Fe$_2$O$_3$ to investigate the long distance transport of\nthermally generated magnonic spin currents. We report on the excitation of\nmagnons generated by the spin Seebeck effect, transported both parallel and\nperpendicular to the antiferromagnetic easy-axis under an applied magnetic\nfield. Making use of an atomistic hematite toy model, we calculate the\ntransport characteristics from the deviation of the antiferromagnetic ordering\nfrom equilibrium under an applied field. We resolve the role of the magnetic\norder parameters in the transport, and experimentally we find significant\nthermal spin transport without the need for a net magnetization.\n"}
+{"abstract": "  We study the finite convergence of iterative methods for solving convex\nfeasibility problems. Our key assumptions are that the interior of the solution\nset is nonempty and that certain overrelaxation parameters converge to zero,\nbut with a rate slower than any geometric sequence. Unlike other works in this\narea, which require divergent series of overrelaxations, our approach allows us\nto consider some summable series. By employing quasi-Fej\\'{e}rian analysis in\nthe latter case, we obtain additional asymptotic convergence guarantees, even\nwhen the interior of the solution set is empty.\n"}
+{"abstract": "  A non-equilibrium model for laser-induced plasmas is used to describe how\nnano-second temporal mode-beating affects plasma kernel formation and growth in\nquiescent air. The chemically reactive Navier-Stokes equations describe the\nhydrodynamics, and non-equilibrium effects are modeled based on a\ntwo-temperature model. Inverse Bremsstrahlung and multiphoton ionization are\nself-consistently taken into account via a coupled solution of the equations\ngoverning plasma dynamics and beam propagation and attenuation (i.e., Radiative\nTransfer Equation). This strategy, despite the additional challenges it may\nbring, allows to minimize empiricism and enables for more accurate simulations\nsince it does not require an artificial plasma seed to trigger breakdown. The\nbenefits of this methodology are demonstrated by the good agreement between the\npredicted and the experimental plasma boundary evolution and absorbed energy.\nThe same goes for the periodic plasma kernel structures which, as suggested by\nexperiments and confirmed by the simulations discussed here, are linked to the\nmodulating frequency.\n"}
+{"abstract": "  We introduce a neural relighting algorithm for captured indoors scenes, that\nallows interactive free-viewpoint navigation. Our method allows illumination to\nbe changed synthetically, while coherently rendering cast shadows and complex\nglossy materials. We start with multiple images of the scene and a 3D mesh\nobtained by multi-view stereo (MVS) reconstruction. We assume that lighting is\nwell-explained as the sum of a view-independent diffuse component and a\nview-dependent glossy term concentrated around the mirror reflection direction.\nWe design a convolutional network around input feature maps that facilitate\nlearning of an implicit representation of scene materials and illumination,\nenabling both relighting and free-viewpoint navigation. We generate these input\nmaps by exploiting the best elements of both image-based and physically-based\nrendering. We sample the input views to estimate diffuse scene irradiance, and\ncompute the new illumination caused by user-specified light sources using path\ntracing. To facilitate the network's understanding of materials and synthesize\nplausible glossy reflections, we reproject the views and compute mirror images.\nWe train the network on a synthetic dataset where each scene is also\nreconstructed with MVS. We show results of our algorithm relighting real indoor\nscenes and performing free-viewpoint navigation with complex and realistic\nglossy reflections, which so far remained out of reach for view-synthesis\ntechniques.\n"}
+{"abstract": "  The temperature dependence of quantum Hall conductivities is studied in the\ncontext of the AdS/CMT paradigm using a model with a bulk theory consisting of\n(3+1)-dimensional Einstein-Maxwell action coupled to a dilaton and an axion,\nwith a negative cosmological constant. We consider a solution which has a\nLifshitz like geometry with a dyonic black-brane in the bulk. There is an\n$Sl(2,R)$ action in the bulk corresponding to electromagnetic duality, which\nmaps between classical solutions, and is broken to $Sl(2,Z)$ by Dirac\nquantisation of dyons. This bulk $Sl(2,Z)$ action translates to an action of\nthe modular group on the 2-dimensional transverse conductivities. The\ntemperature dependence of the infra-red conductivities is then linked to\nmodular forms via gradient flow and the resulting flow diagrams show remarkable\nagreement with existing experimental data on the temperature flow of both\nintegral and fractional quantum Hall conductivities.\n"}
+{"abstract": "  Accurate characterisation of small defects remains a challenge in\nnon-destructive testing (NDT). In this paper, a principle-component\nparametric-manifold mapping approach is applied to single-frequency\neddy-current defect characterisation problems for surface breaking defects in a\nplanar half-space. A broad 1-8 MHz frequency-range FE-circuit model &\ncalibration approach is developed & validated to simulate eddy-current scans of\nsurface-breaking notch defects. This model is used to generate parametric\ndefect databases for surface breaking defects in an aluminium planar half-space\nand defect characterisation of experimental measurements performed.\nParametric-manifold mapping was conducted in N-dimensional principle component\nspace, reducing the dimensionality of the characterisation problem. In a study\ncharacterising slot depth, the model & characterisation approach is shown to\naccurately invert the depth with greater accuracy than a simple amplitude\ninversion method with normalised percentage characterisation errors of 38% and\n17% respectively measured at 2.0 MHz across 5 slot depths between 0.26 - 2.15\nmm. The approach is used to characterise the depth of a sloped slot\ndemonstrating good accuracy up to ~2.0 mm in depth over a broad range of\nsub-resonance frequencies, indicating applications in geometric feature\ninversion. Finally the technique is applied to finite rectangular notch defects\nof surface extents smaller than the diameter of the inspection coil\n(sub-aperture) over a range of frequencies. The results highlight the\nlimitations in characterising these defects and indicate how the inherent\ninstabilities in resonance can severely limit characterisation at these\nfrequencies.\n"}
+{"abstract": "  In this work, we estimate how much bulk viscosity driven by Urca processes is\nlikely to affect the gravitational wave signal of a neutron star coalescence.\nIn the late inspiral, we show that bulk viscosity affects the binding energy at\nfourth post-Newtonian (PN) order. Even though this effect is enhanced by the\nsquare of the gravitational compactness, the coefficient of bulk viscosity is\nlikely too small to lead to observable effects in the waveform during the late\ninspiral, when only considering the orbital motion itself. In the post-merger,\nhowever, the characteristic time-scales and spatial scales are different,\npotentially leading to the opposite conclusion. We post-process data from a\nstate-of-the-art equal-mass binary neutron star merger simulation to estimate\nthe effects of bulk viscosity (which was not included in the simulation\nitself). In that scenario, we find that bulk viscosity can reach high values in\nregions of the merger. We compute several estimates of how much it might\ndirectly affect the global dynamics of the considered merger scenario, and find\nthat it could become significant. Even larger effects could arise in different\nmerger scenarios or in simulations that include non-linear effects. This\nassessment is reinforced by a quantitative comparison with relativistic\nheavy-ion collisions where such effects have been explored extensively.\n"}
+{"abstract": "  Large-scale trademark retrieval is an important content-based image retrieval\ntask. A recent study shows that off-the-shelf deep features aggregated with\nRegional-Maximum Activation of Convolutions (R-MAC) achieve state-of-the-art\nresults. However, R-MAC suffers in the presence of background clutter/trivial\nregions and scale variance, and discards important spatial information. We\nintroduce three simple but effective modifications to R-MAC to overcome these\ndrawbacks. First, we propose the use of both sum and max pooling to minimise\nthe loss of spatial information. We also employ domain-specific unsupervised\nsoft-attention to eliminate background clutter and unimportant regions.\nFinally, we add multi-resolution inputs to enhance the scale-invariance of\nR-MAC. We evaluate these three modifications on the million-scale METU dataset.\nOur results show that all modifications bring non-trivial improvements, and\nsurpass previous state-of-the-art results.\n"}
+{"abstract": "  This is the first one in a series of papers classifying the factorizations of\nalmost simple groups with nonsolvable factors. In this paper we deal with\nalmost simple linear groups.\n"}
+{"abstract": "  In the crowded environment of bio-inspired population-based metaheuristics,\nthe Salp Swarm Optimization (SSO) algorithm recently appeared and immediately\ngained a lot of momentum. Inspired by the peculiar spatial arrangement of salp\ncolonies, which are displaced in long chains following a leader, this algorithm\nseems to provide an interesting optimization performance. However, the original\nwork was characterized by some conceptual and mathematical flaws, which\ninfluenced all ensuing papers on the subject. In this manuscript, we perform a\ncritical review of SSO, highlighting all the issues present in the literature\nand their negative effects on the optimization process carried out by this\nalgorithm. We also propose a mathematically correct version of SSO, named\nAmended Salp Swarm Optimizer (ASSO) that fixes all the discussed problems. We\nbenchmarked the performance of ASSO on a set of tailored experiments, showing\nthat it is able to achieve better results than the original SSO. Finally, we\nperformed an extensive study aimed at understanding whether SSO and its\nvariants provide advantages compared to other metaheuristics. The experimental\nresults, where SSO cannot outperform simple well-known metaheuristics, suggest\nthat the scientific community can safely abandon SSO.\n"}
+{"abstract": "  Neural network architectures in natural language processing often use\nattention mechanisms to produce probability distributions over input token\nrepresentations. Attention has empirically been demonstrated to improve\nperformance in various tasks, while its weights have been extensively used as\nexplanations for model predictions. Recent studies (Jain and Wallace, 2019;\nSerrano and Smith, 2019; Wiegreffe and Pinter, 2019) have showed that it cannot\ngenerally be considered as a faithful explanation (Jacovi and Goldberg, 2020)\nacross encoders and tasks. In this paper, we seek to improve the faithfulness\nof attention-based explanations for text classification. We achieve this by\nproposing a new family of Task-Scaling (TaSc) mechanisms that learn\ntask-specific non-contextualised information to scale the original attention\nweights. Evaluation tests for explanation faithfulness, show that the three\nproposed variants of TaSc improve attention-based explanations across two\nattention mechanisms, five encoders and five text classification datasets\nwithout sacrificing predictive performance. Finally, we demonstrate that TaSc\nconsistently provides more faithful attention-based explanations compared to\nthree widely-used interpretability techniques.\n"}
+{"abstract": "  Phishing attacks have evolved and increased over time and, for this reason,\nthe task of distinguishing between a legitimate site and a phishing site is\nmore and more difficult, fooling even the most expert users. The main proposals\nfocused on addressing this problem can be divided into four approaches:\nList-based, URL based, content-based, and hybrid. In this state of the art, the\nmost recent techniques using web content-based and hybrid approaches for\nPhishing Detection are reviewed and compared.\n"}
+{"abstract": "  Context: The energy-limited (EL) atmospheric escape approach is used to\nestimate mass-loss rates for a broad range of planets that host\nhydrogen-dominated atmospheres as well as for performing atmospheric evolution\ncalculations. Aims: We aim to study the applicability range of the EL\napproximation. Methods: We revise the EL formalism and its assumptions. We also\ncompare its results with those of hydrodynamic simulations, employing a grid\ncovering planets with masses, radii, and equilibrium temperatures ranging\nbetween 1 $M_{\\oplus}$ and 39 $M_{\\oplus}$, 1 $R_{\\oplus}$ and 10 $R_{\\oplus}$,\nand 300 and 2000 K, respectively. Results: Within the grid boundaries, we find\nthat the EL approximation gives a correct order of magnitude estimate for\nmass-loss rates for about 76% of the planets, but there can be departures from\nhydrodynamic simulations by up to three orders of magnitude in individual\ncases. Furthermore, we find that planets for which the mass-loss rates are\ncorrectly estimated by the EL approximation to within one order of magnitude\nhave intermediate gravitational potentials as well as low-to-intermediate\nequilibrium temperatures and irradiation fluxes of extreme ultraviolet and\nX-ray radiation. However, for planets with low or high gravitational\npotentials, or high equilibrium temperatures and irradiation fluxes, the\napproximation fails in most cases. Conclusions: The EL approximation should not\nbe used for planetary evolution calculations that require computing mass-loss\nrates for planets that cover a broad parameter space. In this case, it is very\nlikely that the EL approximation would at times return mass-loss rates of up to\nseveral orders of magnitude above or below those predicted by hydrodynamic\nsimulations. For planetary atmospheric evolution calculations, interpolation\nroutines or approximations based on grids of hydrodynamic models should be used\ninstead.\n"}
+{"abstract": "  Recently, much work has been done to investigate Galois module structure of\nlocal field extensions, particularly through the use of Galois scaffolds. Given\na totally ramified $p$-extension of local fields $L/K$, a Galois Scaffold gives\nus a $K$-basis for $K[G]$ whose effect on the valuation of elements of $L$ is\neasy to determine. In 2013, N.P. Byott and G.G. Elder gave sufficient\nconditions for the existence of Galois scaffolds for cyclic extensions of\ndegree $p^2$ in characteristic $p$. We take their work and adapt it to cyclic\nextensions of degree $p^2$ in characteristic $0$.\n"}
+{"abstract": "  The discoveries of high-temperature superconductivity in H3S and LaH10 have\nexcited the search for superconductivity in compressed hydrides. In contrast to\nrapidly expanding theoretical studies, high-pressure experiments on hydride\nsuperconductors are expensive and technically challenging. Here we\nexperimentally discover superconductivity in two new phases,Fm-3m-CeH10 (SC-I\nphase) and P63/mmc-CeH9 (SC-II phase) at pressures that are much lower (<100\nGPa) than those needed to stabilize other polyhydride superconductors.\nSuperconductivity was evidenced by a sharp drop of the electrical resistance to\nzero, and by the decrease of the critical temperature in deuterated samples and\nin an external magnetic field. SC-I has Tc=115 K at 95 GPa, showing expected\ndecrease on further compression due to decrease of the electron-phonon coupling\n(EPC) coefficient {\\lambda} (from 2.0 at 100 GPa to 0.8 at 200 GPa). SC-II has\nTc = 57 K at 88 GPa, rapidly increasing to a maximum Tc ~100 K at 130 GPa, and\nthen decreasing on further compression. This maximum of Tc is due to a maximum\nof {\\lambda} at the phase transition from P63/mmc-CeH9 into a symmetry-broken\nmodification C2/c-CeH9. The pressure-temperature conditions of synthesis affect\nthe actual hydrogen content, and the actual value of Tc. Anomalously low\npressures of stability of cerium superhydrides make them appealing for studies\nof superhydrides and for designing new superhydrides with even lower pressures\nof stability.\n"}
+{"abstract": "  We consider a linear non-local heat equation in a bounded domain\n$\\Omega\\subset\\mathbb{R}^d$, $d\\geq 1$, with Dirichlet boundary conditions,\nwhere the non-locality is given by the presence of an integral kernel.\nMotivated by several applications in biological systems, in the present paper\nwe study some optimal control problems from a theoretical and numerical point\nof view. In particular, we will employ the classical low-regret approach of\nJ.-L. Lions for treating the problem of incomplete data and provide a simple\ncomputational implementation of the method. The effectiveness of the results\nare illustrated by several examples.\n"}
+{"abstract": "  Gardner conjectured that if two bounded measurable sets $A,B \\subset\n\\mathbb{R}^n$ are equidecomposable by a set of isometries $\\Gamma$ generating\nan amenable group then $A$ and $B$ admit a measurable equidecomposition by all\nisometries. Cie\\'sla and Sabok asked if there is a measurable equidecomposition\nusing isometries only in the group generated by $\\Gamma$. We answer this\nquestion negatively.\n"}
+{"abstract": "  Given a Riemannian manifold $M,$ and an open interval $I\\subset\\mathbb{R},$\nwe characterize nontrivial totally umbilical hypersurfaces of the product\n$M\\times I$ -- as well as of warped products $I\\times_\\omega M$ -- as those\nwhich are local graphs built on isoparametric families of totally umbilical\nhypersurfaces of $M.$ By means of this characterization, we fully extend to\n$\\mathbb{S}^n\\times\\mathbb{R}$ and $\\mathbb{H}^n\\times\\mathbb{R}$ the results\nby Souam and Toubiana on the classification of totally umbilical hypersurfaces\nof $\\mathbb{S}^2\\times\\mathbb{R}$ and $\\mathbb{H}^2\\times\\mathbb{R}.$ It is\nalso shown that an analogous classification holds for arbitrary warped products\n$I\\times_\\omega\\mathbb{S}^n$ and $I\\times_\\omega\\mathbb{H}^n.$\n"}
+{"abstract": "  Zero-shot learning, the task of learning to recognize new classes not seen\nduring training, has received considerable attention in the case of 2D image\nclassification. However, despite the increasing ubiquity of 3D sensors, the\ncorresponding 3D point cloud classification problem has not been meaningfully\nexplored and introduces new challenges. In this paper, we identify some of the\nchallenges and apply 2D Zero-Shot Learning (ZSL) methods in the 3D domain to\nanalyze the performance of existing models. Then, we propose a novel approach\nto address the issues specific to 3D ZSL. We first present an inductive ZSL\nprocess and then extend it to the transductive ZSL and Generalized ZSL (GZSL)\nsettings for 3D point cloud classification. To this end, a novel loss function\nis developed that simultaneously aligns seen semantics with point cloud\nfeatures and takes advantage of unlabeled test data to address some known\nissues (e.g., the problems of domain adaptation, hubness, and data bias). While\ndesigned for the particularities of 3D point cloud classification, the method\nis shown to also be applicable to the more common use-case of 2D image\nclassification. An extensive set of experiments is carried out, establishing\nstate-of-the-art for ZSL and GZSL on synthetic (ModelNet40, ModelNet10, McGill)\nand real (ScanObjectNN) 3D point cloud datasets.\n"}
+{"abstract": "  We prove that it is consistent that Club Stationary Reflection and the\nSpecial Aronszajn Tree Property simultaneously hold on $\\omega_2$, thereby\ncontributing to the study of the tension between compactness and incompactness\nin set theory. The poset which produces the final model follows the collapse of\na weakly compact cardinal first with an iteration of club adding (with\nanticipation) and second with an iteration specializing Aronszajn trees.\n  In the first part of the paper, we prove a general theorem about specializing\nAronszajn trees after forcing with what we call $\\mathcal{F}_{WC}$-Strongly\nProper posets. This type of poset, of which the Levy collapse is a degenerate\nexample, uses systems of exact residue functions to create many strongly\ngeneric conditions. We prove a new result about stationary set preservation by\nquotients of this kind of poset; as a corollary, we show that the original\nLaver-Shelah model satisfies a strong stationary reflection principle, though\nit fails to satisfy the full Club Stationary Reflection. In the second part, we\nshow that the composition of collapsing and club adding (with anticipation) is\nan $\\mathcal{F}_{WC}$-Strongly Proper poset. After proving a new result about\nAronszajn tree preservation, we show how to obtain the final model.\n"}
+{"abstract": "  In this article, we prove Talenti's comparison theorem for Poisson equation\non complete noncompact Riemannian manifold with nonnegative Ricci curvature.\nFurthermore, we obtain the Faber-Krahn inequality for the first eigenvalue of\nDirichlet Laplacian, $L^1$- and $L^\\infty$-moment spectrum, especially\nSaint-Venant theorem for torsional rigidity and a reverse H\\\"older inequality\nfor eigenfunctions of Dirichlet Laplacian.\n"}
+{"abstract": "  IoT systems have been facing increasingly sophisticated technical problems\ndue to the growing complexity of these systems and their fast deployment\npractices. Consequently, IoT managers have to judiciously detect failures\n(anomalies) in order to reduce their cyber risk and operational cost. While\nthere is a rich literature on anomaly detection in many IoT-based systems,\nthere is no existing work that documents the use of ML models for anomaly\ndetection in digital agriculture and in smart manufacturing systems. These two\napplication domains pose certain salient technical challenges. In agriculture\nthe data is often sparse, due to the vast areas of farms and the requirement to\nkeep the cost of monitoring low. Second, in both domains, there are multiple\ntypes of sensors with varying capabilities and costs. The sensor data\ncharacteristics change with the operating point of the environment or machines,\nsuch as, the RPM of the motor. The inferencing and the anomaly detection\nprocesses therefore have to be calibrated for the operating point.\n  In this paper, we analyze data from sensors deployed in an agricultural farm\nwith data from seven different kinds of sensors, and from an advanced\nmanufacturing testbed with vibration sensors. We evaluate the performance of\nARIMA and LSTM models for predicting the time series of sensor data. Then,\nconsidering the sparse data from one kind of sensor, we perform transfer\nlearning from a high data rate sensor. We then perform anomaly detection using\nthe predicted sensor data. Taken together, we show how in these two application\ndomains, predictive failure classification can be achieved, thus paving the way\nfor predictive maintenance.\n"}
+{"abstract": "  Ultraperipheral collisions of high energy protons are a source of\napproximately real photons colliding with each other. Photon fusion can result\nin production of yet unknown charged particles in very clean events. The\ncleanliness of such an event is due to the requirement that the protons survive\nduring the collision. Finite sizes of the protons reduce the probability of\nsuch outcome compared to point-like particles. We calculate the survival\nfactors and cross sections for the production of heavy charged particles at the\nLarge Hadron Collider.\n"}
+{"abstract": "  Animals are capable of extreme agility, yet understanding their complex\ndynamics, which have ecological, biomechanical and evolutionary implications,\nremains challenging. Being able to study this incredible agility will be\ncritical for the development of next-generation autonomous legged robots. In\nparticular, the cheetah (acinonyx jubatus) is supremely fast and maneuverable,\nyet quantifying its whole-body 3D kinematic data during locomotion in the wild\nremains a challenge, even with new deep learning-based methods. In this work we\npresent an extensive dataset of free-running cheetahs in the wild, called\nAcinoSet, that contains 119,490 frames of multi-view synchronized high-speed\nvideo footage, camera calibration files and 7,588 human-annotated frames. We\nutilize markerless animal pose estimation to provide 2D keypoints. Then, we use\nthree methods that serve as strong baselines for 3D pose estimation tool\ndevelopment: traditional sparse bundle adjustment, an Extended Kalman Filter,\nand a trajectory optimization-based method we call Full Trajectory Estimation.\nThe resulting 3D trajectories, human-checked 3D ground truth, and an\ninteractive tool to inspect the data is also provided. We believe this dataset\nwill be useful for a diverse range of fields such as ecology, neuroscience,\nrobotics, biomechanics as well as computer vision.\n"}
+{"abstract": "  We consider gauged U(1) extensions of the standard model of particle physics\nwith three right-handed sterile neutrinos and a singlet scalar. The neutrinos\nobtain mass via the type I seesaw mechanism. We compute the one loop\ncorrections to the elements of the tree level mass matrix of the light\nneutrinos and show explicitly the cancellation of the gauge dependent terms. We\npresent a general formula for the gauge independent, finite one-loop\ncorrections for arbitrary number new U(1) groups, new complex scalars and\nsterile neutrinos. We estimate the size of the corrections relative to the tree\nlevel mass matrix in a particular extension, the super-weak model.\n"}
+{"abstract": "  Reliably assessing the error in an estimated vehicle position is integral for\nensuring the vehicle's safety in urban environments. Many existing approaches\nuse GNSS measurements to characterize protection levels (PLs) as probabilistic\nupper bounds on the position error. However, GNSS signals might be reflected or\nblocked in urban environments, and thus additional sensor modalities need to be\nconsidered to determine PLs. In this paper, we propose a novel approach for\ncomputing PLs by matching camera image measurements to a LiDAR-based 3D map of\nthe environment. We specify a Gaussian mixture model probability distribution\nof position error using deep neural network-based data-driven models and\nstatistical outlier weighting techniques. From the probability distribution, we\ncompute the PLs by evaluating the position error bound using numerical\nline-search methods. Through experimental validation with real-world data, we\ndemonstrate that the PLs computed from our method are reliable bounds on the\nposition error in urban environments.\n"}
+{"abstract": "  \\begin{abstract} By following the paradigm of the global quantisation,\ninstead of the analysis under changes of coordinates, in this work we establish\na global analysis for the explicit computation of the Dixmier trace and the\nWodzicki residue of (elliptic and subelliptic) pseudo-differential operators on\ncompact Lie groups. The regularised determinant for the Dixmier trace is also\ncomputed. We obtain these formulae in terms of the global symbol of the\ncorresponding operators. In particular, our approach links the Dixmier trace\nand Wodzicki residue to the representation theory of the group. Although we\nstart by analysing the case of compact Lie groups, we also compute the Dixmier\ntrace and its regularised determinant on arbitrary closed manifolds $M$, for\nthe class of invariant pseudo-differential operators in terms of their\nmatrix-valued symbols. This analysis includes e.g. the family of positive and\nelliptic pseudo-differential operators on $M$.\n"}
+{"abstract": "  Recently, a universal formula for the Nicolai map in terms of a coupling flow\nfunctional differential operator was found. We present the full perturbative\nexpansion of this operator in Yang-Mills theories where supersymmetry is\nrealized off-shell. Given this expansion, we develop a straightforward method\nto compute the explicit Nicolai map to any order in the gauge coupling. Our\nwork extends the previously known construction method from the Landau gauge to\narbitrary gauges and from the gauge hypersurface to the full gauge-field\nconfiguration space. As an example, we present the map in the axial gauge to\nthe second order.\n"}
+{"abstract": "  In this paper, we investigate dynamic resource scheduling (i.e., joint user,\nsubchannel, and power scheduling) for downlink multi-channel non-orthogonal\nmultiple access (MC-NOMA) systems over time-varying fading channels.\nSpecifically, we address the weighted average sum rate maximization problem\nwith quality-of-service (QoS) constraints. In particular, to facilitate fast\nresource scheduling, we focus on developing a very low-complexity algorithm. To\nthis end, by leveraging Lagrangian duality and the stochastic optimization\ntheory, we first develop an opportunistic MC-NOMA scheduling algorithm whereby\nthe original problem is decomposed into a series of subproblems, one for each\ntime slot. Accordingly, resource scheduling works in an online manner by\nsolving one subproblem per time slot, making it more applicable to practical\nsystems. Then, we further develop a heuristic joint subchannel assignment and\npower allocation (Joint-SAPA) algorithm with very low computational complexity,\ncalled Joint-SAPA-LCC, that solves each subproblem. Finally, through\nsimulation, we show that our Joint-SAPA-LCC algorithm provides good performance\ncomparable to the existing Joint-SAPA algorithms despite requiring much lower\ncomputational complexity. We also demonstrate that our opportunistic MC-NOMA\nscheduling algorithm in which the Joint-SAPA-LCC algorithm is embedded works\nwell while satisfying given QoS requirements.\n"}
+{"abstract": "  The charmonium-like exotic states $Y(4230)$ and the less known $Y(4320)$,\nproduced in $e^+e^-$ collisions, are sources of positive parity exotic hadrons\nin association with photons or pseudoscalar mesons. We analyze the radiative\nand pion decay channels in the compact tetraquark scheme, with a method that\nproves to work equally well in the most studied $D^*\\to \\gamma/\\pi+D$ decays.\nThe decay of the vector $Y$ into a pion and a $Z_c$ state requires a flip of\ncharge conjugation and isospin that is described appropriately in the formalism\nused. Rates however are found to depend on the fifth power of pion momentum\nwhich would make the final states $\\pi Z_c(4020)$ strongly suppressed with\nrespect to $\\pi Z_c(3900)$. The agreement with BES III data would be improved\nconsidering the $\\pi Z_c(4020)$ events to be fed by the tail of the $Y(4320)$\nresonance under the $Y(4230)$. These results should renovate the interest in\nfurther clarifying the emerging experimental picture in this mass region.\n"}
+{"abstract": "  We propose a fully convolutional multi-person pose estimation framework using\ndynamic instance-aware convolutions, termed FCPose. Different from existing\nmethods, which often require ROI (Region of Interest) operations and/or\ngrouping post-processing, FCPose eliminates the ROIs and grouping\npost-processing with dynamic instance-aware keypoint estimation heads. The\ndynamic keypoint heads are conditioned on each instance (person), and can\nencode the instance concept in the dynamically-generated weights of their\nfilters. Moreover, with the strong representation capacity of dynamic\nconvolutions, the keypoint heads in FCPose are designed to be very compact,\nresulting in fast inference and making FCPose have almost constant inference\ntime regardless of the number of persons in the image. For example, on the COCO\ndataset, a real-time version of FCPose using the DLA-34 backbone infers about\n4.5x faster than Mask R-CNN (ResNet-101) (41.67 FPS vs. 9.26FPS) while\nachieving improved performance. FCPose also offers better speed/accuracy\ntrade-off than other state-of-the-art methods. Our experiment results show that\nFCPose is a simple yet effective multi-person pose estimation framework. Code\nis available at: https://git.io/AdelaiDet\n"}
+{"abstract": "  We discuss an approach to the computer assisted proof of the existence of\nbranches of stationary and periodic solutions for dissipative PDEs, using the\nBrussellator system with diffusion and Dirichlet boundary conditions as an\nexample, We also consider the case where the branch of periodic solutions\nemanates from a branch of stationary solutions through a Hopf bifurcation.\n"}
+{"abstract": "  This article presents a randomized matrix-free method for approximating the\ntrace of $f({\\bf A})$, where ${\\bf A}$ is a large symmetric matrix and $f$ is a\nfunction analytic in a closed interval containing the eigenvalues of ${\\bf A}$.\nOur method uses a combination of stochastic trace estimation (i.e.,\nHutchinson's method), Chebyshev approximation, and multilevel Monte Carlo\ntechniques. We establish general bounds on the approximation error of this\nmethod by extending an existing error bound for Hutchinson's method to\nmultilevel trace estimators. Numerical experiments are conducted for common\napplications such as estimating the log-determinant, nuclear norm, and Estrada\nindex, and triangle counting in graphs. We find that using multilevel\ntechniques can substantially reduce the variance of existing single-level\nestimators.\n"}
+{"abstract": "  Computational Social Science (CSS), aiming at utilizing computational methods\nto address social science problems, is a recent emerging and fast-developing\nfield. The study of CSS is data-driven and significantly benefits from the\navailability of online user-generated contents and social networks, which\ncontain rich text and network data for investigation. However, these\nlarge-scale and multi-modal data also present researchers with a great\nchallenge: how to represent data effectively to mine the meanings we want in\nCSS? To explore the answer, we give a thorough review of data representations\nin CSS for both text and network. Specifically, we summarize existing\nrepresentations into two schemes, namely symbol-based and embedding-based\nrepresentations, and introduce a series of typical methods for each scheme.\nAfterwards, we present the applications of the above representations based on\nthe investigation of more than 400 research articles from 6 top venues involved\nwith CSS. From the statistics of these applications, we unearth the strength of\neach kind of representations and discover the tendency that embedding-based\nrepresentations are emerging and obtaining increasing attention over the last\ndecade. Finally, we discuss several key challenges and open issues for future\ndirections. This survey aims to provide a deeper understanding and more\nadvisable applications of data representations for CSS researchers.\n"}
+{"abstract": "  In this paper, we develop a safe decision-making method for self-driving cars\nin a multi-lane, single-agent setting. The proposed approach utilizes deep\nreinforcement learning (RL) to achieve a high-level policy for safe tactical\ndecision-making. We address two major challenges that arise solely in\nautonomous navigation. First, the proposed algorithm ensures that collisions\nnever happen, and therefore accelerate the learning process. Second, the\nproposed algorithm takes into account the unobservable states in the\nenvironment. These states appear mainly due to the unpredictable behavior of\nother agents, such as cars, and pedestrians, and make the Markov Decision\nProcess (MDP) problematic when dealing with autonomous navigation. Simulations\nfrom a well-known self-driving car simulator demonstrate the applicability of\nthe proposed method\n"}
+{"abstract": "  We present a simple lithographic method for fabrication of microresonator\ndevices at the optical fiber surface. First, we undress the predetermined\nsurface areas of a fiber segment from the polymer coating with a focused CO2\nlaser beam. Next, using the remaining coating as a mask, we etch the fiber in a\nhydrofluoric acid solution. Finally, we completely undress the fiber segment\nfrom coating to create a chain of silica bottle microresonators with nanoscale\nradius variation (SNAP microresonators). We demonstrate the developed method by\nfabrication of a chain of five 1 mm long and 30 nm high microresonators at the\nsurface of a 125 micron diameter optical fiber and a single 0.5 mm long and 291\nnm high microresonator at the surface of a 38 micron diameter fiber. As another\napplication, we fabricate a rectangular 5 mm long SNAP microresonator at the\nsurface of a 38 micron diameter fiber and investigate its performance as a\nminiature delay line. The propagation of a 100 ps pulse with 1 ns delay, 0.035c\nvelocity, and negligible dispersion is demonstrated. In contrast to the\npreviously developed approaches in SNAP technology, the developed method allows\nthe introduction of much larger fiber radius variation ranging from nanoscale\nto microscale.\n"}
+{"abstract": "  This study follows many classical approaches to multi-object tracking (MOT)\nthat model the problem using dynamic graphical data structures, and adapts this\nformulation to make it amenable to modern neural networks. Our main\ncontributions in this work are the creation of a framework based on dynamic\nundirected graphs that represent the data association problem over multiple\ntimesteps, and a message passing graph neural network (MPNN) that operates on\nthese graphs to produce the desired likelihood for every association therein.\nWe also provide solutions and propositions for the computational problems that\nneed to be addressed to create a memory-efficient, real-time, online algorithm\nthat can reason over multiple timesteps, correct previous mistakes, update\nbeliefs, and handle missed/false detections. To demonstrate the efficacy of our\napproach, we only use the 2D box location and object category ID to construct\nthe descriptor for each object instance. Despite this, our model performs on\npar with state-of-the-art approaches that make use of additional sensors, as\nwell as multiple hand-crafted and/or learned features. This illustrates that\ngiven the right problem formulation and model design, raw bounding boxes (and\ntheir kinematics) from any off-the-shelf detector are sufficient to achieve\ncompetitive tracking results on challenging MOT benchmarks.\n"}
+{"abstract": "  The model-based investing using financial factors is evolving as a principal\nmethod for quantitative investment. The main challenge lies in the selection of\neffective factors towards excess market returns. Existing approaches, either\nhand-picking factors or applying feature selection algorithms, do not\norchestrate both human knowledge and computational power. This paper presents\niQUANT, an interactive quantitative investment system that assists equity\ntraders to quickly spot promising financial factors from initial\nrecommendations suggested by algorithmic models, and conduct a joint refinement\nof factors and stocks for investment portfolio composition. We work closely\nwith professional traders to assemble empirical characteristics of \"good\"\nfactors and propose effective visualization designs to illustrate the\ncollective performance of financial factors, stock portfolios, and their\ninteractions. We evaluate iQUANT through a formal user study, two case studies,\nand expert interviews, using a real stock market dataset consisting of 3000\nstocks times 6000 days times 56 factors.\n"}
+{"abstract": "  This paper describes considerations behind the organisation of a third\nsemester BSc education. The project aims to facilitate a feedback-oriented\nenvironment using assessment for learning and for incremental measure of\nlearner progress [Vleuten et al, 2012, \"A model for programmatic assessment fit\nfor purpose\"]. Learning outcomes encourage higher order cognitive skills,\nfollowing [Biggs & Tang, 2011,\"Teaching for quality learning at university:\nwhat the student does\"]. Embracing [Dochy et al. 2018, \"Creating Impact Through\nFuture Learning: The High Impact Learning that Lasts (HILL) Model\"], several\nmechanisms encourage focus and motivation.\n"}
+{"abstract": "  Persistent homology is typically computed through persistent cohomology.\nWhile this generally improves the running time significantly, it does not\nfacilitate extraction of homology representatives. The mentioned\nrepresentatives are geometric manifestations of the corresponding holes and\noften carry desirable information.\n  We propose a new method of extraction of persistent homology representatives\nusing cohomology. In a nutshell, we first compute persistent cohomology and use\nthe obtained information to significantly improve the running time of the\ndirect persistent homology computations. This algorithm applied to Rips\nfiltrations generally computes persistent homology representatives much faster\nthan the standard methods.\n"}
+{"abstract": "  The proof of the relative consistency of the axiom of choice has been\nmechanized using Isabelle/ZF. The proof builds upon a previous mechanization of\nthe reflection theorem. The heavy reliance on metatheory in the original proof\nmakes the formalization unusually long, and not entirely satisfactory: two\nparts of the proof do not fit together. It seems impossible to solve these\nproblems without formalizing the metatheory. However, the present development\nfollows a standard textbook, Kunen's Set Theory, and could support the\nformalization of further material from that book. It also serves as an example\nof what to expect when deep mathematics is formalized.\n"}
+{"abstract": "  A short history of Russian researches in Chinese astronomy in 19-20 centuries\n"}
+{"abstract": "  This paper introduces the SemEval-2021 shared task 4: Reading Comprehension\nof Abstract Meaning (ReCAM). This shared task is designed to help evaluate the\nability of machines in representing and understanding abstract concepts. Given\na passage and the corresponding question, a participating system is expected to\nchoose the correct answer from five candidates of abstract concepts in a\ncloze-style machine reading comprehension setup. Based on two typical\ndefinitions of abstractness, i.e., the imperceptibility and nonspecificity, our\ntask provides three subtasks to evaluate the participating models.\nSpecifically, Subtask 1 aims to evaluate how well a system can model concepts\nthat cannot be directly perceived in the physical world. Subtask 2 focuses on\nmodels' ability in comprehending nonspecific concepts located high in a\nhypernym hierarchy given the context of a passage. Subtask 3 aims to provide\nsome insights into models' generalizability over the two types of abstractness.\nDuring the SemEval-2021 official evaluation period, we received 23 submissions\nto Subtask 1 and 28 to Subtask 2. The participating teams additionally made 29\nsubmissions to Subtask 3. The leaderboard and competition website can be found\nat https://competitions.codalab.org/competitions/26153. The data and baseline\ncode are available at\nhttps://github.com/boyuanzheng010/SemEval2021-Reading-Comprehension-of-Abstract-Meaning.\n"}
+{"abstract": "  We study the popular distributed consensus method over networks composed of a\nnumber of densely connected clusters with a sparse connection between them. In\nthese cluster networks, the method often constitutes two-time-scale dynamics,\nwhere the internal nodes within each cluster reach consensus quickly relative\nto the aggregate nodes across clusters. Our main contribution is to provide the\nrate of the distributed consensus method, which characterize explicitly the\nimpacts of the internal and external graphs on the performance of this method.\nOur main result shows that this rate converges exponentially and only scales\nwith a few number of nodes, which is relatively small to the size of the\nnetwork.\n  The key technique in our analysis is to consider a Lyapunov function which\ncaptures the impacts of different time-scale dynamics on the convergence of the\nmethod. Our approach avoids using model reduction, which is the typical way\naccording to singular perturbation theory and relies on relatively simple\ndefinitions of the slow and fast variables. In addition, Lyapunov analysis\nallows us to derive the rate of distributed consensus methods over cluster\nnetworks, which is missing from the existing works using singular perturbation\ntheory. We illustrate our theoretical results by a number of numerical\nsimulations over different cluster networks.\n"}
+{"abstract": "  Classification of galactic morphologies is a crucial task in galactic\nastronomy, and identifying fine structures of galaxies (e.g., spiral arms,\nbars, and clumps) is an essential ingredient in such a classification task.\nHowever, seeing effects can cause images we obtain to appear blurry, making it\ndifficult for astronomers to derive galaxies' physical properties and, in\nparticular, distant galaxies. Here, we present a method that converts blurred\nimages obtained by the ground-based Subaru Telescope into quasi Hubble Space\nTelescope (HST) images via machine learning. Using an existing deep learning\nmethod called generative adversarial networks (GANs), we can eliminate seeing\neffects, effectively resulting in an image similar to an image taken by the\nHST. Using multiple Subaru telescope image and HST telescope image pairs, we\ndemonstrate that our model can augment fine structures present in the blurred\nimages in aid for better and more precise galactic classification. Using our\nfirst of its kind machine learning-based deblurring technique on space images,\nwe can obtain up to 18% improvement in terms of CW-SSIM (Complex Wavelet\nStructural Similarity Index) score when comparing the Subaru-HST pair versus\nSeeingGAN-HST pair. With this model, we can generate HST-like images from\nrelatively less capable telescopes, making space exploration more accessible to\nthe broader astronomy community. Furthermore, this model can be used not only\nin professional morphological classification studies of galaxies but in all\ncitizen science for galaxy classifications.\n"}
+{"abstract": "  Loneliness does not only have emotional implications on a person but also on\nhis/her well-being. The study of loneliness has been challenging and largely\ninconclusive in findings because of the several factors that might correlate to\nthe phenomenon. We present one approach to predicting this event by discovering\npatterns of language associated with loneliness. Our results show insights and\npromising directions for mining text from instant messaging to predict\nloneliness.\n"}
+{"abstract": "  The [CII] deficit, which describes the observed decrease in the ratio of\n[CII] 158 micron emission to continuum infrared emission in galaxies with high\nstar formation surface densities, places a significant challenge to the\ninterpretation of [CII] detections from across the observable universe. In an\nattempt to further decode the cause of the [CII] deficit, the [CII] and dust\ncontinuum emission from 18 Local Volume galaxies has been split based on\nconditions within the interstellar medium where it originated. This is\ncompleted using the Key Insights in Nearby Galaxies: a Far-Infrared Survey with\nHerschel (KINGFISH) and Beyond the Peak (BtP) surveys and the wide-range of\nwavelength information, from UV to far-infrared emission lines, available for a\nselection of star-forming regions within these samples. By comparing these\nsubdivided [CII] emissions to isolated infrared emission and other properties,\nwe find that the thermalization (collisional de-excitation) of the [CII] line\nin HII regions plays a significant role in the deficit observed in our sample.\n"}
+{"abstract": "  We describe recent work on the physics of the Higgs boson at future muon\ncolliders. Starting from the low energy muon collider at the Higgs boson pole\nwe extend our discussion to the multi-TeV muon collider and outline the physics\ncase for such machines about the properties of the Higgs boson and physics\nbeyond the Standard Model that can be possibly discovered.\n"}
+{"abstract": "  A certain class of neutrino mass models predicts long-lived particles whose\nelectric charge is four or three times larger than that of protons. Such\nparticles, if they are light enough, may be produced at the LHC and detected.\nWe investigate the possibility of observing those long-lived multi-charged\nparticles with the MoEDAL detector, which is sensitive to long-lived particles\nwith low velocities ($\\beta$) and a large electric charge ($Z$) with $\\Theta\n\\equiv \\beta/Z \\lesssim 0.15$. We demonstrate that multi-charged scalar\nparticles with a large $Z$ give three-fold advantage for MoEDAL; reduction of\n$\\Theta$ due to strong interactions with the detector, and enhancement of the\nphoton-fusion process, which not only increases the production cross-section\nbut also lowers the average production velocity, reducing $\\Theta$ further. To\ndemonstrate the performance of MoEDAL on multi-charged long-lived particles,\ntwo concrete neutrino mass models are studied. In the first model, the new\nphysics sector is non-coloured and contains long-lived particles with electric\ncharges 2, 3 and 4. A model-independent study finds MoEDAL can expect more than\n1 signal event at the HL-LHC ($L = 300$ fb$^{-1}$) if these particles are\nlighter than 600, 1100 and 1430 GeV, respectively. These compare with the\ncurrent ATLAS limits 650, 780 and 920 GeV for $L = 36$ fb$^{-1}$. The second\nmodel has a coloured new physics sector, which possesses long-lived particles\nwith electric charges 4/3, 7/3 and 10/3. The corresponding MoEDAL's mass\nreaches at the HL-LHC are 1400, 1650 and 1800 GeV, respectively, which compare\nwith the current CMS limits 1450, 1480 and 1510 GeV for $L = 36$ fb$^{-1}$. In\na model-specific study we explore the parameter space of neutrino mass\ngeneration models and identify the regions that can be probed with MoEDAL at\nthe end of Run-3 and the High-Luminosity LHC.\n"}
+{"abstract": "  We investigate the linearized KdV equation on a metric tree consisting of\nthree different types of bonds: incoming unbounded root, two finite bonds, and\nfour outgoing unbounded bonds. Under natural assumptions at the vertices, we\nobtain the uniqueness of a solution. To show the existence we use the theory of\npotentials and reduce the problem to a system of linear algebraic equations. We\nshow that the latter is uniquely solvable under conditions of the uniqueness\ntheorem. Also, we show that the system we consider can be used to model wave\npropagation in pipelines.\n"}
+{"abstract": "  We present an optimisation problem defined using a measure preserving action\nby a group G on a compact probability space X. We show that this problem cannot\nbe solved in any way measurable with respect to a finitely additive measure\nthat extends the original probability measure and such that the group G remains\nmeasure preserving. The proof involves a computer-performed analysis of a\nstochastic process. On the other hand, there are some non-measurable solutions\nthat induce some paradoxical decompositions of the space X. Additionally, we\nshow that, for some small positive epsilon, epsilon-stability also cannot be\nsatisfied in such a measurable way. By epsilon-stability, we mean that the\nexpected gains from local deviations considered independently cannot exceed\nepsilon.\n"}
+{"abstract": "  The amalgamation of different generations of mobile cellular networks around\nthe globe has resulted in diverse data speed experiences for end users. At\npresent there are no defined mechanisms in place for a subscriber of one mobile\nnetwork operator (MNO) to use the services of a WiFi provider. Cellular and\nData Service providers also have no standardized procedures to securely\ninteract with each other, and to allow their subscribers to use third party\nservices on a pay-as-you-go basis. This paper proposes a blockchain-based\noffloading framework that allows a subscriber of a mobile operator to\ntemporarily use another MNO or WiFi provider's higher speed network. Smart\ncontracts allow diverse entities such as MNOs, Brokers and WiFi Providers to\nautomatically execute mutual agreements to enable the utilization of third\nparty infrastructure in a secure and controlled manner. To test the proposed\nframework, the offloading of a subscriber from 3G/4G/4G-LTE/5G networks to a\nfixed broadband WiFi network was carried out and the results analyzed. The\noffloading framework was implemented using the ns-3 network simulator, and the\nEthereum blockchain smart contract features were used for the settlement of\ninvoices.\n"}
+{"abstract": "  In representation learning, there has been recent interest in developing\nalgorithms to disentangle the ground-truth generative factors behind a dataset,\nand metrics to quantify how fully this occurs. However, these algorithms and\nmetrics often assume that both representations and ground-truth factors are\nflat, continuous, and factorized, whereas many real-world generative processes\ninvolve rich hierarchical structure, mixtures of discrete and continuous\nvariables with dependence between them, and even varying intrinsic\ndimensionality. In this work, we develop benchmarks, algorithms, and metrics\nfor learning such hierarchical representations.\n"}
+{"abstract": "  Between January 2017 and January 2021, thousands of local news sources in the\nUnited States reported on over 42,000 protests about topics such as civil\nrights, immigration, guns, and the environment. Given the vast number of local\njournalists that report on protests daily, extracting these events as\nstructured data to understand temporal and geographic trends can empower civic\ndecision-making. However, the task of extracting events from news articles\npresents well known challenges to the NLP community in the fields of domain\ndetection, slot filling, and coreference resolution.\n  To help improve the resources available for extracting structured data from\nnews stories, our contribution is three-fold. We 1) release a manually labeled\ndataset of news article URLs, dates, locations, crowd size estimates, and 494\ndiscrete descriptive tags corresponding to 42,347 reported protest events in\nthe United States between January 2017 and January 2021; 2) describe the\nsemi-automated data collection pipeline used to discover, sort, and review the\n144,568 English articles that comprise the dataset; and 3) benchmark a\nlong-short term memory (LSTM) low dimensional classifier that demonstrates the\nutility of processing news articles based on syntactic structures, such as\nparagraphs and sentences, to count the number of reported protest events.\n"}
+{"abstract": "  It is generally accepted that CMEs result from eruptions of magnetic flux\nropes, which are dubbed as magnetic clouds in interplanetary space. The\ncomposition (including the ionic charge states and elemental abundances) is\ndetermined prior to and/or during CME eruptions in the solar atmosphere, and\ndoes not alter during magnetic cloud propagation to 1 AU and beyond. It has\nbeen known that the composition is not uniform within a cross section\nperpendicular to magnetic cloud axis, and the distribution of ionic charge\nstates within a cross section provides us an important clue to investigate the\nformation and eruption processes of flux ropes due to the freeze-in effect. The\nflux rope is a three dimensional magnetic structure intrinsically, and it\nremains unclear whether the composition is uniform along the flux rope axis as\nmost magnetic clouds are only detected by one spacecraft. In this paper we\nreport a magnetic cloud that was observed by ACE near 1 AU on 1998 March 4--6\nand Ulysses near 5.4 AU on March 24--28 sequentially. At these times, both\nspacecraft were located around the ecliptic plane, and the latitudinal and\nlongitudinal separations between them were $\\sim$2.2$^{\\circ}$ and\n$\\sim$5.5$^{\\circ}$, respectively. It provides us an excellent opportunity to\nexplore the axial inhomogeneity of flux rope composition, as both spacecraft\nalmost intersected the cloud center at different sites along its axis. Our\nstudy shows that the average values of ionic charge states exhibit significant\ndifference along the axis for carbon, and the differences are relatively slight\nbut still obvious for charge states of oxygen and iron, as well as the\nelemental abundances of iron and helium. Besides the means, the composition\nprofiles within the cloud measured by both spacecraft also exhibit some\ndiscrepancies. We conclude that the inhomogeneity of composition exists along\nthe cloud axis.\n"}
+{"abstract": "  While learning-based control techniques often outperform classical controller\ndesigns, safety requirements limit the acceptance of such methods in many\napplications. Recent developments address this issue through so-called\npredictive safety filters, which assess if a proposed learning-based control\ninput can lead to constraint violations and modifies it if necessary to ensure\nsafety for all future time steps. The theoretical guarantees of such predictive\nsafety filters rely on the model assumptions and minor deviations can lead to\nfailure of the filter putting the system at risk. This paper introduces an\nauxiliary soft-constrained predictive control problem that is always feasible\nat each time step and asymptotically stabilizes the feasible set of the\noriginal safety filter, thereby providing a recovery mechanism in\nsafety-critical situations. This is achieved by a simple constraint tightening\nin combination with a terminal control barrier function. By extending\ndiscrete-time control barrier function theory, we establish that the proposed\nauxiliary problem provides a `predictive' control barrier function. The\nresulting algorithm is demonstrated using numerical examples.\n"}
+{"abstract": "  We revisit D3-branes at toric CY$_3$ singularities with orientifolds and\ntheir description in terms of dimer models. We classify orientifold actions on\nthe dimer through smooth involutions of the torus. In particular, we describe\nnew orientifold projections related to maps on the dimer without fixed points,\nleading to Klein bottles. These new orientifolds lead to novel $\\mathcal{N}=1$\nSCFT's that resemble, in many aspects, non-orientifolded theories. For\ninstance, we recover the presence of fractional branes and some of them trigger\na cascading RG-flow \\`a la Klebanov-Strassler. The remaining involutions lead\nto non-supersymmetric setups, thus exhausting the possible orientifolds on\ndimers.\n"}
+{"abstract": "  Domain walls arise in theories where there is spontaneous symmetry breaking\nof a discrete symmetry such as $\\mathbb{Z}_{N}$ and are a feature of many BSM\nmodels. In this work we consider the possibility of detecting domain walls\nthrough their optical effects and specify three different methods of coupling\ndomain walls to the photon. We consider the effects of these couplings in the\ncontext of gravitational wave detectors, such as LIGO, and examine the\nsensitivity of these experiments to domain wall effects. In cases where\ngravitational wave detectors are not sensitive we examine our results in the\ncontext of axion experiments and show how effects of passing domain walls can\nbe detected at interferometers searching for an axion signal.\n"}
+{"abstract": "  We performed $^{59}$Co nuclear magnetic and quadrupole resonance (NMR and\nNQR) measurements under pressure on a single-crystalline CeCoSi, which\nundergoes an unresolved phase transition at $T_0$. The NQR spectra clearly\nshowed that the phase transition at $T_0$ is nonmagnetic, but any symmetry\nlowering at the Co site was not seen irrespective of the feature of\nsecond-order phase transition. By contrast, the NMR spectra were split by the\ninduced magnetic field perpendicular to the external magnetic field. These\nresults show that the phase below $T_0$ is not a simple paramagnetic state but\nis most likely electric multipolar ordered state of Ce $4f$ electrons. The\ndevelopment of the Kondo effect by applying pressure is thought to be crucial\nto stabilize this state and to show novel features beyond commonality of\ntetragonal Ce-based systems.\n"}
+{"abstract": "  Conflicting interpretations of experimental data preclude the understanding\nof the quantum magnetic state of spin-orbit coupled $d^2$ double perovskites.\nWhether the ground state is a Janh-Teller-distorted order of quadrupoles or the\nhitherto elusive octupolar order remains debated. We resolve this uncertainty\nthrough direct calculations of all-rank inter-site exchange interactions and\ninelastic neutron scattering cross-section for the $d^2$ double perovskite\nseries Ba$_2M$OsO$_6$ ($M$= Ca, Mg, Zn). Using advanced many-body first\nprinciples methods we show that the ground state is formed by ferro-ordered\noctupoles coupled by superexchange interactions within the ground-state $E_g$\ndoublet. Computed ordering temperature of the single second-order\nphase-transition is consistent with experimentally observed material-dependent\ntrends. Minuscule distortions of the parent cubic structure are shown to\nqualitatively modify the structure of gaped magnetic excitations.\n"}
+{"abstract": "  We report on a nanomechanical engineering method to monitor matter growth in\nreal time via e-beam electromechanical coupling. This method relies on the\nexceptional mass sensing capabilities of nanomechanical resonators. Focused\nelectron beam induced deposition (FEBID) is employed to selectively grow\nplatinum particles at the free end of singly clamped nanotube cantilevers. The\nelectron beam has two functions: it allows both to grow material on the\nnanotube and to track in real time the deposited mass by probing the\nnoise-driven mechanical resonance of the nanotube. On the one hand, this\ndetection method is highly effective as it can resolve mass deposition with a\nresolution in the zeptogram range; on the other hand, this method is simple to\nuse and readily available to a wide range of potential users, since it can be\noperated in existing commercial FEBID systems without making any modification.\nThe presented method allows to engineer hybrid nanomechanical resonators with\nprecisely tailored functionality. It also appears as a new tool for studying\ngrowth dynamics of ultra-thin nanostructures, opening new opportunities for\ninvestigating so far out-of-reach physics of FEBID and related methods.\n"}
+{"abstract": "  We investigate localised bulging or necking in an incompressible,\nhyperelastic cylindrical tube under axial stretching and surface tension. Three\ncases are considered in which the tube is subjected to different constraints.\nIn case 1 the inner and outer surfaces are traction-free and under surface\ntension, whilst in cases 2 and 3 the inner and outer surfaces (respectively)\nare fixed to prevent radial displacement and surface tension. However, each\nfree surface in these latter two cases is still under surface tension. We first\nstate the analytical bifurcation conditions for localisation and then validate\nthem numerically whilst determining whether localisation is preferred over\nbifurcation into periodic modes. It is shown that bifurcation into a localised\nsolution is unattainable in case 1 but possible and favourable in cases 2 and\n3. In contrast, in case 1 any bifurcation must necessarily take the form of a\nperiodic mode with a non-zero wave number. Our results are validated using\nFinite Element Method (FEM) simulations.\n"}
+{"abstract": "  The study of Ukiyo-e, an important genre of pre-modern Japanese art, focuses\non the object and style like other artwork researches. Such study has benefited\nfrom the renewed interest by the machine learning community in culturally\nimportant topics, leading to interdisciplinary works including collections of\nimages, quantitative approaches, and machine learning-based creativities. They,\nhowever, have several drawbacks, and it remains challenging to integrate these\nworks into a comprehensive view. To bridge this gap, we propose a holistic\napproach We first present a large-scale Ukiyo-e dataset with coherent semantic\nlabels and geometric annotations, then show its value in a quantitative study\nof Ukiyo-e paintings' object using these labels and annotations. We further\ndemonstrate the machine learning methods could help style study through soft\ncolor decomposition of Ukiyo-e, and finally provides joint insights into object\nand style by composing sketches and colors using colorization. Dataset\navailable at https://github.com/rois-codh/arc-ukiyoe-faces\n"}
+{"abstract": "  In this paper, we investigate the ratio of the numbers of odd and even cycles\nin outerplanar graphs. We verify that the ratio generally diverges to infinity\nas the order of a graph diverges to infinity. We also give sharp estimations of\nthe ratio for several classes of outerplanar graphs, and obtain a constant\nupper bound of the ratio for some of them. Furthermore, we consider similar\nproblems in graphs with some pairs of forbidden subgraphs/minors, and propose a\nchallenging problem concerning claw-free graphs.\n"}
+{"abstract": "  Point defects in crystalline materials often occur in multiple charge states.\nAlthough many experimental methods to study and explore point defects are\navailable, techniques to explore the non-equilibrium dynamics of the charge\nstates of these defects at ultrafast (sub-nanosecond) time scales have not been\ndiscussed before. We present results from ultrafast optical-pump\nsupercontinuum-probe spectroscopy measurements on $\\beta$-Ga$_2$O$_3$. The\nstudy of point defects in $\\beta$-Ga$_2$O$_3$ is essential for its\nestablishment as a material platform for high-power electronics and deep-UV\noptoelectronics. Use of a supercontinuum probe allows us to obtain the\ntime-resolved absorption spectra of material defects under non-equilibrium\nconditions with picosecond time resolution. The probe absorption spectra shows\ndefect absorption peaks at two energies, $\\sim$2.2 eV and $\\sim$1.63 eV, within\nthe 1.3-2.5 eV probe energy bandwidth. The strength of the absorption\nassociated with each peak is time-dependent and the spectral weight shifts from\nthe lower energy peak to the higher energy peak with pump-probe delay. Further,\nmaximum defect absorption is seen for probe polarized along the crystal c-axis.\nThe time-dependent probe absorption spectra and the observed dynamics for all\nprobe wavelengths at all pump-probe delays can be fit with a set of rate\nequations for a single multi-level defect. Based on first-principles\ncalculations within hybrid density functional theory we attribute the observed\nabsorption features to optical transitions from the valence band to different\ncharge states of Gallium vacancies. Our results demonstrate that broadband\nultrafast supercontinuum spectroscopy can be a useful tool to explore charge\nstates of defects and defect dynamics in semiconductors.\n"}
+{"abstract": "  We examine the one-dimensional Gross-Pitaevskii lattice at zero temperature\nin the presence of uncorrelated disorder. We obtain analytical expressions for\nthe thermodynamic properties of the ground state field and compare them with\nnumerical simulations both in the weak and strong interaction regimes. We\nanalyze weak excitations above the ground state and compute the localization\nproperties of Bogoliubov-de Gennes modes. In the long-wavelength limit, these\nmodes delocalize in accordance with the extended nature of the ground state.\nFor strong interactions, we observe and derive a divergence of their\nlocalization length at finite energy due to an effective correlated disorder\ninduced by the weak ground state field fluctuations. We derive effective strong\ninteraction field equations for the excitations and generalize to higher\ndimensions.\n"}
+{"abstract": "  We define a variant of real-analytic polylogarithms that are single-valued\nand that satisfy ''clean'' functional relations that do not involve any\nproducts of lower weight functions. We discuss the basic properties of these\nfunctions and, for depths one and two, we present some explicit formulas and\nresults. We also give explicit formulas for the single-valued and clean\nsingle-valued version attached to the Nielsen polylogarithms $S_{n,2}(x)$, and\nwe show how the clean single-valued functions give new evaluations of multiple\npolylogarithms at certain algebraic points.\n"}
+{"abstract": "  We associate with each convex optimization problem, posed on some locally\nconvex space, with infinitely many constraints indexed by the set T, and a\ngiven non-empty family H of finite subsets of T, a suitable Lagrangian-Haar\ndual problem. We obtain necessary and sufficient conditions for H-reducibility,\nthat is, equivalence to some subproblem obtained by replacing the whole index\nset T by some element of H. Special attention is addressed to linear\noptimization, infinite and semi-infinite, and to convex problems with a\ncountable family of constraints. Results on zero H-duality gap and on\nH-(stable) strong duality are provided. Examples are given along the paper to\nillustrate the meaning of the results.\n"}
+{"abstract": "  Online real estate platforms have become significant marketplaces\nfacilitating users' search for an apartment or a house. Yet it remains\nchallenging to accurately appraise a property's value. Prior works have\nprimarily studied real estate valuation based on hedonic price models that take\nstructured data into account while accompanying unstructured data is typically\nignored. In this study, we investigate to what extent an automated visual\nanalysis of apartment floor plans on online real estate platforms can enhance\nhedonic rent price appraisal. We propose a tailored two-staged deep learning\napproach to learn price-relevant designs of floor plans from historical price\ndata. Subsequently, we integrate the floor plan predictions into hedonic rent\nprice models that account for both structural and locational characteristics of\nan apartment. Our empirical analysis based on a unique dataset of 9174 real\nestate listings suggests that current hedonic models underutilize the available\ndata. We find that (1) the visual design of floor plans has significant\nexplanatory power regarding rent prices - even after controlling for structural\nand locational apartment characteristics, and (2) harnessing floor plans\nresults in an up to 10.56% lower out-of-sample prediction error. We further\nfind that floor plans yield a particularly high gain in prediction performance\nfor older and smaller apartments. Altogether, our empirical findings contribute\nto the existing research body by establishing the link between the visual\ndesign of floor plans and real estate prices. Moreover, our approach has\nimportant implications for online real estate platforms, which can use our\nfindings to enhance user experience in their real estate listings.\n"}
+{"abstract": "  HDR reconstruction is an important task in computer vision with many\nindustrial needs. The traditional approaches merge multiple exposure shots to\ngenerate HDRs that correspond to the physical quantity of illuminance of the\nscene. However, the tedious capturing process makes such multi-shot approaches\ninconvenient in practice. In contrast, recent single-shot methods predict a\nvisually appealing HDR from a single LDR image through deep learning. But it is\nnot clear whether the previously mentioned physical properties would still\nhold, without training the network to explicitly model them. In this paper, we\nintroduce the physical illuminance constraints to our single-shot HDR\nreconstruction framework, with a focus on spherical panoramas. By the proposed\nphysical regularization, our method can generate HDRs which are not only\nvisually appealing but also physically plausible. For evaluation, we collect a\nlarge dataset of LDR and HDR images with ground truth illuminance measures.\nExtensive experiments show that our HDR images not only maintain high visual\nquality but also top all baseline methods in illuminance prediction accuracy.\n"}
+{"abstract": "  We analyze the weak-field limit of General Relativity with matter and its\npossible quantisations. This analysis aims towards a predictive quantum theory\nto provide a first-principles description of gravitational effects in\nmacroscopic quantum systems. This includes recently proposed experiments on the\ngeneration of (Newtonian) gravitational forces from quantum distributions of\nmatter, and phenomena like gravity-induced entanglement, gravitational cat\nstates, gravity-induced Rabi oscillations, and quantum causal orderings of\nevents. Our main results include: (i) The demonstration that these phenomena do\nnot involve true gravitational degrees of freedom. (ii) We show that, unlike\nfull general relativity, weak gravity with matter is a parameterised field\ntheory, i.e., a theory obtained by promoting spacetime coordinates to\n`dynamical' variables. (iii) Quantisation via gauge-fixing leads to an\neffective field theory that account for some phenomena, but at the price of\ngauge dependence that manifests more strongly on spacetime observables. This\nambiguity is a manifestation of the problem of time that persists even in weak\ngravity. (iv) A consistent quantisation of parameterised field theories is\nessential for a predictive and spacetime covariant theory of weak gravity that\ndescribes gravitational effects in macroscopic quantum systems. We also discuss\nthe implication of our results to gravitational decoherence theories, the\nnotion of locality in gravity vis-a-vis quantum information theory, and the\nintriguing possibility that proposed solutions to the problem of time can be\ntested in weak-gravity quantum experiments.\n"}
+{"abstract": "  We determine all local smooth or formal CR maps from the unit sphere\n$\\mathbb{S}^3\\subset \\mathbb{C}^2$ into the tube $\\mathcal{T}:= \\mathcal{C}\n\\times i\\mathbb{R}^3 \\subset \\mathbb{C}^3$ over the future light cone\n$\\mathcal{C}:= \\left\\{x\\in \\mathbb{R}^3\\colon x_1^2+x_2^2 = x_3^2, \\ x_3 >\n0\\right\\}$. This result leads to a complete classification of proper\nholomorphic maps from the unit ball in $\\mathbb{C}^2$ into Cartan's classical\ndomain of type IV in $\\mathbb{C}^3$ that extend smoothly to some boundary\npoint. Up to composing with CR automorphisms of the source and target, the\nclassification consists of four algebraic maps. Two maps among them were known\nearlier in the literature, which were shown to be ``rigid'' in the higher\ndimensional case in a recent paper by Xiao and Yuan. Two newly discovered\nquadratic polynomial maps provide counterexamples to a conjecture appeared in\nthe same paper for the case of dimension two.\n"}
+{"abstract": "  Purpose: Grating-based imaging (GBI) and edge-illumination (EI) are two\npromising types of XPCI as the conventional x-ray sources can be directly\nutilized. For GBI and EI systems, the phase-stepping acquisition with multiple\nexposures at a constant fluence is usually adopted in the literature. This\nwork, however, attempts to challenge such a constant fluence concept during the\nphase-stepping process and proposes a fluence adaptation mechanism for dose\nreduction. Method: Recently, analytic multi-order moment analysis has been\nproposed to improve the computing efficiency. In these algorithms, multiple\ncontrasts can be calculated by summing together the weighted phase-stepping\ncurves (PSCs) with some kernel functions, which suggests us that the raw data\nat different steps have different contributions for the noise in retrieved\ncontrasts. Based on analytic retrieval formulas and the Gaussian noise model\nfor detected signals, we derived an optimal adaptive fluence distribution,\nwhich is proportional to the absolute weighting kernel functions and the root\nof original sample PSCs acquired under the constant fluence. Results: To\nvalidate our analyses, simulations and experiments are conducted for GBI and EI\nsystems. Simulated results demonstrate that the dose reduction ratio between\nour proposed fluence distributions and the typical constant one can be about\n20% for the phase contrast, which is consistent with our theoretical\npredictions. Although the experimental noise reduction ratios are a little\nsmaller than the theoretical ones, synthetic and real experiments both observe\nbetter noise performance by our proposed method. Our simulated results also\ngive out the effective ranges of the parameters of the PSCs, such as the\nvisibility in GBI, the standard deviation and the mean value in EI, providing a\nguidance for the use of our proposed approach in practice.\n"}
+{"abstract": "  This paper proposes two algorithms to maximize the minimum array power gain\nin a wide-beam mainlobe by solving the power gain pattern synthesis (PGPS)\nproblem with and without sidelobe constraints. Firstly, the nonconvex PGPS\nproblem is transformed into a nonconvex linear inequality optimization problem\nand then converted to an augmented Lagrangian problem by introducing auxiliary\nvariables via the Alternating Direction Method of Multipliers (ADMM) framework.\nNext,the original intractable problem is converted into a series of nonconvex\nand convex subproblems. The nonconvex subproblems are solved by dividing their\nsolution space into a finite set of smaller ones, in which the solution would\nbe obtained pseudoanalytically. In such a way, the proposed algorithms are\nsuperior to the existing PGPS-based ones as their convergence can be\ntheoretically guaranteed with a lower computational burden. Numerical examples\nwith both isotropic element pattern (IEP) and active element pattern (AEP)\narrays are simulated to show the effectiveness and superiority of the proposed\nalgorithms by comparing with the related existing algorithms.\n"}
+{"abstract": "  The Renyi entropy is an important measure of the information, it is proposed\nby Renyi in the context of entropy theory as a generalization of Shannon\nentropy. We study in detail this measure of multivariate autoregressive moving\naverage (ARMA) control systems. The characteristic function of output process\nis represented from the terms of its residual characteristic functions. Simple\nexpression to compute the Renyi entropy for the output process of these systems\nis derived. In addition, we investigate the covariance matrix for finding the\nupper bound of the entropy. Finally, we present three separate examples that\nserve to illustrate the behavior of information in a multivariate ARMA control\nsystem where the control and noise distributed as Gaussian, Cauchy and Laplace\nprocesses.\n"}
+{"abstract": "  This contribution to \"Solar Magnetic Variability and Climate\" reviews\nsmall-scale magnetic features on the solar surface, in particular the\nstrong-field but tiny magnetic concentrations that constitute network and plage\nand represent most magnetism outside sunspots and filaments. Where these are\nmostly of the same polarity, as in active-region plage, their occurrence varies\nwith the activity variations measured by the sunspot number, but when they\nappear bipolar-mixed on small scales they can also result from granular-scale\ndynamo action that does not vary with the cycle.\n"}
+{"abstract": "  Learning node representations on temporal graphs is a fundamental step to\nlearn real-word dynamic graphs efficiently. Real-world graphs have the nature\nof continuously evolving over time, such as changing edges weights, removing\nand adding nodes and appearing and disappearing of edges, while previous graph\nrepresentation learning methods focused generally on static graphs. We present\nConvDySAT as an enhancement of DySAT, one of the state-of-the-art dynamic\nmethods, by augmenting convolution neural networks with the self-attention\nmechanism, the employed method in DySAT to express the structural and temporal\nevolution. We conducted single-step link prediction on a communication network\nand rating network, Experimental results show significant performance gains for\nConvDySAT over various state-of-the-art methods.\n"}
+{"abstract": "  The amount of remote sensing (RS) data has increased at an unexpected scale,\ndue to the rapid progress of earth-observation and the growth of satellite RS\nand sensor technologies. Traditional relational databases attend their limit to\nmeet the needs of high-resolution and large-scale RS Big Data management. As a\nresult, massive RS data management is currently one of the most imperative\ntopics. To address this problem, this paper describes a distributed\narchitecture for big RS data storage based on a unified metadata file, pyramid\nmodel, and Hilbert curve for data composition and indexing using NoSQL\ndatabases (i.e, Apache Hbase). In this paper, a Hadoop-based framework in\nAzureInsight cloud platform is designed to manage massive RS data in a parallel\nand distributed way. Experimental results prove that our method has the\npotential to overcome the weakness of traditional methods. The proposed model\nis suitable for massive high-resolution image data management.\n"}
+{"abstract": "  For the first time, we present the simultaneous detection and\ncharacterization of three distinct phases at $>10^5$ K in $z=0$ absorption,\nusing deep $\\it{Chandra}$ observations toward Mrk 421. The extraordinarily high\nsignal-to-noise ratio ($\\geqslant60$) of the spectra has allowed us to detect a\n$\\it{hot}$ phase of the Milky Way circumgalactic medium (CGM) at\n3.2$^{+1.5}_{-0.5}\\times$ 10$^7$ K, coexisting with a $\\textit{warm-hot}$ phase\nat 1.5$\\pm$0.1$\\times$10$^6$ K and a $\\textit{warm}$ phase at\n3.0$\\pm$0.4$\\times$10$^5$ K. The $\\textit{warm-hot}$ phase is at the virial\ntemperature of the Galaxy, and the $\\textit{warm}$ phase may have cooled from\nthe $\\textit{warm-hot}$ phase, but the super-virial $\\textit{hot}$ phase\nremains a mystery. We find that [C/O] in the $\\textit{warm}$ and\n$\\textit{warm-hot}$ phases, [Mg/O] in the $\\textit{warm-hot}$ phase and [Ne/O]\nin the $\\textit{hot}$ phase are super-solar, and the $\\textit{hot}$ and the\n$\\textit{warm-hot}$ phases are $\\alpha-$enhanced. Non-thermal line broadening\nis evident in the $\\textit{warm-hot}$ and the $\\textit{hot}$ phases and it\ndominates the total line broadening. Our results indicate that the $>10^5$ K\nCGM is a complex ecosystem. It provides insights on the thermal and chemical\nhistory of the Milky Way CGM, and theories of galaxy evolution.\n"}
+{"abstract": "  A theorem of Brady and Meier states that a right-angled Artin group is a\nduality group if and only if the flag complex of the defining graph is\nCohen--Macaulay. We use this to give an example of a RAAG with the property\nthat its outer automorphism group is not a virtual duality group. This gives a\npartial answer to a question of Vogtmann. In an appendix, Br\\\"uck describes how\nhe used a computer-assisted search to find further examples.\n"}
+{"abstract": "  Crashing ocean waves, cappuccino froths and microfluidic bubble crystals are\nexamples of foamy flows. Foamy flows are critical in numerous natural and\nindustrial processes and remain notoriously difficult to compute as they\ninvolve coupled, multiscale physical processes. Computations need to resolve\nthe interactions of the bubbles with the fluid and complex boundaries, while\ncapturing the drainage and rupture of the microscopic liquid films at their\ninterface. We present a novel multilayer simulation framework (Multi-VOF) that\nadvances the state of the art in simulation capabilities of foamy flows. The\nframework introduces a novel scheme for the distinct handling of multiple\nneighboring bubbles and a new regularization method that produces sharp\ninterfaces and removes spurious fragments. Multi-VOF is verified and validated\nwith experimental results and complemented with open source, efficient scalable\nsoftware. We demonstrate capturing of bubble crystalline structures in\nrealistic microfluidics devices and foamy flows involving tens of thousands of\nbubbles in a waterfall. The present multilayer framework extends the classical\nvolume-of-fluid methodology and allows for unprecedented large scale,\npredictive simulations of flows with multiple interfaces.\n"}
+{"abstract": "  Modern Superconducting QUantum Interference Devices (SQUIDs) are commonly\nfabricated from either Al or Nb electrodes, with an in-situ oxidation process\nto create a weak link between them. However, common problems of such planar\nnano- and micro-SQUIDs are hysteretic current-voltage curves, and a shallow\nflux modulation depth. Here, we demonstrate the formation of both Josephson\njunctions and SQUIDs using a dry transfer technique to stack and\ndeterministically misalign flakes of NbSe$_{2}$; allowing one to overcome these\nissues. The Josephson dynamics of the resulting twisted NbSe$_{2}$-NbSe$_{2}$\njunctions are found to be sensitive to the misalignment angle of the\ncrystallographic axes. A single lithographic process was then implemented to\nshape the Josephson junction into a SQUID geometry with typical loop areas of\n$\\simeq$ 25 $\\mu m^{2}$ and weak links $\\simeq$ 600 nm wide. These devices\ndisplay large stable current and voltage modulation depths of up to $\\Delta\nI_{c} \\simeq$ 75$\\%$ and $\\Delta V \\simeq$ 1.4 mV respectively.\n"}
+{"abstract": "  We demonstrate how to obtain the analytic continuation of formulae for the\nLerch transcendent function, $\\Phi(e^m,k,b)$, and the polylogarithm,\n$\\mathrm{Li}_{k}(e^{m})$, that only hold at the positive integers. The result\nare alternative expressions for these functions and their partial sums valid in\nthe complex half-plane, $\\Re{(k)}>0$.\n"}
+{"abstract": "  Women represent less than 24% of employees in the software development\nindustry and experience various types of prejudice and bias. Despite various\nefforts to increase diversity and multi-gendered participation, women are even\nmore underrepresented in Open Source Software (OSS) projects. In my PhD, I\ninvestigate the following question: How can OSS communities increase women's\nparticipation in their projects? I will identify different OSS career pathways\nand develop a holistic view of women's motivations to join or leave OSS, as\nwell as their definitions of success. Based on this empirical investigation, I\nwill work together with the Linux Foundation to design attraction and retention\nstrategies focused on women. Before and after implementing the strategies, I\nwill conduct empirical studies to evaluate the state of the practice and\nunderstand the implications of the strategies.\n"}
+{"abstract": "  We focus on coherent direction of arrival estimation of wideband sources\nbased on spatial sparsity. This area of research is encountered in many\napplications such as passive radar, sonar, mining, and communication problems,\nin which an increasing attention has been devoted to improving the estimation\naccuracy and robustness to noise. By the development of super-resolution\nalgorithms, narrowband direction of arrival estimation based on gridless sparse\nalgorithms and atomic norm minimization has already been addressed. In this\npaper, a superresolution based method is proposed for coherent direction of\narrival estimation of multiple wideband sources. First, unlike the conventional\ncoherent methods, we develop a new focusing method to map the subband with the\nlargest center frequency to the other ones, which leads to an accurate method\nwith no requirement for initial estimates for DOAs. Then, we introduce an\natomic norm problem by defining a new set of atoms and exploiting the signal\njoint sparsity of different frequency subbands in a continuous spatial domain.\nThis problem is then cast as a semidefinite program, which leads to\nimplementing a new coherent direction of arrival estimation method with higher\nresolution and more robustness to noise. Our method needs only one single\nsnapshot for each frequency subband, leading to a small number of snapshots for\nthe received wideband signal compared to the other coherent DOA techniques.\nNumerical simulations show the outperformance of the proposed method compared\nto the conventional ones.\n"}
+{"abstract": "  The problem of finding the number of ordered commuting tuples of elements in\na finite group is equivalent to finding the size of the solution set of the\nsystem of equations determined by the commutator relations that impose\ncommutativity among any pair of elements from an ordered tuple. We consider\nthis type of systems for the case of ordered triples and express the size of\nthe solution set in terms of the irreducible characters of the group. The\nobtained formulas are natural extensions of Frobenius' character formula that\ncalculates the number of ways a group element is a commutator of an ordered\npair of elements in a finite group. We discuss how our formulas can be used to\nstudy the probability distributions afforded by these systems of equations, and\nwe show explicit calculations for dihedral groups.\n"}
+{"abstract": "  There are no known failures of Bounded Negativity in characteristic 0. In the\nlight of recent work showing the Bounded Negativity Conjecture fails in\npositive characteristics for rational surfaces, we propose new characteristic\nfree conjectures as a replacement. We also develop bounds on numerical\ncharacteristics of curves constraining their negativity. For example, we show\nthat the $H$-constant of a rational curve $C$ with at most $9$ singular points\nsatisfies $H(C)>-2$ regardless of the characteristic.\n"}
+{"abstract": "  International library standards require cataloguers to tediously input\nRomanization of their catalogue records for the benefit of library users\nwithout specific language expertise. In this paper, we present the first\nreported results on the task of automatic Romanization of undiacritized Arabic\nbibliographic entries. This complex task requires the modeling of Arabic\nphonology, morphology, and even semantics. We collected a 2.5M word corpus of\nparallel Arabic and Romanized bibliographic entries, and benchmarked a number\nof models that vary in terms of complexity and resource dependence. Our best\nsystem reaches 89.3% exact word Romanization on a blind test set. We make our\ndata and code publicly available.\n"}
+{"abstract": "  We report experimental and computational studies of thermal transport\nproperties in hexagonal boron nitride (hBN) encapsulated molybdenum disulfide\n(MoS2) structure using refined optothermal Raman techniques, and reveal very\nhigh interfacial thermal conductance between hBN and MoS2. By studying the\nRaman shift of hBN and MoS2 in suspended and substrate-supported thin films\nunder varying laser power and temperature, we calibrate lateral (in-plane)\nthermal conductivity of hBN and MoS2 and the vertical interfacial thermal\nconductance in the hBN/MoS2/hBN heterostructure as well as the interfaces\nbetween heterostructure and substrate. Crucially, we have found that\ninterfacial thermal conductance between hBN and encapsulated MoS2 is 74MW/m2K\nand 72MW/m2K in supported and suspended films, respectively, which are\nsignificantly higher than interfacial thermal conductance between MoS2 and\nother substrates. Molecular dynamics (MD) computations conducted in parallel\nhave shown consistent results. This work provides clear evidence of\nsignificantly efficient heat dissipation in hBN/MoS2/hBN heterostructures and\nsheds light on building novel hBN encapsulated nanoelectronics with efficient\nthermal management.\n"}
+{"abstract": "  Convolutional neural network (CNN) based architectures, such as Mask R-CNN,\nconstitute the state of the art in object detection and segmentation. Recently,\nthese methods have been extended for model-based segmentation where the network\noutputs the parameters of a geometric model (e.g. an ellipse) directly. This\nwork considers objects whose three-dimensional models can be represented as\nellipsoids. We present a variant of Mask R-CNN for estimating the parameters of\nellipsoidal objects by segmenting each object and accurately regressing the\nparameters of projection ellipses. We show that model regression is sensitive\nto the underlying occlusion scenario and that prediction quality for each\nobject needs to be characterized individually for accurate 3D object\nestimation. We present a novel ellipse regression loss which can learn the\noffset parameters with their uncertainties and quantify the overall geometric\nquality of detection for each ellipse. These values, in turn, allow us to fuse\nmulti-view detections to obtain 3D ellipsoid parameters in a principled\nfashion. The experiments on both synthetic and real datasets quantitatively\ndemonstrate the high accuracy of our proposed method in estimating 3D objects\nunder heavy occlusions compared to previous state-of-the-art methods.\n"}
+{"abstract": "  We study constrained reinforcement learning (CRL) from a novel perspective by\nsetting constraints directly on state density functions, rather than the value\nfunctions considered by previous works. State density has a clear physical and\nmathematical interpretation, and is able to express a wide variety of\nconstraints such as resource limits and safety requirements. Density\nconstraints can also avoid the time-consuming process of designing and tuning\ncost functions required by value function-based constraints to encode system\nspecifications. We leverage the duality between density functions and Q\nfunctions to develop an effective algorithm to solve the density constrained RL\nproblem optimally and the constrains are guaranteed to be satisfied. We prove\nthat the proposed algorithm converges to a near-optimal solution with a bounded\nerror even when the policy update is imperfect. We use a set of comprehensive\nexperiments to demonstrate the advantages of our approach over state-of-the-art\nCRL methods, with a wide range of density constrained tasks as well as standard\nCRL benchmarks such as Safety-Gym.\n"}
+{"abstract": "  Grice's Cooperative Principle (1975) describes the implicit maxims that guide\nconversation between humans. As humans begin to interact with non-human\ndialogue systems more frequently and in a broader scope, an important question\nemerges: what principles govern those interactions? The present study addresses\nthis question by evaluating human-AI interactions using Grice's four maxims; we\ndemonstrate that humans do, indeed, apply these maxims to interactions with AI,\neven making explicit references to the AI's performance through a Gricean lens.\nTwenty-three participants interacted with an American English-speaking Alexa\nand rated and discussed their experience with an in-lab researcher. Researchers\nthen reviewed each exchange, identifying those that might relate to Grice's\nmaxims: Quantity, Quality, Manner, and Relevance. Many instances of explicit\nuser frustration stemmed from violations of Grice's maxims. Quantity violations\nwere noted for too little but not too much information, while Quality\nviolations were rare, indicating trust in Alexa's responses. Manner violations\nfocused on speed and humanness. Relevance violations were the most frequent,\nand they appear to be the most frustrating. While the maxims help describe many\nof the issues participants encountered, other issues do not fit neatly into\nGrice's framework. Participants were particularly averse to Alexa initiating\nexchanges or making unsolicited suggestions. To address this gap, we propose\nthe addition of human Priority to describe human-AI interaction. Humans and AIs\nare not conversational equals, and human initiative takes priority. We suggest\nthat the application of Grice's Cooperative Principles to human-AI interactions\nis beneficial both from an AI development perspective and as a tool for\ndescribing an emerging form of interaction.\n"}
+{"abstract": "  End-to-end delay is a critical attribute of quality of service (QoS) in\napplication domains such as cloud computing and computer networks. This metric\nis particularly important in tandem service systems, where the end-to-end\nservice is provided through a chain of services. Service-rate control is a\ncommon mechanism for providing QoS guarantees in service systems. In this\npaper, we introduce a reinforcement learning-based (RL-based) service-rate\ncontroller that provides probabilistic upper-bounds on the end-to-end delay of\nthe system, while preventing the overuse of service resources. In order to have\na general framework, we use queueing theory to model the service systems.\nHowever, we adopt an RL-based approach to avoid the limitations of\nqueueing-theoretic methods. In particular, we use Deep Deterministic Policy\nGradient (DDPG) to learn the service rates (action) as a function of the queue\nlengths (state) in tandem service systems. In contrast to existing RL-based\nmethods that quantify their performance by the achieved overall reward, which\ncould be hard to interpret or even misleading, our proposed controller provides\nexplicit probabilistic guarantees on the end-to-end delay of the system. The\nevaluations are presented for a tandem queueing system with non-exponential\ninter-arrival and service times, the results of which validate our controller's\ncapability in meeting QoS constraints.\n"}
+{"abstract": "  During the last decade or so, there has been an insurgence in the deep\nlearning community to solve health-related issues, particularly breast cancer.\nFollowing the Camelyon-16 challenge in 2016, several researchers have dedicated\ntheir time to build Convolutional Neural Networks (CNNs) to help radiologists\nand other clinicians diagnose breast cancer. In particular, there has been an\nemphasis on Ductal Carcinoma in Situ (DCIS); the clinical term for early-stage\nbreast cancer. Large companies have given their fair share of research into\nthis subject, among these Google Deepmind who developed a model in 2020 that\nhas proven to be better than radiologists themselves to diagnose breast cancer\ncorrectly.\n  We found that among the issues which exist, there is a need for an\nexplanatory system that goes through the hidden layers of a CNN to highlight\nthose pixels that contributed to the classification of a mammogram. We then\nchose an open-source, reasonably successful project developed by Prof. Shen,\nusing the CBIS-DDSM image database to run our experiments on. It was later\nimproved using the Resnet-50 and VGG-16 patch-classifiers, analytically\ncomparing the outcome of both. The results showed that the Resnet-50 one\nconverged earlier in the experiments.\n  Following the research by Montavon and Binder, we used the DeepTaylor\nLayer-wise Relevance Propagation (LRP) model to highlight those pixels and\nregions within a mammogram which contribute most to its classification. This is\nrepresented as a map of those pixels in the original image, which contribute to\nthe diagnosis and the extent to which they contribute to the final\nclassification. The most significant advantage of this algorithm is that it\nperforms exceptionally well with the Resnet-50 patch classifier architecture.\n"}
+{"abstract": "  We present a new tensor network algorithm for calculating the partition\nfunction of interacting quantum field theories in 2 dimensions. It is based on\nthe Tensor Renormalization Group (TRG) protocol, adapted to operate entirely at\nthe level of fields. This strategy was applied in Ref.[1] to the much simpler\ncase of a free boson, obtaining an excellent performance. Here we include an\narbitrary self-interaction and treat it in the context of perturbation theory.\nA real space analogue of the Wilsonian effective action and its expansion in\nFeynman graphs is proposed. Using a $\\lambda \\phi^4$ theory for benchmark, we\nevaluate the order $\\lambda$ correction to the free energy. The results show a\nfast convergence with the bond dimension, implying that our algorithm captures\nwell the effect of interaction on entanglement.\n"}
+{"abstract": "  Quantifying the composition of the material in protoplanetary disks is\nparamount to determining the potential for exoplanetary systems to produce and\nsupport habitable environments. A key complex organic molecule (COM) to detect\nis methanol (CH3OH). CH3OH primarily forms at low temperatures via the\nhydrogenation of CO ice on the surface of icy dust grains and is a necessary\nbasis for the formation of more complex species like amino acids and proteins.\nWe report the detection of CH3OH in a disk around a young, luminous A-type star\nHD100546. This disk is warm and therefore does not host a significant CO ice\nreservoir. We argue that the CH3OH cannot form in situ, and hence, this disk\nhas likely inherited COMs rich ice from an earlier cold dark cloud phase. This\nis strong evidence that at least some of the organic material survives the disk\nformation process and can then be incorporated into forming planets, moons and\ncomets. Therefore, crucial pre-biotic chemical evolution already takes place in\ndark star-forming clouds.\n"}
+{"abstract": "  3D object detection in point clouds is a challenging vision task that\nbenefits various applications for understanding the 3D visual world. Lots of\nrecent research focuses on how to exploit end-to-end trainable Hough voting for\ngenerating object proposals. However, the current voting strategy can only\nreceive partial votes from the surfaces of potential objects together with\nsevere outlier votes from the cluttered backgrounds, which hampers full\nutilization of the information from the input point clouds. Inspired by the\nback-tracing strategy in the conventional Hough voting methods, in this work,\nwe introduce a new 3D object detection method, named as Back-tracing\nRepresentative Points Network (BRNet), which generatively back-traces the\nrepresentative points from the vote centers and also revisits complementary\nseed points around these generated points, so as to better capture the fine\nlocal structural features surrounding the potential objects from the raw point\nclouds. Therefore, this bottom-up and then top-down strategy in our BRNet\nenforces mutual consistency between the predicted vote centers and the raw\nsurface points and thus achieves more reliable and flexible object localization\nand class prediction results. Our BRNet is simple but effective, which\nsignificantly outperforms the state-of-the-art methods on two large-scale point\ncloud datasets, ScanNet V2 (+7.5% in terms of mAP@0.50) and SUN RGB-D (+4.7% in\nterms of mAP@0.50), while it is still lightweight and efficient. Code will be\navailable at https://github.com/cheng052/BRNet.\n"}
+{"abstract": "  Scientists frequently generalize population level causal quantities such as\naverage treatment effect from a source population to a target population. When\nthe causal effects are heterogeneous, differences in subject characteristics\nbetween the source and target populations may make such a generalization\ndifficult and unreliable. Reweighting or regression can be used to adjust for\nsuch differences when generalizing. However, these methods typically suffer\nfrom large variance if there is limited covariate distribution overlap between\nthe two populations. We propose a generalizability score to address this issue.\nThe score can be used as a yardstick to select target subpopulations for\ngeneralization. A simplified version of the score avoids using any outcome\ninformation and thus can prevent deliberate biases associated with inadvertent\naccess to such information. Both simulation studies and real data analysis\ndemonstrate convincing results for such selection.\n"}
+{"abstract": "  Counterfactual inference is a useful tool for comparing outcomes of\ninterventions on complex systems. It requires us to represent the system in\nform of a structural causal model, complete with a causal diagram,\nprobabilistic assumptions on exogenous variables, and functional assignments.\nSpecifying such models can be extremely difficult in practice. The process\nrequires substantial domain expertise, and does not scale easily to large\nsystems, multiple systems, or novel system modifications. At the same time,\nmany application domains, such as molecular biology, are rich in structured\ncausal knowledge that is qualitative in nature. This manuscript proposes a\ngeneral approach for querying a causal biological knowledge graph, and\nconverting the qualitative result into a quantitative structural causal model\nthat can learn from data to answer the question. We demonstrate the\nfeasibility, accuracy and versatility of this approach using two case studies\nin systems biology. The first demonstrates the appropriateness of the\nunderlying assumptions and the accuracy of the results. The second demonstrates\nthe versatility of the approach by querying a knowledge base for the molecular\ndeterminants of a severe acute respiratory syndrome coronavirus 2\n(SARS-CoV-2)-induced cytokine storm, and performing counterfactual inference to\nestimate the causal effect of medical countermeasures for severely ill\npatients.\n"}
+{"abstract": "  In this work, we are dealing with some properties relating the zeros of a\npolynomial and its Mahler measure. We provide estimates on the number of real\nzeros of a polynomial, lower bounds on the distance between the zeros of a\npolynomial and non-zeros located on the unit circle and a lower bound on the\nnumber of zeros of a polynomial in the disk $\\{\\vert x-1\\vert<1\\}$.\n"}
+{"abstract": "  We design blackbox transfer-based targeted adversarial attacks for an\nenvironment where the attacker's source model and the target blackbox model may\nhave disjoint label spaces and training datasets. This scenario significantly\ndiffers from the \"standard\" blackbox setting, and warrants a unique approach to\nthe attacking process. Our methodology begins with the construction of a class\ncorrespondence matrix between the whitebox and blackbox label sets. During the\nonline phase of the attack, we then leverage representations of highly related\nproxy classes from the whitebox distribution to fool the blackbox model into\npredicting the desired target class. Our attacks are evaluated in three complex\nand challenging test environments where the source and target models have\nvarying degrees of conceptual overlap amongst their unique categories.\nUltimately, we find that it is indeed possible to construct targeted\ntransfer-based adversarial attacks between models that have non-overlapping\nlabel spaces! We also analyze the sensitivity of attack success to properties\nof the clean data. Finally, we show that our transfer attacks serve as powerful\nadversarial priors when integrated with query-based methods, markedly boosting\nquery efficiency and adversarial success.\n"}
+{"abstract": "  The heavy traffic and related issues have always been concerns for modern\ncities. With the help of deep learning and reinforcement learning, people have\nproposed various policies to solve these traffic-related problems, such as\nsmart traffic signal control systems and taxi dispatching systems. People\nusually validate these policies in a city simulator, since directly applying\nthem in the real city introduces real cost. However, these policies validated\nin the city simulator may fail in the real city if the simulator is\nsignificantly different from the real world. To tackle this problem, we need to\nbuild a real-like traffic simulation system. Therefore, in this paper, we\npropose to learn the human routing model, which is one of the most essential\npart in the traffic simulator. This problem has two major challenges. First,\nhuman routing decisions are determined by multiple factors, besides the common\ntime and distance factor. Second, current historical routes data usually covers\njust a small portion of vehicles, due to privacy and device availability\nissues. To address these problems, we propose a theory-guided residual network\nmodel, where the theoretical part can emphasize the general principles for\nhuman routing decisions (e.g., fastest route), and the residual part can\ncapture drivable condition preferences (e.g., local road or highway). Since the\ntheoretical part is composed of traditional shortest path algorithms that do\nnot need data to train, our residual network can learn human routing models\nfrom limited data. We have conducted extensive experiments on multiple\nreal-world datasets to show the superior performance of our model, especially\nwith small data. Besides, we have also illustrated why our model is better at\nrecovering real routes through case studies.\n"}
+{"abstract": "  In the Ellis wormhole metrics we study characteristics of fluid dynamics and\nthe properties of linear sound waves. By implying the energy-momentum equation\nand the continuity equation in the general relativistic manner we examine the\nflow dynamics and solve the corresponding equations for a relatively simple\ncase - radial flow. To study the linear sound waves the equations governing the\nmentioned physical system are linearized and solved and interesting\ncharacteristic properties are found.\n"}
+{"abstract": "  Unit tangent bundles $UM$ of semi-Riemannian manifolds $M$ are shown to be\nexamples of dynamical Legendrian contact structures, which were defined in\nrecent work [25] of Sykes-Zelenko to generalize leaf spaces of 2-nondegenerate\nCR manifolds. In doing so, Sykes-Zelenko extended the classification in\nPorter-Zelenko [20] of regular, 2-nondegenerate CR structures to those that can\nbe recovered from their leaf space. The present paper treats dynamical\nLegendrian contact structures associated with 2-nondegenerate CR structures\nwhich were called \"strongly regular\" in Porter-Zelenko, named \"L-contact\nstructures.\" Closely related to Lie-contact structures, L-contact manifolds\nhave homogeneous models given by isotropic Grassmannians of complex 2-planes\nwhose algebra of infinitesimal symmetries is one of $\\mathfrak{so}(p+2,q+2)$ or\n$\\mathfrak{so}^*(2p+4)$ for $p\\geq1$, $q\\geq0$. Each 2-plane in the homogeneous\nmodel is a split-quaternionic or quaternionic line, respectively, and more\ngeneral L-contact structures arise on contact manifolds with hypercomplex\nstructures, unit tangent bundles being a prime example. The Ricci curvature\ntensor of $M$ is used to define the \"Ricci-shifted\" L-contact structure on\n$UM$, whose Nijenhuis tensor vanishes when $M$ is conformally flat. In the\nlanguage of Sykes-Zelenko (for $M$ analytic), such $UM$ is the leaf space of a\n2-nondegenerate CR manifold which is \"recoverable\" from $UM$, providing a new\nsource of examples of 2-nondegenerate CR structures.\n"}
+{"abstract": "  The matching literature often recommends market centralization under the\nassumption that agents know their own preferences and that their preferences\nare fixed. We find counterevidence to this assumption in a quasi-experiment. In\nGermany's university admissions, a clearinghouse implements the early stages of\nthe Gale-Shapley algorithm in real time. We show that early offers made in this\ndecentralized phase, although not more desirable, are accepted more often than\nlater ones. These results, together with survey evidence and a theoretical\nmodel, are consistent with students' costly learning about universities. We\npropose a hybrid mechanism to combine the advantages of decentralization and\ncentralization.\n"}
+{"abstract": "  Dynamical studies of asteroid populations in retrograde orbits, that is with\norbital inclinations greater than 90 degrees, are interesting because the\norigin of such orbits is still unexplained. Generally, the population of\nretrograde asteroids includes mostly Centaurs and transneptunian objects\n(TNOs). A special case is the near-Earth object (343158) 2009 HC82 from the\nApollo group. Another interesting object is the comet 333P/LINEAR, which for\nseveral years was considered the second retrograde object approaching Earth.\nAnother comet in retrograde orbit, 161P Hartley/IRAS appears to be an object of\nsimilar type. Thanks to the large amount of observational data for these two\ncomets, we tested various models of cometary non-gravitational forces applied\nto their dynamics.\n  The goal was to estimate which of non-gravitational perturbations could\naffect the stability of retrograde bodies. In principle, we study the local\nstability by measuring the divergence of nearby orbits.\n  We numerically determined Lyapunov characteristic indicators (LCI) and the\nassociated Lyapunov times (LT). This time, our calculations were extended by\nmore advanced models of non-gravitational perturbations (i.e. Yarkovsky drift\nand in selected cases cometary forces). This allowed us to estimate chaos in\nthe Lyapunov sense.\n  We found that the Yarkovsky effect for obliquities of $\\gamma=0^{\\circ}$ and\n$\\gamma=180^{\\circ}$ can change the LT substantially. In most cases, for the\nprograde rotation, we received more stable solutions. Moreover, we confirmed\nthe role of retrograde resonances in this process. Additionally, the studied\ncometary effects also significantly influence the long-term behaviour of the\nselected comets. The LT can reach values from 100 to over 1000 years.\nConclusions. All of our results indicate that the use of models with\nnon-gravitational effects for retrograde bodies is clearly justified.\n"}
+{"abstract": "  Run-time domain shifts from training-phase domains are common in sensing\nsystems designed with deep learning. The shifts can be caused by sensor\ncharacteristic variations and/or discrepancies between the design-phase model\nand the actual model of the sensed physical process. To address these issues,\nexisting transfer learning techniques require substantial target-domain data\nand thus incur high post-deployment overhead. This paper proposes to exploit\nthe first principle governing the domain shift to reduce the demand on\ntarget-domain data. Specifically, our proposed approach called PhyAug uses the\nfirst principle fitted with few labeled or unlabeled source/target-domain data\npairs to transform the existing source-domain training data into augmented data\nfor updating the deep neural networks. In two case studies of keyword spotting\nand DeepSpeech2-based automatic speech recognition, with 5-second unlabeled\ndata collected from the target microphones, PhyAug recovers the recognition\naccuracy losses due to microphone characteristic variations by 37% to 72%. In a\ncase study of seismic source localization with TDoA fngerprints, by exploiting\nthe frst principle of signal propagation in uneven media, PhyAug only requires\n3% to 8% of labeled TDoA measurements required by the vanilla fingerprinting\napproach in achieving the same localization accuracy.\n"}
+{"abstract": "  We propose Super Odometry, a high-precision multi-modal sensor fusion\nframework, providing a simple but effective way to fuse multiple sensors such\nas LiDAR, camera, and IMU sensors and achieve robust state estimation in\nperceptually-degraded environments. Different from traditional sensor-fusion\nmethods, Super Odometry employs an IMU-centric data processing pipeline, which\ncombines the advantages of loosely coupled methods with tightly coupled methods\nand recovers motion in a coarse-to-fine manner. The proposed framework is\ncomposed of three parts: IMU odometry, visual-inertial odometry, and\nlaser-inertial odometry. The visual-inertial odometry and laser-inertial\nodometry provide the pose prior to constrain the IMU bias and receive the\nmotion prediction from IMU odometry. To ensure high performance in real-time,\nwe apply a dynamic octree that only consumes 10 % of the running time compared\nwith a static KD-tree. The proposed system was deployed on drones and ground\nrobots, as part of Team Explorer's effort to the DARPA Subterranean Challenge\nwhere the team won $1^{st}$ and $2^{nd}$ place in the Tunnel and Urban\nCircuits, respectively.\n"}
+{"abstract": "  Visual Transformers (VTs) are emerging as an architectural paradigm\nalternative to Convolutional networks (CNNs). Differently from CNNs, VTs can\ncapture global relations between image elements and they potentially have a\nlarger representation capacity. However, the lack of the typical convolutional\ninductive bias makes these models more data-hungry than common CNNs. In fact,\nsome local properties of the visual domain which are embedded in the CNN\narchitectural design, in VTs should be learned from samples. In this paper, we\nempirically analyse different VTs, comparing their robustness in a small\ntraining-set regime, and we show that, despite having a comparable accuracy\nwhen trained on ImageNet, their performance on smaller datasets can be largely\ndifferent. Moreover, we propose a self-supervised task which can extract\nadditional information from images with only a negligible computational\noverhead. This task encourages the VTs to learn spatial relations within an\nimage and makes the VT training much more robust when training data are scarce.\nOur task is used jointly with the standard (supervised) training and it does\nnot depend on specific architectural choices, thus it can be easily plugged in\nthe existing VTs. Using an extensive evaluation with different VTs and\ndatasets, we show that our method can improve (sometimes dramatically) the\nfinal accuracy of the VTs. Our code is available at:\nhttps://github.com/yhlleo/VTs-Drloc.\n"}
+{"abstract": "  Occlusion is very challenging in pedestrian detection. In this paper, we\npropose a simple yet effective method named V2F-Net, which explicitly\ndecomposes occluded pedestrian detection into visible region detection and full\nbody estimation. V2F-Net consists of two sub-networks: Visible region Detection\nNetwork (VDN) and Full body Estimation Network (FEN). VDN tries to localize\nvisible regions and FEN estimates full-body box on the basis of the visible\nbox. Moreover, to further improve the estimation of full body, we propose a\nnovel Embedding-based Part-aware Module (EPM). By supervising the visibility\nfor each part, the network is encouraged to extract features with essential\npart information. We experimentally show the effectiveness of V2F-Net by\nconducting several experiments on two challenging datasets. V2F-Net achieves\n5.85% AP gains on CrowdHuman and 2.24% MR-2 improvements on CityPersons\ncompared to FPN baseline. Besides, the consistent gain on both one-stage and\ntwo-stage detector validates the generalizability of our method.\n"}
+{"abstract": "  Flow polytopes are an important class of polytopes in combinatorics whose\nlattice points and volumes have interesting properties and relations. The\nChan-Robbins-Yuen (CRY) polytope is a flow polytope with normalized volume\nequal to the product of consecutive Catalan numbers. Zeilberger proved this by\nevaluating the Morris constant term identity, but no combinatorial proof is\nknown. There is a refinement of this formula that splits the largest Catalan\nnumber into Narayana numbers, which M\\'esz\\'aros gave an interpretation as the\nvolume of a collection of flow polytopes. We introduce a new refinement of the\nMorris identity with combinatorial interpretations both in terms of lattice\npoints and volumes of flow polytopes. Our results generalize M\\'esz\\'aros's\nconstruction and a recent flow polytope interpretation of the Morris identity\nby Corteel-Kim-M\\'esz\\'aros. We prove the product formula of our refinement\nfollowing the strategy of the Baldoni-Vergne proof of the Morris identity.\nLastly, we study a symmetry of the Morris identity bijectively using the\nDanilov-Karzanov-Koshevoy triangulation of flow polytopes and a bijection of\nM\\'esz\\'aros-Morales-Striker.\n"}
+{"abstract": "  Using the recently mooted Galilean gauge theory we have constructed the model\nfor the Schroedinger field interacting wuth gravity which is also dynamical.\nThe dynamics of gravity is dictated by the Newtonian action in the\nNewton-Cartan spacetime. The theory is highly constrained . An elaborate\nanalysis of the constraints of the theory have been performed. The symmetries\nare explicitly verified and the uniqueness of the model has been established.\nTo the best of our knowledge both the model and its constraints structure are\nunique in the literature\n"}
+{"abstract": "  Photo-realistic neural reconstruction and rendering of the human portrait are\ncritical for numerous VR/AR applications. Still, existing solutions inherently\nrely on multi-view capture settings, and the one-shot solution to get rid of\nthe tedious multi-view synchronization and calibration remains extremely\nchallenging. In this paper, we propose MirrorNeRF - a one-shot neural portrait\nfree-viewpoint rendering approach using a catadioptric imaging system with\nmultiple sphere mirrors and a single high-resolution digital camera, which is\nthe first to combine neural radiance field with catadioptric imaging so as to\nenable one-shot photo-realistic human portrait reconstruction and rendering, in\na low-cost and casual capture setting. More specifically, we propose a\nlight-weight catadioptric system design with a sphere mirror array to enable\ndiverse ray sampling in the continuous 3D space as well as an effective online\ncalibration for the camera and the mirror array. Our catadioptric imaging\nsystem can be easily deployed with a low budget and the casual capture ability\nfor convenient daily usages. We introduce a novel neural warping radiance field\nrepresentation to learn a continuous displacement field that implicitly\ncompensates for the misalignment due to our flexible system setting. We further\npropose a density regularization scheme to leverage the inherent geometry\ninformation from the catadioptric data in a self-supervision manner, which not\nonly improves the training efficiency but also provides more effective density\nsupervision for higher rendering quality. Extensive experiments demonstrate the\neffectiveness and robustness of our scheme to achieve one-shot photo-realistic\nand high-quality appearance free-viewpoint rendering for human portrait scenes.\n"}
+{"abstract": "  In this work, we investigate non-Hermitian elastic waveguides with\nperiodically applied proportional feedback efforts, implemented through\npiezoelectric sensors and actuators. Using onedimensional spectral models for\nlongitudinal motion, it is shown that dispersion diagrams of this family of\nstructures exhibit non-reciprocal imaginary frequency components, manifesting\nas wave attenuation or amplification along opposite directions for all pass\nbands. The effects of positive and negative proportional feedback, as well as\nlocal and non-local actuation are investigated. Overall, switching the sign of\nthe feedback effort inverts the amplification direction, while increasing the\ndegree of non-locality produces splitting of the pass bands into multiple bands\nwith interchanging non-reciprocal behavior. Furthermore, skin modes localized\nat the boundaries of finite domains are investigated and successfully predicted\nby the winding number of the complex dispersion bands. These results contribute\nto recent efforts in designing metamaterials with novel properties associated\nwith the physics of non-Hermitian systems, which may find fruitful\ntechnological applications relying on vibration and noise control, wave\nlocalization, filtering and multiplexing.\n"}
+{"abstract": "  We establish an identity for E f (Y) -E f (X), when X and Y both have matrix\nvariateskew-normal distributions and the function f fulfills some weak\nconditions. Thecharacteristic function of matrix variate skew normal\ndistribution is then derived. Finally,we make use of it to derive some\nnecessary and sucient conditions for the comparisonof matrix variate\nskew-normal distributions under six di erent orders, such as usualstochastic\norder, convex order, increasing convex order, upper orthant order,directionally\nconvex order and supermodular order.\n"}
+{"abstract": "  The manifesto of the current article is to investigate the compact\nanisotropic matter profiles in the context of one of the modified gravitational\ntheories, known as $f(\\mathcal{R}, \\mathcal{T})$ gravity, where $\\mathcal{R}$\nis a Ricci Scalar and $\\mathcal{T}$ is the trace of the energy-momentum tensor.\nTo achieve the desired goal, we capitalized on the spherical symmetric\nspace-time and utilized the embedding class-1 solution via Karmarkar's\ncondition in modeling the matter profiles. To calculate the unidentified\nconstraints, Schwarzschild exterior solution along with experimental statistics\nof three different stars LMC X-4, Cen X-3, and EXO 1785-248 are taken under\nconsideration. For the evaluation of the dynamical equations, a unique model\n$f(\\mathcal{R}, \\mathcal{T})=\\mathcal{R}+\\beta \\mathcal{R}^2+\\lambda\n\\mathcal{T}$ has been considered, with $\\beta$ and $\\lambda$ being the real\nconstants. Different physical aspects have been exploited with the help of\nmodified dynamical equations. Conclusively, all the stars under observations\nare realistic, stable, and are free from all singularities.\n"}
+{"abstract": "  With the progress in AI-based facial forgery (i.e., deepfake), people are\nincreasingly concerned about its abuse. Albeit effort has been made for\ntraining classification (also known as deepfake detection) models to recognize\nsuch forgeries, existing models suffer from poor generalization to unseen\nforgery technologies and high sensitivity to changes in image/video quality. In\nthis paper, we advocate adversarial training for improving the generalization\nability to both unseen facial forgeries and unseen image/video qualities. We\nbelieve training with samples that are adversarially crafted to attack the\nclassification models improves the generalization ability considerably.\nConsidering that AI-based face manipulation often leads to high-frequency\nartifacts that can be easily spotted by models yet difficult to generalize, we\nfurther propose a new adversarial training method that attempts to blur out\nthese specific artifacts, by introducing pixel-wise Gaussian blurring models.\nWith adversarial training, the classification models are forced to learn more\ndiscriminative and generalizable features, and the effectiveness of our method\ncan be verified by plenty of empirical evidence. Our code will be made publicly\navailable.\n"}
+{"abstract": "  The surface brightness -- colour relation (SBCR) is a basic tool in\nestablishing precise and accurate distances within the Local Group. Detached\neclipsing binary stars with accurately determined radii and trigonometric\nparallaxes allow for a calibration of the SBCRs with unprecedented accuracy. We\nanalysed four nearby eclipsing binary stars containing late F-type main\nsequence components: AL Ari, AL Dor, FM Leo and BN Scl. We determined very\nprecise spectroscopic orbits and combined them with high precision ground- and\nspace-based photometry. We derived the astrophysical parameters of their\ncomponents with mean errors of 0.1% for mass and 0.4% for radius. We combined\nthose four systems with another 24 nearby eclipsing binaries with accurately\nknown radii from the literature for which $Gaia$ EDR3 parallaxes are available,\nin order to derive the SBCRs. The resulting SBCRs cover stellar spectral types\nfrom B9 V to G7 V. For calibrations we used Johnson optical $B$ and $V$, $Gaia$\n$G_{\\rm BP}$ and $G$ and 2MASS $JHK$ bands. The most precise relations are\ncalibrated using the infrared $K$ band and allow to predict angular diameters\nof A-, F-, and G-type dwarf and subgiant stars with a precision of 1%.\n"}
+{"abstract": "  The Rubin Causal Model (RCM) is a framework that allows to define the causal\neffect of an intervention as a contrast of potential outcomes. In recent years,\nseveral methods have been developed under the RCM to estimate causal effects in\ntime series settings. None of these makes use of ARIMA models, which are\ninstead very common in the econometrics literature. In this paper, we propose a\nnovel approach, C-ARIMA, to define and estimate the causal effect of an\nintervention in a time series setting under the RCM. We first formalize the\nassumptions enabling the definition, the estimation and the attribution of the\neffect to the intervention; we then check the validity of the proposed method\nwith an extensive simulation study, comparing its performance against a\nstandard intervention analysis approach. In the empirical application, we use\nC-ARIMA to assess the causal effect of a permanent price reduction on\nsupermarket sales. The CausalArima R package provides an implementation of our\nproposed approach.\n"}
+{"abstract": "  The renormalization group (RG) equation in the self-consistent local\npotential approximation (SC-LPA) suggested earlier for the description of\ncontinuous phase transitions in lattice models of the Landau-Ginzburg type has\nbeen applied to the solution of the spin-1 Blume-Capel model on the simple\ncubic lattice. The calculated transition temperatures of both continuous and\nthe first-order phase transitions (FOPTs) in zero external field have been\nfound to be in excellent agreement with the best available estimates. It has\nbeen argued that the SC-LPA RG equation may give more accurate and complete\ndescription of the FOPTs than those reported in alternative approaches. It has\nbeen shown that the SC-LPA RG equation can be cast in the form of the\ngeneralized Burgers' equation (GBE). In this formulation of the RG the FOPTs\nhave been shown to assume the form of the shock-wave solutions of GBE in the\ninviscid limit. Universality of the RG flow in the vicinity of the fixed point\ndescribing FOPTs has been discussed.\n"}
+{"abstract": "  In light of recently revised observational measurements of the radius and\nspectroscopic parameters of the extremely old and metal-poor Gaia benchmark\nstar HD 140283 -- also known as the Methuselah star due to prior suggestions\nthat its age is in tension with the age of the Universe -- we present new, best\nestimates for the star's mass and age from stellar modeling. These are derived\nusing 1D stellar evolutionary tracks computed with MESA and the most up-to-date\nmeasurements from CHARA interferometry. Excluding modeling variance from the\nuncertainties, we report a mass of $0.809 \\pm 0.001 M_{\\odot}$ and an age of\n$12.01 \\pm 0.05$ Gyr ($1 \\sigma$). When dominant sources of modeling\nuncertainty are taken into account, we report $0.81 \\pm 0.05 M_{\\odot}$ and $12\n\\pm 0.5$ Gyr, respectively. These results are consistent with recent\nliterature, and the best-fitting age is not in conflict with the currently\naccepted age of the universe ($13.5$ Gyr; arXiv:1303.5089 [astro-ph.CO]).\n"}
+{"abstract": "  Direct Current (DC) power distribution has gained attention in the\nResidential Nanogrids (RNGs) due to the substantial increase in the number of\nroof-top Photovoltaic (PV) systems and internally DC appliances used in\nbuildings. Using DC distribution improves the efficiency of the RNGs compared\nto AC distribution. This paper investigated the efficiency of a DC RNG for low\nand high distribution voltage levels by exploring reasons for power losses. The\nstudied DC RNG consisted of various types of local loads, on-site PV\ngeneration, and battery storage systems. The realistic load, PV profiles, and\nconverter efficiency curves were used to make the analysis more accurate. In\naddition, three load profiles with low, medium, and high power consumptions\nwere considered to study the load impacts on the overall system efficiency.\n"}
+{"abstract": "  We propose a novel method that tackles the problem of unsupervised domain\nadaptation for semantic segmentation by maximizing the cosine similarity\nbetween the source and the target domain at the feature level. A segmentation\nnetwork mainly consists of two parts, a feature extractor and a classification\nhead. We expect that if we can make the two domains have small domain gap at\nthe feature level, they would also have small domain discrepancy at the\nclassification head. Our method computes a cosine similarity matrix between the\nsource feature map and the target feature map, then we maximize the elements\nexceeding a threshold to guide the target features to have high similarity with\nthe most similar source feature. Moreover, we use a class-wise source feature\ndictionary which stores the latest features of the source domain to prevent the\nunmatching problem when computing the cosine similarity matrix and be able to\ncompare a target feature with various source features from various images.\nThrough extensive experiments, we verify that our method gains performance on\ntwo unsupervised domain adaptation tasks (GTA5$\\to$ Cityscaspes and\nSYNTHIA$\\to$ Cityscapes).\n"}
+{"abstract": "  The distributed dual ascent is an established algorithm to solve strongly\nconvex multi-agent optimization problems with separable cost functions, in the\npresence of coupling constraints. In this paper, we study its asynchronous\ncounterpart. Specifically, we assume that each agent only relies on the\noutdated information received from some neighbors. Differently from the\nexisting randomized and dual block-coordinate schemes, we show convergence\nunder heterogeneous delays, communication and update frequencies. Consequently,\nour asynchronous dual ascent algorithm can be implemented without requiring any\ncoordination between the agents.\n"}
+{"abstract": "  The state of the art review broadly oversees the use of novel research\nutilized in the creation of virtual environments applied in interactive art\nexperiences, with a specific focus on the application of procedural animation\nin spatially augmented reality (SAR) exhibitions. These art exhibitions\nfrequently combine sensory displays that appeal, replace, and augment the\nvisual, auditory and touch or haptic senses. We analyze and break down\nart-technology related innovations in the last three years, and thoroughly\nidentify the most recent and vibrant applications of interactive art\nexperiences in the review of numerous installation applications, studies, and\nevents. Display mediums such as virtual reality, augmented reality, mixed\nreality, and robotics are overviewed in the context of art experiences such as\nvisual art museums, park or historic site tours, live concerts, and theatre. We\nexplore research and extrapolate how recent innovations can lead to different\napplications that will be seen in the future.\n"}
+{"abstract": "  Human history has been shaped by armed conflicts. Rather than large-scale\ninterstate wars, low-intensity attacks have been more prevalent in the\npost-World War era. These attacks are often carried out by non-state armed\ngroups (NAGs), which are supported by host states (HSs). We analyze the global\nbipartite network of NAG-HS support and its evolution over the period of\n1945-2010. We find striking parallels to ecological networks such as\nmutualistic and parasitic forms of support, and a nested and modular network\narchitecture. The nestedness emerges from preferential behaviors: highly\nconnected players are more likely to both gain and lose connections.\nLong-persisting major modules are identified, reflecting both regional and\ntrans-regional interests, which show significant turnover in their membership,\ncontrary to the transitory ones. Revealing this architecture further enables\nthe identification of actor's roles and provide insights for effective\nintervention strategies.\n"}
+{"abstract": "  Advertising click fraud detection plays one of the vital roles in current\nE-commerce websites as advertising is an essential component of its business\nmodel. It aims at, given a set of corresponding features, e.g., demographic\ninformation of users and statistical features of clicks, predicting whether a\nclick is fraudulent or not in the community. Recent efforts attempted to\nincorporate attributed behavior sequence and heterogeneous network for\nextracting complex features of users and achieved significant effects on click\nfraud detection. In this paper, we propose a Multimodal and Contrastive\nlearning network for Click Fraud detection (MCCF). Specifically, motivated by\nthe observations on differences of demographic information, behavior sequences\nand media relationship between fraudsters and genuine users on E-commerce\nplatform, MCCF jointly utilizes wide and deep features, behavior sequence and\nheterogeneous network to distill click representations. Moreover, these three\nmodules are integrated by contrastive learning and collaboratively contribute\nto the final predictions. With the real-world datasets containing 2.54 million\nclicks on Alibaba platform, we investigate the effectiveness of MCCF. The\nexperimental results show that the proposed approach is able to improve AUC by\n7.2% and F1-score by 15.6%, compared with the state-of-the-art methods.\n"}
+{"abstract": "  Conformal Ricci solitons are self similar solutions of the conformal Ricci\nflow equation. This paper deals with the study of conformal Ricci solitons\nwithin the framework of warped product manifolds which extends the notion of\nusual Riemannian product manifolds. First, we prove that if a warped product\nmanifold admits conformal Ricci soliton then the base and the fiber also share\nthe same property. In the next section the characterization of conformal Ricci\nsolitons on warped product manifolds in terms of Killing and conformal vector\nfields has been studied. Next, we prove that a warped product manifold\nadmitting conformal Ricci soliton with concurrent potential vector field is\nRicci flat. Finally, an application of conformal Ricci soliton on a class of\nwarped product spacetimes namely, generalized Robertson-Walker spacetimes has\nbeen discussed.\n"}
+{"abstract": "  Visual dynamic complexity is a ubiquitous, hidden attribute of the visual\nworld that every dynamic vision system is faced with. However, it is implicit\nand intractable which has never been quantitatively described due to the\ndifficulty in defending temporal features correlated to spatial image\ncomplexity. To fill this vacancy, we propose a novel bio-robotic approach to\nprofile visual dynamic complexity which can be used as a new metric. Here we\napply a state-of-the-art brain-inspired motion detection neural network model\nto explicitly profile such complexity associated with spatial-temporal\nfrequency (SF-TF) of visual scene. This model is for the first time implemented\nin an autonomous micro-mobile robot which navigates freely in an arena with\nvisual walls displaying moving sine-wave grating or cluttered natural scene.\nThe neural dynamic response can make reasonable prediction on surrounding\ncomplexity since it can be mapped monotonically to varying SF-TF of visual\nscene. The experiments show this approach is flexible to different visual\nscenes for profiling the dynamic complexity. We also use this metric as a\npredictor to investigate the boundary of another collision detection visual\nsystem performing in changing environment with increasing dynamic complexity.\nThis research demonstrates a new paradigm of using biologically plausible\nvisual processing scheme to estimate dynamic complexity of visual scene from\nboth spatial and temporal perspectives, which could be beneficial to predicting\ninput complexity when evaluating dynamic vision systems.\n"}
+{"abstract": "  We use photometric redshifts and statistical background subtraction to\nmeasure stellar mass functions in galaxy group-mass\n($4.5-8\\times10^{13}~\\mathrm{M}_\\odot$) haloes at $1<z<1.5$. Groups are\nselected from COSMOS and SXDF, based on X-ray imaging and sparse spectroscopy.\nStellar mass ($M_{\\mathrm{stellar}}$) functions are computed for quiescent and\nstar-forming galaxies separately, based on their rest-frame $UVJ$ colours. From\nthese we compute the quiescent fraction and quiescent fraction excess (QFE)\nrelative to the field as a function of $M_{\\mathrm{stellar}}$. QFE increases\nwith $M_{\\mathrm{stellar}}$, similar to more massive clusters at $1<z<1.5$.\nThis contrasts with the apparent separability of $M_{\\mathrm{stellar}}$ and\nenvironmental factors on galaxy quiescent fractions at $z\\sim 0$. We then\ncompare our results with higher mass clusters at $1<z<1.5$ and lower redshifts.\nWe find a strong QFE dependence on halo mass at fixed $M_{\\mathrm{stellar}}$;\nwell fit by a logarithmic slope of $\\mathrm{d}(\\mathrm{QFE})/\\mathrm{d}\\log\n(M_{\\mathrm{halo}}) \\sim 0.24 \\pm 0.04$ for all $M_{\\mathrm{stellar}}$ and\nredshift bins. This dependence is in remarkably good qualitative agreement with\nthe hydrodynamic simulation BAHAMAS, but contradicts the observed dependence of\nQFE on $M_{\\mathrm{stellar}}$. We interpret the results using two toy models:\none where a time delay until rapid (instantaneous) quenching begins upon\naccretion to the main progenitor (\"no pre-processing\") and one where it starts\nupon first becoming a satellite (\"pre-processing\"). Delay times appear to be\nhalo mass dependent, with a significantly stronger dependence required without\npre-processing. We conclude that our results support models in which\nenvironmental quenching begins in low-mass ($<10^{14}M_\\odot$) haloes at $z>1$.\n"}
+{"abstract": "  We report an accurate method to determine DNA barcodes from the dwell time\nmeasurement of protein tags (barcodes) along the DNA backbone using Brownian\ndynamics simulation of a model DNA and use a recursive theoretical scheme which\nimproves the measurements to almost 100 % accuracy. The heavier protein tags\nalong the DNA backbone introduce a large speed variation in the chain that can\nbe understood using the idea of non-equilibrium tension propagation theory.\nHowever, from an initial rough characterization of velocities into \"fast\"\n(nucleotides) and \"slow\" (protein tags) domains, we introduce a physically\nmotivated interpolation scheme that enables us to determine the barcode\nvelocities rather accurately. Our theoretical analysis of the motion of the DNA\nthrough a cylindrical nanopore opens up the possibility of its experimental\nrealization and carries over to multi-nanopore devices used for barcoding.\n"}
+{"abstract": "  This paper presents a few observations about pro-Kremlin propaganda between\n2015 and early 2021 with a dataset from the East Stratcom Task Force (ESTF),\nwhich is affiliated with the European Union (EU) but working independently from\nit. Instead of focusing on misinformation and disinformation, the observations\nare motivated by classical propaganda research and the ongoing transformation\nof media systems. According to the tentative results, (i) the propaganda can be\nassumed to target both domestic and foreign audiences. Of the countries and\nregions discussed, (ii) Russia, Ukraine, the United States, and within Europe,\nGermany, Poland, and the EU have been the most frequently discussed. Also other\nconflict regions such as Syria have often appeared in the propaganda. In terms\nof longitudinal trends, however, (iii) most of these discussions have decreased\nin volume after the digital tsunami in 2016, although the conflict in Ukraine\nseems to have again increased the intensity of pro-Kremlin propaganda. Finally,\n(iv) the themes discussed align with state-centric war propaganda and conflict\nzones, although also post-truth themes frequently appear; from conspiracy\ntheories via COVID-19 to fascism -- anything goes, as is typical to propaganda.\n"}
+{"abstract": "  We study Hamiltonicity in graphs obtained as the union of a deterministic\n$n$-vertex graph $H$ with linear degrees and a $d$-dimensional random geometric\ngraph $G^d(n,r)$, for any $d\\geq1$. We obtain an asymptotically optimal bound\non the minimum $r$ for which a.a.s. $H\\cup G^d(n,r)$ is Hamiltonian. Our proof\nprovides a linear time algorithm to find a Hamilton cycle in such graphs.\n"}
+{"abstract": "  Background: Data errors are a common challenge in machine learning (ML)\nprojects and generally cause significant performance degradation in ML-enabled\nsoftware systems. To ensure early detection of erroneous data and avoid\ntraining ML models using bad data, research and industrial practice suggest\nincorporating a data validation process and tool in ML system development\nprocess.\n  Aim: The study investigates the adoption of a data validation process and\ntool in industrial ML projects. The data validation process demands significant\nengineering resources for tool development and maintenance. Thus, it is\nimportant to identify the best practices for their adoption especially by\ndevelopment teams that are in the early phases of deploying ML-enabled software\nsystems.\n  Method: Action research was conducted at a large-software intensive\norganization in telecommunications, specifically within the analytics R\\&D\norganization for an ML use case of classifying faults from returned hardware\ntelecommunication devices.\n  Results: Based on the evaluation results and learning from our action\nresearch, we identified three best practices, three benefits, and two barriers\nto adopting the data validation process and tool in ML projects. We also\npropose a data validation framework (DVF) for systematizing the adoption of a\ndata validation process.\n  Conclusions: The results show that adopting a data validation process and\ntool in ML projects is an effective approach of testing ML-enabled software\nsystems. It requires having an overview of the level of data (feature, dataset,\ncross-dataset, data stream) at which certain data quality tests can be applied.\n"}
+{"abstract": "  Hydrogen intensity mapping is a new field in astronomy that promises to make\nthree-dimensional maps of the matter distribution of the Universe using the\nredshifted $21\\,\\textrm{cm}$ line of neutral hydrogen gas (HI). Several ongoing\nand upcoming radio interferometers, such as Tianlai, CHIME, HERA, HIRAX, etc.\nare using this technique. These instruments are designed to map large swaths of\nthe sky by drift scanning over periods of many months. One of the challenges of\nthe observations is that the daytime data is contaminated by strong radio\nsignals from the Sun. In the case of Tianlai, this results in almost half of\nthe measured data being unusable. We try to address this issue by developing an\nalgorithm for solar contamination removal (AlgoSCR) from the radio data. The\nalgorithm is based on an eigenvalue analysis of the visibility matrix, and\nhence is applicable only to interferometers. We apply AlgoSCR to simulated\nvisibilities, as well as real daytime data from the Tianlai dish array. The\nalgorithm can remove most of the solar contamination without seriously\naffecting other sky signals and thus makes the data usable for certain\napplications.\n"}
+{"abstract": "  Support Vector Machines (SVMs) are one of the most popular supervised\nlearning models to classify using a hyperplane in an Euclidean space. Similar\nto SVMs, tropical SVMs classify data points using a tropical hyperplane under\nthe tropical metric with the max-plus algebra. In this paper, first we show\ngeneralization error bounds of tropical SVMs over the tropical projective\nspace. While the generalization error bounds attained via VC dimensions in a\ndistribution-free manner still depend on the dimension, we also show\ntheoretically by extreme value statistics that the tropical SVMs for\nclassifying data points from two Gaussian distributions as well as empirical\ndata sets of different neuron types are fairly robust against the curse of\ndimensionality. Extreme value statistics also underlie the anomalous scaling\nbehaviors of the tropical distance between random vectors with additional noise\ndimensions. Finally, we define tropical SVMs over a function space with the\ntropical metric and discuss the Gaussian function space as an example.\n"}
+{"abstract": "  We search for triangular numbers that are multiples of other triangular\nnumbers. It is found that for any positive non-square integer multiplier, there\nis an infinity of multiples of triangular numbers that are triangular numbers\nand recurrent relations are deduced theoretically. If the multiplier is a\nsquared integer, there is either one or no solution, depending on the\nmultiplier value.\n"}
+{"abstract": "  Due to the system scaling, transient errors caused by external noises, e.g.,\nheat fluxes and particle strikes, have become a growing concern for the current\nand upcoming extreme-scale high-performance-computing (HPC) systems. However,\nsince such errors are still quite rare as compared to no-fault cases, desirable\nsolutions call for low/no-overhead systems that do not compromise the\nperformance under no-fault conditions and also allow very fast fault recovery\nto minimize downtime. In this paper, we present IterPro, a light-weight\ncompiler-assisted resilience technique to quickly and accurately recover\nprocesses from transient-error-induced crashes. IterPro repairs the corrupted\nprocess states on-the-fly upon occurrences of errors, enabling applications to\ncontinue their executions instead of being terminated. IterPro also exploits\nside effects introduced by induction variable based code optimization\ntechniques to improve its recovery capability. To this end, two new code\ntransformation passes are introduced to expose the side effects for resilience\npurposes. We evaluated IterPro with 4 scientific workloads as well as the NPB\nbenchmarks suite. During their normal execution, IterPro incurs almost zero\nruntime overhead and a small, fixed 27MB memory overhead. Meanwhile, IterPro\ncan recover on an average 83.55% of crash-causing errors within dozens of\nmilliseconds with negligible downtime. With such an effective recovery\nmechanism, IterPro could tremendously mitigate the overheads and resource\nrequirements of the resilience subsystem in future extreme-scale systems.\n"}
+{"abstract": "  The framework of database repairs and consistent answers to queries is a\nprincipled approach to managing inconsistent databases. We describe the first\nsystem able to compute the consistent answers of general aggregation queries\nwith the COUNT(A), COUNT(*), SUM(A), MIN(A), and MAX(A) operators, and with or\nwithout grouping constructs. Our system uses reductions to optimization\nversions of Boolean satisfiability (SAT) and then leverages powerful SAT\nsolvers. We carry out an extensive set of experiments on both synthetic and\nreal-world data that demonstrate the usefulness and scalability of this\napproach.\n"}
+{"abstract": "  We consider the Max-Cut problem. Let $G = (V,E)$ be a graph with adjacency\nmatrix $(a_{ij})_{i,j=1}^{n}$. Burer, Monteiro & Zhang proposed to find, for\n$n$ angles $\\left\\{\\theta_1, \\theta_2, \\dots, \\theta_n\\right\\} \\subset [0,\n2\\pi]$, minima of the energy $$ f(\\theta_1, \\dots, \\theta_n) = \\sum_{i,j=1}^{n}\na_{ij} \\cos{(\\theta_i - \\theta_j)}$$ because configurations achieving a global\nminimum leads to a partition of size 0.878 Max-Cut(G). This approach is known\nto be computationally viable and leads to very good results in practice. We\nprove that by replacing $\\cos{(\\theta_i - \\theta_j)}$ with an explicit function\n$g_{\\varepsilon}(\\theta_i - \\theta_j)$ global minima of this new functional\nlead to a $(1-\\varepsilon)$Max-Cut(G). This suggests some interesting\nalgorithms that perform well. It also shows that the problem of finding\napproximate global minima of energy functionals of this type is NP-hard in\ngeneral.\n"}
+{"abstract": "  Reducing the formation of cracks during growth of GaInP/GaInAs/Ge 3-junction\nsolar cells on Ge|Si virtual substrates has been attempted by thinning the\nstructure, namely the Ge bottom cell and the GaInAs middle cell. The\ntheoretical analysis performed using realistic device parameters indicates that\nthe GaInAs middle cell can be drastically thinned to 1000 nm while increasing\nits In content to 8% with an efficiency loss in the 3-junction cell below 3%.\nThe experimental results show that the formation of macroscopic cracks is\nprevented in thinned GaInAs/Ge 2-junction and GaInP/GaInAs/Ge 3-junction cells.\nThese prototype crack-free multijunction cells demonstrate the concept and were\nused to rule out any possible component integration issue. The performance\nmetrics are limited by the high threading dislocation density over 2e7cm-2 in\nthe virtual substrates used, but an almost current matched, crack-free, thinned\n3-junction solar cell is demonstrated, and the pathway towards solar cells with\nhigher voltages identified.\n"}
+{"abstract": "  Supertree methods are tree reconstruction techniques that combine several\nsmaller gene trees (possibly on different sets of species) to build a larger\nspecies tree. The question of interest is whether the reconstructed supertree\nconverges to the true species tree as the number of gene trees increases (that\nis, the consistency of supertree methods). In this paper, we are particularly\ninterested in the convergence rate of the maximum likelihood supertree.\nPrevious studies on the maximum likelihood supertree approach often formulate\nthe question of interest as a discrete problem and focus on reconstructing the\ncorrect topology of the species tree. Aiming to reconstruct both the topology\nand the branch lengths of the species tree, we propose an analytic approach for\nanalyzing the convergence of the maximum likelihood supertree method.\nSpecifically, we consider each tree as one point of a metric space and prove\nthat the distance between the maximum likelihood supertree and the species tree\nconverges to zero at a polynomial rate under some mild conditions. We further\nverify these conditions for the popular exponential error model of gene trees.\n"}
+{"abstract": "  Semantic segmentation in autonomous driving predominantly focuses on learning\nfrom large-scale data with a closed set of known classes without considering\nunknown objects. Motivated by safety reasons, we address the video class\nagnostic segmentation task, which considers unknown objects outside the closed\nset of known classes in our training data. We propose a novel auxiliary\ncontrastive loss to learn the segmentation of known classes and unknown\nobjects. Unlike previous work in contrastive learning that samples the anchor,\npositive and negative examples on an image level, our contrastive learning\nmethod leverages pixel-wise semantic and temporal guidance. We conduct\nexperiments on Cityscapes-VPS by withholding four classes from training and\nshow an improvement gain for both known and unknown objects segmentation with\nthe auxiliary contrastive loss. We further release a large-scale synthetic\ndataset for different autonomous driving scenarios that includes distinct and\nrare unknown objects. We conduct experiments on the full synthetic dataset and\na reduced small-scale version, and show how contrastive learning is more\neffective in small scale datasets. Our proposed models, dataset, and code will\nbe released at https://github.com/MSiam/video_class_agnostic_segmentation.\n"}
+{"abstract": "  Modern applications, such as social networking systems and e-commerce\nplatforms are centered around using large-scale storage systems for storing and\nretrieving data. In the presence of concurrent accesses, these storage systems\ntrade off isolation for performance. The weaker the isolation level, the more\nbehaviors a storage system is allowed to exhibit and it is up to the developer\nto ensure that their application can tolerate those behaviors. However, these\nweak behaviors only occur rarely in practice, that too outside the control of\nthe application, making it difficult for developers to test the robustness of\ntheir code against weak isolation levels.\n  This paper presents MonkeyDB, a mock storage system for testing\nstorage-backed applications. MonkeyDB supports a Key-Value interface as well as\nSQL queries under multiple isolation levels. It uses a logical specification of\nthe isolation level to compute, on a read operation, the set of all possible\nreturn values. MonkeyDB then returns a value randomly from this set. We show\nthat MonkeyDB provides good coverage of weak behaviors, which is complete in\nthe limit. We test a variety of applications for assertions that fail only\nunder weak isolation. MonkeyDB is able to break each of those assertions in a\nsmall number of attempts.\n"}
+{"abstract": "  The COVID-19 disease spreads swiftly, and nearly three months after the first\npositive case was confirmed in China, Coronavirus started to spread all over\nthe United States. Some states and counties reported high number of positive\ncases and deaths, while some reported lower COVID-19 related cases and\nmortality. In this paper, the factors that could affect the risk of COVID-19\ninfection and mortality were analyzed in county level. An innovative method by\nusing K-means clustering and several classification models is utilized to\ndetermine the most critical factors. Results showed that mean temperature,\npercent of people below poverty, percent of adults with obesity, air pressure,\npopulation density, wind speed, longitude, and percent of uninsured people were\nthe most significant attributes\n"}
+{"abstract": "  Gravitational-wave sources can serve as standard sirens to probe cosmology by\nmeasuring their luminosity distance and redshift. Such standard sirens are also\nuseful to probe theories beyond general relativity with a modified\ngravitational-wave propagation. Many of previous studies on the latter assume\nmulti-messenger observations so that the luminosity distance can be measured\nwith gravitational waves while the redshift is obtained by identifying sources'\nhost galaxies from electromagnetic counterparts. Given that gravitational-wave\nevents of binary neutron star coalescences with associated electromagnetic\ncounterpart detections are expected to be rather rare, it is important to\nexamine the possibility of using standard sirens with gravitational-wave\nobservations alone to probe gravity. In this paper, we achieve this by\nextracting the redshift from the tidal measurement of binary neutron stars that\nwas originally proposed within the context of gravitational-wave cosmology\n(another approach is to correlate \"dark sirens\" with galaxy catalogs that we do\nnot consider here). We consider not only observations with ground-based\ndetectors (e.g. Einstein Telescope) but also multi-band observations between\nground-based and space-based (e.g. DECIGO) interferometers. We find that such\nmulti-band observations with the tidal information can constrain a parametric\nnon-Einsteinian deviation in the luminosity distance (due to the modified\nfriction in the gravitational wave evolution) more stringently than the case\nwith electromagnetic counterparts by a factor of a few. We also map the\nabove-projected constraints on the parametric deviation to those on specific\ntheories and phenomenological models beyond general relativity to put the\nformer into context.\n"}
+{"abstract": "  Galaxy groups host the majority of matter and more than half of all the\ngalaxies in the Universe. Their hot ($10^7$ K), X-ray emitting intra-group\nmedium (IGrM) reveals emission lines typical of many elements synthesized by\nstars and supernovae. Because their gravitational potentials are shallower than\nthose of rich galaxy clusters, groups are ideal targets for studying, through\nX-ray observations, feedback effects, which leave important marks on their gas\nand metal contents. Here, we review the history and present status of the\nchemical abundances in the IGrM probed by X-ray spectroscopy. We discuss the\nlimitations of our current knowledge, in particular due to uncertainties in the\nmodeling of the Fe-L shell by plasma codes, and coverage of the volume beyond\nthe central region. We further summarize the constraints on the abundance\npattern at the group mass scale and the insight it provides to the history of\nchemical enrichment. Parallel to the observational efforts, we review the\nprogress made by both cosmological hydrodynamical simulations and controlled\nhigh-resolution 3D simulations to reproduce the radial distribution of metals\nin the IGrM, the dependence on system mass from group to cluster scales, and\nthe role of AGN and SN feedback in producing the observed phenomenology.\nFinally, we highlight future prospects in this field, where progress will be\ndriven both by a much richer sample of X-ray emitting groups identified with\neROSITA, and by a revolution in the study of X-ray spectra expected from\nmicro-calorimeters onboard XRISM and ATHENA.\n"}
+{"abstract": "  Vaccines are an important public health measure, but vaccine hesitancy and\nrefusal can create clusters of low vaccine coverage and reduce the\neffectiveness of vaccination programs. Social media provides an opportunity to\nestimate emerging risks to vaccine acceptance by including geographical\nlocation and detailing vaccine-related concerns. Methods for classifying social\nmedia posts, such as vaccine-related tweets, use language models (LMs) trained\non general domain text. However, challenges to measuring vaccine sentiment at\nscale arise from the absence of tonal stress and gestural cues and may not\nalways have additional information about the user, e.g., past tweets or social\nconnections. Another challenge in LMs is the lack of commonsense knowledge that\nare apparent in users metadata, i.e., emoticons, positive and negative words\netc. In this study, to classify vaccine sentiment tweets with limited\ninformation, we present a novel end-to-end framework consisting of\ninterconnected components that use domain-specific LM trained on\nvaccine-related tweets and models commonsense knowledge into a bidirectional\ngated recurrent network (CK-BiGRU) with context-aware attention. We further\nleverage syntactical, user metadata and sentiment information to capture the\nsentiment of a tweet. We experimented using two popular vaccine-related Twitter\ndatasets and demonstrate that our proposed approach outperforms\nstate-of-the-art models in identifying pro-vaccine, anti-vaccine and neutral\ntweets.\n"}
+{"abstract": "  Opacity is an information flow property that captures the notion of plausible\ndeniability in dynamic systems, that is whether an intruder can deduce that\n\"secret\" behavior has occurred. In this paper we provide a general framework of\nopacity to unify the many existing notions of opacity that exist for discrete\nevent systems. We use this framework to discuss language-based and state-based\nnotions of opacity over automata. We present several methods for language-based\nopacity verification, and a general approach to transform state-based notions\ninto language-based ones. We demonstrate this approach for current-state and\ninitial-state opacity, unifying existing results. We then investigate the\nnotions of K-step opacity. We provide a language-based view of K-step opacity\nencompassing two existing notions and two new ones. We then analyze the\ncorresponding language-based verification methods both formally and with\nnumerical examples. In each case, the proposed methods offer significant\nreductions in runtime and space complexity.\n"}
+{"abstract": "  The numerical integration of an analytical function $f(x)$ using a finite set\nof equidistant points can be performed by quadrature formulas like the\nNewton-Cotes. Unlike Gaussian quadrature formulas however, higher-order\nNewton-Cotes formulas are not stable, limiting the usable order of such\nformulas. Existing work showed that by the use of orthogonal polynomials,\nstable high-order quadrature formulas with equidistant points can be developed.\nWe improve upon such work by making use of (orthogonal) Gram polynomials and\nderiving an iterative algorithm, together allowing us to reduce the\nspace-complexity of the original algorithm significantly.\n"}
+{"abstract": "  Predicting customer future purchases and lifetime value is a key metrics for\nmanaging marketing campaigns and optimizing marketing spend. This task is\nspecifically challenging when the relationships between the customer and the\nfirm are of a noncontractual nature and therefore the future purchases need to\nbe predicted based mostly on historical purchases. This work compares two\napproaches to predict customer future purchases, first using a\n'buy-till-you-die' statistical model to predict customer behavior and later\nusing a neural network on the same dataset and comparing the results. This\ncomparison will lead to both quantitative and qualitative analysis of those two\nmethods as well as recommendation on how to proceed in different cases and\nopportunities for future research.\n"}
+{"abstract": "  Human affects are complex paradox and an active research domain in affective\ncomputing. Affects are traditionally determined through a self-report based\npsychometric questionnaire or through facial expression recognition. However,\nfew state-of-the-arts pieces of research have shown the possibilities of\nrecognizing human affects from psychophysiological and neurological signals. In\nthis article, electroencephalogram (EEG) signals are used to recognize human\naffects. The electroencephalogram (EEG) of 100 participants are collected where\nthey are given to watch one-minute video stimuli to induce different affective\nstates. The videos with emotional tags have a variety range of affects\nincluding happy, sad, disgust, and peaceful. The experimental stimuli are\ncollected and analyzed intensively. The interrelationship between the EEG\nsignal frequencies and the ratings given by the participants are taken into\nconsideration for classifying affective states. Advanced feature extraction\ntechniques are applied along with the statistical features to prepare a fused\nfeature vector of affective state recognition. Factor analysis methods are also\napplied to select discriminative features. Finally, several popular supervised\nmachine learning classifier is applied to recognize different affective states\nfrom the discriminative feature vector. Based on the experiment, the designed\nrandom forest classifier produces 89.06% accuracy in classifying four basic\naffective states.\n"}
+{"abstract": "  Nonlinear light-matter interactions in structured materials are the source of\nexciting properties and enable vanguard applications in photonics. However, the\nmagnitude of nonlinear effects is generally small, thus requiring high optical\nintensities for their manifestation at the nanoscale. Here, we reveal a large\nnonlinear response of monolayer hexagonal boron nitride (hBN) in the\nmid-infrared phonon-polariton region, triggered by the strongly anharmonic\npotential associated with atomic vibrations in this material. We present robust\nfirst-principles theory predicting a threshold light field $\\sim40\\,$MV/m to\nproduce order-unity effects in Kerr nonlinearities and harmonic generation,\nwhich are made possible by a combination of the long lifetimes exhibited by\noptical phonons and the strongly asymmetric landscape of the configuration\nenergy in hBN. We further foresee polariton blockade at the few-quanta level in\nnanometer-sized structures. In addition, by mixing static and optical fields,\nthe strong nonlinear response of monolayer hBN gives rise to substantial\nfrequency shifts of optical phonon modes, exceeding their spectral width for\nin-plane DC fields that are attainable using lateral gating technology. We\ntherefore predict a practical scheme for electrical tunability of the\nvibrational modes with potential interest in mid-infrared optoelectronics. The\nstrong nonlinear response, low damping, and robustness of hBN polaritons set\nthe stage for the development of applications in light modulation, sensing, and\nmetrology, while triggering the search for intense vibrational nonlinear\nresponse in other ionic materials.\n"}
+{"abstract": "  The present paper is devoted to the description of local and 2-local\nautomorphisms on Cayley algebras over an arbitrary field $\\mathbb{F}$. Given a\nCayley algebra $\\mathcal{C}$ with norm $n$, let $O(\\mathcal{C},n)$ be the\ncorresponding orthogonal group. We prove that the group of all local\nautomorphisms of $\\mathcal{C}$ coincides with the group $\\{\\varphi\\in\nO(\\mathcal{C},n)\\mid \\varphi(1)=1\\}.$ Further we prove that the behavior of\n2-local automorphisms depends on the Cayley algebra being split or division.\nEvery 2-local automorphism on the split Cayley algebra is an automorphism, i.e.\nthey form the exceptional Lie group $G_2(\\mathbb{F})$ if\n$\\textrm{char}\\mathbb{F}\\neq 2,3$. On the other hand, on division Cayley\nalgebras over a field $\\mathbb{F}$, the groups of 2-local automorphisms and\nlocal automorphisms coincide, and they are isomorphic to the group\n$\\{\\varphi\\in O(\\mathcal{C},n)\\mid \\varphi(1)=1\\}.$\n"}
+{"abstract": "  At the fundamental level, quantum communication is ultimately limited by\nnoise. For instance, quantum signals cannot be amplified without the\nintroduction of noise in the amplified states. Furthermore, photon loss reduces\nthe signal-to-noise ratio, accentuating the effect of noise. Thus, most of the\nefforts in quantum communications have been directed towards overcoming noise\nto achieve longer communication distances, larger secret key rates, or to\noperate in noisier environmental conditions. Here, we propose and\nexperimentally demonstrate a platform for quantum communication based on\nultrafast optical techniques. In particular, our scheme enables the\nexperimental realization of high-rates and quantum signal filtering approaching\na single spectro-temporal mode, resulting in a dramatic reduction in channel\nnoise. By experimentally realizing a 1-ps optically induced temporal gate, we\nshow that ultrafast time filtering can result in an improvement in noise\ntolerance by a factor of up to 1200 compared to a 2-ns electronic filter\nenabling daytime quantum key distribution or quantum communication in bright\nfibers.\n"}
+{"abstract": "  Big data is gaining overwhelming attention since the last decade. Almost all\nthe fields of science and technology have experienced a considerable impact\nfrom it. The cloud computing paradigm has been targeted for big data processing\nand mining in a more efficient manner using the plethora of resources available\nfrom computing nodes to efficient storage. Cloud data mining introduces the\nconcept of performing data mining and analytics of huge data in the cloud\navailing the cloud resources. But can we do better? Yes, of course! The main\ncontribution of this chapter is the identification of four game-changing\ntechnologies for the acceleration of computing and analysis of data mining\ntasks in the cloud. Graphics Processing Units can be used to further accelerate\nthe mining or analytic process, which is called GPU accelerated analytics.\nFurther, Approximate Computing can also be introduced in big data analytics for\nbringing efficacy in the process by reducing time and energy and hence\nfacilitating greenness in the entire computing process. Quantum Computing is a\nparadigm that is gaining pace in recent times which can also facilitate\nefficient and fast big data analytics in very little time. We have surveyed\nthese three technologies and established their importance in big data mining\nwith a holistic architecture by combining these three game-changers with the\nperspective of big data. We have also talked about another future technology,\ni.e., Neural Processing Units or Neural accelerators for researchers to explore\nthe possibilities. A brief explanation of big data and cloud data mining\nconcepts are also presented here.\n"}
+{"abstract": "  Ferromagnetic superconductor URhGe has orthorhombic structure and possesses\nspontaneous magnetisation along the c-axis. Magnetic field directed along the\n$b$-axis suppresses ferromagnetism in $c$-direction and leads to a metamagnetic\ntransition into polarised paramagnetic state in the $b$-direction. The theory\nof these phenomena based on the specific magnetic anisotropy of this material\nin $(b,c)$ plane is given. Line of the first order metamagnetic transition ends\nat a critical point. The Van der Waals - type description of behaviour of\nphysical properties near this point is developed. The triplet superconducting\nstate destroyed by orbital effect is recreated in vicinity of the transition.\nIt is shown that the reentrance of superconductivity is caused by the sharp\nincrease of magnetic susceptibility in $b$ direction near the metamagnetic\ntransition. The specific behaviour of the upper critical field in direction of\nspontaneous magnetisation in UCoGe and in UGe$_2$ related to the field\ndependence of magnetic susceptibility is discussed.\n"}
+{"abstract": "  Let $p$ be a prime number, and let $k$ be an algebraically closed field of\ncharacteristic $p$. We show that the tame fundamental group of a smooth affine\ncurve over $k$ is a projective profinite group. We prove that the fundamental\ngroup of a smooth projective variety over $k$ is finitely presented. More\ngenerally we prove that the tame fundamental group of a smooth quasi-projective\nvariety over $k$ which admits a good compactification is finitely presented.\n  v2: references added. Thank you to all for the friendly and fruitful\ncomments.\n"}
+{"abstract": "  Community question answering and discussion platforms such as Reddit, Yahoo!\nanswers or Quora provide users the flexibility of asking open ended questions\nto a large audience, and replies to such questions maybe useful both to the\nuser and the community on certain topics such as health, sports or finance.\nGiven the recent events around COVID-19, some of these platforms have attracted\n2000+ questions from users about several aspects associated with the disease.\nGiven the impact of this disease on general public, in this work we investigate\nways to improve the ranking of user generated answers on COVID-19. We\nspecifically explore the utility of external technical sources of side\ninformation (such as CDC guidelines or WHO FAQs) in improving answer ranking on\nsuch platforms. We found that ranking user answers based on question-answer\nsimilarity is not sufficient, and existing models cannot effectively exploit\nexternal (side) information. In this work, we demonstrate the effectiveness of\ndifferent attention based neural models that can directly exploit side\ninformation available in technical documents or verified forums (e.g., research\npublications on COVID-19 or WHO website). Augmented with a temperature\nmechanism, the attention based neural models can selectively determine the\nrelevance of side information for a given user question, while ranking answers.\n"}
+{"abstract": "  To celebrate Hans Frauenfelder's achievements, we examine energy(-like)\n\"landscapes\" for complex living systems. Energy landscapes summarize all\npossible dynamics of some physical systems. Energy(-like) landscapes can\nexplain some biomolecular processes, including gene expression and, as\nFrauenfelder showed, protein folding. But energy-like landscapes and existing\nframeworks like statistical mechanics seem impractical for describing many\nliving systems. Difficulties stem from living systems being high dimensional,\nnonlinear, and governed by many, tightly coupled constituents that are noisy.\nThe predominant modeling approach is devising differential equations that are\ntailored to each living system. This ad hoc approach faces the notorious\n\"parameter problem\": models have numerous nonlinear, mathematical functions\nwith unknown parameter values, even for describing just a few intracellular\nprocesses. One cannot measure many intracellular parameters or can only measure\nthem as snapshots in time. Another modeling approach uses cellular automata to\nrepresent living systems as discrete dynamical systems with binary variables.\nQuantitative (Hamiltonian-based) rules can dictate cellular automata (e.g.,\nCellular Potts Model). But numerous biological features, in current practice,\nare qualitatively described rather than quantitatively (e.g., gene is (highly)\nexpressed or not (highly) expressed). Cellular automata governed by verbal\nrules are useful representations for living systems and can mitigate the\nparameter problem. However, they can yield complex dynamics that are difficult\nto understand because much of the existing mathematical tools and theorems\napply to continuous but not discrete dynamical systems. Recent studies found\nways to overcome this challenge by discovering a predictive \"landscape\" that\nyield low-dimensional representations of cellular automata dynamics. We review\nthese studies.\n"}
+{"abstract": "  The second phase of the APOGEE survey is providing near-infrared,\nhigh-resolution, high signal-to-noise spectra of stars in the halo, disk, bar\nand bulge of the Milky Way. The near-infrared spectral window is especially\nimportant in the study of the Galactic bulge, where stars are obscured by the\ndust and gas of the disk in its line-of-sight. We present a chemical\ncharacterisation of the globular cluster NGC 6544 with high-resolution\nspectroscopy. The characterisation of the cluster chemical fingerprint, given\nits status of \"interloper\" towards the Galactic bulge and clear signatures of\ntidal disruption in its core is crucial for future chemical tagging efforts.\nCluster members were selected from the DR16 of the APOGEE survey, using\nchemo-dynamical criteria of individual stars. A sample of 23 members of the\ncluster was selected. An analysis considering the intra-cluster abundance\nvariations, known anticorrelations is given. According to the RGB content of\nthe cluster, the iron content and $\\alpha$-enhancement are [Fe/H] $= -1.44 \\pm\n0.04$ dex and [$\\alpha$/Fe] $= 0.20 \\pm 0.04$ dex, respectively. Cluster\nmembers show a significant spread in [Fe/H] and [Al/Fe] that is larger than\nexpected based on measurement errors. An [Al/Fe] spread, signal of an Mg-Al\nanticorrelation is observed and used to constraint the cluster mass budget,\nalong with C, N, Mg, Si, K, Ca, and Ce element variations are discussed. Across\nall the analysed evolutionary stages (RGB and AGB), about $\\sim2/3$ (14 out of\n23) show distinct chemical patterns, possibly associated with second-generation\nstars.\n"}
+{"abstract": "  The transition to a low-carbon economy is one of the ambitions of the\nEuropean Union for 2030. Biobased industries play an essential role in this\ntransition. However, there has been an on-going discussion about the actual\nbenefit of using biomass to produce biobased products, specifically the use of\nagricultural materials (e.g., corn and sugarcane). This paper presents the\nenvironmental impact assessment of 30% and 100% biobased PET (polyethylene\nterephthalate) production using EU biomass supply chains (e.g., sugar beet,\nwheat, and Miscanthus). An integral assessment between the life cycle\nassessment methodology and the global sensitivity assessment is presented as an\nearly-stage support tool to propose and select supply chains that improve the\nenvironmental performance of biobased PET production. From the results,\nMiscanthus is the best option for the production of biobased PET: promoting EU\nlocal supply chains, reducing greenhouse gas (GHG) emissions (process and\nland-use change), and generating lower impacts in midpoint categories related\nto resource depletion, ecosystem quality, and human health. This tool can help\nimproving the environmental performance of processes that could boost the shift\nto a low-carbon economy.\n"}
+{"abstract": "  We demonstrate a low noise short wavelength infrared (SWIR) Sb based type II\nsuperlattice (T2SL) avalanche photodiodes (APD). The SWIR GaSb/(AlAsSb/GaSb)\nAPD structure was designed based on impact ionization engineering and grown by\nmolecular beam epitaxy on GaSb substrate. At room temperature, the device\nexhibits a 50 % cut-off wavelength of 1.74 micron. The device revealed to have\nelectron dominated avalanching mechanism with a gain value of 48 at room\ntemperature. The electron and hole impact ionization coefficients were\ncalculated and compared to give better prospect of the performance of the\ndevice. Low excess noise, as characterized by the carrier ionization ratio of ~\n0.07, has been achieved.\n"}
+{"abstract": "  Multilingual pretrained language models have demonstrated remarkable\nzero-shot cross-lingual transfer capabilities. Such transfer emerges by\nfine-tuning on a task of interest in one language and evaluating on a distinct\nlanguage, not seen during the fine-tuning. Despite promising results, we still\nlack a proper understanding of the source of this transfer. Using a novel layer\nablation technique and analyses of the model's internal representations, we\nshow that multilingual BERT, a popular multilingual language model, can be\nviewed as the stacking of two sub-networks: a multilingual encoder followed by\na task-specific language-agnostic predictor. While the encoder is crucial for\ncross-lingual transfer and remains mostly unchanged during fine-tuning, the\ntask predictor has little importance on the transfer and can be reinitialized\nduring fine-tuning. We present extensive experiments with three distinct tasks,\nseventeen typologically diverse languages and multiple domains to support our\nhypothesis.\n"}
+{"abstract": "  We present a thermodynamics experiment suitable for first year undergraduate\nstudents employing Stirling Engines to create a demonstration of energy\ntransformation and to measure the mechanical efficiency of such engines. Using\nan inexpensive transparent chambered Stirling Engine, students can connect\nconcepts such as the theoretical pressure-volume diagram with the physical\nmovements of the engine's pistons and the resultant useful output work of a\nspinning wheel. We found the majority of students successfully complete this\nexperiment obtaining results similar to when performed by the authors. In\naddition to the core thermodynamics lesson, this experiment incorporates DC\ncircuits, oscilloscopes, and data analysis so it can be integrated into a wider\nundergraduate physics course to combine the teaching of multiple subjects.\n"}
+{"abstract": "  Video super-resolution has recently become one of the most important\nmobile-related problems due to the rise of video communication and streaming\nservices. While many solutions have been proposed for this task, the majority\nof them are too computationally expensive to run on portable devices with\nlimited hardware resources. To address this problem, we introduce the first\nMobile AI challenge, where the target is to develop an end-to-end deep\nlearning-based video super-resolution solutions that can achieve a real-time\nperformance on mobile GPUs. The participants were provided with the REDS\ndataset and trained their models to do an efficient 4X video upscaling. The\nruntime of all models was evaluated on the OPPO Find X2 smartphone with the\nSnapdragon 865 SoC capable of accelerating floating-point networks on its\nAdreno GPU. The proposed solutions are fully compatible with any mobile GPU and\ncan upscale videos to HD resolution at up to 80 FPS while demonstrating high\nfidelity results. A detailed description of all models developed in the\nchallenge is provided in this paper.\n"}
+{"abstract": "  We investigated magnetic textures in a Sc-doped hexaferrite film by means of\nphase microscopy (PM) with a hole-free phase plate in a transmission electron\nmicroscope. In a zero magnetic field, the stripe-shaped magnetic domains\ncoexist with magnetic bubbles. The magnetization in both magnetic domains was\noriented perpendicular to the film and the domain walls have an in-plane\nmagnetization. In the remnant state at 9.2 mT, several magnetic bubbles were\nformed with the formation of stripe-shaped magnetic domains, and the\nout-of-plane component in the stripe-shaped domains gradually appeared as the\nfilm thickness increased. As the film thickness increases further, the magnetic\nbubbles with clockwise or counter-clockwise spin helicities formed a triangular\nlattice. These results in the remnant state suggest that the domain wall energy\nin the magnetic bubble domains is lower in the thicker region.\n"}
+{"abstract": "  This paper addresses the use of data-driven evolving techniques applied to\nfault prognostics. In such problems, accurate predictions of multiple steps\nahead are essential for the Remaining Useful Life (RUL) estimation of a given\nasset. The fault prognostics' solutions must be able to model the typical\nnonlinear behavior of the degradation processes of these assets, and be\nadaptable to each unit's particularities. In this context, the Evolving Fuzzy\nSystems (EFSs) are models capable of representing such behaviors, in addition\nof being able to deal with non-stationary behavior, also present in these\nproblems. Moreover, a methodology to recursively track the model's estimation\nerror is presented as a way to quantify uncertainties that are propagated in\nthe long-term predictions. The well-established NASA's Li-ion batteries data\nset is used to evaluate the models. The experiments indicate that generic EFSs\ncan take advantage of both historical and stream data to estimate the RUL and\nits uncertainty.\n"}
+{"abstract": "  The mutual information is a measure of classical and quantum correlations of\ngreat interest in quantum information. It is also relevant in quantum many-body\nphysics, by virtue of satisfying an area law for thermal states and bounding\nall correlation functions. However, calculating it exactly or approximately is\noften challenging in practice. Here, we consider alternative definitions based\non R\\'enyi divergences. Their main advantage over their von Neumann counterpart\nis that they can be expressed as a variational problem whose cost function can\nbe efficiently evaluated for families of states like matrix product operators\nwhile preserving all desirable properties of a measure of correlations. In\nparticular, we show that they obey a thermal area law in great generality, and\nthat they upper bound all correlation functions. We also investigate their\nbehavior on certain tensor network states and on classical thermal\ndistributions.\n"}
+{"abstract": "  A dilute suspension of motile micro-organisms subjected to a strong ambient\nflow, such as algae in the ocean, can be modelled as a population of\nnon-interacting, orientable active Brownian particles (ABPs). Using the\nSmoluchowski equation (i.e. Fokker-Planck equation in space and orientation),\none can describe the non-trivial transport phenomena of ABPs such as taxes and\nshear-induced migration. This work transforms the Smoluchowski equation into a\ntransport equation, in which the drifts and dispersions can be further\napproximated as a function of the local flow field. The new model can be\napplied to any global flow field due to its local nature, unlike previous\nmethods such as those utilising the generalised Taylor dispersion theory. The\ntransformation shows that the overall drift includes both the biased motility\nof individual particles in the presence of taxis and the shear-induced\nmigration in the absence of taxis. In addition, it uncovers other new drifts\nand dispersions caused by the interactions between the orientational dynamics\nand the passive advection/diffusion of ABPs. Finally, the performance of this\nmodel is assessed using examples of gyrotactic suspensions, where the proposed\nmodel is demonstrated to be most accurate when the biased motility of particles\n(i.e. taxis) is weak.\n"}
+{"abstract": "  With the rapid advancement of information and communication technologies,\nmany researchers have adopted alternative data sources from private data\nvendors to study human movement dynamics in response to large-scale natural or\nsocietal events. Big geosocial data such as georeferenced tweets are publicly\navailable and dynamically evolving as real-world events are happening, making\nit more likely to capture the real-time sentiments and responses of\npopulations. However, precisely-geolocated geosocial data is scarce and biased\ntoward urban population centers. In this research, we developed a big geosocial\ndata analytical framework for extracting human movement dynamics in response to\nlarge-scale events from publicly available georeferenced tweets. The framework\nincludes a two-stage data collection module that collects data in a more\ntargeted fashion in order to mitigate the data scarcity issue of georeferenced\ntweets; in addition, a variable bandwidth kernel density estimation(VB-KDE)\napproach was adopted to fuse georeference information at different spatial\nscales, further augmenting the signals of human movement dynamics contained in\ngeoreferenced tweets. To correct for the sampling bias of georeferenced tweets,\nwe adjusted the number of tweets for different spatial units (e.g., county,\nstate) by population. To demonstrate the performance of the proposed analytic\nframework, we chose an astronomical event that occurred nationwide across the\nUnited States, i.e., the 2017 Great American Eclipse, as an example event and\nstudied the human movement dynamics in response to this event. However, this\nanalytic framework can easily be applied to other types of large-scale events\nsuch as hurricanes or earthquakes.\n"}
+{"abstract": "  The execution of similar units can be compared by their internal behaviors to\ndetermine the causes of their potential performance issues. For instance, by\nexamining the internal behaviors of different fast or slow web requests more\nclosely and by clustering and comparing their internal executions, one can\ndetermine what causes some requests to run slowly or behave in unexpected ways.\nIn this paper, we propose a method of extracting the internal behavior of web\nrequests as well as introduce a pipeline that detects performance issues in web\nrequests and provides insights into their root causes. First, low-level and\nfine-grained information regarding each request is gathered by tracing both the\nuser space and the kernel space. Second, further information is extracted and\nfed into an outlier detector. Finally, these outliers are then clustered by\ntheir behavior, and each group is analyzed separately. Experiments revealed\nthat this pipeline is indeed able to detect slow web requests and provide\nadditional insights into their true root causes. Notably, we were able to\nidentify a real PHP cache contention using the proposed approach.\n"}
+{"abstract": "  The fundamental problem of stabilizing a general non-affine continuous-time\nnonlinear system is investigated via piecewise affine linear models (PALMs) in\nthis paper. A novel integral sliding-mode parallel control (ISMPC) approach is\ndeveloped, where an uncertain piecewise affine system (PWA) is constructed to\nmodel a non-affine continuous-time nonlinear system equivalently on a compact\nregion containing the origin. A piecewise integral sliding-mode parallel\ncontroller is designed to globally stabilize the uncertain PWA and,\nconsequently, to semi-globally stabilize the original nonlinear system. The\nproposed scheme enjoys two favorable features: i) some restrictions on the\nsystem input channel are eliminated, thus the developed method is more relaxed\ncompared with the published approaches; and ii) it is convenient to be used to\ndeal with both matched and unmatched uncertainties of the system. Moreover, we\nprovide discussions about the universality analysis of the developed control\nstrategy for two kinds of typical nonlinear systems. Simulation results from\ntwo numerical examples further demonstrate the performance of the developed\ncontrol approach.\n"}
+{"abstract": "  In this paper we report results of a numerical investigation of turbulent\nnatural gas combustion for jet in a coflow of lean combustion products in the\nDelft-Jet-in-Hot-Coflow (DJHC) burner which emulates MILD (Moderate and Intense\nLow Oxygen Dilution) combustion behavior. The focus is on assessing the\nperformance of the Eddy Dissipation Concept (EDC) model in combination with\ntwo-equation turbulence models and chemical kinetic schemes for about 20\nspecies (Correa mechanism and DRM19 mechanism) by comparing predictions with\nexperimental measurements. We study two different flame conditions\ncorresponding to two different oxygen levels (7.6% and 10.9% by mass) in the\nhot coflow, and for two jet Reynolds number (Re=4100 and Re=8800). The mean\nvelocity and turbulent kinetic energy predicted by different turbulence models\nare in good agreement with data without exhibiting large differences among the\nmodel predictions. The realizable k-e model exhibits better performance in the\nprediction of entrainment. The EDC combustion model predicts too early ignition\nleading to a peak in the radial mean temperature profile at too low axial\ndistance. However the model correctly predicts the experimentally observed\ndecreasing trend of lift-off height with jet Reynolds number. A detailed\nanalysis of the mean reaction rate of the EDC model is made and as possible\ncause for the deviations between model predictions and experiments a low\nturbulent Reynolds number effect is identified. Using modified EDC model\nconstants prediction of too early ignition can be avoided. The results are\nweakly sensitive to the sub-model for laminar viscosity and laminar diffusion\nfluxes.\n"}
+{"abstract": "  The automorphism group $Aut(X,\\mu)$ of a compact, complete metric space $X$\nwith a Radon measure $\\mu$ is a subgroup of $\\mathcal{U}(L^2(X,\\mu))$-the\nunitary group of operators on $L^2(X,\\mu)$. The $Aut(X,\\mu)$-action on the\ngeneralized space $\\mathcal{M}(X)$ is a proper action. Hence, there exists a\nslice at each point of the generalized space $\\mathcal{M}(X)$. Measure Groupoid\n(virtual group) is subsequently employed to analyze the resulting dynamical\nsystem as that of the ergodic action of the commutative algebra (a lattice)\n$C(X)$ on the generalized space $\\mathcal{M}(X)$ which is represented on a\ncommutative von Neumann algebra.\n"}
+{"abstract": "  Drying colloidal droplets have a wide range of applications from medical\ndiagnostics to coatings for industries. This paper explores the effects of the\nsubstrate temperature (ranging from $25$ to $55 ^{\\circ}$C) and various initial\nconcentrations ($\\phi$) of $1$ to $20$ wt% of lysozyme in an aqueous solution\non its drying and final dried film state using bright-field optical microscopy.\nThe $\\phi$ is divided into three regimes, ultra-concentrated ($20$ $<$ $\\phi$\n$\\leq$ $17$ wt%), concentrated ($17$ $<$ $\\phi$ $\\leq$ $9$ wt%) and diluted\n($9$ $<$ $\\phi$ $\\leq$ $1$ wt%). Increasing $\\phi$ in these regimes finds that\nthis movement in the later non-linear region slows down as the front carries\nand deposits protein molecules until the supply in solution is exhausted. In\nthe ultra-concentrated regime, the fluid front moves linearly throughout the\ndrying process. The deposition of protein onto the surface by the fluid front\ncreates the \"coffee-ring\" and increases with increasing $\\phi$. A dimple is\nobserved in a central mound-structure, which grows with increasing $\\phi$. As\nthe temperature increases the drying rate increases, reducing the time for\nconvective flow and the deposition of material at the fluid front.\nInterestingly, at (T, $\\phi$) = ($55 ^{\\circ}$C, $20$ wt%), the droplet forms\nthe thickest film with suppressed ring formation. The dimple diminishes in the\nultra-concentrated regime, whereas it changes to an expanded spot in some\nsamples of the diluted and concentrated regimes with elevated temperatures.\nBoth initial concentration and substrate temperature lead to surface tension\nand temperature gradients across the droplet, affecting the morphological\npatterns. This study provides insights into protein-protein and\nprotein-substrate interactions driven by temperature and concentration for\nbiomedical and biosensing applications.\n"}
+{"abstract": "  The recently introduced anomaly-free twistor string in four dimensions is\nshown to be defined not just in flat but also in curved twistor space. Further,\narguments are given that the classical limit of the corresponding string field\ntheory, if it exists, is related to general relativity, in particular to the\nIsenberg and Yasskin construction using teleparallel gravity. For spacetimes of\nPetrov type D with two shear-free null congruences the construction can be\nsimplified using two-dimensional twistor manifolds.\n"}
+{"abstract": "  We revisit here congruence relations for B\\\"uchi automata, which play a\ncentral role in the automata-based verification. The size of the classical\ncongruence relation is in $3^{\\mathcal{O}(n^2)}$, where $n$ is the number of\nstates of a given B\\\"uchi automaton $\\mathcal{A}$. Here we present improved\ncongruence relations that can be exponentially coarser than the classical one.\nWe further give asymptotically optimal congruence relations of size\n$2^{\\mathcal{O}(n \\log n)}$. Based on these optimal congruence relations, we\nobtain an optimal translation from B\\\"uchi automata to a family of\ndeterministic finite automata (FDFW) that accepts the complementary language.\nTo the best of our knowledge, our construction is the first direct and optimal\ntranslation from B\\\"uchi automata to FDFWs.\n"}
+{"abstract": "  COVID-19 has aided the spread of racism, as well as national insecurity,\ndistrust of immigrants, and general xenophobia, both of which may be linked to\nthe rise in anti-Asian hate crimes during the pandemic. Coronavirus Disease\n2019(COVID19) is thought to have originated in late December 2019 in Wuhan,\nChina, and quickly spread across the world during the spring months of 2020.\nAsian Americans recorded in increase in racially based hate crimes including\nphysical abuse and intimidation as COVID-19 spread throughout the United\nStates. This research study was conducted by high school students in the Bay\nArea to compare the intention and characteristics of hate crimes against Asian\nAmericans to hate crimes against African Americans. According to studies of\nboth victim-related and most offender-related variables, hate crimes against\nAsian Americans have been rapidly growing in the United States and vary from\nthose against African Americans. This leads to an investigation into the racial\ndisparity between Asian American offenders and those of other races. The nature\nand characteristics of hate crimes against Asian Americans are compared to\nthose of hate crimes against African Americans in our research. According to\nstudies of all victim-related factors, hate crimes against Asian Americans are\nsimilar to those against African Americans. Hate crimes against Asian\nAmericans, on the other hand, vary greatly from hate crimes against African\nAmericans in terms of the offender's ethnicity and all incident-related\nvariables.\n"}
+{"abstract": "  For a sum of squares domain of finite D'Angelo 1-type at the origin, we show\nthat the polynomial model obtained from the computation of the Catlin multitype\nat the origin of such a domain is likewise a sum of squares domain. We also\nprove, under the same finite type assumption, that the multitype is an\ninvariant of the ideal of holomorphic functions defining the domain. Both\nresults are proven using Martin Kolar's algorithm for the computation of the\nmultitype introduced in [13]. Given a sum of squares domain, we rewrite the\nKolar algorithm in terms of ideals of holomorphic functions and also introduce\nan approach that explicitly constructs the homogeneous polynomial\ntransformations used in the algorithm.\n"}
+{"abstract": "  Image Captioning, or the automatic generation of descriptions for images, is\none of the core problems in Computer Vision and has seen considerable progress\nusing Deep Learning Techniques. We propose to use Inception-ResNet\nConvolutional Neural Network as encoder to extract features from images,\nHierarchical Context based Word Embeddings for word representations and a Deep\nStacked Long Short Term Memory network as decoder, in addition to using Image\nData Augmentation to avoid over-fitting. For data Augmentation, we use\nHorizontal and Vertical Flipping in addition to Perspective Transformations on\nthe images. We evaluate our proposed methods with two image captioning\nframeworks- Encoder-Decoder and Soft Attention. Evaluation on widely used\nmetrics have shown that our approach leads to considerable improvement in model\nperformance.\n"}
+{"abstract": "  Over the last decade, the vector-apodizing phase plate (vAPP) coronagraph has\nbeen developed from concept to on-sky application in many high-contrast imaging\nsystems on 8-m class telescopes. The vAPP is an geometric-phase patterned\ncoronagraph that is inherently broadband, and its manufacturing is enabled only\nby direct-write technology for liquid-crystal patterns. The vAPP generates two\ncoronagraphic PSFs that cancel starlight on opposite sides of the point spread\nfunction (PSF) and have opposite circular polarization states. The efficiency,\nthat is the amount of light in these PSFs, depends on the retardance offset\nfrom half-wave of the liquid-crystal retarder. Using different liquid-crystal\nrecipes to tune the retardance, different vAPPs operate with high efficiencies\n($>96\\%$) in the visible and thermal infrared (0.55 $\\mu$m to 5 $\\mu$m). Since\n2015, seven vAPPs have been installed in a total of six different instruments,\nincluding Magellan/MagAO, Magellan/MagAO-X, Subaru/SCExAO, and LBT/LMIRcam.\nUsing two integral field spectrographs installed on the latter two instruments,\nthese vAPPs can provide low-resolution spectra (R$\\sim$30) between 1 $\\mu$m and\n5 $\\mu$m. We review the design process, development, commissioning, on-sky\nperformance, and first scientific results of all commissioned vAPPs. We report\non the lessons learned and conclude with perspectives for future developments\nand applications.\n"}
+{"abstract": "  Several methods are available in the literature to stochastically compare\nrandom variables and random vectors. We introduce the notion of asymptotic\nstochastic order for random processes and define four such orders. Various\nproperties and interrelations of the orders are discussed. Sufficient\nconditions for these orders to hold for certain stochastic processes, evolving\nfrom some statistical entities of interest, are derived.\n"}
+{"abstract": "  We reduce the problem of quantization of the Yang-Mills field Hamiltonian to\na problem for defining a probability measure on an infinite-dimensional space\nof gauge equivalence classes of connections on $\\mathbb{R}^3$. We suggest a\nformally self-adjoint expression for the quantized Yang-Mills Hamiltonian as an\noperator on the corresponding Lebesgue $L^2$-space. In the case when the\nYang-Mills field is associated to the Abelian group $U(1)$ we define the\nprobability measure which depends on two real parameters $m>0$ and $c\\neq 0$.\nThis yields a non-standard quantization of the Hamiltonian of the\nelectromagnetic field, and the associated probability measure is Gaussian. The\ncorresponding quantized Hamiltonian is a self-adjoint operator in a Fock space\nthe spectrum of which is $\\{0\\}\\cup[\\frac12m, \\infty)$, i.e. it has a gap.\n"}
+{"abstract": "  The vast majority of stars in galaxy groups are contained within their\nconstituent galaxies. Some small fraction of stars is expected, however, to\nfollow the global dark matter potential of the group. In compact groups,\ninteractions between the galaxies should be frequent. This leads to a more\nintensive material stripping from the group members, which finally forms an\nintra-group light component (IGL). Therefore, the distribution of the IGL\nshould be related to the distribution of the total mass in the compact group\nand its dynamical status. In this study we consider the distribution and\nfraction of the IGL in a sample of 36 Hickson compact groups (HCGs). We use\ndeep observations of these compact groups (down to surface brightness $\\sim 28$\nmag\\,arcsec$^{-2}$ in the $r$ band) obtained with the WISE $28$-inch telescope.\nFor five HCGs with a bright symmetric IGL component, we carry out\nmulticomponent photometric decomposition to simultaneously fit the galaxy\nprofiles and the IGL. For the remaining groups, we only fit the profiles of\ntheir constituent galaxies. We find that the mean surface brightness of the IGL\ncorrelates with the mean morphology of the group: it becomes brighter in the\ngroups with a larger fraction of early-type galaxies. On the other hand, the\nIGL brightness depends on the total luminosity of the group. The IGL profile\ntends to have a S\\'ersic index $n\\sim0.5-1$, which is generally consistent with\nthe mass density profile of dark matter haloes in compact groups obtained from\ncosmological simulations.\n"}
+{"abstract": "  A scheme for the enhanced generation of higher photon-number states is\nrealized, using an optical time-multiplexing setting that exploits a parametric\ndown-conversion source for an iterative state generation. We use a quantum\nfeedback mechanism for already generated photons to induce self-seeding of the\nconsecutive nonlinear process, enabling us to coherently add photons to the\nlight that propagates in the feedback loop. The addition can be carried out for\nany chosen number of round trips, resulting in a successive buildup of\nmultiphoton states. Our system is only limited by loop losses. The looped\ndesign is rendered possible by a carefully engineered waveguide source that is\ncompatible with and preserves the shape of the propagating mode. We compare the\nfidelities and success probabilities of our protocol with the common direct\nheralding of photon-number states. This comparison reveals that, for same the\nfidelity, our feedback-based setup significantly enhances success\nprobabilities, being vital for an efficient utilization in quantum\ntechnologies. Moreover, quantum characteristics of the produced states are\nanalyzed, and the flexibility of producing higher photon-number states with our\nsetup beyond the common direct heralding is demonstrated.\n"}
+{"abstract": "  Attribute-Based Encryption (ABE) is an emerging cryptographic technique that\nallows one to embed a fine-grained access control mechanism into encrypted\ndata. In this paper we propose a novel ABE scheme called SEA-BREW (Scalable and\nEfficient Abe with Broadcast REvocation for Wireless networks), which is suited\nfor Internet of Things (IoT) and Industrial IoT (IIoT) applications. In\ncontrast to state-of-the-art ABE schemes, ours is capable of securely\nperforming key revocations with a single short broadcast message, instead of a\nnumber of unicast messages that is linear with the number of nodes. This is\ndesirable for low-bitrate Wireless Sensor and Actuator Networks (WSANs) which\noften are the heart of (I)IoT systems. In SEA-BREW, sensors, actuators, and\nusers can exchange encrypted data via a cloud server, or directly via wireless\nif they belong to the same WSAN. We formally prove that our scheme is secure\nalso in case of an untrusted cloud server that colludes with a set of users,\nunder the generic bilinear group model. We show by simulations that our scheme\nrequires a constant computational overhead on the cloud server with respect to\nthe complexity of the access control policies. This is in contrast to\nstate-of-the-art solutions, which require instead a linear computational\noverhead.\n"}
+{"abstract": "  Stock trend forecasting, aiming at predicting the stock future trends, is\ncrucial for investors to seek maximized profits from the stock market. Many\nevent-driven methods utilized the events extracted from news, social media, and\ndiscussion board to forecast the stock trend in recent years. However, existing\nevent-driven methods have two main shortcomings: 1) overlooking the influence\nof event information differentiated by the stock-dependent properties; 2)\nneglecting the effect of event information from other related stocks. In this\npaper, we propose a relational event-driven stock trend forecasting (REST)\nframework, which can address the shortcoming of existing methods. To remedy the\nfirst shortcoming, we propose to model the stock context and learn the effect\nof event information on the stocks under different contexts. To address the\nsecond shortcoming, we construct a stock graph and design a new propagation\nlayer to propagate the effect of event information from related stocks. The\nexperimental studies on the real-world data demonstrate the efficiency of our\nREST framework. The results of investment simulation show that our framework\ncan achieve a higher return of investment than baselines.\n"}
+{"abstract": "  The success of neural network embeddings has entailed a renewed interest in\nusing knowledge graphs for a wide variety of machine learning and information\nretrieval tasks. In particular, recent recommendation methods based on graph\nembeddings have shown state-of-the-art performance. In general, these methods\nencode latent rating patterns and content features. Differently from previous\nwork, in this paper, we propose to exploit embeddings extracted from graphs\nthat combine information from ratings and aspect-based opinions expressed in\ntextual reviews. We then adapt and evaluate state-of-the-art graph embedding\ntechniques over graphs generated from Amazon and Yelp reviews on six domains,\noutperforming baseline recommenders. Additionally, our method has the advantage\nof providing explanations that involve the coverage of aspect-based opinions\ngiven by users about recommended items.\n"}
+{"abstract": "  We address the question of transport of heat, in out-of-equilibrium systems.\nThe experimental set-up consists in two coupled granular gas Non-Equilibrium\nSteady State (NESS) heat baths, in which Brownian-like rotors are imbedded.\nThese rotors are electro-mechanically coupled thanks to DC micro-motors,\nthrough a resistor $R$, such that energy flows between them. The average flux\ndepends linearly in the difference of the baths' temperature. Varying $R$\nallows to extrapolate in the non-dissipative coupling limit ($R\\rightarrow0$).\nWe show that, in this limit, the heat flux obeys the Fluctuation Theorem, in a\nform proposed by Jarzynski and W\\'ojcik in $2004$ for the fluctuations of the\nflux between finite size equilibrium heat baths.\n"}
+{"abstract": "  A small-cell network with multiple transmitters and unreliable wireless\nbackhaul is considered for secrecy enhancement. The small-cell network is\noperating under a spectrum sharing agreement with a primary network in a\ncognitive radio system. A constraint on the desired outage probability at the\nprimary receiver is assumed as a part of the spectrum sharing agreement. The\nreliability of the wireless backhaul links are modeled by a set of independent\nand identically distributed Bernoulli random variables. A sub-optimal and an\noptimal small-cell transmitter selection (TS) scheme is proposed to improve the\nperformance of the system, depending on the availability of channel state\ninformation. Selection schemes are designed for the scenario where knowledge is\navailable regarding which backhaul links are active. The corresponding secrecy\noutage probabilities along with their asymptotic expressions are derived. It is\nshown that the secrecy performance is significantly improved compared to the\ncase where knowledge of the active backhaul links is unavailable.\n"}
+{"abstract": "  This work presents a system identification procedure based on Convolutional\nNeural Networks (CNN) for human posture control using the DEC (Disturbance\nEstimation and Compensation) parametric model. The modular structure of the\nproposed control model inspired the design of a modular identification\nprocedure, in the sense that the same neural network is used to identify the\nparameters of the modules controlling different degrees of freedom. In this way\nthe presented examples of body sway induced by external stimuli provide several\ntraining samples at once.\n"}
+{"abstract": "  Coronal holes are the observational manifestation of the solar magnetic field\nopen to the heliosphere and are of pivotal importance for our understanding of\nthe origin and acceleration of the solar wind. Observations from space missions\nsuch as the Solar Dynamics Observatory now allow us to study coronal holes in\nunprecedented detail. Instrumental effects and other factors, however, pose a\nchallenge to automatically detect coronal holes in solar imagery. The science\ncommunity addresses these challenges with different detection schemes. Until\nnow, little attention has been paid to assessing the disagreement between these\nschemes. In this COSPAR ISWAT initiative, we present a comparison of nine\nautomated detection schemes widely-applied in solar and space science. We\nstudy, specifically, a prevailing coronal hole observed by the Atmospheric\nImaging Assembly instrument on 2018 May 30. Our results indicate that the\nchoice of detection scheme has a significant effect on the location of the\ncoronal hole boundary. Physical properties in coronal holes such as the area,\nmean intensity, and mean magnetic field strength vary by a factor of up to 4.5\nbetween the maximum and minimum values. We conclude that our findings are\nrelevant for coronal hole research from the past decade, and are therefore of\ninterest to the solar and space research community.\n"}
+{"abstract": "  Variance-based global sensitivity analysis, in particular Sobol' analysis, is\nwidely used for determining the importance of input variables to a\ncomputational model. Sobol' indices can be computed cheaply based on spectral\nmethods like polynomial chaos expansions (PCE). Another choice are the recently\ndeveloped Poincar\\'e chaos expansions (PoinCE), whose orthonormal\ntensor-product basis is generated from the eigenfunctions of one-dimensional\nPoincar\\'e differential operators. In this paper, we show that the Poincar\\'e\nbasis is the unique orthonormal basis with the property that partial\nderivatives of the basis form again an orthogonal basis with respect to the\nsame measure as the original basis. This special property makes PoinCE ideally\nsuited for incorporating derivative information into the surrogate modelling\nprocess. Assuming that partial derivative evaluations of the computational\nmodel are available, we compute spectral expansions in terms of Poincar\\'e\nbasis functions or basis partial derivatives, respectively, by sparse\nregression. We show on two numerical examples that the derivative-based\nexpansions provide accurate estimates for Sobol' indices, even outperforming\nPCE in terms of bias and variance. In addition, we derive an analytical\nexpression based on the PoinCE coefficients for a second popular sensitivity\nindex, the derivative-based sensitivity measure (DGSM), and explore its\nperformance as upper bound to the corresponding total Sobol' indices.\n"}
+{"abstract": "  The Simon tensor gives rise to a local characterization of the Kerr-NUT\nfamily in the stationary class of vacuum spacetimes. We find that a symmetric\nand traceless tensor in the quotient space of the stationary Killing trajectory\noffers a useful alternative to the Simon tensor. Our tensor is distinct from\nthe spatial dual of the Simon tensor and illustrates the geometric property of\nthe three dimensional quotient space more manifest. The reconstruction\nprocedure of the metric for which the generalized Simon tensor vanishes is\nspelled out in detail. We give a four dimensional description of this tensor in\nterms of the Coulomb part of the imaginary selfdual Weyl tensor, which\ncorresponds to the generalization of the three-index tensor defined by Mars.\nThis allows us to establish a new and simple criterion for the Kerr-NUT family:\nthe gradient of the Ernst potential becomes the non-null eigenvector of the\nCoulomb part of the imaginary selfdual Weyl tensor. We also discuss the ${\\rm\nSU}(1,2)$ covariant extension of the obstruction tensor into the\nEinstein-Maxwell system as an intrinsic characterization of the Kerr-Newman-NUT\nfamily.\n"}
+{"abstract": "  We study the use of the Euler characteristic for multiparameter topological\ndata analysis. Euler characteristic is a classical, well-understood topological\ninvariant that has appeared in numerous applications, including in the context\nof random fields. The goal of this paper is to present the extension of using\nthe Euler characteristic in higher-dimensional parameter spaces. While\ntopological data analysis of higher-dimensional parameter spaces using stronger\ninvariants such as homology continues to be the subject of intense research,\nEuler characteristic is more manageable theoretically and computationally, and\nthis analysis can be seen as an important intermediary step in multi-parameter\ntopological data analysis. We show the usefulness of the techniques using\nartificially generated examples, and a real-world application of detecting\ndiabetic retinopathy in retinal images.\n"}
+{"abstract": "  Photometric observations of the double-mode pulsator VX Hya are presented.\nThey are analyzed with a stroboscopic method, completed by Fourier analysis.\n"}
+{"abstract": "  Charged excitons (trions) are essential for the optical spectra in low\ndimensional doped monolayers (ML) of transitional metal dichalcogenides (TMDC).\nUsing a direct diagonalization of the three-body Hamiltonian, we explore the\nlow-lying trion states in four types of TMDC MLs. We show that the trions fine\nstructure results from the interplay between the spin-valley fine structure of\nthe single-particle bands and the exchange interaction between the composing\nparticles. We demonstrate that by variations of the doping and dielectric\nenvironment, trion energy fine structure can be tuned, leading to anti-crossing\nof the bright and dark states with substantial implications for the optical\nspectra of TMDC MLs.\n"}
+{"abstract": "  Diabetic Retinopathy (DR) is a leading cause of vision loss globally. Yet\ndespite its prevalence, the majority of affected people lack access to the\nspecialized ophthalmologists and equipment required for assessing their\ncondition. This can lead to delays in the start of treatment, thereby lowering\ntheir chances for a successful outcome. Machine learning systems that\nautomatically detect the disease in eye fundus images have been proposed as a\nmeans of facilitating access to DR severity estimates for patients in remote\nregions or even for complementing the human expert's diagnosis. In this paper,\nwe propose a machine learning system for the detection of referable DR in\nfundus images that is based on the paradigm of multiple-instance learning. By\nextracting local information from image patches and combining it efficiently\nthrough an attention mechanism, our system is able to achieve high\nclassification accuracy. Moreover, it can highlight potential image regions\nwhere DR manifests through its characteristic lesions. We evaluate our approach\non publicly available retinal image datasets, in which it exhibits near\nstate-of-the-art performance, while also producing interpretable visualizations\nof its predictions.\n"}
+{"abstract": "  We consider Bayesian optimization in settings where observations can be\nadversarially biased, for example by an uncontrolled hidden confounder. Our\nfirst contribution is a reduction of the confounded setting to the dueling\nbandit model. Then we propose a novel approach for dueling bandits based on\ninformation-directed sampling (IDS). Thereby, we obtain the first efficient\nkernelized algorithm for dueling bandits that comes with cumulative regret\nguarantees. Our analysis further generalizes a previously proposed\nsemi-parametric linear bandit model to non-linear reward functions, and\nuncovers interesting links to doubly-robust estimation.\n"}
+{"abstract": "  This paper studies inference in linear models whose parameter of interest is\na high-dimensional matrix. We focus on the case where the high-dimensional\nmatrix parameter is well-approximated by a ``spiked low-rank matrix'' whose\nrank grows slowly compared to its dimensions and whose nonzero singular values\ndiverge to infinity. We show that this framework covers a broad class of models\nof latent-variables which can accommodate matrix completion problems, factor\nmodels, varying coefficient models, principal components analysis with missing\ndata, and heterogeneous treatment effects. For inference, we propose a new\n``rotation-debiasing\" method for product parameters initially estimated using\nnuclear norm penalization. We present general high-level results under which\nour procedure provides asymptotically normal estimators. We then present\nlow-level conditions under which we verify the high-level conditions in a\ntreatment effects example.\n"}
+{"abstract": "  Let $D\\ne \\mathbb{C}$ be a simply connected domain and $f$ be the Riemann\nmapping from $\\mathbb{D}$ onto $D$. The Hardy number of $D$ is the supremum of\nall $p$ for which $f$ belongs in the Hardy space ${H^p}\\left( \\mathbb{D}\n\\right)$. A comb domain is the entire plane minus an infinite number of\nvertical rays symmetric with respect to the real axis. In this paper we prove\nthat for any $p\\in [1,+\\infty]$, there is a comb domain with Hardy number equal\nto $p$ and this result is sharp. It is known that the Hardy number is related\nwith the moments of the exit time of Brownian motion from the domain. In\nparticular, our result implies that given $ p < q$ there exists a comb domain\nwith finite $p$-th moment but infinite $q$-th moment if and only if $q\\geq\n1/2$. This answers a question posed by Boudabra and Markowsky.\n"}
+{"abstract": "  In this paper, we introduce a new technique that combines two popular methods\nto estimate uncertainty in object detection. Quantifying uncertainty is\ncritical in real-world robotic applications. Traditional detection models can\nbe ambiguous even when they provide a high-probability output. Robot actions\nbased on high-confidence, yet unreliable predictions, may result in serious\nrepercussions. Our framework employs deep ensembles and Monte Carlo dropout for\napproximating predictive uncertainty, and it improves upon the uncertainty\nestimation quality of the baseline method. The proposed approach is evaluated\non publicly available synthetic image datasets captured from sequences of\nvideo.\n"}
+{"abstract": "  Domain generalization is challenging due to the domain shift and the\nuncertainty caused by the inaccessibility of target domain data. In this paper,\nwe address both challenges with a probabilistic framework based on variational\nBayesian inference, by incorporating uncertainty into neural network weights.\nWe couple domain invariance in a probabilistic formula with the variational\nBayesian inference. This enables us to explore domain-invariant learning in a\nprincipled way. Specifically, we derive domain-invariant representations and\nclassifiers, which are jointly established in a two-layer Bayesian neural\nnetwork. We empirically demonstrate the effectiveness of our proposal on four\nwidely used cross-domain visual recognition benchmarks. Ablation studies\nvalidate the synergistic benefits of our Bayesian treatment when jointly\nlearning domain-invariant representations and classifiers for domain\ngeneralization. Further, our method consistently delivers state-of-the-art mean\naccuracy on all benchmarks.\n"}
+{"abstract": "  Photoexcitation is well-known to trigger electronic metastable states and\nlead to phenomena like long-lived photoluminescence and photoconductivity. In\ncontrast, persistent photo-response due to ionic metastable states is rare. In\nthis work, we report persistent structural and ferroelectric photo-responses\ndue to proton metastable states via a nuclear quantum mechanism in\nferroelectric croconic acid, in which the proton-transfer origin of\nferroelectricity is important for the ionic metastable states. We show that,\nafter photoexcitation, the changes of structural and ferroelectric properties\nrelax in about 1000 s, while the photoconductivity decays within 1 s,\nindicating the dominant ionic origin of the responses. The photogenerated\ninternal bias field that survives polarization switching process suggests\nanother proton transfer route and metastable state, in addition to the\nmetastable states resulting from proton transfer along the hydrogen bonds\nproposed previously. Analysis based on Frank Condon principle reveals the\nquantum mechanical nature of the proton-transfer process both within the\nhydrogen bonds and out of the hydrogen bonds, where small mass of proton and\nsignificant change of potential landscape due to the excited electronic states\nare the key. The demonstration of persistent photo-responses due to the proton\nmetastable states unveils a nuclear quantum mechanism for photo-tunability of\nmaterials, which is expected to impact many material properties sensitive to\nionic positions.\n"}
+{"abstract": "  Developing an agent in reinforcement learning (RL) that is capable of\nperforming complex control tasks directly from high-dimensional observation\nsuch as raw pixels is yet a challenge as efforts are made towards improving\nsample efficiency and generalization. This paper considers a learning framework\nfor Curiosity Contrastive Forward Dynamics Model (CCFDM) in achieving a more\nsample-efficient RL based directly on raw pixels. CCFDM incorporates a forward\ndynamics model (FDM) and performs contrastive learning to train its deep\nconvolutional neural network-based image encoder (IE) to extract conducive\nspatial and temporal information for achieving a more sample efficiency for RL.\nIn addition, during training, CCFDM provides intrinsic rewards, produced based\non FDM prediction error, encourages the curiosity of the RL agent to improve\nexploration. The diverge and less-repetitive observations provide by both our\nexploration strategy and data augmentation available in contrastive learning\nimprove not only the sample efficiency but also the generalization. Performance\nof existing model-free RL methods such as Soft Actor-Critic built on top of\nCCFDM outperforms prior state-of-the-art pixel-based RL methods on the DeepMind\nControl Suite benchmark.\n"}
+{"abstract": "  In a decentralized system with $m$ machines, we study the selfish scheduling\nproblem where each user strategically chooses which machine to use. Each\nmachine incurs a cost, which is a function of the total load assigned to it,\nand some cost-sharing mechanism distributes this cost among the machine's\nusers. The users choose a machine aiming to minimize their own share of the\ncost, so the cost-sharing mechanism induces a game among them. We approach this\nproblem from the perspective of a designer who can select which cost-sharing\nmechanism to use, aiming to minimize the price of anarchy (PoA) of the induced\ngames.\n  Recent work introduced the class of \\emph{resource-aware} cost-sharing\nmechanisms, whose decisions can depend on the set of machines in the system,\nbut are oblivious to the total number of users. These mechanisms can guarantee\nlow PoA bounds for instances where the cost functions of the machines are all\nconvex or concave, but can suffer from very high PoA for cost functions that\ndeviate from these families.\n  In this paper we show that if we enhance the class of resource-aware\nmechanisms with some prior information regarding the users, then they can\nachieve low PoA for a much more general family of cost functions. We first show\nthat, as long as the mechanism knows just two of the participating users, then\nit can assign special roles to them and ensure a constant PoA. We then extend\nthis idea to settings where the mechanism has access to the probability with\nwhich each user is present in the system. For all these instances, we provide a\nmechanism that achieves an expected PoA that is logarithmic in the expected\nnumber of users.\n"}
+{"abstract": "  This work is devoted to the structure of the time-discrete Green-Naghdi\nequations including bathymetry. We use the projection structure of the\nequations to characterize homogeneous and inhomogeneous boundary conditions for\nwhich the semi-discrete equations are well-posed. This structure allows us to\npropose efficient and robust numerical treatment of the boundary conditions\nthat ensures entropy stability of the scheme by construction. Numerical\nevidence is provided to illustrate that our approach is suitable for situations\nof practical interest that are not covered by existing theory.\n"}
+{"abstract": "  We describe a new phenomenon in models of coalescence and fragmentation, that\nof gel-shatter cycles. These are dynamical, unforced, stochastic cycles in\nwhich slow, approximately deterministic coalescence up to and beyond gelation\nis followed by abrupt random shattering. We describe their appearance in\nsimulations of stochastic models with multiplicative kernels for coalescence\nand spontaneous fragmentation into monomers (`shattering'). The regime in which\nsuch cycles occur is characterized by a cyclicity order parameter, and we\nprovide a simple scaling argument which describes both this regime and those\nwhich border it.\n"}
+{"abstract": "  We show that every graded ideal of a Leavitt path algebra is graded\nisomorphic to a Leavitt path algebra. It is known that a graded ideal $I$ of a\nLeavitt path algebra is isomorphic to the Leavitt path algebra of a graph,\nknown as the generalized hedgehog graph, which is defined based on certain sets\nof vertices uniquely determined by $I$. However, this isomorphism may not be\ngraded. We show that replacing the short \"spines\" of the generalized hedgehog\ngraph with possibly fewer, but then necessarily longer spines, we obtain a\ngraph (which we call the porcupine graph) such that its Leavitt path algebra is\ngraded isomorphic to $I$. Our proof adapts to show that for every closed\ngauge-invariant ideal $J$ of a graph $C^*$-algebra, there is a gauge-invariant\n$*$-isomorphism mapping the graph $C^*$-algebra of the porcupine graph of $J$\nonto $J.$\n"}
+{"abstract": "  We introduce methods for deriving analytic solutions from\ndifferential-algebraic systems of equations (DAEs), as well as methods for\nderiving governing equations for analytic characterization which is currently\nlimited to very small systems as it is carried out by hand. Analytic solutions\nto the system and analytic characterization through governing equations provide\ninsights into the behaviors of DAEs as well as the parametric regions of\noperation for each potential behavior. For each system (DAEs), and choice of\ndependent variable, there is a corresponding governing equation which is\nunivariate ODE or PDE that is typically higher order than the constitutive\nequations of the system. We first introduce a direct formulation for\nrepresenting systems of linear DAEs. Unlike state space formulations, our\nformulation follows very directly from the system of constitutive equations\nwithout the need for introducing state variables or singular matrices. Using\nthis formulation for the system of constitutive equations (DAEs), we develop\nmethods for deriving analytic expressions for the full solution (complementary\nand particular) for all dependent variables of systems that consist of constant\ncoefficient ordinary-DAEs and special cases of partial-DAEs. We also develop\nmethods for deriving the governing equation for a chosen dependent variable for\nthe constant coefficient ordinary-DAEs and partial-DAEs as well as special\ncases of variable coefficient DAEs. The methods can be automated with symbolic\ncoding environments thereby allowing for dealing with systems of any size while\nretaining analytic nature. This is relevant for interpretable modeling,\nanalytic characterization and estimation, and engineering design in which the\nobjective is to tune parameter values to achieve specific behavior. Such\ninsights cannot directly be obtained using numerical simulations.\n"}
+{"abstract": "  The next decade will be an exciting period for solar astrophysics, as new\nground- and space-based instrumentation will provide unprecedented observations\nof the solar atmosphere and heliosphere. The synergy between modeling effort\nand comprehensive analysis of observations is crucial for the understanding of\nthe physical processes behind the observed phenomena. However, the\nunprecedented wealth of data on one hand, and the complexity of the physical\nphenomena on the other, require the development of new approaches in both data\nanalysis and numerical modeling. In this white paper, we summarize recent\nnumerical achievements to reproduce structure, dynamics, and observed phenomena\nfrom the photosphere to the low corona and outline challenges we expect to face\nfor the interpretation of future observations.\n"}
+{"abstract": "  We study the algebraic properties of binary relations whose underlying\ndigraph is smooth, that is has no source or sink. Such objects have been\nstudied as surjective hyper-operations (shops) on the corresponding vertex set,\nand as binary relations that are defined everywhere and whose inverse is also\ndefined everywhere. In the latter formulation, they have been called\nmultipermutations. We study the lattice structure of sets (monoids) of\nmultipermutations over an n-element domain. Through a Galois connection, these\nmonoids form the algebraic counterparts to sets of relations closed under\ndefinability in positive first-order logic without equality. The first side of\nthis Galois connection has been elaborated previously, we show the other side\nhere. We study the property of inverse on multipermutations and how it connects\nour monoids to groups. We use our results to give a simple dichotomy theorem\nfor the evaluation problem of positive first-order logic without equality on\nthe class of structures whose preserving multipermutations form a monoid closed\nunder inverse. These problems turn out either to be in Logspace or to be\nPspace-complete. We go on to study the monoid of all multipermutations on an\nn-element domain, under usual composition of relations. We characterise its\nGreen relations, regular elements and show that it does not admit a generating\nset that is polynomial on n.\n"}
+{"abstract": "  Portfolio optimization methods suffer from a catalogue of known problems,\nmainly due to the facts that pair correlations of asset returns are unstable,\nand that extremal risk measures such as maximum drawdown are difficult to\npredict due to the non-Gaussianity of portfolio returns. \\\\ In order to look at\noptimal portfolios for arbitrary risk penalty functions, we construct portfolio\nshapes where the penalty is proportional to a moment of the returns of\narbitrary order $p>2$. \\\\ The resulting component weight in the portfolio\nscales sub-linearly with its return, with the power-law $w \\propto\n\\mu^{1/(p-1)}$. This leads to significantly improved diversification when\ncompared to Kelly portfolios, due to the dilution of the winner-takes-all\neffect.\\\\ In the limit of penalty order $p\\rightarrow\\infty$, we recover the\nsimple trading heuristic whereby assets are allocated a fixed positive weight\nwhen their return exceeds the hurdle rate, and zero otherwise. Infinite order\npower-law portfolios thus fall into the class of perfectly diversified\nportfolios.\n"}
+{"abstract": "  Optical wireless communication (OWC) meets the demands of the future\nsix-generation mobile network (6G) as it operates at several hundreds of\nTerahertz and has the potential to enable data rate in the order of Tbps.\nHowever, most beam steering OWC technologies require high-accuracy positioning\nand high-speed control. Resonant beam communication (RBCom), as one kind of\nnon-positioning OWC technologies, has been proposed for high-rate mobile\ncommunications. The mobility of RBCom relies on its self-alignment\ncharacteristic where no positioning is required. In a previous study, an\nexternal-cavity second-harmonic-generation (SHG) RBCom system has been proposed\nfor eliminating the echo interference inside the resonator. However, its energy\nconversion efficiency and complexity are of concern. In this paper, we propose\nan intra-cavity SHG RBCom system to simplify the system design and improve the\nenergy conversion efficiency. We elaborate the system structure and establish\nan analytical model. Numerical results show that the energy consumption of the\nproposed intra-cavity design is reduced to reach the same level of channel\ncapacity at the receiver compared with the external-cavity one.\n"}
+{"abstract": "  A foundational question in the theory of linear compartmental models is how\nto assess whether a model is identifiable -- that is, whether parameter values\ncan be inferred from noiseless data -- directly from the combinatorics of the\nmodel. We completely answer this question for those models (with one input and\none output) in which the underlying graph is a bidirectional tree. Such models\ninclude two families of models appearing often in biological applications:\ncatenary and mammillary models. Our proofs are enabled by two supporting\nresults, which are interesting in their own right. First, we give the first\ngeneral formula for the coefficients of input-output equations (certain\nequations that can be used to determine identifiability). Second, we prove that\nidentifiability is preserved when a model is enlarged in specific ways\ninvolving adding a new compartment with a bidirected edge to an existing\ncompartment.\n"}
+{"abstract": "  Biometric technologies, especially face recognition, have become an essential\npart of identity management systems worldwide. In deployments of biometrics,\nsecure storage of biometric information is necessary in order to protect the\nusers' privacy. In this context, biometric cryptosystems are designed to meet\nkey requirements of biometric information protection enabling a\nprivacy-preserving storage and comparison of biometric data.\n  This work investigates the application of a well-known biometric\ncryptosystem, i.e. the improved fuzzy vault scheme, to facial feature vectors\nextracted through deep convolutional neural networks. To this end, a feature\ntransformation method is introduced which maps fixed-length real-valued deep\nfeature vectors to integer-valued feature sets. As part of said feature\ntransformation, a detailed analysis of different feature quantisation and\nbinarisation techniques is conducted. At key binding, obtained feature sets are\nlocked in an unlinkable improved fuzzy vault. For key retrieval, the efficiency\nof different polynomial reconstruction techniques is investigated. The proposed\nfeature transformation method and template protection scheme are agnostic of\nthe biometric characteristic. In experiments, an unlinkable improved deep face\nfuzzy vault-based template protection scheme is constructed employing features\nextracted with a state-of-the-art deep convolutional neural network trained\nwith the additive angular margin loss (ArcFace). For the best configuration, a\nfalse non-match rate below 1% at a false match rate of 0.01%, is achieved in\ncross-database experiments on the FERET and FRGCv2 face databases. On average,\na security level of up to approximately 28 bits is obtained. This work presents\nan effective face-based fuzzy vault scheme providing privacy protection of\nfacial reference data as well as digital key derivation from face.\n"}
+{"abstract": "  A mapping from Fock space boson states to qubits is given and an underlying\ndigital quantum simulation algorithm of bosons is derived. We realize the\nalgorithm in MPS (matrix product state) which simulating real time dynamics of\nYukawa coupling in varies initial states and coupling constants. This proposal\nmay be achieved in superconductivity NISQ (noisy intermediate-scale quantum)\ncomputer not far future.\n"}
+{"abstract": "  Over the past few years, graph neural networks (GNN) and label\npropagation-based methods have made significant progress in addressing node\nclassification tasks on graphs. However, in addition to their reliance on\nelaborate architectures and algorithms, there are several key technical details\nthat are frequently overlooked, and yet nonetheless can play a vital role in\nachieving satisfactory performance. In this paper, we first summarize a series\nof existing tricks-of-the-trade, and then propose several new ones related to\nlabel usage, loss function formulation, and model design that can significantly\nimprove various GNN architectures. We empirically evaluate their impact on\nfinal node classification accuracy by conducting ablation studies and\ndemonstrate consistently-improved performance, often to an extent that\noutweighs the gains from more dramatic changes in the underlying GNN\narchitecture. Notably, many of the top-ranked models on the Open Graph\nBenchmark (OGB) leaderboard and KDDCUP 2021 Large-Scale Challenge MAG240M-LSC\nbenefit from these techniques we initiated.\n"}
+{"abstract": "  Hypothesis. Isoniazid is one of the primary drugs used in tuberculosis\ntreatment. Isoniazid encapsulation in liposomal vesicles can improve drug\ntherapeutic index and minimize toxic and side effects. In this work, we\nconsider mixtures of hydrogenated soy phosphatidylcholine/phosphatidylglycerol\n(HSPC/DPPG) to get novel biocompatible liposomes for isoniazid pulmonary\ndelivery. Our goal is to understand if the entrapped drug affects bilayer\nstructure.\n  Experiments. HSPC-DPPG unilamellar liposomes are prepared and characterized\nby dynamic light scattering, $\\zeta$-potential, fluorescence anisotropy and\nTransmission Electron Microscopy. Isoniazid encapsulation is determined by UV\nand Laser Transmission Spectroscopy. Calorimetry, light scattering and Surface\nPressure measurements are used to get insight on adsorption and thermodynamic\nproperties of lipid bilayers in the presence of the drug.\n  Findings. We find that INH-lipid interaction can increase the entrapment\ncapability of the carrier due to isoniazid adsorption. The preferential\nINH-HSPC dipole-dipole interaction promotes modification of lipid packing and\nordering and favors the condensation of a HSPC-richer phase in molar excess of\nDPPG. Our findings highlight the importance of fundamental investigations of\ndrug-lipid interactions for the optimal design of liposomal nanocarriers.\n"}
+{"abstract": "  Studies of disordered spin chains have recently experienced a renewed\ninterest, inspired by the question to which extent the exact numerical\ncalculations comply with the existence of a many-body localization phase\ntransition. For the paradigmatic random field Heisenberg spin chains, many\nintriguing features were observed when the disorder is considerable compared to\nthe spin interaction strength. Here, we introduce a phenomenological theory\nthat may explain some of those features. The theory is based on the proximity\nto the noninteracting limit, in which the system is an Anderson insulator.\nTaking the spin imbalance as an exemplary observable, we demonstrate that the\nproximity to the local integrals of motion of the Anderson insulator determines\nthe dynamics of the observable at infinite temperature. In finite interacting\nsystems our theory quantitatively describes its integrated spectral function\nfor a wide range of disorders.\n"}
+{"abstract": "  We investigated the adsorption of iodine on silver (111) in ultra-high\nvacuum. Using low-temperature scanning tunneling microscopy (LT-STM)\nmeasurements we catalog the complex surface structures on the local scale. We\nidentified three distinct phases with increasing iodine coverage which we\ntentatively associate with three phases previously reported in LEED experiments\n(sqrt(3)x sqrt(3)R30, \"triangular\", \"hexagonal\"). We used Fourier space and\nreal space analysis to fully characterize each phase. While Fourier analysis\nmost easily connects our measurements to previous LEED studies, the real space\ninspection reveals local variations in the superstructures of the \"hexagonal\"\nand \"triangular\" phase. The latter, observed here for the first time by LT-STM,\nstabilized by one or two adatoms sitting at the center of a rosette-like iodine\nreconstruction. The most stunning discovery is that variation in the adatom\nseparation of the \"triangular\" phase reconstruct the Ag (111) surface lattice.\n"}
+{"abstract": "  We investigate the impact of different assumptions in the modeling of\none-loop galaxy bias on the recovery of cosmological parameters, as a follow up\nof the analysis done in the first paper of the series at fixed cosmology. We\nuse three different synthetic galaxy samples whose clustering properties match\nthe ones of the CMASS and LOWZ catalogues of BOSS and the SDSS Main Galaxy\nSample. We investigate the relevance of allowing for either short range\nnon-locality or scale-dependent stochasticity by fitting the real-space galaxy\nauto power spectrum or the combination of galaxy-galaxy and galaxy-matter power\nspectrum. From a comparison among the goodness-of-fit ($\\chi^2$), unbiasedness\nof cosmological parameters (FoB), and figure-of-merit (FoM), we find that a\nfour-parameter model (linear, quadratic, cubic non-local bias, and constant\nshot-noise) with fixed quadratic tidal bias provides a robust modelling choice\nfor the auto power spectrum of the three samples, up to $k_{\\rm\nmax}=0.3\\,h\\,\\mathrm{Mpc}^{-1}$ and for an effective volume of\n$6\\,h^{-3}\\,\\mathrm{Gpc}^3$. Instead, a joint analysis of the two observables\nfails at larger scales, and a model extension with either higher derivatives or\nscale-dependent shot-noise is necessary to reach a similar $k_{\\rm max}$, with\nthe latter providing the most stable results. These findings are obtained with\nthree, either hybrid or perturbative, prescriptions for the matter power\nspectrum, \\texttt{RESPRESSO}, gRPT and EFT. In all cases, the inclusion of\nscale-dependent shot-noise increases the range of validity of the model in\nterms of FoB and $\\chi^2$. Interestingly, these model extensions with\nadditional free parameters do not necessarily lead to an increase in the\nmaximally achievable FoM for the cosmological parameters\n$\\left(h,\\,\\Omega_ch^2,\\,A_s\\right)$, which are generally consistent to those\nof the simpler model at smaller $k_{\\rm max}$.\n"}
+{"abstract": "  We consider the two-body problem in a periodic potential, and study the\nbound-state dispersion of a spin-$\\uparrow$ fermion that is interacting with a\nspin-$\\downarrow$ fermion through a short-range attractive interaction. Based\non a variational approach, we obtain the exact solution of the dispersion in\nthe form of a set of self-consistency equations, and apply it to tight-binding\nHamiltonians with onsite interactions. We pay special attention to the\nbipartite lattices with a two-point basis that exhibit time-reversal symmetry,\nand show that the lowest-energy bound states disperse quadratically with\nmomentum, whose effective-mass tensor is partially controlled by the quantum\nmetric tensor of the underlying Bloch states. In particular, we apply our\ntheory to the Mielke checkerboard lattice, and study the special role played by\nthe interband processes in producing a finite effective mass for the bound\nstates in a non-isolated flat band.\n"}
+{"abstract": "  The final aim of this paper is to expand the sparse group of X-ray binaries\nwith gamma-ray counterparts as laboratories to study high-energy processes\nunder physical conditions that periodically repeat. A follow-up of a candidate\nsystem has been carried out. We applied both photometric and spectroscopic\ntechniques in the optical domain together with a period analysis using the\nphase dispersion minimization and CLEAN methods. A tentative period search was\nalso conducted in the gamma-ray domain. Our main result is having established\nthe binary nature of the optical star and X-ray emitter HD 3191 towards the\nFermi gamma-ray source 4FGL J0035.8+6131, the last one proposed to be\nassociated with a blazar of an unknown type. An orbital period close to 16 days\nis reported for HD 3191 together with a likely rotation, or pulsation, period\nof about 0.6 d. Although no convincing evidence for the orbital cycle has been\nfound in the Fermi light curve up to now, the confirmed presence of a high-mass\nX-ray binary towards 4FGL J0035.8+6131 now strengthens the need for caution\nabout its true nature.\n"}
+{"abstract": "  This study addresses the question whether visually grounded speech\nrecognition (VGS) models learn to capture sentence semantics without access to\nany prior linguistic knowledge. We produce synthetic and natural spoken\nversions of a well known semantic textual similarity database and show that our\nVGS model produces embeddings that correlate well with human semantic\nsimilarity judgements. Our results show that a model trained on a small\nimage-caption database outperforms two models trained on much larger databases,\nindicating that database size is not all that matters. We also investigate the\nimportance of having multiple captions per image and find that this is indeed\nhelpful even if the total number of images is lower, suggesting that\nparaphrasing is a valuable learning signal. While the general trend in the\nfield is to create ever larger datasets to train models on, our findings\nindicate other characteristics of the database can just as important important.\n"}
+{"abstract": "  Commonly used automatic speech recognition (ASR) systems can be classified\ninto frame-synchronous and label-synchronous categories, based on whether the\nspeech is decoded on a per-frame or per-label basis. Frame-synchronous systems,\nsuch as traditional hidden Markov model systems, can easily incorporate\nexisting knowledge and can support streaming ASR applications.\nLabel-synchronous systems, based on attention-based encoder-decoder models, can\njointly learn the acoustic and language information with a single model, which\ncan be regarded as audio-grounded language models. In this paper, we propose\nrescoring the N-best hypotheses or lattices produced by a first-pass\nframe-synchronous system with a label-synchronous system in a second-pass. By\nexploiting the complementary modelling of the different approaches, the\ncombined two-pass systems achieve competitive performance without using any\nextra speech or text data on two standard ASR tasks. For the 80-hour AMI IHM\ndataset, the combined system has a 13.7% word error rate (WER) on the\nevaluation set, which is up to a 29% relative WER reduction over the individual\nsystems. For the 300-hour Switchboard dataset, the WERs of the combined system\nare 5.7% and 12.1% on Switchboard and CallHome subsets of Hub5'00, and 13.2%\nand 7.6% on Switchboard Cellular and Fisher subsets of RT03, up to a 33%\nrelative reduction in WER over the individual systems.\n"}
+{"abstract": "  In this paper, we study the general form of three-point functions of\nconserved current multiplets $S_{\\alpha(k)}= S_{(\\alpha_1 \\dots \\alpha_k)}$ of\narbitrary rank in four-dimensional ${\\mathcal N}=1$ superconformal theory. We\nfind that the correlation function of three such operators $\\langle\n\\bar{S}_{\\dot{\\alpha}(k)} (z_1) S_{\\beta(k+l)} (z_2) \\bar{S}_{\\dot{\\gamma}(l)}\n(z_3) \\rangle$ is fixed by the superconformal symmetry up to a single complex\ncoefficient though the precise form of the correlator depends on the values of\n$k$ and $l$. In addition, we present the general structure of mixed correlators\nof the form $\\langle \\bar{S}_{\\dot{\\alpha}(k)} (z_1) S_{\\alpha(k)} (z_2) L(z_3)\n\\rangle$ and $\\langle \\bar{S}_{\\dot{\\alpha}(k)} (z_1) S_{\\alpha(k)} (z_2)\nJ_{\\gamma \\dot{\\gamma}} (z_3) \\rangle$, where $L$ is the flavour current\nmultiplet and $J_{\\gamma \\dot{\\gamma}}$ is the supercurrent.\n"}
+{"abstract": "  Machine Learning (ML) techniques are becoming an invaluable support for\nnetwork intrusion detection, especially in revealing anomalous flows, which\noften hide cyber-threats. Typically, ML algorithms are exploited to\nclassify/recognize data traffic on the basis of statistical features such as\ninter-arrival times, packets length distribution, mean number of flows, etc.\nDealing with the vast diversity and number of features that typically\ncharacterize data traffic is a hard problem. This results in the following\nissues: i) the presence of so many features leads to lengthy training processes\n(particularly when features are highly correlated), while prediction accuracy\ndoes not proportionally improve; ii) some of the features may introduce bias\nduring the classification process, particularly those that have scarce relation\nwith the data traffic to be classified. To this end, by reducing the feature\nspace and retaining only the most significant features, Feature Selection (FS)\nbecomes a crucial pre-processing step in network management and, specifically,\nfor the purposes of network intrusion detection. In this review paper, we\ncomplement other surveys in multiple ways: i) evaluating more recent datasets\n(updated w.r.t. obsolete KDD 99) by means of a designed-from-scratch\nPython-based procedure; ii) providing a synopsis of most credited FS approaches\nin the field of intrusion detection, including Multi-Objective Evolutionary\ntechniques; iii) assessing various experimental analyses such as feature\ncorrelation, time complexity, and performance. Our comparisons offer useful\nguidelines to network/security managers who are considering the incorporation\nof ML concepts into network intrusion detection, where trade-offs between\nperformance and resource consumption are crucial.\n"}
+{"abstract": "  Finding a reduction of complex, high-dimensional dynamics to its essential,\nlow-dimensional \"heart\" remains a challenging yet necessary prerequisite for\ndesigning efficient numerical approaches. Machine learning methods have the\npotential to provide a general framework to automatically discover such\nrepresentations. In this paper, we consider multiscale stochastic systems with\nlocal slow-fast time scale separation and propose a new method to encode in an\nartificial neural network a map that extracts the slow representation from the\nsystem. The architecture of the network consists of an encoder-decoder pair\nthat we train in a supervised manner to learn the appropriate low-dimensional\nembedding in the bottleneck layer. We test the method on a number of examples\nthat illustrate the ability to discover a correct slow representation.\nMoreover, we provide an error measure to assess the quality of the embedding\nand demonstrate that pruning the network can pinpoint an essential coordinates\nof the system to build the slow representation.\n"}
+{"abstract": "  In this paper, we establish optimal Berry--Esseen bounds for the generalized\n$U$-statistics. The proof is based on a new Berry--Esseen theorem for\nexchangeable pair approach by Stein's method under a general linearity\ncondition setting. As applications, an optimal convergence rate of the normal\napproximation for subgraph counts in Erd\\\"os--R\\'enyi graphs and graphon-random\ngraph is obtained.\n"}
+{"abstract": "  High-rate generation of hybrid photon-matter entanglement remains a\nfundamental building block of quantum network architectures enabling protocols\nsuch as quantum secure communication or quantum distributed computing. While a\ntremendous effort has been made to overcome technological constraints limiting\nthe efficiency and coherence times of current systems, an important\ncomplementary approach is to employ parallel and multiplexed architectures.\nHere we follow this approach experimentally demonstrating the generation of\nbipartite polarization-entangled photonic states across more than 500 modes,\nwith a programmable delay for the second photon enabled by qubit storage in a\nwavevector multiplexed cold-atomic quantum memory. We demonstrate Clauser,\nHorne, Shimony, Holt inequality violation by over 3 standard deviations,\nlasting for at least 45 {\\mu}s storage time for half of the modes. The ability\nto shape hybrid entanglement between the polarization and wavevector degrees of\nfreedom provides not only multiplexing capabilities but also brings prospects\nfor novel protocols.\n"}
+{"abstract": "  A cohomology theory, associated to a $n$-Lie algebra and a representation\nspace of it, is introduced. It is observed that this cohomology theory is\nqualified to encode the generalized derivation extensions, and that it\ncoincides, for $n=3$, with the known cohomology of $n$-Lie algebras. The\nabelian extensions and infinitesimal deformations of $n$-Lie algebras, on the\nother hand, are shown to be characterized by the usual cohomology of $n$-Lie\nalgebras. Furthermore, the Hochschild-Serre spectral sequence of the Lie\nalgebra cohomology is upgraded to the level of $n$-Lie algebras, and is applied\nto the cohomology of generalized derivation extensions.\n"}
+{"abstract": "  The scaled relative graph (SRG) of an operator is a subset of the complex\nplane. It captures several salient features of an operator, such as\ncontractiveness, and can be used to reveal the geometric nature of many of the\ninequality based arguments used in the convergence analyses of fixed point\niterations. In this paper we show that the SRG of a linear operator can be\ndetermined from the numerical range of a closely related linear operator.\nFurthermore we demonstrate that the SRG of a linear operator has a range of\nspectral and convexity properties, and satisfies an analogue of Hildebrant's\ntheorem.\n"}
+{"abstract": "  Several approaches to the formulation of a fractional theory of calculus of\n\"variable order\" have appeared in the literature over the years. Unfortunately,\nmost of these proposals lack a rigorous mathematical framework. We consider an\nalternative view on the problem, originally proposed by G. Scarpi in the early\nseventies, based on a naive modification of the representation in the Laplace\ndomain of standard kernels functions involved in (constant-order) fractional\ncalculus. We frame Scarpi's ideas within recent theory of General Fractional\nDerivatives and Integrals, that mostly rely on the Sonine condition, and\ninvestigate the main properties of the emerging variable-order operators. Then,\ntaking advantage of powerful and easy-to-use numerical methods for the\ninversion of Laplace transforms of functions defined in the Laplace domain, we\ndiscuss some practical applications of the variable-order Scarpi integral and\nderivative.\n"}
+{"abstract": "  Time-dependent dynamical properties of a fluid can not be estimated from a\nsingle configuration without performing a simulation. Here we show, however,\nthat the scaling properties of both structure and dynamics can be predicted\nfrom a single configuration. The new method is demonstrated to work very well\nfor equilibrium dynamics of the Kob-Andersen Binary Lennard-Jones mixture.\nFurthermore, the method is applied to isobaric cooling where the liquid falls\nout of equilibrium and forms a glass, demonstrating that the method requires\nneither equilibrium nor constant volume conditions to work, in contrast to\nexisting methods.\n"}
+{"abstract": "  Given an undirected graph, $G$, and vertices, $s$ and $t$ in $G$, the\ntracking paths problem is that of finding the smallest subset of vertices in\n$G$ whose intersection with any $s$-$t$ path results in a unique sequence. This\nproblem is known to be NP-complete and has applications to animal migration\ntracking and detecting marathon course-cutting, but its approximability is\nlargely unknown. In this paper, we address this latter issue, giving novel\nalgorithms having approximation ratios of $(1+\\epsilon)$, $O(\\lg OPT)$ and\n$O(\\lg n)$, for $H$-minor-free, general, and weighted graphs, respectively. We\nalso give a linear kernel for $H$-minor-free graphs and make improvements to\nthe quadratic kernel for general graphs.\n"}
+{"abstract": "  We analyze the well-known equivalence between the quadratic Kontsevich-Penner\nand Hermitian matrix models from the point of view of superintegrability\nrelations, i.e. explicit formulas for character averages. This is not that\ntrivial on the Kontsevich side and seems important for further studies of\nvarious deformations of Kontsevich models. In particular, the Brezin-Hikami\nextension of the above equivalence becomes straightforward.\n"}
+{"abstract": "  Cytology is a low-cost and non-invasive diagnostic procedure employed to\nsupport the diagnosis of a broad range of pathologies. Computer Vision\ntechnologies, by automatically generating quantitative and objective\ndescriptions of examinations' contents, can help minimize the chances of\nmisdiagnoses and shorten the time required for analysis. To identify the\nstate-of-art of computer vision techniques currently applied to cytology, we\nconducted a Systematic Literature Review. We analyzed papers published in the\nlast 5 years. The initial search was executed in September 2020 and resulted in\n431 articles. After applying the inclusion/exclusion criteria, 157 papers\nremained, which we analyzed to build a picture of the tendencies and problems\npresent in this research area, highlighting the computer vision methods,\nstaining techniques, evaluation metrics, and the availability of the used\ndatasets and computer code. As a result, we identified that the most used\nmethods in the analyzed works are deep learning-based (70 papers), while fewer\nworks employ classic computer vision only (101 papers). The most recurrent\nmetric used for classification and object detection was the accuracy (33 papers\nand 5 papers), while for segmentation it was the Dice Similarity Coefficient\n(38 papers). Regarding staining techniques, Papanicolaou was the most employed\none (130 papers), followed by H&E (20 papers) and Feulgen (5 papers). Twelve of\nthe datasets used in the papers are publicly available, with the DTU/Herlev\ndataset being the most used one. We conclude that there still is a lack of\nhigh-quality datasets for many types of stains and most of the works are not\nmature enough to be applied in a daily clinical diagnostic routine. We also\nidentified a growing tendency towards adopting deep learning-based approaches\nas the methods of choice.\n"}
+{"abstract": "  We demonstrate coherent control of photoemission from a gold needle tip using\na two-color laser field. The relative phase between a fundamental field and its\nsecond harmonic imprints a strong modulation on the emitted photocurrent with\nup to 96.5 % contrast. The contrast as a function of the second harmonic\nintensity can be described by three interfering quantum pathways. Increasing\nthe bias voltage applied to the tip reduces the maximum achievable contrast and\nmodifies the weights of the involved pathways. Simulations based on the\ntime-dependent Schr\\\"odinger equation reproduce the characteristic cooperative\nsignal and its dependence on the second harmonic intensity, which further\nconfirms the involvement of three emission pathways.\n"}
+{"abstract": "  In Guo and Peng's article [Spherically convex sets and spherically convex\nfunctions, J. Convex Anal. 28 (2021), 103--122], one defines the notions of\nspherical convex sets and functions on \"general curved surfaces\" in\n$\\mathbb{R}^{n}$ $(n\\ge2)$, one studies several properties of these classes of\nsets and functions, and one establishes analogues of Radon, Helly,\nCarath\\'eodory and Minkowski theorems for spherical convex sets, as well as\nsome properties of spherical convex functions which are analogous to those of\nusual convex functions. In obtaining such results, the authors use an analytic\napproach based on their definitions. Our aim in this note is to provide simpler\nproofs for the results on spherical convex sets; our proofs are based on some\ncharacterizations/representations of spherical convex sets by usual convex sets\nin $\\mathbb{R}^{n}$.\n"}
+{"abstract": "  New categories can be discovered by transforming semantic features into\nsynthesized visual features without corresponding training samples in zero-shot\nimage classification. Although significant progress has been made in generating\nhigh-quality synthesized visual features using generative adversarial networks,\nguaranteeing semantic consistency between the semantic features and visual\nfeatures remains very challenging. In this paper, we propose a novel zero-shot\nlearning approach, GAN-CST, based on class knowledge to visual feature learning\nto tackle the problem. The approach consists of three parts, class knowledge\noverlay, semi-supervised learning and triplet loss. It applies class knowledge\noverlay (CKO) to obtain knowledge not only from the corresponding class but\nalso from other classes that have the knowledge overlay. It ensures that the\nknowledge-to-visual learning process has adequate information to generate\nsynthesized visual features. The approach also applies a semi-supervised\nlearning process to re-train knowledge-to-visual model. It contributes to\nreinforcing synthesized visual features generation as well as new category\nprediction. We tabulate results on a number of benchmark datasets demonstrating\nthat the proposed model delivers superior performance over state-of-the-art\napproaches.\n"}
+{"abstract": "  Almost flat finitely generated projective Hilbert C*-module bundles were\nsuccessfully used by Hanke and Schick to prove special cases of the Strong\nNovikov Conjecture. Dadarlat later showed that it is possible to calculate the\nindex of a K-homology class $\\eta\\in K_*(M)$ twisted with an almost flat bundle\nin terms of the image of $\\eta$ under Lafforgue's assembly map and the almost\nrepresentation associated to the bundle. Mishchenko used flat\ninfinite-dimensional bundles equipped with a Fredholm operator in order to\nprove special cases of the Novikov higher signature conjecture.\n  We show how to generalize Dadarlat's theorem to the case of an\ninfinite-dimensional bundle equipped with a continuous family of Fredholm\noperators on the fibers. Along the way, we show that special cases of the\nStrong Novikov Conjecture can be proven if there exist sufficiently many almost\nflat bundles with Fredholm operator.\n  To this end, we introduce the concept of an asymptotically flat Fredholm\nbundle and its associated asymptotic Fredholm representation, and prove an\nindex theorem which relates the index of the asymptotic Fredholm bundle with\nthe so-called asymptotic index of the associated asymptotic Fredholm\nrepresentation.\n"}
+{"abstract": "  We report the first mode-locked fiber laser to operate in the femtosecond\nregime well beyond 3 {\\mu}m. The laser uses dual-wavelength pumping and\nnon-linear polarisation rotation to produce 3.5 {\\mu}m wavelength pulses with\nminimum duration of 580 fs at a repetition rate of 68 MHz. The pulse energy is\n3.2 nJ, corresponding to a peak power of 5.5 kW.\n"}
+{"abstract": "  Interferometry can completely redirect light, providing the potential for\nstrong and controllable optical forces. However, small particles do not\nnaturally act like interferometric beamsplitters, and the optical scattering\nfrom them is not generally thought to allow efficient interference. Instead,\noptical trapping is typically achieved via deflection of the incident field.\nHere we show that a suitably structured incident field can achieve\nbeamsplitter-like interactions with scattering particles. The resulting trap\noffers order-of-magnitude higher stiffness than the usual Gaussian trap in one\naxis, even when constrained to phase-only structuring. We demonstrate trapping\nof 3.5 to 10.0~$\\mu$m silica spheres, achieving stiffness up to 27.5$\\pm$4.1\ntimes higher than is possible using Gaussian traps, and two orders of magnitude\nhigher measurement signal-to-noise ratio. These results are highly relevant to\nmany applications, including cellular manipulation, fluid dynamics,\nmicro-robotics, and tests of fundamental physics.\n"}
+{"abstract": "  We report the direct observation of intervalley exciton between the Q\nconduction valley and $\\Gamma$ valence valley in bilayer WSe$_2$ by\nphotoluminescence. The Q$\\Gamma$ exciton lies at ~18 meV below the QK exciton\nand dominates the luminescence of bilayer WSe$_2$. By measuring the exciton\nspectra at gate-tunable electric field, we reveal different interlayer electric\ndipole moments and Stark shifts between Q$\\Gamma$ and QK excitons. Notably, we\ncan use the electric field to switch the energy order and dominant luminescence\nbetween Q$\\Gamma$ and QK excitons. Both Q$\\Gamma$ and QK excitons exhibit\npronounced phonon replicas, in which two-phonon replicas outshine the\none-phonon replicas due to the existence of (nearly) resonant exciton-phonon\nscatterings and numerous two-phonon scattering paths. We can simulate the\nreplica spectra by comprehensive theoretical modeling and calculations. The\ngood agreement between theory and experiment for the Stark shifts and phonon\nreplicas strongly supports our assignment of Q$\\Gamma$ and QK excitons.\n"}
+{"abstract": "  Cosmological phase transitions proceed via the nucleation of bubbles that\nsubsequently expand and collide. The resulting gravitational wave spectrum\ndepends crucially on the bubble wall velocity. Microscopic calculations of this\nvelocity are challenging even in weakly coupled theories. We use holography to\ncompute the wall velocity from first principles in a strongly coupled,\nnon-Abelian, four-dimensional gauge theory. The wall velocity is determined\ndynamically in terms of the nucleation temperature. We find an approximately\nlinear relation between the velocity and the ratio $\\Delta\n\\mathcal{P}/\\mathcal{E}$, with $\\Delta \\mathcal{P}$ the pressure difference\nbetween the inside and the outside of the bubble and $\\mathcal{E}$ the energy\ndensity outside the bubble. Up to a rescaling, the wall profile is well\napproximated by that of an equilibrium, phase-separated configuration at the\ncritical temperature. We verify that ideal hydrodynamics provides a good\ndescription of the system everywhere except near the wall.\n"}
+{"abstract": "  Nanocontact properties of two-dimensional (2D) materials are closely\ndependent on their unique nanomechanical systems, such as the number of atomic\nlayers and the supporting substrate. Here, we report a direct observation of\ntoplayer-dependent crystallographic orientation imaging of 2D materials with\nthe transverse shear microscopy (TSM). Three typical nanomechanical systems,\nMoS2 on the amorphous SiO2/Si, graphene on the amorphous SiO2/Si, and MoS2 on\nthe crystallized Al2O3, have been investigated in detail. This experimental\nobservation reveals that puckering behaviour mainly occurs on the top layer of\n2D materials, which is attributed to its direct contact adhesion with the AFM\ntip. Furthermore, the result of crystallographic orientation imaging of\nMoS2/SiO2/Si and MoS2/Al2O3 indicated that the underlying crystalline\nsubstrates almost do not contribute to the puckering effect of 2D materials.\nOur work directly revealed the top layer dependent puckering properties of 2D\nmaterial, and demonstrate the general applications of TSM in the bilayer 2D\nsystems.\n"}
+{"abstract": "  We present a stochastic modeling framework for atomistic propagation of a\nMode I surface crack, with atoms interacting according to the Lennard-Jones\ninteratomic potential at zero temperature. Specifically, we invoke the\nCauchy-Born rule and the maximum entropy principle to infer probability\ndistributions for the parameters of the interatomic potential. We then study\nhow uncertainties in the parameters propagate to the quantities of interest\nrelevant to crack propagation, namely, the critical stress intensity factor and\nthe lattice trapping range. For our numerical investigation, we rely on an\nautomated version of the so-called numerical-continuation enhanced flexible\nboundary (NCFlex) algorithm.\n"}
+{"abstract": "  The count-min sketch (CMS) is a randomized data structure that provides\nestimates of tokens' frequencies in a large data stream using a compressed\nrepresentation of the data by random hashing. In this paper, we rely on a\nrecent Bayesian nonparametric (BNP) view on the CMS to develop a novel\nlearning-augmented CMS under power-law data streams. We assume that tokens in\nthe stream are drawn from an unknown discrete distribution, which is endowed\nwith a normalized inverse Gaussian process (NIGP) prior. Then, using\ndistributional properties of the NIGP, we compute the posterior distribution of\na token's frequency in the stream, given the hashed data, and in turn\ncorresponding BNP estimates. Applications to synthetic and real data show that\nour approach achieves a remarkable performance in the estimation of\nlow-frequency tokens. This is known to be a desirable feature in the context of\nnatural language processing, where it is indeed common in the context of the\npower-law behaviour of the data.\n"}
+{"abstract": "  The collision dynamics of hard spheres and cylindrical pores is solved\nexactly, which is the minimal model for a regularly porous membrane.\n  Nonequilibrium event-driven molecular dynamics simulations are used to show\nthat the permeability $P$ of hard spheres of size $\\sigma$ through cylinderical\npores of size $d$ follow the hindered diffusion mechanism due to size exclusion\nas $P \\propto (1-\\sigma/d)^2$. Under this law, the separation of binary\nmixtures of large and small particles exhibits a linear relationship between\n$\\alpha^{-1/2}$ and $P^{-1/2}$, where $\\alpha$ and $P$ are the selectivity and\npermeability of the smaller particle, respectively. The mean permeability\nthrough polydisperse pores is the sum of permeabilities of individual pores,\nweighted by the fraction of the single pore area over the total pore area.\n"}
+{"abstract": "  We want in this article to show the usefulness of Quantum Turing Machine\n(QTM) in a high-level didactic context as well as in theoretical studies. We\nuse QTM to show its equivalence with quantum circuit model for Deutsch and\nDeutsch-Jozsa algorithms. Further we introduce a strategy of translation from\nQuantum Circuit to Quantum Turing models by these examples. Moreover we\nillustrate some features of Quantum Computing such as superposition from a QTM\npoint of view and starting with few simple examples very known in Quantum\nCircuit form.\n"}
+{"abstract": "  Purpose: This article develops theoretical, algorithmic, perceptual, and\ninteraction aspects of script legibility enhancement in the visible light\nspectrum for the purpose of scholarly editing of papyri texts. - Methods: Novel\nlegibility enhancement algorithms based on color processing and visual\nillusions are compared to classic methods in a user experience experiment. -\nResults: (1) The proposed methods outperformed the comparison methods. (2)\nUsers exhibited a broad behavioral spectrum, under the influence of factors\nsuch as personality and social conditioning, tasks and application domains,\nexpertise level and image quality, and affordances of software, hardware, and\ninterfaces. No single enhancement method satisfied all factor configurations.\nTherefore, it is suggested to offer users a broad choice of methods to\nfacilitate personalization, contextualization, and complementarity. (3) A\ndistinction is made between casual and critical vision on the basis of signal\nambiguity and error consequences. The criteria of a paradigm for enhancing\nimages for critical applications comprise: interpreting images skeptically;\napproaching enhancement as a system problem; considering all image structures\nas potential information; and making uncertainty and alternative\ninterpretations explicit, both visually and numerically.\n"}
+{"abstract": "  We present Omnidirectional Neural Radiance Fields (OmniNeRF), the first\nmethod to the application of parallax-enabled novel panoramic view synthesis.\nRecent works for novel view synthesis focus on perspective images with limited\nfield-of-view and require sufficient pictures captured in a specific condition.\nConversely, OmniNeRF can generate panorama images for unknown viewpoints given\na single equirectangular image as training data. To this end, we propose to\naugment the single RGB-D panorama by projecting back and forth between a 3D\nworld and different 2D panoramic coordinates at different virtual camera\npositions. By doing so, we are able to optimize an Omnidirectional Neural\nRadiance Field with visible pixels collecting from omnidirectional viewing\nangles at a fixed center for the estimation of new viewing angles from varying\ncamera positions. As a result, the proposed OmniNeRF achieves convincing\nrenderings of novel panoramic views that exhibit the parallax effect. We\nshowcase the effectiveness of each of our proposals on both synthetic and\nreal-world datasets.\n"}
+{"abstract": "  Let $X$ be an integrable discrete random variable over $\\{0, 1, 2, \\ldots\\}$\nwith $\\mathbb{P}(X = i + 1) \\leq \\mathbb{P}(X = i)$ for all $i$. Then for any\ninteger $a \\geq 1$, $\\mathbb{P}(X \\leq a) \\leq \\mathbb{E}[X] / (2a - 1)$. Let\n$W$ be an discrete random variable over $\\{\\ldots, -2, -1, 0, 1, 2, \\ldots\\}$\nwith finite second moment where the $\\mathbb{P}(W = i)$ values are unimodal.\nThen $\\mathbb{P}(|W - \\mathbb{E}[W]| \\geq a) \\leq (\\mathbb{V}(W) + 1 / 12) /\n(2(a - 1 / 2)^2)$.\n"}
+{"abstract": "  We point out that light gauge boson mediators could induce new interference\neffects in neutrino-electron scattering that can be used to enhance the\nsensitivity of neutrino-flavor-selective high-intensity neutrino experiments,\nsuch as DUNE. We particularly emphasize a destructive interference effect,\nleading to a deficit between the Standard Model expectation and the\nexperimental measurement of the differential cross-sections, which is prominent\nonly in either the neutrino or the antineutrino mode, depending on the mediator\ncouplings. Therefore, the individual neutrino (or antineutrino) mode could\nallow for sensitivity reaches superior to the combined analysis, and moreover,\ncould distinguish between different types of gauge boson mediators.\n"}
+{"abstract": "  Modeling and simulation of disease spreading in pedestrian crowds has been\nrecently become a topic of increasing relevance. In this paper, we consider the\ninfluence of the crowd motion in a complex dynamical environment on the course\nof infection of the pedestrians. To model the pedestrian dynamics we consider a\nkinetic equation for multi-group pedestrian flow based on a social force model\ncoupled with an Eikonal equation. This model is coupled with a non-local SEIS\ncontagion model for disease spread, where besides the description of local\ncontacts also the influence of contact times has been modelled. Hydrodynamic\napproximations of the coupled system are derived. Finally, simulations of the\nhydrodynamic model are carried out using a mesh-free particle method. Different\nnumerical test cases are investigated including uni- and bi-directional flow in\na passage with and without obstacles.\n"}
+{"abstract": "  We describe the formalization of the existence and uniqueness of Haar measure\nin the Lean theorem prover. The Haar measure is an invariant regular measure on\nlocally compact groups, and it has not been formalized in a proof assistant\nbefore. We will also discuss the measure theory library in Lean's mathematical\nlibrary \\textsf{mathlib}, and discuss the construction of product measures and\nthe proof of Fubini's theorem for the Bochner integral.\n"}
+{"abstract": "  This paper is concerned with linear parameter-dependent systems and considers\nthe notion uniform ensemble reachability. The focus of this work is on\nconstructive methods to compute suitable parameter-independent open-loop inputs\nfor such systems. In contrast to necessary and sufficient conditions for\nensemble reachability, computational methods have to distinguish between\ncontinuous-time and discrete-time systems. Based on recently derived sufficient\nconditions and techniques from complex approximation we present two algorithms\nfor discrete-time singe-input linear systems. Moreover, we illustrate that one\nmethod can also be applied to certain continuous-time single-input systems.\n"}
+{"abstract": "  The orchestra performance is full of sublime rich sounds. In particular, the\nunison of violins sounds different from the solo violin. We try to clarify this\ndifference and similarity of unison and solo numerically analyzing the beat of\n`violins` with timbre, vibrato, melody, and resonance. Characteristic\nproperties appear in the very low-frequency part in the power spectrum of the\nwave amplitude squared. This ultra-buss richness (UBR) can be a new\ncharacteristic of sound on top of the well-known pitch, loudness, and timbre,\nalthough being inaudible directly. We find this UBR is always characterized by\na power-law at low-frequency with the index around -1 and appears everywhere in\nmusic and thus being universal. Furthermore, we explore this power-law property\ntowards much smaller frequency regions and suggest possible relation to the 1/f\nnoise often found in music and many other fields in nature.\n"}
+{"abstract": "  Tackling online hatred using informed textual responses - called counter\nnarratives - has been brought under the spotlight recently. Accordingly, a\nresearch line has emerged to automatically generate counter narratives in order\nto facilitate the direct intervention in the hate discussion and to prevent\nhate content from further spreading. Still, current neural approaches tend to\nproduce generic/repetitive responses and lack grounded and up-to-date evidence\nsuch as facts, statistics, or examples. Moreover, these models can create\nplausible but not necessarily true arguments. In this paper we present the\nfirst complete knowledge-bound counter narrative generation pipeline, grounded\nin an external knowledge repository that can provide more informative content\nto fight online hatred. Together with our approach, we present a series of\nexperiments that show its feasibility to produce suitable and informative\ncounter narratives in in-domain and cross-domain settings.\n"}
+{"abstract": "  Pretraining Bidirectional Encoder Representations from Transformers (BERT)\nfor downstream NLP tasks is a non-trival task. We pretrained 5 BERT models that\ndiffer in the size of their training sets, mixture of formal and informal\nArabic, and linguistic preprocessing. All are intended to support Arabic\ndialects and social media. The experiments highlight the centrality of data\ndiversity and the efficacy of linguistically aware segmentation. They also\nhighlight that more data or more training step do not necessitate better\nmodels. Our new models achieve new state-of-the-art results on several\ndownstream tasks. The resulting models are released to the community under the\nname QARiB.\n"}
+{"abstract": "  We show that any weakly separated Bessel system of model spaces in the Hardy\nspace on the unit disc is a Riesz system and we highlight some applications to\ninterpolating sequences of matrices. This will be done without using the recent\nsolution of the Feichtinger conjecture, whose natural generalization to\nmulti-dimensional model sub-spaces of $\\mathrm{H}^2$ turns out to be false.\n"}
+{"abstract": "  In quantum metrology, entanglement represents a valuable resource that can be\nused to overcome the Standard Quantum Limit (SQL) that bounds the precision of\nsensors that operate with independent particles. Measurements beyond the SQL\nare typically enabled by relatively simple entangled states (squeezed states\nwith Gaussian probability distributions), where quantum noise is redistributed\nbetween different quadratures. However, due to both fundamental limitations and\nthe finite measurement resolution achieved in practice, sensors based on\nsqueezed states typically operate far from the true fundamental limit of\nquantum metrology, the Heisenberg Limit. Here, by implementing an effective\ntime-reversal protocol through a controlled sign change in an optically\nengineered many-body Hamiltonian, we demonstrate atomic-sensor performance with\nnon-Gaussian states beyond the limitations of spin squeezing, and without the\nrequirement of extreme measurement resolution. Using a system of 350 neutral\n$^{171}$Yb atoms, this signal amplification through time-reversed interaction\n(SATIN) protocol achieves the largest sensitivity improvement beyond the SQL\n($11.8 \\pm 0.5$~dB) demonstrated in any interferometer to date. Furthermore, we\ndemonstrate a precision improving in proportion to the particle number\n(Heisenberg scaling), at fixed distance of 12.6~dB from the Heisenberg Limit.\nThese results pave the way for quantum metrology using complex entangled\nstates, with potential broad impact in science and technology. Potential\napplications include searches for dark matter and for physics beyond the\nstandard model, tests of the fundamental laws of physics, timekeeping, and\ngeodesy.\n"}
+{"abstract": "  We prove that many of the recently-constructed algebras and categories which\nappear in categorification can be equipped with an action of the Lie algebra\nsl_2 by derivations. The representations which appear are filtered by tensor\nproducts of coverma modules. In a future paper, we will address the\nimplications of this structure for categorification.\n"}
+{"abstract": "  Modern engineering education tends to focus on mathematics and fundamentals,\neschewing critical reflections on technology and the field of engineering. In\nthis paper, I present an elective engineering course and a 3-lecture module in\nan introductory course that emphasize engaging with the social impacts of\ntechnology.\n"}
+{"abstract": "  We study how the presence of committed volunteers influences the collective\nhelping behavior in emergency evacuation scenarios. In this study, committed\nvolunteers do not change their decision to help injured persons, implying that\nother evacuees may adapt their helping behavior through strategic interactions.\nAn evolutionary game theoretic model is developed which is then coupled to a\npedestrian movement model to examine the collective helping behavior in\nevacuations. By systematically controlling the number of committed volunteers\nand payoff parameters, we have characterized and summarized various collective\nhelping behaviors in phase diagrams. From our numerical simulations, we observe\nthat the existence of committed volunteers can promote cooperation but adding\nadditional committed volunteers is effective only above a minimum number of\ncommitted volunteers. This study also highlights that the evolution of\ncollective helping behavior is strongly affected by the evacuation process.\n"}
+{"abstract": "  Let $(M,g)$ be a smooth Anosov Riemannian manifold and $\\mathcal{C}^\\sharp$\nthe set of its primitive closed geodesics. Given a Hermitian vector bundle\n$\\mathcal{E}$ equipped with a unitary connection $\\nabla^{\\mathcal{E}}$, we\ndefine $\\mathcal{T}^\\sharp(\\mathcal{E}, \\nabla^{\\mathcal{E}})$ as the sequence\nof traces of holonomies of $\\nabla^{\\mathcal{E}}$ along elements of\n$\\mathcal{C}^\\sharp$. This descends to a homomorphism on the additive moduli\nspace $\\mathbb{A}$ of connections up to gauge $\\mathcal{T}^\\sharp: (\\mathbb{A},\n\\oplus) \\to \\ell^\\infty(\\mathcal{C}^\\sharp)$, which we call the\n$\\textit{primitive trace map}$. It is the restriction of the well-known\n$\\textit{Wilson loop}$ operator to primitive closed geodesics.\n  The main theorem of this paper shows that the primitive trace map\n$\\mathcal{T}^\\sharp$ is locally injective near generic points of $\\mathbb{A}$\nwhen $\\dim(M) \\geq 3$. We obtain global results in some particular cases: flat\nbundles, direct sums of line bundles, and general bundles in negative curvature\nunder a spectral assumption which is satisfied in particular for connections\nwith small curvature. As a consequence of the main theorem, we also derive a\nspectral rigidity result for the connection Laplacian.\n  The proofs are based on two new ingredients: a Liv\\v{s}ic-type theorem in\nhyperbolic dynamical systems showing that the cohomology class of a unitary\ncocycle is determined by its trace along closed primitive orbits, and a theorem\nrelating the local geometry of $\\mathbb{A}$ with the Pollicott-Ruelle resonance\nnear zero of a certain natural transport operator.\n"}
+{"abstract": "  We present a detailed investigation of millimeter-wave line emitters ALMA\nJ010748.3-173028 (ALMA-J0107a) and ALMA J010747.0-173010 (ALMA-J0107b), which\nwere serendipitously uncovered in the background of the nearby galaxy VV114\nwith spectral scan observations at $\\lambda$ = 2 - 3 mm. Via Atacama Large\nMillimeter/submillimeter Array (ALMA) detection of CO(4-3), CO(3-2), and\n[CI](1-0) lines for both sources, their spectroscopic redshifts are\nunambiguously determined to be $z= 2.4666\\pm0.0002$ and $z=2.3100\\pm0.0002$,\nrespectively. We obtain the apparent molecular gas masses $M_{\\rm gas}$ of\nthese two line emitters from [CI] line fluxes as $(11.2 \\pm 3.1) \\times 10^{10}\nM_\\odot$ and $(4.2 \\pm 1.2) \\times 10^{10} M_\\odot$, respectively. The observed\nCO(4-3) velocity field of ALMA-J0107a exhibits a clear velocity gradient across\nthe CO disk, and we find that ALMA-J0107a is characterized by an inclined\nrotating disk with a significant turbulence, that is, a deprojected maximum\nrotation velocity to velocity dispersion ratio $v_{\\rm max}/\\sigma_{v}$ of $1.3\n\\pm 0.3$. We find that the dynamical mass of ALMA-J0107a within the CO-emitting\ndisk computed from the derived kinetic parameters, $(1.1 \\pm 0.2) \\times\n10^{10}\\ M_\\odot$, is an order of magnitude smaller than the molecular gas mass\nderived from dust continuum emission, $(3.2\\pm1.6)\\times10^{11}\\ M_{\\odot}$. We\nsuggest this source is magnified by a gravitational lens with a magnification\nof $\\mu \\gtrsim10$, which is consistent with the measured offset from the\nempirical correlation between CO-line luminosity and width.\n"}
+{"abstract": "  Biosignals are nowadays important subjects for scientific researches from\nboth theory and applications especially with the appearance of new pandemics\nthreatening humanity such as the new Coronavirus. One aim in the present work\nis to prove that Wavelets may be successful machinery to understand such\nphenomena by applying a step forward extension of wavelets to multiwavelets. We\nproposed in a first step to improve the multiwavelet notion by constructing\nmore general families using independent components for multi-scaling and\nmultiwavelet mother functions. A special multiwavelet is then introduced,\ncontinuous and discrete multiwavelet transforms are associated, as well as new\nfilters and algorithms of decomposition and reconstruction. The constructed\nmultiwavelet framework is applied for some experimentations showing fast\nalgorithms, ECG signal, and a strain of Coronavirus processing.\n"}
+{"abstract": "  We study the dynamics of the group of holomorphic automorphisms of the affine\ncubic surfaces \\begin{align*} S_{A,B,C,D} = \\{(x,y,z) \\in \\mathbb{C}^3 \\, : \\,\nx^2 + y^2 + z^2 +xyz = Ax + By+Cz+D\\}, \\end{align*} where $A,B,C,$ and $D$ are\ncomplex parameters. We focus on a finite index subgroup $\\Gamma_{A,B,C,D} <\n{\\rm Aut}(S_{A,B,C,D})$ whose action not only describes the dynamics of\nPainlev\\'e 6 differential equations but also arises naturally in the context of\ncharacter varieties. We define the Julia and Fatou sets of this group action\nand prove that there is a dense orbit in the Julia set. In order to show that\nthe Julia set is ``large'' we consider a second dichotomy, between locally\ndiscrete and locally non-discrete dynamics. For an open set in parameter space,\n$\\mathcal{N} \\subset \\mathbb{C}^4$, we show that there simultaneously exists an\nopen set in $S_{A,B,C,D}$ on which $\\Gamma_{A,B,C,D}$ acts locally discretely\nand a second open set in $S_{A,B,C,D}$ on which $\\Gamma_{A,B,C,D}$ acts locally\nnon-discretely. After removing a countable union of real-algebraic\nhypersurfaces from $\\mathcal{N}$ we show that $\\Gamma_{A,B,C,D}$ simultaneously\nexhibits a non-empty Fatou set and also a Julia set having non-trivial\ninterior. The open set $\\mathcal{N}$ contains a natural family of parameters\npreviously studied by Dubrovin-Mazzocco.\n  The interplay between the Fatou/Julia dichotomy and the locally\ndiscrete/non-discrete dichotomy plays a major theme in this paper and seems\nbound to play an important role in further dynamical studies of holomorphic\nautomorphism groups.\n"}
+{"abstract": "  Smart power grids are one of the most complex cyber-physical systems,\ndelivering electricity from power generation stations to consumers. It is\ncritically important to know exactly the current state of the system as well as\nits state variation tendency; consequently, state estimation and state\nforecasting are widely used in smart power grids. Given that state forecasting\npredicts the system state ahead of time, it can enhance state estimation\nbecause state estimation is highly sensitive to measurement corruption due to\nthe bad data or communication failures. In this paper, a hybrid deep\nlearningbased method is proposed for power system state forecasting. The\nproposed method leverages Convolutional Neural Network (CNN) for predicting\nvoltage magnitudes and a Deep Recurrent Neural Network (RNN) for predicting\nphase angels. The proposed CNN-RNN model is evaluated on the IEEE 118-bus\nbenchmark. The results demonstrate that the proposed CNNRNN model achieves\nbetter results than the existing techniques in the literature by reducing the\nnormalized Root Mean Squared Error (RMSE) of predicted voltages by 10%. The\nresults also show a 65% and 35% decrease in the average and maximum absolute\nerror of voltage magnitude forecasting.\n"}
+{"abstract": "  In several previous studies, quasars exhibiting broad emission lines with\n>1000 km/s velocity offsets with respect to the host galaxy rest frame have\nbeen discovered. One leading hypothesis for the origin of these velocity-offset\nbroad lines is the dynamics of a binary supermassive black hole (SMBH). We\npresent high-resolution radio imaging of 34 quasars showing these\nvelocity-offset broad lines with the Very Long Baseline Array (VLBA), aimed at\nfinding evidence for the putative binary SMBHs (such as dual radio cores), and\ntesting the competing physical models. We detect exactly half of the target\nsample from our VLBA imaging, after implementing a 5 detection limit. While we\ndo not resolve double radio sources in any of the targets, we obtain limits on\nthe instantaneous projected separations of a radio-emitting binary for all of\nthe detected sources under the assumption that a binary still exists within our\nVLBA angular resolution limits. We also assess the likelihood that a\nradio-emitting companion SMBH exists outside of our angular resolution limits,\nbut its radio luminosity is too weak to produce a detectable signal in the VLBA\ndata. Additionally, we compare the precise sky positions afforded by these data\nto optical positions from both the SDSS and Gaia DR2 source catalogs. We find\nprojected radio/optical separations on the order of 10 pc for three quasars.\nFinally, we explore how future multi-wavelength campaigns with optical, radio,\nand X-ray observatories can help discriminate further between the competing\nphysical models.\n"}
+{"abstract": "  We compute holographic complexity for the non-supersymmetric Janus\ndeformation of AdS$_5$ according to the volume conjecture. The result is\ncharacterized by a power-law ultraviolet divergence. When a ball-shaped region\nlocated around the interface is considered, a sub-leading logarithmic divergent\nterm and a finite part appear in the corresponding subregion volume complexity.\nUsing two different prescriptions to regularize the divergences, we find that\nthe coefficient of the logarithmic term is universal.\n"}
+{"abstract": "  We consider networks of small, autonomous devices that communicate with each\nother wirelessly. Minimizing energy usage is an important consideration in\ndesigning algorithms for such networks, as battery life is a crucial and\nlimited resource. Working in a model where both sending and listening for\nmessages deplete energy, we consider the problem of finding a maximal matching\nof the nodes in a radio network of arbitrary and unknown topology.\n  We present a distributed randomized algorithm that produces, with high\nprobability, a maximal matching. The maximum energy cost per node is $O(\\log^2\nn)$, where $n$ is the size of the network. The total latency of our algorithm\nis $O(n \\log n)$ time steps. We observe that there exist families of network\ntopologies for which both of these bounds are simultaneously optimal up to\npolylog factors, so any significant improvement will require additional\nassumptions about the network topology.\n  We also consider the related problem of assigning, for each node in the\nnetwork, a neighbor to back up its data in case of node failure. Here, a key\ngoal is to minimize the maximum load, defined as the number of nodes assigned\nto a single node. We present a decentralized low-energy algorithm that finds a\nneighbor assignment whose maximum load is at most a polylog($n$) factor bigger\nthat the optimum.\n"}
+{"abstract": "  Classical approaches for OLAP assume that the data of all tables is complete.\nHowever, in case of incomplete tables with missing tuples, classical approaches\nfail since the result of a SQL aggregate query might significantly differ from\nthe results computed on the full dataset. Today, the only way to deal with\nmissing data is to manually complete the dataset which causes not only high\nefforts but also requires good statistical skills to determine when a dataset\nis actually complete. In this paper, we propose an automated approach for\nrelational data completion called ReStore using a new class of (neural)\nschema-structured completion models that are able to synthesize data which\nresembles the missing tuples. As we show in our evaluation, this efficiently\nhelps to reduce the relative error of aggregate queries by up to 390% on\nreal-world data compared to using the incomplete data directly for query\nanswering.\n"}
+{"abstract": "  Cultural diversity encoded within languages of the world is at risk, as many\nlanguages have become endangered in the last decades in a context of growing\nglobalization. To preserve this diversity, it is first necessary to understand\nwhat drives language extinction, and which mechanisms might enable coexistence.\nHere, we study language shift mechanisms using theoretical and data-driven\nperspectives. A large-scale empirical analysis of multilingual societies using\nTwitter and census data yields a wide diversity of spatial patterns of language\ncoexistence. It ranges from a mixing of language speakers to segregation with\nmultilinguals on the boundaries of disjoint linguistic domains. To understand\nhow these different states can emerge and, especially, become stable, we\npropose a model in which language coexistence is reached when learning the\nother language is facilitated and when bilinguals favor the use of the\nendangered language. Simulations carried out in a metapopulation framework\nhighlight the importance of spatial interactions arising from people mobility\nto explain the stability of a mixed state or the presence of a boundary between\ntwo linguistic regions. Further, we find that the history of languages is\ncritical to understand their present state.\n"}
+{"abstract": "  In this paper we develop a new approach to the study of uncountable\nfundamental groups by using Hurewicz fibrations with the unique path-lifting\nproperty (lifting spaces for short) as a replacement for covering spaces. In\nparticular, we consider the inverse limit of a sequence of covering spaces of\n$X$. It is known that the path-connectivity of the inverse limit can be\nexpressed by means of the derived inverse limit functor $\\varprojlim^1$, which\nis, however, notoriously difficult to compute when the $\\pi_1(X)$ is\nuncountable.To circumvent this difficulty, we express the set of\npath-components of the inverse limit, $\\widehat X$, in terms of the functors\n$\\varprojlim$ and $\\varprojlim^1$ applied to sequences of countable groups\narising from polyhedral approximations of $X$.\n  A consequence of our computation is that path-connectedness of lifting space\nimplies that $\\pi_1(\\tilde X)$ supplements $\\pi_1(X)$ in $\\check\\pi_1(X)$ where\n$\\check\\pi_1(X)$ is the inverse limit of fundamental groups of polyhedral\napproximations of $X$. As an application we show that $\\mathcal G\\cdot\n\\ker_{\\mathbb Z}(\\widehat F)= \\widehat F\\ne\\mathcal G\\cdot\n\\ker_{B(1,n)}(\\widehat F)$, where $\\widehat F$ is the canonical inverse limit\nof finite rank free groups, $\\mathcal G$ is the fundamental group of the\nHawaiian Earring, and $\\ker_A(\\widehat F)$ is the intersection of kernels of\nhomomorphisms from $\\widehat{F}$ to $A$.\n"}
+{"abstract": "  Singular beams have attracted great attention due to their optical properties\nand broad applications from light manipulation to optical communications.\nHowever, there has been a lack of practical schemes with which to achieve\nswitchable singular beams with sub-wavelength resolution using ultrathin and\nflat optical devices. In this work, we demonstrate the generation of switchable\nvector and vortex beams utilizing dynamic metasurfaces at visible frequencies.\nThe dynamic functionality of the metasurface pixels is enabled by the\nutilization of magnesium nanorods, which possess plasmonic reconfigurability\nupon hydrogenation and dehydrogenation. We show that switchable vector beams of\ndifferent polarization states and switchable vortex beams of different\ntopological charges can be implemented through simple hydrogenation and\ndehydrogenation of the same metasurfaces. Furthermore, we demonstrate a\ntwo-cascade metasurface scheme for holographic pattern switching, taking\ninspiration from orbital angular momentum-shift keying. Our work provides an\nadditional degree of freedom to develop high-security optical elements for\nanti-counterfeiting applications.\n"}
+{"abstract": "  In this paper, we considered the gravitational collapse of a symmetric\nradiating star consisting of perfect fluid (baryonic) in the background of dark\nenergy (DE) with general equation of state. The effect of DE on the singularity\nformation has been discussed first separately (only DE present) and then\ncombination of both baryonic and DE interaction. We have also showed that DE\ncomponents play important role in the formation of Black-Hole(BH). In some\ncases the collapse of radiating star leads to black hole formation and in other\ncases it forms Naked-Singularity(or, eternally collapse). The present work is\nin itself remarkable to describe the effect of dark energy on singularity\nformation in radiating star.\n"}
+{"abstract": "  This book chapter describes a novel approach to training machine learning\nsystems by means of a hybrid computer setup i.e. a digital computer tightly\ncoupled with an analog computer. As an example a reinforcement learning system\nis trained to balance an inverted pendulum which is simulated on an analog\ncomputer, thus demonstrating a solution to the major challenge of adequately\nsimulating the environment for reinforcement learning.\n"}
+{"abstract": "  In the present work, we tackle the regular language indexing problem by first\nstudying the hierarchy of $p$-sortable languages: regular languages accepted by\nautomata of width $p$. We show that the hierarchy is strict and does not\ncollapse, and provide (exponential in $p$) upper and lower bounds relating the\nminimum widths of equivalent NFAs and DFAs. Our bounds indicate the importance\nof being able to index NFAs, as they enable indexing regular languages with\nmuch faster and smaller indexes. Our second contribution solves precisely this\nproblem, optimally: we devise a polynomial-time algorithm that indexes any NFA\nwith the optimal value $p$ for its width, without explicitly computing $p$\n(NP-hard to find). In particular, this implies that we can index in polynomial\ntime the well-studied case $p=1$ (Wheeler NFAs). More in general, in polynomial\ntime we can build an index breaking the worst-case conditional lower bound of\n$\\Omega(|P| m)$, whenever the input NFA's width is $p \\in o(\\sqrt{m})$.\n"}
+{"abstract": "  The modern search for extraterrestrial intelligence (SETI) began with the\nseminal publications of Cocconi & Morrison (1959) and Schwartz & Townes (1961),\nwho proposed to search for narrow-band signals in the radio spectrum, and for\noptical laser pulses. Over the last six decades, more than one hundred\ndedicated search programs have targeted these wavelengths; all with null\nresults. All of these campaigns searched for classical communications, that is,\nfor a significant number of photons above a noise threshold; with the\nassumption of a pattern encoded in time and/or frequency space. I argue that\nfuture searches should also target quantum communications. They are preferred\nover classical communications with regards to security and information\nefficiency, and they would have escaped detection in all previous searches. The\nmeasurement of Fock state photons or squeezed light would indicate the\nartificiality of a signal. I show that quantum coherence is feasible over\ninterstellar distances, and explain for the first time how astronomers can\nsearch for quantum transmissions sent by ETI to Earth, using commercially\navailable telescopes and receiver equipment.\n"}
+{"abstract": "  Entanglement entropy (EE) in interacting field theories has two important\nissues: renormalization of UV divergences and non-Gaussianity of the vacuum. In\nthis letter, we investigate them in the framework of the two-particle\nirreducible formalism. In particular, we consider EE of a half space in an\ninteracting scalar field theory. It is formulated as $\\mathbb{Z}_M$ gauge\ntheory on Feynman diagrams: $\\mathbb{Z}_M$ fluxes are assigned on plaquettes\nand summed to obtain EE. Some configurations of fluxes are interpreted as\ntwists of propagators and vertices. The former gives a Gaussian part of EE\nwritten in terms of a renormalized 2-point function while the latter reflects\nnon-Gaussianity of the vacuum.\n"}
+{"abstract": "  It is known that the Cabibbo-Kobayashi-Maskawa (CKM) $n\\times n$ matrix can\nbe represented by a real matrix iff there is no CP-violation, and then the\nJarlskog invariants vanish. We investigate sufficient conditions for the\nopposite statement to hold, paying particular attention to degenerate cases. We\nfind that higher Jarlskog invariants are needed for $n\\geq 4$. One generic\nsufficient condition is provided by the existence of a so-called echelon cross.\n"}
+{"abstract": "  Quantum contextuality takes an important place amongst the concepts of\nquantum computing that bring an advantage over its classical counterpart. For a\nlarge class of contextuality proofs, aka. observable-based proofs of the\nKochen-Specker Theorem, we first formulate the contextuality property as the\nabsence of solutions to a linear system. Then we explain why subgeometries of\nbinary symplectic polar spaces are candidates for contextuality proofs. We\nreport first results of a software that generates these subgeometries and\ndecides their contextuality. The proofs we consider involve more contexts and\nobservables than the smallest known proofs. This intermediate size property of\nthose proofs is interesting for experimental tests, but could also be\ninteresting in quantum game theory.\n"}
+{"abstract": "  A q-Gauss-Newton algorithm is an iterative procedure that solves nonlinear\nunconstrained optimization problems based on minimization of the sum squared\nerrors of the objective function residuals. Main advantage of the algorithm is\nthat it approximates matrix of q-second order derivatives with the first-order\nq-Jacobian matrix. For that reason, the algorithm is much faster than\nq-steepest descent algorithms. The convergence of q-GN method is assured only\nwhen the initial guess is close enough to the solution. In this paper the\ninfluence of the parameter q to the non-linear problem solving is presented\nthrough three examples. The results show that the q-GD algorithm finds an\noptimal solution and speeds up the iterative procedure.\n"}
+{"abstract": "  We study the runtime verification of hyperproperties, expressed in the\ntemporal logic HyperLTL, as a means to inspect a system with respect to\nsecurity polices. Runtime monitors for hyperproperties analyze trace logs that\nare organized by common prefixes in the form of a tree-shaped Kripke structure,\nor are organized both by common prefixes and by common suffixes in the form of\nan acyclic Kripke structure. Unlike runtime verification techniques for trace\nproperties, where the monitor tracks the state of the specification but usually\ndoes not need to store traces, a monitor for hyperproperties repeatedly model\nchecks the growing Kripke structure. This calls for a rigorous complexity\nanalysis of the model checking problem over tree-shaped and acyclic Kripke\nstructures. We show that for trees, the complexity in the size of the Kripke\nstructure is L-complete independently of the number of quantifier alternations\nin the HyperLTL formula. For acyclic Kripke structures, the complexity is\nPSPACE-complete (in the level of the polynomial hierarchy that corresponds to\nthe number of quantifier alternations). The combined complexity in the size of\nthe Kripke structure and the length of the HyperLTL formula is PSPACE-complete\nfor both trees and acyclic Kripke structures, and is as low as NC for the\nrelevant case of trees and alternation-free HyperLTL formulas. Thus, the size\nand shape of both the Kripke structure and the formula have significant impact\non the complexity of the model checking problem.\n"}
+{"abstract": "  Resource allocation is investigated for offloading computational-intensive\ntasks in multi-hop mobile edge computing (MEC) system. The envisioned system\nhas both the cooperative access points (AP) with the computing capability and\nthe MEC servers. A user-device (UD) therefore first uploads a computing task to\nthe nearest AP, and the AP can either locally process the received task or\noffload to MEC server. In order to utilize the radio resource blocks (RRBs) in\nthe APs efficiently, we exploit the non-orthogonal multiple access for\noffloading the tasks from the UDs to the AP(s). For the considered NOMA-enabled\nmulti-hop MEC computing system, our objective is to minimize both the latency\nand energy consumption of the system jointly. Towards this goal, a joint\noptimization problem is formulated by taking the offloading decision of the\nAPs, the scheduling among the UDs, RRBs, and APs, and UDs' transmit power\nallocation into account. To solve this problem efficiently, (i) a conflict\ngraph-based approach is devised that solves the scheduling among the UDs, APs,\nand RRBs, the transmit power control, and the APs' computation resource\nallocation jointly, and (ii) a low-complexity pruning graph-based approach is\nalso devised. The efficiency of the proposed graph-based approaches over\nseveral benchmark schemes is verified via extensive simulations.\n"}
+{"abstract": "  Decentralized optimization over time-varying graphs has been increasingly\ncommon in modern machine learning with massive data stored on millions of\nmobile devices, such as in federated learning. This paper revisits the widely\nused accelerated gradient tracking and extends it to time-varying graphs. We\nprove the $O((\\frac{\\gamma}{1-\\sigma_{\\gamma}})^2\\sqrt{\\frac{L}{\\epsilon}})$\nand\n$O((\\frac{\\gamma}{1-\\sigma_{\\gamma}})^{1.5}\\sqrt{\\frac{L}{\\mu}}\\log\\frac{1}{\\epsilon})$\ncomplexities for the practical single loop accelerated gradient tracking over\ntime-varying graphs when the problems are nonstrongly convex and strongly\nconvex, respectively, where $\\gamma$ and $\\sigma_{\\gamma}$ are two common\nconstants charactering the network connectivity, $\\epsilon$ is the desired\nprecision, and $L$ and $\\mu$ are the smoothness and strong convexity constants,\nrespectively. Our complexities improve significantly over the ones of\n$O(\\frac{1}{\\epsilon^{5/7}})$ and\n$O((\\frac{L}{\\mu})^{5/7}\\frac{1}{(1-\\sigma)^{1.5}}\\log\\frac{1}{\\epsilon})$,\nrespectively, which were proved in the original literature only for static\ngraphs, where $\\frac{1}{1-\\sigma}$ equals $\\frac{\\gamma}{1-\\sigma_{\\gamma}}$\nwhen the network is time-invariant. When combining with a multiple consensus\nsubroutine, the dependence on the network connectivity constants can be further\nimproved to $O(1)$ and $O(\\frac{\\gamma}{1-\\sigma_{\\gamma}})$ for the\ncomputation and communication complexities, respectively. When the network is\nstatic, by employing the Chebyshev acceleration, our complexities exactly match\nthe lower bounds without hiding any poly-logarithmic factor for both\nnonstrongly convex and strongly convex problems.\n"}
+{"abstract": "  Motivated by questions of Fouvry and Rudnick on the distribution of Gaussian\nprimes, we develop a very general setting in which one can study inequities in\nthe distribution of analogues of primes through analytic properties of\ninfinitely many $L$-functions. In particular, we give a heuristic argument for\nthe following claim : for more than half of the prime numbers that can be\nwritten as a sum of two square, the odd square is the square of a positive\ninteger congruent to $1 \\bmod 4$.\n"}
+{"abstract": "  Methane ebullition (bubbling) from lake sediments is an important methane\nflux into the atmosphere. Previous studies have focused on the open-water\nseason, showing that temperature variations, pressure fluctuations and\nwind-induced currents can affect ebullition. However, ebullition surveys during\nthe ice-cover are rare despite the prevalence of seasonally ice-covered lakes,\nand the factors controlling ebullition are poorly understood. Here, we present\na month-long, high frequency record of acoustic ebullition data from an\nice-covered lake. The record shows that ebullition occurs almost exclusively\nwhen atmospheric pressure drops below a threshold that is approximately equal\nto the long-term average pressure. The intensity of ebullition is proportional\nto the amount by which the pressure drops below this threshold. In addition,\nfield measurements of turbidity, in conjunction with laboratory experiments,\nprovide evidence that ebullition is responsible for previously unexplained\nelevated levels of turbidity during ice-cover.\n"}
+{"abstract": "  We present SrvfNet, a generative deep learning framework for the joint\nmultiple alignment of large collections of functional data comprising\nsquare-root velocity functions (SRVF) to their templates. Our proposed\nframework is fully unsupervised and is capable of aligning to a predefined\ntemplate as well as jointly predicting an optimal template from data while\nsimultaneously achieving alignment. Our network is constructed as a generative\nencoder-decoder architecture comprising fully-connected layers capable of\nproducing a distribution space of the warping functions. We demonstrate the\nstrength of our framework by validating it on synthetic data as well as\ndiffusion profiles from magnetic resonance imaging (MRI) data.\n"}
+{"abstract": "  We study the time evolution of the excess value of capacity of entanglement\nbetween a locally excited state and ground state in free, massless fermionic\ntheory and free Yang-Mills theory in four spacetime dimensions. Capacity has\nnon-trivial time evolution and is sensitive to the partial entanglement\nstructure, and shows a universal peak at early times. We define a quantity, the\nnormalized \"Page time\", which measures the timescale when capacity reaches its\npeak. This quantity turns out to be a characteristic property of the inserted\noperator. This firmly establishes capacity as a valuable measure of\nentanglement structure of an operator, especially at early times similar in\nspirit to the Renyi entropies at late times. Interestingly, the time evolution\nof capacity closely resembles its evolution in microcanonical and canonical\nensemble of the replica wormhole model in the context of the black hole\ninformation paradox.\n"}
+{"abstract": "  We have performed density-matrix renormalization group studies of a square\nlattice $t$-$J$ model with small hole doping, $\\delta\\ll 1$, on long 4 and 6\nleg cylinders. We include frustration in the form of a second-neighbor exchange\ncoupling, $J_2 = J_1/2$, such that the undoped ($\\delta=0$) \"parent\" state is a\nquantum spin liquid. In contrast to the relatively short range superconducting\n(SC) correlations that have been observed in recent studies of the 6-leg\ncylinder in the absence of frustration, we find power law SC correlations with\na Luttinger exponent, $K_{sc} \\approx 1$, consistent with a strongly diverging\nSC susceptibility, $\\chi \\sim T^{-(2-K_{sc})}$ as the temperature $T\\to 0$. The\nspin-spin correlations - as in the undoped state - fall exponentially\nsuggesting that the SC \"pairing\" correlations evolve smoothly from the\ninsulating parent state.\n"}
+{"abstract": "  We find a phenomenon in a non-gravitational gauge theory analogous to the\nreplica wormhole in a quantum gravity theory. We consider a reservoir of a\nscalar field coupled with a gauge theory contained in a region with a boundary\nby an axion-like coupling. When the replica trick is used to compute the\nentanglement entropy for a subregion in the reservoir, a tuple of instantons\ndistributed across the replica sheets gives a non-perturbative contribution. As\nan explicit and solvable example, we consider a discrete scalar field coupled\nto a 2d pure gauge theory and observe how the replica instantons reproduce the\nentropy directly calculated from the reduced density matrix. In addition, we\nnotice that the entanglement entropy can detect the confinement of a 2d gauge\ntheory.\n"}
+{"abstract": "  GRB200522A is a short duration gamma-ray burst (GRB) at redshift $z$=0.554\ncharacterized by a bright infrared counterpart. A possible, although not\nunambiguous, interpretation of the observed emission is the onset of a luminous\nkilonova powered by a rapidly rotating and highly-magnetized neutron star,\nknown as magnetar. A bright radio flare, arising from the interaction of the\nkilonova ejecta with the surrounding medium, is a prediction of this model.\nWhereas the available dataset remains open to multiple interpretations (e.g.\nafterglow, r-process kilonova, magnetar-powered kilonova), long-term radio\nmonitoring of this burst may be key to discriminate between models. We present\nour late-time upper limit on the radio emission of GRB200522A, carried out with\nthe Karl G. Jansky Very Large Array at 288 days after the burst. For kilonova\nejecta with energy $E_{\\rm ej} \\approx 10^{53} \\rm erg$, as expected for a\nlong-lived magnetar remnant, we can already rule out ejecta masses $M_{\\rm ej}\n\\lesssim0.03 \\mathrm{M}_\\odot$ for the most likely range of circumburst\ndensities $n\\gtrsim 10^{-3}$ cm$^{-3}$. Observations on timescales of\n$\\approx$3-10 yr after the merger will probe larger ejecta masses up to $M_{\\rm\nej} \\sim 0.1 \\mathrm{M}_\\odot$, providing a robust test to the magnetar\nscenario.\n"}
+{"abstract": "  Most real-world datasets are inherently heterogeneous graphs, which involve a\ndiversity of node and relation types. Heterogeneous graph embedding is to learn\nthe structure and semantic information from the graph, and then embed it into\nthe low-dimensional node representation. Existing methods usually capture the\ncomposite relation of a heterogeneous graph by defining metapath, which\nrepresent a semantic of the graph. However, these methods either ignore node\nattributes, or discard the local and global information of the graph, or only\nconsider one metapath. To address these limitations, we propose a\nMetapaths-guided Neighbors-aggregated Heterogeneous Graph Neural Network(MHN)\nto improve performance. Specially, MHN employs node base embedding to\nencapsulate node attributes, BFS and DFS neighbors aggregation within a\nmetapath to capture local and global information, and metapaths aggregation to\ncombine different semantics of the heterogeneous graph. We conduct extensive\nexperiments for the proposed MHN on three real-world heterogeneous graph\ndatasets, including node classification, link prediction and online A/B test on\nAlibaba mobile application. Results demonstrate that MHN performs better than\nother state-of-the-art baselines.\n"}
+{"abstract": "  Universal quantifiers occur frequently in proof obligations produced by\nprogram verifiers, for instance, to axiomatize uninterpreted functions and to\nexpress properties of arrays. SMT-based verifiers typically reason about them\nvia E-matching, an SMT algorithm that requires syntactic matching patterns to\nguide the quantifier instantiations. Devising good matching patterns is\nchallenging. In particular, overly restrictive patterns may lead to spurious\nverification errors if the quantifiers needed for a proof are not instantiated;\nthey may also conceal unsoundness caused by inconsistent axiomatizations. In\nthis paper, we present the first technique that identifies and helps the users\nremedy the effects of overly restrictive matching patterns. We designed a novel\nalgorithm to synthesize missing triggering terms required to complete a proof.\nTool developers can use this information to refine their matching patterns and\nprevent similar verification errors, or to fix a detected unsoundness.\n"}
+{"abstract": "  The popularity of 3D displays has risen drastically over the past few decades\nbut these displays are still merely a novelty compared to their true potential.\nThe development has mostly focused on Head Mounted Displays (HMD) development\nfor Virtual Reality and in general ignored non-HMD 3D displays. This is due to\nthe inherent difficulty in the creation of these displays and their\nimpracticability in general use due to cost, performance, and lack of\nmeaningful use cases. In fairness to the hardware manufacturers who have made\nstriking innovations in this field, there has been a dereliction of duty of\nsoftware developers and researchers in terms of developing software to best\nutilize these displays.\n  This paper will seek to identify what areas of future software development\ncould mitigate this dereliction. To achieve this goal, the paper will first\nexamine the current state of the art and perform a comparative analysis on\ndifferent types of 3D displays, from this analysis a clear researcher gap\nexists in terms of software development for Light field displays which are the\ncurrent state of the art of non-HMD-based 3D displays.\n  The paper will then outline six distinct areas where the context-awareness\nconcept will allow for non-HMD-based 3D displays in particular light field\ndisplays that can not only compete but surpass their HMD-based brethren for\nmany specific use cases.\n"}
+{"abstract": "  The evolution of the biosphere unfolds as a luxuriant generative process of\nnew living forms and functions. Organisms adapt to their environment, exploit\nnovel opportunities that are created in this continuous blooming dynamics.\nAffordances play a fundamental role in the evolution of the biosphere, for\norganisms can exploit them for new morphological and behavioral adaptations\nachieved by heritable variations and selection. This way, the opportunities\noffered by affordances are then actualized as ever novel adaptations. In this\npaper we maintain that affordances elude a formalization that relies on set\ntheory: we argue that it is not possible to apply set theory to affordances,\ntherefore we cannot devise a set-based mathematical theory of the diachronic\nevolution of the biosphere.\n"}
+{"abstract": "  This paper proposes a deep unfitted Nitsche method for computing elliptic\ninterface problems with high contrasts in high dimensions. To capture\ndiscontinuities of the solution caused by interfaces, we reformulate the\nproblem as an energy minimization involving two weakly coupled components. This\nenables us to train two deep neural networks to represent two components of the\nsolution in high-dimensional. The curse of dimensionality is alleviated by\nusing the Monte-Carlo method to discretize the unfitted Nitsche energy\nfunction. We present several numerical examples to show the performance of the\nproposed method.\n"}
+{"abstract": "  In this paper, we propose a novel multi-color balance method for reducing\ncolor distortions caused by lighting effects. The proposed method allows us to\nadjust three target-colors chosen by a user in an input image so that each\ntarget color is the same as the corresponding destination (benchmark) one. In\ncontrast, white balancing is a typical technique for reducing the color\ndistortions, however, they cannot remove lighting effects on colors other than\nwhite. In an experiment, the proposed method is demonstrated to be able to\nremove lighting effects on selected three colors, and is compared with existing\nwhite balance adjustments.\n"}
+{"abstract": "  Because of the ultrafast and photon-driven nature of the transport in their\nactive region, we demonstrate that quantum cascade lasers can be operated as\nresonantly amplified terahertz detectors. Tunable responsivities up to 50 V/W\nand noise equivalent powers down to 100 pW/sqrt(Hz) are demonstrated at 4.7\nTHz. Constant peak responsivities with respect to the detector temperature are\nobserved up to 80K. Thanks to the sub-ps intersubband lifetime electrical\nbandwidths larger than 20 GHz can be obtained, allowing the detection of\noptical beatnotes from quantum cascade THz frequency combs.\n"}
+{"abstract": "  An almost self-centered graph is a connected graph of order $n$ with exactly\n$n-2$ central vertices, and an almost peripheral graph is a connected graph of\norder $n$ with exactly $n-1$ peripheral vertices. We determine (1) the maximum\ngirth of an almost self-centered graph of order $n;$ (2) the maximum\nindependence number of an almost self-centered graph of order $n$ and radius\n$r;$ (3) the minimum order of a $k$-regular almost self-centered graph and (4)\nthe maximum size of an almost peripheral graph of order $n;$ (5) which numbers\nare possible for the maximum degree of an almost peripheral graph of order $n;$\n(6) the maximum number of vertices of maximum degree in an almost peripheral\ngraph of order $n$ whose maximum degree is the second largest possible.\nWhenever the extremal graphs have a neat form, we also describe them.\n"}
+{"abstract": "  We develop a formalism for constructing stochastic upper bounds on the\nexpected full sample risk for supervised classification tasks via the Hilbert\ncoresets approach within a transductive framework. We explicitly compute tight\nand meaningful bounds for complex datasets and complex hypothesis classes such\nas state-of-the-art deep neural network architectures. The bounds we develop\nexhibit nice properties: i) the bounds are non-uniform in the hypothesis space,\nii) in many practical examples, the bounds become effectively deterministic by\nappropriate choice of prior and training data-dependent posterior distributions\non the hypothesis space, and iii) the bounds become significantly better with\nincrease in the size of the training set. We also lay out some ideas to explore\nfor future research.\n"}
+{"abstract": "  Traditional approaches for data anonymization consider relational data and\ntextual data independently. We propose rx-anon, an anonymization approach for\nheterogeneous semi-structured documents composed of relational and textual\nattributes. We map sensitive terms extracted from the text to the structured\ndata. This allows us to use concepts like k-anonymity to generate a joined,\nprivacy-preserved version of the heterogeneous data input. We introduce the\nconcept of redundant sensitive information to consistently anonymize the\nheterogeneous data. To control the influence of anonymization over unstructured\ntextual data versus structured data attributes, we introduce a modified,\nparameterized Mondrian algorithm. The parameter $\\lambda$ allows to give\ndifferent weight on the relational and textual attributes during the\nanonymization process. We evaluate our approach with two real-world datasets\nusing a Normalized Certainty Penalty score, adapted to the problem of jointly\nanonymizing relational and textual data. The results show that our approach is\ncapable of reducing information loss by using the tuning parameter to control\nthe Mondrian partitioning while guaranteeing k-anonymity for relational\nattributes as well as for sensitive terms. As rx-anon is a framework approach,\nit can be reused and extended by other anonymization algorithms, privacy\nmodels, and textual similarity metrics.\n"}
+{"abstract": "  There are several challenges in creating an electronic archery scoring system\nusing computer vision techniques. Variability of light, reconstruction of the\ntarget from several images, variability of target configuration, and filtering\nnoise were significant challenges during the creation of this scoring system.\nThis paper discusses the approach used to determine where an arrow hits a\ntarget, for any possible single or set of targets and provides an algorithm\nthat balances the difficulty of robust arrow detection while retaining the\nrequired accuracy.\n"}
+{"abstract": "  Facial expressions are the most common universal forms of body language. In\nthe past few years, automatic facial expression recognition (FER) has been an\nactive field of research. However, it is still a challenging task due to\ndifferent uncertainties and complications. Nevertheless, efficiency and\nperformance are yet essential aspects for building robust systems. We proposed\ntwo models, EmoXNet which is an ensemble learning technique for learning\nconvoluted facial representations, and EmoXNetLite which is a distillation\ntechnique that is useful for transferring the knowledge from our ensemble model\nto an efficient deep neural network using label-smoothen soft labels for able\nto effectively detect expressions in real-time. Both of the techniques\nperformed quite well, where the ensemble model (EmoXNet) helped to achieve\n85.07% test accuracy on FER2013 with FER+ annotations and 86.25% test accuracy\non RAF-DB. Moreover, the distilled model (EmoXNetLite) showed 82.07% test\naccuracy on FER2013 with FER+ annotations and 81.78% test accuracy on RAF-DB.\nResults show that our models seem to generalize well on new data and are\nlearned to focus on relevant facial representations for expressions\nrecognition.\n"}
+{"abstract": "  We study the propagation of wavepackets along weakly curved interfaces\nbetween topologically distinct media. Our Hamiltonian is an adiabatic\nmodulation of Dirac operators omnipresent in the topological insulators\nliterature. Using explicit formulas for straight edges, we construct a family\nof solutions that propagates, for long times, unidirectionally and\ndispersion-free along the curved edge. We illustrate our results through\nvarious numerical simulations.\n"}
+{"abstract": "  We study the robust double auction mechanisms, that is, the double auction\nmechanisms that satisfy dominant strategy incentive compatibility, ex-post\nindividual rationality, ex-post budget balance and feasibility. We first\nestablish that the price in any deterministic robust mechanism does not depend\non the valuations of the trading players. We next establish that, with the\nnon-bossiness assumption, the price in any deterministic robust mechanism does\nnot depend on players' valuations at all, whether trading or non-trading, i.e.,\nthe price is posted in advance. Our main result is a characterization result\nthat, with the non-bossiness assumption along with other assumptions on the\nproperties of the mechanism, the posted price mechanism with an exogenous\nrationing rule is the only deterministic robust double auction mechanism. We\nalso show that, even without the non-bossiness assumption, it is quite\ndifficult to find a reasonable robust double auction mechanism other than the\nposted price mechanism with rationing.\n"}
+{"abstract": "  The risk for severe illness and mortality from COVID-19 significantly\nincreases with age. As a result, age-stratified modeling for COVID-19 dynamics\nis the key to study how to reduce hospitalizations and mortality from COVID-19.\nBy taking advantage of network theory, we develop an age-stratified epidemic\nmodel for COVID-19 in complex contact networks. Specifically, we present an\nextension of standard SEIR (susceptible-exposed-infectious-removed)\ncompartmental model, called age-stratified SEAHIR\n(susceptible-exposedasymptomatic-hospitalized-infectious-removed) model, to\ncapture the spread of COVID-19 over multitype random networks with general\ndegree distributions. We derive several key epidemiological metrics and then\npropose an age-stratified vaccination strategy to decrease the mortality and\nhospitalizations. Through extensive study, we discover that the outcome of\nvaccination prioritization depends on the reproduction number R0. Specifically,\nthe elderly should be prioritized only when R0 is relatively high. If ongoing\nintervention policies, such as universal masking, could suppress R0 at a\nrelatively low level, prioritizing the high-transmission age group (i.e.,\nadults aged 20-39) is most effective to reduce both mortality and\nhospitalizations. These conclusions provide useful recommendations for\nage-based vaccination prioritization for COVID-19.\n"}
+{"abstract": "  Large samples of experimentally produced graphene are polycrystalline. For\nthe study of this material, it helps to have realistic computer samples that\nare also polycrystalline. A common approach to produce such samples in computer\nsimulations is based on the method of Wooten, Winer, and Weaire, originally\nintroduced for the simulation of amorphous silicon. We introduce an early\nrejection variation of their method, applied to graphene, which exploits the\nlocal nature of the structural changes to achieve a significant speed-up in the\nrelaxation of the material, without compromising the dynamics. We test it on a\n3,200 atoms sample, obtaining a speedup between one and two orders of\nmagnitude. We also introduce a further variation called early decision\nspecifically for relaxing large samples even faster and we test it on two\nsamples of 10,024 and 20,000 atoms, obtaining a further speed-up of an order of\nmagnitude. Furthermore, we provide a graphical manipulation tool to remove\nunwanted artifacts in a sample, such as bond crossings.\n"}
+{"abstract": "  Popular blockchains such as Ethereum and several others execute complex\ntransactions in blocks through user-defined scripts known as smart contracts.\nSerial execution of smart contract transactions/atomic-units (AUs) fails to\nharness the multiprocessing power offered by the prevalence of multi-core\nprocessors. By adding concurrency to the execution of AUs, we can achieve\nbetter efficiency and higher throughput.\n  In this paper, we develop a concurrent miner that proposes a block by\nexecuting the AUs concurrently using optimistic Software Transactional Memory\nsystems (STMs). It captures the independent AUs in a concurrent bin and\ndependent AUs in the block graph (BG) efficiently. Later, we propose a\nconcurrent validator that re-executes the same AUs concurrently and\ndeterministically using a concurrent bin followed by a BG given by the miner to\nverify the proposed block. We rigorously prove the correctness of concurrent\nexecution of AUs and achieve significant performance gain over the\nstate-of-the-art.\n"}
+{"abstract": "  In this work we study a system of two galaxies, Astarte and Adonis, at z\n$\\sim $2 when the Universe was undergoing its peak of star formation activity.\nAstarte is a dusty star-forming galaxy at the massive-end of the main sequence\n(MS) and Adonis is a less-massive, bright in ultraviolet (UV), companion galaxy\nwith an optical spectroscopic redshift. We analyse the physical properties of\nthis system, and probe the gas mass of Astarte with its ALMA CO emission, to\ninvestigate whether this ultra-massive galaxy is quenching or not. We use\nCIGALE - a spectral energy distribution modeling code - to derive the key\nphysical properties of Astarte and Adonis, mainly their star formation rates\n(SFRs), stellar masses, and dust luminosities. We inspect the variation of the\nphysical parameters depending on the assumed dust attenuation law. We also\nestimate the molecular gas mass of Astarte from its CO emission, using\ndifferent $\\alpha_{CO}$ and transition ratios ($r_{31}$) and discuss the\nimplication of the various assumptions on the gas mass derivation. We find that\nAstarte exhibits a MS-like star formation activity, while Adonis is undergoing\na strong starburst (SB) phase. The molecular gas mass of Astarte is far below\nthe gas fraction of typical star-forming galaxies at z=2. This low gas content\nand high SFR, result in a depletion time of $0.22\\pm0.07$ Gyrs, slightly\nshorter than what is expected for a MS galaxy at this redshift. The CO\nluminosity versus the total IR luminosity suggests a MS-like activity assuming\na galactic conversion factor and a low transition ratio. The SFR of Astarte is\nof the same order using different attenuation laws, unlike its stellar mass\nthat increases using shallow attenuation laws. We discuss these properties and\nsuggest that Astarte might be experiencing a recent decrease of star formation\nactivity and is quenching through the MS following a SB epoch.\n"}
+{"abstract": "  Quantum coherences, correlations and collective effects can be harnessed to\nthe advantage of quantum batteries. Here, we introduce a feasible structure\nengineering scheme that is applicable to spin-based open quantum batteries. Our\nscheme, which builds solely upon a modulation of spin energy gaps, allows\nengineered quantum batteries to exploit spin-spin correlations for mitigating\nenvironment-induced aging. As a result of this advantage, an engineered quantum\nbattery can preserve relatively more energy as compared with its non-engineered\ncounterpart over the course of the storage phase. Particularly, the excess in\nstored energy is independent of system size. This implies a scale-invariant\npassive protection strategy, which we demonstrate on an engineered quantum\nbattery with staggered spin energy gaps. Our findings establish structure\nengineering as a useful route for advancing quantum batteries, and bring new\nperspectives on efficient quantum battery designs.\n"}
+{"abstract": "  Predicting the binding of viral peptides to the major histocompatibility\ncomplex with machine learning can potentially extend the computational\nimmunology toolkit for vaccine development, and serve as a key component in the\nfight against a pandemic. In this work, we adapt and extend USMPep, a recently\nproposed, conceptually simple prediction algorithm based on recurrent neural\nnetworks. Most notably, we combine regressors (binding affinity data) and\nclassifiers (mass spectrometry data) from qualitatively different data sources\nto obtain a more comprehensive prediction tool. We evaluate the performance on\na recently released SARS-CoV-2 dataset with binding stability measurements.\nUSMPep not only sets new benchmarks on selected single alleles, but\nconsistently turns out to be among the best-performing methods or, for some\nmetrics, to be even the overall best-performing method for this task.\n"}
+{"abstract": "  Shift scheduling impacts healthcare workers' well-being because it sets the\nframe for their social life and recreational activities. Since it is complex\nand time-consuming, it has become a target for automation. However, existing\nsystems mostly focus on improving efficiency. The workers' needs and their\nactive participation do not play a pronounced role. Contrasting this trend, we\ndesigned a social practice-based, worker-centered, and well-being-oriented\nself-scheduling system which gives healthcare workers more control during shift\nplanning. In a following nine month appropriation study, we found that workers\nwho were cautious about their social standing in the group or who had a more\nspontaneous personal lifestyle used our system less often than others.\nMoreover, we revealed several conflict prevention practices and suggest to\nshift the focus away from a competitive shift distribution paradigm towards\nsupporting these pro-social practices. We conclude with guidelines to support\nindividual planning practices, self-leadership, and for dealing with conflicts.\n"}
+{"abstract": "  The cost of a partitioned fluid-structure interaction scheme is typically\nassessed by the number of coupling iterations required per time step, while\nignoring the Newton loops within the nonlinear sub-solvers. In this work, we\ndiscuss why these single-field iterations deserve more attention when\nevaluating the coupling's efficiency and how to find the optimal number of\nNewton steps per coupling iteration.\n"}
+{"abstract": "  High-dimensional expanders generalize the notion of expander graphs to\nhigher-dimensional simplicial complexes. In contrast to expander graphs, only a\nhandful of high-dimensional expander constructions have been proposed, and no\nelementary combinatorial construction with near-optimal expansion is known. In\nthis paper, we introduce an improved combinatorial high-dimensional expander\nconstruction, by modifying a previous construction of Liu, Mohanty, and Yang\n(ITCS 2020), which is based on a high-dimensional variant of a tensor product.\nOur construction achieves a spectral gap of $\\Omega(\\frac{1}{k^2})$ for random\nwalks on the $k$-dimensional faces, which is only quadratically worse than the\noptimal bound of $\\Theta(\\frac{1}{k})$. Previous combinatorial constructions,\nincluding that of Liu, Mohanty, and Yang, only achieved a spectral gap that is\nexponentially small in $k$. We also present reasoning that suggests our\nconstruction is optimal among similar product-based constructions.\n"}
+{"abstract": "  We introduce a novel multi-resolution Localized Orthogonal Decomposition\n(LOD) for time-harmonic acoustic scattering problems that can be modeled by the\nHelmholtz equation. The method merges the concepts of LOD and operator-adapted\nwavelets (gamblets) and proves its applicability for a class of complex-valued,\nnon-hermitian and indefinite problems. It computes hierarchical bases that\nblock-diagonalize the Helmholtz operator and thereby decouples the\ndiscretization scales. Sparsity is preserved by a novel localization strategy\nthat improves stability properties even in the elliptic case. We present a\nrigorous stability and a-priori error analysis of the proposed method for\nhomogeneous media. In addition, we investigate the fast solvability of the\nblocks by a standard iterative method. A sequence of numerical experiments\nillustrates the sharpness of the theoretical findings and demonstrates the\napplicability to scattering problems in heterogeneous media.\n"}
+{"abstract": "  In this paper, we introduce the concept of the (higher order) Appell-Carlitz\nnumbers which unifies the definitions of several special numbers in positive\ncharacteristic, such as the Bernoulli-Carlitz numbers and the Cauchy-Carlitz\nnumbers.Their generating function is usually named Hurwitz series in the\nfunction field arithmetic. By using Hasse-Teichm\\\"uller derivatives, we also\nobtain several properties of the (higher order) Appell-Carlitz numbers,\nincluding a recurrence formula, two closed forms expressions, and a determinant\nexpression.\n  The recurrence formula implies Carlitz's recurrence formula for\nBernoulli-Carlitz numbers. Two closed from expressions implies the\ncorresponding results for Bernoulli-Carlitz and Cauchy-Carlitz numbers . The\ndeterminant expression implies the corresponding results for Bernoulli-Carlitz\nand Cauchy-Carlitz numbers, which are analogues of the classical determinant\nexpressions of Bernoulli and Cauchy numbers stated in an article by Glaisher in\n1875.\n"}
+{"abstract": "  We study the phase controlled transmission properties in a compound system\nconsisting of a 3D copper cavity and an yttrium iron garnet (YIG) sphere. By\ntuning the relative phase of the magnon pumping and cavity probe tones,\nconstructive and destructive interferences occur periodically, which strongly\nmodify both the cavity field transmission spectra and the group delay of light.\nMoreover, the tunable amplitude ratio between pump-probe tones allows us to\nfurther improve the signal absorption or amplification, accompanied by either\nsignificantly enhanced optical advance or delay. Both the phase and\namplitude-ratio can be used to realize in-situ tunable and switchable fast-slow\nlight. The tunable phase and amplitude-ratio lead to the zero reflection of the\ntransmitted light and an abrupt fast-slow light transition. Our results confirm\nthat direct magnon pumping through the coupling loops provides a versatile\nroute to achieve controllable signal transmission, storage, and communication,\nwhich can be further expanded to the quantum regime, realizing coherent-state\nprocessing or quantum-limited precise measurements.\n"}
+{"abstract": "  After showing the efficiency of feedforward networks to estimate control in\nhigh dimension in the global optimization of some storages problems, we develop\na modification of an algorithm based on some dynamic programming principle. We\nshow that classical feedforward networks are not effective to estimate Bellman\nvalues for reservoir problems and we propose some neural networks giving far\nbetter results. At last, we develop a new algorithm mixing LP resolution and\nconditional cuts calculated by neural networks to solve some stochastic linear\nproblems.\n"}
+{"abstract": "  In modern networks, the use of drones as mobile base stations (MBSs) has been\ndiscussed for coverage flexibility. However, the realization of drone-based\nnetworks raises several issues. One of the critical issues is drones are\nextremely power-hungry. To overcome this, we need to characterize a new type of\ndrones, so-called charging drones, which can deliver energy to MBS drones.\nMotivated by the fact that the charging drones also need to be charged, we\ndeploy ground-mounted charging towers for delivering energy to the charging\ndrones. We introduce a new energy-efficiency maximization problem, which is\npartitioned into two independently separable tasks. More specifically, as our\nfirst optimization task, two-stage charging matching is proposed due to the\ninherent nature of our network model, where the first matching aims to schedule\nbetween charging towers and charging drones while the second matching solves\nthe scheduling between charging drones and MBS drones. We analyze how to\nconvert the formulation containing non-convex terms to another one only with\nconvex terms. As our second optimization task, each MBS drone conducts\nenergy-aware time-average transmit power allocation minimization subject to\nstability via Lyapunov optimization. Our solutions enable the MBS drones to\nextend their lifetimes; in turn, network coverage-time can be extended.\n"}
+{"abstract": "  Scientific research changed profoundly over the last 30 years, in all its\naspects. Scientific publishing has changed as well, mainly because of the\nstrong increased number of submitted papers and because of the appearance of\nOpen Access journals and publishers. We propose some reflections on these\nissues.\n"}
+{"abstract": "  In marginally jammed solids confined by walls, we calculate the particle and\nensemble averaged value of an order parameter, $\\left<\\Psi(r)\\right>$, as a\nfunction of the distance to the wall, $r$. Being a microscopic indicator of\nstructural disorder and particle mobility in solids, $\\Psi$ is by definition\nthe response of the mean square particle displacement to the increase of\ntemperature in the harmonic approximation and can be directly calculated from\nthe normal modes of vibration of the zero-temperature solids. We find that, in\nconfined jammed solids, $\\left<\\Psi(r)\\right>$ curves at different pressures\ncan collapse onto the same master curve following a scaling function,\nindicating the criticality of the jamming transition. The scaling collapse\nsuggests a diverging length scale and marginal instability at the jamming\ntransition, which should be accessible to sophisticatedly designed experiments.\nMoreover, $\\left<\\Psi(r)\\right>$ is found to be significantly suppressed when\napproaching the wall and anisotropic in directions perpendicular and parallel\nto the wall. This finding can be applied to understand the $r$-dependence and\nanisotropy of the structural relaxation in confined supercooled liquids,\nproviding another example of understanding or predicting behaviors of\nsupercooled liquids from the perspective of the zero-temperature amorphous\nsolids.\n"}
+{"abstract": "  In this paper we present a two-step neural network model to separate\ndetections of solar system objects from optical and electronic artifacts in\ndata obtained with the \"Asteroid Terrestrial-impact Last Alert System\" (ATLAS),\na near-Earth asteroid sky survey system [arXiv:1802.00879]. A convolutional\nneural network [arXiv:1807.10912] is used to classify small \"postage-stamp\"\nimages of candidate detections of astronomical sources into eight classes,\nfollowed by a multi-layered perceptron that provides a probability that a\ntemporal sequence of four candidate detections represents a real astronomical\nsource. The goal of this work is to reduce the time delay between Near-Earth\nObject (NEO) detections and submission to the Minor Planet Center. Due to the\nrare and hazardous nature of NEOs [Harris and D'Abramo, 2015], a low false\nnegative rate is a priority for the model. We show that the model reaches\n99.6\\% accuracy on real asteroids in ATLAS data with a 0.4\\% false negative\nrate. Deployment of this model on ATLAS has reduced the amount of NEO\ncandidates that astronomers must screen by 90%, thereby bringing ATLAS one step\ncloser to full autonomy.\n"}
+{"abstract": "  We study wireless networks where signal propagation delays are multiples of a\ntime interval. Such a network can be modelled as a weighted hypergraph. The\nlink scheduling problem of such a wireless network is closely related to the\nindependent sets of the periodic hypergraph induced by the weighted hypergraph.\nAs the periodic graph has infinitely many vertices, existing characterizations\nof graph independent sets cannot be applied to study link scheduling\nefficiently. To characterize the rate region of link scheduling, a directed\ngraph of finite size called scheduling graph is derived to capture a certain\nconditional independence property of link scheduling over time. A\ncollision-free schedule is equivalent to a path in the scheduling graph, and\nhence the rate region is equivalent to the convex hull of the rate vectors\nassociated with the cycles of the scheduling graph. With the maximum\nindependent set problem as a special case, calculating the whole rate region is\nNP hard and also hard to approximate. We derive two algorithms that benefit\nfrom a partial order on the paths in the scheduling graph, and can potentially\nfind schedules that are not dominated by the existing cycle enumerating\nalgorithms running in a given time. The first algorithm calculates the rate\nregion incrementally in the cycle lengths so that a subset of the rate region\ncorresponding to short cycles can be obtained efficiently. The second algorithm\nenumerates cycles associated with a maximal subgraph of the scheduling graph.\nIn addition to scheduling a wireless network, the independent sets of periodic\nhypergraphs also find applications in some operational research problems.\n"}
+{"abstract": "  Cross-document event coreference resolution is a foundational task for NLP\napplications involving multi-text processing. However, existing corpora for\nthis task are scarce and relatively small, while annotating only modest-size\nclusters of documents belonging to the same topic. To complement these\nresources and enhance future research, we present Wikipedia Event Coreference\n(WEC), an efficient methodology for gathering a large-scale dataset for\ncross-document event coreference from Wikipedia, where coreference links are\nnot restricted within predefined topics. We apply this methodology to the\nEnglish Wikipedia and extract our large-scale WEC-Eng dataset. Notably, our\ndataset creation method is generic and can be applied with relatively little\neffort to other Wikipedia languages. To set baseline results, we develop an\nalgorithm that adapts components of state-of-the-art models for within-document\ncoreference resolution to the cross-document setting. Our model is suitably\nefficient and outperforms previously published state-of-the-art results for the\ntask.\n"}
+{"abstract": "  Few-layered transition metal dichalcogenides (TMDs) are increasingly popular\nmaterials for optoelectronics and catalysis. Amongst the various types of TMDs\navailable today, rhenium-chalcogenides (ReX2) stand out due to their remarkable\nelectronic structure, such as the occurrence of anisotropic excitons and\npotential direct bandgap behavior throughout multi-layered stacks. In this\nletter, we have analyzed the nature and dynamics of charge carriers in highly\ncrystalline liquid-phase exfoliated ReS2, using a unique combination of optical\npump-THz probe and broadband transient absorption spectroscopy. Two distinct\ntime regimes are identified, both of which are dominated by unbound charge\ncarriers despite the high exciton binding energy. In the first time regime, the\nunbound charge carriers cause an increase and a broadening of the exciton\nabsorption band. In the second time regime, a peculiar narrowing of the\nexcitonic absorption profile is observed, which we assign to the presence of\nbuilt-in fields and/or charged defects. Our results pave the way to analyze\nspectrally complex transient absorption measurements on layered TMD materials\nand indicate the potential for ReS2 to produce mobile free charge carriers, a\nfeat relevant for photovoltaic applications.\n"}
+{"abstract": "  We propose a new framework, inspired by random matrix theory, for analyzing\nthe dynamics of stochastic gradient descent (SGD) when both number of samples\nand dimensions are large. This framework applies to any fixed stepsize and the\nfinite sum setting. Using this new framework, we show that the dynamics of SGD\non a least squares problem with random data become deterministic in the large\nsample and dimensional limit. Furthermore, the limiting dynamics are governed\nby a Volterra integral equation. This model predicts that SGD undergoes a phase\ntransition at an explicitly given critical stepsize that ultimately affects its\nconvergence rate, which we also verify experimentally. Finally, when input data\nis isotropic, we provide explicit expressions for the dynamics and average-case\nconvergence rates (i.e., the complexity of an algorithm averaged over all\npossible inputs). These rates show significant improvement over the worst-case\ncomplexities.\n"}
+{"abstract": "  This paper describes our method for tuning a transformer-based pretrained\nmodel, to adaptation with Reliable Intelligence Identification on Vietnamese\nSNSs problem. We also proposed a model that combines bert-base pretrained\nmodels with some metadata features, such as the number of comments, number of\nlikes, images of SNS documents,... to improved results for VLSP shared task:\nReliable Intelligence Identification on Vietnamese SNSs. With appropriate\ntraining techniques, our model is able to achieve 0.9392 ROC-AUC on public test\nset and the final version settles at top 2 ROC-AUC (0.9513) on private test\nset.\n"}
+{"abstract": "  This paper presents the first model-free, simulator-free reinforcement\nlearning algorithm for Constrained Markov Decision Processes (CMDPs) with\nsublinear regret and zero constraint violation. The algorithm is named Triple-Q\nbecause it includes three key components: a Q-function (also called\naction-value function) for the cumulative reward, a Q-function for the\ncumulative utility for the constraint, and a virtual-Queue that\n(over)-estimates the cumulative constraint violation. Under Triple-Q, at each\nstep, an action is chosen based on the pseudo-Q-value that is a combination of\nthe three \"Q\" values. The algorithm updates the reward and utility Q-values\nwith learning rates that depend on the visit counts to the corresponding\n(state, action) pairs and are periodically reset. In the episodic CMDP setting,\nTriple-Q achieves $\\tilde{\\cal O}\\left(\\frac{1 }{\\delta}H^4\nS^{\\frac{1}{2}}A^{\\frac{1}{2}}K^{\\frac{4}{5}} \\right)$ regret, where $K$ is the\ntotal number of episodes, $H$ is the number of steps in each episode, $S$ is\nthe number of states, $A$ is the number of actions, and $\\delta$ is Slater's\nconstant. Furthermore, Triple-Q guarantees zero constraint violation, both on\nexpectation and with a high probability, when $K$ is sufficiently large.\nFinally, the computational complexity of Triple-Q is similar to SARSA for\nunconstrained MDPs and is computationally efficient.\n"}
+{"abstract": "  During the image acquisition process, noise is usually added to the data\nmainly due to physical limitations of the acquisition sensor, and also\nregarding imprecisions during the data transmission and manipulation. In that\nsense, the resultant image needs to be processed to attenuate its noise without\nlosing details. Non-learning-based strategies such as filter-based and noise\nprior modeling have been adopted to solve the image denoising problem.\nNowadays, learning-based denoising techniques showed to be much more effective\nand flexible approaches, such as Residual Convolutional Neural Networks. Here,\nwe propose a new learning-based non-blind denoising technique named Attention\nResidual Convolutional Neural Network (ARCNN), and its extension to blind\ndenoising named Flexible Attention Residual Convolutional Neural Network\n(FARCNN). The proposed methods try to learn the underlying noise expectation\nusing an Attention-Residual mechanism. Experiments on public datasets corrupted\nby different levels of Gaussian and Poisson noise support the effectiveness of\nthe proposed approaches against some state-of-the-art image denoising methods.\nARCNN achieved an overall average PSNR results of around 0.44dB and 0.96dB for\nGaussian and Poisson denoising, respectively FARCNN presented very consistent\nresults, even with slightly worsen performance compared to ARCNN.\n"}
+{"abstract": "  The COVID-19 pandemic has proved to be one of the most disruptive public\nhealth emergencies in recent memory. Among non-pharmaceutical interventions,\nsocial distancing and lockdown measures are some of the most common tools\nemployed by governments around the world to combat the disease. While\nmathematical models of COVID-19 are ubiquitous, few have leveraged network\ntheory in a general way to explain the mechanics of social distancing. In this\npaper, we build on existing network models for heterogeneous, clustered\nnetworks with random link activation/deletion dynamics to put forth realistic\nmechanisms of social distancing using piecewise constant activation/deletion\nrates. We find our models are capable of rich qualitative behavior, and offer\nmeaningful insight with relatively few intervention parameters. In particular,\nwe find that the severity of social distancing interventions and when they\nbegin have more impact than how long it takes for the interventions to take\nfull effect.\n"}
+{"abstract": "  Kubelka-Munk (K-M) theory has been successfully used to estimate pigment\nconcentrations in the pigment mixtures of modern paintings in spectral imagery.\nIn this study the single-constant K-M theory has been utilized for the\nclassification of green pigments in the Selden Map of China, a navigational map\nof the South China Sea likely created in the early seventeenth century.\nHyperspectral data of the map was collected at the Bodleian Library, University\nof Oxford, and can be used to estimate the pigment diversity, and spatial\ndistribution, within the map. This work seeks to assess the utility of\nanalyzing the data in the K/S space from Kubelka-Munk theory, as opposed to the\ntraditional reflectance domain. We estimate the dimensionality of the data and\nextract endmembers in the reflectance domain. Then we perform linear unmixing\nto estimate abundances in the K/S space, and following Bai, et al. (2017), we\nperform a classification in the abundance space. Finally, due to the lack of\nground truth labels, the classification accuracy was estimated by computing the\nmean spectrum of each class as the representative signature of that class, and\ncalculating the root mean squared error with all the pixels in that class to\ncreate a spatial representation of the error. This highlights both the\nmagnitude of, and any spatial pattern in, the errors, indicating if a\nparticular pigment is not well modeled in this approach.\n"}
+{"abstract": "  Objective: The automatic discrimination between the coughing sounds produced\nby patients with tuberculosis (TB) and those produced by patients with other\nlung ailments.\n  Approach: We present experiments based on a dataset of 1358 forced cough\nrecordings obtained in a developing-world clinic from 16 patients with\nconfirmed active pulmonary TB and 35 patients suffering from respiratory\nconditions suggestive of TB but confirmed to be TB negative. Using nested\ncross-validation, we have trained and evaluated five machine learning\nclassifiers: logistic regression (LR), support vector machines (SVM), k-nearest\nneighbour (KNN), multilayer perceptrons (MLP) and convolutional neural networks\n(CNN).\n  Main Results: Although classification is possible in all cases, the best\nperformance is achieved using LR. In combination with feature selection by\nsequential forward selection (SFS), our best LR system achieves an area under\nthe ROC curve (AUC) of 0.94 using 23 features selected from a set of 78\nhigh-resolution mel-frequency cepstral coefficients (MFCCs). This system\nachieves a sensitivity of 93\\% at a specificity of 95\\% and thus exceeds the\n90\\% sensitivity at 70\\% specificity specification considered by the World\nHealth Organisation (WHO) as a minimal requirement for a community-based TB\ntriage test.\n  Significance: The automatic classification of cough audio sounds, when\napplied to symptomatic patients requiring investigation for TB, can meet the\nWHO triage specifications for the identification of patients who should undergo\nexpensive molecular downstream testing. This makes it a promising and viable\nmeans of low cost, easily deployable frontline screening for TB, which can\nbenefit especially developing countries with a heavy TB burden.\n"}
+{"abstract": "  Conformance checking techniques aim to collate observed process behavior with\nnormative/modeled process models. The majority of existing approaches focuses\non completed process executions, i.e., offline conformance checking. Recently,\nnovel approaches have been designed to monitor ongoing processes, i.e., online\nconformance checking. Such techniques detect deviations of an ongoing process\nexecution from a normative process model at the moment they occur. Thereby,\ncountermeasures can be taken immediately to prevent a process deviation from\ncausing further, undesired consequences. Most online approaches only allow to\ndetect approximations of deviations. This causes the problem of falsely\ndetected deviations, i.e., detected deviations that are actually no deviations.\nWe have, therefore, recently introduced a novel approach to compute exact\nconformance checking results in an online environment. In this paper, we focus\non the practical application and present a scalable, distributed implementation\nof the proposed online conformance checking approach. Moreover, we present two\nextensions to said approach to reduce its computational effort and its\npractical applicability. We evaluate our implementation using data sets\ncapturing the execution of real processes.\n"}
+{"abstract": "  Young stellar objects are observed to have large X-ray fluxes and are thought\nto produce commensurate luminosities in energetic particles (cosmic rays). This\nparticle radiation, in turn, can synthesize short-lived radioactive nuclei\nthrough spallation. With a focus on $^{26}$Al, this paper estimates the\nexpected abundances of radioactive nulcei produced by spallation during the\nepoch of planet formation. In this model, cosmic rays are accelerated near the\ninner truncation radii of circumstellar disks, $r_{\\scriptstyle X}\\approx0.1$\nAU, where intense magnetic activity takes place. For planets forming in this\nregion, radioactive abundances can be enhanced over the values inferred for the\nearly solar system (from meteoritic measurements) by factors of $\\sim10-20$.\nThese short-lived radioactive nuclei influence the process of planet formation\nand the properties of planets in several ways. The minimum size required for\nplanetesimals to become fully molten decreases with increasing levels of\nradioactive enrichment, and such melting leads to loss of volatile components\nincluding water. Planets produced with an enhanced radioactive inventory have\nsignificant internal luminosity which can be comparable to that provided by the\nhost star; this additional heating affects both atmospheric mass loss and\nchemical composition. Finally, the habitable zone of red dwarf stars is\ncoincident with the magnetic reconnection region, so that planets forming at\nthose locations will experience maximum exposure to particle radiation, and\nsubsequent depletion of volatiles.\n"}
+{"abstract": "  Using the recently developed time-dependent Landauer-B\\\"uttiker formalism and\nJefimenko's retarded solutions to the Maxwell equations, we show how to compute\nthe time-dependent electromagnetic field produced by the charge and current\ndensities in nanojunctions out of equilibrium. We then apply this formalism to\na benzene ring junction, and show that geometry-dependent quantum interference\neffects can be used to control the magnetic field in the vicinity of the\nmolecule. Then, treating the molecular junction as a quantum emitter, we\ndemonstrate clear signatures of the local molecular geometry in the non-local\nradiated power.\n"}
+{"abstract": "  The perfect case for time-domain investigations are active galactic nuclei\n(AGNs) since they are luminous objects that show strong variability. Key result\nfrom the studies of AGNs variability is the estimated mass of a supermassive\nblack hole (SMBH), which resides in the center of an AGN. Moreover, the\nspectral variability of AGN can be used to study the structure and physics of\nthe broad line region, which in general can be hardly directly observed. Here\nwe review the current status of AGNs variability investigations in Serbia, in\nthe perspectives of the present and future monitoring campaigns.\n"}
+{"abstract": "  In this article, we investigate the problem of state reconstruction of\nfour-level quantum systems. A realistic scenario is considered with measurement\nresults distorted by random unitary operators. Two frames which define\ninjective measurements are applied and compared. By introducing arbitrary\nrotations, we can test the performance of the framework versus the amount of\nexperimental noise. The results of numerical simulations are depicted on graphs\nand discussed. In particular, a class of entangled states is reconstructed. The\nconcurrence is used as a figure of merit in order to quantify how well\nentanglement is preserved through noisy measurements.\n"}
+{"abstract": "  A data representation for system behavior telemetry for scalable big data\nsecurity analytics is presented, affording telemetry consumers comprehensive\nvisibility into workloads at reduced storage and processing overheads. The new\nabstraction, SysFlow, is a compact open data format that lifts the\nrepresentation of system activities into a flow-centric, object-relational\nmapping that records how applications interact with their environment, relating\nprocesses to file accesses, network activities, and runtime information. The\ntelemetry format supports single-event and volumetric flow representations of\nprocess control flows, file interactions, and network communications.\nEvaluation on enterprise-grade benchmarks shows that SysFlow facilitates deeper\nintrospection into attack kill chains while yielding traces orders of magnitude\nsmaller than current state-of-the-art system telemetry approaches --\ndrastically reducing storage requirements and enabling feature-filled system\nanalytics, process-level provenance tracking, and long-term data archival for\ncyber threat discovery and forensic analysis on historical data.\n"}
+{"abstract": "  We introduce a novel class of time integrators for dispersive equations which\nallow us to reproduce the dynamics of the solution from the classical $\n\\varepsilon = 1$ up to long wave limit regime $ \\varepsilon \\ll 1 $ on the\nnatural time scale of the PDE $t= \\mathcal{O}(\\frac{1}{\\varepsilon})$. Most\nnotably our new schemes converge with rates at order $\\tau \\varepsilon$ over\nlong times $t= \\frac{1}{\\varepsilon}$.\n"}
+{"abstract": "  The goal of this paper is an analysis of the geometry of billiards in\nellipses, based on properties of confocal central conics. The extended sides of\nthe billiards meet at points which are located on confocal ellipses and\nhyperbolas. They define the associated Poncelet grid. If a billiard is periodic\nthen it closes for any choice of the initial vertex on the ellipse. This gives\nrise to a continuous variation of billiards which is called billiard's motion\nthough it is neither a Euclidean nor a projective motion. The extension of this\nmotion to the associated Poncelet grid leads to new insights and invariants.\n"}
+{"abstract": "  This paper corrects the characterisation of biautomatic groups presented in\nLemma 2.5.5 in the book Word Processing in Groups by Epstein et al. We present\na counterexample to the lemma, and we reformulate the lemma to give a valid\ncharacterisation of biautomatic groups.\n"}
+{"abstract": "  Data from the space missions {\\it Gaia}, {\\it Kepler}, {\\it CoRoT} and {\\it\nTESS}, make it possible to compare parallax and asteroseismic distances. From\nthe ratio of two densities $\\rho_{\\rm sca}/\\rho_{\\pi}$, we obtain an empirical\nrelation $f_{\\Delta \\nu}$ between the asteroseismic large frequency separation\nand mean density, which is important for more accurate stellar mass and radius.\nThis expression for main-sequence (MS) and subgiant stars with $K$-band\nmagnitude is very close to the one obtained from interior MS models by\nY{\\i}ld{\\i}z, \\c{C}elik \\& Kayhan. We also discuss the effects of effective\ntemperature and parallax offset as the source of the difference between\nasteroseismic and non-asteroseismic stellar parameters. We have obtained our\nbest results for about 3500 red giants (RGs) by using 2MASS data and model\nvalues for $f_{\\Delta \\nu}$ from Sharma et al. Another unknown scaling\nparameter $f_{\\nu_{\\rm max}}$ comes from the relationship between the frequency\nof maximum amplitude and gravity. Using different combinations of $f_{\\nu_{\\rm\nmax}}$ and the parallax offset, we find that the parallax offset is generally a\nfunction of distance. The situation where this slope disappears is accepted as\nthe most reasonable solution. By a very careful comparison of asteroseismic and\nnon-asteroseismic parameters, we obtain very precise values for the parallax\noffset and $f_{\\nu_{\\rm max}}$ for RGs of $-0.0463\\pm0.0007$ mas and\n$1.003\\pm0.001$, respectively. Our results for mass and radius are in perfect\nagreement with those of APOKASC-2: the mass and radius of $\\sim$3500 RGs are in\nthe range of about 0.8-1.8 M$_{\\odot}$ (96 per cent) and 3.8-38 R$_{\\odot}$,\nrespectively.\n"}
+{"abstract": "  In this paper, we investigate the problem of prescribing Webster scalar\ncurvatures on compact pseudo-Hermitian manifolds. In terms of the method of\nupper and lower solutions and the perturbation theory of self-adjoint\noperators, we can describe some sets of Webster scalar curvature functions\nwhich can be realized through pointwise CR conformal deformations and CR\nconformally equivalent deformations respectively from a given pseudo-Hermitian\nstructure.\n"}
+{"abstract": "  We study normal directions to facets of the Newton polytope of the\ndiscriminant of the Laurent polynomial system via the tropical approach. We use\nthe combinatorial construction proposed by Dickenstein, Feichtner and Sturmfels\nfor the tropicalization of algebraic varieties admitting a parametrization by a\nlinear map followed by a monomial map.\n"}
+{"abstract": "  While polarisation sensing is vital in many areas of research, with\napplications spanning from microscopy to aerospace, traditional approaches are\nlimited by method-related error amplification or accumulation, placing\nfundamental limitations on precision and accuracy in single-shot polarimetry.\nHere, we put forward a new measurement paradigm to circumvent this, introducing\nthe notion of a universal full Poincar\\'e generator to map all polarisation\nanalyser states into a single vectorially structured light field, allowing all\nvector components to be analysed in a single-shot with theoretically\nuser-defined precision. To demonstrate the advantage of our approach, we use a\ncommon GRIN optic as our mapping device and show mean errors of <1% for each\nvector component, enhancing the sensitivity by around three times, allowing us\nto sense weak polarisation aberrations not measurable by traditional\nsingle-shot techniques. Our work paves the way for next-generation polarimetry,\nimpacting a wide variety of applications relying on weak vector measurement.\n"}
+{"abstract": "  This paper establishes sufficient conditions that force a graph to contain a\nbipartite subgraph with a given structural property. In particular, let $\\beta$\nbe any of the following graph parameters: Hadwiger number, Haj\\'{o}s number,\ntreewidth, pathwidth, and treedepth. In each case, we show that there exists a\nfunction $f$ such that every graph $G$ with $\\beta(G)\\geq f(k)$ contains a\nbipartite subgraph $\\hat{G}\\subseteq G$ with $\\beta(\\hat{G})\\geq k$.\n"}
+{"abstract": "  We present MAPFF1.0, a determination of unpolarised charged-pion\nfragmentation functions (FFs) from a set of single-inclusive $e^+e^-$\nannihilation and lepton-nucleon semi-inclusive deep-inelastic-scattering\n(SIDIS) data. FFs are parametrised in terms of a neural network (NN) and fitted\nto data exploiting the knowledge of the analytic derivative of the NN itself\nw.r.t. its free parameters. Uncertainties on the FFs are determined by means of\nthe Monte Carlo sampling method properly accounting for all sources of\nexperimental uncertainties, including that of parton distribution functions.\nTheoretical predictions for the relevant observables, as well as evolution\neffects, are computed to next-to-leading order (NLO) accuracy in perturbative\nQCD. We exploit the flavour sensitivity of the SIDIS measurements delivered by\nthe HERMES and COMPASS experiments to determine a minimally-biased set of seven\nindependent FF combinations. Moreover, we discuss the quality of the fit to the\nSIDIS data with low virtuality $Q^2$ showing that, as expected, low-$Q^2$ SIDIS\nmeasurements are generally harder to describe within a NLO-accurate\nperturbative framework.\n"}
+{"abstract": "  Let $G$ be a $2$-generated group. The generating graph $\\Gamma(G)$ is the\ngraph whose vertices are the elements of $G$ and where two vertices $g_1$ and\n$g_2$ are adjacent if $G = \\langle g_1, g_2 \\rangle.$ This graph encodes the\ncombinatorial structure of the distribution of generating pairs across $G.$ In\nthis paper we study some graph theoretic properties of $\\Gamma(G)$, with\nparticular emphasis on those properties that can be formulated in terms of\nforbidden induced subgraphs. In particular we investigate when the generating\ngraph $\\Gamma(G)$ is a cograph (giving a complete description when $G$ is\nsoluble) and when it is perfect (giving a complete description when $G$ is\nnilpotent and proving, among the others, that $\\Gamma(S_n)$ and $\\Gamma(A_n)$\nare perfect if and only if $n\\leq 4$). Finally we prove that for a finite group\n$G$, the properties that $\\Gamma(G)$ is split, chordal or $C_4$-free are\nequivalent.\n"}
+{"abstract": "  In this work, we develop a systematic method of constructing flat-band models\nwith and without band crossings. Our construction scheme utilizes the symmetry\nand spatial shape of a compact localized state (CLS) and also the singularity\nof the flat-band wave function obtained by a Fourier transform of the CLS\n(FT-CLS). In order to construct a flat-band model systematically using these\ningredients, we first choose a CLS with a specific symmetry representation in a\ngiven lattice. Then, the singularity of FT-CLS indicates whether the resulting\nflat band exhibits a band crossing point or not. A tight-binding Hamiltonian\nwith the flat band corresponding to the FT-CLS is obtained by introducing a set\nof basis molecular orbitals, which are orthogonal to the FT-CLS. Our\nconstruction scheme can be systematically applied to any lattice so that it\nprovides a powerful theoretical framework to study exotic properties of both\ngapped and gapless flat bands arising from their wave function singularities.\n"}
+{"abstract": "  One important feature of sunspots is the presence of light bridges. These\nstructures are elongated and bright (as compared to the umbra) features that\nseem to be related to the formation and evolution of sunspots. In this work, we\nstudied the long-term evolution and the stratification of different atmospheric\nparameters of three light bridges formed in the same host sunspot by different\nmechanisms. To accomplish this, we used data taken with the GREGOR Infrared\nSpectrograph installed at the GREGOR telescope. These data were inverted to\ninfer the physical parameters of the atmosphere where the observed spectral\nprofiles were formed of the three light bridges. We find that, in general, the\nbehaviour of the three light bridges is typical of this kind of structure with\nthe magnetic field strength, inclination, and temperature values between the\nvalues at the umbra and the penumbra. We also find that they are of a\nsignificantly non-magnetic character (particularly at the axis of the light\nbridges) as it is deduced from the filling factor. In addition, within the\ncommon behaviour of the physical properties of light bridges, we observe that\neach one exhibits a particular behaviour. Another interesting result is that\nthe light bridge cools down, the magnetic field decreases, and the magnetic\nfield lines get more inclined higher in the atmosphere. Finally, we studied the\nmagnetic and non-magnetic line-of-sight velocities of the light bridges. The\nformer shows that the magnetic component is at rest and, interestingly, its\nvariation with optical depth shows a bi-modal behaviour. For the line-of-sight\nvelocity of the non-magnetic component, we see that the core of the light\nbridge is at rest or with shallow upflows and clear downflows sinking through\nthe edges.\n"}
+{"abstract": "  A vast concourse of events and phenomena occur in nature that may be\ninterrelated by a entropy-maximization technique that provides a comprehensible\nexplanation of a range of physical problems, integrating in a new framework the\nuniversal tendency of energy to a minimum and entropy to a maximum. The outcome\nis a modification of Newton's dynamical equation of motion, grounding the\nprinciples of mechanics on the concepts of energy and entropy, instead on the\nusual definition of force, integrating into a consistent framework the\ndescription of translation and vortical motion. The new method offers a fresh\napproach to traditional problems and can be applied with advantage in the\nsolution of variational problems.\n"}
+{"abstract": "  The generalized Langevin equation (GLE) overcomes the limiting Markov\napproximation of the Langevin equation by an incorporated memory kernel and can\nbe used to model various stochastic processes in many fields of science ranging\nfrom climate modeling over neuroscience to finance. Generally, Bayesian\nestimation facilitates the determination of both suitable model parameters and\ntheir credibility for a measured time series in a straightforward way. In this\nwork we develop a realization of this estimation technique for the GLE in the\ncase of white noise. We assume piecewise constant drift and diffusion functions\nand represent the characteristics of the data set by only a few coefficients,\nwhich leads to a numerically efficient procedure. The kernel function is an\narbitrary time-discrete function with a fixed length $K$. We show how to\ndetermine a reasonable value of $K$ based on the data. We illustrate the\nabilities of both the method and the model by an example from turbulence.\n"}
+{"abstract": "  Complex multivariate time series arise in many fields, ranging from computer\nvision to robotics or medicine. Often we are interested in the independent\nunderlying factors that give rise to the high-dimensional data we are\nobserving. While many models have been introduced to learn such disentangled\nrepresentations, only few attempt to explicitly exploit the structure of\nsequential data. We investigate the disentanglement properties of Gaussian\nprocess variational autoencoders, a class of models recently introduced that\nhave been successful in different tasks on time series data. Our model exploits\nthe temporal structure of the data by modeling each latent channel with a GP\nprior and employing a structured variational distribution that can capture\ndependencies in time. We demonstrate the competitiveness of our approach\nagainst state-of-the-art unsupervised and weakly-supervised disentanglement\nmethods on a benchmark task. Moreover, we provide evidence that we can learn\nmeaningful disentangled representations on real-world medical time series data.\n"}
+{"abstract": "  In this paper we derive some Edmundson-Lah-Ribari\\v{c} type inequalities for\npositive linear functionals and 3-convex functions. Main results are applied to\nthe generalized f-divergence functional. Examples with Zipf Mandelbrot law are\nused to illustrate the results. In addition, obtained results are utilized in\nconstructing some families of exponentially convex functions and Stolarsky-type\nmeans.\n"}
+{"abstract": "  We provide the rigorous derivation of the wave kinetic equation from the\ncubic nonlinear Schr\\\"odinger (NLS) equation at the kinetic timescale, under a\nparticular scaling law that describes the limiting process. This solves a main\nconjecture in the theory of wave turbulence, i.e. the kinetic theory of\nnonlinear wave systems. Our result is the wave analog of Lanford's theorem on\nthe derivation of the Boltzmann kinetic equation from particle systems, where\nin both cases one takes the thermodynamic limit as the size of the system\ndiverges to infinity, and as the interaction strength of waves or radius of\nparticles vanishes to $0$, according to a particular scaling law\n(Boltzmann-Grad in the particle case).\n  More precisely, in dimensions $d\\geq 3$, we consider the (NLS) equation in a\nlarge box of size $L$ with a weak nonlinearity of strength $\\alpha$. In the\nlimit $L\\to\\infty$ and $\\alpha\\to 0$, under the scaling law $\\alpha\\sim\nL^{-1}$, we show that the long-time behavior of (NLS) is statistically\ndescribed by the wave kinetic equation, with well justified approximation, up\nto times that are $O(1)$ (i.e independent of $L$ and $\\alpha$) multiples of the\nkinetic timescale $T_{\\text{kin}}\\sim \\alpha^{-2}$. This is the first result of\nits kind for any nonlinear dispersive system.\n"}
+{"abstract": "  Fixed points in three dimensions described by conformal field theories with\n$MN_{m,n}= O(m)^n\\rtimes S_n$ global symmetry have extensive applications in\ncritical phenomena. Associated experimental data for $m=n=2$ suggest the\nexistence of two non-trivial fixed points, while the $\\varepsilon$ expansion\npredicts only one, resulting in a puzzling state of affairs. A recent numerical\nconformal bootstrap study has found two kinks for small values of the\nparameters $m$ and $n$, with critical exponents in good agreement with\nexperimental determinations in the $m=n=2$ case. In this paper we investigate\nthe fate of the corresponding fixed points as we vary the parameters $m$ and\n$n$. We find that one family of kinks approaches a perturbative limit as $m$\nincreases, and using large spin perturbation theory we construct a large $m$\nexpansion that fits well with the numerical data. This new expansion, akin to\nthe large $N$ expansion of critical $O(N)$ models, is compatible with the fixed\npoint found in the $\\varepsilon$ expansion. For the other family of kinks, we\nfind that it persists only for $n=2$, where for large $m$ it approaches a\nnon-perturbative limit with $\\Delta_\\phi\\approx 0.75$. We investigate the\nspectrum in the case $MN_{100,2}$ and find consistency with expectations from\nthe lightcone bootstrap.\n"}
+{"abstract": "  A major challenge in applying machine learning to automated theorem proving\nis the scarcity of training data, which is a key ingredient in training\nsuccessful deep learning models. To tackle this problem, we propose an approach\nthat relies on training purely with synthetically generated theorems, without\nany human data aside from axioms. We use these theorems to train a\nneurally-guided saturation-based prover. Our neural prover outperforms the\nstate-of-the-art E-prover on this synthetic data in both time and search steps,\nand shows significant transfer to the unseen human-written theorems from the\nTPTP library, where it solves 72\\% of first-order problems without equality.\n"}
+{"abstract": "  Graph embeddings are low dimensional representations of nodes, edges or whole\ngraphs. Such representations allow for data in a network format to be used\nalong with machine learning models for a variety of tasks (e.g., node\nclassification), where using a similarity matrix would be impractical. In\nrecent years, many methods for graph embedding generation have been created\nbased on the idea of random walks. We propose MultiWalk, a framework that uses\nan ensemble of these methods to generate the embeddings. Our experiments show\nthat the proposed framework, using an ensemble composed of two state-of-the-art\nmethods, can generate embeddings that perform better in classification tasks\nthan each method in isolation.\n"}
+{"abstract": "  The Muon g-2 experiment at FERMILAB has confirmed the muon anomalous magnetic\nmoment anomaly with an error bar 15% smaller and a different central value\ncompared with the previous Brookhaven result. The combined results from\nFERMILAB and Brookhaven show a difference with theory at a significance of\n$4.2\\sigma$, strongly indicating the presence of new physics. In light of this\nnew result, we discuss a Two Higgs Doublet model augmented by an Abelian gauge\nsymmetry that can simultaneously accommodate a light dark matter candidate and\n$(g-2)_\\mu$, in agreement with existing bounds.\n"}
+{"abstract": "  We propose and demonstrate a scalable scheme for the simultaneous\ndetermination of internal and motional states in trapped ions with single-site\nresolution. The scheme is applied to the study of polaritonic excitations in\nthe Jaynes- Cummings Hubbard model with trapped ions, in which the internal and\nmotional states of the ions are strongly correlated. We observe quantum phase\ntransitions of polaritonic excitations in two ions by directly evaluating their\nvariances per ion site. Our work establishes an essential technological method\nfor large-scale quantum simulations of polaritonic systems.\n"}
+{"abstract": "  We revisit the renormalizable polynomial inflection point model of inflation,\nfocusing on the small field scenario which can be treated fully analytically.\nIn particular, the running of the spectral index is predicted to be $\\alpha =\n-1.43 \\times 10^{-3} +5.56 \\times 10^{-5} \\left(N_{\\rm CMB}-65 \\right)$, which\nmight be tested in future. We also analyze reheating through perturbative\ninflaton decays to either fermionic or bosonic final states via a trilinear\ncoupling. The lower bound on the reheating temperature from successful Big Bang\nnucleosynthesis gives lower bounds for these couplings; on the other hand\nradiative stability of the inflaton potential leads to upper bounds. In\ncombination this leads to a lower bound on the location $\\phi_0$ of the near\ninflection point, $\\phi_0 > 3 \\cdot 10^{-5}$ in Planckian units. The Hubble\nparameter during inflation can be as low as $H_{\\rm inf} \\sim 1$ MeV, or as\nhigh as $\\sim 10^{10}$ GeV. Similarly, the reheating temperature can lie\nbetween its lower bound of $\\sim 4$ MeV and about $4 \\cdot 10^8 \\ (10^{11})$\nGeV for fermionic (bosonic) inflaton decays. We finally speculate on the\n\"prehistory\" of the universe in this scenario, which might have included an\nepoch of eternal inflation.\n"}
+{"abstract": "  The dynamical history of stars influences the formation and evolution of\nplanets significantly. To explore the influence of dynamical history on planet\nformation and evolution from observations, we assume that stars who experienced\nsignificantly different dynamical histories tend to have different relative\nvelocities. Utilizing the accurate Gaia-Kepler Stellar Properties Catalog, we\nselect single main-sequence stars and divide these stars into three groups\naccording to their relative velocities, i.e. high-V, medium-V, and low-V stars.\nAfter considering the known biases from Kepler data and adopting prior and\nposterior correction to minimize the influence of stellar properties on planet\noccurrence rate, we find that high-V stars have a lower occurrence rate of\nsuper-Earths and sub-Neptunes (1--4 R$_{\\rm \\oplus}$, P<100 days) and higher\noccurrence rate of sub-Earth (0.5--1 R$_{ \\oplus}$, P<30 days) than low-V\nstars. Additionally, high-V stars have a lower occurrence rate of hot Jupiter\nsized planets (4--20 R$_{\\oplus}$, P<10 days) and a slightly higher occurrence\nrate of warm or cold Jupiter sized planets (4--20 R$_{\\oplus}$, 10<P<400 days).\nAfter investigating the multiplicity and eccentricity, we find that high-V\nplanet hosts prefer a higher fraction of multi-planets systems and lower\naverage eccentricity, which is consistent with the eccentricity-multiplicity\ndichotomy of Kepler planetary systems. All these statistical results favor the\nscenario that the high-V stars with large relative velocity may experience\nfewer gravitational events, while the low-V stars may be influenced by stellar\nclustering significantly.\n"}
+{"abstract": "  Determining the mechanism by which high-mass stars are formed is essential\nfor our understanding of the energy budget and chemical evolution of galaxies.\nBy using the New IRAM KIDs Array 2 (NIKA2) camera on the Institut de Radio\nAstronomie Millim\\'etrique (IRAM) 30-m telescope, we have conducted\nhigh-sensitivity and large-scale mapping of a fraction of the Galactic plane in\norder to search for signatures of the transition between the high- and low-mass\nstar-forming modes. Here, we present the first results from the Galactic Star\nFormation with NIKA2 (GASTON) project, a Large Programme at the IRAM 30-m\ntelescope which is mapping $\\approx$2 deg$^2$ of the inner Galactic plane (GP),\ncentred on $\\ell$=23.9$^\\circ$, $b$=0.05$^\\circ$, as well as targets in Taurus\nand Ophiuchus in 1.15 and 2.00 mm continuum wavebands. In this paper we present\nthe first of the GASTON GP data taken, and present initial science results. We\nconduct an extraction of structures from the 1.15 mm maps using a dendrogram\nanalysis and, by comparison to the compact source catalogues from Herschel\nsurvey data, we identify a population of 321 previously-undetected clumps.\nApproximately 80 per cent of these new clumps are 70 $\\mu$m-quiet, and may be\nconsidered as starless candidates. We find that this new population of clumps\nare less massive and cooler, on average, than clumps that have already been\nidentified. Further, by classifying the full sample of clumps based upon their\ninfrared-bright fraction - an indicator of evolutionary stage - we find\nevidence for clump mass growth, supporting models of clump-fed high-mass star\nformation.\n"}
+{"abstract": "  Intersection patterns of convex sets in $\\mathbb{R}^d$ have the remarkable\nproperty that for $d+1 \\le k \\le \\ell$, in any sufficiently large family of\nconvex sets in $\\mathbb{R}^d$, if a constant fraction of the $k$-element\nsubfamilies have nonempty intersection, then a constant fraction of the\n$\\ell$-element subfamilies must also have nonempty intersection. Here, we prove\nthat a similar phenomenon holds for any topological set system $\\mathcal{F}$ in\n$\\mathbb{R}^d$. Quantitatively, our bounds depend on how complicated the\nintersection of $\\ell$ elements of $\\mathcal{F}$ can be, as measured by the sum\nof the $\\lceil\\frac{d}2\\rceil$ first Betti numbers. As an application, we\nimprove the fractional Helly number of set systems with bounded topological\ncomplexity due to the third author, from a Ramsey number down to $d+1$. We also\nshed some light on a conjecture of Kalai and Meshulam on intersection patterns\nof sets with bounded homological VC dimension. A key ingredient in our proof is\nthe use of the stair convexity of Bukh, Matou\\v{s}ek and Nivash to recast a\nsimplicial complex as a homological minor of a cubical complex.\n"}
+{"abstract": "  In this book chapter, we study a problem of distributed content caching in an\nultra-dense edge caching network (UDCN), in which a large number of small base\nstations (SBSs) prefetch popular files to cope with the ever-growing user\ndemand in 5G and beyond. In a UDCN, even a small misprediction of user demand\nmay render a large amount of prefetched data obsolete. Furtherproacmore, the\ninterference variance is high due to the short inter-SBS distances, making it\ndifficult to quantify data downloading rates. Lastly, since the caching\ndecision of each SBS interacts with those of all other SBSs, the problem\ncomplexity of exponentially increases with the number of SBSs, which is unfit\nfor UDCNs. To resolve such challenging issues while reflecting time-varying and\nlocation-dependent user demand, we leverage mean-field game (MFG) theory\nthrough which each SBS interacts only with a single virtual SBS whose state is\ndrawn from the state distribution of the entire SBS population, i.e.,\nmean-field (MF) distribution. This MF approximation asymptotically guarantees\nachieving the epsilon Nash equilibrium as the number of SBSs approaches\ninfinity. To describe such an MFG-theoretic caching framework, this chapter\naims to provide a brief review of MFG, and demonstrate its effectiveness for\nUDCNs.\n"}
+{"abstract": "  Information overload is a prevalent challenge in many high-value domains. A\nprominent case in point is the explosion of the biomedical literature on\nCOVID-19, which swelled to hundreds of thousands of papers in a matter of\nmonths. In general, biomedical literature expands by two papers every minute,\ntotalling over a million new papers every year. Search in the biomedical realm,\nand many other vertical domains is challenging due to the scarcity of direct\nsupervision from click logs. Self-supervised learning has emerged as a\npromising direction to overcome the annotation bottleneck. We propose a general\napproach for vertical search based on domain-specific pretraining and present a\ncase study for the biomedical domain. Despite being substantially simpler and\nnot using any relevance labels for training or development, our method performs\ncomparably or better than the best systems in the official TREC-COVID\nevaluation, a COVID-related biomedical search competition. Using distributed\ncomputing in modern cloud infrastructure, our system can scale to tens of\nmillions of articles on PubMed and has been deployed as Microsoft Biomedical\nSearch, a new search experience for biomedical literature:\nhttps://aka.ms/biomedsearch.\n"}
+{"abstract": "  We analyze the thermodynamic Casimir effect occurring in a gas of\nnon-interacting bosons confined by two parallel walls with a strongly\nanisotropic dispersion inherited from an underlying lattice. In the direction\nperpendicular to the confining walls the standard quadratic dispersion is\nreplaced by the term $|{\\bf p}|^{\\alpha}$ with $\\alpha \\geq 2$ treated as a\nparameter. We derive a closed, analytical expression for the Casimir force\ndepending on the dimensionality $d$ and the exponent $\\alpha$, and analyze it\nfor thermodynamic states in which the Bose-Einstein condensate is present. For\n$\\alpha\\in\\{4,6,8,\\dots\\}$ the exponent governing the decay of the Casimir\nforce with increasing distance between the walls becomes modified and the\nCasimir amplitude $\\Delta_{\\alpha}(d)$ exhibits oscillations of sign as a\nfunction of $d$. Otherwise we find that $\\Delta_{\\alpha}(d)$ features\nsingularities when viewed as a function of $d$ and $\\alpha$. Recovering the\nknown previous results for the isotropic limit $\\alpha=2$ turns out to occur\nvia a cancellation of singular terms.\n"}
+{"abstract": "  The classic searches for supersymmetry have not given any strong indication\nfor new physics. Therefore CMS is designing dedicated searches to target the\nmore difficult and specific supersymmetry scenarios. This contribution present\nthree such recent searches based on 13 TeV proton-proton collisions recorded\nwith the CMS detector in 2016, 2017 and 2018: a search for heavy gluinos\ncascading via heavy next-to-lightest neutralino in final states with boosted Z\nbosons and missing transverse momentum; a search for compressed supersymmetry\nin final states with soft taus; and a search for compressed, long-lived\ncharginos in hadronic final states with disappearing tracks.\n"}
+{"abstract": "  More than a decade has passed since the definition of Globular Cluster (GC)\nchanged, and now we know that they host Multiple Populations (MPs). But few GCs\ndo not share that behaviour and Ruprecht 106 is one of these clusters. We\nanalyzed thirteen member red giant branch stars using spectra in the wavelength\nrange 6120-6405 Angstroms obtained through the GIRAFFE Spectrograph, mounted at\nUT2 telescope at Paranal, as well as the whole cluster using C, V, R and I\nphotometry obtained through the Swope telescope at Las Campanas. Atmospheric\nparameters were determined from the photometry to determine Fe and Na\nabundances. A photometric analysis searching for MPs was also carried out. Both\nanalyses confirm that Ruprecht 106 is indeed one on the few GCs to host Simple\nStellar Population, in agreement with previous studies. Finally, a dynamical\nstudy concerning its orbits was carried out to analyze the possible extra\ngalactic origin of the Cluster. The orbital integration indicates that this GC\nbelongs to the inner halo, while an Energy plane shows that it cannot be\naccurately associated with any known extragalactic progenitor.\n"}
+{"abstract": "  Phase gradient metagratings/metasurfaces (PGMs) have provided a new paradigm\nfor light manipulations. In this work, we will show the existence of gauge\ninvariance in PGMs, i.e., the diffraction law of PGMs is independent of the\nchoice of initial value of abrupt phase shift that induces the phase gradient.\nThis gauge invariance ensures the well-studied ordinary metallic grating that\ncan be regarded as a PGM, with its diffraction properties that can fully\npredicted by generalized diffraction law with phase gradient. The generalized\ndiffraction law presents a new insight for the famous effect of Wood's\nAnomalies and Rayleigh conjecture.\n"}
+{"abstract": "  Hydraulic blockage of cross-drainage structures such as culverts is\nconsidered one of main contributor in triggering urban flash floods. However,\ndue to lack of during floods data and highly non-linear nature of debris\ninteraction, conventional modelling for hydraulic blockage is not possible.\nThis paper proposes to use machine learning regression analysis for the\nprediction of hydraulic blockage. Relevant data has been collected by\nperforming a scaled in-lab study and replicating different blockage scenarios.\nFrom the regression analysis, Artificial Neural Network (ANN) was reported best\nin hydraulic blockage prediction with $R^2$ of 0.89. With deployment of\nhydraulic sensors in smart cities, and availability of Big Data, regression\nanalysis may prove helpful in addressing the blockage detection problem which\nis difficult to counter using conventional experimental and hydrological\napproaches.\n"}
+{"abstract": "  Bi-objective search is a well-known algorithmic problem, concerned with\nfinding a set of optimal solutions in a two-dimensional domain. This problem\nhas a wide variety of applications such as planning in transport systems or\noptimal control in energy systems. Recently, bi-objective A*-based search\n(BOA*) has shown state-of-the-art performance in large networks. This paper\ndevelops a bi-directional and parallel variant of BOA*, enriched with several\nspeed-up heuristics. Our experimental results on 1,000 benchmark cases show\nthat our bi-directional A* algorithm for bi-objective search (BOBA*) can\noptimally solve all of the benchmark cases within the time limit, outperforming\nthe state of the art BOA*, bi-objective Dijkstra and bi-directional\nbi-objective Dijkstra by an average runtime improvement of a factor of five\nover all of the benchmark instances.\n"}
+{"abstract": "  For AI technology to fulfill its full promises, we must have effective means\nto ensure Responsible AI behavior and curtail potential irresponsible use,\ne.g., in areas of privacy protection, human autonomy, robustness, and\nprevention of biases and discrimination in automated decision making. Recent\nliterature in the field has identified serious shortcomings of narrow\ntechnology focused and formalism-oriented research and has proposed an\ninterdisciplinary approach that brings the social context into the scope of\nstudy. In this paper, we take a sociotechnical approach to propose a more\nexpansive framework of thinking about the Responsible AI challenges in both\ntechnical and social context. Effective solutions need to bridge the gap\nbetween a technical system with the social system that it will be deployed to.\nTo this end, we propose human agency and regulation as main mechanisms of\nintervention and propose a decentralized computational infrastructure, or a set\nof public utilities, as the computational means to bridge this gap. A\ndecentralized infrastructure is uniquely suited for meeting this challenge and\nenable technical solutions and social institutions in a mutually reinforcing\ndynamic to achieve Responsible AI goals. Our approach is novel in its\nsociotechnical approach and its aim in tackling the structural issues that\ncannot be solved within the narrow confines of AI technical research. We then\nexplore possible features of the proposed infrastructure and discuss how it may\nhelp solve example problems recently studied in the field.\n"}
+{"abstract": "  In this paper, we argue that models coming from a variety of fields share a\ncommon structure that we call matching function equilibria with partial\nassignment. This structure revolves around an aggregate matching function and a\nsystem of nonlinear equations. This encompasses search and matching models,\nmatching models with transferable, non-transferable and imperfectly\ntransferable utility, and matching with peer effects. We provide a proof of\nexistence and uniqueness of an equilibrium as well as an efficient algorithm to\ncompute it. We show how to estimate parametric versions of these models by\nmaximum likelihood. We also propose an approach to construct counterfactuals\nwithout estimating the matching functions for a subclass of models. We\nillustrate our estimation approach by analyzing the impact of the elimination\nof the Social Security Student Benefit Program in 1982 on the marriage market\nin the United States.\n"}
+{"abstract": "  We prove existence and regularity results for free transmission problems\ngoverned by fully nonlinear elliptic equations with nonhomogeneous\ndegeneracies.\n"}
+{"abstract": "  Non-minimally coupled scalar field models are well-known for providing\ninteresting cosmological features. These include a late time dark energy\nbehavior, a phantom dark energy evolution without singularity, an early time\ninflationary universe, scaling solutions, convergence to the standard\n$\\Lambda$CDM, etc. While the usual stability analysis helps us determine the\nevolution of a model geometrically, bifurcation theory allows us to precisely\nlocate the parameters' values describing the global dynamics without a\nfine-tuning of initial conditions. Using the center manifold theory and\nbifurcation analysis, we show that the general model undergoes a transcritical\nbifurcation, which predicts us to tune our models to have certain desired\ndynamics. We obtained a class of models and a range of parameters capable of\ndescribing a cosmic evolution from an early radiation era towards a late time\ndark energy era over a wide range of initial conditions. There is also a\npossible scenario of crossing the phantom divide line. We also find a class of\nmodels where the late time attractor mechanism is indistinguishable from that\nof a structurally stable general relativity based model; thus, we can elude the\nbig rip singularity generically. Therefore, bifurcation theory allows us to\nselect models that are viable with cosmological observations.\n"}
+{"abstract": "  The objective of this paper is to present some results about viscosity\nsubsolutions of the contact Hamiltonian-Jacobi equations on connected, closed\nmanifold $M$ $$ H(x,\\partial_x u,u)= 0, \\quad x\\in M. $$ Based on implicit\nvariational principles introduced in [12,14], we focus on the monotonicity of\nthe solution semigroups on viscosity subsolutions and the positive invariance\nof the epigraph for viscosity subsolutions. Besides, we show a similar\nconsequence for strict viscosity subsolutions on $M$.\n"}
+{"abstract": "  We analyze the influence of the surface passivation produced by oxides on the\nsuperconducting properties of $\\gamma$-Mo$_2$N ultra-thin films. The\nsuperconducting critical temperature of thin films grown directly on Si (100)\nwith those using a buffer and a capping layer of AlN are compared. The results\nshow that the cover layer avoids the presence of surface oxides, maximizing the\nsuperconducting critical temperature for films with thicknesses of a few\nnanometers. We characterize the flux-flow instability measuring current-voltage\ncurves in a 6.4 nm thick Mo$_2$N film with a superconducting critical\ntemperature of 6.4 K. The data is analyzed using the Larkin and Ovchinnikov\nmodel. Considering self-heating effects due to finite heat removal from the\nsubstrate, we determine a fast quasiparticle relaxation time $\\approx$ 45 ps.\nThis value is promising for its applications in single-photon detectors.\n"}
+{"abstract": "  Understanding the biological function of knots in proteins and their folding\nprocess is an open and challenging question in biology. Recent studies classify\nthe topology and geometry of knotted proteins by analysing the distribution of\na protein's planar projections using topological objects called knotoids. We\napproach the analysis of proteins with the same topology by introducing a\ntopologically inspired statistical metric between their knotoid distributions.\nWe detect geometric differences between trefoil proteins by characterising\ntheir entanglement and we recover a clustering by sequence similarity. By\nlooking directly at the geometry and topology of their native states, we are\nable to probe different folding pathways for proteins forming open-ended\ntrefoil knots. Interestingly, our pipeline reveals that the folding pathway of\nshallow knotted Carbonic Anhydrases involves the creation of a double-looped\nstructure, differently from what was previously observed for deeply knotted\ntrefoil proteins. We validate this with Molecular Dynamics simulations.\n"}
+{"abstract": "  Moving clouds affect the global solar irradiance that reaches the surface of\nthe Earth. As a consequence, the amount of resources available to meet the\nenergy demand in a smart grid powered using Photovoltaic (PV) systems depends\non the shadows projected by passing clouds. This research introduces an\nalgorithm for tracking clouds to predict Sun occlusion. Using thermal images of\nclouds, the algorithm is capable of estimating multiple wind velocity fields\nwith different altitudes, velocity magnitudes and directions.\n"}
+{"abstract": "  In Quantum Field Theory, we discuss the main features of the (non-local)\ncontour gauge which extends the local axial-type gauge used in most approaches.\nBased on the gluon geometry, we demonstrate that the contour gauge does not\nsuffer from the residual gauge. We discuss the useful correspondence between\nthe contour gauge conception and the Hamiltonian (Lagrangian) formalism. Having\ncompared the local and non-local gauges, we again advocate the advantage of the\ncontour gauge use.\n"}
+{"abstract": "  Two decades after its unexpected discovery, the properties of the $X(3872)$\nexotic resonance are still under intense scrutiny. In particular, there are\ndoubts about its nature as an ensemble of mesons or having any other internal\nstructure. We use a Diffusion Monte Carlo method to solve the many-body\nSchr\\\"odinger equation that describes this state as a $c \\bar c n \\bar n$\n($n=u$ or $d$ quark) system. This approach accounts for multi-particle\ncorrelations in physical observables avoiding the usual quark-clustering\nassumed in other theoretical techniques. The most general and accepted pairwise\nCoulomb$\\,+\\,$linear-confining$\\,+\\,$hyperfine spin-spin interaction, with\nparameters obtained by a simultaneous fit of around 100 masses of mesons and\nbaryons, is used. The $X(3872)$ contains light quarks whose masses are given by\nthe mechanism responsible of the dynamical breaking of chiral symmetry. The\nsame mechanisms gives rise to Goldstone-boson exchange interactions between\nquarks that have been fixed in the last 10-20 years reproducing hadron,\nhadron-hadron and multiquark phenomenology. It appears that a meson-meson\nmolecular configuration is preferred but, contrary to the usual assumption of\n$D^0\\bar{D}^{\\ast0}$ molecule for the $X(3872)$, our formalism produces $\\omega\nJ/\\psi$ and $\\rho J/\\psi$ clusters as the most stable ones, which could explain\nin a natural way all the observed features of the $X(3872)$.\n"}
+{"abstract": "  We propose a method to evaluate and improve the validity of required\nspecifications by comparing models from different viewpoints. Inconsistencies\nare automatically extracted from the model in which the analyst defines the\nservice procedure based on the initial requirement; thereafter, the analyst\nautomatically compares it with a state transition model from the same initial\nrequirement that has been created by an evaluator who is different from the\nanalyst. The identified inconsistencies are reported to the analyst to enable\nthe improvement of the required specifications. We develop a tool for\nextraction and comparison and then discuss its effectiveness by applying the\nmethod to a requirements specification example.\n"}
+{"abstract": "  Kappa distributions and with loss cone features have been frequently observed\nwith flares emissions with the signatures of Lower hybrid waves. We have\nanalysed the plasma with Kappa distributions and with loss cone features for\nthe drift wave instabilities in perpendicular propagation for Large flare and\nNormal flare and Coronal condition . While analysing the growth/damping rate,\nwe understand that the growth of propagation of EM waves increases with kappa\ndistribution index for all the three cases. In comparing the propagation large\nflare shows lesser growth in compared with the normal and the coronal plasmas.\nWhen added the loss cone features to Kappa distributions, we find that the\ndamping of EM wave propagation takes place. The damping rate EM waves is\nincreases with perpendicular temperature and loss cone index l, in all the\nthree cases but damping is very high for large flare and then normal in\ncomparision with coronal condition. This shows that the lower hybrid damping\nmay be the source of coronal heating.\n"}
+{"abstract": "  Many software engineering studies or tasks rely on categorizing software\nengineering artifacts. In practice, this is done either by defining simple but\noften imprecise heuristics, or by manual labelling of the artifacts.\nUnfortunately, errors in these categorizations impact the tasks that rely on\nthem. To improve the precision of these categorizations, we propose to gather\nheuristics in a collaborative heuristic repository, to which researchers can\ncontribute a large amount of diverse heuristics for a variety of tasks on a\nvariety of SE artifacts. These heuristics are then leveraged by\nstate-of-the-art weak supervision techniques to train high-quality classifiers,\nthus improving the categorizations. We present an initial version of the\nheuristic repository, which we applied to the concrete task of commit\nclassification.\n"}
+{"abstract": "  Graph embedding is a general approach to tackling graph-analytic problems by\nencoding nodes into low-dimensional representations. Most existing embedding\nmethods are transductive since the information of all nodes is required in\ntraining, including those to be predicted. In this paper, we propose a novel\ninductive embedding method for semi-supervised learning on graphs. This method\ngenerates node representations by learning a parametric function to aggregate\ninformation from the neighborhood using an attention mechanism, and hence\nnaturally generalizes to previously unseen nodes. Furthermore, adversarial\ntraining serves as an external regularization enforcing the learned\nrepresentations to match a prior distribution for improving robustness and\ngeneralization ability. Experiments on real-world clean or noisy graphs are\nused to demonstrate the effectiveness of this approach.\n"}
+{"abstract": "  We propose a new diffusion-asymptotic analysis for sequentially randomized\nexperiments, including those that arise in solving multi-armed bandit problems.\nIn an experiment with $ n $ time steps, we let the mean reward gaps between\nactions scale to the order $1/\\sqrt{n}$ so as to preserve the difficulty of the\nlearning task as $n$ grows. In this regime, we show that the behavior of a\nclass of sequentially randomized Markov experiments converges to a diffusion\nlimit, given as the solution of a stochastic differential equation. The\ndiffusion limit thus enables us to derive refined, instance-specific\ncharacterization of the stochastic dynamics of adaptive experiments. As an\napplication of this framework, we use the diffusion limit to obtain several new\ninsights on the regret and belief evolution of Thompson sampling. We show that\na version of Thompson sampling with an asymptotically uninformative prior\nvariance achieves nearly-optimal instance-specific regret scaling when the\nreward gaps are relatively large. We also demonstrate that, in this regime, the\nposterior beliefs underlying Thompson sampling are highly unstable over time.\n"}
+{"abstract": "  The Embedded-Atom Model (EAM) provides a phenomenological description of\natomic arrangements in metallic systems. It consists of a configurational\nenergy depending on atomic positions and featuring the interplay of two-body\natomic interactions and nonlocal effects due to the corresponding electronic\nclouds. The purpose of this paper is to mathematically investigate the\nminimization of the EAM energy among lattices in two and three dimensions. We\npresent a suite of analytical and numerical results under different reference\nchoices for the underlying interaction potentials. In particular, Gaussian,\ninverse-power, and Lennard-Jones-type interactions are addressed.\n"}
+{"abstract": "  We present a statistical analysis for the characteristics and spatial\nevolution of the interplanetary discontinuities (IDs) in the solar wind, from\n0.13 to 0.9 au, by using the Parker Solar Probe measurements on Orbits 4 and 5.\n3948 IDs have been collected, including 2511 rotational discontinuities (RDs)\nand 557 tangential discontinuities (TDs), with the remnant unidentified. The\nstatistical results show that (1) the ID occurrence rate decreases from 200\nevents/day at 0.13 au to 1 events/day at 0.9 au, following a spatial scaling\nr-2.00, (2) the RD to TD ratio decreases quickly with the heliocentric\ndistance, from 8 at r<0.3 au to 1 at r>0.4 au, (3) the magnetic field tends to\nrotate across the IDs, 45{\\deg} for TDs and 30{\\deg} for RDs in the pristine\nsolar wind within 0.3 au, (4) a special subgroup of RDs exist within 0.3 au,\ncharacterized by small field rotation angles and parallel or antiparallel\npropagations to the background magnetic fields, (5) the TD thicknesses\nnormalized by local ion inertial lengths (di) show no clear spatial scaling and\ngenerally range from 5 to 35 di, and the normalized RD thicknesses follow\nr-1.09 spatial scaling, (6) the outward (anti-sunward) propagating RDs\npredominate in all RDs, with the propagation speeds in the plasma rest frame\nproportional to r-1.03. This work could improve our understandings for the ID\ncharacteristics and evolutions and shed light on the study of the turbulent\nenvironment in the pristine solar wind.\n"}
+{"abstract": "  The paper addresses the problem of defining families of ordered sequences\n$\\{x_i\\}_{i\\in N}$ of elements of a compact subset $X$ of $R^d$ whose prefixes\n$X_n=\\{x_i\\}_{i=1}^{n}$, for all orders $n$, have good space-filling properties\nas measured by the dispersion (covering radius) criterion. Our ultimate aim is\nthe definition of incremental algorithms that generate sequences $X_n$ with\nsmall optimality gap, i.e., with a small increase in the maximum distance\nbetween points of $X$ and the elements of $X_n$ with respect to the optimal\nsolution $X_n^\\star$. The paper is a first step in this direction, presenting\nincremental design algorithms with proven optimality bound for one-parameter\nfamilies of criteria based on coverings and spacings that both converge to\ndispersion for large values of their parameter. The examples presented show\nthat the covering-based method outperforms state-of-the-art competitors,\nincluding coffee-house, suggesting that it inherits from its guaranteed 50\\%\noptimality gap.\n"}
+{"abstract": "  Barchan dunes, or simply barchans, are crescent-shaped dunes found in diverse\nenvironments such as the bottom of rivers, Earth's deserts and the surface of\nMars. In a recent paper [Phys. Rev. E 101, 012905 (2020)], we investigated the\nevolution of subaqueous barchans by using computational fluid dynamics -\ndiscrete element method (CFD-DEM), and our simulations captured well the\nevolution of an initial pile toward a barchan dune in both the bedform and\ngrain scales. The numerical method having shown to be adequate, we obtain now\nthe forces acting on each grain, isolate the contact interactions, and\ninvestigate how forces are distributed and transmitted in a barchan dune. We\npresent force maps and probability density functions (PDFs) for values in the\nstreamwise and spanwise directions, and show that stronger forces are\nexperienced by grains at neither the crest nor leading edge of the barchan, but\nin positions just upstream the dune centroid on the periphery of the dune. We\nshow also that a great part of grains undergo longitudinal forces of the order\nof 10$^{-7}$ N, with negative values around the crest, resulting in\ndecelerations and grain deposition in that region. These data show that the\nforce distribution tends to route a great part of grains toward the crest and\nhorns of subaqueous barchans, being fundamental to comprehend their\nmorphodynamics. However, to the best of the authors' knowledge, they are not\naccessible from current experiments, making of our results an important step\ntoward understanding the behavior of barchan dunes.\n"}
+{"abstract": "  In this paper, we prove that a compact quasi-Einstein manifold\n$(M^n,\\,g,\\,u)$ of dimension $n\\geq 4$ with boundary $\\partial M,$ nonnegative\nsectional curvature and zero radial Weyl tensor is either isometric, up to\nscaling, to the standard hemisphere $\\Bbb{S}^n_+,$ or $g=dt^{2}+\\psi\n^{2}(t)g_{L}$ and $u=u(t),$ where $g_{L}$ is Einstein with nonnegative Ricci\ncurvature. A similar classification result is obtained by assuming a\nfourth-order vanishing condition on the Weyl tensor. Moreover, a new example is\npresented in order to justify our assumptions. In addition, the case of\ndimension $n=3$ is also discussed.\n"}
+{"abstract": "  Normal mode decomposition of atomic vibrations has been used to provide\nmicroscopic under-standing of thermal transport in amorphous solids for\ndecades. In normal mode methods, it is naturally assumed that atoms vibrate\naround their equilibrium positions and that individual normal modes are the\nfundamental vibrational excitations transporting heat. With the abundance of\npredictions from normal mode methods and experimental measurements now\navailable, we care-fully analyze these calculations in amorphous silicon, a\nmodel amorphous solid. We find a number of discrepancies, suggesting that\ntreating individual normal modes as fundamental heat carriers may not be\naccurate in amorphous solids. Further, our classical and ab-initio molecular\ndynamics simulations of amorphous silicon demonstrate a large degree of atomic\ndiffusion, especially at high temperatures, leading to the conclusion that\nthermal transport in amorphous solids could be better described starting from\nthe perspective of liquid dynamics rather than from crystalline solids\n"}
+{"abstract": "  Recent research has confirmed the feasibility of backdoor attacks in deep\nreinforcement learning (RL) systems. However, the existing attacks require the\nability to arbitrarily modify an agent's observation, constraining the\napplication scope to simple RL systems such as Atari games. In this paper, we\nmigrate backdoor attacks to more complex RL systems involving multiple agents\nand explore the possibility of triggering the backdoor without directly\nmanipulating the agent's observation. As a proof of concept, we demonstrate\nthat an adversary agent can trigger the backdoor of the victim agent with its\nown action in two-player competitive RL systems. We prototype and evaluate\nBACKDOORL in four competitive environments. The results show that when the\nbackdoor is activated, the winning rate of the victim drops by 17% to 37%\ncompared to when not activated.\n"}
+{"abstract": "  To complete a previous work, the probability density functions for the errors\nin the center-of-gravity as positioning algorithm are derived with the usual\nmethods of the cumulative distribution functions. These methods introduce\nsubstantial complications compared to the approaches used in a previous\npublication on similar problems. The combinations of random variables\nconsidered are: $X_{g3}=\\theta(x_2-x_1) (x_1-x_3)/(x_1+x_2+x_3) +\n\\theta(x_1-x_2)(x_1+2x_4)/(x_1+x_2+x_4)$ and\n$X_{g4}=(\\theta(x_4-x_5)(2x_4+x_1-x_3)/(x_1+x_2+x_3+x_4)+\n\\theta(x_5-x_4)(x_1-x_3-2x_5)/(x_1+x_2+x_3+x_5)$ The complete and partial forms\nof the probability density functions of these expressions of the\ncenter-of-gravity algorithms are calculated for general probability density\nfunctions of the observation noise. The cumulative probability distributions\nare the essential steps in this study, never calculated elsewhere.\n"}
+{"abstract": "  In this work we derive the junction conditions for the matching between two\nspacetimes at a separation hypersurface in the perfect-fluid version of\n$f\\left(R,T\\right)$ gravity, not only in the usual geometrical representation\nbut also in a dynamically equivalent scalar-tensor representation. We start\nwith the general case in which a thin-shell separates the two spacetimes at the\nseparation hypersurface, for which the general junction conditions are deduced,\nand the particular case for smooth matching is considered when the\nstress-energy tensor of the thin-shell vanishes. The set of junction conditions\nis similar to the one previously obtained for $f\\left(R\\right)$ gravity but\nfeatures also constraints in the continuity of the trace of the stress-energy\ntensor $T_{ab}$ and its partial derivatives, which force the thin-shell to\nsatisfy the equation of state of radiation $\\sigma=2p_t$. As a consequence, a\nnecessary and sufficient condition for spherically symmetric thin-shells to\nsatisfy all the energy conditions is the positivity of its energy density\n$\\sigma$. For specific forms of the function $f\\left(R,T\\right)$, the\ncontinuity of $R$ and $T$ ceases to be mandatory but a gravitational\ndouble-layer arises at the separation hypersurface. The Martinez thin-shell\nsystem and a thin-shell surrounding a central black-hole are provided as\nexamples of application.\n"}
+{"abstract": "  We highlight shortcomings of the dynamical dark energy (DDE) paradigm. For\nparametric models with equation of state (EOS), $w(z) = w_0 + w_a f(z)$ for a\ngiven function of redshift $f(z)$, we show that the errors in $w_a$ are\nsensitive to $f(z)$: if $f(z)$ increases quickly with redshift $z$, then errors\nin $w_a$ are smaller, and vice versa. As a result, parametric DDE models suffer\nfrom a degree of arbitrariness and focusing too much on one model runs the risk\nthat DDE may be overlooked. In particular, we show the ubiquitous\nChevallier-Polarski-Linder model is one of the least sensitive to DDE. We also\ncomment on ``wiggles\" in $w(z)$ uncovered in non-parametric reconstructions.\nConcretely, we isolate the most relevant Fourier modes in the wiggles, model\nthem and fit them back to the original data to confirm the wiggles at\n$\\lesssim2\\sigma$. We delve into the assumptions going into the reconstruction\nand argue that the assumed correlations, which clearly influence the wiggles,\nplace strong constraints on field theory models of DDE.\n"}
+{"abstract": "  Planning to support widespread transportation electrification depends on\ndetailed estimates for the electricity demand from electric vehicles in both\nuncontrolled and controlled or smart charging scenarios. We present a modeling\napproach to rapidly generate charging estimates that include control for\nlarge-scale scenarios with millions of individual drivers. We model\nuncontrolled charging demand using statistical representations of real charging\nsessions. We model the effect of load modulation control on aggregate charging\nprofiles with a novel machine learning approach that replaces traditional\noptimization approaches. We demonstrate its performance modeling workplace\ncharging control with multiple electricity rate schedules, achieving small\nerrors (2.5% to 4.5%), while accelerating computations by more than 4000 times.\nWe illustrate the methodology by generating scenarios for California's 2030\ncharging demand including multiple charging segments and controls, with\nscenarios run locally in under 50 seconds, and for assisting rate design\nmodeling the large-scale impact of a new workplace charging rate.\n"}
+{"abstract": "  The effects of internal adaptation dynamics on the self-organized aggregation\nof chemotactic bacteria are investigated by Monte Carlo (MC) simulations based\non a two-stream kinetic transport equation coupled with a reaction-diffusion\nequation of the chemoattractant that bacteria produce. A remarkable finding is\na nonmonotonic behavior of the peak aggregation density with respect to the\nadaptation time; more specifically, aggregation is the most enhanced when the\nadaptation time is comparable to or moderately larger than the mean run time of\nbacteria. Another curious observation is the formation of a trapezoidal\naggregation profile occurring at a very large adaptation time, where the biased\nmotion of individual cells is rather hindered at the plateau regimes due to the\nboundedness of the tumbling frequency modulation. Asymptotic analysis of the\nkinetic transport system is also carried out, and a novel asymptotic equation\nis obtained at the large adaptation-time regime while the Keller-Segel type\nequations are obtained when the adaptation time is moderate. Numerical\ncomparison of the asymptotic equations with MC results clarifies that\ntrapezoidal aggregation is well described by the novel asymptotic equation, and\nthe nonmonotonic behavior of the peak aggregation density is interpreted as the\ntransient of the asymptotic solutions between different adaptation time\nregimes.\n"}
+{"abstract": "  We investigate the invariance of the Gibbs measure for the fractional\nSchrodinger equation of exponential type (expNLS) $i\\partial_t u +\n(-\\Delta)^{\\frac{\\alpha}2} u = 2\\gamma\\beta e^{\\beta|u|^2}u$ on $d$-dimensional\ncompact Riemannian manifolds $\\mathcal{M}$, for a dispersion parameter\n$\\alpha>d$, some coupling constant $\\beta>0$, and $\\gamma\\neq 0$. (i) We first\nstudy the construction of the Gibbs measure for (expNLS). We prove that in the\ndefocusing case $\\gamma>0$, the measure is well-defined in the whole regime\n$\\alpha>d$ and $\\beta>0$ (Theorem 1.1 (i)), while in the focusing case\n$\\gamma<0$ its partition function is always infinite for any $\\alpha>d$ and\n$\\beta>0$, even with a mass cut-off of arbitrary small size (Theorem 1.1 (ii)).\n(ii) We then study the dynamics (expNLS) with random initial data of low\nregularity. We first use a compactness argument to prove weak invariance of the\nGibbs measure in the whole regime $\\alpha>d$ and $0<\\beta < \\beta^\\star_\\alpha$\nfor some natural parameter $0<\\beta^\\star_\\alpha\\sim (\\alpha-d)$ (Theorem 1.3\n(i)). In the large dispersion regime $\\alpha>2d$, we can improve this result by\nconstructing a local deterministic flow for (expNLS) for any $\\beta>0$. Using\nthe Gibbs measure, we prove that solutions are almost surely global for\n$0<\\beta \\ll\\beta^\\star_\\alpha$, and that the Gibbs measure is invariant\n(Theorem 1.3 (ii)). (iii) Finally, in the particular case $d=1$ and\n$\\mathcal{M}=\\mathbb{T}$, we are able to exploit some probabilistic multilinear\nsmoothing effects to build a probabilistic flow for (expNLS) for\n$1+\\frac{\\sqrt{2}}2<\\alpha \\leq 2$, locally for arbitrary $\\beta>0$ and\nglobally for $0<\\beta \\ll \\beta^\\star_\\alpha$ (Theorem 1.5).\n"}
+{"abstract": "  We propose a novel approximation hierarchy for cardinality-constrained,\nconvex quadratic programs that exploits the rank-dominating eigenvectors of the\nquadratic matrix. Each level of approximation admits a min-max characterization\nwhose objective function can be optimized over the binary variables\nanalytically, while preserving convexity in the continuous variables.\nExploiting this property, we propose two scalable optimization algorithms,\ncoined as the \"best response\" and the \"dual program\", that can efficiently\nscreen the potential indices of the nonzero elements of the original program.\nWe show that the proposed methods are competitive with the existing screening\nmethods in the current sparse regression literature, and it is particularly\nfast on instances with high number of measurements in experiments with both\nsynthetic and real datasets.\n"}
+{"abstract": "  This paper completely characterizes the standard Young tableaux that can be\nreconstructed from their sets or multisets of $1$-minors. In particular, any\nstandard Young tableau with at least $5$ entries can be reconstructed from its\nset of $1$-minors.\n"}
+{"abstract": "  We consider the problem of forecasting the daily number of hospitalized\nCOVID-19 patients at a single hospital site, in order to help administrators\nwith logistics and planning. We develop several candidate hierarchical Bayesian\nmodels which directly capture the count nature of data via a generalized\nPoisson likelihood, model time-series dependencies via autoregressive and\nGaussian process latent processes, and share statistical strength across\nrelated sites. We demonstrate our approach on public datasets for 8 hospitals\nin Massachusetts, U.S.A. and 10 hospitals in the United Kingdom. Further\nprospective evaluation compares our approach favorably to baselines currently\nused by stakeholders at 3 related hospitals to forecast 2-week-ahead demand by\nrescaling state-level forecasts.\n"}
+{"abstract": "  We study theoretically two vibrating quantum emitters trapped near a\none-dimensional waveguide and interacting with propagating photons. We\ndemonstrate, that in the regime of strong optomechanical interaction the\nlight-induced coupling of emitter vibrations can lead to formation of spatially\nlocalized vibration modes, exhibiting parity-time (PT ) symmetry breaking.\nThese localized vibrations can be interpreted as topological defects in the\nquasiclassical energy spectrum.\n"}
+{"abstract": "  The origin of p-type conductivity and the mechanism responsible for low\ncarrier mobility was investigated in pyrite (FeS2) thin films. Temperature\ndependent resistivity measurements were performed on polycrystalline and\nnanostructured thin films prepared by three different methods. Films have a\nhigh hole density and low mobility regardless of the method used for their\npreparation. The charge transport mechanism is determined to be nearest\nneighbour hopping (NNH) at near room temperature with Mott-type variable range\nhopping (VRH) of holes via localized states occurring at lower temperatures.\nDensity functional theory (DFT) predicts that sulfur vacancy induced localized\ndefect states will be situated within the band gap with the charge remaining\nlocalized around the defect. The data indicate that the electronic properties\nincluding hopping transport in pyrite thin films can be correlated to sulfur\nvacancy related defect. The results provide insights on electronic properties\nof pyrite thin films and its implications for charge transport\n"}
+{"abstract": "  In one-shot weight sharing for NAS, the weights of each operation (at each\nlayer) are supposed to be identical for all architectures (paths) in the\nsupernet. However, this rules out the possibility of adjusting operation\nweights to cater for different paths, which limits the reliability of the\nevaluation results. In this paper, instead of counting on a single supernet, we\nintroduce $K$-shot supernets and take their weights for each operation as a\ndictionary. The operation weight for each path is represented as a convex\ncombination of items in a dictionary with a simplex code. This enables a matrix\napproximation of the stand-alone weight matrix with a higher rank ($K>1$). A\n\\textit{simplex-net} is introduced to produce architecture-customized code for\neach path. As a result, all paths can adaptively learn how to share weights in\nthe $K$-shot supernets and acquire corresponding weights for better evaluation.\n$K$-shot supernets and simplex-net can be iteratively trained, and we further\nextend the search to the channel dimension. Extensive experiments on benchmark\ndatasets validate that K-shot NAS significantly improves the evaluation\naccuracy of paths and thus brings in impressive performance improvements.\n"}
+{"abstract": "  We derive the main classical gravitational tests for a recently found vacuum\nsolution with spin and dilation charges in the framework of Metric-Affine gauge\ntheory of gravity. Using the results of the perihelion precession of the star\nS2 by the GRAVITY collaboration and the gravitational redshift of Sirius B\nwhite dwarf we constrain the corrections provided by the torsion and\nnonmetricity fields for these effects.\n"}
+{"abstract": "  We present the results of photometry, linear spectropolarimetry, and imaging\ncircular polarimetry ofcomet C/2009 P1 (Garradd) performed at the 6-m telescope\nBTA of the Special Astrophysical Observatory(Russia) equipped by the multi-mode\nfocal reducer SCORPIO-2. The comet was observed at two epochspost-perihelion:\non February 2-14, 2012 at r=1.6 au and {\\alpha}=36 {\\deg}; and on April 14-21,\n2012 at r=2.2 au and {\\alpha}=27 deg. The spatial maps of the relative\nintensity and circular polarization as well as the spectral distribution of\nlinear polarization are presented. There were two features (dust and gas tails)\norientedin the solar and antisolar directions on February 2 and 14 that allowed\nus to determine rotation periodof the nucleus as 11.1 hours. We detected\nemissions of C2 , C3 , CN, CH, NH2 molecules as well as CO+ and H2O+ ions,\nalong with a high level of the dust continuum. On February 2, the degree of\nlinear polarization in the continuum, within the wavelength range of 0.67-0.68\n{\\mu}m, was about 5% in the near-nucleus region up to near 6000 km and\ndecreased to about 3% at near 40,000 km. The left-handed (negative) circular\npolarization at the level approximately from -0.06% to -0.4% was observed at\nthe distances up to 3*10^4 km from the nucleus on February 14 and April 21,\nrespectively.\n"}
+{"abstract": "  Prospects of the Cherenkov Telescope Array (CTA) for the study of very high\nenergy gamma-ray emission from nearby star-forming galaxies are investigated.\nIn the previous work, we constructed a model to calculate luminosity and energy\nspectrum of pion-decay gamma-ray emission produced by cosmic-ray interaction\nwith the interstellar medium (ISM), from four physical quantities of galaxies\n[star formation rate (SFR), gas mass, stellar mass, and effective radius]. The\nmodel is in good agreement with the observed GeV--TeV emission of several\nnearby galaxies. Applying this model to nearby galaxies that are not yet\ndetected in TeV (mainly from the KINGFISH catalog), their hadronic gamma-ray\nluminosities and spectra are predicted. We identify galaxies of the highest\nchance of detection by CTA, including NGC 5236, M33, NGC 6946, and IC 342.\nConcerning gamma-ray spectra, NGC 1482 is particularly interesting because our\nmodel predicts that this galaxy is close to the calorimetric limit and its\ngamma-ray spectral index in GeV--TeV is close to that of cosmic-ray protons\ninjected into ISM. Therefore this galaxy may be detectable by CTA even though\nits GeV flux is below the {\\it Fermi} Large Area Telescope sensitivity limit.\nIn the TeV regime, most galaxies are not in the calorimetric limit, and the\npredicted TeV flux is lower than that assuming a simple relation between the\nTeV luminosity and SFR of M82 and NGC 253, typically by a factor of 15. This\nmeans that a more sophisticated model beyond the calorimetric limit assumption\nis necessary to study TeV emission from star-forming galaxies.\n"}
+{"abstract": "  Motivated by the needs from an airline crew scheduling application, we\nintroduce structured convolutional kernel networks (Struct-CKN), which combine\nCKNs from Mairal et al. (2014) in a structured prediction framework that\nsupports constraints on the outputs. CKNs are a particular kind of\nconvolutional neural networks that approximate a kernel feature map on training\ndata, thus combining properties of deep learning with the non-parametric\nflexibility of kernel methods. Extending CKNs to structured outputs allows us\nto obtain useful initial solutions on a flight-connection dataset that can be\nfurther refined by an airline crew scheduling solver. More specifically, we use\na flight-based network modeled as a general conditional random field capable of\nincorporating local constraints in the learning process. Our experiments\ndemonstrate that this approach yields significant improvements for the\nlarge-scale crew pairing problem (50,000 flights per month) over standard\napproaches, reducing the solution cost by 17% (a gain of millions of dollars)\nand the cost of global constraints by 97%.\n"}
+{"abstract": "  We study a general convergence theory for the numerical solutions of\ncompressible viscous and electrically conducting fluids with a focus on\nnumerical schemes that preserve the divergence free property of magnetic field\nexactly. Our strategy utilizes the recent concepts of dissipative weak\nsolutions and consistent approximations. First, we show the dissipative\nweak--strong uniqueness principle, meaning a dissipative weak solution\ncoincides with a classical solution as long as they emanate from the same\ninitial data. Next, we show the convergence of consistent approximation towards\nthe dissipative weak solution and thus the classical solution. Upon\ninterpreting the consistent approximation as the stability and consistency of\nsuitable numerical solutions we have established a generalized Lax equivalence\ntheory: convergence $\\Longleftrightarrow$ stability and consistency. Further,\nto illustrate the application of this theory, we propose two novel mixed finite\nvolume-finite element methods with exact divergence-free magnetic field.\nFinally, by showing solutions of these two schemes are consistent\napproximations, we conclude their convergence towards the dissipative weak\nsolution and the classical solution.\n"}
+{"abstract": "  We study the duality between JT gravity and the double-scaled matrix model\nincluding their respective deformations. For these deformed theories we relate\nthe thermal partition function to the generating function of topological\ngravity correlators that are determined as solutions to the KdV hierarchy. We\nspecialise to those deformations of JT gravity coupled to a gas of defects,\nwhich conforms with known results in the literature. We express the\n(asymptotic) thermal partition functions in a low temperature limit, in which\nnon-perturbative corrections are suppressed and the thermal partition function\nbecomes exact. In this limit we demonstrate that there is a Hawking-Page phase\ntransition between connected and disconnected surfaces for this instance of JT\ngravity with a transition temperature affected by the presence of defects.\nFurthermore, the calculated spectral form factors show the qualitative\nbehaviour expected for a Hawking-Page phase transition. The considered\ndeformations cause the ramp to be shifted along the real time axis. Finally, we\ncomment on recent results related to conical Weil-Petersson volumes and the\nanalytic continuation to two-dimensional de Sitter space.\n"}
+{"abstract": "  In this paper, we extend the article that Minkowski problem in Gaussian\nprobability space of Huang et al. to $L_p$-Gaussian Minkowski problem, and\nobtain the existence and uniqueness of $o$-symmetry weak solution in case of\n$p\\geq1$.\n"}
+{"abstract": "  Clustering data into meaningful subsets is a major task in scientific data\nanalysis. To date, various strategies ranging from model-based approaches to\ndata-driven schemes, have been devised for efficient and accurate clustering.\nOne important class of clustering methods that is of a particular interest is\nthe class of exemplar-based approaches. This interest primarily stems from the\namount of compressed information encoded in these exemplars that effectively\nreflect the major characteristics of the respective clusters. Affinity\npropagation (AP) has proven to be a powerful exemplar-based approach that\nrefines the set of optimal exemplars by iterative pairwise message updates.\nHowever, a critical limitation is its inability to capitalize on known\nnetworked relations between data points often available for various scientific\ndatasets. To mitigate this shortcoming, we propose geometric-AP, a novel\nclustering algorithm that effectively extends AP to take advantage of the\nnetwork topology. Geometric-AP obeys network constraints and uses max-sum\nbelief propagation to leverage the available network topology for generating\nsmooth clusters over the network. Extensive performance assessment reveals a\nsignificant enhancement in the quality of the clustering results when compared\nto benchmark clustering schemes. Especially, we demonstrate that geometric-AP\nperforms extremely well even in cases where the original AP fails drastically.\n"}
+{"abstract": "  The effectiveness of fingerprint-based authentication systems on good quality\nfingerprints is established long back. However, the performance of standard\nfingerprint matching systems on noisy and poor quality fingerprints is far from\nsatisfactory. Towards this, we propose a data uncertainty-based framework which\nenables the state-of-the-art fingerprint preprocessing models to quantify noise\npresent in the input image and identify fingerprint regions with background\nnoise and poor ridge clarity. Quantification of noise helps the model two\nfolds: firstly, it makes the objective function adaptive to the noise in a\nparticular input fingerprint and consequently, helps to achieve robust\nperformance on noisy and distorted fingerprint regions. Secondly, it provides a\nnoise variance map which indicates noisy pixels in the input fingerprint image.\nThe predicted noise variance map enables the end-users to understand erroneous\npredictions due to noise present in the input image. Extensive experimental\nevaluation on 13 publicly available fingerprint databases, across different\narchitectural choices and two fingerprint processing tasks demonstrate\neffectiveness of the proposed framework.\n"}
+{"abstract": "  We encounter variables with little variation often in educational data mining\n(EDM) due to the demographics of higher education and the questions we ask.\nYet, little work has examined how to analyze such data. Therefore, we conducted\na simulation study using logistic regression, penalized regression, and random\nforest. We systematically varied the fraction of positive outcomes, feature\nimbalances, and odds ratios. We find the algorithms treat features with the\nsame odds ratios differently based on the features' imbalance and the outcome\nimbalance. While none of the algorithms fully solved how to handle imbalanced\ndata, penalized approaches such as Firth and Log-F reduced the difference\nbetween the built-in odds ratio and value determined by the algorithm. Our\nresults suggest that EDM studies might contain false negatives when determining\nwhich variables are related to an outcome. We then apply our findings to a\ngraduate admissions data set. We end by proposing recommendations that\nresearchers should consider penalized regression for data sets on the order of\nhundreds of cases and should include more context about their data in\npublications such as the outcome and feature imbalances.\n"}
+{"abstract": "  The COVID-19 pandemic started in China in December 2019 and quickly spread to\nseveral countries. The consequences of this pandemic are incalculable, causing\nthe death of millions of people and damaging the global economy. To achieve\nlarge-scale control of this pandemic, fast tools for detection and treatment of\npatients are needed. Thus, the demand for alternative tools for the diagnosis\nof COVID-19 has increased dramatically since accurated and automated tools are\nnot available. In this paper we present the ongoing work on a system for\nCOVID-19 detection using ultrasound imaging and using Deep Learning techniques.\nFurthermore, such a system is implemented on a Raspberry Pi to make it portable\nand easy to use in remote regions without an Internet connection.\n"}
+{"abstract": "  We prove that an inclusion-exclusion inspired expression of Schubert\npolynomials of permutations that avoid the patterns 1432 and 1423 is\nnonnegative. Our theorem implies a partial affirmative answer to a recent\nconjecture of Yibo Gao about principal specializations of Schubert polynomials.\nWe propose a general framework for finding inclusion-exclusion inspired\nexpression of Schubert polynomials of all permutations.\n"}
+{"abstract": "  It is shown that the slopes of the superhorizon hypermagnetic spectra\nproduced by the variation of the gauge couplings are practically unaffected by\nthe relative strength of the parity-breaking terms. A new method is proposed\nfor the estimate of the gauge power spectra in the presence of pseudoscalar\ninteractions during inflation. To corroborate the general results, various\nconcrete examples are explicitly analyzed. Since the large-scale gauge spectra\nalso determine the late-time magnetic fields it turns out that the pseudoscalar\ncontributions have little impact on the magnetogenesis requirement. Conversely\nthe parity-breaking terms crucially affect the gyrotropic spectra that may\nseed, in certain models, the baryon asymmetry of the Universe. In the most\ninteresting regions of the parameter space the modes reentering prior to\nsymmetry breaking lead to a sufficiently large baryon asymmetry while the\nmagnetic power spectra associated with the modes reentering after symmetry\nbreaking may even be of the order of a few hundredths of a nG over typical\nlength scales comparable with the Mpc prior to the collapse of the protogalaxy.\nFrom the viewpoint of the effective field theory description of magnetogenesis\nscenarios these considerations hold generically for the whole class of\ninflationary models where the inflaton is not constrained by any underlying\nsymmetry.\n"}
+{"abstract": "  We study the representation theory of non-admissible simple affine vertex\nalgebra $L_{-5/2} (sl(4))$. We determine an explicit formula for the singular\nvector of conformal weight four in the universal affine vertex algebra\n$V^{-5/2} (sl(4))$, and show that it generates the maximal ideal in $V^{-5/2}\n(sl(4))$. We classify irreducible $L_{-5/2} (sl(4))$--modules in the category\n${\\mathcal O}$, and determine the fusion rules between irreducible modules in\nthe category of ordinary modules $KL_{-5/2}$. It turns out that this fusion\nalgebra is isomorphic to the fusion algebra of $KL_{-1}$. We also prove that\n$KL_{-5/2}$ is a semi-simple, rigid braided tensor category.\n  In our proofs we use the notion of collapsing level for the affine\n$\\mathcal{W}$--algebra, and the properties of conformal embedding $gl(4)\n\\hookrightarrow sl(5)$ at level $k=-5/2$ from arXiv:1509.06512. We show that\n$k=-5/2$ is a collapsing level with respect to the subregular nilpotent element\n$f_{subreg}$, meaning that the simple quotient of the affine\n$\\mathcal{W}$--algebra $W^{-5/2}(sl(4), f_{subreg})$ is isomorphic to the\nHeisenberg vertex algebra $M_J(1)$. We prove certain results on vanishing and\nnon-vanishing of cohomology for the quantum Hamiltonian reduction functor\n$H_{f_{subreg}}$. It turns out that the properties of $H_{f_{subreg}}$ are more\nsubtle than in the case of minimal reducition.\n"}
+{"abstract": "  Over the last years, the number of cyber-attacks on industrial control\nsystems has been steadily increasing. Among several factors, proper software\ndevelopment plays a vital role in keeping these systems secure. To achieve\nsecure software, developers need to be aware of secure coding guidelines and\nsecure coding best practices. This work presents a platform geared towards\nsoftware developers in the industry that aims to increase awareness of secure\nsoftware development. The authors also introduce an interactive game component,\na virtual coach, which implements a simple artificial intelligence engine based\non the laddering technique for interviews. Through a survey, a preliminary\nevaluation of the implemented artifact with real-world players (from academia\nand industry) shows a positive acceptance of the developed platform.\nFurthermore, the players agree that the platform is adequate for training their\nsecure coding skills. The impact of our work is to introduce a new automatic\nchallenge evaluation method together with a virtual coach to improve existing\ncybersecurity awareness training programs. These training workshops can be\neasily held remotely or off-line.\n"}
+{"abstract": "  The long wavelength moir\\'e superlattices in twisted 2D structures have\nemerged as a highly tunable platform for strongly correlated electron physics.\nWe study the moir\\'e bands in twisted transition metal dichalcogenide\nhomobilayers, focusing on WSe$_2$, at small twist angles using a combination of\nfirst principles density functional theory, continuum modeling, and\nHartree-Fock approximation. We reveal the rich physics at small twist angles\n$\\theta<4^\\circ$, and identify a particular magic angle at which the top\nvalence moir\\'e band achieves almost perfect flatness. In the vicinity of this\nmagic angle, we predict the realization of a generalized Kane-Mele model with a\ntopological flat band, interaction-driven Haldane insulator, and Mott\ninsulators at the filling of one hole per moir\\'e unit cell. The combination of\nflat dispersion and uniformity of Berry curvature near the magic angle holds\npromise for realizing fractional quantum anomalous Hall effect at fractional\nfilling. We also identify twist angles favorable for quantum spin Hall\ninsulators and interaction-induced quantum anomalous Hall insulators at other\ninteger fillings.\n"}
+{"abstract": "  Due to the rapid emergence of short videos and the requirement for content\nunderstanding and creation, the video captioning task has received increasing\nattention in recent years. In this paper, we convert traditional video\ncaptioning task into a new paradigm, \\ie, Open-book Video Captioning, which\ngenerates natural language under the prompts of video-content-relevant\nsentences, not limited to the video itself. To address the open-book video\ncaptioning problem, we propose a novel Retrieve-Copy-Generate network, where a\npluggable video-to-text retriever is constructed to retrieve sentences as hints\nfrom the training corpus effectively, and a copy-mechanism generator is\nintroduced to extract expressions from multi-retrieved sentences dynamically.\nThe two modules can be trained end-to-end or separately, which is flexible and\nextensible. Our framework coordinates the conventional retrieval-based methods\nwith orthodox encoder-decoder methods, which can not only draw on the diverse\nexpressions in the retrieved sentences but also generate natural and accurate\ncontent of the video. Extensive experiments on several benchmark datasets show\nthat our proposed approach surpasses the state-of-the-art performance,\nindicating the effectiveness and promising of the proposed paradigm in the task\nof video captioning.\n"}
+{"abstract": "  The $\\phi^4$ double-well theory admits a kink solution, whose rich\nphenomenology is strongly affected by the existence of a single bound\nexcitation called the shape mode. We find that the leading quantum correction\nto the energy needed to excite the shape mode is $-0.115567\\lambda/m$ in terms\nof the coupling $\\lambda/4$ and the meson mass $m$ evaluated at the minimum of\nthe potential. On the other hand, the correction to the continuum threshold is\n$-0.433\\lambda/m$. A naive extrapolation to finite coupling then suggests that\nthe shape mode melts into the continuum at the modest coupling of\n$\\lambda/4\\sim 0.106 m^2$, where the $\\mathbb{Z}_2$ symmetry is still broken.\n"}
+{"abstract": "  Most of the existing formation algorithms for multiagent systems are fully\nlabel-specified, i.e., the desired position for each agent in the formation is\nuniquely determined by its label, which would inevitably make the formation\nalgorithms vulnerable to agent failures. To address this issue, in this paper,\nwe propose a dynamic leader-follower approach to solving the line marching\nproblem for a swarm of planar kinematic robots. In contrast to the existing\nresults, the desired positions for the robots in the line are not fully\nlabel-specified, but determined in a dynamic way according to the current state\nof the robot swarm. By constantly forming a chain of leader-follower pairs,\nexact formation can be achieved by pairwise leader-following tracking. Since\nthe order of the chain of leader-follower pairs is constantly updated, the\nproposed algorithm shows strong robustness against robot failures.\nComprehensive numerical results are provided to evaluate the performance of the\nproposed algorithm.\n"}
+{"abstract": "  A central theme in condensed matter physics is to create and understand the\nexotic states of matter by incorporating magnetism into topological materials.\nOne prime example is the quantum anomalous Hall (QAH) state. Recently, MnBi2Te4\nhas been demonstrated to be an intrinsic magnetic topological insulator and the\nQAH effect was observed in exfoliated MnBi2Te4 flakes. Here, we used molecular\nbeam epitaxy (MBE) to grow MnBi2Te4 films with thickness down to 1 septuple\nlayer (SL) and performed thickness-dependent transport measurements. We\nobserved a non-square hysteresis loop in the antiferromagnetic state for films\nwith thickness greater than 2 SL. The hysteresis loop can be separated into two\nAH components. Through careful analysis, we demonstrated that one AH component\nwith the larger coercive field is from the dominant MnBi2Te4 phase, while the\nother AH component with the smaller coercive field is from the minor Mn-doped\nBi2Te3 phase in the samples. The extracted AH component of the MnBi2Te4 phase\nshows a clear even-odd layer-dependent behavior, a signature of\nantiferromagnetic thin films. Our studies reveal insights on how to optimize\nthe MBE growth conditions to improve the quality of MnBi2Te4 films, in which\nthe QAH and other exotic states are predicted.\n"}
+{"abstract": "  We present the KMOS Galaxy Evolution Survey (KGES), a $K$-band Multi-Object\nSpectrograph (KMOS) study of the H$\\alpha$ and [NII] emission from 288 $K$\nband-selected galaxies at $1.2 \\lesssim z \\lesssim 1.8$, with stellar masses in\nthe range $\\log_{10}(M_{*}/\\rm{M}_{\\odot})\\approx$9-11.5. In this paper, we\ndescribe the survey design, present the sample, and discuss the key properties\nof the KGES galaxies. We combine KGES with appropriately matched samples at\nlower redshifts from the KMOS Redshift One Spectroscopic Survey (KROSS) and the\nSAMI Galaxy Survey. Accounting for the effects of sample selection, data\nquality, and analysis techniques between surveys, we examine the kinematic\ncharacteristics and angular momentum content of star-forming galaxies at\n$z\\approx1.5$, $\\approx1$ and $\\approx0$. We find that stellar mass, rather\nthan redshift, most strongly correlates with the disc fraction amongst\nstar-forming galaxies at $z \\lesssim 1.5$, observing only a modest increase in\nthe prevalence of discs between $z\\approx1.5$ and $z\\approx0.04$ at fixed\nstellar mass. Furthermore, typical star-forming galaxies follow the same median\nrelation between specific angular momentum and stellar mass, regardless of\ntheir redshift, with the normalisation of the relation depending more strongly\non how disc-like a galaxy's kinematics are. This suggests that massive\nstar-forming discs form in a very similar manner across the $\\approx$ 10 Gyr\nencompassed by our study and that the inferred link between the angular\nmomentum of galaxies and their haloes does not change significantly across the\nstellar mass and redshift ranges probed in this work.\n"}
+{"abstract": "  A variety of wireless channel estimation methods, e.g., MUSIC and ESPRIT,\nrely on prior knowledge of the model order. Therefore, it is important to\ncorrectly estimate the number of multipath components (MPCs) which compose such\nchannels. However, environments with many scatterers may generate MPCs which\nare closely spaced. This clustering of MPCs in addition to noise makes the\nmodel order selection task difficult in practice to currently known algorithms.\nIn this paper, we exploit the multidimensional characteristics of MIMO\northogonal frequency division multiplexing (OFDM) systems and propose a machine\nlearning (ML) method capable of determining the number of MPCs with a higher\naccuracy than state of the art methods in almost coherent scenarios. Moreover,\nour results show that our proposed ML method has an enhanced reliability.\n"}
+{"abstract": "  That neural networks may be pruned to high sparsities and retain high\naccuracy is well established. Recent research efforts focus on pruning\nimmediately after initialization so as to allow the computational savings\nafforded by sparsity to extend to the training process. In this work, we\nintroduce a new `DCT plus Sparse' layer architecture, which maintains\ninformation propagation and trainability even with as little as 0.01% trainable\nkernel parameters remaining. We show that standard training of networks built\nwith these layers, and pruned at initialization, achieves state-of-the-art\naccuracy for extreme sparsities on a variety of benchmark network architectures\nand datasets. Moreover, these results are achieved using only simple heuristics\nto determine the locations of the trainable parameters in the network, and thus\nwithout having to initially store or compute with the full, unpruned network,\nas is required by competing prune-at-initialization algorithms. Switching from\nstandard sparse layers to DCT plus Sparse layers does not increase the storage\nfootprint of a network and incurs only a small additional computational\noverhead.\n"}
+{"abstract": "  In this paper we investigate the one dimensional (1D) logarithmic diffusion\nequation with nonlinear Robin boundary conditions, namely, \\[ \\left\\{\n\\begin{array}{l} \\partial_t u=\\partial_{xx} \\log u\\quad \\mbox{in}\\quad\n\\left[-l,l\\right]\\times \\left(0, \\infty\\right)\\\\ \\displaystyle \\partial_x\nu\\left(\\pm l, t\\right)=\\pm 2\\gamma u^{p}\\left(\\pm l, t\\right), \\end{array}\n\\right. \\] where $\\gamma$ is a constant. Let $u_0>0$ be a smooth function\ndefined on $\\left[-l,l\\right]$, and which satisfies the compatibility condition\n$$\\partial_x \\log u_0\\left(\\pm l\\right)= \\pm 2\\gamma u_0^{p-1}\\left(\\pm\nl\\right).$$ We show that for $\\gamma > 0$, $p\\leq \\frac{3}{2}$ solutions to the\nlogarithmic diffusion equation above with initial data $u_0$ are global and\nblow-up in infinite time, and for $p>2$ there is finite time blow-up. Also, we\nshow that in the case of $\\gamma<0$, $p\\geq \\frac{3}{2}$, solutions to the\nlogarithmic diffusion equation with initial data $u_0$ are global and blow-down\nin infinite time, but if $p\\leq 1$ there is finite time blow-down. For some of\nthe cases mentioned above, and some particular families of examples, we provide\nblow-up and blow-down rates. Our approach is partly based on studying the Ricci\nflow on a cylinder endowed with a $\\mathbb{S}^1$-symmetric metric. Then, we\nbring our ideas full circle by proving a new long time existence result for the\nRicci flow on a cylinder without any symmetry assumption. Finally, we show a\nblow-down result for the logarithmic diffusion equation on a disc.\n"}
+{"abstract": "  We present a dynamical mean-field study of antiferromagnetic magnons in one-,\ntwo- and three-orbital Hubbard model of square and bcc cubic lattice at\nintermediate coupling strength. Weinvestigate the effect of anisotropy\nintroduced by an external magnetic field or single-ion anisotropy.For the\nlatter we tune continuously between the easy-axis and easy-plane models. We\nalso analyzea model with spin-orbit coupling in cubic site-symmetry setting.\nThe ordered states as well as themagnetic excitations are sensitive to even a\nsmall breaking ofSU(2)symmetry of the model andfollow the expectations of\nspin-wave theory as well as general symmetry considerations.\n"}
+{"abstract": "  With Rydberg dipole interactions, a mesoscopic atomic ensemble may behave\nlike a two-level single atom, resulting in the so-called picture of superatom.\nIt is in potential a strong candidate as a qubit in quantum information\nscience, especially for efficient coupling with single photons via collective\nenhancement that is essential for building quantum internet to connect remote\nquantum computers. Previously, preliminary studies have been carried out in\ndemonstrating basic concept of Rydberg superatom, a single-photon source, and\nentanglement with a single photon, etc. While a crucial element of single-shot\nqubit measurement is still missing. Here we realize the deterministic\nmeasurement of a superatom qubit via photon burst in a single shot. We make use\nof a low-finesse ring cavity to enhance the atom-photon interaction and obtain\nan in-fiber retrieval efficiency of 44%. Harnessing dipole interaction between\ntwo Rydberg levels, we may either create a sequence of multiple single photons\nor nothing, conditioned on the initial qubit state. We achieve a single-shot\nmeasurement fidelity of 93.2% in 4.8 us. Our work complements the experimental\ntoolbox of harnessing Rydberg superatom for quantum information applications.\n"}
+{"abstract": "  The Wiedemann-Franz law states that the charge conductance and the electronic\ncontribution to the heat conductance are proportional. This sets stringent\nconstraints on efficiency bounds for thermoelectric applications, which seek\nfor large charge conduction in response to a small heat flow. We present\nexperiments based on a quantum dot formed inside a semiconducting InAs nanowire\ntransistor, in which the heat conduction can be tuned significantly below the\nWiedemann-Franz prediction. Comparison with scattering theory shows that this\nis caused by quantum confinement and the resulting energy-selective transport\nproperties of the quantum dot. Our results open up perspectives for tailoring\nindependently the heat and electrical conduction properties in semiconductor\nnanostructures.\n"}
+{"abstract": "  Despite its simple manifestation, triboelectric charging is believed to be\nunthinkably complex because of the unresolvable interfacial interaction between\ntwo rubbing materials. Here, we for the first time reveal a simple physics of\ntriboelectric charging and the origin of triboelectric series based on the\ncausality of friction-originated thermoelectric charging effects at the\ninterface, characterized by the density (\\rho), specific heat (c), thermal\nconductivity (k), and Seebeck coefficient (S) of each material. We demonstrate\nthat energy dissipational heat at the interface induces temperature variations\nin the materials and thus develops electrostatic potentials that will initiate\nthermoelectric charging across the interface. We discovered that the trends and\nquantities of triboelectric charging for various polymers, metals,\nsemiconductors, and even lightning clouds are simply governed by the\ntriboelectric factor, S/\\sqrt{{\\rho}ck}. Our findings will bring significant\nnew opportunities for understanding and management of static electrification.\n"}
+{"abstract": "  In many problem settings, most notably in game playing, an agent receives a\npossibly delayed reward for its actions. Often, those rewards are handcrafted\nand not naturally given. Even simple terminal-only rewards, like winning equals\none and losing equals minus one, can not be seen as an unbiased statement,\nsince these values are chosen arbitrarily, and the behavior of the learner may\nchange with different encodings. It is hard to argue about good rewards and the\nperformance of an agent often depends on the design of the reward signal. In\nparticular, in domains where states by nature only have an ordinal ranking and\nwhere meaningful distance information between game state values is not\navailable, a numerical reward signal is necessarily biased. In this paper we\ntake a look at MCTS, a popular algorithm to solve MDPs, highlight a reoccurring\nproblem concerning its use of rewards, and show that an ordinal treatment of\nthe rewards overcomes this problem. Using the General Video Game Playing\nframework we show dominance of our newly proposed ordinal MCTS algorithm over\nother MCTS variants, based on a novel bandit algorithm that we also introduce\nand test versus UCB.\n"}
+{"abstract": "  The identification of defects in crystal structures is crucial for the\nanalysis of atomistic simulations. Many methods to characterize defects that\nare based on the classification of local atomic arrangement are available for\nsimple crystalline structures. However, there is currently no method to\nidentify both, the crystal structures and internal defects of topologically\nclose-packed (TCP) phases such as Laves phases. We propose a new method, Laves\nphase Crystal Analysis (LaCA), to characterize the atomic arrangement in Laves\ncrystals by interweaving existing structural analysis algorithms. The new\nmethod can identify the polytypes C14 and C15 of Laves phases, typical\ncrystallographic defects in these phases, and common deformation mechanisms\nsuch as synchroshear and non-basal dislocations. Defects in the C36 Laves phase\nare detectable through deviations from the periodic arrangement of the C14 and\nC15 structures that make up this phase. LaCA is robust and extendable to other\nTCP phases.\n"}
+{"abstract": "  It is established existence of ground and bound state solutions for Choquard\nequation considering concave-convex nonlinearities in the following form\n  $$\n  \\begin{array}{rcl}\n  -\\Delta u +V(x) u &=& (I_\\alpha* |u|^p)|u|^{p-2}u+ \\lambda |u|^{q-2}u, \\, u\n\\in H^1(\\mathbb{R}^{N}),\n  \\end{array}\n  $$ where $\\lambda > 0, N \\geq 3, \\alpha \\in (0, N)$. The potential $V$ is a\ncontinuous function and $I_\\alpha$ denotes the standard Riesz potential. Assume\nalso that $1 < q < 2,~2_{\\alpha} < p < 2^*_\\alpha$ where\n$2_\\alpha=(N+\\alpha)/N$, $2_\\alpha=(N+\\alpha)/(N-2)$. Our main contribution is\nto consider a specific condition on the parameter $\\lambda > 0$ taking into\naccount the nonlinear Rayleigh quotient. More precisely, there exists\n$\\lambda_n > 0$ such that our main problem admits at least two positive\nsolutions for each $\\lambda \\in (0, \\lambda_n]$. In order to do that we combine\nNehari method with a fine analysis on the nonlinear Rayleigh quotient. The\nparameter $\\lambda_n > 0$ is optimal in some sense which allow us to apply the\nNehari method.\n"}
+{"abstract": "  We investigate thin and thick stellar disc formation in Milky-Way-mass\ngalaxies using twelve FIRE-2 cosmological zoom-in simulations. All simulated\ngalaxies experience an early period of bursty star formation that transitions\nto a late-time steady phase of near-constant star formation. Stars formed\nduring the late-time steady phase have more circular orbits and thin-disc-like\nmorphology at $z=0$, whilst stars born during the bursty phase have more radial\norbits and thick-disc structure. The median age of thick-disc stars at $z=0$\ncorrelates strongly with this transition time. We also find that galaxies with\nan earlier transition from bursty to steady star formation have a higher\nthin-disc fractions at $z=0$. Three of our systems have minor mergers with\nLMC-size satellites during the thin-disc phase. These mergers trigger short\nstarbursts but do not destroy the thin disc nor alter broad trends between the\nstar formation transition time and thin/thick disc properties. If our\nsimulations are representative of the Universe, then stellar archaeological\nstudies of the Milky Way (or M31) provide a window into past star-formation\nmodes in the Galaxy. Current age estimates of the Galactic thick disc would\nsuggest that the Milky Way transitioned from bursty to steady phase $\\sim$6.5\nGyr ago; prior to that time the Milky Way likely lacked a recognisable thin\ndisc.\n"}
+{"abstract": "  Detection and localization of actions in videos is an important problem in\npractice. A traffic analyst might be interested in studying the patterns in\nwhich vehicles move at a given intersection. State-of-the-art video analytics\nsystems are unable to efficiently and effectively answer such action queries.\nThe reasons are threefold. First, action detection and localization tasks\nrequire computationally expensive deep neural networks. Second, actions are\noften rare events. Third, actions are spread across a sequence of frames. It is\nimportant to take the entire sequence of frames into context for effectively\nanswering the query. It is critical to quickly skim through the irrelevant\nparts of the video to answer the action query efficiently.\n  In this paper, we present Zeus, a video analytics system tailored for\nanswering action queries. We propose a novel technique for efficiently\nanswering these queries using a deep reinforcement learning agent. Zeus trains\nan agent that learns to adaptively modify the input video segments to an action\nclassification network. The agent alters the input segments along three\ndimensions -- sampling rate, segment length, and resolution. Besides\nefficiency, Zeus is capable of answering the query at a user-specified target\naccuracy using a query optimizer that trains the agent based on an\naccuracy-aware reward function. Our evaluation of Zeus on a novel action\nlocalization dataset shows that it outperforms the state-of-the-art frame- and\nwindow-based techniques by up to 1.4x and 3x, respectively. Furthermore, unlike\nthe frame-based technique, it satisfies the user-specified target accuracy\nacross all the queries, at up to 2x higher accuracy, than frame-based methods.\n"}
+{"abstract": "  The impact of global warming and the imperative to limit climate change have\nstimulated the need to develop new solutions based on renewable energy sources.\nOne of the emerging trends in this endeavor are the Electric Vehicles (EVs),\nwhich use electricity instead of traditional fossil fuels as a power source,\nrelying on the Vehicle-to-Grid (V2G) paradigm. The novelty of such a paradigm\nrequires careful analysis to avoid malicious attempts. An attacker can exploit\nseveral surfaces, such as the remote connection between the Distribution Grid\nand Charging Supply or the authentication system between the charging Supply\nEquipment and the Electric Vehicles. However, V2G architecture's high cost and\ncomplexity in implementation can restrain this field's research capability. In\nthis paper, we approach this limitation by proposing MiniV2G, an open-source\nemulator to simulate Electric Vehicle Charging (EVC) built on top of Mininet\nand RiseV2G. MiniV2G is particularly suitable for security researchers to study\nand test real V2G charging scenarios. MiniV2G can reproduce with high fidelity\na V2G architecture to easily simulate an EV charging process. Finally, we\npresent a MiniV2G application and show how MiniV2G can be used to study V2G\ncommunication and develop attacks and countermeasures that can be applied to\nreal systems. Since we believe our tool can be of great help for research in\nthis field, we also made it freely available.\n"}
+{"abstract": "  This note explores state space search to find efficient instruction sequences\nthat perform particular data manipulations. Once found, the instruction\nsequences are hard-wired in the code generator that needs these data\nmanipulations. Since state space is only searched while developing the\ncompiler, search time is not at a premium, which allows exhaustively searching\nfor the best possible instruction sequences.\n"}
+{"abstract": "  As neural networks become more popular, the need for accompanying uncertainty\nestimates increases. The current testing methodology focusses on how good the\npredictive uncertainty estimates explain the differences between predictions\nand observations in a previously unseen test set. Intuitively this is a logical\napproach. The current setup of benchmark data sets also allows easy comparison\nbetween the different methods. We demonstrate, however, through both\ntheoretical arguments and simulations that this way of evaluating the quality\nof uncertainty estimates has serious flaws. Firstly, it cannot disentangle the\naleatoric from the epistemic uncertainty. Secondly, the current methodology\nconsiders the uncertainty averaged over all test samples, implicitly averaging\nout overconfident and underconfident predictions. When checking if the correct\nfraction of test points falls inside prediction intervals, a good score on\naverage gives no guarantee that the intervals are sensible for individual\npoints. We demonstrate through practical examples that these effects can result\nin favoring a method, based on the predictive uncertainty, that has undesirable\nbehaviour of the confidence intervals. Finally, we propose a simulation-based\ntesting approach that addresses these problems while still allowing easy\ncomparison between different methods.\n"}
+{"abstract": "  Acoustic focusing plays a pivotal role in a wide variety of applications\nranging from medical science to nondestructive testing. Previous works have\nshown that acoustic metagratings can overcome the inherent efficiency\nlimitations of gradient metasurfaces in beam steering. In this work, we propose\na new design principle for acoustic metalenses, based on metagratings, to\nachieve efficient ultrasonic focusing. We achieve beam focusing by locally\ncontrolling the excitation of a single diffraction order with the use of\nadiabatically varying metagratings over the lens aperture. A set of\nmetagratings is optimized by a semi-analytical approach using a genetic\nalgorithm, enabling efficient anomalous reflection for a wide range of\nreflection angles. Numerical results reveal that our metalens can effectively\nfocus impinging ultrasonic waves to a focal point of FWHM = 0.364{\\lambda}. The\nfocusing performance of the metalens is demonstrated experimentally, validating\nour proposed approach.\n"}
+{"abstract": "  Internet and social media offer firms novel ways of managing their marketing\nstrategy and gain competitive advantage. The groups of users expressing\nthemselves on the Internet about a particular topic, product, or brand are\nfrequently called a virtual tribe or E-tribe. However, there are no automatic\ntools for identifying and studying the characteristics of these virtual tribes.\nTowards this aim, this paper presents Tribefinder, a system to reveal Twitter\nusers' tribal affiliations, by analyzing their tweets and language use. To show\nthe potential of this instrument, we provide an example considering three\nspecific tribal macro-categories: alternative realities, lifestyle, and\nrecreation. In addition, we discuss the different characteristics of each\nidentified tribe, in terms of use of language and social interaction metrics.\nTribefinder illustrates the importance of adopting a new lens for studying\nvirtual tribes, which is crucial for firms to properly design their marketing\nstrategy, and for scholars to extend prior marketing research.\n"}
+{"abstract": "  We study potential security vulnerabilities of a single-photon detector based\non superconducting transition-edge sensor. In a simple experiment, we show that\nan adversary could fake a photon number result at a certain wavelength by\nsending a larger number of photons at a longer wavelength. In another\nexperiment, we show that the detector can be blinded by bright continuous-wave\nlight and then, a controlled response simulating single-photon detection can be\nproduced by applying a bright light pulse. We model an intercept-and-resend\nattack on a quantum key distribution system that exploits the latter\nvulnerability and, under certain assumptions, succeeds to steal the key.\n"}
+{"abstract": "  Ultrafast laser pulses spatially shaped as Bessel beams in dielectrics create\nhigh aspect ratio plasma channels whose relaxation can lead to the formation of\nnanochannels. We report a strong enhancement of the nanochannel drilling\nefficiency with illumination by double pulses separated by a delay between 10\nto 500 ps. This enables the formation of nanochannels with diameters down to\n100 nm. Experimental absorption measurements demonstrate that the increase of\ndrilling efficiency is due to an increase of the confinement of the energy\ndeposition. Nanochannel formation corresponds to a drastic change in absorption\nof the second pulse demonstrating the occurrence of a phase change produced by\nthe first pulse. This creates a highly absorbing long-living state. Our\nmeasurements show that it is compatible with the semi-metallization of warm\ndense glass which takes place within a timescale of <10 ps after the first\nlaser pulse illumination.\n"}
+{"abstract": "  We introduce a class of Sparse, Physics-based, and partially Interpretable\nNeural Networks (SPINN) for solving ordinary and partial differential equations\n(PDEs). By reinterpreting a traditional meshless representation of solutions of\nPDEs we develop a class of sparse neural network architectures that are\npartially interpretable. The SPINN model we propose here serves as a seamless\nbridge between two extreme modeling tools for PDEs, namely dense neural network\nbased methods like Physics Informed Neural Networks (PINNs) and traditional\nmesh-free numerical methods, thereby providing a novel means to develop a new\nclass of hybrid algorithms that build on the best of both these viewpoints. A\nunique feature of the SPINN model that distinguishes it from other neural\nnetwork based approximations proposed earlier is that it is (i) interpretable,\nin a particular sense made precise in the work, and (ii) sparse in the sense\nthat it has much fewer connections than typical dense neural networks used for\nPDEs. Further, the SPINN algorithm implicitly encodes mesh adaptivity and is\nable to handle discontinuities in the solutions. In addition, we demonstrate\nthat Fourier series representations can also be expressed as a special class of\nSPINN and propose generalized neural network analogues of Fourier\nrepresentations. We illustrate the utility of the proposed method with a\nvariety of examples involving ordinary differential equations, elliptic,\nparabolic, hyperbolic and nonlinear partial differential equations, and an\nexample in fluid dynamics.\n"}
+{"abstract": "  Combinatorial Game Theory has also been called `additive game theory',\nwhenever the analysis involves sums of independent game components. Such {\\em\ndisjunctive sums} invoke comparison between games, which allows abstract values\nto be assigned to them. However, there are rulesets with {\\em entailing moves}\nthat break the alternating play axiom and/or restrict the other player's\noptions within the disjunctive sum components. These situations are exemplified\nin the literature by a ruleset such as {\\sc nimstring}, a normal play variation\nof the classical children's game {\\sc dots \\& boxes}, and {\\sc top~entails}, an\nelegant ruleset introduced in the classical work Winning Ways, by Berlekamp\nConway and Guy. Such rulesets fall outside the scope of the established normal\nplay theory. Here, we axiomatize normal play via two new terminating games,\n$\\infty$ (Left wins) and $\\overline\\infty$ (Right wins), and a more general\ntheory is achieved. We define {\\em affine impartial}, which extends classical\nimpartial games, and we analyze their algebra by extending the established\nSprague-Grundy theory, with an accompanying minimum excluded rule. Solutions of\n{\\sc nimstring} and {\\sc top~entails} are given to illustrate the theory.\n"}
+{"abstract": "  This short paper briefly presents our methodology details of automatic\nintracranial aneurysms segmentation from brain MR scans. We use ensembles of\nmultiple models trained from different loss functions. Our method ranked first\nplace in the ADAM challenge segmentation task. The code and trained models are\npublicly available at https://github.com/JunMa11/ADAM2020.\n"}
+{"abstract": "  Van der Waals heterostructures (vdWHs) allow the assembly of high-crystalline\ntwo-dimensional (2D) materials in order to explore dimensionality effects in\nstrongly correlated systems and the emergence of potential new physical\nscenarios. In this work, it is illustrated the feasibility to integrate 2D\nmaterials in-between 2D superconductors. Particularly, it is presented the\nfabrication and electrical characterization of vertical vdWHs based on\nair-unstable atomically-thin transition metal dichalcogenides formed by\nNbSe2/TaS2/NbSe2 stacks, with TaS2 being the insulator 1T-TaS2 or the metal\n2H-TaS2. Phase transitions as 1T-TaS2 charge density wave and NbSe2\nsuperconductivity are detected. An enhancement of the vdWH resistance due to\nAndreev reflections is observed below the superconducting transition\ntemperature of the NbSe2 flakes. Moreover, in the NbSe2 superconducting state,\nthe field and temperature dependence of the normalized conductance is analyzed\nwithin the Dynes' model and the overall behavior is consistent with the\nBardeen-Cooper-Schrieffer theory. This vdWH approach can be extended to other\n2D materials, such as 2D magnets or topological insulators, with the aim of\nexploring the new emergent properties that may arise from such combinations.\n"}
+{"abstract": "  In this work, we propose a tube-based model predictive control (MPC) scheme\nfor state and input constrained linear systems subject to dynamic uncertainties\ndescribed by integral quadratic constraints (IQCs). In particular, we extend\nthe framework of \\r{ho}-hard IQCs for exponential stability analysis to\nconsider external inputs. This allows us to show that the model error between\nthe true uncertain system and the nominal prediction model is bounded by an\nexponentially stable scalar system. In the proposed tube-based MPC scheme, the\nstate of this error bounding system is predicted along with the nominal model\nand used as a scaling parameter for the tube size. We prove that this MPC\nscheme achieves robust constraint satisfaction and input-to-state stability in\nthe presence of dynamic uncertainties and additive bounded disturbances. In a\nnumerical example, we demonstrate the flexibility of the proposed tube dynamics\nand the reduced conservatism of the IQC approach compared to other\ncharacterizations of dynamic uncertainties that are used in robust MPC.\n"}
+{"abstract": "  Semiquantitative group testing (SQGT) is a pooling method in which the test\noutcomes represent bounded intervals for the number of defectives.\nAlternatively, it may be viewed as an adder channel with quantized outputs.\nSQGT represents a natural choice for Covid-19 group testing as it allows for a\nstraightforward interpretation of the cycle threshold values produced by\npolymerase chain reactions (PCR). Prior work on SQGT did not address the need\nfor adaptive testing with a small number of rounds as required in practice. We\npropose conceptually simple methods for 2-round and nonadaptive SQGT that\nsignificantly improve upon existing schemes by using ideas on nonbinary\nmeasurement matrices based on expander graphs and list-disjunct matrices.\n"}
+{"abstract": "  In causal graphical models based on directed acyclic graphs (DAGs), directed\npaths represent causal pathways between the corresponding variables. The\nvariable at the beginning of such a path is referred to as an ancestor of the\nvariable at the end of the path. Ancestral relations between variables play an\nimportant role in causal modeling. In existing literature on structure\nlearning, these relations are usually deduced from learned structures and used\nfor orienting edges or formulating constraints of the space of possible DAGs.\nHowever, they are usually not posed as immediate target of inference. In this\nwork we investigate the graphical characterization of ancestral relations via\nCPDAGs and d-separation relations. We propose a framework that can learn\ndefinite non-ancestral relations without first learning the skeleton. This\nframe-work yields structural information that can be used in both score- and\nconstraint-based algorithms to learn causal DAGs more efficiently.\n"}
+{"abstract": "  In this article, the hydrodynamic characteristics of Autonomous Underwater\nVehicles (AUVs) are investigated and analyzed under the influence of rotating\nflow fields, that were generated in a recirculating water tank via a rotating\npropeller. Initially, experiments were carried out for the measurement of flow\nfield variables and Quantities of Interest across the AUV in the presence of\nthe rotating propeller while varying the rotational speed and the extent of\nrotational flow strength. The flow field across the AUV was measured using an\nAcoustic Doppler Velocimeter (ADV). These measured turbulent flow statistics\nwere used to validate the Reynolds Stress Model (RSM) based numerical\npredictions in a commercial CFD solver. After preliminary validation of the\nturbulent flow statistics with the numerical predictions, a series of numerical\nsimulations were performed to investigate the effect of the rotational flow\nfield of the propeller on the drag, skin friction and pressure coefficients of\nthe AUV. The operating speed and location of the propeller were also varied to\ncheck their affects on the hydrodynamic performance of AUV. The results\nprovided in this article will be useful for the design optimization of AUVs\ncruising in shallow water where the flow is highly rotational because of\nwave-current interactions. Additionally the results and analysis are relevant\nto study the design and operation of AUVs that have to operate in a group of\nunmanned underwater vehicles or near submarines and ships where the flow field\nis highly complex and such rotational effects are present.\n"}
+{"abstract": "  In this work, we propose iterative access point (AP) selection (APS), linear\nminimum mean-square error (MMSE) precoding and power allocation techniques for\nCell-Free Massive multiple-input multiple-output (MIMO) systems. We consider\nthe downlink channel with single-antenna users and multiple-antenna APs. We\nderive sum-rate expressions for the proposed iterative APS techniques followed\nby MMSE precoding and optimal, adaptive, and uniform power allocation schemes.\nSimulations show that the proposed approach outperforms existing conjugate\nbeamforming (CB) and zero-forcing (ZF) schemes and that performance remains\nexcellent with APS, in the presence of perfect and imperfect channel state\ninformation (CSI).\n"}
+{"abstract": "  The recent growth in multi-fidelity uncertainty quantification has given rise\nto a large set of variance reduction techniques that leverage information from\nmodel ensembles to provide variance reduction for estimates of the statistics\nof a high-fidelity model. In this paper we provide two contributions: (1) we\nutilize an ensemble estimator to account for uncertainties in the optimal\nweights of approximate control variate (ACV) approaches and derive lower bounds\non the number of samples required to guarantee variance reduction; and (2) we\nextend an existing multi-fidelity importance sampling (MFIS) scheme to leverage\ncontrol variates. As such we make significant progress towards both increasing\nthe practicality of approximate control variates$-$for instance, by accounting\nfor the effect of pilot samples$-$and using multi-fidelity approaches more\neffectively for estimating low-probability events. The numerical results\nindicate our hybrid MFIS-ACV estimator achieves up to 50% improvement in\nvariance reduction over the existing state-of-the-art MFIS estimator, which had\nalready shown outstanding convergence rate compared to the Monte Carlo method,\non several problems of computational mechanics.\n"}
+{"abstract": "  A general treatment of the spectral problem of quantum graphs and\ntight-binding models in finite Hilbert spaces is given. The direct spectral\nproblem and the inverse spectral problem are written in terms of simple\nalgebraic equations containing information on the topology of a quantum graph.\nThe inverse problem is shown to be combinatorial, and some low dimensional\nexamples are explicitly solved. For a {\\it window\\ }graph, a commutator and\nanticommutator algebra (superalgebra) is identified as the culprit behind\naccidental degeneracy in the form of triplets, where configurational symmetry\n{\\it alone\\ }fails to explain the result. For a M\\\"obius cycloacene graph, it\nis found that the accidental triplet cannot be explained with a superalgebra,\nbut that the graph can be built unambiguously from the spectrum using\ncombinatorial methods. These examples are compared with a more symmetric but\nless degenerate system, i.e. a {\\it car wheel\\ } graph which possesses neither\ntriplets, nor superalgebra.\n"}
+{"abstract": "  In E-commerce, a key challenge in text generation is to find a good trade-off\nbetween word diversity and accuracy (relevance) in order to make generated text\nappear more natural and human-like. In order to improve the relevance of\ngenerated results, conditional text generators were developed that use input\nkeywords or attributes to produce the corresponding text. Prior work, however,\ndo not finely control the diversity of automatically generated sentences. For\nexample, it does not control the order of keywords to put more relevant ones\nfirst. Moreover, it does not explicitly control the balance between diversity\nand accuracy. To remedy these problems, we propose a fine-grained controllable\ngenerative model, called~\\textit{Apex}, that uses an algorithm borrowed from\nautomatic control (namely, a variant of the \\textit{proportional, integral, and\nderivative (PID) controller}) to precisely manipulate the diversity/accuracy\ntrade-off of generated text. The algorithm is injected into a Conditional\nVariational Autoencoder (CVAE), allowing \\textit{Apex} to control both (i) the\norder of keywords in the generated sentences (conditioned on the input keywords\nand their order), and (ii) the trade-off between diversity and accuracy.\nEvaluation results on real-world datasets show that the proposed method\noutperforms existing generative models in terms of diversity and relevance.\nApex is currently deployed to generate production descriptions and item\nrecommendation reasons in Taobao owned by Alibaba, the largest E-commerce\nplatform in China. The A/B production test results show that our method\nimproves click-through rate (CTR) by 13.17\\% compared to the existing method\nfor production descriptions. For item recommendation reason, it is able to\nincrease CTR by 6.89\\% and 1.42\\% compared to user reviews and top-K item\nrecommendation without reviews, respectively.\n"}
+{"abstract": "  Most restless Markovian bandits problems in infinite horizon can be solved\nquasioptimally: the famous Whittle index policy is known to become\nasymptotically optimal exponentially fast as the number of arms grows, at least\nunder certain conditions (including having a so-called indexable problem). For\nrestless Markovian bandits problems in finite horizons no such optimal policy\nis known. In this paper, we define a new policy, based on a linear program\nrelaxation of the finite horizon problem (called the LP-filling policy), that\nis asymptotically optimal under no condition. Furthermore we show that for\nregular problems (defined in the paper) the LP-filling policy becomes an index\npolicy (called the LP-regular policy) and becomes optimal exponentially fast in\nthe number of arms. We also introduce the LP-update policy that significantly\nimproves the performance compared to the LP-filling policy for large time\nhorizons. We provide numerical studies that show the prevalence of our\nLP-policies over previous solutions.\n"}
+{"abstract": "  With the advent of deep optical-to-near-infrared extragalactic imaging on the\ndegree scale, samples of high-redshift sources are being selected that contain\nboth bright star-forming (SF) galaxies and faint active galactic nuclei (AGN).\nIn this study we investigate the transition between SF and AGN-dominated\nsystems at $z \\simeq 4$ in the rest-frame UV. We find a rapid transition to\nAGN-dominated sources bright-ward of $M_{\\rm UV} \\simeq -23.2$. The effect is\nobserved in the rest-frame UV morphology and size-luminosity relation, where\nextended clumpy systems become point-source dominated, and also in the\navailable spectra for the sample. These results allow us to derive the\nrest-frame UV luminosity function for the SF and AGN-dominated sub-samples. We\nfind the SF-dominated LF is best fit with a double-power law, with a lensed\nSchechter function being unable to explain the existence of extremely luminous\nSF galaxies at $M_{\\rm UV} \\simeq -23.5$. If we identify AGN-dominated sources\naccording to a point-source morphology criterion we recover the relatively flat\nfaint-end slope of the AGN LF determined in previous studies. If we instead\nseparate the LF according to the current spectroscopic AGN fraction, we find a\nsteeper faint-end slope of $\\alpha = -1.83 \\pm 0.11$. Using a simple model to\npredict the rest-frame AGN LF from the $z = 4 $ galaxy LF we find that the\nincreasing impact of host galaxy light on the measured morphology of faint AGN\ncan explain our observations.\n"}
+{"abstract": "  Ultrafinitism postulates that we can only compute on relatively short\nobjects, and numbers beyond certain value are not available. This approach\nwould also forbid many forms of infinitary reasoning and allow to remove\ncertain paradoxes stemming from enumeration theorems.\n  However, philosophers still disagree of whether such a finitist logic would\nbe consistent. We present preliminary work on a proof system based on\nCurry-Howard isomorphism. We also try to present some well-known theorems that\nstop being true in such systems, whereas opposite statements become provable.\n  This approach presents certain impossibility results as logical paradoxes\nstemming from a profligate use of transfinite reasoning.\n"}
+{"abstract": "  We examine the computational complexity of problems in which we are given\ngenerators for a partial bijection semigroup and asked to check properties of\nthe generated semigroup. We prove that the following problems are in AC$^0$:\n(1) enumerating left and right identities and (2) checking if the semigroup is\ncompletely regular. We prove that checking membership of a given idempotent is\na PSPACE-complete problem. We also describe a nondeterministic logspace\nalgorithm for checking if an inverse semigroup given by generators satisfies a\ngiven semigroup identity that may involve a unary inverse operation.\n"}
+{"abstract": "  Personalized conversation models (PCMs) generate responses according to\nspeaker preferences. Existing personalized conversation tasks typically require\nmodels to extract speaker preferences from user descriptions or their\nconversation histories, which are scarce for newcomers and inactive users. In\nthis paper, we propose a few-shot personalized conversation task with an\nauxiliary social network. The task requires models to generate personalized\nresponses for a speaker given a few conversations from the speaker and a social\nnetwork. Existing methods are mainly designed to incorporate descriptions or\nconversation histories. Those methods can hardly model speakers with so few\nconversations or connections between speakers. To better cater for newcomers\nwith few resources, we propose a personalized conversation model (PCM) that\nlearns to adapt to new speakers as well as enabling new speakers to learn from\nresource-rich speakers. Particularly, based on a meta-learning based PCM, we\npropose a task aggregator (TA) to collect other speakers' information from the\nsocial network. The TA provides prior knowledge of the new speaker in its\nmeta-learning. Experimental results show our methods outperform all baselines\nin appropriateness, diversity, and consistency with speakers.\n"}
+{"abstract": "  Important phenomena such as magnetostriction, magnetocaloric, and\nmagnetoelectric effects arise from, or could be enhanced by, the coupling of\nmagnetic and structural degrees of freedom. The coupling of spin and lattice\nalso influence transport and structural properties in magnetic materials in\nparticular around phase transitions. In this paper we propose a method for\nscreening materials for a strong magneto-structural coupling by assessing the\neffect of the local magnetic configuration on the atomic forces using density\nfunctional theory (DFT). We have employed the disordered local moment approach\nin a supercell formulation to probe different magnetic local configurations and\ntheir forces and performed a high-throughput search on binary and ternary\ncompounds available in the Crystallographic Open Database. We identify a list\nof materials with a strong spin-lattice coupling out of which several are\nalready known to display magneto-lattice coupling-phenomena like Fe3O4 and CrN.\nOthers, such as Mn2CrO4 and CaFe7O11 have been less studied and are yet to\nreveal their potentials in experiments and applications.\n"}
+{"abstract": "  We show a few basic results about moduli spaces of semistable modules over\nLie algebroids. The first result shows that such moduli spaces exist for\nrelative projective morphisms of noetherian schemes, removing some earlier\nconstraints. The second result proves general separatedness Langton type\ntheorem for such moduli spaces. More precisely, we prove S-completness of some\nmoduli stacks of semistable modules. In some special cases this result\nidentifies closed points of the moduli space of Gieseker semistable sheaves on\na projective scheme and of the Donaldson--Uhlenbeck compactification of the\nmoduli space of slope stable locally free sheaves on a smooth projective\nsurface. The last result generalizes properness of Hitchin's morphism and it\nshows properness of so called Hodge-Hitchin morphism defined in positive\ncharacteristic on the moduli space of Gieseker semistable integrable\nt-connections in terms of the p-curvature morphism. This last result was proven\nin the curve case by de Cataldo and Zhang using completely different methods.\n"}
+{"abstract": "  We discuss the energy loss due to gravitational radiation of binaries\ncomposed of exotic objects whose horizon boundary conditions are replaced with\nreflective ones. Our focus is on the extreme mass-ratio inspirals, in which the\ncentral heavier black hole is replaced with an exotic compact object. We show,\nin this case, a modulation of the energy loss rate depending on the evolving\norbital frequency occurs and leads to two different types of modifications to\nthe gravitational wave phase evolution; the oscillating part directly\ncorresponding to the modulation in the energy flux, and the non-oscillating\npart coming from the quadratic order in the modulation. This modification can\nbe sufficiently large to detect with future space-borne detectors.\n"}
+{"abstract": "  The metastable Helium (He*) lines near $10830~{\\rm A}$ are ideal probes of\natmospheric erosion--a common phenomenon of close-in exoplanet evolution. A\nhandful of exoplanet observations yielded well-resolved He* absorption features\nin transits, yet they were mostly analyzed with 1D isothermal models\nprescribing mass-loss rates. This work devises 3D hydrodynamics co-evolved with\nray-tracing radiative transfer and non-equilibrium thermochemsitry. Starting\nfrom the observed stellar/planetary properties with reasonable assumptions\nabout the host's high energy irradiation, we predict from first principle the\nmass loss rate, the temperature and ionization profiles, and 3D outflow\nkinematics. Our simulations well reproduce the observed He* line profiles and\nlight curves of WASP-69b. We further investigate the dependence of He*\nobservables on simulation conditions and host radiation. The key findings are:\n(1) Simulations reveal a photoevaporative outflow ($\\sim 0.5~M_{\\oplus}~{\\rm\nGyr}^{-1}$) for WASP-69b without a prominent comet-like tail, consistent with\nthe symmetric transit shape (Vissapragada et al. 2020). (2) 3D simulations are\nmandatory for hydrodynamic features, including Coriolis force, advection, and\nkinematic line broadening. (3) EUV ($>13.6~{\\rm eV}$) photons dominate\nphotoevaporative outflows and populate He* via recombination; FUV is also\ndetrimental by destroying He*; X-ray plays a secondary role. (4) K stars hit\nthe sweet spot of EUV/FUV balance for He* line observation, while G and M stars\nare also worthy targets. (5) Stellar flars create characteristic responses in\nthe He* line profiles.\n"}
+{"abstract": "  Machine learning decision systems are getting omnipresent in our lives. From\ndating apps to rating loan seekers, algorithms affect both our well-being and\nfuture. Typically, however, these systems are not infallible. Moreover, complex\npredictive models are really eager to learn social biases present in historical\ndata that can lead to increasing discrimination. If we want to create models\nresponsibly then we need tools for in-depth validation of models also from the\nperspective of potential discrimination. This article introduces an R package\nfairmodels that helps to validate fairness and eliminate bias in classification\nmodels in an easy and flexible fashion. The fairmodels package offers a\nmodel-agnostic approach to bias detection, visualization and mitigation. The\nimplemented set of functions and fairness metrics enables model fairness\nvalidation from different perspectives. The package includes a series of\nmethods for bias mitigation that aim to diminish the discrimination in the\nmodel. The package is designed not only to examine a single model, but also to\nfacilitate comparisons between multiple models.\n"}
+{"abstract": "  Recent works for attributed network clustering utilize graph convolution to\nobtain node embeddings and simultaneously perform clustering assignments on the\nembedding space. It is effective since graph convolution combines the\nstructural and attributive information for node embedding learning. However, a\nmajor limitation of such works is that the graph convolution only incorporates\nthe attribute information from the local neighborhood of nodes but fails to\nexploit the mutual affinities between nodes and attributes. In this regard, we\npropose a variational co-embedding learning model for attributed network\nclustering (VCLANC). VCLANC is composed of dual variational auto-encoders to\nsimultaneously embed nodes and attributes. Relying on this, the mutual affinity\ninformation between nodes and attributes could be reconstructed from the\nembedding space and served as extra self-supervised knowledge for\nrepresentation learning. At the same time, trainable Gaussian mixture model is\nused as priors to infer the node clustering assignments. To strengthen the\nperformance of the inferred clusters, we use a mutual distance loss on the\ncenters of the Gaussian priors and a clustering assignment hardening loss on\nthe node embeddings. Experimental results on four real-world attributed network\ndatasets demonstrate the effectiveness of the proposed VCLANC for attributed\nnetwork clustering.\n"}
+{"abstract": "  Let $G$ be a simple algebraic group over an algebraically closed field\n$\\mathbb{F}$ of characteristic $p\\geq h$, the Coxeter number of $G$. We observe\nan easy `recursion formula' for computing the Jantzen sum formula of a Weyl\nmodule with $p$-regular highest weight. We also discuss a `duality formula'\nthat relates the Jantzen sum formula to Andersen's sum formula for tilting\nfiltrations and we give two different representation theoretic explanations of\nthe recursion formula. As a corollary, we also obtain an upper bound on the\nlength of the Jantzen filtration of a Weyl module with $p$-regular highest\nweight in terms of the length of the Jantzen filtration of a Weyl module with\nhighest weight in an adjacent alcove.\n"}
+{"abstract": "  Let $A/F$ be an abelian variety over a field. Does there exist a smooth\nprojective $F$-variety $X$, such that $A$ is isomorphic to the automorphism\ngroup scheme of $X/F$? We show that the answer is positive, if and only if $A$\nhas only finitely many automorphisms, over an algebraic closure of $F$. When\n$F=\\mathbb C$, this result is due to Lombardo and Maffei. When $F$ is\nalgebraically closed, it was obtained independently by Blanc and Brion.\n"}
+{"abstract": "  We present novel realizations of Higgs inflation within Supergravity which\nare largely tied to the existence of a pole of order two in the kinetic term of\nthe inflaton field. This pole arises due to the selected Kaehler potentials\nwhich parameterize the (SU(1,1)/U(1))^2 or SU(2,1)/(SU(2)xU(1)) manifolds with\nscalar curvatures R_{(11)^2}=-4/N or R_{21}=-3/N respectively. The associated\nsuperpotential includes, in addition to the Higgs superfields, a stabilizer\nsuperfield, respects the gauge and an R symmetries and contains the first\nallowed nonrenormalizable term. If the coefficient of this term is almost equal\nto that of the renormalizable terms within about 10^-5 and N=1, the\ninflationary observables can be done compatible with the present data and the\nscale M of gauge-symmetry breaking may assume its value within MSSM. Increasing\nM beyond this value, though, inflation may be attained with less tuning.\nModifications to the Kaehler potentials associated with the manifolds above\nallow for inflation, realized with just renormalizable terms, resulting to\nhigher tensor-to-scalar ratios as N approaches its maximum at N=40.\n"}
+{"abstract": "  We develop a unified approach to proving $L^p-L^q$ boundedness of spectral\nprojectors, the resolvent of the Laplace-Beltrami operator and its derivative\non $\\mathbb{H}^d.$ In the case of spectral projectors, and when $p$ and $q$ are\nin duality, the dependence of the implicit constant on $p$ is shown to be\nsharp. We also give partial results on the question of $L^p-L^q$ boundedness of\nthe Fourier extension operator. As an application, we prove smoothing estimates\nfor the free Schr\\\"{o}dinger equation on $\\mathbb{H}^d$ and a limiting\nabsorption principle for the electromagnetic Schr\\\"{o}dinger equation with\nsmall potentials.\n"}
+{"abstract": "  In this paper, we propose an effective method for fast and accurate scene\nparsing called Bidirectional Alignment Network (BiAlignNet). Previously, one\nrepresentative work BiSeNet~\\cite{bisenet} uses two different paths (Context\nPath and Spatial Path) to achieve balanced learning of semantics and details,\nrespectively. However, the relationship between the two paths is not well\nexplored. We argue that both paths can benefit each other in a complementary\nway. Motivated by this, we propose a novel network by aligning two-path\ninformation into each other through a learned flow field. To avoid the noise\nand semantic gaps, we introduce a Gated Flow Alignment Module to align both\nfeatures in a bidirectional way. Moreover, to make the Spatial Path learn more\ndetailed information, we present an edge-guided hard pixel mining loss to\nsupervise the aligned learning process. Our method achieves 80.1\\% and 78.5\\%\nmIoU in validation and test set of Cityscapes while running at 30 FPS with full\nresolution inputs. Code and models will be available at\n\\url{https://github.com/jojacola/BiAlignNet}.\n"}
+{"abstract": "  This paper studies a multi-Intelligent Reflecting Surfaces (IRSs)-assisted\nwireless network consisting of multiple base stations (BSs) serving a set of\nmobile users. We focus on the IRS-BS association problem in which multiple BSs\ncompete with each other for controlling the phase shifts of a limited number of\nIRSs to maximize the long-term downlink data rate for the associated users. We\npropose MDLBI, a Multi-agent Deep Reinforcement Learning-based BS-IRS\nassociation scheme that optimizes the BS-IRS association as well as the\nphase-shift of each IRS when being associated with different BSs. MDLBI does\nnot require information exchanging among BSs. Simulation results show that\nMDLBI achieves significant performance improvement and is scalable for large\nnetworking systems.\n"}
+{"abstract": "  We report an infinite number of orthonormal wave functions bases for the\nquantum problem of a free particle in presence of an applied external magnetic\nfield. Each set of orthonormal wave functions (basis) is labeled by an integer\n$p$, which is the number of magnetic fluxons trapped in the unit cell. These\nbases are suitable to describe particles whose probability density is periodic\nand defines a lattice in position space. The present bases of orthonormal wave\nfunctions unveils fractional effects since the number of particles in the unit\ncell is independent of the number of trapped fluxons. For a single particle\nunder $p$ fluxes in the unit cell, and confined to the lowest Landau level, the\nprobability density vanishes in $p$ points, thus each zero is associated to a\nfraction $1/p$ of the particle. Remarkably the case of $n+1$ filled Landau\nlevels, hence with a total of $N=(n+1)p$ fermions, $n$ being the highest filled\nLandau level, the density displays an egg-box pattern with $p^2$ maxima\n(minima) which means that a $(n+1)/p$ fraction of flux is associated to every\none of these maxima (minima). We also consider the case of particles\ninteracting through the magnetic field energy created by their own motion and\nfind an attractive interaction among them in case they are confined to the\nlowest Landau level ($n=0$). The well-known de Haas-van Alphen oscillations are\nretrieved within the present orthonormal basis of wave functions thus providing\nevidence of its correctness.\n"}
+{"abstract": "  Learning from heterogeneous data poses challenges such as combining data from\nvarious sources and of different types. Meanwhile, heterogeneous data are often\nassociated with missingness in real-world applications due to heterogeneity and\nnoise of input sources. In this work, we propose the variational selective\nautoencoder (VSAE), a general framework to learn representations from\npartially-observed heterogeneous data. VSAE learns the latent dependencies in\nheterogeneous data by modeling the joint distribution of observed data,\nunobserved data, and the imputation mask which represents how the data are\nmissing. It results in a unified model for various downstream tasks including\ndata generation and imputation. Evaluation on both low-dimensional and\nhigh-dimensional heterogeneous datasets for these two tasks shows improvement\nover state-of-the-art models.\n"}
+{"abstract": "  This paper presents simulations of the metastable helium (He*) observations\nof WASP-107b, so far the highest signal-to-noise ratio detection that is\nconfirmed by three different instruments. We employ full 3D hydrodynamics\ncoupled with co-evolving non-equilibrium thermochemistry and ray-tracing\nradiation, predicting mass loss rates, temperature profiles, and synthetic He*\nline profiles and light curves from first principles. We found that a stellar\nwind stronger than solar is demanded by the observed heavily blueshifted line\nprofile and asymmetric transit light curve. Contrary to previous beliefs, we\nargue that radiation pressure can be important for Ly$\\alpha$ observations but\n{\\it not} He*. We found WASP-107b is losing mass at a rate of $\\dot{M} \\simeq\n1.0\\times 10^{-9}~M_\\oplus~{\\rm yr}^{-1}$. Although $\\dot{M}$ varies by $<1~\\%$\ngiven constant wind and irradiation from the host, shear instabilities still\nemerge from wind impacts, producing $\\sim 10~\\%$ fluctuations of He* transit\ndepths over hour-long timescales. The common assumption that He* transit depth\nindicates the fluctuation of $\\dot{M}$ is problematic. The trailing tail is\nmore susceptible than planet adjacency to the shear instabilities, thus the\nline profile is more variable in the blue-shifted wing, while the transit light\ncurve is more variable after mid-transit. We stress the synergy between\nLy$\\alpha$ (higher altitudes, lower density) and He* (lower altitudes, higher\ndensity) transit observations, particularly simultaneous ones, yield better\nunderstanding of planetary outflows and stellar wind properties.\n"}
+{"abstract": "  In this paper, we reformulate conjectural formulas for the arithmetic\nintersection numbers of special cycles on unitary Shimura varieties with\nminuscule parahoric level structure in terms of weighted counting of lattices\ncontaining special homomorphisms.\n"}
+{"abstract": "  In 1909 Einstein described the thermalization of a mirror within a blackbody\ncavity by collisions with thermal photons. While the time to thermalize the\nmotion of even a microscale or nanoscale object is so long that it is not\nfeasible, we show that it is using the high intensity light from an amplified\nthermal light source with a well-defined chemical potential. We predict damping\nof the center-of mass motion due to this effect on times scales of seconds for\nsmall optomechanical systems, such as levitated nanoparticles, allowing\nexperimental observation.\n"}
+{"abstract": "  Near-field radiative heat transfer (NFHT) research currently suffers from an\nimbalance between numerous theoretical studies, as opposed to experimental\nreports that remain, in proportion, relatively scarce. Existing experimental\nplatforms all rely on unique custom-built devices on which it is difficult to\nintegrate new materials and structures for studying the breadth of\ntheoretically proposed phenomena. Here we show high-resolution NFHT\nmeasurements using, as our sensing element, silicon nitride (SiN) freestanding\nnanomembranes$-$a widely available platform routinely used in materials and\ncavity optomechanics research. We measure NFHT by tracking the high mechanical\nquality (Q) factor ($>2\\times10^6$) resonance of a membrane placed in the\nnear-field of a hemispherical hot object. We find that high Q-factor enables a\ntemperature resolution ($1.2\\times10^{-6} \\ \\mathrm{K}$) that is unparalleled\nin previous NFHT experiments. Results are in good agreement with a custom-built\nmodel combining heat transport in nanomembranes and the effect of non-uniform\nstress/temperature on the resonator eigenmodes.\n"}
+{"abstract": "  Despite the fact that it is publicly available, collecting and processing the\nfull bitcoin blockchain data is not trivial. Its mere size, history, and other\nfeatures indeed raise quite specific challenges, that we address in this paper.\nThe strengths of our approach are the following: it relies on very basic and\nstandard tools, which makes the procedure reliable and easily reproducible; it\nis a purely lossless procedure ensuring that we catch and preserve all existing\ndata; it provides additional indexing that makes it easy to further process the\nwhole data and select appropriate subsets of it. We present our procedure in\ndetails and illustrate its added value on large-scale use cases, like address\nclustering. We provide an implementation online, as well as the obtained\ndataset.\n"}
+{"abstract": "  A bi-unitary connection in subfactor theory of Jones producing a subfactor of\nfinite depth gives a 4-tensor appearing in a recent work of\nBultinck-Mariena-Williamson-Sahinoglu-Haegemana-Verstraete on 2-dimensional\ntopological order and anyons. In their work, they have a special projection\ncalled a projector matrix product operator. We prove that the range of this\nprojection of length k is naturally identified with the k-th higher relative\ncommutant of the subfactor arising from the bi-unitary connection. This gives a\nfurther connection between 2-dimensional topological order and subfactor\ntheory.\n"}
+{"abstract": "  In the cold dark matter scenario, galactic dark matter halos are populated\nwith a large number of smaller subhalos. Previous work has shown that dark\nmatter annihilations in subhalos can generate a distinctive, non-Poisson signal\nin the gamma-ray photon counts probability distribution function (PDF). Here we\nshow that the gamma-ray PDF also carries information about the velocity\ndependence of the dark matter annihilation cross section. After calculating the\nPDF assuming $s$-wave and Sommerfeld-enhanced annihilation, we perform a mock\ndata analysis to illustrate how current and future observations can constrain\nthe microphysics of the dark matter annihilation. We find that, with current\nFermi data, and assuming a dark matter annihilation cross section roughly at\nthe limit of current bounds from annihilation in dwarf spheroidal galaxies, one\ncan potentially distinguish the non-Poissonian fluctuations expected from dark\nmatter annihilation in subhalos from Poisson sources, as well as from dark\nmatter models with an incorrect velocity-dependence. We explore how robust\nthese results are to assumptions about the modeling of astrophysical\nbackgrounds. We also point out a four-parameter degeneracy between the velocity\ndependence of the dark matter annihilation, the minimum subhalo mass, the power\nlaw index of the subhalo mass function, and the normalization of the dark\nmatter signal. This degeneracy can be broken with priors from N-body\nsimulations or from observational constraints on the subhalo mass function.\n"}
+{"abstract": "  For certain types of quadratic forms lying in the n-th power of the\nfundamental ideal, we compute upper bounds and where possible exact values for\nthe minimal number of general n-fold Pfister forms, that are needed to write\nthe Witt class of that given form as the sum of the Witt classes of those\nn-fold Pfister forms. We restrict ourselves mostly to the case of so called\nrigid fields, i.e. fields in which binary anisotropic forms represent at most 2\nsquare classes.\n"}
+{"abstract": "  We classify compact manifolds of dimension three equipped with a path\nstructure and a fixed contact form (which we refer to as a strict path\nstructure) under the hypothesis that their automorphism group is non-compact.\nWe use a Cartan connection associated to the structure and show that its\ncurvature is constant.\n"}
+{"abstract": "  We present the results of a single dish survey toward 95 VeLLOs in optically\nthick (HCN 1-0) and thin ($\\rm N_2H^+$ 1-0) lines performed for the purpose of\nunderstanding the physical processes of inward motions in the envelopes of the\nVeLLOs and characterizing their true nature. The normalized velocity\ndifferences ($\\delta V_{HCN}$) between the peak velocities of the two lines\nwere derived for 41 VeLLOs detected in both lines. The $\\delta V$ distribution\nof these VeLLOs is found to be significantly skewed to the blue, indicating the\ndominance of infalling motions in their envelopes. The infall speeds were\nderived for 15 infall candidates by using the HILL5 radiative transfer model.\nThe speeds were in the range of 0.03 $\\rm km~s^{-1}$ to 0.3 $\\rm km~s^{-1}$,\nwith a median value of 0.16 $\\rm km~s^{-1}$, being consistent with the\ngravitational free-fall speeds from pressure-free envelopes. The mass infall\nrates calculated from the infall speeds are mostly of the order of $10^{-6}\nM_{\\odot}~yr^{-1}$ with a median value of $\\rm 3.4 \\pm 1.5 \\times 10^{-6}\nM_{\\odot}~yr^{-1}$. These are found to be also consistent with the values\npredicted with the inside-out collapse model and show a fairly good correlation\nwith the internal luminosities of the VeLLOs. This again indicates that the\ninfall motions observed toward the VeLLOs are likely to be due to the\ngravitational infall motions in their envelopes. Our study suggests that most\nof the VeLLOs are potentially faint protostars while two of the VeLLOs could\npossibly be proto-brown dwarf candidates.\n"}
+{"abstract": "  We address the problem of universal domain adaptation (UDA) in ordinal\nregression (OR), which attempts to solve classification problems in which\nlabels are not independent, but follow a natural order. We show that the UDA\ntechniques developed for classification and based on the clustering assumption,\nunder-perform in OR settings. We propose a method that complements the OR\nclassifier with an auxiliary task of order learning, which plays the double\nrole of discriminating between common and private instances, and expanding\nclass labels to the private target images via ranking. Combined with\nadversarial domain discrimination, our model is able to address the closed set,\npartial and open set configurations. We evaluate our method on three face age\nestimation datasets, and show that it outperforms the baseline methods.\n"}
+{"abstract": "  Perhaps surprisingly sewerage infrastructure is one of the most costly\ninfrastructures in modern society. Sewer pipes are manually inspected to\ndetermine whether the pipes are defective. However, this process is limited by\nthe number of qualified inspectors and the time it takes to inspect a pipe.\nAutomatization of this process is therefore of high interest. So far, the\nsuccess of computer vision approaches for sewer defect classification has been\nlimited when compared to the success in other fields mainly due to the lack of\npublic datasets. To this end, in this work we present a large novel and\npublicly available multi-label classification dataset for image-based sewer\ndefect classification called Sewer-ML.\n  The Sewer-ML dataset consists of 1.3 million images annotated by professional\nsewer inspectors from three different utility companies across nine years.\nTogether with the dataset, we also present a benchmark algorithm and a novel\nmetric for assessing performance. The benchmark algorithm is a result of\nevaluating 12 state-of-the-art algorithms, six from the sewer defect\nclassification domain and six from the multi-label classification domain, and\ncombining the best performing algorithms. The novel metric is a\nclass-importance weighted F2 score, $\\text{F}2_{\\text{CIW}}$, reflecting the\neconomic impact of each class, used together with the normal pipe F1 score,\n$\\text{F}1_{\\text{Normal}}$. The benchmark algorithm achieves an\n$\\text{F}2_{\\text{CIW}}$ score of 55.11% and $\\text{F}1_{\\text{Normal}}$ score\nof 90.94%, leaving ample room for improvement on the Sewer-ML dataset. The\ncode, models, and dataset are available at the project page\nhttps://vap.aau.dk/sewer-ml/\n"}
+{"abstract": "  Quantitative magneto-optical imaging of a type-II superconductor thin film\ncooled under zero, homogeneous, and inhomogeneous applied magnetic fields,\nindicates that the latter procedure leads to an enhancement of the screening\ncapacity. Such an observation is corroborated by both B-independent and\nB-dependent critical state model analyses. Furthermore, repulsive (attractive)\nvortex-(anti)vortex interactions were found to have a decisive role in the\nshielding ability, with initial states prepared with vortices resulting in a\nshorter magnetic flux front penetration depth than those prepared with\nantivortices. The proposed strategy could be implemented to boost the\nperformance of thin superconducting devices.\n"}
+{"abstract": "  We consider the problem of generalization in reinforcement learning where\nvisual aspects of the observations might differ, e.g. when there are different\nbackgrounds or change in contrast, brightness, etc. We assume that our agent\nhas access to only a few of the MDPs from the MDP distribution during training.\nThe performance of the agent is then reported on new unknown test domains drawn\nfrom the distribution (e.g. unseen backgrounds). For this \"zero-shot RL\" task,\nwe enforce invariance of the learned representations to visual domains via a\ndomain adversarial optimization process. We empirically show that this approach\nallows achieving a significant generalization improvement to new unseen\ndomains.\n"}
+{"abstract": "  Let $C$ be a hyperelliptic curve of genus $g>1$ over an algebraically closed\nfield $K$ of characteristic zero and $O$ one of the $(2g+2)$ Weierstrass points\nin $C(K)$. Let $J$ be the jacobian of $C$, which is a $g$-dimensional abelian\nvariety over $K$. Let us consider the canonical embedding of $C$ into $J$ that\nsends $O$ to the zero of the group law on $J$. This embedding allows us to\nidentify $C(K)$ with a certain subset of the commutative group $J(K)$.\n  A special case of the famous theorem of Raynaud (Manin--Mumford conjecture)\nasserts that the set of torsion points in $C(K)$ is finite. It is well known\nthat the points of order 2 in $C(K)$ are exactly the \"remaining\" $(2g+1)$\nWeierstrass points. One of the authors proved that there are no torsion points\nof order $n$ in $C(K)$ if $3\\le n\\le 2g$. So, it is natural to study torsion\npoints of order $2g+1$ (notice that the number of such points in $C(K)$ is\nalways even). Recently, the authors proved that there are infinitely many (for\na given $g$) mutually nonisomorphic pairs $C,O)$ such that $C(K)$ contains at\nleast four points of order $2g+1$.\n  In the present paper we prove that (for a given $g$) there are at most\nfinitely many (up to a isomorphism) pairs $(C,O)$ such that $C(K)$ contains at\nleast six points of order $2g+1$.\n"}
+{"abstract": "  Systems of interacting agents can often be modeled as contextual games, where\nthe context encodes additional information, beyond the control of any agent\n(e.g. weather for traffic and fiscal policy for market economies). In such\nsystems, the most likely outcome is given by a Nash equilibrium. In many\npractical settings, only game equilibria are observed, while the optimal\nparameters for a game model are unknown. This work introduces Nash Fixed Point\nNetworks (N-FPNs), a class of implicit-depth neural networks that output Nash\nequilibria of contextual games. The N-FPN architecture fuses data-driven\nmodeling with provided constraints. Given equilibrium observations of a\ncontextual game, N-FPN parameters are learnt to predict equilibria outcomes\ngiven only the context. We present an end-to-end training scheme for N-FPNs\nthat is simple and memory efficient to implement with existing\nautodifferentiation tools. N-FPNs also exploit a novel constraint decoupling\nscheme to avoid costly projections. Provided numerical examples show the\nefficacy of N-FPNs on atomic and non-atomic games (e.g. traffic routing).\n"}
+{"abstract": "  The Weisfeiler--Leman algorithm is a ubiquitous tool for the Graph\nIsomorphism Problem with various characterisations in e.g. descriptive\ncomplexity and convex optimisation. It is known that graphs that are not\ndistinguished by the two-dimensional variant have cospectral adjacency\nmatrices. We tackle a converse problem by proposing a set of matrices called\nGeneralised Laplacians that characterises the expressiveness of WL in terms of\nspectra. As an application to random walks, we show using Generalised\nLaplacians that the edge colours produced by 2-WL determine commute distances.\n"}
+{"abstract": "  An all-electric unmanned aerial system with both VTOL and Fixed wing\ncapabilities is designed and optimized for long range surveillance and relief\noperations. The UAV is equipped with onboard computers and sensors and is\ncapable of carrying 1kg of relief payload upto 100 Km. The entire low fidelity\ndesign process -- from concept to render -- is carried out using completely\nopen source tools, libraries and in-house code. The challenges faced and\nprimary differences are discussed parallelly. A comparison with commercial\ncodes and programs is also done in some areas to give an overview of key\ncapabilities and caveats.\n"}
+{"abstract": "  We show that for $\\gamma<\\sqrt{4/3}$, it is possible to define the Levy area\nof a planar Brownian motion with the Liouville measure of intermittency\nparameter $\\gamma$ as the underlying area measure. We also consider the case of\nsmoother curves, and study some properties of the integration map thus defined.\n"}
+{"abstract": "  In the pursuit of a deeper understanding of a model's behaviour, there is\nrecent impetus for developing suites of probes aimed at diagnosing models\nbeyond simple metrics like accuracy or BLEU. This paper takes a step back and\nasks an important and timely question: how reliable are these diagnostics in\nproviding insight into models and training setups? We critically examine three\nrecent diagnostic tests for pre-trained language models, and find that\nlikelihood-based and representation-based model diagnostics are not yet as\nreliable as previously assumed. Based on our empirical findings, we also\nformulate recommendations for practitioners and researchers.\n"}
+{"abstract": "  Source independent quantum random number generators (SI-QRNG) are\ncryptographic protocols which attempt to extract random strings from quantum\nsources where the source is under the control of an adversary while the\nmeasurement devices are fully characterized. This represents a middle-ground\nbetween fully-trusted and full-device independence, allowing for fast\nbit-generation rates with current-day technology, while also providing a strong\nsecurity guarantee. In this paper we analyze an SI-QRNG protocol based on\nquantum walks and develop a new proof technique to show security.\n"}
+{"abstract": "  We consider learning the structures of Gaussian latent tree models with\nvector observations when a subset of them are arbitrarily corrupted. First, we\npresent the sample complexities of Recursive Grouping (RG) and Chow-Liu\nRecursive Grouping (CLRG) without the assumption that the effective depth is\nbounded in the number of observed nodes, significantly generalizing the results\nin Choi et al. (2011). We show that Chow-Liu initialization in CLRG greatly\nreduces the sample complexity of RG from being exponential in the diameter of\nthe tree to only logarithmic in the diameter for the hidden Markov model (HMM).\nSecond, we robustify RG, CLRG, Neighbor Joining (NJ) and Spectral NJ (SNJ) by\nusing the truncated inner product. These robustified algorithms can tolerate a\nnumber of corruptions up to the square root of the number of clean samples.\nFinally, we derive the first known instance-dependent impossibility result for\nstructure learning of latent trees. The optimalities of the robust version of\nCLRG and NJ are verified by comparing their sample complexities and the\nimpossibility result.\n"}
+{"abstract": "  One of the promising applications of early quantum computers is the\nsimulation of quantum systems. Variational methods for near-term quantum\ncomputers, such as the variational quantum eigensolver (VQE), are a promising\napproach to finding ground states of quantum systems relevant in physics,\nchemistry, and materials science. These approaches, however, are constrained by\nthe effects of noise as well as the limited quantum resources of near-term\nquantum hardware, motivating the need for quantum error mitigation techniques\nto reduce the effects of noise. Here we introduce $\\textit{neural error\nmitigation}$, a novel method that uses neural networks to improve estimates of\nground states and ground-state observables obtained using VQE on near-term\nquantum computers. To demonstrate our method's versatility, we apply neural\nerror mitigation to finding the ground states of H$_2$ and LiH molecular\nHamiltonians, as well as the lattice Schwinger model. Our results show that\nneural error mitigation improves the numerical and experimental VQE computation\nto yield low-energy errors, low infidelities, and accurate estimations of\nmore-complex observables like order parameters and entanglement entropy,\nwithout requiring additional quantum resources. Additionally, neural error\nmitigation is agnostic to both the quantum hardware and the particular noise\nchannel, making it a versatile tool for quantum simulation. Applying quantum\nmany-body machine learning techniques to error mitigation, our method is a\npromising strategy for extending the reach of near-term quantum computers to\nsolve complex quantum simulation problems.\n"}
+{"abstract": "  Contextual information has been shown to be powerful for semantic\nsegmentation. This work proposes a novel Context-based Tandem Network (CTNet)\nby interactively exploring the spatial contextual information and the channel\ncontextual information, which can discover the semantic context for semantic\nsegmentation. Specifically, the Spatial Contextual Module (SCM) is leveraged to\nuncover the spatial contextual dependency between pixels by exploring the\ncorrelation between pixels and categories. Meanwhile, the Channel Contextual\nModule (CCM) is introduced to learn the semantic features including the\nsemantic feature maps and class-specific features by modeling the long-term\nsemantic dependence between channels. The learned semantic features are\nutilized as the prior knowledge to guide the learning of SCM, which can make\nSCM obtain more accurate long-range spatial dependency. Finally, to further\nimprove the performance of the learned representations for semantic\nsegmentation, the results of the two context modules are adaptively integrated\nto achieve better results. Extensive experiments are conducted on three\nwidely-used datasets, i.e., PASCAL-Context, ADE20K and PASCAL VOC2012. The\nresults demonstrate the superior performance of the proposed CTNet by\ncomparison with several state-of-the-art methods.\n"}
+{"abstract": "  Predictive business process monitoring is concerned with the prediction how a\nrunning process instance will unfold up to its completion at runtime. Most of\nthe proposed approaches rely on a wide number of different machine learning\n(ML) techniques. In the last years numerous comparative studies, reviews, and\nbenchmarks of such approaches where published and revealed that they can be\nsuccessfully applied for different prediction targets. ML techniques require a\nqualitatively and quantitatively sufficient data set. However, there are many\nsituations in business process management (BPM) where only a quantitatively\ninsufficient data set is available. The problem of insufficient data in the\ncontext of BPM is still neglected. Hence, none of the comparative studies or\nbenchmarks investigates the performance of predictive business process\nmonitoring techniques in environments with small data sets. In this paper an\nevaluation framework for comparing existing approaches with regard to their\nsuitability for small data sets is developed and exemplarily applied to\nstate-of-the-art approaches in predictive business process monitoring.\n"}
+{"abstract": "  Neural Module Networks (NMNs) aim at Visual Question Answering (VQA) via\ncomposition of modules that tackle a sub-task. NMNs are a promising strategy to\nachieve systematic generalization, i.e. overcoming biasing factors in the\ntraining distribution. However, the aspects of NMNs that facilitate systematic\ngeneralization are not fully understood. In this paper, we demonstrate that the\nstage and the degree at which modularity is defined has large influence on\nsystematic generalization. In a series of experiments on three VQA datasets\n(MNIST with multiple attributes, SQOOP, and CLEVR-CoGenT), our results reveal\nthat tuning the degree of modularity in the network, especially at the image\nencoder stage, reaches substantially higher systematic generalization. These\nfindings lead to new NMN architectures that outperform previous ones in terms\nof systematic generalization.\n"}
+{"abstract": "  For a generalized holomorphic vector bundle, we introduce the Atiyah class,\nwhich is the obstruction of the existence of generalized holomorphic\nconnections on this bundle. Similar to the holomorphic case, such Atiyah\nclasses can be defined by three approaches: the $\\rm{\\check{C}}$ech cohomology,\nthe extension class of the first jet bundle as well as the Lie pair.\n"}
+{"abstract": "  Kreck's modified surgery theory reduces the classification of closed,\nconnected 4-manifolds, up to connect sum with some number of copies of\n$S^2\\times S^2$, to a series of bordism questions. We implement this in the\ncase of unorientable 4-manifolds M and show that for some choices of\nfundamental groups, the computations simplify considerably. We use this to\nsolve some cases in which $\\pi_1(M)$ is finite of order 2 mod 4: under an\nassumption on cohomology, there are nine stable diffeomorphism classes for\nwhich M is pin$^+$, one stable diffeomorphism class for which M is pin$^-$, and\nfour stable diffeomorphism classes for which M is neither. We also determine\nthe corresponding stable homeomorphism classes.\n"}
+{"abstract": "  Information Causality is a physical principle which states that the amount of\nrandomly accessible data over a classical communication channel cannot exceed\nits capacity, even if the sender and the receiver have access to a source of\nnonlocal correlations. This principle can be used to bound the nonlocality of\nquantum mechanics without resorting to its full formalism, with a notable\nexample of reproducing the Tsirelson's bound of the Clauser-Horne-Shimony-Holt\ninequality. Despite being promising, the latter result found little\ngeneralization to other Bell inequalities because of the limitations imposed by\nthe process of concatenation, in which several nonsignaling resources are put\ntogether to produce tighter bounds. In this work, we show that concatenation\ncan be successfully replaced by limits on the communication channel capacity.\nIt allows us to re-derive and, in some cases, significantly improve all the\npreviously known results in a simpler manner and apply the Information\nCausality principle to previously unapproachable Bell scenarios.\n"}
+{"abstract": "  To obtain the Dirichlet series for complex powers of the Riemann zeta\nfunction, we define and study the basic properties of a sequence of polynomials\nthat, used as coefficients of the respective terms of the Dirichlet series of\nthe Riemann zeta function in the half plane $x > 1$, produces the required\nexponential function. Unlike the method described in ([4], p.~278), which\nrequires more advanced knowledge of the relationships between Dirichlet series\nand multiplicative arithmetic functions, our approach only needs mathematical\ninduction on the total number of prime divisors of $n$, the Dirichlet product\nand the use of an analytic property characteristic of the exponential function\nin the complex plane.\n"}
+{"abstract": "  We introduce a location statistic for distributions on non-linear geometric\nspaces, the diffusion mean, serving both as an extension of and an alternative\nto the Fr\\'echet mean. The diffusion mean arises as the generalization of\nGaussian maximum likelihood analysis to non-linear spaces by maximizing the\nlikelihood of a Brownian motion. The diffusion mean depends on a time parameter\n$t$, which admits the interpretation of the allowed variance of the mean. The\ndiffusion $t$-mean of a distribution $X$ is the most likely origin of a\nBrownian motion at time $t$, given the end-point distribution $X$. We give a\ndetailed description of the asymptotic behavior of the diffusion estimator and\nprovide sufficient conditions for the diffusion estimator to be strongly\nconsistent. Furthermore, we present a smeary central limit theorem for\ndiffusion means and investigate properties of the diffusion mean for\ndistributions on the sphere $\\mathcal{S}^n$. Experimentally, we consider\nsimulated data and data from magnetic pole reversals, all indicating similar or\nimproved convergence rate compared to the Fr\\'echet mean. Here, we additionally\nestimate $t$ and consider its effects on smeariness and uniqueness of the\ndiffusion mean for distributions on the sphere.\n"}
+{"abstract": "  Solar neutrino capture cross-section by 127I nucleus has been studied with\ntaking into account the influence of the resonance structure of the nuclear\nstrength function S(E). Three types of isobaric resonances: giant Gamow-Teller,\nanalog resonance and low-lying Gamow-Teller pigmy resonances has been\ninvestigated on the framework of self-consistent theory of finite Fermi\nsystems. The calculations have been performed considering the resonance\nstructure of the charge-exchange strength function S(E). We analyze the effect\nof each resonance on the energy dependence of the cross-section. It has been\nshown that all high-lying resonances should be considered. Neutron emission\nprocess for high energy nuclear excitation leads to formation 126Xe isotope. We\nevaluate contribution from various sources of solar neutrinos to the\n126Xe/127Xe isotopes ratio formed by energetic neutrinos. 126Xe/127Xe isotope\nratio could be an indicator of high-energy boron neutrinos in the solar\nspectrum. We also discuss the uncertainties in the often used Fermi-functions\ncalculations.\n"}
+{"abstract": "  Coronal mass ejections (CMEs) and solar energetic particles (SEPs) are two\nphenomena that can cause severe space weather effects throughout the\nheliosphere. The evolution of CMEs, especially in terms of their magnetic\nstructure, and the configuration of the interplanetary magnetic field (IMF)\nthat influences the transport of SEPs are currently areas of active research.\nThese two aspects are not necessarily independent of each other, especially\nduring solar maximum when multiple eruptive events can occur close in time.\nAccordingly, we present the analysis of a CME that erupted on 2012 May 11\n(SOL2012-05-11) and an SEP event following an eruption that took place on 2012\nMay 17 (SOL2012-05-17). After observing the May 11 CME using remote-sensing\ndata from three viewpoints, we evaluate its propagation through interplanetary\nspace using several models. Then, we analyse in-situ measurements from five\npredicted impact locations (Venus, Earth, the Spitzer Space Telescope, the Mars\nScience Laboratory en route to Mars, and Mars) in order to search for CME\nsignatures. We find that all in-situ locations detect signatures of an SEP\nevent, which we trace back to the May 17 eruption. These findings suggest that\nthe May 11 CME provided a direct magnetic connectivity for the efficient\ntransport of SEPs. We discuss the space weather implications of CME evolution,\nregarding in particular its magnetic structure, and CME-driven IMF\npreconditioning that facilitates SEP transport. Finally, this work remarks the\nimportance of using data from multiple spacecraft, even those that do not\ninclude space weather research as their primary objective.\n"}
+{"abstract": "  This paper discusses modern Auto Machine Learning (AutoML) tools from the\nperspective of a person with little prior experience in Machine Learning (ML).\nThere are many AutoML tools both ready-to-use and under development, which are\ncreated to simplify and democratize usage of ML technologies in everyday life.\nOur position is that ML should be easy to use and available to a greater number\nof people. Prior research has identified the need for intuitive AutoML tools.\nThis work seeks to understand how well AutoML tools have achieved that goal in\npractice. We evaluate three AutoML Tools to evaluate the end-user experience\nand system performance. We evaluate the tools by having them create models from\na competition dataset on banking data. We report on their performance and the\ndetails of our experience. This process provides a unique understanding of the\nstate of the art of AutoML tools. Finally, we use these experiences to inform a\ndiscussion on how future AutoML tools can improve the user experience for\nneophytes of Machine Learning.\n"}
+{"abstract": "  It is shown that impulsive systems of nonlinear, time-varying and/or switched\nform that allow a stable global state weak linearization are jointly\ninput-to-state stable (ISS) under small inputs and integral ISS (iISS). The\nsystem is said to allow a global state weak linearization if its flow and jump\nequations can be written as a (time-varying, switched) linear part plus a\n(nonlinear) pertubation satisfying a bound of affine form on the state. This\nbound reduces to a linear form under zero input but does not force the system\nto be linear under zero input. The given results generalize and extend\npreviously existing ones in many directions: (a) no (dwell-time or other)\nconstraints are placed on the impulse-time sequence, (b) the system need not be\nlinear under zero input, (c) existence of a (common) Lyapunov function is not\nrequired, (d) the perturbation bound need not be linear on the input.\n"}
+{"abstract": "  The holographic principle implies that quantum field theory overcounts the\nnumber of independent degrees of freedom in quantum gravity. An argument due to\nCohen, Kaplan, and Nelson (CKN) suggests that the number of degrees of freedom\nwell-described by QFT is even smaller than required by holographic bounds, and\nCKN interpreted this result as indicative of a correlation between the UV and\nIR cutoffs on QFT. Here we consider an alternative interpretation in which the\nQFT degrees of freedom are depleted as a function of scale. We use a simple\nrecipe to estimate the impact of depleted densities of states on precision\nobservables, including the Lamb shift and lepton $g-2$. Although these\nobservables are not sensitive to the level of depletion motivated by\ngravitational considerations, the phenomenological exercises also provide an\ninteresting test of quantum field theory that is independent of underlying\nquantum gravity assumptions. A depleted density of states can also render the\nQFT vacuum energy UV-insensitive, reconciling the success of QFT in describing\nordinary particle physics processes and its apparent failure in predicting the\ncosmological constant.\n"}
+{"abstract": "  Photo-induced phase-segregation in mixed halide perovskite MAPb(BrxI1-x)3 is\ninvestigated in the full compositional range by correlative X-ray diffraction\nand photo-luminescence experiments.\n"}
+{"abstract": "  We give a simple proof of a recent result due to Agostiniani, Fogagnolo and\nMazzieri.\n"}
+{"abstract": "  While recent computer vision algorithms achieve impressive performance on\nmany benchmarks, they lack robustness - presented with an image from a\ndifferent distribution, (e.g. weather or lighting conditions not considered\nduring training), they may produce an erroneous prediction. Therefore, it is\ndesired that such a model will be able to reliably predict its confidence\nmeasure. In this work, uncertainty estimation for the task of semantic\nsegmentation is evaluated under a varying level of domain shift: in a\ncross-dataset setting and when adapting a model trained on data from the\nsimulation. It was shown that simple color transformations already provide a\nstrong baseline, comparable to using more sophisticated style-transfer data\naugmentation. Further, by constructing an ensemble consisting of models using\ndifferent backbones and/or augmentation methods, it was possible to improve\nsignificantly model performance in terms of overall accuracy and uncertainty\nestimation under the domain shift setting. The Expected Calibration Error (ECE)\non challenging GTA to Cityscapes adaptation was reduced from 4.05 to the\ncompetitive value of 1.1. Further, an ensemble of models was utilized in the\nself-training setting to improve the pseudo-labels generation, which resulted\nin a significant gain in the final model accuracy, compared to the standard\nfine-tuning (without ensemble).\n"}
+{"abstract": "  Comprehensive benchmarking of clustering algorithms is rendered difficult by\ntwo key factors: (i)~the elusiveness of a unique mathematical definition of\nthis unsupervised learning approach and (ii)~dependencies between the\ngenerating models or clustering criteria adopted by some clustering algorithms\nand indices for internal cluster validation. Consequently, there is no\nconsensus regarding the best practice for rigorous benchmarking, and whether\nthis is possible at all outside the context of a given application. Here, we\nargue that synthetic datasets must continue to play an important role in the\nevaluation of clustering algorithms, but that this necessitates constructing\nbenchmarks that appropriately cover the diverse set of properties that impact\nclustering algorithm performance. Through our framework, HAWKS, we demonstrate\nthe important role evolutionary algorithms play to support flexible generation\nof such benchmarks, allowing simple modification and extension. We illustrate\ntwo possible uses of our framework: (i)~the evolution of benchmark data\nconsistent with a set of hand-derived properties and (ii)~the generation of\ndatasets that tease out performance differences between a given pair of\nalgorithms. Our work has implications for the design of clustering benchmarks\nthat sufficiently challenge a broad range of algorithms, and for furthering\ninsight into the strengths and weaknesses of specific approaches.\n"}
+{"abstract": "  We construct a Floer type boundary operator for generalised Morse-Smale\ndynamical systems on compact smooth manifolds by counting the number of\nsuitable flow lines between closed (both homoclinic and periodic) orbits and\nisolated critical points. The same principle works for the discrete situation\nof general combinatorial vector fields, defined by Forman, on CW complexes. We\ncan thus recover the $\\mathbb{Z}_2$ homology of both smooth and discrete\nstructures directly from the flow lines (V-paths) of our vector field.\n"}
+{"abstract": "  We study the inverse random source problem for the time-space fractional\ndiffusion equation driven by fractional Brownian motion with Hurst index\n$H\\in(0,1)$. With the aid of a novel estimate, by using the operator approach\nwe propose regularity analyses for the direct problem. Then we provide a\nreconstruction scheme for the source terms $f$ and $g$ up to the sign. Next,\ncombining the properties of Mittag-Leffler function, the complete uniqueness\nand instability analyses are provided. It's worth mentioning that all the\nanalyses are unified for $H\\in(0,1)$.\n"}
+{"abstract": "  This work introduces a novel epidemiological model that simultaneously\nconsiders multiple viral strains, reinfections due to waning immunity response\nover time and an optimal control formulation. This enables us to derive optimal\nmitigation strategies over a prescribed time horizon under a more realistic\nframework that does not imply perennial immunity and a single strain, although\nthese can also be derived as particular cases of our formulation. The model\nalso allows estimation of the number of infections over time in the absence of\nmitigation strategies under any number of viral strains. We validate our\napproach in the light of the COVID-19 epidemic and present a number of\nexperiments to shed light on the overall behaviour under one or two strains in\nthe absence of sufficient mitigation measures. We also derive optimal control\nstrategies for distinct mitigation costs and evaluate the effect of these costs\non the optimal mitigation measures over a two-year horizon. The results show\nthat relaxations in the mitigation measures cause a rapid increase in the\nnumber of cases, which then demand more restrictive measures in the future.\n"}
+{"abstract": "  A complex manifold $X$ is \\emph{weakly complete} if it admits a continuous\nplurisubharmonic exhaustion function $\\phi$. The minimal kernels $\\Sigma_X^k, k\n\\in [0,\\infty]$ (the loci where are all $\\mathcal{C}^k$ plurisubharmonic\nexhaustion functions fail to be strictly plurisubharmonic),introduced by\nSlodkowski-Tomassini, and the Levi currents, introduced by Sibony, are both\nconcepts aimed at measuring how far $X$ is from being Stein. We compare these\nnotions, prove that all Levi currents are supported by all the $\\Sigma_X^k$'s,\nand give sufficient conditions for points in $\\Sigma_X^k$ to be in the support\nof some Levi current. When $X$ is a surface and $\\phi$ can be chosen analytic,\nbuilding on previous work by the second author, Slodkowski, and Tomassini,we\nprove the existence of a Levi current precisely supported on $\\Sigma_X^\\infty$,\nand give a classification of Levi currents on $X$. In particular,unless $X$ is\na modification of a Stein space, every point in $X$ is in the support of some\nLevi current.\n"}
+{"abstract": "  Modern engineering alloys have bespoke microstructures, where features such\nas precipitates are used to control properties. In many Ni-based alloys,\ncarbo-nitride precipitates are introduced to strengthen and improve\nperformance. These precipitates can be distributed throughout the\nmicrostructure and niobium rich carbides are often found at grain boundaries.\nIn this work, we used combined energy dispersive X-ray spectroscopy (EDS) and\nelectron backscatter diffraction (EBSD) to characterise the population of these\nprecipitates. Processing of the EDS signal is used to label the Mo/Nb-rich\nprecipitates, and their size and location are measured from maps using a\ncircular Hough transform. This label map is combined with the grain boundary\nnetwork (from EBSD analysis). Statistical analysis, using ANOVA testing,\nreveals differences in chemistry between carbides found in Ni-rich matrix grain\ninteriors, on random high angle boundaries and on special boundaries ({\\Sigma}3\nand {\\Sigma}9). These results are compared between wrought and power metallurgy\nproduct forms. These distributions are discussed in the context of their\nperformance within demanding environments, such as reactor core internals.\n"}
+{"abstract": "  There are many different classifications of entanglement for multipartite\nquantum systems, one of which is based on the number of unentangled particles.\nIn this paper, we mainly study the quantum states containing at most $k-1$\nunentangled particles and provide several entanglement criteria based on\ndifferent forms of inequalities which can both identify quantum states\ncontaining at most $k-1$ unentangled particles. We show that these criteria are\nmore effective for some states by concrete examples.\n"}
+{"abstract": "  Learning node representations that incorporate information from graph\nstructure benefits wide range of tasks on graph. The majority of existing graph\nneural networks (GNNs) have limited power in capturing position information for\na given node. The idea of positioning nodes with selected anchors has been\nexploited, yet mainly relying on explicit labeling of distance information.\nHere we propose Graph Inference Representation (GIR), an anchor based GNN model\nencoding path information related to pre-selected anchors for each node.\nAbilities to get position-aware embeddings are theoretically and experimentally\ninvestigated on GIR and its core variants. Further, the complementarity between\nGIRs and typical GNNs is demonstrated. We show that GIRs get outperformed\nresults in position-aware scenarios, and performances on typical GNNs could be\nimproved by fusing GIR embeddings.\n"}
+{"abstract": "  Nonparametric feature selection in high-dimensional data is an important and\nchallenging problem in statistics and machine learning fields. Most of the\nexisting methods for feature selection focus on parametric or additive models\nwhich may suffer from model misspecification. In this paper, we propose a new\nframework to perform nonparametric feature selection for both regression and\nclassification problems. In this framework, we learn prediction functions\nthrough empirical risk minimization over a reproducing kernel Hilbert space.\nThe space is generated by a novel tensor product kernel which depends on a set\nof parameters that determine the importance of the features. Computationally,\nwe minimize the empirical risk with a penalty to estimate the prediction and\nkernel parameters at the same time. The solution can be obtained by iteratively\nsolving convex optimization problems. We study the theoretical property of the\nkernel feature space and prove both the oracle selection property and the\nFisher consistency of our proposed method. Finally, we demonstrate the superior\nperformance of our approach compared to existing methods via extensive\nsimulation studies and application to a microarray study of eye disease in\nanimals.\n"}
+{"abstract": "  Toxicity evaluation of chemical compounds has traditionally relied on animal\nexperiments;however, the demand for non-animal-based prediction methods for\ntoxicology of compounds is increasing worldwide. Our aim was to provide a\nclassification method for compounds based on \\textit{in vitro} gene expression\nprofiles. The \\textit{in vitro} gene expression data analyzed in the present\nstudy was obtained from our previous study. The data concerned nine compounds\ntypically employed in chemical management.We used agglomerative hierarchical\nclustering to classify the compounds;however, there was a statistical\ndifficulty to be overcome.We needed to properly extract RNAs for clustering\nfrom more than 30,000 RNAs. In order to overcome this difficulty, we introduced\na combinatorial optimization problem with respect to both gene expression\nlevels and the correlation between gene expression profiles. Then, the\nsimulated annealing algorithm was used to obtain a good solution for the\nproblem. As a result, the nine compounds were divided into two groups using\n1,000 extracted RNAs. Our proposed methodology enables read-across, one of the\nframeworks for predicting toxicology, based on \\textit{in vitro} gene\nexpression profiles.\n"}
+{"abstract": "  We report the discovery of the massive hot Jupiter NGTS-13b by the Next\nGeneration Transit Survey (NGTS). The V = 12.7 host star is likely in the\nsubgiant evolutionary phase with log g$_{*}$ = 4.04 $\\pm$ 0.05, T$_{eff}$ =\n5819 $\\pm$ 73 K, M$_{*}$ = 1.30$^{+0.11}_{-0.18}$ M$_{\\odot}$, and R$_{*}$ =\n1.79 $\\pm$ 0.06 R$_{\\odot}$. NGTS detected a transiting planet with a period of\nP = 4.12 days around the star, which was later validated with the Transiting\nExoplanet Survey Satellite (TESS; TIC 454069765). We confirm the planet using\nradial velocities from the CORALIE spectrograph. Using NGTS and TESS full-frame\nimage photometry combined with CORALIE radial velocities we determine NGTS-13b\nto have a radius of R$_{P}$ = 1.142 $\\pm$ 0.046 R$_{Jup}$, mass of M$_{P}$ =\n4.84 $\\pm$ 0.44 M$_{Jup}$ and eccentricity e = 0.086 $\\pm$ 0.034. Some previous\nstudies suggest that $\\sim$4 M$_{Jup}$ may be a border between two separate\nformation scenarios (e.g., core accretion and disk instability) and that\nmassive giant planets share similar formation mechanisms as lower-mass brown\ndwarfs. NGTS-13b is just above 4 M$_{Jup}$ making it an important addition to\nthe statistical sample needed to understand the differences between various\nclasses of substellar companions. The high metallicity, [Fe/H] = 0.25 $\\pm$\n0.17, of NGTS-13 does not support previous suggestions that massive giants are\nfound preferentially around lower metallicity host stars, but NGTS-13b does\nsupport findings that more massive and evolved hosts may have a higher\noccurrence of close-in massive planets than lower-mass unevolved stars.\n"}
+{"abstract": "  Let $f:\\, X\\to Y$ be a semistable non-isotrivial family of $n$-folds over a\nsmooth projective curve with discriminant locus $S \\subseteq Y$ and with\ngeneral fibre $F$ of general type. We show the strict Arakelov inequality\n\\[\\frac{\\mathrm{deg}\\, f_*\\omega_{X/Y}^\\nu}{\\mathrm{rank}\\,\nf_*\\omega_{X/Y}^\\nu} < {n\\nu\\over 2}\\cdot\\mathrm{deg}\\,\\Omega^1_Y(\\log S),\\]\nfor all $\\nu\\in \\mathbb N$ such that the $\\nu$-th pluricanonical linear system\n$|\\omega^\\nu_F|$ is birational. This answers a question asked by M\\\"oller,\nViehweg and the third named author.\n"}
+{"abstract": "  Spin-to-orbit conversion of light is a dynamical optical phenomenon in\nnon-paraxial fields leading to various manifestations of the spin and orbital\nHall effect. However, effects of spin-orbit interaction (SOI) have not been\nexplored extensively for higher order Gaussian beams carrying no intrinsic\norbital angular momentum. Indeed, the SOI effects on such structured beams can\nbe directly visualized due to azimuthal rotation of their transverse intensity\nprofiles - a phenomenon we call the rotational Hall effect. In this paper, we\nshow that for an input circularly polarized (right/left) $HG_{10}$ mode, SOI\nleads to a significant azimuthal rotation of the transverse intensity\ndistribution of both the orthogonal circularly polarized (left/right)\ncomponent, and the transverse component of the longitudinal field intensity\nwith respect to the input intensity profile. We validate our theoretical and\nnumerically simulated results experimentally by tightly focusing a circularly\npolarized $HG_{10}$ beam in an optical tweezers configuration, and projecting\nout the opposite circular polarization component and the transverse component\nof the longitudinal field intensity at the output of the tweezers. We also\nmeasure the rotational shift as a function of the refractive index contrast in\nthe path of the tightly focused light, and in general observe a proportional\nincrease. The enhanced spin-orbit conversion in these cases may lead to\ninteresting applications in inducing complex dynamics in optically trapped\nbirefringent particles using higher order Gaussian beams with no intrinsic\norbital angular momentum.\n"}
+{"abstract": "  The recent development of likelihood-free inference aims training a flexible\ndensity estimator for the target posterior with a set of input-output pairs\nfrom simulation. Given the diversity of simulation structures, it is difficult\nto find a single unified inference method for each simulation model. This paper\nproposes a universally applicable regularization technique, called\nPosterior-Aided Regularization (PAR), which is applicable to learning the\ndensity estimator, regardless of the model structure. Particularly, PAR solves\nthe mode collapse problem that arises as the output dimension of the simulation\nincreases. PAR resolves this posterior mode degeneracy through a mixture of 1)\nthe reverse KL divergence with the mode seeking property; and 2) the mutual\ninformation for the high quality representation on likelihood. Because of the\nestimation intractability of PAR, we provide a unified estimation method of PAR\nto estimate both reverse KL term and mutual information term with a single\nneural network. Afterwards, we theoretically prove the asymptotic convergence\nof the regularized optimal solution to the unregularized optimal solution as\nthe regularization magnitude converges to zero. Additionally, we empirically\nshow that past sequential neural likelihood inferences in conjunction with PAR\npresent the statistically significant gains on diverse simulation tasks.\n"}
+{"abstract": "  Contact tracing is a very effective way to control the COVID-19-like\npandemics. It aims to identify individuals who closely contacted an infected\nperson during the incubation period of the virus and notify them to quarantine.\nHowever, the existing systems suffer from privacy, security, and efficiency\nissues. To address these limitations, in this paper, we propose an efficient\nand privacy-preserving Blockchain-based infection control system. Instead of\ndepending on a single authority to run the system, a group of health\nauthorities, that form a consortium Blockchain, run our system. Using\nBlockchain technology not only secures our system against single point of\nfailure and denial of service attacks, but also brings transparency because all\ntransactions can be validated by different parties. Although contact tracing is\nimportant, it is not enough to effectively control an infection. Thus, unlike\nmost of the existing systems that focus only on contact tracing, our system\nconsists of three integrated subsystems, including contact tracing, public\nplaces access control, and safe-places recommendation. The access control\nsubsystem prevents infected people from visiting public places to prevent\nspreading the virus, and the recommendation subsystem categorizes zones based\non the infection level so that people can avoid visiting contaminated zones.\nOur analysis demonstrates that our system is secure and preserves the privacy\nof the users against identification, social graph disclosure, and tracking\nattacks, while thwarting false reporting (or panic) attacks. Moreover, our\nextensive performance evaluations demonstrate the scalability of our system\n(which is desirable in pandemics) due to its low communication, computation,\nand storage overheads.\n"}
+{"abstract": "  Dynamics of the particle phase in a particle laden turbulent flow is highly\ninfluenced by the fluctuating velocity and vorticity field of the fluid phase.\nThe present work mainly focuses on exploring the possibility of applying a\nLangevin type of random torque model to predict the rotational dynamics of the\nparticle phase. Towards this objective, direct numerical simulations (DNS) have\nbeen carried out for particle laden turbulent shear flow with Reynolds number,\n$Re_\\delta=750$ in presence of sub-Kolmogorov sized inertial particles (Stokes\nnumber >>1). The inter-particle and wall-particle interactions have also been\nconsidered to be elastic. From the particle equation of rotational motion, we\narrive at the expression where the fluctuating angular acceleration fluctuation\n$\\alpha'_i$ of the particle is expressed as the ratio of a linear combination\nof fluctuating rotational velocities of particle $\\omega'_i$ and fluid angular\nvelocity $\\Omega'_i$ to the particle rotational relaxation time $\\tau_r$. The\nanalysis was done using p.d.f plots and Jensen-Shannon Divergence based method\nto assess the similarity of the particle net rotational acceleration\ndistribution $f(\\alpha'_i)$, with (i) the distributions of particle\nacceleration component arising from fluctuating fluid angular velocity computed\nin the particle-Largrangian frame $f(\\Omega'_i/\\tau_r)_{pl}$, (ii) fluctuating\nparticle angular velocity $f(\\omega'_i/\\tau_r)_{pl}$, and (iii) the fluid\nangular velocity $(\\Omega'_i/\\tau_r)_{e}$, computed in the fluid Eulerian\ngrids. The analysis leads to the conclusion that $f(\\alpha'_i)$ can be modeled\nwith a Gaussian white noise using a pre-estimated strength which can be\ncalculated from the temporal correlation of $(\\Omega'_i/\\tau_r)_{e}$.\n"}
+{"abstract": "  The nebular phase of lanthanide-rich ejecta of a neutron star merger (NSM) is\nstudied by using a one-zone model, in which the atomic properties are\nrepresented by a single species, neodymium (Nd). Under the assumption that\nbeta-decay of r-process nuclei is the heat and ionization source, we solve the\nionization and thermal balance of the ejecta under non-local thermodynamic\nequilibrium. The atomic data including energy levels, radiative transition\nrates, collision strengths, and recombination rate coefficients, are obtained\nby using atomic structure codes, GRASP2K and HULLAC. We find that both\npermitted and forbidden lines roughly equally contribute to the cooling rate of\nNd II and Nd III at the nebular temperatures. We show that the kinetic\ntemperature and ionization degree increase with time in the early stage of the\nnebular phase while these quantities become approximately independent of time\nafter the thermalization break of the heating rate because the processes\nrelevant to the ionization and thermalization balance are attributed to\ntwo-body collision between electrons and ions at later times. As a result, in\nspite of the rapid decline of the luminosity, the shape of the emergent\nspectrum does not change significantly with time after the break. We show that\nthe emission-line nebular spectrum of the pure Nd ejecta consists of a broad\nstructure from $0.5\\,\\mu m$ to $20\\,\\mu m$ with two distinct peaks around\n$1\\,\\mu m$ and $10\\,\\mu m$.\n"}
+{"abstract": "  We present new approaches for solving constrained multicomponent nonlinear\nSchr\\\"odinger equations in arbitrary dimensions. The idea is to introduce an\nartificial time and solve an extended damped second order dynamic system whose\nstationary solution is the solution to the time-independent nonlinear\nSchr\\\"odinger equation. Constraints are often considered by projection onto the\nconstraint set, here we include them explicitly into the dynamical system. We\nshow the applicability and efficiency of the methods on examples of relevance\nin modern physics applications.\n"}
+{"abstract": "  This paper proposes an operational measure of non-stochastic information\nleakage to formalize privacy against a brute-force guessing adversary. The\ninformation is measured by non-probabilistic uncertainty of uncertain\nvariables, the non-stochastic counterparts of random variables. For $X$ that is\nrelated to released data $Y$, the non-stochastic brute-force leakage is\nmeasured by the complexity of exhaustively checking all the possibilities of\nthe private attribute $U$ of $X$ by an adversary. The complexity refers to the\nnumber of trials to successfully guess $U$. Maximizing this leakage over all\npossible private attributes $U$ gives rise to the maximal (i.e., worst-case)\nnon-stochastic brute-force guessing leakage. This is proved to be fully\ndetermined by the minimal non-stochastic uncertainty of $X$ given $Y$, which\nalso determines the worst-case attribute $U$ indicating the highest privacy\nrisk if $Y$ is disclosed. The maximal non-stochastic brute-force guessing\nleakage is shown to be proportional to the non-stochastic identifiability of\n$X$ given $Y$ and upper bounds the existing maximin information. The latter\nquantifies the information leakage when an adversary must perfectly guess $U$\nin one-shot via $Y$. Experiments are used to demonstrate the tradeoff between\nthe maximal non-stochastic brute-force guessing leakage and the data utility\n(measured by the maximum quantization error) and to illustrate the relationship\nbetween maximin information and stochastic one-shot maximal leakage.\n"}
+{"abstract": "  Significance: Quantitative measurement of blood oxygen saturation (sO$_2$)\nwith photoacoustic (PA) imaging is one of the most sought after goals of\nquantitative PA imaging research due to its wide range of biomedical\napplications.\n  Aim: A method for accurate and applicable real-time quantification of local\nsO$_2$ with PA imaging.\n  Approach: We combine multiple illumination (MI) sensing with learned spectral\ndecoloring (LSD); training on Monte Carlo simulations of spectrally colored\nabsorbed energy spectra, in order to apply the trained models to real PA\nmeasurements. We validate our combined MI-LSD method on a highly reliable,\nreproducible and easily scalable phantom model, based on copper and nickel\nsulfate solutions.\n  Results: With this sulfate model we see a consistently high estimation\naccuracy using MI-LSD, with median absolute estimation errors of 2.5 to 4.5\npercentage points. We further find fewer outliers in MI-LSD estimates compared\nto LSD. Random forest regressors outperform previously reported neural network\napproaches. Conclusions: Random forest based MI-LSD is a promising method for\naccurate quantitative PA oximetry imaging.\n"}
+{"abstract": "  In the $SO(2,d)$ gauge theory formalism of AdS gravity established in\narXiv:1811.05286, the dynamics of bulk gravity is emergent from the vanishing\nof the boundary covariant anomaly for the $SO(2,d)$ conservation law. Parallel\nwith the known results of chiral anomalies, we establish the descendent\nstructure of the holographic $SO(2,d)$ anomaly. The corresponding anomaly\ncharacteristic class, bulk Chern-Simons like action as well as the boundary\neffective action are constructed systematically. The anomalous conservation law\nis presented both in terms of the covariant and consistent formalisms. Due to\nthe existence of the ruler field, not only the Bardeen-Zumino polynomial, but\nalso the covariant and consistent currents are explicitly constructed.\n"}
+{"abstract": "  We consider a method for estimating asymptotics of arithmetic functions in\nthe natural series in this paper. This question is closely related to the\ndistribution of prime numbers in natural numbers. Problems often arise even\nwith the definition of the asymptotics of the mean value of arithmetic\nfunctions, and even more so in the determination of asymptotics of the moments\nof higher orders. Therefore, the paper proposes a different from the\ntraditional, probabilistic approach based on the limiting distribution of\narithmetic functions. We will consider the general case of a method for\nestimating asymptotics of the moments of additive arithmetic functions having a\nlimit distribution in the paper. Several assertions are proved about the\nestimation of asymptotics of the moments of strongly additive arithmetic\nfunctions, as well as additive functions of the class $H$ having a limit\ndistribution.\n"}
+{"abstract": "  We consider on the torus the scaling limit of stochastic 2D (inviscid) fluid\ndynamical equations with transport noise to deterministic viscous equations.\nQuantitative estimates on the convergence rates are provided by combining\nanalytic and probabilistic arguments, especially heat kernel properties and\nmaximal estimates for stochastic convolutions. Similar ideas are applied to the\nstochastic 2D Keller-Segel model, yielding explicit choice of noise to ensure\nthat the blow-up probability is less than any given threshold. Our approach\nalso gives rise to some mixing property for stochastic linear transport\nequations and dissipation enhancement in the viscous case.\n"}
+{"abstract": "  We consider the problem of discriminating quantum states, where the task is\nto distinguish two different quantum states with a complete classical knowledge\nabout them, and the problem of classifying quantum states, where the task is to\ndistinguish two classes of quantum states where no prior classical information\nis available but a finite number of physical copies of each classes are given.\nIn the case the quantum states are represented by coherent states of light, we\nidentify intermediate scenarios where partial prior information is available.\nWe evaluate an analytical expression for the minimum error when the quantum\nstates are opposite and a prior on the amplitudes is known. Such a threshold is\nattained by complex POVM that involve highly non-linear optical procedure. A\nsuboptimal procedure that can be implemented with current technology is\npresented that is based on a modification of the conventional Dolinar receiver.\nWe study and compare the performance of the scheme under different assumptions\non the prior information available.\n"}
+{"abstract": "  Recently LHCb declared a new structure $X(6900)$ in the final state\ndi-$J/\\psi$ which is popularly regarded as a $cc$-$\\bar c\\bar c$ tetraquark\nstate. %popularly. Within the Bethe-Salpeter (B-S) frame we study the possible\n$cc$-$\\bar c\\bar c$ bound states and the interaction between diquark ($cc$) and\nantidiquark ($\\bar c\\bar c$). In this work $cc$ ($\\bar c\\bar c$) is treated as\na color anti-triplet (triplet) axial-vector so the quantum numbers of\n$cc$-$\\bar c\\bar c$ bound state are $0^+$, $1^+$ and $2^+$. Learning from the\ninteraction in meson case and using the effective coupling we suggest the\ninteraction kernel for diquark and antidiquark system. Then we deduce the B-S\nequations for different quantum numbers. Solving these equations numerically we\nfind the spectra of some excited states can be close to the mass of $X(6900)$\nwhen we assign appropriate values for parameter $\\kappa$ introduced in the\ninteraction (kernel).We also briefly calculate the spectra of $bb$-$\\bar b\\bar\nb$ bound states. Future measurement of $bb$-$\\bar b\\bar b$ state will help us\nto determine the exact form of effective interaction.\n"}
+{"abstract": "  We construct rogue wave solutions of a fifth-order nonlinear Schr\\\"odinger\nequation on the Jacobian elliptic function background. By combining Darboux\ntransformation and the nonlinearization of spectral problem, we generate rogue\nwave solution on two different periodic wave backgrounds. We analyze the\nobtained solutions for different values of system parameter and point out\ncertain novel features of our results. We also compute instability growth rate\nof both $dn$ and $cn$ periodic background waves for the considered system\nthrough spectral stability problem. We show that instability growth rate\ndecreases (increases) for $dn$-$(cn)$ periodic waves when we vary the value of\nthe elliptic modulus parameter.\n"}
+{"abstract": "  There are rich synchronized audio and visual events in our daily life. Inside\nthe events, audio scenes are associated with the corresponding visual objects;\nmeanwhile, sounding objects can indicate and help to separate their individual\nsounds in the audio track. Based on this observation, in this paper, we propose\na cyclic co-learning (CCoL) paradigm that can jointly learn sounding object\nvisual grounding and audio-visual sound separation in a unified framework.\nConcretely, we can leverage grounded object-sound relations to improve the\nresults of sound separation. Meanwhile, benefiting from discriminative\ninformation from separated sounds, we improve training example sampling for\nsounding object grounding, which builds a co-learning cycle for the two tasks\nand makes them mutually beneficial. Extensive experiments show that the\nproposed framework outperforms the compared recent approaches on both tasks,\nand they can benefit from each other with our cyclic co-learning.\n"}
+{"abstract": "  We study gradient-based regularization methods for neural networks. We mainly\nfocus on two regularization methods: the total variation and the Tikhonov\nregularization. Applying these methods is equivalent to using neural networks\nto solve some partial differential equations, mostly in high dimensions in\npractical applications. In this work, we introduce a general framework to\nanalyze the generalization error of regularized networks. The error estimate\nrelies on two assumptions on the approximation error and the quadrature error.\nMoreover, we conduct some experiments on the image classification tasks to show\nthat gradient-based methods can significantly improve the generalization\nability and adversarial robustness of neural networks. A graphical extension of\nthe gradient-based methods are also considered in the experiments.\n"}
+{"abstract": "  We present new conditions on the drift of the Morrey type with mixed norms\nallowing us to obtain Aleksandrov type estimates of potentials of time\ninhomogeneous diffusion processes in spaces with mixed norms and, for instance,\nin $L_{d_{0}+1}$ with $d_{0}<d$.\n"}
+{"abstract": "  Maternal exposure to environmental chemicals during pregnancy can alter birth\nand children's health outcomes. Research seeks to identify critical windows,\ntime periods when the exposures can change future health outcomes, and estimate\nthe exposure-response relationship. Existing statistical approaches focus on\nestimation of the association between maternal exposure to a single\nenvironmental chemical observed at high-temporal resolution, such as weekly\nthroughout pregnancy, and children's health outcomes. Extending to multiple\nchemicals observed at high temporal resolution poses a dimensionality problem\nand statistical methods are lacking. We propose a tree-based model for mixtures\nof exposures that are observed at high temporal resolution. The proposed\napproach uses an additive ensemble of structured tree-pairs that define\nstructured main effects and interactions between time-resolved predictors and\nvariable selection to select out of the model predictors not correlated with\nthe outcome. We apply our method in a simulation and the analysis of the\nrelationship between five exposures measured weekly throughout pregnancy and\nresulting birth weight in a Denver, Colorado birth cohort. We identified\ncritical windows during which fine particulate matter, sulfur dioxide, and\ntemperature are negatively associated with birth weight and an interaction\nbetween fine particulate matter and temperature. Software is made available in\nthe R package dlmtree.\n"}
+{"abstract": "  The general approach taken when training deep learning classifiers is to save\nthe parameters after every few iterations, train until either a human observer\nor a simple metric-based heuristic decides the network isn't learning anymore,\nand then backtrack and pick the saved parameters with the best validation\naccuracy. Simple methods are used to determine if a neural network isn't\nlearning anymore because, as long as it's well after the optimal values are\nfound, the condition doesn't impact the final accuracy of the model. However\nfrom a runtime perspective, this is of great significance to the many cases\nwhere numerous neural networks are trained simultaneously (e.g. hyper-parameter\ntuning). Motivated by this, we introduce a statistical significance test to\ndetermine if a neural network has stopped learning. This stopping criterion\nappears to represent a happy medium compared to other popular stopping\ncriterions, achieving comparable accuracy to the criterions that achieve the\nhighest final accuracies in 77% or fewer epochs, while the criterions which\nstop sooner do so with an appreciable loss to final accuracy. Additionally, we\nuse this as the basis of a new learning rate scheduler, removing the need to\nmanually choose learning rate schedules and acting as a quasi-line search,\nachieving superior or comparable empirical performance to existing methods.\n"}
+{"abstract": "  We study future coherent elastic neutrino-nucleus scattering (CE$\\nu$NS)\nmodifications from a variety of possible models at the Coherent CAPTAIN Mills\n(CCM) experiment at Los Alamos. We show that large regions of Non-Standard\nNeutrino Interaction (NSI) parameter space will be excluded rapidly, and that\nstringent new bounds on the gauge coupling in $Z'$ models will also be placed.\nAs a result, CCM will be able to rule out LMA-D solutions for a large class of\nmodels with MeV-scale mediators.\n"}
+{"abstract": "  Our current understanding of interstellar carbon fractionation hinges on the\ninterpretation of astrochemical kinetic models. Yet, the various reactions\nincluded carry large uncertainties in their (estimated) rate coefficients,\nnotably those involving C with C$_{2}$. In this work, we provide theoretical\nthermal rate coefficients as a function of the temperature for all possible\ngas-phase isotope-exchange reactions of\nC+C$_{2}(X^{1}\\Sigma_{g}^{+},a^{3}\\Pi_{u})$. For this, we employ the\nquasi-classical trajectory method, with the previously obtained potential\nenergy surfaces of C$_{3}$ dictating the forces between the colliding partners.\nThe calculated rate coefficients show a positive temperature dependence and are\nmarkedly different from previous theoretical estimates. While the forward\nreactions are fast and inherently exothermic owing to the lower zero-point\nenergy content of the products, the reverse processes have temperature\nthresholds. For each reaction considered, analytic three-parameter\nArrhenius-Kooij formulas are provided that readily interpolate/extrapolate the\nassociated forward and backward rates. These forms can further be introduced in\nastrochemical networks. Apart from the proper kinetic attributes, we also\nprovide equilibrium constants for these processes, thence confirming their\nprominence in the overall C fractionation chemistry. In this respect, the\n$^{13}$C + $^{12}$C$_{2}(X^{1}{\\Sigma}^{+}_{g})$ and $^{13}$C +\n$^{12}$C$_{2}(a^{3}{\\Pi}_{u})$ reactions are found to be particularly\nconspicuous, notably at the typical temperatures of dense molecular clouds. For\nthese reactions and considering both equilibrium and time-dependent chemistry,\ntheoretical $^{12}$C/$^{13}$C ratios as a function of the gas kinetic\ntemperature are also derived and shown to be consistent with available model\nchemistry and observational data on C$_{2}$\n"}
+{"abstract": "  This paper addresses the problem of detecting multidimensional subspace\nsignals, which model range-spread targets, in noise of unknown covariance. It\nis assumed that a primary channel of measurements, possibly consisting of\nsignal plus noise, is augmented with a secondary channel of measurements\ncontaining only noise. The noises in these two channels share a common\ncovariance matrix, up to a scale, which may be known or unknown. The signal\nmodel is a subspace model with variations: the subspace may be known or known\nonly by its dimension; consecutive visits to the subspace may be unconstrained\nor they may be constrained by a prior distribution. As a consequence, there are\nfour general classes of detectors and, within each class, there is a detector\nfor the case where the scale between the primary and secondary channels is\nknown, and for the case where this scale is unknown. The generalized likelihood\nratio (GLR) based detectors derived in this paper, when organized with\npreviously published GLR detectors, comprise a unified theory of adaptive\nsubspace detection from primary and secondary channels of measurements.\n"}
+{"abstract": "  Recently, significant advancements have been made in face recognition\ntechnologies using Deep Neural Networks. As a result, companies such as\nMicrosoft, Amazon, and Naver offer highly accurate commercial face recognition\nweb services for diverse applications to meet the end-user needs. Naturally,\nhowever, such technologies are threatened persistently, as virtually any\nindividual can quickly implement impersonation attacks. In particular, these\nattacks can be a significant threat for authentication and identification\nservices, which heavily rely on their underlying face recognition technologies'\naccuracy and robustness. Despite its gravity, the issue regarding deepfake\nabuse using commercial web APIs and their robustness has not yet been\nthoroughly investigated. This work provides a measurement study on the\nrobustness of black-box commercial face recognition APIs against Deepfake\nImpersonation (DI) attacks using celebrity recognition APIs as an example case\nstudy. We use five deepfake datasets, two of which are created by us and\nplanned to be released. More specifically, we measure attack performance based\non two scenarios (targeted and non-targeted) and further analyze the differing\nsystem behaviors using fidelity, confidence, and similarity metrics.\nAccordingly, we demonstrate how vulnerable face recognition technologies from\npopular companies are to DI attack, achieving maximum success rates of 78.0%\nand 99.9% for targeted (i.e., precise match) and non-targeted (i.e., match with\nany celebrity) attacks, respectively. Moreover, we propose practical defense\nstrategies to mitigate DI attacks, reducing the attack success rates to as low\nas 0% and 0.02% for targeted and non-targeted attacks, respectively.\n"}
+{"abstract": "  It is shown that for the one-dimensional quantum quartic anharmonic\noscillator the numerical results obtained by Okun-Burke in Ref.2 are easily\nreproduced and can be significantly improved in Lagrange Mesh Method (based on\nnon-uniform lattice).\n"}
+{"abstract": "  The total extragalactic $\\gamma$-ray flux provides a powerful probe into the\norigin and evolution of the highest energy processes in our universe. An\nimportant component of this emission is the isotropic $\\gamma$-ray background\n(IGRB), composed of sources that cannot be individually resolved by current\nexperiments. Previous studies have determined that the IGRB can be dominated by\neither misaligned active galactic nuclei (mAGN) or star-forming galaxies\n(SFGs). However, these analyses are limited, because they have utilized binary\nsource classifications and examined only one source class at a time. We perform\nthe first combined joint-likelihood analysis that simultaneously correlates the\n$\\gamma$-ray luminosity of extragalactic objects with both star-formation and\nmAGN activity. We find that SFGs produce 48$^{+33}_{-20}$\\% of the total IGRB\nat~1 GeV and 56$^{+40}_{-23}$\\% of the total IGRB at 10 GeV. The contribution\nof mAGN is more uncertain, but can also be significant. Future work to quantify\nthe radio and infrared properties of nearby galaxies could significantly\nimprove these constraints.\n"}
+{"abstract": "  In this paper, we derive an analytic characterization of the symmetric\nextension of a Herglotz-Nevanlinna function in several variables. Here, the\nmain tools used are the so-called variable non-dependence property and the\nsymmetry formula satisfied by Herglotz-Nevanlinna function and Cauchy-type\nfunctions. We also provide an extension of the Stieltjes inversion formula for\nCauchy-type functions.\n"}
+{"abstract": "  We consider a wireless source localization network in which a target node\nemits localization signals that are used by anchor nodes to estimate the target\nnode position. In addition to target and anchor nodes, there can also exist\neavesdropper nodes and jammer nodes which aim to estimate the position of the\ntarget node and to degrade the accuracy of localization, respectively. We first\npropose the problem of eavesdropper selection with the goal of optimally\nplacing a given number of eavesdropper nodes to a subset of possible positions\nin the network to estimate the target node position as accurately as possible.\nAs the performance metric, the Cramer-Rao lower bound (CRLB) related to the\nestimation of the target node position by eavesdropper nodes is derived, and\nits convexity and monotonicity properties are investigated. By relaxing the\ninteger constraints, the eavesdropper selection problem is approximated by a\nconvex optimization problem and algorithms are proposed for eavesdropper\nselection. Moreover, in the presence of parameter uncertainty, a robust version\nof the eavesdropper selection problem is developed. Then, the problem of jammer\nselection is proposed where the aim is to optimally place a given number of\njammer nodes to a subset of possible positions for degrading the localization\naccuracy of the network as much as possible. A CRLB expression from the\nliterature is used as the performance metric, and its concavity and\nmonotonicity properties are derived. Also, a convex optimization problem and\nits robust version are derived after relaxation. Moreover, the joint\neavesdropper and jammer selection problem is proposed with the goal of placing\ncertain numbers of eavesdropper and jammer nodes to a subset of possible\npositions. Simulation results are presented to illustrate performance of the\nproposed algorithms.\n"}
+{"abstract": "  We construct and analyze first- and second-order implicit-explicit (IMEX)\nschemes based on the scalar auxiliary variable (SAV) approach for the\nmagneto-hydrodynamic equations. These schemes are linear, only require solving\na sequence of linear differential equations with constant coefficients at each\ntime step, and are unconditionally energy stable. We derive rigorous error\nestimates for the velocity, pressure and magnetic field of the first-order\nscheme in the two dimensional case without any condition on the time step.\nNumerical examples are presented to validate the proposed schemes.\n"}
+{"abstract": "  For crystalline materials with long-range orders, the phonon modes involved\nin the phonon-assisted radiation process are generally involving one or several\nphonons with specific vibration frequencies 1-4. In some glassy material, the\nphonon modes broaden in a short range 5-7. However, the locally distinct\nchemical environments or mass disorder in high-entropy systems can induce an\nanharmonic phonon-phonon coupling and composition disorder, which leads to\nsignificant phonon broadening 8,9.The terminology of high-entropy comes from\nthe high configuration entropy larger than 1.5R (R is the gas constant), which\nresults from randomly distributed multiple nearly equal components in a crystal\nlattice 10,11. Inspired by the high-entropy strategy, we deployed a\nhigh-entropy glass system (HEGS) doped with neodymium ions, which exhibits a\ncomplex structure with tetrahedral voids filled by different ions, including,\nLi+, Zn2+, Si4+, P5+, S6+, etc. Phonon spectral broadening up to thousands of\nwavenumbers in the HEGS allows strong wide-band absorption in both the\nnear-infrared and mid-infrared ranges and assists the system radiation, i.e.,\nbroadened phonon-assisted wide-band radiation (BPAWR). The subsequent\nlow-threshold self-absorption coherence modulation (SACM) was also observed in\nthe HEGS, modulated by changing excitation wavelengths, sample size, and doping\nconcentrations. The time delay of the BPAWR signal is up to 1.66 ns relative to\nthe zero-delay signal, while the time delay of the Raman process is typically\nin the order of fs to ps, rarely up to 10 ps 12-15.The BPAWR-SACM can be\napplied to realize signal amplification of the centered non-absorption band\nwhen dual-wavelength lasers pump the HEGS sample, and signal amplification can\nbe up to 26.02 dB. The spectral characteristics of the BPAWR and the dynamics\nof the energy distribution of the excited species are investigated in detail.\n"}
+{"abstract": "  Certification and quantification of correlations for multipartite states of\nquantum systems appear to be a central task in quantum information theory. We\ngive here a unitary quantum-mechanical perspective of both entanglement and\nEinstein-Podolsky-Rosen (EPR) steering of continuous-variable multimode states.\nThis originates in the Heisenberg uncertainty relations for the canonical\nquadrature operators of the modes. Correlations of two-party $(N\\, \\text{vs}\n\\,1)$-mode states are examined by using the variances of a pair of suitable\nEPR-like observables. It turns out that the uncertainty sum of these nonlocal\nvariables is bounded from below by local uncertainties and is strengthened\ndifferently for separable states and for each one-way unsteerable ones. The\nanalysis of the minimal properly normalized sums of these variances yields\nnecessary conditions of separability and EPR unsteerability of $(N\\, \\text{vs}\n\\,1)$-mode states in both possible ways of steering. When the states and the\nperformed measurements are Gaussian, then these conditions are precisely the\npreviously-known criteria of separability and one-way unsteerability.\n"}
+{"abstract": "  We study identifying user clusters in contextual multi-armed bandits (MAB).\nContextual MAB is an effective tool for many real applications, such as content\nrecommendation and online advertisement. In practice, user dependency plays an\nessential role in the user's actions, and thus the rewards. Clustering similar\nusers can improve the quality of reward estimation, which in turn leads to more\neffective content recommendation and targeted advertising. Different from\ntraditional clustering settings, we cluster users based on the unknown bandit\nparameters, which will be estimated incrementally. In particular, we define the\nproblem of cluster detection in contextual MAB, and propose a bandit algorithm,\nLOCB, embedded with local clustering procedure. And, we provide theoretical\nanalysis about LOCB in terms of the correctness and efficiency of clustering\nand its regret bound. Finally, we evaluate the proposed algorithm from various\naspects, which outperforms state-of-the-art baselines.\n"}
+{"abstract": "  We analyze the structure and stickiness in the chaotic components of generic\nHamiltonian systems with divided phase space. Following the method proposed\nrecently in Lozej and Robnik [Phys. Rev. E 98, 022220 (2018)], the sticky\nregions are identified using the statistics of recurrence times of a single\nchaotic orbit into cells dividing the phase space into a grid. We perform\nextensive numerical studies of three example systems: the Chirikov standard\nmap, the family of Robnik billiards, and the family of lemon billiards. The\nfilling of the cells is compared to the random model of chaotic diffusion,\nintroduced in Robnik et al. [J. Phys. A: Math. Gen. 30, L803 (1997)] for the\ndescription of transport in the phase spaces of ergodic systems. The model is\nbased on the assumption of completely uncorrelated cell visits because of the\nstrongly chaotic dynamics of the orbit and the distribution of recurrence times\nis exponential. In generic systems the stickiness induces correlations in the\ncell visits. The distribution of recurrence times exhibits a separation of\ntimescales because of the dynamical trapping. We model the recurrence time\ndistributions to cells inside sticky areas as a mixture of exponential\ndistributions with different decay times. We introduce the variable S, which is\nthe ratio between the standard deviation and the mean of the recurrence times\nas a measure of stickiness. We use S to globally assess the distributions of\nrecurrence times. We find that in the bulk of the chaotic sea S=1, while S>1 in\nareas of stickiness. We present the results in the form of animated grayscale\nplots of the variable S in the largest chaotic component for the three example\nsystems, included as supplemental material to this paper.\n"}
+{"abstract": "  Within the framework of quantum molecular dynamics transport model, the\nisospin and in-medium effects on the hyperon production in the reaction of\n$^{197}$Au + $^{197}$Au are investigated thoroughly. A repulsive\nhyperon-nucleon potential from the chiral effective field theory is implemented\ninto the model, which is related to the hyperon momentum and baryon density.\nThe correction on threshold energy of the elementary hyperon cross section is\ntaken into account. It is found that the $\\Sigma$ yields are suppressed in the\ndomain of midrapidity and kinetic energy spectra with the potential. The\nhyperons are emitted in the reaction plane because of the strangeness exchange\nreaction and reabsorption process in nuclear medium. The\n$\\Sigma^{-}/\\Sigma^{+}$ ratio depends on the stiffness of nuclear symmetry\nenergy, in particular in the high-energy region (above 500 MeV).\n"}
+{"abstract": "  We consider a biological network of the hippocampal dentate gyrus (DG). The\nDG is a pre-processor for pattern separation which facilitates pattern storage\nand retrieval in the CA3 area of the hippocampus. The main encoding cells in\nthe DG are the granule cells (GCs) which receive the input from the entorhinal\ncortex (EC) and send their output to the CA3. The activation degree of GCs is\nso low (~ 5%). This sparsity has been thought to enhance the pattern\nseparation. We investigate the dynamical origin for winner-take-all (WTA)\ncompetition which leads to sparse activation of the GCs. The whole GCs are\ngrouped into lamellar clusters. In each GC cluster, there is one inhibitory (I)\nbasket cell (BC) along with excitatory (E) GCs. There are three kinds of\nexternal inputs into the GCs; the direct excitatory EC input, the indirect\ninhibitory EC input, mediated by the HIPP (hilar perforant path-associated)\ncells, and the excitatory input from the hilar mossy cells (MCs). The firing\nactivities of the GCs are determined via competition between the external E and\nI inputs. The E-I conductance ratio ${\\cal{R}}_{\\rm E-I}^{\\rm (con)*}$ (given\nby the time average of the external E to I conductances) may represents well\nthe degree of such external E-I input competition. GCs become active when their\n$\\cal{R}_{\\rm E-I}^{\\rm (con)*}$ is larger than a threshold ${\\cal{R}}_{th}^*$,\nand then the mean firing rates of the active GCs are strongly correlated with\n$\\cal{R}_{\\rm E-I}^{\\rm (con)*}$. In each GC cluster, the feedback inhibition\nof the BC may select the winner GCs. GCs with larger $\\cal{R}_{\\rm E-I}^{\\rm\n(con)*}$ than the threshold ${\\cal{R}}_{th}^*$ survive, and they become\nwinners; all the other GCs with smaller $\\cal{R}_{\\rm E-I}^{\\rm (con)*}$ become\nsilent. The WTA competition occurs via competition between the firing activity\nof the GCs and the feedback inhibition from the BC in each GC cluster.\n"}
+{"abstract": "  Narrow escape and narrow capture problems which describe the average times\nrequired to stop the motion of a randomly travelling particle within a domain\nhave applications in various areas of science. While for general domains, it is\nknown how the escape time decreases with the increase of the trap sizes, for\nsome specific 2D and 3D domains, higher-order asymptotic formulas have been\nestablished, providing the dependence of the escape time on the sizes and\nlocations of the traps. Such results allow the use of global optimization to\nseek trap arrangements that minimize average escape times. In a recent paper,\nthe escape time expansion for a 2D elliptic domain was derived, providing the\ndependence of the average MFPT on sizes and locations of small internal traps.\nThe goal of this work is to systematically seek global minima of MFPT for\n$1\\leq N\\leq 50$ traps, and compare the corresponding putative optimal trap\narrangements for different values of the domain eccentricity.\n"}
+{"abstract": "  Agile software teams are expected to follow a number of specific Team\nPractices (TPs) during each iteration, such as estimating the effort (\"points\")\nrequired to complete user stories and coordinating the management of the\ncodebase with the delivery of features. For software engineering instructors\ntrying to teach such TPs to student teams, manually auditing teams if teams are\nfollowing the TPs and improving over time is tedious, time-consuming and\nerror-prone. It is even more difficult when those TPs involve two or more\ntools. For example, starting work on a feature in a project-management tool\nsuch as Pivotal Tracker should usually be followed relatively quickly by the\ncreation of a feature branch on GitHub. Merging a feature branch on GitHub\nshould usually be followed relatively quickly by deploying the new feature to a\nstaging server for customer feedback. Few systems are designed specifically to\naudit such TPs, and existing ones, as far as we know, are limited to a single\nspecific tool.\n  We present Bluejay, an open-source extensible platform that uses the APIs of\nmultiple tools to collect raw data, synthesize it into TP measurements, and\npresent dashboards to audit the TPs. A key insight in Bluejay's design is that\nTPs can be expressed in terminology similar to that used for modeling and\nauditing Service Level Agreement (SLA) compliance. Bluejay therefore builds on\nmature tools used in that ecosystem and adapts them for describing, auditing,\nand reporting on TPs. Bluejay currently consumes data from five different\nwidely-used development tools, and can be customized by connecting it to any\nservice with a REST API. Video showcase available at\ngovernify.io/showcase/bluejay\n"}
+{"abstract": "  Recently, there is an effort to extend fine-grained entity typing by using a\nricher and ultra-fine set of types, and labeling noun phrases including\npronouns and nominal nouns instead of just named entity mentions. A key\nchallenge for this ultra-fine entity typing task is that human annotated data\nare extremely scarce, and the annotation ability of existing distant or weak\nsupervision approaches is very limited. To remedy this problem, in this paper,\nwe propose to obtain training data for ultra-fine entity typing by using a BERT\nMasked Language Model (MLM). Given a mention in a sentence, our approach\nconstructs an input for the BERT MLM so that it predicts context dependent\nhypernyms of the mention, which can be used as type labels. Experimental\nresults demonstrate that, with the help of these automatically generated\nlabels, the performance of an ultra-fine entity typing model can be improved\nsubstantially. We also show that our approach can be applied to improve\ntraditional fine-grained entity typing after performing simple type mapping.\n"}
+{"abstract": "  In this paper, we prove that Crouzeix-Raviart finite elements of polynomial\norder $p\\geq5$, $p$ odd, are inf-sup stable for the Stokes problem on\ntriangulations. For $p\\geq4$, $p$ even, the stability was proved by \\'{A}.\nBaran and G. Stoyan in 2007 by using the \\textit{macroelement technique,} a\n\\textit{dimension formula}, the concept of \\textit{critical points} in a\ntriangulation and a representation of the corresponding \\textit{critical\nfunctions}. Baran and Stoyan proved that these critical functions belong to the\nrange of the divergence operator applied to Crouzeix-Raviart velocity functions\nand the macroelement technique implies the inf-sup stability.\n  The generalization of this theory to cover odd polynomial orders $p\\geq5$ is\ninvolved; one reason is that the macroelement classes, which have been used for\neven $p$, are unsuitable for odd $p$. In this paper, we introduce a new and\nsimple representation of non-conforming Crouzeix-Raviart basis functions of odd\ndegree. We employ only one type of macroelement and derive representations of\nall possible critical functions. Finally, we show that they are in the range of\nthe divergence operator applied to Crouzeix-Raviart velocities from which the\nstability of the discretization follows.\n"}
+{"abstract": "  In this work we study the coexistence in the same Radio Access Network (RAN)\nof two generic services present in the Fifth Generation (5G) of wireless\ncommunication systems: enhanced Mobile BroadBand (eMBB) and massive\nMachine-Type Communications (mMTC). eMBB services are requested for\napplications that demand extremely high data rates and moderate requirements on\nlatency and reliability, whereas mMTC enables applications for connecting a\nmassive number of low-power and low-complexity devices. The coexistence of both\nservices is enabled by means of network slicing and Non-Orthogonal Multiple\nAccess (NOMA) with Successive Interference Cancellation (SIC) decoding. Under\nthe orthogonal slicing, the radio resources are exclusively allocated to each\nservice, while in the non-orthogonal slicing the traffics from both services\noverlap in the same radio resources. We evaluate the uplink performance of both\nservices in a scenario with a multi-antenna Base Station (BS). Our simulation\nresults show that the performance gains obtained through multiple receive\nantennas are more accentuated for the non-orthogonal slicing than for the\northogonal allocation of resources, such that the non-orthogonal slicing\noutperforms its orthogonal counterpart in terms of achievable data rates or\nnumber of connected devices as the number of receive antennas increases.\n"}
+{"abstract": "  Resting-state fMRI has been shown to provide surrogate biomarkers for the\nanalysis of various diseases. In addition, fMRI data helps in understanding the\nbrain's functional working during resting state and task-induced activity. To\nimprove the statistical power of biomarkers and the understanding mechanism of\nthe brain, pooling of multi-center studies has become increasingly popular. But\npooling the data from multiple sites introduces variations due to hardware,\nsoftware, and environment. In this paper, we look at the estimation problem of\nhierarchical Sparsity Connectivity Patterns (hSCPs) in fMRI data acquired on\nmultiple sites. We introduce a simple yet effective matrix factorization based\nformulation to reduce site-related effects while preserving biologically\nrelevant variations. We leverage adversarial learning in the unsupervised\nregime to improve the reproducibility of the components. Experiments on\nsimulated datasets display that the proposed method can estimate components\nwith improved accuracy and reproducibility. We also demonstrate the improved\nreproducibility of the components while preserving age-related variation on a\nreal dataset compiled from multiple sites.\n"}
+{"abstract": "  We propose an end-to-end deep learning framework that comprehensively solves\nthe inverse wave scattering problem across all length scales. Our framework\nconsists of the newly introduced wide-band butterfly network coupled with a\nsimple training procedure that dynamically injects noise during training. While\nour trained network provides competitive results in classical imaging regimes,\nmost notably it also succeeds in the super-resolution regime where other\ncomparable methods fail. This encompasses both (i) reconstruction of scatterers\nwith sub-wavelength geometric features, and (ii) accurate imaging when two or\nmore scatterers are separated by less than the classical diffraction limit. We\ndemonstrate these properties are retained even in the presence of strong noise\nand extend to scatterers not previously seen in the training set. In addition,\nour network is straightforward to train requiring no restarts and has an online\nruntime that is an order of magnitude faster than optimization-based\nalgorithms. We perform experiments with a variety of wave scattering mediums\nand we demonstrate that our proposed framework outperforms both classical\ninversion and competing network architectures that specialize in oscillatory\nwave scattering data.\n"}
+{"abstract": "  Biologically-inspired robots are a very interesting and difficult branch of\nrobotics dues to its very rich dynamical and morphological complexities. Among\nthem, flying animals, such as bats, have been among the most difficult to take\ninspiration from as they exhibit complex wing articulation. We attempt to\ncapture several of the key degrees-of-freedom that are present in the natural\nflapping gait of a bat. In this work, we present the mechanical design and\nanalysis of our flapping wing robot, the Aerobat, where we capture the plunging\nand flexion-extension in the bat's flapping modes. This robot utilizes gears,\ncranks, and four-bar linkage mechanisms to actuate the arm-wing structure\ncomposed of rigid and flexible components monolithically fabricated using\nPolyJet 3D printing. The resulting robot exhibits wing expansion and retraction\nduring the downstroke and upstroke respectively which minimizes the negative\nlift and results in a more efficient flapping gait.\n"}
+{"abstract": "  Many research papers that propose models to predict the course of the\nCOVID-19 pandemic either use handcrafted statistical models or large neural\nnetworks. Even though large neural networks are more powerful than simpler\nstatistical models, they are especially hard to train on small datasets. This\npaper not only presents a model with grater flexibility than the other proposed\nneural networks, but also presents a model that is effective on smaller\ndatasets. To improve performance on small data, six regularisation methods were\ntested. The results show that the GRU combined with 20% Dropout achieved the\nlowest RMSE scores. The main finding was that models with less access to data\nrelied more on the regulariser. Applying Dropout to a GRU model trained on only\n28 days of data reduced the RMSE by 23%.\n"}
+{"abstract": "  Not all experiments publish their results with a description of the\ncorrelations between the data points. This makes it difficult to do hypothesis\ntests or model fits with that data, since just assuming no correlation can lead\nto an over- or underestimation of the resulting uncertainties. This work\npresents robust test statistics that can be used with data sets with missing\ncorrelation information. They are exact in the case of no correlation and\neither guaranteed to be conservative -- i.e. the uncertainty is never\nunderestimated -- in the presence of correlations, or they are also exact in\nthe degenerate case of perfect correlation between the data points.\n"}
+{"abstract": "  We describe a single beam compact Spin Exchange Relaxation Free(SERF)\nmagnetometer whose configuration is compatible with the silicon-glass bonding\nmicro-machining method. A cylindrical vapor cell with 3mm diameter and 3mm in\nlength is utilized in the magnetometer. In order to reduce the wall relaxation\nwhich could not be neglected in micro-machined SERF magnetometer, 3\nAmagats(1Amagat=2.69$\\times$ 10$^{19}$/cm$^3$) neon buffer gas is filled in the\nvapor cell and this is the first demonstration of a Cs-Ne SERF magnetometer. We\nalso did a simulation to show that neon is a better buffer gas than nitrogen\nand helium which is typical utilized in vapor cells. In order to reduce the\nlaser amplitude noise and the large background detection offset which is\nreported to be the main noise source of a single beam absorption SERF\nmagnetometer, we developed a laser power differential method and a factor of 2\nimprovement of the power noise suppression has been demonstrated. Finally, we\ndid an optimization of the magnetometer and sensitivity of 40$fT/Hz^{1/2}$@30Hz\nhas been achieved.\n"}
+{"abstract": "  Current network access infrastructures are characterized by heterogeneity,\nlow latency, high throughput, and high computational capability, enabling\nmassive concurrent connections and various services. Unfortunately, this design\ndoes not pay significant attention to mobile services in underserved areas. In\nthis context, the use of aerial radio access networks (ARANs) is a promising\nstrategy to complement existing terrestrial communication systems. Involving\nairborne components such as unmanned aerial vehicles, drones, and satellites,\nARANs can quickly establish a flexible access infrastructure on demand. ARANs\nare expected to support the development of seamless mobile communication\nsystems toward a comprehensive sixth-generation (6G) global access\ninfrastructure. This paper provides an overview of recent studies regarding\nARANs in the literature. First, we investigate related work to identify areas\nfor further exploration in terms of recent knowledge advancements and analyses.\nSecond, we define the scope and methodology of this study. Then, we describe\nARAN architecture and its fundamental features for the development of 6G\nnetworks. In particular, we analyze the system model from several perspectives,\nincluding transmission propagation, energy consumption, communication latency,\nand network mobility. Furthermore, we introduce technologies that enable the\nsuccess of ARAN implementations in terms of energy replenishment, operational\nmanagement, and data delivery. Subsequently, we discuss application scenarios\nenvisioned for these technologies. Finally, we highlight ongoing research\nefforts and trends toward 6G ARANs.\n"}
+{"abstract": "  The analysis of government proposal during elections from political parties\nis vital to choose the next authorities in any city or country. In this paper,\nwe use a text mining approach to analyze the documents and provide an easy\nvisualization to support an easy analysis. Besides, a comparison with a\nnational plan based on sustainable development objectives of UN(United Nations)\nfrom 2030 Agenda is perfomed using Natural Language techniques.\n"}
+{"abstract": "  We analyse the use of wavelet transform in quantum field theory models\nwritten in light-front coordinates. In a recent paper [W.N Polyzou, Phys. Rev.\nD 101(2020) 096004] W.N.Polyzou used $x^+$ variable as 'time', and applied\nwavelet transform to the 'spatial' coordinates only. This makes the theory\nasymmetric with respect to space and time coordinates. In present paper we\ngeneralise the concept of continuous causal path, which is the basis of path\nintegration, to the sequences of causally ordered spacetime regions, and\npresent evaluation rules for Feynman path integrals over such sequences in\nterms of wavelet transform. Both the path integrals and the wavelet transform\nin our model are symmetric with respect to the light-front variables\n($x^+,x^-$). The definition of a spacetime event in our generalization is very\nmuch like the definition of event in probability theory.\n"}
+{"abstract": "  Machine learning has achieved tremendous success in a variety of domains in\nrecent years. However, a lot of these success stories have been in places where\nthe training and the testing distributions are extremely similar to each other.\nIn everyday situations when models are tested in slightly different data than\nthey were trained on, ML algorithms can fail spectacularly. This research\nattempts to formally define this problem, what sets of assumptions are\nreasonable to make in our data and what kind of guarantees we hope to obtain\nfrom them. Then, we focus on a certain class of out of distribution problems,\ntheir assumptions, and introduce simple algorithms that follow from these\nassumptions that are able to provide more reliable generalization. A central\ntopic in the thesis is the strong link between discovering the causal structure\nof the data, finding features that are reliable (when using them to predict)\nregardless of their context, and out of distribution generalization.\n"}
+{"abstract": "  Deep learning models with large learning capacities often overfit to medical\nimaging datasets. This is because training sets are often relatively small due\nto the significant time and financial costs incurred in medical data\nacquisition and labelling. Data augmentation is therefore often used to expand\nthe availability of training data and to increase generalization. However,\naugmentation strategies are often chosen on an ad-hoc basis without\njustification. In this paper, we present an augmentation policy search method\nwith the goal of improving model classification performance. We include in the\naugmentation policy search additional transformations that are often used in\nmedical image analysis and evaluate their performance. In addition, we extend\nthe augmentation policy search to include non-linear mixed-example data\naugmentation strategies. Using these learned policies, we show that principled\ndata augmentation for medical image model training can lead to significant\nimprovements in ultrasound standard plane detection, with an an average\nF1-score improvement of 7.0% overall over naive data augmentation strategies in\nultrasound fetal standard plane classification. We find that the learned\nrepresentations of ultrasound images are better clustered and defined with\noptimized data augmentation.\n"}
+{"abstract": "  By using extensive tensor network calculations, we map out the phase diagram\nof the frustrated $J_1$-$J_2$ Ising model on the square lattice. In particular,\nwe focus on the cases with controversy in the phase diagram, especially the\nstripe transition in the regime $g = |J_2/J_1|>\\frac{1}{2}$, $(J_2>0,J_1<0)$.\nWhile recent studies claimed that the phase transition is of first order when\n$\\frac{1}{2}<g<g^*$ (with the smallest $g^*$ being $0.67$), our simulations\nsuggest that if there is such a first-order region, it is smaller than those\nfound in earlier studies by other methods. Combining with the analysis of\ncritical properties, we provide evidence that the classical $J_1$-$J_2$ model\nevolves continuously from two decoupled Ising models ($g\\to\\infty$ with central\ncharge $c = 1$) to a point belonging to the tricritical Ising universality\nclass (with $c = 0.7$) as $g$ decreases to $g^*\\simeq 0.54$.\n"}
+{"abstract": "  Plutonium-based compounds establish an ideal platform for exploring the\ninterplay between long-standing itinerant-localized 5$f$ states and strongly\ncorrelated electronic states. In this paper, we exhaustively investigate the\ncorrelated 5$f$ electronic states of PuSn$_3$ dependence on temperature by\nmeans of a combination of the density functional theory and the embedded\ndynamical mean-field theory. It is found that the spectral weight of narrow\n5$f$ band grows significantly and remarkable quasiparticle multiplets appear\naround the Fermi level at low temperature. A striking $c-f$ hybridization and\nprominent valence state fluctuations indicate the advent of coherence and\nitinerancy of 5$f$ states. It is predicted that a 5$f$ localized to itinerant\ncrossover is induced by temperature accompanied by the change in Fermi surface\ntopology. Therefore itinerant 5$f$ states are inclined to take in active\nchemical bonding, suppressing the formation of local magnetic moment of Pu\natoms, which partly elucidates the intrinsic feature of paramagnetic ground\nstate of PuSn$_3$. Furthermore, the 5$f$ electronic correlations are orbital\nselective manifested themselves in differentiated band renormalizations and\nelectron effective masses. Consequently, the convincing results remain crucial\nto our understanding of plutonium-based compounds and promote ongoing research.\n"}
+{"abstract": "  Semi-supervised learning on class-imbalanced data, although a realistic\nproblem, has been under studied. While existing semi-supervised learning (SSL)\nmethods are known to perform poorly on minority classes, we find that they\nstill generate high precision pseudo-labels on minority classes. By exploiting\nthis property, in this work, we propose Class-Rebalancing Self-Training\n(CReST), a simple yet effective framework to improve existing SSL methods on\nclass-imbalanced data. CReST iteratively retrains a baseline SSL model with a\nlabeled set expanded by adding pseudo-labeled samples from an unlabeled set,\nwhere pseudo-labeled samples from minority classes are selected more frequently\naccording to an estimated class distribution. We also propose a progressive\ndistribution alignment to adaptively adjust the rebalancing strength dubbed\nCReST+. We show that CReST and CReST+ improve state-of-the-art SSL algorithms\non various class-imbalanced datasets and consistently outperform other popular\nrebalancing methods. Code has been made available at\nhttps://github.com/google-research/crest.\n"}
+{"abstract": "  We address in this work the instrumental systematic errors that can\npotentially affect the forthcoming and future Cosmic Microwave Background\nexperiments aimed at observing its polarized emission. In particular, we focus\non the systematics induced by the beam and calibration, which are considered\nthe major sources of leakage from total intensity measurements to polarization.\nWe simulated synthetic data sets with Time-Ordered Astrophysics Scalable Tools,\na publicly available simulation and data analysis package. We also propose a\nmitigation technique aiming at reducing the leakage by means of a template\nfitting approach. This technique has shown promising results reducing the\nleakage by 2 orders of magnitude at the power spectrum level when applied to a\nrealistic simulated data set of the LiteBIRD satellite mission.\n"}
+{"abstract": "  Aluminum nitride is piezoelectric and exhibits spontaneous polarization along\nthe $c$-axis, but the polarization cannot be switched by applying an electric\nfield. Adding Sc to AlN enhances the piezoelectric properties, and can make the\nalloy ferroelectric. We perform a detailed first-principles analysis of\nspontaneous and piezoelectric polarization. Comparisons between explicit\nsupercell calculations show that the virtual crystal approximation produces\naccurate results for polarization, but falls short in describing the phase\nstability of the alloy. We relate the behavior of the piezoelectric constant\n$e_{33}$ to the microscopic behavior of the internal displacement parameter\n$u$, finding that the internal strain contribution dominates in the Sc-induced\nenhancement. The value of $u$ increases with scandium concentration, bringing\nthe alloy locally closer to a layered hexagonal structure. Our approach allows\nus to calculate the ferroelectric switching barrier, which we analyze as a\nfunction of Sc concentration and temperature based on Ginzburg-Landau theory.\n"}
+{"abstract": "  We investigate under which assumptions the flow associated to autonomous\nplanar vector fields inherits the Sobolev or BV regularity of the vector field.\nWe consider nearly incompressible and divergence-free vector fields, taking\nadvantage in both cases of the underlying Hamiltonian structure. Finally we\nprovide an example of an autonomous planar Sobolev divergence-free vector\nfield, such that the corresponding regular Lagrangian flow has no bounded\nvariation.\n"}
+{"abstract": "  We study the competition between two different topological orders in three\ndimensions by considering the X-cube model and the three-dimensional toric\ncode. The corresponding Hamiltonian can be decomposed into two commuting parts,\none of which displaying a self-dual spectrum. To determine the phase diagram,\nwe compute the high-order series expansions of the ground-state energy in all\nlimiting cases. Apart from the topological order related to the toric code and\nthe fractonic order related to the X-cube model, we found two new phases which\nare adiabatically connected to classical limits with nontrivial sub-extensive\ndegeneracies. All phase transitions are found to be first order.\n"}
+{"abstract": "  An algebraic group is called semi-reductive if it is a semi-direct product of\na reductive subgroup and the unipotent radical. Such a semi-reductive algebraic\ngroup naturally arises and also plays a key role in the study of modular\nrepresentations of non-classical finite-dimensional simple Lie algebras in\npositive characteristic, and some other cases. Let $G$ ba a connected\nsemi-reductive algebraic group over an algebraically closed field $\\mathbb{F}$\nand $\\mathfrak{g}=Lie(G)$. It turns out that $G$ has many same properties as\nreductive groups, such as the Bruhat decomposition. In this note, we obtain an\nanalogue of classical Chevalley restriction theorem for $\\mathfrak{g}$, which\nsays that the $G$-invariant ring $\\mathbb{F}[\\mathfrak{g}]^G$ is a polynomial\nring if $\\mathfrak{g}$ satisfies a certain \"posivity\" condition suited for lots\nof cases we are interested in. As applications, we further investigate the\nnilpotent cones and resolutions of singularities for semi-reductive Lie\nalgebras.\n"}
+{"abstract": "  We prove that many seemingly simple theories have Borel complete reducts.\nSpecifically, if a countable theory has uncountably many complete 1-types, then\nit has a Borel complete reduct. Similarly, if $Th(M)$ is not small, then\n$M^{eq}$ has a Borel complete reduct, and if a theory $T$ is not\n$\\omega$-stable, then the elementary diagram of some countable model of $T$ has\na Borel complete reduct.\n"}
+{"abstract": "  We provide a method to calculate the rate of false vacuum decay induced by a\nblack hole. The method uses complex tunneling solutions and consistently takes\ninto account the structure of different quantum vacua in the black hole metric\nvia boundary conditions. The latter are connected to the asymptotic behavior of\nthe time-ordered Green's function in the corresponding vacua. We illustrate the\ntechnique on a two-dimensional toy model of a scalar field with inverted\nLiouville potential in an external background of a dilaton black hole. We\nanalytically derive the exponential suppression of tunneling from the Boulware,\nHartle-Hawking and Unruh vacua and show that they are parametrically different.\nThe Unruh vacuum decay rate is exponentially smaller than the decay rate of the\nHartle-Hawking state, though both rates become unsuppressed at high enough\nblack hole temperature. We interpret the vanishing suppression of the Unruh\nvacuum decay at high temperature as an artifact of the two-dimensional model\nand discuss why this result can be modified in the realistic case of black\nholes in four dimensions.\n"}
+{"abstract": "  Conversational recommender systems (CRS) enable the traditional recommender\nsystems to explicitly acquire user preferences towards items and attributes\nthrough interactive conversations. Reinforcement learning (RL) is widely\nadopted to learn conversational recommendation policies to decide what\nattributes to ask, which items to recommend, and when to ask or recommend, at\neach conversation turn. However, existing methods mainly target at solving one\nor two of these three decision-making problems in CRS with separated\nconversation and recommendation components, which restrict the scalability and\ngenerality of CRS and fall short of preserving a stable training procedure. In\nthe light of these challenges, we propose to formulate these three\ndecision-making problems in CRS as a unified policy learning task. In order to\nsystematically integrate conversation and recommendation components, we develop\na dynamic weighted graph based RL method to learn a policy to select the action\nat each conversation turn, either asking an attribute or recommending items.\nFurther, to deal with the sample efficiency issue, we propose two action\nselection strategies for reducing the candidate action space according to the\npreference and entropy information. Experimental results on two benchmark CRS\ndatasets and a real-world E-Commerce application show that the proposed method\nnot only significantly outperforms state-of-the-art methods but also enhances\nthe scalability and stability of CRS.\n"}
+{"abstract": "  Transport services (TAPS) is a working group of the Internet's\nstandardization body, the Internet Engineering Task Force (IETF). TAPS defines\na new recommended API for the Internet's transport layer. This API gives access\nto a wide variety of services from various protocols, and it is\nprotocol-independent: the transport layer becomes adaptive, and applications\nare no longer statically bound to a particular protocol and/or network\ninterface. We give an overview of the TAPS API, and we demonstrate its\nflexibility and ease of use with an example using a Python-based open-source\nimplementation.\n"}
+{"abstract": "  Recently, the inverse electrodynamics model (IEM) was introduced and applied\nto fined Reissner-Nordstr\\\"{o}m black holes in the context of the general\nrelativity coupled minimally with the nonlinear electrodynamics \\cite{1}. The\nsolution consists of both electric and magnetic fields as of the dyonic\nsolutions. Here in this note, we show that the IEM model belongs to a more\ngeneral class of nonlinear electrodynamics with $T=T_{\\mu }^{\\mu }=0$. Here\n$T_{\\mu }^{\\nu }$ is the energy-momentum tensor of the nonlinear electrodynamic\nLagrangian. Naturally, such a dyonic RN black hole solution is the solution for\nthis general class.\n"}
+{"abstract": "  Supermassive stars (SMSs) collapsing via the general-relativistic (GR)\ninstability are invoked as the possible progenitors of supermassive black\nholes. Their mass and angular momentum at the onset of the instability are key\nin many respects, in particular regarding the possibility for observational\nsignatures of direct collapse. Here, we study the stability of rotating,\nrapidly accreting SMSs against GR and derive the properties of these stars at\ndeath. On the basis of hylotropic structures, relevant for rapidly accreting\nSMSs, we define rotation profiles under the assumption of local angular\nmomentum conservation in radiative regions, which allows for differential\nrotation. We find that rotation favours the stability of rapidly accreting SMSs\nas soon as the accreted angular momentum represents a fraction f > 0.1% of the\nKeplerian angular momentum. For f = 0.3%-0.5% the maximum masses consistent\nwith GR stability are increased by an order of magnitude compared to the\nnon-rotating case. For f = 1%, the GR instability cannot be reached if the\nstellar mass does not exceed 10^7-10^8 Msun. These results imply that, like in\nthe non-rotating case, the final masses of the progenitors of direct collapse\nblack holes range in distinct intervals depending on the scenario considered:\n10^5 Msun < M < 10^6 Msun for primordial atomically cooled haloes; 10^6 Msun <\nM < 10^9 Msun for metal-rich galaxy mergers. The models suggest that the\ncentrifugal barrier is inefficient to prevent the direct formation of a\nsupermassive black hole at the collapse of a SMS. Moreover, the conditions of\ngalaxy mergers appear as more favorable than those of atomically cooled haloes\nfor detectable gravitational wave emission and ultra-long gamma-ray bursts at\nblack hole formation.\n"}
+{"abstract": "  In this paper, we study the Joule-Thomson expansion for RN-AdS black holes\nimmersed in perfect fluid dark matter. Firstly, the negative cosmological\nconstant could be interpreted as thermodynamic pressure and its conjugate\nquantity with the volume gave us more physical insights into the black hole. We\nderive the thermodynamic definitions and study the critical behaviour of this\nblack hole. Secondly, the explicit expression of Joule-Thomson coefficient is\nobtained from the basic formulas of enthalpy and temperature. Then, we obtain\nthe isenthalpic curve in $T-P$ graph and demonstrate the cooling-heating region\nby the inversion curve. At last, we derive the ratio of minimum inversion\ntemperature to critical temperature and the inversion curves in terms of charge\n$Q$ and parameter $\\lambda$.\n"}
+{"abstract": "  Although topological artificial systems, like acoustic/photonic crystals and\ncold atoms in optical lattices were initially motivated by simulating\ntopological phases of electronic systems, they have their own unique features\nsuch as the spinless time-reversal symmetry and tunable $\\mathbb{Z}_2$ gauge\nfields. Hence, it is fundamentally important to explore new topological phases\nbased on their unique features. Here, we point out that the $\\mathbb{Z}_2$\ngauge field leads to two fundamental modifications of the conventional $k\\cdot\np$ method: (i) The little co-group must include the translations with\nnontrivial algebraic relations; (ii) The algebraic relations of the little\nco-group are projectively represented. These give rise to higher-dimensional\nirreducible representations and therefore highly degenerate Fermi points.\nBreaking the primitive translations can transform the Fermi points to\ninteresting topological phases. We demonstrate our theory by two models: a\nrectangular $\\pi$-flux model exhibiting graphene-like semimetal phases, and a\ngraphite model with interlayer $\\pi$ flux that realizes the real second-order\nnodal-line semimetal phase with hinge helical modes. Their physical\nrealizations with a general bright-dark mechanism are discussed. Our finding\nopens a new direction to explore novel topological phases unique to artificial\nsystems and establishes the approach to analyze these phases.\n"}
+{"abstract": "  In a recent paper, Iba\\~nez-Azpiroz et al. [Phys. Rev. B 97, 245143 (2018)]\nderive a band-truncation-error-free formula for calculating the generalized\nderivative of the interband dipole matrix using Wannier interpolation. In\npractice, the denominators involving intermediate states are regularized by\nintroducing a finite broadening parameter. In this Comment, I show that when a\nfinite broadening parameter is used, a correction term must be added to the\ngeneralized derivative to obtain results that are independent of the phase\nconvention for the Bloch sums.\n"}
+{"abstract": "  The gauge function, closely related to the atomic norm, measures the\ncomplexity of a statistical model, and has found broad applications in machine\nlearning and statistical signal processing. In a high-dimensional learning\nproblem, the gauge function attempts to safeguard against overfitting by\npromoting a sparse (concise) representation within the learning alphabet.\n  In this work, within the context of linear inverse problems, we pinpoint the\nsource of its success, but also argue that the applicability of the gauge\nfunction is inherently limited by its convexity, and showcase several learning\nproblems where the classical gauge function theory fails. We then introduce a\nnew notion of statistical complexity, gauge$_p$ function, which overcomes the\nlimitations of the gauge function. The gauge$_p$ function is a simple\ngeneralization of the gauge function that can tightly control the sparsity of a\nstatistical model within the learning alphabet and, perhaps surprisingly, draws\nfurther inspiration from the Burer-Monteiro factorization in computational\nmathematics.\n  We also propose a new learning machine, with the building block of gauge$_p$\nfunction, and arm this machine with a number of statistical guarantees. The\npotential of the proposed gauge$_p$ function theory is then studied for two\nstylized applications. Finally, we discuss the computational aspects and, in\nparticular, suggest a tractable numerical algorithm for implementing the new\nlearning machine.\n"}
+{"abstract": "  We have investigated the valence transition in the extended periodic Anderson\nmodel (EPAM) within the framework of dynamical mean field theory. Previous\ntheoretical studies have indicated the presence of this transition in parameter\nranges that are experimentally inaccessible. In this work, we show that,\ninclusion of dynamics beyond static mean-field brings the valence transition in\nphysically relevant regimes. We show the strong dependence of the\nquasiparticle-weight scale across the sharp valence crossover. The density of\nstates also exhibit concomitant large scale spectral weight transfers, which\ncan be observed in experiments.\n"}
+{"abstract": "  Previously, the Einstein equation has been described as an equation of state,\ngeneral relativity as the equilibrium state of gravity, and $f({\\cal R})$\ngravity as a non-equilibrium one. We apply Eckart's first order thermodynamics\nto the effective dissipative fluid describing scalar-tensor gravity.\nSurprisingly, we obtain simple expressions for the effective heat flux,\n\"temperature of gravity\", shear and bulk viscosity, and entropy density, plus a\ngeneralized Fourier law in a consistent Eckart thermodynamical picture.\nWell-defined notions of temperature and approach to equilibrium, missing in the\ncurrent thermodynamics of spacetime scenarios, naturally emerge.\n"}
+{"abstract": "  Recent studies in deep learning have shown significant progress in named\nentity recognition (NER). Most existing works assume clean data annotation, yet\na fundamental challenge in real-world scenarios is the large amount of noise\nfrom a variety of sources (e.g., pseudo, weak, or distant annotations). This\nwork studies NER under a noisy labeled setting with calibrated confidence\nestimation. Based on empirical observations of different training dynamics of\nnoisy and clean labels, we propose strategies for estimating confidence scores\nbased on local and global independence assumptions. We partially marginalize\nout labels of low confidence with a CRF model. We further propose a calibration\nmethod for confidence scores based on the structure of entity labels. We\nintegrate our approach into a self-training framework for boosting performance.\nExperiments in general noisy settings with four languages and distantly labeled\nsettings demonstrate the effectiveness of our method. Our code can be found at\nhttps://github.com/liukun95/Noisy-NER-Confidence-Estimation\n"}
+{"abstract": "  We study general properties of the mapping between 5$d$ and 4$d$\nsuperconformal field theories (SCFTs) under both twisted circle\ncompactification and tuning of local relevant deformation and CB moduli. After\nelucidating in generality when a 5$d$ SCFT reduces to a 4$d$ one, we identify\nnearly all $\\mathcal{N}=1$ 5$d$ SCFT parents of rank-2 4$d$ $\\mathcal{N}=2$\nSCFTs. We then use this result to map out the mass deformation trajectories\namong the rank-2 theories in 4$d$. This can be done by first understanding the\nmass deformations of the 5$d$ $\\mathcal{N}=1$ SCFTs and then map them to 4$d$.\nThe former task can be easily achieved by exploiting the fact that the 5$d$\nparent theories can be obtained as the strong coupling limit of Lagrangian\ntheories, and the latter by understanding the behavior under compactification.\nFinally we identify a set of general criteria that 4$d$ moduli spaces of vacua\nhave to satisfy when the corresponding SCFTs are related by mass deformations\nand check that all our RG-flows satisfy them. Many of the mass deformations we\nfind are not visible from the corresponding complex integrable systems.\n"}
+{"abstract": "  The scalability of photonic implementations of fault-tolerant quantum\ncomputing based on Gottesman-Kitaev-Preskill (GKP) qubits is injured by the\nrequirements of inline squeezing and reconfigurability of the linear optical\nnetwork. In this work we propose a topologically error-corrected architecture\nthat does away with these elements at no cost - in fact, at an advantage - to\nstate preparation overheads. Our computer consists of three modules: a 2D array\nof probabilistic sources of GKP states; a depth-four circuit of static\nbeamsplitters, phase shifters, and single-time-step delay lines; and a 2D array\nof homodyne detectors. The symmetry of our proposed circuit allows us to\ncombine the effects of finite squeezing and uniform photon loss within the\nnoise model, resulting in more comprehensive threshold estimates. These jumps\nover both architectural and analytical hurdles considerably expedite the\nconstruction of a photonic quantum computer.\n"}
+{"abstract": "  The microring resonators (MRR) weight bank, a ubiquitous element in silicon\nphotonics providing weight addition, yet, has long suffered from the weakness\nof low accuracy, so that harming its potential in various promising\napplications. Here, we demonstrated a novel dithering method for MRR weight\ncontrol, reaching an accuracy of up to 8.5 bits and a precision of up to 9.0\nbits, which rivals that of the analog processors. Also, this method maintains\ngood scalability to control multi-MRRs with little additional increase in\nsearching time and system complexity, and has efficacious suppression to the\ninter-channel crosstalk. Given these features, this method could widely benefit\nthe applications of MRR weight banks and facilitate the prevalence of the MRR\nin silicon photonics.\n"}
+{"abstract": "  Smooth minimax games often proceed by simultaneous or alternating gradient\nupdates. Although algorithms with alternating updates are commonly used in\npractice for many applications (e.g., GAN training), the majority of existing\ntheoretical analyses focus on simultaneous algorithms for convenience of\nanalysis. In this paper, we study alternating gradient descent-ascent (Alt-GDA)\nin minimax games and show that Alt-GDA is superior to its simultaneous\ncounterpart (Sim-GDA) in many settings. In particular, we prove that Alt-GDA\nachieves a near-optimal local convergence rate for strongly convex-strongly\nconcave (SCSC) problems while Sim-GDA converges at a much slower rate. To our\nknowledge, this is the \\emph{first} result of any setting showing that Alt-GDA\nconverges faster than Sim-GDA by more than a constant. We further prove that\nthe acceleration effect of alternating updates remains when the minimax problem\nhas only strong concavity in the dual variables. Lastly, we adapt the theory of\nintegral quadratic constraints and show that Alt-GDA attains the same rate\n\\emph{globally} for a class of SCSC minimax problems. Numerical experiments on\nquadratic minimax games validate our claims. Empirically, we demonstrate that\nalternating updates speed up GAN training significantly and the use of optimism\nonly helps for simultaneous algorithms.\n"}
+{"abstract": "  Quantum algorithms designed for noisy intermediate-scale quantum devices\nusually require repeatedly perform a large number of quantum measurements in\nestimating observable expectation values of a many-qubit quantum state.\nExploiting the ideas of importance sampling, observable compatibility, and\nclassical shadows of quantum states, different advanced quantum measurement\nschemes have been proposed to greatly reduce the large measurement cost. Yet,\nthe underline cost reduction mechanisms seem distinct to each other, and how to\nsystematically find the optimal scheme remains a critical theoretical\nchallenge. Here, we address this challenge by firstly proposing a unified\nframework of quantum measurements, incorporating the advanced measurement\nmethods as special cases. Our framework further allows us to introduce a\ngeneral scheme -- overlapped grouping measurement, which simultaneously\nexploits the advantages of the existing methods. We show that an optimal\nmeasurement scheme corresponds to partitioning the observables into overlapped\ngroups with each group consisting of compatible ones. We provide explicit\ngrouping strategies and numerically verify its performance for different\nmolecular Hamiltonians. Our numerical results show great improvements to the\noverall existing measurement schemes. Our work paves the way for efficient\nquantum measurement with near-term quantum devices.\n"}
+{"abstract": "  Magnetic skyrmions are the self-organized topological spin textures behaving\nlike particles. Because of their fast creation and typically long lifetime,\nexperimental verification of skyrmion's creation/annihilation processes has\nbeen challenging. Here we successfully track skyrmions dynamics in\ndefect-introduced Co9Zn9Mn2, by using pump-probe Lorentz transmission electron\nmicroscope. Following the nanosecond-photothermal excitation, we resolve\n160-nm-skyrmion's proliferation at <1 ns, contraction at 5 ns, drift from 10 ns\nto 4 microsecond and coalescence at 5 microsecond. These motions relay the\nmultiscale arrangement and relaxation of skyrmion clusters in a repeatable\ncycle of 20 kHz. Such repeatable dynamics of skyrmions, arising from the\nweakened but still persistent topological protection around defects, enables us\nto visualize the whole life of the skyrmions, as well as demonstrating the\npossible high-frequency manipulations of topological charges brought by\nskyrmions.\n"}
+{"abstract": "  We introduce a definition of thickness in $\\mathbb{R}^d$ and obtain a lower\nbound for the Hausdorff dimension of the intersection of finitely or countably\nmany thick compact sets using a variant of Schmidt's game. As an application we\nprove that given any compact set in $\\mathbb{R}^d$ with thickness $\\tau$, there\nis a number $N(\\tau)$ such that the set contains a translate of all\nsufficiently small similar copies of every set in $\\mathbb{R}^d$ with at most\n$N(\\tau)$ elements; indeed the set of such translations has positive Hausdorff\ndimension. We also prove a gap lemma and bounds relating Hausdorff dimension\nand thickness.\n"}
+{"abstract": "  Many active matter systems are known to perform L\\'{e}vy walks during\nmigration or foraging. Such superdiffusive transport indicates long-range\ncorrelated dynamics. These behavior patterns have been observed for\nmicroswimmers such as bacteria in microfluidic experiments, where Gaussian\nnoise assumptions are insufficient to explain the data. We introduce\n\\textit{active L\\'evy swimmers} to model such behavior. The focus is on ideal\nswimmers that only interact with the walls but not with each other, which\nreduces to the classical L\\'evy walk model but now under confinement. We study\nthe density distribution in the channel and force exerted on the walls by the\nL\\'evy swimmers, where the boundaries require proper explicit treatment. We\nanalyze stronger confinement via a set of coupled kinetics equations and the\nswimmers' stochastic trajectories. Previous literature demonstrated that\npower-law scaling in a multiscale analysis in free space results in a\nfractional diffusion equation. We show that in a channel, in the weak\nconfinement limit active L\\'evy swimmers are governed by a modified Riesz\nfractional derivative. Leveraging recent results on fractional fluxes, we\nderive steady state solutions for the bulk density distribution of active\nL\\'evy swimmers in a channel, and demonstrate that these solutions agree well\nwith particle simulations. The profiles are non-uniform over the entire domain,\nin contrast to constant-in-the-bulk profiles of active Brownian and\nrun-and-tumble particles. Our theory provides a mathematical framework for\nL\\'evy walks under confinement with sliding no-flux boundary conditions and\nprovides a foundation for studies of interacting active L\\'evy swimmers.\n"}
+{"abstract": "  Several European countries have suspended the inoculation of the AstraZeneca\nvaccine out of suspicion of causing deep vein thrombosis. In this letter we\nreport some Fermi estimates performed using a stochastic model aimed at making\na risk-benefit analysis of the interruption of the delivery of the AstraZeneca\nvaccine in France and Italy. Our results clearly show that excess deaths due to\nthe interruption of the vaccination campaign injections largely overrun those\ndue to thrombosis even in worst case scenarios of frequency and gravity of the\nvaccine side effects.\n"}
+{"abstract": "  It was recently argued that one-dimensional systems of several strongly\ninteracting fermions of different mass undergo critical transitions between\ndifferent spatial orderings when the external confinement adiabatically changes\nits shape. In this work, we explore their dynamical properties when finite-time\ndrivings are considered. By detailed analysis of many-body spectra, we show\nthat the dynamics is typically guided only by the lowest eigenstates and may be\nwell-understood in the language of the generalized Landau-Zener mechanism. In\nthis way, we can capture precisely the dynamical response of the system to the\nexternal driving. As consequence, we show that by appropriate tailoring\nparameters of the driving one can target desired many-body state in a\nnon-infinite time. Our theoretical predictions can be straightforwardly\nutilized in upcoming state-of-the-art experiments with ultracold atoms.\n"}
+{"abstract": "  These days, cyber-criminals target humans rather than machines since they try\nto accomplish their malicious intentions by exploiting the weaknesses of end\nusers. Thus, human vulnerabilities pose a serious threat to the security and\nintegrity of computer systems and data. The human tendency to trust and help\nothers, as well as personal, social, and cultural characteristics, are\nindicative of the level of susceptibility that one may exhibit towards certain\nattack types and deception strategies. This work aims to investigate the\nfactors that affect human susceptibility by studying the existing literature\nrelated to this subject. The objective is also to explore and describe state of\nthe art human vulnerability assessment models, current prevention, and\nmitigation approaches regarding user susceptibility, as well as educational and\nawareness raising training strategies. Following the review of the literature,\nseveral conclusions are reached. Among them, Human Vulnerability Assessment has\nbeen included in various frameworks aiming to assess the cyber security\ncapacity of organizations, but it concerns a one time assessment rather than a\ncontinuous practice. Moreover, human maliciousness is still neglected from\ncurrent Human Vulnerability Assessment frameworks; thus, insider threat actors\nevade identification, which may lead to an increased cyber security risk.\nFinally, this work proposes a user susceptibility profile according to the\nfactors stemming from our research.\n"}
+{"abstract": "  News recommendation calls for deep insights of news articles' underlying\nsemantics. Therefore, pretrained language models (PLMs), like BERT and RoBERTa,\nmay substantially contribute to the recommendation quality. However, it's\nextremely challenging to have news recommenders trained together with such big\nmodels: the learning of news recommenders requires intensive news encoding\noperations, whose cost is prohibitive if PLMs are used as the news encoder. In\nthis paper, we propose a novel framework, {SpeedyFeed}, which efficiently\ntrains PLMs-based news recommenders of superior quality. SpeedyFeed is\nhighlighted for its light-weighted encoding pipeline, which gives rise to three\nmajor advantages. Firstly, it makes the intermedia results fully reusable for\nthe training workflow, which removes most of the repetitive but redundant\nencoding operations. Secondly, it improves the data efficiency of the training\nworkflow, where non-informative data can be eliminated from encoding. Thirdly,\nit further saves the cost by leveraging simplified news encoding and compact\nnews representation. Extensive experiments show that SpeedyFeed leads to more\nthan 100$\\times$ acceleration of the training process, which enables big models\nto be trained efficiently and effectively over massive user data. The\nwell-trained PLMs-based model from SpeedyFeed demonstrates highly competitive\nperformance, where it outperforms the state-of-the-art news recommenders with\nsignificant margins. SpeedyFeed is also a model-agnostic framework, which is\npotentially applicable to a wide spectrum of content-based recommender systems;\ntherefore, the whole framework is open-sourced to facilitate the progress in\nrelated areas.\n"}
+{"abstract": "  We prove new complexity results for Feedback Vertex Set and Even Cycle\nTransversal on $H$-free graphs, that is, graphs that do not contain some fixed\ngraph $H$ as an induced subgraph. In particular, we prove that for every $s\\geq\n1$, both problems are polynomial-time solvable for $sP_3$-free graphs and\n$(sP_1+P_5)$-free graphs; here, the graph $sP_3$ denotes the disjoint union of\n$s$ paths on three vertices and the graph $sP_1+P_5$ denotes the disjoint union\nof $s$ isolated vertices and a path on five vertices. Our new results for\nFeedback Vertex Set extend all known polynomial-time results for Feedback\nVertex Set on $H$-free graphs, namely for $sP_2$-free graphs [Chiarelli et al.,\nTCS 2018], $(sP_1+P_3)$-free graphs [Dabrowski et al., Algorithmica 2020] and\n$P_5$-free graphs [Abrishami et al., SODA 2021]. Together, the new results also\nshow that both problems exhibit the same behaviour on $H$-free graphs (subject\nto some open cases). This is in part due to a new general algorithm we design\nfor finding in a ($sP_3)$-free or $(sP_1+P_5)$-free graph $G$ a largest induced\nsubgraph whose blocks belong to some finite class ${\\cal C}$ of graphs. We also\ncompare our results with the state-of-the-art results for the Odd Cycle\nTransversal problem, which is known to behave differently on $H$-free graphs.\n"}
+{"abstract": "  We consider four-point integrals arising in the planar limit of the conformal\n\"fishnet\" theory in four dimensions. They define a two-parameter family of\nhigher-loop Feynman integrals, which extend the series of ladder integrals and\nwere argued, based on integrability and analyticity, to admit matrix-model-like\nintegral and determinantal representations. In this paper, we prove the\nequivalence of all these representations using exact summation and integration\ntechniques. We then analyze the large-order behaviour, corresponding to the\nthermodynamic limit of a large fishnet graph. The saddle-point equations are\nfound to match known two-cut singular equations arising in matrix models,\nenabling us to obtain a concise parametric expression for the free-energy\ndensity in terms of complete elliptic integrals. Interestingly, the latter\ndepends non-trivially on the fishnet aspect ratio and differs from a scaling\nformula due to Zamolodchikov for large periodic fishnets, suggesting a strong\nsensitivity to the boundary conditions. We also find an intriguing connection\nbetween the saddle-point equation and the equation describing the\nFrolov-Tseytlin spinning string in $AdS_{3}\\times S^{1}$, in a generalized\nscaling combining the thermodynamic and short-distance limits.\n"}
+{"abstract": "  This work introduces a new concept of functional areas called Mobility\nFunctional Areas (MFAs), i.e., the geographic zones highly interconnected\naccording to the analysis of mobile positioning data. The MFAs do not coincide\nnecessarily with administrative borders as they are built observing natural\nhuman mobility and, therefore, they can be used to inform, in a bottom-up\napproach, local transportation, spatial planning, health and economic policies.\nAfter presenting the methodology behind the MFAs, this study focuses on the\nlink between the COVID-19 pandemic and the MFAs in Austria. It emerges that the\nMFAs registered an average number of infections statistically larger than the\nareas in the rest of the country, suggesting the usefulness of the MFAs in the\ncontext of targeted re-escalation policy responses to this health crisis. The\nMFAs dataset is openly available to other scholars for further analyses.\n"}
+{"abstract": "  Multi-armed bandit algorithms have been argued for decades as useful for\nadaptively randomized experiments. In such experiments, an algorithm varies\nwhich arms (e.g. alternative interventions to help students learn) are assigned\nto participants, with the goal of assigning higher-reward arms to as many\nparticipants as possible. We applied the bandit algorithm Thompson Sampling\n(TS) to run adaptive experiments in three university classes. Instructors saw\ngreat value in trying to rapidly use data to give their students in the\nexperiments better arms (e.g. better explanations of a concept). Our\ndeployment, however, illustrated a major barrier for scientists and\npractitioners to use such adaptive experiments: a lack of quantifiable insight\ninto how much statistical analysis of specific real-world experiments is\nimpacted (Pallmann et al, 2018; FDA, 2019), compared to traditional uniform\nrandom assignment. We therefore use our case study of the ubiquitous two-arm\nbinary reward setting to empirically investigate the impact of using Thompson\nSampling instead of uniform random assignment. In this setting, using common\nstatistical hypothesis tests, we show that collecting data with TS can as much\nas double the False Positive Rate (FPR; incorrectly reporting differences when\nnone exist) and the False Negative Rate (FNR; failing to report differences\nwhen they exist)...\n"}
+{"abstract": "  Though convolutional neural networks (CNNs) have demonstrated remarkable\nability in learning discriminative features, they often generalize poorly to\nunseen domains. Domain generalization aims to address this problem by learning\nfrom a set of source domains a model that is generalizable to any unseen\ndomain. In this paper, a novel approach is proposed based on probabilistically\nmixing instance-level feature statistics of training samples across source\ndomains. Our method, termed MixStyle, is motivated by the observation that\nvisual domain is closely related to image style (e.g., photo vs.~sketch\nimages). Such style information is captured by the bottom layers of a CNN where\nour proposed style-mixing takes place. Mixing styles of training instances\nresults in novel domains being synthesized implicitly, which increase the\ndomain diversity of the source domains, and hence the generalizability of the\ntrained model. MixStyle fits into mini-batch training perfectly and is\nextremely easy to implement. The effectiveness of MixStyle is demonstrated on a\nwide range of tasks including category classification, instance retrieval and\nreinforcement learning.\n"}
+{"abstract": "  The Department of Homeland Security in the United States estimates that 90%\nof software vulnerabilities can be traced back to defects in design and\nsoftware coding. The financial impact of these vulnerabilities has been shown\nto exceed 380 million USD in industrial control systems alone. Since software\ndevelopers write software, they also introduce these vulnerabilities into the\nsource code. However, secure coding guidelines exist to prevent software\ndevelopers from writing vulnerable code. This study focuses on the human\nfactor, the software developer, and secure coding, in particular secure coding\nguidelines. We want to understand the software developers' awareness and\ncompliance to secure coding guidelines and why, if at all, they aren't\ncompliant or aware. We base our results on a large-scale survey on secure\ncoding guidelines, with more than 190 industrial software developers. Our\nwork's main contribution motivates the need to educate industrial software\ndevelopers on secure coding guidelines, and it gives a list of fifteen\nactionable items to be used by practitioners in the industry. We also make our\nraw data openly available for further research.\n"}
+{"abstract": "  Interaction in quantum systems can spread initially localized quantum\ninformation into the many degrees of freedom of the entire system.\nUnderstanding this process, known as quantum scrambling, is the key to\nresolving various conundrums in physics. Here, by measuring the time-dependent\nevolution and fluctuation of out-of-time-order correlators, we experimentally\ninvestigate the dynamics of quantum scrambling on a 53-qubit quantum processor.\nWe engineer quantum circuits that distinguish the two mechanisms associated\nwith quantum scrambling, operator spreading and operator entanglement, and\nexperimentally observe their respective signatures. We show that while operator\nspreading is captured by an efficient classical model, operator entanglement\nrequires exponentially scaled computational resources to simulate. These\nresults open the path to studying complex and practically relevant physical\nobservables with near-term quantum processors.\n"}
+{"abstract": "  Entity embeddings, which represent different aspects of each entity with a\nsingle vector like word embeddings, are a key component of neural entity\nlinking models. Existing entity embeddings are learned from canonical Wikipedia\narticles and local contexts surrounding target entities. Such entity embeddings\nare effective, but too distinctive for linking models to learn contextual\ncommonality. We propose a simple yet effective method, FGS2EE, to inject\nfine-grained semantic information into entity embeddings to reduce the\ndistinctiveness and facilitate the learning of contextual commonality. FGS2EE\nfirst uses the embeddings of semantic type words to generate semantic\nembeddings, and then combines them with existing entity embeddings through\nlinear aggregation. Extensive experiments show the effectiveness of such\nembeddings. Based on our entity embeddings, we achieved new sate-of-the-art\nperformance on entity linking.\n"}
+{"abstract": "  Adversarial examples contain carefully crafted perturbations that can fool\ndeep neural networks (DNNs) into making wrong predictions. Enhancing the\nadversarial robustness of DNNs has gained considerable interest in recent\nyears. Although image transformation-based defenses were widely considered at\nan earlier time, most of them have been defeated by adaptive attacks. In this\npaper, we propose a new image transformation defense based on error diffusion\nhalftoning, and combine it with adversarial training to defend against\nadversarial examples. Error diffusion halftoning projects an image into a 1-bit\nspace and diffuses quantization error to neighboring pixels. This process can\nremove adversarial perturbations from a given image while maintaining\nacceptable image quality in the meantime in favor of recognition. Experimental\nresults demonstrate that the proposed method is able to improve adversarial\nrobustness even under advanced adaptive attacks, while most of the other image\ntransformation-based defenses do not. We show that a proper image\ntransformation can still be an effective defense approach. Code:\nhttps://github.com/shaoyuanlo/Halftoning-Defense\n"}
+{"abstract": "  2-local derivation is a generalized derivation for a Lie algebra, which plays\nan important role to the study of local properties of the structure of the Lie\nalgebra. In this paper, we prove that every 2-local derivation on the conformal\nGalilei algebra is a derivation.\n"}
+{"abstract": "  Pseudo-goldstinos appear in the scenario of multi-sector SUSY breaking.\nUnlike the true goldstino which is massless and absorbed by the gravitino,\npseudo-goldstinos could obtain mass from radiative effects. In this note,\nworking in the scenario of two-sector SUSY breaking with gauge mediation, we\nexplicitly calculate the pseudo-goldstino mass at the leading three-loop level\nand provide the analytical results. In our calculation we consider the general\ncase of messenger masses (not necessarily equal) and include the higher order\nterms of SUSY breaking scales. Our results can reproduce the numerical value\nestimated previously at the leading order of SUSY breaking scales with the\nassumption of equal messenger masses. It turns out that the results are very\nsensitive to the ratio of messenger masses, while the higher order terms of\nSUSY breaking scales are rather small in magnitude. Depending on the ratio of\nmessenger masses, the pseudo-goldstino mass can be as low as ${{\\cal O} (0.1)}$\nGeV.\n"}
+{"abstract": "  In the field of biometrics, fingerprint recognition systems are vulnerable to\npresentation attacks made by artificially generated spoof fingerprints.\nTherefore, it is essential to perform liveness detection of a fingerprint\nbefore authenticating it. Fingerprint liveness detection mechanisms perform\nwell under the within-dataset environment but fail miserably under cross-sensor\n(when tested on a fingerprint acquired by a new sensor) and cross-dataset (when\ntrained on one dataset and tested on another) settings. To enhance the\ngeneralization abilities, robustness and the interoperability of the\nfingerprint spoof detectors, the learning models need to be adaptive towards\nthe data. We propose a generic model, EaZy learning which can be considered as\nan adaptive midway between eager and lazy learning. We show the usefulness of\nthis adaptivity under cross-sensor and cross-dataset environments. EaZy\nlearning examines the properties intrinsic to the dataset while generating a\npool of hypotheses. EaZy learning is similar to ensemble learning as it\ngenerates an ensemble of base classifiers and integrates them to make a\nprediction. Still, it differs in the way it generates the base classifiers.\nEaZy learning develops an ensemble of entirely disjoint base classifiers which\nhas a beneficial influence on the diversity of the underlying ensemble. Also,\nit integrates the predictions made by these base classifiers based on their\nperformance on the validation data. Experiments conducted on the standard high\ndimensional datasets LivDet 2011, LivDet 2013 and LivDet 2015 prove the\nefficacy of the model under cross-dataset and cross-sensor environments.\n"}
+{"abstract": "  With autonomous vehicles now sharing roads with human drivers, the era of\nmixed autonomy brings new challenges in dealing with congestion. One cause of\ncongestion is when vehicle users choose their routes selfishly to minimize\ntheir personal travel delay rather than a global travel delay, and prior works\naddress this phenomenon using tolling to influence routing choices, but do not\naddress the setting of mixed autonomy. Tolls may be differentiated, meaning\ndifferent users of a road experience different tolls, or they may be anonymous;\nthe latter is desirable to allay concerns of fairness and privacy, as well as\nlogistical challenges. In this work we examine the role of differentiation in\ntraffic networks with mixed autonomy. Specifically, we first establish\ndifferentiated tolls which completely eliminate inefficiency due to selfish\nrouting. We then show the fundamental limitations of anonymous tolls in our\nsetting, and we provide anonymous tolls with mild performance guarantees. We\nshow that in parallel networks, an infinitesimal differentiation in tolls is\nenough to guarantee optimality, and finally we establish a lower bound on the\ninefficiency of variable marginal cost tolling in the mixed autonomy setting.\n"}
+{"abstract": "  The present paper deals with integral classes $\\xi_p\\in\nH_{2p+1}(L_1^{2p+1}\\times L_2^{2p+1})$ which are counterexamples for the\nSteenrod realization problem, where $L_1^{2p+1}$ is the $(2p+1)$-Lens space and\n$p\\geq 3$ is a prime number. For $p=3$, this is the Thom's famous\ncounterexample. We give a geometric description of this class using the theory\nof stratifolds. As a consequence, we obtain a geometric interpretation of the\nobstruction to realizability, in terms of the Atiyah-Hirzebruch spectral\nsequence.\n"}
+{"abstract": "  Opportunities such as higher education can promote intergenerational\nmobility, leading individuals to achieve levels of socioeconomic status above\nthat of their parents. We develop a dynamic model for allocating such\nopportunities in a society that exhibits bottlenecks in mobility; the problem\nof optimal allocation reflects a trade-off between the benefits conferred by\nthe opportunities in the current generation and the potential to elevate the\nsocioeconomic status of recipients, shaping the composition of future\ngenerations in ways that can benefit further from the opportunities. We show\nhow optimal allocations in our model arise as solutions to continuous\noptimization problems over multiple generations, and we find in general that\nthese optimal solutions can favor recipients of low socioeconomic status over\nslightly higher-performing individuals of high socioeconomic status -- a form\nof socioeconomic affirmative action that the society in our model discovers in\nthe pursuit of purely payoff-maximizing goals. We characterize how the\nstructure of the model can lead to either temporary or persistent affirmative\naction, and we consider extensions of the model with more complex processes\nmodulating the movement between different levels of socioeconomic status.\n"}
+{"abstract": "  We present the serendipitous detection of the two main OH maser lines at 1667\nand 1665 MHz associated with IRAS 10597+5926 at z = 0.19612 in the untargeted\nApertif Wide-area Extragalactic Survey (AWES), and the subsequent measurement\nof the OH 1612 MHz satellite line in the same source. With a total OH\nluminosity of log(L/L_Sun) = 3.90 +/- 0.03, IRAS 10597+5926 is the fourth\nbrightest OH megamaser (OHM) known. We measure a lower limit for the 1667/1612\nratio of R_1612 > 45.9 which is the highest limiting ratio measured for the\n1612 MHz OH satellite line to date. OH satellite line measurements provide a\npotentially valuable constraint by which to compare detailed models of OH maser\npumping mechanisms. Optical imaging shows the galaxy is likely a late-stage\nmerger. Based on published infrared and far ultraviolet fluxes, we find that\nthe galaxy is an ultra luminous infrared galaxy (ULIRG) with log(L_TIR/L_Sun) =\n12.24, undergoing a star burst with an estimated star formation rate of 179 +/-\n40 M_Sun/yr. These host galaxy properties are consistent with the physical\nconditions responsible for very bright OHM emission. Finally, we provide an\nupdate on the predicted number of OH masers that may be found in AWES, and\nestimate the total number of OH masers that will be detected in each of the\nindividual main and satellite OH 18 cm lines.\n"}
+{"abstract": "  We investigate two different models. In one of them massive fermions interact\nwith a massive scalar field and in the other the fermion field is in an\nelectrical field (QED2). The chiral condensates are calculated in one-loop\napproximation. We found that the chiral condensate in the case of the Yukawa\ninteraction the fermions and scalar field does not vanish if the mass of the\nfermion field tends to zero. The chiral condensate disappears in QED2, if the\nfermion mass is zero.\n"}
+{"abstract": "  In the family of Intelligent Transportation Systems (ITS), Multimodal\nTransport Systems (MMTS) have placed themselves as a mainstream transportation\nmean of our time as a feasible integrative transportation process. The Global\nEconomy progressed with the help of transportation. The volume of goods and\ndistances covered have doubled in the last ten years, so there is a high demand\nof an optimized transportation, fast but with low costs, saving resources but\nalso safe, with low or zero emissions. Thus, it is important to have an\noverview of existing research in this field, to know what was already done and\nwhat is to be studied next. The main objective is to explore a beneficent\nselection of the existing research, methods and information in the field of\nmultimodal transportation research, to identify industry needs and gaps in\nresearch and provide context for future research. The selective survey covers\nmultimodal transport design and optimization in terms of: cost, time, and\nnetwork topology. The multimodal transport theoretical aspects, context and\nresources are also covering various aspects. The survey's selection includes\nnowadays best methods and solvers for Intelligent Transportation Systems (ITS).\nThe gap between theory and real-world applications should be further solved in\norder to optimize the global multimodal transportation system.\n"}
+{"abstract": "  In this paper, we show that any compact quasi $k$-Yamabe gradient solitons\nmust have constant $\\sigma_{k}$-curvature. Moreover, we provide a certain\ncondition for a compact quasi $k$-Yamabe soliton to be gradient, and for\nnoncompact solitons, we present a geometric rigidity under a decaiment\ncondition on the norm of the soliton field.\n"}
+{"abstract": "  Physical and mental well-being during the COVID-19 pandemic is typically\nassessed via surveys, which might make it difficult to conduct longitudinal\nstudies and might lead to data suffering from recall bias. Ecological momentary\nassessment (EMA) driven smartphone apps can help alleviate such issues,\nallowing for in situ recordings. Implementing such an app is not trivial,\nnecessitates strict regulatory and legal requirements, and requires short\ndevelopment cycles to appropriately react to abrupt changes in the pandemic.\nBased on an existing app framework, we developed Corona Health, an app that\nserves as a platform for deploying questionnaire-based studies in combination\nwith recordings of mobile sensors. In this paper, we present the technical\ndetails of Corona Health and provide first insights into the collected data.\nThrough collaborative efforts from experts from public health, medicine,\npsychology, and computer science, we released Corona Health publicly on Google\nPlay and the Apple App Store (in July, 2020) in 8 languages and attracted 7,290\ninstallations so far. Currently, five studies related to physical and mental\nwell-being are deployed and 17,241 questionnaires have been filled out. Corona\nHealth proves to be a viable tool for conducting research related to the\nCOVID-19 pandemic and can serve as a blueprint for future EMA-based studies.\nThe data we collected will substantially improve our knowledge on mental and\nphysical health states, traits and trajectories as well as its risk and\nprotective factors over the course of the COVID-19 pandemic and its diverse\nprevention measures.\n"}
+{"abstract": "  There is a remarkable connection between the boundary structure of the\npositive kinematic region and branch points of integrated amplitudes in planar\n$\\mathcal{N}=4$ SYM. A long standing question has been precisely how algebraic\nbranch points emerge from this picture. We use wall crossing and scattering\ndiagrams to systematically study the boundary structure of the positive\nkinematic regions associated with MHV amplitudes. The notion of asymptotic\nchambers in the scattering diagram naturally explains the appearance of\nalgebraic branch points. Furthermore, the scattering diagram construction also\nmotivates a new coordinate system for kinematic space that rationalizes the\nrelations between algebraic letters in the symbol alphabet. As a direct\napplication, we conjecture a complete list of all algebraic letters that could\nappear in the symbol alphabet of the 8-point MHV amplitude.\n"}
+{"abstract": "  We describe a systematic procedure to calculate the resolvent operator for a\nlinear pencil on Banach space and thereby simplify, unify and extend known\nmethods for resolution and representation of marginally stable time series. We\npay particular attention to those time series commonly known as unit root\nprocesses. The new method uses infinite-length Jordan chains to find the key\nspectral separation projections which enable separation and solution of the\nfundamental equations for the Laurent series coefficients of the resolvent. It\nis then possible to define the desired Granger-Johansen representation for the\ntime series. The method remains valid when the resolvent has an isolated\nessential singularity at unity.\n"}
+{"abstract": "  The Faddeev equations for the $\\Xi NN$ bound-state problem are solved where\nthe three $S$=$-2$ baryon-baryon interactions of J\\\"ulich-Bonn-M\\\"unchen chiral\nEFT, HAL QCD and Nijmegen ESC08c are used. The $T$-matrix $T_{\\Xi N, \\Xi N}$\nobtained within the original $\\Lambda\\Lambda$-$\\Xi N$-$\\Sigma\\Sigma$ $/$ $\\Xi\nN$-$\\Lambda \\Sigma$-$\\Sigma\\Sigma$ coupled-channel framework is employed as an\ninput to the equations. We found no bound state for J\\\"ulich-Bonn-M\\\"unchen\nchiral EFT and HAL QCD but ESC08c generates a bound state with the total\nisospin and spin-parity $(T,J^{\\pi})=(1/2, 3/2^+)$ where the decays into\n$\\Lambda\\Lambda N$ are suppressed.\n"}
+{"abstract": "  It is shown that for deep neural networks, a single wide layer of width $N+1$\n($N$ being the number of training samples) suffices to prove the connectivity\nof sublevel sets of the training loss function. In the two-layer setting, the\nsame property may not hold even if one has just one neuron less (i.e. width $N$\ncan lead to disconnected sublevel sets).\n"}
+{"abstract": "  We present a Markov Chain Monte Carlo (MCMC)-based parameter estimation\npackage, CosmoReionMC, to jointly constrain cosmological parameters of the\n$\\Lambda$CDM model and the astrophysical parameters related to hydrogen\nreionization. The package is based on a previously developed physically\nmotivated semi-analytical model for reionization, a similar semi-analytical\nmodel for computing the global 21~cm signal during the cosmic dawn and using an\nappropriately modified version of the publicly available CAMB for computing the\nCMB anisotropies. These calculations are then coupled to an MCMC ensemble\nsampler \\texttt{emcee} to compute the posterior distributions of the model\nparameter. The model has twelve free parameters in total: five cosmological and\nseven related to the stellar populations. We constrain the parameters by\nmatching the theoretical predictions with CMB data from Planck, observations\nrelated to the quasar absorption spectra and, for the first time, the global\n21~cm signal from EDGES. We find that incorporating the quasar spectra data in\nthe analysis tightens the bounds on the electron scattering optical depth\n$\\tau$ and consequently the normalization $A_s$ of the primordial matter power\nspectrum (or equivalently $\\sigma_8$). Furthermore, when we include the EDGES\ndata in the analysis, we find that an early population of metal-free stars with\nefficient radio emission is necessary to match the absorption amplitude. The\nCosmoReionMC package should have interesting future applications, e.g., probing\nnon-standard extensions to the $\\Lambda$CDM model.\n"}
+{"abstract": "  Rehearsal is a critical component for class-incremental continual learning,\nyet it requires a substantial memory budget. Our work investigates whether we\ncan significantly reduce this memory budget by leveraging unlabeled data from\nan agent's environment in a realistic and challenging continual learning\nparadigm. Specifically, we explore and formalize a novel semi-supervised\ncontinual learning (SSCL) setting, where labeled data is scarce yet non-i.i.d.\nunlabeled data from the agent's environment is plentiful. Importantly, data\ndistributions in the SSCL setting are realistic and therefore reflect object\nclass correlations between, and among, the labeled and unlabeled data\ndistributions. We show that a strategy built on pseudo-labeling, consistency\nregularization, Out-of-Distribution (OoD) detection, and knowledge distillation\nreduces forgetting in this setting. Our approach, DistillMatch, increases\nperformance over the state-of-the-art by no less than 8.7% average task\naccuracy and up to 54.5% average task accuracy in SSCL CIFAR-100 experiments.\nMoreover, we demonstrate that DistillMatch can save up to 0.23 stored images\nper processed unlabeled image compared to the next best method which only saves\n0.08. Our results suggest that focusing on realistic correlated distributions\nis a significantly new perspective, which accentuates the importance of\nleveraging the world's structure as a continual learning strategy.\n"}
+{"abstract": "  Given restrictions on the availability of data, active learning is the\nprocess of training a model with limited labeled data by selecting a core\nsubset of an unlabeled data pool to label. Although selecting the most useful\npoints for training is an optimization problem, the scale of deep learning data\nsets forces most selection strategies to employ efficient heuristics. Instead,\nwe propose a new integer optimization problem for selecting a core set that\nminimizes the discrete Wasserstein distance from the unlabeled pool. We\ndemonstrate that this problem can be tractably solved with a Generalized\nBenders Decomposition algorithm. Our strategy requires high-quality latent\nfeatures which we obtain by unsupervised learning on the unlabeled pool.\nNumerical results on several data sets show that our optimization approach is\ncompetitive with baselines and particularly outperforms them in the low budget\nregime where less than one percent of the data set is labeled.\n"}
+{"abstract": "  Aberrations limit scanning fluorescence microscopy when imaging in scattering\nmaterials such as biological tissue. Model-based approaches for adaptive optics\ntake advantage of a computational model of the optical setup. Such models can\nbe combined with the optimization techniques of machine learning frameworks to\nfind aberration corrections, as was demonstrated for focusing a laser beam\nthrough aberrations onto a camera [arXiv:2007.13400]. Here, we extend this\napproach to two-photon scanning microscopy. The developed sensorless technique\nfinds corrections for aberrations in scattering samples and will be useful for\na range of imaging application, for example in brain tissue.\n"}
+{"abstract": "  Parallels between the signal processing tasks and biological neurons lead to\nan understanding of the principles of self-organized optimization of input\nsignal recognition. In the present paper, we discuss such similarities among\nbiological and technical systems. We propose adding the well-known STDP\nsynaptic plasticity rule to direct the weight modification towards the state\nassociated with the maximal difference between background noise and correlated\nsignals. We use the principle of physically constrained weight growth as a\nbasis for such weights' modification control. It is proposed that the existence\nand production of bio-chemical 'substances' needed for plasticity development\nrestrict a biological synaptic straight modification. In this paper, the\ninformation about the noise-to-signal ratio controls such a substances'\nproduction and storage and drives the neuron's synaptic pressures towards the\nstate with the best signal-to-noise ratio. We consider several experiments with\ndifferent input signal regimes to understand the functioning of the proposed\napproach.\n"}
+{"abstract": "  Modulations of interfacial Dzyaloshinskii-Moriya interaction (iDMI) on\ncurrent-driven dynamics of 180$^{\\circ}$ domain walls (180DWs) in long and\nnarrow spin valves (LNSVs) with heavy-metal caplayers are systematically\ninvestigated. We focus on LNSVs with in-plane magnetic anisotropy in their free\nlayers. For planar-transverse polarizers (pinned layers of LNSVs), the Walker\nbreakdown can be postponed considerably (practical infinity) by iDMI. More\ninterestingly, the originally unstable traveling mode is also stabilized by\niDMI with high saturation velocity thus serves as fast carrier of information.\nFor parallel polarizers, the Walker limit is increased and the corresponding\nmodifications of iDMI to wall velocity in both steady and precessional flows of\n180DWs are provided. For perpendicular polarizers, precessional flow of 180DWs\nis absent due to the stability of stationary mode beyond the modified Walker\nlimit. For LNSVs with perpendicular magnetic anisotropy in their free layers,\nsimilar results are obtained. Our findings open new possibilities for\ndeveloping magnetic nanodevices based on 180DW propagation with low energy\nconsumption and high robustness.\n"}
+{"abstract": "  In a Type II superconductor, the vortex core behaves like a normal metal.\nConsequently, the single-particle density of states in the vortex core of a\nconventional Type II superconductor remains either flat or (for very clean\nsingle crystals) exhibits a peak at zero bias due to the formation of Caroli-de\nGennes-Matricon bound state inside the core. Here we report an unusual\nobservation from scanning tunneling spectroscopy measurements in a weakly\npinned thin film of the conventional s-wave superconductor a-MoGe, namely, that\na soft gap in the local density of states continues to exist even at the center\nof the vortex core. We ascribe this observation to rapid fluctuation of\nvortices about their mean position that blurs the boundary between the gapless\nnormal core and the gapped superconducting region outside. Analyzing the data\nas a function of magnetic field we show that the variation of fluctuation\namplitude as a function of magnetic field is consistent with quantum zero-point\nmotion of vortices.\n"}
+{"abstract": "  Recent studies on computer vision mainly focus on natural images that express\nreal-world scenes. They achieve outstanding performance on diverse tasks such\nas visual question answering. Diagram is a special form of visual expression\nthat frequently appears in the education field and is of great significance for\nlearners to understand multimodal knowledge. Current research on diagrams\npreliminarily focuses on natural disciplines such as Biology and Geography,\nwhose expressions are still similar to natural images. Another type of diagrams\nsuch as from Computer Science is composed of graphics containing complex\ntopologies and relations, and research on this type of diagrams is still blank.\nThe main challenges of graphic diagrams understanding are the rarity of data\nand the confusion of semantics, which are mainly reflected in the diversity of\nexpressions. In this paper, we construct a novel dataset of graphic diagrams\nnamed Computer Science Diagrams (CSDia). It contains more than 1,200 diagrams\nand exhaustive annotations of objects and relations. Considering the visual\nnoises caused by the various expressions in diagrams, we introduce the topology\nof diagrams to parse topological structure. After that, we propose Diagram\nParsing Net (DPN) to represent the diagram from three branches: topology,\nvisual feature, and text, and apply the model to the diagram classification\ntask to evaluate the ability of diagrams understanding. The results show the\neffectiveness of the proposed DPN on diagrams understanding.\n"}
+{"abstract": "  The concept of literary genre is a highly complex one: not only are different\ngenres frequently defined on several, but not necessarily the same levels of\ndescription, but consideration of genres as cognitive, social, or scholarly\nconstructs with a rich history further complicate the matter. This contribution\nfocuses on thematic aspects of genre with a quantitative approach, namely Topic\nModeling. Topic Modeling has proven to be useful to discover thematic patterns\nand trends in large collections of texts, with a view to class or browse them\non the basis of their dominant themes. It has rarely if ever, however, been\napplied to collections of dramatic texts.\n  In this contribution, Topic Modeling is used to analyze a collection of\nFrench Drama of the Classical Age and the Enlightenment. The general aim of\nthis contribution is to discover what semantic types of topics are found in\nthis collection, whether different dramatic subgenres have distinctive dominant\ntopics and plot-related topic patterns, and inversely, to what extent\nclustering methods based on topic scores per play produce groupings of texts\nwhich agree with more conventional genre distinctions. This contribution shows\nthat interesting topic patterns can be detected which provide new insights into\nthe thematic, subgenre-related structure of French drama as well as into the\nhistory of French drama of the Classical Age and the Enlightenment.\n"}
+{"abstract": "  Dark patterns are interface designs that nudge users towards behavior that is\nagainst their best interests. Since humans are often not even aware that they\nare influenced by these malicious patterns, research has to identify ways to\nprotect web users against them. One approach to this is the automatic detection\nof dark patterns which enables the development of tools that are able to\nprotect users by proactively warning them in cases where they face a dark\npattern. In this paper, we present ongoing work in the direction of automatic\ndetection of dark patterns, and outline an example to detect malicious patterns\nwithin the domain of cookie banners.\n"}
+{"abstract": "  In this work, we present a new semantic segmentation model for historical\ncity maps that surpasses the state of the art in terms of flexibility and\nperformance. Research in automatic map processing is largely focused on\nhomogeneous corpora or even individual maps, leading to inflexible algorithms.\nRecently, convolutional neural networks have opened new perspectives for the\ndevelopment of more generic tools. Based on two new maps corpora, the first one\ncentered on Paris and the second one gathering cities from all over the world,\nwe propose a method for operationalizing the figuration based on traditional\ncomputer vision algorithms that allows large-scale quantitative analysis. In a\nsecond step, we propose a semantic segmentation model based on neural networks\nand implement several improvements. Finally, we analyze the impact of map\nfiguration on segmentation performance and evaluate future ways to improve the\nrepresentational flexibility of neural networks. To conclude, we show that\nthese networks are able to semantically segment map data of a very large\nfigurative diversity with efficiency.\n"}
+{"abstract": "  Within this article, we develop a residual type a posteriori error estimator\nfor a time discrete quasi-static phase-field fracture model. Particular\nemphasize is given to the robustness of the error estimator for the variational\ninequality governing the phase-field evolution with respect to the phase-field\nregularization parameter $\\epsilon$. The article concludes with numerical\nexamples highlighting the performance of the proposed a posteriori error\nestimators on three standard test cases; the single edge notched tension and\nshear test as well as the L-shaped panel test.\n"}
+{"abstract": "  We measure the Shubnikov-de Haas effect in thin-film Sr$_2$RuO$_4$ grown on\nan (LaAlO$_3$)$_{0.29}$-(SrAl$_{1/2}$Ta$_{1/2}$O$_3$)$_{0.71}$ (LSAT)\nsubstrate. We detect all three known Fermi surfaces and extract the Fermi\nsurface volumes, cyclotron effective masses, and quantum lifetimes. We show\nthat the electronic structure is nearly identical to that of single-crystal\nSr$_2$RuO$_4$, and that the quasiparticle lifetime is consistent with the Tc of\ncomparably clean, single-crystal Sr$_2$RuO$_4$. Unlike single-crystal\nSr$_2$RuO$_4$, where the quantum and transport lifetimes are roughly equal, we\nfind that the transport lifetime is $1.3\\pm0.1$ times longer than the quantum\nlifetime. This suggests that extended (rather than point) defects may be the\ndominant source of quasiparticle scattering in these films. To test this idea,\nwe perform cross-sectional STEM and find that out-of-phase boundaries extending\nthe entire thickness of the film occur with a density that is consistent with\nthe quantum mean free path. The long quasiparticle lifetimes make these films\nideal for studying the unconventional superconducting state in Sr$_2$RuO$_4$\nthrough the fabrication of devices -- such as planar tunnel junctions and\nSQUIDs.\n"}
+{"abstract": "  We present the supernova constraints on an axion-photon-dark photon coupling,\nwhich can be the leading coupling to dark sector models and can also lead to\ndramatic changes to axion cosmology. We show that the supernova bound on this\ncoupling has two unusual features. One occurs because the scattering that leads\nto the trapping regime converts axions and dark photons into each other. Thus,\nif one of the two new particles is sufficiently massive, both production and\nscattering become suppressed and the bounds from bulk emission and trapped\n(area) emission both weaken exponentially and do not intersect. The other\nunusual feature occurs because for light dark photons, longitudinal modes\ncouple more weakly than transverse modes do. Since the longitudinal mode is\nmore weakly coupled, it can still cause excessive cooling even if the\ntransverse mode is trapped. Thus, the supernova constraints for massive dark\nphotons look like two independent supernova bounds super-imposed on top of each\nother.\n"}
+{"abstract": "  The Wiener index of a connected graph is the sum of the distances between all\nunordered pairs of vertices. A connected graph is Eulerian if its vertex\ndegrees are all even. In [Gutman, Cruz, Rada, Wiener index of Eulerian Graphs,\nDiscrete Applied Mathematics 132 (2014), 247-250] the authors proved that the\ncycle is the unique graph maximising the Wiener index among all Eulerian graphs\nof given order. They also conjectured that for Eulerian graphs of order $n \\geq\n26$ the graph consisting of a cycle on $n-2$ vertices and a triangle that share\na vertex is the unique Eulerian graph with second largest Wiener index. The\nconjecture is known to hold for all $n\\leq 25$ with exception of six values. In\nthis paper we prove the conjecture.\n"}
+{"abstract": "  The helical edge states of time-reversal invariant two-dimensional\ntopological insulators are protected against backscattering in idealized\nmodels. In more realistic scenarios with a shallow confining potential at the\nsample boundary, additional strongly interacting edge states may arise, that\ncould interfere with the topological protection of edge conduction. We find\nthat interaction effects within the reconstructed edges are well described by\nthe Luttinger liquid model. While interactions between this Luttinger liquid\nand the helical edge states can in principle give rise to dynamical spin\npolarization and the breaking of time-reversal symmetry, we demonstrate that\nrandom spin-orbit coupling strongly suppresses such dynamical spin\npolarization, resulting in the persistence of quantized edge conduction.\n"}
+{"abstract": "  Empirical natural language processing (NLP) systems in application domains\n(e.g., healthcare, finance, education) involve interoperation among multiple\ncomponents, ranging from data ingestion, human annotation, to text retrieval,\nanalysis, generation, and visualization. We establish a unified open-source\nframework to support fast development of such sophisticated NLP workflows in a\ncomposable manner. The framework introduces a uniform data representation to\nencode heterogeneous results by a wide range of NLP tasks. It offers a large\nrepository of processors for NLP tasks, visualization, and annotation, which\ncan be easily assembled with full interoperability under the unified\nrepresentation. The highly extensible framework allows plugging in custom\nprocessors from external off-the-shelf NLP and deep learning libraries. The\nwhole framework is delivered through two modularized yet integratable\nopen-source projects, namely Forte (for workflow infrastructure and NLP\nfunction processors) and Stave (for user interaction, visualization, and\nannotation).\n"}
+{"abstract": "  We propose in this paper a motion planning method for legged robot locomotion\nbased on Geometric Heat Flow framework. The motion planning task is challenging\ndue to the hybrid nature of dynamics and contact constraints. We encode the\nhybrid dynamics and constraints into Riemannian inner product, and this inner\nproduct is defined so that short curves correspond to admissible motions for\nthe system. We rely on the affine geometric heat flow to deform an arbitrary\npath connecting the desired initial and final states to this admissible motion.\nThe method is able to automatically find the trajectory of robot's center of\nmass, feet contact positions and forces on uneven terrain.\n"}
+{"abstract": "  Event generators in high-energy nuclear and particle physics play an\nimportant role in facilitating studies of particle reactions. We survey the\nstate-of-the-art of machine learning (ML) efforts at building physics event\ngenerators. We review ML generative models used in ML-based event generators\nand their specific challenges, and discuss various approaches of incorporating\nphysics into the ML model designs to overcome these challenges. Finally, we\nexplore some open questions related to super-resolution, fidelity, and\nextrapolation for physics event generation based on ML technology.\n"}
+{"abstract": "  We consider an epidemic model of SIR type set on a homogeneous tree and\ninvestigate the spreading properties of the epidemic as a function of the\ndegree of the tree, the intrinsic basic reproduction number and the strength of\nthe interactions within the population of infected individuals. When the degree\nis one, the homogeneous tree is nothing but the standard lattice on the\nintegers and our model reduces to a SIR model with discrete diffusion for which\nthe spreading properties are very similar to the continuous case. On the other\nhand, when the degree is larger than two, we observe some new features in the\nspreading properties. Most notably, there exists a critical value of the\nstrength of interactions above which spreading of the epidemic in the tree is\nno longer possible.\n"}
+{"abstract": "  We present here an explicit form of the random spectral measure element, what\nallows us to express a stationary random field as a stochastic integral\nexplicitly depending on its power spectrum and a spectral tensor if the field\nis a vector one. It has been shown here that convergence mechanism of such\nintegral is significantly different from the one of the Fourier transform and\nthat the traditional formalism is a partial limiting case of the one presented\nhere. The fact that there is an explicit expression of a random field makes\ncalculation of higher order statistics of it much more straightforward (see for\nexample Chepurnov et al. 2020). For a vector field such expression contains a\nprojection of an isotropically distributed random vector by a spectral tensor,\nwhat makes geometrical interpretation of harmonics behavior possible,\nsimplifying its analysis (see Sect. 2). This spectral representation also makes\nstraightforward numerical generation of a random field, what is extensively\nused by Chepurnov et al. 2020. We also present here some practical applications\nof this formalism.\n"}
+{"abstract": "  A fresh approach to the full wave analysis of time evolution of the\npolarization induced in the electromagnetic scattering from dispersive non\nmagnetic particles is presented. It is grounded on the combination of the\nHopfield model for the polarization field, the expansion of the polarization\nfield in terms of static longitudinal and transverse modes of the particle, the\nexpansion of the radiation field in terms of transverse wave modes of free\nspace, and the principle of least action. The polarization field is linearly\ncoupled to the electromagnetic field. The losses of the matter are provided\nthrough a linear coupling of the polarization field to a bath of harmonic\noscillators with a continuous range of natural frequencies. The set of linear\nordinary differential integral equations of convolution type of the overall\nsystem is reduced by eliminating both the radiation degrees of freedoms and the\nbath degrees of freedom, and the reduced system of equations is studied. The\nrole played by the radiation field in the coupling between the longitudinal and\ntransverse mode amplitudes of the polarization is described. The principal\ncharacteristics of the temporal evolution of the mode amplitudes are found as\nthe particle size varies, including the impulse response. Results are presented\nfor the analytically solvable spherical particle. The proposed approach leads\nto a general method for the analysis of the temporal evolution of the\npolarization field induced in dispersive particles of any shape, as well as for\nthe computation of transients and steady states.\n"}
+{"abstract": "  Deep neural networks have achieved remarkable performance on a range of\nclassification tasks, with softmax cross-entropy (CE) loss emerging as the\nde-facto objective function. The CE loss encourages features of a class to have\na higher projection score on the true class-vector compared to the negative\nclasses. However, this is a relative constraint and does not explicitly force\ndifferent class features to be well-separated. Motivated by the observation\nthat ground-truth class representations in CE loss are orthogonal (one-hot\nencoded vectors), we develop a novel loss function termed `Orthogonal\nProjection Loss' (OPL) which imposes orthogonality in the feature space. OPL\naugments the properties of CE loss and directly enforces inter-class separation\nalongside intra-class clustering in the feature space through orthogonality\nconstraints on the mini-batch level. As compared to other alternatives of CE,\nOPL offers unique advantages e.g., no additional learnable parameters, does not\nrequire careful negative mining and is not sensitive to the batch size. Given\nthe plug-and-play nature of OPL, we evaluate it on a diverse range of tasks\nincluding image recognition (CIFAR-100), large-scale classification (ImageNet),\ndomain generalization (PACS) and few-shot learning (miniImageNet, CIFAR-FS,\ntiered-ImageNet and Meta-dataset) and demonstrate its effectiveness across the\nboard. Furthermore, OPL offers better robustness against practical nuisances\nsuch as adversarial attacks and label noise. Code is available at:\nhttps://github.com/kahnchana/opl.\n"}
+{"abstract": "  Computer vision is playing an increasingly important role in automated\nmalware detection with to the rise of the image-based binary representation.\nThese binary images are fast to generate, require no feature engineering, and\nare resilient to popular obfuscation methods. Significant research has been\nconducted in this area, however, it has been restricted to small-scale or\nprivate datasets that only a few industry labs and research teams have access\nto. This lack of availability hinders examination of existing work, development\nof new research, and dissemination of ideas. We introduce MalNet, the largest\npublicly available cybersecurity image database, offering 133x more images and\n27x more classes than the only other public binary-image database. MalNet\ncontains over 1.2 million images across a hierarchy of 47 types and 696\nfamilies. We provide extensive analysis of MalNet, discussing its properties\nand provenance. The scale and diversity of MalNet unlocks new and exciting\ncybersecurity opportunities to the computer vision community--enabling\ndiscoveries and research directions that were previously not possible. The\ndatabase is publicly available at www.mal-net.org.\n"}
+{"abstract": "  The dark matter (DM) can consist of the primordial black holes (PBHs) in\naddition to the conventional weakly interacting massive particles (WIMPs). The\nPoisson fluctuations of the PBH number density produce the isocurvature\nperturbations which can dominate the matter power spectrum at small scales and\nenhance the early structure formation. We study how the WIMP annihilation from\nthose early formed structures can affect the CMB (in particular the E-mode\npolarization anisotropies and $y$-type spectral distortions) and global 21cm\nsignals. Our studies would be of particular interest for the light (sub-GeV)\nWIMP scenarios which have been less explored compared with the mixed DM\nscenarios consisting of PBHs and heavy ($\\gtrsim 1$ GeV) WIMPs. For instance,\nfor the self-annihilating DM mass $m_{\\chi}=1$ MeV and the thermally averaged\nannihilation cross section $\\langle \\sigma v \\rangle \\sim 10^{-30} \\rm cm^3/s$,\nthe latest Planck CMB data requires the PBH fraction with respect to the whole\nDM to be at most ${\\cal O}(10^{-3})$ for the sub-solar mass PBHs and an even\ntighter bound (by a factor $\\sim 5$) can be obtained from the global 21-cm\nmeasurements.\n"}
+{"abstract": "  We propose and demonstrate a high performance four-wavelength erbium-doped\nfiber laser (EDFL), enabled by a figure-8 compound-ring-cavity (F8-CRC) filter\nfor single-longitudinal-mode (SLM) selection and a polarization-managed\nfour-channel filter (PM-FCF) for defining four lasing wavelengths. We introduce\na novel methodology utilizing signal-flow graph combined with Mason's rule to\nanalyze a CRC filter in general and apply it to obtain the important design\nparameters for the F8-CRC filter used in this paper. By combining the functions\nof the F8-CRC filter and the PM-FCF filter assisted by the enhanced\npolarization hole-burning and polarization dependent loss, we achieve the EDFL\nwith fifteen lasing states, including four single-, six dual-, four tri- and\none quad-wavelength lasing operations. In particular, all the four\nsingle-wavelength operations are in stable SLM oscillation, typically with a\nlinewidth of <600 Hz, a RIN of <=-154.58 dB/Hz@>=3 MHz and an output power\nfluctuation of <=+/-3.45%. In addition, all the six dual-wavelength operations\nhave very similar performances, with the performance parameters close to those\nof the single-wavelength lasing operations. Finally, we achieve the wavelength\nspacing tuning of the dual-wavelength operations for the photonic generation of\ntunable microwave signals, and successfully obtain a signal at 23.10 GHz as a\ndemonstration.\n"}
+{"abstract": "  The multifractal superconducting state originates from the interplay of\nAnderson localization and interaction effects. In this article we overview the\nrecent theory of the superconductivity enhancement by multifractality and\nextend it to describe the spectral properties of superconductors on the scales\nof the order of the superconducting gap. Specifically, using the approach based\non renormalization group within the nonlinear sigma model, we develop the\ntheory of a multifractal superconducting state in thin films. We derive a\nmodified Usadel equation that incorporates the interplay of disorder and\ninteractions at energy scales larger than the spectral gap and study the effect\nof such an interplay on the low-energy physics. We determine the spectral gap\nat zero temperature which occurs to be proportional to the multifracally\nenhanced superconducting transition temperature. The modified Usadel equation\nresults in the disorder-averaged density of states that, near the spectral gap,\nresembles the one obtained in the model of a spatially random superconducting\norder parameter. We reveal strong mesoscopic fluctuations of the local density\nof states in the superconducting state. Such strong mesoscopic fluctuations\nimply that the interval of energies in which the superconducting gap\nestablishes is parametrically large in systems with multifractally-enhanced\nsuperconductivity.\n"}
+{"abstract": "  In this paper, we present a task space-based local motion planner that\nincorporates collision avoidance and constraints on end-effector motion during\nthe execution of a task. Our key technical contribution is the development of a\nnovel kinematic state evolution model of the robot where the collision\navoidance is encoded as a complementarity constraint. We show that the\nkinematic state evolution with collision avoidance can be represented as a\nLinear Complementarity Problem (LCP). Using the LCP model along with Screw\nLinear Interpolation (ScLERP) in SE(3), we show that it may be possible to\ncompute a path between two given task space poses by directly moving from the\nstart to the goal pose, even if there are potential collisions with obstacles.\nThe scalability of the planner is demonstrated with experiments using a\nphysical robot. We present simulation and experimental results with both\ncollision avoidance and task constraints to show the efficacy of our approach.\n"}
+{"abstract": "  A new belief space planning algorithm, called covariance steering Belief\nRoadMap (CS-BRM), is introduced, which is a multi-query algorithm for motion\nplanning of dynamical systems under simultaneous motion and observation\nuncertainties. CS-BRM extends the probabilistic roadmap (PRM) approach to\nbelief spaces and is based on the recently developed theory of covariance\nsteering (CS) that enables guaranteed satisfaction of terminal belief\nconstraints in finite-time. The nodes in the CS-BRM are sampled in belief space\nand represent distributions of the system states. A covariance steering\ncontroller steers the system from one BRM node to another, thus acting as an\nedge controller of the corresponding belief graph that ensures belief\nconstraint satisfaction. After the edge controller is computed, a specific edge\ncost is assigned to that edge. The CS-BRM algorithm allows the sampling of\nnon-stationary belief nodes, and thus is able to explore the velocity space and\nfind efficient motion plans. The performance of CS-BRM is evaluated and\ncompared to a previous belief space planning method, demonstrating the benefits\nof the proposed approach.\n"}
+{"abstract": "  How much do individuals contribute to team output? I propose an econometric\nframework to quantify individual contributions when only the output of their\nteams is observed. The identification strategy relies on following individuals\nwho work in different teams over time. I consider two production technologies.\nFor a production function that is additive in worker inputs, I propose a\nregression estimator and show how to obtain unbiased estimates of variance\ncomponents that measure the contributions of heterogeneity and sorting. To\nestimate nonlinear models with complementarity, I propose a mixture approach\nunder the assumption that individual types are discrete, and rely on a\nmean-field variational approximation for estimation. To illustrate the methods,\nI estimate the impact of economists on their research output, and the\ncontributions of inventors to the quality of their patents.\n"}
+{"abstract": "  We study Thompson sampling (TS) in online decision-making problems where the\nuncertain environment is sampled from a mixture distribution. This is relevant\nto multi-task settings, where a learning agent is faced with different classes\nof problems. We incorporate this structure in a natural way by initializing TS\nwith a mixture prior -- dubbed MixTS -- and develop a novel, general technique\nfor analyzing the regret of TS with such priors. We apply this technique to\nderive Bayes regret bounds for MixTS in both linear bandits and tabular Markov\ndecision processes (MDPs). Our regret bounds reflect the structure of the\nproblem and depend on the number of components and confidence width of each\ncomponent of the prior. Finally, we demonstrate the empirical effectiveness of\nMixTS in both synthetic and real-world experiments.\n"}
+{"abstract": "  Synthetic data became already an essential component of machine\nlearning-based perception in the field of autonomous driving. Yet it still\ncannot replace real data completely due to the sim2real domain shift. In this\nwork, we propose a method that leverages the advantages of the augmentation\nprocess and adversarial training to synthesize realistic data for the\npedestrian recognition task. Our approach utilizes an attention mechanism\ndriven by an adversarial loss to learn domain discrepancies and improve\nsim2real adaptation. Our experiments confirm that the proposed adaptation\nmethod is robust to such discrepancies and reveals both visual realism and\nsemantic consistency. Furthermore, we evaluate our data generation pipeline on\nthe task of pedestrian recognition and demonstrate that generated data resemble\nproperties of the real domain.\n"}
+{"abstract": "  Stars within the innermost part of the Nuclear Star Cluster can reach orbital\nvelocities up to a few percent of the light speed. As analyzed by Rafikov\n(2020), Doppler boosting of stellar light may be of relevance at the pericenter\nof stellar orbits, especially with the upcoming high-precision photometry in\nthe near- and mid-infrared bands. Here we analyze the previously neglected\neffect of infrared spectral index of monitored objects on the Doppler-boosted\ncontinuum emission in a narrow band. In contrast to main-sequences stars, the\ndetected compact infrared-excess dust-enshrouded objects have an enhanced\nDoppler-boosting effect by as much as an order of magnitude, with the\nvariability amplitude of the order of ten percent for the most eccentric\norbits. In a similar way, pulsars dominated by non-thermal synchrotron emission\nare also expected to exhibit a stronger Doppler-boosted signal by a factor of\nat least four in comparison with canonical S stars. In case the stellar orbit\nis robustly determined, the relative flux variation can thus provide hints\nabout the nature of the objects. For extended dust-enshrouded objects, such as\nG1, that are variable due to tidal, ellipsoidal, bow-shock, and irradiation\neffects, the subtraction of the expected Doppler-boosting variations will help\nto better comprehend their internal physics. In addition, the relative flux\nvariability due to higher-order relativistic effects is also modified for\ndifferent negative spectral indices in a way that it can obtain both positive\nand negative values with the relative variability of the order of one percent.\n"}
+{"abstract": "  Some years after the appearance of the so-called non-diffracting beams, there\nwas the development of methods capable of structuring them spatially, being the\nso called Frozen Waves method the first and, perhaps, the most efficient one.\nThat method allowed modelling the longitudinal intensity pattern of\nnon-diffracting beams, being, however, little efficient in controlling their\ntransverse spatial pattern, granting only the possibility of choosing their\ntransverse dimensions, which remain invariant throughout the propagation. In\nthis work, we have extended the Frozen Wave method in such a way to control, in\naddition to the longitudinal pattern, the transverse beam structure along the\npropagation. The new transversally and longitudinally structured beams can have\npotential applications in areas such as photonics, optical manipulation,\noptical atom guidance, lithography, etc..\n"}
+{"abstract": "  In this paper, we focus on the solution of a hard single machine scheduling\nproblem by new heuristic algorithms embedding techniques from machine learning\nfield and scheduling theory. These heuristics transform an instance of the hard\nproblem into an instance of a simpler one solved to optimality. The obtained\nschedule is then transposed to the original problem. Computational experiments\nshow that they are competitive with state-of-the-art heuristics, notably on\nlarge instances.\n"}
+{"abstract": "  In several industrial applications, such as crystallization, pollution\ncontrol, and flow assurance, an accurate understanding of the aqueous\nelectrolyte solutions is crucial. Electrolyte equilibrium calculation\ncontributes with the design and optimization of processes by providing\nimportant information, such as species concentration, solution pH and potential\nfor solid formation. In this work, a pure Python library distributed under\nBSD-3 license was developed for the calculation of aqueous electrolyte\nequilibrium. The package takes as inputs the feed components of a given\nsolution, and it automatically identifies its composing ions and the chemical\nreactions involved to calculate equilibrium conditions. Moreover, there is no\nestablished electrolyte activity coefficient model for a broad range of\noperational conditions. Hence, in this package, built-in activity coefficient\nmodels are structured in a modular approach, so that the non-ideality\ncalculation can be performed by a user provided function, which allows further\nresearch in the topic. The package can be used by researchers to readily\nidentify the equilibrium reactions and possible solid phases in a user friendly\nlanguage.\n"}
+{"abstract": "  Medical entity retrieval is an integral component for understanding and\ncommunicating information across various health systems. Current approaches\ntend to work well on specific medical domains but generalize poorly to unseen\nsub-specialties. This is of increasing concern under a public health crisis as\nnew medical conditions and drug treatments come to light frequently. Zero-shot\nretrieval is challenging due to the high degree of ambiguity and variability in\nmedical corpora, making it difficult to build an accurate similarity measure\nbetween mentions and concepts. Medical knowledge graphs (KG), however, contain\nrich semantics including large numbers of synonyms as well as its curated\ngraphical structures. To take advantage of this valuable information, we\npropose a suite of learning tasks designed for training efficient zero-shot\nentity retrieval models. Without requiring any human annotation, our knowledge\ngraph enriched architecture significantly outperforms common zero-shot\nbenchmarks including BM25 and Clinical BERT with 7% to 30% higher recall across\nmultiple major medical ontologies, such as UMLS, SNOMED, and ICD-10.\n"}
+{"abstract": "  We study the local well-posedness of the nonlinear Schr{\\''o}dinger equation\nassociated to the Grushin operator with random initial data. To the best of our\nknowledge, no well-posedness result is known in the Sobolev spaces $H^k$ when\n$k \\leq \\frac{3}{2}$. In this article, we prove that there exists a large\nfamily of initial data such that, with respect to a suitable randomization in\n$H^k$, $k \\in (1,\\frac{3}{2}]$, almost-sure local well-posedness holds. The\nproof relies on bilinear and trilinear estimates.\n"}
+{"abstract": "  Given the omnipresence of non-covalent interactions (NCIs), their accurate\nsimulations are of crucial importance across various scientific disciplines.\nHere we construct accurate models for the description of NCIs by an\ninterpolation along the M{\\o}ller-Plesset adiabatic connection (MP AC). Our\ninterpolation approximates the correlation energy, by recovering MP2 at small\ncoupling strengths and the correct large-coupling strength expansion of the MP\nAC, recently shown to be a functional of the Hartree-Fock density. Our models\nare size consistent for fragments with non-degenerate ground states, have the\nsame cost as double hybrids and require no dispersion corrections to capture\nNCIs accurately. These interpolations greatly reduce large MP2 errors for\ntypical $\\pi$-stacking complexes (e.g., benzene-pyridine dimers) and for the L7\ndataset. They are also competitive with state-of-the-art dispersion enhanced\nfunctionals and can even significantly outperform them for a variety of\ndatasets, such as CT7 and L7.\n"}
+{"abstract": "  Real-time traffic prediction models play a pivotal role in smart mobility\nsystems and have been widely used in route guidance, emerging mobility\nservices, and advanced traffic management systems. With the availability of\nmassive traffic data, neural network-based deep learning methods, especially\nthe graph convolutional networks (GCN) have demonstrated outstanding\nperformance in mining spatio-temporal information and achieving high prediction\naccuracy. Recent studies reveal the vulnerability of GCN under adversarial\nattacks, while there is a lack of studies to understand the vulnerability\nissues of the GCN-based traffic prediction models. Given this, this paper\nproposes a new task -- diffusion attack, to study the robustness of GCN-based\ntraffic prediction models. The diffusion attack aims to select and attack a\nsmall set of nodes to degrade the performance of the entire prediction model.\nTo conduct the diffusion attack, we propose a novel attack algorithm, which\nconsists of two major components: 1) approximating the gradient of the\nblack-box prediction model with Simultaneous Perturbation Stochastic\nApproximation (SPSA); 2) adapting the knapsack greedy algorithm to select the\nattack nodes. The proposed algorithm is examined with three GCN-based traffic\nprediction models: St-Gcn, T-Gcn, and A3t-Gcn on two cities. The proposed\nalgorithm demonstrates high efficiency in the adversarial attack tasks under\nvarious scenarios, and it can still generate adversarial samples under the drop\nregularization such as DropOut, DropNode, and DropEdge. The research outcomes\ncould help to improve the robustness of the GCN-based traffic prediction models\nand better protect the smart mobility systems. Our code is available at\nhttps://github.com/LYZ98/Adversarial-Diffusion-Attacks-on-Graph-based-Traffic-Prediction-Models\n"}
+{"abstract": "  Dipolar Bose-Einstein condensates represent a powerful platform for the\nexploration of quantum many-body phenomena arising from long-range\ninteractions. A series of recent experiments has demonstrated the formation of\nsupersolid states of matter. Subsequent theoretical works have shown that\nquantum fluctuations can affect the underlying phase transition and may lead to\nthe emergence of supersolids with various lattice structures in dipolar\ncondensates. In this work we explore the signatures of such different\ngeometries in confined finite condensates. In addition to previously found\ntriangular lattices, our analysis reveals a rich spectrum of states, from\nhoneycomb patterns and ring structures to striped supersolids. By optimizing\nrelevant parameters we show that transitions between distinct supersolids\nshould be observable in current experiments.\n"}
+{"abstract": "  In this paper, we propose to combine the fifth order Hermite weighted\nessentially non-oscillatory (HWENO) scheme and fast sweeping method (FSM) for\nthe solution of the steady-state $S_{N}$ transport equation in the finite\nvolume framework. It is well-known that the $S_{N}$ transport equation\nasymptotically converges to a macroscopic diffusion equation in the limit of\noptically thick systems with small absorption and sources. Numerical methods\nwhich can preserve the asymptotic limit are referred to as asymptotic\npreserving methods. In the one-dimensional case, we provide the analysis to\ndemonstrate the asymptotic preserving property of the high order finite volume\nHWENO method, by showing that its cell-edge and cell-average fluxes possess the\nthick diffusion limit. Numerical results in both one- and two- dimensions are\npresented to validate its asymptotic preserving property. A hybrid strategy to\ncompute the nonlinear weights in the HWENO reconstruction is introduced to save\ncomputational cost. Extensive one- and two-dimensional numerical experiments\nare performed to verify the accuracy, asymptotic preserving property and\npositivity of the proposed HWENO FSM.\n"}
+{"abstract": "  Many neural networks for graphs are based on the graph convolution operator,\nproposed more than a decade ago. Since then, many alternative definitions have\nbeen proposed, that tend to add complexity (and non-linearity) to the model. In\nthis paper, we follow the opposite direction by proposing simple graph\nconvolution operators, that can be implemented in single-layer graph\nconvolutional networks. We show that our convolution operators are more\ntheoretically grounded than many proposals in literature, and exhibit\nstate-of-the-art predictive performance on the considered benchmark datasets.\n"}
+{"abstract": "  Neural network pruning is a popular technique used to reduce the inference\ncosts of modern, potentially overparameterized, networks. Starting from a\npre-trained network, the process is as follows: remove redundant parameters,\nretrain, and repeat while maintaining the same test accuracy. The result is a\nmodel that is a fraction of the size of the original with comparable predictive\nperformance (test accuracy). Here, we reassess and evaluate whether the use of\ntest accuracy alone in the terminating condition is sufficient to ensure that\nthe resulting model performs well across a wide spectrum of \"harder\" metrics\nsuch as generalization to out-of-distribution data and resilience to noise.\nAcross evaluations on varying architectures and data sets, we find that pruned\nnetworks effectively approximate the unpruned model, however, the prune ratio\nat which pruned networks achieve commensurate performance varies significantly\nacross tasks. These results call into question the extent of \\emph{genuine}\noverparameterization in deep learning and raise concerns about the\npracticability of deploying pruned networks, specifically in the context of\nsafety-critical systems, unless they are widely evaluated beyond test accuracy\nto reliably predict their performance. Our code is available at\nhttps://github.com/lucaslie/torchprune.\n"}
+{"abstract": "  Depth map records distance between the viewpoint and objects in the scene,\nwhich plays a critical role in many real-world applications. However, depth map\ncaptured by consumer-grade RGB-D cameras suffers from low spatial resolution.\nGuided depth map super-resolution (DSR) is a popular approach to address this\nproblem, which attempts to restore a high-resolution (HR) depth map from the\ninput low-resolution (LR) depth and its coupled HR RGB image that serves as the\nguidance.\n  The most challenging problems for guided DSR are how to correctly select\nconsistent structures and propagate them, and properly handle inconsistent\nones. In this paper, we propose a novel attention-based hierarchical\nmulti-modal fusion (AHMF) network for guided DSR. Specifically, to effectively\nextract and combine relevant information from LR depth and HR guidance, we\npropose a multi-modal attention based fusion (MMAF) strategy for hierarchical\nconvolutional layers, including a feature enhance block to select valuable\nfeatures and a feature recalibration block to unify the similarity metrics of\nmodalities with different appearance characteristics. Furthermore, we propose a\nbi-directional hierarchical feature collaboration (BHFC) module to fully\nleverage low-level spatial information and high-level structure information\namong multi-scale features. Experimental results show that our approach\noutperforms state-of-the-art methods in terms of reconstruction accuracy,\nrunning speed and memory efficiency.\n"}
+{"abstract": "  Deep neural networks have usually to be compressed and accelerated for their\nusage in low-power, e.g. mobile, devices. Recently, massively-parallel hardware\naccelerators were developed that offer high throughput and low latency at low\npower by utilizing in-memory computation. However, to exploit these benefits\nthe computational graph of a neural network has to fit into the in-computation\nmemory of these hardware systems that is usually rather limited in size. In\nthis study, we introduce a class of network models that have a small memory\nfootprint in terms of their computational graphs. To this end, the graph is\ndesigned to contain loops by iteratively executing a single network building\nblock. Furthermore, the trade-off between accuracy and latency of these\nso-called iterative neural networks is improved by adding multiple intermediate\noutputs during both training and inference. We show state-of-the-art results\nfor semantic segmentation on the CamVid and Cityscapes datasets that are\nespecially demanding in terms of computational resources. In ablation studies,\nthe improvement of network training by intermediate network outputs as well as\nthe trade-off between weight sharing over iterations and the network size are\ninvestigated.\n"}
+{"abstract": "  We consider an axion cloud around a black hole with background magnetic\nfields. We calculate the decay rate of the axion cloud due to the axion-photon\nconversion associated with the axion-photon coupling. For simplicity, we\nconsider the situation where the axion configuration is dominated by a solution\nfor the eigenvalue equation equivalent to that for the Hydrogen atom, and the\ncoupling term can be evaluated by a successive perturbation method. For the\nmonopole background, we find the decay rate of the axion cloud is given by\n$\\sim q^2\\kappa^2(GM)^5\\mu^8$, where $\\mu$, $M$, $G$, $\\kappa$ and $q$ are the\naxion mass, black hole mass, gravitational constant, coupling constant of the\naxion-photon coupling and monopole charge, respectively. For the uniform\nbackground magnetic field, we obtain the decay rate of the axion cloud $\\sim\nB_0^2\\kappa^2 (GM)^7\\mu^6$, where $B_0$ is the magnetic field strength.\nApplying our formula to the central black hole in our galaxy, we find that the\nvalue of the decay rate for the case of the uniform magnetic field is\ncomparable to the growth rate of the superradiant instability with $\\kappa\\sim\n10^{-12}{\\rm GeV^{-1}}$, $B_0\\sim 10^3{\\rm G}$ and $\\mu\\sim 10^{-18}{\\rm eV}$.\nThe ratio is $10^5$ times larger for the monopole magnetic field with the same\nvalues of the parameters.\n"}
+{"abstract": "  We consider a coupled Patlak-Keller-Segel-Navier-Stokes system in\n$\\mathbb{R}^2$ that describes the collective motion of cells and fluid flow,\nwhere the cells are attracted by a chemical substance and transported by\nambient fluid velocity, and the fluid flow is forced by the friction induced by\nthe cells. The main result of the paper is to show the global existence of\nfree-energy solutions to the 2D Patlak-Keller-Segel-Navier-Stokes system with\ncritical and subcritical mass.\n"}
+{"abstract": "  3D recovery from multi-stereo and stereo images, as an important application\nof the image-based perspective geometry, serves many applications in computer\nvision, remote sensing and Geomatics. In this chapter, the authors utilize the\nimaging geometry and present approaches that perform 3D reconstruction from\ncross-view images that are drastically different in their viewpoints. We\nintroduce our framework that takes ground-view images and satellite images for\nfull 3D recovery, which includes necessary methods in satellite and\nground-based point cloud generation from images, 3D data co-registration,\nfusion and mesh generation. We demonstrate our proposed framework on a dataset\nconsisting of twelve satellite images and 150k video frames acquired through a\nvehicle-mounted Go-pro camera and demonstrate the reconstruction results. We\nhave also compared our results with results generated from an intuitive\nprocessing pipeline that involves typical geo-registration and meshing methods.\n"}
+{"abstract": "  The present paper is to deliberate the geometric composition of a perfect\nfluid spacetime with torse-forming vector field {\\xi} in connection with\nconformal Ricci-Yamabe metric and conformal {\\eta}-Ricci-Yamabe metric. Here we\nhave delineated the conditions for conformal Ricci-Yamabe soliton to be\nexpanding, steady, or shrinking. Later, we have acquired Laplace equation from\nconformal {\\eta}-Ricci-Yamabe soliton equation when the potential vector field\n{\\xi} of the soliton is of gradient type. Lastly, we have designated perfect\nfluid with Robertson-Walker spacetime and some applications of physics and\ngravity.\n"}
+{"abstract": "  AM dependency parsing is a method for neural semantic graph parsing that\nexploits the principle of compositionality. While AM dependency parsers have\nbeen shown to be fast and accurate across several graphbanks, they require\nexplicit annotations of the compositional tree structures for training. In the\npast, these were obtained using complex graphbank-specific heuristics written\nby experts. Here we show how they can instead be trained directly on the graphs\nwith a neural latent-variable model, drastically reducing the amount and\ncomplexity of manual heuristics. We demonstrate that our model picks up on\nseveral linguistic phenomena on its own and achieves comparable accuracy to\nsupervised training, greatly facilitating the use of AM dependency parsing for\nnew sembanks.\n"}
+{"abstract": "  Cyberattacks are a major issues and it causes organizations great financial,\nand reputation harm. However, due to various factors, the current network\nintrusion detection systems (NIDS) seem to be insufficent. Predominant NIDS\nidentifies Cyberattacks through a handcrafted dataset of rules. Although the\nrecent applications of machine learning and deep learning have alleviated the\nenormous effort in NIDS, the security of network data has always been a prime\nconcern. However, to encounter the security problem and enable sharing among\norganizations, Federated Learning (FL) scheme is employed. Although the current\nFL systems have been successful, a network's data distribution does not always\nfit into a single global model as in FL. Thus, in such cases, having a single\nglobal model in FL is no feasible. In this paper, we propose a\nSegmented-Federated Learning (Segmented-FL) learning scheme for a more\nefficient NIDS. The Segmented-FL approach employs periodic local model\nevaluation based on which the segmentation occurs. We aim to bring similar\nnetwork environments to the same group. Further, the Segmented-FL system is\ncoupled with a weighted aggregation of local model parameters based on the\nnumber of data samples a worker possesses to further augment the performance.\nThe improved performance by our system as compared to the FL and centralized\nsystems on standard dataset further validates our system and makes a strong\ncase for extending our technique across various tasks. The solution finds its\napplication in organizations that want to collaboratively learn on diverse\nnetwork environments and protect the privacy of individual datasets.\n"}
+{"abstract": "  The resonance frequency of membranes depends on the gas pressure due to the\nsqueeze-film effect, induced by the compression of a thin gas film that is\ntrapped underneath the resonator by the high frequency motion. This effect is\nparticularly large in low-mass graphene membranes, which makes them promising\ncandidates for pressure sensing applications. Here, we study the squeeze-film\neffect in single layer graphene resonators and find that their resonance\nfrequency is lower than expected from models assuming ideal compression. To\nunderstand this deviation, we perform Boltzmann and continuum finite-element\nsimulations, and propose an improved model that includes the effects of gas\nleakage and can account for the observed pressure dependence of the resonance\nfrequency. Thus, this work provides further understanding of the squeeze-film\neffect and provides further directions into optimizing the design of\nsqueeze-film pressure sensors from 2D materials.\n"}
+{"abstract": "  A new method for simulation of a binary homogeneous Markov process using a\nquantum computer was proposed. This new method allows using the distinguished\nproperties of the quantum mechanical systems -- superposition, entanglement and\nprobability calculations. Implementation of an algorithm based on this method\nrequires the creation of a new quantum logic gate, which creates entangled\nstate between two qubits. This is a two-qubit logic gate and it must perform a\npredefined rotation over the X-axis for the qubit that acts as a target, where\nthe rotation accurately represents the transient probabilities for a given\nMarkov process. This gate fires only when the control qubit is in state |1>. It\nis necessary to develop an algorithm, which uses the distribution for the\ntransient probabilities of the process in a simple and intuitive way and then\ntransform those into X-axis offsets. The creation of a quantum controlled n-th\nroot of X gate using only the existing basic quantum logic gates at the\navailable cloud platforms is possible, although the hardware devices are still\ntoo noisy, which results in a significant measurement error increase. The IBM's\nYorktown 'bow-tie' back-end performs quite better than the 5-qubit T-shaped and\nthe 14-qubit Melbourne quantum processors in terms of quantum fidelity. The\nsimulation of the binary homogeneous Markov process on a real quantum processor\ngives best results on the Vigo and Yorktown (both 5-qubit) back-ends with\nHellinger fidelity of near 0.82. The choice of the right quantum circuit, based\non the available hardware (topology, size, timing properties), would be the\napproach for maximizing the fidelity.\n"}
+{"abstract": "  Conventional machine learning techniques are conducted in a centralized\nmanner. Recently, the massive volume of generated wireless data, the privacy\nconcerns and the increasing computing capabilities of wireless end-devices have\nled to the emergence of a promising decentralized solution, termed as Wireless\nFederated Learning (WFL). In this first of the two parts paper, we present the\napplication of WFL in the sixth generation of wireless networks (6G), which is\nenvisioned to be an integrated communication and computing platform. After\nanalyzing the key concepts of WFL, we discuss the core challenges of WFL\nimposed by the wireless (or mobile communication) environment. Finally, we shed\nlight to the future directions of WFL, aiming to compose a constructive\nintegration of FL into the future wireless networks.\n"}
+{"abstract": "  Sampled environment transitions are a critical input to deep reinforcement\nlearning (DRL) algorithms. Current DRL benchmarks often allow for the cheap and\neasy generation of large amounts of samples such that perceived progress in DRL\ndoes not necessarily correspond to improved sample efficiency. As simulating\nreal world processes is often prohibitively hard and collecting real world\nexperience is costly, sample efficiency is an important indicator for\neconomically relevant applications of DRL. We investigate progress in sample\nefficiency on Atari games and continuous control tasks by comparing the number\nof samples that a variety of algorithms need to reach a given performance level\naccording to training curves in the corresponding publications. We find\nexponential progress in sample efficiency with estimated doubling times of\naround 10 to 18 months on Atari, 5 to 24 months on state-based continuous\ncontrol and of around 4 to 9 months on pixel-based continuous control depending\non the specific task and performance level.\n"}
+{"abstract": "  We present the Australian Square Kilometre Array Pathfinder (ASKAP)\nlocalization and follow-up observations of the host galaxy of the repeating\nfast radio burst (FRB) source, FRB20201124A, the fifth such extragalactic\nrepeating FRB with an identified host. From spectroscopic observations using\nthe 6.5-m MMT Observatory, we derive a redshift of $z=0.0979 \\pm 0.0001$, a\nstar formation rate inferred from H$\\alpha$ emission of SFR(H$\\alpha$) $\\approx\n2.1 M_{\\odot}$ yr$^{-1}$, and a gas-phase metallicity of 12+log(O/H)$\\approx\n9.0$. By jointly modeling the 12-filter optical-mid-infrared (MIR) photometry\nand spectroscopy of the host, we infer a median stellar mass of $\\approx 2\n\\times 10^{10} M_{\\odot}$, internal dust extinction of $A_V\\approx 1-1.5$ mag,\nand a mass-weighted stellar population age of $\\approx 5-6$ Gyr. Connecting\nthese data to the radio and X-ray observations, we cannot reconcile the\nbroad-band behavior with strong AGN activity and instead attribute the dominant\nsource of persistent radio emission to star formation, likely originating from\nthe circumnuclear region of the host. The modeling also indicates a hot dust\ncomponent contributing to the MIR luminosity at a level of $\\approx 10-30\\%$.\nWe model the host galaxy's star formation and mass assembly histories, finding\nthat the host assembled $>90\\%$ of its mass by 1 Gyr ago and exhibited a fairly\nconstant SFR for most of its existence, with no clear evidence of past\nstar-burst activity.\n"}
+{"abstract": "  The concept of negativity is adapted in order to explore the quantum and\nthermal entanglement of the mixed spin-(1/2,$S$) Heisenberg dimers in presence\nof an external magnetic field. The mutual interplay between the spin size $S$,\nXXZ exchange and uniaxial single-ion anisotropy is thoroughly examined with a\ngoal to tune the degree and thermal stability of the pairwise entanglement. It\nturns out that the antiferromagnetic spin-(1/2,$S$) Heisenberg dimers exhibit\nhigher degree of entanglement and higher threshold temperature in comparison\nwith their ferromagnetic counterparts when assuming the same set of model\nparameters. The increasing spin magnitude $S$ accompanied with an easy-plane\nuniaxial single-ion anisotropy can enhance not only the thermal stability but\nsimultaneously the degree of entanglement. It is additionally shown that the\nfurther enhancement of a bipartite entanglement can be achieved in the mixed\nspin-(1/2,$S$) Heisenberg dimers, involving half-odd-integer spins $S$. Under\nthis condition the thermal negativity saturates at low-enough temperatures in\nits maximal value regardless of the magnitude of half-odd-integer spin $S$. The\nmagnetic field induces consecutive discontinuous phase transitions in the mixed\nspin-(1/2,$S$) Heisenberg dimers with $S\\!>\\!1$, which are manifested in a\nsurprising oscillating magnetic-field dependence of the negativity observed at\nlow enough temperature.\n"}
+{"abstract": "  Public blockchains have spurred the growing popularity of decentralized\ntransactions and smart contracts, but they exhibit limitations on the\ntransaction throughput, storage, and computation. To avoid transaction\ngridlock, public blockchains impose large fees and per-block resource limits,\nmaking it difficult to accommodate the ever-growing transaction demand.\nPrevious research endeavors to improve the scalability of blockchain through\nvarious technologies, such as side-chaining, sharding, secured off-chain\ncomputation, communication network optimizations, and efficient consensus\nprotocols. However, these approaches have not attained a widespread adoption\ndue to their inability in delivering a cloud-like performance, in terms of the\nscalability in transaction throughput, storage, and compute capacity. In this\nwork, we determine that the major obstacle to public blockchain scalability is\ntheir underlying unstructured P2P networks. We further show that a centralized\nnetwork can support the deployment of decentralized smart contracts. We propose\na novel approach for achieving scalable decentralization: instead of trying to\nmake blockchain scalable, we deliver decentralization to already scalable cloud\nby using an Ethereum smart contract. We introduce Blockumulus, a framework that\ncan deploy decentralized cloud smart contract environments using a novel\ntechnique called overlay consensus. Through experiments, we demonstrate that\nBlockumulus is scalable in all three dimensions: computation, data storage, and\ntransaction throughput. Besides eliminating the current code execution and\nstorage restrictions, Blockumulus delivers a transaction latency between 2 and\n5 seconds under normal load. Moreover, the stress test of our prototype reveals\nthe ability to execute 20,000 simultaneous transactions under 26 seconds, which\nis on par with the average throughput of worldwide credit card transactions.\n"}
+{"abstract": "  The application of high-dimensional quantum systems (qudits) in quantum\ncomputing and communications seems to be a promising avenue due to the\npossibility of increasing the amount of information encoded in one physical\ncarrier. In this work, we propose a method for implementing single-qudit gates\nfor qudits based on light modes with orbital angular momentum. Method for\nlogical qudits encoding, which ensures the quasi-cyclicity of operations, is\nintroduced. Based on the protocol for converting the orbital angular momentum\nof light in the Raman quantum memory scheme [Vashukevich E.A. et. al. PRA, 101,\n033830 (2020)], we show that the considered gates provide an extremely high\nlevel of fidelity of single-qudit transformations. We also compare quantum\ngates' properties for systems of different dimensions and find the optimal\nconditions for carrying out transformations in the protocol under\nconsideration.\n"}
+{"abstract": "  Let $G$ be a ribbon graph. Matthew Baker and Yao Wang proved that the\nrotor-routing torsor and the Bernardi torsor for $G$, which are two torsor\nstructures on the set of spanning trees for the Picard group of $G$, coincide\nwhen $G$ is planar. We prove the conjecture raised by them that the two torsors\ndisagree when $G$ is non-planar.\n"}
+{"abstract": "  The temperature dependence of AC susceptibility (ACS) has been measured for a\nvery high-quality plate-like slightly overdoped YBCO single crystal for\ndifferent frequencies and AC magnetic field amplitudes. Frequency dependence of\nthe ACS is weak irrespective of the magnetic field orientation but significant\neffects of field orientation with respect to the CuO2 planes and field\nmagnitude on real and imaginary components of fundamental ACS were observed.\nThe height of the loss peak saturates as full penetration of magnetic field is\nachieved. The peak temperature, Tp, in \\c{hi}\" shifts to lower temperatures\nwith increasing magnetic field amplitude for both HIIc and HIIab. The value of\nTp depends on the orientation of the magnetic field with respect to the\ncrystallographic axes, illustrating the anisotropy in the magnetic flux\ndynamics. The superconducting transition width increases weakly with increasing\nmagnetic field. The Cole-Cole plot [\\c{hi}\"(\\c{hi}')] shows qualitatively and\nquantitatively identical features for HIIc and HIIab, independent of the\norientation of the magnetic field with respect to the sample geometry and\nshielding current paths. The general features of \\c{hi}\"(\\c{hi}') implies that,\nthere is no flux creep for the range of frequencies and AC fields employed in\nthis investigation. The maximum value of the loss peak and its position with\nrespect to \\c{hi}' in the Cole-Cole plot are largely consistent with the Bean\ncritical state model. Slightly increased peak value in comparison to the\npredicted peak value within the Bean critical state model is probably due to a\nweak field dependence of Jc. The results obtained here are compared with\nvarious theoretical models and experimental findings. Prominent differences are\nnoted and discussed in details in this study.\n"}
+{"abstract": "  In this paper, we analyse spectral properties of Seidel matrix (denoted by\n$S$) of connected threshold graphs. We compute the characteristic polynomial\nand determinant of Seidel matrix of threshold graphs. We derive formulas for\nthe multiplicity of the eigenvalues $\\pm 1$ of $S$. Further we determine\nthreshold graphs with at most 5 distinct Seidel eigenvalues. Finally we\nconstruct families of Seidel cospectral threshold graphs.\n"}
+{"abstract": "  We describe a theory of logarithmic Chow rings and tautological subrings for\nlogarithmically smooth algebraic stacks, via a generalisation of the notion of\npiecewise-polynomial functions. Using this machinery we prove that the\ndouble-double ramification cycle lies in the tautological subring of the\n(classical) Chow ring of the moduli space of curves, and that the logarithmic\ndouble ramification cycle is divisorial (as conjectured by Molcho,\nPandharipande, and Schmitt).\n"}
+{"abstract": "  We present elemental abundances and stellar population ages for 68 massive\nquiescent galaxies at $0.59\\leq z\\leq0.75$ from the LEGA-C survey. The\nabundance patterns and ages, derived from full-spectrum modeling, are examined\nas a function of stellar mass ($M_*$) and size (i.e., half-light radius;\n$R_e$). We find that both [Mg/H] and [Fe/H] do not vary with stellar mass but\nare correlated with $M_*/R_e$ for quiescent galaxies with $M_*>10^{10.5}\nM_\\odot$. Thus, at fixed mass, compact quiescent galaxies are on average more\nmetal rich. This result reinforces the picture that supernova feedback and\ngravitational potential regulate chemical enrichment. [Mg/Fe] does not vary\nwith $M_*$ or $M_*/R_e$, but there is a marginal positive relation between age\nand mass. Our results support low-redshift findings that more massive galaxies\nform their stars at earlier times. However, in contrast to low-redshift\nstudies, star formation timescale does not appear to depend on mass or size. We\nalso compare the mass-[Fe/H] and mass-[Mg/H] relations to stacks of quiescent\ngalaxies at $z\\sim0$ and find that both relations increase by $\\sim0.2$ dex\nover the past 7 Gyr. Furthermore, at $z\\sim0.7$ we find a clear trend with age,\nsuch that older quiescent galaxies have lower metallicities. Both results can\nbe explained by a chemical evolution model in which galaxies quench via gas\nremoval. Future work, in particular with JWST/NIRSpec, will extend this\nanalysis to higher redshifts, allowing us to fully exploit abundance patterns\nto study the formation histories of quiescent galaxies.\n"}
+{"abstract": "  The two-dimensional (2D) logarithmic character of Coulomb interaction between\ncharges and the resulting logarithmic confinement is a remarkable inherent\nproperty of high dielectric constant (high-$\\kappa$) thin films with far\nreaching implications. Most and foremost, this is the charge\nBerezinskii-Kosterlitz-Thouless transition with the notable manifestation,\nlow-temperature superinsulating topological phase. Here we show that the range\nof the confinement can be tuned by the external gate electrode and unravel a\nvariety of electrostatic interactions in high-$\\kappa$ films. We find that by\nreducing the distance from the gate to the film, we decrease the spatial range\nof the 2D long-range logarithmic interaction, changing it to predominantly\ndipolar or even to exponential one at lateral distances exceeding the dimension\nof the film-gate separation. Our findings offer a unique laboratory for the\nin-depth study of topological phase transitions and related phenomena that\nrange from criticality of quantum metal- and superconductor-insulator\ntransitions to the effects of charge-trapping and Coulomb scalability in memory\nnanodevices.\n"}
+{"abstract": "  The phase III BNT162b2 mRNA COVID-19 vaccine trial is based on a Bayesian\ndesign and analysis, and the main evidence of vaccine efficacy is presented in\nBayesian statistics. Confusion and mistakes are produced in the presentation of\nthe Bayesian results. Some key statistics, such as Bayesian credible intervals,\nare mislabeled and stated as confidence intervals. Posterior probabilities of\nthe vaccine efficacy are not reported as the main results. We illustrate the\nmain differences in the reporting of Bayesian analysis results for a clinical\ntrial and provide four recommendations. We argue that statistical evidence from\na Bayesian trial, when presented properly, is easier to interpret and directly\naddresses the main clinical questions, thereby better supporting regulatory\ndecision making. We also recommend using abbreviation \"BI\" to represent\nBayesian credible intervals as a differentiation to \"CI\" which stands for\nconfidence interval.\n"}
+{"abstract": "  Frequency analysis of long-term ultra-precise photometry can lead to precise\nvalues of rotation frequencies of rotating stars with ``hump and spike''\nfeatures in their periodograms. Using these features, we computed the rotation\nfrequencies and amplitudes. The corresponding equatorial rotational velocity\n($v_{\\rm rot}$) and spot size were estimated. On fitting the autocorrelation\nfunctions of the light-curves with the appropriate model, we determined the\nstarspot decay-time scale. The $v_{\\rm rot}$ agrees well with the projected\nrotational velocity ($v\\,{\\rm sin}\\,i$) in the literature. Considering a single\ncircular and black spot we estimate its radius from the amplitude of the\n``spike''. No evidence for a significant difference in the average ``spike''\namplitude and spot radius was found for Am/Fm and normal A stars. Indeed, we\nderived an average value of $\\rm \\sim 21\\pm2$ and $\\sim 19\\pm2\\,{\\rm ppm}$ for\nthe photometric amplitude and of $\\rm 1.01\\,\\pm\\,0.13$ and\n$1.16\\,\\pm\\,0.12\\,R_{\\rm E}$ for the spot radius (where $R_{\\rm E}$ is the\nEarth radius), respectively. We do find a significant difference for the\naverage spot decay-time scale, which amounts to $3.6\\pm0.2$ and $1.5\\pm0.2$\ndays for Am/Fm and normal A stars, respectively. In general, spots on normal A\nstars are similar in size to those on Am/Fm stars, and both are weaker than\npreviously estimated. The existence of the ``spikes'' in the frequency spectra\nmay not be strongly dependent on the appearance of starspots on the stellar\nsurface. In comparison with G, K and M stars, spots in normal A and Am/Fm stars\nare weak which may indicate the presence of a weak magnetic field.\n"}
+{"abstract": "  Recently, neural architecture search (NAS) has been exploited to design\nfeature pyramid networks (FPNs) and achieved promising results for visual\nobject detection. Encouraged by the success, we propose a novel One-Shot Path\nAggregation Network Architecture Search (OPANAS) algorithm, which significantly\nimproves both searching efficiency and detection accuracy. Specifically, we\nfirst introduce six heterogeneous information paths to build our search space,\nnamely top-down, bottom-up, fusing-splitting, scale-equalizing, skip-connect\nand none. Second, we propose a novel search space of FPNs, in which each FPN\ncandidate is represented by a densely-connected directed acyclic graph (each\nnode is a feature pyramid and each edge is one of the six heterogeneous\ninformation paths). Third, we propose an efficient one-shot search method to\nfind the optimal path aggregation architecture, that is, we first train a\nsuper-net and then find the optimal candidate with an evolutionary algorithm.\nExperimental results demonstrate the efficacy of the proposed OPANAS for object\ndetection: (1) OPANAS is more efficient than state-of-the-art methods (e.g.,\nNAS-FPN and Auto-FPN), at significantly smaller searching cost (e.g., only 4\nGPU days on MS-COCO); (2) the optimal architecture found by OPANAS\nsignificantly improves main-stream detectors including RetinaNet, Faster R-CNN\nand Cascade R-CNN, by 2.3-3.2 % mAP comparing to their FPN counterparts; and\n(3) a new state-of-the-art accuracy-speed trade-off (52.2 % mAP at 7.6 FPS) at\nsmaller training costs than comparable state-of-the-arts. Code will be released\nat https://github.com/VDIGPKU/OPANAS.\n"}
+{"abstract": "  In this work, we introduce the unsupervised Optimum-Path Forest (OPF)\nclassifier for learning visual dictionaries in the context of Barrett's\nesophagus (BE) and automatic adenocarcinoma diagnosis. The proposed approach\nwas validated in two datasets (MICCAI 2015 and Augsburg) using three different\nfeature extractors (SIFT, SURF, and the not yet applied to the BE context\nA-KAZE), as well as five supervised classifiers, including two variants of the\nOPF, Support Vector Machines with Radial Basis Function and Linear kernels, and\na Bayesian classifier. Concerning MICCAI 2015 dataset, the best results were\nobtained using unsupervised OPF for dictionary generation using supervised OPF\nfor classification purposes and using SURF feature extractor with accuracy\nnearly to 78% for distinguishing BE patients from adenocarcinoma ones.\nRegarding the Augsburg dataset, the most accurate results were also obtained\nusing both OPF classifiers but with A-KAZE as the feature extractor with\naccuracy close to 73%. The combination of feature extraction and\nbag-of-visual-words techniques showed results that outperformed others obtained\nrecently in the literature, as well as we highlight new advances in the related\nresearch area. Reinforcing the significance of this work, to the best of our\nknowledge, this is the first one that aimed at addressing computer-aided BE\nidentification using bag-of-visual-words and OPF classifiers, being this\napplication of unsupervised technique in the BE feature calculation the major\ncontribution of this work. It is also proposed a new BE and adenocarcinoma\ndescription using the A-KAZE features, not yet applied in the literature.\n"}
+{"abstract": "  Background and Objectives: Substitution-box (s-box) is one of the essential\ncomponents to create confusion and nonlinear properties in cryptography. To\nstrengthening a cipher against various attacks, including side channel attacks,\nthese boxes need to have numerous security properties. In this paper, a novel\nmethod to generate s-boxes is introduced aimed at improving the resistance of\ns-boxes against side channel attacks. Methods: In the preprocessing phase of\nthis approach, a suitable initial s-box which has some basic security\nproperties is generated by adopting a fast algorithm. Then, in the main stage,\nusing the initial s-box, we generate new s-boxes which not only have the\nproperties of the initial S-box but also have been significantly improved under\nanother set of security properties. To do this, new s-boxes are generated using\na genetic algorithm on a particular subset of the linear combination set of\ncoordinate functions of the initial s-box in the preprocessing stage. Results:\nThe performed experiments demonstrate that the values of all security\nproperties of these new s-boxes, especially the measures of transparency order,\nsignal-to-noise ratio, confusion coefficient, bijection property, fixed point,\nand opposite fixed points, have been substantially improved. For example, our\nexperiments indicate that 70, 220, 2071, 43, and 406 s-boxes are found better\nthan the initial s-box, respectively, in the dimensions of 4x4 through 8x8\nConclusion: In this article, a new s-box construction method is introduced in\nwhich the properties related to side channel attacks are improved, without\nreducing other security properties. Besides, some results obtained from\ngenerated s-boxes in the dimensions of 4x4 through 8x8 demonstrated that the\ngenerated s-boxes are not only improved relative to the initial s-box, but in\nsome cases, considerably better than some well-known s-boxes.\n"}
+{"abstract": "  In 1943 from September to December Kiyoshi Oka wrote a series of papers\nnumbered from VII to XI, as the research reports to Teiji Takagi (then,\nProfessor of Tokyo Imperial University), in which he solved affirmatively the\nso-called Levi Problem (Hartogs' Inverse Problem termed by Oka) for unramified\nRiemann domains over $\\C^n$. This problem which had been left open for more\nthan thirty years then, was the last one of the Three Big Problems summarized\nby Behnke--Thullen 1934.\n  The papers were hand-written in Japanese, consist of pp.~108 in total, and\nhave not been published by themselves. The aim of the present article is to\nprovide an English translation of the most important, last paper (Part II) with\npreparation (Part I). At the end of Part I we will discuss a problem which K.\nOka left and is still open.\n"}
+{"abstract": "  The spin-glass behavior in the ferromagnetic phase of $\\mathrm{Tm_2Cu_2In}$\nwas investigated by dc-, ac-magnetization, and non-equilibrium dynamics\ncharacterizations. Arc-melted polycrystalline compound of $\\mathrm{Tm_2Cu_2In}$\ncrystallizes in $\\mathrm{Mo_2FeB_2}$-type tetragonal structure (space group\n$P_4/mbm$). The temperature variation dc-magnetization exhibits a ferromagnetic\nbehavior with Curie temperature $T_C$ = 32.5 K. The zero-field cooled (ZFC) and\nfield cooled (FC) magnetic curves show a thermomagnetic irreversible behavior\nbelow $T_C$. The field dependence of irreversible temperature follows\nAlmeida-Thouless line. The frequency and ac-driven field-dependent anomalies in\nac-susceptibility results indicate the existence of spin-glass state in\n$\\mathrm{Tm_2Cu_2In}$. The time dependence of magnetization further supports\nthe spin-glass behavior observed in $\\mathrm{Tm_2Cu_2In}$. The frequency\ndependence of the freezing temperature in the real part of ac-susceptibility\nhas been analyzed on the basis power-law divergence and Vogel-Fulcher law. The\nobtained results revealed that $\\mathrm{Tm_2Cu_2In}$ belongs to a ferromagnetic\ncanonical spin-glass class of compounds.\n"}
+{"abstract": "  The Chern-Simons magnetohydrodynamics (CSMHD) is introduced using a\nMaxwell-Chern-Simons (MCS) Lagrangian including an axion-like field $\\Theta$.\nThe MCS equation of motion derived from this Lagrangian consists of a modified\ncurrent, including a chiral magnetic (CM) and an anomalous Hall (AH) current,\nin addition to the ordinary Ohm current of resistive magnetohydrodynamics\n(MHD). The former consists of an axial chemical potential, which is given in\nterms of the temporal comoving derivative of $\\Theta$, and the latter arises\nfrom the spatial gradient of $\\Theta$. As it turns out, the existence of the\naxial chemical potential is a nonequilibrium effect that plays no role in the\nlinear stability analysis, whereas the AH current arises as in the first-order\nlinear perturbation of the thermal equilibrium. We analyze the linear stability\nand causality of the CSMHD in a resistive and chiral medium. We show that the\nAlfven modes propagating sufficiently close to the direction of the magnetic\nfield are unstable but causal. They are also accompanied by a genuine nonhydro\nmode. A stable mode in a particular direction can correspond to an unstable\nmode propagating in the exact opposite direction. The AH instability is a\nmanifestation of a breakdown of the parity. A numerical analysis of the phase\nvelocity confirms these results.\n"}
+{"abstract": "  The ability to accurately evaluate the performance of location determination\nsystems is crucial for many applications. Typically, the performance of such\nsystems is obtained by comparing ground truth locations with estimated\nlocations. However, these ground truth locations are usually obtained by\nclicking on a map or using other worldwide available technologies like GPS.\nThis introduces ground truth errors that are due to the marking process, map\ndistortions, or inherent GPS inaccuracy.\n  In this paper, we present a theoretical framework for analyzing the effect of\nground truth errors on the evaluation of localization systems. Based on that,\nwe design two algorithms for computing the real algorithmic error from the\nvalidation error and marking/map ground truth errors, respectively. We further\nestablish bounds on different performance metrics.\n  Validation of our theoretical assumptions and analysis using real data\ncollected in a typical environment shows the ability of our theoretical\nframework to correct the estimated error of a localization algorithm in the\npresence of ground truth errors. Specifically, our marking error algorithm\nmatches the real error CDF within 4%, and our map error algorithm provides a\nmore accurate estimate of the median/tail error by 150%/72% when the map is\nshifted by 6m.\n"}
+{"abstract": "  We show that a metastable dark matter candidate arises naturally from the\nconformal transformation between the Einstein metric, where gravitons are\nnormalised states, and the Jordan metric dictating the coupling between gravity\nand matter. Despite being secluded from the Standard Model by a large scale\nabove which the Jordan metric shows modifications to the Einstein frame metric,\ndark matter couples to the energy momentum tensor of the Higgs field in the\nprimordial plasma primarily. This allows for the production of dark matter in a\nsufficient amount which complies with observations. The seclusion of dark\nmatter makes it long-lived for masses $\\lesssim 1$ MeV, with a lifetime much\nabove the age of the Universe and the present experimental limits. Such a dark\nmatter scenario has clear monochromatic signatures generated by the decay of\nthe dark matter candidate into neutrino and/or $\\gamma-$rays.\n"}
+{"abstract": "  We study approximation classes for adaptive time-stepping finite element\nmethods for time-dependent Partial Differential Equations (PDE). We measure the\napproximation error in $L_2([0,T)\\times\\Omega)$ and consider the approximation\nwith discontinuous finite elements in time and continuous finite elements in\nspace, of any degree. As a byproduct we define Besov spaces for vector-valued\nfunctions on an interval and derive some embeddings, as well as Jackson- and\nWhitney-type estimates.\n"}
+{"abstract": "  This paper proposes new acoustic feature signatures based on the multiscale\nfractal dimension (MFD), which are robust against the diversity of\nenvironmental sounds, for the content-based similarity search. The diversity of\nsound sources and acoustic compositions is a typical feature of environmental\nsounds. Several acoustic features have been proposed for environmental sounds.\nAmong them is the widely-used Mel-Frequency Cepstral Coefficients (MFCCs),\nwhich describes frequency-domain features. However, in addition to these\nfeatures in the frequency domain, environmental sounds have other important\nfeatures in the time domain with various time scales. In our previous paper, we\nproposed enhanced multiscale fractal dimension signature (EMFD) for\nenvironmental sounds. This paper extends EMFD by using the kernel density\nestimation method, which results in better performance of the similarity search\ntasks. Furthermore, it newly proposes another acoustic feature signature based\non MFD, namely very-long-range multiscale fractal dimension signature (MFD-VL).\nThe MFD-VL signature describes several features of the time-varying envelope\nfor long periods of time. The MFD-VL signature has stability and robustness\nagainst background noise and small fluctuations in the parameters of sound\nsources, which are produced in field recordings. We discuss the effectiveness\nof these signatures in the similarity sound search by comparing with acoustic\nfeatures proposed in the DCASE 2018 challenges. Due to the unique\ndescriptiveness of our proposed signatures, we confirmed the signatures are\neffective when they are used with other acoustic features.\n"}
+{"abstract": "  While predictive policing has become increasingly common in assisting with\ndecisions in the criminal justice system, the use of these results is still\ncontroversial. Some software based on deep learning lacks accuracy (e.g., in\nF-1), and importantly many decision processes are not transparent, causing\ndoubt about decision bias, such as perceived racial and age disparities. This\npaper addresses bias issues with post-hoc explanations to provide a trustable\nprediction of whether a person will receive future criminal charges given one's\nprevious criminal records by learning temporal behavior patterns over twenty\nyears. Bi-LSTM relieves the vanishing gradient problem, attentional mechanisms\nallow learning and interpretation of feature importance, and complex-valued\nnetworks inspired quantum physics to facilitate a certain level of transparency\nin modeling the decision process. Our approach shows a consistent and reliable\nprediction precision and recall on a real-life dataset. Our analysis of the\nimportance of each input feature shows the critical causal impact on\ndecision-making, suggesting that criminal histories are statistically\nsignificant factors, while identifiers, such as race and age, are not. Finally,\nour algorithm indicates that a suspect tends to rather than suddenly increase\ncrime severity level over time gradually.\n"}
+{"abstract": "  This contribution presents a diagnosis scheme for batteries to detect and\nisolate internal faults in the form of small parameter changes. This scheme is\nbased on an electrochemical reduced-order model of the battery, which allows\nthe inclusion of physically meaningful faults that might affect the battery\nperformance. The sensitivity properties of the model are analyzed. The model is\nthen used to compute residuals based on an unscented Kalman filter. Primary\nresiduals and a limiting covariance matrix are obtained thanks to the local\napproach, allowing for fault detection and isolation by chi-squared statistical\ntests. Results show that faults resulting in limited 0.15% capacity and 0.004%\npower fade can be effectively detected by the local approach. The algorithm is\nalso able to correctly isolate faults related with sensitive parameters,\nwhereas parameters with low sensitivity or linearly correlated are more\ndifficult to precise.\n"}
+{"abstract": "  Near Ambient Pressure Scanning Photoelectron Microscopy adds to the widely\nused photoemission spectroscopy and its chemically selective capability two key\nfeatures: (i) the possibility to chemically analyse samples in a more realistic\nenvironmental, gas pressure condition, and (ii) the capability to investigate a\nsystem at the relevant spatial scale. To achieve these goals the approach\ndeveloped at the ESCA Microscopy beamline at the Elettra Synchrotron facility\ncombines the submicron lateral resolution of a Scanning Photoelectron\nMicroscope with a custom designed Near Ambient Pressure Cell where a gas\npressure up to 0.1 mbar is confined inside it around the sample. In this\nmanuscript a review of experiments performed with this unique setup will be\npresented to illustrate its potentiality in both fundamental and applicative\nresearch such as the oxidation reactivity and gas sensitivity of metal oxides\nand semiconductors. In particular the capability to do operando experiment with\nthis setup opens the possibility to perform investigations with active devices\nto properly address the real nature of the studied systems, because it can\nyield to more conclusive results when microscopy and spectroscopy are\nsimultaneously combined in a single technique.\n"}
+{"abstract": "  While most neural generative models generate outputs in a single pass, the\nhuman creative process is usually one of iterative building and refinement.\nRecent work has proposed models of editing processes, but these mostly focus on\nediting sequential data and/or only model a single editing pass. In this paper,\nwe present a generic model for incremental editing of structured data (i.e.,\n\"structural edits\"). Particularly, we focus on tree-structured data, taking\nabstract syntax trees of computer programs as our canonical example. Our editor\nlearns to iteratively generate tree edits (e.g., deleting or adding a subtree)\nand applies them to the partially edited data, thereby the entire editing\nprocess can be formulated as consecutive, incremental tree transformations. To\nshow the unique benefits of modeling tree edits directly, we further propose a\nnovel edit encoder for learning to represent edits, as well as an imitation\nlearning method that allows the editor to be more robust. We evaluate our\nproposed editor on two source code edit datasets, where results show that, with\nthe proposed edit encoder, our editor significantly improves accuracy over\nprevious approaches that generate the edited program directly in one pass.\nFinally, we demonstrate that training our editor to imitate experts and correct\nits mistakes dynamically can further improve its performance.\n"}
+{"abstract": "  Deep learning based methods, especially convolutional neural networks (CNNs)\nhave been successfully applied in the field of single image super-resolution\n(SISR). To obtain better fidelity and visual quality, most of existing networks\nare of heavy design with massive computation. However, the computation\nresources of modern mobile devices are limited, which cannot easily support the\nexpensive cost. To this end, this paper explores a novel frequency-aware\ndynamic network for dividing the input into multiple parts according to its\ncoefficients in the discrete cosine transform (DCT) domain. In practice, the\nhigh-frequency part will be processed using expensive operations and the\nlower-frequency part is assigned with cheap operations to relieve the\ncomputation burden. Since pixels or image patches belong to low-frequency areas\ncontain relatively few textural details, this dynamic network will not affect\nthe quality of resulting super-resolution images. In addition, we embed\npredictors into the proposed dynamic network to end-to-end fine-tune the\nhandcrafted frequency-aware masks. Extensive experiments conducted on benchmark\nSISR models and datasets show that the frequency-aware dynamic network can be\nemployed for various SISR neural architectures to obtain the better tradeoff\nbetween visual quality and computational complexity. For instance, we can\nreduce the FLOPs of SR models by approximate 50% while preserving\nstate-of-the-art SISR performance.\n"}
+{"abstract": "  Sampling-based motion planners rely on incremental densification to discover\nprogressively shorter paths. After computing feasible path $\\xi$ between start\n$x_s$ and goal $x_t$, the Informed Set (IS) prunes the configuration space\n$\\mathcal{C}$ by conservatively eliminating points that cannot yield shorter\npaths. Densification via sampling from this Informed Set retains asymptotic\noptimality of sampling from the entire configuration space. For path length\n$c(\\xi)$ and Euclidean heuristic $h$, $IS = \\{ x | x \\in \\mathcal{C}, h(x_s, x)\n+ h(x, x_t) \\leq c(\\xi) \\}$.\n  Relying on the heuristic can render the IS especially conservative in high\ndimensions or complex environments. Furthermore, the IS only shrinks when\nshorter paths are discovered. Thus, the computational effort from each\niteration of densification and planning is wasted if it fails to yield a\nshorter path, despite improving the cost-to-come for vertices in the search\ntree. Our key insight is that even in such a failure, shorter paths to vertices\nin the search tree (rather than just the goal) can immediately improve the\nplanner's sampling strategy. Guided Incremental Local Densification (GuILD)\nleverages this information to sample from Local Subsets of the IS. We show that\nGuILD significantly outperforms uniform sampling of the Informed Set in\nsimulated $\\mathbb{R}^2$, $SE(2)$ environments and manipulation tasks in\n$\\mathbb{R}^7$.\n"}
+{"abstract": "  The utility of machine learning in understanding the motor system is\npromising a revolution in how to collect, measure, and analyze data. The field\nof movement science already elegantly incorporates theory and engineering\nprinciples to guide experimental work, and in this review we discuss the\ngrowing use of machine learning: from pose estimation, kinematic analyses,\ndimensionality reduction, and closed-loop feedback, to its use in understanding\nneural correlates and untangling sensorimotor systems. We also give our\nperspective on new avenues where markerless motion capture combined with\nbiomechanical modeling and neural networks could be a new platform for\nhypothesis-driven research.\n"}
+{"abstract": "  Zero-shot learning deals with the ability to recognize objects without any\nvisual training sample. To counterbalance this lack of visual data, each class\nto recognize is associated with a semantic prototype that reflects the\nessential features of the object. The general approach is to learn a mapping\nfrom visual data to semantic prototypes, then use it at inference to classify\nvisual samples from the class prototypes only. Different settings of this\ngeneral configuration can be considered depending on the use case of interest,\nin particular whether one only wants to classify objects that have not been\nemployed to learn the mapping or whether one can use unlabelled visual examples\nto learn the mapping. This chapter presents a review of the approaches based on\ndeep neural networks to tackle the ZSL problem. We highlight findings that had\na large impact on the evolution of this domain and list its current challenges.\n"}
+{"abstract": "  We consider fast high-fidelity quantum control by using a shortcut to\nadiabaticity (STA) technique and optimal control theory (OCT). Three specific\nexamples, including expansion of cold atoms from the harmonic trap, atomic\ntransport by moving harmonic trap, and spin dynamics in the presence of\ndissipation, are explicitly detailed. Using OCT as a qualitative guide, we\ndemonstrate how STA protocols designed from inverse engineering method, can\napproach with very high precision optimal solutions built about physical\nconstraints, by a proper choice of the interpolation function and with a very\nreduced number of adjustable parameters.\n"}
+{"abstract": "  Tactile sensing on human feet is crucial for motion control, however, has not\nbeen explored in robotic counterparts. This work is dedicated to endowing\ntactile sensing to legged robot's feet and showing that a single-legged robot\ncan be stabilized with only tactile sensing signals from its foot. We propose a\nrobot leg with a novel vision-based tactile sensing foot system and implement a\nprocessing algorithm to extract contact information for feedback control in\nstabilizing tasks. A pipeline to convert images of the foot skin into\nhigh-level contact information using a deep learning framework is presented.\nThe leg was quantitatively evaluated in a stabilization task on a tilting\nsurface to show that the tactile foot was able to estimate both the surface\ntilting angle and the foot poses. Feasibility and effectiveness of the tactile\nsystem were investigated qualitatively in comparison with conventional\nsingle-legged robotic systems using inertia measurement units (IMU).\nExperiments demonstrate the capability of vision-based tactile sensors in\nassisting legged robots to maintain stability on unknown terrains and the\npotential for regulating more complex motions for humanoid robots.\n"}
+{"abstract": "  Ultralight axions and other bosons are dark matter candidates present in many\nhigh energy physics theories beyond the Standard Model. In particular, the\nstring axiverse postulates the existence of up to $\\mathcal{O}(100)$ light\nscalar bosons constituting the dark sector. Considering a mixture of axions and\ncold dark matter, we obtain upper bounds for the axion relic density $\\Omega_a\nh^2 < 0.004$ for axions of mass $10^{-31}\\;\\mathrm{eV}\\leq m_a \\leq\n10^{-26}\\;\\mathrm{eV}$ at 95% confidence. We also improve existing constraints\nby a factor of over 4.5 and 2.1 for axion masses of $10^{-25}$ eV and\n$10^{-32}$ eV, respectively. We use the Fourier-space galaxy clustering\nstatistics from the Baryon Oscillation Spectroscopic Survey (BOSS) and\ndemonstrate how galaxy surveys break important degeneracies in the axion\nparameter space compared to the cosmic microwave background (CMB). We test the\nvalidity of the effective field theory of large-scale structure approach to\nmixed ultralight axion dark matter by making our own mock galaxy catalogs and\nfind an anisotropic ultralight axion signature in the galaxy quadrupole. We\nalso observe an enhancement of the linear galaxy bias from 1.8 to 2.4 when\nallowing for 5% of the dark matter to be composed of a $10^{-28}$ eV axion in\nour simulations. Finally, we develop an augmented interpolation scheme allowing\na fast computation of the axion contribution to the linear matter power\nspectrum leading to a 70% reduction of the computational cost for the full\nMonte Carlo Markov chains analysis.\n"}
+{"abstract": "  Given a singular projective variety in some projective space, we characterize\nthe smooth curves contracted by the Gauss map in terms of normal bundles. As a\nconsequence, we show that if the variety is normal, then a contracted line\nalways has local obstruction for the embedded deformation and each component of\nthe Hilbert scheme where the line lies is non-reduced everywhere.\n"}
+{"abstract": "  Self-supervised learning algorithms based on instance discrimination train\nencoders to be invariant to pre-defined transformations of the same instance.\nWhile most methods treat different views of the same image as positives for a\ncontrastive loss, we are interested in using positives from other instances in\nthe dataset. Our method, Nearest-Neighbor Contrastive Learning of visual\nRepresentations (NNCLR), samples the nearest neighbors from the dataset in the\nlatent space, and treats them as positives. This provides more semantic\nvariations than pre-defined transformations.\n  We find that using the nearest-neighbor as positive in contrastive losses\nimproves performance significantly on ImageNet classification, from 71.7% to\n75.6%, outperforming previous state-of-the-art methods. On semi-supervised\nlearning benchmarks we improve performance significantly when only 1% ImageNet\nlabels are available, from 53.8% to 56.5%. On transfer learning benchmarks our\nmethod outperforms state-of-the-art methods (including supervised learning with\nImageNet) on 8 out of 12 downstream datasets. Furthermore, we demonstrate\nempirically that our method is less reliant on complex data augmentations. We\nsee a relative reduction of only 2.1% ImageNet Top-1 accuracy when we train\nusing only random crops.\n"}
+{"abstract": "  The problem of classifying tuples of nilpotent matrices over a field under\nsimultaneous conjugation is considered \"hopeless\". However, for any given\nmatrix order over a finite field, the number of concerned orbits is always\nfinite. This paper gives a closed formula for the number of absolutely\nindecomposable orbits using the same methodology as Hua [5]; those orbits are\nnon-splittable over field extensions. As a consequence, those numbers are\nalways polynomials in the cardinality of the base field with integral\ncoefficients. It is conjectured that those coefficients are always\nnon-negative.\n"}
+{"abstract": "  Electronic stopping cross section of tungsten for light ions was\nexperimentally measured in a wide energy interval (20 to 6000 keV for protons\nand 50 to 9000 keV for helium) in backscattering and transmission geometries.\nThe measurements were carried out in three laboratories (Austria, Germany and\nSweden) using five different set-ups, the stopping data deduced from different\ndata sets showed excellent agreement amongst each other, with total uncertainty\nvarying within 1.5 - 3.8\\% for protons and 2.2 - 5.5\\% for helium, averaged\nover the respective energy range of each data set. The final data is compared\nto available data and to widely adopted semi-empirical and theoretical\napproaches, and found to be in good agreement with most adopted models at\nenergies around and above the stopping maximum. Most importantly, our results\nextend the energy regime towards lower energies, and are thus of high\ntechnological relevance, e.g., in fusion research. At these low energies, our\nfindings also revealed that tungsten - featured with fully and partially\noccupied f- and d-subshells, respectively, can be modeled as an electron gas\nfor the energy loss process.\n"}
+{"abstract": "  Fluctuations associated with relaxations in far-from-equilibrium regime is of\nfundamental interest for a large variety of systems within broad scales. Recent\nadvances in techniques such as spectroscopy have generated the possibility for\nmeasuring the fluctuations of the mesoscopic systems in connection to the\nrelaxation processes when driving the underlying quantum systems far from\nequilibrium. We present a general nonequilibrium Fluctuation-Dissipation\nTheorem (FDT) for quantum Markovian processes where the detailed-balance\ncondition is violated. Apart from the fluctuations, the relaxation involves\nextra correlation that is governed by the quantum curl flux emerged in the\nfar-from-equilibrium regime. Such a contribution vanishes for the thermal\nequilibrium, so that the conventional FDT is recovered. We finally apply the\nnonequilibrium FDT to the molecular junctions, elaborating the\ndetailed-balance-breaking effects on the optical transmission spectrum. Our\nresults have the advantage of and exceed the scope of the\nfluctuation-dissipation relation in the perturbative and near equilibrium\nregimes, and is of broad interest for the study of quantum thermodynamics.ation\nin the perturbative and near equilibrium regimes, and is of broad interest for\nthe study of quantum thermodynamics.\n"}
+{"abstract": "  We apply the tools of the dynamical system theory in order to revisit and\nuncover the structure of a nongravitational interaction between pressureless\ndark matter and dark energy described by a scalar field $\\phi$. For a coupling\nfunction $Q = -(\\alpha d\\rho_m/dt + \\beta d\\rho_\\phi/dt )$, where t is the\ncosmic time, we have found that it can be rewritten in the form $Q = 3H (\\alpha\n\\rho_m + \\beta (d\\phi/dt)^2 )/(1-\\alpha +\\beta)$, so that its dependence on the\ndark matter density and on the kinetic term of the scalar field is linear and\nproportional to the Hubble parameter. We analyze the scenarios $\\alpha=0$,\n$\\alpha = \\beta$ and $\\alpha = -\\beta$, separately and in order to describe the\ncosmological evolution we have calculated various observables. A notable result\nof this work is that, unlike for the noninteracting scalar field with\nexponential potential where five critical points appear, in the case studied\nhere, with the exception of the matter dominated solution, the remaining\nsingular points are transformed into scaling solutions enriching the phase\nspace. It is shown that for $\\alpha \\neq 0$, a separatrix arises modifying\nprominently the structure of the phase space. This represents a novel feature\nno mentioned before in the literature.\n"}
+{"abstract": "  Understanding why the scale of emergent hadron mass is obvious in the proton\nbut hidden in the pion may rest on mapping the distribution functions (DFs) of\nall partons within the pion and comparing them with those in the proton; and\nsince glue provides binding in quantum chromodynamics, the glue DF could play a\nspecial role. Producing reliable predictions for the proton's DFs is difficult\nbecause the proton is a three valence-body bound-state problem. As sketched\nherein, the situation for the pion, a two valence-body problem, is much better,\nwith continuum and lattice predictions for the valence-quark and glue DFs in\nagreement. This beginning of theory alignment is timely because experimental\nfacilities now either in operation or planning promise to realise the\nlongstanding goal of providing pion targets, thereby enabling precision\nexperimental tests of rigorous theory predictions concerning Nature's most\nfundamental Nambu-Goldstone bosons.\n"}
+{"abstract": "  The topic of this tutorial is Least Squares Sparse Principal Components\nAnalysis (LS SPCA) which is a simple method for computing approximated\nPrincipal Components which are combinations of only a few of the observed\nvariables. Analogously to Principal Components, these components are\nuncorrelated and sequentially best approximate the dataset. The derivation of\nLS SPCA is intuitive for anyone familiar with linear regression. Since LS SPCA\nis based on a different optimality from other SPCA methods and does not suffer\nfrom their serious drawbacks. I will demonstrate on two datasets how useful and\nparsimonious sparse PCs can be computed. An R package for computing LS SPCA is\navailable for download.\n"}
+{"abstract": "  We present an analysis of hadroproduction of $J/\\psi$ and $\\psi(2S)$ at\nfixed-target energies in the framework of non-relativistic QCD (NRQCD). Using\nboth pion- and proton-induced data, a new determination of the color-octet\nlong-distance matrix elements (LDMEs) is obtained. Compared with previous\nresults, the contributions from the $q \\bar{q}$ and color-octet processes are\nsignificantly enhanced, especially at lower energies. A good agreement between\nthe pion-induced $J/\\psi$ production data and NRQCD calculations using the\nnewly obtained LDMEs is achieved. We find that the pion-induced charmonium\nproduction data are sensitive to the gluon density of pions, and favor pion\nPDFs with relatively large gluon contents at large $x$.\n"}
+{"abstract": "  We investigate the impact of the substrate on the structural properties and\nthe morphology of alkanethiol self-assembled monolayers (SAMs) on gold, using\nfirst principles calculations and atomistic molecular dynamics simulations. We\nconsider hexadecanethiols on Au(211), Au(221) and Au(311) surfaces which\ncontain few-atom wide terraces separated by monoatomic steps similar to the\ncomplex Au surfaces used in experiments. The structure of the SAMs is probed\nvia several structural properties including tilt angles, mean C atom heights\nfrom the surface, precession angles, gauche defects, gyration tensors and\nrelative shape anisotropy. Comparing these properties to those of the\nwell-studied SAMs on Au(111), we observe similarities but also striking\ndifferences. A clear order to disorder transition is observed by changing the\nsubstrate: well-ordered SAMs on (111) and (211) surfaces become mixed\nordered-disordered structures on (311) and fully disordered on (221). The\npresence of steps on the Au surfaces also results in preferential tilt\norientations with long-range order. Our results show that in addition to the\nexpected grafting density dependence, the transition from order to disorder\ncrucially depends on substrate morphology. The onset of ordering behavior is\nrelated to the atomic structure of the surface. The key parameter that affects\nlong-range order is the energy for changing the dihedral angle between\nAu-S-$\\mathrm{C^{(1)}-C^{(2)}}$ of the adsorbed alkanethiol.\n"}
+{"abstract": "  We consider a one-dimensional stochastic differential equation driven by a\nWiener process, where the diffusion coefficient depends on an ergodic fast\nprocess. The averaging principle is satisfied: it is well-known that the slow\ncomponent converges in distribution to the solution of an averaged equation,\nwith generator determined by averaging the square of the diffusion coefficient.\n  We propose a version of the averaging principle, where the solution is\ninterpreted as the sum of two terms: one depending on the average of the\ndiffusion coefficient, the other giving fluctuations around that average. Both\nthe average and fluctuation terms contribute to the limit, which illustrates\nwhy it is required to average the square of the diffusion coefficient to find\nthe limit behavior.\n"}
+{"abstract": "  Researchers, editors, educators and publishers need to understand the mix of\nresearch methods used in their field to guide decision making, with a current\nconcern being that qualitative research is threatened by big data. Although\nthere have been many studies of the prevalence of different methods within\nindividual narrow fields, there have been no systematic studies across\nacademia. In response, this article assesses the prevalence and citation impact\nof academic research 1996-2019 that reports one of four common methods to\ngather qualitative data: interviews; focus groups; case studies; ethnography.\nThe results show that, with minor exceptions, the prevalence of qualitative\ndata has increased, often substantially, since 1996. In addition, all 27 broad\nfields (as classified by Scopus) now publish some qualitative research, with\ninterviewing being by far the most common approach. This suggest that\nqualitative methods teaching and should increase, and researchers, editors and\npublishers should be increasingly open to the value that qualitative data can\nbring.\n"}
+{"abstract": "  Unsupervised Domain Adaptive (UDA) person re-identification (ReID) aims at\nadapting the model trained on a labeled source-domain dataset to a\ntarget-domain dataset without any further annotations. Most successful UDA-ReID\napproaches combine clustering-based pseudo-label prediction with representation\nlearning and perform the two steps in an alternating fashion. However, offline\ninteraction between these two steps may allow noisy pseudo labels to\nsubstantially hinder the capability of the model. In this paper, we propose a\nGroup-aware Label Transfer (GLT) algorithm, which enables the online\ninteraction and mutual promotion of pseudo-label prediction and representation\nlearning. Specifically, a label transfer algorithm simultaneously uses pseudo\nlabels to train the data while refining the pseudo labels as an online\nclustering algorithm. It treats the online label refinery problem as an optimal\ntransport problem, which explores the minimum cost for assigning M samples to N\npseudo labels. More importantly, we introduce a group-aware strategy to assign\nimplicit attribute group IDs to samples. The combination of the online label\nrefining algorithm and the group-aware strategy can better correct the noisy\npseudo label in an online fashion and narrow down the search space of the\ntarget identity. The effectiveness of the proposed GLT is demonstrated by the\nexperimental results (Rank-1 accuracy) for Market1501$\\to$DukeMTMC (82.0\\%) and\nDukeMTMC$\\to$Market1501 (92.2\\%), remarkably closing the gap between\nunsupervised and supervised performance on person re-identification.\n"}
+{"abstract": "  In complex Finsler geometry, an open problem is: does there exist a weakly\nK\\\"ahler Finsler metric which is not K\\\"ahler?\n  In this paper, we give an affirmative answer to this open problem. More\nprecisely, we construct a family of the weakly K\\\"ahler Finsler metrics which\nare non-K\\\"ahler. The examples belong to the unitary invariant complex Randers\nmetrics. Furthermore, a uniformization theorem of the unitary invariant complex\nRanders metrics with constant holomorphic curvature is proved under the weakly\nK\\\"ahler condition.\n"}
+{"abstract": "  The study of pattern containment and avoidance for linear permutations is a\nwell-established area of enumerative combinatorics. A cyclic permutation is the\nset of all rotations of a linear permutation. Callan initiated the study of\npermutation avoidance in cyclic permutations and characterized the avoidance\nclasses for all single permutations of length 4. We continue this work. In\nparticular, we establish a cyclic variant of the Erdos-Szekeres Theorem that\nany linear permutation of length mn+1 must contain either the increasing\npattern of length m+1 or the decreasing pattern of length n+1. We then derive\nresults about avoidance of multiple patterns of length 4. We also determine\ngenerating functions for the cyclic descent statistic on these classes.\nFinally, we end with various open questions and avenues for future research.\n"}
+{"abstract": "  Self-supervised learning has enabled significant improvements on natural\nimage benchmarks. However, there is less work in the medical imaging domain in\nthis area. The optimal models have not yet been determined among the various\noptions. Moreover, little work has evaluated the current applicability limits\nof novel self-supervised methods. In this paper, we evaluate a range of current\ncontrastive self-supervised methods on out-of-distribution generalization in\norder to evaluate their applicability to medical imaging. We show that\nself-supervised models are not as robust as expected based on their results in\nnatural imaging benchmarks and can be outperformed by supervised learning with\ndropout. We also show that this behavior can be countered with extensive\naugmentation. Our results highlight the need for out-of-distribution\ngeneralization standards and benchmarks to adopt the self-supervised methods in\nthe medical imaging community.\n"}
+{"abstract": "  We are interested in multilayer graph clustering, which aims at dividing the\ngraph nodes into categories or communities. To do so, we propose to learn a\nclustering-friendly embedding of the graph nodes by solving an optimization\nproblem that involves a fidelity term to the layers of a given multilayer\ngraph, and a regularization on the (single-layer) graph induced by the\nembedding. The fidelity term uses the contrastive loss to properly aggregate\nthe observed layers into a representative embedding. The regularization pushes\nfor a sparse and community-aware graph, and it is based on a measure of graph\nsparsification called \"effective resistance\", coupled with a penalization of\nthe first few eigenvalues of the representative graph Laplacian matrix to favor\nthe formation of communities. The proposed optimization problem is nonconvex\nbut fully differentiable, and thus can be solved via the descent gradient\nmethod. Experiments show that our method leads to a significant improvement\nw.r.t. state-of-the-art multilayer graph clustering algorithms.\n"}
+{"abstract": "  In this article, we developed and analyzed a thresholding method in which\nsoft thresholding estimators are independently expanded by empirical scaling\nvalues. The scaling values have a common hyper-parameter that is an order of\nexpansion of an ideal scaling value that achieves hard thresholding. We simply\ncall this estimator a scaled soft thresholding estimator. The scaled soft\nthresholding is a general method that includes the soft thresholding and\nnon-negative garrote as special cases and gives an another derivation of\nadaptive LASSO. We then derived the degree of freedom of the scaled soft\nthresholding by means of the Stein's unbiased risk estimate and found that it\nis decomposed into the degree of freedom of soft thresholding and the reminder\nconnecting to hard thresholding. In this meaning, the scaled soft thresholding\ngives a natural bridge between soft and hard thresholding methods. Since the\ndegree of freedom represents the degree of over-fitting, this result implies\nthat there are two sources of over-fitting in the scaled soft thresholding. The\nfirst source originated from soft thresholding is determined by the number of\nun-removed coefficients and is a natural measure of the degree of over-fitting.\nWe analyzed the second source in a particular case of the scaled soft\nthresholding by referring a known result for hard thresholding. We then found\nthat, in a sparse, large sample and non-parametric setting, the second source\nis largely determined by coefficient estimates whose true values are zeros and\nhas an influence on over-fitting when threshold levels are around noise levels\nin those coefficient estimates. In a simple numerical example, these\ntheoretical implications has well explained the behavior of the degree of\nfreedom. Moreover, based on the results here and some known facts, we explained\nthe behaviors of risks of soft, hard and scaled soft thresholding methods.\n"}
+{"abstract": "  Wireless networks are a key component of the telecommunications\ninfrastructure in our society, and wireless services become increasingly\nimportant as the applications of wireless devices have penetrated every aspect\nof our lives. Although wireless technologies have significantly advanced in the\npast decades, most wireless networks are still vulnerable to radio jamming\nattacks due to the openness nature of wireless channels, and the progress in\nthe design of jamming-resistant wireless networking systems remains limited.\nThis stagnation can be attributed to the lack of practical physical-layer\nwireless technologies that can efficiently decode data packets in the presence\nof jamming attacks. This article surveys existing jamming attacks and\nanti-jamming strategies in wireless local area networks (WLANs), cellular\nnetworks, cognitive radio networks (CRNs), ZigBee networks, Bluetooth networks,\nvehicular networks, LoRa networks, RFID networks, and GPS system, with the\nobjective of offering a comprehensive knowledge landscape of existing\njamming/anti-jamming strategies and stimulating more research efforts to secure\nwireless networks against jamming attacks. Different from prior survey papers,\nthis article conducts a comprehensive, in-depth review on jamming and\nanti-jamming strategies, casting insights on the design of jamming-resilient\nwireless networking systems. An outlook on promising antijamming techniques is\noffered at the end of this article to delineate important research directions.\n"}
+{"abstract": "  In this work, we study the Schr\\\"odinger equation\n$i\\partial_t\\psi=-\\Delta\\psi+\\eta(t)\\sum_{j=1}^J\\delta_{x=a_j(t)}\\psi$ on\n$L^2((0,1),C)$ where $\\eta:[0,T]\\longrightarrow R^+$ and\n$a_j:[0,T]\\longrightarrow (0,1)$, $j=1,...,J$. We show how to permute the\nenergy associated to different eigenmodes of the Schr\\\"odinger equation via\nsuitable choice of the functions $\\eta$ and $a_j$. To the purpose, we mime the\ncontrol processes introduced in [17] for a very similar equation where the\nDirac potential is replaced by a smooth approximation supported in a\nneighborhood of $x=a(t)$. We also propose a Galerkin approximation that we\nprove to be convergent and illustrate the control process with some numerical\nsimulations.\n"}
+{"abstract": "  Research on heat waves (periods of excessively hot weather, which may be\naccompanied by high humidity) is a newly emerging research topic within the\nfield of climate change research with high relevance for the whole of society.\nIn this study, we analyzed the rapidly growing scientific literature dealing\nwith heat waves. No summarizing overview has been published on this literature\nhitherto. We developed a suitable search query to retrieve the relevant\nliterature covered by the Web of Science (WoS) as complete as possible and to\nexclude irrelevant literature (n = 8,011 papers). The time-evolution of the\npublications shows that research dealing with heat waves is a highly dynamic\nresearch topic, doubling within about 5 years. An analysis of the thematic\ncontent reveals the most severe heat wave events within the recent decades\n(1995 and 2003), the cities and countries/regions affected (United States,\nEurope, and Australia), and the ecological and medical impacts (drought, urban\nheat islands, excess hospital admissions, and mortality). Risk estimation and\nfuture strategies for adaptation to hot weather are major political issues. We\nidentified 104 citation classics which include fundamental early works of\nresearch on heat waves and more recent works (which are characterized by a\nrelatively strong connection to climate change).\n"}
+{"abstract": "  Graph algorithms can be expressed in terms of linear algebra. GraphBLAS is a\nlibrary of low-level building blocks for such algorithms that targets algorithm\ndevelopers. LAGraph builds on top of the GraphBLAS to target users of graph\nalgorithms with high-level algorithms common in network analysis. In this\npaper, we describe the first release of the LAGraph library, the design\ndecisions behind the library, and performance using the GAP benchmark suite.\nLAGraph, however, is much more than a library. It is also a project to document\nand analyze the full range of algorithms enabled by the GraphBLAS. To that end,\nwe have developed a compact and intuitive notation for describing these\nalgorithms. In this paper, we present that notation with examples from the GAP\nbenchmark suite.\n"}
+{"abstract": "  We propose a deep learning system for attention-guided dual-layer image\ncompression (AGDL). In the AGDL compression system, an image is encoded into\ntwo layers, a base layer and an attention-guided refinement layer. Unlike the\nexisting ROI image compression methods that spend an extra bit budget equally\non all pixels in ROI, AGDL employs a CNN module to predict those pixels on and\nnear a saliency sketch within ROI that are critical to perceptual quality. Only\nthe critical pixels are further sampled by compressive sensing (CS) to form a\nvery compact refinement layer. Another novel CNN method is developed to jointly\ndecode the two compression layers for a much refined reconstruction, while\nstrictly satisfying the transmitted CS constraints on perceptually critical\npixels. Extensive experiments demonstrate that the proposed AGDL system\nadvances the state of the art in perception-aware image compression.\n"}
+{"abstract": "  Recently, using the assumption that the string theory effective action at the\ncritical dimension is background independent, the classical on-shell effective\naction of the bosonic string theory at order $\\alpha'$ in a spacetime manifold\nwithout boundary has been reproduced, up to an overall parameter, by imposing\nthe $O(1,1)$ symmetry when the background has a circle. In the presence of the\nboundary, we consider a background which has boundary and a circle such that\nthe unit normal vector of the boundary is independent of the circle. Then the\n$O(1,1)$ symmetry can fix the bulk action without using the lowest order\nequation of motion. Moreover, the above constraints and the constraint from the\nprinciple of the least action in the presence of boundary can fix the boundary\naction, up to five boundary parameters. In the least action principle, we\nassume that not only the values of the massless fields but also the values of\ntheir first derivatives are arbitrary on the boundary.\n  We have also observed that the cosmological/one-dimensional reduction of the\nleading order action in the presence of the Hawking-Gibbons boundary term,\nproduces zero boundary action. Imposing this as another constraint on the\nboundary couplings at order $\\alpha'$, we find the boundary action up to two\nparameters. For a specific value for these two parameters, the gravity\ncouplings in the boundary become the Chern-Simons gravity plus another term\nwhich has the Laplacian of the extrinsic curvature.\n"}
+{"abstract": "  We analyse the coronal elemental abundances during a small flare using\nHinode/EIS observations. Compared to the pre-flare elemental abundances, we\nobserved a strong increase in coronal abundance of Ca XIV 193.84 {\\AA}, an\nemission line with low first ionisation potential (FIP < 10 eV), as quantified\nby the ratio Ca/Ar during the flare. This is in contrast to the unchanged\nabundance ratio observed using Si X 258.38 {\\AA}/S X 264.23 {\\AA}. We propose\ntwo different mechanisms to explain the different composition results. Firstly,\nthe small flare-induced heating could have ionised S, but not the noble gas Ar,\nso that the flare-driven Alfv\\'en waves brought up Si, S and Ca in tandem via\nthe ponderomotive force which acts on ions. Secondly, the location of the flare\nin strong magnetic fields between two sunspots may suggest fractionation\noccurred in the low chromosphere, where the background gas is neutral H. In\nthis region, high-FIP S could behave more like a low-FIP than a high-FIP\nelement. The physical interpretations proposed generate new insights into the\nevolution of plasma abundances in the solar atmosphere during flaring, and\nsuggests that current models must be updated to reflect dynamic rather than\njust static scenarios.\n"}
+{"abstract": "  The reliable {\\it ab-initio} description of strongly correlated materials is\na long-sought capability in condensed matter physics. The $GW$+EDMFT method is\na promising scheme, which provides a self-consistent description of\ncorrelations and screening, and does not require user-provided parameters. In\norder to test the reliability of this approach we apply it to the\nexperimentally well characterized perovskite compound Ca$_2$RuO$_4$, in which a\ntemperature-dependent structural deformation drives a paramagnetic\nmetal-insulator transition. Our results demonstrate that the nonlocal\npolarization and self-energy components introduced by $GW$ are essential for\nsetting the correct balance between interactions and bandwidths, and that the\n$GW$+EDMFT scheme produces remarkably accurate predictions of the electronic\nproperties of this strongly correlated material.\n"}
+{"abstract": "  X-ray and gamma-ray emissions observed in lightning and long sparks are\nusually connected with the bremsstrahlung of high-energy runaway electrons.\nHere, an alternative physical mechanism for producing X-ray and gamma-ray\nemissions caused by the polarization current and associated electromagnetic\nfield moving with relativistic velocity along a curved discharge channel has\nbeen proposed. It is pointed out that lightning and spark discharges should\nalso produce a coherent radio-frequency radiation. The influence of the\nconductivity and the radius of the lightning channel on the propagation\nvelocity of electromagnetic waves, taking into account the absorption, have\nbeen investigated. The existence of fast electromagnetic surface waves\npropagating along the lightning discharge channel at a speed close to the speed\nof light in vacuum is shown. The possibility of the production of microwave,\nX-ray and gamma-ray emissions by a polarization current pulse moving along a\ncurved path via synchrotron radiation mechanism during the lightning leader\nsteps formation and the very beginning of the return stroke stage is pointed\nout. The existence of long tails in the power spectrum is shown, which explains\nobservations of photon energies in the range of 10-100 MeV in the TGF, as well\nas measured power spectrum of laboratory spark discharge.\n"}
+{"abstract": "  Distance metric learning algorithms aim to appropriately measure similarities\nand distances between data points. In the context of clustering, metric\nlearning is typically applied with the assist of side-information provided by\nexperts, most commonly expressed in the form of cannot-link and must-link\nconstraints. In this setting, distance metric learning algorithms move closer\npairs of data points involved in must-link constraints, while pairs of points\ninvolved in cannot-link constraints are moved away from each other. For these\nalgorithms to be effective, it is important to use a distance metric that\nmatches the expert knowledge, beliefs, and expectations, and the\ntransformations made to stick to the side-information should preserve\ngeometrical properties of the dataset. Also, it is interesting to filter the\nconstraints provided by the experts to keep only the most useful and reject\nthose that can harm the clustering process. To address these issues, we propose\nto exploit the dual information associated with the pairwise constraints of the\nsemi-supervised clustering problem. Experiments clearly show that distance\nmetric learning algorithms benefit from integrating this dual information.\n"}
+{"abstract": "  The elliptic flow coefficient ($v_{2}$) of $\\phi$ mesons at mid-rapidity as a\nfunction of transverse momentum ($p_{T}$) is measured for Au+Au collisions at\ncentre of mass energy ($\\sqrt{s_{NN}}$) 27 and 54.4 GeV. The $v_{2}$\nmeasurement is done using event plane from Time Projection Chamber (TPC,\n$|\\eta|$ $<$1.0) and Event Plane Detectors (EPD, 2.1$<$$|\\eta|$$<$5.1) for 54.4\nand 27 GeV, respectively. A high precision test of the number of constituent\nquark scaling of $\\phi$ meson $v_{2}$ (by including measurements for other\nhadrons) has been shown. The results are compared to transport model (AMPT)\ncalculations.\n"}
+{"abstract": "  The ionic conduction properties of the technologically important\ntwo-dimensional (2D) layered battery material Na2Mn3O7, with exceptional\nsmall-voltage hysteresis between charge and discharge curves, have been\ninvestigated as a function of temperature and frequency by an impedance\nspectroscopy. The detailed analyses of the impedance data in the form of\ndc-conductivity, ac-conductivity, electrical modulus, dielectric constant and\ncomplex polarizability reveal a long-range Na-ionic conductivity with\nnegligible contribution from a local dipole relaxation. A significant\nenhancement (~10^4 times) of the Na-ion conductivity has been found with the\nincreasing temperature from 353 K to 713 K. The temperature dependent\nconductivity reveals thermally activated conduction process with activation\nenergies of 0.161 and 0.377 eV over the two temperature regions of 383-518 K\nand 518-713 K, respectively. AC conductivity study reveals a long-range hopping\nprocess for the conduction of charge carriers with a sharp increase of the\nhopping range at 518 K. The scaling study of the ac-conductivity reveals that\nthe frequency-activated conductivity (above 10^4 Hz at 353 K) is mainly\ncontrolled by the critical frequency that increases with the increasing\ntemperature. The Na-ion conduction in Na2Mn3O7 occurs predominantly by a\ncorrelated barrier hopping process. Besides, a correlation between ionic\nconduction and crystal structure has been established by x-ray and neutron\ndiffraction study. We have further shown that the conductivity of Na2Mn3O7 can\nbe enhanced by reduction of the stacking faults in the crystal structure. Our\nstudy facilitates the understanding of the microscopic ionic conduction\nmechanism in the highly efficient 2D battery material Na2Mn3O7 having high\nenergy storage capacity and high structural stability, paving way for the\ndiscovery of materials for battery applications.\n"}
+{"abstract": "  Scientific literature tends to grow as a function of funding and interest in\na given field. Mining such literature can reveal trends that may not be\nimmediately apparent. The CORD-19 corpus represents a growing corpus of\nscientific literature associated with COVID-19. We examined the intersection of\na set of candidate therapeutics identified in a drug-repurposing study with\ntemporal instances of the CORD-19 corpus to determine if it was possible to\nfind and measure changes associated with them over time. We propose that the\ntechniques we used could form the basis of a tool to pre-screen new candidate\ntherapeutics early in the research process.\n"}
+{"abstract": "  Linear-response time-dependent density-functional theory (TDDFT) can describe\nexcitonic features in the optical spectra of insulators and semiconductors,\nusing exchange-correlation (xc) kernels behaving as $-1/k^{2}$ to leading\norder. We show how excitons can be modeled in real-time TDDFT, using an xc\nvector potential constructed from approximate, long-range corrected xc kernels.\nWe demonstrate for various materials that this real-time approach is consistent\nwith frequency-dependent linear response, gives access to femtosecond exciton\ndynamics following short-pulse excitations, and can be extended with some\ncaution into the nonlinear regime.\n"}
+{"abstract": "  Very recently, Window-based Transformers, which computed self-attention\nwithin non-overlapping local windows, demonstrated promising results on image\nclassification, semantic segmentation, and object detection. However, less\nstudy has been devoted to the cross-window connection which is the key element\nto improve the representation ability. In this work, we revisit the spatial\nshuffle as an efficient way to build connections among windows. As a result, we\npropose a new vision transformer, named Shuffle Transformer, which is highly\nefficient and easy to implement by modifying two lines of code. Furthermore,\nthe depth-wise convolution is introduced to complement the spatial shuffle for\nenhancing neighbor-window connections. The proposed architectures achieve\nexcellent performance on a wide range of visual tasks including image-level\nclassification, object detection, and semantic segmentation. Code will be\nreleased for reproduction.\n"}
+{"abstract": "  Classical random walks on finite graphs have an underrated property: a walk\nfrom any vertex can reach every other vertex in finite time, provided they are\nconnected. Discrete-time quantum walks on finite connected graphs however, can\nhave infinite hitting times. This phenomenon is related to graph symmetry, as\npreviously characterized by the group of direction-preserving graph\nautomorphisms that trivially affect the coin Hilbert space. If a graph is\nsymmetric enough (in a particular sense) then the associated quantum walk\nunitary will contain eigenvectors that do not overlap a set of target vertices,\nfor any coin flip operator. These eigenvectors span the Infinite Hitting Time\n(IHT) subspace. Quantum states in the IHT subspace never reach the target\nvertices, leading to infinite hitting times. However, this is not the whole\nstory: the graph of the 3D cube does not satisfy this symmetry constraint, yet\nquantum walks on this graph with certain symmetric coins can exhibit infinite\nhitting times. We study the effect of coin symmetry by analyzing the group of\ncoin-permutation symmetries (CPS): graph automorphisms that act nontrivially on\nthe coin Hilbert space but leave the coin operator invariant. Unitaries using\nhighly symmetric coins with large CPS groups, such as the permutation-invariant\nGrover coin, are associated with higher probabilities of never arriving, as a\nresult of their larger IHT subspaces.\n"}
+{"abstract": "  Ontology alignment process is overwhelmingly cited in Knowledge Engineering\nas a key mechanism aimed at bypassing heterogeneity and reconciling various\ndata sources, represented by ontologies, i.e., the the Semantic Web\ncornerstone. In such infrastructures and environments, it is inconceivable to\nassume that all ontologies covering a particular domain of knowledge are\naligned in pairs. Moreover, the high performance of alignment approaches is\nclosely related to two factors, i.e., time consumption and machine resource\nlimitations. Thus, good quality alignments are valuable and it would be\nappropriate to exploit them. Based on this observation, this article introduces\na new method of indirect alignment of ontologies in a cross-lingual context.\nIndeed, the proposed method deals with alignments of multilingual ontologies\nand implements an indirect ontology alignment strategy based on a composition\nand reuse of effective direct alignments. The trigger of the proposed method\nprocess is based on alignment algebra which governs the semantics composition\nof relationships and confidence values. The obtained results, after a thorough\nand detailed experiment are very encouraging and highlight many positive\naspects about the new proposed method.\n"}
+{"abstract": "  Optimization-based state estimation is useful for handling of constrained\nlinear or nonlinear dynamical systems. It has an ideal form, known as full\ninformation estimation (FIE) which uses all past measurements to perform state\nestimation, and also a practical counterpart, known as moving-horizon\nestimation (MHE) which uses most recent measurements of a limited length to\nperform the estimation. Due to the theoretical ideal, conditions for robust\nstability of FIE are relatively easier to establish than those for MHE, and\nvarious sufficient conditions have been developed in literature. This work\nreveals a generic link from robust stability of FIE to that of MHE, showing\nthat the former implies at least a weaker robust stability of MHE which\nimplements a long enough horizon. The implication strengthens to strict robust\nstability of MHE if the system satisfies a mild Lipschitz continuity or\nequivalently a robust exponential stability condition. The revealed\nimplications are then applied to derive new sufficient conditions for robust\nstability of MHE, which further reveal an intrinsic relation between the\nexistence of a robustly stable FIE/MHE and the system being incrementally\ninput/output-to-state stable.\n"}
+{"abstract": "  Previous studies have shown that high-quality code comments assist developers\nin program comprehension and maintenance tasks. However, the semi-structured\nnature of comments, unclear conventions for writing good comments, and the lack\nof quality assessment tools for all aspects of comments make their evaluation\nand maintenance a non-trivial problem. To achieve high-quality comments, we\nneed a deeper understanding of code comment characteristics and the practices\ndevelopers follow. In this thesis, we approach the problem of assessing comment\nquality from three different perspectives: what developers ask about commenting\npractices, what they write in comments, and how researchers support them in\nassessing comment quality.\n  Our preliminary findings show that developers embed various kinds of\ninformation in class comments across programming languages. Still, they face\nproblems in locating relevant guidelines to write consistent and informative\ncomments, verifying the adherence of their comments to the guidelines, and\nevaluating the overall state of comment quality. To help developers and\nresearchers in building comment quality assessment tools, we provide: (i) an\nempirically validated taxonomy of comment convention-related questions from\nvarious community forums, (ii) an empirically validated taxonomy of comment\ninformation types from various programming languages, (iii) a\nlanguage-independent approach to automatically identify the information types,\nand (iv) a comment quality taxonomy prepared from a systematic literature\nreview.\n"}
+{"abstract": "  The Lighthouse system was originally developed as tracking system for virtual\nreality applications. Due to its affordable price, it has also found attractive\nuse-cases in robotics in the past. However, existing works frequently rely on\nthe centralized official tracking software, which make the solution less\nattractive for UAV swarms. In this work, we consider an open-source tracking\nsoftware that can run onboard small Unmanned Aerial Vehicles (UAVs) in\nreal-time and enable distributed swarming algorithms. We provide a dataset\nspecifically for the use cases i) flight; and ii) as ground truth for other\ncommonly-used distributed swarming localization systems such as ultra-wideband.\nWe then use this dataset to analyze both accuracy and precision of the\nLighthouse system in different use-cases. To our knowledge, we are the first to\ncompare two different Lighthouse hardware versions with a motion capture system\nand the first to analyze the accuracy using tracking software that runs onboard\na microcontroller.\n"}
+{"abstract": "  In this paper some well-known tests based on empirical distribution functions\n(EDF) with estimated parameters for testing composite normality hypothesis are\nrevisited, and some new results on asymptotic properties are provided. In\nparticular, the approximate Bahadur slopes are obtained -- in the case of close\nalternatives -- for the EDF-based tests as well as the likelihood ratio test.\nThe local approximate efficiencies are calculated for several close\nalternatives. The obtained results could serve as a benchmark for evaluation of\nthe quality of recent and future normality tests.\n"}
+{"abstract": "  We analyze a theory known as extended DBI, which interpolates between DBI and\nthe $U(N)\\times U(N)/U(N)$ non-linear sigma model and represents a nontrivial\nexample of theories with mixed power counting. We discuss symmetries of the\naction and their geometrical origin; the special case of SU(2) extended DBI\ntheory is treated in great detail. The revealed symmetries lead to a new type\nof graded soft theorem that allows us to prove on-shell constructibility of the\ntree-level S-matrix. It turns out that the on-shell constructibility of the\nfull extended DBI remains valid, even if its DBI sub-theory is modified in such\na way to preserve its own on-shell constructibility. We thus propose a slight\ngeneralization of the DBI sub-theory, which we call 2-scale DBI theory. Gluing\nit back to the rest of the extended DBI theory gives a new set of on-shell\nreconstructible theories -- the 2-scale extended DBI theory and its\ndescendants. The uniqueness of the parent theory is confirmed by the bottom-up\napproach that uses on-shell amplitude methods exclusively.\n"}
+{"abstract": "  Neutron stars offer the opportunity to study the behaviour of matter at\ndensities and temperatures inaccessible to terrestrial experiments.\nGravitational-wave observations of binary neutron star coalescences can\nconstrain the neutron-star equation of state before and after merger. After the\nneutron star binary merges, hyperons can form in the remnant, changing the\nbehaviour of the neutron-star equation of state. In this study, we use\nfinite-entropy equations of state to show that a post-merger remnant can spin\nup due to cooling. The magnitude of the spin-up depends on the neutron-star\nequation of state. If hyperons are present, the post-merger spin-up changes the\npeak gravitational-wave frequency by $\\sim 540$ Hz, when the entropy per baryon\ndrops from $s=2$ $k_B$ to $s=0$ $k_B$. If hyperons are not present, the\npost-merger spin-up changes by $\\sim 360$ Hz, providing a gravitational-wave\nsignature for exotic matter. We expect the same qualitative behaviour whenever\ntemperature dependent phase transitions are triggered.\n"}
+{"abstract": "  Intelligent reflecting surface (IRS) has recently been emerged as an\neffective way for improving the performance of wireless networks by\nreconfiguring the propagation environment through a large number of passive\nreflecting elements. This game-changing technology is especially important for\nstepping into the Internet of Everything (IoE) era, where high performance is\ndemanded with very limited available resources. In this paper, we study a\nbackscatter-assisted wireless powered communication network (BS-WPCN), in which\na number of energy-constrained users, powered by a power station (PS), transmit\ninformation to an access point (AP) via backscatter and active wireless\ninformation transfer, with their communication being aided by an IRS. Using a\npractical energy harvesting (EH) model which is able to capture the\ncharacteristics of realistic energy harvesters, we investigate the maximization\nof total network throughput. Specifically, IRS reflection coefficients, PS\ntransmit and AP receive beamforming vectors, power and time allocation are\ndesigned through a two-stage algorithm, assuming minimum mean square error\n(MMSE) receiver at the AP. The effectiveness of the proposed algorithm is\nconfirmed via extensive numerical simulations. We also show that our proposed\nscheme is readily applicable to practical IRS-aided networks with discrete\nphase shift values.\n"}
+{"abstract": "  Spatio-temporal models are widely used in many research areas including\necology. The recent proliferation of the use of in-situ sensors in streams and\nrivers supports space-time water quality modelling and monitoring in near\nreal-time. In this paper, we introduce a new family of dynamic spatio-temporal\nmodels, in which spatial dependence is established based on stream distance and\ntemporal autocorrelation is incorporated using vector autoregression\napproaches. We propose several variations of these novel models using a\nBayesian framework. Our results show that our proposed models perform well\nusing spatio-temporal data collected from real stream networks, particularly in\nterms of out-of-sample RMSPE. This is illustrated considering a case study of\nwater temperature data in the northwestern United States.\n"}
+{"abstract": "  Technology companies have produced varied responses to concerns about the\neffects of the design of their conversational AI systems. Some have claimed\nthat their voice assistants are in fact not gendered or human-like -- despite\ndesign features suggesting the contrary. We compare these claims to user\nperceptions by analysing the pronouns they use when referring to AI assistants.\nWe also examine systems' responses and the extent to which they generate output\nwhich is gendered and anthropomorphic. We find that, while some companies\nappear to be addressing the ethical concerns raised, in some cases, their\nclaims do not seem to hold true. In particular, our results show that system\noutputs are ambiguous as to the humanness of the systems, and that users tend\nto personify and gender them as a result.\n"}
+{"abstract": "  We analyze the rest-optical emission-line ratios of z~1.5 galaxies drawn from\nthe MOSFIRE Deep Evolution Field (MOSDEF) survey. Using composite spectra we\ninvestigate the mass-metallicity relation (MZR) at z~1.5 and measure its\nevolution to z=0. When using gas-phase metallicities based on the N2 line\nratio, we find that the MZR evolution from z~1.5 to z=0 depends on stellar\nmass, evolving by $\\Delta\\rm log(\\rm O/H)\\sim0.25$ dex at\n$M_*<10^{9.75}M_{\\odot}$ down to $\\Delta\\rm log(\\rm O/H)\\sim0.05$ at\n$M_*>10^{10.5}M_{\\odot}$. In contrast, the O3N2-based MZR shows a constant\noffset of $\\Delta\\rm log(\\rm O/H)\\sim0.30$ across all masses, consistent with\nprevious MOSDEF results based on independent metallicity indicators, and\nsuggesting that O3N2 provides a more robust metallicity calibration for our\nz~1.5 sample. We investigated the secondary dependence of the MZR on SFR by\nmeasuring correlated scatter about the mean $M_*$-specific SFR and\n$M_*-\\log(\\rm O3N2)$ relations. We find an anti-correlation between $\\log(\\rm\nO/H)$ and sSFR offsets, indicating the presence of a $M_*$-SFR-Z relation,\nthough with limited significance. Additionally, we find that our z~1.5 stacks\nlie along the z=0 metallicity sequence at fixed\n$\\mu=\\log(M_*/M_{\\odot})-0.6\\times\\log(\\rm SFR / M_{\\odot} yr^{-1})$ suggesting\nthat the z~1.5 stacks can be described by the z=0 fundamental metallicity\nrelation (FMR). However, using different calibrations can shift the calculated\nmetallicities off of the local FMR, indicating that appropriate calibrations\nare essential for understanding metallicity evolution with redshift. Finally,\nunderstanding how [NII]/H$\\alpha$ scales with galaxy properties is crucial to\naccurately describe the effects of blended [NII] and H$\\alpha$ on redshift and\nH$\\alpha$ flux measurements in future large surveys utilizing low-resolution\nspectra such as with Euclid and the Roman Space Telescope.\n"}
+{"abstract": "  The interplay between band topology and magnetism can give rise to exotic\nstates of matter. For example, magnetically doped topological insulators can\nrealize a Chern insulator that exhibits quantized Hall resistance at zero\nmagnetic field. While prior works have focused on ferromagnetic systems, little\nis known about band topology and its manipulation in antiferromagnets. Here, we\nreport that MnBi$_2$Te$_4$ is a rare platform for realizing a\ncanted-antiferromagnetic (cAFM) Chern insulator with electrical control. We\nshow that the Chern insulator state with Chern number $C = 1$ appears as soon\nas the AFM to canted-AFM phase transition happens. The Chern insulator state is\nfurther confirmed by observing the unusual transition of the $C = 1$ state in\nthe cAFM phase to the $C = 2$ orbital quantum Hall states in the magnetic field\ninduced ferromagnetic phase. Near the cAFM-AFM phase boundary, we show that the\nChern number can be toggled on and off by applying an electric field alone. We\nattribute this switching effect to the electrical field tuning of the exchange\ngap alignment between the top and bottom surfaces. Our work paves the way for\nfuture studies on topological cAFM spintronics and facilitates the development\nof proof-of-concept Chern insulator devices.\n"}
+{"abstract": "  Classical T Tauri stars are young low-mass systems still accreting material\nfrom their disks. These systems are dynamic on timescales of hours to years.\nThe observed variability can help us infer the physical processes that occur in\nthe circumstellar environment. We aim at understanding the dynamics of the\nmagnetic interaction between the star and the inner accretion disk in young\nstellar objects. We present the case of the young stellar system V2129 Oph,\nwhich is a well-known T Tauri star. We performed a time series analysis of this\nstar using high-resolution spectroscopic data at optical and infrared\nwavelengths from CFHT/ESPaDOnS, ESO/HARPS and CFHT/SPIRou. The new data sets\nallowed us to characterize the accretion-ejection structure in this system and\nto investigate its evolution over a timescale of a decade via comparisons to\nprevious observational data. We measure radial velocity variations and recover\na stellar rotation period of 6.53d. However, we do not recover the stellar\nrotation period in the variability of various circumstellar lines, such as\nH$\\alpha$ and H$\\beta$ in the optical or HeI 1083nm and Pa$\\beta$ in the\ninfrared. Instead, we show that the optical and infrared line profile\nvariations are consistent with a magnetospheric accretion scenario that shows\nvariability with a period of about 6.0d, shorter than the stellar rotation\nperiod. Additionally, we find a period of 8.5d in H$\\alpha$ and H$\\beta$ lines,\nprobably due to a structure located beyond the corotation radius, at a distance\nof 0.09au. We investigate whether this could be accounted for by a wind\ncomponent, twisted or multiple accretion funnel flows, or an external\ndisturbance in the inner disk. We conclude that the dynamics of the\naccretion-ejection process can vary significantly on a timescale of just a few\nyears, presumably reflecting the evolving magnetic field topology at the\nstellar surface.\n"}
+{"abstract": "  This paper is devoted to proposing a general weighted low-rank recovery model\nand designs a fast SVD-free computational scheme to solve it. First, our\ngeneric weighted low-rank recovery model unifies several existing approaches in\nthe literature.~Moreover, our model readily extends to the non-convex setting.\nAlgorithm-wise, most first-order proximal algorithms in the literature for\nlow-rank recoveries require computing singular value decomposition (SVD). As\nSVD does not scale properly with the dimension of the matrices, these\nalgorithms becomes slower when the problem size becomes larger. By\nincorporating the variational formulation of the nuclear norm into the\nsub-problem of proximal gradient descent, we avoid to compute SVD which results\nin significant speed-up. Moreover, our algorithm preserves the {\\em rank\nidentification property} of nuclear norm [33] which further allows us to design\na rank continuation scheme that asymptotically achieves the minimal iteration\ncomplexity. Numerical experiments on both toy example and real-world problems\nincluding structure from motion (SfM) and photometric stereo, background\nestimation and matrix completion, demonstrate the superiority of our proposed\nalgorithm.\n"}
+{"abstract": "  This paper is the continuation of the previous one \\cite{Cui2021}, where we\nre-proved the area-minimization of cones over Grassmannians of $n$-planes\n$G(n,m;\\mathbb{F})(\\mathbb{F}=\\mathbb{R},\\mathbb{C},\\mathbb{H})$, Cayley plane\n$\\mathbb{O}P^2$ from the point view of Hermitian orthogonal projectors, and\ngave area-minimizing cones associated to oriented real Grassmannians\n$\\widetilde{G}(n,m;\\mathbb{R})$.\n  In this paper, we make a further step on showing that the cones, of dimension\nno less than $\\mathbf{8}$, over minimal products of $G(n,m;\\mathbb{F})$ are\narea-minimizing. Moreover, those cones are very similar to the classical cones\nover products of spheres, and for the critical situation -- the cones of\ndimension $\\mathbf{7}$ \\cite{lawlor1991sufficient}, we gain more\narea-minimizing cones by carefully computing the Jacobian\n$inf_{v}det(I-tH^{v}_{ij})$. Certain minimizing cones among them had been found\nfrom the perspective of $R$-spaces\\cite{Ohno2021area}, or isoparametric\ntheory\\cite{tang2020minimizing}, and others are completely new.\n  We also prove that the cones over minimal product of\n$\\widetilde{G}(n,m;\\mathbb{R})$ are area-minimizing.\n"}
+{"abstract": "  Coronal Mass Ejections (CMEs) are highly dynamic events originating in the\nsolar atmosphere, that show a wide range of kinematic properties and are the\nmajor drivers of the space weather. The angular width of the CMEs is a crucial\nparameter in the study of their kinematics. The fact that whether slow and fast\nCMEs (as based on their relative speed to the average solar wind speed) are\nassociated with different processes at the location of their ejection is still\ndebatable. Thus, in this study, we investigate their angular width to\nunderstand the differences between the slow and fast CMEs. We study the width\ndistribution of slow and fast CMEs and find that they follow different power\nlaw distributions, with a power law indices ($\\alpha$) of -1.1 and -3.7 for\nfast and slow CMEs respectively. To reduce the projection effects, we further\nrestrict our analysis to only limb events as derived from manual catalog and we\nfind similar results. We then associate the slow and fast CMEs to their source\nregions, and classified the sources as Active Regions (ARs) and Prominence\nEruptions (PEs). We find that slow and fast CMEs coming from ARs and PEs, also\nfollow different power laws in their width distributions. This clearly hints\ntowards a possibility that different mechanisms might be involved in the width\nexpansion of slow and fast CMEs coming from different sources.These results are\nalso crucial from the space weather perspective since the width of the CME is\nan important factor in that aspect.\n"}
+{"abstract": "  Understanding multi-node vehicular wireless communication channels is crucial\nfor future time-sensitive safety applications for human-piloted as well as\npartly autonomous vehicles on roads, railways and in the air. These highly\ndynamic wireless communication channels are characterized by rapidly changing\nchannel statistics. In this paper we present the first fully mobile multi-node\nvehicular wireless channel sounding system, which is capable of simultaneously\ncapturing multiple channel frequency responses, ensuring that measurement\nconditions are identical for all observed links. We use it to analyze road\nscenarios with multiple vehicles and a large obstructing double-decker bus. The\nempirical measurement data is used to parametrize a model for the large\nobstructing vehicle within a geometry-based stochastic channel model. We\ncompare the time-variant channel statistics obtained from our channel model\nwith the ones from the measurement campaign. By means of a channel emulator we\nobtain the packet error rates of commercial modems for the measured and the\nsimulated wireless communication channels and compare them, in order to\nvalidate the model at the link level. We find that the path loss, the root mean\nsquare (RMS) delay spread, and the RMS Doppler spread deviate by less than\n$3.6\\,\\mathrm{dB}$, $78\\,\\mathrm{ns}$, and $52\\,\\mathrm{Hz}$, respectively, for\n$80\\%$ of the total simulation duration. The PER obtained from measured data is\nwithin the maximum and minimum bounds of our model for $86\\%$ of the simulation\nduration.\n"}
+{"abstract": "  Graph Neural Network (GNN) is a variant of Deep Neural Networks (DNNs)\noperating on graphs. However, GNNs are more complex compared to traditional\nDNNs as they simultaneously exhibit features of both DNN and graph\napplications. As a result, architectures specifically optimized for either DNNs\nor graph applications are not suited for GNN training. In this work, we propose\na 3D heterogeneous manycore architecture for on-chip GNN training to address\nthis problem. The proposed architecture, ReGraphX, involves heterogeneous ReRAM\ncrossbars to fulfill the disparate requirements of both DNN and graph\ncomputations simultaneously. The ReRAM-based architecture is complemented with\na multicast-enabled 3D NoC to improve the overall achievable performance. We\ndemonstrate that ReGraphX outperforms conventional GPUs by up to 3.5X (on an\naverage 3X) in terms of execution time, while reducing energy consumption by as\nmuch as 11X.\n"}
+{"abstract": "  Connected and automated vehicles (CAVs) provide the most intriguing\nopportunity to reduce pollution, energy consumption, and travel delays. In this\npaper, we address the problem of vehicle platoon formation in a traffic network\nwith partial CAV penetration rates. We investigate the interaction between CAV\nand human-driven vehicle (HDV) dynamics, and provide a rigorous control\nframework that enables platoon formation with the HDVs by only controlling the\nCAVs within the network. We present a complete analytical solution of the CAV\ncontrol policy and the conditions under which a platoon formation is feasible.\nWe evaluate the solution and demonstrate the efficacy of the proposed framework\nusing numerical simulation.\n"}
+{"abstract": "  The last few years have seen tremendous progress in the observation of the\nglobal properties of neutron stars (NSs), e.g. masses, radii and tidal\ndeformabilities. Such properties provide information about possible phase\ntransitions in the inner cores of NSs, provided the connection between observed\nmasses and radii and the equation of state (EoS) is well understood. We focus\nthe present study on first-order phase transitions, which often softens the EoS\nand consequently reduces the maximum mass as well as the radii of NSs. Here, we\nchallenge this conventional expectation by constructing explicit examples of\nEoSs undergoing a first-order phase transition, but which are much stiffer that\ntheir purely hadronic counterparts. We also provide comparisons with the\nrecently proposed quarkyonic EoS which suggests a strong repulsion in the core\nof NSs, and we show that their stiffness can be realistically masqueraded by\nfirst-order phase transitions to exotic matter.\n"}
+{"abstract": "  In this paper, the circle formation control problem is addressed for a group\nof cooperative underactuated fish-like robots involving unknown nonlinear\ndynamics and disturbances. Based on the reinforcement learning and cognitive\nconsistency theory, we propose a decentralized controller without the knowledge\nof the dynamics of the fish-like robots. The proposed controller can be\ntransferred from simulation to reality. It is only trained in our established\nsimulation environment, and the trained controller can be deployed to real\nrobots without any manual tuning. Simulation results confirm that the proposed\nmodel-free robust formation control method is scalable with respect to the\ngroup size of the robots and outperforms other representative RL algorithms.\nSeveral experiments in the real world verify the effectiveness of our RL-based\napproach for circle formation control.\n"}
+{"abstract": "  We introduce the notion of Krull super-dimension of supermodules over certain\nsuper-commutative Noetherian super-rings. We investigate how this notion\nrelates to the notion of odd regular sequence introduced by T.Schmitt and how\nit behaves with respect to the transition to the graded and bigraded\nsupermodules and super-rings associated with the original ones. We also apply\nthese results to the super-dimension theory of superschemes of finite type and\ntheir morphisms.\n"}
+{"abstract": "  We outline and review the computations of polarized and unpolarized nucleon\nstructure functions within the bosonized Nambu-Jona-Lasinio chiral soliton\nmodel. We focus on a consistent regularization prescription for the Dirac sea\ncontribution and present numerical results from that formulation. We also\nreflect on previous calculations on quark distributions in chiral quark soliton\nmodels and attempt to put them into perspective.\n"}
+{"abstract": "  In this paper, we introduce a new benchmark dataset for the challenging\nwriting in the air (WiTA) task -- an elaborate task bridging vision and NLP.\nWiTA implements an intuitive and natural writing method with finger movement\nfor human-computer interaction (HCI). Our WiTA dataset will facilitate the\ndevelopment of data-driven WiTA systems which thus far have displayed\nunsatisfactory performance -- due to lack of dataset as well as traditional\nstatistical models they have adopted. Our dataset consists of five sub-datasets\nin two languages (Korean and English) and amounts to 209,926 video instances\nfrom 122 participants. We capture finger movement for WiTA with RGB cameras to\nensure wide accessibility and cost-efficiency. Next, we propose spatio-temporal\nresidual network architectures inspired by 3D ResNet. These models perform\nunconstrained text recognition from finger movement, guarantee a real-time\noperation by processing 435 and 697 decoding frames-per-second for Korean and\nEnglish, respectively, and will serve as an evaluation standard. Our dataset\nand the source codes are available at https://github.com/Uehwan/WiTA.\n"}
+{"abstract": "  Face recognition has been one of the most relevant and explored fields of\nBiometrics. In real-world applications, face recognition methods usually must\ndeal with scenarios where not all probe individuals were seen during the\ntraining phase (open-set scenarios). Therefore, open-set face recognition is a\nsubject of increasing interest as it deals with identifying individuals in a\nspace where not all faces are known in advance. This is useful in several\napplications, such as access authentication, on which only a few individuals\nthat have been previously enrolled in a gallery are allowed. The present work\nintroduces a novel approach towards open-set face recognition focusing on small\ngalleries and in enrollment detection, not identity retrieval. A Siamese\nNetwork architecture is proposed to learn a model to detect if a face probe is\nenrolled in the gallery based on a verification-like approach. Promising\nresults were achieved for small galleries on experiments carried out on\nPubfig83, FRGCv1 and LFW datasets. State-of-the-art methods like HFCN and HPLS\nwere outperformed on FRGCv1. Besides, a new evaluation protocol is introduced\nfor experiments in small galleries on LFW.\n"}
+{"abstract": "  Through inhalation of, e.g., hyperpolarized $^3$He, it is possible to acquire\ngas diffusion magnetic resonance measurements that depend on the local geometry\nin the vast network of microscopic airways that form the respiratory zone of\nthe human lung. Here, we demonstrate that this can be used to determine the\ndimensions (length and radius) of these airways noninvasively. Specifically,\nthe above technique allows measurement of the weighted time-dependent diffusion\ncoefficient (also called the apparent diffusion coefficient), which we here\nderive in analytic form using symmetries in the airway network. Agreement with\nexperiment is found for the full span of published hyperpolarized $^3$He\ndiffusion magnetic resonance measurements (diffusion times from milliseconds to\nseconds) and published invasive airway dimension measurements.\n"}
+{"abstract": "  The Metric Dimension problem asks for a minimum-sized resolving set in a\ngiven (unweighted, undirected) graph $G$. Here, a set $S \\subseteq V(G)$ is\nresolving if no two distinct vertices of $G$ have the same distance vector to\n$S$. The complexity of Metric Dimension in graphs of bounded treewidth remained\nelusive in the past years. Recently, Bonnet and Purohit [IPEC 2019] showed that\nthe problem is W[1]-hard under treewidth parameterization. In this work, we\nstrengthen their lower bound to show that Metric Dimension is NP-hard in graphs\nof treewidth 24.\n"}
+{"abstract": "  To reduce the stress concentration and ensure the structural safety for\nlattice structure designs, in this paper, a new optimization framework is\ndeveloped for the optimal design of graded lattice structures, innovatively\nintegrating fillet designs as well as yield and elastic buckling constraints.\nBoth strut and fillet radii are defined as design variables. Homogenization\nmethod is employed to characterize the effective elastic constants and yield\nstresses of the lattice metamaterials. Metamaterial models are developed to\nrepresent the relationships between the metamaterial effective properties and\nlattice geometric variables. A yield constraint, based on the modified Hills\nyield criterion, is developed as a function of relative strut radii and fillet\nparameters. An elastic buckling constraint, based on the Euler buckling formula\nand the Johnson formula, is developed as a function of relative strut radii.\nBoth yield and buckling constraints are integrated into an optimization problem\nformulation; a new optimization framework is proposed and a case study of\nminimizing the compliance of a Messerschmitt-Bolkow-Blohm beam is conducted.\nThe yield and buckling constraints guarantee the safety of the optimized beams\ncomposed of BCC and PC lattices. Reductions in compliance and stress\nconcentration are achieved by the optimized MBB beams.\n"}
+{"abstract": "  In this paper, we study the elliptic spectral sequence computing\n$tmf_*(\\mathbb{R} P^2)$ and $tmf_* (\\mathbb{R} P^2 \\wedge \\mathbb{C} P^2)$.\nSpecifically, we compute all differentials and resolve exotic extensions by 2,\n$\\eta$, and $\\nu$. For $tmf_* (\\mathbb{R} P^2 \\wedge \\mathbb{C} P^2)$, we also\ncompute the effect of the $v_1$-self maps of $\\mathbb{R} P^2 \\wedge \\mathbb{C}\nP^2$ on $tmf$-homology.\n"}
+{"abstract": "  Quantum battery is an emerging subject in the field of quantum\nthermodynamics, which is applied to charge, store and dispatch energy in\nquantum systems. In this work, we propose a fast and stable charging protocol\nbased on the adiabatic evolution for the dark state of a three-level quantum\nbattery. It combines the conventional stimulated Raman adiabatic passage\n(STIRAP) and the quantum transitionless driving technique. The charging process\ncan be accelerated up to nearly one order in magnitude even under constraint of\nthe strength of counter-diabatic driving. To perform the charging protocol in\nthe Rydberg atomic system as a typical platform for STIRAP, the prerequisite\ndriving pulses are modified to avoid the constraint of the forbidden\ntransition. Moreover, our protocol is found to be more robust against the\nenvironmental dissipation and dephasing than the conventional STIRAP.\n"}
+{"abstract": "  The goal of this study is to analyze the fine structure of nonlinear modal\ninteractions in different 1D Burgers and 3D Navier-Stokes flows. This analysis\nis focused on preferential alignments characterizing the phases of Fourier\nmodes participating in triadic interactions, which are key to determining the\nnature of energy fluxes between different scales. We develop novel diagnostic\ntools designed to probe the level of coherence among triadic interactions\nrealizing different flow scenarios. We consider extreme 1D viscous Burgers\nflows and 3D Navier-Stokes flows which are complemented by singularity-forming\ninviscid Burgers flows as well as viscous Burgers flows and Navier-Stokes flows\ncorresponding to generic turbulent and simple unimodal initial data, such as\nthe Taylor-Green vortex. The main finding is that while the extreme viscous\nBurgers and Navier-Stokes flows reveal the same relative level of enstrophy\namplification by nonlinear effects, this behaviour is realized via modal\ninteractions with vastly different levels of coherence. In the viscous Burgers\nflows the flux-carrying triads have phase values which saturate the\nnonlinearity thereby maximizing the energy flux towards small scales. On the\nother hand, in 3D Navier-Stokes flows with the extreme initial data the energy\nflux to small scales is realized by a very small subset of helical triads. The\nsecond main finding concerns the role of initial coherence. Comparison of the\nflows resulting from the extreme and generic initial conditions shows striking\nsimilarities between these two types of flows, for the 1D viscous Burgers\nequation as well as the 3D Navier-Stokes equation.\n"}
+{"abstract": "  Digitization, i.e., the process of converting information into a digital\nformat, may provide various opportunities (e.g., increase in productivity,\ndisaster recovery, and environmentally friendly solutions) and challenges for\nbusinesses. In this context, one of the main challenges would be to accurately\nclassify numerous scanned documents uploaded every day by customers as usual\nbusiness processes. For example, processes in banking (e.g., applying for\nloans) or the Government Registry of BDM (Births, Deaths, and Marriages)\napplications may involve uploading several documents such as a driver's license\nand passport. There are not many studies available to address the challenge as\nan application of image classification. Although some studies are available\nwhich used various methods, a more accurate model is still required. The\ncurrent study has proposed a robust fusion model to define the type of identity\ndocuments accurately. The proposed approach is based on two different methods\nin which images are classified based on their visual features and text\nfeatures. A novel model based on statistics and regression has been proposed to\ncalculate the confidence level for the feature-based classifier. A fuzzy-mean\nfusion model has been proposed to combine the classifier results based on their\nconfidence score. The proposed approach has been implemented using Python and\nexperimentally validated on synthetic and real-world datasets. The performance\nof the proposed model is evaluated using the Receiver Operating Characteristic\n(ROC) curve analysis.\n"}
+{"abstract": "  The sunspot umbra harbors the coolest plasma on the solar surface due to the\npresence of strong magnetic fields. The routinely used atomic lines to observe\nthe photosphere have weak signals in the umbra and are often swamped by\nmolecular lines. This makes it harder to infer the properties of the umbra,\nespecially in the darkest regions. The lines of the Ti I multiplet at 2.2\n$\\mu$m are formed mainly at temperatures $\\le\\!4500$ K and are not known to be\naffected by molecular blends in sunspots. Since the first systematic\nobservations in the 1990's, these lines have been seldom observed due to the\ninstrumental challenges involved at these longer wavelengths. We revisit these\nlines and investigate their formation in different solar features. We\nsynthesize the Ti I multiplet using a snapshot from 3D MHD simulation of a\nsunspot and explore the properties of two of its lines in comparison with two\ncommonly used iron lines at 630.25 nm and $1.5648\\,\\mu$m. We find that the Ti I\nlines have stronger signals than the Fe I lines in both intensity and\npolarization in the sunspot umbra and in penumbral spines. They have little to\nno signal in the penumbral filaments and the quiet Sun, at $\\mu=1$. Their\nstrong and well-split profiles in the dark umbra are less affected by stray\nlight. Consequently, inside the sunspot it is easier to invert these lines and\nto infer the atmospheric properties, compared to the iron lines. The Cryo-NIRSP\ninstrument at the DKIST will provide the first ever high resolution\nobservations in this wavelength range. In this preparatory study, we\ndemonstrate the unique temperature and magnetic sensitivities of the Ti\nmultiplet, by probing the Sun's coolest regions which are not favourable for\nthe formation of other commonly used spectral lines. We thus expect such\nobservations to advance our understanding of sunspot properties.\n"}
+{"abstract": "  Document-level relation extraction (DocRE) aims at extracting the semantic\nrelations among entity pairs in a document. In DocRE, a subset of the sentences\nin a document, called the evidence sentences, might be sufficient for\npredicting the relation between a specific entity pair. To make better use of\nthe evidence sentences, in this paper, we propose a three-stage\nevidence-enhanced DocRE framework consisting of joint relation and evidence\nextraction, evidence-centered relation extraction (RE), and fusion of\nextraction results. We first jointly train an RE model with a simple and\nmemory-efficient evidence extraction model. Then, we construct pseudo documents\nbased on the extracted evidence sentences and run the RE model again. Finally,\nwe fuse the extraction results of the first two stages using a blending layer\nand make a final prediction. Extensive experiments show that our proposed\nframework achieves state-of-the-art performance on the DocRED dataset,\noutperforming the second-best method by 0.76/0.82 Ign F1/F1. In particular, our\nmethod significantly improves the performance on inter-sentence relations by\n1.23 Inter F1.\n"}
+{"abstract": "  While the Kerr-Schild double copy of the Coulomb solution in dimensions\nhigher than three is the Schwarzschild black hole, it is known that it should\nbe a non-vacuum solution in three dimensions. We show that the static black\nhole solution of Einstein-Maxwell theory (with one ghost sign in the action) is\nthe double copy with the correct Newtonian limit, which provides an improvement\nover the previous construction with a free scalar field that does not vanish at\ninfinity. By considering a negative cosmological constant, we also study the\ncharged Ba\\~nados-Teitelboim-Zanelli black hole and find that the single copy\ngauge field is the Coulomb solution modified by a term which describes an\nelectric field linearly increasing with the radial coordinate, which is the\nusual behaviour of the Schwarzschild-AdS black hole in higher dimensions when\nwritten around a flat background metric.\n"}
+{"abstract": "  Simultaneous wireless information and power transfer (SWIPT) technique is a\npopular strategy to convey both information and RF energy for harvesting at\nreceivers. In this regard, we consider a two-way relay system with multiple\nusers and a multi-antenna relay employing SWIPT strategy, where splitting the\nreceived signal leads to a rate-energy trade-off. In literature, the works on\ntransceiver design have been studied using computationally intensive and\nsuboptimal convex relaxation based schemes. In this paper, we study the\nbalanced precoder design using chordal distance (CD) decomposition, which\nincurs much lower complexity, and is flexible to dynamic energy requirements.\nIt is analyzed that given a non-negative value of CD, the achieved harvested\nenergy for the proposed balanced precoder is higher than that for the perfect\ninterference alignment (IA) precoder. The corresponding loss in sum rates is\nalso analyzed via an upper bound. Simulation results add that the IA schemes\nbased on mean-squared error are better suited for the SWIPT maximization than\nthe subspace alignment-based methods.\n"}
+{"abstract": "  In this paper we extend the combinatorial theory of poly-Bernoulli numbers.\nWe prove combinatorially some identities involving poly-Bernoulli polynomials.\nWe introduce a combinatorial model for poly-Euler numbers and provide\ncombinatorial proofs of some identities. We redefine $r$-Eulerian polynomials\nand verify Stephan's conjecture concerning poly-Bernoulli numbers and the\ncentral binomial series.\n"}
+{"abstract": "  Training sparse networks to converge to the same performance as dense neural\narchitectures has proven to be elusive. Recent work suggests that\ninitialization is the key. However, while this direction of research has had\nsome success, focusing on initialization alone appears to be inadequate. In\nthis paper, we take a broader view of training sparse networks and consider the\nrole of regularization, optimization, and architecture choices on sparse\nmodels. We propose a simple experimental framework, Same Capacity Sparse vs\nDense Comparison (SC-SDC), that allows for a fair comparison of sparse and\ndense networks. Furthermore, we propose a new measure of gradient flow,\nEffective Gradient Flow (EGF), that better correlates to performance in sparse\nnetworks. Using top-line metrics, SC-SDC and EGF, we show that default choices\nof optimizers, activation functions and regularizers used for dense networks\ncan disadvantage sparse networks. Based upon these findings, we show that\ngradient flow in sparse networks can be improved by reconsidering aspects of\nthe architecture design and the training regime. Our work suggests that\ninitialization is only one piece of the puzzle and taking a wider view of\ntailoring optimization to sparse networks yields promising results.\n"}
+{"abstract": "  A continuous-time multi-state history is semi-Markovian, if an intensity to\nmigrate from one state into another, depends on the duration in the first\nstate. Such duration can be formalised as covariate, entering the intensity\nprocess of the transition counts. We derive the integrated intensity process,\nprove its predictability and the martingale property of the residual. In\nparticular, we verify the usual conditions for the respective filtration. As a\nconsequence, according to Nielsen and Linton (1995), a kernel estimator of the\ntransition intensity, including the duration dependence, converges point-wise\nat a slow rate, compared to the Markovian kernel estimator, i.e when ignoring\ndependence. By using the rate discrepancy, we follow Gozalo (1993) and show\nthat the (properly scaled) maximal difference of the two kernel estimators on a\nrandom grid of points is asymptotically chi-square-1-distributed. As a data\nexample, for a sample of 130,000 German women observed over a period of nine\nyears, we model the mortality after dementia onset, potentially dependent on\nthe disease duration. As usual, the models under both hypotheses need to be\nenlarged to allow for independent right-censoring. We find a significant effect\nof dementia duration, nearly independent of the bandwidth.\n"}
+{"abstract": "  On-device Deep Neural Networks (DNNs) have recently gained more attention due\nto the increasing computing power of the mobile devices and the number of\napplications in Computer Vision (CV), Natural Language Processing (NLP), and\nInternet of Things (IoTs). Unfortunately, the existing efficient convolutional\nneural network (CNN) architectures designed for CV tasks are not directly\napplicable to NLP tasks and the tiny Recurrent Neural Network (RNN)\narchitectures have been designed primarily for IoT applications. In NLP\napplications, although model compression has seen initial success in on-device\ntext classification, there are at least three major challenges yet to be\naddressed: adversarial robustness, explainability, and personalization. Here we\nattempt to tackle these challenges by designing a new training scheme for model\ncompression and adversarial robustness, including the optimization of an\nexplainable feature mapping objective, a knowledge distillation objective, and\nan adversarially robustness objective. The resulting compressed model is\npersonalized using on-device private training data via fine-tuning. We perform\nextensive experiments to compare our approach with both compact RNN (e.g.,\nFastGRNN) and compressed RNN (e.g., PRADO) architectures in both natural and\nadversarial NLP test settings.\n"}
+{"abstract": "  Photoluminescence (PL) intermittency is a ubiquitous phenomenon detrimentally\nreducing the temporal emission intensity stability of single colloidal quantum\ndots (CQDs) and the emission quantum yield of their ensembles. Despite efforts\nfor blinking reduction via chemical engineering of the QD architecture and its\nenvironment, blinking still poses barriers to the application of QDs,\nparticularly in single-particle tracking in biology or in single-photon\nsources. Here, we demonstrate the first deterministic all-optical suppression\nof quantum dot blinking using a compound technique of visible and mid-infrared\n(MIR) excitation. We show that moderate-field ultrafast MIR pulses (5.5 $\\mu$m,\n150 fs) can switch the emission from a charged, low quantum yield 'grey' trion\nstate to the 'bright' exciton state in CdSe/CdS core-shell quantum dots\nresulting in a significant reduction of the QD intensity flicker.\nQuantum-tunneling simulations suggest that the MIR fields remove the excess\ncharge from trions with reduced emission quantum yield to restore higher\nbrightness exciton emission. Our approach can be integrated with existing\nsingle-particle tracking or super-resolution microscopy techniques without any\nmodification to the sample and translates to other emitters presenting\ncharging-induced PL intermittencies, such as single-photon emissive defects in\ndiamond and two-dimensional materials.\n"}
+{"abstract": "  We study the existence of fully nontrivial solutions to the system $$-\\Delta\nu_i+ \\lambda_iu_i = \\sum\\limits_{j=1}^\\ell \\beta_{ij}|u_j|^p|u_i|^{p-2}u_i\\\n\\hbox{in}\\ \\Omega, \\qquad\n  i=1,\\ldots,\\ell,$$ in a bounded or unbounded domain $\\Omega$ in $\\mathbb\nR^N,$ $N\\ge 3$. The $\\lambda_i$'s are real numbers, and the nonlinear term may\nhave subcritical ($1<p<\\frac{N}{N-2}$), critical ($p=\\frac{N}{N-2}$), or\nsupercritical growth ($p>\\frac{N}{N-2}$). The matrix $(\\beta_{ij})$ is\nsymmetric and admits a block decomposition such that the diagonal entries\n$\\beta_{ii}$ are positive, the interaction forces within each block are\nattractive (i.e., all entries $\\beta_{ij}$ in each block are non-negative) and\nthe interaction forces between different blocks are repulsive (i.e., all other\nentries are non-positive). We obtain new existence and multiplicity results of\nfully nontrivial solutions, i.e., solutions where every component $u_i$ is\nnontrivial. We also find fully synchronized solutions (i.e., $u_i=c_i u_1$ for\nall $i=2,\\ldots,\\ell$) in the purely cooperative case whenever $p\\in(1,2).$\n"}
+{"abstract": "  The rare event search experiments using germanium detectors are performed in\nthe underground laboratories to prevent cosmic rays. However, the cosmogenic\nactivation of the cupreous detector components on the ground will generate long\nhalf-life radioisotopes and contribute continually to the expected background\nlevel. We present a study on the cosmogenic activation of copper after 504 days\nof exposure at an altitude of 2469.4 m outside the China Jinping Underground\nLaboratory (CJPL). The specific activities of the cosmogenic nuclides produced\nin the copper bricks were measured using a low background germanium gamma-ray\nspectrometer at CJPL. The production rates at sea level, in units of\nnuclei/kg/day, are 18.6 \\pm 2.0 for Mn-54, 9.9 \\pm 1.3 for Co-56, 48.3 \\pm 5.5\nfor Co-57, 51.8 \\pm 2.5 for Co-58 and 39.7 \\pm 5.7 for Co-60, respectively.\nGiven the expected exposure history of the germanium detectors, a Monte Carlo\nsimulation is conducted to assess the cosmogenic background contributions of\nthe detectors' cupreous components.\n"}
+{"abstract": "  Random lasers (RLs) are intriguing devices with promising applications as\nlight sources for imaging, sensing, super resolution spectral analysis or\ncomplex networks engineering. RLs can be obtained from optically pumped dyes,\noptical fibers and crystals, or electrically pumped semiconductor\nheterostructures. Semicon-ductor RLs are usually fabricated by introducing\nscattering defects into the active layer, adding a degree of complexity to the\nfabrication process and losing the ease of realization potentially offered by\ndisordered structures. Ready availability of electrically pumped RLs, avoiding\ncostly fabrication approach, would boost the use of these devices in research\nand applications. Here, we realize an incoherent semiconductor RL by simply\nprocessing the output mirror of an off-the-shelf Fabry-Perot laser diode via\ncontrolled laser ablation. Optical feedback provided by the intact back mirror\nand the ablated front mirror results in multi-mode ran-dom lasing with low\nspatial coherence and speckled output emission profile.\n"}
+{"abstract": "  Detection of rare traits or diseases in a large population is challenging.\nPool testing allows covering larger swathes of population at a reduced cost,\nwhile simplifying logistics. However, testing precision decreases as it becomes\nunclear which member of a pool made the global test positive.\n  In this paper we discuss testing strategies that provably approach\nbest-possible strategy - optimal in the sense that no other strategy can give\nexact results with fewer tests. Our algorithms guarantee that they provide a\ncomplete and exact result for every individual, without exceeding $1/0.99$\ntimes the number of tests the optimal strategy would require.\n  This threshold is arbitrary: algorithms closer to the optimal bound can be\ndescribed, however their complexity increases, making them less practical.\n  Moreover, the way the algorithms process input samples leads to some\nindividuals' status to be known sooner, thus allowing to take urgency into\naccount when assigning individuals to tests.\n"}
+{"abstract": "  In this note, we study a family of subgraphs of the Farey graph, denoted as\n$\\mathcal{F}_N$ for every $N\\in\\mathbb{N}.$\n  We show that $\\mathcal{F}_N$ is connected if and only if $N$ is either equal\nto one or a prime power. We introduce a class of continued fractions referred\nto as $\\mathcal{F}_N$-continued fractions for each $N>1.$ We establish a\nrelation between $\\mathcal{F}_N$-continued fractions and certain paths from\ninfinity in the graph $\\mathcal{F}_N.$ We discuss existence and uniqueness of\n$\\mathcal{F}_N$-continued fraction expansions of real numbers.\n"}
+{"abstract": "  This work is a theoretical study of the speed at which the material of an\nimpacted target is ejected during the formation of an impact crater. Our model,\nstarting from the first principle of thermodynamics, can describes the speed of\nthe ejecta recursing to considerations that include complex process in simple\ncalculations. The fit of the model with observations shows that the many\ncomplex details implicit in an impact process could be included in some few\nparameters. Ejecta speed could be described independent of impactor parameters.\nThe model is compared with subsonic and supersonic speed experiments showing\ncoincidence in several cases. The model works with subsonic and supersonic\nimpacts. We do not compare the model with hypersonic impacts (> 5 km/s),\nhowever, as the model derivation no depends on the impactor velocity it is\nlikely that also work with this kind of impacts.\n"}
+{"abstract": "  The task of long-form question answering (LFQA) involves retrieving documents\nrelevant to a given question and using them to generate a paragraph-length\nanswer. While many models have recently been proposed for LFQA, we show in this\npaper that the task formulation raises fundamental challenges regarding\nevaluation and dataset creation that currently preclude meaningful modeling\nprogress. To demonstrate these challenges, we first design a new system that\nrelies on sparse attention and contrastive retriever learning to achieve\nstate-of-the-art performance on the ELI5 LFQA dataset. While our system tops\nthe public leaderboard, a detailed analysis reveals several troubling trends:\n(1) our system's generated answers are not actually grounded in the documents\nthat it retrieves; (2) ELI5 contains significant train / validation overlap, as\nat least 81% of ELI5 validation questions occur in paraphrased form in the\ntraining set; (3) ROUGE-L is not an informative metric of generated answer\nquality and can be easily gamed; and (4) human evaluations used for other text\ngeneration tasks are unreliable for LFQA. We offer suggestions to mitigate each\nof these issues, which we hope will lead to more rigorous LFQA research and\nmeaningful progress in the future.\n"}
+{"abstract": "  Consensus protocols have traditionally been studied in a setting where all\nparticipants are known to each other from the start of the protocol execution.\nIn the parlance of the 'blockchain' literature, this is referred to as the\npermissioned setting. What differentiates Bitcoin from these previously studied\nprotocols is that it operates in a permissionless setting, i.e. it is a\nprotocol for establishing consensus over an unknown network of participants\nthat anybody can join, with as many identities as they like in any role. The\narrival of this new form of protocol brings with it many questions. Beyond\nBitcoin, what can we prove about permissionless protocols in a general sense?\nHow does recent work on permissionless protocols in the blockchain literature\nrelate to the well-developed history of research on permissioned protocols in\ndistributed computing?\n  To answer these questions, we describe a formal framework for the analysis of\nboth permissioned and permissionless systems. Our framework allows for\n\"apples-to-apples\" comparisons between different categories of protocols and,\nin turn, the development of theory to formally discuss their relative merits. A\nmajor benefit of the framework is that it facilitates the application of a rich\nhistory of proofs and techniques in distributed computing to problems in\nblockchain and the study of permissionless systems. Within our framework, we\nthen address the questions above. We consider the Byzantine Generals Problem as\na formalisation of the problem of reaching consensus, and address a programme\nof research that asks, \"Under what adversarial conditions, and for what types\nof permissionless protocol, is consensus possible?\" We prove a number of\nresults for this programme, our main result being that deterministic consensus\nis not possible for decentralised permissionless protocols. To close, we give a\nlist of eight open questions.\n"}
+{"abstract": "  Coorbit theory is a powerful machinery that constructs a family of Banach\nspaces, the so-called coorbit spaces, from well-behaved unitary representations\nof locally compact groups. A core feature of coorbit spaces is that they can be\ndiscretized in a way that reflects the geometry of the underlying locally\ncompact group. Many established function spaces such as modulation spaces,\nBesov spaces, Sobolev-Shubin spaces, and shearlet spaces are examples of\ncoorbit spaces. The goal of this survey is to give an overview of coorbit\ntheory with the aim of presenting the main ideas in an accessible manner.\nCoorbit theory is generally seen as a complicated theory, filled with both\ntechnicalities and conceptual difficulties. Faced with this obstacle, we feel\nobliged to convince the reader of the theory's elegance. As such, this survey\nis a showcase of coorbit theory and should be treated as a stepping stone to\nmore complete sources.\n"}
+{"abstract": "  Variational inequalities with thin obstacles and Signorini-type boundary\nconditions are classical problems in the calculus of variations, arising in\nnumerous applications. In the linear case many refined results are known, while\nin the nonlinear setting our understanding is still at a preliminary stage.\n  In this paper we prove $C^1$ regularity for the solutions to a general class\nof quasi-linear variational inequalities with thin obstacles and $C^{1,\n\\alpha}$ regularity for variational inequalities under Signorini-type\nconditions on the boundary of a domain.\n"}
+{"abstract": "  To process data more efficiently, big data frameworks provide data\nabstractions to developers. However, due to the abstraction, there may be many\nchallenges for developers to understand and debug the data processing code. To\nuncover the challenges in using big data frameworks, we first conduct an\nempirical study on 1,000 Apache Spark-related questions on Stack Overflow. We\nfind that most of the challenges are related to data transformation and API\nusage. To solve these challenges, we design an approach, which assists\ndevelopers with understanding and debugging data processing in Spark. Our\napproach leverages statistical sampling to minimize performance overhead, and\nprovides intermediate information and hint messages for each data processing\nstep of a chained method pipeline. The preliminary evaluation of our approach\nshows that it has low performance overhead and we receive good feedback from\ndevelopers.\n"}
+{"abstract": "  We prove an extension of a seminal result of Bertini and Giacomin. Namely we\nconsider weakly asymmetric exclusion processes with several distinct initial\ndata simultaneously, then run according to the basic coupling, and we show\njoint convergence to the solution of the KPZ equation with the same driving\nnoise in the limiting equation. Along the way, we analyze fine properties of\nnontrivially coupled solutions-in-law of KPZ-type equations.\n"}
+{"abstract": "  We consider the optimization of a two-hop relay network based on an\namplify-and-forward Multiple-Input Multiple-Output (MIMO) relay. The relay is\nassumed to derive the output signal by a Relay Transform Matrix (RTM) applied\nto the input signal. Assuming perfect channel state information about the\nnetwork at the relay, the RTM is optimized according to two different criteria:\n{\\bf\\em i)} network capacity; {\\bf\\em ii)} network capacity based on Orthogonal\nSpace--Time Block Codes. The two assumptions have been addressed in part in the\nliterature. The optimization problem is reduced to a manageable convex form,\nwhose KKT equations are explicitly solved. Then, a parametric solution is\ngiven, which yields the power constraint and the capacity achieved with\nuncorrelated transmitted data as functions of a positive indeterminate. The\nsolution for a given average power constraint at the relay is amenable to a\n\\emph{water-filling-like} algorithm, and extends earlier literature results\naddressing the case without the direct link. Simulation results are reported\nconcerning a Rayleigh relay network and the role of the direct link SNR is\nprecisely assessed.\n"}
+{"abstract": "  Path integral contour deformations have been shown to mitigate sign and\nsignal-to-noise problems associated with phase fluctuations in lattice field\ntheories. We define a family of contour deformations applicable to $SU(N)$\nlattice gauge theory that can reduce sign and signal-to-noise problems\nassociated with complex actions and complex observables. For observables, these\ncontours can be used to define deformed observables with identical expectation\nvalue but different variance. As a proof-of-principle, we apply machine\nlearning techniques to optimize the deformed observables associated with Wilson\nloops in two dimensional $SU(2)$ and $SU(3)$ gauge theory. We study loops\nconsisting of up to 64 plaquettes and achieve variance reduction of up to 4\norders of magnitude.\n"}
+{"abstract": "  Gas metallicity $Z$ and the related dust-to-gas ratio $f_\\textrm{d}$ can\ninfluence the growth of HII regions via metal line cooling and UV absorption.\nWe model these effects in star-forming regions containing massive stars. We\ncompute stellar feedback from photoionization and radiation pressure (RP) using\nMonte Carlo radiative transfer coupled with hydrodynamics, including stellar\nand diffuse radiation fields. We follow a $10^5$ M$_\\odot$ turbulent cloud with\n$Z/$Z$_\\odot$ = 2, 1, 0.5, 0.1 and $f_\\textrm{d} = 0.01 Z/$Z$_\\odot$ with a\ncluster-sink particle method for star formation. The models evolve for at least\n1.5 Myr under feedback. Lower $Z$ results in higher temperatures and therefore\nlarger HII regions. For $Z \\ge$Z$_\\odot$, radiation pressure $P_\\textrm{rad}$\ncan dominate locally over the gas pressure $P_\\textrm{gas}$ in the inner\nhalf-parsec around sink particles. Globally, the ratio of\n$P_\\textrm{rad}/P_\\textrm{gas}$ is around 1 (2 Z$_\\odot$), 0.3 (Z$_\\odot$), 0.1\n(0.5 Z$_\\odot$), and 0.03 (0.1 Z$_\\odot$). In the solar model, excluding RP\nresults in an ionized volume several times smaller than the fiducial model with\nboth mechanisms. Excluding RP and UV attenuation by dust results in a larger\nionized volume than the fiducial case. That is, UV absorption hinders growth\nmore than RP helps it. The radial expansion velocity of ionized gas reaches\n$+$15 km/s outwards, while neutral gas has inward velocities for most of the\nruntime, except for 0.1 Z$_\\odot$ which exceeds $+$4 km/s. $Z$ and\n$f_\\textrm{d}$ do not significantly alter the star formation efficiency, rate,\nor cluster half-mass radius, with the exception of 0.1 Z$_\\odot$ due to the\nearlier expulsion of neutral gas.\n"}
+{"abstract": "  We give a generalization of Meeks-Yau's celebrated embeddedness result for\nthe solutions of the Plateau problem for extreme curves.\n"}
+{"abstract": "  In this paper, we study a diffusive Lotka-Volterra competition model under\nhomogeneous Dirichlet boundary conditions. We shall discuss the effects of\ndispersal rate and spatial heterogeneity on population dynamics. More\nprecisely, we establish the main results about the global asymptotic stability\nof semitrivial as well as coexistence steady states.\n"}
+{"abstract": "  We study the dynamics of electroweak phase transition in a simple extension\nof the Standard Model where the Higgs sector is extended by adding an\n$SU(2)_L$-triplet with hypercharge Y=2. By making random scans over the\nparameters of the model, we show that there are regions consistent with\nconstraints from collider experiments and the requirement for a strong\nfirst-order electroweak phase transition which is needed for electroweak\nbaryogenesis. Further, we also study the power spectrum of the gravitational\nwaves which can be generated due to the first-order phase transitions.\nMoreover, the detectability of these gravitational waves, via future\nspace-based detectors, is discussed.\n"}
+{"abstract": "  A major challenge in high-precision light-pulse atom interferometric\nexperiments such as in tests of the weak equivalence principle is the\nuncontrollable dependency of the phase on initial velocity and position of the\natoms in the presence of inhomogeneous gravitational fields. To overcome this\nlimitation, mitigation strategies have been proposed, however, valid only for\nharmonic potentials or only for small branch separations in more general\nsituations. Here we provide a mitigation formula for anharmonic perturbation\npotentials including local gravitational effects that vary on length scales\nmuch smaller than the spatial extent probed by the atoms and originate, e.g.,\nfrom buildings that surround the experiment. Furthermore, our results are\napplicable to general interferometer geometries with arbitrary branch\nseparation and allow for compensation of Coriolis effects in rotating reference\nframes.\n"}
+{"abstract": "  Here we perform a Kaluza-Klein dimensional reduction of Vasiliev's\nfirst-order description of massless spin-s particles from $D=3+1$ to $D=2+1$\nand derive first-order self-dual models describing particles with helicities\n$\\pm s$ for the cases $s=1,2,3$. In the first two cases we recover known\n(parity singlets) self-dual models. In the spin-3 case we derive a new first\norder self-dual model with a local Weyl symmetry which lifts the traceless\nrestriction on the rank-3 tensor. A gauge fixed version of this model\ncorresponds to a known spin-3 self-dual model. We conjecture that our procedure\ncan be generalized to arbitrary integer spins.\n"}
+{"abstract": "  Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine\nlearning method based on evolutionary computation. GSGP performs search\noperations directly at the level of program semantics, which can be done more\nefficiently then operating at the syntax level like most GP systems. Efficient\nimplementations of GSGP in C++ exploit this fact, but not to its full\npotential. This paper presents GSGP-CUDA, the first CUDA implementation of GSGP\nand the most efficient, which exploits the intrinsic parallelism of GSGP using\nGPUs. Results show speedups greater than 1,000X relative to the\nstate-of-the-art sequential implementation.\n"}
+{"abstract": "  In this article, we study bounded solutions of Euler-type equations on\n$\\mathbb{R}^d$ which have no integrability at $|x| \\rightarrow +\\infty$. As has\nbeen previously noted, such solutions fail to achieve uniqueness in an initial\nvalue problem, even under strong smoothness conditions. This contrasts with\nwell-posedness results that have been obtained by using the Leray projection\noperator in these equations. This apparent paradox is solved by noting that\nusing the Leray projector requires an extra condition the solutions must\nfulfill at $|x| \\rightarrow + \\infty$. We find this condition to be necessary\nand sufficient. We deduce a full well-posedness result for the Euler equations\nin the space $C^0_T(B^1_{\\infty, 1})$. We also apply the methods developed for\nthe Euler system to other hydrodynamic models: the Navier-Stokes equations and\nmagnetohydrodynamics.\n"}
+{"abstract": "  We introduce a type of zero-dimensional dynamical system (a pair consisting\nof a totally disconnected compact metrizable space along with a homeomorphism\nof that space), which we call \"fiberwise essentially minimal\", and we prove\nthat the associated crossed product $ C^* $-algebra of such a system is an\nAT-algebra. Under the additional assumption that the system has no periodic\npoints, we prove that the associated crossed product $ C^* $-algebra has real\nrank zero, which tells us that such $ C^* $-algebras are classifiable by $ K\n$-theory. We show that the definition of fiberwise essentially minimality\nallows one to produce many nontrivial examples of such systems (ones that are\nneither minimal nor essentially minimal). The associated crossed product $ C^*\n$-algebras to these nontrivial examples are of particular interest because they\nare non-simple (unlike in the minimal case).\n"}
+{"abstract": "  Next generation neutrino oscillation experiments like DUNE and T2HK are\nmulti-purpose observatories, with a rich physics program beyond oscillation\nmeasurements. A special role is played by their near detector facilities, which\nare particularly well-suited to search for weakly coupled dark sector particles\nproduced in the primary target. In this paper, we demonstrate this by\nestimating the sensitivity of the DUNE near detectors to the scattering of\nsub-GeV DM particles and to the decay of sub-GeV sterile neutrinos (\"heavy\nneutral leptons\"). We discuss in particular the importance of the DUNE-PRISM\ndesign, which allows some of the near detectors to be moved away from the beam\naxis. At such off-axis locations, the signal-to-background ratio improves for\nmany new physics searches. We find that this leads to a dramatic boost in the\nsensitivity to boosted DM particles interacting mainly with hadrons, while for\nboosted DM interacting with leptons, data taken on-axis leads to marginally\nstronger exclusion limits. Searches for heavy neutral leptons perform equally\nwell in both configurations.\n"}
+{"abstract": "  We use the technique of invariant frames to study a left invariant spray\nstructure on a Lie group, and calculate its S-curvature and Riemann curvature,\nwhich generalizes the corresponding formulae in homogeneous Finsler geometry.\nUsing the canonical bi-invariant Berwald spray structure as the reference, any\nleft invariant spray structure can be associated with a spray vector field on\nthe Lie algebra. We find the correspondence between geodesics for a left\ninvariant spray structure and the inverse integral curves of its spray vector\nfield. As an application for this correspondence, we provide an alternative\nproof for Landsberg Conjecture in the case of homogeneous Finsler surfaces.\n"}
+{"abstract": "  Structure factors describe how incident radiation is scattered from materials\nsuch as silicon and germanium and characterize the physical interaction between\nthe material and scattered particles. We use neutron pendell\\\"{o}sung\ninterferometry to make precision measurements of the (220) and (400)\nneutron-silicon structure factors, and achieve a factor of four improvement in\nthe (111) structure factor uncertainty. These data provide measurements of the\nsilicon Debye-Waller factor at room temperature and the mean square neutron\ncharge radius $\\langle r_n^2 \\rangle = -0.1101 \\pm 0.0089 \\, \\mathrm{fm}^2$.\nCombined with existing measurements of the Debye-Waller factor and charge\nradius, the measured structure factors also improve constraints on the strength\nof a Yukawa-modification to gravity by an order of magnitude over the 20 pm to\n10 nm length scale range.\n"}
+{"abstract": "  Using a dataset of 6.32 fb$^{-1}$ of $e^+ e^-$ annihilation data collected\nwith the BESIII detector at center-of-mass energies between 4178 and 4226 MeV,\nwe have measured the absolute branching fraction of the leptonic decay $D_s^+\n\\to \\tau^+ \\nu_{\\tau}$ via $\\tau^+ \\to e^+ \\nu_e \\bar{\\nu}_{\\tau}$, and find\n$\\mathcal{B}_{D_s^+ \\to \\tau^+ \\nu_{\\tau}}=(5.27\\pm0.10\\pm0.12)\\times10^{-2}$,\nwhere the first uncertainty is statistical and the second is systematic. The\nprecision is improved by a factor of 2 compared to the previous best\nmeasurement. Combining with $f_{D_s^+}$ from Lattice quantum chromodynamics\ncalculations or the $|V_{cs}|$ from the CKMfitter group, we extract\n$|V_{cs}|=0.978\\pm0.009\\pm0.012$ and $f_{D_s^+}= (251.1\\pm2.4\\pm3.0)$ MeV,\nrespectively. Combining our result with the world averages of\n$\\mathcal{B}_{D_s^+ \\to \\tau^+ \\nu_{\\tau}}$ and $\\mathcal{B}_{D_s^+ \\to \\mu^+\n\\nu_{\\mu}}$, we obtain the ratio of the branching fractions $\\mathcal{B}_{D_s^+\n\\to \\tau^+ \\nu_{\\tau}} / \\mathcal{B}_{D_s^+ \\to \\mu^+ \\nu_{\\mu}} =\n9.72\\pm0.37$, which is consistent with the standard model prediction of lepton\nflavor universality.\n"}
+{"abstract": "  We show that $3$-graphs on $n$ vertices whose codegree is at least $(2/3 +\no(1))n$ can be decomposed into tight cycles and admit Euler tours, subject to\nthe trivial necessary divisibility conditions. We also provide a construction\nshowing that our bounds are best possible up to the $o(1)$ term. All together,\nour results answer in the negative some recent questions of Glock, Joos,\nK\\\"uhn, and Osthus.\n"}
+{"abstract": "  Motivated by two identities published with Ramanujan's lost notebook and\nconnected, respectively, with the Gauss circle problem and the Dirichlet\ndivisor problem, in an earlier paper, three of the present authors derived\nrepresentations for certain sums of products of trigonometric functions as\ndouble series of Bessel functions. These series are generalized in the present\npaper by introducing the novel notion of balanced derivatives, leading to\nfurther theorems. As we will see below, the regions of convergence in the\nunbalanced case are entirely different than those in the balanced case. From\nthis viewpoint, it is remarkable that Ramanujan had the intuition to formulate\nentries that are, in our new terminology, \"balanced\". If $x$ denotes the number\nof products of the trigonometric functions appearing in our sums, in addition\nto proving the identities mentioned above, theorems and conjectures for upper\nand lower bounds for the sums as $x\\to\\infty$ are established.\n"}
+{"abstract": "  The performance of federated learning (FL) over wireless networks depend on\nthe reliability of the client-server connectivity and clients' local\ncomputation capabilities. In this article we investigate the problem of client\nscheduling and resource block (RB) allocation to enhance the performance of\nmodel training using FL, over a pre-defined training duration under imperfect\nchannel state information (CSI) and limited local computing resources. First,\nwe analytically derive the gap between the training losses of FL with clients\nscheduling and a centralized training method for a given training duration.\nThen, we formulate the gap of the training loss minimization over client\nscheduling and RB allocation as a stochastic optimization problem and solve it\nusing Lyapunov optimization. A Gaussian process regression-based channel\nprediction method is leveraged to learn and track the wireless channel, in\nwhich, the clients' CSI predictions and computing power are incorporated into\nthe scheduling decision. Using an extensive set of simulations, we validate the\nrobustness of the proposed method under both perfect and imperfect CSI over an\narray of diverse data distributions. Results show that the proposed method\nreduces the gap of the training accuracy loss by up to 40.7% compared to\nstate-of-theart client scheduling and RB allocation methods.\n"}
+{"abstract": "  We report the observation of gravitational waves from two compact binary\ncoalescences in LIGO's and Virgo's third observing run with properties\nconsistent with neutron star-black hole (NSBH) binaries. The two events are\nnamed GW200105_162426 and GW200115_042309, abbreviated as GW200105 and\nGW200115; the first was observed by LIGO Livingston and Virgo, and the second\nby all three LIGO-Virgo detectors. The source of GW200105 has component masses\n$8.9^{+1.2}_{-1.5}\\,M_\\odot$ and $1.9^{+0.3}_{-0.2}\\,M_\\odot$, whereas the\nsource of GW200115 has component masses $5.7^{+1.8}_{-2.1}\\,M_\\odot$ and\n$1.5^{+0.7}_{-0.3}\\,M_\\odot$ (all measurements quoted at the 90% credible\nlevel). The probability that the secondary's mass is below the maximal mass of\na neutron star is 89%-96% and 87%-98%, respectively, for GW200105 and GW200115,\nwith the ranges arising from different astrophysical assumptions. The source\nluminosity distances are $280^{+110}_{-110}$ Mpc and $300^{+150}_{-100}$ Mpc,\nrespectively. The magnitude of the primary spin of GW200105 is less than 0.23\nat the 90% credible level, and its orientation is unconstrained. For GW200115,\nthe primary spin has a negative spin projection onto the orbital angular\nmomentum at 88% probability. We are unable to constrain spin or tidal\ndeformation of the secondary component for either event. We infer a NSBH merger\nrate density of $45^{+75}_{-33}\\,\\mathrm{Gpc}^{-3} \\mathrm{yr}^{-1}$ when\nassuming GW200105 and GW200115 are representative of the NSBH population, or\n$130^{+112}_{-69}\\,\\mathrm{Gpc}^{-3} \\mathrm{yr}^{-1}$ under the assumption of\na broader distribution of component masses.\n"}
+{"abstract": "  We show that for every positive integer $k$, any tournament can be\npartitioned into at most $2^{ck}$ $k$-th powers of paths. This result is tight\nup to the exponential constant. Moreover, we prove that for every\n$\\varepsilon>0$ and every integer $k$, any tournament on $n\\ge\n\\varepsilon^{-Ck}$ vertices which is $\\varepsilon$-far from being transitive\ncontains the $k$-th power of a cycle of length $\\Omega(\\varepsilon n)$; both\nbounds are tight up to the implied constants.\n"}
+{"abstract": "  In this paper, we focus on normative systems for online communities. The\npaper addresses the issue that arises when different community members\ninterpret these norms in different ways, possibly leading to unexpected\nbehavior in interactions, usually with norm violations that affect the\nindividual and community experiences. To address this issue, we propose a\nframework capable of detecting norm violations and providing the violator with\ninformation about the features of their action that makes this action violate a\nnorm. We build our framework using Machine Learning, with Logistic Model Trees\nas the classification algorithm. Since norm violations can be highly\ncontextual, we train our model using data from the Wikipedia online community,\nnamely data on Wikipedia edits. Our work is then evaluated with the Wikipedia\nuse case where we focus on the norm that prohibits vandalism in Wikipedia\nedits.\n"}
+{"abstract": "  The dust content of normal galaxies and the dust mass density (DMD) at high-z\n(z>4) are unconstrained given the source confusion and the sensitivity\nlimitations of previous observations. The ALMA Large Program to INvestigate\n[CII] at Early Times (ALPINE), which targeted 118 UV-selected star-forming\ngalaxies at 4.4<z<5.9, provides a new opportunity to tackle this issue for the\nfirst time with a statistically robust dataset. We have exploited the\nrest-frame far-infrared (FIR) fluxes of the 23 continuum individually detected\ngalaxies and stacks of continuum images to measure the dust content of the 118\nUV-selected ALPINE galaxies. We have focused on the dust scaling relations and,\nby comparing them with predictions from chemical evolution models, we have\nprobed the evolutionary stage of UV-selected galaxies at high-z. By using the\nobserved correlation between the UV-luminosity and the dust mass, we have\nestimated the DMD of UV-selected galaxies at z~5, weighting the galaxies by\nmeans of the UV-luminosity function (UVLF). The derived DMD has been compared\nwith the value we have estimated from the 10 ALPINE far-IR continuum blindly\ndetected galaxies at the redshift of the ALPINE targets. The comparison of the\nobserved dust scaling relations with chemical evolution models suggests that\nALPINE galaxies are not likely progenitors of disc galaxies, but of\nintermediate and low mass proto-spheroids, resulting in present-day bulges of\nspiral or elliptical galaxies. Interestingly, this conclusion is in line with\nthe independent morphological analysis, that shows that the majority (~70\\%) of\nthe dust-continuum detected galaxies have a disturbed morphology. The DMD\nobtained at z~5 from UV-selected sources is ~30% of the value obtained from\nblind far-IR selected sources, showing that the UV-selection misses the most\ndust-rich, UV-obscured galaxies.\n"}
+{"abstract": "  Non-autoregressive mechanisms can significantly decrease inference time for\nspeech transformers, especially when the single step variant is applied.\nPrevious work on CTC alignment-based single step non-autoregressive transformer\n(CASS-NAT) has shown a large real time factor (RTF) improvement over\nautoregressive transformers (AT). In this work, we propose several methods to\nimprove the accuracy of the end-to-end CASS-NAT, followed by performance\nanalyses. First, convolution augmented self-attention blocks are applied to\nboth the encoder and decoder modules. Second, we propose to expand the trigger\nmask (acoustic boundary) for each token to increase the robustness of CTC\nalignments. In addition, iterated loss functions are used to enhance the\ngradient update of low-layer parameters. Without using an external language\nmodel, the WERs of the improved CASS-NAT, when using the three methods, are\n3.1%/7.2% on Librispeech test clean/other sets and the CER is 5.4% on the\nAishell1 test set, achieving a 7%~21% relative WER/CER improvement. For the\nanalyses, we plot attention weight distributions in the decoders to visualize\nthe relationships between token-level acoustic embeddings. When the acoustic\nembeddings are visualized, we find that they have a similar behavior to word\nembeddings, which explains why the improved CASS-NAT performs similarly to AT.\n"}
+{"abstract": "  Cherenkov radiation (CR) generated by a charge moving through a hollow\nconical target made of dielectric material is analyzed. We consider two cases:\nthe charge moves from the base of the cone to its top (``straight'' cone) or\nfrom the top to the base (``inverted'' cone). Unlike previous papers, a nonzero\nshift of the charge trajectory from the symmetry axis is taken into account\nwhich leads to generation of asymmetric CR. The most interesting effect is the\nphenomenon of ``Cherenkov spotlight'' which has been reported earlier for\naxially symmetric problems. This effect allows essential enhancement of the CR\nintensity in the far-field region by proper selection of the target's\nparameters and charge velocity. Here we describe the influence of charge shift\non CR far-field patterns paying the main attention to the ``Cherenkov\nspotlight'' regime. Influence of variation of the charge speed on this\nphenomenon is also investigated.\n"}
+{"abstract": "  During its two-year mission at comet 67P, Rosetta nearly continuously\nmonitored the inner coma plasma environment for gas production rates varying\nover three orders of magnitude, at distances to the nucleus from a few to a few\nhundred km. To achieve the best possible measurements, cross-calibration of the\nplasma instruments is needed. We construct with two different physical models\nto cross-calibrate the electron density as measured by the Mutual Impedance\nProbe (MIP) to the ion current and spacecraft potential as measured by the\nRosetta Langmuir Probe (LAP), the latter validated with the Ion Composition\nAnalyser (ICA). We retrieve a continuous plasma density dataset for the entire\ncometary mission with a much improved dynamical range compared to any plasma\ninstrument alone and, at times, improve the temporal resolution from\n0.24-0.74~Hz to 57.8~Hz. The new density dataset is consistent with the\nexisting MIP density dataset and covers long time periods where densities were\ntoo low to be measured by MIP. The physical model also yields, at 3~hour time\nresolution, ion flow speeds as well as a proxy for the solar EUV flux from the\nphotoemission from the Langmuir Probes. We report on two independent\nmission-wide estimates of the ion flow speed which are consistent with the bulk\nH$_2$O$^+$ ion velocities as measured by ICA. We find the ion flow to\nconsistently be much faster than the neutral gas over the entire mission,\nlending further evidence that the ions are collisionally decoupled from the\nneutrals in the coma. RPC measurements of ion speeds are therefore not\nconsistent with the assumptions made in previously published plasma density\nmodels of the comet ionosphere at the start and end of the mission. Also, the\nmeasured EUV flux is perfectly consistent with independently derived values\npreviously published from Johansson et al. (2017) and lends support for the\nconclusions drawn therein.\n"}
+{"abstract": "  Recent observations revealed a coherence between the spin vector of a galaxy\nand the orbital motion of its neighbors. We refer to the phenomenon as \"the\nspin$-$orbit alignment (SOA)\" and explore its physical origin via the\nIllustrisTNG simulation. This is the first study to utilize a cosmological\nhydrodynamic simulation to investigate the SOA of galaxy pairs. In particular,\nwe identify paired galaxies at $z = 0$ having the nearest neighbor with mass\nratios from 1/10 to 10 and calculate the spin$-$orbit angle for each pair. Our\nresults are as follows. (a) There exists a clear preference for prograde\norientations (i.e., SOA) for galaxy pairs, qualitatively consistent with\nobservations. (b) The SOA is significant for both baryonic and dark matter\nspins, being the strongest for gas and the weakest for dark matter. (c) The SOA\nis stronger for less massive targets and for targets having closer neighbors.\n(d) The SOA strengthens for galaxies in low-density regions, and the signal is\ndominated by central$-$satellite pairs in low-mass halos. (e) There is an\nexplicit dependence of the SOA on the duration of interaction with its current\nneighbor. Taken together, we propose that the SOA witnessed at $z = 0$ has been\ndeveloped mainly by interactions with a neighbor for an extended period of\ntime, rather than tidal torque from the ambient large-scale structure.\n"}
+{"abstract": "  It is shown that the physical damage resulting from severe weather phenomenon\nsuch as hurricanes and tornadoes encapsulated in various scales such as\nSaffir-Simpson, TORRO and the Enhanced Fujita Scale, and measured by the Power\nDissipation Index can be predicted using the concept of the Peak Virtual Power.\nThis corresponds to the maximum rate of Entropy production, which is considered\nto be the basic physical principle underlying the damage.\n"}
+{"abstract": "  We provide some basic and sensible definitions of different types of digital\ntwins and recommendations on when and how to use them. Following up on our\nrecent publication of the Learning Causal Digital Twin, this article reports on\na stochastic formulation and solution of the problem. Structural Vector\nAutoregressive Model (SVAR) for Causal estimation is recast as a state-space\nmodel. Kalman filter (and smoother) is then employed to estimate causal factors\nin a system of connected machine bearings. The previous neural network\nalgorithm and Kalman Smoother produced very similar results; however, Kalman\nFilter/Smoother may show better performance for noisy data from industrial IoT\nsources.\n"}
+{"abstract": "  We propose a design pattern for tackling text ranking problems, dubbed\n\"Expando-Mono-Duo\", that has been empirically validated for a number of ad hoc\nretrieval tasks in different domains. At the core, our design relies on\npretrained sequence-to-sequence models within a standard multi-stage ranking\narchitecture. \"Expando\" refers to the use of document expansion techniques to\nenrich keyword representations of texts prior to inverted indexing. \"Mono\" and\n\"Duo\" refer to components in a reranking pipeline based on a pointwise model\nand a pairwise model that rerank initial candidates retrieved using keyword\nsearch. We present experimental results from the MS MARCO passage and document\nranking tasks, the TREC 2020 Deep Learning Track, and the TREC-COVID challenge\nthat validate our design. In all these tasks, we achieve effectiveness that is\nat or near the state of the art, in some cases using a zero-shot approach that\ndoes not exploit any training data from the target task. To support\nreplicability, implementations of our design pattern are open-sourced in the\nPyserini IR toolkit and PyGaggle neural reranking library.\n"}
+{"abstract": "  We investigate the cyclotron dynamics of Bose-Einstein condensate (BEC) in a\nquadruple-well potential with synthetic gauge fields. We use laser-assisted\ntunneling to generate large tunable effective magnetic fields for BEC. The mean\nposition of BEC follows an orbit that simulated the cyclotron orbits of charged\nparticles in a magnetic field. In the absence of atomic interaction, atom\ndynamics may exhibit periodic or quasi-periodic cyclotron orbits. In the\npresence of atomic interaction, the system may exhibit self-trapping, which\ndepends on synthetic gauge fields and atomic interaction strength. In\nparticular, the competition between synthetic gauge fields and atomic\ninteraction leads to the generation of several discontinuous parameter windows\nfor the transition to self-trapping, which is obviously different from that\nwithout synthetic gauge fields.\n"}
+{"abstract": "  Training machine learning (ML) models typically involves expensive iterative\noptimization. Once the model's final parameters are released, there is\ncurrently no mechanism for the entity which trained the model to prove that\nthese parameters were indeed the result of this optimization procedure. Such a\nmechanism would support security of ML applications in several ways. For\ninstance, it would simplify ownership resolution when multiple parties contest\nownership of a specific model. It would also facilitate the distributed\ntraining across untrusted workers where Byzantine workers might otherwise mount\na denial-of-service by returning incorrect model updates.\n  In this paper, we remediate this problem by introducing the concept of\nproof-of-learning in ML. Inspired by research on both proof-of-work and\nverified computations, we observe how a seminal training algorithm, stochastic\ngradient descent, accumulates secret information due to its stochasticity. This\nproduces a natural construction for a proof-of-learning which demonstrates that\na party has expended the compute require to obtain a set of model parameters\ncorrectly. In particular, our analyses and experiments show that an adversary\nseeking to illegitimately manufacture a proof-of-learning needs to perform *at\nleast* as much work than is needed for gradient descent itself.\n  We also instantiate a concrete proof-of-learning mechanism in both of the\nscenarios described above. In model ownership resolution, it protects the\nintellectual property of models released publicly. In distributed training, it\npreserves availability of the training procedure. Our empirical evaluation\nvalidates that our proof-of-learning mechanism is robust to variance induced by\nthe hardware (ML accelerators) and software stacks.\n"}
+{"abstract": "  Modern data analytics take advantage of ensemble learning and transfer\nlearning approaches to tackle some of the most relevant issues in data\nanalysis, such as lack of labeled data to use to train the analysis models,\nsparsity of the information, and unbalanced distributions of the records.\nNonetheless, when applied to multimodal datasets (i.e., datasets acquired by\nmeans of multiple sensing techniques or strategies), the state-of-theart\nmethods for ensemble learning and transfer learning might show some\nlimitations. In fact, in multimodal data analysis, not all observations would\nshow the same level of reliability or information quality, nor an homogeneous\ndistribution of errors and uncertainties. This condition might undermine the\nclassic assumptions ensemble learning and transfer learning methods rely on. In\nthis work, we propose an adaptive approach for dimensionality reduction to\novercome this issue. By means of a graph theory-based approach, the most\nrelevant features across variable size subsets of the considered datasets are\nidentified. This information is then used to set-up ensemble learning and\ntransfer learning architectures. We test our approach on multimodal datasets\nacquired in diverse research fields (remote sensing, brain-computer interfaces,\nphotovoltaic energy). Experimental results show the validity and the robustness\nof our approach, able to outperform state-of-the-art techniques.\n"}
+{"abstract": "  Sonorant sounds are characterized by regions with prominent formant\nstructure, high energy and high degree of periodicity. In this work, the\nvocal-tract system, excitation source and suprasegmental features derived from\nthe speech signal are analyzed to measure the sonority information present in\neach of them. Vocal-tract system information is extracted from the Hilbert\nenvelope of numerator of group delay function. It is derived from zero time\nwindowed speech signal that provides better resolution of the formants. A\nfive-dimensional feature set is computed from the estimated formants to measure\nthe prominence of the spectral peaks. A feature representing strength of\nexcitation is derived from the Hilbert envelope of linear prediction residual,\nwhich represents the source information. Correlation of speech over ten\nconsecutive pitch periods is used as the suprasegmental feature representing\nperiodicity information. The combination of evidences from the three different\naspects of speech provides better discrimination among different sonorant\nclasses, compared to the baseline MFCC features. The usefulness of the proposed\nsonority feature is demonstrated in the tasks of phoneme recognition and\nsonorant classification.\n"}
+{"abstract": "  We introduce an algorithm for a search of extremal fractal curves in large\ncurve classes. It heavily uses SAT-solvers~ -- heuristic algorithms that find\nmodels for CNF boolean formulas. Our algorithm was implemented and applied to\nthe search of fractal surjective curves $\\gamma\\colon[0,1]\\to[0,1]^d$ with\nminimal dilation $$\n\\sup_{t_1<t_2}\\frac{\\|\\gamma(t_2)-\\gamma(t_1)\\|^d}{t_2-t_1}. $$\n  We report new results of that search in the case of Euclidean norm. We have\nfound a new curve that we call \"YE\", a self-similar (monofractal) plane curve\nof genus $5\\times 5$ with dilation $5\\frac{43}{73}=5.5890\\ldots$. In dimension\n$3$ we have found facet-gated bifractals (that we call \"Spring\") of genus\n$2\\times2\\times 2$ with dilation $<17$. In dimension $4$ we obtained that there\nis a curve with dilation $<62$.\n  Some lower bounds on the dilation for wider classes of cubically decomposable\ncurves are proved.\n"}
+{"abstract": "  Graph neural networks (GNNs) have been proposed for a wide range of\ngraph-related learning tasks. In particular, in recent years, an increasing\nnumber of GNN systems were applied to predict molecular properties. However, a\ndirect impediment is to select appropriate hyperparameters to achieve\nsatisfactory performance with lower computational cost. Meanwhile, many\nmolecular datasets are far smaller than many other datasets in typical deep\nlearning applications. Most hyperparameter optimization (HPO) methods have not\nbeen explored in terms of their efficiencies on such small datasets in the\nmolecular domain. In this paper, we conducted a theoretical analysis of common\nand specific features for two state-of-the-art and popular algorithms for HPO:\nTPE and CMA-ES, and we compared them with random search (RS), which is used as\na baseline. Experimental studies are carried out on several benchmarks in\nMoleculeNet, from different perspectives to investigate the impact of RS, TPE,\nand CMA-ES on HPO of GNNs for molecular property prediction. In our\nexperiments, we concluded that RS, TPE, and CMA-ES have their individual\nadvantages in tackling different specific molecular problems. Finally, we\nbelieve our work will motivate further research on GNN as applied to molecular\nmachine learning problems in chemistry and materials sciences.\n"}
+{"abstract": "  We study the moments $M_k(T;\\alpha) = \\int_T^{2T} |\\zeta(s,\\alpha)|^{2k}\\,dt$\nof the Hurwitz zeta function $\\zeta(s,\\alpha)$ on the critical line, $s = 1/2 +\nit$ with a rational shift $\\alpha \\in \\mathbb Q$. We conjecture, in analogy\nwith the Riemann zeta function, that $M_k(T;\\alpha) \\sim c_k(\\alpha) T (\\log\nT)^{k^2}$ . Using heuristics from analytic number theory and random matrix\ntheory, we conjecturally compute $c_k(\\alpha)$. In the process, we investigate\nmoments of products of Dirichlet $L$-functions on the critical line. We prove\nour conjectures for the cases $k = 1,2$.\n"}
+{"abstract": "  We theoretically investigate the phase and voltage correlation dynamics,\nwhich includes both the deterministic contribution and stochastic fluctuations,\nunder a current noise generated by a resistor including thermal and quantum\nfluctuations in a resistively and capacitively shunted Josephson junction. An\nexternal current is found to shift and intensify the deterministic\ncontributions in phase and voltage. In addition to effects of external current,\nwe observe the relaxation of autocorrelation functions of phase and voltage,\nwhich includes the variances due to the current noise, to finite values in the\nlong-time limit. In particular, we find that the asymptotic correlations depend\non the resistance as a consequence of quantum effects. We also find an earlier\ndecay of coherence at a higher temperature in which thermal fluctuations\ndominate over quantum ones. These theoretical predictions can be tested in the\nnext future experiments.\n"}
+{"abstract": "  In this paper, we consider downlink low Earth orbit (LEO) satellite\ncommunication systems where multiple LEO satellites are uniformly distributed\nover a sphere at a certain altitude according to a homogeneous binomial point\nprocess (BPP). Based on the characteristics of the BPP, we analyze the distance\ndistributions and the distribution cases for the serving satellite. We\nanalytically derive the exact outage probability, and the approximated\nexpression is obtained using the Poisson limit theorem. With these derived\nexpressions, the system throughput maximization problem is formulated under the\nsatellite-visibility and outage constraints. To solve this problem, we\nreformulate it with bounded feasible sets and propose an iterative algorithm to\nobtain near-optimal solutions. Simulation results perfectly match the derived\nexact expressions for the outage probability and system throughput. The\nanalytical results of the approximated expressions are fairly close to those of\nthe exact ones. It is also shown that the proposed algorithm for the throughput\nmaximization is very close to the optimal performance obtained by a\ntwo-dimensional exhaustive search.\n"}
+{"abstract": "  We study the problem of performing automated experiment design for drug\nscreening through Bayesian inference and optimisation. In particular, we\ncompare and contrast the behaviour of linear-Gaussian models and Gaussian\nprocesses, when used in conjunction with upper confidence bound algorithms,\nThompson sampling, or bounded horizon tree search. We show that non-myopic\nsophisticated exploration techniques using sparse tree search have a distinct\nadvantage over methods such as Thompson sampling or upper confidence bounds in\nthis setting. We demonstrate the significant superiority of the approach over\nexisting and synthetic datasets of drug toxicity.\n"}
+{"abstract": "  Neural network architectures are achieving superhuman performance on an\nexpanding range of tasks. To effectively and safely deploy these systems, their\ndecision-making must be understandable to a wide range of stakeholders. Methods\nto explain AI have been proposed to answer this challenge, but a lack of theory\nimpedes the development of systematic abstractions which are necessary for\ncumulative knowledge gains. We propose Bayesian Teaching as a framework for\nunifying explainable AI (XAI) by integrating machine learning and human\nlearning. Bayesian Teaching formalizes explanation as a communication act of an\nexplainer to shift the beliefs of an explainee. This formalization decomposes\nany XAI method into four components: (1) the inference to be explained, (2) the\nexplanatory medium, (3) the explainee model, and (4) the explainer model. The\nabstraction afforded by Bayesian Teaching to decompose any XAI method\nelucidates the invariances among them. The decomposition of XAI systems enables\nmodular validation, as each of the first three components listed can be tested\nsemi-independently. This decomposition also promotes generalization through\nrecombination of components from different XAI systems, which facilitates the\ngeneration of novel variants. These new variants need not be evaluated one by\none provided that each component has been validated, leading to an exponential\ndecrease in development time. Finally, by making the goal of explanation\nexplicit, Bayesian Teaching helps developers to assess how suitable an XAI\nsystem is for its intended real-world use case. Thus, Bayesian Teaching\nprovides a theoretical framework that encourages systematic, scientific\ninvestigation of XAI.\n"}
+{"abstract": "  The superconformal index of the $\\mathcal{N}=4$ $SU(N)$ supersymmetric\nYang-Mills theory counts the 1/16-BPS states in this theory, and has been used\nvia the AdS/CFT correspondence to count black hole microstates of 1/16-BPS\nblack holes. On one hand, this index may be related to the Euclidean partition\nfunction of the theory on $S^3\\times S^1$ with complex chemical potentials,\nwhich maps by the AdS/CFT correspondence to a sum over Euclidean gravity\nsolutions. On the other hand, the index may be expressed as a sum over\nsolutions to Bethe Ansatz Equations (BAEs). We show that the known solutions to\nthe BAEs that have a good large $N$ limit, for the case of equal chemical\npotentials for the two angular momenta, have a one-to-one mapping to (complex)\nEuclidean black hole solutions on the gravity side. This mapping captures both\nthe leading contribution from the classical gravity action (of order $N^2$), as\nwell as non-perturbative corrections in $1/N$, which on the gravity side are\nrelated to wrapped D3-branes. Some of the BA solutions map to orbifolds of the\nstandard Euclidean black hole solutions (that obey exactly the same boundary\nconditions as the other solutions). A priori there are many more gravitational\nsolutions than Bethe Ansatz solutions, but we show that by considering the\nnon-perturbative effects, the extra solutions are ruled out, leading to a\nprecise match between the solutions on both sides.\n"}
+{"abstract": "  Crowdsourced testing is increasingly dominant in mobile application (app)\ntesting, but it is a great burden for app developers to inspect the incredible\nnumber of test reports. Many researches have been proposed to deal with test\nreports based only on texts or additionally simple image features. However, in\nmobile app testing, texts contained in test reports are condensed and the\ninformation is inadequate. Many screenshots are included as complements that\ncontain much richer information beyond texts. This trend motivates us to\nprioritize crowdsourced test reports based on a deep screenshot understanding.\n  In this paper, we present a novel crowdsourced test report prioritization\napproach, namely DeepPrior. We first represent the crowdsourced test reports\nwith a novelly introduced feature, namely DeepFeature, that includes all the\nwidgets along with their texts, coordinates, types, and even intents based on\nthe deep analysis of the app screenshots, and the textual descriptions in the\ncrowdsourced test reports. DeepFeature includes the Bug Feature, which directly\ndescribes the bugs, and the Context Feature, which depicts the thorough context\nof the bug. The similarity of the DeepFeature is used to represent the test\nreports' similarity and prioritize the crowdsourced test reports. We formally\ndefine the similarity as DeepSimilarity. We also conduct an empirical\nexperiment to evaluate the effectiveness of the proposed technique with a large\ndataset group. The results show that DeepPrior is promising, and it outperforms\nthe state-of-the-art approach with less than half the overhead.\n"}
+{"abstract": "  We introduce a notion of the \\emph{crux} of a graph $G$, measuring the order\nof a smallest dense subgraph in $G$. This simple-looking notion leads to some\ngeneralisations of known results about cycles, offering an interesting paradigm\nof `replacing average degree by crux'. In particular, we prove that\n\\emph{every} graph contains a cycle of length linear in its crux.\n  Long proved that every subgraph of a hypercube $Q^m$ (resp. discrete torus\n$C_3^m$) with average degree $d$ contains a path of length $2^{d/2}$ (resp.\n$2^{d/4}$), and conjectured that there should be a path of length $2^{d}-1$\n(resp. $3^{d/2}-1$). As a corollary of our result, together with isoperimetric\ninequalities, we close these exponential gaps giving asymptotically optimal\nbounds on long paths in hypercubes, discrete tori, and more generally Hamming\ngraphs.\n  We also consider random subgraphs of $C_4$-free graphs and hypercubes,\nproving near optimal bounds on lengths of long cycles.\n"}
+{"abstract": "  We perform a detailed computational study of the recently introduced Sombor\nindices on random graphs. Specifically, we apply Sombor indices on three models\nof random graphs: Erd\\\"os-R\\'enyi graphs, random geometric graphs, and\nbipartite random graphs. Within a statistical random matrix theory approach, we\nshow that the average values of Sombor indices, normalized to the order of the\ngraph, scale with the graph average degree. Moreover, we discuss the\napplication of average Sombor indices as complexity measures of random graphs\nand, as a consequence, we show that selected normalized Sombor indices are\nhighly correlated with the Shannon entropy of the eigenvectors of the graph\nadjacency matrix.\n"}
+{"abstract": "  It has been known that the Hosoya index of caterpillar graph can be\ncalculated as the numerator of the simple continued fraction. Recently, the\nauthor \\cite{Komatsu2020} introduces a more general graph called\ncaterpillar-bond graph and shows that its Hosoya index can be calculated as the\nnumerator of the general continued fraction.\n  In this paper, we show how the Hosoya index of the graph with non-uniform\nring structure can be calculated from the negative continued fraction. We also\ngive the relation between some radial graphs and multidimensional continued\nfractions in the sense of the Hosoya index.\n"}
+{"abstract": "  Bilingual English speakers speak English as one of their languages. Their\nEnglish is of a non-native kind, and their conversations are of a code-mixed\nfashion. The intelligibility of a bilingual text-to-speech (TTS) system for\nsuch non-native English speakers depends on a lexicon that captures the phoneme\nsequence used by non-native speakers. However, due to the lack of non-native\nEnglish lexicon, existing bilingual TTS systems employ native English lexicons\nthat are widely available, in addition to their native language lexicon. Due to\nthe inconsistency between the non-native English pronunciation in the audio and\nnative English lexicon in the text, the intelligibility of synthesized speech\nin such TTS systems is significantly reduced.\n  This paper is motivated by the knowledge that the native language of the\nspeaker highly influences non-native English pronunciation. We propose a\ngeneric approach to obtain rules based on letter to phoneme alignment to map\nnative English lexicon to their non-native version. The effectiveness of such\nmapping is studied by comparing bilingual (Indian English and Hindi) TTS\nsystems trained with and without the proposed rules. The subjective evaluation\nshows that the bilingual TTS system trained with the proposed non-native\nEnglish lexicon rules obtains a 6% absolute improvement in preference.\n"}
+{"abstract": "  We solve the equations of two-dimensional hydrodynamics describing a\ncircumbinary disk accreting onto an eccentric, equal-mass binary. We compute\nthe time rate of change of the binary semimajor axis $a$ and eccentricity $e$\nover a continuous range of eccentricities spanning $e=0$ to $e=0.9$. We find\nthat binaries with initial eccentricities $e_0 \\lesssim 0.1$ tend to $e=0$,\nwhere the binary semimajor axis expands. All others are attracted to $e \\approx\n0.4$, where the binary semimajor axis decays. The $e \\approx 0.4$ attractor is\ncaused by a rapid change in the disk response from a nearly origin-symmetric\nstate to a precessing asymmetric state. The state change causes the time rates\nof change $\\dot{a}$ and $\\dot{e}$ to steeply change sign at the same critical\neccentricity resulting in an attracting solution where $\\dot{a} = \\dot{e} = 0$.\nThis does not, however, result in a stalled, eccentric binary. The\nfinite-transition time between disk states causes the binary eccentricity to\nevolve beyond the attracting eccentricity in both directions resulting in\noscillating orbital parameters and a drift of the semimajor axis. For the\nchosen disk parameters, binaries with $e_0 \\gtrsim 0.1$ evolve toward and then\noscillate around $e \\approx 0.4$ where they shrink in semimajor axis. Because\nunequal mass binaries grow toward equal mass through preferential accretion,\nour results are applicable to a wide range of initial binary mass ratios.\nHence, these findings merit further investigations of this disk transition;\nunderstanding its dependence on disk parameters is vital for determining the\nfate of binaries undergoing orbital evolution with a circumbinary disk.\n"}
+{"abstract": "  This paper is, in a first stage, devoted to establish a\ntopological--algebraic characterization of the principal component,\n$\\mathcal{U}^0 (M)$, of the set of unitary elements, $\\mathcal{U} (M)$, in a\nunital JB$^*$-algebra $M$. We arrive to the conclusion that, as in the case of\nunital C$^*$-algebras, $$\\begin{aligned}\\mathcal{U}^0(M) &=\nM^{-1}_{\\textbf{1}}\\cap\\mathcal{U} (M) =\\left\\lbrace U_{e^{i h_n}}\\cdots\nU_{e^{i h_1}}(\\textbf{1}) \\colon \\begin{array}{c}\n  n\\in \\mathbb{N}, \\ h_j\\in M_{sa}\n  \\forall\\ 1\\leq j \\leq n\n  \\end{array}\n  \\right\\rbrace \\end{aligned}$$ is analytically arcwise connected. Our second\ngoal is to provide a complete description of the surjective isometries between\nthe principal components of two unital JB$^*$-algebras $M$ and $N$. Contrary to\nthe case of unital C$^*$-algebras, we shall deduce the existence of connected\ncomponents in $\\mathcal{U} (M)$ which are not isometric as metric spaces. We\nshall also establish necessary and sufficient conditions to guarantee that a\nsurjective isometry $\\Delta: \\mathcal{U}(M)\\to \\mathcal{U} (N)$ admits an\nextension to a surjective linear isometry between $M$ and $N$, a conclusion\nwhich is not always true. Among the consequences it is proved that $M$ and $N$\nare Jordan $^*$-isomorphic if, and only if, their principal components are\nisometric as metric spaces if, and only if, there exists a surjective isometry\n$\\Delta: \\mathcal{U}(M)\\to \\mathcal{U}(N)$ mapping the unit of $M$ to an\nelement in $\\mathcal{U}^0(N)$. These results provide an extension to the\nsetting of unital JB$^*$-algebras of the results obtained by O. Hatori for\nunital C$^*$-algebras.\n"}
+{"abstract": "  A flavour-tagged time-dependent angular analysis of $B^0_s\\to J/\\psi\\phi$\ndecays is presented where the $J/\\psi$ meson is reconstructed through its decay\nto an $e^+e^-$ pair. The analysis uses a sample of $pp$ collision data recorded\nwith the LHCb experiment at centre-of-mass energies of 7 and 8 TeV,\ncorresponding to an integrated luminosity of 3 fb$^{-1}$. The $CP$-violating\nphase and lifetime parameters of the $B^0_s$ system are measured to be\n$\\phi_s=0.00\\pm0.28\\pm0.07$ rad, $\\Delta\\Gamma_s=0.115\\pm0.045\\pm0.011$\nps$^{-1}$ and $\\Gamma_s=0.608\\pm0.018\\pm0.012$ ps$^{-1}$ where the first\nuncertainty is statistical and the second systematic. This is the first time\nthat $CP$-violating parameters are measured in the $B^0_s\\to J/\\psi\\phi$ decay\nwith an $e^+e^-$ pair in the final state. The results are consistent with\nprevious measurements in other channels and with the Standard Model\npredictions.\n"}
+{"abstract": "  We find a new contribution in wave-packet scatterings, which has been\noverlooked in the standard formulation of S-matrix. As a concrete example, we\nconsider a two-to-two scattering of light scalars $\\phi$ by another\nintermediate heavy scalar $\\Phi$, in the Gaussian wave-packet formalism:\n$\\phi\\phi\\to\\Phi\\to\\phi\\phi$. This contribution can be interpreted as an\n\"in-time-boundary effect\" of $\\Phi$ for the corresponding $\\Phi\\to\\phi\\phi$\ndecay, proposed by Ishikawa et al., with a newly found modification that would\ncure the previously observed ultraviolet divergence. We show that such an\neffect can be understood as a Stokes phenomenon in an integral over complex\nenergy plane: The number of relevant saddle points and Lefschetz thimbles\n(steepest descent paths) discretely changes depending on the configurations of\ninitial and final states in the scattering.\n"}
+{"abstract": "  In this paper we generalize a weak sequential result of \\cite{fan20182} to a\nnon-scattering solutions in dimension $d \\geq 2$. No symmetry assumptions are\nrequired for the initial data. We build on a previous result of\n\\cite{dodson20202} for one dimension.\n"}
+{"abstract": "  In this paper, we present novel secret key distribution protocols using\nlow-cost, hardware chipsets containing millions of synchronized self-powered\ntimers. The secret keys are derived based on the timers' physical dynamic\nresponses which provide security against any potential side-channel attacks,\nmalicious tampering, or snooping. Using the behavioral model of the\nself-powered timers, we first show that the key-strings derived from the timers\ncan pass the randomness test as defined by the National Institute of Standards\nand Technology (NIST) suite. The key-strings are then used in two key exchange\nprotocols which exploit elapsed time as one-way functions. The protocols\nproposed in this paper facilitate secure communication between (a) a user and a\nremote Server; and (b) two users. Using Monte-Carlo simulations, we investigate\nthe scalability and the security of these protocols against different\nadversarial attacks. We also investigate the robustness of these protocols in\nthe presence of real-world operating conditions and we investigate the use of\nerror-correcting codes to mitigate noise-related artifacts.\n"}
+{"abstract": "  Simulation environments are good for learning different driving tasks like\nlane changing, parking or handling intersections etc. in an abstract manner.\nHowever, these simulation environments often restrict themselves to operate\nunder conservative interaction behavior amongst different vehicles. But, as we\nknow, real driving tasks often involve very high risk scenarios where other\ndrivers often don't behave in the expected sense. There can be many reasons for\nthis behavior like being tired or inexperienced. The simulation environment\ndoesn't take this information into account while training the navigation agent.\nTherefore, in this study we especially focus on systematically creating these\nrisk prone scenarios with heavy traffic and unexpected random behavior for\ncreating better model-free learning agents. We generate multiple autonomous\ndriving scenarios by creating new custom Markov Decision Process (MDP)\nenvironment iterations in the highway-env simulation package. The behavior\npolicy is learnt by agents trained with the help from deep reinforcement\nlearning models. Our behavior policy is deliberated to handle collisions and\nrisky randomized driver behavior. We train model free learning agents with\nsupplement information of risk prone driving scenarios and compare their\nperformance with baseline agents. Finally, we casually measure the impact of\nadding these perturbations in the training process to precisely account for the\nperformance improvement obtained from utilizing the learnings from these\nscenarios.\n"}
+{"abstract": "  Recently, adversarial attack methods have been developed to challenge the\nrobustness of machine learning models. However, mainstream evaluation criteria\nexperience limitations, even yielding discrepancies among results under\ndifferent settings. By examining various attack algorithms, including\ngradient-based and query-based attacks, we notice the lack of a consensus on a\nuniform standard for unbiased performance evaluation. Accordingly, we propose a\nPiece-wise Sampling Curving (PSC) toolkit to effectively address the\naforementioned discrepancy, by generating a comprehensive comparison among\nadversaries in a given range. In addition, the PSC toolkit offers options for\nbalancing the computational cost and evaluation effectiveness. Experimental\nresults demonstrate our PSC toolkit presents comprehensive comparisons of\nattack algorithms, significantly reducing discrepancies in practice.\n"}
+{"abstract": "  In this paper, we propose a Bayesian switching dynamical model for\nsegmentation of 3D pose data over time that uncovers interpretable patterns in\nthe data and is generative. Our model decomposes highly correlated skeleton\ndata into a set of few spatial basis of switching temporal processes in a\nlow-dimensional latent framework. We parameterize these temporal processes with\nregard to a switching deep vector autoregressive prior in order to accommodate\nboth multimodal and higher-order nonlinear inter-dependencies. This results in\na dynamical deep generative latent model that parses the meaningful intrinsic\nstates in the dynamics of 3D pose data using approximate variational inference,\nand enables a realistic low-level dynamical generation and segmentation of\ncomplex skeleton movements. Our experiments on four biological motion data\ncontaining bat flight, salsa dance, walking, and golf datasets substantiate\nsuperior performance of our model in comparison with the state-of-the-art\nmethods.\n"}
+{"abstract": "  In this paper, we continue the investigation of topological properties of\nunbounded norm (un-)topology in normed lattices. We characterize separability\nand second countability of un-topology in terms of properties of the underlying\nnormed lattice. We apply our results to prove that an order continuous Banach\nfunction space $X$ over a semi-finite measure space is separable if and only if\nit has a $\\sigma$-finite carrier and is separable with respect to the topology\nof local convergence in measure. We also address the question when a normed\nlattice is a normal space with respect to the un-topology.\n"}
+{"abstract": "  Reconfigurable intelligent surfaces (RISs) are considered as potential\ntechnologies for the upcoming sixth-generation (6G) wireless communication\nsystem. Various benefits brought by deploying one or multiple RISs include\nincreased spectrum and energy efficiency, enhanced connectivity, extended\ncommunication coverage, reduced complexity at transceivers, and even improved\nlocalization accuracy. However, to unleash their full potential, fundamentals\nrelated to RISs, ranging from physical-layer (PHY) modelling to RIS phase\ncontrol, need to be addressed thoroughly. In this paper, we provide an overview\nof some timely research problems related to the RIS technology, i.e., PHY\nmodelling (including also physics), channel estimation, potential RIS\narchitectures, and RIS phase control (via both model-based and data-driven\napproaches), along with recent numerical results. We envision that more efforts\nwill be devoted towards intelligent wireless environments, enabled by RISs.\n"}
+{"abstract": "  Deep packet inspection (DPI) has been extensively investigated in\nsoftware-defined networking (SDN) as complicated attacks may intractably inject\nmalicious payloads in the packets. Existing proprietary pattern-based or\nport-based third-party DPI tools can suffer from limitations in efficiently\nprocessing a large volume of data traffic. In this paper, a novel\nOpenFlow-enabled deep packet inspection (OFDPI) approach is proposed based on\nthe SDN paradigm to provide adaptive and efficient packet inspection. First,\nOFDPI prescribes an early detection at the flow-level granularity by checking\nthe IP addresses of each new flow via OpenFlow protocols. Then, OFDPI allows\nfor deep packet inspection at the packet-level granularity: (i) for unencrypted\npackets, OFDPI extracts the features of accessible payloads, including tri-gram\nfrequency based on Term Frequency and Inverted Document Frequency (TF-IDF) and\nlinguistic features. These features are concatenated into a sparse matrix\nrepresentation and are then applied to train a binary classifier with logistic\nregression rather than matching with specific pattern combinations. In order to\nbalance the detection accuracy and performance bottleneck of the SDN\ncontroller, OFDPI introduces an adaptive packet sampling window based on the\nlinear prediction; and (ii) for encrypted packets, OFDPI extracts notable\nfeatures of packets and then trains a binary classifier with a decision tree,\ninstead of decrypting the encrypted traffic to weaken user privacy. A prototype\nof OFDPI is implemented on the Ryu SDN controller and the Mininet platform. The\nperformance and the overhead of the proposed sulotion are assessed using the\nreal-world datasets through experiments. The numerical results indicate that\nOFDPI can provide a significant improvement in detection accuracy with\nacceptable overheads.\n"}
+{"abstract": "  Visual context provides grounding information for multimodal machine\ntranslation (MMT). However, previous MMT models and probing studies on visual\nfeatures suggest that visual information is less explored in MMT as it is often\nredundant to textual information. In this paper, we propose an object-level\nvisual context modeling framework (OVC) to efficiently capture and explore\nvisual information for multimodal machine translation. With detected objects,\nthe proposed OVC encourages MMT to ground translation on desirable visual\nobjects by masking irrelevant objects in the visual modality. We equip the\nproposed with an additional object-masking loss to achieve this goal. The\nobject-masking loss is estimated according to the similarity between masked\nobjects and the source texts so as to encourage masking source-irrelevant\nobjects. Additionally, in order to generate vision-consistent target words, we\nfurther propose a vision-weighted translation loss for OVC. Experiments on MMT\ndatasets demonstrate that the proposed OVC model outperforms state-of-the-art\nMMT models and analyses show that masking irrelevant objects helps grounding in\nMMT.\n"}
+{"abstract": "  Abstractive summarization is the task of compressing a long document into a\ncoherent short document while retaining salient information. Modern abstractive\nsummarization methods are based on deep neural networks which often require\nlarge training datasets. Since collecting summarization datasets is an\nexpensive and time-consuming task, practical industrial settings are usually\nlow-resource. In this paper, we study a challenging low-resource setting of\nsummarizing long legal briefs with an average source document length of 4268\nwords and only 120 available (document, summary) pairs. To account for data\nscarcity, we used a modern pretrained abstractive summarizer BART (Lewis et\nal., 2020), which only achieves 17.9 ROUGE-L as it struggles with long\ndocuments. We thus attempt to compress these long documents by identifying\nsalient sentences in the source which best ground the summary, using a novel\nalgorithm based on GPT-2 (Radford et al., 2019) language model perplexity\nscores, that operates within the low resource regime. On feeding the compressed\ndocuments to BART, we observe a 6.0 ROUGE-L improvement. Our method also beats\nseveral competitive salience detection baselines. Furthermore, the identified\nsalient sentences tend to agree with an independent human labeling by domain\nexperts.\n"}
+{"abstract": "  The face-centered cubic (fcc) antiferromagnet is a typical example of a\ngeometrically frustrated system in three dimensions: the classical\nnearest-neighbor fcc Heisenberg model exhibits a ground state manifold with\nlarge accidental degeneracy. Here, we study the most general nearest- and\nnext-nearest-neighbor exchange model allowed by the space-group symmetry of the\nnoncentrosymmetric half-Heusler compounds, which includes a variety of\nanisotropic coupling terms. We show that, while the so-called Kitaev and\nsymmetric off-diagonal ($\\Gamma$) interactions do not fully lift the accidental\nground state degeneracy of the isotropic $J_1$-$J_2$ model,\nDzyaloshinskii-Moriya interactions which are allowed in the half-Heuslers\nunambiguously select a noncollinear ground state. We then discuss the role of\nother effects in the ground state selection, such as quantum fluctuations and\nthe coupling to a magnetic field, and show that these effects can stabilize\nnoncoplanar (triple-q) orders. These results suggest that some half-Heusler\nantiferromagnets might host rare noncollinear/noncoplanar orders, which may in\nturn affect the transport properties of these semimetals.\n"}
+{"abstract": "  We investigate the effect of 2-beam coupling in different imaging geometries\nin generating intensity-difference squeezing from four-wave mixing (4WM) in Rb\natomic vapors. A recently-introduced dual-seeding technique can cancel out the\nclassical noise in a seeded four-wave mixing process. This dual-seeding\ntechnique, however, can introduce new complications that involve 2-beam\ncoupling between different seeded spatial modes in the atomic vapor and can\nruin squeezing at frequencies on the order of the atomic linewidth and below.\nThis complicates some forms of quantum imaging using these systems. Here we\nshow that seeding the 4WM process with skew rays can eliminate the excess noise\ncaused by 2-beam coupling. To avoid 2-beam coupling in bright, seeded images,\nit is important to re-image the object in the gain medium, instead of focussing\nthrough it.\n"}
+{"abstract": "  Given their location on the Hertzsprung-Russell (HR) diagram, thoroughly\ncharacterized subgiant stars can place stringent constraints on a wide range of\nastrophysical problems. Accordingly, they are prime asteroseismic targets for\nthe Transiting Exoplanet Survey Satellite (TESS) mission. In this work, we\ninfer stellar properties for a sample of 347 subgiants located in the TESS\nContinuous Viewing Zones (CVZs), which we select based on their likelihood of\nshowing asteroseismic oscillations. We investigate how well they can be\ncharacterized using classical constraints (photometry, astrometry), and\nvalidate our results using spectroscopic values. We derive luminosities,\neffective temperatures, and radii with mean 1$\\sigma$ random (systematic)\nuncertainties of 4.5% (2%), 33 K (60 K), and 2.2% (2%), as well as more\nmodel-dependent quantities such as surface gravities, masses, and ages. We use\nour sample to demonstrate that subgiants are ideal targets for mass and age\ndetermination based on HR diagram location alone, discuss the advantages of\nstellar parameters derived from a detailed characterization over widely\navailable catalogs, show that the generally used 3D extinction maps tend to\noverestimate the extinction for nearby stars (distance $\\lesssim$ 500 pc), and\nfind a correlation that supports the rotation-activity connection in post main\nsequence stars. The complementary roles played by classical and asteroseismic\ndata sets will open a window to unprecedented astrophysical studies using\nsubgiant stars.\n"}
+{"abstract": "  We generalize Nakajima-Yoshioka's blowup formula to calculate the partition\nfunctions counting the spectrum of bound states to half-BPS Wilson loop\noperators in 5d (and 6d) supersymmetric field theories. The partition function\nin the presence of a Wilson loop operator on the $\\Omega$-background is\nfactorized when put on the blowup $\\hat{\\mathbb{C}}^2$ into two Wilson loop\npartition functions under the localization. This structure provides a set of\nblowup equations for Wilson loop operators. We explain how to formulate the\nblowup equations and solve them to compute the partition functions of Wilson\nloop operators. We test this idea by explicitly calculating the Wilson loop\npartition functions in various 5d/6d field theories and comparing them against\nknown results and expected dualities.\n"}
+{"abstract": "  An effective field theory of composite Dirac fermions was proposed by Son\n[Phys. Rev. X 5, 031027 (2015)] as a theory of the half-filled Landau level\nwith explicit particle-hole symmetry. We compute the electromagnetic response\nof this Son-Dirac theory on the level of the random phase approximation (RPA),\nwhere we pay particular attention to the effect of an additional\ncomposite-fermion dipole term that is needed to restore Galilean invariance. We\nfind that once this dipole correction is taken into account, spurious interband\ntransitions and collective modes that are present in the response of the\nunmodified theory either cancel or are strongly suppressed. We demonstrate that\nthis gives rise to a consistent theory of the half-filled Landau level valid at\nall frequencies, at least to leading order in the momentum. In addition, the\ndipole contribution modifies the Fermi-liquid response at small frequency and\nmomentum, which is a prediction of the Son-Dirac theory within the RPA that\ndistinguishes it from a separate description of the half-filled Landau level by\nHalperin, Lee, and Read within the RPA.\n"}
+{"abstract": "  Score-based generative models (SGMs) have recently demonstrated impressive\nresults in terms of both sample quality and distribution coverage. However,\nthey are usually applied directly in data space and often require thousands of\nnetwork evaluations for sampling. Here, we propose the Latent Score-based\nGenerative Model (LSGM), a novel approach that trains SGMs in a latent space,\nrelying on the variational autoencoder framework. Moving from data to latent\nspace allows us to train more expressive generative models, apply SGMs to\nnon-continuous data, and learn smoother SGMs in a smaller space, resulting in\nfewer network evaluations and faster sampling. To enable training LSGMs\nend-to-end in a scalable and stable manner, we (i) introduce a new\nscore-matching objective suitable to the LSGM setting, (ii) propose a novel\nparameterization of the score function that allows SGM to focus on the mismatch\nof the target distribution with respect to a simple Normal one, and (iii)\nanalytically derive multiple techniques for variance reduction of the training\nobjective. LSGM obtains a state-of-the-art FID score of 2.10 on CIFAR-10,\noutperforming all existing generative results on this dataset. On\nCelebA-HQ-256, LSGM is on a par with previous SGMs in sample quality while\noutperforming them in sampling time by two orders of magnitude. In modeling\nbinary images, LSGM achieves state-of-the-art likelihood on the binarized\nOMNIGLOT dataset. Our project page and code can be found at\nhttps://nvlabs.github.io/LSGM .\n"}
+{"abstract": "  This paper proposes an efficient and robust algorithm to estimate target\ntrajectories with unknown target detection profiles and clutter rates using\nmeasurements from multiple sensors. In particular, we propose to combine the\nmulti-sensor Generalized Labeled Multi-Bernoulli (MS-GLMB) filter to estimate\ntarget trajectories and robust Cardinalized Probability Hypothesis Density\n(CPHD) filters to estimate the clutter rates. The target detection probability\nis augmented to the filtering state space for joint estimation. Experimental\nresults show that the proposed robust filter exhibits near-optimal performance\nin the sense that it is comparable to the optimal MS-GLMB operating with true\nclutter rate and detection probability. More importantly, it outperforms other\nstudied filters when the detection profile and clutter rate are unknown and\ntime-variant. This is attributed to the ability of the robust filter to learn\nthe background parameters on-the-fly.\n"}
+{"abstract": "  Viscous vortex layers subject to a more general uniform strain are\nconsidered. They include Townsend's steady solution for plane strain\n(corresponding to a parameter $a = 1$) in which all the strain in the plane of\nthe layer goes toward vorticity stretching, as well as Migdal's recent steady\nasymmetric solution for axisymmetric strain ($a = 1/2$) in which half of the\nstrain goes into vorticity stretching. In addition to considering asymmetric,\nsymmetric and antisymmetric steady solutions $\\forall a \\ge 0$, it is shown\nthat for $a < 1$, i.e., anything less than the Townsend case, the vorticity\ninherently decays in time: only boundary conditions that maintain a supply of\nvorticity at one or both ends lead to a non-zero steady state. For the\nsuper-Townsend case $a > 1$, steady states have a sheath of opposite sign\nvorticity. Comparison is made with homogeneous-isotropic turbulence in which\ncase the average vorticity in the strain eigenframe is layer-like, has wings of\nopposite vorticity, and the strain configuration is found to be super-Townsend.\nOnly zero-integral perturbations of the $a > 1$ steady solutions are stable;\notherwise, the solution grows. Finally, the appendix shows that the average\nflow in the strain eigenframe is (apart from an extra term) the\nReynolds-averaged Navier-Stokes equation.\n"}
+{"abstract": "  It is known that, for any positive non-square integer multiplier $k$, there\nis an infinity of multiples of triangular numbers which are triangular numbers.\nWe analyze the congruence properties of the indices $\\xi$ of triangular numbers\nthat are multiples of other triangular numbers. We show that the remainders in\nthe congruence relations of $\\xi$ modulo k come always in pairs whose sum\nalways equal $\\left(k-1\\right)$, always include 0 and $\\left(k-1\\right)$, and\nonly 0 and $\\left(k-1\\right)$ if $k$ is prime, or an odd power of a prime, or\nan even square plus one or an odd square minus one or minus two. If the\nmultiplier $k$ is twice the triangular number of $n$, the set of remainders\nincludes also $n$ and $\\left(n^{2}-1\\right)$ and if $k$ has integer factors,\nthe set of remainders include multiples of a factor following certain rules.\nFinally, algebraic expressions are found for remainders in function of $k$ and\nits factors. Several exceptions are noticed and superseding rules exist between\nvarious rules and expressions of remainders. This approach allows to eliminate\nin numerical searches those $\\left(k-\\upsilon\\right)$ values of $\\xi_{i}$ that\nare known not to provide solutions, where $\\upsilon$ is the even number of\nremainders. The gain is typically in the order of $k/\\upsilon$, with\n$\\upsilon\\ll k$ for large values of $k$.\n"}
+{"abstract": "  We present a deterministic $(1+\\varepsilon)$-approximate maximum matching\nalgorithm in $\\mathsf{poly} 1/\\varepsilon$ passes in the semi-streaming model,\nsolving the long-standing open problem of breaking the exponential barrier in\nthe dependence on $1/\\varepsilon$. Our algorithm exponentially improves on the\nwell-known randomized $(1/\\varepsilon)^{O(1/\\varepsilon)}$-pass algorithm from\nthe seminal work by McGregor~[APPROX05], the recent deterministic algorithm by\nTirodkar with the same pass complexity~[FSTTCS18]. Up to polynomial factors in\n$1/\\varepsilon$, our work matches the state-of-the-art deterministic $(\\log n /\n\\log \\log n) \\cdot (1/\\varepsilon)$-pass algorithm by Ahn and Guha~[TOPC18],\nthat is allowed a dependence on the number of nodes $n$. Our result also makes\nprogress on the Open Problem 60 at sublinear.info.\n  Moreover, we design a general framework that simulates our approach for the\nstreaming setting in other models of computation. This framework requires\naccess to an algorithm computing a maximal matching and an algorithm for\nprocessing disjoint $(\\mathsf{poly} 1 / \\varepsilon)$-size connected\ncomponents. Instantiating our framework in $\\mathsf{CONGEST}$ yields a\n$\\mathsf{poly}(\\log{n}, 1/\\varepsilon)$ round algorithm for computing\n$(1+\\varepsilon$)-approximate maximum matching. In terms of the dependence on\n$1/\\varepsilon$, this result improves exponentially state-of-the-art result by\nLotker, Patt-Shamir, and Pettie~[LPSP15]. Our framework leads to the same\nquality of improvement in the context of the Massively Parallel Computation\nmodel as well.\n"}
+{"abstract": "  We develop a new algorithm for non-convex stochastic optimization that finds\nan $\\epsilon$-critical point in the optimal $O(\\epsilon^{-3})$ stochastic\ngradient and Hessian-vector product computations. Our algorithm uses\nHessian-vector products to \"correct\" a bias term in the momentum of SGD with\nmomentum. This leads to better gradient estimates in a manner analogous to\nvariance reduction methods. In contrast to prior work, we do not require\nexcessively large batch sizes, and are able to provide an adaptive algorithm\nwhose convergence rate automatically improves with decreasing variance in the\ngradient estimates. We validate our results on a variety of large-scale deep\nlearning architectures and benchmarks tasks.\n"}
+{"abstract": "  Three-dimensional shell-like structures can be obtained spontaneously at the\nmicroscale from the self-folding of 2D templates of rigid panels. At least for\nsimple structures, the motion of each panel is consistent with a Brownian\nprocess and folding occurs through a sequence of binding events, where pairs of\npanels meet at a specific closing angle. Here, we propose a lattice model to\ndescribe the dynamics of self-folding. As an example, we study the folding of a\npyramid of N lateral faces. We combine analytical and numerical Monte Carlo\nsimulations to find how the folding time depends on the number of faces,\nclosing angle, and initial configuration. Implications for the study of more\ncomplex structures are discussed.\n"}
+{"abstract": "  This study presents a new risk-averse multi-stage stochastic\nepidemics-ventilator-logistics compartmental model to address the resource\nallocation challenges of mitigating COVID-19. This epidemiological logistics\nmodel involves the uncertainty of untested asymptomatic infections and\nincorporates short-term human migration. Disease transmission is also\nforecasted through a new formulation of transmission rates that evolve over\nspace and time with respect to various non-pharmaceutical interventions, such\nas wearing masks, social distancing, and lockdown. The proposed multi-stage\nstochastic model overviews different scenarios on the number of asymptomatic\nindividuals while optimizing the distribution of resources, such as\nventilators, to minimize the total expected number of newly infected and\ndeceased people. The Conditional Value at Risk (CVaR) is also incorporated into\nthe multi-stage mean-risk model to allow for a trade-off between the weighted\nexpected loss due to the outbreak and the expected risks associated with\nexperiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk\nepidemics-ventilator-logistics model to the case of controlling the COVID-19 in\nhighly-impacted counties of New York and New Jersey. We calibrate, validate,\nand test our model using actual infection, population, and migration data. The\nresults indicate that short-term migration influences the transmission of the\ndisease significantly. The optimal number of ventilators allocated to each\nregion depends on various factors, including the number of initial infections,\ndisease transmission rates, initial ICU capacity, the population of a\ngeographical location, and the availability of ventilator supply. Our\ndata-driven modeling framework can be adapted to study the disease transmission\ndynamics and logistics of other similar epidemics and pandemics.\n"}
+{"abstract": "  In this paper we study the non-relativistic dynamic of a charged particle in\nthe electromagnetic field induced by a periodically time dependent current J\nalong an infinitely long and infinitely thin straight wire. The motions are\ndescribed by the Lorentz-Newton equation, in which the electromagnetic field is\nobtained by solving the Maxwell's equations with the current distribution J as\ndata. We prove that many features of the integrable time independent case are\npreserved. More precisely, introducing cylindrical coordinates, we prove the\nexistence of (non-resonant) radially periodic motions that are also of twist\ntype. In particular, these solutions are Lyapunov stable and accumulated by\nsubharmonic and quasiperiodic motions.\n"}
+{"abstract": "  Operator inference learns low-dimensional dynamical-system models with\npolynomial nonlinear terms from trajectories of high-dimensional physical\nsystems (non-intrusive model reduction). This work focuses on the large class\nof physical systems that can be well described by models with quadratic\nnonlinear terms and proposes a regularizer for operator inference that induces\na stability bias onto quadratic models. The proposed regularizer is physics\ninformed in the sense that it penalizes quadratic terms with large norms and so\nexplicitly leverages the quadratic model form that is given by the underlying\nphysics. This means that the proposed approach judiciously learns from data and\nphysical insights combined, rather than from either data or physics alone.\nAdditionally, a formulation of operator inference is proposed that enforces\nmodel constraints for preserving structure such as symmetry and definiteness in\nthe linear terms. Numerical results demonstrate that models learned with\noperator inference and the proposed regularizer and structure preservation are\naccurate and stable even in cases where using no regularization or Tikhonov\nregularization leads to models that are unstable.\n"}
+{"abstract": "  The coronavirus disease (COVID-19) pandemic affected our lives deeply, just\nlike everyone else, the children also suffered from the restrictions due to\nCOVID-19 affecting their education and social interactions with others, being\nrestricted from play areas and schools for a long time. Although social robots\nprovide a promising solution to support children in their education, healthcare\nand social interaction with others, the precautions due to COVID-19 also\nintroduced new constraints in the social robotics research. In this paper, we\nwill discuss the benefits and challenges encountered in child-robot interaction\ndue to COVID-19 based on two user studies. The first study involves children\nwith hearing disabilities, and Pepper humanoid robot to support their\naudiometry tests. The second study includes the child-sized humanoid robot\nKaspar and interaction games with children with autism spectrum disorder (ASD).\n"}
+{"abstract": "  Standard optical potentials use off-resonant laser standing wave induced\nAC-Stark shift. In a recent development [Phys. Rev. Lett. {\\bf 117}, 233001\n(2016)] a three-level scheme in $\\Lambda$ configuration coupled coherently by\nresonant laser fields was introduced leading to an effective lattice with\nsubwavelength potential peaks. Here as an extension of that work to a four\nlevel atomic setup in the tripod configuration is used to create spin\n$1/2$-like two-dimensional dark-space with 1D motion and the presence of\nexternal gauge fields. Most interestingly for a possible application, the\nlifetime for a dark subspace motion is up to two orders of magnitude larger\nthan for a similar $\\Lambda$ system. The model is quite flexible leading to\nlattices with significant nearest, next-nearest, or next-next-nearest hopping\nrates, $J_1,J_2,J_3$ opening up new intriguing possibilities to study, e.g.\nfrustrated systems. The characteristic Wannier functions lead also to new type\nof inter-site interactions not realizable in typical optical lattices.\n"}
+{"abstract": "  In this paper we consider well-posedness properties of vector optimization\nproblems with objective function $f: X \\to Y$ where $X$ and $Y$ are Banach\nspaces and $Y$ is partially ordered by a closed convex pointed cone with\nnonempty interior. The vector well-posedness notion considered in this paper is\nthe one due to Dentcheva and Helbig, which is a natural extension of Tykhonov\nwell-posedness for scalar optimization problems. When a scalar optimization\nproblem is considered it is possible to prove that under some assumptions the\nset of functions for which the related optimzation problem is well-posed is\ndense or even more in \"big\" i.e. contains a dense $G_{\\delta}$ set (these\nresults are called genericity results). The aim of this paper is to extend\nthese genericity results to vector optimization problems.\n"}
+{"abstract": "  Self-duality is a very important concept in the study and applications of\ntopological solitons in many areas of Physics. The rich mathematical structures\nunderlying it lead, in many cases, to the development of exact and\nnon-perturbative methods. We present a generalization of the Yang-Mills-Higgs\nsystem by the introduction of scalar fields assembled in a symmetric and\ninvertible matrix h of the same dimension as the gauge group. The coupling of\nsuch new fields to the gauge and Higgs fields is made by replacing the Killing\nform, in the contraction of the group indices, by the matrix h in the kinetic\nterm for the gauge fields, and by its inverse in the Higgs field kinetic term.\nThe theory is conformally invariant in the three dimensional space R^3. An\nimportant aspect of the model is that for practically all configurations of the\ngauge and Higgs fields the new scalar fields adjust themselves to solve the\nmodified self-duality equations. We construct solutions using a spherically\nsymmetric ans\\\"atz and show that the 't Hooft-Polyakov monopole becomes a\nself-dual solution of such modified Yang-Mills-Higgs system. We use an ans\\\"atz\nbased on the conformal symmetry to construct vacuum solutions presenting\nnon-trivial toroidal magnetic fields.\n"}
+{"abstract": "  Based on the resource theory for quantifying the coherence of quantum\nchannels, we introduce a new coherence quantifier for quantum channels via\nmaximum relative entropy. We prove that the maximum relative entropy for\ncoherence of quantum channels is directly related to the maximally coherent\nchannels under a particular class of superoperations, which results in an\noperational interpretation of the maximum relative entropy for coherence of\nquantum channels. We also introduce the conception of sub-superchannels and\nsub-superchannel discrimination. For any quantum channels, we show that the\nadvantage of quantum channels in sub-superchannel discrimination can be exactly\ncharacterized by the maximum relative entropy of coherence for quantum\nchannels. Similar to the maximum relative entropy of coherence for channels,\nthe robustness of coherence for quantum channels has also been investigated. We\nshow that the maximum relative entropy of coherence for channels provides new\noperational interpretations of robustness of coherence for quantum channels and\nillustrates the equivalence of the dephasing-covariant superchannels,\nincoherent superchannels, and strictly incoherent superchannels in these two\noperational tasks.\n"}
+{"abstract": "  In this paper, we consider the problem of joint beam selection and link\nactivation across a set of communication pairs to effectively control the\ninterference between communication pairs via inactivating part communication\npairs in ultra-dense device-to-device (D2D) mmWave communication networks. The\nresulting optimization problem is formulated as an integer programming problem\nthat is nonconvex and NP-hard. Consequently, the global optimal solution, even\nthe local optimal solution, cannot be generally obtained. To overcome this\nchallenge, this paper resorts to design a deep learning architecture based on\ngraph neural network to finish the joint beam selection and link activation,\nwith taking the network topology information into account. Meanwhile, we\npresent an unsupervised Lagrangian dual learning framework to train the\nparameters of the GBLinks model. Numerical results show that the proposed\nGBLinks model can converges to a stable point with the number of iterations\nincreases, in terms of the weighted sum rate. Furthermore, the GBLinks model\ncan reach near-optimal solution through comparing with the exhaustive search\nscheme in small-scale ultra-dense D2D mmWave communication networks and\noutperforms GreedyNoSched and the SCA-based method. It also shows that the\nGBLinks model can generalize to varying densities and coverage regions of\nultra-dense D2D mmWave communication networks.\n"}
+{"abstract": "  Retinal degenerative diseases cause profound visual impairment in more than\n10 million people worldwide, and retinal prostheses are being developed to\nrestore vision to these individuals. Analogous to cochlear implants, these\ndevices electrically stimulate surviving retinal cells to evoke visual percepts\n(phosphenes). However, the quality of current prosthetic vision is still\nrudimentary. Rather than aiming to restore \"natural\" vision, there is potential\nmerit in borrowing state-of-the-art computer vision algorithms as image\nprocessing techniques to maximize the usefulness of prosthetic vision. Here we\ncombine deep learning--based scene simplification strategies with a\npsychophysically validated computational model of the retina to generate\nrealistic predictions of simulated prosthetic vision, and measure their ability\nto support scene understanding of sighted subjects (virtual patients) in a\nvariety of outdoor scenarios. We show that object segmentation may better\nsupport scene understanding than models based on visual saliency and monocular\ndepth estimation. In addition, we highlight the importance of basing\ntheoretical predictions on biologically realistic models of phosphene shape.\nOverall, this work has the potential to drastically improve the utility of\nprosthetic vision for people blinded from retinal degenerative diseases.\n"}
+{"abstract": "  This paper considers the problem of unsupervised person re-identification\n(re-ID), which aims to learn discriminative models with unlabeled data. One\npopular method is to obtain pseudo-label by clustering and use them to optimize\nthe model. Although this kind of approach has shown promising accuracy, it is\nhampered by 1) noisy labels produced by clustering and 2) feature variations\ncaused by camera shift. The former will lead to incorrect optimization and thus\nhinders the model accuracy. The latter will result in assigning the intra-class\nsamples of different cameras to different pseudo-label, making the model\nsensitive to camera variations. In this paper, we propose a unified framework\nto solve both problems. Concretely, we propose a Dynamic and Symmetric\nCross-Entropy loss (DSCE) to deal with noisy samples and a camera-aware\nmeta-learning algorithm (MetaCam) to adapt camera shift. DSCE can alleviate the\nnegative effects of noisy samples and accommodate the change of clusters after\neach clustering step. MetaCam simulates cross-camera constraint by splitting\nthe training data into meta-train and meta-test based on camera IDs. With the\ninteracted gradient from meta-train and meta-test, the model is enforced to\nlearn camera-invariant features. Extensive experiments on three re-ID\nbenchmarks show the effectiveness and the complementary of the proposed DSCE\nand MetaCam. Our method outperforms the state-of-the-art methods on both fully\nunsupervised re-ID and unsupervised domain adaptive re-ID.\n"}
+{"abstract": "  Objective: The objective of this study is to develop a deep learning pipeline\nto detect signals on dietary supplement-related adverse events (DS AEs) from\nTwitter. Material and Methods: We obtained 247,807 tweets ranging from 2012 to\n2018 that mentioned both DS and AE. We annotated biomedical entities and\nrelations on 2,000 randomly selected tweets. For the concept extraction task,\nwe compared the performance of traditional word embeddings with SVM, CRF and\nLSTM-CRF classifiers to BERT models. For the relation extraction task, we\ncompared GloVe vectors with CNN classifiers to BERT models. We chose the best\nperforming models in each task to assemble an end-to-end deep learning pipeline\nto detect DS AE signals and compared the results to the known DS AEs from a DS\nknowledge base (i.e., iDISK). Results: In both tasks, the BERT-based models\noutperformed traditional word embeddings. The best performing concept\nextraction model is the BioBERT model that can identify supplement, symptom,\nand body organ entities with F1-scores of 0.8646, 0.8497, and 0.7104,\nrespectively. The best performing relation extraction model is the BERT model\nthat can identify purpose and AE relations with F1-scores of 0.8335 and 0.7538,\nrespectively. The end-to-end pipeline was able to extract DS indication and DS\nAEs with an F1-score of 0.7459 and 0,7414, respectively. Comparing to the\niDISK, we could find both known and novel DS-AEs. Conclusion: We have\ndemonstrated the feasibility of detecting DS AE signals from Twitter with a\nBioBERT-based deep learning pipeline.\n"}
+{"abstract": "  A key aspect for the forklifts is the state-of-health (SoH) assessment to\nensure the safety and the reliability of uninterrupted power source.\nForecasting the battery SoH well is imperative to enable preventive maintenance\nand hence to reduce the costs. This paper demonstrates the capabilities of\ngradient boosting regression for predicting the SoH timeseries under\ncircumstances when there is little prior information available about the\nbatteries. We compared the gradient boosting method with light gradient\nboosting, extra trees, extreme gradient boosting, random forests, long\nshort-term memory networks and with combined convolutional neural network and\nlong short-term memory networks methods. We used multiple predictors and lagged\ntarget signal decomposition results as additional predictors and compared the\nyielded prediction results with different sets of predictors for each method.\nFor this work, we are in possession of a unique data set of 45 lithium-ion\nbattery packs with large variation in the data. The best model that we derived\nwas validated by a novel walk-forward algorithm that also calculates point-wise\nconfidence intervals for the predictions; we yielded reasonable predictions and\nconfidence intervals for the predictions. Furthermore, we verified this model\nagainst five other lithium-ion battery packs; the best model generalised to\ngreater extent to this set of battery packs. The results about the final model\nsuggest that we were able to enhance the results in respect to previously\ndeveloped models. Moreover, we further validated the model for extracting cycle\ncounts presented in our previous work with data from new forklifts; their\nbattery packs completed around 3000 cycles in a 10-year service period, which\ncorresponds to the cycle life for commercial Nickel-Cobalt-Manganese (NMC)\ncells.\n"}
+{"abstract": "  The tunneling of electrons and holes in quantum structures plays a crucial\nrole in studying the transport properties of materials and the related devices.\n8-Pmmn borophene is a new two-dimensional Dirac material, which hosts tilted\nDirac cone and chiral, anisotropic massless Dirac fermions. We develop the\ntransfer matrix method to investigate the Klein tunneling of massless fermions\nacross the smooth NP junctions and NPN junctions of 8-Pmmn borophene. Like the\nsharp NP junctions of 8-Pmmn borophene, the tilted Dirac cones induce the\noblique Klein tunneling. The angle of perfect transmission to the normal\nincidence is 20.4 degrees, a constant determined by the Hamiltonian of 8-Pmmn\nborophene. For the NPN junction, there are branches of the Klein tunneling in\nthe phase diagram. We find that the asymmetric Klein tunneling is induced by\nthe chirality and anisotropy of the carriers. Furthermore, we show the\noscillation of electrical resistance related to the Klein tunneling in the NPN\njunctions. One may analyze the pattern of electrical resistance and verify the\nexistence of asymmetric Klein tunneling experimentally.\n"}
+{"abstract": "  Numerical simulations of systems at coexistence are known to yield unstable\nfields in some regions of the density parameters, as well as inequivalence of\nensembles. The Van der Waals-like loops are attributed to effects of the\ninterface between the coexisting phases, but the question of convexity remains\nunresolved. We recover the theory developed by Hill in the 1960's, and adapt it\nto include the interface free energy. Our adapted theory is verified through\ncarefully planned simulations, and gives a thermodynamic description of the\ninterface behaviour inside the coexistence region which restores the proper\nconvexity of the thermodynamic potentials, as well as yields convergence of\ngrandcanonical and canonical results. As a bonus, our interpretation allows\ndirect calculation of surface tension in very good accordance with Onsager's\nanalytic prediction.\n"}
+{"abstract": "  We study the (characteristic) Cauchy problem for the Maxwell-Bloch equations\nof light-matter interaction via asymptotics, under assumptions that prevent the\ngeneration of solitons. Our analysis clarifies some features of the sense in\nwhich physically-motivated initial/boundary conditions are satisfied. In\nparticular, we present a proper Riemann-Hilbert problem that generates the\nunique causal solution to the Cauchy problem, that is, the solution vanishes\noutside of the light cone. Inside the light cone, we relate the leading-order\nasymptotics to self-similar solutions that satisfy a system of ordinary\ndifferential equations related to the Painlev\\'e-III (PIII) equation. We\nidentify these solutions and show that they are related to a family of PIII\nsolutions recently discovered in connection with several limiting processes\ninvolving the focusing nonlinear Schr\\\"odinger equation. We fully explain a\nresulting boundary layer phenomenon in which, even for smooth initial data (an\nincident pulse), the solution makes a sudden transition over an infinitesimally\nsmall propagation distance. At a formal level, this phenomenon has been\ndescribed by other authors in terms of the PIII self-similar solutions. We make\nthis observation precise and for the first time we relate the PIII self-similar\nsolutions to the Cauchy problem. Our analysis of the asymptotic behavior\nsatisfied by the optical field and medium density matrix reveals slow decay of\nthe optical field in one direction that is actually inconsistent with the\nsimplest version of scattering theory. Our results identify a precise generic\ncondition on an optical pulse incident on an initially-unstable medium\nsufficient for the pulse to stimulate the decay of the medium to its stable\nstate.\n"}
+{"abstract": "  The traditional adjacency matrix of a mixed graph is not symmetric in\ngeneral, hence its eigenvalues may be not real. To overcome this obstacle,\nseveral authors have recently defined and studied various Hermitian adjacency\nmatrices of digraphs or mixed graphs. The usual idea is to choose a complex\nnumber $\\alpha \\in \\mathbb{C}$ with $\\vert \\alpha\\vert=1$. The traditional\nadjacency matrix is modified such that the value $\\alpha$ (resp.\\ $\\bar\\alpha$)\nreplaces the previous value at position $(i,j)$ whenever the only edge between\nthe vertices $i$ and $j$ is an arc from $i$ to $j$ (resp.\\ from $j$ to $i$). So\nfar, the choices $\\alpha=i$ and $\\alpha=e^{\\frac{\\pi}{3}i}$ have attracted\nparticular interest. It has even been argued that the latter choice might be\nideal, in a certain sense. The present paper extends and generalizes previous\nresults and, moreover, proposes another particular choice for $\\alpha$ that\nappears just as favourable, if not more.\n"}
+{"abstract": "  Microbiome studies have recently transitioned from experimental designs with\na few hundred samples to designs spanning tens of thousands of samples. Modern\nstudies such as the Earth Microbiome Project (EMP) afford the statistics\ncrucial for untangling the many factors that influence microbial community\ncomposition. Analyzing those data used to require access to a compute cluster,\nmaking it both expensive and inconvenient. We show that recent improvements in\nboth hardware and software now allow to compute key bioinformatics tasks on\nEMP-sized data in minutes using a gaming-class laptop, enabling much faster and\nbroader microbiome science insights.\n"}
+{"abstract": "  The rising need for hybrid physical platforms has triggered a renewed\ninterest for the development of agile radio-frequency phononic circuits with\ncomplex functionalities. The combination of travelling waves with resonant\nmechanical elements appears as an appealing means of harnessing elastic\nvibration. In this work, we demonstrate that this combination can be further\nenriched by the occurrence of elastic non-linearities induced travelling\nsurface acoustic waves (SAW) interacting with a pair of otherwise linear\nmicron-scale mechanical resonators. Reducing the resonator gap distance and\nincreasing the SAW amplitude results in a frequency softening of the resonator\npair response that lies outside the usual picture of geometrical Duffing\nnon-linearities. The dynamics of the SAW excitation scheme allows further\ncontrol of the resonator motion, notably leading to circular polarization\nstates. These results paves the way towards versatile high-frequency\nphononic-MEMS/NEMS circuits fitting both classical and quantum technologies.\n"}
+{"abstract": "  Entangled photon spectroscopy is a nascent field that has important\nimplications for measurement and imaging across chemical, biology, and\nmaterials fields. Entangled photon spectroscopy potentially offers improved\nspatial and temporal-frequency resolutions, increased cross sections for\nmultiphoton and nonlinear measurements, and new abilities in inducing or\nmeasuring quantum correlations. A critical step in enabling entangled photon\nspectroscopies is the creation of high-flux entangled sources that can use\nconventional detectors, as well as provide redundancy for the losses in\nrealistic samples. Here, we report a periodically poled, chirped, lithium\ntantalate platform that generates entangled photon pairs with a $10^{-7}$\nefficiency. For a near watt level diode laser, this results in a near\n$\\mu$W-level flux. The single photon per mode limit that is necessary to\nmaintain non-classical photon behavior is still satisfied by distributing this\npower over up to an octave-spanning bandwidth. The spectral-temporal photon\ncorrelations are observed via a Michelson-type interferometer that measures the\nbroadband Hong-Ou-Mandel two-photon interference. A coherence time of 245 fs\nfor a 10 nm bandwidth in the collinear case and a 62 fs for a 125 nm bandwidth\nin the non-collinear case is measured using a CW pump laser, and, essentially,\ncollecting the full photon cone. We outline in detail the numerical methods\nused for designing and tailoring the entangled photons source, such as changing\ncenter wavelength or bandwidth, with the ultimate aim of increasing the\navailability of high-flux UV-Vis entangled photon sources in the optical\nspectroscopy community.\n"}
+{"abstract": "  We introduce a new approach for audio-visual speech separation. Given a\nvideo, the goal is to extract the speech associated with a face in spite of\nsimultaneous background sounds and/or other human speakers. Whereas existing\nmethods focus on learning the alignment between the speaker's lip movements and\nthe sounds they generate, we propose to leverage the speaker's face appearance\nas an additional prior to isolate the corresponding vocal qualities they are\nlikely to produce. Our approach jointly learns audio-visual speech separation\nand cross-modal speaker embeddings from unlabeled video. It yields\nstate-of-the-art results on five benchmark datasets for audio-visual speech\nseparation and enhancement, and generalizes well to challenging real-world\nvideos of diverse scenarios. Our video results and code:\nhttp://vision.cs.utexas.edu/projects/VisualVoice/.\n"}
+{"abstract": "  We propose to address the issue of sample efficiency, in Deep Convolutional\nNeural Networks (DCNN), with a semi-supervised training strategy that combines\nHebbian learning with gradient descent: all internal layers (both convolutional\nand fully connected) are pre-trained using an unsupervised approach based on\nHebbian learning, and the last fully connected layer (the classification layer)\nis trained using Stochastic Gradient Descent (SGD). In fact, as Hebbian\nlearning is an unsupervised learning method, its potential lies in the\npossibility of training the internal layers of a DCNN without labels. Only the\nfinal fully connected layer has to be trained with labeled examples.\n  We performed experiments on various object recognition datasets, in different\nregimes of sample efficiency, comparing our semi-supervised (Hebbian for\ninternal layers + SGD for the final fully connected layer) approach with\nend-to-end supervised backprop training, and with semi-supervised learning\nbased on Variational Auto-Encoder (VAE). The results show that, in regimes\nwhere the number of available labeled samples is low, our semi-supervised\napproach outperforms the other approaches in almost all the cases.\n"}
+{"abstract": "  In this paper, we analyze 10 globular clusters in order to measure their\nrotational properties by using high precision radial velocity data from the\nSDSS-IV APOGEE-2 survey. Out of the 10 clusters we were able to successfully\nmeasure the rotation speed and position angle of the rotation axis for 9\nclusters (M2, M3, M5, M12, M13, M15, M53, M92, M107). The comparison between\nour results and previous ones shows a really good agreement within our\nuncertainties. For four of the globular clusters, M3, M13, M5 and M15, we\nseparated the sample into two generation of stars using their [Al/Fe]\nabundances and examined the kinematic features of these generations separately\nfrom one another. In case of M3, we found significant difference between the\nrotational properties of first and second populations, confirming for the first\ntime the predictions of several numerical simulations from the literature. The\nother three clusters (M5, M13, M15) also show smaller deviation between the two\ngroups of stars, but those deviations are comparable to our errors.\n"}
+{"abstract": "  Spin defect centers with long quantum coherence times ($T_2$) are key\nsolid-state platforms for a variety of quantum applications. Recently, cluster\ncorrelation expansion (CCE) techniques have emerged as a powerful tool to\nsimulate the $T_2$ of defect electron spins in these solid-state systems with\ngood accuracy. Here, based on CCE, we uncover an algebraic expression for $T_2$\ngeneralized for host compounds with dilute nuclear spin baths, which enables a\nquantitative and comprehensive materials exploration with a near instantaneous\nestimate of the coherence. We investigate more than 12,000 host compounds at\nnatural isotopic abundance, and find that silicon carbide (SiC), a prominent\nwidegap semiconductor for quantum applications, possesses the longest coherence\ntimes among widegap non-chalcogenides. In addition, more than 700 chalcogenides\nare shown to possess a longer $T_2$ than SiC. We suggest new potential host\ncompounds with promisingly long $T_2$ up to 47 ms, and pave the way to explore\nunprecedented functional materials for quantum applications.\n"}
+{"abstract": "  The Transiting Exoplanet Survey Satellite (TESS) is the latest observational\neffort to find exoplanets and map bright transient optical phenomena.\nSupernovae (SN) are particularly interesting as cosmological standard candles\nfor cosmological distance measures. The limiting magnitude of TESS strongly\nconstrains supernova detection to the very nearby Universe ($m \\sim$ 19,\n$z<0.05$). We explore the possibility that more distant supernovae that are\ngravitationally lensed and magnified by a foreground galaxy can be detected by\nTESS, an opportunity to measure the time delay between light paths and\nconstrain the Hubble constant independently.\n  We estimate the rate of occurrence of such systems, assuming reasonable\ndistributions of magnification, host dust attenuation and redshift. There are\napproximately 16 type Ia and 43 core-collapse SN (SNcc) expected to be\nobservable with TESS each year, which translates to 18% and 43% chance of\ndetection per year, respectively. Monitoring the largest collections of known\nstrong galaxy-galaxy lenses from Petrillo et al., this translates into 0.6% and\n1.3% chances of a SNIa and SNcc per year. The TESS all-sky detection rates are\nlower than those of the Zwicky Transient Facility (ZTF) and Vera Rubin\nObservatory. However, on the ecliptic poles, TESS performs almost as well as\nits all-sky search thanks to its continuous coverage: 2 and 4% chance of an\nobserved SN (Ia or cc) each year. These rates argue for timely processing of\nfull-frame TESS imaging to facilitate follow-up and should motivate further\nsearches for low-redshift lensing system.\n"}
+{"abstract": "  In this paper, we develop topological data analysis methods for\nclassification tasks on univariate time series. As an application, we perform\nbinary and ternary classification tasks on two public datasets that consist of\nphysiological signals collected under stress and non-stress conditions. We\naccomplish our goal by using persistent homology to engineer stable topological\nfeatures after we use a time delay embedding of the signals and perform a\nsubwindowing instead of using windows of fixed length. The combination of\nmethods we use can be applied to any univariate time series and in this\napplication allows us to reduce noise and use long window sizes without\nincurring an extra computational cost. We then use machine learning models on\nthe features we algorithmically engineered to obtain higher accuracies with\nfewer features.\n"}
+{"abstract": "  We prove the existence of semi-infinite geodesics for Brownian last-passage\npercolation (BLPP). Specifically, on a single event of probability one, there\nexist semi-infinite geodesics started from every space-time point and traveling\nin every asymptotic direction. Properties of these geodesics include uniqueness\nfor a fixed initial point and direction, non-uniqueness for fixed direction but\nrandom initial points, and coalescence of all geodesics traveling in a common,\nfixed direction. Along the way, we prove that for fixed northeast and southwest\ndirections, there almost surely exist no bi-infinite geodesics in the given\ndirections. The semi-infinite geodesics are constructed from Busemann\nfunctions. Our starting point is a result of Alberts, Rassoul-Agha and Simper\nthat established Busemann functions for fixed points and directions. Out of\nthis, we construct the global process of Busemann functions simultaneously for\nall initial points and directions, and then the family of semi-infinite\nBusemann geodesics. The uncountable space of the semi-discrete setting requires\nextra consideration and leads to new phenomena, compared to discrete models.\n"}
+{"abstract": "  The need for accurate yield estimates for viticulture is becoming more\nimportant due to increasing competition in the wine market worldwide. One of\nthe most promising methods to estimate the harvest is berry counting, as it can\nbe approached non-destructively, and its process can be automated. In this\narticle, we present a method that addresses the challenge of occluded berries\nwith leaves to obtain a more accurate estimate of the number of berries that\nwill enable a better estimate of the harvest. We use generative adversarial\nnetworks, a deep learning-based approach that generates a likely scenario\nbehind the leaves exploiting learned patterns from images with non-occluded\nberries. Our experiments show that the estimate of the number of berries after\napplying our method is closer to the manually counted reference. In contrast to\napplying a factor to the berry count, our approach better adapts to local\nconditions by directly involving the appearance of the visible berries.\nFurthermore, we show that our approach can identify which areas in the image\nshould be changed by adding new berries without explicitly requiring\ninformation about hidden areas.\n"}
+{"abstract": "  Machine learning and deep learning algorithms can be used to classify\nencrypted Internet traffic. Classification of encrypted traffic can become more\nchallenging in the presence of adversarial attacks that target the learning\nalgorithms. In this paper, we focus on investigating the effectiveness of\ndifferent evasion attacks and see how resilient machine and deep learning\nalgorithms are. Namely, we test C4.5 Decision Tree, K-Nearest Neighbor (KNN),\nArtificial Neural Network (ANN), Convolutional Neural Networks (CNN) and\nRecurrent Neural Networks (RNN). In most of our experimental results, deep\nlearning shows better resilience against the adversarial samples in comparison\nto machine learning. Whereas, the impact of the attack varies depending on the\ntype of attack.\n"}
+{"abstract": "  A 2-structure $\\sigma$ consists of a vertex set $V(\\sigma)$ and of an\nequivalence relation $\\equiv_\\sigma$ defined on $(V(\\sigma)\\times\nV(\\sigma))\\setminus\\{(v,v):v\\in V(\\sigma)\\}$. Given a 2-structure $\\sigma$, a\nsubset $M$ of $V(\\sigma)$ is a module of $\\sigma$ if for $x,y\\in M$ and $v\\in\nV(\\sigma)\\setminus M$, $(x,v)\\equiv_{\\sigma}(y,v)$ and\n$(v,x)\\equiv_{\\sigma}(v,y)$. For instance, $\\emptyset$, $V(\\sigma)$ and\n$\\{v\\}$, for $v\\in V(\\sigma)$, are modules of $\\sigma$ called trivial modules\nof $\\sigma$. A 2-structure $\\sigma$ is prime if $v(\\sigma)\\geq 3$ and all the\nmodules of $\\sigma$ are trivial. A prime 2-structure $\\sigma$ is critical if\nfor each $v\\in V(\\sigma)$, $\\sigma-v$ is not prime. A prime 2-structure\n$\\sigma$ is partially critical if there exists $X\\subsetneq V(\\sigma)$ such\nthat $\\sigma[X]$ is prime, and for each $v\\in V(\\sigma)\\setminus X$, $\\sigma-v$\nis not prime. We characterize finite or infinite partially critical\n2-structures.\n"}
+{"abstract": "  Cross-chain interaction is among different blockchains. When the number of\nblockchains increases, it is difficult for blockchains to form a single star\ntopology or a fully connected topology. Meanwhile, different from legacy\nnetworks, the propagation method is required to keep the data validity. Thus,\nthe blockchain topology and associated propagation methods are two key issues,\nwhich should be ensured during the propagation. In this paper, we first propose\nthe confirmation-based propagation method to keep the validity of the\ncross-chain data. The confirmation method is to seal the cross-chain\ntransaction synchronized from other blockchains. Second, we point out that a\nvalid topology requires blockchains to be strongly connected. With this\nrequirement, we propose several topologies, which match different connection\nscenarios. The simulation results show that the proposed methods can securely\npropagate the cross-chain data and the connection way is flexible.\n"}
+{"abstract": "  This agent-based model contributes to a theory of corporate culture in which\ncompany performance and employees' behaviour result from the interaction\nbetween financial incentives, motivational factors and endogenous social norms.\nEmployees' personal values are the main drivers of behaviour. They shape\nagents' decisions about how much of their working time to devote to individual\ntasks, cooperative, and shirking activities. The model incorporates two aspects\nof the management style, analysed both in isolation and combination: (i)\nmonitoring efforts affecting intrinsic motivation, i.e. the firm is either\ntrusting or controlling, and (ii) remuneration schemes affecting extrinsic\nmotivation, i.e. individual or group rewards. The simulations show that\nfinancial incentives can (i) lead to inefficient levels of cooperation, and\n(ii) reinforce value-driven behaviours, amplified by emergent social norms. The\ncompany achieves the highest output with a flat wage and a trusting management.\nEmployees that value self-direction highly are pivotal, since they are strongly\n(de-)motivated by the management style.\n"}
+{"abstract": "  Atomically thin two-dimensional (2D) materials can be vertically stacked with\nvan der Waals bonds, which enable interlayer coupling. In the particular case\nof transition metal dichalcogenide (TMD) bilayers, the relative direction\nbetween the two monolayers, coined as twist-angle, modifies the crystal\nsymmetry and creates a superlattice with exciting properties. Here, we\ndemonstrate an all-optical method for pixel-by-pixel mapping of the twist-angle\nwith resolution of 0.23 degrees, via polarization-resolved second harmonic\ngeneration (P-SHG) microscopy and we compare it with four-dimensional scanning\ntransmission electron microscopy (4D-STEM). It is found that the twist-angle\nimaging of WS2 bilayers, using the P-SHG technique is in excellent agreement\nwith that obtained using electron diffraction. The main advantages of the\noptical approach are that the characterization is performed on the same\nsubstrate that the device is created on and that it is three orders of\nmagnitude faster than the 4D-STEM. We envisage that the optical P-SHG imaging\ncould become the gold standard for the quality examination of TMD\nsuperlattice-based devices.\n"}
+{"abstract": "  We leverage state-of-the-art machine learning methods and a decade's worth of\narchival data from CFHT to predict observatory image quality (IQ) from\nenvironmental conditions and observatory operating parameters. Specifically, we\ndevelop accurate and interpretable models of the complex dependence between\ndata features and observed IQ for CFHT's wide-field camera, MegaCam. Our\ncontributions are several-fold. First, we collect, collate and reprocess\nseveral disparate data sets gathered by CFHT scientists. Second, we predict\nprobability distribution functions (PDFs) of IQ and achieve a mean absolute\nerror of $\\sim0.07''$ for the predicted medians. Third, we explore the\ndata-driven actuation of the 12 dome \"vents\" installed in 2013-14 to accelerate\nthe flushing of hot air from the dome. We leverage epistemic and aleatoric\nuncertainties in conjunction with probabilistic generative modeling to identify\ncandidate vent adjustments that are in-distribution (ID); for the optimal\nconfiguration for each ID sample, we predict the reduction in required\nobserving time to achieve a fixed SNR. On average, the reduction is $\\sim12\\%$.\nFinally, we rank input features by their Shapley values to identify the most\npredictive variables for each observation. Our long-term goal is to construct\nreliable and real-time models that can forecast optimal observatory operating\nparameters to optimize IQ. We can then feed such forecasts into scheduling\nprotocols and predictive maintenance routines. We anticipate that such\napproaches will become standard in automating observatory operations and\nmaintenance by the time CFHT's successor, the Maunakea Spectroscopic Explorer,\nis installed in the next decade.\n"}
+{"abstract": "  In this paper we characterise the pointwise size and regularity estimates for\nthe Dunkl Riesz transform kernel involving both the Euclidean metric and the\nDunkl metric, where the two metrics are not equivalent. We further establish a\nsuitable version of the pointwise lower bound via the Euclidean metric and then\ncharacterise boundedness of commutator of the Dunkl Riesz transform via the BMO\nspace associated with the Euclidean metric and the Dunkl measure. This shows\nthat BMO space via the Euclidean metric is the suitable one associated to the\nDunkl setting but not the one via the Dunkl metric.\n"}
+{"abstract": "  We propose a novel deep generative model based on causal convolutions for\nmulti-subject motion modeling and synthesis, which is inspired by the success\nof WaveNet in multi-subject speech synthesis. However, it is nontrivial to\nadapt WaveNet to handle high-dimensional and physically constrained motion\ndata. To this end, we add an encoder and a decoder to the WaveNet to translate\nthe motion data into features and back to the predicted motions. We also add 1D\nconvolution layers to take skeleton configuration as an input to model skeleton\nvariations across different subjects. As a result, our network can scale up\nwell to large-scale motion data sets across multiple subjects and support\nvarious applications, such as random and controllable motion synthesis, motion\ndenoising, and motion completion, in a unified way. Complex motions, such as\npunching, kicking and, kicking while punching, are also well handled. Moreover,\nour network can synthesize motions for novel skeletons not in the training\ndataset. After fine-tuning the network with a few motion data of the novel\nskeleton, it is able to capture the personalized style implied in the motion\nand generate high-quality motions for the skeleton. Thus, it has the potential\nto be used as a pre-trained network in few-shot learning for motion modeling\nand synthesis. Experimental results show that our model can effectively handle\nthe variation of skeleton configurations, and it runs fast to synthesize\ndifferent types of motions on-line. We also perform user studies to verify that\nthe quality of motions generated by our network is superior to the motions of\nstate-of-the-art human motion synthesis methods.\n"}
+{"abstract": "  As Europe is experiencing a second violent CoVid-19 storm, with the PCR-based\ntesting system deteriorating due to the high volumes of people to be tested\ndaily, there is a general reconsideration of the mathematical theories at the\nbasis of our contact tracing and testing approaches. Drawing upon the concept\nof super spreader, we propose the use of (less sensitive) rapid tests to detect\nthose secondary infections that do not need the use of PCRs, thus saving the\nmost part of PCR tests currently used. This before the system fails.\n"}
+{"abstract": "  We analyze the residual gauge freedom in gravity, in four dimensions, in the\nlight-cone gauge, in a formulation where unphysical fields are integrated out.\nBy checking the invariance of the light-cone Hamiltonian, we obtain a set of\nresidual gauge transformations, which satisfy the BMS algebra realized on the\ntwo physical fields in the theory. Hence, the BMS algebra appears as a\nconsequence of residual gauge invariance in the bulk and not just at the\nasymptotic boundary. We highlight the key features of the light-cone BMS\nalgebra and discuss its connection with the quadratic form structure of the\nHamiltonian.\n"}
+{"abstract": "  This paper investigates a Hamilton-Jacobi (HJ) analysis to solve\nfinite-horizon optimal control problems for high-dimensional systems. Although\ngrid-based methods, such as the level-set method [1], numerically solve a\ngeneral class of HJ partial differential equations, the computational\ncomplexity is exponential in the dimension of the continuous state. To manage\nthis computational complexity, methods based on Lax-Hopf theory have been\ndeveloped for the state-unconstrained optimal control problem under certain\nassumptions, such as affine dynamics and state-independent stage cost. Based on\nthe Lax formula [2], this paper proposes an HJ formula for the\nstate-constrained optimal control problem for nonlinear systems. We call this\nformula \\textit{the generalized Lax formula} for the optimal control problem.\nThe HJ formula provides both the optimal cost and an optimal control signal. We\nalso provide an efficient computational method for a class of problems for\nwhich the dynamics is affine in the state, and for which the stage and terminal\ncost, as well as the state constraints, are convex in the state. This class of\nproblems does not require affine dynamics and convex stage cost in the control.\nThis paper also provides three practical examples.\n"}
+{"abstract": "  Using the isothermal molecular dynamics (MD), coalescence/sintering of Au\nnanoparticles (NPs) was simulated by employing the Nose-Hoover thermostat. The\nMD simulation was realized by using the well-known open program LAMMPS, its\nversion for parallel calculations on GPUs. We have found that the solid NP\nsintering scenario is switched to the coalescence scenario not at the NP\nmelting temperature Tm exactly but at a lower temperature T0=0.9Tm interpreted\nas the critical temperature corresponding to a coalescence/sintering\nbifurcation phenomenon: in the temperature range from T0-2K to T0+2K the\nresulting (daughter) NPs of the same size can have either liquid-like or\ncrystalline structure after coalescence/sintering at the same fixed\ntemperature. The crystallize and liquid states were identify by analyzing the\ndegree of crystallinity and the radial distribution function. For this purpose\nwe employed the OVITO program.\n"}
+{"abstract": "  In this work we propose 3D-FFS, a novel approach to make sensor fusion based\n3D object detection networks significantly faster using a class of\ncomputationally inexpensive heuristics. Existing sensor fusion based networks\ngenerate 3D region proposals by leveraging inferences from 2D object detectors.\nHowever, as images have no depth information, these networks rely on extracting\nsemantic features of points from the entire scene to locate the object. By\nleveraging aggregated intrinsic properties (e.g. point density) of point cloud\ndata, 3D-FFS can substantially constrain the 3D search space and thereby\nsignificantly reduce training time, inference time and memory consumption\nwithout sacrificing accuracy. To demonstrate the efficacy of 3D-FFS, we have\nintegrated it with Frustum ConvNet (F-ConvNet), a prominent sensor fusion based\n3D object detection model. We assess the performance of 3D-FFS on the KITTI\ndataset. Compared to F-ConvNet, we achieve improvements in training and\ninference times by up to 62.80% and 58.96%, respectively, while reducing the\nmemory usage by up to 58.53%. Additionally, we achieve 0.36%, 0.59% and 2.19%\nimprovements in accuracy for the Car, Pedestrian and Cyclist classes,\nrespectively. 3D-FFS shows a lot of promise in domains with limited computing\npower, such as autonomous vehicles, drones and robotics where LiDAR-Camera\nbased sensor fusion perception systems are widely used.\n"}
+{"abstract": "  The peridynamic stress tensor proposed by Lehoucq and Silling (2008) is\ncumbersome to implement in numerical computations. In this note, we show that\nthe peridynamic stress tensor has the mathematical expression of a weighted\nstatic Virial stress derived by Irving and Kirkwood (1950), which offers a\nsimple and clear expression for numerical calculations of peridynamic stress\ntensor.\n"}
+{"abstract": "  Globular clusters contain a finite number of stars. As a result, they\ninevitably undergo secular evolution (`relaxation') causing their mean\ndistribution function (DF) to evolve on long timescales. On one hand, this\nlong-term evolution may be interpreted as driven by the accumulation of local\ndeflections along each star's mean field trajectory -- so-called `non-resonant\nrelaxation'. On the other hand, it can be thought of as driven by non-local,\ncollectively dressed and resonant couplings between stellar orbits, a process\ntermed `resonant relaxation'. In this paper we consider a model globular\ncluster represented by a spherical, isotropic isochrone DF, and compare in\ndetail the predictions of both resonant and non-resonant relaxation theories\nagainst tailored direct $N$-body simulations. In the space of orbital actions\n(namely the radial action and total angular momentum), we find that both\nresonant and non-resonant theories predict the correct morphology for the\nsecular evolution of the cluster's DF, although non-resonant theory\nover-estimates the amplitude of the relaxation rate by a factor ${\\sim 2}$. We\nconclude that the secular relaxation of hot isotropic spherical clusters is not\ndominated by collectively amplified large-scale potential fluctuations, despite\nthe existence of a strong ${\\ell = 1}$ damped mode. Instead, collective\namplification affects relaxation only marginally even on the largest scales.\nThe predicted contributions to relaxation from smaller scale fluctuations are\nessentially the same from resonant and non-resonant theories.\n"}
+{"abstract": "  There are many open questions surrounding the characterisation of groups with\ncontext-sensitive word problem. Only in 2018 was it shown that all finitely\ngenerated virtually Abelian groups have multiple context-free word problems,\nand it is a long-standing open question as to where to place the word problems\nof hyperbolic groups in the formal language hierarchy. In this paper, we\nintroduce a new language class called the parallel hyperedge replacement string\nlanguages, show that it contains all multiple context-free and ET0L languages,\nand lay down the foundations for future work that may be able to place the word\nproblems of many hyperbolic groups in this class.\n"}
+{"abstract": "  To encourage and guide decarbonization efforts, better tools are needed to\nmonitor real-time CO2 and criteria air pollutant emissions from electricity\nconsumption, production, imports, and exports. Using real-time data from the\nelectricity system is especially challenging for quantitative applications\nrequiring high quality and physically consistent data. Until now,\ntime-intensive, ad-hoc and manual data verification and cleaning strategies\nhave been used to prepare the data for quantitative analysis. As an alternative\nto existing techniques, here we provide a physics-informed framework to greatly\naccelerate and automate data processing to enable internally consistent\nelectric system consumption, production, import, and export data in near\nreal-time. A key component of this framework is an optimization program to\nminimize the data adjustments required to satisfy energy conservation\nequations. The effectiveness of this method is demonstrated by applying it to\nthe continental United States electricity network. The resulting\npublicly-available data set, which provides in near-real time, hourly updates\non electricity generation, consumption, imports, exports and associated\nemissions, is the first of this nature.\n"}
+{"abstract": "  Reinforcement learning (RL) is typically concerned with estimating stationary\npolicies or single-step models, leveraging the Markov property to factorize\nproblems in time. However, we can also view RL as a generic sequence modeling\nproblem, with the goal being to produce a sequence of actions that leads to a\nsequence of high rewards. Viewed in this way, it is tempting to consider\nwhether high-capacity sequence prediction models that work well in other\ndomains, such as natural-language processing, can also provide effective\nsolutions to the RL problem. To this end, we explore how RL can be tackled with\nthe tools of sequence modeling, using a Transformer architecture to model\ndistributions over trajectories and repurposing beam search as a planning\nalgorithm. Framing RL as sequence modeling problem simplifies a range of design\ndecisions, allowing us to dispense with many of the components common in\noffline RL algorithms. We demonstrate the flexibility of this approach across\nlong-horizon dynamics prediction, imitation learning, goal-conditioned RL, and\noffline RL. Further, we show that this approach can be combined with existing\nmodel-free algorithms to yield a state-of-the-art planner in sparse-reward,\nlong-horizon tasks.\n"}
+{"abstract": "  We prove that the critical point and the point $1$ have dense orbits for\nLebesgue-a.e., parameter pairs in the two-parameter skew-tent family and\ngeneralised $\\beta$-transformations. As an application, we show that for the\ngeneralised $\\beta$-transformation with the tribonacci number as slope, there\nis matching (i.e., $T^n(0)=T^n(1)$ for some $n \\geq 1$) for Lebesgue-a.e.\ntranslation parameter.\n"}
+{"abstract": "  With recent advances in data collection from multiple sources, multi-view\ndata has received significant attention. In multi-view data, each view\nrepresents a different perspective of data. Since label information is often\nexpensive to acquire, multi-view clustering has gained growing interest, which\naims to obtain better clustering solution by exploiting complementary and\nconsistent information across all views rather than only using an individual\nview. Due to inevitable sensor failures, data in each view may contain error.\nError often exhibits as noise or feature-specific corruptions or outliers.\nMulti-view data may contain any or combination of these error types. Blindly\nclustering multi-view data i.e., without considering possible error in view(s)\ncould significantly degrade the performance. The goal of error-robust\nmulti-view clustering is to obtain useful outcome even if the multi-view data\nis corrupted. Existing error-robust multi-view clustering approaches with\nexplicit error removal formulation can be structured into five broad research\ncategories - sparsity norm based approaches, graph based methods, subspace\nbased learning approaches, deep learning based methods and hybrid approaches,\nthis survey summarizes and reviews recent advances in error-robust clustering\nfor multi-view data. Finally, we highlight the challenges and provide future\nresearch opportunities.\n"}
+{"abstract": "  The $k$-measure of an integer partition was recently introduced by Andrews,\nBhattacharjee and Dastidar. In this paper, we establish trivariate generating\nfunction identities counting both the length and the $k$-measure for partitions\nand distinct partitions, respectively. The $2$-measure case for partitions\nextends a result of Andrews, Bhattacharjee and Dastidar.\n"}
+{"abstract": "  In this note, we first recall the nonconvex problem setting and introduce the\noptimal PAGE algorithm (Li et al., ICML'21). Then we provide a simple and clean\nconvergence analysis of PAGE for achieving optimal convergence rates. Moreover,\nPAGE and its analysis can be easily adopted and generalized to other works. We\nhope that this note provides the insights and is helpful for future works.\n"}
+{"abstract": "  When implementing a non-continuous controller for a cyber-physical system, it\nmay happen that the evolution of the closed-loop system is not anymore\npiecewise differentiable along the trajectory, mainly due to conditional\nstatements inside the controller. This may lead to some unwanted chattering\neffects than may damage the system. This behavior is difficult to observe even\nin simulation. In this paper, we propose an interval approach to characterize\nthe sliding surface which corresponds to the set of all states such that the\nstate trajectory may jump indefinitely between two distinct behaviors. We show\nthat the recent notion of thick sets will allows us to compute efficiently an\nouter approximation of the sliding surface of a given class of hybrid system\ntaking into account all set-membership uncertainties. An application to the\nverification of the controller of a child swing is considered to illustrate the\nprinciple of the approach.\n"}
+{"abstract": "  Underneath the ear skin there are richly branching vascular and neural\nnetworks that ultimately connecting to our heart and brain. Hence, the\nthree-dimensional (3D) mapping of auricular electrophysiological signals could\nprovide a new perspective for biomedical studies such as diagnosis of\ncardiovascular diseases and neurological disorders. However, it is still\nextremely challenging for current sensing techniques to cover the entire\nultra-curved auricle. Here, we report a graphene-based ear-conformable sensing\ndevice with embedded and distributed 3D electrodes which enable full-auricle\nphysiological monitoring. The sensing device, which incorporates programable 3D\nelectrode thread array and personalized auricular mold, has 3D-conformable\nsensing interfaces with curved auricular skin, and was developed using one-step\nmulti-material 3D-printing process. As a proof-of-concept, spatiotemporal\nauricular electrical skin resistance (AESR) mapping was demonstrated. For the\nfirst time, 3D AESR contours were generated and human subject-specific AESR\ndistributions among a population were observed. From the data of 17 volunteers,\nthe auricular region-specific AESR changes after cycling exercise were observed\nin 98% of the tests and were validated via machine learning techniques.\nCorrelations of AESR with heart rate and blood pressure were also studied using\nstatistical analysis. This 3D electronic platform and AESR-based new\nbiometrical findings show promising biomedical applications.\n"}
+{"abstract": "  We consider the classical problem of optimal portfolio construction with the\nconstraint that no short position is allowed, or equivalently the valid\nequilibria of multispecies Lotka-Volterra equations with self-regulation in the\nspecial case where the interaction matrix is of unit rank, corresponding to\nspecies competing for a common resource. We compute the average number of\nsolutions and show that its logarithm grows as $N^\\alpha$, where $N$ is the\nnumber of assets or species and $\\alpha \\leq 2/3$ depends on the interaction\nmatrix distribution. We conjecture that the most likely number of solutions is\nmuch smaller and related to the typical sparsity $m(N)$ of the solutions, which\nwe compute explicitly. We also find that the solution landscape is similar to\nthat of spin-glasses, i.e. very different configurations are quasi-degenerate.\nCorrespondingly, \"disorder chaos\" is also present in our problem. We discuss\nthe consequence of such a property for portfolio construction and ecologies,\nand question the meaning of rational decisions when there is a very large\nnumber \"satisficing\" solutions.\n"}
+{"abstract": "  In this study, we report on face-centered cubic structured CoCrFeNi\nhigh-entropy alloy thin films with finely dispersed nano-oxide particles which\nare formed by internal oxidation. Analytical scanning transmission electron\nmicroscopy imaging found that the particles are Cr2O3. The oxide particles\ncontribute to the hardening of the film increasing its hardness by 14% compared\nto that of the film without precipitates, through the Orowan-type strengthening\nmechanism. Our novel approach paves the way to design medium- and high-entropy\nalloys with high strength by making use of oxide phases.\n"}
+{"abstract": "  Intelligent reflecting surface (IRS) is deemed as a promising and\nrevolutionizing technology for future wireless communication systems owing to\nits capability to intelligently change the propagation environment and\nintroduce a new dimension into wireless communication optimization. Most\nexisting studies on IRS are based on an ideal reflection model. However, it is\ndifficult to implement an IRS which can simultaneously realize any adjustable\nphase shift for the signals with different frequencies. Therefore, the\npractical phase shift model, which can describe the difference of IRS phase\nshift responses for the signals with different frequencies, should be utilized\nin the IRS optimization for wideband and multi-band systems. In this paper, we\nconsider an IRS-assisted multi-cell multi-band system, in which different base\nstations (BSs) operate at different frequency bands. We aim to jointly design\nthe transmit beamforming of BSs and the reflection beamforming of the IRS to\nminimize the total transmit power subject to signal to interference-plus-noise\nratio (SINR) constraints of individual user and the practical IRS reflection\nmodel. With the aid of the practical phase shift model, the influence between\nthe signals with different frequencies is taken into account during the design\nof IRS. Simulation results illustrate the importance of considering the\npractical communication scenario on the IRS designs and validate the\neffectiveness of our proposed algorithm.\n"}
+{"abstract": "  In recent years, Neural Machine Translation (NMT) has achieved notable\nresults in various translation tasks. However, the word-by-word generation\nmanner determined by the autoregressive mechanism leads to high translation\nlatency of the NMT and restricts its low-latency applications.\nNon-Autoregressive Neural Machine Translation (NAT) removes the autoregressive\nmechanism and achieves significant decoding speedup through generating target\nwords independently and simultaneously. Nevertheless, NAT still takes the\nword-level cross-entropy loss as the training objective, which is not optimal\nbecause the output of NAT cannot be properly evaluated due to the multimodality\nproblem. In this article, we propose using sequence-level training objectives\nto train NAT models, which evaluate the NAT outputs as a whole and correlates\nwell with the real translation quality. Firstly, we propose training NAT models\nto optimize sequence-level evaluation metrics (e.g., BLEU) based on several\nnovel reinforcement algorithms customized for NAT, which outperforms the\nconventional method by reducing the variance of gradient estimation. Secondly,\nwe introduce a novel training objective for NAT models, which aims to minimize\nthe Bag-of-Ngrams (BoN) difference between the model output and the reference\nsentence. The BoN training objective is differentiable and can be calculated\nefficiently without doing any approximations. Finally, we apply a three-stage\ntraining strategy to combine these two methods to train the NAT model. We\nvalidate our approach on four translation tasks (WMT14 En$\\leftrightarrow$De,\nWMT16 En$\\leftrightarrow$Ro), which shows that our approach largely outperforms\nNAT baselines and achieves remarkable performance on all translation tasks. The\nsource code is available at https://github.com/ictnlp/Seq-NAT.\n"}
+{"abstract": "  In nominal mission scenarios, geostationary satellites perform end-of-life\norbit maneuvers to reach suitable disposal orbits, where they do not interfere\nwith operational satellites. This research investigates the long-term orbit\nevolution of decommissioned geostationary satellite under the assumption that\nthe disposal maneuver does not occur and the orbit evolves with no control. The\ndynamical model accounts for all the relevant harmonics of the gravity field at\nthe altitude of geostationary orbits, as well as solar radiation pressure and\nthird-body perturbations caused by the Moon and the Sun. Orbit propagations are\nperformed using two algorithms based on different equations of motion and\nnumerical integration methods: (i) Gauss planetary equations for modified\nequinoctial elements with a Runge-Kutta numerical integration scheme based on\n8-7th-order Dorman and Prince formulas; (ii) Cartesian state equations of\nmotion in an Earth-fixed frame with a Runge-Kutta Fehlberg 7/8 integration\nscheme. The numerical results exhibit excellent agreement over integration\ntimes of decades. Some well-known phenomena emerge, such as the longitudinal\ndrift due to the resonance between the orbital motion and Earth's rotation,\nattributable to the J22 term of the geopotential. In addition, the third-body\nperturbation due to Sun and Moon causes two major effects: (a) a precession of\nthe orbital plane, and (b) complex longitudinal dynamics. This study proposes\nan analytical approach for the prediction of the precessional motion and shows\nits agreement with the orbit evolution obtained numerically. Moreover,\nlong-term orbit propagations show that the above mentioned complex longitudinal\ndynamics persists over time scales of several decades. Frequent and\nunpredictable migrations toward different longitude regions occur, in contrast\nwith the known effects due only to the J22 perturbation.\n"}
+{"abstract": "  We consider a stochastic bandit problem with countably many arms that belong\nto a finite set of types, each characterized by a unique mean reward. In\naddition, there is a fixed distribution over types which sets the proportion of\neach type in the population of arms. The decision maker is oblivious to the\ntype of any arm and to the aforementioned distribution over types, but\nperfectly knows the total number of types occurring in the population of arms.\nWe propose a fully adaptive online learning algorithm that achieves O(log n)\ndistribution-dependent expected cumulative regret after any number of plays n,\nand show that this order of regret is best possible. The analysis of our\nalgorithm relies on newly discovered concentration and convergence properties\nof optimism-based policies like UCB in finite-armed bandit problems with \"zero\ngap,\" which may be of independent interest.\n"}
+{"abstract": "  Educational datamining involves the application of datamining techniques to\nstudent activity. However, in the context of computer programming, many\ndatamining techniques can not be applied because they expect vector-shaped\ninput whereas computer programs have the form of syntax trees. In this paper,\nwe present ast2vec, a neural network that maps Python syntax trees to vectors\nand back, thereby facilitating datamining on computer programs as well as the\ninterpretation of datamining results. Ast2vec has been trained on almost half a\nmillion programs of novice programmers and is designed to be applied across\nlearning tasks without re-training, meaning that users can apply it without any\nneed for (additional) deep learning. We demonstrate the generality of ast2vec\nin three settings: First, we provide example analyses using ast2vec on a\nclassroom-sized dataset, involving visualization, student motion analysis,\nclustering, and outlier detection, including two novel analyses, namely a\nprogress-variance-projection and a dynamical systems analysis. Second, we\nconsider the ability of ast2vec to recover the original syntax tree from its\nvector representation on the training data and two further large-scale\nprogramming datasets. Finally, we evaluate the predictive capability of a\nsimple linear regression on top of ast2vec, obtaining similar results to\ntechniques that work directly on syntax trees. We hope ast2vec can augment the\neducational datamining toolbelt by making analyses of computer programs easier,\nricher, and more efficient.\n"}
+{"abstract": "  Cooling down a trapped ion into its motional ground state is a central step\nfor trapped ions based quantum information processing. State of the art cooling\nschemes often work under a set of optimal cooling conditions derived\nanalytically using a perturbative approach, in which the sideband coupling is\nassumed to be the weakest of all the relevant transitions. As a result the\ncooling rate is severely limited. Here we propose to use quantum control\ntechnique powered with automatic differentiation to speed up the classical\ncooling schemes. We demonstrate the efficacy of our approach by applying it to\nfind the optimal cooling conditions for classical sideband cooling and\nelectromagnetically induced transparency cooling schemes, which are in general\nbeyond the weak sideband coupling regime. Based on those numerically found\noptimal cooling conditions, we show that faster cooling can be achieved while\nat the same time a low average phonon occupation can be retained.\n"}
+{"abstract": "  Let $f$ be an operator convex function on $(0,\\infty)$, and $\\Phi$ be a\nunital positive linear maps on $B(H)$.\n  we give a complementary inequality to\n  Davis-Choi-Jensen's inequality as follows \\begin{equation*} f(\\Phi(A))\\geq\n\\frac{4R(A,B)}{(1+R(A,B))^2}\\Phi(f(A)), \\end{equation*} where $R(A,\nB)=\\max\\{r(A^{-1}B) ,r(B^{-1}A)\\}$ and $r(A)$ is the spectral radius of $A$. We\ninvestigate the complementary inequalities related to the operator power means\nand the Karcher means via unital positive linear maps,\n  and obtain the following result: If $A_{1}$, $A_{2}$,\\dots, $A_{n}$, are\npositive definite operators in $B(H)$, and $0<m_i\\leq A_i\\leq M_i$, then\n\\begin{equation*} \\Lambda( \\omega;\\Phi(\\mathbb{A}))\\geq\\Phi(\\Lambda( \\omega;\n\\mathbb{A}))\\geq \\frac{4\\hbar}{(1+\\hbar)^2}~\\Lambda( \\omega;\\Phi(\\mathbb{A})),\n\\end{equation*} where $\\hbar= \\max\\limits_{1\\leq i\\leq n} \\frac{M_i}{m_i}$.\nFinally, we prove that if $G(A_1,\\dots,A_n)$ is the generalized geometric mean\ndefined by Ando-Li-Mathias for $n$ positive definite operators, then\n  \\begin{align*}\n\\Phi(G(A_1,\\dots,A_n))\\geq\\left(\\frac{2h^\\frac{1}{2}}{1+h}\\right)^{n-1}G(\\Phi(A_1),\\dots,\\Phi(A_n)),\n\\end{align*} where $h=\\max\\limits_{1\\leq i,j\\leq n} R(A_i, A_j)$.\n"}
+{"abstract": "  Differential privacy is a formal, mathematical definition of data privacy\nthat has gained traction in academia, industry, and government. The task of\ncorrectly constructing differentially private algorithms is non-trivial, and\nmistakes have been made in foundational algorithms. Currently, there is no\nautomated support for converting an existing, non-private program into a\ndifferentially private version. In this paper, we propose a technique for\nautomatically learning an accurate and differentially private version of a\ngiven non-private program. We show how to solve this difficult program\nsynthesis problem via a combination of techniques: carefully picking\nrepresentative example inputs, reducing the problem to continuous optimization,\nand mapping the results back to symbolic expressions. We demonstrate that our\napproach is able to learn foundational algorithms from the differential privacy\nliterature and significantly outperforms natural program synthesis baselines.\n"}
+{"abstract": "  We address the issue of the suboptimality in the p-version discontinuous\nGalerkin (dG) methods for first order hyperbolic problems. The convergence rate\nis derived for the upwind dG scheme on tensor product meshes in any dimension.\nThe standard proof in seminal work [14] leads to suboptimal convergence in\nterms of the polynomial degree by 3/2 order for general convection fields, with\nthe exception of piecewise multi-linear convection fields, which rather yield\noptimal convergence. Such suboptimality is not observed numerically. Thus, it\nmight be caused by a limitation of the analysis, which we partially overcome:\nfor a special class of convection fields, we shall show that the dG method has\na p-convergence rate suboptimal by 1/2 order only.\n"}
+{"abstract": "  A new proposal is given for designing a non-volatile, completely spin logic\ndevice, that can be reprogrammed for different functional classical logical\noperations. We use the concept of bias driven spin dependent circular current\nand current induced magnetic field in a quantum ring under asymmetric\nring-to-electrode interface configuration to implement all the Boolean\noperations. We extend our idea to build two kinds of parallel computing\narchitectures for getting parallelized operations, all at a particular time.\nFor one case, different kinds of parallel operations are performed in a single\ndevice, whereas in the other type all the possible inputs of a logic gate are\nprocessed in parallel and all the outputs are read simultaneously. The\nperformance and reliability are investigated in terms of power, delay and\npower-delay-product and finally the system temperature. We find that both the\nindividual and simultaneous logic operations studied here are much superior\ncompared to the operations performed in different conventional logic families\nlike complementary metal oxide semiconductor logic, transistor-transistor\nlogic, etc. The key advantage is that we can perform several logic operations,\nas many as we wish, repeating the same or different logic gates using a single\nsetup, which indeed reduces wiring in the circuits and hence consumes much less\npower. Our analysis can be utilized to design optimized logic circuits at\nnano-scale level.\n"}
+{"abstract": "  We introduce a new causal inference framework for time series data aimed at\nassessing the effectiveness of heat alerts in reducing mortality and\nhospitalization risks. We are interested in addressing the following question:\nhow many deaths and hospitalizations could be averted if we were to increase\nthe frequency of issuing heat alerts in a given location? In the context of\ntime series data, the overlap assumption - each unit must have a positive\nprobability of receiving the treatment - is often violated. This is because, in\na given location, issuing a heat alert is a rare event on an average\ntemperature day as heat alerts are almost always issued on extremely hot days.\nTo overcome this challenge, first we introduce a new class of causal estimands\nunder a stochastic intervention (i.e., increasing the odds of issuing a heat\nalert) for a single time series corresponding to a given location. We develop\nthe theory to show that these causal estimands can be identified and estimated\nunder a weaker version of the overlap assumption. Second, we propose\nnonparametric estimators based on time-varying propensity scores, and derive\npoint-wise confidence bands for these estimators. Third, we extend this\nframework to multiple time series corresponding to multiple locations. Via\nsimulations, we show that the proposed estimator has good performance with\nrespect to bias and root mean squared error. We apply our proposed method to\nestimate the causal effects of increasing the odds of issuing heat alerts in\nreducing deaths and hospitalizations among Medicare enrollees in 2817 U.S.\ncounties. We found weak evidence of a causal link between increasing the odds\nof issuing heat alerts during the warm seasons of 2006-2016 and a reduction in\ndeaths and cause-specific hospitalizations across the 2817 counties.\n"}
+{"abstract": "  Consider the Langevin process, described by a vector (position,momentum) in\n$\\mathbb{R}^{d}\\times\\mathbb{R}^d$. Let $\\mathcal O$ be a $\\mathcal{C}^2$ open\nbounded and connected set of $\\mathbb{R}^d$. We prove the compactness of the\nsemigroup of the Langevin process absorbed at the boundary of the domain\n$D:=\\mathcal{O}\\times\\mathbb{R}^d$. We then obtain the existence of a unique\nquasi-stationary distribution (QSD) for the Langevin process on $D$. We also\nprovide a spectral interpretation of this QSD and obtain an exponential\nconvergence of the Langevin process conditioned on non-absorption towards the\nQSD.\n"}
+{"abstract": "  Spectroscopic studies of Galactic O and B stars show that many stars with\nmasses above 8 M$_{\\odot}$ are observed in the HR diagram just beyond the\nMain-Sequence (MS) band predicted by stellar models computed with a moderate\novershooting. This may be an indication that the convective core sizes in stars\nin the upper part of the HR diagram are larger than predicted by these models.\nCombining stellar evolution models and spectroscopic parameters derived for a\nlarge sample of Galactic O and B stars, including brand new information about\ntheir projected rotational velocities, we reexamine the question of the\nconvective core size in MS massive stars. We confirm that for stars more\nmassive than about 8 M$_{\\odot}$, the convective core size at the end of the MS\nphase increases more rapidly with the mass than in models computed with a\nconstant step overshoot chosen to reproduce the main sequence width in the low\nmass range (around 2 M$_{\\odot}$). This conclusion is valid for both the cases\nof non-rotating models and rotating models either with a moderate or a strong\nangular momentum transport. The increase of the convective core mass with the\nmass obtained from the TAMS position is, however, larger than the one deduced\nfrom the surface velocity drop for masses above about 15 M$_{\\odot}$. Although\nobservations available at the moment cannot decide what is the best choice\nbetween the core sizes given by the TAMS and the velocity drop, we discuss\ndifferent methods to get out of this dilemma. At the moment, comparisons with\neclipsing binaries seem to favor the solution given by the velocity drop. While\nwe confirm the need for larger convective cores at higher masses, we find\ntensions in-between different methods for stars more massive than 15\nM$_{\\odot}$. The use of single-aged stellar populations (non-interacting\nbinaries or stellar clusters) would be a great asset to resolve this tension.\n"}
+{"abstract": "  I present a 3D advancing-front mesh refinement algorithm that generates a\nconstrained Delaunay mesh for any piecewise linear complex (PLC) and extend\nthis algorithm to produce truly Delaunay meshes for any PLC. First, as in my\nrecently published 2D algorithm, I split the input line segments such that the\nlength of the subsegments is asymptotically proportional to the local feature\nsize (LFS). For each facet, I refine the mesh such that the edge lengths and\nthe radius of the circumcircle of every triangular element are asymptotically\nproportional to the LFS. Finally, I refine the volume mesh to produce a\nconstrained Delaunay mesh whose tetrahedral elements are well graded and have a\nradius-edge ratio less than some $\\omega^* > 2/\\sqrt{3}$ (except ``near'' small\ninput angles). I extend this algorithm to generate truly Delaunay meshes by\nensuring that every triangular element on a facet satisfies Gabriel's\ncondition, i.e., its diametral sphere is empty. On an ``apex'' vertex where\nmultiple facets intersect, Gabriel's condition is satisfied by a modified\nsplit-on-a-sphere (SOS) technique. On a line where multiple facets intersect,\nGabriel's condition is satisfied by mirroring meshes near the line of\nintersection. The SOS technique ensures that the triangles on a facet near the\napex vertex have angles that are proportional to the angular feature size\n(AFS), a term I define in the paper. All tetrahedra (except ``near'' small\ninput angles) are well graded and have a radius-edge ratio less than $\\omega^*\n> \\sqrt{2}$ for a truly Delaunay mesh. The upper bounds for the radius-edge\nratio are an improvement by a factor of $\\sqrt{2}$ over current\nstate-of-the-art algorithms.\n"}
+{"abstract": "  Observations that span a broad range of wavelengths are used to examine\nasymmetries in the disk of the nearby late-type spiral galaxy NGC 247. The\nnorthern spiral arm is over-luminous at all wavelengths when compared with\nother parts of the galaxy at similar galactocentric radii, while the density of\nvery luminous red stars in the void that is immediately south of this arm\nmatches that in other parts of the disk at the same galactocentric radius. Two\nbubbles with spatial extents of many kpc are identified in the disk, and many\nof the young stars in the southern disk of NGC 247 are located in the walls of\none of these structures. Dynamical age estimates of these bubbles coincide with\nthe last large-scale star formation event in the nucleus, suggesting that there\nwas large-scale star formation throughout the disk of NGC 247 a few hundred Myr\nin the past. Morphological similarities are seen with the classical lop-sided\ngalaxy NGC 4027, and it is concluded that NGC 247 is a significantly lop-sided\nspiral galaxy. The void in the northern disk is then the area between the main\nbody of the disk and the northern arm viewed in projection. The implications of\na lop-sided morphology for NGC 247 in the context of interactions with its\nnearby starburst galaxy companion NGC 253 are discussed.\n"}
+{"abstract": "  Traceability and auditability are key structures that are vital in supply\nchain management and construction. However, trust is the most important aspect\nof customers in these systems. Also, we have to rely on third parties to trade\nin centralized systems. Although current exist frameworks for these solutions\nin the supply chain, these have work poor traceability and lack of real-time\ninformation, and especially lack of privacy-preserving. In this paper, we\npropose a privacy-preserving framework for supply chain traceability that using\nsmart contracts. Development processes, model implementation, and smart\ncontracts are presented in detail and we show cryptographic techniques in these\ntechnologies to address the aforementioned. Finally, thanks to traceability and\nauditability, customers and other parties can view with a single product ID and\nalso verified with digital signature the claims of the actors in the system.\n"}
+{"abstract": "  In this paper, we show that the finite subalgebra\n$\\mathcal{A}^{\\mathbb{R}}(1)$, generated by $\\mathrm{Sq}^1$ and\n$\\mathrm{Sq}^2$, of the $\\mathbb{R}$-motivic Steenrod algebra\n$\\mathcal{A}^{\\mathbb{R}}$ can be given $128$ different\n$\\mathcal{A}^{\\mathbb{R}}$-module structures. We also show that all of these\n$\\mathcal{A}^{\\mathbb{R}}$-modules can be realized as the cohomology of a\n$2$-local finite $\\mathbb{R}$-motivic spectrum. The realization results are\nobtained using an $\\mathbb{R}$ -motivic analogue of the Toda realization\ntheorem. We notice that each realization of $\\mathcal{A}^{\\mathbb{R}}(1)$ can\nbe expressed as a cofiber of an $\\mathbb{R}$-motivic $v_1$-self-map. The\n$\\mathrm{C}_2$-equivariant analogue of the above results then follows because\nof the Betti realization functor. We identify a relationship between the\n$\\mathrm{RO}(\\mathrm{C}_2)$-graded Steenrod operations on a\n$\\mathrm{C}_2$-equivariant space and the classical Steenrod operations on both\nits underlying space and its fixed-points. This technique is then used to\nidentify the geometric fixed-point spectra of the $\\mathrm{C}_2$-equivariant\nrealizations of $\\mathcal{A}^{\\mathrm{C}_2}(1)$. We find another application of\nthe $\\mathbb{R}$-motivic Toda realization theorem: we produce an\n$\\mathbb{R}$-motivic, and consequently a $\\mathrm{C}_2$-equivariant, analogue\nof the Bhattacharya-Egger spectrum $\\mathcal{Z}$, which could be of independent\ninterest.\n"}
+{"abstract": "  We study synchronization dynamics in binary mixtures of locally coupled\nKuramoto oscillators which perform Brownian motion in a two-dimensional box. We\nintroduce two models, where in model $\\cal I$ there are two type of\noscillators, say $\\cal A$ and $\\cal B$, and any two similar oscillators tend to\nsynchronize their phases, while any two dissimilar ones tend to be out of\nphase. In model $\\cal J$, in contrast, the oscillators in subpopulation $\\cal\nA$ behave as in model $\\cal I$, while the oscillators in subpopulation $\\cal B$\ntend to be out of phase with all the others. In the real space all the\noscillators in both models interact via a soft-core repulsive potential. Both\nsubpopulations of model $\\cal I$ and subpopulation $\\cal A$ of model $\\cal J$,\nby their own, exhibit a phase coherent attractor in a certain region of model\nparameters. The approach to the attractor, after an initial transient regime,\nis exponential with some characteristic synchronization time scale $\\tau$.\nNumerical analysis reveals that the attractors of the two subpopulations\nsurvive within model $\\cal I$, regardless of the composition of the mixture\n$\\phi$ and the strength of the cross-population negative coupling constant $H$,\nand that $\\tau$ sensitively depends on $\\phi$, $H$ and the packing fraction. In\nparticular, the ability of the oscillators to move and exchange neighbours can\nsignificantly decrease $\\tau$. In contrast, model $\\cal J$ predicts suppression\nof the synchronized state in subpopulation $\\cal A$ and emergence of the\ncoherent attractor in the \"contrarians\" subpopulation $\\cal B$ for strong and\nweak cross-population coupling, respectively.\n"}
+{"abstract": "  This paper considers a networked aggregative game (NAG) where the players are\ndistributed over a communication network. By only communicating with a subset\nof players, the goal of each player in the NAG is to minimize an individual\ncost function that depends on its own action and the aggregate of all the\nplayers' actions. To this end, we design a novel distributed algorithm that\njointly exploits the ideas of the consensus algorithm and the conditional\nprojection descent. Under strongly monotone assumption on the pseudo-gradient\nmapping, the proposed algorithm with fixed step-sizes is proved to converge\nlinearly to the unique Nash equilibrium of the NAG. Then the theoretical\nresults are validated by numerical experiments.\n"}
+{"abstract": "  At the end of Silicon roadmap, keeping the leakage power in tolerable limit\nand bridging the bandwidth gap between processor and memory have become some of\nthe biggest challenges. Several promising Non-Volatile Memories (NVMs) such as,\nSpin-Transfer Torque RAM (STTRAM), Magnetic RAM (MRAM), Phase Change Memory\n(PCM), Resistive RAM (RRAM) and Ferroelectric RAM (FeRAM) are being\ninvestigated to address the above issues since they offer high density and\nconsumes zero leakage power. On one hand, the desirable properties of emerging\nNVMs make them suitable candidates for several applications including\nreplacement of conventional memories. On the other hand, their unique\ncharacteristics such as, high and asymmetric read/write current and persistence\nbring new threats to data security and privacy. Some of these memories are\nalready deployed in full systems and as discrete chips and are believed to\nbecome ubiquitous in future computing devices. Therefore, it is of utmost\nimportant to investigate their security and privacy issues. Note that these\nNVMs can be considered for cache, main memory or storage application. They are\nalso suitable to implement in-memory computation which increases system\nthroughput and eliminates Von-Neumann Bottleneck. Compute-capable NVMs impose\nnew security and privacy challenges that are fundamentally different than their\nstorage counterpart. This work identifies NVM vulnerabilities, attack vectors\noriginating from device level all the way to circuits and systems considering\nboth storage and compute applications. We also summarize the circuit/system\nlevel countermeasures to make the NVMs robust against security and privacy\nissues.\n"}
+{"abstract": "  In this paper we discuss a hybridised method for FEM-BEM coupling. The\ncoupling from both sides use a Nitsche type approach to couple to the trace\nvariable. This leads to a formulation that is robust and flexible with respect\nto approximation spaces and can easily be combined as a building block with\nother hybridised methods. Energy error norm estimates and the convergence of\nJacobi iterations are proved and the performance of the method is illustrated\non some computational examples.\n"}
+{"abstract": "  We have developed the dynamical model of a clumpy torus in an active galactic\nnucleus (AGN) and compared to recent ALMA observations. We present $N$-body\nsimulations of a torus in the field of a supermassive black hole (SMBH), made\nof up to $N=10^5$ gravitationally interacting clouds. As initial conditions, we\nchoose random distributions of the orbital elements of the clouds with a\ncut-off in the inclination to mimic the presence of wind cones produced at the\nearly AGN stage. When the torus reaches an equilibrium, it has a doughnut\nshape. We discuss the presence of box orbits. We have then constructed the\nvelocity and velocity dispersion maps using the resulting distributions of the\nclouds at equilibrium. The effects of torus inclination and cloud sizes are\nduly analyzed. We discuss the obscuration effects of the clouds using a ray\ntracing simulation matching the model maps to ALMA resolution. By comparing the\nmodel with the observational maps of NGC 1068 we find that the SMBH mass is\n$M_\\text{smbh}=5\\times 10^6 M_\\odot$ for the range of the torus inclination\nangles $45^\\circ - 60^\\circ$. We also construct the velocity dispersion maps\nfor NGC 1326 and NGC 1672. They show that the peaks in the ALMA dispersion maps\nare related to the emission of the torus throat. Finally, we obtain the\ntemperature distribution maps with parameters that correspond to our model\nvelocity maps for NGC 1068. They show stratification in temperature\ndistribution with the shape of the high temperature region as in the VLTI/MIDI\nmap.\n"}
+{"abstract": "  Intelligent reflecting surface (IRS) is an emerging technology to enhance the\nspectral and energy efficiency of wireless communications cost-effectively.\nThis letter considers a new multi-IRS aided wireless network where a cascaded\nline-of-sight (LoS) link is established between the base station (BS) and a\nremote user by leveraging the multi-hop signal reflection of selected IRSs. As\ncompared to the conventional single-/double-hop IRS system, multi-hop IRS\nsystem provides more pronounced path diversity and cooperative passive\nbeamforming gains, especially in the environment with dense obstacles. However,\na more challenging joint active/passive beamforming and multi-hop beam routing\nproblem also arises for maximizing the end-to-end channel gain. Furthermore,\nthe number of IRS-associated channel coefficients increases drastically with\nthe number of IRS hops. To tackle the above issues, in this letter we propose a\nnew and efficient beam training based solution by considering the use of\npractical codebook-based BS/IRS active/passive beamforming without the need of\nexplicit channel estimation. Instead of exhaustively or sequentially searching\nover all combinations of active and passive beam patterns for each beam route,\na distributed beam training scheme is proposed to reduce the complexity, by\nexploiting the (nearly) time-invariant BS-IRS and inter-IRS channels and the\ncooperative training among the BS and IRSs' controllers. Simulation results\nshow that our proposed design achieves the end-to-end channel gain close to\nthat of the sequential beam search, but at a much lower training overhead and\ncomplexity.\n"}
+{"abstract": "  Anomaly detection systems need to consider a lot of information when scanning\nfor anomalies. One example is the context of the process in which an anomaly\nmight occur, because anomalies for one process might not be anomalies for a\ndifferent one. Therefore data -- such as system events -- need to be assigned\nto the program they originate from. This paper investigates whether it is\npossible to infer from a list of system events the program whose behavior\ncaused the occurrence of these system events. To that end, we model transition\nprobabilities between non-equivalent events and apply the $k$-nearest neighbors\nalgorithm. This system is evaluated on non-malicious, real-world data using\nfour different evaluation scores. Our results suggest that the approach\nproposed in this paper is capable of correctly inferring program names from\nsystem events.\n"}
+{"abstract": "  In an idealized infinite crystal, the material properties are constrained by\nthe symmetries of its unit cell. Naturally, the point-group symmetry is broken\nby the sample shape of any finite crystal, yet this is commonly unobservable in\nmacroscopic metals. To sense the shape-induced symmetry lowering in such\nmetals, long-lived bulk states originating from anisotropic Fermi surfaces are\nneeded. Here we show how strongly facetted Fermi surfaces and long\nquasiparticle mean free paths present in microstructures of PdCoO2 yield an\nin-plane resistivity anisotropy that is forbidden by symmetry on an infinite\nhexagonal lattice. Bar shaped transport devices narrower than the mean free\npath are carved from single crystals using focused ion beam (FIB) milling, such\nthat the ballistic charge carriers at low temperatures frequently collide with\nboth sidewalls defining a channel. Two symmetry-forbidden transport signatures\nappear: the in-plane resistivity anisotropy exceeds a factor of 2, and\ntransverse voltages appear in zero magnetic field. We robustly identify the\nchannel direction as the source of symmetry breaking via ballistic Monte- Carlo\nsimulations and numerical solution of the Boltzmann equation.\n"}
+{"abstract": "  Deep neural networks with applications from computer vision and image\nprocessing to medical diagnosis are commonly implemented using clock-based\nprocessors, where computation speed is limited by the clock frequency and the\nmemory access time. Advances in photonic integrated circuits have enabled\nresearch in photonic computation, where, despite excellent features such as\nfast linear computation, no integrated photonic deep network has been\ndemonstrated to date due to the lack of scalable nonlinear functionality and\nthe loss of photonic devices, making scalability to a large number of layers\nchallenging. Here we report the first integrated end-to-end photonic deep\nneural network (PDNN) that performs instantaneous image classification through\ndirect processing of optical waves. Images are formed on the input pixels and\noptical waves are coupled into nanophotonic waveguides and processed as the\nlight propagates through layers of neurons on-chip. Each neuron generates an\noptical output from input optical signals, where linear computation is\nperformed optically and the nonlinear activation function is realised\nopto-electronically. The output of a laser coupled into the chip is uniformly\ndistributed among all neurons within the network providing the same per-neuron\nsupply light. Thus, all neurons have the same optical output range enabling\nscalability to deep networks with large number of layers. The PDNN chip is used\nfor 2- and 4-class classification of handwritten letters achieving accuracies\nof higher than 93.7% and 90.3%, respectively, with a computation time less than\none clock cycle of state-of-the-art digital computation platforms. Direct\nclock-less processing of optical data eliminates photo-detection, A/D\nconversion, and the requirement for a large memory module, enabling\nsignificantly faster and more energy-efficient neural networks for the next\ngenerations of deep learning systems.\n"}
+{"abstract": "  Swing amplification is a model of spiral arm formation in disk galaxies.\nPrevious $N$-body simulations show that the epicycle phases of stars in spiral\narms are synchronized. However, the elementary process of the phase\nsynchronization is not well understood. In order to investigate phase\nsynchronization, we investigate the orbital evolution of stars due to\ngravitational scattering by a perturber under the epicycle approximation and\nits dependence on orbital elements and a disk parameter. We find that\ngravitational scattering by the perturber can cause phase synchronization of\nstellar orbits. The epicycle phases are better synchronized for smaller initial\nepicycle amplitudes of stars and larger shear rates of galactic disks. The\nvertical motion of stars does not affect the phase synchronization. The phase\nsynchronization forms trailing dense regions, which may correspond to spiral\narms.\n"}
+{"abstract": "  Causal structure discovery (CSD) models are making inroads into several\ndomains, including Earth system sciences. Their widespread adaptation is\nhowever hampered by the fact that the resulting models often do not take into\naccount the domain knowledge of the experts and that it is often necessary to\nmodify the resulting models iteratively. We present a workflow that is required\nto take this knowledge into account and to apply CSD algorithms in Earth system\nsciences. At the same time, we describe open research questions that still need\nto be addressed. We present a way to interactively modify the outputs of the\nCSD algorithms and argue that the user interaction can be modelled as a greedy\nfinding of the local maximum-a-posteriori solution of the likelihood function,\nwhich is composed of the likelihood of the causal model and the prior\ndistribution representing the knowledge of the expert user. We use a real-world\ndata set for examples constructed in collaboration with our co-authors, who are\nthe domain area experts. We show that finding maximally usable causal models in\nthe Earth system sciences or other similar domains is a difficult task which\ncontains many interesting open research questions. We argue that taking the\ndomain knowledge into account has a substantial effect on the final causal\nmodels discovered.\n"}
+{"abstract": "  An open source software package for performing dynamic RMS simulation of\nsmall to medium-sized power systems is presented, written entirely in the\nPython programming language. The main objective is to facilitate fast\nprototyping of new wide area monitoring, control and protection applications\nfor the future power system by enabling seamless integration with other tools\navailable for Python in the open source community, e.g. for signal processing,\nartificial intelligence, communication protocols etc. The focus is thus\ntransparency and expandability rather than computational efficiency and\nperformance. The main purpose of this paper, besides presenting the code and\nsome results, is to share interesting experiences with the power system\ncommunity, and thus stimulate wider use and further development. Two\ninteresting conclusions at the current stage of development are as follows:\nFirst, the simulation code is fast enough to emulate real-time simulation for\nsmall and medium-size grids with a time step of 5 ms, and allows for\ninteractive feedback from the user during the simulation. Second, the\nsimulation code can be uploaded to an online Python interpreter, edited, run\nand shared with anyone with a compatible internet browser. Based on this, we\nbelieve that the presented simulation code could be a valuable tool, both for\nresearchers in early stages of prototyping real-time applications, and in the\neducational setting, for students developing intuition for concepts and\nphenomena through real-time interaction with a running power system model.\n"}
+{"abstract": "  Global quantum networks will benefit from the reliable creation and control\nof high-performance solid-state telecom photon-spin interfaces. T radiation\ndamage centres in silicon provide a promising photon-spin interface due to\ntheir narrow O-band optical transition near 1326 nm and long-lived electron and\nnuclear spin lifetimes. To date, these defect centres have only been studied as\nensembles in bulk silicon. Here, we demonstrate the reliable creation of high\nconcentration T centre ensembles in the 220 nm device layer of\nsilicon-on-insulator (SOI) wafers by ion implantation and subsequent annealing.\nWe then develop a method that uses spin-dependent optical transitions to\nbenchmark the characteristic optical spectral diffusion within these T centre\nensembles. Using this new technique, we show that with minimal optimization to\nthe fabrication process high densities of implanted T centres localized\n$\\lesssim$100 nm from an interface display ~1 GHz characteristic levels of\ntotal spectral diffusion.\n"}
+{"abstract": "  Existing global convergence guarantees of (stochastic) gradient descent do\nnot apply to practical deep networks in the practical regime of deep learning\nbeyond the neural tangent kernel (NTK) regime. This paper proposes an\nalgorithm, which is ensured to have global convergence guarantees in the\npractical regime beyond the NTK regime, under a verifiable condition called the\nexpressivity condition. The expressivity condition is defined to be both\ndata-dependent and architecture-dependent, which is the key property that makes\nour results applicable for practical settings beyond the NTK regime. On the one\nhand, the expressivity condition is theoretically proven to hold\ndata-independently for fully-connected deep neural networks with narrow hidden\nlayers and a single wide layer. On the other hand, the expressivity condition\nis numerically shown to hold data-dependently for deep (convolutional) ResNet\nwith batch normalization with various standard image datasets. We also show\nthat the proposed algorithm has generalization performances comparable with\nthose of the heuristic algorithm, with the same hyper-parameters and total\nnumber of iterations. Therefore, the proposed algorithm can be viewed as a step\ntowards providing theoretical guarantees for deep learning in the practical\nregime.\n"}
+{"abstract": "  In the study of closed many-body quantum systems one is often interested in\nthe evolution of a subset of degrees of freedom. On many occasions it is\npossible to approach the problem by performing an appropriate decomposition\ninto a bath and a system. In the simplest case the evolution of the reduced\nstate of the system is governed by a quantum master equation with a\ntime-independent, i.e. Markovian, generator. Such evolution is typically\nemerging under the assumption of a weak coupling between the system and an\ninfinitely large bath. Here, we are interested in understanding to which extent\na neural network function approximator can predict open quantum dynamics -\ndescribed by time-local generators - from an underlying unitary dynamics. We\ninvestigate this question using a class of spin models, which is inspired by\nrecent experimental setups. We find that indeed time-local generators can be\nlearned. In certain situations they are even time-independent and allow to\nextrapolate the dynamics to unseen times. This might be useful for situations\nin which experiments or numerical simulations do not allow to capture long-time\ndynamics and for exploring thermalization occurring in closed quantum systems.\n"}
+{"abstract": "  This paper revisits the concept of damping torque in a multimachine power\nsystem and its relation to the dissipation of oscillation energy in synchronous\nmachine windings. As a multimachine extension of an existing result on a\nsingle-machine-infinite-bus (SMIB) system, we show that the total damping power\nfor a mode stemming from the interaction of electromagnetic torques and rotor\nspeeds is equal to the sum of average power dissipations in the generator\nwindings corresponding to the modal oscillation. Further, counter-intuitive to\nthe SMIB result, we demonstrate that, although the equality holds on an\naggregate, such is not the case for individual machines in an interconnected\nsystem. To that end, distribution factors are derived for expressing the\naverage damping power of each generator as a linear combination of average\npowers of modal energy dissipation in the windings of all machines in the\nsystem. These factors represent the distribution of damping power in a\nmultimachine system. The results are validated on IEEE 4-machine and 16-machine\ntest systems.\n"}
+{"abstract": "  The multi-terminal Josephson effect allows DC supercurrent to flow at finite\ncommensurate voltages. Existing proposals to realize this effect rely on\nnonlocal Andreev processes in superconductor-normal-superconductor junctions.\nHowever, this approach requires precise control over microscopic states and is\nobscured by dissipative current. We show that standard tunnel Josephson\ncircuits also support multiplet supercurrent mediated only by local tunneling\nprocesses. Furtheremore, we observe that the supercurrents persist even in the\nhigh charging energy regime in which only sequential Cooper transfers are\nallowed. Finally, we demonstrate that the multiplet supercurrent in these\ncircuits has a quantum geometric component that is distinguinshable from the\nwell-known adiabatic contribution.\n"}
+{"abstract": "  Physical property of a single quantum object is governed by its precise\natomic arrangement. The direct correlation of localized physical properties\nwith the atomic structures has been therefore strongly desired but still\nlimited in the theoretical studies. Here, we have successfully examined the\nlocalized electronic properties of individual carbon nanotubes by means of\nhigh-resolution electron energy-loss spectroscopy combined with high-resolution\ntransmission electron microscopy. Well-separated sharp peaks at the carbon\nK(1s) absorption edge and in the valence-loss spectra are obtained from a\nsingle freestanding carbon nanotube with the local chiral index and\nunambiguously identified as the transitions between the van Hove singularities.\nThe spectra features clearly vary upon the different areas even in the\nindividual carbon nanotube. Variations in interband transitions, plasmonic\nbehaviors and unoccupied electronic structures are clearly attributed to the\nlocal irregular atomic arrangement such as topological defect and/or elastic\nbond stretching.\n"}
+{"abstract": "  Over the last few decades, Lung Ultrasound (LUS) has been increasingly used\nto diagnose and monitor different lung diseases in neonates. It is a non\ninvasive tool that allows a fast bedside examination while minimally handling\nthe neonate. Acquiring a LUS scan is easy, but understanding the artifacts\nconcerned with each respiratory disease is challenging. Mixed artifact patterns\nfound in different respiratory diseases may limit LUS readability by the\noperator. While machine learning (ML), especially deep learning can assist in\nautomated analysis, simply feeding the ultrasound images to an ML model for\ndiagnosis is not enough to earn the trust of medical professionals. The\nalgorithm should output LUS features that are familiar to the operator instead.\nTherefore, in this paper we present a unique approach for extracting seven\nmeaningful LUS features that can be easily associated with a specific\npathological lung condition: Normal pleura, irregular pleura, thick pleura,\nAlines, Coalescent B-lines, Separate B-lines and Consolidations. These\nartifacts can lead to early prediction of infants developing later respiratory\ndistress symptoms. A single multi-class region proposal-based object detection\nmodel faster-RCNN (fRCNN) was trained on lower posterior lung ultrasound videos\nto detect these LUS features which are further linked to four common neonatal\ndiseases. Our results show that fRCNN surpasses single stage models such as\nRetinaNet and can successfully detect the aforementioned LUS features with a\nmean average precision of 86.4%. Instead of a fully automatic diagnosis from\nimages without any interpretability, detection of such LUS features leave the\nultimate control of diagnosis to the clinician, which can result in a more\ntrustworthy intelligent system.\n"}
+{"abstract": "  We prove that the topological complexity $\\mathrm{TC}(\\pi)$ equals\n$\\mathrm{cd}(\\pi\\times\\pi)$ for certain toral relatively hyperbolic groups\n$\\pi$.\n"}
+{"abstract": "  In this manuscript, we develop an efficient algorithm to evaluate the\nazimuthal Fourier components of the Green's function for the Helmholtz equation\nin cylindrical coordinates. A computationally efficient algorithm for this\nmodal Green's function is essential for solvers for electromagnetic scattering\nfrom bodies of revolution (e.g., radar cross sections, antennas). Current\nalgorithms to evaluate this modal Green's function become computationally\nintractable when the source and target are close or when the wavenumber is\nlarge. Furthermore, most state of the art methods cannot be easily\nparallelized. In this manuscript, we present an algorithm for evaluating the\nmodal Green's function that has performance independent of both\nsource-to-target proximity and wavenumber, and whose cost grows as $O(m)$,\nwhere $m$ is the Fourier mode. Furthermore, our algorithm is embarrassingly\nparallelizable.\n"}
+{"abstract": "  In order for off-Earth top surface structures built from regolith to protect\nastronauts from radiation, they need to be several meters thick. Technical\nUniversity Delft (TUD) proposes to excavate into the ground to create\nsubsurface habitats. By excavating not only natural protection from radiation\ncan be achieved but also thermal insulation because the temperature is more\nstable underground. At the same time through excavation valuable resources can\nbe mined for through in situ resource utilization (ISRU). The idea is that a\nswarm of autonomous mobile robots excavate the ground in a sloped downwards\nspiral movement. The excavated regolith will be mixed with cement, which can be\nreproduced on Mars through ISRU, in order to create concrete. The concrete is\n3D printed/sprayed on the excavated tunnel to reinforce it. As soon as the\ntunnels are reinforced, the material in-between the tunnels can be removed in\norder to create a larger cavity that can be used for inhabitation. Proposed\napproach relies on Design-to-Robotic-Production (D2RP) technology developed at\nTUD1 for on-Earth applications. The rhizomatic 3D printed structure is a\nstructurally optimized porous shell structure with increased insulation\nproperties. In order to regulate the indoor pressurised environment an\ninflatable structure is placed in the 3D printed cavity. This inflatable\nstructure is made of materials, which can also be at some point reproduced on\nMars through ISRU. Depending on location the habitat and the production system\nare powered by a system combining solar and kite power. The ultimate goal is to\ndevelop an autarkic D2RP system for building subsurface autarkic habitats on\nMars from locally obtained materials.\n"}
+{"abstract": "  Multimodal MRI provides complementary and clinically relevant information to\nprobe tissue condition and to characterize various diseases. However, it is\noften difficult to acquire sufficiently many modalities from the same subject\ndue to limitations in study plans, while quantitative analysis is still\ndemanded. In this work, we propose a unified conditional disentanglement\nframework to synthesize any arbitrary modality from an input modality. Our\nframework hinges on a cycle-constrained conditional adversarial training\napproach, where it can extract a modality-invariant anatomical feature with a\nmodality-agnostic encoder and generate a target modality with a conditioned\ndecoder. We validate our framework on four MRI modalities, including\nT1-weighted, T1 contrast enhanced, T2-weighted, and FLAIR MRI, from the\nBraTS'18 database, showing superior performance on synthesis quality over the\ncomparison methods. In addition, we report results from experiments on a tumor\nsegmentation task carried out with synthesized data.\n"}
+{"abstract": "  A method based on the Fast Fourier Transform is proposed to obtain the\ndispersion relation of acoustic waves in heterogeneous periodic media with\narbitrary microstructures. The microstructure is explicitly considered using a\nvoxelized Representative Volume Element (RVE). The dispersion diagram is\nobtained solving an eigenvalue problem for Bloch waves in Fourier space. To\nthis aim, two linear operators representing stiffness and mass are defined\nthrough the use of differential operators in Fourier space. The smallest\neigenvalues are obtained using the implicitly restarted Lanczos and the\nsubspace iteration methods, and the required inverse of the stiffness operator\nis done using the conjugate gradient with a preconditioner. The method is used\nto study the propagation of acoustic waves in elastic polycrystals, showing the\nstrong effect of crystal anistropy and polycrystaline texture on the\npropagation. It is shown that the method combines the simplicity of classical\nFourier series analysis with the versatility of Finite Elements to account for\ncomplex geometries proving an efficient and general approach which allows the\nuse of large RVEs in 3D.\n"}
+{"abstract": "  With the increasing use of the internet and social media, it is obvious that\ncyberbullying has become a major problem. The most basic way for protection\nagainst the dangerous consequences of cyberbullying is to actively detect and\ncontrol the contents containing cyberbullying. When we look at today's internet\nand social media statistics, it is impossible to detect cyberbullying contents\nonly by human power. Effective cyberbullying detection methods are necessary in\norder to make social media a safe communication space. Current research efforts\nfocus on using machine learning for detecting and eliminating cyberbullying.\nAlthough most of the studies have been conducted on English texts for the\ndetection of cyberbullying, there are few studies in Turkish. Limited methods\nand algorithms were also used in studies conducted on the Turkish language. In\naddition, the scope and performance of the algorithms used to classify the\ntexts containing cyberbullying is different, and this reveals the importance of\nusing an appropriate algorithm. The aim of this study is to compare the\nperformance of different machine learning algorithms in detecting Turkish\nmessages containing cyberbullying. In this study, nineteen different\nclassification algorithms were used to identify texts containing cyberbullying\nusing Turkish natural language processing techniques. Precision, recall,\naccuracy and F1 score values were used to evaluate the performance of\nclassifiers. It was determined that the Light Gradient Boosting Model (LGBM)\nalgorithm showed the best performance with 90.788% accuracy and 90.949% F1\nScore value.\n"}
+{"abstract": "  In this paper we introduce Neural Network Coding(NNC), a data-driven approach\nto joint source and network coding. In NNC, the encoders at each source and\nintermediate node, as well as the decoder at each destination node, are neural\nnetworks which are all trained jointly for the task of communicating correlated\nsources through a network of noisy point-to-point links. The NNC scheme is\napplication-specific and makes use of a training set of data, instead of making\nassumptions on the source statistics. In addition, it can adapt to any\narbitrary network topology and power constraint. We show empirically that, for\nthe task of transmitting MNIST images over a network, the NNC scheme shows\nimprovement over baseline schemes, especially in the low-SNR regime.\n"}
+{"abstract": "  We derive a thermodynamic uncertainty relation for first passage processes in\nquantum Markov chains. We consider first passage processes stopping after a\nfixed number of jumps, which contrasts with typical quantum Markov chains which\nend at a fixed time. We bound observables of first passage processes in quantum\nMarkov chains by the Loschmidt echo, which quantifies the extent of\nirreversibility in quantum many-body systems. Considering a particular case of\nthe bound, we show that the lower bound corresponds to the quantum Fisher\ninformation, which has played a fundamental role in uncertainty relations in\nquantum systems. Moreover, our bound reduces to a thermodynamic uncertainty\nrelation for classical first passage processes when considering classical\ndynamics.\n"}
+{"abstract": "  In this study, we investigated the motion of active SiO2-Pt Janus particles\nin the 2D bath of smaller silica tracers dispersed in varying areal densities.\nThe effect on the organization of the tracer particles around the active JPs\nwas also explored. Our experiments indicate that the interaction between the\ntracers and the active JPs mainly depend on the nature of collision marked by\nthe duration of contact. For all the concentration regimes, we have shown that\nthe short time collisions do not have significant impact on the motion of\nactive JPs, however, during moderate/long-time collisions tracer(s) can lead to\na significant change in active JPs motion and even cause them to rotate. In the\nconcentrated regime, our experiments reveal the emergence of a novel\norganizational behavior of the passive tracers on the trailing Pt and the\nleading SiO2 with a strong dependence on the nature of collision.\n"}
+{"abstract": "  We study scalar, fermionic and gauge fields coupled nonminimally to gravity\nin the Einstein-Cartan formulation. We construct a wide class of models with\nnondynamical torsion whose gravitational spectra comprise only the massless\ngraviton. Eliminating non-propagating degrees of freedom, we derive an\nequivalent theory in the metric formulation of gravity. It features contact\ninteractions of a certain form between and among the matter and gauge currents.\nWe also discuss briefly the inclusion of curvature-squared terms.\n"}
+{"abstract": "  Social comparison-based features are widely used in social computing apps.\nHowever, most existing apps are not grounded in social comparison theories and\ndo not consider individual differences in social comparison preferences and\nreactions. This paper is among the first to automatically personalize social\ncomparison targets. In the context of an m-health app for physical activity, we\nuse artificial intelligence (AI) techniques of multi-armed bandits. Results\nfrom our user study (n=53) indicate that there is some evidence that motivation\ncan be increased using the AI-based personalization of social comparison. The\ndetected effects achieved small-to-moderate effect sizes, illustrating the\nreal-world implications of the intervention for enhancing motivation and\nphysical activity. In addition to design implications for social comparison\nfeatures in social apps, this paper identified the personalization paradox, the\nconflict between user modeling and adaptation, as a key design challenge of\npersonalized applications for behavior change. Additionally, we propose\nresearch directions to mitigate this Personalization Paradox.\n"}
+{"abstract": "  Weeds are a significant threat to the agricultural productivity and the\nenvironment. The increasing demand for sustainable agriculture has driven\ninnovations in accurate weed control technologies aimed at reducing the\nreliance on herbicides. With the great success of deep learning in various\nvision tasks, many promising image-based weed detection algorithms have been\ndeveloped. This paper reviews recent developments of deep learning techniques\nin the field of image-based weed detection. The review begins with an\nintroduction to the fundamentals of deep learning related to weed detection.\nNext, recent progresses on deep weed detection are reviewed with the discussion\nof the research materials including public weed datasets. Finally, the\nchallenges of developing practically deployable weed detection methods are\nsummarized, together with the discussions of the opportunities for future\nresearch.We hope that this review will provide a timely survey of the field and\nattract more researchers to address this inter-disciplinary research problem.\n"}
+{"abstract": "  The axion mass receives a large correction from small instantons if the QCD\ngets strongly coupled at high energies. We discuss the size of the new CP\nviolating phases caused by the fact that the small instantons are sensitive to\nthe UV physics. We also discuss the effects of the mass correction on the axion\nabundance of the Universe. Taking the small-instanton contributions into\naccount, we propose a natural scenario of axion dark matter where the axion\ndecay constant is as large as $10^{15\\text{-}16}$GeV. The scenario works in the\nhigh-scale inflation models.\n"}
+{"abstract": "  Cloud service providers are distributing data centers geographically to\nminimize energy costs through intelligent workload distribution. With\nincreasing data volumes in emerging cloud workloads, it is critical to factor\nin the network costs for transferring workloads across data centers. For\ngeo-distributed data centers, many researchers have been exploring strategies\nfor energy cost minimization and intelligent inter-data-center workload\ndistribution separately. However, prior work does not comprehensively and\nsimultaneously consider data center energy costs, data transfer costs, and data\ncenter queueing delay. In this paper, we propose a novel game theory-based\nworkload management framework that takes a holistic approach to the cloud\noperating cost minimization problem by making intelligent scheduling decisions\naware of data transfer costs and the data center queueing delay. Our framework\nperforms intelligent workload management that considers heterogeneity in data\ncenter compute capability, cooling power, interference effects from task\nco-location in servers, time-of-use electricity pricing, renewable energy, net\nmetering, peak demand pricing distribution, and network pricing. Our\nsimulations show that the proposed game-theoretic technique can minimize the\ncloud operating cost more effectively than existing approaches.\n"}
+{"abstract": "  We describe the full group of isometries of absolutely simple, compact,\nconnected real Lie groups, of SO(4) and of U(n), endowed with suitable\nbi-invariant Riemannian metrics.\n"}
+{"abstract": "  The objective of this work is to annotate sign instances across a broad\nvocabulary in continuous sign language. We train a Transformer model to ingest\na continuous signing stream and output a sequence of written tokens on a\nlarge-scale collection of signing footage with weakly-aligned subtitles. We\nshow that through this training it acquires the ability to attend to a large\nvocabulary of sign instances in the input sequence, enabling their\nlocalisation. Our contributions are as follows: (1) we demonstrate the ability\nto leverage large quantities of continuous signing videos with weakly-aligned\nsubtitles to localise signs in continuous sign language; (2) we employ the\nlearned attention to automatically generate hundreds of thousands of\nannotations for a large sign vocabulary; (3) we collect a set of 37K manually\nverified sign instances across a vocabulary of 950 sign classes to support our\nstudy of sign language recognition; (4) by training on the newly annotated data\nfrom our method, we outperform the prior state of the art on the BSL-1K sign\nlanguage recognition benchmark.\n"}
+{"abstract": "  In this letter, we note that the observed in the LIGO / Virgo experiment\nratio of the detection rate of black holes to the rate of detection of binary\nneutron stars requires the assumption of a \"conservative\" collapse of massive\nstars into a black hole: almost all the mass of the collapsing star goes under\nthe horizon. This is consistent with the large masses of black holes detected\nby LIGO/Virgo. On the other hand, the assumption of a small loss of matter\nduring the collapse into a black hole is in good agreement with the small\neccentricity of Single-lined Binaries. At the same time, the absence of X-rays\nfrom most black holes in binary systems with blue stars is explained. We argue\nthat three sets of LIGO / Virgo observations and data on the Single-lined\nBinary with a Candidate Black Hole Component confirm the scenario of the\nevolution of massive field binaries.\n"}
+{"abstract": "  We explore the phenomenology of a model of monopolium based on an\nelectromagnetic dual formulation of Zwanziger and lattice gauge theory. The\nmonopole is assumed to have a finite-sized inner structure based on a 't\nHooft-Polyakov like solution, with the magnetic charge uniformly distributed on\nthe surface of a sphere. The monopole and anti-monopole potential becomes\nlinear plus Coulomb outside the sphere, analogous to the Cornell potential\nutilised in the study of quarkonium states. Discovery of a resonance feature in\nthe diphoton channel as well as in a higher multiplicity photon channel would\nbe a smoking gun for the existence of monopoles within this monopolium\nconstruction, with the mass and bound state properties extractable. Utilising\nthe current LHC results in the diphoton channel, constraints on the monopole\nmass are determined for a wide range of model parameters. These are compared to\nthe most recent MoEDAL results and found to be significantly more stringent in\ncertain parameter regions, providing strong motivation for exploring higher\nmultiplicity photon final state searches.\n"}
+{"abstract": "  This paper presents an exact formula for calculating the fourth-moment tensor\nfrom the second-moment tensor for the three dimensional Jeffery's equation.\nAlthough this approach falls within the category of a moment tensor closure, it\ndoes not rely upon an approximation, either analytic or curve fit, of the\nfourth-moment tensor as do previous closures. This closure is orthotropic in\nthe sense of \\cite{cintra:95}, or equivalently, a natural closure in the sense\nof \\cite{verleye:93}. The existence of these explicit formulae has been\nasserted previously, but as far as the authors know, the explicit forms have\nyet to be published. The formulae involve elliptic integrals, and are valid\nwhenever fiber orientation was isotropic at some point in time. Finally, this\npaper presents the Fast Exact Closure (FEC), a fast and in principle exact\nmethod for solving Jeffery's equation, which does not require approximate\nclosures, nor the elliptic integral computation.\n"}
+{"abstract": "  In this paper, we present a linear-time approximation scheme for $k$-means\nclustering of \\emph{incomplete} data points in $d$-dimensional Euclidean space.\nAn \\emph{incomplete} data point with $\\Delta>0$ unspecified entries is\nrepresented as an axis-parallel affine subspaces of dimension $\\Delta$. The\ndistance between two incomplete data points is defined as the Euclidean\ndistance between two closest points in the axis-parallel affine subspaces\ncorresponding to the data points. We present an algorithm for $k$-means\nclustering of axis-parallel affine subspaces of dimension $\\Delta$ that yields\nan $(1+\\epsilon)$-approximate solution in $O(nd)$ time. The constants hidden\nbehind $O(\\cdot)$ depend only on $\\Delta, \\epsilon$ and $k$. This improves the\n$O(n^2 d)$-time algorithm by Eiben et al.[SODA'21] by a factor of $n$.\n"}
+{"abstract": "  Context. Requirements engineering remains a discipline that is faced with a\nlarge number of challenges, including the implementation of a requirements\nelicitation process in industry. Although several proposals have been suggested\nby researchers and academics, little is known of the practices that are\nactually followed in industry. Objective. We investigate the SoTA with respect\nto requirements elicitation, examining practitioners' practices. We focus on\nthe techniques, the roles involved, and the challenges associated to the\nprocess. Method. We conducted an interview-based survey study involving 24\npractitioners from 12 different Swedish IT companies. Results. We found that\ngroup interaction techniques, including meetings and workshops, are the most\npopular type of elicitation techniques that are employed by the practitioners,\nexcept in the case of small projects. We noted that customers are frequently\ninvolved in the elicitation process, except in the case of market-driven\norganizations. Technical staff (for example, developers and architects) are\nmore frequently involved in the elicitation process compared to the involvement\nof business- or strategic staff. Finally, we identified a number of challenges\nwith respect to stakeholders. These challenges include difficulties in\nunderstanding and prioritizing their needs. Further, it was noted that\nrequirements instability (i.e., caused by changing needs or priorities) was a\npredominant challenge. These observations need to be interpreted in the context\nof the study. Conclusion. The relevant observations regarding the survey\nparticipants' experiences should be of interest to the industry; experiences\nthat should be analyzed in the practitioners' context. Researchers may find\nevidence for the use of academic results in practice, thereby inspiring future\ntheoretical work, as well as further empirical studies in the same area.\n"}
+{"abstract": "  Measurements at particle collider experiments, even if primarily aimed at\nunderstanding Standard Model processes, can have a high degree of model\nindependence, and implicitly contain information about potential contributions\nfrom physics beyond the Standard Model. The Contur package allows users to\nbenefit from the hundreds of measurements preserved in the Rivet library to\ntest new models against the bank of LHC measurements to date. This method has\nproven to be very effective in several recent publications from the Contur\nteam, but ultimately, for this approach to be successful, the authors believe\nthat the Contur tool needs to be accessible to the wider high energy physics\ncommunity. As such, this manual accompanies the first user-facing version:\nContur v2. It describes the design choices that have been made, as well as\ndetailing pitfalls and common issues to avoid. The authors hope that with the\nhelp of this documentation, external groups will be able to run their own\nContur studies, for example when proposing a new model, or pitching a new\nsearch.\n"}
+{"abstract": "  The combination of data from multiple sensors, also known as sensor fusion or\ndata fusion, is a key aspect in the design of autonomous robots. In particular,\nalgorithms able to accommodate sensor fusion techniques enable increased\naccuracy, and are more resilient against the malfunction of individual sensors.\nThe development of algorithms for autonomous navigation, mapping and\nlocalization have seen big advancements over the past two decades. Nonetheless,\nchallenges remain in developing robust solutions for accurate localization in\ndense urban environments, where the so called last-mile delivery occurs. In\nthese scenarios, local motion estimation is combined with the matching of\nreal-time data with a detailed pre-built map. In this paper, we utilize data\ngathered with an autonomous delivery robot to compare different sensor fusion\ntechniques and evaluate which are the algorithms providing the highest accuracy\ndepending on the environment. The techniques we analyze and propose in this\npaper utilize 3D lidar data, inertial data, GNSS data and wheel encoder\nreadings. We show how lidar scan matching combined with other sensor data can\nbe used to increase the accuracy of the robot localization and, in consequence,\nits navigation. Moreover, we propose a strategy to reduce the impact on\nnavigation performance when a change in the environment renders map data\ninvalid or part of the available map is corrupted.\n"}
+{"abstract": "  We obtain local numerators satisfying the BCJ color-kinematics duality at one\nloop for super-Yang-Mills theory in ten dimensions. This is done explicitly for\nsix points via the field-theory limit of the genus-one open superstring\ncorrelators for different color orderings, in an analogous manner to an earlier\nderivation of local BCJ-satisfying numerators at tree level from disk\ncorrelators. These results solve an outstanding puzzle from a previous analysis\nwhere the six-point numerators did not satisfy the color-kinematics duality.\n"}
+{"abstract": "  In most of computer vision applications, motion blur is regarded as an\nundesirable artifact. However, it has been shown that motion blur in an image\nmay have practical interests in fundamental computer vision problems. In this\nwork, we propose a novel framework to estimate optical flow from a single\nmotion-blurred image in an end-to-end manner. We design our network with\ntransformer networks to learn globally and locally varying motions from encoded\nfeatures of a motion-blurred input, and decode left and right frame features\nwithout explicit frame supervision. A flow estimator network is then used to\nestimate optical flow from the decoded features in a coarse-to-fine manner. We\nqualitatively and quantitatively evaluate our model through a large set of\nexperiments on synthetic and real motion-blur datasets. We also provide\nin-depth analysis of our model in connection with related approaches to\nhighlight the effectiveness and favorability of our approach. Furthermore, we\nshowcase the applicability of the flow estimated by our method on deblurring\nand moving object segmentation tasks.\n"}
+{"abstract": "  Over the last two decades, a large population of close-in planets has been\ndetected around a wide variety of host stars. Such exoplanets are likely to\nundergo planetary migration through magnetic and tidal interactions. We aim to\nfollow the orbital evolution of a planet along the structural and rotational\nevolution of its host star by taking into account simultaneously tidal and\nmagnetic torques, in order to explain some properties of the distribution of\nobserved close-in planets. We rely on a numerical model of a coplanar circular\nstar-planet system taking into account stellar structural changes, wind braking\nand star-planet interactions, called ESPEM. We browse the parameter space of\nstar-planet systems' configurations and assess the relative influence of\nmagnetic and tidal torques on secular evolution. We then synthesize star-planet\npopulations and confront their distribution in orbital and stellar rotation\nperiods to Kepler satellite data. First, we find that after the dissipation of\nthe protoplanetary disk, both types of interactions can dominate secular\nevolution depending on the initial configuration of the system and the\nevolutionary phase considered. Moreover, different populations of star-planet\nsystems emerge from the combined action of both kinds of interactions,\naccording to the evolutionary phase during which the planet migrates\nsignificantly. This may affect significantly the detectability of star-planet\nsystems as well as the validity of gyrochonology. All in all, star-planet\ninteractions significantly impact the global distribution in orbital periods\nduring the main sequence, while the global distribution in stellar rotation\nperiods is marginally affected. More precisely, star-planet magnetic\ninteractions significantly affect the distribution of super-Earths around\nslowly rotating stars, while tidal effects are found to shape the distribution\nof giant planets.\n"}
+{"abstract": "  BatchNorm is a critical building block in modern convolutional neural\nnetworks. Its unique property of operating on \"batches\" instead of individual\nsamples introduces significantly different behaviors from most other operations\nin deep learning. As a result, it leads to many hidden caveats that can\nnegatively impact model's performance in subtle ways. This paper thoroughly\nreviews such problems in visual recognition tasks, and shows that a key to\naddress them is to rethink different choices in the concept of \"batch\" in\nBatchNorm. By presenting these caveats and their mitigations, we hope this\nreview can help researchers use BatchNorm more effectively.\n"}
+{"abstract": "  We study the Hilbert scheme $\\mathcal{H}^\\mathcal{L}_{d,g,r}$ parametrizing\nsmooth, irreducible, non-degenerate and linearly normal curves of degree $d$\nand genus $g$ in $\\mathbb{P}^r$ whose complete and very ample hyperplane linear\nseries $\\mathcal{D}$ have relatively small index of speciality\n$i(\\mathcal{D})=g-d+r$. In particular we show the existence (and non-existence\nas well in some sporadic cases) of every Hilbert scheme of linearly normal\ncurves with $i(\\mathcal{D})=4$. We also determine the irreducibility of\n$\\mathcal{H}^\\mathcal{L}_{2r+4,r+8,r}$ for $3\\le r\\le 8$, which are rather\npeculiar families in a certain sense.\n"}
+{"abstract": "  Semantic information of a sentence is crucial for improving the\nexpressiveness of a text-to-speech (TTS) system, but can not be well learned\nfrom the limited training TTS dataset just by virtue of the nowadays encoder\nstructures. As large scale pre-trained text representation develops,\nbidirectional encoder representations from transformers (BERT) has been proven\nto embody text-context semantic information and applied to TTS as additional\ninput. However BERT can not explicitly associate semantic tokens from point of\ndependency relations in a sentence. In this paper, to enhance expressiveness,\nwe propose a semantic representation learning method based on graph neural\nnetwork, considering dependency relations of a sentence. Dependency graph of\ninput text is composed of edges from dependency tree structure considering both\nthe forward and the reverse directions. Semantic representations are then\nextracted at word level by the relational gated graph network (RGGN) fed with\nfeatures from BERT as nodes input. Upsampled semantic representations and\ncharacter-level embeddings are concatenated to serve as the encoder input of\nTacotron-2. Experimental results show that our proposed method outperforms the\nbaseline using vanilla BERT features both in LJSpeech and Blizzard Challenge\n2013 datasets, and semantic representations learned from the reverse direction\nare more effective for enhancing expressiveness.\n"}
+{"abstract": "  A recently developed high-order implicit shock tracking (HOIST) framework for\nresolving discontinuous solutions of inviscid, steady conservation laws [41,\n43] is extended to the unsteady case. Central to the framework is an\noptimization problem which simultaneously computes a discontinuity-aligned mesh\nand the corresponding high-order approximation to the flow, which provides\nnonlinear stabilization and a high-order approximation to the solution. This\nwork extends the implicit shock tracking framework to the case of unsteady\nconservation laws using a method of lines discretization via a diagonally\nimplicit Runge-Kutta method by \"solving a steady problem at each timestep\". We\nformulate and solve an optimization problem that produces a feature-aligned\nmesh and solution at each Runge-Kutta stage of each timestep, and advance this\nsolution in time by standard Runge-Kutta update formulas. A Rankine-Hugoniot\nbased prediction of the shock location together with a high-order, untangling\nmesh smoothing procedure provides a high-quality initial guess for the\noptimization problem at each time, which results in Newton-like convergence of\nthe sequential quadratic programing (SQP) optimization solver. This method is\nshown to deliver highly accurate solutions on coarse, high-order\ndiscretizations without nonlinear stabilization and recover the design accuracy\nof the Runge-Kutta scheme. We demonstrate this framework on a series of\ninviscid, unsteady conservation laws in both one- and two- dimensions. We also\nverify that our method is able to recover the design order of accuracy of our\ntime integrator in the presence of a strong discontinuity.\n"}
+{"abstract": "  Frequency domain Unsteady Lifting-Line Theory (ULLT) provides a means by\nwhich the aerodynamics of oscillating wings may be studied at low computational\ncost without neglecting the interacting effects of aspect ratio and oscillation\nfrequency. Renewed interest in the method has drawn attention to several\nuncertainties however. Firstly, to what extent is ULLT practically useful for\nrectangular wings, despite theoretical limitations? And secondly, to what\nextent is a complicated wake model needed in the outer solution for good\naccuracy? This paper aims to answer these questions by presenting a complete\nULLT based on the work of Sclavounos, along with a novel ULLT that considers\nonly the streamwise vorticity and a Prandtl-like pseudosteady ULLT. These are\ncompared to high fidelity Euler CFD for cases of rectangular wings at multiple\naspect ratios and oscillation frequencies. The results of this work establish\nULLT as a low computational cost model capable of accounting for interacting\nfinite-wing and oscillation frequency effects, and identify the aspect ratio\nand frequency regimes where the three ULLTs are most accurate. This research\npaves the way towards the construction of time-domain or numerical ULLTs which\nmay be augmented to account for non-linearities such as flow separation.\n"}
+{"abstract": "  Nonlinear quantum graphs are metric graphs equipped with a nonlinear\nSchr{\\\"o}dinger equation. Whereas in the last ten years they have known\nconsiderable developments on the theoretical side, their study from the\nnumerical point of view remains in its early stages. The goal of this paper is\nto present the Grafidi library, a Python library which has been developed with\nthe numerical simulation of nonlinear Schr{\\\"o}dinger equations on graphs in\nmind. We will show how, with the help of the Grafidi library, one can implement\nthe popular normalized gradient flow and nonlinear conjugate gradient flow\nmethods to compute ground states of a nonlinear quantum graph. We will also\nsimulate the dynamics of the nonlinear Schr{\\\"o}dinger equation with a\nCrank-Nicolson relaxation scheme and a Strang splitting scheme. Finally, in a\nseries of numerical experiments on various types of graphs, we will compare the\noutcome of our numerical calculations for ground states with the existing\ntheoretical results, thereby illustrating the versatility and efficiency of our\nimplementations in the framework of the Grafidi library.\n"}
+{"abstract": "  This paper proposes a new simultaneous terahertz (THz) information and power\ntransfer (STIPT) system, which is assisted by reconfigurable intelligent\nsurface (RIS) for both the information data and power transmission. We aim to\nmaximize the information users' (IUs') data rate while guaranteeing the energy\nusers' (EUs') and RIS's power harvesting requirements. To solve the formulated\nnon-convex problem, the block coordinate descent (BCD) based algorithm is\nadopted to alternately optimize the transmit precoding of IUs, RIS's reflecting\ncoefficients, and RIS's coordinate. The Penalty Constrained Convex\nApproximation (PCCA) Algorithm is proposed to solve the intractable\noptimization problem of the RIS's coordinate, where the solution's feasibility\nis guaranteed by the introduced penalties. Simulation results confirm that the\nproposed BCD algorithm can significantly enhance the performance of STIPT by\nemploying RIS.\n"}
+{"abstract": "  Resonant optical excitation of certain molecular vibrations in\n$\\kappa$-(BEDT-TTF)$_2$Cu[N(CN)$_2$]Br has been shown to induce transient\nsuperconducting-like optical properties at temperatures far above equilibrium\n$T_c$. Here, we report experiments across the bandwidth-tuned phase diagram of\nthis class of materials, and study the Mott insulator\n$\\kappa$-(BEDT-TTF)$_2$Cu[N(CN)$_2$]Cl and the metallic compound\n$\\kappa$-(BEDT-TTF)$_2$Cu(NCS)$_2$. We find non-equilibrium photoinduced\nsuperconductivity only in $\\kappa$-(BEDT-TTF)$_2$Cu[N(CN)$_2$]Br, indicating\nthat the proximity to the Mott insulating phase and possibly the presence of\npreexisting superconducting fluctuations are pre-requisites for this effect.\n"}
+{"abstract": "  In this paper, we consider the problem of training neural network (NN)\ncontrollers for nonlinear dynamical systems that are guaranteed to satisfy\nsafety and liveness (e.g., reach-avoid) properties. Our approach is to combine\nmodel-based design methodologies for dynamical systems with data-driven\napproaches to achieve this target. We confine our attention to NNs with\nRectifier Linear Unit (ReLU) nonlinearity which are known to represent\nContinuous Piece-Wise Affine (CPWA) functions. Given a mathematical model of\nthe dynamical system, we compute a finite-state abstract model that captures\nthe closed-loop behavior under all possible CPWA controllers. Using this\nfinite-state abstract model, our framework identifies a family of CPWA\nfunctions guaranteed to satisfy the safety requirements. We augment the\nlearning algorithm with a NN weight projection operator during training that\nenforces the resulting NN to represent a CPWA function from the provably safe\nfamily of CPWA functions. Moreover, the proposed framework uses the\nfinite-state abstract model to identify candidate CPWA functions that may\nsatisfy the liveness properties. Using such candidate CPWA functions, the\nproposed framework biases the NN training to achieve the liveness\nspecification. We show the efficacy of the proposed framework both in\nsimulation and on an actual robotic vehicle.\n"}
+{"abstract": "  We present TICaM, a Time-of-flight In-car Cabin Monitoring dataset for\nvehicle interior monitoring using a single wide-angle depth camera. Our dataset\naddresses the deficiencies of currently available in-car cabin datasets in\nterms of the ambit of labeled classes, recorded scenarios and provided\nannotations; all at the same time. We record an exhaustive list of actions\nperformed while driving and provide for them multi-modal labeled images (depth,\nRGB and IR), with complete annotations for 2D and 3D object detection, instance\nand semantic segmentation as well as activity annotations for RGB frames.\nAdditional to real recordings, we provide a synthetic dataset of in-car cabin\nimages with same multi-modality of images and annotations, providing a unique\nand extremely beneficial combination of synthetic and real data for effectively\ntraining cabin monitoring systems and evaluating domain adaptation approaches.\nThe dataset is available at https://vizta-tof.kl.dfki.de/.\n"}
+{"abstract": "  Many neutron star (NS) properties, such as the proton fraction within a NS,\nreflect the symmetry energy contributions to the Equation of State that\ndominate when neutron and proton densities differ strongly. To constrain these\ncontributions at supra-saturation densities, we measure the spectra of charged\npions produced by colliding rare isotope tin (Sn) beams with isotopically\nenriched Sn targets. Using ratios of the charged pion spectra measured at high\ntransverse momenta, we deduce the slope of the symmetry energy to be $42 < L <\n117$ MeV. This value is slightly lower but consistent with the $L$ values\ndeduced from a recent measurement of the neutron skin thickness of $^{208}$Pb.\n"}
+{"abstract": "  The problem of online prediction with sequential side information under\nlogarithmic loss is studied, and general upper and lower bounds on the minimax\nregret incurred by the predictor is established. The upper bounds on the\nminimax regret are obtained by providing and analyzing a probability assignment\ninspired by mixture probability assignments in universal compression, and the\nlower bounds are obtained by way of a redundancy-capacity theorem. The tight\ncharacterization of the regret is provided in some special settings.\n"}
+{"abstract": "  This paper develops a construction-based dialectometry capable of identifying\npreviously unknown constructions and measuring the degree to which a given\nconstruction is subject to regional variation. The central idea is to learn a\ngrammar of constructions (a CxG) using construction grammar induction and then\nto use these constructions as features for dialectometry. This offers a method\nfor measuring the aggregate similarity between regional CxGs without limiting\nin advance the set of constructions subject to variation. The learned CxG is\nevaluated on how well it describes held-out test corpora while dialectometry is\nevaluated on how well it can model regional varieties of English. Themethod is\ntested using two distinct datasets: First, the International Corpus of English\nrepresenting eight outer circle varieties; Second, a web-crawled corpus\nrepresenting five inner circle varieties. Results show that themethod (1)\nproduces a grammar with stable quality across sub-sets of a single corpus that\nis (2) capable of distinguishing between regional varieties of Englishwith a\nhigh degree of accuracy, thus (3) supporting dialectometricmethods formeasuring\nthe similarity between varieties of English and (4) measuring the degree to\nwhich each construction is subject to regional variation. This is important for\ncognitive sociolinguistics because it operationalizes the idea that competition\nbetween constructions is organized at the functional level so that\ndialectometry needs to represent as much of the available functional space as\npossible.\n"}
+{"abstract": "  Most of our knowledge about the structure of the Milky Way has come from the\nstudy of variable stars. Among the variables, mimicking the periodic variation\nof pulsating stars, are the eclipsing binaries. These stars are important in\nastrophysics because they allow us to directly measure radii and masses of the\ncomponents, as well as the distance to the system, thus being useful in studies\nof Galactic structure alongside pulsating RR Lyrae and Cepheids. Using the\ndistinguishing features of their light curves, one can identify them using a\nsemi-automated process. In this work, we present a strategy to search for\neclipsing variables in the inner VVV bulge across an area of 13.4 sq. deg.\nwithin $1.68^{\\rm o}<l<7.53^{\\rm o}$ and $-3.73^{\\rm o}<b<-1.44^{\\rm o}$,\ncorresponding to the VVV tiles b293 to b296 and b307 to b310. We accurately\nclassify 212 previously unknown eclipsing binaries, including six very reddened\nsources. The preliminary analysis suggests these eclipsing binaries are located\nin the most obscured regions of the foreground disk and bulge of the Galaxy.\nThis search is therefore complementary to other variable stars searches carried\nout at optical wavelengths.\n"}
+{"abstract": "  In this article, we consider the Euclidean dispersion problems. Let\n$P=\\{p_{1}, p_{2}, \\ldots, p_{n}\\}$ be a set of $n$ points in $\\mathbb{R}^2$.\nFor each point $p \\in P$ and $S \\subseteq P$, we define $cost_{\\gamma}(p,S)$ as\nthe sum of Euclidean distance from $p$ to the nearest $\\gamma $ point in $S\n\\setminus \\{p\\}$. We define $cost_{\\gamma}(S)=\\min_{p \\in\nS}\\{cost_{\\gamma}(p,S)\\}$ for $S \\subseteq P$. In the $\\gamma$-dispersion\nproblem, a set $P$ of $n$ points in $\\mathbb{R}^2$ and a positive integer $k\n\\in [\\gamma+1,n]$ are given. The objective is to find a subset $S\\subseteq P$\nof size $k$ such that $cost_{\\gamma}(S)$ is maximized. We consider both\n$2$-dispersion and $1$-dispersion problem in $\\mathbb{R}^2$. Along with these,\nwe also consider $2$-dispersion problem when points are placed on a line. In\nthis paper, we propose a simple polynomial time $(2\\sqrt 3 + \\epsilon )$-factor\napproximation algorithm for the $2$-dispersion problem, for any $\\epsilon > 0$,\nwhich is an improvement over the best known approximation factor $4\\sqrt3$\n[Amano, K. and Nakano, S. I., An approximation algorithm for the $2$-dispersion\nproblem, IEICE Transactions on Information and Systems, Vol. 103(3), pp.\n506-508, 2020]. Next, we develop a common framework for designing an\napproximation algorithm for the Euclidean dispersion problem. With this common\nframework, we improve the approximation factor to $2\\sqrt 3$ for the\n$2$-dispersion problem in $\\mathbb{R}^2$. Using the same framework, we propose\na polynomial time algorithm, which returns an optimal solution for the\n$2$-dispersion problem when points are placed on a line. Moreover, to show the\neffectiveness of the framework, we also propose a $2$-factor approximation\nalgorithm for the $1$-dispersion problem in $\\mathbb{R}^2$.\n"}
+{"abstract": "  We study the thermodynamics of Ayon Beato-Garcia black hole and the relation\nbetween the photon orbits and the thermodynamic phase transition of the black\nhole. Then we study the interaction between the microstructures of the black\nhole using Ruppeiner geometry. The radius of photon orbit and minimum impact\nparameter behave non-monotonously below the critical point, which mimics the\nbehaviour of Hawking temperature and pressure in extended thermodynamics. Their\nchange during the large black hole and small black hole phase transition serve\nas the order parameter, having a critical exponent $1/2$. The results show that\ngravity and thermodynamics of Ayon Beato-Garcia black hole are closely related.\nFollowing this we study the thermodynamic geometry which gives insight of the\nmicrostructure interaction of the black hole. We find that the large black hole\nphase is analogous to bosonic gas with a dominant attractive interaction, and\nthe small black hole phase behaves like an anyonic gas with both attractive and\nrepulsive interactions.\n"}
+{"abstract": "  We investigate how physical quantities associated with relativistic stars in\nthe Jordan and Einstein frames are related by the generalized disformal\ntransformations constructed by the scalar and vector fields within the\nslow-rotation approximation. We consider the most general scalar disformal\ntransformation constructed by the scalar field, and by the vector field without\nand with the $U(1)$ gauge symmetry, respectively. At the zeroth order of the\nslow-rotation approximation, by imposing that both the metrics of the Jordan\nand Einstein frames are asymptotically flat, we show that the\nArnowitt-Deser-Misner mass is frame invariant. At the first order of the\nslow-rotation approximation, we discuss the disformal transformations of the\nframe-dragging function, angular velocity, angular momentum, and moment of\ninertia of the star. We show that the angular velocity of the star is frame\ninvariant in all the cases. While the angular momentum and moment of inertia\nare invariant under the scalar disformal transformation, they are not under the\nvector disformal transformation without and with the $U(1)$ gauge symmetry.\n"}
+{"abstract": "  A type-II InAs/AlAs$_{0.16}$Sb$_{0.84}$ multiple-quantum well sample is\ninvestigated for the photoexcited carrier dynamics as a function of excitation\nphoton energy and lattice temperature. Time-resolved measurements are performed\nusing a near-infrared pump pulse, with photon energies near to and above the\nband gap, probed with a terahertz probe pulse. The transient terahertz\nabsorption is characterized by a multi-rise, multi-decay function that captures\nlong-lived decay times and a metastable state of for an excess-photon energy of\n$>100$ meV. For sufficient excess-photon energy, excitation of the metastable\nstate is followed by a transition to the long-lived states. Excitation\ndependence of the long-lived states map onto a near-direct band gap ($E{_g}$)\ndensity of states with an Urbach tail below $E{_g}$. As temperature increases,\nthe long-lived decay times increase $<E{_g}$, due to the increased phonon\ninteraction of the unintentional defect states, and by phonon stabilization of\nthe hot carriers $>E{_g}$. Additionally, Auger (and/or trap-assisted Auger)\nscattering above the onset of the plateau may also contribute to longer\nhot-carrier lifetimes. Meanwhile, the initial decay component shows strong\ndependence on excitation energy and temperature, reflecting the complicated\ninitial transfer of energy between valence-band and defect states, indicating\nmethods to further prolong hot carriers for technological applications.\n"}
+{"abstract": "  Case-based learning is a powerful pedagogical method of creating dialogue\nbetween theory and practice. CBL is particularly suited to executive learning\nas it instigates critical discussion and draws out relevant experiences. In\nthis paper we used a real-world case to teach Information Security Management\nto students in Management Information Systems. The real-world case is described\nin a legal indictment, T-mobile USA Inc v Huawei Device USA Inc. and Huawei\nTechnologies Co. LTD, alleging theft of intellectual property and breaches of\ncontract concerning confidentiality and disclosure of sensitive information.\nThe incident scenario is interesting as it relates to a business asset that has\nboth digital and physical components that has been compromised through an\nunconventional cyber-physical attack facilitated by insiders. The scenario\nsparked an interesting debate among students about the scope and definition of\nsecurity incidents, the role and structure of the security unit, the utility of\ncompliance-based approaches to security, and the inadequate use of threat\nintelligence in modern security strategies.\n"}
+{"abstract": "  This work explores the novel idea of learning a submodular scoring function\nto improve the specificity/selectivity of existing feature attribution methods.\nSubmodular scores are natural for attribution as they are known to accurately\nmodel the principle of diminishing returns. A new formulation for learning a\ndeep submodular set function that is consistent with the real-valued\nattribution maps obtained by existing attribution methods is proposed. This\nformulation not only ensures that the scores for the heat maps that include the\nhighly attributed features across the existing methods are high, but also that\nthe score saturates even for the most specific heat map. The final attribution\nvalue of a feature is then defined as the marginal gain in the induced\nsubmodular score of the feature in the context of other highly attributed\nfeatures, thus decreasing the attribution of redundant yet discriminatory\nfeatures. Experiments on multiple datasets illustrate that the proposed\nattribution method achieves higher specificity while not degrading the\ndiscriminative power.\n"}
+{"abstract": "  In this paper, we theoretically propose a new hashing scheme to establish the\nsparse Fourier transform in high-dimensional space. The estimation of the\nalgorithm complexity shows that this sparse Fourier transform can overcome the\ncurse of dimensionality. To the best of our knowledge, this is the first\npolynomial-time algorithm to recover the high-dimensional continuous\nfrequencies.\n"}
+{"abstract": "  In [1] and [2] we developed a duality theory for multilinear norm\ninequalities. This duality theory is underpinned by a disentanglement theorem,\nwhich states that estimates on a weighted geometric mean over (convex) families\nof functions can be disentangled into quantitatively linked estimates on each\nfamily separately.\n  The disentanglement theorem was proved in [1] via minimax theory together\nwith weak*-compactness arguments in the space of finitely additive measures,\nand an application of the Yosida-Hewitt theory of such measures.\n  In this paper we provide an alternate, rather elementary approach to the\ndisentanglement theorem: we first prove a finite-dimensional case via\nperturbation and strong compactness, and then, building on that, the general\ninfinite-dimensional case via the finite intersection property and weak\ncompactness.\n"}
+{"abstract": "  Due to the vast compositional space of multi-principal element alloys\n(MPEAs), the rational design of MPEAs for optimized microstructures is\ndifficult. Therefore, a high-throughput first-principles study of\nMo-V-Nb-Ti-Zr, a refractory MPEA, was conducted to gain insights into the\nunderlying microstructures. Using Monte-Carlo simulations powered by cluster\nexpansion, we uncover the principles governing the MPEA's microstructures\nacross a large compositional space that includes non-equiatomic compositions\nand encompasses the constituent binaries, ternaries, and quaternaries. In the\nspirit of Hume-Rothery rules for complete solid solubility, we present a\nquantitative expression for predicting solid solution formation from the\ncomposition. Within a consistent framework, our results reproduce the\nmicrostructural observations (solid solution vs. segregation) from numerous\nexperiments and provide microstructural predictions for unexplored regions in\nthe compositional space. Our work illuminates the MPEA's microstructures in\nterms of the separation and clustering tendencies of the elements, presenting a\nset of simple but powerful design principles for future experiments to\nrationally design MPEAs with the desired microstructures for superior\nmechanical properties.\n"}
+{"abstract": "  Many-Worlds quantum mechanics differs from standard quantum mechanics in that\nin Many-Worlds, the wave function is a relative density of universes in the\nmultiverse amplitude rather than a probability amplitude. This means that in\nMany-Worlds, the Born frequencies are approached rather than given a priori.\nThus in Many-Worlds the rate of approach to the final frequencies can be\ncalculated and compared with observation. I use Many-Worlds to derive the rate\nof approach in the double slit experiment, and show that it agrees with\nobservation. Standard quantum theory has never been used to derive an approach\nformula because it cannot be so used, as has been tacitly acknowledged for 70\nyears.\n"}
+{"abstract": "  Lung ultrasound (LUS) imaging is used to assess lung abnormalities, including\nthe presence of B-line artefacts due to fluid leakage into the lungs caused by\na variety of diseases. However, manual detection of these artefacts is\nchallenging. In this paper, we propose a novel methodology to automatically\ndetect and localize B-lines in LUS videos using deep neural networks trained\nwith weak labels. To this end, we combine a convolutional neural network (CNN)\nwith a long short-term memory (LSTM) network and a temporal attention\nmechanism. Four different models are compared using data from 60 patients.\nResults show that our best model can determine whether one-second clips contain\nB-lines or not with an F1 score of 0.81, and extracts a representative frame\nwith B-lines with an accuracy of 87.5%.\n"}
+{"abstract": "  In classification with a reject option, the classifier is allowed in\nuncertain cases to abstain from prediction. The classical cost-based model of a\nreject option classifier requires the cost of rejection to be defined\nexplicitly. An alternative bounded-improvement model, avoiding the notion of\nthe reject cost, seeks for a classifier with a guaranteed selective risk and\nmaximal cover. We coin a symmetric definition, the bounded-coverage model,\nwhich seeks for a classifier with minimal selective risk and guaranteed\ncoverage. We prove that despite their different formulations the three\nrejection models lead to the same prediction strategy: a Bayes classifier\nendowed with a randomized Bayes selection function. We define a notion of a\nproper uncertainty score as a scalar summary of prediction uncertainty\nsufficient to construct the randomized Bayes selection function. We propose two\nalgorithms to learn the proper uncertainty score from examples for an arbitrary\nblack-box classifier. We prove that both algorithms provide Fisher consistent\nestimates of the proper uncertainty score and we demonstrate their efficiency\non different prediction problems including classification, ordinal regression\nand structured output classification.\n"}
+{"abstract": "  Graph neural networks (GNN) have been shown to provide substantial\nperformance improvements for representing and modeling atomistic materials\ncompared with descriptor-based machine-learning models. While most existing GNN\nmodels for atomistic predictions are based on atomic distance information, they\ndo not explicitly incorporate bond angles, which are critical for\ndistinguishing many atomic structures. Furthermore, many material properties\nare known to be sensitive to slight changes in bond angles. We present an\nAtomistic Line Graph Neural Network (ALIGNN), a GNN architecture that performs\nmessage passing on both the interatomic bond graph and its line graph\ncorresponding to bond angles. We demonstrate that angle information can be\nexplicitly and efficiently included, leading to improved performance on\nmultiple atomistic prediction tasks. We use ALIGNN models for predicting 52\nsolid-state and molecular properties available in the JARVIS-DFT, Materials\nproject, and QM9 databases. ALIGNN can outperform some previously reported GNN\nmodels on atomistic prediction tasks by up to 85 % in accuracy with better or\ncomparable model training speed.\n"}
+{"abstract": "  Low congestion shortcuts, introduced by Ghaffari and Haeupler (SODA 2016),\nprovide a unified framework for global optimization problems in the congest\nmodel of distributed computing. Roughly speaking, for a given graph $G$ and a\ncollection of vertex-disjoint connected subsets $S_1,\\ldots, S_\\ell \\subseteq\nV(G)$, $(c,d)$ low-congestion shortcuts augment each subgraph $G[S_i]$ with a\nsubgraph $H_i \\subseteq G$ such that: (i) each edge appears on at most $c$\nsubgraphs (congestion bound), and (ii) the diameter of each subgraph $G[S_i]\n\\cup H_i$ is bounded by $d$ (dilation bound). It is desirable to compute\nshortcuts of small congestion and dilation as these quantities capture the\nround complexity of many global optimization problems in the congest model. For\n$n$-vertex graphs with constant diameter $D=O(1)$, Elkin (STOC 2004) presented\nan (implicit) shortcuts lower bound with\n$c+d=\\widetilde{\\Omega}(n^{(D-2)/(2D-2)})$. A nearly matching upper bound,\nhowever, was only recently obtained for $D \\in \\{3,4\\}$ by Kitamura et al.\n(DISC 2019).\n  In this work, we resolve the long-standing complexity gap of shortcuts in\nconstant diameter graphs, originally posed by Lotker et al. (PODC 2001). We\npresent new shortcut constructions which match, up to poly-logarithmic terms,\nthe lower bounds of Das-Sarma et al. As a result, we provide improved and\nexistentially optimal algorithms for several network optimization tasks in\nconstant diameter graphs, including MST, $(1+\\epsilon)$-approximate minimum\ncuts and more.\n"}
+{"abstract": "  We analyze the Prandtl number (Pr) dependence of spectra and fluxes of\nkinetic energy, as well as the energy injection rates and dissipation rates, of\nturbulent thermal convection using numerical data. As expected, for a flow with\n$ \\mathrm{Pr} \\lessapprox 1 $, the inertial-range kinetic energy flux is\nconstant, and the kinetic energy spectrum is Kolmogorov-like ($ k^{-5/3} $).\nMore importantly, we show that the amplitudes of the kinetic energy fluxes and\nspectra and those of structure functions increase with the decrease of Pr, thus\nexhibiting stronger nonlinearity for flows with small Prandtl numbers.\nConsistent with these observations, the kinetic energy injection rates and the\ndissipation rates too increase with the decrease of Pr. Our results are in\nagreement with earlier studies that report the Reynolds number to be a\ndecreasing function of Prandtl number in turbulent convection. On the other\nhand, the tail of the probability distributions of the local heat flux grows\nwith the increase of Pr, indicating increased fluctuations in the local heat\nflux with Pr.\n"}
+{"abstract": "  We consider the problem of learning a tree-structured Ising model from data,\nsuch that subsequent predictions computed using the model are accurate.\nConcretely, we aim to learn a model such that posteriors $P(X_i|X_S)$ for small\nsets of variables $S$ are accurate. Since its introduction more than 50 years\nago, the Chow-Liu algorithm, which efficiently computes the maximum likelihood\ntree, has been the benchmark algorithm for learning tree-structured graphical\nmodels. A bound on the sample complexity of the Chow-Liu algorithm with respect\nto the prediction-centric local total variation loss was shown in [BK19]. While\nthose results demonstrated that it is possible to learn a useful model even\nwhen recovering the true underlying graph is impossible, their bound depends on\nthe maximum strength of interactions and thus does not achieve the\ninformation-theoretic optimum. In this paper, we introduce a new algorithm that\ncarefully combines elements of the Chow-Liu algorithm with tree metric\nreconstruction methods to efficiently and optimally learn tree Ising models\nunder a prediction-centric loss. Our algorithm is robust to model\nmisspecification and adversarial corruptions. In contrast, we show that the\ncelebrated Chow-Liu algorithm can be arbitrarily suboptimal.\n"}
+{"abstract": "  In this paper, we present a complete proof of the so-called First On-Shell\nTheorem that determines dynamical content of the unfolded equations for free\nsymmetric massless fields of arbitrary integer spin in any dimension and\narbitrary integer or half-integer spin in four dimensions. This is achieved by\ncalculation of the respective $\\sigma_-$ cohomology both in the tensor language\nin Minkowski space of any dimension and in terms of spinors in $AdS_4$. In the\n$d$-dimensional case $H^p(\\sigma_-)$ is computed for any $p$ and interpretation\nof $H^p(\\sigma_-)$ is given both for the original Fronsdal system and for the\nassociated systems of higher form fields.\n"}
+{"abstract": "  We present a drift kinetic model for the free expansion of a thermal plasma\nout of a magnetic nozzle. This problem relates to plasma space propulsion\nsystems, natural environments such as the solar wind, and end losses from the\nexpander region of mirror magnetically confined fusion concepts such as the Gas\nDynamic Trap. The model incorporates trapped and passing orbit types\nencountered in the mirror expander geometry and maps to an upstream thermal\ndistribution. This boundary condition and quasineutrality require the\ngeneration of an ambipolar potential drop of $\\sim5 T_e/e$, forming a thermal\nbarrier for the electrons. The model for the electron and ion velocity\ndistributions and fluid moments is confirmed with data from a fully kinetic\nsimulation. Finally, the model is extended to account for a population of fast\nsloshing ions arising from neutral beam heating within a magnetic mirror, again\nresulting in good agreement with a corresponding kinetic simulation.\n"}
+{"abstract": "  Let $\\mathcal{F}$ be a foliation on a projective manifold $X$ with\n$-K_{\\mathcal{F}}$ nef. Assume that either $\\mathcal{F}$ is regular, or it has\na compact leaf. We prove that there is a locally trivial fibration $f\\colon\nX\\to Y$, and a foliation $\\mathcal{G}$ on $Y$ with $K_{\\mathcal{G}} \\equiv 0$,\nsuch that $\\mathcal{F} = f^{-1}(\\mathcal{G})$.\n"}
+{"abstract": "  We propose a roadmap for leveraging the tremendous opportunities the Internet\nof Things (IoT) has to offer. We argue that the combination of the recent\nadvances in service computing and IoT technology provide a unique framework for\ninnovations not yet envisaged, as well as the emergence of yet-to-be-developed\nIoT applications. This roadmap covers: emerging novel IoT services,\narticulation of major research directions, and suggestion of a roadmap to guide\nthe IoT and service computing community to address key IoT service challenges.\n"}
+{"abstract": "  This paper is concerned with a diffuse interface model called as\nNavier-Stokes/Cahn-Hilliard system. This model is usually used to describe the\nmotion of immiscible two-phase flow with diffusion interface. For the periodic\nboundary value problem of this system in torus $\\mathbb{T}^3$, we prove that\nthere exists a global unique strong solution near the phase separation state,\nwhich means no vacuum, shock wave, mass concentration, interface collision and\nrupture will be developed in finite time. Furthermore, we established the large\ntime behavior of these global strong solution of this system. In particular, we\nfind that the phase field decays algebraically to the phase separation state.\n"}
+{"abstract": "  Recently, several works have addressed modeling of 3D shapes using deep\nneural networks to learn implicit surface representations. Up to now, the\nmajority of works have concentrated on reconstruction quality, paying little or\nno attention to model size or training time. This work proposes LightSAL, a\nnovel deep convolutional architecture for learning 3D shapes; the proposed work\nconcentrates on efficiency both in network training time and resulting model\nsize. We build on the recent concept of Sign Agnostic Learning for training the\nproposed network, relying on signed distance fields, with unsigned distance as\nground truth. In the experimental section of the paper, we demonstrate that the\nproposed architecture outperforms previous work in model size and number of\nrequired training iterations, while achieving equivalent accuracy. Experiments\nare based on the D-Faust dataset that contains 41k 3D scans of human shapes.\nThe proposed model has been implemented in PyTorch.\n"}
+{"abstract": "  We numerically study by lattice Boltzmann simulations the rheological\nproperties of an active emulsion made of a suspension of an active polar gel\nembedded in an isotropic passive background. We find that the hexatic\nequilibrium configuration of polar droplets is highly sensitive to both active\ninjection and external forcing and may either lead to asymmetric unidirectional\nstates which break top-bottom symmetry or symmetric ones. In this latter case,\nfor large enough activity, the system develops a shear-thickening regime at low\nshear rates. Importantly, for larger external forcing a regime with stable\nnegative effective viscosity is found. Moreover, at intermediate activity a\nregion of multistability is encountered and we show that a maximum entropy\nproduction principle holds in selecting the most favorable state.\n"}
+{"abstract": "  We show that minimal-surface non-Euclidean elastic plates share the same\nlow-energy effective theory as Haldane's dimerized quantum spin chain. As a\nresult, such elastic plates support fractional excitations, which take the form\nof charge-$1/2$ solitons between degenerate states of the plates, in strong\nanalogy to their quantum counterpart. These fractional solitons exhibit\nproperties similar to fractional excitations in quantum fractional topological\nstates, including deconfinement and braiding, as well as unique new features\nsuch as holographic properties and diode-like nonlinear response, demonstrating\ngreat potentials for applications as mechanical metamaterials.\n"}
+{"abstract": "  In this note we make progress toward a conjecture of Durham--Fanoni--Vlamis,\nshowing that every infinite-type surface with finite-invariance index 1 and no\nnondisplaceable compact subsurfaces fails to have a good curve graph, that is,\na connected graph where vertices represent homotopy classes of essential simple\nclosed curves and where the natural mapping class group action has infinite\ndiameter orbits. Our arguments use tools developed by Mann--Rafi in their study\nof the coarse geometry of big mapping class groups.\n"}
+{"abstract": "  Thermal images reveal medically important physiological information about\nhuman stress, signs of inflammation, and emotional mood that cannot be seen on\nvisible images. Providing a method to generate thermal faces from visible\nimages would be highly valuable for the telemedicine community in order to show\nthis medical information. To the best of our knowledge, there are limited works\non visible-to-thermal (VT) face translation, and many current works go the\nopposite direction to generate visible faces from thermal surveillance images\n(TV) for law enforcement applications. As a result, we introduce favtGAN, a VT\nGAN which uses the pix2pix image translation model with an auxiliary sensor\nlabel prediction network for generating thermal faces from visible images.\nSince most TV methods are trained on only one data source drawn from one\nthermal sensor, we combine datasets from faces and cityscapes. These combined\ndata are captured from similar sensors in order to bootstrap the training and\ntransfer learning task, especially valuable because visible-thermal face\ndatasets are limited. Experiments on these combined datasets show that favtGAN\ndemonstrates an increase in SSIM and PSNR scores of generated thermal faces,\ncompared to training on a single face dataset alone.\n"}
+{"abstract": "  Mn ions' doping site and valence were studied in PbTiO$_{3}$ (PT) with the\nnative vacancy defects by the first-principles calculations. Firstly, the\nnative vacancy defects of Pb, O and Ti in PT were investigated and it was found\nthat Pb vacancy is preferred to others. And then the growth of Mn doped PT\nshould be preferred to Mn ion substituting for an A-site Pb ion with +3 valence\nwhen Pb is deficient under equilibrium conditions driven solely by minimization\nof the formation energy, and this could result in a larger lattice distortion\nof PT. In addition, when Mn enters the Pb site, the electronegativity of O\nbecomes weaker which makes the domain movement easier in PT to improve the\nperformance of PT, while Mn ion substitution for a B-site Ti ion is the\nopposite.\n"}
+{"abstract": "  Single image super-resolution (SISR) aims to obtain a high-resolution output\nfrom one low-resolution image. Currently, deep learning-based SISR approaches\nhave been widely discussed in medical image processing, because of their\npotential to achieve high-quality, high spatial resolution images without the\ncost of additional scans. However, most existing methods are designed for\nscale-specific SR tasks and are unable to generalise over magnification scales.\nIn this paper, we propose an approach for medical image arbitrary-scale\nsuper-resolution (MIASSR), in which we couple meta-learning with generative\nadversarial networks (GANs) to super-resolve medical images at any scale of\nmagnification in (1, 4]. Compared to state-of-the-art SISR algorithms on\nsingle-modal magnetic resonance (MR) brain images (OASIS-brains) and\nmulti-modal MR brain images (BraTS), MIASSR achieves comparable fidelity\nperformance and the best perceptual quality with the smallest model size. We\nalso employ transfer learning to enable MIASSR to tackle SR tasks of new\nmedical modalities, such as cardiac MR images (ACDC) and chest computed\ntomography images (COVID-CT). The source code of our work is also public. Thus,\nMIASSR has the potential to become a new foundational pre-/post-processing step\nin clinical image analysis tasks such as reconstruction, image quality\nenhancement, and segmentation.\n"}
+{"abstract": "  We propose a new perspective on video understanding by casting the video\nrecognition problem as an image recognition task. We show that an image\nclassifier alone can suffice for video understanding without temporal modeling.\nOur approach is simple and universal. It composes input frames into a super\nimage to train an image classifier to fulfill the task of action recognition,\nin exactly the same way as classifying an image. We prove the viability of such\nan idea by demonstrating strong and promising performance on four public\ndatasets including Kinetics400, Something-to-something (V2), MiT and Jester,\nusing a recently developed vision transformer. We also experiment with the\nprevalent ResNet image classifiers in computer vision to further validate our\nidea. The results on Kinetics400 are comparable to some of the best-performed\nCNN approaches based on spatio-temporal modeling. our code and models will be\nmade available at https://github.com/IBM/sifar-pytorch.\n"}
+{"abstract": "  Competing risks data are common in medical studies, and the sub-distribution\nhazard (SDH) ratio is considered an appropriate measure. However, because the\nlimitations of hazard itself are not easy to interpret clinically and because\nthe SDH ratio is valid only under the proportional SDH assumption, this article\nintroduced an alternative index under competing risks, named restricted mean\ntime lost (RMTL). Several test procedures were also constructed based on RMTL.\nFirst, we introduced the definition and estimation of RMTL based on\nAalen-Johansen cumulative incidence functions. Then, we considered several\ncombined tests based on the SDH and the RMTL difference (RMTLd). The\nstatistical properties of the methods are evaluated using simulations and are\napplied to two examples. The type I errors of combined tests are close to the\nnominal level. All combined tests show acceptable power in all situations. In\nconclusion, RMTL can meaningfully summarize treatment effects for clinical\ndecision making, and three combined tests have robust power under various\nconditions, which can be considered for statistical inference in real data\nanalysis.\n"}
+{"abstract": "  We consider the mean-field Zero-Range process in the regime where the\npotential function $r$ is increasing to infinity at sublinear speed, and the\ndensity of particles is bounded. We determine the mixing time of the system,\nand establish cutoff. We also prove that the Poincar\\'e constant is bounded\naway from zero and infinity. This mean-field estimate extends to arbitrary\ngeometries via a comparison argument. Our proof uses the path-coupling method\nof Bubley and Dyer and stochastic calculus.\n"}
+{"abstract": "  In this paper, we introduce the cohomology theory of $\\mathcal{O}$-operators\non Hom-associative algebras. This cohomology can also be viewed as the\nHochschild cohomology of a certain Hom-associative algebra with coefficients in\na suitable bimodule. Next, we study infinitesimal and formal deformations of an\n$\\mathcal{O}$-operator and show that they are governed by the above-defined\ncohomology. Furthermore, the notion of Nijenhuis elements associated with an\n$\\mathcal{O}$-operator is introduced to characterize trivial infinitesimal\ndeformations.\n"}
+{"abstract": "  Hyperspectral imaging (HSI) unlocks the huge potential to a wide variety of\napplications relied on high-precision pathology image segmentation, such as\ncomputational pathology and precision medicine. Since hyperspectral pathology\nimages benefit from the rich and detailed spectral information even beyond the\nvisible spectrum, the key to achieve high-precision hyperspectral pathology\nimage segmentation is to felicitously model the context along high-dimensional\nspectral bands. Inspired by the strong context modeling ability of\ntransformers, we hereby, for the first time, formulate the contextual feature\nlearning across spectral bands for hyperspectral pathology image segmentation\nas a sequence-to-sequence prediction procedure by transformers. To assist\nspectral context learning procedure, we introduce two important strategies: (1)\na sparsity scheme enforces the learned contextual relationship to be sparse, so\nas to eliminates the distraction from the redundant bands; (2) a spectral\nnormalization, a separate group normalization for each spectral band, mitigates\nthe nuisance caused by heterogeneous underlying distributions of bands. We name\nour method Spectral Transformer (SpecTr), which enjoys two benefits: (1) it has\na strong ability to model long-range dependency among spectral bands, and (2)\nit jointly explores the spatial-spectral features of HSI. Experiments show that\nSpecTr outperforms other competing methods in a hyperspectral pathology image\nsegmentation benchmark without the need of pre-training. Code is available at\nhttps://github.com/hfut-xc-yun/SpecTr.\n"}
+{"abstract": "  The ability to identify stock market trends has obvious advantages for\ninvestors. Buying stock on an upward trend (as well as selling it in case of\ndownward movement) results in profit. Accordingly, the start and end-points of\nthe trend are the optimal points for entering and leaving the market. The\nresearch concentrates on recognizing stock market long-term upward and downward\ntrends. The key results are obtained with the use of gradient boosting\nalgorithms, XGBoost in particular. The raw data is represented by time series\nwith basic stock market quotes with periods labelled by experts as Trend or\nFlat. The features are then obtained via various data transformations, aiming\nto catch implicit factors resulting in a change of stock direction. Modelling\nis done in two stages: stage one aims to detect endpoints of tendencies (i.e.\nsliding windows), stage two recognizes the tendency itself inside the window.\nThe research addresses such issues as imbalanced datasets and contradicting\nlabels, as well as the need for specific quality metrics to keep up with\npractical applicability. The model can be used to design an investment strategy\nthough further research in feature engineering and fine calibration is\nrequired.This paper is the full text of the research, presented at the 20th\nInternational Conference on Artificial Intelligence and Soft Computing Web\nSystem (ICAISC 2021)\n"}
+{"abstract": "  Humans perceive and construct the surrounding world as an arrangement of\nsimple parametric models. In particular, man-made environments commonly consist\nof volumetric primitives such as cuboids or cylinders. Inferring these\nprimitives is an important step to attain high-level, abstract scene\ndescriptions. Previous approaches directly estimate shape parameters from a 2D\nor 3D input, and are only able to reproduce simple objects, yet unable to\naccurately parse more complex 3D scenes. In contrast, we propose a robust\nestimator for primitive fitting, which can meaningfully abstract real-world\nenvironments using cuboids. A RANSAC estimator guided by a neural network fits\nthese primitives to 3D features, such as a depth map. We condition the network\non previously detected parts of the scene, thus parsing it one-by-one. To\nobtain 3D features from a single RGB image, we additionally optimise a feature\nextraction CNN in an end-to-end manner. However, naively minimising\npoint-to-primitive distances leads to large or spurious cuboids occluding parts\nof the scene behind. We thus propose an occlusion-aware distance metric\ncorrectly handling opaque scenes. The proposed algorithm does not require\nlabour-intensive labels, such as cuboid annotations, for training. Results on\nthe challenging NYU Depth v2 dataset demonstrate that the proposed algorithm\nsuccessfully abstracts cluttered real-world 3D scene layouts.\n"}
+{"abstract": "  The novel COVID-19 is a global pandemic disease overgrowing worldwide.\nComputer-aided screening tools with greater sensitivity is imperative for\ndisease diagnosis and prognosis as early as possible. It also can be a helpful\ntool in triage for testing and clinical supervision of COVID-19 patients.\nHowever, designing such an automated tool from non-invasive radiographic images\nis challenging as many manually annotated datasets are not publicly available\nyet, which is the essential core requirement of supervised learning schemes.\nThis article proposes a 3D Convolutional Neural Network (CNN)-based\nclassification approach considering both the inter- and intra-slice spatial\nvoxel information. The proposed system is trained in an end-to-end manner on\nthe 3D patches from the whole volumetric CT images to enlarge the number of\ntraining samples, performing the ablation studies on patch size determination.\nWe integrate progressive resizing, segmentation, augmentations, and\nclass-rebalancing to our 3D network. The segmentation is a critical\nprerequisite step for COVID-19 diagnosis enabling the classifier to learn\nprominent lung features while excluding the outer lung regions of the CT scans.\nWe evaluate all the extensive experiments on a publicly available dataset,\nnamed MosMed, having binary- and multi-class chest CT image partitions. Our\nexperimental results are very encouraging, yielding areas under the ROC curve\nof 0.914 and 0.893 for the binary- and multi-class tasks, respectively,\napplying 5-fold cross-validations. Our method's promising results delegate it\nas a favorable aiding tool for clinical practitioners and radiologists to\nassess COVID-19.\n"}
+{"abstract": "  The orthogonal derivative is defined as a limit of an integral whose kernel\ncontains an orthogonal polynomial with its measure. When in practice no limit\nis taken, it means that the accuracy of the derivative depends on the second\nderivative of the given function. Liptaj shows that it is possible to define a\nkernel in such a way that the accuracy depends on a higher derivative at your\nown choice. The accuracy is therefore much greater than with the orthogonal\nderivative. However Diekema and Koornwinder find a similar extension starting\ndirectly from of the orthogonal derivative. The new kernel is not orthogonal\nfor order greater then one. The transfer function for this new derivative is\ngiven.\n"}
+{"abstract": "  We address the problem of constructing accurate mathematical models of the\ndynamics of complex systems projected on a collective variable. To this aim we\nintroduce a conceptually simple yet effective algorithm for estimating the\nparameters of Langevin and Fokker-Planck equations from a set of short,\npossibly out-of-equilibrium molecular dynamics trajectories, obtained for\ninstance from transition path sampling or as relaxation from high free-energy\nconfigurations. The approach maximizes the model likelihood based on any\nexplicit expression of the short-time propagator, hence it can be applied to\ndifferent evolution equations. We demonstrate the numerical efficiency and\nrobustness of the algorithm on model systems, and we apply it to reconstruct\nthe projected dynamics of pairs of C60 and C240 fullerene molecules in explicit\nwater. Our methodology allows reconstructing the accurate thermodynamics and\nkinetics of activated processes, namely free energy landscapes, diffusion\ncoefficients and kinetic rates. Compared to existing enhanced sampling methods,\nwe directly exploit short unbiased trajectories, at a competitive computational\ncost.\n"}
+{"abstract": "  In recent years, storing large volumes of data on distributed devices has\nbecome commonplace. Applications involving sensors, for example, capture data\nin different modalities including image, video, audio, GPS and others. Novel\nalgorithms are required to learn from this rich distributed data. In this\npaper, we present consensus based multi-layer perceptrons for\nresource-constrained devices. Assuming nodes (devices) in the distributed\nsystem are arranged in a graph and contain vertically partitioned data, the\ngoal is to learn a global function that minimizes the loss. Each node learns a\nfeed-forward multi-layer perceptron and obtains a loss on data stored locally.\nIt then gossips with a neighbor, chosen uniformly at random, and exchanges\ninformation about the loss. The updated loss is used to run a back propagation\nalgorithm and adjust weights appropriately. This method enables nodes to learn\nthe global function without exchange of data in the network. Empirical results\nreveal that the consensus algorithm converges to the centralized model and has\nperformance comparable to centralized multi-layer perceptrons and tree-based\nalgorithms including random forests and gradient boosted decision trees.\n"}
+{"abstract": "  This paper investigates whether computer usage profiles comprised of\nprocess-, network-, mouse-, and keystroke-related events are unique and\nconsistent over time in a naturalistic setting, discussing challenges and\nopportunities of using such profiles in applications of continuous\nauthentication. We collected ecologically-valid computer usage profiles from 31\nMS Windows 10 computer users over 8 weeks and submitted this data to\ncomprehensive machine learning analysis involving a diverse set of online and\noffline classifiers. We found that: (i) profiles were mostly consistent over\nthe 8-week data collection period, with most (83.9%) repeating computer usage\nhabits on a daily basis; (ii) computer usage profiling has the potential to\nuniquely characterize computer users (with a maximum F-score of 99.90%); (iii)\nnetwork-related events were the most relevant features to accurately recognize\nprofiles (95.69% of the top features distinguishing users were\nnetwork-related); and (iv) binary models were the most well-suited for profile\nrecognition, with better results achieved in the online setting compared to the\noffline setting (maximum F-score of 99.90% vs. 95.50%).\n"}
+{"abstract": "  As the demand for vehicles continues to outpace construction of new roads, it\nbecomes imperative we implement strategies that improve utilization of existing\ntransport infrastructure. Traffic sensors form a crucial part of many such\nstrategies, giving us valuable insights into road utilization. However, due to\ncost and lead time associated with installation and maintenance of traffic\nsensors, municipalities and traffic authorities look toward cheaper and more\nscalable alternatives. Due to their ubiquitous nature and wide global\ndeployment, cellular networks offer one such alternative. In this paper we\npresent a novel method for traffic flow estimation using standardized LTE/4G\nradio frequency performance measurement counters. The problem is cast as a\nsupervised regression task using both classical and deep learning methods. We\nfurther apply transfer learning to compensate that many locations lack traffic\nsensor data that could be used for training. We show that our approach benefits\nfrom applying transfer learning to generalize the solution not only in time but\nalso in space (i.e., various parts of the city). The results are very promising\nand, unlike competing solutions, our approach utilizes aggregate LTE radio\nfrequency counter data that is inherently privacy-preserving, readily\navailable, and scales globally without any additional network impact.\n"}
+{"abstract": "  We present a new approach for learning unsupervised node representations in\ncommunity graphs. We significantly extend the Interferometric Graph Transform\n(IGT) to community labeling: this non-linear operator iteratively extracts\nfeatures that take advantage of the graph topology through demodulation\noperations. An unsupervised feature extraction step cascades modulus\nnon-linearity with linear operators that aim at building relevant invariants\nfor community labeling. Via a simplified model, we show that the IGT\nconcentrates around the E-IGT: those two representations are related through\nsome ergodicity properties. Experiments on community labeling tasks show that\nthis unsupervised representation achieves performances at the level of the\nstate of the art on the standard and challenging datasets Cora, Citeseer,\nPubmed and WikiCS.\n"}
+{"abstract": "  We quantify the information content of the non-linear matter power spectrum,\nthe halo mass function, and the void size function, using the Quijote $N$-body\nsimulations. We find that these three statistics exhibit very different\ndegeneracies amongst the cosmological parameters, and thus the combination of\nall three probes enables the breaking of degeneracies, in turn yielding\nremarkably tight constraints. We perform a Fisher analysis using the full\ncovariance matrix, including all auto- and cross-correlations, finding that\nthis increases the information content for neutrino mass compared to a\ncorrelation-free analysis. The multiplicative improvement of the constraints on\nthe cosmological parameters obtained by combining all three probes compared to\nusing the power spectrum alone are: 137, 5, 8, 20, 10, and 43, for $\\Omega_m$,\n$\\Omega_b$, $h$, $n_s$, $\\sigma_8$, and $M_\\nu$, respectively. The marginalized\nerror on the sum of the neutrino masses is $\\sigma(M_\\nu)=0.018\\,{\\rm eV}$ for\na cosmological volume of $1\\,(h^{-1}{\\rm Gpc})^3$, using $k_{\\max}=0.5\\,h{\\rm\nMpc}^{-1}$, and without CMB priors. We note that this error is an underestimate\ninsomuch as we do not consider super-sample covariance, baryonic effects, and\nrealistic survey noises and systematics. On the other hand, it is an\noverestimate insomuch as our cuts and binning are suboptimal due to\nrestrictions imposed by the simulation resolution. Given upcoming galaxy\nsurveys will observe volumes spanning $\\sim 100\\,(h^{-1}{\\rm Gpc})^3$, this\npresents a promising new avenue to measure neutrino mass without being\nrestricted by the need for accurate knowledge of the optical depth, which is\nrequired for CMB-based measurements. Furthermore, the improved constraints on\nother cosmological parameters, notably $\\Omega_m$, may also be competitive with\nCMB-based measurements.\n"}
+{"abstract": "  We study how an observationally-motivated, metallicity-dependent initial mass\nfunction (IMF) affects the feedback budget and observables of an ultra-faint\ndwarf galaxy. We model the evolution of a low-mass ($\\approx 8 \\, \\times \\,\n10^{8} \\, \\rm M_{\\odot}$) dark matter halo with cosmological, zoomed\nhydrodynamical simulations capable of resolving individual supernovae\nexplosions. We complement the EDGE galaxy formation model from Agertz et al.\n(2020) with a new prescription for IMF variations according to Geha et al.\n(2013). At the low metallicities typical of faint dwarf galaxies, the IMF\nbecomes top-heavy, increasing the efficiency of supernova and photo-ionization\nfeedback in regulating star formation. This results in a 100-fold reduction of\nthe final stellar mass of the dwarf compared to a canonical IMF, at fixed\ndynamical mass. The increase in the feedback budget is nonetheless met by\nincreased metal production from more numerous massive stars, leading to nearly\nconstant iron content at $z=0$. A metallicity-dependent IMF therefore provides\na mechanism to produce low-mass ($\\rm M_{\\star}\\sim 10^3 \\rm M_{\\odot}$), yet\nenriched ($\\rm [Fe/H]\\approx -2$) field dwarf galaxies, thus opening a\nself-consistent avenue to populate the plateau in $\\rm [Fe/H]$ at the faintest\nend of the mass-metallicity relation.\n"}
+{"abstract": "  Unsupervised domain adaptation (UDA) and semi-supervised learning (SSL) are\ntwo typical strategies to reduce expensive manual annotations in machine\nlearning. In order to learn effective models for a target task, UDA utilizes\nthe available labeled source data, which may have different distributions from\nunlabeled samples in the target domain, while SSL employs few manually\nannotated target samples. Although UDA and SSL are seemingly very different\nstrategies, we find that they are closely related in terms of task objectives\nand solutions, and SSL is a special case of UDA problems. Based on this\nfinding, we further investigate whether SSL methods work on UDA tasks. By\nadapting eight representative SSL algorithms on UDA benchmarks, we show that\nSSL methods are strong UDA learners. Especially, state-of-the-art SSL methods\nsignificantly outperform existing UDA methods on the challenging UDA benchmark\nof DomainNet, and state-of-the-art UDA methods could be further enhanced with\nSSL techniques. We thus promote that SSL methods should be employed as\nbaselines in future UDA studies and expect that the revealed relationship\nbetween UDA and SSL could shed light on future UDA development. Codes are\navailable at \\url{https://github.com/YBZh}.\n"}
+{"abstract": "  We present BERTGEN, a novel generative, decoder-only model which extends BERT\nby fusing multimodal and multilingual pretrained models VL-BERT and M-BERT,\nrespectively. BERTGEN is auto-regressively trained for language generation\ntasks, namely image captioning, machine translation and multimodal machine\ntranslation, under a multitask setting. With a comprehensive set of\nevaluations, we show that BERTGEN outperforms many strong baselines across the\ntasks explored. We also show BERTGEN's ability for zero-shot language\ngeneration, where it exhibits competitive performance to supervised\ncounterparts. Finally, we conduct ablation studies which demonstrate that\nBERTGEN substantially benefits from multi-tasking and effectively transfers\nrelevant inductive biases from the pre-trained models.\n"}
+{"abstract": "  Quantum Computing is a new paradigm that enables several advances which are\nimpossible using classical technology. With the rise of quantum computers, the\nsoftware is also invited to change so that it can better fit this new\ncomputation way. However, although a lot of research is being conducted in the\nquantum computing field, it is still scarce studies about the differences of\nthe software and software engineering in this new context. Therefore, this\narticle presents a systematic mapping study to present a wide review on the\nparticularities and characteristics of software that are developed for quantum\ncomputers. A total of 24 papers were selected using digital libraries with the\nobjective of answering three research questions elaborated in the conduct of\nthis research.\n"}
+{"abstract": "  We introduce the task of historical text summarisation, where documents in\nhistorical forms of a language are summarised in the corresponding modern\nlanguage. This is a fundamentally important routine to historians and digital\nhumanities researchers but has never been automated. We compile a high-quality\ngold-standard text summarisation dataset, which consists of historical German\nand Chinese news from hundreds of years ago summarised in modern German or\nChinese. Based on cross-lingual transfer learning techniques, we propose a\nsummarisation model that can be trained even with no cross-lingual (historical\nto modern) parallel data, and further benchmark it against state-of-the-art\nalgorithms. We report automatic and human evaluations that distinguish the\nhistoric to modern language summarisation task from standard cross-lingual\nsummarisation (i.e., modern to modern language), highlight the distinctness and\nvalue of our dataset, and demonstrate that our transfer learning approach\noutperforms standard cross-lingual benchmarks on this task.\n"}
+{"abstract": "  Analysing dependent risks is an important task for insurance companies. A\ndependency is reflected in the fact that information about one random variable\nprovides information about the likely distribution of values of another random\nvariable. Insurance companies in particular must investigate such dependencies\nbetween different lines of business and the effects that an extreme loss event,\nsuch as an earthquake or hurricane, has across multiple lines of business\nsimultaneously. Copulas provide a popular model-based approach to analysing the\ndependency between risks, and the coefficient of tail dependence is a measure\nof dependence for extreme losses. Besides commonly used empirical estimators\nfor estimating the tail dependence coefficient, copula fitting can lead to\nestimation of such coefficients directly or can verify their existence.\nGenerally, a range of copula models is available to fit a data set well,\nleading to multiple different tail dependence results; a method based on\nBayesian model averaging is designed to obtain a unified estimate of tail\ndependence. In this article, this model-based coefficient estimation method is\nillustrated through a variety of copula fitting approaches and results are\npresented for several simulated data sets and also a real general insurance\nloss data set.\n"}
+{"abstract": "  Sesqui-pushout (SqPO) rewriting along non-linear rules and for monic matches\nis well-known to permit the modeling of fusing and cloning of vertices and\nedges, yet to date, no construction of a suitable concurrency theorem was\navailable. The lack of such a theorem, in turn, rendered compositional\nreasoning for such rewriting systems largely infeasible. We develop in this\npaper a suitable concurrency theorem for non-linear SqPO-rewriting in\ncategories that are quasi-topoi (subsuming the example of adhesive categories)\nand with matches required to be regular monomorphisms of the given category.\nOur construction reveals an interesting \"backpropagation effect\" in computing\nrule compositions. We derive in addition a concurrency theorem for non-linear\ndouble pushout (DPO) rewriting in rm-adhesive categories. Our results open\nnon-linear SqPO and DPO semantics to the rich static analysis techniques\navailable from concurrency, rule algebra and tracelet theory.\n"}
+{"abstract": "  In this paper, some enhanced error estimates are derived for the augmented\nsubspace methods which are designed for solving eigenvalue problems. We will\nshow that the augmented subspace methods have the second order convergence rate\nwhich is better than the existing results. These sharper estimates provide a\nnew dependence of convergence rate on the coarse spaces in augmented subspace\nmethods. These new results are also validated by some numerical examples.\n"}
+{"abstract": "  In this paper we present a quantum algorithm which increases the amplitude of\nthe states corresponding to the solutions of the search problem by a factor of\nalmost two.\n"}
+{"abstract": "  In this article, high frequency stability estimates for the determination of\nthe potential in the Schr\\\"odinger equation are studied when the boundary\nmeasurements are made on slightly more than half the boundary. The estimates\nreflect the increasing stability property with growing frequency.\n"}
+{"abstract": "  The spread of an infection, a contagion, meme, emotion, message and various\nother spreadable objects have been discussed in several works. Burning and\nfirefighting have been discussed in particular on static graphs. Graph burning\nsimulates the notion of the spread of \"fire\" throughout a graph (plus, one\nunburned node burned at each time-step); graph firefighting simulates the\ndefending of nodes by placing firefighters on the nodes which have not been\nalready burned while the fire is being spread (started by only a single fire\nsource).\n  This article studies a combination of firefighting and burning on a graph\nclass which is a variation (generalization) of temporal graphs. Nodes can be\ninfected from \"outside\" a network. We present a notion of both upgrading (of\nunburned nodes, similar to firefighting) and repairing (of infected nodes). The\nnodes which are burned, firefighted, or repaired are chosen probabilistically.\nSo a variable amount of nodes are allowed to be infected, upgraded and repaired\nin each time step.\n  In the model presented in this article, both burning and firefighting proceed\nconcurrently, we introduce such a system to enable the community to study the\nnotion of spread of an infection and the notion of upgrade/repair against each\nother. The graph class that we study (on which, these processes are simulated)\nis a variation of temporal graph class in which at each time-step,\nprobabilistically, a communication takes place (iff an edge exists in that time\nstep). In addition, a node can be \"worn out\" and thus can be removed from the\nnetwork, and a new healthy node can be added to the network as well. This class\nof graphs enables systems with high complexity to be able to be simulated and\nstudied.\n"}
+{"abstract": "  We prove multi-parameter Leibniz rules corresponding to flag paraproducts of\narbitrary complexity in mixed-norm spaces, including endpoint estimates. The\nproof relies on multi-linear harmonic analysis techniques and a quantitative\ntreatment of the commutators introduced by Bourgain and Li. The argument is\nrobust and applicable to a generic class of multipliers, including (symmetric)\nMikhlin multipliers of positive order and asymmetric variants of partial\ndifferential operators and Mikhlin multipliers of positive order.\n"}
+{"abstract": "  The literature on precision differential abundances (PDAs) in stars is\nextensive. Surveys include sun-like stars in the solar neighborhood, binary\nsystems, and Galactic clusters. Numerous references as well as a discussion of\nrelevant mechanisms may be found in papers by Ramirez, et al. (2019) and Nagar,\net al. (2020). A strong impetus for this work is the probability that the\nabundances have been influenced by exoplanetary systems and their evolution.\n  We calculate the resulting differential abundances ([El/H]) assuming a given\namount of material with the composition of the bulk earth (Wang, et al. 2018)\nwas added to the stellar convection zone of a dwarf G-type star. The mass of\nthe convection zone is uncertain and variable, depending on the spectral type.\nHere, we assume a mass of $5\\cdot 10^{31}$ gm for the stellar convection zone\n(SCZ). This is 0.025 $M_\\odot$ (Pinsonneault, et al. 2001, Chambers, 2010). For\nother SCZ masses, the parameters must be adjusted accordingly. In general, the\nsunlike star will not have exactly the solar composition. This contingency is\nroughly taken into account in our model. An appendix discusses issues of\nvolatility and condensation temperature.\n"}
+{"abstract": "  Simultaneously with the transformation in the energy system, the spot and\nancillary service markets for electricity have become increasingly flexible\nwith shorter service periods and lower minimum powers. This flexibility has\nmade the fastest form of frequency regulation - the frequency containment\nreserve (FCR) - particularly attractive for large-scale battery storage systems\n(BSSs) and led to a market growth of these systems. However, this growth\nresulted in high competition and consequently falling FCR prices, making the\nFCR market increasingly unattractive to large-scale BSSs. In the context of\nmulti-use concepts, this market may be interesting especially for a pool of\nelectric vehicles (EVs), which can generate additional revenue during their\nidle times. In this paper, multi-year measurement data of 22 commercial EVs are\nused for the development of a simulation model for marketing FCR. In addition,\nlogbooks of more than 460 vehicles of different economic sectors are evaluated.\nBased on the simulations, the effects of flexibilization on the marketing of a\npool of EVs are analyzed for the example of the German FCR market design, which\nis valid for many countries in Europe. It is shown that depending on the\nsector, especially the recently made changes of service periods from one week\nto one day and from one day to four hours generate the largest increase in\navailable pool power. Further reductions in service periods, on the other hand,\noffer only a small advantage, as the idle times are often longer than the short\nservice periods. In principle, increasing flexibility overcompensates for\nfalling FCR prices and leads to higher revenues, even if this does not apply\nacross all sectors examined. A pool of 1,000 EVs could theoretically generate\nrevenues of about 5,000 EUR - 8,000 EUR per week on the German FCR market in\n2020.\n"}
+{"abstract": "  Camouflaged object detection (COD) aims to segment camouflaged objects hiding\nin the environment, which is challenging due to the similar appearance of\ncamouflaged objects and their surroundings. Research in biology suggests that\ndepth can provide useful object localization cues for camouflaged object\ndiscovery, as all the animals have 3D perception ability. However, the depth\ninformation has not been exploited for camouflaged object detection. To explore\nthe contribution of depth for camouflage detection, we present a depth-guided\ncamouflaged object detection network with pre-computed depth maps from existing\nmonocular depth estimation methods. Due to the domain gap between the depth\nestimation dataset and our camouflaged object detection dataset, the generated\ndepth may not be accurate enough to be directly used in our framework. We then\nintroduce a depth quality assessment module to evaluate the quality of depth\nbased on the model prediction from both RGB COD branch and RGB-D COD branch.\nDuring training, only high-quality depth is used to update the modal\ninteraction module for multi-modal learning. During testing, our depth quality\nassessment module can effectively determine the contribution of depth and\nselect the RGB branch or RGB-D branch for camouflage prediction. Extensive\nexperiments on various camouflaged object detection datasets prove the\neffectiveness of our solution in exploring the depth information for\ncamouflaged object detection. Our code and data is publicly available at:\n\\url{https://github.com/JingZhang617/RGBD-COD}.\n"}
+{"abstract": "  Due to the large volume of data and information generated by a multitude of\nsocial data sources, it is a huge challenge to manage and extract useful\nknowledge, especially given the different forms of data, streaming data and\nuncertainty and ambiguity of data. Hence, there are still challenges in this\narea of BD analytics research to capture, store, process, visualise, query, and\nmanipulate datasets to derive meaningful information that is specific to an\napplication's domain. This chapter attempts to address this problem by studying\nSemantic Analytics and domain knowledge modelling, and to what extent these\ntechnologies can be utilised toward better understanding to the social textual\ncontents. In particular, the chapter gives an overview of semantic analysis and\ndomain ontology followed by shedding light on domain knowledge modelling,\ninference, semantic storage, and publicly available semantic tools and APIs.\nAlso, the theoretical notion of Knowledge Graphs is reported and their\ninterlinking with SBD is discussed. The utility of the semantic analytics is\ndemonstrated and evaluated through a case study on social data in the context\nof politics domain.\n"}
+{"abstract": "  Temporal logic inference is the process of extracting formal descriptions of\nsystem behaviors from data in the form of temporal logic formulas. The existing\ntemporal logic inference methods mostly neglect uncertainties in the data,\nwhich results in limited applicability of such methods in real-world\ndeployments. In this paper, we first investigate the uncertainties associated\nwith trajectories of a system and represent such uncertainties in the form of\ninterval trajectories. We then propose two uncertainty-aware signal temporal\nlogic (STL) inference approaches to classify the undesired behaviors and\ndesired behaviors of a system. Instead of classifying finitely many\ntrajectories, we classify infinitely many trajectories within the interval\ntrajectories. In the first approach, we incorporate robust semantics of STL\nformulas with respect to an interval trajectory to quantify the margin at which\nan STL formula is satisfied or violated by the interval trajectory. The second\napproach relies on the first learning algorithm and exploits the decision tree\nto infer STL formulas to classify behaviors of a given system. The proposed\napproaches also work for non-separable data by optimizing the worst-case\nrobustness in inferring an STL formula. Finally, we evaluate the performance of\nthe proposed algorithms in two case studies, where the proposed algorithms show\nreductions in the computation time by up to four orders of magnitude in\ncomparison with the sampling-based baseline algorithms (for a dataset with 800\nsampled trajectories in total).\n"}
+{"abstract": "  Formation of hazes at microbar pressures has been explored by theoretical\nmodels of exoplanet atmospheres to explain Rayleigh scattering and/or\nfeatureless transmission spectra, however observational evidence of aerosols in\nthe low pressure formation environments has proved elusive. Here, we show\ndirect evidence of aerosols existing at $\\sim$1 microbar pressures in the\natmosphere of the warm sub-Saturn WASP-69b using observations taken with Space\nTelescope Imaging Spectrograph (STIS) and Wide Field Camera 3 (WFC3)\ninstruments on the Hubble Space Telescope. The transmission spectrum shows a\nwavelength-dependent slope induced by aerosol scattering that covers 11 scale\nheights of spectral modulation. Drawing on the extensive studies of haze in our\nSolar System, we model the transmission spectrum based on a scaled version of\nJupiter's haze density profile to show that WASP-69b transmission spectrum can\nbe produced by scattering from an approximately constant density of particles\nextending throughout the atmospheric column from 40 millibar to microbar\npressures. These results are consistent with theoretical expectations based on\nmicrophysics of the aerosol particles that have suggested haze can exist at\nmicrobar pressures in exoplanet atmospheres.\n"}
+{"abstract": "  Motivated by the recent experimental demonstrations of quantum supremacy,\nproving the hardness of the output of random quantum circuits is an imperative\nnear term goal. We prove under the complexity theoretical assumption of the\nnon-collapse of the polynomial hierarchy that approximating the output\nprobabilities of random quantum circuits to within $\\exp(-\\Omega(m\\log m))$\nadditive error is hard for any classical computer, where $m$ is the number of\ngates in the quantum computation. More precisely, we show that the above\nproblem is $\\#\\mathsf{P}$-hard under $\\mathsf{BPP}^{\\mathsf{NP}}$ reduction. In\nthe recent experiments, the quantum circuit has $n$-qubits and the architecture\nis a two-dimensional grid of size $\\sqrt{n}\\times\\sqrt{n}$. Indeed for constant\ndepth circuits approximating the output probabilities to within\n$2^{-\\Omega(n\\log{n})}$ is hard. For circuits of depth $\\log{n}$ or $\\sqrt{n}$\nfor which the anti-concentration property holds, approximating the output\nprobabilities to within $2^{-\\Omega(n\\log^2{n})}$ and $2^{-\\Omega(n^{3/2}\\log\nn)}$ is hard respectively. We then show that the hardness results extend to any\nopen neighborhood of an arbitrary (fixed) circuit including the trivial circuit\nwith identity gates. We made an effort to find the best proofs and proved these\nresults from first principles, which do not use the standard techniques such as\nthe Berlekamp--Welch algorithm, the usual Paturi's lemma, and Rakhmanov's\nresult.\n"}
+{"abstract": "  We demonstrate efficient generation of mid-infrared frequency combs based on\ncontinuous-wave-seeded femtosecond optical parametric generation in nonlinear\nwaveguides. Conversion of the near-infrared pump to signal and idler light\ntakes place with very high efficiency (74 %) and the threshold (25 pJ) is over\n300 times lower than in bulk analogs. Relative intensity noise of the\nmid-infrared comb is exceptionally low, below 5*10-5 (detector-limited,\nintegrated from 10 Hz to 2 MHz). Furthermore, the mid-infrared bandwidth can be\nincreased by driving the process with broadband pump obtained via\nsupercontinuum generation.\n"}
+{"abstract": "  We inspect the exclusive hadronic decay modes $ B_d\\to \\phi (\\eta^{(')}, \\pi,\n\\omega)$, induced by quark level transition as $b\\to d$ $(\\Delta S=0)$, in\nvector like down quark model. As these decay modes insist highly suppressed\nfollowed by the predicted branching fraction $\\mathcal{O}(10^{-9})$ which\nreflects to scrutinize physics beyond the standard model. We constrain the new\nparameter space inferred from experimental limits on leptonic $B_d\\to \\ell\n\\ell(\\ell=e, \\mu, \\tau)$ and nonleptonic decay modes $B_d \\to \\eta' \\pi^0$ and\n$B_u\\to \\rho^- \\eta'$. We then check the new physics contributions can have\nsignificant impact on the prominent observable so called branching ratio of\n$B_d\\to \\phi (\\eta^{(')},\\pi, \\omega)$ processes.\n"}
+{"abstract": "  This paper deals with the numerical approximation of the biharmonic inverse\nsource problem in an abstract setting in which the measurement data is\nfinite-dimensional. This unified framework in particular covers the conforming\nand nonconforming finite element methods (FEMs). The inverse problem is\nanalysed through the forward problem. Error estimate for the forward solution\nis derived in an abstract set-up that applies to conforming and Morley\nnonconforming FEMs. Since the inverse problem is ill-posed, Tikhonov\nregularisation is considered to obtain a stable approximate solution. Error\nestimate is established for the regularised solution for different\nregularisation schemes. Numerical results that confirm the theoretical results\nare also presented.\n"}
+{"abstract": "  Motivated by the free products of groups, the direct sums of modules, and\nShelah's $(\\lambda,2)$-goodness, we study strong amalgamation properties in\nAbstract Elementary Classes. Such a notion of amalgamation consists of a\nselection of certain amalgams for every triple $M_0\\leq M_1, M_2$, and we show\nthat if $K$ designates a unique strong amalgam to every triple $M_0\\leq M_1,\nM_2$, then $K$ satisfies categoricity transfer at cardinals\n$\\geq\\theta(K)+2^{\\text{LS}(K)}$, where $\\theta(K)$ is a cardinal associated\nwith the notion of amalgamation. We also show that if such a unique choice does\nnot exist, then there is some model $M\\in K$ having $2^{|M|}$ many extensions\nwhich cannot be embedded in each other over $M$. Thus, for AECs which admit a\nnotion of amalgamation, the property of having unique amalgams is a dichotomy\nproperty in the sense of Shelah's classification theory.\n"}
+{"abstract": "  We compute explicitly the least degree of generators of almost all symbolic\npowers of the defining ideal of Fermat-like configuration of lines in\n$\\mathbb{P}^3_\\mathbb{C}$ and provide efficient bounds for remaining cases. We\nwill also compute explicitly those numbers for ideal determining the singular\nlocus of the arrangement of lines given by the pseudoreflection group $A_3$. As\ndirect applications, we verify Chudnovsky's(-like) Conjecture,\nDemailly's(-like) Conjecture and Harbourne-Huneke Containment problem as well\nas calculate the Waldschmidt constant and (asymptotic) resurgence number.\n"}
+{"abstract": "  Instead of using expensive manual annotations, researchers have proposed to\ntrain named entity recognition (NER) systems using heuristic labeling rules.\nHowever, devising labeling rules is challenging because it often requires a\nconsiderable amount of manual effort and domain expertise. To alleviate this\nproblem, we propose \\textsc{GLaRA}, a graph-based labeling rule augmentation\nframework, to learn new labeling rules from unlabeled data. We first create a\ngraph with nodes representing candidate rules extracted from unlabeled data.\nThen, we design a new graph neural network to augment labeling rules by\nexploring the semantic relations between rules. We finally apply the augmented\nrules on unlabeled data to generate weak labels and train a NER model using the\nweakly labeled data. We evaluate our method on three NER datasets and find that\nwe can achieve an average improvement of +20\\% F1 score over the best baseline\nwhen given a small set of seed rules.\n"}
+{"abstract": "  The reaction between atomic oxygen and molecular hydrogen is an important one\nin astrochemistry as it regulates the abundance of the hydroxyl radical and\nserves to open the chemistry of oxygen in diverse astronomical environments.\nHowever, the existence of a high activation barrier in the reaction with ground\nstate oxygen atoms limits its efficiency in cold gas. In this study we\ncalculate the dependence of the reaction rate coefficient on the rotational and\nvibrational state of H$_2$ and evaluate the impact on the abundance of OH in\ninterstellar regions strongly irradiated by far-UV photons, where H2 can be\nefficiently pumped to excited vibrational states. We use a recently calculated\npotential energy surface and carry out time-independent quantum mechanical\nscattering calculations to compute rate coefficients for the reaction O(3P) +\nH2(v,j) -> OH + H, with H2 in vibrational states v = 0-7 and rotational states\nj = 0-10. We find that the reaction becomes significantly faster with\nincreasing vibrational quantum number of H2, although even for high vibrational\nstates of H2 (v = 4-5) for which the reaction is barrierless, the rate\ncoefficient does not strictly attain the collision limit and still maintains a\npositive dependence with temperature. We implemented the calculated\nstate-specific rate coefficients in the Meudon PDR code to model the Orion Bar\nPDR and evaluate the impact on the abundance of the OH radical. We find the\nfractional abundance of OH is enhanced by up to one order of magnitude in\nregions of the cloud corresponding to Av = 1.3-2.3, compared to the use of a\nthermal rate coefficient for O + H2, although the impact on the column density\nof OH is modest, of about 60 %. The calculated rate coefficients will be useful\nto model and interpret JWST observations of OH in strongly UV-illuminated\nenvironments.\n"}
+{"abstract": "  The decrease in the volatility and persistence of US inflation was\naccompanied by a decline in the public's attention to inflation. This decline\nin attention weakens the effectiveness of make-up policies and negative\ninterest rates and can lead to inflation-attention traps: prolonged periods of\na binding lower bound and low inflation due to slowly-adjusting inflation\nexpectations. To mitigate the drawbacks of lower attention, it is optimal to\nincrease the inflation target as attention declines. Accounting for the lower\nbound fundamentally alters the normative implications of declining attention.\nWhile lower attention raises welfare absent the lower-bound constraint, it\ndecreases welfare when accounting for the lower bound.\n"}
+{"abstract": "  Ultra-wideband (UWB) communications have gained popularity in recent years\nfor being able to provide distance measurements and localization with high\naccuracy, which can enhance the capabilities of devices in the Internet of\nThings (IoT). Since energy efficiency is of utmost concern in such\napplications, in this work we evaluate the power and energy consumption,\ndistance measurements, and localization performance of two types of UWB\nphysical interfaces (PHYs), which use either a low- or high-rate pulse\nrepetition (LRP and HRP, respectively). The evaluation is done through\nmeasurements acquired in identical conditions, which is crucial in order to\nhave a fair comparison between the devices. The LRP devices that we tested have\nthe same ranging and localization performance, but ten times (10x) lower power\nconsumption, 6x lower energy consumption per distance measurement, and at least\n8x higher coverage than the HRP devices. Therefore, UWB LRP devices can offer\nhigh-accuracy ranging and localization even to ultra-low-power devices in the\nIoT. We performed measurements in typical LOS and NLOS scenarios and propose\ntheoretical models for the distance errors obtained in these situations. The\nmodels can be used to simulate realistic building deployments and we illustrate\nsuch an example. This paper, therefore, provides a comprehensive overview of\nthe energy demands, ranging characteristics, and localization performance of\nstate-of-the-art UWB devices.\n"}
+{"abstract": "  We present ALADIN (All Layer AdaIN); a novel architecture for searching\nimages based on the similarity of their artistic style. Representation learning\nis critical to visual search, where distance in the learned search embedding\nreflects image similarity. Learning an embedding that discriminates\nfine-grained variations in style is hard, due to the difficulty of defining and\nlabelling style. ALADIN takes a weakly supervised approach to learning a\nrepresentation for fine-grained style similarity of digital artworks,\nleveraging BAM-FG, a novel large-scale dataset of user generated content\ngroupings gathered from the web. ALADIN sets a new state of the art accuracy\nfor style-based visual search over both coarse labelled style data (BAM) and\nBAM-FG; a new 2.62 million image dataset of 310,000 fine-grained style\ngroupings also contributed by this work.\n"}
+{"abstract": "  In recent years, artificial neural networks have developed into a powerful\ntool for dealing with a multitude of problems for which classical solution\napproaches reach their limits. However, it is still unclear why randomly\ninitialized gradient descent optimization algorithms, such as the well-known\nbatch gradient descent, are able to achieve zero training loss in many\nsituations even though the objective function is non-convex and non-smooth. One\nof the most promising approaches to solving this problem in the field of\nsupervised learning is the analysis of gradient descent optimization in the\nso-called overparameterized regime. In this article we provide a further\ncontribution to this area of research by considering overparameterized\nfully-connected rectified artificial neural networks with biases. Specifically,\nwe show that for a fixed number of training data the mean squared error using\nbatch gradient descent optimization applied to such a randomly initialized\nartificial neural network converges to zero at a linear convergence rate as\nlong as the width of the artificial neural network is large enough, the\nlearning rate is small enough, and the training input data are pairwise\nlinearly independent.\n"}
+{"abstract": "  This work presents our advancements in controlling an articulatory speech\nsynthesis engine, \\textit{viz.}, Pink Trombone, with hand gestures. Our\ninterface translates continuous finger movements and wrist flexion into\ncontinuous speech using vocal tract area-function based articulatory speech\nsynthesis. We use Cyberglove II with 18 sensors to capture the kinematic\ninformation of the wrist and the individual fingers, in order to control a\nvirtual tongue. The coordinates and the bending values of the sensors are then\nutilized to fit a spline tongue model that smoothens out the noisy values and\noutliers. Considering the upper palate as fixed and the spline model as the\ndynamically moving lower surface (tongue) of the vocal tract, we compute 1D\narea functional values that are fed to the Pink Trombone, generating continuous\nspeech sounds. Therefore, by learning to manipulate one's wrist and fingers,\none can learn to produce speech sounds just through one's hands, without the\nneed for using the vocal tract.\n"}
+{"abstract": "  In this paper, we propose a blockchain-based cold chain technology for\nvaccine cooling track. The COVID-19 pandemic has caused the death of millions\nof people. An important step towards ending the pandemic is vaccination.\nVaccines must be kept under control temperature during the whole process, from\nfabrication to the hands of the health professionals who will immunize the\npopulation. However, there are numerous reports of vaccine loss due to\ntemperature variations, and, currently, people getting vaccinated have no\ncontrol if their vaccine was kept safe. Blockchain is a technology solution\nthat can provide public and verifiable records. We review the World Health\nOrganization (WHO) cool chain and Blockchain technology. Moreover, we describe\ncurrent IoT temperature monitoring devices and propose Blockcoldchain to track\nvaccine cold chain using blockchain, thus proving an unalterable vaccine\ntemperature history. Our experimental results using smart contracts demonstrate\nthe system's feasibility.\n"}
+{"abstract": "  There is an increasing realization that algorithmic inductive biases are\ncentral in preventing overfitting; empirically, we often see a benign\noverfitting phenomenon in overparameterized settings for natural learning\nalgorithms, such as stochastic gradient descent (SGD), where little to no\nexplicit regularization has been employed. This work considers this issue in\narguably the most basic setting: constant-stepsize SGD (with iterate averaging\nor tail averaging) for linear regression in the overparameterized regime. Our\nmain result provides a sharp excess risk bound, stated in terms of the full\neigenspectrum of the data covariance matrix, that reveals a bias-variance\ndecomposition characterizing when generalization is possible: (i) the variance\nbound is characterized in terms of an effective dimension (specific for SGD)\nand (ii) the bias bound provides a sharp geometric characterization in terms of\nthe location of the initial iterate (and how it aligns with the data covariance\nmatrix). More specifically, for SGD with iterate averaging, we demonstrate the\nsharpness of the established excess risk bound by proving a matching lower\nbound (up to constant factors). For SGD with tail averaging, we show its\nadvantage over SGD with iterate averaging by proving a better excess risk bound\ntogether with a nearly matching lower bound. Moreover, we reflect on a number\nof notable differences between the algorithmic regularization afforded by\n(unregularized) SGD in comparison to ordinary least squares (minimum-norm\ninterpolation) and ridge regression. Experimental results on synthetic data\ncorroborate our theoretical findings.\n"}
+{"abstract": "  While GANs have shown success in realistic image generation, the idea of\nusing GANs for other tasks unrelated to synthesis is underexplored. Do GANs\nlearn meaningful structural parts of objects during their attempt to reproduce\nthose objects? In this work, we test this hypothesis and propose a simple and\neffective approach based on GANs for semantic part segmentation that requires\nas few as one label example along with an unlabeled dataset. Our key idea is to\nleverage a trained GAN to extract pixel-wise representation from the input\nimage and use it as feature vectors for a segmentation network. Our experiments\ndemonstrate that GANs representation is \"readily discriminative\" and produces\nsurprisingly good results that are comparable to those from supervised\nbaselines trained with significantly more labels. We believe this novel\nrepurposing of GANs underlies a new class of unsupervised representation\nlearning that is applicable to many other tasks. More results are available at\nhttps://repurposegans.github.io/.\n"}
+{"abstract": "  Sarcasm is a linguistic expression often used to communicate the opposite of\nwhat is said, usually something that is very unpleasant with an intention to\ninsult or ridicule. Inherent ambiguity in sarcastic expressions, make sarcasm\ndetection very difficult. In this work, we focus on detecting sarcasm in\ntextual conversations from various social networking platforms and online\nmedia. To this end, we develop an interpretable deep learning model using\nmulti-head self-attention and gated recurrent units. Multi-head self-attention\nmodule aids in identifying crucial sarcastic cue-words from the input, and the\nrecurrent units learn long-range dependencies between these cue-words to better\nclassify the input text. We show the effectiveness of our approach by achieving\nstate-of-the-art results on multiple datasets from social networking platforms\nand online media. Models trained using our proposed approach are easily\ninterpretable and enable identifying sarcastic cues in the input text which\ncontribute to the final classification score. We visualize the learned\nattention weights on few sample input texts to showcase the effectiveness and\ninterpretability of our model.\n"}
+{"abstract": "  We study the problem of treasure hunt in a graph by a mobile agent. The nodes\nin the graph are anonymous and the edges at any node $v$ of degree $deg(v)$ are\nlabeled arbitrarily as $0,1,\\ldots, deg(v)-1$. A mobile agent, starting from a\nnode, must find a stationary object, called {\\it treasure} that is located on\nan unknown node at a distance $D$ from its initial position. The agent finds\nthe treasure when it reaches the node where the treasure is present. The {\\it\ntime} of treasure hunt is defined as the number of edges the agent visits\nbefore it finds the treasure. The agent does not have any prior knowledge about\nthe graph or the position of the treasure. An Oracle, that knows the graph, the\ninitial position of the agent, and the position of the treasure, places some\npebbles on the nodes, at most one per node, of the graph to guide the agent\ntowards the treasure.\n  We target to answer the question: what is the fastest possible treasure hunt\nalgorithm regardless of the number of pebbles are placed?\n  We show an algorithm that uses $O(D \\log \\Delta)$ pebbles to find the\ntreasure in a graph $G$ in time $O(D \\log \\Delta + \\log^3 \\Delta)$, where\n$\\Delta$ is the maximum degree of a node in $G$ and $D$ is the distance from\nthe initial position of the agent to the treasure. We show an almost matching\nlower bound of $\\Omega(D \\log \\Delta)$ on time of the treasure hunt using any\nnumber of pebbles.\n"}
+{"abstract": "  We present an auto-differentiable spectral modeling of exoplanets and brown\ndwarfs. This model enables a fully Bayesian inference of the high--dispersion\ndata to fit the ab initio line-by-line spectral computation to the observed\nspectrum by combining it with the Hamiltonian Monte Carlo in recent\nprobabilistic programming languages. An open source code, exojax, developed in\nthis study, was written in Python using the GPU/TPU compatible package for\nautomatic differentiation and accelerated linear algebra, JAX (Bradbury et al.\n2018). We validated the model by comparing it with existing opacity calculators\nand a radiative transfer code and found reasonable agreements of the output. As\na demonstration, we analyzed the high-dispersion spectrum of a nearby brown\ndwarf, Luhman 16 A and found that a model including water, carbon monoxide, and\n$\\mathrm{H_2/He}$ collision induced absorption was well fitted to the observed\nspectrum ($R=10^5$ and 2.28-2.30 $\\mu$m). As a result, we found that\n$T_0=1295_{-32}^{+35}$ K at 1 bar and C/O $=0.62 \\pm 0.03$, which is slightly\nhigher than the solar value. This work demonstrates the potential of full\nBayesian analysis of brown dwarfs and exoplanets as observed by high-dispersion\nspectrographs and also directly-imaged exoplanets as observed by\nhigh-dispersion coronagraphy.\n"}
+{"abstract": "  Large-scale intermittency is a widely observed phenomenon in convective\nsurface layer turbulence that induces non-Gaussian temperature statistics,\nwhile such signature is not observed for velocity signals. Although approaches\nbased on probability density functions have been used so far, those are not\nable to explain to what extent the signals' temporal structure impacts the\nstatistical characteristics of the velocity and temperature fluctuations. To\ntackle this issue, a visibility network analysis is carried out on a\nfield-experimental dataset from a convective atmospheric surface layer flow.\nThrough surrogate data and network-based measures, we demonstrate that the\ntemperature intermittency is related to strong non-linear dependencies in the\ntemperature signals. Conversely, a competition between linear and non-linear\neffects tends to inhibit the temperature-like intermittency behaviour in\nstreamwise and vertical velocities. Based on present findings, new research\navenues are likely to be opened up in studying large-scale intermittency in\nconvective turbulence.\n"}
+{"abstract": "  Rehabilitation is important to improve quality of life for mobility-impaired\npatients. Smart walkers are a commonly used solution that should embed\nautomatic and objective tools for data-driven human-in-the-loop control and\nmonitoring. However, present solutions focus on extracting few specific metrics\nfrom dedicated sensors with no unified full-body approach. We investigate a\ngeneral, real-time, full-body pose estimation framework based on two RGB+D\ncamera streams with non-overlapping views mounted on a smart walker equipment\nused in rehabilitation. Human keypoint estimation is performed using a\ntwo-stage neural network framework. The 2D-Stage implements a detection module\nthat locates body keypoints in the 2D image frames. The 3D-Stage implements a\nregression module that lifts and relates the detected keypoints in both cameras\nto the 3D space relative to the walker. Model predictions are low-pass filtered\nto improve temporal consistency. A custom acquisition method was used to obtain\na dataset, with 14 healthy subjects, used for training and evaluating the\nproposed framework offline, which was then deployed on the real walker\nequipment. An overall keypoint detection error of 3.73 pixels for the 2D-Stage\nand 44.05mm for the 3D-Stage were reported, with an inference time of 26.6ms\nwhen deployed on the constrained hardware of the walker. We present a novel\napproach to patient monitoring and data-driven human-in-the-loop control in the\ncontext of smart walkers. It is able to extract a complete and compact body\nrepresentation in real-time and from inexpensive sensors, serving as a common\nbase for downstream metrics extraction solutions, and Human-Robot interaction\napplications. Despite promising results, more data should be collected on users\nwith impairments, to assess its performance as a rehabilitation tool in\nreal-world scenarios.\n"}
+{"abstract": "  The population of artificial satellites and space debris orbiting the Earth\nimposes non-negligible constraints on both space operations and ground-based\noptical and radio astronomy. The ongoing deployment of several satellite\n`mega-constellations' in the 2020s represents an additional threat that raises\nsignificant concerns. The expected severity of its unwanted consequences is\nstill under study, including radio interference and information loss by\nsatellite streaks appearing in science images. In this Letter, we report a new\nskyglow effect produced by space objects: increased night sky brightness caused\nby sunlight reflected and scattered by that large set of orbiting bodies whose\ndirect radiance is a diffuse component when observed with the naked eye or with\nlow angular resolution photometric instruments. According to our preliminary\nestimates, the zenith luminance of this additional light pollution source may\nhave already reached $\\sim$20 $\\mu$cd m$^{-2}$, which amounts to an\napproximately 10 percent increase over the brightness of the night sky\ndetermined by natural sources of light. This is the critical limit adopted in\n1979 by the International Astronomical Union for the light pollution level not\nto be exceeded at the sites of astronomical observatories.\n"}
+{"abstract": "  We evaluate some new three parameter families of finite reciprocal sums\ninvolving Horadam numbers. We will also be able to state the results for the\ninfinite sums. Some Fibonacci and Lucas sums will be presented as examples.\n"}
+{"abstract": "  Let the finite distributive lattice $D$ be isomorphic to the congruence\nlattice of a finite lattice $L$. Let $Q$ denote those elements of $D$ that\ncorrespond to principal congruences under this isomorphism. Then $Q$ contains\n$0,1 \\in D$ and all the join-irreducible elements of $D$. If $Q$ contains\nexactly these elements, we say that $L$ is a minimal representations of $D$ by\nprincipal congruences of the lattice $L$.\n  We characterize finite distributive lattices $D$ with a minimal\nrepresentation by principal congruences with the property that $D$ has at most\ntwo dual atoms.\n"}
+{"abstract": "  Intelligent agents must pursue their goals in complex environments with\npartial information and often limited computational capacity. Reinforcement\nlearning methods have achieved great success by creating agents that optimize\nengineered reward functions, but which often struggle to learn in sparse-reward\nenvironments, generally require many environmental interactions to perform\nwell, and are typically computationally very expensive. Active inference is a\nmodel-based approach that directs agents to explore uncertain states while\nadhering to a prior model of their goal behaviour. This paper introduces an\nactive inference agent which minimizes the novel free energy of the expected\nfuture. Our model is capable of solving sparse-reward problems with a very high\nsample efficiency due to its objective function, which encourages directed\nexploration of uncertain states. Moreover, our model is computationally very\nlight and can operate in a fully online manner while achieving comparable\nperformance to offline RL methods. We showcase the capabilities of our model by\nsolving the mountain car problem, where we demonstrate its superior exploration\nproperties and its robustness to observation noise, which in fact improves\nperformance. We also introduce a novel method for approximating the prior model\nfrom the reward function, which simplifies the expression of complex objectives\nand improves performance over previous active inference approaches.\n"}
+{"abstract": "  We present the discovery in TMC-1 of vinyl acetylene, CH2CHCCH, and the\ndetection, for the first time in a cold dark cloud, of HCCN, HC4N, and\nCH3CH2CN. A tentative detection of CH3CH2CCH is also reported. The column\ndensity of vinyl acetylene is (1.2 +/- 0.2)e13 cm-2, which makes it one of the\nmost abundant closed-shell hydrocarbons detected in TMC-1. Its abundance is\nonly three times lower than that of propylene, CH3CHCH2. The column densities\nderived for HCCN and HC4N are (4.4 +/- 0.4)e11 cm-2 and (3.7 +/- 0.4)e11 cm-2,\nrespectively. Hence, the HCCN/HC4N abundance ratio is 1.2 +/- 0.3. For ethyl\ncyanide we derive a column density of (1.1 +/- 0.3)e11 cm-2. These results are\ncompared with a state-of-the-art chemical model of TMC-1, which is able to\naccount for the observed abundances of these molecules through gas-phase\nchemical routes.\n"}
+{"abstract": "  We propose a new method to explore a possible departure from the standard\ntime evolution law for the dark matter density. We looked for a violation of\nthis law by using a deformed evolution law, given by $\\rho_c(z) \\propto\n(1+z)^{3+\\epsilon}$, and then constrain $\\epsilon$. The dataset used for this\npurpose consists of Strong Gravitational Lensing data obtained from SLOAN Lens\nACS, BOSS Emission-line Lens Survey, Strong Legacy Survey SL2S, and SLACS;\nalong with galaxy cluster X-ray gas mass fraction measurements obtained using\nthe Chandra Telescope. Our analyses show that $\\epsilon$ is consistent with\nzero within 1 $\\sigma$ c.l., but the current dataset cannot rule out with high\nconfidence level interacting models of dark matter and dark energy.\n"}
+{"abstract": "  We investigate the physics of photonic band structures of the moir\\'e\npatterns that emerged when overlapping two uni-dimensional (1D) photonic\ncrystal slabs with mismatched periods. The band structure of our system is a\nresult of the interplay between intra-layer and inter-layer coupling\nmechanisms, which can be fine-tuned via the distance separating the two layers.\nWe derive an effective Hamiltonian that captures the essential physics of the\nsystem and reproduces all numerical simulations of electromagnetic solutions\nwith high accuracy. Most interestingly, \\textit{magic distances} corresponding\nto the emergence of photonic flatbands within the whole Brillouin zone of the\nmoir\\'e superlattice are observed. We demonstrate that these flatband modes are\ntightly localized within a moir\\'e period. Moreover, we suggest a single-band\ntight-binding model that describes the moir\\'e minibands, of which the\ntunnelling rate can be continuously tuned via the inter-layer strength. Our\nresults show that the band structure of bilayer photonic moir\\'e can be\nengineered in the same fashion as the electronic/excitonic counterparts. It\nwould pave the way to study many-body physics at photonic moir\\'e flatbands and\nnovel optoelectronic devices.\n"}
+{"abstract": "  We provide a boundedness criterion for the integral operator $S_{\\varphi}$ on\nthe fractional Fock-Sobolev space $F^{s,2}(\\mathbb C^n)$, $s\\geq 0$, where\n$S_{\\varphi}$ (introduced by Kehe Zhu) is given by \\begin{eqnarray*}\nS_{\\varphi}F(z):= \\int_{\\mathbb{C}^n} F(w) e^{z \\cdot\\bar{w}} \\varphi(z-\n\\bar{w}) d\\lambda(w) \\end{eqnarray*} with $\\varphi$ in the Fock space\n$F^2(\\mathbb C^n)$ and $d\\lambda(w): = \\pi^{-n} e^{-|w|^2} dw$ the Gaussian\nmeasure on the complex space $\\mathbb{C}^{n}$. This extends the recent result\nin Cao--Li--Shen--Wick--Yan. The main approach is to develop multipliers on the\nfractional Hermite-Sobolev space $W_H^{s,2}(\\mathbb R^n)$.\n"}
+{"abstract": "  In a recent work, two of the authors have formulated the non-linear\nspace-time Hasegawa-Mima plasma equation as a coupled system of two linear\nPDEs, a solution of which is a pair $(u,w)$, with $w=(I-\\Delta)u$. The first\nequation is of hyperbolic type and the second of elliptic type. Variational\nframes for obtaining weak solutions to the initial value Hasegawa-Mima problem\nwith periodic boundary conditions were also derived. Using the Fourier basis in\nthe space variables, existence of solutions were obtained. Implementation of\nalgorithms based on Fourier series leads to systems of dense matrices. In this\npaper, we use a finite element space-domain approach to semi-discretize the\ncoupled variational Hasegawa-Mima model, obtaining global existence of\nsolutions in $H^2$ on any time interval $[0,T]$ for all T. In the sequel,\nfull-discretization using an implicit time scheme on the semi-discretized\nsystem leads to a nonlinear full space-time discrete system with a\nnonrestrictive condition on the time step. Tests on a semi-linear version of\nthe implicit nonlinear full-discrete system are conducted for several initial\ndata, assessing the efficiency of our approach.\n"}
+{"abstract": "  It is widely assumed in the literature that the specularity parameters for\nphonon transmission (forward scattering) and reflection (backward scattering)\nat a boundary are identical, i.e., the statistical distributions of the\ntransition probabilities between an incident phonon and the range of outgoing\nphonon modes are the same for both transmission and reflection. However, it is\nhypothesized by Li and McGaughey that separate specularity parameters are\nneeded to describe the behavior of transmitted and reflected phonons in\nsuperlattices and polycrystalline materials correctly. We test this hypothesis\nby analyzing the mode-resolved specularity parameters computed separately for\ntransmission and reflection processes at a graphene grain boundary. Our results\nshow that backward scattering is considerably more diffuse than forward\nscattering at most frequencies and polarizations, providing strong evidence for\nLi and McGaughey's hypothesis, and shed new light on how surfaces and\ninterfaces modify phonon transport within and between domains in nanostructured\nmaterials.\n"}
+{"abstract": "  We prove that the moduli space of double covers ramified at two points\n$\\mathcal{R}_{g,2}$ is uniruled for $3\\leq g\\leq 6$ and of general type for\n$g\\geq 16$. Furthermore, we consider Prym-canonical divisorial strata in the\nmoduli space $\\overline{\\mathcal{C}^n\\mathcal{R}}_g$ parametrizing $n$-pointed\nPrym curves, and we compute their classes in\n$\\mathrm{Pic}_\\mathbb{Q}(\\overline{\\mathcal{C}^n\\mathcal{R}}_g)$.\n"}
+{"abstract": "  The advancement in distributed generation technologies in modern power\nsystems has led to a widespread integration of renewable power generation at\ncustomer side. However, the intermittent nature of renewable energy pose new\nchallenges to the network operational planning with underlying uncertainties.\nThis paper proposes a novel Bayesian probabilistic technique for forecasting\nrenewable power generation by addressing data and model uncertainties by\nintegrating bidirectional long short-term memory (BiLSTM) neural networks while\ncompressing the weight parameters using variational autoencoder (VAE). Existing\nBayesian deep learning methods suffer from high computational complexities as\nthey require to draw a large number of samples from weight parameters expressed\nin the form of probability distributions. The proposed method can deal with\nuncertainty present in model and data in a more computationally efficient\nmanner by reducing the dimensionality of model parameters. The proposed method\nis evaluated using pinball loss, reconstruction error, and other forecasting\nevaluation metrics. It is inferred from the numerical results that VAE-Bayesian\nBiLSTM outperforms other probabilistic deep learning methods in terms of\nforecasting accuracy and computational efficiency for different sizes of the\ndataset.\n"}
+{"abstract": "  We present methods to strictly calculate the finite-key effects in quantum\nkey distribution (QKD) with error rejection through two-way classical\ncommunication (TWCC) for the sending-or-not-sending twin-field protocol. Unlike\nthe normal QKD without TWCC, here the probability of tagging or untagging for\neach two-bit random group is not independent. We rigorously solve this problem\nby imagining a virtual set of bits where every bit is independent and\nidentical. We show the relationship between the outcome starting from this\nimagined set containing independent and identical bits and the outcome starting\nwith the real set of non-independent bits. With explicit formulas, we show that\nsimply applying Chernoff bound in the calculation gives correct key rate, but\nthe failure probability changes a little bit.\n"}
+{"abstract": "  After reading such a question, any mathematician will say that, according to\na well known result of L.P. Eisenhart found in 1926, the answer is \" One \" of\ncourse, namely the only constant allowing to describe the so-called \" {\\it\nconstant riemannian curvature} \" condition. The purpose of this paper is to\nprove the contrary by studying the case of two dimensional riemannian geometry\nin the light of an old work of E. Vessiot published in 1903 but {\\it still\ntotally unknown today} after more than a century. In fact, we shall compute\nlocally the {\\it Vessiot structure equations} and prove that there are indeed \"\nTwo \" {\\it Vessiot structure constants} satisfying a single {\\it linear Jacobi\ncondition} showing that one of them must vanish while the other one must be\nequal to the known one. This result depends on deep mathematical reasons in the\nformal theory of Lie pseudogroups, which are involving both the Spencer\n$\\delta$-cohomology and diagram chasing in homological algebra. Another similar\nexample will illustrate and justify this comment out of the classical tensorial\nframework of the famous \" {\\it equivalence problem} \". The case of contact\ntransformations will also be studied. Though it is quite unexpected, we shall\nreach the conclusion that the mathematical foundations of both classical and\nconformal riemannian geometry must be revisited. We have treated the case of\nconformal geometry in a recent arXiv preprint.\n"}
+{"abstract": "  We propose a new nonparametric modeling framework for causal inference when\noutcomes depend on how agents are linked in a social or economic network. Such\nnetwork interference describes a large literature on treatment spillovers,\nsocial interactions, social learning, information diffusion, disease and\nfinancial contagion, social capital formation, and more. Our approach works by\nfirst characterizing how an agent is linked in the network using the\nconfiguration of other agents and connections nearby as measured by path\ndistance. The impact of a policy or treatment assignment is then learned by\npooling outcome data across similarly configured agents. We demonstrate the\napproach by proposing an asymptotically valid test for the hypothesis of policy\nirrelevance/no treatment effects and bounding the mean-squared error of a\nk-nearest-neighbor estimator for the average or distributional policy\neffect/treatment response.\n"}
+{"abstract": "  Software defect prediction heavily relies on the metrics collected from\nsoftware projects. Earlier studies often used machine learning techniques to\nbuild, validate, and improve bug prediction models using either a set of\nmetrics collected within a project or across different projects. However,\ntechniques applied and conclusions derived by those models are restricted by\nhow identical those metrics are. Knowledge coming from those models will not be\nextensible to a target project if no sufficient overlapping metrics have been\ncollected in the source projects. To explore the feasibility of transferring\nknowledge across projects without common labeled metrics, we systematically\nintegrated Heterogeneous Defect Prediction (HDP) by replicating and validating\nthe obtained results. Our main goal is to extend prior research and explore the\nfeasibility of HDP and finally to compare its performance with that of its\npredecessor, Cross-Project Defect Prediction. We construct an HDP model on\ndifferent publicly available datasets. Moreover, we propose a new ensemble\nvoting approach in the HDP context to utilize the predictive power of multiple\navailable datasets. The result of our experiment is comparable to that of the\noriginal study. However, we also explored the feasibility of HDP in real cases.\nOur results shed light on the infeasibility of many cases for the HDP algorithm\ndue to its sensitivity to the parameter selection. In general, our analysis\ngives a deep insight into why and how to perform transfer learning from one\ndomain to another, and in particular, provides a set of guidelines to help\nresearchers and practitioners to disseminate knowledge to the defect prediction\ndomain.\n"}
+{"abstract": "  Measuring the interaction between cities is an important research topic in\nmany disciplines, such as sociology, geography, economics and transportation\nscience. The traditional and most widely used spatial interaction model is the\ngravity model, but it requires the parameters to be artificially set. In this\npaper, we propose a parameter-free interactive city choice (ICC) model that\nmeasures intercity interaction from the perspective of individual choice\nbehavior. The ICC model assumes that the probability of an individual choosing\nto interact with a city is proportional to the number of opportunities in the\ndestination city and inversely proportional to the number of intervening\nopportunities between the origin city and the destination city, calculated\nusing the travel time in the transportation network. The intercity interaction\nintensity can be obtained by calculating the product of this probability and\nthe origin city's population. We apply the ICC model to measure the interaction\nintensity among 339 cities in China and analyze the impact of changes in the\nChinese land transportation network from 2005 to 2018 on the intercity and city\ninteraction intensity. Compared with the previously widely used spatial\ninteraction models, the measurement results of the ICC model are more\nconsistent with the actual situation.\n"}
+{"abstract": "  In this paper, we establish a theory of adelic line bundles over\nquasi-projective varieties over finitely generated fields. Besides definitions\nof adelic line bundles, we consider their intersection theory, volume theory,\nand height theory, and apply these to study heights of algebraic points of\nquasi-projective varieties.\n"}
+{"abstract": "  It is known that data rates in standard cellular networks are limited due to\ninter-cell interference. An effective solution of this problem is to use the\nmulti-cell cooperation idea. In Cloud Radio Access Network (C-RAN), which is a\ncandidate solution in 5G and future communication networks, cooperation is\napplied by means of central processors (CPs) connected to simple remote radio\nheads with finite capacity fronthaul links. In this study, we consider a\ndownlink C-RAN with a wireless fronthaul and aim to minimize total power spent\nby jointly designing beamformers for fronthaul and access links. We consider\nthe case where perfect channel state information is not available in the CP. We\nfirst derive a novel theoretical performance bound for the problem defined.\nThen we propose four algorithms with different complexities to show the\ntightness of the bound. The first two algorithms apply successive convex\noptimizations with semi-definite relaxation idea where other two are adapted\nfrom well-known beamforming design methods. The detailed simulations under\nrealistic channel conditions show that as the complexity of the algorithm\nincreases, the corresponding performance becomes closer to the bound.\n"}
+{"abstract": "  In this work, using a detailed dataset furnished by National Health\nAuthorities concerning the Province of Pavia (Lombardy, Italy), we propose to\ndetermine the essential features of the ongoing COVID-19 pandemic in term of\ncontact dynamics. Our contribution is devoted to provide a possible planning of\nthe needs of medical infrastructures in the Pavia Province and to suggest\ndifferent scenarios about the vaccination campaign which possibly help in\nreducing the fatalities and/or reducing the number of infected in the\npopulation. The proposed research combines a new mathematical description of\nthe spread of an infectious diseases which takes into account both age and\naverage daily social contacts with a detailed analysis of the dataset of all\ntraced infected individuals in the Province of Pavia. These information are\nused to develop a data-driven model in which calibration and feeding of the\nmodel are extensively used. The epidemiological evolution is obtained by\nrelying on an approach based on statistical mechanics. This leads to study the\nevolution over time of a system of probability distributions characterizing the\nage and social contacts of the population. One of the main outcomes shows that,\nas expected, the spread of the disease is closely related to the mean number of\ncontacts of individuals. The model permits to forecast thanks to an uncertainty\nquantification approach and in the short time horizon, the average number and\nthe confidence bands of expected hospitalized classified by age and to test\ndifferent options for an effective vaccination campaign with age-decreasing\npriority.\n"}
+{"abstract": "  There is a growing interest in the estimation of the number of unseen\nfeatures, mostly driven by applications in biological sciences. A recent work\nbrought out the upside and the downside of the popular stable-Beta process\nprior, and generalizations thereof, in Bayesian nonparametric inference for the\nunseen-features problem: i) the downside lies in the limited use of the\nsampling information in the posterior distributions, which depend on the\nobservable sample only through the sample size; ii) the upside lies in the\nanalytical tractability and interpretability of the posterior distributions,\nwhich are simple Poisson distributions whose parameters are simple to compute,\nand depend on the sample size and the prior's parameter. In this paper, we\nintroduce and investigate an alternative nonparametric prior, referred to as\nthe stable-Beta scaled process prior, which is the first prior that allows to\nenrich the posterior distribution of the number of unseen features, through the\ninclusion of the sampling information on the number of distinct features in the\nobservable sample, while maintaining the same analytical tractability and\ninterpretability as the stable-Beta process prior. Our prior leads to a\nnegative Binomial posterior distribution, whose parameters depends on the\nsample size, the observed number of distinct features and the prior's\nparameter, providing estimates that are simple, linear in the sampling\ninformation and computationally efficient. We apply our approach to synthetic\nand real genetic data, showing that it outperforms parametric and nonparametric\ncompetitors in terms of estimation accuracy.\n"}
+{"abstract": "  We propose a method to stabilize Laughlin states of a large number of\nstrongly interacting photons by combining a frequency-selective incoherent pump\nwith a step-like potential in the angular momentum basis. Analytical\nexpressions for the preparation efficiency and for the principal error sources\nare obtained. Direct extension of the preparation scheme to states containing\nsingle or multiple quasiholes is discussed.\n"}
+{"abstract": "  The highest-energy known gamma-ray sources are all located within 0.5 degrees\nof extremely powerful pulsars. This raises the question of whether\nultra-high-energy (UHE; $>$ 56 TeV) gamma-ray emission is a universal feature\nexpected near pulsars with a high spin-down power. Using four years of data\nfrom the High Altitude Water Cherenkov (HAWC) Gamma-Ray Observatory, we present\na joint-likelihood analysis of ten extremely powerful pulsars to search for UHE\ngamma-ray emission correlated with these locations. We report a significant\ndetection ($>$ 3$\\sigma$), indicating that UHE gamma-ray emission is a generic\nfeature of powerful pulsars. We discuss the emission mechanisms of the gamma\nrays and the implications of this result. The individual environment that each\npulsar is found in appears to play a role in the amount of emission.\n"}
+{"abstract": "  We study the motion of an impurity in a two-leg ladder interacting with two\nfermionic baths along each leg, a simple model bridging cold atom quantum\nsimulators with an idealised description of the basic transport processes in a\nlayered heterostructure. Using the linked-cluster expansion we obtain exact\nanalytical results for the single-particle Green's function and find that the\nlong-time behaviour is dominated by an intrinsic orthogonality catastrophe\nassociated to the motion of the impurity in each one-dimensional chain. We\nexplore both the case of two identical legs as well as the case where the legs\nare characterised by different interaction strengths: in the latter case we\nobserve a subleading correction which can be relevant for intermediate-time\ntransport at an interface between different materials. In all the cases we do\nnot find significant differences between the intra- and inter-leg Green's\nfunctions in the long-time limit.\n"}
+{"abstract": "  In 1974, Robert Caro published The Power Broker, a critical biography of\nRobert Moses's dictatorial tenure as the \"master builder\" of mid-century New\nYork. Moses profoundly transformed New York's urban fabric and transportation\nsystem, producing the Brooklyn Battery Tunnel, the Verrazano Narrows Bridge,\nthe Westside Highway, the Cross-Bronx Expressway, the Lincoln Center, the UN\nheadquarters, Shea Stadium, Jones Beach State Park and many other projects.\nHowever, The Power Broker did lasting damage to his public image and today he\nremains one of the most controversial figures in city planning history. On\nAugust 26, 1974, Moses issued a turgid 23-page statement denouncing Caro's work\nas \"full of mistakes, unsupported charges, nasty baseless personalities, and\nrandom haymakers.\" Moses's original typewritten statement survives today as a\ngrainy photocopy in the New York City Parks Department archive. To better\npreserve and disseminate it, I have extracted and transcribed its text using\noptical character recognition and edited the result to correct errors. Here I\ncompile my transcription of Moses's statement, alongside Caro's reply to it.\n"}
+{"abstract": "  Our objective in this work is video-text retrieval - in particular a joint\nembedding that enables efficient text-to-video retrieval. The challenges in\nthis area include the design of the visual architecture and the nature of the\ntraining data, in that the available large scale video-text training datasets,\nsuch as HowTo100M, are noisy and hence competitive performance is achieved only\nat scale through large amounts of compute. We address both these challenges in\nthis paper. We propose an end-to-end trainable model that is designed to take\nadvantage of both large-scale image and video captioning datasets. Our model is\nan adaptation and extension of the recent ViT and Timesformer architectures,\nand consists of attention in both space and time. The model is flexible and can\nbe trained on both image and video text datasets, either independently or in\nconjunction. It is trained with a curriculum learning schedule that begins by\ntreating images as 'frozen' snapshots of video, and then gradually learns to\nattend to increasing temporal context when trained on video datasets. We also\nprovide a new video-text pretraining dataset WebVid-2M, comprised of over two\nmillion videos with weak captions scraped from the internet. Despite training\non datasets that are an order of magnitude smaller, we show that this approach\nyields state-of-the-art results on standard downstream video-retrieval\nbenchmarks including MSR-VTT, MSVD, DiDeMo and LSMDC.\n"}
+{"abstract": "  Owing to the remarkable capability of extracting effective graph embeddings,\ngraph convolutional network (GCN) and its variants have been successfully\napplied to a broad range of tasks, such as node classification, link\nprediction, and graph classification. Traditional GCN models suffer from the\nissues of overfitting and oversmoothing, while some recent techniques like\nDropEdge could alleviate these issues and thus enable the development of deep\nGCN. However, training GCN models is non-trivial, as it is sensitive to the\nchoice of hyperparameters such as dropout rate and learning weight decay,\nespecially for deep GCN models. In this paper, we aim to automate the training\nof GCN models through hyperparameter optimization. To be specific, we propose a\nself-tuning GCN approach with an alternate training algorithm, and further\nextend our approach by incorporating the population based training scheme.\nExperimental results on three benchmark datasets demonstrate the effectiveness\nof our approaches on optimizing multi-layer GCN, compared with several\nrepresentative baselines.\n"}
+{"abstract": "  We present the first X-ray dedicated study of the galaxy cluster A795 and of\nthe Fanaroff-Riley Type 0 hosted in its brightest cluster galaxy. Using an\narchival 30 ks \\textit{Chandra} observation we study the dynamical state and\ncooling properties of the intracluster medium, and we investigate whether the\ngrowth of the radio galaxy is prevented by the surrounding environment. We\ndiscover that A795 is a weakly cool core cluster, with an observed mass\ndeposition rate $\\lessapprox 14\\,$ M$_{\\odot}$yr$^{-1}$ in the cooling region\n(central $\\sim$66 kpc). In the inner $\\sim$ 30 kpc we identify two putative\nX-ray cavities, and we unveil the presence of two prominent cold fronts at\n$\\sim$60 kpc and $\\sim$178 kpc from the center, located along a cold ICM spiral\nfeature. The central galaxy, which is offset by 17.7 kpc from the X-ray peak,\nis surrounded by a multi-temperature gas with an average density of\n$n_{\\text{e}} = 2.14 \\times 10^{-2}$ cm$^{-3}$. We find extended radio emission\nat 74-227 MHz centered on the cluster, exceeding the expected flux from the\nradio galaxy extrapolated at low frequency. We propose that sloshing is\nresponsible for the spiral morphology of the gas and the formation of the cold\nfronts, and that the environment alone cannot explain the compactness of the\nradio galaxy. We argue that the power of the two cavities and the sloshing\nkinetic energy can reduce and offset cooling. Considering the spectral and\nmorphological properties of the extended radio emission, we classify it as a\ncandidate radio mini-halo.\n"}
+{"abstract": "  A quantum interface for two-way entanglement transfer between orbital angular\nmomentum degree of freedom in free space and time-energy degree of freedom in\noptical fibers, provides a novel way toward establishing entanglement between\nremote heterogeneous quantum nodes. Here, we experimentally demonstrate this\nkind of transfer interface by using two interferometric cyclic gates. By using\nthis quantum interface, we perform two-way entanglement transfer for the two\ndegrees of freedom. The results show that the quantum entangled state can be\nswitched back and forth between orbital angular momentum and time-energy\ndegrees of freedom, and the fidelity of the state before and after switching is\nhigher than 90%. Our work demonstrates the feasibility and high performance of\nour proposed transfer interface, and paves a route toward building a\nlarge-scale quantum communication network.\n"}
+{"abstract": "  In this paper, we investigate the photon sphere, shadow radius and\nquasinormal modes of a charged black hole in presence of quintessence. The\nresult shows that the shadow radius decreases with the increase of the electric\ncharge. The quasinormal modes are derived by the sixth WKB approximation method\nand shadow radius, respectively. For the fixed electric charge and multipole\nnumber, the values of the real and imaginary parts of the quasinormal modes\ndecrease with the increase of the quintessence charge. When the value of the\nmultipole number is large, the quasinormal modes derived by the two methods are\nconsistent, which shows the correspondence between the quasinormal modes in the\neikonal limit and shadow. When the value of the multipole number is small, the\nquasinormal modes obtained by the two methods are also in good agreement.\n"}
+{"abstract": "  Fuzzy systems have good modeling capabilities in several data science\nscenarios, and can provide human-explainable intelligence models with\nexplainability and interpretability. In contrast to transaction data, which\nhave been extensively studied, sequence data are more common in real-life\napplications. To obtain a human-explainable data intelligence model for\ndecision making, in this study, we investigate explainable fuzzy-theoretic\nutility mining on multi-sequences. Meanwhile, a more normative formulation of\nthe problem of fuzzy utility mining on sequences is formulated. By exploring\nfuzzy set theory for utility mining, we propose a novel method termed pattern\ngrowth fuzzy utility mining (PGFUM) for mining fuzzy high-utility sequences\nwith linguistic meaning. In the case of sequence data, PGFUM reflects the fuzzy\nquantity and utility regions of sequences. To improve the efficiency and\nfeasibility of PGFUM, we develop two compressed data structures with\nexplainable fuzziness. Furthermore, one existing and two new upper bounds on\nthe explainable fuzzy utility of candidates are adopted in three proposed\npruning strategies to substantially reduce the search space and thus expedite\nthe mining process. Finally, the proposed PGFUM algorithm is compared with\nPFUS, which is the only currently available method for the same task, through\nextensive experimental evaluation. It is demonstrated that PGFUM achieves not\nonly human-explainable mining results that contain the original nature of\nrevealable intelligibility, but also high efficiency in terms of runtime and\nmemory cost.\n"}
+{"abstract": "  Let $R$ be a ring and $S$ a multiplicative subset of $R$. An $R$-module $P$\nis called $S$-projective provided that the induced sequence $0\\rightarrow {\\rm\nHom}_R(P,A)\\rightarrow {\\rm Hom}_R(P,B)\\rightarrow {\\rm Hom}_R(P,C)\\rightarrow\n0$ is $S$-exact for any $S$-short exact sequence $0\\rightarrow A\\rightarrow\nB\\rightarrow C\\rightarrow 0$. Some characterizations and properties of\n$S$-projective modules are obtained. The notion of $S$-semisimple modules is\nalso introduced. A ring $R$ is called an $S$-semisimple ring provided that\nevery free $R$-module is $S$-semisimple. Several characterizations of\n$S$-semisimple rings are provided by using $S$-semisimple modules,\n$S$-projective modules, $S$-injective modules and $S$-split $S$-exact\nsequences.\n"}
+{"abstract": "  Satellite imagery is widely used in many application sectors, including\nagriculture, navigation, and urban planning. Frequently, satellite imagery\ninvolves both large numbers of images as well as high pixel counts, making\nsatellite datasets computationally expensive to analyze. Recent approaches to\nsatellite image analysis have largely emphasized deep learning methods. Though\nextremely powerful, deep learning has some drawbacks, including the requirement\nof specialized computing hardware and a high reliance on training data. When\ndealing with large satellite datasets, the cost of both computational resources\nand training data annotation may be prohibitive.\n"}
+{"abstract": "  We apply the coded caching scheme proposed by Maddah-Ali and Niesen to a\nmultipoint multicasting video paradigm. Partially caching the video files on\nthe wireless devices provides an opportunity to decrease data traffic load in\npeak hours via sending multicast coded messages to users. In this paper, we\npropose a two-hop wireless network for video multicasting, where the common\ncoded multicast message is transmitted through different single antenna Edge\nNodes (ENs) to multiple antenna users. Each user can decide to decode any EN by\nusing a zero forcing receiver. Motivated by Scalable Video Coding (SVC), we\nconsider successive refinement source coding in order to provide a ``softer''\ntradeoff between the number of decoded ENs and the source distortion at each\nuser receiver. The resulting coding scheme can be seen as the concatenation of\nMaddah-Ali and Niesen coded caching for each source-coded layer, and multiple\ndescription coding. Using stochastic geometry, we investigate the tradeoff\nbetween delivery time and per-user average source distortion. The proposed\nsystem is spatially scalable in the sense that, for given users' and ENs'\nspatial density, the achieved distortion-delivery time performance is\nindependent of the coverage area (for in the limit of large area).\n"}
+{"abstract": "  After the prediction of many sub- and super-Chandrasekhar (at least a dozen\nfor the latter) limiting mass white dwarfs, hence apparently peculiar class of\nwhite dwarfs, from the observations of luminosity of type Ia supernovae,\nresearchers have proposed various models to explain these two classes of white\ndwarfs separately. We earlier showed that these two peculiar classes of white\ndwarfs, along with the regular white dwarfs, can be explained by a single form\nof the f(R) gravity, whose effect is significant only in the high-density\nregime, and it almost vanishes in the low-density regime. However, since there\nis no direct detection of such white dwarfs, it is difficult to single out one\nspecific theory from the zoo of modified theories of gravity. We discuss the\npossibility of direct detection of such white dwarfs in gravitational wave\nastronomy. It is well-known that in f(R) gravity, more than two polarization\nmodes are present. We estimate the amplitudes of all the relevant modes for the\npeculiar as well as the regular white dwarfs. We further discuss the\npossibility of their detections through future-based gravitational wave\ndetectors, such as LISA, ALIA, DECIGO, BBO, or Einstein Telescope, and thereby\nput constraints or rule out various modified theories of gravity. This\nexploration links the theory with possible observations through gravitational\nwave in f(R) gravity.\n"}
+{"abstract": "  In this paper, we study the mixed problem for new class of nonlinear\nreaction-diffusion PDEs with the nonlocal nonlinearity with variable exponents.\nHere we obtain results on solvability and behavior of solutions both when these\nare yet dissipative and when these get become non-dissipative problems. These\nproblems are possess special properties: these can be to remain dissipative all\ntime or can get become non dissipative after finite time. It is shown that if\nthe studied problems get become non-dissipative can have an infinite number of\ndifferent unstable solutions with varying speeds and also an infinite number of\ndifferent states of spatio-temporal (diffusion) chaos that are generated by\ncascades of bifurcations governed by the corresponding steady states. The\nbehavior of these solutions is analyzed in detail and it is explained how\nspace-time chaos can arise. Since these problems desctibe the dynamics of\ncancer, we explain the behavior of the dynamics of cancer using obtained here\nresults.\n"}
+{"abstract": "  Even a small fraction of nanoparticles in fluids affects the splashing\nbehavior of a droplet upon impact on a smooth surface. Nanofluid drop impact\nonto a smooth sapphire substrate is experimentally investigated over wide\nranges of Reynolds ($10^2<\\mathrm{Re}<10^4$) and Weber ($50<\\mathrm{We}<500$)\nnumbers for three nanofluid mass concentrations (0.01%, 0.1%, 1%) using\nhigh-speed photography. Nanofluids are prepared by diluting a commercial\nAl$_2$O$_3$-water nanofluid in aqueous glycerol solutions without dispersants.\nIn total, 30 samples are prepared and 1799 data points are acquired. Every\nsample is experimentally characterized prior to droplet impact measurements in\nterms of stability, density, viscosity, and surface tension to demonstrate the\nobserved outcomes on the We-Re maps. Each droplet impact condition is repeated\nat least 3 times to ensure good repeatability. The non-monotonic behavior of\nthe spreading-to-splashing transition remains the same for nanofluids. However,\nnanofluids influence this boundary by promoting splashing at low Reynolds\nnumbers. We explain this behavior by increased lamella spreading speed and lift\nduring the lamella spreading stage. Finally, we develop an empirical\ncorrelation which describes the splashing threshold dependency on nanoparticle\nconcentration for the first time.\n"}
+{"abstract": "  We analyze composition methods with complex coefficients exhibiting the\nso-called ``symmetry-conjugate'' pattern in their distribution. In particular,\nwe study their behavior with respect to preservation of qualitative properties\nwhen projected on the real axis and we compare them with the usual left-right\npalindromic compositions. New schemes within this family up to order 8 are\nproposed and their efficiency is tested on several examples. Our analysis shows\nthat higher-order schemes are more efficient even when time step sizes are\nrelatively large.\n"}
+{"abstract": "  We study the emergence of the traveling chimera state in a two-dimensional\nnetwork of Hindmarsh-Rose burst neurons with the mutual presence of local and\nnon-local couplings. We show that in the unique presence of the non-local\nchemical coupling modeled by a nonlinear function, the traveling chimera\nphenomenon occurs with a displacement in both directions of the plane of the\ngrid. The introduction of local electrical coupling shows that the mutual\ninfluence of the two types of coupling can, for certain values, generate\ntraveling chimera, imperfect-traveling, traveling multi-clusters, and\nalternating traveling chimera, ie the presence in the network under study, of\npatterns of coherent elements interspersed by other incoherent elements in\nmovement and alternately changing their position over time. The confirmation of\nthe states of coherence is done by introducing the parameter of instantaneous\nlocal order parameter in two dimensions. We extend our analysis through\nmathematical tools such as the Hamilton energy function to determine the\ndirection of propagation of patterns in two dimensions.\n"}
+{"abstract": "  This paper focuses on the so-called Weighted Inertia-Dissipation-Energy\n(WIDE) variational approach for the approximation of unsteady Leray-Hopf\nsolutions of the incompressible Navier-Stokes system. Initiated in [56], this\nvariational method is here extended to the case of non-Newtonian fluids with\npower-law index $r\\geq11/5$ in three space dimensions and large nonhomogeneous\ndata. Moreover, boundary conditions are not imposed on some parts of\nboundaries, representing, e.g., outflows. Correspondingly, natural boundary\nconditions arise from the minimization. In particular, at walls we recover\nboundary conditions of Navier-slip type. At outflows and inflows, we obtain the\ncondition\n$-\\frac12|\\boldsymbol{v}|^2\\boldsymbol{n}+\\mathbb{T}\\boldsymbol{n}=0$. This\nprovides the first theoretical explanation for the onset of such boundary\nconditions.\n"}
+{"abstract": "  The stabilizer rank of a quantum state $\\psi$ is the minimal $r$ such that\n$\\left| \\psi \\right \\rangle = \\sum_{j=1}^r c_j \\left|\\varphi_j \\right\\rangle$\nfor $c_j \\in \\mathbb{C}$ and stabilizer states $\\varphi_j$. The running time of\nseveral classical simulation methods for quantum circuits is determined by the\nstabilizer rank of the $n$-th tensor power of single-qubit magic states.\n  We prove a lower bound of $\\Omega(n)$ on the stabilizer rank of such states,\nimproving a previous lower bound of $\\Omega(\\sqrt{n})$ of Bravyi, Smith and\nSmolin (arXiv:1506.01396). Further, we prove that for a sufficiently small\nconstant $\\delta$, the stabilizer rank of any state which is $\\delta$-close to\nthose states is $\\Omega(\\sqrt{n}/\\log n)$. This is the first non-trivial lower\nbound for approximate stabilizer rank.\n  Our techniques rely on the representation of stabilizer states as quadratic\nfunctions over affine subspaces of $\\mathbb{F}_2^n$, and we use tools from\nanalysis of boolean functions and complexity theory. The proof of the first\nresult involves a careful analysis of directional derivatives of quadratic\npolynomials, whereas the proof of the second result uses Razborov-Smolensky low\ndegree polynomial approximations and correlation bounds against the majority\nfunction.\n"}
+{"abstract": "  Inspired by the capability of structured illumination microscopy in\nsubwavelength imaging, many researchers devoted themselves to investigating\nthis methodology. However, due to the free propagating feature of the\ntraditional structured illumination fields, the resolution can be only improved\nup to double times compared with the diffractied limited microscopy. Besides,\nmost of the previous studies, relying on incoherent illumination sources, are\nrestricted to fluorescent samples. In this work, a subwavelength nonfluorescent\nimaging method is proposed based on the terahertz traveling wave and plasmonics\nillumination. Excited along with a metal grating, the spoof surface plasmons\nare employed as the plasmonics illumination. When the scattering waves with the\nSSPs illumination are captured, the high order spatial frequency components of\nthe sample are already encoded into the obtainable low order ones. Then, an\nalgorithm is summarized to shift the modulated SF components to their actual\npositions in the Fourier domain. In this manner, high order SF components\ncarrying the fine information are introduced to reconstruct the desired\nimaging, leading to an improvement of the resolution up to 0.12 lambda.\nEncouragingly, the resolution can be further enhanced by tuning the working\nfrequency of the SSPs. This method holds promise for some important\napplications in terahertz nonfluorescent microscopy and sample detection with\nweak scattering.\n"}
+{"abstract": "  Browsers often include security features to detect phishing web pages. In the\npast, some browsers evaluated an unknown URL for inclusion in a list of known\nphishing pages. However, as the number of URLs and known phishing pages\ncontinued to increase at a rapid pace, browsers started to include one or more\nmachine learning classifiers as part of their security services that aim to\nbetter protect end users from harm. While additional information could be used,\nbrowsers typically evaluate every unknown URL using some classifier in order to\nquickly detect these phishing pages. Early phishing detection used standard\nmachine learning classifiers, but recent research has instead proposed the use\nof deep learning models for the phishing URL detection task. Concurrently, text\nembedding research using transformers has led to state-of-the-art results in\nmany natural language processing tasks. In this work, we perform a\ncomprehensive analysis of transformer models on the phishing URL detection\ntask. We consider standard masked language model and additional domain-specific\npre-training tasks, and compare these models to fine-tuned BERT and RoBERTa\nmodels. Combining the insights from these experiments, we propose URLTran which\nuses transformers to significantly improve the performance of phishing URL\ndetection over a wide range of very low false positive rates (FPRs) compared to\nother deep learning-based methods. For example, URLTran yields a true positive\nrate (TPR) of 86.80% compared to 71.20% for the next best baseline at an FPR of\n0.01%, resulting in a relative improvement of over 21.9%. Further, we consider\nsome classical adversarial black-box phishing attacks such as those based on\nhomoglyphs and compound word splits to improve the robustness of URLTran. We\nconsider additional fine tuning with these adversarial samples and demonstrate\nthat URLTran can maintain low FPRs under these scenarios.\n"}
+{"abstract": "  In this work, the results of Ultra-Wideband air-to-ground measurements\ncarried out in a real-world factory environment are presented and discussed.\nWith intelligent in-dustrial deployments in mind, we envision a scenario where\nthe Unmanned Aerial Vehicle can be used as a supplementary tool for factory\noperation, optimization and control. Measurements address narrow band and wide\nband characterization of the wireless radio channel, and can be used for link\nbudget calculation, interference studies and time dispersion assessment in real\nfactories, without the usual limitation for both radio terminals to be close to\nground. The measurements are performed at different locations and different\nheights over the 3.1-5.3 GHz band. Some fundamental propagation parameters\nvalues are determined vs. distance, height and propagation conditions. The\nmeasurements are complemented with, and compared to, conventional\nground-to-ground measurements with the same setup. The conducted measurement\ncampaign gives an insight for realizing wireless applications in smart\nconnected factories, including UAV-assisted applications.\n"}
+{"abstract": "  The optimization of low-thrust, multi-revolution orbit transfer trajectories\nis often regarded as a difficult problem in modern astrodynamics. In this\npaper, a flexible and computationally efficient approach is presented for the\noptimization of low-thrust orbit transfers under eclipse constraints. The\nproposed approach leverages a new dynamic model of the orbital motion and a\nLyapunov-based initial guess generation scheme that is very easy to tune. A\nmulti-objective, single-phase formulation of the optimal control problem is\ndevised, which provides a convenient way to trade off fuel consumption and time\nof flight. A distinctive feature of such a formulation is that it requires no\nprior information about the structure of the optimal solution. Simulation\nresults for two benchmark orbit transfer scenarios indicate that minimum-time,\nminimum-fuel and mixed time/fuel-optimal instances of the control problem can\nbe readily solved via direct collocation, while incurring a significantly lower\ncomputational demand with respect to existing techniques.\n"}
+{"abstract": "  It is a well-established fact in the scientific literature that Picard\niterations of backward stochastic differential equations with globally\nLipschitz continuous nonlinearity converge at least exponentially fast to the\nsolution. In this paper we prove that this convergence is in fact at least\nsquare-root factorially fast. We show for one example that no higher\nconvergence speed is possible in general. Moreover, if the nonlinearity is\n$z$-independent, then the convergence is even factorially fast. Thus we reveal\na phase transition in the speed of convergence of Picard iterations of backward\nstochastic differential equations.\n"}
+{"abstract": "  Automated commonsense reasoning is essential for building human-like AI\nsystems featuring, for example, explainable AI. Event Calculus (EC) is a family\nof formalisms that model commonsense reasoning with a sound, logical basis.\nPrevious attempts to mechanize reasoning using EC faced difficulties in the\ntreatment of the continuous change in dense domains (e.g., time and other\nphysical quantities), constraints among variables, default negation, and the\nuniform application of different inference methods, among others. We propose\nthe use of s(CASP), a query-driven, top-down execution model for Predicate\nAnswer Set Programming with Constraints, to model and reason using EC. We show\nhow EC scenarios can be naturally and directly encoded in s(CASP) and how it\nenables deductive and abductive reasoning tasks in domains featuring\nconstraints involving both dense time and dense fluents.\n"}
+{"abstract": "  This paper aims to construct optimal Z-complementary code set (ZCCS) with\nnon-power-of-two (NPT) lengths to enable interference-free multicarrier\ncode-division multiple access (MC-CDMA) systems. The existing ZCCSs with NPT\nlengths, which are constructed from generalized Boolean functions (GBFs), are\nsub-optimal only with respect to the set size upper bound. For the first time\nin the literature, we advocate the use of pseudo-Boolean functions (PBFs) (each\nof which transforms a number of binary variables to a real number as a natural\ngeneralization of GBF) for direct constructions of optimal ZCCSs with NPT\nlengths.\n"}
+{"abstract": "  Let $A$ be an unital alternative $*$-algebra. Assume that $A$ contains a\nnontrivial symmetric idempotent element $e$ which satisfies $xA \\cdot e = 0$\nimplies $x = 0$ and $xA \\cdot (1_A - e) = 0$ implies $x = 0$. In this paper, it\nis shown that $\\Phi$ is a nonlinear $*$-Jordan-type derivation on A if and only\nif $\\Phi$ is an additive $*$-derivation. As application, we get a result on\nalternative $W^{*}$-algebras.\n"}
+{"abstract": "  Multi-stage learning is an effective technique to invoke multiple\ndeep-learning modules sequentially. This paper applies multi-stage learning to\nspeech enhancement by using a multi-stage structure, where each stage comprises\na self-attention (SA) block followed by stacks of temporal convolutional\nnetwork (TCN) blocks with doubling dilation factors. Each stage generates a\nprediction that is refined in a subsequent stage. A fusion block is inserted at\nthe input of later stages to re-inject original information. The resulting\nmulti-stage speech enhancement system, in short, multi-stage SA-TCN, is\ncompared with state-of-the-art deep-learning speech enhancement methods using\nthe LibriSpeech and VCTK data sets. The multi-stage SA-TCN system's\nhyper-parameters are fine-tuned, and the impact of the SA block, the fusion\nblock and the number of stages are determined. The use of a multi-stage SA-TCN\nsystem as a front-end for automatic speech recognition systems is investigated\nas well. It is shown that the multi-stage SA-TCN systems perform well relative\nto other state-of-the-art systems in terms of speech enhancement and speech\nrecognition scores.\n"}
+{"abstract": "  We calculate transition amplitudes and probabilities between the coherent and\nFock states of a quantum harmonic oscillator with a moving center for an\narbitrary law of motion. These quantities are determined by the Fourier\ntransform of the moving center acceleration. In the case of a constant\nacceleration, the probabilities oscillate with the oscillator frequency, so\nthat no excitation occurs after every period. Examples of oscillating and\nrotating motion of the harmonic trap center are considered too. Estimations\nshow that the effect of excitation of vibration states due to the motion of the\nharmonic trap center can be observed in available atomic traps.\n"}
+{"abstract": "  Reciprocal transformations associated with admitted conservation laws were\noriginally used to derive invariance properties in non-relativistic gasdynamics\nand applied to obtain reduction to tractable canonical forms. They have\nsubsequently been shown to have diverse physical applications to nonlinear\nsystems, notably in the analytic treatment of Stefan-type moving boundary\nproblem and in linking inverse scattering systems and integrable hierarchies in\nsoliton theory. Here,invariance under classes of reciprocal transformations in\nrelativistic gasdynamics is shown to be linked to a Lie group procedure.\n"}
+{"abstract": "  The number of connected smart devices has been increasing exponentially for\ndifferent Internet-of-Things (IoT) applications. Security has been a long run\nchallenge in the IoT systems which has many attack vectors, security flaws and\nvulnerabilities. Securing billions of B connected devices in IoT is a must task\nto realize the full potential of IoT applications. Recently, researchers have\nproposed many security solutions for IoT. Machine learning has been proposed as\none of the emerging solutions for IoT security and Reinforcement learning is\ngaining more popularity for securing IoT systems. Reinforcement learning,\nunlike other machine learning techniques, can learn the environment by having\nminimum information about the parameters to be learned. It solves the\noptimization problem by interacting with the environment adapting the\nparameters on the fly. In this paper, we present an comprehensive survey of\ndifferent types of cyber-attacks against different IoT systems and then we\npresent reinforcement learning and deep reinforcement learning based security\nsolutions to combat those different types of attacks in different IoT systems.\nFurthermore, we present the Reinforcement learning for securing CPS systems\n(i.e., IoT with feedback and control) such as smart grid and smart\ntransportation system. The recent important attacks and countermeasures using\nreinforcement learning B in IoT are also summarized in the form of tables. With\nthis paper, readers can have a more thorough understanding of IoT security\nattacks and countermeasures using Reinforcement Learning, as well as research\ntrends in this area.\n"}
+{"abstract": "  This paper combines the graph-theoretical ideas behind Moravcsik's theorem\nwith a completely analytic derivation of discrete phase-ambiguities, recently\npublished by Nakayama. The result is a new graphical procedure for the\nderivation of certain types of complete sets of observables for an\namplitude-extraction problem with $N$ helicity-amplitudes. The procedure is\napplied to pseudoscalar meson photoproduction ($N = 4$ amplitudes) and\nelectroproduction ($N = 6$ amplitudes), yielding complete sets with minimal\nlength of $2N$ observables. For the case of electroproduction, this is the\nfirst time an extensive list of minimal complete sets is published.\nFurthermore, the generalization of the proposed procedure to processes with a\nlarger number of amplitudes, i.e. $N > 6$ amplitudes, is sketched. The\ngeneralized procedure is outlined for the next more complicated example of\ntwo-meson photoproduction ($N = 8$ amplitudes).\n"}
+{"abstract": "  A constrained multivariate linear model is a multivariate linear model with\nthe columns of its coefficient matrix constrained to lie in a known subspace.\nThis class of models includes those typically used to study growth curves and\nlongitudinal data. Envelope methods have been proposed to improve estimation\nefficiency in the class of unconstrained multivariate linear models, but have\nnot yet been developed for constrained models that we develop in this article.\nWe first compare the standard envelope estimator based on an unconstrained\nmultivariate model with the standard estimator arising from a constrained\nmultivariate model in terms of bias and efficiency. Then, to further improve\nefficiency, we propose a novel envelope estimator based on a constrained\nmultivariate model. Novel envelope-based testing methods are also proposed. We\nprovide support for our proposals by simulations and by studying the classical\ndental data and data from the China Health and Nutrition Survey and a study of\nprobiotic capacity to reduced Salmonella infection.\n"}
+{"abstract": "  We present a new dynamical proof of the Thouless-Anderson-Palmer (TAP)\nequations for the classical Sherrington-Kirkpatrick spin glass at sufficiently\nhigh temperature. In our derivation, the TAP equations are a simple consequence\nof the decay of the two point correlation functions. The methods can also be\nused to establish the decay of higher order correlation functions. We\nillustrate this by proving a suitable decay bound on the three point functions\nfrom which we derive an analogue of the TAP equations for the two point\nfunctions.\n"}
+{"abstract": "  We establish a quantitative relationship between photometric and\nspectroscopic detections of solar-like oscillations using ab initio,\nthree-dimensional (3D), hydrodynamical numerical simulations of stellar\natmospheres. We present a theoretical derivation as proof of concept for our\nmethod. We perform realistic spectral line formation calculations to quantify\nthe ratio between luminosity and radial velocity amplitude for two case\nstudies: the Sun and the red giant $\\epsilon$ Tau. Luminosity amplitudes are\ncomputed based on the bolometric flux predicted by 3D simulations with\ngranulation background modelled the same way as asteroseismic observations.\nRadial velocity amplitudes are determined from the wavelength shift of\nsynthesized spectral lines with methods closely resembling those used in BiSON\nand SONG observations. Consequently, the theoretical luminosity to radial\nvelocity amplitude ratios are directly comparable with corresponding\nobservations. For the Sun, we predict theoretical ratios of 21.0 and 23.7\nppm/[m/s] from BiSON and SONG respectively, in good agreement with observations\n19.1 and 21.6 ppm/[m/s]. For $\\epsilon$ Tau, we predict K2 and SONG ratios of\n48.4 ppm/[m/s], again in good agreement with observations 42.2 ppm/[m/s], and\nmuch improved over the result from conventional empirical scaling relations\nwhich gives 23.2 ppm/[m/s]. This study thus opens the path towards a\nquantitative understanding of solar-like oscillations, via detailed modelling\nof 3D stellar atmospheres.\n"}
+{"abstract": "  This work intends to analyze the nonlinear stochastic dynamics of\ndrillstrings in horizontal configuration. For this purpose, it considers a beam\ntheory, with effects of rotatory inertia and shear deformation, which is\ncapable of reproducing the large displacements that the beam undergoes. The\nfriction and shock effects, due to beam/borehole wall transversal impacts, as\nwell as the force and torque induced by bit-rock interaction, are also\nconsidered in the model. Uncertainties of bit-rock interaction model are taken\ninto account using a parametric probabilistic approach. Numerical simulations\nhave shown that the mechanical system of interest has a very rich nonlinear\nstochastic dynamics, which generate phenomena such as bit-bounce, stick-slip,\nand transverse impacts. A study aiming to maximize the drilling process\nefficiency, varying drillstring velocities of translation and rotation is\npresented. Also, the work presents the definition and solution of two\noptimizations problems, one deterministic and one robust, where the objective\nis to maximize drillstring rate of penetration into the soil respecting its\nstructural limits.\n"}
+{"abstract": "  Future long-baseline neutrino experiments will measure neutrino oscillation\nproperties with unprecedented precision and will search for clear signatures of\nCP violation in the leptonic sector. Near detectors can measure the\nneutrino-electron scattering with high statistics, giving the chance for its\nprecise measurement. We study, in this work, the expectations for the\nmeasurement of radiative corrections in this process. We focus on the\ndetermination of contributions that are exclusive to the neutrino channels,\nparticularly on the neutrino charge radius. We illustrate how the perspectives\nin a first clear measurement of this effective quantity are encouraging.\n"}
+{"abstract": "  Pre-trained multilingual language encoders, such as multilingual BERT and\nXLM-R, show great potential for zero-shot cross-lingual transfer. However,\nthese multilingual encoders do not precisely align words and phrases across\nlanguages. Especially, learning alignments in the multilingual embedding space\nusually requires sentence-level or word-level parallel corpora, which are\nexpensive to be obtained for low-resource languages. An alternative is to make\nthe multilingual encoders more robust; when fine-tuning the encoder using\ndownstream task, we train the encoder to tolerate noise in the contextual\nembedding spaces such that even if the representations of different languages\nare not aligned well, the model can still achieve good performance on zero-shot\ncross-lingual transfer. In this work, we propose a learning strategy for\ntraining robust models by drawing connections between adversarial examples and\nthe failure cases of zero-shot cross-lingual transfer. We adopt two widely used\nrobust training methods, adversarial training and randomized smoothing, to\ntrain the desired robust model. The experimental results demonstrate that\nrobust training improves zero-shot cross-lingual transfer on text\nclassification tasks. The improvement is more significant in the generalized\ncross-lingual transfer setting, where the pair of input sentences belong to two\ndifferent languages.\n"}
+{"abstract": "  Temporal abstractions in the form of options have been shown to help\nreinforcement learning (RL) agents learn faster. However, despite prior work on\nthis topic, the problem of discovering options through interaction with an\nenvironment remains a challenge. In this paper, we introduce a novel\nmeta-gradient approach for discovering useful options in multi-task RL\nenvironments. Our approach is based on a manager-worker decomposition of the RL\nagent, in which a manager maximises rewards from the environment by learning a\ntask-dependent policy over both a set of task-independent discovered-options\nand primitive actions. The option-reward and termination functions that define\na subgoal for each option are parameterised as neural networks and trained via\nmeta-gradients to maximise their usefulness. Empirical analysis on gridworld\nand DeepMind Lab tasks show that: (1) our approach can discover meaningful and\ndiverse temporally-extended options in multi-task RL domains, (2) the\ndiscovered options are frequently used by the agent while learning to solve the\ntraining tasks, and (3) that the discovered options help a randomly initialised\nmanager learn faster in completely new tasks.\n"}
+{"abstract": "  The conceptualization of a claim lies at the core of argument mining. The\nsegregation of claims is complex, owing to the divergence in textual syntax and\ncontext across different distributions. Another pressing issue is the\nunavailability of labeled unstructured text for experimentation. In this paper,\nwe propose LESA, a framework which aims at advancing headfirst into expunging\nthe former issue by assembling a source-independent generalized model that\ncaptures syntactic features through part-of-speech and dependency embeddings,\nas well as contextual features through a fine-tuned language model. We resolve\nthe latter issue by annotating a Twitter dataset which aims at providing a\ntesting ground on a large unstructured dataset. Experimental results show that\nLESA improves upon the state-of-the-art performance across six benchmark claim\ndatasets by an average of 3 claim-F1 points for in-domain experiments and by 2\nclaim-F1 points for general-domain experiments. On our dataset too, LESA\noutperforms existing baselines by 1 claim-F1 point on the in-domain experiments\nand 2 claim-F1 points on the general-domain experiments. We also release\ncomprehensive data annotation guidelines compiled during the annotation phase\n(which was missing in the current literature).\n"}
+{"abstract": "  A star edge coloring of a graph $G$ is a proper edge coloring of $G$ such\nthat every path and cycle of length four in $G$ uses at least three different\ncolors. The star chromatic index of $G$, is the smallest integer $k$ for which\n$G$ admits a star edge coloring with $k$ colors.\n  In this paper, we obtain tight upper bound\n$\\left\\lfloor\\frac{3\\Delta}{2}\\right\\rfloor+2$ for the star chromatic index of\nevery Halin graph, that proves the conjecture of Dvo{\\v{r}}{\\'a}k et al. (J\nGraph Theory, 72 (2013), 313--326) for cubic Halin graphs.\n"}
+{"abstract": "  Having in mind extensions of 2D holography beyond the Jackiw-Teitelboim model\nwe propose holographic counterterms and asymptotic conditions for a family of\nasymptotically AdS$_2$ dilaton gravity models leading to a consistent\nvariational problem and a finite on-shell action. We show the presence of\nasymptotic Virasoro symmetries in all these models. The Schwarzian action\ngenerates (a part) of the equations of motion governing the asymptotic degrees\nof freedom. We also analyse the applicability of various entropy formulae. By a\ndilaton-dependent conformal transformation our results are extended to an even\nlarger class of models having exotic asymptotic behavior. We also analyse\nasymptotic symmetries for some other classes of dilaton gravities without,\nhowever, constructing holographic counterterms.\n"}
+{"abstract": "  In part I of the foundation of the hyperunified field theory, we have shown\nthe presence of entangled hyperqubit-spinor field as fundamental building block\nwith appearance of Minkowski hyper-spacetime as free-motion spacetime and\nemergence of inhomogeneous hyperspin symmetry as fundamental symmetry. In this\npaper as part II, we demonstrate by following along gauge invariance principle\nand scaling invariance hypothesis that the inhomogeneous hyperspin gauge\nsymmetry and scaling gauge symmetry govern fundamental interactions and reveal\nthe nature of gravity and spacetime. With the fiber bundle structure of biframe\nhyper-spacetime and emergence of non-commutative geometry, we explore\nmethodically the gauge-geometry duality and genesis of gravitational\ninteraction in locally flat gravigauge hyper-spacetime. A whole hyperunified\nfield theory in 19-dimensional hyper-spacetime is established to unify not only\nall discovered leptons and quarks into hyperunified qubit-spinor field but also\nall known basic forces into hyperunified interaction governed by inhomogeneous\nhyperspin gauge symmetry. We present a systematic investigate on the\nhyperunified field theory with deriving the dynamics of fundamental fields as\nbasic laws of nature and conservation laws relative to basic symmetries and\nshowing Higgs-like bosons and three families of lepton-quark states. We provide\na detailed analysis on inflationary early universe with evolving graviscalar\nfield and a discussion on scaling gauge field as dark matter candidate and\n$\\cQ_c$-spin scalar field as source of dark energy.\n"}
+{"abstract": "  A new Plug-and-Play (PnP) alternating direction of multipliers (ADMM) scheme\nis proposed in this paper, by embedding a recently introduced adaptive denoiser\nusing the Schroedinger equation's solutions of quantum physics. The potential\nof the proposed model is studied for Poisson image deconvolution, which is a\ncommon problem occurring in number of imaging applications, such as limited\nphoton acquisition or X-ray computed tomography. Numerical results show the\nefficiency and good adaptability of the proposed scheme compared to recent\nstate-of-the-art techniques, for both high and low signal-to-noise ratio\nscenarios. This performance gain regardless of the amount of noise affecting\nthe observations is explained by the flexibility of the embedded quantum\ndenoiser constructed without anticipating any prior statistics about the noise,\nwhich is one of the main advantages of this method. The main novelty of this\nwork resided in the integration of a modified quantum denoiser into the\nPnP-ADMM framework and the numerical proof of convergence of the resulting\nalgorithm.\n"}
+{"abstract": "  Automatic recognition of an online series of unsegmented actions requires a\nmethod for segmentation that determines when an action starts and when it ends.\nIn this paper, a novel approach for recognizing unsegmented actions in online\ntest experiments is proposed. The method uses self-organizing neural networks\nto build a three-layer cognitive architecture. The unique features of an action\nsequence are represented as a series of elicited key activations by the\nfirst-layer self-organizing map. An average length of a key activation vector\nis calculated for all action sequences in a training set and adjusted in\nlearning trials to generate input patterns to the second-layer self-organizing\nmap. The pattern vectors are clustered in the second layer, and the clusters\nare then labeled by an action identity in the third layer neural network. The\nexperiment results show that although the performance drops slightly in online\nexperiments compared to the offline tests, the ability of the proposed\narchitecture to deal with the unsegmented action sequences as well as the\nonline performance makes the system more plausible and practical in real-case\nscenarios.\n"}
+{"abstract": "  The vertex connectivity of an $m$-edge $n$-vertex undirected graph is the\nsmallest number of vertices whose removal disconnects the graph, or leaves only\na singleton vertex. In this paper, we give a reduction from the vertex\nconnectivity problem to a set of maxflow instances. Using this reduction, we\ncan solve vertex connectivity in $\\tilde O(m^{\\alpha})$ time for any $\\alpha\n\\ge 1$, if there is a $m^{\\alpha}$-time maxflow algorithm. Using the current\nbest maxflow algorithm that runs in $m^{4/3+o(1)}$ time (Kathuria, Liu and\nSidford, FOCS 2020), this yields a $m^{4/3+o(1)}$-time vertex connectivity\nalgorithm. This is the first improvement in the running time of the vertex\nconnectivity problem in over 20 years, the previous best being an $\\tilde\nO(mn)$-time algorithm due to Henzinger, Rao, and Gabow (FOCS 1996). Indeed, no\nalgorithm with an $o(mn)$ running time was known before our work, even if we\nassume an $\\tilde O(m)$-time maxflow algorithm. Our new technique is robust\nenough to also improve the best $\\tilde O(mn)$-time bound for directed vertex\nconnectivity to $mn^{1-1/12+o(1)}$ time\n"}
+{"abstract": "  Users of OCR systems, from different institutions and scientific disciplines,\nprefer and produce different transcription styles. This presents a problem for\ntraining of consistent text recognition neural networks on real-world data. We\npropose to extend existing text recognition networks with a Transcription Style\nBlock (TSB) which can learn from data to switch between multiple transcription\nstyles without any explicit knowledge of transcription rules. TSB is an\nadaptive instance normalization conditioned by identifiers representing\nconsistently transcribed documents (e.g. single document, documents by a single\ntranscriber, or an institution). We show that TSB is able to learn completely\ndifferent transcription styles in controlled experiments on artificial data, it\nimproves text recognition accuracy on large-scale real-world data, and it\nlearns semantically meaningful transcription style embedding. We also show how\nTSB can efficiently adapt to transcription styles of new documents from\ntranscriptions of only a few text lines.\n"}
+{"abstract": "  We present an extremely simple Ultra-Resolution Style Transfer framework,\ntermed URST, to flexibly process arbitrary high-resolution images (e.g.,\n10000x10000 pixels) style transfer for the first time. Most of the existing\nstate-of-the-art methods would fall short due to massive memory cost and small\nstroke size when processing ultra-high resolution images. URST completely\navoids the memory problem caused by ultra-high resolution images by (1)\ndividing the image into small patches and (2) performing patch-wise style\ntransfer with a novel Thumbnail Instance Normalization (TIN). Specifically, TIN\ncan extract thumbnail features' normalization statistics and apply them to\nsmall patches, ensuring the style consistency among different patches.\n  Overall, the URST framework has three merits compared to prior arts. (1) We\ndivide input image into small patches and adopt TIN, successfully transferring\nimage style with arbitrary high-resolution. (2) Experiments show that our URST\nsurpasses existing SOTA methods on ultra-high resolution images benefiting from\nthe effectiveness of the proposed stroke perceptual loss in enlarging the\nstroke size. (3) Our URST can be easily plugged into most existing style\ntransfer methods and directly improve their performance even without training.\nCode is available at https://git.io/URST.\n"}
+{"abstract": "  This paper considers the problem of real-time control and learning in dynamic\nsystems subjected to uncertainties. Adaptive approaches are proposed to address\nthe problem, which are combined to with methods and tools in Reinforcement\nLearning (RL) and Machine Learning (ML). Algorithms are proposed in\ncontinuous-time that combine adaptive approaches with RL leading to online\ncontrol policies that guarantee stable behavior in the presence of parametric\nuncertainties that occur in real-time. Algorithms are proposed in discrete-time\nthat combine adaptive approaches proposed for parameter and output estimation\nand ML approaches proposed for accelerated performance that guarantee stable\nestimation even in the presence of time-varying regressors, and for accelerated\nlearning of the parameters with persistent excitation. Numerical validations of\nall algorithms are carried out using a quadrotor landing task on a moving\nplatform and benchmark problems in ML. All results clearly point out the\nadvantage of adaptive approaches for real-time control and learning.\n"}
+{"abstract": "  Neural Architecture Search (NAS) has recently gained increased attention, as\na class of approaches that automatically searches in an input space of network\narchitectures. A crucial part of the NAS pipeline is the encoding of the\narchitecture that consists of the applied computational blocks, namely the\noperations and the links between them. Most of the existing approaches either\nfail to capture the structural properties of the architectures or use\nhand-engineered vector to encode the operator information. In this paper, we\npropose the replacement of fixed operator encoding with learnable\nrepresentations in the optimization process. This approach, which effectively\ncaptures the relations of different operations, leads to smoother and more\naccurate representations of the architectures and consequently to improved\nperformance of the end task. Our extensive evaluation in ENAS benchmark\ndemonstrates the effectiveness of the proposed operation embeddings to the\ngeneration of highly accurate models, achieving state-of-the-art performance.\nFinally, our method produces top-performing architectures that share similar\noperation and graph patterns, highlighting a strong correlation between the\nstructural properties of the architecture and its performance.\n"}
+{"abstract": "  Learned iterative shrinkage thresholding algorithm (LISTA), which adopts deep\nlearning techniques to learn optimal algorithm parameters from labeled training\ndata, can be successfully applied to small-scale multidimensional harmonic\nretrieval (MHR) problems. However, LISTA computationally demanding for\nlarge-scale MHR problems because the matrix size of the learned mutual\ninhibition matrix exhibits quadratic growth with the signal length. These large\nmatrices consume costly memory/computation resources and require a huge amount\nof labeled data for training, restricting the applicability of the LISTA\nmethod. In this paper, we show that the mutual inhibition matrix of a MHR\nproblem naturally has a Toeplitz structure, which means that the degrees of\nfreedom (DoF) of the matrix can be reduced from a quadratic order to a linear\norder. By exploiting this characteristic, we propose a structured\nLISTA-Toeplitz network, which imposes a Toeplitz structure restriction on the\nmutual inhibition matrices and applies linear convolution instead of the\nmatrix-vector multiplication involved in the traditional LISTA network. Both\nsimulation and field test for air target detection with radar are carried out\nto validate the performance of the proposed network. For small-scale MHR\nproblems, LISTAToeplitz exhibits close or even better recovery accuracy than\ntraditional LISTA, while the former significantly reduces the network\ncomplexity and requires much less training data. For large-scale MHR problems,\nwhere LISTA is difficult to implement due to the huge size of the mutual\ninhibition matrices, our proposed LISTA-Toeplitz still enjoys desirable\nrecovery performance.\n"}
+{"abstract": "  Describing networks geometrically through low-dimensional latent metric\nspaces has helped design efficient learning algorithms, unveil network\nsymmetries and study dynamical network processes. However, latent space\nembeddings are limited to specific classes of networks because incompatible\nmetric spaces generally result in information loss. Here, we study arbitrary\nnetworks geometrically by defining a dynamic edge curvature measuring the\nsimilarity between pairs of dynamical network processes seeded at nearby nodes.\nWe show that the evolution of the curvature distribution exhibits gaps at\ncharacteristic timescales indicating bottleneck-edges that limit information\nspreading. Importantly, curvature gaps are robust to large fluctuations in node\ndegrees, encoding communities until the phase transition of detectability,\nwhere spectral and node-clustering methods fail. Using this insight, we derive\ngeometric modularity to find multiscale communities based on deviations from\nconstant network curvature in generative and real-world networks, significantly\noutperforming most previous methods. Our work suggests using network geometry\nfor studying and controlling the structure of and information spreading on\nnetworks.\n"}
+{"abstract": "  During the last two decades, we have progressively turned to the Internet and\nsocial media to find news, entertain conversations and share opinion. Recently,\nOpenAI has developed a ma-chine learning system called GPT-2 for Generative\nPre-trained Transformer-2, which can pro-duce deepfake texts. It can generate\nblocks of text based on brief writing prompts that look like they were written\nby humans, facilitating the spread false or auto-generated text. In line with\nthis progress, and in order to counteract potential dangers, several methods\nhave been pro-posed for detecting text written by these language models. In\nthis paper, we propose a transfer learning based model that will be able to\ndetect if an Arabic sentence is written by humans or automatically generated by\nbots. Our dataset is based on tweets from a previous work, which we have\ncrawled and extended using the Twitter API. We used GPT2-Small-Arabic to\ngenerate fake Arabic Sentences. For evaluation, we compared different recurrent\nneural network (RNN) word embeddings based baseline models, namely: LSTM,\nBI-LSTM, GRU and BI-GRU, with a transformer-based model. Our new\ntransfer-learning model has obtained an accuracy up to 98%. To the best of our\nknowledge, this work is the first study where ARABERT and GPT2 were combined to\ndetect and classify the Arabic auto-generated texts.\n"}
+{"abstract": "  For $s<r$, let $B_{r,s}$ be the graph consisting of two copies of $K_r$,\nwhich share exactly $s$ vertices. Denote by $ex(n, K_r, B_{r,s})$ the maximum\nnumber of copies of $K_r$ in a $B_{r,s}$-free graph on $n$ vertices. In 1976,\nErd\\H{o}s and S\\'{o}s determined $ex(n,K_3,B_{3,1})$. Recently, Gowers and\nJanzer showed that $ex(n,K_r,B_{r,r-1})=n^{r-1-o(1)}$. It is a natural question\nto ask for $ex(n,K_r,B_{r,s})$ for general $r$ and $s$. In this paper, we\nmainly consider the problem for $s=1$. Utilizing the Zykov's symmetrization, we\nshow that $ex(n,K_4, B_{4,1})=\\lfloor (n-2)^2/4\\rfloor$ for $n\\geq 45$. For\n$r\\geq 5$ and $n$ sufficiently large, by the F\\\"{u}redi's structure theorem we\nshow that $ex(n,K_r,B_{r,1}) =\\mathcal{N}(K_{r-2},T_{r-2}(n-2))$, where\n$\\mathcal{N}(K_{r-2},T_{r-2}(n-2))$ represents the number of copies of\n$K_{r-2}$ in the $(r-2)$-partite Tur\\'{a}n graph on $n-2$ vertices.\n"}
+{"abstract": "  With concurrency being integral to most software systems, developers combine\nhigh-level concurrency models in the same application to tackle each problem\nwith appropriate abstractions. While languages and libraries offer a wide range\nof concurrency models, debugging support for applications that combine them has\nnot yet gained much attention. Record & replay aids debugging by\ndeterministically reproducing recorded bugs, but is typically designed for a\nsingle concurrency model only. This paper proposes a practical\nconcurrency-model-agnostic record & replay approach for multi-paradigm\nconcurrent programs, i.e. applications that combine concurrency models. Our\napproach traces high-level nondeterministic events by using a uniform\nmodel-agnostic trace format and infrastructure. This enables orderingbased\nrecord & replay support for a wide range of concurrency models, and thereby\nenables debugging of applications that combine them. In addition, it allows\nlanguage implementors to add new concurrency models and reuse the\nmodel-agnostic record & replay support. We argue that a\nconcurrency-model-agnostic record & replay is practical and enables advanced\ndebugging support for a wide range of concurrency models. The evaluation shows\nthat our approach is expressive and flexible enough to support record & replay\nof applications using threads & locks, communicating event loops, communicating\nsequential processes, software transactional memory and combinations of those\nconcurrency models. For the actor model, we reach recording performance\ncompetitive with an optimized special-purpose record & replay solution. The\naverage recording overhead on the Savina actor benchmark suite is 10% (min. 0%,\nmax. 23%). The performance for other concurrency models and combinations\nthereof is at a similar level. We believe our concurrency-model-agnostic\napproach helps developers of applications that mix and match concurrency\nmodels. We hope that this substrate inspires new tools and languages making\nbuilding and maintaining of multi-paradigm concurrent applications simpler and\nsafer.\n"}
+{"abstract": "  In comparison to the interpretation of classification models, the explanation\nof sequence generation models is also an important problem, however it has seen\nlittle attention. In this work, we study model-agnostic explanations of a\nrepresentative text generation task -- dialogue response generation. Dialog\nresponse generation is challenging with its open-ended sentences and multiple\nacceptable responses. To gain insights into the reasoning process of a\ngeneration model, we propose anew method, local explanation of response\ngeneration (LERG) that regards the explanations as the mutual interaction of\nsegments in input and output sentences. LERG views the sequence prediction as\nuncertainty estimation of a human response and then creates explanations by\nperturbing the input and calculating the certainty change over the human\nresponse. We show that LERG adheres to desired properties of explanations for\ntext generation including unbiased approximation, consistency and cause\nidentification. Empirically, our results show that our method consistently\nimproves other widely used methods on proposed automatic- and human- evaluation\nmetrics for this new task by 4.4-12.8%. Our analysis demonstrates that LERG can\nextract both explicit and implicit relations between input and output segments.\n"}
+{"abstract": "  Signal estimation in the presence of background noise is a common problem in\nseveral scientific disciplines. An 'On/Off' measurement is performed when the\nbackground itself is not known, being estimated from a background control\nsample. The 'frequentist' and Bayesian approaches for signal estimation in\nOn/Off measurements are reviewed and compared, focusing on the weakness of the\nformer and on the advantages of the latter in correctly addressing the\nPoissonian nature of the problem. In this work, we devise a novel\nreconstruction method, dubbed BASiL (Bayesian Analysis including Single-event\nLikelihoods), for estimating the signal rate based on the Bayesian formalism.\nIt uses information on event-by-event individual parameters and their\ndistribution for the signal and background population. Events are thereby\nweighted according to their likelihood of being a signal or a background event\nand background suppression can be achieved without performing fixed fiducial\ncuts. Throughout the work, we maintain a general notation, that allows to apply\nthe method generically, and provide a performance test using real data and\nsimulations of observations with the MAGIC telescopes, as demonstration of the\nperformance for Cherenkov telescopes. BASiL allows to estimate the signal more\nprecisely, avoiding loss of exposure due to signal extraction cuts. We expect\nits applicability to be straightforward in similar cases.\n"}
+{"abstract": "  We extend the Schoenberg correspondence for universal independences by\nSch\\\"urmann and Vo{\\ss} to the multivariate setting of Manzel and Sch\\\"urmann,\ncovering, e.g., Voiculescu's bifreeness as well as Bo\\.zejko and Speicher's\nc-free independence. At the same time, we free the proof in the univariate\nsituation from its dependence on Muraki's classification theorem (which states\nthat there are only five univariate independences: tensor, free, Boolean,\nmonotone, and antimonotone independence). In contrast to the univariate case,\nthe class of multivariate independences considered here is infinite and not yet\nfully understood. Furthermore, helpful tools such quantum stochastic calculi\nand Fock space constructions, which are used by Sch\\\"urmann and Vo{\\ss} to\nprove Schoenberg correspondence in the univariate case, are not available in\ngeneral. This problem is overcome by relating convolution exponentials with\nrespect to different independences to each other, which ultimately allows to\nreduce all Schoenberg correspondences for multivariate independences to the\ncase of convolution semigroups on *-bialgebras.\n"}
+{"abstract": "  In this paper we give two characterizations of the $p \\times q$-grid graphs\nas co-edge-regular graphs with four distinct eigenvalues.\n"}
+{"abstract": "  Developments in statistical learning have fueled the analysis of\nhigh-dimensional time series. However, even in low-dimensional contexts the\nissues arising from ill-conditioned regression problems are well-known. Because\nlinear time series modeling is naturally prone to such issues, I propose to\napply ridge regression to the estimation of dense VAR models. Theoretical\nnon-asymptotic results concerning the addition of a ridge-type penalty to the\nleast squares estimator are discussed, while standard asymptotic and inference\ntechniques are proven to be valid under mild conditions on the regularizer. The\nproposed estimator is then applied to the problem of sieve approximation of\nVAR($\\infty$) processes under moderately harsh sample sizes. Simulation\nevidence is used to discuss the small sample properties of the ridge estimator\n(RLS) when compared to least squares and local projection approaches: I use a\nMonte Carlo exercise to argue that RLS with a lag-adapted cross-validated\nregularizer achieve meaningfully better performance in recovering impulse\nresponse functions and asymptotic confidence intervals than other common\napproaches.\n"}
+{"abstract": "  This submission has been removed by arXiv administrators as the submitter did\nnot have the right to agree to the license at the time of submission.\n"}
+{"abstract": "  Dwarf carbon (dC) stars, main sequence stars showing carbon molecular bands,\nare enriched by mass transfer from a previous asymptotic-giant-branch (AGB)\ncompanion, which has since evolved to a white dwarf. While previous studies\nhave found radial-velocity variations for large samples of dCs, there are still\nrelatively few dC orbital periods in the literature and no dC eclipsing\nbinaries have yet been found. Here, we analyze photometric light curves from\nDR5 of the Zwicky Transient Facility for a sample of 944 dC stars. From these\nlight curves, we identify 34 periodically variable dC stars. Remarkably, of the\nperiodic dCs, 82\\% have periods less than two days. We also provide\nspectroscopic follow-up for four of these periodic systems, measuring radial\nvelocity variations in three of them. Short-period dCs are almost certainly\npost-common-envelope binary systems, since the periodicity is most likely\nrelated to the orbital period, with tidally locked rotation and photometric\nmodulation on the dC either from spots or from ellipsoidal variations. We\ndiscuss evolutionary scenarios that these binaries may have taken to accrete\nsufficient C-rich material while avoiding truncation of the thermally pulsing\nAGB phase needed to provide such material in the first place. We compare these\ndCs to common-envelope models to show that dC stars probably cannot accrete\nenough C-rich material during the common-envelope phase, suggesting another\nmechanism like wind-Roche lobe overflow is necessary. The periodic dCs in this\npaper represent a prime sample for spectroscopic follow-up and for comparison\nto future models of wind-Roche lobe overflow mass transfer.\n"}
+{"abstract": "  It is of paramount importance to achieve efficient data collection in the\nInternet of Things (IoT). Due to the inherent structural properties (e.g.,\nsparsity) existing in many signals of interest, compressive sensing (CS)\ntechnology has been extensively used for data collection in IoT to improve both\naccuracy and energy efficiency. Apart from the existing works which leverage CS\nas a channel coding scheme to deal with data loss during transmission, some\nrecent results have started to employ CS as a source coding strategy. The\nfrequently used projection matrices in these CS-based source coding schemes\ninclude dense random matrices (e.g., Gaussian matrices or Bernoulli matrices)\nand structured matrices (e.g., Toeplitz matrices). However, these matrices are\neither difficult to be implemented on resource-constrained IoT sensor nodes or\nhave limited applicability. To address these issues, in this paper, we design a\nnovel simple and efficient projection matrix, named sparse Gaussian matrix,\nwhich is easy and resource-saving to be implemented in practical IoT\napplications. We conduct both theoretical analysis and experimental evaluation\nof the designed sparse Gaussian matrix. The results demonstrate that employing\nthe designed projection matrix to perform CS-based source coding could\nsignificantly save time and memory cost while ensuring satisfactory signal\nrecovery performance.\n"}
+{"abstract": "  This paper introduces an agent-centric approach to handle novelty in the\nvisual recognition domain of handwriting recognition (HWR). An ideal\ntranscription agent would rival or surpass human perception, being able to\nrecognize known and new characters in an image, and detect any stylistic\nchanges that may occur within or across documents. A key confound is the\npresence of novelty, which has continued to stymie even the best machine\nlearning-based algorithms for these tasks. In handwritten documents, novelty\ncan be a change in writer, character attributes, writing attributes, or overall\ndocument appearance, among other things. Instead of looking at each aspect\nindependently, we suggest that an integrated agent that can process known\ncharacters and novelties simultaneously is a better strategy. This paper\nformalizes the domain of handwriting recognition with novelty, describes a\nbaseline agent, introduces an evaluation protocol with benchmark data, and\nprovides experimentation to set the state-of-the-art. Results show feasibility\nfor the agent-centric approach, but more work is needed to approach\nhuman-levels of reading ability, giving the HWR community a formal basis to\nbuild upon as they solve this challenging problem.\n"}
+{"abstract": "  Change detection, which aims to distinguish surface changes based on\nbi-temporal images, plays a vital role in ecological protection and urban\nplanning. Since high resolution (HR) images cannot be typically acquired\ncontinuously over time, bi-temporal images with different resolutions are often\nadopted for change detection in practical applications. Traditional\nsubpixel-based methods for change detection using images with different\nresolutions may lead to substantial error accumulation when HR images are\nemployed; this is because of intraclass heterogeneity and interclass\nsimilarity. Therefore, it is necessary to develop a novel method for change\ndetection using images with different resolutions, that is more suitable for HR\nimages. To this end, we propose a super-resolution-based change detection\nnetwork (SRCDNet) with a stacked attention module. The SRCDNet employs a super\nresolution (SR) module containing a generator and a discriminator to directly\nlearn SR images through adversarial learning and overcome the resolution\ndifference between bi-temporal images. To enhance the useful information in\nmulti-scale features, a stacked attention module consisting of five\nconvolutional block attention modules (CBAMs) is integrated to the feature\nextractor. The final change map is obtained through a metric learning-based\nchange decision module, wherein a distance map between bi-temporal features is\ncalculated. The experimental results demonstrate the superiority of the\nproposed method, which not only outperforms all baselines -with the highest F1\nscores of 87.40% on the building change detection dataset and 92.94% on the\nchange detection dataset -but also obtains the best accuracies on experiments\nperformed with images having a 4x and 8x resolution difference. The source code\nof SRCDNet will be available at https://github.com/liumency/SRCDNet.\n"}
+{"abstract": "  In the proofs of most cases of the Andr\\'e-Oort conjecture, there are two\ndifferent steps whose effectivity is unclear: the use of generalizations of\nBrauer-Siegel and the use of Pila-Wilkie. Only the case of curves in ${\\bf\nC}^2$ is currently known effectively (by other methods).\n  We give an effective proof of Andr\\'e-Oort for non-compact curves in every\nHilbert modular surface and every Hilbert modular variety of odd genus (under a\nminor generic simplicity condition). In particular we show that in these cases\nthe first step may be replaced by the endomorphism estimates of W\\\"ustholz and\nthe second author together with the specialization method of Andr\\'e via\nG-functions, and the second step may be effectivized using the Q-functions of\nNovikov, Yakovenko and the first author.\n"}
+{"abstract": "  The COVID-19 crisis has shown that we can only prevent the risk of mass\ncontagion through timely, large-scale, coordinated, and decisive actions.\nHowever, frequently the models used by experts [from whom decision-makers get\ntheir main advice] focus on a single perspective [for example, the\nepidemiological one] and do not consider many of the multiple forces that\naffect the COVID-19 outbreak patterns. The epidemiological, socioeconomic, and\nhuman mobility context of COVID-19 can be considered as a complex adaptive\nsystem. So, these interventions (for example, lock-downs) could have many\nand/or unexpected ramifications. This situation makes it difficult to\nunderstand the overall effect produced by any public policy measure and,\ntherefore, to assess its real effectiveness and convenience. By using mobile\nphone data, socioeconomic data, and COVID-19 cases data recorded throughout the\npandemic development, we aim to understand and explain [make sense of] the\nobserved heterogeneous regional patterns of contagion across time and space. We\nwill also consider the causal effects produced by confinement policies by\ndeveloping data-based models to explore, simulate, and estimate these policies'\neffectiveness. We intend to develop a methodology to assess and improve public\npolicies' effectiveness associated with the fight against the pandemic,\nemphasizing its convenience, the precise time of its application, and\nextension. The contributions of this work can be used regardless of the region.\nThe only likely impediment is the availability of the appropriate data.\n"}
+{"abstract": "  We establish an analog of the Hardy-Ramanujan inequality for counting members\nof sifted sets with a given number of distinct prime factors. In particular, we\nestablish a bound for the number of shifted primes p+a below x with k distinct\nprime factors, uniformly for all positive integers k.\n"}
+{"abstract": "  Measuring time lags between time-series or lighcurves at different\nwavelengths from a variable or transient source in astronomy is an essential\nprobe of physical mechanisms causing multiwavelength variability. Time-lags are\ntypically quantified using discrete correlation functions (DCF) which are\nappropriate for linear relationships. However, in variable sources like X-ray\nbinaries, active galactic nuclei (AGN) and other accreting systems, the\nradiative processes and the resulting multiwavelength lightcurves often have\nnon-linear relationships. For such systems it is more appropriate to use\nnon-linear information-theoretic measures of causation like mutual information,\nroutinely used in other disciplines. We demonstrate with toy models loopholes\nof using the standard DCF & show improvements when using the mutual information\ncorrelation function (MICF). For non-linear correlations, the latter accurately\n& sharply identifies the lag components as opposed to the DCF which can be\nerroneous. Following that we apply the MICF to the multiwavelength lightcurves\nof AGN NGC 4593. We find that X-ray fluxes lead UVW2 fluxes by ~0.2 days,\ncloser to model predictions from reprocessing by the accretion disk than the\nDCF estimate. The uncertainties with the current lightcurves are too large\nthough to rule out -ve lags. Additionally, we find another delay component at\n~-1 day i.e. UVW2 leading X-rays consistent with inward propagating\nfluctuations in the accretion disk scenario. This is not detected by the DCF.\nKeeping in mind the non-linear relation between X-ray & UVW2, this is worthy of\nfurther theoretical investigation. From both toy models & real observations, it\nis clear that the mutual information based estimator is highly sensitive to\ncomplex non-linear correlations. With sufficiently high temporal resolution, we\nwill precisely detect each of the lag features corresponding to these\ncorrelations.\n"}
+{"abstract": "  We now turn to understanding the impact that COVID-19 had on the personal\nproductivity and well-being of information workers as their work practices were\nimpacted by remote work. This chapter overviews people's productivity,\nsatisfaction, and work patterns, and shows that the challenges and benefits of\nremote work are closely linked. Looking forward, the infrastructure surrounding\nwork will need to evolve to help people adapt to the challenges of remote and\nhybrid work.\n"}
+{"abstract": "  We present a method for automatically labelling all faces in video archives,\nsuch as TV broadcasts, by combining multiple evidence sources and multiple\nmodalities (visual and audio). We target the problem of ever-growing online\nvideo archives, where an effective, scalable indexing solution cannot require a\nuser to provide manual annotation or supervision. To this end, we make three\nkey contributions: (1) We provide a novel, simple, method for determining if a\nperson is famous or not using image-search engines. In turn this enables a\nface-identity model to be built reliably and robustly, and used for high\nprecision automatic labelling; (2) We show that even for less-famous people,\nimage-search engines can then be used for corroborative evidence to accurately\nlabel faces that are named in the scene or the speech; (3) Finally, we\nquantitatively demonstrate the benefits of our approach on different video\ndomains and test settings, such as TV shows and news broadcasts. Our method\nworks across three disparate datasets without any explicit domain adaptation,\nand sets new state-of-the-art results on all the public benchmarks.\n"}
+{"abstract": "  The main aim of this paper is to investigate Darboux rectifying curves on a\nsmooth surface immersed in the Euclidean space. First, we discuss the component\nof the position vector of a Darboux rectifying curve on a smooth immersed\nsurface under the isometry of surfaces. Next we find a sufficient condition for\nthe conformal invariance of Darboux rectifying curve.\n"}
+{"abstract": "  An Euler tour in a hypergraph (also called a rank-2 universal cycle or\n1-overlap cycle in the context of designs) is a closed walk that traverses\nevery edge exactly once. In this paper, we define a covering $k$-hypergraph to\nbe a non-empty $k$-uniform hypergraph in which every $(k-1)$-subset of vertices\nappear together in at least one edge. We then show that every covering\n$k$-hypergraph, for $k\\geq 3$, admits an Euler tour if and only if it has at\nleast two edges.\n"}
+{"abstract": "  The quasi-chemical organization of the potential distribution theorem --\nmolecular quasi-chemical theory (QCT) -- enables practical calculations and\nalso provides a conceptual framework for molecular hydration phenomena. QCT can\nbe viewed from multiple perspectives: (a) As a way to regularize an\nill-conditioned statistical thermodynamic problem; (b) As an introduction of\nand emphasis on the neighborship characteristics of a solute of interest; (c)\nOr as a way to include accurate electronic structure descriptions of\nnear-neighbor interactions in defensible statistical thermodynamics by clearly\ndefining neighborship clusters. The theory has been applied to solutes of a\nwide range of chemical complexity, ranging from ions that interact with water\nwith both long-ranged and chemically intricate short-ranged interactions, to\nsolutes that interact with water solely through traditional van~der~Waals\ninterations, and including water itself. The solutes range in variety from\nmonoatomic ions to chemically heterogeneous macromolecules. A notable feature\nof QCT is that in applying the theory to this range of solutes, the theory\nitself provides guidance on the necessary approximations and simplifications\nthat can facilitate the calculations. In this Perspective, we develop these\nideas and document them with examples that reveal the insights that can be\nextracted using the QCT formulation.\n"}
+{"abstract": "  In optical communications, space-division multiplexing is a promising\nstrategy to augment the fiber network capacity. It relies on modern fiber\ndesigns that support the propagation of multiple spatial modes. One of these\nfibers, the ring-core fiber (RCF), is able to propagate modes that carry\norbital angular momentum (OAM), and has been shown to enhance not only\nclassical, but also quantum communication systems. Typically, the RCF spatial\nmodes are used as orthogonal transmission channels for data streams that are\ncoupled into the fiber using different Laguerre-Gaussian (LG) beams. Here, we\nstudy the optimal conditions to multiplex information into ring-core fibers in\nthis scheme. We determine which are the most relevant LG beams to be\nconsidered, and how their coupling efficiency can be maximized by properly\nadjusting the beam width with respect to the fiber parameters. Our results show\nthat the coupling efficiency depends upon the OAM value, and that this can\nlimit the achievable transmission rates. In this regard, we show that LG beams\nare not the optimal choice to couple information into RCF. Rather, another\nclass of OAM-carrying beam, the perfect vortex beam, allows for nearly perfect\ncoupling efficiencies for all spatial modes supported by these fibers.\n"}
+{"abstract": "  We study the clustering of HI intensity maps produced from simulations with a\nfocus on baryonic acoustic oscillations (BAO) and the effects induced by\ntelescope beam smoothing and foreground cleaning. We start by creating a HI\ncatalogue at $z=1.321$ based on the Semi-Analytic Galaxy Evolution (SAGE) model\napplied to the UNIT simulations. With this catalogue we investigate the\nrelation between model HI and the dark matter haloes and we also study the\nabundance of HI, $\\Omega_{\\rm HI}$, predicted by this model. We then create\nsynthetic HI intensity maps with a Nearest-Grid-Point approach. In order to\nsimulate the telescope beam effect, a Gaussian smoothing is applied on the\nplane perpendicular to the line of sight. The effect of foreground removal\nmethods is simulated by exponentially damping the largest wavelength Fourier\nmodes on the radial direction. We study the anisotropic 2-point correlation\nfunction (2PCF) $\\xi(r_\\perp,r_\\parallel)$ and how it is affected by the\naforementioned observational effects. In order to better isolate the BAO\nsignal, we study several 2PCF $\\mu$-wedges (with a restricted range of\norientations $\\mu$) tailored to address the systematics effects and we compare\nthem with different definitions of radial 2PCFs. Finally, we discuss our\nfindings in the context of an SKA-like survey, finding a clear BAO signal in\nmost of the estimators here proposed.\n"}
+{"abstract": "  The existence of adversarial examples poses a real danger when deep neural\nnetworks are deployed in the real world. The go-to strategy to quantify this\nvulnerability is to evaluate the model against specific attack algorithms. This\napproach is however inherently limited, as it says little about the robustness\nof the model against more powerful attacks not included in the evaluation. We\ndevelop a unified mathematical framework to describe relaxation-based\nrobustness certification methods, which go beyond adversary-specific robustness\nevaluation and instead provide provable robustness guarantees against attacks\nby any adversary. We discuss the fundamental limitations posed by single-neuron\nrelaxations and show how the recent ``k-ReLU'' multi-neuron relaxation\nframework of Singh et al. (2019) obtains tighter correlation-aware activation\nbounds by leveraging additional relational constraints among groups of neurons.\nSpecifically, we show how additional pre-activation bounds can be mapped to\ncorresponding post-activation bounds and how they can in turn be used to obtain\ntighter robustness certificates. We also present an intuitive way to visualize\ndifferent relaxation-based certification methods. By approximating multiple\nnon-linearities jointly instead of separately, the k-ReLU method is able to\nbypass the convex barrier imposed by single neuron relaxations.\n"}
+{"abstract": "  We present a new model of collective decision making that captures important\ncrowd-funding and donor coordination scenarios. In the setting, there is a set\nof projects (each with its own cost) and a set of agents (that have their\nbudgets as well as preferences over the projects). An outcome is a set of\nprojects that are funded along with the specific contributions made by the\nagents. For the model, we identify meaningful axioms that capture concerns\nincluding fairness, efficiency, and participation incentives. We then propose\ndesirable rules for the model and study, which sets of axioms can be satisfied\nsimultaneously. An experimental study indicates the relative performance of\ndifferent rules as well as the price of enforcing fairness axioms.\n"}
+{"abstract": "  The '$S_8$ tension' is a longstanding discrepancy between the cosmic\nmicrowave background (CMB) and weak gravitational lensing determination of the\namplitude of matter fluctuations, parametrized as\n$S_8\\equiv\\sigma_8(\\Omega_m/0.3)^{0.5}$, where $\\sigma_8$ is the root mean\nsquare of matter fluctuations on a 8 $h^{-1}$Mpc scale, and $\\Omega_m$ is the\ntotal matter abundance. It was recently shown that dark matter (DM) decaying\ninto a massless (dark radiation) and a massive (warm DM) species, with a\nlifetime $\\Gamma^{-1} \\simeq 55~(\\varepsilon/0.007)^{1.4}$ Gyrs -- where\n$\\varepsilon$ represent the mass-energy fraction transferred to the massless\ncomponent -- can ease the tension. Thanks to a fast and accurate fluid\napproximation scheme for the warm species, we perform a comprehensive study of\nthis 2-body decaying DM scenario, discussing in detail its dynamics and its\nimpact on the CMB and linear matter power spectra. We then investigate the\nimplications for the '$S_8$ tension' against a number of changes in the\nanalysis: different $S_8$ priors, marginalization over the lensing information\nin Planck data, trading Planck high$-\\ell$ polarization data for those from the\nSPTpol and ACTPol surveys, and the inclusion of the recent results from the\nXenon1T collaboration. We conclude that the preference for decaying DM,\napparent only when the $S_8$ value determined from weak lensing data is added\nto the analysis, does not sensibly degrade the fit to any of the cosmological\ndata-sets considered, and that the model could potentially explain the\nanomalous electron recoil excess reported by the Xenon1T collaboration.\nFurthermore, we explictly show that while current CMB data alone are not\nsensitive enough to distinguish between standard CDM and decaying DM,\nnext-generation CMB observations (CMB-S4) can unambiguously detect its\nsignature.\n"}
+{"abstract": "  While adversarial neural networks have been shown successful for static image\nattacks, very few approaches have been developed for attacking online image\nstreams while taking into account the underlying physical dynamics of\nautonomous vehicles, their mission, and environment. This paper presents an\nonline adversarial machine learning framework that can effectively misguide\nautonomous vehicles' missions. In the existing image attack methods devised\ntoward autonomous vehicles, optimization steps are repeated for every image\nframe. This framework removes the need for fully converged optimization at\nevery frame to realize image attacks in real-time. Using reinforcement\nlearning, a generative neural network is trained over a set of image frames to\nobtain an attack policy that is more robust to dynamic and uncertain\nenvironments. A state estimator is introduced for processing image streams to\nreduce the attack policy's sensitivity to physical variables such as unknown\nposition and velocity. A simulation study is provided to validate the results.\n"}
+{"abstract": "  A critical question in astrobiology is whether exoEarth candidates (EECs) are\nEarth-like, in that they originate life that progressively oxygenates their\natmospheres similarly to Earth. We propose answering this question\nstatistically by searching for O2 and O3 on EECs with missions such as HabEx or\nLUVOIR. We explore the ability of these missions to constrain the fraction, fE,\nof EECs that are Earth-like in the event of a null detection of O2 or O3 on all\nobserved EECs. We use the Planetary Spectrum Generator to simulate observations\nof EECs with O2 and O3 levels based on Earth's history. We consider four\ninstrument designs: LUVOIR-A (15m), LUVOIR-B (8m), HabEx with a starshade (4m,\n\"HabEx/SS\"), HabEx without a starshade (4m, \"HabEx/no-SS\"); as well as three\nestimates of the occurrence rate of EECs (eta_earth): 24%, 5%, and 0.5%. In the\ncase of a null-detection, we find that for eta_earth = 24%, LUVOIR-A, LUVOIR-B,\nand HabEx/SS would constrain fE to <= 0.094, <= 0.18, and <= 0.56,\nrespectively. This also indicates that if fE is greater than these upper\nlimits, we are likely to detect O3 on at least 1 EEC. Conversely, we find that\nHabEx/no-SS cannot constrain fE, due to the lack of an coronagraph ultraviolet\nchannel. For eta_earth = 5%, only LUVOIR-A and LUVOIR-B would be able to\nconstrain fE, to <= 0.45 and <= 0.85, respectively. For eta_earth = 0.5%, none\nof the missions would allow us to constrain fE, due to the low number of\ndetectable EECs. We conclude that the ability to constrain fE is more robust to\nuncertainties in eta_earth for missions with larger aperture mirrors. However\nall missions are susceptible to an inconclusive null detection if eta_earth is\nsufficiently low.\n"}
+{"abstract": "  Visual perception of the objects in a 3D environment is a key to successful\nperformance in autonomous driving and simultaneous localization and mapping\n(SLAM). In this paper, we present a real time approach for estimating the\ndistances to multiple objects in a scene using only a single-shot image. Given\na 2D Bounding Box (BBox) and object parameters, a 3D distance to the object can\nbe calculated directly using 3D reprojection; however, such methods are prone\nto significant errors because an error from the 2D detection can be amplified\nin 3D. In addition, it is also challenging to apply such methods to a real-time\nsystem due to the computational burden. In the case of the traditional\nmulti-object detection methods, %they mostly pay attention to existing works\nhave been developed for specific tasks such as object segmentation or 2D BBox\nregression. These methods introduce the concept of anchor BBox for elaborate 2D\nBBox estimation, and predictors are specialized and trained for specific 2D\nBBoxes. In order to estimate the distances to the 3D objects from a single 2D\nimage, we introduce the notion of \\textit{anchor distance} based on an object's\nlocation and propose a method that applies the anchor distance to the\nmulti-object detector structure. We let the predictors catch the distance prior\nusing anchor distance and train the network based on the distance. The\npredictors can be characterized to the objects located in a specific distance\nrange. By propagating the distance prior using a distance anchor to the\npredictors, it is feasible to perform the precise distance estimation and\nreal-time execution simultaneously. The proposed method achieves about 30 FPS\nspeed, and shows the lowest RMSE compared to the existing methods.\n"}
+{"abstract": "  Unsupervised Domain Adaptation (UDA) aims to align the labeled source\ndistribution with the unlabeled target distribution to obtain domain invariant\npredictive models. However, the application of well-known UDA approaches does\nnot generalize well in Semi-Supervised Domain Adaptation (SSDA) scenarios where\nfew labeled samples from the target domain are available. In this paper, we\npropose a simple Contrastive Learning framework for semi-supervised Domain\nAdaptation (CLDA) that attempts to bridge the intra-domain gap between the\nlabeled and unlabeled target distributions and inter-domain gap between source\nand unlabeled target distribution in SSDA. We suggest employing class-wise\ncontrastive learning to reduce the inter-domain gap and instance-level\ncontrastive alignment between the original (input image) and strongly augmented\nunlabeled target images to minimize the intra-domain discrepancy. We have shown\nempirically that both of these modules complement each other to achieve\nsuperior performance. Experiments on three well-known domain adaptation\nbenchmark datasets namely DomainNet, Office-Home, and Office31 demonstrate the\neffectiveness of our approach. CLDA achieves state-of-the-art results on all\nthe above datasets.\n"}
+{"abstract": "  A key issue in system design is the lack of communication between hardware,\nsoftware and domain expert. Recent research work shows progress in automatic\nHW/SW co-design flows of neural accelerators that seems to make this kind of\ncommunication obsolete. Most real-world systems, however, are a composition of\nmultiple processing units, communication networks and memories. A HW/SW\nco-design process of (reconfigurable) neural accelerators, therefore, is an\nimportant sub-problem towards a common co-design methodology. The ultimate\nchallenge is to define the constraints for the design space exploration on\nsystem level - a task which requires deep knowledge and understanding of\nhardware architectures, mapping of workloads onto hardware and the application\ndomain, e.g. artificial intelligence.\n  For most projects, these skills are distributed among several people or even\ndifferent teams which is one of the major reasons why there is no established\nend-to-end development methodology for digital systems. This position paper\ndiscusses possibilities how to establish such a methodology for systems that\ninclude (reconfigurable) dedicated accelerators and outlines the central role\nthat languages and tools play in the process.\n"}
+{"abstract": "  The Pr$_{0.50}$Sr$_{0.5}$0CoO$_3$ perovskite exhibits unique\nmagnetostructural properties among the rest of ferromagnetic/metallic\nLn$_{0.50}$Sr$_{0.50}$CoO$_3$ compounds. The sudden orthorhombic-tetragonal\n(Imma $\\to$ I4/mcm) structural transition produces an unusual magnetic behavior\nversus temperature and external magnetic fields. In particular, the symmetry\nchange is responsible for a spontaneous spin rotation in this metallic oxide.\nWe have studied half-doped Ln$_{0.50}$(Sr$_{1-x}$A$_x$)$_{0.50}$CoO$_3$\ncobaltites varying the ionic radius rA of A-site cations (divalent cations and\nlanthanides) in order to complete the T-rA phase diagram. The influence of the\nstructural distortion and the A-cations size for the occurrence of a\nspontaneous spin reorientation in the metallic state has been investigated. As\nthe reorientation of the magnetization is driven by the temperature induced\ncollapse of the orthorhombic distortion, a careful investigation of the\nstructural symmetry is presented varying the structural distortion of the\nSr-rich half-doped cobaltites by means of both compositional and temperature\nchanges. The region in the phase diagram of these perovskites where the phase\nof magnetic symmetry Fm'm'm replaces that of Im'm'a symmetry was determined in\nthis family of ferromagnetic/metallic cobaltites. In that region the\nmagnetization direction has rotated 45 degrees within the a-b plane with\nrespect to the second.\n"}
+{"abstract": "  The new experiment data of muon g-2 is reported by the workers at Fermilab\nNational Accelerator Laboratory(FNAL). Combined with the previous Brookhaven\nNational Laboratory(BNL) E821 result, the departure from the standard model\nprediction is about 4.2 $\\sigma$. It strengthens our faith in the new physics.\n$U(1)_X$SSM is the U(1) extension of the minimal supersymmetric standard model,\nwhere we study the electroweak corrections to the anomalous magnetic dipole\nmoment of muon from the one-loop diagrams and some two-loop diagrams possessing\nimportant contributions. These two-loop diagrams include Barr-Zee type, rainbow\ntype and diamond type. The virtual supersymmetric particles in these two-loop\ndiagrams are chargino, scalar neutrino, neutralino, scalar lepton, which are\nsupposed not very heavy to make relatively large corrections. We obtain the\nWilson coefficients of the dimension 6 operators inducing the anomalous\nmagnetic dipole moment of muon. The numerical results can reach $25\\times\n10^{-10}$ and even larger.\n"}
+{"abstract": "  Starting from the idea of Quantum Computing which is a concept that dates\nback to 80s, we come to the present day where we can perform calculations on\nreal quantum computers. This sudden development of technology opens up new\nscenarios that quickly lead to the desire and the real possibility of\nintegrating this technology into current software architectures. The usage of\nframeworks that allow computation to be performed directly on quantum hardware\nposes a series of challenges. This document describes a an architectural\nframework that addresses the problems of integrating an API exposed Quantum\nprovider in an existing Enterprise architecture and it provides a minimum\nviable product (MVP) solution that really merges classical quantum computers on\na basic scenario with reusable code on GitHub repository. The solution\nleverages a web-based frontend where user can build and select applications/use\ncases and simply execute it without any further complication. Every triggered\nrun leverages on multiple backend options, that include a scheduler managing\nthe queuing mechanism to correctly schedule jobs and final results retrieval.\nThe proposed solution uses the up-to-date cloud native technologies (e.g. Cloud\nFunctions, Containers, Microservices) and serves as a general framework to\ndevelop multiple applications on the same infrastructure.\n"}
+{"abstract": "  The two-loop correction to the mass of the $\\phi^4$ kink is $0.0126\\lambda/m$\nin terms of the coupling $\\lambda$ and the meson mass $m$ evaluated at the\nminimum of the potential. This is calculated using a recently proposed\nalternative to collective coordinates. Both the kink energy and the vacuum\nenergy are IR divergent at this order. To cancel the divergence, the two energy\ndensities are subtracted before integrating over space, or equivalently a\nfinite counterterm is added to the Hamiltonian density to cancel the vacuum\nenergy density. All spatial integrals are performed analytically. However in\nthe last step of our calculation, integrals over virtual momenta are performed\nnumerically.\n"}
+{"abstract": "  We tackle the problem of generalization to unseen configurations for dynamic\ntasks in the real world while learning from high-dimensional image input. The\nfamily of nonlinear dynamical system-based methods have successfully\ndemonstrated dynamic robot behaviors but have difficulty in generalizing to\nunseen configurations as well as learning from image inputs. Recent works\napproach this issue by using deep network policies and reparameterize actions\nto embed the structure of dynamical systems but still struggle in domains with\ndiverse configurations of image goals, and hence, find it difficult to\ngeneralize. In this paper, we address this dichotomy by leveraging embedding\nthe structure of dynamical systems in a hierarchical deep policy learning\nframework, called Hierarchical Neural Dynamical Policies (H-NDPs). Instead of\nfitting deep dynamical systems to diverse data directly, H-NDPs form a\ncurriculum by learning local dynamical system-based policies on small regions\nin state-space and then distill them into a global dynamical system-based\npolicy that operates only from high-dimensional images. H-NDPs additionally\nprovide smooth trajectories, a strong safety benefit in the real world. We\nperform extensive experiments on dynamic tasks both in the real world (digit\nwriting, scooping, and pouring) and simulation (catching, throwing, picking).\nWe show that H-NDPs are easily integrated with both imitation as well as\nreinforcement learning setups and achieve state-of-the-art results. Video\nresults are at https://shikharbahl.github.io/hierarchical-ndps/\n"}
+{"abstract": "  The internal conversion from the optically bright S$_2$\n($^1$B$_{2\\mathrm{u}}$, $\\pi\\pi^*$) state to the dark S$_1$\n($^1$B$_{3\\mathrm{u}}$, n$\\pi^*$) state in pyrazine is a standard benchmark for\nexperimental and theoretical studies on ultrafast radiationless decay. Since\n2008 a few theoretical groups have suggested significant contributions of other\ndark states S$_3$ ($^1$A$_\\mathrm{u}$, n$\\pi^*$) and S$_4$\n($^1$B$_{2\\mathrm{g}}$, n$\\pi^*$) to the decay of S$_2$. We have previously\nreported the results of nuclear wave packet simulations [Phys. Chem. Chem.\nPhys. 17, 2012 (2015)] and photoelectron spectrum calculations [Chem. Phys.\n515, 704 (2018)] that support the conventional two-state picture. In this\narticle, the two different approaches, i.e., wave packet simulation and\nphotoelectron spectrum calculation are combined: We computed the time-resolved\nvacuum ultraviolet photoelectron spectrum and photoelectron angular\ndistribution for the ionization of the wave packet transferred from S$_2$ to\nS$_1$. The present results reproduce almost all the characteristic features of\nthe corresponding experimental time-resolved spectrum [T. Horio et al., J.\nChem. Phys. 145, 044306 (2016)] such as a rapid change from a three-band to\ntwo-band structure. This further supports the existence and character of the\nwidely accepted pathway (S$_2$ $\\rightarrow$ S$_1$) of ultrafast internal\nconversion in pyrazine.\n"}
+{"abstract": "  We consider the classical uncoded caching problem from an online learning\npoint-of-view. A cache of limited storage capacity can hold $C$ files at a time\nfrom a large catalog. A user requests an arbitrary file from the catalog at\neach time slot. Before the file request from the user arrives, a caching policy\npopulates the cache with any $C$ files of its choice. In the case of a\ncache-hit, the policy receives a unit reward and zero rewards otherwise. In\naddition to that, there is a cost associated with fetching files to the cache,\nwhich we refer to as the switching cost. The objective is to design a caching\npolicy that incurs minimal regret while considering both the rewards due to\ncache-hits and the switching cost due to the file fetches. The main\ncontribution of this paper is the switching regret analysis of a Follow the\nPerturbed Leader-based anytime caching policy, which is shown to have an order\noptimal switching regret. In this pursuit, we improve the best-known switching\nregret bound for this problem by a factor of $\\Theta(\\sqrt{C}).$ We conclude\nthe paper by comparing the performance of different popular caching policies\nusing a publicly available trace from a commercial CDN server.\n"}
+{"abstract": "  Bulk nanobubbles (NBs) generated electrochemically by short voltage pulses of\nalternating polarity behave differently from those produced by regular methods.\nOnly bubbles smaller than $200\\;$nm are formed in the process and their\nconcentration is very high. Moreover, the bubbles containing both H2 and O2\ngases disappear quickly via the combustion reaction, although the reaction in\nsuch a small volume cannot happen according to the classical combustion theory.\nExperimental facts about these unusual NBs are reviewed and current\nunderstanding of the observed phenomena is provided. Visualisation methods of a\ncloud of NBs above the electrodes are briefly discussed. Experimental\nsignatures demonstrating the reaction between the gases in NBs are considered.\nA surface-assisted mechanism proposed for the combustion reactions in\nrestricted volumes with a high surface-to-volume ratio is discussed. It it\nexplained how the same mechanism may describe the audible explosion of\nmicrobubbles, that is observed in certain circumstances.\n"}
+{"abstract": "  Accurate localization is of crucial importance for autonomous driving tasks.\nNowadays, we have seen a lot of sensor-rich vehicles (e.g. Robo-taxi) driving\non the street autonomously, which rely on high-accurate sensors (e.g. Lidar and\nRTK GPS) and high-resolution map. However, low-cost production cars cannot\nafford such high expenses on sensors and maps. How to reduce costs? How do\nsensor-rich vehicles benefit low-cost cars? In this paper, we proposed a\nlight-weight localization solution, which relies on low-cost cameras and\ncompact visual semantic maps. The map is easily produced and updated by\nsensor-rich vehicles in a crowd-sourced way. Specifically, the map consists of\nseveral semantic elements, such as lane line, crosswalk, ground sign, and stop\nline on the road surface. We introduce the whole framework of on-vehicle\nmapping, on-cloud maintenance, and user-end localization. The map data is\ncollected and preprocessed on vehicles. Then, the crowd-sourced data is\nuploaded to a cloud server. The mass data from multiple vehicles are merged on\nthe cloud so that the semantic map is updated in time. Finally, the semantic\nmap is compressed and distributed to production cars, which use this map for\nlocalization. We validate the performance of the proposed map in real-world\nexperiments and compare it against other algorithms. The average size of the\nsemantic map is $36$ kb/km. We highlight that this framework is a reliable and\npractical localization solution for autonomous driving.\n"}
+{"abstract": "  Chest radiographs are one of the most common diagnostic modalities in\nclinical routine. It can be done cheaply, requires minimal equipment, and the\nimage can be diagnosed by every radiologists. However, the number of chest\nradiographs obtained on a daily basis can easily overwhelm the available\nclinical capacities. We propose RATCHET: RAdiological Text Captioning for Human\nExamined Thoraces. RATCHET is a CNN-RNN-based medical transformer that is\ntrained end-to-end. It is capable of extracting image features from chest\nradiographs, and generates medically accurate text reports that fit seamlessly\ninto clinical work flows. The model is evaluated for its natural language\ngeneration ability using common metrics from NLP literature, as well as its\nmedically accuracy through a surrogate report classification task. The model is\navailable for download at: http://www.github.com/farrell236/RATCHET.\n"}
+{"abstract": "  Supervised machine learning models and their evaluation strongly depends on\nthe quality of the underlying dataset. When we search for a relevant piece of\ninformation it may appear anywhere in a given passage. However, we observe a\nbias in the position of the correct answer in the text in two popular Question\nAnswering datasets used for passage re-ranking. The excessive favoring of\nearlier positions inside passages is an unwanted artefact. This leads to three\ncommon Transformer-based re-ranking models to ignore relevant parts in unseen\npassages. More concerningly, as the evaluation set is taken from the same\nbiased distribution, the models overfitting to that bias overestimate their\ntrue effectiveness. In this work we analyze position bias on datasets, the\ncontextualized representations, and their effect on retrieval results. We\npropose a debiasing method for retrieval datasets. Our results show that a\nmodel trained on a position-biased dataset exhibits a significant decrease in\nre-ranking effectiveness when evaluated on a debiased dataset. We demonstrate\nthat by mitigating the position bias, Transformer-based re-ranking models are\nequally effective on a biased and debiased dataset, as well as more effective\nin a transfer-learning setting between two differently biased datasets.\n"}
+{"abstract": "  Widespread adoption of deep models has motivated a pressing need for\napproaches to interpret network outputs and to facilitate model debugging.\nInstance attribution methods constitute one means of accomplishing these goals\nby retrieving training instances that (may have) led to a particular\nprediction. Influence functions (IF; Koh and Liang 2017) provide machinery for\ndoing this by quantifying the effect that perturbing individual train instances\nwould have on a specific test prediction. However, even approximating the IF is\ncomputationally expensive, to the degree that may be prohibitive in many cases.\nMight simpler approaches (e.g., retrieving train examples most similar to a\ngiven test point) perform comparably? In this work, we evaluate the degree to\nwhich different potential instance attribution agree with respect to the\nimportance of training samples. We find that simple retrieval methods yield\ntraining instances that differ from those identified via gradient-based methods\n(such as IFs), but that nonetheless exhibit desirable characteristics similar\nto more complex attribution methods. Code for all methods and experiments in\nthis paper is available at:\nhttps://github.com/successar/instance_attributions_NLP.\n"}
+{"abstract": "  The quadratic computational and memory complexities of the Transformer's\nattention mechanism have limited its scalability for modeling long sequences.\nIn this paper, we propose Luna, a linear unified nested attention mechanism\nthat approximates softmax attention with two nested linear attention functions,\nyielding only linear (as opposed to quadratic) time and space complexity.\nSpecifically, with the first attention function, Luna packs the input sequence\ninto a sequence of fixed length. Then, the packed sequence is unpacked using\nthe second attention function. As compared to a more traditional attention\nmechanism, Luna introduces an additional sequence with a fixed length as input\nand an additional corresponding output, which allows Luna to perform attention\noperation linearly, while also storing adequate contextual information. We\nperform extensive evaluations on three benchmarks of sequence modeling tasks:\nlong-context sequence modeling, neural machine translation and masked language\nmodeling for large-scale pretraining. Competitive or even better experimental\nresults demonstrate both the effectiveness and efficiency of Luna compared to a\nvariety\n"}
+{"abstract": "  We show that molecular spin qudits provide an ideal platform to simulate the\nquantum dynamics of photon fields strongly interacting with matter. The basic\nunit of the proposed molecular quantum simulator can be realized by a simple\ndimer of a spin 1/2 and a spin $S$ transition metal ion, solely controlled by\nmicrowave pulses. The spin $S$ ion is exploited to encode the photon field in a\nflexible architecture, which enables the digital simulation of a wide range of\nspin-boson models much more efficiently than by using a multi-qubit register.\nThe effectiveness of our proposal is demonstrated by numerical simulations\nusing realistic molecular parameters, whose prerequisites delineating possible\nchemical approaches are also discussed.\n"}
+{"abstract": "  Extreme debris disks (EDDs) are rare systems with peculiarly large amounts of\nwarm dust that may stem from recent giant impacts between planetary embryos\nduring the final phases of terrestrial planet growth. Here we report on the\nidentification and characterization of six new EDDs. These disks surround F5-G9\ntype main-sequence stars with ages >100 Myr, have dust temperatures higher than\n300K and fractional luminosities between 0.01 and 0.07. Using time-domain\nphotometric data at 3.4 and 4.6$\\mu$m from the WISE all sky surveys, we\nconclude that four of these disks exhibited variable mid-infrared emission\nbetween 2010 and 2019. Analyzing the sample of all known EDDs, now expanded to\n17 objects, we find that 14 of them showed changes at 3-5$\\mu$m over the past\ndecade suggesting that mid-infrared variability is an inherent characteristic\nof EDDs. We also report that wide-orbit pairs are significantly more common in\nEDD systems than in the normal stellar population. While current models of\nrocky planet formation predict that the majority of giant collisions occur in\nthe first 100 Myr, we find that the sample of EDDs is dominated by systems\nolder than this age. This raises the possibility that the era of giant impacts\nmay be longer than we think, or that some other mechanism(s) can also produce\nEDDs. We examine a scenario where the observed warm dust stems from the\ndisruption and/or collisions of comets delivered from an outer reservoir into\nthe inner regions, and explore what role the wide companions could play in this\nprocess.\n"}
+{"abstract": "  Variational autoencoders (VAEs) often suffer from posterior collapse, which\nis a phenomenon in which the learned latent space becomes uninformative. This\nis often related to a hyperparameter resembling the data variance. It can be\nshown that an inappropriate choice of this parameter causes oversmoothness and\nleads to posterior collapse in the linearly approximated case and can be\nempirically verified for the general cases. Therefore, we propose AR-ELBO\n(Adaptively Regularized Evidence Lower BOund), which controls the smoothness of\nthe model by adapting this variance parameter. In addition, we extend VAE with\nalternative parameterizations on the variance parameter to deal with\nnon-uniform or conditional data variance. The proposed VAE extensions trained\nwith AR-ELBO show improved Fr\\'echet inception distance (FID) on images\ngenerated from the MNIST and CelebA datasets.\n"}
+{"abstract": "  We introduce Metatheory.jl: a lightweight and performant general purpose\nsymbolics and metaprogramming framework meant to simplify the act of writing\ncomplex Julia metaprograms and to significantly enhance Julia with a native\nterm rewriting system, based on state-of-the-art equality saturation\ntechniques, and a dynamic first class Abstract Syntax Tree (AST) pattern\nmatching system that is dynamically composable in an algebraic fashion, taking\nfull advantage of the language's powerful reflection capabilities. Our\ncontribution allows to perform general purpose symbolic mathematics,\nmanipulation, optimization, synthesis or analysis of syntactically valid Julia\nexpressions with a clean and concise programming interface, both during\ncompilation or execution of programs.\n"}
+{"abstract": "  Background. Developers use Automated Static Analysis Tools (ASATs) to control\nfor potential quality issues in source code, including defects and technical\ndebt. Tool vendors have devised quite a number of tools, which makes it harder\nfor practitioners to select the most suitable one for their needs. To better\nsupport developers, researchers have been conducting several studies on ASATs\nto favor the understanding of their actual capabilities.\n  Aims. Despite the work done so far, there is still a lack of knowledge\nregarding (1) which source quality problems can actually be detected by static\nanalysis tool warnings, (2) what is their agreement, and (3) what is the\nprecision of their recommendations. We aim at bridging this gap by proposing a\nlarge-scale comparison of six popular static analysis tools for Java projects:\nBetter Code Hub, CheckStyle, Coverity Scan, Findbugs, PMD, and SonarQube.\n  Method. We analyze 47 Java projects and derive a taxonomy of warnings raised\nby 6 state-of-the-practice ASATs. To assess their agreement, we compared them\nby manually analyzing - at line-level - whether they identify the same issues.\nFinally, we manually evaluate the precision of the tools.\n  Results. The key results report a comprehensive taxonomy of ASATs warnings,\nshow little to no agreement among the tools and a low degree of precision.\n  Conclusions. We provide a taxonomy that can be useful to researchers,\npractitioners, and tool vendors to map the current capabilities of the tools.\nFurthermore, our study provides the first overview on the agreement among\ndifferent tools as well as an extensive analysis of their precision.\n"}
+{"abstract": "  We introduce a simplified model of the electron-beam/plasma system to model\nthe electrical breakdown caused by the inductive electric field created by a\nrapidly rising electron beam current. The rigid-beam model is a reduction to\nthe problem geometry to cylindrical coordinated and simplifications to\nMaxwell's equations that are driven by a prescribed electron beam current\ndensity. The model is very convenient for comparing various reductions of the\nplasma dynamics and air chemistry equation while maintaining a good\napproximation to the overall magnitude of the beam-created electric field. The\nusefulness of this model is demonstrated by comparing results for two different\nfluid reductions of the plasma dynamics: one where the collision rates are\ncomputed from the local reduced electric field (E/p) and another where the\ncollision rates are determined from the mean energy per particle. We find that\nthe two methods give similar results at higher pressures where the energy\nrelation rate is large but differs significantly at lower pressures where the\ncharacteristic inelastic energy loss time scale is comparable to or greater\nthan the rise time of the electron beam current.\n"}
+{"abstract": "  For hypocoercive linear kinetic equations we first formulate an optimisation\nproblem on a spatially dependent jump rate in order to find the fastest decay\nrate of perturbations. In the Goldstein-Taylor model we show (i) that for a\nlocally optimal jump rate the spectral gap is determined by multiple, possible\ndegenerate, eigenvectors and (ii) that globally the fastest decay is obtained\nwith a spatially homogeneous jump rate. Our proofs rely on a connection to\ndamped wave equations and a relationship to the spectral theory of\nSchr{\\\"o}dinger operators.\n"}
+{"abstract": "  Generative Adversarial Networks (GANs) are capable of generating convincing\nimitations of elements from a training set, but the distribution of elements in\nthe training set affects to difficulty of properly training the GAN and the\nquality of the outputs it produces. This paper looks at six different GANs\ntrained on different subsets of data from the game Lode Runner. The quality\ndiversity algorithm MAP-Elites was used to explore the set of quality levels\nthat could be produced by each GAN, where quality was defined as being beatable\nand having the longest solution path possible. Interestingly, a GAN trained on\nonly 20 levels generated the largest set of diverse beatable levels while a GAN\ntrained on 150 levels generated the smallest set of diverse beatable levels,\nthus challenging the notion that more is always better when training GANs.\n"}
+{"abstract": "  Lattice structure can dictate electronic and magnetic properties of a\nmaterial. Especially, reconstruction at a surface or heterointerface can create\nproperties that are fundamentally different from those of the corresponding\nbulk material. We have investigated the lattice structure on the surface and in\nthe thin films of epitaxial SrRuO3 with the film thickness up to 22\npseudo-cubic unit cells (u.c.), using the combination of surface sensitive low\nenergy electron diffraction and bulk sensitive scanning transmission electron\nmicroscopy. Our analysis indicates that, in contrast to many perovskite oxides,\nthe RuO6 tilt and rotational distortions appear even in single unit cell SrRuO3\nthin films on cubic SrTiO3, while the full relaxation to the bulk-like\northorhombic structure takes 3-4 u.c. from the interface for thicker films. Yet\nthe TiO6 octahedra of the substrate near the interface with SrRuO3 films show\nno sign of distortion, unlike those near the interface with CaRuO3 films. Two\northogonal in-plane rotated structural domains are identified. These structural\ndistortions are essential for the nature of the thickness dependent transport\nand magnetism in ultrathin films.\n"}
+{"abstract": "  We present a detailed low-energy muon spin rotation and x-ray magnetic\ncircular dichroism (XMCD) investigation of the magnetic structure in ultra-thin\ntetragonal (T)-CuO films. The measured muon-spin polarization decay indicates\nan antiferromagnetic (AFM) order with a transition temperature higher than\n200K. The XMCD signal obtained around the Cu $L_{2,3}$ edges indicates the\npresence of pinned Cu$^{2+}$ moments that are parallel to the sample surface,\nand additionally, isotropic paramagnetic moments. The pinning of some of the Cu\nmoments is caused by an AFM ordering consisting of moments that lie most likely\nin the plane of the film. Moreover, pinned moments show a larger orbital\nmagnetic moment contribution with an approximate ratio of $m_{orb}/m_{spin} =\n2$, indicating that these spins are located at sites with reduced symmetry.\nSome fractions of the pinned moments remain pinned from an AFM background even\nat 360K, indicating that $T_N >$ 360K. A simple model could explain\nqualitatively these experimental findings; however, it is in contrast to\ntheoretical predictions, showing that the magnetic properties of ultra-thin\nT-CuO films differ from bulk expectations and is more complex.\n"}
+{"abstract": "  We analyze a dataset of 51 current (2019-2020) Distributed Systems syllabi\nfrom top Computer Science programs, focusing on finding the prevalence and\ncontext in which topics related to performance are being taught in these\ncourses. We also study the scale of the infrastructure mentioned in DS courses,\nfrom small client-server systems to cloud-scale, peer-to-peer, global-scale\nsystems. We make eight main findings, covering goals such as performance, and\nscalability and its variant elasticity; activities such as performance\nbenchmarking and monitoring; eight selected performance-enhancing techniques\n(replication, caching, sharding, load balancing, scheduling, streaming,\nmigrating, and offloading); and control issues such as trade-offs that include\nperformance and performance variability.\n"}
+{"abstract": "  This study explores the impact of high PV penetration on the inter-area\noscillation modes of large-scale power grids. A series of dynamic models with\nvarious PV penetration levels are developed based on a detailed model\nrepresenting the U.S. Eastern Interconnection (EI). Transient simulations are\nperformed to investigate the change of inter-area oscillation modes with PV\npenetration. The impact of PV control strategies and parameter settings on\ninter-area oscillations is studied. This study finds that as PV increases, the\ndamping of the dominant oscillation mode decreases monotonically. It is also\nobserved that the mode shape varies with the PV control strategy and new\noscillation modes may emerge under inappropriate parameter settings in PV plant\ncontrols.\n"}
+{"abstract": "  Urban green space has been regarded as contributing to citizen happiness by\npromoting physical and mental health. However, how urban green space and\nhappiness are related across many countries of different socioeconomic\nconditions has not been explained well. By measuring urban green space score\n(UGS) from high-resolution Sentinel-2 satellite imagery of 90 global cities\nthat in total cover 179,168 km$^2$ and include 230 million people in 60\ndeveloped countries, we reveal that the amount of urban green space and the GDP\ncan explain the happiness level of the country. More precisely, urban green\nspace and GDP are each individually associated with happiness; happiness in the\n30 wealthiest countries is explained only by urban green space, whereas GDP\nalone explains happiness in the 30 other countries in this study. Lastly, we\nfurther show that the relationship between urban green space and happiness is\nmediated by social support and that GDP moderates the relationship between\nsocial support and happiness, which underlines the importance of maintaining\nurban green space as a place for social cohesion in promoting people's\nhappiness.\n"}
+{"abstract": "  Context: The Sun is by far a privileged target for testing stellar models\nwith unique precision. A recent concern appeared with the progress in the solar\nsurface abundances derivation that has led to a decrease of the solar\nmetallicity. While the ancient high-metallicity models were in fair agreement\nwith other solar observational indicators, it is no longer the case for\nlow-metallicity models. Recent collection of data are however promising to shed\na new light on it. For instance, the Borexino collaboration released in 2020\nthe first-ever complete estimate of neutrinos emitted in the CNO cycle. It has\nreaffirmed the role of the neutrino constraints in the solar modelling process\nand its associated issues. In parallel, newly claimed detection of solar\ngravity modes of oscillations offers another opportunity of probing the\nstratification in the Sun's central layers. Aims: We propose to combine the\ndiagnoses from neutrinos and helioseismology, both from pressure and gravity\nmodes, for assessing the predictions of solar models. We compare in detail the\ndifferent physical prescriptions currently at disposal for stellar model\ncomputations. Results: The CNO neutrino flux confirms a preference for\nhigh-metallicity models. Nevertheless, we found that mild modification of the\nnuclear screening factors can re-match low-metallicity model predictions to\nobserved fluxes, although it does not restore the agreement with the\nhelioseismic frequency ratios. Neither the high-metallicity or low-metallicity\nmodels are able to reproduce the gravity-mode period spacing. The disagreement\nis huge, more than 100$\\sigma$ to the reported value. Reversely, the family of\nstandard models narrows the expected range of the Sun's period spacing: between\n$\\sim$2150 to $\\sim$2190~s. Moreover, we show this indicator can constrain the\nchemical mixture, opacity, and to a lower extent nuclear reactions in solar\nmodels.\n"}
+{"abstract": "  This work is an analytical study of the main differences between classical\ninflationary effects of a fast-roll regime and imprints of hybrid Loop Quantum\nCosmology (LQC) on the primordial power spectrum. Effective LQC solutions of\nphenomenological interest typically contain a classical period of kinetic\ndominance prior to slow-roll inflation, with consequences on the power spectrum\nthat here we try and tell apart from those of phenomena occuring in the\nvicinity of a bounce in hybrid LQC. For this comparison, we use the same\ncriterion for the choice of a vacuum state of the primordial perturbations,\nboth in the context of relativistic cosmology and on the LQC cosmological\nbackground. As a first approximation to the problem, our study ignores the\ninfluence of a quasi-de Sitter evolution (rather than de Sitter) in the\nslow-roll inflationary regime, and of a typically short transition between the\nkinetically dominated era and the slow-roll period. Our results show that,\nwhile the two studied types of effects can lead to a drastic power suppression\nin the infrared sector of the spectrum, the mode scale at which this happens is\nmuch larger for LQC than for classical relativistic cosmology, the\ncorresponding scales being related to the spacetime curvature at the bounce and\nat the onset of inflation, respectively.\n"}
+{"abstract": "  The pairwise velocity generating function $G$ has deep connection with both\nthe pairwise velocity probability distribution function and modeling of\nredshift space distortion (RSD). Its implementation into RSD modeling is often\nfaciliated by expansion into series of pairwise velocity moments $\\langle\nv_{12}^n\\rangle$. Motivated by the logrithmic transformation of the cosmic\ndensity field, we investigate an alternative expansion into series of pairwise\nvelocity cumulants $\\langle v_{12}^n\\rangle_c$ . We numerically evaluate the\nconvergence rate of the two expansions, with three $3072^3$ particle\nsimulations of the CosmicGrowth N-body simulation series. (1) We find that the\ncumulant expansion performs significantly better, for all the halo samples and\nredshifts investigated. (2) For modeling RSD at $k_{\\|}<0.1 h$ Mpc$^{-1}$,\nincluding only the $n=1,2$ cumulants is sufficient. (3) But for modeling RSD at\n$k_\\parallel=0.2 h$ Mpc$^{-1}$, we need and only need the $n=1,2,3,4$\ncumulants. These results provide specific requirements on RSD modeling in terms\nof $m$-th order statistics of the large scale strucure.\n"}
+{"abstract": "  Bayesian optimization (BO) is a sample efficient approach to automatically\ntune the hyperparameters of machine learning models. In practice, one\nfrequently has to solve similar hyperparameter tuning problems sequentially.\nFor example, one might have to tune a type of neural network learned across a\nseries of different classification problems. Recent work on multi-task BO\nexploits knowledge gained from previous tuning tasks to speed up a new tuning\ntask. However, previous approaches do not account for the fact that BO is a\nsequential decision making procedure. Hence, there is in general a mismatch\nbetween the number of evaluations collected in the current tuning task compared\nto the number of evaluations accumulated in all previously completed tasks. In\nthis work, we enable multi-task BO to compensate for this mismatch, such that\nthe transfer learning procedure is able to handle different data regimes in a\nprincipled way. We propose a new multi-task BO method that learns a set of\nordered, non-linear basis functions of increasing complexity via nested\ndrop-out and automatic relevance determination. Experiments on a variety of\nhyperparameter tuning problems show that our method improves the sample ef\n"}
+{"abstract": "  Context. Bulge globular clusters (BGCs) are exceptional tracers of the\nformation and chemodynamical evolution of this oldest Galactic component.\nHowever, until now, observational difficulties have prevented us from taking\nfull advantage of these powerful Galactic archeological tools. Aims. CAPOS, the\nbulge Cluster APOgee Survey, addresses this key topic by observing a large\nnumber of BGCs, most of which have only been poorly studied previously. Even\ntheir most basic parameters, such as metallicity, [{\\alpha}/Fe], and radial\nvelocity, are generally very uncertain. We aim to obtain accurate mean values\nfor these parameters, as well as abundances for a number of other elements, and\nexplore multiple populations. In this first paper, we describe the CAPOS\nproject and present initial results for seven BGCs. Methods. CAPOS uses the\nAPOGEE-2S spectrograph observing in the H band to penetrate obscuring dust\ntoward the bulge. For this initial paper, we use abundances derived from\nASPCAP, the APOGEE pipeline. Results. We derive mean [Fe/H] values of\n$-$0.85$\\pm$0.04 (Terzan 2), $-$1.40$\\pm$0.05 (Terzan 4), $-$1.20$\\pm$0.10 (HP\n1), $-$1.40$\\pm$0.07 (Terzan 9), $-$1.07$\\pm$0.09 (Djorg 2), $-$1.06$\\pm$0.06\n(NGC 6540), and $-$1.11$\\pm$0.04 (NGC 6642) from three to ten stars per\ncluster. We determine mean abundances for eleven other elements plus the mean\n[$\\alpha$/Fe] and radial velocity. CAPOS clusters significantly increase the\nsample of well-studied Main Bulge globular clusters (GCs) and also extend them\nto lower metallicity. We reinforce the finding that Main Bulge and Main Disk\nGCs, formed in situ, have [Si/Fe] abundances slightly higher than their\naccreted counterparts at the same metallicity. We investigate multiple\npopulations and find our clusters generally follow the light-element\n(anti)correlation trends of previous studies of GCs of similar metallicity. We\nfinally explore the abundances ...\n"}
+{"abstract": "  We show that the Euclidean and Lorentzian EPRL vertex amplitudes of covariant\nLoop Quantum Gravity are related through a ``Wick rotation'' of the real\nImmirzi parameter to purely imaginary values. Our result follows from the\nsimultaneous analytic continuation of the algebras, group elements and unitary\nirreducible representations of the gauge groups $Spin(4)$ and\n$SL(2,\\mathbb{C})$, applied to the decomposition of the two models in terms of\n$SU(2)$ invariants and booster functions.\n"}
+{"abstract": "  Data privacy concerns often prevent the use of cloud-based machine learning\nservices for sensitive personal data. While homomorphic encryption (HE) offers\na potential solution by enabling computations on encrypted data, the challenge\nis to obtain accurate machine learning models that work within the\nmultiplicative depth constraints of a leveled HE scheme. Existing approaches\nfor encrypted inference either make ad-hoc simplifications to a pre-trained\nmodel (e.g., replace hard comparisons in a decision tree with soft comparators)\nat the cost of accuracy or directly train a new depth-constrained model using\nthe original training set. In this work, we propose a framework to transfer\nknowledge extracted by complex decision tree ensembles to shallow neural\nnetworks (referred to as DTNets) that are highly conducive to encrypted\ninference. Our approach minimizes the accuracy loss by searching for the best\nDTNet architecture that operates within the given depth constraints and\ntraining this DTNet using only synthetic data sampled from the training data\ndistribution. Extensive experiments on real-world datasets demonstrate that\nthese characteristics are critical in ensuring that DTNet accuracy approaches\nthat of the original tree ensemble. Our system is highly scalable and can\nperform efficient inference on batched encrypted (134 bits of security) data\nwith amortized time in milliseconds. This is approximately three orders of\nmagnitude faster than the standard approach of applying soft comparison at the\ninternal nodes of the ensemble trees.\n"}
+{"abstract": "  The wireless communications landscape is anticipated to offer new service\nlevels following the introduction of the millimeter-wave (mmWave) spectrum to\nconsumer electronics. With their broad bandwidths and corresponding multi-Gbps\ndata rates, these mmWaves are a perfect fit for data hungry applications, such\nas streaming video to extended reality devices. However, the latter are also\nbound by maximal latency constraints as low as 1 ms. Understanding where such\nminuscule time delays lurk requires a close-up study of individual layers in\nthe network stack. Starting from the bottom up, the present work describes an\nendeavor at uncloaking the origins of physical layer (PHY) latency in mmWave\nWi-Fi networks. It proposes a newly designed simulation framework and sheds\nlight on how any conventional laboratory can be turned into a virtual\nexperiment setting, speeding up computation. A case study based on the IEEE\n802.11ad standard demonstrates the framework's ability to track packet latency\nat the PHY-level and identify individual bottlenecks. In particular, it\nevaluates the impact of the number of LDPC decoding iterations on latency in\nshort transmission sequences.\n"}
+{"abstract": "  Probabilistic machine learning models are often insufficient to help with\ndecisions on interventions because those models find correlations - not causal\nrelationships. If observational data is only available and experimentation are\ninfeasible, the correct approach to study the impact of an intervention is to\ninvoke Pearl's causality framework. Even that framework assumes that the\nunderlying causal graph is known, which is seldom the case in practice. When\nthe causal structure is not known, one may use out-of-the-box algorithms to\nfind causal dependencies from observational data. However, there exists no\nmethod that also accounts for the decision-maker's prior knowledge when\ndeveloping the causal structure either. The objective of this paper is to\ndevelop rational approaches for making decisions from observational data in the\npresence of causal graph uncertainty and prior knowledge from the\ndecision-maker. We use ensemble methods like Bayesian Model Averaging (BMA) to\ninfer set of causal graphs that can represent the data generation process. We\nprovide decisions by computing the expected value and risk of potential\ninterventions explicitly. We demonstrate our approach by applying them in\ndifferent example contexts.\n"}
+{"abstract": "  Federated learning (FL) is a paradigm that allows distributed clients to\nlearn a shared machine learning model without sharing their sensitive training\ndata. While largely decentralized, FL requires resources to fund a central\norchestrator or to reimburse contributors of datasets to incentivize\nparticipation. Inspired by insights from prior-independent auction design, we\npropose a mechanism, FIPIA (Federated Incentive Payments via Prior-Independent\nAuctions), to collect monetary contributions from self-interested clients. The\nmechanism operates in the semi-honest trust model and works even if clients\nhave a heterogeneous interest in receiving high-quality models, and the server\ndoes not know the clients' level of interest. We run experiments on the MNIST,\nFashionMNIST, and CIFAR-10 datasets to test clients' model quality under FIPIA\nand FIPIA's incentive properties.\n"}
+{"abstract": "  In this paper we develop the theory of train track maps on graphs of groups.\nWe prove the existence of CTs representing outer automorphisms of free\nproducts. We generalize an index inequality due to Feighn-Handel to the graph\nof groups setting, sharpening a result of Martino.\n"}
+{"abstract": "  We formulate a mean field game where each player stops a privately observed\nBrownian motion with absorption. Players are ranked according to their level of\nstopping and rewarded as a function of their relative rank. There is a unique\nmean field equilibrium and it is shown to be the limit of associated $n$-player\ngames. Conversely, the mean field strategy induces $n$-player\n$\\varepsilon$-Nash equilibria for any continuous reward function -- but not for\ndiscontinuous ones. In a second part, we study the problem of a principal who\ncan choose how to distribute a reward budget over the ranks and aims to\nmaximize the performance of the median player. The optimal reward design\n(contract) is found in closed form, complementing the merely partial results\navailable in the $n$-player case. We then analyze the quality of the mean field\ndesign when used as a proxy for the optimizer in the $n$-player game.\nSurprisingly, the quality deteriorates dramatically as $n$ grows. We explain\nthis with an asymptotic singularity in the induced $n$-player equilibrium\ndistributions.\n"}
+{"abstract": "  While ultrahigh-baud-rate optical signals are effective for extending the\ntransmission distance of large capacity signals, they also reduce the number of\nwavelengths that can be arranged in a band because of their wider bandwidth.\nThis reduces the flexibility of optical path configuration in reconfigurable\noptical add/drop multiplexing (ROADM) networks. In colorless, directionless and\ncontentionless (CDC)-ROADM in particular, the effect reduces the add/drop ratio\nat a node. Multiband ROADM systems are an effective countermeasure for\novercoming this issue, but they make the node configuration more complicated\nand its operation more difficult. In this paper, we analyze the challenges of C\n+ L band CDC-ROADM and show that optical switch devices that operate over\nmultiple bands are effective in meeting them. For this purpose, we built a C +\nL band CDC-ROADM node based on C + L band wavelength selective switches (WSSs)\nand multicast switches (MCSs) and confirmed its effectiveness experimentally.\nIn particular, to simplify the node configuration, we propose a reduction in\nthe number of optical amplifiers used for node loss compensation and\nexperimentally verify its feasibility.\n"}
+{"abstract": "  Federated learning is promising for its ability to collaboratively train\nmodels with multiple clients without accessing their data, but vulnerable when\nclients' data distributions diverge from each other. This divergence further\nleads to a dilemma: \"Should we prioritize the learned model's generic\nperformance (for future use at the server) or its personalized performance (for\neach client)?\" These two, seemingly competing goals have divided the community\nto focus on one or the other, yet in this paper we show that it is possible to\napproach both at the same time. Concretely, we propose a novel federated\nlearning framework that explicitly decouples a model's dual duties with two\nprediction tasks. On the one hand, we introduce a family of losses that are\nrobust to non-identical class distributions, enabling clients to train a\ngeneric predictor with a consistent objective across them. On the other hand,\nwe formulate the personalized predictor as a lightweight adaptive module that\nis learned to minimize each client's empirical risk on top of the generic\npredictor. With this two-loss, two-predictor framework which we name Federated\nRobust Decoupling Fed-RoD, the learned model can simultaneously achieve\nstate-of-the-art generic and personalized performance, essentially bridging the\ntwo tasks.\n"}
+{"abstract": "  We study the free data in the Fefferman-Graham expansion of asymptotically\nEinstein metrics with non-zero cosmological constant. We prove that if\n$\\mathscr{I}$ is conformally flat, the rescaled Weyl tensor at $\\mathscr{I}$\nagrees up to a constant with the free data at $\\mathscr{I}$ , namely the\ntraceless part of the $n$-th order coefficient of the expansion. In the\nnon-conformally flat case, the rescaled Weyl tensor is generically divergent at\n$\\mathscr{I}$ but one can still extract the free data in terms of the\ndifference of the Weyl tensors of suitably constructed metrics, in full\ngenerality when the spacetime dimension $D$ is even and provided the so-called\nobstruction tensor at $\\mathscr{I}$ is identically zero when $D$ is odd. These\nresults provide a geometric definition of the data, particularly relevant for\nthe asymptotic Cauchy problem of even dimensional Einstein metrics with\npositive $\\Lambda$ and also for the odd dimensional analytic case\nirrespectively of the sign of $\\Lambda$. We establish a Killing initial data\nequation at spacelike $\\mathscr{I}$ in all dimension for analytic data. These\nresults are used to find a geometric characterization of the Kerr-de Sitter\nmetrics in all dimensions in terms of its geometric data at null infinity.\n"}
+{"abstract": "  The d-band center descriptor based on the adsorption strength of adsorbate\nhas been widely used in understanding and predicting the catalytic activity in\nvarious metal catalysts. However, its applicability is unsure for the\nsingle-atom-anchored two-dimensional (2D) catalysts. Here, taking the hydrogen\n(H) adsorption on the single-atom-anchored 2D basal plane as example, we\nexamine the influence of orbitals interaction on the bond strength of hydrogen\nadsorption. We find that the adsorption of H is formed mainly via the\nhybridization between the 1s orbital of H and the vertical dz2 orbital of\nanchored atoms. The other four projected d orbitals (dxy/dx2-y2, dxz/dyz) have\nno contribution to the H chemical bond. There is an explicit linear relation\nbetween the dz2-band center and the H bond strength. The dz2-band center is\nproposed as an activity descriptor for hydrogen evolution reaction (HER). We\ndemonstrate that the dz2-band center is valid for the single-atom active sites\non a single facet, such as the basal plane of 2D nanosheets. For the surface\nwith multiple facets, such as the surface of three-dimensional (3D) polyhedral\nnanoparticles, the d-band center is more suitable.\n"}
+{"abstract": "  Inspired by the idea that quantum computers can be useful in advancing basic\nscience, we use a quantum processor to experimentally validate a number of\ntheoretical results in non-equilibrium quantum thermodynamics, that were not\n(or were very little) corroborated so far. In order to do so, we first put\nforward a novel method to implement the so called two point measurement scheme,\nwhich is at the basis of the study of non-equilibrium energetic exchanges in\nquantum systems. Like the well-established interferometric method, our method\nuses an ancillary system, but at variance with it, it provides direct access to\nthe energy exchange statistics, rather than its Fourier transform, thus being\nextremely more effective. We first experimentally validate our ancilla-assisted\ntwo point measurement scheme, and then apply it to i) experimentally verify\nthat fluctuation theorems are robust against projective measurements, a\ntheoretical prediction which was not validated so far, ii) experimentally\nverify the so called heat engine fluctuation relation, by implementing a SWAP\nquantum heat engine. iii) experimentally verify that the heat engine\nfluctuation relation continues to hold in presence of intermediate\nmeasurements, by implementing the design at the basis of the so called\nquantum-measurement-cooling concept. For both engines, we report the measured\naverage heat and work exchanged and single out their operation mode. Our\nexperiments constitute the experimental basis for the understanding of the\nnon-equilibrium energetics of quantum computation and for the implementation of\nenergy management devices on quantum processors.\n"}
+{"abstract": "  In this paper, we hypothesize that the effects of the degree of typicality in\nnatural semantic categories can be generated based on the structure of\nartificial categories learned with deep learning models. Motivated by the human\napproach to representing natural semantic categories and based on the Prototype\nTheory foundations, we propose a novel Computational Prototype Model (CPM) to\nrepresent the internal structure of semantic categories. Unlike other prototype\nlearning approaches, our mathematical framework proposes a first approach to\nprovide deep neural networks with the ability to model abstract semantic\nconcepts such as category central semantic meaning, typicality degree of an\nobject's image, and family resemblance relationship. We proposed several\nmethodologies based on the typicality's concept to evaluate our CPM-model in\nimage semantic processing tasks such as image classification, a global semantic\ndescription, and transfer learning. Our experiments on different image\ndatasets, such as ImageNet and Coco, showed that our approach might be an\nadmissible proposition in the effort to endow machines with greater power of\nabstraction for the semantic representation of objects' categories.\n"}
+{"abstract": "  In e-commerce, opinion tags refer to a ranked list of tags provided by the\ne-commerce platform that reflect characteristics of reviews of an item. To\nassist consumers to quickly grasp a large number of reviews about an item,\nopinion tags are increasingly being applied by e-commerce platforms. Current\nmechanisms for generating opinion tags rely on either manual labelling or\nheuristic methods, which is time-consuming and ineffective. In this paper, we\npropose the abstractive opinion tagging task, where systems have to\nautomatically generate a ranked list of opinion tags that are based on, but\nneed not occur in, a given set of user-generated reviews.\n  The abstractive opinion tagging task comes with three main challenges: (1)\nthe noisy nature of reviews; (2) the formal nature of opinion tags vs. the\ncolloquial language usage in reviews; and (3) the need to distinguish between\ndifferent items with very similar aspects. To address these challenges, we\npropose an abstractive opinion tagging framework, named AOT-Net, to generate a\nranked list of opinion tags given a large number of reviews. First, a\nsentence-level salience estimation component estimates each review's salience\nscore. Next, a review clustering and ranking component ranks reviews in two\nsteps: first, reviews are grouped into clusters and ranked by cluster size;\nthen, reviews within each cluster are ranked by their distance to the cluster\ncenter. Finally, given the ranked reviews, a rank-aware opinion tagging\ncomponent incorporates an alignment feature and alignment loss to generate a\nranked list of opinion tags. To facilitate the study of this task, we create\nand release a large-scale dataset, called eComTag, crawled from real-world\ne-commerce websites. Extensive experiments conducted on the eComTag dataset\nverify the effectiveness of the proposed AOT-Net in terms of various evaluation\nmetrics.\n"}
+{"abstract": "  Coronal extreme-ultraviolet (EUV) waves are large-scale disturbances\npropagating in the corona, whose physical nature and origin have been discussed\nfor decades. We report the first three dimensional (3D) radiative\nmagneto-hydrodynamic (RMHD) simulation of a coronal EUV wave and the\naccompanying quasi-periodic wave trains. The numerical experiment is conducted\nwith the MURaM code and simulates the formation of solar active regions through\nmagnetic flux emergence from the convection zone to the corona. The coronal EUV\nwave is driven by the eruption of a magnetic flux rope that also gives rise to\na C-class flare. It propagates in a semi-circular shape with an initial speed\nranging from about 550 to 700 km s$^{-1}$, which corresponds to an average Mach\nnumber (relative to fast magnetoacoustic waves) of about 1.2. Furthermore, the\nabrupt increase of the plasma density, pressure and tangential magnetic field\nat the wavefront confirms fast-mode shock nature of the coronal EUV wave.\nQuasi-periodic wave trains with a period of about 30 s are found as multiple\nsecondary wavefronts propagating behind the leading wavefront and ahead of the\nerupting magnetic flux rope. We also note that the true wavefront in the 3D\nspace can be very inhomogeneous, however, the line-of-sight integration of EUV\nemission significantly smoothes the sharp structures in 3D and leads to a more\ndiffuse wavefront.\n"}
+{"abstract": "  This Colloquium is a fast journey through the build-up of key thermodynamical\nconcepts, i.e. work, heat and irreversibility -- and how they relate to\ninformation. Born at the time of industrial revolution to optimize the\nexploitation of thermal resources, these concepts have been adapted to small\nsystems where thermal fluctuations are predominant. Extending the framework to\nquantum fluctuations is a great challenge of quantum thermodynamics, that opens\nexciting research lines e.g. measurement fueled engines or thermodynamics of\ndriven-dissipative systems. On a more applied side, it provides the tools to\noptimize the energetic consumption of future quantum computers.\n"}
+{"abstract": "  In this work we address the issue of validating the monodomain equation used\nin combination with the Bueno-Orovio ionic model for the prediction of the\nactivation times in cardiac electro-physiology of the left ventricle. To this\naim, we consider our patients who suffered from Left Bundle Branch Block\n(LBBB). We use activation maps performed at the septum as input data for the\nmodel and maps at the epicardial veins for the validation. In particular, a\nfirst set (half) of the latter are used to estimate the conductivities of the\npatient and a second set (the remaining half) to compute the errors of the\nnumerical simulations. We find an excellent agreement between measures and\nnumerical results. Our validated computational tool could be used to accurately\npredict activation times at the epicardial veins with a short mapping, i.e. by\nusing only a part (the most proximal) of the standard acquisition points, thus\nreducing the invasive procedure and exposure to radiation.\n"}
+{"abstract": "  Understanding how the dynamics of a given quantum system with many degrees of\nfreedom is altered by the presence of a generic perturbation is a notoriously\ndifficult question. Recent works predict that, in the overwhelming majority of\ncases, the unperturbed dynamics is just damped by a simple function, e.g.,\nexponentially as expected from Fermi's golden rule. While these predictions\nrely on random-matrix arguments and typicality, they can only be verified for a\nspecific physical situation by comparing to the actual solution or measurement.\nCrucially, it also remains unclear how frequent and under which conditions\ncounterexamples to the typical behavior occur. In this work, we discuss this\nquestion from the perspective of projection-operator techniques, where\nexponential damping of a density matrix occurs in the interaction picture but\nnot necessarily in the Schr\\\"odinger picture. We show that a nontrivial damping\nin the Schr\\\"odinger picture can emerge if the dynamics in the unperturbed\nsystem possesses rich features, for instance due to the presence of strong\ninteractions. This suggestion has consequences for the time dependence of\ncorrelation functions. We substantiate our theoretical arguments by large-scale\nnumerical simulations of charge transport in the extended Fermi-Hubbard chain,\nwhere the nearest-neighbor interactions are treated as a perturbation to the\nintegrable reference system.\n"}
+{"abstract": "  Continuous practices are a staple of the modern software development\nworkflow. Automation, in particular, is widely adopted due to its benefits\nrelated to quality and productivity. However, automation, similarly to all\nother aspects of the software development workflow, interacts with humans (in\nthis case developers). While some work has investigated the impact of\nautomation on developers, it is not clear to what extent context and process\ninfluence that impact. We present our ADEPT theory of developers and\nautomation, in an attempt to bridge this gap and identify the possible ways\ncontext, process, and other factors may influence how developers perceive,\ninterpret, and interact with automation.\n"}
+{"abstract": "  The goal of electronic monitoring (EM) of longline fishing is to monitor the\nfish catching activities on fishing vessels, either for the regulatory\ncompliance or catch counting. Hierarchical classification based on videos\nallows for inexpensive and efficient fish species identification of catches\nfrom longline fishing, where fishes are under severe deformation and\nself-occlusion during the catching process. More importantly, the flexibility\nof hierarchical classification mitigates the laborious efforts of human reviews\nby providing confidence scores in different hierarchical levels. Some related\nworks either use cascaded models for hierarchical classification or make\npredictions per image or predict one overlapping hierarchical data structure of\nthe dataset in advance. However, with a known non-overlapping hierarchical data\nstructure provided by fisheries scientists, our method enforces the\nhierarchical data structure and introduces an efficient training and inference\nstrategy for video-based fisheries data. Our experiments show that the proposed\nmethod outperforms the classic flat classification system significantly and our\nablation study justifies our contributions in CNN model design, training\nstrategy, and the video-based inference schemes for the hierarchical fish\nspecies classification task.\n"}
+{"abstract": "  Anomaly detection for time-series data becomes an essential task for many\ndata-driven applications fueled with an abundance of data and out-of-the-box\nmachine-learning algorithms. In many real-world settings, developing a reliable\nanomaly model is highly challenging due to insufficient anomaly labels and the\nprohibitively expensive cost of obtaining anomaly examples. It imposes a\nsignificant bottleneck to evaluate model quality for model selection and\nparameter tuning reliably. As a result, many existing anomaly detection\nalgorithms fail to show their promised performance after deployment.\n  In this paper, we propose LaF-AD, a novel anomaly detection algorithm with\nlabel-free model selection for unlabeled times-series data. Our proposed\nalgorithm performs a fully unsupervised ensemble learning across a large number\nof candidate parametric models. We develop a model variance metric that\nquantifies the sensitivity of anomaly probability with a bootstrapping method.\nThen it makes a collective decision for anomaly events by model learners using\nthe model variance. Our algorithm is easily parallelizable, more robust for\nill-conditioned and seasonal data, and highly scalable for a large number of\nanomaly models. We evaluate our algorithm against other state-of-the-art\nmethods on a synthetic domain and a benchmark public data set.\n"}
+{"abstract": "  We introduce a new type of estimator for the spectral tail process of a\nregularly varying time series. The approach is based on a characterizing\ninvariance property of the spectral tail process, which is incorporated into\nthe new estimator via a projection technique. We show uniform asymptotic\nnormality of this estimator, both in the case of known and of unknown index of\nregular variation. In a simulation study the new procedure shows a more stable\nperformance than previously proposed estimators.\n"}
+{"abstract": "  We propose a universal building block of Convolutional Neural Network\n(ConvNet) to improve the performance without any inference-time costs. The\nblock is named Diverse Branch Block (DBB), which enhances the representational\ncapacity of a single convolution by combining diverse branches of different\nscales and complexities to enrich the feature space, including sequences of\nconvolutions, multi-scale convolutions, and average pooling. After training, a\nDBB can be equivalently converted into a single conv layer for deployment.\nUnlike the advancements of novel ConvNet architectures, DBB complicates the\ntraining-time microstructure while maintaining the macro architecture, so that\nit can be used as a drop-in replacement for regular conv layers of any\narchitecture. In this way, the model can be trained to reach a higher level of\nperformance and then transformed into the original inference-time structure for\ninference. DBB improves ConvNets on image classification (up to 1.9% higher\ntop-1 accuracy on ImageNet), object detection and semantic segmentation. The\nPyTorch code and models are released at\nhttps://github.com/DingXiaoH/DiverseBranchBlock.\n"}
+{"abstract": "  Attributed network representation learning aims at learning node embeddings\nby integrating network structure and attribute information. It is a challenge\nto fully capture the microscopic structure and the attribute semantics\nsimultaneously, where the microscopic structure includes the one-step, two-step\nand multi-step relations, indicating the first-order, second-order and\nhigh-order proximity of nodes, respectively. In this paper, we propose a deep\nattributed network representation learning via attribute enhanced neighborhood\n(DANRL-ANE) model to improve the robustness and effectiveness of node\nrepresentations. The DANRL-ANE model adopts the idea of the autoencoder, and\nexpands the decoder component to three branches to capture different order\nproximity. We linearly combine the adjacency matrix with the attribute\nsimilarity matrix as the input of our model, where the attribute similarity\nmatrix is calculated by the cosine similarity between the attributes based on\nthe social homophily. In this way, we preserve the second-order proximity to\nenhance the robustness of DANRL-ANE model on sparse networks, and deal with the\ntopological and attribute information simultaneously. Moreover, the sigmoid\ncross-entropy loss function is extended to capture the neighborhood character,\nso that the first-order proximity is better preserved. We compare our model\nwith the state-of-the-art models on five real-world datasets and two network\nanalysis tasks, i.e., link prediction and node classification. The DANRL-ANE\nmodel performs well on various networks, even on sparse networks or networks\nwith isolated nodes given the attribute information is sufficient.\n"}
+{"abstract": "  Let $\\Sigma$ be a surface equipped with an area form. There is an long\nstanding open question by Katok, which, in particular, asks whether every\nentropy-zero Hamiltonian diffeomorphism of a surface lies in the $C^0$-closure\nof the set of integrable diffeomorphisms. A slightly weaker version of this\nquestion asks: ``Does every entropy-zero Hamiltonian diffeomorphism of a\nsurface lie in the $C^0$-closure of the set of autonomous diffeomorphisms?''\n  In this paper we answer in negative the later question. In particular, we\nshow that on a surface $\\Sigma$ the set of autonomous Hamiltonian\ndiffeomorphisms is not $C^0$-dense in the set of entropy-zero Hamiltonians. We\nexplicitly construct examples of such Hamiltonians which cannot be approximated\nby autonomous diffeomorphisms.\n"}
+{"abstract": "  We consider different generalizations of the Brachistochrone Problem in the\ncontext of fundamental concepts of classical mechanics. The correct statement\nfor the Brachistochrone problem for nonholonomic systems is proposed. It is\nshown that the Brachistochrone problem is closely related to vakonomic\nmechanics.\n"}
+{"abstract": "  Biomonitoring wearable sensors based on two-dimensional nanomaterials have\nlately elicited keen research interest and potential for a new range of\nflexible nanoelectronic devices. Practical nanomaterial-based devices suited\nfor real-world service, which have first-rate performance while being an\nattractive accessory, are still distant. We report a multifunctional flexible\nwearable sensor fabricated using an ultra-thin percolative layer of microwave\nexfoliated graphene nanosheets on laser-patterned gold circular inter-digitated\nelectrodes for monitoring vital human physiological parameters. This Graphene\non Laser-patterned Electrodes (GLE) sensor displays an ultra-high strain\nresolution of 0.024% and a record gauge factor of 6.3e7 and exceptional\nstability and repeatability in its operating range. The sensor was subjected to\nbiomonitoring experiments like measurement of heart rate, breathing rate, body\ntemperature, and hydration level, which are vital health parameters, especially\nconsidering the current pandemic scenario. The sensor also served in\napplications such as a pedometer, limb movement tracking, and control switch\nfor human interaction. The innovative laser-etch process used to pattern gold\nthin-film electrodes and shapes, with the multifunctional incognizable graphene\nlayer, provides a technique for integrating multiple sensors in a wearable\nfashion accessory. The reported work marks a giant leap from the conventional\nbanal devices to a highly marketable multifunctional sensor array as a\nbiomonitoring fashion accessory.\n"}
+{"abstract": "  Research collaborations provide the foundation for scientific advances, but\nwe have only recently begun to understand how they form and grow on a global\nscale. Here we analyze a model of the growth of research collaboration networks\nto explain the empirical observations that the number of collaborations scales\nsuperlinearly with institution size, though at different rates (heterogeneous\ndensification), the number of institutions grows as a power of the number of\nresearchers (Heaps' law) and institution sizes approximate Zipf's law. This\nmodel has three mechanisms: (i) researchers are preferentially hired by large\ninstitutions, (ii) new institutions trigger more potential institutions, and\n(iii) researchers collaborate with friends-of-friends. We show agreement\nbetween these assumptions and empirical data, through analysis of co-authorship\nnetworks spanning two centuries. We then develop a theoretical understanding of\nthis model, which reveals emergent heterogeneous scaling such that the number\nof collaborations between institutions scale with an institution's size.\n"}
+{"abstract": "  Parallelization of A* path planning is mostly limited by the number of\npossible motions, which is far less than the level of parallelism that modern\nprocessors support. In this paper, we go beyond the limitations of traditional\nparallelism of A* and propose Speculative Path Planning to accelerate the\nsearch when there are abundant idle resources. The key idea of our approach is\npredicting future state expansions relying on patterns among expansions and\naggressively parallelize the computations of prospective states (i.e.\npre-evaluate the expensive collision checking operation of prospective nodes).\nThis method allows us to maintain the same search order as of vanilla A* and\nsafeguard any optimality guarantees. We evaluate our method on various\nconfigurations and show that on a machine with 32 physical cores, our method\nimproves the performance around 11x and 10x on average over counterpart\nsingle-threaded and multi-threaded implementations respectively. The code to\nour paper can be found here:\nhttps://github.com/bakhshalipour/speculative-path-planning.\n"}
+{"abstract": "  In this paper, we explore a canonical connection between the algebra of\n$q$-difference operators $\\widetilde{V}_{q}$, affine Lie algebra and affine\nvertex algebras associated to certain subalgebra $\\mathcal{A}$ of the Lie\nalgebra $\\mathfrak{gl}_{\\infty}$. We also introduce and study a category\n$\\mathcal{O}$ of $\\widetilde{V}_{q}$-modules. More precisely, we obtain a\nrealization of $\\widetilde{V}_{q}$ as a covariant algebra of the affine Lie\nalgebra $\\widehat{\\mathcal{A}^{*}}$, where $\\mathcal{A}^{*}$ is a 1-dimensional\ncentral extension of $\\mathcal{A}$. We prove that restricted\n$\\widetilde{V_{q}}$-modules of level $\\ell_{12}$ correspond to\n$\\mathbb{Z}$-equivariant $\\phi$-coordinated quasi-modules for the vertex\nalgebra $V_{\\widetilde{\\mathcal{A}}}(\\ell_{12},0)$, where\n$\\widetilde{\\mathcal{A}}$ is a generalized affine Lie algebra of $\\mathcal{A}$.\nIn the end, we show that objects in the category $\\mathcal{O}$ are restricted\n$\\widetilde{V_{q}}$-modules, and we classify simple modules in the category\n$\\mathcal{O}$.\n"}
+{"abstract": "  Cooling the centre-of-mass motion is an important tool for levitated\noptomechanical systems, but it is often not clear which method can practically\nreach lower temperatures for a particular experiment. We directly compare the\nparametric and velocity feedback damping methods, which are used extensively\nfor cooling the motion of single trapped particles in a range of traps. By\nperforming experiments on the same particle, and with the same detection\nsystem, we demonstrate that velocity damping cools the oscillator to lower\ntemperatures and is more resilient to imperfect experimental conditions. We\nshow that these results are consistent with analytical limits as well as\nnumerical simulations that include experimental noise.\n"}
+{"abstract": "  Given access to a single long trajectory generated by an unknown irreducible\nMarkov chain $M$, we simulate an $\\alpha$-lazy version of $M$ which is ergodic.\nThis enables us to generalize recent results on estimation and identity testing\nthat were stated for ergodic Markov chains in a way that allows fully empirical\ninference. In particular, our approach shows that the pseudo spectral gap\nintroduced by Paulin [2015] and defined for ergodic Markov chains may be given\na meaning already in the case of irreducible but possibly periodic Markov\nchains.\n"}
+{"abstract": "  Let $D^\\alpha, \\alpha>0$, be the Vladimirov-Taibleson fractional\ndifferentiation operator acting on complex-valued functions on a\nnon-Archimedean local field. The identity $D^\\alpha D^{-\\alpha}f=f$ was known\nonly for the case where $f$ has a compact support. Following a result by Samko\nabout the fractional Laplacian of real analysis, we extend the above identity\nin terms of $L^p$-convergence of truncated integrals. Differences between real\nand non-Archimedean cases are discussed.\n"}
+{"abstract": "  Music is often considered as the language of emotions. It has long been known\nto elicit emotions in human being and thus categorizing music based on the type\nof emotions they induce in human being is a very intriguing topic of research.\nWhen the task comes to classify emotions elicited by Indian Classical Music\n(ICM), it becomes much more challenging because of the inherent ambiguity\nassociated with ICM. The fact that a single musical performance can evoke a\nvariety of emotional response in the audience is implicit to the nature of ICM\nrenditions. With the rapid advancements in the field of Deep Learning, this\nMusic Emotion Recognition (MER) task is becoming more and more relevant and\nrobust, hence can be applied to one of the most challenging test case i.e.\nclassifying emotions elicited from ICM. In this paper we present a new dataset\ncalled JUMusEmoDB which presently has 400 audio clips (30 seconds each) where\n200 clips correspond to happy emotions and the remaining 200 clips correspond\nto sad emotion. For supervised classification purposes, we have used 4 existing\ndeep Convolutional Neural Network (CNN) based architectures (resnet18,\nmobilenet v2.0, squeezenet v1.0 and vgg16) on corresponding music spectrograms\nof the 2000 sub-clips (where every clip was segmented into 5 sub-clips of about\n5 seconds each) which contain both time as well as frequency domain\ninformation. The initial results are quite inspiring, and we look forward to\nsetting the baseline values for the dataset using this architecture. This type\nof CNN based classification algorithm using a rich corpus of Indian Classical\nMusic is unique even in the global perspective and can be replicated in other\nmodalities of music also. This dataset is still under development and we plan\nto include more data containing other emotional features as well. We plan to\nmake the dataset publicly available soon.\n"}
+{"abstract": "  Unsupervised domain adaptation has been widely adopted to generalize models\nfor unlabeled data in a target domain, given labeled data in a source domain,\nwhose data distributions differ from the target domain. However, existing works\nare inapplicable to face recognition under privacy constraints because they\nrequire sharing sensitive face images between two domains. To address this\nproblem, we propose a novel unsupervised federated face recognition approach\n(FedFR). FedFR improves the performance in the target domain by iteratively\naggregating knowledge from the source domain through federated learning. It\nprotects data privacy by transferring models instead of raw data between\ndomains. Besides, we propose a new domain constraint loss (DCL) to regularize\nsource domain training. DCL suppresses the data volume dominance of the source\ndomain. We also enhance a hierarchical clustering algorithm to predict pseudo\nlabels for the unlabeled target domain accurately. To this end, FedFR forms an\nend-to-end training pipeline: (1) pre-train in the source domain; (2) predict\npseudo labels by clustering in the target domain; (3) conduct\ndomain-constrained federated learning across two domains. Extensive experiments\nand analysis on two newly constructed benchmarks demonstrate the effectiveness\nof FedFR. It outperforms the baseline and classic methods in the target domain\nby over 4% on the more realistic benchmark. We believe that FedFR will shed\nlight on applying federated learning to more computer vision tasks under\nprivacy constraints.\n"}
+{"abstract": "  In this paper we show that generic continuous Lebesgue measure preserving\ncircle maps have the s-limit shadowing property. In addition we obtain that\ns-limit shadowing is a generic property also for continuous circle maps. In\nparticular, this implies that classical shadowing, periodic shadowing and limit\nshadowing are generic in these two settings as well.\n"}
+{"abstract": "  In this study, the standard IEEE 754 2008 and modulo-based floor functions\nfor rounding non-integers have been presented. Their effects on the accuracy of\nthe bilinear interpolation algorithm have been demonstrated. The improved floor\nuses the modulo operator in an effort to make each non-integer addend an\ninteger when the remainder between the multiplicative inverse of the fractional\nfactor and the numerator is greater than zero. The experiments demonstrated\nrelatively positive effects with the improved floor function and the\nalternativeness to the standard round function.\n"}
+{"abstract": "  To predict line emission in the solar atmosphere requires models which are\nfundamentally different depending on whether the emission is from the\nchromosphere or the corona. At some point between the two regions, there must\nbe a change between the two modelling regimes. Recent extensions to the coronal\nmodelling for carbon and oxygen lines in the solar transition region have shown\nimprovements in the emission of singly- and doubly-charged ions, along with\nLi-like ions. However, discrepancies still remain, particularly for\nsingly-charged ions and intercombination lines. The aim of this work is to\nexplore additional atomic processes that could further alter the charge state\ndistribution and the level populations within ions, in order to resolve some of\nthe discrepancies. To this end, excitation and ionisation caused by both the\nradiation field and by atom-ion collisions have been included, along with\nrecombination through charge transfer. The modelling is carried out using\nconditions which would be present in the quiet Sun, which allows an assessment\nof the part atomic processes play in changing coronal modelling, separately\nfrom dynamic and transient events taking place in the plasma. The effect the\nprocesses have on the fractional ion populations are presented, as well as the\nchange in level populations brought about by the new excitation mechanisms.\nContribution functions of selected lines from low charge states are also shown,\nto demonstrate the extent to which line emission in the lower atmosphere could\nbe affected by the new modelling.\n"}
+{"abstract": "  Researchers help operators of vulnerable and non-compliant internet services\nby individually notifying them about security and privacy issues uncovered in\ntheir research. To improve efficiency and effectiveness of such efforts,\ndedicated notification studies are imperative. As of today, there is no\ncomprehensive documentation of pitfalls and best practices for conducting such\nnotification studies, which limits validity of results and impedes\nreproducibility. Drawing on our experience with such studies and guidance from\nrelated work, we present a set of guidelines and practical recommendations,\nincluding initial data collection, sending of notifications, interacting with\nthe recipients, and publishing the results. We note that future studies can\nespecially benefit from extensive planning and automation of crucial processes,\ni.e., activities that take place well before the first notifications are sent.\n"}
+{"abstract": "  This paper investigates the simultaneous wireless information and power\ntransfer (SWIPT) precoding scheme for K-user multiple-input-multiple-output\n(MIMO) interference channels (IC), for which interference alignment (IA)\nschemes provide optimal precoders to achieve full degrees-of-freedom (DoF)\ngain. However, harvesting RF energy simultaneously reduces the achievable DoFs.\nTo study a trade-off between harvested energy and sum rate, the transceiver\ndesign problem is suboptimally formulated in literature via convex relaxations,\nwhich is still computationally intensive, especially for battery limited nodes\nrunning on harvested energy. In this paper, we propose a systematic method\nusing chordal distance (CD) decomposition to obtain the balanced precoding,\nwhich improves the trade-off. Analysis shows that given the nonnegative value\nof CD, the achieved harvested energy for the proposed precoder is higher than\nthat for perfect IA precoder. Moreover, energy constraints can be achieved,\nwhile maintaining a constant rate loss without losing DoFs via tuning the CD\nvalue and splitting factor. Simulation results verify the analysis and add that\nthe IA schemes based on max-SINR or mean-squared error are better suited for\nSWIPT maximization than subspace or leakage minimization methods.\n"}
+{"abstract": "  This paper analyzes the discretization of a Neumann boundary control problem\nwith a stochastic parabolic equation, where an additive noise occurs in the\nNeumann boundary condition. The convergence is established for general\nfiltrations, and the convergence rate $ O(\\tau^{1/4-\\epsilon} +\nh^{3/2-\\epsilon}) $ is derived for the natural filtration of the Q-Wiener\nprocess.\n"}
+{"abstract": "  Aligning partial views of a scene into a single whole is essential to\nunderstanding one's environment and is a key component of numerous robotics\ntasks such as SLAM and SfM. Recent approaches have proposed end-to-end systems\nthat can outperform traditional methods by leveraging pose supervision.\nHowever, with the rising prevalence of cameras with depth sensors, we can\nexpect a new stream of raw RGB-D data without the annotations needed for\nsupervision. We propose UnsupervisedR&R: an end-to-end unsupervised approach to\nlearning point cloud registration from raw RGB-D video. The key idea is to\nleverage differentiable alignment and rendering to enforce photometric and\ngeometric consistency between frames. We evaluate our approach on indoor scene\ndatasets and find that we outperform existing traditional approaches with\nclassic and learned descriptors while being competitive with supervised\ngeometric point cloud registration approaches.\n"}
+{"abstract": "  Locally recoverable codes were introduced by Gopalan et al. in 2012, and in\nthe same year Prakash et al. introduced the concept of codes with locality,\nwhich are a type of locally recoverable codes. In this work we introduce a new\nfamily of codes with locality, which are subcodes of a certain family of\nevaluation codes. We determine the dimension of these codes, and also bounds\nfor the minimum distance. We present the true values of the minimum distance in\nspecial cases, and also show that elements of this family are \"optimal codes\",\nas defined by Prakash et al.\n"}
+{"abstract": "  Environmental Microorganism Data Set Fifth Version (EMDS-5) is a microscopic\nimage dataset including original Environmental Microorganism (EM) images and\ntwo sets of Ground Truth (GT) images. The GT image sets include a single-object\nGT image set and a multi-object GT image set. The EMDS-5 dataset has 21 types\nof EMs, each of which contains 20 original EM images, 20 single-object GT\nimages and 20 multi-object GT images. EMDS-5 can realize to evaluate image\npreprocessing, image segmentation, feature extraction, image classification and\nimage retrieval functions. In order to prove the effectiveness of EMDS-5, for\neach function, we select the most representative algorithms and price\nindicators for testing and evaluation. The image preprocessing functions\ncontain two parts: image denoising and image edge detection. Image denoising\nuses nine kinds of filters to denoise 13 kinds of noises, respectively. In the\naspect of edge detection, six edge detection operators are used to detect the\nedges of the images, and two evaluation indicators, peak-signal to noise ratio\nand mean structural similarity, are used for evaluation. Image segmentation\nincludes single-object image segmentation and multi-object image segmentation.\nSix methods are used for single-object image segmentation, while k-means and\nU-net are used for multi-object segmentation.We extract nine features from the\nimages in EMDS-5 and use the Support Vector Machine classifier for testing. In\nterms of image classification, we select the VGG16 feature to test different\nclassifiers. We test two types of retrieval approaches: texture feature\nretrieval and deep learning feature retrieval. We select the last layer of\nfeatures of these two deep learning networks as feature vectors. We use mean\naverage precision as the evaluation index for retrieval.\n"}
+{"abstract": "  Sparse adversarial attacks can fool deep neural networks (DNNs) by only\nperturbing a few pixels (regularized by l_0 norm). Recent efforts combine it\nwith another l_infty imperceptible on the perturbation magnitudes. The\nresultant sparse and imperceptible attacks are practically relevant, and\nindicate an even higher vulnerability of DNNs that we usually imagined.\nHowever, such attacks are more challenging to generate due to the optimization\ndifficulty by coupling the l_0 regularizer and box constraints with a\nnon-convex objective. In this paper, we address this challenge by proposing a\nhomotopy algorithm, to jointly tackle the sparsity and the perturbation bound\nin one unified framework. Each iteration, the main step of our algorithm is to\noptimize an l_0-regularized adversarial loss, by leveraging the nonmonotone\nAccelerated Proximal Gradient Method (nmAPG) for nonconvex programming; it is\nfollowed by an l_0 change control step, and an optional post-attack step\ndesigned to escape bad local minima. We also extend the algorithm to handling\nthe structural sparsity regularizer. We extensively examine the effectiveness\nof our proposed homotopy attack for both targeted and non-targeted attack\nscenarios, on CIFAR-10 and ImageNet datasets. Compared to state-of-the-art\nmethods, our homotopy attack leads to significantly fewer perturbations, e.g.,\nreducing 42.91% on CIFAR-10 and 75.03% on ImageNet (average case, targeted\nattack), at similar maximal perturbation magnitudes, when still achieving 100%\nattack success rates. Our codes are available at:\nhttps://github.com/VITA-Group/SparseADV_Homotopy.\n"}
+{"abstract": "  Data augmentation has been widely used to improve deep neural networks in\nmany research fields, such as computer vision. However, less work has been done\nin the context of text, partially due to its discrete nature and the complexity\nof natural languages. In this paper, we propose to improve the standard maximum\nlikelihood estimation (MLE) paradigm by incorporating a self-imitation-learning\nphase for automatic data augmentation. Unlike most existing sentence-level\naugmentation strategies, which are only applied to specific models, our method\nis more general and could be easily adapted to any MLE-based training\nprocedure. In addition, our framework allows task-specific evaluation metrics\nto be designed to flexibly control the generated sentences, for example, in\nterms of controlling vocabulary usage and avoiding nontrivial repetitions.\nExtensive experimental results demonstrate the superiority of our method on two\nsynthetic and several standard real datasets, significantly improving related\nbaselines.\n"}
+{"abstract": "  There has been tremendous interest in the development of formal trust models\nand metrics through the use of analytics (e.g., Belief Theory and Bayesian\nmodels), logics (e.g., Epistemic and Subjective Logic) and other mathematical\nmodels. The choice of trust metric will depend on context, circumstance and\nuser requirements and there is no single best metric for use in all\ncircumstances. Where different users require different trust metrics to be\nemployed the trust score calculations should still be based on all available\ntrust evidence. Trust is normally computed using past experiences but, in\npractice (especially in centralised systems), the validity and accuracy of\nthese experiences are taken for granted. In this paper, we provide a formal\nframework and practical blockchain-based implementation that allows independent\ntrust providers to implement different trust metrics in a distributed manner\nwhile still allowing all trust providers to base their calculations on a common\nset of trust evidence. Further, our design allows experiences to be provably\nlinked to interactions without the need for a central authority. This leads to\nthe notion of evidence-based trust with provable interactions. Leveraging\nblockchain allows the trust providers to offer their services in a competitive\nmanner, charging fees while users are provided with payments for recording\nexperiences. Performance details of the blockchain implementation are provided.\n"}
+{"abstract": "  The \\textit{KArlsruhe TRItium Neutrino} (KATRIN) experiment aims to measure\nthe neutrino mass with a sensitivity of \\SI{0.2}{\\electronvolt}\n(\\SI{90}{\\percent} CL). This will be achieved by a precision measurement of the\nendpoint region of the $\\upbeta$-electron spectrum of tritium decay. The\n$\\upbeta$-electrons are produced in the \\textit{Windowless Gaseous Tritium\nSource} (WGTS) and guided magnetically through the beamline. In order to\naccurately extract the neutrino mass the source activity is required to be\nstable and known to a high precision. The WGTS therefore undergoes constant\nextensive monitoring from several measurement systems. The \\textit{Forward Beam\nMonitor} (FBM) is one such monitoring system. The FBM system comprises a\ncomplex mechanical setup capable of inserting a detector board into the KATRIN\nbeamline with a positioning precision of better than \\SI{0.3}{\\milli\\metre}.\nThe electron flux density at that position is on the order of\n\\SI{e6}{\\per\\second\\per\\milli\\metre\\squared}. The detector board contains two\nsilicon detector chips of \\pin diode type which can measure the\n$\\upbeta$-electron flux from the source with a precision of \\SI{0.1}{\\percent}\nwithin \\SI{60}{\\second} with an energy resolution of FWHM $=$\n\\SI{2}{\\kilo\\electronvolt}. The unique challenge in developing the FBM arise\nfrom its designated operating environment inside the Cryogenic Pumping Section\nwhich is a potentially tritium contaminated ultra-high vacuum chamber at\ncryogenic temperatures in the presence of a \\SI{1}{\\tesla} strong magnetic\nfield. Each of theses parameters do strongly limit the choice of possible\nmaterials which e.g. caused difficulties in detector noise reduction, heat\ndissipation and lubrication. In order to completely remove the FBM from the\nbeam tube a \\SI{2}{\\meter} long traveling distance into the beamline is needed\ndemanding a robust as well as highly precise moving mechanism.\n"}
+{"abstract": "  We propose a fully automated system that simultaneously estimates the camera\nintrinsics, the ground plane, and physical distances between people from a\nsingle RGB image or video captured by a camera viewing a 3-D scene from a fixed\nvantage point. To automate camera calibration and distance estimation, we\nleverage priors about human pose and develop a novel direct formulation for\npose-based auto-calibration and distance estimation, which shows\nstate-of-the-art performance on publicly available datasets. The proposed\napproach enables existing camera systems to measure physical distances without\nneeding a dedicated calibration process or range sensors, and is applicable to\na broad range of use cases such as social distancing and workplace safety.\nFurthermore, to enable evaluation and drive research in this area, we\ncontribute to the publicly available MEVA dataset with additional distance\nannotations, resulting in MEVADA -- the first evaluation benchmark in the world\nfor the pose-based auto-calibration and distance estimation problem.\n"}
+{"abstract": "  For a prime number $p$, we study the asymptotic distribution of CM points on\nthe moduli space of elliptic curves over $\\mathbb{C}_p$. In stark contrast to\nthe complex case, in the $p$-adic setting there are infinitely many different\nmeasures describing the asymptotic distribution of CM points. In this paper we\nidentify all of these measures. A key insight is to translate this problem into\na $p$-adic version of Linnik's classical problem on the asymptotic distribution\nof integer points on spheres. To do this translation, we use the close\nrelationship between the deformation theories of elliptic curves and formal\nmodules and then apply results of Gross and Hopkins. We solve this $p$-adic\nLinnik problem using a deviation estimate extracted from the bounds for the\nFourier coefficients of cuspidal modular forms of Deligne, Iwaniec and Duke. We\nalso identify all accumulation measures of an arbitrary Hecke orbit.\n"}
+{"abstract": "  Terrain adaptation is an essential capability for a ground robot to\neffectively traverse unstructured off-road terrain in real-world field\nenvironments such as forests. However, the expected robot behaviors generated\nby terrain adaptation methods cannot always be executed accurately due to\nsetbacks such as wheel slip and reduced tire pressure. To address this problem,\nwe propose a novel approach for consistent behavior generation that enables the\nground robot's actual behaviors to more accurately match expected behaviors\nwhile adapting to a variety of unstructured off-road terrain. Our approach\nlearns offset behaviors that are used to compensate for the inconsistency\nbetween the actual and expected behaviors without requiring the explicit\nmodeling of various setbacks. Our approach is also able to estimate the\nimportance of the multi-modal features to improve terrain representations for\nbetter adaptation. In addition, we develop an algorithmic solver for our\nformulated regularized optimization problem, which is guaranteed to converge to\nthe global optimal solution. To evaluate the method, we perform extensive\nexperiments using various unstructured off-road terrain in real-world field\nenvironments. Experimental results have validated that our approach enables\nrobots to traverse complex unstructured off-road terrain with more navigational\nbehavior consistency, and it outperforms previous methods, particularly so on\nchallenging terrain.\n"}
+{"abstract": "  Smart contract risk can be defined as a financial risk of loss due to cyber\nattacks on or contagious failures of smart contracts. Its quantification is of\nparamount importance to technology platform providers as well as companies and\nindividuals when considering the deployment of this new technology. That is\nwhy, as our primary contribution, we propose a structural framework of\naggregate loss distribution for smart contract risk under the assumption of a\ntree-stars graph topology representing the network of interactions among smart\ncontracts and their users. Up to our knowledge, there exist no theoretical\nframeworks or models of an aggregate loss distribution for smart contracts in\nthis setting. To achieve our goal, we contextualize the problem in the\nprobabilistic graph-theoretical framework using bond percolation models. We\nassume that the smart contract network topology is represented by a random tree\ngraph of finite size, and that each smart contract is the center of a {random}\nstar graph whose leaves represent the users of the smart contract. We allow for\nheterogeneous loss topology superimposed on this smart contract and user\ntopology and provide analytical results and instructive numerical examples.\n"}
+{"abstract": "  A novel confidence interval estimator is proposed for the risk difference in\nnoninferiority binomial trials. The confidence interval is consistent with an\nexact unconditional test that preserves the type-I error, and has improved\npower, particularly for smaller sample sizes, compared to the confidence\ninterval by Chan & Zhang (1999). The improved performance of the proposed\nconfidence interval is theoretically justified and demonstrated with\nsimulations and examples. An R package is also distributed that implements the\nproposed methods along with other confidence interval estimators.\n"}
+{"abstract": "  Interaction of electromagnetic, acoustic and even gravitational waves with\naccelerating bodies forms a class of nonstationary time-variant processes.\nScattered waves contain intrinsic signatures of motion, which manifest in a\nbroad range of phenomena, including Sagnac interference, Doppler and\nmicro-Doppler frequency shifts. While general relativity is often required to\naccount for motion, instantaneous rest frame approaches are frequently used to\ndescribe interactions with slowly accelerating objects. Here we investigate\ntheoretically and experimentally an interaction regime, which is neither\nrelativistic nor adiabatic. The test model considers an accelerating scatterer\nwith a long-lasting relaxation memory. The slow decay rates violate the\ninstantaneous reaction assumption of quasi-stationarity, introducing\nnon-Markovian contributions to the scattering process. Memory signatures in\nscattering from a rotating dipole are studied theoretically, showing symmetry\nbreaking of micro-Doppler combs. A quasi-stationary numeric analysis of\nscattering in the short memory limit is proposed and validated experimentally\nwith an example of electromagnetic pulses interacting with a rotating wire.\n"}
+{"abstract": "  Based on results of Digne-Michel-Lehrer (2003) we give two formulae for\ntwo-variable Green functions attached to Lusztig induction in a finite\nreductive group. We present applications to explicit computation of these Green\nfunctions, to conjectures of Malle and Rotilio, and to scalar products between\nLusztig inductions of Gelfand-Graev characters.\n"}
+{"abstract": "  We study how a general configuration of finitely-many point vortices, in a\nstate of uniform rotation or translation with Newtonian interaction or\ngeneralized surface quasi-geostrophic interactions, can be desingularized into\na steady configuration of vortex patches. Using a technique first introduced by\nHmidi and Mateu for vortex pairs, we reformulate the problem for the patch\nboundaries so that it no longer appears singular, at which point classical\ntechniques such as Lyapunov--Schmidt reduction can be used. Provided the point\nvortex equilibrium is non-degenerate in a natural sense, solutions can be\nconstructed directly using the implicit function theorem, yielding asymptotics\nfor the shape of the patch boundaries. As an application, we prove the\nexistence of various families of solutions with patches arranged in asymmetric\npairs, regular polygons, body-centered polygons, and nested regular polygons.\nThese configurations are degenerate due to their additional symmetries, but\nthis difficulty can be overcome by integrating the appropriate symmetries into\nthe formulation of the problem.\n"}
+{"abstract": "  We investigate the mass spectra of exotic hadrons known as hexaquarks in the\nform of dibaryons. We use a phenomenological model based on an extended version\nof the G\\\"ursey-Radicati mass formula for hadrons to include non-charmed\nbaryons, charmed baryons, and non-strange dibaryons to be able to predict\nmasses of potential dibaryon states. We perform six numerical fits of this\nmodel to input data for three different sets of masses of baryons and\ndibaryons. We find that the model can fit some of the data sets well,\nespecially the sets including charmed baryons and non-strange dibaryons, and\nobserve that the predicted mass of one of the dibaryons is close to the\nmeasured mass of the observed hexaquark candidate $d^*(2380)$ reported by the\nWASA-at-COSY experiment. The predicted mass of the deuteron is slightly larger\nthan its measured mass. Finally, for the data sets including charmed baryon and\nnon-strange dibaryon masses, we find that the predicted masses of potential\ndibaryon states are all in the range from 1900 MeV to 3700 MeV.\n"}
+{"abstract": "  Despite impressive results, deep learning-based technologies also raise\nsevere privacy and environmental concerns induced by the training procedure\noften conducted in datacenters. In response, alternatives to centralized\ntraining such as Federated Learning (FL) have emerged. Perhaps unexpectedly,\nFL, in particular, is starting to be deployed at a global scale by companies\nthat must adhere to new legal demands and policies originating from governments\nand civil society for privacy protection. However, the potential environmental\nimpact related to FL remains unclear and unexplored. This paper offers the\nfirst-ever systematic study of the carbon footprint of FL. First, we propose a\nrigorous model to quantify the carbon footprint, hence facilitating the\ninvestigation of the relationship between FL design and carbon emissions. Then,\nwe compare the carbon footprint of FL to traditional centralized learning. Our\nfindings show that FL, despite being slower to converge in some cases, may\nresult in a comparatively greener impact than a centralized equivalent setup.\nWe performed extensive experiments across different types of datasets,\nsettings, and various deep learning models with FL. Finally, we highlight and\nconnect the reported results to the future challenges and trends in FL to\nreduce its environmental impact, including algorithms efficiency, hardware\ncapabilities, and stronger industry transparency.\n"}
+{"abstract": "  By employing the ${\\rm AdS}_3/{\\rm CFT}_2$ correspondence in this note we\nobserve an analogy between the structures found in connection with the\nArkani-Hamed-Bai-He-Yan (ABHY) associahedron used for understanding scattering\namplitudes, and the one used for understanding space-time emerging from\npatterns of entanglement. The analogy suggests the natural interpretation for\nthe associahedron as a holographic entanglement polytope associated to the\n${\\rm CFT}_2$ vacuum. Our observations hint at the possibility that the\nfactorization properties of scattering amplitudes are connected to the notion\nof separability of space-time as used in the theory of holographic quantum\nentanglement.\n"}
+{"abstract": "  This paper uses linear magnetohydrodynamics to model resonant absorption in\ncoronal plasma with a Cartesian coordinate system. We impose line-tied boundary\nconditions and tilt the background magnetic field to be oblique to the\ntransition region. Halberstadt & Goedbloed (1993, 1995); Goedbloed& Halberstadt\n(1994); Arregui et al. (2003) show that line-tied boundary conditions cause\ntheir resonant absorption models to produce steep boundary layers/evanescent\nfast waves. We aim to study the importance of the boundary layers and assess\ntheir significance in a solar context. We calculate the solutions in a model\nwhere we impose line-tied boundary conditions and compare this with a model\nwhere we include the chromosphere instead. Results are calculated analytically\nand then verified numerically. We show that line-tied boundary conditions can\ncause the model to overestimate the boundary layers' amplitude significantly.\nIf the fast waves can propagate in the chromosphere, then the line-tied model\naccurately predicts the boundary layers' amplitude. However, if the fast waves\nare evanescent, then the boundary layers' size is reduced significantly, and\nthe line-tied model overestimates their amplitude. This leads to the\ncounter-intuitive result that the length scales tangential to the transition\nregion can play an essential role in determining line-tied boundary conditions'\nvalidity. The results suggest that line-tied boundary conditions can cause the\nmodel to generate unphysically large boundary layers. However, researchers may\nwish to continue to use them in their models for their simplicity and ability\nto significantly reduce computation time if they understand and are aware of\ntheir flaws.\n"}
+{"abstract": "  In this article, Joseph-Louis Lagrange analyzed those numbers which may be\nrepresented by the quadratic form $Bt^2 + Ctu + Du^2$. After proving a few\ntheorems on the divisors of such numbers (and their possible forms), Lagrange\ndeveloped a method for minimizing $C$ in order to determine systematically the\npossible forms for the divisors of numbers represented by the forms $t^2 \\pm\nau^2$.\n  This article was originally published as \"Recherches d'Arithm\\'etique\" in the\n1773 volume of the Nouveaux M\\'emoires de l'Acad\\'emie royale des Sciences et\nBelles-Lettres de Berlin (printed 1775), pp. 265-312. It is the first of two\narticles on this subject, the latter of which appeared in the 1775 volume of\nthe Nouveaux M\\'emoires.\n"}
+{"abstract": "  As the COVID-19 pandemic continues to devastate globally, the use of chest\nX-ray (CXR) imaging as a complimentary screening strategy to RT-PCR testing\ncontinues to grow given its routine clinical use for respiratory complaint. As\npart of the COVID-Net open source initiative, we introduce COVID-Net CXR-2, an\nenhanced deep convolutional neural network design for COVID-19 detection from\nCXR images built using a greater quantity and diversity of patients than the\noriginal COVID-Net. To facilitate this, we also introduce a new benchmark\ndataset composed of 19,203 CXR images from a multinational cohort of 16,656\npatients from at least 51 countries, making it the largest, most diverse\nCOVID-19 CXR dataset in open access form. The COVID-Net CXR-2 network achieves\nsensitivity and positive predictive value of 95.5%/97.0%, respectively, and was\naudited in a transparent and responsible manner. Explainability-driven\nperformance validation was used during auditing to gain deeper insights in its\ndecision-making behaviour and to ensure clinically relevant factors are\nleveraged for improving trust in its usage. Radiologist validation was also\nconducted, where select cases were reviewed and reported on by two\nboard-certified radiologists with over 10 and 19 years of experience,\nrespectively, and showed that the critical factors leveraged by COVID-Net CXR-2\nare consistent with radiologist interpretations. While not a production-ready\nsolution, we hope the open-source, open-access release of COVID-Net CXR-2 and\nthe respective CXR benchmark dataset will encourage researchers, clinical\nscientists, and citizen scientists to accelerate advancements and innovations\nin the fight against the pandemic.\n"}
+{"abstract": "  In this paper, we present Uformer, an effective and efficient\nTransformer-based architecture for image restoration, in which we build a\nhierarchical encoder-decoder network using the Transformer block. In Uformer,\nthere are two core designs. First, we introduce a novel locally-enhanced window\n(LeWin) Transformer block, which performs nonoverlapping window-based\nself-attention instead of global self-attention. It significantly reduces the\ncomputational complexity on high resolution feature map while capturing local\ncontext. Second, we propose a learnable multi-scale restoration modulator in\nthe form of a multi-scale spatial bias to adjust features in multiple layers of\nthe Uformer decoder. Our modulator demonstrates superior capability for\nrestoring details for various image restoration tasks while introducing\nmarginal extra parameters and computational cost. Powered by these two designs,\nUformer enjoys a high capability for capturing both local and global\ndependencies for image restoration. To evaluate our approach, extensive\nexperiments are conducted on several image restoration tasks, including image\ndenoising, motion deblurring, defocus deblurring and deraining. Without bells\nand whistles, our Uformer achieves superior or comparable performance compared\nwith the state-of-the-art algorithms. The code and models are available at\nhttps://github.com/ZhendongWang6/Uformer.\n"}
+{"abstract": "  Brain rhythms emerge as a result of synchronization among interconnected\nspiking neurons. Key properties of such rhythms can be gleaned from the\nphase-resetting curve (PRC). Inferring the macroscopic PRC and developing a\nsystematic phase reduction theory for emerging rhythms remains an outstanding\ntheoretical challenge. Here we present a practical theoretical framework to\ncompute the PRC of generic spiking networks with emergent collective\noscillations. To do so, we adopt a refractory density approach where neurons\nare described by the time since their last action potential. In the\nthermodynamic limit, the network dynamics are captured by a continuity equation\nknown as the refractory density equation. We develop an appropriate adjoint\nmethod for this equation which in turn gives a semi-analytical expression of\nthe infinitesimal PRC. We confirm the validity of our framework for specific\nexamples of neural networks. Our theoretical findings highlight the\nrelationship between key biological properties at the individual neuron scale\nand the macroscopic oscillatory properties assessed by the PRC.\n"}
+{"abstract": "  Quality diversity (QD) is a growing branch of stochastic optimization\nresearch that studies the problem of generating an archive of solutions that\nmaximize a given objective function but are also diverse with respect to a set\nof specified measure functions. However, even when these functions are\ndifferentiable, QD algorithms treat them as \"black boxes\", ignoring gradient\ninformation. We present the differentiable quality diversity (DQD) problem, a\nspecial case of QD, where both the objective and measure functions are first\norder differentiable. We then present MAP-Elites via a Gradient Arborescence\n(MEGA), a DQD algorithm that leverages gradient information to efficiently\nexplore the joint range of the objective and measure functions. Results in two\nQD benchmark domains and in searching the latent space of a StyleGAN show that\nMEGA significantly outperforms state-of-the-art QD algorithms, highlighting\nDQD's promise for efficient quality diversity optimization when gradient\ninformation is available. Source code is available at\nhttps://github.com/icaros-usc/dqd.\n"}
+{"abstract": "  We show that there is an action of the symmetric group on the Hochschild\ncochain complex of a twisted group algebra with coefficients in a bimodule.\nThis allows us to define the symmetric Hochschild cohomology of twisted group\nalgebras, similarly to th construction of symmetric group cohomology due to\nStaic. We give explicit embeddings and connecting homomorphisms between the\nsymmetric cohomology spaces and symmetric Hochschild cohomology of twisted\ngroup algebras.\n"}
+{"abstract": "  Most online handwriting recognition systems require the use of specific\nwriting surfaces to extract positional data. In this paper we present a online\nhandwriting recognition system for word recognition which is based on inertial\nmeasurement units (IMUs) for digitizing text written on paper. This is obtained\nby means of a sensor-equipped pen that provides acceleration, angular velocity,\nand magnetic forces streamed via Bluetooth. Our model combines convolutional\nand bidirectional LSTM networks, and is trained with the Connectionist Temporal\nClassification loss that allows the interpretation of raw sensor data into\nwords without the need of sequence segmentation. We use a dataset of words\ncollected using multiple sensor-enhanced pens and evaluate our model on\ndistinct test sets of seen and unseen words achieving a character error rate of\n17.97% and 17.08%, respectively, without the use of a dictionary or language\nmodel\n"}
+{"abstract": "  In this paper, the author studies the boundedness for a large class of\nsublinear operator $T_\\alpha, \\alpha\\in[0,n)$ generated by Calder{\\'o}n-Zygmund\noperators ($\\alpha=0$) and generated by fractional integral operator\n($\\alpha>0$) on generalized mixed Morrey spaces $M^\\varphi_{\\vec{q}}(\\Bbb\nR^n)$. Moreover, the boundeness for the commutators of $T_\\alpha,\n\\alpha\\in[0,n)$ on generalized mixed Morrey spaces $M^\\varphi_{\\vec{q}}(\\Bbb\nR^n)$ is also studied. As applications, we obtain the boundedness for\nHardy-Littlewood maximal operator, Calder\\'on-Zygmund singular integral\noperators, fractional integral operator, fractional maximal operator and their\ncommutators on generalzied mixed Morrey spaces.\n"}
+{"abstract": "  The purpose of this paper is to show three general formulas of three global\ncharacteristic coefficients of Collatz function. The Collatz function is\ndefined by the following operation on an arbitrary positive integer if N is odd\nmultiply it by 3 and add 1 then the sum obtained is divided by 2, if N is even\ndivide it by 2. Based on the principle, we define the n-order function denoted\nby T^n such as the different expressions of that function are results of\napplying Collatz function n times to a natural numbers which are expressed in\nwell determined forms. Based on these expressions, we can characterize that\nn-order function by three global characteristic coefficients. In the first, we\ndefine these three global coefficients. Secondly, we show that each global\ncharacteristic coefficient has a general expression as a function of n.\n"}
+{"abstract": "  The spin structure function of the neutron is traditionally determined by\nmeasuring the spin asymmetry of inclusive electron deep inelastic scattering\n(DIS) off polarized3He nuclei. In such experiments, nuclear effects can lead to\nlarge model dependencies in the interpretation of experimental data. Here we\nstudy the feasibility of suppressing such model dependencies by tagging both\nspectator protons in the process of DIS off neutrons in3He at the forthcoming\nElectron-Ion Collider (EIC). This allows reconstructing the momentum of the\nstruck neutron to ensure it was nearly at rest in the initial state, thereby\nreducing sensitivity to nuclear corrections, and suppress contributions from\nelectron DIS off protonsin3He. Using realistic accelerator and detector\nconfigurations, we find that the EIC can probe the neutron spin structure from\nxB of 0.003 to 0.651. We further find that the double spectator tagging method\nresults in reduced uncertainties bya factor of 4 on the extracted neutron spin\nasymmetries over all kinematics, and by a factor of 10 in the low-xB\nregion,thereby providing valuable insight to the spin and flavor structure of\nnucleons\n"}
+{"abstract": "  Order dependencies (ODs) capture relationships between ordered domains of\nattributes. Approximate ODs (AODs) capture such relationships even when there\nexist exceptions in the data. During automated discovery of ODs, validation is\nthe process of verifying whether an OD holds. We present an algorithm for\nvalidating approximate ODs with significantly improved runtime performance over\nexisting methods for AODs, and prove that it is correct and has optimal\nruntime. By replacing the validation step in a leading algorithm for\napproximate OD discovery with ours, we achieve orders-of-magnitude improvements\nin performance.\n"}
+{"abstract": "  We consider the discrete defocusing nonlinear Schr\\\"odinger equation in its\nintegrable version, which is called defocusing Ablowitz-Ladik lattice. We\nconsider periodic boundary conditions with period $N$ and initial data sample\naccording to the Generalized Gibbs ensemble. In this setting, the Lax matrix of\nthe Ablowitz-Ladik lattice is a random CMV-periodic matrix and it is related to\nthe Killip-Nenciu Circular $\\beta$-ensemble at high-temperature. We obtain the\ngeneralized free energy of the Ablowitz-Ladik lattice and the density of states\nof the random Lax matrix by establishing a mapping to the one-dimensional\nlog-gas. For the Gibbs measure related to the Hamiltonian of the Ablowitz-Ladik\nflow, we obtain the density of states via a particular solution of the\ndouble-confluent Heun equation.\n"}
+{"abstract": "  We characterize absorption-time distributions for birth-death Markov chains\nwith an absorbing boundary state. Based on generic features of the transition\nrates, the asymptotic distribution for such \"extinction-prone\" chains is either\nGaussian, Gumbel, or a convolution of Gumbel distributions. We show that\nseveral birth-death models of evolutionary dynamics, epidemiology, and chemical\nreactions fall into these classes, establishing new results for the\nabsorption-time distribution in each case. Possible connections to African\nsleeping sickness are discussed.\n"}
+{"abstract": "  The emergent universe scenario is a proposal for resolving the Big Bang\nsingularity problem in the standard Friedmann-Lemaitre-Robertson-Walker\ncosmology. In the context of this scenario, the Universe originates from a\nnonsingular static state. In the present work, considering the realization of\nthe emergent universe scenario, we address the possibility of having a\nnonsingular Kantowski-Sachs type static state. Considering four and five\ndimensional models (with and without brane), it is shown that both the\nexistence and stability of a nonsingular state depend on the dimensions of the\nspacetime and the nature of the fluid supporting the geometry.\n"}
+{"abstract": "  In this article, we summarize two agnostic approaches in the framework of\nspatially curved Friedmann-Robertson-Walker (FRW) cosmologies discussed in\ndetail in (Kerachian et al., 2020, 2019). The first case concerns the dynamics\nof a fluid with an unspecified barotropic equation of state (EoS), for which\nthe only assumption made is the non-negativity of the fluid's energy density.\nThe second case concerns the dynamics of a non-minimally coupled real scalar\nfield with unspecified positive potential. For each of these models, we define\na new set of dimensionless variables and a new evolution parameter. In the\nframework of these agnostic setups, we are able to identify several general\nfeatures, like symmetries, invariant subsets and critical points, and provide\ntheir cosmological interpretation.\n"}
+{"abstract": "  Background: The Pauli exclusion principle plays a crucial role as a building\nblock of many-body quantal systems comprised of fermions. It also induces a\n\"Pauli repulsion\" in the interaction between di-nuclear systems. It has been\nshown in [Phys. Rev. C 95, 031601 (2017)] that the Pauli repulsion widens the\nnucleus-nucleus potential barrier, thus hindering sub-barrier fusion. Purpose:\nTo investigate the proton and neutron contributions to the Pauli repulsion,\nboth in the bare potential neglecting shape polarization and transfer between\nthe reactants, as well as in the dynamical potential obtained by accounting for\nsuch dynamical rearrangements. Methods: As the basis of our study we utilize\nthe Pauli kinetic energy (PKE) obtained by studying the nuclear localization\nfunction (NLF). Recently this approach has been generalized to incorporate all\nof the dynamical and time-odd terms present in the nuclear energy density\nfunctional. This approach is employed in the DCFHF and DC-TDHF methods.\nResults: The PKE spatial distribution shows that a repulsion occurs in the neck\nbetween the nuclei when they first touch. Inside the barrier, neutrons can\ncontribute significantly more to the Pauli repulsion in neutron-rich systems.\nDynamical effects tend to lower the Pauli repulsion near the barrier. Proton\nand neutron dynamical contributions to the PKE significantly differ inside the\nbarrier for asymmetric collisions, which is interpreted as an effect of\nmultinucleon transfer. Conclusions: The PKE is shown to make a significant\ncontribution to nuclear interaction potentials. Protons and neutrons can play\nvery different roles in both the bare potential and in the dynamical\nrearrangement. Further microscopic studies are required to better understand\nthe role of transfer and to investigate the effect of pairing and deformation.\n"}
+{"abstract": "  We study the early kinetic decoupling effect in a pseudo-Nambu-Goldstone\n(pNG) dark matter (DM) model. The pNG DM scattering processes with particles in\nthe thermal bath in the early Universe are suppressed by the small momentum\ntransfer. As a result, kinetic equilibrium is not maintained, and the\ntemperature of DM is different from the temperature of the thermal bath at the\nfreeze-out era. This temperature difference affects the thermal relic abundance\nof DM. We investigate the early kinetic decoupling in the Higgs resonance\nregion, 50 GeV $\\lesssim m_\\chi \\lesssim m_h/2$, where $m_\\chi$ is the mass of\nthe DM, and $m_h/2 \\simeq$ 62.5 GeV. We find that the DM-Higgs coupling\ndetermined to obtain the measured value of the DM energy density is\nunderestimated in the literature. The enhancement in the coupling leads larger\nvalue of the Higgs invisible decay rate. It enlarges the capability to discover\nthe DM signals from the decay of the Higgs bosons at collider experiments.\n"}
+{"abstract": "  Semantic segmentation aims to robustly predict coherent class labels for\nentire regions of an image. It is a scene understanding task that powers\nreal-world applications (e.g., autonomous navigation). One important\napplication, the use of imagery for automated semantic understanding of\npedestrian environments, provides remote mapping of accessibility features in\nstreet environments. This application (and others like it) require detailed\ngeometric information of geographical objects. Semantic segmentation is a\nprerequisite for this task since it maps contiguous regions of the same class\nas single entities. Importantly, semantic segmentation uses like ours are not\npixel-wise outcomes; however, most of their quantitative evaluation metrics\n(e.g., mean Intersection Over Union) are based on pixel-wise similarities to a\nground-truth, which fails to emphasize over- and under-segmentation properties\nof a segmentation model. Here, we introduce a new metric to assess region-based\nover- and under-segmentation. We analyze and compare it to other metrics,\ndemonstrating that the use of our metric lends greater explainability to\nsemantic segmentation model performance in real-world applications.\n"}
+{"abstract": "  Generative Adversarial Networks (GANs) have swiftly evolved to imitate\nincreasingly complex image distributions. However, majority of the developments\nfocus on performance of GANs on balanced datasets. We find that the existing\nGANs and their training regimes which work well on balanced datasets fail to be\neffective in case of imbalanced (i.e. long-tailed) datasets. In this work we\nintroduce a novel theoretically motivated Class Balancing regularizer for\ntraining GANs. Our regularizer makes use of the knowledge from a pre-trained\nclassifier to ensure balanced learning of all the classes in the dataset. This\nis achieved via modelling the effective class frequency based on the\nexponential forgetting observed in neural networks and encouraging the GAN to\nfocus on underrepresented classes. We demonstrate the utility of our\nregularizer in learning representations for long-tailed distributions via\nachieving better performance than existing approaches over multiple datasets.\nSpecifically, when applied to an unconditional GAN, it improves the FID from\n$13.03$ to $9.01$ on the long-tailed iNaturalist-$2019$ dataset.\n"}
+{"abstract": "  Building on H. Tran's study of Loewner hulls generated by complex-valued\ndriving functions, which showed the existence of a phase transition, we answer\nthe question of whether the phase transition for complex-driven hulls matches\nthe phase transition for real-driven hulls. This is accomplished through a\ndetailed study of the Loewner hulls generated by driving functions\n$c\\sqrt{1-t}$ and $c\\sqrt{\\tau + t}$ for $c \\in \\mathbb{C}$ and $\\tau \\geq 0$.\nThis family also provides examples of new geometric behavior that is possible\nfor complex-driven hulls but prohibited for real-driven hulls.\n"}
+{"abstract": "  This paper presents NEUROSPF, a tool for the symbolic analysis of neural\nnetworks. Given a trained neural network model, the tool extracts the\narchitecture and model parameters and translates them into a Java\nrepresentation that is amenable for analysis using the Symbolic PathFinder\nsymbolic execution tool. Notably, NEUROSPF encodes specialized peer classes for\nparsing the model's parameters, thereby enabling efficient analysis. With\nNEUROSPF the user has the flexibility to specify either the inputs or the\nnetwork internal parameters as symbolic, promoting the application of program\nanalysis and testing approaches from software engineering to the field of\nmachine learning. For instance, NEUROSPF can be used for coverage-based testing\nand test generation, finding adversarial examples and also constraint-based\nrepair of neural networks, thus improving the reliability of neural networks\nand of the applications that use them. Video URL: https://youtu.be/seal8fG78LI\n"}
+{"abstract": "  Processing and analyzing time series data\\-sets have become a central issue\nin many domains requiring data management systems to support time series as a\nnative data type. A crucial prerequisite of these systems is time series\nmatching, which still is a challenging problem. A time series is a\nhigh-dimensional data type, its representation is storage-, and its comparison\nis time-consuming. Among the representation techniques that tackle these\nchallenges, the symbolic aggregate approximation (SAX) is the current state of\nthe art. This technique reduces a time series to a low-dimensional space by\nsegmenting it and discretizing each segment into a small symbolic alphabet.\nHowever, SAX ignores the deterministic behavior of time series such as cyclical\nrepeating patterns or trend component affecting all segments and leading to a\ndistortion of the symbolic distribution. In this paper, we present a season-\nand a trend-aware symbolic approximation. We show that this improves the\nsymbolic distribution and increase the representation accuracy without\nincreasing its memory footprint. Most importantly, this enables a more\nefficient time series matching by providing a match up to three orders of\nmagnitude faster than SAX.\n"}
+{"abstract": "  The achievement of all-fibre functional nano-modules for subcellular\nlabel-free measurement has long been pursued due to the limitations of\nmanufacturing techniques. In this paper, a compact all-fibre label-free\nnano-sensor composed of a fibre taper and zinc oxide nano-gratings is designed\nand applied for the early monitoring of apoptosis in single living cells.\nBecause of its nanoscale dimensions, mechanical flexibility and minimal\ncytotoxicity to cells, the sensing module can be loaded in cells for long-term\nin situ tracking with high sensitivity. A gradual increase in the nuclear\nrefractive index during the apoptosis process is observed, revealing the\nincrease in molecular density and the decrease in cell volume. The strategy\nused in this study not only contributes to the understanding of internal\nenvironmental variations during cellular apoptosis but also provides a new\nplatform for non-fluorescent all-fibre devices to investigate cellular events\nand to promote new progress in fundamental cell biochemical engineering.\n"}
+{"abstract": "  A radial velocity study by Donati et al. (2016) reported the detection of a\nclose-in giant planet in a 4.93 d orbit around the ~2 Myr old weak-lined T\nTauri star V830 Tau. Because of the stringent timescale constraints that a very\nyoung host star like V830 Tau would place on hot Jupiter formation models and\ninward migration mechanisms, independent confirmation of the planet's existence\nis needed but so far has not been obtained. We present new Chandra X-ray\nobservations of V830 Tau. The Chandra observations in combination with previous\nXMM-Newton observations reveal strong variable X-ray emission with an X-ray\nluminosity spanning the range log Lx = 30.10 - 30.87 ergs/s. Chandra High\nEnergy Transmission Grating (HETG) spectra show emission lines formed over a\nrange of plasma temperatures from ~4 MK (Ne IX) to ~16 MK (S XV). At the\nseparation of the reported planet (0.057 au) the X-ray flux is ~10$^{6}$ -\n10$^{7}$ times greater than the Sun's X-ray flux at Jupiter. We provide\nestimates of the X-ray ionization and atmospheric heating rates at the planet's\nseparation and identify areas of uncertainty that will need to be addressed in\nany future atmospheric models.\n"}
+{"abstract": "  We study the Gluino-Glue operator in the context of Supersymmetric ${\\cal\nN}{=}1$ Yang-Mills (SYM) theory. This composite operator is gauge invariant,\nand it is directly connected to light bound states of the theory; its\nrenormalization is very important as a necessary step for the study of\nlow-lying bound states via numerical simulations. We make use of a\nGauge-Invariant Renormalization Scheme (GIRS). This requires the calculation of\nthe Green's function of a product of two Gluino-Glue operators, situated at\ndistinct space-time points. Within this scheme, the mixing with non-gauge\ninvariant operators which have the same quantum numbers is inconsequential. We\ncompute the one-loop conversion factor relating the GIRS scheme to\n$\\overline{\\rm MS}$. This conversion factor can be used in order to convert to\n$\\overline{\\rm MS}$ Green's functions which are obtained via lattice\nsimulations and are renormalized nonperturbatively in GIRS.\n"}
+{"abstract": "  We provide a lowerbound on the sample complexity of distribution-free parity\nlearning in the realizable case in the shuffle model of differential privacy.\nNamely, we show that the sample complexity of learning $d$-bit parity functions\nis $\\Omega(2^{d/2})$. Our result extends a recent similar lowerbound on the\nsample complexity of private agnostic learning of parity functions in the\nshuffle model by Cheu and Ullman. We also sketch a simple shuffle model\nprotocol demonstrating that our results are tight up to $poly(d)$ factors.\n"}
+{"abstract": "  Although the security benefits of domain name encryption technologies such as\nDNS over TLS (DoT), DNS over HTTPS (DoH), and Encrypted Client Hello (ECH) are\nclear, their positive impact on user privacy is weakened by--the still\nexposed--IP address information. However, content delivery networks, DNS-based\nload balancing, co-hosting of different websites on the same server, and IP\naddress churn, all contribute towards making domain-IP mappings unstable, and\nprevent straightforward IP-based browsing tracking.\n  In this paper, we show that this instability is not a roadblock (assuming a\nuniversal DoT/DoH and ECH deployment), by introducing an IP-based website\nfingerprinting technique that allows a network-level observer to identify at\nscale the website a user visits. Our technique exploits the complex structure\nof most websites, which load resources from several domains besides their\nprimary one. Using the generated fingerprints of more than 200K websites\nstudied, we could successfully identify 84% of them when observing solely\ndestination IP addresses. The accuracy rate increases to 92% for popular\nwebsites, and 95% for popular and sensitive websites. We also evaluated the\nrobustness of the generated fingerprints over time, and demonstrate that they\nare still effective at successfully identifying about 70% of the tested\nwebsites after two months. We conclude by discussing strategies for website\nowners and hosting providers towards hindering IP-based website fingerprinting\nand maximizing the privacy benefits offered by DoT/DoH and ECH.\n"}
+{"abstract": "  Online learning methods, like the online gradient algorithm (OGA) and\nexponentially weighted aggregation (EWA), often depend on tuning parameters\nthat are difficult to set in practice. We consider an online meta-learning\nscenario, and we propose a meta-strategy to learn these parameters from past\ntasks. Our strategy is based on the minimization of a regret bound. It allows\nto learn the initialization and the step size in OGA with guarantees. It also\nallows to learn the prior or the learning rate in EWA. We provide a regret\nanalysis of the strategy. It allows to identify settings where meta-learning\nindeed improves on learning each task in isolation.\n"}
+{"abstract": "  We examine a real electroweak triplet scalar field as dark matter, abandoning\nthe requirement that its relic abundance is determined through freeze out in a\nstandard cosmological history (a situation which we refer to as a 'miracle-less\nWIMP'). We extract the bounds on such a particle from collider searches,\nsearches for direct scattering with terrestrial targets, and searches for the\nindirect products of annihilation. Each type of search provides complementary\ninformation, and each is most effective in a different region of parameter\nspace. LHC searches tend to be highly dependent on the mass of the SU(2)\ncharged partner state, and are effective for very large or very tiny mass\nsplitting between it and the neutral dark matter component. Direct searches are\nvery effective at bounding the Higgs portal coupling, but ineffective once it\nfalls below $\\lambda_{\\text{eff}} \\lesssim 10^{-3}$. Indirect searches suffer\nfrom large astrophysical uncertainties due to the backgrounds and $J$-factors,\nbut do provide key information for $\\sim$ 100 GeV to TeV masses. Synthesizing\nthe allowed parameter space, this example of WIMP dark matter remains viable,\nbut only in miracle-less regimes.\n"}
+{"abstract": "  With technological advancements and the exponential growth of data, we have\nbeen unfolding different capabilities of neural networks in different sectors.\nIn this paper, I have tried to use a specific type of Neural Network known as\nConvolutional Neural Network(CNN/ConvNet) in the stock market. In other words,\nI have tried to construct and train a convolutional neural network on past\nstock prices data and then tried to predict the movement of stock price i.e.\nwhether the stock price would rise or fall, in the coming time.\n"}
+{"abstract": "  In the present paper, we investigate the regularity and symmetry properties\nof weak solutions to semilinear elliptic equations which are locally stable.\n"}
+{"abstract": "  It is common to assume that all narrow absorption lines (NALs) at extreme\nhigh-velocity shifts form in cosmologically intervening gas or galaxies\nunrelated to quasars. However, previous detailed studies of individual quasars\nhave shown that some NALs at these large velocity shifts do form in high-speed\nquasar ejecta. We search for extreme high-velocity NAL outflows (with speeds\n$\\sim$0.1-0.2c) based on relationships with associated absorption lines (AALs)\nand broad absorption-line (BAL) outflows. We find that high-velocity NALs are\nstrongly correlated with AALs, BALs, and radio loudness, indicating that a\nsignificant fraction of high-velocity systems are either ejected from the\nquasars or form in material swept up by the radio jets (and are not unrelated\nintervening gas). We also consider line-locked C IV doublets as another\nindicator of high-velocity NALs formed in outflows. The fact that line-locked\nNALs are highly ionized and correlated with BAL outflows and radio-loud quasars\nimplies that physical line locking due to radiative forces is both common and\nreal, which provides indirect evidence that a significant fraction of\nhigh-velocity NALs are intrinsic to quasars.\n"}
+{"abstract": "  The uncanny ability of multiple particles to interfere with one another is\none of the core principles of quantum mechanics, and serves as foundation for\nquantum information processing. In particular, the interplay of constructive\nand destructive interference with the characteristic exchange statistics of\nindistinguishable particles give rise to the Hong-Ou-Mandel (HOM) effect, where\nthe bunching of bosons can lead to a perfect suppression of two-particle\ncoincidences between the output ports of a balanced beam splitter. Conversely,\nin the case of two fermions, anti-bunching can systematically enhance these\ncoincidences up to twice the baseline value of distinguishable particles. As\nsuch, the respective emergence of dips or peaks in the HOM experiment may at\nfirst glance appear to be indicative of the bosonic/fermionic nature of the\nincident particles. In this work, we demonstrate experimentally that the\ntwo-particle coincidence statistics of two bosons can instead be seamlessly\ntuned from the expected case of suppression to substantial enhancement by an\nappropriate choice of the observation basis. To this end, our photonic setting\nleverages birefringent polarization couplers to selectively introduce\ndissipation in the photons polarization degree of freedom. Notably, the\nmechanism underpinning this this highly unusual behaviour does not act on the\nindividual phases accumulated by pairs of particles along specific paths, but\ninstead allows them to jointly evade losses, while indistinguishable photons\nare prevented from being simultaneously detected in orthogonal modes. Our\nfindings reveal a new approach to harnessing non-Hermitian settings for the\nmanipulation of multi-particle quantum states and as functional elements in\nquantum simulation and information processing.\n"}
+{"abstract": "  We prove localization with high probability on sets of size of order $N/\\log\nN$ for the eigenvectors of non-Hermitian finitely banded $N\\times N$ Toeplitz\nmatrices $P_N$ subject to small random perturbations, in a very general\nsetting. As perturbation we consider $N\\times N$ random matrices with\nindependent entries of zero mean, finite moments, and which satisfy an\nappropriate anti-concentration bound. We show via a Grushin problem that an\neigenvector for a given eigenvalue $z$ is well approximated by a random linear\ncombination of the singular vectors of $P_N-z$ corresponding to its small\nsingular values. We prove precise probabilistic bounds on the local\ndistribution of the eigenvalues of the perturbed matrix and provide a detailed\nanalysis of the singular vectors to conclude the localization result.\n"}
+{"abstract": "  Let $X$ be a smooth compact connected manifold. Let $G=\\mbox{Diff}\\, X$ be\nthe group of diffeomorphisms of $X$, equipped with the $C^\\infty$-topology, and\nlet $H$ be the stabilizer of some point in $X$. Then the inclusion $H\\to G$,\nwhich is a morphism of two regular Fr\\'echet--Lie groups, is an epimorphism in\nthe category of smooth Lie groups modelled on complete locally convex spaces.\nAt the same time, in the latter category, epimorphisms between finite\ndimensional Lie groups have dense range. We also prove that if $G$ is a\nBanach--Lie group and $H$ is a proper closed subgroup, the inclusion $H\\to G$\nis not an epimorphism in the category of Hausdorff groups.\n"}
+{"abstract": "  The theory of constructing instantaneous equilibrium (ieq) transition under\narbitrary time-dependent temperature and potential variation for a Brownian\nparticle is developed. It is shown that it is essential to consider the\nunderdamped dynamics for temperature-changing transitions. The ieq is\nmaintained by a time-dependent auxiliary position and momentum potential, which\ncan be calculated for given time-dependent transition protocols. Explicit\nanalytic results are derived for the work and heat statistics, energy, and\nentropy changes for harmonic and non-harmonic trapping potential with arbitrary\ntime-dependent potential parameters and temperature protocols. Numerical\nsolutions of the corresponding Langevin dynamics are computed to confirm the\ntheoretical results. Although ieq transition of the reverse process is not the\ntime-reversal of the ieq transition of the forward process due to the\nodd-parity of controlling parameters, their phase-space distribution functions\nrestore the time-reversal symmetry, and hence the energy and entropy changes of\nthe ieq of the reverse process are simply the negative of that of the forward\nprocess. Furthermore, it is shown that it is possible to construct an ieq\ntransition that has zero entropy production at a finite transition rate, i.e.,\na fast ieq isentropic process, and is further demonstrated by explicit Langevin\ndynamics simulations. Our theory provides fundamental building blocks for\ndesigning controlled microscopic heat engine cycles. Implications for\nconstructing an efficient Brownian heat engine are also discussed.\n"}
+{"abstract": "  We prove that the G\\\"{a}rtner--Ellis generating function of probability\ndistributions associated with KMS states of weakly interacting fermions on the\nlattice can be written as the limit of logarithms of Gaussian Berezin\nintegrals. The covariances of the Gaussian integrals are shown to have a\nuniform Pfaffian bound and to be summable in general cases of interest,\nincluding systems that are not translation invariant. The Berezin integral\nrepresentation can thus be used to obtain convergent expansions of the\ngenerating function in terms of powers of its parameter. The derivation and\nanalysis of the expansions of logarithms of Berezin integrals are the subject\nof the second part of the present work. Such technical results are also useful,\nfor instance, in the context of quantum information theory, in the computation\nof relative entropy densities associated with fermionic Gibbs states, and in\nthe theory of quantum normal fluctuations for weakly interacting fermion\nsystems.\n"}
+{"abstract": "  This study examined the problems of personal income tax on revenue generation\nin Gombe state. The methodology used in data collection is survey, which\nutilized both primary and secondary types of data. Purposive sampling technique\nwas adopted in selecting a sample of 150 respondents from both employees of\nstate board of internal revenue service and tax payers in the state. The chi\nsquare statistics test was used in testing the hypotheses. The study found that\ntax avoidance/evasion and complete absences of information technology are\nserious problems affecting revenue generation in the state. It recommends that\ngovernment should device strict measures in dealing and punishing individuals\nengage in tax avoidance and evasion. It should also employ the use of\ninformation technology as it is the only way problems experience in personal\nincome tax collection can be reduced drastically.\n"}
+{"abstract": "  This paper introduces a novel geometric multigrid solver for unstructured\ncurved surfaces. Multigrid methods are highly efficient iterative methods for\nsolving systems of linear equations. Despite the success in solving problems\ndefined on structured domains, generalizing multigrid to unstructured curved\ndomains remains a challenging problem. The critical missing ingredient is a\nprolongation operator to transfer functions across different multigrid levels.\nWe propose a novel method for computing the prolongation for triangulated\nsurfaces based on intrinsic geometry, enabling an efficient geometric multigrid\nsolver for curved surfaces. Our surface multigrid solver achieves better\nconvergence than existing multigrid methods. Compared to direct solvers, our\nsolver is orders of magnitude faster. We evaluate our method on many geometry\nprocessing applications and a wide variety of complex shapes with and without\nboundaries. By simply replacing the direct solver, we upgrade existing\nalgorithms to interactive frame rates, and shift the computational bottleneck\naway from solving linear systems.\n"}
+{"abstract": "  We prove an analogue of Selberg's zero density estimate for $\\zeta(s)$ that\nholds for any $\\mathrm{GL}_2$ $L$-function. We use this estimate to study the\ndistribution of the vector of fractional parts of $\\gamma\\mathbf{\\alpha}$,\nwhere $\\mathbf{\\alpha}\\in\\mathbb{R}^n$ is fixed and $\\gamma$ varies over the\nimaginary parts of the nontrivial zeros of a $\\mathrm{GL}_2$ $L$-function.\n"}
+{"abstract": "  This article investigates the Mahler measure of a family of 2-variate\npolynomials, denoted by $P_d, d\\geq 1$, unbounded in both degree and genus. By\nusing a closed formula for the Mahler measure introduced in \"Volume function\nand Mahler measure of exact polynomials\" (by Guilloux and March\\'e), we are\nable to compute $m(P_d)$, for arbitrary $d$, as a sum of the values of\ndilogarithm at special roots of unity. We prove that $m(P_d)$ converges and the\nlimit is proportional to $\\zeta(3)$, where $\\zeta$ is the Riemann zeta\nfunction.\n"}
+{"abstract": "  Lattice-Boltzmann methods are known for their simplicity, efficiency and ease\nof parallelization, usually relying on uniform Cartesian meshes with a strong\nbond between spatial and temporal discretization. This fact complicates the\ncrucial issue of reducing the computational cost and the memory impact by\nautomatically coarsening the grid where a fine mesh is unnecessary, still\nensuring the overall quality of the numerical solution through error control.\nThis work provides a possible answer to this interesting question, by\nconnecting, for the first time, the field of lattice-Boltzmann Methods (LBM) to\nthe adaptive multiresolution (MR) approach based on wavelets. To this end, we\nemploy a MR multi-scale transform to adapt the mesh as the solution evolves in\ntime according to its local regularity. The collision phase is not affected due\nto its inherent local nature and because we do not modify the speed of the\nsound, contrarily to most of the LBM/Adaptive Mesh Refinement (AMR) strategies\nproposed in literature, thus preserving the original structure of any LBM\nscheme. Besides, an original use of the MR allows the scheme to resolve the\nproper physics by efficiently controlling the accuracy of the transport phase.\nWe carefully test our method to conclude on its adaptability to a wide family\nof existing lattice Boltzmann schemes, treating both hyperbolic and parabolic\nsystems of equation, thus being less problem-dependent than the AMR approaches,\nwhich have a hard time granting an effective control on the error. The ability\nof the method to yield a very efficient compression rate and thus a\ncomputational cost reduction for solutions involving localized structures with\nloss of regularity is also shown, while guaranteeing a precise control on the\napproximation error introduced by the spatial adaptation of the mesh. The\nnumerical strategy is implemented on a specific open-source platform called\nSAMURAI with a dedicated data-structure relying on set algebra.\n"}
+{"abstract": "  The neuromorphic event cameras, which capture the optical changes of a scene,\nhave drawn increasing attention due to their high speed and low power\nconsumption. However, the event data are noisy, sparse, and nonuniform in the\nspatial-temporal domain with an extremely high temporal resolution, making it\nchallenging to design backend algorithms for event-based vision. Existing\nmethods encode events into point-cloud-based or voxel-based representations,\nbut suffer from noise and/or information loss. Additionally, there is little\nresearch that systematically studies how to handle static and dynamic scenes\nwith one universal design for event-based vision. This work proposes the\nAligned Event Tensor (AET) as a novel event data representation, and a neat\nframework called Event Frame Net (EFN), which enables our model for event-based\nvision under static and dynamic scenes. The proposed AET and EFN are evaluated\non various datasets, and proved to surpass existing state-of-the-art methods by\nlarge margins. Our method is also efficient and achieves the fastest inference\nspeed among others.\n"}
+{"abstract": "  A major shift is happening within neurophysiology: a population doctrine is\ndrawing level with the single-neuron doctrine that has long dominated the\nfield. Population-level ideas have so far had their greatest impact in motor\nneuroscience, but they hold great promise for resolving open questions in\ncognition as well. Here, we codify the population doctrine and survey recent\nwork that leverages this view to specifically probe cognition. Our discussion\nis organized around five core concepts that provide a foundation for\npopulation-level thinking: (1) state spaces, (2) manifolds, (3) coding\ndimensions, (4) subspaces, and (5) dynamics. The work we review illustrates the\nprogress and promise that population neurophysiology holds for cognitive\nneuroscience$-$for delivering new insight into attention, working memory,\ndecision-making, executive function, learning, and reward processing.\n"}
+{"abstract": "  Dependency networks (Heckerman et al., 2000) are potential probabilistic\ngraphical models for systems comprising a large number of variables. Like\nBayesian networks, the structure of a dependency network is represented by a\ndirected graph, and each node has a conditional probability table. Learning and\ninference are realized locally on individual nodes; therefore, computation\nremains tractable even with a large number of variables. However, the\ndependency network's learned distribution is the stationary distribution of a\nMarkov chain called pseudo-Gibbs sampling and has no closed-form expressions.\nThis technical disadvantage has impeded the development of dependency networks.\nIn this paper, we consider a certain manifold for each node. Then, we can\ninterpret pseudo-Gibbs sampling as iterative m-projections onto these\nmanifolds. This interpretation provides a theoretical bound for the location\nwhere the stationary distribution of pseudo-Gibbs sampling exists in\ndistribution space. Furthermore, this interpretation involves structure and\nparameter learning algorithms as optimization problems. In addition, we compare\ndependency and Bayesian networks experimentally. The results demonstrate that\nthe dependency network and the Bayesian network have roughly the same\nperformance in terms of the accuracy of their learned distributions. The\nresults also show that the dependency network can learn much faster than the\nBayesian network.\n"}
+{"abstract": "  We study the fair division problem of allocating multiple resources among a\nset of agents with Leontief preferences that are each required to complete a\nfinite amount of work, which we term \"limited demands\". We examine the behavior\nof the classic Dominant Resource Fairness (DRF) mechanism in this setting and\nshow it is fair but only weakly Pareto optimal and inefficient in many natural\nexamples. We propose as an alternative the Least Cost Product (LCP) mechanism,\na natural adaptation of Maximum Nash Welfare to this setting. We characterize\nthe structure of allocations of the LCP mechanism in this setting, show that it\nis Pareto efficient, and that it satisfies the relatively weak fairness\nproperty of sharing incentives. While we prove it satisfies the stronger\nfairness property of (expected) envy freeness in some special cases, we provide\na counterexample showing it does not do so in general, a striking contrast to\nthe \"unreasonable fairness\" of Maximum Nash Welfare in other settings.\nSimulations suggest, however, that these violations of envy freeness are rare\nin randomly generated examples.\n"}
+{"abstract": "  The Multi-speaker Multi-style Voice Cloning Challenge (M2VoC) aims to provide\na common sizable dataset as well as a fair testbed for the benchmarking of the\npopular voice cloning task. Specifically, we formulate the challenge to adapt\nan average TTS model to the stylistic target voice with limited data from\ntarget speaker, evaluated by speaker identity and style similarity. The\nchallenge consists of two tracks, namely few-shot track and one-shot track,\nwhere the participants are required to clone multiple target voices with 100\nand 5 samples respectively. There are also two sub-tracks in each track. For\nsub-track a, to fairly compare different strategies, the participants are\nallowed to use only the training data provided by the organizer strictly. For\nsub-track b, the participants are allowed to use any data publicly available.\nIn this paper, we present a detailed explanation on the tasks and data used in\nthe challenge, followed by a summary of submitted systems and evaluation\nresults.\n"}
+{"abstract": "  With the recent advances of open-domain story generation, the lack of\nreliable automatic evaluation metrics becomes an increasingly imperative issue\nthat hinders the fast development of story generation. According to conducted\nresearches in this regard, learnable evaluation metrics have promised more\naccurate assessments by having higher correlations with human judgments. A\ncritical bottleneck of obtaining a reliable learnable evaluation metric is the\nlack of high-quality training data for classifiers to efficiently distinguish\nplausible and implausible machine-generated stories. Previous works relied on\n\\textit{heuristically manipulated} plausible examples to mimic possible system\ndrawbacks such as repetition, contradiction, or irrelevant content in the text\nlevel, which can be \\textit{unnatural} and \\textit{oversimplify} the\ncharacteristics of implausible machine-generated stories. We propose to tackle\nthese issues by generating a more comprehensive set of implausible stories\nusing {\\em plots}, which are structured representations of controllable factors\nused to generate stories. Since these plots are compact and structured, it is\neasier to manipulate them to generate text with targeted undesirable\nproperties, while at the same time maintain the grammatical correctness and\nnaturalness of the generated sentences. To improve the quality of generated\nimplausible stories, we further apply the adversarial filtering procedure\npresented by \\citet{zellers2018swag} to select a more nuanced set of\nimplausible texts. Experiments show that the evaluation metrics trained on our\ngenerated data result in more reliable automatic assessments that correlate\nremarkably better with human judgments compared to the baselines.\n"}
+{"abstract": "  Alzheimer's Disease (AD) is a severe brain disorder, destroying memories and\nbrain functions. AD causes chronically, progressively, and irreversibly\ncognitive declination and brain damages. The reliable and effective evaluation\nof early dementia has become essential research with medical imaging\ntechnologies and computer-aided algorithms. This trend has moved to modern\nArtificial Intelligence (AI) technologies motivated by deeplearning success in\nimage classification and natural language processing. The purpose of this\nreview is to provide an overview of the latest research involving deep-learning\nalgorithms in evaluating the process of dementia, diagnosing the early stage of\nAD, and discussing an outlook for this research. This review introduces various\napplications of modern AI algorithms in AD diagnosis, including Convolutional\nNeural Network (CNN), Recurrent Neural Network (RNN), Automatic Image\nSegmentation, Autoencoder, Graph CNN (GCN), Ensemble Learning, and Transfer\nLearning. The advantages and disadvantages of the proposed methods and their\nperformance are discussed. The conclusion section summarizes the primary\ncontributions and medical imaging preprocessing techniques applied in the\nreviewed research. Finally, we discuss the limitations and future outlooks.\n"}
+{"abstract": "  We present results of time-series data simulation. We aimed at estimating the\nthreshold used for detecting signals in amplitude spectra, calculated from\nsimulating TESS photometry of up to one year duration. We selected the\nthreshold at a false alarm probability FAP=0.1% and derived S/N ratios between\n4.6 and 5.7 depending on the data cadence and coverage. We also provide a\nformula to estimate the threshold for any FAP adopted and a given number of\ndata points. Our result confirms that, to avoid spurious detection, space-based\nphotometry may require substantially higher S/N than that typically being\nemployed for ground-based data.\n"}
+{"abstract": "  In this paper we introduce a new method simulating stent graft deployment for\nassisting endovascular repair of abdominal aortic aneurysms. The method relies\non intraoperative images coupled with mechanical models. A multi-step algorithm\nhas been developed to increase the reliability of simulations. The first step\npredicts the position of the stent graft within the aorta. The second step is\nan axisymmetric geometric reconstruction of each individual stent. The third\nstep minimizes the rotation of each stent around its main axis. Finally, the\nlast step virtually deploys each stent within a deployment box extracted from\nthe preoperative CT scan. A proof of concept is performed on a patient. The\naccuracy is compatible with the clinical threshold of 3 mm: the average\ndistance between target and simulated stents is 1.73 $\\pm$ 0.37 mm.\nFenestrations of the stent-graft are reconstructed with a maximum error of less\nthan 2.5 mm, which is enables a secure catheterization of secondary arteries.\nIn summary, the method is able to assist EVAR practitioners by providing all\nnecessary information for a fast and accurate stent graft positioning,\ncombining intraoperative data and a mechanical model in a very low cost\nframework.\n"}
+{"abstract": "  We study the cross-stitch flat band lattice with a $\\mathcal{PT}$-symmetric\non-site potential and uncover mobility edges with exact solutions. Furthermore,\nwe study the relationship between the $\\mathcal{PT}$ symmetry broken point and\nthe localization-delocalization transition point, and verify that mobility\nedges in this non-Hermitian model is available to signal the $\\mathcal{PT}$\nsymmetry breaking.\n"}
+{"abstract": "  In cerebrovascular networks, some vertices are more connected to each other\nthan with the rest of the vasculature, defining a community structure. Here, we\nintroduce a class of model networks built by rewiring Random Regular Graphs,\nwhich enables to reproduce this community structure and other topological\nproperties of cerebrovascular networks. We use these model networks to study\nthe global flow reduction induced by the removal of a single edge. We\nanalytically show that this global flow reduction can be expressed as a\nfunction of the initial flow rate in the removed edge and of a topological\nquantity, both of which display probability distributions following Cauchy\nlaws, i.e. with large tails. As a result, we show that the distribution of\nblood flow reductions is strongly influenced by the community structure. In\nparticular, the probability of large flow reductions increases substantially\nwhen the community structure is stronger, weakening the network resilience to\nsingle capillary occlusions. We discuss the implications of these findings in\nthe context of Alzheimer's Disease, in which the importance of vascular\nmechanisms, including capillary occlusions, is beginning to be uncovered.\n"}
+{"abstract": "  This note concerns Loomis-Whitney inequalities in Heisenberg groups\n$\\mathbb{H}^n$: $$|K| \\lesssim\n\\prod_{j=1}^{2n}|\\pi_j(K)|^{\\frac{n+1}{n(2n+1)}}, \\qquad K \\subset\n\\mathbb{H}^n.$$ Here $\\pi_{j}$, $j=1,\\ldots,2n$, are the vertical Heisenberg\nprojections to the hyperplanes $\\{x_j=0\\}$, respectively, and $|\\cdot|$ refers\nto a natural Haar measure on either $\\mathbb{H}^n$, or one of the hyperplanes.\nThe Loomis-Whitney inequality in the first Heisenberg group $\\mathbb{H}^1$ is a\ndirect consequence of known $L^p$ improving properties of the standard Radon\ntransform in $\\mathbb{R}^2$. In this note, we show how the Loomis-Whitney\ninequalities in higher dimensional Heisenberg groups can be deduced by an\nelementary inductive argument from the inequality in $\\mathbb{H}^1$. The same\napproach, combined with multilinear interpolation, also yields the following\nstrong type bound: $$\\int_{\\mathbb{H}^n} \\prod_{j=1}^{2n}\nf_j(\\pi_j(p))\\;dp\\lesssim \\prod_{j=1}^{2n} \\|f_j\\|_{\\frac{n(2n+1)}{n+1}}$$ for\nall nonnegative measurable functions $f_1,\\ldots,f_{2n}$ on $\\mathbb{R}^{2n}$.\nThese inequalities and their geometric corollaries are thus ultimately based on\nplanar geometry. Among the applications of Loomis-Whitney inequalities in\n$\\mathbb{H}^n$, we mention the following sharper version of the classical\ngeometric Sobolev inequality in $\\mathbb{H}^n$: $$\\|u\\|_{\\frac{2n+2}{2n+1}}\n\\lesssim \\prod_{j=1}^{2n}\\|X_ju\\|^{\\frac{1}{2n}}, \\qquad u \\in\nBV(\\mathbb{H}^n),$$ where $X_j$, $j=1,\\ldots,2n$, are the standard horizontal\nvector fields in $\\mathbb{H}^n$.\n"}
+{"abstract": "  We study the classical weighted perfect matchings problem for bipartite\ngraphs or sometimes referred to as the assignment problem, i.e., given a\nweighted bipartite graph $G = (U\\cup V,E)$ with weights $w : E \\rightarrow\n\\mathcal{R}$ we are interested to find the maximum matching in $G$ with the\nminimum/maximum weight. In this work we present a new and arguably simpler\nanalysis of one of the earliest techniques developed for solving the assignment\nproblem, namely the auction algorithm. Using our analysis technique we present\ntighter and improved bounds on the runtime complexity for finding an\napproximate minumum weight perfect matching in $k$-left regular sparse\nbipartite graphs.\n"}
+{"abstract": "  There has been a growing interest in model-agnostic methods that can make\ndeep learning models more transparent and explainable to a user. Some\nresearchers recently argued that for a machine to achieve a certain degree of\nhuman-level explainability, this machine needs to provide human causally\nunderstandable explanations, also known as causability. A specific class of\nalgorithms that have the potential to provide causability are counterfactuals.\nThis paper presents an in-depth systematic review of the diverse existing body\nof literature on counterfactuals and causability for explainable artificial\nintelligence. We performed an LDA topic modelling analysis under a PRISMA\nframework to find the most relevant literature articles. This analysis resulted\nin a novel taxonomy that considers the grounding theories of the surveyed\nalgorithms, together with their underlying properties and applications in\nreal-world data. This research suggests that current model-agnostic\ncounterfactual algorithms for explainable AI are not grounded on a causal\ntheoretical formalism and, consequently, cannot promote causability to a human\ndecision-maker. Our findings suggest that the explanations derived from major\nalgorithms in the literature provide spurious correlations rather than\ncause/effects relationships, leading to sub-optimal, erroneous or even biased\nexplanations. This paper also advances the literature with new directions and\nchallenges on promoting causability in model-agnostic approaches for\nexplainable artificial intelligence.\n"}
+{"abstract": "  We revisit large spectroscopic data sets for field stars from the literature\nto derive the upper Li envelope in the high metallicity regime in our Galaxy.\nWe take advantage of Gaia EDR3 data and state-of-the-art stellar models to\nprecisely determine the position of the sample dwarf stars in the\nHertzsprung-Russell diagram. The highest Li abundances are found in field\nmetal-rich warm dwarfs from the GALAH survey, located on the hot side of the\nLi-dip. Their mean Li value agrees with what was recently derived for warm\ndwarfs in metal-rich clusters, pointing towards a continuous increase of Li up\nto super-solar metallicity. However, if only cool dwarfs are considered in\nGALAH, as done in the other literature surveys, it is found that the upper Li\nenvelope decreases at super-solar metallicities, blurring the actual Li\nevolution picture. We confirm the suggestion that field and open cluster\nsurveys that found opposite Li behaviour in the high metallicity regime do not\nsample the same types of stars: The first ones, with the exception of GALAH,\nmiss warm dwarfs that can potentially preserve their original Li content.\nAlthough we can discard the bending of the Li upper envelope at high\nmetallicity derived from the analysis of cool star samples, we still need to\nevaluate the effects of atomic diffusion on warm, metal-rich early-F and late-A\ntype dwarfs before deriving the actual Li abundance at high metallicity.\n"}
+{"abstract": "  Multi-modal Probabilistic Active Sensing (MMPAS) uses sensor fusion and\nprobabilistic models to control the perception process of robotic sensing\nplatforms. MMPAS is successfully employed in environmental exploration,\ncollaborative mobile robotics, and target tracking, being fostered by the high\nperformance guarantees on autonomous perception. In this context, we propose a\nbi-Radio-Visual PAS scheme to solve the transmitter discovery problem.\nSpecifically, we firstly exploit the correlation between radio and visual\nmeasurements to learn a target detection model in a self-supervised manner.\nThen, the model is combined with antenna radiation anisotropies into a Bayesian\nOptimization framework that controls the platform. We show that the proposed\nalgorithm attains an accuracy of 92%, overcoming two other probabilistic active\nsensing baselines.\n"}
+{"abstract": "  Query optimizer is at the heart of the database systems. Cost-based optimizer\nstudied in this paper is adopted in almost all current database systems. A\ncost-based optimizer introduces a plan enumeration algorithm to find a\n(sub)plan, and then uses a cost model to obtain the cost of that plan, and\nselects the plan with the lowest cost. In the cost model, cardinality, the\nnumber of tuples through an operator, plays a crucial role. Due to the\ninaccuracy in cardinality estimation, errors in cost model, and the huge plan\nspace, the optimizer cannot find the optimal execution plan for a complex query\nin a reasonable time. In this paper, we first deeply study the causes behind\nthe limitations above. Next, we review the techniques used to improve the\nquality of the three key components in the cost-based optimizer, cardinality\nestimation, cost model, and plan enumeration. We also provide our insights on\nthe future directions for each of the above aspects.\n"}
+{"abstract": "  In this paper we study the boundary controllability for a system of two\ncoupled degenerate/singular parabolic equations with a control acting on only\none equation. We analyze both approximate and null boundary controllability\nproperties. Besides, we provide an estimate on the null-control cost. The\nproofs are based on the use of the moment method by Fattorini and Russell\ntogether with some results on biorthogonal families.\n"}
+{"abstract": "  In the non-negative matrix factorization (NMF) problem, the input is an\n$m\\times n$ matrix $M$ with non-negative entries and the goal is to factorize\nit as $M\\approx AW$. The $m\\times k$ matrix $A$ and the $k\\times n$ matrix $W$\nare both constrained to have non-negative entries. This is in contrast to\nsingular value decomposition, where the matrices $A$ and $W$ can have negative\nentries but must satisfy the orthogonality constraint: the columns of $A$ are\northogonal and the rows of $W$ are also orthogonal. The orthogonal non-negative\nmatrix factorization (ONMF) problem imposes both the non-negativity and the\northogonality constraints, and previous work showed that it leads to better\nperformances than NMF on many clustering tasks. We give the first\nconstant-factor approximation algorithm for ONMF when one or both of $A$ and\n$W$ are subject to the orthogonality constraint. We also show an interesting\nconnection to the correlation clustering problem on bipartite graphs. Our\nexperiments on synthetic and real-world data show that our algorithm achieves\nsimilar or smaller errors compared to previous ONMF algorithms while ensuring\nperfect orthogonality (many previous algorithms do not satisfy the hard\northogonality constraint).\n"}
+{"abstract": "  NLP Ghana is an open-source non-profit organization aiming to advance the\ndevelopment and adoption of state-of-the-art NLP techniques and digital\nlanguage tools to Ghanaian languages and problems. In this paper, we first\npresent the motivation and necessity for the efforts of the organization; by\nintroducing some popular Ghanaian languages while presenting the state of NLP\nin Ghana. We then present the NLP Ghana organization and outline its aims,\nscope of work, some of the methods employed and contributions made thus far in\nthe NLP community in Ghana.\n"}
+{"abstract": "  Value-at-risk (VaR) is an established measure to assess risks in critical\nreal-world applications with random environmental factors. This paper presents\na novel VaR upper confidence bound (V-UCB) algorithm for maximizing the VaR of\na black-box objective function with the first no-regret guarantee. To realize\nthis, we first derive a confidence bound of VaR and then prove the existence of\nvalues of the environmental random variable (to be selected to achieve no\nregret) such that the confidence bound of VaR lies within that of the objective\nfunction evaluated at such values. Our V-UCB algorithm empirically demonstrates\nstate-of-the-art performance in optimizing synthetic benchmark functions, a\nportfolio optimization problem, and a simulated robot task.\n"}
+{"abstract": "  In the old spirit of Kaluza-Klein, we consider a spacetime of the form $P =\nM_4 \\times K$, where $K$ is the Lie group $\\mathrm{SU}(3)$ equipped with a\nleft-invariant metric that is not fully right-invariant. We observe that a\ncomplete generation of fermionic fields can be encoded in the 64 components of\na single spinor over the 12-dimensional spacetime. The behaviour of the\nspinorial function along the internal space $K$ can be chosen so that, after\npairing and fibre-integration over $K$, the resulting Dirac kinetic terms in\nfour dimensions couple to the $\\mathfrak{u}(1) \\oplus \\mathfrak{su}(2) \\oplus\n\\mathfrak{su}(3)$ gauge fields in the exact chiral representations present in\nthe Standard Model. Although we describe the action of the internal Dirac\noperator on the 12-dimensional spinor, the full calculation of the fermionic\nmass terms produced by the model is longer and is not carried out here. We\ncalculate instead the action of the internal Laplace operator on the spinor\ncomponents.\n"}
+{"abstract": "  Let (X j , d j , $\\mu$ j), j = 0, 1,. .. , m be metric measure spaces. Given\n0 < p $\\kappa$ $\\le$ $\\infty$ for $\\kappa$ = 1,. .. , m and an analytic family\nof multilinear operators T z : L p 1 (X 1) x $\\bullet$ $\\bullet$ $\\bullet$ L p\nm (X m) $\\rightarrow$ L 1 loc (X 0), for z in the complex unit strip, we prove\na theorem in the spirit of Stein's complex interpolation for analytic families.\nAnalyticity and our admissibility condition are defined in the weak (integral)\nsense and relax the pointwise definitions given in [9]. Continuous functions\nwith compact support are natural dense subspaces of Lebesgue spaces over metric\nmeasure spaces and we assume the operators T z are initially defined on them.\nOur main lemma concerns the approximation of continuous functions with compact\nsupport by similar functions that depend analytically in an auxiliary parameter\nz. An application of the main theorem concerning bilinear estimates for\nSchr{\\\"o}dinger operators on L p is included.\n"}
+{"abstract": "  We propose a new framework for understanding and representing related salient\nevents in a video using visual semantic role labeling. We represent videos as a\nset of related events, wherein each event consists of a verb and multiple\nentities that fulfill various roles relevant to that event. To study the\nchallenging task of semantic role labeling in videos or VidSRL, we introduce\nthe VidSitu benchmark, a large-scale video understanding data source with $29K$\n$10$-second movie clips richly annotated with a verb and semantic-roles every\n$2$ seconds. Entities are co-referenced across events within a movie clip and\nevents are connected to each other via event-event relations. Clips in VidSitu\nare drawn from a large collection of movies (${\\sim}3K$) and have been chosen\nto be both complex (${\\sim}4.2$ unique verbs within a video) as well as diverse\n(${\\sim}200$ verbs have more than $100$ annotations each). We provide a\ncomprehensive analysis of the dataset in comparison to other publicly available\nvideo understanding benchmarks, several illustrative baselines and evaluate a\nrange of standard video recognition models. Our code and dataset is available\nat vidsitu.org.\n"}
+{"abstract": "  We compare the contribution of Active Galactic Nuclei (AGN) and\nstar-formation towards dust heating in sub-mm galaxies (SMGs). We have used\nALMA at $0.\"1$ resolution to image a complete flux-limited sample of seven\nsub-mm sources previously shown to have spectral energy distributions (SEDs)\nthat were as well-fitted by obscured AGN as star-forming galaxy templates.\nIndeed, two sub-mm sources were known to be quasars from their absorbed X-ray\nemission. We find the sub-mm sizes of all SMGs to be small ($\\approx1-2$kpc)\nand generally $\\sim3$ times smaller than any host detected in the\nNear-Infra-Red (NIR). In all cases, the five SMGs are comparable in sub-mm size\nto the two known quasars and four $z\\approx6$ quasars, also observed with ALMA.\nWe detect no evidence of diffuse spiral arms in this complete sample. We then\nconvert the Far-Infra-Red (FIR) luminosities to star-formation rate (SFR)\nsurface densities and find that the SMGs occupy the same range as the known\nquasars in our sample. We conclude that in terms of sub-mm size, extent\nrelative to host and SFR density as well as luminosity and Mid-IR (MIR) colour,\nthere is little distinction between the SMGs and sub-mm bright quasars.\nFinally, we present preliminary evidence that SMGs with higher MIR luminosities\nand sub-mm loud quasars tend to have dust components that range to hotter\ntemperatures than their less luminous SMG counterparts. In light of these\nresults, we continue to suggest that luminous SMGs may host dust-absorbed\nquasars that may simultaneously dominate the FIR and hard X-ray backgrounds.\n"}
+{"abstract": "  The Proper Forcing Axiom implies that compact Hausdorff spaces are either\nfirst-countable or contain a converging $\\omega_1$-sequence.\n"}
+{"abstract": "  In recent years, the task of video prediction-forecasting future video given\npast video frames-has attracted attention in the research community. In this\npaper we propose a novel approach to this problem with Vector Quantized\nVariational AutoEncoders (VQ-VAE). With VQ-VAE we compress high-resolution\nvideos into a hierarchical set of multi-scale discrete latent variables.\nCompared to pixels, this compressed latent space has dramatically reduced\ndimensionality, allowing us to apply scalable autoregressive generative models\nto predict video. In contrast to previous work that has largely emphasized\nhighly constrained datasets, we focus on very diverse, large-scale datasets\nsuch as Kinetics-600. We predict video at a higher resolution on unconstrained\nvideos, 256x256, than any other previous method to our knowledge. We further\nvalidate our approach against prior work via a crowdsourced human evaluation.\n"}
+{"abstract": "  Recently pre-trained multimodal models, such as CLIP, have received a surge\nof attention for their exceptional capabilities towards connecting images and\nnatural language. The textual representations in English can be desirably\ntransferred to multilingualism and support promising downstream multimodal\ntasks for different languages. Nevertheless, previous fairness discourse in\nvision-and-language learning mainly focuses on monolingual representational\nbiases, and rarely scrutinizes the principles of multilingual fairness in this\nmultimodal setting, where one language is equated to a group of individuals and\nimages provide the universal grounding for bridging different languages.\n  In this paper, we provide a nuanced understanding of individual fairness and\ngroup fairness by viewing language as the recipient of fairness notions. We\ndefine new fairness notions within multilingual context and analytically\narticulate that, pre-trained vision-and-language representations are\nindividually fair across languages but not guaranteed to group fairness.\nFurthermore, we conduct extensive experiments to explore the prevalent group\ndisparity across languages and protected groups including race, gender and age.\n"}
+{"abstract": "  Knowledge graph embedding techniques are key to making knowledge graphs\namenable to the plethora of machine learning approaches based on vector\nrepresentations. Link prediction is often used as a proxy to evaluate the\nquality of these embeddings. Given that the creation of benchmarks for link\nprediction is a time-consuming endeavor, most work on the subject matter uses\nonly a few benchmarks. As benchmarks are crucial for the fair comparison of\nalgorithms, ensuring their quality is tantamount to providing a solid ground\nfor developing better solutions to link prediction and ipso facto embedding\nknowledge graphs. First studies of benchmarks pointed to limitations pertaining\nto information leaking from the development to the test fragments of some\nbenchmark datasets. We spotted a further common limitation of three of the\nbenchmarks commonly used for evaluating link prediction approaches:\nout-of-vocabulary entities in the test and validation sets. We provide an\nimplementation of an approach for spotting and removing such entities and\nprovide corrected versions of the datasets WN18RR, FB15K-237, and YAGO3-10. Our\nexperiments on the corrected versions of WN18RR, FB15K-237, and YAGO3-10\nsuggest that the measured performance of state-of-the-art approaches is altered\nsignificantly with p-values <1%, <1.4%, and <1%, respectively. Overall,\nstate-of-the-art approaches gain on average absolute $3.29 \\pm 0.24\\%$ in all\nmetrics on WN18RR. This means that some of the conclusions achieved in previous\nworks might need to be revisited. We provide an open-source implementation of\nour experiments and corrected datasets at at\nhttps://github.com/dice-group/OOV-In-Link-Prediction.\n"}
+{"abstract": "  Our paper explores the game theoretic value of the 7-in-a-row game. We reduce\nthe problem to solving a finite board game, which we target using Proof Number\nSearch. We present a number of heuristic improvements to Proof Number Search\nand examine their effect within the context of this particular game. Although\nour paper does not solve the 7-in-a-row game, our experiments indicate that we\nhave made significant progress towards it.\n"}
+{"abstract": "  Memory bandwidth is strongly correlated to the complexity of the memory\naccess pattern of a running application. To improve memory performance of\napplications with irregular and/or unpredictable memory patterns, we need tools\nto analyze these patterns during application development. In this work, we\npresent a software tool for the analysis and visualization of memory access\npatterns. We perform memory tracing and profiling, we do data processing and\nfiltering, and we use visualization algorithms to produce three dimensional\ngraphs that describe the patterns both in space and in time. Finally, we\nevaluate our toolflow on a variety of applications.\n"}
+{"abstract": "  We present MeerKAT neutral hydrogen (HI) observations of the Fornax A group,\nthat is likely falling into the Fornax cluster for the first time. Our HI image\nis sensitive to 1.4 x 10$^{19}$ cm$^{-2}$ over 44.1 km s$^{-1}$, where we\ndetect HI in 10 galaxies and a total of 1.12 x 10$^{9}$ Msol of HI in the\nintra-group medium (IGM). We search for signs of pre-processing in the 12 group\ngalaxies with confirmed optical redshifts that reside within our HI image.\nThere are 9 galaxies that show evidence of pre-processing and we classify the\npre-processing status of each galaxy, according to their HI morphology and gas\n(atomic and molecular) scaling relations. Galaxies yet to experience\npre-processing have extended HI disks, a high HI content with a H$_2$-to-HI\nratio an order of magnitude lower than the median for their stellar mass.\nGalaxies currently being pre-processed display HI tails, truncated HI disks\nwith typical gas ratios. Galaxies in the advanced stages of pre-processing are\nHI deficient. If there is any HI, they have lost their outer HI disk and\nefficiently converted their HI to H$_2$, resulting in H$_2$-to-HI ratios an\norder of magnitude higher than the median for their stellar mass. The central,\nmassive galaxy in our group underwent a 10:1 merger 2 Gyr ago, and ejected 6.6\n- 11.2 x 10$^{8}$ Msol of HI that we detect as clouds and streams in the IGM,\nsome forming coherent structures up to 220 kpc in length. We also detect giant\n(100 kpc) ionised hydrogen (H$\\alpha$) filaments in the IGM, likely from cool\ngas being removed (and ionised) from an infalling satellite. The H$\\alpha$\nfilaments are situated within the hot halo of NGC 1316 and some regions contain\nHI. We speculate that the H$\\alpha$ and multiphase gas is supported by magnetic\npressure (possibly assisted by the AGN), such that the hot gas can condense and\nform HI that survives in the hot halo for cosmological timescales.\n"}
+{"abstract": "  Motivated by the possibility of observing the co-existence between magnetism\nand unconventional superconductivity in heavy-fermion Ce$_{1-x}$Sm$_x$CoIn$_5$\nalloys, we studied how the samarium substitution on the cerium site affects the\nmagnetic field-tuned-quantum criticality of stoicheometric CeCoIn$_5$ by\nperforming specific heat and resistivity measurements. By applying an external\nmagnetic field, we have observed Fermi-liquid to non-Fermi-liquid crossovers in\nthe temperature dependence of the electronic specific heat normalized by\ntemperature and of the resistivity. We obtained the magnetic-field-induced\nquantum critical point (QCP) by extrapolating to zero temperature the\ntemperature - magnetic field dependence at which the crossovers take place.\nFurthermore, a scaling analysis of the electronic specific heat is used to\nconfirm the existence of the QCP. We have found that the magnitude of the\nmagnetic-field-induced QCP decreases with increasing samarium concentration.\nOur analysis of heat capacity and resistivity data reveals a zero-field QCP for\n$x_\\textrm{cr} \\approx 0.15$, which falls inside the region where Sm ions\nantiferromagnetism and superconductivity co-exist.\n"}
+{"abstract": "  Demixing of multicomponent biomolecular systems via liquid-liquid phase\nseparation (LLPS) has emerged as a potentially unifying mechanism governing the\nformation of several membrane-less intracellular organelles (\"condensates\"),\nboth in the cytoplasm (e.g., stress granules) and in the nucleoplasm (e.g.,\nnucleoli). While both in vivo experiments and studies of synthetic systems\ndemonstrate that LLPS is strongly affected by the presence of a macromolecular\nelastic network, a fundamental understanding of the role of such networks on\nLLPS is still lacking. Here we show that, upon accounting for capillary forces\nresponsible for network expulsion, small-scale heterogeneity of the network,\nand its nonlinear mechanical properties, an intriguing picture of LLPS emerges.\nSpecifically, we predict that, in addition to the experimentally observed\ncavitated droplets which fully exclude the network, two other phases are\nthermodynamically possible: elastically arrested, size-limited droplets at the\nnetwork pore scale, and network-including macroscopic droplets. In particular,\npore size-limited droplets may emerge in chromatin networks, with implications\nfor structure and function of nucleoplasmic condensates.\n"}
+{"abstract": "  Label noise in multiclass classification is a major obstacle to the\ndeployment of learning systems. However, unlike the widely used\nclass-conditional noise (CCN) assumption that the noisy label is independent of\nthe input feature given the true label, label noise in real-world datasets can\nbe aleatory and heavily dependent on individual instances. In this work, we\ninvestigate the instance-dependent noise (IDN) model and propose an efficient\napproximation of IDN to capture the instance-specific label corruption.\nConcretely, noting the fact that most columns of the IDN transition matrix have\nonly limited influence on the class-posterior estimation, we propose a\nvariational approximation that uses a single-scalar confidence parameter. To\ncope with the situation where the mapping from the instance to its confidence\nvalue could vary significantly for two adjacent instances, we suggest using\ninstance embedding that assigns a trainable parameter to each instance. The\nresulting instance-confidence embedding (ICE) method not only performs well\nunder label noise but also can effectively detect ambiguous or mislabeled\ninstances. We validate its utility on various image and text classification\ntasks.\n"}
+{"abstract": "  Since language processing systems generally allocate/discard memory with\ncomplex reference relationships, including circular and indirect references,\ntheir implementation is often not trivial. Here, the allocated memory and the\nreferences can be abstracted to the labeled vertices and edges of a graph. And\nthere exists a graph rewriting language, a programming language or a\ncalculation model that can handle graph intuitively, safely and efficiently.\nTherefore, the implementation of a language processing system can be highly\nexpected as an application field of graph rewriting language. To show this, in\nthis research, we implemented G-machine, the virtual machine for lazy\nevaluation, in hypergraph rewriting language, HyperLMNtal.\n"}
+{"abstract": "  Multiple molecular lines are useful for studying the physical properties of\nmultiphase star-forming gas in different types of galaxies. We probe the\nmolecular gas throughout the disc of the spiral galaxy NGC~5033, hosting an\nactive galactic nucleus (AGN), using multiple low-$J$ CO lines [$^{12}$CO(1-0,\n2-1, 3-2 and $^{13}$CO(1-0, 2-1)] and dense gas tracers [HCN(1-0) and\nHCO$^{+}$(1-0)]. Firstly, we determine the ratios of the integrated intensity\nmaps and the ratio of intensities in position velocity diagrams. Secondly, we\nobtain the ratios of CO lines and high-density tracers at the centre; and\nthirdly, we model these line ratios using a radiative transfer code. Line ratio\ndiagnostics reveal that the south of the gaseous disc contains cooler gas than\nthe northern part, and the centre hosts warmer and less tenuous gas with a\nsimilar dense gas fraction compared to most galaxies of similar type. Our model\nresults mostly agree with the empirical ones in the sense that the central\nregion of NGC~5033 harbours warmer gas than that in the centres of normal\nspirals and lenticulars without showing AGN activity. Finally, the\nbeam-averaged total molecular gas mass and gas surface density along the\ngalaxy's major axis show a radial gradient, i.e. increasing from the outskirts\nup to the central region of size $1$~kpc where there is a depression in both\ngas mass and surface density.\n"}
+{"abstract": "  Electron spins are amongst the most coherent solid-state systems known,\nhowever, to be used in devices for quantum sensing and information processing\napplications, they must be typically placed near interfaces. Understanding and\nmitigating the impacts of such interfaces on the coherence and spectral\nproperties of electron spins is critical to realize such applications, but is\nalso challenging: inferring such data from single-spin studies requires many\nmeasurements to obtain meaningful results, while ensemble measurements\ntypically give averaged results that hide critical information. Here, we report\na comprehensive study of the coherence of near-surface bismuth donor spins in\n28-silicon at millikelvin temperatures. In particular, we use strain-induced\nfrequency shifts caused by a metallic electrode to make spatial maps of spin\ncoherence as a function of depth and position relative to the electrode. By\nmeasuring magnetic-field-insensitive clock transitions we separate magnetic\nnoise caused by surface spins from charge noise. Our results include\nquantitative models of the strain-split spin resonance spectra and extraction\nof paramagnetic impurity concentrations at the silicon surface. The interplay\nof these decoherence mechanisms for such near-surface electron spins is\ncritical for their application in quantum technologies, while the combination\nof the strain splitting and clock transition extends the coherence lifetimes by\nup to two orders of magnitude, reaching up to 300 ms at a mean depth of only\n100nm. The technique we introduce here to spatially map coherence in\nnear-surface ensembles is directly applicable to other spin systems of active\ninterest, such as defects in diamond, silicon carbide, and rare earth ions in\noptical crystals.\n"}
+{"abstract": "  This paper studies the traffic monitoring problem in a road network using a\nteam of aerial robots. The problem is challenging due to two main reasons.\nFirst, the traffic events are stochastic, both temporally and spatially.\nSecond, the problem has a non-homogeneous structure as the traffic events\narrive at different locations of the road network at different rates.\nAccordingly, some locations require more visits by the robots compared to other\nlocations. To address these issues, we define an uncertainty metric for each\nlocation of the road network and formulate a path planning problem for the\naerial robots to minimize the network's average uncertainty. We express this\nproblem as a partially observable Markov decision process (POMDP) and propose a\ndistributed and scalable algorithm based on deep reinforcement learning to\nsolve it. We consider two different scenarios depending on the communication\nmode between the agents (aerial robots) and the traffic management center\n(TMC). The first scenario assumes that the agents continuously communicate with\nthe TMC to send/receive real-time information about the traffic events. Hence,\nthe agents have global and real-time knowledge of the environment. However, in\nthe second scenario, we consider a challenging setting where the observation of\nthe aerial robots is partial and limited to their sensing ranges. Moreover, in\ncontrast to the first scenario, the information exchange between the aerial\nrobots and the TMC is restricted to specific time instances. We evaluate the\nperformance of our proposed algorithm in both scenarios for a real road network\ntopology and demonstrate its functionality in a traffic monitoring system.\n"}
+{"abstract": "  The joint use of multiple imaging modalities for medical image segmentation\nhas been widely studied in recent years. The fusion of information from\ndifferent modalities has demonstrated to improve the segmentation accuracy,\nwith respect to mono-modal segmentations, in several applications. However,\nacquiring multiple modalities is usually not possible in a clinical setting due\nto a limited number of physicians and scanners, and to limit costs and scan\ntime. Most of the time, only one modality is acquired. In this paper, we\npropose KD-Net, a framework to transfer knowledge from a trained multi-modal\nnetwork (teacher) to a mono-modal one (student). The proposed method is an\nadaptation of the generalized distillation framework where the student network\nis trained on a subset (1 modality) of the teacher's inputs (n modalities). We\nillustrate the effectiveness of the proposed framework in brain tumor\nsegmentation with the BraTS 2018 dataset. Using different architectures, we\nshow that the student network effectively learns from the teacher and always\noutperforms the baseline mono-modal network in terms of segmentation accuracy.\n"}
+{"abstract": "  Teleoperation provides a way for human operators to guide robots in\nsituations where full autonomy is challenging or where direct human\nintervention is required. It can also be an important tool to teach robots in\norder to achieve autonomous behaviour later on. The increased availability of\ncollaborative robot arms and Virtual Reality (VR) devices provides ample\nopportunity for development of novel teleoperation methods. Since robot arms\nare often kinematically different from human arms, mapping human motions to a\nrobot in real-time is not trivial. Additionally, a human operator might steer\nthe robot arm toward singularities or its workspace limits, which can lead to\nundesirable behaviour. This is further accentuated for the orchestration of\nmultiple robots. In this paper, we present a VR interface targeted to multi-arm\npayload manipulation, which can closely match real-time input motion. Allowing\nthe user to manipulate the payload rather than mapping their motions to\nindividual arms we are able to simultaneously guide multiple collaborative\narms. By releasing a single rotational degree of freedom, and by using a local\noptimization method, we can improve each arm's manipulability index, which in\nturn lets us avoid kinematic singularities and workspace limitations. We apply\nour approach to predefined trajectories as well as real-time teleoperation on\ndifferent robot arms and compare performance in terms of end effector position\nerror and relevant joint motion metrics.\n"}
+{"abstract": "  This Full Paper in the Research-To-Practice Category presents a long-term\nstudy about the effects of a student-centered course on communication and\nprofessional skills on students' thoughts, attitudes, and behavior. The course\nis offered at a European university as part of a computer science master's\nprogram. This paper shares the design and challenges of a longitudinal study\nthat reaches ten years behind and employs a mixed-methods approach. Besides\npresenting and interpreting the findings, we shed light on which features tend\nto stay on students' minds and impact their way of being and acting in society.\nMoreover, we suggest implications for the design and practice in comparable\ncourses to maximize constructive, sustainable effects, such as improved active\nlistening, presentation skills, and openness to other perspectives. These are\nessential (not only) for computer science professionals. Our findings suggest\nthat the course provided significant learning for the vast majority of\nrespondents, including aspects such as presenting while keeping the other side\nin mind, managing one's stress, and becoming less shy to speak up. All in all,\nwe aim to contribute an evidence-based source of motivation for instructors in\ntechnically focused curricula who hold a student-centered stance.\n"}
+{"abstract": "  Training convolutional neural networks with a Lipschitz constraint under the\n$l_{2}$ norm is useful for provable adversarial robustness, interpretable\ngradients, stable training, etc. While 1-Lipschitz networks can be designed by\nimposing a 1-Lipschitz constraint on each layer, training such networks\nrequires each layer to be gradient norm preserving (GNP) to prevent gradients\nfrom vanishing. However, existing GNP convolutions suffer from slow training,\nlead to significant reduction in accuracy and provide no guarantees on their\napproximations. In this work, we propose a GNP convolution layer called Skew\nOrthogonal Convolution (SOC) that uses the following mathematical property:\nwhen a matrix is {\\it Skew-Symmetric}, its exponential function is an {\\it\northogonal} matrix. To use this property, we first construct a convolution\nfilter whose Jacobian is Skew-Symmetric. Then, we use the Taylor series\nexpansion of the Jacobian exponential to construct the SOC layer that is\northogonal. To efficiently implement SOC, we keep a finite number of terms from\nthe Taylor series and provide a provable guarantee on the approximation error.\nOur experiments on CIFAR-10 and CIFAR-100 show that SOC allows us to train\nprovably Lipschitz, large convolutional neural networks significantly faster\nthan prior works while achieving significant improvements for both standard and\ncertified robust accuracies.\n"}
+{"abstract": "  The plants, Althaea officinalis, Tilia cordata and Psidium guaja have been\nused traditionally to treat respiratory infection symptoms. Flowers of A.\nofficinalis and leaves of T. cordata and P. guaja have been used to treat\ncough, sore throat, catarrh, oral and pharyngeal mucosa irritation. Therefore,\nthis study was designed to examine the antibacterial and antibiofilm effects of\nthese plants individually as well as in combination, as a formula against\nrespiratory infections causing pathogens. The tested pathogens were Extended\nSpectrum Beta-Lactamase producing Escherichia coli (ESBL), Beta-Lactamase\nproducing Escherichia coli (BL), Beta-Lactamase producing Klebsiella pneumoniae\n(BL), Beta-Lactamase producing Pseudomonas aeruginosa (BL), Enterobacter\ncloacae, and Beta-Lactamase producing Staphylococcus aureus (BL). The tested\nplants were extracted using ethanol and then fractionated using different\npolarity solvents (hexane, ethyl acetate and water). Disc diffusion and\nmicrodilution (Minimum Inhibitory Concentration) methods were used to evaluate\nthe antibacterial activity while the antibiofilm activity was tested using\ncrystal violet assay. The results showed that A. officinalis and T. cordata\nextracts and fractions exhibited weak antibacterial activity having MIC values\nranged from 6.25 to 12.5 mg/mL. P. guaja exhibited moderate antibacterial\nactivity with MIC values ranged from 6.25 to 1.56 mg/mL. Combination between\nthese plants extracts and fractions in equal proportion provides stronger\nantibacterial (with MIC values ranged from 6.25 to 0.8 mg/mL) and antibiofilm\nactivities (MBIC50 was 0.2 mg/mL). Therefore, this study provides a valuable\nscientific knowledge to support the use of plants in combination rather than\nindividually.\n"}
+{"abstract": "  The coronavirus disease (COVID-19) outbreak was declared a pandemic in March\n2020 and since then it has had a significant effect on all aspects of life.\nAlthough we live in an information era, we do not have accurate information\nabout this disease. Online social networks (OSNs) play a vital role in society,\nespecially people who do not have trust in the government would tend to have\nmore confidence in the evidence that is formed by social networks. The\nadvantages of OSNs in the COVID-19 era are clear. For instance, social media\nenables people to connect with each other without the need for real-world\nface-to-face social interaction. Social media networks also act as a collective\nintelligence in the absence of world leadership. Therefore, in this study,\nconsidering the phenomenon of information diffusion in OSNs, we focus on the\neffects of COVID-19 on user sentiment and show the user behavior trend during\nthe early months of the pandemic through mining and analyzing OSN data.\nMoreover, we propose a data-driven model to demonstrate how user sentiment\nchanges over a period of time and how OSNs help us to obtain information on\nuser behavior that is very important for the accurate prediction of future\nbehavior. For this purpose, this study uses tweet texts about COVID-19 and the\nrelated network structure to extract significant features, and then presents a\nmodel attempting to provide a more comprehensive real picture of current and\nfuture conditions.\n"}
+{"abstract": "  In this paper, we propose a unified SWIPT signal and its architecture design\nin order to take advantage of both single tone and multi-tone signaling by\nadjusting only the power allocation ratio of a unified signal. For this, we\ndesign a novel unified and integrated receiver architecture for the proposed\nunified SWIPT signaling, which consumes low power with an envelope detection.\nTo relieve the computational complexity of the receiver, we propose an adaptive\ncontrol algorithm by which the transmitter adjusts the communication mode\nthrough temporal convolutional network (TCN) based asymmetric processing. To\nthis end, the transmitter optimizes the modulation index and power allocation\nratio in short-term scale while updating the mode switching threshold in\nlong-term scale. We demonstrate that the proposed unified SWIPT system improves\nthe achievable rate under the self-powering condition of low-power IoT devices.\nConsequently it is foreseen to effectively deploy low-power IoT networks that\nconcurrently supply both information and energy wirelessly to the devices by\nusing the proposed unified SWIPT and adaptive control algorithm in place at the\ntransmitter side.\n"}
+{"abstract": "  Observation of high energy cosmic neutrinos by ICECUBE has ushered in a new\nera in exploring both cosmos and new physics beyond the Standard Model (SM). In\nthe standard picture, although mostly $\\nu_\\mu$ and $\\nu_e$ are produced in the\nsource, oscillation will produce $\\nu_\\tau$ {\\it en route}. Certain beyond SM\nscenarios, like interaction with ultralight DM can alter this picture. Thus,\nthe flavor composition of the cosmic neutrino flux can open up the possibility\nof exploring certain beyond the SM scenarios that are inaccessible otherwise.\nWe show that the $\\tau$ flavor holds a special place among the neutrino flavors\nin elucidating new physics. Interpreting the two anomalous events observed by\nANITA as $\\nu_\\tau$ events makes the tau flavor even more intriguing. We study\nhow the detection of the two tau events by ICECUBE constrains the interaction\nof the neutrinos with ultralight dark matter and discuss the implications of\nthis interaction for even higher energy cosmic neutrinos detectable by future\nradio telescopes such as ARA, ARIANNA and GRAND. We also revisit the $3+1$\nneutrino scheme as a solution to the two anomalous ANITA events and clarify a\nmisconception that exists in the literature about the evolution of high energy\nneutrinos in matter within the $3+1$ scheme with a possibility of scattering\noff nuclei. We show that the existing bounds on the flux of $\\nu_\\tau$ with\nenergy of EeV rules out this solution for the ANITA events. We show that the\n$3+1$ solution can be saved from both this bound and from the bound on the\nextra relativistic degrees of freedom in the early universe by turning on the\ninteraction of neutrinos with ultralight dark matter.\n"}
+{"abstract": "  The spectatorship experience for virtual reality (VR) games differs strongly\nfrom its non-VR precursor. When watching non-VR games on platforms such as\nTwitch, spectators just see what the player sees, as the physical interaction\nis mostly unimportant for the overall impression. In VR, the immersive\nfull-body interaction is a crucial part of the player experience. Hence,\ncontent creators, such as streamers, often rely on green screens or similar\nsolutions to offer a mixed-reality third-person view to disclose their\nfull-body actions. Our work compares the most popular realizations of the\nfirst-person and the third-person perspective in an online survey (N=217) with\nthree different VR games. Contrary to the current trend to stream in\nthird-person, our key result is that most viewers prefer the first-person\nversion, which they attribute mostly to the better focus on in-game actions and\nhigher involvement. Based on the study insights, we provide design\nrecommendations for both perspectives.\n"}
+{"abstract": "  Various planning-based know-how logics have been studied in the recent\nliterature. In this paper, we use such a logic to do know-how-based planning\nvia model checking. In particular, we can handle the higher-order epistemic\nplanning involving know-how formulas as the goal, e.g., find a plan to make\nsure p such that the adversary does not know how to make p false in the future.\nWe give a PTIME algorithm for the model checking problem over finite epistemic\ntransition systems and axiomatize the logic under the assumption of perfect\nrecall.\n"}
+{"abstract": "  The speculation that gravity is the key to solving the quantum measurement\nproblem has been alive for decades, without any convincing demonstration of a\nsolution. One necessary factor in the relevant proposals is that the\ngravitational energy of mutual interaction, which scales quadratically with the\nmass, facilitates the spontaneous collapse of the wavefunctions in spatially\nseparated superpositions. Relying on a simple physical input from\nelectrodynamics, supported by robust first principle calculations, we show that\nthe speculations connecting gravity and the hypothetical spontaneous collapse\nof the wavefunction are inconsistent and not tenable. The result suggests that\nthe gravitational solution to the problem of the collapse of the wavefunction\nbe put to rest.\n"}
+{"abstract": "  We performed a systematic search for X-ray bursts of the SGR J1935+2154 using\nthe Fermi Gamma-ray Burst Monitor continuous data dated from Jan 2013 to\nOctober 2021. Eight bursting phases, which consist of a total of 353 individual\nbursts, are identified. We further analyze the periodic properties of our\nsample using the Lomb-Scargle periodogram. The result suggests that those\nbursts exhibit a period of ~ 238 days with a ~ 63.2% duty cycle. Based on our\nanalysis, we further predict two upcoming active windows of the X-ray bursts.\nSince July 2021, the beginning date of our first prediction has been confirmed\nby the ongoing X-ray activities of the SGR J1935+2154.\n"}
+{"abstract": "  The Norton product is defined on each eigenspace of a distance regular graph\nby the orthogonal projection of the entry-wise product. The resulting algebra,\nknown as the Norton algebra, is a commutative nonassociative algebra that is\nuseful in group theory due to its interesting automorphism group. We provide a\nformula for the Norton product on each eigenspace of a Hamming graph using\nlinear characters. We construct a large subgroup of automorphisms of the Norton\nalgebra of a Hamming graph and completely describe the automorphism group in\nsome cases. We also show that the Norton product on each eigenspace of a\nHamming graph is as nonassociative as possible, except for some special cases\nin which it is either associative or equally as nonassociative as the so-called\ndouble minus operation previously studied by the author, Mickey, and Xu. Our\nresults restrict to the hypercubes and extend to the halved and/or folded\ncubes, the bilinear forms graphs, and more generally, all Cayley graphs of\nfinite abelian groups.\n"}
+{"abstract": "  Current approaches to design flood-sensitive infrastructure typically assume\na stationary rainfall distribution and neglect many uncertainties. These\nassumptions are inconsistent with observations that suggest intensifying\nextreme precipitation events and the uncertainties surrounding projections of\nthe coupled natural-human systems. Here we demonstrate a safety factor approach\nto designing urban infrastructure in a changing climate. Our results show that\nassuming climate stationarity and neglecting deep uncertainties can drastically\nunderestimate flood risks and lead to poor infrastructure design choices. We\nfind that climate uncertainty dominates the socioeconomic and engineering\nuncertainties that impact the hydraulic reliability in stormwater drainage\nsystems. We quantify the upfront costs needed to achieve higher hydraulic\nreliability and robustness against the deep uncertainties surrounding\nprojections of rainfall, surface runoff characteristics, and infrastructure\nlifetime. Depending on the location, we find that adding safety factors of 1.4\nto 1.7 to the standard stormwater pipe design guidance produces robust\nperformance to the considered deep uncertainties. The insights gained from this\nstudy highlight the need for updating traditional engineering design strategies\nto improve infrastructure reliability under socioeconomic and environmental\nchanges.\n"}
+{"abstract": "  The partially overlapped ferroelastic/martensitic and para-ferromagnetic\nphase transitions of a Ni$_{50.53}$Mn${33.65}$In$_{15.82}$ metamagnetic shape\nmemory alloy have been studied from calorimetric, magnetic and acoustic\nemission measurement. We have taken advantage of the existence of thermal\nhysteresis of the first order ferroelastic/martensitic phase transition\n($\\sim2.5$K) to discriminate the latent heat contribution $\\Delta$Ht = 7.21(15)\nkJ/kg and the specific heat contribution $\\Delta$Hc = 216(1) J/kg to the total\nexcess enthalpy of the phase transition. The specific heat was found to follow\na step-like behavior at this phase transition. The intermittent dynamics of the\nferroelastic/martensitic transition has been characterized as a series of\navalanches detected both from acoustic emission and calorimetric measurements.\nThe energy distribution of these avalanche events was found to follow a power\nlaw with a characteristic energy exponent $\\epsilon\\sim2$ which is in agreement\nwith the expected value for a system undergoing a symmetry change from cubic to\nmonoclinic. Finally, the critical behavior of the para-ferromagnetic austenite\nphase transition that takes place at $\\sim 311$K has been studied from the\nbehavior of the specific heat. A critical exponent $\\alpha\\sim0.09$ has been\nobtained, which has been shown to be in agreement with previous values reported\nfor Ni-Mn-Ga alloys but different from the critical divergence reported for\npure Ni.\n"}
+{"abstract": "  We study the causal interpretation of regressions on multiple dependent\ntreatments and flexible controls. Such regressions are often used to analyze\nrandomized control trials with multiple intervention arms, and to estimate\ninstitutional quality (e.g. teacher value-added) with observational data. We\nshow that, unlike with a single binary treatment, these regressions do not\ngenerally estimate convex averages of causal effects-even when the treatments\nare conditionally randomly assigned and the controls fully address omitted\nvariables bias. We discuss different solutions to this issue, and propose as a\nsolution anew class of efficient estimators of weighted average treatment\neffects.\n"}
+{"abstract": "  In partially observable reinforcement learning, offline training gives access\nto latent information which is not available during online training and/or\nexecution, such as the system state. Asymmetric actor-critic methods exploit\nsuch information by training a history-based policy via a state-based critic.\nHowever, many asymmetric methods lack theoretical foundation, and are only\nevaluated on limited domains. We examine the theory of asymmetric actor-critic\nmethods which use state-based critics, and expose fundamental issues which\nundermine the validity of a common variant, and its ability to address high\npartial observability. We propose an unbiased asymmetric actor-critic variant\nwhich is able to exploit state information while remaining theoretically sound,\nmaintaining the validity of the policy gradient theorem, and introducing no\nbias and relatively low variance into the training process. An empirical\nevaluation performed on domains which exhibit significant partial observability\nconfirms our analysis, and shows the unbiased asymmetric actor-critic converges\nto better policies and/or faster than symmetric actor-critic and standard\nasymmetric actor-critic baselines.\n"}
+{"abstract": "  Magnonics attracts increasing attention in the view of novel low-energy\ncomputation technologies based on spin waves. Recently, spin-wave propagation\nin longitudinally magnetized nano-scaled spin-wave conduits was demonstrated,\nproving the fundamental feasibility of magnonics at the sub-100 nm scale.\nTransversely magnetized nano-conduits, which are of great interest in this\nregard as they offer a large group velocity and a potentially chirality-based\nprotected transport of energy, have not yet been investigated due to their\ncomplex internal magnetic field distribution. Here, we present a study of\npropagating spin waves in a transversely magnetized nanoscopic yttrium iron\ngarnet conduit of 50 nm width. Space and time-resolved micro-focused\nBrillouin-light-scattering spectroscopy is employed to measure the spin-wave\ngroup velocity and decay length. A long-range spin-wave propagation is observed\nwith a decay length of up to (8.0+-1.5) {\\mu}m and a large spin-wave lifetime\nof up to (44.7+-9.1) ns. The results are supported with micromagnetic\nsimulations, revealing a single-mode dispersion relation in contrast to the\ncommon formation of localized edge modes for microscopic systems. Furthermore,\na frequency non-reciprocity for counter-propagating spin waves is observed in\nthe simulations and the experiment, caused by the trapezoidal cross-section of\nthe structure. The revealed long-distance spin-wave propagation on the\nnanoscale is particularly interesting for an application in spin-wave devices,\nallowing for long-distance transport of information in magnonic circuits, as\nwell as novel low-energy device architectures.\n"}
+{"abstract": "  We analyze equilibrium behavior and optimal policy within a\nSusceptible-Infected-Recovered epidemic model augmented with potentially\nundiagnosed agents who infer their health status and a social planner with\nimperfect enforcement of social distancing. We define and prove the existence\nof a perfect Bayesian Markov competitive equilibrium and contrast it with the\nefficient allocation subject to the same informational constraints. We identify\ntwo externalities, static (individual actions affect current risk of infection)\nand dynamic (individual actions affect future disease prevalence), and study\nhow they are affected by limitations on testing and enforcement. We prove that\na planner with imperfect enforcement will always wish to curtail activity, but\nthat its incentives vanish as testing becomes perfect. When a vaccine arrives\nfar into the future, the planner with perfect enforcement may encourage\nactivity before herd immunity. We find that lockdown policies have modest\nwelfare gains, whereas quarantine policies are effective even with imperfect\ntesting.\n"}
+{"abstract": "  The correct evaluation of gradients is at the cornerstone of the smoothed\nparticle hydrodynamics (SPH) technique. Using an integral approach to estimate\ngradients has proven to enhance accuracy substantially. Such approach retains\nthe Lagrangian structure of SPH equations and is fully conservative. But, in\npractice, it is difficult to make the Lagrangian formulation totally consistent\nto an exact partition of unit.\n  In this paper we study, among other things, the connection between the choice\nof the volume elements (VEs), which enters in the SPH summations, and the\naccuracy in the gradient estimation within the integral approach scheme (ISPH).\nA new variant of VEs are proposed which improve the partition of unit and is\nfully compatible with the Lagrangian formulation of SPH, including the grad-h\ncorrections. Using analytic considerations, simple static toy models in 1D, and\na few full 3D test cases, we show that any improvement in the partition of unit\nalso leads to a better calculation of gradients when the integral approach is\nused jointly. Additionally, we propose an easy-to-implement modification of the\nISPH scheme, which makes it more flexible and better suited to handle sharp\ndensity contrasts.\n  The ISPH code built with the proposed scheme has been validated with a good\nnumber of standard tests, some of them involving contact discontinuities. The\nperformance of the code was excellent in all of them, always showing that an\nimprovement in the partition of unit is not detrimental of the good\nconservation of energy, momentum, and entropy typical of Lagrangian schemes.\n"}
+{"abstract": "  Recently, sparse training methods have started to be established as a de\nfacto approach for training and inference efficiency in artificial neural\nnetworks. Yet, this efficiency is just in theory. In practice, everyone uses a\nbinary mask to simulate sparsity since the typical deep learning software and\nhardware are optimized for dense matrix operations. In this paper, we take an\northogonal approach, and we show that we can train truly sparse neural networks\nto harvest their full potential. To achieve this goal, we introduce three novel\ncontributions, specially designed for sparse neural networks: (1) a parallel\ntraining algorithm and its corresponding sparse implementation from scratch,\n(2) an activation function with non-trainable parameters to favour the gradient\nflow, and (3) a hidden neurons importance metric to eliminate redundancies. All\nin one, we are able to break the record and to train the largest neural network\never trained in terms of representational power -- reaching the bat brain size.\nThe results show that our approach has state-of-the-art performance while\nopening the path for an environmentally friendly artificial intelligence era.\n"}
+{"abstract": "  Debiased machine learning is a meta algorithm based on bias correction and\nsample splitting to calculate confidence intervals for functionals (i.e. scalar\nsummaries) of machine learning algorithms. For example, an analyst may desire\nthe confidence interval for a treatment effect estimated with a neural network.\nWe provide a nonasymptotic debiased machine learning theorem that encompasses\nany global or local functional of any machine learning algorithm that satisfies\na few simple, interpretable conditions. Formally, we prove consistency,\nGaussian approximation, and semiparametric efficiency by finite sample\narguments. The rate of convergence is root-n for global functionals, and it\ndegrades gracefully for local functionals. Our results culminate in a simple\nset of conditions that an analyst can use to translate modern learning theory\nrates into traditional statistical inference. The conditions reveal a new\ndouble robustness property for ill posed inverse problems.\n"}
+{"abstract": "  We present observations of ionized gas outflows in eleven z$ =1.39-2.59$\nradio-loud quasar host galaxies. Data was taken with the integral field\nspectrograph (IFS) OSIRIS and the adaptive optics system at the W.M. Keck\nObservatory targeting nebular emission lines (H$\\beta$, [OIII], H$\\alpha$,\n[NII] and [SII]) redshifted into the near-infrared (1-2.4 \\micron). Outflows\nwith velocities of 500 - 1700 km\\,s$^{-1}$ are detected in 10 systems on scales\nranging from $<1$ kpc to 10 kpc with outflow rates from 8-2400\nM$_\\odot$yr$^{-1}$. For five sources, the outflow momentum rates are 4-80 times\n$L_{AGN}$/c, consistent with outflows being driven by an energy conserving\nshock. The five other outflows are either driven by radiation pressure or an\nisothermal shock. The outflows are the dominant source of gas depletion, and we\nfind no evidence for star formation along the outflow paths. For eight objects,\nthe outflow paths are consistent with the orientation of the jets. Yet, given\nthe calculated pressures, we find no evidence of the jets currently doing work\non these galactic-scale ionized outflows. We find that galactic-scale feedback\noccurs well before galaxies establish a substantial fraction of their stellar\nmass, as expected from local scaling relationships.\n"}
+{"abstract": "  Task-driven semantic video/image coding has drawn considerable attention with\nthe development of intelligent media applications, such as license plate\ndetection, face detection, and medical diagnosis, which focuses on maintaining\nthe semantic information of videos/images. Deep neural network (DNN)-based\ncodecs have been studied for this purpose due to their inherent end-to-end\noptimization mechanism. However, the traditional hybrid coding framework cannot\nbe optimized in an end-to-end manner, which makes task-driven semantic fidelity\nmetric unable to be automatically integrated into the rate-distortion\noptimization process. Therefore, it is still attractive and challenging to\nimplement task-driven semantic coding with the traditional hybrid coding\nframework, which should still be widely used in practical industry for a long\ntime. To solve this challenge, we design semantic maps for different tasks to\nextract the pixelwise semantic fidelity for videos/images. Instead of directly\nintegrating the semantic fidelity metric into traditional hybrid coding\nframework, we implement task-driven semantic coding by implementing semantic\nbit allocation based on reinforcement learning (RL). We formulate the semantic\nbit allocation problem as a Markov decision process (MDP) and utilize one RL\nagent to automatically determine the quantization parameters (QPs) for\ndifferent coding units (CUs) according to the task-driven semantic fidelity\nmetric. Extensive experiments on different tasks, such as classification,\ndetection and segmentation, have demonstrated the superior performance of our\napproach by achieving an average bitrate saving of 34.39% to 52.62% over the\nHigh Efficiency Video Coding (H.265/HEVC) anchor under equivalent task-related\nsemantic fidelity.\n"}
+{"abstract": "  Future prospects for solar spectroscopy missions operating in the extreme\nultraviolet (EUV) and soft X-ray (SXR) wavelength ranges, 1.2-1600 Angstroms,\nare discussed. NASA is the major funder of Solar Physics missions, and brief\nsummaries of the opportunities for mission development under NASA are given.\nUpcoming major solar missions from other nations are also described. The\nmethods of observing the Sun in the two wavelength ranges are summarized with a\ndiscussion of spectrometer types, imaging techniques and detector options. The\nmajor spectral features in the EUV and SXR regions are identified, and then the\nupcoming instruments and concepts are summarized. The instruments range from\nlarge spectrometers on dedicated missions, to tiny, low-cost CubeSats launched\nthrough rideshare opportunities.\n"}
+{"abstract": "  Increasing the light scattering efficiency of nanostructured materials is\nbecoming an active field of research both in fundamental science and commercial\napplications. In this context, the challenge is to use inexpensive organic\nmaterials that come with a lower refractive index than currently used mineral\nnanoparticles, which are under increased scrutiny for their toxicity. Although\nseveral recent investigations have reported different disordered systems to\noptimise light scattering by morphological design, no systematic studies\ncomparing and explaining how different topological features contribute to\noptical properties have been reported yet. Using in silico synthesis and\nnumerical simulations, we demonstrate that the reflectance is primarily\ndetermined by second order statistics. While remaining differences are\nexplained by surface area and integrated mean curvature, an equal reflectance\ncan be obtained by further tuning the structural anisotropy. Our results\nsuggest a topological invariance for light scattering, demonstrating that any\ndisordered system can be optimised for whiteness.\n"}
+{"abstract": "  Massive multiple-input multiple-output (MIMO) systems are considered as one\nof the leading technologies employed in the next generations of wireless\ncommunication networks (5G), which promise to provide higher spectral\nefficiency, lower latency, and more reliability. Due to the massive number of\ndevices served by the base stations (BS) equipped with large antenna arrays,\nmassive-MIMO systems need to perform high-dimensional signal processing in a\nconsiderably short amount of time. The computational complexity of such data\nprocessing, while satisfying the energy and latency requirements, is beyond the\ncapabilities of the conventional widely-used digital electronics-based\ncomputing, i.e., Field-Programmable Gate Arrays (FPGAs) and\nApplication-Specific Integrated Circuits (ASICs). In this paper, the speed and\nlossless propagation of light is exploited to introduce a photonic computing\napproach that addresses the high computational complexity required by\nmassive-MIMO systems. The proposed computing approach is based on photonic\nimplementation of multiply and accumulate (MAC) operation achieved by\nbroadcast-and-weight (B&W) architecture. The B&W protocol is limited to real\nand positive values to perform MAC operations. In this work, preprocessing\nsteps are developed to enable the proposed photonic computing architecture to\naccept any arbitrary values as the input. This is a requirement for wireless\ncommunication systems that typically deal with complex values. Numerical\nanalysis shows that the performance of the wireless communication system is not\ndegraded by the proposed photonic computing architecture, while it provides\nsignificant improvements in time and energy efficiency for massive-MIMO systems\nas compared to the most powerful Graphics Processing Units (GPUs).\n"}
+{"abstract": "  Dark patterns have emerged as a set of methods to exploit cognitive biases to\ntrick users to make decisions that are more aligned with a third party than to\ntheir own. These patterns can have consequences that might range from\ninconvenience to global disasters. We present a case of a drug company and an\nelectronic medical record vendor who colluded to modify the medical record's\ninterface to induce clinicians to increase the prescription of extended-release\nopioids, a class of drugs that has a high potential for addiction and has\ncaused almost half a million additional deaths in the past two decades. Through\nthis case, we present the use and effects of dark patterns in healthcare,\ndiscuss the current challenges, and offer some recommendations on how to\naddress this pressing issue.\n"}
+{"abstract": "  A representation of general translation-invariant star products in the\nalgebra of M(C) = lim_N\\to \\infty M_N (C) is introduced which results in the\nMoyal-Weyl-Wigner quantization. It provides a matrix model for general\ntranslation-invariant noncommutative quantum field theories in terms of the\nnoncommutative calculus on differential graded algebras. Upon this machinery a\ncohomology theory, the so called star-cohomology, with groups H^k_*(C), is\nworked out which provides a cohomological framework to formulate general\ntranslation-invariant noncommutative quantum field theories based on the\nachievements for the commutative fields, and is comparable to the\nSeiberg-Witten map for the Moyal case. Employing the Chern-Weil theory via the\nintegral classes of H^k_*(Z) a noncommutative version of the Chern character is\ndefined as an equivariant form which contains topological information about the\ncorresponding translation-invariant noncommutative Yang-Mills theory. Thereby\nwe study the mentioned Yang-Mills theories with three types of actions of the\ngauge fields on the spinors, the ordinary, the inverse, and the adjoint action,\nand then some exact solutions for their anomalous behaviors are worked out via\nemploying the homotopic correlation on the integral classes of *-cohomology.\nFinally, the corresponding consistent anomalies are also derived from this\ntopological Chern character in the *-cohomology.\n"}
+{"abstract": "  Simple upper and lower bounds are established for the integral\n$\\int_0^x\\mathrm{e}^{-\\beta u}u^\\nu t_{\\mu,\\nu}(u)\\,\\mathrm{d}u$, where $x>0$,\n$0<\\beta<1$, $\\mu+\\nu>-2$, $\\mu-\\nu\\geq-3$, and $t_{\\mu,\\nu}(x)$ is the\nmodified Lommel function of the first kind. Our bounds complement and improve\non existing bounds for this integral, by either being sharper or increasing the\nrange of validity. Our bounds also generalise recent bounds for an integral\ninvolving the modified Struve function of the first kind, and in some cases\nmore a direct approach lead to sharper bounds when our general bounds are\nspecialised to the modified Struve case.\n"}
+{"abstract": "  Decentralized finance (DeFi) has become one of the most successful\napplications of blockchain and smart contracts. The DeFi ecosystem enables a\nwide range of crypto-financial activities, while the underlying smart contracts\noften contain bugs, with many vulnerabilities arising from the unforeseen\nconsequences of composing DeFi protocols together. In this paper, we propose a\nformal process-algebraic technique that models DeFi protocols in a\ncompositional manner to allow for efficient property verification. We also\nconduct a case study to demonstrate the proposed approach in analyzing the\ncomposition of two interacting DeFi protocols, namely, Curve and Compound.\nFinally, we discuss how the proposed modeling and verification approach can be\nused to analyze financial and security properties of interest.\n"}
+{"abstract": "  Motivated by applications in cybersecurity and epidemiology, we consider the\nproblem of detecting an abrupt change in the intensity of a Poisson process,\ncharacterised by a jump (non transitory change) or a bump (transitory change)\nfrom constant. We propose a complete study from the nonasymptotic minimax\ntesting point of view, when the constant baseline intensity is known or\nunknown. The question of minimax adaptation with respect to each parameter\n(height, location, length) of the change is tackled, leading to a comprehensive\noverview of the various minimax separation rate regimes. We exhibit three such\nregimes and identify the factors of the two phase transitions, by giving the\ncost of adaptation to each parameter. For each alternative hypothesis,\ndepending on the knowledge or not of each change parameter, we propose minimax\nor minimax adaptive tests based on linear statistics, close to CUSUM\nstatistics, or quadratic statistics more adapted to the L 2-distance considered\nin our minimax criteria and typically more powerful in practice, as our\nsimulation study shows. When the change location or length is unknown, our\nadaptive tests are constructed from a scan aggregation principle combined with\nBonferroni or min-p level correction, and a conditioning trick when the\nbaseline intensity is unknown.\n"}
+{"abstract": "  Abstract. We determine the Grothendieck rings of the category of\nfinite-dimensional modules over Queer Lie superalgebras via their rings of\ncharacters. In particular, we show that the ring of characters of the Queer Lie\nsupergroup $Q(n)$ is isomorphic to the ring of Laurent polynomials in\n$x_{1},\\ldots,x_{n}$ for which the evaluation $x_{n-1}=-x_{n}=t$ is independent\nof $t$. We thus complete the description of Grothendieck rings for all\nclassical Lie superalgebras.\n"}
+{"abstract": "  Feature alignment is an approach to improving robustness to distribution\nshift that matches the distribution of feature activations between the training\ndistribution and test distribution. A particularly simple but effective\napproach to feature alignment involves aligning the batch normalization\nstatistics between the two distributions in a trained neural network. This\ntechnique has received renewed interest lately because of its impressive\nperformance on robustness benchmarks. However, when and why this method works\nis not well understood. We investigate the approach in more detail and identify\nseveral limitations. We show that it only significantly helps with a narrow set\nof distribution shifts and we identify several settings in which it even\ndegrades performance. We also explain why these limitations arise by\npinpointing why this approach can be so effective in the first place. Our\nfindings call into question the utility of this approach and Unsupervised\nDomain Adaptation more broadly for improving robustness in practice.\n"}
+{"abstract": "  Elemental gallium can exist in several phases under ambient conditions. The\nstable $\\alpha$ phase has a superconducting transition temperature, $T_c$, of\n0.9~K. By contrast, the $T_c$ of the metastable $\\beta$ phase is around 6~K. To\nunderstand the significant improvement in $T_c$ in the $\\beta$ phase, we first\ncalculate the electronic structure, phonon dispersion, and the electron-phonon\ncoupling of gallium in the $\\alpha$ and $\\beta$ phase. Next, we solve the\nEliashberg equations to obtain the superconducting gaps and the transition\ntemperatures. Using these results, we relate the increased $T_c$ in the $\\beta$\nphase to structural differences between the phases that affect the electronic\nand phonon properties. The structure motif of the $\\alpha$ phase is Ga$_2$\ndimers, which form strong covalent bonds leading to bonding and antibonding\nstates that suppress the density of states at the Fermi level. The $\\beta$-Ga\nstructure consists of arrays of Ga chains that favor strong coupling between\nthe lattice vibrations and the electronic states near the Fermi level. The\nincreased density of states and strong coupling to the phonons for the\n$\\beta$-Ga chains compared to the $\\alpha$ Ga$_2$ dimers enhance\nsuperconductivity in the $\\beta$-Ga phase.\n"}
+{"abstract": "  We analyze the instability of a vortex column in a dilute polymer solution at\nlarge $\\textit{Re}$ and $\\textit{De}$ with $\\textit{El} =\n\\textit{De}/\\textit{Re}$, the elasticity number, being finite. Here,\n$\\textit{Re} = \\Omega_0 a^2/\\nu_s$ and $\\textit{De} = \\Omega_0 \\tau$ are,\nrespectively, the Reynolds and Deborah numbers based on the core angular\nvelocity ($\\Omega_0$), the radius of the column ($a$), the solvent-based\nkinematic viscosity ($\\nu_s = \\mu_s/\\rho$), and the polymeric relaxation time\n($\\tau$). The stability of small-amplitude perturbations in this distinguished\nlimit is governed by the elastic Rayleigh equation whose spectrum is\nparameterized by $ \\textrm{E} = \\textit{El}(1-\\beta)$, $\\beta$ being the ratio\nof the solvent to the solution viscosity. The neglect of the relaxation terms,\nin the said limit, implies that the polymer solution supports undamped elastic\nshear waves propagating relative to the base-state flow. The existence of these\nshear waves leads to multiple (three) continuous spectra associated with the\nelastic Rayleigh equation in contrast to just one for the original Rayleigh\nequation. Further, unlike the neutrally stable inviscid case, an instability of\nthe vortex column arises for finite E due to a pair of elastic shear waves\nbeing driven into a resonant interaction under the differential convection by\nthe irrotational shearing flow outside the core. An asymptotic analysis for the\nRankine profile shows the absence of an elastic threshold; although, for small\nE, the growth rate of the unstable discrete mode is transcendentally small,\nbeing O$(\\textrm{E}^2e^{-1/\\textrm{E}^{\\frac{1}{2}}})$. An accompanying\nnumerical investigation shows that the instability persists for smooth\nvorticity profiles, provided the radial extent of the transition region (from\nthe rotational core to the irrotational exterior) is less than a certain\n$\\textrm{E}$-dependent threshold.\n"}
+{"abstract": "  It is known that $G$-functions solutions of a linear differential equation of\norder 1 with coefficients in $\\overline{\\mathbb{Q}}(z)$, are algebraic (of a\nvery precise form). No general result is known when the order is 2. In this\npaper, we determine the form of a $G$-function solution of an inhomogeneous\nequation of order 1 with coefficients in $\\overline{\\mathbb{Q}}(z)$, as well as\nthat of a $G$-function $f$ of differential order 2 over\n$\\overline{\\mathbb{Q}}(z)$, and such that $f$ and $f'$ are algebraically\ndependent over $\\mathbb{C}(z)$. Our results apply more generally to\nNilsson-Gevrey arithmetic series of order 0 that encompass $G$-functions.\n"}
+{"abstract": "  Traditional aerial vehicles are usually custom-designed for specific tasks.\nAlthough they offer an efficient solution, they are not always able to adapt to\nchanges in the task specification, e.g., increasing the payload. This applies\nto quadrotors, having a maximum payload and only four controllable degrees of\nfreedom, limiting their adaptability to the task's variations. We propose a\nversatile modular robotic system that can increase its payload and degrees of\nfreedom by assembling heterogeneous modules; we call it H-ModQuad. It consists\nof cuboid modules propelled by quadrotors with tilted propellers that can\ngenerate forces in different directions. By connecting different types of\nmodules, an H-ModQuad can increase its controllable degrees of freedom from 4\nto 5 and 6. We model the general structure and propose three controllers, one\nfor each number of controllable degrees of freedom. We extend the concept of\nthe actuation ellipsoid to find the best reference orientation that can\nmaximize the performance of the structure. Our approach is validated with\nexperiments using actual robots, showing the independence of the translation\nand orientation of a structure.\n"}
+{"abstract": "  Wide dynamic range (WDR) image tone mapping is in high demand in many\napplications like film production, security monitoring, and photography. It is\nespecially crucial for mobile devices because most of the images taken today\nare from mobile phones, hence such technology is highly demanded in the\nconsumer market of mobile devices and is essential for a good customer\nexperience. However, high-quality and high-performance WDR image tone mapping\nimplementations are rarely found in the mobile-end. In this paper, we introduce\na high performance, mobile-end WDR image tone mapping implementation. It\nleverages the tone mapping results of multiple receptive fields and calculates\na suitable value for each pixel. The utilization of integral image and integral\nhistogram significantly reduce the required computation. Moreover, GPU parallel\ncomputation is used to increase the processing speed. The experimental results\nindicate that our implementation can process a high-resolution WDR image within\na second on mobile devices and produce appealing image quality.\n"}
+{"abstract": "  Teeth gestures become an alternative input modality for different situations\nand accessibility purposes. In this paper, we present TeethTap, a novel\neyes-free and hands-free input technique, which can recognize up to 13 discrete\nteeth tapping gestures. TeethTap adopts a wearable 3D printed earpiece with an\nIMU sensor and a contact microphone behind both ears, which works in tandem to\ndetect jaw movement and sound data, respectively. TeethTap uses a support\nvector machine to classify gestures from noise by fusing acoustic and motion\ndata, and implements K-Nearest-Neighbor (KNN) with a Dynamic Time Warping (DTW)\ndistance measurement using motion data for gesture classification. A user study\nwith 11 participants demonstrated that TeethTap could recognize 13 gestures\nwith a real-time classification accuracy of 90.9% in a laboratory environment.\nWe further uncovered the accuracy differences on different teeth gestures when\nhaving sensors on single vs. both sides. Moreover, we explored the activation\ngesture under real-world environments, including eating, speaking, walking and\njumping. Based on our findings, we further discussed potential applications and\npractical challenges of integrating TeethTap into future devices.\n"}
+{"abstract": "  Shape completion is the problem of completing partial input shapes such as\npartial scans. This problem finds important applications in computer vision and\nrobotics due to issues such as occlusion or sparsity in real-world data.\nHowever, most of the existing research related to shape completion has been\nfocused on completing shapes by learning a one-to-one mapping which limits the\ndiversity and creativity of the produced results. We propose a novel multimodal\nshape completion technique that is effectively able to learn a one-to-many\nmapping and generates diverse complete shapes. Our approach is based on the\nconditional Implicit MaximumLikelihood Estimation (IMLE) technique wherein we\ncondition our inputs on partial 3D point clouds. We extensively evaluate our\napproach by comparing it to various baselines both quantitatively and\nqualitatively. We show that our method is superior to alternatives in terms of\ncompleteness and diversity of shapes.\n"}
+{"abstract": "  We consider a minimal residual discretization of a simultaneous space-time\nvariational formulation of parabolic evolution equations. Under the usual `LBB'\nstability condition on pairs of trial- and test spaces we show quasi-optimality\nof the numerical approximations without assuming symmetry of the spatial part\nof the differential operator. Under a stronger LBB condition we show error\nestimates in an energy-norm which are independent of this spatial differential\noperator.\n"}
+{"abstract": "  Until recently, artificial neural networks were typically designed with a\nfixed network structure. Here, I argue that network structure is highly\nrelevant to function, and therefore neural networks should be livewired\n(Eagleman 2020): dynamically rewired to reflect relationships between higher\norder representations of the external environment identified by coincident\nactivations in individual neurons. I discuss how this approach may enable such\nnetworks to build compositional world models that operate on symbols and that\nachieve few-shot learning, capabilities thought by many to be critical to\nhuman-level cognition. Here, I also 1) discuss how such livewired neural\nnetworks maximize the information the environment provides to a model, 2)\nexplore evidence indicating that livewiring is implemented in the brain, guided\nby glial cells, 3) discuss how livewiring may give rise to the associative\nemergent behaviors of brains, and 4) suggest paths for future research using\nlivewired networks to understand and create human-like reasoning.\n"}
+{"abstract": "  Let $A$ and $B$ be rings, $U$ a $(B,A)$-bimodule and $T=\\begin{pmatrix}\nA&0\\\\U&B \\end{pmatrix}$ the triangular matrix ring. In this paper, several\nnotions in relative Gorenstein algebra over a triangular matrix ring are\ninvestigated. We first study how to construct w-tilting (tilting,\nsemidualizing) over $T$ using the corresponding ones over $A$ and $B$. We show\nthat when $U$ is relative (weakly) compatible we are able to describe the\nstructure of $G_C$-projective modules over $T$. As an application, we study\nwhen a morphism in $T$-Mod has a special $G_CP(T)$-precover and when the class\n$G_CP(T)$ is a special precovering class. In addition, we study the relative\nglobal dimension of $T$. In some cases, we show that it can be computed from\nthe relative global dimensions of $A$ and $B$. We end the paper with a\ncounterexample to a result that characterizes when a $T$-module has a finite\nprojective dimension.\n"}
+{"abstract": "  A dataset is a shred of crucial evidence to describe a task. However, each\ndata point in the dataset does not have the same potential, as some of the data\npoints can be more representative or informative than others. This unequal\nimportance among the data points may have a large impact in rehearsal-based\ncontinual learning, where we store a subset of the training examples (coreset)\nto be replayed later to alleviate catastrophic forgetting. In continual\nlearning, the quality of the samples stored in the coreset directly affects the\nmodel's effectiveness and efficiency. The coreset selection problem becomes\neven more important under realistic settings, such as imbalanced continual\nlearning or noisy data scenarios. To tackle this problem, we propose Online\nCoreset Selection (OCS), a simple yet effective method that selects the most\nrepresentative and informative coreset at each iteration and trains them in an\nonline manner. Our proposed method maximizes the model's adaptation to a target\ndataset while selecting high-affinity samples to past tasks, which directly\ninhibits catastrophic forgetting. We validate the effectiveness of our coreset\nselection mechanism over various standard, imbalanced, and noisy datasets\nagainst strong continual learning baselines, demonstrating that it improves\ntask adaptation and prevents catastrophic forgetting in a sample-efficient\nmanner.\n"}
+{"abstract": "  There is a striking similarity between Macdonald's reduced word formula and\nthe image of the Schubert class in the cohomology ring of the permutahedral\nvariety $\\mathrm{Perm}_n$ as computed by Klyachko. Toward understanding this\nbetter, we undertake an in-depth study of a $q$-deformation of the\n$\\mathbb{S}_n$-invariant part of the rational cohomology ring of\n$\\mathrm{Perm}_n$, which we call the $q$-Klyachko algebra. We uncover intimate\nlinks between expansions in the basis of squarefree monomials in this algebra\nand various notions in algebraic combinatorics, thereby connecting seemingly\nunrelated results by finding a common ground to study them.\n  Our main results are as follows. 1) A $q$-analog of divided symmetrization\n($q$-DS) using Yang-Baxter elements in the Hecke algebra. It is a linear form\nthat picks up coefficients in the squarefree basis. 2) A relation between\n$q$-DS and the ideal of quasisymmetric polynomials involving work of\nAval--Bergeron--Bergeron. 3) A family of polynomials in $q$ with nonnegative\nintegral coefficients that specialize to Postnikov's mixed Eulerian numbers\nwhen $q=1$. We refer to these new polynomials as remixed Eulerian numbers. For\n$q>0$, their normalized versions occur as probabilities in the internal\ndiffusion limited aggregation (IDLA) stochastic process. 4) A lift of\nMacdonald's reduced word identity in the $q$-Klyachko algebra. 5) The Schubert\nexpansion of the Chow class of the standard split Deligne--Lusztig variety in\ntype $A$, when $q$ is a prime power.\n"}
+{"abstract": "  We consider the homogenization of a Poisson problem or a Stokes system in a\nrandomly punctured domain with Dirichlet boundary conditions. We assume that\nthe holes are spherical and have random centres and radii. We impose that the\naverage distance between the balls is of size $\\eps$ and their average radius\nis $\\varepsilon^{\\alpha}$, $\\alpha \\in (1; 3)$. We prove that, as in the\nperiodic case [G. Allaire, ``Homogenization of the Navier-Stokes equations in\ndomains perforated with tiny holes. II''], the solutions converge to the\nsolution of Darcy's law (or its scalar analogue in the case of Poisson). In the\nsame spirit of [A. Giunti, R. H\\\"ofer and J. Vel\\'azquez, ``Homogenization of\nthe Poisson equation in randomly perforated domains under minimal assumptions\non the size of the holes''], we work under minimal conditions on the\nintegrability of the random radii. These ensure that the problem is\nwell-defined but do not rule out the onset of clusters of holes.\n"}
+{"abstract": "  Policymakers worldwide draft privacy laws that require trading-off between\nsafeguarding consumer privacy and preventing economic loss to companies that\nuse consumer data. However, little empirical knowledge exists as to how privacy\nlaws affect companies' performance. Accordingly, this paper empirically\nquantifies the effects of the enforcement of the EU's General Data Protection\nRegulation (GDPR) on online user behavior over time, analyzing data from 6,286\nwebsites spanning 24 industries during the 10 months before and 18 months after\nthe GDPR's enforcement in 2018. A panel differences estimator, with a synthetic\ncontrol group approach, isolates the short- and long-term effects of the GDPR\non user behavior. The results show that, on average, the GDPR's effects on user\nquantity and usage intensity are negative; e.g., the numbers of total visits to\na website decrease by 4.9% and 10% due to GDPR in respectively the short- and\nlong-term. These effects could translate into average revenue losses of $7\nmillion for e-commerce websites and almost $2.5 million for ad-based websites\n18 months after GDPR. The GDPR's effects vary across websites, with some\nindustries even benefiting from it; moreover, more-popular websites suffer\nless, suggesting that the GDPR increased market concentration.\n"}
+{"abstract": "  Developing the isolation and control of ultracold atomic systems to the level\nof single quanta has led to significant advances in quantum sensing, yet\ndemonstrating a quantum advantage in real world applications by harnessing\nentanglement remains a core task. Here, we realize a many-body quantum-enhanced\nsensor to detect weak displacements and electric fields using a large crystal\nof $\\sim 150$ trapped ions. The center of mass vibrational mode of the crystal\nserves as high-Q mechanical oscillator and the collective electronic spin as\nthe measurement device. By entangling the oscillator and the collective spin\nbefore the displacement is applied and by controlling the coherent dynamics via\na many-body echo we are able to utilize the delicate spin-motion entanglement\nto map the displacement into a spin rotation such that we avoid quantum\nback-action and cancel detrimental thermal noise. We report quantum enhanced\nsensitivity to displacements of $8.8 \\pm 0.4~$dB below the standard quantum\nlimit and a sensitivity for measuring electric fields of\n$240\\pm10~\\mathrm{nV}\\mathrm{m}^{-1}$ in $1$ second\n($240~\\mathrm{nV}\\mathrm{m}^{-1}/\\sqrt{\\mathrm{Hz}}$).\n"}
+{"abstract": "  Prescribed burns have been increasingly administered to forest management\nwith the assumption that they help reduce the risk of wildfires; however, this\nhypothesis has yet to be rigorously tested. We leverage the historical data of\nforest fires from multiple states to show that the sandpile model of\nself-organized criticality accurately represents the real-world incidence of\nfire by describing a negative linear relationship between the logarithm of fire\nsize and the logarithm of the fire incidence number of that size. We then\ninvestigate the association between the size of prescribed burn and the slope\nof the negative linear relationship which represents the relative risk of\ndestructive wildfires. The results demonstrate that increases in the area\nsubject to prescribed burning generally reduce the risk of destructive\nwildfires. This is consistent with the Florida data, which shows a trend in\nreduction of destructive wildfires as prescribed burns have been progressively\nintroduced to forest management. Our study justifies the application of the\nsandpile model to wildfire research and establishes a novel method of the\nanalysis of slope estimated from the sandpile model for facilitating the\ninvestigation of potential risk factors of destructive wildfires and the\ndevelopment of an optimal strategy for prescribed burning.\n"}
+{"abstract": "  To get channel state information (CSI) at a base station (BS), most of\nresearches on massive multiple-input multiple-output (MIMO) systems consider\ntime division duplexing (TDD) to get benefit from the uplink and downlink\nchannel reciprocity. Even in TDD, however, the BS still needs to transmit\ndownlink training signals, which are referred to as channel state information\nreference signals (CSI-RSs) in the 3GPP standard, to support spatial\nmultiplexing in practice. This is because there are many cases that the number\nof transmit antennas is less than the number of receive antennas at a user\nequipment (UE) due to power consumption and circuit complexity issues. Because\nof this mismatch, uplink sounding reference signals (SRSs) from the UE are not\nenough for the BS to obtain full downlink MIMO CSI. Therefore, after receiving\nthe downlink CSI-RSs, the UE needs to feed back quantized CSI to the BS using a\npre-defined codebook to support spatial multiplexing. In this paper, possible\napproaches to reconstruct full downlink MIMO CSI at the BS are proposed by\nexploiting both the SRS and quantized downlink CSI considering practical\nantenna structures with reduced downlink CSI-RS overhead. Numerical results\nshow that the spectral efficiencies by spatial multiplexing based on the\nproposed downlink MIMO CSI reconstruction techniques outperform the\nconventional methods solely based on the quantized CSI.\n"}
+{"abstract": "  Two gravitational wave events, i.e. GW200105 and GW200115, were observed by\nthe Advanced LIGO and Virgo detectors recently. In this work, we show that they\ncan be explained by a scenario of primordial black hole binaries that are\nformed in the early Universe. The merger rate predicted by such a scenario\ncould be consistent with the one estimated from LIGO and Virgo, even if\nprimordial black holes constitute a fraction of cold dark matter. The required\nabundance of primordial black holes is compatible with the existing upper\nlimits from microlensing, caustic crossing and cosmic microwave background\nobservations.\n"}
+{"abstract": "  Recent studies have shown that it is possible to characterize subject bias\nand variance in subjective assessment tests. Apparent differences among\nsubjects can, for the most part, be explained by random factors. Building on\nthat theory, we propose a subjective test design where three to four team\nmembers each rate the stimuli multiple times. The results are comparable to a\nhigh performing objective metric. This provides a quick and simple way to\nanalyze new technologies and perform pre-tests for subjective assessment.\n"}
+{"abstract": "  At the time of publication of H. Everett's Relative-State Formulation (1957)\nand DeWitt's Many-Worlds Interpretation (1970), quantum mechanics was available\nin a more modern and adequate version than the one used by these authors. We\nshow that with the more modern quantum theory, quantum measurement could have\nbeen analyzed along more conventional lines in a one-world cosmology. Bell\ncriticized the Everett-DeWitt theory quite sharply in 1987 but this seems not\nto have affected the acceptance of the old quantum mechanics as the framework\nfor analysis of measurement.\n"}
+{"abstract": "  We develop the general many-body perturbation theory for a superconducting\nquantum dot represented by a single-impurity Anderson model attached to\nsuperconducting leads. We build our approach on a thermodynamically consistent\nmean-field approximation with a two-particle self-consistency of the parquet\ntype. The two-particle self-consistency leading to a screening of the bare\ninteraction proves substantial for suppressing the spurious transitions of the\nHartree-Fock solution. We demonstrate that the magnetic field plays a\nfundamental role in the extension of the perturbation theory beyond the weakly\ncorrelated $0$-phase. It controls the critical behavior of the $0-\\pi$ quantum\ntransition, lifts the degeneracy in the $\\pi$-phase, where the limits to zero\ntemperature and zero magnetic field do not commute. The response to the\nmagnetic field is quite different in $0$- and $\\pi$-phases. While the magnetic\nsusceptibility vanishes in the $0$-phase it becomes of the Curie type and\ndiverges in the $\\pi$-phase at zero temperature.\n"}
+{"abstract": "  Training a large multilayer neural network can present many difficulties due\nto the large number of useless stationary points. These points usually attract\nthe minimization algorithm used during the training phase, which therefore\nresults inefficient. Extending some results proposed in literature for shallow\nnetworks, we propose the mathematical characterization of a class of such\nstationary points that arise in deep neural networks training. Availing such a\ndescription, we are able to define an incremental training algorithm that\navoids getting stuck in the region of attraction of these undesirable\nstationary points.\n"}
+{"abstract": "  Localization on 3D data is a challenging task for unmanned vehicles,\nespecially in long-term dynamic urban scenarios. Due to the generality and\nlong-term stability, the pole-like objects are very suitable as landmarks for\nunmanned vehicle localization in time-varing scenarios. In this paper, a\nlong-term LiDAR-only localization algorithm based on semantic cluster map is\nproposed. At first, the Convolutional Neural Network(CNN) is used to infer the\nsemantics of LiDAR point clouds. Combined with the point cloud segmentation,\nthe long-term static objects pole/trunk in the scene are extracted and\nregistered into a semantic cluster map. When the unmanned vehicle re-enters the\nenvironment again, the relocalization is completed by matching the clusters of\nthe local map with the clusters of the global map. Furthermore, the continuous\nmatching between the local and global maps stably outputs the global pose at\n2Hz to correct the drift of the 3D LiDAR odometry. The proposed approach\nrealizes localization in the long-term scenarios without maintaining the\nhigh-precision point cloud map. The experimental results on our campus dataset\ndemonstrate that the proposed approach performs better in localization accuracy\ncompared with the current state-of-the-art methods. The source of this paper is\navailable at: http://www.github.com/HITSZ-NRSL/long-term-localization.\n"}
+{"abstract": "  While the Berezinskii-Kosterlitz-Thouless transition (BKT) has been under\nintense scrutiny for decades, unambiguous experimental signatures in magnetic\nsystems remain elusive. Here, we investigate the interplay between electronic\nand magnetic degrees of freedom near the BKT transition. Focusing on a metal\nwith easy-plane ferromagnetic order, we establish a framework that accounts\nboth for the coupling between the charge current and the flow of topological\nmagnetic defects and for electron scattering on their inhomogeneous spin\ntexture. We show that electron scattering is responsible for a\ntemperature-dependent magnetoresistance effect scaling as the density of the\ntopological defects, which is expected to increase dramatically above the BKT\ntransition temperature. Our findings call for further experimental\ninvestigations.\n"}
+{"abstract": "  Anterior uveitis is the most common form of intraocular inflammation, and one\nof its main signs is the presence of white blood cells (WBCs) in the anterior\nchamber (AC). Clinically, the true composition of cells can currently only be\nobtained using AC paracentesis, an invasive procedure to obtain AC fluid\nrequiring needle insertion into the AC. We previously developed a spectroscopic\noptical coherence tomography (SOCT) analysis method to differentiate between\npopulations of RBCs and subtypes of WBCs, including granulocytes, lymphocytes\nand monocytes, both in vitro and in ACs of excised porcine eyes. We have shown\nthat different types of WBCs have distinct characteristic size distributions,\nextracted from the backscattered reflectance spectrum of individual cells using\nMie theory. Here, we further develop our method to estimate the composition of\nblood cell mixtures, both in vitro and in vivo. To do so, we estimate the size\ndistribution of unknown cell mixtures by fitting the distribution observed\nusing SOCT with a weighted combination of reference size distributions of each\nWBC type calculated using kernel density estimation. We validate the accuracy\nof our estimation in an in vitro study, by comparing our results for a given\nWBC sample mixture with the cellular concentrations measured by a hemocytometer\nand SOCT images before mixing. We also conducted a small in vivo quantitative\ncell mixture validation pilot study which demonstrates congruence between our\nmethod and AC paracentesis in two patients with uveitis. The SOCT based method\nappears promising to provide quantitative diagnostic information of cellular\nresponses in the ACs of patients with uveitis.\n"}
+{"abstract": "  In order to understand stellar evolution, it is crucial to efficiently\ndetermine stellar surface rotation periods. An efficient tool to automatically\ndetermine reliable rotation periods is needed when dealing with large samples\nof stellar photometric datasets. The objective of this work is to develop such\na tool. Random forest learning abilities are exploited to automate the\nextraction of rotation periods in Kepler light curves. Rotation periods and\ncomplementary parameters are obtained from three different methods: a wavelet\nanalysis, the autocorrelation function of the light curve, and the composite\nspectrum. We train three different classifiers: one to detect if rotational\nmodulations are present in the light curve, one to flag close binary or\nclassical pulsators candidates that can bias our rotation period determination,\nand finally one classifier to provide the final rotation period. We test our\nmachine learning pipeline on 23,431 stars of the Kepler K and M dwarf reference\nrotation catalog of Santos et al. (2019) for which 60% of the stars have been\nvisually inspected. For the sample of 21,707 stars where all the input\nparameters are provided to the algorithm, 94.2% of them are correctly\nclassified (as rotating or not). Among the stars that have a rotation period in\nthe reference catalog, the machine learning provides a period that agrees\nwithin 10% of the reference value for 95.3% of the stars. Moreover, the yield\nof correct rotation periods is raised to 99.5% after visually inspecting 25.2%\nof the stars. Over the two main analysis steps, rotation classification and\nperiod selection, the pipeline yields a global agreement with the reference\nvalues of 92.1% and 96.9% before and after visual inspection. Random forest\nclassifiers are efficient tools to determine reliable rotation periods in large\nsamples of stars. [abridged]\n"}
+{"abstract": "  This paper presents a compositional framework for the construction of\nsymbolic models for a network composed of a countably infinite number of\nfinite-dimensional discrete-time control subsystems. We refer to such a network\nas infinite network. The proposed approach is based on the notion of\nalternating simulation functions. This notion relates a concrete network to its\nsymbolic model with guaranteed mismatch bounds between their output behaviors.\nWe propose a compositional approach to construct a symbolic model for an\ninfinite network, together with an alternating simulation function, by\ncomposing symbolic models and alternating simulation functions constructed for\nsubsystems. Assuming that each subsystem is incrementally input-to-state stable\nand under some small-gain type conditions, we present an algorithm for orderly\nconstructing local symbolic models with properly designed quantization\nparameters. In this way, the proposed compositional approach can provide us a\nguideline for constructing an overall symbolic model with any desired\napproximation accuracy. A compositional controller synthesis scheme is also\nprovided to enforce safety properties on the infinite network in a\ndecentralized fashion. The effectiveness of our result is illustrated through a\nroad traffic network consisting of infinitely many road cells.\n"}
+{"abstract": "  A well-known strategy for building effective preconditioners for higher-order\ndiscretizations of some PDEs, such as Poisson's equation, is to leverage\neffective preconditioners for their low-order analogs. In this work, we show\nthat high-quality preconditioners can also be derived for the Taylor-Hood\ndiscretization of the Stokes equations in much the same manner. In particular,\nwe investigate the use of geometric multigrid based on the $\\boldsymbol{\n\\mathbb{Q}}_1iso\\boldsymbol{ \\mathbb{Q}}_2/ \\mathbb{Q}_1$ discretization of the\nStokes operator as a preconditioner for the $\\boldsymbol{\n\\mathbb{Q}}_2/\\mathbb{Q}_1$ discretization of the Stokes system. We utilize\nlocal Fourier analysis to optimize the damping parameters for Vanka and\nBraess-Sarazin relaxation schemes and to achieve robust convergence. These\nresults are then verified and compared against the measured multigrid\nperformance. While geometric multigrid can be applied directly to the\n$\\boldsymbol{ \\mathbb{Q}}_2/\\mathbb{Q}_1$ system, our ultimate motivation is to\napply algebraic multigrid within solvers for $\\boldsymbol{\n\\mathbb{Q}}_2/\\mathbb{Q}_1$ systems via the $\\boldsymbol{\n\\mathbb{Q}}_1iso\\boldsymbol{ \\mathbb{Q}}_2/ \\mathbb{Q}_1$ discretization, which\nwill be considered in a companion paper.\n"}
+{"abstract": "  Given a projective smooth curve $X$ over any field $k$ of characteristic not\n$2$, we define the global $\\mathbb{A}^1$ degree of a finite morphism of smooth\ncurves $f: X \\to \\mathbb{P}^1_k$ satisfying certain conditions, a\ngeneralization of Morel's construction of $\\mathbb{A}^1$-Brouwer degree of a\nmorphism $f: \\mathbb{P}^1_k \\to \\mathbb{P}^1_k$ as well as Kass and\nWickelgren's construction of Euler numbers of global sections. We then prove\nthat under certain conditions on $N$, the global $\\mathbb{A}^1$ degrees of\ncovering maps between modular curves $X_0(N) \\to X(1)$, $X_1(N) \\to X(1)$, and\n$X(N) \\to X(1)$ are sums of hyperbolic elements $\\langle 1 \\rangle + \\langle -1\n\\rangle$ in the Grothendieck-Witt ring $\\operatorname{GW}(k)$ for any field $k$\nwhose characteristic is coprime to $N$ and the pullback of\n$\\mathscr{O}_{\\operatorname{P}^1}(1)$ is relatively orientable.\n"}
+{"abstract": "  Spectral properties of many finite convolution integral operators have been\nunderstood by finding differential operators that commute with them. In this\npaper we compile a complete list of such commuting pairs, extending previous\nwork to complex-valued and non self-adjoint operators. In addition, we\nintroduce a new kind of commutation relation, which we call sesquicommutation,\nthat also has implications for the spectral properties of the integral\noperator. In this case we also compute a complete list of sequicommuting pairs\nof integral and differential operators.\n"}
+{"abstract": "  Here, we conducted a photometric and astrometric study of two open stellar\nclusters Dolidze-18 and Ruprecht-70, which have not been photometrically\nstudied before. The most important thing for using Gaia (DR2) database lies in\nthe positions, parallax, and proper motions, which make us split cluster\nmembers from the field ones and get precise astrophysical parameters. On\nstudying the radial density profiles of these clusters, the actual sizes are\nestimated and found larger using Gaia. From the color-magnitude diagrams and\ntheoretical isochrones, we simultaneously determined the ages, distance moduli,\nand reddening of the two clusters. However, considering the parallaxes of Gaia\n(DR2) for the cluster members, we calculated the cluster distance and confirmed\nwhat we obtained from the color-magnitude diagram. Then, the Cartesian\ngalactocentric coordinates (Xo, Yo, Zo), and the distances from the galactic\ncenter (Rg) were also estimated. According to the luminosity and mass\nfunctions, the total luminosity and total mass of the clusters are estimated.\nOur study shows that Ruprecht-70 is recently dynamically relaxed, while Dolidze\n18 is not relaxed yet.\n"}
+{"abstract": "  We develop a theory of multiplicities and mixed multiplicities of\nfiltrations, extending the theory for filtrations of $m$-primary ideals to\narbitrary (not necessarily Noetherian) filtrations. The mixed multiplicities of\n$r$ filtrations on an analytically unramified local ring $R$ come from the\ncoefficients of a suitable homogeneous polynomial in $r$ variables of degree\nequal to the dimension of the ring, analogously to the classical case of the\nmixed multiplicities of $m$-primary ideals in a local ring. We prove that the\nMinkowski inequalities hold for arbitrary filtrations. The characterization of\nequality in the Minkowski inequality for m-primary ideals in a local ring by\nTeissier, Rees and Sharp and Katz does not extend to arbitrary filtrations, but\nwe show that they are true in a large and important subcategory of filtrations.\nWe define divisorial and bounded filtrations. The filtration of powers of a\nfixed ideal is a bounded filtration, as is a divisorial filtration. We show\nthat in an excellent local domain, the characterization of equality in the\nMinkowski equality is characterized by the condition that the integral closures\nof suitable Rees like algebras are the same, strictly generalizing the theorem\nof Teissier, Rees and Sharp and Katz. We also prove that a theorem of Rees\ncharacterizing the inclusion of ideals with the same multiplicity generalizes\nto bounded filtrations in excellent local domains. We give a number of other\napplications, extending classical theorems for ideals.\n"}
+{"abstract": "  Aims. We present observations of the first coronal mass ejection (CME)\nobserved at the Solar Orbiter spacecraft on April 19, 2020, and the associated\nForbush decrease (FD) measured by its High Energy Telescope (HET). This CME is\na multispacecraft event also seen near Earth the next day. Methods. We\nhighlight the capabilities of HET for observing small short-term variations of\nthe galactic cosmic ray count rate using its single detector counters. The\nanalytical ForbMod model is applied to the FD measurements to reproduce the\nForbush decrease at both locations. Input parameters for the model are derived\nfrom both in situ and remote-sensing observations of the CME. Results. The very\nslow (~350 km/s) stealth CME caused a FD with an amplitude of 3 % in the\nlow-energy cosmic ray measurements at HET and 2 % in a comparable channel of\nthe Cosmic Ray Telescope for the Effects of Radiation (CRaTER) on the Lunar\nReconnaissance Orbiter, as well as a 1 % decrease in neutron monitor\nmeasurements. Significant differences are observed in the expansion behavior of\nthe CME at different locations, which may be related to influence of the\nfollowing high speed solar wind stream. Under certain assumptions, ForbMod is\nable to reproduce the observed FDs in low-energy cosmic ray measurements from\nHET as well as CRaTER, but with the same input parameters, the results do not\nagree with the FD amplitudes at higher energies measured by neutron monitors on\nEarth. We study these discrepancies and provide possible explanations.\nConclusions. This study highlights that the novel measurements of the Solar\nOrbiter can be coordinated with other spacecraft to improve our understanding\nof space weather in the inner heliosphere. Multi-spacecraft observations\ncombined with data-based modeling are also essential to understand the\npropagation and evolution of CMEs as well as their space weather impacts.\n"}
+{"abstract": "  This paper presents flexible storage codes, a class of error-correcting codes\nthat can recover information from a flexible number of storage nodes. As a\nresult, one can make a better use of the available storage nodes in the\npresence of unpredictable node failures and reduce the data access latency. Let\nus assume a storage system encodes $k\\ell$ information symbols over a finite\nfield $\\mathbb{F}$ into $n$ nodes, each of size $\\ell$ symbols. The code is\nparameterized by a set of tuples $\\{(R_j,k_j,\\ell_j): 1 \\le j \\le a\\}$,\nsatisfying $k_1\\ell_1=k_2\\ell_2=...=k_a\\ell_a$ and $k_1>k_2>...>k_a = k,\n\\ell_a=\\ell$, such that the information symbols can be reconstructed from any\n$R_j$ nodes, each node accessing $\\ell_j$ symbols. In other words, the code\nallows a flexible number of nodes for decoding to accommodate the variance in\nthe data access time of the nodes. Code constructions are presented for\ndifferent storage scenarios, including LRC (locally recoverable) codes, PMDS\n(partial MDS) codes, and MSR (minimum storage regenerating) codes. We analyze\nthe latency of accessing information and perform simulations on Amazon clusters\nto show the efficiency of presented codes.\n"}
+{"abstract": "  Correlations between a system and its environment lead to errors in an open\nquantum system. Detecting those correlations would be valuable for avoiding\nand/or correcting those errors. Here we show that we can detect correlations by\nonly measuring the system itself if we know the cause of the interaction\nbetween the two, for example in the case of a dipole-dipole interaction. We\ninvestigate the unitary $U$ which is associated with the exchange Hamiltonian\nand examine the ability to detect initial correlations between a system and its\nenvironment for various types of initial states. The states we select are\nmotivated by realistic experimental conditions and we provide bounds for when\nwe can state with certainty that there are initial system-environment\ncorrelations given experimental data.\n"}
+{"abstract": "  This open-source book represents our attempt to make deep learning\napproachable, teaching readers the concepts, the context, and the code. The\nentire book is drafted in Jupyter notebooks, seamlessly integrating exposition\nfigures, math, and interactive examples with self-contained code. Our goal is\nto offer a resource that could (i) be freely available for everyone; (ii) offer\nsufficient technical depth to provide a starting point on the path to actually\nbecoming an applied machine learning scientist; (iii) include runnable code,\nshowing readers how to solve problems in practice; (iv) allow for rapid\nupdates, both by us and also by the community at large; (v) be complemented by\na forum for interactive discussion of technical details and to answer\nquestions.\n"}
+{"abstract": "  We present a ground-based optical transmission spectrum for the warm\nSaturn-mass exoplanet WASP-110b from two transit observations made with the\nFOcal Reducer and Spectrograph (FORS2) on the Very Large Telescope (VLT). The\nspectrum covers the wavelength range from 4000 to 8333\\AA, which is binned in\n46 transit depths measured to an averaged precision of 220 parts per million\n(ppm) over an averaged 80\\AA~bin for a Vmag=12.8 star. The measured transit\ndepths are unaffected by a dilution from a close A-type field dwarf, which was\nfully resolved. The overall main characteristic of the transmission spectrum is\nan increasing radius with wavelength and a lack of the theoretically predicted\npressure-broadened sodium and potassium absorption features for a cloud-free\natmosphere. We analyze archival high-resolution optical spectroscopy and find\nevidence for low to moderate activity of the host star, which we take into\naccount in the atmospheric retrieval analysis. Using the AURA retrieval code,\nwe find that the observed transmission spectrum can be best explained by a\ncombination of unocculted stellar faculae and a cloud deck. Transmission\nspectra of cloud-free and hazy atmospheres are rejected at a high confidence.\nWith a possible cloud deck at its terminator, WASP-110b joins the increasing\npopulation of irradiated hot-Jupiter exoplanets with cloudy atmospheres\nobserved in transmission.\n"}
+{"abstract": "  Equivariant neural networks have been successful in incorporating various\ntypes of symmetries, but are mostly limited to vector representations of\ngeometric objects. Despite the prevalence of higher-order tensors in various\napplication domains, e.g. in quantum chemistry, equivariant neural networks for\ngeneral tensors remain underexplored. Previous strategies for learning\nequivariant functions on tensors mostly rely on expensive tensor factorization\nwhich is not scalable when the dimensionality of the problem becomes large. In\nthis work, we propose unitary $N$-body tensor equivariant neural network\n(UNiTE), an architecture for a general class of symmetric tensors called\n$N$-body tensors. The proposed neural network is equivariant with respect to\nthe actions of a unitary group, such as the group of 3D rotations. Furthermore,\nit has a linear time complexity with respect to the number of non-zero elements\nin the tensor. When applied to quantum chemistry, UNiTE in combination with a\nlow-cost physics-based molecular representation outperforms state-of-the-art\nmachine learning methods on multiple benchmarks. Finally, we show that UNiTE\nachieves a robust zero-shot generalization performance on diverse down stream\nchemistry tasks, while being three orders of magnitude faster than conventional\nnumerical methods with competitive accuracy.\n"}
+{"abstract": "  Traffic light management is a broad subject with various papers published\nthat put forth algorithms to efficiently manage traffic using traffic lights.\nTwo such algorithms are the OAF (oldest arrival first) and ITLC (intelligent\ntraffic light controller) algorithms. However, many traffic light algorithms do\nnot consider future traffic flow and therefore cannot mitigate traffic in such\na way as to reduce future traffic in the present. This paper presents the Edge\nLoad Management and Optimization through Pseudoflow Prediction (ELMOPP)\nalgorithm, which aims to solve problems detailed in previous algorithms;\nthrough machine learning with nested long short-term memory (NLSTM) modules and\ngraph theory, the algorithm attempts to predict the near future using past data\nand traffic patterns to inform its real-time decisions and better mitigate\ntraffic by predicting future traffic flow based on past flow and using those\npredictions to both maximize present traffic flow and decrease future traffic\ncongestion. Furthermore, while ITLC and OAF require the use of GPS\ntransponders; and GPS, speed sensors, and radio, respectively, ELMOPP only uses\ntraffic light camera footage, something that is almost always readily available\nin contrast to GPS and speed sensors. ELMOPP was tested against the ITLC and\nOAF traffic management algorithms using a simulation modeled after the one\npresented in the ITLC paper, a single-intersection simulation, and the\ncollected data supports the conclusion that ELMOPP statistically significantly\noutperforms both algorithms in throughput rate, a measure of how many vehicles\nare able to exit inroads every second.\n"}
+{"abstract": "  We consider abstraction-based design of output-feedback controllers for\nnon-linear dynamical systems against specifications over state-based predicates\nin linear-time temporal logic (LTL). In this context, our contribution is\ntwo-fold: (I) we generalize feedback-refinement relations for abstraction-based\noutput-feedback control to systems with arbitrary predicate and observation\nmaps, and (II) we introduce a new algorithm for the synthesis of abstract\noutput-feedback controllers w.r.t. LTL specifications over unobservable\nstate-based predicates.\n  Our abstraction-based output-feedback controller synthesis algorithm consists\nof two steps. First, we compute a finite state abstraction of the original\nsystem using existing techniques. This process typically leads to an abstract\nsystem with non-deterministic predicate and observation maps which are not\nnecessarily related to each other. Second, we introduce an algorithm to compute\nan output-feedback controller for such abstract systems. Our algorithm is\ninspired by reactive synthesis under partial observation and utilizes bounded\nsynthesis.\n"}
+{"abstract": "  We study solution sensitivity for nonlinear programs (NLPs) whose structures\nare induced by graphs. These NLPs arise in many applications such as dynamic\noptimization, stochastic optimization, optimization with partial differential\nequations, and network optimization. We show that for a given pair of nodes,\nthe sensitivity of the primal-dual solution at one node against a data\nperturbation at the other node decays exponentially with respect to the\ndistance between these two nodes on the graph. In other words, the solution\nsensitivity decays as one moves away from the perturbation point. This result,\nwhich we call exponential decay of sensitivity, holds under the strong\nsecond-order sufficiency condition and the linear independence constraint\nqualification. We also present conditions under which the decay rate remains\nuniformly bounded; this allows us to characterize the sensitivity behavior of\nNLPs defined over subgraphs of infinite graphs. The theoretical developments\nare illustrated with numerical examples.\n"}
+{"abstract": "  We investigate the geometry of $C(K,X)$ and $\\ell_{\\infty}(X)$ spaces through\ncomplemented subspaces of the form $\\left(\\bigoplus_{i\\in\n\\varGamma}X_i\\right)_{c_0}$. Concerning the geometry of $C(K,X)$ spaces we\nextend some results of D. Alspach and E. M. Galego from \\cite{AlspachGalego}.\nOn $\\ell_{\\infty}$-sums of Banach spaces we prove that if $\\ell_{\\infty}(X)$\nhas a complemented subspace isomorphic to $c_0(Y)$, then, for some $n \\in\n\\mathbb{N}$, $X^n$ has a subspace isomorphic to $c_0(Y)$. We further prove the\nfollowing:\n  (1) If $C(K)\\sim c_0(C(K))$ and $C(L)\\sim c_0(C(L))$ and\n$\\ell_{\\infty}(C(K))\\sim \\ell_{\\infty}(C(L))$, then $K$ and $L$ have the same\ncardinality.\n  (2) If $K_1$ and $K_2$ are infinite metric compacta, then\n$\\ell_{\\infty}(C(K_1))\\sim \\ell_{\\infty}(C(K_2))$ if and only if $C(K_1)$ is\nisomorphic to $C(K_2)$.\n"}
+{"abstract": "  We analyse the forward error in the floating point summation of real numbers,\nfrom algorithms that do not require recourse to higher precision or better\nhardware. We derive informative explicit expressions, and new deterministic and\nprobabilistic bounds for errors in three classes of algorithms: general\nsummation,shifted general summation, and compensated (sequential) summation.\nOur probabilistic bounds for general and shifted general summation hold to all\norders. For compensated summation, we also present deterministic and\nprobabilistic first and second order bounds, with a first order bound that\ndiffers from existing ones. Numerical experiments illustrate that the bounds\nare informative and that among the three algorithm classes, compensated\nsummation is generally the most accurate method.\n"}
+{"abstract": "  We discuss equilibration process in expanding universes as compared to the\nthermalization process in Minkowski space--time. The final goal is to answer\nthe following question: Is the equilibrium reached before the rapid expansion\nstops and quantum effects have a negligible effect on the background geometry\nor stress--energy fluxes in a highly curved early Universe have strong effects\non the expansion rate and the equilibrium is reached only after the drastic\ndecrease of the space--time curvature? We argue that consideration of more\ngeneric non--invariant states in theories with invariant actions is a necessary\ningredient to understand quantum field dynamics in strongly curved backgrounds.\nWe are talking about such states in which correlation functions are not\nfunctions of such isometry invariants as geodesic distances, while having\ncorrect UV behaviour. The reason to consider such states is the presence of IR\nsecular memory effects for generic time dependent backgrounds, which are\ntotally absent in equilibrium. These effects strongly affect the destiny of\nobservables in highly curved space--times.\n"}
+{"abstract": "  Quantum resource theory formulations of thermodynamics offer a versatile tool\nfor the study of fundamental limitations to the efficiency of physical\nprocesses, independently of the microscopic details governing their dynamics.\nDespite the ubiquitous presence of non-Markovian dynamics in open quantum\nsystems at the nanoscale, rigorous proofs of their beneficial effects on the\nefficiency of quantum dynamical processes at the bio-molecular level have not\nbeen reported yet. Here we combine the resource theory of athermality with\nconcepts from the theory of divisibility classes for quantum channels, to prove\nthat memory effects can increase the efficiency of photoisomerization to levels\nthat are not achievable under a purely thermal Markovian (i.e. memoryless)\nevolution. This provides rigorous evidence that memory effects can provide a\nresource in biological quantum dynamics, and, more generally, quantum\nthermodynamics at the nanoscale.\n"}
+{"abstract": "  Let $\\mathcal{T}$ be a rooted tree endowed with the natural partial order\n$\\preceq$. Let $(Z(v))_{v\\in \\mathcal{T}}$ be a sequence of independent\nstandard Gaussian random variables and let $\\alpha = (\\alpha_k)_{k=1}^\\infty$\nbe a sequence of real numbers with $\\sum_{k=1}^\\infty \\alpha_k^2<\\infty$. Set\n$\\alpha_0 =0$ and define a Gaussian process on $\\mathcal{T}$ in the following\nway: \\[ G(\\mathcal{T}, \\alpha; v): = \\sum_{u\\preceq v} \\alpha_{|u|} Z(u), \\quad\nv \\in \\mathcal{T}, \\] where $|u|$ denotes the graph distance between the vertex\n$u$ and the root vertex. Under mild assumptions on $\\mathcal{T}$, we obtain a\nnecessary and sufficient condition for the almost sure boundedness of the above\nGaussian process. Our condition is also necessary and sufficient for the almost\nsure uniform convergence of the Gaussian process $G(\\mathcal{T}, \\alpha; v)$\nalong all rooted geodesic rays in $\\mathcal{T}$.\n"}
+{"abstract": "  We study the effect of combining spin fluctuations and forward scattering\nelectron-phonon ({\\eph}) coupling on the superconductivity in the\nFeSe/SrTiO$_3$ system modeled by a phenomenological two-band Hubbard model with\nlong-range {\\eph} interactions. We treat the electron and phonon degrees of\nfreedom on an equal footing using a \\emph{fully} self-consistent FLEX plus\nMigdal-Eliashberg calculation, which includes a self-consistent determination\nof the spin fluctuation spectrum. Based on FeSe monolayers, we focus on the\ncase where one of the bands lies below the Fermi level (i.e. incipient), and\ndemonstrate that the combined interactions can enhance or suppress $T_c$,\ndepending on their relative strength. For a suitable choice of parameters, the\nspin-fluctuation mechanism yields a $T_c \\approx 46.8$ K incipient $s_\\pm$\nsuperconductor, consistent with surface-doped FeSe thin films. A\nforward-focused {\\eph} interaction further enhances the $T_c$, as observed in\nmonolayer FeSe on SrTiO$_3$.\n"}
+{"abstract": "  We study mechanisms for selling a single item when buyers have private values\nfor their outside options, which they forego by participating in the mechanism.\nThis substantially changes the revenue maximization problem. For example, the\nseller can strictly benefit from selling lotteries already in the single-buyer\nsetting. We bound the menu size and the sample complexity for the optimal\nsingle-buyer mechanism. We then show that posting a single price is in fact\noptimal under the assumption that either (1) the outside option value is\nindependent of the item value, and the item value distribution has decreasing\nmarginal revenue or monotone hazard rate; or (2) the outside option value is a\nconcave function of the item value. Moreover, when there are multiple buyers,\nwe show that sequential posted pricing guarantees a large fraction of the\noptimal revenue under similar conditions.\n"}
+{"abstract": "  In this paper we prove the case $dim(V_3)=3$ of a conjecture about the\nexterior operad ${\\Lambda}^{S^2}_{V_d}$. For this we introduce a collection of\nnatural involutions on the set of homogeneous cycle-free $d$-partitions of the\ncomplete graph $K_{2d}$, and show that these involutions correspond to the\nrelations in ${\\Lambda}^{S^2}_{V_d}(2d+1)$. When $d=3$ this correspondence\nallows us to give an explicit description of a determinant-like map and to\nsettle the above mentioned conjecture.\n"}
+{"abstract": "  Stochastic hybrid systems involve a coupling between a discrete Markov chain\nand a continuous stochastic process. If the latter evolves deterministically\nbetween jumps in the discrete state, then the system reduces to a piecewise\ndeterministic Markov process (PDMP). Well known examples include stochastic\ngene expression, voltage fluctuations in neurons, and motor-driven\nintracellular transport. In this paper we use coherent spin states to construct\na new path integral representation of the probability density functional for\nstochastic hybrid systems, which holds outside the weak noise regime. We use\nthe path integral to derive a system of Langevin equations in the\nsemi-classical limit, which extends previous diffusion approximations based on\na quasi-steady-state reduction. We then show how in the weak noise limit the\npath integral is equivalent to an alternative representation that was\npreviously derived using Doi-Peliti operators. The action functional of the\nlatter is related to a large deviation principle for stochastic hybrid systems.\n"}
+{"abstract": "  Due to the low error tolerance of a qubit, detecting and correcting errors on\nit is essential for fault-tolerant quantum computing. Surface code (SC)\nassociated with its decoding algorithm is one of the most promising quantum\nerror correction (QEC) methods. % One of the challenges of QEC is its high\ncomplexity and computational demand. QEC needs to be very power-efficient since\nthe power budget is limited inside of a dilution refrigerator for\nsuperconducting qubits by which one of the most successful quantum computers\n(QCs) is built. In this paper, we propose an online-QEC algorithm and its\nhardware implementation with SFQ-based superconducting digital circuits. We\ndesign a key building block of the proposed hardware with an SFQ cell library\nand evaluate it by the SPICE-level simulation. Each logic element is composed\nof about 3000 Josephson junctions and power consumption is about 2.78 uW when\noperating with 2 GHz clock frequency which meets the required decoding speed.\nOur decoder is simulated on a quantum error simulator for code distances 5 to\n13 and achieves a 1.0% accuracy threshold.\n"}
+{"abstract": "  We study scalarization of slowly rotating black holes in the\nEinstein-scalar-Gauss-Bonnet (GB)-Chern-Simons (CS) theory. In the slow\nrotation approximation of $a\\ll1$ with rotation parameter $a$, the GB term is\ngiven by a term for Schwarzschild black hole, whereas the CS term takes a\nlinear term of $a$. The tachyonic instability for slowly rotating black holes\nrepresents the onset of spontaneous scalarization. We use the (2+1)-dimensional\nhyperboloidal foliation method to show the tachyonic instability for slowly\nrotating black holes by considering the time evolution of a spherically\nsymmetric scalar mode. A threshold (existence) curve is obtained from the\nconstant scalar modes under time evolution, which means the boundary between\nstable and unstable black holes. It is found that the slowly rotating black\nholes turn out to be unstable against a spherically symmetric scalar-mode\npropagation for positive coupling $\\alpha$. However, we could not find\ntachyonic instability and any $a$-bound for scalarization for negative coupling\n$\\alpha$.\n"}
+{"abstract": "  The interplay between set-theoretic solutions of the Yang--Baxter equation of\nMathematical Physics, skew braces, regular subgroups, and Hopf--Galois\nstructures has spawned a considerable body of literature in recent years. In a\nrecent paper, Alan Koch generalised a construction of Lindsay N.~Childs,\nshowing how one can obtain bi-skew braces $(G, \\cdot, \\circ)$ from an\nendomorphism of a group $(G, \\cdot)$ whose image is abelian. In this paper, we\ncharacterise the endomorphisms of a group $(G, \\cdot)$ for which Koch's\nconstruction, and a variation on it, yield (bi-)skew braces. We show how the\nset-theoretic solutions of the Yang--Baxter equation derived by Koch's\nconstruction carry over to our more general situation, and discuss the related\nHopf--Galois structures.\n"}
+{"abstract": "  We calculate the NNLO QCD corrections to diphoton production with an\nadditional jet at the LHC. Our calculation represents the first NNLO-accurate\nprediction for the transverse momentum distribution of the diphoton system. The\nimprovement in the accuracy of the theoretical prediction is significant, by a\nfactor of up to four relative to NLO QCD. Our calculation is exact except for\nthe finite remainder of the two-loop amplitude which is included at leading\ncolor. The numerical impact of this approximated contribution is small. The\nresults of this work are expected to further our understanding of the Higgs\nboson sector and of the behavior of higher-order corrections to LHC processes.\n"}
+{"abstract": "  Image captioning model is a cross-modality knowledge discovery task, which\ntargets at automatically describing an image with an informative and coherent\nsentence. To generate the captions, the previous encoder-decoder frameworks\ndirectly forward the visual vectors to the recurrent language model, forcing\nthe recurrent units to generate a sentence based on the visual features.\nAlthough these sentences are generally readable, they still suffer from the\nlack of details and highlights, due to the fact that the substantial gap\nbetween the image and text modalities is not sufficiently addressed. In this\nwork, we explicitly build a Modality Transition Module (MTM) to transfer visual\nfeatures into semantic representations before forwarding them to the language\nmodel. During the training phase, the modality transition network is optimised\nby the proposed modality loss, which compares the generated preliminary textual\nencodings with the target sentence vectors from a pre-trained text\nauto-encoder. In this way, the visual vectors are transited into the textual\nsubspace for more contextual and precise language generation. The novel MTM can\nbe incorporated into most of the existing methods. Extensive experiments have\nbeen conducted on the MS-COCO dataset demonstrating the effectiveness of the\nproposed framework, improving the performance by 3.4% comparing to the\nstate-of-the-arts.\n"}
+{"abstract": "  In this paper, firstly, we introduce the concept of convexity in A-metric\nspaces and show that Mann iteration process converges to the unique fixed point\nof Zamfirescu type contractions in this newly defined convex A-metric space.\nSecondly, we define the concept of stability in convex A-metric spaces and\nestablish stability result for the Mann iteration process considered in such\nspaces. Our results carry some well-known results from the literature to convex\nA-metric spaces.\n"}
+{"abstract": "  Compressive sensing (CS) has been applied to estimate the direction of\narrival (DOA) in underwater acoustics. However, the key problem needed to be\nresolved in a {multipath} propagation environment is to suppress the\ninterferences between the raypaths. Thus, in this paper, {a subspace-based\ncompressive sensing algorithm that formulates the statistic information of the\nsignal subspace in a CS framework is proposed.} The experiment results show\nthat (1) the proposed algorithm enables the separation of raypaths that arrive\nclosely at the {receiver} array and (2) the existing algorithms fail,\nespecially in a low signal-to-noise ratio (SNR) environment.\n"}
+{"abstract": "  We propose a new concept of two-dimensional (2D) Dirac semiconductor which is\ncharacterized by the emergence of fourfold degenerate band crossings near the\nband edge and provide a generic approach to realize this novel semiconductor in\nthe community of material science. Based on the first-principle calculations\nand symmetry analysis, we discover recently synthesised triple-layer (TL)-BiOS2\nis such Dirac semiconductor that features Dirac cone at X/Y point, protected by\nnonsymmorphic symmetry. Due to sandwich-like structure, each Dirac fermion in\nTL-BiOS2 can be regarded as a combination of two Weyl fermions with opposite\nchiralities, degenerate in momentum-energy space but separated in real space.\nSuch Dirac semiconductor carries layer-dependent helical spin textures that\nnever been reported before. Moreover, novel topological phase transitions are\nflexibly achieved in TL-BiOS2: (i) an vertical electric field can drive it into\nWeyl semiconductor with switchable spin polarization direction, (ii) an\nextensive strain is able to generate ferroelectric polarization and actuate it\ninto Weyl nodal ring around X point and into another type of four-fold\ndegenerate point at Y point. Our work extends the Dirac fermion into\nsemiconductor systems and provides a promising avenue to integrate spintronics\nand optoelectronics in topological materials.\n"}
+{"abstract": "  At the Planck scale the distinction between elementary particles and black\nholes becomes fuzzy. The very definition of a \"quantum black hole\" (QBH) is an\nopen issue. Starting from the idea that, at the Planck scale, the radius of the\nevent horizon undergoes quantum oscillations, we introduce a black hole\nmass-radius Generalised Uncertainty Principle (GUP) and derive a corresponding\ngravitational wavelength. Next we recover a GUP encoding effective geometry.\nThis semi-classical gravitational description admits black hole configurations\nonly for masses higher than the Planck mass. Quantum corrections lead to a\nvanishing Hawking temperature when the Planck mass is approached from above.\nFinally we replace our semi-classical model by a relativistic wave equation for\nthe \"horizon wave function\". The solution admits a discrete mass spectrum which\nis bounded from below by a stable ground state with energy close to the Planck\nmass. Interestingly higher angular momentum states fit onto Regge trajectories\nindicating their stringy nature.\n"}
+{"abstract": "  In this paper, we introduce the notion of Grothendieck enriched categories\nfor categories enriched over a sufficiently nice Grothendieck monoidal category\n$\\mathcal{V}$, generalizing the classical notion of Grothendieck categories.\nThen we establish the Gabriel-Popescu type theorem for Grothendieck enriched\ncategories. We also prove that the property of being Grothendieck enriched\ncategories is preserved under the change of the base monoidal categories by a\nmonoidal right adjoint functor. In particular, if we take as $\\mathcal{V}$ the\nmonoidal category of complexes of abelian groups, we obtain the notion of\nGrothendieck dg categories. As an application of the main results, we see that\nthe dg category of complexes of quasi-coherent sheaves on a quasi-compact and\nquasi-separated scheme is an example of Grothendieck dg categories.\n"}
+{"abstract": "  We study the propagation coherence for neutrino oscillations in media with\ndifferent density profiles. For each profile, we find the dependence of the\ncoherence length, $L_{coh}$, on neutrino energy and address the issue of\ncorrespondence of results in the distance and energy-momentum representations.\nThe key new feature in matter is existence of energy ranges with enhanced\ncoherence around the energies $E_0$ of \"infinite coherence\" at which $L_{coh}\n\\rightarrow \\infty$. In the configuration space, the infinite coherence\ncorresponds to equality of the (effective) group velocities of the eigenstates.\nIn constant density medium, there is a unique $E_0$, which coincides with the\nMSW resonance energy of oscillations of mass states and is close to the MSW\nresonance energy of flavor states. In the case of massless neutrinos or\nnegligible masses in a very dense medium the coherence persists continuously.\nIn the adiabatic case, the infinite coherence is realized for periodic density\nchange. Adiabaticity violation changes the shape factors of the wave packets\n(WPs) and leads to their spread. In a medium with sharp density changes\n(jumps), splitting of the eigenstates occurs at crossing of each jump. We study\nthe increase of the coherence length in a single jump and periodic density\njumps - castle-wall (CW) profiles. For the CW profile, there are several $E_0$\ncorresponding to parametric resonances. We outlined applications of the results\nfor supernova neutrinos. In particular, we show that coherence between two\nshock wave fronts leads to observable oscillation effects, and our analysis\nsuggests that the decoherence can be irrelevant for flavor transformations in\nthe central parts of collapsing stars.\n"}
+{"abstract": "  Gathering manually annotated images for the purpose of training a predictive\nmodel is far more challenging in the medical domain than for natural images as\nit requires the expertise of qualified radiologists. We therefore propose to\ntake advantage of past radiological exams (specifically, knee X-ray\nexaminations) and formulate a framework capable of learning the correspondence\nbetween the images and reports, and hence be capable of generating diagnostic\nreports for a given X-ray examination consisting of an arbitrary number of\nimage views. We demonstrate how aggregating the image features of individual\nexams and using them as conditional inputs when training a language generation\nmodel results in auto-generated exam reports that correlate well with\nradiologist-generated reports.\n"}
+{"abstract": "  We develop a method to generate prediction intervals that have a\nuser-specified coverage level across all regions of feature-space, a property\ncalled conditional coverage. A typical approach to this task is to estimate the\nconditional quantiles with quantile regression -- it is well-known that this\nleads to correct coverage in the large-sample limit, although it may not be\naccurate in finite samples. We find in experiments that traditional quantile\nregression can have poor conditional coverage. To remedy this, we modify the\nloss function to promote independence between the size of the intervals and the\nindicator of a miscoverage event. For the true conditional quantiles, these two\nquantities are independent (orthogonal), so the modified loss function\ncontinues to be valid. Moreover, we empirically show that the modified loss\nfunction leads to improved conditional coverage, as evaluated by several\nmetrics. We also introduce two new metrics that check conditional coverage by\nlooking at the strength of the dependence between the interval size and the\nindicator of miscoverage.\n"}
+{"abstract": "  We compute the decomposition numbers of the unipotent characters lying in the\nprincipal $\\ell$-block of a finite group of Lie type $B_{2n}(q)$ or $C_{2n}(q)$\nwhen $q$ is an odd prime power and $\\ell$ is an odd prime number such that the\norder of $q$ mod $\\ell$ is $2n$. Along the way, we extend to these finite\ngroups the results of \\cite{DVV19} on the branching graph for Harish-Chandra\ninduction and restriction.\n"}
+{"abstract": "  Experimentally observed complex networks are often scale-free, small-world\nand have unexpectedly large number of small cycles. Apollonian network is one\nnotable example of a model network respecting simultaneously having all three\nof these properties. This network is constructed by a deterministic procedure\nof consequentially splitting a triangle into smaller and smaller triangles.\nHere we present a similar construction based on consequential splitting of\ntetragons and other polygons with even number of edges. The suggested procedure\nis stochastic and results in the ensemble of planar scale-free graphs, in the\nlimit of large number of splittings the degree distribution of the graph\nconverges to a true power law with exponent, which is smaller than 3 in the\ncase of tetragons, and larger than 3 for polygons with larger number of edges.\nWe show that it is possible to stochastically mix tetragon-based and\nhexagon-based constructions to obtain an ensemble of graphs with tunable\nexponent of degree distribution. Other possible planar generalizations of the\nApollonian procedure are also briefly discussed.\n"}
+{"abstract": "  Learned indices have been proposed to replace classic index structures like\nB-Tree with machine learning (ML) models. They require to replace both the\nindices and query processing algorithms currently deployed by the databases,\nand such a radical departure is likely to encounter challenges and obstacles.\nIn contrast, we propose a fundamentally different way of using ML techniques to\nimprove on the query performance of the classic R-Tree without the need of\nchanging its structure or query processing algorithms. Specifically, we develop\nreinforcement learning (RL) based models to decide how to choose a subtree for\ninsertion and how to split a node when building an R-Tree, instead of relying\non hand-crafted heuristic rules currently used by R-Tree and its variants.\nExperiments on real and synthetic datasets with up to more than 100 million\nspatial objects clearly show that our RL based index outperforms R-Tree and its\nvariants in terms of query processing time.\n"}
+{"abstract": "  We develop new algebraic tools to reason about concurrent behaviours modelled\nas languages of Mazurkiewicz traces and asynchronous automata. These tools\nreflect the distributed nature of traces and the underlying causality and\nconcurrency between events, and can be said to support true concurrency. They\ngeneralize the tools that have been so efficient in understanding, classifying\nand reasoning about word languages. In particular, we introduce an asynchronous\nversion of the wreath product operation and we describe the trace languages\nrecognized by such products (the so-called asynchronous wreath product\nprinciple). We then propose a decomposition result for recognizable trace\nlanguages, analogous to the Krohn-Rhodes theorem, and we prove this\ndecomposition result in the special case of acyclic architectures. Finally, we\nintroduce and analyze two distributed automata-theoretic operations. One, the\nlocal cascade product, is a direct implementation of the asynchronous wreath\nproduct operation. The other, global cascade sequences, although conceptually\nand operationally similar to the local cascade product, translates to a more\ncomplex asynchronous implementation which uses the gossip automaton of Mukund\nand Sohoni. This leads to interesting applications to the characterization of\ntrace languages definable in first-order logic: they are accepted by a\nrestricted local cascade product of the gossip automaton and 2-state\nasynchronous reset automata, and also by a global cascade sequence of 2-state\nasynchronous reset automata. Over distributed alphabets for which the\nasynchronous Krohn-Rhodes theorem holds, a local cascade product of such\nautomata is sufficient and this, in turn, leads to the identification of a\nsimple temporal logic which is expressively complete for such alphabets.\n"}
+{"abstract": "  The rise of the Internet of Things (IoT) has opened new research lines that\nfocus on applying IoT applications to domains further beyond basic user-grade\napplications, such as Industry or Healthcare. These domains demand a very high\nQuality of Service (QoS), mainly a very short response time. In order to meet\nthese demands, some works are evaluating how to modularize and deploy IoT\napplications in different nodes of the infrastructure (edge, fog, cloud), as\nwell as how to place the network controllers, since these decisions affect the\nresponse time of the application. Some works in the literature have approached\nthis problem by providing separate plans for deployment and placing of\ncontrollers. However, this approach makes sub-optimal decisions, that\ncomplicate guaranteeing the demanded response time. To guarantee an optimal\nresponse time, it is crucial to solve the problem in a single effort that\nconsiders both, the networking and computing dimensions. In this work, we\nanalyze the influences between the response time of computing and networking in\nedge computing environments with SDN networks, merging both optimization\nefforts into a single one and proposing a solution to the joint problem. Our\nevaluation shows that our proposal can shorten response time by up to 28.97%\n"}
+{"abstract": "  The B-emission line stars are rapid rotators that were probably spun up by\nmass and angular momentum accretion through mass transfer in an interacting\nbinary. Mass transfer will strip the donor star of its envelope to create a\nsmall and hot subdwarf remnant. Here we report on Hubble Space Telescope/STIS\nfar-ultraviolet spectroscopy of a sample of Be stars that reveals the presence\nof the hot sdO companion through the calculation of cross-correlation functions\nof the observed and model spectra. We clearly detect the spectral signature of\nthe sdO star in 10 of the 13 stars in the sample, and the spectral signals\nindicate that the sdO stars are hot, relatively faint, and slowly rotating as\npredicted by models. A comparison of their temperatures and radii with\nevolutionary tracks indicates that the sdO stars occupy the relatively\nlong-lived, He-core burning stage. Only one of the ten detections was a known\nbinary prior to this investigation, which emphasizes the difficulty of finding\nsuch Be+sdO binaries through optical spectroscopy. However, these results and\nothers indicate that many Be stars probably host hot subdwarf companions.\n"}
+{"abstract": "  In this study, we investigate an extreme ultraviolet (EUV) wave event on 2010\nFebruary 11, which occurred as a limb event from the Earth viewpoint and a disk\nevent from the STEREO--B viewpoint. We use the data obtained by the Atmospheric\nImaging Assembly (AIA) aboard the Solar Dynamics Observatory (SDO) in various\nEUV channels. The EUV wave event was launched by a partial prominence eruption.\nSimilar to some EUV wave events in previous works, this EUV wave event contains\na faster wave with a speed of $\\sim$445$\\pm$6 km s$^{-1}$, which we call\ncoronal Moreton wave, and a slower wave with a speed of $\\sim$298$\\pm$5 km\ns$^{-1}$, which we call \"EIT wave\". The coronal Moreton wave is identified as a\nfast-mode wave and the \"EIT wave\" is identified as an apparent propagation due\nto successive field-line stretching. We also observe a stationary front\nassociated with the fast mode EUV wave. This stationary front is explained as\nmode conversion from the coronal Moreton wave to a slow-mode wave near a\nstreamer.\n"}
+{"abstract": "  We present a framework for deterministically rounding a dynamic fractional\nmatching. Applying our framework in a black-box manner on top of existing\nfractional matching algorithms, we derive the following new results: (1) The\nfirst deterministic algorithm for maintaining a $(2-\\delta)$-approximate\nmaximum matching in a fully dynamic bipartite graph, in arbitrarily small\npolynomial update time. (2) The first deterministic algorithm for maintaining a\n$(1+\\delta)$-approximate maximum matching in a decremental bipartite graph, in\npolylogarithmic update time. (3) The first deterministic algorithm for\nmaintaining a $(2+\\delta)$-approximate maximum matching in a fully dynamic\ngeneral graph, in small polylogarithmic (specifically, $O(\\log^4 n)$) update\ntime. These results are respectively obtained by applying our framework on top\nof the fractional matching algorithms of Bhattacharya et al. [STOC'16],\nBernstein et al. [FOCS'20], and Bhattacharya and Kulkarni [SODA'19].\n  Prior to our work, there were two known general-purpose rounding schemes for\ndynamic fractional matchings. Both these schemes, by Arar et al. [ICALP'18] and\nWajc [STOC'20], were randomized.\n  Our rounding scheme works by maintaining a good {\\em matching-sparsifier}\nwith bounded arboricity, and then applying the algorithm of Peleg and Solomon\n[SODA'16] to maintain a near-optimal matching in this low arboricity graph. To\nthe best of our knowledge, this is the first dynamic matching algorithm that\nworks on general graphs by using an algorithm for low-arboricity graphs as a\nblack-box subroutine. This feature of our rounding scheme might be of\nindependent interest.\n"}
+{"abstract": "  We present a biologically inspired design for swarm foraging based on ant's\npheromone deployment, where the swarm is assumed to have very restricted\ncapabilities. The robots do not require global or relative position\nmeasurements and the swarm is fully decentralized and needs no infrastructure\nin place. Additionally, the system only requires one-hop communication over the\nrobot network, we do not make any assumptions about the connectivity of the\ncommunication graph and the transmission of information and computation is\nscalable versus the number of agents. This is done by letting the agents in the\nswarm act as foragers or as guiding agents (beacons). We present experimental\nresults computed for a swarm of Elisa-3 robots on a simulator, and show how the\nswarm self-organizes to solve a foraging problem over an unknown environment,\nconverging to trajectories around the shortest path. At last, we discuss the\nlimitations of such a system and propose how the foraging efficiency can be\nincreased.\n"}
+{"abstract": "  There is recurrent debate about how useful science is for technological\ndevelopment, but we know little about what kinds of science are more useful for\ntechnology. This paper fills this gap in the literature by exploring how the\nvalue of a patent (as measured by patent forward citations and the stock market\nresponse to the issuing of the patent) depends on the characteristics of the\nscientific papers that it builds on, specifically, basicness,\ninterdisciplinarity, novelty, and scientific citations. Using a dataset of\n33,337 USPTO biotech utility patents and their 860,879 in-text references to\nWeb of Science journal articles, we find (1) a positive effect of the number of\nreferenced scientific papers, (2) an inverted U-shaped effect of basicness, (3)\nan insignificant effect of interdisciplinarity, (4) a discontinuous and\nnonlinear effect of novelty, and (5) a positive effect of scientific citations\nfor patent market value but an insignificant effect on patent citations. In\naddition, in-text referenced papers have a higher chance of being listed on the\nfront-page of the same patent when they are moderately basic, less\ninterdisciplinary, less novel, and more highly cited. Accordingly, using\nfront-page reference yields substantially different results than using in-text\nreferences.\n"}
+{"abstract": "  A graph $G$ is $F$-saturated if it contains no copy of $F$ as a subgraph but\nthe addition of any new edge to $G$ creates a copy of $F$. We prove that for $s\n\\geq 3$ and $t \\geq 2$, the minimum number of copies of $K_{1,t}$ in a\n$K_s$-saturated graph is $\\Theta ( n^{t/2})$. More precise results are obtained\nwhen $t = 2$ where the problem is related to Moore graphs with diameter 2 and\ngirth 5. We prove that for $s \\geq 4$ and $t \\geq 3$, the minimum number of\ncopies of $K_{2,t}$ in an $n$-vertex $K_s$-saturated graph is at least $\\Omega(\nn^{t/5 + 8/5})$ and at most $O(n^{t/2 + 3/2})$. These results answer a question\nof Chakraborti and Loh. General estimates on the number of copies of $K_{a,b}$\nin a $K_s$-saturated graph are also obtained, but finding an asymptotic formula\nremains open.\n"}
+{"abstract": "  This paper identifies a model of software evolution that is prevalent in\nlarge, long-lived academic research tool suites (3L-ARTS). This model results\nin an \"archipelago\" of related but haphazardly organized architectural\n\"islands\", and inherently induces technical debt. We illustrate the archipelago\nmodel with examples from two 3L-ARTS archipelagos identified in literature.\n"}
+{"abstract": "  The dominant approach in probing neural networks for linguistic properties is\nto train a new shallow multi-layer perceptron (MLP) on top of the model's\ninternal representations. This approach can detect properties encoded in the\nmodel, but at the cost of adding new parameters that may learn the task\ndirectly. We instead propose a subtractive pruning-based probe, where we find\nan existing subnetwork that performs the linguistic task of interest. Compared\nto an MLP, the subnetwork probe achieves both higher accuracy on pre-trained\nmodels and lower accuracy on random models, so it is both better at finding\nproperties of interest and worse at learning on its own. Next, by varying the\ncomplexity of each probe, we show that subnetwork probing Pareto-dominates MLP\nprobing in that it achieves higher accuracy given any budget of probe\ncomplexity. Finally, we analyze the resulting subnetworks across various tasks\nto locate where each task is encoded, and we find that lower-level tasks are\ncaptured in lower layers, reproducing similar findings in past work.\n"}
+{"abstract": "  In this thesis, we study chiral topological phases of 2+1 dimensional quantum\nmatter. Such phases are abstractly characterized by their non-vanishing chiral\ncentral charge $c$, a topological invariant which appears as the coefficient of\na gravitational Chern-Simons (gCS) action in bulk, and of corresponding\ngravitational anomalies at boundaries. The chiral central charge is of\nparticular importance in chiral superfluids and superconductors (CSF/Cs), where\n$U(1)$ particle-number symmetry is broken, and $c$ is, in some cases, the only\ntopological invariant characterizing the system. However, as opposed to\ninvariants which can be probed by gauge fields in place of gravity, the\nconcrete physical implications of $c$ in the context of condensed matter\nphysics is quite subtle, and has been the subject of ongoing research and\ncontroversy. The first two parts of this thesis are devoted to the physical\ninterpretation of the gCS action and gravitational anomalies in the context of\nCSF/Cs, where they are of particular importance, but have nevertheless remained\npoorly understood. We then turn to a seemingly unrelated aspect of chiral\ntopological phases - their computational complexity. The infamous $sign\\\nproblem$ leads to an exponential complexity in Monte Carlo simulations of\ngeneric many-body quantum systems. Nevertheless, many phases of matter are\nknown to admit a sign-problem-free representative, allowing an efficient\nclassical simulation. The possibility of $intrinsic$ sign problems, where a\nphase of matter admits no sign-problem-free representative, was recently raised\nbut remains largely unexplored. Here, we establish the existence of an\nintrinsic sign problem in a broad class of chiral topological phases, both\nbosonic and fermionic, defined by the requirement that $e^{2\\pi i c/24}$ is\n$not$ the topological spin of an anyon.\n"}
+{"abstract": "  The central region of NGC 1068 is one of the closest and most studied Active\nGalactic Nucleus. It is known to be of type 2, meaning that its accretion disk\nis obscured by a large amount of dust and gas. The main properties of the\nobscuring structure are still to be determined. We model the inner edge of this\nstructure, where the hot dust responsible for the near-infrared emission\nreaches its sublimation temperature. At first, we use simple geometrical models\nin image reconstructions to determine the main 2D geometrical properties of the\nsource. In a second step, we try to reproduce the observables with K-band\nimages produced by 3D radiative transfer simulations of a heated dusty disk. We\nexplore various parameters to find an optimal solution and a model consistent\nwith all the observables. The three methods indicate an elongated structure\nwith 4 x 6 mas dimensions and its major axis along the NW/SE direction. All\nthree of them suggest that the object looks like an elongated ring rather than\nan elongated thin disk, with its northeast edge less luminous than the\nSouth-West one. The best 3D model is a thick disk with an inner radius r=0.21\n+/- 0.03 pc and a half-opening angle $\\alpha_{1/2}=21\\pm8\\degree$ observed with\nan inclination $i=44_{-6}^{10}$\\,$\\degree$ and $PA=150_{-13}^{8}$\\,$\\degree$. A\nhigh density of dust $n=5_{-2.5}^{+5}\\ M_{\\odot}.pc^{-3}$ is required to\nexplain the contrast between the two edges by self-absorption from the closer\none. The overall structure is obscured by a large foreground obscuration $A_V\n\\sim 75$. The hot dust is not responsible for the obscuration of the central\nengine. The geometry and the orientation of the structure are different from\nthose of the previously observed maser and molecular disks. A single disk is\nunable to account for these differences, and we favor a description of the\nsource where multiple rings are entangled around the central mass.\n"}
+{"abstract": "  We provide a synthesis of different topos-theoretical approaches of the\ngeneral construction of spectra, exploring jointly its purely categorical,\nmodel theoretic and geometric aspects. We detail also the exact relation with\nDiers notion of spectrum. Then we give a more detailed account of Cole's\noriginal construction from bicategorical universal properties. We also provide\nnew formulas to compute the spectrum of arbitrary etale maps as pseudolimits,\nas well as generalizations of results on fibered topos we then apply to compute\nspectrum of a modelled topos.\n"}
+{"abstract": "  In this paper, we consider a three-zero texture with diagonal reflection\nsymmetries in the SM. The three-zero texture has two less assumptions\n($(M_{u})_{11} , (M_{\\nu})_{11} \\neq 0$) than the universal four-zero texture\nof mass matrices $(M_{f})_{11} = (M_{f})_{13,31} = 0$ for $f = u,d,\\nu, e$. The\ntexture allows diagonal reflection symmetries to be almost exact and\n$d$\\,-\\,$e$ unification. They reproduce the CKM and MNS matrices with\naccuracies of $O(10^{-4})$ and $O(10^{-3})$. Some perturbative diagonalizations\nyield two relations with good accuracy for quark mass and mixing. Although this\ncalculation is done in a special basis, it is a general result in a sense,\nbecause other textures and generalized $CP$ symmetries exist by some weak basis\ntransformation.\n  By assuming a $d$\\,-\\,$e$ unified relation ($M_{d} \\sim M_{e}$), we obtain\nthe lightest neutrino mass $m_{1} \\simeq 2.12\\, - \\, 5.64\\,$[meV] and the\neffective mass of the double beta decay $|m_{ee}| \\simeq 1.23 - 2.15 \\,$[meV].\n"}
+{"abstract": "  The results of Kermack-McKendrick SIR model are planned to be reproduced by\ncellular automata (CA) lattice model. The CA algorithms are proposed to study\nthe model of epidemic, systematically. The basic goal is to capture the effects\nof spreading of infection over a scale of length. This CA model can provide the\nrate of growth of the infection over the space which was lacking in the\nmean-field like SIR model. The motion of the circular front of an infected\ncluster shows a linear behaviour in time. The correlation of a particular site\nto be infected, with respect to the central site is also studied. The outcomes\nof CA model are in good agreement with that obtained from SIR model. The\nresults of vaccination have been also incorporated in the CA algorithm with a\nsatisfactory degree of success. The advantage of the present model is that it\ncan shed considerable amount of light to the physical properties of the spread\nof a typical epidemic in a simple, yet robust way.\n"}
+{"abstract": "  Testing for causation, defined as the preceding impact of the past values of\none variable on the current value of another one when all other pertinent\ninformation is accounted for, is increasingly utilized in empirical research of\nthe time-series data in different scientific disciplines. A relatively recent\nextension of this approach has been allowing for potential asymmetric impacts\nsince it is harmonious with the way reality operates in many cases according to\nHatemi-J (2012). The current paper maintains that it is also important to\naccount for the potential change in the parameters when asymmetric causation\ntests are conducted, as there exists a number of reasons for changing the\npotential causal connection between variables across time. The current paper\nextends therefore the static asymmetric causality tests by making them dynamic\nvia the usage of subsamples. An application is also provided consistent with\nmeasurable definitions of economic or financial bad as well as good news and\ntheir potential interaction across time.\n"}
+{"abstract": "  We consider the problem of estimating the 3D orientation of a user, using the\ndownlink mmWave signals received from multiple base stations. We show that the\nreceived signals from several base stations, having known positions, can be\nused to estimate the unknown orientation of the user. We formulate the\nestimation problem as a maximum likelihood estimation problem in the the\nmanifold of rotation matrices. In order to provide an initial estimate to solve\nthe non-linear non-convex optimization problem, we resort to a least squares\nestimation problem that exploits the underlying geometry. Our numerical results\nshow that the problem of orientation estimation can be solved when the signals\nfrom at least two base stations are received. We also provide the orientation\nlower error bound, showing a narrow gap between the performance of the proposed\nestimators and the bound.\n"}
+{"abstract": "  Low dose computed tomography (LDCT) has attracted more and more attention in\nroutine clinical diagnosis assessment, therapy planning, etc., which can reduce\nthe dose of X-ray radiation to patients. However, the noise caused by low X-ray\nexposure degrades the CT image quality and then affects clinical diagnosis\naccuracy. In this paper, we train a transformer-based neural network to enhance\nthe final CT image quality. To be specific, we first decompose the noisy LDCT\nimage into two parts: high-frequency (HF) and low-frequency (LF) compositions.\nThen, we extract content features (X_{L_c}) and latent texture features\n(X_{L_t}) from the LF part, as well as HF embeddings (X_{H_f}) from the HF\npart. Further, we feed X_{L_t} and X_{H_f} into a modified transformer with\nthree encoders and decoders to obtain well-refined HF texture features. After\nthat, we combine these well-refined HF texture features with the pre-extracted\nX_{L_c} to encourage the restoration of high-quality LDCT images with the\nassistance of piecewise reconstruction. Extensive experiments on Mayo LDCT\ndataset show that our method produces superior results and outperforms other\nmethods.\n"}
+{"abstract": "  We consider two models of interacting dark energy, both of which interact\nonly through momentum exchange. One is a phenomenological one-parameter\nextension to $w$CDM, and the other is a coupled quintessence model described by\na Lagrangian formalism. Using a variety of high and low redshift data sets, we\nperform a global fitting of cosmological parameters and compare to\n$\\Lambda$CDM, uncoupled quintessence, and $w$CDM. We find that the models are\ncompetitive with $\\Lambda$CDM, even obtaining a better fit when certain data\nsets are included.\n"}
+{"abstract": "  Young stellar associations represent a key site for the study of star\nformation, but to accurately compare observations to models of stellar\nevolution, the age of an association must be determined. The Upper Scorpius\nregion is the youngest section of the Scorpius-Centaurus OB association, which\nis the largest collection of nearby, young, low-mass stars. The true age of\nUpper Scorpius is not clear, and an observed mass-dependent age gradient in\nUpper Scorpius, as well as in other star-forming regions, complicates age\nmeasurements. The age gradient may indicate a genuine astrophysical feature or\nmay be an artifact of unrecognized systematic effects in stellar age\nmeasurements. We have conducted a synthetic red-optical low-resolution\nspectroscopic survey of a simulated analog to the Upper Scorpius star-forming\nregion to investigate the effects of unresolved binary stars (which have\nmass-dependent demographics) on age measurements of a stellar population. We\nfind that the observed mass-dependent age gradient in Upper Scorpius can be\nexplained by a population of undetected binary stars. For a simulated\npopulation with an age of 10 (RMS = 2) Myr, we measure an age of 10.5 (RMS =\n3.5) Myr for F stars, and 7.5 (RMS = 5.8) Myr for M stars. This discrepancy is\ncaused by the mass-dependent mass ratio distribution and the variable steepness\nof the mass-luminosity relation. Our results support the previously suggested\n10 Myr age for Upper Scorpius, with a small intrinsic age spread.\n"}
+{"abstract": "  In this paper, we describe an open source Python toolkit named Uncertainty\nQuantification 360 (UQ360) for the uncertainty quantification of AI models. The\ngoal of this toolkit is twofold: first, to provide a broad range of\ncapabilities to streamline as well as foster the common practices of\nquantifying, evaluating, improving, and communicating uncertainty in the AI\napplication development lifecycle; second, to encourage further exploration of\nUQ's connections to other pillars of trustworthy AI such as fairness and\ntransparency through the dissemination of latest research and education\nmaterials. Beyond the Python package (\\url{https://github.com/IBM/UQ360}), we\nhave developed an interactive experience (\\url{http://uq360.mybluemix.net}) and\nguidance materials as educational tools to aid researchers and developers in\nproducing and communicating high-quality uncertainties in an effective manner.\n"}
+{"abstract": "  We study the feasibility of strong first-order electroweak phase transition\n(EWPT) in a degenerate-scalar scenario of a complex singlet extension of the\nStandard Model, in which a mass of an additional scalar is nearly degenerate\nwith that of the Higgs boson, 125 GeV. This scenario is known to provide an\nexquisite solution for circumventing constraints from dark matter direct\ndetection experiments due to cancellations between two scattering amplitudes\nmediated by two scalars. In the analysis of EWPT, we employ two gauge-invariant\ncalculation schemes on the scalar potential and two familiar resummation\nmethods in evaluating one-loop (gauge dependent) effective potential. We point\nout that one of the conditions for the strong first-order EWPT is incompatible\nwith the known suppression mechanism of a dark matter cross-section scattering\noff the nucleons. Nevertheless, we find that strong first-order EWPT is still\npossible in the degenerate-scalar scenario by dodging dark matter constraints\ndifferently.\n"}
+{"abstract": "  As a new degree of freedom for optical manipulation, recently spatiotemporal\noptical vortices (STOVs) carrying transverse orbital angular momentums have\nbeen experimentally demonstrated with bulky optical systems. Here we propose a\nspatiotemporal differentiator to generate STOVs with pure transverse orbital\nangular momentum. In order to create phase singularity in the spatiotemporal\ndomain, we design a spatiotemporal differentiator by breaking spatial mirror\nsymmetry. In contrast to the complex bulky systems, the device we propose here\nis a simple one-dimensional periodic nanostructure and thus it is much more\ncompact. We show that for a normal incident pulse, the differentiator generates\na transmitted STOV pulse with transverse orbital angular momentum. Furthermore,\nwe demonstrate that the interference of the generated STOVs can be used to\ndetect the sharp changes of pulse envelopes, in both spatial and temporal\ndimensions.\n"}
+{"abstract": "  We report on a spectroscopic program to search for dual quasars using Subaru\nHyper Suprime-Cam (HSC) images of SDSS quasars which represent an important\nstage during galaxy mergers. Using Subaru/FOCAS and Gemini-N/GMOS, we identify\nthree new physically associated quasar pairs having projected separations less\nthan 20 kpc, out of 26 observed candidates. These include the discovery of the\nhighest redshift ($z=3.1$) quasar pair with a separation $<$ 10 kpc. Based on\nthe sample acquired to date, the success rate of identifying physically\nassociated dual quasars is $19\\%$ when excluding stars based on their HSC\ncolors. Using the full sample of six spectroscopically confirmed dual quasars,\nwe find that the black holes in these systems have black hole masses ($M_{BH}\n\\sim 10^{8-9}M_{\\odot}$) similar to single SDSS quasars as well as their\nbolometric luminosities and Eddington ratios. We measure the stellar mass of\ntheir host galaxies based on 2D image decomposition of the five-band ($grizy$)\noptical emission and assess the mass relation between supermassive black holes\n(SMBHs) and their hosts. Dual SMBHs appear to have elevated masses relative to\ntheir host galaxies. Thus mergers may not necessarily align such systems onto\nthe local mass relation, as suggested by the Horizon-AGN simulation. This study\nsuggests that dual luminous quasars are triggered prior to the final\ncoalescence of the two SMBHs, resulting in early mass growth of the black holes\nrelative to their host galaxies.\n"}
+{"abstract": "  Book covers are intentionally designed and provide an introduction to a book.\nHowever, they typically require professional skills to design and produce the\ncover images. Thus, we propose a generative neural network that can produce\nbook covers based on an easy-to-use layout graph. The layout graph contains\nobjects such as text, natural scene objects, and solid color spaces. This\nlayout graph is embedded using a graph convolutional neural network and then\nused with a mask proposal generator and a bounding-box generator and filled\nusing an object proposal generator. Next, the objects are compiled into a\nsingle image and the entire network is trained using a combination of\nadversarial training, perceptual training, and reconstruction. Finally, a Style\nRetention Network (SRNet) is used to transfer the learned font style onto the\ndesired text. Using the proposed method allows for easily controlled and unique\nbook covers.\n"}
+{"abstract": "  Background: Subgroup analyses are frequently conducted in randomized clinical\ntrials to assess evidence of heterogeneous treatment effect across patient\nsubpopulations. Although randomization balances covariates within subgroups in\nexpectation, chance imbalance may be amplified in small subgroups and harm the\nprecision. Covariate adjustment in overall analysis of RCT is often conducted,\nvia either ANCOVA or propensity score weighting, but for subgroup analysis has\nbeen rarely discussed. In this article, we develop propensity score weighting\nmethodology for covariate adjustment to improve the precision and power of\nsubgroup analyses in RCTs. Methods: We extend the propensity score weighting\nmethodology to subgroup analyses by fitting a logistic propensity model with\npre-specified covariate-subgroup interactions. We show that, by construction,\noverlap weighting exactly balances the covariates with interaction terms in\neach subgroup. Extensive simulations were performed to compare the operating\ncharacteristics of unadjusted, different propensity score weighting and the\nANCOVA estimator. We apply these methods to the HF-ACTION trial to evaluate the\neffect of exercise training on 6-minute walk test in pre-specified subgroups.\nResults: Standard errors of the adjusted estimators are smaller than those of\nthe unadjusted estimator. The propensity score weighting estimator is as\nefficient as ANCOVA, and is often more efficient when subgroup sample size is\nsmall (e.g.<125), and/or when outcome model is misspecified. The weighting\nestimators with full-interaction propensity model consistently outperform the\nstandard main-effect propensity model. Conclusion: Propensity score weighting\nis a transparent and objective method to adjust chance imbalance of important\ncovariates in subgroup analyses of RCTs. It is crucial to include the full\ncovariate-subgroup interactions in the propensity score model.\n"}
+{"abstract": "  Motivated by the recent findings of unconventional superconductivity in\n$\\mathrm{CoSi_2 / TiSi_2}$ heterostructures, we study the effect of interface\ninduced Rashba spin orbit coupling on the conductance of a three terminal \"T\"\nshape superconducting device. We calculate the differential conductance for\nthis device within the quasi-classical formalism that includes the mixing of\ntriplet-singlet pairing due to the Rashba spin orbit coupling. We discuss our\nresult in the light of the conductance spectra reported by Chiu {\\it et al.}\nfor $\\mathrm{CoSi_2 / TiSi_2}$ heterostructures.\n"}
+{"abstract": "  We give a new derivation of mean-field percolation critical behaviour from\nthe triangle condition that is quantitatively much better than previous proofs\nwhen the triangle diagram $\\nabla_{p_c}$ is large. In contrast to earlier\nmethods, our approach continues to yield bounds of reasonable order when the\ntriangle diagram $\\nabla_p$ is unbounded but diverges slowly as $p \\uparrow\np_c$, as is expected to occur in percolation on $\\mathbb{Z}^d$ at the\nupper-critical dimension $d=6$. Indeed, we show in particular that if the\ntriangle diagram diverges polylogarithmically as $p \\uparrow p_c$ then\nmean-field critical behaviour holds to within a polylogarithmic factor. We\napply the methods we develop to deduce that for long-range percolation on the\nhierarchical lattice, mean-field critical behaviour holds to within\npolylogarithmic factors at the upper-critical dimension.\n  As part of the proof, we introduce a new method for comparing diagrammatic\nsums on general transitive graphs that may be of independent interest.\n"}
+{"abstract": "  In Cyber Physical Systems humans are often kept in the loop as operators\nand/or service users. Yet in many cases, humans and machines collaborate and\nprovide services to each other. Research on service models and service\ncomposition for CPS exist; however, humans as service providers have not been\nadequately considered as part of the CPS service composition model. We provide\na classification of human-as-a-service in CPS, and we propose a Service\nOriented Architecture (SOA) ontology model for the CPS environment as part of\nthe Everything-as-a-Service paradigm. The model considers human characteristics\nand their dynamics, as a service provider or collaborator with the machine. As\nthe ontology model is an enabler for engineering a self-adaptive CPS with\nhuman-machine collaboration as service providers, we describe how a commonly\nused self-adaptive reference model can be refined to benefit from the vision.\nWe evaluate the ontological contribution against criteria that relates to\naccuracy, completeness, adaptability, clarity, and consistency. We demonstrate\nthe feasibility of our conceptual reference model using a use case from the\nmedical domain and we show how human-machine service provision is possible.\n"}
+{"abstract": "  Automated tools for the computation of particle physics' processes have\nbecome the backbone of phenomenological studies beyond the standard model.\nHere, we present MadDM v3.2. This release enables the fully automated\ncomputation of loop-induced dark-matter annihilation processes, relevant for\nindirect detection observables. Special emphasis lies on the annihilation into\n$\\gamma X$, where $X=\\gamma, Z, h$ or any new particle even under the dark\nsymmetry. These processes lead to the sharp spectral feature of monochromatic\ngamma lines - a smoking-gun signature of dark matter in our Galaxy. MadDM\nprovides the predictions for the respective fluxes near-Earth and derives\nconstraints from the gamma-ray line searches by Fermi-LAT and HESS. As an\napplication, we discuss the implications for the viable parameter space of a\ntop-philic $t$-channel mediator model and the inert doublet model.\n"}
+{"abstract": "  We study chiral symmetry restoration by analyzing thermal properties of QCD's\n(pseudo-)Goldstone bosons, especially the pion. The meson properties are\nobtained from the spectral densities of mesonic imaginary-time correlation\nfunctions. To obtain the correlation functions, we solve the Dyson-Schwinger\nequations and the inhomogeneous Bethe-Salpeter equations in the leading\nsymmetry-preserving rainbow-ladder approximation. In the chiral limit, the pion\nand its partner sigma degenerate at the critical temperature $T_c$. At $T\n\\gtrsim T_c$, it is found that the pion rapidly dissociates, which signals\ndeconfinement phase transition. Beyond the chiral limit, the pion dissociation\ntemperature can be used to define the pseudo-critical temperature of chiral\nphase crossover, which is consistent with that obtained by the maximum point of\nthe chiral susceptibility. The parallel analysis for kaon and pseudoscalar\n$s\\bar{s}$ suggests that heavy mesons may survive above $T_c$.\n"}
+{"abstract": "  The number of maximal abelian subgroups of a finite p-group is shown to be\ncongruent to 1 modulo p.\n"}
+{"abstract": "  The effectiveness of Machine Learning (ML) methods depend on access to large\nsuitable datasets. In this article, we present how we build the LS-CAT\n(Large-Scale CUDA AutoTuning) dataset sourced from GitHub for the purpose of\ntraining NLP-based ML models. Our dataset includes 19 683 CUDA kernels focused\non linear algebra. In addition to the CUDA codes, our LS-CAT dataset contains 5\n028 536 associated runtimes, with different combinations of kernels, block\nsizes and matrix sizes. The runtime are GPU benchmarks on both Nvidia GTX 980\nand Nvidia T4 systems. This information creates a foundation upon which\nNLP-based models can find correlations between source-code features and optimal\nchoice of thread block sizes.\n  There are several results that can be drawn out of our LS-CAT database. E.g.,\nour experimental results show that an optimal choice in thread block size can\ngain an average of 6% for the average case. We thus also analyze how much\nperformance increase can be achieved in general, finding that in 10% of the\ncases more than 20% performance increase can be achieved by using the optimal\nblock. A description of current and future work is also included.\n"}
+{"abstract": "  In this paper, we present a new tracking architecture with an encoder-decoder\ntransformer as the key component. The encoder models the global spatio-temporal\nfeature dependencies between target objects and search regions, while the\ndecoder learns a query embedding to predict the spatial positions of the target\nobjects. Our method casts object tracking as a direct bounding box prediction\nproblem, without using any proposals or predefined anchors. With the\nencoder-decoder transformer, the prediction of objects just uses a simple\nfully-convolutional network, which estimates the corners of objects directly.\nThe whole method is end-to-end, does not need any postprocessing steps such as\ncosine window and bounding box smoothing, thus largely simplifying existing\ntracking pipelines. The proposed tracker achieves state-of-the-art performance\non five challenging short-term and long-term benchmarks, while running at\nreal-time speed, being 6x faster than Siam R-CNN. Code and models are\nopen-sourced at https://github.com/researchmm/Stark.\n"}
+{"abstract": "  There has been an increasing consensus in learning based face anti-spoofing\nthat the divergence in terms of camera models is causing a large domain gap in\nreal application scenarios. We describe a framework that eliminates the\ninfluence of inherent variance from acquisition cameras at the feature level,\nleading to the generalized face spoofing detection model that could be highly\nadaptive to different acquisition devices. In particular, the framework is\ncomposed of two branches. The first branch aims to learn the camera invariant\nspoofing features via feature level decomposition in the high frequency domain.\nMotivated by the fact that the spoofing features exist not only in the high\nfrequency domain, in the second branch the discrimination capability of\nextracted spoofing features is further boosted from the enhanced image based on\nthe recomposition of the high-frequency and low-frequency information. Finally,\nthe classification results of the two branches are fused together by a\nweighting strategy. Experiments show that the proposed method can achieve\nbetter performance in both intra-dataset and cross-dataset settings,\ndemonstrating the high generalization capability in various application\nscenarios.\n"}
+{"abstract": "  Ensemble-based modifications of the well-known SHapley Additive exPlanations\n(SHAP) method for the local explanation of a black-box model are proposed. The\nmodifications aim to simplify SHAP which is computationally expensive when\nthere is a large number of features. The main idea behind the proposed\nmodifications is to approximate SHAP by an ensemble of SHAPs with a smaller\nnumber of features. According to the first modification, called ER-SHAP,\nseveral features are randomly selected many times from the feature set, and\nShapley values for the features are computed by means of \"small\" SHAPs. The\nexplanation results are averaged to get the final Shapley values. According to\nthe second modification, called ERW-SHAP, several points are generated around\nthe explained instance for diversity purposes, and results of their explanation\nare combined with weights depending on distances between points and the\nexplained instance. The third modification, called ER-SHAP-RF, uses the random\nforest for preliminary explanation of instances and determining a feature\nprobability distribution which is applied to selection of features in the\nensemble-based procedure of ER-SHAP. Many numerical experiments illustrating\nthe proposed modifications demonstrate their efficiency and properties for\nlocal explanation.\n"}
+{"abstract": "  A Lagrangian field on a symplectic manifold $M$ is a family\n$\\Lambda=\\{\\Lambda_x|x \\in M\\}$ of pointed Lagrangian submanifolds of $M$. This\nnotion is a generalization of a real Lagrangian polarization for which each\n$\\Lambda_x$ is the leaf containing $x$. Two Lagrangian fields $\\Lambda$ and\n$\\tilde \\Lambda$ are called transversal if $\\Lambda_x$ intersects\n$\\tilde\\Lambda_x$ transversally at $x$ for every $x$. Two transversal\nLagrangian fields determine an almost para-K\\\"ahler structure on $M$. We\nconstruct a local symplectic groupoid on a neighborhood of the zero section of\n$T^\\ast M$ from two transversal Lagrangian fields on $M$. The Lagrangian\nmanifold of $n$-cycles of this groupoid in $(T^\\ast M)^n$ has a generating\nfunction whose germ around the diagonal of $M^n$ is given by the $n$-point\ncyclic Calabi function of a closed (1,1)-form on a neighborhood of the diagonal\nof $M^2$ obtained from the symplectic form on $M$.\n"}
+{"abstract": "  The Laser Interferometer Gravitational-wave Observatory Scientific\nCollaboration and Virgo Collaboration (LVC) sent out 56 gravitational-wave (GW)\nnotices during the third observing run (O3). Japanese collaboration for\nGravitational wave ElectroMagnetic follow-up (J-GEM) performed optical and\nnear-infrared observations to identify and observe an electromagnetic (EM)\ncounterpart. We constructed web-based system which enabled us to obtain and\nshare information of candidate host galaxies for the counterpart, and status of\nour observations. Candidate host galaxies were selected from the GLADE catalog\nwith a weight based on the three-dimensional GW localization map provided by\nLVC. We conducted galaxy-targeted and wide-field blind surveys, real-time data\nanalysis, and visual inspection of observed galaxies. We performed\ngalaxy-targeted follow-ups to 23 GW events during O3, and the maximum\nprobability covered by our observations reached to 9.8%. Among them, we\nsuccessfully started observations for 10 GW events within 0.5 days after the\ndetection. This result demonstrates that our follow-up observation has a\npotential to constrain EM radiation models for a merger of binary neutron stars\nat a distance of up to $\\sim$100~Mpc with a probability area of $\\leq$\n500~deg$^2$.\n"}
+{"abstract": "  Using firm-level survey- and register-data for both Sweden and Denmark we\nshow systematic mis-measurement in both vacancy measures. While the\nregister-based measure on the aggregate constitutes a quarter of the\nsurvey-based measure, the latter is not a super-set of the former. To obtain\nthe full set of unique vacancies in these two databases, the number of survey\nvacancies should be multiplied by approximately 1.2. Importantly, this\nadjustment factor varies over time and across firm characteristics. Our\nfindings have implications for both the search-matching literature and policy\nanalysis based on vacancy measures: Observed changes in vacancies can be an\noutcome of changes in mis-measurement, and are not necessarily changes in the\nactual number of vacancies.\n"}
+{"abstract": "  Surface stress and surface energy are two fundamental parameters that\ndetermine the surface properties of any materials. While it is commonly\nbelieved that the surface stress and surface energy of liquids are identical,\nthe relationship between the two parameters in soft polymeric gels remains\ndebatable. In this work, we measured the surface stress and surface energy of\nsoft silicone gels with varying weight ratios of crosslinkers in soft wetting\nexperiments. Above a critical density, $k_0$, the surface stress was found to\nincrease significantly with crosslinking density while the surface energy\nremained unchanged. In this regime, we can estimate a non-zero surface elastic\nmodulus that also increases with the ratio of crosslinkers. By comparing the\nsurface mechanics of the soft gels with their bulk rheology, the surface\nproperties near the critical density $k_0$ were found to be closely related to\nthe underlying percolation transition of the polymer networks.\n"}
+{"abstract": "  In order to face the complexity of business environments and detect\npriorities while triggering contingency strategies, we propose a new\nmethodological approach that combines text mining, social network and big data\nanalytics, with the assessment of stakeholders' attitudes towards company core\nvalues. This approach was applied in a case study where we considered the\nTwitter discourse about core values in Italy. We collected more than 94,000\ntweets related to the core values of the firms listed in Fortune's ranking of\nthe World's Most Admired Companies (2013-2017). For the Italian scenario, we\nfound three predominant core values orientations (Customers, Employees and\nExcellence) - which should be at the basis of any business strategy - and three\nlatent ones (Economic-Financial Growth, Citizenship and Social Responsibility),\nwhich need periodic attention. Our contribution is mostly methodological and\nextends the research on text mining and on online big data analytics applied in\ncomplex business contexts.\n"}
+{"abstract": "  The HIBEAM/NNBAR experiment is a free-neutron search for $n \\rightarrow$\nsterile $n$ and $n \\rightarrow \\bar{n}$ oscillations planned to be installed at\nthe European Spallation Source under construction in Lund, Sweden. A key\ncomponent in the experiment is the detector to identify $n-\\bar{n}$\nannihilation events, which will produce on average four pions with a final\nstate invariant mass of two nucleons, around $1.9\\,$GeV. The beamline and\nexperiment are shielded from magnetic fields which would suppress $n\n\\rightarrow \\bar{n}$ transitions, thus no momentum measurement will be\npossible. Additionally, calorimetry for particles with kinetic energies below\n$600\\,$MeV is challenging, as traditional sampling calorimeters used in HEP\nwould suffer from poor shower statistics. A design study is underway to use a\nnovel approach of a hadronic range measurement in multiple plastic scintillator\nlayers, followed by EM calorimetery with lead glass. A prototype calorimeter\nsystem is being built, and will eventually be installed at an ESS test beam\nline for \\textit{in situ} neutron background studies.\n"}
+{"abstract": "  We present an empirical model for nitric oxide NO in the mesosphere\n($\\approx$60--90 km) derived from SCIAMACHY (SCanning Imaging Absorption\nspectroMeter for Atmospheric CHartoghraphY) limb scan data. This work\ncomplements and extends the NOEM (Nitric Oxide Empirical Model; Marsh et al.,\n2004) and SANOMA (SMR Acquired Nitric Oxide Model Atmosphere; Kiviranta et al.,\n2018) empirical models in the lower thermosphere. The regression ansatz builds\non the heritage of studies by Hendrickx et al. (2017) and the superposed epoch\nanalysis by Sinnhuber et al. (2016) which estimate NO production from particle\nprecipitation.\n  Our model relates the daily (longitudinally) averaged NO number densities\nfrom SCIAMACHY (Bender et al., 2017a, b) as a function of geomagnetic latitude\nto the solar Lyman-alpha and the geomagnetic AE (auroral electrojet) indices.\nWe use a non-linear regression model, incorporating a finite and seasonally\nvarying lifetime for the geomagnetically induced NO. We estimate the parameters\nby finding the maximum posterior probability and calculate the parameter\nuncertainties using Markov chain Monte Carlo sampling. In addition to providing\nan estimate of the NO content in the mesosphere, the regression coefficients\nindicate regions where certain processes dominate.\n"}
+{"abstract": "  In this paper, we propose a novel score-base generative model for\nunconditional raw audio synthesis. Our proposal builds upon the latest\ndevelopments on diffusion process modeling with stochastic differential\nequations, which already demonstrated promising results on image generation. We\nmotivate novel heuristics for the choice of the diffusion processes better\nsuited for audio generation, and consider the use of a conditional U-Net to\napproximate the score function. While previous approaches on diffusion models\non audio were mainly designed as speech vocoders in medium resolution, our\nmethod termed CRASH (Controllable Raw Audio Synthesis with High-resolution)\nallows us to generate short percussive sounds in 44.1kHz in a controllable way.\nThrough extensive experiments, we showcase on a drum sound generation task the\nnumerous sampling schemes offered by our method (unconditional generation,\ndeterministic generation, inpainting, interpolation, variations,\nclass-conditional sampling) and propose the class-mixing sampling, a novel way\nto generate \"hybrid\" sounds. Our proposed method closes the gap with GAN-based\nmethods on raw audio, while offering more flexible generation capabilities with\nlighter and easier-to-train models.\n"}
+{"abstract": "  The Apollo 11 Eagle Lunar Module ascent stage was abandoned in lunar orbit\nafter the historic landing in 1969. Its fate is unknown. Numerical analysis\ndescribed here provides evidence that this object might have remained in lunar\norbit to the present day. The simulations show a periodic variation in\neccentricity of the orbit, correlated to the selenographic longitude of the\napsidal line. The rate of apsidal precession is correlated to eccentricity.\nThese two factors appear to interact to stabilize the orbit over the long term.\n"}
+{"abstract": "  Millimeter wave wireless spectrum deployments will allow vehicular\ncommunications to share high data rate vehicular sensor data in real-time. The\nhighly directional nature of wireless links in millimeter spectral bands will\nrequire continuous channel measurements to ensure the transmitter (TX) and\nreceiver (RX) beams are aligned to provide the best channel. Using real-world\nvehicular mmWave measurement data at 28 GHz, we determine the optimal beam\nsweeping period, i.e. the frequency of the channel measurements, to align the\nRX beams to the best channel directions for maximizing the\nvehicle-to-infrastructure (V2I) throughput. We show that in a realistic\nvehicular traffic environment in Austin, TX, for a vehicle traveling at an\naverage speed of 10.5 mph, a beam sweeping period of 300 ms in future V2I\ncommunication standards would maximize the V2I throughput, using a system of\nfour RX phased arrays that scanned the channel 360 degrees in the azimuth and\n30 degrees above and below the boresight. We also investigate the impact of the\nnumber of active RX chains controlling the steerable phased arrays on V2I\nthroughput. Reducing the number of RX chains controlling the phased arrays\nhelps reduce the cost of the vehicular mmWave hardware while multiple RX\nchains, although more expensive, provide more robustness to beam direction\nchanges at the vehicle, allowing near maximum throughput over a wide range of\nbeam sweep periods. We show that the overhead of utilizing one RX chain instead\nof four leads to a 10% drop in mean V2I throughput over six non-line-of-sight\nruns in real traffic conditions, with each run being 10 to 20 seconds long over\na distance of 40 to 90 meters.\n"}
+{"abstract": "  Generating synthesised singing voice with models trained on speech data has\nmany advantages due to the models' flexibility and controllability. However,\nsince the information about the temporal relationship between segments and\nbeats are lacking in speech training data, the synthesised singing may sound\noff-beat at times. Therefore, the availability of the information on the\ntemporal relationship between speech segments and music beats is crucial. The\ncurrent study investigated the segment-beat synchronisation in singing data,\nwith hypotheses formed based on the linguistics theories of P-centre and\nsonority hierarchy. A Mandarin corpus and an English corpus of professional\nsinging data were manually annotated and analysed. The results showed that the\npresence of musical beats was more dependent on segment duration than sonority.\nHowever, the sonority hierarchy and the P-centre theory were highly related to\nthe location of beats. Mandarin and English demonstrated cross-linguistic\nvariations despite exhibiting common patterns.\n"}
+{"abstract": "  Attribution of paintings is a critical problem in art history. This study\nextends machine learning analysis to surface topography of painted works. A\ncontrolled study of positive attribution was designed with paintings produced\nby a class of art students. The paintings were scanned using a confocal optical\nprofilometer to produce surface data. The surface data were divided into\nvirtual patches and used to train an ensemble of convolutional neural networks\n(CNNs) for attribution. Over a range of patch sizes from 0.5 to 60 mm, the\nresulting attribution was found to be 60 to 96% accurate, and, when comparing\nregions of different color, was nearly twice as accurate as CNNs using color\nimages of the paintings. Remarkably, short length scales, as small as twice a\nbristle diameter, were the key to reliably distinguishing among artists. These\nresults show promise for real-world attribution, particularly in the case of\nworkshop practice.\n"}
+{"abstract": "  Several challenging optimization problems arise while considering the\ndeployment of the space-air-ground integrated networks (SAGINs), among which\nthe optimal satellite gateway deployment problem is of significant importance.\nMoreover, with the increasing interest in the software-defined integration of\n5G networks and satellites, the existence of an effective scheme for optimal\nplacement of SDN controllers is essential. In this paper, we discuss the\ninterrelation between the two problems above and propose suitable methods to\nsolve them under various network design criteria. We first provide a MILP model\nfor solving the joint problem, and then motivate the decomposition of the model\ninto two disjoint MILPs. We then show that the resulting problems can be\nmodeled as the optimization of submodular set functions and can be solved\nefficiently with provable optimality gaps.\n"}
+{"abstract": "  A key element in solving real-life data science problems is selecting the\ntypes of models to use. Tree ensemble models (such as XGBoost) are usually\nrecommended for classification and regression problems with tabular data.\nHowever, several deep learning models for tabular data have recently been\nproposed, claiming to outperform XGBoost for some use cases. This paper\nexplores whether these deep models should be a recommended option for tabular\ndata by rigorously comparing the new deep models to XGBoost on various\ndatasets. In addition to systematically comparing their performance, we\nconsider the tuning and computation they require. Our study shows that XGBoost\noutperforms these deep models across the datasets, including the datasets used\nin the papers that proposed the deep models. We also demonstrate that XGBoost\nrequires much less tuning. On the positive side, we show that an ensemble of\ndeep models and XGBoost performs better on these datasets than XGBoost alone.\n"}
+{"abstract": "  For each $\\lambda>0$ and every square-integrable infinitely-divisible (ID)\ndistribution there exists at least one stationary stochastic process $t\\mapsto\nX_t$ with the specified distribution for $X_1$ and with first-order\nautoregressive (AR(1)) structure in the sense that the autocorrelation of $X_s$\nand $X_t$ is $\\exp(-\\lambda|s-t|)$ for all indices $s,t$. For the special case\nof the standard Normal distribution, the process $X_t$ is unique -- namely, the\nfirst-order autoregressive Ornstein-Uhlenbeck velocity process. The process\n$X_t$ is also uniquely determined if $X_1$ is accorded the unit rate Poisson\ndistribution.\n  For the Gamma distribution, however, $X_t$ is \\emph{not} determined uniquely.\nIn these lecture notes we describe six distinct processes with the same\nunivariate marginal distributions and AR(1) autocorrelation function. We\nexplore a few of their properties and describe methods of simulating their\nsample paths.\n"}
+{"abstract": "  Predicting multiple plausible future trajectories of the nearby vehicles is\ncrucial for the safety of autonomous driving. Recent motion prediction\napproaches attempt to achieve such multimodal motion prediction by implicitly\nregularizing the feature or explicitly generating multiple candidate proposals.\nHowever, it remains challenging since the latent features may concentrate on\nthe most frequent mode of the data while the proposal-based methods depend\nlargely on the prior knowledge to generate and select the proposals. In this\nwork, we propose a novel transformer framework for multimodal motion\nprediction, termed as mmTransformer. A novel network architecture based on\nstacked transformers is designed to model the multimodality at feature level\nwith a set of fixed independent proposals. A region-based training strategy is\nthen developed to induce the multimodality of the generated proposals.\nExperiments on Argoverse dataset show that the proposed model achieves the\nstate-of-the-art performance on motion prediction, substantially improving the\ndiversity and the accuracy of the predicted trajectories. Demo video and code\nare available at https://decisionforce.github.io/mmTransformer.\n"}
+{"abstract": "  The quest of utilizing neutral particles to simulate the behaviour of charged\nparticles in a magnetic field makes the generation of artificial magnetic field\nof great interest. The previous and the only proposal for the production of\nsynthetic magnetic field for the dark state polaritons in electromagnetically\ninduced transparency invokes the mechanical rotation of a sample. Here, we put\nforward an optical scheme to generate effective gauge potentials for\nstationary-light polaritons. 5To demonstrate the capabilities of our approach,\nwe present recipes for having dark state polaritons in degenerate Landau levels\nand in driven quantum harmonic oscillator. Our scheme paves a novel way towards\nthe investigation of the bosonic analogue of the fractional quantum Hall effect\nby electromagnetically induced transparency.\n"}
+{"abstract": "  We investigate the solution landscapes of a simplified Ericksen--Leslie (sEL)\nvector model for nematic liquid crystals, confined in a two-dimensional square\ndomain with tangent boundary conditions. An efficient numerical algorithm is\ndeveloped to construct the solution landscapes by utilizing the symmetry\nproperties of the model and the domain. Since the sEL model and the reduced\nLandau--de Gennes (rLdG) models can be viewed as Ginzburg--Landau functionals,\nwe systematically compute the solution landscapes of the sEL model, for\ndifferent domain sizes, and compare with the solution landscapes of the\ncorresponding rLdG models. There are many similarities, including the stable\ndiagonal and rotated states, bifurcation behaviors, and sub-solution landscapes\nwith low-index saddle solutions. Significant disparities also exist between the\ntwo models. The sEL vector model exhibits the stable solution $C\\pm$ with\ninterior defects, high-index \"fake defects\" solutions, novel tessellating\nsolutions, and certain types of distinctive dynamical pathways. The solution\nlandscape approach provides a comprehensive and efficient way for model\ncomparison and is applicable to a wide range of mathematical models in physics.\n"}
+{"abstract": "  Several machine learning-based spoiler detection models have been proposed\nrecently to protect users from spoilers on review websites. Although dependency\nrelations between context words are important for detecting spoilers, current\nattention-based spoiler detection models are insufficient for utilizing\ndependency relations. To address this problem, we propose a new spoiler\ndetection model called SDGNN that is based on syntax-aware graph neural\nnetworks. In the experiments on two real-world benchmark datasets, we show that\nour SDGNN outperforms the existing spoiler detection models.\n"}
+{"abstract": "  Squeezed light finds many important applications in quantum information\nscience and quantum metrology, and has been produced in a variety of physical\nsystems involving optical nonlinear processes. Here, we show how a nonlinear\nmagnetostrictive interaction in a ferrimagnet in cavity magnomechanics can be\nused to reduce quantum noise of the electromagnetic field. We show optimal\nparameter regimes where a substantial and stationary squeezing of the microwave\noutput field can be achieved. The scheme can be realized within the reach of\ncurrent technology in cavity electromagnonics and magnomechanics. Our work\nprovides a new and practicable approach for producing squeezed vacuum states of\nelectromagnetic fields, and may find promising applications in quantum\ninformation processing and quantum metrology.\n"}
+{"abstract": "  The presented compact algorithm for recognizing handwritten digits of the\nMNIST database, created on the LogNNet reservoir neural network, reaches the\nrecognition accuracy of 82%. The algorithm was tested on a low-memory Arduino\nboard with 2 Kb static RAM low-power microcontroller. The dependences of the\naccuracy and time of image recognition on the number of neurons in the\nreservoir have been investigated. The memory allocation demonstrates that the\nalgorithm stores all the necessary information in RAM without using additional\ndata storage, and operates with original images without preliminary processing.\nThe simple structure of the algorithm, with appropriate training, can be\nadapted for wide practical application, for example, for creating mobile\nbiosensors for early diagnosis of adverse events in medicine. The study results\nare important for the implementation of artificial intelligence on peripheral\nconstrained IoT devices and for edge computing.\n"}
+{"abstract": "  We present results from the NIRVANDELS survey investigating the gas-phase\nmetallicity ($\\mathrm{Z}_{\\mathrm{gas}}$, tracing O/H) and stellar metallicity\n($Z_{\\star}$, tracing Fe/H) of 33 star-forming galaxies at redshifts $2.95 < z\n< 3.80$. Based on a combined analysis of deep optical and near-IR spectra,\ntracing the rest-frame far ultraviolet and rest-frame optical respectively, we\npresent the first simultaneous determination of the stellar and gas-phase\nmass-metallicity relationships (MZRs) at $z\\simeq3.4$. In both cases, we find\nthat metallicity increases with increasing stellar mass ($M_{\\star}$), and that\nthe power-law slope at $M_{\\star} \\lesssim 10^{10} \\mathrm{M}_{\\odot}$ of both\nMZRs scales as $Z \\propto M_{\\star}^{0.3}$. Comparing the stellar and gas-phase\nMZRs, we present direct evidence for super-solar O/Fe ratios (i.e.,\n$\\alpha$-enhancement) at $z>3$, finding $\\mathrm{(O/Fe)}\\simeq (2.54 \\pm 0.38)\n\\times \\mathrm{(O/Fe)}_{\\odot}$, with no clear dependence on $M_{\\star}$.\n"}
+{"abstract": "  We consider the Navier-Stokes equations on the two-dimensional unit sphere\nand study the linear stability of the two-jet Kolmogorov type flow which is a\nstationary solution given by the zonal spherical harmonic function of degree\ntwo. We prove the linear stability of the two-jet Kolmogorov type flow for an\narbitrary viscosity coefficient by showing the exponential decay of a solution\nto the linearized equation towards an equilibrium which grows as the viscosity\ncoefficient tends to zero. The main result of this paper is the nonexistence of\nnonzero eigenvalues of the perturbation operator appearing in the linearized\nequation. By making use of the mixing property of the perturbation operator\nwhich is expressed by a recurrence relation for the spherical harmonics, we\nshow that the perturbation operator does not have not only nonreal but also\nnonzero real eigenvalues. As an application of this result, we get the enhanced\ndissipation for the two-jet Kolmogorov type flow in the sense that a solution\nto the linearized equation rescaled in time decays arbitrarily fast as the\nviscosity coefficient tends to zero.\n"}
+{"abstract": "  Effective conservation actions require effective population monitoring.\nHowever, accurately counting animals in the wild to inform conservation\ndecision-making is difficult. Monitoring populations through image sampling has\nmade data collection cheaper, wide-reaching and less intrusive but created a\nneed to process and analyse this data efficiently. Counting animals from such\ndata is challenging, particularly when densely packed in noisy images.\nAttempting this manually is slow and expensive, while traditional computer\nvision methods are limited in their generalisability. Deep learning is the\nstate-of-the-art method for many computer vision tasks, but it has yet to be\nproperly explored to count animals. To this end, we employ deep learning, with\na density-based regression approach, to count fish in low-resolution sonar\nimages. We introduce a large dataset of sonar videos, deployed to record wild\nmullet schools (Mugil liza), with a subset of 500 labelled images. We utilise\nabundant unlabelled data in a self-supervised task to improve the supervised\ncounting task. For the first time in this context, by introducing uncertainty\nquantification, we improve model training and provide an accompanying measure\nof prediction uncertainty for more informed biological decision-making.\nFinally, we demonstrate the generalisability of our proposed counting framework\nthrough testing it on a recent benchmark dataset of high-resolution annotated\nunderwater images from varying habitats (DeepFish). From experiments on both\ncontrasting datasets, we demonstrate our network outperforms the few other deep\nlearning models implemented for solving this task. By providing an open-source\nframework along with training data, our study puts forth an efficient deep\nlearning template for crowd counting aquatic animals thereby contributing\neffective methods to assess natural populations from the ever-increasing visual\ndata.\n"}
+{"abstract": "  Sensitivity limits are usually determined using the Cram\\'er-Rao bound.\nRecently this approach has been used to obtain the ultimate resolution limit\nfor the estimation of the separation between two incoherent point sources.\nHowever, methods that saturate these resolution limits, usually require the\nfull measurement statistics, which can be challenging to access. In this work,\nwe introduce a simple superresolution protocol to estimate the separation\nbetween two thermal sources which relies only on the average value of a single\naccessible observable. We show how optimal observables for this technique may\nbe constructed for arbitrary thermal sources, and we study their sensitivities\nwhen one has access to spatially resolved intensity measurements (direct\nimaging) and photon counting after spatial mode demultiplexing. For\ndemultiplexing, our method is optimal, i.e. it saturates the quantum\nCram\\'er-Rao bound. We also investigate the impact of noise on the optimal\nobservables, their measurement sensitivity and on the scaling with the number\nof detected photons of the smallest resolvable separation. For low signals in\nthe image plane, we demonstrate that our method saturates the Cram\\'er-Rao\nbound even in the presence of noise.\n"}
+{"abstract": "  This work focuses on the problem of unraveling nonlinearly mixed latent\ncomponents in an unsupervised manner. The latent components are assumed to\nreside in the probability simplex, and are transformed by an unknown\npost-nonlinear mixing system. This problem finds various applications in signal\nand data analytics, e.g., nonlinear hyperspectral unmixing, image embedding,\nand nonlinear clustering. Linear mixture learning problems are already\nill-posed, as identifiability of the target latent components is hard to\nestablish in general. With unknown nonlinearity involved, the problem is even\nmore challenging. Prior work offered a function equation-based formulation for\nprovable latent component identification. However, the identifiability\nconditions are somewhat stringent and unrealistic. In addition, the\nidentifiability analysis is based on the infinite sample (i.e., population)\ncase, while the understanding for practical finite sample cases has been\nelusive. Moreover, the algorithm in the prior work trades model expressiveness\nwith computational convenience, which often hinders the learning performance.\nOur contribution is threefold. First, new identifiability conditions are\nderived under largely relaxed assumptions. Second, comprehensive sample\ncomplexity results are presented -- which are the first of the kind. Third, a\nconstrained autoencoder-based algorithmic framework is proposed for\nimplementation, which effectively circumvents the challenges in the existing\nalgorithm. Synthetic and real experiments corroborate our theoretical analyses.\n"}
+{"abstract": "  Generative adversarial networks (GANs) have proven to be surprisingly\nefficient for image editing by inverting and manipulating the latent code\ncorresponding to a natural image. This property emerges from the disentangled\nnature of the latent space. In this paper, we identify two geometric\nlimitations of such latent space: (a) euclidean distances differ from image\nperceptual distance, and (b) disentanglement is not optimal and facial\nattribute separation using linear model is a limiting hypothesis. We thus\npropose a new method to learn a proxy latent representation using normalizing\nflows to remedy these limitations, and show that this leads to a more efficient\nspace for face image editing.\n"}
+{"abstract": "  The Dark Matter Particle Explorer (DAMPE) is a space-borne particle detector\nand cosmic ray observatory in operation since 2015, designed to probe electrons\nand gamma rays from a few GeV to 10 TeV energy, as well as cosmic protons and\nnuclei up to 100 TeV. Among the main scientific objectives is the precise\nmeasurement of the cosmic electron+positron flux, which due to the very large\nproton background in orbit requires a powerful particle identification method.\nIn the past decade, the field of machine learning has provided us the needed\ntools. This paper presents a neural network based approach to cosmic electron\nidentification and proton rejection and showcases its performances based on\nsimulated Monte Carlo data. The neural network reaches significantly lower\nbackground than the classical, cut-based method for the same detection\nefficiency, especially at highest energies. A good matching between simulations\nand real data completes the picture.\n"}
+{"abstract": "  A simple yet effective achiral platform using elliptical nanoholes for\nchiroptical analysis is demonstrated. Under linearly polarized excitation, an\nelliptical nanohole in a thin gold film can generate a localized chiral optical\nfield for chiroptical analysis and simultaneously serve as a near-field optical\ntrap to capture dielectric and plasmonic nanospheres. In particular, the\ntrapping potential is enantioselective for dielectric nanospheres, i.e., the\nhole traps or repels the dielectric nanoparticles depending on the sample\nchirality. For plasmonic nanospheres, the trapping potential well is much\ndeeper than that for dielectric particles, rendering the enantioselectivity\nless pronounced. This platform is suitable for chiral analysis with\nnanoparticle-based solid-state extraction and pre-concentration. Compared to\nplasmonic chiroptical sensing using chiral structures or circularly polarized\nlight, elliptical nanoholes are a simple and effective platform, which is\nexpected to have a relatively low background because chiroptical noise from the\nstructure or chiral species outside the nanohole is minimized. The usage of\nlinearly polarized excitation also makes the platform easily compatible with a\ncommercial optical microscope.\n"}
+{"abstract": "  This paper describes how to augment techniques such as Distributed Shared\nMemory with recent trends on disaggregated Non Volatile Memory in the data\ncentre so that the combination can be used in an edge environment with\npotentially volatile and mobile resources. This article identifies the main\nadvantages and challenges, and offers an architectural evolution to incorporate\nrecent research trends into production-ready disaggregated edges. We also\npresent two prototypes showing the feasibility of this proposal.\n"}
+{"abstract": "  We investigate the Cauchy problem and the diffusion asymptotics for a\nspatially inhomogeneous kinetic model associated to a nonlinear Fokker-Planck\noperator. Its solution describes the density evolution of interacting particles\nwhose mobility is hampered by their aggregation. When the initial data lies\nbelow a Maxwellian, we derive the global well-posedness with instantaneous\nsmoothness. The proof relies on hypoelliptic analogue of the classical\nparabolic theory, as well as a positivity-spreading result based on the Harnack\ninequality and barrier function methods. Moreover, the scaled equation leads to\nthe fast diffusion flow under the low field limit. The relative phi-entropy\nmethod enables us to see the connection between the overdamped dynamics of the\nnonlinearly coupled kinetic model and the correlated fast diffusion. The global\nin time quantitative diffusion asymptotics is then derived by combining\nentropic hypocoercivity, relative phi-entropy and barrier function methods.\n"}
+{"abstract": "  The quantum confinement effect on the spectrum of near-field thermal\nradiation by periodic and random arrays of quantum dots (QDs) is investigated.\nThe local density of states (LDOS) thermally emitted by QD arrays made of three\nlead chalcogenides, namely, lead sulfide, lead selenide, and lead telluride, is\ncomputed at a near-field distance from the arrays. The dielectric function of\nthe QDs is extracted from their absorption spectra by utilizing an optimization\ntechnique. The thermal discrete dipole approximation is used for computing the\nLDOS. It is shown that the peak wavenumber of near-field LDOS emitted by\nperiodic arrays of lead chalcogenide QDs can be significantly modulated (up to\n4490 cm-1) by varying the size of the dots. The LDOS is proportional to the\nimaginary part of the QDs' polarizability which peaks at the bandgap energy of\nthe QDs. The bandgap energy of the QDs (and thus the LDOS peak) is\nsignificantly affected by the quantum confinement effect which is\nsize-dependent. While the magnitude of thermal radiation by random arrays of\nQDs can be different from the periodic arrays with the same filling factor by\nup to 26%, the LDOS spectrum and peak location are the same for both periodic\nand random arrays. The peak wavenumber of near-field radiative heat transfer\nbetween the QD arrays is also strongly affected by quantum confinement in the\nQDs, and thus it can be tuned by changing the size of the QDs.\n"}
+{"abstract": "  Gradient descent (GD) type optimization schemes are the standard methods to\ntrain artificial neural networks (ANNs) with rectified linear unit (ReLU)\nactivation. Such schemes can be considered as discretizations of gradient flows\n(GFs) associated to the training of ANNs with ReLU activation and most of the\nkey difficulties in the mathematical convergence analysis of GD type\noptimization schemes in the training of ANNs with ReLU activation seem to be\nalready present in the dynamics of the corresponding GF differential equations.\nIt is the key subject of this work to analyze such GF differential equations in\nthe training of ANNs with ReLU activation and three layers (one input layer,\none hidden layer, and one output layer). In particular, in this article we\nprove in the case where the target function is possibly multi-dimensional and\ncontinuous and in the case where the probability distribution of the input data\nis absolutely continuous with respect to the Lebesgue measure that the risk of\nevery bounded GF trajectory converges to the risk of a critical point. In\naddition, in this article we show in the case of a 1-dimensional affine linear\ntarget function and in the case where the probability distribution of the input\ndata coincides with the standard uniform distribution that the risk of every\nbounded GF trajectory converges to zero if the initial risk is sufficiently\nsmall. Finally, in the special situation where there is only one neuron on the\nhidden layer (1-dimensional hidden layer) we strengthen the above named result\nfor affine linear target functions by proving that that the risk of every (not\nnecessarily bounded) GF trajectory converges to zero if the initial risk is\nsufficiently small.\n"}
+{"abstract": "  Hashing has been widely used in approximate nearest neighbor search for its\nstorage and computational efficiency. Deep supervised hashing methods are not\nwidely used because of the lack of labeled data, especially when the domain is\ntransferred. Meanwhile, unsupervised deep hashing models can hardly achieve\nsatisfactory performance due to the lack of reliable similarity signals. To\ntackle this problem, we propose a novel deep unsupervised hashing method,\nnamely Distilled Smooth Guidance (DSG), which can learn a distilled dataset\nconsisting of similarity signals as well as smooth confidence signals. To be\nspecific, we obtain the similarity confidence weights based on the initial\nnoisy similarity signals learned from local structures and construct a priority\nloss function for smooth similarity-preserving learning. Besides, global\ninformation based on clustering is utilized to distill the image pairs by\nremoving contradictory similarity signals. Extensive experiments on three\nwidely used benchmark datasets show that the proposed DSG consistently\noutperforms the state-of-the-art search methods.\n"}
+{"abstract": "  Mutation analysis assesses a test suite's adequacy by measuring its ability\nto detect small artificial faults, systematically seeded into the tested\nprogram. Mutation analysis is considered one of the strongest test-adequacy\ncriteria. Mutation testing builds on top of mutation analysis and is a testing\ntechnique that uses mutants as test goals to create or improve a test suite.\nMutation testing has long been considered intractable because the sheer number\nof mutants that can be created represents an insurmountable problem -- both in\nterms of human and computational effort. This has hindered the adoption of\nmutation testing as an industry standard. For example, Google has a codebase of\ntwo billion lines of code and more than 500,000,000 tests are executed on a\ndaily basis. The traditional approach to mutation testing does not scale to\nsuch an environment. To address these challenges, this paper presents a\nscalable approach to mutation testing based on the following main ideas: (1)\nMutation testing is done incrementally, mutating only changed code during code\nreview, rather than the entire code base; (2) Mutants are filtered, removing\nmutants that are likely to be irrelevant to developers, and limiting the number\nof mutants per line and per code review process; (3) Mutants are selected based\non the historical performance of mutation operators, further eliminating\nirrelevant mutants and improving mutant quality. Evaluation in a\ncode-review-based setting with more than 24,000 developers on more than 1,000\nprojects shows that the proposed approach produces orders of magnitude fewer\nmutants and that context-based mutant filtering and selection improve mutant\nquality and actionability. Overall, the proposed approach represents a mutation\ntesting framework that seamlessly integrates into the software development\nworkflow and is applicable up to large-scale industrial settings.\n"}
+{"abstract": "  Previous studies have verified that the functionality of black-box models can\nbe stolen with full probability outputs. However, under the more practical\nhard-label setting, we observe that existing methods suffer from catastrophic\nperformance degradation. We argue this is due to the lack of rich information\nin the probability prediction and the overfitting caused by hard labels. To\nthis end, we propose a novel hard-label model stealing method termed\n\\emph{black-box dissector}, which consists of two erasing-based modules. One is\na CAM-driven erasing strategy that is designed to increase the information\ncapacity hidden in hard labels from the victim model. The other is a\nrandom-erasing-based self-knowledge distillation module that utilizes soft\nlabels from the substitute model to mitigate overfitting. Extensive experiments\non four widely-used datasets consistently demonstrate that our method\noutperforms state-of-the-art methods, with an improvement of at most $8.27\\%$.\nWe also validate the effectiveness and practical potential of our method on\nreal-world APIs and defense methods. Furthermore, our method promotes other\ndownstream tasks, \\emph{i.e.}, transfer adversarial attacks.\n"}
+{"abstract": "  The dark photon is a new gauge boson which arises from an extra U'(1) gauge\nsymmetry. In this paper, a novel dark photon production mechanism based on\nMeV-scale $\\gamma$-$\\gamma$ collider is considered: $\\gamma \\gamma \\rightarrow\n\\gamma A'$. With the aid of PACKAGE-X, differential cross section of $\\gamma\n\\gamma \\rightarrow \\gamma A'$ is obtained, as a function of the kinetic mixing\nparameter $\\varepsilon$ and dark photon mass $m_{A'}$. Taking the\nlight-by-light scattering as background, the constraints on the dark photon\nparameter space for different time intervals in a MeV-scale $\\gamma$-$\\gamma$\ncollider are also given.\n"}
+{"abstract": "  The generation of robot motions in the real world is difficult by using\nconventional controllersalone and requires highly intelligent processing. In\nthis regard, learning-based motion generations are currently being\ninvestigated. However, the main issue has been improvements of the adaptability\nto spatially varying environments, but a variation of the operating speed has\nnot been investigated in detail. In contact-rich tasks, it is especially\nimportant to be able to adjust the operating speed because a nonlinear\nrelationship occurs between the operating speed and force (e.g., inertial and\nfrictional forces), and it affects the results of the tasks. Therefore, in this\nstudy, we propose a method for generating variable operating speeds while\nadapting to spatial perturbations in the environment. The proposed method can\nbe adapted to nonlinearities by utilizing a small amount of motion data. We\nexperimentally evaluated the proposed method by erasing a line using an eraser\nfixed to the tip of the robot as an example of a contact-rich task.\nFurthermore, the proposed method enables a robot to perform a task faster than\na human operator and is capable of operating close to the control bandwidth.\n"}
+{"abstract": "  The amount of information available to the general public is enormous, and it\nis challenging to extract meaningful and reliable content. The availability of\nnews sources and their trustability are the biggest problems for selecting the\nproper authority. Moreover, news providers' inability to publish good news on\nsocial media creates a gap for young people to access trustworthy information.\nThis article describes an automated system that can extract the essential news\nand publish them in a different form on social media networks.\n"}
+{"abstract": "  Understanding black hole microstructure via the thermodynamic geometry can\nprovide us with more deeper insight into black hole thermodynamics in modified\ngravities. In this paper, we study the black hole phase transition and\nRuppeiner geometry for the $d$-dimensional charged Gauss-Bonnet anti-de Sitter\nblack holes. The results show that the small-large black hole phase transition\nis universal in this gravity. By reducing the thermodynamic quantities with the\nblack hole charge, we clearly exhibit the phase diagrams in different parameter\nspaces. Of particular interest is that the radius of the black hole horizon can\nact as the order parameter to characterize the black hole phase transition. We\nalso disclose that different from the five-dimensional neutral black holes, the\ncharged ones allow the repulsive interaction among its microstructure for small\nblack hole of higher temperature. Another significant difference between them\nis that the microscopic interaction changes during the small-large black hole\nphase transition for the charged case, where the black hole microstructure\nundergoes a sudden change. These results are helpful for peeking into the\nmicrostructure of charged black holes in the Gauss-Bonnet gravity.\n"}
+{"abstract": "  The enrichment of coronal loops and the slow solar wind with elements that\nhave low First Ionisation Potential, known as the FIP effect, has often been\ninterpreted as the tracer of a common origin. A current explanation for this\nFIP fractionation rests on the influence of ponderomotive forces and turbulent\nmixing acting at the top of the chromosphere. The implied wave transport and\nturbulence mechanisms are also key to wave-driven coronal heating and solar\nwind acceleration models. This work makes use of a shell turbulence model run\non open and closed magnetic field lines of the solar corona to investigate with\na unified approach the influence of magnetic topology, turbulence amplitude and\ndissipation on the FIP fractionation. We try in particular to assess whether\nthere is a clear distinction between the FIP effect on closed and open field\nregions.\n"}
+{"abstract": "  Let $F_n$ denote the $n$-th Fibonacci number and $L_n$ the $n$-th Lucas\nnumber. We completely solve the family of cubic Thue equations\n$${(X-F_nY)(X-L_nY)X-Y^3=\\pm1}$$ and show that there are no non-trivial\nsolutions for $n\\neq 1,3$.\n"}
+{"abstract": "  Background: Blog posts are frequently used by software practitioners to share\ninformation about their practice. Blog posts therefore provide a potential\nsource of evidence for software engineering (SE) research. The use of blog\nposts as evidence for research appears contentious amongst some SE researchers.\nObjective: To better understand the actual and perceived value of blog posts as\nevidence for SE research, and to develop guidance for SE researchers on the use\nof blog posts as evidence. Method: We further analyse responses from a\npreviously conducted attitudinal survey of 44 software engineering researchers.\nWe conduct a heatmap analysis, simple statistical analysis, and a thematic\nanalysis. Results: We find no clear consensus from respondents on researchers'\nattitudes to the credibility of blog posts, or on a standard set of criteria to\nevaluate blog-post credibility. We show that some of the responses to the\nsurvey exhibit characteristics similar to the content of blog posts, e.g.,\nasserting prior beliefs as claims, with no citations and little supporting\nrationale. We illustrate our insights with ~60 qualitative examples from the\nsurvey ~40% of the total responses. We complement our quantitative and\nqualitative analyses with preliminary checklists to guide SE researchers.\nConclusion: Blog posts are relevant to research because they are written by\nsoftware practitioners describing their practice and experience. But evaluating\nthe credibility of blog posts, so as to select the higher-quality content,\nremains an ongoing challenge. The quantitative and qualitative results, with\nthe proposed checklists, are intended to stimulate reflection and action in the\nresearch community on the role of blog posts as evidence in software\nengineering research. Finally, our findings on researchers' attitudes to blog\nposts also provide more general insights into researchers' values for SE\nresearch.\n"}
+{"abstract": "  We study the collective dynamics of identical phase oscillators on globally\ncoupled networks whose interactions are asymmetric and mediated by positive and\nnegative couplings. We split the set of oscillators into two interconnected\nsubpopulations. In this setup, oscillators belonging to the same group interact\nvia symmetric couplings while the interaction between subpopulations occurs in\nan asymmetric fashion. By employing the dimensional reduction scheme of the\nOtt-Antonsen (OA) theory, we verify the existence of traveling-wave and\n$\\pi$-states, in addition to the classical fully synchronized and incoherent\nstates. Bistability between all collective states is reported. Analytical\nresults are generally in excellent agreement with simulations; for some\nparameters and initial conditions, however, we numerically detect chimera-like\nstates which are not captured by the OA theory.\n"}
+{"abstract": "  We present a new hybrid, local search algorithm for quantum approximate\noptimization of constrained combinatorial optimization problems. We focus on\nthe Maximum Independent Set problem and demonstrate the ability of quantum\nlocal search to solve large problem instances on quantum devices with few\nqubits. The quantum local search algorithm iteratively finds independent sets\nover carefully constructed neighborhoods and combines these solutions to obtain\na global solution. We compare the performance of this algorithm on 3-regular\ngraphs with up to 100 nodes against the well known classical\nBoppana-Halld\\'{o}rsson algorithm for the Maximum Independent Set problem.\n"}
+{"abstract": "  This paper proposes an efficient autonomous valet parking system utilizing\nonly cameras which are the most widely used sensor. To capture more information\ninstantaneously and respond rapidly to changes in the surrounding environment,\nfisheye cameras which have a wider angle of view compared to pinhole cameras\nare used. Accordingly, visual simultaneous localization and mapping is used to\nidentify the layout of the parking lot and track the location of the vehicle.\nIn addition, the input image frames are converted into around view monitor\nimages to resolve the distortion of fisheye lens because the algorithm to\ndetect edges are supposed to be applied to images taken with pinhole cameras.\nThe proposed system adopts a look up table for real time operation by\nminimizing the computational complexity encountered when processing AVM images.\nThe detection rate of each process and the success rate of autonomous parking\nwere measured to evaluate performance. The experimental results confirm that\nautonomous parking can be achieved using only visual sensors.\n"}
+{"abstract": "  A local UV cutoff $\\Lambda(x)$ transforming under Weyl rescalings allows to\nconstruct Weyl invariant kinetic terms for scalar fields including Wilsonian\ncutoff functions. First we consider scalar fields in curved space-time with\nlocal bare couplings of any canonical dimension, and anomalous dimensions which\ndescribe their dependence on the UV cutoff. The local component of the UV\ncutoff plays the role of an additional coupling, albeit with a trivial constant\n$\\beta$ function. This approach allows to derive Weyl consistency conditions\nfor the corresponding anomalous dimensions which assume the form of an exact\ngradient flow. For renormalizable theories the Weyl consistency conditions are\ninitially of the form of an approximate gradient flow for the $\\beta$\nfunctions, and we derive conditions under which it becomes the form of an exact\ngradient flow.\n"}
+{"abstract": "  Quantum computers have the potential to speed up certain computational tasks.\nA possibility this opens up within the field of machine learning is the use of\nquantum techniques that may be inefficient to simulate classically but could\nprovide superior performance in some tasks. Machine learning algorithms are\nubiquitous in particle physics and as advances are made in quantum machine\nlearning technology there may be a similar adoption of these quantum\ntechniques. In this work a quantum support vector machine (QSVM) is implemented\nfor signal-background classification. We investigate the effect of different\nquantum encoding circuits, the process that transforms classical data into a\nquantum state, on the final classification performance. We show an encoding\napproach that achieves an average Area Under Receiver Operating Characteristic\nCurve (AUC) of 0.848 determined using quantum circuit simulations. For this\nsame dataset the best classical method tested, a classical Support Vector\nMachine (SVM) using the Radial Basis Function (RBF) Kernel achieved an AUC of\n0.793. Using a reduced version of the dataset we then ran the algorithm on the\nIBM Quantum ibmq_casablanca device achieving an average AUC of 0.703. As\nfurther improvements to the error rates and availability of quantum computers\nmaterialise, they could form a new approach for data analysis in high energy\nphysics.\n"}
+{"abstract": "  Structural features are important features in a geometrical graph. Although\nthere are some correlation analysis of features based on covariance, there is\nno relevant research on structural feature correlation analysis with graph\nneural networks. In this paper, we introuduce graph feature to feature\n(Fea2Fea) prediction pipelines in a low dimensional space to explore some\npreliminary results on structural feature correlation, which is based on graph\nneural network. The results show that there exists high correlation between\nsome of the structural features. An irredundant feature combination with\ninitial node features, which is filtered by graph neural network has improved\nits classification accuracy in some graph-based tasks. We compare differences\nbetween concatenation methods on connecting embeddings between features and\nshow that the simplest is the best. We generalize on the synthetic geometric\ngraphs and certify the results on prediction difficulty between structural\nfeatures.\n"}
+{"abstract": "  We study elliptic and parabolic problems governed by the singular elliptic\noperators Delta_x+c\\yD_y-b\\y^2 on the half-space R^{N+1}_+.\n"}
+{"abstract": "  A variety of emergent phenomena has been enabled by interface engineering in\nthe complex oxides heterostructures. While extensive attention has been\nattracted to LaMnO3 (LMO) thin films for observing the control of\nfunctionalities at its interface with substrate, the nature of the magnetic\nphases in the thin film is, however,controversial. Here, it is reported that\nthe ferromagnetism in 2 and 5 unit cells thick LMO films epitaxially deposited\n(001)-SrTiO3 substrates ferromagnetic/ferromagnetic coupling in 8 and\n10-unit-cell ones, and a striking ferromagnetic/antiferromagnetic pinning\neffect with apparent positive exchange bias in 15 and 20-unit-cell ones are\nobserved. This novel phenomenon in both 15 and 20-unit-cell films indicates a\ncoexistence of three magnetic orderings in a single LMO film.The\nhigh-resolution scanning transmission electron microscopy suggests a P21/n to\nPbnm symmetry transition from interface to surface, with the spatial\nstratification of MnO6 octahedral morphology, corresponding to different\nmagnetic orderings. These results should shed some new lights on manipulating\nthe functionality of oxides by interface engineering.\n"}
+{"abstract": "  The ground truth used for training image, video, or speech quality prediction\nmodels is based on the Mean Opinion Scores (MOS) obtained from subjective\nexperiments. Usually, it is necessary to conduct multiple experiments, mostly\nwith different test participants, to obtain enough data to train quality models\nbased on machine learning. Each of these experiments is subject to an\nexperiment-specific bias, where the rating of the same file may be\nsubstantially different in two experiments (e.g. depending on the overall\nquality distribution). These different ratings for the same distortion levels\nconfuse neural networks during training and lead to lower performance. To\novercome this problem, we propose a bias-aware loss function that estimates\neach dataset's biases during training with a linear function and considers it\nwhile optimising the network weights. We prove the efficiency of the proposed\nmethod by training and validating quality prediction models on synthetic and\nsubjective image and speech quality datasets.\n"}
+{"abstract": "  Automatic speaker verification (ASV) is a well developed technology for\nbiometric identification, and has been ubiquitous implemented in\nsecurity-critic applications, such as banking and access control. However,\nprevious works have shown that ASV is under the radar of adversarial attacks,\nwhich are very similar to their original counterparts from human's perception,\nyet will manipulate the ASV render wrong prediction. Due to the very late\nemergence of adversarial attacks for ASV, effective countermeasures against\nthem are limited. Given that the security of ASV is of high priority, in this\nwork, we propose the idea of \"voting for the right answer\" to prevent risky\ndecisions of ASV in blind spot areas, by employing random sampling and voting.\nExperimental results show that our proposed method improves the robustness\nagainst both the limited-knowledge attackers by pulling the adversarial samples\nout of the blind spots, and the perfect-knowledge attackers by introducing\nrandomness and increasing the attackers' budgets.\n"}
+{"abstract": "  People's visual experiences of the world are easy to carve up and examine\nalong natural language boundaries, e.g., by category labels, attribute labels,\netc. However, it is more difficult to elicit detailed visuospatial information\nabout what a person attends to, e.g., the specific shape of a tree. Paying\nattention to the shapes of things not only feeds into well defined tasks like\nvisual category learning, but it is also what enables us to differentiate\nsimilarly named objects and to take on creative visual pursuits, like\npoetically describing the shape of a thing, or finding shapes in the clouds or\nstars. We use a new data collection method that elicits people's prioritized\nattention to shapes during visual photo inspection by asking them to trace\nimportant parts of the image under varying time constraints. Using data\ncollected via crowdsourcing over a set of 187 photographs, we examine changes\nin patterns of visual attention across individuals, across image types, and\nacross time constraints.\n"}
+{"abstract": "  In a recent work we presented a recursive algorithm to compute the matrix\nelements of a generic Gaussian transformation in the photon-number basis. Its\npurpose was to evolve a quantum state by building the transformation matrix and\nsubsequently computing the matrix-vector product. Here we present a faster\nalgorithm that computes the final state without having to generate the full\ntransformation matrix first. With this algorithm we bring the time complexity\nof computing the Gaussian evolution of an $N$-dimensional $M$-mode state from\n$O(MN^{2M})$ to $O(M(N^2/2)^M)$, which is an exponential improvement in the\nnumber of modes. In the special case of high squeezing, the evolved state can\nbe approximated with complexity $O(MN^{M})$. Our new algorithm is\ndifferentiable, which means we can use it in conjunction with gradient-based\noptimizers for circuit optimization tasks. We benchmark our algorithm by\noptimizing circuits to produce single photons, Gottesman-Kitaev-Preskill states\nand NOON states, showing that it is up to one order of magnitude faster than\nthe state of the art.\n"}
+{"abstract": "  The rapid increase of distributed energy resources has led to the widespread\ndeployment of microgrids. These flexible and efficient local energy grids are\nable to operate in both grid-connected mode and islanded mode; they are\ninterfaced to the main power system by a fast semiconductor switch and commonly\nmake use of inverter-interfaced generation. This paper focuses on inverter\ninterfaced microgrids, which present a challenge for protection as they do not\nprovide the high short-circuit current necessary for conventional\ntime-overcurrent protection. The application of admittance relaying for the\nprotection of inverter-interfaced microgrids is investigated as a potential\nsolution. The comparison of analytical and simulated results of performed four\nexperiments prove the suitability of admittance relaying for microgrids\nprotection.\n"}
+{"abstract": "  The task of semantic role labeling (SRL) is dedicated to finding the\npredicate-argument structure. Previous works on SRL are mostly supervised and\ndo not consider the difficulty in labeling each example which can be very\nexpensive and time-consuming. In this paper, we present the first neural\nunsupervised model for SRL. To decompose the task as two argument related\nsubtasks, identification and clustering, we propose a pipeline that\ncorrespondingly consists of two neural modules. First, we train a neural model\non two syntax-aware statistically developed rules. The neural model gets the\nrelevance signal for each token in a sentence, to feed into a BiLSTM, and then\nan adversarial layer for noise-adding and classifying simultaneously, thus\nenabling the model to learn the semantic structure of a sentence. Then we\npropose another neural model for argument role clustering, which is done\nthrough clustering the learned argument embeddings biased towards their\ndependency relations. Experiments on CoNLL-2009 English dataset demonstrate\nthat our model outperforms previous state-of-the-art baseline in terms of\nnon-neural models for argument identification and classification.\n"}
+{"abstract": "  This paper presents a neural-network-based solution to recover pixels\noccluded by clouds in satellite images. We leverage radio frequency (RF)\nsignals in the ultra/super-high frequency band that penetrate clouds to help\nreconstruct the occluded regions in multispectral images. We introduce the\nfirst multi-modal multi-temporal cloud removal model. Our model uses publicly\navailable satellite observations and produces daily cloud-free images.\nExperimental results show that our system significantly outperforms baselines\nby 8dB in PSNR. We also demonstrate use cases of our system in digital\nagriculture, flood monitoring, and wildfire detection. We will release the\nprocessed dataset to facilitate future research.\n"}
+{"abstract": "  Empirical mode decomposition (EMD) has developed into a prominent tool for\nadaptive, scale-based signal analysis in various fields like robotics, security\nand biomedical engineering. Since the dramatic increase in amount of data puts\nforward higher requirements for the capability of real-time signal analysis, it\nis difficult for existing EMD and its variants to trade off the growth of data\ndimension and the speed of signal analysis. In order to decompose\nmulti-dimensional signals at a faster speed, we present a novel\nsignal-serialization method (serial-EMD), which concatenates multi-variate or\nmulti-dimensional signals into a one-dimensional signal and uses various\none-dimensional EMD algorithms to decompose it. To verify the effects of the\nproposed method, synthetic multi-variate time series, artificial 2D images with\nvarious textures and real-world facial images are tested. Compared with\nexisting multi-EMD algorithms, the decomposition time becomes significantly\nreduced. In addition, the results of facial recognition with Intrinsic Mode\nFunctions (IMFs) extracted using our method can achieve a higher accuracy than\nthose obtained by existing multi-EMD algorithms, which demonstrates the\nsuperior performance of our method in terms of the quality of IMFs.\nFurthermore, this method can provide a new perspective to optimize the existing\nEMD algorithms, that is, transforming the structure of the input signal rather\nthan being constrained by developing envelope computation techniques or signal\ndecomposition methods. In summary, the study suggests that the serial-EMD\ntechnique is a highly competitive and fast alternative for multi-dimensional\nsignal analysis.\n"}
+{"abstract": "  The magnetar SGR J1745-2900 located in the vicinity of the supermassive black\nhole Sgr A$^{\\star}$ was detected during its X-ray outburst with the Swifht/XRT\ntelescope in April 2013. For several months after its detection the source was\nobserved with the NuSTAR observatory, which allowed pulsations with a period\n$\\sim3.76$ s to be recorded. Using these observations, we have studied in\ndetail the dependence of the pulse profile and the pulsed fraction on the\nenergy and intensity of the magnetar. The pulsed fraction in the 3-5 and 5-10\nkeV energy bands is shown to be 40-50%, slightly increasing with decreasing\nflux. We have performed phase-resolved spectroscopy for the source in the\nenergy band from 3 to $\\sim$40 keV and show that the temperature of the\nemitting regions remains fairly stable during the pulse, while their apparent\nsize changes significantly with phase.\n"}
+{"abstract": "  We relate the machinery of persistence modules to the Legendrian contact\nhomology theory and to Poisson bracket invariants, and use it to show the\nexistence of connecting trajectories of contact and symplectic Hamiltonian\nflows.\n"}
+{"abstract": "  Deep neural networks have been the driving force behind the success in\nclassification tasks, e.g., object and audio recognition. Impressive results\nand generalization have been achieved by a variety of recently proposed\narchitectures, the majority of which are seemingly disconnected. In this work,\nwe cast the study of deep classifiers under a unifying framework. In\nparticular, we express state-of-the-art architectures (e.g., residual and\nnon-local networks) in the form of different degree polynomials of the input.\nOur framework provides insights on the inductive biases of each model and\nenables natural extensions building upon their polynomial nature. The efficacy\nof the proposed models is evaluated on standard image and audio classification\nbenchmarks. The expressivity of the proposed models is highlighted both in\nterms of increased model performance as well as model compression. Lastly, the\nextensions allowed by this taxonomy showcase benefits in the presence of\nlimited data and long-tailed data distributions. We expect this taxonomy to\nprovide links between existing domain-specific architectures.\n"}
+{"abstract": "  Recently, much progress in natural language processing has been driven by\ndeep contextualized representations pretrained on large corpora. Typically, the\nfine-tuning on these pretrained models for a specific downstream task is based\non single-view learning, which is however inadequate as a sentence can be\ninterpreted differently from different perspectives. Therefore, in this work,\nwe propose a text-to-text multi-view learning framework by incorporating an\nadditional view -- the text generation view -- into a typical single-view\npassage ranking model. Empirically, the proposed approach is of help to the\nranking performance compared to its single-view counterpart. Ablation studies\nare also reported in the paper.\n"}
+{"abstract": "  This paper presents a techno-economic optimization model to analyze the\neconomic viability of a PV-battery system for different customer groups in\nSwitzerland clustered based on their annual electricity consumption, rooftop\nsize, annual irradiation and location. The simulations for a static investment\nmodel are carried out for years 2020-2050 and a comprehensive sensitivity\nanalysis is conducted to investigate the impacts of individual parameters such\nas costs, load profiles, electricity prices and tariffs, etc. Results show that\nwhile combining PV with batteries already results in better net present values\nthan PV alone for some customer groups today, the payback periods fluctuate\nbetween 2020 and 2035 due to the mixed effects of policy changes, costs and\nelectricity price developments. The optimal PV and battery sizes increase over\ntime and in 2050 the PV investment is mostly limited by the rooftop size. The\neconomic viability of PV-battery system investments varies between different\ncustomer groups and the most attractive investment (i.e., that has the shortest\npayback period) is mostly accessible to customer groups with higher annual\nirradiation and electricity demand. In addition, investment decisions are\nhighly sensitive to payback periods, future costs, electricity prices and\ntariff developments. Lastly, the grid impact of the PV-battery system\ndeployments are investigated by analyzing the residual Swiss system load\nprofiles. The dynamics of residual load profiles caused by the seasonal, daily\nand hourly patterns of the solar generation emphasizes the need for flexible\nresources with fast ramping capabilities.\n"}
+{"abstract": "  I simulate the evolution of a stellar system hosting two stellar populations\nwhose initial set up is defined according to the two main scenarios proposed\nfor the origin of multiple populations in Galactic globular clusters: (i)\nformation of a second generation from a cooling flow of pristine+polluted gas\nand (ii) accretion of polluted gas onto the proto-stellar disks of a fraction\nof low-mass stars. For this purpose, Monte Carlo simulations containing from\n$10^{5}$ up to $3\\cdot 10^{6}$ particles have been run including the effect of\nstellar evolution, binary interactions, external tidal field and a detailed\nmodelling of the proto-stellar disk structure. The early accretion of gas onto\nproto-stellar disks is unable to produce discrete populations and to alter the\nchemical composition of a significant ($>10\\%$) fraction of stars unless a disk\nlifetime larger ($t_{disk}\\sim20~Myr$) than that predicted by models is\nassumed. Moreover, in this scenario the mixing timescale of the two populations\nis too short to reproduce the observed segregation of the chemically enriched\npopulation. On the other hand, simulations run within the cooling flow scenario\ncan evolve after a Hubble time into stellar systems with a first-to-second\npopulation mass ratio similar to that observed in globular clusters, provided\nthat an initial filling-factor $r_{h}/r_{J}>0.15$ is adopted. However, in the\nweak tidal field regime a radial segregation of the second population stronger\nthan what observed in Milky Way globular clusters at large Galactocentric\ndistances is predicted. This discrepancy disappears in simulations following\neccentric orbits in a realistic axisymmetric potential.\n"}
+{"abstract": "  We consider the problem of jointly modeling and clustering populations of\ntensors by introducing a high-dimensional tensor mixture model with\nheterogeneous covariances. To effectively tackle the high dimensionality of\ntensor objects, we employ plausible dimension reduction assumptions that\nexploit the intrinsic structures of tensors such as low-rankness in the mean\nand separability in the covariance. In estimation, we develop an efficient\nhigh-dimensional expectation-conditional-maximization (HECM) algorithm that\nbreaks the intractable optimization in the M-step into a sequence of much\nsimpler conditional optimization problems, each of which is convex, admits\nregularization and has closed-form updating formulas. Our theoretical analysis\nis challenged by both the non-convexity in the EM-type estimation and having\naccess to only the solutions of conditional maximizations in the M-step,\nleading to the notion of dual non-convexity. We demonstrate that the proposed\nHECM algorithm, with an appropriate initialization, converges geometrically to\na neighborhood that is within statistical precision of the true parameter. The\nefficacy of our proposed method is demonstrated through comparative numerical\nexperiments and an application to a medical study, where our proposal achieves\nan improved clustering accuracy over existing benchmarking methods.\n"}
+{"abstract": "  Microarray gene expression data are often accompanied by a large number of\ngenes and a small number of samples. However, only a few of these genes are\nrelevant to cancer, resulting in signigicant gene selection challenges. Hence,\nwe propose a two-stage gene selection approach by combining extreme gradient\nboosting (XGBoost) and a multi-objective optimization genetic algorithm\n(XGBoost-MOGA) for cancer classification in microarray datasets. In the first\nstage, the genes are ranked use an ensemble-based feature selection using\nXGBoost. This stage can effectively remove irrelevant genes and yield a group\ncomprising the most relevant genes related to the class. In the second stage,\nXGBoost-MOGA searches for an optimal gene subset based on the most relevant\ngenes's group using a multi-objective optimization genetic algorithm. We\nperformed comprehensive experiments to compare XGBoost-MOGA with other\nstate-of-the-art feature selection methods using two well-known learning\nclassifiers on 13 publicly available microarray expression datasets. The\nexperimental results show that XGBoost-MOGA yields significantly better results\nthan previous state-of-the-art algorithms in terms of various evaluation\ncriteria, such as accuracy, F-score, precision, and recall.\n"}
+{"abstract": "  We propose a fast partial video copy detection framework in this paper. In\nthis framework all frame features of the reference videos are organized in a\nKNN searchable database. Instead of scanning all reference videos, the query\nvideo segment does a fast KNN search in the global feature database. The\nreturned results are used to generate a short list of candidate videos. A\nmodified temporal network is then used to localize the copy segment in the\ncandidate videos. We evaluate different choice of CNN features on the VCDB\ndataset. Our benchmark F1 score exceeds the state of the art by a big margin.\n"}
+{"abstract": "  We prove that any length metric space homeomorphic to a surface may be\ndecomposed into non-overlapping convex triangles of arbitrarily small diameter.\nThis generalizes a previous result of Alexandrov--Zalgaller for surfaces of\nbounded curvature.\n"}
+{"abstract": "  In this paper, we propose a new approach to infer state machine models from\nprotocol implementations. Our method, STATEINSPECTOR, learns protocol states by\nusing novel program analyses to combine observations of run-time memory and\nI/O. It requires no access to source code and only lightweight execution\nmonitoring of the implementation under test. We demonstrate and evaluate\nSTATEINSPECTOR's effectiveness on numerous TLS and WPA/2 implementations. In\nthe process, we show STATEINSPECTOR enables deeper state discovery, increased\nlearning efficiency, and more insightful post-mortem analyses than existing\napproaches. Further to improved learning, our method led us to discover several\nconcerning deviations from the standards and a high impact vulnerability in a\nprominent Wi-Fi implementation.\n"}
+{"abstract": "  In this paper, we investigate the stabilization of a linear Bresse system\nwith one singular local frictional damping acting in the longitudinal\ndisplacement, under fully Dirichlet boundary conditions. First, we prove the\nstrong stability of our system. Next, using a frequency domain approach\ncombined with the multiplier method, we establish the exponential stability of\nthe solution if and only if the three waves have the same speed of propagation.\nOn the contrary, we prove that the energy of our system decays polynomially\nwith rates $t^{-1}$ or $t^{-\\frac{1}{2}}$.\n"}
+{"abstract": "  We scrutinize the red dwarf component in the eclipsing binary system V471 Tau\nin order to unravel relations between different activity layers from the\nstellar surface through the chromosphere up to the corona. We aim at studying\nhow the magnetic dynamo in the late-type component is affected by the close\nwhite dwarf companion. We use space photometry, high resolution spectroscopy\nand X-ray observations from different space instruments to explore the main\ncharacteristics of magnetic activity. From K2 photomery we find that 5-10 per\ncent of the apparent surface of the red dwarf is covered by cool starspots.\nFrom seasonal photometric period changes we estimate a weak differential\nrotation. From the flare activity we derive a cumulative flare frequency\ndiagram which suggests that frequent flaring could have a significant role in\nheating the corona. Using high resolution spectroscopy we reconstruct four\nDoppler images for different epochs which reveal an active longitude, that is,\na permanent dominant spot facing the white dwarf. From short term changes in\nthe consecutive Doppler images we derive a weak solar-type surface differential\nrotation with 0.0026 shear coefficient, similar to that provided by photometry.\nThe long-term evolution of X-ray luminosity reveals a possible activity cycle\nlength of 12.7 ys, traces of which were discovered also in the H$\\alpha$\nspectra. We conclude that the magnetic activity of the red dwarf component in\nV471 Tau is strongly influenced by the close white dwarf companion. We confirm\nthe presence of a permanent dominant spot (active longitude) on the red dwarf\nfacing the white dwarf. The weak differential rotation of the red dwarf is very\nlikely the result of tidal confinement by the companion. We find that the\nperiodic appearance of the inter-binary H$\\alpha$ emission from the vicinity of\nthe inner Lagrangian point is correlated with the activity cycle.\n"}
+{"abstract": "  Visual navigation and three-dimensional (3D) scene reconstruction are\nessential for robotics to interact with the surrounding environment.\nLarge-scale scenes and critical camera motions are great challenges facing the\nresearch community to achieve this goal. We raised a pose-only imaging geometry\nframework and algorithms that can help solve these challenges. The\nrepresentation is a linear function of camera global translations, which allows\nfor efficient and robust camera motion estimation. As a result, the spatial\nfeature coordinates can be analytically reconstructed and do not require\nnonlinear optimization. Experiments demonstrate that the computational\nefficiency of recovering the scene and associated camera poses is significantly\nimproved by 2-4 orders of magnitude. This solution might be promising to unlock\nreal-time 3D visual computing in many forefront applications.\n"}
+{"abstract": "  We propose a simple method for automatic speech recognition (ASR) by\nfine-tuning BERT, which is a language model (LM) trained on large-scale\nunlabeled text data and can generate rich contextual representations. Our\nassumption is that given a history context sequence, a powerful LM can narrow\nthe range of possible choices and the speech signal can be used as a simple\nclue. Hence, comparing to conventional ASR systems that train a powerful\nacoustic model (AM) from scratch, we believe that speech recognition is\npossible by simply fine-tuning a BERT model. As an initial study, we\ndemonstrate the effectiveness of the proposed idea on the AISHELL dataset and\nshow that stacking a very simple AM on top of BERT can yield reasonable\nperformance.\n"}
+{"abstract": "  Expansions in non-integer bases have been extensively investigated since a\npioneering work of R\\'enyi. We introduce a more general framework of\nalphabet-base systems that also includes Pedicini's general alphabets and the\nmultiple-base expansions of Neunh\\\"auserer and Li. We extend the Parry type\nlexicographic theory to this setup, and we improve and generalize various\nformer results on unique expansions.\n"}
+{"abstract": "  We present a novel finite element analysis of inelastic structures containing\nShape Memory Alloys (SMAs). Phenomenological constitutive models for SMAs lead\nto material nonlinearities, that require substantial computational effort to\nresolve. Finite element analysis methods, which rely on Gauss quadrature\nintegration schemes, must solve two sets of coupled differential equations: one\nat the global level and the other at the local, i.e. Gauss point level. In\ncontrast to the conventional return mapping algorithm, which solves these two\nsets of coupled differential equations separately using a nested Newton\nprocedure, we propose a scheme to solve the local and global differential\nequations simultaneously. In the process we also derive closed-form expressions\nused to update the internal/constitutive state variables, and unify the popular\nclosest-point and cutting plane methods with our formulas. Numerical testing\nindicates that our method allows for larger thermomechanical loading steps and\nprovides increased computational efficiency, over the standard return mapping\nalgorithm.\n"}
+{"abstract": "  The concept of inertial frame of reference is analysed. It has been shown\nthat this fundamental concept of physics is not clear enough. A definition of\ninertial frame of reference is proposed which expresses its key inherent\nproperty. The definition is operational and powerful. Many other properties of\ninertial frames follow from the definition or it makes them plausible. In\nparticular, the definition shows why physical laws obey space and time\nsymmetries and the principle of relativity, it resolves the problem of clock\nsynchronization and the role of light in it, as well as the problem of the\ngeometry of inertial frames.\n"}
+{"abstract": "  The homologous collapse from rest of a uniform density sphere under its self\ngravity is a well-known toy model for the formation dynamics of astronomical\nobjects ranging from stars to galaxies. Equally well-known is that the\nevolution of the radius with time cannot be explicitly obtained because of the\ntranscendental nature of the differential equation solution. Rather, both\nradius and time are written parametrically in terms of the development angle\n$\\theta$. We here present an explicit integral solution for radius as a\nfunction of time, exploiting methods from complex analysis recently applied to\nthe mathematically-similar 'geometric goat problem'. Our solution can be\nefficiently evaluated using a Fast Fourier Transform and allows for arbitrary\nsampling in time, with a simple Python implementation that is $\\sim$$100\\times$\nfaster than using numerical root-finding to achieve arbitrary sampling. Our\nexplicit solution is advantageous relative to the usual approach of first\ngenerating a uniform grid in $\\theta$, since this latter results in a\nnon-uniform radial or time sampling, less useful for applications such as\ngeneration of sub-grid physics models.\n"}
+{"abstract": "  Users online tend to consume information adhering to their system of beliefs\nand to ignore dissenting information. During the COVID-19 pandemic, users get\nexposed to a massive amount of information about a new topic having a high\nlevel of uncertainty. In this paper, we analyze two social media that enforced\nopposite moderation methods, Twitter and Gab, to assess the interplay between\nnews consumption and content regulation concerning COVID-19. We compare the two\nplatforms on about three million pieces of content analyzing user interaction\nwith respect to news articles. We first describe users' consumption patterns on\nthe two platforms focusing on the political leaning of news outlets. Finally,\nwe characterize the echo chamber effect by modeling the dynamics of users'\ninteraction networks. Our results show that the presence of moderation pursued\nby Twitter produces a significant reduction of questionable content, with a\nconsequent affiliation towards reliable sources in terms of engagement and\ncomments. Conversely, the lack of clear regulation on Gab results in the\ntendency of the user to engage with both types of content, showing a slight\npreference for the questionable ones which may account for a\ndissing/endorsement behavior. Twitter users show segregation towards reliable\ncontent with a uniform narrative. Gab, instead, offers a more heterogeneous\nstructure where users, independently of their leaning, follow people who are\nslightly polarized towards questionable news.\n"}
+{"abstract": "  We consider the problem of semantic matching in product search: given a\ncustomer query, retrieve all semantically related products from a huge catalog\nof size 100 million, or more. Because of large catalog spaces and real-time\nlatency constraints, semantic matching algorithms not only desire high recall\nbut also need to have low latency. Conventional lexical matching approaches\n(e.g., Okapi-BM25) exploit inverted indices to achieve fast inference time, but\nfail to capture behavioral signals between queries and products. In contrast,\nembedding-based models learn semantic representations from customer behavior\ndata, but the performance is often limited by shallow neural encoders due to\nlatency constraints. Semantic product search can be viewed as an eXtreme\nMulti-label Classification (XMC) problem, where customer queries are input\ninstances and products are output labels. In this paper, we aim to improve\nsemantic product search by using tree-based XMC models where inference time\ncomplexity is logarithmic in the number of products. We consider hierarchical\nlinear models with n-gram features for fast real-time inference.\nQuantitatively, our method maintains a low latency of 1.25 milliseconds per\nquery and achieves a 65% improvement of Recall@100 (60.9% v.s. 36.8%) over a\ncompeting embedding-based DSSM model. Our model is robust to weight pruning\nwith varying thresholds, which can flexibly meet different system requirements\nfor online deployments. Qualitatively, our method can retrieve products that\nare complementary to existing product search system and add diversity to the\nmatch set.\n"}
+{"abstract": "  The existence of multiple solutions to AC optimal power flow (ACOPF) problems\nhas been noted for decades. Existing solvers are generally successful in\nfinding local solutions, which are stationary points but may not be globally\noptimal. In this paper, we propose a simple iterative approach to find globally\noptimal solutions to ACOPF problems. First, we call an existing solver for the\nACOPF problem. From the solution and the associated dual variables, we form a\npartial Lagrangian. Then we optimize this partial Lagrangian and use its\nsolution as a warm start to call the solver again for the ACOPF problem. By\nrepeating this process, we can iteratively improve the solution quality, moving\nfrom local solutions to global ones. We show the effectiveness our algorithm on\nstandard IEEE networks. The simulation results show that our algorithm can\nescape from local solutions to achieve global optimums within a few iterations.\n"}
+{"abstract": "  GRO J1008-57, as a Be/X-ray transient pulsar, is considered to have the\nhighest magnetic field in known neutron star X-ray binary systems.\nObservational data of the X-ray outbursts in GRO J1008-57 from 2017 to 2020\nwere collected by the Insight-HXMT satellite. In this work, the spin period of\nthe neutron star in GRO J1008-57 was determined to be about 93.28 seconds in\nAugust 2017, 93.22 seconds in February 2018, 93.25 seconds in June 2019 and\n93.14 seconds in June 2020. GRO J1008-57 evolved in the spin-up process with a\nmean rate of $-(2.10\\pm 0.05)\\times$10$^{-4}$ s/d from 2009 -- 2018, and turned\ninto a spin down process with a rate of $(6.7\\pm 0.6)\\times$10$^{-5}$ s/d from\nFeb 2018 to June 2019. During the type II outburst of 2020, GRO J1008-57 had\nthe spin-up torque again. During the torque reversals, the pulse profiles and\ncontinuum X-ray spectra did not change significantly, and the cyclotron\nresonant scattering feature around 80 keV was only detected during the\noutbursts in 2017 and 2020. Based on the observed mean spin-up rate, we\nestimated the inner accretion disk radius in GRO J1008-57 (about 1 - 2 times of\nthe Alfv\\'{e}n radius) by comparing different accretion torque models of\nmagnetic neutron stars. During the spin-down process, the magnetic torque\nshould dominate over the matter accreting inflow torque, and we constrained the\nsurface dipole magnetic field $B\\geq 6\\times 10^{12}$ G for the neutron star in\nGRO J1008-57, which is consistent with the magnetic field strength obtained by\ncyclotron line centroid energy.\n"}
+{"abstract": "  Question answering (QA) systems provide a way of querying the information\navailable in various formats including, but not limited to, unstructured and\nstructured data in natural languages. It constitutes a considerable part of\nconversational artificial intelligence (AI) which has led to the introduction\nof a special research topic on Conversational Question Answering (CQA), wherein\na system is required to understand the given context and then engages in\nmulti-turn QA to satisfy the user's information needs. Whilst the focus of most\nof the existing research work is subjected to single-turn QA, the field of\nmulti-turn QA has recently grasped attention and prominence owing to the\navailability of large-scale, multi-turn QA datasets and the development of\npre-trained language models. With a good amount of models and research papers\nadding to the literature every year recently, there is a dire need of arranging\nand presenting the related work in a unified manner to streamline future\nresearch. This survey, therefore, is an effort to present a comprehensive\nreview of the state-of-the-art research trends of CQA primarily based on\nreviewed papers from 2016-2021. Our findings show that there has been a trend\nshift from single-turn to multi-turn QA which empowers the field of\nConversational AI from different perspectives. This survey is intended to\nprovide an epitome for the research community with the hope of laying a strong\nfoundation for the field of CQA.\n"}
+{"abstract": "  The data clustering problem consists in dividing a data set into prescribed\ngroups of homogeneous data. This is a NP-hard problem that can be relaxed in\nthe spectral graph theory, where the optimal cuts of a graph are related to the\neigenvalues of graph $1$-Laplacian. In this paper, we firstly give new\nnotations to describe the paths, among critical eigenvectors of the graph\n$1$-Laplacian, realizing sets with prescribed genus.\n  We introduce the pseudo-orthogonality to characterize $m_3(G)$, a special\neigenvalue for the graph $1$-Laplacian. Furthermore, we use it to give an upper\nbound for the third graph Cheeger constant $h_3(G)$, that is $h_3(G) \\le\nm_3(G)$. This is a first step for proving that the $k$-th Cheeger constant is\nthe minimum of the $1$-Laplacian Raylegh quotient among vectors that are\npseudo-orthogonal to the vectors realizing the previous $k-1$ Cheeger\nconstants.\n  Eventually, we apply these results to give a method and a numerical algorithm\nto compute $m_3(G)$, based on a generalized inverse power method.\n"}
+{"abstract": "  An atomic monoid is length-factorial if each two distinct factorizations of\nany element have distinct factorization lengths. We provide a characterization\nof length-factorial Krull monoids in terms of their class groups and the\ndistribution of prime divisors in the classes.\n"}
+{"abstract": "  Matter distribution around clusters is highly anisotropic from their being\nthe nodes of the cosmic web. Clusters' shape and the number of filaments they\nare connected to, i.e., their connectivity, should reflect their level of\nanisotropic matter distribution and must be, in principle, related to their\nphysical properties. We investigate the influence of the dynamical state and\nthe formation history on both the morphology and local connectivity of about\n2400 groups and clusters of galaxies from the large hydrodynamical simulation\nIllustrisTNG at z=0. We find that the mass of groups and clusters mainly\ninfluences the geometry of the matter distribution: massive halos are\nsignificantly more elliptical, and more connected to the cosmic web than\nlow-mass ones. Beyond the mass-driven effect, ellipticity and connectivity are\ncorrelated and are imprints of the growth rate of groups and clusters. Both\nanisotropy measures appear to trace different dynamical states, such that\nunrelaxed groups and clusters are more elliptical and more connected than\nrelaxed ones. This relation between matter anisotropies and dynamical state is\nthe sign of different accretion histories. Relaxed groups and clusters are\nmostly formed long time ago, and slowly accreting matter at the present time.\nThey are rather spherical and weakly connected to their environment, mostly\nbecause they had enough time to relax and, hence, lost the connection with\ntheir preferential directions of accretion and merging. In contrast,\nlate-formed unrelaxed objects are highly anisotropic with large connectivities\nand ellipticities. These groups and clusters are in formation phase and must be\nstrongly affected by the infalling of materials from filaments.\n"}
+{"abstract": "  We derive a series of quantitative bulk-boundary correspondences for 3D\nbosonic and fermionic symmetry-protected topological (SPT) phases under the\nassumption that the surface is gapped, symmetric and topologically ordered,\ni.e., a symmetry-enriched topological (SET) state. We consider those SPT phases\nthat are protected by the mirror symmetry and continuous symmetries that form a\ngroup of $U(1)$, $SU(2)$ or $SO(3)$. In particular, the fermionic cases\ncorrespond to a crystalline version of 3D topological insulators and\ntopological superconductors in the famous ten-fold-way classification, with the\ntime-reversal symmetry replaced by the mirror symmetry and with strong\ninteraction taken into account. For surface SETs, the most general interplay\nbetween symmetries and anyon excitations is considered. Based on the previously\nproposed dimension reduction and folding approaches, we re-derive the\nclassification of bulk SPT phases and define a \\emph{complete} set of bulk\ntopological invariants for every symmetry group under consideration, and then\nderive explicit expressions of the bulk invariants in terms of surface\ntopological properties (such as topological spin, quantum dimension) and\nsymmetry properties (such as mirror fractionalization, fractional charge or\nspin). These expressions are our quantitative bulk-boundary correspondences.\nMeanwhile, the bulk topological invariants can be interpreted as \\emph{anomaly\nindicators} for the surface SETs which carry 't Hooft anomalies of the\nassociated symmetries whenever the bulk is topologically non-trivial. Hence,\nthe quantitative bulk-boundary correspondences provide an easy way to compute\nthe 't Hooft anomalies of the surface SETs. Moreover, our anomaly indicators\nare complete. Our derivations of the bulk-boundary correspondences and anomaly\nindicators are explicit and physically transparent.\n"}
+{"abstract": "  We establish general \"collapse to the mean\" principles that provide\nconditions under which a law-invariant functional reduces to an expectation. In\nthe convex setting, we retrieve and sharpen known results from the literature.\nHowever, our results also apply beyond the convex setting. We illustrate this\nby providing a complete account of the \"collapse to the mean\" for quasiconvex\nfunctionals. In the special cases of consistent risk measures and Choquet\nintegrals, we can even dispense with quasiconvexity. In addition, we relate the\n\"collapse to the mean\" to the study of solutions of a broad class of\noptimisation problems with law-invariant objectives that appear in mathematical\nfinance, insurance, and economics. We show that the corresponding quantile\nformulations studied in the literature are sometimes illegitimate and require\nfurther analysis.\n"}
+{"abstract": "  Most of the knowledge in materials science literature is in the form of\nunstructured data such as text and images. Here, we present a framework\nemploying natural language processing, which automates text and image\ncomprehension and precision knowledge extraction from inorganic glasses'\nliterature. The abstracts are automatically categorized using latent Dirichlet\nallocation (LDA), providing a way to classify and search semantically linked\npublications. Similarly, a comprehensive summary of images and plots are\npresented using the 'Caption Cluster Plot' (CCP), which provides direct access\nto the images buried in the papers. Finally, we combine the LDA and CCP with\nthe chemical elements occurring in the manuscript to present an 'Elemental\nmap', a topical and image-wise distribution of chemical elements in the\nliterature. Overall, the framework presented here can be a generic and powerful\ntool to extract and disseminate material-specific information on\ncomposition-structure-processing-property dataspaces, allowing insights into\nfundamental problems relevant to the materials science community and\naccelerated materials discovery.\n"}
+{"abstract": "  We study a novel task, Video Question-Answer Generation (VQAG), for\nchallenging Video Question Answering (Video QA) task in multimedia. Due to\nexpensive data annotation costs, many widely used, large-scale Video QA\ndatasets such as Video-QA, MSVD-QA and MSRVTT-QA are automatically annotated\nusing Caption Question Generation (CapQG) which inputs captions instead of the\nvideo itself. As captions neither fully represent a video, nor are they always\npractically available, it is crucial to generate question-answer pairs based on\na video via Video Question-Answer Generation (VQAG). Existing video-to-text\n(V2T) approaches, despite taking a video as the input, only generate a question\nalone. In this work, we propose a novel model Generator-Pretester Network that\nfocuses on two components: (1) The Joint Question-Answer Generator (JQAG) which\ngenerates a question with its corresponding answer to allow Video Question\n\"Answering\" training. (2) The Pretester (PT) verifies a generated question by\ntrying to answer it and checks the pretested answer with both the model's\nproposed answer and the ground truth answer. We evaluate our system with the\nonly two available large-scale human-annotated Video QA datasets and achieves\nstate-of-the-art question generation performances. Furthermore, using our\ngenerated QA pairs only on the Video QA task, we can surpass some supervised\nbaselines. We apply our generated questions to Video QA applications and\nsurpasses some supervised baselines using generated questions only. As a\npre-training strategy, we outperform both CapQG and transfer learning\napproaches when employing semi-supervised (20%) or fully supervised learning\nwith annotated data. These experimental results suggest the novel perspectives\nfor Video QA training.\n"}
+{"abstract": "  A nonsingular rational curve $C$ in a complex manifold $X$ whose normal\nbundle is isomorphic to $${\\mathcal O}_{{\\mathbb P}^1}(1)^{\\oplus p} \\oplus\n{\\mathcal O}_{{\\mathbb P}^1}^{\\oplus q}$$ for some nonnegative integers $p$ and\n$q$ is called an unbendable rational curve on $X$. Associated with it is the\nvariety of minimal rational tangents (VMRT) at a point $x \\in C,$ which is the\ngerm of submanifolds ${\\mathcal C}^C_x \\subset {\\mathbb P} T_x X$ consisting of\ntangent directions of small deformations of $C$ fixing $x$. Assuming that there\nexists a distribution $D \\subset TX$ such that all small deformations of $C$\nare tangent to $D$, one asks what kind of submanifolds of projective space can\nbe realized as the VMRT ${\\mathcal C}^C_x \\subset {\\mathbb P} D_x$. When $D\n\\subset TX$ is a contact distribution, a well-known necessary condition is that\n${\\mathcal C}_x^C$ should be Legendrian with respect to the induced contact\nstructure on ${\\mathbb P} D_x$. We prove that this is also a sufficient\ncondition: we construct a complex manifold $X$ with a contact structure $D\n\\subset TX$ and an unbendable rational curve $C \\subset X$ such that all small\ndeformations of $C$ are tangent to $D$ and the VMRT ${\\mathcal C}^C_x \\subset\n{\\mathbb P} D_x$ at some point $x\\in C$ is projectively isomorphic to an\narbitrarily given Legendrian submanifold. Our construction uses the geometry of\ncontact lines on the Heisenberg group and a technical ingredient is the\nsymplectic geometry of distributions the study of which has originated from\ngeometric control theory.\n"}
+{"abstract": "  The classifying space for the framed Haefliger structures of codimension $q$\nand class $C^r$ is $(2q-1)$-connected, for $1\\le r\\le\\infty$. The corollaries\ndeal with the existence of foliations, with the homology and the perfectness of\nthe diffeomorphism groups, with the existence of foliated products, and of\nfoliated bundles.\n"}
+{"abstract": "  We present an astrometric study of the proper motions (PMs) in the core of\nthe globular cluster NGC 6441. The core of this cluster has a high density and\nobservations with current instrumentation are very challenging. We combine\nground-based, high-angular-resolution NACO@VLT images with Hubble Space\nTelescope ACS/HRC data and measure PMs with a temporal baseline of 15 yr for\nabout 1400 stars in the centermost 15 arcseconds of the cluster. We reach a PM\nprecision of $\\sim$30 $\\mu$as yr$^{-1}$ for bright, well-measured stars.\n  Our results for the velocity dispersion are in good agreement with other\nstudies and extend already-existing analyses of the stellar kinematics of NGC\n6441 to its centermost region never probed before. In the innermost arcsecond\nof the cluster, we measure a velocity dispersion of (19.1 $\\pm$ 2.0) km\ns$^{-1}$ for evolved stars. Because of its high mass, NGC 6441 is a promising\ncandidate for harbouring an intermediate-mass black hole (IMBH). We combine our\nmeasurements with additional data from the literature and compute dynamical\nmodels of the cluster. We find an upper limit of $M_{\\rm IMBH} < 1.32 \\times\n10^4\\,\\textrm{M}_\\odot$ but we can neither confirm nor rule out its presence.\nWe also refine the dynamical distance of the cluster to $12.74^{+0.16}_{-0.15}$\nkpc.\n  Although the hunt for an IMBH in NGC 6441 is not yet concluded, our results\nshow how future observations with extremely-large telescopes will benefit from\nthe long temporal baseline offered by existing high-angular-resolution data.\n"}
+{"abstract": "  In recent years, thanks to the continuous surveys performed by INTEGRAL and\nSwift satellites, our knowledge of the hard X-ray/soft gamma-ray sky has\ngreatly improved. As a result it is now populated with about 2000 sources, both\nGalactic and extra-galactic, mainly discovered by IBIS and BAT instruments.\nMany different follow-up campaigns have been successfully performed by using a\nmulti-wavelength approach, shedding light on the nature of a number of these\nnew hard X-ray sources. However, a fraction are still of a unidentified nature.\nThis is mainly due to the lack of lower energy observations, which usually\ndeliver a better constrained position for the sources, and the unavailability\nof the key observational properties, needed to obtain a proper physical\ncharacterization. Here we report on the classification of two poorly studied\nGalactic X-ray transients IGR J20155+3827 and Swift J1713.4-4219, for which the\ncombination of new and/or archival X-ray and Optical/NIR observations have\nallowed us to pinpoint their nature. In particular, thanks to XMM\\Newton\narchival data together with new optical spectroscopic and archival Optical/NIR\nphotometric observations, we have been able to classify IGR J20155+3827 as a\ndistant HMXB. The new INTEGRAL and Swift data collected during the 2019 X-ray\noutburst of Swift J1713.4-4219, in combination with the archival optical/NIR\nobservations, suggest a LMXB classification for this source.\n"}
+{"abstract": "  We calculate the centrality dependence for coherent photoproduction of very\nlow-$p_{T}$ $J/\\psi$ at Relativistic Heavy Ion Collider (RHIC) and Large Hadron\nCollider (LHC) energies within the impact parameter dependent saturated color\ndipole model. By using the large equivalent photon fluxes, we present the\ndifferential cross section of very low-$p_{T}$ $J/\\psi$ produced by coherent\nphotonuclear in peripheral heavy-ion collisions. The numerical results\ndemonstrate that our calculation are agree with $J/\\psi$ data in peripheral\nheavy ion collisions at Relativistic Heavy Ion Collider (RHIC) energies.\n"}
+{"abstract": "  In this article we generalize a classical result by Passman on primeness of\nunital strongly group graded rings to the class of nearly epsilon-strongly\ngroup graded rings which are not necessarily unital. Using this result, we\nobtain (i) a characterization of prime $s$-unital strongly group graded rings,\nand, in particular, of infinite matrix rings and of group rings over $s$-unital\nrings, thereby generalizing a well-known result by Connell; (ii)\ncharacterizations of prime $s$-unital partial skew group rings and of prime\nunital partial crossed products; (iii) a generalization of the well-known\ncharacterizations of prime Leavitt path algebras, by Larki and by\nAbrams-Bell-Rangaswamy.\n"}
+{"abstract": "  Although there is a rapidly growing literature on dynamic connectivity\nmethods, the primary focus has been on separate network estimation for each\nindividual, which fails to leverage common patterns of information. We propose\nnovel graph-theoretic approaches for estimating a population of dynamic\nnetworks that are able to borrow information across multiple heterogeneous\nsamples in an unsupervised manner and guided by covariate information.\nSpecifically, we develop a Bayesian product mixture model that imposes\nindependent mixture priors at each time scan and uses covariates to model the\nmixture weights, which results in time-varying clusters of samples designed to\npool information. The computation is carried out using an efficient\nExpectation-Maximization algorithm. Extensive simulation studies illustrate\nsharp gains in recovering the true dynamic network over existing dynamic\nconnectivity methods. An analysis of fMRI block task data with behavioral\ninterventions reveal sub-groups of individuals having similar dynamic\nconnectivity, and identifies intervention-related dynamic network changes that\nare concentrated in biologically interpretable brain regions. In contrast,\nexisting dynamic connectivity approaches are able to detect minimal or no\nchanges in connectivity over time, which seems biologically unrealistic and\nhighlights the challenges resulting from the inability to systematically borrow\ninformation across samples.\n"}
+{"abstract": "  The recently introduced topological quantum chemistry (TQC) framework has\nprovided a description of universal topological properties of all possible band\ninsulators in all space groups based on crystalline unitary symmetries and time\nreversal. While this formalism filled the gap between the mathematical\nclassification and the practical diagnosis of topological materials, an obvious\nlimitation is that it only applies to weakly interacting systems-which can be\ndescribed within band theory. It is an open question to which extent this\nformalism can be generalized to correlated systems that can exhibit symmetry\nprotected topological phases which are not adiabatically connected to any band\ninsulator. In this work we address the many facettes of this question by\nconsidering the specific example of a Hubbard diamond chain. This model\nfeatures a Mott insulator, a trivial insulating phase and an obstructed atomic\nlimit phase. Here we discuss the nature of the Mott insulator and determine the\nphase diagram and topology of the interacting model with infinite density\nmatrix renormalization group calculations, variational Monte Carlo simulations\nand with many-body topological invariants. We then proceed by considering a\ngeneralization of the TQC formalism to Green's functions combined with the\nconcept of topological Hamiltonian to identify the topological nature of the\nphases, using cluster perturbation theory to calculate the Green's functions.\nThe results are benchmarked with the above determined phase diagram and we\ndiscuss the applicability and limitations of the approach and its possible\nextensions.\n"}
+{"abstract": "  Today's advanced Reinforcement Learning algorithms produce black-box\npolicies, that are often difficult to interpret and trust for a person. We\nintroduce a policy distilling algorithm, building on the CN2 rule mining\nalgorithm, that distills the policy into a rule-based decision system. At the\ncore of our approach is the fact that an RL process does not just learn a\npolicy, a mapping from states to actions, but also produces extra\nmeta-information, such as action values indicating the quality of alternative\nactions. This meta-information can indicate whether more than one action is\nnear-optimal for a certain state. We extend CN2 to make it able to leverage\nknowledge about equally-good actions to distill the policy into fewer rules,\nincreasing its interpretability by a person. Then, to ensure that the rules\nexplain a valid, non-degenerate policy, we introduce a refinement algorithm\nthat fine-tunes the rules to obtain good performance when executed in the\nenvironment. We demonstrate the applicability of our algorithm on the Mario AI\nbenchmark, a complex task that requires modern reinforcement learning\nalgorithms including neural networks. The explanations we produce capture the\nlearned policy in only a few rules, that allow a person to understand what the\nblack-box agent learned. Source code:\nhttps://gitlab.ai.vub.ac.be/yocoppen/svcn2\n"}
+{"abstract": "  Despite the considerable success of neural networks in security settings such\nas malware detection, such models have proved vulnerable to evasion attacks, in\nwhich attackers make slight changes to inputs (e.g., malware) to bypass\ndetection. We propose a novel approach, \\emph{Fourier stabilization}, for\ndesigning evasion-robust neural networks with binary inputs. This approach,\nwhich is complementary to other forms of defense, replaces the weights of\nindividual neurons with robust analogs derived using Fourier analytic tools.\nThe choice of which neurons to stabilize in a neural network is then a\ncombinatorial optimization problem, and we propose several methods for\napproximately solving it. We provide a formal bound on the per-neuron drop in\naccuracy due to Fourier stabilization, and experimentally demonstrate the\neffectiveness of the proposed approach in boosting robustness of neural\nnetworks in several detection settings. Moreover, we show that our approach\neffectively composes with adversarial training.\n"}
+{"abstract": "  Underwater object detection for robot picking has attracted a lot of\ninterest. However, it is still an unsolved problem due to several challenges.\nWe take steps towards making it more realistic by addressing the following\nchallenges. Firstly, the currently available datasets basically lack the test\nset annotations, causing researchers must compare their method with other SOTAs\non a self-divided test set (from the training set). Training other methods lead\nto an increase in workload and different researchers divide different datasets,\nresulting there is no unified benchmark to compare the performance of different\nalgorithms. Secondly, these datasets also have other shortcomings, e.g., too\nmany similar images or incomplete labels. Towards these challenges we introduce\na dataset, Detecting Underwater Objects (DUO), and a corresponding benchmark,\nbased on the collection and re-annotation of all relevant datasets. DUO\ncontains a collection of diverse underwater images with more rational\nannotations. The corresponding benchmark provides indicators of both efficiency\nand accuracy of SOTAs (under the MMDtection framework) for academic research\nand industrial applications, where JETSON AGX XAVIER is used to assess detector\nspeed to simulate the robot-embedded environment.\n"}
+{"abstract": "  The escaping atmospheres of hydrogen driven by stellar X-ray and extreme\nUltraviolet (XUV) have been detected around some exoplanets by the excess\nabsorption of Ly$\\alpha$ in far ultraviolet band. In the optical band the\nexcess absorption of H$\\alpha$ is also found by the ground-based instruments.\nHowever, it is not certain so far if the escape of the atmosphere driven by XUV\ncan result in such absorption. Here we present the XUV driven hydrodynamic\nsimulation coupled with the calculation of detailed level population and the\nprocess of radiative transfer for WASP-121b. Our fiducial model predicts a mass\nloss rate of $\\sim$1.28$\\times$10$^{12}$g/s for WASP-121b. Due to the high\ntemperature and Ly$\\alpha$ intensity predicted by the fiducial model, many\nhydrogen atoms are populated into the first excited state. As a consequence,\nthe transmission spectrum of H$\\alpha$ simulated by our model is broadly\nconsistent with the observation. Comparing with the absorption of H$\\alpha$ at\ndifferent observation times, the stellar XUV emission varies in the range of\n0.5-1.5 times fiducial value, which may reflect the variation of the stellar\nactivity. Finally, we find that the supersonic regions of the planetary wind\ncontribute a prominent portion to the absorption of H$\\alpha$ by comparing the\nequivalent width of H$\\alpha$, which hints that a transonic outflow of the\nupper atmosphere driven by XUV irradiation of the host star can be detected by\nthe ground-based telescope and the H$\\alpha$ can be a good indicator of\nescaping atmosphere.\n"}
+{"abstract": "  We propose a method to non-rigidly align a three-dimensional (3D) volumetric\nimage with a two-dimensional (2D) planar image representing a projection of the\ndeformed volume. The application in mind comes from biological studies in which\n2D intravital microscopy videos of living tissue are recorded, after which the\ntissue is excised and a more detailed 3D volume microscopy is performed.\nCoregistration of both data sets allows to combine the temporal (but 2D)\ninformation with more detailed spatial 3D information.\n  Our approach is variational and uses a hyperelastic deformation\nregularization, as is appropriate for biological material. As a particular\nfeature, the out of plane deformation is estimated based on the out of focus\nblur inside the 2D microscopy image. The approach becomes computationally\nfeasible through the use of a coarse-to-fine optimization strategy and higher\norder optimization methods.\n"}
+{"abstract": "  Assigning geospatial objects with specific categories at the pixel level is a\nfundamental task in remote sensing image analysis. Along with rapid development\nin sensor technologies, remotely sensed images can be captured at multiple\nspatial resolutions (MSR) with information content manifested at different\nscales. Extracting information from these MSR images represents huge\nopportunities for enhanced feature representation and characterisation.\nHowever, MSR images suffer from two critical issues: 1) increased scale\nvariation of geo-objects and 2) loss of detailed information at coarse spatial\nresolutions. To bridge these gaps, in this paper, we propose a novel\nscale-aware neural network (SaNet) for semantic segmentation of MSR remotely\nsensed imagery. SaNet deploys a densely connected feature network (DCFFM)\nmodule to capture high-quality multi-scale context, such that the scale\nvariation is handled properly and the quality of segmentation is increased for\nboth large and small objects. A spatial feature recalibration (SFRM) module is\nfurther incorporated into the network to learn intact semantic content with\nenhanced spatial relationships, where the negative effects of information loss\nare removed. The combination of DCFFM and SFRM allows SaNet to learn\nscale-aware feature representation, which outperforms the existing multi-scale\nfeature representation. Extensive experiments on three semantic segmentation\ndatasets demonstrated the effectiveness of the proposed SaNet in\ncross-resolution segmentation.\n"}
+{"abstract": "  Recent advances in automatic speech recognition (ASR) have achieved accuracy\nlevels comparable to human transcribers, which led researchers to debate if the\nmachine has reached human performance. Previous work focused on the English\nlanguage and modular hidden Markov model-deep neural network (HMM-DNN) systems.\nIn this paper, we perform a comprehensive benchmarking for end-to-end\ntransformer ASR, modular HMM-DNN ASR, and human speech recognition (HSR) on the\nArabic language and its dialects. For the HSR, we evaluate linguist performance\nand lay-native speaker performance on a new dataset collected as a part of this\nstudy. For ASR the end-to-end work led to 12.5%, 27.5%, 33.8% WER; a new\nperformance milestone for the MGB2, MGB3, and MGB5 challenges respectively. Our\nresults suggest that human performance in the Arabic language is still\nconsiderably better than the machine with an absolute WER gap of 3.5% on\naverage.\n"}
+{"abstract": "  We consider the reconstruction problem of video snapshot compressive imaging\n(SCI), which captures high-speed videos using a low-speed 2D sensor (detector).\nThe underlying principle of SCI is to modulate sequential high-speed frames\nwith different masks and then these encoded frames are integrated into a\nsnapshot on the sensor and thus the sensor can be of low-speed. On one hand,\nvideo SCI enjoys the advantages of low-bandwidth, low-power and low-cost. On\nthe other hand, applying SCI to large-scale problems (HD or UHD videos) in our\ndaily life is still challenging and one of the bottlenecks lies in the\nreconstruction algorithm. Exiting algorithms are either too slow (iterative\noptimization algorithms) or not flexible to the encoding process (deep learning\nbased end-to-end networks). In this paper, we develop fast and flexible\nalgorithms for SCI based on the plug-and-play (PnP) framework. In addition to\nthe PnP-ADMM method, we further propose the PnP-GAP (generalized alternating\nprojection) algorithm with a lower computational workload. We first employ the\nimage deep denoising priors to show that PnP can recover a UHD color video with\n30 frames from a snapshot measurement. Since videos have strong temporal\ncorrelation, by employing the video deep denoising priors, we achieve a\nsignificant improvement in the results. Furthermore, we extend the proposed PnP\nalgorithms to the color SCI system using mosaic sensors, where each pixel only\ncaptures the red, green or blue channels. A joint reconstruction and\ndemosaicing paradigm is developed for flexible and high quality reconstruction\nof color video SCI systems. Extensive results on both simulation and real\ndatasets verify the superiority of our proposed algorithm.\n"}
+{"abstract": "  The radio spectra of main-sequence stars remain largely unconstrained due to\nthe lack of observational data to inform stellar atmosphere models. As such,\nthe dominant emission mechanisms at long wavelengths, how they vary with\nspectral type, and how much they contribute to the expected brightness at a\ngiven radio wavelength are still relatively unknown for most spectral types. We\npresent radio continuum observations of Altair, a rapidly rotating A-type star.\nWe observed Altair with NOEMA in 2018 and 2019 at 1.34 mm, 2.09 mm, and 3.22 mm\nand with the VLA in 2019 at 6.7 mm and 9.1 mm. In the radio spectra, we see a\nbrightness temperature minimum at millimeter wavelengths followed by a steep\nrise to temperatures larger than the optical photosphere, behavior that is\nunexpected for A-type stars. We use these data to produce the first\nsub-millimeter to centimeter spectrum of a rapidly rotating A-type star\ninformed by observations. We generated both PHOENIX and KINICH-PAKAL model\natmospheres and determine the KINICH-PAKAL model better reproduces Altair's\nradio spectrum. The synthetic spectrum shows a millimeter brightness\ntemperature minimum followed by significant emission over that of the\nphotosphere at centimeter wavelengths. Together, these data and models show how\nthe radio spectrum of an A-type star can reveal the presence of a chromosphere,\nlikely induced by rapid rotation, and that a Rayleigh Jean's extrapolation of\nthe stellar photosphere is not an adequate representation of a star's radio\nspectrum.\n"}
+{"abstract": "  In this early draft, we present an end-to-end decentralized protocol for the\nsecure and privacy preserving workflow of vaccination, vaccination status\nverification, and adverse reactions or symptoms reporting. The proposed system\nimproves the efficiency, privacy, equity, and effectiveness of the existing\nmanual system while remaining interoperable with its capabilities. We also\ndiscuss various security concerns and alternate methodologies based on the\nproposed protocols.\n"}
+{"abstract": "  Despite the dependency of electric vehicle (EV) fleets on charging station\navailability, charging infrastructure remains limited in many cities. Three\ncontributions are made. First, we propose an EV-to-charging station user\nequilibrium (UE) assignment model with a M/D/C queue approximation as a\nnondifferentiable nonlinear program. Second, to address the\nnon-differentiability of the queue delay function, we propose an original\nsolution algorithm based on the derivative-free Method of Successive Averages.\nComputational tests with a toy network show that the model converges to a UE. A\nworking code in Python is provided free on Github with detailed test cases.\nThird, the model is applied to the large-scale case study of New York City\nDepartment of Citywide Administrative Services (NYC DCAS) fleet and EV charging\nstation configuration as of July 8, 2020, which includes unique, real data for\n563 Level 2 chargers and 4 Direct Current Fast Chargers (DCFCs) and 1484 EVs\ndistributed over 512 Traffic Analysis Zones. The arrival rates of the\nassignment model are calibrated in the base scenario to fit an observed average\nutilization ratio of 7.6% in NYC. The model is then applied to compare charging\nstation investment policies of DCFCs to Level 2 charging stations based on two\nalternative criteria. Results suggest a policy based on selecting locations\nwith high utilization ratio instead of with high queue delay.\n"}
+{"abstract": "  Underwater images suffer from color casts and low contrast due to wavelength-\nand distance-dependent attenuation and scattering. To solve these two\ndegradation issues, we present an underwater image enhancement network via\nmedium transmission-guided multi-color space embedding, called Ucolor.\nConcretely, we first propose a multi-color space encoder network, which\nenriches the diversity of feature representations by incorporating the\ncharacteristics of different color spaces into a unified structure. Coupled\nwith an attention mechanism, the most discriminative features extracted from\nmultiple color spaces are adaptively integrated and highlighted. Inspired by\nunderwater imaging physical models, we design a medium transmission (indicating\nthe percentage of the scene radiance reaching the camera)-guided decoder\nnetwork to enhance the response of the network towards quality-degraded\nregions. As a result, our network can effectively improve the visual quality of\nunderwater images by exploiting multiple color spaces embedding and the\nadvantages of both physical model-based and learning-based methods. Extensive\nexperiments demonstrate that our Ucolor achieves superior performance against\nstate-of-the-art methods in terms of both visual quality and quantitative\nmetrics.\n"}
+{"abstract": "  Using a special complex K3 surface and its blow up, we construct a smooth\ncomplex projective rational surface with infinitely many mutually\nnon-isomorphic real forms. This gives a negative answer to a long standing open\nquestion if a smooth complex projective rational surface has only finitely many\nnon-isomorphic real forms or not.\n"}
+{"abstract": "  Refinement types decorate types with assertions that enable automatic\nverification. Like assertions, refinements are limited to binders that are in\nscope, and hence, cannot express higher-order specifications. Ghost variables\ncircumvent this limitation but are prohibitively tedious to use as the\nprogrammer must divine and explicate their values at all call-sites. We\nintroduce Implicit Refinement Types which turn ghost variables into implicit\npair and function types, in a way that lets the refinement typechecker\nautomatically synthesize their values at compile time. Implicit Refinement\nTypes further take advantage of refinement type information, allowing them to\nbe used as a lightweight verification tool, rather than merely as a technique\nto automate programming tasks. We evaluate the utility of Implicit Refinement\nTypes by showing how they enable the modular specification and automatic\nverification of various higher-order examples including stateful protocols,\naccess control, and resource usage.\n"}
+{"abstract": "  Direct Multisearch (DMS) is a Derivative-free Optimization class of\nalgorithms suited for computing approximations to the complete Pareto front of\na given Multiobjective Optimization problem. It has a well-supported\nconvergence analysis and simple implementations present a good numerical\nperformance, both in academic test sets and in real applications. Recently,\nthis numerical performance was improved with the definition of a search step\nbased on the minimization of quadratic polynomial models, corresponding to the\nalgorithm BoostDMS.\n  In this work, we propose and numerically evaluate the performance of\nparallelization strategies for this solver, applied to the search and to the\npoll steps. The final parallelized version not only considerably decreases the\ncomputational time required for solving a Multiobjective Optimization problem,\nbut also increases the quality of the computed approximation to the Pareto\nfront. Extensive numerical results will be reported in an academic test set and\nin a chemical engineering application.\n"}
+{"abstract": "  We consider a class of 2d $\\sigma$-models on products of group spaces that\nprovide new examples of a close connection between integrability and stability\nunder the RG flow. We first study the integrable $G \\times G$ model derived\nfrom the affine Gaudin construction (for which the 1-loop $\\beta$-functions\nwere found in arXiv:2010.07879) and show that its condition of integrability is\npreserved also by the 2-loop RG flow. We then investigate the RG flow in the\ngauged $G \\times G /H$ model, in particular the integrable $T^{1,1}$ model\nfound in arXiv:2010.05573. We also construct a new class of integrable $G\n\\times G /H$ models in the case when the subgroup $H$ is abelian. In the\nsimplest case of $G=SU_2$, $H=U_1$ this leads to an integrable $\\sigma$-model\non the $T^{1,q}$ space (with a particular $B$-field). This model is also shown\nto be stable under the 2-loop RG flow, and we relate this property to its\ninvariance under T-duality in an isometric $U_1$ direction. This $T^{1,q}$\nmodel may be interpreted as an integrable deformation of the GMM model (of two\ncoupled WZW theories with generic levels) away from the conformal point.\n"}
+{"abstract": "  The interlayer coupling mediated by fermions in ferromagnets brings about\nparallel and anti-parallel magnetization orientations of two magnetic layers,\nresulting in the giant magnetoresistance, which forms the foundation in\nspintronics and accelerates the development of information technology. However,\nthe interlayer coupling mediated by another kind of quasi-particle, boson, is\nstill lacking. Here we demonstrate such a static interlayer coupling at room\ntemperature in an antiferromagnetic junction Fe2O3/Cr2O3/Fe2O3, where the two\nantiferromagnetic Fe2O3 layers are functional materials and the\nantiferromagnetic Cr2O3 layer serves as a spacer. The N\\'eel vectors in the top\nand bottom Fe2O3 are strongly orthogonally coupled, which is bridged by a\ntypical bosonic excitation (magnon) in the Cr2O3 spacer. Such an orthogonally\ncoupling exceeds the category of traditional collinear interlayer coupling via\nfermions in ground state, reflecting the fluctuating nature of the magnons, as\nsupported by our magnon quantum well model. Besides the fundamental\nsignificance on the quasi-particle-mediated interaction, the strong coupling in\nan antiferromagnetic magnon junction makes it a realistic candidate for\npractical antiferromagnetic spintronics and magnonics with ultrahigh-density\nintegration.\n"}
+{"abstract": "  We study a broad class of quantum process discrimination problems that can\nhandle many optimization strategies such as the Bayes, Neyman-Pearson, and\nunambiguous strategies, where each process can consist of multiple time steps\nand can have an internal memory. Given a collection of candidate processes, our\ntask is to find a discrimination strategy, which may be adaptive and/or\nentanglement-assisted, that maximizes a given objective function subject to\ngiven constraints. Our problem can be formulated as a convex problem. Its\nLagrange dual problem with no duality gap and necessary and sufficient\nconditions for an optimal solution are derived. We also show that if a problem\nhas a certain symmetry and at least one optimal solution exists, then there\nalso exists an optimal solution with the same type of symmetry. A minimax\nstrategy for a process discrimination problem is also discussed. As\napplications of our results, we provide some problems in which an adaptive\nstrategy is not necessary for optimal discrimination. We also present an\nexample of single-shot channel discrimination for which an analytical solution\ncan be obtained.\n"}
+{"abstract": "  Hyperbolic ordinal embedding (HOE) represents entities as points in\nhyperbolic space so that they agree as well as possible with given constraints\nin the form of entity i is more similar to entity j than to entity k. It has\nbeen experimentally shown that HOE can obtain representations of hierarchical\ndata such as a knowledge base and a citation network effectively, owing to\nhyperbolic space's exponential growth property. However, its theoretical\nanalysis has been limited to ideal noiseless settings, and its generalization\nerror in compensation for hyperbolic space's exponential representation ability\nhas not been guaranteed. The difficulty is that existing generalization error\nbound derivations for ordinal embedding based on the Gramian matrix do not work\nin HOE, since hyperbolic space is not inner-product space. In this paper,\nthrough our novel characterization of HOE with decomposed Lorentz Gramian\nmatrices, we provide a generalization error bound of HOE for the first time,\nwhich is at most exponential with respect to the embedding space's radius. Our\ncomparison between the bounds of HOE and Euclidean ordinal embedding shows that\nHOE's generalization error is reasonable as a cost for its exponential\nrepresentation ability.\n"}
+{"abstract": "  Electron spin resonance (ESR) pulsed dipolar spectroscopy (PDS) is used\neffectively in measuring nano-meter range distances for protein structure\nprediction. The current global approach in extracting the distance distribution\nfrom time domain PDS signal has multiple limitations. We present a parameter\nfree global method, which is more efficacious and less sensitive to signal\nnoise compared to the current method.\n"}
+{"abstract": "  This work re-implements the OpenAI Gym multi-goal robotic manipulation\nenvironment, originally based on the commercial Mujoco engine, onto the\nopen-source Pybullet engine. By comparing the performances of the Hindsight\nExperience Replay-aided Deep Deterministic Policy Gradient agent on both\nenvironments, we demonstrate our successful re-implementation of the original\nenvironment. Besides, we provide users with new APIs to access a joint control\nmode, image observations and goals with customisable camera and a built-in\non-hand camera. We further design a set of multi-step, multi-goal, long-horizon\nand sparse reward robotic manipulation tasks, aiming to inspire new\ngoal-conditioned reinforcement learning algorithms for such challenges. We use\na simple, human-prior-based curriculum learning method to benchmark the\nmulti-step manipulation tasks. Discussions about future research opportunities\nregarding this kind of tasks are also provided.\n"}
+{"abstract": "  Compositional generalization is the capacity to recognize and imagine a large\namount of novel combinations from known components. It is a key in human\nintelligence, but current neural networks generally lack such ability. This\nreport connects a series of our work for compositional generalization, and\nsummarizes an approach. The first part contains concepts and properties. The\nsecond part looks into a machine learning approach. The approach uses\narchitecture design and regularization to regulate information of\nrepresentations. This report focuses on basic ideas with intuitive and\nillustrative explanations. We hope this work would be helpful to clarify\nfundamentals of compositional generalization and lead to advance artificial\nintelligence.\n"}
+{"abstract": "  A multiring ([Mar3]) is a kind of ring where is allowed the sum of two\nelements to be anon-empty subset of the structure instead of just one element\n-and an hyperring is a multiring with a strong distributive property. Thus a\nreduced hyperring where the prime spec is a Boolean topological space is called\nvon Neumann regular hyperring (vNH). It is possible to associate to every such\nobject a structural presheaf in the same way it is made with rings but there\nare some vNH such that this presheaf is not a sheaf. In this sense, we give a\nfirst-order characterization of vNH with a structural sheaf (geometric vNH or\njust GvNH) and how to transform a vNH in aGvNH -in fact, this transformation\nshows that the category GvNH is a reflexive subcategory of vNH. We also build a\nvon Neumann regular hull for multirings and use this to give applications for\nalgebraic theory of quadratic forms. More precisely, we work with Real Reduced\nMultiring (RRM, [Mar3]) -also known as Real Semigroup (RS, [DP1])-, a special\nkind of multirings that is useful to explore the real structure of rings, and\nshow that a von Neumann hull of a RRM is again a RRM. This gives a\ngeneralization of sheafs arguments present in [DM4].\n"}
+{"abstract": "  Current vision and language tasks usually take complete visual data (e.g.,\nraw images or videos) as input, however, practical scenarios may often consist\nthe situations where part of the visual information becomes inaccessible due to\nvarious reasons e.g., restricted view with fixed camera or intentional vision\nblock for security concerns. As a step towards the more practical application\nscenarios, we introduce a novel task that aims to describe a video using the\nnatural language dialog between two agents as a supplementary information\nsource given incomplete visual data. Different from most existing\nvision-language tasks where AI systems have full access to images or video\nclips, which may reveal sensitive information such as recognizable human faces\nor voices, we intentionally limit the visual input for AI systems and seek a\nmore secure and transparent information medium, i.e., the natural language\ndialog, to supplement the missing visual information. Specifically, one of the\nintelligent agents - Q-BOT - is given two semantic segmented frames from the\nbeginning and the end of the video, as well as a finite number of opportunities\nto ask relevant natural language questions before describing the unseen video.\nA-BOT, the other agent who has access to the entire video, assists Q-BOT to\naccomplish the goal by answering the asked questions. We introduce two\ndifferent experimental settings with either a generative (i.e., agents generate\nquestions and answers freely) or a discriminative (i.e., agents select the\nquestions and answers from candidates) internal dialog generation process. With\nthe proposed unified QA-Cooperative networks, we experimentally demonstrate the\nknowledge transfer process between the two dialog agents and the effectiveness\nof using the natural language dialog as a supplement for incomplete implicit\nvisions.\n"}
+{"abstract": "  In 2014, a union of German research organisations established Projekt DEAL, a\nnational-level project to negotiate licensing agreements with large scientific\npublishers. Negotiations between DEAL and Elsevier began in 2016, and broke\ndown without a successful agreement in 2018; in this time, around 200 German\nresearch institutions cancelled their license agreements with Elsevier, leading\nElsevier to restrict journal access at those institutions from July 2018\nonwards. We investigated the effect of these access restrictions on\nresearchers' publishing and citing behaviours from a bibliometric perspective,\nusing a dataset of ~410,000 articles published by researchers at the affected\nDEAL institutions between 2012-2020. We further investigated these effects with\nrespect to the timing of contract cancellations with Elsevier, research\ndisciplines, collaboration patterns, and article open-access status. We find\nevidence for a decrease in Elsevier's market share of articles from DEAL\ninstitutions, from a peak of 25.3% in 2015 to 20.6% in 2020, with the largest\nyear-on-year market share decreases occurring in 2019 (-1.1%) and 2020 (-1.6%)\nfollowing the implementation of access restrictions. We also observe\nyear-on-year decreases in the proportion of citations made from articles\npublished by authors at DEAL institutions to articles in Elsevier journals\npost-2018, although the decrease is smaller (-0.4% in 2019 and -0.6% in 2020)\nthan changes in publishing volume. We conclude that Elsevier access\nrestrictions have led to some reduced willingness of researchers at DEAL\ninstitutions to publish their research in Elsevier journals, but that\nresearchers are not strongly affected in their ability to cite Elsevier\narticles, with the implication that researchers use a variety of other methods\n(e.g. interlibrary loans, sharing between colleagues, or \"shadow libraries\") to\naccess scientific literature.\n"}
+{"abstract": "  Preventable or undiagnosed visual impairment and blindness affect billion of\npeople worldwide. Automated multi-disease detection models offer great\npotential to address this problem via clinical decision support in diagnosis.\nIn this work, we proposed an innovative multi-disease detection pipeline for\nretinal imaging which utilizes ensemble learning to combine the predictive\ncapabilities of several heterogeneous deep convolutional neural network models.\nOur pipeline includes state-of-the-art strategies like transfer learning, class\nweighting, real-time image augmentation and Focal loss utilization.\nFurthermore, we integrated ensemble learning techniques like heterogeneous deep\nlearning models, bagging via 5-fold cross-validation and stacked logistic\nregression models. Through internal and external evaluation, we were able to\nvalidate and demonstrate high accuracy and reliability of our pipeline, as well\nas the comparability with other state-of-the-art pipelines for retinal disease\nprediction.\n"}
+{"abstract": "  In this paper, we analyze the performance of orthogonal time frequency space\n(OTFS) modulation with antenna selection at the receiver, where $n_s$ out of\n$n_r$ receive antennas with maximum channel Frobenius norms in the\ndelay-Doppler (DD) domain are selected. Single-input multiple-output OTFS\n(SIMO-OTFS), multiple-input multiple-output OTFS (MIMO-OTFS), and space-time\ncoded OTFS (STC-OTFS) systems with receive antenna selection (RAS) are\nconsidered. We consider these systems without and with phase rotation. Our\ndiversity analysis results show that, with no phase rotation, SIMO-OTFS and\nMIMO-OTFS systems with RAS are rank deficient, and therefore they do not\nextract the full receive diversity as well as the diversity present in the DD\ndomain. Also, Alamouti coded STC-OTFS system with RAS and no phase rotation\nextracts the full transmit diversity, but it fails to extract the DD diversity.\nOn the other hand, SIMO-OTFS and STC-OTFS systems with RAS become full-ranked\nwhen phase rotation is used, because of which they extract the full spatial as\nwell as the DD diversity present in the system. Also, when phase rotation is\nused, MIMO-OTFS systems with RAS extract the full DD diversity, but they do not\nextract the full receive diversity because of rank deficiency. Simulation\nresults are shown to validate the analytically predicted diversity performance.\n"}
+{"abstract": "  In this work, we outline a cross-domain assurance process for safety-relevant\nsoftware in embedded systems. This process aims to be applied in various\ndifferent application domains and in conjunction with any development\nmethodology. With this approach we plan to reduce the growing effort for safety\nassessment in embedded systems by reusing safety analysis techniques and tools\nfor the product development in different domains.\n"}
+{"abstract": "  Gamma-ray spectral data were collected from sensors mounted to traffic\nsignals around Northern Virginia. The data were collected over a span of\napproximately fifteen months. A subset of the data were analyzed manually and\nsubsequently used to train machine-learning models to facilitate the evaluation\nof the remaining 50k anomalous events identified in the dataset. We describe\nthe analysis approach used here and discuss the results in terms of\nradioisotope classes and frequency patterns over day-of-week and time-of-day\nspans. Data from this work has been archived and is available for future and\nongoing research applications.\n"}
+{"abstract": "  Magnetic clouds (MCs) are transient structures containing large-scale\nmagnetic flux ropes from solar eruptions. The twist of magnetic field lines\naround the rope axis reveals information about flux rope formation processes\nand geoeffectivity. During propagation, MC flux ropes may erode via\nreconnection with the ambient solar wind. Any erosion reduces the magnetic flux\nand helicity of the ropes, and changes their cross-sectional twist profiles.\nThis study relates twist profiles in MC flux ropes observed at 1 AU to the\namount of erosion undergone by the MCs in interplanetary space. The twist\nprofiles of two well-identified MC flux ropes associated with the clear\nappearance of post eruption arcades in the solar corona are analysed. To infer\nthe amount of erosion, the magnetic flux content of the ropes in the solar\natmosphere is estimated, and compared to estimates at 1 AU. The first MC shows\na monotonically decreasing twist from the axis to periphery, while the second\ndisplays high twist at the axis, rising twist near the edges, and lower twist\nin between. The first MC displays a larger reduction in magnetic flux between\nthe Sun and 1 AU, suggesting more erosion, than that seen in the second MC. In\nthe second cloud, rising twist at the rope edges may have been due to an\nenvelope of overlying coronal field lines with relatively high twist, formed by\nreconnection beneath the erupting flux rope in the low corona. This high-twist\nenvelope remained almost intact from the Sun to 1 AU due to the low erosion\nlevels. In contrast, the high-twist envelope of the first cloud may have been\nentirely peeled away via erosion by the time it reaches 1 AU.\n"}
+{"abstract": "  We propose a method to define quasiprobability distributions for general\nspin-$j$ systems of dimension $n=2j+1$, where $n$ is a prime or power of prime.\nThe method is based on a complete set of orthonormal commuting operators\nrelated to Mutually Unbiased Bases which enable (i) a parameterisation of the\ndensity matrix and (ii) construction of measurement operators that can be\nphysically realised. As a result we geometrically characterise the set of\nstates for which the quasiprobability distribution is non-negative, and can be\nviewed as a joint distribution of classical random variables assuming values in\na finite set of outcomes. The set is an $(n^2-1)$-dimensional convex polytope\nwith $n+1$ vertices as the only pure states, $n^{n+1}$ number of higher\ndimensional faces, and $n^3(n+1)/2$ edges.\n"}
+{"abstract": "  Deep neural networks (DNNs) represent the mainstream methodology for\nsupervised speech enhancement, primarily due to their capability to model\ncomplex functions using hierarchical representations. However, a recent study\nrevealed that DNNs trained on a single corpus fail to generalize to untrained\ncorpora, especially in low signal-to-noise ratio (SNR) conditions. Developing a\nnoise, speaker, and corpus independent speech enhancement algorithm is\nessential for real-world applications. In this study, we propose a\nself-attending recurrent neural network(SARNN) for time-domain speech\nenhancement to improve cross-corpus generalization. SARNN comprises of\nrecurrent neural networks (RNNs) augmented with self-attention blocks and\nfeedforward blocks. We evaluate SARNN on different corpora with nonstationary\nnoises in low SNR conditions. Experimental results demonstrate that SARNN\nsubstantially outperforms competitive approaches to time-domain speech\nenhancement, such as RNNs and dual-path SARNNs. Additionally, we report an\nimportant finding that the two popular approaches to speech enhancement:\ncomplex spectral mapping and time-domain enhancement, obtain similar results\nfor RNN and SARNN with large-scale training. We also provide a challenging\nsubset of the test set used in this study for evaluating future algorithms and\nfacilitating direct comparisons.\n"}
+{"abstract": "  In this paper, we propose a $\\mu$-mode integrator for computing the solution\nof stiff evolution equations. The integrator is based on a $d$-dimensional\nsplitting approach and uses exact (usually precomputed) one-dimensional matrix\nexponentials. We show that the action of the exponentials, i.e. the\ncorresponding batched matrix-vector products, can be implemented efficiently on\nmodern computer systems. We further explain how $\\mu$-mode products can be used\nto compute spectral transforms efficiently even if no fast transform is\navailable. We illustrate the performance of the new integrator by solving,\namong the others, three-dimensional linear and nonlinear Schr\\\"odinger\nequations, and we show that the $\\mu$-mode integrator can significantly\noutperform numerical methods well established in the field. We also discuss how\nto efficiently implement this integrator on both multi-core CPUs and GPUs.\nFinally, the numerical experiments show that using GPUs results in performance\nimprovements between a factor of $10$ and $20$, depending on the problem.\n"}
+{"abstract": "  Plastic scintillators are widely used in particle detectors when precise\ntiming information is required. As a basis for comparing different detectors we\nuse such a characteristic as the time resolution per 1MeV detected energy, i.e.\nthe expected time resolution assuming the deposited energy in the scintillator\nequals to 1MeV and all the scintillation light is collected to the photosensor.\nIn this work we measure this parameter with BC422 plastic scintillator read out\nby different SiPMs. The best obtained value is about 6ps, which is almost a\nfactor of 3 better compared to our earlier result from 2012. Such an\nimprovement represents the progress in the development of SiPMs made over these\nyears and, driven by this progress, an improved knowledge and know-how on such\ndetectors.\n"}
+{"abstract": "  Semi-supervision in Machine Learning can be used in searches for new physics\nwhere the signal plus background regions are not labelled. This strongly\nreduces model dependency in the search for signals Beyond the Standard Model.\nThis approach displays the drawback in that over-fitting can give rise to fake\nsignals. Tossing toy Monte Carlo (MC) events can be used to estimate the\ncorresponding trials factor through a frequentist inference. However, MC events\nthat are based on full detector simulations are resource intensive. Generative\nAdversarial Networks (GANs) can be used to mimic MC generators. GANs are\npowerful generative models, but often suffer from training instability. We\nhenceforth show a review of GANs. We advocate the use of Wasserstein GAN (WGAN)\nwith weight clipping and WGAN with gradient penalty (WGAN-GP) where the norm of\ngradient of the critic is penalized with respect to its input. Following the\nemergence of multi-lepton anomalies, we apply GANs for the generation of\ndi-leptons final states in association with $b$-quarks at the LHC. A good\nagreement between the MC and the WGAN-GP generated events is found for the\nobservables selected in the study.\n"}
+{"abstract": "  Detection of entanglement in quantum states is one of the most important\nproblems in quantum information processing. However, it is one of the most\nchallenging tasks to find a universal scheme which is also desired to be\noptimal to detect entanglement for all states of a specific class--as always\npreferred by experimentalists. Although, the topic is well studied at least in\ncase of lower dimensional compound systems, e.g., two-qubit systems, but in the\ncase of continuous variable systems, this remains as an open problem. Even in\nthe case of two-mode Gaussian states, the problem is not fully solved. In our\nwork, we have tried to address this issue. At first, a limited number of\nHermitian operators is given to test the necessary and sufficient criterion on\nthe covariance matrix of separable two-mode Gaussian states. Thereafter, we\npresent an interferometric scheme to test the same separability criterion in\nwhich the measurements are being done via Stokes-like operators. In such case,\nwe consider only single-copy measurements on a two-mode Gaussian state at a\ntime and the scheme amounts to the full state tomography. Although this latter\napproach is a linear optics based one, nevertheless it is not an economic\nscheme. Resource-wise a more economical scheme than the full state tomography\nis obtained if we consider measurements on two copies of the state at a time.\nHowever, optimality of the scheme is not yet known.\n"}
+{"abstract": "  In this paper we study the $\\mathbb{C}^*$-fixed points in moduli spaces of\nHiggs bundles over a compact Riemann surface for a complex semisimple Lie group\nand its real forms. These fixed points are called Hodge bundles and correspond\nto complex variations of Hodge structure. We introduce a topological invariant\nfor Hodge bundles that generalizes the Toledo invariant appearing for Hermitian\nLie groups. A main result of this paper is a bound on this invariant which\ngeneralizes both the Milnor-Wood inequality of the Hermitian case and the\nArakelov inequalities of classical variations of Hodge structure. When the\ngeneralized Toledo invariant is maximal, we establish rigidity results for the\nassociated variations of Hodge structure which generalize known rigidity\nresults for maximal Higgs bundles and their associated maximal representations\nin the Hermitian case.\n"}
+{"abstract": "  We derive conditions for the existence of fixed points of nonnegative neural\nnetworks, an important research objective to understand the behavior of neural\nnetworks in modern applications involving autoencoders and loop unrolling\ntechniques, among others. In particular, we show that neural networks with\nnonnegative inputs and nonnegative parameters can be recognized as monotonic\nand (weakly) scalable functions within the framework of nonlinear\nPerron-Frobenius theory. This fact enables us to derive conditions for the\nexistence of a nonempty fixed point set of the nonnegative neural networks, and\nthese conditions are weaker than those obtained recently using arguments in\nconvex analysis, which are typically based on the assumption of nonexpansivity\nof the activation functions. Furthermore, we prove that the shape of the fixed\npoint set of monotonic and weakly scalable neural networks is often an\ninterval, which degenerates to a point for the case of scalable networks. The\nchief results of this paper are verified in numerical simulations, where we\nconsider an autoencoder-type network that first compresses angular power\nspectra in massive MIMO systems, and, second, reconstruct the input spectra\nfrom the compressed signals.\n"}
+{"abstract": "  We present a first-principles computational study of cation-Se $\\Sigma$3\n(112) grain boundaries in CuGaSe$_2$. We discuss the structure of these grain\nboundaries, as well as the effect of native defects and Na impurities on their\nelectronic properties. The formation energies show that the defects will tend\nto form preferentially at the grain boundaries, rather than in the grain\ninteriors. We find that in Ga-rich growth conditions Cu vacancies as well as Ga\nat Cu and Cu at Ga antisites are mainly responsible for having the equilibrium\nFermi level pinned toward the middle of the gap, resulting in carrier\ndepletion. The Na at Cu impurity in its +1 charge state contributes to this. In\nGa-poor growth conditions, on the other hand, the formation energies of Cu\nvacancies and Ga at Cu antisites are comparatively too high for any significant\ninfluence on carrier density or on the equilibrium Fermi level position. Thus,\nunder these conditions, the Cu at Ga antisites give rise to a $p$-type grain\nboundary. Also, their formation energy is lower than the formation energy of Na\nat Cu impurities. Thus, the latter will fail to act as a hole barrier\npreventing recombination at the grain boundary, in contrast to what occurs in\nCuInSe$_2$ grain boundaries. We also discuss the effect of the defects on the\nelectronic properties of bulk CuGaSe$_2$, which we assume reflect the\nproperties of the grain interiors.\n"}
+{"abstract": "  Background and Objective: The new type of coronavirus is also called\nCOVID-19. It began to spread at the end of 2019 and has now spread across the\nworld. Until October 2020, It has infected around 37 million people and claimed\nabout 1 million lives. We propose a deep learning model that can help\nradiologists and clinicians use chest X-rays to diagnose COVID-19 cases and\nshow the diagnostic features of pneumonia. Methods: The approach in this study\nis: 1) we propose a data enhancement method to increase the diversity of the\ndata set, thereby improving the generalization performance of the model. 2) Our\ndeep convolution neural network model DPN-SE adds a self-attention mechanism to\nthe DPN network. The addition of a self-attention mechanism has greatly\nimproved the performance of the network. 3) Use the Lime interpretable library\nto mark the feature regions on the X-ray medical image that helps doctors more\nquickly diagnose COVID-19 in people. Results: Under the same network model, the\ndata with and without data enhancement is put into the model for training\nrespectively. At last, comparing two experimental results: among the 10 network\nmodels with different structures, 7 network models have improved their effects\nafter using data enhancement, with an average improvement of 1% in recognition\naccuracy. We propose that the accuracy and recall rates of the DPN-SE network\nare 93% and 98% of cases (COVID vs. pneumonia bacteria vs. viral pneumonia vs.\nnormal). Compared with the original DPN, the respective accuracy is improved by\n2%. Conclusion: The data augmentation method we used has achieved effective\nresults on a small amount of data set, showing that a reasonable data\naugmentation method can improve the recognition accuracy without changing the\nsample size and model structure. Overall, the proposed method and model can\neffectively become a very useful tool for clinical radiologists.\n"}
+{"abstract": "  This paper has three main aims: first, to give a pedagogical introduction to\nNoether's two theorems and their implications for energy conservation in\ngeneral relativity, which was a central point of discussion between Hilbert,\nKlein, Noether and Einstein. Second, it introduces and compares two proposals\nfor gravitational energy and momentum, one of which is very influential in\nphysics: and, so far as I know, neither of the two has been discussed in the\nphilosophical literature. Third, it assesses these proposals in connection with\nrecent philosophical discussions of energy and momentum in general relativity.\n  After briefly reviewing the debates about energy conservation between\nHilbert, Klein, Noether and Einstein, I give Noether's two theorems. I show\nthat Einstein's gravitational energy-momentum pseudo-tensor, including its\nsuperpotential, is fixed, through Noether's theorem, by the boundary terms in\nthe action. That is, the freedom to add an arbitrary superpotential to the\ngravitational pseudo-tensor corresponds to the freedom to add boundary terms to\nthe action without changing the equations of motion. This freedom is fixed in\nthe same way for both problems. I also review two proposals for energy and\nmomentum in GR, of which one is a quasi-local alternative to the local\nexpressions, and the other builds on Einstein's local pseudo-tensor approach. I\ndiscuss the recent philosophical literature on the conservation of energy and\nmomentum in general relativity, and I assess and compare the two proposals in\nthe light of this literature: especially, in light of questions about\ndiffeomorphism invariance and background-independence.\n"}
+{"abstract": "  It is well-known that the chances of success of SETI depend on the longevity\nof technological civilizations or, more broadly, on the duration of the signs\nof their existence, or technosignatures. Here, we re-examine this general tenet\nin more detail, and we show that its broader implications were not given the\nproper significance. In particular, an often overlooked aspect is that the\nduration of a technosignature is in principle almost entirely separable from\nthe age of the civilization that produces it. We propose a classification\nscheme of technosignatures based on their duration and, using Monte Carlo\nsimulations, we show that, given an initial generic distribution of Galactic\ntechnosignatures, only the ones with the longest duration are likely to be\ndetected. This tells us, among other things, that looking for a large number of\nshort-lived technosignatures is a weaker observational strategy than focusing\nthe search on a few long-lived ones. It also suggests to abandon any\nanthropocentric bias in approaching the question of extraterrestrial\nintelligence. We finally give some ideas of possible pathways that can lead to\nthe establishment of long-lived technosignatures.\n"}
+{"abstract": "  We apply the specialization technique based on the decomposition of the\ndiagonal to find an explicit example over $\\mathbb{Q}$ of a quadric and cubic\nhypersurface in $\\mathbb{P}^6$ such that their intersection is a smooth stably\nirrational fourfold. Using the same degeneration, Nicaise and Ottem have\nalready proven that the the very general complete intersection of this type is\nstably irrational using the motivic volume.\n"}
+{"abstract": "  A constellation of $N=d-1$ Majorana stars represents an arbitrary pure\nquantum state of dimension $d$ or a permutation-symmetric state of a system\nconsisting of $n$ qubits. We generalize the latter construction to represent in\na similar way an arbitrary symmetric pure state of $k$ subsystems with $d$\nlevels each. For $d\\geq 3$, such states are equivalent, as far as rotations are\nconcerned, to a collection of various spin states, with definite relative\ncomplex weights. Following Majorana's lead, we introduce a multiconstellation,\nconsisting of the Majorana constellations of the above spin states, augmented\nby an auxiliary, \"spectator\" constellation, encoding the complex weights.\nExamples of stellar representations of symmetric states of four qutrits, and\ntwo spin-$3/2$ systems, are presented. We revisit the Hermite and Murnaghan\nisomorphisms, which relate multipartite states of various spins, number of\nparties, and even symmetries. We show how the tools introduced can be used to\nanalyze multipartite entanglement and to identify optimal quantum rotosensors,\ni.e., pure states which are maximally sensitive to rotations around a specified\naxis, or averaged over all axes.\n"}
+{"abstract": "  Recently, pre-training multilingual language models has shown great potential\nin learning multilingual representation, a crucial topic of natural language\nprocessing. Prior works generally use a single mixed attention (MA) module,\nfollowing TLM (Conneau and Lample, 2019), for attending to intra-lingual and\ncross-lingual contexts equivalently and simultaneously. In this paper, we\npropose a network named decomposed attention (DA) as a replacement of MA. The\nDA consists of an intra-lingual attention (IA) and a cross-lingual attention\n(CA), which model intralingual and cross-lingual supervisions respectively. In\naddition, we introduce a language-adaptive re-weighting strategy during\ntraining to further boost the model's performance. Experiments on various\ncross-lingual natural language understanding (NLU) tasks show that the proposed\narchitecture and learning strategy significantly improve the model's\ncross-lingual transferability.\n"}
+{"abstract": "  Considering Limit Cycles as one of the limits of Lienard equation, an analyis\nanalogous to centre manifold analysis has been done for a $3-D$ nonlinear\nsystem exhibiting Limit Cycle. A rigorous study on radius of the Limit Cycle\norbit has been done by considering $\\lambda-\\omega$ equations for the\nparticular system and subsequently converting the system equations from\ncartesian to polar form. It has been shown through an analysis analogous to\nCentre Manifold Analysis and reduction of the system dynamics on a lower\ndimensional space, the Limit cycle radius undergoes an increment change. One\nexample is provided to support the theoretical predictions.\n"}
+{"abstract": "  In this paper, we propose to use the HLL finite volume scheme combined with\nimplicit techniques for modelling the coupled surface and subsurface water\nflows. In our approach, we used the shallow water equations modelling surface\nwater flow with different source terms such as variable bottom topography,\nfriction effect, precipitation and infiltration. For subsurface water flow, the\nGreen-Amp equation is used to simulate the infiltration process through soils.\nFor solving the resulting nonlinear-coupled system of shallow water flow and\nthe Green-Ampt infiltration equations, the HLL finite volume schemes with\nlinear reconstructions of the solutions at the discrete level are implemented\nin order to achieve the second-order accuracy of the scheme. Appropriate\ndiscretization techniques are used for the source terms to guarantee the\nwell-balanced property of our numerical scheme. Numerical experiments are\nperformed to test the capability of the developed numerical scheme to simulate\nthe coupled surface and subsurface water flows.\n"}
+{"abstract": "  The UNESCO World Heritage List (WHL) includes the exceptionally valuable\ncultural and natural heritage to be preserved for mankind. Evaluating and\njustifying the Outstanding Universal Value (OUV) is essential for each site\ninscribed in the WHL, and yet a complex task, even for experts, since the\nselection criteria of OUV are not mutually exclusive. Furthermore, manual\nannotation of heritage values and attributes from multi-source textual data,\nwhich is currently dominant in heritage studies, is knowledge-demanding and\ntime-consuming, impeding systematic analysis of such authoritative documents in\nterms of their implications on heritage management. This study applies\nstate-of-the-art NLP models to build a classifier on a new dataset containing\nStatements of OUV, seeking an explainable and scalable automation tool to\nfacilitate the nomination, evaluation, research, and monitoring processes of\nWorld Heritage sites. Label smoothing is innovatively adapted to improve the\nmodel performance by adding prior inter-class relationship knowledge to\ngenerate soft labels. The study shows that the best models fine-tuned from BERT\nand ULMFiT can reach 94.3% top-3 accuracy. A human study with expert evaluation\non the model prediction shows that the models are sufficiently generalizable.\nThe study is promising to be further developed and applied in heritage research\nand practice.\n"}
+{"abstract": "  Motivated by the recent discovery of a dispersive-to-nondispersive transition\nfor linear waves in shear flows, we accurately explored the wavenumber-Reynolds\nnumber parameter map of the plane Poiseuille flow, in the limit of least-damped\nwaves. We have discovered the existence of regions of the map where the\ndispersion and propagation features vary significantly from their surroundings.\nThese regions are nested in the dispersive, low-wavenumber part of the map.\nThis complex dispersion scenario demonstrates the existence of linear\ndispersive focusing in wave envelopes evolving out of an initial, spatially\nlocalized, three-dimensional perturbation. An asymptotic wave packet's\nrepresentation, based on the saddle-point method, allows to enlighten the\nnature of the packet's morphology, in particular the arrow-shaped structure and\nspatial spreading rates. A correlation is also highlighted between the regions\nof largest dispersive focusing and the regions which are most subject to strong\nnonlinear coupling in observations.\n"}
+{"abstract": "  In this article we compute analytically the number of Nash Equilibria (NE)\nfor a two-choice game played on a (circular) ladder graph with $2n$ players. We\nconsider a set of games with generic payoff parameters, with the only\nrequirement that a NE occurs if the players choose opposite strategies\n(anti-coordination game). The results show that for both, the ladder and\ncircular ladder, the number of NE grows exponentially with (half) the number of\nplayers $n$, as $N_{NE}(2n)\\sim C(\\varphi)^n$, where $\\varphi=1.618..$ is the\ngolden ratio and $C_{circ}>C_{ladder}$. In addition, the value of the scaling\nfactor $C_{ladder}$ depends on the value of the payoff parameters. However,\nthat is no longer true for the circular ladder (3-degree graph), that is\n$C_{circ}$ is constant, which might suggest that the topology of the graph\nindeed plays an important role for setting the number of NE.\n"}
+{"abstract": "  We consider solving a generalized Allen-Cahn equation coupled with a passive\nconvection for a given incompressible velocity field. The numerical scheme\nconsists of the first order accurate stabilized implicit explicit time\ndiscretization and a fourth order accurate finite difference scheme, which is\nobtained from the finite difference formulation of the $Q^2$ spectral element\nmethod. We prove that the discrete maximum principle holds under suitable mesh\nsize and time step constraints. The same result also applies to construct a\nbound-preserving scheme for any passive convection with an incompressible\nvelocity field.\n"}
+{"abstract": "  We propose a new method for estimating the relative pose between two images,\nwhere we jointly learn keypoint detection, description extraction, matching and\nrobust pose estimation. While our architecture follows the traditional pipeline\nfor pose estimation from geometric computer vision, all steps are learnt in an\nend-to-end fashion, including feature matching. We demonstrate our method for\nthe task of visual localization of a query image within a database of images\nwith known pose. Pairwise pose estimation has many practical applications for\nrobotic mapping, navigation, and AR. For example, the display of persistent AR\nobjects in the scene relies on a precise camera localization to make the\ndigital models appear anchored to the physical environment. We train our\npipeline end-to-end specifically for the problem of visual localization. We\nevaluate our proposed approach on localization accuracy, robustness and runtime\nspeed. Our method achieves state of the art localization accuracy on the 7\nScenes dataset.\n"}
+{"abstract": "  We investigate how symmetries and conserved quantities relate to the\noccurrence of the boundary time crystal (BTC) phase in a generalized spin model\nwith Lindblad dissipation. BTCs are a non-equilibrium phase of matter in which\nthe system, coupled to an external environment, breaks the continuous time\ntranslational invariance. We perform a detailed mean-field study aided by a\nfinite-size analysis of the quantum model of a p,q-spin-interaction system, a\ngeneralized p-spin interaction system, which can be implemented in\nfully-connected spin-1/2 ensembles. We find the following conditions for the\nobservation of the BTC phase: First, the BTC appears when the discrete symmetry\nheld by the Hamiltonian, $\\mathbb{Z}_2$ in the considered models, is explicitly\nbroken by the Lindblad jump operators. Second, the system must be coupled\nuniformly to the same bath in order to preserve the total angular momentum\nduring the time evolution. If these conditions are not satisfied, any\noscillatory behavior appears only as a transient in the dynamics and a\ntime-independent stationary state is eventually reached. Our results suggest\nthat these two elements may be general requirements for the observation of a\nstable BTC phase relating symmetries and conserved quantities in arbitrary spin\nmodels.\n"}
+{"abstract": "  We establish a non-local integral difference quotient representation for\nsymmetric gradient semi-norms in $BD(\\Omega)$ and $LD(\\Omega)$, which does not\nrequire the manipulation of distributional derivatives. Our representation\nextends the formulas for the symmetric gradient established by Mengesha for\nvector-fields in $W^{1,p}(\\Omega;\\mathbb R^d)$, which are inspired by the\ngradient semi-norm formulas introduced by Bourgain, Brezis and Mironescu in\n$W^{1,p}(\\Omega)$ and by D\\'avila in $BV(\\Omega)$.\n"}
+{"abstract": "  In this paper, we develop a family of third order asymptotic-preserving (AP)\nand asymptotically accurate (AA) diagonally implicit Runge-Kutta (DIRK) time\ndiscretization methods for the stiff hyperbolic relaxation systems and kinetic\nBhatnagar-Gross-Krook (BGK) model in the semi-Lagrangian (SL) setting. The\nmethods are constructed based on an accuracy analysis of the SL scheme for\nstiff hyperbolic relaxation systems and kinetic BGK model in the limiting fluid\nregime when the Knudsen number approaches $0$. An extra order condition for the\nasymptotic third order accuracy in the limiting regime is derived. Linear Von\nNeumann stability analysis of the proposed third order DIRK methods are\nperformed to a simplified two-velocity linear kinetic model. Extensive\nnumerical tests are presented to demonstrate the AA, AP and stability\nproperties of our proposed schemes.\n"}
+{"abstract": "  We study families of depth measures defined by natural sets of axioms. We\nshow that any such depth measure is a constant factor approximation of Tukey\ndepth. We further investigate the dimensions of depth regions, showing that the\nCascade conjecture, introduced by Kalai for Tverberg depth, holds for all depth\nmeasures which satisfy our most restrictive set of axioms, which includes Tukey\ndepth. Along the way, we introduce and study a new depth measure called\nenclosing depth, which we believe to be of independent interest, and show its\nrelation to a constant-fraction Radon theorem on certain two-colored point\nsets.\n"}
+{"abstract": "  Following the recent surge in adoption of machine learning (ML), the negative\nimpact that improper use of ML can have on users and society is now also widely\nrecognised. To address this issue, policy makers and other stakeholders, such\nas the European Commission or NIST, have proposed high-level guidelines aiming\nto promote trustworthy ML (i.e., lawful, ethical and robust). However, these\nguidelines do not specify actions to be taken by those involved in building ML\nsystems. In this paper, we argue that guidelines related to the development of\ntrustworthy ML can be translated to operational practices, and should become\npart of the ML development life cycle. Towards this goal, we ran a multi-vocal\nliterature review, and mined operational practices from white and grey\nliterature. Moreover, we launched a global survey to measure practice adoption\nand the effects of these practices. In total, we identified 14 new practices,\nand used them to complement an existing catalogue of ML engineering practices.\nInitial analysis of the survey results reveals that so far, practice adoption\nfor trustworthy ML is relatively low. In particular, practices related to\nassuring security of ML components have very low adoption. Other practices\nenjoy slightly larger adoption, such as providing explanations to users. Our\nextended practice catalogue can be used by ML development teams to bridge the\ngap between high-level guidelines and actual development of trustworthy ML\nsystems; it is open for review and contribution\n"}
+{"abstract": "  Distribution network topology detection and state estimation in real-time are\ncritical for modern distribution systems management and control. However,\nnumber of sensors in distribution networks are limited and communication links\nbetween switch devices and distribution management system are not\nwell-established. In this regard, this paper proposes mixed-integer quadratic\nprogramming (MIQP) formulations to determine the topology of distribution\nnetwork and estimate distribution system states simultaneously using\nmicro-phasor measurement units (micro-PMUs) and smart meter data. Two\napproaches based on AC optimal power flow are proposed and analyzed: (i) polar\npower-voltage (PPV) formulation, and (ii) rectangular current-voltage (RIV)\nformulation. The proposed models include convex objective function while\nconstraints are linearized using first-order approximation of Taylor series and\nBig M method. The proposed models can identify multiple simultaneous switching\nactions at each time instant and different topology configurations including\nradial and meshed networks. Only measurement data at each time interval is\nneeded to identify topology and states of the system correctly. The proposed\nmodels are tested on a modified IEEE-33 bus system with realistic load data\nfrom Pecan Street Inc. database. The results confirm that both models can\nidentify system topology and states with remarkable accuracy in real-time,\nwhile RIV model outperforms PPV model.\n"}
+{"abstract": "  In the present study, simulations are directed to capture the dynamics of\nevacuating inner gas of a bubble bursting at the free surface, using Eulerian\nbased volume of fluid (VOF) method. The rate by which surrounding air rushing\ninside the bubble cavity through the inner gas evacuation is estimated and\ncompared by the collapsing bubble cavity during the sequential stages of the\nbubble bursting at the free surface. Further, the reachability of inner gas\nover the free surface is evaluated by establishing the comparison of the same\nthrough various horizontal planes, lying at different altitudes above the\nunperturbed surface. The evacuating inner gas accompanies vortex rings, which\nentrains the surrounding gas-phase. During the successive stages of air\nentrainment, spatiotemporal characteristics of the vortex ring are obtained. At\nlow Bond numbers (< 1), after comparing the phase contours of evacuating inner\ngas from the bubble cavity, the consequences at the axial growth of gas jet and\nthe radial expansion of the jet tip is discussed separately. Furthermore, under\nthe respiration process, the axial growth of rising inner gas over the free\nsurface and the radial expansion of vortex rings of a bubble bursting at the\nfree surface is compared with the quiescent surrounding air. At last, the\neffects of various possible asymmetric perforation of the bubble cap keeping\nthe same Bo are studied. The cause of bent gas jet, as a consequence of\nperforation of the bubble cap, asymmetrically, is explained by plotting the\nvelocity vectors.\n"}
+{"abstract": "  Thanks to the combination of state-of-the-art accelerators and highly\noptimized open software frameworks, there has been tremendous progress in the\nperformance of deep neural networks. While these developments have been\nresponsible for many breakthroughs, progress towards solving large-scale\nproblems, such as video encoding and semantic segmentation in 3D, is hampered\nbecause access to on-premise memory is often limited. Instead of relying on\n(optimal) checkpointing or invertibility of the network layers -- to recover\nthe activations during backpropagation -- we propose to approximate the\ngradient of convolutional layers in neural networks with a multi-channel\nrandomized trace estimation technique. Compared to other methods, this approach\nis simple, amenable to analyses, and leads to a greatly reduced memory\nfootprint. Even though the randomized trace estimation introduces stochasticity\nduring training, we argue that this is of little consequence as long as the\ninduced errors are of the same order as errors in the gradient due to the use\nof stochastic gradient descent. We discuss the performance of networks trained\nwith stochastic backpropagation and how the error can be controlled while\nmaximizing memory usage and minimizing computational overhead.\n"}
+{"abstract": "  We present ASTRODEEP-GS43, a new multiwavelength photometric catalogue of the\nGOODS-South field, which builds and improves upon the previously released\nCANDELS catalogue. We provide photometric fluxes and corresponding\nuncertainties in 43 optical and infrared bands (25 wide and 18 medium filters),\nas well as photometric redshifts and physical properties of the 34930 CANDELS\n$H$-detected objects, plus an additional sample of 178 $H$-dropout sources, of\nwhich 173 are $Ks$-detected and 5 IRAC-detected. We keep the CANDELS photometry\nin 7 bands (CTIO $U$, Hubble Space Telescope WFC3 and ISAAC-$K$), and measure\nfrom scratch the fluxes in the other 36 (VIMOS, HST ACS, HAWK-I $Ks$, Spitzer\nIRAC, and 23 from Subaru SuprimeCAM and Magellan-Baade Fourstar) with\nstate-of-the-art techniques of template-fitting. We then compute new\nphotometric redshifts with three different software tools, and take the median\nvalue as best estimate. We finally evaluate new physical parameters from SED\nfitting, comparing them to previously published ones. Comparing to a sample of\n3931 high quality spectroscopic redshifts, for the new photo-$z$'s we obtain a\nnormalized median absolute deviation (NMAD) of 0.015 with 3.01$\\%$ of outliers\n(0.011, 0.22$\\%$ on the bright end at $I814$<22.5), similarly to the best\navailable published samples of photometric redshifts, such as the COSMOS\nUltraVISTA catalogue. The ASTRODEEP-GS43 results are in qualitative agreement\nwith previously published catalogues of the GOODS-South field, improving on\nthem particularly in terms of SED sampling and photometric redshift estimates.\nThe catalogue is available for download from the Astrodeep website.\n"}
+{"abstract": "  In chemical graph theory, caterpillar trees have been an appealing model to\nrepresent the molecular structures of benzenoid hydrocarbon. Meanwhile,\ntopological index has been thought of as a powerful tool for modeling\nquantitative structure-property relationship and quantitative\nstructure-activity between molecules in chemical compounds. In this article, we\nconsider a class of caterpillar trees that are incorporated with randomness,\ncalled random caterpillars, and investigate several popular topological indices\nof this random class, including Zagreb index, Randi\\'{c} index and Wiener\nindex, etc. Especially, a central limit theorem is developed for the asymptotic\ndistribution of the Zagreb index of random caterpillars.\n"}
+{"abstract": "  Quasi-one-dimensional (1D) materials provide a superior platform for\ncharacterizing and tuning topological phases for two reasons: i) existence for\nmultiple cleavable surfaces that enables better experimental identification of\ntopological classification, and ii) stronger response to perturbations such as\nstrain for tuning topological phases compared to higher dimensional crystal\nstructures. In this paper, we present experimental evidence for a\nroom-temperature topological phase transition in the quasi-1D material\nBi$_4$I$_4$, mediated via a first order structural transition between two\ndistinct stacking orders of the weakly-coupled chains. Using high resolution\nangle-resolved photoemission spectroscopy on the two natural cleavable\nsurfaces, we identify the high temperature $\\beta$ phase to be the first weak\ntopological insulator with gapless Dirac cones on the (100) surface and no\nDirac crossing on the (001) surface, while in the low temperature $\\alpha$\nphase, the topological surface state on the (100) surface opens a gap,\nconsistent with a recent theoretical prediction of a higher-order topological\ninsulator beyond the scope of the established topological materials databases\nthat hosts gapless hinge states. Our results not only identify a rare\ntopological phase transition between first-order and second-order topological\ninsulators but also establish a novel quasi-1D material platform for exploring\nunprecedented physics.\n"}
+{"abstract": "  This paper develops new tools to quantify uncertainty in optimal decision\nmaking and to gain insight into which variables one should collect information\nabout given the potential cost of measuring a large number of variables. We\ninvestigate simultaneous inference to determine if a group of variables is\nrelevant for estimating an optimal decision rule in a high-dimensional\nsemiparametric framework. The unknown link function permits flexible modeling\nof the interactions between the treatment and the covariates, but leads to\nnonconvex estimation in high dimension and imposes significant challenges for\ninference. We first establish that a local restricted strong convexity\ncondition holds with high probability and that any feasible local sparse\nsolution of the estimation problem can achieve the near-oracle estimation error\nbound. We further rigorously verify that a wild bootstrap procedure based on a\ndebiased version of the local solution can provide asymptotically honest\nuniform inference for the effect of a group of variables on optimal decision\nmaking. The advantage of honest inference is that it does not require the\ninitial estimator to achieve perfect model selection and does not require the\nzero and nonzero effects to be well-separated. We also propose an efficient\nalgorithm for estimation. Our simulations suggest satisfactory performance. An\nexample from a diabetes study illustrates the real application.\n"}
+{"abstract": "  We study a simple lattice model with local symmetry, whose construction is\nbased on a crossed module of finite groups. Its dynamical degrees of freedom\nare associated both to links and faces of a four-dimensional lattice. In\nspecial limits the discussed model reduces to certain known topological quantum\nfield theories. In this work we focus on its dynamics, which we study both\nanalytically and using Monte Carlo simulations. We prove a factorization\ntheorem which reduces computation of correlation functions of local observables\nto known, simpler models. This, combined with standard Krammers-Wannier type\ndualities, allows us to propose a detailed phase diagram, which form is then\nconfirmed in numerical simulations. We describe also topological charges\npresent in the model, its symmetries and symmetry breaking patterns. The\ncorresponding order parameters are the Polyakov loop and its generalization,\nwhich we call a Polyakov surface. The latter is particularly interesting, as it\nis beyond the scope of the factorization theorem. As shown by the numerical\nresults, expectation value of Polyakov surface may serve to detects all phase\ntransitions and is sensitive to a value of the topological charge.\n"}
+{"abstract": "  Despite the proliferation of mobile devices in various wide-area Internet of\nThings applications (e.g., smart city, smart farming), current Low-Power\nWide-Area Networks (LPWANs) are not designed to effectively support mobile\nnodes. In this paper, we propose to handle mobility in SNOW (Sensor Network\nOver White spaces), an LPWAN that operates in the TV white spaces. SNOW\nsupports massive concurrent communication between a base station (BS) and\nnumerous low-power nodes through a distributed implementation of OFDM. In SNOW,\ninter-carrier interference (ICI) is more pronounced under mobility due to its\nOFDM based design. Geospatial variation of white spaces also raises challenges\nin both intra- and inter-network mobility as the low-power nodes are not\nequipped to determine white spaces. To handle mobility impacts on ICI, we\npropose a dynamic carrier frequency offset estimation and compensation\ntechnique which takes into account Doppler shifts without requiring to know the\nspeed of the nodes. We also propose to circumvent the mobility impacts on\ngeospatial variation of white space through a mobility-aware spectrum\nassignment to nodes. To enable mobility of the nodes across different SNOWs, we\npropose an efficient handoff management through a fast and energy-efficient BS\ndiscovery and quick association with the BS by combining time and frequency\ndomain energy-sensing. Experiments through SNOW deployments in a large\nmetropolitan city and indoors show that our proposed approaches enable mobility\nacross multiple different SNOWs and provide robustness in terms of reliability,\nlatency, and energy consumption under mobility.\n"}
+{"abstract": "  A long-standing goal of materials science is to understand, predict and\ncontrol the evolution of microstructures in crystalline materials. Most\nmicrostructure evolution is controlled by interface motion; hence, the\nestablishment of rigorous interface equations of motion is a universal goal of\nmaterials science. We present a new model for the motion of arbitrarily curved\ninterfaces that respects the underlying crystallography of the two\nphases/domains meeting at the interface and is consistent with microscopic\nmechanisms of interface motion; i.e., disconnection migration (line defects in\nthe interface with step and dislocation character). We derive the equation of\nmotion for interface migration under the influence of a wide range of driving\nforces. In Part II of this paper [Salvalaglio, Han and Srolovitz, 2021], we\nimplement the interface model and the equation of motion proposed in this paper\nin a diffuse interface simulation approach for complex morphology and\nmicrostructure evolution.\n"}
+{"abstract": "  We identify a class of modal solutions for spatio-temporal optical vortex\n(STOV) electromagnetic pulses propagating in dispersive media with orbital\nangular momentum (OAM) orthogonal to propagation. We find that symmetric STOVs\nin vacuum can carry half-integer intrinsic orbital angular momentum (OAM); for\ngeneral asymmetric STOVs in a dispersive medium, the OAM is quantized in\ninteger multiples of a parameter that depends on the STOV symmetry and the\ngroup velocity dispersion. Our results suggest that STOVs propagating in\ndispersive media are accompanied by a polariton-like quasiparticle. The modal\ntheory is in excellent agreement with measurements of free space propagation of\nSTOVs.\n"}
+{"abstract": "  In this paper, we study the Cauchy and Goursat problems of the spin-$n/2$\nzero rest-mass equations on Minkowski spacetime by using the conformal\ngeometric method. In our strategy, we prove the wellposedness of the Cauchy\nproblem in Einstein's cylinder. Then we establish pointwise decays of the\nfields and prove the energy equalities of the conformal fields between the null\nconformal boundaries $\\scri^\\pm$ and the hypersurface $\\Sigma_0=\\left\\{ t=0\n\\right\\}$. Finally, we prove the wellposedness of the Goursat problem in the\npartial conformal compactification by using the energy equalities and the\ngeneralisation of H\\\"ormander's result.\n"}
+{"abstract": "  Recent work demonstrated the lack of robustness of optical flow networks to\nphysical, patch-based adversarial attacks. The possibility to physically attack\na basic component of automotive systems is a reason for serious concerns. In\nthis paper, we analyze the cause of the problem and show that the lack of\nrobustness is rooted in the classical aperture problem of optical flow\nestimation in combination with bad choices in the details of the network\narchitecture. We show how these mistakes can be rectified in order to make\noptical flow networks robust to physical, patch-based attacks. Additionally, we\ntake a look at global white-box attacks in the scope of optical flow. We find\nthat targeted white-box attacks can be crafted to bias flow estimation models\ntowards any desired output, but this requires access to the input images and\nmodel weights. Our results indicate that optical flow networks are robust to\nuniversal attacks.\n"}
+{"abstract": "  In this paper, for a family of second-order elliptic equations with rapidly\noscillating periodic coefficients, we are interested in a Carleman-type\ninequality for these solutions satisfying an additional growth condition in\nelliptic periodic homogenization, which implies a three-ball inequality without\nan error term at a macroscopic scale. Moreover, if we replace the additional\ngrowth condition by the doubling condition at a macroscopic scale, then the\nthree-ball inequality without an error term holds at any scale. The proof\nrelies on the convergence of $H^1$-norm for the solution and the compactness\nargument.\n"}
+{"abstract": "  A detailed numerical study on the phenomenon of Shock Wave focusing in air is\ncarried out. The focusing phenomenon is achieved with the help of a shock tube\nand a converging section attached to it. The planar shock generated inside the\nshock tube is converted to spherical shock with the help of the converging\nsection and is focused to a point. High-temperature effects like\ntemperature-dependent Cp variation and chemical reactions corresponding to\ndissociated air are included in the simulation. The chemical reactions\nincluding the dissociation, recombination and ionization of nine species of air\nincluding three ions (N2, O2, N, O, NO, Ar, NO+, O+ and Ar+) are monitored\nthroughout the simulation. The effect of driven section filling conditions such\nas initial pressure and temperature on focusing parameters is studied. The\nvariation in the initial fill temperature is found to affect the flow\nproperties much more as compared to the change in initial fill pressure, while\nmaintaining the same shock strength. The effect of incident shock strength on\nshock wave focusing is also investigated. It is observed that as the strength\nof the shock increases, the conditions like temperature and pressure at the\nfocusing point increases and thereby increasing the reaction rate of all the\nreactions.\n"}
+{"abstract": "  In this paper, we propose a Monocular 3D Single Stage object Detector\n(M3DSSD) with feature alignment and asymmetric non-local attention. Current\nanchor-based monocular 3D object detection methods suffer from feature\nmismatching. To overcome this, we propose a two-step feature alignment\napproach. In the first step, the shape alignment is performed to enable the\nreceptive field of the feature map to focus on the pre-defined anchors with\nhigh confidence scores. In the second step, the center alignment is used to\nalign the features at 2D/3D centers. Further, it is often difficult to learn\nglobal information and capture long-range relationships, which are important\nfor the depth prediction of objects. Therefore, we propose a novel asymmetric\nnon-local attention block with multi-scale sampling to extract depth-wise\nfeatures. The proposed M3DSSD achieves significantly better performance than\nthe monocular 3D object detection methods on the KITTI dataset, in both 3D\nobject detection and bird's eye view tasks.\n"}
+{"abstract": "  In this article, we estimate the cost of simulating electrolyte molecules in\nLi-ion batteries on a fault-tolerant quantum computer, focusing on the\nmolecules that can provide practical solutions to industrially relevant\nproblems. Our resource estimate is based on the fusion-based quantum computing\nscheme using photons, but can be modified easily to the more conventional\ngate-based model as well. We find the cost of the magic state factory to\nconstitute no more than $\\sim 2\\%$ of the total footprint of the quantum\ncomputer, which suggests that it is more advantageous to use algorithms that\nconsume many magic states at the same time. We suggest architectural and\nalgorithmic changes that can accommodate such a capability. On the architecture\nside, we propose a method to consume multiple magic states at the same time,\nwhich can potentially lead to an order of magnitude reduction in the overall\ncomputation time without incurring additional expense in the footprint. This is\nbased on a fault-tolerant measurement of a Pauli product operator in constant\ntime, which may be useful in other contexts as well. We also introduce a method\nto implement an arbitrary fermionic basis change in logarithmic depth, which\nmay be of independent interest.\n"}
+{"abstract": "  The extreme miniaturization of a cold-atom interferometer accelerometer\nrequires the development of novel technologies and architectures for the\ninterferometer subsystems. We describe several component technologies and a\nlaser system architecture to enable a path to such miniaturization. We\ndeveloped a custom, compact titanium vacuum package containing a\nmicrofabricated grating chip for a tetrahedral grating magneto-optical trap\n(GMOT) using a single cooling beam. The vacuum package is integrated into the\noptomechanical design of a compact cold-atom sensor head with fixed optical\ncomponents. In addition, a multichannel laser system driven by a single seed\nlaser has been implemented with time-multiplexed frequency shifting using\nsingle sideband modulators, reducing the number of optical channels connected\nto the sensor head. This laser system architecture is compatible with a highly\nminiaturized photonic integrated circuit approach, and by demonstrating\natom-interferometer operation with this laser system, we show feasibility for\nthe integrated photonic approach. In the compact sensor head, sub-Doppler\ncooling in the GMOT produces 15 uK temperatures, which can operate at a 20 Hz\ndata rate for the atom interferometer sequence. After validating atomic\ncoherence with Ramsey interferometry, we demonstrate a light-pulse atom\ninterferometer in a gravimeter configuration without vibration isolation for 10\nHz measurement cycle rate and T = 0 - 4.5 ms interrogation time, resulting in\n$\\Delta$g / g = 2.0e-6. All these efforts demonstrate progress towards\ndeployable cold-atom inertial sensors under large amplitude motional dynamics.\n"}
+{"abstract": "  Offensive language detection is an ever-growing natural language processing\n(NLP) application. This growth is mainly because of the widespread usage of\nsocial networks, which becomes a mainstream channel for people to communicate,\nwork, and enjoy entertainment content. Many incidents of sharing aggressive and\noffensive content negatively impacted society to a great extend. We believe\ncontributing to improving and comparing different machine learning models to\nfight such harmful contents is an important and challenging goal for this\nthesis. We targeted the problem of offensive language detection for building\nefficient automated models for offensive language detection. With the recent\nadvancements of NLP models, specifically, the Transformer model, which tackled\nmany shortcomings of the standard seq-to-seq techniques. The BERT model has\nshown state-of-the-art results on many NLP tasks. Although the literature still\nexploring the reasons for the BERT achievements in the NLP field. Other\nefficient variants have been developed to improve upon the standard BERT, such\nas RoBERTa and ALBERT. Moreover, due to the multilingual nature of text on\nsocial media that could affect the model decision on a given tween, it is\nbecoming essential to examine multilingual models such as XLM-RoBERTa trained\non 100 languages and how did it compare to unilingual models. The RoBERTa based\nmodel proved to be the most capable model and achieved the highest F1 score for\nthe tasks. Another critical aspect of a well-rounded offensive language\ndetection system is the speed at which a model can be trained and make\ninferences. In that respect, we have considered the model run-time and\nfine-tuned the very efficient implementation of FastText called BlazingText\nthat achieved good results, which is much faster than BERT-based models.\n"}
+{"abstract": "  In this work we study 35 stellar clusters in the Small Magellanic Cloud (SMC)\nin order to provide their mean metallicities and ages. We also provide mean\nmetallicities of the fields surrounding the clusters. We used Str\\\"omgren\nphotometry obtained with the 4.1 m SOAR telescope and take advantage of $(b -\ny)$ and $m1$ colors for which there is a metallicity calibration presented in\nthe literature. The spatial metallicity and age distributions of clusters\nacross the SMC are investigated using the results obtained by Str\\\"omgren\nphotometry. We confirm earlier observations that younger, more metal-rich star\nclusters are concentrated in the central regions of the galaxy, while older,\nmore metal-poor clusters are located farther from the SMC center. We construct\nthe age-metallicity relation for the studied clusters and find good agreement\nwith theoretical models of chemical enrichment, and with other literature age\nand metallicity values for those clusters. We also provide the mean\nmetallicities for old and young populations of the field stars surrounding the\nclusters, and find the latter to be in good agreement with recent studies of\nthe SMC Cepheid population. Finally, the Str\\\"omgren photometry obtained for\nthis study is made publicly available.\n"}
+{"abstract": "  Although much has been published regarding street protests on social media,\nfew works have attempted to characterize social media users' spatial behavior\nin such events. The research reported here uses spatial capture-recapture\nmethods to determine the influence of the built environment, physical proximity\nto protest location, and collective posting rhythm on variations in users'\nspatial detectability and density during a protest in Mexico City. The\nbest-obtained model, together with explaining the spatial density of users,\nshows that there is high variability in the detectability of social media user\nprotest supporters and that the collective posting rhythm and the day of\nobservation are significant explanatory factors. The implication is that\nstudies of collective spatial behavior would benefit by focussing on users'\nactivity centres and their urban environment, rather than their physical\nproximity to the protest location, the latter being unable to adequately\nexplain spatial variations in users' detectability and density during the\nprotest event.\n"}
+{"abstract": "  Let $\\boldsymbol{X}$ be a $d$-dimensional random array on $[n]$ whose entries\ntake values in a finite set $\\mathcal{X}$, that is, $\\boldsymbol{X}=\\langle\nX_s:s\\in {[n]\\choose d}\\rangle$ is an $\\mathcal{X}$-valued stochastic process\nindexed by the set ${[n]\\choose d}$ of all $d$-element subsets of\n$[n]:=\\{1,\\dots,n\\}$. We give easily checked conditions on $\\boldsymbol{X}$\nwhich ensure, for instance, that for \\emph{every} function $f\\colon\n\\mathcal{X}^{{[n]\\choose d}}\\to\\mathbb{R}$ which satisfies\n$\\mathbb{E}[f(\\boldsymbol{X})]=0$ and $\\|f(\\boldsymbol{X})\\|_{L_p}=1$ for some\n$p>1$, the random variable $f(\\boldsymbol{X})$ becomes concentrated after\nconditioning it on a large subarray of $\\boldsymbol{X}$. These conditions cover\nseveral classes of random arrays with not necessarily independent entries.\nApplications are given in combinatorics, and examples are also presented which\nshow~the optimality of various aspects of the results.\n"}
+{"abstract": "  Across the United States, a growing number of school districts are turning to\nmatching algorithms to assign students to public schools. The designers of\nthese algorithms aimed to promote values such as transparency, equity, and\ncommunity in the process. However, school districts have encountered practical\nchallenges in their deployment. In fact, San Francisco Unified School District\nvoted to stop using and completely redesign their student assignment algorithm\nbecause it was not promoting educational equity in practice. We analyze this\nsystem using a Value Sensitive Design approach and find that one reason values\nare not met in practice is that the system relies on modeling assumptions about\nfamilies' priorities, constraints, and goals that clash with the real world.\nThese assumptions overlook the complex barriers to ideal participation that\nmany families face, particularly because of socioeconomic inequalities. We\nargue that direct, ongoing engagement with stakeholders is central to aligning\nalgorithmic values with real world conditions. In doing so we must broaden how\nwe evaluate algorithms while recognizing the limitations of purely algorithmic\nsolutions in addressing complex socio-political problems.\n"}
+{"abstract": "  Learning from non-stationary data streams and overcoming catastrophic\nforgetting still poses a serious challenge for machine learning research.\nRather than aiming to improve state-of-the-art, in this work we provide insight\ninto the limits and merits of rehearsal, one of continual learning's most\nestablished methods. We hypothesize that models trained sequentially with\nrehearsal tend to stay in the same low-loss region after a task has finished,\nbut are at risk of overfitting on its sample memory, hence harming\ngeneralization. We provide both conceptual and strong empirical evidence on\nthree benchmarks for both behaviors, bringing novel insights into the dynamics\nof rehearsal and continual learning in general. Finally, we interpret important\ncontinual learning works in the light of our findings, allowing for a deeper\nunderstanding of their successes.\n"}
+{"abstract": "  We study the coherent properties of fully screened and overscreened Kondo\neffects based on the Nozieres local Fermi-liquid theory and Affleck-Ludwig\nboundary conformal-field-theory approach, respectively. Coherent transports\nthrough an SU($N)$ generalization of fully screened and the\nmulti-$\\mathcal{K}$-channel overscreened Kondo impurities at and beyond the\nparticle-hole (PH) symmetric point are thoroughly investigated. We report\ndistinctive Fermi-liquid coefficients characterizing the finite temperature\ncorrection to the transmission phase shift and normalized visibility in fully\nscreened Kondo regime, which can be measured with the existing experimental\nsetups. Our work equally uncovers the significance of temperature correction to\nthe transmission phase shift and visibility in non Fermi-liquid regime\nassociated with the PH asymmetric overscreened Kondo effects with an arbitrary\nnumber of conduction channels $\\mathcal{K}$. We propose viable roots of\nverifying our predictions in connection to the recent experiments, in\nparticular with the experiments studying highly symmetric forms of fully\nscreened Kondo effects and two-channel overscreened Kondo effects realized in\nquantum dot nano-structures.\n"}
+{"abstract": "  In this letter, we study the resource allocation for a multiuser intelligent\nreflecting surface (IRS)-aided simultaneous wireless information and power\ntransfer (SWIPT) system. Specifically, a multi-antenna base station (BS)\ntransmits energy and information signals simultaneously to multiple energy\nharvesting receivers (EHRs) and information decoding receivers (IDRs) assisted\nby an IRS. Under this setup, we introduce a multi-objective optimization (MOOP)\nframework to investigate the fundamental trade-off between the data sum-rate\nmaximization and the total harvested energy maximization, by jointly optimizing\nthe energy/information beamforming vectors at the BS and the phase shifts at\nthe IRS. This MOOP problem is first converted to a single-objective\noptimization problem (SOOP) via the $\\epsilon$-constraint method and then\nsolved by majorization minimization (MM) and inner approximation (IA)\ntechniques. Simulation results unveil a non-trivial trade-off between the\nconsidered competing objectives, as well as the superior performance of the\nproposed scheme as compared to various baseline schemes.\n"}
+{"abstract": "  We discuss the application of random projections to conic programming:\nnotably linear, second-order and semidefinite programs. We prove general\napproximation results on feasibility and optimality using the framework of\nformally real Jordan algebras. We then discuss some computational experiments\non randomly generated semidefinite programs in order to illustrate the\npractical applicability of our ideas\n"}
+{"abstract": "  Let $R$ be a commutative ring: we explain the Beilinson-Bernstein\nlocalisation mechanism for sheaves of homogeneous twisted differential\noperators defined over a smooth, separated, locally of finite type $R$-scheme.\nAs an application, we give a new proof of Duflo's theorem characterising the\nprimitive ideals of the enveloping algebra of a complex semisimple Lie algebra.\n"}
+{"abstract": "  Frequent Subgraph Mining (FSM) is the key task in many graph mining and\nmachine learning applications. Numerous systems have been proposed for FSM in\nthe past decade. Although these systems show good performance for small\npatterns (with no more than four vertices), we found that they have difficulty\nin mining larger patterns. In this work, we propose a novel two-vertex\nexploration strategy to accelerate the mining process. Compared with the\nsingle-vertex exploration adopted by previous systems, our two-vertex\nexploration avoids the large memory consumption issue and significantly reduces\nthe memory access overhead. We further enhance the performance through an\nindex-based quick pattern technique that reduces the overhead of isomorphism\nchecks, and a subgraph sampling technique that mitigates the issue of subgraph\nexplosion. The experimental results show that our system achieves significant\nspeedups against the state-of-the-art graph pattern mining systems and supports\nlarger pattern mining tasks that none of the existing systems can handle.\n"}
+{"abstract": "  In the diffraction-limited near-field propagation regime, free-space optical\nquantum key distribution (QKD) systems can employ multiple spatial modes to\nimprove their key rate. Here, we analyze QKD using the non-orthogonal flat-top\nfocused beams. Although they suffer from a rate penalty, their ease of\nimplementation makes them an attractive alternative to the well-studied\northonormal Laguerre-Gauss (LG) modes. Indeed, in the presence of turbulence,\nthe non-orthogonal modes may achieve higher QKD rate than the LG modes.\n"}
+{"abstract": "  Charney and DeVore [J. Atmos. Sci. 36 (1979), 1205-1216] found multiple\nequilibrium states as a consequence of bottom topography in their pioneering\nwork on the quasi-geostrophic barotropic flow over topography in a\n$\\beta$-plane channel. In the present paper, we prove that the basic flow is\nasymptotically stable in a parameter region, including the flat topography\nsituation, which excludes the existence of multiple equilibrium states therein.\nMoreover, we show that an additional condition on the average zonal force or\nthe average zonal velocity is indispensable to the well-posedness of the\nCharney-DeVore quasi-geostrophic equation. Coexistence of at least three\nequilibrium states is confirmed by a pseudo-arclength continuation method for\ndifferent topographic amplitudes. The stabilities of the equilibrium states are\nexamined by high-resolution direct numerical simulations.\n"}
+{"abstract": "  Over the last several decades, software has been woven into the fabric of\nevery aspect of our society. As software development surges and code\ninfrastructure of enterprise applications ages, it is now more critical than\never to increase software development productivity and modernize legacy\napplications. Advances in deep learning and machine learning algorithms have\nenabled numerous breakthroughs, motivating researchers to leverage AI\ntechniques to improve software development efficiency. Thus, the fast-emerging\nresearch area of AI for Code has garnered new interest and gathered momentum.\nIn this paper, we present a large-scale dataset CodeNet, consisting of over 14\nmillion code samples and about 500 million lines of code in 55 different\nprogramming languages, which is aimed at teaching AI to code. In addition to\nits large scale, CodeNet has a rich set of high-quality annotations to\nbenchmark and help accelerate research in AI techniques for a variety of\ncritical coding tasks, including code similarity and classification, code\ntranslation between a large variety of programming languages, and code\nperformance (runtime and memory) improvement techniques. Additionally, CodeNet\nprovides sample input and output test sets for 98.5% of the code samples, which\ncan be used as an oracle for determining code correctness and potentially guide\nreinforcement learning for code quality improvements. As a usability feature,\nwe provide several pre-processing tools in CodeNet to transform source code\ninto representations that can be readily used as inputs into machine learning\nmodels. Results of code classification and code similarity experiments using\nthe CodeNet dataset are provided as a reference. We hope that the scale,\ndiversity and rich, high-quality annotations of CodeNet will offer\nunprecedented research opportunities at the intersection of AI and Software\nEngineering.\n"}
+{"abstract": "  Consider a compact surface of genus $\\geq 2$ equipped with a metric that is\nflat everywhere except at finitely many cone points with angles greater than\n$2\\pi$. Following the technique in the work of Burns, Climenhaga, Fisher, and\nThompson, we prove that sufficiently regular potential functions have unique\nequilibrium states if the singular set does not support the full pressure.\nMoreover, we show that the pressure gap holds for any potential which is\nlocally constant on a neighborhood of the singular set. Finally, we establish\nthat the corresponding equilibrium states have the $K$-property, and closed\nregular geodesics equidistribute.\n"}
+{"abstract": "  By restricting the spatial energy transmission of an electromagnetic wave,\ndielectric waveguides transmit light over long distances at sustained\nintensity. Waveguides have been used in the microwave and optical range to\nmaintain strong signal intensities in connection with lasers, but guiding of\nintense short-wavelength radiation such as x-rays has proven more cumbersome.\nHere we investigate theoretically how tapered x-ray waveguides can focus and\nguide radiation from x-ray free electron lasers. Elliptical waveguides using a\ncladding material with high atomic number such as platinum can maintain an\nx-ray intensity up to three orders of magnitude larger than in free space. This\nfeature can be used to significantly enhance resonant interactions of x-rays,\nfor instance driving nuclear transitions up to transient nuclear population\ninversion. This could be the first breakthrough in nuclear state population\ncontrol. Our results anticipate the important role of tapered x-ray waveguides\nin the emerging field of x-ray quantum optics with nuclear transitions.\n"}
+{"abstract": "  Tukey's halfspace depth can be seen as a stochastic program and as such it is\nnot guarded against optimizer's curse, so that a limited training sample may\neasily result in a poor out-of-sample performance. We propose a generalized\nhalfspace depth concept relying on the recent advances in distributionally\nrobust optimization, where every halfspace is examined using the respective\nworst-case distribution in the Wasserstein ball of radius $\\delta\\geq 0$\ncentered at the empirical law. This new depth can be seen as a smoothed and\nregularized classical halfspace depth which is retrieved as $\\delta\\downarrow\n0$. It inherits most of the main properties of the latter and, additionally,\nenjoys various new attractive features such as continuity and strict positivity\nbeyond the convex hull of the support. We provide numerical illustrations of\nthe new depth and its advantages, and develop some fundamental theory. In\nparticular, we study the upper level sets and the median region including their\nbreakdown properties.\n"}
+{"abstract": "  This year marks the eightieth anniversary of the invention of the finite\nelement method (FEM). FEM has become the computational workhorse for\nengineering design analysis and scientific modeling of a wide range of physical\nprocesses, including material and structural mechanics, fluid flow and heat\nconduction, various biological processes for medical diagnosis and surgery\nplanning, electromagnetics and semi-conductor circuit and chip design and\nanalysis, additive manufacturing, i.e. virtually every conceivable problem that\ncan be described by partial differential equations (PDEs). FEM has\nfundamentally revolutionized the way we do scientific modeling and engineering\ndesign, ranging from automobiles, aircraft, marine structures, bridges,\nhighways, and high-rise buildings. Associated with the development of finite\nelement methods has been the concurrent development of an engineering science\ndiscipline called computational mechanics, or computational science and\nengineering.\n  In this paper, we present a historical perspective on the developments of\nfinite element methods mainly focusing on its applications and related\ndevelopments in solid and structural mechanics, with limited discussions to\nother fields in which it has made significant impact, such as fluid mechanics,\nheat transfer, and fluid-structure interaction. To have a complete storyline,\nwe divide the development of the finite element method into four time periods:\nI. (1941-1965) Early years of FEM; II. (1966-1991) Golden age of FEM; III.\n(1992-2017) Large scale, industrial applications of FEM and development of\nmaterial modeling, and IV (2018-) the state-of-the-art FEM technology for the\ncurrent and future eras of FEM research. Note that this paper may not strictly\nfollow the chronological order of FEM developments, because often time these\ndevelopments were interwoven across different time periods.\n"}
+{"abstract": "  We study soluble groups G in which each subnormal subgroup H with infinite\nrank is commensurable with a normal subgroup, i.e. there exists a normal\nsubgroup N such that the intersection of H and N has finite index in both H and\nN. We show that if such a G is periodic, then all subnormal subgroups are\ncommensurable with a normal subgroup, provided either the Hirsch-Plotkin\nradical of G has infinite rank or G is nilpotent-by-abelian (and has infinite\nrank).\n"}
+{"abstract": "  A right Engel sink of an element $g$ of a group $G$ is a set ${\\mathscr\nR}(g)$ such that for every $x\\in G$ all sufficiently long commutators\n$[...[[g,x],x],\\dots ,x]$ belong to ${\\mathscr R}(g)$. (Thus, $g$ is a right\nEngel element precisely when we can choose ${\\mathscr R}(g)=\\{ 1\\}$.) We prove\nthat if a profinite group $G$ admits a coprime automorphism $\\varphi $ of prime\norder such that every fixed point of $\\varphi $ has a finite right Engel sink,\nthen $G$ has an open locally nilpotent subgroup.\n  A left Engel sink of an element $g$ of a group $G$ is a set ${\\mathscr E}(g)$\nsuch that for every $x\\in G$ all sufficiently long commutators\n$[...[[x,g],g],\\dots ,g]$ belong to ${\\mathscr E}(g)$. (Thus, $g$ is a left\nEngel element precisely when we can choose ${\\mathscr E}(g)=\\{ 1\\}$.) We prove\nthat if a profinite group $G$ admits a coprime automorphism $\\varphi $ of prime\norder such that every fixed point of $\\varphi $ has a finite left Engel sink,\nthen $G$ has an open pronilpotent-by-nilpotent subgroup.\n"}
+{"abstract": "  Representing surfaces as zero level sets of neural networks recently emerged\nas a powerful modeling paradigm, named Implicit Neural Representations (INRs),\nserving numerous downstream applications in geometric deep learning and 3D\nvision. Training INRs previously required choosing between occupancy and\ndistance function representation and different losses with unknown limit\nbehavior and/or bias. In this paper we draw inspiration from the theory of\nphase transitions of fluids and suggest a loss for training INRs that learns a\ndensity function that converges to a proper occupancy function, while its log\ntransform converges to a distance function. Furthermore, we analyze the limit\nminimizer of this loss showing it satisfies the reconstruction constraints and\nhas minimal surface perimeter, a desirable inductive bias for surface\nreconstruction. Training INRs with this new loss leads to state-of-the-art\nreconstructions on a standard benchmark.\n"}
+{"abstract": "  In 1989 Kusuoka started the study of probability measures on the shift space\nthat are defined with the help of products of matrices. In particular, he\nderived a sufficient condition for the ergodicity of such measures, which have\nsince been referred to as Kusuoka measures. We observe that repeated\nmeasurements of a unitarily evolving quantum system generate a Kusuoka measure\non the space of sequences of measurement outcomes. We show that if the\nmeasurement consists of scaled projections, then Kusuoka's sufficient\nergodicity condition can be significantly simplified. We then prove that this\ncondition is also necessary for ergodicity if the measurement consists of\nuniformly scaled rank-1 projections (i.e., it is a rank-1 POVM), or of exactly\ntwo projections, one of which is rank-1. For the latter class of measurements\nwe also show that the Kusuoka measure is reversible in the sense that every\nstring of outcomes has the same probability of being emitted by the system as\nits reverse.\n"}
+{"abstract": "  In this paper, we investigate the constraint typology of mixed-integer linear\nprogramming MILP formulations. MILP is a commonly used mathematical programming\ntechnique for modelling and solving real-life scheduling, routing, planning,\nresource allocation, timetabling optimization problems, providing optimized\nbusiness solutions for industry sectors such as: manufacturing, agriculture,\ndefence, healthcare, medicine, energy, finance, and transportation. Despite the\nnumerous real-life Combinatorial Optimization Problems found and solved, and\nmillions yet to be discovered and formulated, the number of types of\nconstraints, the building blocks of a MILP, is relatively much smaller. In the\nsearch of a suitable machine readable knowledge representation for MILPs, we\npropose an optimization modelling tree built based upon an MILP ontology that\ncan be used as a guidance for automated systems to elicit an MILP model from\nend-users on their combinatorial business optimization problems.\n"}
+{"abstract": "  In this study different types of intuitionistic fuzzy continuities (IFCs) and\nintuitionistic fuzzy boundedness (IFBs) in intuitionistic fuzzy pseudo normed\nlinear spaces are studied. Relations (intra and inter) on intuitionistic fuzzy\ncontinuities and intuitionistic fuzzy boundedness are investigated.\n"}
+{"abstract": "  We consider one-loop five-point QCD amplitudes in next-to-multi-Regge\nkinematics, and evaluate the one-loop impact factor for the emission of two\ngluons. This is the last ingredient which is necessary to evaluate the\ngluon-jet impact factor at NNLO accuracy in $\\as$. It is also the first\ninstance in which loop-level QCD amplitudes are evaluated in\nnext-to-multi-Regge kinematics, which requires to apply a different\nreggeisation ansatz to each colour-ordered amplitude.\n"}
+{"abstract": "  Interferometers provide a highly sensitive means to investigate and exploit\nthe coherence properties of light in metrology applications. However,\ninterferometers come in various forms and exploit different properties of the\noptical states within. In this paper, we introduce a classification scheme that\ncharacterizes any interferometer based on the number of involved nonlinear\nelements by studying their influence on single-photon and photon-pair states.\nSeveral examples of specific interferometers from these more general classes\nare discussed, and the theory describing the expected first-order and\nsecond-order coherence measurements for single-photon and single-photon-pair\ninput states is summarized and compared. These theoretical predictions are then\ntested in an innovative experimental setup that is easily able to switch\nbetween implementing an interferometer consisting of only one or two nonlinear\nelements. The resulting singles and coincidence rates are measured in both\nconfigurations and the results are seen to fit well with the presented theory.\nThe measured results of coherence are tied back to the presented classification\nscheme, revealing that our experimental design can be useful in gaining insight\ninto the properties of the various interferometeric setups containing different\ndegrees of nonlinearity.\n"}
+{"abstract": "  Methods for global measurement of transcript abundance such as microarrays\nand RNA-seq generate datasets in which the number of measured features far\nexceeds the number of observations. Extracting biologically meaningful and\nexperimentally tractable insights from such data therefore requires\nhigh-dimensional prediction. Existing sparse linear approaches to this\nchallenge have been stunningly successful, but some important issues remain.\nThese methods can fail to select the correct features, predict poorly relative\nto non-sparse alternatives, or ignore any unknown grouping structures for the\nfeatures. We propose a method called SuffPCR that yields improved predictions\nin high-dimensional tasks including regression and classification, especially\nin the typical context of omics with correlated features. SuffPCR first\nestimates sparse principal components and then estimates a linear model on the\nrecovered subspace. Because the estimated subspace is sparse in the features,\nthe resulting predictions will depend on only a small subset of genes. SuffPCR\nworks well on a variety of simulated and experimental transcriptomic data,\nperforming nearly optimally when the model assumptions are satisfied. We also\ndemonstrate near-optimal theoretical guarantees.\n"}
+{"abstract": "  A decentralized federated learning architecture is proposed to apply to the\nBusinesses-to-Businesses scenarios by introducing the consortium blockchain in\nthis paper. We introduce a model verification mechanism to ensure the quality\nof local models trained by participators. To analyze the latency of the system,\na latency model is constructed by considering the work flow of the\narchitecture. Finally the experiment results show that our latency model does\nwell in quantifying the actual delays.\n"}
+{"abstract": "  Deep convolutional neural networks (DCNN) aided high dynamic range (HDR)\nimaging recently received a lot of attention. The quality of DCNN generated HDR\nimages have overperformed the traditional counterparts. However, DCNN is prone\nto be computationally intensive and power-hungry. To address the challenge, we\npropose LightFuse, a lightweight CNN-based algorithm for extreme dual-exposure\nimage fusion, which can be implemented on various embedded computing platforms\nwith limited power and hardware resources. Two sub-networks are utilized: a\nGlobalNet (G) and a DetailNet (D). The goal of G is to learn the global\ninformation on the spatial dimension, whereas D aims to enhance local details\non the channel dimension. Both G and D are based solely on depthwise\nconvolution (D Conv) and pointwise convolution (P Conv) to reduce required\nparameters and computations. Experimental results display that the proposed\ntechnique could generate HDR images with plausible details in extremely exposed\nregions. Our model outperforms other state-of-the-art approaches in PSNR score\nby 0.7 to 8.5 while achieving 1.4 to 25.9 times parameter reduction.\n"}
+{"abstract": "  We report the detection of two lines at millimetre wavelengths towards the\nimmediate surroundings of the post-giant and most likely post-common-envelope\nstar HD101584 using high-angular-resolution ALMA observations. The\ncircumstellar environment of this object is rich in different molecular\nspecies, but we find no viable identifications in terms of molecular lines.\n  We aim to determine whether or not these lines can be attributed to the\nRydberg transitions -- X30alpha and X26alpha -- of neutral atoms of elements\nheavier than carbon.\n  A simple model in strict local thermodynamic equilibrium for a warm-gas\nenvironment of the moderate-temperature star (T_eff about 8500 K) was\nconstructed to corroborate our findings. A geometrically thin, disc-like\ngeometry seen face-on was chosen and a distance of 1 kpc.\n  The observed flux densities of the lines and the continuum at 232 and 354 GHz\ncan be reproduced using 10^(-3) M_sun of gas at a temperature of about 2800 K\nand a hydrogen density of about 10^(12) cm(-3), assuming solar abundances for\nthe elements. The gas lies within a distance of about 5 au from the star\n(assuming a distance of 1 kpc). The ionisation fraction is low, about\n3x10^(-5). The origin of such a region is not clear, but it may be related to a\ncommon-envelope-evolution phase. With these conditions, the line emissions are\ndominated by Rydberg transitions within the stable isotopes of Mg. A turbulent\nvelocity field in the range 5.5 - 7.5 km s^(-1) is required to fit the Gaussian\nline shapes. An upper limit to the average magnetic field in the line-emitting\nregion of 1G is set using the Zeeman effect in these lines.\n  We speculate that Rydberg transitions of heavy elements may be an interesting\nprobe for the close-in environments of other moderate-temperature objects like\nAGB stars, red supergiants, yellow hypergiants, and binaries of various types.\n"}
+{"abstract": "  Bereg et al. (2012) introduced the Boxes Class Cover problem, which has its\nroots in classification and clustering applications: Given a set of n points in\nthe plane, each colored red or blue, find the smallest cardinality set of\naxis-aligned boxes whose union covers the red points without covering any blue\npoint. In this paper we give an alternative proof of APX-hardness for this\nproblem, which also yields an explicit lower bound on its approximability. Our\nproof also directly applies when restricted to sets of points in general\nposition and to the case where so-called half-strips are considered instead of\nboxes, which is a new result.\n  We also introduce a symmetric variant of this problem, which we call\nSimultaneous Boxes Class Cover and can be stated as follows: Given a set S of n\npoints in the plane, each colored red or blue, find the smallest cardinality\nset of axis-aligned boxes which together cover S such that all boxes cover only\npoints of the same color and no box covering a red point intersects a box\ncovering a blue point. We show that this problem is also APX-hard and give a\npolynomial-time constant-factor approximation algorithm.\n"}
+{"abstract": "  The vertical shear instability (VSI) is a hydrodynamical instability that\nrequires rapid gas cooling and has been suggested to operate in outer regions\nof protoplanetary disks. The VSI drives turbulence with strong vertical\nmotions, which could regulate the dust growth and settling. However, dust\ngrowth and settling can regulate the VSI because dust depletion makes gas\ncooling inefficient in outer disk regions that are optically thin to their own\nthermal emission. In this study, we quantify this potentially stabilizing\neffects of dust evolution on the VSI based on the linear analysis. We construct\na model for calculating the cooling timescale, taking into account dust growth\nbeyond micron sizes and size-dependent settling. Combining the model with the\nlinear stability analysis, we map the region where the VSI operates, which we\ncall the VSI zone, and estimate the maximum growth rate at each radial\nposition. We find that dust growth as well as settling makes the VSI zone more\nconfined around the midplane. This causes a decrease in the growth rate because\nthe vertical shear of the rotation velocity, which is the source of the\ninstability, is weaker at lower altitude. In our default disk model with 0.01\nsolar masses, dust growth from 10 micron to 1 mm causes a decrease in the\ngrowth rate by a factor of more than 10. The suppression of VSI-driven\nturbulence by dust evolution may promote further dust evolution in the outer\nregions and also explain a high degree of dust settling observed in the disk\naround HL Tau.\n"}
+{"abstract": "  We posit a new mechanism for cooperation in multi-agent reinforcement\nlearning (MARL) based upon any nonlinear function of the team's long-term\nstate-action occupancy measure, i.e., a \\emph{general utility}. This subsumes\nthe cumulative return but also allows one to incorporate risk-sensitivity,\nexploration, and priors. % We derive the {\\bf D}ecentralized {\\bf S}hadow\nReward {\\bf A}ctor-{\\bf C}ritic (DSAC) in which agents alternate between policy\nevaluation (critic), weighted averaging with neighbors (information mixing),\nand local gradient updates for their policy parameters (actor). DSAC augments\nthe classic critic step by requiring agents to (i) estimate their local\noccupancy measure in order to (ii) estimate the derivative of the local utility\nwith respect to their occupancy measure, i.e., the \"shadow reward\". DSAC\nconverges to $\\epsilon$-stationarity in $\\mathcal{O}(1/\\epsilon^{2.5})$\n(Theorem \\ref{theorem:final}) or faster $\\mathcal{O}(1/\\epsilon^{2})$\n(Corollary \\ref{corollary:communication}) steps with high probability,\ndepending on the amount of communications. We further establish the\nnon-existence of spurious stationary points for this problem, that is, DSAC\nfinds the globally optimal policy (Corollary \\ref{corollary:global}).\nExperiments demonstrate the merits of goals beyond the cumulative return in\ncooperative MARL.\n"}
+{"abstract": "  Phase wrapping is a major problem in direction-of-arrival (DOA) estimation\nusing phase-difference observations. For a sensor pair with an inter-sensor\nspacing greater than half of the wavelength ($\\lambda/2$) of the signal, phase\nwrapping occurs at certain DOA angles leading to phase-difference ambiguities.\nExisting phase unwrapping methods exploit either frequency or spatial\ndiversity. These techniques work by imposing restrictions on the utilized\nfrequencies or the receiver array geometry. In addition to sensitivity to noise\nand calibration errors, these methods may also have high computational\ncomplexity. We propose a grid-less \\emph{phase-difference projection} (PDP) DOA\nalgorithm to overcome these issues. The concept of \\emph{wrapped\nphased-difference pattern} (WPDP) is introduced, which allows the proposed\nalgorithm to compute most of the parameters required for DOA estimation in an\noffline manner, hence resulting in a superior computational speed in realtime.\nSimulation results demonstrate the excellent performance of the proposed\nalgorithm, both in terms of accuracy and speed.\n"}
+{"abstract": "  This paper presents a new collision avoidance procedure for unmanned aerial\nvehicles in the presence of static and moving obstacles. The proposed procedure\nis based on a new form of local parametrized guidance vector fields, called\ncollision avoidance vector fields, that produce smooth and intuitive maneuvers\naround obstacles. The maneuvers follow nominal collision-free paths which we\nrefer to as streamlines of the collision avoidance vector fields. In the case\nof multiple obstacles, the proposed procedure determines a mixed vector field\nthat blends the collision avoidance vector field of each obstacle and assumes\nits form whenever a pre-defined distance threshold is reached. Then, in\naccordance to the computed guidance vector fields, different collision\navoidance controllers that generate collision-free maneuvers are developed.\nFurthermore, it is shown that any tracking controller with convergence\nguarantees can be used with the avoidance controllers to track the streamlines\nof the collision avoidance vector fields. Finally, numerical simulations\ndemonstrate the efficacy of the proposed approach and its ability to avoid\ncollisions with static and moving pop-up threats in three different practical\nscenarios.\n"}
+{"abstract": "  In this paper, we study how the evaporation of PBHs can affect the production\nof dark matter (DM) particles through thermal processes. We consider fermionic\nDM interacting with Standard Model particles via a spin-1 mediator in the\ncontext of a Freeze-Out or Freeze-In mechanism. We show that when PBHs\nevaporate after dominating the Universe's energy density, PBHs act as a source\nof DM and continuously inject entropy into the visible sector that can affect\nthe thermal production in three qualitatively different ways. We compute the\nannihilation cross-sections which account for the interactions between and\nwithin the PBH produced and thermally produced DM populations, and establish a\nset of Boltzmann equations which we solve to obtain the correct relic abundance\nin those different regimes and confront the results with a set of different\ncosmological constraints. We provide analytic formulae to calculate the relic\nabundance for the Freeze-Out and Freeze-In mechanism in a PBH dominated early\nUniverse. We identify regions of the parameter space where the PBHs dilute the\nrelic density and thermalization occurs. Furthermore, we have made our code\nthat numerically solves the Boltzmann equations publicly available.\n"}
+{"abstract": "  We study the influence of the factor of electron-ion collisions on the\nsolution of the Cauchy problem in the one-dimensional relativistic model of\ncold plasma and show that, depending on their intensity and initial data, two\nscenarios are possible: either the solution remains smooth and stabilizes to a\nstationary state, or during a finite time the oscillations blowup.\n  In contrast to the nonrelativistic model, when exact conditions can be\nobtained separating the two behaviors, in a much more complicated relativistic\nsituation, it turns out to be possible to analytically estimate from below the\ntime during which the existence of a smooth solution and the guaranteed number\nof oscillations during this time. In addition, we show that in contrast to the\nrelativistic case without taking into account collisions, when oscillations\ncorresponding to arbitrarily small deviations from the zero equilibrium\nposition blow up, the presence of electron collisions can suppress the blow-up\nof sufficiently small oscillations. Further, based on the analysis of\ncharacteristics, a numerical algorithm is constructed, the order of accuracy of\nwhich is determined only by the smoothness of the initial data. Numerical\nexperiments are presented to illustrate the theoretical results. The initial\nconditions are chosen as reasonably as possible from the point of view of\nfull-scale physical experiments.\n"}
+{"abstract": "  We construct a new factorized waveform including\n$(l,|m|)=(2,2),(2,1),(3,3),(4,4)$ modes based on effective-one-body (EOB)\nformalism, which is valid for spinning binary black holes (BBH) in general\nequatorial orbit. When combined with the dynamics of $\\texttt{SEOBNRv4}$, the\n$(l,|m|)=(2,2)$ mode waveform generated by this new waveform can fit the\noriginal $\\texttt{SEOBNRv4}$ waveform very well in the case of a quasi-circular\norbit. We have calibrated our new waveform model to the Simulating eXtreme\nSpacetimes (SXS) catalog. The comparison is done for BBH with total mass in\n$(20,200)M_\\odot$ using Advanced LIGO designed sensitivity. For the\nquasi-circular cases we have compared our $(2,2)$ mode waveforms to the 281\nnumerical relativity (NR) simulations of BBH along quasi-circular orbits. All\nof the matching factors are bigger than 98\\%. For the elliptical cases, 24\nnumerical relativity simulations of BBH along an elliptic orbit are used. For\neach elliptical BBH system, we compare our modeled gravitational polarizations\nagainst the NR results for different combinations of the inclination angle, the\ninitial orbit phase and the source localization in the sky. We use the the\nminimal matching factor respect to the inclination angle, the initial orbit\nphase and the source localization to quantify the performance of the higher\nmodes waveform. We found that after introducing the high modes, the minimum of\nthe minimal matching factor among the 24 tested elliptical BBHs increases from\n90\\% to 98\\%. Following our previous $\\texttt{SEOBNRE}$ waveform model, we call\nour new waveform model $\\texttt{SEOBNREHM}$. Our $\\texttt{SEOBNREHM}$ waveform\nmodel can match all tested 305 SXS waveforms better than 98\\% including highly\nspinning ($\\chi=0.99$) BBH, highly eccentric ($e\\approx0.15$) BBH and large\nmass ratio ($q=10$) BBH.\n"}
+{"abstract": "  We develop a mathematical model to capture the web dynamics of slingshot\nspiders (Araneae: Theridiosomatidae), which utilize a tension line to deform\ntheir orb webs into conical springs to hunt flying insects. Slingshot spiders\nare characterized by their ultrafast launch speeds and accelerations (exceeding\n1300 $m/s^2$), however a theoretical approach to characterize the underlying\nspatiotemporal web dynamics remains missing. To address this knowledge gap, we\ndevelop a 2D-coupled damped oscillator model of the web. Our model reveals\nthree key insights into the dynamics of slingshot motion. First, the tension\nline plays a dual role: enabling the spider to load elastic energy into the web\nfor a quick launch (in milliseconds) to displacements of 10-15 body lengths,\nbut also enabling the spider to halt quickly, attenuating inertial\noscillations. Second, the dominant energy dissipation mechanism is viscous drag\nby the silk lines - acting as a low Reynolds number parachute. Third, the web\nexhibits underdamped oscillatory dynamics through a finely-tuned balance\nbetween the radial line forces, the tension line force and viscous drag\ndissipation. Together, our work suggests that the conical geometry and\ntension-line enables the slingshot web to act as both an elastic spring and a\nshock absorber, for the multi-functional roles of risky predation and\nself-preservation.\n"}
+{"abstract": "  In this work we present a gauge-invariant three-dimensional teleparallel\nsupergravity theory using the Chern-Simons formalism. The present construction\nis based on a supersymmetric extension of a particular deformation of the\nPoincar\\'e algebra. At the bosonic level the theory describes a non-Riemannian\ngeometry with a non-vanishing torsion. In presence of supersymmetry, the\nteleparallel supergravity theory is characterized by a non-vanishing\nsuper-torsion in which the cosmological constant can be seen as a source for\nthe torsion. We show that the teleparallel supergravity theory presented here\nreproduces the Poincar\\'e supergravity in the vanishing cosmological limit. The\nextension of our results to $\\mathcal{N}=p+q$ supersymmetries is also explored.\n"}
+{"abstract": "  In this article, we obtain several new weighted bounds for the numerical\nradius of a Hilbert space operator. The significance of the obtained results is\nthe way they generalize many existing results in the literature; where certain\nvalues of the weights imply some known results, or refinements of these\nresults. In the end, we present some numerical examples that show how our\nresults refine the well known results in the literature, related to this topic.\n"}
+{"abstract": "  White dwarfs with metal-polluted atmospheres have been studied widely in the\ncontext of the accretion of rocky debris from evolved planetary systems. One\nopen question is the geometry of accretion and how material arrives and mixes\nin the white dwarf surface layers. Using the 3D radiation-hydrodynamics code\nCO$^5$BOLD, we present the first transport coefficients in degenerate star\natmospheres which describe the advection-diffusion of a passive scalar across\nthe surface-plane. We couple newly derived horizontal diffusion coefficients\nwith previously published vertical diffusion coefficients to provide\ntheoretical constraints on surface spreading of metals in white dwarfs. Our\ngrid of 3D simulations probes the vast majority of the parameter space of\nconvective white dwarfs, with pure-hydrogen atmospheres in the effective\ntemperature range 6000-18000 K and pure-helium atmospheres in the range\n12000-34000 K. Our results suggest that warm hydrogen-rich atmospheres (DA;\n$\\gtrsim$13000 K) and helium-rich atmospheres (DB, DBA; $\\gtrsim$30000 K) are\nunable to efficiently spread the accreted metals across their surface,\nregardless of the time dependence of accretion. This result may be at odds with\nthe current non-detection of surface abundance variations at white dwarfs with\ndebris discs. For cooler hydrogen- and helium-rich atmospheres, we predict a\nlargely homogeneous distribution of metals across the surface within a vertical\ndiffusion timescale. This is typically less than 0.1 per cent of disc lifetime\nestimates, a quantity which is revisited in this paper using the overshoot\nresults. These results have relevance for studies of the bulk composition of\nevolved planetary systems and models of accretion disc physics.\n"}
+{"abstract": "  Responsible AI is becoming critical as AI is widely used in our everyday\nlives. Many companies that deploy AI publicly state that when training a model,\nwe not only need to improve its accuracy, but also need to guarantee that the\nmodel does not discriminate against users (fairness), is resilient to noisy or\npoisoned data (robustness), is explainable, and more. In addition, these\nobjectives are not only relevant to model training, but to all steps of\nend-to-end machine learning, which include data collection, data cleaning and\nvalidation, model training, model evaluation, and model management and serving.\nFinally, responsible AI is conceptually challenging, and supporting all the\nobjectives must be as easy as possible. We thus propose three key research\ndirections towards this vision - depth, breadth, and usability - to measure\nprogress and introduce our ongoing research. First, responsible AI must be\ndeeply supported where multiple objectives like fairness and robust must be\nhandled together. To this end, we propose FR-Train, a holistic framework for\nfair and robust model training in the presence of data bias and poisoning.\nSecond, responsible AI must be broadly supported, preferably in all steps of\nmachine learning. Currently we focus on the data pre-processing steps and\npropose Slice Tuner, a selective data acquisition framework for training fair\nand accurate models, and MLClean, a data cleaning framework that also improves\nfairness and robustness. Finally, responsible AI must be usable where the\ntechniques must be easy to deploy and actionable. We propose FairBatch, a batch\nselection approach for fairness that is effective and simple to use, and Slice\nFinder, a model evaluation tool that automatically finds problematic slices. We\nbelieve we scratched the surface of responsible AI for end-to-end machine\nlearning and suggest research challenges moving forward.\n"}
+{"abstract": "  We study the problem of incentivizing exploration for myopic users in linear\nbandits, where the users tend to exploit arm with the highest predicted reward\ninstead of exploring. In order to maximize the long-term reward, the system\noffers compensation to incentivize the users to pull the exploratory arms, with\nthe goal of balancing the trade-off among exploitation, exploration and\ncompensation. We consider a new and practically motivated setting where the\ncontext features observed by the user are more informative than those used by\nthe system, e.g., features based on users' private information are not\naccessible by the system. We propose a new method to incentivize exploration\nunder such information gap, and prove that the method achieves both sublinear\nregret and sublinear compensation. We theoretical and empirically analyze the\nadded compensation due to the information gap, compared with the case that the\nsystem has access to the same context features as the user, i.e., without\ninformation gap. We also provide a compensation lower bound of our problem.\n"}
+{"abstract": "  Leibniz algebras are a non-anticommutative version of Lie algebras. They play\nan important role in different areas of mathematics and physics and have\nattracted much attention over the last thirty years. In this paper we\ninvestigate whether conditions such as being a Lie algebra, cyclic, simple,\nsemisimple, solvable, supersolvable or nilpotent in such an algebra are\npreserved by lattice isomorphisms.\n"}
+{"abstract": "  We propose a study of the Adaptive Biasing Force method's robustness under\ngeneric (possibly non-conservative) forces. We first ensure the flat histogram\nproperty is satisfied in all cases. We then introduce a fixed point problem\nyielding the existence of a stationary state for both the Adaptive Biasing\nForce and Projected Adapted Biasing Force algorithms, relying on generic bounds\non the invariant probability measures of homogeneous diffusions. Using\nclassical entropy techniques, we prove the exponential convergence of both\nbiasing force and law as time goes to infinity, for both the Adaptive Biasing\nForce and the Projected Adaptive Biasing Force methods.\n"}
+{"abstract": "  A network is said to have the properties of a small world if a suitably\ndefined average distance between any two nodes is proportional to the logarithm\nof the number of nodes, $N$. In this paper, we present a novel derivation of\nthe small-world property for Gilbert-Erd\\\"os-Renyi random networks. We employ a\nmean field approximation that permits the analytic derivation of the\ndistribution of shortest paths that exhibits logarithmic scaling away from the\nphase transition, inferable via a suitably interpreted order parameter. We\nbegin by framing the problem in generality with a formal generating functional\nfor undirected weighted random graphs with arbitrary disorder, recovering the\nresult that the free energy associated with an ensemble of Gilbert graphs\ncorresponds to a system of non-interacting fermions identified with the edge\nstates. We then present a mean field solution for this model and extend it to\nmore general realizations of network randomness. For a two family class of\nstochastic block models that we refer to as dimorphic networks, which allow for\nlinks within the different families to be drawn from two independent discrete\nprobability distributions, we find the mean field approximation maps onto a\nspin chain combinatorial problem and again yields useful approximate analytic\nexpressions for mean path lengths. Dimorophic networks exhibit a richer phase\nstructure, where distinct small world regimes separate in analogy to the\nspinodal decomposition of a fluid. We find that is it possible to induce small\nworld behavior in sub-networks that by themselves would not be in the\nsmall-world regime.\n"}
+{"abstract": "  We propose a reduced-form benchmark predictive model (BPM) for fixed-target\nforecasting of Arctic sea ice extent, and we provide a case study of its\nreal-time performance for target date September 2020. We visually detail the\nevolution of the statistically-optimal point, interval, and density forecasts\nas time passes, new information arrives, and the end of September approaches.\nComparison to the BPM may prove useful for evaluating and selecting among\nvarious more sophisticated dynamical sea ice models, which are widely used to\nquantify the likely future evolution of Arctic conditions and their two-way\ninteraction with economic activity.\n"}
+{"abstract": "  We demonstrate the underwater quantum key distribution (UWQKD) over a\n10.4-meter Jerlov type III seawater channel by building a complete UWQKD system\nwith all-optical transmission of quantum signals, synchronization signal and\nclassical communication signal. The wavelength division multiplexing and the\nspace-time-wavelength filtering technology are applied to ensure that the\noptical signals do not interfere with each other. The system is controlled by\nFPGA, and can be easily integrated into watertight cabins to perform field\nexperiment. By using the decoy-state BB84 protocol with polarization encoding,\nwe obtain a secure key rate of 1.82Kbps and an error rate of 1.55% at the\nattenuation of 13.26dB. We prove that the system can tolerate the channel loss\nup to 23.7dB, therefore may be used in the 300-meter-long Jerlov type I clean\nseawater channel.\n"}
+{"abstract": "  The formulas for local root numbers of abelian varieties of dimension one are\nknown. In this paper we treat the simplest unknown case in dimension two by\nconsidering a curve of genus 2 defined over a $5$-adic field such that the\ninertia acts on the first $\\ell$-adic cohomology group through the largest\npossible finite quotient, isomorphic to $C_5\\rtimes C_8$. We give a few\ncriteria to identify such curves and prove a formula for their local root\nnumbers in terms of invariants associated to a Weierstrass equation.\n"}
+{"abstract": "  The low lying spectrum of the massless overlap Dirac operator coupled to\nabelian fields in three dimensions with three different measures are shown to\nexhibit two phases: a strong coupling gapped phase and a weak coupling gapless\nphase. The vanishing of the gap from the strong coupling side with a Maxwell\nand a conformal measure is governed by a Gaussian exponent. Contrary to this\nresult, the vanishing of the gap from the strong coupling side with a compact\nThirring measure is not consistent with a Gaussian exponent. The low lying\nspectrum with a non-compact Thirring measure does not exhibit a simple\nnon-monotonic behavior as a function of the lattice size on the weak coupling\nside. Our combined analysis suggests exploring the possibility of a strongly\ncoupled continuum theory starting from a compact lattice Thirring model where a\ncompact U(1) gauge field with a single link action is coupled to even number of\nflavors of massless overlap Dirac fermions.\n"}
+{"abstract": "  We report observations of the total solar eclipse of 14 December 2020, during\nwhich a coronal mass ejection can be see n propagating. A comprehensive set of\nphotographs covering a high dynamic range of exposure allow to characterize its\ndimensions. The displacement of the front can be seen occurring during the few\nminutes of totality.\n"}
+{"abstract": "  Ferroelectricity in hafnia is often regarded as a breakthrough discovery in\nferroelectrics, potentially able to revolutionize the whole field. Despite\nincreasing interests, a comprehensive understanding of the many factors driving\nthe ferroelectric stabilization is still lacking. We here address the phase\ntransition in terms of a Landau-theory-based approach, by analyzing\nsymmetry-allowed distortions connecting the high-symmetry paraelectric\ntetragonal phase to the low-symmetry polar orthorhombic phase. By means of\nfirst-principles simulations, we find that the $\\Gamma_{3-}$ polar mode is only\nweakly unstable, whereas the other two symmetry-allowed distortions, non-polar\nY$_{2+}$ and anti-polar Y$_{4-}$ are hard modes. None of the modes, taken alone\nor combined with one other mode, is able to drive the transition: the key\nfactor in stabilizing the polar phase is identified as the strong trilinear\ncoupling among the three modes. Furthermore, the experimentally acknowledged\nimportance of substrate-induced effects in the growth of HfO$_2$ ferroelectric\nthin films, along with the lack of a clear order parameter in the transition,\nsuggested the extension of our analysis to strain effects. Our findings suggest\na complex behaviour of the Y$_{2+}$ mode, which become unstable under certain\nstrain conditions and an overall unstable behaviour for the $\\Gamma_{3-}$ polar\nmode for all the strain states. A robust result emerges from our analysis:\nindependently of the different applied strain (compressive or tensile, applied\nalong orthorhombic axes), the need of a simultaneous excitation of the three\ncoupled modes remain unaltered. Finally, when applied to mimic experimental\ngrowth conditions under strain, our analysis show a further stabilization of\nthe ferroelectric phase with respect to the unstrained case, in agreeement with\nexperimental findings.\n"}
+{"abstract": "  Scaling blockchain efficiency is crucial to its widespread usage in which the\npayment channel is one of the most prominent approaches. With payment channels\nand the network they construct, two users can move some transactions off-chain\nin a predetermined duration to avoid expensive and time-consuming on-chain\nsettlements. Existing work is devoted to designing high-throughput payment\nchannel networks (PCNs) or efficient PCN routing policies to reduce the fee. In\nthis paper, we investigate the PCN routing from a different perspective by\nanswering whether the routing fee of transactions can be saved by being a bit\nmore patient. The key idea is to reorder the processing sequence of atomic\ntransactions, other than to handle each of them separately and immediately. We\npresent two mechanisms, one is periodic transaction processing assisted by a\nPCN broker and the other is purely strategic waiting. In the former, all the\nincoming transactions in a short time interval are processed collectively. We\nformulate an optimization model to minimize their total routing fee and derive\nthe optimal permutation of processing transactions as well as the routing\npolicy for each of them. A Shapley value based scheme is presented to\nredistribute the benefit of reordering among the transactions efficiently and\nfairly. In the latter, we model the waiting time of a strategic transaction on\na single payment channel as the first passage time problem in queueing theory\nwhen the transaction value is higher than the edge capacity upon its arrival.\nBy capturing the capacity dynamics, we are able to calculate the recursive\nexpression of waiting for time distribution that is useful to gauge a user's\ncost of patience. Experimental results manifest that our cost redistribution\nmechanism can effectively save routing fees for all the transactions, and the\nwaiting time distribution coincides with the model well.\n"}
+{"abstract": "  We show that Steklov eigenfunctions in a bounded Lipschitz domain have\nwavelength dense nodal sets near the boundary, in contrast to what can happen\ndeep inside the domain. As a converse, in a two-dimensional Lipschitz domain\n$\\Omega$, we prove that any nodal domain of a Steklov eigenfunction contains a\nhalf-ball centered at $\\partial\\Omega$ of radius $c_{\\Omega}/\\lambda$.\n"}
+{"abstract": "  Spinal codes are known to be capacity achieving over both the additive white\nGaussian noise (AWGN) channel and the binary symmetric channel (BSC). Over\nwireless channels, Spinal encoding can also be regarded as an\nadaptive-coded-modulation (ACM) technique due to its rateless property, which\nfits it with mobile communications. Due to lack of tight analysis on error\nprobability of Spinal codes, optimization of transmission scheme using Spinal\ncodes has not been fully explored. In this work, we firstly derive new tight\nupper bounds of the frame error rate (FER) of Spinal codes for both the AWGN\nchannel and the BSC in the finite block-length (FBL) regime. Based on the\nderived upper bounds, we then design the optimal transmission scheme.\nSpecifically, we formulate a rate maximization problem as a nonlinear integer\nprogramming problem, and solve it by an iterative algorithm for its dual\nproblem. As the optimal solution exhibits an incremental-tail-transmission\npattern, we propose an improved transmission scheme for Spinal codes. Moreover,\nwe develop a bubble decoding with memory (BD-M) algorithm to reduce the\ndecoding time complexity without loss of rate performance. The improved\ntransmission scheme at the transmitter and the BD-M algorithm at the receiver\njointly constitute an \"encoding-decoding\" system of Spinal codes. Simulation\nresults demonstrate that it can improve both the rate performance and the\ndecoding throughput of Spinal codes.\n"}
+{"abstract": "  Human-Object Interaction (HOI) detection is an important problem to\nunderstand how humans interact with objects. In this paper, we explore\ninteractiveness knowledge which indicates whether a human and an object\ninteract with each other or not. We found that interactiveness knowledge can be\nlearned across HOI datasets and bridge the gap between diverse HOI category\nsettings. Our core idea is to exploit an interactiveness network to learn the\ngeneral interactiveness knowledge from multiple HOI datasets and perform\nNon-Interaction Suppression (NIS) before HOI classification in inference. On\naccount of the generalization ability of interactiveness, interactiveness\nnetwork is a transferable knowledge learner and can be cooperated with any HOI\ndetection models to achieve desirable results. We utilize the human instance\nand body part features together to learn the interactiveness in hierarchical\nparadigm, i.e., instance-level and body part-level interactivenesses.\nThereafter, a consistency task is proposed to guide the learning and extract\ndeeper interactive visual clues. We extensively evaluate the proposed method on\nHICO-DET, V-COCO, and a newly constructed PaStaNet-HOI dataset. With the\nlearned interactiveness, our method outperforms state-of-the-art HOI detection\nmethods, verifying its efficacy and flexibility. Code is available at\nhttps://github.com/DirtyHarryLYL/Transferable-Interactiveness-Network.\n"}
+{"abstract": "  Actin and spectrin are important constituents of axonal cytoskeleton.\nPeriodic actin and spectrin structures are found in dendrites, initial segment\nof axon, and main axon. Actin and spectrin periodicity has been hypothesized to\nbe manipulating the axon stability and mechanical behavior. Several\nexperimental and computational studies have been performed focusing on the\nmechanical behavior of actin, spectrin, and actin and spectrin network.\nHowever, most of the actin studies focus on typical long F actin and do not\nprovide quantitative comparison between the mechanical behavior of short and\nlong actin filaments. Also, most of the spectrin studies focus on erythrocytic\nspectrin and do not shed light on the behavior of structurally different axonal\nspectrin. Only a few studies have highlighted forced unfolding of axonal\nspectrin which are relevant to brain injury scenario. A comprehensive, strain\nrate dependent mechanical study is still absent in the literature. Moreover,\nthe current opinions regarding periodic actin and spectrin network structure in\naxon are disputed due to conflicting results on actin ring organization as\nargued by recent superresolution microscopy studies. This review summarizes the\nongoing limitations in this regard and provides insights on possible approaches\nto address them. This study will invoke further investigation into relevant\nhigh strain rate response of actin, spectrin, and actin and spectrin network\nshedding light into brain pathology scenario such as traumatic brain injury.\n"}
+{"abstract": "  This paper generalizes the concept of Bayes correlated equilibrium (Bergemann\nand Morris, 2016) to multi-stage games. We demonstrate the power of our\ncharacterization results by applying them to a number of illustrative examples\nand applications.\n"}
+{"abstract": "  We derive a Matern Gaussian process (GP) on the vertices of a hypergraph.\nThis enables estimation of regression models of observed or latent values\nassociated with the vertices, in which the correlation and uncertainty\nestimates are informed by the hypergraph structure. We further present a\nframework for embedding the vertices of a hypergraph into a latent space using\nthe hypergraph GP. Finally, we provide a scheme for identifying a small number\nof representative inducing vertices that enables scalable inference through\nsparse GPs. We demonstrate the utility of our framework on three challenging\nreal-world problems that concern multi-class classification for the political\nparty affiliation of legislators on the basis of voting behaviour,\nprobabilistic matrix factorisation of movie reviews, and embedding a hypergraph\nof animals into a low-dimensional latent space.\n"}
+{"abstract": "  Experiments involving optical traps often require careful control of the ac\nStark shifts induced by strong confining light fields. By carefully balancing\nlight shifts between two atomic states of interest, optical traps at the magic\nwavelength have been especially effective at suppressing deleterious effects\nstemming from such shifts. Highlighting the power of this technique, optical\nclocks today exploit Lamb-Dicke confinement in magic-wavelength optical traps,\nin some cases realizing shift cancellation at the ten parts per billion level.\nTheory and empirical measurements can be used at varying levels of precision to\ndetermine the magic wavelength where shift cancellation occurs. However, lasers\nexhibit background spectra from amplified spontaneous emission or other lasing\nmodes which can easily contaminate measurement of the magic wavelength and its\nreproducibility in other experiments or conditions. Indeed, residual light\nshifts from laser background have plagued optical lattice clock measurements\nfor years. In this work, we develop a simple theoretical model allowing\nprediction of light shifts from measured background spectra. We demonstrate\ngood agreement between this model and measurements of the background light\nshift from an amplified diode laser in an Yb optical lattice clock.\nAdditionally, we model and experimentally characterize the filtering effect of\na volume Bragg grating bandpass filter, demonstrating that application of the\nfilter can reduce background light shifts from amplified spontaneous emission\nwell below the $10^{-18}$ fractional clock frequency level. This demonstration\nis corroborated by direct clock comparisons between a filtered amplified diode\nlaser and a filtered titanium:sapphire laser.\n"}
+{"abstract": "  Let $F$ be a Bedford-McMullen carpet defined by independent integer\nexponents. We prove that for every line $\\ell \\subseteq \\mathbb{R}^2$ not\nparallel to the major axes, $$ \\dim_H (\\ell \\cap F) \\leq \\max \\left\\lbrace 0,\\,\n\\frac{\\dim_H F}{\\dim^* F} \\cdot (\\dim^* F-1) \\right\\rbrace$$ and $$ \\dim_P\n(\\ell \\cap F) \\leq \\max \\left\\lbrace 0,\\, \\frac{\\dim_P F}{\\dim^* F} \\cdot\n(\\dim^* F-1) \\right\\rbrace$$ where $\\dim^*$ is Furstenberg's star dimension\n(maximal dimension of microsets). This improves the state of art results on\nFurstenberg type slicing Theorems for affine invariant carpets.\n"}
+{"abstract": "  Unmanned aerial vehicles (UAVs) are now beginning to be deployed for\nenhancing the network performance and coverage in wireless communication.\nHowever, due to the limitation of their on-board power and flight time, it is\nchallenging to obtain an optimal resource allocation scheme for the\nUAV-assisted Internet of Things (IoT). In this paper, we design a new\nUAV-assisted IoT systems relying on the shortest flight path of the UAVs while\nmaximising the amount of data collected from IoT devices. Then, a deep\nreinforcement learning-based technique is conceived for finding the optimal\ntrajectory and throughput in a specific coverage area. After training, the UAV\nhas the ability to autonomously collect all the data from user nodes at a\nsignificant total sum-rate improvement while minimising the associated\nresources used. Numerical results are provided to highlight how our techniques\nstrike a balance between the throughput attained, trajectory, and the time\nspent. More explicitly, we characterise the attainable performance in terms of\nthe UAV trajectory, the expected reward and the total sum-rate.\n"}
+{"abstract": "  After averaging over fermion couplings, SYK has a collective field\ndescription that sometimes has \"wormhole\" solutions. We study the fate of these\nwormholes when the couplings are fixed. Working mainly in a simple model, we\nfind that the wormhole saddles persist, but that new saddles also appear\nelsewhere in the integration space -- \"half-wormholes.\" The wormhole\ncontributions depend only weakly on the specific choice of couplings, while the\nhalf-wormhole contributions are strongly sensitive. The half-wormholes are\ncrucial for factorization of decoupled systems with fixed couplings, but they\nvanish after averaging, leaving the non-factorizing wormhole behind.\n"}
+{"abstract": "  We investigate fully self-consistent multiscale quantum-classical algorithms\non current generation superconducting quantum computers, in a unified approach\nto tackle the correlated electronic structure of large systems in both quantum\nchemistry and condensed matter physics. In both of these contexts, a strongly\ncorrelated quantum region of the extended system is isolated and\nself-consistently coupled to its environment via the sampling of reduced\ndensity matrices. We analyze the viability of current generation quantum\ndevices to provide the required fidelity of these objects for a robust and\nefficient optimization of this subspace. We show that with a simple error\nmitigation strategy and optimization of compact tensor product bases to\nminimize the number of terms to sample, these self-consistent algorithms are\nindeed highly robust, even in the presence of significant noises on quantum\nhardware. Furthermore, we demonstrate the use of these density matrices for the\nsampling of non-energetic properties, including dipole moments and Fermi liquid\nparameters in condensed phase systems, achieving a reliable accuracy with\nsparse sampling. It appears that uncertainties derived from the iterative\noptimization of these subspaces is smaller than variances in the energy for a\nsingle subspace optimization with current quantum hardware. This boosts the\nprospect for routine self-consistency to improve the choice of correlated\nsubspaces in hybrid quantum-classical approaches to electronic structure for\nlarge systems in this multiscale fashion.\n"}
+{"abstract": "  Previous calculations of the dielectric and optical properties of 2D\nmaterials often overlooked or circumvented the influence of vacuum spacing\nintroduced in periodic calculations, which gave rise to mispredictions of the\nintrinsic properties of 2D materials or merely qualitative results. We first\nelucidate the relationship between the vacuum spacing and the dielectric and\noptical properties of 2D materials in periodic calculations, and then bring\nforward an effective method to accurately predict the dielectric and optical\nproperties of 2D materials by restoring the intrinsic dielectric functions of\n2D materials independent of the additional vacuum spacing. As examples, the\nintrinsic dielectric and optical properties of ultrathin h-BN and MoS2 from\nmonolayer to pentalayer, including dielectric functions, optical absorption\ncoefficients, refraction indexes, reflectivities, extinction coefficients, and\nenergy loss functions, have been calculated by our method. Our calculations\nreveal that the out-of-plane optical dielectric constants, static refraction\nindexes, and static reflectivities of 2D h-BN and MoS2 increase as the number\nof layers increases, while the in-plane counterparts remain unchanged.\nExcitonic frequency-dependent optical properties of h-BN and MoS2 from\nmonolayer to bulk are also calculated by solving the Bethe-Salpeter equation\nand show strong anisotropy. In better agreement with experimental results than\nprevious calculations, the presented method demonstrates enormous potential to\ninvestigate the dielectric and optical properties of other 2D materials\nextensively and quantitively.\n"}
+{"abstract": "  Model-based fault injection methods are widely used for the evaluation of\nfault tolerance in safety-critical control systems. In this paper, we introduce\na new model-based fault injection method implemented as a highlycustomizable\nSimulink block called FIBlock. It supports the injection of typical faults of\nessential heterogeneous components of Cyber-Physical Systems, such as sensors,\ncomputing hardware, and network. The FIBlock GUI allows the user to select a\nfault type and configure multiple parameters to tune error magnitude, fault\nactivation time, and fault exposure duration. Additional trigger inputs and\noutputs of the block enable the modeling of conditional faults. Furthermore,\ntwo or more FIBlocks connected with these trigger signals can model chained\nerrors. The proposed fault injection method is demonstrated with a lower-limb\nEXO-LEGS exoskeleton, an assistive device for the elderly in everyday life. The\nEXO-LEGS model-based dynamic control is realized in the Simulink environment\nand allows easy integration of the aforementioned FIBlocks. Exoskeletons, in\ngeneral, being a complex CPS with multiple sensors and actuators, are prone to\nhardware and software faults. In the case study, three types of faults were\ninvestigated: 1) sensor freeze, 2) stuck-at-0, 3) bit-flip. The fault injection\nexperiments helped to determine faults that have the most significant effects\non the overall system reliability and identify the fine line for the critical\nfault duration after that the controller could no longer mitigate faults.\n"}
+{"abstract": "  Excitons, quasiparticles of electrons and holes bound by Coulombic\nattraction, are created transiently by light and play an important role in\noptoelectronics, photovoltaics and photosynthesis. While they are also\npredicted to form spontaneously in a small gap semiconductor or a semimetal,\nleading to a Bose-Einstein condensate at low temperature, their material\nrealization has been elusive without any direct evidence. Here we detect the\ndirect photoemission signal from spontaneously formed excitons in a debated\nexcitonic insulator candidate Ta2NiSe5. Our symmetry-selective angle-resolved\nphotoemission spectroscopy reveals a characteristic excitonic feature above the\ntransition temperature, which provides detailed properties of excitons such as\nanisotropic Bohr radius. The present result evidences so called preformed\nexcitons and guarantees the excitonic insulator nature of Ta2NiSe5 at low\ntemperature. Direct photoemission can be an important tool to characterize\nsteady-state excitons.\n"}
+{"abstract": "  We establish an explicit asymptotic formula for the number of rational\nsolutions of intrinsic Diophantine inequalities on simply-connected simple\nalgebraic groups, at arbitrarily small scales.\n"}
+{"abstract": "  This paper studies construction of above-ground biomass (AGB) prediction maps\nfrom synthetic aperture radar (SAR) intensity images. The purpose is to improve\ntraditional regression models based on SAR intensity, trained with a limited\namount of AGB in situ measurements. Although it is costly to collect, data from\nairborne laser scanning (ALS) sensors are highly correlated with AGB.\nTherefore, we propose using AGB predictions based on ALS data as surrogate\nresponse variables for SAR data in a sequential modelling fashion. This\nincreases the amount of training data dramatically. To model the regression\nfunction between SAR intensity and ALS-predicted AGB we propose to utilise a\nconditional generative adversarial network (cGAN), i.e. the Pix2Pix\nconvolutional neural network. This enables the recreation of existing ALS-based\nAGB prediction maps. The generated synthesised ALS-based AGB predictions are\nevaluated qualitatively and quantitatively against ALS-based AGB predictions\nretrieved from a traditional non-sequential regression model trained in the\nsame area. Results show that the proposed architecture manages to capture\ncharacteristics of the actual data. This suggests that the use of ALS-guided\ngenerative models is a promising avenue for AGB prediction from SAR intensity.\nFurther research on this area has the potential of providing both large-scale\nand low-cost predictions of AGB.\n"}
+{"abstract": "  We calculate the extremal cross sectional areas and cyclotron masses for the\nFermi-surface pockets in Dirac and Weyl topological semimetals. The calculation\nis carried out for the most general form of the electron energy bands in the\nvicinity of the Weyl and Dirac points. Using the obtained formulas, one can\nfind parameters characterizing the Dirac and Weyl electrons in the topological\nsemimetals from appropriate experimental data. As an example, we consider the\nW1 electrons in TaAs.\n"}
+{"abstract": "  Training deep networks with limited labeled data while achieving a strong\ngeneralization ability is key in the quest to reduce human annotation efforts.\nThis is the goal of semi-supervised learning, which exploits more widely\navailable unlabeled data to complement small labeled data sets. In this paper,\nwe propose a novel framework for discriminative pixel-level tasks using a\ngenerative model of both images and labels. Concretely, we learn a generative\nadversarial network that captures the joint image-label distribution and is\ntrained efficiently using a large set of unlabeled images supplemented with\nonly few labeled ones. We build our architecture on top of StyleGAN2, augmented\nwith a label synthesis branch. Image labeling at test time is achieved by first\nembedding the target image into the joint latent space via an encoder network\nand test-time optimization, and then generating the label from the inferred\nembedding. We evaluate our approach in two important domains: medical image\nsegmentation and part-based face segmentation. We demonstrate strong in-domain\nperformance compared to several baselines, and are the first to showcase\nextreme out-of-domain generalization, such as transferring from CT to MRI in\nmedical imaging, and photographs of real faces to paintings, sculptures, and\neven cartoons and animal faces. Project Page:\n\\url{https://nv-tlabs.github.io/semanticGAN/}\n"}
+{"abstract": "  We present the largest systematic, HST-based search to date for luminous\n$z\\gtrsim8$ galaxy candidates using $\\sim$1267 arcmin$^{2}$ of (pure-)parallel\nobservations from a compilation of 288 random sightlines with ACS and WFC3\nobservations, derived from the SuperBoRG data set and together representing a\nfactor $\\sim1.12\\times$ larger than existing space-based data sets. Using NIR\ncolor cuts and careful photo-$z$ analyses, we find 31 $z\\gtrsim8$ galaxy\ncandidates over 29 unique sightlines, and derive global galaxy properties such\nas UV magnitudes and continuum slopes, sizes, and rest-frame optical properties\n(e.g., SFRs, stellar masses, $A_{\\rm v}$). Taking advantage of the\n(pure-)parallel nature of our data set - making it one of the most\nrepresentative thus far - and derived SFRs, we evaluate the cosmic star\nformation rate density for the bright end of the UV luminosity function at\n$z\\sim8-10$ and test the validity of luminosity function-derived results using\na conversion factor. We find our method yields comparable results to those\nderived with luminosity functions. Furthermore, we present follow up\nobservations of 5 (Super)BoRG targets with Keck/MOSFIRE, finding no evidence of\nLy$\\alpha$ in $>3$ hrs of $Y-$band observations in either, consistent with a\nlargely neutral medium at $z\\sim8$. Our results offer a definitive HST legacy\non the bright end of the luminosity function and provide a valuable benchmark\nas well as targets for follow up with JWST.\n"}
+{"abstract": "  Frequency perspective recently makes progress in understanding deep learning.\nIt has been widely verified in both empirical and theoretical studies that deep\nneural networks (DNNs) often fit the target function from low to high\nfrequency, namely Frequency Principle (F-Principle). F-Principle sheds light on\nthe strength and the weakness of DNNs and inspires a series of subsequent\nworks, including theoretical studies, empirical studies and the design of\nefficient DNN structures etc. Previous works examine the F-Principle in\ngradient-descent-based training. It remains unclear whether\ngradient-descent-based training is a necessary condition for the F-Principle.\nIn this paper, we show that the F-Principle exists stably in the training\nprocess of DNNs with non-gradient-descent-based training, including\noptimization algorithms with gradient information, such as conjugate gradient\nand BFGS, and algorithms without gradient information, such as Powell's method\nand Particle Swarm Optimization. These empirical studies show the universality\nof the F-Principle and provide hints for further study of F-Principle.\n"}
+{"abstract": "  For a generalized soliton $(g,\\xi,\\eta,\\beta,\\gamma,\\delta)$, we provide\nnecessary and sufficient conditions for the dual $1$-form $\\xi^{\\flat}$ of the\npotential vector field $\\xi$ to be a solution of the Schr\\\"{o}dinger-Ricci\nequation, a harmonic or a Schr\\\"{o}dinger-Ricci harmonic form. We also\ncharacterize the $1$-forms orthogonal to $\\xi^{\\flat}$, underlying the results\nobtained for the Ricci and Yamabe solitons. Further, we formulate the results\nfor the case of gradient generalized solitons. Several applications and\nexamples are also presented.\n"}
+{"abstract": "  In the standard framework of self-consistent many-body perturbation theory,\nthe skeleton series for the self-energy is truncated at a finite order $N$ and\nplugged into the Dyson equation, which is then solved for the propagator $G_N$.\nFor two simple examples of fermionic models -- the Hubbard atom at half filling\nand its zero space-time dimensional simplified version -- we find that $G_N$\nconverges when $N\\to\\infty$ to a limit $G_\\infty\\,$, which coincides with the\nexact physical propagator $G_{\\rm exact}\\ \\,$ at small enough coupling, while\n$G_\\infty \\neq G_{\\rm exact}\\ \\,$ at strong coupling. We also demonstrate that\nit is possible to discriminate between these two regimes thanks to a criterion\nwhich does not require the knowledge of $G_{\\rm exact}\\ \\,$, as proposed in\n[Rossi et al., PRB 93, 161102(R) (2016)].\n"}
+{"abstract": "  We consider structural vector autoregressions subject to 'narrative\nrestrictions', which are inequality restrictions on functions of the structural\nshocks in specific periods. These restrictions raise novel problems related to\nidentification and inference, and there is currently no frequentist procedure\nfor conducting inference in these models. We propose a solution that is valid\nfrom both Bayesian and frequentist perspectives by: 1) formalizing the\nidentification problem under narrative restrictions; 2) correcting a feature of\nthe existing (single-prior) Bayesian approach that can distort inference; 3)\nproposing a robust (multiple-prior) Bayesian approach that is useful for\nassessing and eliminating the posterior sensitivity that arises in these models\ndue to the likelihood having flat regions; and 4) showing that the robust\nBayesian approach has asymptotic frequentist validity. We illustrate our\nmethods by estimating the effects of US monetary policy under a variety of\nnarrative restrictions.\n"}
+{"abstract": "  Inspired by our serendipitous discovery of six AGNs with varying broad-Halpha\nfluxes over years out of our searching for intermediate-mass black holes\n(IMBHs), we conduct a systematic investigation of changing-look (CL) and\nlarge-variability AGNs. We collect all the CL AGNs at z<0.15 and the\nreverberation mapped AGNs with strongly variable broad Halpha, and perform\ncareful decomposition fittings to both their images and spectra. We find two\nobservational facts: (1) The host galaxies of local CL and large-variability\nAGNs, mainly being Seyferts, are in the red (gas-poor) tail of the general\nSeyfert galaxy population. (2) In contrast, there is a significant trend that\ntheir more luminous counterparts namely CL and extremely variable quasars (CLQs\nand EVQs) are different: CLQs are generally in blue galaxies; in terms of the\ndiagram of SFR and M* local CL Seyfert galaxies are located in the green\nvalley, whereas CLQ hosts are in the star-forming main sequence. We propose\nexplanations for those strongly variable Seyferts and quasars, respectively,\nunder the thought that accretion disks broadly depend on nuclear fueling modes.\nLocal large-variability and CL Seyferts are in nuclear famine mode, where\ncold-gas clumps can be formed stochastically in the fueling flow, and their\nepisodic infall produces sharp peaks in the accretion-rate curve. CLQs and EVQs\nare in feast fueling mode, which may account for both their preference to blue\ngalaxies and their variability pattern (high-amplitude tail of the continuous\ndistribution). Lastly, we propose a new thinking: to search for IMBHs by\noptical variability in red galaxies.\n"}
+{"abstract": "  We design a proof system for propositional classical logic that integrates\ntwo languages for Boolean functions: standard conjunction-disjunction-negation\nand binary decision trees. We give two reasons to do so. The first is\nproof-theoretical naturalness: the system consists of all and only the\ninference rules generated by the single, simple, linear shape of the recently\nintroduced subatomic logic. Thanks to this regularity, cuts are eliminated via\na natural construction. The second reason is that the system generates\nefficient proofs. Indeed, we show that a certain class of tautologies due to\nStatman, which cannot have better than exponential cut-free proofs in the\nsequent calculus, have polynomial cut-free proofs in our system. We achieve\nthis by using the same construction that we use for cut elimination. In\nsummary, by expanding the language of propositional logic, we make its proof\ntheory more regular and generate more proofs, some of which are very efficient.\n  That design is made possible by considering propositional variables as\nsuperpositions of their truth values, which are connected by self-dual,\nnon-commutative connectives. A proof can then be projected via each\npropositional variable into two proofs, one for each truth value, without a\nneed for cuts. Those projections are semantically natural and are at the heart\nof all the constructions in this paper. To accommodate self-dual\nnon-commutativity, we compose proofs by deep inference.\n"}
+{"abstract": "  Primitive recursion is a mature, well-understood topic in the theory and\npractice of programming. Yet its dual, primitive corecursion, is\nunderappreciated and still seen as exotic. We aim to put them both on equal\nfooting by giving a foundation for primitive corecursion based on computation,\ngiving a terminating calculus analogous to the original computational\nfoundation of recursion. We show how the implementation details in an abstract\nmachine strengthens their connection, syntactically deriving corecursion from\nrecursion via logical duality. We also observe the impact of evaluation\nstrategy on the computational complexity of primitive (co)recursive\ncombinators: call-by-name allows for more efficient recursion, but\ncall-by-value allows for more efficient corecursion.\n"}
+{"abstract": "  We prove the existence of a crepant sdlt model for slc pairs whose\nirreducible components are normal in codimension one.\n"}
+{"abstract": "  Regular variations of the pulse period of Her X-1 with X-ray flux observed by\nFermi-GBM are examined. We argue that these regular variations result from the\nfree precession of the neutron star in Her X-1.\n"}
+{"abstract": "  We study photon orbits in the background of $(1+3)$-dimensional static,\nspherically symmetric geometries. In particular, we have obtained exact\nanalytical solutions to the null geodesic equations for light rays in terms of\nthe Weierstra{\\ss} function for space-times arising in the context of\nscale-dependent gravity. The trajectories in the $(x-y)$ plane are shown\ngraphically, and we make a comparison with similar geometries arising in\ndifferent contexts. The light deflection angle is computed as a function of the\nrunning parameter $\\xi$, and an upper bound for the latter is obtained.\n"}
+{"abstract": "  Deep neural networks for natural language processing are fragile in the face\nof adversarial examples -- small input perturbations, like synonym substitution\nor word duplication, which cause a neural network to change its prediction. We\npresent an approach to certifying the robustness of LSTMs (and extensions of\nLSTMs) and training models that can be efficiently certified. Our approach can\ncertify robustness to intractably large perturbation spaces defined\nprogrammatically in a language of string transformations. Our evaluation shows\nthat (1) our approach can train models that are more robust to combinations of\nstring transformations than those produced using existing techniques; (2) our\napproach can show high certification accuracy of the resulting models.\n"}
+{"abstract": "  A promising trend in deep learning replaces traditional feedforward networks\nwith implicit networks. Unlike traditional networks, implicit networks solve a\nfixed point equation to compute inferences. Solving for the fixed point varies\nin complexity, depending on provided data and an error tolerance. Importantly,\nimplicit networks may be trained with fixed memory costs in stark contrast to\nfeedforward networks, whose memory requirements scale linearly with depth.\nHowever, there is no free lunch -- backpropagation through implicit networks\noften requires solving a costly Jacobian-based equation arising from the\nimplicit function theorem. We propose Jacobian-Free Backpropagation (JFB), a\nfixed-memory approach that circumvents the need to solve Jacobian-based\nequations. JFB makes implicit networks faster to train and significantly easier\nto implement, without sacrificing test accuracy. Our experiments show implicit\nnetworks trained with JFB are competitive with feedforward networks and prior\nimplicit networks given the same number of parameters.\n"}
+{"abstract": "  We study distributed estimation of a high-dimensional static parameter vector\nthrough a group of sensors whose communication network is modeled by a fixed\ndirected graph. Different from existing time-triggered communication schemes,\nan event-triggered asynchronous scheme is investigated in order to reduce\ncommunication while preserving estimation convergence. A distributed estimation\nalgorithm with a single step size is first proposed based on an event-triggered\ncommunication scheme with a time-dependent decaying threshold. With the\nevent-triggered scheme, each sensor sends its estimate to neighbor sensors only\nwhen the difference between the current estimate and the last sent-out estimate\nis larger than the triggering threshold. We prove that the proposed algorithm\nhas mean-square and almost-sure convergence respectively, under an integrated\ncondition of sensor network topology and sensor measurement matrices. The\ncondition is satisfied if the topology is a balanced digraph containing a\nspanning tree and the system is collectively observable. Moreover, we provide\nestimates for the convergence rates, which are related to the step size as well\nas the triggering threshold. Furthermore, as an essential metric of sensor\ncommunication intensity in the event-triggered distributed algorithms, the\ncommunication rate is proved to decay to zero with a certain speed almost\nsurely as time goes to infinity. We show that given the step size, adjusting\nthe decay speed of the triggering threshold can lead to a tradeoff between the\nconvergence rate of the estimation error and the decay speed of the\ncommunication rate. Specifically, increasing the decay speed of the threshold\nwould make the communication rate decay faster, but reduce the convergence rate\nof the estimation error. Numerical simulations are provided to illustrate the\ndeveloped results.\n"}
+{"abstract": "  Previous studies have demonstrated that commonly studied (vanilla)\ntouch-based continuous authentication systems (V-TCAS) are susceptible to\npopulation attack. This paper proposes a novel Generative Adversarial Network\nassisted TCAS (G-TCAS) framework, which showed more resilience to the\npopulation attack. G-TCAS framework was tested on a dataset of 117 users who\ninteracted with a smartphone and tablet pair. On average, the increase in the\nfalse accept rates (FARs) for V-TCAS was much higher (22%) than G-TCAS (13%)\nfor the smartphone. Likewise, the increase in the FARs for V-TCAS was 25%\ncompared to G-TCAS (6%) for the tablet.\n"}
+{"abstract": "  In the currently favored cosmological paradigm galaxies form hierarchically\nthrough the accretion of numerous satellite galaxies. Since the satellites are\nmuch less massive than the host halo, they occupy a small fraction of the\nvolume in action space defined by the potential of the host halo. Since actions\nare conserved when the potential of the host halo changes adiabatically, stars\nfrom an accreted satellite are expected to remain clustered in action space as\nthe host halo evolves. In this paper, we identify accreted satellites in three\nMilky Way like disk galaxies from the cosmological baryonic FIRE-2 simulations\nby tracking satellite galaxies through simulation snapshots. We then try to\nrecover these satellites by applying the cluster analysis algorithm Enlink to\nthe orbital actions of accreted star particles in the present-day snapshot. We\ndefine several metrics to quantify the success of the clustering algorithm and\nuse these metrics to identify well-recovered and poorly-recovered satellites.\nWe plot these satellites in the infall time-progenitor mass (or stellar mass)\nspace, and determine the boundaries between the well-recovered and\npoorly-recovered satellites in these two spaces with classification tree\nmethod. The groups found by Enlink are more likely to correspond to a real\nsatellite if they have high significance, a quantity assigned by Enlink. Since\ncosmological simulations predict that most stellar halos have a population of\ninsitu stars, we test the ability of Enlink to recover satellites when the\nsample is contaminated by 10-50% of insitu star particles, and show that most\nof the satellites well-recovered by Enlink in the absence of insitu stars, stay\nwell-recovered even with 50% contamination. We thus expect that, in the future,\ncluster analysis in action space will be useful in upcoming data sets (e.g.\nGaia) for identifying accreted satellites in the Milky Way.\n"}
+{"abstract": "  Mobile edge computing pushes computationally-intensive services closer to the\nuser to provide reduced delay due to physical proximity. This has led many to\nconsider deploying deep learning models on the edge -- commonly known as edge\nintelligence (EI). EI services can have many model implementations that provide\ndifferent QoS. For instance, one model can perform inference faster than\nanother (thus reducing latency) while achieving less accuracy when evaluated.\nIn this paper, we study joint service placement and model scheduling of EI\nservices with the goal to maximize Quality-of-Servcice (QoS) for end users\nwhere EI services have multiple implementations to serve user requests, each\nwith varying costs and QoS benefits. We cast the problem as an integer linear\nprogram and prove that it is NP-hard. We then prove the objective is equivalent\nto maximizing a monotone increasing, submodular set function and thus can be\nsolved greedily while maintaining a (1-1/e)-approximation guarantee. We then\npropose two greedy algorithms: one that theoretically guarantees this\napproximation and another that empirically matches its performance with greater\nefficiency. Finally, we thoroughly evaluate the proposed algorithm for making\nplacement and scheduling decisions in both synthetic and real-world scenarios\nagainst the optimal solution and some baselines. In the real-world case, we\nconsider real machine learning models using the ImageNet 2012 data-set for\nrequests. Our numerical experiments empirically show that our more efficient\ngreedy algorithm is able to approximate the optimal solution with a 0.904\napproximation on average, while the next closest baseline achieves a 0.607\napproximation on average.\n"}
+{"abstract": "  We propose a method for extracting physics-based biomarkers from a single\nmultiparametric Magnetic Resonance Imaging (mpMRI) scan bearing a glioma tumor.\nWe account for mass effect, the deformation of brain parenchyma due to the\ngrowing tumor, which on its own is an important radiographic feature but its\nautomatic quantification remains an open problem. In particular, we calibrate a\npartial differential equation (PDE) tumor growth model that captures mass\neffect, parameterized by a single scalar parameter, tumor proliferation,\nmigration, while localizing the tumor initiation site. The single-scan\ncalibration problem is severely ill-posed because the precancerous, healthy,\nbrain anatomy is unknown. To address the ill-posedness, we introduce an\nensemble inversion scheme that uses a number of normal subject brain templates\nas proxies for the healthy precancer subject anatomy. We verify our solver on a\nsynthetic dataset and perform a retrospective analysis on a clinical dataset of\n216 glioblastoma (GBM) patients. We analyze the reconstructions using our\ncalibrated biophysical model and demonstrate that our solver provides both\nglobal and local quantitative measures of tumor biophysics and mass effect. We\nfurther highlight the improved performance in model calibration through the\ninclusion of mass effect in tumor growth models -- including mass effect in the\nmodel leads to 10% increase in average dice coefficients for patients with\nsignificant mass effect. We further evaluate our model by introducing novel\nbiophysics-based features and using them for survival analysis. Our preliminary\nanalysis suggests that including such features can improve patient\nstratification and survival prediction.\n"}
+{"abstract": "  Conformal defects describe the universal behaviors of a conformal field\ntheory (CFT) in the presence of a boundary or more general impurities. The\ncoupled critical system is characterized by new conformal anomalies which are\nanalogous to, and generalize those of standalone CFTs. Here we study the\nconformal $a$- and $c$-anomalies of four dimensional defects in CFTs of general\nspacetime dimensions greater than four. We prove that under unitary defect\nrenormalization group (RG) flows, the defect $a$-anomaly must decrease, thus\nestablishing the defect $a$-theorem. For conformal defects preserving minimal\nsupersymmetry, the full defect symmetry contains a distinguished $U(1)_R$\nsubgroup. We derive the anomaly multiplet relations that express the defect\n$a$- and $c$-anomalies in terms of the defect (mixed) 't Hooft anomalies for\nthis $U(1)_R$ symmetry. Once the $U(1)_R$ symmetry is identified using the\ndefect $a$-maximization principle which we prove, this enables a\nnon-perturbative pathway to the conformal anomalies of strongly coupled\ndefects. We illustrate our methods by discussing a number of examples including\nboundaries in five dimensions and codimension-two defects in six dimensions. We\nalso comment on chiral algebra sectors of defect operator algebras and\npotential conformal collider bounds on defect anomalies.\n"}
+{"abstract": "  In the presence of attraction, the jamming transition of packings of\nfrictionless particles corresponds to the rigidity percolation. When the range\nof attraction is long, the distribution of the size of rigid clusters, $P(s)$,\nis continuous and shows a power-law decay. For systems with short-range\nattractions, however, $P(s)$ appears discontinuous. There is a power-law decay\nfor small cluster sizes, followed by a low probability gap and a peak near the\nsystem size. We find that this appearing ``discontinuity'' does not mean that\nthe transition is discontinuous. In fact, it signifies the coexistence of two\ndistinct length scales, associated with the largest cluster and smaller ones,\nrespectively. The comparison between the largest and second largest clusters\nindicates that their growth rates with system size are rather different.\nHowever, both cluster sizes tend to diverge in the large system size limit,\nsuggesting that the jamming transition of systems with short-range attractions\nis still continuous. In the framework of the two-scale scenario, we also derive\na generalized hyperscaling relation. With robust evidence, our work challenges\nthe former single-scale view of the rigidity percolation.\n"}
+{"abstract": "  This paper is concerned with the existence of positive solutions for a\nfractional population model with the homogeneous Dirichlet condition on the\nexterior of a bounded domain. The approach is based on the sub-super solutions\nmethod. Our results generalize some recent results in the literature.\n"}
+{"abstract": "  The distribution of liquid water in ice-free clouds determines their\nradiative properties, a significant source of uncertainty in weather and\nclimate models. Evaporation and turbulent mixing cause a cloud to display large\nvariations in droplet-number density, but quite small variations in droplet\nsize [Beals et al. (2015)]. Yet direct numerical simulations of the joint\neffect of evaporation and mixing near the cloud edge predict quite different\nbehaviors, and it remains an open question how to reconcile these results with\nthe experimental findings. To infer the history of mixing and evaporation from\nobservational snapshots of droplets in clouds is challenging because clouds are\ntransient systems. We formulated a statistical model that provides a reliable\ndescription of the evaporation-mixing process as seen in direct numerical\nsimulations, and allows to infer important aspects of the history of observed\ndroplet populations, highlighting the key mechanisms at work, and explaining\nthe differences between observations and simulations.\n"}
+{"abstract": "  In 2018, XMM-Newton observed the awakening in X-rays of the Be/X-ray binary\n(Be/XRB) A0538-66. It showed bright and fast flares close to periastron with\nproperties that had never been observed in other Be/XRBs before. We report the\nresults from the observations of A0538-66 collected during the first all-sky\nsurvey of eROSITA, an X-ray telescope (0.2-10 keV) on board the\nSpektrum-Roentgen-Gamma (SRG) satellite. eROSITA caught two flares within one\norbital cycle at orbital phases $\\phi = 0.29$ and $\\phi = 0.93$ (where $\\phi=0$\ncorresponds to periastron), with peak luminosities of $\\sim 2-4 \\times 10^{36}$\nerg/s (0.2-10 keV) and durations of $42 \\leq \\Delta t_{\\rm fl} \\leq 5.7\\times\n10^4$ s. The flare observed at $\\phi \\approx 0.29$ shows that the neutron star\ncan accrete considerably far from periastron, although it is expected to be\noutside of the circumstellar disk, thus providing important new information\nabout the plasma environment surrounding the binary system. We also report the\nresults from the photometric monitoring of A0538-66 carried out with the REM,\nOGLE, and MACHO telescopes from January 1993 until March 2020. We found that\nthe two sharp peaks that characterize the orbital modulation in the optical\noccur asymmetrically in the orbit, relative to the position of the donor star.\n"}
+{"abstract": "  A coastal eddy is modelled as a barotropic vortex propagating along a coastal\nshelf. If the vortex speed matches the phase speed of any coastal trapped shelf\nwave modes, a shelf wave wake is generated leading to a flux of energy from the\nvortex into the wave field. Using a simply shelf geometry, we determine\nanalytic expressions for the wave wake and the leading order flux of wave\nenergy. By considering the balance of energy between the vortex and wave field,\nthis energy flux is then used to make analytic predictions for the evolution of\nthe vortex speed and radius under the assumption that the vortex structure\nremains self similar. These predictions are examined in the asymptotic limit of\nsmall rotation rate and shelf slope and tested against numerical simulations.\n  If the vortex speed does not match the phase speed of any shelf wave, steady\nvortex solutions are expected to exist. We present a numerical approach for\nfinding these nonlinear solutions and examine the parameter dependence of their\nstructure.\n"}
+{"abstract": "  Scarcity of parallel data causes formality style transfer models to have\nscarce success in preserving content. We show that fine-tuning pre-trained\nlanguage (GPT-2) and sequence-to-sequence (BART) models boosts content\npreservation, and that this is possible even with limited amounts of parallel\ndata. Augmenting these models with rewards that target style and content -- the\ntwo core aspects of the task -- we achieve a new state-of-the-art.\n"}
+{"abstract": "  We present ten different characterizations of functions satisfying a weak\nreverse H\\\"older inequality on an open subset of a metric measure space with a\ndoubling measure. Among others, we describe these functions as a class of weak\n$A_\\infty$ weights, which is a generalization of Muckenhoupt weights that\nallows for nondoubling weights. Although our main results are modeled after\nconditions that hold true for Muckenhoupt weights, we also discuss two\nconditions for Muckenhoupt $A_\\infty$ weights that fail to hold for weak\n$A_\\infty$ weights.\n"}
+{"abstract": "  Given a D-brane background in string theory (or equivalently boundary\nconditions in a two-dimensional conformal field theory), classical solutions of\nopen string field theory equations of motion are conjectured to describe new\nD-brane backgrounds (boundary conditions). In this thesis, we study these\nsolutions in bosonic open string field theory using the level truncation\napproach, which is a numerical approach where the string field is truncated to\na finite number of degrees of freedom.\n  We start with a review of the theoretical background and numerical methods\nwhich are needed in the level truncation approach and then we discuss solutions\non several different backgrounds. First, we discuss universal solutions, which\ndo not depend on the open string background, then we analyze solutions of the\nfree boson theory compactified on a circle or on a torus, then marginal\nsolutions in three different approaches and finally solutions in theories which\ninclude the A-series of Virasoro minimal models. In addition to known D-branes,\nwe find so-called exotic solutions which potentially describe yet unknown\nboundary states.\n  This paper is based on my doctoral thesis submitted to the Faculty of\nMathematics and Physics at Charles University in Prague.\n"}
+{"abstract": "  The tight correlations between the mass of supermassive black holes ($M_{\\rm\nBH}$) and their host-galaxy properties have been of great interest to the\nastrophysical community, but a clear understanding of their origin and\nfundamental drivers still eludes us. The local relations for active galaxies\nare interesting in their own right and form the foundation for any evolutionary\nstudy over cosmic time. We present Hubble Space Telescope optical imaging of a\nsample of 66 local active galactic nuclei (AGNs); for 14 objects, we also\nobtained Gemini near-infrared images. We use state of the art methods to\nperform surface photometry of the AGN host galaxies, decomposing them in\nspheroid, disk and bar (when present) and inferring the luminosity and stellar\nmass of the components. We combine this information with spatially-resolved\nkinematics obtained at the Keck Telescopes to study the correlations between\n$M_{\\rm BH}$ (determined from single-epoch virial estimators) and host galaxy\nproperties. The correlations are uniformly tight for our AGN sample, with\nintrinsic scatter 0.2-0.4 dex, smaller than or equal to that of quiescent\ngalaxies. We find no difference between pseudo and classical bulges or barred\nand non-barred galaxies. We show that all the tight correlations can be\nsimultaneously satisfied by AGN hosts in the 10$^7$-10$^9$ $M_{\\odot}$ regime,\nwith data of sufficient quality. The MBH-$\\sigma$ relation is also in agreement\nwith that of AGNs with $M_{\\rm BH}$ obtained from reverberation mapping,\nproviding an indirect validation of single-epoch virial estimators of $M_{\\rm\nBH}$.\n"}
+{"abstract": "  We present Syntroids, a case study for the automatic synthesis of hardware\nfrom a temporal logic specification. Syntroids is a space shooter arcade game\nrealized on an FPGA, where the control flow architecture has been completely\nspecified in Temporal Stream Logic (TSL) and implemented using reactive\nsynthesis. TSL is a recently introduced temporal logic that separates control\nand data. This leads to scalable synthesis, because the cost of the synthesis\nprocess is independent of the complexity of the handled data.\n  In this case study, we report on our experience with the TSL-based\ndevelopment of the Syntroids game and on the implementation quality obtained\nwith synthesis in comparison to manual programming. We also discuss solved and\nopen challenges with respect to currently available synthesis tools.\n"}
+{"abstract": "  Topological phases such as polar skyrmions have been a fertile playground for\nferroelectric oxide superlattices, with exotic physical phenomena such as\nnegative capacitance. Herein, using phase-field simulations, we demonstrate the\nlocal control of the skyrmion phase with electric potential applied through a\ntop electrode. Under a relatively small electric potential, the skyrmions\nunderneath the electrode can be erased and recovered reversibly. A\ntopologically protected transition from the symmetric to asymmetric skyrmion\nbubbles is observed at the edge of the electrode. While a topological\ntransition to a labyrinthine domain requires a high applied potential, it can\nswitch back to the skyrmion state with a relatively small electric potential.\nThe topological transition from +1 to 0 occurs before the full destruction of\nthe bubble state. It is shown that the shrinking and bursting of the skyrmions\nleads to a large reduction in the dielectric permittivity, the magnitude of\nwhich depends on the size of the electrode.\n"}
+{"abstract": "  We consider solutions of the 2D Toda lattice hierarchy which are elliptic\nfunctions of the zeroth time t_0=x. It is known that their poles as functions\nof t_1 move as particles of the elliptic Ruijsenaars-Schneider model. The goal\nof this paper is to extend this correspondence to the level of hierarchies. We\nshow that the Hamiltonians which govern the dynamics of poles with respect to\nthe m-th hierarchical times t_m and \\bar t_m of the 2D Toda lattice hierarchy\nare obtained from expansion of the spectral curve for the Lax matrix of the\nRuijsenaars-Schneider model at the marked points.\n"}
+{"abstract": "  This paper proposes a general framework for nonperturbatively defining\ncontinuum quantum field theories. Unlike most such frameworks, the one offered\nhere is finitary: continuum theories are defined by reducing large but finite\nquantum systems to subsystems with conserved entanglement patterns at short\ndistances. This makes it possible to start from a lattice theory and use rather\nelementary mathematics to isolate the entire algebraic structure of the\ncorresponding low-energy continuum theory.\n  The first half of this paper illustrates this approach through a persnickety\nstudy of (1 + 1)D continuum theories that emerge from the $\\mathbb{Z}_K$ clock\nmodel at large $K$. This leads to a direct lattice derivation of many known\ncontinuum results, such as the operator product expansion of vertex operators\nin the free scalar CFT. Many new results are obtained too. For example,\nself-consistency of the lattice-continuum correspondence leads to a rich, novel\nproposal for the symmetry breaking structure of the clock model at weak\ncoupling, deep in the BKT regime. This also makes precise what one means by\n\"continuous\" when saying that continuous symmetries cannot be broken in (1+1)D.\n  The second half of this paper is devoted to path integrals for continuum\ntheories of bosons and fermions defined in this finitary formalism. The path\nintegrals constructed here have both nonperturbative lattice definitions and\nmanifest continuum properties, such as symmetries under infinitesimal rotations\nor dilatations. Remarkably, this setup also makes it possible to generalize\nNoether's theorem to discrete symmetries.\n"}
+{"abstract": "  We study the average behaviour of the Iwasawa invariants for Selmer groups of\nelliptic curves. These results lie at the intersection of arithmetic statistics\nand Iwasawa theory. We obtain unconditional lower bounds for the density of\nrational elliptic curves with prescribed Iwasawa invariants.\n"}
+{"abstract": "  The goal of this paper is to characterize Gaussian-Process optimization in\nthe setting where the function domain is large relative to the number of\nadmissible function evaluations, i.e., where it is impossible to find the\nglobal optimum. We provide upper bounds on the suboptimality (Bayesian simple\nregret) of the solution found by optimization strategies that are closely\nrelated to the widely used expected improvement (EI) and upper confidence bound\n(UCB) algorithms. These regret bounds illuminate the relationship between the\nnumber of evaluations, the domain size (i.e. cardinality of finite domains /\nLipschitz constant of the covariance function in continuous domains), and the\noptimality of the retrieved function value. In particular, we show that even\nwhen the number of evaluations is far too small to find the global optimum, we\ncan find nontrivial function values (e.g. values that achieve a certain ratio\nwith the optimal value).\n"}
+{"abstract": "  We consider flows $(X,T)$, given by actions $(t, x) \\to tx$, on a compact\nmetric space $X$ with a discrete $T$ as an acting group. We study a new class\nof flows - the \\textsc{Strongly Rigid} ($ \\mathbf {SR} $) \\ flows, that are\nproperly contained in the class of distal ($ \\mathbf D $) flows and properly\ncontain the class of all equicontinuous ($ \\mathbf {EQ} $) flows. Thus,\n$\\mathbf {EQ} \\ \\text{flows} \\subsetneqq \\mathbf {SR} \\ \\text{flows}\n\\subsetneqq \\mathbf{ D} \\ \\text{flows}$.\n  The concepts of equicontinuity, strong rigidity and distality coincide for\nthe induced flow $(2^X,T)$. We observe that strongly rigid $(X,T)$ gives\ndistinct properties for the induced flow $(2^X,T)$ and its enveloping semigroup\n$E(2^X)$. We further study strong rigidity in case of particular semiflows\n$(X,S)$, with $S$ being a discrete acting semigroup.\n"}
+{"abstract": "  This paper describes an image based visual servoing (IBVS) system for a\nnonholonomic robot to achieve good trajectory following without real-time robot\npose information and without a known visual map of the environment. We call it\ntrajectory servoing. The critical component is a feature-based, indirect SLAM\nmethod to provide a pool of available features with estimated depth, so that\nthey may be propagated forward in time to generate image feature trajectories\nfor visual servoing. Short and long distance experiments show the benefits of\ntrajectory servoing for navigating unknown areas without absolute positioning.\nTrajectory servoing is shown to be more accurate than pose-based feedback when\nboth rely on the same underlying SLAM system.\n"}
+{"abstract": "  This paper shows that not only do the codimension one spurious poles of $N^k\nMHV$ tree level diagrams in N=4 SYM theory cancel in the tree level amplitude\nas expected, but their vanishing loci have a geometric interpretation that is\ntightly connected to their representation in the positive Grassmannians. In\ngeneral, given a positroid variety, $\\Sigma$, and a minimal matrix\nrepresentation of it in terms of independent variable valued matrices,\n$M_{\\mathcal{V}}$, one can define a polynomial, $R({\\mathcal{V}})$ that is\nuniquely defined by the Grassmann necklace, ${\\mathcal{I}}$, of the positroid\ncell. The vanishing locus of $R({\\mathcal{V}})$ lies on the boundary of the\npositive variety $\\overline{\\Sigma} \\setminus \\Sigma$, but not all boundaries\nintersect the vanishing loci of a factor of $R({\\mathcal{V}})$. We use this to\nshow that the codimension one spurious poles of N=4 SYM, represented in twistor\nspace, cancel in the tree level amplitude.\n"}
+{"abstract": "  From physics to engineering, biology and social science, natural and\nartificial systems are characterized by interconnected topologies whose\nfeatures - e.g., heterogeneous connectivity, mesoscale organization, hierarchy\n- affect their robustness to external perturbations, such as targeted attacks\nto their units. Identifying the minimal set of units to attack to disintegrate\na complex network, i.e. network dismantling, is a computationally challenging\n(NP-hard) problem which is usually attacked with heuristics. Here, we show that\na machine trained to dismantle relatively small systems is able to identify\nhigher-order topological patterns, allowing to disintegrate large-scale social,\ninfrastructural and technological networks more efficiently than human-based\nheuristics. Remarkably, the machine assesses the probability that next attacks\nwill disintegrate the system, providing a quantitative method to quantify\nsystemic risk and detect early-warning signals of system's collapse. This\ndemonstrates that machine-assisted analysis can be effectively used for policy\nand decision making to better quantify the fragility of complex systems and\ntheir response to shocks.\n"}
+{"abstract": "  We provide a comprehensive quantitative analysis of localized and extended\ntopological defects in the steady state of 2D passive and active repulsive\nBrownian disk systems. We show that, both in and out-of-equilibrium, the\npassage from the solid to the hexatic is driven by the unbinding of\ndislocations, in quantitative agreement with the KTHNY singularity. Instead,\nalthough disclinations dissociate as soon as the liquid phase appears, extended\nclusters of defects largely dominate below the solid-hexatic critical line. The\nlatter percolate in the liquid phase very close to the hexatic-liquid\ntransition, both for continuous and discontinuous transitions, in the\nhomogeneous liquid regime. At critical percolation the clusters of defects are\nfractal with statistical and geometric properties that, within our numerical\naccuracy, are independent of the activity and compatible with the universality\nclass of uncorrelated critical percolation. We also characterize the spatial\norganization of different kinds of point-like defects and we show that the\ndisclinations are not free, but rather always very near more complex defect\nstructures. At high activity, the bulk of the dense phase generated by\nMotility-Induced Phase Separation is characterized by a density of point-like\ndefects, and statistics and morphology of defect clusters, set by the amount of\nactivity and not the packing fraction. Hexatic domains within the dense phase\nare separated by grain-boundaries along which a finite network of topological\ndefects resides, interrupted by gas bubbles in cavitation. The fractal\ndimension of this network diminishes for increasing activity. This structure is\ndynamic in the sense that the defect network allows for an unzipping mechanism\nthat leaves free space for gas bubbles to appear, close, and even be released\ninto the dilute phase.\n"}
+{"abstract": "  The Strict Avalanche Criterion (SAC) is a property of vectorial Boolean\nfunctions that is used in the construction of strong S-boxes. We show in this\npaper how to generalize the concept of SAC to address possible c-differential\nattacks, in the realm of finite fields. We define the concepts of c-Strict\nAvalanche Criterion (c-SAC) and c-Strict Avalanche Criterion of order m\n(c-SAC(m)), and generalize results of (Li and Cusick, 2005). We also show\ncomputationally how the new definition is not equivalent to the existing\nconcepts of c-bent1-ness (Stanica et al., 2020), nor (for n = m) PcN-ness\n(Ellingsen et al., 2020)\n"}
+{"abstract": "  Spectral reconstructions provide rigorous means to remove the Gibbs\nphenomenon and accelerate the convergence of spectral solutions in non-smooth\ndifferential equations. In this paper, we show the concurrent emergence of\ntruncated convolution errors could entirely disrupt the performance of most\nreconstruction techniques in the vicinity of discontinuities. They arise when\nthe Fourier coefficients of the product of two discontinuous functions, namely\n$f=gh$, are approximated via truncated convolution of the corresponding Fourier\nseries, i.e. $\\hat{f}_k\\approx \\sum_{|\\ell|\\leqslant\nN}{\\hat{g}_\\ell\\hat{h}_{k-\\ell}}$. Nonetheless, we numerically illustrate and\nrigorously prove that the classical Gegenbauer method remains exceptionally\nrobust against this phenomenon, with the reconstruction error still diminishing\nproportional to $\\mathcal{O}(N^{-1})$ for the Fourier order $N$, and\nexponentially fast regardless of a constant. Finally, as a case study and a\nproblem of interest in grating analysis whence the phenomenon initially was\nnoticed, we demonstrate the emergence and practical resolution of truncated\nconvolution errors in grating modes, which constitute the basis of Fourier\nmodal methods.\n"}
+{"abstract": "  Peters' formula is an analytical estimate of the time-scale of gravitational\nwave (GW)-induced coalescence of binary systems. It is used in countless\napplications, where the convenience of a simple formula outweighs the need for\nprecision. However, many promising sources of the Laser Interferometer Space\nAntenna (LISA), such as supermassive black hole binaries and extreme mass-ratio\ninspirals (EMRIs), are expected to enter the LISA band with highly eccentric\n($e \\gtrsim$ 0.9) and highly relativistic orbits. These are exactly the two\nlimits in which Peters' estimate performs the worst. In this work, we expand\nupon previous results and give simple analytical fits to quantify how the\ninspiral time-scale is affected by the relative 1.5 post-Newtonian (PN)\nhereditary fluxes and spin-orbit couplings. We discuss several cases that\ndemand a more accurate GW time-scale. We show how this can have a major\ninfluence on quantities that are relevant for LISA event-rate estimates, such\nas the EMRI critical semi-major axis. We further discuss two types of\nenvironmental perturbations that can play a role in the inspiral phase: the\ngravitational interaction with a third massive body and the energy loss due to\ndynamical friction and torques from a surrounding gas medium ubiquitous in\ngalactic nuclei. With the aid of PN corrections to the time-scale in vacuum, we\nfind simple analytical expressions for the regions of phase space in which\nenvironmental perturbations are of comparable strength to the effects of any\nparticular PN order, being able to qualitatively reproduce the results of much\nmore sophisticated analyses.\n"}
+{"abstract": "  Transfer learning with pre-training on large-scale datasets has played an\nincreasingly significant role in computer vision and natural language\nprocessing recently. However, as there exist numerous application scenarios\nthat have distinctive demands such as certain latency constraints and\nspecialized data distributions, it is prohibitively expensive to take advantage\nof large-scale pre-training for per-task requirements. In this paper, we focus\non the area of object detection and present a transfer learning system named\nGAIA, which could automatically and efficiently give birth to customized\nsolutions according to heterogeneous downstream needs. GAIA is capable of\nproviding powerful pre-trained weights, selecting models that conform to\ndownstream demands such as latency constraints and specified data domains, and\ncollecting relevant data for practitioners who have very few datapoints for\ntheir tasks. With GAIA, we achieve promising results on COCO, Objects365, Open\nImages, Caltech, CityPersons, and UODB which is a collection of datasets\nincluding KITTI, VOC, WiderFace, DOTA, Clipart, Comic, and more. Taking COCO as\nan example, GAIA is able to efficiently produce models covering a wide range of\nlatency from 16ms to 53ms, and yields AP from 38.2 to 46.5 without whistles and\nbells. To benefit every practitioner in the community of object detection, GAIA\nis released at https://github.com/GAIA-vision.\n"}
+{"abstract": "  Efficient characterization of quantum devices is a significant challenge\ncritical for the development of large scale quantum computers. We consider an\nexperimentally motivated situation, in which we have a decent estimate of the\nHamiltonian, and its parameters need to be characterized and fine-tuned\nfrequently to combat drifting experimental variables. We use a machine learning\ntechnique known as meta-learning to learn a more efficient optimizer for this\ntask. We consider training with the nearest-neighbor Ising model and study the\ntrained model's generalizability to other Hamiltonian models and larger system\nsizes. We observe that the meta-optimizer outperforms other optimization\nmethods in average loss over test samples. This advantage follows from the\nmeta-optimizer being less likely to get stuck in local minima, which highly\nskews the distribution of the final loss of the other optimizers. In general,\nmeta-learning decreases the number of calls to the experiment and reduces the\nneeded classical computational resources.\n"}
+{"abstract": "  In this paper we consider utilizing a residual neural network (ResNet) to\nsolve ordinary differential equations. Stochastic gradient descent method is\napplied to obtain the optimal parameter set of weights and biases of the\nnetwork. We apply forward Euler, Runge-Kutta2 and Runge-Kutta4 finite\ndifference methods to generate three sets of targets training the ResNet and\ncarry out the target study. The well trained ResNet behaves just as its\ncounterpart of the corresponding one-step finite difference method. In\nparticular, we carry out (1) the architecture study in terms of number of\nhidden layers and neurons per layer to find the optimal ResNet structure; (2)\nthe target study to verify the ResNet solver behaves as accurate as its finite\ndifference method counterpart; (3) solution trajectory simulation. Even the\nResNet solver looks like and is implemented in a way similar to forward Euler\nscheme, its accuracy can be as high as any one step method. A sequence of\nnumerical examples are presented to demonstrate the performance of the ResNet\nsolver.\n"}
+{"abstract": "  There has been increasing interest in modeling survival data using deep\nlearning methods in medical research. In this paper, we proposed a Bayesian\nhierarchical deep neural networks model for modeling and prediction of survival\ndata. Compared with previously studied methods, the new proposal can provide\nnot only point estimate of survival probability but also quantification of the\ncorresponding uncertainty, which can be of crucial importance in predictive\nmodeling and subsequent decision making. The favorable statistical properties\nof point and uncertainty estimates were demonstrated by simulation studies and\nreal data analysis. The Python code implementing the proposed approach was\nprovided.\n"}
+{"abstract": "  The pre-trained neural models have recently achieved impressive performances\nin understanding multimodal content. However, it is still very challenging to\npre-train neural models for video and language understanding, especially for\nChinese video-language data, due to the following reasons. Firstly, existing\nvideo-language pre-training algorithms mainly focus on the co-occurrence of\nwords and video frames, but ignore other valuable semantic and structure\ninformation of video-language content, e.g., sequential order and\nspatiotemporal relationships. Secondly, there exist conflicts between video\nsentence alignment and other proxy tasks. Thirdly, there is a lack of\nlarge-scale and high-quality Chinese video-language datasets (e.g., including\n10 million unique videos), which are the fundamental success conditions for\npre-training techniques.\n  In this work, we propose a novel video-language understanding framework named\nVICTOR, which stands for VIdeo-language understanding via Contrastive\nmulTimOdal pRe-training. Besides general proxy tasks such as masked language\nmodeling, VICTOR constructs several novel proxy tasks under the contrastive\nlearning paradigm, making the model be more robust and able to capture more\ncomplex multimodal semantic and structural relationships from different\nperspectives. VICTOR is trained on a large-scale Chinese video-language\ndataset, including over 10 million complete videos with corresponding\nhigh-quality textual descriptions. We apply the pre-trained VICTOR model to a\nseries of downstream applications and demonstrate its superior performances,\ncomparing against the state-of-the-art pre-training methods such as VideoBERT\nand UniVL. The codes and trained checkpoints will be publicly available to\nnourish further developments of the research community.\n"}
+{"abstract": "  Existing graph neural networks (GNNs) largely rely on node embeddings, which\nrepresent a node as a vector by its identity, type, or content. However, graphs\nwith unattributed nodes widely exist in real-world applications (e.g.,\nanonymized social networks). Previous GNNs either assign random labels to nodes\n(which introduces artefacts to the GNN) or assign one embedding to all nodes\n(which fails to explicitly distinguish one node from another). Further, when\nthese GNNs are applied to unattributed node classification problems, they have\nan undesired equivariance property, which are fundamentally unable to address\nthe data with multiple possible outputs. In this paper, we analyze the\nlimitation of existing approaches to node classification problems. Inspired by\nour analysis, we propose a generalized equivariance property and a Preferential\nLabeling technique that satisfies the desired property asymptotically.\nExperimental results show that we achieve high performance in several\nunattributed node classification tasks.\n"}
+{"abstract": "  Gradient estimation and vector space projection have been studied as two\ndistinct topics. We aim to bridge the gap between the two by investigating how\nto efficiently estimate gradient based on a projected low-dimensional space. We\nfirst provide lower and upper bounds for gradient estimation under both linear\nand nonlinear projections, and outline checkable sufficient conditions under\nwhich one is better than the other. Moreover, we analyze the query complexity\nfor the projection-based gradient estimation and present a sufficient condition\nfor query-efficient estimators. Built upon our theoretic analysis, we propose a\nnovel query-efficient Nonlinear Gradient Projection-based Boundary Blackbox\nAttack (NonLinear-BA). We conduct extensive experiments on four image datasets:\nImageNet, CelebA, CIFAR-10, and MNIST, and show the superiority of the proposed\nmethods compared with the state-of-the-art baselines. In particular, we show\nthat the projection-based boundary blackbox attacks are able to achieve much\nsmaller magnitude of perturbations with 100% attack success rate based on\nefficient queries. Both linear and nonlinear projections demonstrate their\nadvantages under different conditions. We also evaluate NonLinear-BA against\nthe commercial online API MEGVII Face++, and demonstrate the high blackbox\nattack performance both quantitatively and qualitatively. The code is publicly\navailable at https://github.com/AI-secure/NonLinear-BA.\n"}
+{"abstract": "  Non-von Neumann computational hardware, based on neuron-inspired, non-linear\nelements connected via linear, weighted synapses -- so-called neuromorphic\nsystems -- is a viable computational substrate. Since neuromorphic systems have\nbeen shown to use less power than CPUs for many applications, they are of\npotential use in autonomous systems such as robots, drones, and satellites, for\nwhich power resources are at a premium. The power used by neuromorphic systems\nis approximately proportional to the number of spiking events produced by\nneurons on-chip. However, typical information encoding on these chips is in the\nform of firing rates that unarily encode information. That is, the number of\nspikes generated by a neuron is meant to be proportional to an encoded value\nused in a computation or algorithm. Unary encoding is less efficient (produces\nmore spikes) than binary encoding. For this reason, here we present\nneuromorphic computational mechanisms for implementing binary two's complement\noperations. We use the mechanisms to construct a neuromorphic, binary matrix\nmultiplication algorithm that may be used as a primitive for linear\ndifferential equation integration, deep networks, and other standard\ncalculations. We also construct a random walk circuit and apply it in Brownian\nmotion simulations. We study how both algorithms scale in circuit size and\niteration time.\n"}
+{"abstract": "  We report gamma ray spectroscopy measurements of trinitite samples and\nanalogous samples obtained from detonation sites in Nevada and Semipalatinsk,\nas well as in situ measurements of topsoil at the Trinity site. We also report\nthe first isotopic composition measurements of trinitite using the novel\nforensics technique of decay energy spectroscopy (DES) as a complement to\ntraditional forensics techniques. Our measurements are compared to other\npublished results.\n"}
+{"abstract": "  In adversarial training (AT), the main focus has been the objective and\noptimizer while the model has been less studied, so that the models being used\nare still those classic ones in standard training (ST). Classic network\narchitectures (NAs) are generally worse than searched NAs in ST, which should\nbe the same in AT. In this paper, we argue that NA and AT cannot be handled\nindependently, since given a dataset, the optimal NA in ST would be no longer\noptimal in AT. That being said, AT is time-consuming itself; if we directly\nsearch NAs in AT over large search spaces, the computation will be practically\ninfeasible. Thus, we propose a diverse-structured network (DS-Net), to\nsignificantly reduce the size of the search space: instead of low-level\noperations, we only consider predefined atomic blocks, where an atomic block is\na time-tested building block like the residual block. There are only a few\natomic blocks and thus we can weight all atomic blocks rather than find the\nbest one in a searched block of DS-Net, which is an essential trade-off between\nexploring diverse structures and exploiting the best structures. Empirical\nresults demonstrate the advantages of DS-Net, i.e., weighting the atomic\nblocks.\n"}
+{"abstract": "  In asynchronous games, Melli{\\`e}s proved that innocent strategies are\npositional: their behaviour only depends on the position, not the temporal\norder used to reach it. This insightful result shaped our understanding of the\nlink between dynamic (i.e. game) and static (i.e. relational) semantics. In\nthis paper, we investigate the positionality of innocent strategies in the\ntraditional setting of Hyland-Ong-Nickau-Coquand pointer games. We show that\nthough innocent strategies are not positional, total finite innocent strategies\nstill enjoy a key consequence of positionality, namely positional injectivity:\nthey are entirely determined by their positions. Unfortunately, this does not\nhold in general: we show a counterexample if finiteness and totality are\nlifted. For finite partial strategies we leave the problem open; we show\nhowever the partial result that two strategies with the same positions must\nhave the same P-views of maximal length.\n"}
+{"abstract": "  Many problems related to the quality of requirements arise during elicitation\nand specification activities since they are written in natural language. The\nflexibility and inherent nature of language make requirements prone to\ninconsistencies, redundancies, and ambiguities, and consequently, this\ninfluences negatively the later phases of the software life cycle. To address\nthis problem, this paper proposes an innovative approach that combines natural\nlanguage processing techniques and recurrent neural networks to automatically\nassess the quality of software requirements. Initially, the analysis of\nsingular, complete, correct, and appropriate quality properties defined in the\nIEEE 29148: 2018 standard is addressed. The proposed neural models are trained\nwith a data set composed of 1000 software requirements. The proposal provides\nan average accuracy of 75%. These promising results were a motivation to\nexplore its application in the evaluation of other quality properties\n"}
+{"abstract": "  Computing the similarity of two point sets is a ubiquitous task in medical\nimaging, geometric shape comparison, trajectory analysis, and many more\nsettings. Arguably the most basic distance measure for this task is the\nHausdorff distance, which assigns to each point from one set the closest point\nin the other set and then evaluates the maximum distance of any assigned pair.\nA drawback is that this distance measure is not translational invariant, that\nis, comparing two objects just according to their shape while disregarding\ntheir position in space is impossible.\n  Fortunately, there is a canonical translational invariant version, the\nHausdorff distance under translation, which minimizes the Hausdorff distance\nover all translations of one of the point sets. For point sets of size $n$ and\n$m$, the Hausdorff distance under translation can be computed in time $\\tilde\nO(nm)$ for the $L_1$ and $L_\\infty$ norm [Chew, Kedem SWAT'92] and $\\tilde O(nm\n(n+m))$ for the $L_2$ norm [Huttenlocher, Kedem, Sharir DCG'93].\n  As these bounds have not been improved for over 25 years, in this paper we\napproach the Hausdorff distance under translation from the perspective of\nfine-grained complexity theory. We show (i) a matching lower bound of\n$(nm)^{1-o(1)}$ for $L_1$ and $L_\\infty$ (and all other $L_p$ norms) assuming\nthe Orthogonal Vectors Hypothesis and (ii) a matching lower bound of\n$n^{2-o(1)}$ for $L_2$ in the imbalanced case of $m = O(1)$ assuming the 3SUM\nHypothesis.\n"}
+{"abstract": "  Breakthrough advances in reinforcement learning (RL) research have led to a\nsurge in the development and application of RL. To support the field and its\nrapid growth, several frameworks have emerged that aim to help the community\nmore easily build effective and scalable agents. However, very few of these\nframeworks exclusively support multi-agent RL (MARL), an increasingly active\nfield in itself, concerned with decentralised decision-making problems. In this\nwork, we attempt to fill this gap by presenting Mava: a research framework\nspecifically designed for building scalable MARL systems. Mava provides useful\ncomponents, abstractions, utilities and tools for MARL and allows for simple\nscaling for multi-process system training and execution, while providing a high\nlevel of flexibility and composability. Mava is built on top of DeepMind's Acme\n\\citep{hoffman2020acme}, and therefore integrates with, and greatly benefits\nfrom, a wide range of already existing single-agent RL components made\navailable in Acme. Several MARL baseline systems have already been implemented\nin Mava. These implementations serve as examples showcasing Mava's reusable\nfeatures, such as interchangeable system architectures, communication and\nmixing modules. Furthermore, these implementations allow existing MARL\nalgorithms to be easily reproduced and extended. We provide experimental\nresults for these implementations on a wide range of multi-agent environments\nand highlight the benefits of distributed system training.\n"}
+{"abstract": "  End-to-end speech translation models have become a new trend in research due\nto their potential of reducing error propagation. However, these models still\nsuffer from the challenge of data scarcity. How to effectively use unlabeled or\nother parallel corpora from machine translation is promising but still an open\nproblem. In this paper, we propose Cross Speech-Text Network (XSTNet), an\nend-to-end model for speech-to-text translation. XSTNet takes both speech and\ntext as input and outputs both transcription and translation text. The model\nbenefits from its three key design aspects: a self-supervised pre-trained\nsub-network as the audio encoder, a multi-task training objective to exploit\nadditional parallel bilingual text, and a progressive training procedure. We\nevaluate the performance of XSTNet and baselines on the MuST-C En-X and\nLibriSpeech En-Fr datasets. In particular, XSTNet achieves state-of-the-art\nresults on all language directions with an average BLEU of 28.8, outperforming\nthe previous best method by 3.2 BLEU. Code, models, cases, and more detailed\nanalysis are available at https://github.com/ReneeYe/XSTNet.\n"}
+{"abstract": "  In this paper, we describe how the electronic rolling shutter in CMOS image\nsensors can be exploited using a bright, modulated light source (e.g., an\ninexpensive, off-the-shelf laser), to inject fine-grained image disruptions. We\ndemonstrate the attack on seven different CMOS cameras, ranging from cheap IoT\nto semi-professional surveillance cameras, to highlight the wide applicability\nof the rolling shutter attack. We model the fundamental factors affecting a\nrolling shutter attack in an uncontrolled setting. We then perform an\nexhaustive evaluation of the attack's effect on the task of object detection,\ninvestigating the effect of attack parameters. We validate our model against\nempirical data collected on two separate cameras, showing that by simply using\ninformation from the camera's datasheet the adversary can accurately predict\nthe injected distortion size and optimize their attack accordingly. We find\nthat an adversary can hide up to 75% of objects perceived by state-of-the-art\ndetectors by selecting appropriate attack parameters. We also investigate the\nstealthiness of the attack in comparison to a na\\\"{i}ve camera blinding attack,\nshowing that common image distortion metrics can not detect the attack\npresence. Therefore, we present a new, accurate and lightweight enhancement to\nthe backbone network of an object detector to recognize rolling shutter\nattacks. Overall, our results indicate that rolling shutter attacks can\nsubstantially reduce the performance and reliability of vision-based\nintelligent systems.\n"}
+{"abstract": "  We analyze the charmonium states by testing a phenomenological\nnonrelativistic potential and propose a new set of parameters. This new set of\nparameters are fixed using only the lowest lying S-wave states of charmonia\nwhere the spin-orbit and tensor interactions will not contribute. After fitting\nthe parameters we analyze the whole fine structure of charmonium states taking\ninto account the spin-orbit and tensor interactions too. Calculations showed\nthat the nonrelativistic potential model with the phenomenologically defined\nparameters is indeed well approximation for describing the charmonium states.\n"}
+{"abstract": "  This work presents the dynamic properties of charged test particles\ninfluenced by the gravitational and electromagnetic fields. Accordingly, we\nconcentrate on the simplest static and axially symmetric metric in this work,\nwhich contains two quadrupole parameters. One relates to the central object,\nand another relates to the distortion parameter, which is linked to the\nexternal distribution of matter. This metric may associate the observable\neffects to these parameters as dynamical degrees of freedom. The astrophysical\nmotivation for choosing such fields is the possibility to constitute a\nreasonable model for a real situation occurring in these objects' vicinity. To\ntest the role of large-scale magnetic fields in accretion processes, we start\nby analyzing different bound orbits of timelike orbits under the influence of\nthe system's different parameters. In particular, studying the influence of the\nstrength of the magnetic field. This leads to the examine their stability\nconcerning radial and/or vertical oscillations. The final goal is to discuss\nthe oscillation modes' resonant phenomena using different resonant models for\ndisk-oscillation modes.\n"}
+{"abstract": "  The capacity of multiple-input multiple-output additive white Gaussian noise\nchannels is investigated under peak amplitude constraints on the norm of the\ninput vector. New insights on the capacity-achieving input distribution are\npresented. Furthermore, it is provided an iterative algorithm to numerically\nevaluate both the information capacity and the optimal input distribution of\nsuch channel.\n"}
+{"abstract": "  We consider the problems of sampling and counting edges from a graph on $n$\nvertices where our basic access is via uniformly sampled vertices. When we have\na vertex, we can see its degree, and access its neighbors. Eden and Rosenbaum\n[SOSA 2018] have shown it is possible to sample an edge $\\epsilon$-uniformly in\n$O(\\sqrt{1/\\epsilon}\\frac{n}{\\sqrt{m}})$ vertex accesses. Here, we get down to\nexpected $O(\\log(1/\\epsilon)\\frac{n}{\\sqrt{m}})$ vertex accesses. Next, we\nconsider the problem of sampling $s>1$ edges. For this we introduce a model\nthat we call hash-based neighbor access. We show that, w.h.p, we can sample $s$\nedges exactly uniformly at random, with or without replacement, in\n$\\tilde{O}(\\sqrt{s} \\frac{n}{\\sqrt{m}} + s)$ vertex accesses. We present a\nmatching lower bound of $\\Omega(\\sqrt{s} \\frac{n}{\\sqrt{m}} + s)$ which holds\nfor $\\epsilon$-uniform edge multi-sampling with some constant $\\epsilon>0$ even\nthough our positive result has $\\epsilon=0$.\n  We then give an algorithm for edge counting. W.h.p., we count the number of\nedges to within error $\\epsilon$ in time $\\tilde{O}(\\frac{n}{\\epsilon\\sqrt{m}}\n+ \\frac{1}{\\epsilon^2})$. When $\\epsilon$ is not too small (for $\\epsilon \\geq\n\\frac{\\sqrt m}{n}$), we present a near-matching lower-bound of\n$\\Omega(\\frac{n}{\\epsilon \\sqrt{m}})$. In the same range, the previous best\nupper and lower bounds were polynomially worse in $\\epsilon$.\n  Finally, we give an algorithm that instead of hash-based neighbor access uses\nthe more standard pair queries (``are vertices $u$ and $v$ adjacent''). W.h.p.\nit returns $1+\\epsilon$ approximation of the number of edges and runs in\nexpected time $\\tilde{O}(\\frac{n}{\\epsilon \\sqrt{m}} + \\frac{1}{\\epsilon^4})$.\nThis matches our lower bound when $\\epsilon$ is not too small, specifically for\n$\\epsilon \\geq \\frac{m^{1/6}}{n^{1/3}}$.\n"}
+{"abstract": "  In this paper, we build on the work from our previous paper\n(arXiv:2002.01556) to show that periodic rational $G$-equivariant topological\n$K$-theory has a unique genuine-commutative ring structure for $G$ a finite\nabelian group. This means that every genuine-commutative ring spectrum whose\nhomotopy groups are those of $KU_{\\mathbb{Q},G}$ is weakly equivalent, as a\ngenuine-commutative ring spectrum, to $KU_{\\mathbb{Q},G}$. In contrast, the\nconnective rational equivariant $K$-theory spectrum does not have this type of\nuniqueness of genuine-commutative ring structure.\n"}
+{"abstract": "  We establish new Liouville-type theorems for the two-dimensional stationary\nmagneto-hydrodynamic incompressible system assuming that the velocity and\nmagnetic field have bounded Dirichlet integral. The key tool in our proof is\nobserving that the stream function associated to the magnetic field satisfies a\nsimple drift-diffusion equation for which a maximum principle is available.\n"}
+{"abstract": "  Although much progress has been made recently in 3D face reconstruction, most\nprevious work has been devoted to predicting accurate and fine-grained 3D\nshapes. In contrast, relatively little work has focused on generating\nhigh-fidelity face textures. Compared with the prosperity of photo-realistic 2D\nface image generation, high-fidelity 3D face texture generation has yet to be\nstudied. In this paper, we proposed a novel UV map generation model that\npredicts the UV map from a single face image. The model consists of a UV\nsampler and a UV generator. By selectively sampling the input face image's\npixels and adjusting their relative locations, the UV sampler generates an\nincomplete UV map that could faithfully reconstruct the original face. Missing\ntextures in the incomplete UV map are further full-filled by the UV generator.\nThe training is based on pseudo ground truth blended by the 3DMM texture and\nthe input face texture, thus weakly supervised. To deal with the artifacts in\nthe imperfect pseudo UV map, multiple partial UV map discriminators are\nleveraged.\n"}
+{"abstract": "  This paper develops a new method for identifying and estimating production\nfunctions with partially latent inputs. Such data structures arise naturally\nwhen data are collected using an \"input-based sampling\" strategy, e.g., if the\nsampling unit is one of multiple labor input factors. We show that the latent\ninputs can be nonparametrically identified, if they are strictly monotone\nfunctions of a scalar shock a la Olley & Pakes (1996). With the latent inputs\nidentified, semiparametric estimation of the production function proceeds\nwithin an IV framework that accounts for the endogeneity of the covariates. We\nillustrate the usefulness of our method using two applications. The first\nfocuses on pharmacies: we find that production function differences between\nchains and independent pharmacies may partially explain the observed\ntransformation of the industry structure. Our second application investigates\nskill production functions and illustrates important differences in child\ninvestments between married and divorced couples.\n"}
+{"abstract": "  Atom-in-jellium calculations of the Einstein frequency in condensed matter\nand of the equation of state were used to predict the variation of shear\nmodulus from zero pressure to ~$10^7$ g/cm$^3$, for several elements relevant\nto white dwarf (WD) stars and other self-gravitating systems. This is by far\nthe widest range reported electronic structure calculation of shear modulus,\nspanning from ambient through the one-component plasma to extreme relativistic\nconditions. The predictions were based on a relationship between Debye\ntemperature and shear modulus which we assess to be accurate at the o(10%)\nlevel, and is the first known use of atom-in-jellium theory to calculate a\nshear modulus. We assessed the overall accuracy of the method by comparing with\nexperimental measurements and more detailed electronic structure calculations\nat lower pressures.\n"}
+{"abstract": "  This work presents a non-intrusive surrogate modeling scheme based on machine\nlearning technology for predictive modeling of complex systems, described by\nparametrized time-dependent PDEs. For these problems, typical finite element\napproaches involve the spatiotemporal discretization of the PDE and the\nsolution of the corresponding linear system of equations at each time step.\nInstead, the proposed method utilizes a convolutional autoencoder in\nconjunction with a feed forward neural network to establish a low-cost and\naccurate mapping from the problem's parametric space to its solution space. For\nthis purpose, time history response data are collected by solving the\nhigh-fidelity model via FEM for a reduced set of parameter values. Then, by\napplying the convolutional autoencoder to this data set, a low-dimensional\nrepresentation of the high-dimensional solution matrices is provided by the\nencoder, while the reconstruction map is obtained by the decoder. Using the\nlatent representation given by the encoder, a feed-forward neural network is\nefficiently trained to map points from the problem's parametric space to the\ncompressed version of the respective solution matrices. This way, the encoded\nresponse of the system at new parameter values is given by the neural network,\nwhile the entire response is delivered by the decoder. This approach\neffectively bypasses the need to serially formulate and solve the system's\ngoverning equations at each time increment, thus resulting in a significant\ncost reduction and rendering the method ideal for problems requiring repeated\nmodel evaluations or 'real-time' computations. The elaborated methodology is\ndemonstrated on the stochastic analysis of time-dependent PDEs solved with the\nMonte Carlo method, however, it can be straightforwardly applied to other\nsimilar-type problems, such as sensitivity analysis, design optimization, etc.\n"}
+{"abstract": "  With the success of pre-trained language models in recent years, more and\nmore researchers focus on opening the \"black box\" of these models. Following\nthis interest, we carry out a qualitative and quantitative analysis of\nconstituency grammar in attention heads of BERT and RoBERTa. We employ the\nsyntactic distance method to extract implicit constituency grammar from the\nattention weights of each head. Our results show that there exist heads that\ncan induce some grammar types much better than baselines, suggesting that some\nheads act as a proxy for constituency grammar. We also analyze how attention\nheads' constituency grammar inducing (CGI) ability changes after fine-tuning\nwith two kinds of tasks, including sentence meaning similarity (SMS) tasks and\nnatural language inference (NLI) tasks. Our results suggest that SMS tasks\ndecrease the average CGI ability of upper layers, while NLI tasks increase it.\nLastly, we investigate the connections between CGI ability and natural language\nunderstanding ability on QQP and MNLI tasks.\n"}
+{"abstract": "  Image recovery from compressive measurements requires a signal prior for the\nimages being reconstructed. Recent work has explored the use of deep generative\nmodels with low latent dimension as signal priors for such problems. However,\ntheir recovery performance is limited by high representation error. We\nintroduce a method for achieving low representation error using generators as\nsignal priors. Using a pre-trained generator, we remove one or more initial\nblocks at test time and optimize over the new, higher-dimensional latent space\nto recover a target image. Experiments demonstrate significantly improved\nreconstruction quality for a variety of network architectures. This approach\nalso works well for out-of-training-distribution images and is competitive with\nother state-of-the-art methods. Our experiments show that test-time\narchitectural modifications can greatly improve the recovery quality of\ngenerator signal priors for compressed sensing.\n"}
+{"abstract": "  Consider a nonlinear wave equation for a massless scalar field with\nself-interaction in the spatially flat Friedmann-Lema\\^{i}tre-Robertson-Walker\nspacetimes. We treat the so-called heatlike case where the critical exponent is\naffected by the Fujita exponent. We show upper bounds of the lifespan of\nblow-up solutions by distinguishing subcritical and critical cases.\n"}
+{"abstract": "  In certain instances an electric network transforms in natural ways by the\naddition or removal of an edge. This can have interesting consequences for\nrandom walks, in light of the known relationships between electric resistance\nand random walks. We exhibit several instances in which this can be used to\nprove facts or simplify calculations. In particular, a new proof is given for\nthe formula for the expected return time of a random walk on a graph. We also\nshow how hitting times can be calculated in certain instances when a network\ndiffers from a highly symmetric one by one edge.\n"}
+{"abstract": "  It has been shown that placing an obstacle in front of an exit door has\nproven to be a successful method to improve pedestrian evacuations. In this\nwork, we will focus on the space limited by the exit and the obstacles (i.e.\nthe vestibule structure). We analyzed two different types of vestibules: the\ntwo-entry vestibule (which consists of a single panel-like obstacle) and the\nthree-entry vestibule (which consists of two panel-like obstacles). In the\nformer, we studied the effects of varying the walls' friction coefficient\n$\\kappa_w$ and the distance from the obstacle to the exit door $d$. In the\nlatter, we varied the space between the two panels (gap). We found that the\nthree above mentioned parameters control the vestibule's density, which\nsubsequently affects the evacuation flow (fundamental diagram). We have also\nfound that reducing the distance $d$ or increasing the friction facilitates the\nformation of blocking clusters at the vestibule entries, and hence, diminishes\nthe density. If the density is too large or too low, the evacuation flow is\nsuboptimal, whereas if the density is around $2 \\pm 2$, the flow is maximized.\nOur most important result is that the density (and therefore the evacuation\nflow) can be precisely controlled by $\\kappa_w$, $d$, and the gap. Moreover,\nthe three-entry vestibule produced the highest evacuation flow for specific\nconfigurations of the gap and the distance from the panels to the door.\n"}
+{"abstract": "  We study 10 C/C++ projects that have been using a static analysis security\ntesting tool. We analyze the historical scan reports generated by the tool and\nstudy how frequently memory-related alerts appeared. We also studied the\nsubsequent developer action on those alerts. We also look at the CVEs published\nfor these projects within the study timeline and investigate how many of them\nare memory related. Moreover, for one of this project, Linux, we investigate if\nthe involved flaws in the CVE were identified by the studied security tool when\nthey were first introduced in the code. We found memory related alerts to be\nfrequently detected during static analysis security testing. However, based on\nhow actively the project developers are monitoring the tool alerts, these\nerrors can take years to get fixed. For the ten studied projects, we found a\nmedian lifespan of 77 days before memory alerts get fixed. We also find that\naround 40% of the published CVEs for the studied C/C++ projects are related to\nmemory. These memory CVEs have higher CVSS severity ratings and likelihood of\nhaving an exploit script public than non-memory CVEs. We also found only 2.5%\nLinux CVEs were possibly detected during static analysis security testing.\n"}
+{"abstract": "  I analyse the extension of the Ryu Takayanagi formula from the perspective of\nthe universal coefficient theorem and cohomology with different coefficients.\nLooking at the axioms of cohomology, the so called Eilenberg-Steenrod axioms,\nthe third axiom states that the cohomology of a point is trivial in all orders.\nIf this axiom is abandoned the point may have additional structure. This\nphenomenon is not unusual in physics, a simple example for that being the spin\nstructure or even the point particle to string transition. I studied the\ntransition between different coefficients in cohomology and hence between\ndifferent structures of the geometrical point by means of the universal\ncoefficient theorem. The results may have impact on recent island calculations\nof the black hole radiation entropy and the Page curve as well as on a\npotential mapping of higher genus diagrams to genus one diagrams in the\ntopological expansion of string theory.\n"}
+{"abstract": "  In this work we study the issue of geodesic extendibility on complete and\nlocally compact metric length spaces. We focus on the geometric structure of\nthe space $(\\Sigma (X),d_H)$ of compact balls endowed with the Hausdorff\ndistance and give an explicit isometry between $(\\Sigma (X),d_H)$ and the\nclosed half-space $ X\\times \\mathbb{R}_{\\ge 0}$ endowed with a taxicab metric.\nAmong the applications we establish a group isometry between $\\mbox{Iso} (X,d)$\nand $\\mbox{Iso} (\\Sigma (X),d_H)$ when $(X,d)$ is a Hadamard space.\n"}
+{"abstract": "  In this paper, we characterize the extremal digraphs with the maximal or\nminimal $\\alpha$-spectral radius among some digraph classes such as rose\ndigraphs, generalized theta digraphs and tri-ring digraphs with given size $m$.\nThese digraph classes are denoted by $\\mathcal{R}_{m}^k$,\n$\\widetilde{\\boldsymbol{\\Theta}}_k(m)$ and $\\INF(m)$ respectively. The main\nresults about spectral extremal digraph by Guo and Liu in \\cite{MR2954483} and\nLi and Wang in \\cite{MR3777498} are generalized to $\\alpha$-spectral graph\ntheory. As a by-product of our main results, an open problem in\n\\cite{MR3777498} is answered.\n  Furthermore, we determine the digraphs with the first three minimal\n$\\alpha$-spectral radius among all strongly connected digraphs. Meanwhile, we\ndetermine the unique digraph with the fourth minimal $\\alpha$-spectral radius\namong all strongly connected digraphs for $0\\le \\alpha \\le \\frac{1}{2}$.\n"}
+{"abstract": "  Physics-Informed Neural Networks (PINNs) have recently shown great promise as\na way of incorporating physics-based domain knowledge, including fundamental\ngoverning equations, into neural network models for many complex engineering\nsystems. They have been particularly effective in the area of inverse problems,\nwhere boundary conditions may be ill-defined, and data-absent scenarios, where\ntypical supervised learning approaches will fail. Here, we further explore the\nuse of this modeling methodology to surrogate modeling of a fluid dynamical\nsystem, and demonstrate additional undiscussed and interesting advantages of\nsuch a modeling methodology over conventional data-driven approaches: 1)\nimproving the model's predictive performance even with incomplete description\nof the underlying physics; 2) improving the robustness of the model to noise in\nthe dataset; 3) reduced effort to convergence during optimization for a new,\npreviously unseen scenario by transfer optimization of a pre-existing model.\nHence, we noticed the inclusion of a physics-based regularization term can\nsubstantially improve the equivalent data-driven surrogate model in many\nsubstantive ways, including an order of magnitude improvement in test error\nwhen the dataset is very noisy, and a 2-3x improvement when only partial\nphysics is included. In addition, we propose a novel transfer optimization\nscheme for use in such surrogate modeling scenarios and demonstrate an\napproximately 3x improvement in speed to convergence and an order of magnitude\nimprovement in predictive performance over conventional Xavier initialization\nfor training of new scenarios.\n"}
+{"abstract": "  An equation is derived for the energy of a small disturbance in a system that\nis generated by a distribution function (DF) of the form $f(\\vJ)$ -- most\ngalaxies and star clusters can be closely approximated by such a DF. The theory\nof van Kampen modes is extended to such general systems. An inner product on\nthe space of DFs is defined such that the energy of a disturbance is its norm\nunder this product. It is shown that van Kampen modes that differ in frequency\nare then orthogonal, with the consequence that the energies of van Kampen modes\nare additive. Consequently, most of the insight into the dynamics of ergodic\nsystems that was gained in a recent paper on the van Kampen modes of ergodic\nsystems applies to real clusters and galaxies.\n"}
+{"abstract": "  We present self-supervised geometric perception (SGP), the first general\nframework to learn a feature descriptor for correspondence matching without any\nground-truth geometric model labels (e.g., camera poses, rigid\ntransformations). Our first contribution is to formulate geometric perception\nas an optimization problem that jointly optimizes the feature descriptor and\nthe geometric models given a large corpus of visual measurements (e.g., images,\npoint clouds). Under this optimization formulation, we show that two important\nstreams of research in vision, namely robust model fitting and deep feature\nlearning, correspond to optimizing one block of the unknown variables while\nfixing the other block. This analysis naturally leads to our second\ncontribution -- the SGP algorithm that performs alternating minimization to\nsolve the joint optimization. SGP iteratively executes two meta-algorithms: a\nteacher that performs robust model fitting given learned features to generate\ngeometric pseudo-labels, and a student that performs deep feature learning\nunder noisy supervision of the pseudo-labels. As a third contribution, we apply\nSGP to two perception problems on large-scale real datasets, namely relative\ncamera pose estimation on MegaDepth and point cloud registration on 3DMatch. We\ndemonstrate that SGP achieves state-of-the-art performance that is on-par or\nsuperior to the supervised oracles trained using ground-truth labels.\n"}
+{"abstract": "  While large-scale pretrained language models have significantly improved\nwriting assistance functionalities such as autocomplete, more complex and\ncontrollable writing assistants have yet to be explored. We leverage advances\nin language modeling to build an interactive writing assistant that generates\nand rephrases text according to fine-grained author specifications. Users\nprovide input to our Intent-Guided Assistant (IGA) in the form of text\ninterspersed with tags that correspond to specific rhetorical directives (e.g.,\nadding description or contrast, or rephrasing a particular sentence). We\nfine-tune a language model on a dataset heuristically-labeled with author\nintent, which allows IGA to fill in these tags with generated text that users\ncan subsequently edit to their liking. A series of automatic and crowdsourced\nevaluations confirm the quality of IGA's generated outputs, while a small-scale\nuser study demonstrates author preference for IGA over baseline methods in a\ncreative writing task. We release our dataset, code, and demo to spur further\nresearch into AI-assisted writing.\n"}
+{"abstract": "  This thesis work aims to study the possibility of energy production on\nMartian soil and, in particular, to establish what might be an optimal\nconfiguration for an energy system. This goal has been contextualized in the\nwill to feed a scientific base, based the concept of \"Mars Direct\" (Robert\nZubrin, 1990). This habitat has been recreated in its thermal features, in\norder to perform an analysis of the heat loss over a Martian year (1,88\nterrestrial years). As part of this analysis, two possible scenarios have been\nstudied: clear sky with medium solar radiation (\"sun season\") and sand storm\nseason (\"storm season\"). Subsequently, a basic life support system have been\nsimulated thanks to Aspen PLUS. Using the results of the thermal analysis, it\nhas been possible to obtain a thermal and electrical demand profile for the\nHab. After identifying every possible energy source (solar, wind, nuclear, fuel\ncells, rtg), a calculation on Excel has been set with the purpose of finding\none of the configurations with the lowest possible mass and pave the way for a\nfurther, more rigorous, optimization. It is indeed clear that shipping 1\nkilogram to Mars has a cost of hundreds of thousand of dollars.\n"}
+{"abstract": "  We report the systematic analysis of knots, hotspots, and lobes in 57 active\ngalactic nuclei (AGNs) to investigate the variation of the magnetic field along\nwith the jet from the sub-pc base to the terminus in kpc-to-Mpc scales.\nExpanding the number of radio/X-ray samples in Kataoka & Stawarz (2005), we\nanalyzed the data in 12 FR I and 30 FR II radio galaxies, 12 quasars, and 3 BL\nLacs that contained 76 knots, 42 hotspots, and 29 radio lobes. We first derived\nthe equipartition magnetic fields in the cores and then estimated those in\nvarious jet components by assuming $B_{\\rm est}$ $\\propto$ $d^{-1}$, where $d$\nis the distance from the jet base. On the other hand, the magnetic field in\nlarge-scale jets (knots, hotspots, and lobes), $B_{\\rm eq}$, can be estimated\nfrom the observed flux and spatial extent under the equipartition hypothesis.\nWe show that the magnetic field decreases as the distance along the jet\nincreases, but generally gentler than $\\propto d^{-1}$. The increase in $B_{\\rm\neq}/B_{\\rm est}$ at a larger $d$ may suggest the deceleration of the jet around\nthe downstream, but there is no difference between FR I and FR II jets.\nMoreover, the magnetic fields in the hotspots are systematically larger than\nthose of knots and lobes. Finally, we applied the same analysis to knots and\nlobes in Centaurus A to check whether the above discussion will hold even in a\nsingle jet source.\n"}
+{"abstract": "  Federated learning (FL) is a promising approach to distributed compute, as\nwell as distributed data, and provides a level of privacy and compliance to\nlegal frameworks. This makes FL attractive for both consumer and healthcare\napplications. While the area is actively being explored, few studies have\nexamined FL in the context of larger language models and there is a lack of\ncomprehensive reviews of robustness across tasks, architectures, numbers of\nclients, and other relevant factors. In this paper, we explore the fine-tuning\nof Transformer-based language models in a federated learning setting. We\nevaluate three popular BERT-variants of different sizes (BERT, ALBERT, and\nDistilBERT) on a number of text classification tasks such as sentiment analysis\nand author identification. We perform an extensive sweep over the number of\nclients, ranging up to 32, to evaluate the impact of distributed compute on\ntask performance in the federated averaging setting. While our findings suggest\nthat the large sizes of the evaluated models are not generally prohibitive to\nfederated training, we found that the different models handle federated\naveraging to a varying degree. Most notably, DistilBERT converges significantly\nslower with larger numbers of clients, and under some circumstances, even\ncollapses to chance level performance. Investigating this issue presents an\ninteresting perspective for future research.\n"}
+{"abstract": "  We investigate theoretically the Bose-Hubbard version of the celebrated\nSu-Schrieffer-Heeger topological model, which essentially describes a\none-dimensional dimerized array of coupled oscillators with on-site\ninteractions. We study the physics arising from the whole gamut of possible\ndimerizations of the chain, including both the weakly and the strongly\ndimerized limiting cases. Focusing on two-excitation subspace, we\nsystematically uncover and characterize the different types of states which may\nemerge due to the competition between the inter-oscillator couplings, the\nintrinsic topology of the lattice, and the strength of the on-site\ninteractions. In particular, we discuss the formation of scattering bands full\nof extended states, bound bands full of two-particle pairs (including so-called\n`doublons', when the pair occupies the same lattice site), and different\nflavors of topological edge states. The features we describe may be realized in\na plethora of systems, including nanoscale architectures such as photonic\ncavities and optical lattices, and provide perspectives for topological\nmany-body physics.\n"}
+{"abstract": "  Periodic quasars have been suggested to host supermassive binary black holes\n(BBHs) in their centers, and their optical/UV periodicities are interpreted as\ncaused by either the Doppler-boosting (DB) effect of continuum emission from\nthe disk around the secondary black hole (BH) or intrinsic accretion rate\nvariation. However, no other definitive evidence has been found to confirm such\na BBH interpretation(s). In this paper, we investigate the responses of broad\nemission lines (BELs) to the continuum variations for these quasars under two\nBBH scenarios, and check whether they can be distinguished from each other and\nfrom that of a single BH system. We assume a simple circumbinary broad-line\nregion (BLR) model, compatible with BLR size estimates, with a standard\n$\\Gamma$ distribution of BLR clouds. We find that BELs may change significantly\nand periodically under the BBH scenarios due to (1) the position variation of\nthe secondary BH and (2) the DB effect, if significant, and/or intrinsic\nvariation, which is significantly different from the case of a single BH\nsystem. For the two BBH scenarios, the responses of BELs to (apparent)\ncontinuum variations, caused by the DB effect or intrinsic rate variation, are\nalso significantly different from each other, mainly because the DB effect has\na preferred direction along the direction of motion of the secondary BH, while\nthat due to intrinsic variation does not. Such differences in the responses of\nBELs from different scenarios may offer a robust way to distinguish different\ninterpretations of periodic quasars and to identify BBHs, if any, in these\nsystems.\n"}
+{"abstract": "  Due to the resource consumption for transmitting massive data and the concern\nfor exposing sensitive data, it is impossible or undesirable to upload clients'\nlocal databases to a central server. Thus, federated learning has become a hot\nresearch area in enabling the collaborative training of machine learning models\namong multiple clients that hold sensitive local data. Nevertheless,\nunconstrained federated optimization has been studied mainly using stochastic\ngradient descent (SGD), which may converge slowly, and constrained federated\noptimization, which is more challenging, has not been investigated so far. This\npaper investigates sample-based and feature-based federated optimization,\nrespectively, and considers both the unconstrained problem and the constrained\nproblem for each of them. We propose federated learning algorithms using\nstochastic successive convex approximation (SSCA) and mini-batch techniques. We\nshow that the proposed algorithms can preserve data privacy through the model\naggregation mechanism, and their security can be enhanced via additional\nprivacy mechanisms. We also show that the proposed algorithms converge to\nKarush-Kuhn-Tucker (KKT) points of the respective federated optimization\nproblems. Besides, we customize the proposed algorithms to application examples\nand show that all updates have closed-form expressions. Finally, numerical\nexperiments demonstrate the inherent advantages of the proposed algorithms in\nconvergence speeds, communication costs, and model specifications.\n"}
+{"abstract": "  We propose the first approach to automatically and jointly synthesize both\nthe synchronous 3D conversational body and hand gestures, as well as 3D face\nand head animations, of a virtual character from speech input. Our algorithm\nuses a CNN architecture that leverages the inherent correlation between facial\nexpression and hand gestures. Synthesis of conversational body gestures is a\nmulti-modal problem since many similar gestures can plausibly accompany the\nsame input speech. To synthesize plausible body gestures in this setting, we\ntrain a Generative Adversarial Network (GAN) based model that measures the\nplausibility of the generated sequences of 3D body motion when paired with the\ninput audio features. We also contribute a new way to create a large corpus of\nmore than 33 hours of annotated body, hand, and face data from in-the-wild\nvideos of talking people. To this end, we apply state-of-the-art monocular\napproaches for 3D body and hand pose estimation as well as dense 3D face\nperformance capture to the video corpus. In this way, we can train on orders of\nmagnitude more data than previous algorithms that resort to complex in-studio\nmotion capture solutions, and thereby train more expressive synthesis\nalgorithms. Our experiments and user study show the state-of-the-art quality of\nour speech-synthesized full 3D character animations.\n"}
+{"abstract": "  Er3+:Y2SiO5 is a material of particular interest due to its compatibility in\nrealizing telecom-band optical quantum memories and in the implementation of\nquantum transducers interfacing optical communication with quantum computers\nworking in the microwave regime. Extending the coherence lifetimes of the\nelectron spins and the nuclear spins is the essential prerequisite for\nimplementing efficient quantum information processing. The electron spin\ncoherence time of this material is so far limited to several microseconds, and\nthere are significant challenges in optimizing coherence lifetimes\nsimultaneously for both the electron and nuclear spins. Here we perform to our\nknowledge the first pulsed-ENDOR (Electron Nuclear DOuble Resonance)\ninvestigation for an Er3+-doped material at sub-Kelvin temperatures, based on a\nhome-built sub-Kelvin pulsed ENDOR spectrometer. At the lowest working\ntemperature, the electron spin coherence time reaches 273 us, which is enhanced\nby more than 40 times compared with the previous results. In the sub-Kelvin\nregime, a rapid increase in the nuclear spin coherence time is observed, and\nthe longest coherence time of 738 us is obtained. These results are obtained\nwith the compatibility of fast and efficient operations, which establish the\nfoundation for quantum storage and quantum transduction from microwave to\noptical frequencies at telecom C-band.\n"}
+{"abstract": "  The Gini index is a popular inequality measure with many applications in\nsocial and economic studies. This paper studies semiparametric inference on the\nGini indices of two semicontinuous populations. We characterize the\ndistribution of each semicontinuous population by a mixture of a discrete point\nmass at zero and a continuous skewed positive component. A semiparametric\ndensity ratio model is then employed to link the positive components of the two\ndistributions. We propose the maximum empirical likelihood estimators of the\ntwo Gini indices and their difference, and further investigate the asymptotic\nproperties of the proposed estimators. The asymptotic results enable us to\nconstruct confidence intervals and perform hypothesis tests for the two Gini\nindices and their difference. We show that the proposed estimators are more\nefficient than the existing fully nonparametric estimators. The proposed\nestimators and the asymptotic results are also applicable to cases without\nexcessive zero values. Simulation studies show the superiority of our proposed\nmethod over existing methods. Two real-data applications are presented using\nthe proposed methods.\n"}
+{"abstract": "  The advent of AI and ML algorithms has led to opportunities as well as\nchallenges. In this paper, we provide an overview of bias and fairness issues\nthat arise with the use of ML algorithms. We describe the types and sources of\ndata bias, and discuss the nature of algorithmic unfairness. This is followed\nby a review of fairness metrics in the literature, discussion of their\nlimitations, and a description of de-biasing (or mitigation) techniques in the\nmodel life cycle.\n"}
+{"abstract": "  Aims. To explore the chemical pattern of early-type stars with planets,\nsearching for a possible signature of planet formation. In particular, we study\na likely relation between the lambda Bootis chemical pattern and the presence\nof giant planets. Methods. We performed a detailed abundance determination in a\nsample of early-type stars with and without planets via spectral synthesis.\nResults. We compared the chemical pattern of the stars in our sample (13 stars\nwith planets and 24 stars without detected planets) with those of lambda Bootis\nand other chemically peculiar stars. We have found four lambda Bootis stars in\nour sample, two of which present planets and circumstellar disks (HR 8799 and\nHD 169142) and one without planets detected (HD 110058). We have also\nidentified the first lambda Bootis star orbited by a brown dwarf (zeta Del).\nThis interesting pair lambda Bootis star + brown dwarf could help to test\nstellar formation scenarios. We found no unique chemical pattern for the group\nof early-type stars bearing giant planets. However, our results support, in\nprinciple, a suggested scenario in which giant planets orbiting\npre-main-sequence stars possibly block the dust of the disk and result in a\nlambda Bootis-like pattern. On the other hand, we do not find a lambda Bootis\npattern in different hot-Jupiter planet host stars, which do not support the\nidea of possible accretion from the winds of hot-Jupiters, recently proposed in\nthe literature. Then, other mechanisms should account for the presence of the\nlambda Bootis pattern between main-sequence stars. Finally, we suggest that the\nformation of planets around lambda Bootis stars such as HR 8799 and HD 169142\nis also possible through the core accretion process and not only gravitational\ninstability [abridged]\n"}
+{"abstract": "  The quest for Kitaev quantum spin liquids has led to great interest in\nhoneycomb quantum magnets with strong spin-orbit coupling. It has been recently\nproposed that even Mott insulators with $3d$ transition metal ions, having\nnominally weak spin-orbit coupling, can realize such exotic physics. Motivated\nby this, we study the rhombohedral honeycomb cobaltates CoTiO$_3$,\nBaCo$_2$(PO$_4$)$_2$, and BaCo$_2$(AsO$_4$)$_2$, using $\\textit{ab initio}$\ndensity functional theory, which takes into account realistic crystal field\ndistortions and chemical information, in conjunction with exact diagonalization\nnumerics. We show that these Co$^{2+}$ magnets host $j_\\text{eff}=1/2$ local\nmoments with highly anisotropic $g$-factors, and we extract their full spin\nHamiltonians including longer-range and anisotropic exchange couplings. For\nCoTiO$_3$, we find a nearest-neighbor easy-plane ferromagnetic $XXZ$ model with\nadditional bond-dependent anisotropies and interlayer exchange, which supports\nthree-dimensional (3D) Dirac nodal line magnons. In contrast, for\nBaCo$_2$(PO$_4$)$_2$ and BaCo$_2$(AsO$_4$)$_2$, we find a strongly suppressed\ninterlayer coupling, and significant frustration from additional third-neighbor\nantiferromagnetic exchange mediated by P/As. Such bond-anisotropic $J_1$-$J_3$\nspin models can support collinear zig-zag or coplanar spiral ground states; we\ndiscuss their dynamical spin correlations which reveal a gapped Goldstone mode,\nand argue that the effective parameters of these pseudospin-$1/2$ models may be\nstrongly renormalized by coupling to a low energy spin-exciton. Our results\ncall for re-examining proposals for realizing Kitaev spin liquids in the\nhoneycomb cobaltates.\n"}
+{"abstract": "  Recently a number of empirical \"universal\" scaling law papers have been\npublished, most notably by OpenAI. `Scaling laws' refers to power-law decreases\nof training or test error w.r.t. more data, larger neural networks, and/or more\ncompute. In this work we focus on scaling w.r.t. data size $n$. Theoretical\nunderstanding of this phenomenon is largely lacking, except in\nfinite-dimensional models for which error typically decreases with $n^{-1/2}$\nor $n^{-1}$, where $n$ is the sample size. We develop and theoretically analyse\nthe simplest possible (toy) model that can exhibit $n^{-\\beta}$ learning curves\nfor arbitrary power $\\beta>0$, and determine whether power laws are universal\nor depend on the data distribution.\n"}
+{"abstract": "  Inland waterway transportation network significantly supports the overall\nfreight transportation of the nation. In order to ensure efficient and timely\ncommodity transportation through this network, this study aims at developing a\nreliable inland waterway transportation network considering the interactions\nbetween different transportation entities along with considering the uncertain\ncommodity supply and unpredictable waterway conditions over time. A\ncapacitated, multi-commodity, multi-period, stochastic, two-stage mixed-integer\nlinear programming (MILP) model is proposed to capture this stochastic, time\nvariant behavior of the water-depth along any link of the inland waterway under\nconsideration. Additionally, we proposed a parallelized hybrid decomposition\nalgorithm to solve the real-life test instances of this complex NP-hard\nproblem. The proposed algorithm is capable of producing high quality solutions\nwithin a reasonable amount of time. Further, a case study is demonstrated for\nthe Southeast region of the United States and a number of managerial insights\nare drawn that magnifies the impact of different key input parameters on the\noverall inland waterway transportation network under consideration.\n"}
+{"abstract": "  We prove that a generic holomorphic foliation on a weighted projective plane\nhas no algebraic solutions when the degree is big enough. We also prove an\nanalogous result for foliations on Hirzebruch surfaces.\n"}
+{"abstract": "  We demonstrate theoretically that an off-resonant circularly polarized\nelectromagnetic field can induce a persistent current in carbon nanotubes,\nwhich corresponds to electron rotation about the nanotube axis. As a\nconsequence, the nanotubes acquire magnetic moment along the axis, which\ndepends on their crystal structure and can be detected in state-of-the-art\nmeasurements. This effect and related phenomena are analyzed within the\ndeveloped Floquet theory describing the electronic properties of the nanotubes\nirradiated by the field.\n"}
+{"abstract": "  In the present paper, we study the inflationary phenomenology of a\n$k$-inflation corrected Einstein-Gauss-Bonnet theory. Non-canonical kinetic\nterms are known for producing Jean instabilities or superluminal sound wave\nvelocities in the aforementioned era, but we demonstrate in this work that by\nadding Gauss-Bonnet string corrections and assuming that the non-canonical\nkinetic term $\\omega X^\\gamma$ is in quadratic, one can obtain a ghost free\ndescription. Demanding compatibility with the recent GW170817 event forces one\nto accept that the relation $\\ddot\\xi=H\\dot\\xi$ for the scalar coupling\nfunction $\\xi (\\phi)$. As a result, the scalar functions of the theory are\nrevealed to be interconnected and by assuming a specific form for one of them,\nspecifies immediately the other. Here, we shall assume that the scalar\npotential is directly derivable from the equations of motion, once the\nGauss-Bonnet coupling is appropriately chosen, but obviously the opposite is\nfeasible as well. As a result, each term entering the equations of motion, can\nbe written in terms of the scalar field and a relatively tractable\nphenomenology is produced. For quadratic kinetic terms, the resulting scalar\npotential is quite elegant functionally. Different exponents, which lead to\neither a more perplexed solution for the scalar potential, are still a\npossibility which was not further studied. We also discuss in brief the\nnon-Gaussianities issue under the slow-roll and constant-roll conditions\nholding true, and we demonstrate that the predicted amount of non-Gaussianities\nis significantly enhanced in comparison to the $k$-inflation free\nEinstein-Gauss-Bonnet theory.\n"}
+{"abstract": "  In the hydrodynamic model description of heavy ion collisions, the\nfinal-state anisotropic flow $v_n$ are linearly related to the strength of the\nmulti-pole shape of the distribution of nucleons in the transverse plane\n$\\varepsilon_n$, $v_n\\propto \\varepsilon_n$. The $\\varepsilon_n$, for\n$n=1,2,3,4$, are sensitive to the shape of the colliding ions, characterized by\nthe quadrupole $\\beta_2$, octupole $\\beta_3$ and hexadecapole $\\beta_4$\ndeformations. This sensitivity is investigated analytically and also in a Monte\nCarlo Glauber model. One observes a robust linear relation,\n$\\langle\\varepsilon_n^2\\rangle = a_n'+b_n'\\beta_n^2$, for events in a fixed\ncentrality. The $\\langle\\varepsilon_1^2\\rangle$ has a contribution from\n$\\beta_3$ and $\\beta_4$, and $\\langle\\varepsilon_3^2\\rangle$ from $\\beta_4$. In\nthe ultra-central collisions, there are little cross contributions between\n$\\beta_2$ and $\\varepsilon_3$ and between $\\beta_3$ and $\\varepsilon_2$, but\nclear cross contributions are present in non-central collisions. Additionally,\n$\\langle\\varepsilon_n^2\\rangle$ are insensitive to non-axial shape parameters\nsuch as the triaxiality. This is good news because the measurements of $v_2$,\n$v_3$ and $v_4$ can be used to constrain simultaneously the $\\beta_2$,\n$\\beta_3$, and $\\beta_4$ values. This is best done by comparing two colliding\nions with similar mass numbers and therefore nearly identical $a_n'$, to obtain\nsimple equation that relates the $\\beta_n$ of the two species. This opens up\nthe possibility to map the shape of the atomic nuclei at a timescale\n($<10^{-24}$s) much shorter than probed by low-energy nuclear structure physics\n($<10^{-21}$s), which ultimately may provide information complementary to those\nobtained in the nuclear structure experiments.\n"}
+{"abstract": "  The possibility of creating a UCN source on a periodic pulsed reactor is\nconsidered. It is shown that the implementation of the time focusing principle\nbased on non-stationary neutron diffraction and the idea of the UCN trap pulse\nfilling allow creating a sufficiently intense UCN source in a moderate-power\npulsed reactor.\n"}
+{"abstract": "  Because loops execute their body many times, compiler developers place much\nemphasis on their optimization. Nevertheless, in view of highly diverse source\ncode and hardware, compilers still struggle to produce optimal target code. The\nsheer number of possible loop optimizations, including their combinations,\nexacerbates the problem further. Today's compilers use hard-coded heuristics to\ndecide when, whether, and which of a limited set of optimizations to apply.\nOften, this leads to highly unstable behavior, making the success of compiler\noptimizations dependent on the precise way a loop has been written. This paper\npresents LoopLearner, which addresses the problem of compiler instability by\npredicting which way of writing a loop will lead to efficient compiled code. To\nthis end, we train a neural network to find semantically invariant source-level\ntransformations for loops that help the compiler generate more efficient code.\nOur model learns to extract useful features from the raw source code and\npredicts the speedup that a given transformation is likely to yield. We\nevaluate LoopLearner with 1,895 loops from various performance-relevant\nbenchmarks. Applying the transformations that our model deems most favorable\nprior to compilation yields an average speedup of 1.14x. When trying the top-3\nsuggested transformations, the average speedup even increases to 1.29x.\nComparing the approach with an exhaustive search through all available code\ntransformations shows that LoopLearner helps to identify the most beneficial\ntransformations in several orders of magnitude less time.\n"}
+{"abstract": "  Stateful and stateless web tracking gathered much attention in the last\ndecade, however they were always measured separately. To the best of our\nknowledge, our study is the first to detect and measure cookie respawning with\nbrowser and machine fingerprinting. We develop a detection methodology that\nallows us to detect cookies dependency on browser and machine features. Our\nresults show that 1,150 out of the top 30, 000 Alexa websites deploy this\ntracking mechanism. We further uncover how domains collaborate to respawn\ncookies through fingerprinting. We find out that this technique can be used to\ntrack users across websites even when third-party cookies are deprecated.\nTogether with a legal scholar, we conclude that cookie respawning with browser\nfingerprinting lacks legal interpretation under the GDPR and the ePrivacy\ndirective, but its use in practice may breach them, thus subjecting it to fines\nup to 20 million euro.\n"}
+{"abstract": "  We address the problem of performing backpropagation for computation graphs\ninvolving 3D transformation groups SO(3), SE(3), and Sim(3). 3D transformation\ngroups are widely used in 3D vision and robotics, but they do not form vector\nspaces and instead lie on smooth manifolds. The standard backpropagation\napproach, which embeds 3D transformations in Euclidean spaces, suffers from\nnumerical difficulties. We introduce a new library, which exploits the group\nstructure of 3D transformations and performs backpropagation in the tangent\nspaces of manifolds. We show that our approach is numerically more stable,\neasier to implement, and beneficial to a diverse set of tasks. Our\nplug-and-play PyTorch library is available at\nhttps://github.com/princeton-vl/lietorch.\n"}
+{"abstract": "  Supersingular isogeny graphs are known to have very few loops and\nmulti-edges. We formalize this idea by studying and finding bounds for the\nnumber of loops and multi-edges in such graphs. We also find conditions under\nwhich the supersingular isogeny graph $\\Lambda_p(\\ell)$ is simple. The methods\npresented in this paper can be used to study many kinds of collisions in\nsupersingular isogeny graphs. As an application, we introduce the notion of\nbi-route number for two graphs $\\Lambda_p(\\ell_1),\\Lambda_p(\\ell_2)$ and\ncompute bounds for it. We also study the number of edges in common between the\ngraphs $\\Lambda_p(\\ell_1),\\Lambda_p(\\ell_2)$.\n"}
+{"abstract": "  Unlike the brain, artificial neural networks, including state-of-the-art deep\nneural networks for computer vision, are subject to \"catastrophic forgetting\":\nthey rapidly forget the previous task when trained on a new one. Neuroscience\nsuggests that biological synapses avoid this issue through the process of\nsynaptic consolidation and metaplasticity: the plasticity itself changes upon\nrepeated synaptic events. In this work, we show that this concept of\nmetaplasticity can be transferred to a particular type of deep neural networks,\nbinarized neural networks, to reduce catastrophic forgetting.\n"}
+{"abstract": "  The small silicon chip of Schottky diode (0.8x0.8x0.4 mm3) with planar\narrangement of electrodes (chip PSD) as temperature sensor, which functions\nunder the operating conditions of pressure sensor, was developed. The forward\nI-V characteristic of chip PSD is determined by potential barrier between Mo\nand n-Si (ND = 3x1015 cm-3). Forward voltage UF = 208 mV and temperature\ncoefficient TC = -1.635 mV/C (with linearity kT <0.4% for temperature range of\n-65 to +85 C) at supply current IF = 1 mA is achieved. The reverse I-V\ncharacteristic has high breakdown voltage UBR > 85 V and low leakage current IL\n< 5 {\\mu}A at 25 C and IL < 130 {\\mu}A at 85 C (UR = 20 V) because chip PSD\ncontains the structure of two p-type guard rings along the anode perimeter. The\napplication of PSD chip for wider temperature range from -65 to +115 C is\nproved. The separate chip PSD of temperature sensor located at a distance of\nless than 1.5 mm from the pressure sensor chip. The PSD chip transmits input\ndata for temperature compensation of pressure sensor errors by ASIC and for\ndirect temperature measurement.\n"}
+{"abstract": "  Building up on classical linear formulations, we posit that a broad class of\nproblems in signal synthesis and in signal recovery are reducible to the basic\ntask of finding a point in a closed convex subset of a Hilbert space that\nsatisfies a number of nonlinear equations involving firmly nonexpansive\noperators. We investigate this formalism in the case when, due to inaccurate\nmodeling or perturbations, the nonlinear equations are inconsistent. A relaxed\nformulation of the original problem is proposed in the form of a variational\ninequality. The properties of the relaxed problem are investigated and a\nprovenly convergent block-iterative algorithm, whereby only blocks of the\nunderlying firmly nonexpansive operators are activated at a given iteration, is\ndevised to solve it. Numerical experiments illustrate robust recoveries in\nseveral signal and image processing applications.\n"}
+{"abstract": "  We consider the novel task of learning disentangled representations of object\nshape and appearance across multiple domains (e.g., dogs and cars). The goal is\nto learn a generative model that learns an intermediate distribution, which\nborrows a subset of properties from each domain, enabling the generation of\nimages that did not exist in any domain exclusively. This challenging problem\nrequires an accurate disentanglement of object shape, appearance, and\nbackground from each domain, so that the appearance and shape factors from the\ntwo domains can be interchanged. We augment an existing approach that can\ndisentangle factors within a single domain but struggles to do so across\ndomains. Our key technical contribution is to represent object appearance with\na differentiable histogram of visual features, and to optimize the generator so\nthat two images with the same latent appearance factor but different latent\nshape factors produce similar histograms. On multiple multi-domain datasets, we\ndemonstrate our method leads to accurate and consistent appearance and shape\ntransfer across domains.\n"}
+{"abstract": "  We propose PHASER, a correspondence-free global registration of\nsensor-centric pointclouds that is robust to noise, sparsity, and partial\noverlaps. Our method can seamlessly handle multimodal information and does not\nrely on keypoint nor descriptor preprocessing modules. By exploiting properties\nof Fourier analysis, PHASER operates directly on the sensor's signal, fusing\nthe spectra of multiple channels and computing the 6-DoF transformation based\non correlation. Our registration pipeline starts by finding the most likely\nrotation followed by computing the most likely translation. Both estimates are\ndistributed according to a probability distribution that takes the underlying\nmanifold into account, i.e., a Bingham and Gaussian distribution, respectively.\nThis further allows our approach to consider the periodic-nature of rotations\nand naturally represent its uncertainty. We extensively compare PHASER against\nseveral well-known registration algorithms on both simulated datasets, and\nreal-world data acquired using different sensor configurations. Our results\nshow that PHASER can globally align pointclouds in less than 100ms with an\naverage accuracy of 2cm and 0.5deg, is resilient against noise, and can handle\npartial overlap.\n"}
+{"abstract": "  We develop a fully automatic Bayesian Lasso via variational inference. This\nis a scalable procedure for approximating the posterior distribution. Special\nattention is driven to the knot selection in regression spline. In order to\ncarry through our proposal, a full automatic variational Bayesian Lasso, a\nJefferey's prior is proposed for the hyperparameters and a decision theoretical\napproach is introduced to decide if a knot is selected or not. Extensive\nsimulation studies were developed to ensure the effectiveness of the proposed\nalgorithms. The performance of the algorithms were also tested in some real\ndata sets, including data from the world pandemic Covid-19. Again, the\nalgorithms showed a very good performance in capturing the data structure.\n"}
+{"abstract": "  In this work we revisit the steady state, spherically symmetric gas accretion\nproblem from the non-relativistic regime to the ultra-relativistic one. We\nfirst perform a detailed comparison between the Bondi and Michel models, and\nshow how the mass accretion rate in the Michel solution approaches a constant\nvalue as the fluid temperature increases, whereas the corresponding Bondi value\ncontinually decreases, the difference between these two predicted values\nbecoming arbitrarily large at ultra-relativistic temperatures. Additionally, we\nextend the Michel solution to the case of a fluid with an equation of state\ncorresponding to a monoatomic, relativistic gas. Finally, using general\nrelativistic hydrodynamic simulations, we study spherical accretion onto a\nrotating black hole, exploring the influence of the black hole spin on the mass\naccretion rate, the flow morphology and characteristics, and the sonic surface.\nThe effect of the black hole spin becomes more significant as the gas\ntemperature increases and as the adiabatic index $\\gamma$ stiffens. For an\nideal gas in the ultra-relativistic limit ($\\gamma=4/3$), we find a reduction\nof 10 per cent in the mass accretion rate for a maximally rotating black hole\nas compared to a non-rotating one, while this reduction is of up to 50 per cent\nfor a stiff fluid ($\\gamma=2$).\n"}
+{"abstract": "  K-Means++ and its distributed variant K-Means$\\|$ have become de facto tools\nfor selecting the initial seeds of K-means. While alternatives have been\ndeveloped, the effectiveness, ease of implementation, and theoretical grounding\nof the K-means++ and $\\|$ methods have made them difficult to \"best\" from a\nholistic perspective. By considering the limited opportunities within seed\nselection to perform pruning, we develop specialized triangle inequality\npruning strategies and a dynamic priority queue to show the first acceleration\nof K-Means++ and K-Means$\\|$ that is faster in run-time while being\nalgorithmicly equivalent. For both algorithms we are able to reduce distance\ncomputations by over $500\\times$. For K-means++ this results in up to a\n17$\\times$ speedup in run-time and a $551\\times$ speedup for K-means$\\|$. We\nachieve this with simple, but carefully chosen, modifications to known\ntechniques which makes it easy to integrate our approach into existing\nimplementations of these algorithms.\n"}
+{"abstract": "  We investigate the stellar populations for a sample of 161 massive, mainly\nquiescent galaxies at $\\langle z_{\\rm obs} \\rangle=0.8$ with deep Keck/DEIMOS\nrest-frame optical spectroscopy (HALO7D survey). With the fully Bayesian\nframework Prospector, we simultaneously fit the spectroscopic and photometric\ndata with an advanced physical model (including non-parametric star-formation\nhistories, emission lines, variable dust attenuation law, and dust and AGN\nemission) together with an uncertainty and outlier model. We show that both\nspectroscopy and photometry are needed to break the dust-age-metallicity\ndegeneracy. We find a large diversity of star-formation histories: although the\nmost massive ($M_{\\star}>2\\times10^{11}~M_{\\odot}$) galaxies formed the\nearliest (formation redshift of $z_{\\rm f}\\approx5-10$ with a short\nstar-formation timescale of $\\tau_{\\rm SF}\\lesssim1~\\mathrm{Gyr}$), lower-mass\ngalaxies have a wide range of formation redshifts, leading to only a weak trend\nof $z_{\\rm f}$ with $M_{\\star}$. Interestingly, several low-mass galaxies with\nhave formation redshifts of $z_{\\rm f}\\approx5-8$. Star-forming galaxies evolve\nabout the star-forming main sequence, crossing the ridgeline several times in\ntheir past. Quiescent galaxies show a wide range and continuous distribution of\nquenching timescales ($\\tau_{\\rm quench}\\approx0-5~\\mathrm{Gyr}$) with a median\nof $\\langle\\tau_{\\rm quench}\\rangle=1.0_{-0.9}^{+0.8}~\\mathrm{Gyr}$ and of\nquenching epochs of $z_{\\rm quench}\\approx0.8-5.0$ ($\\langle z_{\\rm\nquench}\\rangle=1.3_{-0.4}^{+0.7}$). This large diversity of quenching\ntimescales and epochs points toward a combination of internal and external\nquenching mechanisms. In our sample, rejuvenation and \"late bloomers\" are\nuncommon. In summary, our analysis supports the \"grow & quench\" framework and\nis consistent with a wide and continuously-populated diversity of quenching\ntimescales.\n"}
+{"abstract": "  We study the quantum phase transitions of frustrated antiferromagnetic\nHeisenberg spin-1 systems on the 3/4 and 3/5 skewed two leg ladder geometries.\nThese systems can be viewed as arising by periodically removing rung bonds from\na zigzag ladder. We find that in large systems, the ground state (gs) of the\n3/4 ladder switches from a singlet to a magnetic state for $J_1 \\ge 1.82$; the\ngs spin corresponds to ferromagnetic alignment of effective $S = 2$ objects on\neach unit cell. The gs of antiferromagnetic exchange Heisenberg spin-1 system\non a 3/5 skewed ladder is highly frustrated and has spiral spin arrangements.\nThe amplitude of the spin density wave in the 3/5 ladder is significantly\nlarger compared to that in the magnetic state of the 3/4 ladder. The gs of the\nsystem switches between singlet state and low spin magnetic states multiple\ntimes on tuning $J_1$ in a finite size system. The switching pattern is\nnonmonotonic as a function of $J_1$, and depends on the system size. It appears\nto be the consequence of higher $J_1$ favoring higher spin magnetic state and\nthe finite system favoring a standing spin wave. For some specific parameter\nvalues, the magnetic gs in the 3/5 system is doubly degenerate in two different\nmirror symmetry subspaces. This degeneracy leads to spontaneous spin parity and\nmirror symmetry breaking giving rise to spin current in the gs of the system.\n"}
+{"abstract": "  A new ternary intermetallic compound $\\mathrm{Pr_2Rh_2Ga}$ was synthesized by\narc-melting and was characterized by powder X-ray diffraction (PXRD),\nmagnetization, heat capacity $\\mathrm{C}_p(\\textit{T})$, and electrical\nresistivity $\\rho(T)$ measurements. PXRD patterns revealed that\n$\\mathrm{Pr_2Rh_2Ga}$ crystallizes in the $\\rm{La_2Ni_3}$-type of orthorhombic\nstructure with the space group $Cmca$. The temperature variation of magnetic\nsusceptibility, $\\mathrm {C}_p(\\textit{T})$ and $\\rho(T)$ confirmed that\n$\\mathrm{Pr_2Rh_2Ga}$ exhibits a ferromagnetic behavior with the transition\ntemperature of 18 K. The estimated Sommerfeld coefficient $\\gamma$ = 640\nmJ/($\\mathrm{Pr.mole.K^2}$) from the $\\mathrm {C}_p(\\textit{T})$ results in the\nparamagnetic region just above $T_{C}$~was large in comparison to ordinary\nmetals. In the paramagnetic region $\\rho(T)$ data showed a metallic behavior\ncharacteristic of electron - phonon scattering. The maximum negative\nmagneto-resistance at high field occurs in the region near the magnetic phase\ntransition temperature. The maximum value of magnetic entropy change ($\\rm\n-\\Delta \\textit{S}_{M}$) and adiabatic temperature change ($\\rm \\Delta\n\\textit{T}_{ad}$) are $\\rm8.2~J/kg.K$ and $\\rm3.6~K$, respectively, around the\ntransition temperature for the change of magnetic field 0-9 T. The calculated\nrefrigerant capacity is $\\rm70~J/kg$, and $\\rm135~J/kg$ for a change of\nmagnetic field 0-5 T and 0-9 T, respectively. Arrott plot derived from\nisothermal magnetization and the universal scaling plot by normalizing $\\rm\n-\\Delta \\textit{S}_{M}$ confirm that the compound undergoes a second order\nferromagnetic to paramagnetic phase transition.\n"}
+{"abstract": "  We remark on the use of regularized Stokeslets in the slender body theory\n(SBT) approximation of Stokes flow about a thin fiber of radius $\\epsilon>0$.\nDenoting the regularization parameter by $\\delta$, we consider regularized SBT\nbased on the most common regularized Stokeslet plus a regularized doublet\ncorrection. Given sufficiently smooth force data along the filament, we derive\n$L^\\infty$ bounds for the difference between regularized SBT and its classical\ncounterpart in terms of $\\delta$, $\\epsilon$, and the force data. We show that\nthe regularized and classical expressions for the velocity of the filament\nitself differ by a term proportional to $\\log(\\delta/\\epsilon)$ -- in\nparticular, $\\delta=\\epsilon$ is necessary to avoid an $O(1)$ discrepancy\nbetween the theories. However, the flow at the surface of the fiber differs by\nan expression proportional to $\\log(1+\\delta^2/\\epsilon^2)$, and any choice of\n$\\delta\\propto\\epsilon$ will result in an $O(1)$ discrepancy as $\\epsilon\\to\n0$. Consequently, the flow around a slender fiber due to regularized SBT does\nnot converge to the solution of the well-posed \\emph{slender body PDE} which\nclassical SBT is known to approximate. Numerics verify this $O(1)$ discrepancy\nbut also indicate that the difference may have little impact in practice.\n"}
+{"abstract": "  In the multicentric calculus one takes a polynomial with simple roots as a\nnew global variable and replaces scalar functions {\\varphi} by functions f\ntaking values in C^d with d the degree of the polynomial leading to an\nefficient holomorphic functional calculus for bounded operators. This calculus\nwas extended for non-holomorphic functions, so that if A is not diagonalizable\none can find a p such that p(A) is diagonalizable and apply the calculus to all\nmatrices. This was done in [7] by creating a Banach algebra for C^d-valued\ncontinuous functions in such a way that the original functions {\\varphi} appear\nas Gelfand transforms of f. In this paper we consider constructing a Banach\nalgebra for functions from C^2 into C^(d_1 x d_2) which then likewise leads to\na functional calculus for commuting pairs of matrices.\n"}
+{"abstract": "  This paper addresses the general problem of designing ordinal classification\nmethods based on comparing actions with limiting boundaries of ordered classes\n(categories). The fundamental requirement of the method consists of setting a\nrelational system (D,S), where S and D are reflexive and transitive relations,\nrespectively, S should be compatible with the order of the set of classes, and\nD is a subset of S. An asymmetric preference relation P is defined from S.\nOther requirements are imposed on the actions which compose the limiting\nboundaries between adjacent classes, in such a way that each class is closed\nbelow and above. The paper proposes S-based and P-based assignment procedures.\nEach of them is composed of two complementary assignment procedures, which\ncorrespond through the transposition operation and should be used conjointly.\nThe methods work under several basic conditions on the set of limiting\nboundaries. Under other more demanding separability requirements, each\nprocedure fulfills the set of structural properties established for other\noutranking-based ordinal classification methods. Our proposal avoids the\nconflict between the required correspondence through the transposition\noperation and the assignment of limiting actions to the classes to which they\nbelong. We thus propose very diverse S and P-based ordinal classification\napproaches with desirable properties, which can be designed by using decision\nmodels with the capacity to build preference relations fulfilling the basic\nrequirements to S and D.\n"}
+{"abstract": "  Lane formation in bidirectional pedestrian streams is based on a\nstimulus-response mechanism and strategies of navigation in a fast-changing\nenvironment. Although microscopic models that only guarantee volume exclusion\ncan qualitatively reproduce this phenomenon, they are not sufficient for a\nquantitative description. To quantitatively describe this phenomenon, a minimal\nanticipatory collision-free velocity model is introduced. Compared to the\noriginal velocity model, the new model reduces the occurrence of gridlocks and\nreproduces the movement of pedestrians more realistically. For a quantitative\ndescription of the phenomenon, the definition of an order parameter is used to\ndescribe the formation of lanes at transient states and to show that the\nproposed model compares relatively well with experimental data. Furthermore,\nthe model is validated by the experimental fundamental diagrams of\nbidirectional flows.\n"}
+{"abstract": "  We carried out a search for unknown dwarf novae in a public data release of\nthe Zwicky Transient Facility survey and suspected that the object ZTF18abdlzhd\nis a SU UMa-type star. Performed multicolor CCD observations permit us to\nfollow its fading from an outburst in August and an entire superoutburst in\nOctober 2020. The duration of the superoutburst is 13 days. We detected\nsuperhumps with period P = 0.06918(3) d that are characteristic of UGSU type\nstars.\n"}
+{"abstract": "  We study the strong constraining problem in Lagrangian dynamics in the\ndegenerate codimension one case. This is the first time that degenerate\npotentials at the constraint are considered for this problem. These new results\ncover several real analytic potentials that the previous do not. Some\ncounterintuitive effects of the degenerate constraining problem are discussed.\n"}
+{"abstract": "  To any Frobenius superalgebra $A$ we associate an oriented Frobenius Brauer\ncategory and an affine oriented Frobenius Brauer categeory. We define natural\nactions of these categories on categories of supermodules for general linear\nLie superalgebras $\\mathfrak{gl}_{m|n}(A)$ with entries in $A$. These actions\ngeneralize those on module categories for general linear Lie superalgebras and\nqueer Lie superalgebras, which correspond to the cases where $A$ is the ground\nfield and the two-dimensional Clifford algebra, respectively.\n"}
+{"abstract": "  We show that it is possible to realize a cosmological bouncing solution in an\nanisotropic but homogeneous Bianchi-I background in a class of non-local,\ninfinite derivative theories of gravity. We show that the anisotropic shear\ngrows slower than in general relativity during the contraction phase, peaks to\na finite value at the bounce point, and then decreases as the universe\nasymptotes towards isotropy and homogeneity, and ultimately to de Sitter. Along\nwith a cosmological constant, the matter sector required to drive such a bounce\nis found to consist of three components - radiation, stiff matter and\n$k$-matter (whose energy density decays like the inverse square of the average\nscale factor). Generically, $k$-matter exerts anisotropic pressures. We will\ntest the bouncing solution in local and non-local gravity and show that in the\nlatter case it is possible to simultaneously satisfy positivity of energy\ndensity and, at least in the late time de Sitter phase, avoid the introduction\nof propagating ghost/tachyonic modes.\n"}
+{"abstract": "  Objective: Neural network de-identification studies have focused on\nindividual datasets. These studies assume the availability of a sufficient\namount of human-annotated data to train models that can generalize to\ncorresponding test data. In real-world situations, however, researchers often\nhave limited or no in-house training data. Existing systems and external data\ncan help jump-start de-identification on in-house data; however, the most\nefficient way of utilizing existing systems and external data is unclear. This\narticle investigates the transferability of a state-of-the-art neural clinical\nde-identification system, NeuroNER, across a variety of datasets, when it is\nmodified architecturally for domain generalization and when it is trained\nstrategically for domain transfer. Methods and Materials: We conducted a\ncomparative study of the transferability of NeuroNER using four clinical note\ncorpora with multiple note types from two institutions. We modified NeuroNER\narchitecturally to integrate two types of domain generalization approaches. We\nevaluated each architecture using three training strategies. We measured:\ntransferability from external sources; transferability across note types; the\ncontribution of external source data when in-domain training data are\navailable; and transferability across institutions. Results and Conclusions:\nTransferability from a single external source gave inconsistent results. Using\nadditional external sources consistently yielded an F1-score of approximately\n80%. Fine-tuning emerged as a dominant transfer strategy, with or without\ndomain generalization. We also found that external sources were useful even in\ncases where in-domain training data were available. Transferability across\ninstitutions differed by note type and annotation label but resulted in\nimproved performance.\n"}
+{"abstract": "  Parametric 3D models have enabled a wide variety of tasks in computer\ngraphics and vision, such as modeling human bodies, faces, and hands. However,\nthe construction of these parametric models is often tedious, as it requires\nheavy manual tweaking, and they struggle to represent additional complexity and\ndetails such as wrinkles or clothing. To this end, we propose Neural Parametric\nModels (NPMs), a novel, learned alternative to traditional, parametric 3D\nmodels, which does not require hand-crafted, object-specific constraints. In\nparticular, we learn to disentangle 4D dynamics into latent-space\nrepresentations of shape and pose, leveraging the flexibility of recent\ndevelopments in learned implicit functions. Crucially, once learned, our neural\nparametric models of shape and pose enable optimization over the learned spaces\nto fit to new observations, similar to the fitting of a traditional parametric\nmodel, e.g., SMPL. This enables NPMs to achieve a significantly more accurate\nand detailed representation of observed deformable sequences. We show that NPMs\nimprove notably over both parametric and non-parametric state of the art in\nreconstruction and tracking of monocular depth sequences of clothed humans and\nhands. Latent-space interpolation as well as shape/pose transfer experiments\nfurther demonstrate the usefulness of NPMs. Code is publicly available at\nhttps://pablopalafox.github.io/npms.\n"}
+{"abstract": "  Multi-agent imitation learning aims to train multiple agents to perform tasks\nfrom demonstrations by learning a mapping between observations and actions,\nwhich is essential for understanding physical, social, and team-play systems.\nHowever, most existing works on modeling multi-agent interactions typically\nassume that agents make independent decisions based on their observations,\nignoring the complex dependence among agents. In this paper, we propose to use\ncopula, a powerful statistical tool for capturing dependence among random\nvariables, to explicitly model the correlation and coordination in multi-agent\nsystems. Our proposed model is able to separately learn marginals that capture\nthe local behavioral patterns of each individual agent, as well as a copula\nfunction that solely and fully captures the dependence structure among agents.\nExtensive experiments on synthetic and real-world datasets show that our model\noutperforms state-of-the-art baselines across various scenarios in the action\nprediction task, and is able to generate new trajectories close to expert\ndemonstrations.\n"}
+{"abstract": "  Reed-Muller (RM) codes are conjectured to achieve the capacity of any\nbinary-input memoryless symmetric (BMS) channel, and are observed to have a\ncomparable performance to that of random codes in terms of scaling laws. On the\nnegative side, RM codes lack efficient decoders with performance close to that\nof a maximum likelihood decoder for general parameters. Also, they only admit\ncertain discrete sets of rates. In this paper, we focus on subcodes of RM codes\nwith flexible rates that can take any code dimension from 1 to n, where n is\nthe blocklength. We first extend the recursive projection-aggregation (RPA)\nalgorithm proposed recently by Ye and Abbe for decoding RM codes. To lower the\ncomplexity of our decoding algorithm, referred to as subRPA in this paper, we\ninvestigate different ways for pruning the projections. We then derive the\nsoft-decision based version of our algorithm, called soft-subRPA, that is shown\nto improve upon the performance of subRPA. Furthermore, it enables training a\nmachine learning (ML) model to search for \\textit{good} sets of projections in\nthe sense of minimizing the decoding error rate. Training our ML model enables\nachieving very close to the performance of full-projection decoding with a\nsignificantly reduced number of projections. For instance, our simulation\nresults on a (64,14) RM subcode show almost identical performance for\nfull-projection decoding and pruned-projection decoding with 15 projections\npicked via training our ML model. This is equivalent to lowering the complexity\nby a factor of more than 4 without sacrificing the decoding performance.\n"}
+{"abstract": "  In 1999 Khabibullin established the best estimate in Paley problem for a\nplurisubharmonic function $u$ of finite lower order $0\\leq\\rho\\leq 1$. For\n$\\rho>1$ obtaining a sharp estimate has remained an open question. In this work\nwe solve this problem. We also provide some estimates for the types of the\ncharacteristic functions $T(r,u)$ and $M(r,u)$.\n"}
+{"abstract": "  Depth maps obtained by commercial depth sensors are always in low-resolution,\nmaking it difficult to be used in various computer vision tasks. Thus, depth\nmap super-resolution (SR) is a practical and valuable task, which upscales the\ndepth map into high-resolution (HR) space. However, limited by the lack of\nreal-world paired low-resolution (LR) and HR depth maps, most existing methods\nuse downsampling to obtain paired training samples. To this end, we first\nconstruct a large-scale dataset named \"RGB-D-D\", which can greatly promote the\nstudy of depth map SR and even more depth-related real-world tasks. The \"D-D\"\nin our dataset represents the paired LR and HR depth maps captured from mobile\nphone and Lucid Helios respectively ranging from indoor scenes to challenging\noutdoor scenes. Besides, we provide a fast depth map super-resolution (FDSR)\nbaseline, in which the high-frequency component adaptively decomposed from RGB\nimage to guide the depth map SR. Extensive experiments on existing public\ndatasets demonstrate the effectiveness and efficiency of our network compared\nwith the state-of-the-art methods. Moreover, for the real-world LR depth maps,\nour algorithm can produce more accurate HR depth maps with clearer boundaries\nand to some extent correct the depth value errors.\n"}
+{"abstract": "  In this work, new finite difference schemes are presented for dealing with\nthe upper-convected time derivative in the context of the generalized Lie\nderivative. The upper-convected time derivative, which is usually encountered\nin the constitutive equation of the popular viscoelastic models, is\nreformulated in order to obtain approximations of second-order in time for\nsolving a simplified constitutive equation in one and two dimensions. The\ntheoretical analysis of the truncation errors of the methods takes into account\nthe linear and quadratic interpolation operators based on a Lagrangian\nframework. Numerical experiments illustrating the theoretical results for the\nmodel equation defined in one and two dimensions are included. Finally, the\nfinite difference approximations of second-order in time are also applied for\nsolving a two-dimensional Oldroyd-B constitutive equation subjected to a\nprescribed velocity field at different Weissenberg numbers.\n"}
+{"abstract": "  A cycle $C$ in a graph $G$ is called a Tutte cycle if, after deleting $C$\nfrom $G$, each component has at most three neighbors on $C$. Tutte cycles play\nan important role in the study of Hamiltonicity of planar graphs. Thomas and Yu\nand independently Sanders proved the existence of Tutte cycles containining\nthree specified edges of a facial cycle in a 2-connected plane graph. We prove\na quantitative version of this result, bounding the number of components of the\ngraph obtained by deleting a Tutte cycle. As a corollary, we can find long\ncycles in essentially 4-connected plane graphs that also contain three\nprescribed edges of a facial cycle.\n"}
+{"abstract": "  Dynamic and steady state aspects of wave propagation are deeply connected in\nlossless open systems in which the scattering matrix is unitary. There is then\nan equivalence among the energy excited within the medium, the Wigner time\ndelay, and the density of states. But these equivalences fall away in the\npresence of material loss or gain. In this paper, we use microwave\nmeasurements, numerical simulations, and theoretical analysis to discover the\nchanging relationships among the internal field, transmission, transmission\ntime, dwell time, total excited energy, and the density of states in with loss\nand gain, and their dependence upon dimensionality and spectral overlap. The\ntotal energy excited is equal to the dwell time. In 1D, the average\ntransmission time is independent of loss, gain, and scattering strength. In\nhigher dimensions, however, the average transmission time falls with loss,\nscattering strength and channel number, and increases with gain. It is the sum\nover Lorentzian functions associated with the zeros as well as the poles of the\ntransmission matrix. This endows transmission zeros with robust topological\ncharacteristics: in unitary media, zeros occur either singly on the real axis\nor as conjugate pairs in the complex frequency plane. In weakly absorbing\nmedia, the spectrum of the transmission time of the lowest transmission\neigenchannel is the sum of Lorentzians due to transmission zeros plus a\nbackground due to far-off-resonance poles. The average density of transmission\nzeros in the complex plane is found from the fall of the average transmission\ntime with absorption as zeros are swept into the lower half of the complex\nfrequency plane. We show that the disposition of poles and zeros in the complex\nfrequency plane provides a framework for understanding and controlling wave\npropagation in non-Hermitian systems.\n"}
+{"abstract": "  The relaxation dynamics of hot carriers in silicon (100) is studied via a\nnovel holistic approach based on phase-resolved transient absorption\nspectroscopy with few-cycle optical pulses. After excitation by a sub-5 fs\nlight pulse, strong electron-phonon coupling leads to an ultrafast momentum\nrelaxation with time constant of 10 fs. The thermalization of the hot carriers\noccurs on a time constant of 150 fs, visible in the temporal evolution of the\ncollision time as extracted from the Drude model. We find an increase of the\ncollision time from 3 fs for the shortest timescales with a saturation at\napproximately 18 fs. Moreover, the optical effective mass of the hot carrier\nensemble evolves on ultrafast timescales as well, with a bi-exponential\ndecrease from 0.7 $m_e$ to about 0.125 $m_e$ and time constants of 4 fs and 58\nfs. The presented information on the electron mass dynamics as well as the\nmomentum-, energy-, and collision-scattering times with unprecedented time\nresolution is important for all hot carrier optoelectronic devices.\n"}
+{"abstract": "  The Semi Symmetric Nonnegative Factorization is a factorization of an $n\n\\times n$ nonnegative symmetric matrix $A$ of the form $BCB^T$, where $C$ is a\n$k \\times k$ symmetric matrix, and both $B$ and $C$ are required to be\nnonnegative. The minimal $k$ for which such factorization exists is called the\nSSNF-rank of $A$. In this work, basic properties of the SSNF-rank are given,\nSSNF-rank of low rank matrices is explored, and selected constructions of\nmatrices that control the SSNF-rank are presented.\n"}
+{"abstract": "  We study the effect of reaction times on the kinetics of relaxation to\nstationary states and on congestion transitions in heterogeneous traffic.\nHeterogeneity is modeled as quenched disorders in the parameters of the car\nfollowing model and in the reaction times of the drivers. We observed that at\nlow densities, the relaxation to stationary state from a homogeneous initial\nstate is governed by the same power laws as derived by E. Ben-Naim et al.,\nKinetics of clustering in traffic flow, Phys. Rev. E 50, 822 (1994). The\nstationary state, at low densities, is a single giant platoon of vehicles with\nthe slowest vehicle being the leader. We observed formation of spontaneous jams\ninside the giant platoon which move upstream as stop-go waves and dissipate at\nits tail. The transition happens when the head of the giant platoon interacts\nwith its tail, stable stop-go waves form, which circulate in the ring without\ndissipating. We observed that the system behaves differently when the\ntransition density is approached from above that it does when approached from\nbelow. When the transition density is approached from below, the gap\ndistribution behind the leader has a double peak and is fat-tailed but has a\nbounded support and thus the maximum gap in the system and the variance of the\ngap distribution tend to size-independent values. When the transition density\nis approached from above, the gap distribution becomes a power law and,\nconsequently, the maximum gap in the system and the variance in the gaps\ndiverge as a power law, thereby creating a discontinuity at the transition.\nThus, we observe a phase transition of unusual kind in which both a\ndiscontinuity and a power law are observed at the transition density. These\nunusual features vanish in the absence of reaction time (e.g., automated\ndriving).\n"}
+{"abstract": "  We study acceleration phenomena in monostable integro-differential equations\nwith ignition nonlinearity. Our results cover fractional Laplace operators and\nstandard convolutions in a unified way, which is also a contribution of this\npaper. To achieve this, we construct a sub-solution that captures the expected\ndynamics of the accelerating solution, and this is here the main difficulty.\nThis study involves the flattening effect occurring in accelerated propagation\nphenomena.\n"}
+{"abstract": "  We show that the difference between the topological 4-genus of a knot and the\nminimal genus of a surface bounded by that knot that can be decomposed into a\nsmooth concordance followed by an algebraically simple locally flat surface can\nbe arbitrarily large. This extends work of Hedden-Livingston-Ruberman showing\nthat there are topologically slice knots which are not smoothly concordant to\nany knot with trivial Alexander polynomial.\n"}
+{"abstract": "  During daily activities, humans use their hands to grasp surrounding objects\nand perceive sensory information which are also employed for perceptual and\nmotor goals. Multiple cortical brain regions are known to be responsible for\nsensory recognition, perception and motor execution during sensorimotor\nprocessing. While various research studies particularly focus on the domain of\nhuman sensorimotor control, the relation and processing between motor execution\nand sensory processing is not yet fully understood. Main goal of our work is to\ndiscriminate textured surfaces varying in their roughness levels during active\ntactile exploration using simultaneously recorded electroencephalogram (EEG)\ndata, while minimizing the variance of distinct motor exploration movement\npatterns. We perform an experimental study with eight healthy participants who\nwere instructed to use the tip of their dominant hand index finger while\nrubbing or tapping three different textured surfaces with varying levels of\nroughness. We use an adversarial invariant representation learning neural\nnetwork architecture that performs EEG-based classification of different\ntextured surfaces, while simultaneously minimizing the discriminability of\nmotor movement conditions (i.e., rub or tap). Results show that the proposed\napproach can discriminate between three different textured surfaces with\naccuracies up to 70%, while suppressing movement related variability from\nlearned representations.\n"}
+{"abstract": "  Many-body localization (MBL) is well characterized in Fock space. To quantify\nthe degree of this Fock space localization, the multifractal dimension $D_q$ is\nemployed; it has been claimed that $D_q$ shows a jump from the delocalized\nvalue $D_q=1$ in the ETH phase (ETH: eigenstate thermalization hypothesis) to a\nsmaller value $0<D_q<1$ at the ETH-MBL transition, yet exhibiting a conspicuous\ndiscrepancy from the fully localized value $D_q=0$, which indicate that\nmultifractality remains inside the MBL phase. Here, to better quantify the\nsituation we employ, instead of the commonly used computational basis, the\none-particle density matrix (OPDM) and use its eigenstates (natural orbitals)\nas a Fock state basis for representing many-body eigenstates $|\\psi\\rangle$ of\nthe system. Using this basis, we compute $D_q$ and other indices quantifying\nthe Fock space localization, such as the local purity $S$, which is derived\nfrom the occupation spectrum $\\{n_\\alpha\\}$ (eigenvalues of the OPDM). We\nhighlight the statistical distribution of Hamming distance $x_{\\mu\\nu}$\noccurring in the pair-wise coefficients $|a_\\mu|^2|a_\\nu|^2$ in $S$, and\ncompare this with a related quantity considered in the literature.\n"}
+{"abstract": "  We investigate the nonexistence and existence of nontrivial positive\nsolutions to $\\Delta_m u+u^p|\\nabla u|^q\\leq0$ on noncompact geodesically\ncomplete Riemannian manifolds, where $m>1$, and $(p,q)\\in \\mathbb{R}^2$.\nAccording to classification of $(p, q)$, we establish different volume growth\nconditions to obtain Liouville's theorems for the above quasilinear\ndifferential inequalities, and we also show these volume growth conditions are\nsharp in most cases. Moreover, the results are completely new for $(p, q)$ of\nnegative pair, even in the Euclidean space.\n"}
+{"abstract": "  Recent architectural approaches that address speculative side-channel attacks\naim to prevent software from exposing the microarchitectural state changes of\ntransient execution. The Delay-on-Miss technique is one such approach, which\nsimply delays loads that miss in the L1 cache until they become\nnon-speculative, resulting in no transient changes in the memory hierarchy.\nHowever, this costs performance, prompting the use of value prediction (VP) to\nregain some of the delay. However, the problem cannot be solved by simply\nintroducing a new kind of speculation (value prediction). Value-predicted loads\nhave to be validated, which cannot be commenced until the load becomes\nnon-speculative. Thus, value-predicted loads occupy the same amount of precious\ncore resources (e.g., reorder buffer entries) as Delay-on-Miss. The end result\nis that VP only yields marginal benefits over Delay-on-Miss. In this paper, our\ninsight is that we can achieve the same goal as VP (increasing performance by\nproviding the value of loads that miss) without incurring its negative\nside-effect (delaying the release of precious resources), if we can safely,\nnon-speculatively, recompute a value in isolation (without being seen from the\noutside), so that we do not expose any information by transferring such a value\nvia the memory hierarchy. Value Recomputation, which trades computation for\ndata transfer was previously proposed in an entirely different context: to\nreduce energy-expensive data transfers in the memory hierarchy. In this paper,\nwe demonstrate the potential of value recomputation in relation to the\nDelay-on-Miss approach of hiding speculation, discuss the trade-offs, and show\nthat we can achieve the same level of security, reaching 93% of the unsecured\nbaseline performance (5% higher than Delay-on-miss), and exceeding (by 3%) what\neven an oracular (100% accuracy and coverage) value predictor could do.\n"}
+{"abstract": "  Giant exoplanets on wide orbits have been directly imaged around young stars.\nIf the thermal background in the mid-infrared can be mitigated, then exoplanets\nwith lower masses can also be imaged. Here we present a ground-based\nmid-infrared observing approach that enables imaging low-mass temperate\nexoplanets around nearby stars, and in particular within the closest stellar\nsystem, Alpha Centauri. Based on 75-80% of the best quality images from 100\nhours of cumulative observations, we demonstrate sensitivity to warm\nsub-Neptune-sized planets throughout much of the habitable zone of Alpha\nCentauri A. This is an order of magnitude more sensitive than state-of-the-art\nexoplanet imaging mass detection limits. We also discuss a possible exoplanet\nor exozodiacal disk detection around Alpha Centauri A. However, an instrumental\nartifact of unknown origin cannot be ruled out. These results demonstrate the\nfeasibility of imaging rocky habitable-zone exoplanets with current and\nupcoming telescopes.\n"}
+{"abstract": "  We describe a quantum-assisted machine learning (QAML) method in which\nmultivariate data is encoded into quantum states in a Hilbert space whose\ndimension is exponentially large in the length of the data vector. Learning in\nthis space occurs through applying a low-depth quantum circuit with a tree\ntensor network (TTN) topology, which acts as an unsupervised feature extractor\nto identify the most relevant quantum states in a data-driven fashion. This\nunsupervised feature extractor then feeds a supervised linear classifier and\nencodes the output in a small-dimensional quantum register. In contrast to\nprevious work on \\emph{quantum-inspired} TTN classifiers, in which the\nembedding map and class decision weights did not map the data to well-defined\nquantum states, we present an approach that can be implemented on gate-based\nquantum computing devices. In particular, we identify an embedding map with\naccuracy similar to exponential machines (Novikov \\emph{et al.},\narXiv:1605.03795), but which produces valid quantum states from classical data\nvectors, and utilize manifold-based gradient optimization schemes to produce\nisometric operations mapping quantum states to a register of qubits defining a\nclass decision. We detail methods for efficiently obtaining one- and two-point\ncorrelation functions of the decision boundary vectors of the quantum model,\nwhich can be used for model interpretability, as well as methods for obtaining\nclassifications from partial data vectors. Further, we show that the use of\nisometric tensors can significantly aid in the human interpretability of the\ncorrelation functions extracted from the decision weights, and may produce\nmodels that are less susceptible to adversarial perturbations. We demonstrate\nour methodologies in applications utilizing the MNIST handwritten digit dataset\nand a multivariate timeseries dataset of human activity recognition.\n"}
+{"abstract": "  Linear carbon-based materials such as polyyne and cumulene oligomers provide\na versatile platform for nano-physics and engineering. Direct gap quasi-1D\npolyyne structures are promising for the observation of strong and unusual\nexcitonic eects arising due to the two-dimensional quantum connement. Recently,\nwe reported on the observation of sharp exciton peaks in low temperature\nphotoluminescence spectra of polyyne chains. Here, we analyse the time-resolved\noptical response of this system. We extend the non-local dielectric response\ntheory to predict the exciton radiative lifetime dependence on the band-gap\nvalue and on the length of the chain. A good agreement between the experiment\nand the theory is achieved.\n"}
+{"abstract": "  The ability of near-term quantum computers to represent\nclassically-intractable quantum states has brought much interest in using such\ndevices for estimating the ground and excited state energies of fermionic\nHamiltonians. The usefulness of such near-term techniques, generally based on\nthe Variational Quantum Eigensolver (VQE), however, is limited by device noise\nand the need to perform many circuit repetitions. This paper addresses these\nchallenges by generalizing VQE to consider wavefunctions in a subspace spanned\nby classically tractable states and states that can be prepared on a quantum\ncomputer. The manuscript shows how the ground and excited state energies can be\nestimated using such \"classical-boosting\" and how this approach can be combined\nwith VQE Hamiltonian decomposition techniques. Unlike existing VQE approaches,\nthe sensitivity to sampling error and device noise approaches zero in the limit\nwhere the classically tractable states are able to describe an eigenstate. A\ndetailed analysis of the measurement requirements in the simplest case, where a\nsingle computational basis state is used to boost conventional VQE, shows that\nthe ground-state energy estimation of several closed-shell homonuclear diatomic\nmolecules can be accelerated by a factor of approximately 10-1000. The analysis\nalso shows that the measurement reduction of such single basis state boosting,\nrelative to conventional VQE, can be estimated using only the overlap between\nthe ground state and the computational basis state used for boosting.\n"}
+{"abstract": "  We have adapted the use of exponentially averaged momentum in PSO to\nmulti-objective optimization problems. The algorithm was built on top of SMPSO,\na state-of-the-art MOO solver, and we present a novel mathematical analysis of\nconstriction fairness. We extend this analysis to the use of momentum and\npropose rich alternatives of parameter sets which are theoretically sound. We\ncall our proposed algorithm \"Fairly Constricted PSO with Exponentially-Averaged\nMomentum\", FCPSO-em.\n"}
+{"abstract": "  Capturing scene dynamics and predicting the future scene state is challenging\nbut essential for robotic manipulation tasks, especially when the scene\ncontains both rigid and deformable objects. In this work, we contribute a\nsimulation environment and generate a novel dataset for task-specific\nmanipulation, involving interactions between rigid objects and a deformable\nbag. The dataset incorporates a rich variety of scenarios including different\nobject sizes, object numbers and manipulation actions. We approach dynamics\nlearning by proposing an object-centric graph representation and two modules\nwhich are Active Prediction Module (APM) and Position Prediction Module (PPM)\nbased on graph neural networks with an encode-process-decode architecture. At\nthe inference stage, we build a two-stage model based on the learned modules\nfor single time step prediction. We combine modules with different prediction\nhorizons into a mixed-horizon model which addresses long-term prediction. In an\nablation study, we show the benefits of the two-stage model for single time\nstep prediction and the effectiveness of the mixed-horizon model for long-term\nprediction tasks. Supplementary material is available at\nhttps://github.com/wengzehang/deformable_rigid_interaction_prediction\n"}
+{"abstract": "  We perform an analysis in harmonic space of the Dark Energy Survey Year 1\nData (DES-Y1) galaxy clustering data using products obtained for the real-space\nanalysis. We test our pipeline with a suite of lognormal simulations, which are\nused to validate scale cuts in harmonic space as well as to provide a\ncovariance matrix that takes into account the DES-Y1 mask. We then apply this\npipeline to DES-Y1 data taking into account survey property maps derived for\nthe real-space analysis. We compare with real-space DES-Y1 results obtained\nfrom a similar pipeline. We show that the harmonic space analysis we develop\nyields results that are compatible with the real-space analysis for the bias\nparameters. This verification paves the way to performing a harmonic space\nanalysis for the upcoming DES-Y3 data.\n"}
+{"abstract": "  Online Learning to Rank (OL2R) eliminates the need of explicit relevance\nannotation by directly optimizing the rankers from their interactions with\nusers. However, the required exploration drives it away from successful\npractices in offline learning to rank, which limits OL2R's empirical\nperformance and practical applicability. In this work, we propose to estimate a\npairwise learning to rank model online. In each round, candidate documents are\npartitioned and ranked according to the model's confidence on the estimated\npairwise rank order, and exploration is only performed on the uncertain pairs\nof documents, i.e., \\emph{divide-and-conquer}. Regret directly defined on the\nnumber of mis-ordered pairs is proven, which connects the online solution's\ntheoretical convergence with its expected ranking performance. Comparisons\nagainst an extensive list of OL2R baselines on two public learning to rank\nbenchmark datasets demonstrate the effectiveness of the proposed solution.\n"}
+{"abstract": "  As the market for commercially available unmanned aerial vehicles (UAVs)\nbooms, there is an increasing number of small, teleoperated or autonomous\naircraft found in protected or sensitive airspace. Existing solutions for\nremoval of these aircraft are either military-grade and too disruptive for\ndomestic use, or compose of cumbersomely teleoperated counter-UAV vehicles that\nhave proven ineffective in high-profile domestic cases. In this work, we\nexamine the use of a quadrotor for autonomously targeting semi-stationary and\nmoving aerial objects with little or no prior knowledge of the target's flight\ncharacteristics. Guidance and control commands are generated with information\njust from an onboard monocular camera. We draw inspiration from literature in\nmissile guidance, and demonstrate an optimal guidance method implemented on a\nquadrotor but not usable by missiles. Results are presented for first-pass hit\nsuccess and pursuit duration with various methods. Finally, we cover the CMU\nTeam Tartan entry in the MBZIRC 2020 Challenge 1 competition, demonstrating the\neffectiveness of simple line-of-sight guidance methods in a structured\ncompetition setting.\n"}
+{"abstract": "  Phosphorus-doped diamond is relevant for applications in sensing,\noptoelectronics and quantum photonics, since the unique optical properties of\ncolor centers in diamond can be combined with the n-type conductivity attained\nby the inclusion of phosphorus. Here, we investigate the photoluminescence\nsignal of the nitrogen-vacancy and silicon-vacancy color centers in\nphosphorus-doped diamond as a function of temperature starting from ambient\nconditions up to about 100$^\\circ$ Celsius, focusing on the zero-phonon line\n(ZPL). We find that the wavelength and width of the ZPL of the two color\ncenters exhibit a comparable dependence on temperature, despite the strong\ndifference in the photoluminescence spectra. Moreover, the temperature\nsensitivity of the ZPL of the silicon-vacancy center is not significantly\naffected by phosphorus-doping, as we infer by comparison with silicon-vacancy\ncenters in electronic-grade single-crystal diamond.\n"}
+{"abstract": "  Context. The eROSITA X-ray telescope onboard the Spectrum-Roentgen-Gamma\n(SRG) observatory combines a large field of view and collecting area in the\nenergy range $\\sim$0.2 to $\\sim$8.0 keV with the capability to perform uniform\nscanning observations of large sky areas.\n  Aims. SRG/eROSITA performed scanning observations of the $\\sim$140 square\ndegrees eROSITA Final Equatorial Depth Survey (eFEDS) field as part of its\nperformance verification phase. The observing time was chosen to slightly\nexceed the depth of equatorial fields after the completion of the eROSITA\nall-sky survey. We present a catalog of detected X-ray sources in the eFEDS\nfield providing source positions and extent information, as well as fluxes in\nmultiple energy bands and document a suite of tools and procedures developed\nfor eROSITA data processing and analysis, validated and optimized by the eFEDS\nwork.\n  Methods. A multi-stage source detection procedure was optimized and\ncalibrated by performing realistic simulations of the eROSITA eFEDS\nobservations. We cross-matched the eROSITA eFEDS source catalog with previous\nXMM-ATLAS observations, confirming excellent agreement of the eROSITA and\nXMM-ATLAS source fluxes.\n  Result. We present a primary catalog of 27910 X-ray sources, including 542\nwith significant spatial extent, detected in the 0.2-2.3 keV energy range with\ndetection likelihoods $\\ge 6$, corresponding to a point source flux limit of\n$\\approx 7 \\times 10^{-15}$ erg/cm$^2$/s in the 0.5-2.0 keV energy band. A\nsupplementary catalog contains 4774 low-significance source candidates with\ndetection likelihoods between 5 and 6. In addition, a hard band sample of 246\nsources detected in the energy range 2.3-5.0 keV above a detection likelihood\nof 10 is provided. A description of the dedicated data analysis software,\ncalibration database and standard calibrated data products is provided in\nappendix.\n"}
+{"abstract": "  Seven million people suffer complications after surgery each year. With\nsufficient surgical training and feedback, half of these complications could be\nprevented. Automatic surgical video analysis, especially for minimally invasive\nsurgery, plays a key role in training and review, with increasing interests\nfrom recent studies on tool and workflow detection. In this research, a novel\nmachine learning architecture, RPM-CNN, is created to perform real-time\nsurgical video analysis. This architecture, for the first time, integrates\nvisual simultaneous localization and mapping (vSLAM) into Mask R-CNN.\nSpatio-temporal information, in addition to the visual features, is utilized to\nincrease the accuracy to 96.8 mAP for tool detection and 97.5 mean Jaccard for\nworkflow detection, surpassing all previous works via the same benchmark\ndataset. As a real-time prediction, the RPM-CNN model reaches a 50 FPS runtime\nperformance speed, 10x faster than region based CNN, by modeling the\nspatio-temporal information directly from surgical videos during the vSLAM 3D\nmapping. Additionally, this novel Region Proposal Module (RPM) replaces the\nregion proposal network (RPN) in Mask R-CNN, accurately placing bounding-boxes\nand lessening the annotation requirement. In principle, this architecture\nintegrates the best of both worlds, inclusive of (1) vSLAM on object detection,\nthrough focusing on geometric information for region proposals and (2) CNN on\nobject recognition, through focusing on semantic information for image\nclassification; the integration of these two technologies into one joint\ntraining process opens a new door in computer vision. Furthermore, to apply\nRPM-CNN's real-time top performance to the real world, a Microsoft HoloLens 2\napplication is developed to provide an augmented reality (AR) based solution\nfor both surgical training and assistance.\n"}
+{"abstract": "  Dynamical systems are ubiquitous in science and engineering as models of\nphenomena that evolve over time. Although complex dynamical systems tend to\nhave important modular structure, conventional modeling approaches suppress\nthis structure. Building on recent work in applied category theory, we show how\ndeterministic dynamical systems, discrete and continuous, can be composed in a\nhierarchical style. In mathematical terms, we reformulate some existing operads\nof wiring diagrams and introduce new ones, using the general formalism of\n$\\mathcal{C}$-sets (copresheaves). We then establish dynamical systems as\nalgebras of these operads. In a computational vein, we show that Euler's method\nis functorial for undirected systems, extending a previous result for directed\nsystems. All the ideas in this paper are implemented as practical software\nusing Catlab and the AlgebraicJulia ecosystem, written in the Julia programming\nlanguage for scientific computing.\n"}
+{"abstract": "  The goal of this article is two-fold: in a first part, we prove\nAzuma-Hoeffding type concentration inequalities around the drift for the\ndisplacement of non-elementary random walks on hyperbolic spaces. For a proper\nhyperbolic space $M$, we obtain explicit bounds that depend only on $M$, the\nsize of support of the measure as in the classical case of sums of independent\nrandom variables, and on the norm of the driving probability measure in the\nleft regular representation of the group of isometries. We obtain uniform\nbounds in the case of hyperbolic groups and effective bounds for simple linear\ngroups of rank-one. In a second part, using our concentration inequalities, we\ngive quantitative finite-time estimates on the probability that two independent\nrandom walks on the isometry group of a hyperbolic space generate a free\nnon-abelian subgroup. Our concentration results follow from a more general, but\nless explicit statement that we prove for cocycles which satisfy a certain\ncohomological equation. For example, this also allows us to obtain subgaussian\nconcentration bounds around the top Lyapunov exponent of random matrix products\nin arbitrary dimension.\n"}
+{"abstract": "  Reconstructing an indoor scene and generating a layout/floor plan in 3D or 2D\nis a widely known problem. Quite a few algorithms have been proposed in the\nliterature recently. However, most existing methods either use RGB-D images,\nthus requiring a depth camera, or depending on panoramic photos, assuming that\nthere is little to no occlusion in the rooms. In this work, we proposed GRIHA\n(Generating Room Interior of a House using ARCore), a framework for generating\na layout using an RGB image captured using a simple mobile phone camera. We\ntake advantage of Simultaneous Localization and Mapping (SLAM) to assess the 3D\ntransformations required for layout generation. SLAM technology is built-in in\nrecent mobile libraries such as ARCore by Google. Hence, the proposed method is\nfast and efficient. It gives the user freedom to generate layout by merely\ntaking a few conventional photos, rather than relying on specialized depth\nhardware or occlusion-free panoramic images. We have compared GRIHA with other\nexisting methods and obtained superior results. Also, the system is tested on\nmultiple hardware platforms to test the dependency and efficiency.\n"}
+{"abstract": "  Hidden semi-Markov models generalise hidden Markov models by explicitly\nmodelling the time spent in a given state, the so-called dwell time, using some\ndistribution defined on the natural numbers. While the (shifted) Poisson and\nnegative binomial distribution provide natural choices for such distributions,\nin practice, parametric distributions can lack the flexibility to adequately\nmodel the dwell times. To overcome this problem, a penalised maximum likelihood\napproach is proposed that allows for a flexible and data-driven estimation of\nthe dwell-time distributions without the need to make any distributional\nassumption. This approach is suitable for direct modelling purposes or as an\nexploratory tool to investigate the latent state dynamics. The feasibility and\npotential of the suggested approach is illustrated by modelling muskox\nmovements in northeast Greenland using GPS tracking data. The proposed method\nis implemented in the R-package PHSMM which is available on CRAN.\n"}
+{"abstract": "  Nondeterminism in neural network optimization produces uncertainty in\nperformance, making small improvements difficult to discern from run-to-run\nvariability. While uncertainty can be reduced by training multiple model\ncopies, doing so is time-consuming, costly, and harms reproducibility. In this\nwork, we establish an experimental protocol for understanding the effect of\noptimization nondeterminism on model diversity, allowing us to isolate the\neffects of a variety of sources of nondeterminism. Surprisingly, we find that\nall sources of nondeterminism have similar effects on measures of model\ndiversity. To explain this intriguing fact, we identify the instability of\nmodel training, taken as an end-to-end procedure, as the key determinant. We\nshow that even one-bit changes in initial parameters result in models\nconverging to vastly different values. Last, we propose two approaches for\nreducing the effects of instability on run-to-run variability.\n"}
+{"abstract": "  It is prevalent to utilize external knowledge to help machine answer\nquestions that need background commonsense, which faces a problem that\nunlimited knowledge will transmit noisy and misleading information. Towards the\nissue of introducing related knowledge, we propose a semantic-driven\nknowledge-aware QA framework, which controls the knowledge injection in a\ncoarse-to-careful fashion. We devise a tailoring strategy to filter extracted\nknowledge under monitoring of the coarse semantic of question on the knowledge\nextraction stage. And we develop a semantic-aware knowledge fetching module\nthat engages structural knowledge information and fuses proper knowledge\naccording to the careful semantic of questions in a hierarchical way.\nExperiments demonstrate that the proposed approach promotes the performance on\nthe CommonsenseQA dataset comparing with strong baselines.\n"}
+{"abstract": "  System state estimation constitutes a key problem in several applications\ninvolving multi-agent system architectures. This rests upon the estimation of\nthe state of each agent in the group, which is supposed to access only relative\nmeasurements w.r.t. some neighbors state. Exploiting the standard least-squares\nparadigm, the system state estimation task is faced in this work by deriving a\ndistributed Proximal Point-based iterative scheme. This solution entails the\nemergence of interesting connections between the structural properties of the\nstochastic matrices describing the system dynamics and the convergence behavior\ntoward the optimal estimate. A deep analysis of such relations is provided,\njointly with a further discussion on the penalty parameter that characterizes\nthe Proximal Point approach.\n"}
+{"abstract": "  Bohmian mechanics was designed to give rise to predictions identical to those\nderived by standard quantum mechanics, while invoking a specific interpretation\nof it - one which allows the classical notion of a particle to be maintained\nalongside a guiding wave. For this, the Bohmian model makes use of a unique\nquantum potential which governs the trajectory of the particle. In this work we\nshow that this interpretation of quantum theory naturally leads to the\nderivation of interesting new phenomena. Specifically, we demonstrate how the\nfundamental Casimir-Polder force, by which atoms are attracted to a surface,\nmay be temporarily suppressed by utilizing a specially designed quantum\npotential. We show that when harnessing the quantum potential via a suitable\natomic wavepacket engineering, the absorption by the surface can be\ndramatically reduced. This is proven both analytically and numerically.\nFinally, an experimental scheme is proposed for achieving the required shape\nfor the atomic wavepacket. All these may enable new insights into Bohmian\nmechanics as well as new applications to metrology and sensing.\n"}
+{"abstract": "  Segmentation of organs or lesions from medical images plays an essential role\nin many clinical applications such as diagnosis and treatment planning. Though\nConvolutional Neural Networks (CNN) have achieved the state-of-the-art\nperformance for automatic segmentation, they are often limited by the lack of\nclinically acceptable accuracy and robustness in complex cases. Therefore,\ninteractive segmentation is a practical alternative to these methods. However,\ntraditional interactive segmentation methods require a large amount of user\ninteractions, and recently proposed CNN-based interactive segmentation methods\nare limited by poor performance on previously unseen objects. To solve these\nproblems, we propose a novel deep learning-based interactive segmentation\nmethod that not only has high efficiency due to only requiring clicks as user\ninputs but also generalizes well to a range of previously unseen objects.\nSpecifically, we first encode user-provided interior margin points via our\nproposed exponentialized geodesic distance that enables a CNN to achieve a good\ninitial segmentation result of both previously seen and unseen objects, then we\nuse a novel information fusion method that combines the initial segmentation\nwith only few additional user clicks to efficiently obtain a refined\nsegmentation. We validated our proposed framework through extensive experiments\non 2D and 3D medical image segmentation tasks with a wide range of previous\nunseen objects that were not present in the training set. Experimental results\nshowed that our proposed framework 1) achieves accurate results with fewer user\ninteractions and less time compared with state-of-the-art interactive\nframeworks and 2) generalizes well to previously unseen objects.\n"}
+{"abstract": "  The theory of discrete stochastic systems has been initiated by the work of\nShannon and von Neumann. While Shannon has considered memory-less communication\nchannels and their generalization by introducing states, von Neumann has\nstudied the synthesis of reliable systems from unreliable components. The\nfundamental work of Rabin and Scott about deterministic finite-state automata\nhas led to two generalizations. First, the generalization of transition\nfunctions to conditional distributions studied by Carlyle and Starke. This in\nturn has led to a generalization of time-discrete Markov chains in which the\nchains are governed by more than one transition probability matrix. Second, the\ngeneralization of regular sets by introducing stochastic automata as described\nby Rabin. Stochastic automata are well-investigated. This report provides a\nshort introduction to stochastic automata based on the valuable book of Claus.\nThis includes the basic topics of the theory of stochastic automata:\nequivalence, minimization, reduction, covering, observability, and determinism.\nThen stochastic versions of Mealy and Moore automata are studied and finally\nstochastic language acceptors are considered as a generalization of\nnondeterministic finite-state acceptors.\n"}
+{"abstract": "  We consider a 1D topological superconductor (TSC) constructed by coupling a\npair of Kitaev's Majorana chains with opposite spin configurations. Such a 1D\nlattice model is known to be protected by a $T^2 = -1$ time-reversal symmetry.\nFurthermore, we consider a modeled Rashba spin-orbit coupling on such a system\nof $T^2=-1$ time-reversal symmetric TSC. The Rashba spin-orbit coupling\ntogether with the chemical potential engineered the phase transitions of the\nedge states in the system and consequently the number of Majorona's zero-energy\nedge modes (MZM's) emerging at the edge of the coupled chains. Correspondingly,\nthe topological nature of the system is described by a phase diagram consisting\nof three different phases. The three phases are characterized by a topological\nwinding number, $\\mathcal{W}=1$, $2$ (with one and two MZM's: topological\nphases) and $\\mathcal{W}=0$ (devoid of any MZM: trivial insulating phase).\n"}
+{"abstract": "  In this letter, we provide fundamental insights into the dynamic\ntranscritical transition process using molecular dynamics simulations. A\ntranscritical region, which covers three different fluid states, was discovered\nas a substitute for the traditional interface. The physical properties, such as\ntemperature and density, exhibited a highly non-linear distribution in the\ntranscritical region. Meanwhile, the surface tension was found to exist\nthroughout the transcritical region, and the magnitude was directly\nproportional to $ - \\rho \\cdot {\\nabla ^2}\\rho $\n"}
+{"abstract": "  In this paper, we prove new upper bounds for sums of reciprocals of\nfractional parts over general aligned boxes, thus extending a previous result\nof the author concerning bounds for sums of reciprocals over symmetric boxes.\nThese new upper bounds depend solely on the volume of the boxes, and not on\ntheir diameter. This generalisation relies a novel lattice-point counting\ntechnique involving estimates for the higher successive minima of certain\nnaturally arising lattices.\n"}
+{"abstract": "  We use magneto-conductivity to study the magnetic proximity effect on surface\nstates of doped topological insulators. Our bilayers consist of a layer of\nFe$_7$Se$_8$, which is a metallic ferrimagnet and a layer of\nBi$_{0.8}$Sb$_{1.2}$Te$_{3}$ which is a highly hole-doped topological\ninsulator. Using transport measurements and a modified Hikami-Larkin-Nagaoka\nmodel, we show that the ferromagnet shortens significantly the effective\ncoherence length of the surface states, suggesting that a gap is opened at the\nDirac point. We show that the magnetically induced gap persists on surface\nstates which are separated from the magnet by a topological insulator layer as\nthick as 170 [nm]. Furthermore, the size of the gap is found to be proportional\nto the magnetization that we extract from the anomalous Hall effect.\n  Our results give information on the ties between carrier density, induced\nmagnetization and magnetically induced gap in topological insulator/ferromagnet\nbilayers. This information is important both for a theoretical understanding of\nmagnetic interactions in topological insulators and for the practical\nfabrication of such bilayers, which are the basis of various suggested\ntechnologies, such as spintronic devices, far infra-red detectors etc.\n"}
+{"abstract": "  We study small field models of inflation, which, against previous\nexpectations, yield significant Gravitational Wave (GW) signal while\nreproducing other measured observable quantities in the Cosmic Microwave\nBackground (CMB). We numerically study these, using previously published\nanalytic works as general guidelines. We first discuss the framework necessary\nto understand model building, and some of its motivations. We review the\nslow-roll paradigm, derive the slow-roll parameters, and discuss different\nformulations thereof. We review the Lyth bound and its theoretical descendants,\nwe outline the small/large field taxonomy and their characterization in the\ncurrent nomenclature. We then present our models and the methods employed in\ntheir building and examination. We employ MCMC simulations to evaluate model\nlikelihood and by process of marginalization extract the most probable\ncoefficients for these inflationary potentials. An additional method employed\nis a multinomial fit, where we create a correspondence between coefficients and\nobservables. This allows the use of observable values directly to yield the\nmost likely coefficients. We compare the results of the two methods and\nevaluate the level of tuning required for these models. We discuss an apparent\ndiscrepancy between analytical approaches of evaluating Primordial Power\nSpectrum (PPS) observables and the precise numerical results in our models. We\nidentify some of the sources of this discrepancy and remark on their meaning in\nthe age of precision cosmology. Finally, we present the results of our study,\nfor the most likely inflationary models with polynomial potentials of degree 5,\nand 6. We demonstrate our ability to produce potentials that yield GW with a\ntensor-to-scalar ratio r = 0.03. This is a realistic expectation of GW\ndetection sensitivity in the near future.\n"}
+{"abstract": "  E-textiles has received tremendous attention in recent years due to the\ncapability of integrating sensors into a garment to provide high precision\nsensing of the human body. Besides sensing, a number of solutions for e-textile\ngarments have also integrated wireless interfaces allowing these sensing data\nto be transmitted and also sensors that allow users to provide instructions\nthrough touching. While this has provided a new level of sensing that can\nresult in unprecedented applications, there has been little attention placed on\non-body computing for e-textiles. Facilitating computing on e-textiles can\nresult in a new form of On-body Edge Computing, where sensor information are\nprocessed very close to the body before being transmitted to an external device\nor wireless access point. This form of computing can provide new security and\ndata privacy capabilities and at the same time provide opportunities for new\nenergy harvesting mechanisms to process the data through the garment. This\npaper proposes this concept through embroidered Programmable Logic Array (PLA)\nintegrated into e-textiles. In the way that PLAs have programmable logic\ncircuits by interconnecting different AND, NOT and OR gates, we propose\ne-textile based gates that are sewn into a garment and connected through\nconductive thread stitching. Two designs are proposed and this includes Single\nand Multi-Layered PLA. Experimental validations have been conducted at the\nindividual gates as well as the entire PLA circuits to determine the voltage\nutilization as well as logic computing reliability. Our proposed approach can\nusher in a new form of On-Body Edge Computing for e-textile garments for future\nwearable technologies\n"}
+{"abstract": "  The layered vanadium antimonides AV3Sb5 (A = K, Rb, Cs) are a recently\ndiscovered family of topological kagome metals with a rich phenomenology of\nstrongly correlated electronic phases including charge order and\nsuperconductivity. Understanding how the singularities inherent to the kagome\nelectronic structure are linked to the observed many-body phases is a topic of\ngreat interest and relevance. Here, we combine angle-resolved photoemission\nspectroscopy and density functional theory to reveal multiple kagome-derived\nvan Hove singularities (vHs) coexisting near the Fermi level of CsV3Sb5 and\nanalyze their contribution to electronic symmetry breaking. Intriguingly, the\nvHs in CsV3Sb5 have two distinct flavors - p-type and m-type - which originate\nfrom their pure and mixed sublattice characters, respectively. This twofold vHs\nis unique property of the kagome lattice, and its flavor critically determines\nthe pairing symmetry and ground states emerging in AV3Sb5 series. We establish\nthat, among the multiple vHs in CsV3Sb5, the m-type vHs of the dxz/dyz kagome\nband and the p-type vHs of the dxy/dx2-y2 kagome band cross the Fermi level to\nset the stage for electronic symmetry breaking. The former band exhibits\npronounced Fermi surface nesting, while the latter contributes via higher-order\nvHs. Our work reveals the essential role of kagome-derived vHs for the\ncollective phenomena realized in the AV3Sb5 family, paving the way to a deeper\nunderstanding of strongly correlated topological kagome systems.\n"}
+{"abstract": "  Quantum computing is quickly turning from a promise to a reality, witnessing\nthe launch of several cloud-based, general-purpose offerings, and IDEs.\nUnfortunately, however, existing solutions typically implicitly assume intimate\nknowledge about quantum computing concepts and operators. This paper introduces\nQuantum Service-Oriented Computing (QSOC), including a model-driven methodology\nto allow enterprise DevOps teams to compose, configure and operate enterprise\napplications without intimate knowledge on the underlying quantum\ninfrastructure, advocating knowledge reuse, separation of concerns, resource\noptimization, and mixed quantum- & conventional QSOC applications.\n"}
+{"abstract": "  Let $\\mathcal{H}$ be a separable infinite-dimensional complex Hilbert space,\n$\\mathcal{B}(\\mathcal{H})$ the algebra of bounded linear operators acting on\n$\\mathcal{H}$ and $\\mathcal{J}$ a proper two-sided ideal of\n$\\mathcal{B}(\\mathcal{H})$. Denote by $\\mathcal{U}_\\mathcal{J}(\\mathcal{H})$\nthe group of all unitary operators of the form $I+\\mathcal{J}$. Recall that an\noperator $A \\in \\mathcal{B}(\\mathcal{H})$ is diagonalizable if there exists a\nunitary operator $U$ such that $UAU^*$ is diagonal with respect to some\northonormal basis. A more restrictive notion of diagonalization can be\nformulated with respect to a fixed orthonormal basis $\\mathrm{e}=\\{\ne_n\\}_{n\\geq 1}$ and a proper operator ideal $\\mathcal{J}$ as follows: $A \\in\n\\mathcal{B}(\\mathcal{H})$ is called restricted diagonalizable if there exists\n$U\\in \\mathcal{U}_\\mathcal{J}(\\mathcal{H})$ such that $UAU^*$ is diagonal with\nrespect to $\\mathrm{e}$. In this work we give necessary and sufficient\nconditions for a diagonalizable operator to be restricted diagonalizable. Our\nconditions become a characterization of those diagonalizable operators which\nare restricted diagonalizable when the ideal is arithmetic mean closed. Then we\nobtain results on the structure of the set of all restricted diagonalizable\noperators. In this way we answer several open problems recently raised by\nBelti\\c{t}$\\breve{\\text{a}}$, Patnaik and Weiss.\n"}
+{"abstract": "  Query-based explanations for missing answers identify which operators of a\nquery are responsible for the failure to return a missing answer of interest.\nThis type of explanations has proven to be useful in a variety of contexts\nincluding debugging of complex analytical queries. Such queries are frequent in\nbig data systems such as Apache Spark. We present a novel approach for\nproducing query-based explanations. Our approach is the first to support nested\ndata and to consider operators that modify the schema and structure of the data\n(e.g., nesting and projections) as potential causes of missing answers. To\nefficiently compute explanations, we propose a heuristic algorithm that applies\ntwo novel techniques: (i) reasoning about multiple schema alternatives for a\nquery and (ii) re-validating at each step whether an intermediate result can\ncontribute to the missing answer. Using an implementation of our approach on\nSpark, we demonstrate that it is the first to scale to large datasets and that\nit often finds explanations that existing techniques fail to identify.\n"}
+{"abstract": "  We show that there is essentially a unique elliptic curve $E$ defined over a\ncubic Galois extension $K$ of $\\mathbb Q$ with a $K$-rational point of order 13\nand such that $E$ is not defined over $\\mathbb Q$.\n"}
+{"abstract": "  Kirchhoff's law of thermal radiation imposes a constraint on photon-based\nenergy harvesting processes since part of the incident energy flux is\ninevitably emitted back to the source. By breaking the reciprocity of the\nsystem, it is possible to overcome this restriction and improve the efficiency\nof energy harvesting. Here, we design and analyze a semitransparent emitter\nthat fully absorbs normally incident energy from a given direction with zero\nbackward and unity forward emissivity. The nearly ideal performance with\nwavelength-scale thickness is achieved due to the magneto-optical effect and\nthe guided-mode resonance engineered in the emitter structure. We derive the\ngeneral requirements for the nonreciprocal emitter using the temporal coupled\nmode theory and the symmetry considerations. Finally, we provide a realistic\nemitter design based on a photonic crystal slab consisting of a magnetic Weyl\nsemimetal and silicon.\n"}
+{"abstract": "  The purpose of this paper is to study the phenomenon of large intersections\nin the framework of multiple recurrence for measure-preserving actions of\ncountable abelian groups. Among other things, we show: (1) If $G$ is a\ncountable abelian group and $\\varphi, \\psi : G \\to G$ are homomorphisms such\nthat $\\varphi(G)$, $\\psi(G)$, and $(\\psi - \\varphi)(G)$ have finite index in\n$G$, then for every ergodic measure-preserving system $(X, \\mathcal{B}, \\mu,\n(T_g)_{g \\in G})$, every set $A \\in \\mathcal{B}$, and every $\\varepsilon > 0$,\nthe set $\\{g \\in G : \\mu(A \\cap T_{\\varphi(g)}^{-1}A \\cap T_{\\psi(g)}^{-1}A) >\n\\mu(A)^3 - \\varepsilon\\}$ is syndetic. (2) If $G$ is a countable abelian group\nand $r,s \\in \\mathbb{Z}$ are integers such that $rG$, $sG$, and $(r \\pm s)G$\nhave finite index in $G$, then for every ergodic measure-preserving system $(X,\n\\mathcal{B}, \\mu, (T_g)_{g \\in G})$, every set $A \\in \\mathcal{B}$, and every\n$\\varepsilon > 0$, the set $\\{g \\in G : \\mu(A \\cap T_{rg}^{-1}A \\cap\nT_{sg}^{-1}A \\cap T_{(r+s)g}^{-1}A) > \\mu(A)^4 - \\varepsilon\\}$ is syndetic. In\nparticular, these extend and generalize results of Bergelson, Host, and Kra\nconcerning $\\mathbb{Z}$-actions and of Bergelson, Tao, and Ziegler concerning\n$\\mathbb{F}_p^{\\infty}$-actions. Using an ergodic version of the Furstenberg\ncorrespondence principle, we obtain new combinatorial applications. We also\ndiscuss numerous examples shedding light on the necessity of the various\nhypotheses above. Our results lead to a number of interesting questions and\nconjectures, formulated in the introduction and at the end of the paper.\n"}
+{"abstract": "  We give an algorithm for computing exact maximum flows on graphs with $m$\nedges and integer capacities in the range $[1, U]$ in\n$\\widetilde{O}(m^{\\frac{3}{2} - \\frac{1}{328}} \\log U)$ time. For sparse graphs\nwith polynomially bounded integer capacities, this is the first improvement\nover the $\\widetilde{O}(m^{1.5} \\log U)$ time bound from [Goldberg-Rao JACM\n`98].\n  Our algorithm revolves around dynamically maintaining the augmenting\nelectrical flows at the core of the interior point method based algorithm from\n[M\\k{a}dry JACM `16]. This entails designing data structures that, in limited\nsettings, return edges with large electric energy in a graph undergoing\nresistance updates.\n"}
+{"abstract": "  Using the Laplace derivative a Perron type integral, the Laplace integral, is\ndefined. Moreover, it is shown that this integral includes Perron integral and\nto show that the inclusion is proper, an example of a function is constructed,\nwhich is Laplace integrable but not Perron integrable. Properties of integrals\nsuch as fundamental theorem of calculus, Hake's theorem, integration by parts,\nconvergence theorems, mean value theorems, the integral remainder form of\nTaylor's theorem with an estimation of the remainder, are established. It turns\nout that concerning the Alexiewicz's norm, the space of all Laplace integrable\nfunctions is incomplete and contains the set of all polynomials densely.\nApplications are shown to Poisson integral, a system of generalised ordinary\ndifferential equations and higher-order generalised ordinary differential\nequation.\n"}
+{"abstract": "  Recent multi-wavelength ALMA observations of the protoplanetary disk orbiting\naround Elias 2-27 revealed a two armed spiral structure. The observed\nmorphology together with the young age of the star and the disk-to-star mass\nratio estimated from dust continuum emission make this system a perfect\nlaboratory to investigate the role of self-gravity in the early phases of star\nformation. This is particularly interesting if we consider that gravitational\ninstabilities could be a fundamental first step for the formation of\nplanetesimals and planets. In this Letter, we model the rotation curve obtained\nby CO data of Elias 2-27 with a theoretical rotation curve including both the\ndisk self-gravity and the star contribution to the gravitational potential. We\ncompare this model with a purely Keplerian one and with a simple power-law\nfunction. We find that (especially for the $^{13}$CO isotopologue) the rotation\ncurve is better described by considering not only the star, but also the disk\nself-gravity. We are thus able to obtain for the first time a dynamical\nestimate of the disk mass of $0.08\\pm0.04\\,M_{\\odot}$ and the star mass of\n$0.46\\pm0.03\\,M_{\\odot}$ (in the more general case), the latter being\ncomparable with previous estimates. From these values, we derive that the disk\nis 17$\\%$ of the star mass, meaning that it could be prone to gravitational\ninstabilities. This result would strongly support the hypothesis that the two\nspiral arms are generated by gravitational instabilities.\n"}
+{"abstract": "  Dusty, neutral outflows and inflows are a common feature of nearby\nstar-forming galaxies. We characterize these flows in eight galaxies -- mostly\nAGN -- selected for their widespread NaI D signatures from the Siding Spring\nSouthern Seyfert Spectroscopic Snapshot Survey (S7). This survey employs deep,\nwide field-of-view integral field spectroscopy at moderate spectral resolution\n(R=7000 at NaI D). We significantly expand the sample of sightlines in external\ngalaxies in which the spatially-resolved relationship has been studied between\ncool, neutral gas properties -- N(NaI), Weq(NaI D) -- and dust -- E(B-V) from\nboth stars and gas. Our sample shows strong, significant correlations of total\nWeq with E(B-V)_stars and g-i colour within individual galaxies; correlations\nwith E(B-V)_gas are present but weaker. Regressions yield slope variations from\ngalaxy to galaxy and intrinsic scatter ~1 Angstrom. The sample occupies regions\nin the space of N(NaI) and Weq^abs vs. E(B-V)_gas that are consistent with\nextrapolations from other studies to higher colour excess [E(B-V)_gas ~ 1]. For\nperhaps the first time in external galaxies, we detect inverse P Cygni profiles\nin the NaI D line, presumably due to inflowing gas. Via Doppler shifted NaI D\nabsorption and emission lines, we find ubiquitous flows that differ from\nstellar rotation by >100 km/s or have |v,abs - v,em| > 100 km/s. Inflows and\noutflows extend toward the edge of the detected stellar disk/FOV, together\nsubtend 10-40% of the projected disk, and have similar mean N(NaI) and Weq(NaI\nD). Outflows are consistent with minor-axis or jet-driven flows, while inflows\ntend toward the projected major axis. The inflows may result from\nnon-axisymmetric potentials, tidal motions, or halo infall.\n"}
+{"abstract": "  Objective measurement of perceptually motivated music attributes has\napplication in both target driven mixing and mastering methodologies and music\ninformation retrieval. This work proposes a perceptual model of mix clarity\nwhich decomposes a mixed input signal into transient, steady-state, and\nresidual components. Masking thresholds are calculated for each component and\ntheir relative relationship is used to determine an overall masking score as\nthe model's output. Three variants of the model were tested against subjective\nmix clarity scores gathered from a controlled listening test. The best\nperforming variant achieved a Spearman's rank correlation of rho = 0.8382\n(p<0.01). Furthermore, the model output was analysed using an independent\ndataset generated by progressively applying degradation effects to the test\nstimuli. Analysis of the model suggested a close relationship between the\nproposed model and the subjective mix clarity scores particularly when masking\nwas measured using linearly spaced analysis bands. Moreover, the presence of\nnoise-like residual signals was shown to have a negative effect on the\nperceived mix clarity.\n"}
+{"abstract": "  The fundamental relationship of the atomic structure (represented by its\natomic property parameters, APPs) and its physical properties of a specific\ninorganic substance can be realized in the bottom-up data-centric and the\ntop-down knowledge physics-centric ways. Nowadays these two approaches compete\nand enhance one another qualitatively and quantitatively. We present our own\nholistic method and implementation, based on the PAULING FILE peer-reviewed\ninorganic substances database, the world largest materials database containing\nunder one shelter crystallographic structures, phase diagrams and large variety\nof physical properties of single-phase inorganic substances. In addition we\npresent generated machine-learning data, as well as simulated DFT\nphysics-centered data, which are in close connection and comparison with the\nPAULING FILE peer-reviewed reference data.\n"}
+{"abstract": "  The advent of noisy intermediate-scale quantum (NISQ) devices offers crucial\nopportunities for the development of quantum algorithms. Here we evaluate the\nnoise tolerance of two quantum neural network (QNN) architectures on IBM's NISQ\ndevices, namely, dissipative QNN (DQNN) whose building-block perceptron is a\ncompletely positive map, and the quantum approximate optimization algorithm\n(QAOA). We compare these two approaches to learning an unknown unitary. While\nboth networks succeed in this learning task, we find that a DQNN learns an\nunknown unitary more reliably than QAOA and is less susceptible to gate noise.\n"}
+{"abstract": "  We study the laser-induced torques in the antiferromagnet (AFM) Mn$_2$Au. We\nfind that even linearly polarized light may induce laser-induced torques in\nMn$_2$Au, i.e., the light does not have to be circularly polarized. The\nlaser-induced torques in Mn$_2$Au are comparable in magnitude to those in the\nferromagnets Fe, Co and FePt at optical frequencies. We also compute the\nlaser-induced torques at terahertz (THz) frequencies and compare them to the\nspin-orbit torques (SOTs) excited by THz laser-pulses. We find the SOTs to be\ndominant at THz frequencies for the laser-field strengths used in experiments.\nAdditionally, we show that the matrix elements of the spin-orbit interaction\n(SOI) can be used to add SOI only during the Wannier interpolation, which we\ncall Wannier interpolation of SOI (WISOI). This technique allows us to perform\nthe Wannier interpolation conveniently for many magnetization directions from a\nsingle set of Wannier functions.\n"}
+{"abstract": "  We analyze decoherence and objectivization processes in spin-spin models for\narbitrary spins. We first derive the most general analytic form of the\ndecoherence factor in the measurement limit, where the interaction Hamiltonian\ndominates the rest. We then analyze thermal environments and derive exact,\nanalytic formulas for both the decoherence factor and the state fidelity of\npost-interaction environment states. This allows to analyze the objectivization\nprocess of the state of the central spin during the interaction. We do so\nusing, so called, Spectrum Broadcast Structures (SBS), which are specific\nmultipartite quantum states encoding a certain operation notion of objectivity.\nWe analyze analytically (for short times) and numerically how higher spin\ninfluences the efficiency of decoherence and objectivization processes. As\nexpected, the higher the spin of the environment, the more efficient\ndecoherence and objectivization become. This work is a generalization of\nprevious studies, limited to spin-$1/2$ systems only, and we hope will be\nuseful in future objectivity experiments.\n"}
+{"abstract": "  Applicability of associated plasticity for particulate materials such as\nsoils does not yield satisfactory results when Coulomb's theory of shear\nstrength of soils is assumed, and the yield function derived accordingly is\nused to define both the stress state and the direction of plastic flow. The\nlimitation mainly stems from the fact that Coulomb's theory (and its\nderivatives) is a simplification that intentionally ignores deformation\ncharacteristics that manifest from the particulate nature of such materials. It\nis thus customary to apply a branch of plasticity called non-associated\nplasticity for soils and similar materials. In the non-associated plasticity\nframework, yield functions and plastic potential functions are different. The\nformer defines the mobilization of the stress state while the later defines the\ndirection of plastic flow. For soils, stress-dilatancy theories have become\ncentral in the formulation of non-associated flow rules. In this paper, cyclic\nstress-dilatancy relations are derived based on complementarity hypothesis of\nstress-dilatancy conjugates. Both loading and unloading are explicitly\nconsidered. Then, yield functions are derived based on the resulting\nstress-dilatancy relations. In so doing, the resulting yield functions are\nrendered with a quality to be used for the modelling of deformation behavior of\nsoils subjected to monotonic and cyclic loading conditions. The newly derived\nyield functions are called Associated Cyclic Stress Dilatancy yield functions\nfor which the abbreviation ACStD is used. The theoretical framework is\nestablished first for special cases of deformation modes-plane strain and\naxisymmetric. The framework is generalized for considering Lode angle\ndependency of the yield function and extending the Matusoka-Nakai criterion.\n"}
+{"abstract": "  Let $d \\ge 3$ be an integer and let $P \\in \\mathbb{Z}[x]$ be a polynomial of\ndegree $d$ whose Galois group is $S_d$. Let $(a_n)$ be a linearly recuresive\nsequence of integers which has $P$ as its characteristic polynomial. We prove,\nunder the generalized Riemann hypothesis, that the lower density of the set of\nprimes which divide at least one element of the sequence $(a_n)$ is positive.\n"}
+{"abstract": "  Symbol alphabets of n-particle amplitudes in N=4 super-Yang-Mills theory are\nknown to contain certain cluster variables of Gr(4,n) as well as certain\nalgebraic functions of cluster variables. In this paper we solve the C Z = 0\nmatrix equations associated to several cells of the totally non-negative\nGrassmannian, combining methods of arXiv:2012.15812 for rational letters and\narXiv:2007.00646 for algebraic letters. We identify sets of parameterizations\nof the top cell of Gr_+(5,9) for which the solutions produce all of (and only)\nthe cluster variable letters of the 2-loop nine-particle NMHV amplitude, and\nidentify plabic graphs from which all of its algebraic letters originate.\n"}
+{"abstract": "  In this paper we give a geometric condition which ensures that\n$(q,p)$-Poincar\\'e-Sobolev inequalities are implied from generalized\n$(1,1)$-Poincar\\'e inequalities related to $L^1$ norms in the context of\nproduct spaces. The concept of eccentricity plays a central role in the paper.\nWe provide several $(1,1)$-Poincar\\'e type inequalities adapted to different\ngeometries and then show that our selfimproving method can be applied to obtain\nspecial interesting Poincar\\'e-Sobolev estimates. Among other results, we prove\nthat for each rectangle $R$ of the form $R=I_1\\times I_2 \\subset\n\\mathbb{R}^{n}$ where $I_1\\subset \\mathbb{R}^{n_1}$ and $I_2\\subset\n\\mathbb{R}^{n_2}$ are cubes with sides parallel to the coordinate axes, we have\nthat % \\begin{equation*} \\left( \\frac{1}{w(R)}\\int_{ R } |f\n-f_{R}|^{p_{\\delta,w}^*} \\,wdx\\right)^{\\frac{1}{p_{\\delta,w}^*}} \\leq\nc\\,\\delta^{\\frac1p}(1-\\delta)^{\\frac1p}\\,[w]_{A_{1,\\mathfrak{R}}}^{\\frac1p}\\,\n\\Big(a_1(R)+a_2(R)\\Big), \\end{equation*} % where $\\delta \\in (0,1)$, $w \\in\nA_{1,\\mathfrak{R}}$, $\\frac{1}{p} -\\frac{1}{ p_{\\delta,w}^* }= \\frac{\\delta}{n}\n\\, \\frac{1}{1+\\log [w]_{A_{1,\\mathfrak{R}}}}$ and $a_i(R)$ are bilinear analog\nof the fractional Sobolev seminorms $[u]_{W^{\\delta,p}(Q)}$ (See Theorem 2.18).\nThis is a biparameter weighted version of the celebrated fractional\nPoincar\\'e-Sobolev estimates with the gain\n$\\delta^{\\frac1p}(1-\\delta)^{\\frac1p}$.\n"}
+{"abstract": "  We report growth, electronic structure and superconductivity of ultrathin\nepitaxial CoSi2 films on Si(111). At low coverages, preferred islands with 2, 5\nand 6 monolayers height develop, which agrees well with the surface energy\ncalculation. We observe clear quantum well states as a result of electronic\nconfinement and their dispersion agrees well with density functional theory\ncalculations, indicating weak correlation effect despite strong contributions\nfrom Co 3d electrons. Ex-situ transport measurements show that\nsuperconductivity persists down to at least 10 monolayers, with reduced Tc but\nlargely enhanced upper critical field. Our study opens up the opportunity to\nstudy the interplay between quantum confinement, interfacial symmetry breaking\nand superconductivity in an epitaxial silicide film, which is technologically\nrelevant in microelectronics.\n"}
+{"abstract": "  The auditory attention decoding (AAD) approach was proposed to determine the\nidentity of the attended talker in a multi-talker scenario by analyzing\nelectroencephalography (EEG) data. Although the linear model-based method has\nbeen widely used in AAD, the linear assumption was considered oversimplified\nand the decoding accuracy remained lower for shorter decoding windows.\nRecently, nonlinear models based on deep neural networks (DNN) have been\nproposed to solve this problem. However, these models did not fully utilize\nboth the spatial and temporal features of EEG, and the interpretability of DNN\nmodels was rarely investigated. In this paper, we proposed novel convolutional\nrecurrent neural network (CRNN) based regression model and classification\nmodel, and compared them with both the linear model and the state-of-the-art\nDNN models. Results showed that, our proposed CRNN-based classification model\noutperformed others for shorter decoding windows (around 90% for 2 s and 5 s).\nAlthough worse than classification models, the decoding accuracy of the\nproposed CRNN-based regression model was about 5% greater than other regression\nmodels. The interpretability of DNN models was also investigated by visualizing\nlayers' weight.\n"}
+{"abstract": "  Geometric theories based on classical logic are conservative over their\nintuitionistic counterparts for geometric implications. The latter result\n(sometimes referred to as Barr's theorem) is squarely a consequence of\nGentzen's Hauptsatz. Prima facie though, cut elimination can result in\nsuperexponentially longer proofs. In this paper it is shown that the\ntransformation of a classical proof of a geometric implication in a geometric\ntheory into an intuitionistic proof can be achieved in feasibly many steps.\n"}
+{"abstract": "  We extend the well-known formula for the Euler class of a real oriented\neven-dimensional vector bundle in terms of the curvature of a metric connection\nto the case of a general linear connection provided a metric is present. We\nrewrite the classical Gauss-Bonnet theorem in dimension two in light of this\nformula. We also discuss a potential application to a conjecture of Chern, and\nmake a brief digression to discuss $m$-quasi-Einstein manifolds.\n"}
+{"abstract": "  We study rainbow-free colourings of $k$-uniform hypergraphs; that is,\ncolourings that use $k$ colours but with the property that no hyperedge attains\nall colours. We show that $p^*=(k-1)(\\ln n)/n$ is the threshold function for\nthe existence of a rainbow-free colouring in a random $k$-uniform hypergraph.\n"}
+{"abstract": "  This paper investigates the distributed optimal output consensus problem of\nsecond-order uncertain nonlinear multi-agent systems over weight-unbalanced\ndirected networks. Under the standard assumption that local cost functions are\nstrongly convex with globally Lipschitz gradients, a novel distributed dynamic\nstate feedback controller is developed such that the outputs of all the agents\nreach the optimal solution to minimize the global cost function which is the\nsum of all the local cost functions. The controller design is based on a\ntwo-layer strategy, where a distributed optimal coordinator and a\nreference-tracking controller are proposed to address the challenges arising\nfrom unbalanced directed networks and uncertain nonlinear functions\nrespectively. A key feature of the proposed controller is that the nonlinear\nfunctions containing the uncertainties and disturbances are not required to be\nglobally Lipschitz. Furthermore, by exploiting adaptive control technique, no\nprior knowledge of the uncertainties or disturbances is required either. Two\nsimulation examples are finally provided to illustrate the effectiveness of the\nproposed control scheme.\n"}
+{"abstract": "  In the present work, we report a comprehensive theoretical study of the $\\log\nft$ values for the allowed and forbidden $\\beta^-$ decay transitions of\n$^{46}$K and $^{47}$K corresponding to recently available experimental data\nfrom the GRIFFIN spectrometer at TRIUMF-ISAC [Phys. Rev. C {\\bf 100}, 054327\n(2019); Phys. Rev. C {\\bf 102}, 054314 (2020)]. We perform the nuclear\nshell-model calculation in $sdpf$-valence space, with the SDPF-MU interaction,\nto calculate low-lying energy spectra of $^{46,47}$K and $^{46,47}$Ca. For\nfurther investigation, we also calculate spectroscopic properties of\n$^{46,47}$K, $^{46,47}$Ca and compare the results with the experimental data.\nWe suggest spin-parity of several levels, which were previously tentative, in\nboth the calcium isotopes. Based on the energy and $\\log ft$ values, we\nconclude that the state at 3.984 MeV in $^{46}$Ca could be either $3^{-}$ or\n$2^{+}$ and a state at 4.432 MeV might be $3^{-}$. The spin-parity of the\nstates at 2.875, 3.951 and 4.453 MeV are predicted to be $1/2^-$, $3/2^-$ and\n$1/2^-$ or $1/2^+$, respectively, in $^{47}$Ca. We also give the confirmation\nto spin-parity of 2.850, 3.889 and 4.606 MeV states for $^{47}$Ca. Our results\nof level schemes of $^{46,47}$K are in a reasonable agreement with the\nexperimental data, while the agreement of calculated $\\log ft$ values for the\n$\\beta^-$ decay with the experimental data is limited.\n"}
+{"abstract": "  This paper presents a method for learning logical task specifications and\ncost functions from demonstrations. Linear temporal logic (LTL) formulas are\nwidely used to express complex objectives and constraints for autonomous\nsystems. Yet, such specifications may be challenging to construct by hand.\nInstead, we consider demonstrated task executions, whose temporal logic\nstructure and transition costs need to be inferred by an autonomous agent. We\nemploy a spectral learning approach to extract a weighted finite automaton\n(WFA), approximating the unknown logic structure of the task. Thereafter, we\ndefine a product between the WFA for high-level task guidance and a labeled\nMarkov decision process for low-level control. An inverse reinforcement\nlearning (IRL) problem is considered to learn a cost function by\nbackpropagating the loss between agent and expert behaviors through the\nplanning algorithm. Our proposed model, termed WFA-IRL, is capable of\ngeneralizing the execution of the inferred task specification in a suite of\nMiniGrid environments.\n"}
+{"abstract": "  This paper establishes bounds on the performance of empirical risk\nminimization for large-dimensional linear regression. We generalize existing\nresults by allowing the data to be dependent and heavy-tailed. The analysis\ncovers both the cases of identically and heterogeneously distributed\nobservations. Our analysis is nonparametric in the sense that the relationship\nbetween the regressand and the regressors is assumed to be unknown. The main\nresults of this paper indicate that the empirical risk minimizer achieves the\noptimal performance (up to a logarithmic factor) in a dependent data setting.\n"}
+{"abstract": "  As more robots are implemented for contact-rich tasks, tactile sensors are in\nincreasing demand. For many circumstances, the contact is required to be\ncompliant, and soft sensors are in need. This paper introduces a novelly\ndesigned soft sensor that can simultaneously estimate the contact force and\ncontact location. Inspired by humans' skin, which contains multi-layers of\nreceptors, the designed tactile sensor has a dual-layer structure. The first\nlayer is made of a conductive fabric that is responsible for sensing the\ncontact force. The second layer is composed of four small conductive rubbers\nthat can detect the contact location. Signals from the two layers are firstly\nprocessed by Wheatstone bridges and amplifier circuits so that the measurement\nnoises are eliminated, and the sensitivity is improved. An Arduino chip is used\nfor processing the signal and analyzing the data. The contact force can be\nobtained by a pre-trained model that maps from the voltage to force, and the\ncontact location is estimated by the voltage signal from the conductive rubbers\nin the second layer. In addition, filtering methods are applied to eliminate\nthe estimation noise. Finally, experiments are provided to show the accuracy\nand robustness of the sensor.\n"}
+{"abstract": "  Suppose two quantum circuit chips are located at different places, for which\nwe do not have any prior knowledge, and cannot see the internal structures\neither. If we want to find out whether they have the same functions or not with\ncertainty, what should we do? In this paper, we show that this realistic\nproblem can be solved from the viewpoints of quantum nonlocality. Specifically,\nwe design an elegant protocol that examines underlying quantum nonlocality. We\nprove that in the protocol the strongest nonlocality can be observed if and\nonly if two quantum circuits are equivalent to each other. We show that the\nprotocol also works approximately, where the distance between two quantum\ncircuits can be lower and upper bounded analytically by observed quantum\nnonlocality. Furthermore, we also discuss the possibility to generalize the\nprotocol to multipartite cases, i.e., if we do equivalence checking for\nmultiple quantum circuits, we try to solve the problem in one go. Our work\nintroduces a nontrivial application of quantum nonlocality in quantum\nengineering.\n"}
+{"abstract": "  Bayesian persuasion studies how an informed sender should partially disclose\ninformation to influence the behavior of a self-interested receiver. Classical\nmodels make the stringent assumption that the sender knows the receiver's\nutility. This can be relaxed by considering an online learning framework in\nwhich the sender repeatedly faces a receiver of an unknown, adversarially\nselected type. We study, for the first time, an online Bayesian persuasion\nsetting with multiple receivers. We focus on the case with no externalities and\nbinary actions, as customary in offline models. Our goal is to design no-regret\nalgorithms for the sender with polynomial per-iteration running time. First, we\nprove a negative result: for any $0 < \\alpha \\leq 1$, there is no\npolynomial-time no-$\\alpha$-regret algorithm when the sender's utility function\nis supermodular or anonymous. Then, we focus on the case of submodular sender's\nutility functions and we show that, in this case, it is possible to design a\npolynomial-time no-$(1 - \\frac{1}{e})$-regret algorithm. To do so, we introduce\na general online gradient descent scheme to handle online learning problems\nwith a finite number of possible loss functions. This requires the existence of\nan approximate projection oracle. We show that, in our setting, there exists\none such projection oracle which can be implemented in polynomial time.\n"}
+{"abstract": "  We present newly-calibrated period-$\\phi_{31}$-[Fe/H] relations for\nfundamental mode RR Lyrae stars in the optical and, for the first time,\nmid-infrared. This work's calibration dataset provides the largest and most\ncomprehensive span of parameter space to date with homogeneous metallicities\nfrom $-3<\\textrm{[Fe/H]}<0.4$ and accurate Fourier parameters derived from 1980\nASAS-SN ($V$-band) and 1083 WISE (NEOWISE extension, $W1$ and $W2$ bands) RR\nLyrae stars with well-sampled light curves. We compare our optical\nperiod-$\\phi_{31}$-[Fe/H] with those available in the literature and\ndemonstrate that our relation minimizes systematic trends in the lower and\nhigher metallicity range. Moreover, a direct comparison shows that our optical\nphotometric metallicities are consistent with both those from high-resolution\nspectroscopy and globular clusters, supporting the good performance of our\nrelation. We found an intrinsic scatter in the photometric metallicities (0.41\ndex in the $V$-band and 0.50 dex in the infrared) by utilizing large\ncalibration datasets covering a broad metallicity range. This scatter becomes\nsmaller when optical and infrared bands are used together (0.37 dex). Overall,\nthe relations derived in this work have many potential applications, including\nlarge-area photometric surveys with JWST in the infrared and LSST in the\noptical.\n"}
+{"abstract": "  Vortices are whirling disturbances commonly found in nature ranging from\ntremendously small scales in Bose-Einstein condensates to cosmologically\ncolossal scales in spiral galaxies. An optical vortex, generally associated\nwith a spiral phase, can carry orbital angular momentum (OAM). The optical OAM\ncan either be in the longitudinal direction if the spiral phase twists in the\nspatial domain or in the transverse direction if the phase rotates in the\nspatiotemporal domain. In this article, we demonstrate the intersection of\nspatiotemporal vortices and spatial vortices in a wave packet. As a result of\nthis intersection, the wave packet hosts a tilted OAM that provides an\nadditional degree of freedom to the applications that harness the OAM of\nphotons.\n"}
+{"abstract": "  With the rapid advances in IoT, edge and cloud computing solutions, it is\ncritical to educate and train students in computer science and engineering in\nvarious aspects of IoT-edge-cloud (IoT-E-C) system architecture\nimplementations. We outline the design and development of an undergraduate\nlaboratory course that sets the goal of implementing various interfaces and\ncommunication protocols to connect IoT, edge and cloud computing systems and\nevaluating their performance. The lab setup is modular and based on open source\ntools. In the IoT context, it consists of low-cost processing platforms with\nvarious sensors and actuators. In the edge and cloud computing context, we\nimplement and deploy single board computers and Firebase cloud solutions,\nrespectively. The modular lab setup allows students to engineer and integrate\nvarious communication protocol solutions, including MQTT, COAP and HTTP. In\naddition to the system implementation, students can evaluate and benchmark the\nperformance of the entire system.\n"}
+{"abstract": "  We find the Hecke-Rogers type series representations of generating functions\nof the Hurwitz class numbers which is very close to certain mock theta\nfunctions. We also prove two combinatorial interpretation of Hurwitz class\nnumbers appeared on OEIS.\n"}
+{"abstract": "  The efficiency and accuracy of mapping are crucial in a large scene and\nlong-term AR applications. Multi-agent cooperative SLAM is the precondition of\nmulti-user AR interaction. The cooperation of multiple smart phones has the\npotential to improve efficiency and robustness of task completion and can\ncomplete tasks that a single agent cannot do. However, it depends on robust\ncommunication, efficient location detection, robust mapping, and efficient\ninformation sharing among agents. We propose a multi-intelligence collaborative\nmonocular visual-inertial SLAM deployed on multiple ios mobile devices with a\ncentralized architecture. Each agent can independently explore the environment,\nrun a visual-inertial odometry module online, and then send all the measurement\ninformation to a central server with higher computing resources. The server\nmanages all the information received, detects overlapping areas, merges and\noptimizes the map, and shares information with the agents when needed. We have\nverified the performance of the system in public datasets and real\nenvironments. The accuracy of mapping and fusion of the proposed system is\ncomparable to VINS-Mono which requires higher computing resources.\n"}
+{"abstract": "  The oscillation model, based on the wave equation on networks, can describe\nuser dynamics in online social networks. The fundamental equation of user\ndynamics can be introduced into the oscillation model to explicitly describe\nthe causal relation of user dynamics yielded by certain specific network\nstructures. Moreover, by considering the sparseness of the link structure of\nonline social networks, a novel fundamental equation of different forms has\nbeen devised. In this paper, we derive a closed-form solution of the new\nfundamental equation. Also, we show that the closed-form solution of the new\nfundamental equation can generate the general solution of the original wave\nequation and investigate the characteristics of the derived general solution.\n"}
+{"abstract": "  A multitude of wireless devices for radio local area networks (RLAN) use\nfrequency bands that are shared with other radio services or applications.\nEuropean market surveillance activities on 5 GHz RLAN indicate a growing number\nof non-compliant devices that do not fulfill all radio regulation requirements\nand may cause interference. This paper describes a concept and method for\nmitigating the use of non-compliant equipment based on a public key\ninfrastructure. If every RLAN device holds a security certificate, which is\nrevoked when non-compliance is found, then the connection establishment between\ndevices can be made dependent on compliance. The aim is that legitimate\nequipment rejects connections to non-compliant devices, so that non-compliant\ndevices will become isolated.\n"}
+{"abstract": "  Under an applied traction, highly concentrated suspensions of solid particles\nin fluids can turn from a state in which they flow to a state in which they\ncounteract the traction as an elastic solid: a shear-jammed state. Remarkably,\nthe suspension can turn back to the flowing state simply by inverting the\ntraction. A tensorial model is presented and tested in paradigmatic cases. We\nshow that, to reproduce the phenomenology of shear jamming in generic\ngeometries, it is necessary to link this effect to the elastic response\nsupported by the suspension microstructure rather than to a divergence of the\nviscosity.\n"}
+{"abstract": "  The flow past a fixed finite-length circular cylinder, the axis of which\nmakes a nonzero angle with the incoming stream, is studied through\nfully-resolved simulations, from creeping-flow conditions to strongly inertial\nregimes. The investigation focuses on the way the body aspect ratio $\\chi$\n(defined as as the length-to-diameter ratio), the inclination angle $\\theta$\nwith respect to the incoming flow and the Reynolds number $\\text{Re}$ (based on\nthe cylinder diameter) affect the flow structure past the body and therefore\nthe hydrodynamic loads acting on it. The configuration $\\theta=0^\\circ$ (where\nthe cylinder is aligned with the flow) is considered first, from creeping-flow\nconditions up to $\\text{Re}=400$, with aspect ratios up to $20$ ($10$) for\n$\\text{Re}\\leq10$ ($\\text{Re}\\geq10$). In the low-to-moderate Reynolds number\nregime ($\\text{Re}\\lesssim5$), influence or the aspect ratio, inclination (from\n$0^{\\circ}$ to $30^{\\circ}$), and inertial effects is examined by comparing\nnumerical results for the axial and transverse force components and the\nspanwise torque with theoretical predictions based on the slender-body\napproximation, possibly incorporating finite-Reynolds-number corrections.\nSemiempirical models based on these predictions and incorporating finite-length\nand inertial corrections extracted from the numerical data are derived. For\nlarge enough Reynolds numbers ($\\text{Re}\\gtrsim10^2$), separation takes place\nalong the upstream part of the lateral surface of the cylinder, deeply\ninfluencing the surface stress distribution. Numerical results are used to\nbuild empirical models for the force components and the torque, valid for\nmoderately inclined cylinders ($|\\theta|\\lesssim30^\\circ$) of arbitrary aspect\nratio up to $\\text{Re}\\approx300$ and matching those obtained at\nlow-to-moderate Reynolds number.\n"}
+{"abstract": "  Fine-grained image recognition is very challenging due to the difficulty of\ncapturing both semantic global features and discriminative local features.\nMeanwhile, these two features are not easy to be integrated, which are even\nconflicting when used simultaneously. In this paper, a retrieval-based\ncoarse-to-fine framework is proposed, where we re-rank the TopN classification\nresults by using the local region enhanced embedding features to improve the\nTop1 accuracy (based on the observation that the correct category usually\nresides in TopN results). To obtain the discriminative regions for\ndistinguishing the fine-grained images, we introduce a weakly-supervised method\nto train a box generating branch with only image-level labels. In addition, to\nlearn more effective semantic global features, we design a multi-level loss\nover an automatically constructed hierarchical category structure. Experimental\nresults show that our method achieves state-of-the-art performance on three\nbenchmarks: CUB-200-2011, Stanford Cars, and FGVC Aircraft. Also,\nvisualizations and analysis are provided for better understanding.\n"}
+{"abstract": "  The gradient descent-ascent (GDA) algorithm has been widely applied to solve\nminimax optimization problems. In order to achieve convergent policy parameters\nfor minimax optimization, it is important that GDA generates convergent\nvariable sequences rather than convergent sequences of function values or\ngradient norms. However, the variable convergence of GDA has been proved only\nunder convexity geometries, and there lacks understanding for general nonconvex\nminimax optimization. This paper fills such a gap by studying the convergence\nof a more general proximal-GDA for regularized nonconvex-strongly-concave\nminimax optimization. Specifically, we show that proximal-GDA admits a novel\nLyapunov function, which monotonically decreases in the minimax optimization\nprocess and drives the variable sequence to a critical point. By leveraging\nthis Lyapunov function and the K{\\L} geometry that parameterizes the local\ngeometries of general nonconvex functions, we formally establish the variable\nconvergence of proximal-GDA to a critical point $x^*$, i.e., $x_t\\to x^*,\ny_t\\to y^*(x^*)$. Furthermore, over the full spectrum of the\nK{\\L}-parameterized geometry, we show that proximal-GDA achieves different\ntypes of convergence rates ranging from sublinear convergence up to finite-step\nconvergence, depending on the geometry associated with the K{\\L} parameter.\nThis is the first theoretical result on the variable convergence for nonconvex\nminimax optimization.\n"}
+{"abstract": "  After an artificial model background subtraction, the pixels have been\nlabelled as foreground and background. Previous approaches to secondary\nprocessing the output for denoising usually use traditional methods such as the\nBayesian refinement method. In this paper, we focus on using a modified U-Net\nmodel to approximate the result of the Bayesian refinement method and improve\nthe result. In our modified U-Net model, the result of background subtraction\nfrom other models will be combined with the source image as input for learning\nthe statistical distribution. Thus, the losing information caused by the\nbackground subtraction model can be restored from the source image. Moreover,\nsince the part of the input image is already the output of the other background\nsubtraction model, the feature extraction should be convenient, it only needs\nto change the labels of the noise pixels. Compare with traditional methods,\nusing deep learning methods superiority in keeping details.\n"}
+{"abstract": "  In both flat and curved spacetimes, there are weak and strong versions of the\nanti-Unruh/anti-Hawking effects, in which the KMS field temperature is\nanti-correlated with the response of a detector and its inferred temperature.\nWe investigate for the first time the effects on the weak and strong\nanti-Hawking effects for an Unruh-DeWitt detector orbiting a BTZ black hole in\nthe co-rotating frame. We find that rotation can significantly amplify the\nstrength of the weak anti-Hawking effect, whereas it can either amplify or\nreduce the strength of the strong anti-Hawking effect depending on boundary\nconditions. For the strong anti-Hawking effect, we find a non-monotonic\nrelationship between the angular momentum and detector temperature for each\nboundary condition. In addition, we note that the weak anti-Hawking effect is\nindependent of a changing AdS length, while a longer AdS length increases the\ntemperature range of the strong anti-Hawking effect.\n"}
+{"abstract": "  In this work, we explore the constant-Q transform (CQT) for speech emotion\nrecognition (SER). The CQT-based time-frequency analysis provides variable\nspectro-temporal resolution with higher frequency resolution at lower\nfrequencies. Since lower-frequency regions of speech signal contain more\nemotion-related information than higher-frequency regions, the increased\nlow-frequency resolution of CQT makes it more promising for SER than standard\nshort-time Fourier transform (STFT). We present a comparative analysis of\nshort-term acoustic features based on STFT and CQT for SER with deep neural\nnetwork (DNN) as a back-end classifier. We optimize different parameters for\nboth features. The CQT-based features outperform the STFT-based spectral\nfeatures for SER experiments. Further experiments with cross-corpora evaluation\ndemonstrate that the CQT-based systems provide better generalization with\nout-of-domain training data.\n"}
+{"abstract": "  Does a physical field exist independently of the interaction between the\nfield source and the test body used to measure it at a given point? What does\npropagate between two physical bodies when they interact? These are the\nfundamental questions we answer in the present work for electromagnetic and\ngravitational interactions. We come to the conclusion that gravitational fields\nas well as spacetime curvature cannot depend on the passive-gravitational to\ninertial mass ratio of the test bodies, because this would lead to energy\nconservation and causality violation except when this ratio is universally\nexactly equal to one. For what concerns the hypothetical dependence of\nelectromagnetic fields on the charge to inertial-mass ratio of test bodies,\nthis would call for the propagation of an operationally ill defined quantity\n(Coulomb . Meter$^2$ / Second$^2$) that would replace energy (Joule = Kilogram\n. Meter$^2$ / Second$^2$) transfer in the interaction between two electrically\ncharged bodies. Hence electromagnetic fields cannot depend on the charge to\ninertial-mass ratio of test bodies. A consequence of these results is also the\ndisqualification of Murat Ozer's unification theory of gravitation and\nelectromagnetism.\n"}
+{"abstract": "  A qualitative and quantitative extension of the chaotic models used to\ngenerate self-similar traffic with long-range dependence (LRD) is presented by\nmeans of the formulation of a model that considers the use of piecewise affine\none-dimensional maps. Based on the disaggregation of the temporal series\ngenerated, a valid explanation of the behavior of the values of Hurst exponent\nis proposed and the feasibility of their control from the parameters of the\nproposed model is shown.\n"}
+{"abstract": "  We investigate the local differential privacy (LDP) guarantees of a\nrandomized privacy mechanism via its contraction properties. We first show that\nLDP constraints can be equivalently cast in terms of the contraction\ncoefficient of the $E_\\gamma$-divergence. We then use this equivalent formula\nto express LDP guarantees of privacy mechanisms in terms of contraction\ncoefficients of arbitrary $f$-divergences. When combined with standard\nestimation-theoretic tools (such as Le Cam's and Fano's converse methods), this\nresult allows us to study the trade-off between privacy and utility in several\ntesting and minimax and Bayesian estimation problems.\n"}
+{"abstract": "  In various provers and deductive verification tools, logical transformations\nare used extensively in order to reduce a proof task into a number of simpler\ntasks. Logical transformations are often part of the trusted base of such\ntools. In this paper, we develop a framework to improve confidence in their\nresults. We follow a modular and skeptical approach: transformations are\ninstrumented independently of each other and produce certificates that are\nchecked by a third-party tool. Logical transformations are considered in a\nhigher-order logic, with type polymorphism and built-in theories such as\nequality and integer arithmetic. We develop a language of proof certificates\nfor them and use it to implement the full chain of certificate generation and\ncertificate verification.\n"}
+{"abstract": "  We study the growth of linear matter density perturbations in a modified\ngravity approach of scalar field couplings with metric and torsion. In the\nequivalent scalar-tensor formulation, the matter fields in the Einstein frame\ninteract as usual with an effective dark energy component, whose dynamics is\npresumably governed by a scalar field that sources a torsion mode. As a\nconsequence, the matter density ceases to be self-conserved, thereby making an\nimpact not only on the background cosmological evolution but also on the\nperturbative spectrum of the local inhomogeneities. In order to estimate the\neffect on the growth of the linear matter perturbations, with the least\npossible alteration of the standard parametric form of the growth factor, we\nresort to a suitable Taylor expansion of the corresponding exponent, known as\nthe growth index, about the value of the cosmic scale factor at the present\nepoch. In particular, we obtain an appropriate fitting formula for the growth\nindex in terms of the coupling function and the matter density parameter. While\nthe overall parametric formulation of the growth factor is found to fit well\nwith the latest redshift-space-distortion (RSD) and the observational Hubble\n(OH) data at low redshifts, the fitting formula enables us to constrain the\ngrowth index to well within the concordant cosmological limits, thus ensuring\nthe viability of the formalism.\n"}
+{"abstract": "  Traditionally, promoted by the internet companies, continuous delivery is\nmore and more appealing to industries which develop systems with\nsafety-critical functions. Since safety-critical systems must meet regulatory\nrequirements and require specific safety assessment processes in addition to\nthe normal development steps, enabling continuous delivery of software in\nsafety-critical systems requires the automation of the safety assessment\nprocess in the delivery pipeline. In this paper, we outline a continuous\ndelivery pipeline for realizing continuous safety assessment in\nsoftware-intensive safety-critical systems based on model-based safety\nassessment methods.\n"}
+{"abstract": "  A domain in $\\mathbb{R}^3$ that touches the $x_3$ axis at one point is found\nwith the following property. For any initial value in a $C^2$ class, the\naxially symmetric Navier Stokes equations with Navier slip boundary condition\nhas a finite energy solution that stays bounded for any given time, i.e. no\nfinite time blow up of the fluid velocity occurs. The result seems to be the\nfirst case where the Navier-Stokes regularity problem is solved beyond\ndimension 2.\n"}
+{"abstract": "  With the development of computer-aided diagnosis (CAD) and image scanning\ntechnology, Whole-slide Image (WSI) scanners are widely used in the field of\npathological diagnosis. Therefore, WSI analysis has become the key to modern\ndigital pathology. Since 2004, WSI has been used more and more in CAD. Since\nmachine vision methods are usually based on semi-automatic or fully automatic\ncomputers, they are highly efficient and labor-saving. The combination of WSI\nand CAD technologies for segmentation, classification, and detection helps\nhistopathologists obtain more stable and quantitative analysis results, save\nlabor costs and improve diagnosis objectivity. This paper reviews the methods\nof WSI analysis based on machine learning. Firstly, the development status of\nWSI and CAD methods are introduced. Secondly, we discuss publicly available WSI\ndatasets and evaluation metrics for segmentation, classification, and detection\ntasks. Then, the latest development of machine learning in WSI segmentation,\nclassification, and detection are reviewed continuously. Finally, the existing\nmethods are studied, the applicabilities of the analysis methods are analyzed,\nand the application prospects of the analysis methods in this field are\nforecasted.\n"}
+{"abstract": "  We show that Riemann-Hurwitz-style translation formulas obtained by Kuz'min,\nKida, Iwasawa, Wingberg et alii for the lambda invariant attached to certain\nIwasawa moduli in cyclotomic Z{\\ell}-extension of number fields are essentially\nequivalent. More precisely, we prove that all these formulas,including those\nstated in terms of representations, resul tidentically for purely algebraic\nreasons from the arithmetic computation of a suitable Herbrand quotient which\nit suffices to carry out in the cyclic case of prime degree {\\ell}.\n"}
+{"abstract": "  New measurement and assessment techniques have been applied to the\nradiochemical re-evaluation of the Trinity Event. Thirteen trinitite samples\nwere dissolved and analyzed using a combination of traditional decay counting\nmethods and the mass spectrometry techniques. The resulting data were assessed\nusing advanced simulation tools to afford a final yield determination of $24.8\n\\pm 2$ kilotons TNT equivalent, substantially higher than the previous DOE\nreleased value of 21 kilotons. This article is intended to complement the work\nof Susan Hanson and Warren Oldham, seen elsewhere in this issue.\n"}
+{"abstract": "  Code generation aims to automatically generate a piece of code given an input\nnatural language utterance. Currently, among dominant models, it is treated as\na sequence-to-tree task, where a decoder outputs a sequence of actions\ncorresponding to the pre-order traversal of an Abstract Syntax Tree. However,\nsuch a decoder only exploits the preorder traversal based preceding actions,\nwhich are insufficient to ensure correct action predictions. In this paper, we\nfirst throughly analyze the context modeling difference between neural code\ngeneration models with different traversals based decodings (preorder traversal\nvs breadth-first traversal), and then propose to introduce a mutual learning\nframework to jointly train these models. Under this framework, we continuously\nenhance both two models via mutual distillation, which involves synchronous\nexecutions of two one-to-one knowledge transfers at each training step. More\nspecifically, we alternately choose one model as the student and the other as\nits teacher, and require the student to fit the training data and the action\nprediction distributions of its teacher. By doing so, both models can fully\nabsorb the knowledge from each other and thus could be improved simultaneously.\nExperimental results and in-depth analysis on several benchmark datasets\ndemonstrate the effectiveness of our approach. We release our code at\nhttps://github.com/DeepLearnXMU/CGML.\n"}
+{"abstract": "  Integration of modern defence weapons into ship power systems poses a\nchallenge in terms of meeting the high ramp rate requirements of those loads.\nIt might be demanding for the generators to meet the ramp rates of these loads.\nFailure to meet so, might lead to stability issues. This is addressed by\nconglomeration of generators and energy storage elements to handle the required\npower demand posed by loads. This paper proposes an energy management strategy\nbased on model predictive control that incorporates the uncertainty in the load\nprediction. The proposed controller optimally coordinates the power split\nbetween the generators and energy storage elements to guarantee that the power\ndemand is met taking into considerations the ramp rate limitations and the load\nuncertainty. A low bandwidth model consisting of a single generator and a\nsingle energy storage element is used to validate the results of the proposed\nenergy management strategy. The results demonstrate the robustness of the\ncontroller under load prediction uncertainty and demonstrate the effect of load\nuncertainty on battery capacity loss.\n"}
+{"abstract": "  Recently, a lot of effort has been dedicated to developing next-generation\noptoelectronic devices based on two-dimensional materials, thanks to their\nunique optical properties that are significantly different from those of their\nbulk counterparts. In order to implement high-performance nanoscale optical\ndevices, an in-depth study of how linear and non-linear propagation occurs in\ntwo-dimensional materials is required. Here, we focus on the theory behind the\npropagation of electromagnetic waves in two-dimensional materials as well as\nemerging applications in the fields of electronics, optics, sensors, which are\nsummarized and discussed in the paper.\n"}
+{"abstract": "  Non-Volatile Random Access Memory (NVRAM) is a novel type of hardware that\ncombines the benefits of traditional persistent memory (persistency of data\nover hardware failures) and DRAM (fast random access). In this work, we\ndescribe an algorithm that can be used to execute NVRAM programs and recover\nthe system after a hardware failure while taking the architecture of real-world\nNVRAM systems into account. Moreover, the algorithm can be used to execute\nNVRAM-destined programs on commodity persistent hardware, such as hard drives.\nThat allows us to test NVRAM algorithms using only cheap hardware, without\nhaving access to the NVRAM. We report the usage of our algorithm to implement\nand test NVRAM CAS algorithm.\n"}
+{"abstract": "  In this paper, we consider a future electricity market consisting of\naggregated energy prosumers, who are equipped with local wind power plants\n(WPPs) to support (part of) their energy demands and can also trade energy with\nday-ahead market (DAM) and energy balancing market (EBM). In addition, an\nenergy aggregator (EA) is established, who can provide the trading gateways\nbetween prosumers and the markets. The EA is responsible for making pricing\nstrategies on the prosumers to influence their trading behaviours such that the\nsocial benefit of the prosumer community is improved. Specifically, two price\npackages are provided by the EA: wholesale price (WP) package and lump-sum (LS)\npackage, which can be flexibly selected by prosumers based on their own\npreferences. Analytical energy-trading strategies will be derived for WP\nprosumers and LS prosumers based on non-cooperative games and Nash resource\nallocation strategies, respectively. In this work, a social cost optimization\nproblem will be formulated for the EA, where the detailed WP/LS selection plans\nare unknown in advance. Consequently, a stochastic Stackelberg game between\nprosumers and the EA is formulated, and a two-level stochastic convex\nprogramming algorithm is proposed to minimize the expectation of the social\ncost. The performance of the proposed algorithm is demonstrated with a\ntwo-settlement based market model in the simulation.\n"}
+{"abstract": "  Hand tracking has become an integral feature of recent generations of\nimmersive virtual reality head-mounted displays. With the widespread adoption\nof this feature, hardware engineers and software developers are faced with an\nexciting array of opportunities and a number of challenges, mostly in relation\nto the human user. In this article, I outline what I see as the main\npossibilities for hand tracking to add value to immersive virtual reality as\nwell as some of the potential challenges in the context of the psychology and\nneuroscience of the human user. It is hoped that this paper serves as a roadmap\nfor the development of best practices in the field for the development of\nsubsequent generations of hand tracking and virtual reality technologies.\n"}
+{"abstract": "  Let $(X,d,\\mathfrak{m})$ be a metric measure space. The study of the\nWasserstein space $(\\mathbb{P}_p(X),\\mathbb{W}_p)$ associated to $X$ has proved\nuseful in describing several geometrical properties of $X.$ In this paper we\nfocus on the study of isometries of $\\mathbb{P}_p(X)$ for $p \\in (1,\\infty)$\nunder the assumption that there is some characterization of optimal maps\nbetween measures, the so called Good transport behaviour $GTB_p$. Our first\nresult states that the set of Dirac deltas is invariant under isometries of the\nWasserstein space. Additionally we obtain that the isometry groups of the base\nRiemannian manifold $M$ coincides with the one of the Wasserstein space\n$\\mathbb{P}_p(M)$ under assumptions on the manifold; namely, for $p=2$ that the\nsectional curvature is strictly positive and for general $p\\in (1,\\infty)$ that\n$M$ is a Compact Rank One Symmetric Space.\n"}
+{"abstract": "  We report the discovery of a new high mass X-ray binary pulsar, XMMU\nJ050722.1-684758, possibly associated with the supernova remnant MCSNR\nJ0507-6847 in the Large Magellanic Cloud, using XMM-Newton X-ray observations.\nPulsations with a periodicity of 570 s are discovered from the Be X-ray binary\nXMMU J050722.1-684758 confirming its nature as a HMXB pulsar. The HMXB is\nlocated near the geometric centre of the supernova remnant MCSNR J0507-6847\n(0.9 arcmin from the centre) which supports the XRB-SNR association. The\nestimated age of the supernova remnant is 43-63 kyr which points to a middle\naged to old supernova remnant. The large diameter of the supernova remnant\ncombined with the lack of distinctive shell counterparts in optical and radio\nindicates that the SNR is expanding into the tenous environment of the\nsuperbubble N103. The estimated magnetic field strength of the neutron star is\n$B\\gtrsim10^{14}$ G assuming a spin equilibrium condition which is expected\nfrom the estimated age of the parent remnant and assuming that the measured\nmass-accretion rate remained constant throughout.\n"}
+{"abstract": "  We consider the problem of coded distributed computing where a large linear\ncomputational job, such as a matrix multiplication, is divided into $k$ smaller\ntasks, encoded using an $(n,k)$ linear code, and performed over $n$ distributed\nnodes. The goal is to reduce the average execution time of the computational\njob. We provide a connection between the problem of characterizing the average\nexecution time of a coded distributed computing system and the problem of\nanalyzing the error probability of codes of length $n$ used over erasure\nchannels. Accordingly, we present closed-form expressions for the execution\ntime using binary random linear codes and the best execution time any\nlinear-coded distributed computing system can achieve. It is also shown that\nthere exist \\textit{good} binary linear codes that not only attain\n(asymptotically) the best performance that any linear code (not necessarily\nbinary) can achieve but also are numerically stable against the inevitable\nrounding errors in practice. We then develop a low-complexity algorithm for\ndecoding Reed-Muller (RM) codes over erasure channels. Our decoder only\ninvolves additions, subtractions, {and inversion of relatively small matrices\nof dimensions at most $\\log n+1$}, and enables coded computation over\nreal-valued data. Extensive numerical analysis of the fundamental results as\nwell as RM- and polar-coded computing schemes demonstrate the excellence of the\nRM-coded computation in achieving close-to-optimal performance while having a\nlow-complexity decoding and explicit construction. The proposed framework in\nthis paper enables efficient designs of distributed computing systems given the\nrich literature in the channel coding theory.\n"}
+{"abstract": "  GuessWhat?! is a two-player visual dialog guessing game where player A asks a\nsequence of yes/no questions (Questioner) and makes a final guess (Guesser)\nabout a target object in an image, based on answers from player B (Oracle).\nBased on this dialog history between the Questioner and the Oracle, a Guesser\nmakes a final guess of the target object. Previous baseline Oracle model\nencodes no visual information in the model, and it cannot fully understand\ncomplex questions about color, shape, relationships and so on. Most existing\nwork for Guesser encode the dialog history as a whole and train the Guesser\nmodels from scratch on the GuessWhat?! dataset. This is problematic since\nlanguage encoder tend to forget long-term history and the GuessWhat?! data is\nsparse in terms of learning visual grounding of objects. Previous work for\nQuestioner introduces state tracking mechanism into the model, but it is\nlearned as a soft intermediates without any prior vision-linguistic insights.\nTo bridge these gaps, in this paper we propose Vilbert-based Oracle, Guesser\nand Questioner, which are all built on top of pretrained vision-linguistic\nmodel, Vilbert. We introduce two-way background/target fusion mechanism into\nVilbert-Oracle to account for both intra and inter-object questions. We propose\na unified framework for Vilbert-Guesser and Vilbert-Questioner, where\nstate-estimator is introduced to best utilize Vilbert's power on single-turn\nreferring expression comprehension. Experimental results show that our proposed\nmodels outperform state-of-the-art models significantly by 7%, 10%, 12% for\nOracle, Guesser and End-to-End Questioner respectively.\n"}
+{"abstract": "  A classical density functional theory (cDFT) based on the PC-SAFT equation of\nstate is proposed for the calculation of adsorption equilibria of pure\nsubstances and their mixtures in covalent organic frameworks (COFs). Adsorption\nisotherms of methane, ethane, n-butane and nitrogen in the COFs TpPa-1 and\n2,3-DhaTph are calculated and compared to results from grand canonical Monte\nCarlo (GCMC) simulations. Mixture adsorption is investigated for the\nmethane/ethane and methane/n-butane binary systems. Excellent agreement between\nPC-SAFT DFT and GCMC is obtained for all adsorption isotherms up to pressures\nof 50 bar. The cDFT formalism accurately predicts the selective accumulation of\nlonger hydrocarbons for binary mixtures in the considered COFs. This\napplication shows substantial predictive power of PC-SAFT DFT solved in\nthree-dimensional geometries and the results suggest the method can in the\nfuture also be applied for efficient optimization of force field parameters or\nof structural properties of the porous material based on an analytical theory\nas opposed to a stochastic simulation.\n"}
+{"abstract": "  Factorization-based gradient descent is a scalable and efficient algorithm\nfor solving low-rank matrix completion. Recent progress in structured\nnon-convex optimization has offered global convergence guarantees for gradient\ndescent under certain statistical assumptions on the low-rank matrix and the\nsampling set. However, while the theory suggests gradient descent enjoys fast\nlinear convergence to a global solution of the problem, the universal nature of\nthe bounding technique prevents it from obtaining an accurate estimate of the\nrate of convergence. In this paper, we perform a local analysis of the exact\nlinear convergence rate of gradient descent for factorization-based matrix\ncompletion for symmetric matrices. Without any additional assumptions on the\nunderlying model, we identify the deterministic condition for local convergence\nof gradient descent, which only depends on the solution matrix and the sampling\nset. More crucially, our analysis provides a closed-form expression of the\nasymptotic rate of convergence that matches exactly with the linear convergence\nobserved in practice. To the best of our knowledge, our result is the first one\nthat offers the exact rate of convergence of gradient descent for matrix\nfactorization in Euclidean space for matrix completion.\n"}
+{"abstract": "  The importance of digital identity as a foundation for digital public\nservices is considered. As the classical, centralised model digital identity\nhas proven to be subject to several limitations, self-sovereign identities are\nproposed as replacement, especially in the context of e-government platforms\nand direct participation to policymaking (e.g. through e-voting tools).\n"}
+{"abstract": "  The US Food and Drug Administration (FDA) has been actively promoting the use\nof real-world data (RWD) in drug development. RWD can generate important\nreal-world evidence reflecting the real-world clinical environment where the\ntreatments are used. Meanwhile, artificial intelligence (AI), especially\nmachine- and deep-learning (ML/DL) methods, have been increasingly used across\nmany stages of the drug development process. Advancements in AI have also\nprovided new strategies to analyze large, multidimensional RWD. Thus, we\nconducted a rapid review of articles from the past 20 years, to provide an\noverview of the drug development studies that use both AI and RWD. We found\nthat the most popular applications were adverse event detection, trial\nrecruitment, and drug repurposing. Here, we also discuss current research gaps\nand future opportunities.\n"}
+{"abstract": "  We present Aurora, an open-source package for particle transport, neutrals\nand radiation modeling in magnetic confinement fusion plasmas. Aurora's modern\nmulti-language interface enables simulations of 1.5D impurity transport within\nhigh-performance computing frameworks, particularly for the inference of\nparticle transport coefficients. A user-friendly Python library allows simple\ninteraction with atomic rates from the Atomic Data and Atomic Structure\ndatabase as well as other sources. This enables a range of radiation\npredictions, both for power balance and spectroscopic analysis. We discuss here\nthe superstaging approximation for complex ions, as a way to group charge\nstates and reduce computational cost, demonstrating its wide applicability\nwithin the Aurora forward model and beyond. Aurora also facilitates neutral\nparticle analysis, both from experimental spectroscopic data and other\nsimulation codes. Leveraging Aurora's capabilities to interface SOLPS-ITER\nresults, we demonstrate that charge exchange is unlikely to affect the total\nradiated power from the ITER core during high performance operation. Finally,\nwe describe the ImpRad module in the OMFIT framework, developed to enable\nexperimental analysis and transport inferences on multiple devices using\nAurora.\n"}
+{"abstract": "  We consider the inference problem for high-dimensional linear models, when\ncovariates have an underlying spatial organization reflected in their\ncorrelation. A typical example of such a setting is high-resolution imaging, in\nwhich neighboring pixels are usually very similar. Accurate point and\nconfidence intervals estimation is not possible in this context with many more\ncovariates than samples, furthermore with high correlation between covariates.\nThis calls for a reformulation of the statistical inference problem, that takes\ninto account the underlying spatial structure: if covariates are locally\ncorrelated, it is acceptable to detect them up to a given spatial uncertainty.\nWe thus propose to rely on the $\\delta$-FWER, that is the probability of making\na false discovery at a distance greater than $\\delta$ from any true positive.\nWith this target measure in mind, we study the properties of ensembled\nclustered inference algorithms which combine three techniques: spatially\nconstrained clustering, statistical inference, and ensembling to aggregate\nseveral clustered inference solutions. We show that ensembled clustered\ninference algorithms control the $\\delta$-FWER under standard assumptions for\n$\\delta$ equal to the largest cluster diameter. We complement the theoretical\nanalysis with empirical results, demonstrating accurate $\\delta$-FWER control\nand decent power achieved by such inference algorithms.\n"}
+{"abstract": "  Current black-box variational inference (BBVI) methods require the user to\nmake numerous design choices -- such as the selection of variational objective\nand approximating family -- yet there is little principled guidance on how to\ndo so. We develop a conceptual framework and set of experimental tools to\nunderstand the effects of these choices, which we leverage to propose best\npractices for maximizing posterior approximation accuracy. Our approach is\nbased on studying the pre-asymptotic tail behavior of the density ratios\nbetween the joint distribution and the variational approximation, then\nexploiting insights and tools from the importance sampling literature. Our\nframework and supporting experiments help to distinguish between the behavior\nof BBVI methods for approximating low-dimensional versus\nmoderate-to-high-dimensional posteriors. In the latter case, we show that\nmass-covering variational objectives are difficult to optimize and do not\nimprove accuracy, but flexible variational families can improve accuracy and\nthe effectiveness of importance sampling -- at the cost of additional\noptimization challenges. Therefore, for moderate-to-high-dimensional posteriors\nwe recommend using the (mode-seeking) exclusive KL divergence since it is the\neasiest to optimize, and improving the variational family or using model\nparameter transformations to make the posterior and optimal variational\napproximation more similar. On the other hand, in low-dimensional settings, we\nshow that heavy-tailed variational families and mass-covering divergences are\neffective and can increase the chances that the approximation can be improved\nby importance sampling.\n"}
+{"abstract": "  The notion of primitive chaos was proposed [J. Phys. Soc. Jpn. {\\bf 79},\n15002 (2010)] as a notion closely related to the fundamental problems of\nphysics itself such as determinism, causality, free will, predictability, and\nirreversibility. In this letter, we introduce the notion of bird's-eye view\ninto the primitive chaos, and we find a new hierarchic structure of the\nprimitive chaos. This means that if we find a chaos in a real phenomenon or a\ncomputer simulation, behind it, we can clearly realize the possibility of\ntremendous varieties of chaos in the hierarchic structure unless we can see\nthem visually.\n"}
+{"abstract": "  Thanks to Atkinson (1938), we know the first two terms of the asymptotic\nformula for the square mean integral value of the Riemann zeta function $\\zeta$\non the critical line. Following both his work and the approach of Titchmarsh\n(1986), we present an explicit version of the Atkinson formula, improving on a\nrecent bound by Simoni\\v{c} (2020). Moreover, we extend the Atkinson formula to\nthe range $\\Re(s)\\in\\left[\\frac{1}{4},\\frac{3}{4}\\right]$, giving an explicit\nbound for the square mean integral value of $\\zeta$ and improving on a bound by\nHelfgott and the authors (2019). We use mostly classical tools, such as the\napproximate functional equation and the explicit convexity bounds of the zeta\nfunction given by Backlund (1918).\n"}
+{"abstract": "  We prove hybrid subconvexity bounds twisted L-functions $L(s,f\\times \\chi)$\nat the central point using a fourth moment estimate, including a new instance\nof the Burgess subconvexity bound.\n"}
+{"abstract": "  The paper is devoted to model-theoretic properties of Kac-Moody groups with\nthe focus on elementary equivalence of Kac-Moody groups. We show that\nelementary equivalence of (untwisted) affine Kac-Moody groups implies\ncoincidence of their generalized Cartan matrices and the elementary equivalence\nof their ground fields. We also show that elementary equivalence of arbitrary\nKac-Moody groups over finite fields implies coincidence of these fields and an\nisomorphism of their twin root data. The similar result is established for\nKac-Moody groups defined over infinite subfields of the algebraic closures of\nfinite fields.\n"}
+{"abstract": "  The Chernoff bound is an important inequality relation in probability theory.\nThe original version of the Chernoff bound is to give an exponential decreasing\nbound on the tail distribution of sums of independent random variables. Recent\nyears, several works have been done by extending the original version of the\nChernoff bound to high-dimensional random objects, e.g., random matrices,\nor/and to consider the relaxation that there is no requirement of independent\nassumptions among random objects. In this work, we generalize the matrix\nexpander Chernoff bound studied by Garg et al. at work: A Matrix Expander\nChernoff Bound, to tensor expander Chernoff bounds. Our main tool is to develop\nnew tensor norm inequalities based on log-majorization techniques. These new\ntensor norm inequalities are used to bound the expectation of Ky Fan norm of\nthe random tensor exponential function, then tensor expander Chernoff bounds\ncan be established. Compared with the matrix expander Chernoff bound, the\ntensor expander Chernoff bounds proved at this work contributes following\naspects: (1) the random objects dimensions are increased from matrices\n(two-dimensional data array) to tensors (multidimensional data array); (2) this\nbound generalizes the identity map of the random objects summation to any\npolynomial function of the random objects summation; (3) Ky Fan norm, instead\nonly the maximum or the minimum eigenvalues, for the function of the random\nobjects summation is considered; (4) we remove the restriction about the\nsummation of all mapped random objects is zero, which is required in the matrix\nexpander Chernoff bound derivation.\n"}
+{"abstract": "  In this paper, we introduce a new type of generalized neural network where\nneurons and synapses maintain multiple states. We show that classical\ngradient-based backpropagation in neural networks can be seen as a special case\nof a two-state network where one state is used for activations and another for\ngradients, with update rules derived from the chain rule. In our generalized\nframework, networks have neither explicit notion of nor ever receive gradients.\nThe synapses and neurons are updated using a bidirectional Hebb-style update\nrule parameterized by a shared low-dimensional \"genome\". We show that such\ngenomes can be meta-learned from scratch, using either conventional\noptimization techniques, or evolutionary strategies, such as CMA-ES. Resulting\nupdate rules generalize to unseen tasks and train faster than gradient descent\nbased optimizers for several standard computer vision and synthetic tasks.\n"}
+{"abstract": "  We present an efficient low-rank approximation algorithm for non-negative\ntensors. The algorithm is derived from our two findings: First, we show that\nrank-1 approximation for tensors can be viewed as a mean-field approximation by\ntreating each tensor as a probability distribution. Second, we theoretically\nprovide a sufficient condition for distribution parameters to reduce Tucker\nranks of tensors; interestingly, this sufficient condition can be achieved by\niterative application of the mean-field approximation. Since the mean-field\napproximation is always given as a closed formula, our findings lead to a fast\nlow-rank approximation algorithm without using a gradient method. We\nempirically demonstrate that our algorithm is faster than the existing\nnon-negative Tucker rank reduction methods and achieves competitive or better\napproximation of given tensors.\n"}
+{"abstract": "  We construct Kahler-Einstein metrics with negative scalar curvature near an\nisolated log canonical (non-log terminal) singularity. Such metrics are\ncomplete near the singularity if the underlying space has complex dimension 2\nor if the singularity is smoothable. In complex dimension 2, we show that any\ncomplete Kahler-Einstein metric of negative scalar curvature near an isolated\nlog canonical (non-log terminal) singularity is smoothly asymptotically close\nto one of the model metrics constructed by Kobayashi and Nakamura arising from\nhyperbolic geometry.\n"}
+{"abstract": "  We introduce and characterize extremal 2-cuts for good fractal necklaces.\nUsing the characterization and the related topological properties of extremal\n2-cuts, we prove that every good necklace has a unique necklace IFS in a\ncertain sense. Also, we prove that two good necklaces admit only rigid\nhomeomorphisms and thus the group of self-homeomorphisms of a good necklace is\ncountable. In addition, a certain weaker co-Hopfian property of good necklaces\nis also obtained.\n"}
+{"abstract": "  Demand forecasting is a crucial component of demand management. While\nshortening the forecasting horizon allows for more recent data and less\nuncertainty, this frequently means lower data aggregation levels and a more\nsignificant data sparsity. Sparse demand data usually results in lumpy or\nintermittent demand patterns, which have sparse and irregular demand intervals.\nUsual statistical and machine learning models fail to provide good forecasts in\nsuch scenarios. Our research shows that competitive demand forecasts can be\nobtained through two models: predicting the demand occurrence and estimating\nthe demand size. We analyze the usage of local and global machine learning\nmodels for both cases and compare results against baseline methods. Finally, we\npropose a novel evaluation criterion of lumpy and intermittent demand\nforecasting models' performance. Our research shows that global classification\nmodels are the best choice when predicting demand event occurrence. When\npredicting demand sizes, we achieved the best results using Simple Exponential\nSmoothing forecast. We tested our approach on real-world data consisting of 516\nthree-year-long time series corresponding to European automotive original\nequipment manufacturers' daily demand.\n"}
+{"abstract": "  The strong force that binds atomic nuclei is governed by the rules of Quantum\nChromodynamics. Here we consider the suggestion the internal quark structure of\na nucleon will adjust self-consistently to the local mean scalar field in a\nnuclear medium and that this may play a profound role in nuclear structure. We\nshow that one can derive an energy density functional based on this idea, which\nsuccessfully describes the properties of atomic nuclei across the periodic\ntable in terms of a small number of physically motivated parameters. Because\nthis approach amounts to a new paradigm for nuclear theory, it is vital to find\nways to test it experimentally and we review a number of the most promising\npossibilities.\n"}
+{"abstract": "  In this work, the possibility of using niobium oxide recovered from tailings\nfrom the Penouta Sn-Nb-Ta deposit (located at the North of Spain) as starting\nmaterial for growing microstructures is demonstrated. The properties of the\nstarting material have been studied to understand its crystal structure,\nquality and purity. Recovered niobium oxide powders are mainly of TT-Nb2O5.\nThese powders have been used to grow Nb22O54 microrods by an evaporation method\nin an argon atmosphere. Different characterization techniques (X-Ray\ndiffraction, scanning electron microscopy, micro-Raman spectroscopy,\nluminescence) have been used to determine the properties of Nb22O54 microrods,\nmainly focusing on the crystal quality and refractive index. The present study\nopens the way to the transformation of waste (mine tailings) into a material of\nhigh technological value as niobium oxide, and its reintroduction into the\nvalue chain for a wide range of applications, from coatings to batteries and\nsupercapacitors.\n"}
+{"abstract": "  In the modern city, the utilization rate of public transportation attached\nimportance to the efficiency of public traffic. However, the unreasonable\ndistribution of transportation platforms results in a low utilization rate. In\nthis paper, we researched and evaluated the distribution of platforms -- bus\nand subway -- and proposed a method, called \"partial area cluster\" (PAC), to\nimprove the utilization by changing and renewing the original distribution. The\nnovel method was based on the K-means algorithm in the field of machine\nlearning. PAC worked to search the suitable bus platforms as the center and\nmodified the original one to the subway. Experience has shown that the use of\npublic transport resources has increased by 20%. The study uses a similar\ncluster algorithm to solve transport networks' problems in a novel but\npractical term. As a result, the PAC is expected to be used extensively in the\ntransportation system construction process.\n"}
+{"abstract": "  A growing number of commercial satellite companies provide easily accessible\nsatellite imagery. Overhead imagery is used by numerous industries including\nagriculture, forestry, natural disaster analysis, and meteorology. Satellite\nimages, just as any other images, can be tampered with image manipulation\ntools. Manipulation detection methods created for images captured by \"consumer\ncameras\" tend to fail when used on satellite images due to the differences in\nimage sensors, image acquisition, and processing. In this paper we propose an\nunsupervised technique that uses a Vision Transformer to detect spliced areas\nwithin satellite images. We introduce a new dataset which includes manipulated\nsatellite images that contain spliced objects. We show that our proposed\napproach performs better than existing unsupervised splicing detection\ntechniques.\n"}
+{"abstract": "  A new era of exploration of the low radio frequency Universe from the Moon\nwill soon be underway with landed payload missions facilitated by NASA's\nCommercial Lunar Payload Services (CLPS) program. CLPS landers are scheduled to\ndeliver two radio science experiments, ROLSES to the nearside and LuSEE to the\nfarside, beginning in 2021. These instruments would be pathfinders for a 10-km\ndiameter interferometric array, FARSIDE, composed of 128 pairs of dipole\nantennas proposed to be delivered to the lunar surface later in the decade.\nROLSES and LuSEE, operating at frequencies from 100 kHz to a few tens of MHz,\nwill investigate the plasma environment above the lunar surface and measure the\nfidelity of radio spectra on the surface. Both use electrically-short,\nspiral-tube deployable antennas and radio spectrometers based upon previous\nflight models. ROLSES will measure the photoelectron sheath density to better\nunderstand the charging of the lunar surface via photoionization and impacts\nfrom the solar wind, charged dust, and current anthropogenic radio frequency\ninterference. LuSEE will measure the local magnetic field and exo-ionospheric\ndensity, interplanetary radio bursts, Jovian and terrestrial natural radio\nemission, and the galactic synchrotron spectrum. FARSIDE, and its precursor\nrisk-reduction six antenna-node array PRIME, would be the first radio\ninterferometers on the Moon. FARSIDE would break new ground by imaging radio\nemission from Coronal Mass Ejections (CME) beyond 2 solar radii, monitor\nauroral radiation from the B-fields of Uranus and Neptune (not observed since\nVoyager), and detect radio emission from stellar CMEs and the magnetic fields\nof nearby potentially habitable exoplanets.\n"}
+{"abstract": "  The motive associated to the second Symanzik polynomial of the double-box\ntwo-loop Feynman graph with generic masses and momenta is shown to be an\nelliptic curve.\n"}
+{"abstract": "  The diagnosis of superficial fungal infections in dermatology is still mostly\nbased on manual direct microscopic examination with Potassium Hydroxide (KOH)\nsolution. However, this method can be time consuming and its diagnostic\naccuracy rates vary widely depending on the clinician's experience. With the\nincrease of neural network applications in the field of clinical microscopy, it\nis now possible to automate such manual processes increasing both efficiency\nand accuracy. This study presents a deep neural network structure that enables\nthe rapid solutions for these problems and can perform automatic fungi\ndetection in grayscale images without dyes. 160 microscopic field photographs\ncontaining the fungal element, obtained from patients with onychomycosis, and\n297 microscopic field photographs containing dissolved keratin obtained from\nnormal nails were collected. Smaller patches containing 4234 fungi and 4981\nkeratin were extracted from these images. In order to detect fungus and\nkeratin, VGG16 and InceptionV3 models were developed. The VGG16 model had\n95.98% accuracy, and the area under the curve (AUC) value of 0.9930, while the\nInceptionV3 model had 95.90% accuracy and the AUC value of 0.9917. However,\naverage accuracy and AUC value of clinicians is 72.8% and 0.87, respectively.\nThis deep learning model allows the development of an automated system that can\ndetect fungi within microscopic images.\n"}
+{"abstract": "  System diagnosis is process of identifying faulty nodes in a system. An\nefficient diagnosis is crucial for a multiprocessor system. The BGM diagnosis\nmodel is a modification of the PMC diagnosis model, which is a test-based\ndiagnosis. In this paper, we present a specific structure and propose an\nalgorithm for diagnosing a node in a system under the BGM model. We also give a\npolynomial-time algorithm that a node in a hypercube-like network can be\ndiagnosed correctly in three test rounds under the BGM diagnosis model.\n"}
+{"abstract": "  To realise a planned high-luminosity and high-energy $e^+e^-$-collider, as\nthe ILC, a large amount of positrons have to be produced and the accelerated\nparticles have to be captured and matched according to the damping ring\nacceptances. %There exist several technical possibilities. In this contribution\na new promising alternative method for capturing positrons will be presented,\nthe application of the plasma lens as an optical matching device. It will be\ncompared with the current matching device proposed for the ILC, namely the\nquarter wave transformer. An advantage of the plasma lens is the different\nmagnetic field component, which focuses the divergent beam in a more effective\nmanner. Therefore it will be shown in this paper that the yield requirements\ncould be achieved more easily. The plasma lens can actually be a promising\nalternative for focusing beams as soon as the technical feasibility has been\napproved.\\\\ In the simulation, a tapered active plasma lens has been optimized\nusing the approximation of a homogeneous electric current density constant in\ntime. The optimization process led to a plasma lens design that improves on the\nILC's currently proposed optical matching device, namely the quarter wave\ntransformer, by approximately $50-100\\%$. Furthermore the design has been shown\nto guarantee a stable captured positron yield within $\\pm1.5\\%$ for single,\nindependent parameter deviations of about $\\pm10\\%$.\n"}
+{"abstract": "  The present work reports on LES of turbulent reacting flow of the\nDelft-Jet-in-Hot-Coflow (DJHC) burner, emulating MILD combustion, with\ntransported PDF based combustion models using ANSYS FLUENT 13.0. Two different\neddy viscosity models for LES (Dynamic Smagorinsky and Kinetic Energy\nTransport) along with two solution approaches for PDF transport equation, i.e.\nEulerian and Lagrangian, have been used in the present study. Moreover, the\neffects of chemical kinetics and the micro-mixing models have also been\ninvestigated for two different fuel jet Reynolds number (Re = 4,100 and Re =\n8,800). The mean velocity and turbulent kinetic energy predicted by the\ndifferent models are in good agreement with experimental data. Both the\ncomposition PDF models predict an early ignition resulting in higher radial\nmean temperature predictions at burner exit. The models, however, correctly\npredict the formation mechanism of ignition kernels and the decreasing trend of\nthe lift-off height with increasing jet Reynolds number, as observed\nexperimentally.\n"}
+{"abstract": "  A valley-contrasting Berry curvature in bilayer transition metal\ndichalcogenides with spin-orbit coupling can generate valley magnetization when\nthe inversion symmetry is broken, for example, by an electric field, regardless\nof time-reversal symmetry. A nontrivial Berry curvature can also lead to\nanomalous transport responses, such as the anomalous Hall effect and the\nanomalous Nernst effect. Applied to a bilayer WSe$_2$, an electric field can\ntune the Berry curvature and orbital magnetic moment, which has important\nconsequences for the orbital magnetization and the anomalous Nernst responses.\nThe orbital magnetization and its two contributions, one due to the magnetic\nmoment and one due to the Berry curvature, are calculated and interpreted in\nterms of opposite circulating currents of the bands in the two layers. The\nvalley anomalous Nernst coefficient and spin Nernst coefficient are also\ncalculated. We find that a finite electric field leads to peaks and dips in the\nNernst responses that have the signs of the Berry curvatures of the bands and\nare proportional to their magnitudes; it also enhances the valley Nernst\nresponses. These experimentally verifiable findings may be promising for\ncaloritronic applications.\n"}
+{"abstract": "  We present in this work the calibration procedure and a performance study of\nlong scintillator bars used for the time-of-flight (TOF) measurement in the\nHADES experiment. The digital front-end electronics installed at the TOF\ndetector required to develop novel calibration methods. The exceptional\nperformance of the spectrometer for particle identification and pointing\naccuracy allows one to determine in great detail the response of scintillators\nto minimum ionizing particles. A substantial position sensitivity of the\ncalibration parameters has been found, in particular for the signal time walk.\nAfter including the position dependence, the timing accuracy for minimum\nionizing particles was improved from 190~ps to 135~ps for the shortest rods\n(1475 mm) and to 165~ps for the longest (2356 mm). These results are in\naccordance with the time degradation length of the scintillator bars, as\ndetermined from previous measurements.\n"}
+{"abstract": "  Sequential recommender systems aim to model users' evolving interests from\ntheir historical behaviors, and hence make customized time-relevant\nrecommendations. Compared with traditional models, deep learning approaches\nsuch as CNN and RNN have achieved remarkable advancements in recommendation\ntasks. Recently, the BERT framework also emerges as a promising method,\nbenefited from its self-attention mechanism in processing sequential data.\nHowever, one limitation of the original BERT framework is that it only\nconsiders one input source of the natural language tokens. It is still an open\nquestion to leverage various types of information under the BERT framework.\nNonetheless, it is intuitively appealing to utilize other side information,\nsuch as item category or tag, for more comprehensive depictions and better\nrecommendations. In our pilot experiments, we found naive approaches, which\ndirectly fuse types of side information into the item embeddings, usually bring\nvery little or even negative effects. Therefore, in this paper, we propose the\nNOninVasive self-attention mechanism (NOVA) to leverage side information\neffectively under the BERT framework. NOVA makes use of side information to\ngenerate better attention distribution, rather than directly altering the item\nembedding, which may cause information overwhelming. We validate the NOVA-BERT\nmodel on both public and commercial datasets, and our method can stably\noutperform the state-of-the-art models with negligible computational overheads.\n"}
+{"abstract": "  One strategy for arresting propagating detonation waves in pipes is by\nimposing a sudden area enlargement, which provides a rapid lateral divergence\nof the gases in the reaction zone and attenuates the leading shock. For\nsufficiently small tube diameter, the detonation decays to a deflagration and\nthe shock decays to negligible strengths. This is known as the critical tube\ndiameter problem. In the present study, we provide a closed form model to\npredict the detonation quenching for 2D channels. Whitham's geometric shock\ndynamics, coupled with a shock evolution law based on shocks sustained by a\nconstant source obtained by the shock change equations of Radulescu, is shown\nto capture the lateral shock dynamics response to the failure wave originating\nat the expansion corner. A criterion for successful detonation transmission to\nopen space is that the lateral strain rate provided by the failure wave not\nexceed the critical strain rate of steady curved detonations. Using the\ncritical lateral strain rate obtained by He and Clavin, a closed form solution\nis obtained for the critical channel opening permitting detonation\ntransmission. The predicted critical channel width is found in very good\nagreement with our recent experiments and simulations of diffracting\nH$_2$/O$_2$/Ar detonations.\n"}
+{"abstract": "  We present the two-point cross-correlation function between high-mass X-ray\nbinaries (HMXBs) in the Small Magellanic Cloud (SMC) and their likely\nbirthplaces (OB Associations: OBAs). This function compares the spatial\ncorrelation between the observed HMXB and OBA populations against mock catalogs\nin which the members are distributed randomly across the sky. A significant\ncorrelation (15 sigma) is found for the HMXB and OBA populations when compared\nwith a randomized catalog in which the OBAs are distributed uniformly over the\nSMC. A less significant correlation (4 sigma) is found for a randomized catalog\nof OBAs built with a bootstrap method. However, no significant correlation is\ndetected when the randomized catalogs assume the form of a Gaussian ellipsoid\nor a distribution that reflects the star-formation history from 40 Myr ago.\nBased on their observed distributions and assuming a range of migration\ntimescales, we infer that the average value of the kick velocity inherited by\nan HMXB during the formation of its compact object is 2-34 km/s. This is\nconsiderably less than the value obtained for their counterparts in the Milky\nWay hinting that the galactic environment affecting stellar evolution plays a\nrole in setting the average kick velocity of HMXBs.\n"}
+{"abstract": "  Knowledge-intensive tasks such as question answering often require\nassimilating information from different sections of large inputs such as books\nor article collections. We propose ReadTwice, a simple and effective technique\nthat combines several strengths of prior approaches to model long-range\ndependencies with Transformers. The main idea is to read text in small\nsegments, in parallel, summarizing each segment into a memory table to be used\nin a second read of the text. We show that the method outperforms models of\ncomparable size on several question answering (QA) datasets and sets a new\nstate of the art on the challenging NarrativeQA task, with questions about\nentire books. Source code and pre-trained checkpoints for ReadTwice can be\nfound at https://goo.gle/research-readtwice.\n"}
+{"abstract": "  The monogamy relations of entanglement are highly significant. However, they\ninvolve only amounts of entanglement shared by different subsystems. Results on\nmonogamy relations between entanglement and other kinds of correlations, and\nparticularly classical correlations, are very scarce. Here we experimentally\nobserve a tradeoff relation between internal quantum nonseparability and\nexternal total correlations in a photonic system and found that even purely\nclassical external correlations have a detrimental effect on internal\nnonseparability. The nonseparability we consider, measured by the concurrence,\nis between different degrees of freedom within the same photon, and the\nexternal classical correlations, measured by the standard quantum mutual\ninformation, are generated between the photons of a photon pair using the\ntime-bin method. Our observations show that to preserve the internal\nentanglement in a system, it is necessary to maintain low external\ncorrelations, including classical ones, between the system and its environment.\n"}
+{"abstract": "  A very intense gamma beam of the Gamma Factory facility proposed at CERN can\nbe used to generate radioactive ion beams (RIBs) with high production yields\nand study the structure of exotic neutron-rich nuclei. The radioactive nuclides\nare generated via photo-fission in several actinide targets and thermalized in\nhigh-purity cryogenic helium, filling a gas cell which is enclosing the\ntargets. Electric fields are used to extract heavy ions and form RIBs which can\nbe sent to various selection and measurement stations. Estimates for the\nproduction and extraction yields of exotic neutron-rich nuclei with such a\nsetup are provided. A study of the impact of space charge, build-up inside the\ngas cell, on the extraction properties is presented and it is demonstrated that\nthe beam needs to be chopped for achieving optimal extraction yields.\n"}
+{"abstract": "  In this note we study strong contractivity of monotone systems and\nequilibrium-contractivity of positive systems with respect to non-Euclidean\nnorms. We first introduce the notion of conic matrix measure and study its\nproperties. Using conic matrix measures and weak pairings, we characterize\nstrongly contracting monotone systems with respect to non-Euclidean norms. This\nframework leads to novel results on (i) the stability of monotone separable\nsystems, (ii) the strong contractivity of excitatory Hopfield neural networks,\nand (iii) a strong version of the Matrosov-Bellman comparison lemma. We also\ncharacterize equilibrium-contracting positive systems with respect to\nnon-Euclidean norms and provide a sufficient condition for\nequilibrium-contractivity using conic matrix measures. This framework leads to\nnovel results on (i) the equilibrium-contractivity of positive separable\nsystems, and (ii) a comparison-based framework for interconnected systems.\n"}
+{"abstract": "  Transformer-based models have achieved state-of-the-art results in a wide\nrange of natural language processing (NLP) tasks including document\nsummarization. Typically these systems are trained by fine-tuning a large\npre-trained model to the target task. One issue with these transformer-based\nmodels is that they do not scale well in terms of memory and compute\nrequirements as the input length grows. Thus, for long document summarization,\nit can be challenging to train or fine-tune these models. In this work, we\nexploit large pre-trained transformer-based models and address long-span\ndependencies in abstractive summarization using two methods: local\nself-attention; and explicit content selection. These approaches are compared\non a range of network configurations. Experiments are carried out on standard\nlong-span summarization tasks, including Spotify Podcast, arXiv, and PubMed\ndatasets. We demonstrate that by combining these methods, we can achieve\nstate-of-the-art results on all three tasks in the ROUGE scores. Moreover,\nwithout a large-scale GPU card, our approach can achieve comparable or better\nresults than existing approaches.\n"}
+{"abstract": "  The specific heat is a central property of condensed matter systems including\npolymers and oligomers in their condensed phases. Yet, predictions of this\nquantity from molecular simulations and successful comparisons to experimental\ndata are scarce if existing at all. One reason for this may be that the\ninternal energy and thus the specific heat cannot be coarse-grained so that\nthey defy their rigorous computation with united-atom models. Moreover, many\nmodes in a polymer barely contribute to the specific heat because of their\nquantum mechanical nature. Here, we demonstrate that an analysis of the\nmass-weighted velocity autocorrelation function allows specific heat\npredictions to be corrected for quantum effects so that agreement with\nexperimental data is on par with predictions of other routinely computed\nquantities. We outline how to construct corrections for both all-atom and\nunited-atom descriptions of chain molecules. Corrections computed for eleven\nhydrocarbon oligomers and commodity polymers deviate by less than\n$k_\\textrm{B}/10$ within a subset of nine molecules. Our results may benefit\nthe prediction of heat conductivity.\n"}
+{"abstract": "  In an incomplete market underpinned by the trinomial model, we consider two\ninvestors : an ordinary agent whose decisions are driven by public information\nand an insider who possesses from the beginning a surplus of information\nencoded through a random variable for which he or she knows the outcome.\nThrough the definition of an auxiliary model based on a marked binomial\nprocess, we handle the trinomial model as a volatility one, and use the\nstochastic analysis and Malliavin calculus toolboxes available in that context.\nIn particular, we connect the information drift, the drift to eliminate in\norder to preserve the martingale property within an initial enlargement of\nfiltration in terms of the Malliavin derivative. We solve explicitly the agent\nand the insider expected logarithmic utility maximisation problems and provide\na hedging formula for replicable claims. We identify the insider expected\nadditional utility with the Shannon entropy of the extra information, and\nexamine then the existence of arbitrage opportunities for the insider.\n"}
+{"abstract": "  Algorithmic risk assessments are used to inform decisions in a wide variety\nof high-stakes settings. Often multiple predictive models deliver similar\noverall performance but differ markedly in their predictions for individual\ncases, an empirical phenomenon known as the \"Rashomon Effect.\" These models may\nhave different properties over various groups, and therefore have different\npredictive fairness properties. We develop a framework for characterizing\npredictive fairness properties over the set of models that deliver similar\noverall performance, or \"the set of good models.\" Our framework addresses the\nempirically relevant challenge of selectively labelled data in the setting\nwhere the selection decision and outcome are unconfounded given the observed\ndata features. Our framework can be used to 1) replace an existing model with\none that has better fairness properties; or 2) audit for predictive bias. We\nillustrate these uses cases on a real-world credit-scoring task and a\nrecidivism prediction task.\n"}
+{"abstract": "  Neural networks have achieved remarkable performance in computer vision,\nhowever they are vulnerable to adversarial examples. Adversarial examples are\ninputs that have been carefully perturbed to fool classifier networks, while\nappearing unchanged to humans. Based on prior works on detecting adversaries,\nwe propose a structured methodology of augmenting a deep neural network (DNN)\nwith a detector subnetwork. We use $\\textit{Adversarial Noise Sensitivity}$\n(ANS), a novel metric for measuring the adversarial gradient contribution of\ndifferent intermediate layers of a network. Based on the ANS value, we append a\ndetector to the most sensitive layer. In prior works, more complex detectors\nwere added to a DNN, increasing the inference computational cost of the model.\nIn contrast, our structured and strategic addition of a detector to a DNN\nreduces the complexity of the model while making the overall network\nadversarially resilient. Through comprehensive white-box and black-box\nexperiments on MNIST, CIFAR-10, and CIFAR-100, we show that our method improves\nstate-of-the-art detector robustness against adversarial examples. Furthermore,\nwe validate the energy efficiency of our proposed adversarial detection\nmethodology through an extensive energy analysis on various hardware scalable\nCMOS accelerator platforms. We also demonstrate the effects of quantization on\nour detector-appended networks.\n"}
+{"abstract": "  In this work, we give a proof of the globally sharp asymptotic profiles for\nthe spin-$\\mathfrak{s}$ fields on a Schwarzschild background, including the\nscalar field $(\\mathfrak{s}=0)$, the Maxwell field $(\\mathfrak{s}=\\pm 1)$ and\nthe linearized gravity $(\\mathfrak{s}=\\pm 2)$. The conjectured Price's law in\nthe physics literature which predicts the sharp estimates of the spin $s=\\pm\n\\mathfrak{s}$ components towards the future null infinity as well as in a\ncompact region is shown. Further, we confirm the heuristic claim by Barack and\nOri that the spin $+1, +2$ components have an extra power of decay at the event\nhorizon than the conjectured Price's law. The asymptotics are derived via a\nunified, detailed analysis of the Teukolsky master equation that is satisfied\nby all these components.\n"}
+{"abstract": "  With every locally compact group $G$, one can associate several interesting\nbi-invariant subspaces $X(G)$ of the weakly almost periodic functions\n$\\mathrm{WAP}(G)$ on $G$, each of which captures parts of the representation\ntheory of $G$. Under certain natural assumptions, such a space $X(G)$ carries a\nunique invariant mean and has a natural predual, and we view the\nweak$^*$-continuity of this mean as a rigidity property of $G$. Important\nexamples of such spaces $X(G)$, which we study explicitly, are the algebra\n$M_{\\mathrm{cb}}A_p(G)$ of $p$-completely bounded multipliers of the\nFig\\`a-Talamanca-Herz algebra $A_p(G)$ and the $p$-Fourier-Stieltjes algebra\n$B_p(G)$. In the setting of connected Lie groups $G$, we relate the\nweak$^*$-continuity of the mean on these spaces to structural properties of\n$G$. Our results generalise results of Bekka, Kaniuth, Lau and Schlichting.\n"}
+{"abstract": "  We prove an exponential separation for the sample complexity between the\nstandard PAC-learning model and a version of the Equivalence-Query-learning\nmodel. We then show that this separation has interesting implications for\nadversarial robustness. We explore a vision of designing an adaptive defense\nthat in the presence of an attacker computes a model that is provably robust.\nIn particular, we show how to realize this vision in a simplified setting.\n  In order to do so, we introduce a notion of a strong adversary: he is not\nlimited by the type of perturbations he can apply but when presented with a\nclassifier can repetitively generate different adversarial examples. We explain\nwhy this notion is interesting to study and use it to prove the following.\nThere exists an efficient adversarial-learning-like scheme such that for every\nstrong adversary $\\mathbf{A}$ it outputs a classifier that (a) cannot be\nstrongly attacked by $\\mathbf{A}$, or (b) has error at most $\\epsilon$. In both\ncases our scheme uses exponentially (in $\\epsilon$) fewer samples than what the\nPAC bound requires.\n"}
+{"abstract": "  In this short paper an idea is sketched, how to support drivers of an\nautonomous vehicle in taking back control of the vehicle after a longer section\nof autonomous cruising. The hypothesis is that a clear communication about the\nlocation and behavior of relevant objects in the environment will help the\ndriver to quickly grasp the situational context and thus support drivers in\nsafely handling the ongoing driving situation manually after take-over. Based\non this hypothesis, a research concept is sketched, which entails the necessary\ncomponents as well as the disciplines involved.\n"}
+{"abstract": "  Inspired by Fr\\\"{o}hlich-Spencer and subsequent authors who introduced the\nnotion of contour for long-range systems, we provide a definition of contour\nand a direct proof for the phase transition for ferromagnetic long-range Ising\nmodels on $\\mathbb{Z}^d$, $d\\geq 2$. The argument, which is based on a\nmulti-scale analysis, works for the sharp region $\\alpha>d$ and improves\nprevious results obtained by Park for $\\alpha>3d+1$, and by Ginibre, Grossmann,\nand Ruelle for $\\alpha> d+1$, where $\\alpha$ is the power of the coupling\nconstant. The key idea is to avoid a large number of small contours. As an\napplication, we prove the persistence of the phase transition when we add a\npolynomial decaying magnetic field with power $\\delta>0$ as $h^*|x|^{-\\delta}$,\nwhere $h^* >0$. For $d<\\alpha<d+1$, the phase transition occurs when\n$\\delta>d-\\alpha$, and when $h^*$ is small enough over the critical line\n$\\delta=d-\\alpha$. For $\\alpha \\geq d+1$, $\\delta>1$ it is enough to prove the\nphase transition, and for $\\delta=1$ we have to ask $h^*$ small. The natural\nconjecture is that this region is also sharp for the phase transition problem\nwhen we have a decaying field.\n"}
+{"abstract": "  The field of e-learning has emerged as a topic of interest in academia due to\nthe increased ease of accessing the Internet using using smart-phones and\nwireless devices. One of the challenges facing e-learning platforms is how to\nkeep students motivated and engaged. Moreover, it is also crucial to identify\nthe students that might need help in order to make sure their academic\nperformance doesn't suffer. To that end, this paper tries to investigate the\nrelationship between student engagement and their academic performance. Apriori\nassociation rules algorithm is used to derive a set of rules that relate\nstudent engagement to academic performance. Experimental results' analysis done\nusing confidence and lift metrics show that a positive correlation exists\nbetween students' engagement level and their academic performance in a blended\ne-learning environment. In particular, it is shown that higher engagement often\nleads to better academic performance. This cements the previous work that\nlinked engagement and academic performance in traditional classrooms.\n"}
+{"abstract": "  As software systems become more complex, modern software development requires\nmore attention to human perspectives, and active participation of development\nteams in requirements elicitation tasks. In this context, incomplete or\nambiguous requirements descriptions do not guide the development of good\nsoftware products. We hypothesize that the text auto-completion feature\nimproves the quality of the software requirements artifacts. We present the\nmotivation for this study, related works, our approach and future research\nefforts.\n"}
+{"abstract": "  We study mass preserving transport of passive tracers in the low-diffusivity\nlimit using Lagrangian coordinates. Over finite-time intervals, the\nsolution-operator of the nonautonomous diffusion equation is approximated by\nthat of a time-averaged diffusion equation. We show that leading order\nasymptotics that hold for functions [Krol, 1991] extend to the dominant\nnontrivial singular value. This answers questions raised in [Karrasch & Keller,\n2020]. The generator of the time-averaged diffusion/heat semigroup is a Laplace\noperator associated to a weighted manifold structure on the material manifold.\nWe show how geometrical properties of this weighted manifold directly lead to\nphysical transport quantities of the nonautonomous equation in the\nlow-diffusivity limit.\n"}
+{"abstract": "  Creating scalable, high performance PDE-based simulations requires a suitable\ncombination of discretizations, differential operators, preconditioners and\nsolvers. The required combination changes with the application and with the\navailable hardware, yet software development time is a severely limited\nresource for most scientists and engineers. Here we demonstrate that generating\nsimulation code from a high-level Python interface provides an effective\nmechanism for creating high performance simulations from very few lines of user\ncode. We demonstrate that moving from one supercomputer to another can require\nsignificant algorithmic changes to achieve scalable performance, but that the\ncode generation approach enables these algorithmic changes to be achieved with\nminimal development effort.\n"}
+{"abstract": "  Multispecies swarms are found for microorganisms living in microfluidic\nenvironments where they can take advantage of collective motions during\ntransport and spreading. Nevertheless, there is a general lack of physical\nunderstandings of the origins of the multiscale unstable dynamics. Here we\nbuild a computation model to study the binary suspensions of rear- and\nfront-actuated microswimmers, or respectively the so-called \"pusher\" and\n\"puller\" particles, that have different populations and swimming speeds. We\nperform direct particle simulations to reveal that even in the scenarios of\n\"stress-balanced\" mixtures which produce approximately zero net extra stresses,\nthe longtime dynamics can exhibit non-trivial density fluctuations and\nspatially-correlated motions. We then construct a continuum kinetic model and\nperform linear stability analysis to reveal the underlying mechanisms of\nhydrodynamic instabilities. Our theoretical predictions qualitatively agree\nwith numerical results and explain the onsets of the observed collective\nmotions.\n"}
+{"abstract": "  We conducted a set of Rapid Reviews to characterize Internet of Things\nfacets. We formatted a generic meta-protocol that was instantiated for each of\nthe six facets presented (Connectivity, Things, Behavior, Smartness,\nInteractivity, and Environment)and considering the issue of Security, one of\nthe most important and frequent challenges in the context of IoT. The\nmeta-protocol is detailed and the results of each review are presented.\n"}
+{"abstract": "  We revisit the arguments underlying two well-known arrival-time distributions\nin quantum mechanics, viz., the Aharonov-Bohm and Kijowski (ABK) distribution,\napplicable for freely moving particles, and the quantum flux (QF) distribution.\nAn inconsistency in the original axiomatic derivation of Kijowski's result is\npointed out, along with an inescapable consequence of the \"negative arrival\ntimes\" inherent to this proposal (and generalizations thereof). The ABK\nfree-particle restriction is lifted in a discussion of an explicit arrival-time\nsetup featuring a charged particle moving in a constant magnetic field. A\nnatural generalization of the ABK distribution is in this case shown to be\ncritically gauge-dependent. A direct comparison to the QF distribution, which\ndoes not exhibit this flaw, is drawn (its acknowledged drawback concerning the\nquantum backflow effect notwithstanding).\n"}
+{"abstract": "  In this paper, we propose a matching theory based multi-user cooperative\ncomputing (MUCC) scheme to minimize the overall energy consumption of a group\nof user equipments (UEs), where the UEs can be classified into the following\nroles: resource demander (RD), resource provider (RP), and standalone UE (SU).\nWe first determine the role of each UE by leveraging the roommate matching\nmethod. Then, we propose the college admission based algorithm to divide the\nUEs into multiple cooperation groups, each consisting of one RP and multiple\nRDs. Next, we propose the rotation swap operation to further improve the\nperformance without deteriorating the system stability. Finally, we present an\neffective task offloading algorithm to minimize the energy consumption of all\nthe cooperation groups. The simulation results verify the effectiveness of the\nproposed scheme.\n"}
+{"abstract": "  Motivated by problems of learning to rank long item sequences, we introduce a\nvariant of the cascading bandit model that considers flexible length sequences\nwith varying rewards and losses. We formulate two generative models for this\nproblem within the generalized linear setting, and design and analyze upper\nconfidence algorithms for it. Our analysis delivers tight regret bounds which,\nwhen specialized to vanilla cascading bandits, results in sharper guarantees\nthan previously available in the literature. We evaluate our algorithms on a\nnumber of real-world datasets, and show significantly improved empirical\nperformance as compared to known cascading bandit baselines.\n"}
+{"abstract": "  In this paper the finite-time stabilization problem is solved for a linear\ntime-varying system with unknown control direction by exploiting a modified\nversion of the classical extremum seeking algorithm. We propose to use a\nsuitable oscillatory input to modify the system dynamics, at least in an\naverage sense, so as to satisfy a Differential Linear Matrix Inequality (DLMI)\ncondition which in turns guarantees that the system's state remains inside a\nprescribed time varying hyper-ellipsoid in the state space. The finite-time\nstability (FTS) of the averaged dynamics implies the FTS of the original\nsystem, as the distance between the original and the averaged dynamics can be\nmade arbitrarily small by choosing a sufficiently high value of the dithering\nfrequency used by the extremum seeking algorithm. An estimate of the necessary\nminimum dithering/mixing frequency is provided, and the effectiveness of the\nproposed finite-time stabilization approach is analysed by means of numerical\nexamples.\n"}
+{"abstract": "  Mechanical modelling of poroelastic media under finite strain is usually\ncarried out via phenomenological models neglecting complex micro-macro scales\ninterdependency. One reason is that the mathematical two-scale analysis is only\nstraightforward assuming infinitesimal strain theory. Exploiting the potential\nof ANNs for fast and reliable upscaling and localisation procedures, we propose\nan incremental numerical approach that considers rearrangement of the cell\nproperties based on its current deformation, which leads to the remodelling of\nthe macroscopic model after each time increment. This computational framework\nis valid for finite strain and large deformation problems while it ensures\ninfinitesimal strain increments within time steps. The full effects of the\ninterdependency between the properties and response of macro and micro scales\nare considered for the first time providing more accurate predictive analysis\nof fluid-saturated porous media which is studied via a numerical consolidation\nexample. Furthermore, the (nonlinear) deviation from Darcy's law is captured in\nfluid filtration numerical analyses. Finally, the brain tissue mechanical\nresponse under uniaxial cyclic test is simulated and studied.\n"}
+{"abstract": "  Choi and Shin (2020) have constructed a bootstrap-based test for\nchange-points in panels with temporal and and/or cross-sectional dependence.\nThey have compared their test to several other proposed tests. We demonstrate\nthat by an appropriate, data-adaptive choice of the block length, the\nchange-point test by Sharipov, Tewes, Wendler (2016) can at least cope with\nmild temporal dependence, the size distortion of this test is not as severe as\nclaimed by Choi and Shin (2020).\n"}
+{"abstract": "  In this article, we interpolate a given real analytic spacelike curve $a$ in\nLorentz-Minkowski space $\\mathbb{L}^3$ to another real analytic spacelike curve\n$c$, which is \"close\" enough to $a$ in a certain sense, by a maximal surface\nusing inverse function theorem for Banach spaces. Using the same method we also\ninterpolate a given real analytic curve $a$ in Euclidean space $\\mathbb{E}^3$\nto another real analytic curve $c$, which is \"close\" enough to $a$ in a certain\nsense, by a minimal surface. The Bj\\\"orling problem and Schwartz's solution to\nit play an important role.\n"}
+{"abstract": "  Decomposable tasks are complex and comprise of a hierarchy of sub-tasks.\nSpoken intent prediction, for example, combines automatic speech recognition\nand natural language understanding. Existing benchmarks, however, typically\nhold out examples for only the surface-level sub-task. As a result, models with\nsimilar performance on these benchmarks may have unobserved performance\ndifferences on the other sub-tasks. To allow insightful comparisons between\ncompetitive end-to-end architectures, we propose a framework to construct\nrobust test sets using coordinate ascent over sub-task specific utility\nfunctions. Given a dataset for a decomposable task, our method optimally\ncreates a test set for each sub-task to individually assess sub-components of\nthe end-to-end model. Using spoken language understanding as a case study, we\ngenerate new splits for the Fluent Speech Commands and Snips SmartLights\ndatasets. Each split has two test sets: one with held-out utterances assessing\nnatural language understanding abilities, and one with held-out speakers to\ntest speech processing skills. Our splits identify performance gaps up to 10%\nbetween end-to-end systems that were within 1% of each other on the original\ntest sets. These performance gaps allow more realistic and actionable\ncomparisons between different architectures, driving future model development.\nWe release our splits and tools for the community.\n"}
+{"abstract": "  We propose BaySize, a sample size calculator for phase I clinical trials\nusing Bayesian models. BaySize applies the concept of effect size in dose\nfinding, assuming the MTD is defined based on an equivalence interval.\nLeveraging a decision framework that involves composite hypotheses, BaySize\nutilizes two prior distributions, the fitting prior (for model fitting) and\nsampling prior (for data generation), to conduct sample size calculation under\ndesirable statistical power. Look-up tables are generated to facilitate\npractical applications. To our knowledge, BaySize is the first sample size tool\nthat can be applied to a broad range of phase I trial designs.\n"}
+{"abstract": "  In their early stages, protoplanetary discs are sufficiently massive to\nundergo gravitational instability (GI). This instability is thought to be\ninvolved in mass accretion, planet formation via gas fragmentation, the\ngeneration of spiral density waves, and outbursts. A key and very recent area\nof research is the interaction between the GI and magnetic fields in young\nprotoplanetary discs, in particular whether this instability is able to sustain\na magnetic field via a dynamo. We conduct three-dimensional, stratified\nshearing-box simulations using two independent codes, PLUTO and Athena++, to\ncharacterise the GI dynamo in poorly ionised protostellar discs subject to\nambipolar diffusion. We find that the dynamo operates across a large range of\nambipolar Elssaser number Am (which characterises the strength of ambipolar\ndiffusion) and is particularly strong in the regime Am=10-100, with typical\nmagnetic to thermal energy ratios of order unity. The dynamo is only weakly\ndependent on resolution (at least for Am <100), box size, and cooling law. The\nmagnetic field is produced by the combination of differential rotation and\nlarge-scale vertical roll motions associated with spiral density waves. Our\nresults have direct implications for the dynamo process in young protoplanetary\ndiscs and possibly some regions of AGN discs.\n"}
+{"abstract": "  In this paper, we take a critical and fresh look at the downlink\nmulti-antenna NOMA literature. Instead of contrasting NOMA with OMA, we\ncontrast NOMA with two other baselines. The first is conventional Multi-User\nLinear Precoding (MULP). The second is Rate-Splitting Multiple Access (RSMA)\nbased on multi-antenna Rate-Splitting (RS) and SIC. We show that there is some\nconfusion about the benefits of NOMA, and we dispel the associated\nmisconceptions. First, we highlight why NOMA is inefficient in multi-antenna\nsettings based on basic multiplexing gain analysis. We stress that the issue\nlies in how the NOMA literature has been hastily applied to multi-antenna\nsetups, resulting in a misuse of spatial dimensions and therefore loss in\nmultiplexing gains and rate. Second, we show that NOMA incurs a severe\nmultiplexing gain loss despite an increased receiver complexity due to an\ninefficient use of SIC receivers. Third, we emphasize that much of the merits\nof NOMA are due to the constant comparison to OMA instead of comparing it to\nMULP and RS baselines. We then expose the pivotal design constraint that\nmulti-antenna NOMA requires one user to fully decode the messages of the other\nusers. This design constraint is responsible for the multiplexing gain erosion,\nrate loss, and inefficient use of SIC receivers in multi-antenna settings. Our\nresults confirm that NOMA should not be applied blindly to multi-antenna\nsettings, highlight the scenarios where MULP outperforms NOMA and vice versa,\nand demonstrate the inefficiency, performance loss and complexity disadvantages\nof NOMA compared to RS. The first takeaway message is that, while NOMA is not\nbeneficial in most multi-antenna deployments. The second takeaway message is\nthat other non-orthogonal transmission frameworks, such as RS, exist which\nfully exploit the multiplexing gain and the benefits of SIC to boost the rate\nin multi-antenna settings.\n"}
+{"abstract": "  We consider representation learning from 3D graphs in which each node is\nassociated with a spatial position in 3D. This is an under explored area of\nresearch, and a principled framework is currently lacking. In this work, we\npropose a generic framework, known as the 3D graph network (3DGN), to provide a\nunified interface at different levels of granularity for 3D graphs. Built on\n3DGN, we propose the spherical message passing (SMP) as a novel and specific\nscheme for realizing the 3DGN framework in the spherical coordinate system\n(SCS). We conduct formal analyses and show that the relative location of each\nnode in 3D graphs is uniquely defined in the SMP scheme. Thus, our SMP\nrepresents a complete and accurate architecture for learning from 3D graphs in\nthe SCS. We derive physically-based representations of geometric information\nand propose the SphereNet for learning representations of 3D graphs. We show\nthat existing 3D deep models can be viewed as special cases of the SphereNet.\nExperimental results demonstrate that the use of complete and accurate 3D\ninformation in 3DGN and SphereNet leads to significant performance improvements\nin prediction tasks.\n"}
+{"abstract": "  Many real-world classification problems often have classes with very few\nlabeled training samples. Moreover, all possible classes may not be initially\navailable for training, and may be given incrementally. Deep learning models\nneed to deal with this two-fold problem in order to perform well in real-life\nsituations. In this paper, we propose a novel Few-Shot Lifelong Learning (FSLL)\nmethod that enables deep learning models to perform lifelong/continual learning\non few-shot data. Our method selects very few parameters from the model for\ntraining every new set of classes instead of training the full model. This\nhelps in preventing overfitting. We choose the few parameters from the model in\nsuch a way that only the currently unimportant parameters get selected. By\nkeeping the important parameters in the model intact, our approach minimizes\ncatastrophic forgetting. Furthermore, we minimize the cosine similarity between\nthe new and the old class prototypes in order to maximize their separation,\nthereby improving the classification performance. We also show that integrating\nour method with self-supervision improves the model performance significantly.\nWe experimentally show that our method significantly outperforms existing\nmethods on the miniImageNet, CIFAR-100, and CUB-200 datasets. Specifically, we\noutperform the state-of-the-art method by an absolute margin of 19.27% for the\nCUB dataset.\n"}
+{"abstract": "  Fraud acts as a major deterrent to a companys growth if uncontrolled. It\nchallenges the fundamental value of Trust in the Insurance business. COVID-19\nbrought additional challenges of increased potential fraud to health insurance\nbusiness. This work describes implementation of existing and enhanced fraud\ndetection methods in the pre-COVID-19 and COVID-19 environments. For this\npurpose, we have developed an innovative enhanced fraud detection framework\nusing actuarial and data science techniques. Triggers specific to COVID-19 are\nidentified in addition to the existing triggers. We have also explored the\nrelationship between insurance fraud and COVID-19. To determine this we\ncalculated Pearson correlation coefficient and fitted logarithmic regression\nmodel between fraud in health insurance and COVID-19 cases. This work uses two\ndatasets: health insurance dataset and Kaggle dataset on COVID-19 cases for the\nsame select geographical location in India. Our experimental results shows\nPearson correlation coefficient of 0.86, which implies that the month on month\nrate of fraudulent cases is highly correlated with month on month rate of\nCOVID-19 cases. The logarithmic regression performed on the data gave the\nr-squared value of 0.91 which indicates that the model is a good fit. This work\naims to provide much needed tools and techniques for health insurance business\nto counter the fraud.\n"}
+{"abstract": "  It is challenging to deal with censored data, where we only have access to\nthe incomplete information of survival time instead of its exact value.\nFortunately, under linear predictor assumption, people can obtain guaranteed\ncoverage for the confidence band of survival time using methods like Cox\nRegression. However, when relaxing the linear assumption with neural networks\n(e.g., Cox-MLP (Katzman et al., 2018; Kvamme et al., 2019)), we lose the\nguaranteed coverage. To recover the guaranteed coverage without linear\nassumption, we propose two algorithms based on conformal inference. In the\nfirst algorithm WCCI, we revisit weighted conformal inference and introduce a\nnew non-conformity score based on partial likelihood. We then propose a\ntwo-stage algorithm T-SCI, where we run WCCI in the first stage and apply\nquantile conformal inference to calibrate the results in the second stage.\nTheoretical analysis shows that T-SCI returns guaranteed coverage under milder\nassumptions than WCCI. We conduct extensive experiments on synthetic data and\nreal data using different methods, which validate our analysis.\n"}
+{"abstract": "  A complex function is associated to each bounded linear operator\n"}
+{"abstract": "  In Heisenberg models with exchange anisotropy, transverse spin components are\nnot conserved and can decay not only by transport, but also by dephasing. Here\nwe utilize ultracold atoms to simulate the dynamics of 1D Heisenberg spin\nchains, and observe fast, local spin decay controlled by the anisotropy.\nAdditionally, we directly observe an effective magnetic field created by\nsuperexchange which causes an inhomogeneous decay mechanism due to variations\nof lattice depth between chains, as well as dephasing within each chain due to\nthe twofold reduction of the effective magnetic field at the edges of the\nchains and due to fluctuations of the effective magnetic field in the presence\nof mobile holes. The latter is a new coupling mechanism between holes and\nmagnons. All these dephasing mechanisms, corroborated by extensive numerical\nsimulations, have not been observed before with ultracold atoms and illustrate\nbasic properties of the underlying Hubbard model.\n"}
+{"abstract": "  Let $K$ be a field. Let $f\\in K[[x_{1},...,x_{r}]]$ and $g\\in\nK[[y_{1},...,y_{s}]]$ be nonzero elements. If $X$ (resp. $Y$) is a matrix\nfactorization of $f$ (resp. $g$), Yoshino had constructed a tensor product (of\nmatrix factorizations) $\\widehat{\\otimes}$ such that $X\\widehat{\\otimes}Y$ is a\nmatrix factorization of $f+g\\in K[[x_{1},...,x_{r},y_{1},...,y_{s}]]$. In this\npaper, we propose a bifunctorial operation $\\widetilde{\\otimes}$ and its\nvariant $\\widetilde{\\otimes}'$ such that $X\\widetilde{\\otimes}Y$ and\n$X\\widetilde{\\otimes}' Y$ are two different matrix factorizations of $fg\\in\nK[[x_{1},...,x_{r},y_{1},...,y_{s}]]$. We call $\\widetilde{\\otimes}$ the\nmultiplicative tensor product of $X$ and $Y$. Several properties of\n$\\widetilde{\\otimes}$ are proved. Moreover, we find three functorial variants\nof Yoshino's tensor product $\\widehat{\\otimes}$. Then, $\\widetilde{\\otimes}$\n(or its variant) is used in conjunction with $\\widehat{\\otimes}$ (or any of its\nvariants) to give an improved version of the standard algorithm for factoring\npolynomials using matrices on the class of summand-reducible polynomials\ndefined in this paper. Our algorithm produces matrix factors whose size is at\nmost one half the size one obtains using the standard method.\n"}
+{"abstract": "  Natural forking in blockchain refers to a phenomenon that there are a set of\nblocks at one block height at the same time, implying that various nodes have\ndifferent perspectives of the main chain. Natural forking might give rise to\nmultiple adverse impacts on blockchain, jeopardizing the performance and\nsecurity of the system consequently. However, the ongoing literature in\nanalyzing natural forking is mainly from the macro point of view, which is not\nsufficient to incisively understand this phenomenon. In this paper, we fill\nthis gap through leveraging the large deviation theory to conduct a microscopic\nstudy of natural forking, which resorts to investigating the instantaneous\ndifference between block generation and dissemination in blockchain. Our work\nis derived comprehensively and complementarily via a three-step process, where\nboth the natural forking probability and its decay rate are presented. Through\nsolid theoretical derivation and extensive numerical simulations, we find 1)\nthe probability of the mismatch between block generation and dissemination\nexceeding a given threshold dwindles exponentially with the increase of natural\nforking robustness related parameter or the difference between the block\ndissemination rate and block creation rate; 2) the natural forking robustness\nrelated parameter may emphasize a more dominant effect on accelerating the\nabortion of natural forking in some cases; 3) when the self-correlated block\ngeneration rate is depicted as the stationary autoregressive process with a\nscaling parameter, it is found that setting a lower scaling parameter may speed\nup the failure of natural forking. These findings are valuable since they offer\na fresh theoretical basis to engineer optimal countermeasures for thwarting\nnatural forking and thereby enlivening the blockchain network.\n"}
+{"abstract": "  The integration of multi-access edge computing (MEC) and RAFT consensus makes\nit feasible to deploy blockchain on trustful base stations and gateways to\nprovide efficient and tamper-proof edge data services for Internet of Things\n(IoT) applications. However, reducing the latency of storing data on blockchain\nremains a challenge, especially when an anomalytriggered data flow in a certain\narea exceeds the block generation speed. This letter proposes an intelligent\ntransaction migration scheme for RAFT-based private blockchain in IoT\napplications to migrate transactions in busy areas to idle regions\nintelligently. Simulation results show that the proposed scheme can apparently\nreduce the latency in high data flow circumstances.\n"}
+{"abstract": "  (RFT) allows very high-Q active mode resonators, promising crystal-less\nmonolithic clock generation for mmWave systems. However, there is a strong need\nfor design of mmWave oscillators that utilize the high-Q of active-mode RFT\n(AM-RFT) optimally, while handling unique challenges such as resonator's low\nelectromechanical transduction. In this brief, we develop a theory and through\ndesign and post-layout simulations in 14 nm Global Foundry process, we show the\nfirst active oscillator with AM-RFT at 30 GHz, which improves the fundamental\nlimits of phase noise and figure-of-merit as compared to the oscillators with\nconventional LC resonators. For AM-RFT with Q factor of 10K, post layout\nsimulation results show that the proposed oscillator exhibits phase noise less\nthan -140 dBc per Hz and figure-of-merit greater than 228 dBc per Hz at 1 MHz\noffset for 30 GHz center frequency, which are more than 25 dB better than the\nexisting monolithic LC oscillators.\n"}
+{"abstract": "  We study the question for which Tychonoff spaces $X$ and locally convex\nspaces $E$ the space $C_p(X,E)$ of continuous $E$-valued functions on $X$\ncontains a complemented copy of the space\n$(c_0)_p=\\{x\\in\\mathbb{R}^\\omega\\colon x(n)\\to0\\}$, both endowed with the\npointwise topology. We provide a positive answer for a vast class of spaces,\nextending classical theorems of Cembranos, Freniche, and Doma\\'nski and\nDrewnowski, proved for the case of Banach and Fr\\'echet spaces $C_k(X,E)$. For\ngiven infinite Tychonoff spaces $X$ and $Y$, using the results of Henriksen and\nWoods and analysing relations between the space $C_p(X,C_p(Y))$ and the space\n$SC_p(X\\times Y)$ of separately continuous real-valued functions on the product\n$X\\times Y$, we show that $C_p(X,C_p(Y))$ contains a complemented copy of\n$(c_0)_p$ if and only if any of the spaces $C_p(X)$ and $C_p(Y)$ contains such\na subspace. This approach applies also to show that, contrary to the case of\nthe compact-open topology, for every infinite compact spaces $X$ and $Y$ the\nspace $C_p(X\\times Y)$ cannot be mapped onto the space $C_p(X,C_p(Y))$ by a\ncontinuous linear operator.\n"}
+{"abstract": "  Tensor-based methods have been widely studied to attack inverse problems in\nhyperspectral imaging since a hyperspectral image (HSI) cube can be naturally\nrepresented as a third-order tensor, which can perfectly retain the spatial\ninformation in the image. In this article, we extend the linear tensor method\nto the nonlinear tensor method and propose a nonlinear low-rank tensor unmixing\nalgorithm to solve the generalized bilinear model (GBM). Specifically, the\nlinear and nonlinear parts of the GBM can both be expressed as tensors.\nFurthermore, the low-rank structures of abundance maps and nonlinear\ninteraction abundance maps are exploited by minimizing their nuclear norm, thus\ntaking full advantage of the high spatial correlation in HSIs. Synthetic and\nreal-data experiments show that the low rank of abundance maps and nonlinear\ninteraction abundance maps exploited in our method can improve the performance\nof the nonlinear unmixing. A MATLAB demo of this work will be available at\nhttps://github.com/LinaZhuang for the sake of reproducibility.\n"}
+{"abstract": "  Even though machine learning algorithms already play a significant role in\ndata science, many current methods pose unrealistic assumptions on input data.\nThe application of such methods is difficult due to incompatible data formats,\nor heterogeneous, hierarchical or entirely missing data fragments in the\ndataset. As a solution, we propose a versatile, unified framework called\n`HMill' for sample representation, model definition and training. We review in\ndepth a multi-instance paradigm for machine learning that the framework builds\non and extends. To theoretically justify the design of key components of HMill,\nwe show an extension of the universal approximation theorem to the set of all\nfunctions realized by models implemented in the framework. The text also\ncontains a detailed discussion on technicalities and performance improvements\nin our implementation, which is published for download under the MIT License.\nThe main asset of the framework is its flexibility, which makes modelling of\ndiverse real-world data sources with the same tool possible. Additionally to\nthe standard setting in which a set of attributes is observed for each object\nindividually, we explain how message-passing inference in graphs that represent\nwhole systems of objects can be implemented in the framework. To support our\nclaims, we solve three different problems from the cybersecurity domain using\nthe framework. The first use case concerns IoT device identification from raw\nnetwork observations. In the second problem, we study how malicious binary\nfiles can be classified using a snapshot of the operating system represented as\na directed graph. The last provided example is a task of domain blacklist\nextension through modelling interactions between entities in the network. In\nall three problems, the solution based on the proposed framework achieves\nperformance comparable to specialized approaches.\n"}
+{"abstract": "  Recently, deep neural networks have become to be used in a variety of\napplications. While the accuracy of deep neural networks is increasing, the\nconfidence score, which indicates the reliability of the prediction results, is\nbecoming more important. Deep neural networks are seen as highly accurate but\nknown to be overconfident, making it important to calibrate the confidence\nscore. Many studies have been conducted on confidence calibration. They\ncalibrate the confidence score of the model to match its accuracy, but it is\nnot clear whether these confidence scores can improve the performance of\nsystems that use confidence scores. This paper focuses on cascade inference\nsystems, one kind of systems using confidence scores, and discusses the desired\nconfidence score to improve system performance in terms of inference accuracy\nand computational cost. Based on the discussion, we propose a new confidence\ncalibration method, Learning to Cascade. Learning to Cascade is a simple but\nnovel method that optimizes the loss term for confidence calibration\nsimultaneously with the original loss term. Experiments are conducted using two\ndatasets, CIFAR-100 and ImageNet, in two system settings, and show that naive\napplication of existing calibration methods to cascade inference systems\nsometimes performs worse. However, Learning to Cascade always achieves a better\ntrade-off between inference accuracy and computational cost. The simplicity of\nLearning to Cascade allows it to be easily applied to improve the performance\nof existing systems.\n"}
+{"abstract": "  Imbalanced datasets widely exist in practice and area great challenge for\ntraining deep neural models with agood generalization on infrequent classes. In\nthis work, wepropose a new rare-class sample generator (RSG) to solvethis\nproblem. RSG aims to generate some new samplesfor rare classes during training,\nand it has in particularthe following advantages: (1) it is convenient to use\nandhighly versatile, because it can be easily integrated intoany kind of\nconvolutional neural network, and it works wellwhen combined with different\nloss functions, and (2) it isonly used during the training phase, and\ntherefore, no ad-ditional burden is imposed on deep neural networks duringthe\ntesting phase. In extensive experimental evaluations, weverify the\neffectiveness of RSG. Furthermore, by leveragingRSG, we obtain competitive\nresults on Imbalanced CIFARand new state-of-the-art results on Places-LT,\nImageNet-LT, and iNaturalist 2018. The source code is available at\nhttps://github.com/Jianf-Wang/RSG.\n"}
+{"abstract": "  Weaving frames in separable Hilbert spaces have been recently introduced by\nBemrose et al. to deal with some problems in distributed signal processing and\nwireless sensor networks. In this paper, we study the notion of excess for\nwoven frames and prove that any two frames in a separable Hilbert space that\nare woven have the same excess. We also show that every frame with a large\nclass of duals is woven provided that its redundant elements have small enough\nnorm. Also, we try to transfer the woven property from frames to their duals\nand vise versa. Finally, we look at which perturbations of dual frames preserve\nthe woven property, moreover it is shown that under some conditions the\ncanonical Pareseval frame of two woven frames are also woven.\n"}
+{"abstract": "  Detecting and responding to novel situations in open-world environments is a\nkey capability of human cognition. Current artificial intelligence (AI)\nresearchers strive to develop systems that can perform in open-world\nenvironments. Novelty detection is an important ability of such AI systems. In\nan open-world, novelties appear in various forms and the difficulty to detect\nthem varies. Therefore, to accurately evaluate the detection capability of AI\nsystems, it is necessary to investigate the difficulty to detect novelties. In\nthis paper, we propose a qualitative physics-based method to quantify the\ndifficulty of novelty detection focusing on open-world physical domains. We\napply our method in a popular physics simulation game, Angry Birds. We conduct\nan experiment with human players with different novelties in Angry Birds to\nvalidate our method. Results indicate that the calculated difficulty values are\nin line with the detection difficulty of the human players.\n"}
+{"abstract": "  Digital fabrication courses that relied on physical makerspaces were severely\ndisrupted by COVID-19. As universities shut down in Spring 2020, instructors\ndeveloped new models for digital fabrication at a distance. Through interviews\nwith faculty and students and examination of course materials, we recount the\nexperiences of eight remote digital fabrication courses. We found that learning\nwith hobbyist equipment and online social networks could emulate using\nindustrial equipment in shared workshops. Furthermore, at-home digital\nfabrication offered unique learning opportunities including more iteration,\nmachine tuning, and maintenance. These opportunities depended on new forms of\nlabor and varied based on student living situations. Our findings have\nimplications for remote and in-person digital fabrication instruction. They\nindicate how access to tools was important, but not as critical as providing\nopportunities for iteration; they show how remote fabrication exacerbated\nstudent inequities; and they suggest strategies for evaluating trade-offs in\nremote fabrication models with respect to learning objectives.\n"}
+{"abstract": "  We solve the pointwise Landau-Kolmogorov problem on the interval $\\mathbb{I}\n= [-1,1]$ on finding $\\left|f^{(k)}(t)\\right|\\to\\sup$ under constraints\n$\\|f\\|_2 \\leqslant \\delta$ and $\\left\\|f^{(r)}\\right\\|_2\\leqslant 1$, where\n$t\\in\\mathbb{I}$ and $\\delta > 0$ are fixed. For $r = 1$ and $r = 2$, we solve\nthe uniform version of the Landau-Kolmogorov problem on the interval\n$\\mathbb{I}$ in the Taikov case by proving the Karlin-type conjecture\n$\\sup\\limits_{t\\in \\mathbb{I}}\\left|f^{(k)}(t)\\right| =\n\\left|f^{(k)}(-1)\\right|$ under above constraints. The proof relies on the\nanalysis of the dependence of the norm of the solution to higher-order\nSturm-Liouville equation $(-1)^ru^{(2r)} + \\lambda u = -\\lambda f$ with\nboundary conditions $u^{(s)}(-1) = u^{(s)}(1) = 0$, $s = 0,1,\\ldots,r-1$, on\nnon-negative parameter $\\lambda$, where $f$ is some piece-wise polynomial\nfunction. Furthermore, we find sharp inequality $\\left\\|f^{(k)}\\right\\|_\\infty\n\\leqslant A\\|f\\|_2 + B\\left\\|f^{(r)}\\right\\|_2$ with the smallest possible\nconstant $A > 0$ and the smallest possible constant $B = B(A)$ for $k \\in\n\\{r-2, r-1\\}$.\n"}
+{"abstract": "  Pairwise comparison matrices have received substantial attention in a variety\nof applications, especially in rank aggregation, the task of flattening items\ninto a one-dimensional (and thus transitive) ranking. However, non-transitive\npreference cycles can arise in practice due to the fact that making a decision\noften requires a complex evaluation of multiple factors. In some applications,\nit may be important to identify and preserve information about the inherent\nnon-transitivity, either in the pairwise comparison data itself or in the\nlatent feature space. In this work, we develop structured models for\nnon-transitive pairwise comparison matrices that can be exploited to recover\nsuch matrices from incomplete noisy data and thus allow the detection of\nnon-transitivity. Considering that individuals' tastes and items' latent\nfeatures may change over time, we formulate time-varying pairwise comparison\nmatrix recovery as a dynamic skew-symmetric matrix recovery problem by modeling\nchanges in the low-rank factors of the pairwise comparison matrix. We provide\ntheoretical guarantees for the recovery and numerically test the proposed\ntheory with both synthetic and real-world data.\n"}
+{"abstract": "  Stochastic gravitational wave background (SGWB) is a promising tool to probe\nthe very early universe where the standard model of particle physics and\ncosmology are connected closely. As a possible component of SGWB, gravitational\nwaves (GW) from bubble collisions during the first order cosmological phase\ntransitions deserve comprehensive analyses. In 2017, Ryusuke Jinno and Masahiro\nTakimoto proposed an elegant analysis approach to derive the analytical\nexpressions of energy spectra of GW from bubble collisions in Minkowski\nspacetime avoiding large-scale numerical simulations for the first time[1].\nHowever, they neglect the expansion of the universe and regard the duration of\nphase transitions as infinity in their derivation which could deviate their\nestimations from true values. For these two reasons, we give a new expression\nof GW spectra by adopting their method, switching spacetime background to FLRW\nspacetime and considering a finite duration of phase transitions. By denoting\n$\\sigma$ as the fraction of the speed of phase transitions to the expansion\nspeed of the universe, we find for different $\\sigma$, the maxima of GW energy\nspectra drop by around 1-3 orders of magnitude than the results given by their\nprevious work. Such a significant decrease may bring about new challenges for\ndetectability of GW from bubble collisions. Luckily, by comparing new spectra\nwith PLI (power-law integrated) sensitivity curves of GW detectors, we find GW\nfrom bubble collisions still could be detectable for BBO and LISA in future\ndetection.\n"}
+{"abstract": "  We propose a conjectural construction of global points on modular elliptic\ncurves over arbitrary number fields, generalizing both the p-adic construction\nof Heegner points via Cerednik-Drinfeld uniformization and the definition of\nclassical Stark-Heegner points. In alignment with Nekovar and Scholl's plectic\nconjectures, we expect the non-triviality of these plectic Stark-Heegner points\nto control the Mordell-Weil group of higher rank elliptic curves. We provide\nsome indirect evidence for our conjectures by showing that higher order\nderivatives of anticyclotomic p-adic L-functions compute plectic invariants.\n"}
+{"abstract": "  Continuous deep learning architectures enable learning of flexible\nprobabilistic models for predictive modeling as neural ordinary differential\nequations (ODEs), and for generative modeling as continuous normalizing flows.\nIn this work, we design a framework to decipher the internal dynamics of these\ncontinuous depth models by pruning their network architectures. Our empirical\nresults suggest that pruning improves generalization for neural ODEs in\ngenerative modeling. We empirically show that the improvement is because\npruning helps avoid mode-collapse and flatten the loss surface. Moreover,\npruning finds efficient neural ODE representations with up to 98% less\nparameters compared to the original network, without loss of accuracy. We hope\nour results will invigorate further research into the performance-size\ntrade-offs of modern continuous-depth models.\n"}
+{"abstract": "  It is evident that deep text classification models trained on human data\ncould be biased. In particular, they produce biased outcomes for texts that\nexplicitly include identity terms of certain demographic groups. We refer to\nthis type of bias as explicit bias, which has been extensively studied.\nHowever, deep text classification models can also produce biased outcomes for\ntexts written by authors of certain demographic groups. We refer to such bias\nas implicit bias of which we still have a rather limited understanding. In this\npaper, we first demonstrate that implicit bias exists in different text\nclassification tasks for different demographic groups. Then, we build a\nlearning-based interpretation method to deepen our knowledge of implicit bias.\nSpecifically, we verify that classifiers learn to make predictions based on\nlanguage features that are related to the demographic attributes of the\nauthors. Next, we propose a framework Debiased-TC to train deep text\nclassifiers to make predictions on the right features and consequently mitigate\nimplicit bias. We conduct extensive experiments on three real-world datasets.\nThe results show that the text classification models trained under our proposed\nframework outperform traditional models significantly in terms of fairness, and\nalso slightly in terms of classification performance.\n"}
+{"abstract": "  We consider convolutional neural network (CNN) ensemble learning with the\nobjective function inspired by least action principle; it includes resource\nconsumption component. We teach an agent to perceive images through the set of\npre-trained classifiers and want the resulting dynamically configured system to\nunfold the computational graph with the trajectory that refers to the minimal\nnumber of operations and maximal expected accuracy. The proposed agent's\narchitecture implicitly approximates the required classifier selection function\nwith the help of reinforcement learning. Our experimental results prove, that\nif the agent exploits the dynamic (and context-dependent) structure of\ncomputations, it outperforms conventional ensemble learning.\n"}
+{"abstract": "  This paper presents a novel framework for unsupervised anomaly detection on\nmasked objects called ODDObjects, which stands for Out-of-Distribution\nDetection on Objects. ODDObjects is designed to detect anomalies of various\ncategories using unsupervised autoencoders trained on COCO-style datasets. The\nmethod utilizes autoencoder-based image reconstruction, where high\nreconstruction error indicates the possibility of an anomaly. The framework\nextends previous work on anomaly detection with autoencoders, comparing\nstate-of-the-art models trained on object recognition datasets. Various model\narchitectures were compared, and experimental results show that\nmemory-augmented deep convolutional autoencoders perform the best at detecting\nout-of-distribution objects.\n"}
+{"abstract": "  Biological and physical systems that can be classified as oscillatory media\ngive rise to interesting phenomena like target patterns and spiral waves. The\nexistence of these structures has been proven in the case of systems with local\ndiffusive interactions. In this paper the more general case of oscillatory\nmedia with nonlocal coupling is considered. We model these systems using\nevolution equations where the nonlocal interactions are expressed via a\ndiffusive convolution kernel, and prove the existence of rotating wave\nsolutions for these systems. Since the nonlocal nature of the equations\nprecludes the use of standard techniques from spatial dynamics, the method we\nuse relies instead on a combination of a multiple-scales analysis and a\nconstruction similar to Lyapunov-Schmidt. This approach then allows us to\nderive a normal form, or reduced equation, that captures the leading order\nbehavior of these solutions.\n"}
+{"abstract": "  In this paper, we will provide constructions of D-module structures on the\ncomplex computing the periodic cyclic homology of an $A$-linear stable\ninfinity-category in characteristic zero. We give two methods. The first one is\nbased on a canonical extension of factorization homology to the mapping stack\nand relation between sheaves on free loop space and D-modules. The second one\nuses the algebraic structure on Hochschild pairs, its moduli-theoretic\ninterpretation, Kodaira-Spencer morphisms, and the relation between dg Lie\nalgebras and pointed formal stacks.\n"}
+{"abstract": "  Maximum distance separable (MDS) codes are very important in both theory and\npractice. There is a classical construction of a family of $[2^m+1, 2u-1,\n2^m-2u+3]$ MDS codes for $1 \\leq u \\leq 2^{m-1}$, which are cyclic, reversible\nand BCH codes over $\\mathrm{GF}(2^m)$. The objective of this paper is to study\nthe quaternary subfield subcodes and quaternary subfield codes of a subfamily\nof the MDS codes for even $m$. A family of quaternary cyclic codes is obtained.\nThese quaternary codes are distance-optimal in some cases and very good in\ngeneral. Furthermore, infinite families of $3$-designs from these quaternary\ncodes are presented.\n"}
+{"abstract": "  We will introduce the notion of strong Morita equivalence for completely\npositive linear maps and study its basic properties. Also, we will discuss the\nrelation between strong Morita equivalence for bounded $C^*$-bimodule linear\nmaps and strong Morita equivalence for completely positive linear maps.\nFurthermore, we will show that if two unital $C^*$-algebras are strongly Morita\nequivalent, then there is a $1-1$ correspondence between the two sets of all\nstrong Morita equivalence classes of completely positive linear maps on the two\nunital $C^*$-algebras and we will show that the corresponding two classes of\nthe completely positive linear maps are also strongly Morita equivalent.\n"}
+{"abstract": "  An open problem posed by Milner asks for a proof that a certain\naxiomatisation, which Milner showed is sound with respect to bisimilarity for\nregular expressions, is also complete. One of the main difficulties of the\nproblem is the lack of a full Kleene theorem, since there are automata that can\nnot be specified, up to bisimilarity, by an expression. Grabmayer and Fokkink\n(2020) characterise those automata that can be expressed by regular expressions\nwithout the constant 1, and use this characterisation to give a positive answer\nto Milner's question for this subset of expressions. In this paper, we analyse\nGrabmayer and Fokkink's proof of completeness from the perspective of universal\ncoalgebra, and thereby give an abstract account of their proof method. We then\ncompare this proof method to another approach to completeness proofs from\ncoalgebraic language theory. This culminates in two abstract proof methods for\ncompleteness, what we call the local and global approaches, and a description\nof when one method can be used in place of the other.\n"}
+{"abstract": "  We study the collision between the cue and the ball in the game of billiards.\nAfter studying the collision process in detail, we write the (rotational)\nvelocities of the ball and the cue after the collision. We also find the squirt\nangle of the ball for an oblique collision which represents the deviation of\nthe ball from the intended direction.\n"}
+{"abstract": "  The recurrent mechanism has recently been introduced into U-Net in various\nmedical image segmentation tasks. Existing studies have focused on promoting\nnetwork recursion via reusing building blocks. Although network parameters\ncould be greatly saved, computational costs still increase inevitably in\naccordance with the pre-set iteration time. In this work, we study a\nmulti-scale upgrade of a bi-directional skip connected network and then\nautomatically discover an efficient architecture by a novel two-phase Neural\nArchitecture Search (NAS) algorithm, namely BiX-NAS. Our proposed method\nreduces the network computational cost by sifting out ineffective multi-scale\nfeatures at different levels and iterations. We evaluate BiX-NAS on two\nsegmentation tasks using three different medical image datasets, and the\nexperimental results show that our BiX-NAS searched architecture achieves the\nstate-of-the-art performance with significantly lower computational cost.\n"}
+{"abstract": "  In the first half of twentieth century the theory of complex analytic\nfunctions and of their zerosets was fully developed. The definition of\nholomorphic function has a local nature. Germs of holomorphic functions form a\ndistinguished subring of the ring of germs of continuous functions. This way\ncame out the notion of analytic space. The definition of a complex analytic set\nis by local models as in the case of complex manifolds. But while local models\nfor manifolds are open sets of ${\\mathbb C}^n$, a local model of an analytic\nspace is the zeroset of finitely many analytic functions on an open set of\n${\\mathbb C}^n$ together with a sheaf of continuous function to be called\nholomorphic.\n  Towards the years $50$ of the last century, Cartan, Whitney, Bruhat and\nothers tried to formulate the notion of analytic space over ${\\mathbb R}$.\nImmediately they realize that the real sets verifying a definition similar to\nthe complex one form a cathegory whose elements can get unpleasant behaviour.\nIn particular this cathegory does not get the good properties of complex\nanalytic spaces, as for instance coherence of their structural sheaves and\nTheorems A and B do not hold in general.\n"}
+{"abstract": "  In this paper, we present a Symbolic Reinforcement Learning (SRL) based\narchitecture for safety control of Radio Access Network (RAN) applications. In\nparticular, we provide a purely automated procedure in which a user can specify\nhigh-level logical safety specifications for a given cellular network topology\nin order for the latter to execute optimal safe performance which is measured\nthrough certain Key Performance Indicators (KPIs). The network consists of a\nset of fixed Base Stations (BS) which are equipped with antennas, which one can\ncontrol by adjusting their vertical tilt angle. The aforementioned process is\ncalled Remote Electrical Tilt (RET) optimization. Recent research has focused\non performing this RET optimization by employing Reinforcement Learning (RL)\nstrategies due to the fact that they have self-learning capabilities to adapt\nin uncertain environments. The term safety refers to particular constraints\nbounds of the network KPIs in order to guarantee that when the algorithms are\ndeployed in a live network, the performance is maintained. In our proposed\narchitecture the safety is ensured through model-checking techniques over\ncombined discrete system models (automata) that are abstracted through the\nlearning process. We introduce a user interface (UI) developed to help a user\nset intent specifications to the system, and inspect the difference in agent\nproposed actions, and those that are allowed and blocked according to the\nsafety specification.\n"}
+{"abstract": "  Given a graph $G$ whose edges are labeled by ideals of a commutative ring $R$\nwith identity, a generalized spline is a vertex labeling of $G$ by the elements\nof $R$ so that the difference of labels on adjacent vertices is an element of\nthe corresponding edge ideal. The set of all generalized splines on a graph $G$\nwith base ring $R$ has a ring and an $R$-module structure.\n  In this paper, we focus on the freeness of generalized spline modules over\ncertain graphs with the base ring $R = k[x_1 , \\ldots , x_d]$ where $k$ is a\nfield. We first show the freeness of generalized spline modules on graphs with\nno interior edges over $k[x,y]$ such as cycles or a disjoint union of cycles\nwith free edges. Later, we consider graphs that can be decomposed into disjoint\ncycles without changing the isomorphism class of the syzygy modules. Then we\nuse this decomposition to show that generalized spline modules are free over\n$k[x , y]$ and later we extend this result to the base ring $R = k[x_1 , \\ldots\n, x_d]$ under some restrictions.\n"}
+{"abstract": "  Despite the significant progress over the last 50 years in simulating flow\nproblems using numerical discretization of the Navier-Stokes equations (NSE),\nwe still cannot incorporate seamlessly noisy data into existing algorithms,\nmesh-generation is complex, and we cannot tackle high-dimensional problems\ngoverned by parametrized NSE. Moreover, solving inverse flow problems is often\nprohibitively expensive and requires complex and expensive formulations and new\ncomputer codes. Here, we review flow physics-informed learning, integrating\nseamlessly data and mathematical models, and implementing them using\nphysics-informed neural networks (PINNs). We demonstrate the effectiveness of\nPINNs for inverse problems related to three-dimensional wake flows, supersonic\nflows, and biomedical flows.\n"}
+{"abstract": "  Text classification is a widely studied problem and has broad applications.\nIn many real-world problems, the number of texts for training classification\nmodels is limited, which renders these models prone to overfitting. To address\nthis problem, we propose SSL-Reg, a data-dependent regularization approach\nbased on self-supervised learning (SSL). SSL is an unsupervised learning\napproach which defines auxiliary tasks on input data without using any\nhuman-provided labels and learns data representations by solving these\nauxiliary tasks. In SSL-Reg, a supervised classification task and an\nunsupervised SSL task are performed simultaneously. The SSL task is\nunsupervised, which is defined purely on input texts without using any\nhuman-provided labels. Training a model using an SSL task can prevent the model\nfrom being overfitted to a limited number of class labels in the classification\ntask. Experiments on 17 text classification datasets demonstrate the\neffectiveness of our proposed method.\n"}
+{"abstract": "  Epilepsy is a chronic neurological disorder affecting more than 50 million\npeople globally. An epileptic seizure acts like a temporary shock to the\nneuronal system, disrupting normal electrical activity in the brain. Epilepsy\nis frequently diagnosed with electroencephalograms (EEGs). Current methods\nstudy the time-varying spectra and coherence but do not directly model changes\nin extreme behavior. Thus, we propose a new approach to characterize brain\nconnectivity based on the joint tail behavior of the EEGs. Our proposed method,\nthe conditional extremal dependence for brain connectivity (Conex-Connect), is\na pioneering approach that links the association between extreme values of\nhigher oscillations at a reference channel with the other brain network\nchannels. Using the Conex-Connect method, we discover changes in the extremal\ndependence driven by the activity at the foci of the epileptic seizure. Our\nmodel-based approach reveals that, pre-seizure, the dependence is notably\nstable for all channels when conditioning on extreme values of the focal\nseizure area. Post-seizure, by contrast, the dependence between channels is\nweaker, and dependence patterns are more \"chaotic\". Moreover, in terms of\nspectral decomposition, we find that high values of the high-frequency\nGamma-band are the most relevant features to explain the conditional extremal\ndependence of brain connectivity.\n"}
+{"abstract": "  We consider a cosmological model with dark matter in the form of\n$\\sim10^{-12}M_\\odot$ primordial black holes in dense weakly relativistic\nclusters with masses $18-560M_\\odot$. It is shown that during the multiple\ncollisions of the black holes the $\\sim10$\\% of the initial cluster mass can be\ntransformed into gravitational waves in the time interval from recombination to\nthe redshifts $z\\geq 10$. At the recombination epoch, the density of matter was\nlarger by $\\sim10$\\% and, accordingly, the universe expansion rate was higher.\nThis leads to a shortening of the sound horizon scale, as is necessary to solve\nthe \"$H_0$ tension\" problem.\n"}
+{"abstract": "  Whitney proved in 1931 that every 4-connected planar triangulation is\nhamiltonian. Later in 1979, Hakimi, Schmeichel and Thomassen conjectured that\nevery such triangulation on $n$ vertices has at least $2(n - 2)(n - 4)$\nhamiltonian cycles. Along this direction, Brinkmann, Souffriau and Van Cleemput\nestablished a linear lower bound on the number of hamiltonian cycles in\n4-connected planar triangulations. In stark contrast, Alahmadi, Aldred and\nThomassen showed that every 5-connected triangulation of the plane or the\nprojective plane has exponentially many hamiltonian cycles. This gives the\nmotivation to study the number of hamiltonian cycles of 4-connected\ntriangulations with few 4-separators. Recently, Liu and Yu showed that every\n4-connected planar triangulation with $O(n / \\log n)$ 4-separators has a\nquadratic number of hamiltonian cycles. By adapting the framework of Alahmadi\net al. we strengthen the last two aforementioned results. We prove that every\n4-connected planar or projective planar triangulation with $O(n)$ 4-separators\nhas exponentially many hamiltonian cycles.\n"}
+{"abstract": "  Recent theoretical works on effective, four-band models of three-dimensional,\nDirac semimetals suggest the generic planes in momentum space, orthogonal to\nthe direction of nodal separation, and lying between two Dirac points are\nhigher-order topological insulators, supporting gapped, edge-states.\nFurthermore, the second homotopy classification of four-band models shows the\nhigher-order topological insulators support quantized, non-Abelian Berry's flux\nand the Dirac points are monopoles of $SO(5)$ Berry's connections. Due to the\nlack of suitable computational scheme, such bulk topological properties are yet\nto be determined from the \\emph{ab initio} band structures of Dirac materials.\nIn this work, we report first, comprehensive topological classification of\n\\emph{ab initio} band structures of Na$_3$Bi, by computing Wilson loops of\nnon-Abelian, Berry's connections for several, Kramers-degenerate bands. Our\nwork shows the quantized, non-Abelian, Berry's flux can be used as a stable,\nbulk invariant for describing higher-order topology and topological phase\ntransitions.\n"}
+{"abstract": "  Encoder pre-training is promising in end-to-end Speech Translation (ST),\ngiven the fact that speech-to-translation data is scarce. But ST encoders are\nnot simple instances of Automatic Speech Recognition (ASR) or Machine\nTranslation (MT) encoders. For example, we find that ASR encoders lack the\nglobal context representation, which is necessary for translation, whereas MT\nencoders are not designed to deal with long but locally attentive acoustic\nsequences. In this work, we propose a Stacked Acoustic-and-Textual Encoding\n(SATE) method for speech translation. Our encoder begins with processing the\nacoustic sequence as usual, but later behaves more like an MT encoder for a\nglobal representation of the input sequence. In this way, it is straightforward\nto incorporate the pre-trained models into the system. Also, we develop an\nadaptor module to alleviate the representation inconsistency between the\npre-trained ASR encoder and MT encoder, and develop a multi-teacher knowledge\ndistillation method to preserve the pre-training knowledge. Experimental\nresults on the LibriSpeech En-Fr and MuST-C En-De ST tasks show that our method\nachieves state-of-the-art BLEU scores of 18.3 and 25.2. To our knowledge, we\nare the first to develop an end-to-end ST system that achieves comparable or\neven better BLEU performance than the cascaded ST counterpart when large-scale\nASR and MT data is available.\n"}
+{"abstract": "  Cardiac auscultation is an essential point-of-care method used for the early\ndiagnosis of heart diseases. Automatic analysis of heart sounds for abnormality\ndetection is faced with the challenges of additive noise and sensor-dependent\ndegradation. This paper aims to develop methods to address the cardiac\nabnormality detection problem when both types of distortions are present in the\ncardiac auscultation sound. We first mathematically analyze the effect of\nadditive and convolutional noise on short-term filterbank-based features and a\nConvolutional Neural Network (CNN) layer. Based on the analysis, we propose a\ncombination of linear and logarithmic spectrogram-image features. These 2D\nfeatures are provided as input to a residual CNN network (ResNet) for heart\nsound abnormality detection. Experimental validation is performed on an\nopen-access heart sound abnormality detection dataset involving noisy\nrecordings obtained from multiple stethoscope sensors. The proposed method\nachieves significantly improved results compared to the conventional\napproaches, with an area under the ROC (receiver operating characteristics)\ncurve (AUC) of 91.36%, F-1 score of 84.09%, and Macc (mean of sensitivity and\nspecificity) of 85.08%. We also show that the proposed method shows the best\nmean accuracy across different source domains including stethoscope and noise\nvariability, demonstrating its effectiveness in different recording conditions.\nThe proposed combination of linear and logarithmic features along with the\nResNet classifier effectively minimizes the impact of background noise and\nsensor variability for classifying phonocardiogram (PCG) signals. The proposed\nmethod paves the way towards developing computer-aided cardiac auscultation\nsystems in noisy environments using low-cost stethoscopes.\n"}
+{"abstract": "  We present a first 3D magnetohydrodynamic (MHD) simulation of convective\noxygen and neon shell burning in a non-rotating $18\\, M_\\odot$ star shortly\nbefore core collapse to study the generation of magnetic fields in supernova\nprogenitors. We also run a purely hydrodynamic control simulation to gauge the\nimpact of the magnetic fields on the convective flow and on convective boundary\nmixing. After about 17 convective turnover times, the magnetic field is\napproaching saturation levels in the oxygen shell with an average field\nstrength of $\\mathord{\\sim}10^{10}\\, \\mathrm{G}$, and does not reach kinetic\nequipartition. The field remains dominated by small to medium scales, and the\ndipole field strength at the base of the oxygen shell is only $10^{9}\\,\n\\mathrm{G}$. The angle-averaged diagonal components of the Maxwell stress\ntensor mirror those of the Reynolds stress tensor, but are about one order of\nmagnitude smaller. The shear flow at the oxygen-neon shell interface creates\nrelatively strong fields parallel to the convective boundary, which noticeably\ninhibit the turbulent entrainment of neon into the oxygen shell. The reduced\ningestion of neon lowers the nuclear energy generation rate in the oxygen shell\nand thereby slightly slows down the convective flow. Aside from this indirect\neffect, we find that magnetic fields do not appreciably alter the flow inside\nthe oxygen shell. We discuss the implications of our results for the subsequent\ncore-collapse supernova and stress the need for longer simulations, resolution\nstudies, and an investigation of non-ideal effects for a better understanding\nof magnetic fields in supernova progenitors.\n"}
+{"abstract": "  Recently, $O(\\alpha^3)$ corrections to the muon decay rate and\n$O(\\alpha_s^3)$ to heavy quark decays have been determined by Fael,\nSch\\\"onwald, and Steinhauser. This is the first such perturbative improvement\nof these important quantities in more than two decades. We reveal and explain a\nsymmetry pattern in these new corrections, and confirm the most technically\ndifficult parts of their evaluation.\n"}
+{"abstract": "  We consider reaction-diffusion systems where components diffuse inside the\ndomain and react on the surface through mass transport type boundary conditions\non an evolving domain. Using Lyapunov functional and duality arguments, we\nestablish the existence of component-wise non-negative global solutions.\n"}
+{"abstract": "  Electron-tracking Compton camera, which is a complete Compton camera with\ntracking Compton scattering electron by a gas micro time projection chamber, is\nexpected to open up MeV gamma-ray astronomy. The technical challenge for\nachieving several degrees of the point spread function is the precise\ndetermination of the electron-recoil direction and the scattering position from\ntrack images. We attempted to reconstruct these parameters using convolutional\nneural networks. Two network models were designed to predict the recoil\ndirection and the scattering position. These models marked 41$~$degrees of the\nangular resolution and 2.1$~$mm of the position resolution for 75$~$keV\nelectron simulation data in Argon-based gas at 2$~$atm pressure. In addition,\nthe point spread function of ETCC was improved to 15$~$degrees from\n22$~$degrees for experimental data of 662$~$keV gamma-ray source. These\nperformances greatly surpassed that using the traditional analysis.\n"}
+{"abstract": "  The simplest non-trivial 5d superconformal field theories (SCFT) are the\nfamous rank-one theories with $E_n$ flavour symmetry. We study their $U$-plane,\nwhich is the one-dimensional Coulomb branch of the theory on $\\mathbb{R}^4\n\\times S^1$. The total space of the Seiberg-Witten (SW) geometry -- the $E_n$\nSW curve fibered over the $U$-plane -- is described as a rational elliptic\nsurface with a singular fiber of type $I_{9-n}$ at infinity. A classification\nof all possible Coulomb branch configurations, for the $E_n$ theories and their\n4d descendants, is given by Persson's classification of rational elliptic\nsurfaces. We show that the global form of the flavour symmetry group is encoded\nin the Mordell-Weil group of the SW elliptic fibration. We study in detail many\nspecial points in parameters space, such as points where the flavour symmetry\nenhances, and/or where Argyres-Douglas and Minahan-Nemeschansky theories\nappear. In a number of important instances, including in the massless limit,\nthe $U$-plane is a modular curve, and we use modularity to investigate aspects\nof the low-energy physics, such as the spectrum of light particles at strong\ncoupling and the associated BPS quivers. We also study the gravitational\ncouplings on the $U$-plane, matching the infrared expectation for the couplings\n$A(U)$ and $B(U)$ to the UV computation using the Nekrasov partition function.\n"}
+{"abstract": "  Standard methods for video recognition use large CNNs designed to capture\nspatio-temporal data. However, training these models requires a large amount of\nlabeled training data, containing a wide variety of actions, scenes, settings\nand camera viewpoints. In this paper, we show that current convolutional neural\nnetwork models are unable to recognize actions from camera viewpoints not\npresent in their training data (i.e., unseen view action recognition). To\naddress this, we develop approaches based on 3D representations and introduce a\nnew geometric convolutional layer that can learn viewpoint invariant\nrepresentations. Further, we introduce a new, challenging dataset for unseen\nview recognition and show the approaches ability to learn viewpoint invariant\nrepresentations.\n"}
+{"abstract": "  For superconducting nanowire with the pairing of extended s-type symmetry,\nRashba spin-orbit interaction in a magnetic field, the influence of strong\nintersite charge correlations on single-particle Majorana excitations is\nanalyzed. This problem is investigated on the basis of the density matrix\nrenormalization group numerical method. It is shown that with an increase in\nthe repulsion intensity of electrons located at the neighboring sites, two\nsubbands emerge in the lower Hubbard band of the open system. Based on\ncalculations of the Majorana polarization and degeneracy of the entanglement\nspectrum, it was found that a topologically nontrivial phase with one edge\nstate survives at the edge of each of the subbands where the concentration of\nelectrons or holes is minimal.\n"}
+{"abstract": "  This paper describes the AS-NU systems for two tracks in MultiSpeaker\nMulti-Style Voice Cloning Challenge (M2VoC). The first track focuses on using a\nsmall number of 100 target utterances for voice cloning, while the second track\nfocuses on using only 5 target utterances for voice cloning. Due to the serious\nlack of data in the second track, we selected the speaker most similar to the\ntarget speaker from the training data of the TTS system, and used the speaker's\nutterances and the given 5 target utterances to fine-tune our model. The\nevaluation results show that our systems on the two tracks perform similarly in\nterms of quality, but there is still a clear gap between the similarity score\nof the second track and the similarity score of the first track.\n"}
+{"abstract": "  When a bug manifests in a user-facing application, it is likely to be exposed\nthrough the graphical user interface (GUI). Given the importance of visual\ninformation to the process of identifying and understanding such bugs, users\nare increasingly making use of screenshots and screen-recordings as a means to\nreport issues to developers. However, when such information is reported en\nmasse, such as during crowd-sourced testing, managing these artifacts can be a\ntime-consuming process. As the reporting of screen-recordings in particular\nbecomes more popular, developers are likely to face challenges related to\nmanually identifying videos that depict duplicate bugs. Due to their graphical\nnature, screen-recordings present challenges for automated analysis that\npreclude the use of current duplicate bug report detection techniques. To\novercome these challenges and aid developers in this task, this paper presents\nTango, a duplicate detection technique that operates purely on video-based bug\nreports by leveraging both visual and textual information. Tango combines\ntailored computer vision techniques, optical character recognition, and text\nretrieval. We evaluated multiple configurations of Tango in a comprehensive\nempirical evaluation on 4,860 duplicate detection tasks that involved a total\nof 180 screen-recordings from six Android apps. Additionally, we conducted a\nuser study investigating the effort required for developers to manually detect\nduplicate video-based bug reports and compared this to the effort required to\nuse Tango. The results reveal that Tango's optimal configuration is highly\neffective at detecting duplicate video-based bug reports, accurately ranking\ntarget duplicate videos in the top-2 returned results in 83% of the tasks.\nAdditionally, our user study shows that, on average, Tango can reduce developer\neffort by over 60%, illustrating its practicality.\n"}
+{"abstract": "  Federated Learning (FL) is an emerging learning scheme that allows different\ndistributed clients to train deep neural networks together without data\nsharing. Neural networks have become popular due to their unprecedented\nsuccess. To the best of our knowledge, the theoretical guarantees of FL\nconcerning neural networks with explicit forms and multi-step updates are\nunexplored. Nevertheless, training analysis of neural networks in FL is\nnon-trivial for two reasons: first, the objective loss function we are\noptimizing is non-smooth and non-convex, and second, we are even not updating\nin the gradient direction. Existing convergence results for gradient\ndescent-based methods heavily rely on the fact that the gradient direction is\nused for updating. This paper presents a new class of convergence analysis for\nFL, Federated Learning Neural Tangent Kernel (FL-NTK), which corresponds to\noverparamterized ReLU neural networks trained by gradient descent in FL and is\ninspired by the analysis in Neural Tangent Kernel (NTK). Theoretically, FL-NTK\nconverges to a global-optimal solution at a linear rate with properly tuned\nlearning parameters. Furthermore, with proper distributional assumptions,\nFL-NTK can also achieve good generalization.\n"}
+{"abstract": "  To provide spectroscopic data for lowly charged tungsten ions relevant to\nfusion research, this work focuses on the W8+ ion. Six visible spectra lines in\nthe range of 420-660 nm are observed with a compact electron-beam ion trap in\nShanghai. These lines are assigned to W8+ based on their intensity variations\nas increasing electron-beam energy and the M1 line from the ground\nconfiguration in W7+. Furthermore, transition energies are calculated for the\n30 lowest levels of the 4f14 5s2 5p4, 4f13 5s2 5p5 and 4f12 5s2 5p6\nconfigurations of W8+ by using the flexible atomic code (FAC) and GRASP\npackage, respectively. Reasonably good agreement is found between our two\nindependent atomic-structure calculations. The resulting atomic parameters are\nadopted to simulate the spectra based on the collisional-radiative model\nimplemented in the FAC code. This assists us with identification of six strong\nM1 transitions in 4f13 5s2 5p5 and 4f12 5s2 5p6 configurations from our\nexperiments\n"}
+{"abstract": "  During the night between 9 and 10 September 2017, multiple flash floods\nassociated to a heavy-precipitation event affected the town of Livorno, located\nin Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours,\nassociated with a return period higher than 200 years, caused all the largest\nstreams of the Livorno municipality to flood several areas of the town. We used\nthe limited-area Weather Research and Forecasting (WRF) model, in a\nconvection-permitting setup, to reconstruct the extreme event leading to the\nflash floods. We evaluated possible forecasting improvements emerging from the\nassimilation of local ground stations and X- and S-band radar data into the\nWRF, using the configuration operational at the meteorological center of\nTuscany region (LaMMA) at the time of the event. Simulations were verified\nagainst weather station observations, through an innovative method aimed at\ndisentangling the positioning and intensity errors of precipitation forecasts.\nBy providing more accurate descriptions of the low-level flow and a better\nassessment of the atmospheric water vapour, the results demonstrate that\nassimilating radar data improved the quantitative precipitation forecasts.\n"}
+{"abstract": "  Particle swarm optimization (PSO) is an iterative search method that moves a\nset of candidate solution around a search-space towards the best known global\nand local solutions with randomized step lengths. PSO frequently accelerates\noptimization in practical applications, where gradients are not available and\nfunction evaluations expensive. Yet the traditional PSO algorithm ignores the\npotential knowledge that could have been gained of the objective function from\nthe observations by individual particles. Hence, we draw upon concepts from\nBayesian optimization and introduce a stochastic surrogate model of the\nobjective function. That is, we fit a Gaussian process to past evaluations of\nthe objective function, forecast its shape and then adapt the particle\nmovements based on it. Our computational experiments demonstrate that baseline\nimplementations of PSO (i.e., SPSO2011) are outperformed. Furthermore, compared\nto, state-of-art surrogate-assisted evolutionary algorithms, we achieve\nsubstantial performance improvements on several popular benchmark functions.\nOverall, we find that our algorithm attains desirable properties for\nexploratory and exploitative behavior.\n"}
+{"abstract": "  Two-point time-series data, characterized by baseline and follow-up\nobservations, are frequently encountered in health research. We study a novel\ntwo-point time series structure without a control group, which is driven by an\nobservational routine clinical dataset collected to monitor key risk markers of\ntype-$2$ diabetes (T2D) and cardiovascular disease (CVD). We propose a\nresampling approach called 'I-Rand' for independently sampling one of the two\ntime points for each individual and making inference on the estimated causal\neffects based on matching methods. The proposed method is illustrated with data\nfrom a service-based dietary intervention to promote a low-carbohydrate diet\n(LCD), designed to impact risk of T2D and CVD. Baseline data contain a\npre-intervention health record of study participants, and health data after LCD\nintervention are recorded at the follow-up visit, providing a two-point\ntime-series pattern without a parallel control group. Using this approach we\nfind that obesity is a significant risk factor of T2D and CVD, and an LCD\napproach can significantly mitigate the risks of T2D and CVD. We provide code\nthat implements our method.\n"}
+{"abstract": "  In theory, the Lanczos algorithm generates an orthogonal basis of the\ncorresponding Krylov subspace. However, in finite precision arithmetic, the\northogonality and linear independence of the computed Lanczos vectors is\nusually lost quickly. In this paper we study a class of matrices and starting\nvectors having a special nonzero structure that guarantees exact computations\nof the Lanczos algorithm whenever floating point arithmetic satisfying the IEEE\n754 standard is used. Analogous results are formulated also for a variant of\nthe conjugate gradient method that produces then almost exact results. The\nresults are extended to the Arnoldi algorithm, the nonsymmetric Lanczos\nalgorithm, the Golub-Kahan bidiagonalization, the block-Lanczos algorithm and\ntheir counterparts for solving linear systems.\n"}
+{"abstract": "  We present a theoretical investigation of the resonant elastic scattering of\ntwisted light, carrying angular momentum, by partially stripped ions. Special\nemphasis is placed on a question of whether the scattered radiation is also\ntwisted. In order to investigate such an \"angular momentum transfer\", we\ndevelop an approach that allows us to find a quantum state of the final photon\nwithout projecting it onto a detector state. A general expression for this\nso--called \\textit{evolved} state of outgoing radiation is derived and it can\nbe used to analyze the resonant scattering by any ion, independently of its\nshell structure. Here, we illustrate our approach with the strong electric\ndipole $n S_{0} \\to n' P_{1} \\to n S_{0}$ transitions, which play an important\nrole for the Gamma Factory project at CERN. For the incident Bessel light, the\nscattered radiation is shown to be in a superposition of twisted modes with the\nprojections of the total angular momentum $m_f = 0, \\pm 1$. The larger values\nof $m_f$ can be efficiently generated by inducing transitions of higher\nmultiplicity. This angular momentum transfer, together with a remarkable cross\nsection that is many orders of magnitude larger than that of the backward\nCompton scattering, makes the resonant photon scattering an effective tool for\nthe production of twisted x-- and even gamma--rays at the Gamma Factory\nfacility.\n"}
+{"abstract": "  We study double gamma ($\\gamma\\gamma$) decay nuclear matrix elements (NMEs)\nfor a wide range of nuclei from titanium to xenon, and explore their relation\nto neutrinoless double-beta ($0\\nu\\beta\\beta$) NMEs. To favor the comparison,\nwe focus on double-magnetic dipole transitions in the final $\\beta\\beta$\nnuclei, in particular the $\\gamma\\gamma$ decay of the double isobaric analog of\nthe initial $\\beta\\beta$ state into the ground state. For the most probable\ndecay with equal-energy photons, our large-scale nuclear shell model results\nshow a good linear correlation between the $\\gamma\\gamma$ and $0\\nu\\beta\\beta$\nNMEs. Our analysis reveals that the correlation holds for $\\gamma\\gamma$\ntransitions driven by the spin or orbital angular momentum due to the dominance\nof zero-coupled nucleon pairs, a feature common to $0\\nu\\beta\\beta$ decay. Our\nfindings point out the potential of future $\\gamma\\gamma$ decay measurements to\nconstrain $0\\nu\\beta\\beta$ NMEs, which are key to answer fundamental physics\nquestions based on $0\\nu\\beta\\beta$ experiments.\n"}
+{"abstract": "  Fix a square-free monomial $m \\in S = \\mathbb{K}[x_1,\\ldots,x_n]$. The\nsquare-free principal Borel ideal generated by $m$, denoted ${\\rm sfBorel}(m)$,\nis the ideal generated by all the square-free monomials that can be obtained\nvia Borel moves from the monomial $m$. We give upper and lower bounds for the\nWaldschmidt constant of ${\\rm sfBorel}(m)$ in terms of the support of $m$, and\nin some cases, exact values. For any rational $\\frac{a}{b} \\geq 1$, we show\nthat there exists a square-free principal Borel ideal with Waldschmidt constant\nequal to $\\frac{a}{b}$.\n"}
+{"abstract": "  A major goal for reduced-order models of unsteady fluid flows is to uncover\nand exploit latent low-dimensional structure. Proper orthogonal decomposition\n(POD) provides an energy-optimal linear basis to represent the flow kinematics,\nbut converges slowly for advection-dominated flows and tends to overestimate\nthe number of dynamically relevant variables. We show that nonlinear\ncorrelations in the temporal POD coefficients can be exploited to identify the\nunderlying attractor, characterized by a minimal set of driving modes and a\nmanifold equation for the remaining modes. By viewing these nonlinear\ncorrelations as an invariant manifold reduction, this least-order\nrepresentation can be used to stabilize POD-Galerkin models or as a state space\nfor data-driven model identification. In the latter case, we use sparse\npolynomial regression to learn a compact, interpretable dynamical system model\nfrom the time series of the active modal coefficients. We demonstrate this\nperspective on a quasiperiodic shear-driven cavity flow and show that the\ndynamics evolve on a torus generated by two independent Stuart-Landau\noscillators. These results emphasize the importance of nonlinear dimensionality\nreduction to reveal underlying structure in complex flows.\n"}
+{"abstract": "  In this paper, we study the market-oriented online bi-objective service\nscheduling problem for pleasingly parallel jobs with variable resources in\ncloud environments, from the perspective of SaaS (Software-as-as-Service)\nproviders who provide job-execution services. The main process of scheduling\nSaaS services in clouds is: a SaaS provider purchases cloud instances from IaaS\nproviders to schedule end users' jobs and charges users accordingly. This\nproblem has several particular features, such as the job-oriented end users,\nthe pleasingly parallel jobs with soft deadline constraints, the online\nsettings, and the variable numbers of resources. For maximizing both the\nrevenue and the user satisfaction rate, we design an online algorithm for SaaS\nproviders to optimally purchase IaaS instances and schedule pleasingly parallel\njobs. The proposed algorithm can achieve competitive objectives in polynomial\nrun-time. The theoretical analysis and simulations based on real-world Google\njob traces as well as synthetic datasets validate the effectiveness and\nefficiency of our algorithm.\n"}
+{"abstract": "  The hadronic contribution to the muon anomalous magnetic moment\n$a_\\mu=(g_\\mu-2)/2$ has to be determined at the per-mille level for the\nStandard Model prediction to match the expected final uncertainty of the\nongoing E989 experiment. That is 3 times better than the current precision from\nthe dispersive approach, and 5-15 times smaller than the uncertainty based on\nthe purely theoretical determinations from lattice QCD. So far the\nstumbling-block is the large statistical error in the Monte Carlo evaluation of\nthe required correlation functions which can hardly be tamed by brute force. In\nthis talk we present our proposal to solve this problem by multi-level Monte\nCarlo integration, a technique which reduces the variance of correlators\nexponentially in the distance of the fields. We report the results of our\nfeasibility tests for the computation of the Hadronic Vacuum Polarization on a\nlattice with a linear extension of 3~fm, a spacing of 0.065 fm, and a pion mass\nof 270 MeV. Indeed the two-level integration makes the contribution to the\nstatistical error from long-distances de-facto negligible by accelerating its\ninverse scaling with the cost of the simulation. These findings establish\nmulti-level Monte Carlo as a solid and efficient method for a precise lattice\ndetermination of the hadronic contribution to $a_\\mu$.\n"}
+{"abstract": "  Representations in the form of Symmetric Positive Definite (SPD) matrices\nhave been popularized in a variety of visual learning applications due to their\ndemonstrated ability to capture rich second-order statistics of visual data.\nThere exist several similarity measures for comparing SPD matrices with\ndocumented benefits. However, selecting an appropriate measure for a given\nproblem remains a challenge and in most cases, is the result of a\ntrial-and-error process. In this paper, we propose to learn similarity measures\nin a data-driven manner. To this end, we capitalize on the \\alpha\\beta-log-det\ndivergence, which is a meta-divergence parametrized by scalars \\alpha and\n\\beta, subsuming a wide family of popular information divergences on SPD\nmatrices for distinct and discrete values of these parameters. Our key idea is\nto cast these parameters in a continuum and learn them from data. We\nsystematically extend this idea to learn vector-valued parameters, thereby\nincreasing the expressiveness of the underlying non-linear measure. We conjoin\nthe divergence learning problem with several standard tasks in machine\nlearning, including supervised discriminative dictionary learning and\nunsupervised SPD matrix clustering. We present Riemannian gradient descent\nschemes for optimizing our formulations efficiently, and show the usefulness of\nour method on eight standard computer vision tasks.\n"}
+{"abstract": "  We propose a new method to estimate ion escape from unmagnetized planets that\ncombines observations and models. Assuming that upstream solar wind conditions\nare known, a computer model of the interaction between the solar wind and the\nplanet is executed for different ionospheric ion production rates. This results\nin different amounts of mass loading of the solar wind. Then we obtain the ion\nescape rate from the model run that best fit observations of the bow shock\nlocation. As an example of the method we estimate the heavy ion escape from\nMars on 2015-03-01 to be $2\\cdot 10^{24}$ ions per second, using a hybrid\nplasma model and observations by MAVEN and Mars Express. This method enables\nstudies of how escape depend on different parameters, and also escape rates\nduring extreme solar wind conditions, applicable to studies of escape in the\nearly solar system, and at exoplanets.\n"}
+{"abstract": "  Ontology-mediated query answering is a popular paradigm for enriching answers\nto user queries with background knowledge. For querying the absence of\ninformation, however, there exist only few ontology-based approaches. Moreover,\nthese proposals conflate the closed-domain and closed-world assumption, and\ntherefore are not suited to deal with the anonymous objects that are common in\nontological reasoning. Many real-world applications, like processing electronic\nhealth records (EHRs), also contain a temporal dimension, and require efficient\nreasoning algorithms. Moreover, since medical data is not recorded on a regular\nbasis, reasoners must deal with sparse data with potentially large temporal\ngaps. Our contribution consists of two main parts: In the first part we\nintroduce a new closed-world semantics for answering conjunctive queries with\nnegation over ontologies formulated in the description logic ELHb, which is\nbased on the minimal canonical model. We propose a rewriting strategy for\ndealing with negated query atoms, which shows that query answering is possible\nin polynomial time in data complexity. In the second part, we extend this\nminimal-world semantics for answering metric temporal conjunctive queries with\nnegation over the lightweight temporal logic TELHb and obtain similar\nrewritability and complexity results. This paper is under consideration in\nTheory and Practice of Logic Programming (TPLP).\n"}
+{"abstract": "  Since the earliest conceptualizations by Lee and Markus, and Propoi in the\n1960s, Model Predictive Control (MPC) has become a major success story of\nsystems and control with respect to industrial impact and with respect to\ncontinued and wide-spread research interest. The field has evolved from\nconceptually simple linear-quadratic (convex) settings in discrete and\ncontinuous time to nonlinear and distributed settings including hybrid,\nstochastic, and infinite-dimensional systems. Put differently, essentially the\nentire spectrum of dynamic systems can be considered in the MPC framework with\nrespect to both -- system theoretic analysis and tailored numerics. Moreover,\nrecent developments in machine learning also leverage MPC concepts and\nlearning-based and data-driven MPC have become highly active research areas.\n  However, this evident and continued success renders it increasingly complex\nto live up to industrial expectations while enabling graduate students for\nstate-of-the-art research in teaching MPC. Hence, this position paper attempts\nto trigger a discussion on teaching MPC. To lay the basis for a fruitful\ndebate, we subsequently investigate the prospect of covering MPC in\nundergraduate courses; we comment on teaching textbooks; and we discuss the\nincreasing complexity of research-oriented graduate teaching of~MPC.\n"}
+{"abstract": "  While supervised learning is widely used for perception modules in\nconventional autonomous driving solutions, scalability is hindered by the huge\namount of data labeling needed. In contrast, while end-to-end architectures do\nnot require labeled data and are potentially more scalable, interpretability is\nsacrificed. We introduce a novel architecture that is trained in a fully\nself-supervised fashion for simultaneous multi-step prediction of space-time\ncost map and road dynamics. Our solution replaces the manually designed cost\nfunction for motion planning with a learned high dimensional cost map that is\nnaturally interpretable and allows diverse contextual information to be\nintegrated without manual data labeling. Experiments on real world driving data\nshow that our solution leads to lower number of collisions and road violations\nin long planning horizons in comparison to baselines, demonstrating the\nfeasibility of fully self-supervised prediction without sacrificing either\nscalability or interpretability.\n"}
+{"abstract": "  Autoencoders are among the earliest introduced nonlinear models for\nunsupervised learning. Although they are widely adopted beyond research, it has\nbeen a longstanding open problem to understand mathematically the feature\nextraction mechanism that trained nonlinear autoencoders provide.\n  In this work, we make progress in this problem by analyzing a class of\ntwo-layer weight-tied nonlinear autoencoders in the mean field framework. Upon\na suitable scaling, in the regime of a large number of neurons, the models\ntrained with stochastic gradient descent are shown to admit a mean field\nlimiting dynamics. This limiting description reveals an asymptotically precise\npicture of feature learning by these models: their training dynamics exhibit\ndifferent phases that correspond to the learning of different principal\nsubspaces of the data, with varying degrees of nonlinear shrinkage dependent on\nthe $\\ell_{2}$-regularization and stopping time. While we prove these results\nunder an idealized assumption of (correlated) Gaussian data, experiments on\nreal-life data demonstrate an interesting match with the theory.\n  The autoencoder setup of interests poses a nontrivial mathematical challenge\nto proving these results. In this setup, the \"Lipschitz\" constants of the\nmodels grow with the data dimension $d$. Consequently an adaptation of previous\nanalyses requires a number of neurons $N$ that is at least exponential in $d$.\nOur main technical contribution is a new argument which proves that the\nrequired $N$ is only polynomial in $d$. We conjecture that $N\\gg d$ is\nsufficient and that $N$ is necessarily larger than a data-dependent intrinsic\ndimension, a behavior that is fundamentally different from previously studied\nsetups.\n"}
+{"abstract": "  We demonstrate the generation of a trapped ion oscillator having large\noscillation amplitude of $16.9~{\\rm \\mu m}$. Applying an offset voltage to the\nion trap electrode helped achieve a displacement of the trap center within the\ntime scale of 5 ns. The fluorescence dynamics of the ion were analyzed after\nthe displacement to estimate the oscillation amplitude. The realized trap\ndisplacement is one order magnitude larger than that achieved in the previous\nwork. Thus, this result is an important step toward the realization of a\ngyroscope using a single trapped ion.\n"}
+{"abstract": "  Boundary Representations (B-Reps) are the industry standard in 3D Computer\nAided Design/Manufacturing (CAD/CAM) and industrial design due to their\nfidelity in representing stylistic details. However, they have been ignored in\nthe 3D style research. Existing 3D style metrics typically operate on meshes or\npointclouds, and fail to account for end-user subjectivity by adopting fixed\ndefinitions of style, either through crowd-sourcing for style labels or\nhand-crafted features. We propose UVStyle-Net, a style similarity measure for\nB-Reps that leverages the style signals in the second order statistics of the\nactivations in a pre-trained (unsupervised) 3D encoder, and learns their\nrelative importance to a subjective end-user through few-shot learning. Our\napproach differs from all existing data-driven 3D style methods since it may be\nused in completely unsupervised settings, which is desirable given the lack of\npublicly available labelled B-Rep datasets. More importantly, the few-shot\nlearning accounts for the inherent subjectivity associated with style. We show\nquantitatively that our proposed method with B-Reps is able to capture stronger\nstyle signals than alternative methods on meshes and pointclouds despite its\nsignificantly greater computational efficiency. We also show it is able to\ngenerate meaningful style gradients with respect to the input shape, and that\nfew-shot learning with as few as two positive examples selected by an end-user\nis sufficient to significantly improve the style measure. Finally, we\ndemonstrate its efficacy on a large unlabeled public dataset of CAD models.\nSource code and data will be released in the future.\n"}
+{"abstract": "  In the absence of any hints of new physics in LHC, TeV dark matter candidates\ninteracting through electroweak force (EWDM) are still highly motivated. We\nextend the Standard Model by adding an arbitrary SU(2) DM multiplet in\nnon-chiral representation. In addition to the well-known real representation\nwhich has no coupling to the nuclei at tree level, the complex representation\ncan lead to a new DM candidate providing that one includes a higher dimensional\nmass-splitting operator, which survives the current direct detection bounds.\nSince the masses of gauge mediators are light compared to the dark particles,\nSommerfeld effect is dominant and affects the value of annihilation\ncross-section in both the early universe and current time. We computed the\nrelic abundance through freeze-out mechanism in order to determine DM mass.\nGamma ray fluxes in our galaxy and dwarf satellites provide a promising tool to\nprobe EWDM theory. We confronted the four fermionic representations of the\nmodel with the latest astrophysical observations. It can be concluded that the\nmodel passes the current experimental constraints successfully, and it is\naccessible to future observations.\n"}
+{"abstract": "  We propose a method to accurately obtain the ratio of tumor cells over an\nentire histological slide. We use deep fully convolutional neural network\nmodels trained to detect and classify cells on images of H&E-stained tissue\nsections. Pathologists' labels consisting of exhaustive nuclei locations and\ntumor regions were used to trained the model in a supervised fashion. We show\nthat combining two models, each working at a different magnification allows the\nsystem to capture both cell-level details and surrounding context to enable\nsuccessful detection and classification of cells as either tumor-cell or\nnormal-cell. Indeed, by conditioning the classification of a single cell on a\nmulti-scale context information, our models mimic the process used by\npathologists who assess cell neoplasticity and tumor extent at different\nmicroscope magnifications. The ratio of tumor cells can then be readily\nobtained by counting the number of cells in each class. To analyze an entire\nslide, we split it into multiple tiles that can be processed in parallel. The\noverall tumor cell ratio can then be aggregated. We perform experiments on a\ndataset of 100 slides with lung tumor specimens from both resection and tissue\nmicro-array (TMA). We train fully-convolutional models using heavy data\naugmentation and batch normalization. On an unseen test set, we obtain an\naverage mean absolute error on predicting the tumor cell ratio of less than 6%,\nwhich is significantly better than the human average of 20% and is key in\nproperly selecting tissue samples for recent genetic panel tests geared at\nprescribing targeted cancer drugs. We perform ablation studies to show the\nimportance of training two models at different magnifications and to justify\nthe choice of some parameters, such as the size of the receptive field.\n"}
+{"abstract": "  From a physical/dynamical system perspective, the potential well represents\nthe proportional mass of points that escape the neighbourhood of a given point.\nIn the last 20 years, several works have shown the importance of this quantity\nto obtain precise approximations for several recurrence time distributions in\nmixing stochastic processes and dynamical systems. Besides providing a review\nof the different scaling factors used in the literature in recurrence times,\nthe present work contributes with two new results: (1) for $\\phi$-mixing and\n$\\psi$-mixing processes, we give a new exponential approximation for hitting\nand return times using the potential well as scaling parameter. The error terms\nare explicit and sharp. (2) We analyse the uniform positivity of the potential\nwell.\n"}
+{"abstract": "  This paper proposes a question-answering (QA) benchmark for spatial reasoning\non natural language text which contains more realistic spatial phenomena not\ncovered by prior work and is challenging for state-of-the-art language models\n(LM). We propose a distant supervision method to improve on this task.\nSpecifically, we design grammar and reasoning rules to automatically generate a\nspatial description of visual scenes and corresponding QA pairs. Experiments\nshow that further pretraining LMs on these automatically generated data\nsignificantly improves LMs' capability on spatial understanding, which in turn\nhelps to better solve two external datasets, bAbI, and boolQ. We hope that this\nwork can foster investigations into more sophisticated models for spatial\nreasoning over text.\n"}
+{"abstract": "  This work presents a method for designing the weighting parameter required by\nWiener-based binaural noise reduction methods. This parameter establishes the\ndesired tradeoff between noise reduction and binaural cue preservation in\nhearing aid applications. The proposed strategy was specially derived for the\npreservation of interaural level difference, interaural time difference and\ninteraural coherence binaural cues. It is defined as a function of the average\ninput noise power at the microphones, providing robustness against the\ninfluence of joint changes in noise and speech power (Lombard effect), as well\nas to signal to noise ratio (SNR) variations. A theoretical framework, based on\nthe mathematical definition of the homogeneity degree, is presented and applied\nto a generic augmented Wiener-based cost function. The theoretical insights\nobtained are supported bycomputational simulations and psychoacoustic\nexperiments using the multichannel Wiener filter with interaural transfer\nfunction preservation technique (MWF-ITF), as a case study. Statistical\nanalysis indicates that the proposed dynamic structure for the weighting\nparameter and the design method of its fixed part provide significant\nrobustness against changes in the original binaural cues of both speech and\nresidual noise, at the cost of a small decrease in the noise reduction\nperformance, as compared to the use of a purely fixed weighting parameter.\n"}
+{"abstract": "  Quantum networks are complex systems formed by the interaction among quantum\nprocessors through quantum channels. Analogous to classical computer networks,\nquantum networks allow for the distribution of quantum computation among\nquantum computers. In this work, we describe a quantum walk protocol to perform\ndistributed quantum computing in a quantum network. The protocol uses a quantum\nwalk as a quantum control signal to perform distributed quantum operations. We\nconsider a generalization of the discrete-time coined quantum walk model that\naccounts for the interaction between a quantum walker system in the network\ngraph with quantum registers inside the network nodes. The protocol logically\ncaptures distributed quantum computing, abstracting hardware implementation and\nthe transmission of quantum information through channels. Control signal\ntransmission is mapped to the propagation of the walker system across the\nnetwork, while interactions between the control layer and the quantum registers\nare embedded into the application of coin operators. We demonstrate how to use\nthe quantum walker system to perform a distributed CNOT operation, which shows\nthe universality of the protocol for distributed quantum computing.\nFurthermore, we apply the protocol to the task of entanglement distribution in\na quantum network.\n"}
+{"abstract": "  Reweighting adversarial data during training has been recently shown to\nimprove adversarial robustness, where data closer to the current decision\nboundaries are regarded as more critical and given larger weights. However,\nexisting methods measuring the closeness are not very reliable: they are\ndiscrete and can take only a few values, and they are path-dependent, i.e.,\nthey may change given the same start and end points with different attack\npaths. In this paper, we propose three types of probabilistic margin (PM),\nwhich are continuous and path-independent, for measuring the aforementioned\ncloseness and reweighting adversarial data. Specifically, a PM is defined as\nthe difference between two estimated class-posterior probabilities, e.g., such\nthe probability of the true label minus the probability of the most confusing\nlabel given some natural data. Though different PMs capture different geometric\nproperties, all three PMs share a negative correlation with the vulnerability\nof data: data with larger/smaller PMs are safer/riskier and should have\nsmaller/larger weights. Experiments demonstrate that PMs are reliable\nmeasurements and PM-based reweighting methods outperform state-of-the-art\nmethods.\n"}
+{"abstract": "  This is loosely a continuation of the author's previous paper\narXiv:1802.09496. In the first part, given a fibered variety, we pull back the\nLeray filtration to the Chow group, and use this to give some criteria for the\nHodge and Tate conjectures to hold for such varieties. In the second part, we\nshow that the Hodge conjecture holds for a good desingularization of a self\nfibre product of a non-isotrivial elliptic surface. We also show that the Hodge\nand Tate conjectures hold for natural families of abelian varieties\nparameterized by certain Shimura curves. This uses Zucker's description of the\nmixed Hodge structure on the cohomology of a variation of Hodge structures on a\ncurve, along with appropriate \"vanishing\" theorems.\n"}
+{"abstract": "  The aim of this work is to investigate the occurrence of two different\nspontaneous symmetry breakings in two levels of the description of\nfermion-scalar field model by means of a set of gap equations and of a\nbackground field effective action. For that, we consider the Yukawa model, as a\ntoy model for interactions between non-massive fermions intermediated by a\nself-interacting real scalar field. This model has at stakes two symmetries at\nthe classical level that, as we know, might be spontaneously or dynamically\nbroken with mass generation for the particles. The auxiliary field method is\nconsidered and it produces coupled renormalized gap equations. The effective\naction is then written with quantum contributions by external background fields\nmethod. We brought to light how the renormalization procedure affects the\nphysical gaps, investigate its proprieties, and discuss the connection of the\nauxiliary fields not only to define composite states but also to compute the\neffective action.\n"}
+{"abstract": "  Since the first experimental observation of all-optical switching phenomena,\nintensive research has been focused on finding suitable magnetic systems that\ncan be integrated as storage elements within spintronic devices and whose\nmagnetization can be controlled through ultra-short single laser pulses. We\nreport here atomistic spin simulations of all-optical switching in multilayered\nstructures alternating n monolayers of Tb and m monolayers of Co. By using a\ntwo temperature model, we numerically calculate the thermal variation of the\nmagnetization of each sublattice as well as the magnetization dynamics of\n[Tbn/Com] multilayers upon incidence of a single laser pulse. In particular,\nthe condition to observe thermally-induced magnetization switching is\ninvestigated upon varying systematically both the composition of the sample\n(n,m) and the laser fluence. The samples with one monolayer of Tb as [Tb1/Co2]\nand [Tb1/Co3] are showing thermally induced magnetization switching above a\nfluence threshold. The reversal mechanism is mediated by the residual\nmagnetization of the Tb lattice while the Co is fully demagnetized in agreement\nwith the models developed for ferrimagnetic alloys. The switching is however\nnot fully deterministic but the error rate can be tuned by the damping\nparameter. Increasing the number of monolayers the switching becomes completely\nstochastic. The intermixing at the Tb/Co interfaces appears to be a promising\nway to reduce the stochasticity. These results predict for the first time the\npossibility of TIMS in [Tb/Co] multilayers and suggest the occurrence of\nsub-picosecond magnetization reversal using single laser pulses.\n"}
+{"abstract": "  In completely generic four-dimensional gauge-Yukawa theories, the\nrenormalization group $ \\beta $-functions are known to the 3-2-2 loop order in\ngauge, Yukawa, and quartic couplings, respectively. It does, however, remain\ndifficult to apply these results to specific models without the use of\ndedicated computer tools. We describe a procedure for extracting $ \\beta\n$-functions using the general results and introduce RGBeta, a dedicated\nMathematica package for extracting the $ \\overline{\\text{MS}} $ $ \\beta\n$-functions in broad classes of models. The package and example notebooks are\navailable from the GitHub repository at https://github.com/aethomsen/RGBeta .\n"}
+{"abstract": "  The cosmological implications of the Covariant Canonical Gauge Theory of\nGravity (CCGG) are investigated. CCGG is a Palatini theory derived from first\nprinciples using the canonical transformation formalism in the covariant\nHamiltonian formulation. The Einstein-Hilbert theory is thereby extended by a\nquadratic Riemann-Cartan term in the Lagrangian. Moreover, the requirement of\ncovariant conservation of the stress-energy tensor leads to necessary presence\nof torsion. In the Friedman universe that promotes the cosmological constant to\na time-dependent function, and gives rise to a geometrical correction with the\nEOS of dark radiation. The resulting cosmology, compatible with the\n$\\Lambda$CDM parameter set, encompasses bounce and bang scenarios with graceful\nexits into the late dark energy era. Testing those scenarios against low-z\nobservations shows that CCGG is a viable theory.\n"}
+{"abstract": "  Manual and computer aided methods to perform semen analysis are\ntime-consuming, requires extensive training and prone to human error. The use\nof classical machine learning and deep learning based methods using videos to\nperform semen analysis have yielded good results. The state-of-the-art method\nuses regular convolutional neural networks to perform quality assessments on a\nvideo of the provided sample. In this paper we propose an improved deep\nlearning based approach using three-dimensional convolutional neural networks\nto predict sperm motility from microscopic videos of the semen sample. We make\nuse of the VISEM dataset that consists of video and tabular data of semen\nsamples collected from 85 participants. We were able to achieve good results\nfrom significantly less data points. Our models indicate that deep learning\nbased automatic semen analysis may become a valuable and effective tool in\nfertility and IVF labs.\n"}
+{"abstract": "  In this work, we investigate differential chaos shift keying (DCSK), a\ncommunication-based waveform, in the context of wireless power transfer (WPT).\nParticularly, we present a DCSK-based WPT architecture, that employs an analog\ncorrelator at the receiver in order to boost the energy harvesting (EH)\nperformance. By taking into account the nonlinearities of the EH process, we\nderive closed-form analytical expressions for the peak-to-average-power-ratio\nof the received signal as well as the harvested power. Nontrivial design\ninsights are provided, where it is shown how the parameters of the transmitted\nwaveform affects the EH performance. Furthermore, it is demonstrated that the\nemployment of a correlator at the receiver achieves significant EH gains in\nDCSK-based WPT systems.\n"}
+{"abstract": "  We study primordial non-gaussianity in supersolid inflation. The dynamics of\nsupersolid is formulated in terms of an effective field theory based on four\nscalar fields with a shift symmetric action minimally coupled with gravity. In\nthe scalar sector, there are two phonon-like excitations with a kinetic mixing\nstemming from the completely spontaneous breaking of diffeomorphism. In a\nsqueezed configuration, $f_{\\text{NL}}$ of scalar perturbations is angle\ndependent and not proportional to slow-roll parameters showing a blunt\nviolation of the Maldacena consistency relation. Contrary to solid inflation,\nthe violation persists even after an angular average and generically the amount\nof non-gaussianity is significant. During inflation, non-gaussianity in the TSS\nand TTS sector is enhanced in the same region of the parameters space where the\nsecondary production of gravitational waves is sizeable enough to enter in the\nsensitivity region of LISA, while the scalar $f_{\\text{NL}}$ is still within\nthe current experimental limits.\n"}
+{"abstract": "  Research in the field of malware classification often relies on machine\nlearning models that are trained on high-level features, such as opcodes,\nfunction calls, and control flow graphs. Extracting such features is costly,\nsince disassembly or code execution is generally required. In this paper, we\nconduct experiments to train and evaluate machine learning models for malware\nclassification, based on features that can be obtained without disassembly or\nexecution of code. Specifically, we visualize malware samples as images and\nemploy image analysis techniques. In this context, we focus on two machine\nlearning models, namely, Convolutional Neural Networks (CNN) and Extreme\nLearning Machines (ELM). Surprisingly, we find that ELMs can achieve accuracies\non par with CNNs, yet ELM training requires less than~2\\%\\ of the time needed\nto train a comparable CNN.\n"}
+{"abstract": "  A model is developed, based on the density functional perturbation theory and\nthe inverse Kohn-Sham method, that can be used to improve relativistic nuclear\nenergy density functionals towards an exact but unknown Kohn-Sham\nexchange-correlation functional. The improved functional is determined by\nempirical exact ground-state densities of finite systems. A test of the model\nand an illustrative calculation are performed, starting from two different\napproximate functionals, to reproduce the parameters and density dependence of\na target functional, using exact ground-state densities of symmetric N=Z\nsystems.\n"}
+{"abstract": "  We consider the problem of finite resistance $R$ in superconducting films\nwith geometry of a strip of width $W$ near zero temperature. The resistance is\ngenerated by vortex configurations of the phase field. In the first type of\nprocess, quantum phase slip, the vortex worldline in 2+1 dimensional space-time\nis space-like (i.e., the superconducting phase winds in time and space). In the\nsecond type, vortex tunneling, the worldline is time-like (i.e., the phase\nwinds in the two spatial directions) and connects opposite edges of the film.\nFor moderately disordered samples, processes of second type favor a train of\nvortices, each of which tunnels only across a fraction of the sample.\nOptimization with respect to the number of vortices yields a tunneling distance\nof the order of the coherence length $\\xi$, and the train of vortices becomes\nequivalent to a quantum phase slip. Based on this theory, we find the\nresistance $\\ln R \\sim -g W/\\xi$, where $g$ is the dimensionless normal-state\nconductance. Incorporation of quantum fluctuations indicates a quantum phase\ntransition to an insulating state for $g \\lesssim 1$.\n"}
+{"abstract": "  We present a neural modeling framework for Non-Line-of-Sight (NLOS) imaging.\nPrevious solutions have sought to explicitly recover the 3D geometry (e.g., as\npoint clouds) or voxel density (e.g., within a pre-defined volume) of the\nhidden scene. In contrast, inspired by the recent Neural Radiance Field (NeRF)\napproach, we use a multi-layer perceptron (MLP) to represent the neural\ntransient field or NeTF. However, NeTF measures the transient over spherical\nwavefronts rather than the radiance along lines. We therefore formulate a\nspherical volume NeTF reconstruction pipeline, applicable to both confocal and\nnon-confocal setups. Compared with NeRF, NeTF samples a much sparser set of\nviewpoints (scanning spots) and the sampling is highly uneven. We thus\nintroduce a Monte Carlo technique to improve the robustness in the\nreconstruction. Comprehensive experiments on synthetic and real datasets\ndemonstrate NeTF provides higher quality reconstruction and preserves fine\ndetails largely missing in the state-of-the-art.\n"}
+{"abstract": "  The horizontal orientation angle and vertical inclination angle of an\nelongated subsurface object are key parameters for object identification and\nimaging in ground penetrating radar (GPR) applications. Conventional methods\ncan only extract the horizontal orientation angle or estimate both angles in\nnarrow ranges due to limited polarimetric information and detection capability.\nTo address these issues, this letter, for the first time, explores the\npossibility of leveraging neural networks with multi-polarimetric GPR data to\nestimate both angles of an elongated subsurface object in the entire spatial\nrange. Based on the polarization-sensitive characteristic of an elongated\nobject, we propose a multi-polarization aggregation and selection neural\nnetwork (MASNet), which takes the multi-polarimetric radargrams as inputs,\nintegrates their characteristics in the feature space, and selects\ndiscriminative features of reflected signal patterns for accurate orientation\nestimation. Numerical results show that our proposed MASNet achieves high\nestimation accuracy with an angle estimation error of less than 5{\\deg}. The\npromising results obtained by the proposed method encourages one to think of\nnew solutions for GPR-related tasks by integrating multi-polarization\ninformation with deep learning techniques.\n"}
+{"abstract": "  Most existing metric learning methods focus on learning a similarity or\ndistance measure relying on similar and dissimilar relations between sample\npairs. However, pairs of samples cannot be simply identified as similar or\ndissimilar in many real-world applications, e.g., multi-label learning, label\ndistribution learning. To this end, relation alignment metric learning (RAML)\nframework is proposed to handle the metric learning problem in those scenarios.\nBut RAML learn a linear metric, which can't model complex datasets. Combining\nwith deep learning and RAML framework, we propose a hierarchical relationship\nalignment metric leaning model HRAML, which uses the concept of relationship\nalignment to model metric learning problems under multiple learning tasks, and\nmakes full use of the consistency between the sample pair relationship in the\nfeature space and the sample pair relationship in the label space. Further we\norganize several experiment divided by learning tasks, and verified the better\nperformance of HRAML against many popular methods and RAML framework.\n"}
+{"abstract": "  The chiral anomaly underlies a broad number of phenomena, from enhanced\nelectronic transport in topological metals to anomalous currents in the\nquark-gluon plasma. The discovery of topological states of matter in\nnon-Hermitian systems -- effective descriptions of dissipative systems --\nraises the question of whether there are anomalous conservation laws that\nremain unaccounted for. To answer this question, we consider both $1+1$ and\n$3+1$ dimensions, presenting a unified formulation to calculate anomalous\nresponses in Hermitianized, anti-Hermitianized and non-Hermitian systems of\nmassless electrons with complex Fermi velocities coupled to non-Hermitian gauge\nfields. Our results indicate that the quantum conservation laws of chiral\ncurrents of non-Hermitian systems are not related to those in Hermitianized and\nanti-Hermitianized systems, as would be expected classically, due to novel\nanomalous terms that we derive. These may have implications for a broad class\nof emerging experimental systems that realize non-Hermitian Hamiltonians.\n"}
+{"abstract": "  We consider the classical Yaglom limit theorem for the Neutron Branching\nProcess (NBP) in the setting that the mean semigroup is critical, i.e. its\nleading eigenvalue is zero. We show that the law of the process conditioned on\nsurvival is asymptotically equivalent to an exponential distribution. As part\nof the proof, we also show that the probability of survival decays inversely\nproportionally to time. Although Yaglom limit theorems have recently been\nhandled in the setting of spatial branching processes and superprocesses, [22,\n23], as well as in the setting of isotropic homogeneous Neutron Branching\nProcesses, [21], our approach and the main novelty of this work is based around\na precise result for the scaled asymptotics for the k-th martingale moments of\nthe NBP (rather than the Yaglom limit itself). Our proof of the asymptotic\nmartingale moments turns out to offer a general approach to asymptotic\nmartingale moments of critical branching Markov processes with a non-local\nbranching mechanism. Indeed this is the context in which we give both our\nmoment proofs and the Yaglom limit.\n"}
+{"abstract": "  The field of mathematical morphology offers well-studied techniques for image\nprocessing. In this work, we view morphological operations through the lens of\npersistent homology, a tool at the heart of the field of topological data\nanalysis. We demonstrate that morphological operations naturally form a\nmultiparameter filtration and that persistent homology can then be used to\nextract information about both topology and geometry in the images as well as\nto automate methods for optimizing the study and rendering of structure in\nimages. For illustration, we apply this framework to analyze noisy binary,\ngrayscale, and color images.\n"}
+{"abstract": "  Artificial intelligence (AI) algorithms using deep learning have advanced the\nclassification of skin disease images; however these algorithms have been\nmostly applied \"in silico\" and not validated clinically. Most dermatology AI\nalgorithms perform binary classification tasks (e.g. malignancy versus benign\nlesions), but this task is not representative of dermatologists' diagnostic\nrange. The American Academy of Dermatology Task Force on Augmented Intelligence\npublished a position statement emphasizing the importance of clinical\nvalidation to create human-computer synergy, termed augmented intelligence\n(AuI). Liu et al's recent paper, \"A deep learning system for differential\ndiagnosis of skin diseases\" represents a significant advancement of AI in\ndermatology, bringing it closer to clinical impact. However, significant issues\nmust be addressed before this algorithm can be integrated into clinical\nworkflow. These issues include accurate and equitable model development,\ndefining and assessing appropriate clinical outcomes, and real-world\nintegration.\n"}
+{"abstract": "  Complex A is a high-velocity cloud that is traversing through the Galactic\nhalo toward the Milky Way's disk. We combine both new and archival Green Bank\nTelescope observations to construct a spectroscopically resolved HI~21-cm map\nof this entire complex at a $17.1\\lesssim\\log{\\left({N_{\\rm HI},\\,1\\sigma}/{\\rm\ncm}^{-2}\\right)}\\lesssim17.9$ sensitivity for a ${\\rm FWHM}=20~{\\rm km}\\,{\\rm\ns}^{-1}$ line and $\\Delta\\theta=9.1\\,{\\rm arcmins}$ or $17\\lesssim\\Delta\nd_{\\theta}\\lesssim30~\\rm pc$ spatial resolution. We find that that Complex A is\nhas a Galactic standard of rest frame velocity gradient of $\\Delta\\rm\nv_{GSR}/\\Delta L=25~{\\rm km}\\,{\\rm s}^{-1}/{\\rm kpc}$ along its length, that it\nis decelerating at a rate of $\\langle a\\rangle_{\\rm GSR}=55~{\\rm km}/{\\rm\nyr}^2$, and that it will reach the Galactic plane in $\\Delta t\\lesssim70~{\\rm\nMyrs}$ if it can survive the journey. We have identify numerous signatures of\ngas disruption. The elongated and multi-core structure of Complex A indicates\nthat either thermodynamic instabilities or shock-cascade processes have\nfragmented this stream. We find Rayleigh-Taylor fingers on the low-latitude\nedge of this HVC; many have been pushed backward by ram-pressure stripping. On\nthe high-latitude side of the complex, Kelvin-Helmholtz instabilities have\ngenerated two large wings that extend tangentially off Complex A. The tips of\nthese wings curve slightly forward in the direction of motion and have an\nelevated \\hi\\ column density, indicating that these wings are forming\nRayleigh-Taylor globules at their tips and that this gas is becoming entangled\nwith unseen vortices in the surrounding coronal gas. These observations provide\nnew insights on the survivability of low-metallicity gas streams that are\naccreting onto $L_\\star$ galaxies.\n"}
+{"abstract": "  In many applications, it is desirable that a classifier not only makes\naccurate predictions, but also outputs calibrated posterior probabilities.\nHowever, many existing classifiers, especially deep neural network classifiers,\ntend to be uncalibrated. Post-hoc calibration is a technique to recalibrate a\nmodel by learning a calibration map. Existing approaches mostly focus on\nconstructing calibration maps with low calibration errors, however, this\nquality is inadequate for a calibrator being useful. In this paper, we\nintroduce two constraints that are worth consideration in designing a\ncalibration map for post-hoc calibration. Then we present Meta-Cal, which is\nbuilt from a base calibrator and a ranking model. Under some mild assumptions,\ntwo high-probability bounds are given with respect to these constraints.\nEmpirical results on CIFAR-10, CIFAR-100 and ImageNet and a range of popular\nnetwork architectures show our proposed method significantly outperforms the\ncurrent state of the art for post-hoc multi-class classification calibration.\n"}
+{"abstract": "  In matrix theory and numerical analysis there are two very famous and\nimportant results. One is Gersgorin circle theorem, the other is strictly\ndiagonally dominant theorem. They have important application and research\nvalue, and have been widely used and studied. In this paper, we investigate\ngeneralized diagonally dominant matrices and matrix eigenvalue inclusion\nregions. A class of G-function pairs is proposed, which extends the concept of\nG-functions. Thirteen kind of G-function pairs are established. Their\nproperties and characteristics are studied. By using these special G-function\npairs, we construct a large number of sufficient and necessary conditions for\nstrictly diagonally dominant matrices and matrix eigenvalue inclusion regions.\nThese conditions and regions are composed of different combinations of\nG-function pairs, deleted absolute row sums and column sums of matrices. The\nresults extend, include and are better than some classical results.\n"}
+{"abstract": "  In this paper, first we propose a novel hybrid renewable energy-based\nsolution for frost prevention in horticulture applications involving active\nheaters. Then, we develop a multi-objective robust optimization-based\nformulation to optimize the distribution of a given number of active heaters in\na given large-scale orchard. The objectives are to optimally heat the orchard\nby the proposed frost prevention system and to minimize the total length of the\nenergy distribution pipe network (which is directly related to the installation\ncost and the cost of energy losses during energy transfer). Next, the resulting\noptimization problem is approximated using a discretization scheme. A case\nstudy is provided to give an idea of the potential savings using the proposed\noptimization method compared to the result from a heuristic-based design, which\nshowed a 24.13% reduction in the total pipe length and a 54.29% increase in\nfrost prevention.\n"}
+{"abstract": "  Machine learning (ML) is an important part of modern data science\napplications. Data scientists today have to manage the end-to-end ML life cycle\nthat includes both model training and model serving, the latter of which is\nessential, as it makes their works available to end-users. Systems for model\nserving require high performance, low cost, and ease of management. Cloud\nproviders are already offering model serving options, including managed\nservices and self-rented servers. Recently, serverless computing, whose\nadvantages include high elasticity and fine-grained cost model, brings another\npossibility for model serving.\n  In this paper, we study the viability of serverless as a mainstream model\nserving platform for data science applications. We conduct a comprehensive\nevaluation of the performance and cost of serverless against other model\nserving systems on two clouds: Amazon Web Service (AWS) and Google Cloud\nPlatform (GCP). We find that serverless outperforms many cloud-based\nalternatives with respect to cost and performance. More interestingly, under\nsome circumstances, it can even outperform GPU-based systems for both average\nlatency and cost. These results are different from previous works' claim that\nserverless is not suitable for model serving, and are contrary to the\nconventional wisdom that GPU-based systems are better for ML workloads than\nCPU-based systems. Other findings include a large gap in cold start time\nbetween AWS and GCP serverless functions, and serverless' low sensitivity to\nchanges in workloads or models. Our evaluation results indicate that serverless\nis a viable option for model serving. Finally, we present several practical\nrecommendations for data scientists on how to use serverless for scalable and\ncost-effective model serving.\n"}
+{"abstract": "  We propose a nonlinear reduced basis method for the efficient approximation\nof parametrized partial differential equations (PDEs), exploiting kernel proper\northogonal decomposition (KPOD) for the generation of a reduced-order space and\nneural networks for the evaluation of the reduced-order approximation. In\nparticular, we use KPOD in place of the more classical POD, on a set of\nhigh-fidelity solutions of the problem at hand to extract a reduced basis. This\nmethod provides a more accurate approximation of the snapshots' set featuring a\nlower dimension, while maintaining the same efficiency as POD. A neural network\n(NN) is then used to find the coefficients of the reduced basis by following a\nsupervised learning paradigm and shown to be effective in learning the map\nbetween the time/parameter values and the projection of the high-fidelity\nsnapshots onto the reduced space. In this NN, both the number of hidden layers\nand the number of neurons vary according to the intrinsic dimension of the\ndifferential problem at hand and the size of the reduced space. This adaptively\nbuilt NN attains good performances in both the learning and the testing phases.\nOur approach is then tested on two benchmark problems, a one-dimensional wave\nequation and a two-dimensional nonlinear lid-driven cavity problem. We finally\ncompare the proposed KPOD-NN technique with a POD-NN strategy, showing that\nKPOD allows a reduction of the number of modes that must be retained to reach a\ngiven accuracy in the reduced basis approximation. For this reason, the NN\nbuilt to find the coefficients of the KPOD expansion is smaller, easier and\nless computationally demanding to train than the one used in the POD-NN\nstrategy.\n"}
+{"abstract": "  As software systems are becoming more pervasive, they are also becoming more\nsusceptible to failures, resulting in potentially lethal combinations. Software\ntesting is critical to preventing software failures but is, arguably, the least\nunderstood part of the software life cycle and the toughest to perform\ncorrectly. Adequate research has been carried out in both the process and\ntechnology dimensions of testing, but not in the human dimensions. This paper\nattempts to fill in the gap by exploring the human dimension, i.e., trying to\nunderstand the motivation of software professionals to take up and sustain\ntesting careers. Towards that end, a survey was conducted in four countries -\nIndia, Canada, Cuba, and China - to try to understand how professional software\ntesters perceive and value work-related factors that could influence their\nmotivation to take up and sustain testing careers. With a sample of 220\nsoftware professionals, we observed that very few professionals are keen to\ntake up testing careers. Some aspects of software testing, such as the learning\nopportunities, appear to be a common motivator across the four countries;\nwhereas the treatment meted out to testers as second-class citizens and the\ncomplexity of the job appeared to be the most important de-motivators. This\ncomparative study offers useful insights that can help global software industry\nleaders to come up with an action plan to put the software testing profession\nunder a new light. That could increase the number of software engineers\nchoosing testing careers, which would facilitate quality testing.\n"}
+{"abstract": "  We give a possible splitting method to a Hamiltonian for the description of\ncharged particles moving around the Reissner-Nordstrom-(anti)-de Sitter black\nhole with an external magnetic field. This Hamiltonian can be separated into\nsix analytical solvable pieces, whose solutions are explicit functions of\nproper time. In this case, second- and fourth-order explicit symplectic\nintegrators are easily available. They exhibit excellent long-term behavior in\nmaintaining the boundness of Hamiltonian errors regardless of ordered or\nchaotic orbits if appropriate step-sizes are chosen. Under some circumstances,\nan increase of positive cosmological constant gives rise to strengthening the\nextent of chaos from the global phase space; namely, chaos of charged particles\noccurs easily for the accelerated expansion of the universe. However, an\nincrease of the magnitude of negative cosmological constant does not. The\ndifferent contributions on chaos are because the cosmological constant acts as\na repulsive force in the Reissner-Nordstrom-de Sitter black hole, but an\nattractive force in the Reissner-Nordstrom-anti-de Sitter black hole.\n"}
+{"abstract": "  Weakly supervised temporal action localization aims to detect and localize\nactions in untrimmed videos with only video-level labels during training.\nHowever, without frame-level annotations, it is challenging to achieve\nlocalization completeness and relieve background interference. In this paper,\nwe present an Action Unit Memory Network (AUMN) for weakly supervised temporal\naction localization, which can mitigate the above two challenges by learning an\naction unit memory bank. In the proposed AUMN, two attention modules are\ndesigned to update the memory bank adaptively and learn action units specific\nclassifiers. Furthermore, three effective mechanisms (diversity, homogeneity\nand sparsity) are designed to guide the updating of the memory network. To the\nbest of our knowledge, this is the first work to explicitly model the action\nunits with a memory network. Extensive experimental results on two standard\nbenchmarks (THUMOS14 and ActivityNet) demonstrate that our AUMN performs\nfavorably against state-of-the-art methods. Specifically, the average mAP of\nIoU thresholds from 0.1 to 0.5 on the THUMOS14 dataset is significantly\nimproved from 47.0% to 52.1%.\n"}
+{"abstract": "  Determining when two individuals are within close distance is key to contain\na pandemic, e.g., to alert individuals in real-time and trace their social\ncontacts. Common approaches rely on either Bluetooth Low Energy (BLE) or\nultra-wideband (UWB) radios, that nonetheless strike opposite tradeoffs for\nenergy efficiency vs. accuracy of distance estimates.\n  Janus reconciles these dimensions with a dual-radio protocol enabling\nefficient and accurate social contact detection. Measurements show that Janus\nachieves weeks to months of autonomous operation, depending on the\nconfiguration. Several large-scale campaigns in real-world contexts confirm its\nreliability and practical usefulness in enabling insightful analysis of contact\ndata.\n"}
+{"abstract": "  In this work, an optic fiber based $\\textit{in situ}$ illumination system\nintegrated into an aberration-corrected environmental transmission electron\nmicroscope (ETEM) is designed, built, characterized and applied. With this\nillumination system, the dynamic responses of photoactive materials to photons\ncan be directly observed at the atomic level, and other stimuli including\nheating and various gases can also be applied simultaneously. Either a\nbroadband light source or a high power laser source aiming to expedite\nphotoreactions can be utilized, fitting different application needs. The optic\nfiber enters the ETEM through the objective aperture port, with a carefully\ndesigned curvature and a 30{\\deg} cut at the tip to orient the emitted light\nupwards onto the TEM specimen. The intensity distributions striking the sample\nfrom the broadband and laser sources are both measured, and due to the\nnon-uniform distributions, an alignment procedure has been developed to align\nthe bright spot with the electron optical axis of the TEM. The imaging and\nspectroscopy performances of the ETEM are proved to be maintained after\nincorporating this illumination system. Furthermore, Langmuir evaporation is\nobserved when in situ laser light is applied to GaAs, demonstrating the\nphenomenon of optical heating on suitable semiconductor materials.\n"}
+{"abstract": "  Repeated local measurements of quantum many body systems can induce a phase\ntransition in their entanglement structure. These measurement-induced phase\ntransitions (MIPTs) have been studied for various types of dynamics, yet most\ncases yield quantitatively similar values of the critical exponents, making it\nunclear if there is only one underlying universality class. Here, we directly\nprobe the properties of the conformal field theories governing these MIPTs\nusing a numerical transfer-matrix method, which allows us to extract the\neffective central charge, as well as the first few low-lying scaling dimensions\nof operators at these critical points. Our results provide convincing evidence\nthat the generic and Clifford MIPTs for qubits lie in different universality\nclasses and that both are distinct from the percolation transition for qudits\nin the limit of large onsite Hilbert space dimension. For the generic case, we\nfind strong evidence of multifractal scaling of correlation functions at the\ncritical point, reflected in a continuous spectrum of scaling dimensions.\n"}
+{"abstract": "  In this paper, we give a brief survey of recent results on axially symmetric\nNavier-Stokes equations (ASNS) in the following categories: regularity\ncriterion, Liouville property for ancient solutions, decay and vanishing of\nstationary solutions. Some discussions also touch on the full 3 dimensional\nequations. Two results, closing of the scaling gap for ASNS and vanishing of\nhomogeneous D solutions in 3 dimensional slabs will be described in more\ndetail. In the addendum, two new results in the 3rd category will also be\npresented, which are generalizations of recently published results by the\nauthor and coauthors.\n"}
+{"abstract": "  DNA-driven self-assembly enables precise positioning of the colloidal\nnanoparticles owing to specific Watson-Crick interactions. Another important\nfeature of this self-assembly method is its reversibility by controlling the\ntemperature of the medium. In this work, we study the potential of another\nmechanism to control binding/unbinding process of the DNA-functionalized gold\nnanoparticles. We employ the laser radiation that can be absorbed by the gold\nnanoparticles to heat their network and disassociate it. Here, we show that we\ncan actively control the optical properties of the nanoparticle network by an\nexternal optical excitation. We find out that by irradiating the structure with\na green hand-held laser the total transmittance can increase by ~30% compared\nto the transmittance of the sample not irradiated by the laser. Similarly, the\noptical microscopy images indicate the transformation of the nanoparticle\nnetwork from opaque to transparent while the nanoparticles formed a network\nagain after the laser irradiation stopped. Our results prove that the optical\nexcitation can be used to tailor the structure and thus the optical properties\nof the DNA-self-assembled nanoparticle networks.\n"}
+{"abstract": "  Aerial scene recognition is a fundamental visual task and has attracted an\nincreasing research interest in the last few years. Most of current researches\nmainly deploy efforts to categorize an aerial image into one scene-level label,\nwhile in real-world scenarios, there often exist multiple scenes in a single\nimage. Therefore, in this paper, we propose to take a step forward to a more\npractical and challenging task, namely multi-scene recognition in single\nimages. Moreover, we note that manually yielding annotations for such a task is\nextraordinarily time- and labor-consuming. To address this, we propose a\nprototype-based memory network to recognize multiple scenes in a single image\nby leveraging massive well-annotated single-scene images. The proposed network\nconsists of three key components: 1) a prototype learning module, 2) a\nprototype-inhabiting external memory, and 3) a multi-head attention-based\nmemory retrieval module. To be more specific, we first learn the prototype\nrepresentation of each aerial scene from single-scene aerial image datasets and\nstore it in an external memory. Afterwards, a multi-head attention-based memory\nretrieval module is devised to retrieve scene prototypes relevant to query\nmulti-scene images for final predictions. Notably, only a limited number of\nannotated multi-scene images are needed in the training phase. To facilitate\nthe progress of aerial scene recognition, we produce a new multi-scene aerial\nimage (MAI) dataset. Experimental results on variant dataset configurations\ndemonstrate the effectiveness of our network. Our dataset and codes are\npublicly available.\n"}
+{"abstract": "  Code review is a key development practice that contributes to improve\nsoftware quality and to foster knowledge sharing among developers. However,\ncode review usually takes time and demands detailed and time-consuming analysis\nof textual diffs. Particularly, detecting refactorings during code reviews is\nnot a trivial task, since they are not explicitly represented in diffs. For\nexample, a Move Function refactoring is represented by deleted (-) and added\nlines (+) of code which can be located in different and distant source code\nfiles. To tackle this problem, we introduce RAID, a refactoring-aware and\nintelligent diff tool. Besides proposing an architecture for RAID, we\nimplemented a Chrome browser plug-in that supports our solution. Then, we\nconducted a field experiment with eight professional developers who used RAID\nfor three months. We concluded that RAID can reduce the cognitive effort\nrequired for detecting and reviewing refactorings in textual diff. Besides\ndocumenting refactorings in diffs, RAID reduces the number of lines required\nfor reviewing such operations. For example, the median number of lines to be\nreviewed decreases from 14.5 to 2 lines in the case of move refactorings and\nfrom 113 to 55 lines in the case of extractions.\n"}
+{"abstract": "  We present basic constructions and properties in arithmetic Chern-Simons\ntheory with finite gauge group along the line of topological quantum field\ntheory. For a finite set $S$ of finite primes of a number field $k$, we\nconstruct arithmetic analogues of the Chern-Simons 1-cocycle, the\nprequantization bundle for a surface and the Chern-Simons functional for a\n$3$-manifold. We then construct arithmetic analogues for $k$ and $S$ of the\nquantum Hilbert space (space of conformal blocks) and the Dijkgraaf-Witten\npartition function in (2+1)-dimensional Chern-Simons TQFT. We show some basic\nand functorial properties of those arithmetic analogues. Finally we show\ndecomposition and gluing formulas for arithmetic Chern-Simons invariants and\narithmetic Dijkgraaf-Witten partition functions.\n"}
+{"abstract": "  Fluctuation-dissipation relations or \"theorems\" (FDTs) are fundamental for\nstatistical physics and can be rigorously derived for equilibrium systems.\nTheir applicability to non-equilibrium systems is, however, debated. Here, we\nsimulate an active microrheology experiment, in which a spherical colloid is\npulled with a constant external force through a fluid, creating\nnear-equilibrium and far-from-equilibrium systems. We characterize the\nstructural and dynamical properties of these systems, and reconstruct an\neffective generalized Langevin equation (GLE) for the colloid dynamics.\nSpecifically, we test the validity of two FDTs: The first FDT relates the\nnon-equilibrium response of a system to equilibrium correlation functions, and\nthe second FDT relates the memory friction kernel in the GLE to the stochastic\nforce. We find that the validity of the first FDT depends strongly on the\nstrength of the external driving: it is fulfilled close to equilibrium and\nbreaks down far from it. In contrast, we observe that the second FDT is always\nfulfilled. We provide a mathematical argument why this generally holds for\nmemory kernels reconstructed from a deterministic Volterra equation for\ncorrelation functions, even for non-stationary non-equilibrium systems.\n  Motivated by the Mori-Zwanzig formalism, we therefore suggest to impose an\northogonality constraint on the stochastic force, which is in fact equivalent\nto the validity of this Volterra equation. Such GLEs automatically satisfy the\nsecond FDT and are unique, which is desirable when using GLEs for\ncoarse-grained modeling.\n"}
+{"abstract": "  A simple closed-form solution to the discrete Lyapunov equation (DLE) is\nestablished for certain families of matrices. This solution is expressed in\nterms of the eigen decomposition (ED) for which closed-form solutions are known\nfor all 2x2; 3x3 and certain families of matrices. For general matrices, the\nproposed ED based closed-form solution can be used as an efficient numerical\nsolution when the ED can be computed. The result is then extended to give\nclosed-form solutions for a generalization of the DLE, called the Stein\nequation. The proposed explicit solution's complexity is of the same order as\niterative solutions and significantly smaller than known closed-form solutions.\nThese solutions may prove convenient for analysis and synthesis problems\nrelated to these equations due to their compact form.\n"}
+{"abstract": "  We develop an algorithm for the concurrent (on-the-fly) estimation of\nparameters for a system of evolutionary dissipative partial differential\nequations in which the state is partially observed. The intuitive nature of the\nalgorithm makes its extension to several different systems immediate, and it\nallows for recovery of multiple parameters simultaneously. We test this\nalgorithm on the Kuramoto-Sivashinsky equation in one dimension and demonstrate\nits efficacy in this context.\n"}
+{"abstract": "  This short paper introduces a recently patented line based numbering system.\nThe last allows a best concordance with decimal digits values, and open up new\nopportunities, which are not possible with the classical decimal numeration\nsystem. Proposed OILU symbolic allows generating a new type of number series,\nbased on multi facets numbers splitting process. On the other hand, this new\nsymbolic is used in the development of new visual markers, highly required in\naugmented reality and UAV's navigation applications.\n"}
+{"abstract": "  We develop a simple toy model for polarized images of synchrotron emission\nfrom an equatorial source around a Kerr black hole by using a semi-analytic\nsolution of the null geodesic equation and conservation of the Penrose-Walker\nconstant. Our model is an extension of Narayan et al. (2021), which presented\nresults for a Schwarzschild black hole, including a fully analytic\napproximation. Our model includes an arbitrary observer inclination, black hole\nspin, local boost, and local magnetic field configuration. We study the\ngeometric effects of black hole spin on photon parallel transport and isolate\nthese effects from the complicated combination of relativistic, gravitational,\nand electromagnetic processes in the emission region. We find an analytic\napproximation, consistent with previous work, for the subleading geometric\neffect of spin on observed face-on polarization rotation in the direct image:\n$\\Delta {\\rm EVPA} \\sim -2a/r_{\\rm s}^2$, where $a$ is the black hole spin and\n$r_{\\rm s}$ is the emission radius. We further show that spin introduces an\norder unity effect on face-on subimages: $\\Delta {\\rm EVPA} \\sim \\pm\na/\\sqrt{27}$. We also use our toy model to analyze polarization \"loops\"\nobserved during flares of orbiting hotspots. Our model provides insight into\npolarimetric simulations and observations of black holes such as those made by\nthe EHT and GRAVITY.\n"}
+{"abstract": "  Current and future optical and near-infrared wide-field surveys have the\npotential of finding kilonovae, the optical and infrared counterparts to\nneutron star mergers, independently of gravitational-wave or high-energy\ngamma-ray burst triggers. The ability to discover fast and faint transients\nsuch as kilonovae largely depends on the area observed, the depth of those\nobservations, the number of re-visits per field in a given time frame, and the\nfilters adopted by the survey; it also depends on the ability to perform rapid\nfollow-up observations to confirm the nature of the transients. In this work,\nwe assess kilonova detectability in existing simulations of the LSST strategy\nfor the Vera C. Rubin Wide Fast Deep survey, with focus on comparing rolling to\nbaseline cadences. Although currently available cadences can enable the\ndetection of more than 300 kilonovae out to 1400 Mpc over the ten-year survey,\nwe can expect only 3-32 kilonovae similar to GW170817 to be recognizable as\nfast-evolving transients. We also explore the detectability of kilonovae over\nthe plausible parameter space, focusing on viewing angle and ejecta masses. We\nfind that observations in redder izy bands are crucial for identification of\nnearby (within 300 Mpc) kilonovae that could be spectroscopically classified\nmore easily than more distant sources. Rubin's potential for serendipitous\nkilonova discovery could be increased by gain of efficiency with the employment\nof individual 30s exposures (as opposed to 2x15s snap pairs), with the addition\nof red-band observations coupled with same-night observations in g- or r-bands,\nand possibly with further development of a new rolling-cadence strategy.\n"}
+{"abstract": "  Metal trihalides (MX$_3$) are the most important family of 2D magnetic\nmaterials, being the chromium trihalides the most studied 2D magnets. The\ndiscovery of CrI$_3$, the recent obtention of CrCl$_3$ in-plane magnetic\nmonolayer and the role of CrBr$_3$ inducing topological superconductivity in\nNbSe$_2$, may serve to showcase the active research being done in this family\nnowadays. Despite the high impact of these materials, most of the members in\nthe MX$_3$ family are still unexplored and constitute an untapped source of\ninteresting physical properties. Stimulated by the most recent advances in\nstraintronics and aware of the crucial role of the dielectric screening, we\npresent here a high throughput methodology to automatize the exploration of 2D\nmaterials. Employing this methodology, we studied the MX$_3$ family (M= Cr, Fe,\nV, Ti; X= Cl, Br, I) with the goal of advancing towards the solution of the\nmost problematic issue in these materials, namely the Curie temperature. We use\na particular case to show how this methodology allows us to obtain a complete\ndescription of the magnetic interaction picture (J$_{iso}$, J$_{xx}$, J$_{yy}$,\nJ$_{zz}$) up to third neighbors condensed in a single effective equation per\nparameter, describing magnetic interaction in terms of the strain and the\nHubbard U parameter. Additionally, and because of the important role of the\nHubbard U in MX$_3$ materials, we provide a library of self-consistent\ncalculated Hubbard U for the principal pseudopotential families. The work\npresented herein advances in the description of the still unexplored MX$_3$\nmaterials, opening the door to a rational design of 2D magnetic materials.\n"}
+{"abstract": "  The paper presents a two-stage stochastic program to model a routing problem\ninvolving an Unmanned Aerial Vehicle (UAV) in the context of patrolling\nmissions. In particular, given a set of targets and a set of supplemental\ntargets corresponding to each target, the first stage decisions involve finding\nthe sequence in which the vehicle has to visit the set of targets. Upon\nreaching each target, the UAV collects information and if the operator of the\nUAV deems that the information collected is not of sufficient fidelity, then\nthe UAV has to visit all the supplemental targets corresponding to that target\nto collect additional information before proceeding to visit the next target.\nThe problem is solved using a progressive hedging algorithm and extensive\ncomputational results corroborating the effectiveness of the proposed model and\nthe solution methodology is presented.\n"}
+{"abstract": "  This study examines the influence of grandchildren's gender on grandparents'\nvoting behavior using independently collected individual-level data. The survey\nwas conducted immediately after the House of Councilors election in Japan. I\nobserved that individuals with a granddaughter were more likely to vote for\nfemale candidates by around 10 % than those without. However, having a daughter\ndid not affect the parents' voting behavior. Furthermore, having a son or a\ngrandson did not influence grandparents' voting behavior. This implies that\ngrandparents voted for their granddaughter's future benefit because\ngranddaughters may be too young vote in a male-dominated and aging society.\n"}
+{"abstract": "  The \\textit{Collatz's conjecture} is an unsolved problem in mathematics. It\nis named after Lothar Collatz in 1973. The conjecture also known as Syrucuse\nconjecture or problem.\n  Take any positive integer $ n $. If $ n $ is even then divide it by $ 2 $,\nelse do \"triple plus one\" and get $ 3n+1 $. The conjecture is that for all\nnumbers, this process converges to one.\n  In the modular arithmetic notation, define a function $ f $ as follows:\n\\[f(x)= \\left\\{ \\begin{array}{lll} \\frac{n}{2} &if & n\\equiv 0 \\pmod 2\\\\ 3n+1&\nif& n\\equiv 1 \\pmod 2. \\end{array}\\right. \\] In this paper, we present the\nproof of the Collatz conjecture for many types of sets defined by the remainder\ntheorem of arithmetic. These sets are defined in mods\n  $6, 12, 24, 36, 48, 60, 72, 84, 96, 108$ and we took only odd positive\nremainders to work with. It is not difficult to prove that the same results are\ntrue for any mod $12m, $ for positive integers $m$.\n"}
+{"abstract": "  Embedding learning (EL) and feature synthesizing (FS) are two of the popular\ncategories of fine-grained GZSL methods. EL or FS using global features cannot\ndiscriminate fine details in the absence of local features. On the other hand,\nEL or FS methods exploiting local features either neglect direct attribute\nguidance or global information. Consequently, neither method performs well. In\nthis paper, we propose to explore global and direct attribute-supervised local\nvisual features for both EL and FS categories in an integrated manner for\nfine-grained GZSL. The proposed integrated network has an EL sub-network and a\nFS sub-network. Consequently, the proposed integrated network can be tested in\ntwo ways. We propose a novel two-step dense attention mechanism to discover\nattribute-guided local visual features. We introduce new mutual learning\nbetween the sub-networks to exploit mutually beneficial information for\noptimization. Moreover, we propose to compute source-target class similarity\nbased on mutual information and transfer-learn the target classes to reduce\nbias towards the source domain during testing. We demonstrate that our proposed\nmethod outperforms contemporary methods on benchmark datasets.\n"}
+{"abstract": "  The stringent constraints from the direct searches for exotic scalars at the\nLHC as well as indirect bounds from flavor physics measurements have imposed\nsevere restrictions on the parameter space of new physics models featuring\nextended Higgs sectors. In the Type-II 2HDM, this implies a lower bound on the\ncharged Higgs masses of $\\cal O$(600 GeV). In this work we analyze the\nphenomenology of a Z3HDM in the alignment limit focusing on the impact of\nflavor physics constraints on its parameter space. We show that the couplings\nof the two charged Higgs bosons in this model feature an additional suppression\nfactor compared to Type-II 2HDM. This gives rise to a significant relaxation of\nthe flavor physics constraints in this model, allowing the charged Higgs masses\nto be as low as $\\cal O$(200 GeV). We also consider the constraints coming from\nprecision electroweak observables and the observed diphoton decay rate of the\n125 GeV Higgs boson at the LHC. The bounds coming from the direct searches of\nnonstandard Higgs bosons at the LHC, particularly those from resonance searches\nin the ditau channel, prove to be very effective in constraining this scenario\nfurther.\n"}
+{"abstract": "  Slowly changing variables in a continuous state space constitute an important\ncategory of reinforcement learning and see its application in many domains,\nsuch as modeling a climate control system where temperature, humidity, etc.\nchange slowly over time. However, this subject is less addressed in recent\nstudies. Classical methods with certain variants, such as Dynamic Programming\nwith Tile Coding which discretizes the state space, fail to handle slowly\nchanging variables because those methods cannot capture the tiny changes in\neach transition step, as it is computationally expensive or impossible to\nestablish an extremely granular grid system. In this paper, we introduce a\nHyperspace Neighbor Penetration (HNP) approach that solves the problem. HNP\ncaptures in each transition step the state's partial \"penetration\" into its\nneighboring hyper-tiles in the gridded hyperspace, thus does not require the\ntransition to be inter-tile in order for the change to be captured. Therefore,\nHNP allows for a very coarse grid system, which makes the computation feasible.\nHNP assumes near linearity of the transition function in a local space, which\nis commonly satisfied. In summary, HNP can be orders of magnitude more\nefficient than classical method in handling slowly changing variables in\nreinforcement learning. We have made an industrial implementation of NHP with a\ngreat success.\n"}
+{"abstract": "  While pre-trained language models (e.g., BERT) have achieved impressive\nresults on different natural language processing tasks, they have large numbers\nof parameters and suffer from big computational and memory costs, which make\nthem difficult for real-world deployment. Therefore, model compression is\nnecessary to reduce the computation and memory cost of pre-trained models. In\nthis work, we aim to compress BERT and address the following two challenging\npractical issues: (1) The compression algorithm should be able to output\nmultiple compressed models with different sizes and latencies, in order to\nsupport devices with different memory and latency limitations; (2) The\nalgorithm should be downstream task agnostic, so that the compressed models are\ngenerally applicable for different downstream tasks. We leverage techniques in\nneural architecture search (NAS) and propose NAS-BERT, an efficient method for\nBERT compression. NAS-BERT trains a big supernet on a search space containing a\nvariety of architectures and outputs multiple compressed models with adaptive\nsizes and latency. Furthermore, the training of NAS-BERT is conducted on\nstandard self-supervised pre-training tasks (e.g., masked language model) and\ndoes not depend on specific downstream tasks. Thus, the compressed models can\nbe used across various downstream tasks. The technical challenge of NAS-BERT is\nthat training a big supernet on the pre-training task is extremely costly. We\nemploy several techniques including block-wise search, search space pruning,\nand performance approximation to improve search efficiency and accuracy.\nExtensive experiments on GLUE and SQuAD benchmark datasets demonstrate that\nNAS-BERT can find lightweight models with better accuracy than previous\napproaches, and can be directly applied to different downstream tasks with\nadaptive model sizes for different requirements of memory or latency.\n"}
+{"abstract": "  We present results on the number of linear regions of the functions that can\nbe represented by artificial feedforward neural networks with maxout units. A\nrank-k maxout unit is a function computing the maximum of $k$ linear functions.\nFor networks with a single layer of maxout units, the linear regions correspond\nto the upper vertices of a Minkowski sum of polytopes. We obtain face counting\nformulas in terms of the intersection posets of tropical hypersurfaces or the\nnumber of upper faces of partial Minkowski sums, along with explicit sharp\nupper bounds for the number of regions for any input dimension, any number of\nunits, and any ranks, in the cases with and without biases. Based on these\nresults we also obtain asymptotically sharp upper bounds for networks with\nmultiple layers.\n"}
+{"abstract": "  A new formulation for light propagation in geometric optics by means of the\nBi-local Geodesic Operators is considered. We develop the BiGONLight\nMathematica package, uniquely designed to apply this framework to compute\noptical observables in Numerical Relativity. Our package can be used for light\npropagation on a wide range of scales and redshifts and accepts numerical as\nwell as analytical input for the spacetime metric. In this paper we focus on\ntwo cosmological observables, the redshift and the angular diameter distance,\nspecializing our analysis to a wall universe modeled within the post-Newtonian\napproximation. With this choice and the input metric in analytical form, we are\nable to estimate non-linearities of light propagation by comparing and\nisolating the contributions coming from Newtonian and post-Newtonian\napproximations as opposed to linear perturbation theory. We also clarify the\nrole of the dominant post-Newtonian contribution represented by the linear\ninitial seed which, strictly speaking, is absent in the Newtonian treatment. We\nfound that post-Newtonian non-linear corrections are below $1\\%$, in agreement\nwith previous results in the literature.\n"}
+{"abstract": "  In this article we prove that, over complete manifolds of dimension $n$ with\nvanishing curvature at infinity, the essential spectrum of the Hodge Laplacian\non differential $k$-forms is a connected interval for $0\\leq k\\leq n$. We also\nprove that, under a weaker condition where the Ricci curvature is\nasymptotically nonnegative, the essential spectrum on $k$-forms is\n$[0,\\infty)$, but only for $0\\leq k\\leq q$ and $n-q \\leq k \\leq n$ for some\ninteger $ q\\geq 1$ which depends the structure of the manifolds at infinity. We\nachieve these general results through a novel method that combines collapsing\ntheory and a generalized Weyl criterion. More specifically, we use\nCheeger-Fukaya-Gromov and Cheeger-Colding theory to describe large balls of\nthese manifolds at infinity which capture their spectrum, and obtain very rough\ntest-forms. These rough test-forms are then shown to satisfy a new generalized\nversion of the classical Weyl criterion which is also proved in this article.\n"}
+{"abstract": "  We point out that fractionalized bosonic charge excitations can explain the\nrecently discovered photo-induced superconducting-like response in\n$\\kappa\\text{-(ET})_2\\text{Cu}[\\text{N(CN)}_2]\\text{Br}$, an organic metal\nclose to the Mott transition. The pump laser exerts a periodic drive on the\nfractionalized field, creating a non-equilibrium condensate, which gives a\nDrude peak much narrower than the equilibrium scattering rate, hence\nsuperconducting-like response. Our proposal illuminates new possibilities of\ndetecting fractionalization and can be readily tested in spin liquid candidates\nand in cold atom systems.\n"}
+{"abstract": "  Deploying Reinforcement Learning (RL) agents in the real-world require that\nthe agents satisfy safety constraints. Current RL agents explore the\nenvironment without considering these constraints, which can lead to damage to\nthe hardware or even other agents in the environment. We propose a new method,\nLBPO, that uses a Lyapunov-based barrier function to restrict the policy update\nto a safe set for each training iteration. Our method also allows the user to\ncontrol the conservativeness of the agent with respect to the constraints in\nthe environment. LBPO significantly outperforms state-of-the-art baselines in\nterms of the number of constraint violations during training while being\ncompetitive in terms of performance. Further, our analysis reveals that\nbaselines like CPO and SDDPG rely mostly on backtracking to ensure safety\nrather than safe projection, which provides insight into why previous methods\nmight not have effectively limit the number of constraint violations.\n"}
+{"abstract": "  The key to realizing fault-tolerant quantum computation for singlet-triplet\n(ST) qubits in semiconductor double quantum dot (DQD) is to operate both the\nsingle- and two-qubit gates with high fidelity. The feasible way includes\noperating the qubit near the transverse sweet spot (TSS) to reduce the leading\norder of the noise, as well as adopting the proper pulse sequences which are\nimmune to noise. The single-qubit gates can be achieved by introducing an AC\ndrive on the detuning near the TSS. The large dipole moment of the DQDs at the\nTSS has enabled strong coupling between the qubits and the cavity resonator,\nwhich leads to a two-qubit entangling gates. When operating in the proper\nregion and applying modest pulse sequences, both single- and two-qubit gates\nare having fidelity higher than 99%. Our results suggest that taking advantage\nof the appropriate pulse sequences near the TSS can be effective to obtain\nhigh-fidelity ST qubits.\n"}
+{"abstract": "  In this paper, we present algorithms for synthesizing controllers to\ndistribute a group (possibly swarms) of homogeneous robots (agents) over\nheterogeneous tasks which are operated in parallel. We present algorithms as\nwell as analysis for global and local-feedback-based controller for the swarms.\nUsing ergodicity property of irreducible Markov chains, we design a controller\nfor global swarm control. Furthermore, to provide some degree of autonomy to\nthe agents, we augment this global controller by a local feedback-based\ncontroller using Language measure theory. We provide analysis of the proposed\nalgorithms to show their correctness. Numerical experiments are shown to\nillustrate the performance of the proposed algorithms.\n"}
+{"abstract": "  The goal of this PhD thesis is to study a diophantine approximation problem\nstated by Schmidt in 1967. The problem aim to study the approximation of a\nsubspace of $\\mathbb{R}^n$ by rational subspaces, not necessarily of the same\ndimension, by determining the exponents of approximation associated to the\nvarious angles between the subspaces.\n"}
+{"abstract": "  In this work, we study the dynamics of a single active Brownian particle, as\nwell as the collective behavior of interacting active Brownian particles, in a\nfluctuating heterogeneous environment. We employ a variant of the diffusing\ndiffusivity model where the equation of motion of the active particle involves\na time-dependent motility and diffusivities. Within our model, those\nfluctuations are coupled to each other. Using analytical methods, we obtain the\nprobability distribution function of particle displacement and its moments for\na single particle. We then investigate the impact of the environmental\nfluctuations on the collective behavior of the active Brownian particles by\nmeans of extensive numerical simulations. Our results show that the\nfluctuations hinder the motility-induced phase separation, accompanied by a\nsignificant change of the density dependence of particle velocities. These\neffects are interpreted using our analytical results for the dynamics of a\nsingle particle.\n"}
+{"abstract": "  Recently demonstrated physical-world adversarial attacks have exposed\nvulnerabilities in perception systems that pose severe risks for\nsafety-critical applications such as autonomous driving. These attacks place\nadversarial artifacts in the physical world that indirectly cause the addition\nof a universal patch to inputs of a model that can fool it in a variety of\ncontexts. Adversarial training is the most effective defense against\nimage-dependent adversarial attacks. However, tailoring adversarial training to\nuniversal patches is computationally expensive since the optimal universal\npatch depends on the model weights which change during training. We propose\nmeta adversarial training (MAT), a novel combination of adversarial training\nwith meta-learning, which overcomes this challenge by meta-learning universal\npatches along with model training. MAT requires little extra computation while\ncontinuously adapting a large set of patches to the current model. MAT\nconsiderably increases robustness against universal patch attacks on image\nclassification and traffic-light detection.\n"}
+{"abstract": "  Let $F(z)$ be an arbitrary complex polynomial. We introduce the local root\nclustering problem, to compute a set of natural $\\varepsilon$-clusters of roots\nof $F(z)$ in some box region $B_0$ in the complex plane. This may be viewed as\nan extension of the classical root isolation problem. Our contribution is\ntwo-fold: we provide an efficient certified subdivision algorithm for this\nproblem, and we provide a bit-complexity analysis based on the local geometry\nof the root clusters.\n  Our computational model assumes that arbitrarily good approximations of the\ncoefficients of $F$ are provided by means of an oracle at the cost of reading\nthe coefficients. Our algorithmic techniques come from a companion paper\n(Becker et al., 2018) and are based on the Pellet test, Graeffe and Newton\niterations, and are independent of Sch\\\"onhage's splitting circle method. Our\nalgorithm is relatively simple and promises to be efficient in practice.\n"}
+{"abstract": "  Recently, Mutual Information (MI) has attracted attention in bounding the\ngeneralization error of Deep Neural Networks (DNNs). However, it is intractable\nto accurately estimate the MI in DNNs, thus most previous works have to relax\nthe MI bound, which in turn weakens the information theoretic explanation for\ngeneralization. To address the limitation, this paper introduces a\nprobabilistic representation of DNNs for accurately estimating the MI.\nLeveraging the proposed MI estimator, we validate the information theoretic\nexplanation for generalization, and derive a tighter generalization bound than\nthe state-of-the-art relaxations.\n"}
+{"abstract": "  Background: Network Analysis (NA) is a method that has been used in various\ndisciplines such as Social sciences and Ecology for decades. So far, NA has not\nbeen used extensively in studies of medication use. Only a handful of papers\nhave used NA in Drug Prescription Networks (DPN). We provide an introduction to\nNA terminology alongside a guide to creating and extracting results from the\nmedication networks. Objective: To introduce the readers to NA as a tool to\nstudy medication use by demonstrating how to apply different NA measures on 3\ngenerated medication networks. Methods: We used the Norwegian Prescription\nDatabase (NorPD) to create a network that describes the co-medication in\nelderly persons in Norway on January 1, 2013. We used the Norwegian Electronic\nPrescription Support System (FEST) to create another network of severe\ndrug-drug interactions (DDIs). Lastly, we created a network combining the two\nnetworks to show the actual use of drugs with severe DDIs. We used these\nnetworks to elucidate how to apply and interpret different network measures in\nmedication networks. Results: Interactive network graphs are made available\nonline, Stata and R syntaxes are provided. Various useful network measures for\nmedication networks were applied such as network topological features,\nmodularity analysis and centrality measures. Edge lists data used to generate\nthe networks are openly available for readers in an open data repository to\nexplore and use. Conclusion: We believe that NA can be a useful tool in\nmedication use studies. We have provided information and hopefully inspiration\nfor other researchers to use NA in their own projects. While network analyses\nare useful for exploring and discovering structures in medication use studies,\nit also has limitations. It can be challenging to interpret and it is not\nsuitable for hypothesis testing.\n"}
+{"abstract": "  To address the dynamic nature of real-world networks, we generalize\ncompetitive diffusion games and Voronoi games from static to temporal graphs,\nwhere edges may appear or disappear over time. This establishes a new direction\nof studies in the area of graph games, motivated by applications such as\ninfluence spreading. As a first step, we investigate the existence of Nash\nequilibria in competitive diffusion and Voronoi games on different temporal\ngraph classes. Even when restricting our studies to temporal paths and cycles,\nthis turns out to be a challenging undertaking, revealing significant\ndifferences between the two games in the temporal setting. Notably, both games\nare equivalent on static paths and cycles. Our two main technical results are\n(algorithmic) proofs for the existence of Nash equilibria in temporal\ncompetitive diffusion and temporal Voronoi games when the edges are restricted\nnot to disappear over time.\n"}
+{"abstract": "  In this paper, we present a new control policy parametrization for the\nfinite-horizon covariance steering problem for discrete-time Gaussian linear\nsystems (DTGLS) which can reduce the latter stochastic optimal control problem\nto a tractable optimization problem. The covariance steering problem seeks for\na feedback control policy that will steer the state covariance of a DTGLS to a\ndesired positive definite matrix in finite time. We consider two different\nformulations of the covariance steering problem, one with hard terminal LMI\nconstraints (hard-constrained covariance steering) and another one with soft\nterminal constraints in the form of a terminal cost which corresponds to the\nsquared Wasserstein distance between the actual terminal state (Gaussian)\ndistribution and the desired one (soft-constrained covariance steering). We\npropose a solution approach that relies on the affine disturbance feedback\nparametrization for both problem formulations. We show that this particular\nparametrization allows us to reduce the hard-constrained covariance steering\nproblem into a semi-definite program (SDP) and the soft-constrained covariance\nsteering problem into a difference of convex functions program(DCP). Finally,\nwe show the advantages of our approach over other covariance steering\nalgorithms in terms of computational complexity and computation time by means\nof theoretical analysis and numerical simulations.\n"}
+{"abstract": "  The minimum sum-of-squares clustering problem (MSSC) consists of partitioning\n$n$ observations into $k$ clusters in order to minimize the sum of squared\ndistances from the points to the centroid of their cluster. In this paper, we\npropose an exact algorithm for the MSSC problem based on the branch-and-bound\ntechnique. The lower bound is computed by using a cutting-plane procedure where\nvalid inequalities are iteratively added to the Peng-Wei SDP relaxation. The\nupper bound is computed with the constrained version of k-means where the\ninitial centroids are extracted from the solution of the SDP relaxation. In the\nbranch-and-bound procedure, we incorporate instance-level must-link and\ncannot-link constraints to express knowledge about which data points should or\nshould not be grouped together. We manage to reduce the size of the problem at\neach level preserving the structure of the SDP problem itself. The obtained\nresults show that the approach allows to successfully solve for the first time\nreal-world instances up to 4000 data points.\n"}
+{"abstract": "  Recently, orthogonal recurrent neural networks (RNNs) have emerged as\nstate-of-the-art models for learning long-term dependencies. This class of\nmodels mitigates the exploding and vanishing gradients problem by design. In\nthis work, we employ tools and insights from differential geometry to offer a\nnovel perspective on orthogonal RNNs. We show that orthogonal RNNs may be\nviewed as optimizing in the space of divergence-free vector fields.\nSpecifically, based on a well-known result in differential geometry that\nrelates vector fields and linear operators, we prove that every divergence-free\nvector field is related to a skew-symmetric matrix. Motivated by this\nobservation, we study a new recurrent model, which spans the entire space of\nvector fields. Our method parameterizes vector fields via the directional\nderivatives of scalar functions. This requires the construction of latent inner\nproduct, gradient, and divergence operators. In comparison to state-of-the-art\northogonal RNNs, our approach achieves comparable or better results on a\nvariety of benchmark tasks.\n"}
+{"abstract": "  Attention mechanisms, especially self-attention, have played an increasingly\nimportant role in deep feature representation for visual tasks. Self-attention\nupdates the feature at each position by computing a weighted sum of features\nusing pair-wise affinities across all positions to capture the long-range\ndependency within a single sample. However, self-attention has quadratic\ncomplexity and ignores potential correlation between different samples. This\npaper proposes a novel attention mechanism which we call external attention,\nbased on two external, small, learnable, shared memories, which can be\nimplemented easily by simply using two cascaded linear layers and two\nnormalization layers; it conveniently replaces self-attention in existing\npopular architectures. External attention has linear complexity and implicitly\nconsiders the correlations between all data samples. We further incorporate the\nmulti-head mechanism into external attention to provide an all-MLP\narchitecture, external attention MLP (EAMLP), for image classification.\nExtensive experiments on image classification, object detection, semantic\nsegmentation, instance segmentation, image generation, and point cloud analysis\nreveal that our method provides results comparable or superior to the\nself-attention mechanism and some of its variants, with much lower\ncomputational and memory costs.\n"}
+{"abstract": "  In 1977, Gauduchon proved that on every compact hermitian manifold $(X,\n\\omega)$ there exists a conformally equivalent hermitian metric\n$\\omega_{\\mathrm{G}}$ which satisfies $\\mathrm{dd}^c \\omega_{\\mathrm{G}}^{n-1}\n= 0$. In this note, we extend this result to irreducible compact singular\nhermitian varieties which admit a smoothing.\n"}
+{"abstract": "  Modern hydrodynamical simulations reproduce many properties of the real\nuniverse. These simulations model various physical processes, but many of these\nare included using `subgrid models' due to resolution limits. Although\ndifferent subgrid models have been successful in modelling the effects of\nsupernovae (SNe) feedback on galactic properties, it remains unclear if, and by\nhow much, these differing implementations affect observable halo gas\nproperties. In this work, we use `zoom-in' cosmological initial conditions of\ntwo volumes selected to resemble the Local Group (LG) evolved with both the\nAuriga and EAGLE galaxy formation models. While the subgrid physics models in\nboth simulations reproduce realistic stellar components of $L^\\star$ galaxies,\nthey exhibit different gas properties. Namely, Auriga predicts that the Milky\nWay (MW) is almost baryonically closed, whereas EAGLE suggests that only half\nof the expected baryons reside within the halo. Furthermore, EAGLE predicts\nthat this baryon deficiency extends to the LG, ($r \\leq 1 \\mathrm{~Mpc}$). The\nbaryon deficiency in EAGLE is likely due to SNe feedback at high redshift,\nwhich generates halo-wide outflows, with high covering fractions and radial\nvelocities, which both eject baryons and significantly impede cosmic gas\naccretion. Conversely, in Auriga, gas accretion is almost unaffected by\nfeedback. These differences appear to be the result of the different energy\ninjection methods from SNe to gas. Our results suggest that both quasar\nabsorption lines and fast radio burst dispersion measures could constrain these\ntwo regimes with future observations.\n"}
+{"abstract": "  We develop a new method for the construction of one-dimensional integrable\nLindblad systems, which describe quantum many body models in contact with a\nMarkovian environment. We find several new models with interesting features,\nsuch as annihilation-diffusion processes, a mixture of coherent and classical\nparticle propagation, and a rectified steady state current. We also find new\nways to represent known classical integrable stochastic equations by integrable\nLindblad operators. Our method can be extended to various other situations and\nit establishes a structured approach to the study of solvable open quantum\nsystems.\n"}
+{"abstract": "  Electrohydrodynamic instabilities of fluid-fluid interfaces can be exploited\nin various microfluidic applications in order to enhance mixing, replicate\nwell-controlled patterns or generate drops of a particular size. In this work,\nwe study the stability and dynamics of a system of three superimposed layers of\ntwo immiscible fluids subject to a normal electric field. Following the\nTaylor-Melcher leaky dielectric model, the bulk remains electroneutral while a\nnet charge accumulates on the interfaces. The interfacial charge dynamics is\ncaptured by a conservation equation accounting for Ohmic conduction, advection\nby the flow and finite charge relaxation. Using this model, we perform a linear\nstability analysis and identify different modes of instability, and we\ncharacterize the behavior of the system as a function of the relevant\ndimensionless groups in each mode. Further, we perform numerical simulations\nusing the boundary element method in order to study the effect of\nnonlinearities on long-time interfacial dynamics. We demonstrate how the\ncoupling of flow and surface charge transport in different modes of instability\ncan give rise to nonlinear phenomena such as tip streaming or pinching of the\nfilm into droplets.\n"}
+{"abstract": "  We consider time-harmonic electromagnetic wave equations in composites of a\ndispersive material surrounded by a classical material. In certain frequency\nranges this leads to sign-changing permittivity and/or permeability. Previously\nmeshing rules were reported, which guarantee the convergence of finite element\napproximations to the related scalar source problems. Here we generalize these\nresults to the electromagnetic two dimensional vectorial equations and the\nrelated holomorphic eigenvalue problems. Different than for the analysis on the\ncontinuous level, we require an assumption on both contrasts of the\npermittivity and the permeability. We confirm our theoretical results with\ncomputational studies.\n"}
+{"abstract": "  In this short note, we consider the relative Gersten's complex for Milnor\n$K$-theory over a regular Henselian domain $S$ and prove that in degrees $\\geq\n\\dim S\\geq 1$, the Gersten's complex of an essentially smooth Henselian local\n$S$-scheme is exact.\n"}
+{"abstract": "  Results on unconditional convergence in the Maximum norm for ADI-type\nmethods, such as the Douglas method, applied to the time integration of\nsemilinear parabolic problems are quite difficult to get, mainly when the\nnumber of space dimensions $m$ is greater than two. Such a result is obtained\nhere under quite general conditions on the PDE problem in case that\ntime-independent Dirichlet boundary conditions are imposed. To get these\nbounds, a theorem that guarantees, in some sense, power-boundeness of the\nstability function independently of both the space and time resolutions is\nproved.\n"}
+{"abstract": "  Light fields are 4D scene representation typically structured as arrays of\nviews, or several directional samples per pixel in a single view. This highly\ncorrelated structure is not very efficient to transmit and manipulate\n(especially for editing), though. To tackle these problems, we present a novel\ncompact and editable light field representation, consisting of a set of visual\nchannels (i.e. the central RGB view) and a complementary meta channel that\nencodes the residual geometric and appearance information. The visual channels\nin this representation can be edited using existing 2D image editing tools,\nbefore accurately reconstructing the whole edited light field back. We propose\nto learn this representation via an autoencoder framework, consisting of an\nencoder for learning the representation, and a decoder for reconstructing the\nlight field. To handle the challenging occlusions and propagation of edits, we\nspecifically designed an editing-aware decoding network and its associated\ntraining strategy, so that the edits to the visual channels can be consistently\npropagated to the whole light field upon reconstruction.Experimental results\nshow that our proposed method outperforms related existing methods in\nreconstruction accuracy, and achieves visually pleasant performance in editing\npropagation.\n"}
+{"abstract": "  The concept of Bohr radius for the class of bounded analytic functions was\nintroduced by Harald Bohr in 1914. His initial result received greater interest\nand was sharpened-refined-generalized by several mathematicians in various\nsettings--which is now called Bohr phenomenon. Various generalization of Bohr's\nclassical theorem is now an active area of research and has been a source of\ninvestigation in numerous other function spaces and including holomorphic\nfunctions of several complex variables. Recently, a new generalization of\nBohr's ideas was introduced and investigated by Kayumov et al.. In this note,\nwe investigate and refine generalized Bohr's inequality for the class of\nquasi-subordinations.\n"}
+{"abstract": "  This chapter outlines the relation between artificial intelligence (AI) /\nmachine learning (ML) algorithms and digital games. This relation is two-fold:\non one hand, AI/ML researchers can generate large, in-the-wild datasets of\nhuman affective activity, player behaviour (i.e. actions within the game\nworld), commercial behaviour, interaction with graphical user interface\nelements or messaging with other players, while games can utilise intelligent\nalgorithms to automate testing of game levels, generate content, develop\nintelligent and responsive non-player characters (NPCs) or predict and respond\nplayer behaviour across a wide variety of player cultures. In this work, we\ndiscuss some of the most common and widely accepted uses of AI/ML in games and\nhow intelligent systems can benefit from those, elaborating on estimating\nplayer experience based on expressivity and performance, and on generating\nproper and interesting content for a language learning game.\n"}
+{"abstract": "  Hole spin qubits in planar Ge heterostructures are one of the frontrunner\nplatforms for scalable quantum computers. In these systems, the spin-orbit\ninteractions permit efficient all-electric qubit control. We propose a minimal\ndesign modification of planar devices that enhances these interactions by\norders of magnitude and enables low power ultrafast qubit operations in the GHz\nrange. Our approach is based on an asymmetric potential that strongly squeezes\nthe quantum dot in one direction. This confinement-induced spin-orbit\ninteraction does not rely on microscopic details of the device such as growth\ndirection or strain, and could be turned on and off on demand in\nstate-of-the-art qubits.\n"}
+{"abstract": "  Two-dimensional (2D) materials have received considerable attention as\npossible electrodes in Li-ion batteries (LIBs), although a deeper understanding\nof the Li adsorption behavior as well as broad screening of the materials space\nis still needed. In this work, we build a high-throughput screening scheme that\nincorporates a learned interaction. First, density functional theory and graph\nconvolution networks are utilized to calculate minimum Li adsorption energies\nfor a small set of 2D metallic materials. The data is then used to find a\ndependence of the minimum Li adsorption energies on the sum of ionization\npotential, work function of the 2D metal, and coupling energy between Li+ and\nsubstrate. Our results show that variances of elemental properties and density\nare the most correlated features with coupling. To illustrate the applicability\nof this approach, the model is employed to show that some fluorides and\nchromium oxides are potential high-voltage materials with adsorption energies <\n-7 eV, and the found physics is used as the design principle to enhance the Li\nadsorption ability of graphene. This physics-driven approach shows higher\naccuracy and transferability compared with purely data-driven models.\n"}
+{"abstract": "  We propose a new swampland conjecture stating that the limit of vanishing\ngravitino mass corresponds to the massless limit of an infinite tower of states\nand to the consequent breakdown of the effective field theory. We test our\nproposal in large classes of models coming from compactification of string\ntheory to four dimensions, where we identify the Kaluza-Klein nature of the\ntower of states becoming light. We point out a general relation between the\ngravitino mass and abelian gauge coupling in models with extended\nsupersymmetry, which can survive also in examples with minimal supersymmetry.\nThis allows us to connect our conjecture to other well established swampland\nconjectures, such as the weak gravity conjecture or the absence of global\nsymmetries in quantum gravity. We discuss phenomenological implications of our\nconjecture in (quasi-)de Sitter backgrounds and extract a lower bound for the\ngravitino mass in terms of the Hubble parameter.\n"}
+{"abstract": "  Efficient exploration in bandits is a fundamental online learning problem. We\npropose a variant of Thompson sampling that learns to explore better as it\ninteracts with bandit instances drawn from an unknown prior. The algorithm\nmeta-learns the prior and thus we call it MetaTS. We propose several efficient\nimplementations of MetaTS and analyze it in Gaussian bandits. Our analysis\nshows the benefit of meta-learning and is of a broader interest, because we\nderive a novel prior-dependent Bayes regret bound for Thompson sampling. Our\ntheory is complemented by empirical evaluation, which shows that MetaTS quickly\nadapts to the unknown prior.\n"}
+{"abstract": "  Let $X=\\Lambda\\backslash\\mathbb{H}$ be a Schottky surface, that is, a\nconformally compact hyperbolic surface of infinite area. Let $\\delta$ denote\nthe Hausdorff dimension of the limit set of $\\Lambda$.\n  We prove that for any compact subset $\\mathcal{K}\n\\subset\\{\\,s\\,:\\,\\Re(s)>\\frac{\\delta}{2}\\,\\}$, if one picks a random degree $n$\ncover $X_{n}$ of $X$ uniformly at random, then with probability tending to one\nas $n\\to\\infty$, there are no resonances of $X_{n}$ in $\\mathcal{K}$ other than\nthose already belonging to $X$ (and with the same multiplicity). This result is\nconjectured to be the optimal one for bounded frequency resonances and is\nanalogous to both Alon's and Friedman's conjectures for random graphs, which\nare now theorems due to Friedman and Bordenave-Collins, respectively.\n"}
+{"abstract": "  The aim of this work is to study the structure of bounded finite potent\nendomorphisms on Hilbert spaces. In particular, for these operators, an answer\nto the Invariant Subspace Problem is given and the main properties of its\nadjoint operator are offered. Moreover, for every bounded finite potent\nendomorphism we show that Tate's trace coincides with the Leray trace and with\nthe trace defined by R. Elliott for Riesz Trace Class operators.\n"}
+{"abstract": "  We describe the topology of singular real algebraic curves in a smooth\nsurface. We enumerate and bound in terms of the degree the number of\ntopological types of singular algebraic curves in the real projective plane.\n"}
+{"abstract": "  Speech sounds of spoken language are obtained by varying configuration of the\narticulators surrounding the vocal tract. They contain abundant information\nthat can be utilized to better understand the underlying mechanism of human\nspeech production. We propose a novel deep neural network-based learning\nframework that understands acoustic information in the variable-length sequence\nof vocal tract shaping during speech production, captured by real-time magnetic\nresonance imaging (rtMRI), and translate it into text. The proposed framework\ncomprises of spatiotemporal convolutions, a recurrent network, and the\nconnectionist temporal classification loss, trained entirely end-to-end. On the\nUSC-TIMIT corpus, the model achieved a 40.6% PER at sentence-level, much better\ncompared to the existing models. To the best of our knowledge, this is the\nfirst study that demonstrates the recognition of entire spoken sentence based\non an individual's articulatory motions captured by rtMRI video. We also\nperformed an analysis of variations in the geometry of articulation in each\nsub-regions of the vocal tract (i.e., pharyngeal, velar and dorsal, hard\npalate, labial constriction region) with respect to different emotions and\ngenders. Results suggest that each sub-regions distortion is affected by both\nemotion and gender.\n"}
+{"abstract": "  This paper investigates the impact of the charging level of high penetration\nlevel of Electric Vehicles (EVs) on the power quality of the electricity\ndistribution network. The EV owners tend to charge their EVs as fast as\npossible. The charging levels of EVs within the distribution network affect the\nvoltage profile of buses of the network. In this paper, an exact Second-Order\nCone Programming (SOCP) formulation of the full AC optimal power flow (ACOPF)\nproblem of the distribution network is presented. The network includes solar\ngeneration units and EVs as Distributed Energy Resources (DERs). Different\ncharging levels are considered to analyze the impact of EVs on the distribution\nnetwork. The performance of the proposed model is illustrated for the modified\nIEEE-33 bus system for different charging levels for EVs. Besides, the impact\nof available solar power and battery degradation cost of EVs on the\ndistribution network is investigated. It is illustrated that how EV charging\nwill cause voltage deviation challenges for the distribution network.\n"}
+{"abstract": "  Multilingualism refers to the high degree of proficiency in two or more\nlanguages in the written and oral communication modes. It often results in\nlanguage mixing, a.k.a. code-mixing, when a multilingual speaker switches\nbetween multiple languages in a single utterance of a text or speech. This\npaper discusses the current state of the NLP research, limitations, and\nforeseeable pitfalls in addressing five real-world applications for social good\ncrisis management, healthcare, political campaigning, fake news, and hate\nspeech for multilingual societies. We also propose futuristic datasets, models,\nand tools that can significantly advance the current research in multilingual\nNLP applications for the societal good. As a representative example, we\nconsider English-Hindi code-mixing but draw similar inferences for other\nlanguage pairs\n"}
+{"abstract": "  We have used the central 44 antennas of the new 64-dish MeerKAT radio\ntelescope array to conduct a deep search for new pulsars in the core of nine\nglobular clusters. This has led to the discovery of eight new millisecond\npulsars in six different clusters. Two new binaries, 47 Tuc ac and 47 Tuc ad,\nare eclipsing \"spiders\", featuring compact orbits ($\\lesssim 0.32$ days), very\nlow-mass companions and regular occultations of their pulsed emission. The\nother three new binary pulsars (NGC 6624G, M62G, and Ter 5 an) are in wider ($>\n0.7$ days) orbits, with companions that are likely to be white dwarfs or\nneutron stars. NGC 6624G has a large eccentricity of $e\\simeq 0.38$, which\nenabled us to detect the rate of advance of periastron. This suggests that the\nsystem is massive, with a total mass of $M{\\rm tot} = 2.65 \\pm 0.07$\nM$_{\\odot}$. Likewise, for Ter 5 an, with $e \\simeq 0.0066$, we obtain $M{\\rm\ntot}= 2.97 \\pm 0.52$ M$_{\\odot}$. The other three new discoveries (NGC 6522D,\nNGC 6624H and NGC 6752F) are faint isolated pulsars. Finally, we have used the\nwhole MeerKAT array and synthesized 288 beams, covering an area of $\\sim2$\narcmin in radius around the center of NGC 6624. This has allowed us to localize\nmany of the pulsars in the cluster, demonstrating the beamforming capabilities\nof the TRAPUM software backend and paving the way for the upcoming MeerKAT\nglobular cluster pulsar survey.\n"}
+{"abstract": "  Intrusion Detection Systems (IDS) are key components for securing critical\ninfrastructures, capable of detecting malicious activities on networks or\nhosts. The procedure of implementing a IDS for Internet of Things (IoT)\nnetworks is not without challenges due to the variability of these systems and\nspecifically the difficulty in accessing data. The specifics of these very\nconstrained devices render the design of an IDS capable of dealing with the\nvaried attacks a very challenging problem and a very active research subject.\nIn the current state of literature, a number of approaches have been proposed\nto improve the efficiency of intrusion detection, catering to some of these\nlimitations, such as resource constraints and mobility. In this article, we\nreview works on IDS specifically for these kinds of devices from 2008 to 2018,\ncollecting a total of 51 different IDS papers. We summarise the current themes\nof the field, summarise the techniques employed to train and deploy the IDSs\nand provide a qualitative evaluations of these approaches. While these works\nprovide valuable insights and solutions for sub-parts of these constraints, we\ndiscuss the limitations of these solutions as a whole, in particular what kinds\nof attacks these approaches struggle to detect and the setup limitations that\nare unique to this kind of system. We find that although several paper claim\nnovelty of their approach little inter paper comparisons have been made, that\nthere is a dire need for sharing of datasets and almost no shared code\nrepositories, consequently raising the need for a thorough comparative\nevaluation.\n"}
+{"abstract": "  The local existence of smooth solutions to the inviscid Hall-MHD system has\nbeen obtained since Chae, Degond and Liu [Ann. Inst. H. Poincar\\'e Anal. Non\nLin\\'eaire, {31} (2014), 555--565]. However, as we known, how to construct the\nglobal small solutions to the inviscid Hall-MHD system is still an open\nproblem. In the present paper, we give a positive answer in $ \\mathbb{T}^3$\nwhen the initial magnetic field is close to a background magnetic field\nsatisfying the Diophantine condition.\n"}
+{"abstract": "  Originating from neutron star-neutron star (NS-NS) or neutron star-black hole\n(NS-BH) mergers, short gamma-ray bursts (SGRBs) are the first electromagnetic\nemitters associated with gravitational waves. This association makes the\ndetermination of SGRB formation rate (FR) a critical issue. We determine the\ntrue SGRB FR and its relation to the cosmic star formation rate (SFR). This can\nhelp in determining the expected Gravitation Wave (GW) rate involving small\nmass mergers. We present non-parametric methods for the determination of the\nevolutions of the luminosity function (LF) and the FR using SGRBs observed by\n{\\it Swift}, without any assumptions. These are powerful tools for small\nsamples, such as our sample of 68 SGRBs. We combine SGRBs with and without\nextended emission (SEE), assuming that both descend from the same progenitor.\nTo overcome the incompleteness introduced by redshift measurements we use the\nKolmogorov-Smirnov (KS) test to find flux thresholds yielding a sample of\nsources with a redshift drawn from the parent sample including all sources.\nUsing two subsamples of SGRBs with flux limits of $4.57 \\times 10^{-7}$ and\n$2.15 \\times 10^{-7}$ erg cm$^{-2}$ s$^{-1}$ with respective KS {\\it p=(1,\n0.9)}, we find a 3 $\\sigma$ evidence for luminosity evolution (LE), a broken\npower-law LF with significant steepening at $L\\sim 10^{50}$ erg s$^{-1}$, and a\nFR evolution that decreases monotonically with redshift (independent of LE and\nthe thresholds). Thus, SGRBs may have been more luminous in the past with a FR\ndelayed relative to the SFR as expected in the merger scenario.\n"}
+{"abstract": "  Fuzzy dark matter (FDM) has been a promising alternative to standard cold\ndark matter. The model consists of ultralight bosons with mass $m_b \\sim\n10^{-22}$ eV and features a quantum-pressure-supported solitonic core that\noscillates. In this work, we show that the soliton density oscillations persist\neven after significant tidal stripping of the outer halo. We report two\nintrinsic yet distinct timescales associated, respectively, with the\nground-state soliton wavefunction $\\tau_{00}$ and the soliton density\noscillations $\\tau_\\text{soliton}$, obeying $\\tau_\\text{soliton} /\\tau_{00}\n\\simeq 2.3$. The central star cluster (SC) in Eridanus II has a characteristic\ntimescale $\\tau_\\text{soliton} / \\tau_\\text{SC} \\sim 2$ to $3$ that deviates\nsubstantially from unity. As a result, we demonstrate, both analytically and\nnumerically with three-dimensional self-consistent FDM simulations, that the\ngravitational heating of the SC owing to soliton density oscillations is\nnegligible irrespective of $m_b$. We also show that the subhalo mass function\nto form Eridanus II does not place a strong constraint on $m_b$. These results\nare contrary to the previous findings by Marsh & Niemeyer (2019).\n"}
+{"abstract": "  Machine learning may enable the automated generation of test oracles. We have\ncharacterized emerging research in this area through a systematic literature\nreview examining oracle types, researcher goals, the ML techniques applied, how\nthe generation process was assessed, and the open research challenges in this\nemerging field.\n  Based on a sample of 22 relevant studies, we observed that ML algorithms\ngenerated test verdict, metamorphic relation, and - most commonly - expected\noutput oracles. Almost all studies employ a supervised or semi-supervised\napproach, trained on labeled system executions or code metadata - including\nneural networks, support vector machines, adaptive boosting, and decision\ntrees. Oracles are evaluated using the mutation score, correct classifications,\naccuracy, and ROC. Work-to-date show great promise, but there are significant\nopen challenges regarding the requirements imposed on training data, the\ncomplexity of modeled functions, the ML algorithms employed - and how they are\napplied - the benchmarks used by researchers, and replicability of the studies.\nWe hope that our findings will serve as a roadmap and inspiration for\nresearchers in this field.\n"}
+{"abstract": "  We present exact solutions for the size of the giant connected component\n(GCC) of graphs composed of higher-order homogeneous cycles, including weak\ncycles and cliques, following bond percolation. We use our theoretical result\nto find the location of the percolation threshold of the model, providing\nanalytical solutions where possible. We expect the results derived here to be\nuseful to a wide variety of applications including graph theory, epidemiology,\npercolation and lattice gas models as well as fragmentation theory. We also\nexamine the Erd\\H{o}s-Gallai theorem as a necessary condition on the\ngraphicality of configuration model networks comprising higher-order clique\nsub-graphs.\n"}
+{"abstract": "  We present an event generator for the process $pp\\to \\ell\\nu_\\ell\\gamma$ at\nnext-to-next-to-leading order (NNLO) in QCD and matched to the PYTHIA8 parton\nshower. The calculation makes use of the GENEVA framework, which combines a\nresummed calculation obtained via Soft-Collinear Effective Theory (SCET) with\nthe fixed-order result. We validate the NNLO accuracy of our results by\ncomparing predictions for inclusive quantities with an independent fixed-order\ncalculation, and then present the resummed 0-jettiness spectrum at\nnext-to-next-to-leading logarithmic (NNLL$'$) accuracy. Finally, we compare our\npredictions against data collected by the ATLAS experiment at the Large Hadron\nCollider during its 7 TeV run, finding good agreement.\n"}
+{"abstract": "  The existence of the shortest confidence interval for Poisson mean is shown.\nThe method of obtaining such an interval is presented as well.\n"}
+{"abstract": "  In this document, we report an analysis of the Public MeSH Note field of the\nnew descriptors introduced in the MeSH thesaurus between 2006 and 2020. The aim\nof this analysis was to extract information about the previous status of these\nnew descriptors as Supplementary Concept Records. The Public MeSH Note field\ncontains information in semi-structured text, meant to be read by humans.\nTherefore, we adopted a semi-automated approach, based on regular expressions,\nto extract information from it. In the large majority of cases, we managed to\nminimize the required manual effort for extracting the previous state of a new\ndescriptor as a Supplementary Concept Record. The source code for this analysis\nis openly available on GitHub.\n"}
+{"abstract": "  Inverse Compton scattering (ICS) can obtain quasi-monochromatic and\ndirectional EUV radiation via a MeV-scale energy electron beam and a\nmicron-scale wavelength laser beam, which enables a dramatic reduction in\ndimension and expense of the system, and makes it an attractive technology in\nresearch, industry, medicine and homeland security. Here we propose an EUV\nsource based on high repetition ICS system. The scheme exploits the output from\nthe laser-electron interaction between a MW-ps laser at MHz repetition-rate and\na high quality electron beam with an energy of a few MeV at MHz\nrepetition-rate.\n"}
+{"abstract": "  We report on the expected sensitivity of dedicated scintillator-based\ndetectors at the LHC for elementary particles with charges much smaller than\nthe electron charge. The dataset provided by a prototype scintillator-based\ndetector is used to characterise the performance of the detector and provide an\naccurate background projection. Detector designs, including a novel slab\ndetector configuration, are considered for the data taking period of the LHC to\nstart in 2022 (Run 3) and for the high luminosity LHC. With the Run 3 dataset,\nthe existence of new particles with masses between 10 MeV and 45 GeV could be\nexcluded at 95% confidence level for charges between 0.003e and 0.3e, depending\non their mass. With the high luminosity LHC dataset, the expected limits would\nreach between 10 MeV and 80 GeV for charges between 0.0018e and 0.3e, depending\non their mass\n"}
+{"abstract": "  Recommender systems play a vital role in modern online services, such as\nAmazon and Taobao. Traditional personalized methods, which focus on user-item\n(UI) relations, have been widely applied in industrial settings, owing to their\nefficiency and effectiveness. Despite their success, we argue that these\napproaches ignore local information hidden in similar users. To tackle this\nproblem, user-based methods exploit similar user relations to make\nrecommendations in a local perspective. Nevertheless, traditional user-based\nmethods, like userKNN and matrix factorization, are intractable to be deployed\nin the real-time applications since such transductive models have to be\nrecomputed or retrained with any new interaction. To overcome this challenge,\nwe propose a framework called self-complementary collaborative filtering~(SCCF)\nwhich can make recommendations with both global and local information in real\ntime. On the one hand, it utilizes UI relations and user neighborhood to\ncapture both global and local information. On the other hand, it can identify\nsimilar users for each user in real time by inferring user representations on\nthe fly with an inductive model. The proposed framework can be seamlessly\nincorporated into existing inductive UI approach and benefit from user\nneighborhood with little additional computation. It is also the first attempt\nto apply user-based methods in real-time settings. The effectiveness and\nefficiency of SCCF are demonstrated through extensive offline experiments on\nfour public datasets, as well as a large scale online A/B test in Taobao.\n"}
+{"abstract": "  Spectrum anomaly detection is of great importance in wireless communication\nto secure safety and improve spectrum efficiency. However, spectrum anomaly\ndetection faces many difficulties, especially in unauthorized frequency bands.\nFor example, the composition of unauthorized frequency bands is very complex\nand the abnormal usage patterns are unknown in prior. In this paper, a noise\nattention method is proposed for unsupervised spectrum anomaly detection in\nunauthorized bands. First of all, we theoretically prove that the anomalies in\nunauthorized bands will raise the noise floor of spectrogram after VAE\nreconstruction. Then, we introduce a novel anomaly metric named as noise\nattention score to more effectively capture spectrum anomaly. The effectiveness\nof the proposed method is experimentally verified in 2.4 GHz ISM band.\nLeveraging the noise attention score, the AUC metric of anomaly detection is\nincreased by 0.193. The proposed method is beneficial to reliably detecting\nabnormal spectrum while keeping low false alarm rate.\n"}
+{"abstract": "  We present analytical results for the distribution of first return (FR) times\nof random walks (RWs) on random regular graphs (RRGs) consisting of $N$ nodes\nof degree $c \\ge 3$. Starting from a random initial node $i$ at time $t=0$, at\neach time step $t \\ge 1$ an RW hops into a random neighbor of its previous\nnode. We calculate the distribution $P ( T_{\\rm FR} = t )$ of first return\ntimes to the initial node $i$. We distinguish between first return trajectories\nin which the RW retrocedes its own steps backwards all the way back to the\ninitial node $i$ and those in which the RW returns to $i$ via a path that does\nnot retrocede its own steps. In the retroceding scenario, each edge that\nbelongs to the RW trajectory is crossed the same number of times in the forward\nand backward directions. In the non-retroceding scenario the subgraph that\nconsists of the nodes visited by the RW and the edges it has crossed between\nthese nodes includes at least one cycle. In the limit of $N \\rightarrow \\infty$\nthe RRG converges towards the Bethe lattice. The Bethe lattice exhibits a tree\nstructure, in which all the first return trajectories belong to the retroceding\nscenario. Moreover, in the limit of $N \\rightarrow \\infty$ the trajectories of\nRWs on RRGs are transient in the sense that they return to the initial node\nwith probability $<1$. In this sense they resemble the trajectories of RWs on\nregular lattices of dimensions $d \\ge 3$. The analytical results are found to\nbe in excellent agreement with the results obtained from computer simulations.\n"}
+{"abstract": "  Filter data structures over-approximate a set of hashable keys, i.e. set\nmembership queries may incorrectly come out positive. A filter with false\npositive rate $f \\in (0,1]$ is known to require $\\ge \\log_2(1/f)$ bits per key.\nAt least for larger $f \\ge 2^{-4}$, existing practical filters require a space\noverhead of at least 20% with respect to this information-theoretic bound.\n  We introduce the Ribbon filter: a new filter for static sets with a broad\nrange of configurable space overheads and false positive rates with competitive\nspeed over that range, especially for larger $f \\ge 2^{-7}$. In many cases,\nRibbon is faster than existing filters for the same space overhead, or can\nachieve space overhead below 10% with some additional CPU time. An experimental\nRibbon design with load balancing can even achieve space overheads below 1%.\n  A Ribbon filter resembles an Xor filter modified to maximize locality and is\nconstructed by solving a band-like linear system over Boolean variables. In\nprevious work, Dietzfelbinger and Walzer describe this linear system and an\nefficient Gaussian solver. We present and analyze a faster, more adaptable\nsolving process we call \"Rapid Incremental Boolean Banding ON the fly,\" which\nresembles hash table construction. We also present and analyze an attractive\nRibbon variant based on making the linear system homogeneous, and describe\nseveral more practical enhancements.\n"}
+{"abstract": "  We evolve PyDTNN, a framework for distributed parallel training of Deep\nNeural Networks (DNNs), into an efficient inference tool for convolutional\nneural networks. Our optimization process on multicore ARM processors involves\nseveral high-level transformations of the original framework, such as the\ndevelopment and integration of Cython routines to exploit thread-level\nparallelism; the design and development of micro-kernels for the matrix\nmultiplication, vectorized with ARMs NEON intrinsics, that can accommodate\nlayer fusion; and the appropriate selection of several cache configuration\nparameters tailored to the memory hierarchy of the target ARM processors. Our\nexperiments evaluate both inference throughput (measured in processed images/s)\nand inference latency (i.e., time-to-response) as well as energy consumption\nper image when varying the level of thread parallelism and the processor power\nmodes. The experiments with the new inference engine are reported for the\nResNet50 v1.5 model on the ImageNet dataset from the MLPerf suite using the ARM\nv8.2 cores in the NVIDIA Jetson AGX Xavier board. These results show superior\nperformance compared with the well-spread TFLite from Google and slightly\ninferior results when compared with ArmNN, the native library from ARM for DNN\ninference.\n"}
+{"abstract": "  This paper explores how the current paradigm of vulnerability management\nmight adapt to include machine learning systems through a thought experiment:\nwhat if flaws in machine learning (ML) were assigned Common Vulnerabilities and\nExposures (CVE) identifiers (CVE-IDs)? We consider both ML algorithms and model\nobjects. The hypothetical scenario is structured around exploring the changes\nto the six areas of vulnerability management: discovery, report intake,\nanalysis, coordination, disclosure, and response. While algorithm flaws are\nwell-known in the academic research community, there is no apparent clear line\nof communication between this research community and the operational\ncommunities that deploy and manage systems that use ML. The thought experiments\nidentify some ways in which CVE-IDs may establish some useful lines of\ncommunication between these two communities. In particular, it would start to\nintroduce the research community to operational security concepts, which\nappears to be a gap left by existing efforts.\n"}
+{"abstract": "  In this work we introduce an implementation for which machine learning\ntechniques helped improve the overall performance of an evolutionary algorithm\nfor an optimization problem, namely a variation of robust minimum-cost path in\ngraphs. In this big data optimization problem, a path achieving a good cost in\nmost scenarios from an available set of scenarios (generated by a simulation\nprocess) must be obtained. The most expensive task of our evolutionary\nalgorithm, in terms of computational resources, is the evaluation of candidate\npaths: the fitness function must calculate the cost of the candidate path in\nevery generated scenario. Given the large number of scenarios, this task must\nbe implemented in a distributed environment. We implemented gradient boosting\ndecision trees to classify candidate paths in order to identify good\ncandidates. The cost of the not-so-good candidates is simply forecasted. We\nstudied the training process, gain performance, accuracy, and other variables.\nOur computational experiments show that the computational performance was\nsignificantly improved at the expense of a limited loss of accuracy.\n"}
+{"abstract": "  We present an efficient approach for doing approximate Bayesian inference\nwhen only a limited number of noisy likelihood evaluations can be obtained due\nto computational constraints, which is becoming increasingly common for\napplications of complex models. Our main methodological innovation is to model\nthe log-likelihood function using a Gaussian process (GP) in a local fashion\nand apply this model to emulate the progression that an exact\nMetropolis-Hastings (MH) algorithm would take if it was applicable. New\nlog-likelihood evaluation locations are selected using sequential experimental\ndesign strategies such that each MH accept/reject decision is done within a\npre-specified error tolerance. The resulting approach is conceptually simple\nand sample-efficient as it takes full advantage of the GP model. It is also\nmore robust to violations of GP modelling assumptions and better suited for the\ntypical situation where the posterior is substantially more concentrated than\nthe prior, compared with various existing inference methods based on global GP\nsurrogate modelling. We discuss the probabilistic interpretations and central\ntheoretical aspects of our approach, and we then demonstrate the benefits of\nthe resulting algorithm in the context of likelihood-free inference for\nsimulator-based statistical models.\n"}
+{"abstract": "  Entanglement concurrence has been widely used for featuring entanglement in\nquantum experiments. As an entanglement monotone it is related to specific\nquantum Tsallis entropy. Our goal in this paper is to propose a new\nparameterized bipartite entanglement monotone which is named as $q$-concurrence\ninspired by general Tsallis entropy. We derive an analytical lower bound for\nthe $q$-concurrence of any bipartite quantum entanglement state by employing\npositive partial transposition criterion and realignment criterion, which shows\nan interesting relationship to the strong separability criteria. The new\nentanglement monotone is used to characterize bipartite isotropic states.\nFinally, we provide a computational method to estimate the $q$-concurrence for\nany entanglement by superposing two bipartite pure states. It shows that the\nsuperposition operations can at most increase one ebit for the $q$-concurrence\nin the case that the two states being superposed are bi-orthogonal or one-sided\northogonal. These results reveal a series of new phenomena about the\nentanglement, which may be interesting in quantum communication and quantum\ninformation processing.\n"}
+{"abstract": "  Underwater image enhancement is an important low-level computer vision task\nfor autonomous underwater vehicles and remotely operated vehicles to explore\nand understand the underwater environments. Recently, deep convolutional neural\nnetworks (CNNs) have been successfully used in many computer vision problems,\nand so does underwater image enhancement. There are many deep-learning-based\nmethods with impressive performance for underwater image enhancement, but their\nmemory and model parameter costs are hindrances in practical application. To\naddress this issue, we propose a lightweight adaptive feature fusion network\n(LAFFNet). The model is the encoder-decoder model with multiple adaptive\nfeature fusion (AAF) modules. AAF subsumes multiple branches with different\nkernel sizes to generate multi-scale feature maps. Furthermore, channel\nattention is used to merge these feature maps adaptively. Our method reduces\nthe number of parameters from 2.5M to 0.15M (around 94% reduction) but\noutperforms state-of-the-art algorithms by extensive experiments. Furthermore,\nwe demonstrate our LAFFNet effectively improves high-level vision tasks like\nsalience object detection and single image depth estimation.\n"}
+{"abstract": "  The predominant reason for the damaging power of high-energy radiation is\nmultiple ionization of a molecule, either direct or via the decay of highly\nexcited intermediates, as e.g., in the case of X-ray irradiation. Consequently,\nthe molecule is irreparably damaged by the subsequent fragmentation in a\nCoulomb explosion. In an aqueous environment, however, it has been observed\nthat irradiated molecules may be saved from fragmentation presumably by charge\nand energy dissipation mechanisms. Here, we show that the protective effect of\nthe environment sets in even earlier than hitherto expected, namely immediately\nafter single inner-shell ionization. By combining coincidence measurements of\nthe fragmentation of X-ray-irradiated microsolvated pyrimidine molecules with\ntheoretical calculations, we identify direct intermolecular electronic decay as\nthe protective mechanism, outrunning the usually dominant Auger decay. Our\nresults demonstrate that such processes play a key role in charge\ndelocalization and have to be considered in investigations and models on\nhigh-energy radiation damage in realistic environments.\n"}
+{"abstract": "  In this work we study the metric distortion problem in voting theory under a\nlimited amount of ordinal information. Our primary contribution is threefold.\nFirst, we consider mechanisms which perform a sequence of pairwise comparisons\nbetween candidates. We show that a widely-popular deterministic mechanism\nemployed in most knockout phases yields distortion $\\mathcal{O}(\\log m)$ while\neliciting only $m-1$ out of $\\Theta(m^2)$ possible pairwise comparisons, where\n$m$ represents the number of candidates. Our analysis for this mechanism\nleverages a powerful technical lemma recently developed by Kempe\n\\cite{DBLP:conf/aaai/000120a}. We also provide a matching lower bound on its\ndistortion. In contrast, we prove that any mechanism which performs fewer than\n$m-1$ pairwise comparisons is destined to have unbounded distortion. Moreover,\nwe study the power of deterministic mechanisms under incomplete rankings. Most\nnotably, when every agent provides her $k$-top preferences we show an upper\nbound of $6 m/k + 1$ on the distortion, for any $k \\in \\{1, 2, \\dots, m\\}$.\nThus, we substantially improve over the previous bound of $12 m/k$ recently\nestablished by Kempe \\cite{DBLP:conf/aaai/000120a,DBLP:conf/aaai/000120b}, and\nwe come closer to matching the best-known lower bound. Finally, we are\nconcerned with the sample complexity required to ensure near-optimal distortion\nwith high probability. Our main contribution is to show that a random sample of\n$\\Theta(m/\\epsilon^2)$ voters suffices to guarantee distortion $3 + \\epsilon$\nwith high probability, for any sufficiently small $\\epsilon > 0$. This result\nis based on analyzing the sensitivity of the deterministic mechanism introduced\nby Gkatzelis, Halpern, and Shah \\cite{DBLP:conf/focs/Gkatzelis0020}.\nImportantly, all of our sample-complexity bounds are distribution-independent.\n"}
+{"abstract": "  Satellite images are snapshots of the Earth surface. We propose to forecast\nthem. We frame Earth surface forecasting as the task of predicting satellite\nimagery conditioned on future weather. EarthNet2021 is a large dataset suitable\nfor training deep neural networks on the task. It contains Sentinel 2 satellite\nimagery at 20m resolution, matching topography and mesoscale (1.28km)\nmeteorological variables packaged into 32000 samples. Additionally we frame\nEarthNet2021 as a challenge allowing for model intercomparison. Resulting\nforecasts will greatly improve (>x50) over the spatial resolution found in\nnumerical models. This allows localized impacts from extreme weather to be\npredicted, thus supporting downstream applications such as crop yield\nprediction, forest health assessments or biodiversity monitoring. Find data,\ncode, and how to participate at www.earthnet.tech\n"}
+{"abstract": "  Numerous smart city testbeds and system deployments have surfaced around the\nworld, aiming to provide services over unified large heterogeneous IoT\ninfrastructures. Although we have achieved new scales in smart city\ninstallations and systems, so far the focus has been to provide diverse sources\nof data to smart city services consumers, while neglecting to provide ways to\nsimplify making good use of them. We believe that knowledge creation in smart\ncities through data annotation, supported in both an automated and a\ncrowdsourced manner, is an aspect that will bring additional value to smart\ncities. We present here our approach, aiming to utilize an existing smart city\ndeployment and the OrganiCity software ecosystem. We discuss key challenges\nalong with characteristic use cases, and report on our design and\nimplementation, along with preliminary results.\n"}
+{"abstract": "  Self- and mutually-exciting point processes are popular models in machine\nlearning and statistics for dependent discrete event data. To date, most\nexisting models assume stationary kernels (including the classical Hawkes\nprocesses) and simple parametric models. Modern applications with complex event\ndata require more general point process models that can incorporate contextual\ninformation of the events, called marks, besides the temporal and location\ninformation. Moreover, such applications often require non-stationary models to\ncapture more complex spatio-temporal dependence. To tackle these challenges, a\nkey question is to devise a versatile influence kernel in the point process\nmodel. In this paper, we introduce a novel and general neural network-based\nnon-stationary influence kernel with high expressiveness for handling complex\ndiscrete events data while providing theoretical performance guarantees. We\ndemonstrate the superior performance of our proposed method compared with the\nstate-of-the-art on synthetic and real data.\n"}
+{"abstract": "  The problem for molecular identification knows many solutions which include\nmass spectrometers whose mass sensitivity depends on the performance of the\ndetector involved. The purpose of this article is to show by means of molecular\ndynamics simulations, how a laser-cooled ion cloud, confined in a linear\nradio-frequency trap, can reach the ultimate sensitivity providing the\ndetection of individual charged heavy molecular ions. In our simulations, we\nmodel the laser-cooled Ca + ions as two-level atoms, confined thanks to a set\nof constant and time oscillating electrical fields. A singly-charged molecular\nion with a mass of 10 6 amu is propelled through the ion cloud. The induced\nchange in the fluorescence rate of the lather is used as the detection signal.\nWe show that this signal is due to a significant temperature variation\ntriggered by the Coulombian repulsion and amplified by the radio-frequency\nheating induced by the trap itself. We identify the optimum initial energy for\nthe molecular ion to be detected and furthermore, we characterize the\nperformance of the detector for a large range of confinement voltages.\n"}
+{"abstract": "  In this paper we introduce general transfer operators between high-order and\nlow-order refined finite element spaces that can be used to couple high-order\nand low-order simulations. Under natural restrictions on the low-order refined\nspace we prove that both the high-to-low-order and low-to-high-order linear\nmappings are conservative, constant preserving and high-order accurate. While\nthe proofs apply to affine geometries, numerical experiments indicate that the\nresults hold for more general curved and mixed meshes. These operators also\nhave applications in the context of coarsening solution fields defined on\nmeshes with nonconforming refinement. The transfer operators for $H^1$ finite\nelement spaces require a globally coupled solve, for which robust and efficient\npreconditioners are developed. We present several numerical results confirming\nour analysis and demonstrate the utility of the new mappings in the context of\nadaptive mesh refinement and conservative multi-discretization coupling.\n"}
+{"abstract": "  REBCO tape has remarkable performance for super-conducting technology, but it\nis extremely expensive and it is a strongly anisotropic superconductor. The\ndesign of a 3.5 T collider dipole is presented in which all turns of the body\nwinding are oriented so that the all turns of REBCO tape operate with maximum\ncurrent density. Each turn within the winding contains a stack of Cu-clad REBCO\ntapes; all tapes within the turn are compressed to provide low-resistance Cu-Cu\ncontact, but the successive turns are electrically isolated. Each turn is\noriented so that the local magnetic field at the tapes is closely parallel to\nthe tape surface. Sextupole is controlled using a current-programmed turn to\nprovide excellent homogeneity over a 20:1 dynamic range. Issues of current\nre-distribution during ramping, quench stability, AC losses, synchrotron\nradiation, and cryogenics have been considered, and appear to provide favorable\nproperties for an ultimate-energy hadron collider.\n"}
+{"abstract": "  The paper presents some exact solutions of Bianchi types I, III and\nKantowski-Sachs cosmological models consisting of a dissipative fluid along\nwith an axial magnetic field. A barytropic equation of state between the\nthermodynamic pressure and the matter density, together with a pair of linear\nrelations between the matter density, the shear scalar, and the expansion\nscalar have been assumed for simplicity. The solutions are basically of two\ndifferent types, one for the Bianchi-I and the other for Bianchi-III and\nKantowski-Sachs type. The presence of the magnetic field, however, does not\nchange the fundamental nature of the initial singularity.\n"}
+{"abstract": "  The paper proposes an approach for the efficient model order reduction of\ndynamic contact problems in linear elasticity. Instead of the augmented\nLagrangian method that is widely used for mechanical contact problems, we\nprefer here the Linear Complementarity Programming (LCP) method as basic\nmethodology. It has the advantage of resulting in the much smaller dual problem\nthat is associated with the governing variational principle and that turns out\nto be beneficial for the model order reduction. Since the shape of the contact\nzone depends strongly on the acting outer forces, the LCP for the Lagrange\nmultipliers has to be solved in each time step. The model order reduction\nscheme, on the other hand, is applied to the large linear system for the\ndisplacements and computed in advance by means of an Arnoldi process. In terms\nof computational effort the reduction scheme is very appealing because the\ncontact constraints are fully satisfied while the reduction acts only on the\ndisplacements. As an extension of our approach, we furthermore take up the idea\nof the Craig-Bampton method in order to distinguish between interior nodes and\nthe nodes in the contact zone. A careful performance analysis closes the paper.\n"}
+{"abstract": "  Electrochemical impedance spectroscopy (EIS) is a widely used tool for\ncharacterization of fuel cells and other electrochemical conversion systems.\nWhen applied to the on-line monitoring in the context of in-field applications,\nthe disturbances, drifts and sensor noise may cause severe distortions in the\nevaluated spectra, especially in the low-frequency part. Failure to ignore the\nrandom effects can result in misinterpreted spectra and, consequently, in\nmisleading diagnostic reasoning. This fact has not been often addressed in the\nresearch so far. In this paper, we propose an approach to the quantification of\nthe spectral uncertainty, which relies on evaluating the uncertainty of the\nequivalent circuit model (ECM). We apply the computationally efficient\nvariational Bayes (VB) method and compare the quality of the results with those\nobtained with the Markov chain Monte Carlo (MCMC) algorithm. Namely, MCMC\nalgorithm returns accurate distributions of the estimated model parameters,\nwhile VB approach provides the approximate distributions. By using simulated\nand real data we show that approximate results provided by VB approach,\nalthough slightly over-optimistic, are still close to the more realistic MCMC\nestimates. A great advantage of the VB method for online monitoring is low\ncomputational load, which is several orders of magnitude lower compared to\nMCMC. The performance of VB algorithm is demonstrated on a case of ECM\nparameters estimation in a 6 cell solid oxide fuel cell (SOFC) stack. The\ncomplete numerical implementation for recreating the results can be found at\nhttps://repo.ijs.si/lznidaric/variational-bayes-supplementary-material.\n"}
+{"abstract": "  Reply suggestion models help users process emails and chats faster. Previous\nwork only studies English reply suggestion. Instead, we present MRS, a\nmultilingual reply suggestion dataset with ten languages. MRS can be used to\ncompare two families of models: 1) retrieval models that select the reply from\na fixed set and 2) generation models that produce the reply from scratch.\nTherefore, MRS complements existing cross-lingual generalization benchmarks\nthat focus on classification and sequence labeling tasks. We build a generation\nmodel and a retrieval model as baselines for MRS. The two models have different\nstrengths in the monolingual setting, and they require different strategies to\ngeneralize across languages. MRS is publicly available at\nhttps://github.com/zhangmozhi/mrs.\n"}
+{"abstract": "  We show that universal rogue wave patterns exist in integrable systems. These\nrogue patterns comprise fundamental rogue waves arranged in shapes such as\ntriangle, pentagon and heptagon, with a possible lower-order rogue wave at the\ncenter. These patterns appear when one of the internal parameters in bilinear\nexpressions of rogue waves gets large. Analytically, these patterns are\ndetermined by the root structures of the Yablonskii-Vorob'ev polynomial\nhierarchy through a linear transformation. Thus, the induced rogue patterns in\nthe space-time plane are simply the root structures of Yablonskii-Vorob'ev\npolynomials under actions such as dilation, rotation, stretch, shear and\ntranslation. As examples, these universal rogue patterns are explicitly\ndetermined and graphically illustrated for the generalized derivative nonlinear\nSchr\\\"odinger equations, the Boussinesq equation, and the Manakov system.\nSimilarities and differences between these rogue patterns and those reported\nearlier in the nonlinear Schr\\\"odinger equation are discussed.\n"}
+{"abstract": "  We propose to learn a generative model via entropy interpolation with a\nSchr\\\"{o}dinger Bridge. The generative learning task can be formulated as\ninterpolating between a reference distribution and a target distribution based\non the Kullback-Leibler divergence. At the population level, this entropy\ninterpolation is characterized via an SDE on $[0,1]$ with a time-varying drift\nterm. At the sample level, we derive our Schr\\\"{o}dinger Bridge algorithm by\nplugging the drift term estimated by a deep score estimator and a deep density\nratio estimator into the Euler-Maruyama method. Under some mild smoothness\nassumptions of the target distribution, we prove the consistency of both the\nscore estimator and the density ratio estimator, and then establish the\nconsistency of the proposed Schr\\\"{o}dinger Bridge approach. Our theoretical\nresults guarantee that the distribution learned by our approach converges to\nthe target distribution. Experimental results on multimodal synthetic data and\nbenchmark data support our theoretical findings and indicate that the\ngenerative model via Schr\\\"{o}dinger Bridge is comparable with state-of-the-art\nGANs, suggesting a new formulation of generative learning. We demonstrate its\nusefulness in image interpolation and image inpainting.\n"}
+{"abstract": "  Timely evacuation is crucial to disaster response, as people can avoid\nsuffering and loss of lives when a major disaster happens. With the development\nof sharing economy, ridesharing has the advantage of reducing congestion,\nsaving travel time, and optimizing transportation mode to improve disaster\nevacuation efficiency. The paper proposes to integrate the concept of\nridesharing into evacuation and develops a mixed-integer programming model for\nthis problem. A real-world case study based on Houston is used to validate the\nproposed model. A series of instances are designed to compare the evacuation\nefficiency using two indicators, evacuation percentage and average travel\ndistance. Results reveal that increasing the number of vehicles to help carless\nindividuals might not be the most efficient method in this model. Moreover,\nthis model offers a specific response strategy based on different disaster\nscales, which not only develops a better evacuation plan for the people but\nalso provides relief agencies insights on resource utilization.\n"}
+{"abstract": "  Weakly supervised semantic segmentation is receiving great attention due to\nits low human annotation cost. In this paper, we aim to tackle bounding box\nsupervised semantic segmentation, i.e., training accurate semantic segmentation\nmodels using bounding box annotations as supervision. To this end, we propose\nAffinity Attention Graph Neural Network ($A^2$GNN). Following previous\npractices, we first generate pseudo semantic-aware seeds, which are then formed\ninto semantic graphs based on our newly proposed affinity Convolutional Neural\nNetwork (CNN). Then the built graphs are input to our $A^2$GNN, in which an\naffinity attention layer is designed to acquire the short- and long- distance\ninformation from soft graph edges to accurately propagate semantic labels from\nthe confident seeds to the unlabeled pixels. However, to guarantee the\nprecision of the seeds, we only adopt a limited number of confident pixel seed\nlabels for $A^2$GNN, which may lead to insufficient supervision for training.\nTo alleviate this issue, we further introduce a new loss function and a\nconsistency-checking mechanism to leverage the bounding box constraint, so that\nmore reliable guidance can be included for the model optimization. Experiments\nshow that our approach achieves new state-of-the-art performances on Pascal VOC\n2012 datasets (val: 76.5\\%, test: 75.2\\%). More importantly, our approach can\nbe readily applied to bounding box supervised instance segmentation task or\nother weakly supervised semantic segmentation tasks, with state-of-the-art or\ncomparable performance among almot all weakly supervised tasks on PASCAL VOC or\nCOCO dataset. Our source code will be available at\nhttps://github.com/zbf1991/A2GNN.\n"}
+{"abstract": "  In the PAC-Bayesian literature, the C-Bound refers to an insightful relation\nbetween the risk of a majority vote classifier (under the zero-one loss) and\nthe first two moments of its margin (i.e., the expected margin and the voters'\ndiversity). Until now, learning algorithms developed in this framework minimize\nthe empirical version of the C-Bound, instead of explicit PAC-Bayesian\ngeneralization bounds. In this paper, by directly optimizing PAC-Bayesian\nguarantees on the C-Bound, we derive self-bounding majority vote learning\nalgorithms. Moreover, our algorithms based on gradient descent are scalable and\nlead to accurate predictors paired with non-vacuous guarantees.\n"}
+{"abstract": "  We report spectropolarimetric observations of a supersonic downflow impacting\nthe lower atmosphere within a large sunspot umbra. This work is an extension of\nSchad et al. 2016 using observations acquired in the He I 10830 Angstrom\ntriplet by the Facility Infrared Spectropolarimeter. Downflowing material\naccelerating along a cooled coronal loop reaches peak speeds near 200 km\ns$^{-1}$ and exhibits both high speed emission and absorption within the umbra,\nwhich we determine to be a consequence of the strong height dependence of the\nradiatively-controlled source function above the sunspot umbra. Strong emission\nprofiles close to the rest wavelengths but with long red-shifted tails are also\nobserved at the downflow terminus. From the polarized spectra, we infer\nlongitudinal magnetic field strengths of ${\\sim}2.4$ kG in the core portion of\nthe He I strong emission, which we believe is the strongest ever reported in\nthis line. Photospheric field strengths along the same line-of-sight are\n${\\sim}2.8$ kG as inferred using the Ca I 10839 Angstrom spectral line. The\ntemperatures of the highest speed He I absorption and the near rest emission\nare similar (${\\sim}$10 kK), while a differential emission measure analysis\nusing SDO/AIA data indicates significant increases in radiative cooling for\ntemperatures between $\\sim$0.5 and 1 MK plasma associated with the downflow\nterminus. Combined we interpret these observations in the context of a strong\nradiative shock induced by the supersonic downflow impacting the low sunspot\natmosphere.\n"}
+{"abstract": "  It has been recently discovered that some random processes may satisfy limit\ntheorems even though they exhibit intermittency, namely an unusual growth of\nmoments. In this paper we provide a deeper understanding of these intricate\nlimiting phenomena. We show that intermittent processes may exhibit a\nmultiscale behavior involving growth at different rates. To these rates\ncorrespond different scales. In addition to a dominant scale, intermittent\nprocesses may exhibit secondary scales. The probability of these scales\ndecreases to zero as a power function of time. For the analysis, we consider\nlarge deviations of the rate of growth of the processes. Our approach is quite\ngeneral and covers different possible scenarios with special focus on the\nso-called supOU processes.\n"}
+{"abstract": "  We report, for the first time, the long-awaited detection of diffuse gamma\nrays with energies between 100 TeV and 1 PeV in the Galactic disk.\nParticularly, all gamma rays above 398 TeV are observed apart from known TeV\ngamma-ray sources and compatible with expectations from the hadronic emission\nscenario in which gamma rays originate from the decay of $\\pi^0$'s produced\nthrough the interaction of protons with the interstellar medium in the Galaxy.\nThis is strong evidence that cosmic rays are accelerated beyond PeV energies in\nour Galaxy and spread over the Galactic disk.\n"}
+{"abstract": "  We study the evolution of step bunches on vicinal surfaces using a\nthermodynamically consistent step-flow model that (i) circumvents the\nquasistatic approximation that prevails in the literature by accounting for the\ndynamics of adatom diffusion on terraces and attachment-detachment at steps\n(referred to as the dynamical effect), and (ii) generalizes the expression of\nthe step chemical potential by incorporating the necessary coupling between the\ndiffusion fields on adjacent terraces (referred to as the chemical effect).\nHaving previously shown that these effects can explain the onset of step\nbunching without recourse to the inverse Ehrlich-Schwoebel (iES) barrier or\nother extraneous mechanisms, we are here interested in the evolution of step\nbunches beyond the linear-stability regime. In particular, the numerical\nresolution of the step-flow problem yields a robust power-law coarsening of the\nsurface profile, with the bunch height growing in time as $H\\sim t^{1/2}$ and\nthe minimal interstep distance as a function of the number of steps in the\nbunch cell obeying $\\ell_{min}\\sim N^{-2/3}$. Although these exponents have\npreviously been reported, this is the first time such scaling laws are obtained\nin the absence of an iES barrier or adatom electromigration. In order to\nvalidate our simulations, we take the continuum limit of the discrete step-flow\nsystem, leading to a novel nonlinear evolution equation for the surface height.\nWe investigate the existence of self-similar solutions of this equation and\nconfirm the 1/2 coarsening exponent obtained numerically for $H$. We highlight\nthe influence of the combined dynamical-chemical effect and show that it can be\ninterpreted as an effective iES barrier in the standard BCF theory. Finally, we\nuse a Pad\\'e approximant to derive an analytical expression for the velocity of\nsteadily moving step bunches and compare it to numerical simulations.\n"}
+{"abstract": "  We present temporally abstract actor-critic (TAAC), a simple but effective\noff-policy RL algorithm that incorporates closed-loop temporal abstraction into\nthe actor-critic framework. TAAC adds a second-stage binary policy to choose\nbetween the previous action and a new action output by an actor. Crucially, its\n\"act-or-repeat\" decision hinges on the actually sampled action instead of the\nexpected behavior of the actor. This post-acting switching scheme let the\noverall policy make more informed decisions. TAAC has two important features:\na) persistent exploration, and b) a new compare-through Q operator for\nmulti-step TD backup, specially tailored to the action repetition scenario. We\ndemonstrate TAAC's advantages over several strong baselines across 14\ncontinuous control tasks. Our surprising finding reveals that while achieving\ntop performance, TAAC is able to \"mine\" a significant number of repeated\nactions with the trained policy even on continuous tasks whose problem\nstructures on the surface seem to repel action repetition. This suggests that\naside from encouraging persistent exploration, action repetition can find its\nplace in a good policy behavior. Code is available at\nhttps://github.com/hnyu/taac.\n"}
+{"abstract": "  Scene classification, aiming at classifying a scene image to one of the\npredefined scene categories by comprehending the entire image, is a\nlongstanding, fundamental and challenging problem in computer vision. The rise\nof large-scale datasets, which constitute the corresponding dense sampling of\ndiverse real-world scenes, and the renaissance of deep learning techniques,\nwhich learn powerful feature representations directly from big raw data, have\nbeen bringing remarkable progress in the field of scene representation and\nclassification. To help researchers master needed advances in this field, the\ngoal of this paper is to provide a comprehensive survey of recent achievements\nin scene classification using deep learning. More than 200 major publications\nare included in this survey covering different aspects of scene classification,\nincluding challenges, benchmark datasets, taxonomy, and quantitative\nperformance comparisons of the reviewed methods. In retrospect of what has been\nachieved so far, this paper is also concluded with a list of promising research\nopportunities.\n"}
+{"abstract": "  We present analytic threshold formulae applicable to both dispersive\n(time-domain) and diffractive (pattern-forming) instabilities in Fabry-Perot\nKerr cavities of arbitrary finesse. We do so by extending the gain-circle\ntechnique, recently developed for counter-propagating fields in\nsingle-mirror-feedback systems, to allow for an input mirror. In time-domain\ncounter-propagating systems walk-off effects are known to suppress cross-phase\nmodulation contributions to dispersive instabilities. Applying the gain-circle\napproach with appropriately-adjusted cross-phase couplings extends previous\nresults to arbitrary finesse, beyond mean-field approximations, and describes\nIkeda instabilities.\n"}
+{"abstract": "  We report intertwined Weyl phases, which come from superposing topological\nphases by crystalline symmetry. In the intertwined Weyl phases, an\nunconventional Weyl phase where Weyl points possess a higher charge (monopole\ncharge>1) due to rotation symmetry, and a higher-order topological phase\nenforced by rotation symmetry, are superposed. The two phases are no longer\nseparable, but intertwine with each other, resulting in the novel phase.\nRemarkably, the intertwining leads to a prominent characteristic feature of the\nintertwined Weyl phases: $\\textit{the change of Fermi-arc topology}$ in a\nperiodic pattern, i.e., the way how Fermi arcs connect to Weyl points changes\ndrastically with respect to surface orientation, which exhibits a periodic\npattern. Such a phenomenon is absent in any individual phase alone. Moreover,\nwe elaborate on how to emulate the intertwined double-Weyl phase in cold atoms.\nOur theory is quite promising for generating new topological phases based on\nexisting ones.\n"}
+{"abstract": "  We consider a one-dimensional chain of N equidistantly spaced noninteracting\nqubits embedded in an open waveguide. In the frame of single-excitation\nsubspace, we systematically study the evolution of qubits amplitudes if the\nonly qubit in the chain was initially excited. We show that the temporal\ndynamics of qubits amplitudes crucially depend on the value of kd, where k is\nthe wave vector, d is a distance between neighbor qubits. If kd is equal to an\ninteger multiple of $\\pi$, then the qubits are excited to a stationary level\nwhich scales as SN^{-1}S. We show that in this case, it is the dark states\nwhich prevent qubits from decaying to zero even though they do not contribute\nto the output spectrum of photon emission. For other values of kd the\nexcitations of qubits have the form of damping oscillations, which represent\nthe vacuum Rabi oscillations in a multi-qubit system. In this case, the output\nspectrum of photon radiation is defined by a subradiant state with the smallest\nwidth.\n"}
+{"abstract": "  We present a catalog of eclipsing binaries in the northern Galactic Plane\nfrom the Kiso Wide-Field Camera Intensive Survey of the Galactic Plane\n(KISOGP). We visually identified 7055 eclipsing binaries spread across\n$\\sim$330 square degrees, including 4197 W Ursa Majoris/EW-, 1458 $\\beta$\nLyrae/EB-, and 1400 Algol/EA-type eclipsing binaries. For all systems, $I$-band\nlight curves were used to obtain accurate system parameters. We derived the\ndistances and extinction values for the EW-type objects from their\nperiod--luminosity relation. We also obtained the structure of the thin disk\nfrom the distribution of our sample of eclipsing binary systems, combined with\nthose of high-mass star-forming regions and Cepheid tracers. We found that the\nthin disk is inhomogeneous in number density as a function of Galactic\nlongitude. Using this new set of distance tracers, we constrain the detailed\nstructure of the thin disk. Finally, we report a global parallax zero-point\noffset of $ \\Delta \\pi=-42.1\\pm1.9\\mbox{(stat.)}\\pm12.9\\mbox{(syst.)}$ $\\mu$as\nbetween our carefully calibrated EW-type eclipsing binary positions and those\nprovided by Gaia Early Data Release 3. Implementation of the officially\nrecommended parallax zero-point correction results in a significantly reduced\noffset. Additionally, we provide a photometric characterization of our EW-type\neclipsing binaries that can be applied to further analyses.\n"}
+{"abstract": "  Taxonomies are an important ingredient of knowledge organization, and serve\nas a backbone for more sophisticated knowledge representations in intelligent\nsystems, such as formal ontologies. However, building taxonomies manually is a\ncostly endeavor, and hence, automatic methods for taxonomy induction are a good\nalternative to build large-scale taxonomies. In this paper, we propose TIEmb,\nan approach for automatic unsupervised class subsumption axiom extraction from\nknowledge bases using entity and text embeddings. We apply the approach on the\nWebIsA database, a database of subsumption relations extracted from the large\nportion of the World Wide Web, to extract class hierarchies in the Person and\nPlace domain.\n"}
+{"abstract": "  Crystalline defects can modify quantum interactions in solids, causing\nunintuitive, even favourable, properties such as quantum Hall effect or\nsuperconducting vortex pinning. Here we present another example of this notion\n- an unexpected unidirectional Kondo scattering in single crystals of 2H-NbS2.\nThis manifests as a pronounced low-temperature enhancement in the out-of-plane\nresistivity and thermopower below 40 K, hidden for the in-plane charge\ntransport. The anomaly can be suppressed by the c-axis-oriented magnetic field,\nbut is unaffected by field applied along the planes. The magnetic moments\noriginate from layers of 1T-NbS2, which inevitably form during the growth,\nundergoing a charge-density-wave reconstruction with each superlattice cell\n(David-star-shaped cluster of Nb atoms) hosting a localised spin. Our results\ndemonstrate the unique and highly anisotropic response of a spontaneously\nformed Kondo lattice heterostructure, intercalated in a layered conductor.\n"}
+{"abstract": "  Outflows from super-massive black holes (SMBHs) play an important role in the\nco-evolution of themselves, their host galaxies, and the larger scale\nenvironments. Such outflows are often characterized by emission and absorption\nlines in various bands and in a wide velocity range blueshifted from the\nsystematic redshift of the host quasar. In this paper, we report a strong broad\nline region (BLR) outflow from the z~4.7 quasar BR 1202-0725 based on the\nhigh-resolution optical spectrum taken with the Magellan Inamori Kyocera\nEchelle (MIKE) spectrograph installed on the 6.5m Magellan/Clay telescope,\nobtained from the `Probing the He II re-Ionization ERa via Absorbing C IV\nHistorical Yield' (HIERACHY) project. This rest-frame ultraviolet (UV) spectrum\nis characterized by a few significantly blueshifted broad emission lines from\nhigh ions; the most significant one is the C IV line at a velocity of -6500\nkm/s relative to the H{\\alpha} emission line, which is among the highest\nvelocity BLR outflows in observed quasars at z > 4. The measured properties of\nUV emission lines from different ions, except for O I and Ly{\\alpha}, also\nfollow a clear trend that higher ions tend to be broader and outflow at higher\naverage velocities. There are multiple C IV and Si IV absorbing components\nidentified on the blue wings of the corresponding emission lines, which may be\nproduced by either the outflow or the intervening absorbers.\n"}
+{"abstract": "  Quantum nonlocality, one of the most important features of quantum mechanics,\nis normally connected in experiments with the violation of Bell-Clauser-Horne\n(Bell-CH) inequalities. We propose effective methods for the rearrangement and\nlinear inequality to prove a large variety of Bell-CH inequalities. We also\nderive a set of Bell-CH inequalities by using these methods which can be\nviolated in some quantum entangled states.\n"}
+{"abstract": "  Some differential implications of classical Marx-Strohh\\\"acker theorem are\nextended for multivalent functions. These results are also generalized for\nfunctions with fixed second coefficient by using the theory of first order\ndifferential subordination which in turn, corrects the results of Selvaraj and\nStelin [On multivalent functions associated with fixed second coefficient and\nthe principle of subordination, Int. J. Math. Anal. {\\bf 9} (2015), no.~18,\n883--895].\n"}
+{"abstract": "  Due to the limited smartness and abilities of machine intelligence, currently\nautonomous vehicles are still unable to handle all kinds of situations and\ncompletely replace drivers. Because humans exhibit strong robustness and\nadaptability in complex driving scenarios, it is of great importance to\nintroduce humans into the training loop of artificial intelligence, leveraging\nhuman intelligence to further advance machine learning algorithms. In this\nstudy, a real-time human-guidance-based deep reinforcement learning (Hug-DRL)\nmethod is developed for policy training of autonomous driving. Leveraging a\nnewly designed control transfer mechanism between human and automation, human\nis able to intervene and correct the agent's unreasonable actions in real time\nwhen necessary during the model training process. Based on this\nhuman-in-the-loop guidance mechanism, an improved actor-critic architecture\nwith modified policy and value networks is developed. The fast convergence of\nthe proposed Hug-DRL allows real-time human guidance actions to be fused into\nthe agent's training loop, further improving the efficiency and performance of\ndeep reinforcement learning. The developed method is validated by\nhuman-in-the-loop experiments with 40 subjects and compared with other\nstate-of-the-art learning approaches. The results suggest that the proposed\nmethod can effectively enhance the training efficiency and performance of the\ndeep reinforcement learning algorithm under human guidance, without imposing\nspecific requirements on participant expertise and experience.\n"}
+{"abstract": "  For one 3-body and two 5-body planar choreographies on the same algebraic\nLemniscate by Bernoulli we found explicitly a maximal possible set of\n(particular) Liouville integrals, 7 and 15, respectively, (including the total\nangular momentum), which Poisson commute with the corresponding Hamiltonian\nalong the trajectory. Thus, these choreographies are particularly maximally\nsuperintegrable. It is conjectured that the total number of (particular)\nLiouville integrals is maximal possible for any odd number of bodies $(2n+1)$\nmoving choreographically (without collisions) along given algebraic Lemniscate,\nthus, the corresponding trajectory is particularly, maximally superintegrable.\nSome of these Liouville integrals are presented explicitly. The limit $n \\rar\n\\infty$ is studied: it is predicted that one-dimensional liquid with\nnearest-neighbor interactions occurs, it moves along algebraic Lemniscate and\nit is characterized by infinitely-many constants of motion.\n"}
+{"abstract": "  Human resource management technologies have moved from biometric surveillance\nto emotional artificial intelligence (AI) that monitor employees' engagement\nand productivity, analyze video interviews and CVs of job applicants. The rise\nof the US$20 billion emotional AI industry will transform the future workplace.\nYet, besides no international consensus on the principles or standards for such\ntechnologies, there is a lack of cross-cultural research on future job seekers'\nattitude toward such use of AI technologies. This study collects a\ncross-sectional dataset of 1,015 survey responses of international students\nfrom 48 countries and 8 regions worldwide. A majority of the respondents (52%)\nare concerned about being managed by AI. Following the hypothetico-deductivist\nphilosophy of science, we use the MCMC Hamiltonian approach and conduct a\ndetailed comparison of 10 Bayesian network models with the PSIS-LOO method. We\nconsistently find having a higher income, being male, majoring in business,\nand/or self-rated familiarity with AI correlate with a more positive view of\nemotional AI in the workplace. There is also a stark cross-cultural and\ncross-regional difference. Our analysis shows people from economically less\ndeveloped regions (Africa, Oceania, Central Asia) tend to exhibit less concern\nfor AI managers. And for East Asian countries, 64% of the Japanese, 56% of the\nSouth Korean, and 42% of the Chinese professed the trusting attitude. In\ncontrast, an overwhelming majority of 75% of the European and Northern American\npossesses the worrying/neutral attitude toward being managed by AI. Regarding\nreligion, Muslim students correlate with the most concern toward emotional AI\nin the workplace. When religiosity is higher, the correlation becomes stronger\nfor Muslim and Buddhist students.\n"}
+{"abstract": "  Earlier studies using extreme-ultraviolet images and line-of-sight\nmagnetograms from the Solar Dynamics Observatory (SDO) have suggested that\nactive region (AR) plages and strong network concentrations often have small,\nlooplike features embedded within them, even though no minority-polarity flux\nis visible in the corresponding magnetograms. Because of the unexpected nature\nof these findings, we have searched the SDO database for examples of inverted-Y\nstructures rooted inside \"unipolar\" plages, with such jetlike structures being\ninterpreted as evidence for magnetic reconnection between small bipoles and the\ndominant-polarity field. Several illustrative cases are presented from the\nperiod 2013--2015, all of which are associated with transient outflows from AR\n\"moss.\" The triangular or dome-shaped bases have horizontal dimensions of\n$\\sim$2--4 Mm, corresponding to $\\sim$1--3 granular diameters. We also note\nthat the spongy-textured Fe IX 17.1 nm moss is not confined to plages, but may\nextend into regions where the photospheric field is relatively weak or even has\nmixed polarity. We again find a tendency for bright coronal loops seen in the\n17.1, 19.3, and 21.1 nm passbands to show looplike fine structure and compact\nbrightenings at their footpoints. These observations provide further\nconfirmation that present-day magnetograms are significantly underrepresenting\nthe amount of minority-polarity flux inside AR plages and again suggest that\nfootpoint reconnection and small-scale flux cancellation may play a major role\nin coronal heating, both inside and outside ARs.\n"}
+{"abstract": "  The Artin stack $\\mathcal M_n$ of $0$-dimensional sheaves of length $n$ on\n$\\mathbb A^3$ carries two natural d-critical structures in the sense of Joyce.\nOne comes from its description as a quotient stack\n$[\\textrm{crit}(f_n)/\\textrm{GL}_n]$, another comes from derived deformation\ntheory of sheaves. We show that these d-critical structures agree. We use this\nresult to prove the analogous statement for the Quot scheme of points\n$\\textrm{Quot}_{\\mathbb A^3}(\\mathscr O^{\\oplus r},n) =\n\\textrm{crit}(f_{r,n})$, which is a global critical locus for every $r>0$, and\nalso carries a derived-in-flavour d-critical structure besides the one induced\nby the potential $f_{r,n}$. Again, we show these two d-critical structures\nagree. Moreover, we prove that they locally model the d-critical structure on\n$\\textrm{Quot}_X(F,n)$, where $F$ is a locally free sheaf of rank $r$ on a\nprojective Calabi-Yau $3$-fold $X$.\n  Finally, we prove that the perfect obstruction theory on\n$\\textrm{Hilb}^n\\mathbb A^3=\\textrm{crit}(f_{1,n})$ induced by the Atiyah class\nof the universal ideal agrees with the \\emph{critical} obstruction theory\ninduced by the Hessian of the potential $f_{1,n}$.\n"}
+{"abstract": "  The amount of biomedical data continues to grow rapidly. However, the ability\nto analyze these data is limited due to privacy and regulatory concerns.\nMachine learning approaches that require data to be copied to a single location\nare hampered by the challenges of data sharing. Federated Learning is a\npromising approach to learn a joint model over data silos. This architecture\ndoes not share any subject data across sites, only aggregated parameters, often\nin encrypted environments, thus satisfying privacy and regulatory requirements.\nHere, we describe our Federated Learning architecture and training policies. We\ndemonstrate our approach on a brain age prediction model on structural MRI\nscans distributed across multiple sites with diverse amounts of data and\nsubject (age) distributions. In these heterogeneous environments, our\nSemi-Synchronous protocol provides faster convergence.\n"}
+{"abstract": "  Analog electronic and optical computing exhibit tremendous advantages over\ndigital computing for accelerating deep learning when operations are executed\nat low precision. In this work, we derive a relationship between analog\nprecision, which is limited by noise, and digital bit precision. We propose\nextending analog computing architectures to support varying levels of precision\nby repeating operations and averaging the result, decreasing the impact of\nnoise. Such architectures enable programmable tradeoffs between precision and\nother desirable performance metrics such as energy efficiency or throughput. To\nutilize dynamic precision, we propose a method for learning the precision of\neach layer of a pre-trained model without retraining network weights. We\nevaluate this method on analog architectures subject to a variety of noise\nsources such as shot noise, thermal noise, and weight noise and find that\nemploying dynamic precision reduces energy consumption by up to 89% for\ncomputer vision models such as Resnet50 and by 24% for natural language\nprocessing models such as BERT. In one example, we apply dynamic precision to a\nshot-noise limited homodyne optical neural network and simulate inference at an\noptical energy consumption of 2.7 aJ/MAC for Resnet50 and 1.6 aJ/MAC for BERT\nwith <2% accuracy degradation.\n"}
+{"abstract": "  This paper presents the findings about the impact of heat waves on a real\nurban distribution system. A data-driven methodology is proposed to simulate\nthe portion of faults that can be associated to normal conditions (and hence to\nreliability) and the portion correlated to the heat wave occurrence. Based on\nreal data collected in the years 2012-2017, the fault rates associated to\nreliability and resilience have been calculated and then used to feed a Monte\nCarlo simulation aiming to manage the uncertainty in the fault occurrence.\nFinally, based on the Italian legislation, the benefits deriving by the\nsubstitution of the faulted portion of the system have been calculated.\n"}
+{"abstract": "  In this note, we prove the Bogomolov's inequality over a reduced, compact,\nirreducible, K\\\"ahler complex space that is smooth in codimension 2. The proof\nis obtained by a reduction to the smooth case, using Hironaka's resolution of\nsingularities.\n"}
+{"abstract": "  The convergence of simultaneous and marginal predictive classifiers under\npartition exchangeability in supervised classification is obtained. The result\nshows the asymptotic convergence of these classifiers under infinite amount of\ntraining or test data, such that after observing umpteen amount of data, the\ndifferences between these classifiers would be negligible. This is an important\nresult from the practical perspective as under the presence of sufficiently\nlarge amount of data, one can replace the simpler marginal classifier with\ncomputationally more expensive simultaneous one.\n"}
+{"abstract": "  Based on results by recent surveys, the number of bright quasars at redshifts\nz>3 is being constantly revised upwards. Current consensus is that at bright\nmagnitudes ($M_{1450}\\le -27$) the number densities of such sources could have\nbeen underestimated by a factor of 30-40%. In the framework of the QUBRICS\nsurvey, we identified 58 bright QSOs at 3.6$\\le z \\le $4.2, with magnitudes\n$i_{psf}\\le$18, in an area of 12400$deg^{2}$. The uniqueness of our survey is\nunderlined by the fact that it allows us, for the first time, to extend the\nsampled absolute magnitude range up to $M_{1450}= -29.5$. We derived a\nbright-end slope of $\\beta=-4.025$ and a space density at $<M_{1450}>=-28.75$\nof 2.61$\\times 10^{-10} Mpc^{-3}$ comoving, after taking into account the\nestimated incompleteness of our observations. Taking into account the results\nof fainter surveys, AGN brighter than $M_{1450}=-23$ could produce at least\nhalf of the ionizing emissivity at z$\\sim$4. Considering a mean escape fraction\nof 0.7 for the QSO and AGN population, combined with a mean free path of 41.3\nproper Mpc at $z=3.9$, we derive a photoionization rate of $Log(\\Gamma\n[s^{-1}])=-12.17^{+0.13}_{-0.07}$, produced by AGN at M$_{1450}<-18$, i.e.\n~100% of the measured ionizing background at z~4.\n"}
+{"abstract": "  One of the main obstacles to 3D semantic segmentation is the significant\namount of endeavor required to generate expensive point-wise annotations for\nfully supervised training. To alleviate manual efforts, we propose GIDSeg, a\nnovel approach that can simultaneously learn segmentation from sparse\nannotations via reasoning global-regional structures and individual-vicinal\nproperties. GIDSeg depicts global- and individual- relation via a dynamic edge\nconvolution network coupled with a kernelized identity descriptor. The ensemble\neffects are obtained by endowing a fine-grained receptive field to a\nlow-resolution voxelized map. In our GIDSeg, an adversarial learning module is\nalso designed to further enhance the conditional constraint of identity\ndescriptors within the joint feature distribution. Despite the apparent\nsimplicity, our proposed approach achieves superior performance over\nstate-of-the-art for inferencing 3D dense segmentation with only sparse\nannotations. Particularly, with $5\\%$ annotations of raw data, GIDSeg\noutperforms other 3D segmentation methods.\n"}
+{"abstract": "  Jets constructed via clustering algorithms (e.g., anti-$k_T$, soft-drop) have\nbeen proposed for many precision measurements, such as the strong coupling\n$\\alpha_s$ and the nucleon intrinsic dynamics. However, the theoretical\naccuracy is affected by missing QCD corrections at higher orders for the jet\nfunctions in the associated factorization theorems. Their calculation is\ncomplicated by the jet clustering procedure. In this work, we propose a method\nto evaluate jet functions at higher orders in QCD. The calculation involves the\nphase space sector decomposition with suitable soft subtractions. As a concrete\nexample, we present the quark-jet function using the anti-$k_T$ algorithm with\nE-scheme recombination at next-to-next-to-leading order.\n"}
+{"abstract": "  The Gamow shell model has shown to efficiently describe weakly bound and\nunbound nuclear systems, as internucleon correlations and continuum coupling\nare both taken into account in this model. In the present work, we study\nneutron-dripline oxygen isotopes. It is hereby demonstrated that the presence\nof continuum coupling is important for the description of oxygen isotopes at\ndripline, and especially to assess the eventual bound or unbound character of\n$^{28}$O. Our results suggest that the ground state of $^{28}$O is weakly\nunbound and is similar to the narrow resonant $^{26}$O ground state.\nPredictions of weakly bound and resonance excited states in $^{24\\text-26}$O\nare also provided. The asymptotes of the studied many-body states are analyzed\nvia one-body densities, whereby the different radial properties of well bound,\nloosely bound, resonance states are clearly depicted.\n"}
+{"abstract": "  In the field of human-robot interaction, teaching learning agents from human\ndemonstrations via supervised learning has been widely studied and successfully\napplied to multiple domains such as self-driving cars and robot manipulation.\nHowever, the majority of the work on learning from human demonstrations\nutilizes only behavioral information from the demonstrator, i.e. what actions\nwere taken, and ignores other useful information. In particular, eye gaze\ninformation can give valuable insight towards where the demonstrator is\nallocating their visual attention, and leveraging such information has the\npotential to improve agent performance. Previous approaches have only studied\nthe utilization of attention in simple, synchronous environments, limiting\ntheir applicability to real-world domains. This work proposes a novel imitation\nlearning architecture to learn concurrently from human action demonstration and\neye tracking data to solve tasks where human gaze information provides\nimportant context. The proposed method is applied to a visual navigation task,\nin which an unmanned quadrotor is trained to search for and navigate to a\ntarget vehicle in a real-world, photorealistic simulated environment. When\ncompared to a baseline imitation learning architecture, results show that the\nproposed gaze augmented imitation learning model is able to learn policies that\nachieve significantly higher task completion rates, with more efficient paths,\nwhile simultaneously learning to predict human visual attention. This research\naims to highlight the importance of multimodal learning of visual attention\ninformation from additional human input modalities and encourages the community\nto adopt them when training agents from human demonstrations to perform\nvisuomotor tasks.\n"}
+{"abstract": "  Adaptive control approaches yield high-performance controllers when a precise\nsystem model or suitable parametrizations of the controller are available.\nExisting data-driven approaches for adaptive control mostly augment standard\nmodel-based methods with additional information about uncertainties in the\ndynamics or about disturbances. In this work, we propose a purely data-driven,\nmodel-free approach for adaptive control. Tuning low-level controllers based\nsolely on system data raises concerns on the underlying algorithm safety and\ncomputational performance. Thus, our approach builds on GoOSE, an algorithm for\nsafe and sample-efficient Bayesian optimization. We introduce several\ncomputational and algorithmic modifications in GoOSE that enable its practical\nuse on a rotational motion system. We numerically demonstrate for several types\nof disturbances that our approach is sample efficient, outperforms constrained\nBayesian optimization in terms of safety, and achieves the performance optima\ncomputed by grid evaluation. We further demonstrate the proposed adaptive\ncontrol approach experimentally on a rotational motion system.\n"}
+{"abstract": "  This paper proposes two important contributions for conditional Generative\nAdversarial Networks (cGANs) to improve the wide variety of applications that\nexploit this architecture. The first main contribution is an analysis of cGANs\nto show that they are not explicitly conditional. In particular, it will be\nshown that the discriminator and subsequently the cGAN does not automatically\nlearn the conditionality between inputs. The second contribution is a new\nmethod, called a contrario cGAN, that explicitly models conditionality for both\nparts of the adversarial architecture via a novel a contrario loss that\ninvolves training the discriminator to learn unconditional (adverse) examples.\nThis leads to a novel type of data augmentation approach for GANs (a contrario\nlearning) which allows to restrict the search space of the generator to\nconditional outputs using adverse examples. Extensive experimentation is\ncarried out to evaluate the conditionality of the discriminator by proposing a\nprobability distribution analysis. Comparisons with the cGAN architecture for\ndifferent applications show significant improvements in performance on well\nknown datasets including, semantic image synthesis, image segmentation,\nmonocular depth prediction and \"single label\"-to-image using different metrics\nincluding Fr\\'echet Inception Distance (FID), mean Intersection over Union\n(mIoU), Root Mean Square Error log (RMSE log) and Number of\nstatistically-Different Bins (NDB).\n"}
+{"abstract": "  We study how the typical gradient and typical height of a random surface are\nmodified by the addition of quenched disorder in the form of a random\nindependent external field. The results provide quantitative estimates, sharp\nup to multiplicative constants, in the following cases.\n  It is shown that for real-valued disordered random surfaces of the $\\nabla\n\\phi$ type with a uniformly convex interaction potential: (i) The gradient of\nthe surface delocalizes in dimensions $1\\le d\\le 2$ and localizes in dimensions\n$d\\ge3$. (ii) The surface delocalizes in dimensions $1\\le d\\le 4$ and localizes\nin dimensions $d\\ge 5$.\n  It is further shown that for the integer-valued disordered Gaussian free\nfield: (i) The gradient of the surface delocalizes in dimensions $d=1,2$ and\nlocalizes in dimensions $d\\ge3$. (ii) The surface delocalizes in dimensions\n$d=1,2$. (iii) The surface localizes in dimensions $d\\ge 3$ at weak disorder\nstrength. The behavior in dimensions $d\\ge 3$ at strong disorder is left open.\n  The proofs rely on several tools: explicit identities satisfied by the\nexpectation of the random surface, the Efron--Stein concentration inequality, a\ncoupling argument for Langevin dynamics (originally due to Funaki and Spohn)\nand the Nash--Aronson estimate.\n"}
+{"abstract": "  Deep Learning has become a very valuable tool in different fields, and no one\ndoubts the learning capacity of these models. Nevertheless, since Deep Learning\nmodels are often seen as black boxes due to their lack of interpretability,\nthere is a general mistrust in their decision-making process. To find a balance\nbetween effectiveness and interpretability, Explainable Artificial Intelligence\n(XAI) is gaining popularity in recent years, and some of the methods within\nthis area are used to generate counterfactual explanations. The process of\ngenerating these explanations generally consists of solving an optimization\nproblem for each input to be explained, which is unfeasible when real-time\nfeedback is needed. To speed up this process, some methods have made use of\nautoencoders to generate instant counterfactual explanations. Recently, a\nmethod called Deep Guided Counterfactual Explanations (DGCEx) has been\nproposed, which trains an autoencoder attached to a classification model, in\norder to generate straightforward counterfactual explanations. However, this\nmethod does not ensure that the generated counterfactual instances are close to\nthe data manifold, so unrealistic counterfactual instances may be generated. To\novercome this issue, this paper presents Distribution Aware Deep Guided\nCounterfactual Explanations (DA-DGCEx), which adds a term to the DGCEx cost\nfunction that penalizes out of distribution counterfactual instances.\n"}
+{"abstract": "  The one-dimensional compressible Navier-Stokes-Vlasov-Fokker-Planck system\nwith density-dependent viscosity and drag force coefficients is investigated in\nthe present paper. The existence, uniqueness, and regularity of global weak\nsolution to the initial value problem for general initial data are established\nin spatial periodic domain. Moreover, the long time behavior of the weak\nsolution is analyzed. It is shown that as the time grows, the distribution\nfunction of the particles converges to the global Maxwellian, and both the\nfluid velocity and the macroscopic velocity of the particles converge to the\nsame speed.\n"}
+{"abstract": "  Approximate inference in Bayesian deep networks exhibits a dilemma of how to\nyield high fidelity posterior approximations while maintaining computational\nefficiency and scalability. We tackle this challenge by introducing a novel\nvariational structured approximation inspired by the Bayesian interpretation of\nDropout regularization. Concretely, we focus on the inflexibility of the\nfactorized structure in Dropout posterior and then propose an improved method\ncalled Variational Structured Dropout (VSD). VSD employs an orthogonal\ntransformation to learn a structured representation on the variational Gaussian\nnoise with plausible complexity, and consequently induces statistical\ndependencies in the approximate posterior. Theoretically, VSD successfully\naddresses the pathologies of previous Variational Dropout methods and thus\noffers a standard Bayesian justification. We further show that VSD induces an\nadaptive regularization term with several desirable properties which contribute\nto better generalization. Finally, we conduct extensive experiments on standard\nbenchmarks to demonstrate the effectiveness of VSD over state-of-the-art\nvariational methods on predictive accuracy, uncertainty estimation, and\nout-of-distribution detection.\n"}
+{"abstract": "  We prove a weak version of the Perdomo Conjecture, namely, there is a\npositive constant $\\delta(n)>0$ depending only on $n$ such that on any closed\nembedded, non-totally geodesic, minimal hypersurface $M^n$ in\n$\\mathbb{S}^{n+1}$,\n  $$\\int_{M}S \\geq \\delta(n){\\rm Vol}(M^n),$$ where $S$ is the squared length\nof the second fundamental form of $M^n$. The Perdomo Conjecture asserts that\n$\\delta(n)=n$ which is still open in general. As byproducts, we also obtain\nsome integral inequalities and Simons-type pinching results on closed embedded\n(or immersed) minimal hypersurfaces, with the first positive eigenvalue\n$\\lambda_1(M)$ of the Laplacian involved.\n"}
+{"abstract": "  Initiated by Davis, Nelson, Petersen and Tenner (2018), the enumerative study\nof pinnacle sets of permutations has attracted a fair amount of attention\nrecently. In this article, we provide a recurrence that can be used to compute\nefficiently the number $|\\sym_n(P)|$ of permutations of size $n$ with a given\npinnacle set $P$, with arithmetic complexity $O(k^4 + k\\log n)$ for $P$ of size\n$k$. A symbolic expression can also be computed in this way for pinnacle sets\nof fixed size. A weighted sum $q_n(P)$ of $|\\sym_n(P)|$ proposed in Davis,\nNelson, Petersen and Tenner (2018) seems to have a simple form, and a\nconjectural form is given recently by Flaque, Novelli and Thibon (2021+). We\nsettle the problem by providing and proving an alternative form of $q_n(P)$,\nwhich has a strong combinatorial flavor. We also study admissible orderings of\na given pinnacle set, first considered by Rusu (2020) and characterized by Rusu\nand Tenner (2021), and we give an efficient algorithm for their counting.\n"}
+{"abstract": "  In this paper we study the cohomology of PEL-type Rapoport-Zink spaces\nassociated to unramified unitary similitude groups over $\\Q_p$ in an odd number\nof variables. We extend the results of Kaletha-Minguez-Shin-White to construct\na local Langlands correspondence for these groups and prove an averaging\nformula relating the cohomology of Rapport-Zink spaces to this correspondence.\nWe use this formula to prove the Kottwitz conjecture for the groups we\nconsider.\n"}
+{"abstract": "  Many real life situations require a set of items to be repeatedly placed in a\nrandom sequence. In such circumstances, it is often desirable to test whether\nsuch randomization indeed obtains, yet this problem has received very limited\nattention in the literature. This paper articulates the key features of this\nproblem and presents three \"untargeted\" tests that require no a priori\ninformation from the analyst. These methods are used to analyze the order in\nwhich lottery numbers are drawn in Powerball, the order in which contestants\nperform on American Idol, and the order of candidates on primary election\nballots in Texas and West Virginia. In this last application, multiple\ndeviations from full randomization are detected, with potentially serious\npolitical and legal consequences. The form these deviations take varies,\ndepending on institutional factors, which sometimes necessitates the use of\ntests that exchange power for increased robustness.\n"}
+{"abstract": "  We systematically study the hyperon global polarization's sensitivity to the\ncollision systems' initial longitudinal flow velocity in hydrodynamic\nsimulations. By explicitly imposing local energy-momentum conservation when\nmapping the initial collision geometry to macroscopic hydrodynamic fields, we\nstudy the evolution of systems' orbital angular momentum (OAM) and fluid\nvorticity. We find that a simultaneous description of the $\\Lambda$ hyperons'\nglobal polarization and the slope of pion's directed flow can strongly\nconstrain the size of longitudinal flow at the beginning of hydrodynamic\nevolution. We extract the size of the initial longitudinal flow and the\nfraction of orbital angular momentum in the produced QGP fluid as a function of\ncollision energy with the STAR measurements in the RHIC Beam Energy Scan\nprogram. We find that there is about 100-200 $\\hbar$ OAM that remains in the\nmid-rapidity fluid at the beginning of hydrodynamic evolution. We further exam\nthe effects of different hydrodynamic gradients on the spin polarization of\n$\\Lambda$ and $\\bar{\\Lambda}$. The gradients of $\\mu_B/T$ can change the\nordering between $\\Lambda$'s and $\\bar{\\Lambda}$'s polarization.\n"}
+{"abstract": "  Tree path minimum query problem is a fundamental problem while processing\ntrees, and is used widely in minimum spanning tree verification and randomized\nminimum spanning tree algorithms. In this paper, we study the possibility of\nbuilding an oracle in advance, which is able to answer the queries efficiently.\nWe present an algorithm based on Boruvka trees. Our algorithm is the first to\nachieve a near-optimal bound on query time, while matching the currently\noptimal trade-off between construction time and the number of comparisons\nrequired at query. Particularly, in order to answer each query within $2k$\ncomparisons, our algorithm requires $O(n \\log \\lambda_k(n))$ time and space to\nconstruct the oracle, and the oracle can answer queries in $O(k + \\log\n\\lambda_k(n))$ time. Here $\\lambda_k(n)$ is the inverse of the Ackermann\nfunction along the $k$-th column. This algorithm not only is simpler than the\nprevious ones, but also gives a completely different method of solving this\nproblem.\n"}
+{"abstract": "  In this paper, we provide rigorous justification of the hydrostatic\napproximation and the derivation of primitive equations as the small aspect\nratio limit of the incompressible three-dimensional Navier-Stokes equations in\nthe anisotropic horizontal viscosity regime. Setting $\\varepsilon >0$ to be the\nsmall aspect ratio of the vertical to the horizontal scales of the domain, we\ninvestigate the case when the horizontal and vertical viscosities in the\nincompressible three-dimensional Navier-Stokes equations are of orders $O(1)$\nand $O(\\varepsilon^\\alpha)$, respectively, with $\\alpha>2$, for which the\nlimiting system is the primitive equations with only horizontal viscosity as\n$\\varepsilon$ tends to zero. In particular we show that for \"well prepared\"\ninitial data the solutions of the scaled incompressible three-dimensional\nNavier-Stokes equations converge strongly, in any finite interval of time, to\nthe corresponding solutions of the anisotropic primitive equations with only\nhorizontal viscosities, as $\\varepsilon$ tends to zero, and that the\nconvergence rate is of order $O\\left(\\varepsilon^\\frac\\beta2\\right)$, where\n$\\beta=\\min\\{\\alpha-2,2\\}$. Note that this result is different from the case\n$\\alpha=2$ studied in [Li, J.; Titi, E.S.: \\emph{The primitive equations as the\nsmall aspect ratio limit of the Navier-Stokes equations: Rigorous justification\nof the hydrostatic approximation}, J. Math. Pures Appl., \\textbf{124}\n\\rm(2019), 30--58], where the limiting system is the primitive equations with\nfull viscosities and the convergence is globally in time and its rate of order\n$O\\left(\\varepsilon\\right)$.\n"}
+{"abstract": "  Transformer based architectures are recently used for the task of answering\nquestions over tables. In order to improve the accuracy on this task,\nspecialized pre-training techniques have been developed and applied on millions\nof open-domain web tables. In this paper, we propose two novel approaches\ndemonstrating that one can achieve superior performance on table QA task\nwithout even using any of these specialized pre-training techniques. The first\nmodel, called RCI interaction, leverages a transformer based architecture that\nindependently classifies rows and columns to identify relevant cells. While\nthis model yields extremely high accuracy at finding cell values on recent\nbenchmarks, a second model we propose, called RCI representation, provides a\nsignificant efficiency advantage for online QA systems over tables by\nmaterializing embeddings for existing tables. Experiments on recent benchmarks\nprove that the proposed methods can effectively locate cell values on tables\n(up to ~98% Hit@1 accuracy on WikiSQL lookup questions). Also, the interaction\nmodel outperforms the state-of-the-art transformer based approaches,\npre-trained on very large table corpora (TAPAS and TaBERT), achieving ~3.4% and\n~18.86% additional precision improvement on the standard WikiSQL benchmark.\n"}
+{"abstract": "  The majority of scientific papers are distributed in PDF, which pose\nchallenges for accessibility, especially for blind and low vision (BLV)\nreaders. We characterize the scope of this problem by assessing the\naccessibility of 11,397 PDFs published 2010--2019 sampled across various fields\nof study, finding that only 2.4% of these PDFs satisfy all of our defined\naccessibility criteria. We introduce the SciA11y system to offset some of the\nissues around inaccessibility. SciA11y incorporates several machine learning\nmodels to extract the content of scientific PDFs and render this content as\naccessible HTML, with added novel navigational features to support screen\nreader users. An intrinsic evaluation of extraction quality indicates that the\nmajority of HTML renders (87%) produced by our system have no or only some\nreadability issues. We perform a qualitative user study to understand the needs\nof BLV researchers when reading papers, and to assess whether the SciA11y\nsystem could address these needs. We summarize our user study findings into a\nset of five design recommendations for accessible scientific reader systems.\nUser response to SciA11y was positive, with all users saying they would be\nlikely to use the system in the future, and some stating that the system, if\navailable, would become their primary workflow. We successfully produce HTML\nrenders for over 12M papers, of which an open access subset of 1.5M are\navailable for browsing at https://scia11y.org/\n"}
+{"abstract": "  We prove new vanishing results on the growth of higher torsion homologies for\nsuitable arithmetic lattices, Artin groups and mapping class groups. The growth\nis understood along Farber sequences, in particular, along residual chains. For\nprincipal congruence subgroups, we also obtain strong asymptotic bounds for the\ntorsion growth.\n  As a central tool, we introduce a quantitative homotopical method called\neffective rebuilding. This constructs small classifying spaces of finite index\nsubgroups, at the same time controlling the complexity of the homotopy. The\nmethod easily applies to free abelian groups and then extends recursively to a\nwide class of residually finite groups.\n"}
+{"abstract": "  We discuss the impact of the new LHCb fixed-target $p\\!+\\!^{4}\\!He$ data for\n$D^0/{\\bar D}^0$ production on the intrinsic $c \\bar c$ component in the\nnucleon wave function. Within the scenario presented here neither the\ntraditional gluon-gluon fusion or quark-antiquark annihilation mechanisms\ncalculated in the $k_T$-factorization approach nor their counterparts from the\ncollinear next-to-leading order collinear framework are not sufficient to\ndescribe the transverse momentum and rapidity distributions of $D^0/{\\bar D}^0$\nmesons. First the $c{\\bar c}$-pair production within the standard frameworks is\nconsidered. Here a crucial role of the $c \\to D$ hadronization effects at low\nenergies and low transverse momenta is found and discussed, which was not\nanalyzed in previous studies. A contribution related to intrinsic $c \\bar c$\ncomponent in the nucleon wave function is included in addition. Two models of\nthe symmetric ($c(x) = \\bar c(x)$) intrinsic charm (IC) component are\nconsidered. At present the intrinsic charm $g^* c \\to g c$ (or $g^* {\\bar c}\n\\to g {\\bar c}$) contribution can be calculated only in the massless framework\nand needs to be regularized due to collinear singularity. We show that the\nregularization parameter can be obtained from the fit to the LHCb fixed-target\ndata under consideration here. According to our model the $1 \\%$ intrinsic\ncharm component allows to significantly improve description of the LHCb data.\n"}
+{"abstract": "  It is well known that, with a particular choice of norm, the classical\ndouble-layer potential operator $D$ has essential norm $<1/2$ as an operator on\nthe natural trace space $H^{1/2}(\\Gamma)$ whenever $\\Gamma$ is the boundary of\na bounded Lipschitz domain. This implies, for the standard second-kind boundary\nintegral equations for the interior and exterior Dirichlet and Neumann problems\nin potential theory, convergence of the Galerkin method in $H^{1/2}(\\Gamma)$\nfor any sequence of finite-dimensional subspaces $(\\mathcal{H}_N)_{N=1}^\\infty$\nthat is asymptotically dense in $H^{1/2}(\\Gamma)$. Long-standing open questions\nare whether the essential norm is also $<1/2$ for $D$ as an operator on\n$L^2(\\Gamma)$ for all Lipschitz $\\Gamma$ in 2-d; or whether, for all Lipschitz\n$\\Gamma$ in 2-d and 3-d, or at least for the smaller class of Lipschitz\npolyhedra in 3-d, the weaker condition holds that the operators $\\pm\n\\frac{1}{2}I+D$ are compact perturbations of coercive operators -- this a\nnecessary and sufficient condition for the convergence of the Galerkin method\nfor every sequence of subspaces $(\\mathcal{H}_N)_{N=1}^\\infty$ that is\nasymptotically dense in $L^2(\\Gamma)$. We settle these open questions\nnegatively. We give examples of 2-d and 3-d Lipschitz domains with Lipschitz\nconstant equal to one for which the essential norm of $D$ is $\\geq 1/2$, and\nexamples with Lipschitz constant two for which the operators $\\pm \\frac{1}{2}I\n+D$ are not coercive plus compact. We also give, for every $C>0$, examples of\nLipschitz polyhedra for which the essential norm is $\\geq C$ and for which\n$\\lambda I+D$ is not a compact perturbation of a coercive operator for any real\nor complex $\\lambda$ with $|\\lambda|\\leq C$. Finally, we resolve negatively a\nrelated open question in the convergence theory for collocation methods.\n"}
+{"abstract": "  We propose a new solvable class of multidimensional quantum harmonic\noscillators for a linear diffusive particle and a quadratic energy absorbing\nwell associated with a semi-definite positive matrix force. Under natural and\neasily checked controllability conditions, the ground state and the zero-point\nenergy are explicitly computed in terms of a positive fixed point of a\ncontinuous time algebraic Riccati matrix equation. We also present an explicit\nsolution of normalized and time dependent Feynman-Kac measures in terms of a\ntime varying linear dynamical system coupled with a differential Riccati matrix\nequation. A refined non asymptotic analysis of the stability of these models is\ndeveloped based on a recently developed Floquet-type representation of time\nvarying exponential semigroups of Riccati matrices. We provide explicit and non\nasymptotic estimates of the exponential decays to equilibrium of Feynman-Kac\nsemigroups in terms of Wasserstein distances or Boltzmann-relative entropy. For\nreversible models we develop a series of functional inequalities including de\nBruijn identity, Fisher's information decays, log-Sobolev inequalities, and\nentropy contraction estimates. In this context, we also provide a complete and\nexplicit description of all the spectrum and the excited states of the\nHamiltonian, yielding what seems to be the first result of this type for this\nclass of models. We illustrate these formulae with the traditional harmonic\noscillator associated with real time Brownian particles and Mehler's formula.\nThe analysis developed in this article can also be extended to solve time\ndependent Schrodinger equations equipped with time varying linear diffusions\nand quadratic potential functions.\n"}
+{"abstract": "  Approximate agreement is one of the few variants of consensus that can be\nsolved in a wait-free manner in asynchronous systems where processes\ncommunicate by reading and writing to shared memory. In this work, we consider\na natural generalisation of approximate agreement on arbitrary undirected\nconnected graphs. Each process is given a vertex of the graph as input and, if\nnon-faulty, must output a vertex such that\n  - all the outputs are within distance 1 of one another, and\n  - each output value lies on a shortest path between two input values.\n  From prior work, it is known that there is no wait-free algorithm among $n\n\\ge 3$ processes for this problem on any cycle of length $c \\ge 4$, by\nreduction from 2-set agreement (Casta\\~neda et al., 2018).\n  In this work, we investigate the solvability and complexity of this task on\ngeneral graphs. We give a new, direct proof of the impossibility of approximate\nagreement on cycles of length $c \\ge 4$, via a generalisation of Sperner's\nLemma to convex polygons. We also extend the reduction from 2-set agreement to\na larger class of graphs, showing that approximate agreement on on these graphs\nis unsolvable. Furthermore, we show that combinatorial arguments, used by both\nexisting proofs, are necessary, by showing that the impossibility of a\nwait-free algorithm in the nonuniform iterated snapshot model cannot be proved\nvia an extension-based proof. On the positive side, we present a wait-free\nalgorithm for a class of graphs that properly contains the class of chordal\ngraphs.\n"}
+{"abstract": "  Image-based geometric modeling and novel view synthesis based on sparse,\nlarge-baseline samplings are challenging but important tasks for emerging\nmultimedia applications such as virtual reality and immersive telepresence.\nExisting methods fail to produce satisfactory results due to the limitation on\ninferring reliable depth information over such challenging reference\nconditions. With the popularization of commercial light field (LF) cameras,\ncapturing LF images (LFIs) is as convenient as taking regular photos, and\ngeometry information can be reliably inferred. This inspires us to use a sparse\nset of LF captures to render high-quality novel views globally. However, fusion\nof LF captures from multiple angles is challenging due to the scale\ninconsistency caused by various capture settings. To overcome this challenge,\nwe propose a novel scale-consistent volume rescaling algorithm that robustly\naligns the disparity probability volumes (DPV) among different captures for\nscale-consistent global geometry fusion. Based on the fused DPV projected to\nthe target camera frustum, novel learning-based modules have been proposed\n(i.e., the attention-guided multi-scale residual fusion module, and the\ndisparity field guided deep re-regularization module) which comprehensively\nregularize noisy observations from heterogeneous captures for high-quality\nrendering of novel LFIs. Both quantitative and qualitative experiments over the\nStanford Lytro Multi-view LF dataset show that the proposed method outperforms\nstate-of-the-art methods significantly under different experiment settings for\ndisparity inference and LF synthesis.\n"}
+{"abstract": "  E-commerce companies have to face abnormal sellers who sell potentially-risky\nproducts. Typically, the risk can be identified by jointly considering product\ncontent (e.g., title and image) and seller behavior. This work focuses on\nbehavior feature extraction as behavior sequences can provide valuable clues\nfor the risk discovery by reflecting the sellers' operation habits. Traditional\nfeature extraction techniques heavily depend on domain experts and adapt poorly\nto new tasks. In this paper, we propose a self-supervised method InfoBehavior\nto automatically extract meaningful representations from ultra-long raw\nbehavior sequences instead of the costly feature selection procedure.\nInfoBehavior utilizes Bidirectional Transformer as feature encoder due to its\nexcellent capability in modeling long-term dependency. However, it is\nintractable for commodity GPUs because the time and memory required by\nTransformer grow quadratically with the increase of sequence length. Thus, we\npropose a hierarchical grouping strategy to aggregate ultra-long raw behavior\nsequences to length-processable high-level embedding sequences. Moreover, we\nintroduce two types of pretext tasks. Sequence-related pretext task defines a\ncontrastive-based training objective to correctly select the masked-out\ncoarse-grained/fine-grained behavior sequences against other \"distractor\"\nbehavior sequences; Domain-related pretext task designs a classification\ntraining objective to correctly predict the domain-specific statistical results\nof anomalous behavior. We show that behavior representations from the\npre-trained InfoBehavior can be directly used or integrated with features from\nother side information to support a wide range of downstream tasks.\nExperimental results demonstrate that InfoBehavior significantly improves the\nperformance of Product Risk Management and Intellectual Property Protection.\n"}
+{"abstract": "  This work is devoted to show a sufficient and necessary description for the\nmost probable transition paths of stochastic dynamical systems with Brownian\nnoise. The equivalence is established by studying the relations between\nMarkovian bridge measures and the Onsgaer-Machlup action functional. This\ncannot be achieved by existing methods because Markovian bridge measures are\nnot quasi translation invariant. We overcome this problem by showing that the\nOnsager-Machlup action functional can be derived from a Markovian bridge\nprocess. We then demonstrate that, for some special cases of which one is a\nclass of linear stochastic systems and another is a class of nonlinear\nstochastic systems with small noise, the most probable transition path can be\ndetermined by a first order deterministic differential equation with an initial\nvalue.Finally, we illustrate our results with several examples.\n"}
+{"abstract": "  The statistical convergence is defined for sequences with the asymptotic\ndensity on the natural numbers, in general. In this paper, we introduce the\nstatistical convergence for nets in Riesz spaces by using the finite additive\nmeasures on directed sets. Moreover, we give some relations among the\nstatistical convergence and the lattice properties such as the order\nconvergence and lattice operators.\n"}
+{"abstract": "  The ability of Machine-Learning (ML) based model components to generalize to\nthe previously unseen inputs, and the resulting stability of the models that\nuse these components, has been receiving a lot of recent attention, especially\nwhen it comes to ML-based parameterizations. At the same time, ML-based\nemulators of existing parameterizations can be stable, accurate, and fast when\nused in the model they were specifically designed for. In this work we show\nthat shallow-neural-network-based emulators of radiative transfer\nparameterizations developed almost a decade ago for a state-of-the-art GCM are\nrobust with respect to the substantial structural and parametric change in the\nhost model: when used in the AMIP-like experiment with the new model, they not\nonly remain stable, but generate realistic output. Aspects of neural network\narchitecture and training set design potentially contributing to stability of\nML-based model components are discussed.\n"}
+{"abstract": "  We present a new RF fingerprinting technique for wireless emitters that is\nbased on a simple, easily and efficiently retrainable Ridge Regression (RR)\nclassifier. The RR learns to identify devices using bursts of waveform samples,\nconveniently transformed and preprocessed by delay-loop reservoirs. Deep delay\nLoop Reservoir Computing (DLR) is our processing architecture that supports\ngeneral machine learning algorithms on resource-constrained devices by\nleveraging delay-loop reservoir computing (RC) and innovative architectures of\nloop trees. In prior work, we trained and evaluated DLR using high SNR device\nemissions in clean channels. We here demonstrate how to use DLR for IoT\nauthentication by performing RF-based Specific Emitter Identification (SEI),\neven in the presence of fading channels and heavy in-band jamming by leveraging\na matched filter (MF) extension, dubbed MF-DLR. We show that the MF processing\nimproves the SEI performance of RR without the RC transformation (MF-RR), but\nthe MF-DLR is more robust and applicable for addressing signatures beyond\nwaveform transients (e.g. turn-on).\n"}
+{"abstract": "  We explore the notion of a category with coproducts $\\cup$ and a monoidal\nstructure $\\otimes$ distributing over it, endowed with an endo-functor\n$\\partial$ which is \"linear and Leibniz\". Such $\\partial$ can be legitimately\ncalled a *derivation* on $\\mathcal{C}$, and the pair $(\\mathcal{C},\\partial)$\nappears as the categorification of a differential ri(n)g: a differential 2-rig.\n  We explore a number of possible special examples: when $\\mathcal{C}$ is\nsemicartesian (i.e., its $\\otimes$-monoidal unit is the terminal object),\nderivations are better behaved in some respect; interesting examples arise when\n$\\mathcal{C}=[\\mathcal{A},\\textsf{Set}]$ is a presheaf topos endowed with a\nconvolution product: among these, the operation of Brzozowski's derivative in\nformal language theory; we retrieve the \"chain rule\" in a category of\n\"non-symmetric species\" $[\\textsf{Fin},\\textsf{Set}]$, obtained when a\nsubstitution product $F\\diamond G$ is interpreted as composition $T_F\\circ T_G$\nof the associated finitary functors, regarded as formal power series.\n  Derivations on a 2-rig are tightly related to *tensorial strengths*; this\ndraws a connection between the present theory and applicative structures in\nfunctional programming. Moreover, given a derivation on $\\mathcal{C}$, the\nderivative of the $\\otimes$-monoidal unit $\\partial I$ is a *self-similar*\nobject, i.e. a solution to the equation $X\\cong X\\cup X$; we explore the many\nconsequences of this apparently innocuous observation. This has connection with\nthe category-theoretic analysis of dynamical systems.\n"}
+{"abstract": "  Existing video super-resolution methods often utilize a few neighboring\nframes to generate a higher-resolution image for each frame. However, the\nredundant information between distant frames has not been fully exploited in\nthese methods: corresponding patches of the same instance appear across distant\nframes at different scales. Based on this observation, we propose a video\nsuper-resolution method with long-term cross-scale aggregation that leverages\nsimilar patches (self-exemplars) across distant frames. Our model also consists\nof a multi-reference alignment module to fuse the features derived from similar\npatches: we fuse the features of distant references to perform high-quality\nsuper-resolution. We also propose a novel and practical training strategy for\nreferenced-based super-resolution. To evaluate the performance of our proposed\nmethod, we conduct extensive experiments on our collected CarCam dataset and\nthe Waymo Open dataset, and the results demonstrate our method outperforms\nstate-of-the-art methods. Our source code will be publicly available.\n"}
+{"abstract": "  A recent 3-XORSAT challenge required to minimize a very complex and rough\nenergy function, typical of glassy models with a random first order transition\nand a golf course like energy landscape. We present the ideas beyond the\nquasi-greedy algorithm and its very efficient implementation on GPUs that are\nallowing us to rank first in such a competition. We suggest a better protocol\nto compare algorithmic performances and we also provide analytical predictions\nabout the exponential growth of the times to find the solution in terms of\nfree-energy barriers.\n"}
+{"abstract": "  ECM components, such as the Perineuronal net (PNN), one of the most prevalent\nparts surrounding the neuronal cell. PNN is a protective net-like structure\nregulating neuronal activity such as neurotransmission, charge balance and\ngenerates an action potential. Shock induced damage of this essential component\nmay cause neuronal cell death and potentially leads to CTE, AD diseases, PTSD,\netc. The shock generated possibly during an accident, improvised devie\nexplosion or collision between NFL players may lead to damage to this safety\nnet. The goal is to investigate the mechanics of PNN under shock wave. To\nunderstand the mechanics of PNN, mechanical properties of different PNN\ncomponents such as glycan, GAG, and protein need to be evaluated. In this\nstudy, we evaluated the mechanical strength of PNN molecules and the\ninterfacial strength between the components of PNN. Afterward, we have assessed\nthe PNN molecules' damage efficiency at various conditions such as shock speed,\npreexisting bubble, and boundary conditions. The secondary structure\naltercation of the protein molecules of the PNN has been analyzed to evaluate\ndamage intensity under varying shock loading. At higher shock speed, damage\nintensity is more elevated, and hyaluronan is most likely to break at the rigid\njunction. The primary structure of the protein molecules is most unlikely to\nfail. Instead, the molecules' secondary bonds will be altered. Our study\nsuggests that the number of hydrogen bonds during the shock wave propagation\ndecreased.\n"}
+{"abstract": "  Roboticists usually test new control software in simulation environments\nbefore evaluating its functionality on real-world robots. Simulations reduce\nthe risk of damaging the hardware and can significantly increase the\ndevelopment process's efficiency in the form of automated system tests.\n  However, many flaws in the software remain undetected in simulation data,\nrevealing their harmful effects on the system only in time-consuming\nexperiments. In reality, such irregularities are often easily recognized solely\nby the robot's airborne noise during operation. We propose a simulated noise\nestimate for the detection of failures in automated system tests of robots. The\nclassification of flaws uses classical machine learning - a support vector\nmachine - to identify different failure classes from the scalar noise estimate.\n  The methodology is evaluated on simulation data from the humanoid robot LOLA.\nThe approach yields high failure detection accuracy with a low false-positive\nrate, enabling its use for stricter automated system tests. Results indicate\nthat a single trained model may work for different robots. The proposed\ntechnique is provided to the community in the form of the open-source tool\nNoisyTest, making it easy to test data from any robot. In a broader scope, the\ntechnique may empower real-world automated system tests without human\nevaluation of success or failure.\n"}
+{"abstract": "  A wide range of n- and p-type doping levels in GaAs layers grown by chemical\nbeam epitaxy is achieved using H$_{2}$-diluted DTBSi and CBr$_{4}$ as gas\nprecursors for Si and C. We show that the doping level can be varied by\nmodifying either the concentration or the flux of the diluted precursor.\nSpecifically, we demonstrate carrier concentrations of\n6$\\times$10$^{17}$-1.2$\\times$10$^{19}$ cm$^{-3}$ for Si, and\n9$\\times$10$^{16}$-3.7$\\times$10$^{20}$ cm$^{-3}$ for C, as determined by Hall\neffect measurements. The incorporation of Si and C as a function of the flux of\nthe corresponding diluted precursor is found to follow, respectively, a first\nand a fourth order power law. The dependence of the electron and hole mobility\nvalues on the carrier concentration as well as the analysis of the layers by\nlow-temperature (12 K) photoluminescence spectroscopy indicate that the use of\nH$_{2}$ for diluting DTBSi or CBr$_{4}$ has no effect on the electrical and\noptical properties of GaAs.\n"}
+{"abstract": "  This paper pays close attention to the cross-modality visible-infrared person\nre-identification (VI Re-ID) task, which aims to match human samples between\nvisible and infrared modes. In order to reduce the discrepancy between features\nof different modalities, most existing works usually use constraints based on\nEuclidean metric. Since the Euclidean based distance metric cannot effectively\nmeasure the internal angles between the embedded vectors, the above methods\ncannot learn the angularly discriminative feature embedding. Because the most\nimportant factor affecting the classification task based on embedding vector is\nwhether there is an angularly discriminativ feature space, in this paper, we\npropose a new loss function called Enumerate Angular Triplet (EAT) loss. Also,\nmotivated by the knowledge distillation, to narrow down the features between\ndifferent modalities before feature embedding, we further present a new\nCross-Modality Knowledge Distillation (CMKD) loss. The experimental results on\nRegDB and SYSU-MM01 datasets have shown that the proposed method is superior to\nthe other most advanced methods in terms of impressive performance.\n"}
+{"abstract": "  Probing quantum mechanics, quantum aspects of general relativity along with\nthe sensing and the constraining of classical gravity can all be enabled by\nunprecedented spatial sizes of superpositions of massive objects. In this\npaper, we show that there is a feasible setup sourced by realizable magnetic\nfield gradients ${\\cal O}(10-100)$ Tm$^{-1}$ to construct a large spatial\nsuperposition of ${\\cal O}(10^{-4}-10^{-8})$ m for masses ${\\cal\nO}(10^{-16}-10^{-14})$ kg over a time period of up to $0.1-10$ seconds.\n"}
+{"abstract": "  We study the thermal conductivity in the excitonic insulator using a simple\nquasi one-dimensional two-band model consisting of electron and hole bands with\nthe Coulomb interactions between these bands. Based on the linear response\ntheory within a mean-field scheme, we develop a method to identify the\ncontributions to thermal conductivity driven by excitonic insulator. It is\nfound that there is an additional heat current operator owing to the excitonic\nphase transition, and that it gives contributions to thermal conductivity which\nare not expressed in the form of Sommerfeld-Bethe relations written in the form\nof an imaginary-time derivative of the electric current operator. Finally, we\ndiscuss the relationship between the newly-found additional contribution and\nthe heat current carried by excitons.\n"}
+{"abstract": "  In this paper, we present a control-design method based on the energy-Casimir\nmethod for infinite-dimensional, boundary-actuated port-Hamiltonian systems\nwith two-dimensional spatial domain and second-order Hamiltonian. The resulting\ncontrol law depends on distributed system states that cannot be measured, and\ntherefore, we additionally design an infinite-dimensional observer by\nexploiting the port-Hamiltonian system representation. A Kirchhoff-Love plate\nserves as an example in order to demonstrate the proposed approaches.\n"}
+{"abstract": "  Objectively quantifying the value of player actions in football (soccer) is a\nchallenging problem. To date, studies in football analytics have mainly focused\non the attacking side of the game, while there has been less work on\nevent-driven metrics for valuing defensive actions (e.g., tackles and\ninterceptions). Therefore in this paper, we use deep learning techniques to\ndefine a novel metric that values such defensive actions by studying the threat\nof passages of play that preceded them. By doing so, we are able to value\ndefensive actions based on what they prevented from happening in the game. Our\nDefensive Action Expected Threat (DAxT) model has been validated using\nreal-world event-data from the 2017/2018 and 2018/2019 English Premier League\nseasons, and we combine our model outputs with additional features to derive an\noverall rating of defensive ability for players. Overall, we find that our\nmodel is able to predict the impact of defensive actions allowing us to better\nvalue defenders using event-data.\n"}
+{"abstract": "  The cosmic distance duality relation (CDDR), eta(z)=(1+z)^2 d_A(z)/d_L(z)=1,\nis one of the most fundamental and crucial formulae in cosmology. This relation\ncouples the luminosity and angular diameter distances, two of the most often\nused measures of structure in the Universe. We here propose a new\nmodel-independent method to test this relation, using strong gravitational\nlensing (SGL) and the high-redshift quasar Hubble diagram reconstructed with a\nBezier parametric fit. We carry out this test without pre-assuming a zero\nspatial curvature, adopting instead the value Omega_K=0.001 +/- 0.002 optimized\nby Planck in order to improve the reliability of our result. We parametrize the\nCDDR using eta(z)=1 + eta_0 z, 1 + eta_1 z + eta_2 z^2 and 1 + eta_3 z/(1+z),\nand consider both the SIS and non-SIS lens models for the strong lensing. Our\nbest fit results are: eta_0=-0.021^{+0.068}_{-0.048},\neta_1=-0.404^{+0.123}_{-0.090}, eta_2=0.106^{+0.028}_{-0.034}, and\neta_3=-0.507^{+0.193}_{-0.133} for the SIS model, and\neta_0=-0.109^{+0.044}_{-0.031} for the non-SIS model. The measured eta(z),\nbased on the Planck parameter Omega_K, is essentially consistent with the value\n(=1) expected if the CDDR were fully respected. For the sake of comparison, we\nalso carry out the test for other values of Omega_K, but find that deviations\nof spatial flatness beyond the Planck optimization are in even greater tension\nwith the CDDR. Future measurements of SGL may improve the statistics and alter\nthis result but, as of now, we conclude that the CDDR favours a flat Universe.\n"}
+{"abstract": "  Prior work has shown that Twitter users use skin-toned emoji as an act of\nself-representation to express their racial/ethnic identity. We test whether\nthis signal of identity can influence readers' perceptions about the content of\na post containing that signal. In a large scale (n=944) pre-registered\ncontrolled experiment, we manipulate the presence of skin-toned emoji and\nprofile photos in a task where readers rate obscure trivia facts (presented as\ntweets) as true or false. Using a Bayesian statistical analysis, we find that\nneither emoji nor profile photo has an effect on how readers rate these facts.\nThis result will be of some comfort to anyone concerned about the manipulation\nof online users through the crafting of fake profiles.\n"}
+{"abstract": "  American and Bermudan-type financial instruments are often priced with\nspecific Monte Carlo techniques whose efficiency critically depends on the\neffective dimensionality of the problem and the available computational power.\nIn our work we focus on Bermudan Swaptions, well-known interest rate\nderivatives embedded in callable debt instruments or traded in the OTC market\nfor hedging or speculation purposes, and we adopt an original pricing approach\nbased on Supervised Learning (SL) algorithms. In particular, we link the price\nof a Bermudan Swaption to its natural hedges, i.e. the underlying European\nSwaptions, and other sound financial quantities through SL non-parametric\nregressions. We test different algorithms, from linear models to decision\ntree-based models and Artificial Neural Networks (ANN), analyzing their\npredictive performances. All the SL algorithms result to be reliable and fast,\nallowing to overcome the computational bottleneck of standard Monte Carlo\nsimulations; the best performing algorithms for our problem result to be Ridge,\nANN and Gradient Boosted Regression Tree. Moreover, using feature importance\ntechniques, we are able to rank the most important driving factors of a\nBermudan Swaption price, confirming that the value of the maximum underlying\nEuropean Swaption is the prevailing feature.\n"}
+{"abstract": "  We introduce a ramified monoid, attached to each Brauer--type monoid, that\nis, to the symmetric group, to the Jones and Brauer monoids among others.\nRamified monoids correspond to a class of tied monoids which arise from knot\ntheory and are interesting in itself. The ramified monoid attached to the\nsymmetric group is the Coxeter-like version of the so--called tied braid\nmonoid. We give a presentation of the ramified monoid attached to the Brauer\nmonoid. Also, we introduce and studied two tied-like monoids that cannot be\ndescribed as ramified monoids. However, these monoids can also be regarded as\ntied versions of the Jones and Brauer monoids.\n"}
+{"abstract": "  The NA62 experiment reports the branching ratio measurement BR$(K^+\n\\rightarrow \\pi^+ \\nu\\bar{\\nu}) = (10.6^{+4.0}_{-3.4} |_{\\rm stat} \\pm 0.9_{\\rm\nsyst}) \\times 10 ^{-11}$ at 68% CL, based on the observation of 20 signal\ncandidates with an expected background of 7.0 events from the total data sample\ncollected at the CERN SPS during 2016-2018. This provides evidence for the very\nrare $K^+ \\rightarrow \\pi^+ \\nu\\bar{\\nu}$ decay, observed with a significance\nof 3.4$\\sigma$. The experiment achieves a single event sensitivity of\n$(0.839\\pm 0.054)\\times 10^{-11}$, corresponding to 10.0 events assuming the\nStandard Model branching ratio of $(8.4\\pm1.0)\\times10^{-11}$. This measurement\nis also used to set limits on BR($K^+ \\to \\pi^+ X$), where $X$ is a scalar or\npseudo-scalar particle. Details are given of the analysis of the 2018 data\nsample, which corresponds to about 80% of the total data sample.\n"}
+{"abstract": "  In [Haglund, Remmel, Wilson 2018] the authors state two versions of the so\ncalled Delta conjecture, the rise version and the valley version. Of the\nformer, they also give a more general statement in which zero labels are also\nallowed. In [Qiu, Wilson 2020], the corresponding generalisation of the valley\nversion is also formulated.\n  In [D'Adderio, Iraci, Vanden Wyngaerd 2020], the authors use a pushing\nalgorithm to prove the generalised version of the shuffle theorem. An extension\nof that argument is used in [Iraci, Vanden Wyngaerd 2020] to formulate a valley\nversion of the (generalised) Delta square conjecture, and to suggest a\nsymmetric function identity later stated and proved in [D'Adderio, Romero\n2020].\n  In this paper, we use the pushing algorithm together with the aforementioned\nsymmetric function identity in order to prove that the valley version of the\nDelta conjecture implies the valley version of the generalised Delta\nconjecture, which means that they are actually equivalent.\n  Combining this with the results in [Iraci, Vanden Wyngaerd 2020], we prove\nthat the valley version of the Delta conjecture also implies the corresponding\ngeneralised Delta square conjecture.\n"}
+{"abstract": "  The susceptibility of deep neural networks to untrustworthy predictions,\nincluding out-of-distribution (OOD) data and adversarial examples, still\nprevent their widespread use in safety-critical applications. Most existing\nmethods either require a re-training of a given model to achieve robust\nidentification of adversarial attacks or are limited to out-of-distribution\nsample detection only. In this work, we propose a geometric gradient analysis\n(GGA) to improve the identification of untrustworthy predictions without\nretraining of a given model. GGA analyzes the geometry of the loss landscape of\nneural networks based on the saliency maps of their respective input. To\nmotivate the proposed approach, we provide theoretical connections between\ngradients' geometrical properties and local minima of the loss function.\nFurthermore, we demonstrate that the proposed method outperforms prior\napproaches in detecting OOD data and adversarial attacks, including\nstate-of-the-art and adaptive attacks.\n"}
+{"abstract": "  This paper presents comprehensive studies on frequency response optimized\nintegrators considering second order derivative regarding their numerical\nerror, numerical stability and transient performance. Frequency domain error\nanalysis is conducted on these numerical integrators to reveal their accuracy.\nNumerical stability of the numerical integrators is investigated. Interesting\nnew types of numerical stability are recognized. Transient performance of the\nnumerical integrators is defined to qualitatively characterize their ability to\ntrack fast decaying transients. This property is related to unsatisfactory\nphenomena such as numerical oscillation which frequently appear in time domain\nsimulation of circuits and systems. Transient performance analysis of the\nnumerical integrators is provided. Theoretical observations from the analysis\nof the numerical integrators are verified via time domain case studies.\n"}
+{"abstract": "  Modern data-intensive applications demand high computation capabilities with\nstrict power constraints. Unfortunately, such applications suffer from a\nsignificant waste of both execution cycles and energy in current computing\nsystems due to the costly data movement between the computation units and the\nmemory units. Genome analysis and weather prediction are two examples of such\napplications. Recent FPGAs couple a reconfigurable fabric with high-bandwidth\nmemory (HBM) to enable more efficient data movement and improve overall\nperformance and energy efficiency. This trend is an example of a paradigm shift\nto near-memory computing. We leverage such an FPGA with high-bandwidth memory\n(HBM) for improving the pre-alignment filtering step of genome analysis and\nrepresentative kernels from a weather prediction model. Our evaluation\ndemonstrates large speedups and energy savings over a high-end IBM POWER9\nsystem and a conventional FPGA board with DDR4 memory. We conclude that\nFPGA-based near-memory computing has the potential to alleviate the data\nmovement bottleneck for modern data-intensive applications.\n"}
+{"abstract": "  Music Genres, as a popular meta-data of music, are very useful to organize,\nexplore or search music datasets. Soft music genres are weighted multiple-genre\nannotations to songs. In this initial work, we propose horizontally stacked bar\ncharts to represent a music dataset annotated by these soft music genres. For\nthis purpose, we take an example of a toy dataset consisting of songs labelled\nwith help of three music genres; Blues, Jazz and Country. We demonstrate how\nsuch a stacked bar chart can be used as a slider for user-input in an\ninterface. We implement this by embedding this genre bar in a streaming\napplication prototype and show its utility in choosing playlists. We finally\nconclude by proposing further work and future explorations on our proposed\npreliminary research.\n"}
+{"abstract": "  Recent numerical studies indicate that the antiferromagnetic Kitaev honeycomb\nlattice model undergoes a magnetic-field-induced quantum phase transition into\na new spin-liquid phase. This intermediate-field phase has been previously\ncharacterized as a gapless spin liquid. By implementing a recently developed\nvariational approach based on the exact fractionalized excitations of the\nzero-field model, we demonstrate that the field-induced spin liquid is gapped\nand belongs to Kitaev's 16-fold way. Specifically, the low-field non-Abelian\nliquid with Chern number $C=\\pm 1$ transitions into an Abelian liquid with\n$C=\\pm 4$. The critical field and the field-dependent behaviors of key physical\nquantities are in good quantitative agreement with published numerical results.\nFurthermore, we derive an effective field theory for the field-induced critical\npoint which readily explains the ostensibly gapless nature of the\nintermediate-field spin liquid.\n"}
+{"abstract": "  We evaluated recent CLAS Collaboration measurements for the $90^\\circ$ meson\nphotoproduction off the nucleon using a tagged photon beam spanning the energy\ninterval $s = 3 - 11$ GeV$^2$. The results are compared with the \"Quark\nCounting Rules\" predictions.\n"}
+{"abstract": "  This document is an introduction to designing a multiple degree of freedom\nrobotic manipulator. The goal of the robot is to sort an object based upon the\nobjects color. The robot will be a synthesis of several linkages, servo motors,\nan Arduino system, and an end-effector. The design must be able to autonomously\ndetermine what object to move and where to place it. The robot will have a\nmaximum reach of 24 horizontally. The operating conditions include being able\nto sort several different colored spheres within the area of a 180{\\deg} up to\n16 from the base of the arm. Sorting consists of picking up the object from a\nrandom position near the base and moving it to a storing location near the edge\nof the defined operating range. With the following performance criteria and a\nlimited budget, the final design will enter a competition to determine the most\nrobust, and effective robotic manipulator.\n"}
+{"abstract": "  We consider the problem of sinusoidal parameter estimation using signed\nobservations obtained via one-bit sampling with fixed as well as time-varying\nthresholds. In a previous paper, a relaxation-based algorithm, referred to as\n1bRELAX, has been proposed to iteratively maximize the likelihood function.\nHowever, the exhaustive search procedure used in each iteration of 1bRELAX is\ntime-consuming. In this paper, we present a majorization-minimization (MM)\nbased 1bRELAX algorithm, referred to as 1bMMRELAX, to enhance the computational\nefficiency of 1bRELAX. Using the MM technique, 1bMMRELAX maximizes the\nlikelihood function iteratively using simple FFT operations instead of the more\ncomputationally intensive search used by 1bRELAX. Both simulated and\nexperimental results are presented to show that 1bMMRELAX can significantly\nreduce the computational cost of 1bRELAX while maintaining its excellent\nestimation accuracy.\n"}
+{"abstract": "  We have developed a method that maps large astronomical images onto a\ntwo-dimensional map and clusters them. A combination of various\nstate-of-the-art machine learning (ML) algorithms is used to develop a fully\nunsupervised image quality assessment and clustering system. Our pipeline\nconsists of a data pre-processing step where individual image objects are\nidentified in a large astronomical image and converted to smaller pixel images.\nThis data is then fed to a deep convolutional autoencoder jointly trained with\na self-organizing map (SOM). This part can be used as a recommendation system.\nThe resulting output is eventually mapped onto a two-dimensional grid using a\nsecond, deep, SOM. We use data taken from ground-based telescopes and, as a\ncase study, compare the system's ability and performance with the results\nobtained by supervised methods presented by Teimoorinia et al. (2020). The\navailability of target labels in this data allowed a comprehensive performance\ncomparison between our unsupervised and supervised methods. In addition to\nimage-quality assessments performed in this project, our method can have\nvarious other applications. For example, it can help experts label images in a\nconsiderably shorter time with minimum human intervention. It can also be used\nas a content-based recommendation system capable of filtering images based on\nthe desired content.\n"}
+{"abstract": "  We present a residual-based a posteriori error estimator for the hybrid\nhigh-order (HHO) method for the Stokes model problem. Both the proposed HHO\nmethod and error estimator are valid in two and three dimensions and support\narbitrary approximation orders on fairly general meshes. The upper bound and\nlower bound of the error estimator are proved, in which proof, the key\ningredient is a novel stabilizer employed in the discrete scheme. By using the\ngiven estimator, adaptive algorithm of HHO method is designed to solve model\nproblem. Finally, the expected theoretical results are numerically demonstrated\non a variety of meshes for model problem.\n"}
+{"abstract": "  We prove that vector fields described by the generalized Proca class of\ntheories do not admit a consistent coupling to a gravitational sector defined\nby a scalar-tensor theory of the degenerate type. Under the assumption that\nthere exists a frame in which the Proca field interacts with gravity only\nthrough the metric tensor, our analysis shows that at least one of the\nconstraints associated with the degeneracy of the scalar-tensor sector is\ninevitably lost whenever the vector theory includes a coupling to the\nChristoffel connection.\n"}
+{"abstract": "  In superconductors, the motion of vortices introduces unwanted dissipation\nthat is disruptive to applications. Fortunately, material defects can\nimmobilize vortices, acting as vortex pinning centers, which engenders dramatic\nimprovements in superconductor material properties and device operation. This\nhas motivated decades of research into developing methods of tailoring the\ndisorder landscape in superconductors to increase the strength of vortex\npinning. Yet efficacious materials engineering still alludes us. The\nelectromagnetic properties of real (disordered) superconducting materials\ncannot yet be reliably predicted, such that designing superconductors for\napplications remains a largely inefficient process of trial and error. This is\nultimately due to large gaps in our knowledge of vortex dynamics: the field is\nchallenged by the extremely complex interplay between vortex elasticity,\nvortex-vortex interactions, and material disorder.\n  In this Perspective, we review obstacles and recent successes in\nunderstanding and controlling vortex dynamics in superconducting materials and\ndevices. We further identify major open questions and discuss opportunities for\ntransformative research in the field. This includes improving our understanding\nof vortex creep, determining and reaching the ceiling for the critical current,\nadvanced microscopy to garner accurate structure-property relationships,\nfrontiers in predictive simulations and the benefits of artificial\nintelligence, as well as controlling and exploiting vortices in quantum\ninformation applications.\n"}
+{"abstract": "  We examine the stochastic dynamics of two enzymes that are mechanically\ncoupled to each other, e.g., through an elastic substrate or a fluid medium.\nThe enzymes undergo conformational changes during their catalytic cycle, which\nitself is driven by stochastic steps along a biased chemical free energy\nlandscape. We find conditions under which the enzymes can synchronize their\ncatalytic steps, and discover that the coupling can lead to a significant\nenhancement in their overall catalytic rate. Both effects can be understood as\narising from a global bifurcation in the underlying dynamical system at\nsufficiently strong coupling. Our findings suggest that, despite their\nmolecular scale, enzymes can be cooperative and improve their performance in\nmetabolic clusters.\n"}
+{"abstract": "  We study multiply recurrent and hypercyclic operators as a special case of\n$\\mathcal F$-hypercyclicity, where $\\mathcal F$ is the family of subsets of the\nnatural numbers containing arbitrarily long arithmetic progressions. We prove\nseveral properties of hypercyclic multiply recurrent operators, we characterize\nthose operators which are weakly mixing and multiply recurrent, and we show\nthat there are operators that are multiply recurrent and hypercyclic but not\nweakly mixing.\n"}
+{"abstract": "  Secondary animation effects are essential for liveliness. We propose a\nsimple, real-time solution for adding them on top of standard skinning,\nenabling artist-driven stylization of skeletal motion. Our method takes a\nstandard skeleton animation as input, along with a skin mesh and rig weights.\nIt then derives per-vertex deformations from the different linear and angular\nvelocities along the skeletal hierarchy. We highlight two specific applications\nof this general framework, namely the cartoonlike \"squashy\" and \"floppy\"\neffects, achieved from specific combinations of velocity terms. As our results\nshow, combining these effects enables to mimic, enhance and stylize\nphysical-looking behaviours within a standard animation pipeline, for arbitrary\nskinned characters. Interactive on CPU, our method allows for GPU\nimplementation, yielding real-time performances even on large meshes. Animator\ncontrol is supported through a simple interface toolkit, enabling to refine the\ndesired type and magnitude of deformation at relevant vertices by simply\npainting weights. The resulting rigged character automatically responds to new\nskeletal animation, without further input.\n"}
+{"abstract": "  The Gibbs state is widely taken to be the equilibrium state of a system in\ncontact with an environment at temperature $T$. However, non-negligible\ninteractions between system and environment can give rise to an altered state.\nHere we derive general expressions for this mean force Gibbs state, valid for\nany system that interacts with a bosonic reservoir. First, we derive the state\nin the weak coupling limit and find that, in general, it maintains coherences\nwith respect to the bare system Hamiltonian. Second, we develop a new expansion\nmethod suited to investigate the ultrastrong coupling regime. This allows us to\nderive the explicit form for the mean force Gibbs state, and we find that it\nbecomes diagonal in the basis set by the system-reservoir interaction instead\nof the system Hamiltonian. Several examples are discussed including a single\nqubit, a three-level V-system and two coupled qubits all interacting with\nbosonic reservoirs. The results shed light on the presence of coherences in the\nstrong coupling regime, and provide key tools for nanoscale thermodynamics\ninvestigations.\n"}
+{"abstract": "  Cyber-attacks on Industrial Automation and Control Systems (IACS) are rising\nin numbers and sophistication. Embedded controller devices such as Programmable\nLogic Controllers (PLCs), which are central to controlling physical processes,\nmust be secured against attacks on confidentiality, integrity and availability.\nThe focus of this paper is to add design-level support for security in IACS\napplications, especially around inter-PLC communications. We propose an\nend-to-end solution to develop IACS applications with inherent, and parametric\nsupport for security. Built using the IEC 61499 Function Blocks standard, this\nsolution allows us to annotate certain communications as 'secure' during design\ntime. When the application is compiled, these annotations are transformed into\na security layer that implements encrypted communication between PLCs. In this\npaper, we implement a part of this security layer focussed on confidentiality,\ncalled Confidentiality Layer for Function Blocks (CL4FB), which provides a\nrange of encryption/decryption and secure key exchange functionalities. We\nstudy the impact of using CL4FB in IACS applications with real-time\nconstraints. Through a case study focussing on protection functions in\nsmart-grids, we show that varying levels of confidentiality can be achieved\nwhile also meeting hard real-time deadlines.\n"}
+{"abstract": "  Motivated by the variation in reported lattice parameters of\nfloating-zone-grown Nd$_{2}$Zr$_{2}$O$_{7}$ crystals, we have performed a\ndetailed study of the relationship between synthesis environment, structural\ndisorder, and magnetic properties. Using a combination of polycrystalline\nstandards, electron-probe microanalysis and scattering techniques, we show that\ncrystals grown under atmospheric conditions have a reduced lattice parameter -\nrelative to pristine polycrystalline powders - due to occupation of the Nd-site\nby excess Zr (i.e. \"negative\" stuffing). In contrast, crystals grown under\nhigh-pressure Ar are nearly stoichiometric with an average lattice parameter\napproaching the polycrystalline value. While minimal disorder of the oxygen\nsublattices is observed on the scale of the average structure, neutron\npair-distribution function analysis indicates a highly local disorder of the\noxygen coordination, which is only weakly dependent on growth environment. Most\nimportantly, our magnetization, heat capacity and single-crystal neutron\nscattering data show that the magnetic properties of crystals grown under\nhigh-pressure Ar match closely with those of stoichiometric powders. Neutron\nscattering measurements reveal that the signature of magnetic moment\nfragmentation - the coexistence of all-in-all-out (AIAO) magnetic Bragg peaks\nand diffuse pinch-point scattering due to spin-ice correlations - persists in\nthese nearly stoichiometric crystals. However, in addition to an increased AIAO\ntransition temperature, the diffuse signal is seemingly stabilized and remains\nnearly unchanged upon warming to 800 mK. This behavior indicates that both the\nAIAO magnetic order and spin-ice correlations are sensitive to deviations of\nthe Nd stoichiometry.\n"}
+{"abstract": "  Code search is a common practice for developers during software\nimplementation. The challenges of accurate code search mainly lie in the\nknowledge gap between source code and natural language (i.e., queries). Due to\nthe limited code-query pairs and large code-description pairs available, the\nprior studies based on deep learning techniques focus on learning the semantic\nmatching relation between source code and corresponding description texts for\nthe task, and hypothesize that the semantic gap between descriptions and user\nqueries is marginal. In this work, we found that the code search models trained\non code-description pairs may not perform well on user queries, which indicates\nthe semantic distance between queries and code descriptions. To mitigate the\nsemantic distance for more effective code search, we propose QueCos, a\nQuery-enriched Code search model. QueCos learns to generate semantic enriched\nqueries to capture the key semantics of given queries with reinforcement\nlearning (RL). With RL, the code search performance is considered as a reward\nfor producing accurate semantic enriched queries. The enriched queries are\nfinally employed for code search. Experiments on the benchmark datasets show\nthat QueCos can significantly outperform the state-of-the-art code search\nmodels.\n"}
+{"abstract": "  Agent Based Modelling (ABM) is a computational framework for simulating the\nbehaviours and interactions of autonomous agents. As Agent Based Models are\nusually representative of complex systems, obtaining a likelihood function of\nthe model parameters is nearly always intractable. There is a necessity to\nconduct inference in a likelihood free context in order to understand the model\noutput. Approximate Bayesian Computation is a suitable approach for this\ninference. It can be applied to an Agent Based Model to both validate the\nsimulation and infer a set of parameters to describe the model. Recent research\nin ABC has yielded increasingly efficient algorithms for calculating the\napproximate likelihood. These are investigated and compared using a pedestrian\nmodel in the Hamilton CBD.\n"}
+{"abstract": "  We review spherical and inhomogeneous analytic solutions of the field\nequations of Einstein and of scalar-tensor gravity, including Brans-Dicke\ntheory, non-minimally (possibly conformally) coupled scalar fields, Horndeski,\nand beyond Horndeski/DHOST gravity. The zoo includes both static and dynamic\nsolutions, asymptotically flat, and asymptotically\nFriedmann-Lema\\^itre-Robertson-Walker ones. We minimize overlap with existing\nbooks and reviews and we place emphasis on scalar field spacetimes and on\ngeometries that are \"general\" within certain classes. Relations between various\nsolutions, which have largely emerged during the last decade, are pointed out.\n"}
+{"abstract": "  We study the gravitational radiation emitted during the scattering of two\nspinless bodies in the post-Minkowskian Effective Field Theory approach. We\nderive the conserved stress-energy tensor linearly coupled to gravity and the\nclassical probability amplitude of graviton emission at leading and\nnext-to-leading order in the Newton's constant $G$. The amplitude can be\nexpressed in compact form as one-dimensional integrals over a Feynman parameter\ninvolving Bessel functions. We use it to recover the leading-order radiated\nangular momentum expression. Upon expanding it in the relative velocity between\nthe two bodies $v$, we compute the total four-momentum radiated into\ngravitational waves at leading-order in $G$ and up to an order $v^8$, finding\nagreement with what was recently computed using scattering amplitude methods.\nOur results also allow us to investigate the zero frequency limit of the\nemitted energy spectrum.\n"}
+{"abstract": "  We consider the problem of estimating volatility based on high-frequency data\nwhen the observed price process is a continuous It\\^o semimartingale\ncontaminated by microstructure noise. Assuming that the noise process is\ncompatible across different sampling frequencies, we argue that it typically\nhas a similar local behavior to fractional Brownian motion. For the resulting\nclass of processes, which we call mixed semimartingales, we derive consistent\nestimators and asymptotic confidence intervals for the roughness parameter of\nthe noise and the integrated price and noise volatilities, in all cases where\nthese quantities are identifiable. Our model can explain key features of recent\nstock price data, most notably divergence rates in volatility signature plots\nthat vary considerably over time and between assets.\n"}
+{"abstract": "  Many studies have proposed a relationship between COVID-19 transmissibility\nand ambient pollution levels. However, a major limitation in establishing such\nassociations is to adequately account for complex disease dynamics, influenced\nby e.g. significant differences in control measures and testing policies.\nAnother difficulty is appropriately controlling the effects of other\npotentially important factors, due to both their mutual correlations and a\nlimited dataset. To overcome these difficulties, we will here use the basic\nreproduction number ($R_0$) that we estimate for USA states using non-linear\ndynamics methods. To account for a large number of predictors (many of which\nare mutually strongly correlated), combined with a limited dataset, we employ\nmachine-learning methods. Specifically, to reduce dimensionality without\ncomplicating the variable interpretation, we employ Principal Component\nAnalysis on subsets of mutually related (and correlated) predictors. Methods\nthat allow feature (predictor) selection, and ranking their importance, are\nthen used, including both linear regressions with regularization and feature\nselection (Lasso and Elastic Net) and non-parametric methods based on ensembles\nof weak-learners (Random Forest and Gradient Boost). Through these\nsubstantially different approaches, we robustly obtain that PM$_{2.5}$ is a\nmajor predictor of $R_0$ in USA states, with corrections from factors such as\nother pollutants, prosperity measures, population density, chronic disease\nlevels, and possibly racial composition. As a rough magnitude estimate, we\nobtain that a relative change in $R_0$, with variations in pollution levels\nobserved in the USA, is typically ~30%, which further underscores the\nimportance of pollution in COVID-19 transmissibility.\n"}
+{"abstract": "  The paper introduces a general mathematical framework for action based field\ntheories on Finsler spacetimes. As most often fields on Finsler spacetime\n(e.g., the Finsler fundamental function or the resulting metric tensor) have a\nhomogeneous dependence on the tangent directions of spacetime, we construct the\nappropriate configuration bundles whose sections are such homogeneous fields;\non these configuration bundles, the tools of coordinate free calculus of\nvariations can be consistently applied to obtain field equations. Moreover, we\nprove that general covariance of natural Finsler field Lagrangians leads to an\naveraged energy-momentum conservation law which, in the particular case of\nLorentzian spacetimes, is equivalent to the usual, pointwise energy-momentum\ncovariant conservation law.\n"}
+{"abstract": "  In e-commerce industry, user behavior sequence data has been widely used in\nmany business units such as search and merchandising to improve their products.\nHowever, it is rarely used in financial services not only due to its 3V\ncharacteristics - i.e. Volume, Velocity and Variety - but also due to its\nunstructured nature. In this paper, we propose a Financial Service scenario\nDeep learning based Behavior data representation method for Clustering\n(FinDeepBehaviorCluster) to detect fraudulent transactions. To utilize the\nbehavior sequence data, we treat click stream data as event sequence, use time\nattention based Bi-LSTM to learn the sequence embedding in an unsupervised\nfashion, and combine them with intuitive features generated by risk experts to\nform a hybrid feature representation. We also propose a GPU powered HDBSCAN\n(pHDBSCAN) algorithm, which is an engineering optimization for the original\nHDBSCAN algorithm based on FAISS project, so that clustering can be carried out\non hundreds of millions of transactions within a few minutes. The computation\nefficiency of the algorithm has increased 500 times compared with the original\nimplementation, which makes flash fraud pattern detection feasible. Our\nexperimental results show that the proposed FinDeepBehaviorCluster framework is\nable to catch missed fraudulent transactions with considerable business values.\nIn addition, rule extraction method is applied to extract patterns from risky\nclusters using intuitive features, so that narrative descriptions can be\nattached to the risky clusters for case investigation, and unknown risk\npatterns can be mined for real-time fraud detection. In summary,\nFinDeepBehaviorCluster as a complementary risk management strategy to the\nexisting real-time fraud detection engine, can further increase our fraud\ndetection and proactive risk defense capabilities.\n"}
+{"abstract": "  In the emerging field of molecular communication (MC), testbeds are needed to\nvalidate theoretical concepts, motivate applications, and guide further\nmodeling efforts. To this end, this paper presents a flexible and extendable\nin-vessel MC testbed based on superparamagnetic iron oxide nanoparticles\n(SPIONs) dispersed in an aqueous suspension as they are also used for drug\ntargeting in biotechnology. The transmitter is realized by an electronic pump\nfor injection of the SPIONs stored in a syringe via a Y-tubing-connector. A\nsecond pump generates a background flow for signal propagation in the main\ntube, e.g., modeling a part of a chemical reactor or a blood vessel. For signal\nreception, we employ a commercial susceptometer, an electronic device including\na coil, through which the magnetic particles move and non-intrusively generate\nan electrical signal. We identify the physical mechanisms governing\ntransmission, propagation, and reception of SPIONs as signal carriers and\npropose a simple two-parameter mathematical model for the system's channel\nimpulse response (CIR). Reliable communication is demonstrated for\nmodel-agnostic and model-based detection methods for experiments with 400\nrandom symbols transmitted via on-off keying modulation with a 1 s symbol\ninterval. Moreover, the proposed CIR model is shown to consistently capture the\nexperimentally observed distance-dependent impulse response peak heights and\npeak decays for transmission distances from 5 to 40 cm.\n"}
+{"abstract": "  Three decades of intensive research in molecular nanomagnets have brought the\nmagnetic memory in molecules from liquid helium to liquid nitrogen temperature.\nThe enhancement of this operational temperature relies on a wise choice of the\nmagnetic ion and the coordination environment. However, serendipity,\noversimplified theories and chemical intuition have played the main role. In\norder to establish a powerful framework for statistically driven chemical\ndesign, we collected chemical and physical data for lanthanide-based\nnanomagnets to create a catalogue of over 1400 published experiments, developed\nan interactive dashboard (SIMDAVIS) to visualise the dataset, and applied\ninferential statistical analysis to it. We found that the effective energy\nbarrier derived from the Arrhenius equation displays an excellent correlation\nwith the magnetic memory, and that among all chemical families studied, only\nterbium bis-phthalocyaninato sandwiches and dysprosium metallocenes\nconsistently present magnetic memory up to high temperature, but that there are\nsome promising strategies for improvement.\n"}
+{"abstract": "  We present Self-Ensembling Single-Stage object Detector (SE-SSD) for accurate\nand efficient 3D object detection in outdoor point clouds. Our key focus is on\nexploiting both soft and hard targets with our formulated constraints to\njointly optimize the model, without introducing extra computation in the\ninference. Specifically, SE-SSD contains a pair of teacher and student SSDs, in\nwhich we design an effective IoU-based matching strategy to filter soft targets\nfrom the teacher and formulate a consistency loss to align student predictions\nwith them. Also, to maximize the distilled knowledge for ensembling the\nteacher, we design a new augmentation scheme to produce shape-aware augmented\nsamples to train the student, aiming to encourage it to infer complete object\nshapes. Lastly, to better exploit hard targets, we design an ODIoU loss to\nsupervise the student with constraints on the predicted box centers and\norientations. Our SE-SSD attains top performance compared with all prior\npublished works. Also, it attains top precisions for car detection in the KITTI\nbenchmark (ranked 1st and 2nd on the BEV and 3D leaderboards, respectively)\nwith an ultra-high inference speed. The code is available at\nhttps://github.com/Vegeta2020/SE-SSD.\n"}
+{"abstract": "  In the last few years, Motor Current Signature Analysis (MCSA) has proven to\nbe an effective method for electrical machines condition monitoring. Indeed,\nmany mechanical and electrical faults manifest as side-band spectral components\ngenerated around the fundamental frequency component of the motor current.\nThese components are called interharmonics and they are a major focus of fault\ndetection using MCSA. However, the main drawback of this approach is that the\ninterference of other more prevalent components can obstruct the effect of\ninterharmonics in the spectrum and may therefore impede fault detection\naccuracy. Thus, we propose in this paper an alternative approach that\ndecomposes the different current components based on the Vandermonde model and\nimplements the tracking of each distinct component in time and spectral\ndomains. This is achieved by estimating their respective relevant parameters\nusing the Gradient Descent algorithm. The results of this work prove to be\npromising and establish the parametric tracking of the electrical current\ncomponents using the Gradient Descent algorithm as a reliable monitoring\napproach.\n"}
+{"abstract": "  The photospheric emission in the prompt phase is the natural prediction of\nthe original fireball model for gamma-ray burst (GRB) due to the large optical\ndepth ($\\tau >1$) at the base of the outflow, which is supported by the\nquasi-thermal components detected in several Fermi GRBs. However, which\nradiation mechanism (photosphere or synchrotron) dominates in most GRB spectra\nis still under hot debate. The shape of the observed photosphere spectrum from\na pure hot fireball or a pure Poynting-flux-dominated outflow has been\ninvestigated before. In this work, we further study the photosphere spectrum\nfrom a hybrid outflow containing both a thermal component and a magnetic\ncomponent with moderate magnetization ($\\sigma_{0}=L_{P}/L_{\\text{Th}}\\sim\n1-10$), by invoking the probability photosphere model. The high-energy spectrum\nfrom such a hybrid outflow is a power law rather than an exponential cutoff,\nwhich is compatible with the observed Band function in large amounts of GRBs.\nAlso, the distribution of the low-energy indices (corresponding to the\npeak-flux spectra) is found to be quite consistent with the statistical result\nfor the peak-flux spectra of GRBs best-fitted by the Band function, with\nsimilar angular profiles of structured jet in our previous works. Finally, the\nobserved distribution of the high-energy indices can be well understood after\nconsidering the different magnetic acceleration (due to magnetic reconnection\nand kink instability) and the angular profiles of dimensionless entropy with\nthe narrower core.\n"}
+{"abstract": "  Bayesian parameter estimation of gravitational waves from compact binary\ncoalescence (CBC) typically requires more than millions of evaluations of\ncomputationally expensive template waveforms. We propose a technique to reduce\nthe cost of waveform generation by exploiting the chirping behavior of CBC\nsignal. Our technique does not require waveforms at all frequencies in the\nfrequency range used in the analysis, and does not suffer from the fixed cost\ndue to the upsampling of waveforms. Our technique speeds up the parameter\nestimation of typical binary neutron star signal by a factor of\n$\\mathcal{O}(10)$ for the low-frequency cutoff of $20\\,\\mathrm{Hz}$, and\n$\\mathcal{O}(10^2)$ for $5\\,\\mathrm{Hz}$. It does not require any offline\npreparations or accurate estimates of source parameters provided by detection\npipelines.\n"}
+{"abstract": "  Background: By the beginning of December 2020, some vaccines against COVID-19\nalready presented efficacy and security, which qualify them to be used in mass\nvaccination campaigns. Thus, setting up strategies of vaccination became\ncrucial to control the COVID19 pandemic. Methods: We use daily COVID-19 reports\nfrom Chicago and NYC from 01-Mar2020 to 28- Nov-2020 to estimate the parameters\nof an SEIR-like epidemiological model that accounts for different severity\nlevels. To achieve data adherent predictions, we let the model parameters to be\ntime-dependent. The model is used to forecast different vaccination scenarios,\nwhere the campaign starts at different dates, from 01-Oct-2020 to 01-Apr-2021.\nTo generate realistic scenarios, disease control strategies are implemented\nwhenever the number of predicted daily hospitalizations reaches a preset\nthreshold. Results: The model reproduces the empirical data with remarkable\naccuracy. Delaying the vaccination severely affects the mortality,\nhospitalization, and recovery projections. In Chicago, the disease spread was\nunder control, reducing the mortality increment as the start of the vaccination\nwas postponed. In NYC, the number of cases was increasing, thus, the estimated\nmodel predicted a much larger impact, despite the implementation of contention\nmeasures. The earlier the vaccination campaign begins, the larger is its\npotential impact in reducing the COVID-19 cases, as well as in the\nhospitalizations and deaths. Moreover, the rate at which cases,\nhospitalizations and deaths increase with the delay in the vaccination\nbeginning strongly depends on the shape of the incidence of infection in each\ncity.\n"}
+{"abstract": "  In this paper we introduce a $(p, q)$-deformed analogues of the generalized\nTouchard polynomials and Stirling numbers, the post-quantum analogues of the\n$q$-deformed generalized Touchard polynomials and Stirling numbers. The\nconnection between these deformations is established. A recurrence relation for\nthe $(p, q)$-deformed generalized Touchard polynomials is expounded,\nelucidating a $(p, q)$-deformation of Spivey's relation.\n"}
+{"abstract": "  On July 16, 1945, the Trinity nuclear test exploded in the desert near\nAlamogordo, NM. A variety of new diagnostic experiments were fielded in an\neffort to understand the detailed performance of the nuclear device. This\narticle describes a series of radiochemical experiments that were designed to\nmeasure the efficiency and neutron fluence of the test. These experiments, and\nthe scientists who led them, laid the foundation of weapons radiochemistry for\ndecades to come.\n"}
+{"abstract": "  We consider the fundamental problem of assigning distinct labels to agents in\nthe probabilistic model of population protocols. Our protocols operate under\nthe assumption that the size $n$ of the population is embedded in the\ntransition function. Our labeling protocols are silent w.h.p., i.e., eventually\neach agent reaches its final state and remains in it forever w.h.p., as well as\nsafe, i.e., never update the label assigned to any single agent. We first\npresent a fast, silent w.h.p.and safe labeling protocol for which the required\nnumber of interactions is asymptotically optimal, i.e., $O(n \\log n/\\epsilon)$\nw.h.p. It uses $(2+\\epsilon)n+O(n^c)$ states, for any $c<1,$ and the label\nrange $1,\\dots,(1+\\epsilon)n.$ Furthermore, we consider the so-called pool\nlabeling protocols that include our fast protocol. We show that the expected\nnumber of interactions required by any pool protocol is $\\ge \\frac{n^2}{r+1}$,\nwhen the labels range is $1,\\dots, n+r<2n.$ Next, we provide a protocol which\nis silent and safe once a unique leader is provided, and uses only $n+5\\sqrt n\n+O(n^c)$ states, for any $c<1,$ and draws labels from the range $1,\\dots,n.$\nThe expected number of interactions required by the protocol is $O(n^3).$ On\nthe other hand, we show that (even if a unique leader is given in advance) any\nsilent protocol that produces a valid labeling and is safe with probability\n$>1-\\frac 1n$, uses $\\ge n+\\sqrt {n-1} -1$ states. Hence, our protocol is\nalmost state-optimal. We also present a generalization of the protocol to\ninclude a trade-off between the number of states and the expected number of\ninteractions. Furthermore, we show that for any silent and safe labeling\nprotocol utilizing $n+t<2n$ states the expected number of interactions required\nto achieve a valid labeling is $\\ge \\frac{n^2}{t+1}$.\n"}
+{"abstract": "  How can a text corpus stored in a customer relationship management (CRM)\ndatabase be used for data mining and segmentation? In order to answer this\nquestion we inherited the state of the art methods commonly used in natural\nlanguage processing (NLP) literature, such as word embeddings, and deep\nlearning literature, such as recurrent neural networks (RNN). We used the text\nnotes from a CRM system which are taken by customer representatives of an\ninternet ads consultancy agency between years 2009 and 2020. We trained word\nembeddings by using the corresponding text corpus and showed that these word\nembeddings can not only be used directly for data mining but also be used in\nRNN architectures, which are deep learning frameworks built with long short\nterm memory (LSTM) units, for more comprehensive segmentation objectives. The\nresults prove that structured text data in a CRM can be used to mine out very\nvaluable information and any CRM can be equipped with useful NLP features once\nthe problem definitions are properly built and the solution methods are\nconveniently implemented.\n"}
+{"abstract": "  Describing properties of a strongly interacting quantum many-body system\nposes a serious challenge both for theory and experiment. In this work, we\nstudy excitations of one-dimensional repulsive Bose gas for arbitrary\ninteraction strength using a hydrodynamic approach. We use linearization to\nstudy particle (type-I) excitations and numerical minimization to study hole\n(type-II) excitations. We observe a good agreement between our approach and\nexact solutions of the Lieb-Liniger model for the particle modes and\ndiscrepancies for the hole modes. Therefore, the hydrodynamical equations find\nto be useful for long-wave structures like phonons and of a limited range of\napplicability for short-wave ones like narrow solitons. We discuss potential\nfurther applications of the method.\n"}
+{"abstract": "  Solving partial differential equations (PDEs) is the canonical approach for\nunderstanding the behavior of physical systems. However, large scale solutions\nof PDEs using state of the art discretization techniques remains an expensive\nproposition. In this work, a new physics-constrained neural network (NN)\napproach is proposed to solve PDEs without labels, with a view to enabling\nhigh-throughput solutions in support of design and decision-making. Distinct\nfrom existing physics-informed NN approaches, where the strong form or weak\nform of PDEs are used to construct the loss function, we write the loss\nfunction of NNs based on the discretized residual of PDEs through an efficient,\nconvolutional operator-based, and vectorized implementation. We explore an\nencoder-decoder NN structure for both deterministic and probabilistic models,\nwith Bayesian NNs (BNNs) for the latter, which allow us to quantify both\nepistemic uncertainty from model parameters and aleatoric uncertainty from\nnoise in the data. For BNNs, the discretized residual is used to construct the\nlikelihood function. In our approach, both deterministic and probabilistic\nconvolutional layers are used to learn the applied boundary conditions (BCs)\nand to detect the problem domain. As both Dirichlet and Neumann BCs are\nspecified as inputs to NNs, a single NN can solve for similar physics, but with\ndifferent BCs and on a number of problem domains. The trained surrogate PDE\nsolvers can also make interpolating and extrapolating (to a certain extent)\npredictions for BCs that they were not exposed to during training. Such\nsurrogate models are of particular importance for problems, where similar types\nof PDEs need to be repeatedly solved for many times with slight variations. We\ndemonstrate the capability and performance of the proposed framework by\napplying it to steady-state diffusion, linear elasticity, and nonlinear\nelasticity.\n"}
+{"abstract": "  Topological phases of matter became a new standard to classify quantum\nsystems in many cases, yet key quantities like the quantum geometric tensor\nproviding local information about topological properties are still\nexperimentally hard to access. In non-Abelian systems this accessibility to\ngeometric properties can be even more restrictive due to the degeneracy of the\nstates. We propose universal protocols to determine quantum geometric\nproperties in non-Abelian systems. First, we show that for a weak resonant\ndriving of the local parameters the coherent Rabi oscillations are related to\nthe quantum geometric tensor. Second, we derive that in a Landau-Zener like\ntransition the final probability of an avoided energy crossing is proportional\nto elements of the non-Abelian quantum geometric tensor. Our schemes suggest a\nway to prepare eigenstates of the quantum metric, a task that is difficult\notherwise in a degenerate subspace.\n"}
+{"abstract": "  Recent research has shown that numerous human-interpretable directions exist\nin the latent space of GANs. In this paper, we develop an automatic procedure\nfor finding directions that lead to foreground-background image separation, and\nwe use these directions to train an image segmentation model without human\nsupervision. Our method is generator-agnostic, producing strong segmentation\nresults with a wide range of different GAN architectures. Furthermore, by\nleveraging GANs pretrained on large datasets such as ImageNet, we are able to\nsegment images from a range of domains without further training or finetuning.\nEvaluating our method on image segmentation benchmarks, we compare favorably to\nprior work while using neither human supervision nor access to the training\ndata. Broadly, our results demonstrate that automatically extracting\nforeground-background structure from pretrained deep generative models can\nserve as a remarkably effective substitute for human supervision.\n"}
+{"abstract": "  We show that a popular self-supervised learning method, InfoNCE, is a special\ncase of a new family of unsupervised learning methods, the self-supervised\nvariational autoencoder (SSVAE). SSVAEs circumvent the usual VAE requirement to\nreconstruct the data by using a carefully chosen implicit decoder. The InfoNCE\nobjective was motivated as a simplified parametric mutual information\nestimator. Under one choice of prior, the SSVAE objective (i.e. the ELBO) is\nexactly equal to the mutual information (up to constants). Under an alternative\nchoice of prior, the SSVAE objective is exactly equal to the simplified\nparametric mutual information estimator used in InfoNCE (up to constants).\nImportantly, the use of simplified parametric mutual information estimators is\nbelieved to be critical to obtain good high-level representations, and the\nSSVAE framework naturally provides a principled justification for using prior\ninformation to choose these estimators.\n"}
+{"abstract": "  In this article, we investigate the convergence rate of the discrete-time\nClark--Ocone formula provided by Akahori--Amaba--Okuma [1]. In that paper, they\nmainly focus on the $L_{2}$-convergence rate of the first-order error estimate\nrelated to the tracking error of the delta hedge in mathematical finance. Here,\nas two extensions, we estimate \"the higher order error\" for Wiener functionals\nwith an integrability index $2$ and \"an arbitrary differentiability index.\"\n"}
+{"abstract": "  Simple graph algorithms such as PageRank have been the target of numerous\nhardware accelerators. Yet, there also exist much more complex graph mining\nalgorithms for problems such as clustering or maximal clique listing. These\nalgorithms are memory-bound and thus could be accelerated by hardware\ntechniques such as Processing-in-Memory (PIM). However, they also come with\nnonstraightforward parallelism and complicated memory access patterns. In this\nwork, we address this problem with a simple yet surprisingly powerful\nobservation: operations on sets of vertices, such as intersection or union,\nform a large part of many complex graph mining algorithms, and can offer rich\nand simple parallelism at multiple levels. This observation drives our\ncross-layer design, in which we (1) expose set operations using a novel\nprogramming paradigm, (2) express and execute these operations efficiently with\ncarefully designed set-centric ISA extensions called SISA, and (3) use PIM to\naccelerate SISA instructions. The key design idea is to alleviate the bandwidth\nneeds of SISA instructions by mapping set operations to two types of PIM:\nin-DRAM bulk bitwise computing for bitvectors representing high-degree\nvertices, and near-memory logic layers for integer arrays representing\nlow-degree vertices. Set-centric SISA-enhanced algorithms are efficient and\noutperform hand-tuned baselines, offering more than 10x speedup over the\nestablished Bron-Kerbosch algorithm for listing maximal cliques. We deliver\nmore than 10 SISA set-centric algorithm formulations, illustrating SISA's wide\napplicability.\n"}
+{"abstract": "  Redundant storage maintains the performance of distributed systems under\nvarious forms of uncertainty. This paper considers the uncertainty in node\naccess and download service. We consider two access models under two download\nservice models. In one access model, a user can access each node with a fixed\nprobability, and in the other, a user can access a random fixed-size subset of\nnodes. We consider two download service models. In the first (small file)\nmodel, the randomness associated with the file size is negligible. In the\nsecond (large file) model, randomness is associated with both the file size and\nthe system's operations. We focus on the service rate of the system. For a\nfixed redundancy level, the systems' service rate is determined by the\nallocation of coded chunks over the storage nodes. We consider quasi-uniform\nallocations, where coded content is uniformly spread among a subset of nodes.\nThe question we address asks what the size of this subset (spreading) should\nbe. We show that in the small file model, concentrating the coded content to a\nminimum-size subset is universally optimal. For the large file model, the\noptimal spreading depends on the system parameters. These conclusions hold for\nboth access models.\n"}
+{"abstract": "  Thanks to the increasing growth of computational power and data availability,\nthe research in machine learning has advanced with tremendous rapidity.\nNowadays, the majority of automatic decision making systems are based on data.\nHowever, it is well known that machine learning systems can present problematic\nresults if they are built on partial or incomplete data. In fact, in recent\nyears several studies have found a convergence of issues related to the ethics\nand transparency of these systems in the process of data collection and how\nthey are recorded. Although the process of rigorous data collection and\nanalysis is fundamental in the model design, this step is still largely\noverlooked by the machine learning community. For this reason, we propose a\nmethod of data annotation based on Bayesian statistical inference that aims to\nwarn about the risk of discriminatory results of a given data set. In\nparticular, our method aims to deepen knowledge and promote awareness about the\nsampling practices employed to create the training set, highlighting that the\nprobability of success or failure conditioned to a minority membership is given\nby the structure of the data available. We empirically test our system on three\ndatasets commonly accessed by the machine learning community and we investigate\nthe risk of racial discrimination.\n"}
+{"abstract": "  Light scattering is the main limitation for optical imaging. However, light\ncan be focused through or inside turbid media by spatially shaping the incident\nwavefront. Wavefront shaping is ultimately limited by the available photon\nbudget. We developed a new `dual reference' wavefront shaping algorithm that\noptimally uses the available light. Our method allows for multi-target\nwavefront shaping, making it suitable for transmission matrix measurements or\ntransmitting images. We experimentally confirmed the improvement of the focus\nintensity compared to existing methods.\n"}
+{"abstract": "  We present unsupervised parameter learning in a Gaussian variational\ninference setting that combines classic trajectory estimation for mobile robots\nwith deep learning for rich sensor data, all under a single learning objective.\nThe framework is an extension of an existing system identification method that\noptimizes for the observed data likelihood, which we improve with modern\nadvances in batch trajectory estimation and deep learning. Though the framework\nis general to any form of parameter learning and sensor modality, we\ndemonstrate application to feature and uncertainty learning with a deep network\nfor 3D lidar odometry. Our framework learns from only the on-board lidar data,\nand does not require any form of groundtruth supervision. We demonstrate that\nour lidar odometry performs better than existing methods that learn the full\nestimator with a deep network, and comparable to state-of-the-art ICP-based\nmethods on the KITTI odometry dataset. We additionally show results on lidar\ndata from the Oxford RobotCar dataset.\n"}
+{"abstract": "  Mutual learning, in which multiple networks learn by sharing their knowledge,\nimproves the performance of each network. However, the performance of ensembles\nof networks that have undergone mutual learning does not improve significantly\nfrom that of normal ensembles without mutual learning, even though the\nperformance of each network has improved significantly. This may be due to the\nrelationship between the knowledge in mutual learning and the individuality of\nthe networks in the ensemble. In this study, we propose an ensemble method\nusing knowledge transfer to improve the accuracy of ensembles by introducing a\nloss design that promotes diversity among networks in mutual learning. We use\nan attention map as knowledge, which represents the probability distribution\nand information in the middle layer of a network. There are many ways to\ncombine networks and loss designs for knowledge transfer methods. Therefore, we\nuse the automatic optimization of knowledge-transfer graphs to consider a\nvariety of knowledge-transfer methods by graphically representing conventional\nmutual-learning and distillation methods and optimizing each element through\nhyperparameter search. The proposed method consists of a mechanism for\nconstructing an ensemble in a knowledge-transfer graph, attention loss, and a\nloss design that promotes diversity among networks. We explore optimal ensemble\nlearning by optimizing a knowledge-transfer graph to maximize ensemble\naccuracy. From exploration of graphs and evaluation experiments using the\ndatasets of Stanford Dogs, Stanford Cars, and CUB-200-2011, we confirm that the\nproposed method is more accurate than a conventional ensemble method.\n"}
+{"abstract": "  Automatic International Classification of Diseases (ICD) coding is defined as\na kind of text multi-label classification problem, which is difficult because\nthe number of labels is very large and the distribution of labels is\nunbalanced. The label-wise attention mechanism is widely used in automatic ICD\ncoding because it can assign weights to every word in full Electronic Medical\nRecords (EMR) for different ICD codes. However, the label-wise attention\nmechanism is computational redundant and costly. In this paper, we propose a\npseudo label-wise attention mechanism to tackle the problem. Instead of\ncomputing different attention modes for different ICD codes, the pseudo\nlabel-wise attention mechanism automatically merges similar ICD codes and\ncomputes only one attention mode for the similar ICD codes, which greatly\ncompresses the number of attention modes and improves the predicted accuracy.\nIn addition, we apply a more convenient and effective way to obtain the ICD\nvectors, and thus our model can predict new ICD codes by calculating the\nsimilarities between EMR vectors and ICD vectors. Extensive experiments show\nthe superior performance of our model. On the public MIMIC-III dataset and\nprivate Xiangya dataset, our model achieves micro f1 of 0.583 and 0.806,\nrespectively, which outperforms other competing models. Furthermore, we verify\nthe ability of our model in predicting new ICD codes. The case study shows how\npseudo label-wise attention works, and demonstrates the effectiveness of pseudo\nlabel-wise attention mechanism.\n"}
+{"abstract": "  We have carried out a 3D ideal-MHD (Magnetohydrodynamic) simulation to study\nthe evolution of laser generated plasma plume in a moderate external magnetic\nfield (0.13 T) oriented perpendicular to the flow direction of the plasma\nplume. The simulation shows that the plasma plume pushes the external magnetic\nfield lines outward in the direction of the expansion. This leads to\ncompression and bending of the magnetic field lines.The force resulting from\nthe change in shape and the density of magnetic field lines opposes the\nexpansion of the plume. An elliptic layer of shocked plasma is formed at the\nplasma/external field interface leaving a cavity in the plume core due to the\noutward expansion and the inertia of the plume. As the plasma pressure drops\ndue to expansion, the imbalance between the magnetic energy and the internal\nenergy results in the collapse of the cavity. These observations have striking\nsimilarities with the observations of the experiments [Phys. Plasmas 24, 033511\n(2017)] performed recently to study the plasma plume expansion in the presence\nof an external transverse magnetic field. This similarity indicates that the\nphysical mechanisms dominantly governing the plasma plume expansion in the\nmoderate magnetic field are aptly described in the ideal MHD regime. The\nstudies thus show that the laser generated plasma plume can be utilized to\ncarry out interesting experiments on MHD phenomena in a simple laboratory set\nup.\n"}
+{"abstract": "  The perturbative approach was adopted to develop a temperature-dependent\nversion of non-relativistic quantum mechanics in the limit of low-enough\ntemperatures. A generalized, self-consistent Hamiltonian was therefore\nconstructed for an arbitrary quantum-mechanical system in a way that the\nground-state Hamiltonian turned out to be just a limiting case at absolute\nzero. The weak-coupling term connecting the system of interest and its\nimmediate environment was accordingly treated as the perturbation. Applying the\nobtained generalized Hamiltonian to some typical quantum systems with exact\nzero-temperature solutions, including the free particle in a box, the free\nparticle in vacuum, and the harmonic oscillator, up to the first order of\nself-consistency, therefore corrected their associated Hamiltonians, energy\nspectrums, and wavefunctions to be consistent with the low-temperature limit.\nFurther investigation revealed some kind of quantum tunneling effect by a\nresidual probability for the free particle in a box, as a chief consequence of\nthermally coupling to the reservoir. The possible effects of thermal\nenvironment on the main properties of the wavefunctions were also thoroughly\nexamined and discussed.\n"}
+{"abstract": "  We continue our endeavor to investigate lepton number violating (LNV)\nprocesses at low energy in the framework of effective field theory (EFT). In\nthis work we study the LNV tau decays $\\tau^+\\rightarrow \\ell^-P_i^{+}P_j^{+}$,\nwhere $\\ell=e,~\\mu$ and $P^+_{i,j}$ are the lowest-lying charged pseudoscalars\n$\\pi^+,~K^+$. We analyze the dominant contributions in a series of EFTs from\nhigh to low energy scales, namely, the standard model effective field theory\n(SMEFT), the low-energy effective field theory (LEFT), and the chiral\nperturbation theory ($\\chi$PT). The decay branching ratios are expressed in\nterms of the Wilson coefficients of dimension-five and -seven operators in\nSMEFT and hadronic low energy constants. These Wilson coefficients involve the\nfirst and second generations of quarks and all generations of leptons and thus\ncannot be explored in low energy processes such as nuclear neutrinoless double\ndecay or LNV kaon decays. Unfortunately, the current experimental upper limits\non the branching ratios are too weak to set useful constraints on those\ncoefficients. Or, if we assume the new physics scale is larger than 1 TeV, the\nbranching ratios are well below the current experimental bounds. We also\nestimate hadronic uncertainties incurred in applying $\\chi$PT to $\\tau$ decays\nby computing one-loop chiral logarithms and attempt to improve convergence of\nchiral perturbation by employing dispersion relations in the short-distance\npart of the decay amplitudes.\n"}
+{"abstract": "  Decision support systems have become increasingly popular in the domain of\nagriculture. With the development of automated machine learning, agricultural\nexperts are now able to train, evaluate and make predictions using cutting edge\nmachine learning (ML) models without the need for much ML knowledge. Although\nthis automated approach has led to successful results in many scenarios, in\ncertain cases (e.g., when few labeled datasets are available) choosing among\ndifferent models with similar performance metrics is a difficult task.\nFurthermore, these systems do not commonly allow users to incorporate their\ndomain knowledge that could facilitate the task of model selection, and to gain\ninsight into the prediction system for eventual decision making. To address\nthese issues, in this paper we present AHMoSe, a visual support system that\nallows domain experts to better understand, diagnose and compare different\nregression models, primarily by enriching model-agnostic explanations with\ndomain knowledge. To validate AHMoSe, we describe a use case scenario in the\nviticulture domain, grape quality prediction, where the system enables users to\ndiagnose and select prediction models that perform better. We also discuss\nfeedback concerning the design of the tool from both ML and viticulture\nexperts.\n"}
+{"abstract": "  Main challenges in long-tailed recognition come from the imbalanced data\ndistribution and sample scarcity in its tail classes. While techniques have\nbeen proposed to achieve a more balanced training loss and to improve tail\nclasses data variations with synthesized samples, we resort to leverage readily\navailable unlabeled data to boost recognition accuracy. The idea leads to a new\nrecognition setting, namely semi-supervised long-tailed recognition. We argue\nthis setting better resembles the real-world data collection and annotation\nprocess and hence can help close the gap to real-world scenarios. To address\nthe semi-supervised long-tailed recognition problem, we present an alternate\nsampling framework combining the intuitions from successful methods in these\ntwo research areas. The classifier and feature embedding are learned separately\nand updated iteratively. The class-balanced sampling strategy has been\nimplemented to train the classifier in a way not affected by the pseudo labels'\nquality on the unlabeled data. A consistency loss has been introduced to limit\nthe impact from unlabeled data while leveraging them to update the feature\nembedding. We demonstrate significant accuracy improvements over other\ncompetitive methods on two datasets.\n"}
+{"abstract": "  Let $F^{2,m}(\\mathbb{C})$ denote the Fock-Sobolev space of complex plane. In\nthis paper, we complete characterize the semi-commutator\n$(T_f,T_{\\overline{g}}]$ on $F^{2,m}(\\mathbb{C})(m>0)$ is zero, where $f$ and\n$g$ are functions in the set of all finite linear combinations of kernel\nfunctions. We obtain $(T_f,T_{\\overline{g}}]=0$ if and only if at least one of\n$f$ and $g$ is a constant function. The result is different from the result of\nBauer, Choe, and Koo. (J. Funct. Anal., 268 (2015): 3017-3060.)\n"}
+{"abstract": "  Aim: This study is looking for a common pathogenicity between SARS-CoV-2 and\nplasmodium species, in individuals with certain HLA serotypes. Methods: 1-)\nTblastx searches of SARS-CoV-2 are performed by limiting searches to plasmodium\nspecies that infect human. 2-) Aligned sequences in the respective organisms'\nproteomes are searched with blastp. 3-) Binding predictions of the identified\nSARS-CoV-2 peptide to MHC class I supertype representatives are performed. 4-)\nBlastp searches of predicted-epitopes that bind strongly to the identified HLA\nallele are performed by limiting searches to human and to the plasmodium\nspecies. 5-) Peptides with minimum 60 % identity to the predicted-epitopes are\nfound in results. 6-) Peptides among those, which bind strongly to the same HLA\nallele, are predicted. 7-) Step-4 is repeated by limiting searches to human,\nfor peptides sourced by limiting searches to plasmodium species at step-4. 8-)\nStep-5 and 6 are performed with results of 7. Results: CFLGYFCTCYFGLFC peptide\nof SARS-CoV-2 has the highest identity to P. vivax. Its GYFCTCYFGLF and\nYFCTCYFGLF parts are predicted to bind strongly to HLA-A*24:02. Results\nobtained only for peptides homologous to YFCTCYFGLF, as follows: YYCARRFGLF,\nYYCHCPFGVF, and YYCQQYFFLF are potential HLA-A*24:02 epitopes in the human\nproteome. Among FFYTFYFELF, YFVACLFILF, and YFPTITFHLF peptides in the\nplasmodium species' proteomes with strong binding affinity to HLA-A*24:02, only\nFFYTFYFELF of P. falciparum is homologous to the potential HLA-A*24:02 epitope\nYFYLFSLELF in the human proteome. Conclusion: Immune responses to the\nidentified-peptides with similar sequences and strong binding affinities to\nHLA-A*24:02 may lead to autoimmune response risk in individuals with\nHLA-A*24:02 serotypes, upon getting infected with SARS-CoV-2 or P. falciparum.\n"}
+{"abstract": "  The purpose of this paper is to study the existence and uniqueness of\nsolutions to a system of Stochastic Differential Equations (SDEs). The\ncoordinates are bounded by zero and one, and repulse each other according to a\nCoulombian like interaction force. We show the existence of strong and pathwise\nunique solutions to the system until the first multiple collision at zero or\none, and give a sufficient condition on the parameters of the SDEs for this\nmultiple collision not to occur in finite time.\n"}
+{"abstract": "  Since the end of 2019, the SARS-CoV-2 virus known as COVID-19 has spread\nrapidly around the world, forcing many governments to impose restrictive\nblocking or lockdown to combat the pandemic. With locomotion restriction of\npeople in almost of countries of the world, workers and students needed to keep\ntheir activities at home. As a result, people's behavior, habits, and the way\nthey started using the Internet changed significantly. Like professionals of\noffices, the younger played an important role in this behavior, especially in\nthe type of resources used by them. As result, the characterization and traffic\nof communication networks were affected in some way. In this perspective\narticle, we join from many available studies about the COVID-19 effect at\nnetworks and investigate the effects on the Internet traffic of using services\nsuch as video streaming, video conferencing, and gaming during 2020's months of\nthe pandemic.\n"}
+{"abstract": "  We propose a feature generator backbone composed of an ensemble of\nconvolutional neuralnetworks (CNNs) to improve the recently emerging Vision\nTransformer (ViT) models. We tackled the RSNA intracranial hemorrhage\nclassification problem, i.e., identifying various hemorrhage types from\ncomputed tomography (CT) slices. We show that by gradually stacking several\nfeature maps extracted using multiple Xception CNNs, we can develop a\nfeature-rich input for the ViT model. Our approach allowed the ViT model to pay\nattention to relevant features at multiple levels. Moreover, pretraining the n\nCNNs using various paradigms leads to a diverse feature set and further\nimproves the performance of the proposed n-CNN-ViT. We achieved a test accuracy\nof 98.04% with a weighted logarithmic loss value of 0.0708. The proposed\narchitecture is modular and scalable in both the number of CNNs used for\nfeature extraction and the size of the ViT.\n"}
+{"abstract": "  Refractory masonry (refractories) is exposed to in-service loads of different\ntypes. To rationalise the masonry design and failure analysis, differences of\nfailure under cyclic and monotonic loading were studied. For samples of silica\nrefractories tested in wedge splitting set-up global failure parameters and\ncrack trajectories were assessed. Under cyclic loading, higher fracture energy\nand lower brittleness at failure were seen. Cracks of different modes had\nsimilar non-linearity and branching. However, the size and microstructural\ncharacteristics of the fracture process zone was different. In addition, higher\nenergy dissipation during cyclic loading is promoted by repetitive friction\nevents along the crack trajectory.\n"}
+{"abstract": "  We present a way to transfer maximally- or partially-entangled states of n\nsingle-photon-state (SPS) qubits onto n coherent-state (CS) qubits, by\nemploying 2n microwave cavities coupled to a superconducting flux qutrit. The\ntwo logic states of a SPS qubit here are represented by the vacuum state and\nthe single-photon state of a cavity, while the two logic states of a CS qubit\nare encoded with two coherent states of a cavity. Because of using only one\nsuperconducting qutrit as the coupler, the circuit architecture is\nsignificantly simplified. The operation time for the state transfer does not\nincrease with the increasing of the number of qubits. When the dissipation of\nthe system is negligible, the quantum state can be transferred in a\ndeterministic way since no measurement is required. Furthermore, the\nhigher-energy intermediate level of the coupler qutrit is not excited during\nthe entire operation and thus decoherence from the qutrit is greatly\nsuppressed. As a specific example, we numerically demonstrate that the\nhigh-fidelity transfer of a Bell state of two SPS qubits onto two CS qubits is\nachievable within the present-day circuit QED technology. Finally, it is worthy\nto note that when the dissipation is negligible, entangled states of n CS\nqubits can be transferred back onto n SPS qubits by performing reverse\noperations. This proposal is quite general and can be extended to accomplish\nthe same task, by employing a natural or artificial atom to couple 2n microwave\nor optical cavities.\n"}
+{"abstract": "  This paper is concerned with the development, analysis and numerical\nrealization of a novel variational model for the regularization of inverse\nproblems in imaging. The proposed model is inspired by the architecture of\ngenerative convolutional neural networks; it aims to generate the unknown from\nvariables in a latent space via multi-layer convolutions and non-linear\npenalties, and penalizes an associated cost. In contrast to conventional\nneural-network-based approaches, however, the convolution kernels are learned\ndirectly from the measured data such that no training is required. The present\nwork provides a mathematical analysis of the proposed model in a function space\nsetting, including proofs for regularity and existence/stability of solutions,\nand convergence for vanishing noise. Moreover, in a discretized setting, a\nnumerical algorithm for solving various types of inverse problems with the\nproposed model is derived. Numerical results are provided for applications in\ninpainting, denoising, deblurring under noise, super-resolution and JPEG\ndecompression with multiple test images.\n"}
+{"abstract": "  We study the front of the solution to the F-KPP equation with randomized\nnon-linearity. Under suitable assumptions on the randomness involving spatial\nmixing behavior and boundedness, we show that the front of the solution lags at\nmost logarithmically in time behind the front of the solution of the\ncorresponding linearized equation, i.e. the parabolic Anderson model. This can\nbe interpreted as a partial generalization of Bramson's findings for the\nhomogeneous setting. Building on this result, we establish functional central\nlimit theorems for the fronts of the solutions to both equations.\n"}
+{"abstract": "  The young (50-400 Myr) A3V star $\\beta$ Leo is a primary target to study the\nformation history and evolution of extrasolar planetary systems as one of the\nfew stars with known hot ($\\sim$1600$^\\circ$K), warm ($\\sim$600$^\\circ$K), and\ncold ($\\sim$120$^\\circ$K) dust belt components. In this paper, we present deep\nmid-infrared measurements of the warm dust brightness obtained with the Large\nBinocular Telescope Interferometer (LBTI) as part of its exozodiacal dust\nsurvey (HOSTS). The measured excess is 0.47\\%$\\pm$0.050\\% within the central\n1.5 au, rising to 0.81\\%$\\pm$0.026\\% within 4.5 au, outside the habitable zone\nof $\\beta$~Leo. This dust level is 50 $\\pm$ 10 times greater than in the solar\nsystem's zodiacal cloud. Poynting-Robertson drag on the cold dust detected by\nSpitzer and Herschel under-predicts the dust present in the habitable zone of\n$\\beta$~Leo, suggesting an additional delivery mechanism (e.g.,~comets) or an\nadditional belt at $\\sim$5.5 au. A model of these dust components is provided\nwhich implies the absence of planets more than a few Saturn masses between\n$\\sim$5 au and the outer belt at $\\sim$40 au. We also observationally constrain\ngiant planets with the LBTI imaging channel at 3.8~$\\mu$m wavelength. Assuming\nan age of 50 Myr, any planet in the system between approximately 5 au to 50 au\nmust be less than a few Jupiter masses, consistent with our dust model. Taken\ntogether, these observations showcase the deep contrasts and detection\ncapabilities attainable by the LBTI for both warm exozodiacal dust and giant\nexoplanets in or near the habitable zone of nearby stars.\n"}
+{"abstract": "  Blockchain technologies originate from cryptocurrencies. Thus, most\nblockchain technologies assume an environment with a fast and stable network.\nHowever, in some blockchain-based systems, e.g., supply chain management (SCM)\nsystems, some Internet of Things (IOT) nodes can only rely on the low-quality\nnetwork sometimes to achieve consensus. Thus, it is critical to understand the\napplicability of existing consensus algorithms in such environments. We\nperformed a systematic mapping study to evaluate and compare existing consensus\nmechanisms' capability to provide integrity and security with varying network\nproperties. Our study identified 25 state-of-the-art consensus algorithms from\npublished and preprint literature. We categorized and compared the consensus\nalgorithms qualitatively based on established performance and integrity metrics\nand well-known blockchain security issues. Results show that consensus\nalgorithms rely on the synchronous network for correctness cannot provide the\nexpected integrity. Such consensus algorithms may also be vulnerable to\ndistributed-denial-of-service (DDOS) and routing attacks, given limited network\nthroughput. Conversely, asynchronous consensus algorithms, e.g.,\nHoney-BadgerBFT, are deemed more robust against many of these attacks and may\nprovide high integrity in asynchrony events.\n"}
+{"abstract": "  The segmentation of medical images is a fundamental step in automated\nclinical decision support systems. Existing medical image segmentation methods\nbased on supervised deep learning, however, remain problematic because of their\nreliance on large amounts of labelled training data. Although medical imaging\ndata repositories continue to expand, there has not been a commensurate\nincrease in the amount of annotated data. Hence, we propose a new spatial\nguided self-supervised clustering network (SGSCN) for medical image\nsegmentation, where we introduce multiple loss functions designed to aid in\ngrouping image pixels that are spatially connected and have similar feature\nrepresentations. It iteratively learns feature representations and clustering\nassignment of each pixel in an end-to-end fashion from a single image. We also\npropose a context-based consistency loss that better delineates the shape and\nboundaries of image regions. It enforces all the pixels belonging to a cluster\nto be spatially close to the cluster centre. We evaluated our method on 2\npublic medical image datasets and compared it to existing conventional and\nself-supervised clustering methods. Experimental results show that our method\nwas most accurate for medical image segmentation.\n"}
+{"abstract": "  In this paper, we introduce a framework for segmenting instances of a common\nobject class by multiple active contour evolution over semantic segmentation\nmaps of images obtained through fully convolutional networks. The contour\nevolution is cast as an energy minimization problem, where the aggregate energy\nfunctional incorporates a data fit term, an explicit shape model, and accounts\nfor object overlap. Efficient solution neighborhood operators are proposed,\nenabling optimization through metaheuristics such as simulated annealing. We\ninstantiate the proposed framework in the context of segmenting individual\nfallen stems from high-resolution aerial multispectral imagery. We validated\nour approach on 3 real-world scenes of varying complexity. The test plots were\nsituated in regions of the Bavarian Forest National Park, Germany, which\nsustained a heavy bark beetle infestation. Evaluations were performed on both\nthe polygon and line segment level, showing that the multi-contour segmentation\ncan achieve up to 0.93 precision and 0.82 recall. An improvement of up to 7\npercentage points (pp) in recall and 6 in precision compared to an iterative\nsample consensus line segment detection was achieved. Despite the simplicity of\nthe applied shape parametrization, an explicit shape model incorporated into\nthe energy function improved the results by up to 4 pp of recall. Finally, we\nshow the importance of using a deep learning based semantic segmentation method\nas the basis for individual stem detection. Our method is a step towards\nincreased accessibility of automatic fallen tree mapping, due to higher cost\nefficiency of aerial imagery acquisition compared to laser scanning. The\nprecise fallen tree maps could be further used as a basis for plant and animal\nhabitat modeling, studies on carbon sequestration as well as soil quality in\nforest ecosystems.\n"}
+{"abstract": "  Isolating neutral and charged particles from the environment is essential in\nprecision experiments. For decades, this has been achieved by trapping ions\nwith radio-frequency (rf) fields and neutral particles with optical fields.\nRecently, trapping of ions by interaction with light has been demonstrated.\nThis might permit combining the advantages of optical trapping and ions. For\nexample, by superimposing optical traps to investigate ensembles of ions and\natoms in absence of any radiofrequency fields, as well as to benefit from the\nversatile and scalable trapping geometries featured by optical lattices. In\nparticular, ions provide individual addressability, electronic and motional\ndegrees of freedom that can be coherently controlled and detected via high\nfidelity, state-dependent operations. Their long-range Coulomb interaction is\nsignificantly larger compared to those of neutral atoms and molecules. This\nqualifies to study ultra-cold interaction and chemistry of trapped ions and\natoms, as well as to provide a novel platform for higher-dimensional\nexperimental quantum simulations. The aim of this topical review is to present\nthe current state of the art and to discuss current challenges and the\nprospects of the emerging field.\n"}
+{"abstract": "  Let $R>1$ and let $B$ be the Euclidean $4$-ball of radius $R$ with a closed\nsubset ${E}$ removed. Suppose that $B$ embeds symplectically into the unit\ncylinder $\\mathbb{D}^2 \\times \\mathbb{R}^2$. By Gromov's non-squeezing theorem,\n${E}$ must be non-empty. We prove that the Minkowski dimension of ${E}$ is at\nleast $2$, and we exhibit an explicit example showing that this result is\noptimal at least for $R \\leq \\sqrt{2}$. We also discuss the minimum volume of\n${E}$ in the case that the symplectic embedding extends, with bounded Lipschitz\nconstant, to the entire ball.\n"}
+{"abstract": "  The contextual combinatorial semi-bandit problem with linear payoff functions\nis a decision-making problem in which a learner chooses a set of arms with the\nfeature vectors in each round under given constraints so as to maximize the sum\nof rewards of arms. Several existing algorithms have regret bounds that are\noptimal with respect to the number of rounds $T$. However, there is a gap of\n$\\tilde{O}(\\max(\\sqrt{d}, \\sqrt{k}))$ between the current best upper and lower\nbounds, where $d$ is the dimension of the feature vectors, $k$ is the number of\nthe chosen arms in a round, and $\\tilde{O}(\\cdot)$ ignores the logarithmic\nfactors. The dependence of $k$ and $d$ is of practical importance because $k$\nmay be larger than $T$ in real-world applications such as recommender systems.\nIn this paper, we fill the gap by improving the upper and lower bounds. More\nprecisely, we show that the C${}^2$UCB algorithm proposed by Qin, Chen, and Zhu\n(2014) has the optimal regret bound $\\tilde{O}(d\\sqrt{kT} + dk)$ for the\npartition matroid constraints. For general constraints, we propose an algorithm\nthat modifies the reward estimates of arms in the C${}^2$UCB algorithm and\ndemonstrate that it enjoys the optimal regret bound for a more general problem\nthat can take into account other objectives simultaneously. We also show that\nour technique would be applicable to related problems. Numerical experiments\nsupport our theoretical results and considerations.\n"}
+{"abstract": "  It has recently been argued that the geometric-optics mean path length of\nrays inside a refractive object under Lambertian illumination is independent of\nthe scattering strength of the medium [Savo et al., Science 358, 765 (2017)].\nWe here show that it is in fact different in the case of zero-scattering. We\nuncover and explain the role of trapped ray trajectories in creating this\nunexpected discontinuity from zero- to low-scattering. This allows us to derive\nnew analytic results for the zero-scattering mean path length of simple\nrefractive shapes. This work provides a fresh perspective on the study of path\nlength inside refractive objects, with possible applications in for example the\nstudy of scattering by large particles or the design of optical systems.\n"}
+{"abstract": "  We establish quantitative upper and lower bounds for Schr\\\"odinger operators\nwith complex potentials that satisfy some weak form of sparsity. Our first\nresult is a quantitative version of an example, due to S.\\ Boegli (Comm. Math.\nPhys., 2017, 352, 629-639), of a Schr\\\"odinger operator with eigenvalues\naccumulating to every point of the essential spectrum. The second result shows\nthat the eigenvalue bounds of Frank (Bull. Lond. Math. Soc., 2011, 43, 745-750\nand Trans. Amer. Math. Soc., 2018, 370, 219-240) can be improved for sparse\npotentials. The third result generalizes a theorem of Klaus (Ann. Inst. H.\nPoincar\\'e Sect. A (N.S.), 1983, 38, 7-13) on the characterization of the\nessential spectrum to the multidimensional non-selfadjoint case. The fourth\nresult shows that, in one dimension, the purely imaginary (non-sparse) step\npotential has unexpectedly many eigenvalues, comparable to the number of\nresonances. Our examples show that several known upper bounds are sharp.\n"}
+{"abstract": "  The solar radio flux at F10.7 cm and F30 cm is required by most models\ncharacterizing the state of the Earth's upper atmosphere, such as the\nthermosphere and ionosphere to specify satellite orbits, re-entry services,\ncollision avoidance maneuvers and modeling of space debris evolution. We\ndevelop a method called RESONANCE (\"Radio Emissions from the Sun: ONline\nANalytical Computer-aided Estimator\") for the prediction of the 13-month\nsmoothed monthly mean F10.7 and F30 indices 1-24 months ahead. The prediction\nalgorithm includes three steps. First, we apply a 13-month optimized running\nmean technique to effectively reduce the noise in the radio flux data. Second,\nwe provide initial predictions of the F10.7 and F30 indices using the\nMcNish-Lincoln method. Finally, we improve these initial predictions by\ndeveloping an adaptive Kalman filter with the error statistics identification.\nThe root-mean-square-error of predictions with lead times from 1 to 24 months\nis 5-27 sfu for the F10.7 and 3-16 sfu for F30 index, which statistically\noutperforms current algorithms in use. The proposed approach based on Kalman\nfilter is universal and can be applied to improve the initial predictions of a\nprocess under study provided by any other forecasting method. Furthermore, we\npresent a systematic evaluation of re-entry forecast as an application to test\nthe performance of F10.7 predictions on past ESA re-entry campaigns for\npayloads, rocket bodies, and space debris that re-entered from June 2006 to\nJune 2019. The test results demonstrate that the predictions obtained by\nRESONANCE in general also lead to improvements in the forecasts of re-entry\nepochs.\n"}
+{"abstract": "  We propose a framework for deformable linear object prediction. Prediction of\ndeformable objects (e.g., rope) is challenging due to their non-linear dynamics\nand infinite-dimensional configuration spaces. By mapping the dynamics from a\nnon-linear space to a linear space, we can use the good properties of linear\ndynamics for easier learning and more efficient prediction. We learn a locally\nlinear, action-conditioned dynamics model that can be used to predict future\nlatent states. Then, we decode the predicted latent state into the predicted\nstate. We also apply a sampling-based optimization algorithm to select the\noptimal control action. We empirically demonstrate that our approach can\npredict the rope state accurately up to ten steps into the future and that our\nalgorithm can find the optimal action given an initial state and a goal state.\n"}
+{"abstract": "  Recent measurements performed by the ATLAS and CMS collaborations on the Run\n2 dataset are testing QCD with unprecedented precision. The wealth of data, an\never-improving experimental understanding of jets and photons, and novel\nmeasurements of jet substructure enable an improved understanding.\n"}
+{"abstract": "  Hierarchical text classification consists in classifying text documents into\na hierarchy of classes and sub-classes. Although artificial neural networks\nhave proved useful to perform this task, unfortunately they can leak training\ndata information to adversaries due to training data memorization. Using\ndifferential privacy during model training can mitigate leakage attacks against\ntrained models, enabling the models to be shared safely at the cost of reduced\nmodel accuracy. This work investigates the privacy-utility trade-off in\nhierarchical text classification with differential privacy guarantees, and\nidentifies neural network architectures that offer superior trade-offs. To this\nend, we use a white-box membership inference attack to empirically assess the\ninformation leakage of three widely used neural network architectures. We show\nthat large differential privacy parameters already suffice to completely\nmitigate membership inference attacks, thus resulting only in a moderate\ndecrease in model utility. More specifically, for large datasets with long\ntexts we observed Transformer-based models to achieve an overall favorable\nprivacy-utility trade-off, while for smaller datasets with shorter texts\nconvolutional neural networks are preferable.\n"}
+{"abstract": "  Image composition assessment is crucial in aesthetic assessment, which aims\nto assess the overall composition quality of a given image. However, to the\nbest of our knowledge, there is neither dataset nor method specifically\nproposed for this task. In this paper, we contribute the first composition\nassessment dataset CADB with composition scores for each image provided by\nmultiple professional raters. Besides, we propose a composition assessment\nnetwork SAMP-Net with a novel Saliency-Augmented Multi-pattern Pooling (SAMP)\nmodule, which analyses visual layout from the perspectives of multiple\ncomposition patterns. We also leverage composition-relevant attributes to\nfurther boost the performance, and extend Earth Mover's Distance (EMD) loss to\nweighted EMD loss to eliminate the content bias. The experimental results show\nthat our SAMP-Net can perform more favorably than previous aesthetic assessment\napproaches.\n"}
+{"abstract": "  As proposed to describe putative continuous phase transitions between two\nordered phases, the deconfined quantum critical point (DQCP) goes beyond the\nprevalent Landau-Ginzburg-Wilson (LGW) paradigm since its critical theory is\nnot expressed in terms of the order parameters characterizing either state, but\ninvolves fractionalized degrees of freedom and an emergent symmetry. So far,\ngreat efforts have been spent on its equilibrium properties, but the\nnonequilibrium properties therein are largely unknown. Here we study the\nnonequilibrium dynamics of the DQCP via the imaginary-time evolution in the\ntwo-dimensional (2D) J-Q$_3$ model. We discover fascinating nonequilibrium\nscaling behaviors hinging on the process of fractionization and the dynamics of\nemergent symmetry associated with two length scales. Our findings not only\nconstitute a new realm of nonequilibrium criticality in DQCP, but also offer a\ncontrollable knob by which to investigate the dynamics in strongly correlated\nsystems.\n"}
+{"abstract": "  For deployment, neural architecture search should be hardware-aware, in order\nto satisfy the device-specific constraints (e.g., memory usage, latency and\nenergy consumption) and enhance the model efficiency. Existing methods on\nhardware-aware NAS collect a large number of samples (e.g., accuracy and\nlatency) from a target device, either builds a lookup table or a latency\nestimator. However, such approach is impractical in real-world scenarios as\nthere exist numerous devices with different hardware specifications, and\ncollecting samples from such a large number of devices will require prohibitive\ncomputational and monetary cost. To overcome such limitations, we propose\nHardware-adaptive Efficient Latency Predictor (HELP), which formulates the\ndevice-specific latency estimation problem as a meta-learning problem, such\nthat we can estimate the latency of a model's performance for a given task on\nan unseen device with a few samples. To this end, we introduce novel hardware\nembeddings to embed any devices considering them as black-box functions that\noutput latencies, and meta-learn the hardware-adaptive latency predictor in a\ndevice-dependent manner, using the hardware embeddings. We validate the\nproposed HELP for its latency estimation performance on unseen platforms, on\nwhich it achieves high estimation performance with as few as 10 measurement\nsamples, outperforming all relevant baselines. We also validate end-to-end NAS\nframeworks using HELP against ones without it, and show that it largely reduces\nthe total time cost of the base NAS method, in latency-constrained settings.\nCode is available at https://github.com/HayeonLee/HELP.\n"}
+{"abstract": "  Canonical quantization of gravitational systems is obstructed by the problem\nof time. Due to diffeomorphism symmetry the Hamiltonian vanishes: dynamics with\nrespect to a background time parameter appears \"frozen.\" Two strategies towards\nthe quantization of such systems are the identification of a clock degree of\nfreedom before quantization (deparametrization), and quantization on a\nkinematical Hilbert space which is subject to constraints (Dirac quantization).\nThe usual canonical quantization in quantum field theory is analogous to\ndeparametrization. Here we introduce a frozen formalism and Dirac quantization\nfor a complex Klein-Gordon scalar field, and show that the resulting theory is\nequivalent to usual canonical quantization. We then apply the formalism to the\ngroup field theory formalism for quantum gravity, for which both\ndeparametrization and a \"timeless\" quantization have been proposed in past\nwork. We show how a frozen formalism for group field theory links between these\ntwo existing approaches, and illustrate in particular the construction of\nphysical observables. We derive effective cosmological dynamics for group field\ntheory in the new formalism and compare these to previous work. The frozen\nformalism could be extended to other approaches to quantum gravity that do not\nuse a preferred time parameter.\n"}
+{"abstract": "  Intelligent reflection surface (IRS) is emerging as a promising technique for\nfuture wireless communications. Considering its excellent capability in\ncustomizing the channel conditions via energy-focusing and energy-nulling, it\nis an ideal technique for enhancing wireless communication security and\nprivacy, through the theories of physical layer security and covert\ncommunications, respectively. In this article, we first present some results on\napplying IRS to improve the average secrecy rate in wiretap channels, to enable\nperfect communication covertness, and to deliberately create extra randomness\nin wireless propagations for hiding active wireless transmissions. Then, we\nidentify multiple challenges for future research to fully unlock the benefits\noffered by IRS in the context of physical layer security and covert\ncommunications. With the aid of extensive numerical studies, we demonstrate the\nnecessity of designing the amplitudes of the IRS elements in wireless\ncommunications with the consideration of security and privacy, where the\noptimal values are not always $1$ as commonly adopted in the literature.\nFurthermore, we reveal the tradeoff between the achievable secrecy performance\nand the estimation accuracy of the IRS's channel state information (CSI) at\nboth the legitimate and malicious users, which presents the fundamental\nresource allocation challenge in the context of IRS-aided physical layer\nsecurity. Finally, a passive channel estimation methodology exploiting deep\nneural networks and scene images is discussed as a potential solution to\nenabling CSI availability without utilizing resource-hungry pilots. This\nmethodology serves as a visible pathway to significantly improving the covert\ncommunication rate in IRS-aided wireless networks.\n"}
+{"abstract": "  In a time-reversal invariant system, while the anomalous Hall effect\nidentically vanishes in the linear response regime due to the constraint of\ntime-reversal symmetry on the distribution of Berry curvature, a nonlinear Hall\neffect can emerge in the second-order response regime if the inversion symmetry\nis broken to allow a nonzero Berry curvature dipole (BCD) on the Fermi surface.\nIn this work, we study the nonlinear Hall effect of the BCD origin in\ntwo-dimensional doped insulators and semimetals belonging to the symmetry class\nAI which has spinless time-reversal symmetry. Despite that the class AI does\nnot host any strong topological insulator phase in two dimensions, we find that\nthey can still be classified as topologically obstructed insulators and trivial\ninsulators if putting certain constraint on the Hamiltonians. When the\ninsulator gets closer to the phase boundary of the two distinct phases, we find\nthat the BCDs will become more prominent if the doping level is located near\nthe band edge. Moreover, when the insulator undergoes a phase transition\nbetween the two distinct phases, we find that the BCDs will dramatically change\ntheir signs. For the semimetals without inversion symmetry, we find that the\nBCDs will sharply reverse their signs when the doping level crosses the Dirac\npoints. With the shift of the locations of Dirac points in energy, the critical\ndoping level at which the BCDs sharply reverse their signs will accordingly\nchange. Our study reveals that class AI materials can also have interesting\ngeometrical and topological properties, and remarkable nonlinear Hall effect\ncan also appear in this class of materials even though the spin-orbit coupling\nis negligible. Our findings broaden the scope of materials to study the\nnonlinear Hall effect and provide new perspectives for the application of this\neffect.\n"}
+{"abstract": "  We study Choquard type equation of the form $$-\\Delta u +\\varepsilon\nu-(I_{\\alpha}*|u|^p)|u|^{p-2}u+|u|^{q-2}u=0\\quad in \\quad {\\mathbb\nR}^N,\\qquad\\qquad(P_\\varepsilon)$$ where $N\\geq3$, $I_\\alpha$ is the Riesz\npotential with $\\alpha\\in(0,N)$, $p>1$, $q>2$ and $\\varepsilon\\ge 0$. Equations\nof this type describe collective behaviour of self-interacting many-body\nsystems. The nonlocal nonlinear term represents long-range attraction while the\nlocal nonlinear term represents short-range repulsion. In the first part of the\npaper for a nearly optimal range of parameters we prove the existence and study\nregularity and qualitative properties of positive groundstates of $(P_0)$ and\nof $(P_\\varepsilon)$ with $\\varepsilon>0$. We also study the existence of a\ncompactly supported groundstate for an integral Thomas-Fermi type equation\nassociated to $(P_\\varepsilon)$. In the second part of the paper, for\n$\\varepsilon\\to 0$ we identify six different asymptotic regimes and provide a\ncharacterisation of the limit profiles of the groundstates of $(P_\\varepsilon)$\nin each of the regimes. We also outline three different asymptotic regimes in\nthe case $\\varepsilon\\to\\infty$. In one of the asymptotic regimes positive\ngroundstates of $(P_\\varepsilon)$ converge to a compactly supported\nThomas-Fermi limit profile. This is a new and purely nonlocal phenomenon that\ncan not be observed in the local prototype case of $(P_\\varepsilon)$ with\n$\\alpha=0$. In particular, this provides a justification for the Thomas-Fermi\napproximation in astrophysical models of self-gravitating Bose-Einstein\ncondensate.\n"}
+{"abstract": "  We study the thermodynamics of a $d$-dimensional Schwarzschild black hole in\nthe canonical ensemble. This generalizes York's formalism to any number $d$ of\ndimensions. The canonical ensemble, characterized by a cavity of fixed radius\n$r$ and fixed temperature $T$ at the boundary, allows for two possible\nsolutions in thermal equilibrium, a small and a large black hole. From the\nEuclidean action and the path integral approach, we obtain the free energy, the\nthermodynamic energy, the pressure, and the entropy, of the black hole plus\ncavity system. The entropy is given by the Bekenstein-Hawking area law. The\nheat capacity shows that the smaller black hole is in unstable equilibrium and\nthe larger is stable. The photon sphere radius divides the stability criterion.\nTo study perturbations, a generalized free energy function is obtained that\nallows to understand the possible phase transitions between classical hot flat\nspace and the black holes. The Buchdahl radius, that appears naturally in the\ngeneral relativistic study of star structure, also shows up in our context, the\nfree energy is zero when the cavity's radius has the Buchdahl radius value.\nThen, if the cavity's radius is smaller than the Buchdahl radius classical hot\nflat space can nucleate a black hole. It is also pointed out the link between\nthe canonical analysis performed and the direct perturbation of the path\nintegral. Since gravitational hot flat space is a quantum system made purely of\ngravitons it is of interest to compare the free energies of quantum hot flat\nspace and the stable black hole to find for which ranges of $r$ and $T$ one\nphase predominates over the other. Phase diagrams are displayed. The density of\nstates at a given energy is found. Further calculations and comments are\ncarried out, notably, a connection to thin shells in $d$ spacetime dimensions\nwhich are systems that are also apt to rigorous thermodynamics.\n"}
+{"abstract": "  Useful information (UI) is an elusive concept in neural networks. A\nquantitative measurement of UI is absent, despite the variations of UI can be\nrecognized by prior knowledge. The communication bandwidth of feature maps\ndecreases after downscaling operations, but UI flows smoothly after training\ndue to lower Nyquist frequency. Inspired by the low-Nyqusit-frequency nature of\nUI, we propose the use of spectral roll-off points (SROPs) to estimate UI on\nvariations. The computation of an SROP is extended from a 1-D signal to a 2-D\nimage by the required rotation invariance in image classification tasks. SROP\nstatistics across feature maps are implemented as layer-wise useful information\nestimates. We design sanity checks to explore SROP variations when UI\nvariations are produced by variations in model input, model architecture and\ntraining stages. The variations of SROP is synchronizes with UI variations in\nvarious randomized and sufficiently trained model structures. Therefore, SROP\nvariations is an accurate and convenient sign of UI variations, which promotes\nthe explainability of data representations with respect to frequency-domain\nknowledge.\n"}
+{"abstract": "  We study linear stability of solutions to the Navier\\textendash Stokes\nequations with stochastic viscosity. Specifically, we assume that the viscosity\nis given in the form of a~stochastic expansion. Stability analysis requires a\nsolution of the steady-state Navier-Stokes equation and then leads to a\ngeneralized eigenvalue problem, from which we wish to characterize the real\npart of the rightmost eigenvalue. While this can be achieved by Monte Carlo\nsimulation, due to its computational cost we study three surrogates based on\ngeneralized polynomial chaos, Gaussian process regression and a shallow neural\nnetwork. The results of linear stability analysis assessment obtained by the\nsurrogates are compared to that of Monte Carlo simulation using a set of\nnumerical experiments.\n"}
+{"abstract": "  Low-quality face image restoration is a popular research direction in today's\ncomputer vision field. It can be used as a pre-work for tasks such as face\ndetection and face recognition. At present, there is a lot of work to solve the\nproblem of low-quality faces under various environmental conditions. This paper\nmainly focuses on the restoration of motion-blurred faces. In increasingly\nabundant mobile scenes, the fast recovery of motion-blurred faces can bring\nhighly effective speed improvements in tasks such as face matching. In order to\nachieve this goal, a deblurring method for motion-blurred facial image signals\nbased on generative adversarial networks(GANs) is proposed. It uses an\nend-to-end method to train a sharp image generator, i.e., a processor for\nmotion-blurred facial images. This paper introduce the processing progress of\nmotion-blurred images, the development and changes of GANs and some basic\nconcepts. After that, it give the details of network structure and training\noptimization design of the image processor. Then we conducted a motion blur\nimage generation experiment on some general facial data set, and used the pairs\nof blurred and sharp face image data to perform the training and testing\nexperiments of the processor GAN, and gave some visual displays. Finally, MTCNN\nis used to detect the faces of the image generated by the deblurring processor,\nand compare it with the result of the blurred image. From the results, the\nprocessing effect of the deblurring processor on the motion-blurred picture has\na significant improvement both in terms of intuition and evaluation indicators\nof face detection.\n"}
+{"abstract": "  This paper introduces recurrent equilibrium networks (RENs), a new class of\nnonlinear dynamical models for applications in machine learning, system\nidentification and control. The new model class has ``built in'' guarantees of\nstability and robustness: all models in the class are contracting - a strong\nform of nonlinear stability - and models can satisfy prescribed incremental\nintegral quadratic constraints (IQC), including Lipschitz bounds and\nincremental passivity. RENs are otherwise very flexible: they can represent all\nstable linear systems, all previously-known sets of contracting recurrent\nneural networks and echo state networks, all deep feedforward neural networks,\nand all stable Wiener/Hammerstein models. RENs are parameterized directly by a\nvector in R^N, i.e. stability and robustness are ensured without parameter\nconstraints, which simplifies learning since generic methods for unconstrained\noptimization can be used. The performance and robustness of the new model set\nis evaluated on benchmark nonlinear system identification problems, and the\npaper also presents applications in data-driven nonlinear observer design and\ncontrol with stability guarantees.\n"}
+{"abstract": "  Multi-Party Non-Interactive Key Exchange (MP-NIKE) is a fundamental\ncryptographic primitive in which users register into a key generation centre\nand receive a public/private key pair each. After that, any subset of these\nusers can compute a shared key without any interaction. Nowadays, IoT devices\nsuffer from a high number and large size of messages exchanged in the Key\nManagement Protocol (KMP). To overcome this, an MP-NIKE scheme can eliminate\nthe airtime and latency of messages transferred between IoT devices. MP-NIKE\nschemes can be realized by using multilinear maps. There are several attempts\nfor constructing multilinear maps based on indistinguishable obfuscation,\nlattices and the Chinese Remainder Theorem (CRT). Nevertheless, these schemes\nare inefficient in terms of computation cost and memory overhead. Besides,\nseveral attacks have been recently reported against CRT-based and lattice-based\nmultilinear maps. There is only one modular exponentiation-based MP-NIKE scheme\nin the literature which has been claimed to be both secure and efficient. In\nthis article, we present an attack on this scheme based on the Euclidean\nalgorithm, in which two colluding users can obtain the shared key of any\narbitrary subgroup of users. We also propose an efficient and secure MP-NIKE\nscheme. We show how our proposal is secure in the random oracle model assuming\nthe hardness of the root extraction modulo a composite number.\n"}
+{"abstract": "  The task of voice activity detection (VAD) is an often required module in\nvarious speech processing, analysis and classification tasks. While\nstate-of-the-art neural network based VADs can achieve great results, they\noften exceed computational budgets and real-time operating requirements. In\nthis work, we propose a computationally efficient real-time VAD network that\nachieves state-of-the-art results on several public real recording datasets. We\ninvestigate different training targets for the VAD and show that using the\nsegmental voice-to-noise ratio (VNR) is a better and more noise-robust training\ntarget than the clean speech level based VAD. We also show that multi-target\ntraining improves the performance further.\n"}
+{"abstract": "  Event detection is an important step in extracting knowledge from the video.\nIn this paper, we propose a deep learning approach to detect events in a soccer\nmatch emphasizing the distinction between images of red and yellow cards and\nthe correct detection of the images of selected events from other images. This\nmethod includes the following three modules: i) the variational autoencoder\n(VAE) module to differentiate between soccer images and others image, ii) the\nimage classification module to classify the images of events, and iii) the\nfine-grain image classification module to classify the images of red and yellow\ncards. Additionally, a new dataset was introduced for soccer images\nclassification that is employed to train the networks mentioned in the paper.\nIn the final section, 10 UEFA Champions League matches are used to evaluate the\nnetworks' performance and precision in detecting the events. The experiments\ndemonstrate that the proposed method achieves better performance than\nstate-of-the-art methods.\n"}
+{"abstract": "  We propose a novel type of Rydberg dimer, consisting of a Rydberg-state atom\nbound to a distant positive ion. The molecule is formed through long-range\nelectric-multipole interaction between the Rydberg atom and the point-like ion.\nWe present potential energy curves (PECs) that are asymptotically connected\nwith Rydberg $nP$- or $nD$-states of rubidium or cesium. The PECs exhibit deep,\nlong-range wells which support many vibrational states of Rydberg-atom-ion\nmolecules (RAIMs). We consider photo-association of RAIMs in both the weak and\nthe strong optical-coupling regimes between initial and Rydberg states of the\nneutral atom. Experimental considerations for the realization of RAIMs are\ndiscussed.\n"}
+{"abstract": "  The conventional classification of electrolyte solutions as 'strong' or\n'weak' accounts for their charge transport properties, but neglects their mass\ntransport properties, and is not readily applicable to highly concentrated\nsolutions. Here, we use the Onsager transport formalism in combination with\nlinear response theory to attain a more general classification taking into\naccount both charge and mass transport properties. To this end, we define a\nmolar mass transport coefficient {\\Lambda}_mass, which is related to\nequilibrium center-of-mass fluctuations of the mobile ions and which is the\nmass-transport analogue of the molar ionic conductivity {\\Lambda}_charge. Three\nclasses of electrolyte solution are then distinguished: (i) 'Strong'\nelectrolytes with 4{\\Lambda}_mass \\approx {\\Lambda}_charge; (ii) 'weak charge\ntransport electrolytes' with {\\Lambda}_charge << 4{\\Lambda}_mass; and (iii)\n'weak mass transport electrolytes' with 4{\\Lambda}_mass << {\\Lambda}_charge.\nWhile classes (i) and (ii) encompass the classical 'strong' and 'weak'\nelectrolytes, respectively, many highly concentrated electrolytes fall into\nclass (iii) and thus exhibit transport properties clearly distinct from\nclassical 'strong' and 'weak' electrolytes.\n"}
+{"abstract": "  Non-pharmaceutical interventions have been critical in the fight against the\nCOVID-19 pandemic. However, these sanitary measures have been partially lifted\ndue to socioeconomic factors causing a worrisome rebound of the epidemic in\nseveral countries. In this work, we assess the effectiveness of the mitigation\nimplemented to constrain the spread of SARS-CoV-2 in the Mexican territory\nduring 2020. We also investigate to what extent the initial deployment of the\nvaccine will help to mitigate the pandemic and reduce the need for social\ndistancing and other mobility restrictions. Our modeling approach is based on a\nsimple mechanistic Kermack-McKendrick-type model. To quantify the effect of\nNPIs, we perform a monthly Bayesian inference using officially published data.\nThe results suggest that in the absence of the sanitary measures, the\ncumulative number of infections, hospitalizations, and deaths would have been\nat least twice the official number. Moreover, for low vaccine coverage levels,\nrelaxing NPIs may dramatically increase the disease burden; therefore, safety\nmeasures are of critical importance at the early stages of vaccination. The\nsimulations also suggest that it may be more desirable to employ a vaccine with\nlow efficacy but reach a high coverage than a vaccine with high effectiveness\nbut low coverage levels. This supports the hypothesis that single doses to more\nindividuals will be more effective than two doses for every person.\n"}
+{"abstract": "  We consider a nonlinear chain of coupled oscillators, which is a direct\ngeneralization of the classical FPU lattice and exhibits, besides the usual\nnearest neighbor interaction, also next-to-nearest neighbor interaction. For\nthe case of nearest neighbor attraction and next-to-nearest neighbor repulsion\nwe prove that such a lattice admits, in contrast to the classical FPU model,\nmoving modulating front solutions of permanent form, which have small\nconverging tails at infinity and can be approximated by solitary wave solutions\nof the Nonlinear Schr\\\"odinger equation. When the associated potentials are\neven, then the proof yields moving modulating pulse solutions of permanent\nform, whose profiles are spatially localized. Our analysis employs the spatial\ndynamics approach as developed by Iooss and Kirchg\\\"assner. The relevant\nsolutions are constructed on a five-dimensional center manifold and their\npersistence is guaranteed by reversibility arguments.\n"}
+{"abstract": "  Typical use cases of sentiment analysis usually revolve around assessing the\nprobability of a text belonging to a certain sentiment and deriving insight\nconcerning it; little work has been done to explore further use cases derived\nusing those probabilities in the context of rating. In this paper, we redefine\nthe sentiment proportion values as building blocks for a triangle structure,\nallowing us to derive variables for a new formula for classifying text given in\nthe form of product reviews into a group of higher and a group of lower ratings\nand prove a dependence exists between the sentiments and the ratings.\n"}
+{"abstract": "  Object localization in 3D space is a challenging aspect in monocular 3D\nobject detection. Recent advances in 6DoF pose estimation have shown that\npredicting dense 2D-3D correspondence maps between image and object 3D model\nand then estimating object pose via Perspective-n-Point (PnP) algorithm can\nachieve remarkable localization accuracy. Yet these methods rely on training\nwith ground truth of object geometry, which is difficult to acquire in real\noutdoor scenes. To address this issue, we propose MonoRUn, a novel detection\nframework that learns dense correspondences and geometry in a self-supervised\nmanner, with simple 3D bounding box annotations. To regress the pixel-related\n3D object coordinates, we employ a regional reconstruction network with\nuncertainty awareness. For self-supervised training, the predicted 3D\ncoordinates are projected back to the image plane. A Robust KL loss is proposed\nto minimize the uncertainty-weighted reprojection error. During testing phase,\nwe exploit the network uncertainty by propagating it through all downstream\nmodules. More specifically, the uncertainty-driven PnP algorithm is leveraged\nto estimate object pose and its covariance. Extensive experiments demonstrate\nthat our proposed approach outperforms current state-of-the-art methods on\nKITTI benchmark.\n"}
+{"abstract": "  We find algebraic conditions on a group equivalent to the position of its\nDiophantine problem in the Chomsky Hierarchy. In particular, we prove that a\nfinitely generated group has a context-free Diophantine problem if and only if\nit is finite.\n"}
+{"abstract": "  Until now, the second law of thermodynamics is not formulated for a single\n(deterministic and time-symmetric) microscopic trajectory. Here, such a\nformulation is suggested by introducing information as a full-valuable physical\nquantity defining (a) microscopic entropy and (b) information difference\nbetween an observed process and its time reverse. (a)+(b) then form the second\nlaw and imply that macroscopic irreversibility and mesoscopic stochasticity are\nrelative phenomena depending on the used information reference frame. The\nresults provide fundamental insights for further applications in microphysics\n(informational engines, quantum computing, etc.).\n"}
+{"abstract": "  The complete band representations (BRs) have been constructed in the work of\ntopological quantum chemistry. Each BR is expressed by either a certain orbital\nat a set of Wyckoff sites in realspace, or by a set of irreducible\nrepresentations in momentum space. In this work, we define unconventional\nmaterials as the topologically trivial compounds whose occupied bands can be\nexpressedas a sum of elementary BRs, but not a sum of atomic-orbital-induced\nBRs (aBRs). Namely, these materials possess the unconventional feature of the\nmismatch between average electronic centers and atomic positions. The existence\nof an essential BR at an empty site is described by nonzero real-space\ninvariants. The \"valence\" states can be derived by the aBR decomposition, and\nunconventional materials are supposed to have an uncompensatedtotal \"valence\"\nstate. The high-throughput screening for unconventional materials has been\nperformed through the first-principles calculations. We have discovered 392\nunconventional compounds with detailed information in the table of the results,\nincluding thermoelectronic materials, higher-order topological insulators,\nelectrides, hydrogenstorage materials, hydrogen evolution reaction\nelectrocatalysts, electrodes, and superconductors. The diversity of their\ninteresting properties and applications would be widely studied in the future.\n"}
+{"abstract": "  Accurate determination of the full momentum-dependent spin susceptibility\n$\\chi(\\mathbf{q}) $ is very important for the description of magnetism and\nsuperconductivity. While in principle the formalism for calculating\n$\\chi(\\mathbf{q})$ in the linear response density functional theory (DFT) is\nwell established, hardly any publicly available code includes this capability.\nHere, we describe an alternative way to calculate the static\n$\\chi(\\mathbf{q})$, which can be applied to most common DFT codes without\nadditional programming. The method combined standard fixed-spin-moment\ncalculations of $\\chi(\\mathbf{0}) $ with direct calculations of the energy of\nspin spirals stabilized by an artificial Hubbard interaction. From these\ncalculations, $\\chi_{DFT}(\\mathbf{q} )$ can be extracted by inverting the RPA\nformula. We apply this recipe to the recently discovered Ising\nsuperconductivity in NbSe$_2$ monolayer, one of the most exciting findings in\nsuperconductivity in recent years. It was proposed that spin fluctuations may\nstrongly affect the parity of the order parameter. Previous estimates suggested\nproximity to ferromagnetism, $i.e.$, $\\chi(\\mathbf{q})$ peaked at\n$\\mathbf{q}=0$. We find that the structure of spin fluctuations is more\ncomplicated, with the fluctuation spectrum sharply peaked at $\\mathbf{q}\\approx\n(0.2,0)$. Such a spectrum would change the interband pairing interaction and\nconsiderably affect the superconducting state.\n"}
+{"abstract": "  Many people consider news articles to be a reliable source of information on\ncurrent events. However, due to the range of factors influencing news agencies,\nsuch coverage may not always be impartial. Media bias, or slanted news\ncoverage, can have a substantial impact on public perception of events, and,\naccordingly, can potentially alter the beliefs and views of the public. The\nmain data gap in current research on media bias detection is a robust,\nrepresentative, and diverse dataset containing annotations of biased words and\nsentences. In particular, existing datasets do not control for the individual\nbackground of annotators, which may affect their assessment and, thus,\nrepresents critical information for contextualizing their annotations. In this\nposter, we present a matrix-based methodology to crowdsource such data using a\nself-developed annotation platform. We also present MBIC (Media Bias Including\nCharacteristics) - the first sample of 1,700 statements representing various\nmedia bias instances. The statements were reviewed by ten annotators each and\ncontain labels for media bias identification both on the word and sentence\nlevel. MBIC is the first available dataset about media bias reporting detailed\ninformation on annotator characteristics and their individual background. The\ncurrent dataset already significantly extends existing data in this domain\nproviding unique and more reliable insights into the perception of bias. In\nfuture, we will further extend it both with respect to the number of articles\nand annotators per article.\n"}
+{"abstract": "  In this paper, we discuss a few recent conjectures made by George Beck\nrelated to the ranks and cranks of partitions. The conjectures for the rank of\na partition were proved by Andrews by using results due to Atkin and\nSwinnerton-Dyer on a suitable generating function, while the conjectures\nrelated to cranks were studied by Shane Chern using weighted partition moments.\nWe revisit the conjectures on the crank of a partition by decomposing the\nrelevant generating function and further explore connections with Apple-Lerch\nseries and tenth order mock theta functions.\n"}
+{"abstract": "  In this paper, we introduce a additive Tridiagonal and Double-Tridiagonal\ncodes over $\\mathbb{F}_4$ and then we study the properties of the code. Also,\nwe find the number of additive Tridiagonal codes over $\\mathbb{F}_4.$ Finally,\nwe study the applications of Double-Tridiagonal codes to secret sharing scheme\nbased on matrix projection.\n"}
+{"abstract": "  Images have become an integral part of online media. This has enhanced\nself-expression and the dissemination of knowledge, but it poses serious\naccessibility challenges. Adequate textual descriptions are rare. Captions are\nmore abundant, but they do not consistently provide the needed descriptive\ndetails, and systems trained on such texts inherit these shortcomings. To\naddress this, we introduce the publicly available Wikipedia-based corpus\nConcadia, which consists of 96,918 images with corresponding English-language\ndescriptions, captions, and surrounding context. We use Concadia to further\ncharacterize the commonalities and differences between descriptions and\ncaptions, and this leads us to the hypothesis that captions, while not\nsubstitutes for descriptions, can provide a useful signal for creating\neffective descriptions. We substantiate this hypothesis by showing that image\ncaptioning systems trained on Concadia benefit from having caption embeddings\nas part of their inputs. These experiments also begin to show how Concadia can\nbe a powerful tool in addressing the underlying accessibility issues posed by\nimage data.\n"}
+{"abstract": "  We study the function space characterization of the inductive bias resulting\nfrom controlling the $\\ell_2$ norm of the weights in linear convolutional\nnetworks. We view this in terms of an induced regularizer in the function space\ngiven by the minimum norm of weights required to realize a linear function. For\ntwo layer linear convolutional networks with $C$ output channels and kernel\nsize $K$, we show the following: (a) If the inputs to the network have a single\nchannel, the induced regularizer for any $K$ is a norm given by a semidefinite\nprogram (SDP) that is independent of the number of output channels $C$. (b) In\ncontrast, for networks with multi-channel inputs, multiple output channels can\nbe necessary to merely realize all matrix-valued linear functions and thus the\ninductive bias does depend on $C$. Further, for sufficiently large $C$, the\ninduced regularizer for $K=1$ and $K=D$ are the nuclear norm and the\n$\\ell_{2,1}$ group-sparse norm, respectively, of the Fourier coefficients. (c)\nComplementing our theoretical results, we show through experiments on MNIST and\nCIFAR-10 that our key findings extend to implicit biases from gradient descent\nin overparameterized networks.\n"}
+{"abstract": "  Loop Quantum Gravity (LQG) is a non-perturbative attempt at quantization of a\nclassical phase space description of gravity in terms of $SU(2)$ connections\nand electric fields. As emphasized recently [1], on this phase space, classical\ngravitational evolution in $time$ can be understood in terms of certain gauge\ncovariant generalizations of Lie derivatives with respect to a $spatial$\n$SU(2)$ Lie algebra valued vector field called the Electric Shift. We present a\nderivation of a quantum dynamics for Euclidean LQG which is informed by this\nunderstanding. In addition to the physically motivated nature of the action of\nthe Euclidean Hamiltonian constraint so derived, the derivation implies that\nthe spin labels of regulating holonomies are determined by corresponding labels\nof the spin network state being acted upon thus eliminating the `spin\n$j$-ambiguity' pointed out by Perez. By virtue of Thiemann's seminal work, the\nEuclidean quantum dynamics plays a crucial role in the construction of the\nLorentzian quantum dynamics so that our considerations also have application to\nLorentzian LQG.\n"}
+{"abstract": "  Previous work has argued that atomic gas mass estimates of galaxies from 21\ncm HI emission are systematically low due to a cold opaque atomic gas\ncomponent. If true, this opaque component necessitates a ~35% correction factor\nrelative to the mass from assuming optically-thin HI emission. These mass\ncorrections are based on fitting HI spectra with a single opaque component\nmodel that produces a distinct \"top-hat\" shaped line profile. Here, we\ninvestigate this issue using deep, high spectral resolution HI VLA observations\nof M31 and M33 to test if these top-hat profiles are instead superpositions of\nmultiple HI components along the line-of-sight. We fit both models and find\nthat >80% of the spectra strongly prefer a multi-component Gaussian model while\n<2% prefer the single opacity-corrected component model. This strong preference\nfor multiple components argues against previous findings of lines-of-sight\ndominated by only cold HI. Our findings are enabled by the improved spectral\nresolution (0.42 km/s), whereas coarser spectral resolution blends multiple\ncomponents together. We also show that the inferred opaque atomic ISM mass\nstrongly depends on the goodness-of-fit definition and is highly uncertain when\nthe inferred spin temperature has a large uncertainty. Finally, we find that\nthe relation of the HI surface density with the dust surface density and\nextinction has significantly more scatter when the inferred HI opacity\ncorrection is applied. These variations are difficult to explain without\nadditionally requiring large variations in the dust properties. Based on these\nfindings, we suggest that the opaque HI mass is best constrained by HI\nabsorption studies.\n"}
+{"abstract": "  The theoretical parametrisation of mixing and time-dependent $CP$ violation\nfor $D^0$decays into two hadrons is reviewed, along with the experimental\nmethods to measure them. While these phenomena are usually neglected for $D^0\n\\to K^- \\pi^+$ decays, this approximation is not always justified at the\ncurrent level of experimental precision. In particular, it is shown to produce\na bias on the measurement of the parameter $y_{CP}$, when this is performed by\nrelying on $D^0 \\to K^- \\pi^+$ decays as normalisation channel, whose size is\naround 40% of the precision of the current world average. The sensitivity to\n$CP$ violation in the mixing achievable by studying $D^0 \\to K^- \\pi^+$ and\nuntagged $D \\to K^- \\pi^+$ decays, where $D$ stands for either of $D^0$ or\n$\\bar{D}^0$ mesons, is also estimated.\n"}
+{"abstract": "  The unique optical properties of phase change materials (PCMs) can be\nexploited to develop efficient reconfigurable photonic devices. Here, we\ndesign, model, and compare the performance of programmable 1X2 optical couplers\nbased on: Ge$_2$Sb$_2$Te$_5$, Ge$_2$Sb$_2$Se$_4$Te$_1$, Sb$_2$Se$_3$, and\nSb$_2$S$_3$ PCMs. Once programmed, these devices are passive, which can reduce\nthe overall energy consumed compared to thermo-optic or electro-optic\nreconfigurable devices. Of all the PCMs studied, our ellipsometry refractive\nindex measurements show that Sb$_2$S$_3$ has the lowest absorption in the\ntelecommunications wavelength band. Moreover, Sb$_2$S$_3$-based couplers show\nthe best overall performance, with the lowest insertion losses in both the\namorphous and crystalline states. We show that by growth crystallization tuning\nat least four different coupling ratios can be reliably programmed into the\nSb$_2$S$_3$ directional couplers. We used this effect to design a 2-bit\ntuneable Sb$_2$S$_3$ directional coupler with a dynamic range close to 32 dB.\nThe bit-depth of the coupler appears to be limited by the crystallization\nstochasticity.\n"}
+{"abstract": "  Programmable photonic circuits of reconfigurable interferometers can be used\nto implement arbitrary operations on optical modes, facilitating a flexible\nplatform for accelerating tasks in quantum simulation, signal processing, and\nartificial intelligence. A major obstacle to scaling up these systems is static\nfabrication error, where small component errors within each device accrue to\nproduce significant errors within the circuit computation. Mitigating this\nerror usually requires numerical optimization dependent on real-time feedback\nfrom the circuit, which can greatly limit the scalability of the hardware. Here\nwe present a deterministic approach to correcting circuit errors by locally\ncorrecting hardware errors within individual optical gates. We apply our\napproach to simulations of large scale optical neural networks and infinite\nimpulse response filters implemented in programmable photonics, finding that\nthey remain resilient to component error well beyond modern day process\ntolerances. Our results highlight a new avenue for scaling up programmable\nphotonics to hundreds of modes within current day fabrication processes.\n"}
+{"abstract": "  We consider the 2D Boussinesq equations with viscous but without thermal\ndissipation and observe that in any neighborhood of Couette flow and\nhydrostatic balance (with respect to local norms) there are time-dependent\ntraveling wave solutions of the form $\\omega=-1+f(t)\\cos(x-ty)$, $\\theta=\\alpha\ny + g(t)\\sin(x-ty)$. As our main result we show that the linearized equations\naround these waves for $\\alpha=0$ exhibit echo chains and norm inflation\ndespite viscous dissipation of the velocity. Furthermore, we construct initial\ndata in a critical Gevrey 3 class, for which temperature and vorticity diverge\nto infinity in Sobolev regularity as $t\\rightarrow \\infty$ but for which the\nvelocity still converges.\n"}
+{"abstract": "  It has been postulated and observed in practice that for prediction problems\nin which covariate data can be naturally partitioned into clusters, ensembling\nalgorithms based on suitably aggregating models trained on individual clusters\noften perform substantially better than methods that ignore the clustering\nstructure in the data. In this paper, we provide theoretical support to these\nempirical observations by asymptotically analyzing linear least squares and\nrandom forest regressions under a linear model. Our main results demonstrate\nthat the benefit of ensembling compared to training a single model on the\nentire data, often termed 'merging', might depend on the underlying bias and\nvariance interplay of the individual predictors to be aggregated. In\nparticular, under both fixed and high dimensional linear models, we show that\nmerging is asymptotically superior to optimal ensembling techniques for linear\nleast squares regression due to the unbiased nature of least squares\nprediction. In contrast, for random forest regression under fixed dimensional\nlinear models, our bounds imply a strict benefit of ensembling over merging.\nFinally, we also present numerical experiments to verify the validity of our\nasymptotic results across different situations.\n"}
+{"abstract": "  Although knowledge bases play an important role in many domains (including in\narchives, where they are sometimes used for entity extraction and semantic\nannotation tasks), it is challenging to build knowledge bases by hand. This is\nowing to a number of factors: Knowledge bases must be accurate, up-to-date,\ncomprehensive, and as flexible and as efficient as possible. These requirements\nmean a large undertaking, in the form of extensive work by subject matter\nexperts (such as scientists, programmers, archivists, and other information\nprofessionals). Even when successfully engineered, manually built knowledge\nbases are typically one-off, use-case-specific, non-standardized,\nhard-to-maintain solutions. We present a scalable, flexible, and extensible\narchitecture for knowledge base construction frameworks. As a work in progress,\nwe extend a specific framework to address some of its design limitations. The\ncontributions presented in this short paper can shed a light on the suitability\nof using AKBC frameworks for computational use cases in this domain and provide\nfuture directions for building improved AKBC frameworks.\n"}
+{"abstract": "  This paper introduces NVCache, an approach that uses a non-volatile main\nmemory (NVMM) as a write cache to improve the write performance of legacy\napplications. We compare NVCache against file systems tailored for NVMM\n(Ext4-DAX and NOVA) and with I/O-heavy applications (SQLite, RocksDB). Our\nevaluation shows that NVCache reaches the performance level of the existing\nstate-of-the-art systems for NVMM, but without their limitations: NVCache does\nnot limit the size of the stored data to the size of the NVMM, and works\ntransparently with unmodified legacy applications, providing additional\npersistence guarantees even when their source code is not available.\n"}
+{"abstract": "  We obtain exact formulas for the time-dependence of a few physical\nobservables for the open XX spin chain with Lindbladian dynamics. Our analysis\nis based on the fact that the Lindblad equation for an arbitrary open quadratic\nsystem of $N$ fermions is explicitly solved in terms of diagonalization of a\n$4N\\times4N$ matrix called structure matrix by following the scheme of the\nthird quantization. We mainly focus on the time-dependence of magnetization and\nspin current. We observe that the spatio-temporal dependence of magnetization\nand spin current show the light-cone structure. Moreover, for a fixed site, we\nobserve specific behaviors which are not seen in the closed XX spin chain. Near\nthe center of the chain, these quantities exhibit an exponential approach to\nthe steady state values and the decay time is given by the Liouvillian gap. The\ncontribution of the corresponding state becomes smaller for sites near a\nboundary, where we observe a plateau regime where magnetization does not change\nover a duration of time. Using the fact that the time derivative of\nmagnetization is written as a product of the Bessel function and exponential\nfunction, we can explain various properties of the plateau regime analytically.\n"}
+{"abstract": "  In this work I investigate the finite temperature Casimir effect due to a\nmassive and charged scalar field that breaks Lorentz invariance in a CPT-even,\naether-like way. I study the cases of Dirichlet and mixed (Dirichlet-Neumann)\nboundary conditions on a pair of parallel plates. I will not examine the case\nof Neumann boundary conditions since it produces the same results as Dirichlet\nboundary conditions. The main tool used in this investigation is the\n$\\zeta$-function technique that allows me to obtain the Helmoltz free energy\nand Casimir pressure in the presence of a uniform magnetic field perpendicular\nto the plates. Three cases of Lorentz asymmetry are studied: timelike,\nspacelike and perpendicular to the magnetic field, spacelike and parallel to\nthe magnetic field. Asymptotic cases of small plate distance, high temperature,\nstrong magnetic field, and large mass will be considered for each of the three\ntypes of Lorentz asymmetry and each of the two types of boundary conditions\nexamined. In all these cases simple and very accurate analytic expressions of\nthe thermal corrections to the Casimir energy and pressure are obtained and I\ndiscover that these corrections strongly depend on the direction of the unit\nvector that produces the breaking of the Lorentz symmetry.\n"}
+{"abstract": "  In the field of animal behavior and behavioral ecology, many standardized\nmethods to observe animal behavior were established in the last decades. While\nthe protocols remained similar, behavioral researchers can take advantage of\ntechnological advancements to enter observations directly onto a handheld\ncomputer (phone, tablet, etc.), saving time and potentially increasing fidelity\nof recordings. However, we now have the choice between many different platforms\nfor recording behavioral observations. Our challenge is choosing the most\nappropriate platform that fits a particular study question, research design,\nbudget, and desired amount of preparatory time. Here, we review six low-cost\nsoftware applications for handheld computers that are available for real-time\nentry of behavioral observations: Animal Behaviour Pro, Animal Observer, BORIS,\nCyberTracker, Prim8, and ZooMonitor. We discuss the preliminary decisions that\nhave to be made about the study design, and we assess the six applications by\nproviding the advantages and disadvantages of each platform and an overall\napplication comparison. Our goal is to help researchers make calculated\ndecisions about what behavioral observation platform is best for their study\nsystem and question.\n"}
+{"abstract": "  The propagating intensity disturbances (PIDs) in plumes are still poorly\nunderstood and their identity (magnetoacoustic waves or flows) remains an open\nquestion. We investigate PIDs in five plumes located in the northern polar\ncoronal hole observed during the 2017 total solar eclipse. Three plumes are\nassociated with coronal bright points, jets and macrospicules at their base\n(active plumes) and the other two plumes are not (quiet plumes). The electron\ntemperature at the base of the plumes is obtained from the filter ratio of\nimages taken with the X-ray Telescope on board Hinode and the passband ratio\naround 400 nm from an eclipse instrument, the Diagnostic Coronagraph Experiment\n(DICE). The phase speed (v_r), frequency (omega), and wavenumber (k) of the\nPIDs in the plumes are obtained by applying a Fourier transformation to the\nspace-time (r-t plane) plots in images taken with the Atmospheric Imaging\nAssembly (AIA) in three different wavelength channels (171 A, 193 A, and 211\nA). We found that the PIDs in the higher temperature AIA channels, 193 and 211\nA, are faster than that of the cooler AIA 171 A channel. This tendency is more\nsignificant for the active plumes than the quiet ones. The observed speed ratio\n(~1.3) between the AIA 171 and 193 A channels is similar to the theoretical\nvalue (1.25) of a slow magnetoacoustic wave. Our results support the idea that\nPIDs in plumes represent a superposition of slow magnetoacoustic waves and\nplasma outflows that consist of dense cool flows and hot coronal jets.\n"}
+{"abstract": "  In order to increase the resilience of distribution systems against\nhigh-impact low-probability (HILP) events, it is important to prioritize assets\ndamaged by these events so that the lost loads, especially sensitive and\nimportant loads, can be recovered faster. For this reason, this paper discusses\nthe prioritization of electricity supply lines for scheduling and prioritizing\nrepair actions. To this end, the economic value of distribution system lines\nhas been considered as a criterion representing the sensitivity of the network\nto hurricanes. The modeling is based on value, in which the load value,\nlifetime-based failure probability of the poles, fragility curve, duration of\nline repair by the maintenance team, and the topology factor have been\nconsidered. This is so that the significance of the demand side, the failure\nextent and accessibility of the lines, the importance of time, and the network\nconfiguration are considered. The results provide an order of line priority for\nfault resolution, in which the topology factor has a larger effect. This\nmodeling has been tested on an IEEE 33-bus network.\n"}
+{"abstract": "  This paper proposes a blind estimation method based on the modulation\ntransfer function and Schroeder model for estimating reverberation time in\nseven-octave bands. Therefore, the speech transmission index and five\nroom-acoustic parameters can be estimated.\n"}
+{"abstract": "  Spectral clustering is one of the most popular clustering methods. However,\nhow to balance the efficiency and effectiveness of the large-scale spectral\nclustering with limited computing resources has not been properly solved for a\nlong time. In this paper, we propose a divide-and-conquer based large-scale\nspectral clustering method to strike a good balance between efficiency and\neffectiveness. In the proposed method, a divide-and-conquer based landmark\nselection algorithm and a novel approximate similarity matrix approach are\ndesigned to construct a sparse similarity matrix within extremely low cost.\nThen clustering results can be computed quickly through a bipartite graph\npartition process. The proposed method achieves the lower computational\ncomplexity than most existing large-scale spectral clustering. Experimental\nresults on ten large-scale datasets have demonstrated the efficiency and\neffectiveness of the proposed methods. The MATLAB code of the proposed method\nand experimental datasets are available at\nhttps://github.com/Li-Hongmin/MyPaperWithCode.\n"}
+{"abstract": "  Given a set $S$ of $n$ distinct keys, a function $f$ that bijectively maps\nthe keys of $S$ into the range $\\{0,\\ldots,n-1\\}$ is called a minimal perfect\nhash function for $S$. Algorithms that find such functions when $n$ is large\nand retain constant evaluation time are of practical interest; for instance,\nsearch engines and databases typically use minimal perfect hash functions to\nquickly assign identifiers to static sets of variable-length keys such as\nstrings. The challenge is to design an algorithm which is efficient in three\ndifferent aspects: time to find $f$ (construction time), time to evaluate $f$\non a key of $S$ (lookup time), and space of representation for $f$. Several\nalgorithms have been proposed to trade-off between these aspects. In 1992, Fox,\nChen, and Heath (FCH) presented an algorithm at SIGIR providing very fast\nlookup evaluation. However, the approach received little attention because of\nits large construction time and higher space consumption compared to other\nsubsequent techniques. Almost thirty years later we revisit their framework and\npresent an improved algorithm that scales well to large sets and reduces space\nconsumption altogether, without compromising the lookup time. We conduct an\nextensive experimental assessment and show that the algorithm finds functions\nthat are competitive in space with state-of-the art techniques and provide\n$2-4\\times$ better lookup time.\n"}
+{"abstract": "  Interior-point methods for linear programming problems require the repeated\nsolution of a linear system of equations. Solving these linear systems is\nnon-trivial due to the severe ill-conditioning of the matrices towards\nconvergence. This issue can be alleviated by incorporating suitable\nregularization terms in the linear programming problem. Regularization also\nallows us to efficiently handle rank deficient constraint matrices. We provide\na convergence analysis of a regularized inexact interior-point method. The term\n`inexact' refers to the fact that we do not need to compute the true solution\nof the linear system of equations, only an approximation thereof. The\nformulation of the algorithm is sufficiently general such that specialized\nlinear algebra routines developed in other work on inexact interior-point\nmethods can also be incorporated in our regularized framework. In this work, we\nexploit the inexactness by using a mixed-precision solver for the linear system\nof equations. More specifically, we perform a Cholesky factorization in IEEE\nsingle precision and use it as a preconditioner for the Conjugate Gradient\nmethod. Numerical experiments illustrate the benefits of this approach applied\nto linear programming problems with a dense constraint matrix.\n"}
+{"abstract": "  The semi-random graph process is a single player game in which the player is\ninitially presented an empty graph on $n$ vertices. In each round, a vertex $u$\nis presented to the player independently and uniformly at random. The player\nthen adaptively selects a vertex $v$, and adds the edge $uv$ to the graph. For\na fixed monotone graph property, the objective of the player is to force the\ngraph to satisfy this property with high probability in as few rounds as\npossible.\n  We focus on the problem of constructing a perfect matching in as few rounds\nas possible. In particular, we present an adaptive strategy for the player\nwhich achieves a perfect matching in $\\beta n$ rounds, where the value of\n$\\beta < 1.206$ is derived from a solution to some system of differential\nequations. This improves upon the previously best known upper bound of\n$(1+2/e+o(1)) \\, n < 1.736 \\, n$ rounds. We also improve the previously best\nlower bound of $(\\ln 2 + o(1)) \\, n > 0.693 \\, n$ and show that the player\ncannot achieve the desired property in less than $\\alpha n$ rounds, where the\nvalue of $\\alpha > 0.932$ is derived from a solution to another system of\ndifferential equations. As a result, the gap between the upper and lower bounds\nis decreased roughly four times.\n"}
+{"abstract": "  Although the use of RGB photometry has exploded in the last decades due to\nthe advent of high-quality and inexpensive digital cameras equipped with\nBayer-like color filter systems, there is surprisingly no catalogue of bright\nstars that can be used for calibration purposes. Since due to their excessive\nbrightness, accurate enough spectrophotometric measurements of bright stars\ntypically cannot be performed with modern large telescopes, we have employed\nhistorical 13-color medium-narrow-band photometric data, gathered with quite\nreliable photomultipliers, to fit the spectrum of 1346 bright stars using\nstellar atmosphere models. This not only constitutes a useful compilation of\nbright spectrophotometric standards well spread in the celestial sphere, the\nUCM library of spectrophotometric spectra, but allows the generation of a\ncatalogue of reference RGB magnitudes, with typical random uncertainties $\\sim\n0.01$ mag. For that purpose, we have defined a new set of spectral sensitivity\ncurves, computed as the median of 28 sets of empirical sensitivity curves from\nthe literature, that can be used to establish a standard RGB photometric\nsystem. Conversions between RGB magnitudes computed with any of these sets of\nempirical RGB curves and those determined with the new standard photometric\nsystem are provided. Even though particular RGB measurements from single\ncameras are not expected to provide extremely accurate photometric data, the\nrepeatability and multiplicity of observations will allow access to a large\namount of exploitable data in many astronomical fields, such as the detailed\nmonitoring of light pollution and its impact on the night sky brightness, or\nthe study of meteors, solar system bodies, variable stars, and transient\nobjects. In addition, the RGB magnitudes presented here make the sky an\naccessible and free laboratory for the calibration of the cameras themselves.\n"}
+{"abstract": "  Generalizing our recent joint paper with Vasily Pestun (arXiv:2001.04929), we\nconstruct a family of $SO(2r),Sp(2r),SO(2r+1)$ rational Lax matrices,\npolynomial in the spectral parameter, parametrized by the divisors on the\nprojective line with coefficients being dominant integral coweights of\nassociated Lie algebras. To this end, we provide the RTT realization of the\nantidominantly shifted extended Drinfeld Yangians of $\\mathfrak{so}_{2r},\n\\mathfrak{sp}_{2r}, \\mathfrak{so}_{2r+1}$, and of their coproduct\nhomomorphisms. This establishes some of the recent conjectures in the physics\nliterature by Costello-Gaiotto-Yagi (arXiv:2103.01835) in the classical types.\n"}
+{"abstract": "  Deep deterministic policy gradient (DDPG) based car-following strategy can\nbreak through the constraints of the differential equation model due to the\nability of exploration on complex environments. However, the car-following\nperformance of DDPG is usually degraded by unreasonable reward function design,\ninsufficient training and low sampling efficiency. In order to solve this kind\nof problem, a hybrid car-following strategy based on DDPG and cooperative\nadaptive cruise control (CACC) is proposed. Firstly, the car-following process\nis modeled as markov decision process to calculate CACC and DDPG simultaneously\nat each frame. Given a current state, two actions are obtained from CACC and\nDDPG, respectively. Then an optimal action, corresponding to the one offering a\nlarger reward, is chosen as the output of the hybrid strategy. Meanwhile, a\nrule is designed to ensure that the change rate of acceleration is smaller than\nthe desired value. Therefore, the proposed strategy not only guarantees the\nbasic performance of car-following through CACC, but also makes full use of the\nadvantages of exploration on complex environments via DDPG. Finally, simulation\nresults show that the car-following performance of proposed strategy is\nimproved significantly as compared with that of DDPG and CACC in the whole\nstate space.\n"}
+{"abstract": "  Optical wireless communication is an effective potential solution for\nenabling high speed next generation cellular networks. In this paper, laser\nsources, in particular, vertical-cavity surface-emitting (VCSEL) lasers are\nused for data transmission due to their high modulation speed compared with\nlight emitting diode (LED) sources. To manage multi-user interference, rate\nsplitting (RS) is implemented where the message of a user is split into common\nand private parts. However, the performance of RS is limited in high density\nnetworks. Therefore, hierarchical rate splitting (HRS) particularly suited in\nhigh density networks is considered. The results demonstrate the high data rate\nachieved by using VCSELs. Moreover, HRS is more suitable for achieving high\nperformance in optical networks compared with RS.\n"}
+{"abstract": "  The pseudogap (PG) regime of the underdoped cuprates arguably remains one of\nthe most enigmatic phenomena of correlated quantum matter. Recent theoretical\nideas suggest that \"fractionalized\" bosonic fields can lead to the PG phase, by\nopening a gap in the anti-nodal (AN) region of the Brillouin zone. Such\nfractionalized boson can originate from modulated particle-particle pairs or\npair density wave (PDW), a magnetic stripe, or a modulated spin one\nparticle-hole pair like a spin density wave (SDW) boson, among others. The main\npicture goes as follows. Electrons under strong coupling tend to form different\ntypes of unstable bosons at high temperatures. As the temperature goes down,\nthe compact object gets extremely unstable, and to minimize the entropy, it\nfinally fractionalizes into elementary components, linked by a constraint. The\nprocess of fractionalization involves, in this way, an emergent gauge field\ndirectly linked to the constraint. This, in turn, couples to the Fermi surface\nof electronic carriers and opens a gap in the AN region, which is partly\nresponsible for the PG phase. Alternative theoretical approaches invoke a\nsimple coexistence between the multiple quasi-degenerate orders like charge\ndensity wave (CDW), superconductivity (SC), and magnetic orders at low\ntemperatures. This scenario attributes the PG formation as a \"vestigial\" order\nshowing up at $T^{*}$, which acts as a precursor to the zero temperature\norders. This intricate situation calls for a key experimental test, enabling us\nto discriminate between the various theoretical scenarios. In this paper, we\nfocus on the case where the PDW boson has fractionalized into a CDW and SC\norder below $T^{*}$, and we compare this to the situation where the two orders\nsimply coexist.\n"}
+{"abstract": "  Scene understanding based on LiDAR point cloud is an essential task for\nautonomous cars to drive safely, which often employs spherical projection to\nmap 3D point cloud into multi-channel 2D images for semantic segmentation. Most\nexisting methods simply stack different point attributes/modalities (e.g.\ncoordinates, intensity, depth, etc.) as image channels to increase information\ncapacity, but ignore distinct characteristics of point attributes in different\nimage channels. We design FPS-Net, a convolutional fusion network that exploits\nthe uniqueness and discrepancy among the projected image channels for optimal\npoint cloud segmentation. FPS-Net adopts an encoder-decoder structure. Instead\nof simply stacking multiple channel images as a single input, we group them\ninto different modalities to first learn modality-specific features separately\nand then map the learned features into a common high-dimensional feature space\nfor pixel-level fusion and learning. Specifically, we design a residual dense\nblock with multiple receptive fields as a building block in the encoder which\npreserves detailed information in each modality and learns hierarchical\nmodality-specific and fused features effectively. In the FPS-Net decoder, we\nuse a recurrent convolution block likewise to hierarchically decode fused\nfeatures into output space for pixel-level classification. Extensive\nexperiments conducted on two widely adopted point cloud datasets show that\nFPS-Net achieves superior semantic segmentation as compared with\nstate-of-the-art projection-based methods. In addition, the proposed modality\nfusion idea is compatible with typical projection-based methods and can be\nincorporated into them with consistent performance improvements.\n"}
+{"abstract": "  Deep learning-based fluence map prediction(DL-FMP) method has been reported\nin the literature, which generated fluence maps for desired dose by deep neural\nnetwork(DNN)-based inverse mapping. We hypothesized that DL-FMP is similar to\ngeneral fluence map optimization(FMO) because it's theoretically based on a\ngeneral inverse mapping. We designed four experiments to validate the\ngeneralizability of DL-FMP to other types of plans apart from the training\ndata, which contained only clinical head and neck(HN) full-arc VMAT plans. The\nfirst three experiments quantified the generalizability of DL-FMP to multiple\nanatomical sites, different delivery modalities, and various degree of\nmodulation(DOM), respectively. The fourth experiment explored the\ngeneralizability and stability to infeasible dose inputs. Results of the first\nexperiment manifested that DL-FMP can generalize to lung, liver, esophagus and\nprostate, with gamma passing rates (GPR) higher than 95%(2%/2mm). The second\nexperiment showed that DL-FMP can generalize to partial-arc plans and predict\npartial-arc fluences. GPR(3mm/3%) ranged from 96% to 99%. DL-FMP cannot\ngenerate fluence maps in discrete beam angles for IMRT input. But the predicted\ndose still agreed with ground truth dose with 93% GPR(5%/5mm). The third\nexperiment demonstrated that DL-FMP can generalize to various DOMs, with\nGPRs(3%/3mm) ranged in 94%-98%. Moreover, the DOM of predicted fluence maps\ncorrelated to the optimality of the input dose accordingly. The fourth\nexperiment exemplified that DL-FMP can make stable predictions for infeasible\ndose input. In conclusion, we validated that DL-FMP can generalize to plans for\nmultiple anatomical sites, plans of different delivery modalities and plans\nwith various DOM. It can also make stable prediction for infeasible input.\n"}
+{"abstract": "  This survey provides an overview of the evolution of visually grounded models\nof spoken language over the last 20 years. Such models are inspired by the\nobservation that when children pick up a language, they rely on a wide range of\nindirect and noisy clues, crucially including signals from the visual modality\nco-occurring with spoken utterances. Several fields have made important\ncontributions to this approach to modeling or mimicking the process of learning\nlanguage: Machine Learning, Natural Language and Speech Processing, Computer\nVision and Cognitive Science. The current paper brings together these\ncontributions in order to provide a useful introduction and overview for\npractitioners in all these areas. We discuss the central research questions\naddressed, the timeline of developments, and the datasets which enabled much of\nthis work. We then summarize the main modeling architectures and offer an\nexhaustive overview of the evaluation metrics and analysis techniques.\n"}
+{"abstract": "  We present a comprehensive mechanism for the emergence of rotational\nhorseshoes and strange attractors in a class of two-parameter families of\nperiodically-perturbed differential equations defining a flow on a\nthree-dimensional manifold. When both parameters are zero, its flow exhibits an\nattracting heteroclinic network associated to two periodic solutions. After\nslightly increasing both parameters, while keeping a two-dimensional connection\nunaltered, we focus our attention in the case where the two-dimensional\ninvariant manifolds of the periodic solutions do not intersect. We prove a wide\nrange of dynamical behaviour, ranging from an attracting quasi-periodic torus\nto rotational horseshoes and H\\'enon-like strange attractors. We illustrate our\nresults with an explicit example.\n"}
+{"abstract": "  We investigate the temperature-dependent behavior emerging in the vicinity of\nthe superfluid (SF) to Mott insulator (MI) transition of interacting bosons in\na two-dimensional optical lattice, described by the Bose-Hubbard model. The\nequilibrium phase diagram at finite temperatures is computed by means of the\ncluster mean-field theory (CMF) where the effect of non-local correlations is\nanalyzed systematically by finite-size scaling of the cluster size. The phase\ndiagram exhibits a rich structure including a transition and a crossover of the\nSF and MI phases respectively to a normal fluid (NF) state at finite\ntemperature. In order to characterize these phases, and the NF transition and\ncrossover scales, we calculate, in addition to the condensate amplitude, the\nsuperfluid fraction, sound velocity and compressibility. The phase boundaries\nobtained by CMF with finite-size scaling agree quantitatively with quantum\nMonte Carlo (QMC) results as well as with experiments. The von Neumann\nentanglement entropy of a cluster exhibits critical enhancement near the SF-MI\nquantum critical point (QCP). We also discuss the behavior of the transition\nlines near this QCP at the particle-hole symmetric point located at the tip of\na Mott lobe as well as away from particle-hole symmetry.\n"}
+{"abstract": "  In this paper, we propose a maximum entropy method for predicting disease\nrisks. It is based on a patient's medical history with diseases coded in ICD-10\nwhich can be used in various cases. The complete algorithm with strict\nmathematical derivation is given. We also present experimental results on a\nmedical dataset, demonstrating that our method performs well in predicting\nfuture disease risks and achieves an accuracy rate twice that of the\ntraditional method. We also perform a comorbidity analysis to reveal the\nintrinsic relation of diseases.\n"}
+{"abstract": "  High-Level Synthesis (HLS) frameworks allow to easily specify a large number\nof variants of the same hardware design by only acting on optimization\ndirectives. Nonetheless, the hardware synthesis of implementations for all\npossible combinations of directive values is impractical even for simple\ndesigns. Addressing this shortcoming, many HLS Design Space Exploration (DSE)\nstrategies have been proposed to devise directive settings leading to\nhigh-quality implementations while limiting the number of synthesis runs. All\nthese works require considerable efforts to validate the proposed strategies\nand/or to build the knowledge base employed to tune abstract models, as both\ntasks mandate the syntheses of large collections of implementations. Currently,\nsuch data gathering is performed ad-hoc, a) leading to a lack of\nstandardization, hampering comparisons between DSE alternatives, and b) posing\na very high burden to researchers willing to develop novel DSE strategies.\nAgainst this backdrop, we here introduce DB4HLS, a database of exhaustive HLS\nexplorations comprising more than 100000 design points collected over 4 years\nof synthesis time. The open structure of DB4HLS allows the incremental\nintegration of new DSEs, which can be easily defined with a dedicated\ndomain-specific language. We think that of our database, available at\nhttps://www.db4hls.inf.usi.ch/, will be a valuable tool for the research\ncommunity investigating automated strategies for the optimization of HLS-based\nhardware designs.\n"}
+{"abstract": "  Path planning for a robot is one of the major problems in the area of\nrobotics. When a robot is given a task in the form of a Linear Temporal Logic\n(LTL) specification such that the task needs to be carried out repetitively, we\nwant the robot to follow the shortest cyclic path so that the number of times\nthe robot completes the mission within a given duration gets maximized. In this\npaper, we address the LTL path planning problem in a dynamic environment where\nthe newly arrived dynamic obstacles may invalidate some of the available paths\nat any arbitrary point in time. We present DT*, an SMT-based receding horizon\nplanning strategy that solves an optimization problem repetitively based on the\ncurrent status of the workspace to lead the robot to follow the best available\npath in the current situation. We implement our algorithm using the Z3 SMT\nsolver and evaluate it extensively on an LTL specification capturing a\npick-and-drop application in a warehouse environment. We compare our SMT-based\nalgorithm with two carefully crafted greedy algorithms. Our experimental\nresults show that the proposed algorithm can deal with the dynamism in the\nworkspace in LTL path planning effectively.\n"}
+{"abstract": "  We provide a simple forest model to encode the genealogical structure of a\nmultitype Galton-Watson process with immigration. We provide two encodings of\nthese forests by stochastic processes. We show, under appropriate conditions,\nthe depth-first encodings of each particular type converge to a solution to a\nsystem of stochastic integral equations involving height processes perturbed by\nfunctionals of their local times. The forest picture allows us to extend the\nRay-Knight theorem and show that local time of the solution to the system of\nequations form a multitype continuous state branching process with immigration.\nThese assumptions underlying our weak convergence arguments are easily seen to\nbe met in the Brownian setting, and more generally an $\\alpha$-stable setting\nfor any $\\alpha\\in(1,2]$.\n"}
+{"abstract": "  We construct a (1+1)$d$ topological field theory (TFT) whose topological\ndefect lines (TDLs) realize the transparent Haagerup $\\mathcal{H}_3$ fusion\ncategory. This TFT has six vacua, and each of the three non-invertible simple\nTDLs hosts three defect operators, giving rise to a total of 15 point-like\noperators. The TFT data, including the three-point coefficients and lasso\ndiagrams, are determined by solving all the sphere four-point crossing\nequations and torus one-point modular invariance equations. We further verify\nthat the Cardy states furnish a non-negative integer matrix representation\nunder TDL fusion. While many of the constraints we derive are not limited to\nthe this particular TFT with six vacua, we leave open the construction of TFTs\nwith two or four vacua. Finally, TFTs realizing the Haagerup $\\mathcal{H}_1$\nand $\\mathcal{H}_2$ fusion categories can be obtained by gauging algebra\nobjects. This note makes a modest offering in our pursuit of exotica and the\nquest for their eventual conformity.\n"}
+{"abstract": "  Human-Computer Interfaces have always played a fundamental role in usability\nand commands' interpretability of the modern software systems. With the\nexplosion of the Artificial Intelligence concept, such interfaces have begun to\nfill the gap between the user and the system itself, further evolving in\nAdaptive User Interfaces (AUI). Meta Interfaces are a further step towards the\nuser, and they aim at supporting the human activities in an ambient interactive\nspace; in such a way, the user can control the surrounding space and interact\nwith it. This work aims at proposing a meta user interface that exploits the\nPut That There paradigm to enable the user to fast interaction by employing\nnatural language and gestures. The application scenario is a video surveillance\ncontrol room, in which the speed of actions and reactions is fundamental for\nurban safety and driver and pedestrian security. The interaction is oriented\ntowards three environments: the first is the control room itself, in which the\noperator can organize the views of the monitors related to the cameras on site\nby vocal commands and gestures, as well as conveying the audio on the headset\nor in the speakers of the room. The second one is related to the control of the\nvideo, in order to go back and forth to a particular scene showing specific\nevents, or zoom in/out a particular camera; the third allows the operator to\nsend rescue vehicle in a particular street, in case of need. The gestures data\nare acquired through a Microsoft Kinect 2 which captures pointing and gestures\nallowing the user to interact multimodally thus increasing the naturalness of\nthe interaction; the related module maps the movement information to a\nparticular instruction, also supported by vocal commands which enable its\nexecution. (cont...)\n"}
+{"abstract": "  In this paper, as the first part of the third step of our study on developing\ndata analysis procedures for using 3-dimensional information offered by\ndirectional direct Dark Matter detection experiments in the future, we present\nour double-Monte Carlo \"scattering-by-scattering\" simulation of the\n3-dimensional elastic WIMP-nucleus scattering process, which can provide 3-D\nvelocity information (the magnitude, the direction, and the incoming/scattering\ntime) of each incident halo WIMP as well as the recoil direction and the recoil\nenergy of the scattered target nucleus in different celestial coordinate\nsystems. For readers' reference, (animated) simulation plots with different\nWIMP masses and several frequently used target nuclei for all functionable\nunderground laboratories can be found and downloaded on our online\n(interactive) demonstration webpage (http://www.tir.tw/phys/hep/dm/amidas-2d/).\n"}
+{"abstract": "  We examine the basic conservation laws for diffeomorphism symmetry in the\ncontext of spontaneous diffeomorphism and local Lorentz-symmetry breaking. The\nconservation laws are used as constraints on a generic series of terms in an\nexpansion around a flat background. We find all such terms for a two-tensor\ncoupling to cubic order in the metric and tensor field fluctuations. The\nresults are presented in a form that can be used for phenomenological\ncalculations. One key result is that if we preserve the underlying\ndiffeomorphism symmetry in a spontaneous-symmetry breaking scenario, one cannot\ndecouple the two-tensor fluctuations from the metric fluctuations at the level\nof the action, except in special cases of the quadratic actions.\n"}
+{"abstract": "  The assumption that wave function collapse is induced by the interactions\nthat generate decoherence leads to a stochastic collapse equation that does not\nrequire the introduction of any new physical constants and that is consistent\nwith conservation laws. The collapse operator is based on the interaction\nenergy, with a variable timing parameter related to the rate at which\nindividual interactions generate the branching process. The approximate\nlocalization of physical systems follows from the distance-dependent nature of\nthe interactions. The equation is consistent with strict conservation of\nmomentum and orbital angular momentum, and it is also consistent with energy\nconservation within the accuracy allowed by the limited forms of energy that\ncan be described within nonrelativistic theory. A relativistic extension of the\nproposal is outlined.\n"}
+{"abstract": "  In response to the soaring needs of human mobility data, especially during\ndisaster events such as the COVID-19 pandemic, and the associated big data\nchallenges, we develop a scalable online platform for extracting, analyzing,\nand sharing multi-source multi-scale human mobility flows. Within the platform,\nan origin-destination-time (ODT) data model is proposed to work with scalable\nquery engines to handle heterogenous mobility data in large volumes with\nextensive spatial coverage, which allows for efficient extraction, query, and\naggregation of billion-level origin-destination (OD) flows in parallel at the\nserver-side. An interactive spatial web portal, ODT Flow Explorer, is developed\nto allow users to explore multi-source mobility datasets with user-defined\nspatiotemporal scales. To promote reproducibility and replicability, we further\ndevelop ODT Flow REST APIs that provide researchers with the flexibility to\naccess the data programmatically via workflows, codes, and programs.\nDemonstrations are provided to illustrate the potential of the APIs integrating\nwith scientific workflows and with the Jupyter Notebook environment. We believe\nthe platform coupled with the derived multi-scale mobility data can assist\nhuman mobility monitoring and analysis during disaster events such as the\nongoing COVID-19 pandemic and benefit both scientific communities and the\ngeneral public in understanding human mobility dynamics.\n"}
+{"abstract": "  This document presents the OpenSHMEM extension for the Special Karlsruhe MPI\nbenchmark and the measurement algorithms used to measure the routines.\n"}
+{"abstract": "  The location of the obscuring \"torus\" in an active galactic nucleus (AGN) is\nstill an unresolved issue. The line widths of X-ray fluorescence lines\noriginated from the torus, particularly Fe K$\\alpha$, carry key information on\nthe radii of line emitting regions. Utilizing XCLUMPY (Tanimoto et al. 2019),\nan X-ray clumpy torus model, we develop a realistic model of emission line\nprofiles from an AGN torus where we take into account line broadening due to\nthe Keplerian motion around the black hole. Then, we apply the updated model to\nthe best available broadband spectra (3-100 keV) of the Circinus galaxy\nobserved with Suzaku, XMM-Newton, Nuclear Spectroscopic Telescope Array\n(NuSTAR), and Chandra, including 0.62 Ms Chandra/HETG data. We confirm that the\ntorus is Compton-thick (hydrogen column-density along the equatorial plane is\n$N_\\mathrm{H}^\\mathrm{Equ}=2.16^{+0.24}_{-0.16}\\times 10^{25}\\\n\\mathrm{cm}^{-2}$), geometrically thin (torus angular width\n$\\sigma=10.3^{+0.7}_{-0.3}\\ \\mathrm{degrees}$), viewed edge-on (inclination\n$i=78.3^{+0.4}_{-0.9}\\ \\mathrm{degrees}$), and has super-solar abundance\n($1.52^{+0.04}_{-0.06}$ times solar). Simultaneously analyzing the Chandra/HETG\nfirst, second, and third order spectra with consideration of the spatial extent\nof the Fe K$\\alpha$ line emitting region, we constrain the inner radius of the\ntorus to be $1.9^{+3.1}_{-0.8}\\times 10^5$ times the gravitational radius, or\n$1.6^{+1.5}_{-0.9}\\times 10^{-2}\\ \\mathrm{pc}$ for a black hole mass of\n$(1.7\\pm 0.3)\\times 10^6\\ M_{\\odot}$. This is about 3 times smaller than that\nestimated from the dust sublimation radius, suggesting that the inner side of\nthe dusty region of the torus is composed of dust-free gas.\n"}
+{"abstract": "  Let $\\Gamma=(\\mathcal{V},\\mathcal{E})$ be a graph, whose vertices $v\\in\n\\mathcal{V}$ are colored black and white and labeled with invertible elements\n$\\lambda_v$ from a commutative and associative ring $R$ containing $\\pm 1$.\nThen we consider the associative algebra $\\mathfrak{C}(\\Gamma)$ with identity\nelement $\\mathbf{1}$ generated by the elements of $\\mathcal{V}$ such that for\nall $v,w\\in \\mathcal{V}$ we have\n  \\[\\begin{array}{lll}v^2 &=\\lambda_v\\mathbf{1}&\\textrm{if } v \\textrm{ is\nwhite},\n  v^2 &=-\\lambda_v\\mathbf{1}&\\textrm{if } v \\textrm{ is black},\n  vw+wv&=0&\\textrm{if } \\{v,w\\}\\in \\mathcal{E},\n  vw-wv&=0&\\textrm{if } \\{v,w\\}\\not\\in \\mathcal{E}.\\\\ \\end{array}\\] If $\\Gamma$\nis the complete graph, $\\mathfrak{C}(\\Gamma)$ is a Clifford algebra, otherwise\nit is a so-called quasi-Clifford algebra. We describe this algebra as a twisted\ngroup algebra with the help of a quadratic space $(V,Q)$ over the field\n$\\mathbb{F}_2$. Using this description, we determine the isomorphism type of\n$\\mathfrak{C}(\\Gamma)$ in several interesting examples. As the algebra\n$\\mathfrak{C}(\\Gamma)$ is associative, we can also consider the corresponding\nLie algebra and some of its subalgebras. In case $\\lambda_v=1$ for all $v\\in\n\\mathcal{V}$, and all vertices are black, we find that the elements $v,w\\in\n\\mathcal{V}$ satisfy the following relations $$\\begin{array}{lll}\n  [v,w]&=0&\\textrm{if } \\{v,w\\}\\not\\in \\mathcal{E},\n  {[v,[v,w]]}&=-w&\\textrm{if } \\{v,w\\}\\in \\mathcal{E}.\\\\ \\end{array}$$ In case\n$R$ is a field of characteristic $0$, we identify these algebras as quotients\nof the compact subalgebras of Kac-Moody Lie algebras and prove that they admit\na so-called generalized spin representation.\n"}
+{"abstract": "  Franson interferometry is a well-known quantum measurement technique for\nprobing photon-pair frequency correlations that is often used to certify\ntime-energy entanglement. We demonstrate the complementary technique in the\ntime basis, called conjugate-Franson interferometry, that measures photon-pair\narrival-time correlations, thus providing a valuable addition to the quantum\ntoolbox. We obtain a conjugate-Franson interference visibility of $96\\pm 1$%\nwithout background subtraction for entangled photon pairs generated by\nspontaneous parametric down-conversion. Our measured result surpasses the\nquantum-classical threshold by 25 standard deviations and validates the\nconjugate-Franson interferometer (CFI) as an alternative method for certifying\ntime-energy entanglement. Moreover, the CFI visibility is a function of the\nbiphoton's joint temporal intensity and is therefore sensitive to that state's\nspectral phase variation, something which is not the case for Franson\ninterferometry or Hong-Ou-Mandel interferometry. We highlight the CFI's utility\nby measuring its visibilities for two different biphoton states, one without\nand the other with spectral phase variation, and observing a 21% reduction in\nthe CFI visibility for the latter. The CFI is potentially useful for\napplications in areas of photonic entanglement, quantum communications, and\nquantum networking.\n"}
+{"abstract": "  The recent development of high-altitude platforms (HAPs) has attracted\nincreasing attention since they can serve as a promising communication method\nto assist satellite-terrestrial networks. In this paper, we consider an\nintegrated three-layer satellite-HAP-terrestrial network where the HAP support\ndual-band connectivity. Specifically, the HAP can not only communicate with\nterrestrial users over C-band directly, but also provide backhaul services to\nterrestrial user terminals over Ka-band. We formulate a sum-rate maximization\nproblem and then propose a fractional programming based algorithm to solve the\nproblem by optimizing the bandwidth and power allocation iteratively. The\nclosed-form optimal solutions for bandwidth allocation and power allocation in\neach iteration are also derived. Simulation results show the capacity\nenhancement brought by the dual-band connectivity of the HAP. The influence of\nthe power of the HAP and the power of the satellite is also discussed.\n"}
+{"abstract": "  Radio astronomy and Space Infrastructures in the Azores have a great\nscientific and industrial interest because they benefit from a unique\ngeographical location in the middle of the North Atlantic allowing a vast\nimprovement in the sky coverage. This fact obviously has a very high added\nvalue for: i) the establishment of space tracking and communications networks\nfor the emergent global small satellite fleets ii) it is invaluable to connect\nthe radio astronomy infrastructure networks in Africa, Europe and America\ncontinents using Very Large Baseline Interferometry (VLBI) techniques, iii) it\nallows excellent potential for monitoring space debris and Near Earth Objects\n(NEOs). There is in S. Miguel island a 32-metre SATCOM antenna that could be\nintegrated in advanced VLBI networks and be capable of additional Deep Space\nNetwork ground support. This paper explores the space science opportunities\noffered by the upgrade of the S. Miguel 32-metre SATCOM antenna into a\nworld-class infrastructure for radio astronomy and space exploration: it would\nenable a Deep Space Network mode and would constitute a key space facility for\ndata production, promoting local digital infrastructure investments and the\ntesting of cutting-edge information technologies. Its Atlantic location also\nenables improvements in angular resolution, provides many baseline in East-West\nand North-South directions connecting the emergent VLBI stations in America to\nEurope and Africa VLBI arrays therefore contributing for greater array imaging\ncapabilities especially for sources or well studied fields close to or below\nthe celestial equator, where ESO facilities, ALMA, SKA and its precursors do or\nwill operate and observe in the coming decades.\n"}
+{"abstract": "  In cohort studies, non-random medication use can pose barriers to estimation\nof the natural history trend in a mean biomarker value (namely, the association\nbetween a predictor of interest and a biomarker outcome that would be observed\nin the absence of biomarker-specific treatment). Common causes of treatment and\noutcomes are often unmeasured, obscuring our ability to easily account for\nmedication use with commonly invoked assumptions such as ignorability. Further,\nabsent some variable satisfying the exclusion restriction, use of instrumental\nvariable approaches may be difficult to justify. Heckman's hybrid model with\nstructural shift (sometimes referred to less specifically as the treatment\neffects model) can be used to correct endogeneity bias via a homogeneity\nassumption (i.e., that average treatment effects do not vary across covariates)\nand parametric specification of a joint model for the outcome and treatment. In\nrecent work, we relaxed the homogeneity assumption by allowing observed\ncovariates to serve as treatment effect modifiers. While this method has been\nshown to be reasonably robust in settings of cross-sectional data, application\nof this methodology to settings of longitudinal data remains unexplored. We\ndemonstrate how the assumptions of the treatment effects model can be extended\nto accommodate clustered data arising from longitudinal studies. Our proposed\napproach is semi-parametric in nature in that valid inference can be obtained\nwithout the need to specify the longitudinal correlation structure. As an\nillustrative example, we use data from the Multi-Ethnic Study of\nAtherosclerosis to evaluate trends in low-density lipoprotein by age and\ngender. We confirm that our generalization of the treatment effects model can\nserve as a useful tool to uncover natural history trends in longitudinal data\nthat are obscured by endogenous treatment.\n"}
+{"abstract": "  We study the weighted Fock spaces in one and several complex variables. We\nevaluate the dimension of these spaces in terms of the weight function\nextending and completing earlier results by Rozenblum-Shirokov and Shigekawa.\n"}
+{"abstract": "  Let $\\alpha$ and $\\beta$ be positive real numbers. Let $F(x) \\in\nK[[x^\\Gamma]]$ be a Hahn series. We prove that if $F(x)$ is both\n$\\alpha$-Mahler and $\\beta$-Mahler then it must be a rational function, $F(x)\n\\in K(x)$, assuming some non-degeneracy conditions on $\\alpha$ and $\\beta$.\n"}
+{"abstract": "  Graph Neural Networks (GNNs), a generalization of deep neural networks on\ngraph data have been widely used in various domains, ranging from drug\ndiscovery to recommender systems. However, GNNs on such applications are\nlimited when there are few available samples. Meta-learning has been an\nimportant framework to address the lack of samples in machine learning, and in\nrecent years, researchers have started to apply meta-learning to GNNs. In this\nwork, we provide a comprehensive survey of different meta-learning approaches\ninvolving GNNs on various graph problems showing the power of using these two\napproaches together. We categorize the literature based on proposed\narchitectures, shared representations, and applications. Finally, we discuss\nseveral exciting future research directions and open problems.\n"}
+{"abstract": "  We numerically investigate the topological phase transition induced purely by\ndisorder in a spring-mass chain. We employ two types of disorders - chiral and\nrandom types - to explore the interplay between topology and disorder. By\ntracking the evolution of real space topological invariants, we obtain the\ntopological phase diagrams and demonstrate the bilateral capacity of disorder\nto drive topological transitions, from topologically nontrivial to trivial and\nvice versa. The corresponding transition is accompanied by the realization of a\nmechanical Topological Anderson Insulator. The findings from this study hint\nthat the combination of disorder and topology can serve as an efficient control\nknob to manipulate the transfer of mechanical energy.\n"}
+{"abstract": "  Jupiter co-orbital comets have orbits that are not long-term stable. They may\nexperience flybys with Jupiter close enough to trigger tidal disruptions like\nthe one suffered by comet Shoemaker-Levy 9. Our aim was to study the activity\nand dynamical evolution of the Jupiter co-orbital comet P/2019 LD2 (ATLAS). We\npresent results of an observational study carried out with the 10.4m Gran\nTelescopio Canarias (GTC) that includes image analyses using a MC dust tail\nfitting code to characterize its activity, and spectroscopic studies to search\nfor gas emission. We also present N-body simulations to explore its orbital\nevolution. Images of LD2 obtained on 2020 May 16 show a conspicuous coma and\ntail. The spectrum does not exhibit any evidence of CN, C2, or C3 emission. The\ncomet brightness in a 2.6 arcsec aperture is r'=19.34+/-0.02 mag, with colors\n(g'-r')=0.78+/-0.03, (r'-i')=0.31+/-0.03, and (i'-z')=0.26+/-0.03. The temporal\ndependence of the dust loss rate can be parameterized by a Gaussian having a\nFWHM of 350 days and a maximum of 60 kg/s reached on 2019 August 15. The total\ndust loss rate is 1.9e09 kg. LD2 is now following what looks like a short arc\nof a quasi-satellite cycle that started in 2017 and will end in 2028. On 2063\nJanuary 23, it will experience a very close encounter with Jupiter at 0.016 au.\nIts probability of escaping the solar system during the next 0.5 Myr is\n0.53+/-0.03. LD2 is a kilometer-sized object, in the size range of the\nJupiter-family comets, with a typical comet-like activity likely linked to\nsublimation of crystalline water ice and clathrates. Its origin is still an\nopen question. We report a probability of LD2 having been captured from\ninterstellar space during the last 0.5 Myr of 0.49+/-0.02, 0.67+/-0.06 during\nthe last 1 Myr, 0.83+/-0.06 over 3 Myr, and 0.91+/-0.09 during the last 5 Myr.\n"}
+{"abstract": "  Motivated by upcoming photometric and spectroscopic surveys (Vera C. Rubin\nObservatory Legacy Survey of Space and Time (LSST), Manuakea Spectroscopic\nExplorer), we design the statistical proxies to measure the cadence effects on\nactive galactic nuclei (AGN) variability-observables (time-lags, periodicity,\nand structure-function (SF)). We constructed a multiple-regression model to\nstatistically identify the cadence-formal error pattern knowing AGN time-lags\nand periodicity from different surveys. We defined the simple metric for the\nSF's properties, accounting for the 'observed' SF's deviation relative to those\nobtained from the homogenously-sampled light curves. We tested the regression\nmodels on different observing strategies: the optical dataset of long\nlight-curves of eight AGN with peculiarities and the artificial datasets based\non several idealized and LSST-like cadences. The SFs metric is assessed on\nsynthetic datasets. The regression models (for both data types) predict similar\ncadences for time-lags and oscillation detection, whereas for light curves with\nlow variability ($\\sim 10\\%$), cadences for oscillation detection differ. For\nhigher variability ($\\sim20\\%$), predicted cadences are larger than for\n$F_{var}\\sim 10\\%$. The predicted cadences are decreasing with redshift. SFs\nwith dense and homogenous cadences are more likely to behave similarly. SFs\nwith oscillatory signals are sensitive to the cadences, possibly impacting\nLSST-like operation strategy.\n  The proposed proxies can help to select spectroscopic and photometric-surveys\ncadence strategies, and they will be tested further in larger samples of\nobjects.\n"}
+{"abstract": "  Accurate prediction of future person location and movement trajectory from an\negocentric wearable camera can benefit a wide range of applications, such as\nassisting visually impaired people in navigation, and the development of\nmobility assistance for people with disability. In this work, a new egocentric\ndataset was constructed using a wearable camera, with 8,250 short clips of a\ntargeted person either walking 1) toward, 2) away, or 3) across the camera\nwearer in indoor environments, or 4) staying still in the scene, and 13,817\nperson bounding boxes were manually labelled. Apart from the bounding boxes,\nthe dataset also contains the estimated pose of the targeted person as well as\nthe IMU signal of the wearable camera at each time point. An LSTM-based\nencoder-decoder framework was designed to predict the future location and\nmovement trajectory of the targeted person in this egocentric setting.\nExtensive experiments have been conducted on the new dataset, and have shown\nthat the proposed method is able to reliably and better predict future person\nlocation and trajectory in egocentric videos captured by the wearable camera\ncompared to three baselines.\n"}
+{"abstract": "  Solving inverse problems by dynamical variant of the BC-method is basically\nreduced to inverting the connecting operator $C^T$ of the dynamical system, for\nwhich the problem is stated. Realizing the method numerically, one needs to\ninvert the Gram matrix $\\hat C^T=\\{(C^Tf_i,f_j)\\}_{i,j=1}^N$ for a\nrepresentative set of controls $f_i$. To raise the accuracy of determination of\nthe solution, one has to increase the size $N$, which, especially in the\nmultidimensional case, leads to a rapid increase in the amount of computations.\nHowever, there is a way to reduce it by the proper choice of $f_j$, due to\nwhich the matrix $\\hat C^T$ gets a specific block-Toeplitz structure. In the\npaper, we explain, where this property comes from, and outline a way to use it\nin numerical implementation of the BC-algorithms.\n"}
+{"abstract": "  The size of Transformer models is growing at an unprecedented pace. It has\nonly taken less than one year to reach trillion-level parameters after the\nrelease of GPT-3 (175B). Training such models requires both substantial\nengineering efforts and enormous computing resources, which are luxuries most\nresearch teams cannot afford. In this paper, we propose PipeTransformer, which\nleverages automated and elastic pipelining and data parallelism for efficient\ndistributed training of Transformer models. PipeTransformer automatically\nadjusts the pipelining and data parallelism by identifying and freezing some\nlayers during the training, and instead allocates resources for training of the\nremaining active layers. More specifically, PipeTransformer dynamically\nexcludes converged layers from the pipeline, packs active layers into fewer\nGPUs, and forks more replicas to increase data-parallel width. We evaluate\nPipeTransformer using Vision Transformer (ViT) on ImageNet and BERT on GLUE and\nSQuAD datasets. Our results show that PipeTransformer attains a 2.4 fold\nspeedup compared to the state-of-the-art baseline. We also provide various\nperformance analyses for a more comprehensive understanding of our algorithmic\nand system-wise design. We also develop open-sourced flexible APIs for\nPipeTransformer, which offer a clean separation among the freeze algorithm,\nmodel definitions, and training accelerations, hence allowing it to be applied\nto other algorithms that require similar freezing strategies.\n"}
+{"abstract": "  Conventional wisdom dictates that learning rate should be in the stable\nregime so that gradient-based algorithms don't blow up. This letter introduces\na simple scenario where an unstably large learning rate scheme leads to a super\nfast convergence, with the convergence rate depending only logarithmically on\nthe condition number of the problem. Our scheme uses a Cyclical Learning Rate\n(CLR) where we periodically take one large unstable step and several small\nstable steps to compensate for the instability. These findings also help\nexplain the empirical observations of [Smith and Topin, 2019] where they show\nthat CLR with a large maximum learning rate can dramatically accelerate\nlearning and lead to so-called \"super-convergence\". We prove that our scheme\nexcels in the problems where Hessian exhibits a bimodal spectrum and the\neigenvalues can be grouped into two clusters (small and large). The unstably\nlarge step is the key to enabling fast convergence over the small\neigen-spectrum.\n"}
+{"abstract": "  Deep neural networks can be trained in reciprocal space, by acting on the\neigenvalues and eigenvectors of suitable transfer operators in direct space.\nAdjusting the eigenvalues, while freezing the eigenvectors, yields a\nsubstantial compression of the parameter space. This latter scales by\ndefinition with the number of computing neurons. The classification scores, as\nmeasured by the displayed accuracy, are however inferior to those attained when\nthe learning is carried in direct space, for an identical architecture and by\nemploying the full set of trainable parameters (with a quadratic dependence on\nthe size of neighbor layers). In this Letter, we propose a variant of the\nspectral learning method as appeared in Giambagli et al {Nat. Comm.} 2021,\nwhich leverages on two sets of eigenvalues, for each mapping between adjacent\nlayers. The eigenvalues act as veritable knobs which can be freely tuned so as\nto (i) enhance, or alternatively silence, the contribution of the input nodes,\n(ii) modulate the excitability of the receiving nodes with a mechanism which we\ninterpret as the artificial analogue of the homeostatic plasticity. The number\nof trainable parameters is still a linear function of the network size, but the\nperformances of the trained device gets much closer to those obtained via\nconventional algorithms, these latter requiring however a considerably heavier\ncomputational cost. The residual gap between conventional and spectral\ntrainings can be eventually filled by employing a suitable decomposition for\nthe non trivial block of the eigenvectors matrix. Each spectral parameter\nreflects back on the whole set of inter-nodes weights, an attribute which we\nshall effectively exploit to yield sparse networks with stunning classification\nabilities, as compared to their homologues trained with conventional means.\n"}
+{"abstract": "  Let $P$ be a geodesic plane in a convex cocompact, acylindrical hyperbolic\n3-manifold $M$. Assume that $P^*=M^*\\cap P$ is nonempty, where $M^*$ is the\ninterior of the convex core of $M$. Does this condition imply that $P$ is\neither closed or dense in $M$? A positive answer would furnish an analogue of\nRatner's theorem in the infinite volume setting. In arXiv:1802.03853 it is\nshown that $P^*$ is either closed or dense in $M^*$. Moreover, there are at\nmost countably many planes with $P^*$ closed, and in all previously known\nexamples, $P$ was also closed in $M$. In this note we show more exotic behavior\ncan occur: namely, we give an explicit example of a pair $(M,P)$ such that\n$P^*$ is closed in $M^*$ but $P$ is not closed in $M$. In particular, the\nanswer to the question above is no. Thus Ratner's theorem fails to generalize\nto planes in acylindrical 3-manifolds, without additional restrictions.\n"}
+{"abstract": "  Retinal Vessel Segmentation is important for the diagnosis of various\ndiseases. The research on retinal vessel segmentation focuses mainly on the\nimprovement of the segmentation model which is usually based on U-Net\narchitecture. In our study, we use the U-Net architecture and we rely on heavy\ndata augmentation in order to achieve better performance. The success of the\ndata augmentation relies on successfully addressing the problem of input\nimages. By analyzing input images and performing the augmentation accordingly\nwe show that the performance of the U-Net model can be increased dramatically.\nResults are reported using the most widely used retina dataset, DRIVE.\n"}
+{"abstract": "  We give a formula for the geometric fixed-points spectrum of the real\ntopological cyclic homology of a bounded below ring spectrum, as an equaliser\nof two maps between tensor products of modules over the norm. We then use this\nformula to carry out computations in the fundamental examples of spherical\ngroup-rings, perfect $\\mathbb{F}_p$-algebras, and $2$-torsion free rings with\nperfect modulo $2$ reduction. Our calculations agree with the normal L-theory\nspectrum in the cases where the latter is known, as conjectured by Nikolaus.\n"}
+{"abstract": "  We present a Monte Carlo based analysis of the combined world data on\npolarized lepton-nucleon deep-inelastic scattering at small Bjorken $x$ within\nthe polarized quark dipole formalism. We show for the first time that\ndouble-spin asymmetries at $x<0.1$ can be successfully described using only\nsmall-$x$ evolution derived from first-principles QCD, allowing predictions to\nbe made for the $g_1$ structure function at much smaller $x$. Anticipating\nfuture data from the Electron-Ion Collider, we assess the impact of\nelectromagnetic and parity-violating polarization asymmetries on $g_1$ and\ndemonstrate an extraction of the individual flavor helicity PDFs at small $x$.\n"}
+{"abstract": "  Most speech separation methods, trying to separate all channel sources\nsimultaneously, are still far from having enough general- ization capabilities\nfor real scenarios where the number of input sounds is usually uncertain and\neven dynamic. In this work, we employ ideas from auditory attention with two\nears and propose a speaker and direction inferred speech separation network\n(dubbed SDNet) to solve the cocktail party problem. Specifically, our SDNet\nfirst parses out the respective perceptual representations with their speaker\nand direction characteristics from the mixture of the scene in a sequential\nmanner. Then, the perceptual representations are utilized to attend to each\ncorresponding speech. Our model gener- ates more precise perceptual\nrepresentations with the help of spatial features and successfully deals with\nthe problem of the unknown number of sources and the selection of outputs. The\nexperiments on standard fully-overlapped speech separation benchmarks, WSJ0-\n2mix, WSJ0-3mix, and WSJ0-2&3mix, show the effectiveness, and our method\nachieves SDR improvements of 25.31 dB, 17.26 dB, and 21.56 dB under anechoic\nsettings. Our codes will be released at https://github.com/aispeech-lab/SDNet.\n"}
+{"abstract": "  Non-adiabatic photo-ionization is difficult to control as it relies on the\nderivatives of the envelope and not on phase-details of the short ionizing\npulse. Here, we introduce a catalyzing state, whose presence render\nnon-adiabatic ionization sensitive to phase-details of tailored pulses. Since a\ncatalyzing state is in general easy to create, this opens a perspective for\ncoherent control of ultra-fast ionization.\n"}
+{"abstract": "  We propose a high-performance atomic clock based on the 1.81 PHz transition\nbetween the ground and first-excited state of doubly ionized lead. Utilizing an\neven isotope of lead, both clock states have $I=J=F=0$, where $I$, $J$, and $F$\nare the conventional quantum numbers specifying nuclear, electronic, and total\nangular momentum, respectively. The clock states are nondegenerate and\ncompletely immune to nonscalar perturbations, including first order Zeeman and\nelectric quadrupole shifts. Additionally, the proposed clock is relatively\ninsusceptible to other frequency shifts (blackbody radiation, second order\nZeeman, Doppler), accommodates \"magic\" rf trapping, and is robust against\ndecoherence mechanisms that can otherwise limit clock stability. By driving the\ntransition as a two-photon $E1$+$M1$ process, the accompanying probe Stark\nshift is appreciable yet manageable for practical Rabi frequencies.\n"}
+{"abstract": "  In high-throughput genetics studies, an important aim is to identify\ngene-environment interactions associated with the clinical outcomes. Recently,\nmultiple marginal penalization methods have been developed and shown to be\neffective in G$\\times$E studies. However, within the Bayesian framework,\nmarginal variable selection has not received much attention. In this study, we\npropose a novel marginal Bayesian variable selection method for G$\\times$E\nstudies. In particular, our marginal Bayesian method is robust to data\ncontamination and outliers in the outcome variables. With the incorporation of\nspike-and-slab priors, we have implemented the Gibbs sampler based on MCMC. The\nproposed method outperforms a number of alternatives in extensive simulation\nstudies. The utility of the marginal robust Bayesian variable selection method\nhas been further demonstrated in the case studies using data from the Nurse\nHealth Study (NHS). Some of the identified main and interaction effects from\nthe real data analysis have important biological implications.\n"}
+{"abstract": "  We show that the recipe for computing the expansions $\\theta_\\ell$ and\n$\\theta_n$ of outgoing and ingoing null geodesics normal to a surface admits a\ncovariance group with nonconstant scalar $\\kappa(x)$, corresponding to the\nmapping $\\theta_\\ell \\to \\kappa \\theta_\\ell$, $\\theta_n \\to \\kappa^{-1}\n\\theta_n$. Under this mapping, the product $\\theta_\\ell \\theta_n$ is invariant,\nand thus the marginal surface computed from the vanishing of $\\theta_\\ell$,\nwhich is used to define the apparent horizon, is invariant. This covariance\ngroup naturally appears in comparing the expansions computed with different\nchoices of coordinate system.\n"}
+{"abstract": "  Brazil is home to over 200M people, the majority of which have access to the\nInternet. Over 11M Brazilians live in favelas, or informal settlements with no\noutside government regulation, often ruled by narcos or militias. Victims of\nintimate partner violence (IPV) in these communities are made extra vulnerable\nnot only by lack of access to resources, but by the added layer of violence\ncaused by criminal activity and police confrontations. In this paper, we use an\nunintended harms framework to analyze the unique online privacy needs of favela\nwomen and present research questions that we urge tech abuse researchers to\nconsider.\n"}
+{"abstract": "  Hypergraphs, a generalization of graphs, naturally represent groupwise\nrelationships among multiple individuals or objects, which are common in many\napplication areas, including web, bioinformatics, and social networks. The\nflexibility in the number of nodes in each hyperedge, which provides the\nexpressiveness of hypergraphs, brings about structural differences between\ngraphs and hypergraphs. Especially, the overlaps of hyperedges lead to complex\nhigh-order relations beyond pairwise relations, raising new questions that have\nnot been considered in graphs: How do hyperedges overlap in real-world\nhypergraphs? Are there any pervasive characteristics? What underlying process\ncan cause such patterns? In this work, we closely investigate thirteen\nreal-world hypergraphs from various domains and share interesting observations\nof overlaps of hyperedges. To this end, we define principled measures and\nstatistically compare the overlaps of hyperedges in real-world hypergraphs and\nthose in null models. Additionally, based on the observations, we propose\nHyperLap, a realistic hypergraph generative model. HyperLap is (a) Realistic:\nit accurately reproduces overlapping patterns of real-world hypergraphs, (b)\nAutomatically Fittable: its parameters can be tuned automatically using\nHyperLap+ to generate hypergraphs particularly similar to a given target\nhypergraph, (c) Scalable: it generates and fits a hypergraph with 0.7 billion\nhyperedges within a few hours.\n"}
+{"abstract": "  The vector $U$-bosons, or so called 'dark photons', are one of the possible\ncandidates for the dark matter mediators. They are supposed to interact with\nthe standard matter via a 'vector portal' due to the $U(1)-U(1)^\\prime$\nsymmetry group mixing which might make them visible in particle and heavy-ion\nexperiments. While there is no confirmed observation of dark photons, the\ndetailed analysis of different experimental data allows to estimate the upper\nlimit for the kinetic mixing parameter $\\epsilon^2$ depending on the mass $M_U$\nof $U$-bosons which is also unknown. In this study we present theoretical\nconstraints on the upper limit of $\\epsilon^2(M_U)$ in the mass range $M_U \\le\n0.6$ GeV from the comparison of the calculated dilepton spectra with the\nexperimental data from the HADES Collaboration at SIS18 energies where the dark\nphotons are not observed. Our analysis is based on the microscopic\nParton-Hadron-String Dynamics (PHSD) transport approach which reproduces well\nthe measured dilepton spectra in $p+p$, $p+A$ and $A+A$ collisions.\nAdditionally to the different dilepton channels originating from interactions\nand decays of ordinary matter particles (mesons and baryons), we incorporate\nthe decay of hypothetical $U$-bosons to dileptons, $U\\to e^+e^-$, where the\n$U$-bosons themselves are produced by the Dalitz decay of pions $\\pi^0\\to\n\\gamma U$, $\\eta$-mesons $\\eta \\to \\gamma U$ and Delta resonances $\\Delta \\to N\nU$. Our analysis can help to estimate the requested accuracy for future\nexperimental searches of 'light' dark photons by dilepton experiments.\n"}
+{"abstract": "  First investigations regarding dosimetric properties of the hybrid,\npixelated, photon-counting Dosepix detector in a pulsed photon field (RQR8) for\nthe personal dose equivalent $H\\mathrm{_p(10)}$ are presented. The influence\nquantities such as pulse duration and dose rate were varied, and their\nresponses were compared to the legal limits provided in PTB-A 23.2. The\nvariation of pulse duration at a nearly constant dose rate of 3.7$\\,$Sv/h shows\na flat response around 1.0 from 3.6$\\,$s down to 2$\\,$ms. A response close to\n1.0 is achieved for dose rates from 0.07$\\,$mSv/h to 35$\\,$Sv/h for both pixel\nsizes. Above this dose rate, the large pixels (220$\\,\\mathrm{\\mu}$m edge\nlength) are below the lower limit. The small pixels (55$\\,\\mathrm{\\mu}$m edge\nlength) stay within limits up to 704$\\,$Sv/h. The count rate linearity is\ncompared to previous results, confirming the saturating count rate for high\ndose rates.\n"}
+{"abstract": "  This work develops problem statements related to encoders and autoencoders\nwith the goal of elucidating variational formulations and establishing clear\nconnections to information-theoretic concepts. Specifically, four problems with\nvarying levels of input are considered : a) The data, likelihood and prior\ndistributions are given, b) The data and likelihood are given; c) The data and\nprior are given; d) the data and the dimensionality of the parameters is\nspecified. The first two problems seek encoders (or the posterior) and the\nlatter two seek autoencoders (i.e. the posterior and the likelihood). A\nvariational Bayesian setting is pursued, and detailed derivations are provided\nfor the resulting optimization problem. Following this, a linear Gaussian\nsetting is adopted, and closed form solutions are derived. Numerical\nexperiments are also performed to verify expected behavior and assess\nconvergence properties. Explicit connections are made to rate-distortion\ntheory, information bottleneck theory, and the related concept of sufficiency\nof statistics is also explored. One of the motivations of this work is to\npresent the theory and learning dynamics associated with variational inference\nand autoencoders, and to expose information theoretic concepts from a\ncomputational science perspective.\n"}
+{"abstract": "  Tree edit distance is a well-studied measure of dissimilarity between rooted\ntrees with node labels. It can be computed in $O(n^3)$ time [Demaine, Mozes,\nRossman, and Weimann, ICALP 2007], and fine-grained hardness results suggest\nthat the weighted version of this problem cannot be solved in truly subcubic\ntime unless the APSP conjecture is false [Bringmann, Gawrychowski, Mozes, and\nWeimann, SODA 2018].\n  We consider the unweighted version of tree edit distance, where every\ninsertion, deletion, or relabeling operation has unit cost. Given a parameter\n$k$ as an upper bound on the distance, the previous fastest algorithm for this\nproblem runs in $O(nk^3)$ time [Touzet, CPM 2005], which improves upon the\ncubic-time algorithm for $k\\ll n^{2/3}$. In this paper, we give a faster\nalgorithm taking $O(nk^2 \\log n)$ time, improving both of the previous results\nfor almost the full range of $\\log n \\ll k\\ll n/\\sqrt{\\log n}$.\n"}
+{"abstract": "  Natural and anthropogenic disasters frequently affect both the supply and\ndemand side of an economy. A striking recent example is the Covid-19 pandemic\nwhich has created severe disruptions to economic output in most countries.\nThese direct shocks to supply and demand will propagate downstream and upstream\nthrough production networks. Given the exogenous shocks, we derive a lower\nbound on total shock propagation. We find that even in this best case scenario\nnetwork effects substantially amplify the initial shocks. To obtain more\nrealistic model predictions, we study the propagation of shocks bottom-up by\nimposing different rationing rules on industries if they are not able to\nsatisfy incoming demand. Our results show that economic impacts depend strongly\non the emergence of input bottlenecks, making the rationing assumption a key\nvariable in predicting adverse economic impacts. We further establish that the\nmagnitude of initial shocks and network density heavily influence model\npredictions.\n"}
+{"abstract": "  Recent progress on muon $g-2$ measurement prompts one to take it even more\nseriously. In the general two Higgs doublet model that allows extra Yukawa\ncouplings, we take a simplified approach of single enhanced coupling. We fix\nthe charged lepton flavor violating coupling, $\\rho_{\\tau\\mu} =\n\\rho_{\\mu\\tau}$, via the one-loop mechanism, for illustrative masses of the\nheavy scalar $H$ and pseudoscalar $A$, where we assume $m_A = m_{H^+}$. Since\nextra top Yukawa couplings are plausibly the largest, we turn on $\\rho_{tt}$\nand find that LHC search for $gg \\to H,\\,A \\to \\tau\\mu$ gives more stringent\nbound than from $\\tau\\to \\mu\\gamma$ with two-loop mechanism. Turning on a\nsecond extra top Yukawa coupling, $\\rho_{tc}$, can loosen the bound on\n$\\rho_{tt}$, but LHC constraints can again be more stringent than from $B \\to\nD\\mu\\nu$ vs $De\\nu$ universality. This means that evidence for $H,\\,A \\to\n\\tau\\mu$ may yet emerge with full LHC Run 2 data, while direct search for\n$\\tau^\\pm\\mu^\\mp bW^+$ or $t\\bar cbW^+$ (plus conjugate) may also bear fruit.\n"}
+{"abstract": "  Monocular depth estimation (MDE) is a fundamental task in many applications\nsuch as scene understanding and reconstruction. However, most of the existing\nmethods rely on accurately labeled datasets. A weakly-supervised framework\nbased on attention nested U-net (ANU) named as ANUW is introduced in this paper\nfor cases with wrong labels. The ANUW is trained end-to-end to convert an input\nsingle RGB image into a depth image. It consists of a dense residual network\nstructure, an adaptive weight channel attention (AWCA) module, a patch second\nnon-local (PSNL) module and a soft label generation method. The dense residual\nnetwork is the main body of the network to encode and decode the input. The\nAWCA module can adaptively adjust the channel weights to extract important\nfeatures. The PSNL module implements the spatial attention mechanism through a\nsecond-order non-local method. The proposed soft label generation method uses\nthe prior knowledge of the dataset to produce soft labels to replace false\nones. The proposed ANUW is trained on a defective monocular depth dataset and\nthe trained model is tested on three public datasets, and the results\ndemonstrate the superiority of ANUW in comparison with the state-of-the-art MDE\nmethods.\n"}
+{"abstract": "  We have implemented a GPU version of the Jefimenko's equations -- JefiGPU.\nGiven the proper distributions of the source terms $\\rho$ (charge density) and\n$\\mathbf{J}$ (current density) in the source volume, the algorithm gives the\nelectromagnetic fields in the observational region (not necessarily overlaps\nthe vicinity of the sources). To verify the accuracy of the GPU implementation,\nwe have compared the obtained results with that of the theoretical ones. Our\nresults show that the deviations of the GPU results from the theoretical ones\nare around 5\\%. Meanwhile, we have also compared the performance of the GPU\nimplementation with a CPU version. The simulation results indicate that the GPU\ncode is significantly faster than the CPU version. Finally, we have studied the\nparameter dependence of the execution time and memory consumption on one NVIDIA\nTesla V100 card. Our code can be consistently coupled to RBG (Relativistic\nBoltzmann equations on GPUs) and many other GPU-based algorithms in physics.\n"}
+{"abstract": "  The standard approach to analyzing brain electrical activity is to examine\nthe spectral density function (SDF) and identify predefined frequency bands\nthat have the most substantial relative contributions to the overall variance\nof the signal. However, a limitation of this approach is that the precise\nfrequency and bandwidth of oscillations vary with cognitive demands. Thus they\nshould not be arbitrarily defined a priori in an experiment. In this paper, we\ndevelop a data-driven approach that identifies (i) the number of prominent\npeaks, (ii) the frequency peak locations, and (iii) their corresponding\nbandwidths (or spread of power around the peaks). We propose a Bayesian mixture\nauto-regressive decomposition method (BMARD), which represents the standardized\nSDFas a Dirichlet process mixture based on a kernel derived from second-order\nauto-regressive processes which completely characterize the location (peak)and\nscale (bandwidth) parameters. We present a Metropolis-Hastings within Gibbs\nalgorithm to sample from the posterior distribution of the mixture parameters.\nSimulation studies demonstrate the robustness and performance of the BMARD\nmethod. Finally, we use the proposed BMARD method to analyze local field\npotential (LFP) activity from the hippocampus of laboratory rats across\ndifferent conditions in a non-spatial sequence memory experiment to identify\nthe most interesting frequency bands and examine the link between specific\npatterns of activity and trial-specific cognitive demands.\n"}
+{"abstract": "  In this work, we propose a fully coupled multiscale strategy for components\nmade from short fiber reinforced composites, where each Gauss point of the\nmacroscopic finite element model is equipped with a deep material network (DMN)\nwhich covers the different fiber orientation states varying within the\ncomponent. These DMNs need to be identified by linear elastic precomputations\non representative volume elements, and serve as high-fidelity surrogates for\nfull-field simulations on microstructures with inelastic constituents. We\ndiscuss how to extend direct DMNs to account for varying fiber orientation, and\npropose a simplified sampling strategy which significantly speeds up the\ntraining process. To enable concurrent multiscale simulations, evaluating the\nDMNs efficiently is crucial. We discuss dedicated techniques for exploiting\nsparsity and high-performance linear algebra modules, and demonstrate the power\nof the proposed approach on an industrial-scale three-dimensional component.\nIndeed, the DMN is capable of accelerating two-scale simulations significantly,\nproviding possible speed-ups of several magnitudes.\n"}
+{"abstract": "  The Automatic Quasi-clique Merger algorithm is a new algorithm adapted from\nearly work published under the name QCM (quasi-clique merger) [Ou2006, Ou2007,\nZhao2011, Qi2014]. The AQCM algorithm performs hierarchical clustering in any\ndata set for which there is an associated similarity measure quantifying the\nsimilarity of any data i and data j. Importantly, the method exhibits two\nvaluable performance properties: 1) the ability to automatically return either\na larger or smaller number of clusters depending on the inherent properties of\nthe data rather than on a parameter 2) the ability to return a very large\nnumber of relatively small clusters automatically when such clusters are\nreasonably well defined in a data set. In this work we present the general idea\nof a quasi-clique agglomerative approach, provide the full details of the\nmathematical steps of the AQCM algorithm, and explain some of the motivation\nbehind the new methodology. The main achievement of the new methodology is that\nthe agglomerative process now unfolds adaptively according to the inherent\nstructure unique to a given data set, and this happens without the time-costly\nparameter adjustment that drove the previous QCM algorithm. For this reason we\ncall the new algorithm \\emph{automatic}. We provide a demonstration of the\nalgorithm's performance at the task of community detection in a social media\nnetwork of 22,900 nodes.\n"}
+{"abstract": "  The paper discusses design techniques for a seamless architecture of\ninformation systems (IS). A seamless architecture is understood as such an\narchitectural de-scription of an IS, that defines explicit connections between\nelements of architec-tural models of various architectural representations.\nDesign techniques are based on the adaptive clustering method developed by the\nauthor, which allows one to bridge technological gaps between architectural\nabstracts of different levels and to link architectural models in such a way as\nto ensure the design of a more detailed model based on a model of a higher\nlevel of abstraction.\n"}
+{"abstract": "  Utilizing a data set corresponding to an integrated luminosity of 6.32~$\\rm\nfb^{-1}$, recorded by the BESIII detector at center-of-mass energies between\n4.178 and 4.226~GeV, we perform an amplitude analysis of the decay $D_{s}^{+}\n\\to K_{S}^{0}\\pi^{+}\\pi^{0}$ and determine the relative fractions and phase\ndifferences of different intermediate processes, which include\n$K_{S}^{0}\\rho(770)^{+}$, $K_{S}^{0}\\rho(1450)^{+}$, $K^{*}(892)^{0}\\pi^{+}$,\n$K^{*}(892)^{+}\\pi^{0}$, and $K^{*}(1410)^{0}\\pi^{+}$. Using a double-tag\ntechnique, and making an efficiency correction that relies on our knowledge of\nthe phase-space distribution of the decays coming from the amplitude analysis,\n  the absolute branching fraction is measured to be $\\mathcal{B}(D_{s}^{+} \\to\nK_{S}^{0}\\pi^{+}\\pi^{0})=(5.43\\pm0.30_{\\text{stat}}\\pm\n0.15_{\\text{syst}})\\times 10^{-3}$.\n"}
+{"abstract": "  We study the Solid Isotropic Material Penalisation (SIMP) method with a\ndensity field generated by a fully-connected neural network, taking the\ncoordinates as inputs. In the large width limit, we show that the use of DNNs\nleads to a filtering effect similar to traditional filtering techniques for\nSIMP, with a filter described by the Neural Tangent Kernel (NTK). This filter\nis however not invariant under translation, leading to visual artifacts and\nnon-optimal shapes. We propose two embeddings of the input coordinates, which\nlead to (approximate) spatial invariance of the NTK and of the filter. We\nempirically confirm our theoretical observations and study how the filter size\nis affected by the architecture of the network. Our solution can easily be\napplied to any other coordinates-based generation method.\n"}
+{"abstract": "  Convolutional neural networks have been successful lately enabling companies\nto develop neural-based products, which demand an expensive process, involving\ndata acquisition and annotation; and model generation, usually requiring\nexperts. With all these costs, companies are concerned about the security of\ntheir models against copies and deliver them as black-boxes accessed by APIs.\nNonetheless, we argue that even black-box models still have some\nvulnerabilities. In a preliminary work, we presented a simple, yet powerful,\nmethod to copy black-box models by querying them with natural random images. In\nthis work, we consolidate and extend the copycat method: (i) some constraints\nare waived; (ii) an extensive evaluation with several problems is performed;\n(iii) models are copied between different architectures; and, (iv) a deeper\nanalysis is performed by looking at the copycat behavior. Results show that\nnatural random images are effective to generate copycats for several problems.\n"}
+{"abstract": "  The goal of this article is to exhibit two new families of finitely generated\nsimple groups of homeomorphisms of $\\mathbf{R}$. These families are strikingly\ndifferent from existing families owing to the nature of their actions on\n$\\mathbf{R}$, and exhibit surprising algebraic and dynamical features. In\nparticular, one construction provides the first examples of finitely generated\nsimple groups of homeomorphisms of the real line which also admit a minimal\naction by homeomorphisms on the circle. This provides new examples of finitely\ngenerated simple groups with infinite commutator width, and the first such left\norderable examples. Another construction provides the first examples of\nfinitely generated simple left orderable groups that admit minimal actions by\nhomeomorphisms on the torus.\n"}
+{"abstract": "  We present a machine learning approach that allows to characterize the\ndisorder potential of a two-dimensional electronic system from its quantum\ntransport properties. Numerically simulated transport data for a large number\nof disorder configurations is used for the training of artificial neural\nnetworks. We show that the trained networks are able to recognize details of\nthe disorder potential of an unknown sample from its transport properties, and\nthat they can even reconstruct the complete potential landscape seen by the\nelectrons.\n"}
+{"abstract": "  In this paper, we fill the research gap by adopting state-of-the-art computer\nvision techniques for the data extraction stage in a data mining system. As\nshown in Fig.1, this stage contains two subtasks, namely, plot element\ndetection and data conversion. For building a robust box detector, we\ncomprehensively compare different deep learning-based methods and find a\nsuitable method to detect box with high precision. For building a robust point\ndetector, a fully convolutional network with feature fusion module is adopted,\nwhich can distinguish close points compared to traditional methods. The\nproposed system can effectively handle various chart data without making\nheuristic assumptions. For data conversion, we translate the detected element\ninto data with semantic value. A network is proposed to measure feature\nsimilarities between legends and detected elements in the legend matching\nphase. Furthermore, we provide a baseline on the competition of Harvesting raw\ntables from Infographics. Some key factors have been found to improve the\nperformance of each stage. Experimental results demonstrate the effectiveness\nof the proposed system.\n"}
+{"abstract": "  We extract verified algorithms for exact real number computation from\nconstructive proofs. To this end we use a coinductive representation of reals\nas streams of binary signed digits. The main objective of this paper is the\nformalisation of a constructive proof that real numbers are closed with respect\nto limits. All the proofs of the main theorem and the first application are\nimplemented in the Minlog proof system and the extracted terms are further\ntranslated into Haskell. We compare two approaches. The first approach is a\ndirect proof. In the second approach we make use of the representation of reals\nby a Cauchy-sequence of rationals. Utilizing translations between the two\nrepresenation and using the completeness of the Cauchy-reals, the proof is very\nshort. In both cases we use Minlog's program extraction mechanism to\nautomatically extract a formally verified program that transforms a converging\nsequence of reals, i.e. a sequence of streams of binary signed digits together\nwith a modulus of convergence, into the binary signed digit representation of\nits limit. The correctness of the extracted terms follows directly from the\nsoundness theorem of program extraction. As a first application we use the\nextracted algorithms together with Heron's method to construct an algorithm\nthat computes square roots with respect to the binary signed digit\nrepresentation. In a second application we use the convergence theorem to show\nthat the signed digit representation of real numbers is closed under\nmultiplication.\n"}
+{"abstract": "  A discontinuous model of computation called one-way jumping finite automata\nwas defined by H. Chigahara et. al. This model was a restricted version of the\nmodel jumping finite automata. One-way jumping finite automata change their\nstates after deleting a letter of an input and jump only in one direction.\nAllowing a state to delete a subword instead of a letter, we define a new model\ngeneralized circular one-way jumping finite automata. These automata work on an\ninput in a circular manner. Similar to one-way jumping finite automata,\ngeneralized circular one-way jumping finite automata also jump only in one\ndirection. We show that this newly defined model is powerful than one-way\njumping finite automata. We define new variants(right and left) of the model\ngeneralized circular one-way jumping finite automata and compare them. We also\ncompare the newly defined model with Chomsky hierarchy. Finally, we explore\nclosure properties of the model.\n"}
+{"abstract": "  We present a kinematic study of ionised extraplanar gas in two\nlow-inclination late-type galaxies (NGC 3982 and NGC 4152) using integral field\nspectroscopy data from the DiskMass H$\\alpha$ sample. We first isolate the\nextraplanar gas emission by masking the H$\\alpha$ flux from the regularly\nrotating disc. The extraplanar gas emission is then modelled in the\nthree-dimensional position-velocity domain using a parametric model described\nby three structural and four kinematic parameters. Best-fit values for the\nmodel are determined via a Bayesian MCMC approach. The reliability and accuracy\nof our modelling method are carefully determined via tests using mock data. We\ndetect ionised extraplanar gas in both galaxies, with scale heights\n$0.83^{+0.27}_{-0.40}\\,\\mathrm{kpc}$ (NGC 3982) and\n$1.87^{+0.43}_{-0.56}\\,\\mathrm{kpc}$ (NGC 4152) and flux fraction between the\nextraplanar gas and the regularly rotating gas within the disc of 27% and 15%\nrespectively, consistent with previous determinations in other systems. We find\nlagging rotation of the ionized extraplanar gas in both galaxies, with vertical\nrotational gradients $-22.24^{+6.60}_{-13.13} \\,\\mathrm{km\\,s^{-1}\\,kpc^{-1}}$\nand $-11.18^{+3.49}_{-4.06}\\,\\mathrm{km\\,s^{-1}\\,kpc^{-1}}$, respectively, and\nweak evidence for vertical and radial inflow in both galaxies. The above\nresults are similar to the kinematics of the neutral extraplanar gas found in\nseveral galaxies, though this is the first time that 3D kinematic modelling of\nionised extraplanar gas has been carried out. Our results are broadly\nconsistent with a galactic fountain origin combined with gas accretion.\nHowever, a dynamical model is required to better understand the formation of\nionised extraplanar gas.\n"}
+{"abstract": "  The increased demand for machine learning applications made companies offer\nMachine-Learning-as-a-Service (MLaaS). In MLaaS (a market estimated 8000M USD\nby 2025), users pay for well-performing ML models without dealing with the\ncomplicated training procedure. Among MLaaS, text-based applications are the\nmost popular ones (e.g., language translators). Given this popularity, MLaaS\nmust provide resiliency to adversarial manipulations. For example, a wrong\ntranslation might lead to a misunderstanding between two parties. In the text\ndomain, state-of-the-art attacks mainly focus on strategies that leverage ML\nmodels' weaknesses. Unfortunately, not much attention has been given to the\nother pipeline' stages, such as the indexing stage (i.e., when a sentence is\nconverted from a textual to a numerical representation) that, if manipulated,\ncan significantly affect the final performance of the application.\n  In this paper, we propose a novel text evasion technique called\n\"\\textit{Zero-Width} attack\" (ZeW) that leverages the injection of human\nnon-readable characters, affecting indexing stage mechanisms. We demonstrate\nthat our simple yet effective attack deceives MLaaS of \"giants\" such as Amazon,\nGoogle, IBM, and Microsoft. Our case study, based on the manipulation of\nhateful tweets, shows that out of 12 analyzed services, only one is resistant\nto our injection strategy. We finally introduce and test a simple \\textit{input\nvalidation} defense that can prevent our proposed attack.\n"}
+{"abstract": "  To assess the effectiveness of any medical intervention, researchers must\nconduct a time-intensive and highly manual literature review. NLP systems can\nhelp to automate or assist in parts of this expensive process. In support of\nthis goal, we release MS^2 (Multi-Document Summarization of Medical Studies), a\ndataset of over 470k documents and 20k summaries derived from the scientific\nliterature. This dataset facilitates the development of systems that can assess\nand aggregate contradictory evidence across multiple studies, and is the first\nlarge-scale, publicly available multi-document summarization dataset in the\nbiomedical domain. We experiment with a summarization system based on BART,\nwith promising early results. We formulate our summarization inputs and targets\nin both free text and structured forms and modify a recently proposed metric to\nassess the quality of our system's generated summaries. Data and models are\navailable at https://github.com/allenai/ms2\n"}
+{"abstract": "  We describe via simulation novel optimization algorithms for a Hopfield\nneural network constructed using manufacturable three-terminal\nSilicon-Oxide-Nitride-Oxide-Silicon (SONOS) synaptic devices. We first present\na computationally-light, memristor-based, highly accurate compact model for the\nSONOS. Using the compact model, we describe techniques of simulated annealing\nin Hopfield networks by exploiting imperfect problem definitions, current\nleakage, and the continuous tunability of the SONOS to enable transient chaotic\ngroup dynamics. We project improvements in energy consumption and latency for\noptimization relative to the best CPUs and GPUs by at least 4 orders of\nmagnitude, and also exceeding the best projected memristor-based hardware;\nalong with a 100-fold increase in error-resilient hardware size (i.e., problem\nsize).\n"}
+{"abstract": "  Video analytics systems critically rely on video cameras, which capture\nhigh-quality video frames, to achieve high analytics accuracy. Although modern\nvideo cameras often expose tens of configurable parameter settings that can be\nset by end-users, deployment of surveillance cameras today often uses a fixed\nset of parameter settings because the end-users lack the skill or understanding\nto reconfigure these parameters.\n  In this paper, we first show that in a typical surveillance camera\ndeployment, environmental condition changes can significantly affect the\naccuracy of analytics units such as person detection, face detection and face\nrecognition, and how such adverse impact can be mitigated by dynamically\nadjusting camera settings. We then propose CAMTUNER, a framework that can be\neasily applied to an existing video analytics pipeline (VAP) to enable\nautomatic and dynamic adaptation of complex camera settings to changing\nenvironmental conditions, and autonomously optimize the accuracy of analytics\nunits (AUs) in the VAP. CAMTUNER is based on SARSA reinforcement learning (RL)\nand it incorporates two novel components: a light-weight analytics quality\nestimator and a virtual camera. CAMTUNER is implemented in a system with AXIS\nsurveillance cameras and several VAPs (with various AUs) that processed\nday-long customer videos captured at airport entrances. Our evaluations show\nthat CAMTUNER can adapt quickly to changing environments. We compared CAMTUNER\nwith two alternative approaches where either static camera settings were used,\nor a strawman approach where camera settings were manually changed every hour\n(based on human perception of quality). We observed that for the face detection\nand person detection AUs, CAMTUNER is able to achieve up to 13.8% and 9.2%\nhigher accuracy, respectively, compared to the best of the two approaches\n(average improvement of 8% for both AUs).\n"}
+{"abstract": "  The properties of semiconductors can be crucially impacted by midgap states\ninduced by dopants, which can be native or intentionally incorporated in the\ncrystal lattice. For Bernal-stacked bilayer graphene (BLG), which has a tunable\nbandgap, the existence of midgap states induced by dopants has been\nconjectured, but never confirmed experimentally. Here, we report scanning\ntunneling microscopy and spectroscopy results, supported by tight-binding\ncalculations, that demonstrate the existence of midgap states in BLG. We show\nthat the midgap state in BLG -- for which we demonstrate gate-tunability --\nappears when the dopant is hosted on the non-dimer sublattice sites. We further\nevidence the presence of narrow resonances at the onset of the high energy\nbands (valence or conduction, depending on the dopant type) when the dopants\nlie on the dimer sublattice sites. These results suggest that dopants/defects\ncan play an important role in the transport and optical properties of\nmultilayer graphene samples, especially at energies close to the band extrema.\n"}
+{"abstract": "  We analyze the new redefinitions of heat Q and work W recently presented in\n[arXiv: 1912.01939; arXiv:1912.01983v5] in the quantum thermodynamics domain.\nAccording to these redefinitions, heat must be associated with the variation of\nentropy, while work must be associated with variation of state vectors.\nAnalyzing the behavior of two specific examples, we show some peculiarities of\nthese new redefinitions which, based on the counterexample presented, seems to\npoint to a possible inadequacy of these redefinitions.\n"}
+{"abstract": "  The impact parameter-dependent dipole model is an ideal tool for\ninvestigating the spatial structure of the proton. We investigate the\nincoherent $ep$ crosssection in exclusive $J/\\psi$ photoproduction as measured\nby HERA, and find that as $|t|$ increases, the model needs several levels of\nthe substructure of gluon density fluctuations in order to describe the\nmeasured data well. This substructure is modelled as hotspots within hotspots.\nWe find that three levels of the substructure are adequate for a good\ndescription of all available data, up to $|t|=30~$GeV$^2$. We note that the\ngluonic density fluctuation structure follows a scaling behaviour, such that\nthe logarithms of the number of hotspots and their size fall on a line. We\nfurther conclude that a more advanced model containing gluon-gluon correlations\nneeds also will need increased complexity as $|t|$ increases. Our findings in\nthis paper systematically constrain which features such a model must have.\n"}
+{"abstract": "  A wide variety of deep reinforcement learning (DRL) models have recently been\nproposed to learn profitable investment strategies. The rules learned by these\nmodels outperform the previous strategies specially in high frequency trading\nenvironments. However, it is shown that the quality of the extracted features\nfrom a long-term sequence of raw prices of the instruments greatly affects the\nperformance of the trading rules learned by these models. Employing a neural\nencoder-decoder structure to extract informative features from complex input\ntime-series has proved very effective in other popular tasks like neural\nmachine translation and video captioning in which the models face a similar\nproblem. The encoder-decoder framework extracts highly informative features\nfrom a long sequence of prices along with learning how to generate outputs\nbased on the extracted features. In this paper, a novel end-to-end model based\non the neural encoder-decoder framework combined with DRL is proposed to learn\nsingle instrument trading strategies from a long sequence of raw prices of the\ninstrument. The proposed model consists of an encoder which is a neural\nstructure responsible for learning informative features from the input\nsequence, and a decoder which is a DRL model responsible for learning\nprofitable strategies based on the features extracted by the encoder. The\nparameters of the encoder and the decoder structures are learned jointly, which\nenables the encoder to extract features fitted to the task of the decoder DRL.\nIn addition, the effects of different structures for the encoder and various\nforms of the input sequences on the performance of the learned strategies are\ninvestigated. Experimental results showed that the proposed model outperforms\nother state-of-the-art models in highly dynamic environments.\n"}
+{"abstract": "  Does the ability to protect an asset from unsecured creditors affect its\nprice? This paper identifies the impact of bankruptcy protection on house\nprices using 139 changes in homestead exemptions. Large increases in the\nhomestead exemption raised house prices 3% before 2005. Smaller exemption\nincreases, to adjust for inflation, did not affect house prices. The effect\ndisappeared after BAPCPA, a 2005 federal law designed to prevent bankruptcy\nabuse. The effect was bigger in inelastic locations.\n"}
+{"abstract": "  The overlapping of multiple beams is common in inertial confinement fusion\n(ICF), making the collective stimulated Brillouin scattering (SBS) with shared\nion acoustic wave (IAW) potentially important because of the effectively larger\nlaser intensities to drive the instability. In this work, based on a linear\nkinetic model, an exact analytic solution for the convective amplification of\nSBS with the shared IAW modes stimulated by two overlapped beams is presented.\nFrom this solution, effects of the wavelength difference, crossing angle,\npolarization states, and finite beam overlapping volume of the two laser beams\non the shared IAW modes are studied. It is found that a wavelength difference\nof several nanometers between the laser beams has negligible effects, except\nfor a very small crossing angle about one degree. However, the crossing angle,\nbeam polarization states, and finite beam overlapping volume can have\nsignificant influences on the shared IAW modes. Furthermore, the out-of-plane\nmodes, in which the wavevectors of daughter waves lie in the different planes\nfrom the two overlapped beams, are found to be important for certain\npolarization states and crossing angles of the laser beams with the finite beam\noverlapping volume. This work is helpful to comprehend and estimate the\ncollective SBS with shared IAW in ICF experiments.\n"}
+{"abstract": "  Neurite orientation dispersion and density imaging (NODDI) enables the\nassessment of intracellular, extracellular and free water signals from\nmulti-shell diffusion MRI data. It is an insightful approach to characterize\nbrain tissue microstructure. Single-shell reconstruction for NODDI parameters\nhas been discouraged in previous studies caused by failure when fitting,\nespecially for the neurite density index (NDI). Here, we investigated the\npossibility of creating robust NODDI parameter maps with single-shell data,\nusing the isotropic volume fraction (fISO) as prior. Prior estimation was made\nindependent of the NODDI model constraint using a dictionary learning approach.\nFirst, we used a stochastic sparse dictionary-based network (DictNet) in\npredicting fISO which is trained with data obtained from in vivo and simulated\ndiffusion MRI data. In single-shell cases, the mean diffusivity (MD) and raw T2\nsignal with no diffusion weighting (S0) was incorporated in the dictionary for\nthe fISO estimation. Then, the NODDI framework was used with the known fISO to\nestimate the NDI and orientation dispersion index (ODI). The fISO estimated by\nour model was compared with other fISO estimators in the simulation. Further,\nusing both synthetic data simulation and human data collected on a 3T scanner,\nwe compared the performance of our dictionary-based learning prior NODDI (DLpN)\nwith the original NODDI for both single-shell and multi-shell data. Our results\nsuggest that DLpN derived NDI and ODI parameters for single-shell protocols are\ncomparable with original multi-shell NODDI, and protocol with b=2000 s/mm2\nperforms the best (error ~5% in white and grey matter). This may allow NODDI\nevaluation of studies on single-shell data by multi-shell scanning of two\nsubjects for DictNet fISO training.\n"}
+{"abstract": "  In this paper, we investigate the optimal design of a wireless-powered covert\ncommunication (WP-CC) system, in which a probabilistic accumulate-then-transmit\n(ATT) protocol is proposed to maximize the communication covertness subject to\na quality-of-service (QoS) requirement on communication. Specifically, in the\nconsidered WP-CC system, a full-duplex (FD) receiver transmits artificial noise\n(AN) to simultaneously charge an energy-constrained transmitter and to confuse\na warden's detection on the transmitter's communication activity. With the\nprobabilistic ATT protocol, the transmitter sends its information with a prior\nprobability, i.e., $p$, conditioned on the available energy being sufficient.\nOur analysis shows that the probabilistic ATT protocol can achieve higher\ncovertness than the traditional ATT protocol with $p=1$. In order to facilitate\nthe optimal design of the WP-CC system, we also derive the warden's minimum\ndetection error probability and characterize the effective covert rate from the\ntransmitter to the receiver to quantify the communication covertness and\nquality, respectively. The derived analytical results facilitate the joint\noptimization of the probability $p$ and the information transmit power. We\nfurther present the optimal design of a cable-powered covert communication\n(CP-CC) system as a benchmark for comparison. Our simulation shows that the\nproposed probabilistic ATT protocol (with a varying $p$) can achieve the\ncovertness upper bound determined by the CP-CC system, while the traditional\nATT protocol (with $p=1$) cannot, which again confirms the benefits brought by\nthe proposed probabilistic ATT in covert communications.\n"}
+{"abstract": "  Entangled systems in experiments may be lost or offline in distributed\nquantum information processing. This inspires a general problem to characterize\nquantum operations which result in breaking of entanglement or not. Our goal in\nthis work is to solve this problem both in single entanglement and network\nscenarios. We firstly propose a local model for characterizing all entangled\nstates that are breaking for losing particles. This implies a simple criterion\nfor witnessing single entanglement such as generalized GHZ states and Dicke\nstates. It further provides an efficient witness for entangled quantum networks\ndepending on its connectivity such as $k$-independent quantum networks,\ncompletely connected quantum networks, and $k$-connected quantum networks.\nThese networks are universal resources for measurement-based quantum\ncomputations. The strong nonlocality can be finally verified by using nonlinear\ninequalities. These results show distinctive features of both single entangled\nsystems and entangled quantum networks.\n"}
+{"abstract": "  Deep learning has been used to image compressive sensing (CS) for enhanced\nreconstruction performance. However, most existing deep learning methods train\ndifferent models for different subsampling ratios, which brings additional\nhardware burden. In this paper, we develop a general framework named scalable\ndeep compressive sensing (SDCS) for the scalable sampling and reconstruction\n(SSR) of all existing end-to-end-trained models. In the proposed way, images\nare measured and initialized linearly. Two sampling masks are introduced to\nflexibly control the subsampling ratios used in sampling and reconstruction,\nrespectively. To make the reconstruction model adapt to any subsampling ratio,\na training strategy dubbed scalable training is developed. In scalable\ntraining, the model is trained with the sampling matrix and the initialization\nmatrix at various subsampling ratios by integrating different sampling matrix\nmasks. Experimental results show that models with SDCS can achieve SSR without\nchanging their structure while maintaining good performance, and SDCS\noutperforms other SSR methods.\n"}
+{"abstract": "  We consider the setting in which a strong binary instrument is available for\na binary treatment. The traditional LATE approach assumes the monotonicity\ncondition stating that there are no defiers (or compliers). Since this\ncondition is not always obvious, we investigate the sensitivity and testability\nof this condition. In particular, we focus on the question: does a slight\nviolation of monotonicity lead to a small problem or a big problem? We find a\nphase transition for the monotonicity condition. On one of the boundary of the\nphase transition, it is easy to learn the sign of LATE and on the other side of\nthe boundary, it is impossible to learn the sign of LATE. Unfortunately, the\nimpossible side of the phase transition includes data-generating processes\nunder which the proportion of defiers tends to zero. This boundary of phase\ntransition is explicitly characterized in the case of binary outcomes. Outside\na special case, it is impossible to test whether the data-generating process is\non the nice side of the boundary. However, in the special case that the\nnon-compliance is almost one-sided, such a test is possible. We also provide\nsimple alternatives to monotonicity.\n"}
+{"abstract": "  Situation awareness (SA) is critical to improving takeover performance during\nthe transition period from automated driving to manual driving. Although many\nstudies measured SA during or after the driving task, few studies have\nattempted to predict SA in real time in automated driving. In this work, we\npropose to predict SA during the takeover transition period in conditionally\nautomated driving using eye-tracking and self-reported data. First, a tree\nensemble machine learning model, named LightGBM (Light Gradient Boosting\nMachine), was used to predict SA. Second, in order to understand what factors\ninfluenced SA and how, SHAP (SHapley Additive exPlanations) values of\nindividual predictor variables in the LightGBM model were calculated. These\nSHAP values explained the prediction model by identifying the most important\nfactors and their effects on SA, which further improved the model performance\nof LightGBM through feature selection. We standardized SA between 0 and 1 by\naggregating three performance measures (i.e., placement, distance, and speed\nestimation of vehicles with regard to the ego-vehicle) of SA in recreating\nsimulated driving scenarios, after 33 participants viewed 32 videos with six\nlengths between 1 and 20 s. Using only eye-tracking data, our proposed model\noutperformed other selected machine learning models, having a root-mean-squared\nerror (RMSE) of 0.121, a mean absolute error (MAE) of 0.096, and a 0.719\ncorrelation coefficient between the predicted SA and the ground truth. The code\nis available at https://github.com/refengchou/Situation-awareness-prediction.\nOur proposed model provided important implications on how to monitor and\npredict SA in real time in automated driving using eye-tracking data.\n"}
+{"abstract": "  Slang is a common type of informal language, but its flexible nature and\npaucity of data resources present challenges for existing natural language\nsystems. We take an initial step toward machine generation of slang by\ndeveloping a framework that models the speaker's word choice in slang context.\nOur framework encodes novel slang meaning by relating the conventional and\nslang senses of a word while incorporating syntactic and contextual knowledge\nin slang usage. We construct the framework using a combination of probabilistic\ninference and neural contrastive learning. We perform rigorous evaluations on\nthree slang dictionaries and show that our approach not only outperforms\nstate-of-the-art language models, but also better predicts the historical\nemergence of slang word usages from 1960s to 2000s. We interpret the proposed\nmodels and find that the contrastively learned semantic space is sensitive to\nthe similarities between slang and conventional senses of words. Our work\ncreates opportunities for the automated generation and interpretation of\ninformal language.\n"}
+{"abstract": "  We present three alternative derivations of the method of characteristics\n(MOC) for a second order nonlinear hyperbolic partial differential equation.\nThe MOC gives rise to two mutually coupled systems of ordinary differential\nequations. As a special case we consider the Monge-Amp\\`ere equation, for which\nwe solve the system of ODE's using explicit one-step methods (Euler,\nRunge-Kutta) and spline interpolation. Numerical examples demonstrate the\nperformance of the methods.\n"}
+{"abstract": "  Large-volume liquid scintillator detectors with ultra-low background levels\nhave been widely used to study neutrino physics and search for dark matter.\nEvent vertex and event time are not only useful for event selection but also\nessential for the reconstruction of event energy. In this study, four event\nvertex and event time reconstruction algorithms using charge and time\ninformation collected by photomultiplier tubes were analyzed comprehensively.\nThe effects of photomultiplier tube properties were also investigated. The\nresults indicate that the transit time spread is the main effect degrading the\nvertex reconstruction, while the effect of dark noise is limited. In addition,\nwhen the event is close to the detector boundary, the charge information\nprovides better performance for vertex reconstruction than the time\ninformation.\n"}
+{"abstract": "  Our contribution concerns interactive decision support systems for group\ndecision support. Through this study, we apply to implement a decisional\nprocess aiming to represent the multiplicity of actors, their diversity, their\nbehaviors and their interactions. In this context, we contribute to the design\nand development of a group decision support system. The system is modeled by a\nmulti agents system while exploiting a negotiation protocol based on mediation\nand concession. This protocol allows decision-makers to express their\npreferences using multicriteria analysis methods, mainly the method by total\naggregation AHP (Hierarchical Process Analysis) and the method by partial\naggregation PROMETHEE II .\n"}
+{"abstract": "  Decomposable graphical models, also known as perfect DAG models, play a\nfundamental role in standard approaches to probabilistic inference via graph\nrepresentations in modern machine learning and statistics. However, such models\nare limited by the assumption that the data-generating distribution does not\nentail strictly context-specific conditional independence relations. The family\nof staged tree models generalizes DAG models so as to accommodate\ncontext-specific knowledge. We provide a new characterization of perfect\ndiscrete DAG models in terms of their staged tree representations. This\ncharacterization identifies the family of balanced staged trees as the natural\ngeneralization of discrete decomposable models to the context-specific setting.\n"}
+{"abstract": "  Studying the human factors that impact on software development, and assigning\nindividuals with specific competencies and qualities to particular software\nroles, have been shown to aid software project performance. For instance, prior\nevidence suggests that extroverted software project leaders are most\nsuccessful. Role assignment based on individuals' competencies and behaviors\nmay be especially relevant in distributed software development contexts where\nteams are often affected by distance, cultural, and personality issues. Project\nleaders in these environments need to possess high levels of inter-personal,\nintra-personal and organizational competencies if they are to appropriately\nmanage such issues and maintain positive project performance. With a view to\nunderstanding and explaining the specific competencies and behaviors that are\nrequired of project leaders in these settings, we used psycholinguistic and\ndirected content analysis to study the way six successful IBM Rational Jazz\nleaders operated while coordinating their three distributed projects. Contrary\nto previous evidence reported in personality studies, our results did not\nreveal universal competencies and behaviors among these Jazz leaders. Instead,\nJazz project leaders' competencies and behaviors varied with their project\nportfolio of tasks. Our findings suggest that a pragmatic approach that\nconsiders the nature of the software tasks being developed is likely to be a\nmore effective strategy for assigning leaders to distributed software teams, as\nagainst a strategy that promotes a specific personality type. We discuss these\nfindings and outline implications for distributed software project governance.\n"}
+{"abstract": "  The extraction of filamentary structure from a point cloud is discussed. The\nfilaments are modeled as ridge lines or higher dimensional ridges of an\nunderlying density. We propose two novel algorithms, and provide theoretical\nguarantees for their convergences. We consider the new algorithms as\nalternatives to the Subspace Constraint Mean Shift (SCMS) algorithm that do not\nsuffer from a shortcoming of the SCMS that is also revealed in this paper.\n"}
+{"abstract": "  Video conferencing applications (VCAs) have become a critical Internet\napplication, even more so during the COVID-19 pandemic, as users worldwide now\nrely on them for work, school, and telehealth. It is thus increasingly\nimportant to understand the resource requirements of different VCAs and how\nthey perform under different network conditions, including: how much speed\n(upstream and downstream throughput) a VCA needs to support high quality of\nexperience; how VCAs perform under temporary reductions in available capacity;\nhow they compete with themselves, with each other, and with other applications;\nand how usage modality (e.g., number of participants) affects utilization. We\nstudy three modern VCAs: Zoom, Google Meet, and Microsoft Teams. Answers to\nthese questions differ substantially depending on VCA. First, the average\nutilization on an unconstrained link varies between 0.8 Mbps and 1.9 Mbps.\nGiven temporary reduction of capacity, some VCAs can take as long as 50 seconds\nto recover to steady state. Differences in proprietary congestion control\nalgorithms also result in unfair bandwidth allocations: in constrained\nbandwidth settings, one Zoom video conference can consume more than 75% of the\navailable bandwidth when competing with another VCA (e.g., Meet, Teams). For\nsome VCAs, client utilization can decrease as the number of participants\nincreases, due to the reduced video resolution of each participant's video\nstream given a larger number of participants. Finally, one participant's\nviewing mode (e.g., pinning a speaker) can affect the upstream utilization of\nother participants.\n"}
+{"abstract": "  PSR J0740$+$6620 has a gravitational mass of $2.08\\pm 0.07~M_\\odot$, which is\nthe highest reliably determined mass of any neutron star. As a result, a\nmeasurement of its radius will provide unique insight into the properties of\nneutron star core matter at high densities. Here we report a radius measurement\nbased on fits of rotating hot spot patterns to Neutron Star Interior\nComposition Explorer (NICER) and X-ray Multi-Mirror (XMM-Newton) X-ray\nobservations. We find that the equatorial circumferential radius of PSR\nJ0740$+$6620 is $13.7^{+2.6}_{-1.5}$ km (68%). We apply our measurement,\ncombined with the previous NICER mass and radius measurement of PSR\nJ0030$+$0451, the masses of two other $\\sim 2~M_\\odot$ pulsars, and the tidal\ndeformability constraints from two gravitational wave events, to three\ndifferent frameworks for equation of state modeling, and find consistent\nresults at $\\sim 1.5-3$ times nuclear saturation density. For a given\nframework, when all measurements are included the radius of a $1.4~M_\\odot$\nneutron star is known to $\\pm 4$% (68% credibility) and the radius of a\n$2.08~M_\\odot$ neutron star is known to $\\pm 5$%. The full radius range that\nspans the $\\pm 1\\sigma$ credible intervals of all the radius estimates in the\nthree frameworks is $12.45\\pm 0.65$ km for a $1.4~M_\\odot$ neutron star and\n$12.35\\pm 0.75$ km for a $2.08~M_\\odot$ neutron star.\n"}
+{"abstract": "  Sensor-based human activity recognition (HAR), i.e., the ability to discover\nhuman daily activity patterns from wearable or embedded sensors, is a key\nenabler for many real-world applications in smart homes, personal healthcare,\nand urban planning. However, with an increasing number of applications being\ndeployed, an important question arises: how can a HAR system autonomously learn\nnew activities over a long period of time without being re-engineered from\nscratch? This problem is known as continual learning and has been particularly\npopular in the domain of computer vision, where several techniques to attack it\nhave been developed. This paper aims to assess to what extent such continual\nlearning techniques can be applied to the HAR domain. To this end, we propose a\ngeneral framework to evaluate the performance of such techniques on various\ntypes of commonly used HAR datasets. We then present a comprehensive empirical\nanalysis of their computational cost and effectiveness of tackling HAR-specific\nchallenges (i.e., sensor noise and labels' scarcity). The presented results\nuncover useful insights on their applicability and suggest future research\ndirections for HAR systems. Our code, models and data are available at\nhttps://github.com/srvCodes/continual-learning-benchmark.\n"}
+{"abstract": "  Data protection across organizations is limiting the application of\ncentralized learning (CL) techniques. Federated learning (FL) enables multiple\nparticipants to build a learning model without sharing data. Nevertheless,\nthere are very few research works on FL in intelligent manufacturing. This\npaper presents the results of an empirical study on failure prediction in the\nproduction line based on FL. This paper (1) designs Federated Support Vector\nMachine (FedSVM) and Federated Random Forest (FedRF) algorithms for the\nhorizontal FL and vertical FL scenarios, respectively; (2) proposes an\nexperiment process for evaluating the effectiveness between the FL and CL\nalgorithms; (3) finds that the performance of FL and CL are not significantly\ndifferent on the global testing data, on the random partial testing data, and\non the estimated unknown Bosch data, respectively. The fact that the testing\ndata is heterogeneous enhances our findings. Our study reveals that FL can\nreplace CL for failure prediction.\n"}
+{"abstract": "  We propose a generalized Kuramoto model with relativistic effects and\ninvestigate emergent asymptotic behaviors. The proposed generalized Kuramoto\nmodel incorporates relativistic Kuramoto(RK) type models which can be derived\nfrom the relativistic Cucker-Smale (RCS) on the unit sphere under suitable\napproximations. We present several sufficient frameworks leading to complete\nsynchronization in terms of initial data and system parameters. For the\nrelativistic Kuramoto model, we show that it can be reduced to the Kuramoto\nmodel in any finite time interval in a non-relativistic limit. We also provide\nseveral numerical examples for two approximations of the relativistic Kuramoto\nmodel, and compare them with analytical results.\n"}
+{"abstract": "  Pixel scaling effects have been a major issue for the development of\nhigh-resolution color image sensors due to the reduced photoelectric signal and\nthe color crosstalk. Various structural color techniques have been proposed and\ndemonstrated the large freedom in color manipulation by the structure design.\nHowever, the optical efficiency and the color distortion limit the practical\napplications due to its intrinsic filtering mechanism. Instead, on-chip\nfull-color routing is quite charming for improving the signal-to-noise ratio.\nIn this paper, a single-layer quick response code like nanorouter is proposed\nfor the full-color light routing in a pixel level of image sensors. It shows\nmuch higher routing efficiency than various planar lens schemes for signal\nwavelength focusing. Moreover, over 60% signal enhancement with robust\npolarization insensitivity is obtained in all three primary color bands with a\nsame nanorouter by a multi-objective optimization method. Negligible color\ndistortion is observed from the reconstructed color image. Such a simple\nnanorouter structure is promising for the development of image sensor,\nphotovoltaics and display.\n"}
+{"abstract": "  The mechanism of atomic collisions in excited bands plays an important role\nin the study of the orbital physics in optical lattices and simulation of\ncondensed matter physics. Atoms distributing in one excited bands of an optical\nlattice would collide and decay to other bands through different scattering\nchannels. In excited bands of one dimensional lattice, due to lack of geometry,\nthere is no significant difference between cross section of scattering\nchannels. Here, we investigate the collisional scattering channels for atoms in\nthe excited bands of a triangular optical lattice and demonstrate a dominant\nscattering channel in the experiment. A shortcut method is utilized to load\nBose-Einstein condensates of $^{87} {\\rm Rb}$ atoms into the first D band with\nzero quasi-momentum. After some time for evolution, the number of atoms\nscattering to S band due to two-body collisions is around four times more than\nthat to the second most band. We reveal that the scattering channel to $ss$\nband is dominant by theoretical calculation, which agrees with experimental\nmeasurements. The appearance of dominant scattering channels in triangular\noptical lattice is owing to geometric dimension coupling. This work is helpful\nfor the study of many-body systems and directional enhancement in optical\nlattices.\n"}
+{"abstract": "  Accurate multi-class segmentation is a long-standing challenge in medical\nimaging, especially in scenarios where classes share strong similarity.\nSegmenting retinal blood vessels in retinal photographs is one such scenario,\nin which arteries and veins need to be identified and differentiated from each\nother and from the background. Intra-segment misclassification, i.e. veins\nclassified as arteries or vice versa, frequently occurs when arteries and veins\nintersect, whereas in binary retinal vessel segmentation, error rates are much\nlower. We thus propose a new approach that decomposes multi-class segmentation\ninto multiple binary, followed by a binary-to-multi-class fusion network. The\nnetwork merges representations of artery, vein, and multi-class feature maps,\neach of which are supervised by expert vessel annotation in adversarial\ntraining. A skip-connection based merging process explicitly maintains\nclass-specific gradients to avoid gradient vanishing in deep layers, to favor\nthe discriminative features. The results show that, our model respectively\nimproves F1-score by 4.4\\%, 5.1\\%, and 4.2\\% compared with three\nstate-of-the-art deep learning based methods on DRIVE-AV, LES-AV, and HRF-AV\ndata sets. Code: https://github.com/rmaphoh/Learning-AVSegmentation\n"}
+{"abstract": "  We combine ideas from uni-directional and bi-directional heuristic search,\nand approximation algorithms for the Traveling Salesman Problem, to develop a\nnovel framework for a Multi-Goal Path Finding (MGPF) problem that provides a\n2-approximation guarantee. MGPF aims to find a least-cost path from an origin\nto a destination such that each node in a given set of goals is visited at\nleast once along the path. We present numerical results to illustrate the\nadvantages of our framework over conventional alternates in terms of the number\nof expanded nodes and run time.\n"}
+{"abstract": "  In this paper, we propose a gradient boosting algorithm for large-scale\nregression problems called \\textit{Gradient Boosted Binary Histogram Ensemble}\n(GBBHE) based on binary histogram partition and ensemble learning. From the\ntheoretical perspective, by assuming the H\\\"{o}lder continuity of the target\nfunction, we establish the statistical convergence rate of GBBHE in the space\n$C^{0,\\alpha}$ and $C^{1,0}$, where a lower bound of the convergence rate for\nthe base learner demonstrates the advantage of boosting. Moreover, in the space\n$C^{1,0}$, we prove that the number of iterations to achieve the fast\nconvergence rate can be reduced by using ensemble regressor as the base\nlearner, which improves the computational efficiency. In the experiments,\ncompared with other state-of-the-art algorithms such as gradient boosted\nregression tree (GBRT), Breiman's forest, and kernel-based methods, our GBBHE\nalgorithm shows promising performance with less running time on large-scale\ndatasets.\n"}
+{"abstract": "  Over the years motor deficit in Parkinson's Disease (PD) patients was largely\nstudied, however, no consistent pattern of relations between quantitative\nelectroencephalography (qEEG) and motor scales emerged. There is a general lack\nof information on the relation between EEG changes and scales related to\nspecific motor deficits. Therefore, the study aimed to investigate the relation\nbetween brain oscillatory activity alterations (EEG power bands) and most used\nPD-related motor deficit scales. A positive correlation was found between the\nfreezing of the gait questionnaire (FOGQ) and delta spectral power band\n(rho=0.67; p=0.008), while a negative correlation with the same scale was\nobserved in the alpha spectral power band (rho=-0.59, p=0.027). Additionally,\nmotor scores measure by motor part of Unified Parkinson's Disease Rating Scale\n(UPDRS) correlated directly with theta (rho=0.55, p=0.040) and inversely with\nbeta EEG power band (rho=-0.77, p=0.001). No significant correlation was found\nbetween spectral powers and Hoehn and Yahr (H&Y), BERG (Berg K. et. al. 1995),\nModified Parkinson Activity Scale (MPAS), Six-Minute Walk Test (6MWT) and Timed\nUp and Go Test (TUG). In conclusion, our study supports the earlier findings\nsuggesting a link between EEG slowing and motor decline, providing more insight\ninto the relation between EEG alteration and deficits in different motor\ndomains. These findings indicate that EEG assessment may be a useful biomarker\nfor objective monitoring of progression and neurophysiological effect of\nrehabilitation approaches in PD's.\n"}
+{"abstract": "  One of the primary sources for the future space-based gravitational wave\ndetector, the Laser Interferometer Space Antenna, are the inspirals of small\ncompact objects into massive black holes in the centres of galaxies. The\ngravitational waveforms from such Extreme Mass Ratio Inspiral (EMRI) systems\nwill provide measurements of their parameters with unprecedented precision, but\nonly if the waveforms are accurately modeled. Here we explore the impact of\ntransient orbital resonances which occur when the ratio of radial and polar\nfrequencies is a rational number. We introduce a new Effective Resonance Model,\nwhich is an extension of the numerical kludge EMRI waveform approximation to\ninclude the effect of resonances, and use it to explore the impact of\nresonances on EMRI parameter estimation. For one-year inspirals, we find that\nthe few cycle dephasings induced by 3:2 resonances can lead to systematic\nerrors in parameter estimates, that are up to several times the typical\nmeasurement precision of the parameters. The bias is greatest for 3:2\nresonances that occur closer to the central black hole. By regarding them as\nunknown model parameters, we further show that observations will be able to\nconstrain the size of the changes in the orbital parameters across the\nresonance to a relative precision of 10% for a typical one-year EMRI\nobservation with signal-to-noise ratio of 20. Such measurements can be regarded\nas tests of fundamental physics, by comparing the measured changes to those\npredicted in general relativity.\n"}
+{"abstract": "  Statistical arbitrage identifies and exploits temporal price differences\nbetween similar assets. We propose a unifying conceptual framework for\nstatistical arbitrage and develop a novel deep learning solution, which finds\ncommonality and time-series patterns from large panels in a data-driven and\nflexible way. First, we construct arbitrage portfolios of similar assets as\nresidual portfolios from conditional latent asset pricing factors. Second, we\nextract the time series signals of these residual portfolios with one of the\nmost powerful machine learning time-series solutions, a convolutional\ntransformer. Last, we use these signals to form an optimal trading policy, that\nmaximizes risk-adjusted returns under constraints. We conduct a comprehensive\nempirical comparison study with daily large cap U.S. stocks. Our optimal\ntrading strategy obtains a consistently high out-of-sample Sharpe ratio and\nsubstantially outperforms all benchmark approaches. It is orthogonal to common\nrisk factors, and exploits asymmetric local trend and reversion patterns. Our\nstrategies remain profitable after taking into account trading frictions and\ncosts. Our findings suggest a high compensation for arbitrageurs to enforce the\nlaw of one price.\n"}
+{"abstract": "  Criticality experiments with $^{235}$U (metal and hydride) and $^{239}$Pu\n(metal) were performed during the Manhattan Project. Results from these\nexperiments provided necessary information for the success of the Manhattan\nProject. These experiments have been previously described in compilations made\nafter the Manhattan Project, but those works are either lacking in technical\ndetails or are not publicly available. This work aims to provide detailed\ninformation while showcasing the enduring impact of these experiments 75 years\nafter they were performed. Furthermore, we use modern computational methods\nembodied in the MCNP6 code and ENDF data to analyze and interpret these\nhistoric measurements. The world's first four criticality accidents are also\ndiscussed, as lessons learned from these helped shape the field of criticality\nexperiments.\n"}
+{"abstract": "  Dietary supplements (DS) have been widely used by consumers, but the\ninformation around the efficacy and safety of DS is disparate or incomplete,\nthus creating barriers for consumers to find information effectively.\nConversational agent (CA) systems have been applied to the healthcare domain,\nbut there is no such a system to answer consumers regarding DS use, although\nwidespread use of DS. In this study, we develop the first CA system for DS use\n"}
+{"abstract": "  The functional object-oriented network (FOON) has been introduced as a\nknowledge representation, which takes the form of a graph, for symbolic task\nplanning. To get a sequential plan for a manipulation task, a robot can obtain\na task tree through a knowledge retrieval process from the FOON. To evaluate\nthe quality of an acquired task tree, we compare it with a conventional form of\ntask knowledge, such as recipes or manuals. We first automatically convert task\ntrees to recipes, and we then compare them with the human-created recipes in\nthe Recipe1M+ dataset via a survey. Our preliminary study finds no significant\ndifference between the recipes in Recipe1M+ and the recipes generated from FOON\ntask trees in terms of correctness, completeness, and clarity.\n"}
+{"abstract": "  This paper studies model-based bandit and reinforcement learning (RL) with\nnonlinear function approximations. We propose to study convergence to\napproximate local maxima because we show that global convergence is\nstatistically intractable even for one-layer neural net bandit with a\ndeterministic reward. For both nonlinear bandit and RL, the paper presents a\nmodel-based algorithm, Virtual Ascent with Online Model Learner (ViOlin), which\nprovably converges to a local maximum with sample complexity that only depends\non the sequential Rademacher complexity of the model class. Our results imply\nnovel global or local regret bounds on several concrete settings such as linear\nbandit with finite or sparse model class, and two-layer neural net bandit. A\nkey algorithmic insight is that optimism may lead to over-exploration even for\ntwo-layer neural net model class. On the other hand, for convergence to local\nmaxima, it suffices to maximize the virtual return if the model can also\nreasonably predict the size of the gradient and Hessian of the real return.\n"}
+{"abstract": "  Learning-based stereo matching and depth estimation networks currently excel\non public benchmarks with impressive results. However, state-of-the-art\nnetworks often fail to generalize from synthetic imagery to more challenging\nreal data domains. This paper is an attempt to uncover hidden secrets of\nachieving domain robustness and in particular, discovering the important\ningredients of generalization success of stereo matching networks by analyzing\nthe effect of synthetic image learning on real data performance. We provide\nevidence that demonstrates that learning of features in the synthetic domain by\na stereo matching network is heavily influenced by two \"shortcuts\" presented in\nthe synthetic data: (1) identical local statistics (RGB colour features)\nbetween matching pixels in the synthetic stereo images and (2) lack of realism\nin synthetic textures on 3D objects simulated in game engines. We will show\nthat by removing such shortcuts, we can achieve domain robustness in the\nstate-of-the-art stereo matching frameworks and produce a remarkable\nperformance on multiple realistic datasets, despite the fact that the networks\nwere trained on synthetic data, only. Our experimental results point to the\nfact that eliminating shortcuts from the synthetic data is key to achieve\ndomain-invariant generalization between synthetic and real data domains.\n"}
+{"abstract": "  An increasing number of emerging applications in data science and engineering\nare based on multidimensional and structurally rich data. The irregularities,\nhowever, of high-dimensional data often compromise the effectiveness of\nstandard machine learning algorithms. We hereby propose the Rank-R Feedforward\nNeural Network (FNN), a tensor-based nonlinear learning model that imposes\nCanonical/Polyadic decomposition on its parameters, thereby offering two core\nadvantages compared to typical machine learning methods. First, it handles\ninputs as multilinear arrays, bypassing the need for vectorization, and can\nthus fully exploit the structural information along every data dimension.\nMoreover, the number of the model's trainable parameters is substantially\nreduced, making it very efficient for small sample setting problems. We\nestablish the universal approximation and learnability properties of Rank-R\nFNN, and we validate its performance on real-world hyperspectral datasets.\nExperimental evaluations show that Rank-R FNN is a computationally inexpensive\nalternative of ordinary FNN that achieves state-of-the-art performance on\nhigher-order tensor data.\n"}
+{"abstract": "  Cooper pair splitters are promising candidates for generating spin-entangled\nelectrons. However, the splitting of Cooper pairs is a random and noisy\nprocess, which hinders further synchronized operations on the entangled\nelectrons. To circumvent this problem, we here propose and analyze a dynamic\nCooper pair splitter that produces a noiseless and regular flow of\nspin-entangled electrons. The Cooper pair splitter is based on a superconductor\ncoupled to quantum dots, whose energy levels are tuned in and out of resonance\nto control the splitting process. We identify the optimal operating conditions\nfor which exactly one Cooper pair is split per period of the external drive and\nthe flow of entangled electrons becomes noiseless. To characterize the\nregularity of the Cooper pair splitter in the time domain, we analyze the\n$g^{(2)}$-function of the output currents and the distribution of waiting times\nbetween split Cooper pairs. Our proposal is feasible using current technology,\nand it paves the way for dynamic quantum information processing with\nspin-entangled electrons.\n"}
+{"abstract": "  We investigate swampland conjectures for quantum gravity in the context of\nM-theory compactified on Calabi-Yau threefolds which admit infinite sequences\nof flops. Naively, the moduli space of such compactifications contains paths of\narbitrary geodesic length traversing an arbitrarily large number of K\\\"ahler\ncones, along which the low-energy spectrum remains virtually unchanged. In\ncases where the infinite chain of Calabi-Yau manifolds involves only a finite\nnumber of isomorphism classes, the moduli space has an infinite discrete\nsymmetry which relates the isomorphic manifolds connected by flops. This is a\nremnant of the 11D Poincare symmetry and consequently gauged, as it has to be\nby the no-global symmetry conjecture. The apparent contradiction with the\nswampland distance conjecture is hence resolved after dividing by this discrete\nsymmetry. If the flop sequence involves infinitely many non-isomorphic\nmanifolds, this resolution is no longer available. However, such a situation\ncannot occur if the Kawamata-Morrison conjecture for Calabi-Yau threefolds is\ntrue. Conversely, the swampland distance conjecture, when applied to infinite\nflop chains, implies the Kawamata-Morrison conjecture under a plausible\nassumption on the diameter of the K\\\"ahler cones.\n"}
+{"abstract": "  We consider entanglement of first-quantized identical particles by adopting\nan algebraic approach. In particular, we investigate fermions whose wave\nfunctions are given by the Slater determinants, as for singlet sectors of\none-matrix models. We show that the upper bounds of the general R\\'enyi\nentropies are $N \\log 2$ for $N$ particles or an $N\\times N$ matrix. We compute\nthe target space entanglement entropy and the mutual information in a free\none-matrix model. We confirm the area law: the single-interval entropy for the\nground state scales as $\\frac{1}{3}\\log N$ in the large $N$ model. We obtain an\nanalytical $\\mathcal{O}(N^0)$ expression of the mutual information for two\nintervals in the large $N$ expansion.\n"}
+{"abstract": "  Collaboration is identified as a required and necessary skill for students to\nbe successful in the fields of Science, Technology, Engineering and Mathematics\n(STEM). However, due to growing student population and limited teaching staff\nit is difficult for teachers to provide constructive feedback and instill\ncollaborative skills using instructional methods. Development of simple and\neasily explainable machine-learning-based automated systems can help address\nthis problem. Improving upon our previous work, in this paper we propose using\nsimple temporal-CNN deep-learning models to assess student group collaboration\nthat take in temporal representations of individual student roles as input. We\ncheck the applicability of dynamically changing feature representations for\nstudent group collaboration assessment and how they impact the overall\nperformance. We also use Grad-CAM visualizations to better understand and\ninterpret the important temporal indices that led to the deep-learning model's\ndecision.\n"}
+{"abstract": "  For a group $G$, let $\\mathcal{F}_{n}$ be the family of all the subgroups of\n$G$ containing a subgroup isomorphic to $\\mathbb{Z}^{r}$ for some\n$r=0,1,2,\\dots ,n$ of finite index. Joecken, Lafont and S\\'anchez Salda\\~na\ncomputed the $\\mathcal{F}_1$-dimension of 3-manifold groups. The goal of this\narticle is to compute the $\\mathcal{F}_n$-geometric dimension of 3-manifold\ngroups for all $n\\geq 2$.\n"}
+{"abstract": "  The Atoyac River is among the two most polluted in Mexico. Water quality in\nthe Upper Atoyac River Basin (UARB) has been devastated by industrial and\nmunicipal wastewater, as well as from effluents from local dwellers, that go\nthrough little to no treatment, affecting health, production, ecosystems and\nproperty value. We did a systematic review and mapping of the costs that\npollution imposes on different sectors and localities in the UARB, and\ninitially found 358 studies, of which 17 were of our particular interest. We\nfocus on estimating the cost of pollution through different valuation methods\nsuch as averted costs, hedonic pricing, and contingent valuation, and for that\nwe only use 10 studies. Costs range from less than a million to over $16\nmillion dollars a year, depending on the sector, with agriculture, industry and\ntourism yielding the highest costs. This exercise is the first of its kind in\nthe UARB that maps costs for sectors and localities affected, and sheds light\non the need of additional research to estimate the total cost of pollution\nthroughout the basin. This information may help design further research needs\nin the region.\n"}
+{"abstract": "  Physics in schools is distinctly different from, and struggles to capture the\nexcitement of, university research-level work. Initiatives where students\nengage in independent research linked to cutting-edge physics within their\nschool over several months might help mitigate this, potentially facilitating\nthe uptake of science in higher education. However, how such initiatives are\nbest supported remains unclear and understudied. This paper evaluates a\nprovision framework, `Physics Research in School Environments' (PRiSE), using\nsurvey data from participating 14-18 year-old students and their teachers to\nunderstand their experience of the programme. The results show that PRiSE\nappears to provide much more positive experiences than typical university\noutreach initiatives due to the nature of the opportunities afforded over\nseveral months, which schools would not be able to provide without external\ninput. The intensive support offered is deemed necessary, with all elements\nappearing equally important. Based on additional feedback from independent\nresearchers and engagement professionals, we also suggest the framework could\nbe adopted at other institutions and applied to their own areas of scientific\nresearch, something which has already started to occur.\n"}
+{"abstract": "  The Stochastic Abacus is can employed to compute winning probabilities for\neach vertex of a rooted tree in the game \"Pass the Buck\", with the starting\nvertex being the root of the tree. For all but the simplest trees, the abacus\ncan't really be implemented due to the large number of steps needed for\ncompletion. In this paper, a technique for anticipating the outcome is\nintroduced.\n"}
+{"abstract": "  In this paper we prove the generalized Kaplansky conjecture for the Jordan\nalgebras of the type $J_n$ in particular for self adjoint $2\\times 2$ matrices\nover $\\R$, over $\\C$, $\\HH$ and $\\Oct$. In fact, we prove that the image of\nmultilinear polynomial must be either $\\{0\\}$, $\\mathbb{R}$, the space of pure\nelements $V$, or $J_n$.\n"}
+{"abstract": "  Let $G$ be a linear algebraic group over an algebraically closed field $k$\nand $Aut(G)$ the group of all algebraic group automorphisms of $G$. For every\n$\\varphi\\in Aut(G)$ let $\\mathcal{R}(\\varphi)$ denote the set of all orbits of\nthe $\\varphi$-twisted conjugacy action of $G$ on itself (given by $(g,x)\\mapsto\ngx\\varphi(g^{-1})$, for all $g,x\\in G$). We say that $G$ satisfies the\n$R_\\infty$-property if $\\mathcal{R}(\\varphi)$ is infinite for every $\\varphi\\in\nAut(G)$. In an earlier work we have shown that this property is satisfied by\nevery connected non-solvable algebraic group. From a theorem due to Steinberg\nit follows that if a connected algebraic group $G$ has the $R_\\infty$-property\nthen $G^\\varphi$ is infinite for all $\\varphi\\in Aut(G)$. In this article we\nshow that the condition is also sufficient. In particular we deduce that a\nBorel subgroup of any semisimple algebraic group has the $R_\\infty$-property\nand identify certain classes of solvable algebraic groups for which the\nproperty fails.\n"}
+{"abstract": "  We perform a Hadamard point-split renormalization of the current density\n$j_\\nu$ of a charged scalar field in a Reissner-Nordstr\\\"om-de Sitter\nspacetime. We compute numerically the expectation values of the components\n$j_r$ and $j_t$ in the Unruh state in the exterior region and study their\ndependence on the field parameters and the position.\n"}
+{"abstract": "  We investigate the concept of molecular-sized outward-swinging gate, which\nallows for entropy decrease in an isolated system. The theoretical analysis,\nthe Monte Carlo simulation, and the direct solution of governing equations all\nsuggest that under the condition of local nonchaoticity, the probability of\nparticle crossing is asymmetric. It is demonstrated by an experiment on a\nnanoporous membrane one-sidedly surface-grafted with bendable organic chains.\nRemarkably, through the membrane, gas spontaneously and repeatedly flows from\nthe low-pressure side to the high-pressure side. While this phenomenon seems\ncounterintuitive, it is compatible with the principle of maximum entropy. The\nlocally nonchaotic gate interrupts the probability distribution of the local\nmicrostates, and imposes additional constraints on the global microstates, so\nthat entropy reaches a nonequilibrium maximum. Such a mechanism is\nfundamentally different from Maxwell's demon and Feynman's ratchet, and is\nconsistent with microscopic reversibility. It implies that useful work may be\nproduced in a cycle from a single thermal reservoir. A generalized form of the\nsecond law of thermodynamics is proposed.\n"}
+{"abstract": "  We prove existence, uniqueness and Sobolev regularity of weak solution of the\nCauchy problem of the stochastic transport equation with drift in a large class\nof singular vector fields containing, in particular, the $L^d$ class, the weak\n$L^d$ class, as well as some vector fields that are not even in $L_{\\rm\nloc}^{2+\\varepsilon}$ for any $\\varepsilon>0$.\n"}
+{"abstract": "  Activity generation plays an important role in activity-based demand\nmodelling systems. While machine learning, especially deep learning, has been\nincreasingly used for mode choice and traffic flow prediction, much less\nresearch exploiting the advantage of deep learning for activity generation\ntasks. This paper proposes a novel activity pattern generation framework by\nincorporating deep learning with travel domain knowledge. We model each\nactivity schedule as one primary activity tour and several secondary activity\ntours. We then develop different deep neural networks with entity embedding and\nrandom forest models to classify activity type, as well as to predict activity\ntimes. The proposed framework can capture the activity patterns for individuals\nin both training and validation sets. Results show high accuracy for the start\ntime and end time of work and school activities. The framework also replicates\nthe start time patterns of stop-before and stop-after primary work activity\nwell. This provides a promising direction to deploy advanced machine learning\nmethods to generate more reliable activity-travel patterns for transport demand\nsystems and their applications.\n"}
+{"abstract": "  The identification of rare diseases from clinical notes with Natural Language\nProcessing (NLP) is challenging due to the few cases available for machine\nlearning and the need of data annotation from clinical experts. We propose a\nmethod using ontologies and weak supervision. The approach includes two steps:\n(i) Text-to-UMLS, linking text mentions to concepts in Unified Medical Language\nSystem (UMLS), with a named entity linking tool (e.g. SemEHR) and weak\nsupervision based on customised rules and Bidirectional Encoder Representations\nfrom Transformers (BERT) based contextual representations, and (ii)\nUMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease\nOntology (ORDO). Using MIMIC-III US intensive care discharge summaries as a\ncase study, we show that the Text-to-UMLS process can be greatly improved with\nweak supervision, without any annotated data from domain experts. Our analysis\nshows that the overall pipeline processing discharge summaries can surface rare\ndisease cases, which are mostly uncaptured in manual ICD codes of the hospital\nadmissions.\n"}
+{"abstract": "  We investigate the adversarial robustness of CNNs from the perspective of\nchannel-wise activations. By comparing \\textit{non-robust} (normally trained)\nand \\textit{robustified} (adversarially trained) models, we observe that\nadversarial training (AT) robustifies CNNs by aligning the channel-wise\nactivations of adversarial data with those of their natural counterparts.\nHowever, the channels that are \\textit{negatively-relevant} (NR) to predictions\nare still over-activated when processing adversarial data. Besides, we also\nobserve that AT does not result in similar robustness for all classes. For the\nrobust classes, channels with larger activation magnitudes are usually more\n\\textit{positively-relevant} (PR) to predictions, but this alignment does not\nhold for the non-robust classes. Given these observations, we hypothesize that\nsuppressing NR channels and aligning PR ones with their relevances further\nenhances the robustness of CNNs under AT. To examine this hypothesis, we\nintroduce a novel mechanism, i.e., \\underline{C}hannel-wise\n\\underline{I}mportance-based \\underline{F}eature \\underline{S}election (CIFS).\nThe CIFS manipulates channels' activations of certain layers by generating\nnon-negative multipliers to these channels based on their relevances to\npredictions. Extensive experiments on benchmark datasets including CIFAR10 and\nSVHN clearly verify the hypothesis and CIFS's effectiveness of robustifying\nCNNs. \\url{https://github.com/HanshuYAN/CIFS}\n"}
+{"abstract": "  Purpose: Recent studies suggest ultra-high dose rate (FLASH) irradiation can\nspare normal tissues from radiotoxicity, while efficiently controlling the\ntumor, and this is known as the FLASH effect. This study performed theoretical\nanalyses about the impact of radiolytic oxygen depletion (ROD) on the cellular\nresponses after FLASH irradiation. Methods: Monte Carlo simulation was used to\nmodel the ROD process, determine the DNA damage, and calculate the amount of\noxygen depleted (LROD) during FLASH exposure. A mathematical model was applied\nto analyze oxygen tension (pO2) distribution in human tissues and the recovery\nof pO2 after FLASH irradiation. DNA damage and cell survival fractions (SFs)\nafter FLASH irradiation were calculated. The impact of initial cellular pO2,\nFLASH pulse number, pulse interval, and radiation quality of the source\nparticles on ROD and subsequent cellular responses were systematically\nevaluated. Results: The simulated electron LROD range was 0.38-0.43 {\\mu}M/Gy\nwhen pO2 ranged from 7.5-160 mmHg. The calculated DNA damage and SFs show that\nradioprotective effect is only evident in cells with a lower pO2. Different\nirradiation setups alter the cellular responses by modifying the pO2. Single\npulse delivery or multi-pulse delivery with pulse intervals shorter than 10-50\nms resulted in fewer DNA damages and higher SFs. Source particles with a low\nradiation quality have a higher capacity to deplete oxygen, and thus, lead to a\nmore conspicuous radioprotective effect. Conclusions: The FLASH radioprotective\neffect due to ROD may only be observed in cells with a low pO2. Single pulse\ndelivery or multi-pulse delivery with short pulse intervals are suggested for\nFLASH irradiation to avoid oxygen tension recovery during pulse intervals.\nSource particles with low radiation quality are preferred for their conspicuous\nradioprotective effects.\n"}
+{"abstract": "  Particles kicked by external forces to produce mobility distinct from thermal\ndiffusion are an iconic feature of the active matter problem. Here, we map this\nonto a minimal model for experiment and theory covering the wide time and\nlength scales of usual active matter systems. A particle diffusing in a\nharmonic potential generated by an optical trap is kicked by programmed forces\nwith time correlation at random intervals following the Poisson process. The\nmodel's generic simplicity allows us to find conditions for which displacements\nare Gaussian (or not), how diffusion is perturbed (or not) by kicks, and\nquantifying heat dissipation to maintain the non-equilibrium steady state in an\nactive bath. The model reproduces experimental results of tracer mobility in an\nactive bath of swimming algal cells. It can be used as a stochastic dynamic\nsimulator for Brownian objects in various active baths without mechanistic\nunderstanding, owing to the generic framework of the protocol.\n"}
+{"abstract": "  Let $(M_j)_{j=1}^\\infty\\in\\mathbb{N}$ and $(r_j)_{j=1}^\\infty\\in\\mathbb{R}^+$\nbe increasing sequences satisfying some mild rate of growth conditions. We\nprove that there is an entire function $f: \\mathbb{C} \\rightarrow\\mathbb{C}$\nwhose behavior in the large annuli $\\{ z\\in\\mathbb{C} :\nr_{j}\\cdot\\exp(\\pi/M_{j})\\leq|z|\\leq r_{j+1}\\}$ is given by a perturbed\nrescaling of $z\\mapsto z^{M_j}$, such that the only singular values of $f$ are\nrescalings of $\\pm r_j^{M_j}$. We describe several applications to the dynamics\nof entire functions.\n"}
+{"abstract": "  A novel compressed matrix format is proposed that combines an adaptive\nhierarchical partitioning of the matrix with low-rank approximation. One\ntypical application is the approximation of discretized functions on\nrectangular domains; the flexibility of the format makes it possible to deal\nwith functions that feature singularities in small, localized regions. To deal\nwith time evolution and relocation of singularities, the partitioning can be\ndynamically adjusted based on features of the underlying data. Our format can\nbe leveraged to efficiently solve linear systems with Kronecker product\nstructure, as they arise from discretized partial differential equations\n(PDEs). For this purpose, these linear systems are rephrased as linear matrix\nequations and a recursive solver is derived from low-rank updates of such\nequations. We demonstrate the effectiveness of our framework for stationary and\ntime-dependent, linear and nonlinear PDEs, including the Burgers' and\nAllen-Cahn equations.\n"}
+{"abstract": "  In the paper a simple explicit formula for an arbitrary $3j$-symbol for the\nLie algebra $\\mathfrak{gl}_3$ is given. More precise necessary conditions for\nnon-vanishing of a $3j$-symbol are given, in the case when these conditions\nhold we give an explicit expression for a $3j$-symbol. It is expressed through\na value of an $A$-hypergeometric function when one substitutes $1$ instead of\nall it's arguments. The problem of calculation of an arbitrary $3j$-symbol is\nequivalent to the problem of calculation of an arbitrary Clebsh-Gordan\ncoefficient for the algebra $\\mathfrak{gl}_3$. These coefficients play an\nimportant role in quantum mechanics in the theory of quarks.\n"}
+{"abstract": "  We prove several consistency results in choiceless set theory ZF+DC regarding\ncountable chromatic numbers of various algebraic hypergraphs on Euclidean\nspaces.\n"}
+{"abstract": "  We develop an algorithm to train individually fair learning-to-rank (LTR)\nmodels. The proposed approach ensures items from minority groups appear\nalongside similar items from majority groups. This notion of fair ranking is\nbased on the definition of individual fairness from supervised learning and is\nmore nuanced than prior fair LTR approaches that simply ensure the ranking\nmodel provides underrepresented items with a basic level of exposure. The crux\nof our method is an optimal transport-based regularizer that enforces\nindividual fairness and an efficient algorithm for optimizing the regularizer.\nWe show that our approach leads to certifiably individually fair LTR models and\ndemonstrate the efficacy of our method on ranking tasks subject to demographic\nbiases.\n"}
+{"abstract": "  In this work, we present EvoK, a new way of sharing one's heart rate with\nfeedback from their close contacts to alleviate social isolation and\nloneliness. EvoK consists of a pair of wearable prototype devices (i.e., sender\nand receiver). The sender is designed as a headband enabling continuous sensing\nof heart rate with aesthetic designs to maximize social acceptance. The\nreceiver is designed as a wristwatch enabling unobtrusive receiving of the\nloved one's continuous heart rate with multi-modal notification systems.\n"}
+{"abstract": "  Detailed chemistry-based computational fluid dynamics (CFD) simulations are\ncomputationally expensive due to the solution of the underlying chemical\nkinetics system of ordinary differential equations (ODEs). Here, we introduce a\nnovel open-source library aiming at speeding up such reactive flow simulations\nusing OpenFOAM, an open-source C++ software for CFD. First, our dynamic load\nbalancing model DLBFoam (Tekg\\\"ul et al., 2021) is utilized to mitigate the\ncomputational imbalance due to chemistry solution in multiprocessor reactive\nflow simulations. Then, the individual (cell-based) chemistry solutions are\noptimized by implementing an analytical Jacobian formulation using the\nopen-source library pyJac, and by increasing the efficiency of the ODE solvers\nby utilizing the linear algebra package LAPACK. We demonstrate the speed-up\ncapabilities of this new library on various combustion problems. These test\nproblems include a 2D turbulent reacting shear layer and 3D stratified\ncombustion to highlight the favorable scaling aspects of the library on\nignition/flame front initiation setups for dual-fuel combustion. Furthermore,\ntwo fundamental 3D demonstrations are provided on non-premixed and partially\npremixed flames, namely the ECN Spray A and the Sandia flame D experimental\nconfigurations. The novel model offers up to two orders of magnitude speed-up\nfor most of the investigated cases. The openly shared code along with the test\ncase setups represent a radically new enabler for reactive flow simulations in\nthe OpenFOAM framework.\n"}
+{"abstract": "  We have performed cough detection based on measurements from an accelerometer\nattached to the patient's bed. This form of monitoring is less intrusive than\nbody-attached accelerometer sensors, and sidesteps privacy concerns encountered\nwhen using audio for cough detection. For our experiments, we have compiled a\nmanually-annotated dataset containing the acceleration signals of approximately\n6000 cough and 68000 non-cough events from 14 adult male patients in a\ntuberculosis clinic. As classifiers, we have considered convolutional neural\nnetworks (CNN), long-short-term-memory (LSTM) networks, and a residual neural\nnetwork (Resnet50). We find that all classifiers are able to distinguish\nbetween the acceleration signals due to coughing and those due to other\nactivities including sneezing, throat-clearing and movement in the bed with\nhigh accuracy. The Resnet50 performs the best, achieving an area under the ROC\ncurve (AUC) exceeding 0.98 in cross-validation experiments. We conclude that\nhigh-accuracy cough monitoring based only on measurements from the\naccelerometer in a consumer smartphone is possible. Since the need to gather\naudio is avoided and therefore privacy is inherently protected, and since the\naccelerometer is attached to the bed and not worn, this form of monitoring may\nrepresent a more convenient and readily accepted method of long-term patient\ncough monitoring.\n"}
+{"abstract": "  We introduce DatasetGAN: an automatic procedure to generate massive datasets\nof high-quality semantically segmented images requiring minimal human effort.\nCurrent deep networks are extremely data-hungry, benefiting from training on\nlarge-scale datasets, which are time consuming to annotate. Our method relies\non the power of recent GANs to generate realistic images. We show how the GAN\nlatent code can be decoded to produce a semantic segmentation of the image.\nTraining the decoder only needs a few labeled examples to generalize to the\nrest of the latent space, resulting in an infinite annotated dataset generator!\nThese generated datasets can then be used for training any computer vision\narchitecture just as real datasets are. As only a few images need to be\nmanually segmented, it becomes possible to annotate images in extreme detail\nand generate datasets with rich object and part segmentations. To showcase the\npower of our approach, we generated datasets for 7 image segmentation tasks\nwhich include pixel-level labels for 34 human face parts, and 32 car parts. Our\napproach outperforms all semi-supervised baselines significantly and is on par\nwith fully supervised methods, which in some cases require as much as 100x more\nannotated data as our method.\n"}
+{"abstract": "  Histopathological characterization of colorectal polyps allows to tailor\npatients' management and follow up with the ultimate aim of avoiding or\npromptly detecting an invasive carcinoma. Colorectal polyps characterization\nrelies on the histological analysis of tissue samples to determine the polyps\nmalignancy and dysplasia grade. Deep neural networks achieve outstanding\naccuracy in medical patterns recognition, however they require large sets of\nannotated training images. We introduce UniToPatho, an annotated dataset of\n9536 hematoxylin and eosin (H&E) stained patches extracted from 292 whole-slide\nimages, meant for training deep neural networks for colorectal polyps\nclassification and adenomas grading. We present our dataset and provide\ninsights on how to tackle the problem of automatic colorectal polyps\ncharacterization.\n"}
+{"abstract": "  Automated skin lesion analysis for simultaneous detection and recognition is\nstill challenging for inter-class homogeneity and intra-class heterogeneity,\nleading to low generic capability of a Single Convolutional Neural Network\n(CNN) with limited datasets. This article proposes an end-to-end deep CNN-based\nframework for simultaneous detection and recognition of the skin lesions, named\nDermo-DOCTOR, consisting of two encoders. The feature maps from two encoders\nare fused channel-wise, called Fused Feature Map (FFM). The FFM is utilized for\ndecoding in the detection sub-network, concatenating each stage of two\nencoders' outputs with corresponding decoder layers to retrieve the lost\nspatial information due to pooling in the encoders. For the recognition\nsub-network, the outputs of three fully connected layers, utilizing feature\nmaps of two encoders and FFM, are aggregated to obtain a final lesion class. We\ntrain and evaluate the proposed Dermo-Doctor utilizing two publicly available\nbenchmark datasets, such as ISIC-2016 and ISIC-2017. The achieved segmentation\nresults exhibit mean intersection over unions of 85.0 % and 80.0 % respectively\nfor ISIC-2016 and ISIC-2017 test datasets. The proposed Dermo-DOCTOR also\ndemonstrates praiseworthy success in lesion recognition, providing the areas\nunder the receiver operating characteristic curves of 0.98 and 0.91\nrespectively for those two datasets. The experimental results show that the\nproposed Dermo-DOCTOR outperforms the alternative methods mentioned in the\nliterature, designed for skin lesion detection and recognition. As the\nDermo-DOCTOR provides better-results on two different test datasets, even with\nlimited training data, it can be an auspicious computer-aided assistive tool\nfor dermatologists.\n"}
+{"abstract": "  Within the past two decades, Gaussian process regression has been\nincreasingly used for modeling dynamical systems due to some beneficial\nproperties such as the bias variance trade-off and the strong connection to\nBayesian mathematics. As data-driven method, a Gaussian process is a powerful\ntool for nonlinear function regression without the need of much prior\nknowledge. In contrast to most of the other techniques, Gaussian Process\nmodeling provides not only a mean prediction but also a measure for the model\nfidelity. In this article, we give an introduction to Gaussian processes and\nits usage in regression tasks of dynamical systems. Try Gaussian process\nregression yourself: https://gpr.tbeckers.com\n"}
+{"abstract": "  The non-trivial magnetic texture in real space gives rise to the intriguing\nphenomenon of topological Hall effect (THE), which is relatively less explored\nin topological semimetals. Here, we report large THE in the antiferromagnetic\n(AFM) state in single crystals of EuAgAs, an AFM Dirac semimetal. EuAgAs hosts\nAFM ground state below $T_N$ = 12 K with a weak ferromagnetic component. The\nin-plane isothermal magnetization below $T_N$ exhibits a weak metamagnetic\ntransition. We also observe chiral anomaly induced positive longitudinal\nmagnetoconductivity which indicates a Weyl fermion state under applied magnetic\nfield. The first-principles calculations reveal that EuAgAs is an AFM Dirac\nsemimetal with a pair of Dirac cones, and therefore, a Weyl semimetailic state\ncan be realized under time-reversal symmetry breaking via an applied magnetic\nfield. Our study establishes that EuAgAs is a novel system for exploiting the\ninterplay of band topology and the topology of the magnetic texture.\n"}
+{"abstract": "  This chapter presents a quantum computing-based approach to study and harness\nneuronal correlates of mental activity for the development of Brain-Computer\nInterface (BCI) systems. It introduces the notion of a logic of the mind, where\nneurophysiological data are encoded as logical expressions representing mental\nactivity. Effective logical expressions are likely to be extensive, involving\ndozens of variables. Large expressions require considerable computational power\nto be processed. This is problematic for BCI applications because they require\nfast reaction times to execute sequences of commands. Quantum computers hold\nmuch promise in terms of processing speed for some problems, including those\ninvolving logical expressions. Hence, we propose to use quantum computers to\nprocess the logic of the mind. The chapter begins with an introduction to BCI\nand the electroencephalogram, which is the neurophysiological signal that is\nnormally used in BCI. Then, it briefly discusses how the EEG corresponds to\nmental states, followed by an introduction to the logic of the mind. After\nthat, there is an overview of quantum computing, focusing on the basics deemed\nnecessary to understand how it processes logical expressions. An example of a\nBCI system is presented. In a nutshell, the system reads the EEG and builds\nlogical expressions, which are sent to a quantum computer to solve them. In\nturn, the system converts the results into sounds by means of a bespoke\nsynthesiser. Essentially, the BCI here is a musical instrument controlled by\nthe mind of the player. Our BCI is a proof-of-concept aimed at demonstrating\nhow quantum computing may support the development of sophisticated BCI systems.\nThe remaining of the chapter is devoted to technical and practical\nconsiderations on the limitations of current quantum computing hardware\ntechnology and scalability of the system.\n"}
+{"abstract": "  The high solubility of oxygen in Ti, Zr and Hf makes it difficult to\nstabilize the protective oxide scales on their surfaces as the subsurface\nregions can serve as boundless sinks that continuously dissolve oxygen.\nAlloying elements are crucial to reduce the oxygen solubility and diffusivity\nwithin early transition metals. Past studies have shown that substitutional\nalloying additions to titanium repel interstitial oxygen. Here we use\nfirst-principles calculations to show that this repulsion is short ranged and\nidentify a variety of factors that are likely responsible for the repulsive\ninteraction. We identify a unique hybridization phenomenon between dissolved\nsubstitutional elements and interstitial oxygen within hcp Ti that leads to a\nrepulsive interaction at short distances, similar to that between closed-shell\natoms. Calculations of Bader charges also suggest the existence of short-range\nCoulomb interactions due to the accumulation of charge on the substitutional\nsolute and interstitial oxygen that is drawn from the Ti host.\n"}
+{"abstract": "  We study the 3-\\textsc{Coloring} problem in graphs with small diameter. In\n2013, Mertzios and Spirakis showed that for $n$-vertex diameter-2 graphs this\nproblem can be solved in subexponential time $2^{\\mathcal{O}(\\sqrt{n \\log\nn})}$. Whether the problem can be solved in polynomial time remains a\nwell-known open question in the area of algorithmic graphs theory.\n  In this paper we present an algorithm that solves 3-\\textsc{Coloring} in\n$n$-vertex diameter-2 graphs in time $2^{\\mathcal{O}(n^{1/3} \\log^{2} n)}$.\nThis is the first improvement upon the algorithm of Mertzios and Spirakis in\nthe general case, i.e., without putting any further restrictions on the\ninstance graph.\n  In addition to standard branchings and reducing the problem to an instance of\n2-\\textsc{Sat}, the crucial building block of our algorithm is a combinatorial\nobservation about 3-colorable diameter-2 graphs, which is proven using a\nprobabilistic argument.\n  As a side result, we show that 3-\\textsc{Coloring} can be solved in time\n$2^{\\mathcal{O}( (n \\log n)^{2/3})}$ in $n$-vertex diameter-3 graphs. We also\ngeneralize our algorithms to the problem of finding a list homomorphism from a\nsmall-diameter graph to a cycle.\n"}
+{"abstract": "  We give a general framework for the universality classes of $ \\sigma $-finite\nmeasures in penalisation problems with multiplicative weights. We discuss\npenalisation problems for Brownian motions, L\\'evy processes and Langevin\nprocesses in our framework.\n"}
+{"abstract": "  The approximation of functions in Orlicz space by multivariate operators on\nsimplex is considered. The convergence rate is given by using modulus of\nsmoothness.\n"}
+{"abstract": "  In 1999, Katona and Kierstead conjectured that if a $k$-uniform hypergraph\n$\\cal H$ on $n$ vertices has minimum co-degree $\\lfloor\n\\frac{n-k+3}{2}\\rfloor$, i.e., each set of $k-1$ vertices is contained in at\nleast $\\lfloor \\frac{n-k+3}{2}\\rfloor$ edges, then it has a Hamiltonian cycle.\nR\\\"{o}dl, Ruci\\'{n}ski and Szemer\\'{e}di in 2011 proved that the conjecture is\ntrue when $k=3$ and $n$ is large. We show that this Katona-Kierstead conjecture\nholds if $k=4$, $n$ is large, and $V({\\cal H})$ has a partition $A$, $B$ such\nthat $|A|=\\lceil n/2\\rceil$, $|\\{e\\in E({\\cal H}):|e \\cap A|=2\\}| <\\epsilon\nn^4$.\n"}
+{"abstract": "  We propose a novel method to reconstruct volumetric flows from sparse views\nvia a global transport formulation. Instead of obtaining the space-time\nfunction of the observations, we reconstruct its motion based on a single\ninitial state. In addition we introduce a learned self-supervision that\nconstrains observations from unseen angles. These visual constraints are\ncoupled via the transport constraints and a differentiable rendering step to\narrive at a robust end-to-end reconstruction algorithm. This makes the\nreconstruction of highly realistic flow motions possible, even from only a\nsingle input view. We show with a variety of synthetic and real flows that the\nproposed global reconstruction of the transport process yields an improved\nreconstruction of the fluid motion.\n"}
+{"abstract": "  In a typical supervised machine learning setting, the predictions on all test\ninstances are based on a common subset of features discovered during model\ntraining. However, using a different subset of features that is most\ninformative for each test instance individually may not only improve prediction\naccuracy, but also the overall interpretability of the model. At the same time,\nfeature selection methods for classification have been known to be the most\neffective when many features are irrelevant and/or uncorrelated. In fact,\nfeature selection ignoring correlations between features can lead to poor\nclassification performance. In this work, a Bayesian network is utilized to\nmodel feature dependencies. Using the dependency network, a new method is\nproposed that sequentially selects the best feature to evaluate for each test\ninstance individually, and stops the selection process to make a prediction\nonce it determines that no further improvement can be achieved with respect to\nclassification accuracy. The optimum number of features to acquire and the\noptimum classification strategy are derived for each test instance. The\ntheoretical properties of the optimum solution are analyzed, and a new\nalgorithm is proposed that takes advantage of these properties to implement a\nrobust and scalable solution for high dimensional settings. The effectiveness,\ngeneralizability, and scalability of the proposed method is illustrated on a\nvariety of real-world datasets from diverse application domains.\n"}
+{"abstract": "  Maximum likelihood estimation is widely used in training Energy-based models\n(EBMs). Training requires samples from an unnormalized distribution, which is\nusually intractable, and in practice, these are obtained by MCMC algorithms\nsuch as Langevin dynamics. However, since MCMC in high-dimensional space\nconverges extremely slowly, the current understanding of maximum likelihood\ntraining, which assumes approximate samples from the model can be drawn, is\nproblematic. In this paper, we try to understand this training procedure by\nreplacing Langevin dynamics with deterministic solutions of the associated\ngradient descent ODE. Doing so allows us to study the density induced by the\ndynamics (if the dynamics are invertible), and connect with GANs by treating\nthe dynamics as generator models, the initial values as latent variables and\nthe loss as optimizing a critic defined by the very same energy that determines\nthe generator through its gradient. Hence the term - self-adversarial loss. We\nshow that reintroducing the noise in the dynamics does not lead to a\nqualitative change in the behavior, and merely reduces the quality of the\ngenerator. We thus show that EBM training is effectively a self-adversarial\nprocedure rather than maximum likelihood estimation.\n"}
+{"abstract": "  In this work we investigate the boundedness of Fourier multipliers on\nTriebel-Lizorkin spaces associated to positive Rockland operators on a graded\nLie group. The found criterion is expressed in terms of the H\\\"ormander-Mihlin\ncondition on the global symbol of a Fourier multiplier.\n"}
+{"abstract": "  We study Chern insulators from the point of view of K\\\"ahler geometry, i.e.\nthe geometry of smooth manifolds equipped with a compatible triple consisting\nof a symplectic form, an integrable almost complex structure and a Riemannian\nmetric. The Fermi projector, i.e. the projector onto the occupied bands,\nprovides a map to a K\\\"ahler manifold. The quantum metric and Berry curvature\nof the occupied bands are then related to the Riemannian metric and symplectic\nform, respectively, on the target space of quantum states. We find that the\nminimal volume of a parameter space with respect to the quantum metric is $\\pi\n|\\mathcal{C}|$, where $\\mathcal{C}$ is the first Chern number. We determine the\nconditions under which the minimal volume is achieved both for the Brillouin\nzone and the twist-angle space. The minimal volume of the Brillouin zone,\nprovided the quantum metric is everywhere non-degenerate, is achieved when the\nlatter is endowed with the structure of a K\\\"ahler manifold inherited from the\none of the space of quantum states. If the quantum volume of the twist-angle\ntorus is minimal, then both parameter spaces have the structure of a K\\\"ahler\nmanifold inherited from the space of quantum states. These conditions turn out\nto be related to the stability of fractional Chern insulators. For two-band\nsystems, the volume of the Brillouin zone is naturally minimal provided the\nBerry curvature is everywhere non-negative or nonpositive, and we additionally\nshow how the latter, which in this case is proportional to the quantum volume\nform, necessarily has zeros due to topological constraints.\n"}
+{"abstract": "  Finding efficient, easily implementable differentially private (DP)\nalgorithms that offer strong excess risk bounds is an important problem in\nmodern machine learning. To date, most work has focused on private empirical\nrisk minimization (ERM) or private population loss minimization. However, there\nare often other objectives--such as fairness, adversarial robustness, or\nsensitivity to outliers--besides average performance that are not captured in\nthe classical ERM setup. To this end, we study a completely general family of\nconvex, Lipschitz loss functions and establish the first known DP excess risk\nand runtime bounds for optimizing this broad class. We provide similar bounds\nunder additional assumptions of smoothness and/or strong convexity. We also\naddress private stochastic convex optimization (SCO). While $(\\epsilon,\n\\delta)$-DP ($\\delta > 0$) has been the focus of much recent work in private\nSCO, proving tight population loss bounds and runtime bounds for $(\\epsilon,\n0)$-DP remains a challenging open problem. We provide the tightest known\n$(\\epsilon, 0)$-DP population loss bounds and fastest runtimes under the\npresence of (or lack of) smoothness and strong convexity. Our methods extend to\nthe $\\delta > 0$ setting, where we offer the unique benefit of ensuring\ndifferential privacy for arbitrary $\\epsilon > 0$ by incorporating a new form\nof Gaussian noise. Finally, we apply our theory to two learning frameworks:\ntilted ERM and adversarial learning. In particular, our theory quantifies\ntradeoffs between adversarial robustness, privacy, and runtime. Our results are\nachieved using perhaps the simplest DP algorithm: output perturbation. Although\nthis method is not novel conceptually, our novel implementation scheme and\nanalysis show that the power of this method to achieve strong privacy, utility,\nand runtime guarantees has not been fully appreciated in prior works.\n"}
+{"abstract": "  We compute a formula for the discriminant of tautological bundles on\nsymmetric powers of a complex smooth projective curve. It follows that the\nBogomolov inequality does not give a new restriction to stability of these\ntautological bundles. It only rules out tautological bundles which are already\nknown to have the structure sheaf as a destabilising subbundle.\n"}
+{"abstract": "  Analysis of competing risks data plays an important role in the lifetime data\nanalysis. Recently Feizjavadian and Hashemi (Computational Statistics and Data\nAnalysis, vol. 82, 19-34, 2015) provided a classical inference of a competing\nrisks data set using four-parameter Marshall-Olkin bivariate Weibull\ndistribution when the failure of an unit at a particular time point can happen\ndue to more than one cause. The aim of this paper is to provide the Bayesian\nanalysis of the same model based on a very flexible Gamma-Dirichlet prior on\nthe scale parameters. It is observed that the Bayesian inference has certain\nadvantages over the classical inference in this case. We provide the Bayes\nestimates of the unknown parameters and the associated highest posterior\ndensity credible intervals based on Gibbs sampling technique. We further\nconsider the Bayesian inference of the model parameters assuming partially\nordered Gamma-Dirichlet prior on the scale parameters when one cause is more\nsevere than the other cause. We have extended the results for different\ncensoring schemes also.\n"}
+{"abstract": "  With availability of huge amounts of labeled data, deep learning has achieved\nunprecedented success in various object detection tasks. However, large-scale\nannotations for medical images are extremely challenging to be acquired due to\nthe high demand of labour and expertise. To address this difficult issue, in\nthis paper we propose a novel semi-supervised deep metric learning method to\neffectively leverage both labeled and unlabeled data with application to\ncervical cancer cell detection. Different from previous methods, our model\nlearns an embedding metric space and conducts dual alignment of semantic\nfeatures on both the proposal and prototype levels. First, on the proposal\nlevel, we generate pseudo labels for the unlabeled data to align the proposal\nfeatures with learnable class proxies derived from the labeled data.\nFurthermore, we align the prototypes generated from each mini-batch of labeled\nand unlabeled data to alleviate the influence of possibly noisy pseudo labels.\nMoreover, we adopt a memory bank to store the labeled prototypes and hence\nsignificantly enrich the metric learning information from larger batches. To\ncomprehensively validate the method, we construct a large-scale dataset for\nsemi-supervised cervical cancer cell detection for the first time, consisting\nof 240,860 cervical cell images in total. Extensive experiments show our\nproposed method outperforms other state-of-the-art semi-supervised approaches\nconsistently, demonstrating efficacy of deep semi-supervised metric learning\nwith dual alignment on improving cervical cancer cell detection performance.\n"}
+{"abstract": "  Several recent works [40, 24] observed an interesting phenomenon in neural\nnetwork pruning: A larger finetuning learning rate can improve the final\nperformance significantly. Unfortunately, the reason behind it remains elusive\nup to date. This paper is meant to explain it through the lens of dynamical\nisometry [42]. Specifically, we examine neural network pruning from an unusual\nperspective: pruning as initialization for finetuning, and ask whether the\ninherited weights serve as a good initialization for the finetuning? The\ninsights from dynamical isometry suggest a negative answer. Despite its\ncritical role, this issue has not been well-recognized by the community so far.\nIn this paper, we will show the understanding of this problem is very important\n-- on top of explaining the aforementioned mystery about the larger finetuning\nrate, it also unveils the mystery about the value of pruning [5, 30]. Besides a\nclearer theoretical understanding of pruning, resolving the problem can also\nbring us considerable performance benefits in practice.\n"}
+{"abstract": "  Knowledge graph completion refers to predicting missing triples. Most\napproaches achieve this goal by predicting entities, given an entity and a\nrelation. We predict missing triples via the relation prediction. To this end,\nwe frame the relation prediction problem as a multi-label classification\nproblem and propose a shallow neural model (SHALLOM) that accurately infers\nmissing relations from entities. SHALLOM is analogous to C-BOW as both\napproaches predict a central token (p) given surrounding tokens ((s,o)). Our\nexperiments indicate that SHALLOM outperforms state-of-the-art approaches on\nthe FB15K-237 and WN18RR with margins of up to $3\\%$ and $8\\%$ (absolute),\nrespectively, while requiring a maximum training time of 8 minutes on these\ndatasets. We ensure the reproducibility of our results by providing an\nopen-source implementation including training and evaluation scripts at\n{\\url{https://github.com/dice-group/Shallom}.}\n"}
+{"abstract": "  Recently, we obtained the simple metrics of a spherically symmetric black\nhole in a dark matter halo, and extended to the case of rotation. As the\ncharacteristic sound of black holes, quasinormal modes (QNMs) are one of the\nimportant means to understand black holes currently. Based on these two metrics\nof a spherically symmetric black hole, we study the QNMs of cold dark matter\n(CDM) and scalar field dark matter (SFDM) models using the methods of the\nmaterial field perturbations and the gravitational perturbation, and make\ncomparisons with the Schwarzschild black hole. Our results show that black hole\nQNMs of CDM and SFDM in a dark matter halo are different from the Schwarzschild\nblack hole, unlike a Schwarzschild black hole with a prominent power-law tail.\nThe different kinds models of dark matter can be distinguished by their QNMs.\nThe time of QNMs ringing and frequencies increase with increasing parameter\n$l$. The overall QNMs of CDM are stronger than that of SFDM in the same\ncondition, which is easier to be detected. In addition, QNMs frequencies using\nthe sixth-order WKB method and the Prony method are in good agreement.\n"}
+{"abstract": "  We study the collision of a kink and an antikink in the double sine-Gordon\nmodel with and without the excited vibrational mode. In the latter case, we\nfind that there is a limited range of the parameters where the resonance\nwindows exist, despite the existence of a vibrational mode. Still, when the\nvibrational mode is initially excited, its energy can turn into translational\nenergy after the collision. This creates one-bounce as well as a rich structure\nof higher-bounce resonance windows that depend on the wobbling phase being in\nor out of phase at the collision and the wobbling amplitude being sufficiently\nlarge. When the vibrational mode is excited, the modified structure of\none-bounce windows is observed in the whole range of the model's parameters,\nand the resonant interval with higher-bounce windows gradually increases with\nthe wobbling amplitude. We estimated the center of the one-bounce windows using\na simple analytical approximation for the wobbling evolution. The kinks' final\nwobbling frequency is Lorentz contracted, which is simply derived from our\nequations. We also report that the maximum energy density value always has a\nsmooth behavior in the resonance windows.\n"}
+{"abstract": "  We demonstrate ferroelectric (FE) memory transistors on a crystalline silicon\nchannel with endurance exceeding $10^{10}$ cycles. The ferroelectric\ntransistors (FeFETs) incorporate a high-$\\kappa$ interfacial layer (IL) of\nthermally grown silicon nitride (SiN$_x$) and a thin 4.5 nm layer of Zr-doped\nFE-HfO$_2$ on a $\\sim$30 nm SOI channel. The device shows a $\\sim$ 1V memory\nwindow in a DC sweep of just $\\pm$ 2.5V, and can be programmed and erased with\nvoltage pulses of $V_G= \\pm$ 3V at a pulse width of 250 ns. The device also\nshows very good retention behavior. These results indicate that appropriate\nengineering of the IL layer could substantially improve FeFET device\nperformance and reliability.\n"}
+{"abstract": "  The objective of the present paper is to investigate the decay of solutions\nfor a laminated Timoshenko beam with interfacial slip in the whole space R\nsubject to a thermal effect acting only on one component modelled by either\nFourier or Cattaneo law. When the thermal effect is acting via the second or\nthird component of the laminated Timoshenko beam (rotation angle displacement\nor dynamic of the slip), we obtain that both systems, Timoshenko-Fourier and\nTimoshenko-Cattaneo systems, satisfy the same polynomial stability estimates in\nthe L2 -norm of the solution and its higher order derivatives with respect of\nthe space variable. The decay rate depends on the regularity of the initial\ndata. In addition, the presence and absence of the regularity-loss type\nproperty are determined by some relations between the parameters of systems.\nHowever, when the thermal effect is acting via the first comoponent of the\nsystem (transversal displacement), a new stability condition is introduced for\nboth TimoshenkoFourier and Timoshenko-Cattaneo systems. This stability\ncondition is in the form of threshold between polynomial stability and\nconvergence to zero. To prove our results, we use the energy method in Fourier\nspace combined with judicious choices of weight functions to build appropriate\nLyapunov functionals.\n"}
+{"abstract": "  We consider a two-component Fermi gas with a contact interaction from the BCS\nregime to the unitary limit. Starting from the idea that many-body effects\nshould not depend on short-distance or high-momentum physics which is encoded\nin the s-wave scattering length, but only on momentum scales of the order of\nthe Fermi momentum, we build effective low-momentum interactions that reproduce\nthe scattering phase shifts of the contact interaction below some momentum\ncutoff. Inspired from recent successes of this method in nuclear structure\ntheory, we use these interactions to describe the equation of state of the\nFermi gas in the framework of Hartree-Fock-Bogliubov theory with perturbative\ncorrections. In the BCS regime, there is a range of cutoffs where we obtain\nfully converged results. Near unitarity, convergence is not yet reached, but we\nobtain promising results for the ground-state energies close to the\nexperimental ones. Limitations and possible extensions of the approach are\ndiscussed.\n"}
+{"abstract": "  We prove the persistence of boundary smoothness of vortex patches for a\nnon-linear transport equation in $\\mathbb{R}^n$ with velocity field given by\nconvolution of the density with an odd kernel, homogeneous of degree $-(n-1)$\nand of class $C^2(\\mathbb{R}^n\\setminus\\{0\\}, \\mathbb{R}^n).$ This allows the\nvelocity field to have non-trivial divergence. The quasi-geostrophic equation\nin $\\mathbb{R}^3$ and the Cauchy transport equation in the plane are examples.\n"}
+{"abstract": "  We found evidence for critical internal heat values during significant\ngeophysical transitions in the inner solar system planetary bodies in\nassociation with volcanism. From a simple rocky planet thermal evolution model,\nwe could infer critical surface heat flux values during peak phases (~1.2 W/m2)\nand cessation phases (~ 0.092 W/m2) of major volcanism in Earth, Moon, Mars,\nVenus and Mercury. The above phases of volcanism are accompanied by significant\ngeophysical transitions like growth and decay of global planetary magnetic\nfields which is likely to be related to systematic changes in core-mantle\nboundary heat flux values. The above results suggest the that planets are\npossibly self-organised physical systems with strong core-mantle-crust coupling\n. The present study will have implications on the search for habitable\nextrasolar planets.\n"}
+{"abstract": "  Chain conditions are one of the major tools used in the theory of forcing. We\nsay that a partial order has the countable chain condition if every antichain\n(in the sense of forcing) is countable. Without the axiom of choice antichains\ntend to be of little use, for various reasons, and in this short note we study\na number of conditions which in ZFC are equivalent to the countable chain\ncondition.\n"}
+{"abstract": "  In this paper, an intelligent reflecting surface (IRS) assisted spectrum\nsharing underlay cognitive radio (CR) wiretap channel (WTC) is studied, and we\naim at enhancing the secrecy rate of secondary user in this channel subject to\ntotal power constraint at secondary transmitter (ST), interference power\nconstraint (IPC) at primary receiver (PR) as well as unit modulus constraint at\nIRS. Due to extra IPC and eavesdropper (Eve) are considered, all the existing\nsolutions for enhancing secrecy rate of IRS-assisted non-CR WTC as well as\nenhancing transmission rate in IRS-assisted CR channel without eavesdropper\nfail in this work. Therefore, we propose new numerical solutions to optimize\nthe secrecy rate of this channel under full primary, secondary users' channel\nstate information (CSI) and three different cases of Eve's CSI: full CSI,\nimperfect CSI with bounded estimation error, and no CSI. Simulation results\nshow that our proposed solutions for the IRS-assisted design greatly enhance\nthe secrecy performance compared with the existing numerical solutions with and\nwithout IRS under full and imperfect Eve's CSI. And positive secrecy rate can\nbe achieved by our proposed AN aided approach given most channel realizations\nunder no Eve's CSI case so that secure communication also can be guaranteed.\nAll of the proposed AO algorithms are guaranteed to monotonic convergence.\n"}
+{"abstract": "  Fisher forecasts are a common tool in cosmology with applications ranging\nfrom survey planning to the development of new cosmological probes. While\nfrequently adopted, they are subject to numerical instabilities that need to be\ncarefully investigated to ensure accurate and reproducible results. This\nresearch note discusses these challenges using the example of a weak lensing\ndata vector and proposes procedures that can help in their solution.\n"}
+{"abstract": "  The influence of hypothetical new interactions beyond the Standard Model on\natomic spectra has attracted recent interest. In the present work,\ninterelectronic photon-exchange corrections and radiative quantum\nelectrodynamic corrections to the hypothetical contribution to the energy\nlevels of few-electron ions from a new interaction are calculated. The $1s$,\n$2s$ and $2p_{1/2}$ ground states of H-like, Li-like and B-like ions are\nconsidered, as motivated by proposals to use isotope shift spectroscopy of\nfew-electron ions in order to set stringent constraints on hypothetical new\ninteractions. It is shown that, for light Li-like and B-like ions,\nphoton-exchange corrections are comparable to or even larger, by up to several\norders of magnitude, than the leading one-electron contribution from the new\ninteraction, when the latter is mediated by heavy bosons.\n"}
+{"abstract": "  Located in the Large Magellanic cloud and mostly irradiated by a massive-star\ncluster R$\\,$136, 30 Doradus is an ideal target to test the leading theory of\nthe grain alignment and rotational disruption by RAdiative Torques (RATs).\nHere, we use publicly available polarized thermal dust emission observations of\n30 Doradus at 89, 154, and 214$\\,\\mu$m using SOFIA/HAWC+. We analyse the\nvariation of the dust polarization degree ($p$) with the total emission\nintensity ($I$), the dust temperature ($T_{\\rm d}$), and the gas column density\n($N_{\\rm H}$) constructed from ${\\it Herschel}$ data. The 30 Doradus complex is\ndivided into two main regions relative to R$\\,$136, namely North and South. In\nthe North, we find that the polarization degree first decreases and then\nincreases before decreasing again when the dust temperature increases toward\nthe irradiating cluster R$\\,$136. The first depolarization likely arises from\nthe decrease of grain alignment efficiency toward the dense medium due to the\nattenuation of the interstellar radiation field and the increase of the gas\ndensity. The second trend (the increase of $p$ with $T_{\\rm d}$) is consistent\nwith the RAT alignment theory. The final trend (the decrease of $p$ with\n$T_{\\rm d}$) is consistent with the RAT alignment theory only when the grain\nrotational disruption by RATs is taken into account. In the South, we find that\nthe polarization degree is nearly independent of the dust temperature, while\nthe grain alignment efficiency is higher around the peak of the gas column\ndensity and decreases toward the radiation source. The latter feature is also\nconsistent with the prediction of the rotational disruption by RATs.\n"}
+{"abstract": "  Predicting the next interaction of a short-term interaction session is a\nchallenging task in session-based recommendation. Almost all existing works\nrely on item transition patterns, and neglect the impact of user historical\nsessions while modeling user preference, which often leads to non-personalized\nrecommendation. Additionally, existing personalized session-based recommenders\ncapture user preference only based on the sessions of the current user, but\nignore the useful item-transition patterns from other user's historical\nsessions. To address these issues, we propose a novel Heterogeneous Global\nGraph Neural Networks (HG-GNN) to exploit the item transitions over all\nsessions in a subtle manner for better inferring user preference from the\ncurrent and historical sessions. To effectively exploit the item transitions\nover all sessions from users, we propose a novel heterogeneous global graph\nthat contains item transitions of sessions, user-item interactions and global\nco-occurrence items. Moreover, to capture user preference from sessions\ncomprehensively, we propose to learn two levels of user representations from\nthe global graph via two graph augmented preference encoders. Specifically, we\ndesign a novel heterogeneous graph neural network (HGNN) on the heterogeneous\nglobal graph to learn the long-term user preference and item representations\nwith rich semantics. Based on the HGNN, we propose the Current Preference\nEncoder and the Historical Preference Encoder to capture the different levels\nof user preference from the current and historical sessions, respectively. To\nachieve personalized recommendation, we integrate the representations of the\nuser current preference and historical interests to generate the final user\npreference representation. Extensive experimental results on three real-world\ndatasets show that our model outperforms other state-of-the-art methods.\n"}
+{"abstract": "  The surface plasmon-polaritons (SPPs) switch is the key element of the\nintegrated devices in optical computation and terahertz (THz) communications.\nIn this paper, we propose a novel design of THz SPPs switch based on quantum\nengineering. Due to the robustness of coherent quantum control technique, our\nswitch is very robust against with perturbations of geometrical parameters and\npresents a good performance at on-state (and off-state) from 0.5 THz to 0.7\nTHz. The on-state and off-state of our device can be controlled by the external\nvoltage. We believe this finding will be the great improvement for the\nintegrated optical computing and THz communications.\n"}
+{"abstract": "  Here we report on studying the electronic and optical material properties of\nthe technologically-relevant material indium tin oxide (ITO) as a function of\nthermal annealing. In this work, ITO powder has been prepared utilizing\nsolid-state reaction methods. An electron beam gun technology has been used to\nprepare a ITO film (325 nm). The ITO window layer has been investigated at\nvarious temperatures. The effects of absolute temperature on the structural,\noptical, and electrical properties of the prepared ITO thin film layer are\ninvestigated. The energy band type corresponding to the orbital transitions has\nbeen determined, and the energies of the orbital transitions have been\ncalculated in the Tauc region, HOMO/LUMO gap, and charge transfer gap. In\nadditions, the exciton and Urbach energies have been computed. It has been\nfound that these energies increase with increasing the annealing temperature,\nexcept for Urbach's energies which behave differently. Thin-film quality\ncoefficient, surface resistance, and thermal emission in addition to the angle\nof refraction as a function of wavelength, have been determined.\n"}
+{"abstract": "  The Kochen-Specker theorem is one of the fundamental no-go theorems in\nquantum theory. It has far-reaching consequences for all attempts trying to\ngive an interpretation of the quantum formalism. In this work, we examine the\nhypotheses that, at the ontological level, lead to the KochenSpecker\ncontradiction. We emphasize the role of the assumptions about identity and\ndistinguishability of quantum objects in the argument.\n"}
+{"abstract": "  In this paper, we design a deep learning based resource allocation framework,\nin the form of an auction, for simultaneous information and power transfer from\na hybrid access point (AP) to information devices and energy harvesting\ndevices, respectively. Using Myerson's lemma and the concept of virtual welfare\nmaximization, we develop an optimal dominant-strategy incentive-compatible\nmechanism for the AP to maximize its expected revenue, based on the devices'\nbid profiles, valuation distributions, demand profiles, and channel state\ninformation. In so doing, we formulate the revenue maximization problem, which\nis a mixed-integer non-linear program, and propose an efficient\nBranch-and-Bound (BnB) algorithm to solve the problem using semidefinite\nrelaxation technique in each branch. Since the problem has exponential time\ncomplexity, using BnB algorithms can be impractical for real-time applications.\nTo circumvent this, a deep neural network (DNN) is proposed, and trained to\npredict the optimal mechanism for beamforming the data and the energy towards\nthe information and energy devices, respectively. We use the BnB algorithm to\nsolve the problem offline and populate the training dataset. The proposed DNN\narchitecture is indeed a multi-layer perceptron, which is trained well to map\nthe heterogeneous input to the desired output with high accuracy. Furthermore,\nwe propose a heuristic iterative solution whose accuracy performance is\ncomparable to that of the DNN-based solution. The heuristic solution has\npolynomial time complexity whereas the DNN-based solution has linear time\ncomplexity.\n"}
+{"abstract": "  Einstein-Maxwell dilaton-axion gravity is a string-inspired model arising\nfrom the low energy effective action of heterotic string theory and an\nimportant candidate as alternative to General Relativity. Recently, some\nauthors have explored its astrophysical implications in the spectra of\naccreting black holes and inferred the constraint $r_2 < 0.1$, where $r_2 \\ge\n0$ is the black hole dilaton charge and General Relativity is recovered for\n$r_2 = 0$. In the present paper, we study the impact of a non-vanishing black\nhole dilaton charge on the reflection spectrum of the disk. From the analysis\nof a NuSTAR spectrum of the black hole binary EXO 1846-031, we find the\nconstraint $r_2 < 0.011$ (90% CL), which is an order of magnitude more\nstringent.\n"}
+{"abstract": "  By globally analyzing nuclear Drell-Yan data including all incident energies,\nthe nuclear effects of nPDFs and initial-state parton energy loss are\ninvestigated. Based on Landau-Pomeranchuk-Migdal (LPM) regime, the calculations\nare carried out by means of the analytic parametrizations of quenching weights\nderived from the Baier-Dokshitzer-Mueller-Peign$\\acute{e}$-Schiff (BDMPS)\nformalism and using the new EPPS16 nPDFs. It is found that the results are in\ngood agreement with the data and the role of the energy loss effect on the\nsuppression of Drell-Yan ratios is prominent, especially for low-mass Drell-Yan\nmeasurements. The nuclear effects of nPDFs becomes more obvious with the\nnuclear mass number A, the same as the energy loss effect. By global fit, the\ntransport coefficient extracted is $\\hat{q}=0.26\\pm0.04$ GeV$^{2}$/fm. In\naddition, to avoid diminishing the QCD NLO correction on the data form of\nDrell-Yan ratios, the separate calculations about the Compton differential\ncross section ratios $R_{Fe(W)/C}(x_{F})$ at 120GeV are performed, which\nprovides a feasible way to better distinguish the gluon energy loss in Compton\nscattering. It is found that the role of the initial-state gluon energy loss on\nthe suppression of Compton scattering rations is not very important and becomes\ndisappear with the increase of $x_{F}$.\n"}
+{"abstract": "  Colloidal semiconductor nanoplatelets combine weak lateral confinement with\nstrong Coulomb interactions, enhanced by dielectric confinement. When the\nplatelets are charged with carriers of the same sign, this results in severe\nCoulomb repulsions which shape the electronic structure. To illustrate this\npoint, the shell filling of type-I (CdSe/CdS) and type-II (CdSe/CdTe)\ncore/crown nanoplatelets with up to 4 electrons or holes is investigated\ntheoretically. We find that Coulomb repulsions enable addition energies\nexceeding room temperature thermal energy and promote the occupation of\nhigh-spin states. For charged excitons and biexcitons in CdSe/CdTe\nnanoplatelets, the repulsions further give rise to multi-peaked emission\nspectra with widely tunable (over 100 meV) energy, and a transition from\ntype-II to quasi-type-II band profile as the number of electrons confined in\nthe core increases. We conclude that the number of excess carriers injected in\nnanoplatelets is a versatile degree of freedom to modulate their magnetic and\noptoelectronic properties.\n"}
+{"abstract": "  While the twin prime conjecture is still famously open, it holds true in the\nsetting of finite fields: There are infinitely many pairs of monic irreducible\npolynomials over $\\mathbb{F}_q$ that differ by a fixed constant, for each $q\n\\geq 3$. Elementary, constructive proofs were given for different cases by Hall\nand Pollack. In the same spirit, we discuss the construction of a further\ninfinite family of twin prime tuples of odd degree, and its relations to the\nexistence of certain Wieferich primes and to arithmetic properties of the\ncombinatorial Bell numbers.\n"}
+{"abstract": "  Gaussian processes (GPs) are frequently used in machine learning and\nstatistics to construct powerful models. However, when employing GPs in\npractice, important considerations must be made, regarding the high\ncomputational burden, approximation of the posterior, choice of the covariance\nfunction and inference of its hyperparmeters. To address these issues, Hensman\net al. (2015) combine variationally sparse GPs with Markov chain Monte Carlo\n(MCMC) to derive a scalable, flexible and general framework for GP models.\nNevertheless, the resulting approach requires intractable likelihood\nevaluations for many observation models. To bypass this problem, we propose a\npseudo-marginal (PM) scheme that offers asymptotically exact inference as well\nas computational gains through doubly stochastic estimators for the intractable\nlikelihood and large datasets. In complex models, the advantages of the PM\nscheme are particularly evident, and we demonstrate this on a two-level GP\nregression model with a nonparametric covariance function to capture\nnon-stationarity.\n"}
+{"abstract": "  We present OrbNet Denali, a machine learning model for electronic structure\nthat is designed as a drop-in replacement for ground-state density functional\ntheory (DFT) energy calculations. The model is a message-passing neural network\nthat uses symmetry-adapted atomic orbital features from a low-cost quantum\ncalculation to predict the energy of a molecule. OrbNet Denali is trained on a\nvast dataset of 2.3 million DFT calculations on molecules and geometries. This\ndataset covers the most common elements in bio- and organic chemistry (H, Li,\nB, C, N, O, F, Na, Mg, Si, P, S, Cl, K, Ca, Br, I) as well as charged\nmolecules. OrbNet Denali is demonstrated on several well-established benchmark\ndatasets, and we find that it provides accuracy that is on par with modern DFT\nmethods while offering a speedup of up to three orders of magnitude. For the\nGMTKN55 benchmark set, OrbNet Denali achieves WTMAD-1 and WTMAD-2 scores of\n7.19 and 9.84, on par with modern DFT functionals. For several GMTKN55 subsets,\nwhich contain chemical problems that are not present in the training set,\nOrbNet Denali produces a mean absolute error comparable to those of DFT\nmethods. For the Hutchison conformers benchmark set, OrbNet Denali has a median\ncorrelation coefficient of R^2=0.90 compared to the reference DLPNO-CCSD(T)\ncalculation, and R^2=0.97 compared to the method used to generate the training\ndata (wB97X-D3/def2-TZVP), exceeding the performance of any other method with a\nsimilar cost. Similarly, the model reaches chemical accuracy for non-covalent\ninteractions in the S66x10 dataset. For torsional profiles, OrbNet Denali\nreproduces the torsion profiles of wB97X-D3/def2-TZVP with an average MAE of\n0.12 kcal/mol for the potential energy surfaces of the diverse fragments in the\nTorsionNet500 dataset.\n"}
+{"abstract": "  Multi-agent systems are designed to concurrently accomplish a diverse set of\ntasks at unprecedented scale. Here, the central problems faced by a system\noperator are to decide (i) how to divide available resources amongst the agents\nassigned to tasks and (ii) how to coordinate the behavior of the agents to\noptimize the efficiency of the resulting collective behavior. The focus of this\npaper is on problem (i), where we seek to characterize the impact of the\ndivision of resources on the best-case efficiency of the resulting collective\nbehavior. Specifically, we focus on a team Colonel Blotto game where there are\ntwo sub-colonels competing against a common adversary in a two battlefield\nenvironment. Here, each sub-colonel is assigned a given resource budget and is\nrequired to allocate these resources independent of the other sub-colonel.\nHowever, their success is dependent on the allocation strategy of both\nsub-colonels. The central focus of this manuscript is on how to divide a common\npool of resources among the two sub-colonels to optimize the resulting\nbest-case efficiency guarantees. Intuitively, one would imagine that the more\nbalanced the division of resources, the worse the performance, as such\ndivisions restrict the sub-colonels' ability to employ joint randomized\nstrategies that tend to be necessary for optimizing performance guarantees.\nHowever, the main result of this paper demonstrates that this intuition is\nactually incorrect. A more balanced division of resources can offer better\nperformance guarantees than a more centralized division. Hence, this paper\ndemonstrates that the resource division problem is highly non-trivial in such\nenmeshed environments and worthy of significant future research efforts.\n"}
+{"abstract": "  Most clinical trials involve the comparison of a new treatment to a control\narm (e.g., the standard of care) and the estimation of a treatment effect.\nExternal data, including historical clinical trial data and real-world\nobservational data, are commonly available for the control arm. Borrowing\ninformation from external data holds the promise of improving the estimation of\nrelevant parameters and increasing the power of detecting a treatment effect if\nit exists. In this paper, we propose to use Bayesian additive regression trees\n(BART) for incorporating external data into the analysis of clinical trials,\nwith a specific goal of estimating the conditional or population average\ntreatment effect. BART naturally adjusts for patient-level covariates and\ncaptures potentially heterogeneous treatment effects across different data\nsources, achieving flexible borrowing. Simulation studies demonstrate that BART\ncompares favorably to a hierarchical linear model and a normal-normal\nhierarchical model. We illustrate the proposed method with an acupuncture\ntrial.\n"}
+{"abstract": "  A pair of triply charmed baryons, $\\Omega_{ccc}\\Omega_{ccc}$, is studied as\nan ideal dibaryon system by (2+1)-flavor lattice QCD with nearly physical\nlight-quark masses and the relativistic heavy quark action with the physical\ncharm quark mass. The spatial baryon-baryon correlation is related to their\nscattering parameters on the basis of the HAL QCD method. The\n$\\Omega_{ccc}\\Omega_{ccc}$ in the ${^1S_0}$ channel taking into account the\nCoulomb repulsion with the charge form factor of $\\Omega_{ccc}$ leads to the\nscattering length $a^{\\rm C}_0\\simeq -19~\\text{fm}$ and the effective range\n$r^{\\rm C}_{\\mathrm{eff}}\\simeq 0.45~\\text{fm}$. The ratio $r^{\\rm\nC}_{\\mathrm{eff}}/a^{\\rm C}_0 \\simeq -0.024$, whose magnitude is considerably\nsmaller than that of the dineutron ($-0.149$), indicates that\n$\\Omega_{ccc}\\Omega_{ccc}$ is located in the unitary regime.\n"}
+{"abstract": "  Video grounding aims to localize a moment from an untrimmed video for a given\ntextual query. Existing approaches focus more on the alignment of visual and\nlanguage stimuli with various likelihood-based matching or regression\nstrategies, i.e., P(Y|X). Consequently, these models may suffer from spurious\ncorrelations between the language and video features due to the selection bias\nof the dataset. 1) To uncover the causality behind the model and data, we first\npropose a novel paradigm from the perspective of the causal inference, i.e.,\ninterventional video grounding (IVG) that leverages backdoor adjustment to\ndeconfound the selection bias based on structured causal model (SCM) and\ndo-calculus P(Y|do(X)). Then, we present a simple yet effective method to\napproximate the unobserved confounder as it cannot be directly sampled from the\ndataset. 2) Meanwhile, we introduce a dual contrastive learning approach (DCL)\nto better align the text and video by maximizing the mutual information (MI)\nbetween query and video clips, and the MI between start/end frames of a target\nmoment and the others within a video to learn more informative visual\nrepresentations. Experiments on three standard benchmarks show the\neffectiveness of our approaches. Our code is available on GitHub:\nhttps://github.com/nanguoshun/IVG.\n"}
+{"abstract": "  Based on improving the classical Bohr inequality, we get in this paper some\nrefined versions for a quasi-subordination family of functions, one of which is\nkey to build our results. By means of these investigations, for a family of\nharmonic mappings defined in the unit disk $\\D$, we establish an improved Bohr\ninequality with refined Bohr radius under particular conditions. Along the line\nof extremal problems concerning the refined Bohr radius, we derive a series of\nresults. % in a logical way. Here the family of harmonic mappings have the form\n$f=h+\\overline{g}$, where $g(0)=0$, the analytic part $h$ is bounded by 1 and\nthat $|g'(z)|\\leq k|h'(z)|$ in $\\D$ and for some $k\\in[0,1]$.\n"}
+{"abstract": "  Recent renewed interest in multi-agent reinforcement learning (MARL) has\ngenerated an impressive array of techniques that leverage deep reinforcement\nlearning, primarily actor-critic architectures, and can be applied to a limited\nrange of settings in terms of observability and communication. However, a\ncontinuing limitation of much of this work is the curse of dimensionality when\nit comes to representations based on joint actions, which grow exponentially\nwith the number of agents. In this paper, we squarely focus on this challenge\nof scalability. We apply the key insight of action anonymity, which leads to\npermutation invariance of joint actions, to two recently presented deep MARL\nalgorithms, MADDPG and IA2C, and compare these instantiations to another recent\ntechnique that leverages action anonymity, viz., mean-field MARL. We show that\nour instantiations can learn the optimal behavior in a broader class of agent\nnetworks than the mean-field method, using a recently introduced pragmatic\ndomain.\n"}
+{"abstract": "  Let $T$ be a compact torus. We prove that, up to equivariant rational\nequivalence, the category of $T$-simply connected, $T$-finite type $T$-spaces\nwith finitely many isotropy types is completely described by certain finite\nsystems of commutative differential graded algebras with consistent choices of\ndegree $2$ cohomology classes. We show that the algebraic systems corresponding\nto finite $T$-CW-complexes are exactly those which satisfy the necessary\ncondition imposed by the Borel localization theorem along with certain\nfiniteness conditions. We derive an algebraic characterization of when an\nalgebra over a polyonmial ring is realized as the rational equivariant\ncohomology of a finite $T$-CW-complex. As further applications we prove that\nany GKM graph cohomology is realized by a finite $T$-CW-complex and classify\nequivariant cohomology algebras of finite $S^1$-CW-complexes with discrete\nfixed points.\n"}
+{"abstract": "  In Rayleigh-B\\'enard convection, a fluid volume is heated from below and\ncooled at the top. Due to buoyancy forces, hot fluid rises and cold fluid\nsinks, leading to an overall upward heat flux that has a power-law dependence\non the imposed temperature difference. Our experiment shows that, adding\nsidewall heating modifies this power-law relation, thus brings control to this\nclassical fluidic system. Through careful flow, temperature, and heat flux\nmeasurements, we investigate the effects of such side-heating to\nRayleigh-B\\'enard convection and rationalize our observations with simple\nrelations that describe how side-heating controls the overall heat transfer.\nSuch control-response mechanism is conceptually analogous to that of electrical\ntransistors, and our \"thermal transistor\" may serve as a potential building\nblock for more complex thermal-fluidic circuits.\n"}
+{"abstract": "  ALICE will significantly increase its Pb--Pb data taking rate from the 1\\,kHz\nof triggered readout in Run 2 to 50 kHz of continuous readout for LHC Run 3.\nUpdated tracking detectors are installed for Run 3 and a new two-phase\ncomputing strategy is employed. In the first synchronous phase during the data\ntaking, the raw data is compressed for storage to an on-site disk buffer and\nthe required data for the detector calibration is collected. In the second\nasynchronous phase the compressed raw data is reprocessed using the final\ncalibration to produce the final reconstruction output. Traditional CPUs are\nunable to cope with the huge data rate and processing demands of the\nsynchronous phase, therefore ALICE employs GPUs to speed up the processing.\nSince the online computing farm performs a part of the asynchronous processing\nwhen there is no beam in the LHC, ALICE plans to use the GPUs also for this\nsecond phase. This paper gives an overview of the GPU processing in the\nsynchronous phase, the full system test to validate the reference GPU\narchitecture, and the prospects for the GPU usage in the asynchronous phase.\n"}
+{"abstract": "  Quantum fluctuations are expected to lead to highly entangled spin-liquid\nstates in some two-dimensional spin-1/2 compounds. We have synthesized and\nmeasured thermodynamic properties and muon relaxation rates in Cu-based\ntwo-dimensional triangular-lattice spin liquids, one of which is the least\ndisordered of this kind synthesized hitherto. Its measured properties can all\nbe simply characterized by scale-invariant time-dependent fluctuations with a\nsingle parameter. The specific heat divided by temperature and muon relaxation\nrates are both temperature-independent at low temperatures, followed by a\nlogarithmic decrease with increasing temperature. Even more remarkably,\n$\\sim$57\\% of the magnetic entropy is missing down to temperatures of\n\\textit{O}(10$^{-3}$) the exchange energy, independent of magnetic field up to\n$g\\mu_BH > k_BT$\\@. These properties are intrinsic. They are evidence that\nquantum fluctuations lead either to a gigantic specific heat peak from\ntopological singlet excitations below such temperatures, or to an extensively\ndegenerate topological singlet ground state. This is an ultra-quantum state of\nmatter.\n"}
+{"abstract": "  Dickson conjectured that a set of polynomials will take on infinitely many\nsimultaneous prime values. Later others, such as Hardy and Littlewood, gave\nestimates for the number of these primes. In this article we look at this\nconjecture, develop a simple heuristic and rederive these classic estimates. We\nthen apply them to several special forms of primes and compare the estimates\nwith the actual numbers.\n"}
+{"abstract": "  We define a probabilistic programming language for Gaussian random variables\nwith a first-class exact conditioning construct. We give operational,\ndenotational and equational semantics for this language, establishing\nconvenient properties like exchangeability of conditions. Conditioning on\nequality of continuous random variables is nontrivial, as the exact observation\nmay have probability zero; this is Borel's paradox. Using categorical\nformulations of conditional probability, we show that the good properties of\nour language are not particular to Gaussians, but can be derived from universal\nproperties, thus generalizing to wider settings. We define the Cond\nconstruction, which internalizes conditioning as a morphism, providing general\ncompositional semantics for probabilistic programming with exact conditioning.\n"}
+{"abstract": "  In this work, we present a novel methodology for texture image recognition\nusing a partial differential equation modeling. More specifically, we employ\nthe pseudo-parabolic Buckley-Leverett equation to provide a dynamics to the\ndigital image representation and collect local descriptors from those images\nevolving in time. For the local descriptors we employ the magnitude and signal\nbinary patterns and a simple histogram of these features was capable of\nachieving promising results in a classification task. We compare the accuracy\nover well established benchmark texture databases and the results demonstrate\ncompetitiveness, even with the most modern deep learning approaches. The\nachieved results open space for future investigation on this type of modeling\nfor image analysis, especially when there is no large amount of data for\ntraining deep learning models and therefore model-based approaches arise as\nsuitable alternatives.\n"}
+{"abstract": "  A new analysis of the modification of the hadronization process in the\nnuclear medium for pions is presented. The effective description is condensed\nin a set of medium modified fragmentation functions (nFFs) obtained at\nnext-to-leading order (NLO) accuracy. The study was made using the open-source\ntool xFitter, conveniently modified to incorporate semi-inclusive processes.\nTheoretical uncertainties for the nFFs are also provided.\n"}
+{"abstract": "  According to the classical view of globular clusters, stars inside globular\nclusters are evolved from the same giant molecular cloud. Then their stars'\nchemical compositions must be the same. But recent photometric and\nspectroscopic studies of globular clusters reveal the presence of more-than-one\nstellar populations inside globular clusters. This finding challenges our\nclassical view of globular clusters.\n  In this work, we investigated the possibility of solving multiple stellar\npopulations problem in globular clusters using dark matter assumptions. We\nshowed that the presence of dark matter inside globular clusters changes the\nphysical parameters (e.g. chemical composition, luminosity, temperature, age,\netc.) of stars inside them. We supposed that dark matter distributed\nnon-uniformly inside globular clusters. It means stars in high dark matter\ndensity environments (like the central region of globular clusters) are more\naffected by the presence of dark matter. Using this assumption, we showed that\nstars in different locations of globular clusters (corresponding to different\ndark matter densities) follow different evolutionary paths (e.g. on\nHertzsprung-Russell diagram). We used this note to infer that the presence of\ndark matter inside globular clusters can be the reason for the multiple stellar\npopulations.\n"}
+{"abstract": "  In order to process efficiently ever-higher dimensional data such as images,\nsentences, or audio recordings, one needs to find a proper way to reduce the\ndimensionality of such data. In this regard, SVD-based methods including PCA\nand Isomap have been extensively used. Recently, a neural network alternative\ncalled autoencoder has been proposed and is often preferred for its higher\nflexibility. This work aims to show that PCA is still a relevant technique for\ndimensionality reduction in the context of classification. To this purpose, we\nevaluated the performance of PCA compared to Isomap, a deep autoencoder, and a\nvariational autoencoder. Experiments were conducted on three commonly used\nimage datasets: MNIST, Fashion-MNIST, and CIFAR-10. The four different\ndimensionality reduction techniques were separately employed on each dataset to\nproject data into a low-dimensional space. Then a k-NN classifier was trained\non each projection with a cross-validated random search over the number of\nneighbours. Interestingly, our experiments revealed that k-NN achieved\ncomparable accuracy on PCA and both autoencoders' projections provided a big\nenough dimension. However, PCA computation time was two orders of magnitude\nfaster than its neural network counterparts.\n"}
+{"abstract": "  Supernovae are the dominant source of chemical enrichment of galaxies, and\nthey are an important source of energy to heat the interstellar medium and\naccelerate cosmic rays. Our knowledge of supernovae in the Milky Way is based\nmostly on the study of Galactic supernova remnants (SNRs), providing an\n(incomplete) record to supernova activity over the last ~100,000 yr. Here we\nreport on an investigation of the spatial distribution of Galactic SNRs. Given\nthe limited number of SNRs it is common to assume a functional form for the\nGalactocentric distribution of SNRs. However, several functional forms have\nbeen used in the past, without much justification for the radial distribution.\nFor example, one often used functional form implies that no supernova activity\nis present in the Galactic Centre region. However, the presence of a magnetar\nand a SNR near the Galactic Centre suggest that a spatial distribution with\nzero SNRs at the Galactic Centre is not realistic. In light of these concerns\nwe reevaluate the Galactic SNR distribution. We provide a brief outline of the\nmain detection biases in finding SNRs and we investigate whether or not the use\nof the most common functional form is justified and how it compares to other\nmodels for the SNR distribution. We do this by analysing the longitudinal\ndistribution of SNRs. We find that a simple exponential distribution is the\nmost consistent and simplest model for describing the radial SNR distribution\nin the Galaxy and draw comparisons with the massive star formation and\nmetallicity distributions.\n"}
+{"abstract": "  Most ultraluminous X-ray sources (ULXs) are believed to be stellar mass black\nholes or neutron stars accreting beyond the Eddington limit. Determining the\nnature of the compact object and the accretion mode from broadband spectroscopy\nis currently a challenge, but the observed timing properties provide insight\ninto the compact object and details of the geometry and accretion processes.\nHere we report a timing analysis for an 800 ks XMM-Newton campaign on the\nsupersoft ultraluminous X-ray source, NGC 247 ULX-1. Deep and frequent dips\noccur in the X-ray light curve, with the amplitude increasing with increasing\nenergy band. Power spectra and coherence analysis reveals the dipping\npreferentially occurs on $\\sim 5$ ks and $\\sim 10$ ks timescales. The dips can\nbe caused by either the occultation of the central X-ray source by an optically\nthick structure, such as warping of the accretion disc, or from obscuration by\na wind launched from the accretion disc, or both. This behaviour supports the\nidea that supersoft ULXs are viewed close to edge-on to the accretion disc.\n"}
+{"abstract": "  Monocular depth estimation is an especially important task in robotics and\nautonomous driving, where 3D structural information is essential. However,\nextreme lighting conditions and complex surface objects make it difficult to\npredict depth in a single image. Therefore, to generate accurate depth maps, it\nis important for the model to learn structural information about the scene. We\npropose a novel Patch-Wise EdgeConv Module (PEM) and EdgeConv Attention Module\n(EAM) to solve the difficulty of monocular depth estimation. The proposed\nmodules extract structural information by learning the relationship between\nimage patches close to each other in space using edge convolution. Our method\nis evaluated on two popular datasets, the NYU Depth V2 and the KITTI Eigen\nsplit, achieving state-of-the-art performance. We prove that the proposed model\npredicts depth robustly in challenging scenes through various comparative\nexperiments.\n"}
+{"abstract": "  In this paper, we have proposed a dispersive formulation of scalar-based\nmeshless method for time-domain analysis of electromagnetic wave propagation\nthrough left-handed (LH) materials. Moreover, we have incorporated Berenger's\nperfectly matched layer (PML) absorbing boundary condition (ABC) into the\ndispersive formulation to truncate open-domain structures. In general, the LH\nmedium as a kind of dispersive media can be described by frequency-dependent\nconstitutive parameters. The most appropriate numerical techniques for analysis\nof LH media are dispersive formulations of conventional numerical methods. In\ncomparison to the conventional grid-based numerical methods, it is proved that\nmeshless methods not only are strong tools for accurate approximation of\nderivatives in Maxwell's equations but also can provide more flexibility in\nmodeling the spatial domain of problems. However, we have not seen any reports\non using dispersive forms of meshless methods for simulation of wave\npropagation in metamaterials and applying any PML ABCs to dispersive\nformulation of meshless method. The proposed formulation in this paper enables\nus to take advantage of meshless methods in analysis of LH media. For modeling\nthe frequency behavior of the medium in the proposed dispersive formulation, we\nhave used auxiliary differential equation (ADE) method based on the relations\nbetween electromagnetic fields intensities and current densities. Effectiveness\nof the proposed formulation is verified by a numerical example; also, some\nbasic factors which affect the accuracy and computational cost of the\nsimulations are studied.\n"}
+{"abstract": "  For quantum search via the continuous-time quantum walk, the evolution of the\nwhole system is usually limited in a small subspace. In this paper, we discuss\nhow the symmetries of the graphs are related to the existence of such an\ninvariant subspace, which also suggests a dimensionality reduction method based\non group representation theory. We observe that in the one-dimensional subspace\nspanned by each desired basis state which assembles the identically evolving\noriginal basis states, we always get a trivial representation of the symmetry\ngroup. So we could find the desired basis by exploiting the projection operator\nof the trivial representation. Besides being technical guidance in this type of\nproblem, this discussion also suggests that all the symmetries are used up in\nthe invariant subspace and the asymmetric part of the Hamiltonian is very\nimportant for the purpose of quantum search.\n"}
+{"abstract": "  We present a deep learning based approach to containerized application\nruntime stability analysis, and an intelligent publishing algorithm that can\ndynamically adjust the depth of process-level forensics published to a backend\nincident analysis repository. The approach applies variational autoencoders\n(VAEs) to learn the stable runtime patterns of container images, and then\ninstantiates these container-specific VAEs to implement stability detection and\nadaptive forensics publishing. In performance comparisons using a 50-instance\ncontainer workload, a VAE-optimized service versus a conventional eBPF-based\nforensic publisher demonstrates 2 orders of magnitude (OM) CPU performance\nimprovement, a 3 OM reduction in network transport volume, and a 4 OM reduction\nin Elasticsearch storage costs. We evaluate the VAE-based stability detection\ntechnique against two attacks, CPUMiner and HTTP-flood attack, finding that it\nis effective in isolating both anomalies. We believe this technique provides a\nnovel approach to integrating fine-grained process monitoring and\ndigital-forensic services into large container ecosystems that today simply\ncannot be monitored by conventional techniques\n"}
+{"abstract": "  We make variational estimates for the binding energies of\n$^2H,~^3H,~^4He,~^{16}O,~^{40}Ca$, showing their running with the Similarity\nRenormalization Group (SRG) cutoff towards the infrared region and show the\ngeneralized Tjon lines that emerge from the calculations. We infer the SRG\nevolution of the three-body contributions for the $^3H$ and $^4He$ binding\nenergies by computing the two-body contributions with a variational approach\nassuming that four-body forces are negligible. At any given SRG cutoff, the\nthree-body contributions may then be inferred as being the experimental value\nminus the two-body contributions. The off-shell / on-shell transition at a\ncritical SRG cutoff $\\lambda_c$ drives the behavior of all binding energies and\nthis scale is the turning point of the generalized Tjon lines. Also, at\n$\\lambda_c$ the ratios between three-body and two-body contributions to the\nbinding energies, $B_{\\lambda_c}(3) ~/~ B_{\\lambda_c}(2)$, are the same in both\n$^3H$ and $^4He$ systems and equal to $1/4$, so that $B_{\\lambda_c}(2) ~=~\n4~B_{\\lambda_c}(3)$. All calculations are carried out with the Idaho-Salamanca\nN3LO potential, which is evolved with the SRG up to the infrared fixed point\n($\\lambda \\to 0$) in all S, P, D, F and G partial-wave channels in order to\ncompute the variational binding energies at several SRG cutoff scales.\n"}
+{"abstract": "  Products between phase-type distributed random variables and any independent,\npositive and continuous random variable are studied. Their asymptotic\nproperties are established, and an expectation-maximization algorithm for their\neffective statistical inference is derived and implemented using real-world\ndatasets. In contrast to discrete scaling studied in earlier literature, in the\npresent continuous case closed-form formulas for various functionals of the\nresulting distributions are obtained, which facilitates both their analysis and\nimplementation. The resulting mixture distributions are very often heavy-tailed\nand yet retain various properties of phase-type distributions, such as being\ndense (in weak convergence) on the set of distributions with positive support.\n"}
+{"abstract": "  A viable approach for building large-scale quantum computers is to interlink\nsmall-scale quantum computers with a quantum network to create a larger\ndistributed quantum computer. When designing quantum algorithms for such a\ndistributed quantum computer, one can make use of the added parallelization and\ndistribution abilities inherent in the system. An added difficulty to then\novercome for distributed quantum computing is that a complex control system to\norchestrate the various components is required. In this work, we aim to address\nthese issues. We explicitly define what it means for a quantum algorithm to be\ndistributed and then present various quantum algorithms that fit the\ndefinition. We discuss potential benefits and propose a high-level scheme for\ncontrolling the system. With this, we present our software framework called\nInterlin-q, a simulation platform that aims to simplify designing and verifying\nparallel and distributed quantum algorithms. We demonstrate Interlin-q by\nimplementing some of the discussed algorithms using Interlin-q and layout\nfuture steps for developing Interlin-q into a control system for distributed\nquantum computers.\n"}
+{"abstract": "  Perovskites have been the focus of attention due to their multitude of\noutstanding optoelectronic properties and structural versatility.\nTwo-dimensional halide perovskite such as (C_6H_5C_2H_4NH_3)_2PbI_4, or simply\nPEPI, forms natural multiple quantum wells with enhanced light-matter\ninteractions, making them attractive systems for further investigation. This\nwork reports tunable splitting of exciton modes in PEPI resulting from strong\nlight-matter interactions, manifested as multiple dips (modes) in the\nreflection spectra. While the origin of the redder mode is well understood,\nthat for the bluer dip at room temperature is still lacking. Here, it is\nrevealed that the presence of the multiple modes originates from an indirect\ncoupling between excitons in different quantum wells. The long-range\ncharacteristic of the mediated coupling between excitons in distant quantum\nwells is also demonstrated in a structure design along with its tunability.\nMoreover, a device architecture involving an end silver layer enhances the two\nexcitonic modes and provides further tunability. Importantly, this work will\nmotivate the possibility of coupling of the excitonic modes with a confined\nlight mode in a microcavity to produce multiple exciton-polariton modes.\n"}
+{"abstract": "  In a continuous-variable optical system, the Gottesman-Kitaev-Preskill (GKP)\nqubit is a promising candidate for fault-tolerant quantum computation. To\nimplement non-Clifford operations on GKP qubits, non-Gaussian operations are\nrequired. In this context, the implementation of a cubic phase gate by\ncombining nonlinear feedforward with ancillary states has been widely\nresearched. Recently, however, it is pointed out that the cubic phase gate is\nnot the most suitable for non-Clifford operations on GKP qubits. In this work,\nwe show that we can achieve linear optical implementation of non-Clifford\noperations on GKP qubit with high fidelity by applying the nonlinear\nfeedforward originally developed for the cubic phase gate and using a\nGKP-encoded ancillary state. Our work shows the versatility of nonlinear\nfeedforward technique important for optical implementation of the\nfault-tolerant continuous-variable quantum computation.\n"}
+{"abstract": "  Cross-modal retrieval methods build a common representation space for samples\nfrom multiple modalities, typically from the vision and the language domains.\nFor images and their captions, the multiplicity of the correspondences makes\nthe task particularly challenging. Given an image (respectively a caption),\nthere are multiple captions (respectively images) that equally make sense. In\nthis paper, we argue that deterministic functions are not sufficiently powerful\nto capture such one-to-many correspondences. Instead, we propose to use\nProbabilistic Cross-Modal Embedding (PCME), where samples from the different\nmodalities are represented as probabilistic distributions in the common\nembedding space. Since common benchmarks such as COCO suffer from\nnon-exhaustive annotations for cross-modal matches, we propose to additionally\nevaluate retrieval on the CUB dataset, a smaller yet clean database where all\npossible image-caption pairs are annotated. We extensively ablate PCME and\ndemonstrate that it not only improves the retrieval performance over its\ndeterministic counterpart but also provides uncertainty estimates that render\nthe embeddings more interpretable. Code is available at\nhttps://github.com/naver-ai/pcme\n"}
+{"abstract": "  The recent opening of gravitational wave astronomy has shifted the debate\nabout black hole mimickers from a purely theoretical arena to a\nphenomenological one. In this respect, missing a definitive quantum gravity\ntheory, the possibility to have simple, meta-geometries describing in a compact\nway alternative phenomenologically viable scenarios is potentially very\nappealing. A recently proposed metric by Simpson and Visser is exactly an\nexample of such meta-geometry describing, for different values of a single\nparameter, different non-rotating black hole mimickers. Here, we employ the\nNewman--Janis procedure to construct a rotating generalisation of such\ngeometry. We obtain a stationary, axially symmetric metric that depends on\nmass, spin and an additional real parameter $\\ell$. According to the value of\nsuch parameter, the metric may represent a rotating traversable wormhole, a\nrotating regular black hole with one or two horizons, or three more limiting\ncases. By studying the internal and external rich structure of such solutions,\nwe show that the obtained metric describes a family of interesting and simple\nregular geometries providing viable Kerr black hole mimickers for future\nphenomenological studies.\n"}
+{"abstract": "  The mechanisms that drive metal-to-insulator transitions (MIT) in correlated\nsolids are not fully understood. For example, the perovskite (PV) SrCoO3 is a\nFM metal while the oxygen-deficient (n-doped) brownmillerite (BM) SrCoO2.5 is\nan anti-ferromagnetic (AFM) insulator. Given the magnetic and structural\ntransitions that accompany the MIT, the driver for such a MIT transition is\nunclear. We also observe that the perovskite metals LaNiO3, SrFeO3, and SrCoO3\nalso undergo MIT when n-doped via high-to-low valence compositional changes.\nAlso, pressurizing the insulating BM SrCoO2.5 phase, drives a gap closing.\nUsing DFT and correlated diffusion Monte Carlo approaches we demonstrate that\nthe ABO3 perovskites most prone to MIT are self hole-doped materials,\nreminiscent of a negative charge-transfer system. Upon n-doping away from the\nnegative-charge transfer metallic phase, an underlying charge-lattice (or\ne-phonon) coupling drives the system to a bond-disproportionated gapped state,\nthereby achieving ligand hole passivation at certain sites only, leading to\ncharge-disproportionated states. The size of the gap opened is correlated with\nthe size of the hole-filling at these ligand sites. This suggests that the\ninteractions driving the gap opening to realize a MIT even in correlated metals\nis the charge-transfer energy, but it couples with the underlying phonons to\nenable the transition to the insulating phase. Other orderings (magnetic,\ncharge, etc.) driven by weaker interactions are secondary and may assist gap\nopenings at small dopings, but its the charge-transfer energy that\npredominantly determines the bandgap, with a negative energy preferring the\nmetallic phase. This n-doping can be achieved by modulations in stoichiometry\nor composition or pressure. Hence, controlling the amount of the ligand-hole is\nkey in controlling MIT. We compare our predictions to experiments where\npossible.\n"}
+{"abstract": "  A single-spin qubit placed near the surface of a conductor acquires an\nadditional contribution to its $1/T_1$ relaxation rate due to magnetic noise\ncreated by electric current fluctuations in the material. We analyze this\ntechnique as a wireless probe of superconductivity in atomically thin two\ndimensional materials. At temperatures $T \\lesssim T_c$, the dominant\ncontribution to the qubit relaxation rate is due to transverse electric current\nfluctuations arising from quasiparticle excitations. We demonstrate that this\nmethod enables detection of metal-to-superconductor transitions, as well as\ninvestigation of the symmetry of the superconducting gap function, through the\nnoise scaling with temperature. We show that scaling of the noise with\nsample-probe distance provides a window into the non-local quasi-static\nconductivity of superconductors, both clean and disordered. At low temperatures\nthe quasiparticle fluctuations get suppressed, yet the noise can be substantial\ndue to resonant contributions from collective longitudinal modes, such as\nplasmons in monolayers and Josephson plasmons in bilayers. Potential\nexperimental implications are discussed.\n"}
+{"abstract": "  Data-driven dimensionality reduction methods such as proper orthogonal\ndecomposition (POD) and dynamic mode decomposition (DMD) have proven to be\nuseful for exploring complex phenomena within fluid dynamics and beyond. A\nwell-known challenge for these techniques is posed by the continuous\nsymmetries, e.g. translations and rotations, of the system under consideration\nas drifts in the data dominate the modal expansions without providing an\ninsight into the dynamics of the problem. In the present study, we address this\nissue for the pressure-driven flow in a rectangular channel by formulating a\ncontinuous symmetry reduction method that eliminates the translations\nsimultaneously in the streamwise and spanwise directions. As an application, we\nconsider turbulence in a minimal flow unit at a Reynolds number (based on the\ncenterline velocity and half-channel height) Re = 2000 and compute the\nsymmetry-reduced dynamic mode decomposition (SRDMD) of sliding data windows of\nvarying durations. SRDMD of channel flow reveals episodes of turbulent time\nevolution that can be approximated by a low-dimensional linear expansion.\n"}
+{"abstract": "  We propose a binary representation of categorical values using a linear map.\nThis linear representation preserves the neighborhood structure of categorical\nvalues. In the context of evolutionary algorithms, it means that every\ncategorical value can be reached in a single mutation. The linear\nrepresentation is embedded into standard metaheuristics, applied to the problem\nof Sudoku puzzles, and compared to the more traditional direct binary encoding.\nIt shows promising results in fixed-budget experiments and empirical cumulative\ndistribution functions with high dimension instances, and also in fixed-target\nexperiments with small dimension instances.\n"}
+{"abstract": "  Current dynamic networks and dynamic pruning methods have shown their\npromising capability in reducing theoretical computation complexity. However,\ndynamic sparse patterns on convolutional filters fail to achieve actual\nacceleration in real-world implementation, due to the extra burden of indexing,\nweight-copying, or zero-masking. Here, we explore a dynamic network slimming\nregime, named Dynamic Slimmable Network (DS-Net), which aims to achieve good\nhardware-efficiency via dynamically adjusting filter numbers of networks at\ntest time with respect to different inputs, while keeping filters stored\nstatically and contiguously in hardware to prevent the extra burden. Our DS-Net\nis empowered with the ability of dynamic inference by the proposed\ndouble-headed dynamic gate that comprises an attention head and a slimming head\nto predictively adjust network width with negligible extra computation cost. To\nensure generality of each candidate architecture and the fairness of gate, we\npropose a disentangled two-stage training scheme inspired by one-shot NAS. In\nthe first stage, a novel training technique for weight-sharing networks named\nIn-place Ensemble Bootstrapping is proposed to improve the supernet training\nefficacy. In the second stage, Sandwich Gate Sparsification is proposed to\nassist the gate training by identifying easy and hard samples in an online way.\nExtensive experiments demonstrate our DS-Net consistently outperforms its\nstatic counterparts as well as state-of-the-art static and dynamic model\ncompression methods by a large margin (up to 5.9%). Typically, DS-Net achieves\n2-4x computation reduction and 1.62x real-world acceleration over ResNet-50 and\nMobileNet with minimal accuracy drops on ImageNet. Code release:\nhttps://github.com/changlin31/DS-Net .\n"}
+{"abstract": "  We extract the imaginary part of the heavy-quark potential using\nclassical-statistical simulations of real-time Yang-Mills dynamics in classical\nthermal equilibrium. The $r$-dependence of the imaginary part of the potential\nis extracted by measuring the temporal decay of Wilson loops of spatial length\n$r$. We compare our results to continuum expressions obtained using hard\nthermal loop theory and to semi-analytic lattice perturbation theory\ncalculations using the hard classical loop formalism. We find that, when\nplotted as a function of $m_D r$, where $m_D$ is the hard classical loop Debye\nmass, the imaginary part of the heavy-quark potential shows little sensitivity\nto the lattice spacing at small $m_D r \\lesssim 1$ and agrees well with the\nsemi-analytic hard classical loop result. For large quark-antiquark\nseparations, we quantify the magnitude of the non-perturbative long-range\ncorrections to the imaginary part of the heavy-quark potential. We present our\nresults for a wide range of temperatures, lattice spacings, and lattice\nvolumes. This work sets the stage for extracting the imaginary part of the\nheavy-quark potential in an expanding non-equilibrium Yang Mills plasma.\n"}
+{"abstract": "  The millihertz gravitational-wave frequency band is expected to contain a\nrich symphony of signals with sources ranging from galactic white dwarf\nbinaries to extreme mass ratio inspirals. Many of these gravitational-wave\nsignals will not be individually resolvable. Instead, they will incoherently\nadd to produce stochastic gravitational-wave confusion noise whose frequency\ncontent will be governed by the dynamics of the sources. The angular structure\nof the power of the confusion noise will be modulated by the distribution of\nthe sources across the sky. Measurement of this structure can yield important\ninformation about the distribution of sources on galactic and extra-galactic\nscales, their astrophysics and their evolution over cosmic timescales.\nMoreover, since the confusion noise is part of the noise budget of LISA,\nmapping it will also be essential for studying resolvable signals. In this\npaper, we present a Bayesian algorithm to probe the angular distribution of the\nstochastic gravitational-wave confusion noise with LISA using a spherical\nharmonic basis. We develop a technique based on Clebsch-Gordan coefficients to\nmathematically constrain the spherical harmonics to yield a non-negative\ndistribution, making them optimal for expanding the gravitational-wave power\nand amenable to Bayesian inference. We demonstrate these techniques using a\nseries of simulations and analyses, including recovery of simulated distributed\nand localized sources of gravitational-wave power. We also apply this method to\nmap the gravitational-wave foreground from galactic white-dwarfs using a\nsimplified model of the galactic white dwarf distribution.\n"}
+{"abstract": "  Although AI holds promise for improving human decision making in societally\ncritical domains, it remains an open question how human-AI teams can reliably\noutperform AI alone and human alone in challenging prediction tasks (also known\nas complementary performance). We explore two directions to understand the gaps\nin achieving complementary performance. First, we argue that the typical\nexperimental setup limits the potential of human-AI teams. To account for lower\nAI performance out-of-distribution than in-distribution because of distribution\nshift, we design experiments with different distribution types and investigate\nhuman performance for both in-distribution and out-of-distribution examples.\nSecond, we develop novel interfaces to support interactive explanations so that\nhumans can actively engage with AI assistance. Using virtual pilot studies and\nlarge-scale randomized experiments across three tasks, we demonstrate a clear\ndifference between in-distribution and out-of-distribution, and observe mixed\nresults for interactive explanations: while interactive explanations improve\nhuman perception of AI assistance's usefulness, they may reinforce human biases\nand lead to limited performance improvement. Overall, our work points out\ncritical challenges and future directions towards enhancing human performance\nwith AI assistance.\n"}
+{"abstract": "  The $\\gamma$-metric is a static, axially-symmetric singular solution of the\nvacuum Einstein's equations without an event horizon. This is a two-parameter\nfamily of solutions, generic values of one of which (called $\\gamma$) measure\nthe deviation from spherical symmetry. Here, we first study the shadow cast by\nthis geometry, in order to constrain the $\\gamma$-metric from observations. We\nfind that for $\\gamma < 1/2$, there are, in principle, no shadows cast. On the\nother hand, shadows cast for all values of $\\gamma \\geq 1/2$ are consistent\nwith observations of M$87^*$ by the Event Horizon Telescope. We also study\nimages of thin accretion disks in the $\\gamma$-metric background. In situations\nwhere the $\\gamma$-metric possesses light rings, these qualitatively mimic\nSchwarzschild black holes with the same ADM mass, while in the absence of such\nrings, they are drastically different from the black hole case.\n"}
+{"abstract": "  By strengthening one of the hypotheses of a well-known sufficient condition\nfor the hypercyclicity of linear operators in Banach spaces, we arrive at a\nsufficient condition for linear chaos and reveal consequences of the latter for\ninverses, powers, multiples, and spectral properties. Extending the results,\nfamiliar for bounded linear operators, we also show that the hypercyclicity of\nunbounded linear operators subject to the sufficient condition for\nhypercyclicity is inherited by their bounded inverses, powers, and unimodular\nmultiples and that necessary conditions for linear hypercyclicity stretch to\nthe unbounded case.\n"}
+{"abstract": "  Many researchers have developed VR systems for people with visual impairments\nby using various audio feedback techniques. However, there has been much less\nstudy of collaborative VR systems in which people with visual impairments and\npeople with able-body can participate together. Therefore, we developed a VR\nshowdown game which is similar to a real Showdown game in which two players can\nplay together in the same virtual environment. We incorporate auditory distance\nperception using the HRTF (Head Related Transform Function) based on a spatial\nposition in VR. We developed two modes in the showdown game. One is the PVA\n(Player vs. Agent) mode in which people with visual impairments can play alone\nand the PVP (Player vs. Player) mode in which people with visual impairments\ncan play with another player in the network environment. We conducted our user\nstudies by comparing the performances of people with visual impairments and\npeople with able-body. The user study results show that people with visual\nimpairments won 67.6% of the games when competing against people with\nable-body. This paper reports an example of a collaborative VR system for\npeople with visual impairments and also design guideline for developing VR\nsystems for people with visual impairments.\n"}
+{"abstract": "  Typical fact verification models use retrieved written evidence to verify\nclaims. Evidence sources, however, often change over time as more information\nis gathered and revised. In order to adapt, models must be sensitive to subtle\ndifferences in supporting evidence. We present VitaminC, a benchmark infused\nwith challenging cases that require fact verification models to discern and\nadjust to slight factual changes. We collect over 100,000 Wikipedia revisions\nthat modify an underlying fact, and leverage these revisions, together with\nadditional synthetically constructed ones, to create a total of over 400,000\nclaim-evidence pairs. Unlike previous resources, the examples in VitaminC are\ncontrastive, i.e., they contain evidence pairs that are nearly identical in\nlanguage and content, with the exception that one supports a given claim while\nthe other does not. We show that training using this design increases\nrobustness -- improving accuracy by 10% on adversarial fact verification and 6%\non adversarial natural language inference (NLI). Moreover, the structure of\nVitaminC leads us to define additional tasks for fact-checking resources:\ntagging relevant words in the evidence for verifying the claim, identifying\nfactual revisions, and providing automatic edits via factually consistent text\ngeneration.\n"}
+{"abstract": "  Intrinsically disordered proteins (IDPs) constitute a broad set of proteins\nwith few uniting and many diverging properties. IDPs-and intrinsically\ndisordered regions (IDRs) interspersed between folded domains-are generally\ncharacterized as having no persistent tertiary structure; instead they\ninterconvert between a large number of different and often expanded structures.\nIDPs and IDRs are involved in an enormously wide range of biological functions\nand reveal novel mechanisms of interactions, and while they defy the common\nstructure-function paradigm of folded proteins, their structural preferences\nand dynamics are important for their function. We here discuss open questions\nin the field of IDPs and IDRs, focusing on areas where machine learning and\nother computational methods play a role. We discuss computational methods aimed\nto predict transiently formed local and long-range structure, including methods\nfor integrative structural biology. We discuss the many different ways in which\nIDPs and IDRs can bind to other molecules, both via short linear motifs, as\nwell as in the formation of larger dynamic complexes such as biomolecular\ncondensates. We discuss how experiments are providing insight into such\ncomplexes and may enable more accurate predictions. Finally, we discuss the\nrole of IDPs in disease and how new methods are needed to interpret the\nmechanistic effects of genomic variants in IDPs.\n"}
+{"abstract": "  We study the problem of distributed multi-robot coverage over an unknown,\nnonuniform sensory field. Modeling the sensory field as a realization of a\nGaussian Process and using Bayesian techniques, we devise a policy which aims\nto balance the tradeoff between learning the sensory function and covering the\nenvironment. We propose an adaptive coverage algorithm called Deterministic\nSequencing of Learning and Coverage (DSLC) that schedules learning and coverage\nepochs such that its emphasis gradually shifts from exploration to exploitation\nwhile never fully ceasing to learn. Using a novel definition of coverage regret\nwhich characterizes overall coverage performance of a multi-robot team over a\ntime horizon $T$, we analyze DSLC to provide an upper bound on expected\ncumulative coverage regret. Finally, we illustrate the empirical performance of\nthe algorithm through simulations of the coverage task over an unknown\ndistribution of wildfires.\n"}
+{"abstract": "  Leaf hydraulic networks play an important role not only in fluid transport\nbut also in maintaining whole-plant water status through transient\nenvironmental changes in soil-based water supply or air humidity. Both water\npotential and hydraulic resistance vary spatially throughout the leaf transport\nnetwork, consisting of xylem, stomata and water-storage cells, and portions of\nleaf areas far from the leaf base can be disproportionately disadvantaged under\nwater stress. Besides the suppression of transpiration and reduction of water\nloss caused by stomatal closure, the leaf capacitance of water storage, which\ncan also vary locally, is thought to be crucial for the maintenance of leaf\nwater status. In order to study the fluid dynamics in these networks, we\ndevelop a spatially explicit, capacitive model which is able to capture the\nlocal spatiotemporal changes of water potential and flow rate. In\nelectrical-circuit analogues described by Ohm's law, we implement linear\ncapacitors imitating water storage, and we present both analytical calculations\nof a uniform one-dimensional model and numerical simulation methods for general\nspatially explicit network models, and their relation to conventional\nlumped-element models. Calculation and simulation results are shown for the\nuniform model, which mimics key properties of a monocotyledonous grass leaf. We\nillustrate water status of a well-watered leaf, and the lowering of water\npotential and transpiration rate caused by excised water source or reduced air\nhumidity. We show that the time scales of these changes under water stress are\nhugely affected by leaf capacitance and resistances to capacitors, in addition\nto stomatal resistance. Through this modeling of a grass leaf, we confirm the\npresence of uneven water distribution over leaf area, and also discuss the\nimportance of considering the spatial variation of leaf hydraulic traits in\nplant biology.\n"}
+{"abstract": "  Conventional stochastic rounding (CSR) is widely employed in the training of\nneural networks (NNs), showing promising training results even in low-precision\ncomputations. We introduce an improved stochastic rounding method, that is\nsimple and efficient. The proposed method succeeds in training NNs with 16-bit\nfixed-point numbers and provides faster convergence and higher classification\naccuracy than both CSR and deterministic rounding-to-the-nearest method.\n"}
+{"abstract": "  We propose a novel infection spread model based on a random connection graph\nwhich represents connections between $n$ individuals. Infection spreads via\nconnections between individuals and this results in a probabilistic cluster\nformation structure as well as a non-i.i.d. (correlated) infection status for\nindividuals. We propose a class of two-step sampled group testing algorithms\nwhere we exploit the known probabilistic infection spread model. We investigate\nthe metrics associated with two-step sampled group testing algorithms. To\ndemonstrate our results, for analytically tractable exponentially split cluster\nformation trees, we calculate the required number of tests and the expected\nnumber of false classifications in terms of the system parameters, and identify\nthe trade-off between them. For such exponentially split cluster formation\ntrees, for zero-error construction, we prove that the required number of tests\nis $O(\\log_2n)$. Thus, for such cluster formation trees, our algorithm\noutperforms any zero-error non-adaptive group test, binary splitting algorithm,\nand Hwang's generalized binary splitting algorithm. Our results imply that, by\nexploiting probabilistic information on the connections of individuals, group\ntesting can be used to reduce the number of required tests significantly even\nwhen infection rate is high, contrasting the prevalent belief that group\ntesting is useful only when infection rate is low.\n"}
+{"abstract": "  We develop a novel model of the magnetized spin-1 Bose-Einstein condensate\n(BEC) of neutral atoms, using the method of many-particle quantum hydrodynamic\n(QHD) and propose an original derivation of the system of continual equations.\nWe consider bosons with a spin-spin interaction and a short range interaction\nin the first order in the interaction radius, on the of basis of the\nself-consistent field approximation of the QHD equations. We demonstrate that\nthe dynamics of the fluid velocity and magnetization is determined by a\nnontrivial modification of the Euler and Landau-Lifshitz equation, and show\nthat a nontrivial modification of the spin density evolution equation contains\nthe spin torque effect that arises from the self-interactions between spins of\nthe bosons. The properties of the dispersion spectrum of collective excitations\nare described. We obtain the new contribution of the self-interaction of spins\nin the spin wave spectrum together with the influence of an external magnetic\nfield and spin-spin interactions between polarized particles. The anisotropic\nspin wave instability is predicted.\n"}
+{"abstract": "  Recommendation and ranking systems are known to suffer from popularity bias;\nthe tendency of the algorithm to favor a few popular items while\nunder-representing the majority of other items. Prior research has examined\nvarious approaches for mitigating popularity bias and enhancing the\nrecommendation of long-tail, less popular, items. The effectiveness of these\napproaches is often assessed using different metrics to evaluate the extent to\nwhich over-concentration on popular items is reduced. However, not much\nattention has been given to the user-centered evaluation of this bias; how\ndifferent users with different levels of interest towards popular items are\naffected by such algorithms. In this paper, we show the limitations of the\nexisting metrics to evaluate popularity bias mitigation when we want to assess\nthese algorithms from the users' perspective and we propose a new metric that\ncan address these limitations. In addition, we present an effective approach\nthat mitigates popularity bias from the user-centered point of view. Finally,\nwe investigate several state-of-the-art approaches proposed in recent years to\nmitigate popularity bias and evaluate their performances using the existing\nmetrics and also from the users' perspective. Our experimental results using\ntwo publicly-available datasets show that existing popularity bias mitigation\ntechniques ignore the users' tolerance towards popular items. Our proposed\nuser-centered method can tackle popularity bias effectively for different users\nwhile also improving the existing metrics.\n"}
+{"abstract": "  In this study, we improve the topological complexity computations on digital\nimages with introducing the digital topological complexity computations of a\nsurjective and digitally continuous map between digital images. We also reveal\ndifferences in topological complexity computations of maps between digital\nimages and topological spaces. Moreover, we emphasize the importance of the\nadjacency relation on the domain and the range of a digital map in these\ncomputations.\n"}
+{"abstract": "  We introduce a non-local $L^2$-gradient flow for the Willmore energy of\nimmersed surfaces which preserves the isoperimetric ratio. For initial data\nwith energy below an explicit threshold, we show long-time existence and\nconvergence to a Helfrich immersion.\n"}
+{"abstract": "  We find a novel confinement mechanism in the two-flavor dense quark matter\nproposed recently, that consists of the 2SC condensates and the $P$-wave\ndiquark condensates of $d$-quarks. This quark matter exhibiting color\nsuperconductivity as well as superfluidity is classified into two phases;\nconfined and deconfined phases of vortices. We establish that the criterion of\nthe confinement is color neutrality of Aharonov-Bohm (AB) phases: vortices\nexhibiting color non-singlet AB phases are confined by the so-called AB defects\nto form color-singlet bound states. In the deconfined phase, the most stable\nvortices are non-Abelian Alice strings, which are superfluid vortices with\nfractional circulation and non-Abelian color magnetic fluxes therein,\nexhibiting color non-singlet AB phases. On the other hand, in the confined\nphase, these non-Abelian vortices are confined to either a baryonic or mesonic\nbound state in which constituent vortices are connected by AB defects. The\nbaryonic bound state consists of three non-Abelian Alice strings with different\ncolor magnetic fluxes with the total flux canceled out connected by a domain\nwall junction, while the mesonic bound state consists of two non-Abelian Alice\nstrings with the same color magnetic fluxes connected by a single domain wall.\nInterestingly, the latter contains a color magnetic flux in its core, but this\ncan exist because of color neutrality of its AB phase.\n"}
+{"abstract": "  We investigate the effect of longitudinal and transverse calorimeter\nsegmentation on event-by-event software compensation for hadronic showers. To\nfactorize out sampling and electronics effects, events are simulated in which a\nsingle charged pion is shot at a homogenous lead glass calorimeter, split into\nlongitudinal and transverse segments of varying size. As an approximation of an\noptimal reconstruction, a neural network-based energy regression is trained.\nThe architecture is based on blocks of convolutional kernels customized for\nshower energy regression using local energy densities; biases at the edges of\nthe training dataset are mitigated using a histogram technique. With this\napproximation, we find that a longitudinal and transverse segment size less\nthan or equal to 0.5 and 1.3 nuclear interaction lengths, respectively, is\nnecessary to achieve an optimal energy measurement. In addition, an intrinsic\nenergy resolution of $8\\%/\\sqrt{E}$ for pion showers is observed.\n"}
+{"abstract": "  Combining the representations of the words that make up a sentence into a\ncohesive whole is difficult, since it needs to account for the order of words,\nand to establish how the words present relate to each other. The solution we\npropose consists in iteratively adjusting the context. Our algorithm starts\nwith a presumably erroneous value of the context, and adjusts this value with\nrespect to the tokens at hand. In order to achieve this, representations of\nwords are built combining their symbolic embedding with a positional encoding\ninto single vectors. The algorithm then iteratively weighs and aggregates these\nvectors using our novel second-order attention mechanism. Our models report\nstrong results in several well-known text classification tasks.\n"}
+{"abstract": "  Place/Transition Petri nets with inhibitor arcs (PTI nets for short), which\nare a well-known Turing-complete, distributed model of computation, are\nequipped with a decidable, behavioral equivalence, called pti-place\nbisimilarity, that conservatively extends place bisimilarity defined over\nPlace/Transition nets (without inhibitor arcs). We prove that pti-place\nbisimilarity is sensible, as it respects the causal semantics of PTI nets.\n"}
+{"abstract": "  We present a resource-planning game for the cybersecurity of network control\nsystems (NCS). The NCS is assumed to be operating in closed-loop using a linear\nstate-feedback $\\mathcal{H}_2$ controller. A zero-sum, two-player Stackelberg\ngame (SG) is developed between an attacker and a defender for this NCS. The\nattacker aims to disable communication of selected nodes and thereby render the\nfeedback gain matrix to be sparse, leading to degradation of closed-loop\nperformance. The defender, on the other hand, aims to prevent this loss by\ninvesting in the protection of targeted nodes. Both players trade their\n$\\mathcal{H}_2$-performance objectives for the costs of their actions. The\nstandard backward induction method is modified to determine a cost-based\nStackelberg equilibrium (CBSE) that provides the optimal performance payoffs to\nboth players while saving their individual costs. We analyze the dependency of\na CBSE on the relative budgets of the players as well as on the \"important\"\nnetwork nodes, which must be preserved to achieve a reliable\n$\\mathcal{H}_2$-performance. Additionally, we consider NCS models with\nuncertainties and develop robust, long-term security investment games based on\nthis cost-based SG. The proposed games are validated using an example of\nwide-area control of electric power systems, where it is demonstrated that\nreliable and robust defense is feasible unless the defender is much more\nresource-limited than the attacker.\n"}
+{"abstract": "  In the past few years, numerous deep learning methods have been proposed to\naddress the task of segmenting salient objects from RGB images. However, these\napproaches depending on single modality fail to achieve the state-of-the-art\nperformance on widely used light field salient object detection (SOD) datasets,\nwhich collect large-scale natural images and provide multiple modalities such\nas multi-view, micro-lens images and depth maps. Most recently proposed light\nfield SOD methods have acquired improving detecting accuracy, yet still predict\nrough objects' structures and perform slow inference speed. To this end, we\npropose CMA-Net, which consists of two novel cascaded mutual attention modules\naiming at fusing the high level features from the modalities of all-in-focus\nand depth. Our proposed CMA-Net outperforms 30 SOD methods on two widely\napplied light field benchmark datasets. Besides, the proposed CMA-Net is able\nto inference at the speed of 53 fps. Extensive quantitative and qualitative\nexperiments illustrate both the effectiveness and efficiency of our CMA-Net.\n"}
+{"abstract": "  We address a system of equations modeling an incompressible fluid interacting\nwith an elastic body. We prove the local existence when the initial velocity\nbelongs to the space $H^{s}$, where $s>3/2$ and the initial structure velocity\nis in $H^{s-1/2}$.\n"}
+{"abstract": "  Quantum devices can be used to solve constrained combinatorial optimization\n(COPT) problems thanks to the use of penalization methods to embed the COPT\nproblem's constraints in its objective to obtain a quadratic unconstrained\nbinary optimization (QUBO) reformulation of the COPT. However, the particular\nway in which this penalization is carried out, affects the value of the penalty\nparameters, as well as the number of additional binary variables that are\nneeded to obtain the desired QUBO reformulation. In turn, these factors\nsubstantially affect the ability of quantum computers to efficiently solve\nthese constrained COPT problems. This efficiency is key towards the goal of\nusing quantum computers to solve constrained COPT problems more efficiently\nthan with classical computers. Along these lines, we consider an important\nconstrained COPT problem; namely, the maximum $k$-colorable subgraph (M$k$CS)\nproblem, in which the aim is to find an induced $k$-colorable subgraph with\nmaximum cardinality in a given graph. This problem arises in channel assignment\nin spectrum sharing networks, VLSI design, human genetic research, and\ncybersecurity. We derive two QUBO reformulations for the M$k$CS problem, and\nfully characterize the range of the penalty parameters that can be used in the\nQUBO reformulations. Further, one of the QUBO reformulations of the M$k$CS\nproblem is obtained without the need to introduce additional binary variables.\nTo illustrate the benefits of obtaining and characterizing these QUBO\nreformulations, we benchmark different QUBO reformulations of the M$k$CS\nproblem by performing numerical tests on D-Wave's quantum annealing devices.\nThese tests also illustrate the numerical power gained by using the latest\nD-Wave's quantum annealing device.\n"}
+{"abstract": "  Self-training is a competitive approach in domain adaptive segmentation,\nwhich trains the network with the pseudo labels on the target domain. However\ninevitably, the pseudo labels are noisy and the target features are dispersed\ndue to the discrepancy between source and target domains. In this paper, we\nrely on representative prototypes, the feature centroids of classes, to address\nthe two issues for unsupervised domain adaptation. In particular, we take one\nstep further and exploit the feature distances from prototypes that provide\nricher information than mere prototypes. Specifically, we use it to estimate\nthe likelihood of pseudo labels to facilitate online correction in the course\nof training. Meanwhile, we align the prototypical assignments based on relative\nfeature distances for two different views of the same target, producing a more\ncompact target feature space. Moreover, we find that distilling the already\nlearned knowledge to a self-supervised pretrained model further boosts the\nperformance. Our method shows tremendous performance advantage over\nstate-of-the-art methods. We will make the code publicly available.\n"}
+{"abstract": "  Density matrix quantum Monte Carlo (DMQMC) is a recently-developed method for\nstochastically sampling the $N$-particle thermal density matrix to obtain\nexact-on-average energies for model and \\emph{ab initio} systems. We report a\nsystematic numerical study of the sign problem in DMQMC based on simulations of\natomic and molecular systems. In DMQMC, the density matrix is written in an\nouter product basis of Slater determinants and has a size of space which is the\nsquare of the number of Slater determinants. In principle this means DMQMC\nneeds to sample a space which scales in the system size, $N$, as\n$\\mathcal{O}[(\\exp(N))^2]$. In practice, there is a system-dependent critical\nwalker population ($N_c$) which must be exceeded in order to remove the sign\nproblem, and this imposes limitations by way of storage and computer time. We\nestablish that $N_c$ for DMQMC is the square of $N_c$ for FCIQMC. By contrast,\nthe minimum $N_c$ in the interaction picture modification of DMQMC (IP-DMQMC)\nonly is directly proportionate to the $N_c$ for FCIQMC. We find that this comes\nfrom the asymmetric propagation of IP-DMQMC compared to the symmetric\npropagation of canonical DMQMC. An asymmetric mode of propagation is\nprohibitively expensive for DMQMC because it has a much greater stochastic\nerror. Finally, we find that the equivalence between IP-DMQMC and FCIQMC seems\nto extend to the initiator approximation, which is often required to study\nlarger basis sets and other systems. This suggests IP-DMQMC offers a way to\nameliorate the cost of moving between a Slater determinant space and an outer\nproduct basis.\n"}
+{"abstract": "  In this paper, we study the existence of global Yamabe flow on asymptotically\nflat (in short, AF or ALE) manifolds. Note that the ADM mass is preserved in\ndimensions 3,4 and 5. We present a new general local existence of Yamabe flow\non a complete Riemannian manifold with the initial metric quasi-isometric to a\nbackground metric of bounded scalar curvature. Asymptotic behaviour of the\nYamabe flow on ALE manifolds is also addressed provided the initial scalar\ncurvature is non-negative and there is a bounded subsolution to the\ncorresponding Poisson equation. We also present a maximum principle for a very\ngeneral parabolic equations on the complete Riemannian manifolds.\n"}
+{"abstract": "  The present article revisits the Diffusion Operator Integral (DOI) variance\nreduction technique originally proposed in Heath and Platen (2002) and extends\nits theoretical concept to the pricing of American-style options under\n(time-homogeneous) L\\'evy stochastic differential equations. The resulting Jump\nDiffusion Operator Integral (JDOI) method can be combined with numerous Monte\nCarlo based stopping-time algorithms, including the ubiquitous least-squares\nMonte Carlo (LSMC) algorithm of Longstaff and Schwartz (cf. Carriere (1996),\nLongstaff and Schwartz (2001)). We exemplify the usefulness of our theoretical\nderivations under a concrete, though very general jump-diffusion stochastic\nvolatility dynamics and test the resulting LSMC based version of the JDOI\nmethod. The results provide evidence of a strong variance reduction when\ncompared with a simple application of the LSMC algorithm and proves that\napplying our technique on top of Monte Carlo based pricing schemes provides a\npowerful way to speed-up these methods.\n"}
+{"abstract": "  Implicit equilibrium models, i.e., deep neural networks (DNNs) defined by\nimplicit equations, have been becoming more and more attractive recently. In\nthis paper, we investigate an emerging question: can an implicit equilibrium\nmodel's equilibrium point be regarded as the solution of an optimization\nproblem? To this end, we first decompose DNNs into a new class of unit layer\nthat is the proximal operator of an implicit convex function while keeping its\noutput unchanged. Then, the equilibrium model of the unit layer can be derived,\nnamed Optimization Induced Equilibrium Networks (OptEq), which can be easily\nextended to deep layers. The equilibrium point of OptEq can be theoretically\nconnected to the solution of its corresponding convex optimization problem with\nexplicit objectives. Based on this, we can flexibly introduce prior properties\nto the equilibrium points: 1) modifying the underlying convex problems\nexplicitly so as to change the architectures of OptEq; and 2) merging the\ninformation into the fixed point iteration, which guarantees to choose the\ndesired equilibrium point when the fixed point set is non-singleton. We show\nthat deep OptEq outperforms previous implicit models even with fewer\nparameters. This work establishes the first step towards the\noptimization-guided design of deep models.\n"}
+{"abstract": "  Secure integer comparison has been a popular research topic in cryptography,\nboth for its simplicity to describe and for its applications. The aim is to\nenable two parties to compare their inputs without revealing the exact value of\nthose inputs.\n  In this paper, we highlight three-party integer comparison (TPIC), where a\n\\emph{judge}, with no private input, wants to know the comparison result, while\ntwo \\emph{competitors} hold secret integers to do privacy-preserving\ncomparison. The judge actively obtains the result rather than passively waiting\nfor it sent by a competitor. We give two TPIC constructions considering\n\\emph{Mixed adversaries}, who have with different capabilities. One is secure\nagainst a semi-honest adversary with low computation and communication cost,\nwhile the other is secure against a malicious adversary.\n  Basing on TPIC, we present multi-party comparisons through concrete\napplications, including a joint bidding scheme and a practical auction. Brief\nsecurity proofs and analysis for the applications are presented. In comparison,\nour auction scheme is more efficient with lower cost, making it feasible in\npractice rather than a theoretical design. All the comparisons and application\nschemes run on top of blockchain requiring a constant number of rounds.\n"}
+{"abstract": "  In this work we investigate the parallel scalability of the numerical method\ndeveloped in Guthrey and Rossmanith [The regionally implicit discontinuous\nGalerkin method: Improving the stability of DG-FEM, SIAM J. Numer. Anal.\n(2019)]. We develop an implementation of the regionally-implicit discontinuous\nGalerkin (RIDG) method in DoGPack, which is an open source C++ software package\nfor discontinuous Galerkin methods. Specifically, we develop and test a hybrid\nOpenMP and MPI parallelized implementation of DoGPack with the goal of\nexploring the efficiency and scalability of RIDG in comparison to the popular\nstrong stability-preserving Runge-Kutta discontinuous Galerkin (SSP-RKDG)\nmethod. We demonstrate that RIDG methods are able to hide communication latency\nassociated with distributed memory parallelism, due to the fact that almost all\nof the work involved in the method is highly localized to each element,\nproducing a localized prediction for each region. We demonstrate the enhanced\nefficiency and scalability of the of the RIDG method and compare it to SSP-RKDG\nmethods and show extensibility to very high order schemes. The two-dimensional\nscaling study is performed on machines at the Institute for Cyber-Enabled\nResearch at Michigan State University, using up to 1440 total cores on Intel(R)\nXeon(R) Gold 6148 CPU @ 2.40GHz CPUs. The three dimensional scaling study is\nperformed on Livermore Computing clusters at at Lawrence Livermore National\nLaboratory, using up to 28672 total cores on Intel Xeon CLX-8276L CPUs with\nOmni-Path interconnects.\n"}
+{"abstract": "  We discuss relations between different notions of ranks for multilinear\nforms. In particular we show that the Schmidt and the analytic ranks for\ntrilinear forms are essentially proportional.\n"}
+{"abstract": "  The study of multi-type Protein-Protein Interaction (PPI) is fundamental for\nunderstanding biological processes from a systematic perspective and revealing\ndisease mechanisms. Existing methods suffer from significant performance\ndegradation when tested in unseen dataset. In this paper, we investigate the\nproblem and find that it is mainly attributed to the poor performance for\ninter-novel-protein interaction prediction. However, current evaluations\noverlook the inter-novel-protein interactions, and thus fail to give an\ninstructive assessment. As a result, we propose to address the problem from\nboth the evaluation and the methodology. Firstly, we design a new evaluation\nframework that fully respects the inter-novel-protein interactions and gives\nconsistent assessment across datasets. Secondly, we argue that correlations\nbetween proteins must provide useful information for analysis of novel\nproteins, and based on this, we propose a graph neural network based method\n(GNN-PPI) for better inter-novel-protein interaction prediction. Experimental\nresults on real-world datasets of different scales demonstrate that GNN-PPI\nsignificantly outperforms state-of-the-art PPI prediction methods, especially\nfor the inter-novel-protein interaction prediction.\n"}
+{"abstract": "  In this paper, we consider numerical approximation to periodic measure of a\ntime periodic stochastic differential equations (SDEs) under weakly dissipative\ncondition. For this we first study the existence of the periodic measure\n$\\rho_t$ and the large time behaviour of $\\mathcal{U}(t+s,s,x) :=\n\\mathbb{E}\\phi(X_{t}^{s,x})-\\int\\phi d\\rho_t,$ where $X_t^{s,x}$ is the\nsolution of the SDEs and $\\phi$ is a test function being smooth and of\npolynomial growth at infinity. We prove $\\mathcal{U}$ and all its spatial\nderivatives decay to 0 with exponential rate on time $t$ in the sense of\naverage on initial time $s$. We also prove the existence and the geometric\nergodicity of the periodic measure of the discretized semi-flow from the\nEuler-Maruyama scheme and moment estimate of any order when the time step is\nsufficiently small (uniform for all orders). We thereafter obtain that the weak\nerror for the numerical scheme of infinite horizon is of the order $1$ in terms\nof the time step. We prove that the choice of step size can be uniform for all\ntest functions $\\phi$. Subsequently we are able to estimate the average\nperiodic measure with ergodic numerical schemes.\n"}
+{"abstract": "  Perturbative supersymmetry breaking on the landscape of string vacua is\nexpected to favor large soft terms as a power-law or log distribution, but\ntempered by an anthropic veto of inappropriate vacua or vacua leading to too\nlarge a value for the derived weak scale -- a violation of the atomic\nprinciple. Indeed, scans of such vacua yield a statistical prediction for light\nHiggs boson mass m_h~ 125 GeV with sparticles (save possibly light higgsinos)\ntypically beyond LHC reach. In contrast, models of dynamical SUSY breaking\n(DSB) -- with a hidden sector gauge coupling g^2 scanned uniformly -- lead to\ngaugino condensation and a uniform distribution of soft parameters on a log\nscale. Then soft terms are expected to be distributed as $m_{\\rm soft}^{-1}$\nfavoring small values. A scan of DSB soft terms generally leads to $m_h\\ll 125$\nGeV and sparticle masses usually below LHC limits. Thus, the DSB landscape\nscenario seems excluded from LHC search results. An alternative is that the\nexponential suppression of the weak scale is set anthropically on the landscape\nvia the atomic principle.\n"}
+{"abstract": "  We propose an approach to saddle point optimization relying only on oracles\nthat solve minimization problems approximately. We analyze its convergence\nproperty on a strongly convex--concave problem and show its linear convergence\ntoward the global min--max saddle point. Based on the convergence analysis, we\ndevelop a heuristic approach to adapt the learning rate. An implementation of\nthe developed approach using the (1+1)-CMA-ES as the minimization oracle,\nnamely Adversarial-CMA-ES, is shown to outperform several existing approaches\non test problems. Numerical evaluation confirms the tightness of the\ntheoretical convergence rate bound as well as the efficiency of the learning\nrate adaptation mechanism. As an example of real-world problems, the suggested\noptimization method is applied to automatic berthing control problems under\nmodel uncertainties, showing its usefulness in obtaining solutions robust to\nuncertainty.\n"}
+{"abstract": "  Chest X-ray (CXR) is the most typical diagnostic X-ray examination for\nscreening various thoracic diseases. Automatically localizing lesions from CXR\nis promising for alleviating radiologists' reading burden. However, CXR\ndatasets are often with massive image-level annotations and scarce lesion-level\nannotations, and more often, without annotations. Thus far, unifying different\nsupervision granularities to develop thoracic disease detection algorithms has\nnot been comprehensively addressed. In this paper, we present OXnet, the first\ndeep omni-supervised thoracic disease detection network to our best knowledge\nthat uses as much available supervision as possible for CXR diagnosis. We first\nintroduce supervised learning via a one-stage detection model. Then, we inject\na global classification head to the detection model and propose dual attention\nalignment to guide the global gradient to the local detection branch, which\nenables learning lesion detection from image-level annotations. We also impose\nintra-class compactness and inter-class separability with global prototype\nalignment to further enhance the global information learning. Moreover, we\nleverage a soft focal loss to distill the soft pseudo-labels of unlabeled data\ngenerated by a teacher model. Extensive experiments on a large-scale chest\nX-ray dataset show the proposed OXnet outperforms competitive methods with\nsignificant margins. Further, we investigate omni-supervision under various\nannotation granularities and corroborate OXnet is a promising choice to\nmitigate the plight of annotation shortage for medical image diagnosis.\n"}
+{"abstract": "  In system identification, estimating parameters of a model using limited\nobservations results in poor identifiability. To cope with this issue, we\npropose a new method to simultaneously select and estimate sensitive parameters\nas key model parameters and fix the remaining parameters to a set of typical\nvalues. Our method is formulated as a nonlinear least squares estimator with\nL1-regularization on the deviation of parameters from a set of typical values.\nFirst, we provide consistency and oracle properties of the proposed estimator\nas a theoretical foundation. Second, we provide a novel approach based on\nLevenberg-Marquardt optimization to numerically find the solution to the\nformulated problem. Third, to show the effectiveness, we present an application\nidentifying a biomechanical parametric model of a head position tracking task\nfor 10 human subjects from limited data. In a simulation study, the variances\nof estimated parameters are decreased by 96.1% as compared to that of the\nestimated parameters without L1-regularization. In an experimental study, our\nmethod improves the model interpretation by reducing the number of parameters\nto be estimated while maintaining variance accounted for (VAF) at above 82.5%.\nMoreover, the variances of estimated parameters are reduced by 71.1% as\ncompared to that of the estimated parameters without L1-regularization. Our\nmethod is 54 times faster than the standard simplex-based optimization to solve\nthe regularized nonlinear regression.\n"}
+{"abstract": "  In this paper we continue the stability analysis of the model for coinfection\nwith density dependent susceptible population introduced in the 1st part of the\npaper. We look for coexistence equilibrium points, their stability and\ndependence on the carrying capacity $K$. Two sets of parameter value are\ndetermined, each giving rise to different scenarios for the equilibrium branch\nparametrized by $K$. In both scenarios the branch includes coexistence points\nimplying that both coinfection and single infection of both diseases can exist\ntogether in a stable state. There are no simple explicit expression for these\nequilibrium points and we will require a more delicate analysis of these points\nwith a new bifurcation technique adapted to such epidemic related problems. The\nfirst scenario is described by the branch of stable equilibrium points which\nincludes a section of coexistence points starting at a bifurcation equilibrium\npoint with zero second single infections and finishing at another bifurcation\npoint with zero first single infections. In the second scenario the branch also\nincludes a section of coexistence equilibrium points with the same type of\nstarting point but the branch stays inside the positive cone after this. The\ncoexistence equilibrium points are stable at the start of the section. It stays\nstable as long as the product of $K$ and the rate $\\bar \\gamma$ of coinfection\nresulting from two single infections is small but, after this it can reach a\nHopf bifurcation and periodic orbits will appear.\n"}
+{"abstract": "  Mike Lockwood and Mathew Owens discuss how eclipse observations are aiding\nthe development of a climatology of near-Earth space\n"}
+{"abstract": "  Compound ion distributions, fi(v), have been measured by NASA's\nMagnetospheric Multi-Scale Mission (MMS) and have been found in reconnection\nsimulations. A complex distribution, fi(v), consisting, for example, of\nessentially disjoint pieces will be called a multi-beam distribution and\nmodeled as a sum of \"beams,\" fi(v) = f1(v) + ... +fN(v). Velocity moments of\nfi(v) are taken beam by beam and summed. Such multi-beam moments of fi(v) have\nadvantages over the customary standard velocity moments of fi(v), forwhich\nthere is only one mean flow velocity. For example, the standard thermal energy\nmomentof a pair of equal and opposite cold particle beams is non-zero even\nthough each beam has zero thermal energy. We therefore call this thermal energy\npseudo-thermal. By contrast, a multi-beam moment of two or more beams has no\npseudo-thermal energy. We develop three different ways of decomposing into a\nsum and finding multi-beam moments for both a multi-beam fi(v) measured by MMS\nin the dayside magnetosphere during reconnection and a multi-beam fi(v) found\nin a PIC simulation of magnetotail reconnection. The three methods are: A\nvisual method in which the velocity centroid of each beam is estimated and its\ndensity determined self-consistently; A k-means method in which particles in a\nparticle-representation of fi(v) are sorted into a minimum energy configuration\nof N (= k) clusters; A nonlinear least squares method based on a fit to a sum\nof N kappa functions.\n  Multi-beam energy moments are calculated and compared with standard moments\nfor the thermal energy density, pressure tensor, thermal energy flux (heat plus\nenthalpy fluxes), bulk kinetic energy density, RAM pressure and bulk kinetic\nenergy flux. Applying this new formalism to real data demonstrates in detail\nhow multi-beam techniques may provide significant insight into the properties\nof observed space plasmas.\n"}
+{"abstract": "  Various algorithms in reinforcement learning exhibit dramatic variability in\ntheir convergence rates and ultimate accuracy as a function of the problem\nstructure. Such instance-specific behavior is not captured by existing global\nminimax bounds, which are worst-case in nature. We analyze the problem of\nestimating optimal $Q$-value functions for a discounted Markov decision process\nwith discrete states and actions and identify an instance-dependent functional\nthat controls the difficulty of estimation in the $\\ell_\\infty$-norm. Using a\nlocal minimax framework, we show that this functional arises in lower bounds on\nthe accuracy on any estimation procedure. In the other direction, we establish\nthe sharpness of our lower bounds, up to factors logarithmic in the state and\naction spaces, by analyzing a variance-reduced version of $Q$-learning. Our\ntheory provides a precise way of distinguishing \"easy\" problems from \"hard\"\nones in the context of $Q$-learning, as illustrated by an ensemble with a\ncontinuum of difficulty.\n"}
+{"abstract": "  We study the dependent type theory CaTT, introduced by Finster and Mimram,\nwhich presents the theory of weak $\\omega$-categories, following the idea that\ntype theories can be considered as presentations of generalized algebraic\ntheories. Our main contribution is a formal proof that the models of this type\ntheory correspond precisely to weak $\\omega$-categories, as defined by\nMaltsiniotis, by generalizing a definition proposed by Grothendieck for weak\n$\\omega$-groupoids: Those are defined as suitable presheaves over a\ncat-coherator, which is a category encoding structure expected to be found in\nan $\\omega$-category. This comparison is established by proving the initiality\nconjecture for the type theory CaTT, in a way which suggests the possible\ngeneralization to a nerve theorem for a certain class of dependent type\ntheories\n"}
+{"abstract": "  Differentiable particle filters provide a flexible mechanism to adaptively\ntrain dynamic and measurement models by learning from observed data. However,\nmost existing differentiable particle filters are within the bootstrap particle\nfiltering framework and fail to incorporate the information from latest\nobservations to construct better proposals. In this paper, we utilize\nconditional normalizing flows to construct proposal distributions for\ndifferentiable particle filters, enriching the distribution families that the\nproposal distributions can represent. In addition, normalizing flows are\nincorporated in the construction of the dynamic model, resulting in a more\nexpressive dynamic model. We demonstrate the performance of the proposed\nconditional normalizing flow-based differentiable particle filters in a visual\ntracking task.\n"}
+{"abstract": "  As Facial Expression Recognition (FER) systems become integrated into our\ndaily lives, these systems need to prioritise making fair decisions instead of\naiming at higher individual accuracy scores. Ranging from surveillance systems\nto diagnosing mental and emotional health conditions of individuals, these\nsystems need to balance the accuracy vs fairness trade-off to make decisions\nthat do not unjustly discriminate against specific under-represented\ndemographic groups. Identifying bias as a critical problem in facial analysis\nsystems, different methods have been proposed that aim to mitigate bias both at\ndata and algorithmic levels. In this work, we propose the novel usage of\nContinual Learning (CL), in particular, using Domain-Incremental Learning\n(Domain-IL) settings, as a potent bias mitigation method to enhance the\nfairness of FER systems while guarding against biases arising from skewed data\ndistributions. We compare different non-CL-based and CL-based methods for their\nclassification accuracy and fairness scores on expression recognition and\nAction Unit (AU) detection tasks using two popular benchmarks, the RAF-DB and\nBP4D datasets, respectively. Our experimental results show that CL-based\nmethods, on average, outperform other popular bias mitigation techniques on\nboth accuracy and fairness metrics.\n"}
+{"abstract": "  Accurate modeling of the electronic structure of warm dense matter is a\nchallenging problem whose solution would allow a better understanding of\nmaterial properties like equation of state, opacity, and conductivity, with\nresulting applications from astrophysics to fusion energy research. Here we\nexplore the real-space Green's function method as a technique for solving the\nKohn-Sham density functional theory equations under warm dense matter\nconditions. We find the method to be tractable and accurate throughout the\ndensity and temperature range of interest, in contrast to other approaches.\nGood agreement on equation of state is found when comparing to other methods,\nwhere they are thought to be accurate.\n"}
+{"abstract": "  If $f$ is an entire function and $a$ is a complex number, $a$ is said to be\nan asymptotic value of $f$ if there exists a path $\\gamma$ from $0$ to infinity\nsuch that $f(z) - a$ tends to $0$ as $z$ tends to infinity along $\\gamma$. The\nDenjoy--Carleman--Ahlfors Theorem asserts that if $f$ has $n$ distinct\nasymptotic values, then the rate of growth of $f$ is at least order $n/2$, mean\ntype. A long-standing problem asks whether this conclusion holds for entire\nfunctions having $n$ distinct asymptotic (entire) functions, each of growth at\nmost order $1/2$, minimal type. In this paper conditions on the function $f$\nand associated asymptotic paths are obtained that are sufficient to guarantee\nthat $f$ satisfies the conclusion of the Denjoy--Carleman--Ahlfors Theorem. In\naddition, for each positive integer $n$, an example is given of an entire\nfunction of order $n$ having $n$ distinct, prescribed asymptotic functions,\neach of order less than $1/2$.\n"}
+{"abstract": "  Taking the recently reported non-zero rotation angle of the cosmic microwave\nbackground (CMB) linear polarization $\\beta=0.35\\pm0.14{\\rm\\, deg}$ as the hint\nfor a pseudo Nambu-Goldstone boson quintessence dark energy (DE), we study the\nelectroweak (EW) axion quintessence DE model where the axion mass is generated\nby the EW instantons. We find that the observed value of $\\beta$ implies a\nnon-trivial $U(1)$ electromagnetic anomaly coefficient ($c_{\\gamma}$), once the\ncurrent constraint on the DE equation of state is also taken into account. With\nthe aid of the hypothetical high energy structure of the model inspired by the\nexperimentally inferred $c_{\\gamma}$, the model is shown to be able to make\nprediction for the current equation of state ($w_{\\rm DE,0}$) of the\nquintessence DE. This is expected to make our scenario distinguishable in\ncomparison with the cosmological constant ($w=-1$) and testable in future when\nthe error in the future measurement of $w_{\\rm DE,0}$ is reduced to\n$\\mathcal{O}(1)\\%$ level ($\\delta w=\\mathcal{O}(10^{-2})$).\n"}
+{"abstract": "  Graph Convolution Networks (GCNs) manifest great potential in recommendation.\nThis is attributed to their capability on learning good user and item\nembeddings by exploiting the collaborative signals from the high-order\nneighbors. Like other GCN models, the GCN based recommendation models also\nsuffer from the notorious over-smoothing problem - when stacking more layers,\nnode embeddings become more similar and eventually indistinguishable, resulted\nin performance degradation. The recently proposed LightGCN and LR-GCN alleviate\nthis problem to some extent, however, we argue that they overlook an important\nfactor for the over-smoothing problem in recommendation, that is, high-order\nneighboring users with no common interests of a user can be also involved in\nthe user's embedding learning in the graph convolution operation. As a result,\nthe multi-layer graph convolution will make users with dissimilar interests\nhave similar embeddings. In this paper, we propose a novel Interest-aware\nMessage-Passing GCN (IMP-GCN) recommendation model, which performs high-order\ngraph convolution inside subgraphs. The subgraph consists of users with similar\ninterests and their interacted items. To form the subgraphs, we design an\nunsupervised subgraph generation module, which can effectively identify users\nwith common interests by exploiting both user feature and graph structure. To\nthis end, our model can avoid propagating negative information from high-order\nneighbors into embedding learning. Experimental results on three large-scale\nbenchmark datasets show that our model can gain performance improvement by\nstacking more layers and outperform the state-of-the-art GCN-based\nrecommendation models significantly.\n"}
+{"abstract": "  Because manufacturing processes evolve fast, and since production visual\naspect can vary significantly on a daily basis, the ability to rapidly update\nmachine vision based inspection systems is paramount. Unfortunately, supervised\nlearning of convolutional neural networks requires a significant amount of\nannotated images for being able to learn effectively from new data.\nAcknowledging the abundance of continuously generated images coming from the\nproduction line and the cost of their annotation, we demonstrate it is possible\nto prioritize and accelerate the annotation process. In this work, we develop a\nmethodology for learning actively, from rapidly mined, weakly (i.e. partially)\nannotated data, enabling a fast, direct feedback from the operators on the\nproduction line and tackling a big machine vision weakness: false positives. We\nalso consider the problem of covariate shift, which arises inevitably due to\nchanging conditions during data acquisition. In that regard, we show\ndomain-adversarial training to be an efficient way to address this issue.\n"}
+{"abstract": "  A family of original formulae for computing number PI and its proof are\npresented. An algorithm is proposed to validate the results of this new\nalgorithm.\n"}
+{"abstract": "  Time-lapse fluorescent microscopy (TLFM) combined with predictive\nmathematical modelling is a powerful tool to study the inherently dynamic\nprocesses of life on the single-cell level. Such experiments are costly,\ncomplex and labour intensive. A complimentary approach and a step towards in\nsilico experimentation, is to synthesise the imagery itself. Here, we propose\nMulti-StyleGAN as a descriptive approach to simulate time-lapse fluorescence\nmicroscopy imagery of living cells, based on a past experiment. This novel\ngenerative adversarial network synthesises a multi-domain sequence of\nconsecutive timesteps. We showcase Multi-StyleGAN on imagery of multiple live\nyeast cells in microstructured environments and train on a dataset recorded in\nour laboratory. The simulation captures underlying biophysical factors and time\ndependencies, such as cell morphology, growth, physical interactions, as well\nas the intensity of a fluorescent reporter protein. An immediate application is\nto generate additional training and validation data for feature extraction\nalgorithms or to aid and expedite development of advanced experimental\ntechniques such as online monitoring or control of cells.\n  Code and dataset is available at\nhttps://git.rwth-aachen.de/bcs/projects/tp/multi-stylegan.\n"}
+{"abstract": "  Microservices have become a popular architectural style for data-driven\napplications, given their ability to functionally decompose an application into\nsmall and autonomous services to achieve scalability, strong isolation, and\nspecialization of database systems to the workloads and data formats of each\nservice. Despite the accelerating industrial adoption of this architectural\nstyle, an investigation of the state of the practice and challenges\npractitioners face regarding data management in microservices is lacking. To\nbridge this gap, we conducted a systematic literature review of representative\narticles reporting the adoption of microservices, we analyzed a set of popular\nopen-source microservice applications, and we conducted an online survey to\ncross-validate the findings of the previous steps with the perceptions and\nexperiences of over 120 experienced practitioners and researchers.\n  Through this process, we were able to categorize the state of practice of\ndata management in microservices and observe several foundational challenges\nthat cannot be solved by software engineering practices alone, but rather\nrequire system-level support to alleviate the burden imposed on practitioners.\nWe discuss the shortcomings of state-of-the-art database systems regarding\nmicroservices and we conclude by devising a set of features for\nmicroservice-oriented database systems.\n"}
+{"abstract": "  We review recent studies of the operator product expansion of the plaquette\nand of the associated determination of the gluon condensate. One first needs\nthe perturbative expansion to orders high enough to reach the asymptotic regime\nwhere the renormalon behavior sets in. The divergent perturbative series is\nformally regulated using the principal value prescription for its Borel\nintegral. Subtracting the perturbative series truncated at the minimal term, we\nobtain the leading non-perturbative correction of the operator product\nexpansion, i.e. the gluon condensate, with superasymptotic accuracy. It is then\nexplored how to increase such precision within the context of the\nhyperasymptotic expansion. The results fully confirm expectations from\nrenormalons and the operator product expansion.\n"}
+{"abstract": "  We propose and demonstrate that thermal laser evaporation can be applied to\nall solid, non-radioactive elements in the periodic table. By depositing thin\nfilms, we achieve growth rates exceeding 1 angstrom/s with output laser powers\nless than 500 W, using identical beam parameters for many different elements.\nThe source temperature is found to vary linearly with laser power within the\nexamined power range. High growth rates are possible using free-standing\nsources for most of the elements tested, eliminating the need for crucibles.\n"}
+{"abstract": "  Magnetic skyrmions are topologically stable nanoscale particle-like objects\nthat were discovered in 2009. Since that time, intense research interest has\nled to the identification of numerous compounds that support skyrmions over a\nrange of conditions spanning cryogenic to room temperatures. Skyrmions can be\nset into motion under various types of driving, and the combination of their\nsize, stability, and dynamics makes them ideal candidates for numerous\napplications. Skyrmions represent a new class of system in which the energy\nscales of the skyrmion-skyrmion interactions, sample disorder, temperature, and\ndrive can compete. A growing body of work indicates that the static and dynamic\nstates of skyrmions can be influenced strongly by pinning or disorder in the\nsample; thus, an understanding of such effects is essential for the eventual\nuse of skyrmions in applications. In this article we review the current state\nof knowledge regarding individual skyrmions and skyrmion assemblies interacting\nwith quenched disorder or pinning. We outline the microscopic mechanisms for\nskyrmion pinning, including the repulsive and attractive interactions that can\narise from impurities, grain boundaries, or nanostructures. This is followed by\ndescriptions of depinning phenomena, sliding states over disorder, the effect\nof pinning on the skyrmion Hall angle, the competition between thermal and\npinning effects, the control of skyrmion motion using ordered potential\nlandscapes such as one- or two-dimensional periodic asymmetric substrates, the\ncreation of skyrmion diodes, and skyrmion ratchet effects. We highlight the\ndistinctions arising from internal modes and the strong gyroscopic or Magnus\nforces that cause the dynamical states of skyrmions to differ from those of\nother systems with pinning. We also discuss future directions and open\nquestions related to the pinning and dynamics in skyrmion systems.\n"}
+{"abstract": "  The Municipality of Rondon\\'opolis possesses several touristic attractions\nsuch as a great diversity of waterfalls and little beaches located in the\nsurroundings of the urban area, which attract tourists from various locations.\nAiming to understand how ecotourism can contribute to the conservation of water\nresources in the leisure areas, as well as their potential development of\ntouristic activities in those places. The procedures included the use of\nvarious techniques subsidized in remote sensing and geoprocessing tools that\nallowed the analysis and spatial distribution of tourism activities of the main\nleisure areas. The spatial distribution of the waterfalls and its surroundings,\nwe observe the biophysical characters such as: the endemic vegetation, the\ncachoeiras, the waterfalls, the rocky outcrops, rivers, little beaches and\nespraiados. The results showed a correct perception of respondents on existing\ninter-relationships between ecotourism practices and the sustainable use of\nwater resources. In conclusion though, a long way must be performed in order to\nprevent the economic benefits of ecotourism generate an inappropriate\nexploitation of natural resources, causing environmental problems, particularly\nto water resources in the surroundings.\n"}
+{"abstract": "  We present a search for continuous gravitational waves emitted by neutron\nstars in binary systems conducted on data from the early third observing run of\nthe Advanced LIGO and Advanced Virgo detectors using the semicoherent,\nGPU-accelerated, BinarySkyHough pipeline. The search analyzes the most\nsensitive frequency band of the LIGO detectors, 50 - 300 Hz. Binary orbital\nparameters are split into four regions, comprising orbital periods of 3 - 45\ndays and projected semimajor axes of 2 - 40 light-seconds. No detections are\nreported. We estimate the sensitivity of the search using simulated continuous\nwave signals, achieving the most sensitive results to date across the analyzed\nparameter space.\n"}
+{"abstract": "  Twisted spin waves attracted research attentions lately and the orbital\nangular momentum they carry may be utilized in communication and computing\ntechnologies. In this work, we manifest by micromagnetic simulations that\ntwisted spin wave modes naturally exist in thick ferromagnetic disks. The\ntwisted spin waves can be readily stimulated using rotating magnetic field when\nit is tuned to the eigenfrequency of corresponding modes. We analytically\nderive dispersion relation of the twisted spin waves and the results agree well\nwith the numerical studies. Lastly, we demonstrate that the topological charge\nof twisted spin waves can be controlled by the exciting field.\n"}
+{"abstract": "  Periodic stacking of topologically trivial and non-trivial layers with\nopposite symmetry of the valence and conduction bands induces topological\ninterface states that, in the strong coupling limit, hybridize both across the\ntopological and normal insulator layers. Using band structure engineering, such\nsuperlattices can be effectively realized using the IV-VI lead tin\nchalcogenides. This leads to emergent minibands with a tunable topology as\ndemonstrated both by theory and experiments. The topological minibands are\nproven by magneto-optical spectroscopy, revealing Landau level transitions both\nat the center and edges of the artificial superlattice mini Brillouin zone.\nTheir topological character is identified by the topological phase transitions\nwithin the minibands observed as a function of temperature. The critical\ntemperature of this transition as well as the miniband gap and miniband width\ncan be precisely controlled by the layer thicknesses and compositions. This\nwitnesses the generation of a new fully tunable quasi-3D topological state that\nprovides a template for realization of magnetic Weyl semimetals and other\nstrongly interacting topological phases.\n"}
+{"abstract": "  Modern key-value stores, object stores, Internet proxy caches, as well as\nContent Delivery Networks (CDN) often manage objects of diverse sizes, e.g.,\nblobs, video files of different lengths, images with varying resolution, and\nsmall documents. In such workloads, size-aware cache policies outperform\nsize-oblivious algorithms. Unfortunately, existing size-aware algorithms tend\nto be overly complicated and computationally~expensive.\n  Our work follows a more approachable pattern; we extend the prevalent\n(size-oblivious) TinyLFU cache admission policy to handle variable sized items.\nImplementing our approach inside two popular caching libraries only requires\nminor changes. We show that our algorithms yield competitive or better\nhit-ratios and byte hit-ratios compared to the state of the art size-aware\nalgorithms such as AdaptSize, LHD, LRB, and GDSF. Further, a runtime comparison\nindicates that our implementation is faster by up to x3 compared to the best\nalternative, i.e., it imposes much lower CPU overhead.\n"}
+{"abstract": "  In this paper, we consider the image captioning task from a new\nsequence-to-sequence prediction perspective and propose CaPtion TransformeR\n(CPTR) which takes the sequentialized raw images as the input to Transformer.\nCompared to the \"CNN+Transformer\" design paradigm, our model can model global\ncontext at every encoder layer from the beginning and is totally\nconvolution-free. Extensive experiments demonstrate the effectiveness of the\nproposed model and we surpass the conventional \"CNN+Transformer\" methods on the\nMSCOCO dataset. Besides, we provide detailed visualizations of the\nself-attention between patches in the encoder and the \"words-to-patches\"\nattention in the decoder thanks to the full Transformer architecture.\n"}
+{"abstract": "  Wikipedia abstract generation aims to distill a Wikipedia abstract from web\nsources and has met significant success by adopting multi-document\nsummarization techniques. However, previous works generally view the abstract\nas plain text, ignoring the fact that it is a description of a certain entity\nand can be decomposed into different topics. In this paper, we propose a\ntwo-stage model TWAG that guides the abstract generation with topical\ninformation. First, we detect the topic of each input paragraph with a\nclassifier trained on existing Wikipedia articles to divide input documents\ninto different topics. Then, we predict the topic distribution of each abstract\nsentence, and decode the sentence from topic-aware representations with a\nPointer-Generator network. We evaluate our model on the WikiCatSum dataset, and\nthe results show that \\modelnames outperforms various existing baselines and is\ncapable of generating comprehensive abstracts. Our code and dataset can be\naccessed at \\url{https://github.com/THU-KEG/TWAG}\n"}
+{"abstract": "  When probabilistic classifiers are trained and calibrated, the so-called\ngrouping loss component of the calibration loss can easily be overlooked.\nGrouping loss refers to the gap between observable information and information\nactually exploited in the calibration exercise. We investigate the relation\nbetween grouping loss and the concept of sufficiency, identifying\ncomonotonicity as a useful criterion for sufficiency. We revisit the probing\nreduction approach of Langford & Zadrozny (2005) and find that it produces an\nestimator of probabilistic classifiers that reduces grouping loss. Finally, we\ndiscuss Brier curves as tools to support training and 'sufficient' calibration\nof probabilistic classifiers.\n"}
+{"abstract": "  Data hiding is one widely used approach for protecting authentication and\nownership. Most multimedia content like images and videos are transmitted or\nsaved in the compressed form. This kind of lossy compression, such as JPEG, can\ndestroy the hidden data, which raises the need of robust data hiding. It is\nstill an open challenge to achieve the goal of data hiding that can be against\nthese compressions. Recently, deep learning has shown large success in data\nhiding, while non-differentiability of JPEG makes it challenging to train a\ndeep pipeline for improving robustness against lossy compression. The existing\nSOTA approaches replace the non-differentiable parts with differentiable\nmodules that perform similar operations. Multiple limitations exist: (a) large\nengineering effort; (b) requiring a white-box knowledge of compression attacks;\n(c) only works for simple compression like JPEG. In this work, we propose a\nsimple yet effective approach to address all the above limitations at once.\nBeyond JPEG, our approach has been shown to improve robustness against various\nimage and video lossy compression algorithms.\n"}
+{"abstract": "  Short product lifecycles and a high variety of products force industrial\nmanufacturing processes to change frequently. Due to the manual approach of\nmany quality analysis techniques, they can significantly slow down adaption\nprocesses of production systems or make production unprofitable. Therefore,\nautomating them can be a key technology for keeping pace with market demand of\nthe future. The methodology presented here aims at a meta-model supporting\nautomation for PFMEA. The method differentiates product requirements,\nproduction steps and quality measures in such a way, that complex quality\nrequirements can be addressed in any instance of a factory using a common\nmeta-modeling language.\n"}
+{"abstract": "  Sarcasm is a sophisticated way of wrapping any immanent truth, mes-sage, or\neven mockery within a hilarious manner. The advent of communications using\nsocial networks has mass-produced new avenues of socialization. It can be\nfurther said that humor, irony, sarcasm, and wit are the four chariots of being\nsocially funny in the modern days. In this paper, we manually extract the\nsarcastic word distribution features of a benchmark pop culture sarcasm corpus,\ncontaining sarcastic dialogues and monologues. We generate input sequences\nformed of the weighted vectors from such words. We further propose an\namalgamation of four parallel deep long-short term networks (pLSTM), each with\ndistinctive activation classifier. These modules are primarily aimed at\nsuccessfully detecting sarcasm from the text corpus. Our proposed model for\ndetecting sarcasm peaks a training accuracy of 98.95% when trained with the\ndiscussed dataset. Consecutively, it obtains the highest of 98.31% overall\nvalidation accuracy on two handpicked Project Gutenberg English humor\nliterature among all the test cases. Our approach transcends previous\nstate-of-the-art works on several sarcasm corpora and results in a new gold\nstandard performance for sarcasm detection.\n"}
+{"abstract": "  Quantum corrections to holographic entanglement entropy require knowledge of\nthe bulk quantum state. In this paper, we derive a novel dual prescription for\nthe generalized entropy that allows us to interpret the leading quantum\ncorrections in a geometric way with minimal input from the bulk state. The\nequivalence is proven using tools borrowed from convex optimization. The new\nprescription does not involve bulk surfaces but instead uses a generalized\nnotion of a flow, which allows for possible sources or sinks in the bulk\ngeometry. In its discrete version, our prescription can alternatively be\ninterpreted in terms of a set of Planck-thickness bit threads, which can be\neither classical or quantum. This interpretation uncovers an aspect of the\ngeneralized entropy that admits a neat information-theoretic description,\nnamely, the fact that the quantum corrections can be cast in terms of\nentanglement distillation of the bulk state. We also prove some general\nproperties of our prescription, including nesting and a quantum version of the\nmax multiflow theorem. These properties are used to verify that our proposal\nrespects known inequalities that a von Neumann entropy must satisfy, including\nsubadditivity and strong subadditivity, as well as to investigate the fate of\nthe holographic monogamy. Finally, using the Iyer-Wald formalism we show that\nfor cases with a local modular Hamiltonian there is always a canonical solution\nto the program that exploits the property of bulk locality. Combining with\nprevious results by Swingle and Van Raamsdonk, we show that the consistency of\nthis special solution requires the semi-classical Einstein's equations to hold\nfor any consistent perturbative bulk quantum state.\n"}
+{"abstract": "  Assurance of asymptotic trajectory tracking in robotic manipulators with a\ncontinuous control law in the presence of unmodeled dynamics or external\ndisturbance is a challenging problem. Recently, it is asserted that this aim is\nachieved by designing a traditional model-free controller together with time\ndelay estimation (TDE) such that neither dynamical parameters nor conservative\nassumptions on the external disturbance are required. In this note, the purpose\nis to show that this claim is wrong. Additionally, modification of this method\nfor some special cases is presented.\n"}
+{"abstract": "  Summarization systems face the core challenge of identifying and selecting\nimportant information. In this paper, we tackle the problem of content\nselection in unsupervised extractive summarization of long, structured\ndocuments. We introduce a wide range of heuristics that leverage cognitive\nrepresentations of content units and how these are retained or forgotten in\nhuman memory. We find that properties of these representations of human memory\ncan be exploited to capture relevance of content units in scientific articles.\nExperiments show that our proposed heuristics are effective at leveraging\ncognitive structures and the organization of the document (i.e.\\ sections of an\narticle), and automatic and human evaluations provide strong evidence that\nthese heuristics extract more summary-worthy content units.\n"}
+{"abstract": "  Chernoff approximations to strongly continuous one-parameter semigroups give\nsolutions to a wide class of differential equations. This paper studies the\nrate of convergence of the Chernoff approximations. We provide simple natural\nexamples for which the convergence is arbitrary fast, is arbitrary slow, and\nholds in the strong operator topology but does not hold in the norm operator\ntopology. We also prove a general theorem that gives an upper estimate for the\nspeed of decay of the norm of the residual term of the Chernoff approximations.\nThe result is applied to one-dimensional parabolic differential equations with\nvariable coefficients. The obtained estimates can be used for the numerical\nsolution of PDEs.\n"}
+{"abstract": "  Let $G$ be a transitive permutation group on a finite set $\\Omega$ and recall\nthat a base for $G$ is a subset of $\\Omega$ with trivial pointwise stabiliser.\nThe base size of $G$, denoted $b(G)$, is the minimal size of a base. If\n$b(G)=2$ then we can study the Saxl graph $\\Sigma(G)$ of $G$, which has vertex\nset $\\Omega$ and two vertices are adjacent if they form a base. This is a\nvertex-transitive graph, which is conjectured to be connected with diameter at\nmost $2$ when $G$ is primitive. In this paper, we combine probabilistic and\ncomputational methods to prove a strong form of this conjecture for all almost\nsimple primitive groups with soluble point stabilisers. In this setting, we\nalso establish best possible lower bounds on the clique and independence\nnumbers of $\\Sigma(G)$ and we determine the groups with a unique regular\nsuborbit, which can be interpreted in terms of the valency of $\\Sigma(G)$.\n"}
+{"abstract": "  This paper investigates bilateral control of teleoperators with closed\narchitecture and subjected to arbitrary bounded time-varying delay. A prominent\nchallenge for bilateral control of such teleoperators lies in the closed\narchitecture, especially in the context not involving interaction force/torque\nmeasurement. This yields the long-standing situation that most bilateral\ncontrol rigorously developed in the literature is hard to be justified as\napplied to teleoperators with closed architecture. With a new class of dynamic\nfeedback, we propose kinematic and adaptive dynamic controllers for\nteleoperators with closed architecture, and we show that the proposed kinematic\nand dynamic controllers are robust with respect to arbitrary bounded\ntime-varying delay. In addition, by exploiting the input-output properties of\nan inverted form of the dynamics of robot manipulators with closed\narchitecture, we remove the assumption of uniform exponential stability of a\nlinear time-varying system due to the adaptation to the gains of the inner\ncontroller in demonstrating stability of the presented adaptive dynamic\ncontrol. The application of the proposed approach is illustrated by the\nexperimental results using a Phantom Omni and a UR10 robot.\n"}
+{"abstract": "  In this paper, we explore open-domain sketch-to-photo translation, which aims\nto synthesize a realistic photo from a freehand sketch with its class label,\neven if the sketches of that class are missing in the training data. It is\nchallenging due to the lack of training supervision and the large geometric\ndistortion between the freehand sketch and photo domains. To synthesize the\nabsent freehand sketches from photos, we propose a framework that jointly\nlearns sketch-to-photo and photo-to-sketch generation. However, the generator\ntrained from fake sketches might lead to unsatisfying results when dealing with\nsketches of missing classes, due to the domain gap between synthesized sketches\nand real ones. To alleviate this issue, we further propose a simple yet\neffective open-domain sampling and optimization strategy to \"fool\" the\ngenerator into treating fake sketches as real ones. Our method takes advantage\nof the learned sketch-to-photo and photo-to-sketch mapping of in-domain data\nand generalizes it to the open-domain classes. We validate our method on the\nScribble and SketchyCOCO datasets. Compared with the recent competing methods,\nour approach shows impressive results in synthesizing realistic color, texture,\nand maintaining the geometric composition for various categories of open-domain\nsketches. Our code is available at https://github.com/Mukosame/AODA\n"}
+{"abstract": "  Aluminum nitride (AlN) is an emerging material for integrated quantum\nphotonics with its excellent linear and nonlinear optical properties. In\nparticular, its second-order nonlinear susceptibility $\\chi^{(2)}$ allows\nsingle-photon generation. We have grown AlN thin films on silicon nitride via\nreactive DC magnetron sputtering. The thin films have been characterized using\nX-ray diffraction, optical reflectometry, atomic force microscopy, and scanning\nelectron microscopy. The crystalline properties of the thin films have been\nimproved by optimizing the nitrogen to argon ratio and the magnetron DC power\nof the deposition process. X-ray diffraction measurements confirm the\nfabrication of high-quality c-axis oriented thin films with a full width at\nhalf maximum of the rocking curves of 3.9 deg. for 300-nm-thick films. Atomic\nforce microscopy measurements reveal a root mean square surface roughness below\n1 nm. The AlN deposition on SiN allows us to fabricate hybrid photonic circuits\nwith a new approach that avoids the challenging patterning of AlN.\n"}
+{"abstract": "  An intuitive and straightforward modelling of many locally-interacting\nquantum system requires a local description of the quantised electromagnetic\nfield. In a recent paper [Southall et al., J. Mod. Opt. 68, 647 (2021)], we\nshowed that such a description, which must overcome several no-go theorems, is\nindeed possible. To better justify our approach, this paper presents a\nsystematic quantisation of one-dimensional electromagnetic fields in position\nspace. Starting from the assumption that its basic building blocks are local\nbosonic particles with a clear direction of propagation--so-called bosons\nlocalised in position (BLiPs)--we identify the relevant Schroedinger equation\nand construct Lorentz-covariant electric and magnetic field observables. In\naddition we show that our approach simplifies to the standard description of\nquantum electrodynamics when restricted to a subspace of monochromatic photon\nFock states.\n"}
+{"abstract": "  Modern wide-field high-cadence surveys have revealed the significant\ndiversity of optical transient phenomena in their luminosity and timescale\ndistributions, which led to the discovery of some mysterious fast optical\ntransients (FOTs). These FOTs can usually rise and decline remarkably in a\ntimescale of a few days to weeks, which are obviously much rapider than\nordinary supernovae. SN 2019bkc/ATLAS19dqr is one of the fastest detected FOTs\nso far and, meanwhile, it was found to be un-associated with a host galaxy.\nThese discoveries provide a good chance to explore the possible origins of\nFOTs. So, we model the light curves of SN 2019bkc in details. It is found that\nSN 2019bkc can be well explained by the thermal emission of an explosion ejecta\nthat is powered by a long-lasting central engine. The engine could be a\nspinning-down millisecond magnetar or a fallback accretion onto a compact\nobject. Combining the engine property, the mass of the ejecta, and the\nhostlessness of SN 2019bkc, we suggest that this FOT is likely to originate\nfrom a merger of a white dwarf and a neutron star.\n"}
+{"abstract": "  Decentralised government levels are often entrusted with the management of\npublic works and required to ensure well-timed infrastructure delivery to their\ncommunities. We investigate whether monitoring the activity of local procuring\nauthorities during the execution phase of the works they manage may expedite\nthe infrastructure delivery process. Focussing on an Italian regional law which\nimposes monitoring by the regional government on \"strategic\" works carried out\nby local buyers, we draw causal claims using a regression-discontinuity\napproach, made unusual by the presence of multiple assignment variables.\nEstimation is performed through discrete-time survival analysis techniques.\nResults show that monitoring does expedite infrastructure delivery.\n"}
+{"abstract": "  Our motivation stems from current medical research aiming at personalized\ntreatment using a molecular-based approach. The goal is to develop a more\nprecise and targeted decision making process, relative to traditional\ntreatments based primarily on clinical diagnoses. A challenge we address is\nevaluating treatment effects for individuals affected by Glioblastoma (GBM), a\nbrain cancer where targeted therapy is essential to improve patients'\nprospects. Specifically, we consider the pathway associated to cytokine\nTGF-beta, whose abnormal signalling activity has been found to be linked to the\nprogression of GBM and other tumors. We analyze treatment effects within a\ncausal framework represented by a Directed Acyclic Graph (DAG) model, whose\nvertices are the variables belonging to the TGF-beta pathway. A major obstacle\nin implementing the above program is represented by individual heterogeneity,\nimplying that patients will respond differently to the same therapy. We address\nthis issue through an infinite mixture of Gaussian DAG-models where both the\ngraphical structure as well as the allied model parameters are regarded as\nuncertain. Our procedure determines a clustering structure of the units\nreflecting the underlying heterogeneity, and produces subject-specific causal\neffects through Bayesian model averaging across a variety of model features.\nWhen applied to the GBM dataset, it reveals that regulation of TGF-beta\nproteins produces heterogeneous effects, represented by clusters of patients\npotentially benefiting from selective interventions.\n"}
+{"abstract": "  Galaxy lenses are frequently modeled as an elliptical mass distribution with\nexternal shear and isothermal spheres to account for secondary and\nline-of-sight galaxies. There is statistical evidence that some fraction of\nobserved quads are inconsistent with these assumptions, and require a\ndipole-like contribution to the mass with respect to the light. Simplifying\nassumptions about the shape of mass distributions can lead to the incorrect\nrecovery of parameters such as $H_0$. We create several tests of synthetic quad\npopulations with different deviations from an elliptical shape, then fit them\nwith an ellipse+shear model, and measure the recovered values of $H_0$.\nKinematic constraints are not included. We perform two types of fittings -- one\nwith a single point source and one with an array of sources emulating an\nextended source. We carry out two model-free comparisons between our mock quads\nand the observed population. One result of these comparisons is a statistical\ninconsistency not yet mentioned in the literature: the image distance ratios\nwith respect to the lens center of observed quads appear to span a much wider\nrange than those of synthetic or simulated quads. Bearing this discrepancy in\nmind, our mock populations can result in biases on $H_0$ $\\sim10\\%$.\n"}
+{"abstract": "  Transformers and variational autoencoders (VAE) have been extensively\nemployed for symbolic (e.g., MIDI) domain music generation. While the former\nboast an impressive capability in modeling long sequences, the latter allow\nusers to willingly exert control over different parts (e.g., bars) of the music\nto be generated. In this paper, we are interested in bringing the two together\nto construct a single model that exhibits both strengths. The task is split\ninto two steps. First, we equip Transformer decoders with the ability to accept\nsegment-level, time-varying conditions during sequence generation.\nSubsequently, we combine the developed and tested in-attention decoder with a\nTransformer encoder, and train the resulting MuseMorphose model with the VAE\nobjective to achieve style transfer of long musical pieces, in which users can\nspecify musical attributes including rhythmic intensity and polyphony (i.e.,\nharmonic fullness) they desire, down to the bar level. Experiments show that\nMuseMorphose outperforms recurrent neural network (RNN) based baselines on\nnumerous widely-used metrics for style transfer tasks.\n"}
+{"abstract": "  Consider a two-person zero-sum search game between a hider and a searcher.\nThe hider hides among $n$ discrete locations, and the searcher successively\nvisits individual locations until finding the hider. Known to both players, a\nsearch at location $i$ takes $t_i$ time units and detects the hider -- if\nhidden there -- independently with probability $q_i$, for $i=1,\\ldots,n$. The\nhider aims to maximize the expected time until detection, while the searcher\naims to minimize it. We prove the existence of an optimal strategy for each\nplayer. In particular, the hider's optimal mixed strategy hides in each\nlocation with a nonzero probability, and the searcher's optimal mixed strategy\ncan be constructed with up to $n$ simple search sequences. We develop an\nalgorithm to compute an optimal strategy for each player, and compare the\noptimal hiding strategy with the simple hiding strategy which gives the\nsearcher no location preference at the beginning of the search.\n"}
+{"abstract": "  We consider the generalized Langevin equation (GLE) in a harmonic potential\nwith power law decay memory. We study the anomalous diffusion of the particle's\ndisplacement and velocity. By comparison with the free particle situation in\nwhich the velocity was previously shown to be either diffusive or subdiffusive,\nwe find that, when trapped in a harmonic potential, the particle's displacement\nmay either be diffusive or superdiffusive. Under slightly stronger assumptions\non the memory kernel, namely, for kernels related to the broad class of\ncompletely monotonic functions, we show that both the free particle and the\nharmonically bounded GLE satisfy the equipartition of energy condition. This\ngeneralizes previously known results for the GLE under particular kernel\ninstances such as the generalized Rouse kernel or (exactly) a power law\nfunction.\n"}
+{"abstract": "  Recent works [PhysRevLett.125.170402 (2020) and PhysRevLett.125.043603\n(2020)] verify that the perfect single-photon character is a global property of\nresonance fluorescence including all the spectral components, and then the\nabsence of some components can spoil the single-photon character, which\nconsiderably limits the application of filtering scheme to obtain single-photon\nsource (SPS) with subnatural linewidth. Here, we note that the linewidth of\nfluorescent photon is not always limited by the intrinsic linewidth of the\nemitter, and provide a scheme where the linewidth of the fluorescent single\nphoton can be much smaller than the natural linewidth of emitter by\nmanipulating external coherent fields. By simulating the response of the filter\nor detection setup with a bandwidth approaching and even much smaller than the\nnatural linewidth of emitter, the realization of ultranarrow SPS can be\nconfirmed. Besides, we demonstrate that this scheme of ultranarrow SPS can be\nimplemented based on various real physical platforms.\n"}
+{"abstract": "  In this paper, we present a Modern Standard Arabic (MSA) Sentence difficulty\nclassifier, which predicts the difficulty of sentences for language learners\nusing either the CEFR proficiency levels or the binary classification as simple\nor complex. We compare the use of sentence embeddings of different kinds\n(fastText, mBERT , XLM-R and Arabic-BERT), as well as traditional language\nfeatures such as POS tags, dependency trees, readability scores and frequency\nlists for language learners. Our best results have been achieved using\nfined-tuned Arabic-BERT. The accuracy of our 3-way CEFR classification is F-1\nof 0.80 and 0.75 for Arabic-Bert and XLM-R classification respectively and 0.71\nSpearman correlation for regression. Our binary difficulty classifier reaches\nF-1 0.94 and F-1 0.98 for sentence-pair semantic similarity classifier.\n"}
+{"abstract": "  For $n\\geq 4$ (even), the function\n$\\varphi_{n\\mathcal{L}}(z)=1+nz/(n+1)+z^n/(n+1)$ maps the unit disk\n$\\mathbb{D}$ onto a domain bounded by an epicycloid with $n-1$ cusps. In this\npaper, the class $\\mathcal{S}^*_{n\\mathcal{L}} =\n\\mathcal{S}^*(\\varphi_{n\\mathcal{L}})$ is studied and various inclusion\nrelations are established with other subclasses of starlike functions. The\nbounds on initial coefficients is also computed. Various radii problems are\nalso solved for the class $\\mathcal{S}^*_{n\\mathcal{L}}$.\n"}
+{"abstract": "  Motivated by the study of physical models associated with General Relativity,\nwe review some finite-dimensional, geometric and covariant formulations that\nallow us to characterize in a simple manner the symmetries for classical field\ntheory by implementing an appropriate fibre-bundle structure, either at the\nLagrangian, the multisymplectic or the polysymplectic levels. In particular, we\nare able to formulate Noether's theorems by means of the covariant momentum\nmaps and to systematically introduce a covariant Poisson-Hamiltonian framework.\nAlso, by focusing on the space plus time decomposition for a generic classical\nfield theory and its relation to these geometric formulations, we are able to\nsuccessfully recover the gauge content and the true local degrees of freedom\nfor the theory. In order to illustrate the relevance of these geometric\nframeworks, we center our attention to the analysis of a model for\n$3$-dimensional theory of General Relativity that involves an arbitrary\nImmirzi-like parameter. At the Lagrangian level, we reproduce the field\nequations of the system which for this model turn out to be equivalent to the\nvanishing torsion condition and the Einstein equations. We also concentrate on\nthe analysis of the gauge symmetries of the system in order to obtain the\nLagrangian covariant momentum map associated with the theory and, consequently,\nits corresponding Noether currents. Next, we aim our attention to describing\nhow the gauge symmetries of the model yield covariant canonical transformations\non the covariant multimomenta phase-space, thus giving rise to the existence of\na covariant momentum map. Besides, we analyze the physical system under\nconsideration within the De Donder-Weyl canonical theory implemented at the\npolysymplectic level, thus establishing a relation from the covariant momentum\nmap to the conserved currents of the theory.\n"}
+{"abstract": "  Given the hard problem of consciousness (Chalmers, 1995) there are no brain\nelectrophysiological correlates of the subjective experience (the felt quality\nof redness or the redness of red, the experience of dark and light, the quality\nof depth in a visual field, the sound of a clarinet, the smell of mothball,\nbodily sensations from pains to orgasms, mental images that are conjured up\ninternally, the felt quality of emotion, the experience of a stream of\nconscious thought or the phenomenology of thought). However, there are brain\noccipital and left temporal electrophysiological correlates of the subjective\nexperience (Pereira, 2015). Notwithstanding, as evoked signal, the change in\nevent-related brain potentials phase (frequency is the change in phase over\ntime) is instantaneous, that is, the frequency will transiently be infinite: a\ntransient peak in frequency (positive or negative), if any, is instantaneous in\nelectroencephalogram averaging or filtering that the event-related brain\npotentials required and the underlying structure of the event-related brain\npotentials in the frequency domain cannot be accounted, for example, by the\nWavelet Transform (WT) or the Fast Fourier Transform (FFT) analysis, because\nthey require that frequency is derived by convolution rather than by\ndifferentiation. However, as I show in the current original research report,\none suitable method for analyse the instantaneous change in event-related brain\npotentials phase and accounted for a transient peak in frequency (positive or\nnegative), if any, in the underlying structure of the event-related brain\npotentials is the Empirical Mode Decomposition with post processing (Xie et\nal., 2014) Ensemble Empirical Mode Decomposition (postEEMD) and Hilbert-Huang\nTransform (HHT).\n"}
+{"abstract": "  Several encryption proposals for DNS have been presented since 2016, but\ntheir adoption was not comprehensively studied yet. This research measured the\ncurrent adoption of DoH (DNS over HTTPS), DoT (DNS over TLS), and DoQ (DNS over\nQUIC) for five months at the beginning of 2021 by three different organizations\nwith global coverage. By comparing the total values, amount of requests per\nuser, and the seasonality of the traffic, it was possible to obtain the current\nadoption trends. Moreover, we actively scanned the Internet for still-unknown\nworking DoH servers and we compared them with a novel curated list of\nwell-known DoH servers. We conclude that despite growing in 2020, during the\nfirst five months of 2021 there was statistically significant evidence that the\naverage amount of Internet traffic for DoH, DoT and DoQ remained stationary.\nHowever, we found that the amount of, still unknown and ready to use, DoH\nservers grew 4 times. These measurements suggest that even though the amount of\nencrypted DNS is currently not growing, there may probably be more connections\nsoon to those unknown DoH servers for benign and malicious purposes.\n"}
+{"abstract": "  In this comment we discuss relative strengths and weaknesses of simplex and\nDirichlet Dempster-Shafer inference as applied to multi-resolution tests of\nindependence.\n"}
+{"abstract": "  Deep learning has become the leading approach to assisted target recognition.\nWhile these methods typically require large amounts of labeled training data,\ndomain adaptation (DA) or transfer learning (TL) enables these algorithms to\ntransfer knowledge from a labelled (source) data set to an unlabelled but\nrelated (target) data set of interest. DA enables networks to overcome the\ndistribution mismatch between the source and target that leads to poor\ngeneralization in the target domain. DA techniques align these distributions by\nminimizing a divergence measurement between source and target, making the\ntransfer of knowledge from source to target possible. While these algorithms\nhave advanced significantly in recent years, most do not explicitly leverage\nglobal data manifold structure in aligning the source and target. We propose to\nleverage global data structure by applying a topological data analysis (TDA)\ntechnique called persistent homology to TL.\n  In this paper, we examine the use of persistent homology in a domain\nadversarial (DAd) convolutional neural network (CNN) architecture. The\nexperiments show that aligning persistence alone is insufficient for transfer,\nbut must be considered along with the lifetimes of the topological\nsingularities. In addition, we found that longer lifetimes indicate robust\ndiscriminative features and more favorable structure in data. We found that\nexisting divergence minimization based approaches to DA improve the topological\nstructure, as indicated over a baseline without these regularization\ntechniques. We hope these experiments highlight how topological structure can\nbe leveraged to boost performance in TL tasks.\n"}
+{"abstract": "  Speaker diarization is the process of labeling different speakers in a speech\nsignal. Deep speaker embeddings are generally extracted from short speech\nsegments and clustered to determine the segments belong to same speaker\nidentity. The x-vector, which embeds segment-level speaker characteristics by\nstatistically pooling frame-level representations, is one of the most widely\nused deep speaker embeddings in speaker diarization. Multi-scale aggregation,\nwhich employs multi-scale representations from different layers, has recently\nsuccessfully been used in short duration speaker verification. In this paper,\nwe investigate a multi-scale aggregation approach in an x-vector embedding\nframework for speaker diarization by exploiting multiple statistics pooling\nlayers from different frame-level layers. Thus, it is expected that x-vectors\nwith multi-scale aggregation have the potential to capture meaningful speaker\ncharacteristics from short segments, effectively taking advantage of different\ninformation at multiple layers. Experimental evaluation on the CALLHOME dataset\nshowed that our approach provides substantial improvement over the baseline\nx-vectors.\n"}
+{"abstract": "  Microscopic studies on thin film superconductors play an important role for\nprobing non-equilibrium phase transitions and revealing dynamics at the\nnanoscale. However, magnetic sensors with nanometer scale spatial and\npicosecond temporal resolution are essential for exploring these. Here, we\npresent an all-optical, microwave-free method, that utilizes the negatively\ncharged nitrogen-vacancy (NV) center in diamond as a non-invasive quantum\nsensor and enables the spatial detection of the Meissner state in a\nsuperconducting thin film. We place an NV implanted diamond membrane on a\nsuperconducting LSCO thin film. The strong B-field dependence of the NV\nphotoluminescence (PL) allows us to investigate the Meissner screening in LSCO\nunder an externally applied magnetic field in a non-resonant manner.\n"}
+{"abstract": "  Unsupervised video-based person re-identification (re-ID) methods extract\nricher features from video tracklets than image-based ones. The\nstate-of-the-art methods utilize clustering to obtain pseudo-labels and train\nthe models iteratively. However, they underestimate the influence of two kinds\nof frames in the tracklet: 1) noise frames caused by detection errors or heavy\nocclusions exist in the tracklet, which may be allocated with unreliable labels\nduring clustering; 2) the tracklet also contains hard frames caused by pose\nchanges or partial occlusions, which are difficult to distinguish but\ninformative. This paper proposes a Noise and Hard frame Aware Clustering (NHAC)\nmethod. NHAC consists of a graph trimming module and a node re-sampling module.\nThe graph trimming module obtains stable graphs by removing noise frame nodes\nto improve the clustering accuracy. The node re-sampling module enhances the\ntraining of hard frame nodes to learn rich tracklet information. Experiments\nconducted on two video-based datasets demonstrate the effectiveness of the\nproposed NHAC under the unsupervised re-ID setting.\n"}
+{"abstract": "  Diverse cognitive processes set different demands on locally segregated and\nglobally integrated brain activity. However, it remains unclear how resting\nbrains configure their functional organization to balance the demands on\nnetwork segregation and integration to best serve cognition. Here, we use an\neigenmode-based approach to identify hierarchical modules in functional brain\nnetworks, and quantify the functional balance between network segregation and\nintegration. In a large sample of healthy young adults (n=991), we combine the\nwhole-brain resting state functional magnetic resonance imaging (fMRI) data\nwith a mean-filed model on the structural network derived from diffusion tensor\nimaging and demonstrate that resting brain networks are on average close to a\nbalanced state. This state allows for a balanced time dwelling at segregated\nand integrated configurations, and highly flexible switching between them.\nFurthermore, we employ structural equation modelling to estimate general and\ndomain-specific cognitive phenotypes from nine tasks, and demonstrate that\nnetwork segregation, integration and their balance in resting brains predict\nindividual differences in diverse cognitive phenotypes. More specifically,\nstronger integration is associated with better general cognitive ability,\nstronger segregation fosters crystallized intelligence and processing speed,\nand individual's tendency towards balance supports better memory. Our findings\nprovide a comprehensive and deep understanding of the brain's functioning\nprinciples in supporting diverse functional demands and cognitive abilities,\nand advance modern network neuroscience theories of human cognition.\n"}
+{"abstract": "  A reasonable amount of annotated data is required for fine-tuning pre-trained\nlanguage models (PLM) on downstream tasks. However, obtaining labeled examples\nfor different language varieties can be costly. In this paper, we investigate\nthe zero-shot performance on Dialectal Arabic (DA) when fine-tuning a PLM on\nmodern standard Arabic (MSA) data only -- identifying a significant performance\ndrop when evaluating such models on DA. To remedy such performance drop, we\npropose self-training with unlabeled DA data and apply it in the context of\nnamed entity recognition (NER), part-of-speech (POS) tagging, and sarcasm\ndetection (SRD) on several DA varieties. Our results demonstrate the\neffectiveness of self-training with unlabeled DA data: improving zero-shot\nMSA-to-DA transfer by as large as \\texttildelow 10\\% F$_1$ (NER), 2\\% accuracy\n(POS tagging), and 4.5\\% F$_1$ (SRD). We conduct an ablation experiment and\nshow that the performance boost observed directly results from the unlabeled DA\nexamples used for self-training. Our work opens up opportunities for leveraging\nthe relatively abundant labeled MSA datasets to develop DA models for zero and\nlow-resource dialects. We also report new state-of-the-art performance on all\nthree tasks and open-source our fine-tuned models for the research community.\n"}
+{"abstract": "  A typical deep neural network (DNN) has a large number of trainable\nparameters. Choosing a network with proper capacity is challenging and\ngenerally a larger network with excessive capacity is trained. Pruning is an\nestablished approach to reducing the number of parameters in a DNN. In this\npaper, we propose a framework for pruning DNNs based on a population-based\nglobal optimization method. This framework can use any pruning objective\nfunction. As a case study, we propose a simple but efficient objective function\nbased on the concept of energy-based models. Our experiments on ResNets,\nAlexNet, and SqueezeNet for the CIFAR-10 and CIFAR-100 datasets show a pruning\nrate of more than $50\\%$ of the trainable parameters with approximately $<5\\%$\nand $<1\\%$ drop of Top-1 and Top-5 classification accuracy, respectively.\n"}
+{"abstract": "  The self-attention-based model, transformer, is recently becoming the leading\nbackbone in the field of computer vision. In spite of the impressive success\nmade by transformers in a variety of vision tasks, it still suffers from heavy\ncomputation and intensive memory costs. To address this limitation, this paper\npresents an Interpretability-Aware REDundancy REDuction framework (IA-RED$^2$).\nWe start by observing a large amount of redundant computation, mainly spent on\nuncorrelated input patches, and then introduce an interpretable module to\ndynamically and gracefully drop these redundant patches. This novel framework\nis then extended to a hierarchical structure, where uncorrelated tokens at\ndifferent stages are gradually removed, resulting in a considerable shrinkage\nof computational cost. We include extensive experiments on both image and video\ntasks, where our method could deliver up to 1.4x speed-up for state-of-the-art\nmodels like DeiT and TimeSformer, by only sacrificing less than 0.7% accuracy.\nMore importantly, contrary to other acceleration approaches, our method is\ninherently interpretable with substantial visual evidence, making vision\ntransformer closer to a more human-understandable architecture while being\nlighter. We demonstrate that the interpretability that naturally emerged in our\nframework can outperform the raw attention learned by the original visual\ntransformer, as well as those generated by off-the-shelf interpretation\nmethods, with both qualitative and quantitative results. Project Page:\nhttp://people.csail.mit.edu/bpan/ia-red/.\n"}
+{"abstract": "  This paper studies the problem of recovering the hidden vertex correspondence\nbetween two edge-correlated random graphs. We focus on the Gaussian model where\nthe two graphs are complete graphs with correlated Gaussian weights and the\nErd\\H{o}s-R\\'enyi model where the two graphs are subsampled from a common\nparent Erd\\H{o}s-R\\'enyi graph $\\mathcal{G}(n,p)$. For dense graphs with\n$p=n^{-o(1)}$, we prove that there exists a sharp threshold, above which one\ncan correctly match all but a vanishing fraction of vertices and below which\ncorrectly matching any positive fraction is impossible, a phenomenon known as\nthe \"all-or-nothing\" phase transition. Even more strikingly, in the Gaussian\nsetting, above the threshold all vertices can be exactly matched with high\nprobability. In contrast, for sparse Erd\\H{o}s-R\\'enyi graphs with\n$p=n^{-\\Theta(1)}$, we show that the all-or-nothing phenomenon no longer holds\nand we determine the thresholds up to a constant factor. Along the way, we also\nderive the sharp threshold for exact recovery, sharpening the existing results\nin Erd\\H{o}s-R\\'enyi graphs.\n  The proof of the negative results builds upon a tight characterization of the\nmutual information based on the truncated second-moment computation and an\n\"area theorem\" that relates the mutual information to the integral of the\nreconstruction error. The positive results follows from a tight analysis of the\nmaximum likelihood estimator that takes into account the cycle structure of the\ninduced permutation on the edges.\n"}
+{"abstract": "  Aircraft trajectory prediction requires the determination of the atmospheric\nproperties (pressure, temperature, and density) encountered by the aircraft\nduring its flight. This is accomplished by employing a tabulated prediction\npublished by a meteorological service, a static atmosphere model that does not\nconsider the atmosphere variation with time or horizontal position, such as the\nInternational Civil Aviation Organization (ICAO) Standard Atmosphere (ISA), or\na variation to the later so it better conforms with the expected flight\nconditions. This article proposes an easy-to-use quasi static model that\nintroduces temperature and pressure variations while respecting all the\nhypotheses of the ISA model, resulting in more realistic trajectory predictions\nas the obtained atmospheric properties bear a higher resemblance with the local\nconditions encountered during the flight. The proposed model relies on two\nparameters, the temperature and pressure offsets, and converges to the ISA\nmodel when both are zero. Expressions to obtain the atmospheric properties are\nestablished, and their dependencies with both parameters explained. The author\ncalls this model INSA (ICAO Non Standard Atmosphere) and releases its C++\nimplementation as open-source software.\n"}
+{"abstract": "  We extend our BDI (birth-death-immigration) process based stochastic model of\nan infectious disease to time-nonhomogeneous cases. First, we discuss the\ndeterministic model, and derive the expected value of the infection process.\nThen as an application we consider that a government issues a decree to its\ncitizens to curtail their activities that may incur further infections and show\nhow the public's tardy response may further increase infections and prolong the\nepidemic much longer than one might think. We seek to solve a partial\ndifferential equation for the probability generating function. We find,\nhowever, that an exact solution is obtainable only for the BD process, i.e., no\narrivals of the infected from outside. The coefficient of variation for the\nnonhomogeneous BD process is found to be well over unity. This result implies\nthat the variations among different sample paths will be as large as in the\nnegative binomial distribution with r<1, which was found in Part I for the\nhomogeneous BDI model. In the final section, we illustrate, using our running\nexample, how much information we can derive from the time dependent PMF\n(probability mass function) P_k(t)=Pr[I(t)=k]. We present graphical plots of\nthe PMF at various t's, and cross-sections of this function at various k's. A\nmesh plot of the function over the (k, t) plane summarizes the above numerous\nplots. The results of this paper reinforce our earlier claim (see Abstract of\nPart II) that it would be a futile effort to attempt to identify all possible\nreasons why environments of similar situations differ so much in their epidemic\npatterns. Mere \"luck\" plays a more significant role than most of us may\nbelieve. We should be prepared for a worse possible scenario, which only a\nstochastic model can provide with probabilistic qualification. An empirical\nvalidation of the above results will be given in Part III-B.\n"}
+{"abstract": "  Chemical abundances studies of cataclysmic variables have revealed high\nnitrogen to carbon ratios in a number of of cataclysmic variable white dwarfs\n(based on ultraviolet emission and absorption lines), as well as possible\ncarbon deficiency in many secondaries (based on the absence of infrared CO\nabsorption lines). These indicate that the accreted material on the white dwarf\nsurface and the donor itself might be contaminated with CNO processed material.\nTo further understand the origin of this abundance anomaly, there is a need for\nfurther chemical abundance study. In the present work, we carry out a far\nultraviolet spectral analysis of the extreme SU UMa dwarf nova TU Men and the U\nGem dwarf nova SS Aur using archival spectra. We derive the mass and\ntemperature of the WD using the recently available DR2 Gaia parallaxes. The\nanalysis of HST STIS spectra yields a WD mass $M_{\\rm\nwd}=0.77^{+0.16}_{-0.13}M_{\\odot}$ with a temperature of $27,750 \\pm 1000$~K\nfor TU Men, and a WD mass $M_{\\rm wd} \\sim 0.80 \\pm 0.15$ with a temperature of\n$\\sim 30,000 \\pm 1000$~K for SS Aur. However, the analysis of a FUSE spectrum\nfor SS Aur gives to a higher temperature $\\sim 33,375 \\pm 1875 $~K, yielding to\na higher WD mass $\\sim 1 \\pm 0.25 M_{\\odot}$, which could be due to the effect\nof a second hot emitting component present in the short wavelengths of FUSE.\nMost importantly, based on the white dwarf far ultraviolet absorption lines, we\nfind that both systems have subsolar carbon and silicon abundances. For TU Men\nwe also find suprasolar nitrogen abundance, evidence of CNO processing.\n"}
+{"abstract": "  This study analyzes the impact of the COVID-19 pandemic on the subjective\nwell-being as measured through Twitter data indicators for Japan and Italy. It\nturns out that, overall, the subjective well-being dropped by 11.7% for Italy\nand 8.3% for Japan in the first nine months of 2020 compared to the last two\nmonths of 2019 and even more compared to the historical mean of the indexes.\nThrough a data science approach we try to identify the possible causes of this\ndrop down by considering several explanatory variables including, climate and\nair quality data, number of COVID-19 cases and deaths, Facebook Covid and flu\nsymptoms global survey, Google Trends data and coronavirus-related searches,\nGoogle mobility data, policy intervention measures, economic variables and\ntheir Google Trends proxies, as well as health and stress proxy variables based\non big data. We show that a simple static regression model is not able to\ncapture the complexity of well-being and therefore we propose a dynamic elastic\nnet approach to show how different group of factors may impact the well-being\nin different periods, even over a short time length, and showing further\ncountry-specific aspects. Finally, a structural equation modeling analysis\ntries to address the causal relationships among the COVID-19 factors and\nsubjective well-being showing that, overall, prolonged mobility\nrestrictions,flu and Covid-like symptoms, economic uncertainty, social\ndistancing and news about the pandemic have negative effects on the subjective\nwell-being.\n"}
+{"abstract": "  In the celebrated paper by Jean Leray, published in JMPA journal in 1933, the\ninvading domains method was proposed to construct D-solutions for the\nstationary Navier-Stokes flow around obstacle problem. In two dimensions,\nwhether Leray's D-solution achieves the prescribed limiting velocity at spatial\ninfinity became a major open problem since then. In this paper, we solve this\nproblem at small Reynolds numbers. The proof builds on a novel blow-down\nargument which rescales the invading domains to the unit disc, and the ideas\ndeveloped in a recent paper [Korobkov-Pileckas-Russo2020], where the\nnontriviality of Leray solutions in the general case was proved, and\n[Korobkov-Ren-2021], where the uniqueness result for small Reynolds number was\nestablished.\n"}
+{"abstract": "  The attention module, which is a crucial component in Transformer, cannot\nscale efficiently to long sequences due to its quadratic complexity. Many works\nfocus on approximating the dot-then-exponentiate softmax function in the\noriginal attention, leading to sub-quadratic or even linear-complexity\nTransformer architectures. However, we show that these methods cannot be\napplied to more powerful attention modules that go beyond the\ndot-then-exponentiate style, e.g., Transformers with relative positional\nencoding (RPE). Since in many state-of-the-art models, relative positional\nencoding is used as default, designing efficient Transformers that can\nincorporate RPE is appealing. In this paper, we propose a novel way to\naccelerate attention calculation for Transformers with RPE on top of the\nkernelized attention. Based upon the observation that relative positional\nencoding forms a Toeplitz matrix, we mathematically show that kernelized\nattention with RPE can be calculated efficiently using Fast Fourier Transform\n(FFT). With FFT, our method achieves $\\mathcal{O}(n\\log n)$ time complexity.\nInterestingly, we further demonstrate that properly using relative positional\nencoding can mitigate the training instability problem of vanilla kernelized\nattention. On a wide range of tasks, we empirically show that our models can be\ntrained from scratch without any optimization issues. The learned model\nperforms better than many efficient Transformer variants and is faster than\nstandard Transformer in the long-sequence regime.\n"}
+{"abstract": "  The purpose of this paper is to develop a shared control takeover strategy\nfor smooth and safety control transition from an automation driving system to\nthe human driver and to approve its positive impacts on drivers' behavior and\nattitudes. A \"human-in-the-loop\" driving simulator experiment was conducted to\nevaluate the impact of the proposed shared control takeover strategy under\ndifferent disengagement conditions. Results of thirty-two drivers showed shared\ncontrol takeover strategy could improve safety performance at the aggregated\nlevel, especially at non-driving related disengagements. For more urgent\ndisengagements caused by another vehicle's sudden brake, a shared control\nstrategy enlarges individual differences. The primary reason is that some\ndrivers had higher self-reported mental workloads in response to the shared\ncontrol takeover strategy. Therefore, shared control between driver and\nautomation can involve driver's training to avoid mental overload when\ndeveloping takeover strategies.\n"}
+{"abstract": "  We propose a new architecture for artificial neural networks called\nHouseholder-absolute neural layers, or Han-layers for short, that use\nHouseholder reflectors as weight matrices and the absolute-value function for\nactivation. Han-layers, functioning as fully connected layers, are motivated by\nrecent results on neural-network variability and are designed to increase\nactivation ratio and reduce the chance of Collapse to Constants. Neural\nnetworks constructed chiefly from Han-layers are called HanNets. By\nconstruction, HanNets enjoy a theoretical guarantee that vanishing or exploding\ngradient never occurs. We conduct several proof-of-concept experiments. Some\nsurprising results obtained on styled test problems suggest that, under certain\nconditions, HanNets exhibit an unusual ability to produce nearly perfect\nsolutions unattainable by fully connected networks. Experiments on regression\ndatasets show that HanNets can significantly reduce the number of model\nparameters while maintaining or improving the level of generalization accuracy.\nIn addition, by adding a few Han-layers into the pre-classification FC-layer of\na convolutional neural network, we are able to quickly improve a\nstate-of-the-art result on CIFAR10 dataset. These proof-of-concept results are\nsufficient to necessitate further studies on HanNets to understand their\ncapacities and limits, and to exploit their potentials in real-world\napplications.\n"}
+{"abstract": "  We introduce a new method for generating color images from sketches or edge\nmaps. Current methods either require some form of additional user-guidance or\nare limited to the \"paired\" translation approach. We argue that segmentation\ninformation could provide valuable guidance for sketch colorization. To this\nend, we propose to leverage semantic image segmentation, as provided by a\ngeneral purpose panoptic segmentation network, to create an additional\nadversarial loss function. Our loss function can be integrated to any baseline\nGAN model. Our method is not limited to datasets that contain segmentation\nlabels, and it can be trained for \"unpaired\" translation tasks. We show the\neffectiveness of our method on four different datasets spanning scene level\nindoor, outdoor, and children book illustration images using qualitative,\nquantitative and user study analysis. Our model improves its baseline up to 35\npoints on the FID metric. Our code and pretrained models can be found at\nhttps://github.com/giddyyupp/AdvSegLoss.\n"}
+{"abstract": "  Cavity magnonics deals with the interaction of magnons - elementary\nexcitations in magnetic materials - and confined electromagnetic fields. We\nintroduce the basic physics and review the experimental and theoretical\nprogress of this young field that is gearing up for integration in future\nquantum technologies. Much of its appeal is derived from the strong\nmagnon-photon coupling and the easily-reached nonlinear regime in microwave\ncavities. The interaction of magnons with light as detected by Brillouin light\nscattering is enhanced in magnetic optical resonators, which can be employed to\nmanipulate magnon distributions. The cavity photon-mediated coupling of a\nmagnon mode to a superconducting qubit enables measurements in the single\nmagnon limit.\n"}
+{"abstract": "  The influence of the orientation of gold nanorods in different assemblies has\nbeen investigated using the Finite Difference Time Domain (FDTD) simulation\nmethod. To understand the relative orientation, we vary the size and angle in\ndimer geometries. Significant effects of plasmon coupling emerged in\nlongitudinal resonances having end-to-end configurations of gold nanorods. The\neffect of orientational plasmon coupling in dimers gives rise to both bonding\nand anti-bonding plasmon modes. Effects of various geometries like primary\nmonomer, dimer, trimer, and tetramer structures have been explored and compared\nwith their higher nanorod ensembles. The asymmetric spectral response in a 4 *\n4 gold nanorods array indicates a Fano-like resonance. The variation of gap\ndistance in ordered arrays allowed modulation of the Fano resonance mode. The\nplasmon modes' resonance wavelength and field enhancement have been tuned by\nvarying the gap distance, angular orientation, size irregularity between the\nnanorods, and nanorod numbers in an array. The integrated nanostructures\nstudied here are not only significant for fundamental research but also\napplications in plasmon-based devices.\n"}
+{"abstract": "  Let $E \\subset \\mathbb R^{n+1}$ be a parabolic uniformly rectifiable set. We\nprove that every bounded solution $u$ to $$\\partial_tu- \\Delta u=0, \\quad\n\\text{in} \\quad \\mathbb R^{n+1}\\setminus E$$ satisfies a Carleson measure\nestimate condition. An important technical novelty of our work is that we\ndevelop a corona domain approximation scheme for $E$ in terms of regular\nLip(1/2,1) graph domains. This approximation scheme has an analogous elliptic\nversion which is an improvement of the known results in that setting.\n"}
+{"abstract": "  For robots to understand human instructions and perform meaningful tasks in\nthe near future, it is important to develop learned models that comprehend\nreferential language to identify common objects in real-world 3D scenes. In\nthis paper, we introduce a spatial-language model for a 3D visual grounding\nproblem. Specifically, given a reconstructed 3D scene in the form of point\nclouds with 3D bounding boxes of potential object candidates, and a language\nutterance referring to a target object in the scene, our model successfully\nidentifies the target object from a set of potential candidates. Specifically,\nLanguageRefer uses a transformer-based architecture that combines spatial\nembedding from bounding boxes with fine-tuned language embeddings from\nDistilBert to predict the target object. We show that it performs competitively\non visio-linguistic datasets proposed by ReferIt3D. Further, we analyze its\nspatial reasoning task performance decoupled from perception noise, the\naccuracy of view-dependent utterances, and viewpoint annotations for potential\nrobotics applications.\n"}
+{"abstract": "  This paper examines the stability of the quantum random walk search\nalgorithm, when the walk coin is constructed by generalized Householder\nreflection and additional phase shift, against inaccuracies in the phases used\nto construct the coin. The optimization of the algorithm is done by numerical\nmethods - Monte Carlo, neural networks, and supervised machine learning. The\nresults of numerical simulations show that, with such a construction of the\nHouseholder reflection, the algorithm is more stable to inaccuracies in the\nspecific values of these phases, as long as it is possible to control the phase\ndifference between the phase shift and the phase involved in the Householder\nreflection. This paper explicitly shows as an example, how achieving a properly\ndesigned phase difference would make quantum random walk search on a hypercube\nmore stable for coin register consisting of one, two, and three qubits.\n"}
+{"abstract": "  In this paper we consider the coalescing random walk model on general graphs\n$G=(V,E)$. We set up a unified framework to study the leading order of decay\nrate of $P_t$, the expectation of the fraction of occupied sites at time $t$,\nparticularly for the `Big Bang' regime where $t\\ll\nt_{\\text{coal}}:=\\mathbb{E}[\\inf\\{s:\\text{There is only one particle at time\n}s\\}]$. Our results show that $P_t$ satisfies certain mean field behavior, if\nthe graphs satisfy certain transience-like conditions.\n  We apply this framework to two families of graphs: (1) graphs given by\nconfiguration model with degree $\\ge 3$, and (2) finite and infinite\nvertex-transitive graphs. In the first case, we show that for $1 \\ll t \\ll\n|V|$, $P_t$ decays in the order of $t^{-1}$, and $(tP_t)^{-1}$ is approximately\nthe probability that two particles starting from the root of the corresponding\nunimodular Galton-Watson tree never collide after one of them leaves the root,\nwhich is also roughly $|V|/(2t_{\\text{meet}})$, where $t_{\\text{meet}}$ is the\nmean meeting time of two independent walkers. By taking the local weak limit,\nfor the corresponding unimodular Galton-Watson tree we prove convergence of\n$tP_t$ as $t\\to\\infty$. For the second family of graphs, if we take a sequence\nof finite vertex-transitive graphs $G_n=(V_n, E_n)$, such that\n$t_{\\text{meet}}=O(|V_n|)$ and the inverse of the spectral gap $t_{\\text{rel}}$\nis $o(|V_n|)$, then for $t_{\\text{rel}}\\ll t\\ll t_{\\text{coal}}$, $(tP_t)^{-1}$\nis approximately the probability that two random walks never meet before time\n$t$, and also $|V|/(2t_{\\text{meet}})$. In addition, we define a natural\nuniform transience condition, and show that in the transitive setup it implies\nthe above for all $1\\ll t\\ll t_{\\text{coal}}$. Such estimates of $tP_t$ are\nalso obtained for all infinite transient transitive unimodular graphs, in\nparticular, all transient transitive amenable graphs.\n"}
+{"abstract": "  Estimating and testing for differences in molecular phenotypes (e.g. gene\nexpression, chromatin accessibility, transcription factor binding) across\nconditions is an important part of understanding the molecular basis of gene\nregulation. These phenotypes are commonly measured using high-throughput\nsequencing assays (e.g., RNA-seq, ATAC-seq, ChIP-seq), which provide\nhigh-resolution count data that reflect how the phenotypes vary along the\ngenome. Multiple methods have been proposed to help exploit these\nhigh-resolution measurements for differential expression analysis. However,\nthey ignore the count nature of the data, instead using normal approximations\nthat work well only for data with large sample sizes or high counts. Here we\ndevelop count-based methods to address this problem. We model the data for each\nsample using an inhomogeneous Poisson process with spatially structured\nunderlying intensity function, and then, building on multi-scale models for the\nPoisson process, estimate and test for differences in the underlying intensity\nfunction across samples (or groups of samples). Using both simulation and real\nATAC-seq data we show that our method outperforms previous normal-based\nmethods, especially in situations with small sample sizes or low counts.\n"}
+{"abstract": "  Coreference resolution is an important component in analyzing narrative text\nfrom administrative data (e.g., clinical or police sources). However, existing\ncoreference models trained on general language corpora suffer from poor\ntransferability due to domain gaps, especially when they are applied to\ngender-inclusive data with lesbian, gay, bisexual, and transgender (LGBT)\nindividuals. In this paper, we analyzed the challenges of coreference\nresolution in an exemplary form of administrative text written in English:\nviolent death narratives from the USA's Centers for Disease Control's (CDC)\nNational Violent Death Reporting System. We developed a set of data\naugmentation rules to improve model performance using a probabilistic data\nprogramming framework. Experiments on narratives from an administrative\ndatabase, as well as existing gender-inclusive coreference datasets,\ndemonstrate the effectiveness of data augmentation in training coreference\nmodels that can better handle text data about LGBT individuals.\n"}
+{"abstract": "  The Kerr nonlinear optical performance of silicon nanowire waveguides\nintegrated with 2D layered graphene oxide (GO) films is theoretically studied\nand optimized based on experimentally measured linear and nonlinear optical\nparameters of the GO films. The strong mode overlap between the silicon\nnanowires and highly nonlinear GO films yields a significantly enhanced Kerr\nnonlinearity for the hybrid waveguides. A detailed analysis for the influence\nof waveguide geometry and GO film thickness on the propagation loss, nonlinear\nparameter, and nonlinear figure of merit (FOM) is performed. The results show\nthat the effective nonlinear parameter and nonlinear FOM can be increased by up\nto 52 and 79 times relative to bare silicon nanowires, respectively. Self-phase\nmodulation (SPM)-induced spectral broadening of optical pulses is used as a\nbenchmark to evaluate the nonlinear performance, examining the tradeoff between\nenhancing Kerr nonlinearity and minimizing loss. By optimizing the device\nparameters to balance this, a high spectral broadening factor of 27.8 can be\nachieved, more than 6 times that achieved in previous experiments. Finally, the\ninfluence of pulse chirp, material anisotropy, and the interplay between\nsaturable absorption and SPM is also discussed, together with the comparison\nbetween the spectral broadening after going through GO-coated and\ngraphene-coated silicon waveguides. These results provide useful guidance for\noptimizing the Kerr nonlinear optical performance of silicon waveguides\nintegrated with 2D layered GO films.\n"}
+{"abstract": "  This work proposes a detectability condition for linear time-varying systems\nbased on the exponential dichotomy spectrum. The condition guarantees the\nexistence of an observer, whose gain is determined only by the unstable modes\nof the system. This allows for an observer design with low computational\ncomplexity compared to classical estimation approaches. An extension of this\nobserver design to a class of nonlinear systems is proposed and local\nconvergence of the corresponding estimation error dynamics is proven. Numerical\nresults show the efficacy of the proposed observer design technique.\n"}
+{"abstract": "  We present a novel approach for high-order accurate numerical differentiation\non unstructured meshes of quadrilateral elements. To differentiate a given\nfunction, an auxiliary function with greater smoothness properties is defined\nwhich when differentiated provides the derivatives of the original function.\nThe method generalises traditional finite difference methods to meshes of\narbitrary topology in any number of dimensions for any order of derivative and\naccuracy. We demonstrate the accuracy of the numerical scheme using dual\nquadrilateral meshes and a refinement method based on subdivision surfaces. The\nscheme is applied to the solution of a range of partial differential equations,\nincluding both linear and nonlinear, and second and fourth order equations.\n"}
+{"abstract": "  Deep learning models (with neural networks) have been widely used in\nchallenging tasks such as computer-aided disease diagnosis based on medical\nimages. Recent studies have shown deep diagnostic models may not be robust in\nthe inference process and may pose severe security concerns in clinical\npractice. Among all the factors that make the model not robust, the most\nserious one is adversarial examples. The so-called \"adversarial example\" is a\nwell-designed perturbation that is not easily perceived by humans but results\nin a false output of deep diagnostic models with high confidence. In this\npaper, we evaluate the robustness of deep diagnostic models by adversarial\nattack. Specifically, we have performed two types of adversarial attacks to\nthree deep diagnostic models in both single-label and multi-label\nclassification tasks, and found that these models are not reliable when\nattacked by adversarial example. We have further explored how adversarial\nexamples attack the models, by analyzing their quantitative classification\nresults, intermediate features, discriminability of features and correlation of\nestimated labels for both original/clean images and those adversarial ones. We\nhave also designed two new defense methods to handle adversarial examples in\ndeep diagnostic models, i.e., Multi-Perturbations Adversarial Training (MPAdvT)\nand Misclassification-Aware Adversarial Training (MAAdvT). The experimental\nresults have shown that the use of defense methods can significantly improve\nthe robustness of deep diagnostic models against adversarial attacks.\n"}
+{"abstract": "  In \\cite{Bahamonde:2019zea}, a spherically symmetric black hole (BH) was\nderived from the quadratic form of $f(T)$. Here we derive the associated\nenergy, invariants of curvature, and torsion of this BH and demonstrate that\nthe higher-order contribution of torsion renders the singularity weaker\ncompared with the Schwarzschild BH of general relativity (GR). Moreover, we\ncalculate the thermodynamic quantities and reveal the effect of the\nhigher--order contribution on these quantities. Therefore, we derive a new\nspherically symmetric BH from the cubic form of\n$f(T)=T+\\epsilon\\Big[\\frac{1}{2}\\alpha T^2+\\frac{1}{3}\\beta T^3\\Big]$, where\n$\\epsilon<<1$, $\\alpha$, and $\\beta$ are constants. The new BH is characterized\nby the two constants $\\alpha$ and $\\beta$ in addition to $\\epsilon$. At\n$\\epsilon=0$ we return to GR. We study the physics of these new BH solutions\nvia the same procedure that was applied for the quadratic BH. Moreover, we\ndemonstrate that the contribution of the higher-order torsion,\n$\\frac{1}{2}\\alpha T^2+\\frac{1}{3}\\beta T^3$, may afford an interesting\nphysics.\n"}
+{"abstract": "  Let $a_1, \\dots, a_n \\in \\mathbb{R}$ satisfy $\\sum_i a_i^2 = 1$, and let\n$\\varepsilon_1, \\ldots, \\varepsilon_n$ be uniformly random $\\pm 1$ signs and $X\n= \\sum_{i=1}^{n} a_i \\varepsilon_i$. It is conjectured that $X = \\sum_{i=1}^{n}\na_i \\varepsilon_i$ has $\\Pr[X \\geq 1] \\geq 7/64$. The best lower bound so far\nis $1/20$, due to Oleszkiewicz. In this paper we improve this to $\\Pr[X \\geq 1]\n\\geq 6/64$.\n"}
+{"abstract": "  This article aims to study the existence of stable Bloch oscillations and\nLandau-Zener tunneling in a non-Hermitian system when exposed to external\nfields. We investigate a non-Hermitian $\\cal{PT}$-symmetric diamond chain\nnetwork and its transport dynamics in two different situations, namely in a\nflat band case and a non-flat band case. The considered system does not support\nunbroken-$\\cal{PT}$ phase or completely real eigenspectra in any of the\nparametric regions in both the flat and non-flat band cases. In the flat band\ncase, up to a critical value of the gain-loss parameter, the bands are found to\nbe gapless or inseparable, and for other values the bands are isolated.\nConsidering the non-flat band case, all the bands are found to be complex\ndispersive and are also isolated. In the case of completely broken $\\cal{PT}$\nphase, we look upon the possibility to have stable dynamics or Bloch\noscillations upon the application of external fields like synthetic electric\nfield. In particular, when the complex bands are isolated, we point out that\nthe Landau-Zener tunneling induced by the synthetic electric field can enable\nBloch oscillations. The amplitude of these Bloch oscillations is large and\npersists for a long propagation distance which reveals that super Bloch\noscillations can be observed in the broken $\\cal{PT}$ phase of the system. We\nalso report the amplified Bloch oscillations which pave the way towards\ncontrolling transport phenomena in non-Hermitian systems.\n"}
+{"abstract": "  Emergent electromagnetic induction based on electrodynamics of noncollinear\nspin states may enable dramatic miniaturization of inductor elements widely\nused in electric circuits, yet many issues are to be solved toward application.\nOne such problem is how to increase working temperature. We report the large\nemergent electromagnetic induction achieved around and above room temperature\nbased on short-period ($\\leq$ 3 nm) spin-spiral states of a metallic helimagnet\n${\\rm YMn}_{6}{\\rm Sn}_{6}$. The observed inductance value $L$ and its sign are\nobserved to vary to a large extent, depending not only on the spin helix\nstructure controlled by temperature and magnetic field but also on the current\ndensity. The present finding on room-temperature operation and possible sign\ncontrol of $L$ may provide a new step toward realizing microscale quantum\ninductors.\n"}
+{"abstract": "  Scattering immune propagation of light in topological photonic systems may\nrevolutionarize the design of integrated photonic circuits for information\nprocessing and communications. In optics, various photonic topological circuits\nhave been developed, which were based on classical emulation of either quantum\nspin Hall effect or quantum valley Hall effect. On the other hand, the\ncombination of both the valley and spin degrees of freedom can lead to a new\nkind of topological transport phenomenon, dubbed quantum spin valley Hall\neffect (QSVH), which can further expand the number of topologically protected\nedge channels and would be useful for information multiplexing. However, it is\nchallenging to realize QSVH in most known material platforms, due to the\nrequirement of breaking both the (pseudo-)fermionic time-reversal (T) and\nparity symmetries (P) individually, but leaving the combined symmetry S=TP\nintact. Here, we propose an experimentally feasible platform to realize QSVH\nfor light, based on coupled ring resonators mediated by optical Kerr\nnonlinearity. Thanks to the inherent flexibility of cross-mode modulation\n(XMM), the coupling between the probe light can be engineered in a controllable\nway such that spin-dependent staggered sublattice potential emerges in the\neffective Hamiltonian. With delicate yet experimentally feasible pump\nconditions, we show the existence of spin valley Hall induced topological edge\nstates. We further demonstrate that both degrees of freedom, i.e., spin and\nvalley, can be manipulated simultaneously in a reconfigurable manner to realize\nspin-valley photonics, doubling the degrees of freedom for enhancing the\ninformation capacity in optical communication systems.\n"}
+{"abstract": "  As quantum information processors grow in quantum bit (qubit) count and\nfunctionality, the control and measurement system becomes a limiting factor to\nlarge scale extensibility. To tackle this challenge and keep pace with rapidly\nevolving classical control requirements, full control stack access is essential\nto system level optimization. We design a modular FPGA (field-programmable gate\narray) based system called QubiC to control and measure a superconducting\nquantum processing unit. The system includes room temperature electronics\nhardware, FPGA gateware, and engineering software. A prototype hardware module\nis assembled from several commercial off-the-shelf evaluation boards and\nin-house developed circuit boards. Gateware and software are designed to\nimplement basic qubit control and measurement protocols. System functionality\nand performance are demonstrated by performing qubit chip characterization,\ngate optimization, and randomized benchmarking sequences on a superconducting\nquantum processor operating at the Advanced Quantum Testbed at Lawrence\nBerkeley National Laboratory. The single-qubit and two-qubit process fidelities\nare measured to be 0.9980$\\pm$0.0001 and 0.948$\\pm$0.004 by randomized\nbenchmarking. With fast circuit sequence loading capability, the QubiC performs\nrandomized compiling experiments efficiently and improves the feasibility of\nexecuting more complex algorithms.\n"}
+{"abstract": "  Consider a PPT two-party protocol $\\pi=(A,B)$ in which the parties get no\nprivate inputs and obtain outputs $O^A,O^B\\in \\{0,1\\}$, and let $V^A$ and $V^B$\ndenote the parties' individual views. Protocol $\\pi$ has $\\alpha$-agreement if\n$Pr[O^A=O^B]=1/2+\\alpha$. The leakage of $\\pi$ is the amount of information a\nparty obtains about the event $\\{O^A=O^B\\}$; that is, the leakage $\\epsilon$ is\nthe maximum, over $P\\in\\{A,B\\}$, of the distance between $V^P|OA=OB$ and\n$V^P|OA\\neq OB$. Typically, this distance is measured in statistical distance,\nor, in the computational setting, in computational indistinguishability. For\nthis choice, Wullschleger [TCC 09] showed that if $\\alpha>>\\epsilon$ then the\nprotocol can be transformed into an OT protocol.\n  We consider measuring the protocol leakage by the log-ratio distance (which\nwas popularized by its use in the differential privacy framework). The\nlog-ratio distance between X,Y over domain \\Omega is the minimal $\\epsilon>0$\nfor which, for every $v\\in\\Omega$, $log(Pr[X=v]/Pr[Y=v])\\in\n[-\\epsilon,\\epsilon]$. In the computational setting, we use computational\nindistinguishability from having log-ratio distance $\\epsilon$. We show that a\nprotocol with (noticeable) accuracy $\\alpha\\in\\Omega(\\epsilon^2)$ can be\ntransformed into an OT protocol (note that this allows $\\epsilon>>\\alpha$). We\ncomplete the picture, in this respect, showing that a protocol with $\\alpha\\in\no(\\epsilon^2)$ does not necessarily imply OT. Our results hold for both the\ninformation theoretic and the computational settings, and can be viewed as a\n\"fine grained\" approach to \"weak OT amplification\".\n  We then use the above result to fully characterize the complexity of\ndifferentially private two-party computation for the XOR function, answering\nthe open question put by Goyal, Khurana, Mironov, Pandey, and Sahai [ICALP 16]\nand Haitner, Nissim, Omri, Shaltiel, and Silbak [FOCS 18].\n"}
+{"abstract": "  Internet of Things (IoT) consists of a large number of devices connected\nthrough a network, which exchange a high volume of data, thereby posing new\nsecurity, privacy, and trust issues. One way to address these issues is\nensuring data confidentiality using lightweight encryption algorithms for IoT\nprotocols. However, the design and implementation of such protocols is an\nerror-prone task; flaws in the implementation can lead to devastating security\nvulnerabilities. Here we propose a new verification approach named\nEncryption-BMC and Fuzzing (EBF), which combines Bounded Model Checking (BMC)\nand Fuzzing techniques to check for security vulnerabilities that arise from\nconcurrent implementations of cyrptographic protocols, which include data race,\nthread leak, arithmetic overflow, and memory safety. EBF models IoT protocols\nas a client and server using POSIX threads, thereby simulating both entities'\ncommunication. It also employs static and dynamic verification to cover the\nsystem's state-space exhaustively. We evaluate EBF against three benchmarks.\nFirst, we use the concurrency benchmark from SV-COMP and show that it\noutperforms other state-of-the-art tools such as ESBMC, AFL, Lazy-CSeq, and\nTSAN with respect to bug finding. Second, we evaluate an open-source\nimplementation called WolfMQTT. It is an MQTT client implementation that uses\nthe WolfSSL library. We show that \\tool detects a data race bug, which other\napproaches are unable to find. Third, to show the effectiveness of EBF, we\nreplicate some known vulnerabilities in OpenSSL and CyaSSL (lately WolfSSL)\nlibraries. EBF can detect the bugs in minimum time.\n"}
+{"abstract": "  There has been a growing interest in defining models of automata enriched\nwith time, such as finite automata extended with clocks (timed automata). In\nthis paper, we study deterministic timed finite state machines (TFSMs), i.e.,\nfinite state machines with a single clock, timed guards and timeouts which\ntransduce timed input words into timed output words. We solve the problem of\nequivalence checking by defining a bisimulation from timed FSMs to untimed ones\nand viceversa. Moreover, we apply these bisimulation relations to build the\nintersection of two timed finite state machines by untiming them, intersecting\nthem and transforming back to the timed intersection.\n"}
+{"abstract": "  Given an elliptic curve $E$ over a global function field $K$, the Galois\naction on the $n$-torsion points of $E$ gives rise to a mod-n Galois\nrepresentation $\\rho_{E,n}$. For $K$ satisfying some mild conditions, we show\nthat the set of $E$ for which $\\rho_{E,n}$ is as large as possible for all $n$,\nhas density $1$.\n"}
+{"abstract": "  Knowledge of the spatial organisation of economic activity within a city is\nkey to policy concerns. However, in developing cities with high levels of\ninformality, this information is often unavailable. Recent progress in machine\nlearning together with the availability of street imagery offers an affordable\nand easily automated solution. Here we propose an algorithm that can detect\nwhat we call 'visible firms' using street view imagery. Using Medell\\'in,\nColombia as a case study, we illustrate how this approach can be used to\nuncover previously unseen economic activity. Applying spatial analysis to our\ndataset we detect a polycentric structure with five distinct clusters located\nin both the established centre and peripheral areas. Comparing the density of\nvisible and registered firms, we find that informal activity concentrates in\npoor but densely populated areas. Our findings highlight the large gap between\nwhat is captured in official data and the reality on the ground.\n"}
+{"abstract": "  State-of-the-art object detection systems for autonomous driving achieve\npromising results in clear weather conditions. However, such autonomous safety\ncritical systems also need to work in degrading weather conditions, such as\nrain, fog and snow. Unfortunately, most approaches evaluate only on the KITTI\ndataset, which consists only of clear weather scenes. In this paper we address\nthis issue and perform one of the most detailed evaluation on single and dual\nmodality architectures on data captured in real weather conditions. We analyse\nthe performance degradation of these architectures in degrading weather\nconditions. We demonstrate that an object detection architecture performing\ngood in clear weather might not be able to handle degrading weather conditions.\nWe also perform ablation studies on the dual modality architectures and show\ntheir limitations.\n"}
+{"abstract": "  The based loop space of the configuration space of $m$ points in\n$\\mathbb{R}^n$ can be viewed as the space of pure braids on $m$ strands in\n$\\mathbb{R}^{n+1}$. For $n>2$, Cohen and Gitler calculated its homology,\nrelating it to earlier work of Kohno on the case $n=2$ of the classical pure\nbraid group. We continue our study of spaces of pure braids in terms of\nKontsevich's CDGA of diagrams, the integration map for formality of\nconfiguration spaces, and Chen's iterated integrals. In particular, we\nconstruct a power series connection whose dual yields a Hopf algebra\nisomorphism between the cohomology of braids and the bar construction on\ndiagrams. It further maps iterated Whitehead products in homotopy to trivalent\ntrees modulo the IHX relation. As an application, we establish a correspondence\nbetween Milnor invariants of Brunnian spherical links and certain Chen\nintegrals. We finally show that a graphing map induces an injection of a\ncertain subspace of the homotopy of configuration spaces into the homotopy of\nmany spaces of $k$-dimensional string links in $\\mathbb{R}^{n+k}$. We\nconjecture that this graphing map is more generally injective on the rational\nhomotopy and rational homology of the $k$-fold loop space of configuration\nspace.\n"}
+{"abstract": "  High internal phase emulsions (HIPEs) are considered as an important\nfunctional material and have been the focus of intense development effort, but\nit has not been possible to alter their fundamental attributes at either the\nmicrocosmic or macroscopic level, which severely limits their practical\napplications in various areas. In this work, we report a general strategy for\ncreating complex HIPEs and we additionally show a route to forming HIPE films\nat liquid interfaces. Double HIPEs and Janus HIPEs are both realized for the\nfirst time. They feature complex microscopic patterns with a short-range\nanisotropy, and exhibit non-Newtonian pseudoplastic flow behavior. By taking\nadvantage of their response to a high-pH subphase, interfacial films can be\nsuccessfully obtained, which are tunable in thickness and morphologies under\ncompression.\n"}
+{"abstract": "  We present the main sequence for all galaxies and star-forming galaxies for a\nsample of 28,469 massive ($M_\\star \\ge 10^{11}$M$_\\odot$) galaxies at cosmic\nnoon ($1.5 < z < 3.0$), uniformly selected from a 17.5 deg$^2$ area (0.33\nGpc$^3$ comoving volume at these redshifts). Our large sample allows for a\nnovel approach to investigating the galaxy main sequence that has not been\naccessible to previous studies. We measure the main sequence in small mass bins\nin the SFR-M$_{\\star}$ plane without assuming a functional form for the main\nsequence. With a large sample of galaxies in each mass bin, we isolate\nstar-forming galaxies by locating the transition between the star-forming and\ngreen valley populations in the SFR-M$_{\\star}$ plane. This approach eliminates\nthe need for arbitrarily defined fixed cutoffs when isolating the star-forming\ngalaxy population, which often biases measurements of the scatter around the\nstar-forming galaxy main sequence. We find that the main sequence for all\ngalaxies becomes increasingly flat towards present day at the high-mass end,\nwhile the star-forming galaxy main sequence does not. We attribute this\ndifference to the increasing fraction of the collective green valley and\nquiescent galaxy population from $z=3.0$ to $z=1.5$. Additionally, we measure\nthe total scatter around the star-forming galaxy main sequence and find that it\nis $\\sim0.5-1.0$ dex with little evolution as a function of mass or redshift.\nWe discuss the implications that these results have for pinpointing the\nphysical processes driving massive galaxy evolution.\n"}
+{"abstract": "  Let $a \\in \\mathbb{Z}_{>0}$ and $\\epsilon, \\epsilon_1, \\epsilon_2, \\epsilon_3\n\\in \\{\\pm 1\\}$. We classify explicitly all singular moduli $x_1, x_2, x_3$\nsatisfying either $\\epsilon_1 x_1^a + \\epsilon_2 x_2^a + \\epsilon_3 x_3^a \\in\n\\mathbb{Q}$ or $(x_1 x_2 x_3)^{\\epsilon a} \\in \\mathbb{Q}^{\\times}$. In\nparticular, we show that all solutions in singular moduli $x_1, x_2, x_3$ to\nthe Fermat equations $x_1^a + x_2^a + x_3^a= 0$ and $x_1^a + x_2^a - x_3^a= 0$\nsatisfy $x_1 x_2 x_3 = 0$. Our proofs use a generalisation of a result of Faye\nand Riffaut on the fields generated by sums and products of two singular\nmoduli, which we also establish.\n"}
+{"abstract": "  We investigate the Andreev reflection and Josephson effect in the $\\alpha$-T3\nlattice which falls between graphene ($\\alpha = 0$) and the dice lattice\n($\\alpha = 1$) by adjusting the parameter $\\alpha$. In the regime of specular\nAndreev reflection, when the incident energy of electron is small, the\nprobability of Andreev reflection decreases as the parameter $\\alpha$\nincreases. On the contrary, when the incident energy is large, the probability\nof Andreev reflection increases as the parameter $\\alpha$ increases.\nInterestingly, when the incident energy approaches the superconducting\nenergy-gap function, the Andreev reflection with approximate all-angle perfect\ntransmission happens in the case of $\\alpha = 1$. In the regime of Andreev\nretro-reflection, when the parameter $\\alpha$ increases, the probability of\nAndreev reflection increases regardless of the value of incident energy. When\nthe incident energy approaches the superconducting energy-gap function, the\nAndreev reflection with approximate all-angle perfect transmission happens\nregardless of the value of $\\alpha$. We also give the differential conductance\nin these two regimes and find that the differential conductance increases as\nthe parameter $\\alpha$ increases generally. In addition, the $\\alpha$-T3\nlattice-based Josephson current increases as $\\alpha$ increases. When the\nlength of junction approaches zero, the critical Josephson currents in the\ndifferent values of $\\alpha$ approach the same value.\n"}
+{"abstract": "  This paper addresses text recognition for domains with limited manual\nannotations by a simple self-training strategy. Our approach should reduce\nhuman annotation effort when target domain data is plentiful, such as when\ntranscribing a collection of single person's correspondence or a large\nmanuscript. We propose to train a seed system on large scale data from related\ndomains mixed with available annotated data from the target domain. The seed\nsystem transcribes the unannotated data from the target domain which is then\nused to train a better system. We study several confidence measures and\neventually decide to use the posterior probability of a transcription for data\nselection. Additionally, we propose to augment the data using an aggressive\nmasking scheme. By self-training, we achieve up to 55 % reduction in character\nerror rate for handwritten data and up to 38 % on printed data. The masking\naugmentation itself reduces the error rate by about 10 % and its effect is\nbetter pronounced in case of difficult handwritten data.\n"}
+{"abstract": "  We consider the long time dynamics of nonlinear Schr\\\"odinger equations with\nan external potential. More precisely, we look at Hartree type equations in\nthree or higher dimensions with small initial data. We prove an optimal decay\nestimate, which is comparable to the decay of free solutions. Our proof relies\non good control on a high Sobolev norm of the solution to estimate the terms in\nDuhamel's formula.\n"}
+{"abstract": "  We investigate the spatial distribution of spin orientation in magnetic\nnanoparticles consisting of hard and soft magnetic layers. The nanoparticles\nare synthesized in a core / shell spherical morphology where the magnetically\nhard, high anisotropy layer is CoFe$_2$O$_4$ (CFO) while the lower anisotropy\nmaterial is Fe$_3$O$_4$ (FO). The nanoparticles have a mean diameter of\n$\\sim$9.2 - 9.6 nm and are synthesized as two variants: a conventional hard /\nsoft core / shell structure with a CFO core / FO shell (CFO@FO) and the\ninverted structure FO core / CFO shell (FO@CFO). High resolution electron\nmicroscopy confirms the coherent spinel structure across the core / shell\nboundary in both variants while magnetometry indicates the nanoparticles are\nsuperparamagnetic at 300 K and develop a considerable anisotropy at reduced\ntemperatures. Low temperature \\textit{M vs. H} loops suggest a multi-step\nreversal process. Temperature dependent small angle neutron scattering (SANS)\nwith full polarization analysis reveals a strong perpendicular plane alignment\nof the spins near zero field, indicative of spin canting, but the perpendicular\nalignment quickly disappears upon application of a weak field and little spin\nordering parallel to the field until the coercive field is reached. Above the\ncoercive field of the sample, spins orient predominantly along the field\ndirection. At both zero field and near saturation, the parallel magnetic SANS\npeak coincides with the structural peak, indicating the magnetization is\nuniform throughout the nanoparticle volume, while near the coercive field the\nparallel scattering peak shifts to higher momentum transfer (Q), suggesting\nthat the coherent scattering volume is smaller and likely originates in the\nsofter Fe$_3$O$_4$ portion of the nanoparticle.\n"}
+{"abstract": "  We present the Open Predicate Query Language (OPQL); a method for\nconstructing a virtual KB (VKB) trained entirely from text. Large Knowledge\nBases (KBs) are indispensable for a wide-range of industry applications such as\nquestion answering and recommendation. Typically, KBs encode world knowledge in\na structured, readily accessible form derived from laborious human annotation\nefforts. Unfortunately, while they are extremely high precision, KBs are\ninevitably highly incomplete and automated methods for enriching them are far\ntoo inaccurate. Instead, OPQL constructs a VKB by encoding and indexing a set\nof relation mentions in a way that naturally enables reasoning and can be\ntrained without any structured supervision. We demonstrate that OPQL\noutperforms prior VKB methods on two different KB reasoning tasks and,\nadditionally, can be used as an external memory integrated into a language\nmodel (OPQL-LM) leading to improvements on two open-domain question answering\ntasks.\n"}
+{"abstract": "  We consider the classical Yang-Mills system coupled with a Dirac equation in\n3+1 dimensions. Using that most of the nonlinear terms fulfill a null condition\nwe prove local well-posedness for data with minimal regularity assumptions.\nThis problem for smooth data was solved forty years ago by Y. Choquet-Bruhat\nand D. Christodoulou. Our result generalizes a similar result for the\nYang-Mills equation by S. Selberg and A. Tesfahun.\n"}
+{"abstract": "  We present spectroscopic observations of superthin galaxies. Superthin\ngalaxies have the thinnest stellar disks among disk galaxies. A sample of 138\nsuperthins was observed in visible light with the 3.5 m telescope at Apache\nPoint Observatory in New Mexico to obtain the rotation curves of the ionized\ngas in the galaxies. The sample represents the largest survey of superthin\ngalaxies so far and provides a database to investigate the kinematic and\ndynamic properties of this special type of extragalactic objects. Here we\npresent the rotation curves of our sample objects.\n"}
+{"abstract": "  Controlling the spin dynamics and spin lifetimes is one of the main\nchallenges in spintronics. To this end, the study of the spin diffusion in\ntwo-dimensional electron gases (2DEGs) shows that when the Rashba and\nDresselhaus spin-orbit couplings (SOC) are balanced, a persistent spin helix\nregime arises. There, a striped spin pattern shows a long lifetime, limited\nonly by the cubic Dresselhaus SOC, and its dynamics can be controlled by\nin-plane drift fields. Here, we derive a spin diffusion equation for\nnon-degenerate two-subbands 2DEGs. We show that the intersubband scattering\nrate, which is defined by the overlap of the subband densities, enters as a new\nnob to control the spin dynamics, and can be controlled by electric fields,\nbeing maximum for symmetric quantum wells. We find that for large intersubband\ncouplings the dynamics follow an effective diffusion matrix given by\napproximately half of the subband-averaged matrices. This extra 1/2 factor\narises from Matthiessen's rule summing over the intra- and intersubband\nscattering rates, and leads to a reduced diffusion constant and larger spin\nlifetimes. We illustrate our findings with numerical solutions of the diffusion\nequation with parameters extracted from realistic Schr\\\"odinger-Poisson\ncalculations.\n"}
+{"abstract": "  Distributed data analysis without revealing the individual data has recently\nattracted significant attention in several applications. A collaborative data\nanalysis through sharing dimensionality reduced representations of data has\nbeen proposed as a non-model sharing-type federated learning. This paper\nanalyzes the accuracy and privacy evaluations of this novel framework. In the\naccuracy analysis, we provided sufficient conditions for the equivalence of the\ncollaborative data analysis and the centralized analysis with dimensionality\nreduction. In the privacy analysis, we proved that collaborative users' private\ndatasets are protected with a double privacy layer against insider and external\nattacking scenarios.\n"}
+{"abstract": "  There has recently been considerable interest in addressing the problem of\nunifying distributed statistical analyses into a single coherent inference.\nThis problem naturally arises in a number of situations, including in big-data\nsettings, when working under privacy constraints, and in Bayesian model choice.\nThe majority of existing approaches have relied upon convenient approximations\nof the distributed analyses. Although typically being computationally\nefficient, and readily scaling with respect to the number of analyses being\nunified, approximate approaches can have significant shortcomings -- the\nquality of the inference can degrade rapidly with the number of analyses being\nunified, and can be substantially biased even when unifying a small number of\nanalyses that do not concur. In contrast, the recent Fusion approach of Dai et\nal. (2019) is a rejection sampling scheme which is readily parallelisable and\nis exact (avoiding any form of approximation other than Monte Carlo error),\nalbeit limited in applicability to unifying a small number of low-dimensional\nanalyses. In this paper we introduce a practical Bayesian Fusion approach. We\nextend the theory underpinning the Fusion methodology and, by embedding it\nwithin a sequential Monte Carlo algorithm, we are able to recover the correct\ntarget distribution. By means of extensive guidance on the implementation of\nthe approach, we demonstrate theoretically and empirically that Bayesian Fusion\nis robust to increasing numbers of analyses, and coherently unifying analyses\nwhich do not concur. This is achieved while being computationally competitive\nwith approximate schemes.\n"}
+{"abstract": "  The liquid argon time projection chamber (LArTPC) detector technology has an\nexcellent capability to measure properties of low-energy neutrinos produced by\nthe sun and supernovae and to look for exotic physics at very low energies. In\norder to achieve those physics goals, it is crucial to identify and reconstruct\nsignals in the waveforms recorded on each TPC wire. In this paper, we report on\na novel algorithm based on a one-dimensional convolutional neural network (CNN)\nto look for the region-of-interest (ROI) in raw waveforms. We test this\nalgorithm using data from the ArgoNeuT experiment in conjunction with an\nimproved noise mitigation procedure and a more realistic data-driven noise\nmodel for simulated events. This deep-learning ROI finder shows promising\nperformance in extracting small signals and gives an efficiency approximately\ntwice that of the traditional algorithm in the low energy region of\n$\\sim$0.03-0.1 MeV. This method offers great potential to explore low-energy\nphysics using LArTPCs.\n"}
+{"abstract": "  The dataset described in this paper contains daily data about COVID-19 cases\nthat occurred in Italy over the period from Jan. 28, 2020 to March 20, 2021,\ndivided into ten age classes of the population, the first class being 0-9\nyears, the tenth class being 90 years and over. The dataset contains eight\ncolumns, namely: date (day), age class, number of new cases, number of newly\nhospitalized patients, number of patients entering intensive care, number of\ndeceased patients, number of recovered patients, number of active infected\npatients. This data has been officially released for research purposes by the\nItalian authority for COVID-19 epidemiologic surveillance (Istituto Superiore\ndi Sanit\\`a - ISS), upon formal request by the authors, in accordance with the\nOrdonnance of the Chief of the Civil Protection Department n. 691 dated Aug. 4\n2020. A separate file contains the numerosity of the population in each age\nclass, according to the National Institute of Statistics (ISTAT) data of the\nresident population of Italy as of Jan. 2020. This data has potential use, for\ninstance, in epidemiologic studies of the effects of the COVID-19 contagion in\nItaly, in mortality analysis by age class, and in the development and testing\nof dynamical models of the contagion.\n"}
+{"abstract": "  Optimal transport distances have become a classic tool to compare probability\ndistributions and have found many applications in machine learning. Yet,\ndespite recent algorithmic developments, their complexity prevents their direct\nuse on large scale datasets. To overcome this challenge, a common workaround is\nto compute these distances on minibatches i.e. to average the outcome of\nseveral smaller optimal transport problems. We propose in this paper an\nextended analysis of this practice, which effects were previously studied in\nrestricted cases. We first consider a large variety of Optimal Transport\nkernels. We notably argue that the minibatch strategy comes with appealing\nproperties such as unbiased estimators, gradients and a concentration bound\naround the expectation, but also with limits: the minibatch OT is not a\ndistance. To recover some of the lost distance axioms, we introduce a debiased\nminibatch OT function and study its statistical and optimisation properties.\nAlong with this theoretical analysis, we also conduct empirical experiments on\ngradient flows, generative adversarial networks (GANs) or color transfer that\nhighlight the practical interest of this strategy.\n"}
+{"abstract": "  Fast scramblers are dynamical quantum systems that produce many-body\nentanglement on a timescale that grows logarithmically with the system size\n$N$. We propose and investigate a family of deterministic, fast scrambling\nquantum circuits realizable in near-term experiments with arrays of neutral\natoms. We show that three experimental tools -- nearest-neighbour Rydberg\ninteractions, global single-qubit rotations, and shuffling operations\nfacilitated by an auxiliary tweezer array -- are sufficient to generate\nnonlocal interaction graphs capable of scrambling quantum information using\nonly $O(\\log N)$ parallel applications of nearest-neighbor gates. These tools\nenable direct experimental access to fast scrambling dynamics in a highly\ncontrolled and programmable way, and can be harnessed to produce highly\nentangled states with varied applications.\n"}
+{"abstract": "  Anomalies in the flux-ratios of the images of quadruply-lensed quasars have\nbeen used to constrain the nature of dark matter. Assuming these lensing\nperturbations are caused by dark matter haloes, it is possible to constrain the\nmass of a hypothetical Warm Dark Matter (WDM) particle to be $m_\\chi > 5.2$\nkeV. However, the assumption that perturbations are only caused by DM haloes\nmight not be correct as other structures, such as filaments and pancakes, exist\nand make up a significant fraction of the mass in the universe, ranging between\n5$\\%$ -- 50$\\%$ depending on the dark matter model. Using novel\nfragmentation-free simulations of 1 and 3keV WDM cosmologies we study these\n\"non-halo\" structures and estimate their impact on flux-ratio observations. We\nfind that these structures display sharp density gradients with short\ncorrelation lengths, and can contribute more to the lensing signal than all\nhaloes up to the half-mode mass combined, thus reducing the differences\nexpected among WDM models. We estimate that this becomes especially important\nfor any flux-ratio based constraint sensitive to haloes of mass $M \\sim 10^8\nM_\\odot$. We conclude that accounting for all types structures in\nstrong-lensing observations is required to improve the accuracy of current and\nfuture constraints.\n"}
+{"abstract": "  The source of diffuse aurora has been widely studied and linked to electron\ncyclotron harmonic (ECH) and upper-band chorus (UBC) waves. It is known that\nthese waves scatter 100s of eV to 10s of keV electrons from the plasma sheet,\nbut the relative contribution of each wave type is still an open question. In\nthis paper, we report on an interesting and unusual auroral feature observed on\nMarch 15, 2002. We believe that these observations could help further our\nunderstanding of waves associated with diffuse aurora. This diffuse auroral\nfeature is characterized by four phases: (1) the initial phase exhibiting\nregular diffuse aurora, (2) the brightening phase, where an east-west auroral\nstripe rapidly brightens, (3) the eraser phase, where the stripe dims to below\nits initial state, and (4) the recovery phase, where the diffuse aurora returns\nto its original brightness. Using a superposed epoch analysis of 22 events, we\ncalculate the average recovery phase time to be 20 seconds, although this\nvaries widely between events. We hypothesize that the process responsible for\nthese auroral eraser events could be the result of chorus waves modulating\ndiffuse aurora.\n"}
+{"abstract": "  We provide sufficient conditions for the occurrence of time-periodic Hopf\nbifurcation for the coupled system constituted by a rigid sphere, $\\mathscr S$,\nfreely moving under gravity in a Navier-Stokes liquid. Since the region of flow\nis unbounded (namely, the whole space outside $\\mathscr S$), the main\ndifficulty consists in finding the appropriate functional setting where general\ntheory may apply. In this regard, we are able to show that the problem can be\nformulated as a suitable system of coupled operator equations in Banach spaces,\nwhere the relevant operators are Fredholm of index 0. In such a way, we can use\nthe theory recently introduced by the author, and give sufficient conditions\nfor time-periodic bifurcation to take place.\n"}
+{"abstract": "  Purpose of this research is to forecast the development of sand bodies in\nproductive sediments based on well log data and seismic attributes. The object\nof the study is the productive intervals of Achimov sedimentary complex in the\npart of oil field located in Western Siberia. The research shows a\ntechnological stack of machine learning algorithms, methods for enriching the\nsource data with synthetic ones and algorithms for creating new features. The\nresult was the model of regression relationship between the values of natural\nradioactivity of rocks and seismic wave field attributes with an acceptable\nprediction quality. Acceptable quality of the forecast is confirmed both by\nmodel cross validation, and by the data obtained following the results of new\nwell.\n"}
+{"abstract": "  American football is a leading sport for contact-related injuries such as\ncervical spine injuries, some of which result from an unforeseen hit. The use\nof a feedback mechanism to alert an athlete of a potential hit may mitigate the\nrisk for sport-related concussion and catastrophic injury by allowing athletes\nto effectively brace for an otherwise unforeseen impact. In this project, we\ncreated a proximity sensor system using radio frequency identification (RFID)\ntechnology to send haptic feedback to the wearer before a potential impact. Our\npilot test consisted of three participants who wore our novel sensor system,\nwho ran simulated routes to represent player-to-player contact. Additionally,\nwe used simulated data from a Network Live Football (NFL) league game to\nfurther validate our contact prediction algorithm. Results show that our method\ncan predict players' collisions with less than 14% false-alarms.\n"}
+{"abstract": "  The direct and indirect boundary element methods, accelerated via the fast\nmultipole method, are applied to numerical simulation of room acoustics for\nlarge rooms of volume $\\sim 150$ $m^{3}$ and frequencies up to 5 kHz on a\nworkstation. As the parameter $kD$ (wavenumber times room diameter) is large,\nstabilization of the previously developed FMM algorithms is required for\naccuracy. A stabilization scheme is one of the key contribution of this paper.\nThe computations are validated using well-known image source solutions for\nshoebox shaped rooms. Computations for L-shaped rooms are performed to\nillustrate the ability to capture diffractions. The ability to model in-room\nbaffles, and boundary openings (doors/windows) is also demonstrated. The\nlargest case has $kD>1100$ with a discretization of size 6 million elements.\nThe performance of different boundary integral formulations was compared, and\ntheir rates of convergence using a preconditioned flexible GMRES were found to\nbe substantially different. These promising results suggest a path to efficient\nsimulations of room acoustics via high performance boundary element methods.\n"}
+{"abstract": "  One of the most used priors in Bayesian clustering is the Dirichlet prior. It\ncan be expressed as a Chinese Restaurant Process. This process allows\nnonparametric estimation of the number of clusters when partitioning datasets.\nIts key feature is the \"rich-get-richer\" property, which assumes a cluster has\nan a priori probability to get chosen linearly dependent on population. In this\npaper, we show that such prior is not always the best choice to model data. We\nderive the Powered Chinese Restaurant process from a modified version of the\nDirichlet-Multinomial distribution to answer this problem. We then develop some\nof its fundamental properties (expected number of clusters, convergence).\nUnlike state-of-the-art efforts in this direction, this new formulation allows\nfor direct control of the importance of the \"rich-get-richer\" prior.\n"}
+{"abstract": "  We study the well-posedness and the spatial behavior at infinity of perfect\nfluid flows on $\\R^d$ with initial data in a scale of weighted Sobolev spaces\nthat allow spatial growth/decay at infinity as $|x|^\\beta$ with $\\beta<1/2$. In\nparticular, we show that the solution of the Euler equation generically\ndevelops an asymptotic expansion at infinity with non-vanishing asymptotic\nterms that depend analytically on time and the initial data. We identify the\nevolution space for initial data in the Schwartz class with a certain space of\nsymbols.\n"}
+{"abstract": "  In this work we study microlocal regularity of hyperfunctions defining in\nthis context a class of generalized FBI transforms first introduced for\ndistributions by Berhanu and Hounie. Using a microlocal decomposition of a\nhyperfunction and the generalized FBI transforms we were able to characterize\nthe wave-front set of hyperfunctions according several types of regularity. The\nmicrolocal decomposition allowed us to recover and generalize both classical\nand recent results and, in particular, we proved for differential operators\nwith real-analytic coefficients that if the elliptic regularity theorem\nregarding any reasonable regularity holds for distributions, then it is\nautomatically true for hyperfunctions.\n"}
+{"abstract": "  We report generation of ultrashort UV pulses by soliton self-compression in\nkagom\\'e-style hollow-core photonic crystal fiber filled with ambient air. Pump\npulses with energy 2.6 uJ and duration 54 fs at 400 nm were compressed\ntemporally by a factor of 5, to a duration of ~11 fs. The experimental results\nare supported by numerical simulations, showing that both Raman and Kerr\neffects play a role in the compression dynamics. The convenience of using\nambient air, and the absence of glass windows that would distort the compressed\npulses, makes the setup highly attractive as the basis of an efficient\ntable-top UV pulse compressor.\n"}
+{"abstract": "  Transition-edge sensors (TESs) are capable of highly accurate single particle\nenergy measurement. TESs have been used for a wide range of photon detection\napplications, particularly in astronomy, but very little consideration has been\ngiven to their capabilities as electron calorimeters. Existing electron\nspectrometers require electron filtering optics to achieve energy\ndiscrimination, but this step discards the vast majority of electrons entering\nthe instrument. TESs require no such energy filtering, meaning they could\nprovide orders of magnitude improvement in measurement rate. To investigate the\ncapabilities of TESs in electron spectroscopy, a simulation pipeline has been\ndevised. The pipeline allows the results of a simulated experiment to be\ncompared with the actual spectrum of the incident beam, thereby allowing\nmeasurement accuracy and efficiency to be studied. Using Fisher information,\nthe energy resolution of the simulated detectors was also calculated, allowing\nthe intrinsic limitations of the detector to be separated from the specific\ndata analysis method used. The simulation platform has been used to compare the\nperformance of TESs with existing X-ray photoelectron spectroscopy (XPS)\nanalysers. TESs cannot match the energy resolution of XPS analysers for\nhigh-precision measurements but have comparable or better resolutions for high\ncount rate applications. The measurement rate of a typical XPS analyser can be\nmatched by an array of 10 TESs with 120 microsecond response times and there is\nsignificant scope for improvement, without compromising energy resolution, by\nincreasing array size.\n"}
+{"abstract": "  A key functionality of emerging connected autonomous systems such as smart\ncities, smart transportation systems, and the industrial Internet-of-Things, is\nthe ability to process and learn from data collected at different physical\nlocations. This is increasingly attracting attention under the terms of\ndistributed learning and federated learning. However, in connected autonomous\nsystems, data transfer takes place over communication networks with often\nlimited resources. This paper examines algorithms for communication-efficient\nlearning for linear regression tasks by exploiting the informativeness of the\ndata. The developed algorithms enable a tradeoff between communication and\nlearning with theoretical performance guarantees and efficient practical\nimplementations.\n"}
+{"abstract": "  In this paper we introduce OperA, a transformer-based model that accurately\npredicts surgical phases from long video sequences. A novel attention\nregularization loss encourages the model to focus on high-quality frames during\ntraining. Moreover, the attention weights are utilized to identify\ncharacteristic high attention frames for each surgical phase, which could\nfurther be used for surgery summarization. OperA is thoroughly evaluated on two\ndatasets of laparoscopic cholecystectomy videos, outperforming various\nstate-of-the-art temporal refinement approaches.\n"}
+{"abstract": "  In this paper, we consider the Cauchy problem to the 3D MHD equations. We\nshow that the Serrin--type conditions imposed on one component of the velocity\n$u_{3}$ and one component of magnetic fields $b_{3}$ with $$ u_{3} \\in\nL^{p_{0},1}(-1,0;L^{q_{0}}(B(2))),\\ b_{3} \\in\nL^{p_{1},1}(-1,0;L^{q_{1}}(B(2))), $$\n$\\frac{2}{p_{0}}+\\frac{3}{q_{0}}=\\frac{2}{p_{1}}+\\frac{3}{q_{1}}=1$ and\n$3<q_{0},q_{1}<+\\infty$ imply that the suitable weak solution is regular at\n$(0,0)$. The proof is based on the new local energy estimates introduced by\nChae-Wolf (Arch. Ration. Mech. Anal. 2021) and Wang-Wu-Zhang\n(arXiv:2005.11906).\n"}
+{"abstract": "  We have searched for axion-like resonance states by colliding optical photons\nin a focused laser field (creation beam) by adding another laser field\n(inducing beam) for stimulation of the resonance decays, where\nfrequency-converted signal photons can be created as a result of stimulated\nphoton-photon scattering via exchanges of axion-like resonances. A\nquasi-parallel collision system (QPS) in such a focused field allows access to\nthe sub-eV mass range of resonance particles. In past searches in QPS, for\nsimplicity, we interpreted the scattering rate based on an analytically\ncalculable symmetric collision geometry in both incident angles and incident\nenergies by partially implementing the asymmetric nature to meet the actual\nexperimental conditions. In this paper, we present new search results based on\na complete parameterization including fully asymmetric collisional geometries.\nIn particular, we combined a linearly polarized creation laser and a circularly\npolarized inducing laser to match the new parameterization. A 0.10 mJ / 31 fs\nTi:sapphire laser pulse and a 0.20 mJ / 9 ns Nd:YAG laser pulse were\nspatiotemporally synchronized by sharing a common optical axis and focused into\nthe vacuum system. Under a condition in which atomic background processes were\ncompletely negligible, no significant scattering signal was observed at the\nvacuum pressure of $2.6 \\times 10^{-5}$ Pa, thereby providing upper bounds on\nthe coupling-mass relation by assuming exchanges of scalar and pseudoscalar\nfields at a 95 % confidence level in the sub-eV mass range.\n"}
+{"abstract": "  Small scale jet-induced erosion experiments are useful for identifying the\nscaling of erosion with respect to the various physical parameters (gravity,\ngrain size, gas velocity, gas density, grain density, etc.), and because they\nprovide a data set for benchmarking numerical flow codes. We have performed\nexperiments varying the physical parameters listed above (e.g., gravity was\nvaried in reduced gravity aircraft flights). In all these experiments, a\nsubsonic jet of gas impinges vertically on a bed of sand or lunar soil simulant\nforming a localized scour hole beneath the jet. Videography captures the\nerosion and scour hole formation processes, and analysis of these videos\npost-test identifies the scaling of these processes. This has produced\nimportant new insights into the physics of erosion. Based on these insights, we\nhave developed an erosion rate model that can be applied to generalized\nsituations, such as the erosion of soil beneath a horizontal gas flow on a\nplanetary surface. This is important to lunar exploration because the rate of\nerosion beneath the rocket exhaust plume of a landing spacecraft will determine\nthe amount of sand-blasting damage that can be inflicted upon surrounding\nhardware. Although the rocket exhaust plume at the exit of the nozzle is\nsupersonic, the boundary layer on the lunar surface where erosion occurs is\nsubsonic. The model has been benchmarked through comparison with the Apollo\nlanding videos, which show the blowing lunar soil, and computational fluid\ndynamics simulations of those landings.\n"}
+{"abstract": "  In the traditional quantum theory, one-dimensional quantum spin models\npossess a factorization surface where the ground states are fully separable\nhaving vanishing bipartite as well as multipartite entanglement. We report that\nin the non-Hermitian counterpart of these models, these factorization surfaces\neither can predict the exceptional points where the unbroken to the broken\ntransition occurs or can guarantee the reality of the spectrum, thereby\nproposing a procedure to reveal the unbroken phase. We first analytically\ndemonstrate it for the nearest-neighbor rotation-time RT-symmetric XY model\nwith uniform and alternating transverse magnetic fields, referred to as the\niATXY model. Exact diagonalization techniques are then employed to establish\nthis fact for the RT-symmetric XYZ model with short- and long-range\ninteractions as well as for the variable-ranged iATXY model. Moreover, we show\nthat although the factorization surface prescribes the unbroken phase of the\nnon-Hermitian model, the bipartite nearest-neighbor entanglement at the\nexceptional point is nonvanishing.\n"}
+{"abstract": "  Usually, the realizations of the noncommutative Snyder model lead to a\nnonassociative star product. However, it has been shown that this problem can\nbe avoided by adding to the spacetime coordinates new tensorial degrees of\nfreedom. The model so obtained, called extended Snyder model, can be subject to\na $\\kappa$-deformation, giving rise to a unification of the Snyder and the\n$\\kappa$-Poincar\\'e algebras in the formalism of extended spacetime. In this\npaper we review this construction and consider the generic realizations of the\n$\\kappa$-deformed extended Snyder model, calculating the associated star\nproduct, coproduct and twist in a perturbative setting. We also introduce a\nrepresentation of the Lorentz algebra in the extended space and speculate on\npossible interpretations of the tensorial degrees of freedom.\n"}
+{"abstract": "  In the present paper, we study the distribution of the return points in the\nfibers for a RDS (random dynamical systems) nonuniformly expanding preserving\nan ergodic probability, we also show the abundance of nonlacunarity of\nhyperbolic times that are obtained along the orbits through the fibers. We\nconclude that any ergodic measure with positive Lyapunov exponents satisfies\nthe nonuniform specification property between fibers. As consequences, we prove\nthat any expanding measure is the limit of probability measure whose measures\nof disintegration on the fibers are supported by a finite number of return\npoints and we prove that the average of the measures on the fibers\ncorresponding to a disintegration, along an orbit in the base dynamics is the\nlimit of Dirac measures supported in return orbits on the fibers.\n"}
+{"abstract": "  In forensic investigations it is often of value to establish whether two\nphones were used by the same person during a given time period. We present a\nmethod that uses time and location of cell tower registrations of mobile phones\nto assess the strength of evidence that any pair of phones were used by the\nsame person. The method is transparent as it uses logistic regression to\ndiscriminate between the hypotheses of same and different user, and a standard\nkernel density estimation to quantify the weight of evidence in terms of a\nlikelihood ratio. We further add to previous theoretical work by training and\nvalidating our method on real world data, paving the way for application in\npractice. The method shows good performance under different modeling choices\nand robustness under lower quantity or quality of data. We discuss practical\nusage in court.\n"}
+{"abstract": "  Viral fusion proteins are attached to the membrane of enveloped viruses (a\ngroup that includes Coronaviruses, Dengue, HIV and Influenza) and catalyze\nfusion between the viral and host membranes, enabling the virus to insert its\ngenetic material into the host cell. Given the importance of these\nbiomolecules, this work presents a centralized database containing the most\nrelevant information on viral fusion proteins, available through a free-to-use\nweb server accessible through the URL https://viralfp.bio.di.uminho.pt/. This\nweb application contains several bioinformatic tools, such as Clustal sequence\nalignment and Weblogo, including as well a machine learning-based tool capable\nof predicting the location of fusion peptides (the component of fusion proteins\nthat inserts into the host's cell membrane) within the fusion protein sequence.\nGiven the crucial role of these proteins in viral infection, their importance\nas natural targets of our immune system and their potential as therapeutic\ntargets, this web application aims to foster our ability to fight pathogenic\nviruses.\n"}
+{"abstract": "  We define an involution on the space of compact tempered unipotent\nrepresentations of inner twists of a split simple $p$-adic group $G$ and\ninvestigate its behaviour with respect to restrictions to reductive quotients\nof maximal compact open subgroups. In particular, we formulate a precise\nconjecture about the relation with a version of Lusztig's nonabelian Fourier\ntransform on the space of unipotent representations of the (possibly\ndisconnected) reductive quotients of maximal compact subgroups. We give\nevidence of the conjecture, including proofs for $\\mathsf{SL}_n$ and\n$\\mathsf{PGL}_n$.\n"}
+{"abstract": "  In recent years, resistive RAM often referred to as memristor is actively\npursued as a replacement for nonvolatile-flash memory due to its superior\ncharacteristics such as high density, scalability, low power operation, high\nendurance, and fast operating speed. However, one of the challenges that need\nto be overcome is the loss of retention for both ON- and OFF-states; the\nretention loss. While various models are proposed to explain the retention loss\nin memristors consisting of a switching layer, in this paper, we propose that\nthe nucleation of clusters made of electrical charges, charge-clusters, in the\nswitching layer acts as a potential root cause for the retention loss. The\nnucleation results from localized electric-field produced intermittently during\ncyclic switching operations. We use the phase-field method to illustrate how\nthe nucleation of charge-clusters gives rise to the retention loss. Our results\nsuggest that the degree at which the retention loss arises is linked to the\nnumber of cyclic switching operations since the probability at which nucleation\ncenters form increases with the number of cycle switching operations, which is\nconsistent with a range of experimental findings previously reported.\n"}
+{"abstract": "  Recent advances in protecting node privacy on graph data and attacking graph\nneural networks (GNNs) gain much attention. The eye does not bring these two\nessential tasks together yet. Imagine an adversary can utilize the powerful\nGNNs to infer users' private labels in a social network. How can we\nadversarially defend against such privacy attacks while maintaining the utility\nof perturbed graphs? In this work, we propose a novel research task,\nadversarial defenses against GNN-based privacy attacks, and present a graph\nperturbation-based approach, NetFense, to achieve the goal. NetFense can\nsimultaneously keep graph data unnoticeability (i.e., having limited changes on\nthe graph structure), maintain the prediction confidence of targeted label\nclassification (i.e., preserving data utility), and reduce the prediction\nconfidence of private label classification (i.e., protecting the privacy of\nnodes). Experiments conducted on single- and multiple-target perturbations\nusing three real graph data exhibit that the perturbed graphs by NetFense can\neffectively maintain data utility (i.e., model unnoticeability) on targeted\nlabel classification and significantly decrease the prediction confidence of\nprivate label classification (i.e., privacy protection). Extensive studies also\nbring several insights, such as the flexibility of NetFense, preserving local\nneighborhoods in data unnoticeability, and better privacy protection for\nhigh-degree nodes.\n"}
+{"abstract": "  Formation of quantum scars in many-body systems provides a novel mechanism\nfor enhancing coherence of weakly entangled states. At the same time, coherence\nof edge modes in certain symmetry protected topological (SPT) phases can\npersist away from the ground state. In this work we show the existence of\nmany-body scars and their implications on bulk coherence in such an SPT phase.\nTo this end, we study the eigenstate properties and the dynamics of an\ninteracting spin-$1/2$ chain with three-site \"cluster\" terms hosting a\n$\\mathbb{Z}_2 \\times \\mathbb{Z}_2$ SPT phase. Focusing on the weakly\ninteracting regime, we find that eigenstates with volume-law entanglement\ncoexist with area-law entangled eigenstates throughout the spectrum. We show\nthat a subset of the latter can be constructed by virtue of repeated cluster\nexcitations on the even or odd sublattice of the chain, resulting in an\nequidistant \"tower\" of states, analogous to the phenomenology of quantum\nmany-body scars. We further demonstrate that these scarred eigenstates support\nnonthermal expectation values of local cluster operators in the bulk and\nexhibit signatures of topological order even at finite energy densities.\nStudying the dynamics for out-of-equilibrium states drawn from the\nnoninteracting \"cluster basis\", we unveil that nonthermalizing bulk dynamics\ncan be observed on long time scales if clusters on odd and even sites are\nenergetically detuned. In this case, cluster excitations remain essentially\nconfined to one of the two sublattices such that inhomogeneous cluster\nconfigurations cannot equilibrate and thermalization of the full system is\nimpeded. Our work sheds light on the role of quantum many-body scars in\npreserving SPT order at finite temperature and the possibility of coherent bulk\ndynamics in models with SPT order beyond the existence of long-lived edge\nmodes.\n"}
+{"abstract": "  Many emerging reconfigurable optical systems are limited by routing\ncomplexity when producing dynamic, two-dimensional (2D) electric fields. Using\na gradient-based inverse designed, static phase-mask doublet, we propose an\noptical system to produce 2D intensity wavefronts using a one-dimensional (1D)\nintensity Spatial Light Modulator (SLM). We show the capability of mapping each\npoint in a 49 element 1D array to a distinct 7x7 2D spatial distribution. Our\nproposed method will significantly relax the routing complexity of 2D\nsub-wavelength SLMs, possibly enabling next-generation SLMs to leverage novel\npixel architectures and new materials.\n"}
+{"abstract": "  Permalloy Ni$_{80}$Fe$_{20}$ is one of the key magnetic materials in the\nfield of magnonics. Its potential would be further unveiled if it could be\ndeposited in three dimensional (3D) architectures of sizes down to the\nnanometer. Atomic Layer Deposition, ALD, is the technique of choice for\ncovering arbitrary shapes with homogeneous thin films. Early successes with\nferromagnetic materials include nickel and cobalt. Still, challenges in\ndepositing ferromagnetic alloys reside in the synthesis via decomposing the\nconsituent elements at the same temperature and homogeneously. We report\nplasma-enhanced ALD to prepare permalloy Ni$_{80}$Fe$_{20}$ thin films and\nnanotubes using nickelocene and iron(III) tert-butoxide as metal precursors,\nwater as the oxidant agent and an in-cycle plasma enhanced reduction step with\nhydrogen. We have optimized the ALD cycle in terms of Ni:Fe atomic ratio and\nfunctional properties. We obtained a Gilbert damping of 0.013, a resistivity of\n28 $\\mu\\Omega$cm and an anisotropic magnetoresistance effect of 5.6 $\\%$ in the\nplanar thin film geometry. We demonstrate that the process also works for\ncovering GaAs nanowires, resulting in permalloy nanotubes with high aspect\nratios and diameters of about 150 nm. Individual nanotubes were investigated in\nterms of crystal phase, composition and spin-dynamic response by microfocused\nBrillouin Light Scattering. Our results enable NiFe-based 3D spintronics and\nmagnonic devices in curved and complex topology operated in the GHz frequency\nregime.\n"}
+{"abstract": "  Extensive works have tackled Language Identification (LID) in the speech\ndomain, however their application to the singing voice trails and performances\non Singing Language Identification (SLID) can be improved leveraging recent\nprogresses made in other singing related tasks. This work presents a modernized\nphonotactic system for SLID on polyphonic music: phoneme recognition is\nperformed with a Connectionist Temporal Classification (CTC)-based acoustic\nmodel trained with multilingual data, before language classification with a\nrecurrent model based on the phonemes estimation. The full pipeline is trained\nand evaluated with a large and publicly available dataset, with unprecedented\nperformances. First results of SLID with out-of-set languages are also\npresented.\n"}
+{"abstract": "  We present full-sky maps of the Integrated Sachs-Wolfe effect (ISW) for the\nMICE Grand Challenge lightcone simulation up to redshift 1.4. The maps are\nconstructed in the linear regime using spherical Bessel transforms. We compare\nand contrast this procedure against analytical approximations found in the\nliterature. By computing the ISW in the linear regime, we remove the\nsubstantial computing and storage resources required to calculate the\nnon-linear Rees-Sciama effect. Since the linear ISW at low redshift $z\\leq1$,\nat large angular scales, and after matter domination is $\\sim 10^{2}\\times$\nlarger in $\\Delta T/T$, this has a negligible impact on the maps produced and\nonly becomes relevant on scales which are dominated by cosmic microwave\nbackground (CMB) anisotropies. The MICE simulation products have been\nextensively used for studies involving current and future galaxy surveys. The\navailability of these maps will allow MICE to be used for future galaxy and CMB\ncross-correlation studies, ISW reconstruction studies, and ISW void-stacking\nstudies probed by galaxy surveys such as Dark Energy Survey, Dark Energy\nSpectroscopic Instrument, Euclid, and Rubin Legacy Survey of Space and Time.\nThe pipeline developed in this study is provided as a public Python package\npyGenISW. This could be used in the future studies for constructing the ISW\nfrom existing and future simulation suites probing vast sets of cosmological\nparameters and models.\n"}
+{"abstract": "  Using a lifecycle framework with Epstein-Zin (1989) utility and a\nmixed-integer optimization approach, we compute the optimal age to claim Social\nSecurity benefits. Taking advantage of homogeneity, a sufficient statistic is\nthe ratio of wealth to the primary insurance amount (PIA). If the investor's\nwealth to PIA ratio exceeds a certain threshold, individuals should defer\nSocial Security for at least a year. The optimal threshold depends on mortality\nassumptions and an individual's utility preferences, but is less sensitive to\ncapital market assumptions. The threshold wealth to PIA ratio increases from\n5.5 for men and 5.2 for women at age 62 to 11.1 for men and 10.4 for women at\nage 69. Below the threshold wealth to PIA ratio, individuals claim Social\nSecurity to raise consumption. Above this level, investors can afford to fund\nconsumption out of wealth for at least one year, and then claim a higher\nbenefit.\n"}
+{"abstract": "  We study a class of optimization problems including matrix scaling, matrix\nbalancing, multidimensional array scaling, operator scaling, and tensor scaling\nthat arise frequently in theory and in practice. Some of these problems, such\nas matrix and array scaling, are convex in the Euclidean sense, but others such\nas operator scaling and tensor scaling are geodesically convex on a different\nRiemannian manifold. Trust region methods, which include box-constrained\nNewton's method, are known to produce high precision solutions very quickly for\nmatrix scaling and matrix balancing (Cohen et. al., FOCS 2017, Allen-Zhu et.\nal. FOCS 2017), and result in polynomial time algorithms for some geodesically\nconvex problems like operator scaling (Garg et. al. STOC 2018, B\\\"urgisser et.\nal. FOCS 2019). One is led to ask whether these guarantees also hold for\nmultidimensional array scaling and tensor scaling.\n  We show that this is not the case by exhibiting instances with exponential\ndiameter bound: we construct polynomial-size instances of 3-dimensional array\nscaling and 3-tensor scaling whose approximate solutions all have doubly\nexponential condition number. Moreover, we study convex-geometric notions of\ncomplexity known as margin and gap, which are used to bound the running times\nof all existing optimization algorithms for such problems. We show that margin\nand gap are exponentially small for several problems including array scaling,\ntensor scaling and polynomial scaling. Our results suggest that it is\nimpossible to prove polynomial running time bounds for tensor scaling based on\ndiameter bounds alone. Therefore, our work motivates the search for analogues\nof more sophisticated algorithms, such as interior point methods, for\ngeodesically convex optimization that do not rely on polynomial diameter\nbounds.\n"}
+{"abstract": "  In the present work, we investigate the hidden-strangeness production process\nin the $S=+1$ channel via $K^+p\\to K^+\\phi\\,p$, focussing on the exotic\n\\textit{pentaquark} molecular $K^*\\Sigma$ bound state, assigned by\n$P^+_s(2071,3/2^-)$. For this purpose, we employ the effective Lagrangian\napproach in the tree-level Born approximation. Using the experimental and\ntheoretical inputs for the exotic state and for the ground-state hadron\ninteractions, the numerical results show a small but obvious peak structure\nfrom $P^+_s$ with the signal-to-background ratio $\\approx1.7\\,\\%$, and it is\nenhanced in the backward-scattering region of the outgoing $K^+$ in the\ncenter-of-mass frame. We also find that the contribution from the\n$K^*(1680,1^-)$ meson plays an important role to reproduce the data. The\nproton-spin polarizations are taken into account to find a way to reduce the\nbackground. The effects of the possible $27$-plet pentaquark $\\Theta^{++}_{27}$\nis discussed as well.\n"}
+{"abstract": "  Single image pose estimation is a fundamental problem in many vision and\nrobotics tasks, and existing deep learning approaches suffer by not completely\nmodeling and handling: i) uncertainty about the predictions, and ii) symmetric\nobjects with multiple (sometimes infinite) correct poses. To this end, we\nintroduce a method to estimate arbitrary, non-parametric distributions on\nSO(3). Our key idea is to represent the distributions implicitly, with a neural\nnetwork that estimates the probability given the input image and a candidate\npose. Grid sampling or gradient ascent can be used to find the most likely\npose, but it is also possible to evaluate the probability at any pose, enabling\nreasoning about symmetries and uncertainty. This is the most general way of\nrepresenting distributions on manifolds, and to showcase the rich expressive\npower, we introduce a dataset of challenging symmetric and nearly-symmetric\nobjects. We require no supervision on pose uncertainty -- the model trains only\nwith a single pose per example. Nonetheless, our implicit model is highly\nexpressive to handle complex distributions over 3D poses, while still obtaining\naccurate pose estimation on standard non-ambiguous environments, achieving\nstate-of-the-art performance on Pascal3D+ and ModelNet10-SO(3) benchmarks.\n"}
+{"abstract": "  This report bases itself on the idea of using concrete examples to verify\nconceptual schemas, and in particular cardinality constraints. When novice ORM\nmodellers model domains, the selection of proper cardinality constraints for\nrelationship types is quite often prone to errors. In this report we propose a\nmechanism for the generation of significant examples for selected subschemas.\nThe generated examples are significant in the sense that they illustrate the\npossible combinations of instances that are allowed with respect to the\ncardinality constraints on the involved relationship types. In this report we\nfirstly provide a brief informal discussion of the basic idea. Then we present\na syntactic mechanism to select the subschema for which example instances are\nto be generated. This is followed by the actual example generation algorithm\nitself. We will also present, as a {\\em spin-off}, an algorithm that allows us\nto detect possible flaws in the conceptual schema by calculating the number of\ninstances that can be used to populate the types in the schema.\n"}
+{"abstract": "  Over the last few years, neural network derived word embeddings became\npopular in the natural language processing literature. Studies conducted have\nmostly focused on the quality and application of word embeddings trained on\npublic available corpuses such as Wikipedia or other news and social media\nsources. However, these studies are limited to generic text and thus lack\ntechnical and scientific nuances such as domain specific vocabulary,\nabbreviations, or scientific formulas which are commonly used in academic\ncontext. This research focuses on the performance of word embeddings applied to\na large scale academic corpus. More specifically, we compare quality and\nefficiency of trained word embeddings to TFIDF representations in modeling\ncontent of scientific articles. We use a word2vec skip-gram model trained on\ntitles and abstracts of about 70 million scientific articles. Furthermore, we\nhave developed a benchmark to evaluate content models in a scientific context.\nThe benchmark is based on a categorization task that matches articles to\njournals for about 1.3 million articles published in 2017. Our results show\nthat content models based on word embeddings are better for titles (short text)\nwhile TFIDF works better for abstracts (longer text). However, the slight\nimprovement of TFIDF for larger text comes at the expense of 3.7 times more\nmemory requirement as well as up to 184 times higher computation times which\nmay make it inefficient for online applications. In addition, we have created a\n2-dimensional visualization of the journals modeled via embeddings to\nqualitatively inspect embedding model. This graph shows useful insights and can\nbe used to find competitive journals or gaps to propose new journals.\n"}
+{"abstract": "  Due to the increasing connectivity of modern vehicles, collected data is no\nlonger only stored in the vehicle itself but also transmitted to car\nmanufacturers and vehicle assistant apps. This development opens up new\npossibilities for digital forensics in criminal investigations involving modern\nvehicles. This paper deals with the digital forensic analysis of vehicle\nassistant apps of eight car manufacturers. We reconstruct the driver's\nactivities based on the data stored on the smartphones and in the\nmanufacturer's backend.\n  For this purpose, data of the Android and iOS apps of the car manufacturers\nAudi, BMW, Ford, Mercedes, Opel, Seat, Tesla, and Volkswagen were extracted\nfrom the smartphone and examined using digital forensic methods in accordance\nwith lawful government-approved forensics guidelines. Additionally,\nmanufacturer data was retrieved using Subject Access Requests. Using the\nextensive data gathered, we successfully reconstruct trips and refueling\nprocesses, determine parking positions and duration, and track the locking and\nunlocking of the vehicle.\n  These findings show that the digital forensic investigation of smartphone\napplications is a useful addition to vehicle forensics and should therefore be\ntaken into account in the strategic preparation of future digital forensic\ninvestigations.\n"}
+{"abstract": "  Collaborative Filtering (CF) has become the standard approach to solve\nrecommendation systems (RS) problems. Collaborative Filtering algorithms try to\nmake predictions about interests of a user by collecting the personal interests\nfrom multiple users. There are multiple CF algorithms, each one of them with\nits own biases. It is the Machine Learning practitioner that has to choose the\nbest algorithm for each task beforehand. In Recommender Systems, different\nalgorithms have different performance for different users within the same\ndataset. Meta Learning (MtL) has been used to choose the best algorithm for a\ngiven problem. Meta Learning is usually applied to select algorithms for a\nwhole dataset. Adapting it to select the to the algorithm for a single user in\na RS involves several challenges. The most important is the design of the\nmetafeatures which, in typical meta learning, characterize datasets while here,\nthey must characterize a single user. This work presents a new meta-learning\nbased framework named $\\mu$-cf2vec to select the best algorithm for each user.\nWe propose using Representation Learning techniques to extract the\nmetafeatures. Representation Learning tries to extract representations that can\nbe reused in other learning tasks. In this work we also implement the framework\nusing different RL techniques to evaluate which one can be more useful to solve\nthis task. In the meta level, the meta learning model will use the metafeatures\nto extract knowledge that will be used to predict the best algorithm for each\nuser. We evaluated an implementation of this framework using MovieLens 20M\ndataset. Our implementation achieved consistent gains in the meta level,\nhowever, in the base level we only achieved marginal gains.\n"}
+{"abstract": "  We study four-dimensional gauge theories with arbitrary simple gauge group\nwith $1$-form global center symmetry and $0$-form parity or discrete chiral\nsymmetry. We canonically quantize on $\\mathbb{T}^3$, in a fixed background\nfield gauging the $1$-form symmetry. We show that the mixed $0$-form/$1$-form\n't Hooft anomaly results in a central extension of the global-symmetry operator\nalgebra. We determine this algebra in each case and show that the anomaly\nimplies degeneracies in the spectrum of the Hamiltonian at any finite-size\ntorus. We discuss the consistency of these constraints with both older and\nrecent semiclassical calculations in $SU(N)$ theories, with or without adjoint\nfermions, as well as with their conjectured infrared phases.\n"}
+{"abstract": "  We introduce a novel method to aggregate Bipolar Argumentation (BA)\nFrameworks expressing opinions by different parties in debates. We use Bipolar\nAssumption-based Argumentation (ABA) as an all-encompassing formalism for BA\nunder different semantics. By leveraging on recent results on judgement\naggregation in Social Choice Theory, we prove several preservation results,\nboth positive and negative, for relevant properties of Bipolar ABA.\n"}
+{"abstract": "  We show that the $n$th derivative with maximal domain is a chaotic operator\nin the spaces $C[a,b]$ and $L_p(a,b)$ ($-\\infty<a<b<\\infty$, $1\\le p<\\infty$)\nfor each $n\\in \\mathbb{N}$.\n"}
+{"abstract": "  We study the multi-user Bayesian persuasion game between one encoder and two\ndecoders, where the first decoder is better informed than the second decoder.\nWe consider two perfect links, one to the first decoder only, and the other to\nboth decoders. We consider that the encoder and both decoders are endowed with\ndistinct and arbitrary distortion functions. We investigate the strategic\nsource coding problem in which the encoder commits to an encoding while the\ndecoders select the sequences of symbols that minimize their respective\ndistortion functions. We characterize the optimal encoder distortion value by\nconsidering successive refinement coding with respect to a specific probability\ndistribution which involves two auxiliary random variables, and captures the\nincentives constraints of both decoders.\n"}
+{"abstract": "  This paper addresses the problem of picking up only one object at a time\navoiding any entanglement in bin-picking. To cope with a difficult case where\nthe complex-shaped objects are heavily entangled together, we propose a\ntopology-based method that can generate non-tangle grasp positions on a single\ndepth image. The core technique is entanglement map, which is a feature map to\nmeasure the entanglement possibilities obtained from the input image. We use\nthe entanglement map to select probable regions containing graspable objects.\nThe optimum grasping pose is detected from the selected regions considering the\ncollision between robot hand and objects. Experimental results show that our\nanalytic method provides a more comprehensive and intuitive observation of\nentanglement and exceeds previous learning-based work in success rates.\nEspecially, our topology-based method does not rely on any object models or\ntime-consuming training process, so that it can be easily adapted to more\ncomplex bin-picking scenes.\n"}
+{"abstract": "  Searching for network width is an effective way to slim deep neural networks\nwith hardware budgets. With this aim, a one-shot supernet is usually leveraged\nas a performance evaluator to rank the performance \\wrt~different width.\nNevertheless, current methods mainly follow a manually fixed weight sharing\npattern, which is limited to distinguish the performance gap of different\nwidth. In this paper, to better evaluate each width, we propose a locally free\nweight sharing strategy (CafeNet) accordingly. In CafeNet, weights are more\nfreely shared, and each width is jointly indicated by its base channels and\nfree channels, where free channels are supposed to loCAte FrEely in a local\nzone to better represent each width. Besides, we propose to further reduce the\nsearch space by leveraging our introduced FLOPs-sensitive bins. As a result,\nour CafeNet can be trained stochastically and get optimized within a min-min\nstrategy. Extensive experiments on ImageNet, CIFAR-10, CelebA and MS COCO\ndataset have verified our superiority comparing to other state-of-the-art\nbaselines. For example, our method can further boost the benchmark NAS network\nEfficientNet-B0 by 0.41\\% via searching its width more delicately.\n"}
+{"abstract": "  Modern neural networks obtain information about the problem and calculate the\noutput solely from the input values. We argue that it is not always optimal,\nand the network's performance can be significantly improved by augmenting it\nwith a query mechanism that allows the network to make several solution trials\nat run time and get feedback on the loss value on each trial. To demonstrate\nthe capabilities of the query mechanism, we formulate an unsupervised (not\ndependant on labels) loss function for Boolean Satisfiability Problem (SAT) and\ntheoretically show that it allows the network to extract rich information about\nthe problem. We then propose a neural SAT solver with a query mechanism called\nQuerySAT and show that it outperforms the neural baseline on a wide range of\nSAT tasks and the classical baselines on SHA-1 preimage attack and 3-SAT task.\n"}
+{"abstract": "  In this work, we consider networks of so-called geometrically exact beams,\nnamely, shearable beams that may undergo large motions. The corresponding\nmathematical model, commonly written in terms of displacements and rotations\nexpressed in a fixed basis (Geometrically Exact Beam model, or GEB), has a\nquasilinear governing system. However, the model may also be written in terms\nof intrinsic variables expressed in a moving basis attached to the beam\n(Intrinsic GEB model, or IGEB) and while the number of equations is then\ndoubled, the latter model has the advantage of being of first-order, hyperbolic\nand only semilinear. First, for any network, we show the existence and\nuniqueness of semi-global in time classical solutions to the IGEB model (i.e.,\nfor arbitrarily large time intervals, provided that the data are small enough).\nThen, for a specific network containing a cycle, we address the problem of\nlocal exact controllability of nodal profiles for the IGEB model -- we steer\nthe solution to satisfy given profiles at one of the multiple nodes by means of\ncontrols applied at the simple nodes -- by using the constructive method of\nZhuang, Leugering and Li [Exact boundary controllability of nodal profile for\nSaint-Venant system on a network with loops, in J. Math. Pures Appl., 2018].\nAfterwards, for any network, we show that the existence of a unique classical\nsolution to the IGEB network implies the same for the corresponding GEB\nnetwork, by using that these two models are related by a nonlinear\ntransformation. In particular, this allows us to give corresponding existence,\nuniqueness and controllability results for the GEB network.\n"}
+{"abstract": "  In the current picture of cosmology and astrophysics, the formation and\nevolution of galaxies is closely linked to that of their dark matter haloes.\nThe best representation of this galaxy-dark matter halo co-evolution is the M*\n- Mhalo relation. In this study we investigate how the radio-mode feedback from\nactive galactic nuclei (AGN) affects the M* - Mhalo relation at redshifts 0.08\n< z < 1.53. We use a set of 111 radio-selected AGN at 3 GHz VLA-COSMOS within\nthe X-ray galaxy groups in the COSMOS field. We compare these results to the\nones of 171 star-forming galaxies (SFGs), using the theoretical relation of\nMoster et al. (2013). We find that AGN agree within 1% with the Moster et al.\n(2013) relation, SFGs show an offset of 37%, suggesting that the radio-mode\nfeedback from AGN at a median redshift of ~ 0.5 still plays a significant role\nin the M* - Mhalo relation.\n"}
+{"abstract": "  Ride Hailing Services (RHS) have become a popular means of transportation,\nand with its popularity comes the concerns of privacy of riders and drivers.\nORide is a privacy-preserving RHS proposed at the USENIX Security Symposium\n2017 and uses Somewhat Homomorphic Encryption (SHE). In their protocol, a rider\nand all drivers in a zone send their encrypted coordinates to the RHS Service\nProvider (SP) who computes the squared Euclidean distances between them and\nforwards them to the rider. The rider decrypts these and selects the optimal\ndriver with least Euclidean distance. In this work, we demonstrate a\nlocation-harvesting attack where an honest-but-curious rider, making only a\nsingle ride request, can determine the exact coordinates of about half the\nnumber of responding drivers even when only the distance between the rider and\ndrivers are given. The significance of our attack lies in inferring locations\nof other drivers in the zone, which are not (supposed to be) revealed to the\nrider as per the protocol. We validate our attack by running experiments on\nzones of varying sizes in arbitrarily selected big cities. Our attack is based\non enumerating lattice points on a circle of sufficiently small radius and\neliminating solutions based on conditions imposed by the application scenario.\nFinally, we propose a modification to ORide aimed at thwarting our attack and\nshow that this modification provides sufficient driver anonymity while\npreserving ride matching accuracy.\n"}
+{"abstract": "  We associate with each convex optimization problem posed on some locally\nconvex space with an infinite index set T, and a given non-empty family H\nformed by finite subsets of T, a suitable Lagrangian-Haar dual problem. We\nprovide reverse H-strong duality theorems, H-Farkas type lemmas and optimality\ntheorems. Special attention is addressed to infinite and semi-infinite linear\noptimization problems.\n"}
+{"abstract": "  We derive a Doi-Peliti field theory for free active Ornstein-Uhlenbeck\nparticles, or, equivalently, free inertial Brownian particles, and present a\nway to diagonalise the Gaussian part of the action and calculate the\npropagator. Unlike previous course-grained approaches this formulation\ncorrectly tracks particle identity and yet can easily be expanded to include\npotentials and arbitrary reactions.\n"}
+{"abstract": "  Domain adaptation is an important problem and often needed for real-world\napplications. In this problem, instead of i.i.d. datapoints, we assume that the\nsource (training) data and the target (testing) data have different\ndistributions. With that setting, the empirical risk minimization training\nprocedure often does not perform well, since it does not account for the change\nin the distribution. A common approach in the domain adaptation literature is\nto learn a representation of the input that has the same distributions over the\nsource and the target domain. However, these approaches often require\nadditional networks and/or optimizing an adversarial (minimax) objective, which\ncan be very expensive or unstable in practice. To tackle this problem, we first\nderive a generalization bound for the target loss based on the training loss\nand the reverse Kullback-Leibler (KL) divergence between the source and the\ntarget representation distributions. Based on this bound, we derive an\nalgorithm that minimizes the KL term to obtain a better generalization to the\ntarget domain. We show that with a probabilistic representation network, the KL\nterm can be estimated efficiently via minibatch samples without any additional\nnetwork or a minimax objective. This leads to a theoretically sound alignment\nmethod which is also very efficient and stable in practice. Experimental\nresults also suggest that our method outperforms other representation-alignment\napproaches.\n"}
+{"abstract": "  Nonlinear terms in standard models of ship maneuvering entail continuous\nnon-smooth terms so that bifurcations of straight motion are not amenable to\nstandard center manifold reduction. In this paper, we determine the criticality\nof Hopf bifurcations that arise in stabilizing the straight motion based on a\nrecently developed analytical approach. For such a 3 DOF model of ship motion\nwith yaw damping and yaw restoring control, we present a detailed analysis of\nthe possibilities to stabilize the straight motion, and the resulting nonlinear\neffects. To facilitate the analysis, we consider a combination of rudder and\npropeller forces into an effective thruster force. We identify existence,\nlocation and geometry of the stability boundary in terms of the controls,\nincluding the dependence on the propeller diameter and the thruster position.\nWe find that `safe' supercritical Andronov-Hopf bifurcations are predominant\nand by means of numerical continuation we provide a global bifurcation\nanalysis, which identifies the arrangement and relative location of stable and\nunstable equilibria and periodic orbits. We illustrate the resulting stable\nquasi-periodic ship motions in Earth-fixed coordinates and present some direct\nnumerical simulations.\n"}
+{"abstract": "  Secure and scalable device provisioning is a notorious challenge in Wi-Fi.\nWPA2/WPA3 solutions take user interaction and a strong passphrase for granted.\nHowever, the often weak passphrases are subject to guessing attacks. Notably,\nthere has been a significant rise of cyberattacks on Wi-Fi home or small office\nnetworks during the COVID-19 pandemic. This paper addresses the device\nprovisioning problem in Wi-Fi (personal mode) and proposes ComPass protocol to\nsupplement WPA2/WPA3. ComPass replaces the pre-installed or user-selected\npassphrases with automatically generated ones. For this, ComPass employs\nPhysical Layer Security and extracts credentials from common random physical\nlayer parameters between devices. Two major features make ComPass unique and\nsuperior compared to previous proposals: First, it employs phase information\n(rather than amplitude or signal strength) to generate the passphrase so that\nit is robust, scaleable, and impossible to guess. Our analysis showed that\nComPass generated passphrases have 3 times more entropy than human generated\npassphrases (113-bits vs. 34-bits). Second, ComPass selects parameters such\nthat two devices bind only within a certain proximity (less than 3m), hence\nproviding practically useful in-build PLS-based authentiation. ComPass is\navailable as a kernel module or as full firmware.\n"}
+{"abstract": "  Templated copying is the central operation by which biology produces complex\nmolecules. Cells copy sequence information from DNA to RNA and on into\nproteins, which are the molecules responsible for the function and regulation\nof cellular systems. In the templated copying process the template catalyses\nthe formation of a second molecule carrying the same sequence. Traditionally,\npeople have ignored the separation of the template and copy at the end of the\nprocess, but separation is necessary and fundamentally changes the\nthermodynamics of the process. In general, creating an accurate polymer costs\nfree energy. Omitting separation, this cost can be compensated for by the extra\nfree energy released by \"correct\" copy/template bonds. Separation requires\nthese bonds be broken, so true copying requires an input of free energy.\nEqually the fact that copy/template bonds are temporary means there is no\nthermodynamic bias towards accuracy, instead copying relies only on kinetic\neffects to promote accuracy. In general, transducing energy can only happen\nreversibly when the transduction process is quasistatic and time varying;\nsomething that cannot be true when you are relying on kinetic discrimination.\n  Copying is a far from equilibrium process. This thesis explores the\nconsequences of this observation. We start in the limit of infinite length\ncopies where the costs of accuracy represent hard thermodynamic bounds and then\nmoves to the finite length limit where these same limits can be understood as\nkinetic barriers. We then discuss copying systems as non-equilibrium steady\nstates, which can be analysed as information engines moving free energy between\nout-of-equilibrium baths.\n"}
+{"abstract": "  The few-shot multi-speaker multi-style voice cloning task is to synthesize\nutterances with voice and speaking style similar to a reference speaker given\nonly a few reference samples. In this work, we investigate different speaker\nrepresentations and proposed to integrate pretrained and learnable speaker\nrepresentations. Among different types of embeddings, the embedding pretrained\nby voice conversion achieves the best performance. The FastSpeech 2 model\ncombined with both pretrained and learnable speaker representations shows great\ngeneralization ability on few-shot speakers and achieved 2nd place in the\none-shot track of the ICASSP 2021 M2VoC challenge.\n"}
+{"abstract": "  Knowledge distillation is a method of transferring the knowledge from a\npretrained complex teacher model to a student model, so a smaller network can\nreplace a large teacher network at the deployment stage. To reduce the\nnecessity of training a large teacher model, the recent literatures introduced\na self-knowledge distillation, which trains a student network progressively to\ndistill its own knowledge without a pretrained teacher network. While\nSelf-knowledge distillation is largely divided into a data augmentation based\napproach and an auxiliary network based approach, the data augmentation\napproach looses its local information in the augmentation process, which\nhinders its applicability to diverse vision tasks, such as semantic\nsegmentation. Moreover, these knowledge distillation approaches do not receive\nthe refined feature maps, which are prevalent in the object detection and\nsemantic segmentation community. This paper proposes a novel self-knowledge\ndistillation method, Feature Refinement via Self-Knowledge Distillation\n(FRSKD), which utilizes an auxiliary self-teacher network to transfer a refined\nknowledge for the classifier network. Our proposed method, FRSKD, can utilize\nboth soft label and feature-map distillations for the self-knowledge\ndistillation. Therefore, FRSKD can be applied to classification, and semantic\nsegmentation, which emphasize preserving the local information. We demonstrate\nthe effectiveness of FRSKD by enumerating its performance improvements in\ndiverse tasks and benchmark datasets. The implemented code is available at\nhttps://github.com/MingiJi/FRSKD.\n"}
+{"abstract": "  The colocation of individuals in different environments is an important\nprerequisite for exposure to infectious diseases on a social network. Standard\nepidemic models fail to capture the potential complexity of this scenario by\n(1) neglecting the higher-order structure of contacts which typically occur\nthrough environments like workplaces, restaurants, and households; and by (2)\nassuming a linear relationship between the exposure to infected contacts and\nthe risk of infection. Here, we leverage a hypergraph model to embrace the\nheterogeneity of environments and the heterogeneity of individual participation\nin these environments. We find that combining heterogeneous exposure with the\nconcept of minimal infective dose induces a universal nonlinear relationship\nbetween infected contacts and infection risk. Under nonlinear infection\nkernels, conventional epidemic wisdom breaks down with the emergence of\ndiscontinuous transitions, super-exponential spread, and hysteresis.\n"}
+{"abstract": "  We make use of Hecke operators and arithmetic of imaginary quadratic fields\nto derive an explicit version of a special case of Siegel's mass formula.\n"}
+{"abstract": "  In this paper, we prove both of the Titchmarsh theorems associated with\nTwo-Sided Quaternionic Fourier Transform and we conclude about Short-Time\nTwo-Sided Quaternionic Fourier Transform.\n"}
+{"abstract": "  This article presents the results of a study involving the translation of a\nfictional story from English into Catalan in three modalities:\nmachine-translated (MT), post-edited (MTPE) and translated without aid (HT).\nEach translation was analysed to evaluate its creativity. Subsequently, a\ncohort of 88 Catalan participants read the story in a randomly assigned\nmodality and completed a survey. The results show that HT presented a higher\ncreativity score if compared to MTPE and MT. HT also ranked higher in narrative\nengagement, and translation reception, while MTPE ranked marginally higher in\nenjoyment. HT and MTPE show no statistically significant differences in any\ncategory, whereas MT does in all variables tested. We conclude that creativity\nis highest when professional translators intervene in the process, especially\nwhen working without any aid. We hypothesize that creativity in translation\ncould be the factor that enhances reading engagement and the reception of\ntranslated literary texts.\n"}
+{"abstract": "  Lithium metal batteries are seen as a critical piece towards electrifying\naviation. During charging, plating of lithium metal, a critical failure\nmechanism, has been studied and mitigation strategies have been proposed. For\nelectric aircraft, high discharge power requirements necessitate stripping of\nlithium metal in an uniform way and recent studies have identified the\nevolution of surface voids and pits as a potential failure mechanism. In this\nwork, using density functional theory calculations and thermodynamic analysis,\nwe investigate the discharge process on lithium metal surfaces. In particular,\nwe calculate the tendency for vacancy congregation on lithium metal surfaces,\nwhich constitutes the first step in the formation of voids and pits. We find\nthat among the low Miller index surfaces, the (111) surface is the least likely\nto exhibit pitting issues. Our analysis suggests that faceting control during\nelectrodeposition could be a key pathway towards simultaneously enabling both\nfast charge and fast discharge.\n"}
+{"abstract": "  Pastures are a class of field-like algebraic objects which include both\npartial fields hyperfields and have nice categorical properties. We prove\nseveral lift theorems for representations of matroids over pastures, including\na generalization of Pendavingh and van Zwam's Lift Theorem for partial fields.\nBy embedding the earlier theory into a more general framework, we are able to\nestablish new results even in the case of lifts of partial fields, for example\nthe conjecture of Pendavingh--van Zwam that their lift construction is\nidempotent. We give numerous applications to matroid representations, e.g. we\nshow that, up to projective equivalence, every pair consisting of a hexagonal\nrepresentation and an orientation lifts uniquely to a near-regular\nrepresentation. The proofs are different from the arguments used by Pendavingh\nand van Zwam, relying instead on a result of Gelfand-Rybnikov-Stone inspired by\nTutte's homotopy theorem.\n"}
+{"abstract": "  The latent knowledge in the emotions and the opinions of the individuals that\nare manifested via social networks are crucial to numerous applications\nincluding social management, dynamical processes, and public security.\nAffective computing, as an interdisciplinary research field, linking artificial\nintelligence to cognitive inference, is capable to exploit emotion-oriented\nknowledge from brief contents. The textual contents convey hidden information\nsuch as personality and cognition about corresponding authors that can\ndetermine both correlations and variations between users. Emotion recognition\nfrom brief contents should embrace the contrast between authors where the\ndifferences in personality and cognition can be traced within emotional\nexpressions. To tackle this challenge, we devise a framework that, on the one\nhand, infers latent individual aspects, from brief contents and, on the other\nhand, presents a novel ensemble classifier equipped with dynamic dropout\nconvnets to extract emotions from textual context. To categorize short text\ncontents, our proposed method conjointly leverages cognitive factors and\nexploits hidden information. We utilize the outcome vectors in a novel\nembedding model to foster emotion-pertinent features that are collectively\nassembled by lexicon inductions. Experimental results show that compared to\nother competitors, our proposed model can achieve a higher performance in\nrecognizing emotion from noisy contents.\n"}
+{"abstract": "  Can a neural network minimizing cross-entropy learn linearly separable data?\nDespite progress in the theory of deep learning, this question remains\nunsolved. Here we prove that SGD globally optimizes this learning problem for a\ntwo-layer network with Leaky ReLU activations. The learned network can in\nprinciple be very complex. However, empirical evidence suggests that it often\nturns out to be approximately linear. We provide theoretical support for this\nphenomenon by proving that if network weights converge to two weight clusters,\nthis will imply an approximately linear decision boundary. Finally, we show a\ncondition on the optimization that leads to weight clustering. We provide\nempirical results that validate our theoretical analysis.\n"}
+{"abstract": "  Rule ensembles are designed to provide a useful trade-off between predictive\naccuracy and model interpretability. However, the myopic and random search\ncomponents of current rule ensemble methods can compromise this goal: they\noften need more rules than necessary to reach a certain accuracy level or can\neven outright fail to accurately model a distribution that can actually be\ndescribed well with a few rules. Here, we present a novel approach aiming to\nfit rule ensembles of maximal predictive power for a given ensemble size (and\nthus model comprehensibility). In particular, we present an efficient\nbranch-and-bound algorithm that optimally solves the per-rule objective\nfunction of the popular second-order gradient boosting framework. Our main\ninsight is that the boosting objective can be tightly bounded in linear time of\nthe number of covered data points. Along with an additional novel pruning\ntechnique related to rule redundancy, this leads to a computationally feasible\napproach for boosting optimal rules that, as we demonstrate on a wide range of\ncommon benchmark problems, consistently outperforms the predictive performance\nof boosting greedy rules.\n"}
+{"abstract": "  Temporal aggregation is an intuitively appealing approach to deal with demand\nuncertainty. There are two types of temporal aggregation: non-overlapping and\noverlapping. Most of the supply chain forecasting literature has focused so far\non the former and there is no research that analyses the latter for\nauto-correlated demands. In addition, most of the analytical research to-date\nassumes infinite demand series' lengths whereas, in practice, forecasting is\nbased on finite demand histories. The length of the demand history is an\nimportant determinant of the comparative performance of the two approaches but\nhas not been given sufficient attention in the literature. In this paper we\nexamine the effectiveness of temporal aggregation for forecasting finite\nauto-correlated demand. We do so by means of an analytical study of the\nforecast accuracy of aggregation and non-aggregation approaches based on mean\nsquared error. We complement this with a numerical analysis to explore the\nimpact of demand parameters and the length of the series on (comparative)\nperformance. We also conduct an empirical evaluation to validate the analytical\nresults using monthly time series of the M4-competition dataset. We find the\ndegree of auto-correlation, the forecast horizon and the length of the series\nto be important determinants of forecast accuracy. We discuss the merits of\neach approach and highlight their implications for real world practices.\n"}
+{"abstract": "  Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art deep\nlearning model for representation learning on graphs. However, it remains\nnotoriously challenging to train and inference GCNs over large graph datasets,\nlimiting their application to large real-world graphs and hindering the\nexploration of deeper and more sophisticated GCN graphs. This is because as the\ngraph size grows, the sheer number of node features and the large adjacency\nmatrix can easily explode the required memory and data movements. To tackle the\naforementioned challenges, we explore the possibility of drawing lottery\ntickets when sparsifying GCN graphs, i.e., subgraphs that largely shrink the\nadjacency matrix yet are capable of achieving accuracy comparable to or even\nbetter than their full graphs. Specifically, we for the first time discover the\nexistence of graph early-bird (GEB) tickets that emerge at the very early stage\nwhen sparsifying GCN graphs, and propose a simple yet effective detector to\nautomatically identify the emergence of such GEB tickets. Furthermore, we\nadvocate graph-model co-optimization and develop a generic efficient GCN\nearly-bird training framework dubbed GEBT that can significantly boost the\nefficiency of GCN training by (1) drawing joint early-bird tickets between the\nGCN graphs and models and (2) enabling simultaneously sparsification of both\nthe GCN graphs and models. Experiments on various GCN models and datasets\nconsistently validate our GEB finding and the effectiveness of our GEBT, e.g.,\nour GEBT achieves up to 80.2% ~ 85.6% and 84.6% ~ 87.5% savings of GCN training\nand inference costs while offering a comparable or even better accuracy as\ncompared to state-of-the-art methods. Our source code and supplementary\nappendix are available at https://github.com/RICE-EIC/Early-Bird-GCN.\n"}
+{"abstract": "  Many high-energy astrophysical systems contain magnetized collisionless\nplasmas with relativistic particles, in which turbulence can be driven by an\narbitrary mixture of solenoidal and compressive motions. For example,\nturbulence in hot accretion flows may be driven solenoidally by the\nmagnetorotational instability or compressively by spiral shock waves. It is\nimportant to understand the role of the driving mechanism on kinetic turbulence\nand the associated particle energization. In this work, we compare\nparticle-in-cell simulations of solenoidally driven turbulence with similar\nsimulations of compressively driven turbulence. We focus on plasma that has an\ninitial beta of unity, relativistically hot electrons, and varying ion\ntemperature. Apart from strong large-scale density fluctuations in the\ncompressive case, the turbulence statistics are similar for both drives, and\nthe bulk plasma is described reasonably well by an isothermal equation of\nstate. We find that nonthermal particle acceleration is more efficient when\nturbulence is driven compressively. In the case of relativistically hot ions,\nboth driving mechanisms ultimately lead to similar power-law particle energy\ndistributions, but over a different duration. In the case of non-relativistic\nions, there is significant nonthermal particle acceleration only for\ncompressive driving. Additionally, we find that the electron-to-ion heating\nratio is less than unity for both drives, but takes a smaller value for\ncompressive driving. We demonstrate that this additional ion energization is\nassociated with the collisionless damping of large-scale compressive modes via\nperpendicular electric fields.\n"}
+{"abstract": "  The spin-orbit entangled (SOE) Jeff-state has been a fertile ground to study\nnovel quantum phenomena. Contrary to the conventional weakly correlated\nJeff=1/2 state of 4d and 5d transition metal compounds, the ground state of\nCuAl2O4 hosts a Jeff=1/2 state with a strong correlation of Coulomb U. Here, we\nreport that surprisingly Cu2+ ions of CuAl2O4 overcome the otherwise usually\nstrong Jahn-Teller distortion and instead stabilize the SOE state, although the\ncuprate has relatively small spin-orbit coupling. From the x-ray absorption\nspectroscopy and high-pressure x-ray diffraction studies, we obtained definite\nevidence of the Jeff=1/2 state with a cubic lattice at ambient pressure. We\nalso found the pressure-induced structural transition to a compressed\ntetragonal lattice consisting of the spin-only S=1/2 state for pressure higher\nthan Pc=8 GPa. This phase transition from the Mott insulating Jeff=1/2 to the\nS=1/2 states is a unique phenomenon and has not been reported before. Our study\noffers a rare example of the SOE Jeff-state under strong electron correlation\nand its pressure-induced transition to the S=1/2 state.\n"}
+{"abstract": "  Genetic Algorithm (GA) -- motivated by the natural evolutionary process -- is\na robust method to estimate the optimal solutions of problems involving one or\nmore objective functions. In this article, for the first time, we apply GA to\nreconstruct the cleaned CMB temperature anisotropy map over large angular\nscales of the sky using (internal) linear combination (ILC) of observations\nfrom the final-year WMAP and Planck satellite missions. To avoid getting\ntrapped into a local minimum, we implement the GA with generous diversity in\nthe populations. This is achieved by introducing a small but significant amount\nof mutation of genes during crossover and selecting pairs with diverse fitness\ncoefficients. We find that the new GA-ILC method produces a cleaned CMB map\nwhich agrees very well with the CMB map obtained using the exact and analytical\nexpression of weights in the ILC method. By performing extensive Monte Carlo\nsimulations of the CMB reconstruction using the GA-ILC algorithm we find that\nresidual foregrounds in the cleaned map are minimal and mostly tend to occupy\nlocalized regions along the central galactic plane. The CMB angular power\nspectrum shows no indication of any bias in the entire multipole range $2 \\leq\n\\ell \\leq 32$ studied in this work. The error in the CMB angular power spectrum\nis minimal as well and given entirely by the cosmic variance induced error. Our\nresults agree well with those obtained by various other reconstruction methods\nby different research groups. This problem-independent robust GA-ILC method\nprovides a flexible way towards the complex and challenging task of CMB\ncomponent reconstruction in cosmology.\n"}
+{"abstract": "  This paper deals with the preparation and characterization of conductive and\nflexible sheets based on graphite nanoplates (GNP) and polydimethylsiloxane\n(PDMS). Highly porous GNP nanopaper were prepared by filtration from a GNP\nsuspension in solvent. Subsequently, the impregnation strategy was pursued to\nobtain a composite material. By varying the concentration of the solution to be\nimpregnated and the deposition time, PDMS/GNP nanopapers are produced with a\nrange of PDMS content, porosity and density. Thermal diffusivity of the films\n(both in-plane and cross-plane) were measured and correlated with the structure\nof the nanopapers. Based on the results found and considering the restraints,\nthree formulations were further investigated for their physical, thermal and\nmechanical properties. Highly flexible, tough and conductive (up to 25 W/mK)\nnanopapers were obtained, for possible application in modern electronic\ndevices.\n"}
+{"abstract": "  It has been recently shown that the halo near the Sun contains several\nkinematic substructures associated to past accretion events. For the more\ndistant halo, there is evidence of large-scale density variations -- in the\nform of stellar clouds or overdensities. We study the link between the local\nhalo kinematic groups and three of these stellar clouds: the Hercules-Aquila\ncloud, the Virgo Overdensity, and the Eridanus-Phoenix overdensity. We perform\norbital integrations in a standard Milky Way potential of a local halo sample\nextracted from Gaia eDR3, with the goal of predicting the location of the\nmerger debris elsewhere in the Galaxy. We specifically focus on the regions\noccupied by the three stellar clouds and compare their kinematic and distance\ndistributions with those predicted from the orbits of the nearby debris. We\nfind that the local halo substructures have families of orbits that tend to\npile up in the regions where the stellar clouds have been found. The distances\nand velocities of the cloud's member stars are in good agreement with those\npredicted from the orbit integrations, particularly for Gaia-Enceladus stars.\nThis is the dominant contributor of all three overdensities, with a minor part\nstemming from the Helmi streams and to an even smaller extent from Sequoia. The\norbital integrations predict no asymmetries in the sky distribution of halo\nstars, and they pinpoint where additional debris associated with the local halo\nsubstructures may be located.\n"}
+{"abstract": "  We consider a simple theoretical model to investigate the impact of\ninheritances on the wealth distribution. Wealth is described as a finite\nresource, which remains constant over different generations and is divided\nequally among offspring. All other sources of wealth are neglected. We consider\ndifferent societies characterized by a different offspring probability\ndistribution. We find that, if the population remains constant, the society\nreaches a stationary wealth distribution. We show that inequality emerges every\ntime the number of children per family is not always the same. For realistic\noffspring distributions from developed countries, the model predicts a Gini\ncoefficient of $G\\approx 0.3$. If we divide the society into wealth classes and\nset the probability of getting married to depend on the distance between\nclasses, the stationary wealth distribution crosses over from an exponential to\na power-law regime as the number of wealth classes and the level of class\ndistinction increase.\n"}
+{"abstract": "  Successfully attaining consensus in the absence of a centralized coordinator\nis a fundamental problem in distributed multi-agent systems. We analyze\nprogress in the Synod consensus protocol -- which does not assume a unique\nleader -- under the assumptions of asynchronous communication and potential\nagent failures. We identify a set of sufficient conditions under which it is\npossible to guarantee that a set of agents will eventually attain consensus.\nFirst, a subset of the agents must behave correctly and not permanently fail\nuntil consensus is reached, and second, at least one proposal must be\neventually uninterrupted by higher-numbered proposals. To formally reason about\nagent failures, we introduce a failure-aware actor model (FAM). Using FAM, we\nmodel the identified conditions and provide a formal proof of eventual progress\nin Synod. Our proof has been mechanically verified using the Athena proof\nassistant and, to the best of our knowledge, it is the first machine-checked\nproof of eventual progress in Synod.\n"}
+{"abstract": "  Two automatic threshold selection (TS) methods for Extreme Value analysis\nunder a peaks-over-threshold (POT) approach are presented and evaluated, both\nbuilt on: fitting the Generalized Pareto distribution (GPd) to excesses'\nsamples over candidate levels ; the GPd-specific relation between L-skewness\nand L-kurtosis; the asymptotic behaviour of the matching L-statistics.\nPerformance is illustrated on significant wave heights data sets and compared\nto the L-moment-based heuristic in [10], which is found to be favorable.\n  PUBLISHED VERSION AVAILABLE AT:\nhttps://www.spestatistica.pt/storage/app/uploads/public/609/28f/6d0/60928f6d08a0c016386627.pdf\n"}
+{"abstract": "  Medical steerable needles can move along 3D curvilinear trajectories to avoid\nanatomical obstacles and reach clinically significant targets inside the human\nbody. Automating steerable needle procedures can enable physicians and patients\nto harness the full potential of steerable needles by maximally leveraging\ntheir steerability to safely and accurately reach targets for medical\nprocedures such as biopsies and localized therapy delivery for cancer. For the\nautomation of medical procedures to be clinically accepted, it is critical from\na patient care, safety, and regulatory perspective to certify the correctness\nand effectiveness of the motion planning algorithms involved in procedure\nautomation. In this paper, we take an important step toward creating a\ncertifiable motion planner for steerable needles. We introduce the first motion\nplanner for steerable needles that offers a guarantee, under clinically\nappropriate assumptions, that it will, in finite time, compute an exact,\nobstacle-avoiding motion plan to a specified target, or notify the user that no\nsuch plan exists. We present an efficient, resolution-complete motion planner\nfor steerable needles based on a novel adaptation of multi-resolution planning.\nCompared to state-of-the-art steerable needle motion planners (none of which\nprovide any completeness guarantees), we demonstrate that our new\nresolution-complete motion planner computes plans faster and with a higher\nsuccess rate.\n"}
+{"abstract": "  We prove that Cheng maximal diameter theorem for hypergraphs with positive\ncoarse Ricci curvature.\n"}
+{"abstract": "  The observations of high redshifts quasars at $z\\gtrsim 6$ have revealed that\nsupermassive black holes (SMBHs) of mass $\\sim 10^9\\,\\mathrm{M_{\\odot}}$ were\nalready in place within the first $\\sim$ Gyr after the Big Bang. Supermassive\nstars (SMSs) with masses $10^{3-5}\\,\\mathrm{M_{\\odot}}$ are potential seeds for\nthese observed SMBHs. A possible formation channel of these SMSs is the\ninterplay of gas accretion and runaway stellar collisions inside dense nuclear\nstar clusters (NSCs). However, mass loss due to stellar winds could be an\nimportant limitation for the formation of the SMSs and affect the final mass.\nIn this paper, we study the effect of mass loss driven by stellar winds on the\nformation and evolution of SMSs in dense NSCs using idealised N-body\nsimulations. Considering different accretion scenarios, we have studied the\neffect of the mass loss rates over a wide range of metallicities\n$Z_\\ast=[.001-1]\\mathrm{Z_{\\odot}}$ and Eddington factors $f_{\\rm\nEdd}=L_\\ast/L_{\\mathrm{Edd}}=0.5,0.7,\\,\\&\\, 0.9$. For a high accretion rate of\n$10^{-4}\\,\\mathrm{M_{\\odot}yr^{-1}}$, SMSs with masses $\\gtrsim 10^3\\MSun$\ncould be formed even in a high metallicity environment. For a lower accretion\nrate of $10^{-5}\\,\\mathrm{M_{\\odot}yr^{-1}}$, SMSs of masses $\\sim\n10^{3-4}\\,\\mathrm{M_{\\odot}}$ can be formed for all adopted values of $Z_\\ast$\nand $f_{\\rm Edd}$, except for $Z_\\ast=\\mathrm{Z_{\\odot}}$ and $f_{\\rm Edd}=0.7$\nor 0.9. For Eddington accretion, SMSs of masses $\\sim 10^3\\,\\mathrm{M_{\\odot}}$\ncan be formed in low metallicity environments with $Z_\\ast\\lesssim\n0.01\\mathrm{Z_{\\odot}}$. The most massive SMSs of masses $\\sim\n10^5\\,\\mathrm{M_{\\odot}}$ can be formed for Bondi-Hoyle accretion in\nenvironments with $Z_\\ast \\lesssim 0.5\\mathrm{Z_{\\odot}}$.\n"}
+{"abstract": "  With the rapid growth of data, it is becoming increasingly difficult to train\nor improve deep learning models with the right subset of data. We show that\nthis problem can be effectively solved at an additional labeling cost by\ntargeted data subset selection(TSS) where a subset of unlabeled data points\nsimilar to an auxiliary set are added to the training data. We do so by using a\nrich class of Submodular Mutual Information (SMI) functions and demonstrate its\neffectiveness for image classification on CIFAR-10 and MNIST datasets. Lastly,\nwe compare the performance of SMI functions for TSS with other state-of-the-art\nmethods for closely related problems like active learning. Using SMI functions,\nwe observe ~20-30% gain over the model's performance before re-training with\nadded targeted subset; ~12% more than other methods.\n"}
+{"abstract": "  The loading of resources from third-parties has evoked new security and\nprivacy concerns about the current world wide web. Building on the concepts of\nforced and implicit trust, this paper examines cross-domain transmission\ncontrol protocol (TCP) connections that are initiated to domains other than the\ndomain queried with a web browser. The dataset covers nearly ten thousand\ndomains and over three hundred thousand TCP connections initiated by querying\npopular Finnish websites and globally popular sites. According to the results,\n(i) cross-domain connections are extremely common in the current Web. (ii) Most\nof these transmit encrypted content, although mixed content delivery is\nrelatively common; many of the cross-domain connections deliver unencrypted\ncontent at the same time. (iii) Many of the cross-domain connections are\ninitiated to known web advertisement domains, but a much larger share traces to\nsocial media platforms and cloud infrastructures. Finally, (iv) the results\ndiffer slightly between the Finnish web sites sampled and the globally popular\nsites. With these results, the paper contributes to the ongoing work for better\nunderstanding cross-domain connections and dependencies in the world wide web.\n"}
+{"abstract": "  In this paper we analyze the problem of optimal task scheduling for data\ncenters. Given the available resources and tasks, we propose a fast distributed\niterative algorithm which operates over a large scale network of nodes and\nallows each of the interconnected nodes to reach agreement to an optimal\nsolution in a finite number of time steps. More specifically, the algorithm (i)\nis guaranteed to converge to the exact optimal scheduling plan in a finite\nnumber of time steps and, (ii) once the goal of task scheduling is achieved, it\nexhibits distributed stopping capabilities (i.e., it allows the nodes to\ndistributely determine whether they can terminate the operation of the\nalgorithm). Furthermore, the proposed algorithm operates exclusively with\nquantized values (i.e., the information stored, processed and exchanged between\nneighboring agents is subject to deterministic uniform quantization) and relies\non event-driven updates (e.g., to reduce energy consumption, communication\nbandwidth, network congestion, and/or processor usage). We also provide\nexamples to illustrate the operation, performance, and potential advantages of\nthe proposed algorithm. Finally, by using extensive empirical evaluations\nthrough simulations we show that the proposed algorithm exhibits\nstate-of-the-art performance.\n"}
+{"abstract": "  Within Density Functional Theory, we have studied the interplay between\nvortex arrays and capillary waves in spinning prolate He-4 droplets made of\nseveral thousands of helium atoms. Surface capillary waves are ubiquitous in\nprolate superfluid He-4 droplets and, depending on the size and angular\nmomentum of the droplet, they may coexist with vortex arrays. We have found\nthat the equilibrium configuration of small prolate droplets is vortex-free,\nevolving towards vortex-hosting as the droplet size increases. This result is\nin agreement with a recent experiment [S.M. O'Connell et al., Phys. Rev. Lett.\n124, 215301 (2020)], where it has been disclosed that vortex arrays and\ncapillary waves coexist in the equilibrium configuration of very large drops.\nContrarily to viscous droplets executing rigid body rotation, the stability\nphase diagram of spinning He-4 droplets cannot be universally described in\nterms of dimensionless angular momentum and angular velocity variables:\ninstead, the rotational properties of superfluid helium droplets display a\nclear dependence on the droplet size and the number of vortices they host.\n"}
+{"abstract": "  This editorial article partially informs the algorithmic trading community\nabout launching of the new journal \"Algorithmic Trading and Controls\" (ATC).\nATC is an online open-access journal that publishes novel works on algorithmic\ntrading and its control methodologies. In this inaugural article, we discuss\nnine major challenges that contemporary Algo trading faces. There is nothing\nsuperstitiously magical about the number \"nine,\" but so is any other one.\nSeveral of these challenges are at the strategy level, including for example,\ntrading of illiquid securities or optimal portfolio execution. Others are more\nat the level of risk management and controls, such as on how to develop\nautomated controls, testing and simulations. The editorial views could be\ninevitably personal and biased, but have been explored with the most innocent\nintention of contributing to this important field in modern financial services\nand technologies.\n"}
+{"abstract": "  Prophet inequalities are a useful tool for designing online allocation\nprocedures and comparing their performance to the optimal offline allocation.\nIn the basic setting of $k$-unit prophet inequalities, the magical procedure of\nAlaei (2011) with its celebrated performance guarantee of\n$1-\\frac{1}{\\sqrt{k+3}}$ has found widespread adoption in mechanism design and\nother online allocation problems in online advertising, healthcare scheduling,\nand revenue management. Despite being commonly used for implementing online\nallocation, the tightness of Alaei's procedure for a given $k$ has remained\nunknown. In this paper we resolve this question, characterizing the tight bound\nby identifying the structure of the optimal online implementation, and\nconsequently improving the best-known guarantee for $k$-unit prophet\ninequalities for all $k>1$. We also consider a more general online stochastic\nknapsack problem where each individual allocation can consume an arbitrary\nfraction of the initial capacity. We introduce a new \"best-fit\" procedure for\nimplementing a fractionally-feasible knapsack solution online, with a\nperformance guarantee of $\\frac{1}{3+e^{-2}}\\approx0.319$, which we also show\nis tight. This improves the previously best-known guarantee of 0.2 for online\nknapsack. Our analysis differs from existing ones by eschewing the need to\nsplit items into \"large\" or \"small\" based on capacity consumption, using\ninstead an invariant for the overall utilization on different sample paths.\nFinally, we refine our technique for the unit-density special case of knapsack,\nand improve the guarantee from 0.321 to 0.3557 in the multi-resource\nappointment scheduling application of Stein et al. (2020). All in all, our\nresults imply \\textit{tight} Online Contention Resolution Schemes for\n$k$-uniform matroids and the knapsack polytope, respectively, which has further\nimplications in mechanism design.\n"}
+{"abstract": "  Coherent interconversion of signals between optical and mechanical domains is\nenabled by optomechanical interactions. Extreme light-matter coupling produced\nby confining light to nanoscale mode volumes can then access single\nmid-infrared (MIR) photon sensitivity. Here we utilise the infrared absorption\nand Raman activity of molecular vibrations in plasmonic nanocavities to\ndemonstrate frequency upconversion. We convert {\\lambda}~10 {\\mu}m incoming\nlight to visible via surface-enhanced Raman scattering (SERS) in\ndoubly-resonant antennas that enhance upconversion by >10^10. We show >200%\namplification of the SERS antiStokes emission when a MIR pump is tuned to a\nmolecular vibrational frequency, obtaining lowest detectable powers ~1\n{\\mu}W/{\\mu}m^2 at room temperature. These results have potential for low-cost\nand large-scale infrared detectors and spectroscopic techniques, and bring\nsingle-molecule sensing into the infrared\n"}
+{"abstract": "  Searchable Symmetric Encryption (SSE) allows a data owner to securely\noutsource its encrypted data to a cloud server while maintaining the ability to\nsearch over it and retrieve matched documents. Most existing SSE schemes leak\nwhich documents are accessed per query, i.e., the so-called access pattern, and\nthus are vulnerable to attacks that can recover the database or the queried\nkeywords. Current techniques that fully hide access patterns, such as ORAM or\nPIR, suffer from heavy communication or computational costs, and are not\ndesigned with search capabilities in mind. Recently, Chen et al. (INFOCOM'18)\nproposed an obfuscation framework for SSE that protects the access pattern in a\ndifferentially private way with a reasonable utility cost. However, this scheme\nleaks the so-called search pattern, i.e., how many times a certain query is\nperformed. This leakage makes the proposal vulnerable to certain database and\nquery recovery attacks.\n  In this paper, we propose OSSE (Obfuscated SSE), an SSE scheme that\nobfuscates the access pattern independently for each query performed. This in\nturn hides the search pattern and makes our scheme resistant against attacks\nthat rely on this leakage. Under certain reasonable assumptions, our scheme has\nsmaller communication overhead than ORAM-based SSE. Furthermore, our scheme\nworks in a single communication round and requires very small constant\nclient-side storage. Our empirical evaluation shows that OSSE is highly\neffective at protecting against different query recovery attacks while keeping\na reasonable utility level. Our protocol provides significantly more protection\nthan the proposal by Chen et al.~against some state-of-the-art attacks, which\ndemonstrates the importance of hiding search patterns in designing effective\nprivacy-preserving SSE schemes.\n"}
+{"abstract": "  In ML-aided decision-making tasks, such as fraud detection or medical\ndiagnosis, the human-in-the-loop, usually a domain-expert without technical ML\nknowledge, prefers high-level concept-based explanations instead of low-level\nexplanations based on model features. To obtain faithful concept-based\nexplanations, we leverage multi-task learning to train a neural network that\njointly learns to predict a decision task based on the predictions of a\nprecedent explainability task (i.e., multi-label concepts). There are two main\nchallenges to overcome: concept label scarcity and the joint learning. To\naddress both, we propose to: i) use expert rules to generate a large dataset of\nnoisy concept labels, and ii) apply two distinct multi-task learning strategies\ncombining noisy and golden labels. We compare these strategies with a fully\nsupervised approach in a real-world fraud detection application with few golden\nlabels available for the explainability task. With improvements of 9.26% and of\n417.8% at the explainability and decision tasks, respectively, our results show\nit is possible to improve performance at both tasks by combining labels of\nheterogeneous quality.\n"}
+{"abstract": "  Data augmentation has attracted a lot of research attention in the deep\nlearning era for its ability in alleviating data sparseness. The lack of\nlabeled data for unseen evaluation databases is exactly the major challenge for\ncross-domain text-to-SQL parsing. Previous works either require human\nintervention to guarantee the quality of generated data, or fail to handle\ncomplex SQL queries. This paper presents a simple yet effective data\naugmentation framework. First, given a database, we automatically produce a\nlarge number of SQL queries based on an abstract syntax tree grammar. For\nbetter distribution matching, we require that at least 80% of SQL patterns in\nthe training data are covered by generated queries. Second, we propose a\nhierarchical SQL-to-question generation model to obtain high-quality natural\nlanguage questions, which is the major contribution of this work. Finally, we\ndesign a simple sampling strategy that can greatly improve training efficiency\ngiven large amounts of generated data. Experiments on three cross-domain\ndatasets, i.e., WikiSQL and Spider in English, and DuSQL in Chinese, show that\nour proposed data augmentation framework can consistently improve performance\nover strong baselines, and the hierarchical generation component is the key for\nthe improvement.\n"}
+{"abstract": "  Subcomponent millicharged dark matter that cools baryons via Coulomb\ninteractions has been invoked to explain the EDGES anomaly. However, this model\nis in severe tension with constraints from cosmology and stellar emissions. In\nthis work, we consider the consequences of these millicharged particles\nexisting in composite blobs. The relevant degrees of freedom at high\ntemperature are minuscule elementary charges, which fuse at low temperatures to\nmake up blobs of larger charge. These blobs serve as the degrees of freedom\nrelevant in cooling the baryons sufficiently to account for the EDGES anomaly.\nIn such a model, cosmology and stellar constraints (which involve\nhigh-temperature processes) apply only to the feebly-interacting elementary\ncharges and not to the blobs. This salvages a large range of parameter space\nfor millicharged blobs that can explain the EDGES anomaly. It also opens up new\nparameter space for direct detection, albeit at low momentum transfers.\n"}
+{"abstract": "  We prove that semi-infinite Bruhat order on an affine Weyl group is\ncompletely determined from those on the quotients by affine Weyl subgroups\nassociated with various maximal (standard) parabolic subgroups of finite type.\nFurthermore, for an affine Weyl group of classical type, we give a complete\nclassification of all cover relations of semi-infinite Bruhat order (or\nequivalently, all edges of the quantum Bruhat graphs) on the quotients in terms\nof tableaux. Combining these we obtain a tableau criterion for semi-infinite\nBruhat order on an affine Weyl group of classical type. As an application, we\ngive new tableau models for the crystal bases of a level-zero fundamental\nrepresentation and a level-zero extremal weight module over a quantum affine\nalgebra of classical untwisted type, which we call quantum Kashiwara-Nakashima\ncolumns and semi-infinite Kashiwara-Nakashima tableaux. We give an explicit\ndescription of the crystal isomorphisms among three different realizations of\nthe crystal basis of a level-zero fundamental representation by quantum\nLakshmibai-Seshadri paths, quantum Kashiwara-Nakashima columns, and (ordinary)\nKashiwara-Nakashima columns.\n"}
+{"abstract": "  Aim: To study the multimessenger nature of the signal that can result from\nthe phase transition of a neutron star to a quark star and their corresponding\nastrophysical observations. Methods: The phase transition process is initiated\nby the abrupt pressure and density changes at the star center, giving rise to a\nshock which deconfines matter followed by a weak front converting excess down\nto strange quarks to attain absolute stability. This process's effects are\ninvestigated by understanding how the energy escapes from the star in the form\nof neutrino-antineutrino annihilation. For such annihilation process, the\ncorresponding energy deposition rate is calculated. Structural changes due to\nthe energy loss have been investigated in the likes of misalignment angle\nevolution of the star and its astrophysical observation through gravitational\nwaves. Results: The energy and time signature for the neutrino-antineutrino\nannihilation is compared with the observed isotropic energy for a short\ngamma-ray burst. The misalignment angle evolves to align the star's tilt axis,\nwhich can lead to the sudden increase or decrease of radio intensity from the\npulsar. The corresponding gravitational wave emission, both continuous and\nburst, all lead towards multimessenger signals coming from the phase\ntransition.\n"}
+{"abstract": "  We study the convergence properties of decentralized fictitious play (DFP)\nfor the class of near-potential games where the incentives of agents are nearly\naligned with a potential function. In DFP, agents share information only with\ntheir current neighbors in a sequence of time-varying networks, keep estimates\nof other agents' empirical frequencies, and take actions to maximize their\nexpected utility functions computed with respect to the estimated empirical\nfrequencies. We show that empirical frequencies of actions converge to a set of\nstrategies with potential function values that are larger than the potential\nfunction values obtained by approximate Nash equilibria of the closest\npotential game. This result establishes that DFP has identical convergence\nguarantees in near-potential games as the standard fictitious play in which\nagents observe the past actions of all the other agents.\n"}
+{"abstract": "  Interactions among individuals in social networks lead to echo chambers where\nthe distribution of opinions follows a bimodal distribution with two peaks at\nthe opposite extremes. In issues with clear answers, such as global warming,\none of the echo chambers reflects an inaccurate judgment, potentially from\nmisinformation. However, in issues without clear answers such as elections, the\nneutral consensus is preferable for promoting discourse. In this letter, we use\nan opinion dynamics model to study the effect of a random dynamical nudge where\nwe present random input to each agent from the other individuals in the\nnetwork. We show that random dynamical nudge disallows the formation of echo\nchambers and leads to a normal distribution of opinions centered around the\nneutral consensus. The random dynamical nudge relies on the collective dynamics\nand it does not require surveillance of every person's opinions. Social media\nnetworks could implement a version of this self-feedback mechanism to prevent\nthe formation of segregated online communities on pressing issues such as\nelections.\n"}
+{"abstract": "  Semantic segmentation from very fine resolution (VFR) urban scene images\nplays a significant role in several application scenarios including autonomous\ndriving, land cover classification, and urban planning, etc. However, the\ntremendous details contained in the VFR image severely limit the potential of\nthe existing deep learning approaches. More seriously, the considerable\nvariations in scale and appearance of objects further deteriorate the\nrepresentational capacity of those se-mantic segmentation methods, leading to\nthe confusion of adjacent objects. Addressing such is-sues represents a\npromising research field in the remote sensing community, which paves the way\nfor scene-level landscape pattern analysis and decision making. In this\nmanuscript, we pro-pose a bilateral awareness network (BANet) which contains a\ndependency path and a texture path to fully capture the long-range\nrelationships and fine-grained details in VFR images. Specif-ically, the\ndependency path is conducted based on the ResT, a novel Transformer backbone\nwith memory-efficient multi-head self-attention, while the texture path is\nbuilt on the stacked convo-lution operation. Besides, using the linear\nattention mechanism, a feature aggregation module (FAM) is designed to\neffectively fuse the dependency features and texture features. Extensive\nexperiments conducted on the three large-scale urban scene image segmentation\ndatasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam dataset, and UAVid\ndataset, demonstrate the effective-ness of our BANet. Specifically, a 64.6%\nmIoU is achieved on the UAVid dataset.\n"}
+{"abstract": "  We introduce an approach that accurately reconstructs 3D human poses and\ndetailed 3D full-body geometric models from single images in realtime. The key\nidea of our approach is a novel end-to-end multi-task deep learning framework\nthat uses single images to predict five outputs simultaneously: foreground\nsegmentation mask, 2D joints positions, semantic body partitions, 3D part\norientations and uv coordinates (uv map). The multi-task network architecture\nnot only generates more visual cues for reconstruction, but also makes each\nindividual prediction more accurate. The CNN regressor is further combined with\nan optimization based algorithm for accurate kinematic pose reconstruction and\nfull-body shape modeling. We show that the realtime reconstruction reaches\naccurate fitting that has not been seen before, especially for wild images. We\ndemonstrate the results of our realtime 3D pose and human body reconstruction\nsystem on various challenging in-the-wild videos. We show the system advances\nthe frontier of 3D human body and pose reconstruction from single images by\nquantitative evaluations and comparisons with state-of-the-art methods.\n"}
+{"abstract": "  Salient object detection aims at detecting the most visually distinct objects\nand producing the corresponding masks. As the cost of pixel-level annotations\nis high, image tags are usually used as weak supervisions. However, an image\ntag can only be used to annotate one class of objects. In this paper, we\nintroduce saliency subitizing as the weak supervision since it is\nclass-agnostic. This allows the supervision to be aligned with the property of\nsaliency detection, where the salient objects of an image could be from more\nthan one class. To this end, we propose a model with two modules, Saliency\nSubitizing Module (SSM) and Saliency Updating Module (SUM). While SSM learns to\ngenerate the initial saliency masks using the subitizing information, without\nthe need for any unsupervised methods or some random seeds, SUM helps\niteratively refine the generated saliency masks. We conduct extensive\nexperiments on five benchmark datasets. The experimental results show that our\nmethod outperforms other weakly-supervised methods and even performs comparably\nto some fully-supervised methods.\n"}
+{"abstract": "  In recent years, the unprecedented growth in digital payments fueled\nconsequential changes in fraud and financial crimes. In this new landscape,\ntraditional fraud detection approaches such as rule-based engines have largely\nbecome ineffective. AI and machine learning solutions using graph computing\nprinciples have gained significant interest. Graph neural networks and emerging\nadaptive solutions provide compelling opportunities for the future of fraud and\nfinancial crime detection. However, implementing the graph-based solutions in\nfinancial transaction processing systems has brought numerous obstacles and\napplication considerations to light. In this paper, we overview the latest\ntrends in the financial crimes landscape and discuss the implementation\ndifficulties current and emerging graph solutions face. We argue that the\napplication demands and implementation challenges provide key insights in\ndeveloping effective solutions.\n"}
+{"abstract": "  One of the most challenging problems for the realization of a scalable\nquantum computer is to design a physical device that keeps the error rate for\neach quantum processing operation low. These errors can originate from the\naccuracy of quantum manipulation, such as the sweeping of a gate voltage in\nsolid state qubits or the duration of a laser pulse in optical schemes. Errors\nalso result from decoherence, which is often regarded as more crucial in the\nsense that it is inherent to the quantum system, being fundamentally a\nconsequence of the coupling to the external environment.\n  Grouping small collections of qubits into clusters with symmetries can\nprotect parts of the calculation from decoherence. We use 4-level cores with a\nstraightforward generalization of discrete rotational symmetry, omega-rotation\ninvariance, to encode pairs of coupled qubits and universal 2-qubit logical\ngates. We include quantum errors as a main source of decoherence, and show that\nsymmetry makes logical operations particularly resilient to untimely\nanisotropic qubit rotations. We propose a scalable scheme for universal quantum\ncomputation where cores play the role of quantum-computational transistors,\nquansistors.\n  Initialization and readout are achieved by coupling to leads. The external\nleads are explicitly considered and are assumed to be the other main source of\ndecoherence. We show that quansistors can be dynamically decoupled from the\nleads by tuning their internal parameters, giving them the versatility required\nto act as controllable quantum memory units. With this dynamical decoupling,\nlogical operations within quansistors are also symmetry-protected from unbiased\nnoise in their parameters. We identify technologies that could implement\nomega-rotation invariance. Many of our results can be generalized to\nhigher-level omega-rotation-invariant systems, or adapted to clusters with\nother symmetries.\n"}
+{"abstract": "  Wireless power transfer (WPT) is an emerging paradigm that will enable using\nwireless to its full potential in future networks, not only to convey\ninformation but also to deliver energy. Such networks will enable trillions of\nfuture low-power devices to sense, compute, connect, and energize anywhere,\nanytime, and on the move. The design of such future networks brings new\nchallenges and opportunities for signal processing, machine learning, sensing,\nand computing so as to make the best use of the RF radiations, spectrum, and\nnetwork infrastructure in providing cost-effective and real-time power supplies\nto wireless devices and enable wireless-powered applications. In this paper, we\nfirst review recent signal processing techniques to make WPT and wireless\ninformation and power transfer as efficient as possible. Topics include power\namplifier and energy harvester nonlinearities, active and passive beamforming,\nintelligent reflecting surfaces, receive combining with multi-antenna\nharvester, modulation, coding, waveform, massive MIMO, channel acquisition,\ntransmit diversity, multi-user power region characterization, coordinated\nmultipoint, and distributed antenna systems. Then, we overview two different\ndesign methodologies: the model and optimize approach relying on analytical\nsystem models, modern convex optimization, and communication theory, and the\nlearning approach based on data-driven end-to-end learning and physics-based\nlearning. We discuss the pros and cons of each approach, especially when\naccounting for various nonlinearities in wireless-powered networks, and\nidentify interesting emerging opportunities for the approaches to complement\neach other. Finally, we identify new emerging wireless technologies where WPT\nmay play a key role -- wireless-powered mobile edge computing and\nwireless-powered sensing -- arguing WPT, communication, computation, and\nsensing must be jointly designed.\n"}
+{"abstract": "  In the image fusion field, the design of deep learning-based fusion methods\nis far from routine. It is invariably fusion-task specific and requires a\ncareful consideration. The most difficult part of the design is to choose an\nappropriate strategy to generate the fused image for a specific task in hand.\nThus, devising learnable fusion strategy is a very challenging problem in the\ncommunity of image fusion. To address this problem, a novel end-to-end fusion\nnetwork architecture (RFN-Nest) is developed for infrared and visible image\nfusion. We propose a residual fusion network (RFN) which is based on a residual\narchitecture to replace the traditional fusion approach. A novel\ndetail-preserving loss function, and a feature enhancing loss function are\nproposed to train RFN. The fusion model learning is accomplished by a novel\ntwo-stage training strategy. In the first stage, we train an auto-encoder based\non an innovative nest connection (Nest) concept. Next, the RFN is trained using\nthe proposed loss functions. The experimental results on public domain data\nsets show that, compared with the existing methods, our end-to-end fusion\nnetwork delivers a better performance than the state-of-the-art methods in both\nsubjective and objective evaluation. The code of our fusion method is available\nat https://github.com/hli1221/imagefusion-rfn-nest\n"}
+{"abstract": "  We consider a cache-aided wireless device-to-device (D2D) network under the\nconstraint of one-shot delivery, where the placement phase is orchestrated by a\ncentral server. We assume that the devices' caches are filled with uncoded\ndata, and the whole file database at the server is made available in the\ncollection of caches. Following this phase, the files requested by the users\nare serviced by inter-device multicast communication. For such a system\nsetting, we provide the exact characterization of load-memory trade-off, by\nderiving both the minimum average and the minimum peak sum-loads of links\nbetween devices, for a given individual memory size at disposal of each user.\n"}
+{"abstract": "  The classical notion of retraction map used to approximate geodesics is\nextended and rigorously defined to become a powerful tool to construct\ngeometric integrators. Using the geometry of the tangent and cotangent bundles,\nwe are able to tangently and cotangent lift such a map so that these lifts\ninherit the same properties as the original one and they continue to be\nextended retraction maps. In particular, the cotangent lift of this new notion\nof retraction map is a natural symplectomorphism, what plays a key role for\nconstructing geometric integrators and symplectic methods. As a result, a wide\nrange of numerical methods are recovered and canonically constructed by using\ndifferent extended retraction maps, as well as some operations with Lagrangian\nsubmanifolds.\n"}
+{"abstract": "  Learning with labels noise has gained significant traction recently due to\nthe sensitivity of deep neural networks under label noise under common loss\nfunctions. Losses that are theoretically robust to label noise, however, often\nmakes training difficult. Consequently, several recently proposed methods, such\nas Meta-Weight-Net (MW-Net), use a small number of unbiased, clean samples to\nlearn a weighting function that downweights samples that are likely to have\ncorrupted labels under the meta-learning framework. However, obtaining such a\nset of clean samples is not always feasible in practice. In this paper, we\nanalytically show that one can easily train MW-Net without access to clean\nsamples simply by using a loss function that is robust to label noise, such as\nmean absolute error, as the meta objective to train the weighting network. We\nexperimentally show that our method beats all existing methods that do not use\nclean samples and performs on-par with methods that use gold samples on\nbenchmark datasets across various noise types and noise rates.\n"}
+{"abstract": "  Custom voice, a specific text to speech (TTS) service in commercial speech\nplatforms, aims to adapt a source TTS model to synthesize personal voice for a\ntarget speaker using few speech data. Custom voice presents two unique\nchallenges for TTS adaptation: 1) to support diverse customers, the adaptation\nmodel needs to handle diverse acoustic conditions that could be very different\nfrom source speech data, and 2) to support a large number of customers, the\nadaptation parameters need to be small enough for each target speaker to reduce\nmemory usage while maintaining high voice quality. In this work, we propose\nAdaSpeech, an adaptive TTS system for high-quality and efficient customization\nof new voices. We design several techniques in AdaSpeech to address the two\nchallenges in custom voice: 1) To handle different acoustic conditions, we use\ntwo acoustic encoders to extract an utterance-level vector and a sequence of\nphoneme-level vectors from the target speech during training; in inference, we\nextract the utterance-level vector from a reference speech and use an acoustic\npredictor to predict the phoneme-level vectors. 2) To better trade off the\nadaptation parameters and voice quality, we introduce conditional layer\nnormalization in the mel-spectrogram decoder of AdaSpeech, and fine-tune this\npart in addition to speaker embedding for adaptation. We pre-train the source\nTTS model on LibriTTS datasets and fine-tune it on VCTK and LJSpeech datasets\n(with different acoustic conditions from LibriTTS) with few adaptation data,\ne.g., 20 sentences, about 1 minute speech. Experiment results show that\nAdaSpeech achieves much better adaptation quality than baseline methods, with\nonly about 5K specific parameters for each speaker, which demonstrates its\neffectiveness for custom voice. Audio samples are available at\nhttps://speechresearch.github.io/adaspeech/.\n"}
+{"abstract": "  Autonomous CPSs are often required to handle uncertainties and self-manage\nthe system operation in response to problems and increasing risk in the\noperating paradigm. This risk may arise due to distribution shifts,\nenvironmental context, or failure of software or hardware components.\nTraditional techniques for risk assessment focus on design-time techniques such\nas hazard analysis, risk reduction, and assurance cases among others. However,\nthese static, design-time techniques do not consider the dynamic contexts and\nfailures the systems face at runtime. We hypothesize that this requires a\ndynamic assurance approach that computes the likelihood of unsafe conditions or\nsystem failures considering the safety requirements, assumptions made at design\ntime, past failures in a given operating context, and the likelihood of system\ncomponent failures. We introduce the ReSonAte dynamic risk estimation framework\nfor autonomous systems. ReSonAte reasons over Bow-Tie Diagrams (BTDs) which\ncapture information about hazard propagation paths and control strategies. Our\ninnovation is the extension of the BTD formalism with attributes for modeling\nthe conditional relationships with the state of the system and environment. We\nalso describe a technique for estimating these conditional relationships and\nequations for estimating risk based on the state of the system and environment.\nTo help with this process, we provide a scenario modeling procedure that can\nuse the prior distributions of the scenes and threat conditions to generate the\ndata required for estimating the conditional relationships. To improve\nscalability and reduce the amount of data required, this process considers each\ncontrol strategy in isolation and composes several single-variate distributions\ninto one complete multi-variate distribution for the control strategy in\nquestion.\n"}
+{"abstract": "  Since the beginnings of the electronic age, a quest for ever faster and\nsmaller switches has been initiated, since this element is ubiquitous and\nfoundational in any electronic circuit to regulate the flow of current. Mott\ninsulators are promising candidates to meet this need as they undergo extremely\nfast resistive switching under electric field. However the mechanism of this\ntransition is still under debate. Our spatially-resolved {\\mu}-XRD imaging\nexperiments carried out on the prototypal Mott insulator (V0.95Cr0.05)2O3 show\nthat the resistive switching is associated with the creation of a conducting\nfilamentary path consisting in an isostructural compressed phase without any\nchemical nor symmetry change. This clearly evidences that the resistive\nswitching mechanism is inherited from the bandwidth-controlled Mott transition.\nThis discovery might hence ease the development of a new branch of electronics\ndubbed Mottronics.\n"}
+{"abstract": "  Self-training based unsupervised domain adaptation (UDA) has shown great\npotential to address the problem of domain shift, when applying a trained deep\nlearning model in a source domain to unlabeled target domains. However, while\nthe self-training UDA has demonstrated its effectiveness on discriminative\ntasks, such as classification and segmentation, via the reliable pseudo-label\nselection based on the softmax discrete histogram, the self-training UDA for\ngenerative tasks, such as image synthesis, is not fully investigated. In this\nwork, we propose a novel generative self-training (GST) UDA framework with\ncontinuous value prediction and regression objective for cross-domain image\nsynthesis. Specifically, we propose to filter the pseudo-label with an\nuncertainty mask, and quantify the predictive confidence of generated images\nwith practical variational Bayes learning. The fast test-time adaptation is\nachieved by a round-based alternative optimization scheme. We validated our\nframework on the tagged-to-cine magnetic resonance imaging (MRI) synthesis\nproblem, where datasets in the source and target domains were acquired from\ndifferent scanners or centers. Extensive validations were carried out to verify\nour framework against popular adversarial training UDA methods. Results show\nthat our GST, with tagged MRI of test subjects in new target domains, improved\nthe synthesis quality by a large margin, compared with the adversarial training\nUDA methods.\n"}
+{"abstract": "  In this early draft, we describe a decentralized, app-based approach to\nCOVID-19 vaccine distribution that facilitates zero knowledge verification,\ndynamic vaccine scheduling, continuous symptoms reporting, access to aggregate\nanalytics based on population trends and more. To ensure equity, our solution\nis developed to work with limited internet access as well. In addition, we\ndescribe the six critical functions that we believe last mile vaccination\nmanagement platforms must perform, examine existing vaccine management systems,\nand present a model for privacy-focused, individual-centric solutions.\n"}
+{"abstract": "  Possibility of the diverging gradient of the macroscopic quantity near the\nboundary is investigated by a mono-speed Lorentz-gas model, with a special\nattention to the regularizing effect of the grazing collision for the\ninfinite-range potential on the velocity distribution function (VDF) and its\ninfluence on the macroscopic quantity. By careful numerical analyses of the\nsteady one-dimensional boundary-value problem, it is confirmed that the grazing\ncollision suppresses the occurrence of a jump discontinuity of the VDF on the\nboundary. However, as the price for that regularization, the collision integral\nbecomes no longer finite in the direction of the molecular velocity parallel to\nthe boundary. Consequently, the gradient of the macroscopic quantity diverges,\neven stronger than the case of the finite-range potential. A conjecture about\nthe diverging rate in approaching the boundary is made as well for a wide range\nof the infinite-range potentials, accompanied by the numerical evidence.\n"}
+{"abstract": "  This paper considers a cooperative cognitive radio network with two primary\nusers (PUs) and two secondary users (SUs) that enables two-way communications\nof primary and secondary systems in conjunction with non-linear energy\nharvesting based simultaneous wireless information and power transfer (SWIPT).\nWith the considered network, SUs are able to realize their communications over\nthe licensed spectrum while extending relay assistance to the PUs. The overall\nbidirectional end-to-end transmission takes place in four phases, which include\nboth energy harvesting (EH) and information transfer. A non-linear energy\nharvester with a hybrid SWIPT scheme is adopted in which both power-splitting\nand time-switching EH techniques are used. The SUs aid in relay cooperation by\nperforming an amplify-and-forward operation, whereas selection combining\ntechnique is adopted at the PUs to extract the intended signal from multiple\nreceived signals broadcasted by the SUs. Accurate outage probability\nexpressions for the primary and secondary links are derived under the\nNakagami-$m$ fading environment. Further, the system behavior is analyzed with\nrespect to achievable system throughput and energy efficiency. Since the\nperformance of the considered system is strongly affected by the spectrum\nsharing factor and hybrid SWIPT parameters, particle swarm optimization is\nimplemented to optimize the system parameters so as to maximize the system\nthroughput and energy efficiency. Simulation results are provided to\ncorroborate the performance analysis and give useful insights into the system\nbehavior concerning various system/channel parameters.\n"}
+{"abstract": "  We introduce a model involving two adversaries Buster and Fixer taking turns\nmodifying a connected graph, where each round consists of Buster deleting a\nsubset of edges and Fixer responding by adding edges from a reserve set of\nweighted edges to leave the graph connected. With the weights representing the\ncost for Fixer to use specific reserve edges to reconnect the graph, we provide\na reasonable definition for what should constitute an optimal strategy for\nFixer to keep the graph connected for as long as possible as cheaply as\npossible, and prove that a greedy strategy for Fixer satisfies our conditions\nfor optimality.\n"}
+{"abstract": "  In contexts in which the principle of dependent choice may not be available,\nas toposes or Constructive Set Theory, standard locale theoretic results\nrelated to complete regularity, as complete regularity of compact regular\nlocales, may fail to hold. For resolving this difficulty, B. Banaschewski and\nA. Pultr introduced strongly regular locales. Unfortunately, Banaschewski and\nPultr's notion relies on non-constructive set existence principles that hinder\nits use in Constructive Set Theory. In this article, a fully constructive\nformulation of strong regularity for locales is introduced by replacing\nnon-constructive set existence with coinductive set definitions. As an\napplication, every strongly regular locale $L$ is proved to have a compact\nregular compactification. Furthermore, every compactification is shown to enjoy\na natural extension property for an associated class of continuous maps, and to\nbe the minimal compactification with this property. Finally, an open problem\nrelated to the existence of the compact regular reflection of a locale is\npresented.\n"}
+{"abstract": "  For a finite-index $\\mathrm{II}_1$ subfactor $N \\subset M$, we prove the\nexistence of a universal Hopf $\\ast$-algebra (or, a discrete quantum group in\nthe analytic language) acting on $M$ in a trace-preserving fashion and fixing\n$N$ pointwise. We call this Hopf $\\ast$-algebra the quantum Galois group for\nthe subfactor and compute it in some examples of interest, notably for\narbitrary irreducible finite-index depth-two subfactors. Along the way, we\nprove the existence of universal acting Hopf algebras for more general\nstructures (tensors in enriched categories), in the spirit of recent work by\nAgore, Gordienko and Vercruysse.\n"}
+{"abstract": "  We approach the Torelli problem of recostructing a curve from its Jacobian\nfrom a computational point of view. Following Dubrovin, we design a machinery\nto solve this problem effectively, which builds on methods in numerical\nalgebraic geometry. We verify this methods via numerical experiments with\ncurves up to genus 7.\n"}
+{"abstract": "  Two-dimensional (2D) platinum diselenide (PtSe$_2$) has received significant\nattention for 2D transistor applications due to its high mobility. Here, using\nmolecular beam epitaxy, we investigate the growth of 2D PtSe$_2$ on highly\noriented pyrolytic graphite (HOPG) and unveil their electronic properties via\nX-ray photoelectron spectroscopy, Raman spectra, and scanning tunnelling\nmicroscopy/spectroscopy as well as density functional theory (DFT)\ncalculations. PtSe$_2$ adopts a layer-by-layer growth mode on HOPG and shows a\ndecreasing band gap with increasing layer number. For the layer numbers from\none to four, PtSe$_2$ has band gaps of $2.0 \\pm 0.1$, $1.1 \\pm 0.1$, $0.6 \\pm\n0.1$ and $0.20 \\pm 0.1$ eV, respectively, and becomes semimetal from the fifth\nlayer. DFT calculations reproduce the layer-dependent evolution of both the\nband gap and band edges, suggest an indirect band-gap structure, and elucidate\nthe underlying physics at the atomic level.\n"}
+{"abstract": "  We conduct the first 3D hydrodynamic simulations of oxygen-neon\nwhite-dwarf-neutron star/black hole mergers (ONe WD-NS/BH mergers). Such\nmergers constitute a significant fraction, and may even dominate, the inspiral\nrates of all WD-NS binaries. We post-process our simulations to obtain the\nnuclear evolution of these systems and couple the results to a supernova\nspectral synthesis code to obtain the first light curves and spectra for these\ntransients. We find that the amount of $^{56}$Ni synthesised in these mergers\ngrows as a strong function of the WD mass, reaching typically $0.05$ and up to\n$0.1\\,{\\rm M}_\\odot$ per merger. Photodisintegration leads to similar amounts\nof $^4$He and about a ten times smaller amount of $^1$H. The nuclear yields\nfrom these mergers, in particular those of $^{55}$Mn, may contribute\nsignificantly to Galactic chemical evolution. The transients expected from ONe\nWD-NS mergers are dominantly red/infrared, evolve on month-long timescales and\nreach bolometric magnitudes of up to -16.5. The current surveys must have\nalready detected these transients or are, alternatively, putting strong\nconstraints on merger scenarios. The properties of the expected transients from\nWD-NS mergers best agree with faint type Iax supernovae. The Vera Rubin\nObservatory (LSST) will be detecting up to hundreds of merging ONe WD-NS\nsystems per year. We simulate a subset of our models with 2D axisymmetric FLASH\ncode to investigate why they have been challenging for previous studies. We\nfind that the likely main challenge has been effectively modelling the nuclear\nstatistical equilibrium regime in such mergers.\n"}
+{"abstract": "  In online collaborative learning environments, students create content and\nconstruct their own knowledge through complex interactions over time. To\nfacilitate effective social learning and inclusive participation in this\ncontext, insights are needed into the correspondence between\nstudent-contributed artifacts and their subsequent popularity among peers. In\nthis study, we represent student artifacts by their (a) contextual action logs\n(b) textual content, and (c) set of instructor-specified features, and use\nthese representations to predict artifact popularity measures. Through a\nmixture of predictive analysis and visual exploration, we find that the neural\nembedding representation, learned from contextual action logs, has the\nstrongest predictions of popularity, ahead of instructor's knowledge, which\nincludes academic value and creativity ratings. Because this representation can\nbe learnt without extensive human labeling effort, it opens up possibilities\nfor shaping more inclusive student interactions on the fly in collaboration\nwith instructors and students alike.\n"}
+{"abstract": "  This paper presents a self-supervised learning method for pointer-generator\nnetworks to improve spoken-text normalization. Spoken-text normalization that\nconverts spoken-style text into style normalized text is becoming an important\ntechnology for improving subsequent processing such as machine translation and\nsummarization. The most successful spoken-text normalization method to date is\nsequence-to-sequence (seq2seq) mapping using pointer-generator networks that\npossess a copy mechanism from an input sequence. However, these models require\na large amount of paired data of spoken-style text and style normalized text,\nand it is difficult to prepare such a volume of data. In order to construct\nspoken-text normalization model from the limited paired data, we focus on\nself-supervised learning which can utilize unpaired text data to improve\nseq2seq models. Unfortunately, conventional self-supervised learning methods do\nnot assume that pointer-generator networks are utilized. Therefore, we propose\na novel self-supervised learning method, MAsked Pointer-Generator Network\n(MAPGN). The proposed method can effectively pre-train the pointer-generator\nnetwork by learning to fill masked tokens using the copy mechanism. Our\nexperiments demonstrate that MAPGN is more effective for pointer-generator\nnetworks than the conventional self-supervised learning methods in two\nspoken-text normalization tasks.\n"}
+{"abstract": "  Many people are unaware of the digital dangers that lie around each\ncyber-corner. Teaching people how to recognize dangerous situations is crucial,\nespecially for those who work on or with computers. We postulated that\ninteractive graphic vignettes could be a great way to expose professionals to\ndangerous situations and demonstrate the effects of their choices in these\nsituations. In that way, we aimed to inoculate employees against cybersecurity\nthreats.\n  We used the Comic-BEE platform to create interactive security awareness\nvignettes and evaluated for how employees of a major industrial company\nperceived them. For analysing the potential of these comics, we ran an\nevaluation study as part of a capture-the-flag (CTF) event, an interactive\nexercise for hacking vulnerable software. We evaluated whether the comics\nfulfilled our requirements based on the responses of the participants. We\nshowed the comics, on various cybersecurity concepts, to 20 volunteers. In the\ncontext of a CTF event, our requirements were not fulfilled. Most participants\nconsidered the images distracting, stating a preference for text-only material.\n"}
+{"abstract": "  We introduce the notion of poly-stable pairs of formal schemes over the\nvaluation ring of a non-archimedean field. For such pairs we define and\ninvestigate the dual intersection complex. We proceed to develop the so called\nextended skeleton of a poly-stable pair via an approximation process using the\nclassical skeletons constructed by Berkovich. This is essentially a\ngeneralization of a construction by Gubler, Rabinoff and Werner from the\nstrictly semi-stable case to the arbitrary poly-stable case and we extend their\nresults.\n"}
+{"abstract": "  This study leverages narrative from global newspapers to construct\ntheme-based knowledge graphs about world events, demonstrating that features\nextracted from such graphs improve forecasts of industrial production in three\nlarge economies compared to a number of benchmarks. Our analysis relies on a\nfiltering methodology that extracts \"backbones\" of statistically significant\nedges from large graph data sets. We find that changes in the eigenvector\ncentrality of nodes in such backbones capture shifts in relative importance\nbetween different themes significantly better than graph similarity measures.\nWe supplement our results with an interpretability analysis, showing that the\ntheme categories \"disease\" and \"economic\" have the strongest predictive power\nduring the time period that we consider. Our work serves as a blueprint for the\nconstruction of parsimonious - yet informative - theme-based knowledge graphs\nto monitor in real time the evolution of relevant phenomena in socio-economic\nsystems.\n"}
+{"abstract": "  We revisit the problem of bootstrapping CFT correlators of charged fields.\nAfter discussing in detail how bounds for uncharged fields can be recycled to\nthe charged case, we introduce two sets of analytic functional bases for\ncorrelators on the line. The first, which we call \"simple\", is essentially a\ndirect sum of analytic functionals for the uncharged case. We use it to\nestablish very general bounds on the OPE density appearing in charged\ncorrelators. The second basis is dual to generalized free fields and we explain\nhow it is related to a charged version of the Polyakov bootstrap. We apply\nthese functionals to map out the space of correlators and obtain new improved\nbounds on the 3d Ising twist defect.\n"}
+{"abstract": "  We examine the construction of the spin angular momentum in systems with\npseudoclassical Grassmann variables. In constrained systems there are many\ndifferent algebraic forms for the dynamical variables that will all agree on\nthe constraint surface. For the angular momentum, a particular form of the\ngenerators is preferred, which yields superselection sectors of irreducible\nspin^c(n) representations rather than reducible so(n) representations when\nquantized in the Schr\\\"odinger realization.\n"}
+{"abstract": "  We developed a new class of mono- or few-layered two-dimensional polymers\nbased on dinuclear (arene)ruthenium nodes, obtained by combining the imine\ncondensation with an interfacial chemistry process, and use a modified\nLangmuir-Schaefer method to transfer them onto solid surfaces. Robust\nnano-sheets of 2D polymers including dinuclear complexes of heavy ruthenium\natoms as nodes were synthesised. These nano-sheets, whose thickness is of a few\ntens of nanometers, were suspended onto solid porous membranes. Then, they were\nthoroughly characterised with a combination of local probes, including Raman\nscattering, Fourier transform infrared spectroscopy and transmission electron\nmicroscopy in imaging and diffraction mode.\n"}
+{"abstract": "  Although speaker verification has conventionally been an audio-only task,\nsome practical applications provide both audio and visual streams of input. In\nthese cases, the visual stream provides complementary information and can often\nbe leveraged in conjunction with the acoustics of speech to improve\nverification performance. In this study, we explore audio-visual approaches to\nspeaker verification, starting with standard fusion techniques to learn joint\naudio-visual (AV) embeddings, and then propose a novel approach to handle\ncross-modal verification at test time. Specifically, we investigate unimodal\nand concatenation based AV fusion and report the lowest AV equal error rate\n(EER) of 0.7% on the VoxCeleb1 dataset using our best system. As these methods\nlack the ability to do cross-modal verification, we introduce a multi-view\nmodel which uses a shared classifier to map audio and video into the same\nspace. This new approach achieves 28% EER on VoxCeleb1 in the challenging\ntesting condition of cross-modal verification.\n"}
+{"abstract": "  We present an English translation of Erwin Schr\\\"odinger's paper on \"On the\nReversal of the Laws of Nature\". In this paper Schr\\\"odinger analyses the idea\nof time reversal of a diffusion process. Schr\\\"odinger's paper acted as a\nprominent source of inspiration for the works of Bernstein on reciprocal\nprocesses and of Kolmogorov on time reversal properties of Markov processes and\ndetailed balance. The ideas outlined by Schr\\\"odinger also inspired the\ndevelopment of probabilistic interpretations of quantum mechanics by F\\'enyes,\nNelson and others as well as the notion of \"Euclidean Quantum Mechanics\" as\nprobabilistic analogue of quantization. In the second part of the paper\nSchr\\\"odinger discusses the relation between time reversal and statistical laws\nof physics. We emphasize in our commentary the relevance of Schr\\\"odinger's\nintuitions for contemporary developments in statistical nano-physics.\n"}
+{"abstract": "  Recent work demonstrated the benefits of studying continuous-time dynamics\ngoverning the GAN training. However, this dynamics is analyzed in the model\nparameter space, which results in finite-dimensional dynamical systems. We\npropose a novel perspective where we study the local dynamics of adversarial\ntraining in the general functional space and show how it can be represented as\na system of partial differential equations. Thus, the convergence properties\ncan be inferred from the eigenvalues of the resulting differential operator. We\nshow that these eigenvalues can be efficiently estimated from the target\ndataset before training. Our perspective reveals several insights on the\npractical tricks commonly used to stabilize GANs, such as gradient penalty,\ndata augmentation, and advanced integration schemes. As an immediate practical\nbenefit, we demonstrate how one can a priori select an optimal data\naugmentation strategy for a particular generation task.\n"}
+{"abstract": "  Physics-Informed Neural Networks (PINNs) are a class of deep neural networks\nthat are trained, using automatic differentiation, to compute the response of\nsystems governed by partial differential equations (PDEs). The training of\nPINNs is simulation-free, and does not require any training dataset to be\nobtained from numerical PDE solvers. Instead, it only requires the physical\nproblem description, including the governing laws of physics, domain geometry,\ninitial/boundary conditions, and the material properties. This training usually\ninvolves solving a non-convex optimization problem using variants of the\nstochastic gradient descent method, with the gradient of the loss function\napproximated on a batch of collocation points, selected randomly in each\niteration according to a uniform distribution. Despite the success of PINNs in\naccurately solving a wide variety of PDEs, the method still requires\nimprovements in terms of computational efficiency. To this end, in this paper,\nwe study the performance of an importance sampling approach for efficient\ntraining of PINNs. Using numerical examples together with theoretical\nevidences, we show that in each training iteration, sampling the collocation\npoints according to a distribution proportional to the loss function will\nimprove the convergence behavior of the PINNs training. Additionally, we show\nthat providing a piecewise constant approximation to the loss function for\nfaster importance sampling can further improve the training efficiency. This\nimportance sampling approach is straightforward and easy to implement in the\nexisting PINN codes, and also does not introduce any new hyperparameter to\ncalibrate. The numerical examples include elasticity, diffusion and plane\nstress problems, through which we numerically verify the accuracy and\nefficiency of the importance sampling approach compared to the predominant\nuniform sampling approach.\n"}
+{"abstract": "  A majority of microrobots are constructed using compliant materials that are\ndifficult to model analytically, limiting the utility of traditional\nmodel-based controllers. Challenges in data collection on microrobots and large\nerrors between simulated models and real robots make current model-based\nlearning and sim-to-real transfer methods difficult to apply. We propose a\nnovel framework residual model learning (RML) that leverages approximate models\nto substantially reduce the sample complexity associated with learning an\naccurate robot model. We show that using RML, we can learn a model of the\nHarvard Ambulatory MicroRobot (HAMR) using just 12 seconds of passively\ncollected interaction data. The learned model is accurate enough to be\nleveraged as \"proxy-simulator\" for learning walking and turning behaviors using\nmodel-free reinforcement learning algorithms. RML provides a general framework\nfor learning from extremely small amounts of interaction data, and our\nexperiments with HAMR clearly demonstrate that RML substantially outperforms\nexisting techniques.\n"}
+{"abstract": "  The Zwicky Transient Facility (ZTF) has been observing the entire northern\nsky since the start of 2018 down to a magnitude of 20.5 ($5 \\sigma$ for 30s\nexposure) in $g$, $r$, and $i$ filters. Over the course of two years, ZTF has\nobtained light curves of more than a billion sources, each with 50-1000 epochs\nper light curve in $g$ and $r$, and fewer in $i$. To be able to use the\ninformation contained in the light curves of variable sources for new\nscientific discoveries, an efficient and flexible framework is needed to\nclassify them. In this paper, we introduce the methods and infrastructure which\nwill be used to classify all ZTF light curves. Our approach aims to be flexible\nand modular and allows the use of a dynamical classification scheme and labels,\ncontinuously evolving training sets, and the use of different machine learning\nclassifier types and architectures. With this setup, we are able to\ncontinuously update and improve the classification of ZTF light curves as new\ndata becomes available, training samples are updated, and new classes need to\nbe incorporated.\n"}
+{"abstract": "  The technological exploitation of ferroelectricity in CMOS electron devices\noffers new design opportunities, but also significant challenges from an\nintegration, optimization and modelling perspective. We here revisit the\nworking principle and the modelling of some novel ferroelectric based devices,\nwith an emphasis on energy efficiency and on applications to new computational\nparadigms.\n"}
+{"abstract": "  Accurate prediction of physical properties is critical for discovering and\ndesigning novel materials. Machine learning technologies have attracted\nsignificant attention in the materials science community for their potential\nfor large-scale screening. Graph Convolution Neural Network (GCNN) is one of\nthe most successful machine learning methods because of its flexibility and\neffectiveness in describing 3D structural data. Most existing GCNN models focus\non the topological structure but overly simplify the three-dimensional\ngeometric structure. However, in materials science, the 3D-spatial distribution\nof atoms is crucial for determining the atomic states and interatomic forces.\nThis paper proposes an adaptive GCNN with a novel convolution mechanism that\nsimultaneously models atomic interactions among all neighbor atoms in\nthree-dimensional space. We apply the proposed model to two distinctly\nchallenging problems on predicting material properties. The first is Henry's\nconstant for gas adsorption in Metal-Organic Frameworks (MOFs), which is\nnotoriously difficult because of its high sensitivity to atomic configurations.\nThe second is the ion conductivity in solid-state crystal materials, which is\ndifficult because of few labeled data available for training. The new model\noutperforms existing graph-based models on both data sets, suggesting that the\ncritical three-dimensional geometric information is indeed captured.\n"}
+{"abstract": "  A minimal perfect hash function $f$ for a set $S$ of $n$ keys is a bijective\nfunction of the form $f : S \\rightarrow \\{0,\\ldots,n-1\\}$. These functions are\nimportant for many practical applications in computing, such as search engines,\ncomputer networks, and databases. Several algorithms have been proposed to\nbuild minimal perfect hash functions that: scale well to large sets, retain\nfast evaluation time, and take very little space, e.g., 2 - 3 bits/key. PTHash\nis one such algorithm, achieving very fast evaluation in compressed space,\ntypically several times faster than other techniques. In this work, we propose\na new construction algorithm for PTHash enabling: (1) multi-threading, to\neither build functions more quickly or more space-efficiently, and (2)\nexternal-memory processing to scale to inputs much larger than the available\ninternal memory. Only few other algorithms in the literature share these\nfeatures, despite of their big practical impact. We conduct an extensive\nexperimental assessment on large real-world string collections and show that,\nwith respect to other techniques, PTHash is competitive in construction time\nand space consumption, but retains 2 - 6$\\times$ better lookup time.\n"}
+{"abstract": "  There is a growing need for data-driven research efforts on how the public\nperceives the ethical, moral, and legal issues of autonomous AI systems. The\ncurrent debate on the responsibility gap posed by these systems is one such\nexample. This work proposes a mixed AI ethics model that allows normative and\ndescriptive research to complement each other, by aiding scholarly discussion\nwith data gathered from the public. We discuss its implications on bridging the\ngap between optimistic and pessimistic views towards AI systems' deployment.\n"}
+{"abstract": "  Role of inhomogeneous perturbations of nematic aerogel on the form of the\norder parameter emerging at the transition of liquid $^3$He in the superfluid\nstate is considered. It is shown that a region of stability of the polar\ndistorted ABM phase can begin right from the transition temperature. The\nsymmetry argument is given, which selects the most favorable aerogel for\nstabilization of pure polar phase.\n"}
+{"abstract": "  The standard model characterizing the GRB spectrum invokes a\nfour-free-parameters empirical function, so-called the Band model. One\nalternative model named cutoff power-law (CPL) implements a power-law plus an\nexponential cutoff. These functions obtain almost equally good fits, and are\nadopted in nearly all the GRB literature. Here, we reanalyze the sample defined\nin Li. (39 bursts including 944 spectra). We classify the spectra by two\nmethods: (1) checking their corner-corner plots of the posteriors to determine\nwell-constrained $\\beta$ (Band-better) and un-constrained $\\beta$ (CPL-better)\ncategories; and (2) defining four groups by the different of deviance\ninformation criterion (DIC). We found $\\alpha=-0.64\\pm0.28$ and $\\rm\nlog_{10}(E_{\\rm p})=\\rm log_{10}(191)\\pm0.41$ for the Band-better spectra and\n$\\alpha=-0.96\\pm0.33$ and $\\rm log_{10}(E_{\\rm p})=\\rm log_{10}(249)\\pm0.40$\nfor the CPL-better spectra. With the \"Correct\" model (the \"better\" one between\nBand and CPL) and v.v. the \"Misused\" model defined, we find significant\ndeviations in parameter distributions and parameter relations for the\nBand-\"Correct\" and CPL-\"Misused\" cases. This means if a spectrum is\nbest-described as Band, applying CPL to derive the spectral parameters will\nprominently deviate from their intrinsic shape, therefore affecting the\nphysical interpretation. Our analysis indicates that the \"better\" model should\nbe duly examined during GRB spectral analysis. In addition, the $\\beta$\ndistribution exhibits a bimodal structure containing Band-better and CPL-better\nspectra respectively, inferring Band and CPL both have physical origins.\n"}
+{"abstract": "  The design of optimal auctions is a problem of interest in economics, game\ntheory and computer science. Despite decades of effort, strategyproof,\nrevenue-maximizing auction designs are still not known outside of restricted\nsettings. However, recent methods using deep learning have shown some success\nin approximating optimal auctions, recovering several known solutions and\noutperforming strong baselines when optimal auctions are not known. In addition\nto maximizing revenue, auction mechanisms may also seek to encourage socially\ndesirable constraints such as allocation fairness or diversity. However, these\nphilosophical notions neither have standardization nor do they have widely\naccepted formal definitions. In this paper, we propose PreferenceNet, an\nextension of existing neural-network-based auction mechanisms to encode\nconstraints using (potentially human-provided) exemplars of desirable\nallocations. In addition, we introduce a new metric to evaluate an auction\nallocations' adherence to such socially desirable constraints and demonstrate\nthat our proposed method is competitive with current state-of-the-art\nneural-network based auction designs. We validate our approach through human\nsubject research and show that we are able to effectively capture real human\npreferences. Our code is available at\nhttps://github.com/neeharperi/PreferenceNet\n"}
+{"abstract": "  Ion transport in biological tissues is crucial in the study of many\nbiological and pathological problems. Some multi-cellular structures, like\nsmooth muscles on the vessel walls, could be treated as periodic bi-domain\nstructures, which consist of intracellular space and extracellular space with\nsemipermeable membranes in between. With the aid of two-scale homogenization\ntheory, macro-scale models are proposed based on an electro-neutral (EN)\nmicroscale model with nonlinear interface conditions, where membranes are\ntreated as combinations of capacitors and resistors. The connectivity of\nintracellular space is also taken into consideration. If the intracellular\nspace is fully connected and forms a syncytium, then the macroscale model is a\nbidomain nonlinear coupled partial differential equations system. Otherwise,\nwhen the intracellular cells are not connected, the macroscale model for\nintracellular space is an ordinary differential system with source/sink terms\nfrom the connected extracellular space.\n"}
+{"abstract": "  In a recent paper we have shown that data collected from linear systems\nexcited by persistently exciting inputs during low-complexity experiments, can\nbe used to design state- and output-feedback controllers, including optimal\nLinear Quadratic Regulators (LQR), by solving linear matrix inequalities (LMI)\nand semidefinite programs. We have also shown how to stabilize in the first\napproximation unknown nonlinear systems using data. In contrast to the case of\nlinear systems, however, in the case of nonlinear systems the conditions for\nlearning a controller directly from data may not be fulfilled even when the\ndata are collected in experiments performed using persistently exciting inputs.\nIn this paper we show how to design experiments that lead to the fulfilment of\nthese conditions.\n"}
+{"abstract": "  Since the first signal in 2015, the gravitational-wave detections of merging\nbinary black holes (BBHs) by the LIGO and Virgo collaborations (LVC) have\ncompletely transformed our understanding of the lives and deaths of compact\nobject binaries, and have motivated an enormous amount of theoretical work on\nthe astrophysical origin of these objects. We show that the phenomenological\nfit to the redshift-dependent merger rate of BBHs from Abbott et al. (2020) is\nconsistent with a purely dynamical origin for these objects, and that the\ncurrent merger rate of BBHs from the LVC could be explained entirely with\nglobular clusters alone. While this does not prove that globular clusters are\nthe dominant formation channel, we emphasize that many formation scenarios\ncould contribute a significant fraction of the current LVC rate, and that any\nanalysis that assumes a single (or dominant) mechanism for producing BBH\nmergers is implicitly using a specious astrophysical prior.\n"}
+{"abstract": "  The context of the present paper is stochastic thermodynamics - an approach\nto nonequilibrium thermodynamics rooted within the broader framework of\nstochastic control. In contrast to the classical paradigm of Carnot engines, we\nherein propose to consider thermodynamic processes with periodic continuously\nvarying temperature of a heat bath and study questions of maximal power and\nefficiency for two idealized cases, overdamped (first-order) and underdamped\n(second-order) stochastic models. We highlight properties of optimal periodic\ncontrol, derive and numerically validate approximate formulae for the optimal\nperformance (power and efficiency).\n"}
+{"abstract": "  The use of cash bail as a mechanism for detaining defendants pre-trial is an\noften-criticized system that many have argued violates the presumption of\n\"innocent until proven guilty.\" Many studies have sought to understand both the\nlong-term effects of cash bail's use and the disparate rate of cash bail\nassignments along demographic lines (race, gender, etc). However, such work is\noften susceptible to problems of infra-marginality -- that the data we observe\ncan only describe average outcomes, and not the outcomes associated with the\nmarginal decision.\n  In this work, we address this problem by creating a hierarchical Bayesian\nmodel of cash bail assignments. Specifically, our approach models cash bail\ndecisions as a probabilistic process whereby judges balance the relative costs\nof assigning cash bail with the cost of defendants potentially skipping court\ndates, and where these skip probabilities are estimated based upon features of\nthe individual case. We then use Monte Carlo inference to sample the\ndistribution over these costs for different magistrates and across different\nraces. We fit this model to a data set we have collected of over 50,000 court\ncases in the Allegheny and Philadelphia counties in Pennsylvania. Our analysis\nof 50 separate judges shows that they are uniformly more likely to assign cash\nbail to black defendants than to white defendants, even given identical\nlikelihood of skipping a court appearance. This analysis raises further\nquestions about the equity of the practice of cash bail, irrespective of its\nunderlying legal justification.\n"}
+{"abstract": "  The cybersecurity breaches render surveillance systems vulnerable to video\nforgery attacks, under which authentic live video streams are tampered to\nconceal illegal human activities under surveillance cameras. Traditional video\nforensics approaches can detect and localize forgery traces in each video frame\nusing computationally-expensive spatial-temporal analysis, while falling short\nin real-time verification of live video feeds. The recent work correlates\ntime-series camera and wireless signals to recognize replayed surveillance\nvideos using event-level timing information but it cannot realize fine-grained\nforgery detection and localization on each frame. To fill this gap, this paper\nproposes Secure-Pose, a novel cross-modal forgery detection and localization\nsystem for live surveillance videos using WiFi signals near the camera spot. We\nobserve that coexisting camera and WiFi signals convey common human semantic\ninformation and the presence of forgery attacks on video frames will decouple\nsuch information correspondence. Secure-Pose extracts effective human pose\nfeatures from synchronized multi-modal signals and detects and localizes\nforgery traces under both inter-frame and intra-frame attacks in each frame. We\nimplement Secure-Pose using a commercial camera and two Intel 5300 NICs and\nevaluate it in real-world environments. Secure-Pose achieves a high detection\naccuracy of 95.1% and can effectively localize tampered objects under different\nforgery attacks.\n"}
+{"abstract": "  Recently, deep learning based methods have demonstrated promising results on\nthe graph matching problem, by relying on the descriptive capability of deep\nfeatures extracted on graph nodes. However, one main limitation with existing\ndeep graph matching (DGM) methods lies in their ignorance of explicit\nconstraint of graph structures, which may lead the model to be trapped into\nlocal minimum in training. In this paper, we propose to explicitly formulate\npairwise graph structures as a \\textbf{quadratic constraint} incorporated into\nthe DGM framework. The quadratic constraint minimizes the pairwise structural\ndiscrepancy between graphs, which can reduce the ambiguities brought by only\nusing the extracted CNN features.\n  Moreover, we present a differentiable implementation to the quadratic\nconstrained-optimization such that it is compatible with the unconstrained deep\nlearning optimizer. To give more precise and proper supervision, a\nwell-designed false matching loss against class imbalance is proposed, which\ncan better penalize the false negatives and false positives with less\noverfitting. Exhaustive experiments demonstrate that our method competitive\nperformance on real-world datasets.\n"}
+{"abstract": "  This paper focuses on the dynamics of the eight tridimensional principal\nslices of the tricomplex Mandelbrot set: the Tetrabrot, the Arrowheadbrot, the\nMousebrot, the Turtlebrot, the Hourglassbrot, the Metabrot, the Airbrot\n(octahedron) and the Firebrot (tetrahedron). In particular, we establish a\ngeometrical classification of these 3D slices using the properties of some\nspecific sets that correspond to projections of the bicomplex Mandelbrot set on\nvarious two-dimensional vector subspaces, and we prove that the Firebrot is a\nregular tetrahedron. Finally, we construct the so-called \"Stella octangula\" as\na tricomplex dynamical system composed of the union of the Firebrot and its\ndual, and after defining the idempotent 3D slices of $\\mathcal{M}_{3}$, we show\nthat one of them corresponds to a third Platonic solid: the cube.\n"}
+{"abstract": "  Being one of the most popular generative framework, variational\nautoencoders(VAE) are known to suffer from a phenomenon termed posterior\ncollapse, i.e. the latent variational distributions collapse to the prior,\nespecially when a strong decoder network is used. In this work, we analyze the\nlatent representation of collapsed VAEs, and proposed a novel model, neighbor\nembedding VAE(NE-VAE), which explicitly constraints the encoder to encode\ninputs close in the input space to be close in the latent space. We observed\nthat for VAE variants that report similar ELBO, KL divergence or even mutual\ninformation scores may still behave quite differently in the latent\norganization. In our experiments, NE-VAE can produce qualitatively different\nlatent representations with majority of the latent dimensions remained active,\nwhich may benefit downstream latent space optimization tasks. NE-VAE can\nprevent posterior collapse to a much greater extent than it's predecessors, and\ncan be easily plugged into any autoencoder framework, without introducing\naddition model components and complex training routines.\n"}
+{"abstract": "  In this paper a coalitional control and observation scheme is presented in\nwhich the coalitions are changed online by enabling and disabling communication\nlinks. Transitions between coalitions are made to best balance overall system\nperformance and communication costs. Linear Matrix Inequalities are used to\ndesign the controller and observer, guaranteeing stability of the switching\nsystem. Simulation results for vehicle platoon control are presented to\nillustrate the proposed method.\n"}
+{"abstract": "  We consider online packing problems where we get a stream of axis-parallel\nrectangles. The rectangles have to be placed in the plane without overlapping,\nand each rectangle must be placed without knowing the subsequent rectangles.\nThe goal is to minimize the perimeter or the area of the axis-parallel bounding\nbox of the rectangles. We either allow rotations by 90 degrees or translations\nonly.\n  For the perimeter version we give algorithms with an absolute competitive\nratio slightly less than 4 when only translations are allowed and when\nrotations are also allowed.\n  We then turn our attention to minimizing the area and show that the\nasymptotic competitive ratio of any algorithm is at least $\\Omega(\\sqrt{n})$,\nwhere $n$ is the number of rectangles in the stream, and this holds with and\nwithout rotations. We then present algorithms that match this bound in both\ncases. We also show that the competitive ratio cannot be bounded as a function\nof OPT. We then consider two special cases.\n  The first is when all the given rectangles have aspect ratios bounded by some\nconstant. The particular variant where all the rectangles are squares and we\nwant to minimize the area of the bounding square has been studied before and an\nalgorithm with a competitive ratio of 8 has been given [Fekete and Hoffmann,\nAlgorithmica, 2017]. We improve the analysis of the algorithm and show that the\nratio is at most 6, which is tight.\n  The second special case is when all edges have length at least 1. Here, the\n$\\Omega(\\sqrt n)$ lower bound still holds, and we turn our attention to lower\nbounds depending on OPT. We show that any algorithm has an asymptotic\ncompetitive ratio of at least $\\Omega(\\sqrt{OPT})$ for the translational case\nand $\\Omega(\\sqrt[4]{OPT})$ when rotations are allowed. For both versions, we\ngive algorithms that match the respective lower bounds.\n"}
+{"abstract": "  Extreme multi-label classification (XML) is becoming increasingly relevant in\nthe era of big data. Yet, there is no method for effectively generating\nstratified partitions of XML datasets. Instead, researchers typically rely on\nprovided test-train splits that, 1) aren't always representative of the entire\ndataset, and 2) are missing many of the labels. This can lead to poor\ngeneralization ability and unreliable performance estimates, as has been\nestablished in the binary and multi-class settings. As such, this paper\npresents a new and simple algorithm that can efficiently generate stratified\npartitions of XML datasets with millions of unique labels. We also examine the\nlabel distributions of prevailing benchmark splits, and investigate the issues\nthat arise from using unrepresentative subsets of data for model development.\nThe results highlight the difficulty of stratifying XML data, and demonstrate\nthe importance of using stratified partitions for training and evaluation.\n"}
+{"abstract": "  Inverse molecular design, i.e., designing molecules with specific target\nproperties, can be posed as an optimization problem. High-dimensional\noptimization tasks in the natural sciences are commonly tackled via\npopulation-based metaheuristic optimization algorithms such as evolutionary\nalgorithms. However, expensive property evaluation, which is often required,\ncan limit the widespread use of such approaches as the associated cost can\nbecome prohibitive. Herein, we present JANUS, a genetic algorithm that is\ninspired by parallel tempering. It propagates two populations, one for\nexploration and another for exploitation, improving optimization by reducing\nexpensive property evaluations. Additionally, JANUS is augmented by a deep\nneural network that approximates molecular properties via active learning for\nenhanced sampling of the chemical space. Our method uses the SELFIES molecular\nrepresentation and the STONED algorithm for the efficient generation of\nstructures, and outperforms other generative models in common inverse molecular\ndesign tasks achieving state-of-the-art performance.\n"}
+{"abstract": "  Mentorship in science is crucial for topic choice, career decisions, and the\nsuccess of mentees and mentors. Typically, researchers who study mentorship use\narticle co-authorship and doctoral dissertation datasets. However, available\ndatasets of this type focus on narrow selections of fields and miss out on\nearly career and non-publication-related interactions. Here, we describe\nMENTORSHIP, a crowdsourced dataset of 743176 mentorship relationships among\n738989 scientists across 112 fields that avoids these shortcomings. We enrich\nthe scientists' profiles with publication data from the Microsoft Academic\nGraph and \"semantic\" representations of research using deep learning content\nanalysis. Because gender and race have become critical dimensions when\nanalyzing mentorship and disparities in science, we also provide estimations of\nthese factors. We perform extensive validations of the profile--publication\nmatching, semantic content, and demographic inferences. We anticipate this\ndataset will spur the study of mentorship in science and deepen our\nunderstanding of its role in scientists' career outcomes.\n"}
+{"abstract": "  This paper addresses the standard generalized likelihood ratio test (GLRT)\ndetection problem of weak signals in background noise. In so doing, we consider\na nonfluctuating target embedded in complex white Gaussian noise (CWGN), in\nwhich the amplitude of the target echo and the noise power are assumed to be\nunknown. Important works have analyzed the performance for the referred\nscenario and proposed GLRT-based detectors. Such detectors are projected at an\nearly stage (i.e., prior to the formation of a post-beamforming scalar\nwaveform), thereby imposing high demands on hardware, processing, and data\nstorage. From a hardware perspective, most radar systems fail to meet these\nstrong requirements. In fact, due to hardware and computational constraints,\nmost radars use a combination of analog and digital beamformers (sums) before\nany estimation or further pre-processing. The rationale behind this study is to\nderive a GLRT detector that meets the hardware and system requirements. In this\nwork, we design and analyze a more practical and easy-to-implement GLRT\ndetector, which is projected after the analog beamforming. The performance of\nthe proposed detector is analyzed and the probabilities of detection (PD) and\nfalse alarm (PFA) are derived in closed form. Moreover, we show that in the low\nsignal-to-noise ratio (SNR) regime, the post-beamforming GLRT detector performs\nbetter than both the classic pre-beamforming GLRT detector and the square-law\ndetector. This finding suggests that if the signals are weak, instead of\nprocessing the signals separately, we first must to reinforce the overall\nsignal and then assembling the system's detection statistic. At last, the SNR\nlosses are quantified, in which the superiority of the post-beamforming GLRT\ndetector was evidenced as the number of antennas and samples increase.\n"}
+{"abstract": "  We study multiplicities of jumping numbers of multiplier ideals in a smooth\nvariety of arbitrary dimension. We prove that the multiplicity function is a\nquasi-polynomial, hence proving that the Poincar\\'e series is a rational\nfunction. We further study when the various components of the quasi-polynomial\nhave the highest possible degree and relate it to jumping numbers contributed\nby Rees valuations. Finally, we study the special case of monomial ideals.\n"}
+{"abstract": "  In this work, we study the approximation of expected values of functional\nquantities on the solution of a stochastic differential equation (SDE), where\nwe replace the Monte Carlo estimation with the evaluation of a deep neural\nnetwork. Once the neural network training is done, the evaluation of the\nresulting approximating function is computationally highly efficient so that\nusing deep neural networks to replace costly Monte Carlo integration is\nappealing, e.g., for near real-time computations in quantitative finance.\nHowever, the drawback of these nowadays widespread ideas lies in the fact that\ntraining a suitable neural network is likely to be prohibitive in terms of\ncomputational cost. We address this drawback here by introducing a multilevel\napproach to the training of deep neural networks. More precisely, we combine\nthe deep learning algorithm introduced by Beck et al. with the idea of\nmultilevel Monte Carlo path simulation of Giles. The idea is to train several\nneural networks, each having a certain approximation quality and computational\ncomplexity, with training data computed from so-called level estimators,\nintroduced by Giles. We show that under certain assumptions, the variance in\nthe training process can be reduced by shifting most of the computational\nworkload to training neural nets at coarse levels where producing the training\ndata sets is comparably cheap, whereas training the neural nets corresponding\nto the fine levels requires only a limited number of training data sets. We\nformulate a complexity theorem showing that the multilevel idea can indeed\nreduce computational complexity.\n"}
+{"abstract": "  In this paper we consider Littlewood-Paley functions defined by the\nsemigroups associated with the operator $\\mathcal{A}=-\\frac{\\Delta}{2}-x\\nabla$\nin the inverse Gaussian setting for Banach valued functions. We characterize\nthe uniformly convex and smooth Banach spaces by using\n$L^p(\\mathbb{R}^n,\\gamma_{-1})$- properties of the\n$\\mathcal{A}$-Littlewood-Paley functions. We also use Littlewood-Paley\nfunctions associated with $\\mathcal{A}$ to characterize the K\\\"othe function\nspaces with the UMD property.\n"}
+{"abstract": "  The isospin doublet scalar field with hypercharge 3/2 is introduced in some\nnew physics models such as tiny neutrino masses. Detecting the doubly charged\nscalar bosons from the doublet field can be a good probe of such models.\nHowever, their collider phenomenology has not been examined sufficiently. We\ninvestigate collider signatures of the doubly and singly charged scalar bosons\nat the LHC for the high-luminosity upgraded option (HL-LHC) by looking at\ntransverse mass distributions etc. With the appropriate kinematical cuts we\ndemonstrate the background reduction in the minimal model in the following two\ncases depending on the mass of the scalar bosons. (1) The main decay mode of\nthe singly charged scalar bosons is the tau lepton and missing (as well as\ncharm and strange quarks). (2) That is into a top bottom pair. In the both\ncases, we assume that the doubly charged scalar boson is heavier than the\nsingly charged ones. We conclude that the scalar doublet field with $Y = 3/2$\nis expected to be detectable at the HL-LHC unless the mass is too large.\n"}
+{"abstract": "  $\\alpha\\mu$ is a search algorithm which repairs two defaults of Perfect\nInformation Monte Carlo search: strategy fusion and non locality. In this paper\nwe optimize $\\alpha\\mu$ for the game of Bridge, avoiding useless computations.\nThe proposed optimizations are general and apply to other imperfect information\nturn-based games. We define multiple optimizations involving Pareto fronts, and\nshow that these optimizations speed up the search. Some of these optimizations\nare cuts that stop the search at a node, while others keep track of which\npossible worlds have become redundant, avoiding unnecessary, costly\nevaluations. We also measure the benefits of parallelizing the double dummy\nsearches at the leaves of the $\\alpha\\mu$ search tree.\n"}
+{"abstract": "  We contribute to approximate algorithms for the quadratic assignment problem\nalso known as graph matching. Inspired by the success of the fusion moves\ntechnique developed for multilabel discrete Markov random fields, we\ninvestigate its applicability to graph matching. In particular, we show how\nfusion moves can be efficiently combined with the dedicated state-of-the-art\ndual methods that have recently shown superior results in computer vision and\nbio-imaging applications. As our empirical evaluation on a wide variety of\ngraph matching datasets suggests, fusion moves significantly improve\nperformance of these methods in terms of speed and quality of the obtained\nsolutions. Our method sets a new state-of-the-art with a notable margin with\nrespect to its competitors.\n"}
+{"abstract": "  We consider tunneling of quasiparticles through a rectangular quantum well,\nsubject to periodic driving. The quasiparticles are the itinerant charges in\ntwo-dimensional and three-dimensional semimetals having a quadratic\nband-touching (QBT) point in the Brillouin zone. In order to analyze the\ntime-periodic Hamiltonian, we assume a non-adiabatic limit, where the Floquet\ntheorem is applicable. By deriving the Floquet scattering matrices, we chalk\nout the transmission and shot noise spectra of the QBT semimetals. The spectra\nshow Fano resonances, which we identify with the (quasi)bound states of the\nsystems.\n"}
+{"abstract": "  We prove an inequality of H\\\"older type traducing the unique continuation\nproperty at one time for the heat equation with a potential and Neumann\nboundary condition. The main feature of the proof is to overcome the\npropagation of smallness by a global approach using a refined parabolic\nfrequency function method. It relies with a Carleman commutator estimate to\nobtain the logarithmic convexity property of the frequency function.\n"}
+{"abstract": "  In real-world settings involving consequential decision-making, the\ndeployment of machine learning systems generally requires both reliable\nuncertainty quantification and protection of individuals' privacy. We present a\nframework that treats these two desiderata jointly. Our framework is based on\nconformal prediction, a methodology that augments predictive models to return\nprediction sets that provide uncertainty quantification -- they provably cover\nthe true response with a user-specified probability, such as 90%. One might\nhope that when used with privately-trained models, conformal prediction would\nyield privacy guarantees for the resulting prediction sets; unfortunately this\nis not the case. To remedy this key problem, we develop a method that takes any\npre-trained predictive model and outputs differentially private prediction\nsets. Our method follows the general approach of split conformal prediction; we\nuse holdout data to calibrate the size of the prediction sets but preserve\nprivacy by using a privatized quantile subroutine. This subroutine compensates\nfor the noise introduced to preserve privacy in order to guarantee correct\ncoverage. We evaluate the method on large-scale computer vision datasets.\n"}
+{"abstract": "  In this paper, we consider a large-scale instance of the classical\nPickup-and-Delivery Vehicle Routing Problem (PDVRP) that must be solved by a\nnetwork of mobile cooperating robots. Robots must self-coordinate and\nself-allocate a set of pickup/delivery tasks while minimizing a given cost\nfigure. This results in a large, challenging Mixed-Integer Linear Problem that\nmust be cooperatively solved without a central coordinator. We propose a\ndistributed algorithm based on a primal decomposition approach that provides a\nfeasible solution to the problem in finite time. An interesting feature of the\nproposed scheme is that each robot computes only its portion of solution,\nthereby preserving privacy of sensible information. The algorithm also exhibits\nattractive scalability properties that guarantee solvability of the problem\neven in large networks. To the best of our knowledge, this is the first attempt\nto provide a scalable distributed solution to the problem. The algorithm is\nfirst tested through Gazebo simulations on a ROS 2 platform, highlighting the\neffectiveness of the proposed solution. Finally, experiments on a real testbed\nwith a team of ground and aerial robots are provided.\n"}
+{"abstract": "  Let $L$ be finite extension of $\\mathbb{Q}_p$ with ring of integers\n$\\mathcal{O}_L$. I show that periodic topological cyclic homology of\n$\\mathcal{O}_L$, over the base $\\mathbb{E}_{\\infty}$-ring\n$\\mathbb{S}_{W(\\mathbb{F}_q)}[z]$ carries a $p$-height one formal group law mod\n$p$ that depends on the Eisenstein polynomial of $L$ over $\\mathbb{Q}_p$ for a\nchoice of uniformizer $\\varpi\\in \\mathcal{O}_L$.\n"}
+{"abstract": "  Magnetic resonance imaging systems rely on signal detection via\nradiofrequency coil arrays which, ideally, need to provide both bendability and\nform-fitting stretchability to conform to the imaging volume. However, most\ncommercial coils are rigid and of fixed size with a substantial mean offset\ndistance of the coil from the anatomy, which compromises the spatial resolution\nand diagnostic image quality as well as patient comfort. Here, we propose a\nsoft and stretchable receive coil concept based on liquid metal and\nultra-stretchable polymer that conforms closely to a desired anatomy. Moreover,\nits smart geometry provides a self-tuning mechanism to maintain a stable\nresonance frequency over a wide range of elongation levels. Theoretical\nanalysis and numerical simulations were experimentally confirmed and\ndemonstrated that the proposed coil withstood the unwanted frequency detuning\ntypically observed with other stretchable coils (0.4% for the proposed coil as\ncompared to 4% for a comparable control coil). Moreover, the signal-to-noise\nratio of the proposed coil increased by up to 60% as compared to a typical,\nrigid, commercial coil.\n"}
+{"abstract": "  Magnetic monopoles have been a subject of study for more than a century since\nthe first ideas by A. Vaschy and P. Curie, circa 1890. In 1974, Y. Nambu\nproposed a model for magnetic monopoles exploring a parallelism between the\nbroken symmetry Higgs and the superconductivity Ginzburg-Landau theories in\norder to describe the pions quark-antiquark confinement states. There, Nambu\ndescribes an energetic string where its end points behave like two magnetic\nmonopoles with opposite magnetic charges -- quark and antiquark. Consequently,\nnot only the interaction among monopole and antimonopole, mediated by a massive\nvector boson (Yukawa potential), but also the energetic string (linear\npotential) contributes to the effective interaction potential. We propose here\na monopole-antimonopole non confining attractive interaction of the Nambu-type,\nand then investigate the formation of bound states, the monopolium. Some\nnecessary conditions for the existence of bound states to be fulfilled by the\nproposed Nambu-type potential, Kato weakness, Set\\^o and Bargmann conditions,\nare verified. In the following, ground state energies are estimated for a\nvariety of monopolium reduced mass, from $10^2$MeV to $10^2$TeV, and Compton\ninteraction lengths, from $10^{-2}$am to $10^{-1}$pm, where discussion about\nnon relativistic and relativistic limits validation is carried out.\n"}
+{"abstract": "  Precipitation nowcasting is an important task for weather forecasting. Many\nrecent works aim to predict the high rainfall events more accurately with the\nhelp of deep learning techniques, but such events are relatively rare. The\nrarity is often addressed by formulations that re-weight the rare events.\nSomehow such a formulation carries a side effect of making \"blurry\" predictions\nin low rainfall regions and cannot convince meteorologists to trust its\npractical usability. We fix the trust issue by introducing a discriminator that\nencourages the prediction model to generate realistic rain-maps without\nsacrificing predictive accuracy. Furthermore, we extend the nowcasting time\nframe from one hour to three hours to further address the needs from\nmeteorologists. The extension is based on consecutive attentions across\ndifferent hours. We propose a new deep learning model for precipitation\nnowcasting that includes both the discrimination and attention techniques. The\nmodel is examined on a newly-built benchmark dataset that contains both radar\ndata and actual rain data. The benchmark, which will be publicly released, not\nonly establishes the superiority of the proposed model, but also is expected to\nencourage future research on precipitation nowcasting.\n"}
+{"abstract": "  Designing deep learning-based solutions is becoming a race for training\ndeeper models with a greater number of layers. While a large-size deeper model\ncould provide competitive accuracy, it creates a lot of logistical challenges\nand unreasonable resource requirements during development and deployment. This\nhas been one of the key reasons for deep learning models not being excessively\nused in various production environments, especially in edge devices. There is\nan immediate requirement for optimizing and compressing these deep learning\nmodels, to enable on-device intelligence. In this research, we introduce a\nblack-box framework, Deeplite Neutrino for production-ready optimization of\ndeep learning models. The framework provides an easy mechanism for the\nend-users to provide constraints such as a tolerable drop in accuracy or target\nsize of the optimized models, to guide the whole optimization process. The\nframework is easy to include in an existing production pipeline and is\navailable as a Python Package, supporting PyTorch and Tensorflow libraries. The\noptimization performance of the framework is shown across multiple benchmark\ndatasets and popular deep learning models. Further, the framework is currently\nused in production and the results and testimonials from several clients are\nsummarized.\n"}
+{"abstract": "  In this paper we study the problem of private information retrieval where a\nuser seeks to retrieve one of the $F$ files from a cluster of $N$ non-colluding\nservers without revealing the identity of the requested file. In our setting\nthe servers are storage constrained in that they can only store a fraction\n$\\mu=t/N$ of each file. Furthermore, we assume that the files are stored in an\nuncoded fashion. The rate of a PIR protocol is defined as the ratio of the file\nsize and the total number of bits downloaded. The maximum achievable rate is\nreferred to as capacity. It was previously shown that there are capacity\nachieving PIR protocols when the file size is $N^F$ and complete files were\nstored on all the servers. These results were further extended for the case\nwhen servers store only a fraction of each file. However, the subpacketization\n$v$ of the files required is exponential in the number of servers $N$. We\npropose a novel uncoded PIR protocol based on combinatorial designs that are\nalso capacity achieving when the file size is $v \\times t^F$. Our protocol has\nlinear subpacketization in the number of servers in contrast to previous work\nin storage constrained uncoded PIR schemes. In the proposed PIR protocol, the\ngiven system is projected to multiple instances of reduced systems with\nreplicated servers having full storage capacity. The subfiles stored in these\nvarious instances are separately retrieved and lifted to solve the PIR problem\nfor the original system.\n"}
+{"abstract": "  High-pressure torsion (HPT), a technique of severe plastic deformation (SPD),\nis shown as a promising processing method for exchange-spring magnetic\nmaterials in bulk form. Powder mixtures of Fe and SmCo$_{5}$ are consolidated\nand deformed by HPT exhibiting sample dimensions of several millimetres, being\nessential for bulky magnetic applications. The structural evolution during HPT\ndeformation of Fe-SmCo$_{5}$ compounds at room- and elevated- temperatures of\nchemical compositions consisting of 87, 47, 24 and 10 wt.% Fe is studied and\nmicrostructurally analysed. Electron microscopy and synchrotron X-ray\ndiffraction reveal a dual-phase nanostructured composite for the as-deformed\nsamples with grain refinement after HPT deformation. SQUID magnetometry\nmeasurements show hysteresis curves of an exchange coupled nanocomposite at\nroom temperature, while for low temperatures a decoupling of Fe and SmCo$_{5}$\nis observed. Furthermore, exchange interactions between the hard- and\nsoft-magnetic phase can explain a shift of the hysteresis curve. Strong\nemphasis is devoted to the correlation between the magnetic properties and the\nevolving nano-structure during HPT deformation, which is conducted for a 1:1\ncomposition ratio of Fe to SmCo$_{5}$. SQUID magnetometry measurements show an\nincreasing saturation magnetisation for increasing strain $\\gamma$ and a\nmaximum of the coercive field strength at a shear strain of $\\gamma$ = 75.\n"}
+{"abstract": "  Gravitational waves (GWs) have opened a new window on fundamental physics in\na number of important ways. The next generation of GW detectors may reveal more\ninformation about the polarization structure of GWs. Additionally, there is\ngrowing interest in theories of gravity beyond GR. One such theory which\nremains viable within the context of recent measurements of the speed of\npropagation of GWs is the teleparallel analogue of Horndeski gravity. In this\nwork, we explore the polarization structure of this newly proposed formulation\nof Horndeski theory. In curvature-based gravity, Horndeski theory is almost\nsynonymous with extensions to GR since it spans a large portion of these\npossible extensions. We perform this calculation by taking perturbations about\na Minkowski background and consider which mode propagates. The result is that\nthe polarization structure depends on the choice of model parameters in the\nteleparallel Horndeski Lagrangian with a maximum of seven propagating degrees\nof freedom. While the curvature-based Horndeski results follows as a particular\nlimit within this setup, we find a much richer structure of both massive and\nmassless cases which produce scalar--vector--tensor propagating degrees of\nfreedom. We also find that the GW polarization that emerges from the\nteleparallel analogue of Horndeski gravity results in analogous massive and\nmassless modes which take on at most four polarizations in the massless sector\nand two scalar ones in the massive sector. In none of the cases do we find\nvector polarizations.\n"}
+{"abstract": "  Metamorphic testing seeks to verify software in the absence of test oracles.\nOur application domain is ocean system modeling, where test oracles rarely\nexist, but where symmetries of the simulated physical systems are known. The\ninput data set is large owing to the requirements of the application domain.\nThis paper presents work in progress for the automated generation of\nmetamorphic test scenarios using machine learning. We extended our previously\nproposed method [1] to identify metamorphic relations with reduced\ncomputational complexity. Initially, we represent metamorphic relations as\nidentity maps. We construct a cost function that minimizes for identifying a\nmetamorphic relation orthogonal to previously found metamorphic relations and\npenalize for the identity map. A machine learning algorithm is used to identify\nall possible metamorphic relations minimizing the defined cost function. We\npropose applying dimensionality reduction techniques to identify attributes in\nthe input which have high variance among the identified metamorphic relations.\nWe apply mutation on these selected attributes to identify distinct metamorphic\nrelations with reduced computational complexity. For experimental evaluation,\nwe subject the two implementations of an ocean-modeling application to the\nproposed method to present the use of metamorphic relations to test the two\nimplementations of this application.\n"}
+{"abstract": "  We consider light pulses in a circular array of $2N$ coupled nonlinear\noptical waveguides. The waveguides are either hermitian or alternate gain and\nloss in a $\\mathcal{PT}$-symmetric fashion. Simple patterns in the array\ninclude a ring of $2N$ pulses travelling abreast, and a breather -- a string of\npulses where all even and all odd waveguides flash in turn. In addition, the\nstructure displays solitons gyrating around the necklace by switching from one\nwaveguide to the next. Some of the gyrating solitons are stable while other\nones are weakly unstable and evolve into gyrating multiflash strings. By tuning\nthe gain-loss coefficient, the gyration of solitons in a nonhermitian array may\nbe reversed without changing the direction of their translational motion.\n"}
+{"abstract": "  Despite learning-based visual odometry (VO) has shown impressive results in\nrecent years, the pretrained networks may easily collapse in unseen\nenvironments. The large domain gap between training and testing data makes them\ndifficult to generalize to new scenes. In this paper, we propose an online\nadaptation framework for deep VO with the assistance of scene-agnostic\ngeometric computations and Bayesian inference. In contrast to learning-based\npose estimation, our method solves pose from optical flow and depth while the\nsingle-view depth estimation is continuously improved with new observations by\nonline learned uncertainties. Meanwhile, an online learned photometric\nuncertainty is used for further depth and pose optimization by a differentiable\nGauss-Newton layer. Our method enables fast adaptation of deep VO networks to\nunseen environments in a self-supervised manner. Extensive experiments\nincluding Cityscapes to KITTI and outdoor KITTI to indoor TUM demonstrate that\nour method achieves state-of-the-art generalization ability among\nself-supervised VO methods.\n"}
+{"abstract": "  In this paper, we explore the relation between the index of reducibility and\nthe Hilbert coefficients in local rings. Consequently, the main result of this\nstudy provides a characterization of a sequentially Cohen-Macaulay ring in\nterms of its Hilbert coefficients for non-parameter ideals. As corollaries to\nthe main theorem, we obtain characterizations of a Gorenstein/Cohen-Macaulay\nring in terms of its Chern coefficients for parameter ideals.\n"}
+{"abstract": "  The rapid production of data on the internet and the need to understand how\nusers are feeling from a business and research perspective has prompted the\ncreation of numerous automatic monolingual sentiment detection systems. More\nrecently however, due to the unstructured nature of data on social media, we\nare observing more instances of multilingual and code-mixed texts. This\ndevelopment in content type has created a new demand for code-mixed sentiment\nanalysis systems. In this study we collect, label and thus create a dataset of\nPersian-English code-mixed tweets. We then proceed to introduce a model which\nuses BERT pretrained embeddings as well as translation models to automatically\nlearn the polarity scores of these Tweets. Our model outperforms the baseline\nmodels that use Na\\\"ive Bayes and Random Forest methods.\n"}
+{"abstract": "  We develop a hyperparameter optimisation algorithm, Automated Budget\nConstrained Training (AutoBCT), which balances the quality of a model with the\ncomputational cost required to tune it. The relationship between\nhyperparameters, model quality and computational cost must be learnt and this\nlearning is incorporated directly into the optimisation problem. At each\ntraining epoch, the algorithm decides whether to terminate or continue\ntraining, and, in the latter case, what values of hyperparameters to use. This\ndecision weighs optimally potential improvements in the quality with the\nadditional training time and the uncertainty about the learnt quantities. The\nperformance of our algorithm is verified on a number of machine learning\nproblems encompassing random forests and neural networks. Our approach is\nrooted in the theory of Markov decision processes with partial information and\nwe develop a numerical method to compute the value function and an optimal\nstrategy.\n"}
+{"abstract": "  The current paper is concerned with the spreading speeds of the following\nparabolic-parabolic chemotaxis model with logistic source on $\\mathbb{R}^{N}$,\n\\begin{equation} \\begin{cases} u_t=\\Delta u-\\chi\\nabla\\cdot ( u\\nabla v) +\nu(a-bu),\\quad x\\in\\mathbb{R}^{N},\\cr {v_t}=\\Delta v -\\lambda v+\\mu u,\\quad x\\in\n\\mathbb{R}^{N}. \\end{cases}(1) \\end{equation} where $\\chi, \\ a,\\ b,\\ \\lambda,\\\n\\mu$ are positive constants. Assume $b>\\frac{N\\mu\\chi}{4}$. Among others, it is\nproved that $2\\sqrt{a}$ is the spreading speed of the global classical\nsolutions of (1) with nonempty compactly supported initial functions, that is,\n$$ \\lim_{t\\to\\infty}\\sup_{|x|\\geq ct}u(x,t;u_0,v_0)=0\\quad \\forall\\,\\,\nc>2\\sqrt{a} $$ and $$ \\liminf_{t\\to\\infty}\\inf_{|x|\\leq ct}u(x,t;u_0,v_0)>0\n\\quad \\forall\\,\\, 0<c<2\\sqrt{a}. $$ where $(u(x,t;u_0,v_0), v(x,t;u_0,v_0))$ is\nthe unique global classical solution of (1) with $u(x,0;u_0,v_0)=u_0$,\n$v(x,0;u_0,v_0)=v_0$, and ${\\rm supp}(u_0)$, ${\\rm supp}(v_0)$ are nonempty and\ncompact. It is well known that $2\\sqrt{a}$ is the spreading speed of the\nfollowing Fisher-KPP equation, $$ u_t=\\Delta u+u(a-bu),\\quad \\forall\\,\\\nx\\in\\mathbb{R}^{N}. $$ Hence, if $b>\\frac{N\\mu\\chi}{4}$, the chemotaxis neither\nspeeds up nor slows down the spatial spreading in the Fisher-KPP equation.\n"}
+{"abstract": "  The design of efficient and generic algorithms for solving combinatorial\noptimization problems has been an active field of research for many years.\nStandard exact solving approaches are based on a clever and complete\nenumeration of the solution set. A critical and non-trivial design choice with\nsuch methods is the branching strategy, directing how the search is performed.\nThe last decade has shown an increasing interest in the design of machine\nlearning-based heuristics to solve combinatorial optimization problems. The\ngoal is to leverage knowledge from historical data to solve similar new\ninstances of a problem. Used alone, such heuristics are only able to provide\napproximate solutions efficiently, but cannot prove optimality nor bounds on\ntheir solution. Recent works have shown that reinforcement learning can be\nsuccessfully used for driving the search phase of constraint programming (CP)\nsolvers. However, it has also been shown that this hybridization is challenging\nto build, as standard CP frameworks do not natively include machine learning\nmechanisms, leading to some sources of inefficiencies. This paper presents the\nproof of concept for SeaPearl, a new CP solver implemented in Julia, that\nsupports machine learning routines in order to learn branching decisions using\nreinforcement learning. Support for modeling the learning component is also\nprovided. We illustrate the modeling and solution performance of this new\nsolver on two problems. Although not yet competitive with industrial solvers,\nSeaPearl aims to provide a flexible and open-source framework in order to\nfacilitate future research in the hybridization of constraint programming and\nmachine learning.\n"}
+{"abstract": "  We present high-precision photometry and polarimetry for the protoplanetary\ndisk around HD142527, with a focus on determining the light scattering\nparameters of the dust. We re-reduced polarimetric differential imaging data of\nHD142527 in the VBB (735 nm) and H-band (1625 nm) from the ZIMPOL and IRDIS\nsubinstruments of SPHERE/VLT. With polarimetry and photometry based on\nreference star differential imaging, we were able to measure the linearly\npolarized intensity and the total intensity of the light scattered by the\ncircumstellar disk with high precision. We used simple Monte Carlo simulations\nof multiple light scattering by the disk surface to derive constraints for\nthree scattering parameters of the dust: the maximum polarization of $P_{\\rm\nmax}$, the asymmetry parameter $g$, and the single-scattering albedo $\\omega$.\nWe measure a reflected total intensity of $51.4\\pm1.5$ mJy and $206\\pm12$ mJy\nand a polarized intensity of $11.3\\pm0.3$ mJy and $55.1\\pm3.3$ mJy in the VBB\nand H-band, respectively. We also find in the visual range a degree of\npolarization that varies between $28\\%$ on the far side of the disk and $17\\%$\non the near side. The disk shows a red color for the scattered light intensity\nand the polarized intensity, which are about twice as high in the near-infrared\nwhen compared to the visual. We determine with model calculations the\nscattering properties of the dust particles and find evidence for strong\nforward scattering ($g\\approx 0.5-0.75$), relatively low single-scattering\nalbedo ($\\omega \\approx 0.2-0.5$), and high maximum polarization ($P_{\\rm max}\n\\approx 0.5-0.75$) at the surface on the far side of the disk for both observed\nwavelengths. The optical parameters indicate the presence of large aggregate\ndust particles, which are necessary to explain the high maximum polarization,\nthe strong forward-scattering nature of the dust, and the observed red disk\ncolor.\n"}
+{"abstract": "  Interpretability techniques aim to provide the rationale behind a model's\ndecision, typically by explaining either an individual prediction (local\nexplanation, e.g. `why is this patient diagnosed with this condition') or a\nclass of predictions (global explanation, e.g. `why are patients diagnosed with\nthis condition in general'). While there are many methods focused on either\none, few frameworks can provide both local and global explanations in a\nconsistent manner. In this work, we combine two powerful existing techniques,\none local (Integrated Gradients, IG) and one global (Testing with Concept\nActivation Vectors), to provide local, and global concept-based explanations.\nWe first validate our idea using two synthetic datasets with a known ground\ntruth, and further demonstrate with a benchmark natural image dataset. We test\nour method with various concepts, target classes, model architectures and IG\nbaselines. We show that our method improves global explanations over TCAV when\ncompared to ground truth, and provides useful insights. We hope our work\nprovides a step towards building bridges between many existing local and global\nmethods to get the best of both worlds.\n"}
+{"abstract": "  B\\'ezier curves provide the basic building blocks of graphic design in 2D. In\nthis paper, we port B\\'ezier curves to manifolds. We support the interactive\ndrawing and editing of B\\'ezier splines on manifold meshes with millions of\ntriangles, by relying on just repeated manifold averages. We show that direct\nextensions of the De Casteljau and Bernstein evaluation algorithms to the\nmanifold setting are fragile, and prone to discontinuities when control\npolygons become large. Conversely, approaches based on subdivision are robust\nand can be implemented efficiently. We define B\\'ezier curves on manifolds, by\nextending both the recursive De Casteljau bisection and a new open-uniform\nLane-Riesenfeld subdivision scheme, which provide curves with different degrees\nof smoothness. For both schemes, we present algorithms for curve tracing, point\nevaluation, and point insertion. We test our algorithms for robustness and\nperformance on all watertight, manifold, models from the Thingi10k repository,\nwithout any pre-processing and with random control points. For interactive\nediting, we port all the basic user interface interactions found in 2D tools\ndirectly to the mesh. We also support mapping complex SVG drawings to the mesh\nand their interactive editing.\n"}
+{"abstract": "  Personalized news recommendation techniques are widely adopted by many online\nnews feed platforms to target user interests. Learning accurate user interest\nmodels is important for news recommendation. Most existing methods for news\nrecommendation rely on implicit feedbacks like click behaviors for inferring\nuser interests and model training. However, click behaviors are implicit\nfeedbacks and usually contain heavy noise. In addition, they cannot help infer\ncomplicated user interest such as dislike. Besides, the feed recommendation\nmodels trained solely on click behaviors cannot optimize other objectives such\nas user engagement. In this paper, we present a news feed recommendation method\nthat can exploit various kinds of user feedbacks to enhance both user interest\nmodeling and recommendation model training. In our method we propose a unified\nuser modeling framework to incorporate various explicit and implicit user\nfeedbacks to infer both positive and negative user interests. In addition, we\npropose a strong-to-weak attention network that uses the representations of\nstronger feedbacks to distill positive and negative user interests from\nimplicit weak feedbacks for accurate user interest modeling. Besides, we\npropose a multi-feedback model training framework by jointly training the model\nin the click, finish and dwell time prediction tasks to learn an\nengagement-aware feed recommendation model. Extensive experiments on real-world\ndataset show that our approach can effectively improve the model performance in\nterms of both news clicks and user engagement.\n"}
+{"abstract": "  The evaluation of rare but high-stakes events remains one of the main\ndifficulties in obtaining reliable policies from intelligent agents, especially\nin large or continuous state/action spaces where limited scalability enforces\nthe use of a prohibitively large number of testing iterations. On the other\nhand, a biased or inaccurate policy evaluation in a safety-critical system\ncould potentially cause unexpected catastrophic failures during deployment. In\nthis paper, we propose the Accelerated Policy Evaluation (APE) method, which\nsimultaneously uncovers rare events and estimates the rare event probability in\nMarkov decision processes. The APE method treats the environment nature as an\nadversarial agent and learns towards, through adaptive importance sampling, the\nzero-variance sampling distribution for the policy evaluation. Moreover, APE is\nscalable to large discrete or continuous spaces by incorporating function\napproximators. We investigate the convergence properties of proposed algorithms\nunder suitable regularity conditions. Our empirical studies show that APE\nestimates rare event probability with a smaller variance while only using\norders of magnitude fewer samples compared to baseline methods in both\nmulti-agent and single-agent environments.\n"}
+{"abstract": "  This work is motivated by the sign problem in a logarithmic parameter of\nblack hole entropy and the existing more massive white dwarfs than the\nChandrasekhar mass limit. We examine the quadratic, linear, and\nlinear-quadratic generalized uncertainty principle (GUP) models within the\nvirtue of recent masses and radii of white dwarfs. We consider the modification\ngenerated by introducing the minimal length proposal on the degenerate Fermi\ngas equation of state (EOS) and on the hydrostatic equation. For the latter, we\napplied Verlinde's proposal regarding entropic gravity to derived the quantum\ncorrected Newtonian gravity which in turn responsible for modifying the\nhydrostatic equation. Through the chi-square analysis of the models, we have\nfound that the observation data favor the quadratic dan linear GUP models\nwithout mass limit. However, for the quadratic-linear GUP model, we can obtain\nthe positive value of the free parameter $\\gamma_0$ as well as we can get mass\nlimit more massive than the Chandrasekhar mass limit. In the linear-quadratic\nGUP model, the formation of stable massive white dwarfs than the Chandrasekhar\nlimit is possible only if both parameters are not equal.\n"}
+{"abstract": "  Tidal disruption events (TDEs) can uncover the quiescent black holes (BHs) at\nthe center of galaxies and also offer a promising method to study them. In a\npartial TDE (PTDE), the BH's tidal force cannot fully disrupt the star, so the\nstellar core survives and only a varied portion of the stellar mass is bound to\nthe BH and feeds it. We calculate the event rate of PTDEs and full TDEs\n(FTDEs). In general, the event rate of PTDEs is higher than that of FTDEs,\nespecially for the larger BHs. And the detection rate of PTDEs is about dozens\nper year by Zwicky Transient Factory (ZTF). During the circularization process\nof the debris stream in PTDEs, no outflow can be launched due to the efficient\nradiative diffusion. The circularized debris ring then experiences viscous\nevolution and forms an accretion disk. We calculate the light curves of PTDEs\ncontributed by these two processes, along with their radiation temperature\nevolution. The light curves have double peaks and the spectra peak in UV.\nWithout obscuration or reprocessing of the radiation by an outflow, PTDEs\nprovide a clean environment to study the circularization and transient disk\nformation in TDEs.\n"}
+{"abstract": "  Generative adversarial networks (GANs) have shown promising results when\napplied on partial differential equations and financial time series generation.\nWe investigate if GANs can also be used to approximate one-dimensional Ito\nstochastic differential equations (SDEs). We propose a scheme that approximates\nthe path-wise conditional distribution of SDEs for large time steps. Standard\nGANs are only able to approximate processes in distribution, yielding a weak\napproximation to the SDE. A conditional GAN architecture is proposed that\nenables strong approximation. We inform the discriminator of this GAN with the\nmap between the prior input to the generator and the corresponding output\nsamples, i.e. we introduce a `supervised GAN'. We compare the input-output map\nobtained with the standard GAN and supervised GAN and show experimentally that\nthe standard GAN may fail to provide a path-wise approximation. The GAN is\ntrained on a dataset obtained with exact simulation. The architecture was\ntested on geometric Brownian motion (GBM) and the Cox-Ingersoll-Ross (CIR)\nprocess. The supervised GAN outperformed the Euler and Milstein schemes in\nstrong error on a discretisation with large time steps. It also outperformed\nthe standard conditional GAN when approximating the conditional distribution.\nWe also demonstrate how standard GANs may give rise to non-parsimonious\ninput-output maps that are sensitive to perturbations, which motivates the need\nfor constraints and regularisation on GAN generators.\n"}
+{"abstract": "  The majority of planetary nebulae (PNe) show axisymmetric morphologies, whose\ncauses are not well understood. In this work, we present spatially resolved\nkinematic observations of 14 Galactic PNe surrounding Wolf-Rayet ([WR]) and\nweak emission-line stars ($wels$) based on the H$\\alpha$ and [N II] emission\ntaken with the Wide Field Spectrograph on the ANU 2.3-m telescope.\nVelocity-resolved channel maps and position--velocity diagrams, together with\narchival Hubble Space Telescope ($HST$) and ground-based images, are employed\nto construct three-dimensional morpho-kinematic models of 12 objects using the\nprogram SHAPE. Our results indicate that these 12 PNe have elliptical\nmorphologies with either open or closed outer ends. Kinematic maps also\nillustrate on-sky orientations of elliptically symmetric morphologies of the\ninterior shells in NGC 6578 and NGC 6629, and the compact ($\\leq 6$ arcsec) PNe\nPe1-1, M3-15, M1-25, Hen2-142, and NGC 6567, in agreement with the\nhigh-resolution $HST$ images containing morphological details. Point-symmetric\nknots in Hb4 exhibit deceleration with distance from the nebular center that\ncould be due to shock collisions with the ambient medium. Velocity dispersion\nmaps of Pe1-1 disclose point-symmetric knots similar to those in Hb4.\nCollimated outflows are also visible in the position--velocity diagrams of\nM3-30, M1-32, M3-15, and K2-16, which are reconstructed by tenuous prolate\nellipsoids extending upwardly from thick toroidal shells in our models.\n"}
+{"abstract": "  Artificial intelligence systems, which are designed with a capability to\nlearn from the data presented to them, are used throughout society. These\nsystems are used to screen loan applicants, make sentencing recommendations for\ncriminal defendants, scan social media posts for disallowed content and more.\nBecause these systems don't assign meaning to their complex learned correlation\nnetwork, they can learn associations that don't equate to causality, resulting\nin non-optimal and indefensible decisions being made. In addition to making\ndecisions that are sub-optimal, these systems may create legal liability for\ntheir designers and operators by learning correlations that violate\nanti-discrimination and other laws regarding what factors can be used in\ndifferent types of decision making. This paper presents the use of a machine\nlearning expert system, which is developed with meaning-assigned nodes (facts)\nand correlations (rules). Multiple potential implementations are considered and\nevaluated under different conditions, including different network error and\naugmentation levels and different training levels. The performance of these\nsystems is compared to random and fully connected networks.\n"}
+{"abstract": "  Learning from implicit feedback is challenging because of the difficult\nnature of the one-class problem: we can observe only positive examples. Most\nconventional methods use a pairwise ranking approach and negative samplers to\ncope with the one-class problem. However, such methods have two main drawbacks\nparticularly in large-scale applications; (1) the pairwise approach is severely\ninefficient due to the quadratic computational cost; and (2) even recent\nmodel-based samplers (e.g. IRGAN) cannot achieve practical efficiency due to\nthe training of an extra model.\n  In this paper, we propose a learning-to-rank approach, which achieves\nconvergence speed comparable to the pointwise counterpart while performing\nsimilarly to the pairwise counterpart in terms of ranking effectiveness. Our\napproach estimates the probability densities of positive items for each user\nwithin a rich class of distributions, viz. \\emph{exponential family}. In our\nformulation, we derive a loss function and the appropriate negative sampling\ndistribution based on maximum likelihood estimation. We also develop a\npractical technique for risk approximation and a regularisation scheme. We then\ndiscuss that our single-model approach is equivalent to an IRGAN variant under\na certain condition. Through experiments on real-world datasets, our approach\noutperforms the pointwise and pairwise counterparts in terms of effectiveness\nand efficiency.\n"}
+{"abstract": "  Recent Juno observations have greatly extended the temporal and spatial\ncoverage of lightning detection on Jupiter. We use these data to constrain a\nmodel of moist convection and lightning generation in Jupiter's atmosphere, and\nderive a roughly solar abundance of water at the base of the water cloud.\nShallow lightning, observed by Juno (Becker et al., 2020, Nature, 584, 55-58)\nand defined as flashes originating at altitudes corresponding to pressure less\nthan 2 bars, is reproduced, as is lightning at a deeper range of pressures,\nincluding those below the water cloud base. It is found that the generation of\nlightning requires ammonia to stabilize liquid water at altitudes corresponding\nto sub-freezing temperatures. We find a range of local water abundances in\nwhich lightning is possible, including subsolar values of water--consistent\nwith other determinations of deep oxygen abundance.\n"}
+{"abstract": "  Auditory perception involves cues in the monaural auditory pathways as well\nas binaural cues based on differences between the ears. So far auditory models\nhave often focused on either monaural or binaural experiments in isolation.\nAlthough binaural models typically build upon stages of (existing) monaural\nmodels, only a few attempts have been made to extend a monaural model by a\nbinaural stage using a unified decision stage for monaural and binaural cues.\nIn such approaches, a typical prototype of binaural processing has been the\nclassical equalization-cancelation mechanism, which either involves\nsignal-adaptive delays and provides a single channel output or can be\nimplemented with tapped delays providing a high-dimensional multichannel\noutput. This contribution extends the (monaural) generalized envelope power\nspectrum model by a non-adaptive binaural stage with only a few, fixed output\nchannels. The binaural stage resembles features of physiologically motivated\nhemispheric binaural processing, as simplified signal processing stages,\nyielding a 5-channel monaural and binaural matrix feature \"decoder\" (BMFD). The\nback end of the existing monaural model is applied to the 5-channel BMFD output\nand calculates short-time envelope power and power features. The model is\nevaluated and discussed for a baseline database of monaural and binaural\npsychoacoustic experiments from the literature.\n"}
+{"abstract": "  We report on the first demonstration of fluorescence detection using\nsingle-photon avalanche photodiodes (SPADs) monolithically integrated with a\nmicrofabricated surface ion trap. The SPADs are positioned below the trapping\npositions of the ions, and designed to detect 370 nm photons emitted from\nsingle $^{174}$Yb$^+$ and $^{171}$Yb$^+$ ions. We achieve an ion/no-ion\ndetection fidelity for $^{174}$Yb$^+$ of 0.99 with an average detection window\nof 7.7(1) ms. We report a dark count rate as low as 1.2 kHz at room temperature\noperation. The fidelity is limited by laser scatter, dark counts, and heating\nthat prevents holding the ion directly above the SPAD. We measure count rates\nfrom each of the contributing sources and fluorescence as a function of ion\nposition. Based on the active detector area and using the ion as a calibrated\nlight source we estimate a SPAD quantum efficiency of 24$\\pm$1%.\n"}
+{"abstract": "  We study inflationary universes with an SU(3) gauge field coupled to an\ninflaton through a gauge kinetic function. Although the SU(3) gauge field grows\nat the initial stage of inflation due to the interaction with the inflaton,\nnonlinear self-couplings in the kinetic term of the gauge field become\nsignificant and cause nontrivial dynamics after sufficient growth. We\ninvestigate the evolution of the SU(3) gauge field numerically and reveal\nattractor solutions in the Bianchi type I spacetime. In general cases where all\nthe components of the SU(3) gauge field have the same magnitude initially, they\nall tend to decay eventually because of the nonlinear self-couplings.\nTherefore, the cosmic no-hair conjecture generically holds in a mathematical\nsense. Practically, however, the anisotropy can be generated transiently in the\nearly universe, even for an isotropic initial condition. Moreover, we find\nparticular cases for which several components of the SU(3) gauge field survive\nagainst the nonlinear self-couplings. It occurs due to flat directions in the\npotential of a gauge field for Lie groups whose rank is higher than one. Thus,\nan SU(2) gauge field has a specialty among general non-Abelian gauge fields.\n"}
+{"abstract": "  Network Design problems typically ask for a minimum cost sub-network from a\ngiven host network. This classical point-of-view assumes a central authority\nenforcing the optimum solution. But how should networks be designed to cope\nwith selfish agents that own parts of the network? In this setting, agents will\ndeviate from a minimum cost network if this decreases their individual cost.\nHence, designed networks should be both efficient in terms of total cost and\nstable in terms of the agents' willingness to accept the network.\n  We study this novel type of Network Design problem by investigating the\ncreation of $(\\beta,\\gamma)$-networks, that are in $\\beta$-approximate Nash\nequilibrium and have a total cost of at most $\\gamma$ times the optimal cost,\nfor the recently proposed Euclidean Generalized Network Creation Game by Bil\\`o\net al. [SPAA 2019]. There, $n$ agents corresponding to points in Euclidean\nspace create costly edges among themselves to optimize their centrality in the\ncreated network. Our main result is a simple $\\mathcal{O}(n^2)$-time algorithm\nthat computes a $(\\beta,\\beta)$-network with low $\\beta$ for any given set of\npoints. Moreover, on integer grid point sets or random point sets our algorithm\nachieves a low constant $\\beta$. Besides these results, we discuss a\ngeneralization of our algorithm to instances with arbitrary, even non-metric,\nedge lengths. Moreover, we show that no such positive results are possible when\nfocusing on either optimal networks or perfectly stable networks as in both\ncases NP-hard problems arise, there exist instances with very unstable optimal\nnetworks, and there are instances for perfectly stable networks with high total\ncost. Along the way, we significantly improve several results from Bil\\`o et\nal. and we asymptotically resolve their conjecture about the Price of Anarchy\nby providing a tight bound.\n"}
+{"abstract": "  Background: Despite lacking scientific support, vaccine hesitancy is\nwidespread. While vaccine damage as a scientific fact is statistically highly\nuncommon, emerging social and technological forces have converted probable\nvaccine damage into an alternative fact. Methods: Using the Eurobarometer 91.2\nsurvey on a statistically representative EU27-UK sample interviewed in March\n2019, we documented perceptions of vaccine risks and identified three belief\nconfigurations regarding vaccine effectiveness, safety, and usefulness, through\nexploratory cluster analysis. Results: The public beliefs in vaccine risks are\nfrequent. Approximatively one-tenth of the EU27-UK population consider vaccines\nare not rigorously tested before authorization, one-third believe vaccines can\noverload or weaken the immune system and that they can cause the disease\nagainst which they protect, and almost one-half believe vaccines can cause\nserious side effects. We identified three belief configurations: the skeptical,\nthe confident, and the trade-off clusters. The skeptical type (approx. 11\npercent of EU27-UK respondents) is defined by the belief that vaccines are\nrather ineffective, affected by risks of probable vaccine damage, not\nwell-tested, and useless; the confident type (approx. 59 percent) is defined by\nbeliefs that vaccines are effective, safe, well-tested, and useful; and the\ntrade-off type (approx. 29 percent) combines beliefs that vaccines are\neffective, well-tested and useful, with beliefs of probable vaccine damage.\nConclusions: Probable vaccine damage presently exists as an alternative fact in\nthe public imagination, perceptively available for wide segments of the public,\nincluding those who trust medical science.\n"}
+{"abstract": "  The $b\\to s(d)$ quark-level transitions are flavor-changing neutral current\nprocesses, which are not allowed at tree level in the standard model. These\nprocesses are very rare and constitute a potential probe for new physics. Belle\nII at SuperKEKB is a substantial upgrade of the Belle experiment. It aims to\ncollect 50 ab$^{-1}$ of data with a design peak luminosity of $8\\times 10^{35}$\ncm$^{-2}$s$^{-1}$ that is 40 times more than its predecessor. It has been\nrecording data since 2019 and during these early days of the experiment,\nefforts are being made to detect early signals of the above decays. We report\nthe first reconstrution in Belle II data of a $B\\to K^{*}\\gamma$ signal as well\nas future prospects for radiative and electroweak decays at Belle II.\n"}
+{"abstract": "  Deep learning has provided new ways of manipulating, processing and analyzing\ndata. It sometimes may achieve results comparable to, or surpassing human\nexpert performance, and has become a source of inspiration in the era of\nartificial intelligence. Another subfield of machine learning named\nreinforcement learning, tries to find an optimal behavior strategy through\ninteractions with the environment. Combining deep learning and reinforcement\nlearning permits resolving critical issues relative to the dimensionality and\nscalability of data in tasks with sparse reward signals, such as robotic\nmanipulation and control tasks, that neither method permits resolving when\napplied on its own. In this paper, we present recent significant progress of\ndeep reinforcement learning algorithms, which try to tackle the problems for\nthe application in the domain of robotic manipulation control, such as sample\nefficiency and generalization. Despite these continuous improvements,\ncurrently, the challenges of learning robust and versatile manipulation skills\nfor robots with deep reinforcement learning are still far from being resolved\nfor real world applications.\n"}
+{"abstract": "  We present a systematic study of the productions and decays of light axial\nvector mesons with $J^{PC}=1^{+\\pm}$ in charmonium decays. In the quark model\nscenario, the two axial vector nonets are connected with each other by the\nGell-Mann-Okubo mass relation through the mixing between two $K_1$ states (i.e.\n$K_1(1270)$ and $K_1(1400)$) with configurations of $^3P_1$ and $^1P_1$. The\nmixing angles between $f_1$ and $f_1'$ (i.e. $f_1(1285)$ and $f_1(1420)$), and\nbetween $h_1$ and $h_1'$ (i.e. $h_1(1170)$ and $h_1(1415)$) can be reliably\nconstrained. We then introduce the intermediate $K^*\\bar{K}+c.c.$ meson loop\ntransitions in the description of the productions and decays of these axial\nvector mesons. The presence of the nearby $S$-wave $K^*\\bar{K}+c.c.$ to which\nthese axial vector mesons have strong coupling strengths, turns out to be\ncrucial for understanding many puzzling questions related to their productions\nand decays. This is because that the $S$-wave $K^*\\bar{K}+c.c.$ rescatterings\nby the kaon exchange satisfy the triangle singularity (TS) condition in some of\nthese cases and the TS mechanism can introduce special interference effects in\nthe exclusive decays of these light axial vector mesons.\n"}
+{"abstract": "  Math expressions are important parts of scientific and educational documents,\nbut some of them may be challenging for junior scholars or students to\nunderstand. Nevertheless, constructing textual descriptions for math\nexpressions is nontrivial. In this paper, we explore the feasibility to\nautomatically construct descriptions for math expressions. But there are two\nchallenges that need to be addressed: 1) finding relevant documents since a\nmath equation understanding usually requires several topics, but these topics\nare often explained in different documents. 2) the sparsity of the collected\nrelevant documents making it difficult to extract reasonable descriptions.\nDifferent documents mainly focus on different topics which makes model hard to\nextract salient information and organize them to form a description of math\nexpressions. To address these issues, we propose a hybrid model (MathDes) which\ncontains two important modules: Selector and Summarizer. In the Selector, a\nTopic Relation Graph (TRG) is proposed to obtain the relevant documents which\ncontain the comprehensive information of math expressions. TRG is a graph built\naccording to the citations between expressions. In the Summarizer, a\nsummarization model under the Integer Linear Programming (ILP) framework is\nproposed. This module constructs the final description with the help of a\ntimeline that is extracted from TRG. The experimental results demonstrate that\nour methods are promising for this task and outperform the baselines in all\naspects.\n"}
+{"abstract": "  In this work, based on the Fredkin spin chain, we introduce a family of\nspin-$1/2$ many-body Hamiltonians with a three-site interaction featuring a\nfragmented Hilbert space with coexisting quantum many-body scars. The\nfragmentation results from an emergent kinetic constraint resembling the\nconserved spin configuration in the 1D Fermi-Hubbard model in the infinite\nonsite repulsion limit. To demonstrate the many-body scars, we construct an\nexact eigenstate that is in the middle of the spectrum within each fractured\nsub-sector displaying either logarithmic or area-law entanglement entropy. The\ninterplay between fragmentation and scarring leads to rich tunable non-ergodic\ndynamics by quenching different initial states that is shown through large\nscale matrix product state simulations. In addition, we provide a Floquet\nquantum circuit that displays non-ergodic dynamics as a result of sharing the\nsame fragmentation structure and scarring as the Hamiltonian.\n"}
+{"abstract": "  The rich literature on online Bayesian selection problems has long focused on\nso-called prophet inequalities, which compare the gain of an online algorithm\nto that of a \"prophet\" who knows the future. An equally-natural, though\nsignificantly less well-studied benchmark is the optimum online algorithm,\nwhich may be omnipotent (i.e., computationally-unbounded), but not omniscient.\nWhat is the computational complexity of the optimum online? How well can a\npolynomial-time algorithm approximate it?\n  We study the above questions for the online stochastic maximum-weight\nmatching problem under vertex arrivals. For this problem, a number of\n$1/2$-competitive algorithms are known against optimum offline. This is the\nbest possible ratio for this problem, as it generalizes the original\nsingle-item prophet inequality problem.\n  We present a polynomial-time algorithm which approximates the optimal online\nalgorithm within a factor of $0.51$ -- beating the best-possible prophet\ninequality. In contrast, we show that it is PSPACE-hard to approximate this\nproblem within some constant $\\alpha < 1$.\n"}
+{"abstract": "  Motivated by the industrial processing of chocolate, we study experimentally\nthe fluidisation of a sessile drop of yield-stress fluid on a pre-existing\nlayer of the same fluid under vertical sinusoidal oscillations. We compare the\nbehaviours of molten chocolate and Carbopol which are both shear-thinning with\na similar yield stress but exhibit very different elastic properties. We find\nthat these materials spread when the forcing acceleration exceeds a threshold\nwhich is determined by the initial deposition process. However, they exhibit\nvery different spreading behaviours: whereas chocolate exhibits slow long-term\nspreading, the Carbopol drop rapidly relaxes its stress by spreading to a new\nequilibrium shape with an enlarged footprint. This spreading is insensitive to\nthe history of the forcing. In addition, the Carbopol drop performs\nlarge-amplitude oscillations with the forcing frequency, both above and below\nthe threshold. We investigate these viscoelastic oscillations and provide\nevidence of complex nonlinear viscoelastic behaviour in the vicinity of the\nspreading threshold. In fact, for forcing accelerations greater than the\nspreading threshold, our drop automatically adjusts its shape to remain at the\nyield stress. We discuss how our vibrated-drop experiment offers a new and\npowerful approach to probing the yield transition in elastoviscoplastic fluids.\n"}
+{"abstract": "  In this paper, a video service enhancement strategy is investigated under an\nedge-cloud collaboration framework, where video caching and delivery decisions\nare made in the cloud and edge respectively. We aim to guarantee the user\nfairness in terms of video coding rate under statistical delay constraint and\nedge caching capacity constraint. A hybrid human-artificial intelligence\napproach is developed to improve the user hit rate for video caching.\nSpecifically, individual user interest is first characterized by merging\nfactorization machine (FM) model and multi-layer perceptron (MLP) model, where\nboth low-order and high-order features can be well learned simultaneously.\nThereafter, a social aware similarity model is constructed to transferred\nindividual user interest to group interest, based on which, videos can be\nselected to cache. Furthermore, a double bisection exploration scheme is\nproposed to optimize wireless resource allocation and video coding rate. The\neffectiveness of the proposed video caching scheme and video delivery scheme is\nfinally validated by extensive experiments with a real-world data set.\n"}
+{"abstract": "  Semi-supervised learning has received a lot of recent attention as it\nalleviates the need for large amounts of labelled data which can often be\nexpensive, requires expert knowledge and be time consuming to collect. Recent\ndevelopments in deep semi-supervised classification have reached unprecedented\nperformance and the gap between supervised and semi-supervised learning is\never-decreasing. This improvement in performance has been based on the\ninclusion of numerous technical tricks, strong augmentation techniques and\ncostly optimisation schemes with multi-term loss functions. We propose a new\nframework, LaplaceNet, for deep semi-supervised classification that has a\ngreatly reduced model complexity. We utilise a hybrid energy-neural network\nwhere graph based pseudo-labels, generated by minimising the graphical\nLaplacian, are used to iteratively improve a neural-network backbone. Our model\noutperforms state-of-the-art methods for deep semi-supervised classification,\nover several benchmark datasets. Furthermore, we consider the application of\nstrong-augmentations to neural networks theoretically and justify the use of a\nmulti-sampling approach for semi-supervised learning. We demonstrate, through\nrigorous experimentation, that a multi-sampling augmentation approach improves\ngeneralisation and reduces the sensitivity of the network to augmentation.\n"}
+{"abstract": "  Measuring the information leakage is critical for evaluating practical\nsecurity of cryptographic devices against side-channel analysis. More\nstraightforwardly, it is interesting to have an upper bound on success rate of\nany attack given a (fixed) number of side-channel measurements. Or conversely,\nwe wish to derive a lower bound on the number of queries for a given success\nrate of optimal attacks. In this paper, we derive several bounds in both\ndirections by using information-theoretic tools, particularly for cryptographic\nimplementations protected by masking schemes. We show that a generic upper\nbound on the probability of success, irrespective to specific attacks, is\nlinked to mutual information between side-channel measurements and the secret.\nMoreover, our numerical evaluation confirms that, the success rate of optimal\nmaximum likelihood distinguishers is tightly bounded given a fixed number of\nmeasurements.\n"}
+{"abstract": "  Sparse optimization problems are ubiquitous in many fields such as\nstatistics, signal/image processing and machine learning. This has led to the\nbirth of many iterative algorithms to solve them. A powerful strategy to boost\nthe performance of these algorithms is known as safe screening: it allows the\nearly identification of zero coordinates in the solution, which can then be\neliminated to reduce the problem's size and accelerate convergence. In this\nwork, we extend the existing Gap Safe screening framework by relaxing the\nglobal strong-concavity assumption on the dual cost function. Instead, we\nexploit local regularity properties, that is, strong concavity on well-chosen\nsubsets of the domain. The non-negativity constraint is also integrated to the\nexisting framework. Besides making safe screening possible to a broader class\nof functions that includes beta-divergences (e.g., the Kullback-Leibler\ndivergence), the proposed approach also improves upon the existing Gap Safe\nscreening rules on previously applicable cases (e.g., logistic regression). The\nproposed general framework is exemplified by some notable particular cases:\nlogistic function, beta = 1.5 and Kullback-Leibler divergences. Finally, we\nshowcase the effectiveness of the proposed screening rules with different\nsolvers (coordinate descent, multiplicative-update and proximal gradient\nalgorithms) and different data sets (binary classification, hyperspectral and\ncount data).\n"}
+{"abstract": "  We study colored coverage and clustering problems. Here, we are given a\ncolored point set where the points are covered by (unknown) $k$ clusters, which\nare monochromatic (i.e., all the points covered by the same cluster, have the\nsame color). The access to the colors of the points (or even the points\nthemselves) is provided indirectly via various queries (such as nearest\nneighbor, or separation queries). We show that if the number of clusters is a\nconstant, then one can correctly deduce the color of all the points (i.e.,\ncompute a monochromatic clustering of the points) using a polylogarithmic\nnumber of queries.\n  We investigate several variants of this problem, including Undecided Linear\nProgramming, covering of points by $k$ monochromatic balls, covering by $k$\ntriangles/simplices, and terrain simplification. For the later problem, we\npresent the first near linear time approximation algorithm. While our\napproximation is slightly worse than previous work, this is the first algorithm\nto have subquadratic complexity if the terrain has \"small\" complexity.\n"}
+{"abstract": "  Demographic changes increase the necessity to base the pension system more\nand more on the second and the third pillar, namely the occupational and\nprivate pension plans; this paper deals with Target Date Funds (TDFs), which\nare a typical investment opportunity for occupational pension planners. TDFs\nare usually identified with a decreasing fraction of wealth invested in equity\n(a so-called glide path) as retirement comes closer, i.e., wealth is invested\nmore risky the younger the saver is. We investigate whether this is actually\noptimal in the presence of non-tradable income risk in a stochastic volatility\nenvironment. The retirement planning procedure is formulated as a stochastic\noptimization problem. We find it is the (random) contributions that induce the\noptimal path exhibiting a glide path structure, both in the constant and\nstochastic volatility environment. Moreover, the initial wealth and the initial\ncontribution made to a retirement account strongly influence the fractional\namount of wealth to be invested in risky assets. The risk aversion of an\nindividual mainly determines the steepness of the glide path.\n"}
+{"abstract": "  The number $\\frac{\\pi ^{2}}{6}$ is involved in the variance of several\ndistributions in statistics. At the same time it holds\n$\\sum\\nolimits_{k=1}^{\\infty }k^{-2}= \\frac{\\pi ^{2}}{6}$, which solves the\nfamous Basel problem. We first provide a historical perspective on the Basel\nproblem, and second show how to generate further proofs building on stochastic\nprocesses.\n"}
+{"abstract": "  This paper considers the problem of asynchronous distributed multi-agent\noptimization on server-based system architecture. In this problem, each agent\nhas a local cost, and the goal for the agents is to collectively find a minimum\nof their aggregate cost. A standard algorithm to solve this problem is the\niterative distributed gradient-descent (DGD) method being implemented\ncollaboratively by the server and the agents. In the synchronous setting, the\nalgorithm proceeds from one iteration to the next only after all the agents\ncomplete their expected communication with the server. However, such synchrony\ncan be expensive and even infeasible in real-world applications. We show that\nwaiting for all the agents is unnecessary in many applications of distributed\noptimization, including distributed machine learning, due to redundancy in the\ncost functions (or {\\em data}). Specifically, we consider a generic notion of\nredundancy named $(r,\\epsilon)$-redundancy implying solvability of the original\nmulti-agent optimization problem with $\\epsilon$ accuracy, despite the removal\nof up to $r$ (out of total $n$) agents from the system. We present an\nasynchronous DGD algorithm where in each iteration the server only waits for\n(any) $n-r$ agents, instead of all the $n$ agents. Assuming\n$(r,\\epsilon)$-redundancy, we show that our asynchronous algorithm converges to\nan approximate solution with error that is linear in $\\epsilon$ and $r$.\nMoreover, we also present a generalization of our algorithm to tolerate some\nByzantine faulty agents in the system. Finally, we demonstrate the improved\ncommunication efficiency of our algorithm through experiments on MNIST and\nFashion-MNIST using the benchmark neural network LeNet.\n"}
+{"abstract": "  We develop techniques that lay out a basis for generalizations of the famous\nThurston's Topological Characterization of Rational Functions for an infinite\nset of marked points and branched coverings of infinite degree. Analogously to\nthe classical theorem we consider the Thurston's $\\sigma$-map acting on a\nTeichm\\\"uller space which is this time infinite-dimensional -- and this leads\nto a completely different theory comparing to the classical setting.\n  We demonstrate our techniques by giving an alternative proof of the result by\nMarkus F\\\"orster about the classification of exponential functions with the\nescaping singular value.\n"}
+{"abstract": "  The study of the visual system of the brain has attracted the attention and\ninterest of many neuro-scientists, that derived computational models of some\ntypes of neuron that compose it. These findings inspired researchers in image\nprocessing and computer vision to deploy such models to solve problems of\nvisual data processing. In this paper, we review approaches for image\nprocessing and computer vision, the design of which is based on\nneuro-scientific findings about the functions of some neurons in the visual\ncortex. Furthermore, we analyze the connection between the hierarchical\norganization of the visual system of the brain and the structure of\nConvolutional Networks (ConvNets). We pay particular attention to the\nmechanisms of inhibition of the responses of some neurons, which provide the\nvisual system with improved stability to changing input stimuli, and discuss\ntheir implementation in image processing operators and in ConvNets.\n"}
+{"abstract": "  Small, highly absorbing points are randomly present on the surfaces of the\nmain interferometer optics in Advanced LIGO. The resulting nano-meter scale\nthermo-elastic deformations and substrate lenses from these micron-scale\nabsorbers significantly reduces the sensitivity of the interferometer directly\nthough a reduction in the power-recycling gain and indirect interactions with\nthe feedback control system. We review the expected surface deformation from\npoint absorbers and provide a pedagogical description of the impact on power\nbuild-up in second generation gravitational wave detectors (dual-recycled\nFabry-Perot Michelson interferometers). This analysis predicts that the\npower-dependent reduction in interferometer performance will significantly\ndegrade maximum stored power by up to 50% and hence, limit GW sensitivity, but\nsuggests system wide corrections that can be implemented in current and future\nGW detectors. This is particularly pressing given that future GW detectors call\nfor an order of magnitude more stored power than currently used in Advanced\nLIGO in Observing Run 3. We briefly review strategies to mitigate the effects\nof point absorbers in current and future GW wave detectors to maximize the\nsuccess of these enterprises.\n"}
+{"abstract": "  Interpolation and internal painting are one of the basic approaches in image\ninternal painting, which is used to eliminate undesirable parts that occur in\ndigital images or to enhance faulty parts. This study was designed to compare\nthe interpolation algorithms used in image in-painting in the literature.\nErrors and noise generated on the colour and grayscale formats of some of the\ncommonly used standard images in the literature were corrected by using Cubic,\nKriging, Radial based function and High dimensional model representation\napproaches and the results were compared using standard image comparison\ncriteria, namely, PSNR (peak signal-to-noise ratio), SSIM (Structural\nSIMilarity), Mean Square Error (MSE). According to the results obtained from\nthe study, the absolute superiority of the methods against each other was not\nobserved. However, Kriging and RBF interpolation give better results both for\nnumerical data and visual evaluation for image in-painting problems with large\narea losses.\n"}
+{"abstract": "  We develop a mean-field theory of the growth, exchange and distribution (GED)\nmodel introduced by Kang et al. (preceding paper) that accurately describes the\nphase transition in the limit that the number of agents $N$ approaches\ninfinity. The GED model is a generalization of the Yard-Sale model in which the\nadditional wealth added by economic growth is nonuniformly distributed to the\nagents according to their wealth in a way determined by the parameter\n$\\lambda$. The model was shown numerically to have a phase transition at\n$\\lambda=1$ and be characterized by critical exponents and critical slowing\ndown. Our mean-field treatment of the GED model correctly predicts the\nexistence of the phase transition, critical slowing down, the values of the\ncritical exponents, and introduces an energy whose probability satisfies the\nBoltzmann distribution for $\\lambda < 1$, implying that the system is in\nthermodynamic equilibrium in the limit that $N \\to \\infty$. We show that the\nvalues of the critical exponents obtained by varying $\\lambda$ for a fixed\nvalue of $N$ do not satisfy the usual scaling laws, but do satisfy scaling if a\ncombination of parameters, which we refer to as the Ginzburg parameter, is much\ngreater than one and is held constant. We discuss possible implications of our\nresults for understanding economic systems and the subtle nature of the\nmean-field limit in systems with both additive and multiplicative noise.\n"}
+{"abstract": "  We study the emergence of de Sitter space in Euclidean dynamical\ntriangulations (EDT). Working within the semi-classical approximation, it is\npossible to relate the lattice parameters entering the simulations to the\npartition function of Euclidean quantum gravity. We verify that the EDT\ngeometries behave semi-classically, and by making contact with the Hawking-Moss\ninstanton solution for the Euclidean partition function, we show how to extract\na value of the renormalized Newton's constant from the simulations. This value\nis consistent with that of our previous determination coming from the\ninteraction of scalar particles. That the same universal constant appears in\nthese two different sectors of the theory is a strong indication that EDT\nprovides a viable formulation of quantum gravity.\n"}
+{"abstract": "  Pre-trained language models of the BERT family have defined the\nstate-of-the-arts in a wide range of NLP tasks. However, the performance of\nBERT-based models is mainly driven by the enormous amount of parameters, which\nhinders their application to resource-limited scenarios. Faced with this\nproblem, recent studies have been attempting to compress BERT into a\nsmall-scale model. However, most previous work primarily focuses on a single\nkind of compression technique, and few attention has been paid to the\ncombination of different methods. When BERT is compressed with integrated\ntechniques, a critical question is how to design the entire compression\nframework to obtain the optimal performance. In response to this question, we\nintegrate three kinds of compression methods (weight pruning, low-rank\nfactorization and knowledge distillation (KD)) and explore a range of designs\nconcerning model architecture, KD strategy, pruning frequency and learning rate\nschedule. We find that a careful choice of the designs is crucial to the\nperformance of the compressed model. Based on the empirical findings, our best\ncompressed model, dubbed Refined BERT cOmpreSsion with InTegrAted techniques\n(ROSITA), is $7.5 \\times$ smaller than BERT while maintains $98.5\\%$ of the\nperformance on five tasks of the GLUE benchmark, outperforming the previous\nBERT compression methods with similar parameter budget. The code is available\nat https://github.com/llyx97/Rosita.\n"}
+{"abstract": "  We consider the scattering of $n$ classical particles interacting via pair\npotentials, under the assumption that each pair potential is \"long-range\", i.e.\nbeing of order ${\\cal O}(r^{-\\alpha})$ for some $\\alpha >0$. We define and\nfocus on the \"free region\", the set of states leading to well-defined and\nwell-separated final states at infinity. As a first step, we prove the\nexistence of an explicit, global surface of section for the free region. This\nsurface of section is key to proving the smoothness of the map sending a point\nto its final state and to establishing a forward conjugacy between the $n$-body\ndynamics and free dynamics.\n"}
+{"abstract": "  From global pandemics to geopolitical turmoil, leaders in logistics, product\nallocation, procurement and operations are facing increasing difficulty with\nsafeguarding their organizations against supply chain vulnerabilities. It is\nrecommended to opt for forecasting against trending based benchmark because\nauditing a future forecast puts more focus on seasonality. The forecasting\nmodels provide with end-to-end, real time oversight of the entire supply chain,\nwhile utilizing predictive analytics and artificial intelligence to identify\npotential disruptions before they occur. By combining internal and external\ndata points, coming up with an AI-enabled modelling engine can greatly reduce\nrisk by helping retail companies proactively respond to supply and demand\nvariability. This research paper puts focus on creating an ingenious way to\ntackle the impact of COVID19 on Supply chain, product allocation, trending and\nseasonality.\n  Key words: Supply chain, covid-19, forecasting, coronavirus, manufacturing,\nseasonality, trending, retail.\n"}
+{"abstract": "  Dimensionality reduction (DR) techniques have been consistently supporting\nhigh-dimensional data analysis in various applications. Besides the patterns\nuncovered by these techniques, the interpretation of DR results based on each\nfeature's contribution to the low-dimensional representation supports new finds\nthrough exploratory analysis. Current literature approaches designed to\ninterpret DR techniques do not explain the features' contributions well since\nthey focus only on the low-dimensional representation or do not consider the\nrelationship among features. This paper presents ClusterShapley to address\nthese problems, using Shapley values to generate explanations of dimensionality\nreduction techniques and interpret these algorithms using a cluster-oriented\nanalysis. ClusterShapley explains the formation of clusters and the meaning of\ntheir relationship, which is useful for exploratory data analysis in various\ndomains. We propose novel visualization techniques to guide the interpretation\nof features' contributions on clustering formation and validate our methodology\nthrough case studies of publicly available datasets. The results demonstrate\nour approach's interpretability and analysis power to generate insights about\npathologies and patients in different conditions using DR results.\n"}
+{"abstract": "  The convergence speed of machine learning models trained with Federated\nLearning is significantly affected by heterogeneous data partitions, even more\nso in a fully decentralized setting without a central server. In this paper, we\nshow that the impact of label distribution skew, an important type of data\nheterogeneity, can be significantly reduced by carefully designing the\nunderlying communication topology. We present D-Cliques, a novel topology that\nreduces gradient bias by grouping nodes in sparsely interconnected cliques such\nthat the label distribution in a clique is representative of the global label\ndistribution. We also show how to adapt the updates of decentralized SGD to\nobtain unbiased gradients and implement an effective momentum with D-Cliques.\nOur extensive empirical evaluation on MNIST and CIFAR10 demonstrates that our\napproach provides similar convergence speed as a fully-connected topology,\nwhich provides the best convergence in a data heterogeneous setting, with a\nsignificant reduction in the number of edges and messages. In a 1000-node\ntopology, D-Cliques require 98% less edges and 96% less total messages, with\nfurther possible gains using a small-world topology across cliques.\n"}
+{"abstract": "  The overall predictive uncertainty of a trained predictor can be decomposed\ninto separate contributions due to epistemic and aleatoric uncertainty. Under a\nBayesian formulation, assuming a well-specified model, the two contributions\ncan be exactly expressed (for the log-loss) or bounded (for more general\nlosses) in terms of information-theoretic quantities (Xu and Raginsky, 2020).\nThis paper addresses the study of epistemic uncertainty within an\ninformation-theoretic framework in the broader setting of Bayesian\nmeta-learning. A general hierarchical Bayesian model is assumed in which\nhyperparameters determine the per-task priors of the model parameters. Exact\ncharacterizations (for the log-loss) and bounds (for more general losses) are\nderived for the epistemic uncertainty -quantified by the minimum excess\nmeta-risk (MEMR)- of optimal meta-learning rules. This characterization is\nleveraged to bring insights into the dependence of the epistemic uncertainty on\nthe number of tasks and on the amount of per-task training data. Experiments\nare presented that use the proposed information-theoretic bounds, evaluated via\nneural mutual information estimators, to compare the performance of\nconventional learning and meta-learning as the number of meta-learning tasks\nincreases.\n"}
+{"abstract": "  Neural network (NN) interatomic potentials provide fast prediction of\npotential energy surfaces, closely matching the accuracy of the electronic\nstructure methods used to produce the training data. However, NN predictions\nare only reliable within well-learned training domains, and show volatile\nbehavior when extrapolating. Uncertainty quantification approaches can flag\natomic configurations for which prediction confidence is low, but arriving at\nsuch uncertain regions requires expensive sampling of the NN phase space, often\nusing atomistic simulations. Here, we exploit automatic differentiation to\ndrive atomistic systems towards high-likelihood, high-uncertainty\nconfigurations without the need for molecular dynamics simulations. By\nperforming adversarial attacks on an uncertainty metric, informative geometries\nthat expand the training domain of NNs are sampled. When combined to an active\nlearning loop, this approach bootstraps and improves NN potentials while\ndecreasing the number of calls to the ground truth method. This efficiency is\ndemonstrated on sampling of kinetic barriers and collective variables in\nmolecules, and can be extended to any NN potential architecture and materials\nsystem.\n"}
+{"abstract": "  Normalizing flows are a class of flexible deep generative models that offer\neasy likelihood computation. Despite their empirical success, there is little\ntheoretical understanding of their expressiveness. In this work, we study\nresidual flows, a class of normalizing flows composed of Lipschitz residual\nblocks. We prove residual flows are universal approximators in maximum mean\ndiscrepancy. We provide upper bounds on the number of residual blocks to\nachieve approximation under different assumptions.\n"}
+{"abstract": "  The use of conversational assistants to search for information is becoming\nincreasingly more popular among the general public, pushing the research\ntowards more advanced and sophisticated techniques. In the last few years, in\nparticular, the interest in conversational search is increasing, not only\nbecause of the generalization of conversational assistants but also because\nconversational search is a step forward in allowing a more natural interaction\nwith the system.\n  In this work, the focus is on exploring the context present of the\nconversation via the historical utterances and respective embeddings with the\naim of developing a conversational search system that helps people search for\ninformation in a natural way. In particular, this system must be able to\nunderstand the context where the question is posed, tracking the current state\nof the conversation and detecting mentions to previous questions and answers.\nWe achieve this by using a context-tracking component based on neural\nquery-rewriting models. Another crucial aspect of the system is to provide the\nmost relevant answers given the question and the conversational history. To\nachieve this objective, we used a Transformer-based re-ranking method and\nexpanded this architecture to use the conversational context.\n  The results obtained with the system developed showed the advantages of using\nthe context present in the natural language utterances and in the neural\nembeddings generated throughout the conversation.\n"}
+{"abstract": "  Hand-eye calibration of laser profile sensors is the process of extracting\nthe homogeneous transformation between the laser profile sensor frame and the\nend-effector frame of a robot in order to express the data extracted by the\nsensor in the robot's global coordinate system. For laser profile scanners this\nis a challenging procedure, as they provide data only in two dimensions and\nstate-of-the-art calibration procedures require the use of specialised\ncalibration targets. This paper presents a novel method to extract the\ntranslation-part of the hand-eye calibration matrix with rotation-part known a\npriori in a target-agnostic way. Our methodology is applicable to any 2D image\nor 3D object as a calibration target and can also be performed in situ in the\nfinal application. The method is experimentally validated on a real\nrobot-sensor setup with 2D and 3D targets.\n"}
+{"abstract": "  Many machine learning models have been built to tackle information overload\nissues on Massive Open Online Courses (MOOC) platforms. These models rely on\nlearning powerful representations of MOOC entities. However, they suffer from\nthe problem of scarce expert label data. To overcome this problem, we propose\nto learn pre-trained representations of MOOC entities using abundant unlabeled\ndata from the structure of MOOCs which can directly be applied to the\ndownstream tasks. While existing pre-training methods have been successful in\nNLP areas as they learn powerful textual representation, their models do not\nleverage the richer information about MOOC entities. This richer information\nincludes the graph relationship between the lectures, concepts, and courses\nalong with the domain knowledge about the complexity of a concept. We develop\nMOOCRep, a novel method based on Transformer language model trained with two\npre-training objectives : 1) graph-based objective to capture the powerful\nsignal of entities and relations that exist in the graph, and 2)\ndomain-oriented objective to effectively incorporate the complexity level of\nconcepts. Our experiments reveal that MOOCRep's embeddings outperform\nstate-of-the-art representation learning methods on two tasks important for\neducation community, concept pre-requisite prediction and lecture\nrecommendation.\n"}
+{"abstract": "  To reduce the resource demand of neural network (NN) inference systems, it\nhas been proposed to use approximate memory, in which the supply voltage and\nthe timing parameters are tuned trading accuracy with energy consumption and\nperformance. Tuning these parameters aggressively leads to bit errors, which\ncan be tolerated by NNs when bit flips are injected during training. However,\nbit flip training, which is the state of the art for achieving bit error\ntolerance, does not scale well; it leads to massive overheads and cannot be\napplied for high bit error rates (BERs). Alternative methods to achieve bit\nerror tolerance in NNs are needed, but the underlying principles behind the bit\nerror tolerance of NNs have not been reported yet. With this lack of\nunderstanding, further progress in the research on NN bit error tolerance will\nbe restrained.\n  In this study, our objective is to investigate the internal changes in the\nNNs that bit flip training causes, with a focus on binarized NNs (BNNs). To\nthis end, we quantify the properties of bit error tolerant BNNs with two\nmetrics. First, we propose a neuron-level bit error tolerance metric, which\ncalculates the margin between the pre-activation values and batch normalization\nthresholds. Secondly, to capture the effects of bit error tolerance on the\ninterplay of neurons, we propose an inter-neuron bit error tolerance metric,\nwhich measures the importance of each neuron and computes the variance over all\nimportance values. Our experimental results support that these two metrics are\nstrongly related to bit error tolerance.\n"}
+{"abstract": "  It is a commonly held belief that enforcing invariance improves\ngeneralisation. Although this approach enjoys widespread popularity, it is only\nvery recently that a rigorous theoretical demonstration of this benefit has\nbeen established. In this work we build on the function space perspective of\nElesedy and Zaidi arXiv:2102.10333 to derive a strictly non-zero generalisation\nbenefit of incorporating invariance in kernel ridge regression when the target\nis invariant to the action of a compact group. We study invariance enforced by\nfeature averaging and find that generalisation is governed by a notion of\neffective dimension that arises from the interplay between the kernel and the\ngroup. In building towards this result, we find that the action of the group\ninduces an orthogonal decomposition of both the reproducing kernel Hilbert\nspace and its kernel, which may be of interest in its own right.\n"}
+{"abstract": "  Deeper and wider CNNs are known to provide improved performance for deep\nlearning tasks. However, most such networks have poor performance gain per\nparameter increase. In this paper, we investigate whether the gain observed in\ndeeper models is purely due to the addition of more optimization parameters or\nwhether the physical size of the network as well plays a role. Further, we\npresent a novel rescaling strategy for CNNs based on learnable repetition of\nits parameters. Based on this strategy, we rescale CNNs without changing their\nparameter count, and show that learnable sharing of weights itself can provide\nsignificant boost in the performance of any given model without changing its\nparameter count. We show that small base networks when rescaled, can provide\nperformance comparable to deeper networks with as low as 6% of optimization\nparameters of the deeper one.\n  The relevance of weight sharing is further highlighted through the example of\ngroup-equivariant CNNs. We show that the significant improvements obtained with\ngroup-equivariant CNNs over the regular CNNs on classification problems are\nonly partly due to the added equivariance property, and part of it comes from\nthe learnable repetition of network weights. For rot-MNIST dataset, we show\nthat up to 40% of the relative gain reported by state-of-the-art methods for\nrotation equivariance could actually be due to just the learnt repetition of\nweights.\n"}
+{"abstract": "  We investigate self-gravitating equilibria of halos constituted by dark\nmatter (DM) non-minimally coupled to gravity. In particular, we consider a\ntheoretically motivated non-minimal coupling which may arise when the\naveraging/coherence length $L$ associated to the fluid description of the DM\ncollective behavior is comparable to the local curvature scale. In the\nNewtonian limit, such a non-minimal coupling amounts to a modification of the\nPoisson equation by a term $L^2\\,\\nabla^2\\rho$ proportional to the Laplacian of\nthe DM density $\\rho$ itself. We further adopt a general power-law equation of\nstate $p\\propto \\rho^{\\Gamma}\\, r^\\alpha$ relating the DM dynamical pressure\n$p$ to density $\\rho$ and radius $r$, as expected by phase-space density\nstratification during the gravitational assembly of halos in a cosmological\ncontext. We confirm previous findings that, in absence of the non-minimal\ncoupling, the resulting density $\\rho(r)$ features a steep central cusp and an\noverall shape mirroring the outcomes of $N-$body simulations in the standard\n$\\Lambda$CDM cosmology, as described by the classic NFW or Einasto profiles.\nMost importantly, we find that the non-minimal coupling causes the density\ndistribution to develop an inner core and a shape closely following, out to\nseveral core scale radii, the Burkert profile. In fact, we highlight that the\nresulting mass distributions can fit, with an accuracy comparable to the\nBurkert's one, the co-added rotation curves of dwarf, DM-dominated galaxies.\nFinally, we show that non-minimally coupled DM halos are consistent with the\nobserved scaling relation between the core radius $r_0$ and core density\n$\\rho_0$, in terms of an universal core surface density $\\rho_0\\times r_0$\namong different galaxies.\n"}
+{"abstract": "  This paper studies the application of the Quantum Approximate Optimization\nAlgorithm (QAOA) to spin-glass models with random multi-body couplings in the\nlimit of a large number of spins. We show that for such mixed-spin models the\nperformance of depth $1$ QAOA is independent of the specific instance in the\nlimit of infinite sized systems and we give an explicit formula for the\nexpected performance. We also give explicit expressions for the higher moments\nof the expected energy, thereby proving that the expected performance of QAOA\nconcentrates.\n"}
+{"abstract": "  Code cloning plays a very important role in open-source software engineering.\nThe presence of clones within a project may indicate a need for refactoring,\nand clones between projects are even more interesting, since code migration\ntakes place and violations are possible. But how is code being copied? How\nprevalent is the process and on what level does it happen?\n  In this general study, we attempt to shed some light on these questions by\nsearching for clones in a large dataset of over 23 thousand Java projects on\nthe level of both files and methods, and by studying the code fragments\nthemselves and their clone pairs. We study the size and the age of code\nfragments, the prevalence of their clones, relationships between exact and\nnon-exact clones, as well as between method-level and file-level clones. We\nalso discover and describe various anomalies in the code clones that were\ndetected in the dataset.\n  Our research shows that the copying occurs all through the years of the Java\ncode existence and that method-level copying is much more prevalent than\nfile-level copying, with only 35.4% of methods having no clones at all.\nAdditionally, some of the discovered anomalies can be useful for future\nlarge-scale cloning research as they can be used for removing auto-generated\ncode.\n"}
+{"abstract": "  Driver drowsiness is one of main factors leading to road fatalities and\nhazards in the transportation industry. Electroencephalography (EEG) has been\nconsidered as one of the best physiological signals to detect drivers drowsy\nstates, since it directly measures neurophysiological activities in the brain.\nHowever, designing a calibration-free system for driver drowsiness detection\nwith EEG is still a challenging task, as EEG suffers from serious mental and\nphysical drifts across different subjects. In this paper, we propose a compact\nand interpretable Convolutional Neural Network (CNN) to discover shared EEG\nfeatures across different subjects for driver drowsiness detection. We\nincorporate the Global Average Pooling (GAP) layer in the model structure,\nallowing the Class Activation Map (CAM) method to be used for localizing\nregions of the input signal that contribute most for classification. Results\nshow that the proposed model can achieve an average accuracy of 73.22% on 11\nsubjects for 2-class cross-subject EEG signal classification, which is higher\nthan conventional machine learning methods and other state-of-art deep learning\nmethods. It is revealed by the visualization technique that the model has\nlearned biologically explainable features, e.g., Alpha spindles and Theta\nburst, as evidence for the drowsy state. It is also interesting to see that the\nmodel uses artifacts that usually dominate the wakeful EEG, e.g., muscle\nartifacts and sensor drifts, to recognize the alert state. The proposed model\nillustrates a potential direction to use CNN models as a powerful tool to\ndiscover shared features related to different mental states across different\nsubjects from EEG signals.\n"}
+{"abstract": "  Existing conversational recommendation (CR) systems usually suffer from\ninsufficient item information when conducted on short dialogue history and\nunfamiliar items. Incorporating external information (e.g., reviews) is a\npotential solution to alleviate this problem. Given that reviews often provide\na rich and detailed user experience on different interests, they are potential\nideal resources for providing high-quality recommendations within an\ninformative conversation. In this paper, we design a novel end-to-end\nframework, namely, Review-augmented Conversational Recommender (RevCore), where\nreviews are seamlessly incorporated to enrich item information and assist in\ngenerating both coherent and informative responses. In detail, we extract\nsentiment-consistent reviews, perform review-enriched and entity-based\nrecommendations for item suggestions, as well as use a review-attentive\nencoder-decoder for response generation. Experimental results demonstrate the\nsuperiority of our approach in yielding better performance on both\nrecommendation and conversation responding.\n"}
+{"abstract": "  The evolution of chiral spin structures is studied in ferrimagnet\nTa/Ir/Fe/GdFeCo/Pt multilayers as a function of temperature using scanning\nelectron microscopy with polarization analysis (SEMPA). The GdFeCo ferrimagnet\nexhibits pure right-hand N\\'eel-type domain wall (DW) spin textures over a\nlarge temperature range. This indicates the presence of a negative\nDzyaloshinskii-Moriya interaction (DMI) that can originate from both the top\nFe/Pt and the Co/Pt interfaces. From measurements of the DW width, as well as\ncomplementary magnetic characterization, the exchange stiffness as a function\nof temperature is ascertained. The exchange stiffness is surprisingly mostly\nconstant, which is explained by theoretical predictions. Beyond single\nskyrmions, we find by direct imaging a pure N\\'eel-type skyrmionium, which due\nto the absence of a skyrmion Hall angle is a promising topological spin\nstructure to enable high impact potential applications in the next generation\nof spintronic devices.\n"}
+{"abstract": "  We present a novel deep learning-based framework to generate embedding\nrepresentations of fine-grained emotions that can be used to computationally\ndescribe psychological models of emotions. Our framework integrates a\ncontextualized embedding encoder with a multi-head probing model that enables\nto interpret dynamically learned representations optimized for an emotion\nclassification task. Our model is evaluated on the Empathetic Dialogue dataset\nand shows the state-of-the-art result for classifying 32 emotions. Our layer\nanalysis can derive an emotion graph to depict hierarchical relations among the\nemotions. Our emotion representations can be used to generate an emotion wheel\ndirectly comparable to the one from Plutchik's\\LN model, and also augment the\nvalues of missing emotions in the PAD emotional state model.\n"}
+{"abstract": "  Traditional Cauchy-Stokes decomposition of velocity gradient tensor gives a\nsymmetric and an anti-symmetric subtensors which are called the strain-rate and\nvorticity tensors. There are two problems with Cauchy-Stokes decomposition. The\nfirst one is that the anti-symmetric or vorticity tensor cannot represent the\nfluid rotation or vortex. The second one is that the symmetric (strain-rate)\ntensor cannot distinguish the stretching (compression) and shear. The\nstretching and shear are dependent on the coordinate or are not Galilean\ninvariant. Since vorticity cannot distinguish between the non-rotational shear\nand the rigid rotation, vorticity has been decomposed to a rigid rotation\ncalled Liutex and anti-symmetric shear in our previous work. A Liutex-based\nprincipal coordinate was developed and the velocity gradient tensor was\ndecomposed in the principal coordinate as a rigid rotation (Liutex tensor), a\npure shear tensor and a stretching (compression) tensor, which is called the\nprincipal decomposition. However, the principal decomposition is made at each\npoint which has own principal coordinate different from other points. This\npaper derives the principal decomposition in the original xyz coordinate\nsystem, and, therefore, provides a new tool for fluid kinematics to conduct the\nvelocity gradient tensor decomposition to rigid rotation, pure shear, and\nstretching (compression) which is Galilean invariant and has clear physical\nmeanings. The new velocity gradient tensor decomposition could become a\nfoundation for new fluid kinematics.\n"}
+{"abstract": "  This study aims to assess the properties and classification of 62 variable\nstars in Cygnus, little studied since their discovery and originally reported\nin the Information Bulletin on Variable Stars (IBVS) 1302. Using data from\nprevious studies and several astronomical databases, we performed our analysis\nmainly utilizing a period analysis software and comparing the photometric\ncharacteristics of the variables in a Colour-Absolute Magnitude Diagram. For\nall stars, the variability is confirmed. We discovered new significant results\nfor the period and/or type of 17 variables and highlighted incorrect\ncross-reference names on astronomical databases for 23 stars. For 3 stars,\nwhose original type was unknown, we propose a new type. We calculated an epoch\nof a minimum or a maximum for 24 stars; for 3 of them, the epoch has been\ndefined for the first time. This assessment also identifies some cases for\nwhich results from the ASAS-SN Catalog of Variable Stars are not consistent\nwith results from Gaia DR2 and/or our analysis.\n"}
+{"abstract": "  The classification of glomerular lesions is a routine and essential task in\nrenal pathology. Recently, machine learning approaches, especially deep\nlearning algorithms, have been used to perform computer-aided lesion\ncharacterization of glomeruli. However, one major challenge of developing such\nmethods is the naturally imbalanced distribution of different lesions. In this\npaper, we propose CircleMix, a novel data augmentation technique, to improve\nthe accuracy of classifying globally sclerotic glomeruli with a hierarchical\nlearning strategy. Different from the recently proposed CutMix method, the\nCircleMix augmentation is optimized for the ball-shaped biomedical objects,\nsuch as glomeruli. 6,861 glomeruli with five classes (normal, periglomerular\nfibrosis, obsolescent glomerulosclerosis, solidified glomerulosclerosis, and\ndisappearing glomerulosclerosis) were employed to develop and evaluate the\nproposed methods. From five-fold cross-validation, the proposed CircleMix\naugmentation achieved superior performance (Balanced Accuracy=73.0%) compared\nwith the EfficientNet-B0 baseline (Balanced Accuracy=69.4%)\n"}
+{"abstract": "  Trapped ions are excellent candidates for quantum computing and quantum\nnetworks because of their long coherence times, ability to generate entangled\nphotons as well as high fidelity single- and two-qubit gates. To scale up\ntrapped ion quantum computing, we need a Bell-state analyzer on a\nreconfigurable platform that can herald high fidelity entanglement between\nions. In this work, we design a photonic Bell-state analyzer on a\nreconfigurable thin film lithium niobate platform for polarization-encoded\nqubits. We optimize the device to achieve high fidelity entanglement between\ntwo trapped ions and find >99% fidelity. The proposed device can scale up\ntrapped ion quantum computing as well as other optically active spin qubits,\nsuch as color centers in diamond, quantum dots, and rare-earth ions.\n"}
+{"abstract": "  We present the results of imaging photometric and long-slit spectroscopic\nobservations of comet 2P/Encke performed at the heliocentric distance 0.56 au,\ngeocentric distance 0.65 au, and phase angle 109.2 deg on November 4, 2013 and\nat 1.05 au, 1.34 au, and 46.8 deg on January 23, 2017. Observations were\ncarried out at the 6-m BTA telescope of the Special Astrophysical Observatory\n(Russia) with the multimode focal reducer SCORPIO-2. In 2013, the direct images\nof comet Encke were obtained with the broad-band V filters, whereas in 2017 the\nnarrow-band cometary BC, RC, and NH2 filters as well as the medium-band SED500\nand broad-band r-sdss filters were used for observations. About 60 emissions\nbelonging to the CN, C2, C3, NH2, CH, and CO+ molecules were identified within\nthe range 3750-7100 {\\AA}. The ratios of the production rates C2/CN and C3/CN\ncorrespond to the typical comets, not depleted in the carbon-chain. A complex\nstructure of the coma was detected in both observational periods. In January\n2017, the dust was in general concentrated near the nucleus, the dust/gas ratio\nwas 2.9 in the r-sdss filter, however, this ratio was larger than 1 at\ndistances 3000-40000 km from the nucleus. We found that about 75% of the flux\nof the reflected light in the central pixel was due to the nucleus, whereas the\nnucleus's flux contributed 48% in the total intensity of the 2000 km area of\nthe coma. We found that after correction for the dust coma contamination the\nnucleus magnitude is 18.8m+/-0.2m.\n"}
+{"abstract": "  An effective recipe for building seq2seq, non-autoregressive, task-oriented\nparsers to map utterances to semantic frames proceeds in three steps: encoding\nan utterance $x$, predicting a frame's length |y|, and decoding a |y|-sized\nframe with utterance and ontology tokens. Though empirically strong, these\nmodels are typically bottlenecked by length prediction, as even small\ninaccuracies change the syntactic and semantic characteristics of resulting\nframes. In our work, we propose span pointer networks, non-autoregressive\nparsers which shift the decoding task from text generation to span prediction;\nthat is, when imputing utterance spans into frame slots, our model produces\nendpoints (e.g., [i, j]) as opposed to text (e.g., \"6pm\"). This natural\nquantization of the output space reduces the variability of gold frames,\ntherefore improving length prediction and, ultimately, exact match.\nFurthermore, length prediction is now responsible for frame syntax and the\ndecoder is responsible for frame semantics, resulting in a coarse-to-fine\nmodel. We evaluate our approach on several task-oriented semantic parsing\ndatasets. Notably, we bridge the quality gap between non-autogressive and\nautoregressive parsers, achieving 87 EM on TOPv2 (Chen et al. 2020).\nFurthermore, due to our more consistent gold frames, we show strong\nimprovements in model generalization in both cross-domain and cross-lingual\ntransfer in low-resource settings. Finally, due to our diminished output\nvocabulary, we observe 70% reduction in latency and 83% reduction in memory at\nbeam size 5 compared to prior non-autoregressive parsers.\n"}
+{"abstract": "  Purpose: We seek to explore the realm of literature about digital libraries.\nWe specifically seek to ascertain how interest in this subject has evolved, its\nimpact, the most productive journals and countries, the number of occurrences\nof digital libraries, the relationships and dynamics of the main concepts\nmentioned, and the dynamics of metadata formats.Methods: We extracted corpora\nfrom the Google Scholar and Microsoft Academic Search bibliographic databases.\nWe analyzed the named entities and concepts contained within these corpora with\nthe help of text mining technologies, CorTexT in particular.Results: While the\nnumber of publications on the subject of digital libraries is increasing, their\naverage number of citations is decreasing. China, the United States and India\nare the most productive countries on the subject. Literature about conservation\nand national libraries has gradually been replaced by literature about open\naccess, university libraries and the relationship with users. Internet Archive\nis the most cited digital library in literature and continues to grow. Dublin\nCore is the most talked about metadata format, however the subject of metadata\nformats is declining in the corpus today.Conclusion: Digital libraries now seem\nto be reaching the age of maturity.\n"}
+{"abstract": "  Finding parsimonious models through variable selection is a fundamental\nproblem in many areas of statistical inference. Here, we focus on Bayesian\nregression models, where variable selection can be implemented through a\nregularizing prior imposed on the distribution of the regression coefficients.\nIn the Bayesian literature, there are two main types of priors used to\naccomplish this goal: the spike-and-slab and the continuous scale mixtures of\nGaussians. The former is a discrete mixture of two distributions characterized\nby low and high variance. In the latter, a continuous prior is elicited on the\nscale of a zero-mean Gaussian distribution. In contrast to these existing\nmethods, we propose a new class of priors based on discrete mixture of\ncontinuous scale mixtures providing a more general framework for Bayesian\nvariable selection. To this end, we substitute the observation-specific local\nshrinkage parameters (typical of continuous mixtures) with mixture component\nshrinkage parameters. Our approach drastically reduces the number of parameters\nneeded and allows sharing information across the coefficients, improving the\nshrinkage effect. By using half-Cauchy distributions, this approach leads to a\ncluster-shrinkage version of the Horseshoe prior. We present the properties of\nour model and showcase its estimation and prediction performance in a\nsimulation study. We then recast the model in a multiple hypothesis testing\nframework and apply it to a neurological dataset obtained using a novel\nwhole-brain imaging technique.\n"}
+{"abstract": "  LiteBIRD has been selected as JAXA's strategic large mission in the 2020s, to\nobserve the cosmic microwave background (CMB) $B$-mode polarization over the\nfull sky at large angular scales. The challenges of LiteBIRD are the wide\nfield-of-view (FoV) and broadband capabilities of millimeter-wave polarization\nmeasurements, which are derived from the system requirements. The possible\npaths of stray light increase with a wider FoV and the far sidelobe knowledge\nof $-56$ dB is a challenging optical requirement. A crossed-Dragone\nconfiguration was chosen for the low frequency telescope (LFT : 34--161 GHz),\none of LiteBIRD's onboard telescopes. It has a wide field-of-view ($18^\\circ\n\\times 9^\\circ$) with an aperture of 400 mm in diameter, corresponding to an\nangular resolution of about 30 arcminutes around 100 GHz. The focal ratio f/3.0\nand the crossing angle of the optical axes of 90$^\\circ$ are chosen after an\nextensive study of the stray light. The primary and secondary reflectors have\nrectangular shapes with serrations to reduce the diffraction pattern from the\nedges of the mirrors. The reflectors and structure are made of aluminum to\nproportionally contract from warm down to the operating temperature at $5\\,$K.\nA 1/4 scaled model of the LFT has been developed to validate the wide\nfield-of-view design and to demonstrate the reduced far sidelobes. A\npolarization modulation unit (PMU), realized with a half-wave plate (HWP) is\nplaced in front of the aperture stop, the entrance pupil of this system. A\nlarge focal plane with approximately 1000 AlMn TES detectors and frequency\nmultiplexing SQUID amplifiers is cooled to 100 mK. The lens and sinuous\nantennas have broadband capability. Performance specifications of the LFT and\nan outline of the proposed verification plan are presented.\n"}
+{"abstract": "  In programming education, teachers need to monitor and assess the progress of\ntheir students by investigating the code they write. Code quality of programs\nwritten in traditional programming languages can be automatically assessed with\nautomated tests, verification tools, or linters. In many cases these approaches\nrely on some form of manually written formal specification to analyze the given\nprograms. Writing such specifications, however, is hard for teachers, who are\noften not adequately trained for this task. Furthermore, automated tool support\nfor popular block-based introductory programming languages like Scratch is\nlacking. Anomaly detection is an approach to automatically identify deviations\nof common behavior in datasets without any need for writing a specification. In\nthis paper, we use anomaly detection to automatically find deviations of\nScratch code in a classroom setting, where anomalies can represent erroneous\ncode, alternative solutions, or distinguished work. Evaluation on solutions of\ndifferent programming tasks demonstrates that anomaly detection can\nsuccessfully be applied to tightly specified as well as open-ended programming\ntasks.\n"}
+{"abstract": "  The incorporation of Wilson lines leads to an extension of the modular\nsymmetries of string compactification beyond $\\mathrm{SL}(2,\\mathbb Z)$. In the\nsimplest case with one Wilson line $Z$, K\\\"ahler modulus $T$ and complex\nstructure modulus $U$, we are led to the Siegel modular group\n$\\mathrm{Sp}(4,\\mathbb Z)$. It includes $\\mathrm{SL}(2,\\mathbb\nZ)_T\\times\\mathrm{SL}(2,\\mathbb Z)_U$ as well as $\\mathbb Z_2$ mirror symmetry,\nwhich interchanges $T$ and $U$. Possible applications to flavor physics of the\nStandard Model require the study of orbifolds of $\\mathrm{Sp}(4,\\mathbb Z)$ to\nobtain chiral fermions. We identify the 13 possible orbifolds and determine\ntheir modular flavor symmetries as subgroups of $\\mathrm{Sp}(4,\\mathbb Z)$.\nSome cases correspond to symmetric orbifolds that extend previously discussed\ncases of $\\mathrm{SL}(2,\\mathbb Z)$. Others are based on asymmetric orbifold\ntwists (including mirror symmetry) that do no longer allow for a simple\nintuitive geometrical interpretation and require further study. Sometimes they\ncan be mapped back to symmetric orbifolds with quantized Wilson lines. The\nsymmetries of $\\mathrm{Sp}(4,\\mathbb Z)$ reveal exciting new aspects of modular\nsymmetries with promising applications to flavor model building.\n"}
+{"abstract": "  We study the top quark pair events production in $pp$ collisions in the\n$\\ell$+jets channel at the LHC energy of $\\sqrt{s} = 14$ TeV for Standard Model\nas well as new physics processes. We explore the usage of semi-boosted\ntopologies where the top quark decays into a boosted hadronic $W$-jet and an\nisolated $b$-jet and study their performance in the $t\\bar{t}$ events kinematic\nreconstruction. An efficiency increase is observed and the correlation of\nselected kinematic variables between the detector and particle level is\nstudied. Quality of the reconstructed mass line shape of a $Z'$ model with\napplications in current LHC experiments is evaluated. The results of the\nunfolding procedure for chosen spectra and an explicit proof of a slight\ndegradation in the signal significance after the unfolding procedure are\npresented using the Fully Bayesian unfolding technique.\n"}
+{"abstract": "  It is shown for causal fermion systems describing Minkowski-type spacetimes\nthat an interacting causal fermion system at time $t$ gives rise to a\ndistinguished state on the algebra generated by fermionic and bosonic field\noperators. The proof of positivity of the state is given and representations\nare constructed.\n"}
+{"abstract": "  We show how a complete mathematical description of a complicated physical\nphenomenon can be constructed from observational data via a hybrid approach\ncombining three simple and general ingredients: physical assumptions of\nsmoothness, locality, and symmetry, a weak formulation of differential\nequations, and sparse regression. To illustrate this, we extract a complete\nsystem of governing equations describing flows of incompressible Newtonian\nfluids -- the Navier-Stokes equation, the continuity equation, and the boundary\nconditions -- from numerical data describing a highly turbulent channel flow in\nthree dimensions. The hybrid approach is remarkably robust, yielding accurate\nresults for very high noise levels, and should thus work equally well for a\nwide range of experimental data. In addition, this approach provides easily\ninterpretable information about the relative importance of different physical\neffects (such as viscosity) as well as useful insight into the quality of the\ndata, making it a useful diagnostic tool.\n"}
+{"abstract": "  An optical circuit-switched network core has the potential to overcome the\ninherent challenges of a conventional electrical packet-switched core of\ntoday's compute clusters. As optical circuit switches (OCS) directly handle the\nphoton beams without any optical-electrical-optical (O/E/O) conversion and\npacket processing, OCS-based network cores have the following desirable\nproperties: a) agnostic to data-rate, b) negligible/zero power consumption, c)\nno need of transceivers, d) negligible forwarding latency, and e) no need for\nfrequent upgrade. Unfortunately, OCS can only provide point-to-point (unicast)\ncircuits. They do not have built-in support for one-to-many (multicast)\ncommunication, yet multicast is fundamental to a plethora of data-intensive\napplications running on compute clusters nowadays. In this paper, we propose\nShufflecast, a novel optical network architecture for next-generation compute\nclusters that can support high-performance multicast satisfying all the\nproperties of an OCS-based network core. Shufflecast leverages small fanout,\ninexpensive, passive optical splitters to connect the Top-of-rack (ToR) switch\nports, ensuring data-rate agnostic, low-power, physical-layer multicast. We\nthoroughly analyze Shufflecast's highly scalable data plane, light-weight\ncontrol plane, and graceful failure handling. Further, we implement a complete\nprototype of Shufflecast in our testbed and extensively evaluate the network.\nShufflecast is more power-efficient than the state-of-the-art multicast\nmechanisms. Also, Shufflecast is more cost-efficient than a conventional\npacket-switched network. By adding Shufflecast alongside an OCS-based unicast\nnetwork, an all-optical network core with the aforementioned desirable\nproperties supporting both unicast and multicast can be realized.\n"}
+{"abstract": "  A picture-hanging puzzle is the task of hanging a framed picture with a wire\naround a set of nails in such a way that it can remain hanging on certain\nspecified sets of nails, but will fall if any more are removed. The classical\nbrain teaser asks us to hang a picture on two nails in such a way that it falls\nwhen any one is detached. Demaine et al (2012) proved that all reasonable\npuzzles of this kind are solvable, and that for the $k$-out-of-$n$ problem, the\nsize of a solution can be bounded by a polynomial in $n$. We give simplified\nproofs of these facts, for the latter leading to a reasonable exponent in the\npolynomial bound.\n"}
+{"abstract": "  Detecting and classifying instances of hate in social media text has been a\nproblem of interest in Natural Language Processing in the recent years. Our\nwork leverages state of the art Transformer language models to identify hate\nspeech in a multilingual setting. Capturing the intent of a post or a comment\non social media involves careful evaluation of the language style, semantic\ncontent and additional pointers such as hashtags and emojis. In this paper, we\nlook at the problem of identifying whether a Twitter post is hateful and\noffensive or not. We further discriminate the detected toxic content into one\nof the following three classes: (a) Hate Speech (HATE), (b) Offensive (OFFN)\nand (c) Profane (PRFN). With a pre-trained multilingual Transformer-based text\nencoder at the base, we are able to successfully identify and classify hate\nspeech from multiple languages. On the provided testing corpora, we achieve\nMacro F1 scores of 90.29, 81.87 and 75.40 for English, German and Hindi\nrespectively while performing hate speech detection and of 60.70, 53.28 and\n49.74 during fine-grained classification. In our experiments, we show the\nefficacy of Perspective API features for hate speech classification and the\neffects of exploiting a multilingual training scheme. A feature selection study\nis provided to illustrate impacts of specific features upon the architecture's\nclassification head.\n"}
+{"abstract": "  In this work, we study tracker phantom dark energy models with a general\nparameterization of the scalar potentials. Our analysis also considers the\nscenario of having both phantom field and the cosmological constant as the dark\nenergy components. A detailed statistical analysis with current cosmological\nobservations shows an increase in the value of the Hubble parameter due to the\npresence of phantom dark energy but it can not alleviate the Hubble tension\ncompletely. Our results using Bayesian methods suggests a decisive evidence in\nfavor of a phantom field over a positive cosmological constant, although the\npossibility of a negative cosmological constant cannot be ruled out hidden in\nthe dark sector.\n"}
+{"abstract": "  The predictive power of the deformed relativistic Hartree-Bogoliubov theory\nin continuum (DRHBc) for nuclear mass is examined in the superheavy region,\n$102 \\le Z \\le 120$. The accuracy of predicting the 10 (56) measured (measured\nand empirical) masses is $0.635$ ($0.642$) MeV, in comparison with $0.515$\n($1.360$) MeV by WS4 and $0.910$ ($2.831$) MeV by FRDM. Possible stability\nagainst multineutron emission beyond the two-neutron drip line is explored by\nthe DRHBc theory, which takes into account simultaneously the deformation\neffects, the pairing correlations, and the continuum effects. Nuclei stable\nagainst two- and multineutron emissions beyond the two-neutron drip line are\npredicted in $_{106}$Sg, $_{108}$Hs, $_{110}$Ds, and $_{112}$Cn isotopic\nchains, forming a peninsula of stability adjacent to the nuclear mainland. This\nstability is mainly due to the deformation which significantly affects the\nshell structure around the Fermi surface. The pairing correlations and\ncontinuum influence the stability peninsula in a self-consistent way.\n"}
+{"abstract": "  Two-dimensional metal particle focusing is an essential task for various\nfabrication processes. While acoustofluidic devices can manipulate particles in\ntwo dimensions, the production of these devices often demands a cleanroom\nenvironment. Therefore, acoustically excited glass capillaries present a cheap\nalternative to labour-intensive cleanroom production. Here, we present 2D metal\nmicro-particle focusing in a round glass capillary using bulk acoustic waves.\nExcitation of the piezoelectric transducer at specific frequencies leads to\nmode shapes in the round capillary, concentrating particles towards the\ncapillary centre. We experimentally investigate the particle linewidth for\ndifferent particle materials and concentrations. We demonstrate the focus of\ncopper particles with 1 $\\mu$m in diameter down to a line of width 60.8 $\\pm$\n7.0 $\\mu$m and height 45.2 $\\pm$ 9.3 $\\mu$m, corresponding to a local\nconcentration of 4.5 % v/v, which is 90 times higher than the concentration of\nthe initial solution. Through numerical analysis, we could obtain further\ninsights into the particle manipulation mechanism inside the capillary and\npredict the particle trajectories. We found that a transition of the acoustic\nstreaming pattern enables us to manipulate particles close to the critical\nparticle radius. Finally, we used our method to eject copper particles through\na tapered round capillary with an opening of 25 $\\mu$m in diameter, which would\nnot be possible without particle focusing. Our novel setup can be utilized for\nvarious applications, that otherwise might suffer from abrasion, clogging and\nlimited resolution.\n"}
+{"abstract": "  In this paper we study the problem of classifying pencils of curves of degree\n$d$ in $\\mathbb{P}^2$ using geometric invariant theory. We consider the action\nof $SL(3)$ and we relate the stability of a pencil to the stability of its\ngenerators, to the log canonical threshold of its members, and to the\nmultiplicities of its base points, thus obtaining explicit stability criteria.\n"}
+{"abstract": "  It is widely assumed that long-wavelength infrared (IR) emission from cold\ndust (T~20-40K) is a reliable tracer of star formation even in the presence of\na bright active galactic nucleus (AGN). Based on radiative transfer (RT) models\nof clumpy AGN tori, hot dust emission from the torus contributes negligibly to\nthe galaxy spectral energy distribution (SED) at $\\lambda\\ga100$ \\micron.\nHowever, these models do not include AGN heating of host-galaxy-scale diffuse\ndust, which may have far-IR (FIR) colors comparable to cold diffuse dust heated\nby stars. To quantify the contribution of AGN heating to host-galaxy-scale cold\ndust emission at $\\lambda\\ga100$ \\micron, we perform dust RT calculations on a\nsimulated galaxy merger both including and excluding the bright AGN that it\nhosts. By differencing the SEDs yielded by RT calculations with and without AGN\nthat are otherwise identical, we quantify the FIR cold dust emission arising\nsolely from re-processed AGN photons. In extreme cases, AGN-heated\nhost-galaxy-scale dust can increase galaxy-integrated FIR flux densities by\nfactors of 2-4; star formation rates calculated from the FIR luminosity\nassuming no AGN contribution can overestimate the true value by comparable\nfactors. Because the FIR colors of such systems are similar to those of purely\nstar-forming galaxies and redder than torus models, broadband SED decomposition\nmay be insufficient for disentangling the contributions of stars and heavily\ndust-enshrouded AGN in the most IR-luminous galaxies. We demonstrate how\nkpc-scale resolved observations can be used to identify deeply dust-enshrouded\nAGN with cool FIR colors when spectroscopic and/or X-ray detection methods are\nunavailable.\n"}
+{"abstract": "  We resolve the long-standing \"impossible tuning\" issue for the classic expert\nproblem and show that, it is in fact possible to achieve regret\n$O\\left(\\sqrt{(\\ln d)\\sum_t \\ell_{t,i}^2}\\right)$ simultaneously for all expert\n$i$ in a $T$-round $d$-expert problem where $\\ell_{t,i}$ is the loss for expert\n$i$ in round $t$. Our algorithm is based on the Mirror Descent framework with a\ncorrection term and a weighted entropy regularizer. While natural, the\nalgorithm has not been studied before and requires a careful analysis. We also\ngeneralize the bound to $O\\left(\\sqrt{(\\ln d)\\sum_t\n(\\ell_{t,i}-m_{t,i})^2}\\right)$ for any prediction vector $m_t$ that the\nlearner receives, and recover or improve many existing results by choosing\ndifferent $m_t$. Furthermore, we use the same framework to create a master\nalgorithm that combines a set of base algorithms and learns the best one with\nlittle overhead. The new guarantee of our master allows us to derive many new\nresults for both the expert problem and more generally Online Linear\nOptimization.\n"}
+{"abstract": "  This article describes an action rule induction algorithm based on a\nsequential covering approach. Two variants of the algorithm are presented. The\nalgorithm allows the action rule induction from a source and a target decision\nclass point of view. The application of rule quality measures enables the\ninduction of action rules that meet various quality criteria. The article also\npresents a method for recommendation induction. The recommendations indicate\nthe actions to be taken to move a given test example, representing the source\nclass, to the target one. The recommendation method is based on a set of\ninduced action rules. The experimental part of the article presents the results\nof the algorithm operation on sixteen data sets. As a result of the conducted\nresearch the Ac-Rules package was made available.\n"}
+{"abstract": "  We investigate information theoretic properties of low rank (less than or\nequal to 3) quantum channels with $SU(2)$-symmetry, where we have a complete\ndescription. We prove that PPT property coincides with entanglement-breaking\nproperty and that degradability seldomly holds in this class. In connection\nwith these results we will demonstrate how we can compute Holevo and coherent\ninformation of those channels. In particular, we exhibit a strong form of\nadditivity violation of coherent information, which resembles the\nsuperactivation of coherent information of depolarizing channels.\n"}
+{"abstract": "  Variational auto-encoders (VAEs) are a powerful approach to unsupervised\nlearning. They enable scalable approximate posterior inference in\nlatent-variable models using variational inference (VI). A VAE posits a\nvariational family parameterized by a deep neural network called an encoder\nthat takes data as input. This encoder is shared across all the observations,\nwhich amortizes the cost of inference. However the encoder of a VAE has the\nundesirable property that it maps a given observation and a\nsemantics-preserving transformation of it to different latent representations.\nThis \"inconsistency\" of the encoder lowers the quality of the learned\nrepresentations, especially for downstream tasks, and also negatively affects\ngeneralization. In this paper, we propose a regularization method to enforce\nconsistency in VAEs. The idea is to minimize the Kullback-Leibler (KL)\ndivergence between the variational distribution when conditioning on the\nobservation and the variational distribution when conditioning on a random\nsemantic-preserving transformation of this observation. This regularization is\napplicable to any VAE. In our experiments we apply it to four different VAE\nvariants on several benchmark datasets and found it always improves the quality\nof the learned representations but also leads to better generalization. In\nparticular, when applied to the Nouveau Variational Auto-Encoder (NVAE), our\nregularization method yields state-of-the-art performance on MNIST and\nCIFAR-10. We also applied our method to 3D data and found it learns\nrepresentations of superior quality as measured by accuracy on a downstream\nclassification task.\n"}
+{"abstract": "  We consider here a problem of population dynamics modeled on a logistic\nequation with both classical and nonlocal diffusion, possibly in combination\nwith a pollination term.\n  The environment considered is a niche with zero-flux, according to a new type\nof Neumann condition.\n  We discuss the situations that are more favorable for the survival of the\nspecies, in terms of the first positive eigenvalue.\n  Quite surprisingly, the eigenvalue analysis for the one dimensional case is\nstructurally different than the higher dimensional setting, and it sensibly\ndepends on the nonlocal character of the dispersal.\n  The mathematical framework of this problem takes into consideration the\nequation $$ -\\alpha\\Delta u +\\beta(-\\Delta)^su =(m-\\mu u)u+\\tau\\;J\\star u\n\\qquad{\\mbox{in }}\\; \\Omega,$$ where $m$ can change sign.\n  This equation is endowed with a set of Neumann condition that combines the\nclassical normal derivative prescription and the nonlocal condition introduced\nin [S. Dipierro, X. Ros-Oton, E. Valdinoci, Rev. Mat. Iberoam. (2017)].\n  We will establish the existence of a minimal solution for this problem and\nprovide a throughout discussion on whether it is possible to obtain non-trivial\nsolutions (corresponding to the survival of the population).\n  The investigation will rely on a quantitative analysis of the first\neigenvalue of the associated problem and on precise asymptotics for large lower\nand upper bounds of the resource.\n  In this, we also analyze the role played by the optimization strategy in the\ndistribution of the resources, showing concrete examples that are unfavorable\nfor survival, in spite of the large resources that are available in the\nenvironment.\n"}
+{"abstract": "  The Rosseland mean opacity of dust in protoplanetary disks is often\ncalculated assuming the interstellar medium (ISM) size distribution and a\nconstant dust-to-gas ratio. However, the dust size distribution and the\ndust-to-gas ratio in protoplanetary disks are distinct from those of the ISM.\nHere, we use simple dust evolution models that incorporate grain growth and\ntransport to calculate the time evolution of mean opacity of dust grains as a\nfunction of distance from the star. Dust dynamics and size distribution are\nsensitive to the assumed value of the turbulence strength $\\alpha_{\\rm t}$ and\nthe velocity at which grains fragment $v_{\\rm frag}$. For moderate-to-low\nturbulence strengths of $\\alpha_{\\rm t} \\lesssim 10^{-3}$ and substantial\ndifferences in $v_{\\rm frag}$ for icy and ice-free grains, we find a spatially\nnon-uniform dust-to-gas ratio and grain size distribution that deviate\nsignificantly from the ISM values, in agreement with previous studies. The\neffect of a non-uniform dust-to-gas ratio on the Rosseland mean opacity\ndominates over that of the size distribution. This spatially varying -- that is\nnon-monotonic -- dust-to-gas ratio creates a region in the protoplanetary disk\nthat is optimal for producing hydrogen-rich planets, potentially explaining the\napparent peak in gas giant planet occurrence rate at intermediate distances.\nThe enhanced dust-to-gas ratio within the ice line also suppresses gas\naccretion rates onto sub-Neptune cores, thus stifling their tendency to undergo\nrunaway gas accretion within disk lifetimes. Finally, our work corroborates the\nidea that low mass cores with large primordial gaseous envelopes\n(`super-puffs') originate beyond the ice line.\n"}
+{"abstract": "  Demand response (DR) programs have gained much attention during the last\nthree decades to optimize the decisions of the electricity market participants\nconsidering the demand-side management (DSM). It can potentially enhance system\nreliability and manages price volatility by modifying the amount or time of\nelectricity consumption. This paper proposes a novel game-theoretical model\naccounting for the relationship between retailers (leaders) and consumers\n(followers) in a dynamic price environment where both players optimize their\nrespective economic goals under uncertainty. The model is solved under two\nframeworks. First by considering retailer's market power and second by\naccounting for an equilibrium setting based on a Cournot game. These are\nformulated in terms of a mathematical program with equilibrium constraints\n(MPEC) and with a mixed-integer linear program (MILP), respectively. In\nparticular, the retailers' market power model is first formulated as a bi-level\noptimization problem, and the MPEC is subsequently derived by replacing the\nconsumers' problem (lower level) with its Karush-Kuhn-Tucker (KKT) optimality\nconditions. In contrast, the Cournot equilibrium model is solved as a MILP by\nconcatenating the retailer's and consumers' KKT optimality conditions. The\nsolution sets, the practical approaches for solutions, the required techniques\nto test and compare the performance of the model are undertaken with realistic\ndata. Numerical simulations confirm the applicability and effectiveness of the\nproposed model to explore the interactions of markets power and DR programs.\nThe results confirm that consumers are better off in an equilibrium framework\nwhile the retailer increases its expected profit when the market power is\nconsidered. However, we show how these results are highly affected by the\nlevels of of consumers' flexibility.\n"}
+{"abstract": "  In a human-robot collaborative task where a robot helps its partner by\nfinding described objects, the depth dimension plays a critical role in\nsuccessful task completion. Existing studies have mostly focused on\ncomprehending the object descriptions using RGB images. However, 3-dimensional\nspace perception that includes depth information is fundamental in real-world\nenvironments. In this work, we propose a method to identify the described\nobjects considering depth dimension data. Using depth features significantly\nimproves performance in scenes where depth data is critical to disambiguate the\nobjects and across our whole evaluation dataset that contains objects that can\nbe specified with and without the depth dimension.\n"}
+{"abstract": "  This paper introduces techniques to integrate WordNet into a Fuzzy Logic\nProgramming system. Since WordNet relates words but does not give graded\ninformation on the relation between them, we have implemented standard\nsimilarity measures and new directives allowing the proximity equations linking\ntwo words to be generated with an approximation degree. Proximity equations are\nthe key syntactic structures which, in addition to a weak unification\nalgorithm, make a flexible query-answering process possible in this kind of\nprogramming language. This addition widens the scope of Fuzzy Logic\nProgramming, allowing certain forms of lexical reasoning, and reinforcing\nNatural Language Processing applications.\n  [Under consideration in Theory and Practice of Logic Programming (TPLP)]\n"}
+{"abstract": "  The IoT ecosystem suffers from a variety of problems around security,\nidentity, access control, data flow and data storage that introduce friction\ninto interactions between various parties. In many respects, the situation is\nsimilar to the early days of the Internet, where, prior to the establishment of\nInternet Exchanges, routing between different BGP autonomous systems was often\npoint to point. We propose a similar solution, the IoT Exchange, where IoT\ndevice owners can register their devices and offer data for sale or can upload\ndata into the IoT services of any of the big hyperscale cloud platforms for\nfurther processing. The goal of the IoT Exchange is to break down the silos\nwithin which device wireless connectivity types and cloud provider IoT systems\nconstrain users to operate. In addition, if the device owner needs to maintain\nthe data close to the edge to reduce access latency, the MillenniumDB service\nrunning in an edge data center with minimal latency to the edge device,\nprovides a database with a variety of schema engines (SQL, noSQL, etc). The IoT\nexchange uses decentralized identifiers for identity management and verifiable\ncredentials for authorizing software updates and to control access to the\ndevices, to avoid dependence on certificate authorities and other centralized\nidentity and authorization management systems. In addition, verifiable\ncredentials provide a way whereby privacy preserving processing can be applied\nto traffic between a device and an end data or control customer, if some risk\nof privacy compromise exists.\n"}
+{"abstract": "  In this work, Bernstein's concentration inequalities for squared integrable\nmatrix-valued discrete-time martingales are obtained. Based on Lieb's theory\nand Bernstein's condition, a suitable supermartingale can be constructed. Our\nproof is largely based on this new exponential supermartingale, Freedman's\nmethod, and Doob's stopping theorem. Our result can be regarded as an extension\nof Tropp's work (ECP, 2012).\n"}
+{"abstract": "  We study the interplay between properties of measures on a Boolean algebra A\nand forcing names for ultrafilters on A. We show that several well known\nmeasure theoretic properties of Boolean algebras (such as supporting a strictly\npositive measure or carrying only separable measures) have quite natural\ncharacterizations in the forcing language. We show some applications of this\napproach. In particular, we reprove a theorem of Kunen saying that in the\nclassical random model there are no towers of height $\\omega_2$.\n"}
+{"abstract": "  We show a connection between sampling and optimization on discrete domains.\nFor a family of distributions $\\mu$ defined on size $k$ subsets of a ground set\nof elements that is closed under external fields, we show that rapid mixing of\nnatural local random walks implies the existence of simple approximation\nalgorithms to find $\\max \\mu(\\cdot)$. More precisely we show that if\n(multi-step) down-up random walks have spectral gap at least inverse\npolynomially large in $k$, then (multi-step) local search can find $\\max\n\\mu(\\cdot)$ within a factor of $k^{O(k)}$. As the main application of our\nresult, we show a simple nearly-optimal $k^{O(k)}$-factor approximation\nalgorithm for MAP inference on nonsymmetric DPPs. This is the first nontrivial\nmultiplicative approximation for finding the largest size $k$ principal minor\nof a square (not-necessarily-symmetric) matrix $L$ with $L+L^\\intercal\\succeq\n0$.\n  We establish the connection between sampling and optimization by showing that\nan exchange inequality, a concept rooted in discrete convex analysis, can be\nderived from fast mixing of local random walks. We further connect exchange\ninequalities with composable core-sets for optimization, generalizing recent\nresults on composable core-sets for DPP maximization to arbitrary distributions\nthat satisfy either the strongly Rayleigh property or that have a log-concave\ngenerating polynomial.\n"}
+{"abstract": "  Given any equation and the integers $[n] = \\{1,\\dots,n\\}$, one can ask: does\nevery 2-coloring of $[n]$ contain at least as many monochromatic solutions\n(asymptotically) as a uniformly random 2-coloring? We show that the answer is\n\"no\" for every 3-term linear equation with integer coefficients, i.e. there\nalways exist 2-colorings with asymptotically fewer monochromatic solutions than\nuniformly random ones.\n"}
+{"abstract": "  The proliferation of social media platforms like Twitter has heightened the\nconsequences of the spread of misinformation. To understand and model the\nspread of misinformation, in this paper, we leveraged the SEIZ (Susceptible,\nExposed, Infected, Skeptics) epidemiological model to describe the underlying\nprocess that delineates the spread of misinformation on Twitter. Compared to\nthe other epidemiological models, this model produces broader results because\nit includes the additional Skeptics (Z) compartment, wherein a user may be\nexposed to an item of misinformation but not engage in any reaction to it, and\nthe additional Exposed (E) compartment, wherein the user may need some time\nbefore deciding to spread a misinformation item. We analyzed misinformation\nregarding the unrest in Washington, D.C. in the month of March 2020 which was\npropagated by the use of the #DCblackout hashtag by different users across the\nU.S. on Twitter. Our analysis shows that misinformation can be modeled using\nthe concept of epidemiology. To the best of our knowledge, this research is the\nfirst to attempt to apply the SEIZ epidemiological model to the spread of a\nspecific item of misinformation, which is a category distinct from that of\nrumor, and a hoax on online social media platforms. Applying a mathematical\nmodel can help to understand the trends and dynamics of the spread of\nmisinformation on Twitter and ultimately help to develop techniques to quickly\nidentify and control it.\n"}
+{"abstract": "  Given a degeneration of compact projective complex manifolds $X$ over the\npunctured disc, with meromorphic singularities, and a relatively ample line\nbundle $L$ on $X$, we study spaces of plurisubharmonic metrics on $L$, with\nparticular focus on (relative) finite-energy conditions. We endow the space\n$\\hat \\cE^1(L)$ of relatively maximal, relative finite-energy metrics with a\n$d_1$-type distance given by the Lelong number at zero of the collection of\nfibrewise Darvas $d_1$-distances. We show that this metric structure is\ncomplete and geodesic. Seeing $X$ and $L$ as schemes $X_\\K$, $L_\\K$ over the\ndiscretely-valued field $\\K=\\mathbb{C}((t))$ of complex Laurent series, we show\nthat the space $\\cE^1(L_\\K\\an)$ of non-Archimedean finite-energy metrics over\n$L_\\K\\an$ embeds isometrically and geodesically into $\\hat \\cE^1(L)$, and\ncharacterize its image. This generalizes previous work of\nBerman-Boucksom-Jonsson, treating the trivially-valued case. We investigate\nconsequences regarding convexity of non-Archimedean functionals.\n"}
+{"abstract": "  In this short paper, we will review the proposal of a correspondence between\nthe doubled geometry of Double Field Theory and the higher geometry of bundle\ngerbes. Double Field Theory is T-duality covariant formulation of the\nsupergravity limit of String Theory, which generalises Kaluza-Klein theory by\nunifying metric and Kalb-Ramond field on a doubled-dimensional space. In light\nof the proposed correspondence, this doubled geometry is interpreted as an\natlas description of the higher geometry of bundle gerbes. In this sense,\nDouble Field Theory can be interpreted as a field theory living on the total\nspace of the bundle gerbe, just like Kaluza-Klein theory is set on the total\nspace of a principal bundle. This correspondence provides a higher geometric\ninterpretation for para-Hermitian geometry which opens the door to its\ngeneralisation to the other Extended Field Theories. This review is based on,\nbut not limited to, my talk at the workshop Generalized Geometry and\nApplications at Universit\\\"at Hamburg on 3rd of March 2020.\n"}
+{"abstract": "  Stagnation detection has been proposed as a mechanism for randomized search\nheuristics to escape from local optima by automatically increasing the size of\nthe neighborhood to find the so-called gap size, i.e., the distance to the next\nimprovement. Its usefulness has mostly been considered in simple multimodal\nlandscapes with few local optima that could be crossed one after another. In\nmultimodal landscapes with a more complex location of optima of similar gap\nsize, stagnation detection suffers from the fact that the neighborhood size is\nfrequently reset to $1$ without using gap sizes that were promising in the\npast.\n  In this paper, we investigate a new mechanism called radius memory which can\nbe added to stagnation detection to control the search radius more carefully by\ngiving preference to values that were successful in the past. We implement this\nidea in an algorithm called SD-RLS$^{\\text{m}}$ and show compared to previous\nvariants of stagnation detection that it yields speed-ups for linear functions\nunder uniform constraints and the minimum spanning tree problem. Moreover, its\nrunning time does not significantly deteriorate on unimodal functions and a\ngeneralization of the Jump benchmark. Finally, we present experimental results\ncarried out to study SD-RLS$^{\\text{m}}$ and compare it with other algorithms.\n"}
+{"abstract": "  In this paper we analyze a mixed displacement-pseudostress formulation for\nthe elasticity eigenvalue problem. We propose a finite element method to\napproximate the pseudostress tensor with Raviart-Thomas elements and the\ndisplacement with piecewise polynomials. With the aid of the classic theory for\ncompact operators, we prove that our method is convergent and does not\nintroduce spurious modes. Also, we obtain error estimates for the proposed\nmethod. Finally, we report some numerical tests supporting the theoretical\nresults.\n"}
+{"abstract": "  An impurity hole observed in the Large Helical Device (LHD) is a hollow\ndensity profile of an impurity ion species formed in the core plasma where the\nnegative (inward-pointing) ambipolar radial electric field ($E_r$) exists.\nAlthough local neoclassical models have predicted the sign of $E_r$ in impurity\nhole plasmas is negative for the entire minor radius, an experimental\nmeasurement of an impurity hole plasma has shown that the $E_r$ changes the\nsign from negative to positive along the minor radius. In the present work, we\ninvestigate neoclassical impurity transport in an impurity hole plasma using a\nglobal neoclassical simulation code FORTEC-3D. The variation of electrostatic\npotential on flux surface ($\\Phi_1$) is evaluated from the quasi-neutrality\ncondition in multi-ion-species plasma by the global simulation. The ambipolar\n$E_r$ and neoclassical fluxes are determined in solving a global drift-kinetic\nequation including the effect of $\\Phi_1$. By the global simulation, we show\nthat an $E_r$ which changes the sign along the radius is obtained as a solution\nof the ambipolar condition and with such an $E_r$, impurity carbon flux can be\noutwardly directed even where $E_r<0$ and the carbon density profile is hollow\naround the magnetic axis. Furthermore, it is found that the outward carbon flux\nis only a factor 2-3 from balancing the modeled inward turbulent flux. Our\nresult indicates that we have moved one step closer to reproducing the impurity\ntransport in impurity hole plasmas by kinetic simulation.\n"}
+{"abstract": "  One key property in recommender systems is the long-tail distribution in\nuser-item interactions where most items only have few user feedback. Improving\nthe recommendation of tail items can promote novelty and bring positive effects\nto both users and providers, and thus is a desirable property of recommender\nsystems. Current novel recommendation studies over-emphasize the importance of\ntail items without differentiating the degree of users' intent on popularity\nand often incur a sharp decline of accuracy. Moreover, none of existing methods\nhas ever taken the extreme case of tail items, i.e., cold-start items without\nany interaction, into consideration.\n  In this work, we first disclose the mechanism that drives a user's\ninteraction towards popular or niche items by disentangling her intent into\nconformity influence (popularity) and personal interests (preference). We then\npresent a unified end-to-end framework to simultaneously optimize accuracy and\nnovelty targets based on the disentangled intent of popularity and that of\npreference. We further develop a new paradigm for novel recommendation of\ncold-start items which exploits the self-supervised learning technique to model\nthe correlation between collaborative features and content features. We conduct\nextensive experimental results on three real-world datasets. The results\ndemonstrate that our proposed model yields significant improvements over the\nstate-of-the-art baselines in terms of accuracy, novelty, coverage, and\ntrade-off.\n"}
+{"abstract": "  One of the major open problems in noncommutative algebraic geometry is the\nclassification of noncommutative projective surfaces (or, slightly more\ngenerally, of noetherian connected graded domains of Gelfand-Kirillov dimension\n3). In a companion paper the authors described a noncommutative version of\nblowing down and, for example, gave a noncommutative analogue of Castelnuovo's\nclassic theorem that lines of self-intersection (-1) on a smooth surface can be\ncontracted.\n  In this paper we will use these techniques to construct explicit birational\ntransformations between various noncommutative surfaces containing an elliptic\ncurve. Notably we show that Van den Bergh's quadrics can be obtained from the\nSklyanin algebra by suitably blowing up and down, and we also provide a\nnoncommutative analogue of the classical Cremona transform. This extends and\namplifies earlier work of Presotto and Van den Bergh.\n"}
+{"abstract": "  Millions of people are affected by acute and chronic wounds yearly across the\nworld. Continuous wound monitoring is important for wound specialists to allow\nmore accurate diagnosis and optimization of management protocols. Machine\nLearning-based classification approaches provide optimal care strategies\nresulting in more reliable outcomes, cost savings, healing time reduction, and\nimproved patient satisfaction. In this study, we use a deep learning-based\nmethod to classify burn wound images into two or three different categories\nbased on the wound conditions. A pre-trained deep convolutional neural network,\nAlexNet, is fine-tuned using a burn wound image dataset and utilized as the\nclassifier. The classifier's performance is evaluated using classification\nmetrics such as accuracy, precision, and recall as well as confusion matrix. A\ncomparison with previous works that used the same dataset showed that our\ndesigned classifier improved the classification accuracy by more than 8%.\n"}
+{"abstract": "  Low light conditions in aerial images adversely affect the performance of\nseveral vision based applications. There is a need for methods that can\nefficiently remove the low light attributes and assist in the performance of\nkey vision tasks. In this work, we propose a new method that is capable of\nenhancing the low light image in a self-supervised fashion, and sequentially\napply detection and segmentation tasks in an end-to-end manner. The proposed\nmethod occupies a very small overhead in terms of memory and computational\npower over the original algorithm and delivers superior results. Additionally,\nwe propose the generation of a new low light aerial dataset using GANs, which\ncan be used to evaluate vision based networks for similar adverse conditions.\n"}
+{"abstract": "  Automated machine learning (AutoML) systems aim to enable training machine\nlearning (ML) models for non-ML experts. A shortcoming of these systems is that\nwhen they fail to produce a model with high accuracy, the user has no path to\nimprove the model other than hiring a data scientist or learning ML -- this\ndefeats the purpose of AutoML and limits its adoption. We introduce an\ninterpretable data feedback solution for AutoML. Our solution suggests new data\npoints for the user to label (without requiring a pool of unlabeled data) to\nimprove the model's accuracy. Our solution analyzes how features influence the\nprediction among all ML models in an AutoML ensemble, and we suggest more data\nsamples from feature ranges that have high variance in such analysis. Our\nevaluation shows that our solution can improve the accuracy of AutoML by 7-8%\nand significantly outperforms popular active learning solutions in data\nefficiency, all the while providing the added benefit of being interpretable.\n"}
+{"abstract": "  Over the past several years, the electrocardiogram (ECG) has been\ninvestigated for its uniqueness and potential to discriminate between\nindividuals. This paper discusses how this discriminatory information can help\nin continuous user authentication by a wearable chest strap which uses dry\nelectrodes to obtain a single lead ECG signal. To the best of the authors'\nknowledge, this is the first such work which deals with continuous\nauthentication using a genuine wearable device as most prior works have either\nused medical equipment employing gel electrodes to obtain an ECG signal or have\nobtained an ECG signal through electrode positions that would not be feasible\nusing a wearable device. Prior works have also mainly dealt with using the ECG\nsignal for identification rather than verification, or dealt with using the ECG\nsignal for discrete authentication. This paper presents a novel algorithm which\nuses QRS detection, weighted averaging, Discrete Cosine Transform (DCT), and a\nSupport Vector Machine (SVM) classifier to determine whether the wearer of the\ndevice should be positively verified or not. Zero intrusion attempts were\nsuccessful when tested on a database consisting of 33 subjects.\n"}
+{"abstract": "  Electrochemistry experiments have established that the capacitance of\nelectrode-electrolyte interfaces is much larger for good metals such as gold\nand platinum than for carbon-based materials. Despite the development of\nelaborate electrode interaction potentials, to date molecular dynamics\nsimulations were not able to capture this effect. Here we show that changing\nthe width of the Gaussian charge distribution used to represent the atomic\ncharges in gold is an effective way to tune its metallicity. Larger Gaussian\nwidths lead to a capacitance of aqueous solutions (pure water and 1 molar NaCl)\nin good agreement with recent ab initio molecular dynamics results. For pure\nwater, the increase in the capacitance is not accompanied with structural\nchanges, while in the presence of salt the Na$^+$ cations tend to adsorb\nsignificantly on the surface. For a strongly metallic gold electrode, these\nions can even form inner sphere complexes on hollow sites of the surface.\n"}
+{"abstract": "  We propose a new kind of coherent state for the general $SO(D+1)$ formulation\nof loop quantum gravity in the $(1+D)$-dimensional space-time. Instead of\nThiemann's coherent state for $SO(D+1)$ gauge theory, our coherent spin-network\nstate is given by constructing proper superposition over quantum numbers of the\nspin-networks with vertices labelled by the coherent intertwiners. Such\nsuperposition type coherent states are labelled by the so-called generalized\ntwisted geometric variables which capture the geometric meaning of discretized\ngeneral relativity. We study the basic properties of this kind of coherent\nstates, i.e., the completeness and peakedness property. Moreover, we show that\nthe superposition type coherent states are consistent with Thiemann's coherent\nstate for $SO(D+1)$ gauge theory in large $\\eta$ limit.\n"}
+{"abstract": "  The combination of core/shell geometry and band gap engineering in nanowire\nheterostructures can be employed to realize systems with novel transport and\noptical properties. Here, we report on the growth of InAs/InP/GaAsSb\ncore-dual-shell nanowires by catalyst-free chemical beam epitaxy on Si(111)\nsubstrates. Detailed morphological, structural, and compositional analyses of\nthe nanowires as a function of growth parameters were carried out by scanning\nand transmission electron microscopy and by energy-dispersive X-ray\nspectroscopy. Furthermore, by combining the scanning transmission electron\nmicroscopy-Moire technique with geometric phase analysis, we studied the\nresidual strain and the relaxation mechanisms in this system. We found that InP\nshell facets are well-developed along all the crystallographic directions only\nwhen the nominal thickness is above 1 nm, suggesting an island-growth mode.\nMoreover, the crystallographic analysis indicates that both InP and GaAsSb\nshells grow almost coherently to the InAs core along the 112 direction and\nelastically compressed along the 110 direction. For InP shell thickness above 8\nnm, some dislocations and roughening occur at the interfaces. This study\nprovides useful general guidelines for the fabrication of high-quality devices\nbased on these core-dual-shell nanowires.\n"}
+{"abstract": "  Self-adjusting computation is an approach for automatically producing dynamic\nalgorithms from static ones. The approach works by tracking control and data\ndependencies, and propagating changes through the dependencies when making an\nupdate. Extensively studied in the sequential setting, some results on parallel\nself-adjusting computation exist, but are either only applicable to limited\nclasses of computations, such as map-reduce, or are ad-hoc systems with no\ntheoretical analysis of their performance.\n  In this paper, we present the first system for parallel self-adjusting\ncomputation that applies to a wide class of nested parallel algorithms and\nprovides theoretical bounds on the work and span of the resulting dynamic\nalgorithms. As with bounds in the sequential setting, our bounds relate a\n\"distance\" measure between computations on different inputs to the cost of\npropagating an update. However, here we also consider parallelism in the\npropagation cost. The main innovation in the paper is in using Series-Parallel\ntrees (SP trees) to track sequential and parallel control dependencies to allow\npropagation of changes to be applied safely in parallel. We show both\ntheoretically and through experiments that our system allows algorithms to\nproduce updated results over large datasets significantly faster than\nfrom-scratch execution. We demonstrate our system with several example\napplications, including algorithms for dynamic sequences and dynamic trees. In\nall cases studied, we show that parallel self-adjusting computation can provide\na significant benefit in both work savings and parallel time.\n"}
+{"abstract": "  We study over-the-air model aggregation in federated edge learning (FEEL)\nsystems, where channel state information at the transmitters (CSIT) is assumed\nto be unavailable. We leverage the reconfigurable intelligent surface (RIS)\ntechnology to align the cascaded channel coefficients for CSIT-free model\naggregation. To this end, we jointly optimize the RIS and the receiver by\nminimizing the aggregation error under the channel alignment constraint. We\nthen develop a difference-of-convex algorithm for the resulting non-convex\noptimization. Numerical experiments on image classification show that the\nproposed method is able to achieve a similar learning accuracy as the\nstate-of-the-art CSIT-based solution, demonstrating the efficiency of our\napproach in combating the lack of CSIT.\n"}
+{"abstract": "  Voice authentication is drawing increasing attention and becomes an\nattractive alternative to passwords for mobile authentication. Recent advances\nin mobile technology further accelerate the adoption of voice biometrics in an\narray of diverse mobile applications. However, recent studies show that voice\nauthentication is vulnerable to replay attacks, where an adversary can spoof a\nvoice authentication system using a pre-recorded voice sample collected from\nthe victim. In this paper, we propose VoiceLive, a liveness detection system\nfor both text-dependent and text-independent voice authentication on\nsmartphones. VoiceLive detects a live user by leveraging the user's unique\nvocal system and the stereo recording of smartphones. In particular, utilizing\nthe built-in gyroscope, loudspeaker, and microphone, VoiceLive first measures\nthe smartphone's distance and angle from the user, then it captures the\nposition-specific time-difference-of-arrival (TDoA) changes in a sequence of\nphoneme sounds to the two microphones of the phone, and uses such unique TDoA\ndynamic which doesn't exist under replay attacks for liveness detection.\nVoiceLive is practical as it doesn't require additional hardware but\ntwo-channel stereo recording that is supported by virtually all smartphones.\nOur experimental evaluation with 12 participants and different types of phones\nshows that VoiceLive achieves over 99% detection accuracy at around 1% Equal\nError Rate (EER) on the text-dependent system and around 99% accuracy and 2%\nEER on the text-independent one. Results also show that VoiceLive is robust to\ndifferent phone positions, i.e. the user is free to hold the smartphone with\ndistinct distances and angles.\n"}
+{"abstract": "  This paper investigates how Natural Language Understanding (NLU) could be\napplied in Emotion Recognition, a specific task in affective computing. We\nfinetuned different transformers language models (BERT, DistilBERT, RoBERTa,\nXLNet, and ELECTRA) using a fine-grained emotion dataset and evaluating them in\nterms of performance (f1-score) and time to complete.\n"}
+{"abstract": "  In this paper we revise the theory of turnpikes in discounted Markov decision\nprocesses, prove the turnpike theorem for the undiscounted model and apply the\nresults to the specific random walk.\n"}
+{"abstract": "  Deep neural networks are easily misled by adversarial examples. Although lots\nof defense methods are proposed, many of them are demonstrated to lose\neffectiveness when against properly performed adaptive attacks. How to evaluate\nthe adversarial robustness effectively is important for the realistic\ndeployment of deep models, but yet still unclear. To provide a reasonable\nsolution, one of the primary things is to understand the error (or gap) between\nthe true adversarial robustness and the evaluated one, what is it and why it\nexists. Several works are done in this paper to make it clear. Firstly, we\nintroduce an interesting phenomenon named gradient traps, which lead to\nincompetent adversaries and are demonstrated to be a manifestation of\nevaluation error. Then, we analyze the error and identify that there are three\ncomponents. Each of them is caused by a specific compromise. Moreover, based on\nthe above analysis, we present our evaluation suggestions. Experiments on\nadversarial training and its variations indicate that: (1) the error does exist\nempirically, and (2) these defenses are still vulnerable. We hope these\nanalyses and results will help the community to develop more powerful defenses.\n"}
+{"abstract": "  Differential dynamic microscopy (DDM) is increasingly used in the fields of\nsoft matter physics and biophysics to extract the dynamics of microscopic\nobjects across a range of wavevectors using optical microscopy. Standard DDM is\nlimited to detecting dynamics no faster than the camera frame rate. We report\non an extension to DDM where we sequentially illuminate the sample with\nspectrally-distinct light and image with a color camera. By pulsing blue and\nthen red light separated by a lag time much smaller than the camera's exposure\ntime we are able to use this two-color DDM method to measure dynamics occurring\nmuch faster than the camera frame rate.\n"}
+{"abstract": "  In this paper we make a detailed analysis of conservation principles in the\ncontext of a family of fourth-order gravitational theories generated via a\nquadratic Lagrangian. In particular, we focus on the associated notion of\nenergy and start a program related to its study. We also exhibit examples of\nsolutions which provide intuitions about this notion of energy which allows us\nto interpret it, and introduce several study cases where its analysis seems\ntractable. Finally, positive energy theorems are presented in restricted\nsituations.\n"}
+{"abstract": "  We study automorphic categories of nilpotent sheaves under degenerations of\nsmooth curves to nodal Deligne-Mumford curves. Our constructions realize affine\nHecke operators as the result of bubbling projective lines from marked points.\nWe use this to construct a \"gluing functor\" from the automorphic category of a\nnodal Deligne-Mumford curve to the automorphic category of a smoothing.\n"}
+{"abstract": "  It has been predicted that the momentum and radiation pressure of light in\nnegative-index metamaterials (NIMs) reverse their direction from what is\nobserved for normal materials. The negative refraction and inverse Doppler\neffect of light in NIMs have been experimentally observed, but the equally\nfundamental phenomenon, the negative radiation pressure of light, still lacks\nexperimental verification. Here we show that the negative momentum and\nradiation pressure of light in NIMs are caused by the sum of the positive\nmomentum density of the electromagnetic (EM) field and the negative momentum\ndensity of the atoms due to the optical force, which drives the atoms backward\nand reduces the local density of atoms at the site of the light field. In\ncontrast to earlier works, light in NIMs has both negative total momentum and\nenergy. For the experimental discovery of the negative radiation pressure, one\nmust record the reflection of both momentum components.\n"}
+{"abstract": "  We attribute deviations of the muon and electron magnetic moments from the\ntheoretical predictions to the presence of an additional $U(1)^\\prime$\nsupersymmetric model. We interpret the discrepancies between the muon and\nelectron anomalous magnetic moments to be due to the presence of non-universal\n$U(1)^\\prime$ charges. In a minimally extended model, we show that requiring\nboth deviations to be satisfied imposes constraints on the spectrum of the\nmodel, in particular on dark matter candidates and slepton masses and ordering.\nChoosing three benchmarks with distinct dark matter features, we study\nimplications of the model at colliders, concentrating on variables that can\ndistinguish our non-universal scenario from other $U(1)^\\prime$\nimplementations.\n"}
+{"abstract": "  Differential equations in general and neural ODEs in particular are an\nessential technique in continuous-time system identification. While many\ndeterministic learning algorithms have been designed based on numerical\nintegration via the adjoint method, many downstream tasks such as active\nlearning, exploration in reinforcement learning, robust control, or filtering\nrequire accurate estimates of predictive uncertainties. In this work, we\npropose a novel approach towards estimating epistemically uncertain neural\nODEs, avoiding the numerical integration bottleneck. Instead of modeling\nuncertainty in the ODE parameters, we directly model uncertainties in the state\nspace. Our algorithm - distributional gradient matching (DGM) - jointly trains\na smoother and a dynamics model and matches their gradients via minimizing a\nWasserstein loss. Our experiments show that, compared to traditional\napproximate inference methods based on numerical integration, our approach is\nfaster to train, faster at predicting previously unseen trajectories, and in\nthe context of neural ODEs, significantly more accurate.\n"}
+{"abstract": "  Localization based on received signal strength (RSS) has drawn great interest\nin the wireless sensor network (WSN). In this paper, we investigate the\nRSS-based multi-sources localization problem with unknown transmitted power\nunder shadow fading. The log-normal shadowing effect is approximated through\nFenton-Wilkinson (F-W) method and maximum likelihood estimation is adopted to\noptimize the RSS-based multiple sources localization problem. Moreover, we\nexploit a sparse recovery and weighted average of candidates (SR-WAC) based\nmethod to set up an initiation, which can efficiently approach a superior local\noptimal solution. It is shown from the simulation results that the proposed\nmethod has a much higher localization accuracy and outperforms the other\n"}
+{"abstract": "  We present an optimization framework based on Lagrange duality and the\nscattering $\\mathbb{T}$ operator of electromagnetism to construct limits on the\npossible features that may be imparted to a collection of output fields from a\ncollection of input fields, i.e., constraints on achievable optical\ntransformations and the characteristics of structured materials as\ncommunication channels. Implications of these bounds on the performance of\nrepresentative optical devices having multi-wavelength or multiport\nfunctionalities are examined in the context of electromagnetic shielding,\nfocusing, near-field resolution, and linear computing.\n"}
+{"abstract": "  A common strategy to video understanding is to incorporate spatial and motion\ninformation by fusing features derived from RGB frames and optical flow. In\nthis work, we introduce a new way to leverage semantic segmentation as an\nintermediate representation for video understanding and use it in a way that\nrequires no additional labeling.\n  Second, we propose a general framework which learns the intermediate\nrepresentations (optical flow and semantic segmentation) jointly with the final\nvideo understanding task and allows the adaptation of the representations to\nthe end goal. Despite the use of intermediate representations within the\nnetwork, during inference, no additional data beyond RGB sequences is needed,\nenabling efficient recognition with a single network.\n  Finally, we present a way to find the optimal learning configuration by\nsearching the best loss weighting via evolution. We obtain more powerful visual\nrepresentations for videos which lead to performance gains over the\nstate-of-the-art.\n"}
+{"abstract": "  Automatic readability assessment (ARA) is the task of evaluating the level of\nease or difficulty of text documents for a target audience. For researchers,\none of the many open problems in the field is to make such models trained for\nthe task show efficacy even for low-resource languages. In this study, we\npropose an alternative way of utilizing the information-rich embeddings of BERT\nmodels with handcrafted linguistic features through a combined method for\nreadability assessment. Results show that the proposed method outperforms\nclassical approaches in readability assessment using English and Filipino\ndatasets, obtaining as high as 12.4% increase in F1 performance. We also show\nthat the general information encoded in BERT embeddings can be used as a\nsubstitute feature set for low-resource languages like Filipino with limited\nsemantic and syntactic NLP tools to explicitly extract feature values for the\ntask.\n"}
+{"abstract": "  The oxytocin effects on large-scale brain networks such as Default Mode\nNetwork (DMN) and Frontoparietal Network (FPN) have been largely studied using\nfMRI data. However, these studies are mainly based on the statistical\ncorrelation or Bayesian causality inference, lacking interpretability at\nphysical and neuroscience level. Here, we propose a physics-based framework of\nKuramoto model to investigate oxytocin effects on the phase dynamic neural\ncoupling in DMN and FPN. Testing on fMRI data of 59 participants administrated\nwith either oxytocin or placebo, we demonstrate that oxytocin changes the\ntopology of brain communities in DMN and FPN, leading to higher synchronization\nin the FPN and lower synchronization in the DMN, as well as a higher variance\nof the coupling strength within the DMN and more flexible coupling patterns\nacross time. These results together indicate that oxytocin may increase the\nability to overcome the corresponding internal oscillation dispersion and\nsupport the flexibility in neural synchrony in various social contexts,\nproviding new evidence for explaining the oxytocin modulated social behaviors.\nOur proposed Kuramoto model-based framework can be a potential tool in network\nneuroscience and offers physical and neural insights into phase dynamics of the\nbrain.\n"}
+{"abstract": "  Cell structures were introduced by W. Debski and E. Tymchatyn as a way to\nstudy some classes of topological spaces and their continuous functions by\nmeans of discrete approximations. In this work we weaken the notion of cell\nstructure and prove that the resulting class of topological space admitting\nsuch a generalized cell structure includes non-regular spaces.\n"}
+{"abstract": "  Many crowdsourced NLP datasets contain systematic gaps and biases that are\nidentified only after data collection is complete. Identifying these issues\nfrom early data samples during crowdsourcing should make mitigation more\nefficient, especially when done iteratively. We take natural language inference\nas a test case and ask whether it is beneficial to put a linguist `in the loop'\nduring data collection to dynamically identify and address gaps in the data by\nintroducing novel constraints on the task. We directly compare three data\ncollection protocols: (i) a baseline protocol, (ii) a linguist-in-the-loop\nintervention with iteratively-updated constraints on the task, and (iii) an\nextension of linguist-in-the-loop that provides direct interaction between\nlinguists and crowdworkers via a chatroom. The datasets collected with linguist\ninvolvement are more reliably challenging than baseline, without loss of\nquality. But we see no evidence that using this data in training leads to\nbetter out-of-domain model performance, and the addition of a chat platform has\nno measurable effect on the resulting dataset. We suggest integrating expert\nanalysis \\textit{during} data collection so that the expert can dynamically\naddress gaps and biases in the dataset.\n"}
+{"abstract": "  The shapes and morphology of the organs and tissues are important prior\nknowledge in medical imaging recognition and segmentation. The morphological\noperation is a well-known method for morphological feature extraction. As the\nmorphological operation is performed well in hand-crafted image segmentation\ntechniques, it is also promising to design an approach to approximate\nmorphological operation in the convolutional networks. However, using the\ntraditional convolutional neural network as a black-box is usually hard to\nspecify the morphological operation action. Here, we introduced a 3D\nmorphological operation residual block to extract morphological features in\nend-to-end deep learning models for semantic segmentation. This study proposed\na novel network block architecture that embedded the morphological operation as\nan infinitely strong prior in the convolutional neural network. Several 3D deep\nlearning models with the proposed morphological operation block were built and\ncompared in different medical imaging segmentation tasks. Experimental results\nshowed the proposed network achieved a relatively higher performance in the\nsegmentation tasks comparing with the conventional approach. In conclusion, the\nnovel network block could be easily embedded in traditional networks and\nefficiently reinforce the deep learning models for medical imaging\nsegmentation.\n"}
+{"abstract": "  Let XNLP be the class of parameterized problems such that an instance of size\n$n$ with parameter $k$ can be solved nondeterministically in time\n$f(k)n^{O(1)}$ and space $f(k)\\log(n)$ (for some computable function $f$). We\ngive a wide variety of XNLP-complete problems, such as {\\sc List Coloring} and\n{\\sc Precoloring Extension} with pathwidth as parameter, {\\sc Scheduling of\nJobs with Precedence Constraints}, with both number of machines and partial\norder width as parameter, {\\sc Bandwidth} and variants of {\\sc Weighted\nCNF-Satisfiability} and reconfiguration problems. In particular, this implies\nthat all these problems are $W[t]$-hard for all $t$. This also answers a long\nstanding question on the parameterized complexity of the {\\sc Bandwidth}\nproblem.\n"}
+{"abstract": "  While the Rydberg-blockade regime provides the natural setting for creating\n$W$-type entanglement with cold neutral atoms, it is demonstrated here that a\nscalable entanglement resource of this type can even be obtained under\ncompletely different physical circumstances. To be more precise, a special\ninstance of twisted $W$ states -- namely, $\\pi$-twisted ones -- can be\nengineered in one-dimensional arrays of cold neutral atoms with Rydberg-dressed\nresonant dipole-dipole interaction. In particular, it is shown here that this\nis possible even when a (dressed) Rydberg excitation is coupled to the motional\ndegrees of freedom of atoms in their respective, nearly-harmonic optical-dipole\nmicrotraps, which are quantized into dispersionless (zero-dimensional) bosons.\nFor a specially chosen (\"sweet-spot\") detuning of the off-resonant dressing\nlasers from the relevant internal atomic transitions, the desired $\\pi$-twisted\n$W$ state of Rydberg-dressed qubits is the ground state of the effective\nexcitation -- boson Hamiltonian of the system in a broad window of the relevant\nparameters. Being at the same time separated from the other eigenstates by a\ngap equal to the single-boson energy, this $W$ state can be prepared using a\nRabi-type driving protocol. The corresponding preparation times are independent\nof the system size and several orders of magnitude shorter than the effective\nlifetimes of the relevant atomic states.\n"}
+{"abstract": "  Recently, neural networks have been successfully employed to improve upon\nstate-of-the-art performance in ad-hoc retrieval tasks via machine-learned\nranking functions. While neural retrieval models grow in complexity and impact,\nlittle is understood about their correspondence with well-studied IR\nprinciples. Recent work on interpretability in machine learning has provided\ntools and techniques to understand neural models in general, yet there has been\nlittle progress towards explaining ranking models.\n  We investigate whether one can explain the behavior of neural ranking models\nin terms of their congruence with well understood principles of document\nranking by using established theories from axiomatic IR. Axiomatic analysis of\ninformation retrieval models has formalized a set of constraints on ranking\ndecisions that reasonable retrieval models should fulfill. We operationalize\nthis axiomatic thinking to reproduce rankings based on combinations of\nelementary constraints. This allows us to investigate to what extent the\nranking decisions of neural rankers can be explained in terms of retrieval\naxioms, and which axioms apply in which situations. Our experimental study\nconsiders a comprehensive set of axioms over several representative neural\nrankers. While the existing axioms can already explain the particularly\nconfident ranking decisions rather well, future work should extend the axiom\nset to also cover the other still \"unexplainable\" neural IR rank decisions.\n"}
+{"abstract": "  We introduce a natural temporal analogue of Eulerian circuits and prove that,\nin contrast with the static case, it is NP-hard to determine whether a given\ntemporal graph is temporally Eulerian even if strong restrictions are placed on\nthe structure of the underlying graph and each edge is active at only three\ntimes. However, we do obtain an FPT-algorithm with respect to a new parameter\ncalled interval-membership-width which restricts the times assigned to\ndifferent edges; we believe that this parameter will be of independent interest\nfor other temporal graph problems. Our techniques also allow us to resolve two\nopen question of Akrida, Mertzios and Spirakis [CIAC 2019] concerning a related\nproblem of exploring temporal stars. Furthermore, we introduce a vertex-variant\nof interval-membership-width (which can be arbitrarily larger than its\nedge-counterpart) and use it to obtain an FPT-time algorithm for a natural\nvertex-exploration problem that remains hard even when\ninterval-membership-width is bounded.\n"}
+{"abstract": "  This paper introduces a new benchmarking dataset for marine snow removal of\nunderwater images. Marine snow is one of the main degradation sources of\nunderwater images that are caused by small particles, e.g., organic matter and\nsand, between the underwater scene and photosensors. We mathematically model\ntwo typical types of marine snow from the observations of real underwater\nimages. The modeled artifacts are synthesized with underwater images to\nconstruct large-scale pairs of ground-truth and degraded images to calculate\nobjective qualities for marine snow removal and to train a deep neural network.\nWe propose two marine snow removal tasks using the dataset and show the first\nbenchmarking results of marine snow removal. The Marine Snow Removal\nBenchmarking Dataset is publicly available online.\n"}
+{"abstract": "  We present a study of undoped AlGaN/GaN separate confinement heterostructures\ndesigned to operate as electron beam pumped ultraviolet lasers. We discuss the\neffect of spontaneous and piezoelectric polarization on carrier diffusion,\ncomparing the results of cathodoluminescence with electronic simulations of the\nband structure and Monte Carlo calculations of the electron trajectories.\nCarrier collection is significantly improved using an asymmetric graded-index\nseparate confinement heterostructure (GRINSCH). The graded layers avoid\npotential barriers induced by polarization differences in the heterostructure\nand serve as strain transition buffers which reduce the mosaicity of the active\nregion and the linewidth of spontaneous emission.\n"}
+{"abstract": "  We study Property (T) in the $\\Gamma(n,k,d)$ model of random groups: as $k$\ntends to infinity this gives the Gromov density model, introduced in [Gro93].\nWe provide bounds for Property (T) in the $k$-angular model of random groups,\ni.e. the $ \\Gamma (n,k,d)$ model where $k$ is fixed and $n$ tends to infinity.\nWe also prove that for $d>1\\slash 3$, a random group in the $\\Gamma(n,k,d)$\nmodel has Property (T) with probability tending to $1$ as $k$ tends to\ninfinity, strengthening the results of \\.{Z}uk and Kotowski--Kotowski, who\nconsider only groups in the $\\Gamma (n,3k,d)$ model.\n"}
+{"abstract": "  The characterisation of small conducting inclusions with low conductivities\nin an otherwise uniform background from low-frequency electrical field\nmeasurements has important applications in medical imaging using electrical\nimpedance tomography as well as in geological imaging using electrical\nresistivity tomography. It is known that such objects can be characterised by a\nP\\'oyla-Szeg\\\"o polarizability tensor. The coefficients of this tensor for\ndifferent shapes and contrasts involves the solution of a transmission problem,\nwhich in general must be done numerically. With a dictionary classification\nalgorithm in mind, we provide a series of accurate tensor characterisations of\nobjects that can be used as benchmarks for developers of software tools for\nsolving the transmission problem. To this end, we apply adaptive the boundary\nelement method to achieve our accurate solutions.\n"}
+{"abstract": "  Circular dichroism is the differential rate of absorption of right- and\nleft-handed circularly polarized light by chiral particles. Optical vortices\nwhich convey orbital angular momentum (OAM) possess a chirality associated with\nthe clockwise or anti-clockwise twisting of their wavefront. Here it is\nhighlighted that both oriented and randomly oriented chiral particles absorb\nphotons from twisted beams at different rates depending on whether the vortex\ntwists to the right or the left through a dipole coupling scheme. This is in\ncontrast to previous studies that investigated dipole couplings with vortex\nmodes in the paraxial approximation and showed no such chiral sensitivity to\nthe vortex handedness: only in oriented media where electric quadrupole\ncoupling contributes to optical activity effects due to absorption does such a\nmechanism exist for paraxial vortices. The distinct difference in the scheme\nhighlighted in this work is that longitudinal fields are taken account of and\nthese allow for OAM transfer even during dipole interactions. Due to the vortex\ndichroism persisting in randomly oriented collections of chiral particles, the\nmechanism has a distinct advantage in its potential applicability in chemical\nand biochemical applications where the systems under study are invariably in\nthe liquid phase. Additionally, the result is put into context in terms of the\nquantifiable optical chirality, highlighting that optical OAM can in fact\nincrease the optical chirality density of an electromagnetic field.\n"}
+{"abstract": "  We consider more general framework than the corresponding one considered in\nour previous work on the Hodge-Iwasawa theory. In our current consideration we\nconsider the corresponding more general base spaces, namely the analytic adic\nspaces and analytic perfectoid spaces in Kedlaya's AWS Lecture notes. We hope\nour discussion will also shed some light on further generalization to even more\ngeneral spaces such as those considered by Gabber-Ramero namely one just\nconsiders certain topological rings which satisfy the Fontaine-Wintenberger\nidempotent correspondence and calls them perfectoid generalizing the notions\nfrom Scholze, Fontaine, Kedlaya-Liu and Kedlaya (AWS Lecture notes). Actually\nsome of the discussion we presented here is already in some more general form\nfor this purpose (although we have not made enough efforts to write all the\nthings).\n"}
+{"abstract": "  The onset of rigidity in interacting liquids, as they undergo a transition to\na disordered solid, is associated with a dramatic rearrangement of the\nlow-frequency vibrational spectrum. In this letter, we derive scaling forms for\nthe singular dynamical response of disordered viscoelastic networks near both\njamming and rigidity percolation. Using effective-medium theory, we extract\ncritical exponents, invariant scaling combinations and analytical formulas for\nuniversal scaling functions near these transitions. Our scaling forms describe\nthe behavior in space and time near the various onsets of rigidity, for rigid\nand floppy phases and the crossover region, including diverging length and time\nscales at the transitions. We expect that these behaviors can be measured in\nsystems ranging from colloidal suspensions to anomalous charge-density\nfluctuations of \"strange\" metals.\n"}
+{"abstract": "  The scattering of multispectral incoherent light is a common and unfavorable\nsignal scrambling in natural scenes. However, the blurred light spot due to\nscattering still holds lots of information remaining to be explored. Former\nmethods failed to recover the polarized hyperspectral information from\nscattered incoherent light or relied on additional dispersion elements. Here we\nput forward the transmission matrix (TM) approach for extended objects under\nincoherent illumination by speculating the unknown TM through experimentally\ncalibrated or digitally emulated ways. Employing a fiber bundle as a powerful\nimaging and dispersion element, we recover the spatial information in 252\npolarized-spectral channels from a single speckle, thus achieving single-shot,\nhigh-resolution, broadband hyperspectral imaging for two polarization states\nwith the cheap, compact, fiber-bundle-only system. Based on the scattering\nprinciple itself, our method not only greatly improves the robustness of the TM\napproach to retrieve the input spectral information, but also reveals the\nfeasibility to explore the polarized spatio-spectral information from blurry\nspeckles only with the help of simple optical setups.\n"}
+{"abstract": "  We present a BRST analysis of supersymmetry anomalies of $\\mathcal{N} = 1$\nsupersymmetric quantum field theories with anomalous R symmetry. To this end,\nwe consider the coupling of the matter theory to classical $\\mathcal{N} = 1$\nnew minimal supergravity. We point out that a supersymmetry anomaly cocycle\nassociated to the $\\mathrm{U}(1){}_\\mathrm{R}$ field does exist for this\ntheory. It is non-trivial in the space of supergravity fields (and ghosts), but\nit becomes BRST-exact in the functional space that includes antifields.\nEquivalently, the $\\mathrm{U}(1){}_\\mathrm{R}$ supersymmetry anomaly cocycle\nvanishes \"on-shell\". It is therefore removable. However, to remove it --\nprecisely because it is not trivial in the smaller space of fields -- one needs\nto deform the supergravity BRST operator. This deformation is triggered, at\nfirst order in the anomaly coefficient, by a local operator $S_1$ of ghost\nnumber 1. We give a cohomological characterization of $S_1$ and compute it in\nfull detail. At higher orders in the anomaly coefficient, we expect a priori\nthat further deformations of the BRST rules are necessary.\n"}
+{"abstract": "  The recent advances of Augmented Reality (AR) in healthcare have shown that\ntechnology is a significant part of the current healthcare system. In recent\ndays, augmented reality has proposed numerous smart applications in healthcare\ndomain including wearable access, telemedicine, remote surgery, diagnosis of\nmedical reports, emergency medicine, etc. The aim of the developed augmented\nhealthcare application is to improve patient care, increase efficiency, and\ndecrease costs. This article puts on an effort to review the advances in\nAR-based healthcare technologies and goes to peek into the strategies that are\nbeing taken to further this branch of technology. This article explores the\nimportant services of augmented-based healthcare solutions and throws light on\nrecently invented ones as well as their respective platforms. It also addresses\nconcurrent concerns and their relevant future challenges. In addition, this\npaper analyzes distinct AR security and privacy including security requirements\nand attack terminologies. Furthermore, this paper proposes a security model to\nminimize security risks. Augmented reality advantages in healthcare, especially\nfor operating surgery, emergency diagnosis, and medical training is being\ndemonstrated here thorough proper analysis. To say the least, the article\nillustrates a complete overview of augmented reality technology in the modern\nhealthcare sector by demonstrating its impacts, advancements, current\nvulnerabilities; future challenges, and concludes with recommendations to a new\ndirection for further research.\n"}
+{"abstract": "  We establish the twisted functoriality in nonabelian Hodge theory in positive\ncharacteristic. As an application, we obtain a purely algebraic proof of the\nfact that the pullback of a semistable Higgs bundle with vanishing Chern\nclasses is again semistable.\n"}
+{"abstract": "  The semantic segmentation (SS) task aims to create a dense classification by\nlabeling at the pixel level each object present on images. Convolutional neural\nnetwork (CNN) approaches have been widely used, and exhibited the best results\nin this task. However, the loss of spatial precision on the results is a main\ndrawback that has not been solved. In this work, we propose to use a multi-task\napproach by complementing the semantic segmentation task with edge detection,\nsemantic contour, and distance transform tasks. We propose that by sharing a\ncommon latent space, the complementary tasks can produce more robust\nrepresentations that can enhance the semantic labels. We explore the influence\nof contour-based tasks on latent space, as well as their impact on the final\nresults of SS. We demonstrate the effectiveness of learning in a multi-task\nsetting for hourglass models in the Cityscapes, CamVid, and Freiburg Forest\ndatasets by improving the state-of-the-art without any refinement\npost-processing.\n"}
+{"abstract": "  Lenses are a category theoretic construct and are used in a wide variety of\napplications. Symmetric lenses compose to, of course, form new symmetric\nlenses. Symmetric lenses are usually represented as spans of asymmetric lenses.\nIn many applications, the fact that a symmetric lens might also be represented\nas a cospan of asymmetric lenses is important, especially for implementation\npurposes. However, the composition of symmetric lenses does not preserve the\nproperty that the lenses can be represented by cospans -- two such symmetric\nlenses may (and frequently do) compose to form a symmetric lens which cannot be\nrepresented as a cospan of asymmetric lenses. Thus preserving the factorisation\nto show how cospans of asymmetric lenses might be used in implementations\nbecomes important. In 2018, the first work on multilenses was begun.\nMultilenses can be represented as multi-spans of asymmetric lenses (often\ncalled 'wide spans', these are spans with an arbitrary finite number of legs).\nIn this paper we analyse a small but realistic example of a supply chain in\nwhich the cospan representations would be 'composed away' by ordinary symmetric\nlens composition, and introduce a new kind of composition which we call\n'fusion' in which two ordinary symmetric lenses (spans with two legs) fuse to\nform a multilens with three legs preserving the cospan representations, and\nmore generally, two symmetric multilenses, spans with say m and n legs, fuse to\nform a symmetric multilens with m+n-1 legs, again preserving cospan\nrepresentations.\n"}
+{"abstract": "  Privacy considerations and bias in datasets are quickly becoming\nhigh-priority issues that the computer vision community needs to face. So far,\nlittle attention has been given to practical solutions that do not involve\ncollection of new datasets. In this work, we show that for object detection on\nCOCO, both anonymizing the dataset by blurring faces, as well as swapping faces\nin a balanced manner along the gender and skin tone dimension, can retain\nobject detection performances while preserving privacy and partially balancing\nbias.\n"}
+{"abstract": "  Fine-grained estimation of galaxy merger stages from observations is a key\nproblem useful for validation of our current theoretical understanding of\ngalaxy formation. To this end, we demonstrate a CNN-based regression model that\nis able to predict, for the first time, using a single image, the merger stage\nrelative to the first perigee passage with a median error of 38.3 million years\n(Myrs) over a period of 400 Myrs. This model uses no specific dynamical\nmodeling and learns only from simulated merger events. We show that our model\nprovides reasonable estimates on real observations, approximately matching\nprior estimates provided by detailed dynamical modeling. We provide a\npreliminary interpretability analysis of our models, and demonstrate first\nsteps toward calibrated uncertainty estimation.\n"}
+{"abstract": "  We present a simple algorithm that perfectly samples configurations from the\nunique Gibbs measure of a spin system on a potentially infinite graph $G$. The\nsampling algorithm assumes strong spatial mixing together with subexponential\ngrowth of $G$. It produces a finite window onto a perfect sample from the Gibbs\ndistribution. The run-time is linear in the size of the window.\n"}
+{"abstract": "  In recent years, federated learning has been embraced as an approach for\nbringing about collaboration across large populations of learning agents.\nHowever, little is known about how collaboration protocols should take agents'\nincentives into account when allocating individual resources for communal\nlearning in order to maintain such collaborations. Inspired by game theoretic\nnotions, this paper introduces a framework for incentive-aware learning and\ndata sharing in federated learning. Our stable and envy-free equilibria capture\nnotions of collaboration in the presence of agents interested in meeting their\nlearning objectives while keeping their own sample collection burden low. For\nexample, in an envy-free equilibrium, no agent would wish to swap their\nsampling burden with any other agent and in a stable equilibrium, no agent\nwould wish to unilaterally reduce their sampling burden.\n  In addition to formalizing this framework, our contributions include\ncharacterizing the structural properties of such equilibria, proving when they\nexist, and showing how they can be computed. Furthermore, we compare the sample\ncomplexity of incentive-aware collaboration with that of optimal collaboration\nwhen one ignores agents' incentives.\n"}
+{"abstract": "  With the advent of gravitational wave astronomy and first pictures of the\n\"shadow\" of the central black hole of our milky way, theoretical analyses of\nblack holes (and compact objects mimicking them sufficiently closely) have\nbecome more important than ever. The near future promises more and more\ndetailed information about the observable black holes and black hole\ncandidates. This information could lead to important advances on constraints on\nor evidence for modifications of general relativity. More precisely, we are\nstudying the influence of weak teleparallel perturbations on general\nrelativistic vacuum spacetime geometries in spherical symmetry. We find the\nmost general family of spherically symmetric, static vacuum solutions of the\ntheory, which are candidates for describing teleparallel black holes which\nemerge as perturbations to the Schwarzschild black hole. We compare our\nfindings to results on black hole or static, spherically symmetric solutions in\nteleparallel gravity discussed in the literature, by comparing the predictions\nfor classical observables such as the photon sphere, the perihelion shift, the\nlight deflection, and the Shapiro delay. On the basis of these observables, we\ndemonstrate that among the solutions we found, there exist spacetime geometries\nthat lead to much weaker bounds on teleparallel gravity than those found\nearlier. Finally, we move on to a discussion of how the teleparallel\nperturbations influence the Hawking evaporation in these spacetimes.\n"}
+{"abstract": "  Single channel speech separation has experienced great progress in the last\nfew years. However, training neural speech separation for a large number of\nspeakers (e.g., more than 10 speakers) is out of reach for the current methods,\nwhich rely on the Permutation Invariant Loss (PIT). In this work, we present a\npermutation invariant training that employs the Hungarian algorithm in order to\ntrain with an $O(C^3)$ time complexity, where $C$ is the number of speakers, in\ncomparison to $O(C!)$ of PIT based methods. Furthermore, we present a modified\narchitecture that can handle the increased number of speakers. Our approach\nseparates up to $20$ speakers and improves the previous results for large $C$\nby a wide margin.\n"}
+{"abstract": "  (Partial) ranking loss is a commonly used evaluation measure for multi-label\nclassification, which is usually optimized with convex surrogates for\ncomputational efficiency. Prior theoretical work on multi-label ranking mainly\nfocuses on (Fisher) consistency analyses. However, there is a gap between\nexisting theory and practice -- some pairwise losses can lead to promising\nperformance but lack consistency, while some univariate losses are consistent\nbut usually have no clear superiority in practice. In this paper, we attempt to\nfill this gap through a systematic study from two complementary perspectives of\nconsistency and generalization error bounds of learning algorithms. Our results\nshow that learning algorithms with the consistent univariate loss have an error\nbound of $O(c)$ ($c$ is the number of labels), while algorithms with the\ninconsistent pairwise loss depend on $O(\\sqrt{c})$ as shown in prior work. This\nexplains that the latter can achieve better performance than the former in\npractice. Moreover, we present an inconsistent reweighted univariate loss-based\nlearning algorithm that enjoys an error bound of $O(\\sqrt{c})$ for promising\nperformance as well as the computational efficiency of univariate losses.\nFinally, experimental results validate our theoretical analyses.\n"}
+{"abstract": "  Existing rotated object detectors are mostly inherited from the horizontal\ndetection paradigm, as the latter has evolved into a well-developed area.\nHowever, these detectors are difficult to perform prominently in high-precision\ndetection due to the limitation of current regression loss design, especially\nfor objects with large aspect ratios. Taking the perspective that horizontal\ndetection is a special case for rotated object detection, in this paper, we are\nmotivated to change the design of rotation regression loss from induction\nparadigm to deduction methodology, in terms of the relation between rotation\nand horizontal detection. We show that one essential challenge is how to\nmodulate the coupled parameters in the rotation regression loss, as such the\nestimated parameters can influence to each other during the dynamic joint\noptimization, in an adaptive and synergetic way. Specifically, we first convert\nthe rotated bounding box into a 2-D Gaussian distribution, and then calculate\nthe Kullback-Leibler Divergence (KLD) between the Gaussian distributions as the\nregression loss. By analyzing the gradient of each parameter, we show that KLD\n(and its derivatives) can dynamically adjust the parameter gradients according\nto the characteristics of the object. It will adjust the importance (gradient\nweight) of the angle parameter according to the aspect ratio. This mechanism\ncan be vital for high-precision detection as a slight angle error would cause a\nserious accuracy drop for large aspect ratios objects. More importantly, we\nhave proved that KLD is scale invariant. We further show that the KLD loss can\nbe degenerated into the popular $l_{n}$-norm loss for horizontal detection.\nExperimental results on seven datasets using different detectors show its\nconsistent superiority, and codes are available at\nhttps://github.com/yangxue0827/RotationDetection.\n"}
+{"abstract": "  We analyze the unparticle effects on the lepton flavor universality violating\nratios in $b\\to s$ transition, $R(K^{(*)})$. We concentrate on the vector\nunparticles which contribute to the relevant Wilson coefficients $C_{9,10}$.\nNew effects appear in the form of some powers of the momentum transfer squared,\nwhich is a different feature from other new physics models like leptoquarks or\n$Z'$. Constraints from $B_s$-${\\bar B}_s$ mixing, $B_s\\to\\mu^+\\mu^-$, and the\nelectron/muon anomalous magnetic moments are considered. It is found that the\nvector unparticles successfully explain $R(K^{(*)})$ with moderate values of\nmodel parameters.\n"}
+{"abstract": "  This article proposes a new population-based optimization algorithm called\nthe Tangent Search Algorithm (TSA) to solve optimization problems. The TSA uses\na mathematical model based on the tangent function to move a given solution\ntoward a better solution. The tangent flight function has the advantage to\nbalance between the exploitation and the exploration search. Moreover, a novel\nescape procedure is used to avoid to be trapped in local minima. Besides, an\nadaptive variable step size is also integrated in this algorithm to enhance the\nconvergence capacity. The performance of TSA is assessed in three classes of\ntests: classical tests, CEC benchmarks, and engineering optimization problems.\nMoreover, several studies and metrics have been used to observe the behavior of\nthe proposed TSA. The experimental results show that TSA algorithm is capable\nto provide very promising and competitive results on most benchmark functions\nthanks to better balance between exploration and exploitation of the search\nspace. The main characteristics of this new optimization algorithm is its\nsimplicity and efficiency and it requires only a small number of user-defined\nparameters.\n"}
+{"abstract": "  Recently Hachem et al. formulated a multiaccess coded caching model which\nconsists of a central server connected to $K$ users via an error-free shared\nlink, and $K$ cache-nodes. Each cache-node is equipped with a local cache and\neach user can access $L$ neighbouring cache-nodes with a cyclic wrap-around\nfashion. In this paper, we take the privacy of the users' demands into\nconsideration, i.e., each user can only get its required file and can not get\nany information about the demands of other users. By storing some private keys\nat the cache-nodes, we propose a novel transformation approach to transform a\nnon-private multiaccess coded caching scheme into a private multiaccess coded\ncaching scheme.\n"}
+{"abstract": "  Superconductivity in graphene-based systems has recently attracted much\nattention, as either intrinsic behavior or induced by proximity to a\nsuperconductor may lead to interesting topological phases and symmetries of the\npairing function. A prominent system considers the pairing to have chiral\nsymmetry. The question arises as to the effect of possible spin-orbit coupling\non the resulting superconducting quasiparticle spectrum. Utilizing a\nBogolyubov-de Gennes (BdG) Hamiltonian, we explore the interplay of different\ninteraction terms in the system, and their role in generating complex Berry\ncurvatures in the quasiparticle spectrum, as well as non-trivial topological\nbehavior. We demonstrate that the topology of the BdG Hamiltonian in these\nsystems may result in the appearance of edge states along the zigzag edges of\nnanoribbons in the appropriate regime.\n"}
+{"abstract": "  Post-processing of static embedding has beenshown to improve their\nperformance on both lexical and sequence-level tasks. However, post-processing\nfor contextualized embeddings is an under-studied problem. In this work, we\nquestion the usefulness of post-processing for contextualized embeddings\nobtained from different layers of pre-trained language models. More\nspecifically, we standardize individual neuron activations using z-score,\nmin-max normalization, and by removing top principle components using the\nall-but-the-top method. Additionally, we apply unit length normalization to\nword representations. On a diverse set of pre-trained models, we show that\npost-processing unwraps vital information present in the representations for\nboth lexical tasks (such as word similarity and analogy)and sequence\nclassification tasks. Our findings raise interesting points in relation to\ntheresearch studies that use contextualized representations, and suggest\nz-score normalization as an essential step to consider when using them in an\napplication.\n"}
+{"abstract": "  Let $W_a$ be an affine Weyl group. In 1987 Jian Yi Shi gave a\ncharacterization of the elements $w \\in W_a$ in terms of $\\Phi^+$-tuples\n$(k(w,\\alpha))_{\\alpha \\in \\Phi^+}$ called the Shi vectors. Using these\ncoefficients, a formula is provided to compute the standard length of $W_a$. In\nthis note we express the twisted affine length function of $W_a$ in terms of\nthe Shi coefficients.\n"}
+{"abstract": "  We present lattice calculations of the low-lying spectrum of $^{12}$C using a\nsimple nucleon-nucleon interaction that is independent of spin and isospin and\ntherefore invariant under Wigner's SU(4) symmetry. We find strong signals for\nall excited states up to $\\sim 15$~MeV above the ground state, and explore the\nstructure of each state using a large variety of $\\alpha$ cluster and harmonic\noscillator trial states, projected onto given irreducible representations of\nthe cubic group. We are able to verify earlier findings for the $\\alpha$\nclustering in the Hoyle state and the second $2^+$ state of $^{12}$C. The\nsuccess of these calculations to describe the full low-lying energy spectrum\nusing spin-independent interactions suggest that either the spin-orbit\ninteractions are somewhat weak in the $^{12}$C system, or the effects of\n$\\alpha$ clustering are diminishing their influence. This is in agreement with\nprevious findings from {\\it ab initio} shell model calculations.\n"}
+{"abstract": "  Motivated by an energy efficient building application, we want to optimize a\nquadratic cost functional subject to the Boussinesq approximation of the\nNavier-Stokes equations and to bilateral state and control constraints. Since\nthe computation of such an optimal solution is numerically costly, we design an\nefficient strategy to compute a sub-optimal (but applicationally acceptable)\nsolution with significantly reduced computational effort. We employ an economic\nModel Predictive Control (MPC) strategy to obtain a feedback control. The MPC\nsub-problems are based on a linear-quadratic optimal control problem subjected\nto mixed control and state constraints and a convection-diffusion equation,\nreduced with proper orthogonal decomposition. To solve each sub-problem, we\napply a primal-dual active set strategy. The method can be fully parallelized,\nwhich enables the solution of large problems with real-world parameters.\n"}
+{"abstract": "  In this paper, we introduce PeerGAN, a generative adversarial network (GAN)\nsolution to improve the stability of the generated samples and to mitigate mode\ncollapse. Built upon the Vanilla GAN's two-player game between the\ndiscriminator $D_1$ and the generator $G$, we introduce a peer discriminator\n$D_2$ to the min-max game. Similar to previous work using two discriminators,\nthe first role of both $D_1$, $D_2$ is to distinguish between generated samples\nand real ones, while the generator tries to generate high-quality samples which\nare able to fool both discriminators. Different from existing methods, we\nintroduce another game between $D_1$ and $D_2$ to discourage their agreement\nand therefore increase the level of diversity of the generated samples. This\nproperty alleviates the issue of early mode collapse by preventing $D_1$ and\n$D_2$ from converging too fast. We provide theoretical analysis for the\nequilibrium of the min-max game formed among $G, D_1, D_2$. We offer\nconvergence behavior of PeerGAN as well as stability of the min-max game. It's\nworth mentioning that PeerGAN operates in the unsupervised setting, and the\nadditional game between $D_1$ and $D_2$ does not need any label supervision.\nExperiments results on a synthetic dataset and on real-world image datasets\n(MNIST, Fashion MNIST, CIFAR-10, STL-10, CelebA, VGG) demonstrate that PeerGAN\noutperforms competitive baseline work in generating diverse and high-quality\nsamples, while only introduces negligible computation cost.\n"}
+{"abstract": "  We demarcate the unstable regimes of superfluids in a Rabi-coupled\ntwo-component Bose-Einstein condensate in the presence of optical lattices. We\nfind that the Rabi coupling can stabilize superfluids. A significant separation\nbetween Landau and dynamical instabilities is presented in a Rabi-coupled\nZeeman lattice.\n"}
+{"abstract": "  Searches for counterparts to multimessenger events with optical imagers use\ndifference imaging to detect new transient sources. However, even with existing\nartifact detection algorithms, this process simultaneously returns several\nclasses of false positives: false detections from poor quality image\nsubtractions, false detections from low signal-to-noise images, and detections\nof pre-existing variable sources. Currently, human visual inspection to remove\nthe false positives is a central part of multimessenger follow-up observations,\nbut when next generation gravitational wave and neutrino detectors come online\nand increase the rate of multimessenger events, the visual inspection process\nwill be prohibitively expensive. We approach this problem with two\nconvolutional neural networks operating on the difference imaging outputs. The\nfirst network focuses on removing false detections and demonstrates an accuracy\nof 92 percent on our dataset. The second network focuses on sorting all real\ndetections by the probability of being a transient source within a host galaxy\nand distinguishes between various classes of images that previously required\nadditional human inspection. We find the number of images requiring human\ninspection will decrease by a factor of 1.5 using our approach alone and a\nfactor of 3.6 using our approach in combination with existing algorithms,\nfacilitating rapid multimessenger counterpart identification by the\nastronomical community.\n"}
+{"abstract": "  Cumulative hardness comparisons are a simple but statistically powerful test\nfor the presence of gravitational lensing in gamma-ray bursts (GRBs). Since\ngravitational lensing does not change photon energies, all source images should\nhave the same spectra -- and hence hardness. Applied to the recent claim that\nthe two pulses in GRB 950830 are lensed images of the same pulse, the measured\nflux ratio between the two main pulses should be the same at all energies.\nAfter summing up all the counts in both of GRB 950830's two pulses in all four\nBATSE energy bands, it was found that in energy channel 3, the second pulse\nappears somewhat weak. In comparison with the other BATSE energy channels, the\ndifference was statistically significant at above 90\\%. This model-independent\ntest indicates that the case for GRB 950830 involving a gravitational lens may\nbe intriguing -- but should not be considered proven.\n"}
+{"abstract": "  We introduce a methodology to study the possible matter flows of an ecosystem\ndefined by observational biomass data and realistic biological constraints. The\nflows belong to a polyhedron in a multi dimensional space making statistical\nexploration difficult in practice; instead, we propose to solve a convex\noptimization problem. Five criteria corresponding to ecological network indices\nhave been selected to be used as convex goal functions. Numerical results show\nthat the method is fast and can be used for large systems. Minimum flow\nsolutions are analyzed using flow decomposition in paths and circuits. Their\nconsistency is also tested by introducing a system of differential equations\nfor the biomasses and examining the stability of the biomass fixed point. The\nmethod is illustrated and explained throughout the text on an ecosystem toy\nmodel. It is also applied to realistic food models.\n"}
+{"abstract": "  We study spin excitations and Kondo effect in open-shell nanographenes,\nmotivated by recent scanning tunneling inelastic spectroscopy experiments.\nSpecifically, we consider three systems, the triangulene, the extended\ntriangulene with rocket shape, both with an $S=1$ ground state, and a\ntriangulene dimer with $S=0$ on account of intermolecular exchange. We focus on\nthe consequences of hybridization of the nanographene zero-modes with a\nconducting substrate on the $dI/dV$ lineshapes associated with spin\nexcitations. The partially filled nanographene zero-modes coupled to the\nconduction electrons in the substrate constitute multi-orbital Anderson\nimpurity models that we solve in the one-crossing approximation which treats\nthe coupling to the substrate to infinite order. We find that the coupling to\nthe substrate leads to (i) renormalization of the spin flip excitation energies\nof the bare molecule, (ii) broadening of the spectral features and (iii) the\nemergence of zero bias Kondo peaks for the $S=1$ ground states. The calculated\nsubstrate induced shift of the spin excitation energies is found to be\nsignificantly larger than their broadening, which implies that this effect has\nto be considered when comparing experimental results and theory.\n"}
+{"abstract": "  This paper studies the performance of a model-free LQR based PID (i-LQR-PID)\ncontroller designed for tracking control problem of a 2-DoF laboratory\nhelicopter. The control problem addressed in 2-DoF helicopter system aims to\ntrack the desired pitch and yaw axes trajectories despite disturbed operating\nconditions. In addition to the unpredictable variations, the 2-DoF helicopter\ndynamic is highly nonlinear with having strong cross-couplings in their models\nas well as being open loop unstable system. Thus, we propose a model-free LQR\nbased PID control strategy in order to achieve better trajectory tracking\ncontrol objectives. Robustness tests are performed experimentally to show the\neffectiveness of the model-free control.\n"}
+{"abstract": "  We present a scalable post-processing algorithm for debiasing trained models,\nincluding deep neural networks (DNNs), which we prove to be near-optimal by\nbounding its excess Bayes risk. We empirically validate its advantages on\nstandard benchmark datasets across both classical algorithms as well as modern\nDNN architectures and demonstrate that it outperforms previous post-processing\nmethods while performing on par with in-processing. In addition, we show that\nthe proposed algorithm is particularly effective for models trained at scale\nwhere post-processing is a natural and practical choice.\n"}
+{"abstract": "  In this paper, we propose a hybrid technique for semantic question matching.\nIt uses our proposed two-layered taxonomy for English questions by augmenting\nstate-of-the-art deep learning models with question classes obtained from a\ndeep learning based question classifier. Experiments performed on three\nopen-domain datasets demonstrate the effectiveness of our proposed approach. We\nachieve state-of-the-art results on partial ordering question ranking (POQR)\nbenchmark dataset. Our empirical analysis shows that coupling standard\ndistributional features (provided by the question encoder) with knowledge from\ntaxonomy is more effective than either deep learning (DL) or taxonomy-based\nknowledge alone.\n"}
+{"abstract": "  Much of the structure of cosmological correlators is controlled by their\nsingularities, which in turn are fixed in terms of flat-space scattering\namplitudes. An important challenge is to interpolate between the singular\nlimits to determine the full correlators at arbitrary kinematics. This is\nparticularly relevant because the singularities of correlators are not directly\nobservable, but can only be accessed by analytic continuation. In this paper,\nwe study rational correlators, including those of gauge fields, gravitons, and\nthe inflaton, whose only singularities at tree level are poles and whose\nbehavior away from these poles is strongly constrained by unitarity and\nlocality. We describe how unitarity translates into a set of cutting rules that\nconsistent correlators must satisfy, and explain how this can be used to\nbootstrap correlators given information about their singularities. We also\nderive recursion relations that allow the iterative construction of more\ncomplicated correlators from simpler building blocks. In flat space, all energy\nsingularities are simple poles, so that the combination of unitarity\nconstraints and recursion relations provides an efficient way to bootstrap the\nfull correlators. In many cases, these flat-space correlators can then be\ntransformed into their more complex de Sitter counterparts. As an example of\nthis procedure, we derive the correlator associated to graviton Compton\nscattering in de Sitter space, though the methods are much more widely\napplicable.\n"}
+{"abstract": "  In this paper we explore audiovisual emotion recognition under noisy acoustic\nconditions with a focus on speech features. We attempt to answer the following\nresearch questions: (i) How does speech emotion recognition perform on noisy\ndata? and (ii) To what extend does a multimodal approach improve the accuracy\nand compensate for potential performance degradation at different noise levels?\nWe present an analytical investigation on two emotion datasets with\nsuperimposed noise at different signal-to-noise ratios, comparing three types\nof acoustic features. Visual features are incorporated with a hybrid fusion\napproach: The first neural network layers are separate modality-specific ones,\nfollowed by at least one shared layer before the final prediction. The results\nshow a significant performance decrease when a model trained on clean audio is\napplied to noisy data and that the addition of visual features alleviates this\neffect.\n"}
+{"abstract": "  Smartphones contain information that is more sensitive and personal than\nthose found on computers and laptops. With an increase in the versatility of\nsmartphone functionality, more data has become vulnerable and exposed to\nattackers. Successful mobile malware attacks could steal a user's location,\nphotos, or even banking information. Due to a lack of post-attack strategies\nfirms also risk going out of business due to data theft. Thus, there is a need\nbesides just detecting malware intrusion in smartphones but to also identify\nthe data that has been stolen to assess, aid in recovery and prevent future\nattacks. In this paper, we propose an accessible, non-intrusive machine\nlearning solution to not only detect malware intrusion but also identify the\ntype of data stolen for any app under supervision. We do this with Android\nusage data obtained by utilising publicly available data collection framework-\nSherLock. We test the performance of our architecture for multiple users on\nreal-world data collected using the same framework. Our architecture exhibits\nless than 9% inaccuracy in detecting malware and can classify with 83%\ncertainty on the type of data that is being stolen.\n"}
+{"abstract": "  A scenario of non-Hermitian bulk--boundary correspondence proposed for\none-dimensional topological insulators is adapted to a non-Hermitian Chern\ninsulator to examine its applicability to two-dimensional systems. This\nscenario employs bulk geometry under a modified periodic boundary condition and\nboundary geometry under an open boundary condition. The bulk geometry is used\nto define a topological number, whereas the boundary geometry is used to\nobserve the presence or absence of a topological boundary state. It is\ndemonstrated that the bulk--boundary correspondence holds in a two-dimensional\nChern insulator with gain/loss-type non-Hermiticity; a nontrivial Chern number\ncalculated in the bulk geometry is in one-to-one correspondence with the\npresence of a topological boundary state in the boundary geometry. This\napproach enables us to determine a phase diagram in the boundary geometry.\n"}
+{"abstract": "  In this paper we present the first deterministic polynomial time algorithm\nfor determining the existence of a Hamiltonian cycle and finding a Hamiltonian\ncycle in general graphs. Our algorithm can also resolve the Hamiltonian path\nproblem in the traceable graphs. The space complexity of our algorithm is\nO(n^4). The time complexity are theoretically O(n^5*d^2) on average and\nO(n^6*d^2) in the worst case respectively, where d is the maximum degree of\nvertex. With parallel computing, the space complexity can be improved to O(n^3)\nand the time complexity to O(n^3*d^2) on average and O(n^4*d^2) in the worst\ncase. We construct the corresponding path hologram transformed from the\noriginal graph and compute the path set, which is a collection of segment sets\nconsisting of all the vertices located on the same segment layer among all the\nlongest basic paths, of every vertex with dynamic programming. The path\nhologram is a multi-segment graph with the vertex <u, k> where u is a vertex\nand k is the segment layer of u in the path hologram. To ensure each path\nstored in the path set is legal and each segment set of the path set contains\nonly valid vertices, the key strategy of our method is the \"consecutive\"\ndeleting-replenishing operations recursively on the left/right action field of\na vertex, respectively. The greatest contribution of our method is the path set\nin which all the legal paths can be stored in O(n^2) space for a complete graph\nof order n. In fact, our algorithm can be directly applied to the original\ngraph. Besides, our algorithm can deal with the finite general graphs including\nundirected, directed, and mixed. As a result, the well-known problem HCP in NPC\ncan be now resolved practically in deterministic polynomial time for general\ngraphs in the worst case.\n"}
+{"abstract": "  The paper proves existence of renormalized stationary solutions for a dense\nclass of discrete velocity Boltzmann equations in the plane with given ingoing\nboundary values. The proof is based on the construction of a sequence of\napproximations with L1 compactness for the integrated collision frequency and\ngain term. Compactness is obtained using the Kolmogorov-Riesz theorem.\n"}
+{"abstract": "  Fine time-resolved analysis of matter - i.e. spectroscopy and photon\nscattering - in the linear response regime requires a fs-scale pulsed, high\nrepetition rate, fully coherent X-ray source. A seeded Free-Electron Laser\n(FEL) driven by a Super-Conducting Linac, generating $10^{8}$-$10^{10}$\ncoherent photons at 2-5 keV with abou 0.5 MHz of repetition rate, can address\nthis need. The seeding scheme proposed is the Echo-Enabled Harmonic Generation,\nalimented by a FEL Oscillator working at 13.6 nm with a cavity based on Mo-Si\nmirrors. The whole chain of the X-ray generation is here described by means of\nstart-to-end simulations. Comparisons with the Self Amplified Spontaneus\nEmission and a fresh-bunch harmonic cascade performed with similar electron\nbeams show the validity of this scheme.\n"}
+{"abstract": "  For sequential data, change points are moments of abrupt regime switches.\nSuch changes appear in different scenarios, including complex video\nsurveillance, and we need to detect them as fast as possible. Classic\napproaches for change point detection (CPD) perform poorly for semi-structured\nsequential data because of the absence of adequate data representation learning\nprocedure. We propose a principled loss function that approximates classic\nrigorous solutions but is differentiable and makes possible representation\nlearning. This loss function balances change detection delay and time to false\nalarm to provide a successful model for CPD. In experiments, we consider simple\nseries and more complex real-world image sequences and videos with change\npoints. For more complex problems, we show that we need more meaningful\nrepresentations tailored for the specificity of the CPD task. Taking this into\naccount, the proposed approach InDiD improves baseline results of CPD for\nvarious data types. For explosion detection, F1 score for our method is $0.54$\ncompared to baseline scores $0.46$ and $0.30$.\n"}
+{"abstract": "  We propose an asymptotic-preserving (AP), uniformly convergent numerical\nscheme for the relativistic collisional Drift-Kinetic Equation (rDKE) to\nsimulate runaway electrons in axisymmetric toroidal magnetic field geometries\ntypical of tokamak devices. The approach is derived from an exact Green's\nfunction solution with numerical approximations of quantifiable impact, and\nresults in a simple, two-step operator-split algorithm, consisting of a\ncollisional Eulerian step, and a Lagrangian orbit-integration step with\nanalytically prescribed kernels. The AP character of the approach is\ndemonstrated by analysis of the dominant numerical errors, as well as by\nnumerical experiments. We demonstrate the ability of the algorithm to provide\naccurate answers regardless of plasma collisionality on a circular axisymmetric\ntokamak geometry.\n"}
+{"abstract": "  We study the fundamental problem of counting the number of nodes in a sparse\nnetwork (of unknown size) under the presence of a large number of Byzantine\nnodes. We assume the full information model where the Byzantine nodes have\ncomplete knowledge about the entire state of the network at every round\n(including random choices made by all the nodes), have unbounded computational\npower, and can deviate arbitrarily from the protocol.\n  Our main contribution is a randomized distributed algorithm that estimates\nthe size of a network under the presence of a large number of Byzantine nodes.\nIn particular, our algorithm estimates the size of a sparse, \"small-world\",\nexpander network with up to $O(n^{1 - \\delta})$ Byzantine nodes, where $n$ is\nthe (unknown) network size and $\\delta$ can be be any arbitrarily small (but\nfixed) positive constant. Our algorithm outputs a (fixed) constant factor\nestimate of $\\log(n)$ with high probability; the correct estimate of the\nnetwork size will be known to a large fraction ($(1 - \\epsilon)$-fraction, for\nany fixed positive constant $\\epsilon$) of the honest nodes. Our algorithm is\nfully distributed, lightweight, and simple to implement, runs in $O(\\log^3{n})$\nrounds, and requires nodes to send and receive messages of only small-sized\nmessages per round; any node's local computation cost per round is also small.\n"}
+{"abstract": "  Low dose computed tomography is a mainstream for clinical applications.\nHow-ever, compared to normal dose CT, in the low dose CT (LDCT) images, there\nare stronger noise and more artifacts which are obstacles for practical\napplications. In the last few years, convolution-based end-to-end deep learning\nmethods have been widely used for LDCT image denoising. Recently, transformer\nhas shown superior performance over convolution with more feature interactions.\nYet its ap-plications in LDCT denoising have not been fully cultivated. Here,\nwe propose a convolution-free T2T vision transformer-based Encoder-decoder\nDilation net-work (TED-net) to enrich the family of LDCT denoising algorithms.\nThe model is free of convolution blocks and consists of a symmetric\nencoder-decoder block with sole transformer. Our model is evaluated on the\nAAPM-Mayo clinic LDCT Grand Challenge dataset, and results show outperformance\nover the state-of-the-art denoising methods.\n"}
+{"abstract": "  A wave view of the universe is proposed in which each natural phenomenon is\nequipped with its own unique natural viewing lens. A self-sameness modeling\nprinciple and its systematic application in Fourier-Laplace transform space is\nproposed as a novel, universal discrete modeling paradigm for\nadvection-diffusion-reaction equations (ADREs) across non-integer derivatives,\ntime scales, and wave spectral signatures. Its implementation is a novel exact\nspectral derivative discretization finite difference method (ESDDFD), a way for\ncrafting wave viewing lenses by obtaining discrete wave models from ADRE\nmodels. The template for building these lenses come in the form of natural\nderivative representations obtained from the wave signature probability\ndistribution function and its harmonic oscillation in FL transform space; use\nof the ESDDFD method in the discrete numerical modeling of wave equations\nrequires no a-priori theory of any mathematical derivative. A major\nmathematical consequence of this viewpoint is that all notions of the\nmathematical integer or non-integer derivatives have representation as limits\nof such natural derivative representations; this and other consequences are\ndiscussed and a discretization of a simple integer derivative\ndiffusion-reaction equation is presented to illustrate the method. The\nresulting view lenses, in the form of ESDDFD models, work well in detecting\nboth local and non-local Debye or Kohlrausch-Williams-Watts exponential\npatterns; only Brownian motion and sub-diffusion are discussed in the present\narticle.\n"}
+{"abstract": "  Based on a nonlocal Laplacian operator, a novel edge detection method of the\ngrayscale image is proposed in this paper. This operator utilizes the\ninformation of neighbor pixels for a given pixel to obtain effective and\ndelicate edge detection. The nonlocal edge detection method is used as an\ninitialization for solving the Allen-Cahn equation to achieve two-phase\nsegmentation of the grayscale image. Efficient exponential time differencing\n(ETD) solvers are employed in the time integration, and finite difference\nmethod is adopted in space discretization. The maximum bound principle and\nenergy stability of the proposed numerical schemes are proved. The capability\nof our segmentation method has been verified in numerical experiments for\ndifferent types of grayscale images.\n"}
+{"abstract": "  Steam power turbine-based power plant approximately contributes 90% of the\ntotal electricity produced in the United States. Mainly steam turbine consists\nof multiple types of turbine, boiler, attemperator, reheater, etc. Power is\nproduced through the steam with high pressure and temperature that is conducted\nby the turbines. The total power generation of the power plant is highly\nnonlinear considering all these elements in the model. We perform a predictive\nmodeling approach to detect the power generation from these turbines by the\nGaussian process (GP) model. As there are multiple interconnected turbines, we\nconsider a multivariate Gaussian process (MGP) modeling to predict the power\ngeneration from these turbines which can capture the cross-correlations between\nthe turbines. Also, the sensitivity analysis of the input parameters is\nconstructed for each turbine to find out the most important parameters.\n"}
+{"abstract": "  LB-1 has variously been proposed as either an X-ray dim B-type star plus\nblack hole (B+BH) binary, or a Be star plus an inflated stripped star (Be+Bstr)\nbinary. The Space Telescope Imaging Spectrograph (STIS) on board HST was used\nto obtain a flux-calibrated spectrum that is compared with non-LTE spectral\nenergy distributions (SED) and line profiles for the proposed models. The\nHubble data, together with the Gaia EDR3 parallax, provide tight constraints on\nthe properties and stellar luminosities of the system. In the case of the\nBe+Bstr model we adopt the published flux ratio for the Be and Bstr stars,\nre-determine the T$_{eff}$ of the Bstr using the silicon ionization balance,\nand infer Teff for the Be star from the fit to the SED. We derive stellar\nparameters consistent with previous results, but with greater precision enabled\nby the Hubble SED. While the Be+Bstr model is a better fit to the HeI lines and\ncores of the Balmer lines in the optical, the B+BH model provides a better fit\nto the Si iv resonance lines in the UV. The analysis also implies that the Bstr\nstar has roughly twice solar silicon abundance, difficult to reconcile with a\nstripped star origin. The Be star on the other hand has a rather low\nluminosity, and a spectroscopic mass inconsistent with its possible dynamical\nmass. The fit to the UV can be significantly improved by reducing the T$_{eff}$\nand radius of the Be star, though at the expense of leading to a different mass\nratio. In the B+BH model, the single B-type spectrum is a good match to the UV\nspectrum. Adopting a mass ratio of 5.1$\\pm$0.1 (Liu et al. 2020) implies a BH\nmass of $\\sim$21$^{+9}_{-8}M_{\\odot}$.\n"}
+{"abstract": "  When a rising bubble in a Newtonian liquid reaches the liquid-air interface,\nit can burst, leading to the formation of capillary waves and a jet on the\nsurface. Here, we numerically study this phenomenon in a yield stress fluid. We\nshow how viscoplasticity controls the fate of these capillary waves and their\ninteraction at the bottom of the cavity. Unlike Newtonian liquids, the free\nsurface converges to a non-flat final equilibrium shape once the driving\nstresses inside the pool fall below the yield stress. Details of the dynamics,\nincluding the flow's energy budgets, are discussed. The work culminates in a\nregime map with four main regimes with different characteristic behaviours.\n"}
+{"abstract": "  Telecom industry is significantly evolving all over the globe than ever.\nMobile users number is increasing remarkably. Telecom operators are investing\nto get more users connected and to improve user experience, however, they are\nfacing various challenges. Decrease of main revenue streams of voice calls, SMS\n(Short Message Service) and LDC (Long distance calls) with a significant\nincrease in data traffic. In contrary, with free cost, OTT (Over the top)\nproviders such as WhatsApp and Facebook communication services rendered over\nnetworks that built and owned by MNOs. Recently, OTT services gradually\nsubstituting the traditional MNOs` services and became ubiquitous with the help\nof the underlying data services provided by MNOs. The OTTs` services massive\npenetration into telecom industry is driving the MNOs to reconsider their\nstrategies and revenue sources.\n"}
+{"abstract": "  The Boolean matrix factorization problem consists in approximating a matrix\nby the Boolean product of two smaller Boolean matrices. To obtain optimal\nsolutions when the matrices to be factorized are small, we propose SAT and\nMaxSAT encoding; however, when the matrices to be factorized are large, we\npropose a heuristic based on the search for maximal biclique edge cover. We\nexperimentally demonstrate that our approaches allow a better factorization\nthan existing approaches while keeping reasonable computation times. Our\nmethods also allow the handling of incomplete matrices with missing entries.\n"}
+{"abstract": "  Automatic differentiation is a set of techniques to efficiently and\naccurately compute the derivative of a function represented by a computer\nprogram. Existing C++ libraries for automatic differentiation (e.g. Adept, Stan\nMath Library), however, exhibit large memory consumptions and runtime\nperformance issues. This paper introduces FastAD, a new C++ template library\nfor automatic differentiation, that overcomes all of these challenges in\nexisting libraries by using vectorization, simpler memory management using a\nfully expression-template-based design, and other compile-time optimizations to\nremove some run-time overhead. Benchmarks show that FastAD performs 2-10 times\nfaster than Adept and 2-19 times faster than Stan across various test cases\nincluding a few real-world examples.\n"}
+{"abstract": "  High degree of valley polarization of optically active excitons in\ntransition-metal dichalcogenide monolayers (TMD-MLs) is vital in valley-based\nphotonic applications but known to be likely spoiled by the intrinsic\nelectron-hole exchange interactions. In this study, we present a theoretical\ninvestigation of the valley and optical properties of finite-momentum dark\nexcitons in WSe$_2$-MLs by solving the density-functional-theory(DFT)-based\nBethe-Salpeter equation (BSE) under the guidance of symmetry analysis. %We\nreveal that, in general, finite-momentum excitons are actually well immune from\nthe exchange-induced valley depolarization, except for those with specific\nexciton momenta coincident with the $3\\sigma_v$ and $3C_2'$ symmetries in the\n$D_{3h}$ point group of TMD-MLs. We reveal that, unlike the bright exciton\ninevitably subjected to electron-hole exchange interaction, inter-valley\nfinite-momentum dark excitons in WSe$_2$-MLs are well immune from the\nexchange-induced valley depolarization and inherently highly valley-polarized\nunder the enforcement of the crystal symmetries. More importantly, the superior\nvalley polarizations of the inter-valley dark excitons in WSe$_2$-MLs are shown\nalmost fully transferable to the optical polarization in the phonon-assisted\nphoto-luminescences because of the native suppression of exchange-induced\ndepolarization in the second-order optical processes. The analysis of\nphonon-assisted photo-luminescences accounts for the recently observed\nbrightness, high degree of optical polarization and long lifetime of the\ninter-valley dark exciton states in tungsten-based TMD-MLs.\n"}
+{"abstract": "  Scaled physical modeling is an important means to understand the behavior of\nfluids in nature. However, a common source of errors is conflicting similarity\ncriteria. Here, we present using hypergravity to improve the scaling similarity\nof gravity-dominated fluid convection, e.g. natural convection and multi-phase\nflow. We demonstrate the validity of the approach by investigating water-brine\nbuoyant jet experiments conducted under hypergravity created by a centrifuge.\nResults show that the scaling similarity increases with the gravitational\nacceleration. In particular, the model best represents the prototype under N3g\nwith a spatial scale of 1/N and a time scale of 1/N2 by simultaneously\nsatisfying the Froude and Reynolds criteria. The significance of centrifuge\nradius and fluid velocity in determining the accuracy of the scaled model is\ndiscussed in the light of Coriolis force and turbulence. This study\ndemonstrates a new direction for the physical modeling of fluids subject to\ngravity with broad application prospects.\n"}
+{"abstract": "  We study a class of polaron-type Hamiltonians with sufficiently regular form\nfactor in the interaction term. We investigate the strong-coupling limit of the\nmodel, and prove suitable bounds on the ground state energy as a function of\nthe total momentum of the system. These bounds agree with the semiclassical\napproximation to leading order. The latter corresponds here to the situation\nwhen the particle undergoes harmonic motion in a potential well whose frequency\nis determined by the corresponding Pekar functional. We show that for all such\nmodels the effective mass diverges in the strong coupling limit, in all spatial\ndimensions. Moreover, for the case when the phonon dispersion relation grows at\nleast linearly with momentum, the bounds result in an asymptotic formula for\nthe effective mass quotient, a quantity generalizing the usual notion of the\neffective mass. This asymptotic form agrees with the semiclassical\nLandau--Pekar formula and can be regarded as the first rigorous confirmation,\nin a slightly weaker sense than usually considered, of the validity of the\nsemiclassical formula for the effective mass.\n"}
+{"abstract": "  As current- and next-generation astronomical instruments come online, they\nwill generate an unprecedented deluge of data. Analyzing these data in real\ntime presents unique conceptual and computational challenges, and their\nlong-term storage and archiving is scientifically essential for generating\nreliable, reproducible results. We present here the real-time processing (RTP)\nsystem for the Hydrogen Epoch of Reionization Array (HERA), a radio\ninterferometer endeavoring to provide the first detection of the highly\nredshifted 21 cm signal from Cosmic Dawn and the Epoch of Reionization by an\ninterferometer. The RTP system consists of analysis routines run on raw data\nshortly after they are acquired, such as calibration and detection of\nradio-frequency interference (RFI) events. RTP works closely with the\nLibrarian, the HERA data storage and transfer manager which automatically\ningests data and transfers copies to other clusters for post-processing\nanalysis. Both the RTP system and the Librarian are public and open source\nsoftware, which allows for them to be modified for use in other scientific\ncollaborations. When fully constructed, HERA is projected to generate over 50\nterabytes (TB) of data each night, and the RTP system enables the successful\nscientific analysis of these data.\n"}
+{"abstract": "  We estimate the effect of Medicaid expansion on the adult uninsurance rate in\nstates that did not expand Medicaid in 2014. Using data from the American\nCommunity Survey (ACS), we estimate this effect - the treatment effect on the\ncontrols (ETC) - by re-weighting expansion regions to approximately balance the\ncovariates from non-expansion regions using an extension of the stable\nbalancing weights objective function (Zubizaretta (2015)). We contribute to the\nbalancing weights literature by accounting for hierarchical data structure and\ncovariate measurement error when calculating our weights, and to the synthetic\ncontrols literature (see, e.g. Abadie et al 2010) by outlining a set of\nassumptions that identifies the ETC using time-series cross-sectional data. We\nestimate that Medicaid expansion would have changed the uninsurance rate by\n-2.33 (-3.49, -1.16) percentage points. These results are smaller in absolute\nmagnitude than existing estimates of the treatment effect on the treated (ETT),\nthough may not be directly comparable due to the study design, target\npopulation, and level of analysis. Regardless, we caution against making\ninferences about the ETC using estimates of the ETT, and emphasize the need to\ndirectly estimate the appropriate counterfactual when they are the quantity of\ninterest.\n"}
+{"abstract": "  We study {\\em sign-restricted matrices} (SRMs), a class of rectangular $(0,\n\\pm 1)$-matrices generalizing the alternating sign matrices (ASMs). In an SRM\neach partial column sum, starting from row 1, equals 0 or 1, and each partial\nrow sum, starting from column 1, is nonnegative. We determine the maximum\nnumber of nonzeros in SRMs and characterize the possible row and column sum\nvectors. Moreover, a number of results on interchange operations are shown,\nboth for SRMs and, more generally, for $(0, \\pm 1)$-matrices. The Bruhat order\non ASMs can be extended to SRMs with the result a distributive lattice. Also,\nwe study polytopes associated with SRMs and some relates decompositions.\n"}
+{"abstract": "  We compute the flux-induced F-term potential in 4d F-theory compactifications\nat large complex structure. In this regime, each complex structure field splits\nas an axionic field plus its saxionic partner, and the classical F-term\npotential takes the form $V = Z^{AB} \\rho_A\\rho_B$ up to\nexponentially-suppressed terms, with $\\rho$ depending on the fluxes and axions\nand $Z$ on the saxions. We provide explicit, general expressions for $Z$ and\n$\\rho$, and from there analyse the set of flux vacua, for an arbitrary number\nof fields. We identify two families of vacua with all complex structure fields\nfixed and a flux contribution to the tadpole $N_{\\rm flux}$ which is bounded.\nIn the first and most generic one, the saxion vevs are bounded from above by a\npower of $N_{\\rm flux}$. In the second their vevs may be unbounded and $N_{\\rm\nflux}$ is a product of two arbitrary integers, unlike what is claimed by the\nTadpole Conjecture. We specialise to type IIB orientifolds, where both families\nof vacua are present, and link our analysis with several results in the\nliterature. We finally illustrate our findings with several examples.\n"}
+{"abstract": "  This is a tutorial and survey paper on unification of spectral dimensionality\nreduction methods, kernel learning by Semidefinite Programming (SDP), Maximum\nVariance Unfolding (MVU) or Semidefinite Embedding (SDE), and its variants. We\nfirst explain how the spectral dimensionality reduction methods can be unified\nas kernel Principal Component Analysis (PCA) with different kernels. This\nunification can be interpreted as eigenfunction learning or representation of\nkernel in terms of distance matrix. Then, since the spectral methods are\nunified as kernel PCA, we say let us learn the best kernel for unfolding the\nmanifold of data to its maximum variance. We first briefly introduce kernel\nlearning by SDP for the transduction task. Then, we explain MVU in detail.\nVarious versions of supervised MVU using nearest neighbors graph, by class-wise\nunfolding, by Fisher criterion, and by colored MVU are explained. We also\nexplain out-of-sample extension of MVU using eigenfunctions and kernel mapping.\nFinally, we introduce other variants of MVU including action respecting\nembedding, relaxed MVU, and landmark MVU for big data.\n"}
+{"abstract": "  Placeholder translation systems enable the users to specify how a specific\nphrase is translated in the output sentence. The system is trained to output\nspecial placeholder tokens, and the user-specified term is injected into the\noutput through the context-free replacement of the placeholder token. However,\nthis approach could result in ungrammatical sentences because it is often the\ncase that the specified term needs to be inflected according to the context of\nthe output, which is unknown before the translation. To address this problem,\nwe propose a novel method of placeholder translation that can inflect specified\nterms according to the grammatical construction of the output sentence. We\nextend the sequence-to-sequence architecture with a character-level decoder\nthat takes the lemma of a user-specified term and the words generated from the\nword-level decoder to output the correct inflected form of the lemma. We\nevaluate our approach with a Japanese-to-English translation task in the\nscientific writing domain, and show that our model can incorporate specified\nterms in the correct form more successfully than other comparable models.\n"}
+{"abstract": "  CUDA (formerly an abbreviation of Compute Unified Device Architecture) is a\nparallel computing platform and API model created by Nvidia allowing software\ndevelopers to use a CUDA-enabled graphics processing unit (GPU) for general\npurpose processing. This 90-pages tutorial introduces the CUDA concepts in an\neasy-to-grasp and interactive way with ready-to-run code samples tested on\nWindows and Linux. Starting from scratch, a complete stand-alone GPU tool is\nimplemented which automatically performs a ciphertext-only attack on\nciphertexts encrypted by monoalphabetic substitution and columnar\ntransposition. Throughout this process, you will learn how to architect the\ntool, what optimizations could significantly accelerate the routines, why the\nchoice of an adequate metaheuristic is critical, and how to draw sketches to\nenlighten the design process. This tutorial will be incorporated in the\nCrypTool book as chapter 13.\n"}
+{"abstract": "  We demonstrate phase locking of two ultraviolet laser sources by modulating a\nfundamental infrared laser with 4th-order sidebands using an electro-optic\nmodulator and phase locking of one sideband to a second fundamental infrared\nlaser. Subsequent sum frequency generation and second harmonic generation\nsuccessfully translates the frequency offset to the ultraviolet domain. The\nphase lock at 139 GHz is confirmed through stimulated Raman transitions for\nthermometry of $^9$Be$^+$ ions confined in a cryogenic Penning trap. This\ntechnique might be used for sideband cooling of single $^9$Be$^+$ ions as well\nas sympathetic cooling schemes and quantum logic based measurements in Penning\ntraps in the future.\n"}
+{"abstract": "  Motivated by applications in machine learning and archival data storage, we\nintroduce function-correcting codes, a new class of codes designed to protect a\nfunction evaluation of the data against errors. We show that\nfunction-correcting codes are equivalent to irregular-distance codes, i.e.,\ncodes that obey some given distance requirement between each pair of codewords.\nUsing these connections, we study irregular-distance codes and derive general\nupper and lower bounds on their optimal redundancy. Since these bounds heavily\ndepend on the specific function, we provide simplified, suboptimal bounds that\nare easier to evaluate. We further employ our general results to specific\nfunctions of interest and compare our results to standard error-correcting\ncodes which protect the whole data.\n"}
+{"abstract": "  In this work we demonstrate the use of neural networks for rapid extraction\nof signal parameters of discretely sampled signals. In particular, we use dense\nautoencoder networks to extract the parameters of interest from exponentially\ndecaying signals and decaying oscillations. By using a three-stage training\nmethod and careful choice of the neural network size, we are able to retrieve\nthe relevant signal parameters directly from the latent space of the\nautoencoder network at significantly improved rates compared to traditional\nalgorithmic signal-analysis approaches. We show that the achievable precision\nand accuracy of this method of analysis is similar to conventional\nalgorithm-based signal analysis methods, by demonstrating that the extracted\nsignal parameters are approaching their fundamental parameter estimation limit\nas provided by the Cram\\'er-Rao bound. Furthermore, we demonstrate that\nautoencoder networks are able to achieve signal analysis, and, hence, parameter\nextraction, at rates of 75 kHz, orders-of-magnitude faster than conventional\ntechniques with similar precision. Finally, we explore the limitations of our\napproach, demonstrating that analysis rates of $>$200 kHz are feasible with\nfurther optimization of the transfer rate between the data-acquisition system\nand data-analysis system.\n"}
+{"abstract": "  When a liquid film lies on a non-wettable substrate, the configuration is\nunstable and the film then retracts from a solid substrate to form droplets.\nThis phenomenon, known as dewetting, commonly leads to undesirable\nmorphological changes. Nevertheless, recent works have demonstrated the\npossibility to harness dewetting by employing templated substrates with a\ndegree of precision on par with advanced lithographic processes for\nhigh-performance nanophotonic applications. Since resonant behavior is highly\nsensitive to geometrical changes, predicting quantitatively dewetting dynamics\nis of high interest. In this work, we develop a continuum model that predicts\nthe evolution of a thin film on a patterned substrate, from the initial reflow\nto the nucleation and growth of holes. We provide an operative framework based\non macroscopic measurements to model the intermolecular interactions at the\norigin of the dewetting process, involving length scales that span from\nsub-nanometer to micron range. A comparison of experimental and simulated\nresults shows that the model can accurately predict the final distributions,\nthereby offering novel predictive tools to tailor the optical response of\ndewetted nanostructures.\n"}
+{"abstract": "  Modeling and simulating a power distribution network (PDN) for printed\ncircuit boards (PCBs) with irregular board shapes and multi-layer stackup is\ncomputationally inefficient using full-wave simulations. This paper presents a\nnew concept of using deep learning for PDN impedance prediction. A boundary\nelement method (BEM) is applied to efficiently calculate the impedance for\narbitrary board shape and stackup. Then over one million boards with different\nshapes, stackup, IC location, and decap placement are randomly generated to\ntrain a deep neural network (DNN). The trained DNN can predict the impedance\naccurately for new board configurations that have not been used for training.\nThe consumed time using the trained DNN is only 0.1 seconds, which is over 100\ntimes faster than the BEM method and 5000 times faster than full-wave\nsimulations.\n"}
+{"abstract": "  This paper presents a end-to-end framework for the F0 transformation in the\ncontext of expressive voice conversion. A single neural network is proposed, in\nwhich a first module is used to learn F0 representation over different temporal\nscales and a second adversarial module is used to learn the transformation from\none emotion to another. The first module is composed of a convolution layer\nwith wavelet kernels so that the various temporal scales of F0 variations can\nbe efficiently encoded. The single decomposition/transformation network allows\nto learn in a end-to-end manner the F0 decomposition that are optimal with\nrespect to the transformation, directly from the raw F0 signal.\n"}
+{"abstract": "  One common approach to computing the magnetic field produced by a filamentary\ncurrent-carrying coil is to approximate the coil as a series of straight\nsegments. The Biot-Savart field from each straight segment is known\nanalytically. However, if the endpoints of the straight segments are chosen to\nlie on the coil, then the accuracy of the Biot-Savart computation is generally\nonly second-order in the number of endpoints. We propose a simple modification:\nshift each endpoint off the coil in the outwards normal direction by an amount\nproportional to the local curvature. With this modification, the Biot-Savart\naccuracy increases to fourth order and the numerical error is dramatically\nreduced for a given number of discretization points.\n"}
+{"abstract": "  Stochastic PDE eigenvalue problems often arise in the field of uncertainty\nquantification, whereby one seeks to quantify the uncertainty in an eigenvalue,\nor its eigenfunction. In this paper we present an efficient multilevel\nquasi-Monte Carlo (MLQMC) algorithm for computing the expectation of the\nsmallest eigenvalue of an elliptic eigenvalue problem with stochastic\ncoefficients. Each sample evaluation requires the solution of a PDE eigenvalue\nproblem, and so tackling this problem in practice is notoriously\ncomputationally difficult. We speed up the approximation of this expectation in\nfour ways: 1) we use a multilevel variance reduction scheme to spread the work\nover a hierarchy of FE meshes and truncation dimensions; 2) we use QMC methods\nto efficiently compute the expectations on each level; 3) we exploit the\nsmoothness in parameter space and reuse the eigenvector from a nearby QMC point\nto reduce the number of iterations of the eigensolver; and 4) we utilise a\ntwo-grid discretisation scheme to obtain the eigenvalue on the fine mesh with a\nsingle linear solve. The full error analysis of a basic MLQMC algorithm is\ngiven in the companion paper [Gilbert and Scheichl, 2021], and so in this paper\nwe focus on how to further improve the efficiency and provide theoretical\njustification of the enhancement strategies 3) and 4). Numerical results are\npresented that show the efficiency of our algorithm, and also show that the\nfour strategies we employ are complementary.\n"}
+{"abstract": "  Neuromorphic computing systems are embracing memristors to implement high\ndensity and low power synaptic storage as crossbar arrays in hardware. These\nsystems are energy efficient in executing Spiking Neural Networks (SNNs). We\nobserve that long bitlines and wordlines in a memristive crossbar are a major\nsource of parasitic voltage drops, which create current asymmetry. Through\ncircuit simulations, we show the significant endurance variation that results\nfrom this asymmetry. Therefore, if the critical memristors (ones with lower\nendurance) are overutilized, they may lead to a reduction of the crossbar's\nlifetime. We propose eSpine, a novel technique to improve lifetime by\nincorporating the endurance variation within each crossbar in mapping machine\nlearning workloads, ensuring that synapses with higher activation are always\nimplemented on memristors with higher endurance, and vice versa. eSpine works\nin two steps. First, it uses the Kernighan-Lin Graph Partitioning algorithm to\npartition a workload into clusters of neurons and synapses, where each cluster\ncan fit in a crossbar. Second, it uses an instance of Particle Swarm\nOptimization (PSO) to map clusters to tiles, where the placement of synapses of\na cluster to memristors of a crossbar is performed by analyzing their\nactivation within the workload. We evaluate eSpine for a state-of-the-art\nneuromorphic hardware model with phase-change memory (PCM)-based memristors.\nUsing 10 SNN workloads, we demonstrate a significant improvement in the\neffective lifetime.\n"}
+{"abstract": "  Within the past decade, the rise of applications based on artificial\nintelligence (AI) in general and machine learning (ML) in specific has led to\nmany significant contributions within different domains. The applications range\nfrom robotics over medical diagnoses up to autonomous driving. However, nearly\nall applications rely on trained data. In case this data consists of 3D images,\nit is of utmost importance that the labeling is as accurate as possible to\nensure high-quality outcomes of the ML models. Labeling in the 3D space is\nmostly manual work performed by expert workers, where they draw 3D bounding\nboxes around target objects the ML model should later automatically identify,\ne.g., pedestrians for autonomous driving or cancer cells within radiography.\n  While a small range of recent 3D labeling tools exist, they all share three\nmajor shortcomings: (i) they are specified for autonomous driving applications,\n(ii) they lack convenience and comfort functions, and (iii) they have high\ndependencies and little flexibility in data format. Therefore, we propose a\nnovel labeling tool for 3D object detection in point clouds to address these\nshortcomings.\n"}
+{"abstract": "  Probabilistic message-passing algorithms are developed for routing\ntransmissions in multi-wavelength optical communication networks, under node\nand edge-disjoint routing constraints and for various objective functions.\nGlobal routing optimization is a hard computational task on its own but is made\nmuch more difficult under the node/edge-disjoint constraints and in the\npresence of multiple wavelengths, a problem which dominates routing efficiency\nin real optical communication networks that carry most of the world's Internet\ntraffic. The scalable principled method we have developed is exact on trees but\nprovides good approximate solutions on locally tree-like graphs. It\naccommodates a variety of objective functions that correspond to low latency,\nload balancing and consolidation of routes, and can be easily extended to\ninclude heterogeneous signal-to-noise values on edges and a restriction on the\navailable wavelengths per edge. It can be used for routing and managing\ntransmissions on existing topologies as well as for designing and modifying\noptical communication networks. Additionally, it provides the tool for settling\nan open and much debated question on the merit of wavelength-switching nodes\nand the added capabilities they provide. The methods have been tested on\ngenerated networks such as random-regular, Erd\\H{o}s R\\'{e}nyi and power-law\ngraphs, as well as on the UK and US optical communication networks. They show\nexcellent performance with respect to existing methodology on small networks\nand have been scaled up to network sizes that are beyond the reach of most\nexisting algorithms.\n"}
+{"abstract": "  We present a method of training character manipulation of amorphous materials\nsuch as those often used in cooking. Common examples of amorphous materials\ninclude granular materials (salt, uncooked rice), fluids (honey), and\nvisco-plastic materials (sticky rice, softened butter). A typical task is to\nspread a given material out across a flat surface using a tool such as a\nscraper or knife. We use reinforcement learning to train our controllers to\nmanipulate materials in various ways. The training is performed in a physics\nsimulator that uses position-based dynamics of particles to simulate the\nmaterials to be manipulated. The neural network control policy is given\nobservations of the material (e.g. a low-resolution density map), and the\npolicy outputs actions such as rotating and translating the knife. We\ndemonstrate policies that have been successfully trained to carry out the\nfollowing tasks: spreading, gathering, and flipping. We produce a final\nanimation by using inverse kinematics to guide a character's arm and hand to\nmatch the motion of the manipulation tool such as a knife or a frying pan.\n"}
+{"abstract": "  Worker monitoring and protection in collaborative robot (cobots) industrial\nenvironments requires advanced sensing capabilities and flexible solutions to\nmonitor the movements of the operator in close proximity of moving robots.\nCollaborative robotics is an active research area where Internet of Things\n(IoT) and novel sensing technologies are expected to play a critical role.\nConsidering that no single technology can currently solve the problem of\ncontinuous worker monitoring, the paper targets the development of an IoT\nmultisensor data fusion (MDF) platform. It is based on an edge-cloud\narchitecture that supports the combination and transformation of multiple\nsensing technologies to enable the passive and anonymous detection of workers.\nMultidimensional data acquisition from different IoT sources, signal\npre-processing, feature extraction, data distribution and fusion, along with\nmachine learning (ML) and computing methods are described. The proposed IoT\nplatform also comprises a practical solution for data fusion and analytics. It\nis able to perform opportunistic and real-time perception of workers by fusing\nand analyzing radio signals obtained from several interconnected IoT\ncomponents, namely a multi-antenna WiFi installation (2.4-5 GHz), a sub-THz\nimaging camera (100 GHz), a network of radars (122 GHz) and infrared sensors\n(8-13 {\\mu}m). The performance of the proposed IoT platform is validated\nthrough real use case scenarios inside a pilot industrial plant in which\nprotective human--robot distance must be guaranteed considering latency and\ndetection uncertainties.\n"}
+{"abstract": "  A ring is said to satisfy the $2$-nil-sum property if every non central-unit\nis the sum of two nilpotents. We prove that a ring satisfies the $2$-nil-sum\nproperty iff it is either a simple ring with the $2$-nil-sum property or a\ncommutative local ring with nil Jacobson radical, and we provide an example of\na simple ring with the $2$-nil-sum property that is not commutative. Moreover,\na simple right Goldie ring has the $2$-nil-sum property iff it is a field.\n"}
+{"abstract": "  One of the main challenges of reactive synthesis, an automated procedure to\nobtain a correct-by-construction reactive system, is to deal with unrealizable\nspecifications. One means to deal with unrealizability, in the context of\nGR(1), an expressive assume-guarantee fragment of LTL that enables efficient\nsynthesis, is the computation of an unrealizable core, which can be viewed as a\nfault-localization approach. Existing solutions, however, are computationally\ncostly, are limited to computing a single core, and do not correctly support\nspecifications with constructs beyond pure GR(1) elements.\n  In this work we address these limitations. First, we present QuickCore, a\nnovel algorithm that accelerates unrealizable core computations by relying on\nthe monotonicity of unrealizability, on an incremental computation, and on\nadditional properties of GR(1) specifications. Second, we present Punch, a\nnovel algorithm to efficiently compute all unrealizable cores of a\nspecification. Finally, we present means to correctly handle specifications\nthat include higher-level constructs beyond pure GR(1) elements.\n  We implemented our ideas on top of Spectra, an open-source language and\nsynthesis environment. Our evaluation over benchmarks from the literature shows\nthat QuickCore is in most cases faster than previous algorithms, and that its\nrelative advantage grows with scale. Moreover, we found that most\nspecifications include more than one core, and that Punch finds all the cores\nsignificantly faster than a competing naive algorithm.\n"}
+{"abstract": "  We present a method to linearize, without approximation, a specific class of\neigenvalue problems with eigenvector nonlinearities (NEPv), where the\nnonlinearities are expressed by scalar functions that are defined by a quotient\nof linear functions of the eigenvector. The exact linearization relies on an\nequivalent multiparameter problem (MEP) that contains the exact solutions of\nthe NEPv. Due to the characterization of MEPs in terms of a generalized\neigenvalue problem this provides a direct way to compute all NEPv solutions for\nsmall problems, and it opens up the possibility to develop locally convergent\niterative methods for larger problems. Moreover, the linear formulation allows\nus to easily determine the number of solutions of the NEPv. We propose two\nnumerical schemes that exploit the structure of the linearization: inverse\niteration and residual inverse iteration. We show how symmetry in the MEP can\nbe used to improve reliability and reduce computational cost of both methods.\nTwo numerical examples verify the theoretical results and a third example shows\nthe potential of a hybrid scheme that is based on a combination of the two\nproposed methods.\n"}
+{"abstract": "  In this paper, we derive the action for N = 3 conformal supergravity in four\nspace-time dimensions. We construct a density formula for N = 3 conformal\nsupergravity based on the superform action principle. Finally, we embed the N =\n3 Weyl multiplet in the density formula to obtain the invariant action for N =\n3 conformal supergravity. We will show that there are two inequivalent\nembeddings by changing a particular coefficient from real to imaginary. They\nlead to invariant actions, which will either be the supersymmetrization of the\nWeyl square term or the Pontryagin density in the eventuality of gauge fixing\nto Poincar\\'e supergravity. We also discuss consistent truncation of N = 4 Weyl\nmultiplet to N = 3 Weyl multiplet and use it for a robust check of our results\nusing the earlier known results in N = 4 conformal supergravity.\n"}
+{"abstract": "  Power grid primary frequency response will be significantly impaired by\nPhotovoltaic (PV) penetration increase because of the decrease in inertia and\ngovernor response. PV inertia and governor emulation requires reserving PV\noutput and leads to solar energy waste. This paper exploits current grid\nresources and explores energy storage for primary frequency response under high\nPV penetration at the interconnection level. Based on the actual models of the\nU.S. Eastern Interconnection grid and the Texas grid, effects of multiple\nfactors associated with primary frequency response, including the governor\nratio, governor deadband, droop rate, and fast load response, are assessed\nunder high PV penetration scenarios. In addition, performance of batteries and\nsupercapacitors using different control strategies is studied in the two\ninterconnections. The paper quantifies the potential of various resources to\nimprove interconnection-level primary frequency response under high PV\npenetration without curtailing solar output.\n"}
+{"abstract": "  Model inversion, whose goal is to recover training data from a pre-trained\nmodel, has been recently proved feasible. However, existing inversion methods\nusually suffer from the mode collapse problem, where the synthesized instances\nare highly similar to each other and thus show limited effectiveness for\ndownstream tasks, such as knowledge distillation. In this paper, we propose\nContrastive Model Inversion~(CMI), where the data diversity is explicitly\nmodeled as an optimizable objective, to alleviate the mode collapse issue. Our\nmain observation is that, under the constraint of the same amount of data,\nhigher data diversity usually indicates stronger instance discrimination. To\nthis end, we introduce in CMI a contrastive learning objective that encourages\nthe synthesizing instances to be distinguishable from the already synthesized\nones in previous batches. Experiments of pre-trained models on CIFAR-10,\nCIFAR-100, and Tiny-ImageNet demonstrate that CMI not only generates more\nvisually plausible instances than the state of the arts, but also achieves\nsignificantly superior performance when the generated data are used for\nknowledge distillation. Code is available at\n\\url{https://github.com/zju-vipa/DataFree}.\n"}
+{"abstract": "  Given that there are 94 clinics and more than 200,000 patients treated\nworldwide, proton and carbon are the most used heavily charged particles in\nheavy ion therapy. However, there is a recent increasing trend in using new ion\nbeams. Each heavy ion has a different effect on the target. As each heavy ion\nmoves through the tissue, they lose enormous energy in collisions, so their\nrange is not long. Ionization accounts for the majority of this loss in energy.\nDuring this interaction of the heavily charged particles with the target, the\nparticles do not only ionize, but also lose energy with the recoil. Recoil\noccurs by atom-to-atom collisions. With these collisions, crystalline atoms\nreact with different combinations and form cascades in accordance with their\nenergies. Thus, secondary particles create ionization and recoil. In this\nstudy, recoil values of boron, carbon, nitrogen and oxygen beams in the water\nphantom were computed in the energy range of 2.0-2.5 GeV using Monte Carlo\nsimulation and the results were compared with carbon. It was observed that\nthere is a regular increase in the recoil peak amplitude for carbon and boron\nions, unlike oxygen and nitrogen where such a regularity could not be seen.\nMoreover, the gaps in the crystal structure increased as the energy increases.\n"}
+{"abstract": "  Bias stress degradation in conjugated polymer field-effect transistors is a\nfundamental problem in these disordered materials and can be traced back to\ninteractions of the material with environmental species,1,2,3 as well as\nfabrication-induced defects.4,5 However, the effect of the end groups of the\npolymer gate dielectric and the associated dipole-induced disorder on bias\nstress stability has not been studied so far in high-performing n-type\nmaterials, such as N2200.6,7 In this work, the performance metrics of N2200\ntransistors are examined with respect to dielectrics with different end groups\n(Cytop-M and Cytop-S8). We hypothesize that the polar end groups would lead to\nincreased dipole-induced disorder, and worse performance.1,9,10 The long-time\nannealing scheme at lower temperatures used in the paper is assumed to lead to\nbetter crystallization by allowing the crystalline domains to reorganize in the\npresence of the solvent.11 It is hypothesized that the higher crystallinity\ncould narrow down the range at which energy carriers are induced and thus\ndecrease the gate dependence of the mobility. The results show that the\ndielectric end groups do not influence the bias stress stability of N2200\ntransistors. However, long annealing times result in a dramatic improvement in\nbias stress stability, with the most stable devices having a mobility that is\nonly weakly dependent on or independent of gate voltage.\n"}
+{"abstract": "  In the companion paper [Phys. Rev. D 103 (2021) 10, [2101.02951]] we have\nderived the short-ranged potentials for the Teukolsky equations for massless\nspins $(0,1/2,1,2)$ in general spherically-symmetric and static metrics. Here\nwe apply these results to numerically compute the Hawking radiation spectra of\nsuch particles emitted by black holes (BHs) in three different ansatz: charged\nBHs, higher-dimensional BHs, and polymerized BHs arising from models of quantum\ngravity. In order to ensure the robustness of our numerical procedure, we show\nthat it agrees with newly derived analytic formulas for the cross-sections in\nthe high and low energy limits. We show how the short-ranged potentials and\nprecise Hawking radiation rates can be used inside the code\n$\\texttt{BlackHawk}$ to predict future primordial BH evaporation signals for a\nvery wide class of BH solutions, including the promising regular BH solutions\nderived from loop quantum gravity. In particular, we derive the first Hawking\nradiation constraints on polymerized BHs from AMEGO. We prove that the mass\nwindow $10^{16}-10^{18}\\,$g for all dark matter into primordial BHs can be\nreopened with high values of the polymerization parameter, which encodes the\ntypical scale and strength of quantum gravity corrections.\n"}
+{"abstract": "  The secure domination numbers of the Cartesian products of two small graphs\nwith paths or cycles is determined, as well as for Mobius ladder graphs. Prior\nto this work, in all cases where the secure domination number has been\ndetermined, the proof has either been trivial, or has been derived from lower\nbounds established by considering different forms of domination. However, the\nlatter mode of proof is not applicable for most graphs, including those\nconsidered here. Hence, this work represents the first attempt to determine\nsecure domination numbers via the properties of secure domination itself, and\nit is expected that these methods may be used to determine further results in\nthe future.\n"}
+{"abstract": "  As the major factors affecting the safety of deep learning models, corner\ncases and related detection are crucial in AI quality assurance for\nconstructing safety- and security-critical systems. The generic corner case\nresearches involve two interesting topics. One is to enhance DL models\nrobustness to corner case data via the adjustment on parameters/structure. The\nother is to generate new corner cases for model retraining and improvement.\nHowever, the complex architecture and the huge amount of parameters make the\nrobust adjustment of DL models not easy, meanwhile it is not possible to\ngenerate all real-world corner cases for DL training. Therefore, this paper\nproposes to a simple and novel study aiming at corner case data detection via a\nspecific metric. This metric is developed on surprise adequacy (SA) which has\nadvantages on capture data behaviors. Furthermore, targeting at characteristics\nof corner case data, three modifications on distanced-based SA are developed\nfor classification applications in this paper. Consequently, through the\nexperiment analysis on MNIST data and industrial data, the feasibility and\nusefulness of the proposed method on corner case data detection are verified.\n"}
+{"abstract": "  An analysis of sub-MeV dark photon as dark matter is given which is achieved\nwith two hidden sectors, one of which interacts directly with the visible\nsector while the second has only indirect coupling with the visible sector. The\nformalism for the evolution of three bath temperatures for the visible sector\nand the two hidden sectors is developed and utilized in solution of Boltzmann\nequations coupling the three sectors. We present exclusion plots where the\nsub-MeV dark photon can be dark matter. The analysis can be extended to a\nmulti-temperature universe with multiple hidden sectors and multiple heat\nbaths.\n"}
+{"abstract": "  Power control in decentralized wireless networks poses a complex stochastic\noptimization problem when formulated as the maximization of the average sum\nrate for arbitrary interference graphs. Recent work has introduced data-driven\ndesign methods that leverage graph neural network (GNN) to efficiently\nparametrize the power control policy mapping channel state information (CSI) to\nthe power vector. The specific GNN architecture, known as random edge GNN\n(REGNN), defines a non-linear graph convolutional architecture whose spatial\nweights are tied to the channel coefficients, enabling a direct adaption to\nchannel conditions. This paper studies the higher-level problem of enabling\nfast adaption of the power control policy to time-varying topologies. To this\nend, we apply first-order meta-learning on data from multiple topologies with\nthe aim of optimizing for a few-shot adaptation to new network configurations.\n"}
+{"abstract": "  Concepts similar to emergence have been used since antiquity, but we lack an\nagreed definition of emergence. Still, emergence has been identified as one of\nthe features of complex systems. Most would agree on the statement \"life is\ncomplex\". Thus, understanding emergence and complexity should benefit the study\nof living systems. It can be said that life emerges from the interactions of\ncomplex molecules. But how useful is this to understand living systems?\nArtificial life (ALife) has been developed in recent decades to study life\nusing a synthetic approach: build it to understand it. ALife systems are not so\ncomplex, be them soft (simulations), hard (robots), or wet (protocells). Then,\nwe can aim at first understanding emergence in ALife, for then using this\nknowledge in biology. I argue that to understand emergence and life, it becomes\nuseful to use information as a framework. In a general sense, emergence can be\ndefined as information that is not present at one scale but is present at\nanother scale. This perspective avoids problems of studying emergence from a\nmaterialistic framework, and can be useful to study self-organization and\ncomplexity.\n"}
+{"abstract": "  Primordial Black Holes (PBHs) are remnants of objects formed in the early\nUniverse. Their lifetime is an increasing function of their mass, so PBHs in\nthe right mass range can end their lives in an evaporation event that is\npotentially detectable by our instruments now. This evaporation may result in a\n$\\gamma$-ray flash that can be detected by the current generation of\nVery-High-Energy $\\gamma$-ray detectors. The Southern Wide field of view\nGamma-ray Observatory (SWGO) will be part of the next generation of these\ninstruments. It will be able to establish limits on PBH evaporations for\nintegration windows between 0.5 and 5 s, in a radius of 0.25 pc around the\nEarth, being sensitive to a rate of the order of $\\sim$50 pc$^{-3}$ yr$^{-1}$,\nmore than one order of magnitude more constraining than the currently\nestablished best limits.\n"}
+{"abstract": "  Training with Reinforcement Learning requires a reward function that is used\nto guide the agent towards achieving its objective. However, designing smooth\nand well-behaved rewards is in general not trivial and requires significant\nhuman engineering efforts. Generating rewards in self-supervised way, by\ninspiring the agent with an intrinsic desire to learn and explore the\nenvironment, might induce more general behaviours. In this work, we propose a\ncuriosity-based bonus as intrinsic reward for Reinforcement Learning, computed\nas the Bayesian surprise with respect to a latent state variable, learnt by\nreconstructing fixed random features. We extensively evaluate our model by\nmeasuring the agent's performance in terms of environment exploration, for\ncontinuous tasks, and looking at the game scores achieved, for video games. Our\nmodel is computationally cheap and empirically shows state-of-the-art\nperformance on several problems. Furthermore, experimenting on an environment\nwith stochastic actions, our approach emerged to be the most resilient to\nsimple stochasticity. Further visualization is available on the project\nwebpage.(https://lbsexploration.github.io/)\n"}
+{"abstract": "  Let $\\mathcal{P}_s(n)$ denote the $n$th $s$-gonal number. We consider the\nequation \\[\\mathcal{P}_s(n) = y^m, \\] for integers $n,s,y,$ and $m$. All\nsolutions to this equation are known for $m>2$ and $s \\in \\{3,5,6,8,20 \\}$. We\nconsider the case $s=10$, that of decagonal numbers. Using a descent argument\nand the modular method, we prove that the only decagonal number $>1$\nexpressible as a perfect $m$th power with $m>1$ is $\\mathcal{P}_{10}(3) = 3^3$.\n"}
+{"abstract": "  We investigate the solar wind energy flux in the inner heliosphere using\n12-day observations around each perihelion of Encounter One (E01), Two (E02),\nFour (E04), and Five (E05) of Parker Solar Probe (PSP), respectively, with a\nminimum heliocentric distance of 27.8 solar radii ($R_\\odot{}$). Energy flux\nwas calculated based on electron parameters (density $n_e$, core electron\ntemperature $T_{c}$, and suprathermal electron temperature $T_{h}$) obtained\nfrom the simplified analysis of the plasma quasi-thermal noise (QTN) spectrum\nmeasured by RFS/FIELDS and the bulk proton parameters (bulk speed $V_p$ and\ntemperature $T_p$) measured by the Faraday Cup onboard PSP, SPC/SWEAP.\nCombining observations from E01, E02, E04, and E05, the averaged energy flux\nvalue normalized to 1 $R_\\odot{}$ plus the energy necessary to overcome the\nsolar gravitation ($W_{R_\\odot{}}$) is about 70$\\pm$14 $W m^{-2}$, which is\nsimilar to the average value (79$\\pm$18 $W m^{-2}$) derived by Le Chat et al\nfrom 24-year observations by Helios, Ulysses, and Wind at various distances and\nheliolatitudes. It is remarkable that the distributions of $W_{R_\\odot{}}$ are\nnearly symmetrical and well fitted by Gaussians, much more so than at 1 AU,\nwhich may imply that the small heliocentric distance limits the interactions\nwith transient plasma structures.\n"}
+{"abstract": "  Competing interactions produce finite-size textures in myriad condensed\nmatter systems, typically forming elongated stripe or round bubble domains.\nTransitions between stripe and bubble phases, driven by field or temperature,\nare expected to be reversible in nature. Here we report on the distinct\ncharacter of the analogous transition for nanoscale spin textures in chiral\nCo/Pt-based multilayer films, known to host N\\'{e}el skyrmions, using\nmicroscopy, magnetometry, and micromagnetic simulations. Upon increasing field,\nindividual stripes fission into multiple skyrmions, and this transition\nexhibits a macroscopic signature of irreversibility. Crucially, upon field\nreversal, the skyrmions do not fuse back into stripes, with many skyrmions\nretaining their morphology down to zero field. Both the macroscopic\nirreversibility and the microscopic zero-field skyrmion density are governed by\nthe thermodynamic material parameter determining chiral domain stability. These\nresults establish the thermodynamic and microscopic framework underlying\nambient skyrmion generation and stability in chiral multilayer films and\nprovide immediate directions for their functionalization in devices.\n"}
+{"abstract": "  Reconciling the geology of Mars with models of atmospheric evolution remains\na major challenge. Martian geology is characterized by past evidence for\nepisodic surface liquid water, and geochemistry indicating a slow and\nintermittent transition from wetter to drier and more oxidizing surface\nconditions. Here we present a new model incorporating randomized injection of\nreducing greenhouse gases and oxidation due to hydrogen escape, to investigate\nthe conditions responsible for these diverse observations. We find that Mars\ncould have transitioned repeatedly from reducing (H2-rich) to oxidizing\n(O2-rich) atmospheric conditions in its early history. Our model predicts a\ngenerally cold early Mars, with mean annual temperatures below 240 K. If peak\nreducing gas release rates and background CO2 levels are high enough, it\nnonetheless exhibits episodic warm intervals sufficient to degrade crater\nwalls, form valley networks and create other fluvial/lacustrine features. Our\nmodel also predicts transient buildup of atmospheric O2, which can help explain\nthe occurrence of oxidized mineral species such as manganese oxides at Gale\nCrater. We suggest that the apparent Noachian--Hesperian transition from\nphyllosilicate deposition to sulfate deposition around 3.5 billion years ago\ncan be explained as a combined outcome of increasing planetary oxidation,\ndecreasing groundwater availability and a waning bolide impactor flux, which\ndramatically slowed the remobilization and thermochemical destruction of\nsurface sulfates. Ultimately, rapid and repeated variations in Mars' early\nclimate and surface chemistry would have presented both challenges and\nopportunities for any emergent microbial life.\n"}
+{"abstract": "  Samarium substituted bismuth ferrite (BiFeO3) ceramics prepared by sol-gel\nsynthesis method were studied using both local scale and microscopic\nmeasurement techniques in order to clarify an evolution of the crystal\nstructure of the compounds across the morphotropic phase boundary region. X-ray\ndiffraction analysis, transmission and scanning electron microscopies, XPS,\nEDS/EDX experiments and piezoresponse force microscopy were used to study the\nstructural transitions from the polar active rhombohedral phase to the\nanti-polar orthorhombic phase and then to the non-polar orthorhombic phase,\nobserved in the Bi1-xSmxFeO3 compounds within the concentration range of 0.08 <\nx < 0.2. The results obtained by microscopic techniques testify that the\ncompounds in the range of 0.12 < x < 0.15 are characterized by two phase\nstructural state formed by a coexistence of the rhombohedral and the anti-polar\northorhombic phases; two phase structural state observed in the compounds with\n0.15 < x < 0.18 is associated with a coexistence of the anti-polar orthorhombic\nand the non-polar orthorhombic phases. Local scale measurements have revealed a\nnotable difference in the concentration range ascribed to the morphotropic\nphase boundary estimated by microscopic measurements, the obtained results\ntestify a wider concentration range ascribed to a coexistence of different\nstructural phases, the background of the mentioned difference is discussed.\n"}
+{"abstract": "  In this paper, we address the speech denoising problem, where Gaussian, pink\nand blue additive noises are to be removed from a given speech signal. Our\napproach is based on a redundant, analysis-sparse representation of the\noriginal speech signal. We pick an eigenvector of the Zauner unitary matrix and\n-- under certain assumptions on the ambient dimension -- we use it as window\nvector to generate a spark deficient Gabor frame. The analysis operator\nassociated with such a frame, is a (highly) redundant Gabor transform, which we\nuse as a sparsifying transform in denoising procedure. We conduct computational\nexperiments on real-world speech data, using as baseline three Gabor transforms\ngenerated by state-of-the-art window vectors in time-frequency analysis and\ncompare their performance to the proposed Gabor transform. The results show\nthat our proposed redundant Gabor transform outperforms all others,\nconsistently for all signals.\n"}
+{"abstract": "  We propose a novel class of responsive polymer brushes, where the effective\ngrafting density can be controlled by external stimuli. This is achieved by\nusing end-grafted polymer chains that have an affinity to the substrate. For\nsufficiently strong surface interactions, a fraction of chains condenses into a\nnear-surface layer, while the remaining ones form the outer brush. The dense\nlayer and the more tenuous outer brush can be seen as coexisting microphases.\nThe effective grafting density of the outer brush is controlled by the\nadsorption strength and can be changed reversibly and in a controlled way as a\nresponse to changes in environmental parameters. The effect is demonstrated by\nnumerical SCF calculations and analyzed by scaling arguments. Since the\nthickness of the denser layer is about a few monomer sizes, its capacity to\nform a microphase is limited by the product of the brush chain length and the\ngrafting density. We explore the range of chain lengths and grafting densities\nwhere the effect is most pronounced. In this range, the SCF studies suggest\nthat individual chains inside the brush show large rapid fluctuations between\ntwo states that are separated by only a small free energy barrier. The behavior\nof the brush as a whole, however, does not reflect these large fluctuations,\nand the effective grafting density varies smoothly as a function of the control\nparameters.\n"}
+{"abstract": "  Post-training quantization is a representative technique for compressing\nneural networks, making them smaller and more efficient for deployment on edge\ndevices. However, an inaccessible user dataset often makes it difficult to\nensure the quality of the quantized neural network in practice. In addition,\nexisting approaches may use a single uniform bit-width across the network,\nresulting in significant accuracy degradation at extremely low bit-widths. To\nutilize multiple bit-width, sensitivity metric plays a key role in balancing\naccuracy and compression. In this paper, we propose a novel sensitivity metric\nthat considers the effect of quantization error on task loss and interaction\nwith other layers. Moreover, we develop labeled data generation methods that\nare not dependent on a specific operation of the neural network. Our\nexperiments show that the proposed metric better represents quantization\nsensitivity, and generated data are more feasible to be applied to\nmixed-precision quantization.\n"}
+{"abstract": "  We study acceleration phenomena in monostable integro-differential equations\nwith weak Allee effect. Previous works have shown its occurrence and obtained\ncorrect upper bounds on the rate of expansion, but precise lower bounds were\nleft open. In this paper, we provide a sharp lower bound of acceleration for a\nlarge class of dispersion operators. Indeed, our results cover fractional\nLaplace operators and standard convolutions in a unified way, which is also a\ncontribution of this paper. To achieve this, we construct a refined\nsub-solution that captures the expected dynamics of the accelerating solution,\nand this is here the main difficulty.\n"}
+{"abstract": "  Based on a recent result by de Klerk, Glineur, and Taylor (SIAM J. Optim.,\n30(3):2053--2082, 2020) on the attainable convergence rate of gradient descent\nfor smooth and strongly convex functions in terms of function values, a\nconvergence analysis for general descent methods with fixed step sizes is\npresented. It covers variable metric methods as well as gradient related search\ndirections under angle and scaling conditions. An application to inexact\ngradient methods is also presented.\n"}
+{"abstract": "  When the charge densities on the inner and outer surface of the vesicle\nmembrane are similar one may assume that the potential at a point closer to the\nouter/inner surface is more affected by the charges located at the outer/inner\nsurface of the vesicle membrane. However, because of the curvature of the\nvesicle and the screening effect of the electrolyte ions the situation is more\ncomplicated. When the charge densities at the inner and outer surface of the\nvesicle membrane are the same then at any finite distance Z from the center of\nthe vesicle the contribution of the charges, located at the outer membrane\nsurface, to the vesicle potential Vex is different from the contribution of the\ncharges, located at the inner membrane surface, to the vesicle potential Vin.\nAt low electrolyte ion concentrations (from C=0 to 0.00001 [Mol]) Vex/Vin>1 and\nincreases with increasing distance from the center of the vesicle until the\nexternal surface of the vesicle membrane. With further increasing distance\nVex/Vin remains constant at C=0 [Mol], while at C>0 [Mol] it starts decreasing.\nHowever, at high electrolyte ion concentrations (C>0.01 [Mol]) with increasing\ndistance from the center of the vesicle Vex/Vin reaches its maximum at the\ninternal surface of the vesicle membrane. With further increasing distance,\nclose to the outer surface of the vesicle Vex/Vin becomes less than 1 (i.e. the\ncharges at the inner surface of the membrane contribute to the potential more\nthan the charges at the outer surface). Further increasing the distance Vex/Vin\nreaches its minimum and then increases to 1 (i.e. the charges at the inner\nsurface of the membrane contribute to the potential like the charges at the\nouter surface of the membrane). With increasing electrolyte ion concentration\nthe minimum of Vex/Vin is getting deeper (approaching zero) and its location is\ngetting closer to the outer surface of the vesicle.\n"}
+{"abstract": "  Recently, passive unmanned aerial vehicle (UAV) localization has become\npopular due to mobility and convenience. In this paper, we consider a scenario\nof using distributed drone cluster to estimate the position of a passive\nemitter via received signal strength (RSS). First, a distributed\nmajorizeminimization (DMM) RSS-based localization method is proposed. To\naccelerate its convergence, a tight upper bound of the objective function from\nthe primary one is derived. Furthermore, to reduce communication overhead, a\ndistributed estimation scheme using the Fisher information matrix (DEF) is\npresented, with only requiring one-round communication between edge UAVs and\ncenter UAV. Additionally, a local search solution is used as the initial value\nof DEF. Simulation results show that the proposed DMM performs better than the\nexisting distributed Gauss-Newton method (DGN) in terms of root of mean square\nerror (RMSE) under a limited low communication overhead constraint. Moreover,\nthe proposed DEF performs much better than MM in terms of RMSE, but has a\nhigher computational complexity than the latter.\n"}
+{"abstract": "  We study a version of the fractal uncertainty principle in the joint\ntime-frequency representation. Namely, we consider Daubechies' localization\noperator projecting onto spherically symmetric $n$-iterate Cantor sets with an\narbitrary base $M>1$ and alphabet $\\mathscr{A}$. We derive an upper bound\nasymptote up to a multiplicative constant for the operator norm in terms of the\nbase $M$ and alphabet size $|\\mathscr{A}|$ of the Cantor set. For any fixed\nbase and alphabet size, we show that there are Cantor sets such that the\nasymptote is optimal. In particular, the asymptote is precise for the mid-third\nCantor set, which was studied in part I. Nonetheless, this does not extend to\nevery Cantor set as we provide examples where the optimal asymptote is not\nachieved.\n"}
+{"abstract": "  Most non-professional photo manipulations are not made using propriety\nsoftware like Adobe Photoshop, which is expensive and complicated to use for\nthe average consumer selfie-taker or meme-maker. Instead, these individuals opt\nfor user friendly mobile applications like FaceTune and Pixlr to make human\nface edits and alterations. Unfortunately, there is no existing dataset to\ntrain a model to classify these type of manipulations. In this paper, we\npresent a generative model that approximates the distribution of human face\nedits and a method for detecting Facetune and Pixlr manipulations to human\nfaces.\n"}
+{"abstract": "  Let $p$ be a prime number and $K$ a finite extension of $\\mathbb{Q}_p$. We\nstate conjectures on the smooth representations of $\\mathrm{GL}_n(K)$ that\noccur in spaces of mod $p$ automorphic forms (for compact unitary groups). In\nparticular, when $K$ is unramified, we conjecture that they are of finite\nlength and predict their internal structure (extensions, form of subquotients)\nfrom the structure of a certain algebraic representation of $\\mathrm{GL}_n$.\nWhen $n=2$ and $K$ is unramified, we prove several cases of our conjectures,\nincluding new finite length results.\n"}
+{"abstract": "  A methodology is proposed for formulating dynamic equations in\nthermo-piezoelectric and dissipative media from the first principle of energy\nconservation. The results are in agreement with those from Hamiltonian\nprinciple. Our formulations based on energy conservation are much easier to\nunderstand. What is more is that, the energy conservation based framework is\nable to handle dissipation problems, which is usually behind the scope of\nHamiltonian principle. In our case, the mechanical, electric and thermal\nphenomena firstly are taken into account, and then the medium with dissipation\nis included. The formulations on acoustic dynamic equation and the associated\nconstitutive relations of the medium will also pave an alternative way in\ncomputational dynamic modelling based on weak formulations. In addition, our\nmethodology may be extended to other dynamic equation formulations, such as in\nelectrodynamics, fluid mechanics and quantum mechanics. This is especially true\nfor tackling the problems with multi-physical field interactions and coupling.\n"}
+{"abstract": "  In this paper we investigate two-point algebraic-geometry codes (AG codes)\ncoming from the Beelen-Montanucci (BM) maximal curve. We study properties of\ncertain two-point Weierstrass semigroups of the curve and use them for\ndetermining a lower bound on the minimum distance of such codes. AG codes with\nbetter parameters with respect to comparable two-point codes from the\nGarcia-G\\\"uneri-Stichtenoth (GGS) curve are discovered.\n"}
+{"abstract": "  Let $(K,\\Delta)$ be a Hermitian form field and $n\\geq 3$. We prove that if\n$\\sigma\\in U_{2n+1}(K,\\Delta)$ is a unitary matrix of level $(K,\\Delta)$, then\nany short root transvection $T_{ij}(x)$ is a product of $4$ elementary unitary\nconjugates of $\\sigma$ and $\\sigma^{-1}$. Moreover, the bound $4$ is sharp. We\nalso show that any extra short root transvection $T_i(x,y)$ is a product of\n$12$ elementary unitary conjugates of $\\sigma$ and $\\sigma^{-1}$. If the level\nof $\\sigma$ is $(0,K\\times 0)$, then any $(0,K\\times 0)$-elementary extra short\nroot transvection $T_i(x,0)$ is a product of $2$ elementary unitary conjugates\nof $\\sigma$ and $\\sigma^{-1}$.\n"}
+{"abstract": "  Medication errors continue to be the leading cause of avoidable patient harm\nin hospitals. This paper sets out a framework to assure medication safety that\ncombines machine learning and safety engineering methods. It uses safety\nanalysis to proactively identify potential causes of medication error, based on\nexpert opinion. As healthcare is now data rich, it is possible to augment\nsafety analysis with machine learning to discover actual causes of medication\nerror from the data, and to identify where they deviate from what was predicted\nin the safety analysis. Combining these two views has the potential to enable\nthe risk of medication errors to be managed proactively and dynamically. We\napply the framework to a case study involving thoracic surgery, e.g.\noesophagectomy, where errors in giving beta-blockers can be critical to control\natrial fibrillation. This case study combines a HAZOP-based safety analysis\nmethod known as SHARD with Bayesian network structure learning and process\nmining to produce the analysis results, showing the potential of the framework\nfor ensuring patient safety, and for transforming the way that safety is\nmanaged in complex healthcare environments.\n"}
+{"abstract": "  This paper mainly focuses on solving the dynamic optimization of the planar\ncontrolled crowd motion models with obstacles which is an application of a\nclass of optimal control problems governed by a general perturbed nonconvex\nsweeping process. This can be considered as a significant extension of the\nprevious work regarding the controlled crowd motion models, where the obstacles\nhave not been considered. The necessary optimality conditions for the problem\nunder consideration are established and illustrated by a nontrivial example of\npractical importance.\n"}
+{"abstract": "  With almost 50% of annual commercial drug approvals being Personalised\nMedicine (PM) and its huge potential to improve quality of life, this emerging\nmedical sector has received increased attention from the industry and medical\nresearch, driven by health and care services, and us, the patients.\nNotwithstanding the power of Advanced Therapy Medicinal Products (ATMPs) to\ntreat progressive illnesses and rare genetic conditions, their delivery on\nlarge scale is still problematic. The biopharmaceutical companies are currently\nstruggling to meet timely delivery and, given high prices of up to $2 million\nper patient, prove the cost-effectiveness of their ATMP. The fragility of ATMPs\ncombined with the impossibility for replacements due to the nature of the\ntreatment and the advanced stages of the patient's condition are some of the\nbottlenecks added to a generally critical supply chain. As a consequence, ATMPs\nare currently used in most cases only as a last resort. ATMPs are at the\nintersection of multiple healthcare logistic networks and, due to their\nnovelty, research around their commercialisation is still in its infancy from\nan operations research perspective. To accelerate technology adoption in this\ndomain, we characterize pertinent practical challenges in a PM supply chain and\nthen capture them in a holistic mathematical model ready for optimisation. The\nidentified challenges and derived model will be contrasted with literature of\nrelated supply chains in terms of model formulations and suitable optimisation\nmethods. Finally, needed technological advancements are discussed to pave the\nway to affordable commercialisation of PM.\n"}
+{"abstract": "  Deterministic photon-photon gates enable the controlled generation of\nentanglement between mobile carriers of quantum information. Such gates have\nthus far been exclusively realized in the optical domain and by relying on\npost-selection. Here, we present a non-post-selected, deterministic,\nphoton-photon gate in the microwave frequency range realized using\nsuperconducting circuits. We emit photonic qubits from a source chip and route\nthose qubits to a gate chip with which we realize a universal gate set by\ncombining controlled absorption and re-emission with single-qubit gates and\nqubit-photon controlled-phase gates. We measure quantum process fidelities of\n$75\\,\\%$ for single- and of $57\\,\\%$ for two-qubit gates, limited mainly by\nradiation loss and decoherence. This universal gate set has a wide range of\npotential applications in superconducting quantum networks.\n"}
+{"abstract": "  Presented is a data-driven Machine Learning (ML) framework for the\nidentification and modeling of traveling wave spatiotemporal dynamics. The\npresented framework is based on the steadily-propagating traveling wave ansatz,\n$u(x,t) = U(\\xi=x - ct + a)$. For known evolution equations, this coordinate\ntransformation reduces governing partial differential equations (PDEs) to a set\nof coupled ordinary differential equations (ODEs) in the traveling wave\ncoordinate $\\xi$. Although traveling waves are readily observed in many\nphysical systems, the underlying governing equations may be unknown. For these\ninstances, the traveling wave ODEs can be (i) identified in an interpretable\nmanner through an implementation of sparse regression techniques or (ii)\nmodeled empirically with neural ODEs. Presented are these methods applied to\nseveral physical systems that admit traveling waves. Examples include traveling\nwave fronts, pulses, and wavetrains restricted to one-wave wave propagation in\na single spatial dimension.\n"}
+{"abstract": "  The early Cosmology driven by a single scalar field, both massless and\nmassive, in the context of Eddington-inspired Born-Infeld gravity, is explored.\nWe show the existence of nonsingular solutions of bouncing and loitering type\n(depending on the sign of the gravitational theory's parameter, $\\epsilon$)\nreplacing the Big Bang singularity, and discuss their properties. In addition,\nin the massive case we find some new features of the cosmological evolution\ndepending on the value of the mass parameter, including asymmetries in the\nexpansion/contraction phases, or a continuous transition between a contracting\nphase to an expanding one via an intermediate loitering phase. We also provide\na combined analysis of cosmic chronometers, standard candles, BAO, and CMB data\nto constrain the model, finding that for roughly $\\vert \\epsilon \\vert \\lesssim\n5\\cdot 10^{-8} \\text{ m}^2$ the model is compatible with the latest\nobservations while successfully removing the Big Bang singularity. This bound\nis several orders of magnitude stronger than the most stringent constraints\ncurrently available in the literature\n"}
+{"abstract": "  Photonic crystals (PCs) offer unique ways to control light-matter\ninteractions. The measurement of dispersion relations is a fundamental\nprerequisite if we are to create novel functionalities in PC devices.\nAngle-resolved spectroscopic techniques are commonly used for characterizing\nPCs that work in the visible and near-infrared regions. However, the techniques\ncannot be applied to the mid- and long-wavelength infrared regions due to the\nlimited sensitivity of infrared detectors. Here, we propose an alternative\napproach to measuring infrared dispersion relations. We construct a\nhigh-precision angle-resolved setup compatible with a Fourier-transform\nspectrometer with an angle resolution as high as 0.3 degrees. Hence, the\nreflection spectra are mapped to the 2D photonic band structures of\nIn(Ga,Al)As/InP based PC slabs, which are designed as mid-infrared PC\nsurface-emitting lasers. We identify complex PC modes with the aid of\npolarization selection rules derived by the group theory. Spectral analysis\nmakes it possible to evaluate the mode quality (Q) factors. Therefore,\nangle-resolved reflection is a useful way of optimizing 2D PC parameters for\nmid-infrared devices.\n"}
+{"abstract": "  Given a family $\\mathcal F$ of $k$-element sets, $S_1,\\ldots,S_r\\in\\mathcal\nF$ form an {\\em $r$-sunflower} if $S_i \\cap S_j =S_{i'} \\cap S_{j'}$ for all $i\n\\neq j$ and $i' \\neq j'$. According to a famous conjecture of Erd\\H os and Rado\n(1960), there is a constant $c=c(r)$ such that if $|\\mathcal F|\\ge c^k$, then\n$\\mathcal F$ contains an $r$-sunflower.\n  We come close to proving this conjecture for families of bounded {\\em\nVapnik-Chervonenkis dimension}, VC-dim$(\\mathcal F)\\le d$. In this case, we\nshow that $r$-sunflowers exist under the slightly stronger assumption\n$|\\mathcal F|\\ge2^{10k(dr)^{2\\log^{*} k}}$. Here, $\\log^*$ denotes the iterated\nlogarithm function.\n  We also verify the Erd\\H os-Rado conjecture for families $\\mathcal F$ of\nbounded {\\em Littlestone dimension} and for some geometrically defined set\nsystems.\n"}
+{"abstract": "  Neural image compression leverages deep neural networks to outperform\ntraditional image codecs in rate-distortion performance. However, the resulting\nmodels are also heavy, computationally demanding and generally optimized for a\nsingle rate, limiting their practical use. Focusing on practical image\ncompression, we propose slimmable compressive autoencoders (SlimCAEs), where\nrate (R) and distortion (D) are jointly optimized for different capacities.\nOnce trained, encoders and decoders can be executed at different capacities,\nleading to different rates and complexities. We show that a successful\nimplementation of SlimCAEs requires suitable capacity-specific RD tradeoffs.\nOur experiments show that SlimCAEs are highly flexible models that provide\nexcellent rate-distortion performance, variable rate, and dynamic adjustment of\nmemory, computational cost and latency, thus addressing the main requirements\nof practical image compression.\n"}
+{"abstract": "  Einstein's theory of General Relativity is one of the pillars of modern\nphysics. For decades, the theory has been mainly tested in the weak field\nregime with experiments in the Solar System and observations of binary pulsars.\nThanks to a new generation of observational facilities, the past 5 years have\nseen remarkable changes in this field and there are now numerous efforts for\ntesting General Relativity in the strong field regime with black holes and\nneutron stars using different techniques. Here I will review the work of my\ngroup at Fudan University devoted to test General Relativity with black hole\nX-ray data.\n"}
+{"abstract": "  A commonly used technique for the higher-order PageRank problem is the power\nmethod that is computationally intractable for large-scale problems. The\ntruncated power method proposed recently provides us with another idea to solve\nthis problem, however, its accuracy and efficiency can be poor in practical\ncomputations. In this work, we revisit the higher-order PageRank problem and\nconsider how to solve it efficiently. The contribution of this work is as\nfollows. First, we accelerate the truncated power method for high-order\nPageRank. In the improved version, it is neither to form and store the vectors\narising from the dangling states, nor to store an auxiliary matrix. Second, we\npropose a truncated power method with partial updating to further release the\noverhead, in which one only needs to update some important columns of the\napproximation in each iteration. On the other hand, the truncated power method\nsolves a modified high-order PageRank model for convenience, which is not\nmathematically equivalent to the original one. Thus, the third contribution of\nthis work is to propose a sparse power method with partial updating for the\noriginal higher-order PageRank problem. The convergence of all the proposed\nmethods are discussed. Numerical experiments on large and sparse real-world and\nsynthetic data sets are performed. The numerical results show the superiority\nof our new algorithms over some state-of-the-art ones for large and sparse\nhigher-order PageRank problems.\n"}
+{"abstract": "  We theoretically investigate the one-color injection currents and shift\ncurrents in zigzag graphene nanoribbons with applying a static electric field\nacross the ribbon, which breaks the inversion symmetry to generate nonzero\nsecond order optical responses by dipole interaction. These two types of\ncurrents can be separately excited by specific light polarization, circularly\npolarized lights for injection currents and linearly polarized lights for shift\ncurrents. Based on a tight binding model formed by carbon 2p$_z$ orbitals, we\nnumerically calculate the spectra of injection coefficients and shift\nconductivities, as well as their dependence on the static field strength and\nribbon width. The spectra show many peaks associated with the optical\ntransition between different subbands, and the positions and amplitudes of\nthese peaks can be effectively controlled by the static electric field. By\nconstructing a simple two band model, the static electric fields are found to\nmodify the edge states in a nonperturbative way, and their associated optical\ntransitions dominate the current generation at low photon energies. For typical\nparameters, such as a static field 10$^6$ V/m and light intensity 0.1\nGW/cm$^2$, the magnitude of the injection and shift currents for a ribbon with\nwidth 5 nm can be as large as the order of 1 $\\mu$A. Our results provide a\nphysical basis for realizing passive optoelectronic devices based on graphene\nnanoribbons.\n"}
+{"abstract": "  Graph neural networks (GNNs) have achieved tremendous success in graph\nmining. However, the inability of GNNs to model substructures in graphs remains\na significant drawback. Specifically, message-passing GNNs (MPGNNs), as the\nprevailing type of GNNs, have been theoretically shown unable to distinguish,\ndetect or count many graph substructures. While efforts have been paid to\ncomplement the inability, existing works either rely on pre-defined\nsubstructure sets, thus being less flexible, or are lacking in theoretical\ninsights. In this paper, we propose GSKN, a GNN model with a theoretically\nstronger ability to distinguish graph structures. Specifically, we design GSKN\nbased on anonymous walks (AWs), flexible substructure units, and derive it upon\nfeature mappings of graph kernels (GKs). We theoretically show that GSKN\nprovably extends the 1-WL test, and hence the maximally powerful MPGNNs from\nboth graph-level and node-level viewpoints. Correspondingly, various\nexperiments are leveraged to evaluate GSKN, where GSKN outperforms a wide range\nof baselines, endorsing the analysis.\n"}
+{"abstract": "  Increasingly common open data and open application programming interfaces\n(APIs) together with the progress of data science -- such as artificial\nintelligence (AI) and especially machine learning (ML) -- create opportunities\nto build novel services by combining data from different sources. In this\nexperience report, we describe our firsthand experiences on open data and in\nthe domain of marine traffic in Finland and Sweden and identified technological\nopportunities for novel services. We enumerate five challenges that we have\nencountered with the application of open data: relevant data, historical data,\nlicensing, runtime quality, and API evolution. These challenges affect both\nbusiness model and technical implementation. We discuss how these challenges\ncould be alleviated by better governance practices for provided open APIs and\ndata.\n"}
+{"abstract": "  The chapter presents the problem of the complexity of plastic flow in alloys,\nwhich is manifested by serrated deformation curves and transient plastic strain\nlocalizations. This phenomenon uncovers an inherently collective nature of the\ndynamics of crystal defects. The chapter discusses the state-of-the art of this\nresearch under the angle of distinct regimes of collective dislocation dynamics\nfound in conventional, basically binary alloys, e.g., self-organized\ncriticality, deterministic chaos, or synchronization. It concludes with the\nformulation of diverse open questions, notably concerning manifestations of the\ncollective dislocation dynamics pertaining to finer scales of plastic\ndeformation processes, as compared to the macroscopic plastic instability.\n"}
+{"abstract": "  KPD 0005+5106, with an effective temperature of $\\simeq$200,000 K, is one of\nthe hottest white dwarfs (WDs). ROSAT unexpectedly detected \"hard\" ($\\sim$1\nkeV) X-rays from this apparently single WD. We have obtained Chandra\nobservations that confirm the spatial coincidence of this hard X-ray source\nwith KPD 0005+5106. We have also obtained XMM-Newton observations of KPD\n0005+5106, as well as PG 1159$-$035 and WD 0121$-$756, which are also\napparently single and whose hard X-rays were detected by ROSAT at\n3$\\sigma$-4$\\sigma$ levels. The XMM-Newton spectra of the three WDs show\nremarkably similar shapes that can be fitted by models including a blackbody\ncomponent for the stellar photospheric emission, a thermal plasma emission\ncomponent, and a power-law component. Their X-ray luminosities in the $0.6-3.0$\nkeV band range from $4\\times10^{29}$ to $4\\times10^{30}$ erg~s$^{-1}$. The\nXMM-Newton EPIC-pn soft-band ($0.3-0.5$ keV) lightcurve of KPD 0005+5106 is\nessentially constant, but the hard-band ($0.6-3.0$ keV) lightcurve shows\nperiodic variations. An analysis of the generalized Lomb-Scargle periodograms\nfor the XMM-Newton and Chandra hard-band lightcurves finds a convincing\nmodulation (false alarm probability of 0.41%) with a period of 4.7$\\pm$0.3 hr.\nAssuming that this period corresponds to a binary orbital period, the Roche\nradii of three viable types of companion have been calculated: M9V star, T\nbrown dwarf, and Jupiter-like planet. Only the planet has a size larger than\nits Roche radius, although the M9V star and T brown dwarf may be heated by the\nWD and inflate past the Roche radius. Thus, all three types of companion may be\ndonors to fuel accretion-powered hard X-ray emission.\n"}
+{"abstract": "  As an important perceptual characteristic of the Human Visual System (HVS),\nthe Just Noticeable Difference (JND) has been studied for decades with\nimage/video processing (e.g., perceptual image/video coding). However, there is\nlittle exploration on the existence of JND for AI, like Deep Machine Vision\n(DMV), although the DMV has made great strides in many machine vision tasks. In\nthis paper, we take an initial attempt, and demonstrate that DMV does have the\nJND, termed as DMVJND. Besides, we propose a JND model for the classification\ntask in DMV. It has been discovered that DMV can tolerate distorted images with\naverage PSNR of only 9.56dB (the lower the better), by generating JND via\nunsupervised learning with our DMVJND-NET. In particular, a semantic-guided\nredundancy assessment strategy is designed to constrain the magnitude and\nspatial distribution of the JND. Experimental results on classification tasks\ndemonstrate that we successfully find and model the JND for deep machine\nvision. Meanwhile, our DMV-JND paves a possible direction for DMV oriented\nimage/video compression, watermarking, quality assessment, deep neural network\nsecurity, and so on.\n"}
+{"abstract": "  We use density-functional theory calculations to explore the magnetic\nproperties of perovskite rare-earth nickelates, $\\mathcal{R}$NiO$_3$, by\nconstructing microscopic magnetic models containing all relevant exchange\ninteractions via Wannierization and Green's function techniques. These models\nelucidate the mechanism behind the formation of antiferromagnetic order with\nthe experimentally observed propagation vector, and explain the reason previous\nDFT plus Hubbard $U$ calculations favored ferromagnetic order. We perform\ncalculations of magnetic moments and exchange-coupling parameters for different\namplitudes of the $R_1^+$ breathing mode distortion, which results in expanded\nand compressed NiO$_6$ octahedra. We find that the magnetic moment vanishes for\nthe \"short bond\" nickels, i.e., the ones in the compressed octahedra. The\ninclusion of spin-orbit coupling demonstrates that the magnetic anisotropy is\nvery small, while the magnetic moment of the short bond nickel atoms tend to\nzero even for the noncollinear case. Our results provide a clear picture of the\ntrends of the magnetic order across the nickelate series and give insights into\nthe coupling between magnetic order and structural distortions.\n"}
+{"abstract": "  Demonstrating acrobatic behavior of a humanoid robot such as flips and\nspinning jumps requires systematic approaches across hardware design, motion\nplanning, and control. In this paper, we present a new humanoid robot design,\nan actuator-aware kino-dynamic motion planner, and a landing controller as part\nof a practical system design for highly dynamic motion control of the humanoid\nrobot. To achieve the impulsive motions, we develop two new proprioceptive\nactuators and experimentally evaluate their performance using our\ncustom-designed dynamometer. The actuator's torque, velocity, and power limits\nare reflected in our kino-dynamic motion planner by approximating the\nconfiguration-dependent reaction force limits and in our dynamics simulator by\nincluding actuator dynamics along with the robot's full-body dynamics. For the\nlanding control, we effectively integrate model-predictive control and\nwhole-body impulse control by connecting them in a dynamically consistent way\nto accomplish both the long-time horizon optimal control and high-bandwidth\nfull-body dynamics-based feedback. Actuators' torque output over the entire\nmotion are validated based on the velocity-torque model including battery\nvoltage droop and back-EMF voltage. With the carefully designed hardware and\ncontrol framework, we successfully demonstrate dynamic behaviors such as back\nflips, front flips, and spinning jumps in our realistic dynamics simulation.\n"}
+{"abstract": "  The most simple and famous divergent power series coming from ODE may be the\nso-called Euler series $\\sum_{n\\ge 0}(-1)^n\\,n!\\,x^{n+1}$, that, as well as all\nits positive powers, is Borel-summable in any direction excepted the negative\nreal half-axis. By considering a family of linear $q$-difference operators\nassociated with a given first order non-homogenous $q$-difference equation, it\nwill be shown that the summability order of $q$-analoguous counterparties of\nEuler series depends upon of the degree of power under consideration.\n"}
+{"abstract": "  Certification of nonlocality of quantum mechanics is an important fundamental\ntest that typically requires prolonged data collection and is only revealed in\nan in-depth analysis. These features are often particularly exposed in hybrid\nsystems, such as interfaces between light and atomic ensembles. Certification\nof entanglement from images acquired with single-photon camera can mitigate\nthis issue by exploiting multiplexed photon generation. Here we demonstrate\nthis feature in a quantum memory (QM) operating in a real-time feedback mode.\nThrough spatially-multimode spin-wave storage the QM enables operation of the\nreal-time ghost imaging (GI) protocol. By properly preparing the spatial phase\nof light emitted by the atoms we enable observation of Bell-type nonlocality\nfrom a single image acquired in the far field as witnessed by the Bell\nparameter of $S=2.227\\pm0.007>2$. Our results are an important step towards\nfast and efficient utilization of multimode quantum memories both in protocols\nand in fundamental tests.\n"}
+{"abstract": "  Given a finite tensor category $\\mathcal{C}$, we prove that a modified trace\non the tensor ideal of projective objects can be obtained from a suitable\ntrivialization of the Nakayama functor as right $\\mathcal{C}$-module functor.\nUsing a result of Costello, this allows us to associate to any finite tensor\ncategory equipped with such a trivialization of the Nakayama functor a chain\ncomplex valued topological conformal field theory, the trace field theory. The\ntrace field theory topologically describes the modified trace, the\nHattori-Stallings trace, and also the structures induced by them on the\nHochschild complex of $\\mathcal{C}$. In this article, we focus on implications\nin the linear (as opposed to differential graded) setting: We use the trace\nfield theory to define a non-unital homotopy commutative product on the\nHochschild chains in degree zero. This product is block diagonal and can be\ndescribed through the handle elements of the trace field theory. Taking the\nmodified trace of the handle elements recovers the Cartan matrix of\n$\\mathcal{C}$.\n"}
+{"abstract": "  The Cattle Tracing System database is an online recording system for cattle\nbirths, deaths and between--herd movements in the United Kingdom. Although it\nhas been thoroughly examined, the most recently reported movement analysis is\nfrom 2009. This article uses the database to construct weighted directed\nmonthly movement networks for two distinct periods of time, 2004--2006 and\n2015--2017, to quantify by how much the underlying structure of the network has\nchanged. Substantial changes in network structure may influence policy--makers\ndirectly or may influence models built upon the network data, and these in turn\ncould impact policy--makers and their assessment of risk. Four general network\nmeasures are used (total number of nodes with movements, movements, births and\ndeaths), in conjunction with network metrics to describe each monthly network.\nTwo updates of the database were examined to determine by how much the movement\ndata stored for a particular time period had been cleansed between updates.\nStatistical models show that there is a statistically significant effect of the\ntime period (2004--2006 vs 2015--2017) in the values of all network measures\nand six of nine network metrics. Changes in the sizes of both the Giant and\nWeakly Strongly Connected components predict reductions in the upper and lower\nbounds of the maximum epidemic size. Examination of the updates of the database\nshow that there are differences in records between updates and therefore\nevidence of historical data changing between updates. Accurate modelling of\ndisease spread through a network requires representative descriptions of the\nnetwork. The authors recommend that where possible the most recent available\ndata always be used for network modelling and that methods of network\nprediction be examined to mitigate for the time required for data to become\navailable.\n"}
+{"abstract": "  In this paper we study relations between various natural structures on\nF-manifolds. In particular, given an arbitrary Riemannian F-manifold we present\na construction of a canonical flat F-manifold associated to it. We also\ndescribe a construction of a canonical homogeneous Riemannian F-manifold\nassociated to an arbitrary exact homogeneous flat pencil of metrics satisfying\na certain non-degeneracy assumption. In the last part of the paper we construct\nLegendre transformations for Riemannian F-manifolds.\n"}
+{"abstract": "  In passive fluid-fluid phase separation, a single interfacial tension sets\nboth the capillary fluctuations of the interface and the rate of Ostwald\nripening. We show that these phenomena are governed by two different tensions\nin active systems, and compute the capillary tension $\\sigma_{cw}$ which sets\nthe relaxation rate of interfacial fluctuations in accordance with capillary\nwave theory. We discover that strong enough activity can cause negative\n$\\sigma_{cw}$. In this regime, depending on the global composition, the system\nself-organizes, either into a microphase-separated state in which coalescence\nis highly inhibited, or into an `active foam' state. Our results are obtained\nfor Active Model B+, a minimal continuum model which, although generic, admits\nsignificant analytical progress.\n"}
+{"abstract": "  This paper develops an unified framework to study finite-sample convergence\nguarantees of a large class of value-based asynchronous reinforcement learning\n(RL) algorithms. We do this by first reformulating the RL algorithms as\n\\textit{Markovian Stochastic Approximation} (SA) algorithms to solve\nfixed-point equations. We then develop a Lyapunov analysis and derive\nmean-square error bounds on the convergence of the Markovian SA. Based on this\nresult, we establish finite-sample mean-square convergence bounds for\nasynchronous RL algorithms such as $Q$-learning, $n$-step TD, TD$(\\lambda)$,\nand off-policy TD algorithms including V-trace. As a by-product, by analyzing\nthe convergence bounds of $n$-step TD and TD$(\\lambda)$, we provide theoretical\ninsights into the bias-variance trade-off, i.e., efficiency of bootstrapping in\nRL. This was first posed as an open problem in (Sutton, 1999).\n"}
+{"abstract": "  This note has two related but independent parts. Firstly, we prove a\ngeneralisation of a recent result of Gay on the smooth mapping class group of\n$S^4$. Secondly, we prove that the Dehn twist along the boundary sphere of a\nsimply-connected closed smooth 4-manifold $X$ with $\\partial X\\cong S^3$ is\ntrivial after taking connected sums with enough copies of $S^2\\times S^2$.\n"}
+{"abstract": "  Pre-stellar cores represent the initial conditions of star formation.\nAlthough these initial conditions in nearby low-mass star-forming regions have\nbeen investigated in detail, such initial conditions remain vastly unexplored\nfor massive star-forming regions. We report the detection of a cluster of\nlow-mass starless and pre-stellar core candidates in a massive star\nprotocluster forming cloud, NGC6334S. With the ALMA observations at a\n$\\sim$0.02 pc spatial resolution, we identified 17 low-mass starless core\ncandidates that do not show any evidence of protostellar activity. These\ncandidates present small velocity dispersions, high fractional abundances of\nNH$_{2}$D, high NH$_{3}$ deuterium fractionations, and are completely dark in\nthe infrared wavelengths from 3.6 up to 70~$\\mu$m. Turbulence is significantly\ndissipated and the gas kinematics are dominated by thermal motions toward these\ncandidates. Nine out of the 17 cores are gravitationally bound, and therefore\nare identified as pre-stellar core candidates. The embedded cores of NGC6334S\nshow a wide diversity in masses and evolutionary stages.\n"}
+{"abstract": "  Manually determining concepts present in a group of questions is a\nchallenging and time-consuming process. However, the process is an essential\nstep while modeling a virtual learning environment since a mapping between\nconcepts and questions using mastery level assessment and recommendation\nengines are required. We investigated unsupervised semantic models (known as\ntopic modeling techniques) to assist computer science teachers in this task and\npropose a method to transform Computer Science 1 teacher-provided code\nsolutions into representative text documents, including the code structure\ninformation. By applying non-negative matrix factorization and latent Dirichlet\nallocation techniques, we extract the underlying relationship between questions\nand validate the results using an external dataset. We consider the\ninterpretability of the learned concepts using 14 university professors' data,\nand the results confirm six semantically coherent clusters using the current\ndataset. Moreover, the six topics comprise the main concepts present in the\ntest dataset, achieving 0.75 in the normalized pointwise mutual information\nmetric. The metric correlates with human ratings, making the proposed method\nuseful and providing semantics for large amounts of unannotated code.\n"}
+{"abstract": "  Political organizations worldwide keep innovating their use of social media\ntechnologies. In the 2019 Indian general election, organizers used a network of\nWhatsApp groups to manipulate Twitter trends through coordinated mass postings.\nWe joined 600 WhatsApp groups that support the Bharatiya Janata Party, the\nright-wing party that won the general election, to investigate these campaigns.\nWe found evidence of 75 hashtag manipulation campaigns in the form of\nmobilization messages with lists of pre-written tweets. Building on this\nevidence, we estimate the campaigns' size, describe their organization and\ndetermine whether they succeeded in creating controlled social media\nnarratives. Our findings show that the campaigns produced hundreds of\nnationwide Twitter trends throughout the election. Centrally controlled but\nvoluntary in participation, this hybrid configuration of technologies and\norganizational strategies shows how profoundly online tools transform campaign\npolitics. Trend alerts complicate the debates over the legitimate use of\ndigital tools for political participation and may have provided a blueprint for\nparticipatory media manipulation by a party with popular support.\n"}
+{"abstract": "  Plasmon-induced transparency (PIT) in advanced materials has attracted\nextensive attention for both theoretical and applied physics. Here, we\nconsidered a scheme that can produce PIT and studied the characteristics of\nultraslow low-power magnetic solitons. The PIT metamaterial is constructed as\nan array of unit cells that consist of two coupled varactor-loaded split-ring\nresonators. Simulations verified that ultraslow magnetic solitons can be\ngenerated in this type of metamaterial. To solve nonlinear equations, various\ntypes of numerical methods can be applied by virtue of exact solutions, which\nare always difficult to acquire. However, the initial conditions and\npropagation distance impact the ultimate results. In this article, an\nartificial neural network (ANN) was used as a supervised learning model to\npredict the evolution and final mathematical expressions through training based\non samples with disparate initial conditions. Specifically, the influences of\nthe number of hidden layers were discussed. Additionally, the learning results\nobtained by employing several training algorithms were analyzed and compared.\nOur research opens a route for employing machine learning algorithms to save\ntime in both physical and engineering applications of Schr\\\"odinger-type\nsystems.\n"}
+{"abstract": "  We provide a variety of cases in which two factorizations have Hurwitz orbits\nof the same size. We begin with prototypical results about factorizations of\nlength two, and show that cycling elements or flipping and inverting elements\nin any factorization preserves Hurwitz orbit size. We prove that \"double\nreverse\" factorizations in groups with special presentations have Hurwitz\norbits of equal size, and end with applications to complex reflection groups.\n"}
+{"abstract": "  We demonstrate that the pair-correlation function of Bose-Einstein condensed\natoms, which are confined in a quasi-one-dimensional toroidal/annular\npotential, realizes a quantum time crystal in the ground state of the system.\nWe study this observable, assuming weak repulsive interatomic interactions and\na finite number of atoms.\n"}
+{"abstract": "  Radiation reaction (RR) terms at the third post-Minkowskian (3PM) order have\nrecently been found to be instrumental in restoring smooth continuity between\nthe non-relativistic, relativistic, and ultra-relativistic (including the\nmassless) regimes. Here we propose a new and intriguing connection between RR\nand soft (bremsstrahlung) theorems which short-circuits the more involved\nconventional loop computations. Although first noticed in the context of the\nmaximally supersymmetric theory, unitarity and analyticity arguments support\nthe general validity of this 3PM-order connection that we apply, in particular,\nto Einstein's gravity and to its Jordan-Brans-Dicke extension. In the former\ncase we find full agreement with a recent result by Damour obtained through a\nvery different reasoning.\n"}
+{"abstract": "  We present an open-source library of natively differentiable physics and\nrobotics environments, accompanied by gradient-based control methods and a\nbenchmark-ing suite. The introduced environments allow auto-differentiation\nthrough the simulation dynamics, and thereby permit fast training of\ncontrollers. The library features several popular environments, including\nclassical control settings from OpenAI Gym. We also provide a novel\ndifferentiable environment, based on deep neural networks, that simulates\nmedical ventilation. We give several use-cases of new scientific results\nobtained using the library. This includes a medical ventilator simulator and\ncontroller, an adaptive control method for time-varying linear dynamical\nsystems, and new gradient-based methods for control of linear dynamical systems\nwith adversarial perturbations.\n"}
+{"abstract": "  In this paper we introduce and study the theories of pseudo links and\nsingular links in the Solid Torus, ST. Pseudo links are links with some missing\ncrossing information that naturally generalize the notion of knot diagrams, and\nthat have potential use in molecular biology, while singular links are links\nthat contain a finite number of self-intersections. We consider pseudo links\nand singular links in ST and we set up the appropriate topological theory in\norder to construct invariants for these types of links in ST. In particular, we\nformulate and prove the analogue of the Alexander theorem for pseudo links and\nfor singular links in ST. We then introduce the mixed pseudo braid monoid and\nthe mixed singular braid monoid, with the use of which, we formulate and prove\nthe analogue of the Markov theorem for pseudo links and for singular links in\nST. \\smallbreak Moreover, we introduce the pseudo Hecke algebra of type A,\n$P\\mathcal{H}_n$, the cyclotomic and generalized pseudo Hecke algebras of type\nB, $P\\mathcal{H}_{1, n}$, and discuss how the pseudo braid monoid (cor. the\nmixed pseudo braid monoid) can be represented by $P\\mathcal{H}_{n}$ (cor. by\n$P\\mathcal{H}_{1, n}$). This is the first step toward the construction of\nHOMFLYPT-type invariants for pseudo links in $S^3$ and in ST. We also introduce\nthe cyclotomic and generalized singular Hecke algebras of type B,\n$S\\mathcal{H}_{1, n}$, and we present two sets that we conjecture that they\nform linear bases for $S\\mathcal{H}_{1, n}$. Finally, we generalize the bracket\npolynomial for pseudo links in ST.\n"}
+{"abstract": "  In this paper, we consider the sparse least squares regression problem with\nprobabilistic simplex constraint. Due to the probabilistic simplex constraint,\none could not apply the L1 regularization to the considered regression model.\nTo find a sparse solution, we reformulate the least squares regression problem\nas a nonconvex and nonsmooth L1 regularized minimization problem over the unit\nsphere. Then we propose a geometric proximal gradient method for solving the\nregularized problem, where the explicit expression of the global solution to\nevery involved subproblem is obtained. The global convergence of the proposed\nmethod is established under some mild assumptions. Some numerical results are\nreported to illustrate the effectiveness of the proposed algorithm.\n"}
+{"abstract": "  Recently, methods based on Convolutional Neural Networks (CNN) achieved\nimpressive success in semantic segmentation tasks. However, challenges such as\nthe class imbalance and the uncertainty in the pixel-labeling process are not\ncompletely addressed. As such, we present a new approach that calculates a\nweight for each pixel considering its class and uncertainty during the labeling\nprocess. The pixel-wise weights are used during training to increase or\ndecrease the importance of the pixels. Experimental results show that the\nproposed approach leads to significant improvements in three challenging\nsegmentation tasks in comparison to baseline methods. It was also proved to be\nmore invariant to noise. The approach presented here may be used within a wide\nrange of semantic segmentation methods to improve their robustness.\n"}
+{"abstract": "  Classification between different activities in an indoor environment using\nwireless signals is an emerging technology for various applications, including\nintrusion detection, patient care, and smart home. Researchers have shown\ndifferent methods to classify activities and their potential benefits by\nutilizing WiFi signals. In this paper, we analyze classification of moving\nobjects by employing machine learning on real data from a massive\nmulti-input-multi-output (MIMO) system in an indoor environment. We conduct\nmeasurements for different activities in both line-of-sight and non\nline-of-sight scenarios with a massive MIMO testbed operating at 3.7 GHz. We\npropose algorithms to exploit amplitude and phase-based features classification\ntask. For the considered setup, we benchmark the classification performance and\nshow that we can achieve up to 98% accuracy using real massive MIMO data, even\nwith a small number of experiments. Furthermore, we demonstrate the gain in\nperformance results with a massive MIMO system as compared with that of a\nlimited number of antennas such as in WiFi devices.\n"}
+{"abstract": "  This paper deals with the quasilinear fully parabolic attraction-repulsion\nchemotaxis system \\begin{align*} u_t=\\nabla \\cdot (D(u)\\nabla u)\n  -\\nabla \\cdot (G(u)\\chi(v)\\nabla v)\n  +\\nabla\\cdot(H(u)\\xi(w)\\nabla w), \\quad v_t=d_1\\Delta v+\\alpha u-\\beta v,\n\\quad w_t=d_2\\Delta w+\\gamma u-\\delta w, \\quad x \\in \\Omega,\\ t>0, \\end{align*}\nunder homogeneous Neumann boundary conditions and initial conditions, where\n$\\Omega \\subset \\mathbb{R}^n$ $(n \\ge 1)$ is a bounded domain with smooth\nboundary, $d_1, d_2, \\alpha, \\beta, \\gamma, \\delta>0$ are constants. Also, the\ndiffusivity $D$, the density-dependent sensitivities $G, H$ fulfill\n$D(s)=a_0(s+1)^{m-1}$ with $a_0>0$ and $m \\in \\mathbb{R}$; $0 \\le G(s) \\le\nb_0(s+1)^{q-1}$ with $b_0>0$ and $q<\\min\\{2,\\ m+1\\}$; $0 \\le H(s) \\le\nc_0(s+1)^{r-1}$ with $c_0>0$ and $r<\\min\\{2,\\ m+1\\}$, and the signal-dependent\nsensitivities $\\chi, \\xi$ satisfy $0<\\chi(s)\\le \\frac{\\chi_0}{s^{k_1}}$ with\n$\\chi_0>0$ and $k_1>1$; $0<\\xi(s)\\le \\frac{\\xi_0}{s^{k_2}}$ with $\\xi_0>0$ and\n$k_2>1$. Global existence and boundedness in the case that $w=0$ were proved by\nDing (J. Math. Anal. Appl.; 2018;461;1260-1270) and Jia-Yang (J. Math. Anal.\nAppl.; 2019;475;139-153). However, there is no work on the above fully\nparabolic attraction-repulsion chemotaxis system with nonlinear diffusion and\nsignal-dependent sensitivity. This paper develops global existence and\nboundedness of classical solutions to the above system by introducing a new\ntest function.\n"}
+{"abstract": "  Simple polyhedra are $2$-dimensional polyhedra and important objects in\nlow-dimensional geometry and in the applications of fold maps, defined as\nsmooth maps regarded as higher dimensional variants of Morse functions. They\nare also important since they are locally so-called Reeb spaces of (so-called\nstable) fold maps into the plane and represent the manifolds compactly. The\nReeb space of a fold map is defined as the space of all connected components of\npreimages of the map and is a polyhedron whose dimension is same as that of the\nmanifold of the target. Is a given simple polyhedron homeomorphic to the Reeb\nspace of a suitable stable fold map? What can we say about their global\ntopologies? Previously the author has challenged this for a specific case and\npresented fundamental construction and topological properties of the polyhedra\nas related new results. The present paper extends some of these works and\nresults and present new related results.\n"}
+{"abstract": "  The peculiar motion of the observer, if not accurately accounted for, is\nbound to induce a well-defined clustering signal in the distribution of\ngalaxies. This signal is related to the Kaiser rocket effect. Here we examine\nthe amplitude and form of this effect, both analytically and numerically, and\ndiscuss possible implications for the analysis and interpretation of\nforthcoming cosmological surveys. For an idealistic cosmic variance dominated\nfull-sky survey with a Gaussian selection function peaked at $z\\sim 1.5$ it is\na $> 5\\sigma$ effect and it can in principle bias very significantly the\ninference of cosmological parameters, especially for primordial\nnon-Gaussianity. For forthcoming surveys, with realistic masks and selection\nfunctions, the Kaiser rocket is not a significant concern for cosmological\nparameter inference. However, it is a systematic effect whose origin, nature\nand imprint on galaxy maps are well known and thus should be subtracted or\nmitigated. We present several approaches to do so.\n"}
+{"abstract": "  The space of $n \\times m$ complex matrices can be regarded as an algebraic\nvariety on which the group ${\\bf GL}_n \\times {\\bf GL}_m$ acts. There is a rich\ninteraction between geometry and representation theory in this example. In an\nimportant paper, de Concini, Eisenbud, and Procesi classified the equivariant\nideals in the coordinate ring. More recently, we proved a noetherian result for\nfamilies of equivariant modules as $n$ and $m$ vary. In this paper, we\nestablish analogs of these results for the space of $(n|n) \\times (m|m)$\nisomeric matrices with respect to the action of ${\\bf Q}_n \\times {\\bf Q}_m$,\nwhere ${\\bf Q}_n$ is the automorphism group of the isomeric structure (commonly\nknown as the \"queer supergroup\"). Our work is motivated by connections to the\nBrauer category and the theory of twisted commutative algebras.\n"}
+{"abstract": "  Models focusing on interactions between land-use and transportation mostly\nassume an exogenous provision of transportation infrastructures. We investigate\nhere co-evolutionary processes between land-use and transportation, at the\nscale of Mega-City Regions, by introducing a toy model of corresponding\nprocesses. In particular, our model is specifically tailored to include\ngovernance processes ruling the growth of transportation infrastructure. We\nshow through stylised numerical simulations the potentialities of our model to\nreproduce a variety of dynamics when co-evolution is taken into account. We\nthen apply the model to a case study, by calibrating it for the Pearl River\nDelta Mega-city Region (China, 1990-2010). To go beyond this first modelling\nstep, we elaborate on the challenges to overcome to go further towards more\ncomplex models integrating co-evolution between transportation networks and\nterritories in urban systems.\n"}
+{"abstract": "  This paper is concerned with space-time homogenization problems for damped\nwave equations with spatially periodic oscillating elliptic coefficients and\ntemporally (arithmetic) quasi-periodic oscillating viscosity coefficients. Main\nresults consist of a homogenization theorem, qualitative properties of\nhomogenized matrices which appear in homogenized equations and a corrector\nresult for gradients of solutions. In particular, homogenized equations and\ncell problems will turn out to deeply depend on the quasi-periodicity as well\nas the log ratio of spatial and temporal periods of the coefficients. Even\ntypes of equations will change depending on the log ratio and\nquasi-periodicity. Proofs of the main results are based on a (very weak)\nspace-time two-scale convergence theory.\n"}
+{"abstract": "  The trigonal/bcc orientation relationships (ORs) and their likelihood of\noccurrence are extensively studied using dispersed Y4Zr3O12nano-precipitates in\nbcc Fe matrix by means of transmission electron microscopy, image simulations,\nand a crystallographic model. Two orientation relationships related by a\nrotation:[1-20]||[111] with (21-2)||(-110) and [1-20]||[111] with\n(00-3)||(-110), are established and periodic arrays of misfit dislocation\ndoublets are identified at the strained interface in (110) for both ORs.\nFurther eighteen energetically feasible ORs in Y4Zr3O12/bcc system are deduced\nby combining stereographic projections, which include the two predominant ORs\nin this study and other ORs in literature. The orientation relationship which\ngenerates the interface with a minimum number of dislocation doublets is the\nmost frequent.\n"}
+{"abstract": "  We study the problem of user association, namely finding the optimal\nassignment of user equipment to base stations to achieve a targeted network\nperformance. In this paper, we focus on the knowledge transferability of\nassociation policies. Indeed, traditional non-trivial user association schemes\nare often scenario-specific or deployment-specific and require a policy\nre-design or re-learning when the number or the position of the users change.\nIn contrast, transferability allows to apply a single user association policy,\ndevised for a specific scenario, to other distinct user deployments, without\nneeding a substantial re-learning or re-design phase and considerably reducing\nits computational and management complexity. To achieve transferability, we\nfirst cast user association as a multi-agent reinforcement learning problem.\nThen, based on a neural attention mechanism that we specifically conceived for\nthis context, we propose a novel distributed policy network architecture, which\nis transferable among users with zero-shot generalization capability i.e.,\nwithout requiring additional training.Numerical results show the effectiveness\nof our solution in terms of overall network communication rate, outperforming\ncentralized benchmarks even when the number of users doubles with respect to\nthe initial training point.\n"}
+{"abstract": "  Neuroscientists often describe neural activity as a representation of\nsomething, or claim to have found evidence for a neural representation. But\nwhat do these statements mean? The reasons to call some neural activity a\nrepresentation and the assumptions that come with this term are not generally\nmade clear from its common uses in neuroscience. Representation is a central\nconcept in philosophy of mind, with a rich history going back to the ancient\nperiod. In order to clarify its usage in neuroscience, here we advance a link\nbetween the connotations of this term across these disciplines. We draw on a\nbroad range of discourse in philosophy to distinguish three key aspects of\nrepresentation: correspondence, functional role, and teleology. We argue that\neach of these aspects are implied by the explanatory role the term plays in\nneuroscience. However, evidence related to all three aspects is rarely\npresented or discussed in the course of individual studies that aim to identify\nrepresentations. Overlooking the significance of all three aspects hinders\ncommunication in neuroscience, as it obscures the limitations of experimental\nparadigms and conceals gaps in our understanding of the phenomena of primary\ninterest. Working from this three-part view, we discuss how to move toward\nclearer communication about representations in the brain.\n"}
+{"abstract": "  Dvir and Moran proved the following upper bound for the size of a family\n$\\mbox{$\\cal F$}$ of subsets of $[n]$ with $\\mbox{Vdim}(\\mbox{$\\cal F$} \\Delta\n\\mbox{$\\cal F$})\\leq d$.\n  Let $d\\leq n$ be integers. Let $\\mbox{$\\cal F$}$ be a family of subsets of\n$[n]$ with $\\mbox{Vdim}(\\mbox{$\\cal F$} \\Delta \\mbox{$\\cal F$})\\leq d$. Then \\[\n\\left|\\mbox{$\\cal F$}\\right|\\le 2\\sum_{k=0}^{\\lfloor d/2 \\rfloor}\\binom nk. \\]\n  Our main result is the following uniform version of Dvir and Moran's result.\n  Let $d\\leq n$ be integers. Let $\\mbox{$\\cal F$}$ be an uniform family of\nsubsets of $[n]$ with $\\mbox{Vdim}(\\mbox{$\\cal F$} \\Delta \\mbox{$\\cal F$})\\leq\nd$. Then \\[ \\left|\\mbox{$\\cal F$}\\right|\\le 2 {n \\choose \\lfloor d/2 \\rfloor}.\n\\]\n  Denote by $\\mathbf{v_F}\\in \\{0,1\\}^n$ the characteristic vector of a set $F\n\\subseteq [n]$.\n  Our proof is based on the following uniform version of Croot-Lev-Pach Lemma:\n  Let $0\\leq d\\leq n$ be integers. Let $\\mbox{$\\cal H$}$ be a $k$-uniform\nfamily of subsets of $[n]$. Let $\\mathbb F$ be a field. Suppose that there\nexists a polynomial $P(x_1, \\ldots ,x_n,y_1, \\ldots ,y_n)\\in \\mathbb F[x_1,\n\\ldots ,x_n,y_1, \\ldots ,y_n]$ with $\\mbox{deg}(P)\\leq d$ such that\n$P(\\mathbf{v_F},\\mathbf{v_F})\\ne 0$ for each $F\\in \\mbox{$\\cal H$}$ and\n$P(\\mathbf{v_F},\\mathbf{v_G})= 0$ for each $F\\ne G\\in \\mbox{$\\cal H$}$. Then \\[\n\\left|\\mbox{$\\cal H$}\\right|\\le 2 {n \\choose \\lfloor d/2 \\rfloor}. \\]\n"}
+{"abstract": "  In this article, we study several aspects of the intersections of\nalgorithmically random closed sets. First, we answer a question of Cenzer and\nWeber, showing that the operation of intersecting relatively random closed sets\n(with respect to certain underlying measures induced by Bernoulli measures on\nthe space of codes of closed sets), which preserves randomness, can be\ninverted: a random closed set of the appropriate type can be obtained as the\nintersection of two relatively random closed sets. We then extend the\nCenzer/Weber analysis to the intersection of multiple random closed sets,\nidentifying the Bernoulli measures with respect to which the intersection of\nrelatively random closed sets can be non-empty. We lastly apply our analysis to\nprovide a characterization of the effective Hausdorff dimension of sequences in\nterms of the degree of intersectability of random closed sets that contain\nthem.\n"}
+{"abstract": "  A small group of postdocs, graduate students, and undergraduates\ninadvertently formed a longitudinal study contrasting expected productivity\nlevels with actual productivity levels. Over the last nine months, our group\nself-reported 559 tasks, dates, and completion times -- expected and actual.\nHere, I show which types of tasks we are the worst at completing in the\noriginally planned amount of time (spoiler: coding and writing tasks), whether\nmore senior researchers have more accurate expectations (spoiler: not much),\nand whether our expectations improve with time (spoiler: only a little).\n"}
+{"abstract": "  We consider two conformal defects close to each other in a free theory, and\nstudy what happens as the distance between them goes to zero. This limit is the\nsame as zooming out, and the two defects have fused to another defect. As we\nzoom in we find a non-conformal effective action for the fused defect. Among\nother things this means that we cannot in general decompose the two-point\ncorrelator of two defects in terms of other conformal defects. We prove the\nfusion using the path integral formalism by treating the defects as sources for\na scalar in the bulk.\n"}
+{"abstract": "  In 2017 Bar-Natan and the first author showed that solutions to the\nKashiwara--Vergne equations are in bijection with certain knot invariants:\nhomomorphic expansions of welded foams. Welded foams are a class of knotted\ntubes in $\\mathbb{R}^4$, which can be finitely presented algebraically as a\ncircuit algebra, or, equivalently, a wheeled prop. In this paper we describe\nthe Kashiwara-Vergne groups $\\mathsf{KV}$ and $\\mathsf{KRV}$ -- the symmetry\ngroups of Kashiwara-Vergne solutions -- as automorphisms of the completed\ncircuit algebras of welded foams, and their associated graded circuit algebra\nof arrow diagrams, respectively. Finally, we provide a description of the\ngraded Grothendieck-Teichm\\\"uller group $\\mathsf{GRT}_1$ as automorphisms of\narrow diagrams.\n"}
+{"abstract": "  Face authentication usually utilizes deep learning models to verify users\nwith high recognition accuracy. However, face authentication systems are\nvulnerable to various attacks that cheat the models by manipulating the digital\ncounterparts of human faces. So far, lots of liveness detection schemes have\nbeen developed to prevent such attacks. Unfortunately, the attacker can still\nbypass these schemes by constructing wide-ranging sophisticated attacks. We\nstudy the security of existing face authentication services (e.g., Microsoft,\nAmazon, and Face++) and typical liveness detection approaches. Particularly, we\ndevelop a new type of attack, i.e., the low-cost 3D projection attack that\nprojects manipulated face videos on a 3D face model, which can easily evade\nthese face authentication services and liveness detection approaches. To this\nend, we propose FaceLip, a novel liveness detection scheme for face\nauthentication, which utilizes unforgeable lip motion patterns built upon\nwell-designed acoustic signals to enable a strong security guarantee. The\nunique lip motion patterns for each user are unforgeable because FaceLip\nverifies the patterns by capturing and analyzing the acoustic signals that are\ndynamically generated according to random challenges, which ensures that our\nsignals for liveness detection cannot be manipulated. Specially, we develop\nrobust algorithms for FaceLip to eliminate the impact of noisy signals in the\nenvironment and thus can accurately infer the lip motions at larger distances.\nWe prototype FaceLip on off-the-shelf smartphones and conduct extensive\nexperiments under different settings. Our evaluation with 44 participants\nvalidates the effectiveness and robustness of FaceLip.\n"}
+{"abstract": "  Compact fiber-to-chip couplers play an important role in optical\ninterconnections, especially in data centers. However, the development of\ncouplers has been mostly limited to standard single mode fibers, with few\ndevices compatible with multicore and multimode fibers. Through the use of\nstate-of-the-art optimization algorithms, we designed a compact\ndual-polarization coupler to interface chips and dense multicore fibers,\ndemonstrating, for the first time, coupling to both polarizations of all the\ncores, with measured coupling efficiency of $-$4.3 dB and with a 3 dB bandwidth\nof 48 nm. The dual-polarization coupler has footprint of 200 $\\mu m^2$ per\ncore, which makes it the smallest fiber-to-chip coupler experimentally\ndemonstrated on a standard silicon-on-insulator platform.\n"}
+{"abstract": "  Co-evolving time series appears in a multitude of applications such as\nenvironmental monitoring, financial analysis, and smart transportation. This\npaper aims to address the following challenges, including (C1) how to\nincorporate explicit relationship networks of the time series; (C2) how to\nmodel the implicit relationship of the temporal dynamics. We propose a novel\nmodel called Network of Tensor Time Series, which is comprised of two modules,\nincluding Tensor Graph Convolutional Network (TGCN) and Tensor Recurrent Neural\nNetwork (TRNN). TGCN tackles the first challenge by generalizing Graph\nConvolutional Network (GCN) for flat graphs to tensor graphs, which captures\nthe synergy between multiple graphs associated with the tensors. TRNN leverages\ntensor decomposition to model the implicit relationships among co-evolving time\nseries. The experimental results on five real-world datasets demonstrate the\nefficacy of the proposed method.\n"}
+{"abstract": "  We present a Greens function approach to calculate the Dzyaloshinskii-Moriya\ninteractions (DMI) from first principles electronic structure calculations,\nthat is computationally more efficient and accurate than the most-commonly\nemployed supercell and generalized Bloch-based approaches. The method is\napplied to the (111) Co/Pt bilayer where the Co- and/or Pt-thickness dependence\nof the DMI coefficients are calculated. Overall, the calculated DMI are in\nrelatively good agreement with the corresponding values reported\nexperimentally. Furthermore, we investigate the effect of strain in the DMI\ntensor elements and show that the isotropic N\\'{e}el DMI can be significantly\nmodulated by the normal strains, $\\epsilon_{xx},\\epsilon_{yy}$ and is\nrelatively insensitive to the shear strain, $\\epsilon_{xy}$. Moreover, we show\nthat anisotropic strains, $(\\epsilon_{xx}-\\epsilon_{yy})$ and\n  $\\epsilon_{xy}$, result in the emergence of anisotropic N\\'{e}el- and\nBloch-type DMIs, respectively.\n"}
+{"abstract": "  We prove a number of limiting distributions for statistics for unimodal\nsequences of positive integers by adapting a probabilistic framework for\ninteger partitions introduced by Fristedt. The difficulty in applying the\ndirect analogue of Fristedt's techniques to unimodal sequences lies in the fact\nthat the generating function for partitions is an infinite product, while that\nof unimodal sequences is not. Essentially, we get around this by conditioning\non the size of the largest part and working uniformly on contributing summands.\nOur framework may be used to derive many distributions, and our results include\njoint distributions for largest parts and multiplicities of small parts. We\ndiscuss ranks as well. We further obtain analogous results for strongly\nunimodal sequences.\n"}
+{"abstract": "  Automatic polyp segmentation has proven to be immensely helpful for endoscopy\nprocedures, reducing the missing rate of adenoma detection for endoscopists\nwhile increasing efficiency. However, classifying a polyp as being neoplasm or\nnot and segmenting it at the pixel level is still a challenging task for\ndoctors to perform in a limited time. In this work, we propose a fine-grained\nformulation for the polyp segmentation problem. Our formulation aims to not\nonly segment polyp regions, but also identify those at high risk of malignancy\nwith high accuracy. In addition, we present a UNet-based neural network\narchitecture called NeoUNet, along with a hybrid loss function to solve this\nproblem. Experiments show highly competitive results for NeoUNet on our\nbenchmark dataset compared to existing polyp segmentation models.\n"}
+{"abstract": "  Multiplayer Online Battle Area (MOBA) games are a recent huge success both in\nthe video game industry and the international eSports scene. These games\nencourage team coordination and cooperation, short and long-term planning,\nwithin a real-time combined action and strategy gameplay.\n  Artificial Intelligence and Computational Intelligence in Games research\ncompetitions offer a wide variety of challenges regarding the study and\napplication of AI techniques to different game genres. These events are widely\naccepted by the AI/CI community as a sort of AI benchmarking that strongly\ninfluences many other research areas in the field.\n  This paper presents and describes in detail the Dota 2 Bot competition and\nthe Dota 2 AI framework that supports it. This challenge aims to join both,\nMOBAs and AI/CI game competitions, inviting participants to submit AI\ncontrollers for the successful MOBA \\textit{Defense of the Ancients 2} (Dota 2)\nto play in 1v1 matches, which aims for fostering research on AI techniques for\nreal-time games. The Dota 2 AI framework makes use of the actual Dota 2 game\nmodding capabilities to enable to connect external AI controllers to actual\nDota 2 game matches using the original Free-to-Play game.se of the actual Dota\n2 game modding capabilities to enable to connect external AI controllers to\nactual Dota 2 game matches using the original Free-to-Play game.\n"}
+{"abstract": "  Simulating the dynamics of many-body quantum systems is believed to be one of\nthe first fields that quantum computers can show a quantum advantage over\nclassical computers. Noisy intermediate-scale quantum (NISQ) algorithms aim at\neffectively using the currently available quantum hardware. For quantum\nsimulation, various types of NISQ algorithms have been proposed with individual\nadvantages as well as challenges. In this work, we propose a new algorithm,\ntruncated Taylor quantum simulator (TTQS), that shares the advantages of\nexisting algorithms and alleviates some of the shortcomings. Our algorithm does\nnot have any classical-quantum feedback loop and bypasses the barren plateau\nproblem by construction. The classical part in our hybrid quantum-classical\nalgorithm corresponds to a quadratically constrained quadratic program (QCQP)\nwith a single quadratic equality constraint, which admits a semidefinite\nrelaxation. The QCQP based classical optimization was recently introduced as\nthe classical step in quantum assisted eigensolver (QAE), a NISQ algorithm for\nthe Hamiltonian ground state problem. Thus, our work provides a conceptual\nunification between the NISQ algorithms for the Hamiltonian ground state\nproblem and the Hamiltonian simulation. We recover differential equation-based\nNISQ algorithms for Hamiltonian simulation such as quantum assisted simulator\n(QAS) and variational quantum simulator (VQS) as particular cases of our\nalgorithm. We test our algorithm on some toy examples on current cloud quantum\ncomputers. We also provide a systematic approach to improve the accuracy of our\nalgorithm.\n"}
+{"abstract": "  Wireless blockchain network is proposed to enable a decentralized and safe\nwireless networks for various blockchain applications. To achieve blockchain\nconsensus in wireless network, one of the important steps is to broadcast new\nblock using wireless channel. Under wireless network protocols, the block\ntransmitting will be affected significantly. In this work, we focus on the\nconsensus process in blockchain-based wireless local area network (B-WLAN) by\ninvestigating the impact of the media access control (MAC) protocol, CSMA/CA.\nWith the randomness of the backoff counter in CSMA/CA, it is possible for\nlatter blocks to catch up or outpace the earlier one, which complicates\nblockchain forking problem. In view of this, we propose mining strategies to\npause mining for reducing the forking probability, and a discard strategy to\nremove the forking blocks that already exist in CSMA/CA backoff procedure.\nBased on the proposed strategies, we design Block Access Control (BAC)\napproaches to effectively schedule block mining and transmitting for improving\nthe performance of B-WLAN. Then, Markov chain models are presented to conduct\nperformance analysis in B-WLAN. The results show that BAC approaches can help\nthe network to achieve a high transaction throughput while improving block\nutilization and saving computational power. Meanwhile, the trade-off between\ntransaction throughput and block utilization is demonstrated, which can act as\na guidance for practical deployment of blockchain.\n"}
+{"abstract": "  Intelligent reflecting surface (IRS) is a new and revolutionary technology\ncapable of reconfiguring the wireless propagation environment by controlling\nits massive low-cost passive reflecting elements. Different from prior works\nthat focus on optimizing IRS reflection coefficients or single-IRS placement,\nwe aim to maximize the minimum throughput of a single-cell multiuser system\naided by multiple IRSs, by joint multi-IRS placement and power control at the\naccess point (AP), which is a mixed-integer non-convex problem with drastically\nincreased complexity with the number of IRSs/users. To tackle this challenge, a\nring-based IRS placement scheme is proposed along with a power control policy\nthat equalizes the users' non-outage probability. An efficient searching\nalgorithm is further proposed to obtain a close-to-optimal solution for\narbitrary number of IRSs/rings. Numerical results validate our analysis and\nshow that our proposed scheme significantly outperforms the benchmark schemes\nwithout IRS and/or with other power control policies. Moreover, it is shown\nthat the IRSs are preferably deployed near AP for coverage range extension,\nwhile with more IRSs, they tend to spread out over the cell to cover more and\nget closer to target users.\n"}
+{"abstract": "  Various remote sensing observations have been used so far to probe the\nturbulent properties of the solar wind. Using the recently reported density\nmodulation indices that are derived using angular broadening observations of\nCrab Nebula during 1952 - 2013, we measured the solar wind proton heating using\nthe kinetic $\\rm Alfv\\acute{e}n$ wave dispersion equation. The estimated\nheating rates vary from $\\approx 1.58 \\times 10^{-14}$ to $1.01 \\times 10^{-8}\n~\\rm erg~ cm^{-3}~ s^{-1}$ in the heliocentric distance range 5 - 45 $\\rm\nR_{\\odot}$. Further, we found that heating rates vary with the solar cycle in\ncorrelation with density modulation indices. The models derived using in-situ\nmeasurements (for example, electron/proton density, temperature, and magnetic\nfield) that the recently launched Parker Solar Probe observes (planned closest\nperihelia $\\rm 9.86~ R_{\\odot}$ from the center of the Sun) are useful in the\nestimation of the turbulent heating rate precisely. Further, we compared our\nheating rate estimates with the one derived using previously reported remote\nsensing and in-situ observations.\n"}
+{"abstract": "  The Metis coronagraph is one of the remote sensing instruments hosted on\nboard the ESA/NASA Solar Orbiter mission. Metis is devoted to carry out the\nfirst simultaneous imaging of the solar corona in both visible light (VL) and\nultraviolet (UV). High-energy particles penetrate spacecraft materials and may\nlimit the performance of on-board instruments. A study of galactic cosmic-ray\n(GCR) tracks observed in the first VL images gathered by Metis during the\ncommissioning phase for a total of 60 seconds of exposure time is presented\nhere. A similar analysis is planned for the UV channel. A prediction of the GCR\nflux up to hundreds of GeV is made here for the first part of the Solar Orbiter\nmission to study the Metis coronagraph performance. GCR model predictions are\ncompared to observations gathered on board Solar Orbiter by the EPD/HET\nexperiment in the range 10 MeV-100 MeV in the summer 2020 and with previous\nmeasurements. Estimated cosmic-ray fluxes above 70 MeV n$^{-1}$ have been also\nparameterized and used for Monte Carlo simulations aiming at reproducing the\ncosmic-ray track observations in the Metis coronagraph VL images. The same\nparameterizations can also be used to study the performance of other detectors.\nBy comparing observations of cosmic-ray tracks in the Metis VL images with\nFLUKA Monte Carlo simulations of cosmic-ray interactions in the VL detector, it\nis found that cosmic rays fire a fraction of the order of 10$^{-4}$ of the\nwhole image pixel sample. Therefore, cosmic rays do not affect sensibly the\nquality of Metis VL images. It is also found that the overall efficiency for\ncosmic-ray identification in the Metis VL images is approximately equal to the\ncontribution of Z$>$2 particles. As a result, the Metis coronagraph may play\nthe role of a proton monitor for long-term GCR variations during the overall\nmission duration.\n"}
+{"abstract": "  Recent re-determination of stellar atmospheric parameters for a sample of\nstars observed during the {\\it Kepler} mission allowed to enlarge the number of\n{\\it Kepler} B-type stars. We present the detailed frequency analysis for all\nthese objects. All stars exhibit pulsational variability with maximum\namplitudes at frequencies corresponding to high-order g modes. Peaks that could\nbe identified with low-order p/g modes are also extracted for a few stars. We\nidentified some patters in the oscillation spectra that can be associated with\nthe period spacings that can result from the asymptotic nature of the detected\npulsational modes. We also tentatively confront the observed oscillation\ncharacteristics with predictions from linear nonadiabatic computations of\nstellar pulsations. For high-order g modes the traditional approximation was\nemployed to include the effects of rotation on the frequency values and mode\ninstability.\n"}
+{"abstract": "  Humor is an important social phenomenon, serving complex social and\npsychological functions. However, despite being studied for millennia humor is\ncomputationally not well understood, often considered an AI-complete problem.\nIn this work, we introduce a novel setting in humor mining: automatically\ndetecting funny and unusual scientific papers. We are inspired by the Ig Nobel\nprize, a satirical prize awarded annually to celebrate funny scientific\nachievements (example past winner: \"Are cows more likely to lie down the longer\nthey stand?\"). This challenging task has unique characteristics that make it\nparticularly suitable for automatic learning. We construct a dataset containing\nthousands of funny papers and use it to learn classifiers, combining findings\nfrom psychology and linguistics with recent advances in NLP. We use our models\nto identify potentially funny papers in a large dataset of over 630,000\narticles. The results demonstrate the potential of our methods, and more\nbroadly the utility of integrating state-of-the-art NLP methods with insights\nfrom more traditional disciplines.\n"}
+{"abstract": "  Privacy preference signals are digital representations of how users want\ntheir personal data to be processed. Such signals must be adopted by both the\nsender (users) and intended recipients (data processors). Adoption represents a\ncoordination problem that remains unsolved despite efforts dating back to the\n1990s. Browsers implemented standards like the Platform for Privacy Preferences\n(P3P) and Do Not Track (DNT), but vendors profiting from personal data faced\nfew incentives to receive and respect the expressed wishes of data subjects. In\nthe wake of recent privacy laws, a coalition of AdTech firms published the\nTransparency and Consent Framework (TCF), which defines an opt-in consent\nsignal. This paper integrates post-GDPR developments into the wider history of\nprivacy preference signals. Our main contribution is a high-frequency\nlongitudinal study describing how TCF signal gained dominance as of February\n2021. We explore which factors correlate with adoption at the website level.\nBoth the number of third parties on a website and the presence of Google Ads\nare associated with higher adoption of TCF. Further, we show that vendors acted\nas early adopters of TCF 2.0 and provide two case-studies describing how\nConsent Management Providers shifted existing customers to TCF 2.0. We sketch\nways forward for a pro-privacy signal.\n"}
+{"abstract": "  Concerns for the privacy of communication is widely discussed in research and\noverall society. For the public financial infrastructure of blockchains, this\ndiscussion encompasses the privacy of transaction data and its broadcasting\nthroughout the network. To tackle this problem, we transform a discrete-time\nprotocol for contact networks over infinite trees into a computer network\nprotocol for peer-to-peer networks. Peer-to-peer networks are modeled as\norganically growing graphs. We show that the distribution of shortest paths in\nsuch a network can be modeled using a normal distribution\n$\\mathcal{N}(\\mu,\\sigma^2).$ We determine statistical estimators for\n$\\mu,\\sigma$ via multivariate models. The model behaves logarithmic over the\nnumber of nodes n and proportional to an inverse exponential over the number of\nadded edges k. These results facilitate the computation of optimal forwarding\nprobabilities during the dissemination phase for optimal privacy in a limited\ninformation environment.\n"}
+{"abstract": "  Along with the rapid expansion of information technology and digitalization\nof health data, there is an increasing concern on maintaining data privacy\nwhile garnering the benefits in medical field. Two critical challenges are\nidentified: Firstly, medical data is naturally distributed across multiple\nlocal sites, making it difficult to collectively train machine learning models\nwithout data leakage. Secondly, in medical applications, data are often\ncollected from different sources and views, resulting in heterogeneity and\ncomplexity that requires reconciliation. This paper aims to provide a generic\nFederated Multi-View Learning (FedMV) framework for multi-view data leakage\nprevention, which is based on different types of local data availability and\nenables to accommodate two types of problems: Vertical Federated Multi-View\nLearning (V-FedMV) and Horizontal Federated Multi-View Learning (H-FedMV). We\nexperimented with real-world keyboard data collected from BiAffect study. The\nresults demonstrated that the proposed FedMV approach can make full use of\nmulti-view data in a privacy-preserving way, and both V-FedMV and H-FedMV\nmethods perform better than their single-view and pairwise counterparts.\nBesides, the proposed model can be easily adapted to deal with multi-view\nsequential data in a federated environment, which has been modeled and\nexperimentally studied. To the best of our knowledge, this framework is the\nfirst to consider both vertical and horizontal diversification in the\nmulti-view setting, as well as their sequential federated learning.\n"}
+{"abstract": "  For a list $\\cal{L}$ of finite groups and for a profinite group $G$, we\nconsider the intersection $T(G)$ of all open normal subgroups $N$ of $G$ with\n$G/N$ in $\\cal{L}$. We give a cohomological characterization of the\nepimorphisms $\\pi\\colon S\\to G$ of profinite groups (satisfying some additional\nrequirements) such that $\\pi[T(S)]=T(G)$. For $p$ prime, this is used to\ndescribe cohomologically the profinite groups $G$ whose $n$th term $G_{(n,p)}$\n(resp., $G^{(n,p)}$) in the $p$-Zassenhaus filtration (resp., lower $p$-central\nfiltration) is an intersection of this form. When $G=G_F$ is the absolute\nGalois group of a field $F$ containing a root of unity of order $p$, we recover\nas special cases results by Minac, Spira and the author, describing $G_{(3,p)}$\nand $G^{(3,p)}$ as $T(G)$ for appropriate lists $\\cal{L}$.\n"}
+{"abstract": "  This paper proposes the SPARK dataset as a new unique space object\nmulti-modal image dataset. Image-based object recognition is an important\ncomponent of Space Situational Awareness, especially for applications such as\non-orbit servicing, active debris removal, and satellite formation. However,\nthe lack of sufficient annotated space data has limited research efforts in\ndeveloping data-driven spacecraft recognition approaches. The SPARK dataset has\nbeen generated under a realistic space simulation environment, with a large\ndiversity in sensing conditions for different orbital scenarios. It provides\nabout 150k images per modality, RGB and depth, and 11 classes for spacecrafts\nand debris. This dataset offers an opportunity to benchmark and further develop\nobject recognition, classification and detection algorithms, as well as\nmulti-modal RGB-Depth approaches under space sensing conditions. Preliminary\nexperimental evaluation validates the relevance of the data, and highlights\ninteresting challenging scenarios specific to the space environment.\n"}
+{"abstract": "  We propose a computationally efficient method to construct nonparametric,\nheteroskedastic prediction bands for uncertainty quantification, with or\nwithout any user-specified predictive model. The data-adaptive prediction band\nis universally applicable with minimal distributional assumptions, with strong\nnon-asymptotic coverage properties, and easy to implement using standard convex\nprograms. Our approach can be viewed as a novel variance interpolation with\nconfidence and further leverages techniques from semi-definite programming and\nsum-of-squares optimization. Theoretical and numerical performances for the\nproposed approach for uncertainty quantification are analyzed.\n"}
+{"abstract": "  Failures in optical network backbone can cause significant interruption in\ninternet data traffic. Hence, it is very important to reduce such network\noutages. Prediction of such failures would be a step forward to avoid such\ndisruption of internet services for users as well as operators. Several\nresearch proposals are available in the literature which are applications of\ndata science and machine learning techniques. Most of the techniques rely on\nsignificant amount of real time data collection. Network devices are assumed to\nbe equipped to collect data and these are then analysed by different algorithms\nto predict failures. Every network element which is already deployed in the\nfield may not have these data gathering or analysis techniques designed into\nthem initially. However, such mechanisms become necessary later when they are\nalready deployed in the field. This paper proposes a Bayesian network based\nfailure prediction of network nodes, e.g., routers etc., using very basic\ninformation from the log files of the devices and applying power law based data\naugmentation to complement for scarce real time information. Numerical results\nshow that network node failure prediction can be performed with high accuracy\nusing the proposed mechanism.\n"}
+{"abstract": "  The algorithmic independence of conditionals, which postulates that the\ncausal mechanism is algorithmically independent of the cause, has recently\ninspired many highly successful approaches to distinguish cause from effect\ngiven only observational data. Most popular among these is the idea to\napproximate algorithmic independence via two-part Minimum Description Length\n(MDL). Although intuitively sensible, the link between the original postulate\nand practical two-part MDL encodings is left vague. In this work, we close this\ngap by deriving a two-part formulation of this postulate, in terms of\nKolmogorov complexity, which directly links to practical MDL encodings. To\nclose the cycle, we prove that this formulation leads on expectation to the\nsame inference result as the original postulate.\n"}
+{"abstract": "  We investigate Referring Image Segmentation (RIS), which outputs a\nsegmentation map corresponding to the given natural language description. To\nsolve RIS efficiently, we need to understand each word's relationship with\nother words, each region in the image to other regions, and cross-modal\nalignment between linguistic and visual domains. We argue that one of the\nlimiting factors in the recent methods is that they do not handle these\ninteractions simultaneously. To this end, we propose a novel architecture\ncalled JRNet, which uses a Joint Reasoning Module(JRM) to concurrently capture\nthe inter-modal and intra-modal interactions. The output of JRM is passed\nthrough a novel Cross-Modal Multi-Level Fusion (CMMLF) module which further\nrefines the segmentation masks by exchanging contextual information across\nvisual hierarchy through linguistic features acting as a bridge. We present\nthorough ablation studies and validate our approach's performance on four\nbenchmark datasets, showing considerable performance gains over the existing\nstate-of-the-art methods.\n"}
+{"abstract": "  A relation between matrix-valued complete Bernstein functions and\nmatrix-valued Stieltjes functions is applied to prove that the solutions of\nmatricial convolution equations with extended LICM kernels belong to special\nclasses of functions. In particular the cases of the solutions of the\nviscoelastic duality relation and the solutions of the matricial Sonine\nequation are discussed, with applications in anisotropic linear viscoelasticity\nand a generalization of fractional calculus.\n  In the first case it is in particular shown that duality of completely\nmonotone relaxation functions and Bernstein creep functions in general requires\ninclusion in the relaxation function of a Newtonian viscosity term in addition\nto the memory effects represented by the completely monotone kernel.\n  We define anisotropic generalized fractional derivatives (GFD) by replacing\nthe kernel $t^{-\\alpha}/\\Gamma(1-\\alpha)$ of the Caputo derivatives with\ncompletely monotone matrix-valued kernels which are weakly singular at 0.\n"}
+{"abstract": "  Formal Methods for the Informal Engineer (FMIE) was a workshop held at the\nBroad Institute of MIT and Harvard in 2021 to explore the potential role of\nverified software in the biomedical software ecosystem. The motivation for\norganizing FMIE was the recognition that the life sciences and medicine are\nundergoing a transition from being passive consumers of software and AI/ML\ntechnologies to fundamental drivers of new platforms, including those which\nwill need to be mission and safety-critical. Drawing on conversations leading\nup to and during the workshop, we make five concrete recommendations to help\nsoftware leaders organically incorporate tools, techniques, and perspectives\nfrom formal methods into their project planning and development trajectories.\n"}
+{"abstract": "  In this article, we construct samples of SLE-like curves out of samples of\nCLE and Poisson point process of Brownian excursions. We show that the law of\nthese curves depends continuously on the intensity measure of the Brownian\nexcursions. Using such construction of curves, we extend the notion of level\nlines of GFF to the case when the boundary condition is measure-valued.\n"}
+{"abstract": "  We report on experimental investigation of thermal contact resistance of the\nnoncuring graphene thermal interface materials with the surfaces characterized\nby different degree of roughness. It is found that the thermal contact\nresistance depends on the graphene loading non-monotonically, achieving its\nminimum at the loading fraction of ~15 wt.%. Increasing the surface roughness\nby ~1 micrometer results in approximately the factor of x2 increase in the\nthermal contact resistance for this graphene loading. The obtained dependences\nof the thermal conductivity, thermal contact resistance, and the total thermal\nresistance of the thermal interface material layer on the graphene loading and\nsurface roughness indicate the need for optimization of the loading fraction\nfor specific materials and roughness of the connecting surfaces. Our results\nare important for developing graphene technologies for thermal management of\nhigh-power-density electronics.\n"}
+{"abstract": "  The exact solutions of both the cubic Duffing equation and cubic-quintic\nDuffing equation are presented by using only leaf functions. In previous\nstudies, exact solutions of the cubic Duffing equation have been proposed using\nfunctions that integrate leaf functions in the phase of trigonometric\nfunctions. Because they are not simple, the procedures for transforming the\nexact solutions are complicated and not convenient. The first derivative of the\nleaf function can be derived as the root. This derivative can be factored.\nThese factors or multiplications of factors are exact solutions to the Duffing\nequation. Some of these exact solutions are of the same type as the cubic\nDuffing equation reported in previously. Some of these exact solutions satisfy\nthe exact solutions of the cubic--quintic Duffing equations with high\nnonlinearity. In this study, the relationship between the parameters of these\nexact solutions and the coefficients of the terms of the Duffing equation is\nclarified. We numerically analyze these exact solutions, plot the waveform, and\ndiscuss the periodicity and amplitude of the waveform.\n"}
+{"abstract": "  Recently predicted and observed Pauli crystals are structures formed by\ntrapped ultracold non-interacting particles obeying Fermi statistics. The\nrelative positions of the particles are determined by the trapping potential\nand the Pauli exclusion principle. Measuring all particles at once, their\npositions tend to be close to vertices of non-trivial polyhedrons. Similar\nshapes are found for systems of particles bound to the surface of a sphere.\n"}
+{"abstract": "  Least squares estimators, when trained on a few target domain samples, may\npredict poorly. Supervised domain adaptation aims to improve the predictive\naccuracy by exploiting additional labeled training samples from a source\ndistribution that is close to the target distribution. Given available data, we\ninvestigate novel strategies to synthesize a family of least squares estimator\nexperts that are robust with regard to moment conditions. When these moment\nconditions are specified using Kullback-Leibler or Wasserstein-type\ndivergences, we can find the robust estimators efficiently using convex\noptimization. We use the Bernstein online aggregation algorithm on the proposed\nfamily of robust experts to generate predictions for the sequential stream of\ntarget test samples. Numerical experiments on real data show that the robust\nstrategies may outperform non-robust interpolations of the empirical least\nsquares estimators.\n"}
+{"abstract": "  For the fractional quantum Hall states on a finite disc, we study the\nthermoelectric transport properties under the influence of an edge and its\nreconstruction. In a recent study on a torus [Phys. Rev. B 101, 241101 (2020)],\nSheng and Fu found a universal non-Fermi liquid power-law scaling of the\nthermoelectric conductivity $\\alpha_{xy} \\propto T^{\\eta}$ for the gapless\ncomposite Fermi-liquid state. The exponent $\\eta \\sim 0.5$ appears an\nindependence of the filling factors and the details of the interactions. In the\npresence of an edge, we find the properties of the edge spectrum dominants the\nlow-temperature behaviors and breaks the universal scaling law of the\nthermoelectric conductivity. In order to consider individually the effects of\nthe edge states, the entanglement spectrum in real space is employed and tuned\nby varying the area of subsystem. In non-Abelian Moore-Read state, the Majorana\nneutral edge mode is found to have more significant effect than that of the\ncharge mode in the low temperature.\n"}
+{"abstract": "  The spread of the novel coronavirus disease caused schools in Japan to close\nto cope with the pandemic. In response to this, parents of students were\nobliged to care for their children during the daytime when they were usually at\nschool. Does the increase in burden of childcare influence parents mental\nhealth? Based on short panel data from mid-March to mid-April 2020, we explored\nhow school closures influenced the mental health of parents with school-aged\nchildren. Using the fixed effects model, we found that school closures lead to\nstudents mothers suffering from worse mental health than other females, while\nthe fathers mental health did not differ from other males. This tendency was\nonly observed for less educated mothers who had children attending primary\nschool, but not those attending junior high school. The contribution of this\npaper is to show that school closures increased the inequality of mental health\nbetween genders and the educational background of parents.\n"}
+{"abstract": "  In this paper, we propose an approach named psc2code to denoise the process\nof extracting source code from programming screencasts. First, psc2code\nleverages the Convolutional Neural Network based image classification to remove\nnon-code and noisy-code frames. Then, psc2code performs edge detection and\nclustering-based image segmentation to detect sub-windows in a code frame, and\nbased on the detected sub-windows, it identifies and crops the screen region\nthat is most likely to be a code editor. Finally, psc2code calls the API of a\nprofessional OCR tool to extract source code from the cropped code regions and\nleverages the OCRed cross-frame information in the programming screencast and\nthe statistical language model of a large corpus of source code to correct\nerrors in the OCRed source code.\n  We conduct an experiment on 1,142 programming screencasts from YouTube. We\nfind that our CNN-based image classification technique can effectively remove\nthe non-code and noisy-code frames, which achieves an F1-score of 0.95 on the\nvalid code frames. Based on the source code denoised by psc2code, we implement\ntwo applications: 1) a programming screencast search engine; 2) an\ninteraction-enhanced programming screencast watching tool. Based on the source\ncode extracted from the 1,142 collected programming screencasts, our\nexperiments show that our programming screencast search engine achieves the\nprecision@5, 10, and 20 of 0.93, 0.81, and 0.63, respectively.\n"}
+{"abstract": "  Multipole representation is proposed for the anisotropic Coulomb interactions\nin solids. Any local interactions can be expressed as the product of two\nmultipole operators, and the interaction parameters are systematically\nclassified based on the point group symmetry. The form of the multipole\ninteractions are restricted not only by the symmetry and Hermiticity but also\nby the spatial structure of the interaction, which is closely related to the\npresence or absence of the odd-rank multipoles. As an exemplary demonstration,\nthe screened Coulomb interaction for SrVO$_3$ is considered, where only a few\nparameters are necessary for its description. By comparing it with the\nunscreened version, the totally symmetric $A_1$ representation is found to be\nstrongly suppressed, but the $A_1$ component still gives a dominant\ncontribution for the anisotropic part of the interaction. The anisotropic\ninteractions are also applied to the localized two $f$-electron wave functions,\nwhich give the same-order contribution as the one-body level splitting\nestimated by the band structure calculation.\n"}
+{"abstract": "  Being low-level radiation exposure and less harmful to health, low-dose\ncomputed tomography (LDCT) has been widely adopted in the early screening of\nlung cancer and COVID-19. LDCT images inevitably suffer from the degradation\nproblem caused by complex noises. It was reported that, compared with\ncommercial iterative reconstruction methods, deep learning (DL)-based LDCT\ndenoising methods using convolutional neural network (CNN) achieved competitive\nperformance. Most existing DL-based methods focus on the local information\nextracted by CNN, while ignoring both explicit non-local and context\ninformation (which are leveraged by radiologists). To address this issue, we\npropose a novel deep learning model named radiologist-inspired deep denoising\nnetwork (RIDnet) to imitate the workflow of a radiologist reading LDCT images.\nConcretely, the proposed model explicitly integrates all the local, non-local\nand context information rather than local information only. Our\nradiologist-inspired model is potentially favoured by radiologists as a\nfamiliar workflow. A double-blind reader study on a public clinical dataset\nshows that, compared with state-of-the-art methods, our proposed model achieves\nthe most impressive performance in terms of the structural fidelity, the noise\nsuppression and the overall score. As a physicians-inspired model, RIDnet gives\na new research roadmap that takes into account the behavior of physicians when\ndesigning decision support tools for assisting clinical diagnosis. Models and\ncode are available at https://github.com/tonyckc/RIDnet_demo.\n"}
+{"abstract": "  We introduce posets with interfaces (iposets) and generalise the serial\ncomposition of posets to a new gluing composition of iposets. In partial order\nsemantics of concurrency, this amounts to designate events that continue their\nexecution across components. Alternatively, in terms of decomposing concurrent\nsystems, it allows cutting through some events, whereas serial composition may\ncut through edges only.\n  We show that iposets under gluing composition form a category, extending the\nmonoid of posets under serial composition, and a 2-category when enriched with\na subsumption order and a suitable parallel composition as a lax tensor. This\ngeneralises the interchange monoids used in concurrent Kleene algebra.\n  We also consider gp-iposets, which are generated from singletons by finitary\ngluing and parallel compositions. We show that the class includes the\nseries-parallel posets as well as the interval orders, which are also well\nstudied in concurrency theory. Finally, we show that not all posets are\ngp-iposets, exposing several posets that cannot occur as induced substructures\nof gp-iposets.\n"}
+{"abstract": "  Identifying the origin of a sample image in biometric systems can be\nbeneficial for data authentication in case of attacks against the system and\nfor initiating sensor-specific processing pipelines in sensor-heterogeneous\nenvironments. Motivated by shortcomings of the photo response non-uniformity\n(PRNU) based method in the biometric context, we use a texture classification\napproach to detect the origin of finger vein sample images. Based on eight\npublicly available finger vein datasets and applying eight classical yet simple\ntexture descriptors and SVM classification, we demonstrate excellent sensor\nmodel identification results for raw finger vein samples as well as for the\nmore challenging region of interest data. The observed results establish\ntexture descriptors as effective competitors to PRNU in finger vein sensor\nmodel identification.\n"}
+{"abstract": "  Age-related macular degeneration (AMD) is one of the leading causes of\npermanent vision loss in people aged over 60 years. Accurate segmentation of\nbiomarkers such as drusen that points to the early stages of AMD is crucial in\npreventing further vision impairment. However, segmenting drusen is extremely\nchallenging due to their varied sizes and appearances, low contrast and noise\nresemblance. Most existing literature, therefore, have focused on size\nestimation of drusen using classification, leaving the challenge of accurate\nsegmentation less tackled. Additionally, obtaining the pixel-wise annotations\nis extremely costly and such labels can often be noisy, suffering from\ninter-observer and intra-observer variability. Quantification of uncertainty\nassociated with segmentation tasks offers principled measures to inspect the\nsegmentation output. Realizing its utility in identifying erroneous\nsegmentation and the potential applications in clinical decision making, here\nwe develop a U-Net based drusen segmentation model and quantify the\nsegmentation uncertainty. We investigate epistemic and aleatoric uncertainty\ncapturing model confidence and data uncertainty respectively. We present\nsegmentation results and show how uncertainty can help formulate robust\nevaluation strategies. We visually inspect the pixel-wise uncertainty and\nsegmentation results on test images. We finally analyze the correlation between\nsegmentation uncertainty and accuracy. Our results demonstrate the utility of\nleveraging uncertainties in developing and explaining segmentation models for\nmedical image analysis.\n"}
+{"abstract": "  The dipole strength of the nuclide $^{76}$Ge was studied in photon-scattering\nexperiments using bremsstrahlung produced with electron beams of energies of\n7.8 and 12.3 MeV at the $\\gamma$ELBE facility. We identified 210 levels up to\nan excitation energy of 9.4 MeV and assigned spin $J$ = 1 to most of them. The\nquasicontinuum of unresolved transitions was included in the analysis of the\nspectra and the intensities of branching transitions were estimated on the\nbasis of simulations of statistical $\\gamma$-ray cascades. The photoabsorption\ncross section up to the neutron-separation energy was determined and is\ncompared with predictions of the statistical reaction model. The derived photon\nstrength function is compared with results of experiments using other\nreactions.\n"}
+{"abstract": "  We study rotation programs within the standard implementation frame-work\nunder complete information. A rotation program is a myopic stableset whose\nstates are arranged circularly, and agents can effectively moveonly between two\nconsecutive states. We provide characterizing conditionsfor the implementation\nof efficient rules in rotation programs. Moreover,we show that the conditions\nfully characterize the class of implementablemulti-valued and efficient rules\n"}
+{"abstract": "  Visual steel surface defect detection is an essential step in steel sheet\nmanufacturing. Several machine learning-based automated visual inspection (AVI)\nmethods have been studied in recent years. However, most steel manufacturing\nindustries still use manual visual inspection due to training time and\ninaccuracies involved with AVI methods. Automatic steel defect detection\nmethods could be useful in less expensive and faster quality control and\nfeedback. But preparing the annotated training data for segmentation and\nclassification could be a costly process. In this work, we propose to use the\nTransfer Learning-based U-Net (TLU-Net) framework for steel surface defect\ndetection. We use a U-Net architecture as the base and explore two kinds of\nencoders: ResNet and DenseNet. We compare these nets' performance using random\ninitialization and the pre-trained networks trained using the ImageNet data\nset. The experiments are performed using Severstal data. The results\ndemonstrate that the transfer learning performs 5% (absolute) better than that\nof the random initialization in defect classification. We found that the\ntransfer learning performs 26% (relative) better than that of the random\ninitialization in defect segmentation. We also found the gain of transfer\nlearning increases as the training data decreases, and the convergence rate\nwith transfer learning is better than that of the random initialization.\n"}
+{"abstract": "  Anomalies or failures in large computer systems, such as the cloud, have an\nimpact on a large number of users that communicate, compute, and store\ninformation. Therefore, timely and accurate anomaly detection is necessary for\nreliability, security, safe operation, and mitigation of losses in these\nincreasingly important systems. Recently, the evolution of the software\nindustry opens up several problems that need to be tackled including (1)\naddressing the software evolution due software upgrades, and (2) solving the\ncold-start problem, where data from the system of interest is not available. In\nthis paper, we propose a framework for anomaly detection in log data, as a\nmajor troubleshooting source of system information. To that end, we utilize\npre-trained general-purpose language models to preserve the semantics of log\nmessages and map them into log vector embeddings. The key idea is that these\nrepresentations for the logs are robust and less invariant to changes in the\nlogs, and therefore, result in a better generalization of the anomaly detection\nmodels. We perform several experiments on a cloud dataset evaluating different\nlanguage models for obtaining numerical log representations such as BERT,\nGPT-2, and XL. The robustness is evaluated by gradually altering log messages,\nto simulate a change in semantics. Our results show that the proposed approach\nachieves high performance and robustness, which opens up possibilities for\nfuture research in this direction.\n"}
+{"abstract": "  The categorical modeling of Petri nets has received much attention recently.\nThe Dialectica construction has also had its fair share of attention. We\nrevisit the use of the Dialectica construction as a categorical model for Petri\nnets generalizing the original application to suggest that Petri nets with\ndifferent kinds of transitions can be modeled in the same categorical\nframework. Transitions representing truth-values, probabilities, rates or\nmultiplicities, evaluated in different algebraic structures called lineales are\nuseful and are modeled here in the same category. We investigate (categorical\ninstances of) this generalized model and its connections to more recent models\nof categorical nets.\n"}
+{"abstract": "  Millimeter wave (mmWave) and terahertz MIMO systems rely on pre-defined\nbeamforming codebooks for both initial access and data transmission. Being\npre-defined, however, these codebooks are commonly not optimized for specific\nenvironments, user distributions, and/or possible hardware impairments. This\nleads to large codebook sizes with high beam training overhead which increases\nthe initial access/tracking latency and makes it hard for these systems to\nsupport highly mobile applications. To overcome these limitations, this paper\ndevelops a deep reinforcement learning framework that learns how to iteratively\noptimize the codebook beam patterns (shapes) relying only on the receive power\nmeasurements and without requiring any explicit channel knowledge. The\ndeveloped model learns how to autonomously adapt the beam patterns to best\nmatch the surrounding environment, user distribution, hardware impairments, and\narray geometry. Further, this approach does not require any knowledge about the\nchannel, array geometry, RF hardware, or user positions. To reduce the learning\ntime, the proposed model designs a novel Wolpertinger-variant architecture that\nis capable of efficiently searching for an optimal policy in a large discrete\naction space, which is important for large antenna arrays with quantized phase\nshifters. This complex-valued neural network architecture design respects the\npractical RF hardware constraints such as the constant-modulus and quantized\nphase shifter constraints. Simulation results based on the publicly available\nDeepMIMO dataset confirm the ability of the developed framework to learn\nnear-optimal beam patterns for both line-of-sight (LOS) and non-LOS scenarios\nand for arrays with hardware impairments without requiring any channel\nknowledge.\n"}
+{"abstract": "  In this paper, we propose a new numerical scheme for the coupled Stokes-Darcy\nmodel with Beavers-Joseph-Saffman interface condition. We use the weak Galerkin\nmethod to discretize the Stokes equation and the mixed finite element method to\nthe Darcy equation. A discrete inf-sup condition is proved and optimal error\nestimates are also derived. Numerical experiments validate the theoretical\nanalysis.\n"}
+{"abstract": "  Deep learning models have become increasingly useful in many different\nindustries. On the domain of image classification, convolutional neural\nnetworks proved the ability to learn robust features for the closed set\nproblem, as shown in many different datasets, such as MNIST FASHIONMNIST,\nCIFAR10, CIFAR100, and IMAGENET. These approaches use deep neural networks with\ndense layers with softmax activation functions in order to learn features that\ncan separate classes in a latent space. However, this traditional approach is\nnot useful for identifying classes unseen on the training set, known as the\nopen set problem. A similar problem occurs in scenarios involving learning on\nsmall data. To tackle both problems, few-shot learning has been proposed. In\nparticular, metric learning learns features that obey constraints of a metric\ndistance in the latent space in order to perform classification. However, while\nthis approach proves to be useful for the open set problem, current\nimplementation requires pair-wise training, where both positive and negative\nexamples of similar images are presented during the training phase, which\nlimits the applicability of these approaches in large data or large class\nscenarios given the combinatorial nature of the possible inputs.In this paper,\nwe present a constraint-based approach applied to the representations in the\nlatent space under the normalized softmax loss, proposed by[18]. We\nexperimentally validate the proposed approach for the classification of unseen\nclasses on different datasets using both metric learning and the normalized\nsoftmax loss, on disjoint and joint scenarios. Our results show that not only\nour proposed strategy can be efficiently trained on larger set of classes, as\nit does not require pairwise learning, but also present better classification\nresults than the metric learning strategies surpassing its accuracy by a\nsignificant margin.\n"}
+{"abstract": "  We address the problem of sparsity-promoting optimal control of\ncyber-physical systems (CPSs) in the presence of communication delays. The\ndelays are categorized into two types - namely, an inter-layer delay for\npassing state and control information between the physical layer and the cyber\nlayer, and an intra-layer delay that operates between the computing agents,\nreferred to here as control nodes (CNs), within the cyber-layer. Our objective\nis to minimize the closed-loop H2-norm of the physical system by co-designing\nan optimal combination of these two delays and a sparse state-feedback\ncontroller while respecting a given bandwidth cost constraint. We propose a\ntwo-loop optimization algorithm for this. Based on the alternating directions\nmethod of multipliers (ADMM), the inner loop handles the conflicting directions\nbetween the decreasing H2-norm and the increasing sparsity level of the\ncontroller. The outer loop comprises a semidefinite program (SDP)-based\nrelaxation of non-convex inequalities necessary for closed-loop stability.\nMoreover, for CPSs where the state and control information assigned to the CNs\nare not private, we derive an additional algorithm that further sparsifies the\ncommunication topology by modifying the row and column structures of the\nobtained controller, resulting in reassigning the communication map between the\ncyber and physical layers, and determining which physical agent should send its\nstate information to which CN. Proofs for closed-loop stability and optimality\nare provided for both algorithms, followed by numerical simulations.\n"}
+{"abstract": "  People spend an enormous amount of time and effort looking for lost objects.\nTo help remind people of the location of lost objects, various computational\nsystems that provide information on their locations have been developed.\nHowever, prior systems for assisting people in finding objects require users to\nregister the target objects in advance. This requirement imposes a cumbersome\nburden on the users, and the system cannot help remind them of unexpectedly\nlost objects. We propose GO-Finder (\"Generic Object Finder\"), a\nregistration-free wearable camera based system for assisting people in finding\nan arbitrary number of objects based on two key features: automatic discovery\nof hand-held objects and image-based candidate selection. Given a video taken\nfrom a wearable camera, Go-Finder automatically detects and groups hand-held\nobjects to form a visual timeline of the objects. Users can retrieve the last\nappearance of the object by browsing the timeline through a smartphone app. We\nconducted a user study to investigate how users benefit from using GO-Finder\nand confirmed improved accuracy and reduced mental load regarding the object\nsearch task by providing clear visual cues on object locations.\n"}
+{"abstract": "  We present a simple but effective approach for leveraging Wikipedia for\nneural machine translation as well as cross-lingual tasks of image captioning\nand dependency parsing without using any direct supervision from external\nparallel data or supervised models in the target language. We show that first\nsentences and titles of linked Wikipedia pages, as well as cross-lingual image\ncaptions, are strong signals for a seed parallel data to extract bilingual\ndictionaries and cross-lingual word embeddings for mining parallel text from\nWikipedia. Our final model achieves high BLEU scores that are close to or\nsometimes higher than strong supervised baselines in low-resource languages;\ne.g. supervised BLEU of 4.0 versus 12.1 from our model in English-to-Kazakh.\nMoreover, we tailor our wikily supervised translation models to unsupervised\nimage captioning, and cross-lingual dependency parser transfer. In image\ncaptioning, we train a multi-tasking machine translation and image captioning\npipeline for Arabic and English from which the Arabic training data is a\ntranslated version of the English captioning data, using our wikily-supervised\ntranslation models. Our captioning results on Arabic are slightly better than\nthat of its supervised model. In dependency parsing, we translate a large\namount of monolingual text, and use it as artificial training data in an\nannotation projection framework. We show that our model outperforms recent work\non cross-lingual transfer of dependency parsers.\n"}
+{"abstract": "  With the pandemic of COVID-19, relevant fake news is spreading all over the\nsky throughout the social media. Believing in them without discrimination can\ncause great trouble to people's life. However, universal language models may\nperform weakly in these fake news detection for lack of large-scale annotated\ndata and sufficient semantic understanding of domain-specific knowledge. While\nthe model trained on corresponding corpora is also mediocre for insufficient\nlearning. In this paper, we propose a novel transformer-based language model\nfine-tuning approach for these fake news detection. First, the token vocabulary\nof individual model is expanded for the actual semantics of professional\nphrases. Second, we adapt the heated-up softmax loss to distinguish the\nhard-mining samples, which are common for fake news because of the\ndisambiguation of short text. Then, we involve adversarial training to improve\nthe model's robustness. Last, the predicted features extracted by universal\nlanguage model RoBERTa and domain-specific model CT-BERT are fused by one\nmultiple layer perception to integrate fine-grained and high-level specific\nrepresentations. Quantitative experimental results evaluated on existing\nCOVID-19 fake news dataset show its superior performances compared to the\nstate-of-the-art methods among various evaluation metrics. Furthermore, the\nbest weighted average F1 score achieves 99.02%.\n"}
+{"abstract": "  In this paper, we study the wave propagation in a metallic parallel-plate\nstructure with glide-symmetric elliptical holes. To perform this study, we\nderived a mode-matching technique based on the generalized Floquet theorem for\nglide-symmetric structures. This mode-matching technique benefits from a lower\ncomputational cost since it takes advantage of the glide symmetry in the\nstructure. It also provides physical insight on the specific properties of\nFloquet modes propagating in these specific structures. With our analysis, we\ndemonstrate that glide-symmetric structures with periodic elliptical holes\nexhibit an anisotropic refractive index over a wide range of frequencies. The\nequivalent refractive index can be controlled by tuning the dimensions of the\nholes. Finally, by combining the anisotropy related to the elliptical holes and\ntransformation optics, a Maxwell fish-eye lens with a 33.33% size compression\nis designed. This lens operates in a wideband frequency range from 2.5 GHz to\n10 GHz.\n"}
+{"abstract": "  Context: As Industrial Cyber-Physical Systems (ICPS) become more connected\nand widely-distributed, often operating in safety-critical environments, we\nrequire innovative approaches to detect and diagnose the faults that occur in\nthem. Objective: We profile fault identification and diagnosis techniques\nemployed in the aerospace, automotive, and industrial control domains. By\nexamining both theoretical presentations as well as case studies from\nproduction environments, we present a profile of the current approaches being\nemployed and identify gaps. Methodology: A scoping study was used to identify\nand compare fault detection and diagnosis methodologies that are presented in\nthe current literature. Results: Fault identification and analysis studies from\n127 papers published from 2004 to 2019 reveal a wide diversity of promising\ntechniques, both emerging and in-use. These range from traditional\nPhysics-based Models to Data-Driven Artificial Intelligence (AI) and\nKnowledge-Based approaches. Predictive diagnostics or prognostics featured\nprominently across all sectors, along with discussions of techniques including\nFault trees, Petri nets and Markov approaches. We also profile some of the\ntechniques that have reached the highest Technology Readiness Levels, showing\nhow those methods are being applied in real-world environments beyond the\nlaboratory. Conclusions: Our results suggest that the continuing wide use of\nboth Model-Based and Data-Driven AI techniques across all domains, especially\nwhen they are used together in hybrid configuration, reflects the complexity of\nthe current ICPS application space. While creating sufficiently-complete models\nis labor intensive, Model-free AI techniques were evidenced as a viable way of\naddressing aspects of this challenge, demonstrating the increasing\nsophistication of current machine learning systems.(Abridged)\n"}
+{"abstract": "  Recent cosmic-ray measurements are challenging our models of propagation in\nthe Galaxy. A good characterization of the secondary cosmic rays (B, Be, Li and\nsub-iron species) is crucial to constrain these models and exploit the\nprecision of modern CR experiments. In this work, a Markov chain Monte Carlo\nanalysis has been implemented to fit the experimental flux ratios between B, Be\nand Li and their flux ratios to the primary nuclei C and O. We have fitted the\ndata using two different parametrizations for the spallation cross sections.\nThe uncertainties in the evaluation of the spectra of these secondary cosmic\nrays, due to spallation cross sections, have been considered by introducing\nscale factors as nuisance parameters. We have also tested two different\nformulations for the diffusion coefficient, which differ in the origin of the\nhigh energy hardening of cosmic rays. Additionally, two different approaches\nare used to scale the cross sections, one based on a combined analysis of all\nthe species (\"combined\" analysis) and the other reproducing the high energy\nspectra of the secondary-to-secondary flux ratios of Be/B, Li/B, Li/Be\n(\"scaled\" analysis). This allows us to make a better comparison between the\npropagation parameters inferred from the cross sections parametrizations tested\nin this work. This novel analysis has been successfully implemented using the\nnumerical code DRAGON2 to reproduce the cosmic-ray nuclei data up to $Z=14$\nfrom the AMS-02 experiment. It is found that the ratios of Li favor a harder\nspectral index of the diffusion coefficient, but compatible with the other\nratios inside the observed $2\\sigma$ uncertainties. In addition, it is shown\nthat, including these scale factors, the secondary-to-primary flux ratios can\nbe simultaneously reproduced.\n"}
+{"abstract": "  Long secondary periods (LSPs), observed in a third of pulsating red giant\nstars, are the only unexplained type of large-amplitude stellar variability\nknown at this time. Here we show that this phenomenon is a manifestation of a\nsubstellar or stellar companion orbiting the red giant star. Our investigation\nis based on a sample of about 16,000 well-defined LSP variables detected in the\nlong-term OGLE photometric database of the Milky Way and Magellanic Clouds,\ncombined with the mid-infrared data extracted from the NEOWISE-R archive. From\nthis collection, we selected about 700 objects with stable, large-amplitude,\nwell-sampled infrared light curves and found that about half of them exhibit\nsecondary eclipses, thus presenting an important piece of evidence that the\nphysical mechanism responsible for LSPs is binarity. Namely, the LSP light\nchanges are due to the presence of a dusty cloud orbiting the red giant\ntogether with the companion and obscuring the star once per orbit. The\nsecondary eclipses, visible only in the infrared wavelength, occur when the\ncloud is hidden behind the giant. In this scenario, the low-mass companion is a\nformer planet that has accreted a significant amount of mass from the envelope\nof its host star and grown into a brown dwarf.\n"}
+{"abstract": "  We analyze the Hawking evaporation process of Reissner-Nordstr\\\"om black\nholes. It is shown that the characteristic radiation quanta emitted by the\ncharged black holes may turn near-extremal black-hole spacetimes into\nhorizonless naked singularities. The present analysis therefore reveals the\nintriguing possibility that the semi-classical Hawking evaporation process of\nblack holes may violate the fundamental Penrose cosmic censorship conjecture.\n"}
+{"abstract": "  Wide dynamic range (WDR) images contain more scene details and contrast when\ncompared to common images. However, it requires tone mapping to process the\npixel values in order to display properly. The details of WDR images can\ndiminish during the tone mapping process. In this work, we address the problem\nby combining a novel reformulated Laplacian pyramid and deep learning. The\nreformulated Laplacian pyramid always decompose a WDR image into two frequency\nbands where the low-frequency band is global feature-oriented, and the\nhigh-frequency band is local feature-oriented. The reformulation preserves the\nlocal features in its original resolution and condenses the global features\ninto a low-resolution image. The generated frequency bands are reconstructed\nand fine-tuned to output the final tone mapped image that can display on the\nscreen with minimum detail and contrast loss. The experimental results\ndemonstrate that the proposed method outperforms state-of-the-art WDR image\ntone mapping methods. The code is made publicly available at\nhttps://github.com/linmc86/Deep-Reformulated-Laplacian-Tone-Mapping.\n"}
+{"abstract": "  This paper concerns the modeling and numerical simulation of the process of\nspeciation. In particular, given conditions for which one or more speciation\nevents within an ecosystem occur, our aim is to develop the necessary modeling\nand simulation tools. Care is also taken to establish a solid mathematical\nfoundation on which our modeling framework is built. This is the subject of the\nfirst half of the paper. The second half is devoted to developing a multi-scale\nframework for eco-evolutionary modeling, where the relevant scales are that of\nspecies and individual/population, respectively. Hence, a system of interacting\nspecies can be described at the species level, while for branching species a\npopulation level description is necessary. Our multi-scale framework thus\nconsists of coupling the species and population level models where speciation\nevents are detected in advance and then resolved at the population scale until\nthe branching is complete. Moreover, since the population level model is\nformulated as a PDE, we first establish the well-posedness in the time-discrete\nsetting, and then derive the a posteriori error estimates which provides a\nfully computable upper bound on an energy-type error, including also for the\ncase of general smooth distributions (which will be useful for the detection of\nspeciation events). Several numerical tests validate our framework in practice.\n"}
+{"abstract": "  Visual defect detection (VDD) for high-mix low-volume production of\nnon-convex metal objects, such as high-pressure cylindrical piping joint parts\n(VDD-HPPPs), is challenging because subtle difference in domain (e.g., metal\nobjects, imaging device, viewpoints, lighting) significantly affects the\nspecular reflection characteristics of individual metal object types. In this\npaper, we address this issue by introducing a tailor-made VDD framework that\ncan be automatically adapted to a new domain. Specifically, we formulate this\nadaptation task as the problem of network architecture search (NAS) on a deep\nobject-detection network, in which the network architecture is searched via\nreinforcement learning. We demonstrate the effectiveness of the proposed\nframework using the VDD-HPPPs task as a factory case study. Experimental\nresults show that the proposed method achieved higher burr detection accuracy\ncompared with the baseline method for data with different training/test domains\nfor the non-convex HPPPs, which are particularly affected by domain shifts.\n"}
+{"abstract": "  We present a novel method for efficient acquisition of shape and spatially\nvarying reflectance of 3D objects using polarization cues. Unlike previous\nworks that have exploited polarization to estimate material or object\nappearance under certain constraints (known shape or multiview acquisition), we\nlift such restrictions by coupling polarization imaging with deep learning to\nachieve high quality estimate of 3D object shape (surface normals and depth)\nand SVBRDF using single-view polarization imaging under frontal flash\nillumination. In addition to acquired polarization images, we provide our deep\nnetwork with strong novel cues related to shape and reflectance, in the form of\na normalized Stokes map and an estimate of diffuse color. We additionally\ndescribe modifications to network architecture and training loss which provide\nfurther qualitative improvements. We demonstrate our approach to achieve\nsuperior results compared to recent works employing deep learning in\nconjunction with flash illumination.\n"}
+{"abstract": "  Recent high-profile attacks on the Internet of Things (IoT) have brought to\nthe forefront the vulnerability of \"smart\" devices, and have resulted in\nnumerous IoT-focused security analyses. Many of the attacks had weak device\nconfiguration as the root cause. One potential source of rich and definitive\ninformation about the configuration of an IoT device is the device's firmware.\nHowever, firmware analysis is complex and automated firmware analyses have thus\nfar been confined to devices with more traditional operating systems such as\nLinux or VxWorks. Most IoT peripherals, due to lacking traditional operating\nsystems and implementing a wide variety of communication technologies, have\nonly been the subject of smaller-scale analyses. Peripheral firmware analysis\nis further complicated by the fact that such firmware files are predominantly\navailable as stripped binaries, without the ELF headers and symbol tables that\nwould simplify reverse engineering.\n  In this paper, we present argXtract, an open-source automated static analysis\ntool, which extracts security-relevant configuration information from stripped\nIoT peripheral firmware. Specifically, we focus on binaries that target the ARM\nCortex-M architecture, due to its growing popularity among IoT peripherals.\nargXtract overcomes the challenges associated with stripped Cortex-M analysis\nand is able to retrieve arguments to security-relevant supervisor and function\ncalls, enabling automated bulk analysis of firmware files. We demonstrate this\nvia three real-world case studies. The largest case study covers a dataset of\n243 Bluetooth Low Energy binaries targeting Nordic Semiconductor chipsets,\nwhile the other two focus on Nordic ANT and STMicroelectronics BlueNRG\nbinaries. The results reveal widespread lack of security and privacy controls\nin IoT, such as minimal or no protection for data, fixed passkeys and trackable\ndevice addresses.\n"}
+{"abstract": "  We prove the decay in the energy space for the solution to the defocusing\nbiharmonic Hartree-Fock equations with mass-supercritical and\nenergy-subcritical Choquard-type nonlinearity in space dimension $d\\geq3$. We\ntreat both the free and the perturbed by a potential case. As a direct\nconsequence, we obtain large-data scattering in $H^2(\\mathbb R^d)^N, \\, N\\geq\n1$.\n"}
+{"abstract": "  The BBM equation is a Hamiltonian PDE which revealed to be a very interesting\ntest-model to study the transformation property of Gaussian measures along the\nflow. In this paper we study the BBM equation with critical dispersion (which\nis a Benjamin-Ono type model). We prove that the image of the Gaussian measures\nsupported on fractional Sobolev spaces of increasing regularity are absolutely\ncontinuous, but we cannot identify the density, for which new ideas are needed.\n"}
+{"abstract": "  Access to large and diverse computer-aided design (CAD) drawings is critical\nfor developing symbol spotting algorithms. In this paper, we present\nFloorPlanCAD, a large-scale real-world CAD drawing dataset containing over\n10,000 floor plans, ranging from residential to commercial buildings. CAD\ndrawings in the dataset are all represented as vector graphics, which enable us\nto provide line-grained annotations of 30 object categories. Equipped by such\nannotations, we introduce the task of panoptic symbol spotting, which requires\nto spot not only instances of countable things, but also the semantic of\nuncountable stuff. Aiming to solve this task, we propose a novel method by\ncombining Graph Convolutional Networks (GCNs) with Convolutional Neural\nNetworks (CNNs), which captures both non-Euclidean and Euclidean features and\ncan be trained end-to-end. The proposed CNN-GCN method achieved\nstate-of-the-art (SOTA) performance on the task of semantic symbol spotting,\nand help us build a baseline network for the panoptic symbol spotting task. Our\ncontributions are three-fold: 1) to the best of our knowledge, the presented\nCAD drawing dataset is the first of its kind; 2) the panoptic symbol spotting\ntask considers the spotting of both thing instances and stuff semantic as one\nrecognition problem; and 3) we presented a baseline solution to the panoptic\nsymbol spotting task based on a novel CNN-GCN method, which achieved SOTA\nperformance on semantic symbol spotting. We believe that these contributions\nwill boost research in related areas.\n"}
+{"abstract": "  We devise an efficient scheme to determine vibrational properties from Path\nIntegral Molecular Dynamics (PIMD) simulations. The method is based on\nzero-time Kubo-transformed correlation functions and captures the anharmonicity\nof the potential due to both temperature and quantum effects. Using analytical\nderivations and numerical calculations on toy-model potentials, we show that\ntwo different estimators built upon PIMD correlation functions fully\ncharacterize the phonon spectra and the anharmonicity strength. The first\nestimator is associated with force-force quantum correlators and gives access\nto the fundamental frequencies and thermodynamic properties of the quantum\nsystem. The second one is instead connected to displacement-displacement\ncorrelators and probes the lowest-energy phonon excitations with high accuracy.\nWe also prove that the use of generalized eigenvalue equations, in place of the\nstandard normal mode equations, leads to a significant speed-up in the PIMD\nphonon calculations, both in terms of faster convergence rate and smaller\ntime-step bias. Within this framework, using ab initio PIMD simulations, we\ncompute phonon dispersions of diamond and of the high-pressure I41/amd phase of\natomic hydrogen. We find that, in the latter case, the anharmonicity is\nstronger than previously estimated and yields a sizeable red-shift in the\nvibrational spectrum of atomic hydrogen.\n"}
+{"abstract": "  This three-part paper comprises: (i) a critique by Halvorson of Bell's (1973)\npaper \"Subject and Object\"; (ii) a comment by Butterfield; (iii) a reply by\nHalvorson. An Appendix gives the passage from Bell that is the focus of\nHalvorson's critique.\n"}
+{"abstract": "  We study the order of convergence of Galerkin variational integrators for\nordinary differential equations. Galerkin variational integrators approximate a\nvariational (Lagrangian) problem by restricting the space of curves to the set\nof polynomials of degree at most $s$ and approximating the action integral\nusing a quadrature rule. We show that, if the quadrature rule is sufficiently\naccurate, the order of the integrators thus obtained is $2s$.\n"}
+{"abstract": "  We report herein, the potential of raw pinecone powder (PCP) and pinecone\nbiochar (PCBC) as alternatives to activated carbon used in Permeable Adsorptive\nBarriers (PABs) for the in situ remediation of polluted groundwater. A\nconstructed lab-scale unconfined aquifer ($38\\times30\\times17$ $cm$) fitted\nwith PCP and PCBC PABs ($21\\times3\\times20$ $cm$), was evaluated for the\nremoval of $Pb^{2+}$ ions in a continuous flow setup. Results indicate that\nafter $3600$ minutes, PCP was able to reduce $Pb^{2+}$ ions from a Co=$50$\n$mg/L$ to $7.94$ $mg/L$ for the first run and $19.4$ $mg/L$ for a second run,\nrespectively. Comparatively, PCBC reached $6.5$ $mg/L$ for the first run and\n$8.94$ for the second run. It was confirmed that adsorption was best described\nby the first-order kinetic model with $R^2$ values above $0.95$. Maximum\nadsorption capacity values were found to be $1.00$, $0.63$, $1.08$, and $0.85$\nmg/g for each scenario respectively. In addition, nonlinear regression models\nof exponential and Gaussian Processes are fit to explain remediation by time\nfor $Pb^{2+}$ and Methylene Blue. Gaussian Processes are able to better explain\nthe variation of pollution removal compared to simpler exponential models. When\nregressed against true removal percentages all models are able to provide\n$R^2>0.99$.\n"}
+{"abstract": "  Vision-language Navigation (VLN) tasks require an agent to navigate\nstep-by-step while perceiving the visual observations and comprehending a\nnatural language instruction. Large data bias, which is caused by the disparity\nratio between the small data scale and large navigation space, makes the VLN\ntask challenging. Previous works have proposed various data augmentation\nmethods to reduce data bias. However, these works do not explicitly reduce the\ndata bias across different house scenes. Therefore, the agent would overfit to\nthe seen scenes and achieve poor navigation performance in the unseen scenes.\nTo tackle this problem, we propose the Random Environmental Mixup (REM) method,\nwhich generates cross-connected house scenes as augmented data via mixuping\nenvironment. Specifically, we first select key viewpoints according to the room\nconnection graph for each scene. Then, we cross-connect the key views of\ndifferent scenes to construct augmented scenes. Finally, we generate augmented\ninstruction-path pairs in the cross-connected scenes. The experimental results\non benchmark datasets demonstrate that our augmentation data via REM help the\nagent reduce its performance gap between the seen and unseen environment and\nimprove the overall performance, making our model the best existing approach on\nthe standard VLN benchmark. The code have released:\nhttps://github.com/LCFractal/VLNREM.\n"}
+{"abstract": "  End-to-end multilingual speech recognition involves using a single model\ntraining on a compositional speech corpus including many languages, resulting\nin a single neural network to handle transcribing different languages. Due to\nthe fact that each language in the training data has different characteristics,\nthe shared network may struggle to optimize for all various languages\nsimultaneously. In this paper we propose a novel multilingual architecture that\ntargets the core operation in neural networks: linear transformation functions.\nThe key idea of the method is to assign fast weight matrices for each language\nby decomposing each weight matrix into a shared component and a language\ndependent component. The latter is then factorized into vectors using rank-1\nassumptions to reduce the number of parameters per language. This efficient\nfactorization scheme is proved to be effective in two multilingual settings\nwith $7$ and $27$ languages, reducing the word error rates by $26\\%$ and $27\\%$\nrel. for two popular architectures LSTM and Transformer, respectively.\n"}
+{"abstract": "  People assume different and important roles within social networks. Some\nroles have received extensive study: that of influencers who are\nwell-connected, and that of brokers who bridge unconnected parts of the\nnetwork. However, very little work has explored another potentially important\nrole, that of creating opportunities for people to interact and facilitating\nconversation between them. These individuals bring people together and act as\nsocial catalysts. In this paper, we test for the presence of social catalysts\non the online social network Facebook. We first identify posts that have\nspurred conversations between the poster's friends and summarize the\ncharacteristics of such posts. We then aggregate the number of catalyzed\ncomments at the poster level, as a measure of the individual's \"catalystness.\"\nThe top 1% of such individuals account for 31% of catalyzed interactions,\nalthough their network characteristics do not differ markedly from others who\npost as frequently and have a similar number of friends. By collecting survey\ndata, we also validate the behavioral measure of catalystness: a person is more\nlikely to be nominated as a social catalyst by their friends if their posts\nprompt discussions between other people more frequently. The measure, along\nwith other conversation-related features, is one of the most predictive of a\nperson being nominated as a catalyst. Although influencers and brokers may have\ngotten more attention for their network positions, our findings provide\nconverging evidence that another important role exists and is recognized in\nonline social networks.\n"}
+{"abstract": "  This paper presents a new derivation method of converse bounds on the\nnon-asymptotic achievable rate of memoryless discrete channels. It is based on\nthe finite blocklength statistics of the channel, where with the use of an\nauxiliary channel the converse bound is produced. This methodology is general\nand initially presented for an arbitrary channel. Afterwards, the main result\nis specialized for the $q$-ary erasure (QEC), binary symmetric (BSC), and Z\nchannels.\n"}
+{"abstract": "  Modeling equity in the allocation of scarce resources is a fast-growing\nconcern in the humanitarian logistics field. The Gini coefficient is one of the\nmost widely recognized measures of inequity and it was originally characterized\nby means of the Lorenz curve, which is a mathematical function that links the\ncumulative share of income to rank-ordered groups in a population. So far,\nhumanitarian logistics models that have approached equity using the Gini\ncoefficient do not actually optimize its original formulation, but use\nalternative definitions that do not necessarily replicate that original Gini\nmeasure. In this paper, we derive the original Gini coefficient via the Lorenz\ncurve to optimize the effectiveness-equity trade-off in a humanitarian\nlocation-allocation problem. We also propose new valid inequalities based on an\nupper-bounding Lorenz curve to tighten the linear relaxation of our model and\ndevelop a clustering-based construction of the Lorenz curve that requires fewer\nadditional constraints and variables than the original one. The computational\nstudy, based on the floods and landslides in Rio de Janeiro state, Brazil,\nreveals that while alternative Gini definitions have interesting properties,\nthey can generate vastly different decisions compared to the original Gini\ncoefficient. In addition, viewed from the perspective of the original Gini\ncoefficient, these decisions can be significantly less equitable.\n"}
+{"abstract": "  In topological equivalence, a bounded linear operator between Banach spaces -\nwe focus on the case of Hilbert spaces - is viewed as only acting linearly and\ncontinuously between them qua different spaces with the structure of linear\ntopological space. For instance, invertible operators in Banach spaces (that\nis, isomorphisms among them) will make up one equivalence class for each class\nof isomorphic spaces. On the other hand, compact and non-compact operators, or\noperators with or without a kernel, clearly will not. We make some crucial\nsteps towards describing invariants that will characterize these topological\nequivalence classes.\n"}
+{"abstract": "  We introduce a general framework for large-scale model-based derivative-free\noptimization based on iterative minimization within random subspaces. We\npresent a probabilistic worst-case complexity analysis for our method, where in\nparticular we prove high-probability bounds on the number of iterations before\na given optimality is achieved. This framework is specialized to nonlinear\nleast-squares problems, with a model-based framework based on the Gauss-Newton\nmethod. This method achieves scalability by constructing local linear\ninterpolation models to approximate the Jacobian, and computes new steps at\neach iteration in a subspace with user-determined dimension. We then describe a\npractical implementation of this framework, which we call DFBGN. We outline\nefficient techniques for selecting the interpolation points and search\nsubspace, yielding an implementation that has a low per-iteration linear\nalgebra cost (linear in the problem dimension) while also achieving fast\nobjective decrease as measured by evaluations. Extensive numerical results\ndemonstrate that DFBGN has improved scalability, yielding strong performance on\nlarge-scale nonlinear least-squares problems.\n"}
+{"abstract": "  In this study, we explore the evolution of lithium in giant stars based on\ndata assembled from the literature on asteroseismology and Li abundances for\ngiants. Our final sample of 187 giants consists of 44 red giant branch (RGB),\n140 core He-burning (CHeB) and three giants with an unclassified evolutionary\nphase. For all 187 stars, the seismic parameters $\\nu\\rm_{max}$ (frequency of\nmaximum oscillation power) and $\\Delta \\nu$ (large frequency spacing) are\navailable, while $\\Delta \\Pi\\rm_{1}$ (the asymptotic gravity-mode period\nspacing) is available for a subset of 64. For some of the CHeB giants, mass\nestimates from the asteroseismic scaling relations are found to be\nunderestimated when compared with mass estimates from isochrones based on\nseismic data. Whilst most of the Li-rich giants in the sample have masses less\nthan 1.5 $M_\\odot$, they are also present up to and beyond the maximum mass\nexpected to have suffered a core He-flash, i.e. $M$ $\\leq$ 2.25 $M_\\odot$: this\nsuggests contributions from other processes towards Li enrichment. To\nunderstand the evolution of giants in the $\\Delta \\Pi\\rm_{1}$ $-$ $\\Delta \\nu$\nplane, we use the {\\it Modules for Experiments in Stellar Astrophysics} models\nwhich show the presence of mini He-flashes following the initial strong core\nHe-flash. From the distribution of A(Li) as a function of $\\Delta \\nu$, which\nis similar to the distribution of A(Li) as a function of luminosity, we find no\nindication of Li enrichment near the luminosity bump. Also, A(Li) trends to\n$\\sim$ -1.5 dex near the RGB tip. The data also suggest a decrease in A(Li)\nwith an increase in $\\Delta \\Pi\\rm_{1}$ for CHeB giants.\n"}
+{"abstract": "  We propose the novel use of a generative adversarial network (GAN) (i) to\nmake predictions in time (PredGAN) and (ii) to assimilate measurements\n(DA-PredGAN). In the latter case, we take advantage of the natural adjoint-like\nproperties of generative models and the ability to simulate forwards and\nbackwards in time. GANs have received much attention recently, after achieving\nexcellent results for their generation of realistic-looking images. We wish to\nexplore how this property translates to new applications in computational\nmodelling and to exploit the adjoint-like properties for efficient data\nassimilation. To predict the spread of COVID-19 in an idealised town, we apply\nthese methods to a compartmental model in epidemiology that is able to model\nspace and time variations. To do this, the GAN is set within a reduced-order\nmodel (ROM), which uses a low-dimensional space for the spatial distribution of\nthe simulation states. Then the GAN learns the evolution of the low-dimensional\nstates over time. The results show that the proposed methods can accurately\npredict the evolution of the high-fidelity numerical simulation, and can\nefficiently assimilate observed data and determine the corresponding model\nparameters.\n"}
+{"abstract": "  A high-throughput correlative study of the local mechanical properties,\nchemical composition and crystallographic orientation has been carried out in\nselected areas of cast Inconel 718 specimens subjected to three different\ntempers. The specimens showed a strong Nb segregation at the scale of the\ndendrite arms, with local Nb contents that varied between 2 wt.% in the core of\nthe dendrite arms to 8 wt.% in the interdendritic regions and 25 wt.% within\nthe second phase particles (MC carbides, Laves phases and {\\delta} phase\nneedles). The nanohardness was found to correlate strongly with the local Nb\ncontent and the temper condition. On the contrary, the indentation elastic\nmoduli were not influenced by the local chemical composition or temper\ncondition, but directly correlated with the crystallographic grain orientation,\ndue to the high elastic anisotropy of nickel alloys.\n"}
+{"abstract": "  Cosmological models of the early or late universe exhibit (quasi) de Sitter\nspace-times with different stability properties. Considering models derived\nfrom string theory, the swampland program does not provide for now a definite\ncharacterisation of this stability. In this work we focus on de Sitter\nsolutions of 10d type II supergravities, candidates for classical de Sitter\nstring backgrounds: surprisingly, all known examples are unstable with $\\eta_V\n< -1$. We aim at proving the existence of such a systematic tachyon, and\ngetting formally a bound on the value of $\\eta_V$. To that end, we develop\nthree methods, giving us various sufficient conditions for having a tachyon\nupon assumptions, in analogy with de Sitter no-go theorems. Our analysis\neventually indicates the existence of variety of different tachyons, and\nrelated bounds on $\\eta_V$. We use this knowledge to find 10 new de Sitter\nsolutions of type IIB supergravity, that have tachyons of a different kind,\nhigher $\\eta_V$ values and new 6d geometries. One solution even appears to be\nstable, with however non-compact extra dimensions.\n"}
+{"abstract": "  Hyperspectral images (HSIs) have been widely used in a variety of\napplications thanks to the rich spectral information they are able to provide.\nAmong all HSI processing tasks, HSI denoising is a crucial step. Recently, deep\nlearning-based image denoising methods have made great progress and achieved\ngreat performance. However, existing methods tend to ignore the correlations\nbetween adjacent spectral bands, leading to problems such as spectral\ndistortion and blurred edges in denoised results. In this study, we propose a\nnovel HSI denoising network, termed SSCAN, that combines group convolutions and\nattention modules. Specifically, we use a group convolution with a spatial\nattention module to facilitate feature extraction by directing models'\nattention to band-wise important features. We propose a spectral-spatial\nattention block (SSAB) to exploit the spatial and spectral information in\nhyperspectral images in an effective manner. In addition, we adopt residual\nlearning operations with skip connections to ensure training stability. The\nexperimental results indicate that the proposed SSCAN outperforms several\nstate-of-the-art HSI denoising algorithms.\n"}
+{"abstract": "  We identify an unusual mechanism for quantum oscillations in nodal\nsemimetals, driven by a single pair of Landau levels periodically closing their\ngap at the Fermi energy as a magnetic field is varied. These `zero Landau\nlevel' quantum oscillations (ZQOs) appear in the nodal limit where the\nzero-field Fermi volume vanishes, and have distinctive periodicity and\ntemperature dependence. We link the Landau spectrum of a two-dimensional (2D)\nnodal semimetal to the Rabi model, and show by exact solution that across the\nentire Landau fan, pairs of opposite-parity Landau levels are intertwined in a\n`serpentine' manner. We propose 2D surfaces of topological crystalline\ninsulators as natural settings for ZQOs, and comment on implications for\nanomaly physics in 3D nodal semimetals.\n"}
+{"abstract": "  This paper builds Wasserstein ambiguity sets for the unknown probability\ndistribution of dynamic random variables leveraging noisy partial-state\nobservations. The constructed ambiguity sets contain the true distribution of\nthe data with quantifiable probability and can be exploited to formulate robust\nstochastic optimization problems with out-of-sample guarantees. We assume the\nrandom variable evolves in discrete time under uncertain initial conditions and\ndynamics, and that noisy partial measurements are available. All random\nelements have unknown probability distributions and we make inferences about\nthe distribution of the state vector using several output samples from multiple\nrealizations of the process. To this end, we leverage an observer to estimate\nthe state of each independent realization and exploit the outcome to construct\nthe ambiguity sets. We illustrate our results in an economic dispatch problem\ninvolving distributed energy resources over which the scheduler has no direct\ncontrol.\n"}
+{"abstract": "  Time-resolved photon-counting plays an indispensable role in precision\nmetrology in both classical and quantum regimes. In particular, time-correlated\nsingle-photon counting (TCSPC) has been the key enabling technology for\napplications such as low-light fluorescence lifetime spectroscopy and photon\ncounting time-of-flight (ToF) 3D imaging. However, state-of-the-art TCSPC\nsingle-photon timing resolution (SPTR) is limited in the range of 10-100 ps by\nthe available single-photon detector technology. In this paper, we\nexperimentally demonstrate a time-magnified TCSPC (TM-TCSPC) that achieves an\nunprecedentedly short SPTR of 550 fs for the first time with an off-the-shelf\nsingle-photon detector. The TM-TCSPC can resolve ultrashort pulses with a\n130-fs pulsewidth difference at a 22-fs accuracy. When applied to photon\ncounting ToF 3D imaging, the TM-TCSPC greatly suppresses the range walk error\nthat limits all photon counting ToF 3D imaging systems by 99.2 % (130 times)\nand thus provides unprecedentedly high depth measurement accuracy and precision\nof 26 {\\mu}m and 3 {\\mu}m, respectively.\n"}
+{"abstract": "  Disruptions in tokamak plasmas may lead to the generation of runaway\nelectrons that have the potential to damage plasma-facing components. Improved\nunderstanding of the runaway generation process requires interpretative\nmodelling of experiments. In this work we simulate eight discharges in the\nASDEX Upgrade and JET tokamaks, where argon gas was injected to trigger the\ndisruption. We use a fluid modelling framework with the capability to model the\ngeneration of runaway electrons through the hot-tail, Dreicer and avalanche\nmechanisms, as well as runaway electron losses. Using experimentally based\ninitial values of plasma current and electron temperature and density, we can\nreproduce the plasma current evolution using realistic assumptions about\ntemperature evolution and assimilation of the injected argon in the plasma. The\nassumptions and results are similar for the modelled discharges in ASDEX\nUpgrade and JET, indicating that the implemented models are applicable to\nmachines of varying size, which is important for the modelling of future,\nlarger machines. For the modelled discharges in ASDEX Upgrade, where the\ninitial temperature was comparatively high, we had to assume that a large\nfraction of the hot-tail runaway electrons were lost in order to reproduce the\nmeasured current evolution.\n"}
+{"abstract": "  Metamaterials are artificial structures with unusual and superior properties\nthat come from their carefully designed building blocks -- also called\nmeta-atoms. Metamaterials have permeated large swatches of science, including\nelectromagnetics and mechanics. Although metamaterials hold the promise for\nrealizing technological advances, their potential to enhance interactions\nbetween humans and materials has remained unexplored. Here, we devise\nmeta-atoms with tailored fracture properties to control mouthfeel sensory\nexperience. Using chocolate as a model material, we first use meta-atoms to\ncontrol the fracture anisotropy and the number of cracks and demonstrate that\nthese properties are captured in mouthfeel experience. We further use topology\noptimization to rationally design edible meta-atoms with maximally anisotropic\nfracture strength. Our work opens avenues for the use of meta-atoms and\nmetamaterials to control fracture and to enhance human-matter interactions.\n"}
+{"abstract": "  We define $\\Delta$-Baire spaces. If a paratopological group $G$ is\n$\\Delta$-Baire space, then $G$ is a topological group. Locally pseudocompact\nspaces, Baire $p$-spaces, Baire $\\Sigma$-spaces, products of \\v{C}ech-complete\nspaces are $\\Delta$-Baire spaces.\n"}
+{"abstract": "  The $3x+1$ map $T$ is defined on the $2$-adic integers $\\mathbb{Z}_2$ by\n$T(x)=x/2$ for even $x$ and $T(x)=(3x+1)/2$ for odd $x$. It is still unproved\nthat under iteration of $T$ the trajectory of any rational $2$-adic integer is\neventually cyclic. A $2$-adic integer is rational if and only if its\nrepresentation with $1$'s and $0$'s is eventually periodic. We prove that the\n$3x+1$ conjugacy $\\Phi$ maps aperiodic $v\\in\\mathbb{Z}_2$ onto aperiodic\n$2$-adic integers provided that\n$\\underline{\\lim}\\;(\\frac{h}{\\ell})_{\\ell=1}^{\\infty} > \\frac{\\ln(2)}{\\ln(3)}$\nwhere $h$ is the number of $1$'s in the first $\\ell$ digits of $v$ with the\nfollowing constraint: if there is a rational $2$-adic integer with a non-cyclic\ntrajectory, then necessarily\n$\\underline{\\lim}\\;(\\frac{h}{\\ell})_{\\ell=1}^{\\infty}=\\frac{\\ln(2)}{\\ln(3)}$.\nWe study $\\Phi$ as an infinite series in $\\mathbb{R}$ and obtain negative\nirrational numbers for which we compute their aperiodic $2$-adic expansion. We\nfind prominent behaviors of the orbit of $x$ taking Sturmian words as parity\nvector. We also found amazing results of the terms of $\\Phi$ in $\\mathbb{R}$.\nWe define the $\\ell$'th iterate of $T$ for $\\ell\\rightarrow \\infty$ in the ring\nof $3$-adic integers and obtain positive irrational numbers for which we\ncompute their aperiodic $3$-adic expansion.\n"}
+{"abstract": "  Transverse thermoelectric devices consist of only one thermoelectric\nmaterial, unlike conventional longitudinal thermoelectric devices that require\ntwo types of thermoelectric materials with p- and n-type polarities. However,\nscalable synthesis of materials that demonstrate axis-dependent carrier\npolarity, which is a prospective component to demonstrate the transverse\nthermoelectric device, is challenging. This paper reports that polycrystalline\nNaSn$_2$As$_2$, which was prepared by using uniaxial hot pressing, displayed\naxis-dependent carrier polarity. The preferred orientation of the sample was\nconfirmed through X-ray diffraction measurements. Seebeck coefficient\nmeasurements indicate that carrier polarity depends on the measurement\ndirection, which is consistent with recently reported results on single\ncrystals of NaSn$_2$As$_2$. Given that our sample preparation procedure is\nreadily scalable, the present work shows the possibility for preparing\ntransverse thermoelectric devices using polycrystalline NaSn$_2$As$_2$ with a\npreferred orientation.\n"}
+{"abstract": "  Let $G=SL(2,5)$ be the special linear group of $2 \\times 2$-matrices with\ncoefficients in the field with $5$ elements. We show that the principal block\nover a splitting field $K$ of characteristic two of the group algebra $KG$ has\na $3$-cluster tilting module. This gives the first example of a\nrepresentation-infinite block of a group algebra having a cluster tilting\nmodule and answers a question by Erdmann and Holm.\n"}
+{"abstract": "  ML workloads are becoming increasingly popular in the cloud. Good cloud\ntraining performance is contingent on efficient parameter exchange among VMs.\nWe find that Collectives, the widely used distributed communication algorithms,\ncannot perform optimally out of the box due to the hierarchical topology of\ndatacenter networks and multi-tenancy nature of the cloudenvironment.In this\npaper, we present Cloud Collectives , a prototype that accelerates collectives\nby reordering theranks of participating VMs such that the communication pattern\ndictated by the selected collectives operation best exploits the locality in\nthe network.Collectives is non-intrusive, requires no code changes nor rebuild\nof an existing application, and runs without support from cloud providers. Our\npreliminary application of Cloud Collectives on allreduce operations in public\nclouds results in a speedup of up to 3.7x in multiple microbenchmarks and 1.3x\nin real-world workloads of distributed training of deep neural networks and\ngradient boosted decision trees using state-of-the-art frameworks.\n"}
+{"abstract": "  In rate-distortion (RD) problems one seeks reduced representations of a\nsource that meet a target distortion constraint. Such optimal representations\nundergo topological transitions at some critical rate values, when their\ncardinality or dimensionality change. We study the convergence time of the\nArimoto-Blahut alternating projection algorithms, used to solve such problems,\nnear those critical points, both for the rate-distortion and information\nbottleneck settings. We argue that they suffer from critical slowing down -- a\ndiverging number of iterations for convergence -- near the critical points.\nThis phenomenon can have theoretical and practical implications for both\nmachine learning and data compression problems.\n"}
+{"abstract": "  Besides conventional geostationary (GSO) satellite broadband communication\nservices, non-geostationary (NGSO) satellites are envisioned to support various\nnew communication use cases from countless industries. These new scenarios\nbring many unprecedented challenges that will be discussed in this paper\nalongside with several potential future research opportunities. NGSO systems\nare known for various advantages, including their important features of low\ncost, lower propagation delay, smaller size, and lower losses in comparison to\nGSO satellites. However, there are still many deployment challenges to be\ntackled to ensure seamless integration not only with GSO systems but also with\nterrestrial networks. In this paper, we discuss several key challenges\nincluding satellite constellation and architecture designs, coexistence with\nGSO systems in terms of spectrum access and regulatory issues, resource\nmanagement algorithms, and NGSO networking requirements. Additionally, the\nlatest progress in provisioning secure communication via NGSO systems is\ndiscussed. Finally, this paper identifies multiple important open issues and\nresearch directions to inspire further studies towards the next generation of\nsatellite networks.\n"}
+{"abstract": "  Noisy Intermediate-Scale Quantum (NISQ) hardware has unavoidable noises, and\ncrosstalk error is a significant error source. When multiple quantum operations\nare executed simultaneously, the quantum state can be corrupted due to the\ncrosstalk between gates during simultaneous operations, decreasing the circuit\nfidelity. In this work, we first report on several protocols for characterizing\ncrosstalk. Then, we discuss different crosstalk mitigation methods from the\nhardware and software perspectives. Finally, we perform crosstalk injection\nexperiments on the IBM quantum device and demonstrate the fidelity improvement\nwith the crosstalk mitigation method.\n"}
+{"abstract": "  Spin canting and complex spin textures in antiferromagnetic materials can\noften be described in terms of Dzyaloshinskii-Moriya interactions (DMI). Values\nfor DMI parameters are not easily measurable directly, and often inferred from\nother quantities. In this work, we examine how domain wall dynamics in an\nantiferromagnetic optomagnonic system can display unique features directly\nrelated to the existence of DMI. Our results indicate that the presence of DMI\nenables spin interactions with cavity photons in a geometry which otherwise\nallows no magneto-optical coupling, and on the other hand modulates frequencies\nin a geometry where coupling is already realized in the absence of DMI. This\nresult may be used to measure the DMI constant in optomagnonic experiments by\ncomparing resonances obtained with different polarisations of the exciting\nfield.\n"}
+{"abstract": "  To raise the superconducting-transition temperature (Tc) has been the driving\nforce for the long, sustained effort in superconductivity research. Recent\nprogress in hydrides with Tcs up to 287 K under 267 GPa has heralded a new era\nof room-temperature superconductivity (RTS) with immense technological promise.\nIndeed, RTS has lifted the temperature barrier for the ubiquitous application\nof superconductivity. Unfortunately, formidable pressure is required to attain\nsuch high Tcs. The most effective relief to this impasse is to remove the\npressure needed while retaining the pressure-induced Tc without pressure. Here\nwe show such a possibility in the pure and doped high-temperature\nsuperconductor (HTS) FeSe by retaining, at ambient via pressure-quenching (PQ),\nits Tc up to 37 K (quadrupling that of a pristine FeSe) and other\npressure-induced phases. We have also observed that some phases remain stable\nwithout pressure at up to 300 K and for at least 7 days. The observations are\nin qualitative agreement with our ab initio simulations using the solid-state\nnudged elastic band (SSNEB) method. We strongly believe that the PQ technique\ndeveloped here can be adapted to the RTS hydrides and other materials of value\nwith minimal effort.\n"}
+{"abstract": "  We prove that every elliptic curve defined over a totally real number field\nof degree 4 not containing $\\sqrt{5}$ is modular. To this end, we study the\nquartic points on four modular curves.\n"}
+{"abstract": "  This paper is the first of a three part series in which we explore geometric\nand analytic properties of the Kohn Laplacian and its inverse on general\nquadric submanifolds of $\\mathbb{C}^n\\times\\mathbb{C}^m$. In this paper, we\npresent a streamlined calculation for a general integral formula for the\ncomplex Green operator $N$ and the projection onto the nullspace of $\\Box_b$.\nThe main application of our formulas is the critical case of codimension two\nquadrics in $\\mathbb{C}^4$ where we discuss the known solvability and\nhypoellipticity criteria of Peloso and Ricci \\cite{PeRi03}. We also provide\nexamples to show that our formulas yield explicit calculations in some\nwell-known cases: the Heisenberg group and a Cartesian product of Heisenberg\ngroups.\n"}
+{"abstract": "  Spoken intent detection has become a popular approach to interface with\nvarious smart devices with ease. However, such systems are limited to the\npreset list of intents-terms or commands, which restricts the quick\ncustomization of personal devices to new intents. This paper presents a\nfew-shot spoken intent classification approach with task-agnostic\nrepresentations via meta-learning paradigm. Specifically, we leverage the\npopular representation-based meta-learning learning to build a task-agnostic\nrepresentation of utterances, that then use a linear classifier for prediction.\nWe evaluate three such approaches on our novel experimental protocol developed\non two popular spoken intent classification datasets: Google Commands and the\nFluent Speech Commands dataset. For a 5-shot (1-shot) classification of novel\nclasses, the proposed framework provides an average classification accuracy of\n88.6% (76.3%) on the Google Commands dataset, and 78.5% (64.2%) on the Fluent\nSpeech Commands dataset. The performance is comparable to traditionally\nsupervised classification models with abundant training samples.\n"}
+{"abstract": "  Nitrogen-vacancy (NV) color centers in diamond have been demonstrated as\nuseful magnetic sensors, in particular for measuring spin fluctuations,\nachieving high sensitivity and spatial resolution. These abilities can be used\nto explore various biological and chemical processes, catalyzed by Reactive\nOxygen Species (ROS). Here we demonstrate a novel approach to measure and\nquantify Hydroxyl radicals with high spatial resolution, using the fluorescence\ndifference between NV charged states. According to the results, the achieved NV\nsensitivity is $11 \\pm 4 \\frac{nM}{\\sqrt Hz}$, realized in-situ without spin\nlabels and localized to a volume of $\\sim 10$ picoliter.\n"}
+{"abstract": "  Suppose a researcher observes individuals within a county within a state.\nGiven concerns about correlation across individuals, at which level should they\ncluster their observations for inference? This paper proposes a modified\nrandomization test as a robustness check for their chosen specification in a\nlinear regression setting. Existing tests require either the number of states\nor number of counties to be large. Our method is designed for settings with few\nstates and few counties. While the method is conservative, it has competitive\npower in settings that may be relevant to empirical work.\n"}
+{"abstract": "  Machine learning classification models are vulnerable to adversarial examples\n-- effective input-specific perturbations that can manipulate the model's\noutput. Universal Adversarial Perturbations (UAPs), which identify noisy\npatterns that generalize across the input space, allow the attacker to greatly\nscale up the generation of these adversarial examples. Although UAPs have been\nexplored in application domains beyond computer vision, little is known about\ntheir properties and implications in the specific context of realizable\nattacks, such as malware, where attackers must reason about satisfying\nchallenging problem-space constraints.\n  In this paper, we explore the challenges and strengths of UAPs in the context\nof malware classification. We generate sequences of problem-space\ntransformations that induce UAPs in the corresponding feature-space embedding\nand evaluate their effectiveness across threat models that consider a varying\ndegree of realistic attacker knowledge. Additionally, we propose adversarial\ntraining-based mitigations using knowledge derived from the problem-space\ntransformations, and compare against alternative feature-space defenses. Our\nexperiments limit the effectiveness of a white box Android evasion attack to\n~20 % at the cost of 3 % TPR at 1 % FPR. We additionally show how our method\ncan be adapted to more restrictive application domains such as Windows malware.\n  We observe that while adversarial training in the feature space must deal\nwith large and often unconstrained regions, UAPs in the problem space identify\nspecific vulnerabilities that allow us to harden a classifier more effectively,\nshifting the challenges and associated cost of identifying new universal\nadversarial transformations back to the attacker.\n"}
+{"abstract": "  The quantum to classical transition of fluctuations in the early universe is\nstill not completely understood. Some headway has been made incorporating the\neffects of decoherence and the squeezing of states, though the methods and\nprocedures continue to be challenged. But new developments in the analysis of\nthe most recent Planck data suggest that the primordial power spectrum has a\ncutoff associated with the very first quantum fluctuation to have emerged into\nthe semi-classical universe from the Planck domain at about the Planck time. In\nthis paper, we examine the implications of this result on the question of\nclassicalization, and demonstrate that the birth of quantum fluctuations at the\nPlanck scale would have been a `process' supplanting the need for a\n`measurement' in quantum mechanics. Emerging with a single wavenumber, these\nfluctuations would have avoided the interference between different degrees of\nfreedom in a superposed state. Moreover, the implied scalar-field potential had\nan equation-of-state consistent with the zero active mass condition in general\nrelativity, allowing the quantum fluctuations to emerge in their ground state\nwith a time-independent frequency. They were therefore effectively quantum\nharmonic oscillators with classical correlations in phase space from the very\nbeginning.\n"}
+{"abstract": "  Digital sensing provides an unprecedented opportunity to assess and\nunderstand mobility. However, incompleteness, missing information, possible\ninaccuracies, and temporal heterogeneity in the geolocation data can undermine\nits applicability. As mobility patterns are often repeated, we propose a method\nto use similar trajectory patterns from the local vicinity and\nprobabilistically ensemble them to robustly reconstruct missing or unreliable\nobservations. We evaluate the proposed approach in comparison with traditional\nfunctional trajectory interpolation using a case of sea vessel trajectory data\nprovided by The Automatic Identification System (AIS). By effectively\nleveraging the similarities in real-world trajectories, our pattern ensembling\nmethod helps to reconstruct missing trajectory segments of extended length and\ncomplex geometry. It can be used for locating mobile objects when temporary\nunobserved as well as for creating an evenly sampled trajectory interpolation\nuseful for further trajectory mining.\n"}
+{"abstract": "  We consider the existence and spectral stability of static multi-kink\nstructures in the discrete sine-Gordon equation, as a representative example of\nthe family of discrete Klein-Gordon models. The multi-kinks are constructed\nusing Lin's method from an alternating sequence of well-separated kink and\nantikink solutions. We then locate the point spectrum associated with these\nmulti-kink solutions by reducing the spectral problem to a matrix equation. For\nan $m$-structure multi-kink, there will be $m$ eigenvalues in the point\nspectrum near each eigenvalue of the primary kink, and, as long as the spectrum\nof the primary kink is imaginary, the spectrum of the multi-kink will be as\nwell. We obtain analytic expressions for the eigenvalues of a multi-kink in\nterms of the eigenvalues and corresponding eigenfunctions of the primary kink,\nand these are in very good agreement with numerical results. We also perform\nnumerical time-stepping experiments on perturbations of multi-kinks, and the\noutcomes of these simulations are interpreted using the spectral results.\n"}
+{"abstract": "  Slow or even stop electromagnetic (EM) waves attract researchers' attentions\nfor its potential applications in energy storage, optical buffer and\nnonlinearity enhancement. However, in most cases of the EM waves trapping, the\nEM waves are not truly trapped due to the existence of reflection. In this\npaper, a novel metal-semiconductor-semiconductor-metal (MSSM) structure, and a\nnovel truly rainbow trapping in a tapered MSSM model at terahertz frequencies\nare demonstrated by theoretical analysis and numerical simulations. More\nimportantly, functional devices such as optical buffer, optical switch and\noptical filter are achieved in our designed MSSM structure based on truly\nrainbow trapping theory. Owing to the property of one-way propagation, these\nnew types of optical devices can be high-performance and are expected to be\nused in integrated optical circuits.\n"}
+{"abstract": "  In this work we consider particle creation by the expansion of the universe,\nusing two Bianchi type I anisotropic models. The particles studied are of spin\n0 and 1/2. The cosmological models have rotational symmetry, which allows us to\nsolve exactly the equations of motion. The number density of the created\nparticles is calculated with the method of Bogolubov transformations.\n"}
+{"abstract": "  Aerial scene recognition is a fundamental research problem in interpreting\nhigh-resolution aerial imagery. Over the past few years, most studies focus on\nclassifying an image into one scene category, while in real-world scenarios, it\nis more often that a single image contains multiple scenes. Therefore, in this\npaper, we investigate a more practical yet underexplored task -- multi-scene\nrecognition in single images. To this end, we create a large-scale dataset,\ncalled MultiScene, composed of 100,000 unconstrained high-resolution aerial\nimages. Considering that manually labeling such images is extremely arduous, we\nresort to low-cost annotations from crowdsourcing platforms, e.g.,\nOpenStreetMap (OSM). However, OSM data might suffer from incompleteness and\nincorrectness, which introduce noise into image labels. To address this issue,\nwe visually inspect 14,000 images and correct their scene labels, yielding a\nsubset of cleanly-annotated images, named MultiScene-Clean. With it, we can\ndevelop and evaluate deep networks for multi-scene recognition using clean\ndata. Moreover, we provide crowdsourced annotations of all images for the\npurpose of studying network learning with noisy labels. We conduct experiments\nwith extensive baseline models on both MultiScene-Clean and MultiScene to offer\nbenchmarks for multi-scene recognition in single images and learning from noisy\nlabels for this task, respectively. To facilitate progress, we make our dataset\nand trained models available on\nhttps://gitlab.lrz.de/ai4eo/reasoning/multiscene.\n"}
+{"abstract": "  In this paper, we ask whether vocal source features (pitch, shimmer, jitter,\netc) can improve the performance of automatic sung speech recognition, arguing\nthat conclusions previously drawn from spoken speech studies may not be valid\nin the sung speech domain. We first use a parallel singing/speaking corpus\n(NUS-48E) to illustrate differences in sung vs spoken voicing characteristics\nincluding pitch range, syllables duration, vibrato, jitter and shimmer. We then\nuse this analysis to inform speech recognition experiments on the sung speech\nDSing corpus, using a state of the art acoustic model and augmenting\nconventional features with various voice source parameters. Experiments are run\nwith three standard (increasingly large) training sets, DSing1 (15.1 hours),\nDSing3 (44.7 hours) and DSing30 (149.1 hours). Pitch combined with degree of\nvoicing produces a significant decrease in WER from 38.1% to 36.7% when\ntraining with DSing1 however smaller decreases in WER observed when training\nwith the larger more varied DSing3 and DSing30 sets were not seen to be\nstatistically significant. Voicing quality characteristics did not improve\nrecognition performance although analysis suggests that they do contribute to\nan improved discrimination between voiced/unvoiced phoneme pairs.\n"}
+{"abstract": "  Co$_3$Sn$_2$S$_2$ is a magnetic Weyl semimetal, in which ferromagnetic\nordering at 177K is predicted to stabilize Weyl points. We perform temperature\nand spatial dependent angle--resolved photoemission spectroscopy measurements\nthrough the Curie temperature ($T_c$), which show large band shifts and\nrenormalization concomitant with the onset of magnetism. We argue that\nCo$_3$Sn$_2$S$_2$ evolves from a Mott ferromagnet below $T_c$ to a correlated\nmetallic state above $T_c$. To understand the magnetism, we derive a\ntight-binding model of Co-$3d_{x^2-y^2}$ orbitals on the kagome lattice. At the\nfilling obtained by first-principles calculations, this model reproduces the\nferromagnetic ground state, and results in the reduction of Coulomb\ninteractions due to cluster effects. Using a disordered local moment\nsimulation, we show how this reduced Hubbard-$U$ leads to a collapse of the\nbands across the magnetic transition, resulting in a correlated state which\ncarries associated characteristic photoemission signatures that are distinct\nfrom those of a simple lifting of exchange splitting. The behavior of topology\nacross $T_c$ is discussed in the context of this description of the magnetism.\n"}
+{"abstract": "  The Green Bank North Celestial Cap (GBNCC) survey is a 350-MHz all-sky survey\nfor pulsars and fast radio transients using the Robert C. Byrd Green Bank\nTelescope. To date, the survey has discovered over 190 pulsars, including 33\nmillisecond pulsars (MSPs) and 24 rotating radio transients(RRATs). Several\nexotic pulsars have been discovered in the survey, including PSR J1759+5036, a\nbinary pulsar with a 176-ms spin period in an orbit with a period of 2.04 days,\nan eccentricity of 0.3,and a projected semi-major axis of 6.8 light seconds.\nUsing seven years of timing data, we are able to measure one post-Keplerian\nparameter, advance of periastron, which has allowed us to constrain the total\nsystem mass to 2.62(3) solar masses. This constraint, along with the spin\nperiod and orbital parameters, suggests that this is a double neutron star\nsystem, although we cannot entirely rule out a pulsar-white dwarf binary. This\npulsar is only detectable in roughly 45% of observations, most likely due to\nscintillation. However, additional observations are required to determine\nwhether there may be other contributing effects.\n"}
+{"abstract": "  The class of assignment problems is a fundamental and well-studied class in\nthe intersection of Social Choice, Computational Economics and Discrete\nAllocation. In a general assignment problem, a group of agents expresses\npreferences over a set of items, and the task is to allocate items to agents in\nan \"optimal\" way. A verification variant of this problem includes an allocation\nas part of the input, and the question becomes whether this allocation is\n\"optimal\". In this paper, we generalize the verification variant to the setting\nwhere each agent is equipped with multiple incomplete preference lists: Each\nlist (called a layer) is a ranking of items in a possibly different way\naccording to a different criterion.\n  In particular, we introduce three multi-layer verification problems, each\ncorresponds to an optimality notion that weakens the notion of global\noptimality (that is, pareto optimality in multiple layers) in a different way.\nInformally, the first notion requires that, for each group of agents whose size\nis exactly some input parameter k, the agents in the group will not be able to\ntrade their assigned items among themselves and benefit in at least alpha\nlayers; the second notion is similar, but it concerns all groups of size at\nmost k rather than exactly k; the third notion strengthens these notions by\nrequiring that groups of k agents will not be part of possibly larger groups\nthat benefit in at least alpha layers. We study the three problems from the\nperspective of parameterized complexity under several natural parameterizations\nsuch as the number of layers, the number of agents, the number of items, the\nnumber of allocated items, the maximum length of a preference list, and more.\nWe present an almost comprehensive picture of the parameterized complexity of\nthe problems with respect to these parameters.\n"}
+{"abstract": "  Starting with an entropy that includes volumetric, area and length terms as\nwell as logarithmic contributions, we derive the corresponding modified\nNewtonian gravity and derive the expression for planetary orbits. We calculate\nthe shift of the perihelion of Mercury to find bounds to the parameters\nassociated to the modified Newtonian gravity. We compare the parameter\nassociated to the volumetric contribution in the entropy-area relationship with\nthe value derived for galactic rotation curves and the value obtained from the\ncosmological constant.\n"}
+{"abstract": "  End-to-end models reach state-of-the-art performance for speech recognition,\nbut global soft attention is not monotonic, which might lead to convergence\nproblems, to instability, to bad generalisation, cannot be used for online\nstreaming, and is also inefficient in calculation. Monotonicity can potentially\nfix all of this. There are several ad-hoc solutions or heuristics to introduce\nmonotonicity, but a principled introduction is rarely found in literature so\nfar. In this paper, we present a mathematically clean solution to introduce\nmonotonicity, by introducing a new latent variable which represents the audio\nposition or segment boundaries. We compare several monotonic latent models to\nour global soft attention baseline such as a hard attention model, a local\nwindowed soft attention model, and a segmental soft attention model. We can\nshow that our monotonic models perform as good as the global soft attention\nmodel. We perform our experiments on Switchboard 300h. We carefully outline the\ndetails of our training and release our code and configs.\n"}
+{"abstract": "  Simulated quantum annealing based on the path-integral Monte Carlo is one of\nthe most common tools to simulate quantum annealing on classical hardware.\nNevertheless, it is in principle highly non-trivial whether or not this\nclassical algorithm can correctly reproduce the quantum dynamics of quantum\nannealing, particularly in the diabatic regime. We study this problem\nnumerically through the generalized Kibble-Zurek mechanism of defect\ndistribution in the simplest ferromagnetic one-dimensional transverse-field\nIsing model with and without coupling to the environment. We find that,in the\nabsence of coupling to the environment, simulated quantum annealing correctly\ndescribes the annealing-time dependence of the average number of defects, but a\ndetailed analysis of the defect distribution shows clear deviations from the\ntheoretical prediction. When the system is open (coupled to the environment),\nthe average number of defects does not follow the theoretical prediction but is\nqualitatively compatible with the numerical result by the infinite\ntime-evolving block decimation combined with the quasi-adiabatic propagator\npath integral, which is valid in a very short time region. The distribution of\ndefects in the open system turns out to be not far from the theoretical\nprediction. It is surprising that the classical stochastic dynamics of\nsimulated quantum annealing ostensibly reproduce some aspects of the quantum\ndynamics. However, a serious problem is that it is hard to predict for which\nphysical quantities in which system it is reliable. Those results suggest the\nnecessity to exert a good amount of caution in using simulated quantum\nannealing to study the detailed quantitative aspects of the dynamics of quantum\nannealing.\n"}
+{"abstract": "  We investigate certain properties of tilted (oblique) domains, associated\nwith the Janowski function $(1+Az)/(1+Bz),$ where $A,B\\in\\mathbb{C}$ with\n$A\\neq B$ and $|B|\\leq 1$. We find several bounds for these oblique domains.\nFurther, we extend the various known argument, radius, and subordination\nresults involving Janowski functions with complex parameters.\n"}
+{"abstract": "  We study a model of collective search by teams. Discoveries beget discoveries\nand correlated search results are governed by a Brownian path. Search results'\nvariation at any point -- the search scope -- is jointly controlled. Agents\nindividually choose when to cease search and implement their best discovery. We\ncharacterize equilibrium and optimal policies. Search scope is constant and\nindependent of search outcomes as long as no member leaves. It declines after\ndepartures. A simple drawdown stopping boundary governs each agent's search\ntermination. We show the emergence of endogenous exit waves, whereby possibly\nheterogeneous agents cease search all at once.\n"}
+{"abstract": "  A new approach to normal operators in real Hilbert spaces is discussed, and\nspectral decompositions are obtained, via unique associated real spectral\nmeasures on complex subsets, having a more classical flavor. The results are\nthen applied to quaternionic normal operators, which are regarded as special\nclasses of real normal operators.\n"}
+{"abstract": "  Motivated from the central role of the mean-square displacement and its\nsecond time-derivative -- that is the velocity autocorrelation function\n$\\left\\langle v(0)v(t)\\right\\rangle=\\frac{1}{2} \\frac{\\mathrm{d}^{2}\n\\left\\langle \\Delta r^{2} (t)\\right\\rangle}{\\mathrm{d}t^{2}} $ in the\ndescription of Brownian motion, we revisit the physical meaning of the first\ntime-derivative of the mean-square displacement of Brownian particles. By\nemploying a rheological analogue for Brownian motion, we show that the\ntime-derivative of the mean-square displacement $\\frac{\\mathrm{d}\\left\\langle\n\\Delta r^{2} (t) \\right\\rangle}{\\mathrm{d}t}$ of Brownian microspheres with\nmass $m$ and radius $R$ immersed in any linear, isotropic viscoelastic material\nis identical to $\\frac{N K_B T}{3 \\pi R}h(t)$, where $h(t)$ is the impulse\nresponse function of a rheological network that is a parallel connection of the\nlinear viscoelastic material with an inerter with distributed inertance\n$m_R=\\frac{m}{6 \\pi R}$. The impulse response function $h(t)=\\frac{3\\pi R}{N\nK_B T}\\frac{\\mathrm{d}\\left\\langle \\Delta r^{2} (t)\n\\right\\rangle}{\\mathrm{d}t}$ of the viscoelastic material-inerter parallel\nconnection derived in this paper at the stress-strain level of the rheological\nanalogue is essentially the response function $\\chi(t)=\\frac{h(t)}{6\\pi R}$ of\nthe Brownian particles expressed at the force-displacement level by Nishi\n\\textit{et al.} (2018). By employing the viscoelastic material-inerter\nrheological analogue we derive the mean-square displacement and its\ntime-derivatives of Brownian particles immersed in a viscoelastic material\ndescribed with a Maxwell element connected in parallel with a dashpot which\ncaptures the high-frequency viscous behavior and we show that for Brownian\nmotion in such fluid-like soft matter the impulse response function, $h(t)$\nmaintains a finite constant value in the long term.\n"}
+{"abstract": "  Many preceding works have shown in $^{11}$Li the presence of the halo\nstructure comprised of the weakly bound two neutrons around $^9$Li, and it is\nintriguing to see how this halo structure changes when another $^9$Li\napproaches. In this study, we introduce a four-body model for $^{20}$C with two\n$^9$Li clusters and two valence neutrons. The recent development of the\nantisymmetrized quasi cluster model (AQCM) makes it possible to generate\n$jj$-coupling shell-model wave functions from $\\alpha$ cluster models. Here,\n$jj$-coupling shell model wave function of $^9$Li is regarded as a cluster,\nwhich corresponds to the subclosure configuration of $p_{3/2}$ for the\nneutrons, and we discuss how the two neutrons connect two $^9$Li clusters.\nUntil now, most of the clusters in the conventional models have been limited to\nthe closures of the three-dimensional harmonic oscillators, such as $^4$He,\n$^{16}$O, and $^{40}$Ca; however, owing to AQCM, it is feasible to utilize the\n$jj$-coupling shell model wave functions as plural subsystems quite easily. The\nappearance of a rotational band structure with a cluster structure around the\nfour-body threshold energy is discussed.\n"}
+{"abstract": "  In this paper we introduce SMPLicit, a novel generative model to jointly\nrepresent body pose, shape and clothing geometry. In contrast to existing\nlearning-based approaches that require training specific models for each type\nof garment, SMPLicit can represent in a unified manner different garment\ntopologies (e.g. from sleeveless tops to hoodies and to open jackets), while\ncontrolling other properties like the garment size or tightness/looseness. We\nshow our model to be applicable to a large variety of garments including\nT-shirts, hoodies, jackets, shorts, pants, skirts, shoes and even hair. The\nrepresentation flexibility of SMPLicit builds upon an implicit model\nconditioned with the SMPL human body parameters and a learnable latent space\nwhich is semantically interpretable and aligned with the clothing attributes.\nThe proposed model is fully differentiable, allowing for its use into larger\nend-to-end trainable systems. In the experimental section, we demonstrate\nSMPLicit can be readily used for fitting 3D scans and for 3D reconstruction in\nimages of dressed people. In both cases we are able to go beyond state of the\nart, by retrieving complex garment geometries, handling situations with\nmultiple clothing layers and providing a tool for easy outfit editing. To\nstimulate further research in this direction, we will make our code and model\npublicly available at http://www.iri.upc.edu/people/ecorona/smplicit/.\n"}
+{"abstract": "  In a companion paper [ABS1] we introduced the stable $\\infty$-category of\nnoncommutative CW-spectra, which we denoted $\\mathtt{NSp}$. Let $\\mathcal{M}$\ndenote the full spectrally enriched subcategory of $\\mathtt{NSp}$ whose objects\nare the non-commutative suspension spectra of matrix algebras. In [ABS1] we\nproved that $\\mathtt{NSp}$ is equivalent to the $\\infty$-category of spectral\npresheaves on $\\mathcal{M}$. In this paper we investigate the structure of\n$\\mathcal{M}$, and derive some consequences regarding the structure of\n$\\mathtt{NSp}$.\n  To begin with, we introduce a rank filtration of $\\mathcal{M}$. We show that\nthe mapping spectra of $\\mathcal{M}$ map naturally to the connective $K$-theory\nspectrum $ku$, and that the rank filtration of $\\mathcal{M}$ is a lift of the\nclassical rank filtration of $ku$. We describe the subquotients of the rank\nfiltration in terms of complexes of direct-sum decompositions which also arose\nin the study of $K$-theory and of Weiss's orthogonal calculus. We prove that\nthe rank filtration stabilizes rationally after the first stage. Using this we\ngive an explicit model of the rationalization of $\\mathtt{NSp}$ as presheaves\nof rational spectra on the category of finite-dimensional Hilbert spaces and\nunitary transformations up to scaling. Our results also have consequences for\nthe $p$-localization and the chromatic localization of $\\mathcal{M}$.\n"}
+{"abstract": "  Neyman and Pearson's theory of testing hypotheses does not warrant minimal\nepistemic reliability: the feature of driving to true conclusions more often\nthan to false ones. The theory does not protect from the possible negative\neffects of the pragmatic value-laden unequal setting of error probabilities on\nthe theory's epistemic reliability. Most importantly, in the case of a negative\nimpact, no methodological adjustment is available to neutralize it, so in such\ncases, the discussed pragmatic-value-ladenness of the theory inevitably\ncompromises the goal of attaining truth.\n"}
+{"abstract": "  In this article a new family of preconditioners is introduced for symmetric\npositive definite linear systems. The new preconditioners, called the AWG\npreconditioners (for Algebraic-Woodbury-GenEO) are constructed algebraically.\nBy this, we mean that only the knowledge of the matrix A for which the linear\nsystem is being solved is required. Thanks to the GenEO spectral coarse space\ntechnique, the condition number of the preconditioned operator is bounded\ntheoretically from above. This upper bound can be made smaller by enriching the\ncoarse space with more spectral modes.The novelty is that, unlike in previous\nwork on the GenEO coarse spaces, no knowledge of a partially non-assembled form\nof A is required. Indeed, the spectral coarse space technique is not applied\ndirectly to A but to a low-rank modification of A of which a suitable\nnon-assembled form is known by construction. The extra cost is a second (and to\nthis day rather expensive) coarse solve in the preconditioner. One of the AWG\npreconditioners has already been presented in the short preprint [38]. This\narticle is the first full presentation of the larger family of AWG\npreconditioners. It includes proofs of the spectral bounds as well as numerical\nillustrations.\n"}
+{"abstract": "  We review the derivation of the pulsations equations for spherically\nsymmetric boson stars and then make a thorough study of the radial oscillation\nfrequencies for the fundamental and first excited modes. We do this for\nself-interacting boson stars and consider a range of values for the\nself-coupling constant. We also numerically evolve boson stars and Fourier\ntransform the dynamic solutions. The Fourier transform gives an independent\ncomputation of the radial oscillation frequencies and allows us to verify our\nresults obtained from the pulsation equations. We find excellent agreement\nbetween the two methods.\n"}
+{"abstract": "  Subjective classification of spiral galaxies is not sufficient for studying\nthe effect of bars on their physical characteristics. In reality the problem is\nto comprehend the complex correlations in a multivariate parametric space.\nMultivariate tools are the best ones for understanding this complex\ncorrelation. In this work an objective classification of a large set (26,089)\nof spiral galaxies was compiled as a value added galaxy catalogue from sdss DR\n15 virtual data archive.\n  Initially for dimensionality reduction, Independent Component Analysis is\nperformed to determine a set of Independent Components that are linear\ncombinations of 48 observed features (namely ionised lines, Lick indices,\nphotometric and morphological properties). Subsequently a K-means cluster\nanalysis is carried out on the basis of the 14 best chosen Independent\nComponents to obtain 12 distinct homogeneous groups of spiral galaxies. Amongst\nthese, 3 groups are the oldest ones (1.6 Gyr - 5.9 Gyr), while 5 groups fall in\nthe medium aged category (1.4 Gyr - 1.6 Gyr), 2 groups consist of only unbarred\nspirals, 1 group is the youngest one and the remaining one is an outlier. In\nmany groups there are clear indication of recurrent bar formation phenomena\nwhich is consistent with few previous simulation works. In order to study the\nrobustness a second method of clustering by Gaussian Mixture Modeling Method\n(GMMBC) is applied.\n"}
+{"abstract": "  This article provides the domain of existence $\\Omega$ of the Laplace\ntransform of infinitely divisible negative multinomial distributions. We define\na negative multinomial distribution on $\\mathbb{N}^{n},$ where $\\mathbb{N}$ is\nthe set of nonnegative integers, by its probability generating function which\nwill be of the form $\\left( A\\left( a_{1}z_{1} ,\\ldots,a_{n}z_{n}\\right)\n/A\\left( a_{1},\\ldots,a_{n}\\right) \\right) ^{-\\lambda}$ where $A\\left(\n\\mathbf{z}\\right) = {\\displaystyle\\sum\\limits_{T\\subset\\left\\{\n1,2,\\ldots,n\\right\\} }} a_{T}\\prod\\limits_{i\\in T}z_{i},$ where\n$a_{\\emptyset}\\neq0,$ and where $\\lambda$ is a positive number. Finding couples\n$\\left( A,\\lambda\\right) $ for which we obtain a probability generating\nfunction is a difficult problem. Necessary and sufficient conditions on the\ncoefficients $a_{T}$ of $A$ for which we obtain a probability generating\nfunction for any positive number $\\lambda$ are know by (Bernardoff, 2003). Thus\nwe obtain necessary and sufficient conditions on $\\mathbf{a}=\\left(\na_{1},\\ldots,a_{n}\\right) $ so that $\\mathbf{a}=\\left(\n\\mathbf{e}^{t_{1}},\\ldots,\\mathbf{e}^{t_{n}}\\right) $ with $\\mathbf{t}=\\left(\nt_{1},\\ldots,t_{n}\\right) $ belonging to $\\Omega$. This makes it possible to\nconstruct all the infinitely divisible multinomial distributions on\n$\\mathbb{N}^{n}$. We give examples of construction in dimensions 2 and 3.\n"}
+{"abstract": "  There exists a small family of analytic SO(4)-invariant but time-dependent\nSU(2) Yang-Mills solutions in any conformally flat four-dimensional spacetime.\nThese might play a role in early-universe cosmology for stabilizing the\nsymmetric Higgs vacuum. We analyze the linear stability of these \"cosmic gauge\nfields\" against general gauge-field perturbations while keeping the metric\nfrozen, by diagonalizing the (time-dependent) Yang-Mills fluctuation operator\naround them and applying Floquet theory to its eigenfrequencies and normal\nmodes. Except for the exactly solvable SO(4) singlet perturbation, which is\nfound to be marginally stable linearly but bounded nonlinearly, generic normal\nmodes often grow exponentially due to resonance effects. Even at very high\nenergies, all cosmic Yang-Mills backgrounds are rendered linearly unstable.\n"}
+{"abstract": "  Computing the closed convex envelope or biconjugate is the core operation\nthat bridges the domain of nonconvex with convex analysis. We focus here on\ncomputing the conjugate of a bivariate piecewise quadratic function defined\nover a polytope. First, we compute the convex envelope of each piece, which is\ncharacterized by a polyhedral subdivision such that over each member of the\nsubdivision, it has a rational form (square of a linear function over a linear\nfunction). Then we compute the conjugate of all such rational functions. It is\nobserved that the conjugate has a parabolic subdivision such that over each\nmember of its subdivision, it has a fractional form (linear function over\nsquare root of a linear function). This computation of the conjugate is\nperformed with a worst-case linear time complexity algorithm. Our results are\nan important step toward computing the conjugate of a piecewise quadratic\nfunction, and further in obtaining explicit formulas for the convex envelope of\npiecewise rational functions.\n"}
+{"abstract": "  The 4D 4-point scattering amplitude of massless scalars via a massive\nexchange is expressed in a basis of conformal primary particle wavefunctions.\nThis celestial amplitude is expanded in a basis of 2D conformal partial waves\non the unitary principal series, and then rewritten as a sum over 2D conformal\nblocks via contour deformation. The conformal blocks include intermediate\nexchanges of spinning light-ray states, as well as scalar states with positive\ninteger conformal weights. The conformal block prefactors are found as expected\nto be quadratic in the celestial OPE coefficients.\n"}
+{"abstract": "  Opportunistic relay selection (ORS) has been recognized as a simple but\nefficient method for mobile nodes to achieve cooperative diversity in slow\nfading channels. However, the wrong selection of the best relay arising from\noutdated channel state information (CSI) in fast time-varying channels\nsubstantially degrades its performance. With the proliferation of high-mobility\napplications and the adoption of higher frequency bands in 5G and beyond\nsystems, the problem of outdated CSI will become more serious. Therefore, the\ndesign of a novel cooperative method that is applicable to not only slow fading\nbut also fast fading is increasingly of importance. To this end, we develop and\nanalyze a deep-learning-aided cooperative method coined predictive relay\nselection (PRS) in this article. It can remarkably improve the quality of CSI\nthrough fading channel prediction while retaining the simplicity of ORS by\nselecting a single opportunistic relay so as to avoid the complexity of\nmulti-relay coordination and synchronization. Information-theoretic analysis\nand numerical results in terms of outage probability and channel capacity\nreveal that PRS achieves full diversity gain in slow fading wireless\nenvironments and substantially outperforms the existing schemes in fast fading\nchannels.\n"}
+{"abstract": "  Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs)\nare used for a wide variety of missions related to exploration and scientific\nresearch. Successful navigation by these systems requires a good localization\nsystem. Kalman filter based localization techniques have been prevalent since\nthe early 1960s and extensive research has been carried out using them, both in\ndevelopment and in design. It has been found that the use of a dynamic model\n(instead of a kinematic model) in the Kalman filter can lead to more accurate\npredictions, as the dynamic model takes the forces acting on the AUV into\naccount. Presented in this paper is a motion-predictive extended Kalman filter\n(EKF) for AUVs using a simplified dynamic model. The dynamic model is derived\nfirst and then it was simplified for a RexROV, a type of submarine vehicle used\nin simple underwater exploration, inspection of subsea structures, pipelines\nand shipwrecks. The filter was implemented with a simulated vehicle in an\nopen-source marine vehicle simulator called UUV Simulator and the results were\ncompared with the ground truth. The results show good prediction accuracy for\nthe dynamic filter, though improvements are needed before the EKF can be used\non real-time. Some perspective and discussion on practical implementation is\npresented to show the next steps needed for this concept.\n"}
+{"abstract": "  Profiling of whole transcriptomes has become a cornerstone of molecular\nbiology and an invaluable tool for the characterization of clinical phenotypes\nand the identification of disease subtypes. Analyses of these data are becoming\never more sophisticated as we move beyond simple comparisons to consider\nnetworks of higher-order interactions and associations. Gene regulatory\nnetworks model the regulatory relationships of transcription factors and genes\nand have allowed the identification of differentially regulated processes in\ndisease systems. In this perspective we discuss gene targeting scores, which\nmeasure changes in inferred regulatory network interactions, and their use in\nidentifying disease-relevant processes. In addition, we present an example\nanalysis or pancreatic ductal adenocarcinoma demonstrating the power of gene\ntargeting scores to identify differential processes between complex phenotypes;\nprocesses which would have been missed by only performing differential\nexpression analysis. This example demonstrates that gene targeting scores are\nan invaluable addition to gene expression analysis in the characterization of\ndiseases and other complex phenotypes.\n"}
+{"abstract": "  High-resolution satellite imagery has proven useful for a broad range of\ntasks, including measurement of global human population, local economic\nlivelihoods, and biodiversity, among many others. Unfortunately,\nhigh-resolution imagery is both infrequently collected and expensive to\npurchase, making it hard to efficiently and effectively scale these downstream\ntasks over both time and space. We propose a new conditional pixel synthesis\nmodel that uses abundant, low-cost, low-resolution imagery to generate accurate\nhigh-resolution imagery at locations and times in which it is unavailable. We\nshow that our model attains photo-realistic sample quality and outperforms\ncompeting baselines on a key downstream task -- object counting -- particularly\nin geographic locations where conditions on the ground are changing rapidly.\n"}
+{"abstract": "  Quantum circuit simulators have a long tradition of exploiting massive\nhardware parallelism. Most of the times, parallelism has been supported by\nspecial purpose libraries tailored specifically for the quantum circuits.\nQuantum circuit simulators are integral part of quantum software stacks, which\nare mostly written in Python. Our focus has been on ease of use, implementation\nand maintainability within the Python ecosystem. We report the performance\ngains we obtained by using CuPy, a general purpose library (linear algebra)\ndeveloped specifically for CUDA-based GPUs, to simulate quantum circuits. For\nsupremacy circuits the speedup is around 2x, and for quantum multipliers almost\n22x compared to state-of-the-art C++-based simulators.\n"}
+{"abstract": "  We present sub-arcsecond resolution ALMA imaging of the CO(3-2) emission in\ntwo $z\\sim2.5$ heavily reddened quasars (HRQs) - ULASJ1234+0907 and\nULASJ2315+0143 - and their companion galaxies. Dynamical modeling of the\nresolved velocity fields enables us to constrain the molecular gas morphologies\nand host galaxy masses. Combining the new data with extensive multi-wavelength\nobservations, we are able to study the relative kinematics of different\nmolecular emission lines, the molecular gas fractions and the locations of the\nquasars on the M$_{\\rm{BH}}$-M$_{\\rm{gal}}$ relation. Despite having similar\nblack-hole properties, the two HRQs display markedly different host galaxy\nproperties and local environments. J1234 has a very massive host, M$_{\\rm{dyn}}\n\\sim 5 \\times 10^{11}$M$_\\odot$ and two companion galaxies that are similarly\nmassive located within 200 kpc of the quasar. The molecular gas fraction is low\n($\\sim$6%). The significant ongoing star formation in the host galaxy is\nentirely obscured at rest-frame UV and optical wavelengths. J2315 is resolved\ninto a close-separation major-merger ($\\Delta$r=15 kpc; $\\Delta$v=170 km/s)\nwith a $\\sim$1:2 mass ratio. The total dynamical mass is estimated to be\n$\\lesssim$10$^{11}$M$_\\odot$ and the molecular gas fraction is high ($>$45%). A\nnew HSC image of the galaxy shows unobscured UV-luminous star-forming regions\nco-incident with the extended reservoir of cold molecular gas in the merger. We\nuse the outputs from the Illustris simulations to track the growth of such\nmassive black holes from $z\\sim6$ to the present day. While J1234 is consistent\nwith the simulated $z\\sim2$ relation, J2315 has a black hole that is\nover-massive relative to its host galaxy.\n"}
+{"abstract": "  In this paper, we consider the Cauchy problem for the one-dimensional\ncompressible isentropic magnetohydrodynamic (MHD) equations with no vacuum at\ninfinity, but the initial vacuum can be permitted inside the region. By\nderiving a priori $\\nu $ (resistivity coefficient)-independent estimates, we\nestablish the non-resistive limit of the global strong solutions with large\ninitial data. Moreover, as a by-product, the global well-posedness of strong\nsolutions for both the compressible resistive MHD equations and non-resistive\nMHD equations are also established, respectively.\n"}
+{"abstract": "  In this paper, we develop a new neural network family based on power series\nexpansion, which is proved to achieve a better approximation accuracy in\ncomparison with existing neural networks. This new set of neural networks\nembeds the power series expansion (PSE) into the neural network structure. Then\nit can improve the representation ability while preserving comparable\ncomputational cost by increasing the degree of PSE instead of increasing the\ndepth or width. Both theoretical approximation and numerical results show the\nadvantages of this new neural network.\n"}
+{"abstract": "  We show that the set of all measures on any measurable space is a complete\nlattice, i.e. every collection of measures has both a greatest lower bound and\na least upper bound.\n"}
+{"abstract": "  The ability to automatically detect stuttering events in speech could help\nspeech pathologists track an individual's fluency over time or help improve\nspeech recognition systems for people with atypical speech patterns. Despite\nincreasing interest in this area, existing public datasets are too small to\nbuild generalizable dysfluency detection systems and lack sufficient\nannotations. In this work, we introduce Stuttering Events in Podcasts\n(SEP-28k), a dataset containing over 28k clips labeled with five event types\nincluding blocks, prolongations, sound repetitions, word repetitions, and\ninterjections. Audio comes from public podcasts largely consisting of people\nwho stutter interviewing other people who stutter. We benchmark a set of\nacoustic models on SEP-28k and the public FluencyBank dataset and highlight how\nsimply increasing the amount of training data improves relative detection\nperformance by 28\\% and 24\\% F1 on each. Annotations from over 32k clips across\nboth datasets will be publicly released.\n"}
+{"abstract": "  A simple method to increase the flow power extraction efficiency of a\ncircular cylinder, undergoing vortex-induced vibration (VIV), by confining it\nbetween two parallel plates is proposed. A two-dimensional numerical study was\nperformed on VIV of a circular cylinder inside a parallel plate channel of\nheight H at Reynolds number 150 to quantify the improvement. The cylinder is\nelastically mounted with a spring such that it is only free to vibrate in the\ndirection transverse to the channel flow and has a fixed mass ratio (m*) of 10.\nThe energy extraction process is modelled as a damper, with spatially constant\ndamping ration ((), attached to the cylinder. The simulations are performed by\nvarying the reduced velocity for a set of fixed mass-damping ({\\alpha} = m*()\nvalues ranging between 0 to 1. The blockage ratio (b = D/H) is varied from 0.25\nto 0.5 by changing the channel height. The quasi-periodic initial branch found\nfor the unconfined cylinder shrinks with the increasing blockage. The extracted\npower is found to increase rapidly with the blockage. For maximum blockage (b =\n0.2), the maximum flow power extracted by the cylinder is an order of magnitude\nlarger as compared to what it would extract in an open domain with free stream\nvelocity equal to the channel mean velocity. The optimal mass-damping\n({\\alpha}c ) for extracting maximum power is found to lie between 0.2 to 0.3.\nAn expression is derived to predict the maximum extracted power from the\nundamped response of a confined/unconfined cylinder. With the assumption\n{\\alpha}c = 0.25, the derived expression can predict the maximum power\nextraction within +-20% of the actual values obtained from present and previous\nnumerical and experimental studies.\n"}
+{"abstract": "  We report on the formation of the dilute $Pd_{1-x}Fe_x$ compositions with\ntunable magnetic properties under an ion-beam implantation of epitaxial Pd thin\nfilms. Binary $Pd_{1-x}Fe_x$ alloys with a mean iron content $x$ of $0.025$,\n$0.035$ or $0.075$ were obtained by the implantation of $40 keV$ $Fe^+$ ions\ninto the palladium films on MgO (001) substrate to the doses of\n$0.5\\cdot10^{16}, 1.0\\cdot10^{16}$ and $3.0\\cdot10^{16}$ $ions/cm^2$,\nrespectively. Structural and magnetic studies have shown that iron atoms occupy\nregular fcc-lattice Pd-sites without the formation of any secondary\ncrystallographic phase. All the iron implanted Pd films reveal ferromagnetism\nat low temperatures (below $200 K$) with both the Curie temperature and\nsaturation magnetization determined by the implanted iron dose. In contrast to\nthe magnetic properties of the molecular beam epitaxy grown $Pd_{1-x}Fe_x$\nalloy films with the similar iron contents, the Fe-implanted Pd films possess\nweaker in-plane magnetocrystalline anisotropy, and, accordingly, a lower\ncoercivity. The observed multiple ferromagnetic resonances in the implanted\n$Pd_{1-x}Fe_x$ films indicate a formation of a magnetically inhomogeneous state\ndue to spinodal decomposition into regions, presumably layers, with identical\ncrystal symmetry but different iron contents. The multiphase magnetic structure\nis robust with respect to the vacuum annealing at $770 K$, though develops\ntowards well-defined local $Pd-Fe$ compositions.\n"}
+{"abstract": "  The field of galaxy evolution will make a great leap forward in the next\ndecade as a consequence of the huge effort by the scientific community in\nmulti-object spectroscopic facilities. To maximise the impact of such incoming\ndata, the analysis methods must also step up, extracting reliable information\nfrom the available spectra. In this paper, we aim to investigate the limits and\nthe reliability of different spectral synthesis methods in the estimation of\nthe mean stellar age and metallicity. The main question this work aims to\naddress is which signal-to-noise ratios (S/N) are needed to reliably determine\nthe mean stellar age and metallicity from a galaxy spectrum and how this\ndepends on the tool used to model the spectra. To address this question we\nbuilt a set of realistic simulated spectra containing stellar and nebular\nemission, reproducing the evolution of a galaxy in two limiting cases: a\nconstant star formation rate and an exponentially declining star formation. We\ndegraded the synthetic spectra built from these two star formation histories\n(SFHs) to different S/N and analysed them with three widely used spectral\nsynthesis codes, namely FADO, STECKMAP, and STARLIGHT. For S/N < 5 all three\ntools show a large diversity in the results. The FADO and STARLIGHT tools find\nmedian differences in the light-weighted mean stellar age of ~0.1 dex, while\nSTECKMAP shows a higher value of ~0.2 dex. Detailed investigations of the\nbest-fit spectrum for galaxies with overestimated mass-weighted quantities\npoint towards the inability of purely stellar models to fit the observed\nspectra around the Balmer jump. Our results imply that when a galaxy enters a\nphase of high specific star formation rate the neglect of the nebular continuum\nemission in the fitting process has a strong impact on the estimation of its\nSFH when purely stellar fitting codes are used, even in presence of high S/N\nspectra.\n"}
+{"abstract": "  In this paper we focus on the dynamic state estimation which harnesses a vast\namount of sensing data harvested by multiple parties and recognize that in many\napplications, to improve collaborations between parties, the estimation\nprocedure must be designed with the awareness of protecting participants' data\nand model privacy, where the latter refers to the privacy of key parameters of\nobservation models. We develop a state estimation paradigm for the scenario\nwhere multiple parties with data and model privacy concerns are involved.\nMultiple parties monitor a physical dynamic process by deploying their own\nsensor networks and update the state estimate according to the average state\nestimate of all the parties calculated by a cloud server and security module.\nThe paradigm taps additively homomorphic encryption which enables the cloud\nserver and security module to jointly fuse parties' data while preserving the\ndata privacy. Meanwhile, all the parties collaboratively develop a stable (or\noptimal) fusion rule without divulging sensitive model information. For the\nproposed filtering paradigm, we analyze the stabilization and the optimality.\nFirst, to stabilize the multi-party state estimator while preserving\nobservation model privacy, two stabilization design methods are proposed. For\nspecial scenarios, the parties directly design their estimator gains by the\nmatrix norm relaxation. For general scenarios, after transforming the original\ndesign problem into a convex semi-definite programming problem, the parties\ncollaboratively derive suitable estimator gains based on the ADMM. Second, an\noptimal collaborative gain design method with model privacy guarantees is\nprovided, which results in the asymptotic MMSE state estimation. Finally,\nnumerical examples are presented to illustrate our design and theoretical\nfindings.\n"}
+{"abstract": "  We study the economic interactions among sellers and buyers in online\nmarkets. In such markets, buyers have limited information about the product\nquality, but can observe the sellers' reputations which depend on their past\ntransaction histories and ratings from past buyers. Sellers compete in the same\nmarket through pricing, while considering the impact of their heterogeneous\nreputations. We consider sellers with limited as well as unlimited capacities,\nwhich correspond to different practical market scenarios. In the unlimited\nseller capacity scenario, buyers prefer the seller with the highest\nreputation-price ratio. If the gap between the highest and second highest\nseller reputation levels is large enough, then the highest reputation seller\ndominates the market as a monopoly. If sellers' reputation levels are\nrelatively close to each other, then those sellers with relatively high\nreputations will survive at the equilibrium, while the remaining relatively low\nreputation sellers will get zero market share. In the limited seller capacity\nscenario, we further consider two different cases. If each seller can only\nserve one buyer, then it is possible for sellers to set their monopoly prices\nat the equilibrium while all sellers gain positive market shares; if each\nseller can serve multiple buyers, then it is possible for sellers to set\nmaximum prices at the equilibrium. Simulation results show that the dynamics of\nreputations and prices in the longer-term interactions will converge to stable\nstates, and the initial buyer ratings of the sellers play the critical role in\ndetermining sellers' reputations and prices at the stable state.\n"}
+{"abstract": "  Illuminated gapped-gold-nanorod dimers hold surface plasmon polaritons (SPPs)\nthat can be engineered, by an appropriate choice of geometrical parameters, to\nenhance the electromagnetic field at the gap, allowing applications in\nmolecular detection via surface-enhanced Raman spectroscopy (SERS). Envisioning\nhybrid devices in which the SERS spectroscopy of molecules in the gap is\ncomplemented by electrical measurements, it arises the question of designing\nefficient geometries to contact the nanorods without decreasing the enhancement\nfactor (EF) of the nanoantenna, i.e., the figure of merit for SERS\nspectroscopy. Within this framework we theoretically study the feasibility to\nfabricate designs based on covering with gold the far-from-the-gap areas of the\ndimer. We show that by tuning the geometrical parameters of the designs these\nsystems can reach enhancement factors larger than the best achieved in the\nuncovered dimer: this supremacy survives even in the presence of dimer\nasymmetries and vacancies at the interfaces between the nanorods and the\ncovering layers. Our results show that geometrical modifications away from the\ngap can improve the optical response at the gap, thus enabling the use of these\ndevices both for hybrid and optical applications.\n"}
+{"abstract": "  Non-Independent and Identically Distributed (non- IID) data distribution\namong clients is considered as the key factor that degrades the performance of\nfederated learning (FL). Several approaches to handle non-IID data such as\npersonalized FL and federated multi-task learning (FMTL) are of great interest\nto research communities. In this work, first, we formulate the FMTL problem\nusing Laplacian regularization to explicitly leverage the relationships among\nthe models of clients for multi-task learning. Then, we introduce a new view of\nthe FMTL problem, which in the first time shows that the formulated FMTL\nproblem can be used for conventional FL and personalized FL. We also propose\ntwo algorithms FedU and dFedU to solve the formulated FMTL problem in\ncommunication-centralized and decentralized schemes, respectively.\nTheoretically, we prove that the convergence rates of both algorithms achieve\nlinear speedup for strongly convex and sublinear speedup of order 1/2 for\nnonconvex objectives. Experimentally, we show that our algorithms outperform\nthe conventional algorithm FedAvg in FL settings, MOCHA in FMTL settings, as\nwell as pFedMe and Per-FedAvg in personalized FL settings.\n"}
+{"abstract": "  State-of-the-art human-in-the-loop robot grasping is hugely suffered by\nElectromyography (EMG) inference robustness issues. As a workaround,\nresearchers have been looking into integrating EMG with other signals, often in\nan ad hoc manner. In this paper, we are presenting a method for end-to-end\ntraining of a policy for human-in-the-loop robot grasping on real reaching\ntrajectories. For this purpose we use Reinforcement Learning (RL) and Imitation\nLearning (IL) in DEXTRON (DEXTerity enviRONment), a stochastic simulation\nenvironment with real human trajectories that are augmented and selected using\na Monte Carlo (MC) simulation method. We also offer a success model which once\ntrained on the expert policy data and the RL policy roll-out transitions, can\nprovide transparency to how the deep policy works and when it is probably going\nto fail.\n"}
+{"abstract": "  Equations of motion for excited states of weakly self-interacting bosons\nforming fuzzy dark matter are solved using the WKB approximation. The\ncontribution of self-interactions are neglected in the equations of motion.\nWave functions of excited states are expressed in terms of a yet undetermined\ngravitational potential. At equilibrium, the contributions of states to the\ndensity distribution are summed using Bose-Einstein statistics. Combined with\nthe Poisson equation, a differential equation is obtained for the gravitational\npotential, which has physically acceptable solutions only if the energy\nspectrum of excited states has a finite gap, corresponding to a finite virial\nradius. Such a gap could be created by decay processes, in first order\nperturbation of the self-interaction potential. The obtained density profile is\nfound to be similar to the Burkert profile.\n"}
+{"abstract": "  We re-create the essential results of a 1989 unpublished article by Mazzola\nand Muzzulini that contains musicological aspects of a first-species\ncounterpoint model. We include a summary of the mathematical counterpoint\ntheory and several variations of the model that offer different perspectives on\nMazzola's original principles.\n"}
+{"abstract": "  In this conceptual work, we present Deep Convolutional Gaussian Mixture\nModels (DCGMMs): a new formulation of deep hierarchical Gaussian Mixture Models\n(GMMs) that is particularly suitable for describing and generating images.\nVanilla (i.e., flat) GMMs require a very large number of components to describe\nimages well, leading to long training times and memory issues. DCGMMs avoid\nthis by a stacked architecture of multiple GMM layers, linked by convolution\nand pooling operations. This allows to exploit the compositionality of images\nin a similar way as deep CNNs do. DCGMMs can be trained end-to-end by\nStochastic Gradient Descent. This sets them apart from vanilla GMMs which are\ntrained by Expectation-Maximization, requiring a prior k-means initialization\nwhich is infeasible in a layered structure. For generating sharp images with\nDCGMMs, we introduce a new gradient-based technique for sampling through\nnon-invertible operations like convolution and pooling. Based on the MNIST and\nFashionMNIST datasets, we validate the DCGMMs model by demonstrating its\nsuperiority over flat GMMs for clustering, sampling and outlier detection.\n"}
+{"abstract": "  Active fluids, such as cytoskeletal filaments, bacterial colonies and\nepithelial cell layers, exhibit distinctive orientational coherence, often\ncharacterized by nematic order and topological defects. By contrast, little is\nknown about positional coherence -- i.e., how a hidden dynamic skeleton\norganizes the underlying chaotic motion -- despite this being one of their most\nprominent and experimentally accessible features. Using a combination of\ndynamical systems theory, experiments on two-dimensional mixtures of\nmicrotubules and kinesin and hydrodynamic simulations, we characterize\npositional coherence in active nematics. These coherent structures can be\nidentified in the framework of Lagrangian dynamics as moving attractors and\nrepellers, which orchestrate complex motion. To understand the interaction of\npositional and orientational coherence on the dynamics of defects, we then\nanalysed observations and simulations and see that +1/2 defects move and deform\nthe attractors, thus functioning as control centers for collective motion.\nAdditionally, we find that regions around isolated +1/2 defects undergo high\nbending and low stretching/shearing deformations, consistent with the local\nstress distribution. The stress is minimum at the defect, while high\ndifferential stress along the defect orientation induces folding. Our work\noffers a new perspective to describe self-organization in active fluids, with\npotential applications to multicellular systems.\n"}
+{"abstract": "  Consider a finite collection $\\{T_1, \\ldots, T_J\\}$ of differential operators\nwith constant coefficients on $\\mathbb{T}^n$ ($n\\geq 2$) and the space of\nsmooth functions generated by this collection, namely, the space of functions\n$f$ such that $T_j f \\in C(\\mathbb{T}^n)$, $1\\leq j\\leq J$. We prove that if\nthere are at least two linearly independent operators among their senior parts\n(relative to some mixed pattern of homogeneity), then this space does not have\nlocal unconditional structure. This fact generalizes the previously known\nresult that such spaces are not isomorphic to a complemented subspace of $C(S)$\n"}
+{"abstract": "  This study aimed to compare and investigate the efficacy of the real-world\nexperiences, immersive virtual reality (IVR) experiences, and video walkthrough\nrepresentations on layout-learning in a complex building. A quasi-experimental,\nintervention, and delayed post-test research design was used among three\ngroups: real-world, IVR, and video walkthrough representation. A total of 41\nfirst-year design students from architecture, and game design departments were\nattended the study. Design students were selected as they already know how to\ncommunicate graphically the layout of a building on paper. IVR and video\nwalkthrough groups experienced the representations of the building by\nthemselves, but real-world group experienced the building within a group as\nimitating a design education field trip. After 10 days, a total of 26\nparticipants out of 41 tested for spatial recall performance. Recall\nperformances were measured as an indicator of layout learning by analysing the\nplan sketches drawn by the participants. Results showed IVR group recalled\nsignificantly more spatial memories compared with the real-world and video\nwalkthrough representation groups. Real-world group performed the worst among\nthree groups. This result was interpreted to three conclusions. First, the\nlayout learning tends to be overly sensitive to distractions and lower levels\nof distraction may lead to better levels of layout learning. Second, for the\ndomain of layout knowledge learning, a remarkably simple IVR model is enough.\nThird, although real-world experiences give direct and richer sensory\ninformation about the spaces, layout knowledge acquisition from the surrounding\nenvironment requires psychologically active wayfinding decisions.\n"}
+{"abstract": "  The current concept of Smart Cities influences urban planners and researchers\nto provide modern, secured and sustainable infrastructure and give a decent\nquality of life to its residents. To fulfill this need video surveillance\ncameras have been deployed to enhance the safety and well-being of the\ncitizens. Despite technical developments in modern science, abnormal event\ndetection in surveillance video systems is challenging and requires exhaustive\nhuman efforts. In this paper, we surveyed various methodologies developed to\ndetect anomalies in intelligent video surveillance. Firstly, we revisit the\nsurveys on anomaly detection in the last decade. We then present a systematic\ncategorization of methodologies developed for ease of understanding.\nConsidering the notion of anomaly depends on context, we identify different\nobjects-of-interest and publicly available datasets in anomaly detection. Since\nanomaly detection is considered a time-critical application of computer vision,\nour emphasis is on anomaly detection using edge devices and approaches\nexplicitly designed for them. Further, we discuss the challenges and\nopportunities involved in anomaly detection at the edge.\n"}
+{"abstract": "  In some memory-constrained settings like IoT devices and over-the-network\ndata pipelines, it can be advantageous to have smaller contextual embeddings.\nWe investigate the efficacy of projecting contextual embedding data (BERT) onto\na manifold, and using nonlinear dimensionality reduction techniques to compress\nthese embeddings. In particular, we propose a novel post-processing approach,\napplying a combination of Isomap and PCA. We find that the geodesic distance\nestimations, estimates of the shortest path on a Riemannian manifold, from\nIsomap's k-Nearest Neighbors graph bolstered the performance of the compressed\nembeddings to be comparable to the original BERT embeddings. On one dataset, we\nfind that despite a 12-fold dimensionality reduction, the compressed embeddings\nperformed within 0.1% of the original BERT embeddings on a downstream\nclassification task. In addition, we find that this approach works particularly\nwell on tasks reliant on syntactic data, when compared with linear\ndimensionality reduction. These results show promise for a novel geometric\napproach to achieve lower dimensional text embeddings from existing\ntransformers and pave the way for data-specific and application-specific\nembedding compressions.\n"}
+{"abstract": "  We consider a generalization of the recently proposed gradient coding\nframework where a large dataset is divided across $n$ workers and each worker\ntransmits to a master node one or more linear combinations of the gradients\nover the data subsets assigned to it. Unlike the conventional framework which\nrequires the master node to recover the sum of the gradients over all the data\nsubsets in the presence of $s$ straggler workers, we relax the goal of the\nmaster node to computing the sum of at least some $\\alpha$ fraction of the\ngradients. The broad goal of our work is to study the optimal computation and\ncommunication load per worker for this approximate gradient coding framework.\nWe begin by deriving a lower bound on the computation load of any feasible\nscheme and also propose a strategy which achieves this lower bound, albeit at\nthe cost of high communication load and a number of data partitions which can\nbe polynomial in the number of workers $n$. We then restrict attention to\nschemes which utilize a number of data partitions equal to $n$ and propose\nschemes based on cyclic assignment which have a lower communication load. When\neach worker transmits a single linear combination, we also prove lower bounds\non the computation load of any scheme using $n$ data partitions.\n"}
+{"abstract": "  This paper compares BERT-SQuAD and Ab3P on the Abbreviation Definition\nIdentification (ADI) task. ADI inputs a text and outputs short forms\n(abbreviations/acronyms) and long forms (expansions). BERT with reranking\nimproves over BERT without reranking but fails to reach the Ab3P rule-based\nbaseline. What is BERT missing? Reranking introduces two new features:\ncharmatch and freq. The first feature identifies opportunities to take\nadvantage of character constraints in acronyms and the second feature\nidentifies opportunities to take advantage of frequency constraints across\ndocuments.\n"}
+{"abstract": "  A series of new flow observables mixed harmonic multi-particle cumulants\n(MHC), which allow for the first time to quantify the correlations strength\nbetween different order of flow coefficients with various moments, was\ninvestigated using hydrodynamic model. These new observables are constructed\nbased on multi-particle cumulants, and thus by design will be less sensitive to\nthe non-flow contaminations. In addition to the previous study of correlation\ninvolving two flow coefficients with their second moments, both correlations of\nthree flow coefficients and the correlations of higher order moments of $v_2$\nand $v_3$ are systematically investigated using iEBE-VISHNU hybrid model with\ntwo different initial conditions, AMPT and TRENTo, respectively. These\nsystematic studies using hydrodynamic models will significantly improve the\nunderstanding on the correlations between different orders (and moments) of\nflow coefficients. The hydrodynamic predictions shown in this paper and the\nfuture comparisons to experimental measurements will provide more constraints\non theoretical models and extract more information about the transport\nproperties of the quark-gluon plasma created in heavy-ion collisions.\n"}
+{"abstract": "  An implicit but pervasive hypothesis of modern computer vision research is\nthat convolutional neural network (CNN) architectures that perform better on\nImageNet will also perform better on other vision datasets. We challenge this\nhypothesis through an extensive empirical study for which we train 500 sampled\nCNN architectures on ImageNet as well as 8 other image classification datasets\nfrom a wide array of application domains. The relationship between architecture\nand performance varies wildly, depending on the datasets. For some of them, the\nperformance correlation with ImageNet is even negative. Clearly, it is not\nenough to optimize architectures solely for ImageNet when aiming for progress\nthat is relevant for all applications. Therefore, we identify two\ndataset-specific performance indicators: the cumulative width across layers as\nwell as the total depth of the network. Lastly, we show that the range of\ndataset variability covered by ImageNet can be significantly extended by adding\nImageNet subsets restricted to few classes.\n"}
+{"abstract": "  Maintaining legacy enterprise information systems is a known necessity in\ncompanies. To date, it remains an expensive and time-consuming process,\nrequiring high effort and cost to get small changes implemented. MITRAS seeks\nto reduce the maintenance cost by providing an automatic maintenance system\nmodel based on graph transformations. This article presents Parthenos, a\ndifferent approach to MITRAS, whose goal is to guarantee the correctness of\nintroduced modifications at a syntax and type semantics level of the source\ncode. Along with that, it proposes an extensible architecture, which allows the\nmost varied types of systems to carry out software maintenance. Parthenos was\nevaluated through functional tests to evaluate its effectiveness, using\nmeasures of precision, recall, and f-measure.\n"}
+{"abstract": "  We propose a simple extension of residual networks that works simultaneously\nin multiple resolutions. Our network design is inspired by the iterative\nback-projection algorithm but seeks the more difficult task of learning how to\nenhance images. Compared to similar approaches, we propose a novel solution to\nmake back-projections run in multiple resolutions by using a data pipeline\nworkflow. Features are updated at multiple scales in each layer of the network.\nThe update dynamic through these layers includes interactions between different\nresolutions in a way that is causal in scale, and it is represented by a system\nof ODEs, as opposed to a single ODE in the case of ResNets. The system can be\nused as a generic multi-resolution approach to enhance images. We test it on\nseveral challenging tasks with special focus on super-resolution and raindrop\nremoval. Our results are competitive with state-of-the-arts and show a strong\nability of our system to learn both global and local image features.\n"}
+{"abstract": "  Neural network-based embeddings have been the mainstream approach for\ncreating a vector representation of the text to capture lexical and semantic\nsimilarities and dissimilarities. In general, existing encoding methods dismiss\nthe punctuation as insignificant information; consequently, they are routinely\neliminated in the pre-processing phase as they are shown to improve task\nperformance. In this paper, we hypothesize that punctuation could play a\nsignificant role in sentiment analysis and propose a novel representation model\nto improve syntactic and contextual performance. We corroborate our findings by\nconducting experiments on publicly available datasets and verify that our model\ncan identify the sentiments more accurately over other state-of-the-art\nbaseline methods.\n"}
+{"abstract": "  In Part 1 of the current series of papers, we demonstrate the equivalence\nbetween the Zhang-Zhang polynomial $\\text{ZZ}(\\boldsymbol{S},x)$ of a\nKekul\\'ean regular $m$-tier strip $\\boldsymbol{S}$ of length $n$ and the\nextended strict order polynomial $\\text{E}_{\\mathcal{S}}^{\\circ}(n,x+1)$ of a\ncertain partially ordered set (poset) $\\mathcal{S}$ associated with\n$\\boldsymbol{S}$. The discovered equivalence is a consequence of the one-to-one\ncorrespondence between the set $\\left\\{ K\\right\\}$ of Kekul\\'e structures of\n$\\boldsymbol{S}$ and the set $\\left\\{\n\\mu:\\mathcal{S}\\supset\\mathcal{A}\\rightarrow\\left[\\,n\\,\\right]\\right\\}$ of\nstrictly order-preserving maps from the induced subposets of $\\mathcal{S}$ to\nthe interval $\\left[\\thinspace n\\thinspace\\right]$. As a result, the problems\nof determining the Zhang-Zhang polynomial of $\\boldsymbol{S}$ and of generating\nthe complete set of Clar covers of $\\boldsymbol{S}$ reduce to the problem of\nconstructing the set $\\mathcal{L}(\\mathcal{S})$ of linear extensions of the\ncorresponding poset $\\mathcal{S}$ and studying their basic properties. In\nparticular, the Zhang-Zhang polynomial of $\\boldsymbol{S}$ can be written in a\ncompact form as\n$\\text{ZZ}(\\boldsymbol{S},x)=\\sum_{k=0}^{\\left|\\mathcal{S}\\right|}\\sum_{w\\in\\mathcal{L}(\\mathcal{S})}\\binom{\\left|\\mathcal{S}\\right|-\\text{fix}_{\\mathcal{S}}(w)}{\\,\\,k\\,\\,\\hspace{1pt}-\\text{fix}_{\\mathcal{S}}(w)}\\binom{n+\\text{des}(w)}{k}\\left(1+x\\right)^{k}$,\nwhere $\\text{des}(w)$ and $\\text{fix}_{\\mathcal{S}}(w)$ denote the number of\ndescents and the number of fixed labels, respectively, in the linear extension\n$w\\in\\mathcal{L}(\\mathcal{S})$.\n"}
+{"abstract": "  A large number of particle detectors employ liquid argon as their target\nmaterial owing to its high scintillation yield and its ability to drift\nionization charge over large distances. Scintillation light from argon is\npeaked at 128 nm and a wavelength shifter is required for its efficient\ndetection. In this work, we directly compare the light yield achieved in two\nidentical liquid argon chambers, one of which is equipped with PolyEthylene\nNaphthalate (PEN) and the other with TetraPhenyl Butadiene (TPB) wavelength\nshifter. Both chambers are lined with enhanced specular reflectors and\ninstrumented with SiPMs with a coverage fraction of approximately 1%, which\nrepresents a geometry comparable to the future large scale detectors. We\nmeasured the light yield of the PEN chamber to be\n39.4$\\pm$0.4(stat)$\\pm$1.9(syst)% of the yield of the TPB chamber. Using a\nMonte Carlo simulation this result is used to extract the wavelength shifting\nefficiency of PEN relative to TPB equal to 47.2$\\pm$5.7%. This result paves the\nway for the use of easily available PEN foils as a wavelength shifter, which\ncan substantially simplify the construction of future liquid argon detectors.\n"}
+{"abstract": "  In this paper, we study the linear transformation model in the most general\nsetup. This model includes many important and popular models in statistics and\neconometrics as special cases. Although it has been studied for many years, the\nmethods in the literature are based on kernel-smoothing techniques or make use\nof only the ranks of the responses in the estimation of the parametric\ncomponents. The former approach needs a tuning parameter, which is not easily\noptimally specified in practice; and the latter is computationally expensive\nand may not make full use of the information in the data. In this paper, we\npropose two methods: a pairwise rank likelihood method and a\nscore-function-based method based on this pairwise rank likelihood. We also\nexplore the theoretical properties of the proposed estimators. Via extensive\nnumerical studies, we demonstrate that our methods are appealing in that the\nestimators are not only robust to the distribution of the random errors but\nalso lead to mean square errors that are in many cases comparable to or smaller\nthan those of existing methods.\n"}
+{"abstract": "  Correlated with the trend of increasing degrees of freedom in robotic systems\nis a similar trend of rising interest in Spatio-Temporal systems described by\nPartial Differential Equations (PDEs) among the robotics and control\ncommunities. These systems often exhibit dramatic under-actuation, high\ndimensionality, bifurcations, and multimodal instabilities. Their control\nrepresents many of the current-day challenges facing the robotics and\nautomation communities. Not only are these systems challenging to control, but\nthe design of their actuation is an NP-hard problem on its own. Recent methods\neither discretize the space before optimization, or apply tools from linear\nsystems theory under restrictive linearity assumptions in order to arrive at a\ncontrol solution. This manuscript provides a novel sampling-based stochastic\noptimization framework based entirely in Hilbert spaces suitable for the\ngeneral class of \\textit{semi-linear} SPDEs which describes many systems in\nrobotics and applied physics. This framework is utilized for simultaneous\npolicy optimization and actuator co-design optimization. The resulting\nalgorithm is based on variational optimization, and performs joint episodic\noptimization of the feedback control law and the actuation design over\nepisodes. We study first and second order systems, and in doing so, extend\nseveral results to the case of second order SPDEs. Finally, we demonstrate the\nefficacy of the proposed approach with several simulated experiments on a\nvariety of SPDEs in robotics and applied physics including an infinite\ndegree-of-freedom soft robotic manipulator.\n"}
+{"abstract": "  We study the generalizations of the known equivalent reformulations of\ncondition moderate growth from the single weight sequence to the weight matrix\nsetting. This condition, also known in the literature under the name stability\nunder ultradifferentiable operators, plays a significant role in the theory of\nultradifferentiable (and ultraholomorophic) function classes defined in terms\nof weight sequences and its generalization becomes relevant when dealing with\nclasses defined by weight matrices. In the matrix setting, we prove that the\ndifferent mixed conditions are in general not equivalently satisfied anymore\nand we focus on weight matrices associated with (associated) weight functions.\n"}
+{"abstract": "  The I-table contains luminous intensity values over the range of angles for\nthe luminaires used in the road lighting in accordance with technical report\nCIE 121:1996. A limited number of angles causes smoothing of the luminous\nintensity diagram omitting possible local extremal values which affect the\ncalculations of the photometric parameters such as average illuminance, average\nluminance, uniformity or treshold increment. The interpolating methods used to\ncalculate the luminous intensity can significantly improve the accuracy of the\ncalculations and redound to more effective and reliable road lighting design.\nIn the paper standard interpolation methods used up to now are compared with\nnewly proposed hybrid adaptive splines. Calculated values of luminous intensity\nare compared and verified by goniophotometric measurements.\n"}
+{"abstract": "  We propose an Exclusive Topic Modeling (ETM) for unsupervised text\nclassification, which is able to 1) identify the field-specific keywords though\nless frequently appeared and 2) deliver well-structured topics with exclusive\nwords. In particular, a weighted Lasso penalty is imposed to reduce the\ndominance of the frequently appearing yet less relevant words automatically,\nand a pairwise Kullback-Leibler divergence penalty is used to implement topics\nseparation. Simulation studies demonstrate that the ETM detects the\nfield-specific keywords, while LDA fails. When applying to the benchmark NIPS\ndataset, the topic coherence score on average improves by 22% and 10% for the\nmodel with weighted Lasso penalty and pairwise Kullback-Leibler divergence\npenalty, respectively.\n"}
+{"abstract": "  We present TweeNLP, a one-stop portal that organizes Twitter's natural\nlanguage processing (NLP) data and builds a visualization and exploration\nplatform. It curates 19,395 tweets (as of April 2021) from various NLP\nconferences and general NLP discussions. It supports multiple features such as\nTweetExplorer to explore tweets by topics, visualize insights from Twitter\nactivity throughout the organization cycle of conferences, discover popular\nresearch papers and researchers. It also builds a timeline of conference and\nworkshop submission deadlines. We envision TweeNLP to function as a collective\nmemory unit for the NLP community by integrating the tweets pertaining to\nresearch papers with the NLPExplorer scientific literature search engine. The\ncurrent system is hosted at http://nlpexplorer.org/twitter/CFP .\n"}
+{"abstract": "  The rapid solidification of a binary mixture in the region of the interface\nvelocities $V$ close to the diffusion speed in the bulk of the liquid phase\n$V_D$ is considered within the framework of the local nonequilibrium approach.\nIn this high-speed region the derivation of the analytical expression for the\nresponse function \"temperature-velocity\" representing kinetic phase diagram is\ngiven without using the concept of the equilibrium phase diagram.\n  The modes of movement of the interface both without and with the drag effect\nare analyzed. It is shown that the drag effect can be accompanied by a local\ninterface temperature maximum at $V = V_D$.\n"}
+{"abstract": "  In this work we introduce a new class of gradient-free global optimization\nmethods based on a binary interaction dynamics governed by a Boltzmann type\nequation. In each interaction the particles act taking into account both the\nbest microscopic binary position and the best macroscopic collective position.\nIn the mean-field limit we show that the resulting Fokker-Planck partial\ndifferential equations generalize the current class of consensus based\noptimization (CBO) methods. For the latter methods, convergence to the global\nminimizer can be shown for a large class of functions. Algorithmic\nimplementations inspired by the well-known direct simulation Monte Carlo\nmethods in kinetic theory are derived and discussed. Several examples on\nprototype test functions for global optimization are reported including\napplications to machine learning.\n"}
+{"abstract": "  Deep neural network is an effective choice to automatically recognize human\nactions utilizing data from various wearable sensors. These networks automate\nthe process of feature extraction relying completely on data. However, various\nnoises in time series data with complex inter-modal relationships among sensors\nmake this process more complicated. In this paper, we have proposed a novel\nmulti-stage training approach that increases diversity in this feature\nextraction process to make accurate recognition of actions by combining\nvarieties of features extracted from diverse perspectives. Initially, instead\nof using single type of transformation, numerous transformations are employed\non time series data to obtain variegated representations of the features\nencoded in raw data. An efficient deep CNN architecture is proposed that can be\nindividually trained to extract features from different transformed spaces.\nLater, these CNN feature extractors are merged into an optimal architecture\nfinely tuned for optimizing diversified extracted features through a combined\ntraining stage or multiple sequential training stages. This approach offers the\nopportunity to explore the encoded features in raw sensor data utilizing\nmultifarious observation windows with immense scope for efficient selection of\nfeatures for final convergence. Extensive experimentations have been carried\nout in three publicly available datasets that provide outstanding performance\nconsistently with average five-fold cross-validation accuracy of 99.29% on UCI\nHAR database, 99.02% on USC HAR database, and 97.21% on SKODA database\noutperforming other state-of-the-art approaches.\n"}
+{"abstract": "  Let $G$ be a compact connected Lie group. We show that the category\n$\\mathbf{Loc}_{\\infty}(BG)$ of $\\infty$-local systems on the classifying space\nof $G$, can be described infinitesimally as the category\n$\\mathbf{InfLoc}_{\\infty}(\\mathfrak{g})$ of basic $\\mathfrak{g}$-$L_\\infty$\nspaces. Moreover, we show that, given a principal bundle $\\pi \\colon P\n\\rightarrow X$ with structure group $G$ and any connection $\\theta$ on $P$,\nthere is a DG functor $$\\mathcal{CW}_{\\theta} \\colon\n\\mathbf{InfLoc}_{\\infty}(\\mathfrak{g}) \\longrightarrow\n\\mathbf{Loc}_{\\infty}(X), $$ which corresponds to the pullback functor by the\nclassifying map of $P$. The DG functors associated to different connections are\nrelated by an $A_\\infty$-natural isomorphism. This construction provides a\ncategorification of the Chern-Weil homomorphism, which is recovered by applying\nthe functor $\\mathcal{CW}_{\\theta}$ to the endomorphisms of the constant local\nsystem.\n"}
+{"abstract": "  The Fowler-Nordheim tunneling current formula has been widely used in the\ndesign of devices based on metal/insulator (metal/semiconductor)\nheterojunctions with triangle potential barriers, such as the flash memory.\nHere we adopt the model that was used to derive the Landauer formula at finite\ntemperature, the nearly-free electron approximation to describe the electronic\nstates in semi-infinite metal electrode and the Wentzel-Kramers-Brillouin (WKB)\napproximation to describe the transmission coefficient, and derive a tunneling\ncurrent formula for metal/insulator heterojunctions under large bias and\nelectric field. In contrast to the Fowler-Nordheim formula which is the limit\nat zero temperature, our formula is generalized to the finite temperature (with\nthe thermal excitation of electrons in metal electrode considered) and the\npotential barriers beyond triangle ones, which may be used for the design of\nmore complicated heterojunction devices based on the carrier tunneling.\n"}
+{"abstract": "  We study the problem of matrix completion in this paper. A spectral scaled\nStudent prior is exploited to favour the underlying low-rank structure of the\ndata matrix. Importantly, we provide a thorough theoretical investigation for\nour approach, while such an analysis is hard to obtain and limited in\ntheoretical understanding of Bayesian matrix completion. More precisely, we\nshow that our Bayesian approach enjoys a minimax-optimal oracle inequality\nwhich guarantees that our method works well under model misspecification and\nunder general sampling distribution. Interestingly, we also provide efficient\ngradient-based sampling implementations for our approach by using Langevin\nMonte Carlo which is novel in Bayesian matrix completion. More specifically, we\nshow that our algorithms are significantly faster than Gibbs sampler in this\nproblem. To illustrate the attractive features of our inference strategy, some\nnumerical simulations are conducted and an application to image inpainting is\ndemonstrated.\n"}
+{"abstract": "  We present a time dependent quantum perturbation result, uniform in the\nPlanck constant, for perturbations of potentials whose gradients are Lipschitz\ncontinuous by potentials whose gradients are only bounded a.e.. Though this low\nregularity of the full potential is not enough to provide the existence of the\nclassical underlying dynamics, at variance with the quantum one, our result\nshows that the classical limit of the perturbed quantum dynamics remains in a\ntubular neighbourhood of the classical unperturbed one of size of order of the\nsquare root of the size of the perturbation. We treat both Schr\\\"odinger and\nvon Neumann-Heisenberg equations.\n"}
+{"abstract": "  We review progress in active hydrodynamic descriptions of flowing media on\ncurved and deformable manifolds: the state-of-the-art in continuum descriptions\nof single-layers of epithelial and/or other tissues during development. First,\nafter a brief overview of activity, flows and hydrodynamic descriptions, we\nhighlight the generic challenge of identifying the dependence on dynamical\nvariables of so-called active kinetic coefficients -- active counterparts to\ndissipative Onsager coefficients. We go on to describe some of the subtleties\nconcerning how curvature and active flows interact, and the issues that arise\nwhen surfaces are deformable. We finish with a broad discussion around the\nutility of such theories in developmental biology. This includes limitations to\nanalytical techniques, challenges associated with numerical integration,\nfitting-to-data and inference, and potential tools for the future, such as\ndiscrete differential geometry.\n"}
+{"abstract": "  High-finesse, open-geometry microcavities have recently emerged as a\nversatile tool for enhancing interactions between photons and material systems,\nwith a range of applications in quantum optics and quantum information science.\nHowever, mechanical vibrations pose a considerable challenge to their operation\nwithin a closed-cycle cryostat, particularly when spatial tunability and\nfree-space optical access are required. Here, we present the design and\ncharacterization of a system that can achieve $\\sim$16 pm-rms passive\nmechanical stability between two high-finesse mirrors while permitting both\nthree-dimensional positioning of the cavity mode and free-space confocal\nimaging. The design relies on two cascaded vibration isolation stages connected\nby leaf springs that decouple axial and lateral motion, and incorporates\ntuned-mass and magnetic damping. Furthermore, we present a technique for\nquantifying cavity length displacements similar to or larger than the cavity\nlinewidth, allowing in-situ measurement of vibrations with and without active\nfeedback. Our results facilitate operation of a tunable, high-finesse cavity\nwithin a closed-cycle cryostat, representing an enabling technology for cavity\ncoupling to a variety of solid-state systems.\n"}
+{"abstract": "  Limited-valency colloidal particles can self-assemble into polymeric\nstructures analogous to molecules. While their structural equilibrium\nproperties have attracted wide attention, insight into their dynamics has\nproven challenging. Here, we investigate the polymerization dynamics of\nsemiflexible polymers in two dimensions (2D) by direct observation of\nassembling divalent particles, bonded by critical Casimir forces. The\nreversible critical Casimir force creates living polymerization conditions with\ntunable chain dissociation, association and bending rigidity. We find that\nunlike dilute polymers that show exponential size distributions in excellent\nagreement with Flory theory, concentrated samples exhibit arrest of rotational\nand translational diffusion due to a continuous isotropic-to-nematic transition\nin 2D, slowing down the growth kinetics. These effects are circumvented by\naddition of higher-valency particles, cross-linking the polymers into networks.\nOur results connecting polymer flexibility, polymer interactions and the\npeculiar isotropic-nematic transition in 2D offer insight into polymerization\nprocesses of synthetic two-dimensional polymers, and biopolymers at membranes\nand interfaces.\n"}
+{"abstract": "  Understanding differences in hospital case-fatality rates (HCFRs) of\ncoronavirus disease (COVID-19) may help evaluate its severity and the capacity\nof the healthcare system to reduce mortality. We examined the variability in\nHCFRs of COVID-19 in relation to spatial inequalities in socioeconomic factors\nacross the city of Sao Paulo, Brazil. We found that HCFRs were higher for men\nand for individuals aged 60 years and older. Our models identified per capita\nincome as a significant factor that is negatively associated with the HCFRs of\nCOVID-19, even after adjusting by age, sex and presence of risk factors.\n"}
+{"abstract": "  With the significant rise in demand for same-day instant deliveries, several\ncourier services are exploring alternatives to transport packages in a cost-\nand time-effective, as well as, sustainable manner. Motivated by a real-life\ncase study, this paper focuses on developing a simulation algorithm that\nassists same-day package delivery companies to serve customers instantly. The\nproposed recommender system provides the best solution with respect to three\ncriteria: cost, time, and sustainability, considering the variation in travel\ntime and cost parameters. The decision support tool provides recommendations on\nthe best alternative for transporting products based on factors, such as source\nand destination locations, time of the day, package weight, and volume. Besides\nconsidering existing new technologies like electric-assisted cargo bikes, we\nalso analyze the impact of emerging methods of deliveries, such as robots and\nair taxis. Finally, this paper also considers the best delivery alternative\nduring the presence of a pandemic, such as COVID-19. For the purpose of\nillustrating our approach, we consider the delivery options in New York City.\nWe believe that the proposed tool is the first to provide solutions to courier\ncompanies considering evolving modes of transportation and under logistics\ndisruptions due to pandemic.\n  Keywords: Instant package delivery; Courier services; Simulation algorithm;\nRecommender system; Emerging technologies; COVID-19 pandemic.\n"}
+{"abstract": "  Iron selenide (FeSe) is an iron-based superconductor which shows unique\nproperties, including strongly anisotropic superconducting gap, paramagnetism\nin undoped compound and extremely small Fermi pocket size. In this work, we\ndemonstrate that the sizes of electron and hole pockets in FeSe monolayer\nbecome much smaller than those in bulk. The Fermi energy is in the order of a\nfew meV and can be fine-tuned by the thickness of graphene layers underneath.\nDespite the low carrier density, the FeSe monolayers grown on trilayer or\nmulti-layer graphene are superconducting. The superconducting gap size is\nsensitive to the Fermi energy of the hole band. Remarkably, the FeSe monolayer\nprovides the opportunity to study the physics in the crossover regime where the\nFermi energy and superconducting gap are comparable to each other.\n"}
+{"abstract": "  We define the notion of a specialization morphism from a locally noetherian\nanalytic adic space to a scheme. This captures the (classical) specialization\nmorphism associated to a formal scheme. There is a well behaved theory of\ncompactifications and it turns out that the classical specialization morphism\nis \\emph{proper} in this setup. As an application, we show that the nearby\ncycles functor commutes with lower shriek in great generality.\n"}
+{"abstract": "  We discuss two problems concerning the class Eberlein compacta, i.e., weakly\ncompact subspaces of Banach spaces. The first one deals with preservation of\nsome classes of scattered Eberlein compacta under continuous images. The second\none concerns the known problem of the existence of nonmetrizable compact spaces\nwithout nonmetrizable zero-dimensional closed subspaces. We show that the\nexistence of such Eberlein compacta is consistent with ZFC. We also show that\nit is consistent with ZFC that each Eberlein compact space of weight $>\n\\omega_1$ contains a nonmetrizable closed zero-dimensional subspace.\n"}
+{"abstract": "  We study the cluster structure of 21Ne and 21Na within the framework of the\ncluster shell model (CSM) and show that they have a complex cluster structure\nwith the coexistence of a 20Ne+n, 20}Ne+p structure and a 19Ne+2n, 19}F+2p\nstructure. Seven rotational bands are identified in 21Ne and four in 21Na and\nassigned to single-particle cluster states, single-hole cluster states and\nvibrational states. The single-particle states are associated with the 20Ne+n\nand 20Ne+p cluster structure, while the single-hole states are associated with\nthe 19Ne+2n and 19F+2p structure.\n"}
+{"abstract": "  Clinical trials of a vaccine during an epidemic face particular challenges,\nsuch as the pressure to identify an effective vaccine quickly to control the\nepidemic, and the effect that time-space-varying infection incidence has on the\npower of a trial. We illustrate how the operating characteristics of different\ntrial design elements may be evaluated using a network epidemic and trial\nsimulation model, based on COVID-19 and individually randomised two-arm trials\nwith a binary outcome. We show that \"ring\" recruitment strategies, prioritising\nparticipants at high risk of infection, can result in substantial improvement\nin terms of power, if sufficiently many contacts of observed cases are at high\nrisk. In addition, we introduce a novel method to make more efficient use of\nthe data from the earliest cases of infection observed in the trial, whose\ninfection may have been too early to be vaccine-preventable. Finally, we\ncompare several methods of response-adaptive randomisation, discussing their\nadvantages and disadvantages in this two-arm context and identifying particular\nadaptation strategies that preserve power and estimation properties, while\nslightly reducing the number of infections, given an effective vaccine.\n"}
+{"abstract": "  Medical imaging systems are commonly assessed and optimized by use of\nobjective-measures of image quality (IQ) that quantify the performance of an\nobserver at specific tasks. Variation in the objects to-be-imaged is an\nimportant source of variability that can significantly limit observer\nperformance. This object variability can be described by stochastic object\nmodels (SOMs). In order to establish SOMs that can accurately model realistic\nobject variability, it is desirable to use experimental data. To achieve this,\nan augmented generative adversarial network (GAN) architecture called\nAmbientGAN has been developed and investigated. However, AmbientGANs cannot be\nimmediately trained by use of advanced GAN training methods such as the\nprogressive growing of GANs (ProGANs). Therefore, the ability of AmbientGANs to\nestablish realistic object models is limited. To circumvent this, a\nprogressively-growing AmbientGAN (ProAmGAN) has been proposed. However,\nProAmGANs are designed for generating two-dimensional (2D) images while medical\nimaging modalities are commonly employed for imaging three-dimensional (3D)\nobjects. Moreover, ProAmGANs that employ traditional generator architectures\nlack the ability to control specific image features such as fine-scale textures\nthat are frequently considered when optimizing imaging systems. In this study,\nwe address these limitations by proposing two advanced AmbientGAN\narchitectures: 3D ProAmGANs and Style-AmbientGANs (StyAmGANs). Stylized\nnumerical studies involving magnetic resonance (MR) imaging systems are\nconducted. The ability of 3D ProAmGANs to learn 3D SOMs from imaging\nmeasurements and the ability of StyAmGANs to control fine-scale textures of\nsynthesized objects are demonstrated.\n"}
+{"abstract": "  Semantic segmentation, which refers to pixel-wise classification of an image,\nis a fundamental topic in computer vision owing to its growing importance in\nrobot vision and autonomous driving industries. It provides rich information\nabout objects in the scene such as object boundary, category, and location.\nRecent methods for semantic segmentation often employ an encoder-decoder\nstructure using deep convolutional neural networks. The encoder part extracts\nfeature of the image using several filters and pooling operations, whereas the\ndecoder part gradually recovers the low-resolution feature maps of the encoder\ninto a full input resolution feature map for pixel-wise prediction. However,\nthe encoder-decoder variants for semantic segmentation suffer from severe\nspatial information loss, caused by pooling operations or convolutions with\nstride, and does not consider the context in the scene. In this paper, we\npropose a dense upsampling convolution method based on guided filtering to\neffectively preserve the spatial information of the image in the network. We\nfurther propose a novel local context convolution method that not only covers\nlarger-scale objects in the scene but covers them densely for precise object\nboundary delineation. Theoretical analyses and experimental results on several\nbenchmark datasets verify the effectiveness of our method. Qualitatively, our\napproach delineates object boundaries at a level of accuracy that is beyond the\ncurrent excellent methods. Quantitatively, we report a new record of 82.86% and\n81.62% of pixel accuracy on ADE20K and Pascal-Context benchmark datasets,\nrespectively. In comparison with the state-of-the-art methods, the proposed\nmethod offers promising improvements.\n"}
+{"abstract": "  Recent works have shown exciting results in unsupervised image de-rendering\n-- learning to decompose 3D shape, appearance, and lighting from single-image\ncollections without explicit supervision. However, many of these assume\nsimplistic material and lighting models. We propose a method, termed RADAR,\nthat can recover environment illumination and surface materials from real\nsingle-image collections, relying neither on explicit 3D supervision, nor on\nmulti-view or multi-light images. Specifically, we focus on rotationally\nsymmetric artefacts that exhibit challenging surface properties including\nspecular reflections, such as vases. We introduce a novel self-supervised\nalbedo discriminator, which allows the model to recover plausible albedo\nwithout requiring any ground-truth during training. In conjunction with a shape\nreconstruction module exploiting rotational symmetry, we present an end-to-end\nlearning framework that is able to de-render the world's revolutionary\nartefacts. We conduct experiments on a real vase dataset and demonstrate\ncompelling decomposition results, allowing for applications including\nfree-viewpoint rendering and relighting.\n"}
+{"abstract": "  A novel algorithm that produces a quad layout based on imposed set of\nsingularities is proposed. In this paper, we either use singularities that\nappear naturally, e.g., by minimizing Ginzburg-Landau energy, or use as an\ninput user-defined singularity pattern, possibly with high valence\nsingularities that do not appear naturally in cross-field computations. The\nfirst contribution of the paper is the development of a formulation that allows\ncomputing a cross-field from a given set of singularities through the\nresolution of two linear PDEs. A specific mesh refinement is applied at the\nvicinity of singularities to accommodate the large gradients of cross\ndirections that appear in the vicinity of singularities of high valence. The\nsecond contribution of the paper is a correction scheme that repairs limit\ncycles and/or non-quadrilateral patches. Finally, a high quality\nblock-structured quad mesh is generated from the quad layout and per-partition\nparameterization.\n"}
+{"abstract": "  The temporal Credit Assignment Problem (CAP) is a well-known and challenging\ntask in AI. While Reinforcement Learning (RL), especially Deep RL, works well\nwhen immediate rewards are available, it can fail when only delayed rewards are\navailable or when the reward function is noisy. In this work, we propose\ndelegating the CAP to a Neural Network-based algorithm named InferNet that\nexplicitly learns to infer the immediate rewards from the delayed rewards. The\neffectiveness of InferNet was evaluated on two online RL tasks: a simple\nGridWorld and 40 Atari games; and two offline RL tasks: GridWorld and a\nreal-life Sepsis treatment task. For all tasks, the effectiveness of using the\nInferNet inferred rewards is compared against the immediate and the delayed\nrewards with two settings: with noisy rewards and without noise. Overall, our\nresults show that the effectiveness of InferNet is robust against noisy reward\nfunctions and is an effective add-on mechanism for solving temporal CAP in a\nwide range of RL tasks, from classic RL simulation environments to a real-world\nRL problem and for both online and offline learning.\n"}
+{"abstract": "  We study the stratified gas in a rapidly rotating centrifuge as a model for\nthe Earth's atmosphere. Based on methods of perturbation theory, it is shown\nthat in certain regimes, internal waves in the gas centrifuge have the same\ndispersion relation to leading order as their atmospheric siblings. Assuming an\nair filled centrifuge with a radius of circa 50 cm, the optimal rotational\nfrequency for realistic atmosphere-like waves is around 10 000 rounds per\nminute. Using gases of lower heat capacities at constant pressure, like xenon,\nthe rotational frequencies can be even halved to obtain the same results.\nSimilar to the atmosphere, it is feasible in the gas centrifuge to generate a\nclear scale separation of wave frequencies and therefore phase speeds between\nacoustic waves and internal waves. In addition to the centrifugal force, the\nCoriolis force acts in the same plane. However, its influence on axially\nhomogeneous internal waves appears only as a higher-order correction. We\nconclude that the gas centrifuge provides an unprecedented opportunity to\ninvestigate atmospheric internal waves experimentally with a compressible\nworking fluid.\n"}
+{"abstract": "  Neural networks represent a field of research that can quickly achieve very\ngood results in the field of medical image segmentation using a GPU. A possible\nway to achieve good results without GPUs are random forests. For this purpose,\ntwo random forest approaches were compared with a state-of-the-art deep\nconvolutional neural network. To make the comparison the PhC-C2DH-U373 and the\nretinal imaging datasets were used. The evaluation showed that the deep\nconvolutional neutral network achieved the best results. However, one of the\nrandom forest approaches also achieved a similar high performance. Our results\nindicate that random forest approaches are a good alternative to deep\nconvolutional neural networks and, thus, allow the usage of medical image\nsegmentation without a GPU.\n"}
+{"abstract": "  We have obtained Hubble Space Telescope (HST) coronagraphic observations of\nthe circumstellar disk around M star TWA 7 using the STIS instrument in visible\nlight. Together with archival observations including HST/NICMOS using the F160W\nfilter and Very Large Telescope/SPHERE at $H$-band in polarized light, we\ninvestigate the system in scattered light. By studying this nearly face-on\nsystem using geometric disk models and Henyey--Greenstein phase functions, we\nreport new discovery of a tertiary ring and a clump. We identify a layered\narchitecture: three rings, a spiral, and an ${\\approx}150$ au$^2$ elliptical\nclump. The most extended ring peaks at $28$ au, and the other components are on\nits outskirts. Our point source detection limit calculations demonstrate the\nnecessity of disk modeling in imaging fainter planets. Morphologically, we\nwitness a clockwise spiral motion, and the motion pattern is consistent with\nboth solid body and local Keplerian; we also observe underdensity regions for\nthe secondary ring that might result from mean motion resonance or moving\nshadows: both call for re-observations to determine their nature. Comparing\nmulti-instrument observations, we obtain blue STIS-NICMOS color, STIS-SPHERE\nradial distribution peak difference for the tertiary ring, and high\nSPHERE-NICMOS polarization fraction; these aspects indicate that TWA 7 could\nretain small dust particles. By viewing the debris disk around M star TWA 7 at\na nearly face-on vantage point, our study allows for the understanding of such\ndisks in scattered light in both system architecture and dust property.\n"}
+{"abstract": "  We propose a learning-based compression scheme that envelopes a standard\ncodec between pre and post-processing deep CNNs. Specifically, we demonstrate\nimprovements over prior approaches utilizing a compression-decompression\nnetwork by introducing: (a) an edge-aware loss function to prevent blurring\nthat is commonly occurred in prior works & (b) a super-resolution convolutional\nneural network (CNN) for post-processing along with a corresponding\npre-processing network for improved rate-distortion performance in the low rate\nregime. The algorithm is assessed on a variety of datasets varying from low to\nhigh resolution namely Set 5, Set 7, Classic 5, Set 14, Live 1, Kodak, General\n100, CLIC 2019. When compared to JPEG, JPEG2000, BPG, and recent CNN approach,\nthe proposed algorithm contributes significant improvement in PSNR with an\napproximate gain of 20.75%, 8.47%, 3.22%, 3.23% and 24.59%, 14.46%, 10.14%,\n8.57% at low and high bit-rates respectively. Similarly, this improvement in\nMS-SSIM is approximately 71.43%, 50%, 36.36%, 23.08%, 64.70% and 64.47%,\n61.29%, 47.06%, 51.52%, 16.28% at low and high bit-rates respectively. With\nCLIC 2019 dataset, PSNR is found to be superior with approximately 16.67%,\n10.53%, 6.78%, and 24.62%, 17.39%, 14.08% at low and high bit-rates\nrespectively, over JPEG2000, BPG, and recent CNN approach. Similarly, the\nMS-SSIM is found to be superior with approximately 72%, 45.45%, 39.13%, 18.52%,\nand 71.43%, 50%, 41.18%, 17.07% at low and high bit-rates respectively,\ncompared to the same approaches. A similar type of improvement is achieved with\nother datasets also.\n"}
+{"abstract": "  Although deep-learning has been successfully applied in a variety of science\nand engineering problems owing to its strong high-dimensional nonlinear mapping\ncapability, it is of limited use in scientific knowledge discovery. In this\nwork, we propose a deep-learning based framework to discover the macroscopic\ngoverning equation of viscous gravity current based on high-resolution\nmicroscopic simulation data without the need for prior knowledge of underlying\nterms. For two typical scenarios with different viscosity ratios, the\ndeep-learning based equations exactly capture the same dominated terms as the\ntheoretically derived equations for describing long-term asymptotic behaviors,\nwhich validates the proposed framework. Unknown macroscopic equations are then\nobtained for describing short-term behaviors, and additional deep-learned\ncompensation terms are eventually discovered. Comparison of posterior tests\nshows that the deep-learning based PDEs actually perform better than the\ntheoretically derived PDEs in predicting evolving viscous gravity currents for\nboth long-term and short-term regimes. Moreover, the proposed framework is\nproven to be very robust against non-biased data noise for training, which is\nup to 20%. Consequently, the presented deep-learning framework shows\nconsiderable potential for discovering unrevealed intrinsic laws in scientific\nsemantic space from raw experimental or simulation results in data space.\n"}
+{"abstract": "  One of the main challenges since the advancement of convolutional neural\nnetworks is how to connect the extracted feature map to the final\nclassification layer. VGG models used two sets of fully connected layers for\nthe classification part of their architectures, which significantly increases\nthe number of models' weights. ResNet and next deep convolutional models used\nthe Global Average Pooling (GAP) layer to compress the feature map and feed it\nto the classification layer. Although using the GAP layer reduces the\ncomputational cost, but also causes losing spatial resolution of the feature\nmap, which results in decreasing learning efficiency. In this paper, we aim to\ntackle this problem by replacing the GAP layer with a new architecture called\nWise-SrNet. It is inspired by the depthwise convolutional idea and is designed\nfor processing spatial resolution and also not increasing computational cost.\nWe have evaluated our method using three different datasets: Intel Image\nClassification Challenge, MIT Indoors Scenes, and a part of the ImageNet\ndataset. We investigated the implementation of our architecture on several\nmodels of Inception, ResNet and DensNet families. Applying our architecture has\nrevealed a significant effect on increasing convergence speed and accuracy. Our\nExperiments on images with 224x224 resolution increased the Top-1 accuracy\nbetween 2% to 8% on different datasets and models. Running our models on\n512x512 resolution images of the MIT Indoors Scenes dataset showed a notable\nresult of improving the Top-1 accuracy within 3% to 26%. We will also\ndemonstrate the GAP layer's disadvantage when the input images are large and\nthe number of classes is not few. In this circumstance, our proposed\narchitecture can do a great help in enhancing classification results. The code\nis shared at https://github.com/mr7495/image-classification-spatial.\n"}
+{"abstract": "  Link prediction is a very fundamental task on graphs. Inspired by traditional\npath-based methods, in this paper we propose a general and flexible\nrepresentation learning framework based on paths for link prediction.\nSpecifically, we define the representation of a pair of nodes as the\ngeneralized sum of all path representations, with each path representation as\nthe generalized product of the edge representations in the path. Motivated by\nthe Bellman-Ford algorithm for solving the shortest path problem, we show that\nthe proposed path formulation can be efficiently solved by the generalized\nBellman-Ford algorithm. To further improve the capacity of the path\nformulation, we propose the Neural Bellman-Ford Network (NBFNet), a general\ngraph neural network framework that solves the path formulation with learned\noperators in the generalized Bellman-Ford algorithm. The NBFNet parameterizes\nthe generalized Bellman-Ford algorithm with 3 neural components, namely\nINDICATOR, MESSAGE and AGGREGATE functions, which corresponds to the boundary\ncondition, multiplication operator, and summation operator respectively. The\nNBFNet is very general, covers many traditional path-based methods, and can be\napplied to both homogeneous graphs and multi-relational graphs (e.g., knowledge\ngraphs) in both transductive and inductive settings. Experiments on both\nhomogeneous graphs and knowledge graphs show that the proposed NBFNet\noutperforms existing methods by a large margin in both transductive and\ninductive settings, achieving new state-of-the-art results.\n"}
+{"abstract": "  The standard way to estimate the parameters $\\Theta_\\text{SEIR}$ (e.g., the\ntransmission rate $\\beta$) of an SEIR model is to use grid search, where\nsimulations are performed on each set of parameters, and the parameter set\nleading to the least $L_2$ distance between predicted number of infections and\nobserved infections is selected. This brute-force strategy is not only time\nconsuming, as simulations are slow when the population is large, but also\ninaccurate, since it is impossible to enumerate all parameter combinations. To\naddress these issues, in this paper, we propose to transform the\nnon-differentiable problem of finding optimal $\\Theta_\\text{SEIR}$ to a\ndifferentiable one, where we first train a recurrent net to fit a small number\nof simulation data. Next, based on this recurrent net that is able to\ngeneralize SEIR simulations, we are able to transform the objective to a\ndifferentiable one with respect to $\\Theta_\\text{SEIR}$, and straightforwardly\nobtain its optimal value. The proposed strategy is both time efficient as it\nonly relies on a small number of SEIR simulations, and accurate as we are able\nto find the optimal $\\Theta_\\text{SEIR}$ based on the differentiable objective.\nOn two COVID-19 datasets, we observe that the proposed strategy leads to\nsignificantly better parameter estimations with a smaller number of\nsimulations.\n"}
+{"abstract": "  The thermal transport properties of biphenylene network (BPN), a novel sp2\n-hybridized two-dimensional allotrope of carbon atoms recently realized in\nexperiments [Fan et al., Science, 372 852-856 (2021)], are studied using the\ndensity functional theory-driven solution of the Boltzmann transport equation.\nThe thermal transport in BPN is anisotropic and the obtained thermal\nconductivities are more than an order of magnitude lower than that in graphene,\ndespite similar sp2-hybridized planar-structure of both allotropes. The lower\nthermal conductivity in BPN is found to originate from enhanced anharmonicity\nwhich in turn is a result of reduced crystal symmetry of BPN.\n"}
+{"abstract": "  Modeling complex physical dynamics is a fundamental task in science and\nengineering. Traditional physics-based models are sample efficient,\ninterpretable but often rely on rigid assumptions. Furthermore, direct\nnumerical approximation is usually computationally intensive, requiring\nsignificant computational resources and expertise. While deep learning (DL)\nprovides novel alternatives for efficiently recognizing complex patterns and\nemulating nonlinear dynamics, its predictions do not necessarily obey the\ngoverning laws of physical systems, nor do they generalize well across\ndifferent systems. Thus, the study of physics-guided DL emerged and has gained\ngreat progress. Physics-guided DL aims to take the best from both physics-based\nmodeling and state-of-the-art DL models to better solve scientific problems. In\nthis paper, we provide a structured overview of existing methodologies of\nintegrating prior physical knowledge or physics-based modeling into DL, with a\nspecial emphasis on learning dynamical systems. We also discuss the fundamental\nchallenges and emerging opportunities in the area.\n"}
+{"abstract": "  The Beyond-Linear-Use-of-Equation-Superposition (BLUES) function method is\nextended to coupled nonlinear ordinary differential equations and applied to\nthe epidemiological SIRS model with vaccination. Accurate analytic\napproximations are obtained for the time evolution of the susceptible and\ninfected population fractions. The results are compared with those obtained\nwith alternative methods, notably Adomian decomposition, variational iteration\nand homotopy perturbation. In contrast with these methods, the BLUES iteration\nconverges rapidly, globally, and captures the exact asymptotic behavior for\nlong times. The time of the infection peak is calculated using the BLUES\napproximants and the results are compared with numerical solutions, which\nindicate that the method is able to generate useful analytic expressions that\ncoincide with the (numerically) exact ones already for a small number of\niterations.\n"}
+{"abstract": "  Wide field small aperture telescopes (WFSATs) are mainly used to obtain\nscientific information of point--like and streak--like celestial objects.\nHowever, qualities of images obtained by WFSATs are seriously affected by the\nbackground noise and variable point spread functions. Developing high speed and\nhigh efficiency data processing method is of great importance for further\nscientific research. In recent years, deep neural networks have been proposed\nfor detection and classification of celestial objects and have shown better\nperformance than classical methods. In this paper, we further extend abilities\nof the deep neural network based astronomical target detection framework to\nmake it suitable for photometry and astrometry. We add new branches into the\ndeep neural network to obtain types, magnitudes and positions of different\ncelestial objects at the same time. Tested with simulated data, we find that\nour neural network has better performance in photometry than classical methods.\nBecause photometry and astrometry are regression algorithms, which would obtain\nhigh accuracy measurements instead of rough classification results, the\naccuracy of photometry and astrometry results would be affected by different\nobservation conditions. To solve this problem, we further propose to use\nreference stars to train our deep neural network with transfer learning\nstrategy when observation conditions change. The photometry framework proposed\nin this paper could be used as an end--to--end quick data processing framework\nfor WFSATs, which can further increase response speed and scientific outputs of\nWFSATs.\n"}
+{"abstract": "  A major strategy to prevent the spread of COVID-19 is the limiting of\nin-person contacts. However, this is impractical or impossible for the many\ndisabled people who do not live in care facilities, but still require\ncaregivers. We seek to determine which interventions can prevent infections\namong disabled people and their caregivers. We simulate transmission with a\nmodel that includes susceptible, exposed, asymptomatic, symptomatically ill,\nhospitalized, and removed individuals. The networks on which we simulate\ndisease spread incorporate heterogeneity in the risks of different types of\ninteractions, time-dependent lockdown and reopening measures, and contact\ndistributions for four different groups (caregivers, disabled people, essential\nworkers, and the general population). We find the probability of becoming\ninfected is largest for caregivers and second largest for disabled people. Our\nanalysis of network structure illustrates that caregivers have the largest\nmodal eigenvector centrality. We find that two interventions --\ncontact-limiting by all groups and mask-wearing by disabled people and\ncaregivers -- most reduce the cases among disabled people and caregivers. We\nalso test which group spreads COVID-19 most readily by seeding infections in a\nsubset of each group. We find caregivers are the most potent spreaders of\nCOVID-19, particularly to other caregivers and to disabled people. We test\nwhere to use limited vaccine doses most effectively and find (1) vaccinating\ncaregivers better protects disabled people than vaccinating the general\npopulation or essential workers and (2) vaccinating caregivers protects\ndisabled people about as much as vaccinating disabled people themselves. Our\nresults highlight the potential effectiveness of mask-wearing, contact-limiting\nthroughout society, and strategic vaccination for limiting the exposure of\ndisabled people and their caregivers to COVID-19.\n"}
+{"abstract": "  This paper is concerned with analyzing a class of fractional calculus of\nvariations problems and their associated Euler-Lagrange (fractional\ndifferential) equations. Unlike the existing fractional calculus of variations\nwhich is based on the classical notion of fractional derivatives, the\nfractional calculus of variations considered in this paper is based on a newly\ndeveloped notion of weak fractional derivatives and their associated fractional\norder Sobolev spaces. Since fractional derivatives are direction-dependent,\nusing one-sided fractional derivatives and their combinations leads to new\ntypes of calculus of variations and fractional differential equations as well\nas nonstandard Neumann boundary operators. The primary objective of this paper\nis to establish the well-posedness and regularities for a class of fractional\ncalculus of variations problems and their Euler-Lagrange (fractional\ndifferential) equations. This is achieved first for one-sided Dirichlet energy\nfunctionals which lead to one-sided fractional Laplace equations, then for more\ngeneral energy functionals which give rise to more general fractional\ndifferential equations.\n"}
+{"abstract": "  We employ MUSE/VLT data to study the ionised and highly ionised gas phases of\nthe feedback in Circinus, the closest Seyfert 2 galaxy to us. The analysis of\nthe nebular emission allowed us to detect a remarkable high-ionisation gas\noutflow beyond the galaxy plane traced by the coronal lines [Fe VII]\n$\\lambda$6089 and [Fe X] $\\lambda$6374, extending up to 700 pc and 350 pc NW\nfrom the nucleus, respectively. This is the first time that the [Fe X] emission\nis observed at such distances from the central engine in an AGN. The gas\nkinematics reveals expanding gas shells with velocities of a few hundred km\ns$^{-1}$, spatially coincident with prominent hard X-ray emission detected by\nChandra. Density and temperature sensitive line ratios show that the extended\nhigh-ionisation gas is characterized by a temperature reaching 25000 K and an\nelectron density > 10$^2$ cm$^{-3}$. We found that local gas excitation by\nshocks produced by the passage of a radio jet leads to the spectacular\nhigh-ionisation emission in this object. This hypothesis is fully supported by\nphotoionisation models that accounts for the combined effects of the central\nengine and shocks. They reproduce the observed emission line spectrum at\ndifferent locations inside and outside of the NW ionisation cone. The energetic\noutflow produced by the radio jet is spatially located close to an extended\nmolecular outflow recently reported using ALMA which suggests that they both\nrepresent different phases of the same feedback process acting on the AGN.\n"}
+{"abstract": "  In this paper, we propose an invertible neural network for image denoising\n(DnINN) inspired by the transform-based denoising framework. The proposed DnINN\nconsists of an invertible neural network called LINN whose architecture is\ninspired by the lifting scheme in wavelet theory and a sparsity-driven\ndenoising network which is used to remove noise from the transform\ncoefficients. The denoising operation is performed with a single\nsoft-thresholding operation or with a learned iterative shrinkage thresholding\nnetwork. The forward pass of LINN produces an over-complete representation\nwhich is more suitable for denoising. The denoised image is reconstructed using\nthe backward pass of LINN using the output of the denoising network. The\nsimulation results show that the proposed DnINN method achieves results\ncomparable to the DnCNN method while only requiring 1/4 of learnable\nparameters.\n"}
+{"abstract": "  According to Mostow's celebrated rigidity theorem, the geometry of closed\nhyperbolic 3-manifolds is already determined by their topology. In particular,\nthe volume of such manifolds is a topological invariant and, as such, has been\ninvestigated for half a century.\n  Motivated by the algorithmic study of 3-manifolds, Maria and Purcell have\nrecently shown that every closed hyperbolic 3-manifold M with volume vol(M)\nadmits a triangulation with dual graph of treewidth at most C vol(M), for some\nuniversal constant C.\n  Here we improve on this result by showing that the volume provides a linear\nupper bound even on the pathwidth of the dual graph of some triangulation,\nwhich can potentially be much larger than the treewidth. Our proof relies on a\nsynthesis of tools from 3-manifold theory: generalized Heegaard splittings,\namalgamations, and the thick-thin decomposition of hyperbolic 3-manifolds. We\nprovide an illustrated exposition of this toolbox and also discuss the\nalgorithmic consequences of the result.\n"}
+{"abstract": "  We perform a fit of the finite-volume QCD spectrum of three pions at maximal\nisospin to constrain the three-body force. We use the unitarity-based\nrelativistic three-particle quantization condition, with the GWUQCD spectrum\nobtained at 315 MeV and 220 MeV pion mass in two-flavor QCD. For the heavier\npion mass we find that the data is consistent with a constant contact term\nclose to zero, whereas for the lighter mass we see a statistically significant\nenergy dependence in tension with the prediction of leading order ChPT. Our\nresults also suggest that with enough three-body energy levels, the two-body\namplitude could be constrained.\n"}
+{"abstract": "  In recent years, the implementation of the demand response (DR) programs in\nthe power systems scheduling and operation is increased. DR is used to improve\nthe consumers' and power providers economic condition. That said, optimal power\nflow is a fundamental concept in the power system operation and control. The\nimpact of exploiting DR programs in the power management of the systems is of\nsignificant importance. In this paper, the effect of time-based DR programs on\nthe cost of 24-hour operation of a power system is presented. The effect of the\ntime of use and real-time pricing programs with different participation factors\nare investigated. In addition, the systems operation cost is studied to analyze\nthe DR programs' role in the current power grids. For this aim, the 14-bus IEEE\ntest system is used to properly implement and simulate the proposed approach.\n"}
+{"abstract": "  The focus of the project will be an examination of obtaining the highest\nscore in the Axelrod Tournament. The initial design of the highest score in the\nAxelrod Tournament consisted of looking at the Cooperation rates of the top\nstrategies currently in the Axelrod Library. After creating an initial Finite\nState Machine strategy that utilized the Cooperation Rates of the top players;\nour ten-state FSM finished within the top 35 players out of 220 in the short\nrun time strategies in the Axelrod Library. After a quick evolutionary\nalgorithm, 50 generations, our original ten-state FSM was changed into an\neight-state FSM, which finished within the top 5 of the short run time\nstrategies in the Axelrod Library. This eight-state FSM was then evolved again\nusing a full evolutionary algortihm process, which lasted 500 generations,\nwhere the eight-state FSM evolved into a another eight-state FSM which finishes\nfirst in the Axelrod Library among the short run time strategies and the full\nAxelrod Tournament. From that final FSM, two of the eight states are\ninaccessible, so the final FSM strategy is a six-state FSM that finishes first\nin the Full Axelrod Tournament, against the short run time strategies as well\nas the long run time strategies.\n"}
+{"abstract": "  An important task when working with terrain models is computing viewsheds:\nthe parts of the terrain visible from a given viewpoint. When the terrain is\nmodeled as a polyhedral terrain, the viewshed is composed of the union of all\nthe triangle parts that are visible from the viewpoint. The complexity of a\nviewshed can vary significantly, from constant to quadratic in the number of\nterrain vertices, depending on the terrain topography and the viewpoint\nposition.\n  In this work we study a new topographic attribute, the \\emph{prickliness},\nthat measures the number of local maxima in a terrain from all possible\nperspectives. We show that the prickliness effectively captures the potential\nof 2.5D terrains to have high complexity viewsheds, and we present near-optimal\nalgorithms to compute the prickliness of 1.5D and 2.5D terrains. We also report\non some experiments relating the prickliness of real word 2.5D terrains to the\nsize of the terrains and to their viewshed complexity.\n"}
+{"abstract": "  In this paper we provide a detailed description of the eigenvalue $\nE_{D}(x_0)\\leq 0$ (respectively $ E_{N}(x_0)\\leq 0$) of the self-adjoint\nHamiltonian operator representing the negative Laplacian on the positive\nhalf-line with a Dirichlet (resp. Neuman) boundary condition at the origin\nperturbed by an attractive Dirac distribution $-\\lambda \\delta(x-x_0)$ for any\nfixed value of the magnitude of the coupling constant. We also investigate the\n$\\lambda$-dependence of both eigenvalues for any fixed value of $x_0$.\nFurthermore, we show that both systems exhibit resonances as poles of the\nanalytic continuation of the resolvent. These results will be connected with\nthe study of the ground state energy of two remarkable three-dimensional\nself-adjoint operators, studied in depth in Albeverio's monograph, perturbed by\nan attractive $\\delta$-distribution supported on the spherical shell of radius\n$r_0$.\n"}
+{"abstract": "  The Simons Observatory (SO) is a Cosmic Microwave Background (CMB) experiment\nto observe the microwave sky in six frequency bands from 30GHz to 290GHz. The\nObservatory -- at $\\sim$5200m altitude -- comprises three Small Aperture\nTelescopes (SATs) and one Large Aperture Telescope (LAT) at the Atacama Desert,\nChile. This research note describes the design and current status of the LAT\nalong with its future timeline.\n"}
+{"abstract": "  In this paper we study the elementary theory of graph products of groups and\nshow that under natural conditions on the vertex groups we can recover (the\ncore of) the underlying graph and the associated vertex groups. More precisely,\nwe require the vertex groups to satisfy a non-generic almost positive sentence,\na condition which generalizes a range of natural ``non-freeness conditions\"\nsuch as the satisfaction of a group law, having nontrivial center or being\nboundedly simple.\n  As a corollary, we determine an invariant of the elementary theory of a\nright-angled Artin group, the core of the defining graph, which we conjecture\nto determine the elementary class of the RAAG. We further combine our results\nwith the results of Sela on free products of groups to describe all finitely\ngenerated groups elementarily equivalent to certain RAAGs. We also deduce\nrigidity results on the elementary classification of graph products of groups\nfor specific types of vertex groups, such as finite, nilpotent or classical\nlinear groups.\n"}
+{"abstract": "  Hand Gesture Recognition (HGR) based on inertial data has grown considerably\nin recent years, with the state-of-the-art approaches utilizing a single\nhandheld sensor and a vocabulary comprised of simple gestures.\n  In this work we explore the benefits of using multiple inertial sensors.\nUsing WaveGlove, a custom hardware prototype in the form of a glove with five\ninertial sensors, we acquire two datasets consisting of over $11000$ samples.\n  To make them comparable with prior work, they are normalized along with $9$\nother publicly available datasets, and subsequently used to evaluate a range of\nMachine Learning approaches for gesture recognition, including a newly proposed\nTransformer-based architecture. Our results show that even complex gestures\ninvolving different fingers can be recognized with high accuracy.\n  An ablation study performed on the acquired datasets demonstrates the\nimportance of multiple sensors, with an increase in performance when using up\nto three sensors and no significant improvements beyond that.\n"}
+{"abstract": "  An important result by Agmon implies that an eigenfunction of a\nSchr\\\"{o}dinger operator in $\\mathbb{R}^n$ with eigenvalue $E$ below the bottom\nof the essential spectrum decays exponentially if the associated classically\nallowed region $\\{x \\in \\mathbb{R}^n~:~ V(x) \\leq E \\}$ is compact. We extend\nthis result to a class of Schr\\\"{o}dinger operators with eigenvalues, for which\nthe classically allowed region is not necessarily compactly supported: We show\nthat integrability of the characteristic function of the classically allowed\nregion with respect to an increasing weight function of bounded logarithmic\nderivative leads to $L^2$-decay of the eigenfunction with respect to the same\nweight. Here, the decay is measured in the Agmon metric, which takes into\naccount anisotropies of the potential. In particular, for a power law (or,\nrespectively, exponential) weight, our main result implies that power law (or,\nrespectively, exponential) decay of \"the size of the classically allowed\nregion\" allows to conclude power law (or, respectively, exponential) decay, in\nthe Agmon metric, of the eigenfunction.\n"}
+{"abstract": "  Detailed parcel-level crop type mapping for the whole European Union (EU) is\nnecessary for the evaluation of agricultural policies. The Copernicus program,\nand Sentinel-1 (S1) in particular, offers the opportunity to monitor\nagricultural land at a continental scale and in a timely manner. However, so\nfar the potential of S1 has not been explored at such a scale. Capitalizing on\nthe unique LUCAS 2018 Copernicus in-situ survey, we present the first\ncontinental crop type map at 10-m spatial resolution for the EU based on S1A\nand S1B Synthetic Aperture Radar observations for the year 2018. Random forest\nclassification algorithms are tuned to detect 19 different crop types. We\nassess the accuracy of this EU crop map with three approaches. First, the\naccuracy is assessed with independent LUCAS core in-situ observations over the\ncontinent. Second, an accuracy assessment is done specifically for main crop\ntypes from farmers declarations from 6 EU member countries or regions totaling\n>3M parcels and 8.21 Mha. Finally, the crop areas derived by classification are\ncompared to the subnational (NUTS 2) area statistics reported by Eurostat. The\noverall accuracy for the map is reported as 80.3% when grouping main crop\nclasses and 76% when considering all 19 crop type classes separately. Highest\naccuracies are obtained for rape and turnip rape with user and produced\naccuracies higher than 96%. The correlation between the remotely sensed\nestimated and Eurostat reported crop area ranges from 0.93 (potatoes) to 0.99\n(rape and turnip rape). Finally, we discuss how the framework presented here\ncan underpin the operational delivery of in-season high-resolution based crop\nmapping.\n"}
+{"abstract": "  An investigation to optimize the application of the third-generation charge\noptimized many-body (COMB3) interatomic potential and associated input\nparameters was carried out through the study of solid-liquid interactions in\nclassical molecular dynamics (MD) simulations. The rates of these molecular\ninteractions are understood though the wetting rates of water nano droplets on\na bare copper (111) surface. Implementing the Langevin thermostat, the\ninfluence of simulation time step, the number of atoms in the system, the\nfrequency at which charge equilibration is performed, and the temperature\nrelaxation rate are all examined. The results indicate that time steps of 0.4\nfs are possible when using longer relaxation times for the system temperature,\nwhich is almost double the typical time step used for reactive potentials. The\nuse of the QEq charge equilibration allows for a fewer atomic layers to be used\nin the Cu slab. In addition, charge equilibrium schemes do not need to be\nperformed every time step to ensure accurate charge transfer. Interestingly,\nthe rate of wetting for the nanodroplets is dominantly dependent on the\ntemperature relaxation time which are predicted to significantly change the\nviscosity of the water droplets. This work provides a pathway for optimizing\nsimulations using the COMB3 reactive interatomic potential.\n"}
+{"abstract": "  In this paper we present a direct formula for the solution of the general\nsecond order linear ordinary differential equation as our main result such that\nthe parameters required for the formula are determined using another\ndifferential equation, which happens to be a Riccati Equation. We also derive\nother results based on the main result which include special cases for the\nconcerned differential equation with variable coefficients, formula for\nsolution of concerned differential equation with constant coefficients and\nformula for the solution of the concerned differential equation with one\ncomplimentary solution known.\n"}
+{"abstract": "  Diabetes is currently one of the most common, dangerous, and costly diseases\nin the world that is caused by an increase in blood sugar or a decrease in\ninsulin in the body. Diabetes can have detrimental effects on people's health\nif diagnosed late. Today, diabetes has become one of the challenges for health\nand government officials. Prevention is a priority, and taking care of people's\nhealth without compromising their comfort is an essential need. In this study,\nthe Ensemble training methodology based on genetic algorithms are used to\naccurately diagnose and predict the outcomes of diabetes mellitus. In this\nstudy, we use the experimental data, real data on Indian diabetics on the\nUniversity of California website. Current developments in ICT, such as the\nInternet of Things, machine learning, and data mining, allow us to provide\nhealth strategies with more intelligent capabilities to accurately predict the\noutcomes of the disease in daily life and the hospital and prevent the\nprogression of this disease and its many complications. The results show the\nhigh performance of the proposed method in diagnosing the disease, which has\nreached 98.8%, and 99% accuracy in this study.\n"}
+{"abstract": "  Recent experimental advances in strongly coupled light-matter systems has\nsparked the development of general ab-initio methods capable of describing\ninteracting light-matter systems from first principles. One of these methods,\nquantum-electrodynamical density-functional theory (QEDFT), promises\ncomputationally efficient calculations for large correlated light-matter\nsystems with the quality of the calculation depending on the underlying\napproximation for the exchange-correlation functional. So far no true\ndensity-functional approximation has been introduced limiting the efficient\napplication of the theory. In this paper, we introduce the first gradient-based\ndensity functional for the QEDFT exchange-correlation energy derived from the\nadiabatic-connection fluctuation-dissipation theorem. We benchmark this\nsimple-to-implement approximation on small systems in optical cavities and\ndemonstrate its relatively low computational costs for fullerene molecules up\nto C$_{180}$ coupled to 400,000 photon modes in a dissipative optical cavity.\nThis work now makes first principle calculations of much larger systems\npossible within the QEDFT framework effectively combining quantum optics with\nlarge-scale electronic structure theory.\n"}
+{"abstract": "  We show that the Weyl phase in $\\rm Sm_2Ir_2O_7$ is protracted up to at least\n2~\\% alloying with Bi by an anomalous negative lattice expansion with $\\rm\n\\Delta a \\sim- 0.01$\\AA. With further doping, the magnetic ordering disappears\nand electrical resistivity decreases by orders of magnitude; the resistivity\nupturn remains but with $\\rm 1/T$ dependence of Weyl phase changed to $\\rm\n-lnT$ dependence characteristic of the Quadratic Band Touching (QBT). At the\nWeyl-QBT phase boundary, a new phase is evidenced whose resistivity scales as\n$\\rm -T^{1/4}$ possibly due to proximity to a quantum critical point proposed\nseveral years ago [Phys. Rev. X 4, 041027 (2014)], but whose experimental\nevidence has remained elusive thus far.\n"}
+{"abstract": "  We generalize soft theorems of the nonlinear sigma model beyond the\n$\\mathcal{O} (p^2)$ amplitudes and the coset of $\\text{SU} (N) \\times \\text{SU}\n(N) / \\text{SU} (N) $. We first discuss the universal flavor ordering of the\namplitudes for the Nambu-Goldstone bosons, so that we can reinterpret the known\n$\\mathcal{O} (p^2)$ single soft theorem for $\\text{SU} (N) \\times \\text{SU} (N)\n/ \\text{SU} (N) $ in the context of a general symmetry group representation. We\nthen investigate the special case of the fundamental representation of\n$\\text{SO} (N)$, where a special flavor ordering of the \"pair basis\" is\navailable. We provide novel amplitude relations and a Cachazo-He-Yuan formula\nfor such a basis, and derive the corresponding single soft theorem. Next, we\nextend the single soft theorem for a general group representation to\n$\\mathcal{O} (p^4)$, where for at least two specific choices of the\n$\\mathcal{O} (p^4)$ operators, the leading non-vanishing pieces can be\ninterpreted as new extended theory amplitudes involving bi-adjoint scalars, and\nthe corresponding soft factors are the same as at $\\mathcal{O} (p^2)$. Finally,\nwe compute the general formula for the double soft theorem, valid to all\nderivative orders, where the leading part in the soft momenta is fixed by the\n$\\mathcal{O}(p^2)$ Lagrangian, while any possible corrections to the subleading\npart are determined by the $\\mathcal{O}(p^4)$ Lagrangian alone. Higher order\nterms in the derivative expansion do not contribute any new corrections to the\ndouble soft theorem.\n"}
+{"abstract": "  Unconventional photon blockade refers to the suppression of multi-photon\nstates in weakly nonlinear optical resonators via the destructive interference\nof different excitation pathways. It has been studied in a pair of coupled\nnonlinear resonators and other few-mode systems. Here, we show that\nunconventional photon blockade can be greatly enhanced in a chain of coupled\nresonators. Specifically, the strength of the nonlinearity in each resonator\nneeded to achieve unconventional photon blockade is suppressed exponentially\nwith lattice size. The analytic derivation, based on a weak drive\napproximation, is validated by wavefunction Monte Carlo simulations. These\nfindings show that customized lattices of coupled resonators can be powerful\ntools for controlling multi-photon quantum states.\n"}
+{"abstract": "  For the first time a dedicated investigation of the target size of a\nnanodosimeter device has been carried out in order to investigate to what\nextent measured ionisation cluster size distributions can serve as benchmark\ndata for modelling approaches, with particular focus on the target size in\nterms of liquid H2O. To this end, measurements with alpha particles from a\n241Am source were carried out using three different target gases, H2O, C3H8 and\nC4H8O. For each of the three target gases, three different drift-time windows\nwere applied to realise three different target sizes. A method has been\ndeveloped to determine the dimensions of the simulated nanometric target volume\nin liquid H2O for cylindrical and spherical shape, as often used in approaches\nto model radiation effects to DNA. Simulations with nanometric targets of\ndimensions determined with this method agree very well with the corresponding\nmeasurements. Scaling of the spatial distribution of the extraction efficiency\nfor different target gases and drift-time windows, which corresponds to the\nnanodosimeter's target volume, in terms of liquid H2O using\n($\\rho\\lambda_{ion}$)-ratios has also been investigated and proved to yield an\nestimate of the target volume in liquid H2O. Thus, it can be concluded that\nionisation cluster size distributions measured with a nanodosimeter device are\nsuited as benchmark data for approaches that model radiation induced damage to\nDNA in nanometric volumes of liquid H2O in simple geometries such as cylinders\nor spheres, provided that the nanodosimeter's target volume is characterised\naccordingly. By proper selection of drift-time window length as well as target\ngas and density a wide range of target volume dimensions in terms of liquid H2O\ncan be realised with the PTB Ion Counter nanodosimeter according to specific\nrequirements of modelling approaches.\n"}
+{"abstract": "  The aviation industry is of great importance for a globally connected\neconomy. Customer satisfaction with airlines and airport performance is\nconsiderably influenced by how much flights are delayed. But how should the\ndelay be quantified with thousands of flights for each airport and airline?\nHere, we present a statistical analysis of arrival delays at several UK\nairports between 2018 and 2020. We establish a procedure to compare both mean\ndelay and extreme events among airlines and airports, identifying a power-law\ndecay of large delays. Furthermore, we note drastic changes in plane delay\nstatistics during the COVID-19 pandemic. Finally, we find that delays are\ndescribed by a superposition of simple distributions, leading to a\nsuperstatistics.\n"}
+{"abstract": "  Human re-rendering from a single image is a starkly under-constrained\nproblem, and state-of-the-art algorithms often exhibit undesired artefacts,\nsuch as over-smoothing, unrealistic distortions of the body parts and garments,\nor implausible changes of the texture. To address these challenges, we propose\na new method for neural re-rendering of a human under a novel user-defined pose\nand viewpoint, given one input image. Our algorithm represents body pose and\nshape as a parametric mesh which can be reconstructed from a single image and\neasily reposed. Instead of a colour-based UV texture map, our approach further\nemploys a learned high-dimensional UV feature map to encode appearance. This\nrich implicit representation captures detailed appearance variation across\nposes, viewpoints, person identities and clothing styles better than learned\ncolour texture maps. The body model with the rendered feature maps is fed\nthrough a neural image-translation network that creates the final rendered\ncolour image. The above components are combined in an end-to-end-trained neural\nnetwork architecture that takes as input a source person image, and images of\nthe parametric body model in the source pose and desired target pose.\nExperimental evaluation demonstrates that our approach produces higher quality\nsingle image re-rendering results than existing methods.\n"}
+{"abstract": "  The proliferation of insecure Internet-connected devices gave rise to the IoT\nbotnets which can grow very large rapidly and may perform high-impact\ncyber-attacks. The related studies for tackling IoT botnets are concerned with\neither capturing or analyzing IoT botnet samples, using honeypots and\nsandboxes, respectively. The lack of integration between the two implies that\nthe samples captured by the honeypots must be manually submitted for analysis\nin sandboxes, introducing a delay during which a botnet may change its\noperation. Furthermore, the effectiveness of the proposed sandboxes is limited\nby the potential use of anti-analysis techniques and the inability to identify\nfeatures for effective detection and identification of IoT botnets. In this\npaper, we propose and evaluate a novel framework, the IoT-BDA framework, for\nautomated capturing, analysis, identification, and reporting of IoT botnets.\nThe framework consists of honeypots integrated with a novel sandbox that\nsupports a wider range of hardware and software configurations, and can\nidentify indicators of compromise and attack, along with anti-analysis,\npersistence, and anti-forensics techniques. These features can make botnet\ndetection and analysis, and infection remedy more effective. The framework\nreports the findings to a blacklist and abuse service to facilitate botnet\nsuspension. The paper also describes the discovered anti-honeypot techniques\nand the measures applied to reduce the risk of honeypot detection. Over the\nperiod of seven months, the framework captured, analyzed, and reported 4077\nunique IoT botnet samples. The analysis results show that some IoT botnets used\nanti-analysis, persistence, and anti-forensics techniques typically seen in\ntraditional botnets.\n"}
+{"abstract": "  Laboratory tests play a major role in clinical decision making because they\nare essential for the confirmation of diagnostics suspicions and influence\nmedical decisions. The number of different laboratory tests available to\nphysicians in our age has been expanding very rapidly due to the rapid advances\nin laboratory technology. To find the correct desired tests within this\nexpanding plethora of elements, the Health Information System must provide a\npowerful search engine and the practitioner need to remember the exact name of\nthe laboratory test to correctly select the bag of tests to order. Recommender\nsystems are platforms which suggest appropriate items to a user after learning\nthe users' behaviour. A neighbourhood-based collaborative filtering method was\nused to model the recommender system, where similar bags, clustered using\nnearest neighbours algorithm, are used to make recommendations of tests for\neach other similar bag of laboratory tests. The recommender system developed in\nthis paper achieved 95.54 % in the mean average precision metric. A fully\ndocumented Python package named LaboRecommender was developed to implement the\nalgorithm proposed in this paper\n"}
+{"abstract": "  Purpose: Magnetization transfer (MT) and inhomogeneous MT (ihMT) contrasts\nare used in MRI to provide information about macromolecular tissue content. In\nparticular, MT is sensitive to macromolecules and ihMT appears to be specific\nto myelinated tissue. This study proposes a technique to characterize MT and\nihMT properties from a single acquisition, producing both semiquantitative\ncontrast ratios, and quantitative parameter maps.\n  Theory and Methods: Building upon previous work that uses multiband\nradiofrequency (RF) pulses to efficiently generate ihMT contrast, we propose a\ncyclic-steady-state approach that cycles between multiband and single-band\npulses to boost the achieved contrast. Resultant time-variable signals are\nreminiscent of a magnetic resonance fingerprinting (MRF) acquisition, except\nthat the signal fluctuations are entirely mediated by magnetization transfer\neffects. A dictionary-based low-rank inversion method is used to reconstruct\nthe resulting images and to produce both semiquantitative MT ratio (MTR) and\nihMT ratio (ihMTR) maps, as well as quantitative parameter estimates\ncorresponding to an ihMT tissue model.\n  Results: Phantom and in vivo brain data acquired at 1.5T demonstrate the\nexpected contrast trends, with ihMTR maps showing contrast more specific to\nwhite matter (WM), as has been reported by others. Quantitative estimation of\nsemisolid fraction and dipolar T1 was also possible and yielded measurements\nconsistent with literature values in the brain.\n  Conclusions: By cycling between multiband and single-band pulses, an entirely\nmagnetization transfer mediated 'fingerprinting' method was demonstrated. This\nproof-of-concept approach can be used to generate semiquantitative maps and\nquantitatively estimate some macromolecular specific tissue parameters.\n"}
+{"abstract": "  A recently-derived alternative method for computing the relative gain array\n(RGA) for singular and/or non-square systems has been proposed that provably\nguarantees unit invariance. This property is not offered by the conventional\nmethod that uses the Moore-Penrose (MP) pseudoinverse. In this paper we note\nthat the absence of the scale-invariance property by the conventional MP-RGA\ndoes not necessarily imply a practical disadvantage in real-world applications.\nIn other words, while it is true that performance of a controller should not\ndepend on the choice of units on its input and output variables, this does not\n{\\em necessarily} imply that the resulting MP-RGA measures of component\ninteraction lead to different controller-design input-output pairings. In this\npaper we consider the application of the MP-RGA to a realistic system (a Sakai\nfractional distillation system) to assess whether or not the choice of unit,\nwhich in this case relates to temperature, affects the choice of input-output\npairings determined by the resulting RGA matrix. Our results show that it does,\nthus confirming that unit-sensitivity of the MP-RGA undermines its rigorous use\nfor MIMO controller design.\n"}
+{"abstract": "  This paper presents a Material Mask Overlay Strategy topology optimization\napproach with improved material assignment at the element level for achieving\nclose to black-and-white designs for pressure-loaded problems. Hexagonal\nelements are employed to parametrize the design domain as this tessellation\nprovides nonsingular local connectivity. Elliptical negative masks are used to\nfind the optimized material layout. The material dilation and material erosion\nvariables of each mask are systematically varied in association with a\ngray-scale measure constraint to achieve designs close to 0-1. Darcy's law in\nassociation with a drainage term is used to formulate the pressure field. The\nobtained pressure field is converted into the consistent nodal forces using\nWachspress shape functions. Sensitivities of the objective and pressure load\nare evaluated using the adjoint-variable method. The approach is demonstrated\nby solving various pressure-loaded structures and pressure-actuated compliant\nmechanisms. Compliance is minimized for loadbearing structures, whereas a\nmulticriteria objective is minimized for mechanism designs.\n"}
+{"abstract": "  Small-angle neutron scattering is used in combination with transport\nmeasurements to investigate the current-induced effects on the morphology of\nthe intermediate mixed state domains in the intertype superconductor niobium.\nWe report the robust self-organisation of the vortex lattice domains to\nelongated parallel stripes perpendicular to the applied current in a\nsteady-state. The experimental results for the formation of the superstructure\nare supported by theoretical calculations, which highlight important details of\nthe vortex matter evolution. The investigation demonstrates a mechanism of a\nspontaneous pattern formation that is closely related to the universal physics\ngoverning the intermediate mixed state in low-$\\kappa$ superconductors.\n"}
+{"abstract": "  Causal inference is a challenging problem with observational data alone. The\ntask becomes easier when having access to data from perturbing the underlying\nsystem, even when happening in a non-randomized way: this is the setting we\nconsider, encompassing also latent confounding variables. To identify causal\nrelations among a collections of covariates and a response variable, existing\nprocedures rely on at least one of the following assumptions: i) the response\nvariable remains unperturbed, ii) the latent variables remain unperturbed, and\niii) the latent effects are dense. In this paper, we examine a perturbation\nmodel for interventional data, which can be viewed as a mixed-effects linear\nstructural causal model, over a collection of Gaussian variables that does not\nsatisfy any of these conditions. We propose a maximum-likelihood estimator --\ndubbed DirectLikelihood -- that exploits system-wide invariances to uniquely\nidentify the population causal structure from unspecific perturbation data, and\nour results carry over to linear structural causal models without requiring\nGaussianity. We illustrate the utility of our framework on synthetic data as\nwell as real data involving California reservoirs and protein expressions.\n"}
+{"abstract": "  Through examples, we illustrate how to compute differential operators on a\nquotient of an affine semigroup ring by a radical monomial ideal, when working\nover an algebraically closed field of characteristic 0.\n"}
+{"abstract": "  Although it is possible to increase confidence in Free and Open Source\nSoftware (FOSS) by reviewing its source code, trusting code is not the same as\ntrusting its executable counterparts. These are typically built and distributed\nby third-party vendors, with severe security consequences if their supply\nchains are compromised. In this paper, we present reproducible builds, an\napproach that can determine whether generated binaries correspond with their\noriginal source code. We first define the problem, and then provide insight\ninto the challenges of making real-world software build in a \"reproducible\"\nmanner-this is, when every build generates bit-for-bit identical results.\nThrough the experience of the Reproducible Builds project making the Debian\nLinux distribution reproducible, we also describe the affinity between\nreproducibility and quality assurance (QA).\n"}
+{"abstract": "  Recently, there has been an increasing interest in neural speech synthesis.\nWhile the deep neural network achieves the state-of-the-art result in\ntext-to-speech (TTS) tasks, how to generate a more emotional and more\nexpressive speech is becoming a new challenge to researchers due to the\nscarcity of high-quality emotion speech dataset and the lack of advanced\nemotional TTS model. In this paper, we first briefly introduce and publicly\nrelease a Mandarin emotion speech dataset including 9,724 samples with audio\nfiles and its emotion human-labeled annotation. After that, we propose a simple\nbut efficient architecture for emotional speech synthesis called EMSpeech.\nUnlike those models which need additional reference audio as input, our model\ncould predict emotion labels just from the input text and generate more\nexpressive speech conditioned on the emotion embedding. In the experiment\nphase, we first validate the effectiveness of our dataset by an emotion\nclassification task. Then we train our model on the proposed dataset and\nconduct a series of subjective evaluations. Finally, by showing a comparable\nperformance in the emotional speech synthesis task, we successfully demonstrate\nthe ability of the proposed model.\n"}
+{"abstract": "  A key limiting factor of present day state-of-the-art laser frequency\nstabilization using Fabry-P\\'erot cavities are length changes due to\nthermo-mechanical noise, originating from, for example, Brownian motion. We\npresent a laser-frequency stabilization concept using an optical cavity with a\nstrong slow-light effect to reduce the impact of cavity length changes on the\nfrequency stability. The resulting noise-reduction factor is proportional to\nthe ratio between the light phase and group velocities in the highly dispersive\ncavity spacer. We experimentally demonstrate a proof-of-principle\nimplementation of this laser-frequency stabilization technique using a\nrare-earth doped crystalline cavity spacer in conjunction with semi-permanent\nspectral tailoring to achieve precise control of the dispersive properties of\nthe cavity. A reduction in frequency sensitivity of four orders of magnitude\nwas achieved compared to the same setup in the absence of the slow-light\neffect.\n"}
+{"abstract": "  In this work, we propose the $4S$-$3D$ mixing scheme to assign the\n$\\Upsilon(10753)$ into the conventional bottomonium family. Under this\ninterpretation, we further study its hidden-bottom hadronic decays with a\n$\\eta^{(\\prime)}$ or $\\omega$ emission, which include\n$\\Upsilon(10753)\\to\\Upsilon(1S)\\eta^{(\\prime)}$, $\\Upsilon(10753)\\to\nh_{b}(1P)\\eta$ and $\\Upsilon(10753)\\to\\chi_{bJ}\\omega$ ($J$=0,1,2) processes.\nSince the $\\Upsilon(10753)$ is above the $B\\bar{B}$ threshold, the\ncoupled-channel effect cannot be ignored, thus, when calculating partial decay\nwidths of these $\\Upsilon(10753)$ hidden-bottom decays, we apply the hadronic\nloop mechanism. Our result shows that these discussed decay processes own\nconsiderable branching fractions with the order of magnitude of $10^{-4}\\sim\n10^{-3}$, which can be accessible at Belle II and other future experiments.\n"}
+{"abstract": "  Robust implementable output regulator design approaches are studied for\ngeneral linear continuous-time \\mbox{systems} with periodically sampled\nmeasurements, consisting of both the regulation errors and extra measurements\nthat are generally non-vanishing in steady state. A digital regulator is first\ndeveloped via the conventional emulation-based approach, rendering the\nregulation errors asymptotically bounded with a small sampling period. We then\ndevelop a hybrid design framework by incorporating a generalized hold device,\nwhich transforms the original problem into the problem of designing an output\nfeedback controller fulfilling two conditions for a discrete-time system. We\nshow that such a controller can always be obtained by designing a discrete-time\ninternal model, a discrete-time washout filter, and a discrete-time output\nfeedback stabilizer. As a result, the regulation errors are shown to be\nglobally exponentially convergent to zero, while the sampling period is fixed\nbut can be arbitrarily large. This design framework is further developed for a\nmulti-rate digital regulator with a large sampling period of the measurements\nand a small control execution period.\n"}
+{"abstract": "  We establish an equivalence-singularity dichotomy for a large class of\none-dimensional Markov measures. Our approach is new in that we deal with\none-sided and two-sided chains simultaneously, and in that we do not appeal to\nany 0-1 law. In fact we deduce a new 0-1 law from the dichotomy.\n"}
+{"abstract": "  We study the problem of minimizing the sum of potentially non-differentiable\nconvex cost functions with partially overlapping dependences in an asynchronous\nmanner, where communication in the network is not coordinated. We study the\nbehavior of an asynchronous algorithm based on dual decomposition and block\ncoordinate subgradient methods under assumptions weaker than those used in the\nliterature. At the same time, we allow different agents to use local stepsizes\nwith no global coordination. Sufficient conditions are provided for almost sure\nconvergence to the solution of the optimization problem. Under additional\nassumptions, we establish a sublinear convergence rate that in turn can be\nstrengthened to linear convergence rate if the problem is strongly convex and\nhas Lipschitz gradients. We also extend available results in the literature by\nallowing multiple and potentially overlapping blocks to be updated at the same\ntime with non-uniform and potentially time varying probabilities assigned to\ndifferent blocks. A numerical example is provided to illustrate the\neffectiveness of the algorithm.\n"}
+{"abstract": "  In this article we study and classify optimal martingales in the dual\nformulation of optimal stopping problems. In this respect we distinguish\nbetween weakly optimal and surely optimal martingales. It is shown that the\nfamily of weakly optimal and surely optimal martingales may be quite large. On\nthe other hand it is shown that the Doob-martingale, that is, the martingale\npart of the Snell envelope, is in a certain sense the most robust surely\noptimal martingale under random perturbations. This new insight leads to a\nnovel randomized dual martingale minimization algorithm that doesn't require\nnested simulation. As a main feature, in a possibly large family of optimal\nmartingales the algorithm efficiently selects a martingale that is as close as\npossible to the Doob martingale. As a result, one obtains the dual upper bound\nfor the optimal stopping problem with low variance.\n"}
+{"abstract": "  Topic models are widely used analysis techniques for clustering documents and\nsurfacing thematic elements of text corpora. These models remain challenging to\noptimize and often require a \"human-in-the-loop\" approach where domain experts\nuse their knowledge to steer and adjust. However, the fragility,\nincompleteness, and opacity of these models means even minor changes could\ninduce large and potentially undesirable changes in resulting model. In this\npaper we conduct a simulation-based analysis of human-centered interactions\nwith topic models, with the objective of measuring the sensitivity of topic\nmodels to common classes of user actions. We find that user interactions have\nimpacts that differ in magnitude but often negatively affect the quality of the\nresulting modelling in a way that can be difficult for the user to evaluate. We\nsuggest the incorporation of sensitivity and \"multiverse\" analyses to topic\nmodel interfaces to surface and overcome these deficiencies.\n"}
+{"abstract": "  We present bright [CII] 158 $\\mu$m line detections from a strongly magnified\nand multiply-imaged ($\\mu\\sim20-160$) sub-$L^{*}$ ($M_{\\rm UV}$ =\n$-19.75^{+0.55}_{-0.44}$) Lyman-break galaxy (LBG) at $z=6.0719\\pm0.0004$ from\nthe ALMA Lensing Cluster Survey (ALCS). Emission lines are identified at 268.7\nGHz at $\\geq$ 8$\\sigma$ exactly at positions of two multiple images of the LBG\nbehind the massive galaxy cluster RXCJ0600$-$2007. Our lens models, updated\nwith the latest spectroscopy from VLT/MUSE, indicate that a sub region of the\nLBG crosses the caustic and is lensed into a long ($\\sim6''$) arc with a local\nmagnification of $\\mu\\sim 160$, for which the [CII] line is also significantly\ndetected. The source-plane reconstruction resolves the interstellar medium\n(ISM) structure, showing that the [CII] line is co-spatial with the rest-frame\nUV continuum at the scale of $\\sim$300 pc. The [CII] line properties suggest\nthat the LBG is a rotation-dominated system whose velocity gradient explains a\nslight difference of redshifts between the whole LBG and its sub region. The\nstar formation rate (SFR)-$L_{\\rm [CII]}$ relations from the sub to the whole\nregions of the LBG are consistent with those of local galaxies. We evaluate the\nlower limit of the faint-end of the [CII] luminosity function at $z=6$, and\nfind that it is consistent with predictions from semi analytical models and\nfrom the local SFR-$L_{\\rm [CII]}$ relation with a SFR function at $z=6$. These\nresults imply that the local SFR-$L_{\\rm [CII]}$ relation is universal for a\nwide range of scales including the spatially resolved ISM, the whole region of\ngalaxy, and the cosmic scale, even in the epoch of reionization.\n"}
+{"abstract": "  Recently a continuous description of the particle swarm optimization (PSO)\nbased on a system of stochastic differential equations was proposed by Grassi\nand Pareschi in arXiv:2012.05613 where the authors formally showed the link\nbetween PSO and the consensus based optimization (CBO) through zero-inertia\nlimit. This paper is devoted to solving this theoretical open problem proposed\nin arXiv:2012.05613 by providing a rigorous derivation of CBO from PSO through\nthe limit of zero inertia, and a quantified convergence rate is obtained as\nwell. The proofs are based on a probabilistic approach by investigating the\nweak convergence of the corresponding stochastic differential equations (SDEs)\nof Mckean type in the continuous path space and the results are illustrated\nwith some numerical examples.\n"}
+{"abstract": "  It is generally believed that wormholes are supported by exotic matter\nviolating Null Energy Condition (NEC). However, various studies of wormhole\ngeometries under Lovelock theories of gravity have reported existence of\nwormhole supported by matter satisfying NEC. Being inspired by these results,\nwe explore the possibility of the existence of wormhole supported by normal\nquark matter in third order Lovelock gravity theory. Well known MIT Bag Model\nEquation of state is chosen for describing the quark matter. Taking physically\nacceptable approximations, we solve the field equations for shape function\nwhich satisfies flare out condition. The residual of the approximate solution\nis studied for accuracy and found to be acceptable.\n"}
+{"abstract": "  Knowledge bases are collections of domain-specific and commonsense facts.\nRecently, the sizes of KBs are rocketing due to automatic extraction for\nknowledge and facts. For example, the number of facts in WikiData is up to 974\nmillion! According to our observation, current KBs, especially domain KBs, show\nstrong relevance in relations according to some topics. These patterns can be\nused to conclude and infer for part of facts in the KBs. Therefore, the\noriginal KBs can be minimzed by extracting patterns and essential facts.\n"}
+{"abstract": "  In this work, we consider the problem of learning a feed-forward neural\nnetwork (NN) controller to safely steer an arbitrarily shaped planar robot in a\ncompact and obstacle-occluded workspace. Unlike existing methods that depend\nstrongly on the density of data points close to the boundary of the safe state\nspace to train NN controllers with closed-loop safety guarantees, we propose an\napproach that lifts such assumptions on the data that are hard to satisfy in\npractice and instead allows for graceful safety violations, i.e., of a bounded\nmagnitude that can be spatially controlled. To do so, we employ reachability\nanalysis methods to encapsulate safety constraints in the training process.\nSpecifically, to obtain a computationally efficient over-approximation of the\nforward reachable set of the closed-loop system, we partition the robot's state\nspace into cells and adaptively subdivide the cells that contain states which\nmay escape the safe set under the trained control law. To do so, we first\ndesign appropriate under- and over-approximations of the robot's footprint to\nadaptively subdivide the configuration space into cells. Then, using the\noverlap between each cell's forward reachable set and the set of infeasible\nrobot configurations as a measure for safety violations, we introduce penalty\nterms into the loss function that penalize this overlap in the training\nprocess. As a result, our method can learn a safe vector field for the\nclosed-loop system and, at the same time, provide numerical worst-case bounds\non safety violation over the whole configuration space, defined by the overlap\nbetween the over-approximation of the forward reachable set of the closed-loop\nsystem and the set of unsafe states. Moreover, it can control the tradeoff\nbetween computational complexity and tightness of these bounds. Finally, we\nprovide a simulation study that verifies the efficacy of the proposed scheme.\n"}
+{"abstract": "  The problem of covert communication over Multiple-Input Multiple-Output\n(MIMO) Additive White Gaussian Noise (AWGN) channels is investigated, in which\na transmitter attempts to reliably communicate with a legitimate receiver while\navoiding detection by a passive adversary. The covert capacity of the MIMO AWGN\nis characterized under a variational distance covertness constraint when the\nMIMO channel matrices are static and known. The characterization of the covert\ncapacity is also extended to a class of channels in which the legitimate\nchannel matrix is known but the adversary's channel matrix is only known up to\na rank and a spectral norm constraint.\n"}
+{"abstract": "  We explore emergent geometry of the spacetime at the microscopic scale by\nadiabatic transport of a quasi-coherent state of a fermionic string, with\nquantum spacetime described by the matrix theory (BFSS matrix model). We show\nthat the generator of the Berry phase is the shift vector of the spacetime\nfoliation by spacelike surfaces associated with the quasi-coherent state. The\noperator-valued generator of the geometric phase of weak adiabatic transport is\nthe Lorentz connection of the emergent geometry which is not torsion free at\nthe microscopic scale. The effects of the torsion seem consistent with the\nusual interpretation of the Berry curvature as a pseudo magnetic field.\n"}
+{"abstract": "  A toric code is an error-correcting code determined by a toric variety or its\nassociated integral convex polytope. We investigate $4$- and $5$-dimensional\ntoric $3$-fold codes, which are codes arising from polytopes in $\\mathbf{R}^3$\nwith four and five lattice points, respectively. By computing the minimum\ndistances of each code, we fully classify the $4$-dimensional codes. We further\npresent progress toward the same goal for dimension $5$ codes. In particular,\nwe classify the $5$-dimensional toric $3$-fold codes arising from polytopes of\nwidth 1.\n"}
+{"abstract": "  The aim of this paper is to derive macroscopic equations for processes on\nlarge co-evolving networks, examples being opinion polarization with the\nemergence of filter bubbles or other social processes such as norm development.\nThis leads to processes on graphs (or networks), where both the states of\nparticles in nodes as well as the weights between them are updated in time. In\nour derivation we follow the basic paradigm of statistical mechanics: We start\nfrom paradigmatic microscopic models and derive a Liouville-type equation in a\nhigh-dimensional space including not only the node states in the network\n(corresponding to positions in mechanics), but also the edge weights between\nthem. We then derive a natural (finite) marginal hierarchy and pass to an\ninfinite limit.\n  We will discuss the closure problem for this hierarchy and see that a simple\nmean-field solution can only arise if the weight distributions between nodes of\nequal states are concentrated. In a more interesting general case we propose a\nsuitable closure at the level of a two-particle distribution (including the\nweight between them) and discuss some properties of the arising kinetic\nequations. Moreover, we highlight some structure-preserving properties of this\nclosure and discuss its analysis in a minimal model. We discuss the application\nof our theory to some agent-based models in literature and discuss some open\nmathematical issues.\n"}
+{"abstract": "  We propose a novel technique to analyse spacings of the observed CMB\ntemperature angular power spectrum (APS) motivated by the behaviour of level\nspacings in the presence or absence of correlations between eigenvalues of a\nrandom matrix. We use data from WMAP 9 year ILC and 2018 Planck maps\n(Commander, NILC and SMICA) and estimate minimum, maximum, average spacings and\nratios of maximum to minimum spacings between consecutive multipoles of APS\nmeasures ($C_{\\ell}$ and $\\frac{\\ell(\\ell+1)}{2\\pi}C_\\ell$). Anomalous\nmultipole ranges given by $\\ell_{max}$ and $\\ell_{min}$ exist for multipoles\n$\\in[2,31]$ of this work. Sans parity distinctions, average spacing of\n$8\\leq\\ell \\leq 31$ for $C_\\ell$'s, and maximum and average spacings of\n$\\ell_{min}\\in[8,12]$ of $\\frac{\\ell(\\ell+1)}{2\\pi}C_\\ell$'s are anomalously\nlow for all maps, with most anomalously low maximum spacing at $99.93\\%$ C.L.\nfor $\\frac{\\ell(\\ell+1)}{2\\pi}C_\\ell\\text{ (Planck NILC)}$. With parity\ndistinctions, even multipoles indicate anomalously low maximum and average\nspacings relative to odd multipoles, most consistently for\n$\\ell_{max}\\in[6,30]$ of $C_\\ell$'s of all maps, the most outstanding being the\neven multipole average spacing of $2\\leq\\ell\\leq27$ for\n$\\frac{\\ell(\\ell+1)}{2\\pi}C_\\ell\\text{ (WMAP)}$, at $99.60\\%$ C.L. For spacing\nratios, multipole ranges are similar to those for anomalous spacings, and odd\nmultipole spacing ratios are mostly anomalous, with odd multipole spacing ratio\nof $2\\leq\\ell\\leq7$ being consistently low for all maps. Overall, we infer an\nunusual deficit of correlations between APS measures and deviations from\nisotropy due to consistently low, parity-biased anomalous spacings, which can\nbe argued to have arisen primordially. The physical origin of these findings\nmay be probed in future work.\n"}
+{"abstract": "  Ultracold polar molecules possess long-range, anisotropic, and tunable\ndipolar interactions, providing the opportunities to probe quantum phenomena\ninaccessible with existing cold gas platforms. However, experimental progress\nhas been hindered by the dominance of two-body loss over elastic interactions,\nwhich prevents efficient evaporative cooling. Though recent work has\ndemonstrated controlled interactions by confining molecules to a\ntwo-dimensional geometry, a general approach for tuning molecular interactions\nin a three-dimensional (3D), stable system has been lacking. Here, we\ndemonstrate tunable elastic dipolar interactions in a bulk gas of ultracold\n40K87Rb molecules in 3D, facilitated by an electric field-induced shielding\nresonance which suppresses the reactive loss by a factor of thirty. This\nimprovement in the ratio of elastic to inelastic collisions enables direct\nthermalization. The thermalization rate depends on the angle between the\ncollisional axis and the dipole orientation controlled by an external electric\nfield, a direct manifestation of the anisotropic dipolar interaction. We\nachieve evaporative cooling mediated by the dipolar interactions in three\ndimensions. This work demonstrates full control of a long-lived bulk quantum\ngas system with tunable long-range interactions, paving the way for the study\nof collective quantum many-body physics.\n"}
+{"abstract": "  This paper presents an investigation of local elastic properties of\npolystyrene doped with a SiO2 nanoparticle. The local elasticity is analyzed\nusing the local density fluctuations at various length scales. A significant\nenhancement of local elastic properties of polystyrene was observed around the\nnanoparticle, and it was revealed that the additional stiffness has an\nexponential behavior with a typical length scale R0 ~ 1.4 nm. At the same time,\nit was shown that the density and other structural properties of polystyrene at\nthis distance from the nanoparticle surface are identical to those for the bulk\npolystyrene. The enhancement of the local elastic properties was attributed to\nthe effect of non-affine deformations caused by the strong spatial fluctuations\nof polystyrene stiffness at the length scale below 1 nm. The effect was\nconfirmed using the random matrix model with variable strength of disorder.\n"}
+{"abstract": "  The $\\eta N$ interactions are investigated in the hot magnetized asymmetric\nnuclear matter using chiral SU(3) model and chiral perturbation theory (ChPT).\nIn the chiral model, the in-medium properties of $\\eta$-meson are calculated by\nthe medium modified scalar densities under the influence of an external\nmagnetic field. Further, in the combined approach of chiral model and ChPT,\noff-shell contributions of $\\eta N$ interactions are evaluated from the ChPT\neffective $\\eta N$ Lagrangian, and the in-medium effect of scalar densities are\nincorporated from the chiral SU(3) model. We observe a significant effect of\nmagnetic field on the in-medium mass and optical potential of $\\eta$ meson. We\nobserve a deeper mass-shift in the combined approach of ChPT and chiral model\ncompared to the effect of solo chiral SU(3) model. In both approaches, no\nadditional mass-shift is observed due to the uncharged nature of $\\eta$ mesons\nin the presence of magnetic field.\n"}
+{"abstract": "  Mina and Andersen, authors of the Perspectives in Science: COVID-19 Testing:\nOne Size Does Not Fit All have referred to results and adopted conclusions from\nrecently published governmental report Pavelka et al. \"The effectiveness of\npopulation wide, rapid antigen test based screening in reducing SARS-CoV-2\ninfection prevalence in Slovakia\" without critical consideration, and rigorous\nverification. We demonstrate that the authors refer to conclusions that are not\nsupported by experimental data. Further, there is a lack of objective,\nindependent information and studies regarding the widespread, public testing\nprogram currently in force in the Slovak Republic. We offer an alternative\nexplanation of observed data as they have been provided by the Slovak Republic\ngovernment to fill this information gap. We also provide explanations and\nconclusions that more accurately describe viral spread dynamics. Drawing from\navailable public data and our simple but rigorous analysis, we show that it is\nnot possible to make clear conclusions about any positive impact of the public\ntesting program in the Slovak Republic. In particular, it is not possible to\nconclude that this testing program forces the curve down for the SARS-CoV-2\nvirus outbreak. We think that Pavelka et al. did not consider many fundamental\nphenomena in their proposed computer simulations and data analysis - in\nparticular: the complexity of SARS-CoV-2 virus spread. In complex\nspatio-temporal dynamical systems, small spatio-temporal fluctuations can\ndramatically change the dynamics of virus spreading on large scales.\n"}
+{"abstract": "  In this paper we introduce the class of bilinear Hilbert--Carleson operators\n$\\{BC^a\\}_{a>0}$ defined by $$ BC^{a}(f,g)(x):= \\sup_{\\lambda\\in {\\mathbb R}}\n\\Big|\\int f(x-t)\\, g(x+t)\\, e^{i\\lambda t^a} \\, \\frac{dt}{t} \\Big| $$ and show\nthat in the non-resonant case $a\\in (0,\\infty)\\setminus\\{1,2\\}$ the operator\n$BC^a$ extends continuously from $L^p({\\mathbb R})\\times L^q({\\mathbb R})$ into\n$L^r({\\mathbb R})$ whenever $\\frac{1}{p}+\\frac{1}{q}=\\frac{1}{r}$ with\n$1<p,\\,q\\leq\\infty$ and $\\frac{2}{3}<r<\\infty$.\n  A key novel feature of these operators is that -- in the non-resonant case --\n$BC^{a}$ has a \\emph{hybrid} nature enjoying both (1) ``zero curvature''\nfeatures inherited from the modulation invariance property of the classical\nbilinear Hilbert transform (BHT), and (2) ``non-zero curvature'' features\narising from the Carleson-type operator with nonlinear phase $\\lambda t^a$.\n"}
+{"abstract": "  A path-following collision-avoidance model predictive control (MPC) method is\nproposed which approximates obstacle shapes as convex polygons.\nCollision-avoidance is ensured by means of the signed distance function which\nis calculated efficiently as part of the MPC problem by making use of a dual\nformulation. The overall MPC problem can be solved by standard nonlinear\nprogramming (NLP) solvers. The dual signed distance formulation yields, besides\nthe (dual) collision-avoidance constraints, norm, and consistency constraints.\nA novel approach is presented that combines the arising norm equality with the\ndual collision-avoidance inequality constraints to yield an alternative\nformulation reduced in complexity. Moving obstacles are considered using\nseparate convex sets of linearly predicted obstacle positions in the dual\nproblem. The theoretical findings and simplifications are compared with the\noften-used ellipsoidal obstacle formulation and are analyzed with regard to\nefficiency by the example of a simulated path-following autonomous surface\nvessel during a realistic maneuver and AIS obstacle data from the Kiel bay\narea.\n"}
+{"abstract": "  In this study, recommendations of the AAPM TG- 43 (U1) report have been\nfollowed to characterize the new 192Ir pulse dose rate source, provided by the\nApplied Radiation Research School, Nuclear Science and Technology Research\nInstitute in Iran. Dose rate constant, radial dose function, geometry factors,\nand anisotropy function were calculated according to the relevant American\nAssociation of Physicists in Medicine AAPM and TG43 (U1) reports. In this\nstudy, 192Ir source was characterized using Monte Carlo simulation in water\nphantom, and in addition, experimental measurements were carried out using\nthermoluminescent dosimeters (TLD-100) in plexiglass (PMMA) phantoms. The\ndose-rate constant for the 192Ir PDR was found to be equal to 1.131 cGyh-1U-1\nand 1.173 cGyh-1U-1 with TLD measurement and Monte Carlo simulation,\nrespectively. Also in this study, the geometry function, radial dose functions\ng(r), and the anisotropy function have been calculated at distances from 0.1 to\n16 cm. The dose-rate constant of these calculations has been compared with\nmeasured values for an actual 192Ir seed. The results of dosimetry parameters,\npresented in tabulated and graphical formats, exhibited good agreement to those\nreported from other commercially available PDR 192Ir sources. The results\nobtained in this study are in close agreement with the characteristics of the\ncommercially available 192Ir sources. The results obtained in this study can be\ntreated as an initial assessment of this source to be employed in the\nconventional treatment planning systems subsequent to complementary\ninvestigations.\n"}
+{"abstract": "  CzeV404 is an SU UMa-type dwarf nova in the period gap. Kara et al. (2021)\n(arXiv:2107.02664) recently published photometric and spectroscopic\nobservations and obtained a mass ratio q=0.16, which is in severe disagreement\nof q~0.32 estimated from superhump observations (Bakowska et al., 2014). I here\npresent what analysis was wrong or outdated in Bakowska et al. (2014) and\nprovide a new value of q=0.247(5), consistent with the known behavior of\nsuperhumps and the evolution of cataclysmic variables. CzeV404 does not look\nlike an unusual dwarf nova as suggested by Kara et al. (2021) and I discuss\nthat the link between SW Sex and SU UMa systems suggested by Kara et al. (2021)\nis not supported.\n"}
+{"abstract": "  By planning through a learned dynamics model, model-based reinforcement\nlearning (MBRL) offers the prospect of good performance with little environment\ninteraction. However, it is common in practice for the learned model to be\ninaccurate, impairing planning and leading to poor performance. This paper aims\nto improve planning with an importance sampling framework that accounts and\ncorrects for discrepancy between the true and learned dynamics. This framework\nalso motivates an alternative objective for fitting the dynamics model: to\nminimize the variance of value estimation during planning. We derive and\nimplement this objective, which encourages better prediction on trajectories\nwith larger returns. We observe empirically that our approach improves the\nperformance of current MBRL algorithms on two stochastic control problems, and\nprovide a theoretical basis for our method.\n"}
+{"abstract": "  Research evaluations and comparison of the assessments of academic\ninstitutions (scientific areas, departments, universities etc.) are among the\nmajor issues in recent years in higher education systems. One method, followed\nby some national evaluation agencies, is to assess the research quality by the\nevaluation of a limited number of publications in a way that each publication\nis rated among $n$ classes. This method produces, for each institution, a\ndistribution of the publications in the $n$ classes. In this paper we introduce\na natural geometric way to compare these assessments by introducing an ad hoc\ndistance from the performance of an institution to the best possible achievable\nassessment. Moreover, to avoid the methodological error of comparing\nnon-homogeneous institutions, we introduce a {\\em geometric score} based on\nsuch a distance. The latter represents the probability that an ideal\ninstitution, with the same configuration as the one under evaluation, performs\nworst. We apply our method, based on the geometric score, to rank, in two\nspecific scientific areas, the Italian universities using the results of the\nevaluation exercise VQR 2011-2014.\n"}
+{"abstract": "  A promising method for measuring the cosmological parameter combination\nfsigma_8 is to compare observed peculiar velocities with peculiar velocities\npredicted from a galaxy density field using perturbation theory. We use N-body\nsimulations and semi-analytic galaxy formation models to quantify the accuracy\nand precision of this method. Specifically, we examine a number of technical\naspects, including the optimal smoothing length applied to the density field,\nthe use of dark matter halos or galaxies as tracers of the density field, the\neffect of noise in the halo mass estimates or in the stellar-to-halo mass\nrelation, and the effect of finite survey volumes. We find that for a Gaussian\nsmoothing of 4 Mpc/h, the method has only small systematic biases at the level\nof 5%. Cosmic variance affects current measurements at the 5% level due to the\nvolume of current redshift data sets.\n"}
+{"abstract": "  We measure the scalar induced gravitational waves from the cosmic microwave\nbackground (CMB) observations and the gravitational wave observations. In the\n$\\Lambda$CDM+$r$ model, we constrain the cosmological parameters within the\nevolution of the scalar induced gravitational waves by the additional scalar\npower spectrum. The two special cases called narrow power spectrum and wide\npower spectrum have influence on the cosmological parameters, especially the\ncombinations of Planck18+BAO+BK15+LISA. We also compare these numerical results\nfrom four datasets within LIGO, LISA, IPTA and FAST projects, respectively. The\nconstraints from FAST have a significant impact on tensor-to-scalar ratio.\n"}
+{"abstract": "  Some interpersonal verbs can implicitly attribute causality to either their\nsubject or their object and are therefore said to carry an implicit causality\n(IC) bias. Through this bias, causal links can be inferred from a narrative,\naiding language comprehension. We investigate whether pre-trained language\nmodels (PLMs) encode IC bias and use it at inference time. We find that to be\nthe case, albeit to different degrees, for three distinct PLM architectures.\nHowever, causes do not always need to be implicit -- when a cause is explicitly\nstated in a subordinate clause, an incongruent IC bias associated with the verb\nin the main clause leads to a delay in human processing. We hypothesize that\nthe temporary challenge humans face in integrating the two contradicting\nsignals, one from the lexical semantics of the verb, one from the\nsentence-level semantics, would be reflected in higher error rates for models\non tasks dependent on causal links. The results of our study lend support to\nthis hypothesis, suggesting that PLMs tend to prioritize lexical patterns over\nhigher-order signals.\n"}
+{"abstract": "  This paper proposes a simultaneous control method for the angle and stiffness\nof the joint in an antagonistic pneumatic artificial muscle (PAM) actuator\nsystem using only pressure measurements, and clarifies the allowable references\nfor the PAM actuator system. To achieve a sensor-less control, the proposed\nmethod estimates the joint angle and contraction forces using an unscented\nKalman filter that employs a detailed model of the actuator system. Unlike\nprevious control methods, the proposed method does not require any encoder and\nforce sensor to achieve angle and stiffness control of the PAM actuator system.\nExperimental validations using three control scenarios confirm that the\nproposed method can control the joint angle and stiffness simultaneously and\nindependently. Moreover, it is shown that a reference admissible set can be\nused as an indicator to establish reference values by demonstrating that the\nreference set covers the experimentally obtained trajectories of the angle and\nstiffness.\n"}
+{"abstract": "  We propose to tackle data-to-text generation tasks by directly splicing\ntogether retrieved segments of text from \"neighbor\" source-target pairs. Unlike\nrecent work that conditions on retrieved neighbors but generates text\ntoken-by-token, left-to-right, we learn a policy that directly manipulates\nsegments of neighbor text, by inserting or replacing them in partially\nconstructed generations. Standard techniques for training such a policy require\nan oracle derivation for each generation, and we prove that finding the\nshortest such derivation can be reduced to parsing under a particular weighted\ncontext-free grammar. We find that policies learned in this way perform on par\nwith strong baselines in terms of automatic and human evaluation, but allow for\nmore interpretable and controllable generation.\n"}
+{"abstract": "  We report the optical absorption characteristics and the photocurrent spectra\nfor large-area molecular junctions containing different molecular structures\nand thicknesses. Through experimental assessment of the optical absorption of\nmolecules in solution and chemisorbed onto transparent, conductive carbon\nfilms, the energy ranges and intensity of absorption for a variety of molecular\nlayers as a function of thickness were determined. Our findings show that\noptical absorption is red-shifted for a chemisorbed molecular layer relative to\nthe molecule in solution, indicating electronic coupling between the molecule\nand the surface. However, we also find that from 2-22 nm in thickness, the\nabsorption characteristics follow Beers law, with no further shifts in the\nwavelength of maximum absorption or low-energy absorption onset. For energies\nwhere no absorption occurs, an internal photoemission process takes place\nresulting in photocurrent that can be sustained over a limited range. For\nenergies at which the molecular component absorbs light, photocurrent can\nproceed over longer distances, and the trends in photocurrent yield versus\nthickness shows interesting trends that may be related to the distance scale\nover which charge carriers can communicate with the conductive contacts. This\nmethod is particularly relevant for strongly coupled systems that rely on\nmolecular properties to mediate charge transport, since excitation takes place\ndirectly within the molecular layer, and provides a direct probe of energetics\nin systems that show bulk-limited transport.\n"}
+{"abstract": "  Emotion recognition in conversations is an important step in various virtual\nchat bots which require opinion-based feedback, like in social media threads,\nonline support and many more applications. Current Emotion recognition in\nconversations models face issues like (a) loss of contextual information in\nbetween two dialogues of a conversation, (b) failure to give appropriate\nimportance to significant tokens in each utterance and (c) inability to pass on\nthe emotional information from previous utterances.The proposed model of\nAdvanced Contextual Feature Extraction (AdCOFE) addresses these issues by\nperforming unique feature extraction using knowledge graphs, sentiment lexicons\nand phrases of natural language at all levels (word and position embedding) of\nthe utterances. Experiments on the Emotion recognition in conversations dataset\nshow that AdCOFE is beneficial in capturing emotions in conversations.\n"}
+{"abstract": "  CHARA/SPICA (Stellar Parameters and Images with a Cophased Array) is\ncurrently being developed at Observatoire de la C\\^ote d'Azur. It will be\ninstalled at the visible focus of the CHARA Array by the end of 2021. It has\nbeen designed to perform a large survey of fundamental stellar parameters with,\nin the possible cases, a detailed imaging of the surface or environment of\nstars. To reach the required precision and sensitivity, CHARA/SPICA combines a\nlow spectral resolution mode R = 140 in the visible and single-mode fibers fed\nby the AO stages of CHARA. This setup generates additional needs before the\ninterferometric combination: the compensation of atmospheric refraction and\nlongitudinal dispersion, and the fringe stabilization. In this paper, we\npresent the main features of the 6-telescopes fibered visible beam combiner\n(SPICA-VIS) together with the first laboratory and on-sky results of the fringe\ntracker (SPICA-FT). We describe also the new fringe-tracker simulator developed\nin parallel to SPICA-FT.\n"}
+{"abstract": "  Many computational problems are subject to a quantum speed-up: one might find\nthat a problem having an O(n^3)-time or O(n^2)-time classic algorithm can be\nsolved by a known O(n^1.5)-time or O(n)-time quantum algorithm. The question\nnaturally arises: how much quantum speed-up is possible?\n  The area of fine-grained complexity allows us to prove optimal lower-bounds\non the complexity of various computational problems, based on the conjectured\nhardness of certain natural, well-studied problems. This theory has recently\nbeen extended to the quantum setting, in two independent papers by Buhrman,\nPatro, and Speelman (arXiv:1911.05686), and by Aaronson, Chia, Lin, Wang, and\nZhang (arXiv:1911.01973).\n  In this paper, we further extend the theory of fine-grained complexity to the\nquantum setting. A fundamental conjecture in the classical setting states that\nthe 3SUM problem cannot be solved by (classical) algorithms in time O(n^{2-a}),\nfor any a>0. We formulate an analogous conjecture, the Quantum-3SUM-Conjecture,\nwhich states that there exist no sublinear O(n^{1-b})-time quantum algorithms\nfor the 3SUM problem.\n  Based on the Quantum-3SUM-Conjecture, we show new lower-bounds on the time\ncomplexity of quantum algorithms for several computational problems. Most of\nour lower-bounds are optimal, in that they match known upper-bounds, and hence\nthey imply tight limits on the quantum speedup that is possible for these\nproblems.\n"}
+{"abstract": "  In practice, the use of rounding is ubiquitous. Although researchers have\nlooked at the implications of rounding continuous random variables, rounding\nmay be applied to functions of discrete random variables as well. For example,\nto infer on suicide difference between two time periods, authorities may\nprovide a rounded average of deaths for each period. Suicide rates tend to be\nrelatively low around the world and such rounding may seriously affect\ninference on the change of suicide rate. In this paper, we study the scenario\nwhen a rounded to nearest integer average is used to estimate a non-negative\ndiscrete random variable. Specifically, our interest is in drawing inference on\na parameter from the pmf of Y, when we get U=n[Y/n]as a proxy for Y. The\nprobability generating function of U, E(U), and Var(U) capture the effect of\nthe coarsening of the support of Y. Also, moments and estimators of\ndistribution parameters are explored for some special cases. Under certain\nconditions, there is little impact from rounding. However, we also find\nscenarios where rounding can significantly affect statistical inference as\ndemonstrated in two applications. The simple methods we propose are able to\npartially counter rounding error effects.\n"}
+{"abstract": "  We give an analytic treatment of the time resolution and efficiency of Single\nPhoton Avalanche Diodes (SPADs) and Silicon Photomultipliers (SiPMs). We\nprovide closed-form expressions for structures with uniform electric fields and\nefficient numerical prescriptions for arbitrary electric field configurations.\nWe discuss the sensor performance for single photon detection and also for\ncharged particle detection.\n"}
+{"abstract": "  We consider the family of $r$-parallel sets in $\\mathbb{R}^d$, that is sets\nof the form $A_r=A+rB_2^n$, where $B_2^n$ is the unit Euclidean ball and $A$ is\nan arbitrary Borel set. We show that the ratio between the upper surface area\nmeasure of an $r$-parallel set and its volume is upper bounded by $d/r$.\nEquality is achieved for $A$ being a single point. As a consequence of our main\nresult we show that the Gaussian upper surface area measure of an $r$-parallel\nset is upper bounded by $18d \\max(\\sqrt{d},r^{-1})$. Moreover, we observe that\nthere exists a $1$-parallel set with Gaussian surface area measure at least\n$0.28 \\cdot d^{1/4}$.\n"}
+{"abstract": "  Attractor inflation is a particularly robust framework for developing\ninflationary models that are insensitive to the details of the potential. Such\nmodels are most often considered in the metric formulation of gravity. However,\nnon-minimal models may not necessarily maintain their attractor nature in the\nPalatini formalism where the connection is independent of the metric. In this\nwork, we employ the $\\beta$-function formalism to classify the strong coupling\nlimit of inflationary models in both the metric and the Palatini approaches.\nFurthermore, we determine the range of values for the non-minimal coupling that\nlead to theories being observationally indistinguishable in metric and Palatini\nwithin current accuracy. Finally, we reconstruct the Jordan frame potential for\n$\\xi$-attractors by imposing an explicit form for the $\\beta$-function,\ndemonstrating the effect that the choice of metric or Palatini has on the\ninflationary observables of the theory.\n"}
+{"abstract": "  Pseudo-labeling is a key component in semi-supervised learning (SSL). It\nrelies on iteratively using the model to generate artificial labels for the\nunlabeled data to train against. A common property among its various methods is\nthat they only rely on the model's prediction to make labeling decisions\nwithout considering any prior knowledge about the visual similarity among the\nclasses. In this paper, we demonstrate that this degrades the quality of\npseudo-labeling as it poorly represents visually similar classes in the pool of\npseudo-labeled data. We propose SemCo, a method which leverages label semantics\nand co-training to address this problem. We train two classifiers with two\ndifferent views of the class labels: one classifier uses the one-hot view of\nthe labels and disregards any potential similarity among the classes, while the\nother uses a distributed view of the labels and groups potentially similar\nclasses together. We then co-train the two classifiers to learn based on their\ndisagreements. We show that our method achieves state-of-the-art performance\nacross various SSL tasks including 5.6% accuracy improvement on Mini-ImageNet\ndataset with 1000 labeled examples. We also show that our method requires\nsmaller batch size and fewer training iterations to reach its best performance.\nWe make our code available at https://github.com/islam-nassar/semco.\n"}
+{"abstract": "  In many indigenous societies, people are categorised into several cultural\ngroups, or clans, within which they believe to share ancestors. Clan\nattributions provide certain rules for marriage and descent. Such rules between\nclans constitute kinship structures. Anthropologists have revealed several\nkinship structures. Here, we propose an agent-based model of indigenous\nsocieties to reveal the evolution of kinship structures. In the model, several\nsocieties compete. Societies themselves comprise multiple families with\nparameters for cultural traits and mate preferences. These values determine\nwith whom each family cooperates and competes and are transmitted to a new\ngeneration with mutation. The growth rate of each family is determined by the\nnumber of cooperators and competitors. Through this multi-level evolution,\nfamily traits and preferences diverge to form clusters that can be regarded as\nclans. Subsequently, kinship structures emerge, including dual organisation and\ngeneralised or restricted exchange, as well as patrilineal, matrilineal, and\ndouble descent systems. These structures emerge depending on the necessity of\ncooperation and the strength of mating competition. Their dependence is also\nestimated analytically. Finally, statistical analysis using the Standard\nCross-Cultural Sample, a global ethnographic database, empirically verified\ntheoretical results. Such collaboration between theoretical and empirical\napproaches will unveil universal features in anthropology.\n"}
+{"abstract": "  The univariate generalized extreme value (GEV) distribution is the most\ncommonly used tool for analysing the properties of rare events. The ever\ngreater utilization of Bayesian methods for extreme value analysis warrants\ndetailed theoretical investigation, which has thus far been underdeveloped.\nEven the most basic asymptotic results are difficult to obtain because the GEV\nfails to satisfy standard regularity conditions. Here, we prove that the\nposterior distribution of the GEV parameter vector, given an independent and\nidentically distributed sequence of observations, converges to a normal\ndistribution centred at the true parameter. The proof necessitates analysing\nintegrals of the GEV likelihood function over the entire parameter space, which\nrequires considerable care because the support of the GEV density depends on\nthe parameters in complicated ways.\n"}
+{"abstract": "  A family of subsets of $[n]$ is intersecting if every pair of its sets\nintersects. Determining the structure of large intersecting families is a\ncentral problem in extremal combinatorics. Frankl-Kupavskii and\nBalogh-Das-Liu-Sharifzadeh-Tran independently showed that for $n\\geq 2k +\nc\\sqrt{k\\ln k}$, almost all $k$-uniform intersecting families are stars.\nImproving their result, we show that the same conclusion holds for $n\\geq 2k+\n100\\ln k$. Our proof uses, among others, Sapozhenko's graph container lemma and\nthe Das-Tran removal lemma.\n"}
+{"abstract": "  In this paper, we propose multi-input multi-output (MIMO) beamforming designs\ntowards joint radar sensing and multi-user communications. We employ the\nCram\\'er-Rao bound (CRB) as a performance metric of target estimation, under\nboth point and extended target scenarios. We then propose minimizing the CRB of\nradar sensing while guaranteeing a pre-defined level of\nsignal-to-interference-plus-noise ratio (SINR) for each communication user. For\nthe single-user scenario, we derive a closed form for the optimal solution for\nboth cases of point and extended targets. For the multi-user scenario, we show\nthat both problems can be relaxed into semidefinite programming by using the\nsemidefinite relaxation approach, and prove that the global optimum can always\nbe obtained. Finally, we demonstrate numerically that the globally optimal\nsolutions are reachable via the proposed methods, which provide significant\ngains in target estimation performance over state-of-the-art benchmarks.\n"}
+{"abstract": "  We report on the transport study of a double quantum dot (DQD) device made\nfrom a freestanding, single crystalline InSb nanosheet. The freestanding\nnanosheet is grown by molecular beam epitaxy and the DQD is defined by top gate\ntechnique. Through the transport measurements, we demonstrate how a single\nquantum dot (QD) and a DQD can be defined in an InSb nanosheet by tuning\nvoltages applied to the top gates. We also measure the charge stability\ndiagrams of the DQD and show that the charge states and the inter-dot coupling\nbetween the two individual QDs in the DQD can be efficiently regulated by the\ntop gates. Numerical simulations for the potential profile and charge density\ndistribution in the DQD have been performed and the results support the\nexperimental findings and provide a better understanding of fabrication and\ntransport characteristics of the DQD in the InSb nanosheet. The achieved DQD in\nthe two-dimensional InSb nanosheet possesses pronounced benefits in lateral\nscaling and can thus serve as a new building block for developments of quantum\ncomputation and quantum simulation technologies.\n"}
+{"abstract": "  It has previously been shown that 2D spectral mammography can be used to\ndiscriminate between (likely benign) cystic and (potentially malignant) solid\nlesions in order to reduce unnecessary recalls in mammography. One limitation\nof the technique is, however, that the composition of overlapping tissue needs\nto be interpolated from a region surrounding the lesion. The purpose of this\ninvestigation was to demonstrate that lesion characterization can be done with\nspectral tomosynthesis, and to investigate whether the 3D information available\nin tomosynthesis can reduce the uncertainty from the interpolation of\nsurrounding tissue. A phantom experiment was designed to simulate a cyst and a\ntumor, where the tumor was overlaid with a structure that made it mimic a cyst.\nIn 2D, the two targets appeared similar in composition, whereas spectral\ntomosynthesis revealed the exact compositional difference. However, the loss of\ndiscrimination signal due to spread from the plane of interest was of the same\nstrength as the reduction of anatomical noise. Results from a preliminary\ninvestigation on clinical tomosynthesis images of solid lesions yielded results\nthat were consistent with the phantom experiments, but were still to some\nextent inconclusive. We conclude that lesion characterization is feasible in\nspectral tomosynthesis, but more data, as well as refinement of the calibration\nand discrimination algorithms, are needed to draw final conclusions about the\nbenefit compared to 2D.\n"}
+{"abstract": "  Classification, a heavily-studied data-driven machine learning task, drives\nan increasing number of prediction systems involving critical human decisions\nsuch as loan approval and criminal risk assessment. However, classifiers often\ndemonstrate discriminatory behavior, especially when presented with biased\ndata. Consequently, fairness in classification has emerged as a high-priority\nresearch area. Data management research is showing an increasing presence and\ninterest in topics related to data and algorithmic fairness, including the\ntopic of fair classification. The interdisciplinary efforts in fair\nclassification, with machine learning research having the largest presence,\nhave resulted in a large number of fairness notions and a wide range of\napproaches that have not been systematically evaluated and compared. In this\npaper, we contribute a broad analysis of 13 fair classification approaches and\nadditional variants, over their correctness, fairness, efficiency, scalability,\nrobustness to data errors, sensitivity to underlying ML model, data efficiency,\nand stability using a variety of metrics and real-world datasets. Our analysis\nhighlights novel insights on the impact of different metrics and high-level\napproach characteristics on different aspects of performance. We also discuss\ngeneral principles for choosing approaches suitable for different practical\nsettings, and identify areas where data-management-centric solutions are likely\nto have the most impact.\n"}
+{"abstract": "  Cyber attacks constitute a significant threat to organizations with\nimplications ranging from economic, reputational, and legal consequences. As\ncybercriminals' techniques get sophisticated, information security\nprofessionals face a more significant challenge to protecting information\nsystems. In today's interconnected realm of computer systems, each attack\nvector has a network dimension. The present study investigates network\nintrusion attempts with anomaly-based machine learning models to provide better\nprotection than the conventional misuse-based models. Two models, namely an\nensemble learning model and a convolutional neural network model, were built\nand implemented on a data set gathered from a real-life, institutional\nproduction environment. To demonstrate the models' reliability and validity,\nthey were applied to the UNSW-NB15 benchmarking data set. The type of attack\nwas limited to probing attacks to keep the scope of the study manageable. The\nfindings revealed high accuracy rates, the CNN model being slightly more\naccurate.\n"}
+{"abstract": "  Active learning is a decision-making process. In both abstract and physical\nsettings, active learning demands both analysis and action. This is a review of\nactive learning in robotics, focusing on methods amenable to the demands of\nembodied learning systems. Robots must be able to learn efficiently and\nflexibly through continuous online deployment. This poses a distinct set of\ncontrol-oriented challenges -- one must choose suitable measures as objectives,\nsynthesize real-time control, and produce analyses that guarantee performance\nand safety with limited knowledge of the environment or robot itself. In this\nwork, we survey the fundamental components of robotic active learning systems.\nWe discuss classes of learning tasks that robots typically encounter, measures\nwith which they gauge the information content of observations, and algorithms\nfor generating action plans. Moreover, we provide a variety of examples -- from\nenvironmental mapping to nonparametric shape estimation -- that highlight the\nqualitative differences between learning tasks, information measures, and\ncontrol techniques. We conclude with a discussion of control-oriented open\nchallenges, including safety-constrained learning and distributed learning.\n"}
+{"abstract": "  In this paper we propose a novel observer to solve the problem of visual\nsimultaneous localization and mapping (SLAM), only using the information from a\nsingle monocular camera and an inertial measurement unit (IMU). The system\nstate evolves on the manifold $SE(3)\\times \\mathbb{R}^{3n}$, on which we design\ndynamic extensions carefully in order to generate an invariant foliation, such\nthat the problem is reformulated into online \\emph{constant parameter}\nidentification. Then, following the recently introduced parameter\nestimation-based observer (PEBO) and the dynamic regressor extension and mixing\n(DREM) procedure, we provide a new simple solution. A notable merit is that the\nproposed observer guarantees almost global asymptotic stability requiring\nneither persistency of excitation nor uniform complete observability, which,\nhowever, are widely adopted in most existing works with guaranteed stability.\n"}
+{"abstract": "  PAC-Bayesian bounds are known to be tight and informative when studying the\ngeneralization ability of randomized classifiers. However, when applied to some\nfamily of deterministic models such as neural networks, they require a loose\nand costly derandomization step. As an alternative to this step, we introduce\nnew PAC-Bayesian generalization bounds that have the originality to provide\ndisintegrated bounds, i.e., they give guarantees over one single hypothesis\ninstead of the usual averaged analysis. Our bounds are easily optimizable and\ncan be used to design learning algorithms. We illustrate the interest of our\nresult on neural networks and show a significant practical improvement over the\nstate-of-the-art framework.\n"}
+{"abstract": "  The functionalization of single-walled carbon nanotubes (SWCNTs) with\nluminescent sp$^{3}$ defects has greatly improved their performance in\napplications such as quantum light sources and bioimaging. Here, we report the\ncovalent functionalization of purified semiconducting SWCNTs with stable\norganic radicals (perchlorotriphenylmethyl, PTM) carrying a net spin. This\nmodel system allows us to use the near-infrared photoluminescence arising from\nthe defect-localized exciton as a highly sensitive probe for the short-range\ninteraction between the PTM radical and the SWCNT. Our results point toward an\nincreased triplet exciton population due to radical-enhanced intersystem\ncrossing, which could provide access to the elusive triplet manifold in SWCNTs.\nFurthermore, this simple synthetic route to spin-labeled defects could enable\nmagnetic resonance studies complementary to in vivo fluorescence imaging with\nfunctionalized SWCNTs and facilitate the scalable fabrication of spintronic\ndevices with magnetically switchable charge transport.\n"}
+{"abstract": "  Spike-timing-dependent plasticity(STDP) is a biological process of synaptic\nmodification caused by the difference of firing order and timing between\nneurons. One of the neurodynamical roles of STDP is to form a macroscopic\ngeometrical structure in the neuronal state space in response to a periodic\ninput. This work proposes a practical memory model based on STDP that can store\nand retrieve high-dimensional associative data. The model combines STDP\ndynamics with an encoding scheme for distributed representations and can handle\nmultiple composite data in a continuous manner. In the auto-associative memory\ntask where a group of images is continuously streamed to the model, the images\nare successfully retrieved from an oscillating neural state whenever a proper\ncue is given. In the second task that deals with semantic memories embedded\nfrom sentences, the results show that words can recall multiple sentences\nsimultaneously or one exclusively, depending on their grammatical relations.\n"}
+{"abstract": "  As cameras are increasingly deployed in new application domains such as\nautonomous driving, performing 3D object detection on monocular images becomes\nan important task for visual scene understanding. Recent advances on monocular\n3D object detection mainly rely on the ``pseudo-LiDAR'' generation, which\nperforms monocular depth estimation and lifts the 2D pixels to pseudo 3D\npoints. However, depth estimation from monocular images, due to its poor\naccuracy, leads to inevitable position shift of pseudo-LiDAR points within the\nobject. Therefore, the predicted bounding boxes may suffer from inaccurate\nlocation and deformed shape. In this paper, we present a novel neighbor-voting\nmethod that incorporates neighbor predictions to ameliorate object detection\nfrom severely deformed pseudo-LiDAR point clouds. Specifically, each feature\npoint around the object forms their own predictions, and then the ``consensus''\nis achieved through voting. In this way, we can effectively combine the\nneighbors' predictions with local prediction and achieve more accurate 3D\ndetection. To further enlarge the difference between the foreground region of\ninterest (ROI) pseudo-LiDAR points and the background points, we also encode\nthe ROI prediction scores of 2D foreground pixels into the corresponding\npseudo-LiDAR points. We conduct extensive experiments on the KITTI benchmark to\nvalidate the merits of our proposed method. Our results on the bird's eye view\ndetection outperform the state-of-the-art performance by a large margin,\nespecially for the ``hard'' level detection.\n"}
+{"abstract": "  The singlet pseudoscalar sector of light mesons is investigated in the\nsoft-wall model, a bottom-up approach to the AdS/QCD correspondence. The\n$\\eta^\\prime$ mass results from the mixing among the fields dual to the axial\ncurrent, pseudoscalar current and the $G\\tilde G$ operator. The topological\nsusceptibility is computed for any quark mass, and the Witten-Veneziano\nrelation is obtained in the large $N_c$ limit.\n"}
+{"abstract": "  We give a proof of a recent combinatorial conjecture due to the first author,\nwhich was discovered in the framework of commutative algebra. This result gives\nrise to new companions to the famous Andrews-Gordon identities. Our tools\ninvolve graded quotient rings, Durfee squares and rectangles for integer\npartitions, and $q$-series identities.\n"}
+{"abstract": "  What is the difference between goal-directed and habitual behavior? We\npropose a novel computational framework of decision making with Bayesian\ninference, in which everything is integrated as an entire neural network model.\nThe model learns to predict environmental state transitions by self-exploration\nand generating motor actions by sampling stochastic internal states ${z}$.\nHabitual behavior, which is obtained from the prior distribution of ${z}$, is\nacquired by reinforcement learning. Goal-directed behavior is determined from\nthe posterior distribution of ${z}$ by planning, using active inference which\noptimizes the past, current and future ${z}$ by minimizing the variational free\nenergy for the desired future observation constrained by the observed sensory\nsequence. We demonstrate the effectiveness of the proposed framework by\nexperiments in a sensorimotor navigation task with camera observations and\ncontinuous motor actions.\n"}
+{"abstract": "  We explore a method of encoding secret messages using factoradic numbering of\npermuted lists of text or numeric elements. Encoding and decoding methods are\nprovided, with code, and key aspects of the correctness of the methods are\nformally proven. The method of encoding is simple and provides a working\nexample of using textual and numeric lists as a stenagographic channel. Given\nthe ubiquity of lists, such channels are already present but are often unused.\n"}
+{"abstract": "  In this paper, we consider a problem of Lang about finiteness of torsion\npoints on plane rational curves, and prove some results towards a matrix\nanalogue of this problem, including a full analogue for $2\\times 2$ matrices\ndefined over $\\mathbb{C}$.\n"}
+{"abstract": "  Purpose: Image classification may be the fundamental task in imaging\nartificial intelligence. We have recently shown that reinforcement learning can\nachieve high accuracy for lesion localization and segmentation even with\nminuscule training sets. Here, we introduce reinforcement learning for image\nclassification. In particular, we apply the approach to normal vs.\ntumor-containing 2D MRI brain images.\n  Materials and Methods: We applied multi-step image classification to allow\nfor combined Deep Q learning and TD(0) Q learning. We trained on a set of 30\nimages (15 normal and 15 tumor-containing). We tested on a separate set of 30\nimages (15 normal and 15 tumor-containing). For comparison, we also trained and\ntested a supervised deep-learning classification network on the same set of\ntraining and testing images.\n  Results: Whereas the supervised approach quickly overfit the training data\nand as expected performed poorly on the testing set (57% accuracy, just over\nrandom guessing), the reinforcement learning approach achieved an accuracy of\n100%.\n  Conclusion: We have shown a proof-of-principle application of reinforcement\nlearning to the classification of brain tumors. We achieved perfect testing set\naccuracy with a training set of merely 30 images.\n"}
+{"abstract": "  We present the beta functions of gauge and Yukawa couplings in general\nfour-dimensional quantum field theory, at four and three loops, respectively.\nThe essence of our approach is fixing unknown coefficients in the most general\nansatz for beta-functions by direct calculation in several simplified models.\nWe apply our results to the Standard Model and its extension with an arbitrary\nnumber of Higgs doublets and provide expressions for all four-loop gauge\ncouplings beta-functions with matrix Yukawa interactions.\n"}
+{"abstract": "  In light of a gap found by Krupi\\'{n}ski, we give a new proof of\nassociativity for the Morley (or \"nonforking\") product of invariant measures in\nNIP theories.\n"}
+{"abstract": "  The study of complex oxides and oxide heterostructures have dominated the\nfield of experimental and theoretical condensed matter research for the better\npart of the last few decades. Powerful experimental techniques like molecular\nbeam epitaxy and pulsed laser deposition have made fabrication of oxide\nheterostructures with atomically sharp interfaces possible, while more and more\nsophisticated handling of exchange and correlations within first principles\nmethods including density functional theory (DFT) supplemented with Hubbard U\ncorrections and hybrid functionals, and beyond DFT techniques like dynamical\nmean field theory (DMFT) have made understanding of such correlated oxides and\noxide interfaces easier. The emergence of the high mobility two dimensional\nelectron gas with fascinating properties like giant photoconductance, large\nnegative magnetoresistance, superconductivity, ferromagnetism, and the\nmysterious coexistence of the latter two have indeed caught the attention of\ncondensed matter community at large. Similarly strain tuning of oxides have\ngenerated considerable interest particularly after the recent discovery of\npiezoelectric methods of strain generation. Theoretical understanding and\nprediction of the possible exotic phases emerging in such complex oxides both\nunder strain and in heterostructures will eventually lead to better design of\ndevice applications in this new emerging field of oxide electronics, along with\npossible discovery of exotic physics in condensed matter systems which may be\nof wider significance! In this review we briefly look at theoretical studies of\nnovel phenomena in oxides under strain and oxide heterostructures, and try to\nunderstand the role of exchange and particularly correlation in giving rise to\nsuch exotic electronic states.\n"}
+{"abstract": "  The manpower scheduling problem is a kind of critical combinational\noptimization problem. Researching solutions to scheduling problems can improve\nthe efficiency of companies, hospitals, and other work units. This paper\nproposes a new model combined with deep learning to solve the multi-shift\nmanpower scheduling problem based on the existing research. This model first\nsolves the objective function's optimized value according to the current\nconstraints to find the plan of employee arrangement initially. It will then\nuse the scheduling table generation algorithm to obtain the scheduling result\nin a short time. Moreover, the most prominent feature we propose is that we\nwill use the neural network training method based on the time series to solve\nlong-term and long-period scheduling tasks and obtain manpower arrangement. The\nselection criteria of the neural network and the training process are also\ndescribed in this paper. We demonstrate that our model can make a precise\nforecast based on the improvement of neural networks. This paper also discusses\nthe challenges in the neural network training process and obtains enlightening\nresults after getting the arrangement plan. Our research shows that neural\nnetworks and deep learning strategies have the potential to solve similar\nproblems effectively.\n"}
+{"abstract": "  In this paper we study the identifiability of specific forms (symmetric\ntensors), with the target of extending recent methods for the case of $3$\nvariables to more general cases. In particular, we focus on forms of degree $4$\nin $5$ variables. By means of tools coming from classical algebraic geometry,\nsuch as Hilbert function, liaison procedure and Serre's construction, we give a\ncomplete geometric description and criteria of identifiability for ranks $\\geq\n9$, filling the gap between rank $\\leq 8$, covered by Kruskal's criterion, and\n$15$, the rank of a general quartic in $5$ variables. For the case $r=12$, we\nconstruct an effective algorithm that guarantees that a given decomposition is\nunique.\n"}
+{"abstract": "  The jamming transition is accompanied by a rich phenomenology, such as\nhysteresis or non-local effects, which is still not well understood. Here we\nexperimentally investigate a model frictionless granular layer flowing down an\ninclined plane, as a way to disentangle generic collective effects from those\narising from frictional interactions. We find that thin frictionless granular\nlayers are devoid of hysteresis, yet the layer stability is increased as it\ngets thinner. Rheological laws obtained for different layer thicknesses can be\ncollapsed into a unique master curve, supporting that non-local effects are the\nconsequence of the usual finite-size effects associated to the presence of a\ncritical point. This collapse indicates that the so-called isostatic length\n$l^*$ governs the effect of boundaries on flow, and rules out other\npropositions made in the past.\n"}
+{"abstract": "  In some short gamma-ray bursts, precursor flares occurring $\\sim$ seconds\nprior to the main episode have been observed. These flares may then be\nassociated with the last few cycles of the inspiral when the orbital frequency\nis a few hundred Hz. During these final cycles, tidal forces can resonantly\nexcite quasi-normal modes in the inspiralling stars, leading to a rapid\nincrease in their amplitude. It has been shown that these modes can exert\nsufficiently strong strains onto the neutron star crust to instigate yieldings.\nDue to the typical frequencies of $g$-modes being $\\sim 100\\text{ Hz}$, their\nresonances with the orbital frequency match the precursor timings and warrant\nfurther investigation. Adopting realistic equations of state and solving the\ngeneral-relativistic pulsation equations, we study $g$-mode resonances in\ncoalescing quasi-circular binaries, where we consider various stellar rotation\nrates, degrees of stratification, and magnetic field structures. We show that\nfor some combination of stellar parameters, the resonantly excited $g_1$- and\n$g_2$-modes may lead to crustal failure and trigger precursor flares.\n"}
+{"abstract": "  Ellerman bombs are regions with enhanced Balmer line wing emission and mark\nmagnetic reconnection in the deep solar atmosphere in active regions and quiet\nSun. They are often found in regions where opposite magnetic polarities are in\nclose proximity. Recent high resolution observations suggest that Ellerman\nbombs are more prevalent than thought before. We aim to determine the\noccurrence of Ellerman bombs in the penumbra of sunspots. We analyze high\nspatial resolution observations of sunspots in the Balmer H-alpha and H-beta\nlines as well as auxiliary continuum channels obtained with the Swedish 1-m\nSolar Telescope and apply the k-means clustering technique to systematically\ndetect and characterize Ellerman Bombs. Features with all the defining\ncharacteristics of Ellerman bombs are found in large numbers over the entire\npenumbra. The true prevalence of these events is only fully appreciated in the\nH-beta line due to highest spatial resolution and lower chromospheric opacity.\nWe find that the penumbra hosts some of the highest Ellerman bomb densities,\nonly surpassed by the moat in the immediate surroundings of the sunspot. Some\npenumbral Ellerman bombs show flame morphology and rapid dynamical evolution.\nMany penumbral Ellerman bombs are fast moving with typical speed of 3.7 km/s\nand sometimes more than 10 km/s. Many penumbral Ellerman bombs migrate from the\ninner to the outer penumbra over hundreds of km and some continue moving beyond\nthe outer penumbral boundary into the moat. Many penumbral Ellerman bombs are\nfound in the vicinity of regions with opposite magnetic polarity. We conclude\nthat reconnection is a near continuous process in the low atmosphere of the\npenumbra of sunspots as manifest in the form of penumbral Ellerman bombs. These\nare so prevalent that they may be a major sink of sunspot magnetic energy.\n"}
+{"abstract": "  Spin-orbit coupling is an essential mechanism underlying quantum phenomena\nsuch as the spin Hall effect and topological insulators. It has been widely\nstudied in well-isolated Hermitian systems, but much less is known about the\nrole dissipation plays in spin-orbit-coupled systems. Here, we implement\ndissipative spin-orbit-coupled bands filled with ultracold fermions, and\nobserve parity-time symmetry breaking as a result of the competition between\nthe spin-orbit coupling and dissipation. Tunable dissipation, introduced by\nstate-selective atom loss, enables us to tune the energy gap and close it at\nthe critical dissipation value, the so-called exceptional point. In the\nvicinity of the critical point, the state evolution exhibits a chiral response,\nwhich enables us to tune the spin-orbit coupling and dissipation dynamically,\nrevealing topologically robust chiral spin transfer when the quantum state\nencircles the exceptional point. This demonstrates that we can explore\nnon-Hermitian topological states with spin-orbit coupling.\n"}
+{"abstract": "  We generalize the shuffle theorem and its $(km,kn)$ version, as conjectured\nby Haglund et al. and Bergeron et al., and proven by Carlsson and Mellit, and\nMellit, respectively. In our version the $(km,kn)$ Dyck paths on the\ncombinatorial side are replaced by lattice paths lying under a line segment\nwhose $x$ and $y$ intercepts need not be integers, and the algebraic side is\ngiven either by a Schiffmann algebra operator formula or an equivalent explicit\nraising operator formula.\n  We derive our combinatorial identity as the polynomial truncation of an\nidentity of infinite series of $GL_{l}$ characters, expressed in terms of\ninfinite series versions of LLT polynomials. The series identity in question\nfollows from a Cauchy identity for non-symmetric Hall-Littlewood polynomials.\n"}
+{"abstract": "  We present a new algorithm for estimating the Point Spread Function (PSF) in\nwide-field astronomical images with extreme source crowding. Robust and\naccurate PSF estimation in crowded astronomical images dramatically improves\nthe fidelity of astrometric and photometric measurements extracted from\nwide-field sky monitoring imagery. Our radically new approach utilizes\nconvolutional sparse representations to model the continuous functions involved\nin the image formation. This approach avoids the need to detect and precisely\nlocalize individual point sources that is shared by existing methods. In\nexperiments involving simulated astronomical imagery, it significantly\noutperforms the recent alternative method with which it is compared.\n"}
+{"abstract": "  We report the result of a search for neutrinos in coincidence with solar\nflares from the GOES flare database. The search was performed on a 10.8\nkton-year exposure of KamLAND collected from 2002 to 2019. This large exposure\nallows us to explore previously unconstrained parameter space for solar flare\nneutrinos. We found no statistical excess of neutrinos and established 90%\nconfidence level upper limits of $8.4 \\times 10^7$ cm$^{-2}$ ($3.0 \\times\n10^{9}$ cm$^{-2}$) on electron anti-neutrino (electron neutrino) fluence at 20\nMeV normalized to the X12 flare, assuming that the neutrino fluence is\nproportional to the X-ray intensity.\n"}
+{"abstract": "  In this short review we pass through the milestones in the studies of the\nelectronic topological transitions (ETT) and focus on some recent applications\nof the ideas worked out in their classical theory. These are: two-dimensional\nelectron systems, de Haas-van Alphen effect, classification of ETT in\nmultidimensional systems, superconductivity in systems close to ETT,\nthermoelectricity in heavy-fermion systems, where the cascades of topological\nchanges of Fermi surface (FS) are generated by magnetic field. The history of\nstudies of ETT is inextricably linked with Kharkov school of condensed matter\nphysics, with such names as I.M. Lifshitz, V.G.Bar'yakhtar and many other.\nAmong them is Moisey Isaakovich Kaganov, who contributed much in studies of the\nrole of geometry and topology of FS in physical properties of the metals. Two\nof the authors (A.V. and Y.G.) had a honor and pleasure to work with \"Musik\",\nas all friends called Kaganov; all of us have been learning the niceties of\nscience from his books. \"The Fermi surface is the stage on which the drama of\nthe life of the electron is played out\" wrote Kaganov and Lifshitz. We devote\nthis work to their memory.\n"}
+{"abstract": "  Here we provide the most comprehensive determinations of the rest-frame $UV$\nLF available to date with HST at z~2, 3, 4, 5, 6, 7, 8, and 9. Essentially all\nof the non-cluster extragalactic legacy fields are utilized, including the\nHubble Ultra Deep Field (HUDF), the Hubble Frontier Field parallel fields, and\nall five CANDELS fields, for a total survey area of 1136 arcmin^2. Our\ndeterminations include galaxies at z~2-3 leveraging the deep HDUV, UVUDF, and\nERS WFC3/UVIS observations available over a ~150 arcmin^2 area in the GOODS\nNorth and GOODS South regions. All together, our collective samples include\n>24,000 sources, >2.3x larger than previous selections with HST. 5766, 6332,\n7240, 3449, 1066, 601, 246, and 33 sources are identified at z~2, 3, 4, 5, 6,\n7, 8, and 9, respectively. Combining our results with an earlier z~10 LF\ndetermination by Oesch+2018a, we quantify the evolution of the $UV$ LF. Our\nresults indicate that there is (1) a smooth flattening of the faint-end slope\nalpha from alpha~-2.4 at z~10 to -1.5 at z~2, (2) minimal evolution in the\ncharacteristic luminosity M* at z>~2.5, and (3) a monotonic increase in the\nnormalization log_10 phi* from z~10 to z~2, which can be well described by a\nsimple second-order polynomial, consistent with an \"accelerated\" evolution\nscenario. We find that each of these trends (from z~10 to z~2.5 at least) can\nbe readily explained on the basis of the evolution of the halo mass function\nand a simple constant star formation efficiency model.\n"}
+{"abstract": "  This paper is devoted to the analysis of Steklov eigenvalues and Steklov\neigenfunctions on a class of warped product Riemannian manifolds $(M,g)$ whose\nboundary $\\partial M$ consists in two distinct connected components $\\Gamma_0$\nand $\\Gamma_1$. First, we show that the Steklov eigenvalues can be divided into\ntwo families $(\\lambda_m^\\pm)_{m \\geq 0}$ which satisfy accurate asymptotics as\n$m \\to \\infty$. Second, we consider the associated Steklov eigenfunctions which\nare the harmonic extensions of the boundary Dirichlet to Neumann\neigenfunctions. In the case of symmetric warped product, we prove that the\nSteklov eigenfunctions are exponentially localized on the whole boundary\n$\\partial M$ as $m \\to \\infty$. Whenever we add an asymmetric perturbation to a\nsymmetric warped product, we observe a flea on the elephant effect. Roughly\nspeaking, we prove that \"half\" the Steklov eigenfunctions are exponentially\nlocalized on one connected component of the boundary, say $\\Gamma_0$, and the\nother half on the other connected component $\\Gamma_1$ as $m \\to \\infty$.\n"}
+{"abstract": "  Having numerous potential applications and great impact, end-to-end speech\ntranslation (ST) has long been treated as an independent task, failing to fully\ndraw strength from the rapid advances of its sibling - text machine translation\n(MT). With text and audio inputs represented differently, the modality gap has\nrendered MT data and its end-to-end models incompatible with their ST\ncounterparts. In observation of this obstacle, we propose to bridge this\nrepresentation gap with Chimera. By projecting audio and text features to a\ncommon semantic representation, Chimera unifies MT and ST tasks and boosts the\nperformance on ST benchmarks, MuST-C and Augmented Librispeech, to a new\nstate-of-the-art. Specifically, Chimera obtains 27.1 BLEU on MuST-C EN-DE,\nimproving the SOTA by a +1.9 BLEU margin. Further experimental analyses\ndemonstrate that the shared semantic space indeed conveys common knowledge\nbetween these two tasks and thus paves a new way for augmenting training\nresources across modalities. Code, data, and resources are available at\nhttps://github.com/Glaciohound/Chimera-ST.\n"}
+{"abstract": "  The novel coronavirus disease (COVID-19) pneumonia has posed a great threat\nto the world recent months by causing many deaths and enormous economic damage\nworldwide. The first case of COVID-19 in Morocco was reported on 2 March 2020,\nand the number of reported cases has increased day by day. In this work, we\nextend the well-known SIR compartmental model to deterministic and stochastic\ntime-delayed models in order to predict the epidemiological trend of COVID-19\nin Morocco and to assess the potential role of multiple preventive measures and\nstrategies imposed by Moroccan authorities. The main features of the work\ninclude the well-posedness of the models and conditions under which the\nCOVID-19 may become extinct or persist in the population. Parameter values have\nbeen estimated from real data and numerical simulations are presented for\nforecasting the COVID-19 spreading as well as verification of theoretical\nresults.\n"}
+{"abstract": "  OCTO-TIGER is an astrophysics code to simulate the evolution of\nself-gravitating and rotat-ing systems of arbitrary geometry based on the fast\nmultipole method, using adaptive mesh refinement. OCTO-TIGER is currently\noptimised to simulate the merger of well-resolved stars that can be\napproximated by barotropic structures, such as white dwarfs or main sequence\nstars. The gravity solver conserves angular momentum to machine precision,\nthanks to a correction algorithm. This code uses HPX parallelization, allowing\nthe overlap of work and communication and leading to excellent scaling\nproperties, allowing for the computation of large problems in reasonable\nwall-clock times. In this paper, we investigate the code performance and\nprecision by running benchmarking tests. These include simple problems, such as\nthe Sod shock tube, as well as sophisticated, full, white-dwarf binary\nsimulations. Results are compared to analytic solutions, when known, and to\nother grid based codes such as FLASH. We also compute the interaction between\ntwo white dwarfs from the early mass transfer through to the merger and compare\nwith past simulations of similar systems. We measure OCTO-TIGERs scaling\nproperties up to a core count of 80,000, showing excellent performance for\nlarge problems. Finally, we outline the current and planned areas of\ndevelopment aimed at tackling a number of physical phenomena connected to\nobservations of transients.\n"}
+{"abstract": "  Debris disks are tenuous, dusty belts surrounding main sequence stars\ngenerated by collisions between planetesimals. HD 206893 is one of only two\nstars known to host a directly imaged brown dwarf orbiting interior to its\ndebris ring, in this case at a projected separation of 10.4 au. Here we resolve\nstructure in the debris disk around HD 206893 at an angular resolution of 0.6\"\n(24 au) and wavelength of 1.3 mm with the Atacama Large\nMillimeter/submillimeter Array (ALMA). We observe a broad disk extending from a\nradius of <51 au to 194^{+13}_{-2} au. We model the disk with a continuous,\ngapped, and double power-law model of the surface density profile, and find\nstrong evidence for a local minimum in the surface density distribution near a\nradius of 70 au, consistent with a gap in the disk with an inner radius of\n63^{+8}_{-16} au and width 31^{+11}_{-7} au. Gapped structure has been observed\nin four other debris disks -- essentially every other radially resolved debris\ndisk observed with sufficient angular resolution and sensitivity with ALMA --\nand could be suggestive of the presence of an additional planetary-mass\ncompanion.\n"}
+{"abstract": "  In conjunction with a previous southern-hemisphere work, we present the\nlargest radio survey of jets from massive protostars to date with\nhigh-resolution, ($\\sim 0.04^{\\prime\\prime}$) VLA observations towards two\nsubsamples of massive star-forming regions of different evolutionary statuses:\n48 infrared-bright, massive YSOs and 8 IRDCs containing 16 luminous (${\\rm\nL_{bol}}>10^3\\,{\\rm L_\\odot}$) cores. For $94\\%$ of the MYSO sample we detect\nthermal radio ($\\alpha \\geq -0.1$ whereby $S_\\nu \\propto \\nu^\\alpha$) sources\ncoincident with the protostar, of which $84\\%$ (13 jets and 25 candidates) are\njet-like. Radio luminosity is found to scale with ${\\rm L_{bol}}$ similarly to\nthe low-mass case supporting a common mechanism for jet production across all\nmasses. Associated radio lobes tracing shocks are seen towards $52\\%$ of\njet-like objects and are preferentially detected towards jets of higher radio\nand bolometric luminosities, resulting from our sensitivity limitations. We\nfind jet mass loss rate scales with bolometric luminosity as $\\dot{m}_{\\rm jet}\n\\propto {\\rm L_{bol}}^{0.9\\pm0.2}$, thereby discarding radiative, line-driving\nmechanisms as the dominant jet-launching process. Calculated momenta show that\nthe majority of jets are mechanically capable of driving the massive, molecular\noutflow phenomena since $p_{\\rm jet}>p_{\\rm outflow}$. Finally, from their\nphysical extent we show that the radio emission can not originate from small,\noptically-thick \\textsc{Hii} regions. Towards the IRDC cores, we observe\nincreasing incidence rates/radio fluxes with age using the proxy of increasing\nluminosity-to-mass $\\left( \\frac{L}{M} \\right)$ and decreasing infrared flux\nratios $\\left(\\frac{S_{70 \\rm \\mu m}}{S_{24 \\rm \\mu m}}\\right)$. Cores with\n$\\frac{L}{M}<40\\,{\\rm L_{sol}}{\\rm M_{sol}}^{-1}$ are not detected above\n($5.8{\\rm GHz}$) radio luminosities of $\\sim1{\\rm mJy\\,kpc}^2$.\n"}
+{"abstract": "  We consider the problem of driving a finite-state classical system from some\ninitial distribution $p$ to some final distribution $p'$ with vanishing entropy\nproduction (EP), under the constraint that the driving protocols can only use\nsome limited set of energy functions $\\mathcal{E}$. Assuming no other\nconstraints on the driving protocol, we derive a simple condition that\nguarantees that such a transformation can be carried out, which is stated in\nterms of the smallest probabilities in $\\{p,p'\\}$ and a graph-theoretic\nproperty defined in terms of $\\mathcal{E}$. Our results imply that a\nsurprisingly small amount of control over the energy function is sufficient (in\nparticular, any transformation $p\\to p'$ can be carried out as soon as one can\ncontrol some one-dimensional parameter of the energy function, e.g., the energy\nof a single state). We also derive a lower bound on the EP under more general\nconstraints on the transition rates, which is formulated in terms of a convex\noptimization problem.\n"}
+{"abstract": "  We benchmark the quantum processing units of the largest quantum annealers to\ndate, the 5000+ qubit quantum annealer Advantage and its 2000+ qubit\npredecessor D-Wave 2000Q, using tail assignment and exact cover problems from\naircraft scheduling scenarios. The benchmark set contains small, intermediate,\nand large problems with both sparsely connected and almost fully connected\ninstances. We find that Advantage outperforms D-Wave 2000Q for almost all\nproblems, with a notable increase in success rate and problem size. In\nparticular, Advantage is also able to solve the largest problems with 120\nlogical qubits that D-Wave 2000Q cannot solve anymore. Furthermore, problems\nthat can still be solved by D-Wave 2000Q are solved faster by Advantage. We\nfind that D-Wave 2000Q can only achieve better success rates for a few very\nsparsely connected problems.\n"}
+{"abstract": "  This document describes the Generalized Moving Peaks Benchmark (GMPB) that\ngenerates continuous dynamic optimization problem instances. The landscapes\ngenerated by GMPB are constructed by assembling several components with a\nvariety of controllable characteristics ranging from unimodal to highly\nmultimodal, symmetric to highly asymmetric, smooth to highly irregular, and\nvarious degrees of variable interaction and ill-conditioning. In this document,\nwe explain how these characteristics can be generated by different parameter\nsettings of GMPB. The MATLAB source code of GMPB is also explained.\n"}
+{"abstract": "  The Dicke superradiance from an optically thin nanocomposite slab represented\nby metal nanoparticles dispersed in a dielectric matrix is predicted and its\ntheory is developed from first principles. It is shown that the superradiance\nsignal evolution is determined by the eigenvalues of the field susceptibility\ntensor for the slab. The excitation of the system by a pumping pulse in\ndifferent polarizations as well as in the attenuated total reflection\nconfiguration is considered. It is demonstrated that the relaxation rates are\nenhanced when surface plasmon polaritons are excited at the interface between\nthe substrate and superstrate. These findings can pave a way to the extension\nof the optical techniques based on the Dicke superradiance to the femtosecond\ntime domain.\n"}
+{"abstract": "  The classical nova YZ Reticuli was discovered in July 2020. Shortly after\nthis we commenced a sustained, highly time-sampled coverage of its subsequent\nrapid evolution with time-resolved spectroscopy from the Global Jet Watch\nobservatories. Its H-alpha complex exhibited qualitatively different spectral\nsignatures in the following weeks and months. We find that these H-alpha\ncomplexes are well described by the same five Gaussian emission components\nthroughout the six months following eruption. These five components appear to\nconstitute two pairs of lines, from jet outflows and an accretion disc,\ntogether with an additional central component. The correlated, symmetric\npatterns that these jet/accretion disc pairs exhibit suggest precession,\nprobably in response to the large perturbation caused by the nova eruption. The\njet and accretion disc signatures persist from the first ten days after\nbrightening -- evidence that the accretion disc survived the disruption. We\nalso compare another classical nova (V6568 Sgr) that erupted in July 2020 whose\nH-alpha complex can be described analogously, but with faster line-of-sight jet\nspeeds exceeding 4000 km/s. We suggest that classical novae with higher mass\nwhite dwarfs bridge the gap between recurrent novae and classical novae such as\nYZ Reticuli.\n"}
+{"abstract": "  Training deep neural networks on large datasets containing high-dimensional\ndata requires a large amount of computation. A solution to this problem is\ndata-parallel distributed training, where a model is replicated into several\ncomputational nodes that have access to different chunks of the data. This\napproach, however, entails high communication rates and latency because of the\ncomputed gradients that need to be shared among nodes at every iteration. The\nproblem becomes more pronounced in the case that there is wireless\ncommunication between the nodes (i.e. due to the limited network bandwidth). To\naddress this problem, various compression methods have been proposed including\nsparsification, quantization, and entropy encoding of the gradients. Existing\nmethods leverage the intra-node information redundancy, that is, they compress\ngradients at each node independently. In contrast, we advocate that the\ngradients across the nodes are correlated and propose methods to leverage this\ninter-node redundancy to improve compression efficiency. Depending on the node\ncommunication protocol (parameter server or ring-allreduce), we propose two\ninstances of the LGC approach that we coin Learned Gradient Compression (LGC).\nOur methods exploit an autoencoder (i.e. trained during the first stages of the\ndistributed training) to capture the common information that exists in the\ngradients of the distributed nodes. We have tested our LGC methods on the image\nclassification and semantic segmentation tasks using different convolutional\nneural networks (ResNet50, ResNet101, PSPNet) and multiple datasets (ImageNet,\nCifar10, CamVid). The ResNet101 model trained for image classification on\nCifar10 achieved an accuracy of 93.57%, which is lower than the baseline\ndistributed training with uncompressed gradients only by 0.18%.\n"}
+{"abstract": "  By considering the tiling of an $N$-board (a linear array of $N$ square cells\nof unit width) with new types of tile that we refer to as combs, we give a\ncombinatorial interpretation of the product of two consecutive generalized\nFibonacci numbers $s_n$ (where $s_{n}=\\sum_{i=1}^q v_i s_{n-m_i}$, $s_0=1$,\n$s_{n<0}=0$, where $v_i$ and $m_i$ are positive integers and $m_1<\\cdots<m_q$)\neach raised to an arbitrary non-negative integer power. A $(w,g;m)$-comb is a\ntile composed of $m$ rectangular sub-tiles of dimensions $w\\times1$ separated\nby gaps of width $g$. The interpretation is used to give combinatorial proof of\nnew convolution-type identities relating $s_n^2$ for the cases $q=2$, $v_i=1$,\n$m_1=M$, $m_2=m+1$ for $M=0,m$ to the permanent of a (0,1) Toeplitz matrix with\n3 nonzero diagonals which are $-2$, $M-1$, and $m$ above the leading diagonal.\nWhen $m=1$ these identities reduce to ones connecting the Padovan and\nNarayana's cows numbers.\n"}
+{"abstract": "  In this work, we study two different approaches to defining the entropy of a\nquantum channel. One of these is based on the von Neumann entropy of the\ncorresponding Choi-Jamio{\\l}kowski state. The second one is based on the\nrelative entropy of the output of the extended channel relative to the output\nof the extended completely depolarizing channel. This entropy then needs to be\noptimized over all possible input states. Our results first show that the\nformer entropy provides an upper bound on the latter. Next, we show that for\nunital qubit channels, this bound is saturated. Finally, we conjecture and\nprovide numerical intuitions that the bound can also be saturated for random\nchannels as their dimension tends to infinity.\n"}
+{"abstract": "  In 2021, Japan recorded more than three times as much snowfall as usual, so\nroad user maybe come across dangerous situation. The poor visibility caused by\nsnow triggers traffic accidents. For example, 2021 January 19, due to the dry\nsnow and the strong wind speed of 27 m / s, blizzards occurred and the outlook\nhas been ineffective. Because of the whiteout phenomenon, multiple accidents\nwith 17 casualties occurred, and 134 vehicles were stacked up for 10 hours over\n1 km. At the night time zone, the temperature drops and the road surface tends\nto freeze. CCTV images on the road surface have the advantage that we enable to\nmonitor the status of major points at the same time. Road managers are required\nto make decisions on road closures and snow removal work owing to the road\nsurface conditions even at night. In parallel, they would provide road users to\nalert for hazardous road surfaces. This paper propose a method to automate a\nsnow hazard indicator that the road surface region is generated from the night\nsnow image using the Conditional GAN, pix2pix. In addition, the road surface\nand the snow covered ROI are predicted using the semantic segmentation\nDeepLabv3+ with a backbone MobileNet, and the snow hazard indicator to\nautomatically compute how much the night road surface is covered with snow. We\ndemonstrate several results applied to the cold and snow region in the winter\nof Japan January 19 to 21 2021, and mention the usefulness of high similarity\nbetween snowy night-to-day fake output and real snowy day image for night snow\nvisibility.\n"}
+{"abstract": "  Star clusters form via clustering star formation inside molecular clouds. In\norder to understand the dynamical evolution of star clusters in their early\nphase, in which star clusters are still embedded in their surrounding gas, we\nneed an accurate integration of individual stellar orbits without gravitational\nsoftening in the systems including both gas and stars, as well as modeling\nindividual stars with a realistic mass function. We develop a new tree-direct\nhybrid smoothed particle hydrodynamics/N-body code, ASURA+BRIDGE, in which\nstars are integrated using a direct N-body scheme or PeTar, a particle-particle\nparticle-tree scheme code, without gravitational softening. In ASURA+BRIDGE,\nstars are assumed to have masses randomly drawn from a given initial mass\nfunction. With this code, we perform star-cluster formation simulations\nstarting from molecular clouds without gravitational softening. We find that\nartificial dense cores in star-cluster centers due to the softening disappear\nwhen we do not use softening. We further demonstrate that star clusters are\nbuilt up via mergers of smaller clumps. Star clusters formed in our simulations\ninclude some dynamically formed binaries with the minimum semi-major axes of a\nfew au, and the binary fraction is higher for more massive stars.\n"}
+{"abstract": "  Image harmonization has been significantly advanced with large-scale\nharmonization dataset. However, the current way to build dataset is still\nlabor-intensive, which adversely affects the extendability of dataset. To\naddress this problem, we propose to construct a large-scale rendered\nharmonization dataset RHHarmony with fewer human efforts to augment the\nexisting real-world dataset. To leverage both real-world images and rendered\nimages, we propose a cross-domain harmonization network CharmNet to bridge the\ndomain gap between two domains. Moreover, we also employ well-designed style\nclassifiers and losses to facilitate cross-domain knowledge transfer. Extensive\nexperiments demonstrate the potential of using rendered images for image\nharmonization and the effectiveness of our proposed network. Our dataset and\ncode are available at\nhttps://github.com/bcmi/Rendered_Image_Harmonization_Datasets.\n"}
+{"abstract": "  The human aorta is a high-risk area for vascular diseases, which are commonly\nrestored by thoracic endovascular aortic repair. In this paper, we report a\npromising shear-activated targeted nanoparticle drug delivery strategy to\nassist in the treatment of coarctation of the aorta and aortic aneurysm.\nIdealized three-dimensional geometric models of coarctation of the aorta and\naortic aneurysm are designed, respectively. The unique hemodynamic environment\nof the diseased aorta is used to improve nanoparticle drug delivery.\nMicro-carriers with nanoparticle drugs would be targeting activated to release\nnanoparticle drugs by local abnormal shear stress rate (SSR). Coarctation of\nthe aorta provides a high SSR hemodynamic environment, while the aortic\naneurysm is exposed to low SSR. We propose a method to calculate the SSR\nthresholds for the diseased aorta. Results show that the upstream near-wall\narea of the diseased location is an ideal injection location for the\nmicro-carriers, which could be activated by the abnormal SSR. Released\nnanoparticle drugs would be successfully targeted delivered to the aortic\ndiseased wall. Besides, the high diffusivity of the micro-carriers and\nnanoparticle drugs has a significant impact on the surface drug concentrations\nof the diseased aortic walls, especially for aortic aneurysms. This study\npreliminary demonstrates the feasibility of shear-activated targeted\nnanoparticle drug delivery in the treatment of aortic diseases and provides a\ntheoretical basis for developing the drug delivery system and novel therapy.\n"}
+{"abstract": "  Aerodynamic forces render accurate high-speed trajectory tracking with\nquadrotors extremely challenging. These complex aerodynamic effects become a\nsignificant disturbance at high speeds, introducing large positional tracking\nerrors, and are extremely difficult to model. To fly at high speeds, feedback\ncontrol must be able to account for these aerodynamic effects in real-time.\nThis necessitates a modelling procedure that is both accurate and efficient to\nevaluate. Therefore, we present an approach to model aerodynamic effects using\nGaussian Processes, which we incorporate into a Model Predictive Controller to\nachieve efficient and precise real-time feedback control, leading to up to 70%\nreduction in trajectory tracking error at high speeds. We verify our method by\nextensive comparison to a state-of-the-art linear drag model in synthetic and\nreal-world experiments at speeds of up to 14m/s and accelerations beyond 4g.\n"}
+{"abstract": "  We address the question of whether a non-nomological (i.e., anomic)\ninterpretation of the wavefunction is compatible with the quantum formalism.\nAfter clarifying the distinction between ontic, epistemic, nomic and anomic\nmodels we focus our attention on two famous no-go theorems due to Pusey,\nBarrett, and Rudolph (PBR) on the one side and Hardy on the other side which\nforbid the existence of anomic-epistemic models. Moreover, we demonstrate that\nthe so called restricted ontic indifference introduced by Hardy induces new\nconstraints. We show that after modifications the Hardy theorem actually rules\nout all anomic models of the wavefunction assuming only restricted ontic\nindifference and preparation independence.\n"}
+{"abstract": "  The investigation of evaporating liquid drop deposited onto a flat surface is\nof great importance for physical, engineering and medical applications. Novel\nanalytical expressions are proposed to calculate the evaporation rate of\nsessile drop (mass loss per unit surface area per unit time) and total\nevaporation rate (mass loss per unit time). To obtain these results, the H.M.\nMacdonald's solution for a flat wedge was transformed by method of inversion in\na sphere originally developed by J.C. Maxwell in the Treatise on Electricity\nand Magnetism with further derivation the solution for a lens based on\nconsideration given in bipolar coordinates by G.A. Grinberg. These solutions\nare mathematically equivalent to expressions proposed earlier by Yuri O. Popov\n[Phys. Rev. E 71, 036313 (2005)], but, in some cases, probably, the new\nsolutions can be more useful from a computational point of view.\n"}
+{"abstract": "  The ex-ante evaluation of policies using structural econometric models is\nbased on estimated parameters as a stand-in for the true parameters. This\npractice ignores uncertainty in the counterfactual policy predictions of the\nmodel. We develop a generic approach that deals with parametric uncertainty\nusing uncertainty sets and frames model-informed policy-making as a decision\nproblem under uncertainty. The seminal human capital investment model by Keane\nand Wolpin (1997) provides a well-known, influential, and empirically-grounded\ntest case. We document considerable uncertainty in the models's policy\npredictions and highlight the resulting policy recommendations obtained from\nusing different formal rules of decision-making under uncertainty.\n"}
+{"abstract": "  Consider two independent Poisson point processes of unit intensity in the\nEuclidean space of dimension $d$ at least 3. We construct a perfect matching\nbetween the two point sets that is a factor (i.e., an equivariant measurable\nfunction of the point configurations), and with the property that the distance\nbetween a configuration point and its pair has a tail distribution that decays\nas fast as possible, namely, as $b\\exp (-cr^d)$ with suitable constants\n$b,c>0$. Our proof relies on two earlier results: an allocation rule of similar\ntail for a Poisson point process, and a recent theorem that enables one to\nobtain perfect matchings from fractional perfect matchings in our setup.\n"}
+{"abstract": "  We propose a parameter sharing method for Transformers (Vaswani et al.,\n2017). The proposed approach relaxes a widely used technique, which shares\nparameters for one layer with all layers such as Universal Transformers\n(Dehghani et al., 2019), to increase the efficiency in the computational time.\nWe propose three strategies: Sequence, Cycle, and Cycle (rev) to assign\nparameters to each layer. Experimental results show that the proposed\nstrategies are efficient in the parameter size and computational time.\nMoreover, we indicate that the proposed strategies are also effective in the\nconfiguration where we use many training data such as the recent WMT\ncompetition.\n"}
+{"abstract": "  In inland fisheries, transporting fishery resource individuals from a habitat\nto spatially apart habitat(s) has recently been considered for fisheries stock\nmanagement in the natural environment. However, its mathematical optimization,\nespecially finding when and how much of the population should be transported,\nis still a fundamental unresolved issue. We propose a new impulse control\nframework to tackle this issue based on a simple but new stochastic growth\nmodel of individual fishes. The novel growth model governing individuals' body\nweights uses a Wright-Fisher model as a latent driver to reproduce plausible\ngrowth dynamics. The optimization problem is formulated as an impulse control\nproblem of a cost-benefit functional constrained by a degenerate parabolic\nFokker-Planck equation of the stochastic growth dynamics. Because the growth\ndynamics have an observable variable and an unobservable variable (a variable\ndifficult or impossible to observe), we consider both full-information and\npartial-information cases. The latter is more involved but more realistic\nbecause of not explicitly using the unobservable variable in designing the\ncontrols. In both cases, resolving an optimization problem reduces to solving\nthe associated Fokker-Planck and its adjoint equations, the latter being\nnon-trivial. We present a derivation procedure of the adjoint equation and its\ninternal boundary conditions in time to efficiently derive the optimal\ntransporting strategy. We finally provide a demonstrative computational example\nof a transporting problem of Ayu sweetfish (Plecoglossus altivelis altivelis)\nbased on the latest real data set.\n"}
+{"abstract": "  We present a many-body expansion (MBE) formulation and implementation for\nefficient computation of analytical energy gradients from OSV-MP2 theory based\non our earlier work (Zhou et al. J. Chem. Theory Comput. 2020, 16, 196-210).\nThe third-order MBE(3) expansion of OSV-MP2 wavefunction was developed to adopt\nthe orbital-specific clustering and long-range termination schemes, which\navoids term-by-term differentiations of the MBE energy bodies. We achieve\nbetter efficiency by exploiting the algorithmic sparsity that allows to prune\nout insignificant fitting integrals and OSV relaxations. With these\napproximations, the present implementation is benchmarked on a range of\nmolecules that show an economic scaling in the linear and quadratic regimes for\ncomputing MBE(3)-OSV-MP2 amplitude and gradient equations, respectively, and\nyields normal accuracy comparable to the original OSV-MP2 results. The\nMPI-3-based parallelism through shared memory one-sided communication is\nfurther developed for improving parallel scalability and memory accessibility\nby sorting the MBE(3) orbital clusters into independent tasks that are\ndistributed on multiple processes across many nodes, supporting both global and\nlocal data locations in which selected MBE(3)-OSV-MP2 intermediates of\ndifferent sizes are distinguished and accordingly placed. The accuracy and\nefficiency level of our MBE(3)-OSV-MP2 analytical gradient implementation is\nfinally illustrated in two applications: we show that the subtle coordination\nstructure differences of mechanically interlocked Cu-catenane complexes can be\ndistinguished when tuning ligand lengths; and the porphycene molecular dynamics\nreveals the emergence of the vibrational signature arising from softened N-H\nstretching associated with hydrogen transfer, using an MP2 level of electron\ncorrelation and classical nuclei for the first time.\n"}
+{"abstract": "  We provide a brief review of many aspects of the planetary physics of hot\nJupiters. Our aim is to cover most of the major areas of current study while\nproviding the reader with additional references for more detailed follow-up. We\nfirst discuss giant planet formation and subsequent orbital evolution via\ndisk-driven torques or dynamical interactions. More than one formation pathway\nis needed to understand the population. Next, we examine our current\nunderstanding of the evolutionary history and current interior structure of the\nplanets, where we focus on bulk composition as well as viable models to explain\nthe inflated radii of the population. Finally we discuss aspects of their\natmospheres in the context of observations and 1D and 3D models, including\natmospheric structure and escape, spectroscopic signatures, and complex\natmospheric circulation. The major opacity sources in these atmospheres,\nincluding alkali metals, water vapor, and others, are discussed. We discuss\nphysics that control the 3D atmospheric circulation and day-to-night\ntemperature structures. We conclude by suggesting important future work for\nstill-open questions.\n"}
+{"abstract": "  Manual fact-checking does not scale well to serve the needs of the internet.\nThis issue is further compounded in non-English contexts. In this paper, we\ndiscuss claim matching as a possible solution to scale fact-checking. We define\nclaim matching as the task of identifying pairs of textual messages containing\nclaims that can be served with one fact-check. We construct a novel dataset of\nWhatsApp tipline and public group messages alongside fact-checked claims that\nare first annotated for containing \"claim-like statements\" and then matched\nwith potentially similar items and annotated for claim matching. Our dataset\ncontains content in high-resource (English, Hindi) and lower-resource (Bengali,\nMalayalam, Tamil) languages. We train our own embedding model using knowledge\ndistillation and a high-quality \"teacher\" model in order to address the\nimbalance in embedding quality between the low- and high-resource languages in\nour dataset. We provide evaluations on the performance of our solution and\ncompare with baselines and existing state-of-the-art multilingual embedding\nmodels, namely LASER and LaBSE. We demonstrate that our performance exceeds\nLASER and LaBSE in all settings. We release our annotated datasets, codebooks,\nand trained embedding model to allow for further research.\n"}
+{"abstract": "  We show that if $(\\ell_{\\phi_0}, \\ell_{\\phi_1})$ is a couple of suitable\nOrlicz sequence spaces then the corresponding Rochberg derived spaces of all\norders associated to the complex interpolation method are Fenchel-Orlicz\nspaces. In particular, the induced twisted sums have the $(C[0, 1],\n\\mathbb{C})$-extension property.\n"}
+{"abstract": "  The thermodynamic structure of protoplanetary discs is determined by dust\nopacities, which depend on the size of the dust grains and their chemical\ncomposition. In the inner regions, the grain sizes are regulated by the level\nof turbulence (e.g. $\\alpha$ viscosity) and by the dust fragmentation velocity\nthat represents the maximal velocity that grains can have at a collision before\nthey fragment. Here, we perform self-consistently calculated 2D hydrodynamical\nsimulations that consider a full grain size distribution of dust grains with a\ntransition in the dust fragmentation velocity at the water-ice line. This\napproach accounts for the results of previous particle collision laboratory\nexperiments, in which silicate particles typically have a lower dust\nfragmentation velocity than water-ice particles. Furthermore, we probe the\neffects of variations in the water abundance, the dust-to-gas ratio, and the\nturbulence parameter on the disc structure. For the discs with a transition in\nthe dust fragmentation velocity at the water-ice line, we find a narrow but\nstriking zone of planetary outward migration, including for low viscosities. In\naddition, we find a bump in the radial pressure gradient profile that tends to\nbe located slightly inside the ice line. Both of these features are present for\nall tested disc parameters. Thus, we conclude that the ice line can function\nboth as a migration trap, which can extend the growth times of planets before\nthey migrate to the inner edge of the protoplanetary disc, and as a pressure\ntrap, where planetesimal formation can be initiated or enhanced.\n"}
+{"abstract": "  In this work we consider strange stars formed by quark matter in the\ncolor-flavor-locked (CFL) phase of color superconductivity. The CFL phase is\ndescribed by a Nambu-Jona-Lasinio model with four-fermion vector and diquark\ninteraction channels. The effect of the color superconducting medium on the\ngluons are incorporated into the model by including the gluon self-energy in\nthe thermodynamic potential. We construct parametrizations of the model by\nvarying the vector coupling $G_V$ and comparing the results to the data on\ntidal deformability from the GW170817 event, the observational data on maximum\nmasses from massive pulsars such as the MSP J0740+6620, and the mass/radius\nfits to NICER data for PSR J003+0451. Our results points out to windows for the\n$G_V$ parameter space of the model, with and without gluon effects included,\nthat are compatible with all these astrophysical constraints, namely,\n$0.21<G_V/G_S<0.4$, and $0.02<G_V/G_S<0.1$, respectively. We also observe a\nstrong correlation between the tidal deformabilites of the GW170817 event and\n$G_V$. Our results indicate that strange stars cannot be ruled out in\ncollisions of compact binaries from the structural point of view.\n"}
+{"abstract": "  Understanding the electron pairing in hole-doped cuprate superconductors has\nbeen a challenge, in particular because the \"normal\" state from which it\nevolves is unprecedented. Now, after three and a half decades of research,\ninvolving a wide range of experimental characterizations, it is possible to\ndelineate a clear and consistent cuprate story. It starts with doping holes\ninto a charge-transfer insulator, resulting in in-gap states. These states\nexhibit a pseudogap resulting from the competition between antiferromagnetic\nsuperexchange $J$ between nearest-neighbor Cu atoms (a real-space interaction)\nand the kinetic energy of the doped holes, which, in the absence of\ninteractions, would lead to extended Bloch-wave states whose occupancy is\ncharacterized in reciprocal space. To develop some degree of coherence on\ncooling, the spin and charge correlations must self-organize in a cooperative\nfashion. A specific example of resulting emergent order is that of spin and\ncharge stripes, as observed in La$_{2-x}$Ba$_x$CuO$_4$. While stripe order\nfrustrates bulk superconductivity, it nevertheless develops pairing and\nsuperconducting order of an unusual character. The antiphase order of the spin\nstripes decouples them from the charge stripes, which can be viewed as\nhole-doped, two-leg, spin-$\\frac12$ ladders. To achieve superconducting order,\nthe pair correlations in neighboring ladders must develop phase order. In the\npresence of spin stripe order, antiphase Josephson coupling can lead to\npair-density-wave superconductivity. Alternatively, in-phase superconductivity\nrequires that the spin stripes have an energy gap, which empirically limits the\ncoherent superconducting gap. Hence, superconducting order in the cuprates\ninvolves a compromise between the pairing scale, which is maximized at\n$x\\sim\\frac18$, and phase coherence, which is optimized at $x\\sim0.2$.\n"}
+{"abstract": "  A deconstructed finite temperature gauge theory has the Euclidean time\ndirection kept discrete and finite. The ultraviolet behavior is that of one\ndimension less than at zero temperature. One can add to the action a gauge\ninvariant term dependent on Polyakov loops, without worrying about nonlocality.\nThe deformation of {\\\" U}nsal and Yaffe is best analyzed in this framework. It\nis shown that turning it on causes a dramatic change in the Hilbert space on\nwhich the transfer matrix in the temperature direction acts. The undeformed\nHilbert space is everywhere a gauge singlet while the deformed one includes all\nstates transforming locally, anywhere, under any SU(N)/Z(N) irreducible\nrepresentation. Implications of this in the context of large N reduction are\ndiscussed.\n"}
+{"abstract": "  The paper presents a numerical analysis of the class of mathematical models\nof linear fractional oscillators, which is the Cauchy problem for a\ndifferential equation with derivatives of fractional orders in the sense of\nGerasimov-Caputo. A method based on an explicit nonlocal finite-difference\nscheme (ENFDS) and the Adams-Bashfort-Moulton (ABM) method is considered a\nnumerical analysis tool. An analysis of the errors of the methods is carried\nout, and it is shown that the ABM method is more accurate and converges faster\nto an exact solution than the ENFDS method.\n"}
+{"abstract": "  The ability of a planet to maintain surface water, key to life as we know it,\ndepends on solar and planetary energy. As a star ages, it delivers more energy\nto a planet. As a planet ages it produces less internal heat, which leads to\ncooling. For the Earth, interior cooling connects to plate tectonics - the\nsurface manifestation of convection within the Earth's interior. This process\ncycles volatiles (CO2 and water) between surface and interior reservoirs, which\naffects planetary climate. Cycling rates depend on the efficiency of plate\ntectonic cooling. That efficiency remains debated and multiple hypotheses have\nbeen put forth. Geological proxy data allow us to validate these hypotheses\naccounting for model and data uncertainty. Multiple models pass the validation\ntest. Those models define a distribution for terrestrial exoplanets akin to\nEarth, accounting for variations in tectonic efficiency. Feeding this\ndistribution into climate models indicates that the time at which habitable\nconditions are established can vary by billions of years. Planets of the same\nabsolute age and orbital distance can reside and not reside within the classic\nhabitable zone due to differences in plate tectonic cooling efficiencies. The\nfull model population allows a probability distribution to be constructed for\nthe the time at which habitable conditions are established. The distribution\nindicates that Earth-like exoplanets, of the same age, can be at different\nevolutionary stages. It also indicates that planets around stars whose early\nevolution is unfavorable for life can become habitable later in their energetic\nhistories.\n"}
+{"abstract": "  A systematic study is performed to measure the ion backflow fraction of the\nGEM detectors. The effects of different voltage configurations and Ar/CO_2 gas\nmixtures, in ratios of 70:30, 80:20 and 90:10, on positive ion fraction are\ninvestigated in detail. Moreover, a comparative study is performed between\nsingle and quadruple GEM detectors.The ion current with detector effective gain\nis measured with various field configurations and with three proportions of gas\nmixtures. The ion backflow fraction for the GEM is substantially reduced with\nthe lower drift field. A minimum ion backflow fraction of 18 % is achieved in\nthe single GEM detector with Ar/CO_2 80:20 gas mixture, however, a minimum ion\nbackflow fraction of 3.5 %, 3.0%, and 3.8 % are obtained for a drift field of\n0.1kV/cm with Ar/CO_2 70:30, 80:20 and 90:10 gas mixtures, respectively for\nquadrupole GEM detector. Similar values of effective gain and ion backflow\nfraction have been found by calculating the current from pulse height spectrum\nmethod, obtained in the Multi Channel Analyser.\n"}
+{"abstract": "  Motivated by Yabe's classification of symmetric $2$-generated axial algebras\nof Monster type, we introduce a large class of algebras of Monster type\n$(\\alpha, \\frac{1}{2})$, generalising Yabe's $\\mathrm{III}(\\alpha,\\frac{1}{2},\n\\delta)$ family. Our algebras bear a striking similarity with Jordan spin\nfactor algebras with the difference being that we asymmetrically split the\nidentity as a sum of two idempotents. We investigate the properties of this\nalgebra, including the existence of a Frobenius form and ideals. In the\n$2$-generated case, where our algebra is isomorphic to one of Yabe's examples,\nwe use our new viewpoint to identify the axet, that is, the closure of the two\ngenerating axes.\n"}
+{"abstract": "  Fokker-Planck equations represent a suitable description of the finite-time\nbehavior for a large class of particle systems as the size of the population\ntends to infinity. Recently, the theory of graph limits have been introduced in\nthe classical mean-field framework to account for heterogeneous interactions\namong particles. In many instances, such network heterogeneity is preserved in\nthe limit which turns from being a single Fokker-Planck equation (also known as\nMcKean-Vlasov) to an infinite system of non-linear partial differential\nequations (PDE) coupled by means of a graphon. While appealing from an applied\nviewpoint, few rigorous results exist on the graphon particle system. This note\naddresses such limit system focusing on the relation between initial conditions\nand interaction network: if the system initial datum and the graphon degrees\nsatisfy a suitable condition, a significantly simpler representation of the\nsolution is available.\n  Interesting conclusions can be drawn from this result: substantially\ndifferent graphons can give rise to exactly the same particle behavior, e.g.,\nif the initial condition is independent of the graphon, the particle behavior\nis indistinguishable from the well-known mean-field limit as long as the\ngraphon has constant degree; step-kernels, a building block of graph limit\ntheory, can be used to approximate the graphon particle system with a finite\nsystem of Fokker-Planck equations and an explicit rate of convergence.\n"}
+{"abstract": "  A plane graph is called a rectangular graph if each of its edges can be\noriented either horizontally or vertically, each of its interior regions is a\nfour-sided region and all interior regions can be fitted in a rectangular\nenclosure. Only planar graphs can be dualized. If the dual of a plane graph is\na rectangular graph, then the plane graph is a rectangularly dualizable graph.\n  In 1985, Ko\\'zmi\\'nski and Kinnen presented a necessary and sufficient\ncondition for the existence of a rectangularly dualizable graph for a separable\nconnected plane graph. In this paper, we present a counter example for which\nthe conditions given by them for separable connected plane graphs fail and\nhence, we derive a necessary and sufficient condition for a plane graph to be a\nrectangularly dualizable graph.\n"}
+{"abstract": "  Deep learning compiler frameworks are gaining ground as a more portable\nback-end for deep learning applications on increasingly diverse hardware.\nHowever, they face the daunting challenge of matching performance offered by\nhand-tuned target-specific libraries. While auto-tuning frameworks with\nstatistical cost models can provide dynamic and efficient code optimization,\nthey suffer from large space exploration and cost model training overheads.\nThis paper proposes MetaTune, a meta-learning based cost model that more\nquickly and accurately predicts the performance of optimized codes with\npre-trained model parameters. MetaTune encodes convolution kernel codes as\nstructurally similar graphs to facilitate meta-learning, meta-trains a GNN\nmodel with a very small input data set, and then predicts optimization\nparameters for unseen convolution operations with varying sizes and structures\nduring compilation. The resulting framework with MetaTune provides 8 to 13%\nbetter inference time on average for four CNN models with comparable or lower\noptimization time while outperforming transfer learning by 10% in\ncross-platform cases.\n"}
+{"abstract": "  3D shape reconstruction from a single image has been a long-standing problem\nin computer vision. Recent advances have led to 3D representation learning,\nwherein pixel-aligned 3D reconstruction methods show impressive performance.\nHowever, it is normally hard to exploit meaningful local image features to\ndescribe 3D point samplings from the aligned pixels when large variations of\nocclusions, views, and appearances exist. In this paper, we study a general\nkernel to encode local image features with considering geometric relationships\nof point samplings from the underlying surfaces. The kernel is derived from the\nproposed spatial pattern, in a way the kernel points are obtained as the 2D\nprojections of a number of 3D pattern points around a sampling. Supported by\nthe spatial pattern, the 2D kernel encodes geometric information that is\nessential for 3D reconstruction tasks, while traditional 2D kernels mainly\nconsider appearance information. Furthermore, to enable the network to discover\nmore adaptive spatial patterns for further capturing non-local contextual\ninformation, the spatial pattern is devised to be deformable. Experimental\nresults on both synthetic datasets and real datasets demonstrate the\nsuperiority of the proposed method.\n"}
+{"abstract": "  This paper is concerned with the existence and regularity of mininizers as\nwell as of corresponding multipliers to an optimal control problem governed by\nsemilinear elliptic equations, in which mixed pointwise control-state\nconstraints are considered in a quite general form and the controls act\nsimultaneously in the domain and on the boundary. Under standing assumptions,\nthe minimizers and the corresponding multipliers do exist. Furthermore, by\napplying the bootstrapping technique and establishing some calculation tools\nfor functions in Sobolev spaces of fractional order, the optimal solutions and\nthe associated Lagrange multipliers are shown to be Lipschitz continuous.\n"}
+{"abstract": "  Using ultraviolet (UV) light curves we constrain the circumstellar\nenvironments of 1080 Type Ia supernovae (SNe Ia) within $z<0.5$ from archival\nGalaxy Evolution Explorer (GALEX) observations. All SNe Ia are required to have\npre- and post-explosion GALEX observations to ensure adequate subtraction of\nthe host-galaxy flux. Using the late-time GALEX observations we look for the UV\nexcess expected from any interaction between the SN ejecta and circumstellar\nmaterial (CSM). Four SNe Ia are detected near maximum light and we compare the\nGALEX photometry to archival data, but we find none of our targets show\nconvincing evidence of CSM interaction. A recent Hubble Space Telescope (HST)\nsurvey estimates that $\\sim6\\%$ of SNe Ia may interact with distant CSM, but\nstatistical inferences are complicated by the small sample size and selection\neffects. By injecting model light curves into our data and then recovering\nthem, we constrain a broad range of CSM interactions based on the CSM\ninteraction start time and the maximum luminosity. Combining our GALEX\nnon-detections with the HST results, we constrain occurrence of late-onset CSM\ninteraction among SNe Ia with moderate CSM interaction, similar to that\nobserved in PTF11kx, to $f_\\text{CSM}\\lesssim5.1\\%$ between $0-500$ days after\ndiscovery and $\\lesssim2.7\\%$ between $500-1000$ days after discovery at $90\\%$\nconfidence. For weaker CSM interactions similar to SN 2015cp, we obtain limits\nof $\\lesssim16\\%$ and $\\lesssim4.8\\%$, respectively, for the same time ranges.\n"}
+{"abstract": "  The symmetries of asymptotically flat spacetimes in general relativity are\ngiven by the Bondi-Metzner-Sachs (BMS) group, though there are proposed\ngeneralizations of its symmetry algebra. Associated with each symmetry is a\ncharge and a flux, and the values of these charges and their changes can\ncharacterize a spacetime. The charges of the BMS group are relativistic angular\nmomentum and supermomentum (which includes 4-momentum); the extensions of the\nBMS algebra also include generalizations of angular momentum called \"super\nangular momentum.\" Several different formalisms have been used to define\nangular momentum, and they produce nonequivalent expressions for the charge. It\nwas shown recently that these definitions can be summarized in a two-parameter\nfamily of angular momenta, which we investigate in this paper. We find that\nrequiring that the angular momentum vanishes in flat spacetime restricts the\ntwo parameters to be equal. If we do not require that the angular momentum\nagrees with a common Hamiltonian definition, then we are left with a\none-parameter family of angular momenta that includes the definitions from the\nseveral different formalisms. We then also propose a similar two-parameter\nfamily of super angular momentum. We examine the effect of the free parameters\non the values of the angular momentum and super angular momentum from\nnonprecessing binary-black-hole mergers. The definitions of angular momentum\ndiffer at a high post-Newtonian order for these systems, but only when the\nsystem is radiating gravitational waves. The different super-angular-momentum\ndefinitions occur at lower orders, and there is a difference in the change of\nsuper angular momentum even after the gravitational waves pass, which arises\nbecause of the gravitational-wave memory effect. We estimate the size of these\neffects using numerical-relativity surrogate waveforms and find they are small\nbut resolvable.\n"}
+{"abstract": "  We comment on a recent paper regarding the derivation of the magnetic field\ncomponents of a solenoid in analytical form by proposing a different and\nsimpler method\n"}
+{"abstract": "  We take a geometrical viewpoint and present a unifying view on supervised\ndeep learning with the Bregman divergence loss function - this entails frequent\nclassification and prediction tasks. Motivated by simulations we suggest that\nthere is principally no implicit bias of vanilla stochastic gradient descent\ntraining of deep models towards \"simpler\" functions. Instead, we show that good\ngeneralization may be instigated by bounded spectral products over layers\nleading to a novel geometric regularizer. It is revealed that in deep enough\nmodels such a regularizer enables both, extreme accuracy and generalization, to\nbe reached. We associate popular regularization techniques like weight decay,\ndrop out, batch normalization, and early stopping with this perspective. Backed\nup by theory we further demonstrate that \"generalization by design\" is\npractically possible and that good generalization may be encoded into the\nstructure of the network. We design two such easy-to-use structural\nregularizers that insert an additional \\textit{generalization layer} into a\nmodel architecture, one with a skip connection and another one with drop-out.\nWe verify our theoretical results in experiments on various feedforward and\nconvolutional architectures, including ResNets, and datasets (MNIST, CIFAR10,\nsynthetic data). We believe this work opens up new avenues of research towards\nbetter generalizing architectures.\n"}
+{"abstract": "  The assumption that voters' preferences share some common structure is a\nstandard way to circumvent NP-hardness results in social choice problems. While\nthe Kemeny ranking problem is NP-hard in the general case, it is known to\nbecome easy if the preferences are 1-dimensional Euclidean. In this note, we\nprove that the Kemeny ranking problem is NP-hard for d-dimensional Euclidean\npreferences with d>=2. We note that this result also holds for the Slater\nranking problem.\n"}
+{"abstract": "  The long lifetime and the evolving nature of industrial products make them\nsubject to technical debt at different levels. Despite multiple years of\nresearch on technical debt management, our industrial experience shows that\nintroducing systematic technical debt management in a large-scale company is\nvery challenging. To identify the challenges, we provide a conceptual framework\nfor holistic debt management across the product development value stream, which\ntakes multiple categories of debt and their interplays into account.We use this\nframework to identify multiple challenges that are still open to be explored by\nthe research community. Due to the practical nature of technical debt\nmanagement, we believe this paper can guide the research community on the needs\nof industry for the effective application of technical debt management in\npractice.\n"}
+{"abstract": "  Short-orbit gas giant planet formation/evolution mechanisms are still not\nwell understood. One promising pathway to discriminate between mechanisms is to\nconstrain the occurrence rate of these peculiar exoplanets at the earliest\nstage of the system's life. However, a major limitation when studying newly\nborn stars is stellar activity. This cocktail of phenomena triggered by fast\nrotation, strong magnetic fields and complex internal dynamics, especially\npresent in very young stars, compromises our ability to detect exoplanets. In\nthis paper, we investigated the limitations of such detections in the context\nof already acquired data solely using radial velocity data acquired with a\nnon-stabilised spectrograph. We employed two strategies: Doppler Imaging and\nGaussian Processes and could confidently detect Hot Jupiters with\nsemi-amplitude of 100 $m.s^{-1}$ buried in the stellar activity. We also showed\nthe advantages of the Gaussian Process approach in this case. This study serves\nas a proof of concept to identify potential candidates for follow-up\nobservations or even discover such planets in legacy datasets available to the\ncommunity.\n"}
+{"abstract": "  For a given linear program (LP) a permutation of its variables that sends\nfeasible points to feasible points and preserves the objective function value\nof each of its feasible points is a symmetry of the LP. The set of all\nsymmetries of an LP, denoted by $G^{\\rm LP}$, is the symmetry group of the LP.\nMargot [F. Margot, 50 Years of Integer Programming 1958-2008 (2010), 647-686]\ndescribed a method for computing a subgroup of the symmetry group $G^{\\rm LP}$\nof an LP. This method computes $G^{\\rm LP}$ when the LP has only non-redundant\ninequalities and its feasible set satisfies no equality constraints. However,\nwhen the feasible set of the LP satisfies equality constraints this method\nfinds only a subgroup of $G^{\\rm LP}$ and can miss symmetries. We develop a\nmethod for finding the symmetry group of a feasible LP whose feasible set\nsatisfies equality constraints. We apply this method to find and exploit the\npreviously unexploited symmetries of an orthogonal array defining integer\nlinear program (ILP) within the branch-and-bound (B\\&B) with isomorphism\npruning algorithm [F. Margot, Symmetric ILP: Coloring and small integers,\nDiscrete Optimization 4 (1) (2007), 40-62]. Our method reduced the running time\nfor finding all OD-equivalence classes of OA$(160,8,2,4)$ and OA$(176,8,2,4)$\nby factors of $1/(2.16)$ and $1/(1.36)$ compared to the fastest known method\n[D. A. Bulutoglu and K. J. Ryan, Integer programming for classifying orthogonal\narrays, Australasian Journal of Combinatorics 70 (3) (2018), 362-385]. These\nwere the two bottleneck cases that could not have been solved until the B\\&B\nwith isomorphism pruning algorithm was applied.\n"}
+{"abstract": "  We consider the Hartle-Hawking wavefunction of the universe defined as a\nEuclidean path integral that satisfies the \"no-boundary proposal.\" We focus on\nthe simplest minisuperspace model that comprises a single scale factor degree\nof freedom and a positive cosmological constant. The model can be seen as a\nnon-linear $\\sigma$-model with a line-segment base. We reduce the path integral\nover the lapse function to an integral over the proper length of the base and\nuse diffeomorphism-invariant measures for the ghosts and the scale factor. As a\nresult, the gauge-fixed path integral is independent of the gauge. However, we\npoint out that all field redefinitions of the scale factor degree of freedom\nyield different choices of gauge-invariant path-integral measures. For each\nprescription, we compute the wavefunction at the semi-classical level and find\na different result. We resolve in each case the ambiguity in the form of the\nWheeler-DeWitt equation at this level of approximation. By imposing that the\nHamiltonians associated with these possibly distinct quantum theories are\nHermitian, we determine the inner products of the corresponding Hilbert spaces\nand find that they lead to a universal norm, at least semi-classically. Quantum\npredictions are thus independent of the prescription at this level of\napproximation. Finally, all wavefunctions of the Hilbert spaces of the\nminisuperspace model we consider turn out to be non-normalizable, including the\nno-boundary states.\n"}
+{"abstract": "  In group testing, the goal is to identify a subset of defective items within\na larger set of items based on tests whose outcomes indicate whether at least\none defective item is present. This problem is relevant in areas such as\nmedical testing, DNA sequencing, communication protocols, and many more. In\nthis paper, we study (i) a sparsity-constrained version of the problem, in\nwhich the testing procedure is subjected to one of the following two\nconstraints: items are finitely divisible and thus may participate in at most\n$\\gamma$ tests; or tests are size-constrained to pool no more than $\\rho$ items\nper test; and (ii) a noisy version of the problem, where each test outcome is\nindependently flipped with some constant probability. Under each of these\nsettings, considering the for-each recovery guarantee with asymptotically\nvanishing error probability, we introduce a fast splitting algorithm and\nestablish its near-optimality not only in terms of the number of tests, but\nalso in terms of the decoding time. While the most basic formulations of our\nalgorithms require $\\Omega(n)$ storage for each algorithm, we also provide\nlow-storage variants based on hashing, with similar recovery guarantees.\n"}
+{"abstract": "  In the paper, we prove the local Gan-Gross-Prasad conjecture for special\northogonal groups over archimedean local fields for generic local L-parameters.\nThe non-archimedean counterpart of the conjecture for generic parameters was\nproved by C. Moeglin and J.-L. Waldspurger in [MW12] based on that for tempered\nparameters, which was proved by Waldspurger in [Wal09]. Over archimedean local\nfields, part of the conjecture, the Multiplicity One in Vogan L-packets, was\nproved by Zhilin Luo for tempered L-parameters, and the other part of the\nconjecture for tempered L-parameters is our ongoing work joint with Zhilin Luo.\n"}
+{"abstract": "  The size of rings (also called cyclic polymers) in bidisperse blends of\nchemically identical rings is analyzed by computer simulations. Data of\nentangled ring blends and blends of interpenetrating rings are compared and it\nis shown that the compression of entangled rings can be explained by the\nchanges in the penetrable fraction of the minimal surface bounded by the ring.\nCorrections for small rings can be approximated by a concatenation probability\n$1-P_{OO}$ that a ring entraps at least one other ring. Both results are in\nline with a previous work [1] to explain the compression of entangled rings in\nmonodisperse melts. Bond-bond correlations in melts of interpenetrating rings\nlead to similar corrections for ring sizes as reported previously [2] for\nmonodisperse linear melts. For entangled rings, bond-bond correlations show an\nanti-correlation peak at a curvilinear distance of about ten segments that\ncoincides with a horizontal tangent in the normalized mean square internal\ndistances along the ring. Both observations become independent of melt\nmolecular weight for sufficiently large degrees of polymerization and such\nbehavior is not found in samples with entanglements switched off. In\nconsequence, the length scale of topological interactions (entanglement length)\nin a melt of entangled rings must be considered as constant in contrast to a\nrecent proposal by Sakaue [3].\n"}
+{"abstract": "  Image registration is a fundamental building block for various applications\nin medical image analysis. To better explore the correlation between the fixed\nand moving images and improve registration performance, we propose a novel deep\nlearning network, Co-Attention guided Registration Network (CAR-Net). CAR-Net\nemploys a co-attention block to learn a new representation of the inputs, which\ndrives the registration of the fixed and moving images. Experiments on UK\nBiobank cardiac cine-magnetic resonance image data demonstrate that CAR-Net\nobtains higher registration accuracy and smoother deformation fields than\nstate-of-the-art unsupervised registration methods, while achieving comparable\nor better registration performance than corresponding weakly-supervised\nvariants. In addition, our approach can provide critical structural information\nof the input fixed and moving images simultaneously in a completely\nunsupervised manner.\n"}
+{"abstract": "  We perform a complete next-to-leading order calculation of the non-Abelian\nelectric field correlator in a SU($N_c$) plasma, which encodes properties of\nthe plasma relevant for heavy particle bound state formation and dissociation\nand is different from the correlator for the heavy quark diffusion coefficient.\nThe calculation is carried out in the real-time formalism of thermal field\ntheory and includes both vacuum and finite temperature contributions. By\nworking in the $R_\\xi$ gauge, we explicitly show the results are gauge\nindependent, infrared and collinear safe. The renormalization group equation of\nthis electric field correlator is determined by that of the strong coupling\nconstant. Our next-to-leading order calculation can be directly applied to any\ndipole singlet-adjoint transition of heavy particle pairs. For example, it can\nbe used to describe dissociation and (re)generation of heavy quarkonia inside\nthe quark-gluon plasma well below the melting temperature, as well as heavy\ndark matter pairs (or charged co-annihilating partners) in the early universe.\n"}
+{"abstract": "  Reward engineering and designing an incentive reward function are non-trivial\ntasks to train agents in complex environments. Furthermore, an inaccurate\nreward function may lead to a biased behaviour which is far from an efficient\nand optimised behaviour. In this paper, we focus on training a single agent to\nscore goals with binary success/failure reward function in Half Field Offense\ndomain. As the major advantage of this research, the agent has no presumption\nabout the environment which means it only follows the original formulation of\nreinforcement learning agents. The main challenge of using such a reward\nfunction is the high sparsity of positive reward signals. To address this\nproblem, we use a simple prediction-based exploration strategy (called Curious\nExploration) along with a Return-based Memory Restoration (RMR) technique which\ntends to remember more valuable memories. The proposed method can be utilized\nto train agents in environments with fairly complex state and action spaces.\nOur experimental results show that many recent solutions including our baseline\nmethod fail to learn and perform in complex soccer domain. However, the\nproposed method can converge easily to the nearly optimal behaviour. The video\npresenting the performance of our trained agent is available at\nhttp://bit.ly/HFO_Binary_Reward.\n"}
+{"abstract": "  We consider a model-agnostic solution to the problem of Multi-Domain Learning\n(MDL) for multi-modal applications. Many existing MDL techniques are\nmodel-dependent solutions which explicitly require nontrivial architectural\nchanges to construct domain-specific modules. Thus, properly applying these MDL\ntechniques for new problems with well-established models, e.g. U-Net for\nsemantic segmentation, may demand various low-level implementation efforts. In\nthis paper, given emerging multi-modal data (e.g., various structural\nneuroimaging modalities), we aim to enable MDL purely algorithmically so that\nwidely used neural networks can trivially achieve MDL in a model-independent\nmanner. To this end, we consider a weighted loss function and extend it to an\neffective procedure by employing techniques from the recently active area of\nlearning-to-learn (meta-learning). Specifically, we take inner-loop gradient\nsteps to dynamically estimate posterior distributions over the hyperparameters\nof our loss function. Thus, our method is model-agnostic, requiring no\nadditional model parameters and no network architecture changes; instead, only\na few efficient algorithmic modifications are needed to improve performance in\nMDL. We demonstrate our solution to a fitting problem in medical imaging,\nspecifically, in the automatic segmentation of white matter hyperintensity\n(WMH). We look at two neuroimaging modalities (T1-MR and FLAIR) with\ncomplementary information fitting for our problem.\n"}
+{"abstract": "  We display an application of the notions of kernelization and data reduction\nfrom parameterized complexity to proof complexity: Specifically, we show that\nthe existence of data reduction rules for a parameterized problem having (a). a\nsmall-length reduction chain, and (b). small-size (extended) Frege proofs\ncertifying the soundness of reduction steps implies the existence of\nsubexponential size (extended) Frege proofs for propositional formalizations of\nthe given problem.\n  We apply our result to infer the existence of subexponential Frege and\nextended Frege proofs for a variety of problems. Improving earlier results of\nAisenberg et al. (ICALP 2015), we show that propositional formulas expressing\n(a stronger form of) the Kneser-Lov\\'asz Theorem have polynomial size Frege\nproofs for each constant value of the parameter k. Previously only\nquasipolynomial bounds were known (and only for the ordinary Kneser-Lov\\'asz\nTheorem).\n  Another notable application of our framework is to impossibility results in\ncomputational social choice: we show that, for any fixed number of agents,\npropositional translations of the Arrow and Gibbard-Satterthwaite impossibility\ntheorems have subexponential size Frege proofs.\n"}
+{"abstract": "  We present a rapid-scanning approach to fluorescence-detected two-dimensional\nelectronic spectroscopy that combines acousto-optic phase-modulation with\ndigital lock-in detection. The approach shifts the signal detection window to\nsuppress 1/f laser noise and enables interferometric tracking of the time\ndelays to allow for correction of spectral phase distortions and accurate\nphasing of the data. This use of digital lock-in detection enables acquisition\nof linear and nonlinear signals of interest in a single measurement. We\ndemonstrate the method on a laser dye, measuring the linear fluorescence\nexcitation spectrum, as well as rephasing, non-rephasing and absorptive\nfluorescence-detected two-dimensional electronic spectra.\n"}
+{"abstract": "  We present a novel approach for unsupervised activity segmentation, which\nuses video frame clustering as a pretext task and simultaneously performs\nrepresentation learning and online clustering. This is in contrast with prior\nworks where representation learning and clustering are often performed\nsequentially. We leverage temporal information in videos by employing temporal\noptimal transport. In particular, we incorporate a temporal regularization term\nwhich preserves the temporal order of the activity into the standard optimal\ntransport module for computing pseudo-label cluster assignments. The temporal\noptimal transport module enables our approach to learn effective\nrepresentations for unsupervised activity segmentation. Furthermore, previous\nmethods require storing learned features for the entire dataset before\nclustering them in an offline manner, whereas our approach processes one\nmini-batch at a time in an online manner. Extensive evaluations on three public\ndatasets, i.e. 50-Salads, YouTube Instructions, and Breakfast, and our dataset,\ni.e., Desktop Assembly, show that our approach performs on par or better than\nprevious methods for unsupervised activity segmentation, despite having\nsignificantly less memory constraints.\n"}
+{"abstract": "  This discussion applies quantitative finance methods and economic arguments\nto cryptocurrencies in general and bitcoin in particular -- as there are about\n$10,000$ cryptocurrencies, we focus (unless otherwise specified) on the most\ndiscussed crypto of those that claim to hew to the original protocol (Nakamoto\n2009) and the one with, by far, the largest market capitalization.\n  In its current version, in spite of the hype, bitcoin failed to satisfy the\nnotion of \"currency without government\" (it proved to not even be a currency at\nall), can be neither a short nor long term store of value (its expected value\nis no higher than $0$), cannot operate as a reliable inflation hedge, and,\nworst of all, does not constitute, not even remotely, a safe haven for one's\ninvestments, a shield against government tyranny, or a tail protection vehicle\nfor catastrophic episodes.\n  Furthermore, bitcoin promoters appear to conflate the success of a payment\nmechanism (as a decentralized mode of exchange), which so far has failed, with\nthe speculative variations in the price of a zero-sum maximally fragile asset\nwith massive negative externalities.\n  Going through monetary history, we show how a true numeraire must be one of\nminimum variance with respect to an arbitrary basket of goods and services, how\ngold and silver lost their inflation hedge status during the Hunt brothers\nsqueeze in the late 1970s and what would be required from a true inflation\nhedged store of value.\n"}
+{"abstract": "  It is conjectured that the sum $$ S_r(n)=\\sum_{k=1}^{n}\n\\frac{k}{k+r}\\binom{n}{k} $$ for positive integers $r,n$ is never integral.\nThis has been shown for $r\\le 22$. In this note we study the problem in the\n``$n$ aspect\" showing that the set of $n$ such that $S_r(n)\\in {\\mathbb Z}$ for\nsome $r\\ge 1$ has asymptotic density $0$. Our principal tools are some deep\nresults on the distribution of primes in short intervals.\n"}
+{"abstract": "  In communication systems, there are many tasks, like modulation recognition,\nwhich rely on Deep Neural Networks (DNNs) models. However, these models have\nbeen shown to be susceptible to adversarial perturbations, namely imperceptible\nadditive noise crafted to induce misclassification. This raises questions about\nthe security but also the general trust in model predictions. We propose to use\nadversarial training, which consists of fine-tuning the model with adversarial\nperturbations, to increase the robustness of automatic modulation recognition\n(AMC) models. We show that current state-of-the-art models benefit from\nadversarial training, which mitigates the robustness issues for some families\nof modulations. We use adversarial perturbations to visualize the features\nlearned, and we found that in robust models the signal symbols are shifted\ntowards the nearest classes in constellation space, like maximum likelihood\nmethods. This confirms that robust models not only are more secure, but also\nmore interpretable, building their decisions on signal statistics that are\nrelevant to modulation recognition.\n"}
+{"abstract": "  In April 2021, the European Commission published a proposed regulation on AI.\nIt intends to create a uniform legal framework for AI within the European Union\n(EU). In this chapter, we analyze and assess the proposal. We show that the\nproposed regulation is actually not needed due to existing regulations. We also\nargue that the proposal clearly poses the risk of overregulation. As a\nconsequence, this would make the use or development of AI applications in\nsafety-critical application areas, such as in healthcare, almost impossible in\nthe EU. This would also likely further strengthen Chinese and US corporations\nin their technology leadership. Our assessment is based on the oral evidence we\ngave in May 2021 to the joint session of the European Union affairs committees\nof the German federal parliament and the French National Assembly.\n"}
+{"abstract": "  In this paper, we initiate the study of global stability for anisotropic\nsystems of quasilinear wave equations. Equations of this kind arise naturally\nin the study of crystal optics, and they exhibit birefringence. We introduce a\nphysical space strategy based on bilinear energy estimates that allows us to\nprove decay for the nonlinear problem. This uses decay for the homogeneous wave\nequation as a black box. The proof also requires us to interface this strategy\nwith the vector field method and take advantage of the scaling vector field. A\ncareful analysis of the spacetime geometry of the interaction between waves is\nnecessary in the proof.\n"}
+{"abstract": "  This study aims to review and get information on the different mobile game\napplications and the possibility of being a supplementary tool for learning to\nenhance and empower the e-Learning aspect of a Technical Education and Skills\nDevelopment Authority. It is reviewed to help in the development of the\neLearning department of TESDA. The study aims to facilitate the reorganization\nof the E-learning program of TESDA by using a method called Game Based\nLearning. To accomplish this target, different applications will be reviewed to\ngive an idea on how game-based learning is being used as a supplementary\nlearning tool for different subject areas. This paper will focus on TESDA\nlanguage Skills institute and Technical-Vocational program since there are a\nlot of reviews for main subject areas but only few in this field. The database\nto be used in the methodology is Google Scholar, inclusion and exclusion\ncriteria used is that article should be within the span of five years and\nshould be game based learning mobile applications. Will be selecting 4\napplications as a representation since it is an informational review. No need\nto indicate the other applications as this is just a basis for future studies.\nMobile games have such a powerful instructional potential, by which teaching\ncan be revolutionized, which output is acquiring the praise and judgment of\nacademic professionals and educators. After this review, future studies can be\nmade to support this study like reviewing from other databases like Web of\nScience and Scopus to support the foundation made on this review. The review\nwill open further studies to help TESDA administrator in using mobile games as\na supplementary learning tool for their e-learning.\n"}
+{"abstract": "  We present a margin-free finite mixture model which allows us to\nsimultaneously classify objects into known classes and to identify possible new\nobject types using a set of continuous attributes. This application is\nmotivated by the needs of identifying and possibly detecting new types of a\nparticular kind of stars known as variable stars. We first suitably transform\nthe physical attributes of the stars onto the simplex to achieve scale\ninvariance while maintaining their dependence structure. This allows us to\ncompare data collected by different sky surveys which can have different\nscales. The model hence combines a mixture of Dirichlet mixtures to represent\nthe known classes with the semi-supervised classification strategy of Vatanen\net al. (2012) for outlier detection. In line with previous work on\nsemiparametric model-based clustering, the single Dirichlet distributions can\nbe seen as providing the baseline pattern of the data. These are then combined\nto effectively model the complex distributions of the attributes for the\ndifferent classes. The model is estimated using a hierarchical two-step\nprocedure which combines a suitably adapted version of the\nExpectation-Maximization (EM) algorithm with Bayes' rule. We validate our model\non a reliable sample of periodic variable stars available in the literature\n(Dubath et al., 2011) achieving an overall classification accuracy of 71.95%, a\nsensitivity of 86.11% and a specificity of 99.79% for new class detection.\n"}
+{"abstract": "  Sharding, i.e. splitting the miners or validators to form and run several\nsubchains in parallel, is known as one of the main solutions to the scalability\nproblem of blockchains. The drawback is that as the number of miners expanding\neach subchain becomes small, it becomes vulnerable to security attacks. To\nsolve this problem, a framework, named as \\textit{Polyshard}, has been proposed\nin which each validator verifies a coded combination of the blocks introduced\nby different subchains, thus helping to protect the security of all subchains.\nIn this paper, we introduce an attack on Polyshard, called \\textit{the\ndiscrepancy} attack, which is the result of malicious nodes controlling a few\nsubchains and dispersing different blocks to different nodes. We show that this\nattack undermines the security of Polyshard and is undetectable in its current\nsetting.\n"}
+{"abstract": "  We present multi-band Hubble Space Telescope imaging that spans rest-frame\nnear-ultraviolet through near-infrared wavelengths (0.3-1.1 $\\mu$m) for 12\ncompact starburst galaxies at z=0.4-0.8. These massive galaxies (M_stellar ~\n10^11 M_Sun) are driving very fast outflows ($v_{max}$=1000-3000 km/s), and\ntheir light profiles are dominated by an extremely compact starburst component\n(half-light radius ~ 100 pc). Our goal is to constrain the physical mechanisms\nresponsible for launching these fast outflows by measuring the physical\nconditions within the central kiloparsec. Based on our stellar population\nanalysis, the central component typically contributes $\\approx$25% of the total\nstellar mass and the central escape velocities $v_{esc,central}\\approx900$ km/s\nare a factor of two smaller than the observed outflow velocities. This requires\nphysical mechanisms that can accelerate gas to speeds significantly beyond the\ncentral escape velocities, and it makes clear that these fast outflows are\ncapable of traveling into the circumgalactic medium, and potentially beyond. We\nfind central stellar densities comparable to theoretical estimates of the\nEddington limit, and we estimate $\\Sigma_1$ surface densities within the\ncentral kpc comparable to those of compact massive galaxies at $0.5<z<3.0$.\nRelative to \"red nuggets\" and \"blue nuggets\" at $z\\sim2$, we find significantly\nsmaller $r_e$ values at a given stellar mass, which we attribute to the\ndominance of a young stellar component in our sample and the better physical\nresolution for rest-frame optical observations at $z\\sim0.6$ versus $z\\sim2$.\nWe compare to theoretical scenarios involving major mergers and violent disc\ninstability, and we speculate that our galaxies are progenitors of power-law\nellipticals in the local universe with prominent stellar cusps.\n"}
+{"abstract": "  Internet of Everything (IoE) applications such as haptics, human-computer\ninteraction, and extended reality, using the sixth-generation (6G) of wireless\nsystems have diverse requirements in terms of latency, reliability, data rate,\nand user-defined performance metrics. Therefore, enabling IoE applications over\n6G requires a new framework that can be used to manage, operate, and optimize\nthe 6G wireless system and its underlying IoE services. Such a new framework\nfor 6G can be based on digital twins. Digital twins use a virtual\nrepresentation of the 6G physical system along with the associated algorithms\n(e.g., machine learning, optimization), communication technologies (e.g.,\nmillimeter-wave and terahertz communication), computing systems (e.g., edge\ncomputing and cloud computing), as well as privacy and security-related\ntechnologists (e.g., blockchain). First, we present the key design requirements\nfor enabling 6G through the use of a digital twin. Next, the architectural\ncomponents and trends such as edge-based twins, cloud-based-twins, and\nedge-cloud-based twins are presented. Furthermore, we provide a comparative\ndescription of various twins. Finally, we outline and recommend guidelines for\nseveral future research directions.\n"}
+{"abstract": "  As the real-time digital counterpart of a physical system or process, digital\ntwins are utilized for system simulation and optimization. Neural networks are\none way to build a digital twins model by using data especially when a\nphysics-based model is not accurate or even not available. However, for a newly\ndesigned system, it takes time to accumulate enough data for neural network\nmodel and only an approximate physics-based model is available. To take\nadvantage of both models, this paper proposed a model that combines the\nphysics-based model and the neural network model to improve the prediction\naccuracy for the whole life cycle of a system. The proposed hybrid model\n(PhysiNet) was able to automatically combine the models and boost their\nprediction performance. Experiments showed that the PhysiNet outperformed both\nthe physics-based model and the neural network model.\n"}
+{"abstract": "  Oblique plane microscopy (OPM) enables high speed, volumetric fluorescence\nimaging through a single-objective geometry. While these advantages have\npositioned OPM as a valuable tool to probe biological questions in animal\nmodels, its potential for in vivo human imaging is largely unexplored due to\nits typical use with exogenous fluorescent dyes. Here we introduce a\nscattering-contrast oblique plane microscope (sOPM) and demonstrate label-free\nimaging of blood cells flowing through human capillaries in vivo. The sOPM\nilluminates a capillary bed in the ventral tongue with an oblique light sheet,\nand images side- and back- scattered signal from blood cells. By synchronizing\nsOPM with a conventional capillaroscope, we acquire paired widefield and axial\nimages of blood cells flowing through a capillary loop. The widefield\ncapillaroscope image provides absorption contrast and confirms the presence of\nred blood cells (RBCs), while the sOPM image may aid in determining whether\noptical absorption gaps (OAGs) between RBCs have cellular or acellular\ncomposition. Further, we demonstrate consequential differences between\nfluorescence and scattering versions of OPM by imaging the same polystyrene\nbeads sequentially with each technique. Lastly, we substantiate in vivo\nobservations by imaging isolated red blood cells, white blood cells, and\nplatelets in vitro using 3D agar phantoms. These results demonstrate a\npromising new avenue towards in vivo blood analysis.\n"}
+{"abstract": "  Estimating the gradients for binary variables is a task that arises\nfrequently in various domains, such as training discrete latent variable\nmodels. What has been commonly used is a REINFORCE based Monte Carlo estimation\nmethod that uses either independent samples or pairs of negatively correlated\nsamples. To better utilize more than two samples, we propose ARMS, an\nAntithetic REINFORCE-based Multi-Sample gradient estimator. ARMS uses a copula\nto generate any number of mutually antithetic samples. It is unbiased, has low\nvariance, and generalizes both DisARM, which we show to be ARMS with two\nsamples, and the leave-one-out REINFORCE (LOORF) estimator, which is ARMS with\nuncorrelated samples. We evaluate ARMS on several datasets for training\ngenerative models, and our experimental results show that it outperforms\ncompeting methods. We also develop a version of ARMS for optimizing the\nmulti-sample variational bound, and show that it outperforms both VIMCO and\nDisARM. The code is publicly available.\n"}
+{"abstract": "  Existing physical model-based imaging methods for ultrasound elasticity\nreconstruction utilize fixed variational regularizers that may not be\nappropriate for the application of interest or may not capture complex spatial\nprior information about the underlying tissues. On the other hand, end-to-end\nlearning-based methods count solely on the training data, not taking advantage\nof the governing physical laws of the imaging system. Integrating\nlearning-based priors with physical forward models for ultrasound elasticity\nimaging, we present a joint reconstruction framework which guarantees that\nlearning driven reconstructions are consistent with the underlying physics. For\nsolving the elasticity inverse problem as a regularized optimization problem,\nwe propose a plug-and-play (PnP) reconstruction approach in which each\niteration of the elasticity image estimation process involves separate updates\nincorporating data fidelity and learning-based regularization. In this\nmethodology, the data fidelity term is developed using a statistical linear\nalgebraic model of quasi-static equilibrium equation revealing the relationship\nof the observed displacement fields \\cmmnt{measured deformation data} to the\nunobserved elastic modulus. The regularizer comprises a convolutional neural\nnetwork (CNN) based denoiser that captures the learned prior structure of the\nunderlying tissues. Preliminary simulation results demonstrate the robustness\nand effectiveness of the proposed approach with limited training datasets and\nnoisy displacement measurements.\n"}
+{"abstract": "  Geometric feature extraction is a crucial component of point cloud\nregistration pipelines. Recent work has demonstrated how supervised learning\ncan be leveraged to learn better and more compact 3D features. However, those\napproaches' reliance on ground-truth annotation limits their scalability. We\npropose BYOC: a self-supervised approach that learns visual and geometric\nfeatures from RGB-D video without relying on ground-truth pose or\ncorrespondence. Our key observation is that randomly-initialized CNNs readily\nprovide us with good correspondences; allowing us to bootstrap the learning of\nboth visual and geometric features. Our approach combines classic ideas from\npoint cloud registration with more recent representation learning approaches.\nWe evaluate our approach on indoor scene datasets and find that our method\noutperforms traditional and learned descriptors, while being competitive with\ncurrent state-of-the-art supervised approaches.\n"}
+{"abstract": "  The ignition of Quantum Cascade Lasers can occur from a state of oscillating\nfie ld domains. Here, the interplay between lasing and the kinetics of\ntraveling dom ain boundaries provides complex oscillation scenarios. We analyze\nour numerical findings in detail for a device operating at terahertz\nfrequencies and manifest chaotic evolution by positive Lyapunov exponents. This\nshows that these importan t devices can exhibit chaotic behavior even without\nperiodic driving, which need s to be taken into account in their design.\n"}
+{"abstract": "  With the unprecedented reliance on cloud computing as the backbone for\nstoring today's big data, we argue in this paper that the role of the cloud\nshould be reshaped from being a passive virtual market to become an active\nplatform for monetizing the big data through Artificial Intelligence (AI)\nservices. The objective is to enable the cloud to be an active platform that\ncan help big data service providers reach a wider set of customers and cloud\nusers (i.e., data consumers) to be exposed to a larger and richer variety of\ndata to run their data analytic tasks. To achieve this vision, we propose a\nnovel game theoretical model, which consists of a mix of cooperative and\ncompetitive strategies. The players of the game are the big data service\nproviders, cloud computing platform, and cloud users. The strategies of the\nplayers are modeled using the two-sided market theory that takes into\nconsideration the network effects among involved parties, while integrating the\nexternalities between the cloud resources and consumer demands into the design\nof the game. Simulations conducted using Amazon and google clustered data show\nthat the proposed model improves the total surplus of all the involved parties\nin terms of cloud resources provision and monetary profits compared to the\ncurrent merchant model.\n"}
+{"abstract": "  In this paper, we propose SACHER (soft actor-critic (SAC) with hindsight\nexperience replay (HER)), which constitutes a class of deep reinforcement\nlearning (DRL) algorithms. SAC is known as an off-policy model-free DRL\nalgorithm based on the maximum entropy framework, which outperforms earlier DRL\nalgorithms in terms of exploration, robustness and learning performance.\nHowever, in SAC, maximizing the entropy-augmented objective may degrade the\noptimality of learning outcomes. HER is known as a sample-efficient replay\nmethod that enhances the performance of off-policy DRL algorithms by allowing\nthe agent to learn from both failures and successes. We apply HER to SAC and\npropose SACHER to improve the learning performance of SAC. More precisely,\nSACHER achieves the desired optimal outcomes faster and more accurately than\nSAC, since HER improves the sample efficiency of SAC. We apply SACHER to the\nnavigation and control problem of unmanned aerial vehicles (UAVs), where SACHER\ngenerates the optimal navigation path of the UAV under various obstacles in\noperation. Specifically, we show the effectiveness of SACHER in terms of the\ntracking error and cumulative reward in UAV operation by comparing them with\nthose of state-of-the-art DRL algorithms, SAC and DDPG. Note that SACHER in UAV\nnavigation and control problems can be applied to arbitrary models of UAVs.\n"}
+{"abstract": "  Most of today's security solutions, such as security information and event\nmanagement (SIEM) and signature based IDS, require the operator to evaluate\npotential attack vectors and update detection signatures and rules in a timely\nmanner. However, today's sophisticated and tailored advanced persistent threats\n(APT), malware, ransomware and rootkits, can be so complex and diverse, and\noften use zero day exploits, that a pure signature-based blacklisting approach\nwould not be sufficient to detect them. Therefore, we could observe a major\nparadigm shift towards anomaly-based detection mechanisms, which try to\nestablish a system behavior baseline -- either based on netflow data or system\nlogging data -- and report any deviations from this baseline. While these\napproaches look promising, they usually suffer from scalability issues. As the\namount of log data generated during IT operations is exponentially growing,\nhigh-performance analysis methods are required that can handle this huge amount\nof data in real-time. In this paper, we demonstrate how high-performance\nbioinformatics tools can be applied to tackle this issue. We investigate their\napplication to log data for outlier detection to timely reveal anomalous system\nbehavior that points to cyber attacks. Finally, we assess the detection\ncapability and run-time performance of the proposed approach.\n"}
+{"abstract": "  We show in ZF that: (i) Every subcompact metrizable space is completely\nmetrizable, and every completely metrizable space is countably subcompact. (ii)\nA metrizable space X=(X,T) is countably compact iff it is countably subcompact\nrelative to T. (iii) For every metric space X=(X,d), X is compact iff it is\nsubcompact relative to T. We also show: (iv) The negation of each of the\nstatements, (a) every countably subcompact metrizable space is completely\nmetrizable, (b) every countably subcompact metrizable space is subcompact, (c)\nevery complete metrizable space is subcompact is relatively consistent with ZF.\n"}
+{"abstract": "  We consider an ergodic harvesting problem with model ambiguity that arises\nfrom biology. To account for the ambiguity, the problem is constructed as a\nstochastic game with two players: the decision-maker (DM) chooses the `best'\nharvesting policy and an adverse player chooses the `worst' probability\nmeasure. The main result is establishing an optimal strategy (also referred to\nas a control) of the DM and showing that it is a threshold policy. The optimal\nthreshold and the optimal payoff are obtained by solving a free-boundary\nproblem emerging from the Hamilton--Jacobi--Bellman (HJB) equation. As part of\nthe proof, we fix a gap that appeared in the HJB analysis of [Alvarez and\nHening, {\\em Stochastic Process. Appl.}, 2019], a paper that analyzed the\nrisk-neutral version of the ergodic harvesting problem. Finally, we study the\ndependence of the optimal threshold and the optimal payoff on the ambiguity\nparameter and show that if the ambiguity goes to 0, the problem converges, to\nthe risk-neutral problem.\n"}
+{"abstract": "  We present the application of simultaneous diagonalization and minimum energy\n(SDME) high-order finite element modal bases for simulation of transient\nnon-linear elastodynamic problem, including impact cases with neo-hookean\nhyperelastic materials. The bases are constructed using procedures for\nsimultaneous diagonalization of the internal modes and Schur complement of the\nboundary modes from the standard nodal and modal bases, constructed using\nLagrange and Jacobi polynomials, respectively. The implementation of these\nbases in a high-order finite element code is straightforward, since the\nprocedure is applied only to the one-dimensional expansion bases. Non-linear\ntransient structural problems with large deformation, hyperelastic materials\nand impact are solved using the obtained bases with explicit and implicit time\nintegration procedures. Iterative solutions based on preconditioned conjugate\ngradient methods are considered. The performance of the proposed bases in terms\nof the number of iterations of pre-conditioned conjugate gradient methods and\ncomputational time are compared with the standard nodal and modal bases. Our\nnumerical tests obtained speedups up to 41 using the considered bases when\ncompared to the standard ones.\n"}
+{"abstract": "  Recently, machine learning has been used to substitute parts of conventional\ncomputational fluid dynamics, e.g. the cell-face reconstruction in\nfinite-volume solvers or the curvature computation in the Volume-of-Fluid (VOF)\nmethod. The latter showed improvements in terms of accuracy for coarsely\nresolved interfaces, however at the expense of convergence and symmetry. In\nthis work, a combined approach is proposed, adressing the aforementioned\nshortcomings. We focus on interface reconstruction (IR) in the level-set\nmethod, i.e. the computation of the volume fraction and apertures. The combined\nmodel consists of a classification neural network, that chooses between the\nconventional (linear) IR and the neural network IR depending on the local\ninterface resolution. The proposed approach improves accuracy for coarsely\nresolved interfaces and recovers the conventional IR for high resolutions,\nyielding first order overall convergence. Symmetry is preserved by mirroring\nand rotating the input level-set grid and subsequently averaging the\npredictions. The combined model is implemented into a CFD solver and\ndemonstrated for two-phase flows. Furthermore, we provide details of floating\npoint symmetric implementation and computational efficiency.\n"}
+{"abstract": "  Natural Language Processing (NLP) techniques can be applied to help with the\ndiagnosis of medical conditions such as depression, using a collection of a\nperson's utterances. Depression is a serious medical illness that can have\nadverse effects on how one feels, thinks, and acts, which can lead to emotional\nand physical problems. Due to the sensitive nature of such data, privacy\nmeasures need to be taken for handling and training models with such data. In\nthis work, we study the effects that the application of Differential Privacy\n(DP) has, in both a centralized and a Federated Learning (FL) setup, on\ntraining contextualized language models (BERT, ALBERT, RoBERTa and DistilBERT).\nWe offer insights on how to privately train NLP models and what architectures\nand setups provide more desirable privacy utility trade-offs. We envisage this\nwork to be used in future healthcare and mental health studies to keep medical\nhistory private. Therefore, we provide an open-source implementation of this\nwork.\n"}
+{"abstract": "  High temperature superconducting materials have been known since the\npioneering work of Bednorz and Mueller in 1986. While the microscopic mechanism\nresponsible for high Tc superconductivity is still debated, most materials\nshowing high Tc contain highly electronic polarizable ions, suggesting that the\nmechanism driving high Tc superconductivity can be related to the ion\nelectronic polarizability in high Tc materials. Here we show that a free charge\ncarrier polarizes the ions surrounding it and the total electrical potential\ngenerated by the charge carrier itself and the polarized ions becomes\nattractive in some regions of space. Our results on bulk FeSe, monolayer FeSe\non SrTiO3 and La2CuO4 are in excellent agreement with the experiments. The fact\nthat the electronic polarizability explains correctly and quantitatively the\nsuperconductivity parameters: Tc, gap and paring energies of both pnictides and\ncuprates with similar polarizability parameters, suggests that the same model\nmay be applicable to other material systems within these groups as well as\nother high Tc groups.\n"}
+{"abstract": "  NASA's Double Asteroid Redirection Test (DART) mission will impact its target\nasteroid, Dimorphos, at an oblique angle that will not be known prior to the\nimpact. We computed iSALE-3D simulations of DART-like impacts on asteroid\nsurfaces at different impact angles and found that the the vertical momentum\ntransfer efficiency, $\\beta$, is similar for different impact angles, however,\nthe imparted momentum is reduced as the impact angle decreases. It is expected\nthat the momentum imparted from a 45$^\\circ$ impact is reduced by up to 50\\%\ncompared to a vertical impact. The direction of the ejected momentum is not\nnormal to the surface, however it is observed to `straighten up' with crater\ngrowth. iSALE-2D simulations of vertical impacts provide context for the\niSALE-3D simulation results and show that the ejection angle varies with both\ntarget properties and with crater growth. While the ejection angle is\nrelatively insensitive to the target porosity, it varies by up to 30$^\\circ$\nwith target coefficient of internal friction. The simulation results presented\nin this paper can help constrain target properties from the DART crater ejecta\ncone, which will be imaged by the LICIACube. The results presented here\nrepresent the basis for an empirical scaling relationship for oblique impacts\nand can be used as a framework to determine an analytical approximation of the\nvertical component of the ejecta momentum, $\\beta-1$, given known target\nproperties.\n"}
+{"abstract": "  A relativistic density-functional theory based on a Fock-space effective\nquantum-electrodynamics (QED) Hamiltonian using the Coulomb or Coulomb-Breit\ntwo-particle interaction is developed. This effective QED theory properly\nincludes the effects of vacuum polarization through the creation of\nelectron-positron pairs but does not include explicitly the photon degrees of\nfreedom. It is thus a more tractable alternative to full QED for atomic and\nmolecular calculations. Using the constrained-search formalism, a Kohn-Sham\nscheme is formulated in a quite similar way to non-relativistic\ndensity-functional theory, and some exact properties of the involved density\nfunctionals are studied, namely charge-conjugation symmetry and uniform\ncoordinate scaling. The usual no-pair Kohn-Sham scheme is obtained as a\nwell-defined approximation to this relativistic density-functional theory.\n"}
+{"abstract": "  We present an investigation of the magnetic activity and flare\ncharacteristics of the sub-giant stars mostly from F and G spectral types and\ncompare the results with the main-sequence (MS) stars. The light curve of 352\nstars on the sub-giant branch (SGB) from the Kepler mission is analyzed in\norder to infer stability, relative coverage and contrast of the magnetic\nstructures and also flare properties using three flare indexes. The results\nshow that: (i) Relative coverage and contrast of the magnetic features along\nwith rate, power and magnitude of flares increase on the SGB due to the\ndeepening of the convective zone and more vigorous magnetic field production\n(ii) Magnetic activity of the F and G-type stars on the SGB does not show\ndependency to the rotation rate and does not obey the saturation regime. This\nis the opposite of what we saw for the main sequence, in which the G-, K- and\nM-type stars show clear dependency to the Rossby number; (iii) The positive\nrelationship between the magnetic features stability and their relative\ncoverage and contrast remains true on the SGB, though it has lower dependency\ncoefficient in comparison with the MS; (iv) Magnetic proxies and flare indexes\nof the SGB stars increase with increasing the relative mass of the convective\nzone.\n"}
+{"abstract": "  Few-shot learning (FSL) is the task of learning to recognize previously\nunseen categories of images from a small number of training examples. This is a\nchallenging task, as the available examples may not be enough to unambiguously\ndetermine which visual features are most characteristic of the considered\ncategories. To alleviate this issue, we propose a method that additionally\ntakes into account the names of the image classes. While the use of class names\nhas already been explored in previous work, our approach differs in two key\naspects. First, while previous work has aimed to directly predict visual\nprototypes from word embeddings, we found that better results can be obtained\nby treating visual and text-based prototypes separately. Second, we propose a\nsimple strategy for learning class name embeddings using the BERT language\nmodel, which we found to substantially outperform the GloVe vectors that were\nused in previous work. We furthermore propose a strategy for dealing with the\nhigh dimensionality of these vectors, inspired by models for aligning\ncross-lingual word embeddings. We provide experiments on miniImageNet, CUB and\ntieredImageNet, showing that our approach consistently improves the\nstate-of-the-art in metric-based FSL.\n"}
+{"abstract": "  The phenomenon of low-temperature superconductivity is intimately associated\nwith the condensation of weakly bound, very extended, strongly overlapping\nCooper pairs, and systematic experimental studies of the associated mean square\nradius (coherence length) have been made. While the extension of BCS theory to\nthe atomic nucleus has been successful beyond expectation, to our knowledge, no\nmeasurement of the nuclear coherence length (expected to be much larger than\nnuclear dimensions) has been reported in the literature. Recent studies of\nCooper pair transfer across a Josephson-like junction, transiently established\nin a heavy ion collision between superfluid nuclei, have likely changed the\nsituation, providing the experimental input for a quantitative estimate of the\nnuclear coherence length, as well as the basis for a nuclear analogue of the\n(ac) Josephson effect.\n"}
+{"abstract": "  The extreme intensities obtainable with lasers such as Gemini allow\nnon-linear QED phenomena to be investigated according to our calculations.\nElectron-positron pair production from a pure vacuum target, which has yet to\nbe observed experimentally, is possibly the most iconic process. Beyond\npair-production our campaign will allow the experimental investigation of\ncurrently unexplored extreme radiation regimes, like the quantum radiation\ndominated regime (where quantum and self-field effects become important) and\nnon-linear Compton scattering. This is the first experiment in a multi-part\ncampaign proposed by a major international collaboration to investigate\nnon-linear QED. This proposal is for the first experiment in a series of 3 to\nachieve our most high-profile experimental goal of pair production in vacuum,\nbut each experiment is designed to have its own tangible high-profile outcome.\n"}
+{"abstract": "  We introduce SHARE: a System for Hierarchical Assistive Recipe Editing to\nassist home cooks with dietary restrictions -- a population under-served by\nexisting cooking resources. Our hierarchical recipe editor makes necessary\nsubstitutions to a recipe's ingredients list and re-writes the directions to\nmake use of the new ingredients. We introduce the novel RecipePairs dataset of\n84K pairs of similar recipes in which one recipe satisfies one of seven dietary\nconstraints, allowing for supervised training of such recipe editing models.\nExperiments on this dataset demonstrate that our system produces convincing,\ncoherent recipes that are appropriate for a target dietary constraint (contain\nno prohibited ingredients). We show that this is a challenging task that cannot\nbe adequately solved with human-written ingredient substitution rules or\nstraightforward adaptation of state-of-the-art models for recipe generation. We\nfurther demonstrate through human evaluations and real-world cooking trials\nthat recipes edited by our system can be easily followed by home cooks to\ncreate delicious and satisfactory dishes.\n"}
+{"abstract": "  The center-of-gravity rule is tested for heavy and light-quark mesons. In the\nheavy-meson sector, the rule is excellently satisfied. In the light-quark\nsector, the rule suggests that the $a_0(980)$ could be the spin-partner of\n$a_2(1320)$, $a_1(1260)$, and $b_1(1235)$; $f_0(500)$ the spin-partner of\n$f_2(1270)$, $f_1(1285)$, and $h_1(1170)$; and $f_0(980)$ the spin-partner of\n$f_2'(1525)$, $f_1(1420)$, and $h_1(1415)$. From the decay and the production\nof light scalar mesons we find a consistent mixing angle $\\theta^{\\rm\ns}=(14\\pm4)^\\circ$. We conclude that $f_0(980)$ is likely \"octet-like\" in SU(3)\nwith a slightly larger $s\\bar s$ content and $f_0(500)$ is SU(3) \"singlet-like\"\nwith a larger $n\\bar n$ component. The $a_0(1450)$, $K^*_0(1430)$, $f_0(1500)$\nand $f_0(1370)$ are suggested as nonet of radial excitations. The scalar\nglueball is discussed as part of the wave function of scalar isoscalar mesons\nand not as additional \"intruder\". It seems not to cause supernumerosity.\n"}
+{"abstract": "  Crack detection is of great significance for monitoring the integrity and\nwell-being of the infrastructure such as bridges and underground pipelines,\nwhich are harsh environments for people to access. In recent years, computer\nvision techniques have been applied in detecting cracks in concrete structures.\nHowever, they suffer from variances in light conditions and shadows, lacking\nrobustness and resulting in many false positives. To address the uncertainty in\nvision, human inspectors actively touch the surface of the structures, guided\nby vision, which has not been explored in autonomous crack detection. In this\npaper, we propose a novel approach to detect and reconstruct cracks in concrete\nstructures using vision-guided active tactile perception. Given an RGB-D image\nof a structure, the rough profile of the crack in the structure surface will\nfirst be segmented with a fine-tuned Deep Convolutional Neural Networks, and a\nset of contact points are generated to guide the collection of tactile images\nby a camera-based optical tactile sensor. When contacts are made, a pixel-wise\nmask of the crack can be obtained from the tactile images and therefore the\nprofile of the crack can be refined by aligning the RGB-D image and the tactile\nimages. Extensive experiment results have shown that the proposed method\nimproves the effectiveness and robustness of crack detection and reconstruction\nsignificantly, compared to crack detection with vision only, and has the\npotential to enable robots to help humans with the inspection and repair of the\nconcrete infrastructure.\n"}
+{"abstract": "  We present a catalog of high-velocity CIV $\\lambda$ 1548,1551 mini-Broad\nAbsorption Lines (mini-BALs) in the archives of the VLT-UVES and Keck-HIRES\nspectrographs. We identify high-velocity CIV mini-BALs based on smooth rounded\nBAL-like profiles with velocity blueshifts $<$ $-$4000 km/s and widths in the\nrange 70 $\\lesssim$ FWHM(1548) $\\lesssim$ 2000 km/s (for $\\lambda$1548 alone).\nWe find 105 mini-BALs in 44 quasars from a total sample of 638 quasars. The\nfraction of quasars with at least one mini-BAL meeting our criteria is roughly\n$\\sim9$% after correcting for incomplete velocity coverage. However, the\nnumbers of systems rise sharply at lower velocities and narrower FWHMs,\nsuggesting that many outflow lines are missed by our study. All of the systems\nare highly ionized based on the strong presence of NV and OVI and/or the\nabsence of SiII and CII when within the wavelength coverage. Two of the\nmini-BAL systems in our catalog, plus three others at smaller velocity shifts,\nhave PV $\\lambda$1118,1128 absorption indicating highly saturated CIV\nabsorption and total hydrogen column densities $\\gtrsim 10^{22}$ cm$^{-3}$.\nMost of the mini-BALs are confirmed to have optical depths $\\gtrsim$1 with\npartial covering of the quasar continuum source. The covering fractions are as\nsmall as 0.06 in CIV and 0.03 in SiIV , corresponding to outflow absorbing\nstructures $<0.002$ pc across. When multiple lines are measured, the lines of\nless abundant ions tend to have narrower profiles and smaller covering\nfractions indicative of inhomogeneous absorbers where higher column densities\noccur in smaller clumps. This picture might extend to BAL outflows if the\nbroader and generally deeper BALs form in either the largest clumps or\ncollections of many mini-BAL-like clumps that blend together in observed quasar\nspectra.\n"}
+{"abstract": "  Thouless pumping is a fundamental instance of quantized transport, which is\ntopologically protected. Although its theoretical importance, the adiabaticity\ncondition is an obstacle for further practical applications. Here, focusing on\nthe Rice-Mele model, we provide a family of finite-frequency examples that\nensure both the absence of excitations and the perfect quantization of the\npumped charge at the end of each cycle. This family, which contains an\nadiabatic protocol as a limiting case, is obtained through a mapping onto the\nzero curvature representation of the Euclidean sinh-Gordon equation.\n"}
+{"abstract": "  The consumer Internet of Things (IoT) have developed in recent years. Mass\nIoT devices are constructed to build a huge communications network. But these\ndevices are insecure in reality, it means that the communications network are\nexposed by the attacker. Moreover, the IoT communication network also faces\nwith variety of sudden errors. Therefore, it easily leads to that is vulnerable\nwith the threat of attacker and system failure. The severe situation of IoT\ncommunication network motivates the development of new techniques to\nautomatically detect multi-anomaly. In this paper, we propose SS-VTCN, a\nsemi-supervised network for IoT multiple anomaly detection that works well\neffectively for IoT communication network. SS-VTCN is designed to capture the\nnormal patterns of the IoT traffic data based on the distribution whether it is\nlabeled or not by learning their representations with key techniques such as\nVariational Autoencoders and Temporal Convolutional Network. This network can\nuse the encode data to predict preliminary result, and reconstruct input data\nto determine anomalies by the representations. Extensive evaluation experiments\nbased on a benchmark dataset and a real consumer smart home dataset demonstrate\nthat SS-VTCN is more suitable than supervised and unsupervised method with\nbetter performance when compared other state-of-art semi-supervised method.\n"}
+{"abstract": "  The aim of this article is to introduce the concept of graded $2$-absorbing\ncoprimary submodules as a generalization of graded strongly $2$-absorbing\nsecond submodules, and explore some properties of this class. A non-zero graded\n$R$-submodule $N$ of a graded $R$-module $M$ is called a graded $2$-absorbing\ncoprimary $R$-submodule if whenever $x, y$ are homogeneous elements of $R$ and\n$K$ is a graded $R$-submodule of $M$ such that $xyN\\subseteq K$, then either\n$x$ or $y$ is in the graded radical of $(K :_{R} N)$ or $xy\\in Ann_{R}(N)$.\nSeveral results have been achieved.\n"}
+{"abstract": "  A small number of anomalously bright boulders on the near-Earth, rubble-pile\nasteroid (101955) Bennu were recently identified as eucritic material\noriginating from asteroid (4) Vesta. Building on this discovery, we explored\nthe global presence of exogenic boulders on Bennu. Our analysis focused on\nboulders larger than 1 m that show the characteristic 1-micron pyroxene\nabsorption band in the four-color MapCam data from the OSIRIS-REx mission. We\nconfirm the presence of exogenic boulders similar to eucrites and find that\nmixtures of eucrites with carbonaceous material is also a possible composition\nfor some boulders. Some of the exogenic boulders have spectral properties\nsimilar to those of ordinary chondrite (OC) meteorites, although the laboratory\nspectra of these meteorites have a higher albedo than those measured on Bennu,\nwhich could be explained by either a grain size effect, the presence of impact\nmelt, or optical mixing with carbonaceous material owing to dust coating. Our\nMonte Carlo simulations predict that the median amount of OC mass added to the\nparent body of Bennu is 0.055% and 0.037% of the volume of a 100- and\n200-km-diameter parent body, respectively. If Bennu was a uniformly mixed\nbyproduct of parent body and S-type projectiles, the equivalent mass of OC\nmaterial would be a sphere with a diameter of 36 to 40 m (or a volume of 24,200\nto 33,600 m3). The total amount of OC material in the interior of Bennu\nestimated from the MapCam data is slightly higher (91,000-150,000 m3).\n"}
+{"abstract": "  This is the second paper treating the double coset problem for classical\ngroups. Let $G$ be an algebraic group over an algebraically closed field $K$.\nThe double coset problem consists of classifying the pairs $H,J$ of closed\nconnected subgroups of $G$ with finitely many $(H,J)$-double cosets in $G$. The\ncritical setup occurs when one of $H,J$, say $H$, is reductive, and $J$ is a\nparabolic subgroup. Assume that $G$ is a classical group, $H$ is simple and $J$\nis a maximal parabolic $P_k$, the stabilizer of a totally singular $k$-space.\nThen most candidates have $k=1$ or $k=2$. The case $k=1$ was solved in a\nprevious paper and here we deal with $k=2$. We solve this case by determining\nall faithful irreducible self-dual $H$-modules $V$, such that $H$ has finitely\nmay orbits on totally singular $2$-spaces of $V$.\n"}
+{"abstract": "  We consider the moduli space of vector bundles of rank $n$ and degree $ng$\nover a fixed Riemann surface of genus $g\\geq 2$. We use the explicit\nparametrization in terms of the Tyurin data. In the moduli space there is a\n\"non-abelian\" Theta divisor, consisting of bundles with $h^1\\geq 1$. On the\ncomplement of this divisor we construct a non-abelian Cauchy kernel explicitly\nin terms of the Tyurin data. With the additional datum of a non-special\ndivisor, we can construct a reference flat holomorphic connection which is also\ndependent holomorphically on the moduli of the bundle. This allows us to\nidentify the bundle of Higgs fields, i.e. the cotangent bundle of the moduli\nspace, with the affine bundle of holomorphic connections and provide a\nmonodromy map into the ${\\rm GL}_n$ character variety. We show that the Goldman\nsymplectic structure on the character variety pulls back along this map to the\ncomplex canonical symplectic structure on the cotangent bundle and hence also\non the space of affine connections. The pull-back of the Liouville one-form to\nthe affine bundle of connections is then shown to be a logarithmic form with\npoles along the non-abelian theta divisor and residue given by $h^1$.\n"}
+{"abstract": "  Non-intrusive load monitoring (NILM) as the process of extracting the usage\npattern of appliances from the aggregated power signal is among successful\napproaches aiding residential energy management. In recent years, high volume\ndatasets on power profiles have become available, which has helped make\nclassification methods employed for the NILM purpose more effective and more\naccurate. However, the presence of multi-mode appliances and appliances with\nclose power values have remained influential in worsening the computational\ncomplexity and diminishing the accuracy of these algorithms. To tackle these\nchallenges, we propose an event-based classification process, in the first\nphase of which the $K$-nearest neighbors method, as a fast classification\ntechnique, is employed to extract power signals of appliances with exclusive\nnon-overlapping power values. Then, two deep learning models, which consider\nthe water consumption of some appliances as a novel signature in the network,\nare utilized to distinguish between appliances with overlapping power values.\nIn addition to power disaggregation, the proposed process as well extracts the\nwater consumption profiles of specific appliances. To illustrate the proposed\nprocess and validate its efficiency, seven appliances of the AMPds are\nconsidered, with the numerical classification results showing marked\nimprovement with respect to the existing classification-based NILM techniques.\n"}
+{"abstract": "  Majorana bound states are zero-energy states predicted to emerge in\ntopological superconductors and intense efforts seeking a definitive proof of\ntheir observation are still ongoing. A standard route to realize them involves\nantagonistic orders: a superconductor in proximity to a ferromagnet. Here we\nshow this issue can be resolved using antiferromagnetic rather than\nferromagnetic order. We propose to use a chain of antiferromagnetic skyrmions,\nin an otherwise collinear antiferromagnet, coupled to a bulk conventional\nsuperconductor as a novel platform capable of supporting Majorana bound states\nthat are robust against disorder. Crucially, the collinear antiferromagnetic\nregion neither suppresses superconductivity nor induces topological\nsuperconductivity, thus allowing for Majorana bound states localized at the\nends of the chain. Our model introduces a new class of systems where\ntopological superconductivity can be induced by editing antiferromagnetic\ntextures rather than locally tuning material parameters, opening avenues for\nthe conclusive observation of Majorana bound states.\n"}
+{"abstract": "  We describe the use of machine learning algorithms to select high-quality\nmeasurements for the Mu2e experiment. This technique is important for\nexperiments with backgrounds that arise due to measurement errors. The\nalgorithms use multiple pieces of ancillary information that are sensitive to\nmeasurement quality to separate high-quality and low-quality measurements.\n"}
+{"abstract": "  Family history is a major risk factor for many types of cancer. Mendelian\nrisk prediction models translate family histories into cancer risk predictions\nbased on knowledge of cancer susceptibility genes. These models are widely used\nin clinical practice to help identify high-risk individuals. Mendelian models\nleverage the entire family history, but they rely on many assumptions about\ncancer susceptibility genes that are either unrealistic or challenging to\nvalidate due to low mutation prevalence. Training more flexible models, such as\nneural networks, on large databases of pedigrees can potentially lead to\naccuracy gains. In this paper, we develop a framework to apply neural networks\nto family history data and investigate their ability to learn inherited\nsusceptibility to cancer. While there is an extensive literature on neural\nnetworks and their state-of-the-art performance in many tasks, there is little\nwork applying them to family history data. We propose adaptations of\nfully-connected neural networks and convolutional neural networks to pedigrees.\nIn data simulated under Mendelian inheritance, we demonstrate that our proposed\nneural network models are able to achieve nearly optimal prediction\nperformance. Moreover, when the observed family history includes misreported\ncancer diagnoses, neural networks are able to outperform the Mendelian BRCAPRO\nmodel embedding the correct inheritance laws. Using a large dataset of over\n200,000 family histories, the Risk Service cohort, we train prediction models\nfor future risk of breast cancer. We validate the models using data from the\nCancer Genetics Network.\n"}
+{"abstract": "  Controlling growth via cell division is crucial in the development of higher\norganisms, and yet the mechanisms through which this is achieved, e.g., in\nepithelial tissue, is not yet fully understood. We show that by coupling the\ncell cycle oscillator governing cell division to signals that encode\ninter-cellular contacts, this phenomenon can be seen as a collective dynamical\ntransition in a system of coupled oscillators in lattices with changing degree\nof disorder. As the distribution of cellular morphological characteristics\nbecome more homogeneous over the course of development, the contact-induced\nsignals to the cells increase beyond a critical value to trigger coordinated\ncessation of oscillations, eventually leading to growth arrest. Our results\nsuggest that the global phenomenon of growth rate reduction as a tissue\napproaches its appropriate size is causally related to the increasingly regular\ngeometry of local cell-cell contact interfaces.\n"}
+{"abstract": "  Latent class models with covariates are widely used for psychological,\nsocial, and educational researches. Yet the fundamental identifiability issue\nof these models has not been fully addressed. Among the previous researches on\nthe identifiability of latent class models containing covariates, Huang and\nBandeen-Roche (2004, Psychometrika, 69:5-32) studied the local identifiability\nconditions. However, motivated by recent advances in the identifiability of\nrestricted latent class models, particularly the Cognitive Diagnosis Models\n(CDMs), we show in this work that the conditions in Huang and Bandeen-Roche\n(2004) are only necessary but not sufficient to determine the local\nidentifiability of the model parameters. To address the open identifiability\nissue for latent class models with covariates, this work establishes conditions\nto ensure the global identifiability of the model parameters in both strict and\ngeneric sense. Moreover, our results extend to polytomous-response CDMs with\ncovariates, which generalizes the existing identifiability results for CDMs.\n"}
+{"abstract": "  Inflationary model driven by a scalar field whose potential has a step in the\nsecond derivative with respect to the field is considered. For the best fit\npotential parameter values, the 3-point function and the non-Gaussianity\nassociated with the featured model is calculated. We study the shape and scale\ndependence of the 3-point function. The distinctive feature of this model is\nits characteristic ringing behaviour of $f_{NL}$. We can see that the\noscillations in $f_{NL}$ in this model last for a much longer range of k\nvalues, as compared to the previously studied models. In that sense, this model\nis potentially distinguishable from models with other features in the\npotential.\n"}
+{"abstract": "  Agents operating in unstructured environments often produce negative side\neffects (NSE), which are difficult to identify at design time. While the agent\ncan learn to mitigate the side effects from human feedback, such feedback is\noften expensive and the rate of learning is sensitive to the agent's state\nrepresentation. We examine how humans can assist an agent, beyond providing\nfeedback, and exploit their broader scope of knowledge to mitigate the impacts\nof NSE. We formulate this problem as a human-agent team with decoupled\nobjectives. The agent optimizes its assigned task, during which its actions may\nproduce NSE. The human shapes the environment through minor reconfiguration\nactions so as to mitigate the impacts of the agent's side effects, without\naffecting the agent's ability to complete its assigned task. We present an\nalgorithm to solve this problem and analyze its theoretical properties. Through\nexperiments with human subjects, we assess the willingness of users to perform\nminor environment modifications to mitigate the impacts of NSE. Empirical\nevaluation of our approach shows that the proposed framework can successfully\nmitigate NSE, without affecting the agent's ability to complete its assigned\ntask.\n"}
+{"abstract": "  We find the quantum power emitted and distribution in $3+1$-dimensions of\nrelativistic acceleration radiation using a single perfectly reflecting mirror\nvia Lorentz invariance demonstrating close analogies to point charge radiation\nin classical electrodynamics.\n"}
+{"abstract": "  The minimum height of vertex and edge partition trees are well-studied graph\nparameters known as, for instance, vertex and edge ranking number. While they\nare NP-hard to determine in general, linear-time algorithms exist for trees.\nMotivated by a correspondence with Dasgupta's objective for hierarchical\nclustering we consider the total rather than maximum depth of vertices as an\nalternative objective for minimization. For vertex partition trees this leads\nto a new parameter with a natural interpretation as a measure of robustness\nagainst vertex removal.\n  As tools for the study of this family of parameters we show that they have\nsimilar recursive expressions and prove a binary tree rotation lemma. The new\nparameter is related to trivially perfect graph completion and therefore\nintractable like the other three are known to be. We give polynomial-time\nalgorithms for both total-depth variants on caterpillars and on trees with a\nbounded number of leaf neighbors. For general trees, we obtain a\n2-approximation algorithm.\n"}
+{"abstract": "  Learning with graphs has attracted significant attention recently. Existing\nrepresentation learning methods on graphs have achieved state-of-the-art\nperformance on various graph-related tasks such as node classification, link\nprediction, etc. However, we observe that these methods could leak serious\nprivate information. For instance, one can accurately infer the links (or node\nidentity) in a graph from a node classifier (or link predictor) trained on the\nlearnt node representations by existing methods. To address the issue, we\npropose a privacy-preserving representation learning framework on graphs from\nthe \\emph{mutual information} perspective. Specifically, our framework includes\na primary learning task and a privacy protection task, and we consider node\nclassification and link prediction as the two tasks of interest. Our goal is to\nlearn node representations such that they can be used to achieve high\nperformance for the primary learning task, while obtaining performance for the\nprivacy protection task close to random guessing. We formally formulate our\ngoal via mutual information objectives. However, it is intractable to compute\nmutual information in practice. Then, we derive tractable variational bounds\nfor the mutual information terms, where each bound can be parameterized via a\nneural network. Next, we train these parameterized neural networks to\napproximate the true mutual information and learn privacy-preserving node\nrepresentations. We finally evaluate our framework on various graph datasets.\n"}
+{"abstract": "  We propose a novel framework for controllable pathological image synthesis\nfor data augmentation. Inspired by CycleGAN, we perform cycle-consistent\nimage-to-image translation between two domains: healthy and pathological.\nGuided by a semantic mask, an adversarially trained generator synthesizes\npathology on a healthy image in the specified location. We demonstrate our\napproach on an institutional dataset of cerebral microbleeds in traumatic brain\ninjury patients. We utilize synthetic images generated with our method for data\naugmentation in cerebral microbleeds detection. Enriching the training dataset\nwith synthetic images exhibits the potential to increase detection performance\nfor cerebral microbleeds in traumatic brain injury patients.\n"}
+{"abstract": "  We consider a particle undergoing Brownian motion in Euclidean space of any\ndimension, forced by a Gaussian random velocity field that is white in time and\nsmooth in space. We show that conditional on the velocity field, the quenched\ndensity of the particle after a long time can be approximated pointwise by the\nproduct of a deterministic Gaussian density and a spacetime-stationary random\nfield $U$. If the velocity field is additionally assumed to be incompressible,\nthen $U\\equiv 1$ almost surely and we obtain a local central limit theorem.\n"}
+{"abstract": "  We use the Holder regularity analysis to study the symmetry breaking and\nrecovery due to a parametric potential generated via the strictly isospectral\nfactorization method. The initial potential is two-dimensional and periodic in\nthe two Cartesian directions, with the symmetry group $P_{4mm}$. The resulting\nparametric isospectral potential display a P_m symmetry for values of the\nparameter moderately close to the singular value gamma_s. However, at large\nvalues of the parameter, visually around gamma=gamma_s+110, the original\nsymmetry is recovered. For a much higher precision value of the parameter for\nthis symmetry recovery, we show that the multifractal spectrum of the\nparametric potential can be conveniently used. In the latter case, we obtain\ngamma=gamma_s+201.085 for three decimal digits precision.\n"}
+{"abstract": "  In this note, we explicitly compute the vacuum expectation value of the\ncommutator of scalar fields in a d-dimensional conformal field theory on the\ncylinder. We find from explicit calculations that we need smearing not only in\nspace but also in time to have finite commutators except for those of free\nscalar operators. Thus the equal time commutators of the scalar fields are not\nwell-defined for a non-free conformal field theory, even if which is defined\nfrom the Lagrangian. We also have the commutator for a conformal field theory\non Minkowski space, instead of the cylinder, by taking the small distance\nlimit. For the conformal field theory on Minkowski space, the above statements\nare also applied.\n"}
+{"abstract": "  We investigate contributions to the anomalous magnetic moments of charged\nleptons in the neutrino-extended Standard Model Effective Field Theory\n($\\nu$SMEFT). We discuss how $\\nu$SMEFT operators can contribute to a lepton's\nmagnetic moment at one- and two-loop order. We show that only one operator can\naccount for existing electronic and muonic discrepancies, assuming new physics\nappears above $1$ TeV. In particular, we find that a right-handed charged\ncurrent in combination with minimal sterile-active mixing can explain the\ndiscrepancy for sterile neutrino masses of $\\mathcal O(100)$ GeV while avoiding\ndirect and indirect constraints. We discuss how searches for sterile neutrino\nproduction at the (HL-)LHC, measurements of $h\\rightarrow \\mu^+ \\mu^-$ and\nsearches for $h\\rightarrow e^+ e^-$, neutrinoless double beta decay\nexperiments, and improved unitarity tests of the CKM matrix can further probe\nthe relevant parameter space.\n"}
+{"abstract": "  Joint design of control and communication in Wireless Networked Control\nSystems (WNCS) is a promising approach for future wireless industrial\napplications. In this context, Age of Information (AoI) has been increasingly\nutilized as a metric that is more representative than latency in the context of\nsystems with a sense-compute-actuate cycle. Nevertheless, AoI is commonly\ndefined for a single communication direction, Downlink or Uplink, which does\nnot capture the closed-loop dynamics. In this paper, we extend the concept of\nAoI by defining a new metric, Age of Loop (AoL), relevant for WNCS closed-loop\nsystems. The AoL is defined as the time elapsed since the piece of information\ncausing the latest action or state (depending on the selected time origin) was\ngenerated. We then use the proposed metric to learn the WNCS latency and\nfreshness bounds and we apply such learning methodology to minimize the long\nterm WNCS cost with the least amount of bandwidth. We show that, using the AoL,\nwe can learn the control system requirement and use this information to\noptimize network resources.\n"}
+{"abstract": "  Pre-trained language models have achieved human-level performance on many\nMachine Reading Comprehension (MRC) tasks, but it remains unclear whether these\nmodels truly understand language or answer questions by exploiting statistical\nbiases in datasets. Here, we demonstrate a simple yet effective method to\nattack MRC models and reveal the statistical biases in these models. We apply\nthe method to the RACE dataset, for which the answer to each MRC question is\nselected from 4 options. It is found that several pre-trained language models,\nincluding BERT, ALBERT, and RoBERTa, show consistent preference to some\noptions, even when these options are irrelevant to the question. When\ninterfered by these irrelevant options, the performance of MRC models can be\nreduced from human-level performance to the chance-level performance. Human\nreaders, however, are not clearly affected by these irrelevant options.\nFinally, we propose an augmented training method that can greatly reduce\nmodels' statistical biases.\n"}
+{"abstract": "  We study the problem of entity-relation extraction in the presence of\nsymbolic domain knowledge. Such knowledge takes the form of an ontology\ndefining relations and their permissible arguments. Previous approaches set out\nto integrate such knowledge in their learning approaches either through\nself-training, or through approximations that lose the precise meaning of the\nlogical expressions. By contrast, our approach employs semantic loss which\ncaptures the precise meaning of a logical sentence through maintaining a\nprobability distribution over all possible states, and guiding the model to\nsolutions which minimize any constraint violations. With a focus on low-data\nregimes, we show that semantic loss outperforms the baselines by a wide margin.\n"}
+{"abstract": "  Non-intrusive load monitoring (NILM) is a technique that uses a single sensor\nto measure the total power consumption of a building. Using an energy\ndisaggregation method, the consumption of individual appliances can be\nestimated from the aggregate measurement. Recent disaggregation algorithms have\nsignificantly improved the performance of NILM systems. However, the\ngeneralization capability of these methods to different houses as well as the\ndisaggregation of multi-state appliances are still major challenges. In this\npaper we address these issues and propose an energy disaggregation approach\nbased on the variational autoencoders framework. The probabilistic encoder\nmakes this approach an efficient model for encoding information relevant to the\nreconstruction of the target appliance consumption. In particular, the proposed\nmodel accurately generates more complex load profiles, thus improving the power\nsignal reconstruction of multi-state appliances. Moreover, its regularized\nlatent space improves the generalization capabilities of the model across\ndifferent houses. The proposed model is compared to state-of-the-art NILM\napproaches on the UK-DALE and REFIT datasets, and yields competitive results.\nThe mean absolute error reduces by 18% on average across all appliances\ncompared to the state-of-the-art. The F1-Score increases by more than 11%,\nshowing improvements for the detection of the target appliance in the aggregate\nmeasurement.\n"}
+{"abstract": "  The attempt to include the Pauli principle in the Monte Carlo method by\nacting also on the free flight step and not only at the end of each collision\nis investigated. The charge transport in suspended monolayer graphene is\nconsidered as test case. The results are compared with those obtained in the\nstandard Ensemble Monte Carlo technique and in the new Direct Simulation Monte\nCarlo algorithm which is able to correctly handle with Pauli's principle. The\nphysical aspects of the investigated approach are analyzed as well.\n"}
+{"abstract": "  Inspired by recent work on extended image volumes that lays the ground for\nrandomized probing of extremely large seismic wavefield matrices, we present a\nmemory frugal and computationally efficient inversion methodology that uses\ntechniques from randomized linear algebra. By means of a carefully selected\nrealistic synthetic example, we demonstrate that we are capable of achieving\ncompetitive inversion results at a fraction of the memory cost of conventional\nfull-waveform inversion with limited computational overhead. By exchanging\nmemory for negligible computational overhead, we open with the presented\ntechnology the door towards the use of low-memory accelerators such as GPUs.\n"}
+{"abstract": "  This paper describes the performance of the team cs60075_team2 at SemEval\n2021 Task 1 - Lexical Complexity Prediction. The main contribution of this\npaper is to fine-tune transformer-based language models pre-trained on several\ntext corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the\ncorpora from which the CompLex Dataset was extracted, and others being from\nother specific domains such as Finance, Law, etc. We perform ablation studies\non selecting the transformer models and how their individual complexity scores\nare aggregated to get the resulting complexity scores. Our method achieves a\nbest Pearson Correlation of $0.784$ in sub-task 1 (single word) and $0.836$ in\nsub-task 2 (multiple word expressions).\n"}
+{"abstract": "  Type-2 fuzzy differential equations (T2FDEs) of order 1 are already known and\nthe solution method of type-2 fuzzy initial value problems (T2FIVPs) for them\nwas given by M. Mazandarani and M. Najariyan \\cite{MN} in 2014. We give the\nsolution method of second-order T2FIVPs in this paper. Furthermore, we would\nlike to propose new notations for type-2 fuzzy theory where symbols tend to be\ncomplicated and misleading. In particular, the Hukuhara differential symbols\nintroduced experimentally in this paper will give us clearler meanings and\nexpressions.\n"}
+{"abstract": "  This paper introduces neural architecture search (NAS) for the automatic\ndiscovery of end-to-end keyword spotting (KWS) models in limited resource\nenvironments. We employ a differentiable NAS approach to optimize the structure\nof convolutional neural networks (CNNs) operating on raw audio waveforms. After\na suitable KWS model is found with NAS, we conduct quantization of weights and\nactivations to reduce the memory footprint. We conduct extensive experiments on\nthe Google speech commands dataset. In particular, we compare our end-to-end\napproach to mel-frequency cepstral coefficient (MFCC) based systems. For\nquantization, we compare fixed bit-width quantization and trained bit-width\nquantization. Using NAS only, we were able to obtain a highly efficient model\nwith an accuracy of 95.55% using 75.7k parameters and 13.6M operations. Using\ntrained bit-width quantization, the same model achieves a test accuracy of\n93.76% while using on average only 2.91 bits per activation and 2.51 bits per\nweight.\n"}
+{"abstract": "  We consider the problem of explaining the predictions of graph neural\nnetworks (GNNs), which otherwise are considered as black boxes. Existing\nmethods invariably focus on explaining the importance of graph nodes or edges\nbut ignore the substructures of graphs, which are more intuitive and\nhuman-intelligible. In this work, we propose a novel method, known as\nSubgraphX, to explain GNNs by identifying important subgraphs. Given a trained\nGNN model and an input graph, our SubgraphX explains its predictions by\nefficiently exploring different subgraphs with Monte Carlo tree search. To make\nthe tree search more effective, we propose to use Shapley values as a measure\nof subgraph importance, which can also capture the interactions among different\nsubgraphs. To expedite computations, we propose efficient approximation schemes\nto compute Shapley values for graph data. Our work represents the first attempt\nto explain GNNs via identifying subgraphs explicitly and directly. Experimental\nresults show that our SubgraphX achieves significantly improved explanations,\nwhile keeping computations at a reasonable level.\n"}
+{"abstract": "  We employ model predictive control for a multi-period portfolio optimization\nproblem. In addition to the mean-variance objective, we construct a portfolio\nwhose allocation is given by model predictive control with a risk-parity\nobjective, and provide a successive convex program algorithm that provides 30\ntimes faster and robust solutions in the experiments. Computational results on\nthe multi-asset universe show that multi-period models perform better than\ntheir single period counterparts in out-of-sample period, 2006-2020. The\nout-of-sample risk-adjusted performance of both mean-variance and risk-parity\nformulations beat the fix-mix benchmark, and achieve Sharpe ratio of 0.64 and\n0.97, respectively.\n"}
+{"abstract": "  Anomaly detection with weakly supervised video-level labels is typically\nformulated as a multiple instance learning (MIL) problem, in which we aim to\nidentify snippets containing abnormal events, with each video represented as a\nbag of video snippets. Although current methods show effective detection\nperformance, their recognition of the positive instances, i.e., rare abnormal\nsnippets in the abnormal videos, is largely biased by the dominant negative\ninstances, especially when the abnormal events are subtle anomalies that\nexhibit only small differences compared with normal events. This issue is\nexacerbated in many methods that ignore important video temporal dependencies.\nTo address this issue, we introduce a novel and theoretically sound method,\nnamed Robust Temporal Feature Magnitude learning (RTFM), which trains a feature\nmagnitude learning function to effectively recognise the positive instances,\nsubstantially improving the robustness of the MIL approach to the negative\ninstances from abnormal videos. RTFM also adapts dilated convolutions and\nself-attention mechanisms to capture long- and short-range temporal\ndependencies to learn the feature magnitude more faithfully. Extensive\nexperiments show that the RTFM-enabled MIL model (i) outperforms several\nstate-of-the-art methods by a large margin on four benchmark data sets\n(ShanghaiTech, UCF-Crime, XD-Violence and UCSD-Peds) and (ii) achieves\nsignificantly improved subtle anomaly discriminability and sample efficiency.\nCode is available at https://github.com/tianyu0207/RTFM.\n"}
+{"abstract": "  The attenuation of ultrasound waves in photoacoustic and thermoacoustic\nimaging presents an important drawback in the applicability of these\nmodalities. This issue has been addressed previously in the applied and\ntheoretical literature, and some advances have been made on the topic. In\nparticular, stability inequalities have been proposed for the inverse problem\nof initial source recovery with partial observations under the assumption of\nunique determination of the initial pressure. The main goal of this work is to\nfill this gap, this is, we prove the uniqueness property for the inverse\nproblem and establish the associated stability estimates as well. The problem\nof reconstructing the initial condition of acoustic waves in the complete-data\nsetting is revisited and a new Neumann series reconstruction formula is\nobtained for the case of partial observations in a semi-bounded geometry. A\nnumerical simulation is also included to test the method.\n"}
+{"abstract": "  The production of the hypermagnetic gyrotropy is investigated under the\nassumption that the gauge coupling smoothly evolves during a quasi-de Sitter\nphase and then flattens out in the radiation epoch by always remaining\nperturbative. In the plane defined by the strength of the anomalous\ninteractions and by the rate of evolution of the gauge coupling the actual\nweight of the pseudoscalar interactions turns out to be always rather modest if\nmajor deviations from the homogeneity are to be avoided during the inflationary\nphase. Even if the gauge power spectra are related by duality only in the\nabsence of anomalous contributions, an approximate duality symmetry constrains\nthe late-time form of the hypermagnetic power spectra. Since the hypermagnetic\ngyrotropy associated with the modes reentering prior to the phase transition\nmust be released into fermions later on, the portions of the parameter space\nwhere the obtained baryon asymmetry is close to the observed value are the most\nrelevant for the present ends. For the same range of parameters the magnetic\npower spectra associated with the modes reentering after symmetry breaking may\neven be of the order of a few hundredths of a nG over typical length scales\ncomparable with the Mpc prior to the collapse of the protogalaxy.\n"}
+{"abstract": "  The asymptotic law for the expected nodal volume of random non-Gaussian\nmonochromatic band-limited functions is determined in vast generality. Our\nmethods combine microlocal analytic techniques and modern probability theory. A\nparticularly challenging obstacle needed to overcome is the possible\nconcentration of nodal volume on a small proportion of the manifold, requiring\nsolutions in both disciplines. As for the fine aspects of the distribution of\nnodal volume, such as its variance, it is expected that the non-Gaussian\nmonochromatic functions behave qualitatively differently compared to their\nGaussian counterpart, with some conjectures been put forward.\n"}
+{"abstract": "  In this work, we propose Masked Noun-Phrase Prediction (MNPP), a pre-training\nstrategy to tackle pronoun resolution in a fully unsupervised setting. Firstly,\nWe evaluate our pre-trained model on various pronoun resolution datasets\nwithout any finetuning. Our method outperforms all previous unsupervised\nmethods on all datasets by large margins. Secondly, we proceed to a few-shot\nsetting where we finetune our pre-trained model on WinoGrande-S and XS\nseparately. Our method outperforms RoBERTa-large baseline with large margins,\nmeanwhile, achieving a higher AUC score after further finetuning on the\nremaining three official splits of WinoGrande.\n"}
+{"abstract": "  The abundance of data has given machine learning considerable momentum in\nnatural sciences and engineering. However, the modeling of simulated physical\nprocesses remains difficult. A key problem is the correct handling of geometric\nboundaries. While triangularized geometric boundaries are very common in\nengineering applications, they are notoriously difficult to model by machine\nlearning approaches due to their heterogeneity with respect to size and\norientation. In this work, we introduce Boundary Graph Neural Networks (BGNNs),\nwhich dynamically modify graph structures to address boundary conditions.\nBoundary graph structures are constructed via modifying edges, augmenting node\nfeatures, and dynamically inserting virtual nodes. The new BGNNs are tested on\ncomplex 3D granular flow processes of hoppers and rotating drums which are\nstandard components of industrial machinery. Using precise simulations that are\nobtained by an expensive and complex discrete element method, BGNNs are\nevaluated in terms of computational efficiency as well as prediction accuracy\nof particle flows and mixing entropies. Even if complex boundaries are present,\nBGNNs are able to accurately reproduce 3D granular flows within simulation\nuncertainties over hundreds of thousands of simulation timesteps, and most\nnotably particles completely stay within the geometric objects without using\nhandcrafted conditions or restrictions.\n"}
+{"abstract": "  We resolve the min-max complexity of distributed stochastic convex\noptimization (up to a log factor) in the intermittent communication setting,\nwhere $M$ machines work in parallel over the course of $R$ rounds of\ncommunication to optimize the objective, and during each round of\ncommunication, each machine may sequentially compute $K$ stochastic gradient\nestimates. We present a novel lower bound with a matching upper bound that\nestablishes an optimal algorithm.\n"}
+{"abstract": "  We observe a $n$-sample, the distribution of which is assumed to belong, or\nat least to be close enough, to a given mixture model. We propose an estimator\nof this distribution that belongs to our model and possesses some robustness\nproperties with respect to a possible misspecification of it. We establish a\nnon-asymptotic deviation bound for the Hellinger distance between the target\ndistribution and its estimator when the model consists of a mixture of\ndensities that belong to VC-subgraph classes. Under suitable assumptions and\nwhen the mixture model is well-specified, we derive risk bounds for the\nparameters of the mixture. Finally, we design a statistical procedure that\nallows us to select from the data the number of components as well as suitable\nmodels for each of the densities that are involved in the mixture. These models\nare chosen among a collection of candidate ones and we show that our selection\nrule combined with our estimation strategy result in an estimator which\nsatisfies an oracle-type inequality.\n"}
+{"abstract": "  We define certain arithmetic derivatives on $\\mathbb{Z}$ that respect the\nLeibniz rule, are additive for a chosen equation $a+b=c$, and satisfy a\nsuitable non-degeneracy condition. Using Geometry of Numbers, we\nunconditionally show their existence with controlled size. We prove that any\npower-saving improvement on our size bounds would give a version of the $abc$\nConjecture. In fact, we show that the existence of sufficiently small\narithmetic derivatives in our sense is equivalent to the $abc$ Conjecture. Our\nresults give an explicit manifestation of an analogy suggested by Vojta in the\neighties, relating Geometry of Numbers in arithmetic to derivatives in function\nfields and Nevanlinna theory. In addition, our construction formalizes the\nwidespread intuition that the $abc$ Conjecture should be related to arithmetic\nderivatives of some sort.\n"}
+{"abstract": "  Python libraries are widely used for machine learning and scientific\ncomputing tasks today. APIs in Python libraries are deprecated due to feature\nenhancements and bug fixes in the same way as in other languages. These\ndeprecated APIs are discouraged from being used in further software\ndevelopment. Manually detecting and replacing deprecated APIs is a tedious and\ntime-consuming task due to the large number of API calls used in the projects.\nMoreover, the lack of proper documentation for these deprecated APIs makes the\ntask challenging. To address this challenge, we propose an algorithm and a tool\nAPIScanner that automatically detects deprecated APIs in Python libraries. This\nalgorithm parses the source code of the libraries using abstract syntax tree\n(ASTs) and identifies the deprecated APIs via decorator, hard-coded warning or\ncomments. APIScanner is a Visual Studio Code Extension that highlights and\nwarns the developer on the use of deprecated API elements while writing the\nsource code. The tool can help developers to avoid using deprecated API\nelements without the execution of code. We tested our algorithm and tool on six\npopular Python libraries, which detected 838 of 871 deprecated API elements.\nDemo of APIScanner: https://youtu.be/1hy_ugf-iek. Documentation, tool, and\nsource code can be found here: https://rishitha957.github.io/APIScanner.\n"}
+{"abstract": "  Deep Neural Networks (DNNs) have achieved remarkable performance on a variety\nof applications but are extremely vulnerable to adversarial perturbation. To\naddress this issue, various defense methods have been proposed to enhance model\nrobustness. Unfortunately, the most representative and promising methods, such\nas adversarial training and its variants, usually degrade model accuracy on\nbenign samples, limiting practical utility. This indicates that it is difficult\nto extract both robust and accurate features using a single network under\ncertain conditions, such as limited training data, resulting in a trade-off\nbetween accuracy and robustness. To tackle this problem, we propose an\nAdversarial Feature Stacking (AFS) model that can jointly take advantage of\nfeatures with varied levels of robustness and accuracy, thus significantly\nalleviating the aforementioned trade-off. Specifically, we adopt multiple\nnetworks adversarially trained with different perturbation budgets to extract\neither more robust features or more accurate features. These features are then\nfused by a learnable merger to give final predictions. We evaluate the AFS\nmodel on CIFAR-10 and CIFAR-100 datasets with strong adaptive attack methods,\nwhich significantly advances the state-of-the-art in terms of the trade-off.\nWithout extra training data, the AFS model achieves a benign accuracy\nimprovement of 6% on CIFAR-10 and 9% on CIFAR-100 with comparable or even\nstronger robustness than the state-of-the-art adversarial training methods.\nThis work demonstrates the feasibility to obtain both accurate and robust\nmodels under the circumstances of limited training data.\n"}
+{"abstract": "  The concept of balancedly splittable orthogonal designs is introduced along\nwith a recursive construction. As an application, equiangular tight frames over\nthe real, complex, and quaternions meeting the Delsarte-Goethals-Seidel upper\nbound is obtained.\n"}
+{"abstract": "  In this paper, we introduce a noisy framework for SFTs, allowing some amount\nof forbidden patterns to appear. Using the Besicovitch distance, which permits\na global comparison of configurations, we then study the closeness of noisy\nmeasures to non-noisy ones as the amount of noise goes to 0. Our first main\nresult is the full classification of the (in)stability in the one-dimensional\ncase. Our second main result is a stability property under Bernoulli noise for\nhigher-dimensional periodic SFTs, which we finally extend to an aperiodic\nexample through a variant of the Robinson tiling.\n"}
+{"abstract": "  We study Cayley graphs of abelian groups from the perspective of quantum\nsymmetries. We develop a general strategy for determining the quantum\nautomorphism groups of such graphs. Applying this procedure, we find the\nquantum symmetries of the halved cube graph, the folded cube graph and the\nHamming graphs.\n"}
+{"abstract": "  Gravitational waves are a radically new way to peer into the darkest depths\nof the cosmos. Pulsars can be used to make direct detections of gravitational\nwaves through precision timing. When a gravitational wave passes between a\npulsar and the Earth, it stretches and squeezes the intermediate space-time,\nleading to deviations of the measured pulse arrival times away from model\nexpectations. Combining the data from many Galactic pulsars can corroborate\nsuch a signal, and enhance its detection significance. This technique is known\nas a Pulsar Timing Array (PTA). Here I provide an overview of PTAs as a\nprecision gravitational-wave detection instrument, then review the types of\nsignal and noise processes that we encounter in typical pulsar data analysis. I\ntake a pragmatic approach, illustrating how searches are performed in real\nlife, and where possible directing the reader to codes or techniques that they\ncan explore for themselves. The goal is to provide theoretical background and\npractical recipes for data exploration that allow the reader to join in the\nexciting hunt for very low frequency gravitational waves.\n"}
+{"abstract": "  We study variation in policing outcomes attributable to differential policing\npractices in New York City (NYC) using geographic regression discontinuity\ndesigns. By focusing on small geographic windows near police precinct\nboundaries we can estimate local average treatment effects of precincts on\narrest rates. The standard geographic regression discontinuity design relies on\ncontinuity assumptions of the potential outcome surface or a local\nrandomization assumption within a window around the boundary. While these\nassumptions are often thought to be more realistic than other assumptions used\nto infer causality from observational data, they can easily be violated in\nrealistic applications. We develop a novel and robust approach to testing\nwhether there are differences in policing outcomes that are caused by\ndifferences in police precincts across NYC. In particular, our test is robust\nto violations of the assumptions traditionally made in geographic regression\ndiscontinuity designs and is valid under much weaker assumptions. We use a\nunique form of resampling to identify new geographic boundaries that are known\nto have no treatment effect, which provides a valid estimate of our estimator's\nnull distribution even under violations of standard assumptions. We find that\nthis procedure gives substantially different results in the analysis of NYC\narrest rates than those that rely on standard assumptions, thereby providing\nmore robust estimates of the nature of the effect of police precincts on arrest\nrates in NYC.\n"}
+{"abstract": "  The emergence of Big Data in recent years has resulted in a growing need for\nefficient data processing solutions. While infrastructures with sufficient\ncompute power are available, the I/O bottleneck remains. The Linux page cache\nis an efficient approach to reduce I/O overheads, but few experimental studies\nof its interactions with Big Data applications exist, partly due to limitations\nof real-world experiments. Simulation is a popular approach to address these\nissues, however, existing simulation frameworks do not simulate page caching\nfully, or even at all. As a result, simulation-based performance studies of\ndata-intensive applications lead to inaccurate results.\n  In this paper, we propose an I/O simulation model that includes the key\nfeatures of the Linux page cache. We have implemented this model as part of the\nWRENCH workflow simulation framework, which itself builds on the popular\nSimGrid distributed systems simulation framework. Our model and its\nimplementation enable the simulation of both single-threaded and multithreaded\napplications, and of both writeback and writethrough caches for local or\nnetwork-based filesystems. We evaluate the accuracy of our model in different\nconditions, including sequential and concurrent applications, as well as local\nand remote I/Os. We find that our page cache model reduces the simulation error\nby up to an order of magnitude when compared to state-of-the-art, cacheless\nsimulations.\n"}
+{"abstract": "  Song et al. [Phys. Rev. C 102, 065208 (2020)] presented results for the\n$\\Lambda_c N$ interaction based on an extrapolation of lattice simulations by\nthe HAL QCD Collaboration at unphysical quark masses to the physical point via\ncovariant chiral effective field theory. We point out that their predictions\nfor the $^3D_1$ partial wave disagree with available lattice results. We\ndiscuss the origin of that disagreement and present a comparison with\npredictions from conventional (non-relativistic) chiral effective field theory.\n"}
+{"abstract": "  A $(0,1)$-matrix has the consecutive-ones property (C1P) if its columns can\nbe permuted to make the $1$'s in each row appear consecutively. This property\nwas characterised in terms of forbidden submatrices by Tucker in 1972. Several\ngraph classes were characterised by means of this property, including interval\ngraphs and strongly chordal digraphs. In this work, we define and characterise\n2-nested matrices, which are $(0,1)$-matrices with a variant of the C1P and for\nwhich there is also certain assignment of one of two colors to each block of\nconsecutive $1$'s in each row. The characterization of 2-nested matrices in the\npresent work is of key importance to characterise split graphs that are also\ncircle by minimal forbidden induced subgraphs.\n"}
+{"abstract": "  In this article, we define the $m$-prismatic site and the $m$-$q$-crystalline\nsite, which is a higher level analogue of the prismatic site and the\n$q$-crystalline site respectively. We prove a certain equivalence between the\ncategory of crystals on the $m$-prismatic site (resp. the $m$-$q$-crystalline\nsite) and that on the prismatic site (resp. the $q$-crystalline site), which\ncan be regarded as the prismatic (resp. the $q$-crystalline) analogue of the\nFrobenius descent due to Berthelot and the Cartier transform due to\nOgus-Vologodsky, Oyama and Xu. We also prove the equivalence between the\ncategory of crystals on the $m$-prismatic site and that on the\n$(m-1)$-$q$-crystalline site.\n"}
+{"abstract": "  Gadolinium-based contrast agents (GBCAs) have been widely used to better\nvisualize disease in brain magnetic resonance imaging (MRI). However,\ngadolinium deposition within the brain and body has raised safety concerns\nabout the use of GBCAs. Therefore, the development of novel approaches that can\ndecrease or even eliminate GBCA exposure while providing similar contrast\ninformation would be of significant use clinically. For brain tumor patients,\nstandard-of-care includes repeated MRI with gadolinium-based contrast for\ndisease monitoring, increasing the risk of gadolinium deposition. In this work,\nwe present a deep learning based approach for contrast-enhanced T1 synthesis on\nbrain tumor patients. A 3D high-resolution fully convolutional network (FCN),\nwhich maintains high resolution information through processing and aggregates\nmulti-scale information in parallel, is designed to map pre-contrast MRI\nsequences to contrast-enhanced MRI sequences. Specifically, three pre-contrast\nMRI sequences, T1, T2 and apparent diffusion coefficient map (ADC), are\nutilized as inputs and the post-contrast T1 sequences are utilized as target\noutput. To alleviate the data imbalance problem between normal tissues and the\ntumor regions, we introduce a local loss to improve the contribution of the\ntumor regions, which leads to better enhancement results on tumors. Extensive\nquantitative and visual assessments are performed, with our proposed model\nachieving a PSNR of 28.24dB in the brain and 21.2dB in tumor regions. Our\nresults suggests the potential of substituting GBCAs with synthetic contrast\nimages generated via deep learning.\n"}
+{"abstract": "  In this paper, we investigate the Lie algebra structures of weight one\nsubspaces of $C_2$-cofinite vertex operator superalgebras. We also show that\nfor any positive integer $k$, vertex operator superalgebras\n$L_{sl(1|n+1)}(k,0)$ and $L_{osp(2|2n)}(k,0)$ have infinitely many irreducible\nadmissible modules. As a consequence, we give a proof of the fact that\n$L_{\\mathfrak g}(k,0)$ is $C_2$-cofinite if and only if $\\mathfrak g$ is either\na simple Lie algebra, or $\\mathfrak g=osp(1|2n)$, and $k$ is a nonnegative\ninteger. As an application, we show that $L_{G(3)}(1,0)$ is a vertex operator\nsuperalgebra such that the category of $L_{G(3)}(1,0)$-modules is semisimple\nbut $L_{G(3)}(1,0)$ is not $C_2$-cofinite.\n"}
+{"abstract": "  Organic-inorganic layered perovskites are two-dimensional quantum wells with\nlayers of lead-halide octahedra stacked between organic ligand barriers. The\ncombination of their dielectric confinement and ionic sublattice results in\nexcitonic excitations with substantial binding energies that are strongly\ncoupled to the surrounding soft, polar lattice. However, the ligand environment\nin layered perovskites can significantly alter their optical properties due to\nthe complex dynamic disorder of soft perovskite lattice. Here, we observe the\ndynamic disorder through phonon dephasing lifetimes initiated by ultrafast\nphotoexcitation employing high-resolution resonant impulsive stimulated Raman\nspectroscopy of a variety of ligand substitutions. We demonstrate that\nvibrational relaxation in layered perovskite formed from flexible alkyl-amines\nas organic barriers is fast and relatively independent of the lattice\ntemperature. Relaxation in aromatic amine based layered perovskite is slower,\nthough still fast relative to pure inorganic lead bromide lattices, with a rate\nthat is temperature dependent. Using molecular dynamics simulations, we explain\nthe fast rates of relaxation by quantifying the large anharmonic coupling of\nthe optical modes with the ligand layers and rationalize the temperature\nindependence due to their amorphous packing. This work provides a molecular and\ntime-domain depiction of the relaxation of nascent optical excitations and\nopens opportunities to understand how they couple to the complex layered\nperovskite lattice, elucidating design principles for optoelectronic devices.\n"}
+{"abstract": "  The aim of this paper is to investigate the cardiorespiratory synchronization\nin athletes subjected to extreme physical stress combined with a cognitive\nstress tasks. ECG and respiration were measured in 14 athletes before and after\nthe Ironmen competition. Stroop test was applied between the measurements\nbefore and after the Ironmen competition to induce cognitive stress.\nSynchrogram and empirical mode decomposition analysis were used for the first\ntime to investigate the effects of physical stress, induced by the Ironmen\ncompetition, on the phase synchronization of the cardiac and respiratory\nsystems of Ironmen athletes before and after the competition. A cognitive\nstress task (Stroop test) was performed both pre- and post-Ironman event in\norder to prevent the athletes from cognitively controlling their breathing\nrates. Our analysis showed that cardiorespiratory synchronization increased\npost-Ironman race compared to pre-Ironman. The results suggest that the amount\nof stress the athletes are recovering from post-competition is greater than the\neffects of the Stroop test. This indicates that the recovery phase after the\ncompetition is more important for restoring and maintaining homeostasis, which\ncould be another reason for stronger synchronization.\n"}
+{"abstract": "  Many panel data have the latent subgroup effect on individuals, and it is\nimportant to correctly identify these groups since the efficiency of resulting\nestimators can be improved significantly by pooling the information of\nindividuals within each group. However, the currently assumed parametric and\nsemiparametric relationship between the response and predictors may be\nmisspecified, which leads to a wrong grouping result, and the nonparametric\napproach hence can be considered to avoid such mistakes. Moreover, the response\nmay depend on predictors in different ways at various quantile levels, and the\ncorresponding grouping structure may also vary. To tackle these problems, this\npaper proposes a nonparametric quantile regression method for homogeneity\npursuit, and a pairwise fused penalty is used to automatically select the\nnumber of groups. The asymptotic properties are established, and an ADMM\nalgorithm is also developed. The finite sample performance is evaluated by\nsimulation experiments, and the usefulness of the proposed methodology is\nfurther illustrated by an empirical example.\n"}
+{"abstract": "  Severe geomagnetic disturbances increase the magnitude of the electric field\non the Earth's surface (E-field) and drive geomagnetically-induced currents\n(GICs) along the transmission lines in electric grids. These additional\ncurrents can pose severe risks, such as current distortions, transformer\nsaturation and increased reactive power losses, each of which can lead to\nsystem unreliability. Several mitigation actions (e.g., changing grid topology)\nexist that can reduce the harmful GIC effects on the grids. Making such\ndecisions can be challenging, however, because the magnitude and direction of\nthe E-field are uncertain and non-stationary. In this paper, we model uncertain\nE-fields using the distributionally robust optimization (DRO) approach that\ndetermines optimal transmission grid operations such that the worst-case\nexpectation of the system cost is minimized. We also capture the effect of GICs\non the nonlinear AC power flow equations. For solution approaches, we develop\nan accelerated column-and-constraint generation (CCG) algorithm by exploiting a\nspecial structure of the support set of uncertain parameters representing the\nE-field. Extensive numerical experiments based on \"epri-21\" and \"uiuc-150\"\nsystems, designed for GMD studies, demonstrate (i) the computational\nperformance of the accelerated CCG algorithm, and (ii) the performance of\ndistributionally robust grid operations that satisfy nonlinear, nonconvex AC\npower flow equations and GIC constraints.\n"}
+{"abstract": "  We use 78 reverberation-measured Mg II time-lag quasars (QSOs) in the\nredshift range $0.0033 \\leq z \\leq 1.89$ to constrain cosmological parameters\nin six different cosmological models. The basis of our method is the use of the\nradius-luminosity or $R-L$ relation to standardize these 78 Mg II QSOs. In each\ncosmological model we simultaneously determine $R-L$ relation and cosmological\nmodel parameters, thus avoiding the circularity problem. We find that the $R-L$\nrelation parameter values are independent of the cosmological model used in the\nanalysis thus establishing that current Mg II QSOs are standardizable candles.\nCosmological constraints obtained using these QSOs are significantly weaker\nthan, but consistent with, those obtained from a joint analysis of baryon\nacoustic oscillation (BAO) observations and Hubble parameter [$H(z)$]\nmeasurements. So, we also analyse these QSOs in conjunction with the BAO +\n$H(z)$ data and find cosmological constraints consistent with the standard\nspatially-flat $\\Lambda$CDM model as well as with mild dark energy dynamics and\na little spatial curvature. A larger sample of higher-quality\nreverberation-measured QSOs should have a smaller intrinsic dispersion and so\nshould provide tighter constraints on cosmological parameters.\n"}
+{"abstract": "  Threat actors can be persistent, motivated and agile, and leverage a\ndiversified and extensive set of tactics and techniques to attain their goals.\nIn response to that, defenders establish threat intelligence programs to stay\nthreat-informed and lower risk. Actionable threat intelligence is integrated\ninto security information and event management systems (SIEM) or is accessed\nvia more dedicated tools like threat intelligence platforms. A threat\nintelligence platform gives access to contextual threat information by\naggregating, processing, correlating, and analyzing real-time data and\ninformation from multiple sources, and in many cases, it provides centralized\nanalysis and reporting of an organization's security events. Sysmon logs is a\ndata source that has received considerable attention for endpoint visibility.\nApproaches for threat detection using Sysmon have been proposed, mainly\nfocusing on search engine technologies like NoSQL database systems. This paper\ndemonstrates one of the many use cases of Sysmon and cyber threat intelligence.\nIn particular, we present a threat assessment system that relies on a cyber\nthreat intelligence ontology to automatically classify executed software into\ndifferent threat levels by analyzing Sysmon log streams. The presented system\nand approach augments cyber defensive capabilities through situational\nawareness, prediction, and automated courses of action.\n"}
+{"abstract": "  Stellar Intensity Interferometry is a technique based on the measurement of\nthe second order spatial correlation of the light emitted from a star. The\nphysical information provided by these measurements is the angular size and\nstructure of the emitting source. A worldwide effort is presently under way to\nimplement stellar intensity interferometry on telescopes separated by long\nbaselines and on future arrays of Cherenkov telescopes. We describe an\nexperiment of this type, realized at the Asiago Observatory (Italy), in which\nwe performed for the first time measurements of the correlation counting photon\ncoincidences in post-processing by means of a single photon software correlator\nand exploiting entirely the quantum properties of the light emitted from a\nstar. We successfully detected the temporal correlation of Vega at zero\nbaseline and performed a measurement of the correlation on a projected baseline\nof $\\sim$2 km. The average discrete degree of coherence at zero baseline for\nVega is $< g^{(2)} > \\, = 1.0034 \\pm 0.0008$, providing a detection with a\nsignal-to-noise ratio $S/N \\gtrsim 4$. No correlation is detected over the km\nbaseline. The measurements are consistent with the expected degree of spatial\ncoherence for a source with the 3.3 mas angular diameter of Vega. The\nexperience gained with the Asiago experiment will serve for future\nimplementations of stellar intensity interferometry on long-baseline arrays of\nCherenkov telescopes.\n"}
+{"abstract": "  In recent years, mobile clients' computing ability and storage capacity have\ngreatly improved, efficiently dealing with some applications locally. Federated\nlearning is a promising distributed machine learning solution that uses local\ncomputing and local data to train the Artificial Intelligence (AI) model.\nCombining local computing and federated learning can train a powerful AI model\nunder the premise of ensuring local data privacy while making full use of\nmobile clients' resources. However, the heterogeneity of local data, that is,\nNon-independent and identical distribution (Non-IID) and imbalance of local\ndata size, may bring a bottleneck hindering the application of federated\nlearning in mobile edge computing (MEC) system. Inspired by this, we propose a\ncluster-based clients selection method that can generate a federated virtual\ndataset that satisfies the global distribution to offset the impact of data\nheterogeneity and proved that the proposed scheme could converge to an\napproximate optimal solution. Based on the clustering method, we propose an\nauction-based clients selection scheme within each cluster that fully considers\nthe system's energy heterogeneity and gives the Nash equilibrium solution of\nthe proposed scheme for balance the energy consumption and improving the\nconvergence rate. The simulation results show that our proposed selection\nmethods and auction-based federated learning can achieve better performance\nwith the Convolutional Neural Network model (CNN) under different data\ndistributions.\n"}
+{"abstract": "  Despite the modern level of development of computational chemistry methods\nand techno-logical progress, fast and accurate determination of solvation free\nenergy remains a huge problem for physical chemists. In this paper, we describe\ntwo computational schemes that can potentially solve this problem. We consider\nsystems of poorly soluble drug compounds in supercritical carbon dioxide.\nConsidering that the biggest contribution among all inter-molecular\ninteractions is made by van der Waals interactions, we model solute and solvent\nparticles as coarse-grained ones interacting via the effective Lennard-Jones\npotential. The first proposed approach is based on the classical density\nfunctional theory and the second one relies on molecular dynamics simulation of\nthe Lennard-Jones fluid. Sacrificing the precision of the molecular structure\ndescription while capturing the phase behavior of the fluid with sufficient\naccuracy, we propose computationally advantageous paths to obtaining the\nsolvation free energy values with the accuracy satisfactory for engineering\napplications. The agreement reached between the results of such coarse-graining\nmodels and the experimental data indicates that the use of the all-atom\nmolecular dynamic simulations for the studied systems seems to be excessive.\n"}
+{"abstract": "  This paper illustrates two algorithms designed in Forneron & Ng (2020): the\nresampled Newton-Raphson (rNR) and resampled quasi-Newton (rqN) algorithms\nwhich speed-up estimation and bootstrap inference for structural models. An\nempirical application to BLP shows that computation time decreases from nearly\n5 hours with the standard bootstrap to just over 1 hour with rNR, and only 15\nminutes using rqN. A first Monte-Carlo exercise illustrates the accuracy of the\nmethod for estimation and inference in a probit IV regression. A second\nexercise additionally illustrates statistical efficiency gains relative to\nstandard estimation for simulation-based estimation using a dynamic panel\nregression example.\n"}
+{"abstract": "  Warm Jupiters -- defined here as planets larger than 6 Earth radii with\norbital periods of 8--200 days -- are a key missing piece in our understanding\nof how planetary systems form and evolve. It is currently debated whether Warm\nJupiters form in situ, undergo disk or high eccentricity tidal migration, or\nhave a mixture of origin channels. These different classes of origin channels\nlead to different expectations for Warm Jupiters' properties, which are\ncurrently difficult to evaluate due to the small sample size. We take advantage\nof the \\TESS survey and systematically search for Warm Jupiter candidates\naround main-sequence host stars brighter than the \\TESS-band magnitude of 12 in\nthe Full-Frame Images in Year 1 of the \\TESS Prime Mission data. We introduce a\ncatalog of 55 Warm Jupiter candidates, including 19 candidates that were not\noriginally released as \\TESS Objects of Interest (TOIs) by the \\TESS team. We\nfit their \\TESS light curves, characterize their eccentricities and\ntransit-timing variations (TTVs), and prioritize a list for ground-based\nfollow-up and \\TESS Extended Mission observations. Using hierarchical Bayesian\nmodeling, we find the preliminary eccentricity distributions of our\nWarm-Jupiter-candidate catalog using a Beta distribution, a Rayleigh\ndistribution, and a two-component Gaussian distribution as the functional forms\nof the eccentricity distribution. Additional follow-up observations will be\nrequired to clean the sample of false positives for a full statistical study,\nderive the orbital solutions to break the eccentricity degeneracy, and provide\nmass measurements.\n"}
+{"abstract": "  A hallmark of topological superconductivity is the non-Abelian statistics of\nMajorana bound states (MBS), its chargeless zero-energy emergent\nquasiparticles. The resulting fractionalization of a single electron, stored\nnonlocally as a two spatially separated MBS, provides a powerful platform for\nimplementing fault-tolerant topological quantum computing. However, despite\nintensive efforts, experimental support for MBS remains indirect and does not\nprobe their non-Abelian statistics. Here we propose how to overcome this\nobstacle in mini-gate controlled planar Josephson junctions (JJ) and\ndemonstrate non-Abelian statistics through MBS fusion, detected by charge\nsensing using a quantum point contact. The feasibility of preparing,\nmanipulating, and fusing MBS in two-dimensional (2D) systems is supported in\nour experiments which demonstrate the control of superconducting properties\nwith five mini gates in InAs/Al-based JJs. While we focus on this\nwell-established platform, where the topological superconductivity was already\nexperimentally detected, our proposal to identify elusive non-Abelian\nstatistics motivates also further MBS studies in other gate-controlled 2D\nsystems.\n"}
+{"abstract": "  This tutorial aims at giving an introductory treatment of the circuit\nanalysis of superconducting qubits, i.e., two-level systems in superconducting\ncircuits. It also touches upon couplings between such qubits and how microwave\ndriving and these couplings can be used for single- and two-qubit gates, as\nwell as how to include noise when calculating the dynamics of the system. We\nalso discuss higher-dimensional superconducting qudits. The tutorial is\nintended for new researchers with limited or no experience with the field but\nshould be accessible to anyone with a bachelor's degree in physics. The\ntutorial introduces the basic methods used in quantum circuit analysis,\nstarting from a circuit diagram and ending with a quantized Hamiltonian, that\nmay be truncated to the lowest levels. We provide examples of all the basic\ntechniques throughout the discussion, while in the last part of the tutorial we\ndiscuss several of the most commonly used circuits for quantum-information\napplications. This includes both worked examples of single qubits and examples\nof how to analyze the coupling methods that allow multiqubit operations. In\nseveral detailed appendices, we provide the interested reader with an\nintroduction to more advanced techniques for handling larger circuit designs.\n"}
+{"abstract": "  We give a complete description of the third-moment (skewness) characteristics\nof both linear and nonlinear momentum trading strategies, the latter being\nunderstood as transformations of a normalised moving-average filter (EMA). We\nexplain in detail why the skewness is generally positive and has a term\nstructure.\n  This paper is a synthesis of two papers published by the author in RISK in\n2012, with some updates and comments.\n"}
+{"abstract": "  We propose an agent-based network formation model under uncertainty with the\nobjective of relaxing the common assumption of complete information, calling\nattention to the role beliefs may play in segregation. We demonstrate that our\nmodel is capable of generating a set of networks that encompasses those of a\ncomplete information model. Further, we show that by allowing agents to be\nbiased toward each other based on observable attributes, our model is able to\ngenerate homophilous equilibria with preferences that are indifferent to these\nattributes. We accompany our theoretical results with a simulation-based\ninvestigation of the relationship between beliefs and segregation and show that\nbiased beliefs are an important driver of segregation under incomplete\ninformation.\n"}
+{"abstract": "  Gapped bilayer graphene can support the presence of intragap states due to\nkink gate potentials applied to the graphene layers. Electrons in these states\ndisplay valley-momentum locking, which makes them attractive for topological\nvalleytronics. Here, we show that kink-antikink local potentials enable\nmodulated scattering of topological currents. We find that the kink-antikink\ncoupling leads to anomalous steps in the junction conductance. Further, when\nthe constriction detaches from the propagating modes, forming a loop, the\nconductance reveals the system energy spectrum. Remarkably, these kink-antikink\ndevices can also work as valley filters with tiny magnetic fields by tuning a\ncentral gate.\n"}
+{"abstract": "  Rigidity regulates the integrity and function of many physical and biological\nsystems. This is the first of two papers on the origin of rigidity, wherein we\npropose that \"energetic rigidity,\" in which all non-trivial deformations raise\nthe energy of a structure, is a more useful notion of rigidity in practice than\ntwo more commonly used rigidity tests: Maxwell-Calladine constraint counting\n(first-order rigidity) and second-order rigidity. We find that constraint\ncounting robustly predicts energetic rigidity only when the system has no\nstates of self stress. When the system has states of self stress, we show that\nsecond-order rigidity can imply energetic rigidity in systems that are not\nconsidered rigid based on constraint counting, and is even more reliable than\nshear modulus. We also show that there may be systems for which neither first\nnor second-order rigidity imply energetic rigidity. The formalism of energetic\nrigidity unifies our understanding of mechanical stability and also suggests\nnew avenues for material design.\n"}
+{"abstract": "  We present protocols for quantum key distribution in a prepare-and-measure\nsetup with an asymmetric level of trust. While the device of the sender (Alice)\nis partially characterized, the receiver's (Bob's) device is treated as a\nblack-box. The security of the protocols is based on the assumption that\nAlice's prepared states have limited overlaps, but no explicit bound on the\nHilbert space dimension is required. The protocols are immune to attacks on the\nreceiver's device, such as blinding attacks. The users can establish a secret\nkey while continuously monitoring the correct functioning of their devices\nthrough observed statistics. We report a proof-of-principle demonstration,\ninvolving mostly off-the-shelf equipment, as well as a high-efficiency\nsuperconducting nanowire detector. A positive key rate is demonstrated over a\n4.8 km low-loss optical fiber with finite-key analysis. The prospects of\nimplementing these protocols over longer distances is discussed.\n"}
+{"abstract": "  For robotics systems to be used in high risk, real-world situations, they\nhave to be quickly deployable and robust to environmental changes,\nunder-performing hardware, and mission subtask failures. Robots are often\ndesigned to consider a single sequence of mission events, with complex\nalgorithms lowering individual subtask failure rates under some critical\nconstraints. Our approach is to leverage common techniques in vision and\ncontrol and encode robustness into mission structure through outcome monitoring\nand recovery strategies, aided by a system infrastructure that allows for quick\nmission deployments under tight time constraints and no central communication.\nWe also detail lessons in rapid field robotics development and testing. Systems\nwere developed and evaluated through real-robot experiments at an outdoor test\nsite in Pittsburgh, Pennsylvania, USA, as well as in the 2020 Mohamed Bin Zayed\nInternational Robotics Challenge. All competition trials were completed in\nfully autonomous mode without RTK-GPS. Our system led to 4th place in Challenge\n2 and 7th place in the Grand Challenge, and achievements like popping five\nballoons (Challenge 1), successfully picking and placing a block (Challenge 2),\nand dispensing the most water autonomously with a UAV of all teams onto an\noutdoor, real fire (Challenge 3).\n"}
+{"abstract": "  We consider bijective maps $\\phi$ on the full operator algebra\n$\\mathcal{B}(\\mathcal{H})$ of an infinite dimensional Hilbert space with the\nproperty that, for every $A,B,X\\in \\mathcal{B}(\\mathcal{H})$, $X$ is the\nDouglas solution of the equation $A=BX$ if and only if $Y=\\phi(X)$ is the\nDouglas solution of the equation $\\phi(A)=\\phi(B)Y$. We prove that those maps\nare implemented by a unitary or anti-unitary map $U$, i.e., $\\phi(A)=UAU^*$.\n"}
+{"abstract": "  (Multi-type) branching processes are a natural and well-studied model for\ngenerating random infinite trees. Branching processes feature both\nnondeterministic and probabilistic branching, generalizing both transition\nsystems and Markov chains (but not generally Markov decision processes). We\nstudy the complexity of model checking branching processes against linear-time\nomega-regular specifications: is it the case almost surely that every branch of\na tree randomly generated by the branching process satisfies the omega-regular\nspecification? The main result is that for LTL specifications this problem is\nin PSPACE, subsuming classical results for transition systems and Markov\nchains, respectively. The underlying general model-checking algorithm is based\non the automata-theoretic approach, using unambiguous B\\\"uchi automata.\n"}
+{"abstract": "  Axons in white matter have been shown to have varying geometries within a\nbundle using ex vivo imaging techniques, but what does this mean for diffusion\nMRI (dMRI) based spherical deconvolution (SD)? SD attempts to estimate the\nfibre orientation distribution function (fODF) by assuming a single dMRI fibre\nresponse function (FRF) for all white matter populations and deconvolving this\nFRF from the dMRI signal at each voxel to estimate the fODF. Variable fibre\ngeometry within a bundle however suggests the FRF might not be constant even\nwithin a single voxel. We test what impact realistic fibre geometry has on SD\nby simulating the dMRI signal in a range of realistic white matter numerical\nphantoms, including synthetic phantoms and real axons segmented from electron\nmicroscopy. We demonstrate that variable fibre geometry leads to a variable FRF\nacross axons and that in general no single FRF is effective to recover the\nunderlying fibre orientation distribution function (fODF). This finding\nsuggests that assuming a single FRF can lead to misestimation of the fODF,\ncausing further downstream errors in techniques such as tractography.\n"}
+{"abstract": "  In this paper, we propose LGG, a neurologically inspired graph neural\nnetwork, to learn local-global-graph representations from\nElectroencephalography (EEG) for a Brain-Computer Interface (BCI). A temporal\nconvolutional layer with multi-scale 1D convolutional kernels and kernel-level\nattention fusion is proposed to learn the temporal dynamics of EEG. Inspired by\nneurological knowledge of cognitive processes in the brain, we propose local\nand global graph-filtering layers to learn the brain activities within and\nbetween different functional areas of the brain to model the complex relations\namong them during the cognitive processes. Under the robust nested\ncross-validation settings, the proposed method is evaluated on the publicly\navailable dataset DEAP, and the classification performance is compared with\nstate-of-the-art methods, such as FBFgMDM, FBTSC, Unsupervised learning,\nDeepConvNet, ShallowConvNet, EEGNet, and TSception. The results show that the\nproposed method outperforms all these state-of-the-art methods, and the\nimprovements are statistically significant (p<0.05) in most cases. The source\ncode can be found at: https://github.com/yi-ding-cs/LGG\n"}
+{"abstract": "  The cosmological constant $\\Lambda$ is usually interpreted as Dark Energy\n(DE) or modified gravity (MG). Here we propose instead that $\\Lambda$\ncorresponds to a boundary term in the action of classical General Relativity.\nThe action is zero for a perfect fluid solution and this fixes $\\Lambda$ to the\naverage density $\\rho$ and pressure $p$ inside a primordial causal boundary:\n$\\Lambda = 4\\pi G <\\rho+3p>$. This explains both why the observed value of\n$\\Lambda$ is related to the matter density today and also why other\ncontributions to $\\Lambda$, such as DE or MG, do not produce cosmic expansion.\nCosmic acceleration results from the repulsive boundary force that occurs when\nthe expansion reaches the causal horizon. This universe is similar to the\n$\\Lambda$CDM universe, except on the largest observable scales, where we expect\ndepartures from homogeneity/isotropy, such as CMB anomalies and variations in\ncosmological parameters indicated by recent observations.\n"}
+{"abstract": "  We develop photoelectron interferometry based on laser-assisted extreme\nultraviolet ionization for randomly oriented chiral molecules. As in the well\nestablished 'reconstruction of attosecond beating by interference of two-photon\ntransitions', an infrared or visible laser pulse promotes interferences between\ncomponents of the photoelectron wave packet ionized by a comb of XUV photons,\napplied here to a sample of chiral molecules. We show that the magnitude of the\nresulting chiral signal is simply controlled by the time delay between the XUV\nand laser pulses, the choice of the laser frequency determines the\nphotoelectron energy at which the chiral signal is probed, and comparison of\ndifferent polarization configurations in the two-photon process allows for\ndisentangling the contributions of bound and continuum states to the chiral\nresponse. Our proposal provides a simple, experimentally feasible, robust and\nversatile tool for the control of photoelectron circular dichroism.\n"}
+{"abstract": "  Intelligent reflecting surface (IRS) has recently emerged as a new solution\nto enhance wireless communication performance via passive signal reflection. In\nthis letter, we unlock the potential of IRS controller in relaying information\nand propose a novel IRS-assisted communication system with both IRS passive\nreflection and active relaying. Specifically, we jointly optimize the time\nallocations for decode-and-forward (DF) relaying by the IRS controller and the\nIRS passive beamforming to maximize the achievable rate of the proposed system.\nWe also compare the rate performance of the proposed system with the\nconventional IRS without relaying, and reveal the conditions for one to\noutperform the other. Simulation results demonstrate that the proposed new\ndesign can significantly improve the coverage/rate performance of IRS-assisted\nsystems.\n"}
+{"abstract": "  A salami is a connected, locally finite, weighted graph with non-negative\nOllivier Ricci curvature and at least two ends of infinite volume. We show that\nevery salami has exactly two ends and no vertices with positive curvature. We\nmoreover show that every salami is recurrent and admits harmonic functions with\nconstant gradient. The proofs are based on extremal Lipschitz extensions, a\nvariational principle and the study of harmonic functions. Assuming a lower\nbound on the edge weight, we prove that salamis are quasi-isometric to the\nline, that the space of all harmonic functions has finite dimension, and that\nthe space of subexponentially growing harmonic functions is two-dimensional.\nMoreover, we give a Cheng-Yau gradient estimate for harmonic functions on\nballs.\n"}
+{"abstract": "  Simultaneous, post-hoc inference is desirable in large-scale hypotheses\ntesting as it allows for exploration of data while deciding on criteria for\nproclaiming discoveries. It was recently proved that all admissible post-hoc\ninference methods for the number of true discoveries must be based on closed\ntesting. In this paper we investigate tractable and efficient closed testing\nwith local tests of different properties, such as monotonicty, symmetry and\nseparability, meaning that the test thresholds a monotonic or symmetric\nfunction or a function of sums of test scores for the individual hypotheses.\nThis class includes well-known global null tests by Fisher, Stouffer and\nRuschendorf, as well as newly proposed ones based on harmonic means and Cauchy\ncombinations. Under monotonicity, we propose a new linear time statistic\n(\"coma\") that quantifies the cost of multiplicity adjustments. If the tests are\nalso symmetric and separable, we develop several fast (mostly linear-time)\nalgorithms for post-hoc inference, making closed testing tractable. Paired with\nrecent advances in global null tests based on generalized means, our work\nimmediately instantiates a series of simultaneous inference methods that can\nhandle many complex dependence structures and signal compositions. We provide\nguidance on choosing from these methods via theoretical investigation of the\nconservativeness and sensitivity for different local tests, as well as\nsimulations that find analogous behavior for local tests and full closed\ntesting. One result of independent interest is the following: if\n$P_1,\\dots,P_d$ are $p$-values from a multivariate Gaussian with arbitrary\ncovariance, then their arithmetic average P satisfies $Pr(P \\leq t) \\leq t$ for\n$t \\leq \\frac{1}{2d}$.\n"}
+{"abstract": "  In this paper, a method to evaluate the flexibility of aggregates of domestic\nelectric water heaters is proposed and applied to the Italian case. Flexibility\nis defined as the capability of the aggregate to vary its power demand for a\ngiven time interval. The evaluation method consists of a Monte Carlo analysis,\nthat uses the thermal model of electric water heaters and a proper elaboration\nof the external inputs, such as ambient and cold water temperatures, and hot\nwater demand. The case of large aggregates defined along the Italian territory\nhas been studied showing the dependence of flexibility on seasons and on time.\n"}
+{"abstract": "  In this paper, we investigate representations of links that are either\ncentrally symmetric in $\\mathbb{R}^3$ or antipodally symmetric in\n$\\mathbb{S}^3$. By using the notions of antipodally self-dual and antipodally\nsymmetric maps, introduced and studied by the authors, we are able to present\nsufficient combinatorial conditions for a link $L$ to admit such\nrepresentations. The latter naturally arises sufficient conditions for $L$ to\nbe amphichiral.\n  We also introduce another (closely related) method yielding again to\nsufficient conditions for $L$ to be amphichiral. We finally prove that a link\n$L$, associated to a map $G$, is amphichiral if the self-dual pairing of $G$ is\nnot one of 6 specific ones among the classification of the 24 self-dual pairing\n$Cor(G) \\rhd Aut(G)$.\n"}
+{"abstract": "  This study first reconstructs three deep learning powered stock trading\nmodels and their associated strategies that are representative of distinct\napproaches to the problem and established upon different aspects of the many\ntheories evolved around deep learning. It then seeks to compare the performance\nof these strategies from different perspectives through trading simulations ran\non three scenarios when the benchmarks are kept at historical low points for\nextended periods of time. The results show that in extremely adverse market\nclimates, investment portfolios managed by deep learning powered algorithms are\nable to avert accumulated losses by generating return sequences that shift the\nconstantly negative CSI 300 benchmark return upward. Among the three, the LSTM\nmodel's strategy yields the best performance when the benchmark sustains\ncontinued loss.\n"}
+{"abstract": "  We present a new approach to study (1+1)-dimensional Dirac equation in the\nbackground of an effective mass $M$ by exploiting the possibility of a\nposition-dependent fermi velocity $v_f$. We explore the resulting structure of\nthe coupled equations and arrive at an interesting constraint of $M$ turning\nout to be the inverse square of $v_f$. We address several solutions of the\neffective potential that include the free particle, shifted harmonic\noscillator, Morse potential, and CPRS potential.\n"}
+{"abstract": "  This paper provides a view of Maxwell's equations from the perspective of\ncomplex variables. The study is made through complex differential forms and the\nHodge star operator in $\\mathbb{C}^2$ with respect to the Euclidean and the\nMinkowski metrics. It shows that holomorphic functions give rise to nontrivial\nsolutions, and the inner product between the electric and the magnetic fields\nis considered in this case. Further, it obtains a simple necessary and\nsufficient condition regarding harmonic solutions to the equations. In the end,\nthe paper gives an interpretation of the Lorenz gauge condition in terms of the\ncodifferential operator.\n"}
+{"abstract": "  The demand for higher resolution displays drives the demand for smaller\npixels. Displays show a trend of doubling the pixel number every 4 years and\ndoubling the pixel per inch (PPI) every 6 years. As the prospective candidate\nfor next-generation display technology, microLED (micro Light Emitting Diode)\nwill suffer from sidewall current leakage and poor extraction efficiency as its\nlateral size reduces. Using Finite Element Analysis (FEA) method and\nFinite-Difference Time-Domain (FDTD) method, we find that reducing the\nthickness of the LED can reduce the current leaking to the sidewalls and reduce\nthe total internal reflection simultaneously. A promising solution to this\nproblem is by using atomically thin 2D materials to make LEDs. However,\nmonolayer inorganic 2D materials that can provide red, green and blue emission\nare still lacking. Based on the blue light-emitting material fluorographene\n(CF), partially fluorinated graphene (CFx) is synthesized in this work to emit\nred and green colors from 683 nm to 555 nm (limited by the instrument). This\nwork also demonstrates lithographically defined regions with different colors,\npaving the way for the scaling of microLED.\n"}
+{"abstract": "  We compute the entanglement negativity for various pure and mixed state\nconfigurations in a bath coupled to an evaporating two dimensional non-extremal\nJackiw-Teitelboim (JT) black hole obtained through the partial dimensional\nreduction of a three dimensional BTZ black hole. Our results exactly reproduce\nthe analogues of the Page curve for the entanglement negativity which were\nrecently determined through diagrammatic technique developed in the context of\nrandom matrix theory.\n"}
+{"abstract": "  We present a 5.4$\\sigma$ detection of the pairwise kinematic\nSunyaev-Zel'dovich (kSZ) effect using Atacama Cosmology Telescope (ACT) and\n$\\it{Planck}$ CMB observations in combination with Luminous Red Galaxy samples\nfrom the Sloan Digital Sky Survey (SDSS) DR15 catalog. Results are obtained\nusing three ACT CMB maps: co-added 150 GHz and 98 GHz maps, combining\nobservations from 2008-2018 (ACT DR5), which overlap with SDSS DR15 over 3,700\nsq. deg., and a component-separated map using night-time only observations from\n2014-2015 (ACT DR4), overlapping with SDSS DR15 over 2,089 sq. deg. Comparisons\nof the results from these three maps provide consistency checks in relation to\npotential frequency-dependent foreground contamination. A total of 343,647\ngalaxies are used as tracers to identify and locate galaxy groups and clusters\nfrom which the kSZ signal is extracted using aperture photometry. We consider\nthe impact of various aperture photometry assumptions and covariance estimation\nmethods on the signal extraction. Theoretical predictions of the pairwise\nvelocities are used to obtain best-fit, mass-averaged, optical depth estimates\nfor each of five luminosity-selected tracer samples. A comparison of the\nkSZ-derived optical depth measurements obtained here to those derived from the\nthermal SZ effect for the same sample is presented in a companion paper.\n"}
+{"abstract": "  Detecting an Out-of-Domain (OOD) utterance is crucial for a robust dialog\nsystem. Most dialog systems are trained on a pool of annotated OOD data to\nachieve this goal. However, collecting the annotated OOD data for a given\ndomain is an expensive process. To mitigate this issue, previous works have\nproposed generative adversarial networks (GAN) based models to generate OOD\ndata for a given domain automatically. However, these proposed models do not\nwork directly with the text. They work with the text's latent space instead,\nenforcing these models to include components responsible for encoding text into\nlatent space and decoding it back, such as auto-encoder. These components\nincrease the model complexity, making it difficult to train. We propose OodGAN,\na sequential generative adversarial network (SeqGAN) based model for OOD data\ngeneration. Our proposed model works directly on the text and hence eliminates\nthe need to include an auto-encoder. OOD data generated using OodGAN model\noutperforms state-of-the-art in OOD detection metrics for ROSTD (67% relative\nimprovement in FPR 0.95) and OSQ datasets (28% relative improvement in FPR\n0.95) (Zheng et al., 2020).\n"}
+{"abstract": "  The aim of this notes is to give a concise introduction to control theory for\nsystems governed by stochastic partial differential equations. We shall mainly\nfocus on controllability and optimal control problems for these systems. For\nthe first one, we present results for the exact controllability of stochastic\ntransport equations, null and approximate controllability of stochastic\nparabolic equations and lack of exact controllability of stochastic hyperbolic\nequations. For the second one, we first introduce the stochastic linear\nquadratic optimal control problems and then the Pontryagin type maximum\nprinciple for general optimal control problems. It deserves mentioning that, in\norder to solve some difficult problems in this field, one has to develop new\ntools, say, the stochastic transposition method introduced in our previous\nworks.\n"}
+{"abstract": "  Language models (LMs) for virtual assistants (VAs) are typically trained on\nlarge amounts of data, resulting in prohibitively large models which require\nexcessive memory and/or cannot be used to serve user requests in real-time.\nEntropy pruning results in smaller models but with significant degradation of\neffectiveness in the tail of the user request distribution. We customize\nentropy pruning by allowing for a keep list of infrequent n-grams that require\na more relaxed pruning threshold, and propose three methods to construct the\nkeep list. Each method has its own advantages and disadvantages with respect to\nLM size, ASR accuracy and cost of constructing the keep list. Our best LM gives\n8% average Word Error Rate (WER) reduction on a targeted test set, but is 3\ntimes larger than the baseline. We also propose discriminative methods to\nreduce the size of the LM while retaining the majority of the WER gains\nachieved by the largest LM.\n"}
+{"abstract": "  We study the thermalization of a small $XX$ chain coupled to long, gapped\n$XXZ$ leads at either side by observing the relaxation dynamics of the whole\nsystem. Using extensive tensor network simulations, we show that such systems,\nalthough not integrable, appear to show either extremely slow thermalization or\neven lack thereof since the two can not be distinguished within the accuracy of\nour numerics. We show that the persistent oscillations observed in the spin\ncurrent in the middle of the $XX$ chain are related to eigenstates of the\nentire system located within the gap of the boundary chains. We find from exact\ndiagonalization that some of these states remain strictly localized within the\n$XX$ chain and do not hybridize with the rest of the system. The frequencies of\nthe persistent oscillations determined by numerical simulations of dynamics\nmatch the energy differences between these states exactly. This has important\nimplications for open systems, where the strongly interacting leads are often\nassumed to thermalize the central system. Our results suggest that if we employ\ngapped systems for the leads, this assumption does not hold; this finding is\nparticularly relevant to any potential future experimental studies of open\nquantum systems.\n"}
+{"abstract": "  Background: The novel coronavirus pandemic has affected Brazil's Santa\nCatarina State (SC) severely. At the time of writing (24 March 2021), over\n764,000 cases and over 9,800 deaths by COVID-19 have been confirmed, hospitals\nwere fully occupied with local news reporting at least 397 people in the\nwaiting list for an ICU bed. In an attempt to better inform local policy\nmaking, we applied an existing Bayesian algorithm to model the spread of the\npandemic in the seven geographic macro-regions of the state. Here we propose\nchanges to extend the model and improve its forecasting capabilities.\n  Methods: Our four proposed variations of the original method allow accessing\ndata of daily reported infections and take into account under-reporting of\ncases more explicitly. Two of the proposed versions also attempt to model the\ndelay in test reporting. We simulated weekly forecasting of deaths from the\nperiod from 31/05/2020 until 31/01/2021. First week data were used as a\ncold-start to the algorithm, after which weekly calibrations of the model were\nable to converge in fewer iterations. Google Mobility data were used as\ncovariates to the model, as well as to estimate of the susceptible population\nat each simulated run.\n  Findings: The changes made the model significantly less reactive and more\nrapid in adapting to scenarios after a peak in deaths is observed. Assuming\nthat the cases are under-reported greatly benefited the model in its stability,\nand modelling retroactively-added data (due to the \"hot\" nature of the data\nused) had a negligible impact in performance.\n  Interpretation: Although not as reliable as death statistics, case\nstatistics, when modelled in conjunction with an overestimate parameter,\nprovide a good alternative for improving the forecasting of models, especially\nin long-range predictions and after the peak of an infection wave.\n"}
+{"abstract": "  With the wide use of deep neural networks (DNN), model interpretability has\nbecome a critical concern, since explainable decisions are preferred in\nhigh-stake scenarios. Current interpretation techniques mainly focus on the\nfeature attribution perspective, which are limited in indicating why and how\nparticular explanations are related to the prediction. To this end, an\nintriguing class of explanations, named counterfactuals, has been developed to\nfurther explore the \"what-if\" circumstances for interpretation, and enables the\nreasoning capability on black-box models. However, generating counterfactuals\nfor raw data instances (i.e., text and image) is still in the early stage due\nto its challenges on high data dimensionality and unsemantic raw features. In\nthis paper, we design a framework to generate counterfactuals specifically for\nraw data instances with the proposed Attribute-Informed Perturbation (AIP). By\nutilizing generative models conditioned with different attributes,\ncounterfactuals with desired labels can be obtained effectively and\nefficiently. Instead of directly modifying instances in the data space, we\niteratively optimize the constructed attribute-informed latent space, where\nfeatures are more robust and semantic. Experimental results on real-world texts\nand images demonstrate the effectiveness, sample quality as well as efficiency\nof our designed framework, and show the superiority over other alternatives.\nBesides, we also introduce some practical applications based on our framework,\nindicating its potential beyond the model interpretability aspect.\n"}
+{"abstract": "  Suzuki's split-step quantum walk on the one-dimensional integer lattice can\nbe naturally viewed as a chirally symmetric quantum walk. Given the unitary\ntime-evolution of such a chirally symmetric quantum walk, we can separately\nintroduce well-defined indices for the eigenvalues $\\pm 1.$ The bulk-edge\ncorrespondence for Suzuki's split-step quantum walk is twofold. Firstly, we\nshow that the multiplicities of the eigenvalues $\\pm 1$ coincide with the\nabsolute values of the associated indices. Note that this can be viewed as the\nsymmetry protection of bound states, and that the indices we consider are\nrobust in the sense that these depend only on the asymptotic behaviour of the\nparameters of the given model. Secondly, we show that that such bound states\nexhibit exponential decay at spatial infinity.\n"}
+{"abstract": "  This paper addresses distributional offline continuous-time reinforcement\nlearning (DOCTR-L) with stochastic policies for high-dimensional optimal\ncontrol. A soft distributional version of the classical Hamilton-Jacobi-Bellman\n(HJB) equation is given by a semilinear partial differential equation (PDE).\nThis `soft HJB equation' can be learned from offline data without assuming that\nthe latter correspond to a previous optimal or near-optimal policy. A\ndata-driven solution of the soft HJB equation uses methods of Neural PDEs and\nPhysics-Informed Neural Networks developed in the field of Scientific Machine\nLearning (SciML). The suggested approach, dubbed `SciPhy RL', thus reduces\nDOCTR-L to solving neural PDEs from data. Our algorithm called Deep DOCTR-L\nconverts offline high-dimensional data into an optimal policy in one step by\nreducing it to supervised learning, instead of relying on value iteration or\npolicy iteration methods. The method enables a computable approach to the\nquality control of obtained policies in terms of both their expected returns\nand uncertainties about their values.\n"}
+{"abstract": "  We study the motion of a charged particle with magnetic moment in external\nelectromagnetic fields utilizing covariant unification of Gilbertian and\nAmperian descriptions of particle magnetic dipole moment. Considering the case\nof a current loop, our approach is verified by comparing classical dynamics\nwith the classical limit of relativistic quantum dynamics. We obtain motion of\na charged particle in the presence of an external linearly polarized EM (laser)\nplane wave field incorporating the effect of spin dynamics. For specific\nlaser-particle initial configurations, we determine that the Stern-Gerlach\nforce can have a cumulative effect on the trajectory of charged particles.\n"}
+{"abstract": "  Modeling the burning processes of biomass such as wood, grass, and crops is\ncrucial for the modeling and prediction of wildland and urban fire behavior.\nDespite its importance, the burning of solid fuels remains poorly understood,\nwhich can be partly attributed to the unknown chemical kinetics of most solid\nfuels. Most available kinetic models were built upon expert knowledge, which\nrequires chemical insights and years of experience. This work presents a\nframework for autonomously discovering biomass pyrolysis kinetic models from\nthermogravimetric analyzer (TGA) experimental data using the recently developed\nchemical reaction neural networks (CRNN). The approach incorporated the CRNN\nmodel into the framework of neural ordinary differential equations to predict\nthe residual mass in TGA data. In addition to the flexibility of\nneural-network-based models, the learned CRNN model is fully interpretable, by\nincorporating the fundamental physics laws, such as the law of mass action and\nArrhenius law, into the neural network structure. The learned CRNN model can\nthen be translated into the classical forms of biomass chemical kinetic models,\nwhich facilitates the extraction of chemical insights and the integration of\nthe kinetic model into large-scale fire simulations. We demonstrated the\neffectiveness of the framework in predicting the pyrolysis and oxidation of\ncellulose. This successful demonstration opens the possibility of rapid and\nautonomous chemical kinetic modeling of solid fuels, such as wildfire fuels and\nindustrial polymers.\n"}
+{"abstract": "  This study proposes a real-time distributed energy resource (DER)\ncoordination model that can exploit flexibility from the DERs to solve voltage\nand overloading issues using both active and reactive power. The model\nconsiders time-coupling devices including electric vehicles and heat pumps by\ndeviating as little as possible from their original schedules while\nprioritizing DERs with the most urgent demand using dynamic cost terms. The\nmodel does not require a multi-period setting or a multi-period-ahead forecast,\nwhich enables the model to alleviate the computational difficulty and enhance\nits applicability for DSOs to manage the grids in real time. A case study using\na Dutch low-voltage grid assuming a 100% penetration scenario of electric\nvehicles, heat pumps, and photovoltaics (PVs) in the households validates that\nthe proposed model can resolve the network issues while not affecting user\ncomfort.\n"}
+{"abstract": "  We study the statistics and the arithmetic properties of the Robin spectrum\nof a rectangle. A number of results are obtained for the multiplicities in the\nspectrum, depending on the Diophantine nature of the aspect ratio. In\nparticular, it is shown that for the square, unlike the case of Neumann\neigenvalues where there are unbounded multiplicities of arithmetic origin,\nthere are no multiplicities in the Robin spectrum for sufficiently small (but\nnonzero) Robin parameter except a systematic symmetry. In addition, uniform\nlower and upper bounds are established for the Robin-Neumann gaps in terms of\ntheir limiting mean spacing. Finally, that the pair correlation function of the\nRobin spectrum on a Diophantine rectangle is shown to be Poissonian.\n"}
+{"abstract": "  We investigate the behavior of equilibria in an $M/M/1$ feedback queue where\nprice and time sensitive customers are homogeneous with respect to service\nvaluation and cost per unit time of waiting. Upon arrival, customers can\nobserve the number of customers in the system and then decide to join or to\nbalk. Customers are served in order of arrival. After being served, each\ncustomer either successfully completes the service and departs the system with\nprobability $q$, or the service fails and the customer immediately joins the\nend of the queue to wait to be served again until she successfully completes\nit.\n  We analyse this decision problem as a noncooperative game among the\ncustomers. We show that there exists a unique symmetric Nash equilibrium\nthreshold strategy. We then prove that the symmetric Nash equilibrium threshold\nstrategy is evolutionarily stable. Moreover, if we relax the strategy\nrestrictions by allowing customers to renege, in the new Nash equilibrium,\ncustomers have a greater incentive to join. However, this does not necessarily\nincrease the equilibrium expected payoff, and for some parameter values, it\ndecreases it.\n"}
+{"abstract": "  In this paper we generalize the $j$-invariant criterion for the semistable\nreduction type of an elliptic curve to superelliptic curves $X$ given by\n$y^{n}=f(x)$. We first define a set of tropical invariants for $f(x)$ using\nsymmetrized Pl\\\"{u}cker coordinates and we show that these invariants determine\nthe tree associated to $f(x)$. We then prove that this tree completely\ndetermines the reduction type of $X$ for $n$ that are not divisible by the\nresidue characteristic. The conditions on the tropical invariants that\ndistinguish between the different types are given by half-spaces as in the\nelliptic curve case. These half-spaces arise naturally as the moduli spaces of\ncertain Newton polygon configurations. We give a procedure to write down their\nequations and we illustrate this by giving the half-spaces for polynomials\n$f(x)$ of degree $d\\leq{5}$.\n"}
+{"abstract": "  We use the topology of simplicial complexes to model political structures\nfollowing [1]. Simplicial complexes are a natural tool to encode interactions\nin the structures since a simplex can be used to represent a subset of\ncompatible agents. We translate the wedge, cone, and suspension operations into\nthe language of political structures and show how these constructions\ncorrespond to merging structures and introducing mediators. We introduce the\nnotions of the viability of an agent and the stability of a political system\nand examine their interplay with the simplicial complex topology, casting their\ninteractions in category-theoretic language whenever possible. We introduce a\nrefinement of the model by assigning weights to simplices corresponding to the\nnumber of issues the agents agree on. In addition, homology of simplicial\ncomplexes is used to detect non-viabilities, certain cycles of incompatible\nagents, and the (non)presence of mediators. Finally, we extend some results\nfrom [1], bringing viability and stability into the language of friendly\ndelegations and using homology to examine the existence of R-compromises and\nD-compromises.\n"}
+{"abstract": "  We prove explicit bounds for the number of sums of consecutive prime squares\nbelow a given magnitude.\n"}
+{"abstract": "  It is argued that cosmic chronometers yield estimates of the spatially\naveraged expansion rate even in a universe that is not well described by a\nglobal FLRW model - as long as the Universe is statistically homogeneous and\nisotropic with a sufficiently small homogeneity scale. On the other hand,\nmeasurements of the expansion rate based on observations of redshift drift will\nnot in general yield estimates of the spatially averaged expansion rate - but\nit will in the case where the universe is described well by a single FLRW model\non large scales. Therefore, a disagreement between measurements of the\nexpansion rate based on cosmic chronometers versus redshift drift is an\nexpected signal of non-negligible cosmic backreaction.\n"}
+{"abstract": "  Orbitally ordered states exhibit unique features which make them a promising\nplatform for exploring the ultrafast dynamics of long-range order in solids:\nTheir free energy typically has multiple discrete minima, and electric laser\nfields or selectively excited phonons can exert effective forces that may be\nused to steer the order parameter through these free energy landscapes.\nMoreover, their free energy strongly depends on fluctuations, and in some cases\nrestoring forces close to a minimum are exclusively of entropic origin\n(order-by-disorder mechanisms). This can open pathways to control the dynamics\nof the order parameter via non-thermal fluctuations. In this work, we study the\nlaser-induced non-equilibrium dynamics in a $120^\\circ$ compass model, using\ntime-dependent Ginzburg-Landau theory. We analyze protocols to switch the order\nparameter between equivalent configurations, with a focus on the interplay\nbetween the external force due to the driving field, and the non-thermal\nentropic forces. In particular, we find that remanent non-thermal fluctuations\nafter some excitation can stabilize the high-symmetry phase even when the\nhomogeneous potential has retrieved its low-temperature form, which facilitates\nlaser-induced switching.\n"}
+{"abstract": "  We disclose the effects of Lifshitz dynamical exponent $z$ on the properties\nof holographic paramagnetic-ferromagnetic phase transition in the background of\nLifshitz spacetime. To preserve the conformal invariance in higher dimensions,\nwe consider the Power-Maxwell (PM) electrodynamics as our gauge field. We\nintroduce a massive $2$-form coupled to the PM field and perform the numerical\nshooting method in the probe limit by assuming the PM and the $2$-form fields\ndo not back-react on the background geometry. The obtained results indicate\nthat the critical temperature decreases with increasing the strength of the\npower parameter $q$ and dynamical exponent $z$. Besides, the formation of the\nmagnetic moment in the black hole background is harder in the absence of an\nexternal magnetic field. At low temperatures, and in the absence of an external\nmagnetic field, our result show the spontaneous magnetization and the\nferromagnetic phase transition. We find that the critical exponent takes the\nuniversal value $\\beta= 1/2$ regardless of the parameters $q, z, d$, which is\nin agreement with the mean field theory. In the presence of an external\nmagnetic field, the magnetic susceptibility satisfies the Curie-Weiss law.\n"}
+{"abstract": "  We consider the problem of fair allocation of indivisible items among $n$\nagents with additive valuations, when agents have equal entitlements to the\ngoods, and there are no transfers. Best-of-Both-Worlds (BoBW) fairness\nmechanisms aim to give all agents both an ex-ante guarantee (such as getting\nthe proportional share in expectation) and an ex-post guarantee. Prior BoBW\nresults have focused on ex-post guarantees that are based on the \"up to one\nitem\" paradigm, such as envy-free up to one item (EF1). In this work we attempt\nto give every agent a high value ex-post, and specifically, a constant fraction\nof his maximin share (MMS). The up to one item paradigm fails to give such a\nguarantee, and it is not difficult to present examples in which previous BoBW\nmechanisms give agents only a $\\frac{1}{n}$ fraction of their MMS.\n  Our main result is a deterministic polynomial time algorithm that computes a\ndistribution over allocations that is ex-ante proportional, and ex-post, every\nallocation gives every agent at least his proportional share up to one item,\nand more importantly, at least half of his MMS. Moreover, this last ex-post\nguarantee holds even with respect to a more demanding notion of a share,\nintroduced in this paper, that we refer to as the truncated proportional share\n(TPS). Our guarantees are nearly best possible, in the sense that one cannot\nguarantee agents more than their proportional share ex-ante, and one cannot\nguarantee agents more than a $\\frac{n}{2n-1}$ fraction of their TPS ex-post.\n"}
+{"abstract": "  For the pristine phosphorene nanoribbons (PNRs) with edge states, there exist\ntwo categories of edge bands near the Fermi energy (EF), i.e., the\nshuttle-shaped twofold-degenerate and the near-flat simple degenerate edge\nbands. However, the usual experimental measurement may not distinguish the\ndifference between the two categories of edge bands. Here we study the varying\nrule for the edge bands of PNRs under an external electrostatic field. By using\nthe KWANT code based on the tight-binding approach, we find that the\ntwofold-degenerate edge bands can be divided into two separated shuttles until\nthe degeneracy is completely removed and a gap near EFis opened under a\nsufficiently strong in-plane electric field. Importantly, each shuttle from the\nribbon upper or lower edge outmost atoms is identified according to the local\ndensity of states. However, under a small off-plane field the shuttle-shaped\nbands are easily induced into two near-flat bands contributed from the edge\natoms of the top and bottom sublayers, respectively. The evidence provides the\nedge and sublayer degrees of freedom (DOF) for the PNRs with shuttle-shaped\nedge bands, of which is obviously different from another category PNRs\nintrinsically with near-flat edge bands. This is because that the former\ncategory of ribbons solely have four zigzag-like atomic configurations at the\nedges in each unit cell, which also results in that the property is robust\nagainst the point defect in the ribbon center area. As an application,\nfurthermore, based on this issue we propose a homogenous junction of a\nshuttle-edge-band PNR attached by two electric gates. Interestingly, the\ntransport property of the junction with field manipulation well reflects the\ncharacteristics of the two DOFs. These findings may provide a further\nunderstanding on PNRs and initiate new developments in PNR-based electronics.\n"}
+{"abstract": "  We suggest a novel stochastic approximation algorithm to compute a Symmetric\nNash Equilibrium strategy in a general queueing game with a finite action\nspace. The algorithm involves a single simulation of the queueing process with\ndynamic updating of the strategy at regeneration times. Under mild assumptions\nregarding the regenerative structure of the process the algorithm converges to\na symmetric equilibrium strategy almost surely. This yields a powerful tool\nthat can be used to approximate equilibrium strategies in a broad range of\nstrategic queueing models in which direct analysis is impracticable.\n"}
+{"abstract": "  Multiple-point geostatistics plays an important role in characterizing\ncomplex subsurface aquifer systems such as channelized structures. However,\nonly a few studies have paid attention to how to choose an applicable training\nimage. In this paper, a TI selection method based on Recurrent Neural Networks\nis proposed. A synthetic case is tested using two channelized training images\ngiven the hydraulic head time series. Three different RNNs architectures are\ntested for the selection performance. Various scenarios of the model input are\nalso tested including the number of observation wells, the observation time\nsteps, the influence of observation noise, and the training dataset size. In\nthis TI selection task, the GRU has the best performance among all three\narchitectures and can reach to a 97.63% accuracy.\n"}
+{"abstract": "  Let $p$ be a prime and $n$ a positive integer such that $\\sqrt{\\frac p2} + 1\n\\leq n \\leq \\sqrt{p}$. For any arithmetic progression $A$ of length $n$ in\n$\\mathbb{F}_p$, we establish an asymptotic formula for the number of directions\ndetermined by $A \\times A \\subset \\mathbb{F}_p^2$. The key idea is to reduce\nthe problem to counting the number of solutions to the bilinear Diophantine\nequation $ad+bc=p$ in variables $1\\le a,b,c,d\\le n$; our asymptotic formula for\nthe number of solutions is of independent interest.\n"}
+{"abstract": "  Galaxy clusters host the largest particle accelerators in the Universe: Shock\nwaves in the intracluster medium (ICM), a hot and ionised plasma, that\naccelerate particles to high energies. Radio observations pick up synchrotron\nemission in the ICM, proving the existence of accelerated cosmic-ray electrons.\nHowever, a sign of cosmic-ray protons, in form of $\\gamma$-rays. remains\nundetected. This is know as the missing $\\gamma$-ray problem and it directly\nchallenges the shock acceleration mechanism at work in the ICM. Over the last\ndecade, theoretical and numerical studies focused on improving our knowledge on\nthe microphysics that govern the shock acceleration process in the ICM. These\nnew models are able to predict a $\\gamma$-ray signal, produced by shock\naccelerated cosmic-ray protons, below the detection limits set modern\n$\\gamma$-ray observatories. In this review, we summarise the latest advances in\nsolving the missing $\\gamma$-ray problem.\n"}
+{"abstract": "  The atomically clean surface of the iron-based superconductor FeTe0.55Se0.45\nis investigated by low-temperature STM with different tip apex states. By\nmanipulating a single Fe atom onto the tip apex, signatures of the orbital\nnature of the subsurface Fe layer of FeTe0.55Se0.45 can be identified. By\npreparing a charged tip state, the intrinsic spatial inhomogeneity of the\nchemical potential of FeTe0.55Se0.45 can be revealed. As a result, three\ndifferent types of vortex bound states originating from locally varying\ntopological properties of the FeTe0.55Se0.45 surface are observed by scanning\ntunneling spectroscopy.\n"}
+{"abstract": "  We show that constraints on scalar field potentials and towers of light\nmassive states in asymptotic limits of scalar field space (as posited by the de\nSitter Conjecture and the Swampland Distance Conjecture, respectively) are\ncorrelated with the prospects for defining asymptotic observables in expanding\nFRW cosmologies. The observations of a \"census taker\" in an eternally inflating\ncosmology are further related to the question of whether certain domain walls\nsatisfy a version of the Weak Gravity Conjecture. This suggests that answers to\nfundamental questions about asymptotic observables in cosmology could help shed\nlight on the Swampland program, and vice versa.\n"}
+{"abstract": "  We present a general approach to justify the random phase approximation for\nthe homogeneous Fermi gas in three-dimensions in the mean-field scaling regime.\nWe consider a system of $N$ fermions on a torus, interacting via a two-body\nrepulsive potential proportional to $N^{-\\frac{1}{3}}$. In the limit\n$N\\rightarrow\\infty$, we derive the exact leading order of the correlation\nenergy of the system, which coincides with the prediction of Gell-Mann and\nBrueckner in 1957.\n"}
+{"abstract": "  By tracing the orbital and star formation histories of galaxies within the\nComa cluster, we seek to understand the role of the high-density cluster\nenvironment in quenching these galaxies. We combine star formation histories\nextracted from high-signal-to-noise spectra of 11 early-type galaxies around\nthe center of the Coma cluster with probability distributions for their orbital\nparameters obtained using an N-body simulation to connect their orbital and\nstar formation histories. We find that all 11 galaxies likely quenched near\ntheir first pericentric approach. Higher stellar mass galaxies\n($\\log(M_\\star/\\mathrm{M}_\\odot)>10$) had formed a higher fraction of their\nstellar mass (more than $\\sim90\\%$) than their lower mass counterparts ($\\sim\n80$-$90\\%$) by the time they fell into the cluster (when they cross\n$2.5r_\\mathrm{vir}$). We find that the expected infall occurred around\n$z\\sim0.6$, followed by the first pericentric passage $\\sim4\\,\\mathrm{Gyr}$\nlater. Galaxies in our sample formed a significant fraction of their stellar\nmass, up to $15\\%$, between infall and first pericenter, and had assembled more\nthan $\\sim98\\%$ of their cumulative stellar mass by first pericenter. Unlike\nprevious low-redshift studies that suggest that star formation continues until\nabout first apocenter or later, the high percentage of stellar mass already\nformed by first pericenter in our sample galaxies points to star formation\nceasing within a gigayear after the first pericentric passage. We consider the\npossible physical mechanisms driving quenching and find that our results\nresemble the situation in clusters at $z\\sim1$, where active stripping of gas\n(ram-pressure or tidally driven) seems to be required to quench satellites by\ntheir first pericentric passage. However, a larger sample will be required to\nconclusively account for the unknown fraction of preprocessed satellites in the\nComa cluster.\n"}
+{"abstract": "  We propose a minimal off-lattice model of living organisms where just a very\nfew dynamical rules of growth are assumed. The stable coexistence of many\nclusters is detected when we replace the global restriction rule by a locally\napplied one. A rich variety of evolving patterns is revealed where players\nmovement has a decisive role on the evolutionary outcome. For example,\nintensive individual mobility may jeopardize the survival of the population,\nbut if we increase players movement further then it can save the population.\nNotably, the collective drive of population members is capable to compensate\nthe negative consequence of intensive movement and keeps the system alive. When\nthe drive becomes biased then the resulting unidirectional flow alters the\nstable pattern and produce a stripe-like state instead of the previously\nobserved hexagonal arrangement of clusters. Interestingly, the rotation of\nstripes can be flipped if the individual movement exceeds a threshold value.\n"}
+{"abstract": "  We present an overview of multisoliton chains arising in various\nnon-integrable field theories, and discuss different mechanisms, which may lead\nto the occurrence of such axially-symmetric classical solutions. We explain the\npattern of interactions between different solitons, in particular Q-balls,\nSkyrmions and monopoles and show how chains of interacting non-BPS solitons may\nform in a dynamic equilibrium between repulsive and attractive forces.\n"}
+{"abstract": "  We show that for a closed hyperbolic 3-manifold the size of the first\neigenvalue of the Hodge Laplacian acting on coexact 1-forms is comparable to an\nisoperimetric ratio relating geodesic length and stable commutator length with\ncomparison constants that depend polynomially on the volume and on a lower\nbound on injectivity radius, refining estimates of Lipnowski and Stern. We use\nthis estimate to show that there exist sequences of closed hyperbolic\n3-manifolds with injectivity radius bounded below and volume going to infinity\nfor which the 1-form Laplacian has spectral gap vanishing exponentially fast in\nthe volume.\n"}
+{"abstract": "  We present a methodology for computing the flexibility index when uncertainty\nis characterized using multivariate Gaussian random variables. Our approach\ncomputes the flexibility index by solving a mixed-integer conic program (MICP).\nThis methodology directly characterizes ellipsoidal sets to capture\ncorrelations in contrast to previous methodologies that employ approximations.\nWe also show that, under a Gaussian representation, the flexibility index can\nbe used to obtain a lower bound for the so-called stochastic flexibility index\n(i.e., the probability of having feasible operation). Our results also show\nthat the methodology can be generalized to capture different types of\nuncertainty sets.\n"}
+{"abstract": "  We consider a situation where an $N$-level system (NLS) is coupled to a heat\nbath without being necessarily thermalized. For this situation we derive\ngeneral Jarzinski-type equations and conclude that heat and entropy is flowing\nfrom the hot bath to the cold NLS and, vice versa, from the hot NLS to the cold\nbath. The Clausius relation between increase of entropy and transfer of heat\ndivided by a suitable temperature assumes the form of two inequalities which\nhave already been considered in the literature. Our approach is illustrated by\nan analytical example.\n"}
+{"abstract": "  We derive the Hydrodynamics for a system of N active, spherical, underdamped\nparticles, interacting through conservative forces. At the microscopic level,\nwe represent the evolution of the particles in terms of the Kramers equation\nfor the probability density distribution of their positions, velocities, and\norientations, while at a mesoscopic level we switch to a coarse-grained\ndescription introducing an appropriate set of hydrodynamic fields given by the\nlower-order moments of the distribution. In addition to the usual density and\npolarization fields, the Hydrodynamics developed in this paper takes into\naccount the velocity and kinetic temperature fields, which are crucial to\nunderstanding new aspects of the behavior of active liquids. By imposing a\nsuitable closure of the hydrodynamic moment equations and truncation of the\nBorn-Bogolubov-Green-Kirkwood-Yvon hierarchy, we obtain a closed set of\nmesoscopic balance equations. At this stage, we focus our interest on the small\ndeviations of the hydrodynamic fields from their averages and apply the methods\nof the theory of linear hydrodynamic fluctuations. Our treatment sheds light on\nthe peculiar properties of isotropic active liquids and their emergent\ndynamical collective phenomena, such as the spontaneous alignment of the\nparticle velocities. We predict the existence within the liquid phase of\nspatial equal-time Ornstein-Zernike-like velocity correlations both for the\nlongitudinal and the transverse modes. At variance with active solids, in\nactive liquids, the correlation length of the transverse velocity fluctuations\nis sensibly shorter than the length of the longitudinal fluctuations. In\nparticular, the latter depends on the sound speed and increases with the\npersistence time, while the former displays a weaker dependence on these\nparameters. Finally, within the same framework, we derive the dynamical\nstructure factors ...\n"}
+{"abstract": "  Collective motion in animal groups, such as swarms of insects, flocks of\nbirds, and schools of fish, are some of the most visually striking examples of\nemergent behavior. Empirical analysis of these behaviors in experiment or\ncomputational simulation primarily involves the use of \"swarm-averaged\" metrics\nor order parameters such as velocity alignment and angular momentum. Recently,\ntools from computational topology have been applied to the analysis of swarms\nto further understand and automate the detection of fundamentally different\nswarm structures evolving in space and time. Here, we show how the field of\ngraph signal processing can be used to fuse these two approaches by\ncollectively analyzing swarm properties using graph Fourier harmonics that\nrespect the topological structure of the swarm. This graph Fourier analysis\nreveals hidden structure in a number of common swarming states and forms the\nbasis of a flexible analysis framework for collective motion.\n"}
+{"abstract": "  Over 1000 students over four semesters were given the option of taking an\nintroductory statistics class either by in-person attendance in lectures,\naugmented by online recorded lectures, or by taking the same class without the\nin-person lectures. The all-online students did slightly better on\ncomputer-graded exams. The causal effect of choosing only online lectures was\nestimated by adjusting for potential confounders using four methods. The four\nnearly identical point estimates remained positive but were small and not\nstatistically significant. No statistically significant differences were found\nin preliminary comparisons of effects on females/males, U.S./non-U.S. citizens,\nfreshmen/non-freshman, and lower-scoring/higher-scoring math ACT groups.\n"}
+{"abstract": "  Transformers have shown outstanding results for natural language\nunderstanding and, more recently, for image classification. We here extend this\nwork and propose a transformer-based approach for image retrieval: we adopt\nvision transformers for generating image descriptors and train the resulting\nmodel with a metric learning objective, which combines a contrastive loss with\na differential entropy regularizer. Our results show consistent and significant\nimprovements of transformers over convolution-based approaches. In particular,\nour method outperforms the state of the art on several public benchmarks for\ncategory-level retrieval, namely Stanford Online Product, In-Shop and CUB-200.\nFurthermore, our experiments on ROxford and RParis also show that, in\ncomparable settings, transformers are competitive for particular object\nretrieval, especially in the regime of short vector representations and\nlow-resolution images.\n"}
+{"abstract": "  We study the charge of the 4D-Einstein-Gauss-Bonnet black hole by a negative\ncharge and a positive charge of a particle-antiparticle pair on the horizons r-\nand r+, respectively. We show that there are two types of the Schwarzschild\nblack hole. We show also that the Einstein-Gauss-Bonnet black hole charge has\nquantified values. We obtain the Hawking-Bekenstein formula with two\nlogarithmic corrections, the second correction depends on the cosmological\nconstant and the black hole charge. Finally, we study the thermodynamics of the\nEGB-AdS black hole.\n"}
+{"abstract": "  An introduction to the On-Line Encyclopedia of Integer Sequences (or OEIS,\nhttps://oeis.org) for graduate students in mathematics\n"}
+{"abstract": "  Almost 25 years ago, Jarzynski published a paper in which it was asserted:\nthe work done, W, in driving a system from state A to state B, characterized by\nthe Helmholtz free energies FA and FB, satisfies an equality in which an\naverage over an ensemble of measurements for W determines the difference in\nFree energy. Several features of this result require more detailed description,\nto be given in the text. The equality is significant and unexpected. So is the\nstatement that the equality is independent of the rate of change from state A\nto state B. A few years ago, I had presented three papers in which the\ncontraction of the description from full phase space to coordinate space only\nwas made. This was motivated by the large difference in time scales for momenta\nrelaxation and coordinate relaxation. The Jarzynski equality (JE) will be shown\nhere to be correct only in the limit of extremely slow processes. The proposed\ncounterexample is for a macromolecular harmonic oscillator with a Hooke's\nspring constant, k, that during the time interval linearly changes from k1 to\nk2. When performed very slowly, we obtain JE in a form in which the free energy\nis related to the ratio of the k's. However, when the transition is performed\nmore rapidly the equality is lost. Jarzynski has suggested to me that Hummer\nand Szabo have shown that JE follows directly from the Feynman-Kac formula. We\nshow here that F-K does not reproduce the direct calculation executed in this\npaper and that it has been misapplied in earlier papers.\n"}
+{"abstract": "  Average partial effects (APEs) are generally not point-identified in binary\nresponse panel models with unrestricted unobserved heterogeneity. We show their\npoint-identification under an index sufficiency assumption on the unobserved\nheterogeneity, even when the error distribution is unspecified. This assumption\ndoes not impose parametric restrictions on the unobserved heterogeneity. We\nthen construct a three-step semiparametric estimator for the APE. In the first\nstep, we estimate the common parameters using either a conditional logit or\nsmoothed maximum score estimator. In the second step, we estimate the\nconditional expectation of the outcomes given the indices and a generated\nregressor that depends on first-step estimates. In the third step, we average\nderivatives of this conditional expectation to obtain a partial mean that\nestimates the APE. We show that this proposed three-step APE estimator is\nconsistent and asymptotically normal. We evaluate its finite-sample properties\nin Monte Carlo simulations. We then illustrate our estimator in a study of\ndeterminants of married women's labor supply.\n"}
+{"abstract": "  We first revisit impact-parameter dependent collisions of ultra-relativistic\nparticles in quantum field theory. Two conditions sufficient for defining an\nimpact-parameter dependent cross section are given, which could be violated in\nproton-proton collisions. By imposing these conditions, a general formula for\nthe impact-parameter dependent cross section is derived. Then, using\nsoft-collinear effective theory, we derive a factorization formula for the\nimpact-parameter dependent cross section for inclusive hard processes with only\ncolorless final-state products in hadron and nuclear collisions. It entails\ndefining thickness beam functions, which are Fourier transforms of transverse\nphase-space parton distribution functions. By modelling non-perturbative modes\nin thickness beam functions of large nuclei in heavy-ion collisions, the\nfactorization formula confirms the cross section in the Glauber model for hard\nprocesses. Besides, the factorization formula is verified up to one loop in\nperturbative QCD for the inclusive Drell-Yan process in quark-antiquark\ncollisions at a finite impact parameter.\n"}
+{"abstract": "  The sensitivity to angular rotation of the top class Sagnac gyroscope\nGINGERINO is carefully investigated with standard statistical means, using 103\ndays of continuous operation and the available geodesic measurements of the\nEarth angular rotation rate. All features of the Earth rotation rate are\ncorrectly reproduced. The sensitivity of fractions of frad/s is attained for\nlong term runs. This excellent sensitivity and stability put Sagnac gyroscopes\nat the forefront for fundamental physics, in particular for tests of general\nrelativity and Lorentz violation, where the sensitivity plays the key role to\nprovide reliable data for deeper theoretical investigations. The achieved\nsensitivity overcomes the conventionally expected one for Sagnac ring laser\ngyroscopes.\n"}
+{"abstract": "  A phonoriton is an elementary excitation that is predicted to emerge from\nhybridization between exciton, phonon and photon. Besides the intriguing\nmany-particle structure, phonoritons are of interest as they could serve as\nfunctional nodes in devices that utilize electronic, phononic and photonic\nelements for energy conversion and thermal transport applications. Although\nphonoritons are predicted to emerge in an excitonic medium under intense\nelectromagnetic wave irradiation, the stringent condition for their existence\nhas eluded direct observation in solids. In particular, on-resonance, intense\npumping scheme has been proposed but excessive photoexcitation of carriers\nprevents optical detection. Here we theoretically predict the appearance of\nphonoritonic features in monolayer hexagonal boron nitride (hBN) embedded in an\noptical cavity. The coherent superposition nature of phonoriton states is\nevidenced by the hybridization of exciton-polariton branches with phonon\nreplicas that is tunable by the cavity-matter coupling strength. This finding\nsimultaneously provides an experimental pathway to observe the predicted\nphonoritons and opens a new avenue for tuning materials properties.\n"}
+{"abstract": "  Direct hyperlogarithmic integration offers a strong alternative to\ndifferential equation methods for Feynman integration, particularly for\nmulti-particle diagrams. We review a variety of results by the authors in which\nthis method, employed with some care, can compute diagrams of up to eight\nparticles and four loops. We also highlight situations in which this method\nfails due to an algebraic obstruction. In a large number of cases the\nobstruction can be associated with a Calabi-Yau manifold.\n"}
+{"abstract": "  In precision crop protection, (target-orientated) object detection in image\nprocessing can help navigate Unmanned Aerial Vehicles (UAV, crop protection\ndrones) to the right place to apply the pesticide. Unnecessary application of\nnon-target areas could be avoided. Deep learning algorithms dominantly use in\nmodern computer vision tasks which require high computing time, memory\nfootprint, and power consumption. Based on the Edge Artificial Intelligence, we\ninvestigate the main three paths that lead to dealing with this problem,\nincluding hardware accelerators, efficient algorithms, and model compression.\nFinally, we integrate them and propose a solution based on a light deep neural\nnetwork (DNN), called Ag-YOLO, which can make the crop protection UAV have the\nability to target detection and autonomous operation. This solution is\nrestricted in size, cost, flexible, fast, and energy-effective. The hardware is\nonly 18 grams in weight and 1.5 watts in energy consumption, and the developed\nDNN model needs only 838 kilobytes of disc space. We tested the developed\nhardware and software in comparison to the tiny version of the state-of-art\nYOLOv3 framework, known as YOLOv3-Tiny to detect individual palm in a\nplantation. An average F1 score of 0.9205 at the speed of 36.5 frames per\nsecond (in comparison to similar accuracy at 18 frames per second and 8.66\nmegabytes of the YOLOv3-Tiny algorithm) was reached. This developed detection\nsystem is easily plugged into any machines already purchased as long as the\nmachines have USB ports and run Linux Operating System.\n"}
+{"abstract": "  We introduce a computational method and a user-friendly code with a\nterminal-based graphical user interface (GUI), named ElATools, developed to\nanalyze mechanical and anisotropic elastic properties. ElATools enables facile\nanalysis of the second-order elastic stiffness tensor of two-dimensional (2D)\nand three-dimensional (3D) crystal systems. It computes and displays the main\nmechanical properties including the bulk modulus, Young's modulus, shear\nmodulus, hardness, p-wave modulus, universal anisotropy index, Chung-Buessem\nanisotropy index, log-Euclidean anisotropy parameter, Cauchy pressures,\nPoisson's ratio, and Pugh's ratio, using three averaging schemes of Voigt,\nReuss, and Hill. It includes an online and offline database from the Materials\nProject with more than 13,000 elastic stiffness constants for 3D materials. The\nprogram supports output files of the well-known computational codes IRelast,\nIRelast2D, ElaStic, and AELAS. Four types of plotting and visualization tools\nare integrated to conveniently interface with GNUPLOT, XMGRACE, view3dscene,\nand plotly libraries, offering immediate post-processing of the results. It can\nefficiently identify anomalous mechanical properties, such as negative linear\ncompressibility, negative Poisson's ratio, and highly-anisotropic elastic\nmodulus in 2D and 3D materials, which are central properties to design and\ndevelop high-performance nanoscale electromechanical devices. Six case studies\non selected material systems, namely, ZnAu$_2$ (CN)$_4$, CrB$_2$,\n$\\delta$-phosphorene, Pd$_2$O$_6$Se$_2$ monolayer, and GaAs, and a hypothetical\nset of systems with cubic symmetry are presented to demonstrate the descriptive\nand predictive capabilities of ElATools.\n"}
+{"abstract": "  I want to combine two hitherto largely independent research projects,\nscientific understanding and mechanistic explanations. Understanding is not\nonly achieved by answering why-questions, that is, by providing scientific\nexplanations, but also by answering what-questions, that is, by providing what\nI call scientific descriptions. Based on this distinction, I develop three\nforms of understanding: understanding-what, understanding-why, and\nunderstanding-how. I argue that understanding-how is a particularly deep form\nof understanding, because it is based on mechanistic explanations, which answer\nwhy something happens in virtue of what it is made of. I apply the three forms\nof understanding to two case studies: first, to the historical development of\nthermodynamics and, second, to the differences between the Clausius and the\nBoltzmann entropy in explaining thermodynamic processes.\n"}
+{"abstract": "  We show that the singular set $\\Sigma$ in the classical obstacle problem can\nbe locally covered by a $C^\\infty$ hypersurface, up to an \"exceptional\" set\n$E$, which has Hausdorff dimension at most $n-2$ (countable, in the $n=2$\ncase). Outside this exceptional set, the solution admits a polynomial expansion\nof arbitrarily large order. We also prove that $\\Sigma\\setminus E$ is extremely\nunstable with respect to monotone perturbations of the boundary datum. We apply\nthis result to the planar Hele-Shaw flow, showing that the free boundary can\nhave singular points for at most countable many times.\n"}
+{"abstract": "  Control systems often must satisfy strict safety requirements over an\nextended operating lifetime. Control Barrier Functions (CBFs) are a promising\nrecent approach to constructing simple and safe control policies. This paper\nproposes a framework for verifying that a CBF guarantees safety for all time\nand synthesizing CBFs with verifiable safety in polynomial control systems. Our\napproach is to show that safety of CBFs is equivalent to the non-existence of\nsolutions to a family of polynomial equations, and then prove that this\nnonexistence is equivalent to a pair of sum-of-squares constraints via the\nPositivstellensatz of algebraic geometry. We develop this Positivstellensatz to\nverify CBFs, as well as generalization to high-degree systems and multiple CBF\nconstraints. We then propose a set of heuristics for CBF synthesis, including a\ngeneral alternating-descent heuristic, a specialized approach for compact safe\nregions, and an approach for convex unsafe regions. Our approach is illustrated\non two numerical examples.\n"}
+{"abstract": "  In this paper, we present two sharp upper bounds for the spectral radius of\n(bipartite) graphs with forbidden a star forest and characterize all extremal\ngraphs. Moreover, the minimum least eigenvalue of the adjacency matrix of graph\nwith forbidden a star forest and all extremal graphs for graphs are obtained.\n"}
+{"abstract": "  We present independent determinations of cosmological parameters using the\ndistance estimator based on the established correlation between the Balmer line\nluminosity, L(H$\\beta$), and the velocity dispersion ($\\sigma$) for HII\ngalaxies (HIIG). These results are based on new VLT-KMOS high spectral\nresolution observations of 41 high-z ($1.3 \\leq$ z $\\leq 2.6$) HIIG combined\nwith published data for 45 high-z and 107 z $\\leq 0.15$ HIIG, while the\ncosmological analysis is based on the MultiNest MCMC procedure not considering\nsystematic uncertainties. Using only HIIG to constrain the matter density\nparameter ($\\Omega_m$), we find $\\Omega_m = 0.244^{+0.040}_{-0.049}$ (stat), an\nimprovement over our best previous cosmological parameter constraints, as\nindicated by a 37% increase of the FoM. The marginalised best-fit parameter\nvalues for the plane $\\{\\Omega_m; w_0\\}$ = $\\{0.249^{+0.11}_{-0.065};\n-1.18^{+0.45}_{-0.41}\\}$ (stat) show an improvement of the cosmological\nparameters constraints by 40%. Combining the HIIG Hubble diagram, the cosmic\nmicrowave background (CMB) and the baryon acoustic oscillation (BAO) probes\nyields $\\Omega_m=0.298 \\pm 0.012$ and $w_0=-1.005 \\pm 0.051$, which are\ncertainly compatible -- although less constraining -- than the solution based\non the joint analysis of SNIa/CMB/BAO. An attempt to constrain the evolution of\nthe dark energy with time (CPL model), using a joint analysis of the HIIG, CMB\nand BAO measurements, shows a degenerate 1$\\sigma$ contour of the parameters in\nthe $\\{w_0,w_a\\}$ plane.\n"}
+{"abstract": "  We generalize the Bogomolov-Gieseker inequality for semistable coherent\nsheaves on smooth projective surfaces to smooth Deligne-Mumford surfaces. We\nwork over positive characteristic $p>0$ and generalize Langer's method to\nsmooth Deligne-Mumford stacks. As applications we obtain the Bogomolov\ninequality for semistable coherent sheaves on a Deligne-Mumford surface in\ncharacteristic zero, and the Bogomolov inequality for semistable sheaves on a\nroot stack over a smooth surface which is equivalent to the Bogomolov\ninequality for the rational parabolic sheaves on a smooth surface $S$. In a\njoint appendix with Hao Max Sun, we generalize the Bogomolov inequality formula\nto Simpson Higgs sheaves on tame Deligne-Mumford stacks.\n"}
+{"abstract": "  Fraud has led to a huge addition of expenses in health insurance sector in\nIndia. The work is aimed to provide methods applied to health insurance fraud\ndetection. The work presents two approaches - a markov model and an improved\nmarkov model using gradient boosting method in health insurance claims. The\ndataset 382,587 claims of which 38,082 claims are fraudulent. The markov based\nmodel gave the accuracy of 94.07% with F1-score at 0.6683. However, the\nimproved markov model performed much better in comparison with the accuracy of\n97.10% and F1-score of 0.8546. It was observed that the improved markov model\ngave much lower false positives compared to markov model.\n"}
+{"abstract": "  We propose an efficient framework with compatibility between normal printing\nand printing with special color inks in this paper. Special color inks can be\nused for printing to represent some particular colors and specific optical\nproperties, which are difficult to express using only CMYK inks. Special color\nlayers are required in addition to the general color layer for printing with\nspecial color inks. We introduce a reversible data hiding (RDH) method to embed\nthe special color layers into the general color layer without visible\nartifacts. The proposed method can realize both normal printing and printing\nwith special color inks by using a single layer. Our experimental results show\nthat the quality of the marked image is virtually identical to that of the\noriginal image, i.e., the general color layer.\n"}
+{"abstract": "  Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively\nwith normal (i.e., healthy) images to detect any abnormal (i.e., unhealthy)\nsamples that do not conform to the expected normal patterns. UAD has two main\nadvantages over its fully supervised counterpart. Firstly, it is able to\ndirectly leverage large datasets available from health screening programs that\ncontain mostly normal image samples, avoiding the costly manual labelling of\nabnormal samples and the subsequent issues involved in training with extremely\nclass-imbalanced data. Further, UAD approaches can potentially detect and\nlocalise any type of lesions that deviate from the normal patterns. One\nsignificant challenge faced by UAD methods is how to learn effective\nlow-dimensional image representations to detect and localise subtle\nabnormalities, generally consisting of small lesions. To address this\nchallenge, we propose a novel self-supervised representation learning method,\ncalled Constrained Contrastive Distribution learning for anomaly detection\n(CCD), which learns fine-grained feature representations by simultaneously\npredicting the distribution of augmented data and image contexts using\ncontrastive learning with pretext constraints. The learned representations can\nbe leveraged to train more anomaly-sensitive detection models. Extensive\nexperiment results show that our method outperforms current state-of-the-art\nUAD approaches on three different colonoscopy and fundus screening datasets.\nOur code is available at https://github.com/tianyu0207/CCD.\n"}
+{"abstract": "  On the unit tangent bundle of a nonflat compact nonpositively curved surface,\nwe prove that there is a unique probability Borel measure invariant by the\nhorocyclic flow which gives full measure to the set of rank 1 vectors recurrent\nby the geodesic flow. If we assume in addition that the surface has no flat\nstrips, we show that the horocyclic flow is uniquely ergodic. These results are\nvalid for any parametrization of the horocyclic flow.\n"}
+{"abstract": "  The map of half-BPS line defects under mirror symmetry has previously been\nworked out for 3d $\\mathcal{N}=4$ linear quivers with unitary gauge groups,\nwhere these defects have a clear realization in terms of a brane picture in\nType IIB String Theory. In this work, we initiate the study of line defects and\nthe associated mirror maps for more general 3d $\\mathcal{N}=4$ quiver gauge\ntheories from a QFT approach, using the $S$-type operations introduced in\n\\cite{Dey:2020hfe}. In particular, our construction does not rely on any String\nTheory realization of the quiver gauge theories and the defects. After\ndiscussing the general framework for the construction of these line defects and\ntheir mirror maps, we focus on quiver gauge theories of the $D$-type and the\naffine $D$-type with unitary gauge groups, as a concrete set of examples. Some\nof the line defects we study admit a Hanany-Witten description and we show that\nthe associated mirror maps predicted by the Type IIB construction in these\ncases agree with the QFT computation. In addition, we study an example\ninvolving defects in an affine $D$-type theory, for which the dual theory is\nnot directly realized by the Type IIB description. In a companion paper, we\nwill discuss defects in infinite families of non-ADE quivers using the general\nconstruction developed in this paper.\n"}
+{"abstract": "  This paper introduces the Class-wise Principal Component Analysis, a\nsupervised feature extraction method for hyperspectral data. Hyperspectral\nImaging (HSI) has appeared in various fields in recent years, including Remote\nSensing. Realizing that information extraction tasks for hyperspectral images\nare burdened by data-specific issues, we identify and address two major\nproblems. Those are the Curse of Dimensionality which occurs due to the\nhigh-volume of the data cube and the class imbalance problem which is common in\nhyperspectral datasets. Dimensionality reduction is an essential preprocessing\nstep to complement a hyperspectral image classification task. Therefore, we\npropose a feature extraction algorithm for dimensionality reduction, based on\nPrincipal Component Analysis (PCA). Evaluations are carried out on the Indian\nPines dataset to demonstrate that significant improvements are achieved when\nusing the reduced data in a classification task.\n"}
+{"abstract": "  We describe a system being developed for measuring the shapes of the mirrors\nof the Fred Young Submillimeter Telescope (FYST), now under construction for\nthe CCAT Observatory. \"Holographic\" antenna-measuring techniques are an\nefficient and accurate way of measuring the surfaces of large millimeter-wave\ntelescopes and they have the advantage of measuring the wave-front errors of\nthe whole system under operational conditions, e.g. at night on an exposed\nsite. Applying this to FYST, however, presents significant challenges because\nof the high accuracy needed, the fact that the telescope consists of two large\noff-axis mirrors, and a requirement that measurements can be made without\npersonnel present. We use a high-frequency (~300GHz) source which is relatively\nclose to the telescope aperture (<1/100th of the Fresnel distance) to minimize\natmospheric effects. The main receiver is in the receiver cabin and can be\nmoved under remote control to different positions, so that the wave-front\nerrors in different parts of the focal plane can be measured. A second receiver\nplaced on the yoke provides a phase reference. The signals are combined in a\ndigital cross-correlation spectrometer. Scanning the telescope provides a map\nof the complex beam pattern. The surface errors are found by inference, i.e. we\nmake models of the reflectors with errors and calculate the patterns expected,\nand then iterate to find the best match to the data. To do this we have\ndeveloped a fast and accurate method for calculating the patterns using the\nKirchhoff-Fresnel formulation. This paper presents details of the design and\noutlines the results from simulations of the measurement and inference process.\nThese indicate that a measurement accuracy of ~3 microns rms is achievable.\n"}
+{"abstract": "  Understanding the production mechanism of light (anti-)nuclei in high-energy\nnuclear collisions and cosmic rays has been a long-standing problem in nuclear\nphysics. In the present study, we develop a stochastic method to solve the\nrelativistic kinetic equations for light nuclei production from many-body\nreactions with the inclusion of their finite sizes. The present approach gives\nan excellent description of the deuteron and helium-3 data from central Au+Au\n(Pb+Pb) collisions at $\\sqrt{s_{\\rm NN}}$ $=$ $200~\\rm GeV$ ($2.76~\\rm TeV$).\nIt can also naturally explain their suppressed production in $pp$ collisions at\n7 TeV as a result of their finite sizes.\n"}
+{"abstract": "  Autonomous vehicles interacting with other traffic participants heavily rely\non the perception and prediction of other agents' behaviors to plan safe\ntrajectories. However, as occlusions limit the vehicle's perception ability,\nreasoning about potential hazards beyond the field of view is one of the most\nchallenging issues in developing autonomous driving systems. This paper\nintroduces a novel analytical approach that poses safe trajectory planning\nunder occlusions as a hybrid zero-sum dynamic game between the autonomous\nvehicle (evader) and an initially hidden traffic participant (pursuer). Due to\nocclusions, the pursuer's state is initially unknown to the evader and may\nlater be discovered by the vehicle's sensors. The analysis yields optimal\nstrategies for both players as well as the set of initial conditions from which\nthe autonomous vehicle is guaranteed to avoid collisions. We leverage this\ntheoretical result to develop a novel trajectory planning framework for\nautonomous driving that provides worst-case safety guarantees while minimizing\nconservativeness by accounting for the vehicle's ability to actively avoid\nother road users as soon as they are detected in future observations. Our\nframework is agnostic to the driving environment and suitable for various\nmotion planners. We demonstrate our algorithm on challenging urban and highway\ndriving scenarios using the open-source CARLA simulator.\n"}
+{"abstract": "  The interface between a solid and vacuum can become electronically distinct\nfrom the bulk. This feature, encountered in the case of quantum Hall effect,\nhas a manifestation in insulators with topologically protected metallic surface\nstates. Non-trivial Berry curvature of the Bloch waves or periodically driven\nperturbation are known to generate it. Here, by studying the angle-dependent\nmagnetoresistance in prismatic bismuth crystals of different shapes, we detect\na robust surface contribution to electric conductivity when the magnetic field\nis aligned parallel to a two-dimensional boundary between the three-dimensional\ncrystal and vacuum. The effect is absent in antimony, which has an identical\ncrystal symmetry, a similar Fermi surface structure and equally ballistic\ncarriers, but an inverted band symmetry and a topological invariant of opposite\nsign. Our observation confirms that the boundary interrupting the cyclotron\norbits remains metallic in bismuth, which is in agreement with what was\npredicted by Azbel decades ago. However, the absence of the effect in antimony\nindicates an intimate link between band symmetry and this boundary conductance.\n"}
+{"abstract": "  Transport networks are typically optimized, either by evolutionary pressures\nin biological systems, or by human design, in engineered structures. In the\ncase of systems such as the animal vasculature, the transport of fluids is not\nonly hindered by the inherent resistance to flow but also kept in a dynamic\nstate by the pulsatile nature of the heart and elastic properties of the vessel\nwalls. While this imparted pulsatility necessarily increases the dissipation of\nenergy caused by the resistance, the vessel elasticity helps to reduce overall\ndissipation by attenuating the amplitude of the pulsatile components of the\nflow. However, we find that this reduction in energy loss comes at the price of\nincreasing the time required to respond to changes in the flow boundary\nconditions for vessels longer than a critical size. In this regime, dissipation\nand response time are found to follow a simple power law scaling relation in\nsingle vessels as well as hierarchically structured networks. Applying\nbiologically relevant parameters to the model suggests that animal vascular\nnetworks may have evolved to maintain a minimal response time over lesser\ndissipation.\n"}
+{"abstract": "  In this paper, we introduce Super Luminal (\"xLumi\"), a new payment channel\nprotocol for blockchain systems. xLumi is a simple unidirectional payment\nchannel that can be extended to a bidirectional payment channel or a complete\nnetwork. xLumi guarantees the security of the payment channel's funds by using\na simple set of mathematical rules that can be easily implemented on any\nblockchain with the necessary infrastructure. We also give the detailed\nimplementation methods of this idea using V Systems contract systems in this\npaper.\n"}
+{"abstract": "  Many neural network quantization techniques have been developed to decrease\nthe computational and memory footprint of deep learning. However, these methods\nare evaluated subject to confounding tradeoffs that may affect inference\nacceleration or resource complexity in exchange for higher accuracy. In this\nwork, we articulate a variety of tradeoffs whose impact is often overlooked and\nempirically analyze their impact on uniform and mixed-precision post-training\nquantization, finding that these confounding tradeoffs may have a larger impact\non quantized network accuracy than the actual quantization methods themselves.\nBecause these tradeoffs constrain the attainable hardware acceleration for\ndifferent use-cases, we encourage researchers to explicitly report these design\nchoices through the structure of \"quantization cards.\" We expect quantization\ncards to help researchers compare methods more effectively and engineers\ndetermine the applicability of quantization techniques for their hardware.\n"}
+{"abstract": "  The present work reports on the flow physics of turbulent supersonic flow\nover backward facing step (BFS) at Mach 2 using LES methodology where the\ndynamic Smagorinsky model is used for SGS modeling, while POD is invoked to\nidentify the coherent structures present in the flow. The mean data obtained\nthrough the computations is in good agreement with the experimental\nmeasurements, while the iso-surfaces of Q-criterion at different time instants\nshow the complex flow structures. The presence of counter rotating vortex pair\nin the shear layer along with the complex shock wave/boundary layer interaction\nleading to the separation of boundary layer is also evident from the contours\nof both Q and the modulus of vorticity. Further, the POD analysis reveals the\npresence of coherent structures, where the first and second modes confirm the\nvortical structures near the step as well as along the shear layer in the\ndownstream region; while the second, third and fourth modes confirm the\npresence of vortices along the shear layer due to Kelvin-Helmholtz (K-H)\ninstability. Moreover, POD as well as frequency analysis is extended at\ndifferent planes to extract the detailed flow features.\n"}
+{"abstract": "  We study the generalized Forchheimer flows of slightly compressible fluids in\nrotating porous media. In the problem's model, the varying density in the\nCoriolis force is fully accounted for without any simplifications. It results\nin a doubly nonlinear parabolic equation for the density. We derive a priori\nestimates for the solutions in terms of the initial, boundary data and physical\nparameters, emphasizing on the case of unbounded data. Weighted\nPoincar\\'e-Sobolev inequalities suitable to the equation's nonlinearity,\nadapted Moser's iteration and maximum principle are used and combined to obtain\ndifferent types of estimates.\n"}
+{"abstract": "  Let a group $A$ act on the group $G$ coprimely. Suppose that the order of the\nfixed point subgroup $C_G(A)$ is not divisible by an arbitrary but fixed prime\n$p$. In the present paper we determine bounds for the $p$-length of the group\n$G$ in terms of the order of $A$, and investigate how some $A$-invariant\n$p$-subgroups are embedded in $G$ under various additional assumptions.\n"}
+{"abstract": "  In this work, we propose two algorithms that can be applied in the context of\na networked virtual environment to efficiently handle the interpolation of\ndisplacement data for hand-based VR HMDs. Our algorithms, based on the use of\ndual-quaternions and multivectors respectively, impact the network consumption\nrate and are highly effective in scenarios involving multiple users. We\nillustrate convincing results in a modern game engine and a medical VR\ncollaborative training scenario.\n"}
+{"abstract": "  The collection $\\mathcal{M}$ of all isometry classes of compact metric spaces\nendowed with the Gromov-Hausdorff distance $d_\\mathcal{GH}$ is known to be a\ngeodesic space. However, there is no known structural characterization of\ngeodesics in $\\mathcal{M}$. In this paper we provide two such\ncharacterizations. We first prove that every Gromov-Hausdorff geodesic is in\nfact a geodesic in the Hausdorff hyperspace of some compact metric space, which\nwe call a Hausdorff geodesic. Inspired by this characterization, we further\nelucidate a structural connection between Hausdorff geodesics and Wasserstein\ngeodesics: every Hausdorff geodesic is equivalent to a so-called Hausdorff\ndisplacement interpolation. This equivalence allows us to establish that every\nGromov-Hausdorff geodesic is dynamic, a notion which we develop in analogy with\ndynamic optimal couplings in the theory of optimal transport.\n  Besides geodesics in $\\mathcal{M}$, we also study geodesics on the collection\n$\\mathcal{M}^w$ of isomorphism classes of compact metric measure spaces. Sturm\nconstructed a family of Gromov-type distances on $\\mathcal{M}^w$, which we\ndenote $d_{\\mathcal{GW},{p}}^\\mathrm{S}$ (for $p\\in[1,\\infty)$), and proved\nthat $(\\mathcal{M}^w,d_{\\mathcal{GW},{p}}^\\mathrm{S})$ is also a geodesic\nspace. We are interested in $d_{\\mathcal{GW},{p}}^\\mathrm{S}$ geodesics which\nare (essentially) Wasserstein geodesics. We prove the set of such geodesics is\ndense in the set of all $d_{\\mathcal{GW},{p}}^\\mathrm{S}$ geodesics and\nidentify a rich class of such geodesics.\n"}
+{"abstract": "  A few years ago, the first CNN surpassed human performance on ImageNet.\nHowever, it soon became clear that machines lack robustness on more challenging\ntest cases, a major obstacle towards deploying machines \"in the wild\" and\ntowards obtaining better computational models of human visual perception. Here\nwe ask: Are we making progress in closing the gap between human and machine\nvision? To answer this question, we tested human observers on a broad range of\nout-of-distribution (OOD) datasets, recording 85,120 psychophysical trials\nacross 90 participants. We then investigated a range of promising machine\nlearning developments that crucially deviate from standard supervised CNNs\nalong three axes: objective function (self-supervised, adversarially trained,\nCLIP language-image training), architecture (e.g. vision transformers), and\ndataset size (ranging from 1M to 1B).\n  Our findings are threefold. (1.) The longstanding distortion robustness gap\nbetween humans and CNNs is closing, with the best models now exceeding human\nfeedforward performance on most of the investigated OOD datasets. (2.) There is\nstill a substantial image-level consistency gap, meaning that humans make\ndifferent errors than models. In contrast, most models systematically agree in\ntheir categorisation errors, even substantially different ones like contrastive\nself-supervised vs. standard supervised models. (3.) In many cases,\nhuman-to-model consistency improves when training dataset size is increased by\none to three orders of magnitude. Our results give reason for cautious\noptimism: While there is still much room for improvement, the behavioural\ndifference between human and machine vision is narrowing. In order to measure\nfuture progress, 17 OOD datasets with image-level human behavioural data and\nevaluation code are provided as a toolbox and benchmark at:\nhttps://github.com/bethgelab/model-vs-human/\n"}
+{"abstract": "  Recent years have seen many breakthroughs in natural language processing\n(NLP), transitioning it from a mostly theoretical field to one with many\nreal-world applications. Noting the rising number of applications of other\nmachine learning and AI techniques with pervasive societal impact, we\nanticipate the rising importance of developing NLP technologies for social\ngood. Inspired by theories in moral philosophy and global priorities research,\nwe aim to promote a guideline for social good in the context of NLP. We lay the\nfoundations via the moral philosophy definition of social good, propose a\nframework to evaluate the direct and indirect real-world impact of NLP tasks,\nand adopt the methodology of global priorities research to identify priority\ncauses for NLP research. Finally, we use our theoretical framework to provide\nsome practical guidelines for future NLP research for social good. Our data and\ncode are available at http://github.com/zhijing-jin/nlp4sg_acl2021. In\naddition, we curate a list of papers and resources on NLP for social good at\nhttps://github.com/zhijing-jin/NLP4SocialGood_Papers.\n"}
+{"abstract": "  Consensus of autonomous agents is a benchmark problem in multi-agent control.\nIn this paper, we consider continuous-time averaging consensus policies (or\nLaplacian flows) and their discrete-time counterparts over time-varying graphs\nin presence of unknown but bounded communication delays. It is known that\nconsensus is established (no matter how large the delays are) if the graph is\nperiodically, or uniformly quasi-strongly connected (UQSC). The UQSC condition\nis often believed to be the weakest sufficient condition under which consensus\ncan be proved. We show that the UQSC condition can actually be substantially\nrelaxed and replaced by a condition that we call aperiodic quasi-strong\nconnectivity (AQSC), which, in some sense, proves to be very close to the\nnecessary condition of integral connectivity. Furthermore, in some special\nsituations such as undirected or type-symmetric graph, we find a necessary and\nsufficient condition for consensus in presence of bounded delay; the relevant\nresults have been previously proved only in the undelayed case. The consensus\ncriteria established in this paper generalize a number of results known in the\nliterature.\n"}
+{"abstract": "  We discuss the fundamental noise limitations of a ferromagnetic torque sensor\nbased on a levitated magnet in the tipping regime. We evaluate the optimal\nmagnetic field resolution taking into account the thermomechanical noise and\nthe mechanical detection noise at the standard quantum limit (SQL). We find\nthat the Energy Resolution Limit (ERL), pointed out in recent literature as a\nrelevant benchmark for most classes of magnetometers, can be surpassed by many\norders of magnitude. Moreover, similarly to the case of a ferromagnetic\ngyroscope, it is also possible to surpass the standard quantum limit for\nmagnetometry with independent spins, arising from spin-projection noise. Our\nfinding indicates that magnetomechanical systems optimized for magnetometry can\nachieve a magnetic field resolution per unit volume several orders of magnitude\nbetter than any conventional magnetometer. We discuss possible implications,\nfocusing on fundamental physics problems such as the search for exotic\ninteractions beyond the standard model.\n"}
+{"abstract": "  An analogous construction of simplicial homotopy group for Kan complex can be\napplied to saturated complicial sets to give monoids. In this paper, we\ninvestigate how the construction of loop spaces of Kan complexes lifts to the\ncomplicial setting and relates to such monoids.\n"}
+{"abstract": "  Although Natural Language Processing (NLP) is at the core of many tools young\npeople use in their everyday life, high school curricula (in Italy) do not\ninclude any computational linguistics education. This lack of exposure makes\nthe use of such tools less responsible than it could be and makes choosing\ncomputational linguistics as a university degree unlikely. To raise awareness,\ncuriosity, and longer-term interest in young people, we have developed an\ninteractive workshop designed to illustrate the basic principles of NLP and\ncomputational linguistics to high school Italian students aged between 13 and\n18 years. The workshop takes the form of a game in which participants play the\nrole of machines needing to solve some of the most common problems a computer\nfaces in understanding language: from voice recognition to Markov chains to\nsyntactic parsing. Participants are guided through the workshop with the help\nof instructors, who present the activities and explain core concepts from\ncomputational linguistics. The workshop was presented at numerous outlets in\nItaly between 2019 and 2021, both face-to-face and online.\n"}
+{"abstract": "  We consider the quantum difference equation of the Hilbert scheme of points\nin $\\mathbb{C}^2$. This equation is the K-theoretic generalization of the\nquantum differential equation discovered by A. Okounkov and R. Pandharipande.\nWe obtain two explicit descriptions for the monodromy of these equations -\nrepresentation-theoretic and algebro-geometric. In the representation-theoretic\ndescription, the monodromy acts via certain explicit elements in the quantum\ntoroidal algebra $\\frak{gl}_1$. In the algebro-geometric description, the\nmonodromy features as transition matrices between the stable envelope bases in\nthe equivariant K-theory and elliptic cohomology. Using the second approach we\nidentify the monodromy matrices for the differential equation with the\nK-theoretic $R$-matrices of cyclic quiver varieties, which appear as\nsubvarieties in the $3D$-mirror Hilbert scheme. Most of the results in the\npaper are illustrated by explicit examples for cases $n=2$ and $n=3$ in the\nAppendix.\n"}
+{"abstract": "  Despite the success of convolutional neural networks (CNNs) in many computer\nvision and image analysis tasks, they remain vulnerable against so-called\nadversarial attacks: Small, crafted perturbations in the input images can lead\nto false predictions. A possible defense is to detect adversarial examples. In\nthis work, we show how analysis in the Fourier domain of input images and\nfeature maps can be used to distinguish benign test samples from adversarial\nimages. We propose two novel detection methods: Our first method employs the\nmagnitude spectrum of the input images to detect an adversarial attack. This\nsimple and robust classifier can successfully detect adversarial perturbations\nof three commonly used attack methods. The second method builds upon the first\nand additionally extracts the phase of Fourier coefficients of feature-maps at\ndifferent layers of the network. With this extension, we are able to improve\nadversarial detection rates compared to state-of-the-art detectors on five\ndifferent attack methods.\n"}
+{"abstract": "  The vast majority of existing studies that estimate the average unexplained\ngender pay gap use unnecessarily restrictive linear versions of the\nBlinder-Oaxaca decomposition. Using a notably rich and large data set of 1.7\nmillion employees in Switzerland, we investigate how the methodological\nimprovements made possible by such big data affect estimates of the unexplained\ngender pay gap. We study the sensitivity of the estimates with regard to i) the\navailability of observationally comparable men and women, ii) model flexibility\nwhen controlling for wage determinants, and iii) the choice of different\nparametric and semi-parametric estimators, including variants that make use of\nmachine learning methods. We find that these three factors matter greatly.\nBlinder-Oaxaca estimates of the unexplained gender pay gap decline by up to 39%\nwhen we enforce comparability between men and women and use a more flexible\nspecification of the wage equation. Semi-parametric matching yields estimates\nthat when compared with the Blinder-Oaxaca estimates, are up to 50% smaller and\nalso less sensitive to the way wage determinants are included.\n"}
+{"abstract": "  Python has become the de facto language for scientific computing. Programming\nin Python is highly productive, mainly due to its rich science-oriented\nsoftware ecosystem built around the NumPy module. As a result, the demand for\nPython support in High Performance Computing (HPC) has skyrocketed. However,\nthe Python language itself does not necessarily offer high performance. In this\nwork, we present a workflow that retains Python's high productivity while\nachieving portable performance across different architectures. The workflow's\nkey features are HPC-oriented language extensions and a set of automatic\noptimizations powered by a data-centric intermediate representation. We show\nperformance results and scaling across CPU, GPU, FPGA, and the Piz Daint\nsupercomputer (up to 23,328 cores), with 2.47x and 3.75x speedups over\nprevious-best solutions, first-ever Xilinx and Intel FPGA results of annotated\nPython, and up to 93.16% scaling efficiency on 512 nodes.\n"}
+{"abstract": "  The objective of this study is improving the location estimate of a mobile\nrobot capable of motion on a plane and mounted with a conventional 2D LIDAR\nsensor, given an initial guess for its location on a 2D map of its\nsurroundings. Documented herein is the theoretical reasoning behind solving a\nmatching problem between two homoriented 2D scans, one derived from the robot's\nphysical sensor and one derived by simulating its operation within the map, in\na manner that does not require the establishing of correspondences between\ntheir constituting rays. Two results are proved and subsequently shown through\nexperiments. The first is that the true position of the sensor can be recovered\nwith arbitrary precision when the physical sensor reports faultless\nmeasurements and there is no discrepancy between the environment the robot\noperates in and its perception of it by the robot. The second is that when\neither is affected by disturbance, the location estimate is bound in a\nneighbourhood of the true location whose radius is proportional to the\naffecting disturbance.\n"}
+{"abstract": "  Complex nonlinear turbulent dynamical systems are ubiquitous in many areas.\nRecovering unobserved state variables is an important topic for the data\nassimilation of turbulent systems. In this article, an efficient continuous in\ntime data assimilation scheme is developed, which exploits closed analytic\nformulae for updating the unobserved state variables. Therefore, it is\ncomputationally efficient and accurate. The new data assimilation scheme is\ncombined with a simple reduced order modeling technique that involves a cheap\nclosure approximation and a noise inflation. In such a way, many complicated\nturbulent dynamical systems can satisfy the requirements of the mathematical\nstructures for the proposed efficient data assimilation scheme. The new data\nassimilation scheme is then applied to the Sabra shell model, which is a\nconceptual model for nonlinear turbulence. The goal is to recover the\nunobserved shell velocities across different spatial scales. It has been shown\nthat the new data assimilation scheme is skillful in capturing the nonlinear\nfeatures of turbulence including the intermittency and extreme events in both\nthe chaotic and the turbulent dynamical regimes. It has also been shown that\nthe new data assimilation scheme is more accurate and computationally cheaper\nthan the standard ensemble Kalman filter and nudging data assimilation schemes\nfor assimilating the Sabra shell model.\n"}
+{"abstract": "  Face authentication on mobile end has been widely applied in various\nscenarios. Despite the increasing reliability of cutting-edge face\nauthentication/verification systems to variations like blinking eye and subtle\nfacial expression, anti-spoofing against high-resolution rendering replay of\npaper photos or digital videos retains as an open problem. In this paper, we\npropose a simple yet effective face anti-spoofing system, termed Aurora Guard\n(AG). Our system firstly extracts the normal cues via light reflection\nanalysis, and then adopts an end-to-end trainable multi-task Convolutional\nNeural Network (CNN) to accurately recover subjects' intrinsic depth and\nmaterial map to assist liveness classification, along with the light CAPTCHA\nchecking mechanism in the regression branch to further improve the system\nreliability. Experiments on public Replay-Attack and CASIA datasets demonstrate\nthe merits of our proposed method over the state-of-the-arts. We also conduct\nextensive experiments on a large-scale dataset containing 12,000 live and\ndiverse spoofing samples, which further validates the generalization ability of\nour method in the wild.\n"}
+{"abstract": "  Reconfigurable intelligent surface (RIS) is an excellent use case for\nline-of-sight (LOS) based technologies such as free-space optical (FSO)\ncommunications. In this paper, we analyze the performance of RIS-empowered FSO\n(RISE-FSO) systems by unifying Fisher-Snedecor (F), Gamma-Gamma (GG), and\nMalaga (M) distributions for atmospheric turbulence with zero-boresight\npointing errors over deterministic as well as random path-loss in foggy\nconditions with heterodyne detection (HD) and intensity modulation/direct\ndetection (IM/DD) methods. By deriving the probability density function (PDF)\nand cumulative distribution function (CDF) of the direct-link (DL) with the\nstatistical effect of atmospheric turbulence, pointing errors, and random fog,\nwe develop exact expressions of PDF and CDF of the resultant channel for the\nRISE-FSO system. Using the derived statistical results, we present exact\nexpressions of outage probability, average bit-error-rate (BER), ergodic\ncapacity, and moments of signal-to-noise ratio (SNR) for both DL-FSO and\nRISE-FSO systems. We also develop an asymptotic analysis of the outage\nprobability and average BER and derive the diversity order of the considered\nsystems. We validate the analytical expressions using Monte-Carlo simulations\nand demonstrate the performance scaling of the FSO system with the number of\nRIS elements for various turbulence channels, detection techniques, and weather\nconditions.\n"}
+{"abstract": "  We introduce a framework for learning from unlabeled video what is\npredictable in the future. Instead of committing up front to features to\npredict, our approach learns from data which features are predictable. Based on\nthe observation that hyperbolic geometry naturally and compactly encodes\nhierarchical structure, we propose a predictive model in hyperbolic space. When\nthe model is most confident, it will predict at a concrete level of the\nhierarchy, but when the model is not confident, it learns to automatically\nselect a higher level of abstraction. Experiments on two established datasets\nshow the key role of hierarchical representations for action prediction.\nAlthough our representation is trained with unlabeled video, visualizations\nshow that action hierarchies emerge in the representation.\n"}
+{"abstract": "  We present a novel conditional Generative Adversarial Network (cGAN)\narchitecture that is capable of generating 3D Computed Tomography scans in\nvoxels from noisy and/or pixelated approximations and with the potential to\ngenerate full synthetic 3D scan volumes. We believe conditional cGAN to be a\ntractable approach to generate 3D CT volumes, even though the problem of\ngenerating full resolution deep fakes is presently impractical due to GPU\nmemory limitations. We present results for autoencoder, denoising, and\ndepixelating tasks which are trained and tested on two novel COVID19 CT\ndatasets. Our evaluation metrics, Peak Signal to Noise ratio (PSNR) range from\n12.53 - 46.46 dB, and the Structural Similarity index ( SSIM) range from 0.89\nto 1.\n"}
+{"abstract": "  We propose a new way for sympathetic cooling of ions in an electromagnetic\nPaul trap: it implies the use for this purpose of cold buffer atoms in the\nregion of atom-ion confinement-induced resonance (CIR). The problem is that the\nunavoidable micromotion of the ion and the long-range nature of its interaction\nwith the environment of colder atoms in a hybrid atomic-ion trap prevent its\nsympathetic cooling. We show that the destructive effect of ion micromotion on\nits sympathetic cooling can however be suppressed in the vicinity of the\natom-ion CIR. The origin of this is the \"fermionization\" of the atom-ion wave\nfunction near CIR, where the atom-ion pair behaves as a pair of noninteracting\nidentical fermions. This prevents the complete approach of the atom with the\nion near resonance and does not enhance the ion micromotion, which interferes\nwith its sympathetic cooling. We investigate the effect of sympathetic cooling\naround CIRs in atom-ion and atom-atom confined collisions within the\nqusiclassical-quantum approach using the Li-Yb$^+$ and Li-Yb confined systems\nas an example. In this approach, the Schr\\\"odinger equation for a cold light\natom is integrated simultaneously with the classical Hamilton equations for a\nhotter heavy ion or atom during collision. We have found the region near the\natom-ion CIR where the sympathetic cooling of the ion by cold atoms is possible\nin a hybrid atom-ion trap. We also show that it is possible to improve the\nefficiency of sympathetic cooling in atomic traps by using atomic CIRs.\n"}
+{"abstract": "  Adversarial learning is used to test the robustness of machine learning\nalgorithms under attack and create attacks that deceive the anomaly detection\nmethods in Industrial Control System (ICS). Given that security assessment of\nan ICS demands that an exhaustive set of possible attack patterns is studied,\nin this work, we propose an association rule mining-based attack generation\ntechnique. The technique has been implemented using data from a secure Water\nTreatment plant. The proposed technique was able to generate more than 300,000\nattack patterns constituting a vast majority of new attack vectors which were\nnot seen before. Automatically generated attacks improve our understanding of\nthe potential attacks and enable the design of robust attack detection\ntechniques.\n"}
+{"abstract": "  We study the response of a thermal state of the Hubbard-like system to either\nglobal or local non-Hermitian perturbation, which coalesces the degenerate\nground state within the $U(1)$ symmetry breaking phase. We show that the\ndynamical response of the system is strongly sensitive to the underlying\nquantum phase transition (QPT) from a Mott insulator to a superfluid state. The\nUhlmann fidelity in the superfluid phase decays to a steady value determined by\nthe order of the exceptional point (EP) within the subspace spanned by the\ndegenerate ground states but remains almost unchanged in the Mott insulating\nphase. It demonstrates that the phase diagram at zero temperature is preserved\neven though a local probing field is applied. Specifically, two celebrated\nmodels including the Bose-Hubbard model and the Jaynes-Cummings-Hubbard model\nare employed to demonstrate this property in the finite-size system, wherein\nfluctuations of the boson and polariton number are observed based on EP\ndynamics. This work presents an alternative approach to probe the\nsuperfluid-insulator QPT at non-zero temperature.\n"}
+{"abstract": "  The huge size of the widely used BERT family models has led to recent efforts\nabout model distillation. The main goal of distillation is to create a\ntask-agnostic pre-trained model that can be fine-tuned on downstream tasks\nwithout fine-tuning its full-sized version. Despite the progress of\ndistillation, to what degree and for what reason a task-agnostic model can be\ncreated from distillation has not been well studied. Also, the mechanisms\nbehind transfer learning of those BERT models are not well investigated either.\nTherefore, this work focuses on analyzing the acceptable deduction when\ndistillation for guiding the future distillation procedure. Specifically, we\nfirst inspect the prunability of the Transformer heads in RoBERTa and ALBERT\nusing their head importance estimation proposed by Michel et al. (2019), and\nthen check the coherence of the important heads between the pre-trained task\nand downstream tasks. Hence, the acceptable deduction of performance on the\npre-trained task when distilling a model can be derived from the results, and\nwe further compare the behavior of the pruned model before and after\nfine-tuning. Our studies provide guidance for future directions about BERT\nfamily model distillation.\n"}
+{"abstract": "  We give a new construction of sheaves on a relative site associated to a\nproduct $X\\times S$ where $S$ plays the role of a parameter space, expanding\nthe previous construction by the same authors, where the subanalytic structure\non $S$ was required. Here we let this last condition fall. In this way the\nconstruction becomes much easier to apply when dimension of $S$ is bigger than\none. We also study the functorial properties of base change with respect to the\nparameter space.\n"}
+{"abstract": "  We discuss the extension of the empirical equation: $\\left\\langle\ns_{N}^{2}\\right\\rangle_{0}\\propto g\\,l^{2}$, where the subscript 0 denotes the\nideal value with no excluded volume and $g$ the generation number from the root\nto the youngest (outermost) generation. By analogy with the linear chain\nproblem, we introduce the assumption that the scaling relation, $\\left\\langle\ns_{N}^{2}\\right\\rangle_{0}\\propto g^{2\\lambda}\\,l^{2}$, exists for arbitrary\npolymeric architectures, where $\\lambda$ is an exponent for the backbone\nstructure. Then, making use of the relationship between $g$ and $N$ (monomer\nnumber), we can deduce the exponent, $\\nu$, for polymers with various\narchitectures. The theory of the excluded volume effects impose the severe\nrestriction on the quantities: $\\nu_{0}$, $\\nu$, and $\\lambda$; for instance,\nthe inequality, $\\nu_{0}\\ge\\frac{1}{d+1}$, must be satisfied for isolated\npolymers in good solvents. An intriguing question is whether or not there\nexists an actual molecule that violates this inequality. We take up two\nexamples having $\\nu_{0}=1/4$ for $d=2$ and $\\nu_{0}=1/6$ for $d=3$, and\ndiscuss this question.\n"}
+{"abstract": "  In this work, we propose FastDPM, a unified framework for fast sampling in\ndiffusion probabilistic models. FastDPM generalizes previous methods and gives\nrise to new algorithms with improved sample quality. We systematically\ninvestigate the fast sampling methods under this framework across different\ndomains, on different datasets, and with different amount of conditional\ninformation provided for generation. We find the performance of a particular\nmethod depends on data domains (e.g., image or audio), the trade-off between\nsampling speed and sample quality, and the amount of conditional information.\nWe further provide insights and recipes on the choice of methods for\npractitioners.\n"}
+{"abstract": "  Since its discovery as a Kondo insulator 50 years ago, SmB6 recently received\na revival of interest due to detection of unexpected quantum oscillations in\nthe insulating state, discovery of disorder-immune bulk transport, and\nproposals of correlation-driven topological physics. While recent transport\nresults attribute the anomalous low temperature conduction to two-dimensional\nsurface states, important alternatives, such as conduction channel residing in\none-dimensional dislocation lines, have not been adequately explored. Here we\nstudy SmB6 with scanning microwave impedance microscopy and uncover evidence\nfor conducting one-dimensional states terminating at surface step edges. These\nstates remain conducting up to room temperature, indicating unusual robustness\nagainst scattering and an unconventional origin. Our results bring to light a\nheretofore undetected conduction route in SmB6 that contributes to the low\ntemperature transport. The unique scenario of intrinsic one-dimensional\nconducting channels in a highly insulating correlated bulk offers a\none-dimensional platform that may host exotic physics.\n"}
+{"abstract": "  This study offers a step-by-step practical procedure from the analysis of the\ncurrent status of the spare parts inventory system to advanced service-level\nanalysis by virtue of simulation-optimization technique for a real-world case\nstudy associated with a seaport. The remarkable variety and immense diversity\non one hand, and extreme complexities not only in consumption patterns but in\nthe supply of spare parts in an international port with technically advance\nport operator machinery, on the other hand, have convinced the managers to deal\nwith this issue in a structural framework. The huge available data require\ncleaning and classification to properly process them and derive reorder point\n(ROP) estimation, reorder quantity (ROQ) estimation, and associated service\nlevel analysis. Finally, from 247000 items used in 9 years long, 1416 inventory\nitems are elected as a result of ABC analysis integrating with the Analytic\nHierarchy Process (AHP), which led to the main items that need to be kept under\nstrict inventory control. The ROPs and the pertinent quantities are simulated\nby Arena software for all the main items, each of which took approximately 30\nminutes run-time on a personal computer to determine near-optimal estimations.\n"}
+{"abstract": "  In this paper, we study the optimal control of the mean and variance of the\nnetwork state vector. We develop an algorithm to optimize the control input\nplacement subject to constraints on the state, which must be achieved at a\ngiven time threshold; seeking an input placement which moves the moment at\nminimum cost. First, we solve the state-selection problem for a number of\nvariants of the first and second moment, and find solutions related to the\neigenvalues of the systems' Gramian matrices. Our algorithm then uses this\ninformation to find a locally optimal input placement. This is a Generalization\nof the Projected Gradient Method (GPGM). We solve the problem for some common\nversions of these moments, including the mean state and versions of the second\nmoment which induce discord, repel from a certain state, or encourage\nconvergence. We then perform simulations, and discuss a measure of centrality\nbased on the system flux -- a measure which describes what nodes are most\nimportant to optimal control of the average state.\n"}
+{"abstract": "  The precise simulation of particle transport through detectors remains a key\nelement for the successful interpretation of high energy physics results.\nHowever, Monte Carlo based simulation is extremely demanding in terms of\ncomputing resources. This challenge motivates investigations of faster,\nalternative approaches for replacing the standard Monte Carlo approach.\n  We apply Generative Adversarial Networks (GANs), a deep learning technique,\nto replace the calorimeter detector simulations and speeding up the simulation\ntime by orders of magnitude. We follow a previous approach which used\nthree-dimensional convolutional neural networks and develop new two-dimensional\nconvolutional networks to solve the same 3D image generation problem faster.\nAdditionally, we increased the number of parameters and the neural networks\nrepresentational power, obtaining a higher accuracy. We compare our best\nconvolutional 2D neural network architecture and evaluate it versus the\nprevious 3D architecture and Geant4 data. Our results demonstrate a high\nphysics accuracy and further consolidate the use of GANs for fast detector\nsimulations.\n"}
+{"abstract": "  Tactile Internet (TI) enables the omnipresence and exchange of tactile\nexperiences across the global via the ultra-reliable and ultra-responsive\nconnectivity. This article argues for coordinated multi-point (CoMP) enabled\nradio access network (RAN) slicing as an efficient solution that satisfies the\nstringent reliable and responsive connectivity requirements for supporting\ntactile applications. This article presents the emerging challenges when\naccommodating CoMP-enabled RAN slicing in the TI ecosystem and expounds the\nfunctional split of CoMP-enabled RAN. Besides, this article elaborates on the\nimplementation prototype of CoMP-enabled RAN slicing for TI with the\ncoexistence of diverse vertical applications. Finally, this article studies a\nuse case of enabling TI-included application multiplexing as an example of\nCoMP-enabled RAN slicing for TI.\n"}
+{"abstract": "  The connectivity between vehicles, infrastructure, and other traffic\nparticipants brings a new dimension to automotive safety applications. Soon all\nthe newly produced cars will have Vehicle to Everything (V2X) communication\nmodems alongside the existing Advanced Driver Assistant Systems (ADAS). It is\nessential to identify the different sensor measurements for the same targets\n(Data Association) to use connectivity reliably as a safety feature alongside\nthe standard ADAS functionality. Considering the camera is the most common\nsensor available for ADAS systems, in this paper, we present an experimental\nimplementation of a Mahalanobis distance-based data association algorithm\nbetween the camera and the Vehicle to Vehicle (V2V) communication sensors. The\nimplemented algorithm has low computational complexity and the capability of\nrunning in real-time. One can use the presented algorithm for sensor fusion\nalgorithms or higher-level decision-making applications in ADAS modules.\n"}
+{"abstract": "  The availability of labelled data is one of the main limitations in machine\nlearning. We can alleviate this using weak supervision: a framework that uses\nexpert-defined rules $\\boldsymbol{\\lambda}$ to estimate probabilistic labels\n$p(y|\\boldsymbol{\\lambda})$ for the entire data set. These rules, however, are\ndependent on what experts know about the problem, and hence may be inaccurate\nor may fail to capture important parts of the problem-space. To mitigate this,\nwe propose Active WeaSuL: an approach that incorporates active learning into\nweak supervision. In Active WeaSuL, experts do not only define rules, but they\nalso iteratively provide the true label for a small set of points where the\nweak supervision model is most likely to be mistaken, which are then used to\nbetter estimate the probabilistic labels. In this way, the weak labels provide\na warm start, which active learning then improves upon. We make two\ncontributions: 1) a modification of the weak supervision loss function, such\nthat the expert-labelled data inform and improve the combination of weak\nlabels; and 2) the maxKL divergence sampling strategy, which determines for\nwhich data points expert labelling is most beneficial. Our experiments show\nthat when the budget for labelling data is limited (e.g. $\\leq 60$ data\npoints), Active WeaSuL outperforms weak supervision, active learning, and\ncompeting strategies, with only a handful of labelled data points. This makes\nActive WeaSuL ideal for situations where obtaining labelled data is difficult.\n"}
+{"abstract": "  The half-life of $^{212}$Po was measured with the highest up-to-date accuracy\nas $T_{1/2}=295.1(4)$ ns by using thorium-loaded liquid scintillator.\n"}
+{"abstract": "  This paper presents a data-driven localization framework with high precision\nin time-varying complex multipath environments, such as dense urban areas and\nindoors, where GPS and model-based localization techniques come short. We\nconsider the angle-delay profile (ADP), a linear transformation of channel\nstate information (CSI), in massive MIMO systems and show that ADPs preserve\nusers' motion when stacked temporally. We discuss that given a static\nenvironment, future frames of ADP time-series are predictable employing a video\nframe prediction algorithm. We express that a deep convolutional neural network\n(DCNN) can be employed to learn the background static scattering environment.\nTo detect foreground changes in the environment, corresponding to path blockage\nor addition, we introduce an algorithm taking advantage of the trained DCNN.\nFurthermore, we present DyLoc, a data-driven framework to recover distorted\nADPs due to foreground changes and to obtain precise location estimations. We\nevaluate the performance of DyLoc in several dynamic scenarios employing\nDeepMIMO dataset to generate geo-tagged CSI datasets for indoor and outdoor\nenvironments. We show that previous DCNN-based techniques fail to perform with\ndesirable accuracy in dynamic environments, while DyLoc pursues localization\nprecisely. Moreover, simulations show that as the environment gets richer in\nterms of the number of multipath, DyLoc gets more robust to foreground changes.\n"}
+{"abstract": "  In exploratory data analysis of known classes of high dimensional data, a\ncentral question is how distinct are the classes? The Direction Projection\nPermutation (DiProPerm) hypothesis test provides an answer to this that is\ndirectly connected to a visual analysis of the data. In this paper, we propose\nan improved DiProPerm test that solves 3 major challenges of the original\nversion. First, we implement only balanced permutations to increase the test\npower for data with strong signals. Second, our mathematical analysis leads to\nan adjustment to correct the null behavior of both balanced and the\nconventional all permutations. Third, new confidence intervals (reflecting\npermutation variation) for test significance are also proposed for comparison\nof results across different contexts. This improvement of DiProPerm inference\nis illustrated in the context of comparing cancer types in examples from The\nCancer Genome Atlas.\n"}
+{"abstract": "  We reply to the Comment by Svhaika, Ruocco, Schirmacher and collaborators.\nThere were two accidental mistakes in our original paper (Phys. Rev. Lett. 112,\n145501 (2019)), which have been now corrected. All the physical conclusions and\nresults of the original paper, including the prediction of boson peak due to\nanharmonicity, remain valid in the corrected version.\n"}
+{"abstract": "  Intrinsically broken symmetries in the bulk of topological insulators (TIs)\nare manifested in their surface states. In spite of particle-hole asymmetry in\nTIs, it has often been assumed that their surface states are characterized by a\nparticle-hole symmetric Dirac energy dispersion. In this work we demonstrate\nthat the effect of particle-hole asymmetry is essential to correctly describe\nthe energy spectrum and the magneto-optical response in TIs thin-films. In\nthin-films of TIs with a substantial degree of particle-hole symmetry breaking,\nsuch as Sb$_2$Te$_3$, the longitudinal optical conductivity displays absorption\npeaks arising from optical transitions between bulk and surface Landau levels\nfor low photon energies. The transition energies between the bulk and surface\nLandau levels exhibit clearly discernable signatures from those between surface\nLandau levels due to their distinct magnetic field dependence. Bulk\ncontributions to the magneto-optical conductivity in a TI thin-film are\nenhanced via one type of doping while being suppressed by the other. This\nasymmetric dependence on type of doping aids in revealing the particle-hole\nasymmetry in TI thin-films.\n"}
+{"abstract": "  According to the embodied cognition perspective, linguistic negation may\nblock the motor simulations induced by language processing. Transcranial\nmagnetic stimulation (TMS) was applied to the left primary motor cortex (hand\narea) of monolingual Italian and German healthy participants during a rapid\nserial visual presentation of sentences from their own language. In these\nlanguages, the negative particle is located at the beginning and at the end of\nthe sentence, respectively. The study investigated whether the interruption of\nthe motor simulation processes, accounted for by reduced motor evoked\npotentials (MEPs), takes place similarly in two languages differing on the\nposition of the negative marker. Different levels of sentence concreteness were\nalso manipulated to investigate if negation exerts generalized effects or if it\nis affected by the semantic features of the sentence. Our findings indicate\nthat negation acts as a block on motor representations, but independently from\nthe language and words concreteness level.\n"}
+{"abstract": "  We prove that if $f$ is a polynomial over a number field $K$ with a finite\nsuperattracting periodic point and a non-archimedean place of bad reduction,\nthen there is an $\\epsilon>0$ such that only finitely many $P\\in K^{\\text{ab}}$\nhave canonical height less than $\\epsilon$ with respect to $f$. The key\ningredient is the geometry of the filled Julia set at a place of bad reduction.\nWe also prove a conditional uniform boundedness result for the $K$-rational\npreperiodic points of such polynomials, as well as a uniform lower bound on the\ncanonical height of non-preperiodic points in $K$. We further prove\nunconditional analogues of these results in the function field setting.\n"}
+{"abstract": "  We revisit the proposal that the ensemble average over free boson CFTs in two\ndimensions - parameterized by Narain's moduli space - is dual to an exotic\ntheory of gravity in three dimensions dubbed $U(1)$ gravity. We consider\nflavored partition functions, where the usual genus $g$ partition function is\nweighted by Wilson lines coupled to the conserved $U(1)$ currents of these\ntheories. These flavored partition functions obey a heat equation which relates\ndeformations of the Riemann surface moduli to those of the chemical potentials\nwhich measure these $U(1)$ charges. This allows us to derive a Siegel-Weil\nformula which computes the average of these flavored partition functions. The\nresult takes the form of a \"sum over geometries,\" albeit with modifications\nrelative to the unflavored case.\n"}
+{"abstract": "  Modern trends in data collection are bringing current mainstream techniques\nfor database query processing to their limits. Consequently, various novel\napproaches for efficient query processing are being actively studied. One such\napproach is based on hypertree decompositions (HDs), which have been shown to\ncarry great potential to process complex queries more efficiently and with\nstronger theoretical guarantees. However, using HDs for query execution relies\non the difficult task of computing decompositions of the query structure, which\nguides the efficient execution of the query. From theoretical results we know\nthat the performance of purely sequential methods is inherently limited, yet\nthe problem is susceptible to parallelisation.\n  In this paper we propose the first algorithm for computing hypertree\ndecompositions that is well-suited for parallelisation. The proposed algorithm\nlog-k-decomp requires only a logarithmic number of recursion levels and\nadditionally allows for highly parallelised pruning of the search space by\nrestriction to balanced separators. We provide detailed experimental evaluation\nover the HyperBench benchmark and demonstrate that our approach is highly\neffective especially for complex queries.\n"}
+{"abstract": "  Few-shot intent detection is a challenging task due to the scare annotation\nproblem. In this paper, we propose a Pseudo Siamese Network (PSN) to generate\nlabeled data for few-shot intents and alleviate this problem. PSN consists of\ntwo identical subnetworks with the same structure but different weights: an\naction network and an object network. Each subnetwork is a transformer-based\nvariational autoencoder that tries to model the latent distribution of\ndifferent components in the sentence. The action network is learned to\nunderstand action tokens and the object network focuses on object-related\nexpressions. It provides an interpretable framework for generating an utterance\nwith an action and an object existing in a given intent. Experiments on two\nreal-world datasets show that PSN achieves state-of-the-art performance for the\ngeneralized few shot intent detection task.\n"}
+{"abstract": "  A double-phase Ar detector can efficiently identify particles and reconstruct\ntheir positions. However, the properties of electroluminescence (EL) for\nsecondary light emission in the gas phase are not fully understood. Earlier\nstudies have explained the EL process using an ordinary EL mechanism because of\nan Ar excimer; however, this mechanism does not predict the emission of visible\nlight (VL). Recent measurements have demonstrated VL components in Ar gas EL,\nto explain which a new mechanism called neutral bremsstrahlung (NBrS) was\nproposed. In this study, we investigated gaseous Ar EL in the VL region from\n300 to 600 nm at room temperature and normal pressure using a gaseous time\nprojection chamber (TPC). The secondary emission light from the TPC\nluminescence region was dispersed using a spectrometer. The observed spectrum\nwas interpreted using the ordinary EL and NBrS models, and the effect of\nnitrogen impurities is discussed herein.\n"}
+{"abstract": "  A remarkable theorem of Joris states that a function $f$ is $C^\\infty$ if two\nrelatively prime powers of $f$ are $C^\\infty$. Recently, Thilliez showed that\nan analogous theorem holds in Denjoy--Carleman classes of Roumieu type. We\nprove that a division property, equivalent to Joris's result, is valid in a\nwide variety of ultradifferentiable classes. Generally speaking, it holds in\nall dimensions for non-quasianalytic classes. In the quasianalytic case we have\ngeneral validity in dimension one, but we also get validity in all dimensions\nfor certain quasianalytic classes.\n"}
+{"abstract": "  We present High Sensitivity Array (HSA) and enhanced Multi Element Remotely\nLinked Interferometer Network (eMERLIN) observations of the radio-loud\nbroad-lined type Ic supernova PTF11qcj obtained $\\sim7.5$ years after the\nexplosion. Previous observations of this supernova at 5.5 yrs since explosion\nshowed a double-peaked radio light curve accompanied by a detection in the\nX-rays, but no evidence for broad H$\\alpha$ spectral features. The Very Long\nBaseline Interferometry (VLBI) observations presented here show that the\nPTF11qcj GHz radio ejecta remains marginally resolved at the sub-milliarcsecond\nlevel at $\\approx 7.5$ yrs after the explosion, pointing toward a\nnon-relativistic expansion. Our VLBI observations thus favor a scenario in\nwhich the second peak of the PTF11qcj radio light curve is related to strong\ninteraction of the supernova ejecta with a circumstellar medium of variable\ndensity, rather than to the emergence of an off-axis jet. Continued VLBI\nmonitoring of PTF11qcj in the radio may strengthen further this conclusion.\n"}
+{"abstract": "  In self-supervised representation learning, a common idea behind most of the\nstate-of-the-art approaches is to enforce the robustness of the representations\nto predefined augmentations. A potential issue of this idea is the existence of\ncompletely collapsed solutions (i.e., constant features), which are typically\navoided implicitly by carefully chosen implementation details. In this work, we\nstudy a relatively concise framework containing the most common components from\nrecent approaches. We verify the existence of complete collapse and discover\nanother reachable collapse pattern that is usually overlooked, namely\ndimensional collapse. We connect dimensional collapse with strong correlations\nbetween axes and consider such connection as a strong motivation for feature\ndecorrelation (i.e., standardizing the covariance matrix). The gains from\nfeature decorrelation are verified empirically to highlight the importance and\nthe potential of this insight.\n"}
+{"abstract": "  The study of topological magnetic excitations has attracted widespread\nattention in the past few years. In this thesis, I have studied some examples\nof novel topological magnonic phases/phenomena in low-dimensional quantum\nmagnets. The first chapter motivates the research based on the research gap in\nthis field of study. The second chapter is written to make the thesis\nself-sufficient and the concepts are explained through examples. In the second\nchapter, the following formalisms and physical observables are described:\nHolstein-Primakoff, bond operator, Schwinger boson, Bogoliubov-Valatin, Group\ntheory, Berry-phase, Berry-curvature, Chern number, thermal Hall conductance,\nNernst conductivity, dynamical spin structure factor, edge-current. The main\nresults of the thesis are shown in the third, fourth, and fifth chapters. In\nthe third chapter, I have shown that anti-chiral edge states (co-propagating\nedge states) arise in the ferromagnetic Heisenberg model on the honeycomb\nlattice with Dzyaloshinskii-Moriya (DM) interactions. My results suggest that\nsuch anti-chiral edge states may be induced in certain realistic models of\nquantum magnets. In the fourth chapter, I have found the emergence of many\nmagnon band-topological phases in the flux state of the Shastry-Sutherland\nmodel. I have derived a simple analytical form of the temperature dependence of\nderivative of thermal Hall conductivity near the band topological transition\npoint which I propose to be experimentally useful. In the fifth chapter, I have\ninvestigated the emergence of Weyl triplons due to inter-layer DM-interaction\nin a microscopic model of SrCu2(BO3)2, a widely studied frustrated quantum\nmagnet. I have shown that the thermal magnon Hall conductivity has a\nquasi-linear dependence as a function of the magnetic field in a Weyl-triplon\nregion.\n"}
+{"abstract": "  Large pre-trained language models achieve state-of-the-art results when\nfine-tuned on downstream NLP tasks. However, they almost exclusively focus on\ntext-only representation, while neglecting cell-level layout information that\nis important for form image understanding. In this paper, we propose a new\npre-training approach, StructuralLM, to jointly leverage cell and layout\ninformation from scanned documents. Specifically, we pre-train StructuralLM\nwith two new designs to make the most of the interactions of cell and layout\ninformation: 1) each cell as a semantic unit; 2) classification of cell\npositions. The pre-trained StructuralLM achieves new state-of-the-art results\nin different types of downstream tasks, including form understanding (from\n78.95 to 85.14), document visual question answering (from 72.59 to 83.94) and\ndocument image classification (from 94.43 to 96.08).\n"}
+{"abstract": "  Obtaining viewer responses from videos can be useful for creators and\nstreaming platforms to analyze the video performance and improve the future\nuser experience. In this report, we present our method for 2021 Evoked\nExpression from Videos Challenge. In particular, our model utilizes both audio\nand image modalities as inputs to predict emotion changes of viewers. To model\nlong-range emotion changes, we use a GRU-based model to predict one sparse\nsignal with 1Hz. We observe that the emotion changes are smooth. Therefore, the\nfinal dense prediction is obtained via linear interpolating the signal, which\nis robust to the prediction fluctuation. Albeit simple, the proposed method has\nachieved pearson's correlation score of 0.04430 on the final private test set.\n"}
+{"abstract": "  We often find illegal immigrants moving from one country to another. By means\nof land these illegal immigrants move over the fence cut the border wires and\nmoves to the other part of the land. What do you think our soldiers are not\ndoing their job? It is not that actually. It is very difficult to watch\nconsistently over the border. Soldiers have a limited vision to check around\nthe whole land mass. So for them we have come up with a solution that we have\nbuilt a device that will sense the presence of an intruder (illegal immigrant).\nThis device will be installed over the fences. When an intruder passes over the\nfence this device will transmit the signal to the soldiers Smartphone app\n(BLYNK app). The soldier will be notified with the signal and after receiving\nthe signal, the soldier can switch on the alarm and the emergency lights via\nthe app. By this the soldiers in the camp will be alerted and can take their\nrespective positions and arrest the intruder which was passing the border\nillegally. By this device we can alert the soldiers in the border to take more\nsafety precautions to keep our country safe. Index Terms-Node MCU ESP32,\ninfrared sensor, Blynk app.\n"}
+{"abstract": "  We report a planar 1D photonic bandgap waveguide exhibiting four second-order\nexceptional points (EP2s). The interactions between the selective pairs of\nsupported quasi-guided TE modes are modulated by spatial distribution of\ntransverse in-homogeneous gain-loss profile.\n"}
+{"abstract": "  This paper investigates the security enhancement of an intelligent reflecting\nsurface (IRS) assisted non-orthogonal multiple access (NOMA) network, where a\ndistributed IRS enabled NOMA transmission framework is proposed to serve users\nsecurely in the presence of a passive eavesdropper. Considering that\neavesdropper's instantaneous channel state information (CSI) is challenging to\nacquire in practice, we utilize secrecy outage probability (SOP) as the\nsecurity metric. A problem of maximizing the minimum secrecy rate among users,\nsubject to the successive interference cancellation (SIC) decoding constraints\nand SOP constraints, by jointly optimizing transmit beamforming at the BS and\nphase shifts of IRSs, is formulated. For special case with a single-antenna BS,\nwe derive the exact closed-form SOP expressions and propose a novel\nring-penalty based successive convex approximation (SCA) algorithm to design\npower allocation and phase shifts jointly. While for the more general and\nchallenging case with a multi-antenna BS, we adopt the Bernstein-type\ninequality to approximate the SOP constraints by a deterministic convex form.\nTo proceed, an efficient alternating optimization (AO) algorithm is developed\nto solve the considered problem. Numerical results validate the advantages of\nthe proposed algorithms over the baseline schemes. Particularly, two\ninteresting phenomena on distributed IRS deployment are revealed: 1) the\nsecrecy rate peak is achieved only when distributed IRSs share the reflecting\nelements equally; and 2) the distributed IRS deployment does not always\noutperform the centralized IRS deployment, due to the tradeoff between the\nnumber of IRSs and the reflecting elements equipped at each IRS.\n"}
+{"abstract": "  We report on the design, construction, and commissioning of a prototype\naperture masking technology implemented at the Keck OSIRIS Imager: the\nholographic aperture mask. Holographic aperture masking (HAM) aims at (i)\nincreasing the throughput of sparse aperture masking (SAM) by selectively\ncombining all subapertures across a telescope pupil in multiple interferograms\nusing a phase mask, and (ii) adding low-resolution spectroscopic capabilities.\nUsing liquid-crystal geometric phase patterns, we manufacture a HAM mask that\nuses an 11-hole SAM design as the central component and a holographic component\ncomprising 19 different subapertures. Thanks to a multilayer liquid-crystal\nimplementation, the mask has a diffraction efficiency higher than 96% from 1.1\nto 2.5 micron. We create a pipeline that extracts monochromatic closure phases\nfrom the central component as well as multiwavelength closure phases from the\nholographic component. We test the performance of the HAM mask in the\nlaboratory and on-sky. The holographic component yields 26 closure phases with\nspectral resolutions between R$\\sim$6.5 and R$\\sim$15. On April 19, 2019, we\nobserved the binary star HDS 1507 in the Hbb filter ($\\lambda_0 = 1638$ nm and\n$\\Delta \\lambda = 330$ nm) and retrieved a constant separation of 120.9 $\\pm\n0.5$ mas for the independent wavelength bins, which is in excellent agreement\nwith literature values. For both the laboratory measurements and the\nobservations of unresolved reference stars, we recorded nonzero closure phases\n-- a potential source of systematic error that we traced to polarization\nleakage of the HAM optic. We propose a future upgrade that improves the\nperformance, reducing this effect to an acceptable level. Holographic aperture\nmasking is a simple upgrade of SAM with increased throughput and a new\ncapability of simultaneous low-resolution spectroscopy that provides new\ndifferential observables.\n"}
+{"abstract": "  The numerous compact sources associated with neutron stars and white dwarfs\ndiscovered in recent decades are analyzed in terms of the Gravimagnetic Rotator\nmodel (GMR paradigm - Lipunov, 1987a; Lipunov, 1992). We offer the instrument\nfor understanding of various observed features and evolutionary relationships\nof neutron stars and white dwarfs. We depict in a single diagram all objects\nfrom radio pulsars and dwarf novae to ultra luminous X-ray sources and a radio\npulsating white dwarf. This diagram directly demonstrates the genetic link\nbetween different types of compact sources thereby making it possible to\nconfirm and illustrate clearly the established evolutionary connections - such\nas that between bulge X-ray sources and millisecond pulsars. This approach\nallows us to understand the evolutionary status of Ultra Luminous X-ray\nsources. In addition, we propose an additional evolutionary branch of the\nformation of Magnetars. When our work was completed, an article by Kirsten et\nal.2021, was published, which reports the localization of FRB 20200120 in one\nof the globular clusters of the galaxy M81. This shows that the\naccretion-induced collapse scenario of the white dwarf (Lipunov and Postnov,\n1985), considered in detail in this work, is a possible genealogical branch of\nMagnetar production.\n"}
+{"abstract": "  Automatic segmentation of brain abnormalities is challenging, as they vary\nconsiderably from one pathology to another. Current methods are supervised and\nrequire numerous annotated images for each pathology, a strenuous task. To\ntackle anatomical variability, Unsupervised Anomaly Detection (UAD) methods are\nproposed, detecting anomalies as outliers of a healthy model learned using a\nVariational Autoencoder (VAE). Previous work on UAD adopted a 2D approach,\nmeaning that MRIs are processed as a collection of independent slices. Yet, it\ndoes not fully exploit the spatial information contained in MRI. Here, we\npropose to perform UAD in a 3D fashion and compare 2D and 3D VAEs. As a side\ncontribution, we present a new loss function guarantying a robust training.\nLearning is performed using a multicentric dataset of healthy brain MRIs, and\nsegmentation performances are estimated on White-Matter Hyperintensities and\ntumors lesions. Experiments demonstrate the interest of 3D methods which\noutperform their 2D counterparts.\n"}
+{"abstract": "  The widespread adoption of Free/Libre and Open Source Software (FLOSS) means\nthat the ongoing maintenance of many widely used software components relies on\nthe collaborative effort of volunteers who set their own priorities and choose\ntheir own tasks. We argue that this has created a new form of risk that we call\n'underproduction' which occurs when the supply of software engineering labor\nbecomes out of alignment with the demand of people who rely on the software\nproduced. We present a conceptual framework for identifying relative\nunderproduction in software as well as a statistical method for applying our\nframework to a comprehensive dataset from the Debian GNU/Linux distribution\nthat includes 21,902 source packages and the full history of 461,656 bugs. We\ndraw on this application to present two experiments: (1) a demonstration of how\nour technique can be used to identify at-risk software packages in a large\nFLOSS repository and (2) a validation of these results using an alternate\nindicator of package risk. Our analysis demonstrates both the utility of our\napproach and reveals the existence of widespread underproduction in a range of\nwidely-installed software components in Debian.\n"}
+{"abstract": "  We present Low-Frequency Array (LOFAR) 143.5-MHz radio observations of\nflaring activity during 2019 May from the X-ray binary Cygnus X-3. Similar to\nradio observations of previous outbursts from Cygnus X-3, we find that this\nsource was significantly variable at low frequencies, reaching a maximum flux\ndensity of about 5.8 Jy. We compare our LOFAR light curve with contemporaneous\nobservations taken at 1.25 and 2.3 GHz with the RATAN-600 telescope, and at 15\nGHz with the Arcminute Microkelvin Imager (AMI) Large Array. The initial\n143.5-MHz flux density level, $\\sim$2 Jy, is suggested to be the delayed and\npossibly blended emission from at least some of the flaring activity that had\nbeen detected at higher frequencies before our LOFAR observations had begun.\nThere is also evidence of a delay of more than four days between a bright flare\nthat initially peaked on May 6 at 2.3 and 15 GHz, and the corresponding peak\n($\\gtrsim$ 5.8 Jy) at 143.5 MHz. From the multi-frequency light curves, we\nestimate the minimum energy and magnetic field required to produce this flare\nto be roughly 10$^{44}$ erg and 40 mG, respectively, corresponding to a minimum\nmean power of $\\sim$10$^{38}$ erg s$^{-1}$. Additionally, we show that the\nbroadband radio spectrum evolved over the course of our observing campaign; in\nparticular, the two-point spectral index between 143.5 MHz and 1.25 GHz\ntransitioned from being optically thick to optically thin as the flare\nsimultaneously brightened at 143.5 MHz and faded at GHz frequencies.\n"}
+{"abstract": "  Previous works indicate that the glyph of Chinese characters contains rich\nsemantic information and has the potential to enhance the representation of\nChinese characters. The typical method to utilize the glyph features is by\nincorporating them into the character embedding space. Inspired by previous\nmethods, we innovatively propose a Chinese pre-trained representation model\nnamed as GlyphCRM, which abandons the ID-based character embedding method yet\nsolely based on sequential character images. We render each character into a\nbinary grayscale image and design two-channel position feature maps for it.\nFormally, we first design a two-layer residual convolutional neural network,\nnamely HanGlyph to generate the initial glyph representation of Chinese\ncharacters, and subsequently adopt multiple bidirectional encoder Transformer\nblocks as the superstructure to capture the context-sensitive information.\nMeanwhile, we feed the glyph features extracted from each layer of the HanGlyph\nmodule into the underlying Transformer blocks by skip-connection method to\nfully exploit the glyph features of Chinese characters. As the HanGlyph module\ncan obtain a sufficient glyph representation of any Chinese character, the\nlong-standing out-of-vocabulary problem could be effectively solved. Extensive\nexperimental results indicate that GlyphCRM substantially outperforms the\nprevious BERT-based state-of-the-art model on 9 fine-tuning tasks, and it has\nstrong transferability and generalization on specialized fields and\nlow-resource tasks. We hope this work could spark further research beyond the\nrealms of well-established representation of Chinese texts.\n"}
+{"abstract": "  As the length-scales of materials decrease, heterogeneities associated with\ninterfaces approach the importance of the surrounding materials. Emergent\nelectronic and magnetic interface properties in superlattices have been studied\nextensively by both experiments and theory. $^{1-6}$ However, the presence of\ninterfacial vibrations that impact phonon-mediated responses, like thermal\nconductivity $^{7,8}$, has only been inferred in experiments indirectly. While\nit is accepted that intrinsic phonons change near boundaries $^{9,10}$, the\nphysical mechanisms and length-scales through which interfacial effects\ninfluence materials remain unclear. Herein, we demonstrate the localized\nvibrational response associated with the interfaces in SrTiO$_3$-CaTiO$_3$\nsuperlattices by combining advanced scanning transmission electron microscopy\nimaging and spectroscopy and density-functional-theory calculations. Symmetries\natypical of either constituent material are observed within a few atomic planes\nnear the interface. The local symmetries create local phonon modes that\ndetermine the global response of the superlattice once the spacing of the\ninterfaces approaches the phonon spatial extent. The results provide direct\nvisualization and quantification, illustrating the progression of the local\nsymmetries and interface vibrations as they come to determine the vibrational\nresponse of an entire superlattice; stated differently, the progression from a\nmaterial with interfaces, to a material dominated by interfaces, to a material\nof interfaces as the period decreases. Direct observation of such local atomic\nand vibrational phenomena demonstrates that their spatial extent needs to be\nquantified to understand macroscopic behavior. Tailoring interfaces, and\nknowing their local vibrational response, provides a means of pursuing designer\nsolids having emergent infrared and thermal responses.\n"}
+{"abstract": "  Metastability is a quintessential feature of first order quantum phase\ntransitions, which is lost either by dynamical instability or by nucleating\nbubbles of a true vacuum through quantum tunneling. By considering a drive\nacross the first order quantum phase transition in the quantum Ising chain in\nthe presence of both transverse and longitudinal fields, we reveal multiple\nregions in the parameter space where the initial metastable state loses its\nmetastability in successive stages. The mechanism responsible is found to be\nsemi-degenerate resonant tunnelings to states with specific bubble sizes. We\nshow that such dynamics of quantized bubble nucleations can be understood in\nterms of Landau-Zener transitions, which provide quantitative predictions of\nnucleation probabilities for different bubble sizes.\n"}
+{"abstract": "  Whether tidal disruption events (TDEs) circularise or accrete directly as a\nhighly eccentric disc is the subject of current research and appears to depend\nsensitively on the disc thermodynamics. In a previous paper we applied the\ntheory of eccentric discs to TDE discs using an $\\alpha-$prescription for the\ndisc stress, which leads to solutions that exhibit extreme, potentially\nunphysical, behaviour. In this paper we further explore the dynamical vertical\nstructure of highly eccentric discs using alternative stress models that are\nbetter motivated by the behaviour of magnetic fields in eccentric discs. We\nfind that the presence of a coherent magnetic field has a stabilising effect on\nthe dynamics and can significantly alter the behaviour of highly eccentric\nradiation dominated discs. We conclude that magnetic fields are important for\nthe evolution of TDE discs.\n"}
+{"abstract": "  Awareness of cybersecurity topics facilitates software developers to produce\nsecure code. This awareness is especially important in industrial environments\nfor the products and services in critical infrastructures. In this work, we\naddress how to raise awareness of software developers on the topic of secure\ncoding. We propose the \"CyberSecurity Challenges\", a serious game designed to\nbe used in an industrial environment and address software developers' needs.\nOur work distils the experience gained in conducting these CyberSecurity\nChallenges in an industrial setting. The main contributions are the design of\nthe CyberSecurity Challenges events, the analysis of the perceived benefits,\nand practical advice for practitioners who wish to design or refine these\ngames.\n"}
+{"abstract": "  Motion completion is a challenging and long-discussed problem, which is of\ngreat significance in film and game applications. For different motion\ncompletion scenarios (in-betweening, in-filling, and blending), most previous\nmethods deal with the completion problems with case-by-case designs. In this\nwork, we propose a simple but effective method to solve multiple motion\ncompletion problems under a unified framework and achieves a new state of the\nart accuracy under multiple evaluation settings. Inspired by the recent great\nsuccess of attention-based models, we consider the completion as a sequence to\nsequence prediction problem. Our method consists of two modules - a standard\ntransformer encoder with self-attention that learns long-range dependencies of\ninput motions, and a trainable mixture embedding module that models temporal\ninformation and discriminates key-frames. Our method can run in a\nnon-autoregressive manner and predict multiple missing frames within a single\nforward propagation in real time. We finally show the effectiveness of our\nmethod in music-dance applications.\n"}
+{"abstract": "  The magnetic nature of the formation of solar active regions lies at the\nheart of understanding solar activity and, in particular, solar eruptions. A\nwidespread model, used in many theoretical studies, simulations and the\ninterpretation of observations is that the basic structure of an active region\nis created by the emergence of a large tube of pre-twisted magnetic field.\nDespite plausible reasons and the availability of various proxies suggesting\nthe veracity of this model, there has not yet been any direct observational\nevidence of the emergence of large twisted magnetic flux tubes. Thus, the\nfundamental question, \"are active regions formed by large twisted flux tubes?\"\nhas remained open. In this work, we answer this question in the affirmative and\nprovide direct evidence to support this. We do this by investigating a robust\ntopological quantity, called magnetic winding, in solar observations. This\nquantity, combined with other signatures that are currently available, provides\nthe first direct evidence that large twisted flux tubes do emerge to create\nactive regions.\n"}
+{"abstract": "  Shubnikov-de Haas (SdH) oscillations, the most well-known\nmagneto-oscillations caused by the quantization of electron energy levels in\nthe presence of magnetic fields in two-dimensional (2D) electron systems, can\nbe used to determine Fermi-surface properties and directly measure the Berry\nphase of the 2D systems. It is usually thought that transport measurements are\nrequired to measure the SdH oscillations. Contradicting this belief, we\ndemonstrate that the SdH oscillations can be measured in graphene systems by\ncarrying out scanning tunneling spectroscopy (STS) measurements. The\nenergy-momentum dispersions and Berry phases of monolayer, Bernal-stacked\nbilayer, and ABC-stacked trilayer graphene are obtained according to the\nmeasured SdH oscillations in the STS spectra. It is possible to obtain the SdH\noscillations when the size of the 2D systems is larger than the magnetic length\nand, importantly, no gate electrode is required in the STS measurement,\ntherefore, the reported method in this work is applicable to a wide range of\nmaterials.\n"}
+{"abstract": "  To maximize safety and driving comfort, autonomous driving systems can\nbenefit from implementing foresighted action choices that take different\npotential scenario developments into account. While artificial scene prediction\nmethods are making fast progress, an attentive human driver may still be able\nto identify relevant contextual features which are not adequately considered by\nthe system or for which the human driver may have a lack of trust into the\nsystem's capabilities to treat them appropriately. We implement an approach\nthat lets a human driver quickly and intuitively supplement scene predictions\nto an autonomous driving system by gaze. We illustrate the feasibility of this\napproach in an existing autonomous driving system running a variety of\nscenarios in a simulator. Furthermore, a Graphical User Interface (GUI) was\ndesigned and integrated to enhance the trust and explainability of the system.\nThe utilization of such cooperatively augmented scenario predictions has the\npotential to improve a system's foresighted driving abilities and make\nautonomous driving more trustable, comfortable and personalized.\n"}
+{"abstract": "  The hydrodynamics of droplets is significant in wide-ranging applications\ninvolving immiscible fluids and emulsions in food and pharmaceutical. The\ncontrol and manipulation of droplets are primarily a function of flow governing\nand geometrical parameters. The finite element and level set approaches are\nused in this work to explore the influences of capillary number (Ca) and flow\nrate ratio (Qr) of dispersed and continuous phases on hydrodynamics of droplet\ngeneration in two-phase flow through T-junction cross-flow microfluidic device.\nA mathematical model based on a mass continuity, Navier-Stokes, and level set\nequations are solved computationally using the Eulerian framework for Ca = 1e-4\n- 1 and Qr =0.1 - 10. Both immiscible phases, having equal density and unequal\nviscosity, flow (Re=0.1) through equal-sized channels. In particular,\ninstantaneous phase flow field, droplet size, droplet detachment time and\ngeneration frequency are presented and discussed as a function of governing\nparameters (Ca and Qr). Considered parametric space is characterized as\nsqueezing, first transition, dripping, second transition, parallel, and jet\nflow regimes. In contrast to threshold Ca ~ 0.01 in earlier studies, squeezing\nregime exists for all Ca and Qr = 2- 10. Flow regimes are also mapped into\ndroplets and non-droplet zones. Threshold interfacial Ca, defining the boundary\nbetween droplet and non-droplet zones, scales quadratically with Qr. Droplet\ndynamics shows a complex dependence on Ca and Qr. Droplet length varies\nlinearly with Qr in squeezing regime whereas power-law variation with Ca and Qr\nin dripping regime. Droplet frequency shows a power-law function of Ca and Qr\nin droplet zone. Present results compare excellently with earlier limited\nexperimental and numerical studies. Finally, present results and predictive\ncorrelations can guide engineering and design of droplet microfluidics devices.\n"}
+{"abstract": "  This paper describes the design and development of a prototype technique for\nartificial intelligence based on the fusion of genetic algorithm, neural\nnetwork and fuzzy logic. It starts by establishing a relationship between the\nneural network and fuzzy logic. Then, it combines the genetic algorithm with\nthem. Information fusions are at the confidence level, where matching scores\ncan be reported and discussed. The technique is called the Genetic Neuro-Fuzzy\n(GNF). It can be used for high accuracy real-time environments.\n"}
+{"abstract": "  Understanding of the interaction of antiferromagnetic solitons including\ndomain walls and skyrmions with boundaries of chiral antiferromagnetic slabs is\nimportant for the design of prospective antiferromagnetic spintronic devices.\nHere, we derive the transition from spin lattice to micromagnetic nonlinear\n$\\sigma$-model with the corresponding boundary conditions for a chiral cubic\nG-type antiferromagnet and analyze the impact of the slab boundaries and\nantisymmetric exchange (Dzyaloshinskii--Moriya interaction) on the vector order\nparameter. We apply this model to evaluate modifications of antiferromagnetic\ndomain walls and skyrmions upon interaction with boundaries for different\nstrengths of the antisymmetric exchange. Due to the presence of the\nantisymmetric exchange, both types of antiferromagnetic solitons become broader\nwhen approaching the boundary and transform to a mixed Bloch--N\\'{e}el\nstructure. Both textures feel the boundary at the distance of about 5 magnetic\nlengths. In this respect, our model provides design rules for antiferromagnetic\nracetracks, which can support bulk-like properties of solitons.\n"}
+{"abstract": "  Mesoporous bioactive glasses, MBG, are gaining increasing interest in the\ndesign of new biomaterials for bone defects treatment. An important research\ntrend to enhance their biological behavior is the inclusion of moderate amounts\nof oxides with therapeutical action such as CuO. In this paper, MBG with\ncomposition were synthesized, investigating the influence of the CuO content\nand some synthesis parameters in their properties. Two batch were developed;\nfirst one using HCl as catalyst and chlorides as CaO and CuO precursors, second\none, using HNO3 and nitrates. MBG of chlorides batch exhibited calcium/copper\nphosphate nanoparticles, between 10 and 20 nm. Nevertheless, CuO- containing\nMBG of nitrates batch showed nuclei of metallic copper nanoparticles larger\nthan 50 nm and quicker in vitro bioactive responses. Thus, they were coated by\nan apatite-like layer after 24 h soaked in simulated body fluid, a remarkably\nshort period for MBG containing up to 5 % of CuO. A model, focused in the\ncopper location in the glass network, was proposed to relate nanostructure and\nin vitro behaviour. Moreover, after 24 h in MEM or THB culture media, all the\nMBG released therapeutic amounts of Ca2+ and Cu2+ ions. Because the quick\nbioactive response in SBF, the capacity to host biomolecules in their pores and\nto release therapeutic concentrations of Ca2+ and Cu2+ ions, MBG of the nitrate\nbatch are considered as excellent biomaterials for bone regeneration.\n"}
+{"abstract": "  The radio $k$-coloring is an assignment $l$ of non-negative integers to the\nvertices of a graph in such a way that for two vertices $u,v$ which are $d$\ndistance apart, we must have $|l(u)-l(v)| \\geq k+1 - d$. The difference between\nthe greatest and the least image of $l$ is its span and our objective is to\nminimize the span. That is, the radio $k$-chromatic number $rc_k(G)$ is the\nminimum $\\lambda$ such that it is a span of some radio $k$-coloring of $G$. In\nthis article, we find exact values of $rc_k(P_n^{m})$ when $k > diam(P_n^{m})+\n\\epsilon$, where $P_n^{m}$ denotes the $m^{th}$ power of a path on $(n+1)$\nvertices, and $diam(P_n^{m})$ denotes its diameter.\n"}
+{"abstract": "  Laser-induced-continuum-structure(LICS) allows for coherent control\ntechniques to be applied in a Raman type system with intermediate continuum\nstate. To this end the standard LICS problem treats two bound states coupled to\none or more continua states. In this paper we discuss the simplest non-trivial\nmultilevel generalization of LICS coupling two bound levels, each composed of\ntwo degenerate states through a common continuum state. We reduce the\ncomplexity of the effective system of bound states by switching to a rotated\nbasis in which different sub-systems of lower dimension evolve independently.\nWe derive a trapping condition and explore the dynamics of the sub-systems\nunder different initial conditions.\n"}
+{"abstract": "  Most 3D neural networks are trained from scratch owing to the lack of\nlarge-scale labeled 3D datasets. In this paper, we present a novel 3D\npretraining method by leveraging 2D networks learned from rich 2D datasets. We\npropose the contrastive pixel-to-point knowledge transfer to effectively\nutilize the 2D information by mapping the pixel-level and point-level features\ninto the same embedding space. Due to the heterogeneous nature between 2D and\n3D networks, we introduce the back-projection function to align the features\nbetween 2D and 3D to make the transfer possible. Additionally, we devise an\nupsampling feature projection layer to increase the spatial resolution of\nhigh-level 2D feature maps, which enables learning fine-grained 3D\nrepresentations. With a pretrained 2D network, the proposed pretraining process\nrequires no additional 2D or 3D labeled data, further alleviating the expensive\n3D data annotation cost. To the best of our knowledge, we are the first to\nexploit existing 2D trained weights to pretrain 3D deep neural networks. Our\nintensive experiments show that the 3D models pretrained with 2D knowledge\nboost the performances of 3D networks across various real-world 3D downstream\ntasks.\n"}
+{"abstract": "  We demonstrate direct transport between two opposing sets of Yu-Shiba-Rusinov\n(YSR) subgap states realized in a double quantum dot. This sub-gap transport\nrelies on intrinsic quasiparticle relaxation, but the tunability of the device\nallows us to explore also an additional relaxation mechanism based on charge\ntransferring Andreev reflections. The transition between these two relaxation\nregimes is identified in the experiment as a marked gate-induced stepwise\nchange in conductance. We present a transport calculation, including YSR bound\nstates and multiple Andreev reflections alongside with quasiparticle\nrelaxation, due to a weak tunnel coupling to a nearby normal metal, and obtain\nexcellent agreement with the data.\n"}
+{"abstract": "  The exploration of interstellar space will require autonomous navigation\nsystems that do not rely on tracking from the Earth. Here I develop a method to\ndetermine the 3D position and 3D velocity of a spacecraft in deep space using a\nstar catalogue. As a spacecraft moves away from the Sun, the observed positions\nand velocities of the stars will change relative to those in a Earth-based\ncatalogue due to parallax and aberration. By measuring just the angular\ndistances between pairs of stars, and comparing these to the catalogue, we can\ninfer the coordinates of the spacecraft via an iterative forward-modelling\nprocess. I perform simulations with existing star catalogues to demonstrate the\nmethod and to compute its performance. Using the 20 nearest stars and a modest\nangular distance measurement accuracy of 1\", the position and velocity of a\nspacecraft light years from the Sun moving at relativistic speeds can be\ndetermined to within 3 au and 2 km/s respectively. These accuracies improve\nlinearly with the measurement accuracy, e.g. with angles measured to 1 mas the\nnavigation accuracy is 1000 times better. Performance can also be improved\nusing more stars, or by including onboard measurements of the stars' radial\nvelocities, as these too are affected by the spacecraft's position and motion.\n"}
+{"abstract": "  The well-known Kullback-Leibler divergence is known to be superadditive,\nenabling its use in many information-theoretic applications. However, such a\nproof is still missing for Tsallis divergence to the best of our knowledge. In\nthis work, we first prove that Tsallis divergence is $\\alpha$-superadditive for\n$\\alpha\\in\\mathbb{R}$. Then, we show that this fact entails ordinary\nsuperadditivity in the same aforementioned interval. This opens up a novel\nroute to many future applications of Tsallis divergence in information theory.\n"}
+{"abstract": "  Many differentiated products have key attributes that are unstructured and\nthus high-dimensional (e.g., design, text). Instead of treating unstructured\nattributes as unobservables in economic models, quantifying them can be\nimportant to answer interesting economic questions. To propose an analytical\nframework for this type of products, this paper considers one of the simplest\ndesign products -- fonts -- and investigates merger and product differentiation\nusing an original dataset from the world's largest online marketplace for\nfonts. We quantify font shapes by constructing embeddings from a deep\nconvolutional neural network. Each embedding maps a font's shape onto a\nlow-dimensional vector. In the resulting product space, designers are assumed\nto engage in Hotelling-type spatial competition. From the image embeddings, we\nconstruct two alternative measures that capture the degree of design\ndifferentiation. We then study the causal effects of a merger on the merging\nfirm's creative decisions using the constructed measures in a synthetic control\nmethod. We find that the merger causes the merging firm to increase the visual\nvariety of font design. Notably, such effects are not captured when using\ntraditional measures for product offerings (e.g., specifications and the number\nof products) constructed from structured data.\n"}
+{"abstract": "  Accurate modeling of chemically reactive systems has traditionally relied on\neither expensive ab initio approaches or flexible bond-order force fields such\nas ReaxFF that require considerable time, effort, and expertise to\nparameterize. Here, we introduce FLARE++, a Bayesian active learning method for\ntraining reactive many-body force fields on the fly during molecular dynamics\n(MD) simulations. During the automated training loop, the predictive\nuncertainties of a sparse Gaussian process (SGP) force field are evaluated at\neach timestep of an MD simulation to determine whether additional ab initio\ndata are needed. Once trained, the SGP is mapped onto an equivalent and much\nfaster model that is polynomial in the local environment descriptors and whose\nprediction cost is independent of the training set size. We apply our method to\na canonical reactive system in the field of heterogeneous catalysis, hydrogen\nsplitting and recombination on a platinum (111) surface, obtaining a trained\nmodel within three days of wall time that is twice as fast as a recent Pt/H\nReaxFF force field and considerably more accurate. Our method is fully open\nsource and is expected to reduce the time and effort required to train fast and\naccurate reactive force fields for complex systems.\n"}
+{"abstract": "  We have found stable chaotic solutions for optomechanical systems coupled\nwith a Two-Level System or qubit. In this system methods have been found which\ncan be used to Tune in and out of Chaos as well as various n-period motions.\nThis includes achieving chaos by changing the detuning, coupling parameters,\nand Power of the driving laser. This allows us to manipulate chaos using either\nthe qubit or the optical cavity. Chaotic motion was also observed in both the\nqubit and cavity by only changing the relative phase between of driving fields\nof the two. This gives us the prospect of creating and exploring chaotic motion\nin quantum mechanical systems with further ease\n"}
+{"abstract": "  During the last decades, quantum dots within the Coulomb blockade regime of\ntransport have been proposed as essential building blocks for a wide variety of\nnanomachines. This includes thermoelectric devices, quantum shuttles, quantum\npumps, and even quantum motors. However, in this regime, the role of quantum\nmechanics is commonly limited to provide energy quantization while the working\nprinciple of the devices is ultimately the same as their classic counterparts.\nHere, we study quantum-dot-based nanomachines in the Coulomb blockade regime,\nbut in a configuration where the coherent superpositions of the dots' states\nplays a crucial role. We show that the studied system can be used as the basis\nfor different forms of \"true\" quantum machines that should only work in the\npresence of these coherent superpositions. We analyze the efficiency of these\nmachines against different nonequilibrium sources (bias voltage, temperature\ngradient, and external driving) and the factors that limit it, including\ndecoherence and the role of the different orders appearing in the adiabatic\nexpansion of the charge/heat currents.\n"}
+{"abstract": "  We reconsider the all-optical homodyne-measurement based experimental schemes\nthat aim to reveal Bell nonclassicality of a single photon, often termed\n`nonlocality'. We focus on the schemes put forward by Tan, Walls and Collett\n(TWC, 1991) and Hardy (1994). In the light of our previous work the Tan, Walls\nand Collett setup can be described by a precise local hidden variable model,\nhence the claimed nonclassicality of this proposal is apparent, whereas the\nnonclassicality proof proposed by Hardy is impeccable. In this work we resolve\nthe following problem: which feature of the Hardy's approach is crucial for its\nsuccessful confirmation of nonclassicality. The scheme of Hardy differs from\nthe Tan, Walls and Collett setup in two aspects. (i) It introduces a\nsuperposition of a single photon excitation with vacuum as the initial state of\none of the input modes of a 50-50 beamsplitter, which creates the superposition\nstate of two separable (exit) modes under investigation. (ii) In the final\nmeasurements Hardy's proposal utilises a varying strengths of the local\noscillator fields, whereas in the TWC case they are constant. In fact the local\noscillators in Hardy's scheme are either on or off (the local setting is\nspecified by the presence or absence of the local auxiliary field). We show\nthat it is the varying strength of the local oscillators, from setting to\nsetting, which is the crucial feature enabling violation of local realism in\nthe Hardy setup, whereas it is not necessary to use initial superposition of a\nsingle photon excitation with vacuum as the initial state of the input mode.\nNeither one needs to operate in the fully on/off detection scheme. Despite the\nfailure of the Tan, Walls and Collett scheme in proving Bell nonclassicality,\nwe show that their scheme can serve as an entanglement indicator.\n"}
+{"abstract": "  We give a short analysis of the \\emph{transversal achievement game} on a\nsquare grid due to M. Erickson (2010).\n"}
+{"abstract": "  Women's football is gaining supporters and practitioners worldwide, raising\nquestions about what the differences are with men's football. While the two\nsports are often compared based on the players' physical attributes, we analyze\nthe spatio-temporal events during matches in the last World Cups to compare\nmale and female teams based on their technical performance. We train an\nartificial intelligence model to recognize if a team is male or female based on\nvariables that describe a match's playing intensity, accuracy, and performance\nquality. Our model accurately distinguishes between men's and women's football,\nrevealing crucial technical differences, which we investigate through the\nextraction of explanations from the classifier's decisions. The differences\nbetween men's and women's football are rooted in play accuracy, the recovery\ntime of ball possession, and the players' performance quality. Our methodology\nmay help journalists and fans understand what makes women's football a distinct\nsport and coaches design tactics tailored to female teams.\n"}
+{"abstract": "  In this paper, we review several results from singularly perturbed\ndifferential equations with multiple small parameters. In addition, we develop\na general conceptual framework to compare and contrast the different results by\nproposing a three-step process. First, one specifies the setting and\nrestrictions of the differential equation problem to be studied and identifies\nthe relevant small parameters. Second, one defines a notion of equivalence via\na property/observable for partitioning the parameter space into suitable\nregions near the singular limit. Third, one studies the possible asymptotic\nsingular limit problems as well as perturbation results to complete the\ndiagrammatic subdivision process. We illustrate this approach for two simple\nproblems from algebra and analysis. Then we proceed to the review of several\nmodern double-limit problems including multiple time scales, stochastic\ndynamics, spatial patterns, and network coupling. For each example, we\nillustrate the previously mentioned three-step process and show that already\ndouble-limit parametric diagrams provide an excellent unifying theme. After\nthis review, we compare and contrast the common features among the different\nexamples. We conclude with a brief outlook, how our methodology can help to\nsystematize the field better, and how it can be transferred to a wide variety\nof other classes of differential equations.\n"}
+{"abstract": "  We describe the architecture of Sammen Om Demens (SOD), an application for\nportable devices aiming at helping persons with dementia when wandering and\ngetting lost through the involvement of caregivers, family members, and\nordinary citizens who volunteer.\n  To enable the real-time detection of a person with dementia that has lost\norientation, we transfer location data at high frequency from a frontend on the\nsmartphone of a person with dementia to a backend system. The backend system\nmust be able to cope with the high throughput data and carry out possibly heavy\ncomputations for the detection of anomalous behavior via artificial\nintelligence techniques. This sets certain performance and architectural\nrequirements on the design of the backend.\n  In the paper, we discuss our design and implementation choices for the\nbackend of SOD that involve microservices and serverless services to achieve\nefficiency and scalability. We give evidence of the achieved goals by deploying\nthe SOD backend on a public cloud and measuring the performance on simulated\nload tests.\n"}
+{"abstract": "  The still-maturing noisy intermediate-scale quantum (NISQ) technology faces\nstrict limitations on the algorithms that can be implemented efficiently. In\nquantum chemistry, the variational quantum eigensolver (VQE) algorithm has\nbecome ubiquitous, using the functional form of the ansatz as a degree of\nfreedom, whose parameters are found variationally in a feedback loop between\nthe quantum processor and its conventional counterpart. Alternatively, a\npromising new avenue has been unraveled by the quantum variants of techniques\ngrounded on expansions of the moments of the Hamiltonian, among which two stand\nout: the connected moments expansion (CMX) [Phys. Rev. Lett. 58, 53 (1987)] and\nthe Peeters-Devreese-Soldatov (PDS) functional [J. Phys. A 17, 625 (1984); Int.\nJ. Mod. Phys. B 9, 2899], the latter based on the standard moments <$H^k$>.\nContrasting with VQE-based methods and provided the quantum circuit prepares a\nstate with non-vanishing overlap with the true ground state, CMX often\nconverges to the ground state energy, while PDS is guaranteed to converge by\nvirtue of being variational. However, for a finite CMX/PDS order, the circuit\nmay significantly impact the energy accuracy. Here we use the ADAPT-VQE\nalgorithm to test shallow circuit construction strategies that are not expected\nto impede their implementation in the present quantum hardware while granting\nsizable accuracy improvement in the computed ground state energies. We also\nshow that we can take advantage of the fact that the terms in the connected\nmoments are highly recurring in different powers, incurring a sizable reduction\nin the number of necessary measurements. By coupling this measurement caching\nwith a threshold that determines whether a given term is to be measured based\non its associated scalar coefficient, we observe a further reduction in the\nnumber of circuit implementations while allowing for tunable accuracy.\n"}
+{"abstract": "  The rapid spread of COVID-19 infections on a global level has highlighted the\nneed for accurate, transparent and timely information regarding collective\nmobility patterns to inform de-escalation strategies as well as to provide\nforecasting capacity for re-escalation policies aiming at addressing further\nwaves of the virus. Such information can be extracted using aggregate\nanonymised data from innovative sources such as mobile positioning data. This\npaper presents lessons learnt and results of a unique Business-to-Government\n(B2G) initiative between several Mobile Network Operators in Europe and the\nEuropean Commission. Mobile positioning data have supported policy makers and\npractitioners with evidence and data-driven knowledge to understand and predict\nthe spread of the disease, the effectiveness of the containment measures, their\nsocio-economic impacts while feeding scenarios at EU scale and in a comparable\nway across countries. The challenges of this data sharing initiative are not\nlimited to data quality, harmonisation, and comparability across countries,\nhowever important they are. Equally essential aspects that need to be addressed\nfrom the onset are related to data privacy, security, fundamental rights and\ncommercial sensitivity.\n"}
+{"abstract": "  This paper presents a proof of concept for the usefulness of second-order\ntexture features for the qualitative analysis and classification of chromogenic\nin-situ hybridization whole slide images in high-throughput imaging\nexperiments. The challenge is that currently, the gold standard for gene\nexpression grading in such images is expert assessment. The idea of the\nresearch team is to use different approaches in the analysis of these images\nthat will be used for structural segmentation and functional analysis in gene\nexpression. The article presents such perspective idea to select a number of\ntextural features that are going to be used for classification. In our\nexperiment, natural grouping of image samples (tiles) depending on their local\ntexture properties was explored in an unsupervised classification procedure.\nThe features are reduced to two dimensions with fuzzy c-means clustering. The\noverall conclusion of this experiment is that Haralick features are a viable\nchoice for classification and analysis of chromogenic in-situ hybridization\nimage data. The principal component analysis approach produced slightly more\n\"understandable\" from an annotator's point of view classes.\n"}
+{"abstract": "  Although machine learning (ML) is widely used in practice, little is known\nabout practitioners' actual understanding of potential security challenges. In\nthis work, we close this substantial gap in the literature and contribute a\nqualitative study focusing on developers' mental models of the ML pipeline and\npotentially vulnerable components. Studying mental models has helped in other\nsecurity fields to discover root causes or improve risk communication. Our\nstudy reveals four characteristic ranges in mental models of industrial\npractitioners. The first range concerns the intertwined relationship of\nadversarial machine learning (AML) and classical security. The second range\ndescribes structural and functional components. The third range expresses\nindividual variations of mental models, which are neither explained by the\napplication nor by the educational background of the corresponding subjects.\nThe fourth range corresponds to the varying levels of technical depth, which\nare however not determined by our subjects' level of knowledge. Our\ncharacteristic ranges have implications for the integration of AML into\ncorporate workflows, security enhancing tools for practitioners, and creating\nappropriate regulatory frameworks for AML.\n"}
+{"abstract": "  New programming languages (e.g., Swift, Go, Rust, etc.) are being introduced\nto provide a better opportunity for the developers to make software development\nrobust and easy. At the early stage, a programming language is likely to have\nresource constraints that encourage the developers to seek help frequently from\nexperienced peers active in QA sites such as Stack Overflow (SO). In this\nstudy, we have formally studied the discussions on three popular new languages\nintroduced after the inception of SO (2008) and match those with the relevant\nactivities in GitHub whenever appropriate. For that purpose, we have mined\n4,17,82,536 questions and answers from SO and 7,846 issue information along\nwith 6,60,965 repository information from GitHub. Initially, the development of\nnew languages is relatively slow compared to mature languages (e.g., C, C++,\nJava). The expected outcome of this study is to reveal the difficulties and\nchallenges faced by the developers working with these languages so that\nappropriate measures can be taken to expedite the generation of relevant\nresources. We have used the LDA method on SO's questions and answers to\nidentify different topics of new languages. We have extracted several features\nof the answer pattern of the new languages from SO to study their\ncharacteristics. These attributes were used to identify difficult topics. We\nexplored the background of developers who are contributing to these languages.\nWe have created a model by combining Stack Overflow data and issues,\nrepository, user data of GitHub. Finally, we have used that model to identify\nfactors that affect language evolution. We believe that the outcome of our\nstudy is likely to help the owner/sponsor of these languages to design better\nfeatures and documentation. It will also help the software developers or\nstudents to prepare themselves to work on these languages in an informed way.\n"}
+{"abstract": "  Model checking has been developed for verifying the behaviour of systems with\nstochastic and non-deterministic behavior. It is used to provide guarantees\nabout such systems. While most model checking methods focus on propositional\nmodels, various probabilistic planning and reinforcement frameworks deal with\nrelational domains, for instance, STRIPS planning and relational Markov\nDecision Processes. Using propositional model checking in relational settings\nrequires one to ground the model, which leads to the well known state explosion\nproblem and intractability. We present pCTL-REBEL, a lifted model checking\napproach for verifying pCTL properties on relational MDPs. It extends REBEL,\nthe relational Bellman update operator, which is a lifted value iteration\napproach for model-based relational reinforcement learning, toward relational\nmodel-checking. PCTL-REBEL is lifted, which means that rather than grounding,\nthe model exploits symmetries and reasons at an abstract relational level.\nTheoretically, we show that the pCTL model checking approach is decidable for\nrelational MDPs even for possibly infinite domains provided that the states\nhave a bounded size. Practically, we contribute algorithms and an\nimplementation of lifted relational model checking, and we show that the lifted\napproach improves the scalability of the model checking approach.\n"}
+{"abstract": "  We study $p$-adic $L$-functions $L_p(s,\\chi)$ for Dirichlet characters\n$\\chi$. We show that $L_p(s,\\chi)$ has a Dirichlet series expansion for each\nregularization parameter $c$ that is prime to $p$ and the conductor of $\\chi$.\nThe expansion is proved by transforming a known formula for $p$-adic\n$L$-functions and by controlling the limiting behavior. A finite number of\nEuler factors can be factored off in a natural manner from the $p$-adic\nDirichlet series. We also provide an alternative proof of the expansion using\n$p$-adic measures and give an explicit formula for the values of the\nregularized Bernoulli distribution. The result is particularly simple for\n$c=2$, where we obtain a Dirichlet series expansion that is similar to the\ncomplex case.\n"}
+{"abstract": "  Accretion disks and coronae around massive black holes have been studied\nextensively, and they are known to be coupled. Over a period of 30 years,\nhowever, the X-ray (coronal) flux of Mrk 817 increased by a factor of 40 while\nits UV (disk) flux remained relatively steady. Recent high-cadence monitoring\nfinds that the X-ray and UV continua in Mrk 817 are also decoupled on time\nscales of weeks and months. These findings could require mechanical beaming of\nthe innermost accretion flow, and/or an absorber that shields the disk and/or\nbroad line region (BLR) from the X-ray corona. Herein, we report on a 135 ks\nobservation of Mrk 817 obtained with NuSTAR, complemented by simultaneous X-ray\ncoverage via the Neil Gehrels Swift Observatory. The X-ray data strongly prefer\na standard relativistic disk reflection model over plausible alternatives.\nComparable fits with related models constrain the spin to lie in the range 0.5\n< a < 1, and the viewing angle to lie between 10 deg. < theta < 22 deg.\n(including 1-sigma statistical errors and small systematic errors related to\ndifferences between the models). The spectra also reveal strong evidence of\nmoderately ionized absorption, similar to but likely less extreme than\nobscuring events in NGC 5548 and NGC 3783. Archival Swift data suggest that the\nabsorption may be variable. Particularly if the column density of this absorber\nis higher along the plane of the disk, it may intermittently mask or prevent\ncoupling between the central engine, disk, and BLR in Mrk 817.\n"}
+{"abstract": "  Artificial neural networks perform state-of-the-art in an ever-growing number\nof tasks, and nowadays they are used to solve an incredibly large variety of\ntasks. There are problems, like the presence of biases in the training data,\nwhich question the generalization capability of these models. In this work we\npropose EnD, a regularization strategy whose aim is to prevent deep models from\nlearning unwanted biases. In particular, we insert an \"information bottleneck\"\nat a certain point of the deep neural network, where we disentangle the\ninformation about the bias, still letting the useful information for the\ntraining task forward-propagating in the rest of the model. One big advantage\nof EnD is that we do not require additional training complexity (like decoders\nor extra layers in the model), since it is a regularizer directly applied on\nthe trained model. Our experiments show that EnD effectively improves the\ngeneralization on unbiased test sets, and it can be effectively applied on\nreal-case scenarios, like removing hidden biases in the COVID-19 detection from\nradiographic images.\n"}
+{"abstract": "  Computations on the dendritic trees of neurons have important constraints.\nVoltage dependent conductances in dendrites are not similar to arbitrary\ndirect-current generation, they are the basis for dendritic nonlinearities and\nthey do not allow converting positive currents into negative currents. While it\nhas been speculated that the dendritic tree of a neuron can be seen as a\nmulti-layer neural network and it has been shown that such an architecture\ncould be computationally strong, we do not know if that computational strength\nis preserved under these biological constraints. Here we simulate models of\ndendritic computation with and without these constraints. We find that\ndendritic model performance on interesting machine learning tasks is not hurt\nby these constraints but may benefit from them. Our results suggest that single\nreal dendritic trees may be able to learn a surprisingly broad range of tasks.\n"}
+{"abstract": "  We propose DeRenderNet, a deep neural network to decompose the albedo and\nlatent lighting, and render shape-(in)dependent shadings, given a single image\nof an outdoor urban scene, trained in a self-supervised manner. To achieve this\ngoal, we propose to use the albedo maps extracted from scenes in videogames as\ndirect supervision and pre-compute the normal and shadow prior maps based on\nthe depth maps provided as indirect supervision. Compared with state-of-the-art\nintrinsic image decomposition methods, DeRenderNet produces shadow-free albedo\nmaps with clean details and an accurate prediction of shadows in the\nshape-independent shading, which is shown to be effective in re-rendering and\nimproving the accuracy of high-level vision tasks for urban scenes.\n"}
+{"abstract": "  The experimental thermal neutron cross sections of the twenty proteinogenic\namino acids have been measured over the incident-neutron energy range spanning\nfrom 1 meV to 10 keV and data have been interpreted using the multi-phonon\nexpansion based on first-principles calculations. The scattering cross section,\ndominated by the incoherent inelastic contribution from the hydrogen atoms, can\nbe rationalised in terms of the average contributions of different functional\ngroups, thus neglecting their correlation. These results can be used for\nmodelling the total neutron cross sections of complex organic systems like\nproteins, muscles, or human tissues from a limited number of starting input\nfunctions. This simplification is of crucial importance for fine-tuning of\ntransport simulations used in medical applications, including boron neutron\ncapture therapy as well as secondary neutrons-emission induced during proton\ntherapy. Moreover, the parametrized neutron cross sections allow a better\ntreatment of neutron scattering experiments, providing detailed sample\nself-attenuation corrections for a variety of biological and soft-matter\nsystems.\n"}
+{"abstract": "  We study solutions of the stellar structure equations for spherically\nsymmetric objects in Palatini $f(R)=R+\\alpha R^2$ and $f(R,Q)=R+\\alpha\nR^2+\\beta Q$ in the mass-radius region associated to neutron stars. We\nillustrate the potential impact of the $R^2$ and $Q$ terms by studying a range\nof viable values of $\\alpha$ and $\\beta$. Similarly, we use different equations\nof state (SLy, FPS, HS(DD2) and HS(TMA)) as a simple way to account for the\nequation of state uncertainty. Our results show that for certain combinations\nof the $\\alpha$ and $\\beta$ parameters and equation of state, the effect of\nmodifications of general relativity on the properties of stars is sizeable.\nTherefore, with increasing accuracy in the determination of the equation of\nstate for neutron stars, astrophysical observations may serve as discriminators\nof modifications of General Relativity.\n"}
+{"abstract": "  Success in Open Source Software (OSS) is often perceived as an exclusively\ncode-centric endeavor. This perception can exclude a variety of individuals\nwith a diverse set of skills and backgrounds, in turn helping create the\ncurrent diversity & inclusion imbalance in OSS. Because people's perspectives\nof success affect their personal, professional, and life choices, to be able to\nsupport a diverse class of individuals, we must first understand what OSS\ncontributors consider successful. Thus far, research has used a\nuni-dimensional, code-centric lens to define success. In this paper, we\nchallenge this status-quo and reveal the multi-faceted definition of success\namong OSS contributors. We do so through interviews with 27 OSS contributors\nwho are recognized as successful in their communities, and a follow-up open\nsurvey with 193 OSS contributors. Our study provides nuanced definitions of\nsuccess perceptions in OSS, which might help devise strategies to attract and\nretain a diverse set of contributors, helping them attain their \"pots of gold\nat the end of the rainbow\".\n"}
+{"abstract": "  In a class of neutrino mass models with modular flavor symmetries, it has\nbeen observed that CP symmetry is preserved at the fixed point (or stabilizer)\nof the modulus parameter $\\tau = {\\rm i}$, whereas significant CP violation\nemerges within the neighbourhood of this stabilizer. In this paper, we first\nconstruct a viable model with the modular $A^\\prime_5$ symmetry, and explore\nthe phenomenological implications for lepton masses and flavor mixing. Then, we\nintroduce explicit perturbations to the stabilizer at $\\tau = {\\rm i}$, and\npresent both numerical and analytical results to understand why a small\ndeviation from the stabilizer leads to large CP violation. As low-energy\nobservables are very sensitive to the perturbations to model parameters, we\nfurther demonstrate that the renormalization-group running effects play an\nimportant role in confronting theoretical predictions at the high-energy scale\nwith experimental measurements at the low-energy scale.\n"}
+{"abstract": "  It is generally thought that FRII Radio Galaxies host thin optically thick\ndisks, while FRIs are powered by Advected Dominated Accretion Flows. The\nsources with an efficient engine are optically classified as High Excitation\nRadio Galaxies (HERGs) and those with an inefficient motor as Low Excitation\nRadio Galaxies (LERGs). Recently, the study of Radio Galaxies down to mJy\nfluxes has cast serious doubts on the LERG-FRI and HERG-FRII correspondence,\nrevealing that many LERGs show FRII radio morphologies. The FR catalogs\nrecently compiled by Capetti et al. (2017a,b) and Baldi et al. (2018) have\nallowed us to explore this issue in the local ($z\\le 0.15$) mJy Universe. Our\nstatistical study shows that the majority of nearby mJy objects are in a late\nstage of their life. FRII-LERGs appear more similar to the old FRI-LERGs than\nto the young FRII-HERGs. FRII-LERGs may be aged HERGs that, exhausted the cold\nfuel, have changed their accretion regime or a separate LERG class particularly\nefficient in launching jets. Exploiting the empirical relations which convert\nL$_{\\rm [OIII]}$ and L$_{\\rm 1.4~GHz}$ into accretion power and jet kinetic\npower, respectively, we observed that LERGs with similar masses and accretion\nrates seem to expel jets of different power. We speculate that intrinsic\ndifferences related to the black hole properties (spin and magnetic field at\nits horizon) can determine the observed spread in jet luminosity. In this view,\nFRII-LERGs should have the fastest spinning black holes and/or the most intense\nmagnetic fluxes. On the contrary, compact LERGs (i.e. FR0s) should host\nextremely slow black holes and/or weak magnetic fields.\n"}
+{"abstract": "  In this paper we perform systematic investigation of all possible exponential\nsolutions in Einstein-Gauss-Bonnet gravity with the spatial section being a\nproduct of two subspaces. We describe a scheme which always allow to find\nsolution for a given $\\{p, q\\} > 2$ (number of dimensions of two subspaces) and\n$\\zeta$ (ratio of the expansion rates of these two subspaces). Depending on the\nparameters, for given $\\{\\alpha, \\Lambda\\}$ (Gauss-Bonnet coupling and\ncosmological constant) there could be up to four distinct solutions (with\ndifferent $\\zeta$'s). Stability requirement introduces relation between\n$\\zeta$, $\\{p, q\\}$ and sign of the expansion rate. Nevertheless, for any $\\{p,\nq\\} > 2$ we can always choose sign for expansion rates so that the resulting\nsolution would be stable. The scheme for finding solutions is described and the\nbounds on the parameters are drawn. Specific cases with $\\{p, q\\} = \\{1, 2\\}$\nare also considered. Finally, we separately described physically sensible case\nwith one of the subspaces being three-dimensional and expanding (resembling our\nUniverse) while another to be contracting (resembling extra dimensions),\ndescribing successful compactification; for this case we also drawn bounds on\nthe parameters where such regime occurs.\n"}
+{"abstract": "  In the problem of active sequential hypothesis testing (ASHT), a learner\nseeks to identify the true hypothesis from among a known set of hypotheses. The\nlearner is given a set of actions and knows the random distribution of the\noutcome of any action under any true hypothesis. Given a target error\n$\\delta>0$, the goal is to sequentially select the fewest number of actions so\nas to identify the true hypothesis with probability at least $1 - \\delta$.\nMotivated by applications in which the number of hypotheses or actions is\nmassive (e.g., genomics-based cancer detection), we propose efficient (greedy,\nin fact) algorithms and provide the first approximation guarantees for ASHT,\nunder two types of adaptivity. Both of our guarantees are independent of the\nnumber of actions and logarithmic in the number of hypotheses. We numerically\nevaluate the performance of our algorithms using both synthetic and real-world\nDNA mutation data, demonstrating that our algorithms outperform previously\nproposed heuristic policies by large margins.\n"}
+{"abstract": "  Deep neural networks (DNNs) have recently found emerging use in accelerated\nMRI reconstruction. DNNs typically learn data-driven priors from large datasets\nconstituting pairs of undersampled and fully-sampled acquisitions. Acquiring\nsuch large datasets, however, might be impractical. To mitigate this\nlimitation, we propose a few-shot learning approach for accelerated MRI that\nmerges subject-driven priors obtained via physical signal models with\ndata-driven priors obtained from a few training samples. Demonstrations on\nbrain MR images from the NYU fastMRI dataset indicate that the proposed\napproach requires just a few samples to outperform traditional parallel imaging\nand DNN algorithms.\n"}
+{"abstract": "  In recent years, much work have studied the use of convolutional neural\nnetworks for gravitational-wave detection. However little work pay attention to\nwhether the transient noise can trigger the CNN model or not. In this paper, we\nstudy the responses of the sine-Gaussian glitches, the Gaussian glitches and\nthe ring-down glitches in the trained convolutional neural network classifier.\nWe find that the network is robust to the sine-Gaussian and Gaussian glitches,\nwhose false alarm probabilities are close to that of the LIGO-like noises, in\ncontrast to the case of the ring-down glitches, in which the false alarm\nprobability is far larger than that of the LIGO-like noises. We also\ninvestigate the responses of the glitches with different frequency. We find\nthat when the frequency of the glitches falls in that of the trained GW\nsignals, the false alarm probability of the glitches will be much larger than\nthat of the LIGO-like noises, and the probability of the glitches being\nmisjudged as the GW signals may even exceed 30%.\n"}
+{"abstract": "  Huayi Chen introduces the notion of an approximable graded algebra, which he\nuses to prove a Fujita-type theorem in the arithmetic setting, and asked if any\nsuch algebra is the graded ring of a big line bundle on a projective variety.\nThis was proved to be false in a previous paper of the author's, who\nsubsequently proved that any such algebra is associated to an infinite Weil\ndivisor. In this paper, we show that over the complex numbers, this infinite\nWeil divisor necessarily has finite cohomology class.\n"}
+{"abstract": "  Self-supervision has demonstrated to be an effective learning strategy when\ntraining target tasks on small annotated data-sets. While current research\nfocuses on creating novel pretext tasks to learn meaningful and reusable\nrepresentations for the target task, these efforts obtain marginal performance\ngains compared to fully-supervised learning. Meanwhile, little attention has\nbeen given to study the robustness of networks trained in a self-supervised\nmanner. In this work, we demonstrate that networks trained via self-supervised\nlearning have superior robustness and generalizability compared to\nfully-supervised learning in the context of medical imaging. Our experiments on\npneumonia detection in X-rays and multi-organ segmentation in CT yield\nconsistent results exposing the hidden benefits of self-supervision for\nlearning robust feature representations.\n"}
+{"abstract": "  We investigate the harmonic response of neon atoms to mid-IR laser fields\n(2000-3000 nm) using a single-active electron (SAE) model and the fully ab\ninitio all-electron R-Matrix with Time-dependence (RMT) method. The laser peak\nintensity and wavelength are varied to find optimal parameters for\nhigh-harmonic imaging in the water window. Comparison of the SAE and RMT\nresults shows qualitative agreement between them as well as parameters such as\nthe cutoff frequency predicted by the classical three-step model. However,\nthere are significant differences in the details, thus indicating the\nimportance of multi-electron effects.\n"}
+{"abstract": "  We show that for every integer $m > 0$, there is an ordinary abelian variety\nover ${\\mathbb F}_2$ that has exactly $m$ rational points.\n"}
+{"abstract": "  Autonomous landing of Unmanned Aerial Vehicles (UAVs) in crowded scenarios is\ncrucial for successful deployment of UAVs in populated areas, particularly in\nemergency landing situations where the highest priority is to avoid hurting\npeople. In this work, a new visual-based algorithm for identifying Safe Landing\nZones (SLZ) in crowded scenarios is proposed, considering a camera mounted on\nan UAV, where the people in the scene move with unknown dynamics. To do so, a\ndensity map is generated for each image frame using a Deep Neural Network, from\nwhere a binary occupancy map is obtained aiming to overestimate the people's\nlocation for security reasons. Then, the occupancy map is projected to the\nhead's plane, and the SLZ candidates are obtained as circular regions in the\nhead's plane with a minimum security radius. Finally, to keep track of the SLZ\ncandidates, a multiple instance tracking algorithm is implemented using Kalman\nFilters along with the Hungarian algorithm for data association. Several\nscenarios were studied to prove the validity of the proposed strategy,\nincluding public datasets and real uncontrolled scenarios with people moving in\npublic squares, taken from an UAV in flight. The study showed promising results\nin the search of preventing the UAV from hurting people during emergency\nlanding.\n"}
+{"abstract": "  It has recently been shown that the inner region of protoplanetary disks\n(PPDs) is governed by wind-driven accretion, and the resulting accretion flow\nshowing complex vertical profiles. Such complex flow structures are further\nenhanced due to the Hall effect, especially when the background magnetic field\nis aligned with disk rotation. We investigate how such flow structures impact\nglobal dust transport via Monte-Carlo simulations, focusing on two scenarios.\nIn the first scenario, the toroidal magnetic field is maximized in the miplane,\nleading to accretion and decretion flows above and below. In the second\nscenario, the toroidal field changes sign across the midplane, leading to an\naccretion flow at the disk midplane, with decretion flows above and below. We\nfind that in both cases, the contribution from additional gas flows can still\nbe accurately incorporated into the advection-diffusion framework for\nvertically-integrated dust transport, with enhanced dust radial diffusion up to\nan effective $\\alpha^{\\rm eff}\\sim10^{-2}$ for strongly coupled dust, even when\nbackground turbulence is weak $\\alpha<10^{-4}$. Dust radial drift is also\nmodestly enhanced in the second scenario. We provide a general analytical\ntheory that accurately reproduces our simulation results, thus establishing a\nframework to model global dust transport that realistically incorporates\nvertical gas flow structures. We also note that the theory is equally\napplicable to the transport of chemical species.\n"}
+{"abstract": "  This survey reviews several approaches of data mining (DM) in healthindustry\nfrom many research groups world wide. The focus is on modern multi-core\nprocessors built into today's commodity computers, which are typically found at\nuniversity institutes both as small server and workstation computers. So they\nare deliberately not high-performance computers. Modern multi-core processors\nconsist of several (2 to over 100) computer cores, which work independently of\neach other according to the principle of \"multiple instruction multiple data\"\n(MIMD). They have a common main memory (shared memory). Each of these computer\ncores has several (2-16) arithmetic-logic units, which can simultaneously carry\nout the same arithmetic operation on several data in a vector-like manner\n(single instruction multiple data, SIMD). DM algorithms must use both types of\nparallelism (SIMD and MIMD), with access to the main memory (centralized\ncomponent) being the main barrier to increased efficiency. This is important\nfor DM in healthindustry applications like ECG, EEG, CT, SPECT, fMRI, DTI,\nultrasound, microscopy, dermascopy, etc.\n"}
+{"abstract": "  We tackle the crucial challenge of fusing different modalities of features\nfor multimodal sentiment analysis. Mainly based on neural networks, existing\napproaches largely model multimodal interactions in an implicit and\nhard-to-understand manner. We address this limitation with inspirations from\nquantum theory, which contains principled methods for modeling complicated\ninteractions and correlations. In our quantum-inspired framework, the word\ninteraction within a single modality and the interaction across modalities are\nformulated with superposition and entanglement respectively at different\nstages. The complex-valued neural network implementation of the framework\nachieves comparable results to state-of-the-art systems on two benchmarking\nvideo sentiment analysis datasets. In the meantime, we produce the unimodal and\nbimodal sentiment directly from the model to interpret the entangled decision.\n"}
+{"abstract": "  It is well-known that the embedding of the Sobolev space of weakly\ndifferentiable functions into H\\\"{o}lder spaces holds if the integrability\nexponent is higher than the space dimension of the domain. In this paper, the\nembedding of the Sobolev functions into the H\\\"{o}lder spaces is expressed in\nterms of the minimal weak differentiability requirement independent of the\nintegrability exponent. The proof is based on the generalized Newton-Leibniz\nformula in the rectangular prism and inductive application of the Sobolev trace\nembedding results. The particular motivation for the new embedding theorem\nappears in proving compactness of the family of multilinear interpolations of\ndiscrete grid functions arising as solutions of the discretized PDE problems.\n"}
+{"abstract": "  State-of-the-art convolutional neural networks excel in machine learning\ntasks such as face recognition, and object classification but suffer\nsignificantly when adversarial attacks are present. It is crucial that machine\ncritical systems, where machine learning models are deployed, utilize robust\nmodels to handle a wide range of variability in the real world and malicious\nactors that may use adversarial attacks. In this study, we investigate eye\nclosedness detection to prevent vehicle accidents related to driver\ndisengagements and driver drowsiness. Specifically, we focus on adversarial\nattacks in this application domain, but emphasize that the methodology can be\napplied to many other domains. We develop two models to detect eye closedness:\nfirst model on eye images and a second model on face images. We adversarially\nattack the models with Projected Gradient Descent, Fast Gradient Sign and\nDeepFool methods and report adversarial success rate. We also study the effect\nof training data augmentation. Finally, we adversarially train the same models\non perturbed images and report the success rate for the defense against these\nattacks. We hope our study sets up the work to prevent potential vehicle\naccidents by capturing drivers' face images and alerting them in case driver's\neyes are closed due to drowsiness.\n"}
+{"abstract": "  The generalization ability of most meta-reinforcement learning (meta-RL)\nmethods is largely limited to test tasks that are sampled from the same\ndistribution used to sample training tasks. To overcome the limitation, we\npropose Latent Dynamics Mixture (LDM) that trains a reinforcement learning\nagent with imaginary tasks generated from mixtures of learned latent dynamics.\nBy training a policy on mixture tasks along with original training tasks, LDM\nallows the agent to prepare for unseen test tasks during training and prevents\nthe agent from overfitting the training tasks. LDM significantly outperforms\nstandard meta-RL methods in test returns on the gridworld navigation and MuJoCo\ntasks where we strictly separate the training task distribution and the test\ntask distribution.\n"}
+{"abstract": "  The coarse Ricci curvature of graphs introduced by Ollivier as well as its\nmodification by Lin-Lu-Yau have been studied from various aspects. In this\npaper, we propose a new transport distance appropriate for hypergraphs and\nstudy a generalization of Lin-Lu-Yau type curvature of hypergraphs. As an\napplication, we derive a Bonnet-Myers type estimate for hypergraphs under a\nlower Ricci curvature bound associated with our transport distance. We remark\nthat our transport distance is new even for graphs and worthy of further study.\n"}
+{"abstract": "  Animal-vehicle collisions (AVCs) are common around the world and result in\nconsiderable loss of animal and human life, as well as significant property\ndamage and regular insurance claims. Understanding their occurrence in relation\nto various contributing factors and being able to identify locations of high\nrisk are valuable to AVC prevention, yielding economic, social and\nenvironmental cost savings. However, many challenges exist in the study of AVC\ndatasets. These include seasonality of animal activity, unknown exposure (i.e.,\nthe number of animal crossings), very low AVC counts across most sections of\nextensive roadway networks, and computational burdens that come with discrete\nresponse analysis using large datasets. To overcome these challenges, a\nBayesian hierarchical model is proposed where the exposure is modeled with\nnonparametric Dirichlet process, and the number of segment-level AVCs is\nassumed to follow a Binomial distribution. A P\\'olya-Gamma augmented Gibbs\nsampler is derived to estimate the proposed model. By using the AVC data of\nmultiple years across about 100,000 segments of state-controlled highways in\nTexas, U.S., it is demonstrated that the model is scalable to large datasets,\nwith a preponderance of zeros and clear monthly seasonality in counts, while\nidentifying high-risk locations (for application of design treatments, like\nseparated animal crossings with fencing) and key explanatory factors based on\nsegment-specific factors (such as changes in speed limit) can be done within\nthe modelling framework, which provide useful information for policy-making\npurposes.\n"}
+{"abstract": "  Quantum computing technologies pose a significant threat to the currently\nemployed public-key cryptography protocols. In this paper, we discuss the\nimpact of the quantum threat on public key infrastructures (PKIs), which are\nused as a part of security systems for protecting production environments. We\nanalyze security issues of existing models with a focus on requirements for a\nfast transition to post-quantum solutions. Although our primary focus is on the\nattacks with quantum computing, we also discuss some security issues that are\nnot directly related to the used cryptographic algorithms but are essential for\nthe overall security of the PKI. We attempt to provide a set of security\nrecommendations regarding the PKI from the viewpoints of attacks with quantum\ncomputers.\n"}
+{"abstract": "  This paper investigates the initial boundary value problem for a\nnon-stationary one-dimensional heat equation that simulates the temperature\ndistribution in freshwater ice near the Earth's poles. The mathematical model\nhas been constructed taking into account solid-liquid phase transitions. Data\nfrom meteorological stations were used to determine the model parameters, with\nthe help of which the necessary physical and thermophysical characteristics of\nthe computational domain were obtained. For the numerical solution of the\nproblem, the finite volume method (FVM) was used. For the obtained periodic\nregime, the temperature versus depth dependences for each month were plotted,\nand the depth of the active layer, as well as the depth of zero annual\namplitudes were found for each meteorological station. A forecast of the ice\ntemperature regime for 2100 was modeled for three Representative Concentration\nPathway (RCP) scenarios of global warming: moderate RCP2.6, corresponding to\nthe current emissions of RCP7 and adopted at the Paris Agreement in 2015\nRCP1.9. By analyzing the impact of an additional 0,5 degrees Celsius of warming\non other areas, a reduction in the full range of risks to humanity and the\nplanet as a whole becomes evident with the proper efforts of the global\ncommunity. Thus, the conducted modeling has confirmed the need to reduce the\nrate of global warming.\n"}
+{"abstract": "  Let $\\cX$ be a family of subsets of a finite set $E$. A matroid on $E$ is\ncalled an $\\cX$-matroid if each set in $\\cX$ is a circuit. We consider the\nproblem of determining when there exists a unique maximal $\\cX$-matroid in the\nweak order poset of all $\\cX$-matroids on $E$, and characterizing its rank\nfunction when it exists.\n"}
+{"abstract": "  Hydrogen deuteride (HD) is prevalent in a wide variety of astrophysical\nenvironments, and measuring its large-scale distribution at different epochs\ncan in principle provide information about the properties of these\nenvironments. In this paper, we explore the prospects for accessing this\ndistribution using line intensity mapping of emission from the lowest\nrotational transition in HD, focusing on observations of the epoch of\nreionization ($z\\sim6-10$) and earlier. We find the signal from the epoch of\nreionization to be strongest most promising, through cross-correlations within\nexisting [CII] intensity mapping surveys. While the signal we predict is out of\nreach for current-generation projects, planned future improvements should be\nable to detect reionization-era HD without any additional observations, and\nwould help to constrain the properties of the star-forming galaxies thought to\nplay a key role in reionization. We also investigate several avenues for\nmeasuring HD during \"cosmic dawn\" ($z\\sim10-30$), a period in which HD could\nprovide one of the only complementary observables to 21$\\,$cm intensity maps.\nWe conclude that existing and planned facilities are poorly matched to the\nspecifications desirable for a significant detection, though such a measurement\nmay be achievable with sustained future effort. Finally, we explain why HD\nintensity mapping of the intergalactic medium during the cosmic dark ages\n($z\\gtrsim 30$) appears to be out of reach of any conceivable experiment.\n"}
+{"abstract": "  Human is able to conduct 3D recognition by a limited number of haptic\ncontacts between the target object and his/her fingers without seeing the\nobject. This capability is defined as `haptic glance' in cognitive\nneuroscience. Most of the existing 3D recognition models were developed based\non dense 3D data. Nonetheless, in many real-life use cases, where robots are\nused to collect 3D data by haptic exploration, only a limited number of 3D\npoints could be collected. In this study, we thus focus on solving the\nintractable problem of how to obtain cognitively representative 3D key-points\nof a target object with limited interactions between the robot and the object.\nA novel reinforcement learning based framework is proposed, where the haptic\nexploration procedure (the agent iteratively predicts the next position for the\nrobot to explore) is optimized simultaneously with the objective 3D recognition\nwith actively collected 3D points. As the model is rewarded only when the 3D\nobject is accurately recognized, it is driven to find the sparse yet efficient\nhaptic-perceptual 3D representation of the object. Experimental results show\nthat our proposed model outperforms the state of the art models.\n"}
+{"abstract": "  This paper introduces QAConv, a new question answering (QA) dataset that uses\nconversations as a knowledge source. We focus on informative conversations\nincluding business emails, panel discussions, and work channels. Unlike\nopen-domain and task-oriented dialogues, these conversations are usually long,\ncomplex, asynchronous, and involve strong domain knowledge. In total, we\ncollect 34,204 QA pairs, including span-based, free-form, and unanswerable\nquestions, from 10,259 selected conversations with both human-written and\nmachine-generated questions. We segment long conversations into chunks, and use\na question generator and dialogue summarizer as auxiliary tools to collect\nmulti-hop questions. The dataset has two testing scenarios, chunk mode and full\nmode, depending on whether the grounded chunk is provided or retrieved from a\nlarge conversational pool. Experimental results show that state-of-the-art QA\nsystems trained on existing QA datasets have limited zero-shot ability and tend\nto predict our questions as unanswerable. Fine-tuning such systems on our\ncorpus can achieve significant improvement up to 23.6% and 13.6% in both chunk\nmode and full mode, respectively.\n"}
+{"abstract": "  Based on current trends in computer architectures, faster compute speeds must\ncome from increased parallelism rather than increased clock speeds, which are\ncurrently stagnate. This situation has created the well-known bottleneck for\nsequential time-integration, where each individual time-value (i.e., time-step)\nis computed sequentially. One approach to alleviate this and achieve\nparallelism in time is with multigrid. In this work, we consider\nmultigrid-reduction-in-time (MGRIT), a multilevel method applied to the time\ndimension that computes multiple time-steps in parallel. Like all multigrid\nmethods, MGRIT relies on the complementary relationship between relaxation on a\nfine-grid and a correction from the coarse grid to solve the problem. All\ncurrent MGRIT implementations are based on unweighted-Jacobi relaxation; here\nwe introduce the concept of weighted relaxation to MGRIT. We derive new\nconvergence bounds for weighted relaxation, and use this analysis to guide the\nselection of relaxation weights. Numerical results then demonstrate that\nnon-unitary relaxation weights consistently yield faster convergence rates and\nlower iteration counts for MGRIT when compared with unweighted relaxation. In\nmost cases, weighted relaxation yields a 10%-20% saving in iterations. For\nA-stable integration schemes, results also illustrate that under-relaxation can\nrestore convergence in some cases where unweighted relaxation is not\nconvergent.\n"}
+{"abstract": "  In computer vision and natural language processing, innovations in model\narchitecture that increase model capacity have reliably translated into gains\nin performance. In stark contrast with this trend, state-of-the-art\nreinforcement learning (RL) algorithms often use small MLPs, and gains in\nperformance typically originate from algorithmic innovations. It is natural to\nhypothesize that small datasets in RL necessitate simple models to avoid\noverfitting; however, this hypothesis is untested. In this paper we investigate\nhow RL agents are affected by exchanging the small MLPs with larger modern\nnetworks with skip connections and normalization, focusing specifically on\nactor-critic algorithms. We empirically verify that naively adopting such\narchitectures leads to instabilities and poor performance, likely contributing\nto the popularity of simple models in practice. However, we show that dataset\nsize is not the limiting factor, and instead argue that instability from taking\ngradients through the critic is the culprit. We demonstrate that spectral\nnormalization (SN) can mitigate this issue and enable stable training with\nlarge modern architectures. After smoothing with SN, larger models yield\nsignificant performance improvements -- suggesting that more \"easy\" gains may\nbe had by focusing on model architectures in addition to algorithmic\ninnovations.\n"}
+{"abstract": "  We show that deciding the equality of two Dedekind sums $S(c,b)$, $S(d,b)$ is\nequivalent to deciding whether a Dedekind sum defined by $b, c, d$ takes a\ncertain value. By means of this result we construct infinite sequences of\npairwise equal Dedekind sums. Moreover, we prove a result that says how many\nDedekind sums $S(d,b)$, $1\\le d\\le b-1$, may be equal to a given $S(c,b)$ if\n$b$ is a square-free number.\n"}
+{"abstract": "  Global transformations form a categorical framework adapting graph\ntransformations to describe fully synchronous rule systems on a given data\nstructure.In this work we focus on data structures that can be captured as\npresheaves and study the computational aspects of such synchronous rule\nsystems.To obtain an online algorithm, a complete study of the sub-steps within\neach synchronous step is done at the semantic level.This leads to the\ndefinition of accretive rule systems and a local criterion to characterize\nthese systems.Finally an online computation algorithm for theses systems is\ngiven.\n"}
+{"abstract": "  We clarify universal critical properties of delocalization-localization\ntransitions in three-dimensional (3D) unitary and orthogonal classes with\nparticle-hole and/or chiral symmetries (classes AIII, BDI, D, C and CI). We\nfirst introduce tight-binding models on cubic lattice that belong to these five\nnonstandard symmetry classes respectively. Unlike the Bogoliubov-de Gennes\nHamiltonian for superconductors, all the five models have finite areas of Fermi\nsurfaces in the momentum space in the clean limit. Thereby, the scaling theory\nof the Anderson transition guarantees the presence of the\ndelocalization-localization transitions at finite disorder strength in these\nmodels. Based on this expectation, we carry out extensive transfer matrix\ncalculations of the Lyapunov exponents for zero-energy eigenstates of the\ndisordered tight-binding models with quasi-one-dimensional geometry. Near the\nAnderson transition point, the correlation length diverges with a universal\ncritical exponent $\\nu$. Using finite-size scaling analysis of the localization\nlength, we determine the critical exponent of the transitions and scaling\nfunction of the (normalized) localization length in the three non-standard\nunitary symmetry classes: $\\nu_{\\text{AIII}}=1.06\\pm0.02$,\n$\\nu_{\\text{D}}=0.87\\pm0.03$, and $\\nu_{\\text{C}}=0.996\\pm0.012$. Our result of\nthe class C is consistent with a previous study of classical network model [M.\nOrtu\\~no et al, Phys. Rev. Lett. 102, 070603 (2009)]. The critical exponents of\nthe two non-standard orthogonal classes are estimated as $\\nu_{\\text{CI}}=1.17\n\\pm 0.02$ and $\\nu_{\\text{BDI}}=1.12 \\pm 0.06$. Our result of the class CI is\nconsistent with another previous study of a lattice model, while the exponent\nof the class BDI is at variance with the previous evaluation of nodal Dirac\nring model [X. L. Luo et al, Phys. Rev. B. 101, 020202(R) (2020)].\n"}
+{"abstract": "  The growing popularity of e-scooters and their rapid expansion across urban\nstreets has attracted widespread attention. A major policy question is whether\ne-scooters substitute existing mobility options or fill the service gaps left\nby them. This study addresses this question by analyzing the spatiotemporal\npatterns of e-scooter service availability and use in Washington DC, focusing\non their spatial relationships with public transit and bikesharing. Results\nfrom an analysis of three open big datasets suggest that e-scooters have both\ncompeting and complementary effects on transit and bikesharing services. The\nsupply of e-scooters significantly overlaps with the service areas of transit\nand bikesharing, and we classify a majority of e-scooter trips as substitutes\nto transit and bikesharing uses. A travel-time-based analysis further reveals\nthat when choosing e-scooters over transit, travelers pay a price premium and\nsave some travel time. The price premium is greater during the COVID-19\npandemic but the associated travel-time savings are smaller. This implies that\npublic health considerations rather than time-cost tradeoffs are the main\ndriver for many to choose e-scooters over transit during COVID. In addition, we\nfind that e-scooters complement bikesharing and transit by providing services\nto underserved neighborhoods. A sizeable proportion (about 10 percent) of\ne-scooter trips are taken to connect with the rail services. Future research\nmay combine the big-data-based analysis presented here with traditional methods\nto further shed light on the interactions between e-scooter services,\nbikesharing, and public transit.\n"}
+{"abstract": "  We study the behavior of linear discriminant functions for binary\nclassification in the infinite-imbalance limit, where the sample size of one\nclass grows without bound while the sample size of the other remains fixed. The\ncoefficients of the classifier minimize an expected loss specified through a\nweight function. We show that for a broad class of weight functions, the\nintercept diverges but the rest of the coefficient vector has a finite limit\nunder infinite imbalance, extending prior work on logistic regression. The\nlimit depends on the left tail of the weight function, for which we distinguish\nthree cases: bounded, asymptotically polynomial, and asymptotically\nexponential. The limiting coefficient vectors reflect robustness or\nconservatism properties in the sense that they optimize against certain\nworst-case alternatives. In the bounded and polynomial cases, the limit is\nequivalent to an implicit choice of upsampling distribution for the minority\nclass. We apply these ideas in a credit risk setting, with particular emphasis\non performance in the high-sensitivity and high-specificity regions.\n"}
+{"abstract": "  We propose a new way to share licensed spectrum bandwidth capacity in mobile\nnetworks between operators, service providers and consumers using\nblockchain-based smart contracts. We discuss the foundational building blocks\nin the contract as well as various extensions to support more advanced features\nsuch as bulk purchases, future reservations, and various auction mechanisms.\nFurthermore, we demonstrate how the system can be implemented with an\nopen-source, permissioned Enterprise blockchain, Hyperledger Sawtooth. We show\nthat our smart contract implementation can improve blockchain transaction\nperformance, by approximately four orders of magnitude compared to serial\ntransactions and one order of magnitude compared to parallell transactions,\nusing PKI-driven bulk purchases of mobile access grants, paving the way for\nfully automated, efficient, and fine-grained roaming agreements.\n"}
+{"abstract": "  Redundancy mechanisms consist in sending several copies of a same job to a\nsubset of servers. It constitutes one of the most promising ways to exploit\ndiversity in multiservers applications. However, its pros and cons are still\nnot sufficiently understood in the context of realistic models with generic\nstatistical properties of service-times distributions and correlation\nstructures of copies. We aim at giving a survey of recent results concerning\nthe stability-arguably the first benchmark of performance-of systems with\ncancel-oncompletion redundancy. We also point out open questions and\nconjectures.\n"}
+{"abstract": "  We present an explicit matrix algebra quantization of the algebra of\nvolume-preserving diffeomorphisms of the $n$-torus. That is, we approximate the\ncorresponding classical Nambu brackets using\n$\\mathfrak{sl}(N^{\\lceil\\frac{n}{2}\\rceil},\\mathbb{C})$-matrices equipped with\nthe finite bracket given by the completely anti-symmetrized matrix product,\nsuch that the classical brackets are retrieved in the $N\\rightarrow \\infty$\nlimit. We then apply this approximation to the super $4$-brane in $9$\ndimensions and give a regularized action in analogy with the matrix\nquantization of the supermembrane. This action exhibits a reduced gauge\nsymmetry that we discuss from the viewpoint of $L_\\infty$-algebras in a slight\ngeneralization to the construction of Lie $2$-algebras from Bagger--Lambert\n$3$-algebras.\n"}
+{"abstract": "  Given an undirected graph, a conflict-free coloring (CFON*) is an assignment\nof colors to a subset of the vertices of the graph such that for every vertex\nthere exists a color that is assigned to exactly one vertex in its open\nneighborhood. The minimum number of colors required for such a coloring is\ncalled the conflict-free chromatic number. The decision version of the CFON*\nproblem is NP-complete even on planar graphs. In this paper, we show the\nfollowing results.\n  The CFON* problem is fixed-parameter tractable with respect to the combined\nparameters clique width and the solution size. We study the problem on block\ngraphs and cographs, which have bounded clique width. For both graph classes,\nwe give tight bounds of three and two respectively for the CFON* chromatic\nnumber. We study the problem on the following intersection graphs: interval\ngraphs, unit square graphs and unit disk graphs. We give tight bounds of two\nand three for the CFON* chromatic number for proper interval graphs and\ninterval graphs. Moreover, we give upper bounds for the CFON* chromatic number\non unit square and unit disk graphs. We also study the problem on split graphs\nand Kneser graphs. For split graphs, we show that the problem is NP-complete.\nFor Kneser graphs $K(n,k)$, when $n\\geq k(k+1)^2 + 1$, we show that the CFON*\nchromatic number is $k+1$. We also study the closed neighborhood variant of the\nproblem denoted by CFCN*, and obtain analogous results in some of the above\ncases.\n"}
+{"abstract": "  Let $D\\subset \\mathbb C^n$ be a bounded domain. A pair of distinct boundary\npoints $\\{p,q\\}$ of $D$ has the visibility property provided there exist a\ncompact subset $K_{p,q}\\subset D$ and open neighborhoods $U_p$ of $p$ and $U_q$\nof $q$, such that the real geodesics for the Kobayashi metric of $D$ which join\npoints in $U_p$ and $U_q$ intersect $K_{p,q}$. Every Gromov hyperbolic convex\ndomain enjoys the visibility property for any couple of boundary points. The\nGoldilocks domains introduced by Bharali and Zimmer and the log-type domains of\nLiu and Wang also enjoy the visibility property.\n  In this paper we relate the growth of the Kobayashi distance near the\nboundary with visibility and provide new families of convex domains where that\nproperty holds. We use the same methods to provide refinements of localization\nresults for the Kobayashi distance, and give a localized sufficient condition\nfor visibility. We also exploit visibility to study the boundary behavior of\nbiholomorphic maps.\n"}
+{"abstract": "  The activations of language transformers like GPT-2 have been shown to\nlinearly map onto brain activity during speech comprehension. However, the\nnature of these activations remains largely unknown and presumably conflate\ndistinct linguistic classes. Here, we propose a taxonomy to factorize the\nhigh-dimensional activations of language models into four combinatorial\nclasses: lexical, compositional, syntactic, and semantic representations. We\nthen introduce a statistical method to decompose, through the lens of GPT-2's\nactivations, the brain activity of 345 subjects recorded with functional\nmagnetic resonance imaging (fMRI) during the listening of ~4.6 hours of\nnarrated text. The results highlight two findings. First, compositional\nrepresentations recruit a more widespread cortical network than lexical ones,\nand encompass the bilateral temporal, parietal and prefrontal cortices. Second,\ncontrary to previous claims, syntax and semantics are not associated with\nseparated modules, but, instead, appear to share a common and distributed\nneural substrate. Overall, this study introduces a versatile framework to\nisolate, in the brain activity, the distributed representations of linguistic\nconstructs.\n"}
+{"abstract": "  The paper is concerned with the spatially homogeneous isotropic Boltzmann\nequation for Bose-Einstein particles with quantum collision kernel where the\ninteraction potential $\\phi({\\bf x})$ can be approximately written as the delta\nfunction plus a certain attractive potential such that the Fourier transform\n$\\widehat{\\phi}$ of $\\phi$ behaves like $0 \\le \\widehat{\\phi}(\\xi) \\le {\\rm\nconst.} |\\xi|^{\\eta}$ for $|\\xi|<<1$ for some constant $\\eta\\ge 1$. We prove\nthat in this case, there is no condensation in finite time for all temperatures\nand all solutions, and thus it is completely different from the case\n$\\widehat{\\phi}(\\xi) \\ge {\\rm const.}|\\xi|^{\\eta}$ for $|\\xi|<<1$ with $0\\le\n\\eta<1/4$ as considered in \\cite{Cai-Lu}. For a class of initial data that have\nsome nice integrability near the origin, we also get some regularity, stability\nand $L^{\\infty}$ estimate.\n"}
+{"abstract": "  Charge exchange between highly charged ions and neutral atoms and molecules\nhas been considered as one of the important mechanisms controlling soft X ray\nemissions in many astrophysical objects and environments. However, for modeling\ncharge exchange soft X ray emission, the data of n and l resolved state\nselective capture cross sections are often obtained by empirical and\nsemiclassical theory calculations. With a newly built cold target recoil ion\nmomentum spectroscopy (COLTRIMS) apparatus, we perform a series of measurements\nof the charge exchange of Ne(8,9)+ ions with He and H2 for collision energy\nranging from 1 to 24.75 keV/u. n resolved state selective capture\ncross-sections are reported. By comparing the measured state selective capture\ncross sections to those calculated by the multichannel Landau Zener method\n(MCLZ), it is found that MCLZ calculations are in good agreement with the\nmeasurement for the dominant n capture for He target. Furthermore, by using nl\nresolved cross sections calculated by MCLZ and applying l distributions\ncommonly used in the astrophysical literature to experimentally derived n\nresolved cross sections, we calculate the soft X ray emissions in the charge\nexchange between 4 keV/u Ne8+ and He by considering the radiative cascade from\nthe excited Ne7+ ions. Reasonable agreement is found in comparison to the\nmeasurement for even and separable models, and MCLZ calculations give results\nin a better agreement.\n"}
+{"abstract": "  The brain performs unsupervised learning and (perhaps) simultaneous\nsupervised learning. This raises the question as to whether a hybrid of\nsupervised and unsupervised methods will produce better learning. Inspired by\nthe rich space of Hebbian learning rules, we set out to directly learn the\nunsupervised learning rule on local information that best augments a supervised\nsignal. We present the Hebbian-augmented training algorithm (HAT) for combining\ngradient-based learning with an unsupervised rule on pre-synpatic activity,\npost-synaptic activities, and current weights. We test HAT's effect on a simple\nproblem (Fashion-MNIST) and find consistently higher performance than\nsupervised learning alone. This finding provides empirical evidence that\nunsupervised learning on synaptic activities provides a strong signal that can\nbe used to augment gradient-based methods.\n  We further find that the meta-learned update rule is a time-varying function;\nthus, it is difficult to pinpoint an interpretable Hebbian update rule that\naids in training. We do find that the meta-learner eventually degenerates into\na non-Hebbian rule that preserves important weights so as not to disturb the\nlearner's convergence.\n"}
+{"abstract": "  The microquasar MAXI J\\(1820+070\\) went into outburst from mid-March until\nmid-July 2018 with several faint rebrightenings afterwards. With a peak flux of\napproximately 4 Crab in the \\(20-50\\) keV, energy range the source was\nmonitored across the electromagnetic spectrum with detections from radio to\nhard X-ray frequencies. Using these multi-wavelength observations, we analyzed\nquasi-simultaneous observations from 12 April, near the peak of the outburst\n(\\(\\sim 23\\) March). Spectral analysis of the hard X-rays found a \\(kT_e \\sim\n30 \\) keV and \\( \\tau \\sim 2\\) with a \\texttt{CompTT} model, indicative of an\naccreting black hole binary in the hard state. The flat/inverted radio spectrum\nand the accretion disk winds seen at optical wavelengths are also consistent\nwith the hard state. Then we constructed a spectral energy distribution\nspanning \\(\\sim 12\\) orders of magnitude using modelling in \\texttt{JetSeT}.\nThe model is composed of an irradiated disk with a Compton hump and a leptonic\njet with an acceleration region and a synchrotron-dominated cooling region.\n\\texttt{JetSeT} finds the spectrum is dominated by jet emission up to\napproximately \\(10^{14}\\) Hz after which disk and coronal emission dominate.\nThe acceleration region has a magnetic field of \\( B \\sim 1.6 \\times 10^4 \\) G,\na cross section of \\(R \\sim 2.8 \\times 10^{9} \\) cm, and a flat radio spectral\nshape naturally obtained from the synchroton cooling of the accelerated\nelectrons. The jet luminosity of \\(> 8 \\times 10^{37} \\) erg/s (\\(>\n0.15L_{Edd}\\)) compared to an accretion luminosity of \\( \\sim 6 \\times\n10^{37}\\) erg/s, assuming a distance of 3 kpc. Because these two values are\ncomparable, it is possible the jet is powered predominately via accretion with\nonly a small contribution needed from the Blanford-Znajek mechanism from the\nreportedly slowly spinning black hole.\n"}
+{"abstract": "  We demonstrate a hierarchy of various classes of quantum correlations on\nexperimentally prepared two-qubit Werner-like states with controllable white\nnoise. Werner states, which are white-noise-affected Bell states, are\nprototypal examples for studying such a hierarchy as a function of the amount\nof white noise. We experimentally generated Werner states and their\ngeneralizations, i.e., partially entangled pure states affected by white noise.\nThese states enabled us to study the hierarchy of the following classes of\ncorrelations: separability, entanglement, steering in three- and\ntwo-measurement scenarios, and Bell nonlocality. We show that the generalized\nWerner states (GWSs) reveal fundamentally different aspects of the hierarchy\ncompared to the Werner states. In particular, we find five different parameter\nregimes of the GWSs, including those steerable in a two-measurement scenario\nbut not violating Bell inequalities. This regime cannot be observed for the\nusual Werner states. Furthermore, we find threshold curves separating different\nregimes of the quantum correlations and find the optimal states which allow for\nthe largest amount of white noise, which does not destroy their specific\nquantum correlations (e.g., unsteerable entanglement). Thus, we could identify\nthe optimal Bell-nondiagonal GWSs, which are, for this specific meaning, more\nrobust against white noise compared to the Bell-diagonal GWSs (i.e., Werner\nstates).\n"}
+{"abstract": "  We show that the maximal number of planes in a complex smooth cubic fourfold\nin ${\\mathbb P}^5$ is $405$, realized by the Fermat cubic only; the maximal\nnumber of real planes in a real smooth cubic fourfold is $357$, realized by the\nso-called Clebsch--Segre cubic. Altogether, there are but three (up to\nprojective equivalence) cubics with more than $350$ planes.\n"}
+{"abstract": "  The study of exoplanet atmospheres showed large diversity compared to the\nplanets in our solar system. Especially Jupiter type exoplanets orbiting their\nhost star in close orbits, the so-called hot and ultra-hot Jupiters, have been\nstudied in detail due to their enhanced atmospheric signature. Due to their\ntidally locked status, the temperature difference between the day- and\nnightside triggers atmospheric winds which can lead to various fingerprints in\nthe observations. Spatially resolved absorption lines during transit such as\nsodium (Na) could be a good tracer for such winds. Different works resolved the\nNa$^-$ absorption lines on different exoplanets which show different line\nwidths. Assuming that this could be attributed to such zonal jet streams, this\nwork models the effect of such winds on synthetic absorption lines. For this,\ntransiting Jupiter type planets with rotational velocities similar to hot and\nultra-hot Jupiter are considered. The investigation shows that high wind\nvelocities could reproduce the broadening of Na-line profiles inferred in\ndifferent high-resolution transit observations. There is a tendency that the\nbroadening values decrease for planets with lower equilibrium temperature. This\ncould be explained by atmospheric drag induced by the ionization of alkali\nlines which slow down the zonal jet streams, favoring their existence on hot\nJupiter rather than ultra-hot Jupiter.\n"}
+{"abstract": "  In this work we compute for the first time the so-called Complete-NLO\npredictions for top-quark pair hadroproduction in association with at least one\nisolated photon ($ t \\bar t \\gamma$). We also compute NLO QCD+EW predictions\nfor the similar case with at least two isolated photons ($ t \\bar t\n\\gamma\\gamma$) and for single-top hadroproduction in association with at least\none isolated photon. In addition, we complement our results with NLO QCD+EW\npredictions of the hadronic and leptonic decays of top-quark including an\nisolated photon. All these results have been obtained in a completely automated\napproach, by extending the capabilities of the MadGraph5_aMC@NLO framework and\nenabling the Complete-NLO predictions for processes with isolated photons in\nthe final state. We discuss the technical details of the implementation, which\ninvolves a mixed EW renormalisation scheme for such processes.\n"}
+{"abstract": "  We establish multiplicity results for the following class of quasilinear\nproblems $$ \\left\\{ \\begin{array}{l} -\\Delta_{\\Phi}u=f(x,u) \\quad \\mbox{in}\n\\quad \\Omega, \\\\ u=0 \\quad \\mbox{on} \\quad \\partial \\Omega, \\end{array} \\right.\n\\leqno{(P)} $$ where $\\Delta_{\\Phi}u=\\text{div}(\\varphi(x,|\\nabla u|)\\nabla u)$\nfor a generalized N-function $\\Phi(x,t)=\\int_{0}^{|t|}\\varphi(x,s)s\\,ds$. We\nconsider $\\Omega\\subset\\mathbb{R}^N$ to be a smooth bounded domain that\ncontains two disjoint open regions $\\Omega_N$ and $\\Omega_p$ such that\n$\\overline{\\Omega_N}\\cap\\overline{\\Omega_p}=\\emptyset$. The main feature of the\nproblem $(P)$ is that the operator $-\\Delta_{\\Phi}$ behaves like $-\\Delta_N$ on\n$\\Omega_N$ and $-\\Delta_p$ on $\\Omega_p$. We assume the nonlinearity\n$f:\\Omega\\times\\mathbb{R}\\to\\mathbb{R}$ of two different types, but both\nbehaves like $e^{\\alpha|t|^\\frac{N}{N-1}}$ on $\\Omega_N$ and $|t|^{p^*-2}t$ on\n$\\Omega_p$ as $|t|$ is large enough, for some $\\alpha>0$ and\n$p^*=\\frac{Np}{N-p}$ being the critical Sobolev exponent for $1<p<N$. In this\ncontext, for one type of nonlinearity $f$, we provide multiplicity of solutions\nin a general smooth bounded domain and for another type of nonlinearity $f$, in\nan annular domain $\\Omega$, we establish existence of multiple solutions for\nthe problem $(P)$ that are nonradial and rotationally nonequivalent.\n"}
+{"abstract": "  The representation of functions by artificial neural networks depends on a\nlarge number of parameters in a non-linear fashion. Suitable parameters of\nthese are found by minimizing a 'loss functional', typically by stochastic\ngradient descent (SGD) or an advanced SGD-based algorithm.\n  In a continuous time model for SGD with noise that follows the 'machine\nlearning scaling', we show that in a certain noise regime, the optimization\nalgorithm prefers 'flat' minima of the objective function in a sense which is\ndifferent from the flat minimum selection of continuous time SGD with\nhomogeneous noise.\n"}
+{"abstract": "  This paper establishes the equivalence between Bayesian revealed preference\n(Caplin and Dean, 2015) and classical revealed preference with non-linear\nbudget constraints (Forges and Minelli, 2009). Classical revealed preference\ntests for utility maximization given known budget constraints. Bayesian\nrevealed preference tests for costly information acquisition given a utility\nfunction. Our main result shows that the key theorem in Caplin and Dean (2015)\non Bayesian revealed preference is equivalent to Afriat-type feasibility\ninequalities (Afriat, 1967) for general (non-linear) budget sets. Our second\nresult exploits this connection between classical and Bayesian revealed\npreference to construct a monotone convex information acquisition cost from\ndecision maker's utilities and decisions in Bayesian revealed preference.\nKeywords: Afriat's Theorem, Revealed preference, Bayesian revealed preference,\nCostly Information acquisition, Rational Inattention, Blackwell ordering,\nUtility Theory.\n"}
+{"abstract": "  We consider estimation and control of the cylinder wake at low Reynolds\nnumbers. A particular focus is on the development of efficient numerical\nalgorithms to design optimal linear feedback controllers when there are many\ninputs (disturbances applied everywhere) and many outputs (perturbations\nmeasured everywhere). We propose a resolvent-based iterative algorithm to\nperform i) optimal estimation of the flow using a limited number of sensors;\nand ii) optimal control of the flow when the entire flow is known but only a\nlimited number of actuators are available for control. The method uses\nresolvent analysis to take advantage of the low-rank characteristics of the\ncylinder wake and solutions are obtained without any model-order reduction.\nOptimal feedback controllers are also obtained by combining the solutions of\nthe estimation and control problems. We show that the performance of the\nestimators and controllers converges to the true global optima, indicating that\nthe important physical mechanisms for estimation and control are of low rank.\n"}
+{"abstract": "  Understanding the escape of ionizing (Lyman continuum) photons from galaxies\nis vital for determining how galaxies contributed to reionization in the early\nuniverse. While directly detecting Lyman continuum from high redshift galaxies\nis impossible due to the intergalactic medium, low redshift galaxies in\nprinciple offer this possibility, but requirie observations from space. The\nfirst local galaxy for which Lyman continuum escape was found is Haro11 , a\nluminous blue compact galaxy at z=0.02, where observations with the FUSE\nsatellite revealed an escape fraction of 3.3 %. However the FUSE aperture\ncovers the entire galaxy, and it is not clear from where the Lyman continuum is\nleaking out. Here we utilize HST/COS spectroscopy in the wavelength range\n1100-1700 A of the three knots (A, B, and C) of Haro11 to study the presence of\nLy-$\\alpha$ emission and the properties of intervening gas. We find that all\nknots have bright Ly-$\\alpha$ emission. UV absorption lines, originating in the\nneutral interstellar medium, as well as lines probing the ionized medium, are\nseen extending to blue shifted velocities of 500 km/s in all three knots,\ndemonstrating the presence of an outflowing multiphase medium. We find that\nknots A and B have large covering fractions of neutral gas, making LyC escape\nalong these sightlines improbable, while knot C has a much lower covering\nfraction ($\\lesssim50$%). Knot C also has the the highest Ly-$\\alpha$ escape\nfraction and we conclude that it is the most likely source of the escaping\nLyman continuum detected in Haro11.\n"}
+{"abstract": "  The performance of state-of-the-art neural rankers can deteriorate\nsubstantially when exposed to noisy inputs or applied to a new domain. In this\npaper, we present a novel method for fine-tuning neural rankers that can\nsignificantly improve their robustness to out-of-domain data and query\nperturbations. Specifically, a contrastive loss that compares data points in\nthe representation space is combined with the standard ranking loss during\nfine-tuning. We use relevance labels to denote similar/dissimilar pairs, which\nallows the model to learn the underlying matching semantics across different\nquery-document pairs and leads to improved robustness. In experiments with four\npassage ranking datasets, the proposed contrastive fine-tuning method obtains\nimprovements on robustness to query reformulations, noise perturbations, and\nzero-shot transfer for both BERT and BART based rankers. Additionally, our\nexperiments show that contrastive fine-tuning outperforms data augmentation for\nrobustifying neural rankers.\n"}
+{"abstract": "  We study locally conformally balanced metrics on almost abelian Lie algebras,\nnamely solvable Lie algebras admitting an abelian ideal of codimension one,\nproviding characterizations in every dimension. Moreover, we classify\nsix-dimensional almost abelian Lie algebras admitting locally conformally\nbalanced metrics and study some compatibility results between different types\nof special Hermitian metrics on almost abelian Lie groups and their compact\nquotients. We end by classifying almost abelian Lie algebras admitting locally\nconformally hyperk\\\"ahler structures.\n"}
+{"abstract": "  We show that the homotopy groups of a Moore space $P^n(p^r)$, where $p^r \\neq\n2$, are $\\mathbb{Z}/p^s$-hyperbolic for $s \\leq r$. Combined with work of\nHuang-Wu, Neisendorfer, and Theriault, this completely resolves the question of\nwhen such a Moore space is $\\mathbb{Z}/p^s$-hyperbolic for $p \\geq 5$, or when\n$p=2$ and $r \\geq 6$. We also give a criterion in ordinary homology for a space\nto be $\\mathbb{Z}/p^r$-hyperbolic, and deduce some examples.\n"}
+{"abstract": "  The excessive use of images in social networks, government databases, and\nindustrial applications has posed great privacy risks and raised serious\nconcerns from the public. Even though differential privacy (DP) is a widely\naccepted criterion that can provide a provable privacy guarantee, the\napplication of DP on unstructured data such as images is not trivial due to the\nlack of a clear qualification on the meaningful difference between any two\nimages. In this paper, for the first time, we introduce a novel notion of\nimage-aware differential privacy, referred to as DP-image, that can protect\nuser's personal information in images, from both human and AI adversaries. The\nDP-Image definition is formulated as an extended version of traditional\ndifferential privacy, considering the distance measurements between feature\nspace vectors of images. Then we propose a mechanism to achieve DP-Image by\nadding noise to an image feature vector. Finally, we conduct experiments with a\ncase study on face image privacy. Our results show that the proposed DP-Image\nmethod provides excellent DP protection on images, with a controllable\ndistortion to faces.\n"}
+{"abstract": "  We propose a new approach to constant-roll warm inflation as a generalization\nof constant-roll inflation. Based on the $\\beta$-function formalism, it is\nshown that constant-roll warm inflation models with a natural end fall into\nuniversality classes defined by three different types of $\\beta$-functions,\nunder the assumption that radiation energy density is quasi-stable. Given that\nwarm inflation is completely specified by the $\\beta$-function and dissipation\ncoefficient ratio, we investigate whether or not the inflation can physically\nbe realized for enough number of e-foldings at the background level for some\ncombinations of the $\\beta$-functions and non-trivial dissipation coefficient\nratios.\n"}
+{"abstract": "  Understanding individuals' behavior during hurricane evacuation is of\nparamount importance for local, state, and government agencies hoping to be\nprepared for natural disasters. Complexities involved with human\ndecision-making procedures and lack of data for such disasters are the main\nreasons that make hurricane evacuation studies challenging. In this paper, we\nutilized a large mobile phone Location-Based Services (LBS) data to construct\nthe evacuation pattern during the landfall of Hurricane Irma. By employing our\nproposed framework on more than 11 billion mobile phone location sightings, we\nwere able to capture the evacuation decision of 807,623 smartphone users who\nwere living within the state of Florida. We studied users' evacuation\ndecisions, departure and reentry date distribution, and destination choice. In\naddition to these decisions, we empirically examined the influence of\nevacuation order and low-lying residential areas on individuals' evacuation\ndecisions. Our analysis revealed that 57.92% of people living in mandatory\nevacuation zones evacuated their residences while this ratio was 32.98% and\n33.68% for people living in areas with no evacuation order and voluntary\nevacuation order, respectively. Moreover, our analysis revealed the importance\nof the individuals' mobility behavior in modeling the evacuation decision\nchoice. Historical mobility behavior information such as number of trips taken\nby each individual and the spatial area covered by individuals' location\ntrajectory estimated significant in our choice model and improve the overall\naccuracy of the model significantly.\n"}
+{"abstract": "  We examine the question of the integrability of the recently defined\n$\\mathbb{Z}_2\\times \\mathbb{Z}_2$-graded sine-Gordon model, which is a natural\ngeneralisation of the supersymmetric sine-Gordon equation. We do this via\nappropriate auto-B\\\"{a}cklund transformations, construction of conserved\nspinor-valued currents and a pair of infinite sets of conservation laws.\n"}
+{"abstract": "  DNS over TLS (DoT) and DNS over HTTPS (DoH) promise to improve privacy and\nsecurity of DNS by encrypting DNS messages, especially when messages are padded\nto a uniform size. Firstly, to demonstrate the limitations of recommended\npadding approaches, we present Segram, a novel app fingerprinting attack that\nallows adversaries to infer which mobile apps are executed on a device.\nSecondly, we record traffic traces of 118 Android apps using 10 different\nDoT/DoH resolvers to study the effectiveness of Segram under different\nconditions. According to our results, Segram identifies apps with accuracies of\nup to 72% with padding in a controlled closed world setting. The effectiveness\nof Segram is comparable with state-of-the-art techniques but Segram requires\nless computational effort. We release our datasets and code. Thirdly, we study\nthe prevalence of padding among privacy-focused DoT/DoH resolvers, finding that\nup to 81% of our sample fail to enable padding. Our results suggest that\nrecommended padding approaches are less effective than expected and that\nresolver operators are not sufficiently aware about this feature.\n"}
+{"abstract": "  BG Ind is a well studied, bright, nearby binary consisting of a pair of F\nstars in a 1.46-day orbit. We have discovered in the TESS lightcurve for TIC\n229804573 (aka BG Ind) a second eclipsing binary in the system with a 0.53-day.\nOur subsequent analyses of the recent TESS and archival ground-based\nphotometric and radial velocity data, reveal that the two binaries are\ngravitationally bound in a 721-day period, moderately eccentric orbit. We\npresent the results of a joint spectro-photodynamical analysis of the eclipse\ntiming variation curves of both binaries based on TESS and ground-based\narchival data, the TESS lightcurve, archival radial velocity data and the\nspectral energy distribution, coupled with the use of PARSEC stellar\nisochrones. We confirm prior studies of BG Ind which found that the brighter\nbinary A consists of slightly evolved F-type stars with refined masses of 1.32\nand 1.43 $M_\\odot$, and radii of 1.59 and 2.34 $R_\\odot$. The previously\nunknown binary B has two less massive stars of 0.69 and 0.64 $M_\\odot$ and\nradii of 0.64 and 0.61 $R_\\odot$. Based on a number of different arguments\nwhich we discuss, we conclude that the three orbital planes are likely aligned\nto within 17$^\\circ$.\n"}
+{"abstract": "  We propose an unsupervised approach for learning end-to-end reconstruction\noperators for ill-posed inverse problems. The proposed method combines the\nclassical variational framework with iterative unrolling, which essentially\nseeks to minimize a weighted combination of the expected distortion in the\nmeasurement space and the Wasserstein-1 distance between the distributions of\nthe reconstruction and ground-truth. More specifically, the regularizer in the\nvariational setting is parametrized by a deep neural network and learned\nsimultaneously with the unrolled reconstruction operator. The variational\nproblem is then initialized with the reconstruction of the unrolled operator\nand solved iteratively till convergence. Notably, it takes significantly fewer\niterations to converge, thanks to the excellent initialization obtained via the\nunrolled operator. The resulting approach combines the computational efficiency\nof end-to-end unrolled reconstruction with the well-posedness and\nnoise-stability guarantees of the variational setting. Moreover, we demonstrate\nwith the example of X-ray computed tomography (CT) that our approach\noutperforms state-of-the-art unsupervised methods, and that it outperforms or\nis on par with state-of-the-art supervised learned reconstruction approaches.\n"}
+{"abstract": "  This paper investigates performance attribution measures as a basis for\nconstraining portfolio optimization. We employ optimizations that minimize\nexpected tail loss and investigate both asset allocation (AA) and the selection\neffect (SE) as hard constraints on asset weights. The test portfolio consists\nof stocks from the Dow Jones Industrial Average index; the benchmark is an\nequi-weighted portfolio of the same stocks. Performance of the optimized\nportfolios is judged using comparisons of cumulative price and the\nrisk-measures maximum drawdown, Sharpe ratio, and Rachev ratio. The results\nsuggest a positive role in price and risk-measure performance for the\nimposition of constraints on AA and SE, with SE constraints producing the\nlarger performance enhancement.\n"}
+{"abstract": "  We consider smooth solutions of the Burgers-Hilbert equation that are a small\nperturbation $\\delta$ from a global periodic traveling wave with small\namplitude $\\epsilon$. We use a modified energy method to prove the existence\ntime of smooth solutions on a time scale of $\\frac{1}{\\epsilon\\delta}$ with\n$0<\\delta\\ll\\epsilon\\ll1$ and on a time scale of $\\frac{\\epsilon}{\\delta^2}$\nwith $0<\\delta\\ll\\epsilon^2\\ll1$. Moreover, we show that the traveling wave\nexists for an amplitude $\\epsilon$ in the range $(0,\\epsilon^*)$ with\n$\\epsilon^*\\sim 0.29$ and fails to exist for $\\epsilon>\\frac{2}{e}$.\n"}
+{"abstract": "  Recently, denoising diffusion probabilistic models and generative score\nmatching have shown high potential in modelling complex data distributions\nwhile stochastic calculus has provided a unified point of view on these\ntechniques allowing for flexible inference schemes. In this paper we introduce\nGrad-TTS, a novel text-to-speech model with score-based decoder producing\nmel-spectrograms by gradually transforming noise predicted by encoder and\naligned with text input by means of Monotonic Alignment Search. The framework\nof stochastic differential equations helps us to generalize conventional\ndiffusion probabilistic models to the case of reconstructing data from noise\nwith different parameters and allows to make this reconstruction flexible by\nexplicitly controlling trade-off between sound quality and inference speed.\nSubjective human evaluation shows that Grad-TTS is competitive with\nstate-of-the-art text-to-speech approaches in terms of Mean Opinion Score. We\nwill make the code publicly available shortly.\n"}
+{"abstract": "  COVID-19 patient triaging with predictive outcome of the patients upon first\npresent to emergency department (ED) is crucial for improving patient\nprognosis, as well as better hospital resources management and cross-infection\ncontrol. We trained a deep feature fusion model to predict patient outcomes,\nwhere the model inputs were EHR data including demographic information,\nco-morbidities, vital signs and laboratory measurements, plus patient's CXR\nimages. The model output was patient outcomes defined as the most insensitive\noxygen therapy required. For patients without CXR images, we employed Random\nForest method for the prediction. Predictive risk scores for COVID-19 severe\noutcomes (\"CO-RISK\" score) were derived from model output and evaluated on the\ntesting dataset, as well as compared to human performance. The study's dataset\n(the \"MGB COVID Cohort\") was constructed from all patients presenting to the\nMass General Brigham (MGB) healthcare system from March 1st to June 1st, 2020.\nED visits with incomplete or erroneous data were excluded. Patients with no\ntest order for COVID or confirmed negative test results were excluded. Patients\nunder the age of 15 were also excluded. Finally, electronic health record (EHR)\ndata from a total of 11060 COVID-19 confirmed or suspected patients were used\nin this study. Chest X-ray (CXR) images were also collected from each patient\nif available. Results show that CO-RISK score achieved area under the Curve\n(AUC) of predicting MV/death (i.e. severe outcomes) in 24 hours of 0.95, and\n0.92 in 72 hours on the testing dataset. The model shows superior performance\nto the commonly used risk scores in ED (CURB-65 and MEWS). Comparing with\nphysician's decisions, CO-RISK score has demonstrated superior performance to\nhuman in making ICU/floor decisions.\n"}
+{"abstract": "  We present a new approach ARLPCG: Adversarial Reinforcement Learning for\nProcedural Content Generation, which procedurally generates and tests\npreviously unseen environments with an auxiliary input as a control variable.\nTraining RL agents over novel environments is a notoriously difficult task. One\npopular approach is to procedurally generate different environments to increase\nthe generalizability of the trained agents. ARLPCG instead deploys an\nadversarial model with one PCG RL agent (called Generator) and one solving RL\nagent (called Solver). The Generator receives a reward signal based on the\nSolver's performance, which encourages the environment design to be challenging\nbut not impossible. To further drive diversity and control of the environment\ngeneration, we propose using auxiliary inputs for the Generator. The benefit is\ntwo-fold: Firstly, the Solver achieves better generalization through the\nGenerator's generated challenges. Secondly, the trained Generator can be used\nas a creator of novel environments that, together with the Solver, can be shown\nto be solvable. We create two types of 3D environments to validate our model,\nrepresenting two popular game genres: a third-person platformer and a racing\ngame. In these cases, we shows that ARLPCG has a significantly better solve\nratio, and that the auxiliary inputs renders the levels creation controllable\nto a certain degree. For a video compilation of the results please visit\nhttps://youtu.be/z7q2PtVsT0I.\n"}
+{"abstract": "  In this paper, we study the regulation of the Ground Contact Forces (GRF) in\nthruster-assisted legged locomotion. We will employ Reference Governors (RGs)\nfor enforcing GRF constraints in Harpy model which is a bipedal robot that is\nbeing developed at Northeastern University. Optimization-based methods and\nwhole body control are widely used for enforcing the no-slip constraints in\nlegged locomotion which can be very computationally expensive. In contrast, RGs\ncan enforce these constraints by manipulating joint reference trajectories\nusing Lyapunov stability arguments which can be computed much faster. The\naddition of the thrusters in our model allows to manipulate the gait parameters\nand the GRF without sacrificing the locomotion stability.\n"}
+{"abstract": "  In many real-world applications such as business planning and sensor data\nmonitoring, one important, yet challenging, the task is to rank objects(e.g.,\nproducts, documents, or spatial objects) based on their ranking scores and\nefficiently return those objects with the highest scores. In practice, due to\nthe unreliability of data sources, many real-world objects often contain noises\nand are thus imprecise and uncertain. In this paper, we study the problem of\nprobabilistic top-k dominating(PTD) query on such large-scale uncertain data in\na distributed environment, which retrieves k uncertain objects from distributed\nuncertain databases(on multiple distributed servers), having the largest\nranking scores with high confidences. In order to efficiently tackle the\ndistributed PTD problem, we propose a MapReduce framework for processing\ndistributed PTD queries over distributed uncertain databases. In this MapReduce\nframework, we design effective pruning strategies to filter out false alarms in\nthe distributed setting, propose cost-model-based index distribution mechanisms\nover servers, and develop efficient distributed PTD query processing\nalgorithms. Extensive experiments have demonstrated the efficiency and\neffectiveness of our proposed distributed PTD approach on both real and\nsynthetic data sets through various experimental settings.\n"}
+{"abstract": "  Devising domain- and model-agnostic evaluation metrics for generative models\nis an important and as yet unresolved problem. Most existing metrics, which\nwere tailored solely to the image synthesis setup, exhibit a limited capacity\nfor diagnosing the different modes of failure of generative models across\nbroader application domains. In this paper, we introduce a 3-dimensional\nevaluation metric, ($\\alpha$-Precision, $\\beta$-Recall, Authenticity), that\ncharacterizes the fidelity, diversity and generalization performance of any\ngenerative model in a domain-agnostic fashion. Our metric unifies statistical\ndivergence measures with precision-recall analysis, enabling sample- and\ndistribution-level diagnoses of model fidelity and diversity. We introduce\ngeneralization as an additional, independent dimension (to the\nfidelity-diversity trade-off) that quantifies the extent to which a model\ncopies training data -- a crucial performance indicator when modeling sensitive\ndata with requirements on privacy. The three metric components correspond to\n(interpretable) probabilistic quantities, and are estimated via sample-level\nbinary classification. The sample-level nature of our metric inspires a novel\nuse case which we call model auditing, wherein we judge the quality of\nindividual samples generated by a (black-box) model, discarding low-quality\nsamples and hence improving the overall model performance in a post-hoc manner.\n"}
+{"abstract": "  Summary: R and Matlab are two high-level scientific programming languages\nwhich are frequently applied in computational biology. To extend the wide\nvariety of available and approved implementations, we present the Rcall\ninterface which runs in MATLAB and provides direct access to methods and\nsoftware packages implemented in R. Rcall involves passing the relevant data to\nR, executing the specified R commands and forwarding the results to MATLAB for\nfurther use. The evaluation and conversion of the basic data types in R and\nMATLAB are provided. Due to the easy embedding of R facilities, Rcall greatly\nextends the functionality of the MATLAB programming language.\n  Availability: Source code is freely available at\nhttps://github.com/kreutz-lab/Rcall, implemented in MATLAB and supported on\nLinux, MS Windows and Mac OS X.\n"}
+{"abstract": "  Random projections reduce the dimension of a set of vectors while preserving\nstructural information, such as distances between vectors in the set. This\npaper proposes a novel use of row-product random matrices in random projection,\nwhere we call it Tensor Random Projection (TRP). It requires substantially less\nmemory than existing dimension reduction maps. The TRP map is formed as the\nKhatri-Rao product of several smaller random projections, and is compatible\nwith any base random projection including sparse maps, which enable dimension\nreduction with very low query cost and no floating point operations. We also\ndevelop a reduced variance extension. We provide a theoretical analysis of the\nbias and variance of the TRP, and a non-asymptotic error analysis for a TRP\ncomposed of two smaller maps. Experiments on both synthetic and MNIST data show\nthat our method performs as well as conventional methods with substantially\nless storage.\n"}
+{"abstract": "  Motivated by the issue of inaccurate channel state information (CSI) at the\nbase station (BS), which is commonly due to feedback/processing delays and\ncompression problems, in this paper, we introduce a scalable idea of adopting\nartificial intelligence (AI) aided CSI acquisition. The proposed scheme\nenhances the CSI compression, which is done at the mobile terminal (MT), along\nwith accurate recovery of estimated CSI at the BS. Simulation-based results\ncorroborate the validity of the proposed scheme. Numerically, nearly 100\\%\nrecovery of the estimated CSI is observed with relatively lower overhead than\nthe benchmark scheme. The proposed idea can bring potential benefits in the\nwireless communication environment, e.g., ultra-reliable and low latency\ncommunication (URLLC), where imperfect CSI and overhead is intolerable.\n"}
+{"abstract": "  This paper aims at proposing a sufficient matrix inequality condition to\ncarry out the global exponential stability of the wave equation under an\nevent-triggering mechanism that updates a damping source term. The damping is\ndistributed in the whole space but sampled in time. The wellposedness of the\nclosed-loop event-triggered control system is shown. Furthermore, the avoidance\nof Zeno behavior is ensured provided that the initial data are more regular.\nThe interest of the results is drawn through some numerical simulations.\n"}
+{"abstract": "  Discovery of high-temperature superconductivity in hydrogen-rich compounds\nhas fuelled the enthusiasm for finding materials with more promising\nsuperconducting properties among hydrides. However, the ultrahigh pressure\nneeded to synthesize and maintain high-temperature hydrogen-rich\nsuperconductors hinders the experimental investigation of these materials. For\npractical applications, it is also highly desired to find more hydrogen-rich\nmaterials that superconduct at high temperatures but under relatively lower\npressures. Based on first-principles density functional theory, we calculate\nthe electronic and phonon band structures for a ternary borohydride formed by\nintercalating BH$_4$ tetrahedrons into a face-centered-cubic potassium lattice,\nKB$_2$H$_8$. Remarkably, we find that this material is dynamically stable and\none of its $sp^3$-hybridized $\\sigma$-bonding bands is metallized (i.e.\npartially filled) above a moderate high pressure. This metallized\n$\\sigma$-bonding band couples strongly with phonons, giving rise to a strong\nsuperconducting pairing potential. By solving the anisotropic Eliashberg\nequations, we predict that the superconducting transition temperature of this\ncompound is 134-146 K around 12 GPa.\n"}
+{"abstract": "  Deep neural networks are playing an important role in many real-life\napplications. After being trained with abundant data and computing resources, a\ndeep neural network model providing service is endowed with economic value. An\nimportant prerequisite in commercializing and protecting deep neural networks\nis the reliable identification of their genuine author. To meet this goal,\nwatermarking schemes that embed the author's identity information into the\nnetworks have been proposed. However, current schemes can hardly meet all the\nnecessary requirements for securely proving the authorship and mostly focus on\nmodels for classification. To explicitly meet the formal definitions of the\nsecurity requirements and increase the applicability of deep neural network\nwatermarking schemes, we propose a new framework based on multi-task learning.\nBy treating the watermark embedding as an extra task, most of the security\nrequirements are explicitly formulated and met with well-designed regularizers,\nthe rest is guaranteed by using components from cryptography. Moreover, a\ndecentralized verification protocol is proposed to standardize the ownership\nverification. The experiment results show that the proposed scheme is flexible,\nsecure, and robust, hence a promising candidate in deep learning model\nprotection.\n"}
+{"abstract": "  The effects of Poissonian resetting at a constant rate $r$ on the reaction\ntime between a Brownian particle and a stochastically gated target are studied.\nThe target switches between a reactive state and a non-reactive one. We\ncalculate the mean time at which the particle subject to resetting hits the\ntarget for the first time, while the latter is in the reactive state. The\nsearch time is minimum at a resetting rate that depends on the target\ntransition rates. When the target relaxation rate is much larger than both the\nresetting rate and the inverse diffusion time, the system becomes equivalent to\na partially absorbing boundary problem. In other cases, however, the optimal\nresetting rate can be a non-monotonic function of the target rates, a feature\nnot observed in partial absorption.\n  We compute the relative fluctuations of the first hitting time around its\nmean and compare our results with the ungated case. The usual universal\nbehavior of these fluctuations for resetting processes at their optimum breaks\ndown due to the target internal dynamics.\n"}
+{"abstract": "  There is always a natural embedding of $S_s\\wr S_k$ into the linear\nprogramming (LP) relaxation permutation symmetry group of an orthogonal array\ninteger linear programming (ILP) formulation with equality constraints.\n  The point of this paper is to prove that in the $2$ level, strength $1$ case\nthe LP relaxation permutation symmetry group of this formulation is isomorphic\nto $S_2\\wr S_k$ for all $k$, and in the $2$ level, strength $2$ case it is\nisomorphic to $S_2^k\\rtimes S_{k+1}$ for $k\\geq 4$. The strength $2$ result\nreveals previously unknown permutation symmetries that can not be captured by\nthe natural embedding of $S_2\\wr S_k$. We also conjecture a complete\ncharacterization of the LP relaxation permutation symmetry group of the ILP\nformulation.\n"}
+{"abstract": "  Phase-change memory devices have found applications in in-memory computing\nwhere the physical attributes of these devices are exploited to compute in\nplace without the need to shuttle data between memory and processing units.\nHowever, non-idealities such as temporal variations in the electrical\nresistance have a detrimental impact on the achievable computational precision.\nTo address this, a promising approach is projecting the phase configuration of\nphase change material onto some stable element within the device. Here we\ninvestigate the projection mechanism in a prominent phase-change memory device\narchitecture, namely mushroom-type phase-change memory. Using nanoscale\nprojected Ge2Sb2Te5 devices we study the key attributes of state-dependent\nresistance, drift coefficients, and phase configurations, and using them reveal\nhow these devices fundamentally work.\n"}
+{"abstract": "  The widespread application of modern machine learning has increased the need\nfor robust statistical algorithms. This work studies one such fundamental\nstatistical measure known as the Tukey depth. We study the problem in the\ncontinuum (population) limit. In particular, we derive the associated necessary\nconditions, which take the form of a first-order partial differential equation.\nWe discuss the classical interpretation of this necessary condition as the\nviscosity solution of a Hamilton-Jacobi equation, but with a non-classical\nHamiltonian with discontinuous dependence on the gradient at zero. We prove\nthat this equation possesses a unique viscosity solution and that this solution\nalways bounds the Tukey depth from below. In certain cases, we prove that the\nTukey depth is equal to the viscosity solution, and we give some illustrations\nof standard numerical methods from the optimal control community which deal\ndirectly with the partial differential equation. We conclude by outlining\nseveral promising research directions both in terms of new numerical algorithms\nand theoretical challenges.\n"}
+{"abstract": "  Code reuse is an important part of software development. The adoption of code\nreuse practices is especially noticeable in Node.js. The Node.js package\nmanager, NPM, contains over 1 Million packages and developers often seek out\npackages to solve programming tasks. Due to the vast number of packages,\nselecting the right package is difficult and time consuming. With the goal of\nimproving productivity of developers that heavily reuse code through\nthird-party packages, we present Node Code Query(NCQ), a custom Read-Eval-Print\nLoop environment that allows developers to 1) search for NPM packages using\nnatural language queries, 2) search for code snippets related to those\npackages, 3) quickly setup new environments for testing those snippets, and 4)\ntransition between search and editing modes. Our user study shows that\nparticipants began programming faster and concluded tasks faster with NCQ than\nwith a baseline approach, and that they liked, among other features, the search\nfor code snippets and packages. Our results suggest that NCQ makes Node.js\ndevelopers more efficient in reusing code.\n"}
+{"abstract": "  Unmanned Aerial Vehicles (UAVs) have recently attracted significant attention\ndue to their outstanding ability to be used in different sectors and serve in\ndifficult and dangerous areas. Moreover, the advancements in computer vision\nand artificial intelligence have increased the use of UAVs in various\napplications and solutions, such as forest fires detection and borders\nmonitoring. However, using deep neural networks (DNNs) with UAVs introduces\nseveral challenges of processing deeper networks and complex models, which\nrestricts their on-board computation. In this work, we present a strategy\naiming at distributing inference requests to a swarm of resource-constrained\nUAVs that classifies captured images on-board and finds the minimum\ndecision-making latency. We formulate the model as an optimization problem that\nminimizes the latency between acquiring images and making the final decisions.\nThe formulated optimization solution is an NP-hard problem. Hence it is not\nadequate for online resource allocation. Therefore, we introduce an online\nheuristic solution, namely DistInference, to find the layers placement strategy\nthat gives the best latency among the available UAVs. The proposed approach is\ngeneral enough to be used for different low decision-latency applications as\nwell as for all CNN types organized into the pipeline of layers (e.g., VGG) or\nbased on residual blocks (e.g., ResNet).\n"}
+{"abstract": "  We show that if a measure of dimension $s$ on $\\mathbb{R}^d$ admits $(p,q)$\nFourier restriction for some endpoint exponents allowed by its dimension,\nnamely $q=\\tfrac{s}{d}p'$ for some $p>1$, then it is either absolutely\ncontinuous or $1$-purely unrectifiable.\n"}
+{"abstract": "  Semantic segmentation models only perform well on the domain they are trained\non and datasets for training are scarce and often have a small label-spaces,\nbecause the pixel level annotations required are expensive to make. Thus\ntraining models on multiple existing domains is desired to increase the output\nlabel-space. Current research shows that there is potential to improve accuracy\nacross datasets by using multi-domain training, but this has not yet been\nsuccessfully extended to datasets of three different non-overlapping domains\nwithout manual labelling. In this paper a method for this is proposed for the\ndatasets Cityscapes, SUIM and SUN RGB-D, by creating a label-space that spans\nall classes of the datasets. Duplicate classes are merged and discrepant\ngranularity is solved by keeping classes separate. Results show that accuracy\nof the multi-domain model has higher accuracy than all baseline models\ntogether, if hardware performance is equalized, as resources are not limitless,\nshowing that models benefit from additional data even from domains that have\nnothing in common.\n"}
+{"abstract": "  Effective communication is an important skill for enabling information\nexchange in multi-agent settings and emergent communication is now a vibrant\nfield of research, with common settings involving discrete cheap-talk channels.\nSince, by definition, these settings involve arbitrary encoding of information,\ntypically they do not allow for the learned protocols to generalize beyond\ntraining partners. In contrast, in this work, we present a novel problem\nsetting and the Quasi-Equivalence Discovery (QED) algorithm that allows for\nzero-shot coordination (ZSC), i.e., discovering protocols that can generalize\nto independently trained agents. Real world problem settings often contain\ncostly communication channels, e.g., robots have to physically move their\nlimbs, and a non-uniform distribution over intents. We show that these two\nfactors lead to unique optimal ZSC policies in referential games, where agents\nuse the energy cost of the messages to communicate intent. Other-Play was\nrecently introduced for learning optimal ZSC policies, but requires prior\naccess to the symmetries of the problem. Instead, QED can iteratively discovers\nthe symmetries in this setting and converges to the optimal ZSC policy.\n"}
+{"abstract": "  When to initiate treatment on patients is an important problem in many\nmedical studies such as AIDS and cancer. In this article, we formulate the\ntreatment initiation time problem for time-to-event data and propose an optimal\nindividualized regime that determines the best treatment initiation time for\nindividual patients based on their characteristics. Different from existing\noptimal treatment regimes where treatments are undertaken at a pre-specified\ntime, here new challenges arise from the complicated missing mechanisms in\ntreatment initiation time data and the continuous treatment rule in terms of\ninitiation time. To tackle these challenges, we propose to use restricted mean\nresidual lifetime as a value function to evaluate the performance of different\ntreatment initiation regimes, and develop a nonparametric estimator for the\nvalue function, which is consistent even when treatment initiation times are\nnot completely observable and their distribution is unknown. We also establish\nthe asymptotic properties of the resulting estimator in the decision rule and\nits associated value function estimator. In particular, the asymptotic\ndistribution of the estimated value function is nonstandard, which follows a\nweighted chi-squared distribution. The finite-sample performance of the\nproposed method is evaluated by simulation studies and is further illustrated\nwith an application to a breast cancer data.\n"}
+{"abstract": "  We propose various methods for combining or amalgamating propositional\nlanguages and deductive systems. We make heavy use of quantales and quantale\nmodules in the wake of previous works by the present and other authors. We also\ndescribe quite extensively the relationships among the algebraic and\norder-theoretic constructions and the corresponding ones based on a purely\nlogical approach.\n"}
+{"abstract": "  Inspired by the latest measurement of muon g-2 by the Fermilab Experiment, we\nrevisit a class of supersymmetric models in which non-universality at M_GUT\nallows us to realize relatively light sleptons in the few hundred GeV range.\nThese sleptons provide additional contributions to muon g-2 that can be\narranged to reconcile theory and experiment. The solutions compatible with the\nmuon g-2 resolution typically predict light sleptons, charginos and\nneutralinos. We show how these solutions can be probed during LHC-Run3. A\ndirect impact on the chargino mass is observed such that the chargino can be\nprobed up to about 800 GeV during the Run3 experiments. Despite such a direct\nimpact, it is still possible to realize lighter chargino masses which can\nescape detection due to the chirality mixture of the lighter slepton states.\nThe colored squarks as well as the gluino turn out to be heavier than about 3-4\nTeV if the LSP neutralino satisfies the Planck bound on the dark matter relic\nabundance. We highlight a variety of benchmark points and, in particular,\ncoannihilation scenarios with dark matter candidates that will be tested in the\nongoing and planned direct and indirect detection experiments. By relaxing the\nrequirement that the LSP neutralino saturates the relic dark matter abundance,\nwe are able to find solutions with gluino and squark masses in a range that may\nbe accessible at LHC-Run3.\n"}
+{"abstract": "  The goal of the present paper is to study the viscoelastic wave equation with\nthe time delay \\[ |u_t|^\\rho u_{tt}-\\Delta u-\\Delta u_{tt}+\\int_0^tg(t-s)\\Delta\nu(s)ds+\\mu_1u_t(x,t)+\\mu_2 u_t(x,t-\\tau)=b|u|^{p-2}u\\] under initial boundary\nvalue conditions, where $\\rho,~b,~\\mu_1$ are positive constants, $\\mu_2$ is a\nreal number, $\\tau>0$ represents the time delay. By using the multiplier method\ntogether with some properties of the convex functions, the explicit and general\nstability results of energy are proved under the general assumption on the\nrelaxation function $g$. This work generalizes and improves earlier results on\nthe stability of the viscoelastic equations with the time delay in the\nliterature.\n"}
+{"abstract": "  We show that in large enough rank, the Gromov boundary of the free factor\ncomplex is path connected and locally path connected.\n"}
+{"abstract": "  We study numerically the nonlinear stability of {\\it excited} fermion-boson\nstars in spherical symmetry. Such compound hypothetical stars, composed by\nfermions and bosons, are gravitationally bound, regular, and static\nconfigurations described within the coupled Einstein-Klein-Gordon-Euler\ntheoretical framework. The excited configurations are characterized by the\npresence in the radial profile of the (complex, massive) scalar field -- the\nbosonic piece -- of at least one node across the star. The dynamical emergence\nof one such configuration from the accretion of a cloud of scalar field onto an\nalready-formed neutron star, was numerically revealed in our previous\ninvestigation. Prompted by that finding we construct here equilibrium\nconfigurations of excited fermion-boson stars and study their stability\nproperties using numerical-relativity simulations. In addition, we also analyze\ntheir dynamical formation from generic, constraint-satisfying initial data.\nContrary to purely boson stars in the excited state, which are known to be\ngenerically unstable, our study reveals the appearance of a cooperative\nstabilization mechanism between the fermionic and bosonic constituents of those\nexcited-state mixed stars. While similar examples of stabilization mechanisms\nhave been recently discussed in the context of $\\ell-$boson stars and\nmulti-field, multi-frequency boson stars, our results seem to indicate that the\nstabilization mechanism is a purely gravitational effect and does not depend on\nthe type of matter of the companion star.\n"}
+{"abstract": "  The Labastida-Marin\\~o-Ooguri-Vafa (LMOV) invariants are the open string BPS\ninvariants which are expected to be integers based on the string duality\nconjecture from M-theory. Several explicit formulae of LMOV invariants for\nframed unknot have been obtained in the literature. In this paper, we present a\nunified method to deal with the integrality of such explicit formulae.\nFurthermore, we also prove the integrality of certain LMOV invariants for\nframed unknot in higher genera.\n"}
+{"abstract": "  For reliable environment perception, the use of temporal information is\nessential in some situations. Especially for object detection, sometimes a\nsituation can only be understood in the right perspective through temporal\ninformation. Since image-based object detectors are currently based almost\nexclusively on CNN architectures, an extension of their feature extraction with\ntemporal features seems promising.\n  Within this work we investigate different architectural components for a\nCNN-based temporal information extraction. We present a Temporal Feature\nNetwork which is based on the insights gained from our architectural\ninvestigations. This network is trained from scratch without any ImageNet\ninformation based pre-training as these images are not available with temporal\ninformation. The object detector based on this network is evaluated against the\nnon-temporal counterpart as baseline and achieves competitive results in an\nevaluation on the KITTI object detection dataset.\n"}
+{"abstract": "  This work investigates the structure of rank-metric codes in connection with\nconcepts from finite geometry, most notably the $q$-analogues of projective\nsystems and blocking sets. We also illustrate how to associate a classical\nHamming-metric code to a rank-metric one, in such a way that various\nrank-metric properties naturally translate into the homonymous Hamming-metric\nnotions under this correspondence. The most interesting applications of our\nresults lie in the theory of minimal rank-metric codes, which we introduce and\nstudy from several angles. Our main contributions are bounds for the parameters\nof a minimal rank-metric codes, a general existence result based on a\ncombinatorial argument, and an explicit code construction for some parameter\nsets that uses the notion of a scattered linear set. Throughout the paper we\nalso show and comment on curious analogies/divergences between the theories of\nerror-correcting codes in the rank and in the Hamming metric.\n"}
+{"abstract": "  This paper is concerned with the regularization of large-scale discrete\ninverse problems by means of inexact Krylov methods. Specifically, we derive\ntwo new inexact Krylov methods that can be efficiently applied to unregularized\nor Tikhonov-regularized least squares problems, and we study their theoretical\nproperties, including links with their exact counterparts and strategies to\nmonitor the amount of inexactness. We then apply the new methods to separable\nnonlinear inverse problems arising in blind deblurring. In this setting\ninexactness stems from the uncertainty in the parameters defining the blur,\nwhich may be recovered using a variable projection method leading to an\ninner-outer iteration scheme (i.e., one cycle of inner iterations is performed\nto solve one linear deblurring subproblem for any intermediate values of the\nblurring parameters computed by a nonlinear least squares solver). The new\ninexact solvers can naturally handle varying inexact blurring parameters while\nsolving the linear deblurring subproblems, allowing for a much reduced number\nof total iterations and substantial computational savings with respect to their\nexact counterparts.\n"}
+{"abstract": "  We investigate the Josephson current through a suspended carbon nanotube\ndouble quantum dot which, at sufficiently low temperatures, is characterized by\nthe ground state of the electronic subsystem. Depending on parameters like a\nmagnetic field or the inter-dot coupling, the ground state can either be a\ncurrent-carrying singlet or doublet, or a blockaded triplet state. Since the\nelectron-vibration interaction has been demonstrated to be electrostatically\ntuneable, we study in particular its effect on the current-phase relation. We\nshow that the coupling to the vibration mode can lift the current-suppressing\ntriplet blockade by inducing a quantum phase transition to a ground state of a\ndifferent total spin. Our key finding is the development of a triple point in\nthe Josephson current parameterized by the resonator coupling and the Josephson\nphase. The quantum phase transitions around the triple point are directly\naccessible through the critical current and resilient to moderately finite\ntemperatures. The proposed setup makes the mechanical degree of freedom part of\na superconducting hybrid device which is interesting for ultra-sensitive\ndisplacement detectors.\n"}
+{"abstract": "  Distinguishing classes within substellar objects and understanding their\nformation and evolution need larger samples of substellar companions such as\nexoplanets, brown dwarfs, and low-mass stars. In this paper, we look for\nsubstellar companions using radial velocity surveys of FGK stars with the\nSOPHIE spectrograph at the Observatoire de Haute-Provence. We assign here the\nradial velocity variations of 27 stars to their orbital motion induced by\nlow-mass companions. We also constrained their plane-of-the-sky motion using\nHIPPARCOS and Gaia Data Release 1 measurements, which constrain the true masses\nof some of these companions. We report the detection and characterization of\nsix cool Jupiters, three brown dwarf candidates, and 16 low-mass stellar\ncompanions. We additionally update the orbital parameters of the low-mass star\nHD 8291 B, and we conclude that the radial velocity variations of HD 204277 are\nlikely due to stellar activity despite resembling the signal of a giant planet.\nOne of the new giant planets, BD+631405 b, adds to the population of highly\neccentric cool Jupiters, and it is presently the most massive member. Two of\nthe cool Jupiter systems also exhibit signatures of an additional outer\ncompanion. The orbital periods of the new companions span 30 days to 11.5\nyears, their masses 0.72 Jupiter mass to 0.61 Solar mass, and their\neccentricities 0.04 to 0.88. These discoveries probe the diversity of\nsubstellar objects and low-mass stars, which will help constrain the models of\ntheir formation and evolution.\n"}
+{"abstract": "  The goal of Multi-task Bayesian Optimization (MBO) is to minimize the number\nof queries required to accurately optimize a target black-box function, given\naccess to offline evaluations of other auxiliary functions. When offline\ndatasets are large, the scalability of prior approaches comes at the expense of\nexpressivity and inference quality. We propose JUMBO, an MBO algorithm that\nsidesteps these limitations by querying additional data based on a combination\nof acquisition signals derived from training two Gaussian Processes (GP): a\ncold-GP operating directly in the input domain and a warm-GP that operates in\nthe feature space of a deep neural network pretrained using the offline data.\nSuch a decomposition can dynamically control the reliability of information\nderived from the online and offline data and the use of pretrained neural\nnetworks permits scalability to large offline datasets. Theoretically, we\nderive regret bounds for JUMBO and show that it achieves no-regret under\nconditions analogous to GP-UCB (Srinivas et. al. 2010). Empirically, we\ndemonstrate significant performance improvements over existing approaches on\ntwo real-world optimization problems: hyper-parameter optimization and\nautomated circuit design.\n"}
+{"abstract": "  Liquid-liquid phase separation is emerging as a crucial phenomenon in several\nfundamental cell processes. A range of eukaryotic systems exhibit liquid\ncondensates. However, their function in bacteria, which in general lack\nmembrane-bound compartments, remains less clear. Here, we used high-resolution\noptical microscopy to observe single bacterial aggresomes, nanostructured\nintracellular assemblies of proteins, to undercover their role in cell stress.\nWe find that proteins inside aggresomes are mobile and undergo dynamic\nturnover, consistent with a liquid state. Our observations are in quantitative\nagreement with phase-separated liquid droplet formation driven by interacting\nproteins under thermal equilibrium that nucleate following diffusive collisions\nin the cytoplasm. We have discovered aggresomes in multiple species of\nbacteria, and show that these emergent, metastable liquid-structured protein\nassemblies increase bacterial fitness by enabling cells to tolerate\nenvironmental stresses.\n"}
+{"abstract": "  In this paper we compare the notion of stochastic two-scale convergence in\nthe mean (by Bourgeat, Mikeli\\'c and Wright), the notion of stochastic\nunfolding (recently introduced by the authors), and the quenched notion of\nstochastic two-scale convergence (by Zhikov and Pyatnitskii). In particular, we\nintroduce stochastic two-scale Young measures as a tool to compare mean and\nquenched limits. Moreover, we discuss two examples, which can be naturally\nanalyzed via stochastic unfolding, but which cannot be treated via quenched\nstochastic two-scale convergence.\n"}
+{"abstract": "  Let $n$ and $a$ be positive integers such that $2\\leq a\\leq \\frac{n}{2}$. In\nthis short note, we compute for the exact value of the distance spectral\nradius, vertex-forwarding index, and some distance-based topological indices of\nthe complement of circulant networks $C_n(1,a)$ and\n$C_{m^h}(1,m,m^2,\\ldots,m^{h-1})$. For $a\\neq \\frac{n}{2}$, the circulant\n$C_n(1,a)$ is called a double loop network while the circulant\n$C_{m^h}(1,m,m^2,\\ldots,m^{h-1})$ is called the multiplicative circulant\nnetwork on $m^h$ vertices.\n"}
+{"abstract": "  Effective postprandial glucose control is important to glucose management for\nsubjects with diabetes mellitus. In this work, a data-driven meal bolus\ndecision method is proposed without the need of subject-specific glucose\nmanagement parameters. The postprandial glucose dynamics is learnt using\nGaussian process regression. Considering the asymmetric risks of hyper- and\nhypoglycemia and the uncertainties in the predicted glucose trajectories, an\nasymmetric risk-sensitive cost function is designed. Bayesian optimization is\nutilized to solve the optimization problem, since the gradient of the cost\nfunction is unavailable. The proposed approach is evaluated using the 10-adult\ncohort of the FDA-accepted UVA/Padova T1DM simulator and compared with the\nstandard insulin bolus calculator. For the case of announced meals, the\nproposed method achieves satisfactory and similar performance in terms of mean\nglucose and percentage time in [70, 180] mg/dL without increasing the risk of\nhypoglycemia. Similar results are observed for the case without the meal\ninformation (assuming that the patient follows a consistent diet) and the case\nof basal rate mismatches. In addition, advisory-mode analysis is performed\nbased on clinical data, which indicates that the method can determine safe and\nreasonable meal boluses in real clinical settings. The results verify the\neffectiveness and robustness of the proposed method and indicate the\nfeasibility of achieving improved postprandial glucose regulation through a\ndata-driven optimal control method.\n"}
+{"abstract": "  In an interactive belief model, are the players \"commonly meta-certain\" of\nthe model itself? This paper formalizes such implicit \"common meta-certainty\"\nassumption. To that end, the paper expands the objects of players' beliefs from\nevents to functions defined on the underlying states. Then, the paper defines a\nplayer's belief-generating map: it associates, with each state, whether a\nplayer believes each event at that state. The paper formalizes what it means\nby: \"a player is (meta-)certain of her own belief-generating map\" or \"the\nplayers are (meta-)certain of the profile of belief-generating maps (i.e., the\nmodel).\" The paper shows: a player is (meta-)certain of her own\nbelief-generating map if and only if her beliefs are introspective. The players\nare commonly (meta-)certain of the model if and only if, for any event which\nsome player i believes at some state, it is common belief at the state that\nplayer i believes the event. This paper then asks whether the \"common\nmeta-certainty\" assumption is needed for an epistemic characterization of\ngame-theoretic solution concepts. The paper shows: if each player is logical\nand (meta-)certain of her own strategy and belief-generating map, then each\nplayer correctly believes her own rationality. Consequently, common belief in\nrationality alone leads to actions that survive iterated elimination of\nstrictly dominated actions.\n"}
+{"abstract": "  Can a computer determine a piano player's skill level? Is it preferable to\nbase this assessment on visual analysis of the player's performance or should\nwe trust our ears over our eyes? Since current CNNs have difficulty processing\nlong video videos, how can shorter clips be sampled to best reflect the players\nskill level? In this work, we collect and release a first-of-its-kind dataset\nfor multimodal skill assessment focusing on assessing piano player's skill\nlevel, answer the asked questions, initiate work in automated evaluation of\npiano playing skills and provide baselines for future work. Dataset is\navailable from: https://github.com/ParitoshParmar/Piano-Skills-Assessment.\n"}
+{"abstract": "  Performance modeling of parallel applications on multicore processors remains\na challenge in computational co-design due to multicore processors' complex\ndesign. Multicores include complex private and shared memory hierarchies. We\npresent a Scalable Analytical Shared Memory Model (SASMM). SASMM can predict\nthe performance of parallel applications running on a multicore. SASMM uses a\nprobabilistic and computationally-efficient method to predict the reuse\ndistance profiles of caches in multicores. SASMM relies on a stochastic, static\nbasic block-level analysis of reuse profiles. The profiles are calculated from\nthe memory traces of applications that run sequentially rather than using\nmulti-threaded traces. The experiments show that our model can predict private\nL1 cache hit rates with 2.12% and shared L2 cache hit rates with about 1.50%\nerror rate.\n"}
+{"abstract": "  As machine learning models are increasingly used in critical decision-making\nsettings (e.g., healthcare, finance), there has been a growing emphasis on\ndeveloping methods to explain model predictions. Such \\textit{explanations} are\nused to understand and establish trust in models and are vital components in\nmachine learning pipelines. Though explanations are a critical piece in these\nsystems, there is little understanding about how they are vulnerable to\nmanipulation by adversaries. In this paper, we discuss how two broad classes of\nexplanations are vulnerable to manipulation. We demonstrate how adversaries can\ndesign biased models that manipulate model agnostic feature attribution methods\n(e.g., LIME \\& SHAP) and counterfactual explanations that hill-climb during the\ncounterfactual search (e.g., Wachter's Algorithm \\& DiCE) into\n\\textit{concealing} the model's biases. These vulnerabilities allow an\nadversary to deploy a biased model, yet explanations will not reveal this bias,\nthereby deceiving stakeholders into trusting the model. We evaluate the\nmanipulations on real world data sets, including COMPAS and Communities \\&\nCrime, and find explanations can be manipulated in practice.\n"}
+{"abstract": "  Recent studies of silicon spin qubits at temperatures above 1 K are\nencouraging demonstrations that the cooling requirements for solid-state\nquantum computing can be considerably relaxed. However, qubit readout\nmechanisms that rely on charge sensing with a single-island single-electron\ntransistor (SISET) quickly lose sensitivity due to thermal broadening of the\nelectron distribution in the reservoirs. Here we exploit the tunneling between\ntwo quantised states in a double-island SET (DISET) to demonstrate a charge\nsensor with an improvement in signal-to-noise by an order of magnitude compared\nto a standard SISET, and a single-shot charge readout fidelity above 99 % up to\n8 K at a bandwidth > 100 kHz. These improvements are consistent with our\ntheoretical modelling of the temperature-dependent current transport for both\ntypes of SETs. With minor additional hardware overheads, these sensors can be\nintegrated into existing qubit architectures for high fidelity charge readout\nat few-kelvin temperatures.\n"}
+{"abstract": "  We propose a convolutional-recurrent channel equalizer and experimentally\ndemonstrate 1dB Q-factor improvement both in single-channel and 96 x WDM,\nDP-16QAM transmission over 450km of TWC fiber. The new equalizer outperforms\nprevious NN-based approaches and a 3-steps-per-span DBP.\n"}
+{"abstract": "  Image inpainting is one of the important tasks in computer vision which\nfocuses on the reconstruction of missing regions in an image. The aim of this\npaper is to introduce an image inpainting model based on Wasserstein Generative\nAdversarial Imputation Network. The generator network of the model uses\nbuilding blocks of convolutional layers with different dilation rates, together\nwith skip connections that help the model reproduce fine details of the output.\nThis combination yields a universal imputation model that is able to handle\nvarious scenarios of missingness with sufficient quality. To show this\nexperimentally, the model is simultaneously trained to deal with three\nscenarios given by missing pixels at random, missing various smaller square\nregions, and one missing square placed in the center of the image. It turns out\nthat our model achieves high-quality inpainting results on all scenarios.\nPerformance is evaluated using peak signal-to-noise ratio and structural\nsimilarity index on two real-world benchmark datasets, CelebA faces and Paris\nStreetView. The results of our model are compared to biharmonic imputation and\nto some of the other state-of-the-art image inpainting methods.\n"}
+{"abstract": "  Prior studies have shown that, training machine learning models via empirical\nloss minimization to maximize a utility metric (e.g., accuracy), might yield\nmodels that make discriminatory predictions. To alleviate this issue, we\ndevelop a new training algorithm, named BiFair, which jointly minimizes for a\nutility, and a fairness loss of interest. Crucially, we do so without directly\nmodifying the training objective, e.g., by adding regularization terms. Rather,\nwe learn a set of weights on the training dataset, such that, training on the\nweighted dataset ensures both good utility, and fairness. The dataset weights\nare learned in concurrence to the model training, which is done by solving a\nbilevel optimization problem using a held-out validation dataset. Overall, this\napproach yields models with better fairness-utility trade-offs. Particularly,\nwe compare our algorithm with three other state-of-the-art fair training\nalgorithms over three real-world datasets, and demonstrate that, BiFair\nconsistently performs better, i.e., we reach to better values of a given\nfairness metric under same, or higher accuracy. Further, our algorithm is\nscalable. It is applicable both to simple models, such as logistic regression,\nas well as more complex models, such as deep neural networks, as evidenced by\nour experimental analysis.\n"}
+{"abstract": "  Extracting robust feature representation is one of the key challenges in\nobject re-identification (ReID). Although convolution neural network\n(CNN)-based methods have achieved great success, they only process one local\nneighborhood at a time and suffer from information loss on details caused by\nconvolution and downsampling operators (e.g. pooling and strided convolution).\nTo overcome these limitations, we propose a pure transformer-based object ReID\nframework named TransReID. Specifically, we first encode an image as a sequence\nof patches and build a transformer-based strong baseline with a few critical\nimprovements, which achieves competitive results on several ReID benchmarks\nwith CNN-based methods. To further enhance the robust feature learning in the\ncontext of transformers, two novel modules are carefully designed. (i) The\njigsaw patch module (JPM) is proposed to rearrange the patch embeddings via\nshift and patch shuffle operations which generates robust features with\nimproved discrimination ability and more diversified coverage. (ii) The side\ninformation embeddings (SIE) is introduced to mitigate feature bias towards\ncamera/view variations by plugging in learnable embeddings to incorporate these\nnon-visual clues. To the best of our knowledge, this is the first work to adopt\na pure transformer for ReID research. Experimental results of TransReID are\nsuperior promising, which achieve state-of-the-art performance on both person\nand vehicle ReID benchmarks.\n"}
+{"abstract": "  There have been many successful implementations of neural style transfer in\nrecent years. In most of these works, the stylization process is confined to\nthe pixel domain. However, we argue that this representation is unnatural\nbecause paintings usually consist of brushstrokes rather than pixels. We\npropose a method to stylize images by optimizing parameterized brushstrokes\ninstead of pixels and further introduce a simple differentiable rendering\nmechanism. Our approach significantly improves visual quality and enables\nadditional control over the stylization process such as controlling the flow of\nbrushstrokes through user input. We provide qualitative and quantitative\nevaluations that show the efficacy of the proposed parameterized\nrepresentation.\n"}
+{"abstract": "  We use Higgs bundles to study the 3d $\\mathcal{N} = 1$ vacua obtained from\nM-theory compactified on a local $Spin(7)$ space given as a four-manifold $M_4$\nof ADE singularities with further generic enhancements in the singularity type\nalong one-dimensional subspaces. There can be strong quantum corrections to the\nmassless degrees of freedom in the low energy effective field theory, but\ntopologically robust quantities such as \"parity\" anomalies are still\ncalculable. We show how geometric reflections of the compactification space\ndescend to 3d reflections and discrete symmetries. The \"parity\" anomalies of\nthe effective field theory descend from topological data of the\ncompactification. The geometric perspective also allows us to track various\nperturbative and non-perturbative corrections to the 3d effective field theory.\nWe also provide some explicit constructions of well-known 3d theories,\nincluding those which arise as edge modes of 4d topological insulators, and 3d\n$\\mathcal{N} = 1$ analogs of grand unified theories. An additional result of\nour analysis is that we are able to track the spectrum of extended objects and\ntheir transformations under higher-form symmetries.\n"}
+{"abstract": "  We study lattice two-color QCD (QC$_2$D) with two flavors of staggered\nfermions at imaginary and real quark chemical potential $\\mu_q$ and $T>T_c$. We\nemploy various methods of analytic continuation of the quark number density\nfrom imaginary to real quark chemical potentials $\\mu_q$, including series\nexpansions as well as based on phenomenological models, and study their\naccuracy by comparing the results to the lattice data. Below the Roberge-Weiss\ntemperature, $T<T_{RW}$, we find that the cluster expansion model provides an\naccurate analytic continuation of the baryon number density in the studied\nrange of chemical potentials. On the other hand, the behavior of the\nreconstructed canonical partition functions indicate that the available models\nmay require corrections at high quark densities. At $T>T_{RW}$ we show that the\nanalytic continuation to the real values of $\\mu_q$ based on trigonometric\nfunctions works equally well with the conventional method based on the Taylor\nexpansion in powers of $\\mu_q$.\n"}
+{"abstract": "  The fusion orbit category $\\overline{\\mathcal F _{\\mathcal C}} (G)$ of a\ndiscrete group $G$ over a collection $\\mathcal C$ is the category whose objects\nare the subgroups $H$ in $\\mathcal C$, and whose morphisms $H \\to K$ are given\nby the $G$-maps $G/H \\to G/K$ modulo the action of the centralizer group\n$C_G(H)$. We show that the higher limits over $\\overline{\\mathcal F _{\\mathcal\nC}} (G)$ can be computed using the hypercohomology spectral sequences coming\nfrom the Dwyer $G$-spaces for centralizer and normalizer decompositions for\n$G$.\n  If $G$ is the discrete group realizing a saturated fusion system $\\mathcal\nF$, then these hypercohomology spectral sequences give two spectral sequences\nthat converge to the cohomology of the centric orbit category ${\\mathcal O}^c\n(\\mathcal F)$. This allows us to apply our results to the sharpness problem for\nthe subgroup decomposition of a $p$-local finite group. We prove that the\nsubgroup decomposition for every $p$-local finite group is sharp (over\n$\\mathcal F$-centric subgroups) if it is sharp for every $p$-local finite group\nwith nontrivial center. We also show that for every $p$-local finite group $(S,\n\\mathcal F, \\mathcal L)$, the subgroup decomposition is sharp if and only if\nthe normalizer decomposition is sharp.\n"}
+{"abstract": "  Dynamical multiferroicity features entangled dynamic orders: fluctuating\nelectric dipoles induce magnetization. Hence, the material with paraelectric\nfluctuations can develop magnetic signatures if dynamically driven. We identify\nthe paraelectric KTaO$_3$ (KTO) as a prime candidate for the observation of the\ndynamical multiferroicity. We show that when a KTO sample is exposed to a\ncircularly polarized laser pulse, the dynamically induced ionic magnetic\nmoments are of the order of 5\\% of the nuclear magneton per unit cell. We\ndetermine the phonon spectrum using ab initio methods and identify T$_{1u}$ as\nrelevant soft phonon modes that couple to the external field and induce\nmagnetic polarization. We also predict a corresponding electron effect for the\ndynamically induced magnetic moment which is enhanced by several orders of\nmagnitude due to the significant mass difference between electron and ionic\nnucleus.\n"}
+{"abstract": "  The paper presents the derivation of the asymptotic behavior of $\\nu$-zeros\nof the modified Bessel function of imaginary order $K_{{\\rm i}\\nu}(z)$. This\nderivation is based on the quasiclassical treatment of the exponential\npotential on the positive half axis. The asymptotic expression for the\n$\\nu$-zeros (zeros with respect to order) contains the Lambert $W$ function,\nwhich is readily available in most computer algebra systems and numerical\nsoftware packages. The use of this function provides much higher accuracy of\nthe estimation comparing to known relations containing the logarithm, which is\njust the leading term of $W(x)$ at large $x$. Our result ensures accuracies\nsufficient for practical applications.\n"}
+{"abstract": "  Current domain-independent, classical planners require symbolic models of the\nproblem domain and instance as input, resulting in a knowledge acquisition\nbottleneck. Meanwhile, although deep learning has achieved significant success\nin many fields, the knowledge is encoded in a subsymbolic representation which\nis incompatible with symbolic systems such as planners. We propose Latplan, an\nunsupervised architecture combining deep learning and classical planning. Given\nonly an unlabeled set of image pairs showing a subset of transitions allowed in\nthe environment (training inputs), Latplan learns a complete propositional PDDL\naction model of the environment. Later, when a pair of images representing the\ninitial and the goal states (planning inputs) is given, Latplan finds a plan to\nthe goal state in a symbolic latent space and returns a visualized plan\nexecution. We evaluate Latplan using image-based versions of 6 planning\ndomains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of\nLightsOut.\n"}
+{"abstract": "  Recently, there has been an increased interest in the study of the generation\nof low-energy cosmic rays (CRs; < 1 TeV) in shocks situated on the surface of a\nprotostar or along protostellar jets. These locally accelerated CRs offer an\nattractive explanation for the high levels of non-thermal emission and\nionisation rate, $\\zeta$, observed close to these sources. The high $\\zeta$\nobserved in some protostellar sources is generally attributed to\nshock-generated UV photons. The aim of this article is to show that when\nsynchrotron emission and a high $\\zeta$ are measured in the same spatial\nregion, a locally shock-accelerated CR flux is sufficient to explain both\nphenomena. We assume that relativistic particles are accelerated according to\nthe first-order Fermi acceleration mechanism and compute $\\zeta$ and the\nnon-thermal emission at cm wavelengths. We then apply our model to the\nstar-forming region OMC-2 FIR 3/FIR 4. Using a Bayesian analysis, we constrain\nthe parameters of the model and estimate the spectral indices of the\nnon-thermal radio emission. We demonstrate that the local CR acceleration model\nmakes it possible to simultaneously explain the synchrotron emission along the\nHOPS 370 jet within the FIR 3 region and $\\zeta$ observed near the FIR 4\nprotocluster. Our model constrains the magnetic field strength\n(~250-450$~\\mu$G), its turbulent component (~20-40$~\\mu$G), and the jet\nvelocity in the shock reference frame for the three non-thermal sources of the\nHOPS 370 jet (~350-1000 km s$^{-1}$). Beyond the modelling of the OMC-2 FIR\n3/FIR 4 system, we show how the combination of continuum observations at cm\nwavelengths and molecular transitions is a powerful new tool for the analysis\nof star-forming regions: these two types of observations can be simultaneously\ninterpreted by invoking only the presence of locally accelerated CRs, without\nhaving to resort to shock-generated UV photons.\n"}
+{"abstract": "  A number of approximations have been proposed to estimate basic hydrodynamic\nquantities, in particular the frequency of a limit cycle. One of these, RZIF\n(for Real Zero Imaginary Frequency), calls for linearizing the governing\nequations about the mean flow and estimating the frequency as the imaginary\npart of the leading eigenvalue. A further reduction, the SCM (for\nSelf-Consistent Model), approximates the mean flow as well, as resulting only\nfrom the nonlinear interaction of the leading eigenmode with itself. Both RZIF\nand SCM have proven dramatically successful for the archetypal case of the wake\nof a circular cylinder.\n  Here, the SCM is applied to thermosolutal convection, for which a\nsupercritical Hopf bifurcation gives rise to branches of standing waves and\ntraveling waves. The SCM is solved by means of a full Newton method coupling\nthe approximate mean flow and leading eigenmode. Although the RZIF property is\nverified for the traveling waves, the SCM reproduces the nonlinear frequency\nonly very near the onset of the bifurcation and for another isolated parameter\nvalue. Thus, the nonlinear interaction arising from the leading mode is\ninsufficient to reproduce the nonlinear mean field and frequency.\n"}
+{"abstract": "  Empirical Dynamic Modeling (EDM) is a state-of-the-art non-linear time-series\nanalysis framework. Despite its wide applicability, EDM was not scalable to\nlarge datasets due to its expensive computational cost. To overcome this\nobstacle, researchers have attempted and succeeded in accelerating EDM from\nboth algorithmic and implementational aspects. In previous work, we developed a\nmassively parallel implementation of EDM targeting HPC systems (mpEDM).\nHowever, mpEDM maintains different backends for different architectures. This\ndesign becomes a burden in the increasingly diversifying HPC systems, when\nporting to new hardware. In this paper, we design and develop a\nperformance-portable implementation of EDM based on the Kokkos performance\nportability framework (kEDM), which runs on both CPUs and GPUs while based on a\nsingle codebase. Furthermore, we optimize individual kernels specifically for\nEDM computation, and use real-world datasets to demonstrate up to $5.5\\times$\nspeedup compared to mpEDM in convergent cross mapping computation.\n"}
+{"abstract": "  In this work, we only use data on the unstable manifold to locate the\npartition boundaries by checking folding points at different levels, which\npractically coincide with homoclinic tangencies (HTs). The method is then\napplied to the classic two-dimensional Henon map and a well-known\nthree-dimensional map. Comparison with previous results is made in the Henon\ncase and Lyapunov exponents are computed through the metric entropy based on\nthe partition, to show the validity of the current scheme.\n"}
+{"abstract": "  We study the problem of content delivery in two-user interference channels\nwith altering topology, random available cache at the receivers, and delayed\nchannel knowledge at the transmitter. We establish a new set of outer-bounds on\nthe achievable rates when each receiver has access to a random fraction of the\nmessage intended for the other receiver, and when each transmitter is aware of\nwhich part of its own message is known to the unintended receiver. The\nouter-bounds reveal the significant potential rate boost associated with even a\nsmall amount of side-information at each receiver. The key in deriving the\nbounds is to quantify the baseline entropy that will always become available to\nthe unintended receiver given the altering topology and the already available\nside-information. We will also present matching achievable rates in certain\nscenarios and outline the challenges in more general settings.\n"}
+{"abstract": "  We present a unified and hardware efficient architecture for two stage voice\ntrigger detection (VTD) and false trigger mitigation (FTM) tasks. Two stage VTD\nsystems of voice assistants can get falsely activated to audio segments\nacoustically similar to the trigger phrase of interest. FTM systems cancel such\nactivations by using post trigger audio context. Traditional FTM systems rely\non automatic speech recognition lattices which are computationally expensive to\nobtain on device. We propose a streaming transformer (TF) encoder architecture,\nwhich progressively processes incoming audio chunks and maintains audio context\nto perform both VTD and FTM tasks using only acoustic features. The proposed\njoint model yields an average 18% relative reduction in false reject rate (FRR)\nfor the VTD task at a given false alarm rate. Moreover, our model suppresses\n95% of the false triggers with an additional one second of post-trigger audio.\nFinally, on-device measurements show 32% reduction in runtime memory and 56%\nreduction in inference time compared to non-streaming version of the model.\n"}
+{"abstract": "  Brockett's necessary condition yields a test to determine whether a system\ncan be made to stabilize about some operating point via continuous, purely\nstate-dependent feedback. For many real-world systems, however, one wants to\nstabilize sets which are more general than a single point. One also wants to\ncontrol such systems to operate safely by making obstacles and other\n\"dangerous\" sets repelling.\n  We generalize Brockett's necessary condition to the case of stabilizing\ngeneral compact subsets having a nonzero Euler characteristic. Using this\ngeneralization, we also formulate a necessary condition for the existence of\n\"safe\" control laws. We illustrate the theory in concrete examples and for some\ngeneral classes of systems including a broad class of nonholonomically\nconstrained Lagrangian systems. We also show that, for the special case of\nstabilizing a point, the specialization of our general stabilizability test is\nstronger than Brockett's.\n"}
+{"abstract": "  Fault Tree analysis is a widely used failure analysis methodology to assess a\nsystem in terms of safety or reliability in many industrial application\ndomains. However, with Fault Tree methodology there is no possibility to\nexpress a temporal sequence of events or state-dependent behavior of\nsoftware-controlled systems. In contrast to this, Markov Chains are a\nstate-based analysis technique based on a stochastic model. But the use of\nMarkov Chains for failure analysis of complex safety-critical systems is\nlimited due to exponential explosion of the size of the model. In this paper,\nwe present a concept to integrate Markov Chains in Component Fault Tree models.\nBased on a component concept for Markov Chains, which enables the association\nof Markov Chains to system development elements such as components, complex or\nsoftware-controlled systems can be analyzed w.r.t. safety or reliability in a\nmodular and compositional way. We illustrate this approach using a case study\nfrom the automotive domain.\n"}
+{"abstract": "  Multi-user capabilities in Cisco Packet Tracer provide an incentive for\nimmersive realistic learning to increase the efficiency of teaching and\nlearning in the networking class. It is difficult to evaluate the configuration\nskills of the students in the classroom, particularly during laboratory class,\nwhile teaching networking course, the most problems for lecturers. The\nmulti-user functionality that uses the one-to-many remote relay server concept\nallows lecturers to remotely control and test many students at one time. The\nkey purpose of this paper is to incorporate creativity in teaching by using\nrealistic multi-user practises in the networking class. In this study, the\nmultiuser operation was developed and used via an existing LAN link in the\nclassroom. The focus of the operation is the simple configuration of network\nequipment such as routers and switches. This practise was used during the\nlaboratory session, encouraging lecturers to establish relationships based on a\nlarge audience. The professor is able to evaluate the success of the students\nduring the execution of the activity and to ensure that each student engages in\nthe activity. As a result of introducing this approach, the curiosity of\nstudents in technical subjects can be improved and student success can be\nconveniently tracked on the part of the lecturer. Lecturers are able to monitor\nthe configuration capabilities of students closely to ensure that each student\nengages in the laboratory class. The lecturer will train students not only to\ndevelop their configuration and troubleshooting abilities by using this\nteaching form, but also to accelerate the pace at which students need to fix\nnetwork problems.\n"}
+{"abstract": "  Plastic yielding in solids strongly depends on various conditions, such as\ntemperature and loading rate and indeed, sample-dependent knowledge of yield\npoints in structural materials promotes reliability in mechanical behavior.\nCommonly, yielding is measured through controlled mechanical testing at small\nor large scales, in ways that either distinguish elastic (stress) from total\ndeformation measurements, or by identifying plastic slip contributions. In this\npaper we argue that instead of separate elastic/plastic measurements, yielding\ncan be unraveled through statistical analysis of total strain fluctuations\nduring the evolution sequence of profiles measured in-situ, through digital\nimage correlation. We demonstrate two distinct ways of precisely quantifying\nyield locations in widely applicable crystal plasticity models, that apply in\npolycrystalline solids, either by using principal component analysis or\ndiscrete wavelet transforms. We test and compare these approaches in synthetic\ndata of polycrystal simulations and a variety of yielding responses, through\nchanges of the applied loading rates and the strain-rate sensitivity exponents.\n"}
+{"abstract": "  Semi-algebraic proof systems such as sum-of-squares (SoS) have attracted a\nlot of attention recently due to their relation to approximation algorithms:\nconstant degree semi-algebraic proofs lead to conjecturally optimal\npolynomial-time approximation algorithms for important NP-hard optimization\nproblems. Motivated by the need to allow a more streamlined and uniform\nframework for working with SoS proofs than the restrictive propositional level,\nwe initiate a systematic first-order logical investigation into the kinds of\nreasoning possible in algebraic and semi-algebraic proof systems. Specifically,\nwe develop first-order theories that capture in a precise manner constant\ndegree algebraic and semi-algebraic proof systems: every statement of a certain\nform that is provable in our theories translates into a family of constant\ndegree polynomial calculus or SoS refutations, respectively; and using a\nreflection principle, the converse also holds.\n  This places algebraic and semi-algebraic proof systems in the established\nframework of bounded arithmetic, while providing theories corresponding to\nsystems that vary quite substantially from the usual propositional-logic ones.\n  We give examples of how our semi-algebraic theory proves statements such as\nthe pigeonhole principle, we provide a separation between algebraic and\nsemi-algebraic theories, and we describe initial attempts to go beyond these\ntheories by introducing extensions that use the inequality symbol, identifying\nalong the way which extensions lead outside the scope of constant degree SoS.\nMoreover, we prove new results for propositional proofs, and specifically\nextend Berkholz's dynamic-by-static simulation of polynomial calculus (PC) by\nSoS to PC with the radical rule.\n"}
+{"abstract": "  Recent advancements in machine learning and signal processing domains have\nresulted in an extensive surge of interest in Deep Neural Networks (DNNs) due\nto their unprecedented performance and high accuracy for different and\nchallenging problems of significant engineering importance. However, when such\ndeep learning architectures are utilized for making critical decisions such as\nthe ones that involve human lives (e.g., in control systems and medical\napplications), it is of paramount importance to understand, trust, and in one\nword \"explain\" the argument behind deep models' decisions. In many\napplications, artificial neural networks (including DNNs) are considered as\nblack-box systems, which do not provide sufficient clue on their internal\nprocessing actions. Although some recent efforts have been initiated to explain\nthe behaviors and decisions of deep networks, explainable artificial\nintelligence (XAI) domain, which aims at reasoning about the behavior and\ndecisions of DNNs, is still in its infancy. The aim of this paper is to provide\na comprehensive overview on Understanding, Visualization, and Explanation of\nthe internal and overall behavior of DNNs.\n"}
+{"abstract": "  Reinforcement Learning (RL) algorithms have led to recent successes in\nsolving complex games, such as Atari or Starcraft, and to a huge impact in\nreal-world applications, such as cybersecurity or autonomous driving. In the\nside of the drawbacks, recent works have shown how the performance of RL\nalgorithms decreases under the influence of soft changes in the reward\nfunction. However, little work has been done about how sensitive these\ndisturbances are depending on the aggressiveness of the attack and the learning\nexploration strategy. In this paper, we propose to fill this gap in the\nliterature analyzing the effects of different attack strategies based on reward\nperturbations, and studying the effect in the learner depending on its\nexploration strategy. In order to explain all the behaviors, we choose a\nsub-class of MDPs: episodic, stochastic goal-only-rewards MDPs, and in\nparticular, an intelligible grid domain as a benchmark. In this domain, we\ndemonstrate that smoothly crafting adversarial rewards are able to mislead the\nlearner, and that using low exploration probability values, the policy learned\nis more robust to corrupt rewards. Finally, in the proposed learning scenario,\na counterintuitive result arises: attacking at each learning episode is the\nlowest cost attack strategy.\n"}
+{"abstract": "  State of the art architectures for untrimmed video Temporal Action\nLocalization (TAL) have only considered RGB and Flow modalities, leaving the\ninformation-rich audio modality totally unexploited. Audio fusion has been\nexplored for the related but arguably easier problem of trimmed (clip-level)\naction recognition. However, TAL poses a unique set of challenges. In this\npaper, we propose simple but effective fusion-based approaches for TAL. To the\nbest of our knowledge, our work is the first to jointly consider audio and\nvideo modalities for supervised TAL. We experimentally show that our schemes\nconsistently improve performance for state of the art video-only TAL\napproaches. Specifically, they help achieve new state of the art performance on\nlarge-scale benchmark datasets - ActivityNet-1.3 (54.34 mAP@0.5) and THUMOS14\n(57.18 mAP@0.5). Our experiments include ablations involving multiple fusion\nschemes, modality combinations and TAL architectures. Our code, models and\nassociated data are available at https://github.com/skelemoa/tal-hmo.\n"}
+{"abstract": "  This paper is concerned with quasi-linear parabolic equations driven by an\nadditive forcing $\\xi \\in C^{\\alpha-2}$, in the full sub-critical regime\n$\\alpha \\in (0,1)$. We are inspired by Hairer's regularity structures, however\nwe work with a more parsimonious model indexed by multi-indices rather than\ntrees. This allows us to capture additional symmetries which play a crucial\nrole in our analysis. Assuming bounds on this model, which is modified in\nagreement with the concept of algebraic renormalization, we prove local a\npriori estimates on solutions to the quasi-linear equations modified by the\ncorresponding counter terms.\n"}
+{"abstract": "  There is a great diversity of formal models to understand the dynamics of\ntransport and vehicular flow on a road. Many of these models are inspired by\nthe dynamics of flows governed by partial differential equations. However, it\nis possible to simplify these models to ordinary equations by considering\nconstant variations in some of the input variables in this type of models.\nHowever, given that these types of systems present discrete changes when the\nvehicle density is altered in some sections of the lane, it seems reasonable to\nmake use of hybrid systems to better understand the evolution of these\ndynamics. In this work we are interested in making use of dynamic differential\nlogic to formally verify one of these models proposed in ordinary equations.\nThis verification will be done through a proof assistant specially designed for\nhybrid systems called KeYmaera. Once we adapt the model to a hybrid system\nrepresentation we proceed to use KeYmaera to verify that the proposed model is\nformally correct.\n"}
+{"abstract": "  We compute explicitly the Khovanov polynomials (using the computer program\nfrom katlas.org) for the two simplest families of the satellite knots, which\nare the twisted Whitehead doubles and the two-strand cables. We find that a\nquantum group decomposition for the HOMFLY polynomial of a satellite knot can\nbe extended to the Khovanov polynomial, whose quantum group properties are not\nmanifest. Namely, the Khovanov polynomial of a twisted Whitehead double or\ntwo-strand cable (the two simplest satellite families) can be presented as a\nnaively deformed linear combination of the pattern and companion invariants.\nFor a given companion, the satellite polynomial \"smoothly\" depends on the\npattern but for the \"jump\" at one critical point defined by the s-invariant of\nthe companion knot. A similar phenomenon is known for the knot Floer homology\nand tau-invariant for the same kind of satellites.\n"}
+{"abstract": "  This paper is an introduction to our ongoing more comprehensive work on a\ncritically important period in the history of Russian mathematics education; it\nprovides a glimpse into the socio-political environment in which the famous\nSoviet tradition of mathematics education was born. The authors are\npractitioners of mathematics education in two very different countries, England\nand Russia. We have a chance to see that too many trends and debates in current\neducation policy resemble battles around mathematics education in the 1920s and\n1930s Soviet Russia. This is why this period should be revisited and\nre-analysed, despite quite a considerable amount of previous research. Our main\nconclusion: mathematicians, first of all, were fighting for control over\nselection, education, and career development, of young mathematicians. In the\nharshest possible political environment, they were taking potentially lethal\nrisks.\n"}
+{"abstract": "  Recent observations of globular clusters (GCs) provide evidence that the\nstellar initial mass function (IMF) may not be universal, suggesting\nspecifically that the IMF grows increasingly top-heavy with decreasing\nmetallicity and increasing gas density. Non-canonical IMFs can greatly affect\nthe evolution of GCs, mainly because the high end determines how many black\nholes (BHs) form. Here we compute a new set of GC models, varying the IMF\nwithin observational uncertainties. We find that GCs with top-heavy IMFs lose\nmost of their mass within a few Gyr through stellar winds and tidal stripping.\nHeating of the cluster through BH mass segregation greatly enhances this\nprocess. We show that, as they approach complete dissolution, GCs with\ntop-heavy IMFs can evolve into 'dark clusters' consisting of mostly BHs by\nmass. In addition to producing more BHs, GCs with top-heavy IMFs also produce\nmany more binary BH (BBH) mergers. Even though these clusters are short-lived,\nmergers of ejected BBHs continue at a rate comparable to, or greater than, what\nis found for long-lived GCs with canonical IMFs. Therefore these clusters,\nalthough they are no longer visible today, could still contribute significantly\nto the local BBH merger rate detectable by LIGO/Virgo, especially for sources\nwith higher component masses well into the BH mass gap. We also report that one\nof our GC models with a top-heavy IMF produces dozens of intermediate-mass\nblack holes (IMBHs) with masses $M>100\\,{\\rm M_\\odot}$, including one with\n$M>500\\,{\\rm M_\\odot}$. Ultimately, additional gravitational wave observations\nwill provide strong constraints on the stellar IMF in old GCs and the formation\nof IMBHs at high redshift.\n"}
+{"abstract": "  Within vacuum Weyl gravity, we obtain a solution by which, using different\nchoices of the conformal factor, we derive metrics describing (i)~a bounce of\nthe universe; (ii)~toroidal and spherical wormholes; and (iii)~a change in\nmetric signature. It is demonstrated that singularities occurring in these\nsystems are \"masked\". We give a simple explanation of the possibility of\nmasking the singularities within Weyl gravity. It is shown that in the first\nand third cases the three-dimensional metrics form Ricci flows. The question of\nthe possible applicability of conformal Weyl gravity as some phenomenological\ntheory in an approximate description of quantum gravity is discussed.\n"}
+{"abstract": "  Semidefinite Programming (SDP) is a class of convex optimization programs\nwith vast applications in control theory, quantum information, combinatorial\noptimization and operational research. Noisy intermediate-scale quantum (NISQ)\nalgorithms aim to make an efficient use of the current generation of quantum\nhardware. However, optimizing variational quantum algorithms is a challenge as\nit is an NP-hard problem that in general requires an exponential time to solve\nand can contain many far from optimal local minima. Here, we present a current\nterm NISQ algorithm for SDP. The classical optimization program of our NISQ\nsolver is another SDP over a smaller dimensional ansatz space. We harness the\nSDP based formulation of the Hamiltonian ground state problem to design a NISQ\neigensolver. Unlike variational quantum eigensolvers, the classical\noptimization program of our eigensolver is convex, can be solved in polynomial\ntime with the number of ansatz parameters and every local minimum is a global\nminimum. Further, we demonstrate the potential of our NISQ SDP solver by\nfinding the largest eigenvalue of up to $2^{1000}$ dimensional matrices and\nsolving graph problems related to quantum contextuality. We also discuss NISQ\nalgorithms for rank-constrained SDPs. Our work extends the application of NISQ\ncomputers onto one of the most successful algorithmic frameworks of the past\nfew decades.\n"}
+{"abstract": "  The use of satellites to provide ubiquitous coverage and connectivity for\ndensely deployed Internet of Things (IoT) networks is expected to be a reality\nin emerging 6G networks. Yet the low battery capacity of IoT nodes constitutes\na problem for their direct connectivity to satellites, which are located at\naltitudes of up to 2000 km. In this paper, we propose a novel architecture\ninvolving the use of reconfigurable intelligent surface (RIS) units to mitigate\nthe path loss associated with long transmission distances. These RIS units can\nbe placed on satellite reflectarrays, and, when used in broadcasting and\nbeamforming, they can provide significant gains in signal transmission. This\nstudy shows that RIS-assisted satellites can provide up to 10^5 times higher\ndownlink and achievable uplink rates for IoT networks.\n"}
+{"abstract": "  In this paper, we introduce the notion of periodic safety, which requires\nthat the system trajectories periodically visit a subset of a forward-invariant\nsafe set, and utilize it in a multi-rate framework where a high-level planner\ngenerates a reference trajectory that is tracked by a low-level controller\nunder input constraints. We introduce the notion of fixed-time barrier\nfunctions which is leveraged by the proposed low-level controller in a\nquadratic programming framework. Then, we design a model predictive control\npolicy for high-level planning with a bound on the rate of change for the\nreference trajectory to guarantee that periodic safety is achieved. We\ndemonstrate the effectiveness of the proposed strategy on a simulation example,\nwhere the proposed fixed-time stabilizing low-level controller shows successful\nsatisfaction of control objectives, whereas an exponentially stabilizing\nlow-level controller fails.\n"}
+{"abstract": "  Society is characterized by the presence of a variety of social norms:\ncollective patterns of sanctioning that can prevent miscoordination and\nfree-riding. Inspired by this, we aim to construct learning dynamics where\npotentially beneficial social norms can emerge. Since social norms are\nunderpinned by sanctioning, we introduce a training regime where agents can\naccess all sanctioning events but learning is otherwise decentralized. This\nsetting is technologically interesting because sanctioning events may be the\nonly available public signal in decentralized multi-agent systems where reward\nor policy-sharing is infeasible or undesirable. To achieve collective action in\nthis setting we construct an agent architecture containing a classifier module\nthat categorizes observed behaviors as approved or disapproved, and a\nmotivation to punish in accord with the group. We show that social norms emerge\nin multi-agent systems containing this agent and investigate the conditions\nunder which this helps them achieve socially beneficial outcomes.\n"}
+{"abstract": "  Photon-assisted tunneling frequently provides detailed information on the\nunderlying charge-transfer process. In particular, the Tien-Gordon approach and\nits extensions predict that the sideband spacing in bias voltage is a direct\nfingerprint of the number of electrons transferred in a single tunneling event.\nHere, we analyze photon-assisted tunneling into subgap states in\nsuperconductors in the limit of small temperatures and bias voltages where\ntunneling is dominated by resonant Andreev processes and does not conform to\nthe predictions of simple Tien-Gordon theory. Our analysis is based on a\nsystematic Keldysh calculation of the subgap conductance and provides a\ndetailed analytical understanding of photon-assisted tunneling into subgap\nstates, in excellent agreement with a recent experiment. We focus on tunneling\nfrom superconducting electrodes and into Yu-Shiba-Rusinov states associated\nwith magnetic impurities or adatoms, but we also explicitly extend our results\nto include normal-metal electrodes or other types of subgap states in\nsuperconductors. In particular, we argue that photon-assisted Andreev\nreflections provide a high-accuracy method to measure small, but nonzero\nenergies of subgap states which can be important for distinguishing\nconventional subgap states from Majorana bound states.\n"}
+{"abstract": "  Computing market equilibria is a problem of both theoretical and applied\ninterest. Much research to date focuses on the case of static Fisher markets\nwith full information on buyers' utility functions and item supplies. Motivated\nby real-world markets, we consider an online setting: individuals have linear,\nadditive utility functions; items arrive sequentially and must be allocated and\npriced irrevocably. We define the notion of an online market equilibrium in\nsuch a market as time-indexed allocations and prices which guarantee buyer\noptimality and market clearance in hindsight. We propose a simple, scalable and\ninterpretable allocation and pricing dynamics termed as PACE. When items are\ndrawn i.i.d. from an unknown distribution (with a possibly continuous support),\nwe show that PACE leads to an online market equilibrium asymptotically. In\nparticular, PACE ensures that buyers' time-averaged utilities converge to the\nequilibrium utilities w.r.t. a static market with item supplies being the\nunknown distribution and that buyers' time-averaged expenditures converge to\ntheir per-period budget. Hence, many desirable properties of market\nequilibrium-based fair division such as no envy, Pareto optimality, and the\nproportional-share guarantee are also attained asymptotically in the online\nsetting. Next, we extend the dynamics to handle quasilinear buyer utilities,\nwhich gives the first online algorithm for computing first-price pacing\nequilibria. Finally, numerical experiments on real and synthetic datasets show\nthat the dynamics converges quickly under various metrics.\n"}
+{"abstract": "  As a tool for capturing irregular temporal dependencies (rather than\nresorting to binning temporal observations to construct time series), Hawkes\nprocesses with exponential decay have seen widespread adoption across many\napplication domains, such as predicting the occurrence time of the next\nearthquake or stock market spike. However, practical applications of Hawkes\nprocesses face a noteworthy challenge: There is substantial and often\nunquantified variance in decay parameter estimations, especially in the case of\na small number of observations or when the dynamics behind the observed data\nsuddenly change. We empirically study the cause of these practical challenges\nand we develop an approach to surface and thereby mitigate them. In particular,\nour inspections of the Hawkes process likelihood function uncover the\nproperties of the uncertainty when fitting the decay parameter. We thus propose\nto explicitly capture this uncertainty within a Bayesian framework. With a\nseries of experiments with synthetic and real-world data from domains such as\n\"classical\" earthquake modeling or the manifestation of collective emotions on\nTwitter, we demonstrate that our proposed approach helps to quantify\nuncertainty and thereby to understand and fit Hawkes processes in practice.\n"}
+{"abstract": "  The first step in most text simplification is to predict which words are\nconsidered complex for a given target population before carrying out lexical\nsubstitution. This task is commonly referred to as Complex Word Identification\n(CWI) and it is often modelled as a supervised classification problem. For\ntraining such systems, annotated datasets in which words and sometimes\nmulti-word expressions are labelled regarding complexity are required. In this\npaper we analyze previous work carried out in this task and investigate the\nproperties of complex word identification datasets for English.\n"}
+{"abstract": "  This paper serves as a survey of recent advances in large margin training and\nits theoretical foundations, mostly for (nonlinear) deep neural networks (DNNs)\nthat are probably the most prominent machine learning models for large-scale\ndata in the community over the past decade. We generalize the formulation of\nclassification margins from classical research to latest DNNs, summarize\ntheoretical connections between the margin, network generalization, and\nrobustness, and introduce recent efforts in enlarging the margins for DNNs\ncomprehensively. Since the viewpoint of different methods is discrepant, we\ncategorize them into groups for ease of comparison and discussion in the paper.\nHopefully, our discussions and overview inspire new research work in the\ncommunity that aim to improve the performance of DNNs, and we also point to\ndirections where the large margin principle can be verified to provide\ntheoretical evidence why certain regularizations for DNNs function well in\npractice. We managed to shorten the paper such that the crucial spirit of large\nmargin learning and related methods are better emphasized.\n"}
+{"abstract": "  It is known that interference alignment (IA) plays an important role in\nimproving the degree of freedom (DoF) of multi-input and multi-output (MIMO)\nsystems. However, most of the traditional IA schemes suffer from the high\ncomputational complexity and require the global and instantaneous channel state\ninformation (CSI), both of which make them difficult to be extended to cellular\nMIMO systems. To handle these issues, two new interference alignment schemes,\ni.e., the retrospective interference regeneration (RIR) scheme and the\nbeamforming based distributed retrospective interference alignment (B-DRIA)\nscheme, are proposed for cellular K-user MIMO downlink networks. For the RIR\nscheme, it adopts interference elimination algorithm to erase redundant symbols\nin inter-cell interference (ICI) signals, and then uses interference\nregeneration algorithm to avoid secondary interference. The RIR scheme obtains\ngreater DoF gain than the retrospective interference alignment (RIA) scheme,\nbut incurs performance degradation when the transceiver antennas ratio (TAR)\napproaches 1. Therefore, the B-DRIA scheme is further proposed. For the B-DRIA\nscheme, the cellular beamforming matrix is introduced to eliminate the ICI, and\nmeanwhile distributed retrospective interference alignment algorithm is adopted\nto align inter-user interference (IUI). The simulation results show that the\nB-DRIA scheme obtains larger DoF than the RIR scheme locally. Specifically,\nwhen TAR approaches 1, two schemes obtain the same DoF. While TAR approaches 2,\nthe DoF of the B-DRIA scheme is superior than the RIR scheme.\n"}
+{"abstract": "  Light in hyperbolic dispersion media is known to exhibit an intriguing\nray-like character. However, detailed understanding of ray-like excitations in\nhyperbolic media is surprisingly limited, mostly based on numerical\nsimulations. In our work, we analytically describe the formation of multimodal\nray-like excitations in a planar slab of hyperbolic media. We demonstrate that\nthese rays have an approximately Lorentzian profile and that they propagate in\na zig-zagged manner. These rays acquire phase in a discrete fashion at every\ninternal reflection inside the slab and broaden at a rate which is proportional\nto the rate of optical absorption. Moreover, based on this description, we\nreveal a unique reflection mechnanism where destructive interefernce between\nthe different modes composing the ray can improve the strength of reflection at\nan interface between a dielectric and a metalic substrate. This reflection\nmechanism is highly asymmetric, occuring only when the ray is incident from the\nside of the dielectric substrate to the side of the metallic substrate, and the\nstrength of reflection is shown to be directly related to the width of the ray.\n"}
+{"abstract": "  This article describes a formula for second variation of generalized\nEinstein-Hilbert functional on Riemannian manifolds.\n"}
+{"abstract": "  A connected partition is a partition of the vertices of a graph into sets\nthat induce connected subgraphs. Such partitions naturally occur in many\napplication areas such as road networks, and image processing. We consider\nBalanced Connected Partitions (BCP), where the two classical objectives for BCP\nare to maximize the weight of the smallest, or minimize the weight of the\nlargest component. We study BCP on c-claw-free graphs, the class of graphs that\ndo not have $K_{1,c}$ as an induced subgraph, and present efficient\n(c-1)-approximation algorithms for both objectives. In particular, due to the\n(3-)claw-freeness of line graphs, this also implies a 2-approximations for the\nedge-partition version of BCP in general graphs.\n  In the 1970s Gy\\H{o}ri and Lov\\'{a}sz showed for natural numbers\n$w_1,\\dots,w_k$ where $\\sum_i w_i$ is the vertex size, that if $G$ is\nk-connected, then there exist a connected k-partition with part sizes\n$w_1,\\dots,w_k$. However, to this day no polynomial algorithm to compute such\npartitions exists for k>4. Towards finding such a partition $T_1,\\dots, T_k$,\nwe show how to efficiently compute connected partitions that at least\napproximately meet the target weights, subject to the mild assumption that each\n$w_i$ is greater than the weight of the heaviest vertex. In particular, we give\na 3-approximation for both the lower and the upper bounded version i.e. we\nguarantee that each $T_i$ has weight at least $\\frac{w_i}{3}$ or that each\n$T_i$ has weight most $3w_i$, respectively. Also, we present a both-side\nbounded version that produces a connected partition where each $T_i$ has size\nat least $\\frac{w_i}{3}$ and at most $\\max(\\{r,3\\}) w_i$, where $r \\geq 1$ is\nthe ratio between the largest and smallest value in $w_1, \\dots, w_k$. In\nparticular for the balanced version, i.e.~$w_1=w_2=, \\dots,=w_k$, this gives a\npartition with $\\frac{1}{3}w_i \\leq w(T_i) \\leq 3w_i$.\n"}
+{"abstract": "  Random fields are useful mathematical tools for representing natural\nphenomena with complex dependence structures in space and/or time. In\nparticular, the Gaussian random field is commonly used due to its attractive\nproperties and mathematical tractability. However, this assumption seems to be\nrestrictive when dealing with counting data. To deal with this situation, we\npropose a random field with a Poisson marginal distribution by considering a\nsequence of independent copies of a random field with an exponential marginal\ndistribution as 'inter-arrival times' in the counting renewal processes\nframework. Our proposal can be viewed as a spatial generalization of the\nPoisson process.\n  Unlike the classical hierarchical Poisson Log-Gaussian model, our proposal\ngenerates a (non)-stationary random field that is mean square continuous and\nwith Poisson marginal distributions. For the proposed Poisson spatial random\nfield, analytic expressions for the covariance function and the bivariate\ndistribution are provided. In an extensive simulation study, we investigate the\nweighted pairwise likelihood as a method for estimating the Poisson random\nfield parameters.\n  Finally, the effectiveness of our methodology is illustrated by an analysis\nof reindeer pellet-group survey data, where a zero-inflated version of the\nproposed model is compared with zero-inflated Poisson Log-Gaussian and Poisson\nGaussian copula models. Supplementary materials for this article, include\ntechnical proofs and R code for reproducing the work, are available as an\nonline supplement.\n"}
+{"abstract": "  Modeling networks as different graph types and researching on route finding\nstrategies, to avoid congestion in dense subnetworks via graph-theoretic\napproaches, contributes to overall blocking probability reduction in networks.\nOur main focus is to study methods for modeling congested subnetworks and graph\ndensity measures, in order to identify routes that avoid dense subgraphs for\nglobal congestion avoidance, along with covering related algorithmic issues.\n"}
+{"abstract": "  Heterogeneous presentation of a neurological disorder suggests potential\ndifferences in the underlying pathophysiological changes that occur in the\nbrain. We propose to model heterogeneous patterns of functional network\ndifferences using a demographic-guided attention (DGA) mechanism for recurrent\nneural network models for prediction from functional magnetic resonance imaging\n(fMRI) time-series data. The context computed from the DGA head is used to help\nfocus on the appropriate functional networks based on individual demographic\ninformation. We demonstrate improved classification on 3 subsets of the ABIDE I\ndataset used in published studies that have previously produced\nstate-of-the-art results, evaluating performance under a leave-one-site-out\ncross-validation framework for better generalizeability to new data. Finally,\nwe provide examples of interpreting functional network differences based on\nindividual demographic variables.\n"}
+{"abstract": "  The spin reduced matrix defined by taking partial trace with the momentum\nvariable has been shown not to transform covariantly under Lorentz\ntransformations. In this paper, I suggest another representation space of the\nPoincar\\'e group, the elements of which are in a sense \"apparent wave\nfunctions\". In this new Hilbert space, the naive spin observable\n$\\frac{1}{2}\\boldsymbol{\\tau}$ from nonrelativistic quantum mechanics on the\nold Hilbert space is represented as the Newton-Wigner spin operator. Also on\nthis space, one can naturally associate to each state a Lorentz covariant spin\nreduced density matrix, which is then given a new interpretation.\n"}
+{"abstract": "  We introduce a new class of Wasserstein-type distances specifically designed\nto tackle questions concerning stability and convergence to equilibria for\nkinetic equations. Thanks to these new distances, we improve some classical\nestimates by Loeper and Dobrushin on Vlasov-type equations, and we present an\napplication to quasineutral limits.\n"}
+{"abstract": "  We consider the conjugate gradient algorithm applied to a general class of\nspiked sample covariance matrices. The main result of the paper is that the\nnorms of the error and residual vectors at any finite step concentrate on\ndeterministic values determined by orthogonal polynomials with respect to a\ndeformed Marchenko--Pastur law. The first-order limits and fluctuations are\nshown to be universal. Additionally, for the case where the bulk eigenvalues\nlie in a single interval we show a stronger universality result in that the\nasymptotic rate of convergence of the conjugate gradient algorithm only depends\non the support of the bulk, provided the spikes are well-separated from the\nbulk. In particular, this shows that the classical condition number bound for\nthe conjugate gradient algorithm is pessimistic for spiked matrices.\n"}
+{"abstract": "  To prevent the spread of coronavirus disease 2019 (COVID-19), preliminary\ntemperature measurement and mask detection in public areas are conducted.\nHowever, the existing temperature measurement methods face the problems of\nsafety and deployment. In this paper, to realize safe and accurate temperature\nmeasurement even when a person's face is partially obscured, we propose a\ncloud-edge-terminal collaborative system with a lightweight infrared\ntemperature measurement model. A binocular camera with an RGB lens and a\nthermal lens is utilized to simultaneously capture image pairs. Then, a mobile\ndetection model based on a multi-task cascaded convolutional network (MTCNN) is\nproposed to realize face alignment and mask detection on the RGB images. For\naccurate temperature measurement, we transform the facial landmarks on the RGB\nimages to the thermal images by an affine transformation and select a more\naccurate temperature measurement area on the forehead. The collected\ninformation is uploaded to the cloud in real time for COVID-19 prevention.\nExperiments show that the detection model is only 6.1M and the average\ndetection speed is 257ms. At a distance of 1m, the error of indoor temperature\nmeasurement is about 3%. That is, the proposed system can realize real-time\ntemperature measurement in public areas.\n"}
+{"abstract": "  It is well known that positive values of redshift drift is a signature of\ndark energy within the conventionally studied\nFriedmann-Lema\\^{\\i}tre-Robertson-Walker (FLRW) universe models. Here we show\n-- without making assumptions on the metric tensor of the Universe -- that\nredshift drift is a promising direct probe of violation of the strong energy\ncondition within the theory of general relativity.\n"}
+{"abstract": "  This paper presents an integrated system for performing precision harvesting\nmissions using a legged harvester. Our harvester performs a challenging task of\nautonomous navigation and tree grabbing in a confined, GPS denied forest\nenvironment. Strategies for mapping, localization, planning, and control are\nproposed and integrated into a fully autonomous system. The mission starts with\na human mapping the area of interest using a custom-made sensor module.\nSubsequently, a human expert selects the trees for harvesting. The sensor\nmodule is then mounted on the machine and used for localization within the\ngiven map. A planning algorithm searches for both an approach pose and a path\nin a single path planning problem. We design a path following controller\nleveraging the legged harvester's capabilities for negotiating rough terrain.\nUpon reaching the approach pose, the machine grabs a tree with a\ngeneral-purpose gripper. This process repeats for all the trees selected by the\noperator. Our system has been tested on a testing field with tree trunks and in\na natural forest. To the best of our knowledge, this is the first time this\nlevel of autonomy has been shown on a full-size hydraulic machine operating in\na realistic environment.\n"}
+{"abstract": "  Primordial black holes (PBHs) produced in the early Universe have attracted\nwide interest for their ability to constitute dark matter and explain the\ncompact binary coalescence. We propose a new mechanism of PBH production during\nfirst-order phase transitions (PTs) and find that PBHs are naturally produced\nduring PTs model-independently. Because of the randomness of the quantum\ntunneling, there always exists some probability that the vacuum decay is\npostponed in a whole Hubble volume. Since the vacuum energy density remains\nconstant while radiation is quickly redshifted in the expanding Universe, the\npostponed vacuum decay then results in overdense regions, which finally\ncollapse into PBHs as indicated by numerical simulations. Utilizing this result\none can obtain mutual predictions and constraints between PBHs and GWs from\nPTs. The predicted mass function of PBHs is nearly monochromatic. We\ninvestigate two typical cases and find that 1) PBHs from a PT constitute all\ndark matter and GWs peak at $1$Hz, 2) PBHs from a PT can explain the\ncoalescence events observed by LIGO-Virgo collaboration, and meanwhile GWs can\nexplain the common-spectrum process detected by NANOGrav collaboration.\n"}
+{"abstract": "  We present a numerical method specifically designed for simulating\nthree-dimensional fluid--structure interaction (FSI) problems based on the\nreference map technique (RMT). The RMT is a fully Eulerian FSI numerical method\nthat allows fluids and large-deformation elastic solids to be represented on a\nsingle fixed computational grid. This eliminates the need for meshing complex\ngeometries typical in other FSI approaches, and greatly simplifies the coupling\nbetween fluid and solids. We develop the first three-dimensional implementation\nof the RMT, parallelized using the distributed memory paradigm, to simulate\nincompressible FSI with neo-Hookean solids. As part of our new method, we\ndevelop a new field extrapolation scheme that works efficiently in parallel.\nThrough representative examples, we demonstrate the method's accuracy and\nconvergence, as well as its suitability in investigating many-body and active\nsystems. The examples include settling of a mixture of heavy and buoyant soft\nellipsoids, lid-driven cavity flow containing a soft sphere, and swimmers\nactuated via active stress.\n"}
+{"abstract": "  We study the multiplicity and concentration of complex valued solutions for a\nfractional magnetic Schr\\\"odinger equation involving a scalar continuous\nelectric potential satisfying a local condition and a continuous nonlinearity\nwith subcritical growth. The main results are obtained by applying a\npenalization technique, generalized Nehari manifold method and\nLjusternik-Schnirelman theory. We also prove a Kato's inequality for the\nfractional magnetic Laplacian which we believe to be useful in the study of\nother fractional magnetic problems.\n"}
+{"abstract": "  The set of strata of a reductive group can be viewed as an enlargement of the\nset of unipotent classes. In this paper the notion of distinguished unipotent\nclass is extended to this larger set.\n"}
+{"abstract": "  Algebraic models for the reconstruction problem in X-ray computed tomography\n(CT) provide a flexible framework that applies to many measurement geometries.\nFor large-scale problems we need to use iterative solvers, and we need stopping\nrules for these methods that terminate the iterations when we have computed a\nsatisfactory reconstruction that balances the reconstruction error and the\ninfluence of noise from the measurements. Many such stopping rules are\ndeveloped in the inverse problems communities, but they have not attained much\nattention in the CT world. The goal of this paper is to describe and illustrate\nfour stopping rules that are relevant for CT reconstructions.\n"}
+{"abstract": "  The development of materials with specific structural properties is of huge\npractical interest, for example, for medical applications or for the\ndevelopment of light weight structures in aeronautics. In this article, we\ncombine shape optimization and homogenization for the optimal design of the\nmicrostructure in scaffolds. Given the current microstructure, we apply the\nisogeometric boundary element method to compute the effective tensor and to\nupdate the microstructure by using the shape gradient in order to match the\ndesired effective tensor. Extensive numerical studies are presented to\ndemonstrate the applicability and feasibility of the approach.\n"}
+{"abstract": "  We present an approach to enhancing the realism of synthetic images. The\nimages are enhanced by a convolutional network that leverages intermediate\nrepresentations produced by conventional rendering pipelines. The network is\ntrained via a novel adversarial objective, which provides strong supervision at\nmultiple perceptual levels. We analyze scene layout distributions in commonly\nused datasets and find that they differ in important ways. We hypothesize that\nthis is one of the causes of strong artifacts that can be observed in the\nresults of many prior methods. To address this we propose a new strategy for\nsampling image patches during training. We also introduce multiple\narchitectural improvements in the deep network modules used for photorealism\nenhancement. We confirm the benefits of our contributions in controlled\nexperiments and report substantial gains in stability and realism in comparison\nto recent image-to-image translation methods and a variety of other baselines.\n"}
+{"abstract": "  In this short review we propose a critical assessment of the role of chaos\nfor the thermalization of Hamiltonian systems with high dimensionality. We\ndiscuss this problem for both classical and quantum systems. A comparison is\nmade between the two situations: some examples from recent and past literature\nare presented which support the point of view that chaos is not necessary for\nthermalization. Finally, we suggest that a close analogy holds between the role\nplayed by Kinchin's theorem for high-dimensional classical systems and the role\nplayed by Von Neumann's theorem for many-body quantum systems.\n"}
+{"abstract": "  CONTEXT: [ABRIDGED]. For the Milky Way bulge, there are currently essentially\nno measurements of carbon in un-evolved stars, hampering our abilities to\nproperly compare Galactic chemical evolution models to observational data for\nthis still enigmatic stellar population. AIMS: We aim to determine carbon\nabundances for our sample of 91 microlensed bulge dwarf and subgiant stars.\nTogether with new determinations for oxygen this forms the first statistically\nsignificant sample of bulge stars that have C and O abundances measured, and\nfor which the C abundances have not been altered by the nuclear burning\nprocesses internal to the stars. METHODS: The analysis is based on\nhigh-resolution spectra for a sample of 91 dwarf and subgiant stars that were\nobtained during microlensing events when the brightnesses of the stars were\nhighly magnified. Carbon abundances were determined through spectral line\nsynthesis of five CI lines around 9100 A, and oxygen abundances using the three\nOI lines at about 7770 A. [ABRIDGED] RESULTS: Carbon abundances was possible to\ndetermine for 70 of the 91 stars in the sample and oxygen abundances for 88 of\nthe 91 stars in the sample. The [C/Fe] ratio evolves essentially in lockstep\nwith [Fe/H], centred around solar values at all [Fe/H]. The [O/Fe]-[Fe/H] trend\nhas an appearance very similar to that observed for other alpha-elements in the\nbulge, [ABRIDGED]. When dividing the bulge sample into two sub-groups, one\nyounger than 8 Gyr and one older than 8 Gyr, the stars in the two groups follow\nexactly the elemental abundance trends defined by the solar neighbourhood thin\nand thick disks, respectively. Comparisons with recent models of Galactic\nchemical evolution in the [C/O]-[O/H] plane shows that the models that best\nmatch the data are the ones that have been calculated with the Galactic thin\nand thick disks in mind. [ABRIDGED] ....\n"}
+{"abstract": "  We report on critical phenomena in the gravitational collapse of\nelectromagnetic waves. Generalizing earlier results that focused on dipole\nelectromagnetic waves, we here compare with quadrupole waves in axisymmetry. We\nperform numerical simulations of dipole and quadrupole wave initial data,\nfine-tuning both sets of data to the onset of black hole formation in order to\nstudy the critical solution and related critical phenomena. We observe that\ndifferent multipole moments have different symmetries, indicating that the\ncritical solution for electromagnetic waves cannot be unique, at least not\nglobally. This is confirmed in our numerical simulations: while dipole data\nlead to a single center of collapse, at the center of symmetry, quadrupole data\nfeature two separate centers of collapse on the symmetry axis, above and below\nthe center of symmetry -- reminiscent of similar findings reported for critical\ncollapse of vacuum gravitational waves. While the critical solution for neither\nthe dipole nor the quadrupole data is exactly self-similar, we find that their\napproximate echoing periods appear to differ, as do the critical exponents. We\ndiscuss whether the centers of collapse found for dipole and quadrupole data\nmight all have the same properties, which would suggest a \"local uniqueness\" of\nthe critical solution. Instead, we provide some evidence -- including the\ndiffering echoing periods and critical exponents -- suggesting that the\ncritical solutions are distinct even locally. We speculate on the implications\nof our findings for critical phenomena in the collapse of vacuum gravitational\nwaves, which share with electromagnetic waves the absence of a spherically\nsymmetric critical solution.\n"}
+{"abstract": "  Neutrinoless double-$\\beta$ decay of $^{76}$Ge is searched for with germanium\ndetectors where source and detector of the decay are identical. For the success\nof future experiments it is important to increase the mass of the detectors. We\nreport here on the characterization and testing of five prototype detectors\nmanufactured in inverted coaxial (IC) geometry from material enriched to 88% in\n$^{76}$Ge. IC detectors combine the large mass of the traditional semi-coaxial\nGe detectors with the superior resolution and pulse shape discrimination power\nof point contact detectors which exhibited so far much lower mass. Their\nperformance has been found to be satisfactory both when operated in vacuum\ncryostat and bare in liquid argon within the GERDA setup. The measured\nresolutions at the Q-value for double-$\\beta$ decay of $^{76}$Ge\n(Q$_{\\beta\\beta}$ = 2039 keV) are about 2.1 keV full width at half maximum in\nvacuum cryostat. After 18 months of operation within the ultra-low background\nenvironment of the GERmanium Detector Array (GERDA) experiment and an\naccumulated exposure of 8.5 kg$\\cdot$yr, the background index after analysis\ncuts is measured to be $4.9^{+7.3}_{-3.4}\\times 10^{-4}$ counts\n/(keV$\\cdot$kg$\\cdot$yr) around Q$_{\\beta\\beta}$. This work confirms the\nfeasibility of IC detectors for the next-generation experiment LEGEND.\n"}
+{"abstract": "  Magnetic surveys are conventionally performed by scanning a domain with a\nportable scalar magnetic sensor. Unfortunately, scalar magnetometers are\nexpensive, power consuming and bulky. In many applications, calibrated vector\nmagnetometers can be used to perform magnetic surveys. In recent years\nalgorithms based on artificial intelligence (AI) achieve state-of-the-art\nresults in many modern applications. In this work we investigate an AI\nalgorithm for the classical scalar calibration of magnetometers. A simple, low\ncost method for performing a magnetic survey is presented. The method utilizes\na low power consumption sensor with an AI calibration procedure that improves\nthe common calibration methods and suggests an alternative to the conventional\ntechnology and algorithms. The setup of the survey system is optimized for\nquick deployment in-situ right before performing the magnetic survey. We\npresent a calibration method based on a procedure of rotating the sensor in the\nnatural earth magnetic field for an optimal time period. This technique can\ndeal with a constant field offset and non-orthogonality issues and does not\nrequire any external reference. The calibration is done by finding an estimator\nthat yields the calibration parameters and produces the best geometric fit to\nthe sensor readings. A comprehensive model considering the physical,\nalgorithmic and hardware properties of the magnetometer of the survey system is\npresented. The geometric ellipsoid fitting approach is parametrically tested.\nThe calibration procedure reduced the root-mean-squared noise from the order of\n104 nT to less than 10 nT with variance lower than 1 nT in a complete 360\ndegrees rotation in the natural earth magnetic field.\n"}
+{"abstract": "  This work, Part II, together with its companion Part I develops a new\nframework for stochastic functional Kolmogorov equations, which are nonlinear\nstochastic differential equations depending on the current as well as the past\nstates. Because of the complexity of the problems, it is natural to divide our\ncontributions into two parts to answer a long-standing question in biology and\necology. What are the minimal conditions for long-term persistence and\nextinction of a population? Part I of our work provides characterization of\npersistence, whereas in this part, extinction is the main focus. The main\ntechniques used in this paper are combination of the newly developed functional\nIt^o formula and a dynamical system approach. Compared to the study of\nstochastic Kolmogorov systems without delays, the main difficulty is that\ninfinite dimensional systems have to be treated. The extinction is\ncharacterized after investigating random occupation measures and examining\nbehavior of functional systems around boundaries. Our characterizations of\nlong-term behavior of the systems reduces to that of Kolmogorov systems without\ndelay when there is no past dependence. A number of applications are also\nexamined.\n"}
+{"abstract": "  Learning the structure of Bayesian networks and causal relationships from\nobservations is a common goal in several areas of science and technology. We\nshow that the prequential minimum description length principle (MDL) can be\nused to derive a practical scoring function for Bayesian networks when flexible\nand overparametrized neural networks are used to model the conditional\nprobability distributions between observed variables. MDL represents an\nembodiment of Occam's Razor and we obtain plausible and parsimonious graph\nstructures without relying on sparsity inducing priors or other regularizers\nwhich must be tuned. Empirically we demonstrate competitive results on\nsynthetic and real-world data. The score often recovers the correct structure\neven in the presence of strongly nonlinear relationships between variables; a\nscenario were prior approaches struggle and usually fail. Furthermore we\ndiscuss how the the prequential score relates to recent work that infers causal\nstructure from the speed of adaptation when the observations come from a source\nundergoing distributional shift.\n"}
+{"abstract": "  We analyze the energy transfer for solutions to the defocusing cubic\nnonlinear Schr\\\"odinger (NLS) initial value problem on 2D irrational tori.\nMoreover we complement the analytic study with numerical experimentation. As a\nbiproduct of our investigation we also prove that the quasi-resonant part of\nthe NLS initial value problem we consider, in both the focusing and defocusing\ncase, is globally well-posed for initial data of finite mass.\n"}
+{"abstract": "  Suppression of superconductivity due to the proximity effect between a\nsuperconductor and a ferromagnet can be partially alleviated when a Cooper pair\nsimultaneously samples different directions of the short-range exchange field.\nThe superconductor's critical temperature, TC, is therefore expected to\npartially recover when the ferromagnet is in a multi-domain state, as opposed\nto a single-domain state. Here, we discuss series of experiments performed with\nferromagnet(Pt/Co)/spacer(IrMn and Pt)/superconductor(NbN) heterostructures. By\ntuning the various parameters in play, e.g., superconducting coherence\nlength-to-thicknesses ratio, and domain sizes, we obtained up to 10 percent\nrecovery of the superconducting critical temperature {\\Delta}TC/TC. This\nlarge-scale recovery made novel investigations possible. In particular, from\nthe spacer thickness-dependence of {\\Delta}TC/TC, it was possible to deduce the\ncharacteristic length for Cooper pair penetration in an IrMn antiferromagnet.\nThis information is crucial for electronic transport, and up to now has been\ndifficult to access experimentally for antiferromagnets.\n"}
+{"abstract": "  We study here the observational constraints on single-field inflationary\nmodels achievable with the next generation of CMB experiments. We particularly\nfocus on a Stage-IV like experiment and forecasts its constraints on\ninflationary parameters in the context of $\\alpha$-attractor inflation\ncomprising a large class of single-field models. To tailor our forecasts we use\nas a fiducial model the results obtained with current CMB and LSS data,\nassuming the $\\alpha$-model a priori. We found that current CMB data are able\nto place a tight bound on the ratio of the tensor-to-scalar ratio with the\nalpha parameter $r/\\alpha = 3.87^{+0.78}_{-0.94}\\cdot 10^{-3}$ and on the\nrunning of the scalar index $\\alpha_S = -6.4^{+1.6}_{-1.3}\\cdot 10^{-4}$ with a\nvalue of the scalar index consistent with current constraints. In the\noptimistic scenario of detection of primordial gravitational waves in the CMB\nB-mode polarization power spectra, we found that CMB-S4 will be able to achieve\na $15\\%$ bound on the value of the parameter $\\alpha = 1.01^{+0.14}_{-0.18}$.\nThis bound clearly show the ability of CMB-S4 to constrain not only the energy\nscale of inflation but also the shape of its potential. Enlarging the baseline\nmodel to also include the neutrino sector merely reduce the accuracy of $5\\%$\nleading to $\\alpha = 1.07^{+0.18}_{-0.23}$ so that our main conclusions are\nstill valid.\n"}
+{"abstract": "  Blind image deblurring is an important yet very challenging problem in\nlow-level vision. Traditional optimization based methods generally formulate\nthis task as a maximum-a-posteriori estimation or variational inference\nproblem, whose performance highly relies on the handcraft priors for both the\nlatent image and the blur kernel. In contrast, recent deep learning methods\ngenerally learn, from a large collection of training images, deep neural\nnetworks (DNNs) directly mapping the blurry image to the clean one or to the\nblur kernel, paying less attention to the physical degradation process of the\nblurry image. In this paper, we present a deep variational Bayesian framework\nfor blind image deblurring. Under this framework, the posterior of the latent\nclean image and blur kernel can be jointly estimated in an amortized inference\nfashion with DNNs, and the involved inference DNNs can be trained by fully\nconsidering the physical blur model, together with the supervision of data\ndriven priors for the clean image and blur kernel, which is naturally led to by\nthe evidence lower bound objective. Comprehensive experiments are conducted to\nsubstantiate the effectiveness of the proposed framework. The results show that\nit can not only achieve a promising performance with relatively simple\nnetworks, but also enhance the performance of existing DNNs for deblurring.\n"}
+{"abstract": "  Crystal structure prediction (CSP) has emerged as one of the most important\napproaches for discovering new materials. CSP algorithms based on evolutionary\nalgorithms and particle swarm optimization have discovered a great number of\nnew materials. However, these algorithms based on ab initio calculation of free\nenergy are inefficient. Moreover, they have severe limitations in terms of\nscalability. We recently proposed a promising crystal structure prediction\nmethod based on atomic contact maps, using global optimization algorithms to\nsearch for the Wyckoff positions by maximizing the match between the contact\nmap of the predicted structure and the contact map of the true crystal\nstructure. However, our previous contact map based CSP algorithms have two\nmajor limitations: (1) the loss of search capability due to getting trapped in\nlocal optima; (2) it only uses the connection of atoms in the unit cell to\npredict the crystal structure, ignoring the chemical environment outside the\nunit cell, which may lead to unreasonable coordination environments. Herein we\npropose a novel multi-objective genetic algorithms for contact map-based\ncrystal structure prediction by optimizing three objectives, including contact\nmap match accuracy, the individual age, and the coordination number match.\nFurthermore, we assign the age values to all the individuals of the GA and try\nto minimize the age aiming to avoid the premature convergence problem. Our\nexperimental results show that compared to our previous CMCrystal algorithm,\nour multi-objective crystal structure prediction algorithm (CMCrystalMOO) can\nreconstruct the crystal structure with higher quality and alleviate the problem\nof premature convergence.\n"}
+{"abstract": "  Partial groups are a natural generalization of discrete groups recently\nintroduced by Chermak in connection with the theory of fusion systems. In this\npaper we develop an extension theory for partial groups based in the classical\ntheory of fibre bundles of simplicial sets.\n"}
+{"abstract": "  Highly r-process enhanced metal-poor stars (MP r-II, $\\rm [Eu/Fe]>1$ and $\\rm\n[Fe/H]\\lesssim-1.5$) have been observed in ultra-faint dwarf (UFD) galaxy,\nspecifically in Reticulum~II (Ret~II). The fact that only a few UFDs contain\nsuch stars implies that the r-process site may reflect very rare, but\nindividually prolific events, such as neutron star mergers (NSMs). Considering\nthe relatively short star formation history (SFH) of UFDs, it is puzzling how\nthey could experience such a rare phenomenon. In this work, we show the results\nof cosmological hydrodynamic zoom-in simulations of isolated UFDs\n($M_{vir}\\approx10^7-10^8$ solar mass and $M_{\\ast}\\approx10^3-10^4$ solar mass\nat $z=0$) to explain the formation of MP r-II stars in UFDs. We employ a simple\ntoy model for NSM events, adopting parameters consistent with observations,\nsuch as the NSM rate (1 per $M_{\\ast}\\approx10^5$ solar mass) and europium (Eu)\nmass ($M_{Eu}\\approx10^{-5}$ solar mass). We identify only one simulated galaxy\n($ M_{vir}\\approx4.6\\times10^7$, $M_{\\ast}\\approx 3.4\\times 10^3$ solar mass at\n$z=0$) with abundances similar to Ret~II in a simulation volume that hosts\n$\\sim30$ UFD analogs, indicating that such abundances are possible but rare. By\nexploring a range of key parameters, we demonstrate that the most important\nfactor in determining the formation of MP r-II stars in UFDs is how quickly\nsubsequent stars can be formed out of r-process enriched gas. We find that it\ntakes between 10 to 100~Myr to form the first and second burst of MP r-II\nstars. Over this period, Eu-polluted gas maintains the required high abundance\nratios of $\\rm [Eu/Fe]>1$.\n"}
+{"abstract": "  The climate emergency increasingly threatens our communities, ecosystems,\nfood production, health, and economy. It disproportionately impacts lower\nincome communities, communities of color, and the elderly. Assessments since\nthe 2018 IPCC 1.5 Celsius report show that current national and sub-national\ncommitments and actions are insufficient. Fortunately, a suite of solutions\nexists now to mitigate the climate crisis if we initiate and sustain actions\ntoday. California, which has a strong set of current targets in place and is\nhome to clean energy and high technology innovation, has fallen behind in its\nclimate ambition compared to a number of major governments. California, a\ncatalyst for climate action globally, can and should ramp up its leadership by\naligning its climate goals with the most recent science, coordinating actions\nto make 2030 a point of significant accomplishment. This entails dramatically\naccelerating its carbon neutrality and net-negative emissions goal from 2045 to\n2030, including advancing clean energy and clean transportation standards, and\naccelerating nature-based solutions on natural and working lands. It also means\nchanging its current greenhouse gas reduction goals both in the percentage and\nthe timing: cutting emissions by 80 percent (instead of 40 percent) below 1990\nlevels much closer to 2030 than 2050. These actions will enable California to\nsave lives, benefit underserved and frontline communities, and save trillions\nof dollars. This rededication takes heed of the latest science, accelerating\nequitable, job-creating climate policies. While there are significant\nchallenges to achieving these goals, California can establish policy now that\nwill unleash innovation and channel market forces, as has happened with solar,\nand catalyze positive upward-scaling tipping points for accelerated global\nclimate action.\n"}
+{"abstract": "  Distributions of open source software packages dedicated to specific\nprogramming languages facilitate software development by allowing software\nprojects to depend on the functionality provided by such reusable packages. The\nhealth of a software project can be affected by the maturity of the packages on\nwhich it depends. The version numbers of the used package releases provide an\nindication of their maturity. Packages with a 0.y.z version number are commonly\nassumed to be under initial development, suggesting that they are likely to be\nless stable, and depending on them may be considered as less healthy. In this\npaper, we empirically study, for four open source package distributions (Cargo,\nnpm, Packagist and RubyGems) to which extent 0.y.z package releases and >=1.0.0\npackage releases behave differently. We quantify the prevalence of 0.y.z\nreleases, we explore how long packages remain in the initial development stage,\nwe compare the update frequency of 0.y.z and >=1.0.0 package releases, we study\nhow often 0.y.z releases are required by other packages, we assess whether\nsemantic versioning is respected for dependencies towards them, and we compare\nsome characteristics of 0.y.z and >=1.0.0 package repositories hosted on\nGitHub. Among others, we observe that package distributions are more permissive\nthan what semantic versioning dictates for 0.y.z releases, and that many of the\n0.y.z releases can actually be regarded as mature packages. As a consequence,\nthe version number does not provide a good indication of the maturity of a\npackage release.\n"}
+{"abstract": "  We report the observation of matter-wave jet emission in a strongly\nferromagnetic spinor Bose-Einstein condensate of $^7$Li atoms. Directional\natomic beams with $|{F=1,m_F=1}\\rangle$ and $|{F=1,m_F=-1}\\rangle$ spin states\nare generated from $|{F=1,m_F=0}\\rangle$ state condensates, or vice versa. This\nresults from collective spin-mixing scattering events, where spontaneously\nproduced pairs of atoms with opposite momentum facilitates additional\nspin-mixing collisions as they pass through the condensates. The matter-wave\njets of different spin states ($|{F=1,m_F=\\pm1}\\rangle$) can be a macroscopic\nEinstein-Podolsky-Rosen state with spacelike separation. Its spin-momentum\ncorrelations are studied by using the angular correlation function for each\nspin state. Rotating the spin axis, the inter-spin and intra-spin momentum\ncorrelation peaks display a high contrast oscillation, indicating collective\ncoherence of the atomic ensembles. We provide numerical calculations that\ndescribe the experimental results at a quantitative level and can identify its\nentanglement after 100~ms of a long time-of-flight.\n"}
+{"abstract": "  Observations in the lowest MWA band between $75-100$ MHz have the potential\nto constrain the distribution of neutral hydrogen in the intergalactic medium\nat redshift $\\sim 13-17$. Using 15 hours of MWA data, we analyse systematics in\nthis band such as radio-frequency interference (RFI), ionospheric and wide\nfield effects. By updating the position of point sources, we mitigate the\ndirection independent calibration error due to ionospheric offsets. Our\ncalibration strategy is optimized for the lowest frequency bands by reducing\nthe number of direction dependent calibrators and taking into account radio\nsources within a wider field of view. We remove data polluted by systematics\nbased on the RFI occupancy and ionospheric conditions, finally selecting 5.5\nhours of the cleanest data. Using these data, we obtain two sigma upper limits\non the 21 cm power spectrum in the range of $0.1\\lessapprox k \\lessapprox 1\n~\\rm ~h~Mpc^{-1}$ and at $z$=14.2, 15.2 and 16.5, with the lowest limit being\n$6.3\\times 10^6 ~\\rm mK^2$ at $\\rm k=0.14 \\rm ~h~Mpc^{-1}$ and at $z=15.2$ with\na possibility of a few \\% of signal loss due to direction independent\ncalibration.\n"}
+{"abstract": "  Ageing of society and increase of time spent with chronic conditions\nchallenge the traditional long-term care model. Assistive technology and\neHealth are seen to play an important role when addressing these challenges.\nOne prominent example are patient self-management systems. These systems not\nonly transform the way patients with chronic conditions interact with the\nhealthcare system, but also change work practices of care providers. This\nliterature review addresses sociotechnical challenges of eHealth technologies\nwith a strong collaborative component. As a result, four themes are identified\nand discussed.\n"}
+{"abstract": "  We uncover a highly nontrivial dependence of the spin-noise (SN) resonance\nbroadening induced by the intense probe beam. The measurements were performed\nby probing the cell with cesium vapor at the wavelengths of the transition\n${6}^2S_{1/2} \\leftrightarrow {6}^2P_{3/2}$ ($\\mathrm{D}_2$ line) with the\nunresolved hyperfine structure of the excited state. The light-induced\nbroadening of the SN resonance was found to differ strongly at different slopes\nof the $\\mathrm{D}_2$ line and, generally, varied nonmonotonically with light\npower. We discuss the effect in terms of the phenomenological Bloch equations\nfor the spin fluctuations and demonstrate that the SN broadening behavior\nstrongly depends on the relation between the pumping and excited-level decay\nrates, the spin precession, and decoherence rates. To reconcile the puzzling\nexperimental results, we propose that the degree of optical perturbation of the\nspin-system is controlled by the route of the excited-state relaxation of the\natom or, in other words, that the act of optical excitation of the atom does\nnot necessarily break down completely its ground-state coherence and continuity\nof the spin precession. Spectral asymmetry of the effect, in this case, is\nprovided by the position of the \"closed\" transition $F = 4 \\leftrightarrow F' =\n5$ at the short-wavelength side of the line. This hypothesis, however, remains\nto be proven by microscopic calculations.\n"}
+{"abstract": "  Languages emerge and change over time at the population level though\ninteractions between individual speakers. It is, however, hard to directly\nobserve how a single speaker's linguistic innovation precipitates a\npopulation-wide change in the language, and many theoretical proposals exist.\nWe introduce a very general mathematical model that encompasses a wide variety\nof individual-level linguistic behaviours and provides statistical predictions\nfor the population-level changes that result from them. This model allows us to\ncompare the likelihood of empirically-attested changes in definite and\nindefinite articles in multiple languages under different assumptions on the\nway in which individuals learn and use language. We find that accounts of\nlanguage change that appeal primarily to errors in childhood language\nacquisition are very weakly supported by the historical data, whereas those\nthat allow speakers to change incrementally across the lifespan are more\nplausible, particularly when combined with social network effects.\n"}
+{"abstract": "  This paper addresses the problem of mirror surface reconstruction, and\nproposes a solution based on observing the reflections of a moving reference\nplane on the mirror surface. Unlike previous approaches which require tedious\ncalibration, our method can recover the camera intrinsics, the poses of the\nreference plane, as well as the mirror surface from the observed reflections of\nthe reference plane under at least three unknown distinct poses. We first show\nthat the 3D poses of the reference plane can be estimated from the reflection\ncorrespondences established between the images and the reference plane. We then\nform a bunch of 3D lines from the reflection correspondences, and derive an\nanalytical solution to recover the line projection matrix. We transform the\nline projection matrix to its equivalent camera projection matrix, and propose\na cross-ratio based formulation to optimize the camera projection matrix by\nminimizing reprojection errors. The mirror surface is then reconstructed based\non the optimized cross-ratio constraint. Experimental results on both synthetic\nand real data are presented, which demonstrate the feasibility and accuracy of\nour method.\n"}
+{"abstract": "  I discuss the scope and naturalness of the proton mass decomposition (or sum\nrule) published in PRL74, 1071 (1995) and answer a few criticisms that appeared\nrecently in the literature, focusing particularly on its interpretation and the\nquantum anomalous energy contribution. I comment on the so-called\nframe-independent or invariant-mass decomposition from the trace of the\nenergy-momentum tensor. I stress the importance of measuring the quantum\nanomalous energy through experiments. Finally, I point out a large discrepancy\nin the scalar radius of the nucleon extracted from vector-meson productions and\nlattice QCD calculations.\n"}
+{"abstract": "  Recent advances in wireless communication and solid-state circuits together\nwith the enormous demands of sensing ability have given rise to a new enabling\ntechnology, integrated sensing and communications (ISAC). The ISAC captures two\nmain advantages over dedicated sensing and communication functionalities: 1)\nIntegration gain to efficiently utilize congested resources, and even, 2)\nCoordination gain to balance dual-functional performance or/and perform mutual\nassistance. Meanwhile, triggered by ISAC, we are also witnessing a paradigm\nshift in the ubiquitous IoT architecture, in which the sensing and\ncommunication layers are tending to converge into a new layer, namely, the\nsignaling layer.\n  In this paper, we first attempt to introduce a definition of ISAC, analyze\nthe various influencing forces, and present several novel use cases. Then, we\ncomplement the understanding of the signaling layer by presenting several key\nbenefits in the IoT era. We classify existing dominant ISAC solutions based on\nthe layers in which integration is applied. Finally, several challenges and\nopportunities are discussed. We hope that this overview article will serve as a\nprimary starting point for new researchers and offer a bird's-eye view of the\nexisting ISAC-related advances from academia and industry, ranging from\nsolid-state circuitry, signal processing, and wireless communication to mobile\ncomputing.\n"}
+{"abstract": "  Automatic segmentation of the musculoskeletal system in pediatric magnetic\nresonance (MR) images is a challenging but crucial task for morphological\nevaluation in clinical practice. We propose a deep learning-based regularized\nsegmentation method for multi-structure bone delineation in MR images, designed\nto overcome the inherent scarcity and heterogeneity of pediatric data. Based on\na newly devised shape code discriminator, our adversarial regularization scheme\nenforces the deep network to follow a learnt shape representation of the\nanatomy. The novel shape priors based adversarial regularization (SPAR)\nexploits latent shape codes arising from ground truth and predicted masks to\nguide the segmentation network towards more consistent and plausible\npredictions. Our contribution is compared to state-of-the-art regularization\nmethods on two pediatric musculoskeletal imaging datasets from ankle and\nshoulder joints.\n"}
+{"abstract": "  This paper is devoted to Bresse systems, a robust model for circular beams,\ngiven by a set of three coupled wave equations. The main objective is to\nestablish the existence of global attractors for dynamics of semilinear\nproblems with localized damping. In order to deal with localized damping a\nunique continuation property (UCP) is needed. Therefore we also provide a\nsuitable UCP for Bresse systems. Our strategy is to set the problem in a\nRiemannian geometry framework and see the system as a single equation with\ndifferent Riemann metrics. Then we perform Carleman-type estimates to get our\nresult.\n"}
+{"abstract": "  All light has structure, but only recently it has become possible to\nconstruct highly controllable and precise potentials so that most laboratories\ncan harness light for their specific applications. In this chapter, we review\nthe emerging techniques for high-resolution and configurable optical trapping\nof ultracold atoms. We focus on optical deflectors and spatial light modulators\nin the Fourier and direct imaging configurations. These optical techniques have\nenabled significant progress in studies of superfluid dynamics, single-atom\ntrapping, and underlie the emerging field of atomtronics. The chapter is\nintended as a complete guide to the experimentalist for understanding,\nselecting, and implementing the most appropriate optical trapping technology\nfor a given application. After introducing the basic theory of optical trapping\nand image formation, we describe each of the above technologies in detail,\nproviding a guide to the fundamental operation of optical deflectors, digital\nmicromirror devices, and liquid crystal spatial light modulators. We also\ndescribe the capabilities of these technologies for manipulation of trapped\nultracold atoms, where the potential is dynamically modified to enable\nexperiments, and where time-averaged potentials can realise more complex traps.\nThe key considerations when implementing time-averaged traps are described.\n"}
+{"abstract": "  We observe a novel type of shear banding in the rheology of thixotropic\nyield-stress fluids that is due to the coupling of both non-locality and\nthixotropy. The latter is known to lead to shear banding even in homogeneous\nstress fields, but the bands observed in the presence of non-local effects\nappear different as the shear rate varies continuously over the shear band.\nHere, we introduce a simple non-local model for the shear banding (NL-SB), and\nwe implement it for the analysis of micron-scale rheo-MRI velocimetry\nmeasurements of a milk microgel suspension in a cone-and-plate geometry. The\nproposed NL-SB model accurately quantifies the cooperativity length and yields\nvalues in the order of the aggregate size in the microgel.\n"}
+{"abstract": "  With web and mobile platforms becoming more prominent devices utilized in\ndata analysis, there are currently few systems which are not without flaw. In\norder to increase the performance of these systems and decrease errors of data\noversimplification, we seek to understand how other programming languages can\nbe used across these platforms which provide data and type safety, as well as\nutilizing concurrency to perform complex data manipulation tasks.\n"}
+{"abstract": "  Observations from dynamical systems often exhibit irregularities, such as\ncensoring, where values are recorded only if they fall within a certain range.\nCensoring is ubiquitous in practice, due to saturating sensors,\nlimit-of-detection effects, and image-frame effects. In light of recent\ndevelopments on learning linear dynamical systems (LDSs), and on censored\nstatistics with independent data, we revisit the decades-old problem of\nlearning an LDS, from censored observations (Lee and Maddala (1985); Zeger and\nBrookmeyer (1986)). Here, the learner observes the state $x_t \\in \\mathbb{R}^d$\nif and only if $x_t$ belongs to some set $S_t \\subseteq \\mathbb{R}^d$. We\ndevelop the first computationally and statistically efficient algorithm for\nlearning the system, assuming only oracle access to the sets $S_t$. Our\nalgorithm, Stochastic Online Newton with Switching Gradients, is a novel\nsecond-order method that builds on the Online Newton Step (ONS) of Hazan et al.\n(2007). Our Switching-Gradient scheme does not always use (stochastic)\ngradients of the function we want to optimize, which we call \"censor-aware\"\nfunction. Instead, in each iteration, it performs a simple test to decide\nwhether to use the censor-aware, or another \"censor-oblivious\" function, for\ngetting a stochastic gradient.\n  In our analysis, we consider a \"generic\" Online Newton method, which uses\narbitrary vectors instead of gradients, and we prove an error-bound for it.\nThis can be used to appropriately design these vectors, leading to our\nSwitching-Gradient scheme. This framework significantly deviates from the\nrecent long line of works on censored statistics (e.g., Daskalakis et al.\n(2018); Kontonis et al. (2019); Daskalakis et al. (2019)), which apply\nStochastic Gradient Descent (SGD), and their analysis reduces to establishing\nconditions for off-the-shelf SGD-bounds.\n"}
+{"abstract": "  A new nonlinear integral equation (NLIE) describing the thermodynamics of the\nHeisenberg spin chain is derived based on the t-W relation of the quantum\ntransfer matrices. The free energy of the system in a magnetic field is thus\nobtained by solving the NLIE. This method can be generalized to other lattice\nquantum integrable models. Taking the SU(3)-invariant quantum spin chain as an\nexample, we construct the corresponding NLIEs and compute the free energy. The\npresent results coincide exactly with those obtained via other methods\npreviously.\n"}
+{"abstract": "  The Spatial Convolution Model (SCM) analyzes allostery based on the spatial\nevolution of the docking protein elastic media, whereby convolution of the\nmedia in response to wave propagation is solved as a function of Z fluctuations\nand backbone vibration modes. We show that although the elastic media is a\ncomplex three-dimensional structure allostery behaves as if it occurs along a\nstretched oscillating string, where inhomogeneities along the string effect\nlocal entropies responsible for ligand binding and transduction of allosteric\nwaves. To identify inhomogeneities along the string, we ignored local density\nand tension changes during wave propagation and resolved helix wave and\nphysical properties by applying molecular string and beam bending theories.\nImportantly, we show that allostery occurs at three major scales and that\npropagation of standing waves create a rolling entropy which drives entropy\ntransfers between fields. Conversion of resonance energy to quantum harmonic\noscillators allowed us to consider effects of damping and interactions with the\nsurrounding media as well as to model effects of residue interaction strength\non entropy transfer.\n"}
+{"abstract": "  We introduce a collection of recognizing textual entailment (RTE) datasets\nfocused on figurative language. We leverage five existing datasets annotated\nfor a variety of figurative language -- simile, metaphor, and irony -- and\nframe them into over 12,500 RTE examples.We evaluate how well state-of-the-art\nmodels trained on popular RTE datasets capture different aspects of figurative\nlanguage. Our results and analyses indicate that these models might not\nsufficiently capture figurative language, struggling to perform pragmatic\ninference and reasoning about world knowledge. Ultimately, our datasets provide\na challenging testbed for evaluating RTE models.\n"}
+{"abstract": "  It is well known that impurities play a central role in the linear and\nnonlinear response of graphene at optical and terahertz frequencies. In this\nwork, we calculate the bands and intraband dipole connection elements for\nnitrogen-doped monolayer graphene using a density functional tight binding\napproach. Employing these results, we calculate the linear and nonlinear\nresponse of the doped graphene to terahertz pulses using a density-matrix\napproach in the length gauge. We present the results for the linear and\nnonlinear mobility as well as third harmonic generation in graphene for\nadsorbed and substitutional nitrogen doping for a variety of doping densities.\nWe show that the conduction bands are more parabolic in graphene structures\nwith substitutional nitrogen doping than for those with adsorbed nitrogen. As a\nresult, substitutional doping has a greater impact on the terahertz mobility\nand nonlinear response of graphene than adsorbed nitrogen does.\n"}
+{"abstract": "  In todays world, where the need for security is paramount and biometric\naccess control systems are gaining mass acceptance due to their increased\nreliability, research in this area is quite relevant. Also with the advent of\nIOT devices and increased community support for cheap and small computers like\nRaspberry Pi its convenient than ever to design a complete standalone system\nfor any purpose. This paper proposes a Facial Biometric System built on the\nclient-server paradigm using Raspberry Pi 3 model B running a novel local\ndescriptor based parallel algorithm. This paper also proposes an extended\nversion of Local Gradient Hexa Pattern with improved accuracy. The proposed\nextended version of LGHP improved performance as shown in performance analysis.\nExtended LGHP shows improvement over other state-of-the-art descriptors namely\nLDP, LTrP, MLBP and LVP on the most challenging benchmark facial image\ndatabases, i.e. Cropped Extended Yale-B, CMU-PIE, color-FERET, LFW, and\nGhallager database. Proposed system is also compared with various patents\nhaving similar system design and intent to emphasize the difference and novelty\nof the system proposed.\n"}
+{"abstract": "  Catastrophic forgetting undermines the effectiveness of deep neural networks\n(DNNs) in scenarios such as continual learning and lifelong learning. While\nseveral methods have been proposed to tackle this problem, there is limited\nwork explaining why these methods work well. This paper has the goal of better\nexplaining a popularly used technique for avoiding catastrophic forgetting:\nquadratic regularization. We show that quadratic regularizers prevent\nforgetting of past tasks by interpolating current and previous values of model\nparameters at every training iteration. Over multiple training iterations, this\ninterpolation operation reduces the learning rates of more important model\nparameters, thereby minimizing their movement. Our analysis also reveals two\ndrawbacks of quadratic regularization: (a) dependence of parameter\ninterpolation on training hyperparameters, which often leads to training\ninstability and (b) assignment of lower importance to deeper layers, which are\ngenerally the place forgetting occurs in DNNs. Via a simple modification to the\norder of operations, we show these drawbacks can be easily avoided, resulting\nin 6.2% higher average accuracy at 4.5% lower average forgetting. Code\navailable at \\url{https://github.com/EkdeepSLubana/QRforgetting}\n"}
+{"abstract": "  Video Question Answering is a task which requires an AI agent to answer\nquestions grounded in video. This task entails three key challenges: (1)\nunderstand the intention of various questions, (2) capturing various elements\nof the input video (e.g., object, action, causality), and (3) cross-modal\ngrounding between language and vision information. We propose Motion-Appearance\nSynergistic Networks (MASN), which embed two cross-modal features grounded on\nmotion and appearance information and selectively utilize them depending on the\nquestion's intentions. MASN consists of a motion module, an appearance module,\nand a motion-appearance fusion module. The motion module computes the\naction-oriented cross-modal joint representations, while the appearance module\nfocuses on the appearance aspect of the input video. Finally, the\nmotion-appearance fusion module takes each output of the motion module and the\nappearance module as input, and performs question-guided fusion. As a result,\nMASN achieves new state-of-the-art performance on the TGIF-QA and MSVD-QA\ndatasets. We also conduct qualitative analysis by visualizing the inference\nresults of MASN. The code is available at\nhttps://github.com/ahjeongseo/MASN-pytorch.\n"}
+{"abstract": "  We propose to enhance the performance of localized plasmon structured\nillumination microscopy (LP-SIM) via intensity correlations. LP-SIM uses\nsub-wavelength illumination patterns to encode high spatial frequency\ninformation. It can enhance the resolution up to three-fold before gaps in the\nOTF support arise. For blinking fluorophores or for quantum antibunching an\nintensity correlation analysis induces higher harmonics of the illumination\npattern and enlarges the effective OTF. This enables ultrahigh resolutions\nwithout gaps in the OTF support, and thus a fully deterministic imaging scheme.\nWe present simulations that include shot and external noise and demonstrate the\nresolution power under realistic photon budgets. The technique has potential in\nlight microscopy where low-intensity illumination is paramount while aiming for\nhigh spatial but moderate temporal resolutions.\n"}
+{"abstract": "  We develop a geometric method to establish existence of equilibrium states\nassociated to H\\\"older potentials for center isometries (as are regular\nelements of Anosov actions), in particular the entropy maximizing measure and\nthe SRB measure. In these cases it is established the uniqueness, and it is\ngiven a characterization of the equilibrium states in terms in terms of their\ndisintegrations along stable and unstable foliations. It is proven also that\nthe resulting system is isomorphic to a Bernoulli scheme.\n"}
+{"abstract": "  Frost growth on cold surfaces is a transient process with coupled heat and\nmass transfer. Due to multiple factors such as humidity, temperature, flow\nvelocity and constantly changing thermal properties as frost grows, precise\nprediction can be challenging. Especially when the geometry of the frosting\nsurfaces gets complicated, it requires a balance of computing accuracy and\nefficiency. In this work, a numerical model is developed to predict frost\ngrowth considering the effect of the above parameters. Mixture model is adapted\nto improve the computational efficiency and the unstructured grids add the\nflexibility to extend the model to complex geometries. The predicted frost\ngrowth rate matches well with the experimental data reported in the literature\nunder similar conditions. The model predicts reasonable growth trend of frost\nas the surface temperature, air temperature, humidity and flow velocity vary.\nThe surface wettability effect is well captured at the early stage of frosting\nand it shows a higher frost growth rate on surfaces with a higher wettability.\n"}
+{"abstract": "  The excitation of a black hole by infalling matter or radiation has been\nstudied for a long time, mostly in linear perturbation theory. In this paper we\nstudy numerically the response of a Schwarzschild black hole to an incoming\ngravitational wave pulse. We present a numerically well-posed initial boundary\nvalue problem for the generalized conformal field equations in which a wave\nprofile for the ingoing wave is specified at an outer time-like boundary which\nthen hits an initially static and spherically symmetric black hole. The\nnon-linear interaction of the black hole with the gravitational wave leads to\nscattered radiation moving back out. The clean separation between initial state\nand incoming radiation makes this setup ideal to study scattering problems. The\nuse of the conformal field equations allows us to trace the response of the\nblack hole to null infinity where we can read off the scattered gravitational\nwaves and compute the Bondi-Sachs mass and the gravitational flux through\n$\\mathscr{I}$. In this way we check the Bondi-Sachs mass loss formula directly\non null infinity. We also comment on comparisons with quasinormal modes.\n"}
+{"abstract": "  We investigate strongly asymmetric self-assembled nanostructures with one of\ndimensions reaching hundreds of nanometers. Close to the nanowire-like type of\nconfinement, such objects are sometimes assigned as one-dimensional in nature.\nHere, we directly observe the spectrum of exciton excited states corresponding\nto longitudinal quantization. This is based on probing the optical transitions\nvia polarization-resolved microphotoluminescence excitation ($\\mu$PLE)\nmeasurement performed on single nanostructures combined with theoretical\ncalculation of neutral and charged exciton optical properties. We successfully\nprobe absorption-like spectra for individual bright states forming the exciton\nground-state fine structure, as well as for the negatively charged exciton.\nConfronting the calculated spectrum of excitonic absorption with $\\mu$PLE\ntraces, we identify optical transitions involving states that contain carriers\nat various excited levels related to the longest dimension. Based on\ncross-polarized excitation-detection scheme, we show very well conserved spin\nconfiguration during orbital relaxation of the exciton from a number of excited\nstates comparable to the quasi-resonant pumping via the optical phonon, and no\npolarization memory for the trion, as theoretically expected.\n"}
+{"abstract": "  Throughout the COVID-19 pandemic, the most common symptom displayed by\npatients has been a fever, leading to the use of temperature scanning as a\npreemptive measure to detect potential carriers of the virus. Human employees\nwith handheld thermometers have been used to fulfill this task, however this\nputs them at risk as they cannot be physically distanced and the sequential\nnature of this method leads to great inconveniences and inefficiency. The\nproposed solution is an autonomously navigating robot capable of conversing and\nscanning people's temperature to detect fevers and help screen for COVID-19. To\nsatisfy this objective, the robot must be able to (1) navigate autonomously,\n(2) detect and track people, and (3) get individuals' temperature reading and\nconverse with them if it exceeds 38{\\deg}C. An autonomously navigating mobile\nrobot is used with a manipulator controlled using a face tracking algorithm,\nand an end effector consisting of a thermal camera, smartphone, and chatbot.\nThe goal is to develop a functioning solution that performs the above tasks. In\naddition, technical challenges encountered and their engineering solutions will\nbe presented, and recommendations will be made for enhancements that could be\nincorporated when approaching commercialization.\n"}
+{"abstract": "  We prove a version of Temkin's local altered uniformization theorem. We show\nthat for any rig-smooth, quasi-compact and quasi-separated admissible formal\n$\\mathcal{O}_K$-model $\\mathfrak{X}$, there is a finite extension $K'/K$ such\nthat $\\mathfrak{X}_{\\mathcal{O}_{K'}}$ locally admits a rig-\\'etale morphism\n$g\\colon \\mathfrak{X}' \\to \\mathfrak{X}_{\\mathcal{O}_{K'}}$ and a\nrig-isomorphism $h\\colon \\mathfrak{X}'' \\to \\mathfrak{X}'$ with $\\mathfrak{X}'$\nbeing a successive semi-stable curve fibration over $\\mathcal{O}_{K'}$ and\n$\\mathfrak{X}''$ being a poly-stable formal $\\mathcal{O}_{K'}$-scheme.\nMoreover, $\\mathfrak{X}'$ admits an action of a finite group $G$ such that\n$g\\colon \\mathfrak{X}' \\to \\mathfrak{X}_{\\mathcal{O}_{K'}}$ is $G$-invariant,\nand the adic generic fiber $\\mathfrak{X}'_{K'}$ becomes a $G$-torsor over its\nquasi-compact open image $U=g_{K'}(\\mathfrak{X}'_{K'})$. Also, we study\nproperties of the quotient map $\\mathfrak{X}'/G \\to\n\\mathfrak{X}_{\\mathcal{O}_{K'}}$ and show that it can be obtained as a\ncomposition of open immersions and rig-isomorphisms.\n"}
+{"abstract": "  The operating system is designed to manage the hardware and software\nresources of a computer. With the development of quantum computing, the\nmanagement of quantum resources and cooperation between quantum systems and\nother computing resources (e.g. CPU, GPU and FPGA etc.) become the key\nchallenge for the application of quantum computing to solve real world\nproblems. In this paper we propose a quantum operating system, Origin Pilot.\nOrigin Pilot includes the module of quantum task scheduling, quantum resource\nmanagement, quantum program compilation and qubits' automatic calibration. With\nthese modules, Origin Pilot can manage the quantum computing resources and\nsolve the multi-quantum processor scheduling problem. It can also allow the\nparallel execution of multiple quantum programs and calibrate the quantum\nresource effectively. Thus, the performance of resources is guaranteed and the\nresource utilization is improved. By comparing the results with and without\nOrigin Pilot, we evaluate the impact on a quantum circuit's fidelity of qubits\nmapping algorithm. We also evaluate the effectiveness of automatic calibration\nand parallel execution of multi-quantum processors. Finally, Origin Pilot can\nbe easily customized for hybrid computing resources.\n"}
+{"abstract": "  We demonstrate for the first time the coexistence of a quantum-channel and\n8x200 Gpbs 16-QAM optical channels with launching powers as high as -9dBm per\nchannel in a 2 km HC-NANF. Comparative analysis with single-mode fibre reveals\nthat the quantum channel could not be sustained at such power levels.\n"}
+{"abstract": "  We present an energy scaling function to predict, in a specific range, the\nenergy of bosonic trimers with large scattering lengths and finite range\ninteractions, which is validated by quantum Monte Carlo calculations using\nmicroscopic Hamiltonians with two- and three-body potentials. The proposed\nscaling function depends on the scattering length, effective range, and a\nreference energy, which we chose as the trimer energy at unitarity. We obtained\nthe scaling function as a limit cycle from the solution of the renormalized\nzero-range model with effective range corrections. We proposed a simple\nparametrization of the energy scaling function. Besides the intrinsic interest\nin theoretical and experimental investigations, this scaling function allows\none to probe Efimov physics with only the trimer ground states, which may open\nopportunities to identify Efimov trimers whenever access to excited states is\nlimited.\n"}
+{"abstract": "  In this work, we continue the study of the bifurcations of the critical\npoints in a symmetric Caldera potential energy surface. In particular, we study\nthe influence of the depth of the potential on the trajectory behavior before\nand after the bifurcations of the critical points. We observe two different\ntypes of trajectory behavior: dynamical matching and the non-existence of\ndynamical matching. Dynamical matching is a phenomenon that limits the way in\nwhich a trajectory can exit the Caldera based solely on how it enters the\nCaldera. Furthermore, we discuss two different types of symmetric Caldera\npotential energy surface and the transition from the one type to the other\nthrough the bifurcations of the critical points.\n"}
+{"abstract": "  Existing approaches to unsupervised object discovery (UOD) do not scale up to\nlarge datasets without approximations that compromise their performance. We\npropose a novel formulation of UOD as a ranking problem, amenable to the\narsenal of distributed methods available for eigenvalue problems and link\nanalysis. Through the use of self-supervised features, we also demonstrate the\nfirst effective fully unsupervised pipeline for UOD. Extensive experiments on\nCOCO and OpenImages show that, in the single-object discovery setting where a\nsingle prominent object is sought in each image, the proposed LOD (Large-scale\nObject Discovery) approach is on par with, or better than the state of the art\nfor medium-scale datasets (up to 120K images), and over 37% better than the\nonly other algorithms capable of scaling up to 1.7M images. In the multi-object\ndiscovery setting where multiple objects are sought in each image, the proposed\nLOD is over 14% better in average precision (AP) than all other methods for\ndatasets ranging from 20K to 1.7M images. Using self-supervised features, we\nalso show that the proposed method obtains state-of-the-art UOD performance on\nOpenImages. Our code is publicly available at https://github.com/huyvvo/LOD.\n"}
+{"abstract": "  Future wireless communications are largely inclined to deploy a massive\nnumber of antennas at the base stations (BS) by exploiting energy-efficient and\nenvironmentally friendly technologies. An emerging technology called dynamic\nmetasurface antennas (DMAs) is promising to realize such massive antenna arrays\nwith reduced physical size, hardware cost, and power consumption. This paper\naims to optimize the energy efficiency (EE) performance of DMAs-assisted\nmassive MIMO uplink communications. We propose an algorithmic framework for\ndesigning the transmit precoding of each multi-antenna user and the DMAs tuning\nstrategy at the BS to maximize the EE performance, considering the availability\nof the instantaneous and statistical channel state information (CSI),\nrespectively. Specifically, the proposed framework includes Dinkelbach's\ntransform, alternating optimization, and deterministic equivalent methods. In\naddition, we obtain a closed-form solution to the optimal transmit signal\ndirections for the statistical CSI case, which simplifies the corresponding\ntransmission design. The numerical results show good convergence performance of\nour proposed algorithms as well as considerable EE performance gains of the\nDMAs-assisted massive MIMO uplink communications over the baseline schemes.\n"}
+{"abstract": "  We study $\\infty$-Ground states in convex domains in the plane. In a polygon,\nthe points where an $\\infty$-Ground state does not satisfy the $\\infty$-Laplace\nEquation are characterized: they are restricted to lie on specific curves,\nwhich are acting as attracting (fictitious) streamlines. The gradient is\ncontinuous outside these curves and no streamlines can meet there.\n"}
+{"abstract": "  Persistent homology has undergone significant development in recent years.\nHowever, one outstanding challenge is to build a coherent statistical inference\nprocedure on persistent diagrams. In this paper, we first present a new lattice\npath representation for persistent diagrams. We then develop a new exact\nstatistical inference procedure for lattice paths via combinatorial\nenumerations. The lattice path method is applied to the topological\ncharacterization of the protein structures of the COVID-19 virus. We\ndemonstrate that there are topological changes during the conformational change\nof spike proteins.\n"}
+{"abstract": "  Computationally tractable methods are developed for centralized goal\nassignment and planning of collision-free polynomial-in-time trajectories for\nsystems of multiple aerial robots. The method first assigns robots to goals to\nminimize total time-in-motion based on initial trajectories. By coupling the\nassignment and trajectory generation, the initial motion plans tend to require\nonly limited collision resolution. The plans are then refined by checking for\npotential collisions and resolving them using either start time delays or\naltitude assignment. Numerical experiments using both methods show significant\nreductions in the total time required for agents to arrive at goals with only\nmodest additional computational effort in comparison to state-of-the-art prior\nwork, enabling planning for thousands of agents.\n"}
+{"abstract": "  With this work, we investigate the use of Reinforcement Learning (RL) for the\ngeneration of spatial assemblies, by combining ideas from Procedural Generation\nalgorithms (Wave Function Collapse algorithm (WFC)) and RL for Game Solving.\nWFC is a Generative Design algorithm, inspired by Constraint Solving. In WFC,\none defines a set of tiles/blocks and constraints and the algorithm generates\nan assembly that satisfies these constraints. Casting the problem of generation\nof spatial assemblies as a Markov Decision Process whose states transitions are\ndefined by WFC, we propose an algorithm that uses Reinforcement Learning and\nSelf-Play to learn a policy that generates assemblies that maximize objectives\nset by the designer. Finally, we demonstrate the use of our Spatial Assembly\nalgorithm in Architecture Design.\n"}
+{"abstract": "  The Mann-Kendall test for trend has gained a lot of attention in a range of\ndisciplines, especially in the environmental sciences. One of the drawbacks of\nthe Mann-Kendall test when applied to real data is that no distinction can be\nmade between meaningful and non-meaningful differences in subsequent\nobservations. We introduce the concept of partial ties, which allows inferences\nwhile accounting for (non)meaningful difference. We introduce the modified\nstatistic that accounts for such a concept and derive its variance estimator.\nWe also present analytical results for the behavior of the test in a class of\ncontiguous alternatives. Simulation results which illustrate the added value of\nthe test are presented. We apply our extended version of the test to some real\ndata concerning blood donation in Europe.\n"}
+{"abstract": "  Ground Penetrating Radar (GPR) is one of the most important non-destructive\nevaluation (NDE) devices to detect subsurface objects (i.e., rebars, utility\npipes) and reveal the underground scene. The two biggest challenges in\nGPR-based inspection are the GPR data collection and subsurface target imaging.\nTo address these challenges, we propose a robotic solution that automates the\nGPR data collection process with a free motion pattern. It facilitates the 3D\nmetric GPR imaging by tagging the pose information with GPR measurement in\nreal-time. We also introduce a deep neural network (DNN) based GPR data\nanalysis method which includes a noise removal segmentation module to clear the\nnoise in GPR raw data and a DielectricNet to estimate the dielectric value of\nsubsurface media in each GPR B-scan data. We use both the field and synthetic\ndata to verify the proposed method. Experimental results demonstrate that our\nproposed method can achieve better performance and faster processing speed in\nGPR data collection and 3D GPR imaging than other methods.\n"}
+{"abstract": "  We consider the task of photo-realistic unconditional image generation\n(generate high quality, diverse samples that carry the same visual content as\nthe image) on mobile platforms using Generative Adversarial Networks (GANs). In\nthis paper, we propose a novel approach to trade-off image generation accuracy\nof a GAN for the energy consumed (compute) at run-time called Scale-Energy\nTradeoff GAN (SETGAN). GANs usually take a long time to train and consume a\nhuge memory hence making it difficult to run on edge devices. The key idea\nbehind SETGAN for an image generation task is for a given input image, we train\na GAN on a remote server and use the trained model on edge devices. We use\nSinGAN, a single image unconditional generative model, that contains a pyramid\nof fully convolutional GANs, each responsible for learning the patch\ndistribution at a different scale of the image. During the training process, we\ndetermine the optimal number of scales for a given input image and the energy\nconstraint from the target edge device. Results show that with SETGAN's unique\nclient-server-based architecture, we were able to achieve a 56% gain in energy\nfor a loss of 3% to 12% SSIM accuracy. Also, with the parallel multi-scale\ntraining, we obtain around 4x gain in training time on the server.\n"}
+{"abstract": "  Modern data centers suffer from immense power consumption. The erratic\nbehavior of internet traffic forces data centers to maintain excess capacity in\nthe form of idle servers in case the workload suddenly increases. As an idle\nserver still consumes a significant fraction of the peak energy, data center\noperators have heavily invested in capacity scaling solutions. In simple terms,\nthese aim to deactivate servers if the demand is low and to activate them again\nwhen the workload increases. To do so, an algorithm needs to strike a delicate\nbalance between power consumption, flow-time, and switching costs. Over the\nlast decade, the research community has developed competitive online algorithms\nwith worst-case guarantees. In the presence of historic data patterns,\nprescription from Machine Learning (ML) predictions typically outperform such\ncompetitive algorithms. This, however, comes at the cost of sacrificing the\nrobustness of performance, since unpredictable surges in the workload are not\nuncommon. The current work builds on the emerging paradigm of augmenting\nunreliable ML predictions with online algorithms to develop novel robust\nalgorithms that enjoy the benefits of both worlds.\n  We analyze a continuous-time model for capacity scaling, where the goal is to\nminimize the weighted sum of flow-time, switching cost, and power consumption\nin an online fashion. We propose a novel algorithm, called Adaptive Balanced\nCapacity Scaling (ABCS), that has access to black-box ML predictions, but is\ncompletely oblivious to the accuracy of these predictions. In particular, if\nthe predictions turn out to be accurate in hindsight, we prove that ABCS is\n$(1+\\varepsilon)$-competitive. Moreover, even when the predictions are\ninaccurate, ABCS guarantees a bounded competitive ratio. The performance of the\nABCS algorithm on a real-world dataset positively support the theoretical\nresults.\n"}
+{"abstract": "  Unmanned Aerial vehicles (UAVs) are widely used as network processors in\nmobile networks, but more recently, UAVs have been used in Mobile Edge\nComputing as mobile servers. However, there are significant challenges to use\nUAVs in complex environments with obstacles and cooperation between UAVs. We\nintroduce a new multi-UAV Mobile Edge Computing platform, which aims to provide\nbetter Quality-of-Service and path planning based on reinforcement learning to\naddress these issues. The contributions of our work include: 1) optimizing the\nquality of service for mobile edge computing and path planning in the same\nreinforcement learning framework; 2) using a sigmoid-like function to depict\nthe terminal users' demand to ensure a higher quality of service; 3) applying\nsynthetic considerations of the terminal users' demand, risk and geometric\ndistance in reinforcement learning reward matrix to ensure the quality of\nservice, risk avoidance, and the cost-savings. Simulations have shown the\neffectiveness and feasibility of our platform, which can help advance related\nresearches.\n"}
+{"abstract": "  A convolutional neural network is used to align an orbital angular momentum\nsorter in a transmission electron microscope. The method is demonstrated using\nsimulations and experimentally. As a result of its accuracy and speed, it\noffers the possibility of real-time tuning of other electron optical devices\nand electron beam shaping configurations.\n"}
+{"abstract": "  In this contribution, we investigate the interaction between electric and\nmagnetic fields with an electric quadrupole moment of a spinless particle\nmoving in an elastic medium which has a topological defect (screw dislocation).\nBy considering this interaction, the Schr\\\"odinger equation is exactly solved\nby using the analytical method. Thus, the eigenfunction and energy eigenvalues\nfor two configurations are found. Meanwhile, by observing a shift in the\nangular momentum quantum number, the energy eigenvalues and the wave function\nof the system are modified, due to the screw dislocation in the medium.\n"}
+{"abstract": "  Spatiotemporal properties of two-dimensional (2D) Hall-magnetohydrodynamic\nturbulence at intermediate plasma $\\beta=2$ are studied by means of Fast\nIterative Filtering, a new technique for the decomposition of nonstationary\nnonlinear signals. Results show that the magnetic energy at sub-ion scales is\nconcentrated in perturbations with frequencies smaller than the ion-cyclotron\n(IC) frequency and with polarization properties that are incompatible with both\nkinetic Alfv\\'en waves (KAWs) and IC waves. At higher frequencies, we clearly\nidentify signatures of both whistler waves and KAWs, however their energetic\ncontribution to the magnetic power spectrum is negligible. We conclude that the\ndynamics of 2D Hall-MHD turbulence at sub-ion scales is mainly driven by\nlocalized intermittent structures, with no significant contribution of wavelike\nfluctuations.\n"}
+{"abstract": "  We analyse errors of randomized explicit and implicit Euler schemes for\napproximate solving of ordinary differential equations (ODEs). We consider\nclasses of ODEs for which the right-hand side functions satisfy Lipschitz\ncondition globally or only locally. Moreover, we assume that only inexact\ndiscrete information, corrupted by some noise, about the right-hand side\nfunction is available. Optimality and stability of explicit and implicit\nrandomized Euler algorithms are also investigated.\n"}
+{"abstract": "  Using the secondary Steenrod algebra, we compute all Adams $d_2$\ndifferentials of the sphere up to the 140th stem, and all $d_2$'s of $C2$,\n$C\\eta$, $C\\nu$, $C\\sigma$ and $C2 \\wedge C\\sigma$ up to the 112th stem.\n  This data allows us to compute hidden $2, \\eta, \\nu$ and $\\sigma$ extensions\nof the sphere that jump by one filtration, from which we resolve all remaining\nunknown $d_2$, $d_3$, $d_4$ and $d_5$ differentials of the sphere up to the\n95th stem.\n"}
+{"abstract": "  Mutual entrainment effects in cold neutron-proton mixtures are studied in the\nframework of the self-consistent nuclear energy-density functional theory.\nExact expressions for the mass currents, valid for both homogeneous and\ninhomogeneous systems, are directly derived from the time-dependent\nHartree-Fock equations with no further approximation. The equivalence with the\nFermi-liquid expression is also demonstrated. Focusing on neutron-star cores, a\nconvenient and simple analytical formulation of the entrainment matrix in terms\nof the isovector effective mass is found, thus allowing to relate entrainment\nphenomena in neutron stars to isovector giant dipole resonances in finite\nnuclei. Results obtained with different functionals are presented. These\ninclude the Brussels-Montreal functionals, for which unified equations of state\nof neutron stars have been recently calculated.\n"}
+{"abstract": "  We obtain sharp inequalities between the large scale asymptotic of the $J$\nfunctional with respect to the $d_1$ metric on the space of Kahler metrics.\nApplications regarding the initial value problem for geodesic rays are\npresented.\n"}
+{"abstract": "  We analyse the gas content evolution of infalling haloes in cluster\nenvironments from THE THREE HUNDRED project, a collection of 324 numerically\nmodelled galaxy clusters. The haloes in our sample were selected within\n$5R_{200}$ of the main cluster halo at $z=0$ and have total halo mass\n$M_{200}\\geq10^{11} h^{-1} M_{\\odot}$. We track their main progenitors and\nstudy their gas evolution since their crossing into the infall region, which we\ndefine as $1-4R_{200}$. Studying the radial trends of our populations using\nboth the full phase space information and a line-of-sight projection, we\nconfirm the Arthur et al. (2019) result and identify a characteristic radius\naround $1.7R_{200}$ in 3D and at $R_{200}$ in projection at which infalling\nhaloes lose nearly all of the gas prior their infall. Splitting the trends by\nsubhalo status we show that subhaloes residing in group-mass and low-mass host\nhaloes in the infall region follow similar radial gas-loss trends as their\nhosts, whereas subhaloes of cluster-mass host haloes are stripped of their gas\nmuch further out. Our results show that infalling objects suffer significant\ngaseous disruption that correlates with time-since-infall, cluster-centric\ndistance and host mass, and that the gaseous disruption they experience is a\ncombination of subhalo pre-processing and object gas depletion at a radius\nwhich behaves like an accretion shock.\n"}
+{"abstract": "  Eddington-inspired Born-Infeld gravity is an important modification of\nEinstein's general relativity, which can give rise to non-singular cosmologies\nat the classical level, and avoid the end-stage singularity in a gravitational\ncollapse process. In the Newtonian limit, this theory gives rise to a modified\nPoisson's equation, as a consequence of which stellar observables acquire model\ndependent corrections, compared to the ones computed in the low energy limit of\ngeneral relativity. This can in turn be used to establish astrophysical\nconstraints on the theory. Here, we obtain such a constraint using\nobservational data from cataclysmic variable binaries. In particular, we\nconsider the tidal disruption limit of the secondary star by a white dwarf\nprimary. The Roche lobe filling condition of this secondary star is used to\ncompute stellar observables in the modified gravity theory in a numerical\nscheme. These are then contrasted with the values obtained by using available\ndata on these objects, via a Monte Carlo error progression method. This way, we\nare able to constrain the theory within $5\\sigma$ confidence level.\n"}
+{"abstract": "  Nearest Neighbor Search (NNS) is a central task in knowledge representation,\nlearning, and reasoning. There is vast literature on efficient algorithms for\nconstructing data structures and performing exact and approximate NNS. This\npaper studies NNS under Uncertainty (NNSU). Specifically, consider the setting\nin which an NNS algorithm has access only to a stochastic distance oracle that\nprovides a noisy, unbiased estimate of the distance between any pair of points,\nrather than the exact distance. This models many situations of practical\nimportance, including NNS based on human similarity judgements, physical\nmeasurements, or fast, randomized approximations to exact distances. A naive\napproach to NNSU could employ any standard NNS algorithm and repeatedly query\nand average results from the stochastic oracle (to reduce noise) whenever it\nneeds a pairwise distance. The problem is that a sufficient number of repeated\nqueries is unknown in advance; e.g., a point maybe distant from all but one\nother point (crude distance estimates suffice) or it may be close to a large\nnumber of other points (accurate estimates are necessary). This paper shows how\nideas from cover trees and multi-armed bandits can be leveraged to develop an\nNNSU algorithm that has optimal dependence on the dataset size and the\n(unknown)geometry of the dataset.\n"}
+{"abstract": "  Question generation (QG) is to generate natural and grammatical questions\nthat can be answered by a specific answer for a given context. Previous\nsequence-to-sequence models suffer from a problem that asking high-quality\nquestions requires commonsense knowledge as backgrounds, which in most cases\ncan not be learned directly from training data, resulting in unsatisfactory\nquestions deprived of knowledge. In this paper, we propose a multi-task\nlearning framework to introduce commonsense knowledge into question generation\nprocess. We first retrieve relevant commonsense knowledge triples from mature\ndatabases and select triples with the conversion information from source\ncontext to question. Based on these informative knowledge triples, we design\ntwo auxiliary tasks to incorporate commonsense knowledge into the main QG\nmodel, where one task is Concept Relation Classification and the other is Tail\nConcept Generation. Experimental results on SQuAD show that our proposed\nmethods are able to noticeably improve the QG performance on both automatic and\nhuman evaluation metrics, demonstrating that incorporating external commonsense\nknowledge with multi-task learning can help the model generate human-like and\nhigh-quality questions.\n"}
+{"abstract": "  This paper considers decentralized stochastic optimization over a network of\n$n$ nodes, where each node possesses a smooth non-convex local cost function\nand the goal of the networked nodes is to find an $\\epsilon$-accurate\nfirst-order stationary point of the sum of the local costs. We focus on an\nonline setting, where each node accesses its local cost only by means of a\nstochastic first-order oracle that returns a noisy version of the exact\ngradient. In this context, we propose a novel single-loop decentralized hybrid\nvariance-reduced stochastic gradient method, called GT-HSGD, that outperforms\nthe existing approaches in terms of both the oracle complexity and practical\nimplementation. The GT-HSGD algorithm implements specialized local hybrid\nstochastic gradient estimators that are fused over the network to track the\nglobal gradient. Remarkably, GT-HSGD achieves a network topology-independent\noracle complexity of $O(n^{-1}\\epsilon^{-3})$ when the required error tolerance\n$\\epsilon$ is small enough, leading to a linear speedup with respect to the\ncentralized optimal online variance-reduced approaches that operate on a single\nnode. Numerical experiments are provided to illustrate our main technical\nresults.\n"}
+{"abstract": "  The magnetic activity of the Sun directly impacts the Earth and human life.\nLikewise, other stars will have an impact on the habitability of planets\norbiting these host stars. The lack of information on the magnetic field in the\nhigher atmospheric layers hampers our progress in understanding solar magnetic\nactivity. Overcoming this limitation would allow us to address four paramount\nlong-standing questions: (1) How does the magnetic field couple the different\nlayers of the atmosphere, and how does it transport energy? (2) How does the\nmagnetic field structure, drive and interact with the plasma in the\nchromosphere and upper atmosphere? (3) How does the magnetic field destabilise\nthe outer solar atmosphere and thus affect the interplanetary environment? (4)\nHow do magnetic processes accelerate particles to high energies? New\nground-breaking observations are needed to address these science questions. We\nsuggest a suite of three instruments that far exceed current capabilities in\nterms of spatial resolution, light-gathering power, and polarimetric\nperformance: (a) A large-aperture UV-to-IR telescope of the 1-3 m class aimed\nmainly to measure the magnetic field in the chromosphere by combining high\nspatial resolution and high sensitivity. (b) An extreme-UV-to-IR coronagraph\nthat is designed to measure the large-scale magnetic field in the corona with\nan aperture of about 40 cm. (c) An extreme-UV imaging polarimeter based on a 30\ncm telescope that combines high throughput in the extreme UV with polarimetry\nto connect the magnetic measurements of the other two instruments. This mission\nto measure the magnetic field will unlock the driver of the dynamics in the\nouter solar atmosphere and thereby greatly advance our understanding of the Sun\nand the heliosphere.\n"}
+{"abstract": "  In this work, we investigate the near horizon and asymptotic symmetries of\nstatic and rotating hairy$-$AdS black hole in the framework of general minimal\nmassive gravity. We obtain energy, angular momentum and entropy of the\nsolutions. Then we show that our results for these quantities are consistent\nwith the first law of black hole thermodynamics. By considering the near\nhorizon geometry of black hole, we find near horizon conserved charges and\ntheir algebra. By writing the algebra of conserved charges in terms of Fourier\nmodes we have obtained the conserved charges in terms of zero modes.\n"}
+{"abstract": "  We propose a novel framework for modeling multiple multivariate point\nprocesses, each with heterogeneous event types that share an underlying space\nand obey the same generative mechanism. Focusing on Hawkes processes and their\nvariants that are associated with Granger causality graphs, our model leverages\nan uncountable event type space and samples the graphs with different sizes\nfrom a nonparametric model called {\\it graphon}. Given those graphs, we can\ngenerate the corresponding Hawkes processes and simulate event sequences.\nLearning this graphon-based Hawkes process model helps to 1) infer the\nunderlying relations shared by different Hawkes processes; and 2) simulate\nevent sequences with different event types but similar dynamics. We learn the\nproposed model by minimizing the hierarchical optimal transport distance\nbetween the generated event sequences and the observed ones, leading to a novel\nreward-augmented maximum likelihood estimation method. We analyze the\nproperties of our model in-depth and demonstrate its rationality and\neffectiveness in both theory and experiments.\n"}
+{"abstract": "  The Secured Overnight Funding Rate (SOFR) is becoming the main Risk-Free Rate\nbenchmark in US dollars, thus interest rate term structure models need to be\nupdated to reflect the key features exhibited by the dynamics of SOFR and the\nforward rates implied by SOFR futures. Historically, interest rate term\nstructure modelling has been based on rates of substantially longer time to\nmaturity than overnight, but with SOFR the overnight rate now is the primary\nmarket observable. This means that the empirical idiosyncrasies of the\novernight rate cannot be ignored when constructing interest rate models in a\nSOFR-based world.\n  As a rate reflecting transactions in the Treasury overnight repurchase\nmarket, the dynamics of SOFR are closely linked to the dynamics of the\nEffective Federal Funds Rate (EFFR), which is the interest rate most directly\nimpacted by US monetary policy target rate decisions. Therefore, these rates\nfeature jumps at known times (Federal Open Market Committee meeting dates), and\nmarket expectations of these jumps are reflected in prices for futures written\non these rates. On the other hand, forward rates implied by Fed Funds and SOFR\nfutures continue to evolve diffusively. The model presented in this paper\nreflects the key empirical features of SOFR dynamics and is calibrated to\nfutures prices. In particular, the model reconciles diffusive forward rate\ndynamics with piecewise constant paths of the target short rate.\n"}
+{"abstract": "  I discuss the strong link between the transmission line (TL) equation and the\nTL circuit model for the charging of an electrolyte-filled pore of finite\nlength. In particular, I show how Robin and Neumann boundary conditions to the\nTL equation, proposed by others on physical grounds, also emerge in the TL\ncircuit subject to a stepwise potential. The pore relaxes with a timescale\n$\\tau$, an expression for which consistently follows from the TL circuit, TL\nequation, and from the pore's known impedance. An approximation to $\\tau$\nexplains the numerically determined relaxation time of the stack-electrode\nmodel of Lian et al. [Phys. Rev. Lett. 124, 076001 (2020), arXiv:1911.09924].\n"}
+{"abstract": "  We study the dynamics of a cosmological model with a perfect fluid and\n$\\mathcal{N}$ fields on a hyperbolic field space interacting via a symmetric\npotential. We list all late-time solutions, investigate their stability and\nbriefly discuss predictions of the theory. Moreover, for the case of two scalar\nfields and an exponential potential we prove that the field equations are\nLiouville integrable and we provide for the first time the general solution for\na region of the parameter space.\n"}
+{"abstract": "  We prove foundational results about the set of homomorphisms from a finitely\ngenerated group to the collection of all fundamental groups of compact\n3-manifolds and answer questions of Reid-Wang-Zhou and Agol-Liu.\n"}
+{"abstract": "  For every positive integer $r$, we solve the modular Schwarzian differential\nequation $\\{h,\\tau\\}=2\\pi^2r^2E_4$, where $E_4$ is the weight 4 Eisenstein\nseries, by means of equivariant functions on the upper half-plane. This paper\nsupplements previous works \\cite{forum, ramanujan}, where the same equation has\nbeen solved for infinite families of rational values of $r$. This also leads to\nthe solutions to the modular differential equation $y''+r^2\\pi^2E_4\\,y=0$ for\nevery positive integer $r$. These solutions are quasi-modular forms for\n$\\mbox{SL}_2(\\mathbb Z)$ if $r$ is even or for the subgroup of index 2,\n$\\mbox{SL}_2(\\mathbb Z)^2$, if $r$ is odd.\n"}
+{"abstract": "  Context. Direct observation of gamma-ray emission from the decay of $^{18}$F\nejected in classical nova outbursts remains a major focus of the nuclear\nastrophysics community. However, modeling the abundance of ejected $^{18}$F,\nand thus the predicted detectability distance of a gamma-ray signal near 511\nkeV emitted from these transient thermonuclear episodes, is hampered by\nsignificant uncertainties in our knowledge of the key $^{18}$F(p,$\\alpha$)\nreaction rate. Aims. We analyze uncertainties in the most recent nuclear\nphysics experimental results employed to calculate the $^{18}$F(p,$\\alpha$)\nreaction rate. Our goal is to determine which uncertainties have the most\nprofound influence on the predicted abundance of $^{18}$F ejected from novae,\nin order to guide future experimental works. Methods. We calculated a wide\nrange of $^{18}$F(p,$\\alpha$) reaction rates using R-Matrix formalism, allowing\nus to take into account all interference effects. Using a selection of 16\nevenly-spaced rates over the full range, we performed 16 new hydrodynamic nova\nsimulations. Results. We performed one of the most thorough theoretical studies\nof the impact of the $^{18}$F(p,$\\alpha$) reaction in classical novae to date.\nThe $^{18}$F(p,$\\alpha$) rate remains highly uncertain at nova temperatures,\nresulting in a factor ~10 uncertainty in the predicted abundance of $^{18}$F\nejected from nova explosions. We also found that the abundance of $^{18}$F may\nbe strongly correlated with that of $^{19}$F. Conclusions. Despite numerous\nnuclear physics uncertainties affecting the $^{18}$F(p,$\\alpha$) reaction rate,\nwhich are dominated by unknown interference signs between 1/2$^+$ and 3/2$^+$\nresonances, future experimental work should focus on firmly and precisely\ndetermining the directly measurable quantum properties of the subthreshold\nstates in the compound nucleus $^{19}$Ne near 6.13 and 6.29 MeV.\n"}
+{"abstract": "  We study uncloneable quantum encryption schemes for classical messages as\nrecently proposed by Broadbent and Lord. We focus on the information-theoretic\nsetting and give several limitations on the structure and security of these\nschemes: Concretely, 1) We give an explicit cloning-indistinguishable attack\nthat succeeds with probability $\\frac12 + \\mu/16$ where $\\mu$ is related to the\nlargest eigenvalue of the resulting quantum ciphertexts. 2) For a uniform\nmessage distribution, we partially characterize the scheme with the minimal\nsuccess probability for cloning attacks. 3) Under natural symmetry conditions,\nwe prove that the rank of the ciphertext density operators has to grow at least\nlogarithmically in the number of messages to ensure uncloneable security. 4)\nThe \\emph{simultaneous} one-way-to-hiding (O2H) lemma is an important technique\nin recent works on uncloneable encryption and quantum copy protection. We give\nan explicit example which shatters the hope of reducing the multiplicative\n\"security loss\" constant in this lemma to below 9/8.\n"}
+{"abstract": "  Over the past two decades, CNN architectures have produced compelling models\nof sound perception and cognition, learning hierarchical organizations of\nfeatures. Analogous to successes in computer vision, audio feature\nclassification can be optimized for a particular task of interest, over a wide\nvariety of datasets and labels. In fact similar architectures designed for\nimage understanding have proven effective for acoustic scene analysis. Here we\npropose applying Transformer based architectures without convolutional layers\nto raw audio signals. On a standard dataset of Free Sound 50K,comprising of 200\ncategories, our model outperforms convolutional models to produce state of the\nart results. This is significant as unlike in natural language processing and\ncomputer vision, we do not perform unsupervised pre-training for outperforming\nconvolutional architectures. On the same training set, with respect mean\naver-age precision benchmarks, we show a significant improvement. We further\nimprove the performance of Transformer architectures by using techniques such\nas pooling inspired from convolutional net-work designed in the past few years.\nIn addition, we also show how multi-rate signal processing ideas inspired from\nwavelets, can be applied to the Transformer embeddings to improve the results.\nWe also show how our models learns a non-linear non constant band-width\nfilter-bank, which shows an adaptable time frequency front end representation\nfor the task of audio understanding, different from other tasks e.g. pitch\nestimation.\n"}
+{"abstract": "  Automatic code synthesis from natural language descriptions is a challenging\ntask. We witness massive progress in developing code generation systems for\ndomain-specific languages (DSLs) employing sequence-to-sequence deep learning\ntechniques in the recent past. In this paper, we specifically experiment with\n\\textsc{AlgoLisp} DSL-based generative models and showcase the existence of\nsignificant dataset bias through different classes of adversarial examples. We\nalso experiment with two variants of Transformer-based models that outperform\nall existing \\textsc{AlgoLisp} DSL-based code generation baselines. Consistent\nwith the current state-of-the-art systems, our proposed models, too, achieve\npoor performance under adversarial settings. Therefore, we propose several\ndataset augmentation techniques to reduce bias and showcase their efficacy\nusing robust experimentation.\n"}
+{"abstract": "  We use combined South Pole Telescope (SPT)+Planck temperature maps to analyze\nthe circumgalactic medium (CGM) encompassing 138,235 massive, quiescent 0.5\n$\\leq$ z $\\leq$ 1.5 galaxies selected from data from the Dark Energy Survey\n(DES) and Wide-Field Infrared Survey Explorer (WISE). Images centered on these\ngalaxies were cut from the 1.85 arcmin resolution maps with frequency bands at\n95, 150, and 220 GHz. The images were stacked, filtered, and fit with a\ngray-body dust model to isolate the thermal Sunyaev-Zel'dovich (tSZ) signal,\nwhich is proportional to the total energy contained in the CGM of the galaxies.\nWe separate these $M_{\\star} = 10^{10.9} M_\\odot$ - $10^{12} M_\\odot$ galaxies\ninto 0.1 dex stellar mass bins, detecting tSZ per bin up to $5.6\\sigma$ and a\ntotal signal-to-noise ratio of $10.1\\sigma$. We also detect dust with an\noverall signal-to-noise ratio of $9.8\\sigma$, which overwhelms the tSZ at\n150GHz more than in other lower-redshift studies. We correct for the $0.16$ dex\nuncertainty in the stellar mass measurements by parameter fitting for an\nunconvolved power-law energy-mass relation, $E_{\\rm therm} = E_{\\rm therm,peak}\n\\left(M_\\star/M_{\\star,{\\rm peak}} \\right)^\\alpha$, with the peak stellar mass\ndistribution of our selected galaxies defined as $M_{\\star,{\\rm peak}}= 2.3\n\\times 10^{11} M_\\odot$. This yields an $E_{\\rm therm,peak}=\n5.98_{-1.00}^{+1.02} \\times 10^{60}$ erg and $\\alpha=3.77_{-0.74}^{+0.60}$.\nThese are consistent with $z \\approx 0$ observations and within the limits of\nmoderate models of active galactic nuclei (AGN) feedback. We also compute the\nradial profile of our full sample, which is similar to that recently measured\nat lower-redshift by Schaan et al. (2021).\n"}
+{"abstract": "  Spontaneous scalarization is a gravitational phenomenon in which deviations\nfrom general relativity arise once a certain threshold in curvature is\nexceeded, while being entirely absent below that threshold. For black holes,\nscalarization is known to be triggered by a coupling between a scalar and the\nGauss-Bonnet invariant. A coupling with the Ricci scalar, which can trigger\nscalarization in neutron stars, is instead known to not contribute to the onset\nof black hole scalarization, and has so far been largely ignored in the\nliterature when studying scalarized black holes. In this paper, we study the\ncombined effect of both these couplings on black hole scalarization. We show\nthat the Ricci coupling plays a significant role in the properties of\nscalarized solutions and their domain of existence. This work is an important\nstep in the construction of scalarization models that evade binary pulsar\nconstraints and have general relativity as a cosmological late-time attractor,\nwhile still predicting deviations from general relativity in black hole\nobservations.\n"}
+{"abstract": "  This paper studies the problem of multi-agent computation under the\ndifferential privacy requirement of the agents' local datasets against\neavesdroppers having node-to-node communications. We first propose for the\nnetwork equipped with public-private networks. The private network is sparse\nand not even necessarily connected, over which communications are encrypted and\nsecure along with the intermediate node states; the public network is connected\nand may be dense, over which communications are allowed to be public. In this\nsetting, we propose a multi-gossip PPSC mechanism over the private network,\nwhere at each step, randomly selected node pairs update their states in such a\nway that they are shuffled with random noise while maintaining summation\nconsistency. We show that this mechanism can achieve any desired differential\nprivacy level with any prescribed probability. Next, we embed this mechanism in\ndistributed computing processes, and propose privacy-guarantee protocols for\nthree basic computation tasks, where an adaptive mechanism adjusts the amount\nof noise injected in PPSC steps for privacy protection, and the number of\nregular computation steps for accuracy guarantee. For average consensus, we\ndevelop a PPSC-Gossip averaging consensus algorithm by utilizing the\nmulti-gossip PPSC mechanism for privacy encryption before an averaging\nconsensus algorithm over the public network for local computations. For network\nlinear equations and distributed convex optimization, we develop two respective\ndistributed computing protocols by following the PPSC-Gossip averaging\nconsensus algorithm with an additional projection or gradient descent step\nwithin each step of computation. Given any privacy and accuracy requirements,\nit is shown that all three proposed protocols can compute their corresponding\nproblems with the desired computation accuracy, while achieving the desired\ndifferential privacy.\n"}
+{"abstract": "  Zr and Mo macrosegregations were investigated in Ti6246 titanium alloy\nindustrial-scale ingotby vacuum arc remelting. The formation mechanism of Zr\nand Mo macrosegregations was studied during the solidification process. Zr\nmacrosegregation was characterized by low content in the equiaxed grain zone\nand high content in the hot top zone. Mo exhibits an opposite trend with Zr.\nThe macrosegregations of Zr and Mo were the most pronounced, with a statistic\nsegregation degree higher than Al and Sn. It could be concluded that\ntemperature gradient and solidification rate dominated the macrosegregation\nformations during the solidification process. The thermal buoyancy made the\nnegative segregation Zr be continuously discharged to the front of the\nsolid-liquid interface. Mo was enriched in the solid phase at the solid-liquid\ninterface as the positive segregation.\n"}
+{"abstract": "  In the Demand Strip Packing problem (DSP), we are given a time interval and a\ncollection of tasks, each characterized by a processing time and a demand for a\ngiven resource (such as electricity, computational power, etc.). A feasible\nsolution consists of a schedule of the tasks within the mentioned time\ninterval. Our goal is to minimize the peak resource consumption, i.e. the\nmaximum total demand of tasks executed at any point in time.\n  It is known that DSP is NP-hard to approximate below a factor 3/2, and\nstandard techniques for related problems imply a (polynomial-time)\n2-approximation. Our main result is a (5/3+eps)-approximation algorithm for any\nconstant eps>0. We also achieve best-possible approximation factors for some\nrelevant special cases.\n"}
+{"abstract": "  This paper focuses on error-correcting codes that can handle a predefined set\nof specific error patterns. The need for such codes arises in many settings of\npractical interest, including wireless communication and flash memory systems.\nIn many such settings, a smaller field size is achievable than that offered by\nMDS and other standard codes. We establish a connection between the minimum\nalphabet size for this generalized setting and the combinatorial properties of\na hypergraph that represents the prespecified collection of error patterns. We\nalso show a connection between error and erasure correcting codes in this\nspecialized setting. This allows us to establish bounds on the minimum alphabet\nsize and show an advantage of non-linear codes over linear codes in a\ngeneralized setting. We also consider a variation of the problem which allows a\nsmall probability of decoding error and relate it to an approximate version of\nhypergraph coloring.\n"}
+{"abstract": "  The complexity of cyberattacks in Cyber-Physical Systems (CPSs) calls for a\nmechanism that can evaluate the operational behaviour and security without\nnegatively affecting the operation of live systems. In this regard, Digital\nTwins (DTs) are revolutionizing the CPSs. DTs strengthen the security of CPSs\nthroughout the product lifecycle, while assuming that the DT data is trusted,\nproviding agility to predict and respond to real-time changes. However,\nexisting DTs solutions in CPS are constrained with untrustworthy data\ndissemination among multiple stakeholders and timely course correction. Such\nlimitations reinforce the significance of designing trustworthy distributed\nsolutions with the ability to create actionable insights in real-time. To do\nso, we propose a framework that focuses on trusted and intelligent DT by\nintegrating blockchain and Artificial Intelligence (AI). Following a hybrid\napproach, the proposed framework not only acquires process knowledge from the\nspecifications of the CPS, but also relies on AI to learn security threats\nbased on sensor data. Furthermore, we integrate blockchain to safeguard product\nlifecycle data. We discuss the applicability of the proposed framework for the\nautomotive industry as a CPS use case. Finally, we identify the open challenges\nthat impede the implementation of intelligence-driven architectures in CPSs.\n"}
+{"abstract": "  We extend the notion of singular vectors in the context of Diophantine\napproximation of real numbers with elements of a totally real number field $K$.\nFor $m\\geq1$, we establish a version of Dani correspondence in number fields\nand prove that under a class of `friendly measures' in $K_S^m$, the set of\nsingular vectors has measure zero. Tackling the inheritance question, we show\nthe existence of nondegenerate divergent orbits on manifolds in $K_S^m$ under\nthe action of a certain one parameter subgroup of $\\mathrm{SL}_{m+1}(K_S)$.\n"}
+{"abstract": "  The Louvain algorithm is currently one of the most popular community\ndetection methods. This algorithm finds communities by maximizing a quantity\ncalled modularity. In this work, we describe a metric space of clusterings,\nwhere clusterings are described by a binary vector indexed by the vertex-pairs.\nWe extend this geometry to a hypersphere and prove that maximizing modularity\nis equivalent to minimizing the angular distance to some modularity vector over\nthe set of clustering vectors. This equivalence allows us to view the Louvain\nalgorithm as a nearest-neighbor search that approximately minimizes the\ndistance to this modularity vector. By replacing this modularity vector by a\ndifferent vector, many alternative community detection methods can be obtained.\nWe explore this wider class and compare it to existing modularity-based\nmethods. Our experiments show that these alternatives may outperform\nmodularity-based methods. For example, when communities are large compared to\nvertex neighborhoods, a vector based on numbers of common neighbors outperforms\nexisting community detection methods. While the focus of the present work is\ncommunity detection in networks, the proposed methodology can be applied to any\nclustering problem where pair-wise similarity data is available.\n"}
+{"abstract": "  Based on the recently formulated chiral radiation transport theory for\nleft-handed neutrinos, we study the chiral transport of neutrinos near thermal\nequilibrium in core-collapse supernovae. We first compute the near-equilibrium\nsolution of the chiral radiation transport equation under the relaxation time\napproximation, where the relaxation time is directly derived from the effective\nfield theory of the weak interaction. By using such a solution, we\nsystematically derive analytic expressions for the nonequilibrium corrections\nof the neutrino energy-momentum tensor and neutrino number current induced by\nmagnetic fields via the neutrino absorption on nucleons. In particular, we find\nthe nonequilibrium neutrino energy current proportional to the magnetic field.\nWe also discuss its phenomenological consequences such as the possible relation\nto pulsar kicks.\n"}
+{"abstract": "  Recent advances in self-supervised learning with instance-level contrastive\nobjectives facilitate unsupervised clustering. However, a standalone datum is\nnot perceiving the context of the holistic cluster, and may undergo sub-optimal\nassignment. In this paper, we extend the mainstream contrastive learning\nparadigm to a cluster-level scheme, where all the data subjected to the same\ncluster contribute to a unified representation that encodes the context of each\ndata group. Contrastive learning with this representation then rewards the\nassignment of each datum. To implement this vision, we propose twin-contrast\nclustering (TCC). We define a set of categorical variables as clustering\nassignment confidence, which links the instance-level learning track with the\ncluster-level one. On one hand, with the corresponding assignment variables\nbeing the weight, a weighted aggregation along the data points implements the\nset representation of a cluster. We further propose heuristic cluster\naugmentation equivalents to enable cluster-level contrastive learning. On the\nother hand, we derive the evidence lower-bound of the instance-level\ncontrastive objective with the assignments. By reparametrizing the assignment\nvariables, TCC is trained end-to-end, requiring no alternating steps. Extensive\nexperiments show that TCC outperforms the state-of-the-art on challenging\nbenchmarks.\n"}
+{"abstract": "  Second harmonic generation (SHG) spectroscopy ubiquitously enables the\ninvestigation of surface chemistry, interfacial chemistry as well as symmetry\nproperties in solids. Polarization-resolved SHG spectroscopy in the visible to\ninfrared regime is regularly used to investigate electronic and magnetic orders\nthrough their angular anisotropies within the crystal structure. However, the\nincreasing complexity of novel materials and emerging phenomena hamper the\ninterpretation of experiments solely based on the investigation of hybridized\nvalence states. Here, polarization-resolved SHG in the extreme ultraviolet\n(XUV-SHG) is demonstrated for the first time, enabling element-resolved angular\nanisotropy investigations. In non-centrosymmetric LiNbO$_3$, elemental\ncontributions by lithium and niobium are clearly distinguished by energy\ndependent XUV-SHG measurements. This element-resolved and symmetry-sensitive\nexperiment suggests that the displacement of Li ions in LiNbO$_3$, which is\nknown to lead to ferroelectricity, is accompanied by distortions to the Nb ion\nenvironment that breaks the inversion symmetry of the NbO$_{6}$ octahedron as\nwell. Our simulations show that the measured second harmonic spectrum is\nconsistent with Li ion displacements from the centrosymmetric position by\n$\\sim$0.5 Angstrom while the Nb-O bonds are elongated/contracted by\ndisplacements of the O atoms by $\\sim$0.1 Angstrom. In addition, the\npolarization-resolved measurement of XUV-SHG shows excellent agreement with\nnumerical predictions based on dipole-induced SHG commonly used in the optical\nwavelengths. This constitutes the first verification of the dipole-based SHG\nmodel in the XUV regime. The findings of this work pave the way for future\nangle and time-resolved XUV-SHG studies with elemental specificity in condensed\nmatter systems.\n"}
+{"abstract": "  A subset $X$ of a finite group $G$ is said to be prime-power-independent if\neach element in $X$ has prime power order and there is no proper subset $Y$ of\n$X$ with $\\langle Y, \\Phi(G)\\rangle = \\langle X, \\Phi(G)\\rangle$, where\n$\\Phi(G)$ is the Frattini subgroup of $G$. A group $G$ is $\\mathcal{B}_{pp}$ if\nall prime-power-independent generating sets for $G$ have the same cardinality.\nWe prove that, if $G$ is $\\mathcal{B}_{pp}$, then $G$ is solvable. Pivoting on\nsome recent results of Krempa and Stocka, this yields a complete classification\nof $\\mathcal{B}_{pp}$-groups.\n"}
+{"abstract": "  Projective monomial curves correspond to rings generated by monomials of the\nsame degree in two variables. Such rings always have finite Macaulayfication.\nWe show how to characterize the Buchsbaumness and the Castelnuovo-Mumford\nregularity of these rings by means of their finite Macaulayfication, and we use\nthis method to study the Buchsbaumness and to estimate the Castelnuovo-Mumford\nregularity of large classes of non-smooth monomial curves in terms of the given\nmonomials.\n"}
+{"abstract": "  Context: The analytical results of Chandrasekhar's semi-infinite diffuse\nreflection problem is crucial in the context of stellar or planetary\natmosphere. However, the atmospheric emission effect was not taken into account\nin this model, and the solutions are applicable only for diffusely scattering\natmosphere in absence of emission.\n  Aim: We extend the model of semi-infinite diffuse reflection problem by\nincluding the effects of thermal emission B(T ), and present how this affects\nChandrasekhar's analytical end results. Hence, we aim to generalize\nChandrasekhar's model to provide a complete picture of this problem.\n  Method: We use Invariance Principle Method to find the radiative transfer\nequation accurate for diffuse reflection in presence of B(T ). Then we derive\nthe modified scattering function S(${\\mu},{\\phi}; {\\mu}_0 , {\\phi}_0$ ) for\ndifferent kind of phase functions.\n  Results: We find that, the scattering function S(${\\mu}, {\\phi}; {\\mu}_0 ,\n{\\phi}_0$ ) as well as diffusely reflected specific intensity $I(0, {\\mu};\n{\\mu}_0 )$ for different phase functions are modified due to the emission\n$B(T)$ from layer ${\\tau} = 0$. In both cases, B(T) is added to the results of\nonly scattering case derived by Chandrasekhar, with some multiplicative\nfactors. Thus the diffusely reflected spectra will be enriched and carries the\ntemperature information of ${\\tau} = 0$ layer. As the effects are additive in\nnature, hence our model reduces to the sub-case of Chandrasekhar's scattering\nmodel in case of $B(T) = 0$. We conclude that our generalized model provides\nmore accurate results due to the inclusion of the thermal emission effect in\nChandrasekhar's semi-infinite atmosphere problem.\n"}
+{"abstract": "  In this paper, the notions of nonnil-injective modules and\nnonnil-FP-injective modules are introduced and studied. Especially, we show\nthat a $\\phi$-ring $R$ is an integral domain if and only if any\nnonnil-injective (resp., nonnil-FP-injective) module $R$-module is injective\n(resp., FP-injective). Some new characterizations of $\\phi$-von Neumann regular\nrings, nonnil-Notherian rings and nonnil-coherent rings are given. We finally\ncharacterize $\\phi$-Dedekind rings and $\\phi$-\\Prufer\\ rings in terms of\n$\\phi$-flat modules, nonnil-injective modules and nonnil-FP-injective modules.\n"}
+{"abstract": "  Few-shot segmentation has been attracting a lot of attention due to its\neffectiveness to segment unseen object classes with a few annotated samples.\nMost existing approaches use masked Global Average Pooling (GAP) to encode an\nannotated support image to a feature vector to facilitate query image\nsegmentation. However, this pipeline unavoidably loses some discriminative\ninformation due to the average operation. In this paper, we propose a simple\nbut effective self-guided learning approach, where the lost critical\ninformation is mined. Specifically, through making an initial prediction for\nthe annotated support image, the covered and uncovered foreground regions are\nencoded to the primary and auxiliary support vectors using masked GAP,\nrespectively. By aggregating both primary and auxiliary support vectors, better\nsegmentation performances are obtained on query images. Enlightened by our\nself-guided module for 1-shot segmentation, we propose a cross-guided module\nfor multiple shot segmentation, where the final mask is fused using predictions\nfrom multiple annotated samples with high-quality support vectors contributing\nmore and vice versa. This module improves the final prediction in the inference\nstage without re-training. Extensive experiments show that our approach\nachieves new state-of-the-art performances on both PASCAL-5i and COCO-20i\ndatasets.\n"}
+{"abstract": "  In this review paper, we present an elaborate discussion on wavelength\nselective switches and their demonstrations. We also review packaging and\nelectronic photonic integration of switches; a topic neglected in other review\npapers. We also cover wavelength locking which is paramount in switching\nnetworks with many tunable filters.\n"}
+{"abstract": "  Following Benjamin et al., a matrix with entries being sums of two\nneighbouring Catalan numbers is considered. Its LU-decomposition is given, by\nguessing the results and later prove it by computer algebra, with lots of human\nhelp. Specializing a parameter, the determinant turns out to be a Fibonacci\nnumber with odd index, confirming earlier results, obtained back then by\ncombinatorial methods.\n"}
+{"abstract": "  We present the discovery of rapid photometric variability in three ultra-cool\ndwarfs from long-duration monitoring with the Spitzer Space Telescope. The T7,\nL3.5, and L8 dwarfs have the shortest photometric periods known to date:\n${1.080}^{+0.004}_{-0.005}$ h, ${1.14}^{+0.03}_{-0.01}$ h, and\n${1.23}^{+0.01}_{-0.01}$ h, respectively. We confirm the rapid rotation through\nmoderate-resolution infrared spectroscopy, which reveals projected rotational\nvelocities between 79 and 104 km s$^{-1}$. We compare the near-infrared spectra\nto photospheric models to determine the objects' fundamental parameters and\nradial velocities. We find that the equatorial rotational velocities for all\nthree objects are $\\gtrsim$100 km s$^{-1}$. The three L and T dwarfs reported\nhere are the most rapidly spinning and likely the most oblate field ultra-cool\ndwarfs known to date. Correspondingly, all three are excellent candidates for\nseeking auroral radio emission and net optical/infrared polarization. As of\nthis writing, 78 L-, T-, and Y-dwarf rotation periods have now been measured.\nThe clustering of the shortest rotation periods near 1 h suggests that brown\ndwarfs are unlikely to spin much faster.\n"}
+{"abstract": "  We show that, for a (not necessarily continuous) weakly contractive mean-type\nmapping $\\mathbf{M} \\colon I^p\\to I^p$ (where $I$ is an interval and $p \\in\n\\mathbb{N}$), the functional equation $K \\circ \\mathbf{M}=K$ has at most one\nsolution in the family of continuous means $K \\colon I^p \\to I$.\n  Some general approach to the latter equation is also given.\n"}
+{"abstract": "  Turning principles into practice is one of the most pressing challenges of\nartificial intelligence (AI) governance. In this article, we reflect on a novel\ngovernance initiative by one of the world's largest AI conferences. In 2020,\nthe Conference on Neural Information Processing Systems (NeurIPS) introduced a\nrequirement for submitting authors to include a statement on the broader\nsocietal impacts of their research. Drawing insights from similar governance\ninitiatives, including institutional review boards (IRBs) and impact\nrequirements for funding applications, we investigate the risks, challenges and\npotential benefits of such an initiative. Among the challenges, we list a lack\nof recognised best practice and procedural transparency, researcher opportunity\ncosts, institutional and social pressures, cognitive biases, and the inherently\ndifficult nature of the task. The potential benefits, on the other hand,\ninclude improved anticipation and identification of impacts, better\ncommunication with policy and governance experts, and a general strengthening\nof the norms around responsible research. To maximise the chance of success, we\nrecommend measures to increase transparency, improve guidance, create\nincentives to engage earnestly with the process, and facilitate public\ndeliberation on the requirement's merits and future. Perhaps the most important\ncontribution from this analysis are the insights we can gain regarding\neffective community-based governance and the role and responsibility of the AI\nresearch community more broadly.\n"}
+{"abstract": "  The covariance matrix $\\boldsymbol{\\Sigma}$ of non-linear clustering\nstatistics that are measured in current and upcoming surveys is of fundamental\ninterest for comparing cosmological theory and data and a crucial ingredient\nfor the likelihood approximations underlying widely used parameter inference\nand forecasting methods. The extreme number of simulations needed to estimate\n$\\boldsymbol{\\Sigma}$ to sufficient accuracy poses a severe challenge.\nApproximating $\\boldsymbol{\\Sigma}$ using inexpensive but biased surrogates\nintroduces model error with respect to full simulations, especially in the\nnon-linear regime of structure growth. To address this problem we develop a\nmatrix generalization of Convergence Acceleration by Regression and Pooling\n(CARPool) to combine a small number of simulations with fast surrogates and\nobtain low-noise estimates of $\\boldsymbol{\\Sigma}$ that are unbiased by\nconstruction. Our numerical examples use CARPool to combine GADGET-III $N$-body\nsimulations with fast surrogates computed using COmoving Lagrangian\nAcceleration (COLA). Even at the challenging redshift $z=0.5$, we find variance\nreductions of at least $\\mathcal{O}(10^1)$ and up to $\\mathcal{O}(10^4)$ for\nthe elements of the matter power spectrum covariance matrix on scales\n$8.9\\times 10^{-3}<k_\\mathrm{max} <1.0$ $h {\\rm Mpc^{-1}}$. We demonstrate\ncomparable performance for the covariance of the matter bispectrum, the matter\ncorrelation function and probability density function of the matter density\nfield. We compare eigenvalues, likelihoods, and Fisher matrices computed using\nthe CARPool covariance estimate with the standard sample covariance estimators\nand generally find considerable improvement except in cases where $\\Sigma$ is\nseverely ill-conditioned.\n"}
+{"abstract": "  We introduce the notion of a g-atomic subspace for a bounded linear operator\nand construct several useful resolutions of the identity operator on a Hilbert\nspace using the theory of g-fusion frames. Also we shall describe the concept\nof frame operator for a pair of g-fusion Bessel sequences and some of their\nproperties.\n"}
+{"abstract": "  Recently, atomically thin PdSe$_2$ semiconductors with rare pentagonal\nSe-Pd-Se monolayers were synthesized and were also found to possess superior\nproperties such as ultrahigh air stability, and high carrier mobility, thus\noffering a new family of two-dimensional (2D) materials for exploration of 2D\nsemiconductor physics and for applications in advanced opto-electronic and\nnonlinear photonic devices. In this work, we systematically study the nonlinear\noptical (NLO) responses [namely, bulk photovoltaic effect (BPVE),\nsecond-harmonic generation (SHG) and linear electric-optic (LEO) effect] of\nnoncentrosymmetric bilayer (BL) and four-layer (FL) PdS$_2$ and PdSe$_2$ by\napplying the first-principles density functional theory with the generalized\ngradient approximation plus scissors-correction. First of all, the shift\ncurrent conductivity is in the order of 130 $\\mu$A/V$^2$, being very high\ncompared to known BPVE materials. Similarly, their injection current\nsusceptibilities are in the order of 100$\\times$10$^8$A/V$^2$s, again being\nlarge. Secondly, the SHG coefficients ($\\chi^{(2)}$) of these materials are\nalso large, being one order higher than that of the best-known few-layer group\n6B transition metal dichalcogenides. For example, the maximum magnitude of\n$\\chi^{(2)}$ can reach 1.4$\\times$10$^3$ pm/V for BL PdSe$_2$ at 1.9 eV and\n1.2$\\times$10$^3$ pm/V at 3.1 eV for BL PdS$_2$. Thirdly we find significant\nLEO coefficients for these structures in the low photon energy. All these\nindicate that 2D PdX$_2$ semiconductors will find promising NLO applications.\nFourthly, we find that the large BPVE and SHG of the few-layer PdX$_2$\nstructures are due to strong intralayer directional covalent bonding and also\n2D quantum confinement.\n"}
+{"abstract": "  Borromean ring refers to a peculiar structure where three rings are linked\ntogether while any two of them are unlinked. Here we propose the realization of\nits quantum mechanical analog in a many-body system of three-component\nultracold bosons. Namely, we identify the {\\it Borromean droplet}, where only\nthe ternary bosons can form a self-bound droplet while any binary subsystems\ncannot. Its formation is facilitated by an additional attractive force induced\nby the density fluctuation of a third component, which enlarges the mean-field\ncollapse region in comparison to the binary case and renders the formation of\nBorromean droplet after incorporating the repulsive force from quantum\nfluctuations. Outside the Borromean regime, the competition between ternary and\nbinary droplets leads to an interesting phenomenon of droplet phase separation,\nmanifested by double plateaus in the density profile. We further show that the\ntransition between different droplets and gas phase can be conveniently tuned\nby boson numbers and interaction strengths. The study reveals the possibility\nof Borromean binding in the many-body world and sheds light on more intriguing\nmany-body bound state formed in multi-component systems.\n"}
+{"abstract": "  Observing a set of images and their corresponding paragraph-captions, a\nchallenging task is to learn how to produce a semantically coherent paragraph\nto describe the visual content of an image. Inspired by recent successes in\nintegrating semantic topics into this task, this paper develops a plug-and-play\nhierarchical-topic-guided image paragraph generation framework, which couples a\nvisual extractor with a deep topic model to guide the learning of a language\nmodel. To capture the correlations between the image and text at multiple\nlevels of abstraction and learn the semantic topics from images, we design a\nvariational inference network to build the mapping from image features to\ntextual captions. To guide the paragraph generation, the learned hierarchical\ntopics and visual features are integrated into the language model, including\nLong Short-Term Memory (LSTM) and Transformer, and jointly optimized.\nExperiments on public dataset demonstrate that the proposed models, which are\ncompetitive with many state-of-the-art approaches in terms of standard\nevaluation metrics, can be used to both distill interpretable multi-layer\ntopics and generate diverse and coherent captions.\n"}
+{"abstract": "  We give an inductive procedure for finding the extremal rays of the\nequivariant Littlewood-Richardson cone, which is closely related to the\nsolution space to S. Friedland's majorized Hermitian eigenvalue problem. In so\ndoing, we solve the \"rational version\" of a problem posed by C. Robichaux, H.\nYadav, and A. Yong. Our procedure is a natural extension of P. Belkale's\nalgorithm for the classical Littlewood-Richardson cone. The main tools for\naccommodating the equivariant setting are certain foundational results of D.\nAnderson, E. Richmond, and A. Yong. We also study two families of special rays\nof the cone and make observations about the Hilbert basis of the associated\nlattice semigroup.\n"}
+{"abstract": "  Recent developments in Machine Learning and Deep Learning depend heavily on\ncloud computing and specialized hardware, such as GPUs and TPUs. This forces\nthose using those models to trust private data to cloud servers. Such scenario\nhas prompted a large interest on Homomorphic Cryptography and Secure\nMulti-Party Computation protocols that allow the use of cloud computing power\nin a privacy-preserving manner.\n  When comparing the efficiency of such protocols, most works in literature\nresort to complexity analysis that gives asymptotic higher-bounding limits of\ncomputational cost when input size tends to infinite. These limits may be very\ndifferent from the actual cost or execution time, when performing such\ncomputations over small, or average-sized datasets.\n  We argue that Monte Carlo methods can render better computational cost and\ntime estimates, fostering better design and implementation decisions for\ncomplex systems, such as Privacy-Preserving Machine Learning Frameworks.\n"}
+{"abstract": "  We develop a non-perturbative analysis of the electro-production of heavy\nvector mesons ($\\phi$, $J/\\Psi$) from threshold to high energy. We use the\nholographic construction with bulk confinement enforced through a soft wall.\nUsing Witten diagrams, we evaluate the pertinent cross sections for heavy\nvector mesons ($\\phi$, $J/\\Psi$) production and study their dependence on both\nthe incoming virtual photon polarization as well as the outgoing polarization\nof the heavy meson. Our results for $J/\\Psi$ electro-production compares well\nwith the available HERA data at low and intermediate $Q^2$, and for a wide\nrange of momentum transfer. We also predict the quasi-real electro-production\nof $J/\\Psi$ near threshold.\n"}
+{"abstract": "  In a recent paper~[Nature Catalysis 3, 573 (2020)], Robatjazi {\\em et al.}\ndemonstrate hydrodefluorination on Al nanocrystals decorated by Pd islands\nunder illumination and under external heating. They conclude that\nphotocatalysis accomplishes the desired transformation \\ce{CH3F + D2 -> CH3D +\nDF} efficiently and selectively due to \"hot\" electrons, as evidenced by an\nillumination-induced reduction of the activation energy.\n  Although some of the problems identified in prior work by the same group have\nbeen addressed, scrutiny of the data in~[Nature Catalysis 3, 573 (2020)] raises\ndoubts about both the methodology and the central conclusions. First, we show\nthat the thermal control experiments in~[Nature Catalysis 3, 573 (2020)] do not\nseparate thermal from \"hot electron\" contributions, and therefore any\nconclusions drawn from these experiments are invalid. We then show that an\nimproved thermal control implies that the activation energy of the reaction\ndoes not change, and that an independent purely thermal calculation (based\nsolely on the sample parameters provided in the original manuscript) explains\nthe measured data perfectly. For the sake of completeness, we also address\ntechnical problems in the calibration of the thermal camera, an unjustifiable\ndisqualification of some of the measured data, as well as concerning aspects of\nthe rest of the main results, including the mass spectrometry approach used to\ninvestigate the selectivity of the reaction, and claims about the stoichiometry\nand reaction order. All this shows that the burden of proof for involvement of\nhot electrons has not been met.\n"}
+{"abstract": "  We propose a framework where a phase transition associated with a gauge\nsymmetry breaking that occurs (not far) above the electroweak scale sets a\nstage for baryogenesis similar to the electroweak baryogenesis in the Standard\nModel. A concrete realization utilizes the breaking of $SU(2)_R \\times U(1)_X\n\\rightarrow U(1)_Y$. New chiral fermions charged under the extended gauge\nsymmetry have nonzero lepton numbers, which makes the $B-L$ symmetry anomalous.\nThe new lepton sector contains a large flavor-dependent CP violation, similar\nto the Cabibbo-Kobayashi-Maskawa phase, without inducing sizable electric\ndipole moments of the Standard Model particles. A bubble wall dynamics\nassociated with the first-order phase transition and $SU(2)_R$ sphaleron\nprocesses generate a lepton asymmetry, which is transferred into a baryon\nasymmetry via the ordinary electroweak sphaleron process. Unlike the Standard\nModel electroweak baryogenesis, the new phase transition can be of the strong\nfirst order and the new CP violation is not significantly suppressed by Yukawa\ncouplings, so that the observed asymmetry can be produced. The model can be\nprobed by collider searches for new particles and the observation of\ngravitational waves. One of the new leptons becomes a dark matter candidate.\nThe model can be also embedded into a left-right symmetric theory to solve the\nstrong CP problem.\n"}
+{"abstract": "  Question Answering (QA) models over Knowledge Bases (KBs) are capable of\nproviding more precise answers by utilizing relation information among\nentities. Although effective, most of these models solely rely on fixed\nrelation representations to obtain answers for different question-related KB\nsubgraphs. Hence, the rich structured information of these subgraphs may be\noverlooked by the relation representation vectors. Meanwhile, the direction\ninformation of reasoning, which has been proven effective for the answer\nprediction on graphs, has not been fully explored in existing work. To address\nthese challenges, we propose a novel neural model, Relation-updated\nDirection-guided Answer Selector (RDAS), which converts relations in each\nsubgraph to additional nodes to learn structure information. Additionally, we\nutilize direction information to enhance the reasoning ability. Experimental\nresults show that our model yields substantial improvements on two widely used\ndatasets.\n"}
+{"abstract": "  Here we numerically solve the equations derived in part I of this two-part\nseries and verify their validity. In particular we use the functional\nRenormalisation Group (fRG) flow equations to obtain effective potentials for\ninitially highly anharmonic and non-polynomial potentials, including potentials\nwith multiple trapping wells and barriers, and at different temperatures. The\nnumerical computations determining the effective action are much faster than\nthe direct simulation of the stochastic dynamics to which we compare our fRG\nresults. We benchmark our numerical solutions to the flow equations by\ncomparing the first two equilibrium cumulants from the fRG against the\nBoltzmann distribution. We obtain excellent agreement between the two methods\ndemonstrating that numerical solutions for the effective potential can be\naccurately obtained in all the highly unharmonic cases we examined. We then\nassess the utility of the effective potential to describe the equilibrium\n2-point correlation function $\\langle x(0)x(t) \\rangle$ and the relevant\ncorrelation time. We find that when Wavefunction Renormalisation is also\nutilized, these are obtained to percent accuracy for temperatures down to the\ntypical height of the potentials' barriers but accuracy is quickly lost for\nlower temperatures. We also show how the fRG can offer strong agreement with\ndirect numerical simulation of the nonequilibrium evolution of average position\nand variance. Also, the fRG solution represents the whole ensemble average,\nfurther adding to its convenience over other techniques, such as direct\nnumerical simulations or solving the Fokker-Planck diffusion equation, which\nrequire multiple solutions with different initial conditions to construct\naverages over an ensemble.\n"}
+{"abstract": "  In this article we prove large deviations principles for high minima of\nGaussian processes with nonnegatively correlated increments on arbitrary\nintervals. Furthermore, we prove large deviations principles for the increments\nof such processes on intervals $[a,b]$ where $b-a$ is either less than the\nincrement or twice the increment, assuming stationarity of the increments. As a\nchief example, we consider fractional Brownian motion and fractional Gaussian\nnoise for $H\\geq 1/2$.\n"}
+{"abstract": "  The last decade has witnessed an experimental revolution in data science and\nmachine learning, epitomised by deep learning methods. Indeed, many\nhigh-dimensional learning tasks previously thought to be beyond reach -- such\nas computer vision, playing Go, or protein folding -- are in fact feasible with\nappropriate computational scale. Remarkably, the essence of deep learning is\nbuilt from two simple algorithmic principles: first, the notion of\nrepresentation or feature learning, whereby adapted, often hierarchical,\nfeatures capture the appropriate notion of regularity for each task, and\nsecond, learning by local gradient-descent type methods, typically implemented\nas backpropagation.\n  While learning generic functions in high dimensions is a cursed estimation\nproblem, most tasks of interest are not generic, and come with essential\npre-defined regularities arising from the underlying low-dimensionality and\nstructure of the physical world. This text is concerned with exposing these\nregularities through unified geometric principles that can be applied\nthroughout a wide spectrum of applications.\n  Such a 'geometric unification' endeavour, in the spirit of Felix Klein's\nErlangen Program, serves a dual purpose: on one hand, it provides a common\nmathematical framework to study the most successful neural network\narchitectures, such as CNNs, RNNs, GNNs, and Transformers. On the other hand,\nit gives a constructive procedure to incorporate prior physical knowledge into\nneural architectures and provide principled way to build future architectures\nyet to be invented.\n"}
+{"abstract": "  In this paper, we study the existence of limit cycles in continuous and\ndiscontinuous planar piecewise linear Hamiltonian differential systems with two\nor three zones separated by straight lines and such that the linear systems\nthat define the piecewise one have isolated singular points, i.e. centers or\nsaddles. In this case, we show that if the planar piecewise linear Hamiltonian\ndifferential system is either continuous or discontinuous with two zones, then\nit has no limit cycles. Now, if the planar piecewise linear Hamiltonian\ndifferential system is discontinuous with three zones, then it has at most one\nlimit cycle, and there are examples with one limit cycle. More precisely,\nwithout taking into account the position of the singular points in the zones,\nwe present examples with the unique limit cycle for all possible combinations\nof saddles and centers.\n"}
+{"abstract": "  Many cell membrane proteins that bind to actin form dynamic clusters driven\nby contractile flows generated by the actomyosin machinery at the cell cortex.\nRecent evidence suggests that a necessary condition for the generation of these\nprotein clusters on the membrane is the stratified organization of the active\nagents -formin-nucleated actin, myosin-II minifilaments, and ARP2/3-nucleated\nactin mesh -within the cortex. Further, the observation that these clusters\ndynamically remodel, requires that the components of this active machinery\nundergo turnover. Here we develop a coarse-grained agent-based Brownian\ndynamics simulation that incorporates the effects of stratification, binding of\nmyosin minifilaments to multiple actin filaments and their turnover. We show\nthat these three features of the active cortical machinery -stratification,\nmultivalency and turnover -are critical for the realisation of a nonequilibrium\nsteady state characterised by contractile flows and dynamic orientational\npatterning. We show that this nonequilibrium steady state enabled by the above\nfeatures of the cortex, can facilitate multi-particle encounters of membrane\nproteins that profoundly influence the kinetics of bimolecular reactions at the\ncell surface.\n"}
+{"abstract": "  This master's thesis contains an introduction to $A_\\infty$-algebras and\nhomological perturbation theory. We then discuss the formality of compact\nK\\\"ahler manifolds and present a direct proof of a homotopy transfer principle\nof $A_\\infty$-algebras, also known as Kadeishvili's Theorem.\n"}
+{"abstract": "  We consider the problem of learning stabilizer states with noise in the\nProbably Approximately Correct (PAC) framework of Aaronson (2007) for learning\nquantum states. In the noiseless setting, an algorithm for this problem was\nrecently given by Rocchetto (2018), but the noisy case was left open. Motivated\nby approaches to noise tolerance from classical learning theory, we introduce\nthe Statistical Query (SQ) model for PAC-learning quantum states, and prove\nthat algorithms in this model are indeed resilient to common forms of noise,\nincluding classification and depolarizing noise. We prove an exponential lower\nbound on learning stabilizer states in the SQ model. Even outside the SQ model,\nwe prove that learning stabilizer states with noise is in general as hard as\nLearning Parity with Noise (LPN) using classical examples. Our results position\nthe problem of learning stabilizer states as a natural quantum analogue of the\nclassical problem of learning parities: easy in the noiseless setting, but\nseemingly intractable even with simple forms of noise.\n"}
+{"abstract": "  We introduce DIVE, an end-to-end speaker diarization algorithm. Our neural\nalgorithm presents the diarization task as an iterative process: it repeatedly\nbuilds a representation for each speaker before predicting the voice activity\nof each speaker conditioned on the extracted representations. This strategy\nintrinsically resolves the speaker ordering ambiguity without requiring the\nclassical permutation invariant training loss. In contrast with prior work, our\nmodel does not rely on pretrained speaker representations and optimizes all\nparameters of the system with a multi-speaker voice activity loss. Importantly,\nour loss explicitly excludes unreliable speaker turn boundaries from training,\nwhich is adapted to the standard collar-based Diarization Error Rate (DER)\nevaluation. Overall, these contributions yield a system redefining the\nstate-of-the-art on the standard CALLHOME benchmark, with 6.7% DER compared to\n7.8% for the best alternative.\n"}
+{"abstract": "  The challenge of each organization is how they adapt to the shift of more\ncomplex technology such as mobile, big data, interconnected world, and the\nInternet of things. In order to achieve their objective, they must understand\nhow to take advantage of the interconnected individuals inside and outside the\norganization. Learning organization continues to transform by listening and\nmaintain the connection with their counterparts. Customer relationship\nmanagement is an important source for business organizations to grow and to\nassure their future. The complex social network, where interconnected peoples\nget information and get influenced very quickly, certainly a big challenge for\nbusiness organizations. The combination of these complex technologies provides\nintriguing insight such as the capabilities to listen to what the markets want,\nto understand their market competition, and to understand their market\nsegmentation. In this paper, as a part of organization transformation, we show\nhow a business organization mine online conversational in Twitter related to\ntheir brand issue and analyze them in the context of customer relationship\nmanagement to extract several insights regarding their market.\n"}
+{"abstract": "  There are a large number of optimization problems in physical models where\nthe relationships between model parameters and outputs are unknown or hard to\ntrack. These models are named as black-box models in general because they can\nonly be viewed in terms of inputs and outputs, without knowledge of the\ninternal workings. Optimizing the black-box model parameters has become\nincreasingly expensive and time consuming as they have become more complex.\nHence, developing effective and efficient black-box model optimization\nalgorithms has become an important task. One powerful algorithm to solve such\nproblem is Bayesian optimization, which can effectively estimates the model\nparameters that lead to the best performance, and Gaussian Process (GP) has\nbeen one of the most widely used surrogate model in Bayesian optimization.\nHowever, the time complexity of GP scales cubically with respect to the number\nof observed model outputs, and GP does not scale well with large parameter\ndimension either. Consequently, it has been challenging for GP to optimize\nblack-box models that need to query many observations and/or have many\nparameters. To overcome the drawbacks of GP, in this study, we propose a\ngeneral Bayesian optimization algorithm that employs a Neural Process (NP) as\nthe surrogate model to perform black-box model optimization, namely, Neural\nProcess for Bayesian Optimization (NPBO). In order to validate the benefits of\nNPBO, we compare NPBO with four benchmark approaches on a power system\nparameter optimization problem and a series of seven benchmark Bayesian\noptimization problems. The results show that the proposed NPBO performs better\nthan the other four benchmark approaches on the power system parameter\noptimization problem and competitively on the seven benchmark problems.\n"}
+{"abstract": "  Quantum-access security, where an attacker is granted superposition access to\nsecret-keyed functionalities, is a fundamental security model and its study has\ninspired results in post-quantum security. We revisit, and fill a gap in, the\nquantum-access security analysis of the Lamport one-time signature scheme (OTS)\nin the quantum random oracle model (QROM) by Alagic et al.~(Eurocrypt 2020). We\nthen go on to generalize the technique to the Winternitz OTS. Along the way, we\ndevelop a tool for the analysis of hash chains in the QROM based on the\nsuperposition oracle technique by Zhandry (Crypto 2019) which might be of\nindependent interest.\n"}
+{"abstract": "  The accuracy of smartphone-based positioning methods using WiFi usually\nsuffers from ranging errors caused by non-line-of-sight (NLOS) conditions.\nPrevious research usually exploits several statistical features from a long\ntime series (hundreds of samples) of WiFi received signal strength (RSS) or\nWiFi round-trip time (RTT) to achieve a high identification accuracy. However,\nthe long time series or large sample size attributes to high power and time\nconsumption in data collection for both training and testing. This will also\nundoubtedly be detrimental to user experience as the waiting time of getting\nenough samples is quite long. Therefore, this paper proposes a new real-time\nNLOS/LOS identification method for smartphone-based indoor positioning system\nusing WiFi RTT and RSS. Based on our extensive analysis of RSS and RTT\nfeatures, a machine learning-based method using random forest was chosen and\ndeveloped to separate the samples for NLOS/LOS conditions. Experiments in\ndifferent environments show that our method achieves a discrimination accuracy\nof about 94% with a sample size of 10. Considering the theoretically shortest\nWiFi ranging interval of 100ms of the RTT-enabled smartphones, our algorithm is\nable to provide the shortest latency of 1s to get the testing result among all\nof the state-of-art methods.\n"}
+{"abstract": "  We present a pioneering estimate of the global yearly greenhouse gas\nemissions of a large-scale Astrophysics experiment over several decades: the\nGiant Array for Neutrino Detection (GRAND). The project aims at detecting\nultra-high energy neutrinos with a 200,000 radio antenna array over\n200,000\\,km$^2$ as of the 2030s. With a fully transparent methodology based on\nopen source data, we calculate the emissions related to three unavoidable\nsources: travel, digital technologies and hardware equipment. We find that\nthese emission sources have a different impact depending on the stages of the\nexperiment. Digital technologies and travel prevail for the small-scale\nprototyping phase (GRANDProto300), whereas hardware equipment (material\nproduction and transportation) and data transfer/storage largely outweigh the\nother emission sources in the large-scale phase (GRAND200k). In the mid-scale\nphase (GRAND10k), the three sources contribute equally. This study highlights\nthe considerable carbon footprint of a large-scale astrophysics experiment, but\nalso shows that there is room for improvement. We discuss various lines of\nactions that could be implemented. The GRAND project being still in its\nprototyping stage, our results provide guidance to the future collaborative\npractices and instrumental design in order to reduce its carbon footprint.\n"}
+{"abstract": "  We report a systematic study of the $5d$-electron-doped system\nCe(Fe$_{1-x}$Ir$_x$)$_2$Al$_{10}$ ($0 \\leq x \\leq 0.15$). With increasing $x$,\nthe orthorhombic $b$~axis decreases slightly while accompanying changes in $a$\nand $c$ leave the unit cell volume almost unchanged. Inelastic neutron\nscattering, along with thermal and transport measurements, reveal that for the\nKondo semiconductor CeFe$_2$Al$_{10}$, the low-temperature energy gap which is\nproposed to be a consequence of strong $c \\mhyphen f$ hybridization, is\nsuppressed by a small amount of Ir substitution for Fe, and that the system\nadopts a metallic ground state with an increase in the density of states at the\nFermi level. The charge or transport gap collapses (at $x=$~0.04) faster than\nthe spin gap with Ir substitution. Magnetic susceptibility, heat capacity, and\nmuon spin relaxation measurements demonstrate that the system undergoes\nlong-range antiferromagnetic order below a N\\'eel temperature,\n$T_{\\mathrm{N}}$, of 3.1(2)~K for $x = 0.15$. The ordered moment is estimated\nto be smaller than 0.07(1)~$\\mu_\\mathrm{B}$/Ce although the trivalent state of\nCe is confirmed by Ce L$_3$-edge x-ray absorption near edge spectroscopy. It is\nsuggested that the $c \\mhyphen f$ hybridization gap, which plays an important\nrole in the unusually high ordering temperatures observed in Ce$T_2$Al$_{10}$\n($T$ = Ru and Os), may not be necessary for the onset of magnetic order with a\nlow $T_{\\mathrm{N}}$ seen here in Ce(Fe$_{1-x}$Ir$_x$)$_2$Al$_{10}$.\n"}
+{"abstract": "  In this chapter we review recent results concerning localized and extended\ndissipative solutions of the discrete complex Ginzburg-Landau equation. In\nparticular, we discuss discrete diffraction effects arising both from linear\nand nonlinear properties, the existence of self-localized dissipative solitons\nin the presence of cubic-quintic terms and modulational instability induced by\nsaturable nonlinearities. Dynamical stability properties of localized and\nextended dissipative discrete solitons are also discussed.\n"}
+{"abstract": "  There is a growing privacy concern due to the popularity of social media and\nsurveillance systems, along with advances in face recognition software.\nHowever, established image obfuscation techniques are either vulnerable to\nre-identification attacks by human or deep learning models, insufficient in\npreserving image fidelity, or too computationally intensive to be practical. To\ntackle these issues, we present DeepBlur, a simple yet effective method for\nimage obfuscation by blurring in the latent space of an unconditionally\npre-trained generative model that is able to synthesize photo-realistic facial\nimages. We compare it with existing methods by efficiency and image quality,\nand evaluate against both state-of-the-art deep learning models and industrial\nproducts (e.g., Face++, Microsoft face service). Experiments show that our\nmethod produces high quality outputs and is the strongest defense for most test\ncases.\n"}
+{"abstract": "  The murder of George Floyd by police in May 2020 sparked international\nprotests and renewed attention in the Black Lives Matter movement. Here, we\ncharacterize ways in which the online activity following George Floyd's death\nwas unparalleled in its volume and intensity, including setting records for\nactivity on Twitter, prompting the saddest day in the platform's history, and\ncausing George Floyd's name to appear among the ten most frequently used\nphrases in a day, where he is the only individual to have ever received that\nlevel of attention who was not known to the public earlier that same week.\nFurther, we find this attention extended beyond George Floyd and that more\nBlack victims of fatal police violence received attention following his death\nthan during other past moments in Black Lives Matter's history. We place that\nattention within the context of prior online racial justice activism by showing\nhow the names of Black victims of police violence have been lifted and\nmemorialized over the last 12 years on Twitter. Our results suggest that the\n2020 wave of attention to the Black Lives Matter movement centered past\ninstances of police violence in an unprecedented way, demonstrating the impact\nof the movement's rhetorical strategy to \"say their names.\"\n"}
+{"abstract": "  Nonlinear metasurface holography shows the great potential of metasurfaces to\ncontrol the phase, amplitude, and polarization of light while simultaneously\nconverting the frequency of the light. The possibility of tailoring the\nscattering properties of a coherent beam, as well as the scattering properties\nof nonlinear signals originating from the meta-atoms facilitates a huge degree\nof freedom in beam shaping application. Recently, several approaches showed\nthat virtual objects or any kind of optical information can be generated at a\nwavelength different from the laser input beam. Here, we demonstrate a\nsingle-layer nonlinear geometric-phase metasurface made of plasmonic\nnanostructures for a simultaneous second and third harmonic generation.\nDifferent from previous works, we demonstrate a two-color hologram with\ndissimilar types of nanostructures that generate the color information by\ndifferent nonlinear optical processes. The amplitude ratio of both harmonic\nsignals can be adapted depending on the nanostructures' resonance as well as\nthe power and the wavelength of the incident laser beam. The two-color\nholographic image is reconstructed in the Fourier space at visible wavelengths\nwith equal amplitudes using a single near-infrared wavelength. Nonlinear\nholography using multiple nonlinear processes simultaneously provides an\nalternative path to holographic color display applications, enhanced optical\nencryption schemes, and multiplexed optical data-storage.\n"}
+{"abstract": "  A lot of attention has been paid to a quantum-sensing network for detecting\nmagnetic fields in different positions. Recently, cryptographic quantum\nmetrology was investigated where the information of the magnetic fields is\ntransmitted in a secure way. However, sometimes, the positions where non-zero\nmagnetic fields are generated could carry important information. Here, we\npropose an anonymous quantum sensor where an information of positions having\nnon-zero magnetic fields is hidden after measuring magnetic fields with a\nquantum-sensing network. Suppose that agents are located in different positions\nand they have quantum sensors. After the quantum sensors are entangled, the\nagents implement quantum sensing that provides a phase information if non-zero\nmagnetic fields exist, and POVM measurement is performed on quantum sensors.\nImportantly, even if the outcomes of the POVM measurement is stolen by an\neavesdropper, information of the positions with non-zero magnetic fields is\nstill unknown for the eavesdropper in our protocol. In addition, we evaluate\nthe sensitivity of our proposed quantum sensors by using Fisher information\nwhen there are at most two positions having non-zero magnetic fields. We show\nthat the sensitivity is finite unless these two (non-zero) magnetic fields have\nexactly the same amplitude. Our results pave the way for new applications of\nquantum-sensing network.\n"}
+{"abstract": "  We present the prospects for the pre-merger detection and localization of\nbinary neutron star mergers with third generation gravitational-wave\nobservatories. We consider a wide variety of gravitational-wave networks which\nmay be operating in the 2030's and beyond; these networks include up to two\nCosmic Explorer sites, the Einstein Telescope, and continued observation with\nthe existing second generation ground-based detectors. For a fiducial local\nmerger rate of 300 Gpc$^{-3}$yr$^{-1}$, we find that the Einstein Telescope on\nits own is able to detect 6 and 2 sources per year at 5 and 30 minutes before\nmerger, respectively, while providing a localization of $<10~\\textrm{deg}^2$. A\nsingle Cosmic Explorer would detect but be unable to localize sources on its\nown. A two-detector Cosmic Explorer network, however, would detect 22 and 0.4\nmergers per year using the same criteria. A full three-detector network with\nthe operation of dual Cosmic Explorers and the Einstein Telescope would allow\nfor $<1~\\textrm{deg}^2$ source localization at 5 minutes before merger for\n$\\sim7$ sources per year. Given the dramatic increase in localization and\ndetection capabilities, third generation observatories will enable the regular\nobservation of the prompt emission of mergers by a broad array of observatories\nincluding gamma-ray, x-ray, and optical telescopes. Moreover, sub-degree\nlocalizations minutes before merger, combined with narrow-field-of-view\nhigh-energy telescopes, could strongly constrain the high-energy pre-merger\nemission models proposed in the last decade.\n"}
+{"abstract": "  We investigate the Cauchy problem for the focusing inhomogeneous nonlinear\nSchr\\\"odinger equation $i \\partial_t u + \\Delta u = - |x|^b |u|^{p-1} u$ in the\nradial Sobolev space $H^1_{\\text{rad}}(\\mathbb{R}^N)$, where $b>0$ and $p>1$.\nWe show the global existence and energy scattering in the inter-critical\nregime, i.e., $p>\\frac{N+4+2b}{N}$ and $p<\\frac{N+2+2b}{N-2}$ if $N\\geq 3$. We\nalso obtain blowing-up solutions for the mass-critical and mass-supercritical\nnonlinearities. The main difficulty, coming from the spatial growing\nnonlinearity, is overcome by refined Gagliardo-Nirenberg type inequalities. Our\nproofs are based on improved Gagliardo-Nirenberg inequalities, the\nMorawetz-Sobolev approach of Dodson and Murphy, radial Sobolev embeddings, and\nlocalized virial estimates.\n"}
+{"abstract": "  We are concerned with zeros of random power series with coefficients being a\nstationary, centered, complex Gaussian process. We show that the expected\nnumber of zeros in every smooth domain in the disk of convergence is less than\nthat of the hyperbolic GAF with i.i.d. coefficients. When coefficients are\nfinitely dependent, i.e., the spectral density is a trigonometric polynomial,\nwe derive precise asymptotics of the expected number of zeros inside the disk\nof radius $r$ centered at the origin as $r$ tends to the radius of convergence,\nin the proof of which we clarify that the negative contribution to the number\nof zeros stems from the zeros of the spectral density.\n"}
+{"abstract": "  Context: Galaxy interactions and mergers can lead to supermassive black hole\n(SMBH) binaries which become active galactic nuclei (AGN) pairs when the SMBHs\nstart accreting mass. If there is a third galaxy involved in the interaction,\nthen a triple AGN system can form. Aims: Our goal is to investigate the nature\nof the nuclear emission from the galaxies in the interacting pair\nNGC\\,7733--NGC\\,7734 using archival VLT/MUSE Integral field spectrograph data\nand study its relation to the stellar mass distribution traced by near-infrared\n(NIR) observations from the South African Astronomical Observatory (SAAO).\nMethods:We conducted near-infrared observations using the SAAO and identified\nthe morphological properties of bulges in each galaxy. We used MUSE data to\nobtain a set of ionized emission lines from each galaxy and studied the\nionization mechanism. We also examined the relation of the galaxy pair with any\nnearby companions with Far-UV observations using the UVIT. Conclusions: The\nemission line analysis from the central regions of NGC\\,7733 and NGC\\,7734 show\nSeyfert and LINER type AGN activity. The galaxy pair NGC\\,7733--34 also shows\nevidence of a third component, which has Seyfert-like emission. Hence, the\ngalaxy pair NGC\\,7733--34 forms a triple AGN system. We also detected an\nExtended Narrow-line region (ENLR) associated with the nucleus of NGC\\,7733.\n"}
+{"abstract": "  We distinguish two general modes of testing for Deep Neural Networks (DNNs):\nOffline testing where DNNs are tested as individual units based on test\ndatasets obtained without involving the DNNs under test, and online testing\nwhere DNNs are embedded into a specific application environment and tested in a\nclosed-loop mode in interaction with the application environment. Typically,\nDNNs are subjected to both types of testing during their development life cycle\nwhere offline testing is applied immediately after DNN training and online\ntesting follows after offline testing and once a DNN is deployed within a\nspecific application environment. In this paper, we study the relationship\nbetween offline and online testing. Our goal is to determine how offline\ntesting and online testing differ or complement one another and if offline\ntesting results can be used to help reduce the cost of online testing? Though\nthese questions are generally relevant to all autonomous systems, we study them\nin the context of automated driving systems where, as study subjects, we use\nDNNs automating end-to-end controls of steering functions of self-driving\nvehicles. Our results show that offline testing is less effective than online\ntesting as many safety violations identified by online testing could not be\nidentified by offline testing, while large prediction errors generated by\noffline testing always led to severe safety violations detectable by online\ntesting. Further, we cannot exploit offline testing results to reduce the cost\nof online testing in practice since we are not able to identify specific\nsituations where offline testing could be as accurate as online testing in\nidentifying safety requirement violations.\n"}
+{"abstract": "  The balancing domain decomposition methods (BDDC) are originally introduced\nfor symmetric positive definite systems and have been extended to the\nnonsymmetric positive definite system from the linear finite element\ndiscretization of advection-diffusion equations. In this paper, the convergence\nof the GMRES method is analyzed for the BDDC preconditioned linear system from\nadvection-diffusion equations with the hybridizable discontinuous Galerkin\n(HDG) discretization. Compared to the finite element discretizations, several\nadditional norms for the numerical trace have to be used and the equivalence\nbetween the bilinear forms and norms needs to be established. For large\nviscosity, if the subdomain size is small enough, the number of iterations is\nindependent of the number of subdomains and depends only slightly on the\nsudomain problem size. The convergence deteriorates when the viscosity\ndecreases. These results are similar to those with the finite element\ndiscretizations. Moreover, the effects of the additional primal constraints\nused in the BDDC algorithms are more significant with the higher degree HDG\ndiscretizations. The results of two two-dimensional examples are provided to\nconfirm our theory.\n"}
+{"abstract": "  For a positive integer $b\\ge2$, the $b$-symbol code is a new coding framework\nproposed to combat $b$-errors in $b$-symbol read channels. Especially, a\n$2$-symbol code is called a symbol-pair code. Remarkably, a classical maximum\ndistance separable (MDS) code is also an MDS $b$-symbol code. Recently, for any\nMDS code $\\mathcal{C}$, Ma and Luo determined the symbol-pair weight\ndistribution for $\\mathcal{C}$. In this paper, by calculating the number of\ncodewords in $\\mathcal{C}$ with special shape, we obtain the $b$-weight\ndistribution for $\\mathcal{C}$, and then generalize Theorem $1$ in \\cite{ML}.\n"}
+{"abstract": "  Genuine multipartite entanglement underlies correlation experiments\ncorroborating quantum mechanics and it is an expedient empowering many quantum\ntechnologies. One of many counterintuitive facets of genuine multipartite\nentanglement is its ability to exhibit an emergent character, that is, one can\ninfer its presence in some multipartite states merely from a set of its\nseparable marginals. Here, we show that the effect can be found also in the\ncontext of Gaussian states of bosonic systems. Specifically, we construct\nexamples of multimode Gaussian states carrying genuine multipartite\nentanglement which can be verified solely from separable nearest-neighbour\ntwo-mode marginals. The key tool of our construction is a genuine multipartite\nentanglement witness acting only on some two-mode reductions of the global\ncovariance matrix, which we find by a numerical solution of a semi-definite\nprogramme. We also propose an experimental scheme for preparation of the\nsimplest three-mode state, which requires interference of three correlatively\ndisplaced squeezed beams on two beam splitters. Besides revealing the concept\nof emergent genuine multipartite entanglement in the Gaussian scenario and\nbringing it closer to experimentally testable form, our results pave the way to\neffective diagnostics methods of global properties of multipartite states\nwithout complete tomography.\n"}
+{"abstract": "  We derive a new interior solution for stellar compact objects in\n$f\\mathcal{(R)}$ gravity assuming a differential relation to constrain the\nRicci curvature scalar. To this aim, we consider specific forms for the radial\ncomponent of the metric and the first derivative of $f\\mathcal{(R)}$. After,\nthe time component of the metric potential and the form of $f(\\mathcal R)$\nfunction are derived. From these results, it is possible to obtain the radial\nand tangential components of pressure and the density. The resulting interior\nsolution represents a physically motivated anisotropic neutron star model. It\nis possible to match it with a boundary exterior solution. From this matching,\nthe components of metric potentials can be rewritten in terms of a compactness\nparameter $C$ which has to be $C=2GM/Rc^2 <<0.5$ for physical consistency.\nOther physical conditions for real stellar objects are taken into account\naccording to the solution. We show that the model accurately bypasses\nconditions like the finiteness of radial and tangential pressures, and energy\ndensity at the center of the star, the positivity of these components through\nthe stellar structure, and the negativity of the gradients. These conditions\nare satisfied if the energy-conditions hold. Moreover, we study the stability\nof the model by showing that Tolman-Oppenheimer-Volkoff equation is at\nhydrostatic equilibrium. The solution is matched with observational data of\nmillisecond pulsars with a withe dwarf companion and pulsars presenting\nthermonuclear bursts.\n"}
+{"abstract": "  Cyber-physical systems (CPS) have been increasingly attacked by hackers.\nRecent studies have shown that CPS are especially vulnerable to insider\nattacks, in which case the attacker has full knowledge of the systems\nconfiguration. To better prevent such types of attacks, we need to understand\nhow insider attacks are generated. Typically, there are three critical aspects\nfor a successful insider attack: (i) Maximize damage, (ii) Avoid detection and\n(iii) Minimize the attack cost. In this paper we propose a Stealthy Attack\nGEneration (SAGE) framework by formulizing a novel optimization problem\nconsidering these three objectives and the physical constraints of the CPS. By\nadding small worst-case perturbations to the system, the SAGE attack can\ngenerate significant damage, while remaining undetected by the systems\nmonitoring algorithms. The proposed methodology is evaluated on several anomaly\ndetection algorithms. The results show that SAGE attacks can cause severe\ndamage while staying undetected and keeping the cost of an attack low. Our\nmethod can be accessed in the supplementary material of this paper to aid\nresearcher and practitioners in the design and development of resilient CPS and\ndetection algorithms.\n"}
+{"abstract": "  ASASSN-14ms may represent the most luminous Type Ibn supernova (SN~Ibn) ever\ndetected, with an absolute U-band magnitude brighter than -22.0 mag and a total\nbolometric luminosity >1.0x10^{44} erg/s near maximum light. The early-time\nspectra of this SN are characterized by a blue continuum on which are\nsuperimposed narrow P~Cygni profile lines of He I, suggesting the presence of\nslowly moving (~1000 km/s), He-rich circumstellar material (CSM). At 1--2\nmonths after maximum brightness, the He I line profiles become only slightly\nbroader, with blueshifted velocities of 2000--3000 km/s, consistent with the\nCSM shell being continuously accelerated by the SN light and ejecta. Like most\nSNe~Ibn, the light curves of ASASSN-14ms show rapid post-peak evolution,\ndropping by ~7 mag in the V band over three months. Such a rapid post-peak\ndecline and high luminosity can be explained with interaction between SN ejecta\nand helium-rich CSM of 0.9~M_{\\odot} at a distance of~10^{15} cm. The CSM\naround ASASSN-14ms is estimated to originate from a pre-explosion event with a\nmass-loss rate of 6.7~M_\\odot /yr (assuming a velocity of ~1000 km/s), which is\nconsistent with abundant He-rich material violently ejected during the late\nWolf-Rayet (WN9-11 or Opfe) stage. After examining the light curves for a\nsample of SNe~Ibn, we find that the more luminous ones tend to have slower\npost-peak decline rates, reflecting that the observed differences may arise\nprimarily from discrepancies in the CSM distribution around the massive\nprogenitors.\n"}
+{"abstract": "  Compact varifocal lenses are essential to various imaging and vision\ntechnologies. However, existing varifocal elements typically rely on\nmechanically-actuated systems with limited tuning speeds and scalability. Here,\nan ultrathin electrically controlled varifocal lens based on a liquid crystal\n(LC) encapsulated semiconductor metasurface is demonstrated. Enabled by the\nfield-dependent LC anisotropy, applying a voltage bias across the LC cell\nmodifies the local phase response of the silicon meta-atoms, in turn modifying\nthe focal length of the metalens. In a numerical implementation, a\nvoltage-actuated metalens with continuous zoom and up to 20% total focal shift\nis demonstrated. The concept of LC-based metalens is experimentally verified\nthrough the design and fabrication of a bifocal metalens that facilitates\nhigh-contrast switching between two discrete focal lengths upon application of\na 3.2 V$_{\\rm pp}$ voltage bias. Owing to their ultrathin thickness and\nadaptable design, LC-driven semiconductor metasurfaces open new opportunities\nfor compact varifocal lensing in a diversity of modern imaging applications.\n"}
+{"abstract": "  In this research, we study biological networks at different scales: a gene\nautoregulatory network at the single-cell level and the gut microbiota at the\npopulation level.\n  Proteins are the main actors in cells, they are the building blocks, act as\nenzymes and antibodies. The production of proteins is mediated by transcription\nfactors. In some cases, a protein acts as its own transcription factor, this is\ncalled autoregulation. It is known that autorepression speeds up the response\nand that autoactivation can lead to multiple stable equilibria. In this thesis,\nwe study the effects of the combination of activation and repression in\nautoregulation, as a case study we investigate the possible dynamics of the\nleucine responsive protein B of the archaeon Sulfolobus solfataricus (Ss-LrpB),\na protein that regulates itself in a unique and non-monotonic way via three\nbinding boxes. We examine for which conditions this type of network leads to\noscillations or bistability.\n  In the second part, much larger biological systems are considered. Ecological\nsystems, among which the human gut microbiome, are characterized by\nheavy-tailed abundance profiles. We study how these distributions can arise\nfrom population-based models by adding saturation effects and linear noise.\nMoreover, we examine different characteristics of experimental time series of\nmicrobial communities, such as the noise color and neutrality of the\nbiodiversity, and look at the influence of the parameters on these\ncharacteristics. With the first research topic we want to lay a foundation for\nthe understanding of non-monotonic gene regulation and take the first steps\ntoward synthetic biology in archaea. In the second part of the thesis, we\ninvestigate experimental time series from complex ecosystems and seek\ntheoretical models reproducing all observed characteristics in view of building\npredictive models.\n"}
+{"abstract": "  Materials with ultralow thermal conductivity are of great interest for\nefficient energy conversion and thermal barrier coating. Copper-based\nsemiconductors such as copper chalcogenides and copper halides are known to\npossess extreme low thermal conductivity, whereas the fundamental origin of the\nlow thermal conductivity observed in the copper-based materials remains\nelusive. Here, we reveal that s-d coupling induced giant phonon anharmonicity\nis the fundamental mechanism responsible for the ultralow thermal conductivity\nof copper compounds. The symmetry controlled strong coupling of high-lying\noccupied copper 3d orbital with the unoccupied 4s state under thermal vibration\nremarkably lowers the lattice potential barrier, which enhances anharmonic\nscattering between phonons. This understanding is confirmed by\ntemperature-dependent Raman spectra measurements. Our study offers an insight\nat atomic level connecting electronic structures with phonon vibration modes,\nand thus sheds light on materials properties that rely on electron-phonon\ncoupling, such as thermoelectricity and superconductivity.\n"}
+{"abstract": "  We investigate the propagator of 3d quantum gravity, formulated as a discrete\ntopological path integral. We define it as the Ponzano-Regge amplitude of the\nsolid cylinder swept by a 2d disk evolving in time. Quantum states for a 2d\ndisk live in the tensor products of N spins, where N is the number of holonomy\ninsertions connecting to the disk boundary. We formulate the cylindric\namplitude in terms of a transfer matrix and identify its eigen-modes in terms\nof spin recoupling. We show that the propagator distinguishes the subspaces\nwith different total spin and may select the vanishing total spin sector at\nlate time depending on the chosen cylinder boundary data. We discuss\napplications to quantum circuits and the possibility of experimental\nsimulations of this 3d quantum gravity propagator.\n"}
+{"abstract": "  We show that in electron-hole bilayers with excitonic order arising from\nconduction and valence bands formed by atomic orbitals that have different\nparities, nonzero interlayer tunneling leads to a second order Josephson\neffect. This means the interlayer electrical current is related to the phase of\nthe excitonic order parameter as $J = J_c \\sin2\\theta$ instead of $J = J_c \\sin\n\\theta$, and that the system has two degenerate ground states at $\\theta=0,\n\\pi$ that can be switched by an interlayer voltage pulse. When generalized to a\nthree dimensional stack of alternating electron-hole planes or a two\ndimensional stack of chains, AC Josephson effect implies that electric field\npulses perpendicular to the layers and chains can steer the order parameter\nphase between the two degenerate ground states, making these devices ultrafast\nmemories. The order parameter steering also applies to the excitonic insulator\ncandidate Ta$_2$NiSe$_5$.\n"}
+{"abstract": "  We study the extremal Betti numbers of the class of $t$--spread strongly\nstable ideals. More precisely, we determine the maximal number of admissible\nextremal Betti numbers for such ideals, and thereby we generalize the known\nresults for $t\\in \\{1,2\\}$.\n"}
+{"abstract": "  Multi-goal reinforcement learning is widely applied in planning and robot\nmanipulation. Two main challenges in multi-goal reinforcement learning are\nsparse rewards and sample inefficiency. Hindsight Experience Replay (HER) aims\nto tackle the two challenges via goal relabeling. However, HER-related works\nstill need millions of samples and a huge computation. In this paper, we\npropose Multi-step Hindsight Experience Replay (MHER), incorporating multi-step\nrelabeled returns based on $n$-step relabeling to improve sample efficiency.\nDespite the advantages of $n$-step relabeling, we theoretically and\nexperimentally prove the off-policy $n$-step bias introduced by $n$-step\nrelabeling may lead to poor performance in many environments. To address the\nabove issue, two bias-reduced MHER algorithms, MHER($\\lambda$) and Model-based\nMHER (MMHER) are presented. MHER($\\lambda$) exploits the $\\lambda$ return while\nMMHER benefits from model-based value expansions. Experimental results on\nnumerous multi-goal robotic tasks show that our solutions can successfully\nalleviate off-policy $n$-step bias and achieve significantly higher sample\nefficiency than HER and Curriculum-guided HER with little additional\ncomputation beyond HER.\n"}
+{"abstract": "  Projection-free conditional gradient (CG) methods are the algorithms of\nchoice for constrained optimization setups in which projections are often\ncomputationally prohibitive but linear optimization over the constraint set\nremains computationally feasible. Unlike in projection-based methods, globally\naccelerated convergence rates are in general unattainable for CG. However, a\nvery recent work on Locally accelerated CG (LaCG) has demonstrated that local\nacceleration for CG is possible for many settings of interest. The main\ndownside of LaCG is that it requires knowledge of the smoothness and strong\nconvexity parameters of the objective function. We remove this limitation by\nintroducing a novel, Parameter-Free Locally accelerated CG (PF-LaCG) algorithm,\nfor which we provide rigorous convergence guarantees. Our theoretical results\nare complemented by numerical experiments, which demonstrate local acceleration\nand showcase the practical improvements of PF-LaCG over non-accelerated\nalgorithms, both in terms of iteration count and wall-clock time.\n"}
+{"abstract": "  Let $M$ be the $6$-manifold $M$ as the total space of the sphere bundle of a\nrank $3$ vector bundle over a simply connected closed $4$-manifold. We show\nthat after looping $M$ is homotopy equivalent to a product of loops on spheres\nin general. This particularly implies the cohomology rigidity property of $M$\nafter looping. Furthermore, passing to the rational homotopy, we show that such\n$M$ is Koszul in the sense of Berglund.\n"}
+{"abstract": "  Sc3Mn3Al7Si5 is a rare example of a correlated metal in which the Mn moments\nform a kagome lattice. The absence of magnetic ordering to the lowest\ntemperatures suggests that geometrical frustration of magnetic interactions may\nlead to strong magnetic fluctuations. We have performed inelastic neutron\nscattering measurements on Sc3Mn3Al7Si5, finding that phonon scattering\ndominates for energies from ~20 - 50 meV. These results are in good agreement\nwith ab initio calculations of the phonon dispersions and densities of states,\nand as well reproduce the measured specific heat. A weak magnetic signal was\ndetected at energies less than ~10 meV, present only at the lowest\ntemperatures. The magnetic signal is broad and quasielastic, as expected for\nmetallic paramagnets.\n"}
+{"abstract": "  In two dimensional digital geometry, two lattice points are 4-connected\n(resp. 8-connected) if their Euclidean distance is at most one (resp.\n$\\sqrt{2}$). A set $S \\subset Z^2$ is 4-connected (resp. 8-connected) if for\nall pair of points $p_1, p_2$ in $S$ there is a path connecting $p_1$ to $p_2$\nsuch that every edge consists of a 4-connected (resp. 8-connected) pair of\npoints. The original definition of digital convexity which states that a set $S\n\\subset Z^d$ is digital convex if $\\conv(S) \\cap Z^d= S$, where $\\conv(S)$\ndenotes the convex hull of $S$ does not guarantee connectivity. However,\nmultiple algorithms assume connectivity. In this paper, we show that in two\ndimensional space, any digital convex set $S$ of $n$ points is unimodularly\nequivalent to a 8-connected digital convex set $C$. In fact, the resulting\ndigital convex set $C$ is 4-connected except for at most one point which is\n8-connected to the rest of the set. The matrix of $SL_2(Z)$ defining the affine\nisomorphism of $Z^2$ between the two unimodularly equivalent lattice polytopes\n$S$ and $C$ can be computed in roughly $O(n)$ time. We also show that no\nsimilar result is possible in higher dimension.\n"}
+{"abstract": "  We give a very simple derivation of the Atiyah-Patodi-Singer (APS) index\ntheorem and its small generalization by using the path integral of massless\nDirac fermions. It is based on the Fujikawa's argument for the relation between\nthe axial anomaly and the Atiyah-Singer index theorem, and only a minor\nmodification of that argument is sufficient to show the APS index theorem. The\nkey ingredient is the identification of the APS boundary condition and its\ngeneralization as physical state vectors in the Hilbert space of the massless\nfermion theory. The APS $\\eta$-invariant appears as the axial charge of the\nphysical states.\n"}
+{"abstract": "  Micro-Expression Recognition has become challenging, as it is extremely\ndifficult to extract the subtle facial changes of micro-expressions. Recently,\nseveral approaches proposed several expression-shared features algorithms for\nmicro-expression recognition. However, they do not reveal the specific\ndiscriminative characteristics, which lead to sub-optimal performance. This\npaper proposes a novel Feature Refinement ({FR}) with expression-specific\nfeature learning and fusion for micro-expression recognition. It aims to obtain\nsalient and discriminative features for specific expressions and also predict\nexpression by fusing the expression-specific features. FR consists of an\nexpression proposal module with attention mechanism and a classification\nbranch. First, an inception module is designed based on optical flow to obtain\nexpression-shared features. Second, in order to extract salient and\ndiscriminative features for specific expression, expression-shared features are\nfed into an expression proposal module with attention factors and proposal\nloss. Last, in the classification branch, labels of categories are predicted by\na fusion of the expression-specific features. Experiments on three publicly\navailable databases validate the effectiveness of FR under different protocol.\nResults on public benchmarks demonstrate that our FR provides salient and\ndiscriminative information for micro-expression recognition. The results also\nshow our FR achieves better or competitive performance with the existing\nstate-of-the-art methods on micro-expression recognition.\n"}
+{"abstract": "  We present the first verified implementation of a decision procedure for the\nquantifier-free theory of partial and linear orders. We formalise the procedure\nin Isabelle/HOL and provide a specification that is made executable using\nIsabelle's code generator. The procedure is already part of the development\nversion of Isabelle as a sub-procedure of the simplifier.\n"}
+{"abstract": "  Various applications of advanced air mobility (AAM) in urban environments\nfacilitate our daily life and public services. As one of the key issues of\nrealizing these applications autonomously, path planning problem has been\nstudied with main objectives on minimizing travel distance, flight time and\nenergy cost. However, AAM operations in metropolitan areas bring safety and\nsociety issues. Because most of AAM aircraft are unmanned aerial vehicles\n(UAVs) and they may fail to operate resulting in fatality risk, property damage\nrisk and societal impacts (noise and privacy) to the public. To quantitatively\nassess these risks and mitigate them in planning phase, this paper proposes an\nintegrated risk assessment model and develops a hybrid algorithm to solve the\nrisk-based 3D path planning problem. The integrated risk assessment method\nconsiders probability and severity models of UAV impact ground people and\nvehicle. By introducing gravity model, the population density and traffic\ndensity are estimated in a finer scale, which enables more accurate risk\nassessment. The 3D risk-based path planning problem is first formulated as a\nspecial minimum cost flow problem. Then, a hybrid estimation of distribution\nalgorithm (EDA) and risk-based A* (named as EDA-RA*) algorithm is proposed to\nsolve the problem. To improve computational efficiency, k-means clustering\nmethod is incorporated into EDA-RA* to provide both global and local search\nheuristic information, which formed the EDA and fast risk-based A* algorithm we\ncall EDA-FRA*. Case study results show that the risk assessment model can\ncapture high risk areas and the generated risk map enables safe UAV path\nplanning in urban complex environments.\n"}
+{"abstract": "  Designing efficient fault-tolerant circuits for converting between two\nquantum codes is an effective way to realize universal fault-tolerant quantum\ncomputing. In this work, we adopt the round-robin circuit construction to\ndesign a conversion circuit between CSS 7-qubit error-correcting code and\nReed-Muller 15-qubit code. We take the pieceable fault-tolerant protocol to\nguarantee the fault tolerance of our conversion circuit. A neural network\ndecoding scheme is proposed to suppress the propagation of quantum errors. Our\ndecoding scheme can effectively reduce the damaging effects of imperfect\nsyndrome extraction operations and predict possible logical errors that\noccurred after the error correction process. We provide a detailed description\nof our syndrome data model and analyze the depolarizing noise threshold of our\nconversion circuit through numerical simulations of the stabilizer circuit.\n"}
+{"abstract": "  Robust estimation is an important problem in statistics which aims at\nproviding a reasonable estimator when the data-generating distribution lies\nwithin an appropriately defined ball around an uncontaminated distribution.\nAlthough minimax rates of estimation have been established in recent years,\nmany existing robust estimators with provably optimal convergence rates are\nalso computationally intractable. In this paper, we study several estimation\nproblems under a Wasserstein contamination model and present computationally\ntractable estimators motivated by generative adversarial networks (GANs).\nSpecifically, we analyze properties of Wasserstein GAN-based estimators for\nlocation estimation, covariance matrix estimation, and linear regression and\nshow that our proposed estimators are minimax optimal in many scenarios.\nFinally, we present numerical results which demonstrate the effectiveness of\nour estimators.\n"}
+{"abstract": "  Artificial neural networks will always make a prediction, even when\ncompletely uncertain and regardless of the consequences. This obliviousness of\nuncertainty is a major obstacle towards their adoption in practice. Techniques\nexist, however, to estimate the two major types of uncertainty: model\nuncertainty and observation noise in the data. Bayesian neural networks are\ntheoretically well-founded models that can learn the model uncertainty of their\npredictions. Minor modifications to these models and their loss functions allow\nlearning the observation noise for individual samples as well. This paper is\nthe first to apply these techniques to predictive process monitoring. We found\nthat they contribute towards more accurate predictions and work quickly.\nHowever, their main benefit resides with the uncertainty estimates themselves\nthat allow the separation of higher-quality from lower-quality predictions and\nthe building of confidence intervals. This leads to many interesting\napplications, enables an earlier adoption of prediction systems with smaller\ndatasets and fosters a better cooperation with humans.\n"}
+{"abstract": "  We consider the problem of maximizing the minimum (weighted) value of all\ncomponents of a vector over a polymatroid. This is a special case of the\nlexicographically optimal base problem introduced and solved by Fujishige. We\ngive an alternative formulation of the problem as a zero-sum game between a\nmaximizing player whose mixed strategy set is the base of the polymatroid and a\nminimizing player whose mixed strategy set is a simplex. We show that this game\nand three variations of it unify several problems in search, sequential testing\nand queuing. We give a new, short derivation of optimal strategies for both\nplayers and an expression for the value of the game. Furthermore, we give a\ncharacterization of the set of optimal strategies for the minimizing player and\nwe consider special cases for which optimal strategies can be found\nparticularly easily.\n"}
+{"abstract": "  The strategy adopted in the original Morris-Thorne wormhole was to retain\ncomplete control over the geometry at the expense of certain engineering\nconsiderations. The purpose of this paper is to obtain several complete\nwormhole solutions by assuming a noncommutative-geometry background with a\nconcomitant isotropic-pressure condition. This condition allows us to consider\na cosmological setting with a perfect-fluid equation of state. An extended form\nof the equation generalizes the first solution and subsequently leads to the\ngeneralized Chaplygin-gas model and hence to a third solution. The solutions\nobtained extend several previous results. This paper also reiterates the need\nfor a noncommutative-geometry background to account for the enormous radial\ntension that is characteristic of Morris-Thorne wormholes.\n"}
+{"abstract": "  We adapt the quasi-monotone method from [2] for composite convex minimization\nin the stochastic setting. For the proposed numerical scheme we derive the\noptimal convergence rate in terms of the last iterate, rather than on average\nas it is standard for subgradient methods. The theoretical guarantee for\nindividual convergence of the regularized quasi-monotone method is confirmed by\nnumerical experiments on l1-regularized robust linear regression.\n"}
+{"abstract": "  Understanding the impact of environment on the formation and evolution of\ndark matter halos and galaxies is a crucial open problem. Studying statistical\ncorrelations in large simulated populations sheds some light on these impacts,\nbut the causal effect of an environment on individual objects is harder to\npinpoint. Addressing this, we present a new method for resimulating a single\ndark matter halo in multiple large-scale environments. In the initial\nconditions, we 'splice' (i.e. insert) the Lagrangian region of a halo into\ndifferent Gaussian random fields, while enforcing consistency with the\nstatistical properties of $\\Lambda$CDM. Applying this technique, we demonstrate\nthat the mass of halos is primarily determined by the density structure inside\ntheir Lagrangian patches, while the halos' concentration is more strongly\naffected by environment. The splicing approach will also allow us to study, for\nexample, the impact of the cosmic web on accretion processes and galaxy\nquenching.\n"}
+{"abstract": "  Integration is indispensable, not only in mathematics, but also in a wide\nrange of other fields. A deep learning method has recently been developed and\nshown to be capable of integrating mathematical functions that could not\npreviously be integrated on a computer. However, that method treats integration\nas equivalent to natural language translation and does not reflect mathematical\ninformation. In this study, we adjusted the learning model to take mathematical\ninformation into account and developed a wide range of learning models that\nlearn the order of numerical operations more robustly. In this way, we achieved\na 98.80% correct answer rate with symbolic integration, a higher rate than that\nof any existing method. We judged the correctness of the integration based on\nwhether the derivative of the primitive function was consistent with the\nintegrand. By building an integrated model based on this strategy, we achieved\na 99.79% rate of correct answers with symbolic integration.\n"}
+{"abstract": "  In this paper we use the additive thermodynamic formalism to obtain new\nbounds of the Hausdorff and box-counting dimension of certain non conformal\nhyperbolic repellers defined by $C^r$, $r > 1$ piecewise expanding maps on a\n$d$-dimensional smooth manifold $M$.\n"}
+{"abstract": "  The field of automatic music composition has seen great progress in the last\nfew years, much of which can be attributed to advances in deep neural networks.\nThere are numerous studies that present different strategies for generating\nsheet music from scratch. The inclusion of high-level musical characteristics\n(e.g., perceived emotional qualities), however, as conditions for controlling\nthe generation output remains a challenge. In this paper, we present a novel\napproach for calculating the valence (the positivity or negativity of the\nperceived emotion) of a chord progression within a lead sheet, using\npre-defined mood tags proposed by music experts. Based on this approach, we\npropose a novel strategy for conditional lead sheet generation that allows us\nto steer the music generation in terms of valence, phrasing, and time\nsignature. Our approach is similar to a Neural Machine Translation (NMT)\nproblem, as we include high-level conditions in the encoder part of the\nsequence-to-sequence architectures used (i.e., long-short term memory networks,\nand a Transformer network). We conducted experiments to thoroughly analyze\nthese two architectures. The results show that the proposed strategy is able to\ngenerate lead sheets in a controllable manner, resulting in distributions of\nmusical attributes similar to those of the training dataset. We also verified\nthrough a subjective listening test that our approach is effective in\ncontrolling the valence of a generated chord progression.\n"}
+{"abstract": "  Communication in high frequencies such as millimeter wave and terahertz\nsuffer from high path-loss and intense shadowing which necessitates beamforming\nfor reliable data transmission. On the other hand, at high frequencies the\nchannels are sparse and consist of few spatial clusters. Therefore, beam\nalignment (BA) strategies are used to find the direction of these channel\nclusters and adjust the width of the beam used for data transmission. In this\nwork, a single-user uplink scenario where the channel has one dominant cluster\nis considered. It is assumed that the user transmits a set of BA packets over a\nfixed duration. Meanwhile, the base-station (BS) uses different probing beams\nto scan different angular regions. Since the BS measurements are noisy, it is\nnot possible to find a narrow beam that includes the angle of arrival (AoA) of\nthe user with probability one. Therefore, the BS allocates a narrow beam to the\nuser which includes the AoA of the user with a predetermined error probability\nwhile minimizing the expected beamwidth of the allocated beam. Due to\nintractability of this noisy BA problem, here this problem is posed as an\nend-to-end optimization of a deep neural network (DNN) and effects of different\nloss functions are discussed and investigated. It is observed that the proposed\nDNN based BA, at high SNRs, achieves a performance close to that of the optimal\nBA when there is no-noise and for all SNRs, outperforms state-of-the-art.\n"}
+{"abstract": "  We prove potential automorphy results for a single Galois representation $G_F\n\\rightarrow GL_n(\\overline{\\mathbb{Q}}_l)$ where $F$ is a CM number field. The\nstrategy is to use the $p,q$ switch trick and modify the Dwork motives employed\nin \\cite{HSBT} to break self-duality of the motives, but not the Hodge-Tate\nweights. Another key result to prove is the ordinarity of certain $p$-adic\nrepresentations, which follows from log geometry techniques. One input is the\nautomorphy lifting theorem in \\cite{tap}.\n"}
+{"abstract": "  Histotripsy relies on cavitation to mechanically homogenize soft tissue.\nThere is strong evidence that the high stresses, strains, and strain rates\ndeveloped as bubbles grow and collapse contribute to this tissue\nhomogenization. While such stresses and strains have been examined\ncomputationally in model systems with assumed constitutive models (e.g.,\nfinite-deformation Neo-Hookean model) and viscoelastic properties determined\nunder quasi-static conditions, recent studies proposed that the Quadratic Law\nKelvin-Voigt (QLKV) constitutive model, which additionally accounts for strain\nstiffening, more accurately represents the viscoelastic response of soft\nmaterials subjected to cavitation; this model has also been used to infer\nviscoelastic properties at high rates. In this work, we use the QLKV model and\nthese properties to calculate the time-dependent stress, strain, and strain\nrate fields produced during the growth and collapse of individual bubbles\nsubjected to a histotripsy-relevant pressure waveform in agarose gels of 0.3~\\%\nand 1.0~\\% concentration and corresponding to actual (past) experiments. We\nfind that, as the gel concentration is increased, strain stiffening manifests\nin larger elastic stresses and compressive stresses extending into the collapse\nphase, particularly for the 1.0~\\% concentration gel. As a result, the duration\nof the collapse phase also increases. In comparison with the conventional\nNeo-Hookean model, the compressive stress has a larger magnitude, extends\nfarther into the surrounding medium, and shows an increased departure from\ngrowth/collapse symmetry close to the bubble; all of these effects are\nmagnified in the stiffer gel.\n"}
+{"abstract": "  Plasmas that are strongly magnetized in the sense that the gyrofrequency\nexceeds the plasma frequency exhibit novel transport properties that are not\nwell understood. As a representative example, we compute the friction force\nacting on a massive test charge moving through a strongly coupled and strongly\nmagnetized one-component plasma using a generalized Boltzmann kinetic theory.\nRecent works studying the weakly coupled regime have shown that strong\nmagnetization leads to a transverse component of the friction force that is\nperpendicular to both the Lorentz force and velocity of the test charge; in\naddition to the stopping power component aligned antiparallel to the velocity.\nRecent molecular dynamics simulations have also shown that strong Coulomb\ncoupling in addition to strong magnetization gives rise to a third component of\nthe friction force in the direction of the Lorentz force. Here, we show that\nthe generalized Boltzmann kinetic theory captures these effects, and generally\nagrees well with the molecular dynamics simulations over a broad range of\nCoulomb coupling and magnetization strength regimes. The theory is also used to\nshow that a \"gyro\" component of the friction in the direction of the Lorentz\nforce arises due to asymmetries associated with gyromotion during short-range\ncollisions. Computing the average motion of the test charge through the\nbackground plasma, the transverse force is found to strongly influence the\ntrajectory by changing the gyroradius and the gyro friction force is found to\nslightly change the gyrofrequency of the test charge resulting in a phase\nshift.\n"}
+{"abstract": "  For the first time we search for the $\\eta_{c2}(1D)$ in\n$e^+e^-\\to\\gamma\\eta_{c2}(1D)$ at $\\sqrt{s}$ = 10.52, 10.58, and 10.867 GeV\nwith data samples of 89.5 fb$^{-1}$, 711 fb$^{-1}$, and 121.4 fb$^{-1}$,\nrespectively, accumulated with the Belle detector at the KEKB asymmetric energy\nelectron-positron collider. No significant $\\eta_{c2}(1D)$ signal is observed\nin the mass range between 3.8 and 3.88 GeV/$c^2$. The upper limit at 90\\%\nconfidence level on the product of the Born cross section for $e^+e^- \\to\n\\gamma\\eta_{c2}(1D)$ and branching fraction for $\\eta_{c2}(1D)\\to \\gamma\nh_c(1P)$ is determined to be $\\sigma(e^+e^- \\to \\gamma\\eta_{c2}(1D)){\\cal\nB}(\\eta_{c2}(1D)\\to \\gamma h_c(1P))$ $<$ 4.9 fb at $\\sqrt{s}$ near 10.6 GeV.\n"}
+{"abstract": "  In this paper, we propose a novel Feature Decomposition and Reconstruction\nLearning (FDRL) method for effective facial expression recognition. We view the\nexpression information as the combination of the shared information (expression\nsimilarities) across different expressions and the unique information\n(expression-specific variations) for each expression. More specifically, FDRL\nmainly consists of two crucial networks: a Feature Decomposition Network (FDN)\nand a Feature Reconstruction Network (FRN). In particular, FDN first decomposes\nthe basic features extracted from a backbone network into a set of facial\naction-aware latent features to model expression similarities. Then, FRN\ncaptures the intra-feature and inter-feature relationships for latent features\nto characterize expression-specific variations, and reconstructs the expression\nfeature. To this end, two modules including an intra-feature relation modeling\nmodule and an inter-feature relation modeling module are developed in FRN.\nExperimental results on both the in-the-lab databases (including CK+, MMI, and\nOulu-CASIA) and the in-the-wild databases (including RAF-DB and SFEW) show that\nthe proposed FDRL method consistently achieves higher recognition accuracy than\nseveral state-of-the-art methods. This clearly highlights the benefit of\nfeature decomposition and reconstruction for classifying expressions.\n"}
+{"abstract": "  We show how fitting sparse linear models over learned deep feature\nrepresentations can lead to more debuggable neural networks. These networks\nremain highly accurate while also being more amenable to human interpretation,\nas we demonstrate quantiatively via numerical and human experiments. We further\nillustrate how the resulting sparse explanations can help to identify spurious\ncorrelations, explain misclassifications, and diagnose model biases in vision\nand language tasks. The code for our toolkit can be found at\nhttps://github.com/madrylab/debuggabledeepnetworks.\n"}
+{"abstract": "  We present the analysis of multi-wavelength observations of seven\nextragalactic radio sources, listed as unidentified in the Third Cambridge\nRevised Catalog (3CR). X-ray observations, performed during Chandra Cycle 21,\nwere compared to VLA, WISE and Pan-STARRS observations in the radio, infrared\nand optical bands, respectively. All sources in this sample lack a clear\noptical counterpart, and are thus missing their redshift and optical\nclassification. In order to confirm the X-ray and infrared radio counterparts\nof core and extended components, here we present for the first time radio maps\nobtained manually reducing VLA archival data. As in previous papers on the\nChandra X-ray snapshot campaign, we report X-ray detections of radio cores and\ntwo sources, out of the seven presented here, are found to be members of galaxy\nclusters. For these two cluster sources (namely, 3CR 409 and 3CR 454.2), we\nderived surface brightness profiles in four directions. For all seven sources,\nwe measured X-ray intensities of the radio sources and we also performed\nstandard X-ray spectral analysis for the four sources (namely, 3CR 91, 3CR 390,\n3CR 409 and 3CR 428) with the brightest nuclei (more than 400 photons in the\n2'' nuclear region). We also detected extended X-ray emission around 3CR 390\nand extended X-ray emission associated with the northern jet of 3CR 158. This\npaper represents the first attempt to give a multi-wavelength view of the\nunidentified radio sources listed in the 3CR catalog.\n"}
+{"abstract": "  The lossless data compression algorithm based on Bayesian Attention Networks\nis derived from first principles. Bayesian Attention Networks are defined by\nintroducing an attention factor per a training sample loss as a function of two\nsample inputs, from training sample and prediction sample. By using a sharpened\nJensen's inequality we show that the attention factor is completely defined by\na correlation function of the two samples w.r.t. the model weights. Due to the\nattention factor the solution for a prediction sample is mostly defined by a\nfew training samples that are correlated with the prediction sample. Finding a\nspecific solution per prediction sample couples together the training and the\nprediction. To make the approach practical we introduce a latent space to map\neach prediction sample to a latent space and learn all possible solutions as a\nfunction of the latent space along with learning attention as a function of the\nlatent space and a training sample. The latent space plays a role of the\ncontext representation with a prediction sample defining a context and a\nlearned context dependent solution used for the prediction.\n"}
+{"abstract": "  Collaborative Mobile crowdsourcing (CMCS) allows entities, e.g., local\nauthorities or individuals, to hire a team of workers from the crowd of\nconnected people, to execute complex tasks. In this paper, we investigate two\ndifferent CMCS recruitment strategies allowing task requesters to form teams of\nsocially connected and skilled workers: i) a platform-based strategy where the\nplatform exploits its own knowledge about the workers to form a team and ii) a\nleader-based strategy where the platform designates a group leader that\nrecruits its own suitable team given its own knowledge about its Social Network\n(SN) neighbors. We first formulate the recruitment as an Integer Linear Program\n(ILP) that optimally forms teams according to four fuzzy-logic-based criteria:\nlevel of expertise, social relationship strength, recruitment cost, and\nrecruiter's confidence level. To cope with NP-hardness, we design a novel\nlow-complexity CMCS recruitment approach relying on Graph Neural Networks\n(GNNs), specifically graph embedding and clustering techniques, to shrink the\nworkers' search space and afterwards, exploiting a meta-heuristic genetic\nalgorithm to select appropriate workers. Simulation results applied on a\nreal-world dataset illustrate the performance of both proposed CMCS recruitment\napproaches. It is shown that our proposed low-complexity GNN-based recruitment\nalgorithm achieves close performances to those of the baseline ILP with\nsignificant computational time saving and ability to operate on large-scale\nmobile crowdsourcing platforms. It is also shown that compared to the\nleader-based strategy, the platform-based strategy recruits a more skilled team\nbut with lower SN relationships and higher cost.\n"}
+{"abstract": "  In this work, we perform Bayesian inference tasks for the chemical master\nequation in the tensor-train format. The tensor-train approximation has been\nproven to be very efficient in representing high dimensional data arising from\nthe explicit representation of the chemical master equation solution. An\nadditional advantage of representing the probability mass function in the\ntensor train format is that parametric dependency can be easily incorporated by\nintroducing a tensor product basis expansion in the parameter space. Time is\ntreated as an additional dimension of the tensor and a linear system is derived\nto solve the chemical master equation in time. We exemplify the tensor-train\nmethod by performing inference tasks such as smoothing and parameter inference\nusing the tensor-train framework. A very high compression ratio is observed for\nstoring the probability mass function of the solution. Since all linear algebra\noperations are performed in the tensor-train format, a significant reduction of\nthe computational time is observed as well.\n"}
+{"abstract": "  Single crystal inelastic neutron scattering is used to study spin wave\nexcitations in the fully polarized state of the frustrated quantum\nferro-antiferromagnet BaCdVO(PO$_4$)$_2$. The data analysis is based on a\nHeisenberg spin Hamiltonian that includes as many distinct nearest-neighbor and\nnext-nearest neighbor interactions as allowed by crystal symmetry. All 8 such\nexchange constants are obtained in a simultaneous fit to over 150 scans across\nthe dispersion manifold. This establishes a definitive quantitative model of\nthis material. It turns out to be substantially different from the one assumed\nin numerous previous studies based on powder experiments.\n"}
+{"abstract": "  It is unclear if existing interpretations of deep neural network models\nrespond effectively to the needs of users. This paper summarizes the common\nforms of explanations (such as feature attribution, decision rules, or probes)\nused in over 200 recent papers about natural language processing (NLP), and\ncompares them against user questions collected in the XAI Question Bank. We\nfound that although users are interested in explanations for the road not taken\n-- namely, why the model chose one result and not a well-defined, seemly\nsimilar legitimate counterpart -- most model interpretations cannot answer\nthese questions.\n"}
+{"abstract": "  This study used the recent ATLAS jet substructure measurements to see if any\nimprovements can be made to the commonly used Pythia8 Monash and A14 tunes.\n"}
+{"abstract": "  In this paper we investigate real-time, dynamic traffic optimization in\nrailway systems. In order to enable practical solution times, we operate the\noptimizer in a receding horizon fashion and with optimization horizons that are\nshorter than the full path to destinations, using a model predictive control\n(MPC) approach. We present new procedures to establish safe prediction\nhorizons, providing formal guarantees that the system is operated in a way that\nsatisfies hard safety constraints despite the fact that not all future train\ninteractions are taken into account, by characterizing the minimal required\noptimization horizons. We also show that any feasible solution to our proposed\nmodels is sufficient to maintain a safe, automated operation of the railway\nsystem, providing an upper bound on the computations strictly required.\nAdditionally, we show that these minimal optimization horizons also\ncharacterize an upper bound on computations required to construct a feasible\nsolution for any arbitrary optimization horizon, paving the way for anytime\nalgorithms. Finally, our results enable systematic solution reuse, when\nprevious schedules are available. We test our approach on a detailed simulation\nenvironment of a real-world railway system used for freight transport.\n"}
+{"abstract": "  The problem of nonintegrability of the circular restricted three-body problem\nis very classical and important in the theory of dynamical systems. It was\npartially solved by Poincare in the nineteenth century: He showed that there\nexists no real-analytic first integral which depends analytically on the mass\nratio of the second body to the first one and is functionally independent of\nthe Hamiltonian. When the mass of the second body becomes zero, the restricted\nthree-body problem reduces to the two-body Kepler problem. We prove the\nnonintegrability of the restricted three-body problem both in the planar and\nspatial cases for any nonzero mass of the second body. Our basic tool of the\nproofs is a technique developed here for determining whether perturbations of\nintegrable systems which may be non-Hamiltonian are not meromorphically\nintegrable near resonant periodic orbits such that the first integrals and\ncommutative vector fields also depend meromorphically on the perturbation\nparameter. The technique is based on generalized versions due to Ayoul and Zung\nof the Morales-Ramis and Morales-Ramis-Simo theories.\n"}
+{"abstract": "  Protein fold classification is a classic problem in structural biology and\nbioinformatics. We approach this problem using persistent homology. In\nparticular, we use alpha shape filtrations to compare a topological\nrepresentation of the data with a different representation that makes use of\nknot-theoretic ideas. We use the statistical method of Angle-based Joint and\nIndividual Variation Explained (AJIVE) to understand similarities and\ndifferences between these representations.\n"}
+{"abstract": "  3D point cloud (PC) -- a collection of discrete geometric samples of a\nphysical object's surface -- is typically large in size, which entails\nexpensive subsequent operations like viewpoint image rendering and object\nrecognition. Leveraging on recent advances in graph sampling, we propose a fast\nPC sub-sampling algorithm that reduces its size while preserving the overall\nobject shape. Specifically, to articulate a sampling objective, we first assume\na super-resolution (SR) method based on feature graph Laplacian regularization\n(FGLR) that reconstructs the original high-resolution PC, given 3D points\nchosen by a sampling matrix $\\H$. We prove that minimizing a worst-case SR\nreconstruction error is equivalent to maximizing the smallest eigenvalue\n$\\lambda_{\\min}$ of a matrix $\\H^{\\top} \\H + \\mu \\cL$, where $\\cL$ is a\nsymmetric, positive semi-definite matrix computed from the neighborhood graph\nconnecting the 3D points. Instead, for fast computation we maximize a lower\nbound $\\lambda^-_{\\min}(\\H^{\\top} \\H + \\mu \\cL)$ via selection of $\\H$ in three\nsteps. Interpreting $\\cL$ as a generalized graph Laplacian matrix corresponding\nto an unbalanced signed graph $\\cG$, we first approximate $\\cG$ with a balanced\ngraph $\\cG_B$ with the corresponding generalized graph Laplacian matrix\n$\\cL_B$. Second, leveraging on a recent theorem called Gershgorin disc perfect\nalignment (GDPA), we perform a similarity transform $\\cL_p = \\S \\cL_B \\S^{-1}$\nso that Gershgorin disc left-ends of $\\cL_p$ are all aligned at the same value\n$\\lambda_{\\min}(\\cL_B)$. Finally, we perform PC sub-sampling on $\\cG_B$ using a\ngraph sampling algorithm to maximize $\\lambda^-_{\\min}(\\H^{\\top} \\H + \\mu\n\\cL_p)$ in roughly linear time. Experimental results show that 3D points chosen\nby our algorithm outperformed competing schemes both numerically and visually\nin SR reconstruction quality.\n"}
+{"abstract": "  Theoretical issues: With the explosive growth in the research literature\nproduction, the need for new approaches to structure knowledge emerged. Method:\nSynthetic content analysis was used in our meta-study. Results and discussion:\nOur meta-study showed that content analysis is frequently used in nursing\nresearch in a very wide spectrum of applications. The trend of its use is\npositive and it is used globally in a variety of research settings. The\nsynthetic content analysis used in our study showed to be a very helpful tool\nin performing knowledge synthesis, replacing many of the routine activities of\nconventional synthesis with automated activities this making such studies more\neconomically viable and easier to perform.\n"}
+{"abstract": "  People convey information extremely effectively through spoken interaction\nusing multiple channels of information transmission: the lexical channel of\nwhat is said, and the non-lexical channel of how it is said. We propose\nstudying human perception of spoken communication as a means to better\nunderstand how information is encoded across these channels, focusing on the\nquestion 'What characteristics of communicative context affect listener's\nexpectations of speech?'. To investigate this, we present a novel behavioural\ntask testing whether listeners can discriminate between the true utterance in a\ndialogue and utterances sampled from other contexts with the same lexical\ncontent. We characterize how perception - and subsequent discriminative\ncapability - is affected by different degrees of additional contextual\ninformation across both the lexical and non-lexical channel of speech. Results\ndemonstrate that people can effectively discriminate between different prosodic\nrealisations, that non-lexical context is informative, and that this channel\nprovides more salient information than the lexical channel, highlighting the\nimportance of the non-lexical channel in spoken interaction.\n"}
+{"abstract": "  Social and professional networks affect labor market dynamics, knowledge\ndiffusion and new business creation. To understand the determinants of how\nthese networks are formed in the first place, we analyze a unique dataset of\nbusiness cards exchanges among a sample of over 240,000 users of the\nmulti-platform contact management and professional social networking tool for\nindividuals Eight. We develop a structural model of network formation with\nstrategic interactions, and we estimate users' payoffs that depend on the\ncomposition of business relationships, as well as indirect business\ninteractions. We allow heterogeneity of users in both observable and\nunobservable characteristics to affect how relationships form and are\nmaintained. The model's stationary equilibrium delivers a likelihood that is a\nmixture of exponential random graph models that we can characterize in\nclosed-form. We overcome several econometric and computational challenges in\nestimation, by exploiting a two-step estimation procedure, variational\napproximations and minorization-maximization methods. Our algorithm is\nscalable, highly parallelizable and makes efficient use of computer memory to\nallow estimation in massive networks. We show that users payoffs display\nhomophily in several dimensions, e.g. location; furthermore, users unobservable\ncharacteristics also display homophily.\n"}
+{"abstract": "  Graph neural networks are emerging as continuation of deep learning success\nw.r.t. graph data. Tens of different graph neural network variants have been\nproposed, most following a neighborhood aggregation scheme, where the node\nfeatures are updated via aggregating features of its neighboring nodes from\nlayer to layer. Though related research surges, the power of GNNs are still not\non-par-with their counterpart CNNs in computer vision and RNNs in natural\nlanguage processing. We rethink this problem from the perspective of\ninformation propagation, and propose to enhance information propagation among\nGNN layers by combining heterogeneous aggregations. We argue that as richer\ninformation are propagated from shallow to deep layers, the discriminative\ncapability of features formulated by GNN can benefit from it. As our first\nattempt in this direction, a new generic GNN layer formulation and upon this a\nnew GNN variant referred as HAG-Net is proposed. We empirically validate the\neffectiveness of HAG-Net on a number of graph classification benchmarks, and\nelaborate all the design options and criterions along with.\n"}
+{"abstract": "  Galactic cosmic-rays (GCRs) are thought to be accelerated in strong shocks\ninduced by massive star winds and supernova explosions sweeping across the\ninterstellar medium. But the phase of the interstellar medium from which the\nCRs are extracted has remained elusive until now. Here, we study in detail the\nGCR source composition deduced from recent measurements by the AMS-02, Voyager\n1 and SuperTIGER experiments to obtain information on the composition,\nionisation state and dust content of the GCR source reservoirs. We show that\nthe volatile elements of the CR material are mainly accelerated from a plasma\nof temperature higher than $\\sim 2$ MK, which is typical of the hot medium\nfound in galactic superbubbles energised by the activity of massive star winds\nand supernova explosions. Another GCR component, which is responsible for the\noverabundance of $^{22}$Ne, most likely arises from acceleration of massive\nstar winds in their termination shocks. From the CR-related $\\gamma$-ray\nluminosity of the Milky Way, we estimate that the ion acceleration efficiency\nin both supernova shocks and wind termination shocks is of the order of\n$10^{-5}$. The GCR source composition also shows evidence for a preferential\nacceleration of refractory elements contained in interstellar dust. We suggest\nthat the GCR refractories are also produced in superbubbles, from shock\nacceleration and subsequent sputtering of dust grains continuously incorporated\ninto the hot plasma through thermal evaporation of embedded molecular clouds.\nOur model explains well the measured abundances of all primary and mostly\nprimary CRs from H to Zr, including the overabundance of $^{22}$Ne.\n"}
+{"abstract": "  Developing complex engineered systems (CES) poses significant challenges for\nengineers, managers, designers, and businesspeople alike due to the inherent\ncomplexity of the systems and contexts involved. Furthermore, experts have\nexpressed great interest in filling the gap in theory about how CES develop.\nThis article begins to address that gap in two ways. First, it reviews the\nnumerous definitions of CES along with existing theory and methods on CES\ndevelopment processes. Then, it proposes the ComplEx System Integrated\nUtilities Model (CESIUM), a novel framework for exploring how numerous system\nand development process characteristics may affect the performance of CES.\nCESIUM creates simulated representations of a system architecture, the\ncorresponding engineering organization, and the new product development process\nthrough which the organization designs the system. It does so by representing\nthe system as a network of interdependent artifacts designed by agents. Agents\niteratively design their artifacts through optimization and share information\nwith other agents, thereby advancing the CES toward a solution. This paper\ndescribes the model, conducts a sensitivity analysis, provides validation, and\nsuggests directions for future study.\n"}
+{"abstract": "  Entity alignment (EA) is the task of identifying the entities that refer to\nthe same real-world object but are located in different knowledge graphs (KGs).\nFor entities to be aligned, existing EA solutions treat them separately and\ngenerate alignment results as ranked lists of entities on the other side.\nNevertheless, this decision-making paradigm fails to take into account the\ninterdependence among entities. Although some recent efforts mitigate this\nissue by imposing the 1-to-1 constraint on the alignment process, they still\ncannot adequately model the underlying interdependence and the results tend to\nbe sub-optimal. To fill in this gap, in this work, we delve into the dynamics\nof the decision-making process, and offer a reinforcement learning (RL) based\nmodel to align entities collectively. Under the RL framework, we devise the\ncoherence and exclusiveness constraints to characterize the interdependence and\nrestrict collective alignment. Additionally, to generate more precise inputs to\nthe RL framework, we employ representative features to capture different\naspects of the similarity between entities in heterogeneous KGs, which are\nintegrated by an adaptive feature fusion strategy. Our proposal is evaluated on\nboth cross-lingual and mono-lingual EA benchmarks and compared against\nstate-of-the-art solutions. The empirical results verify its effectiveness and\nsuperiority.\n"}
+{"abstract": "  Emerging applications such as wireless sensing, position location, robotics,\nand many more are driven by the ultra-wide bandwidths available at\nmillimeter-wave (mmWave) and Terahertz (THz) frequencies. The characterization\nand efficient utilization of wireless channels at these extremely high\nfrequencies require detailed knowledge of the radio propagation characteristics\nof the channels. Such knowledge is developed through empirical observations of\noperating conditions using wireless transceivers that measure the impulse\nresponse through channel sounding. Today, cutting-edge channel sounders rely on\nseveral bulky RF hardware components with complicated interconnections, large\nparasitics, and sub-GHz RF bandwidth. This paper presents a compact sliding\ncorrelation-based channel sounder baseband built on a monolithic integrated\ncircuit (IC) using 65 nm CMOS, implemented as an evaluation board achieving a 2\nGHz RF bandwidth. The IC is the worlds first gigabit-per-second channel sounder\nbaseband implemented in low-cost CMOS. The presented single-board system can be\nemployed at both the transmit and receive baseband to study multipath\ncharacteristics and path loss. Thus, the singleboard implementation provides an\ninexpensive and compact solution for sliding correlation-based channel sounding\nwith 1 ns multipath delay resolution.\n"}
+{"abstract": "  We compute asymptotic estimates for the Fourier coefficients of two mixed\nmock modular forms, which come from Bailey pairs derived by Lovejoy and Osburn.\nTo do so, we employ the circle method due to Wright and a modified Tauberian\ntheorem. We encounter cancellation in our estimates for one of the mock theta\nfunctions due to the auxiliary function $\\theta_{n,p}$ arising from the\nsplitting of Hickerson and Mortenson. We deal with this by using higher order\nasymptotic expansions for the Jacobi theta functions.\n"}
+{"abstract": "  Post-starburst (or \"E+A\") galaxies trace the fastest and most dramatic\nprocesses in galaxy evolution. Recent work studying the evolution of galaxies\nthrough this phase have revealed insights on how galaxies undergo structural\nand stellar population changes as well as the role of various feedback\nmechanisms. In this review, I summarize recent work on identifying\npost-starburst galaxies; tracing the role of this phase through cosmic time;\nmeasuring stellar populations, on-going star formation, morphologies,\nkinematics, interstellar medium properties, and AGN activity; mechanisms to\ncause the recent starburst and its end; and the future evolution to quiescence\n(or not). The review concludes with a list of open questions and exciting\npossibilities for future facilities.\n"}
+{"abstract": "  The axial and polar modes for the ring down of a Schwarzschild black hole are\ncalculated, by first deriving the Regge-Wheeler and Zerilli equations,\nrespectively, and finally applying the Asymptotic Iteration Method (AIM). We\nwere able to reach up to 500 iterations, obtaining for the first time\nconvergence for a wide range of large damping modes. The General Relativity\n(GR) and a particular version of an extended model with an r-dependent\nmass-function are compared. This mass-function allows an analytical solution\nfor the Tortoise coordinate. The example of the mass-function corresponds to\nthe leading correction for extended theories and serves as a starting point to\ntreat other r-dependent parameter mass-functions.\n"}
+{"abstract": "  We define and study positional marked patterns, permutations $\\tau$ where one\nof elements in $\\tau$ is underlined. Given a permutation $\\sigma$, we say that\n$\\sigma$ has a $\\tau$-match at position $i$ if $\\tau$ occurs in $\\sigma$ in\nsuch a way that $\\sigma_i$ plays the role of the underlined element in the\noccurrence. We let $pmp_\\tau(\\sigma)$ denote the number of positions $i$ which\n$\\sigma$ has a $\\tau$-match. This defines a new class of statistics on\npermutations, where we study such statistics and prove a number of results. In\nparticular, we prove that two positional marked patterns $1\\underline{2}3$ and\n$1\\underline{3}2$ give rise to two statistics that have the same distribution.\nThe equidistibution phenomenon also occurs in other several collections of\npatterns like $\\left \\{1\\underline{2}3 , 1\\underline{3}2 \\right \\}$, and $\\left\n\\{ 1\\underline234, 1\\underline243, \\underline2134, \\underline2 1 4 3 \\right\n\\}$, as well as two positional marked patterns of any length $n$: $\\left \\{\n1\\underline 2\\tau , \\underline 21\\tau \\right \\}$.\n"}
+{"abstract": "  For the sparsity-rank-aware least squares estimations, the LiGME (Linearly\ninvolved Generalized Moreau Enhanced) model was established recently in [Abe,\nYamagishi, Yamada, 2020] to use certain nonconvex enhancements of linearly\ninvolved convex regularizers without losing their overall convexities. In this\npaper, for further advancement of the LiGME model by incorporating multiple a\npriori knowledge as hard convex constraints, we newly propose a convexly\nconstrained LiGME (cLiGME) model. The cLiGME model can utilize multiple convex\nconstraints while preserving benefits achieved by the LiGME model. We also\npresent a proximal splitting type algorithm for the proposed cLiGME model.\nNumerical experiments demonstrate the efficacy of the proposed model and the\nproposed optimization algorithm in a scenario of signal processing application.\n"}
+{"abstract": "  We develop a novel approach to conformal prediction when the target task has\nlimited data available for training. Conformal prediction identifies a small\nset of promising output candidates in place of a single prediction, with\nguarantees that the set contains the correct answer with high probability. When\ntraining data is limited, however, the predicted set can easily become unusably\nlarge. In this work, we obtain substantially tighter prediction sets while\nmaintaining desirable marginal guarantees by casting conformal prediction as a\nmeta-learning paradigm over exchangeable collections of auxiliary tasks. Our\nconformalization algorithm is simple, fast, and agnostic to the choice of\nunderlying model, learning algorithm, or dataset. We demonstrate the\neffectiveness of this approach across a number of few-shot classification and\nregression tasks in natural language processing, computer vision, and\ncomputational chemistry for drug discovery.\n"}
+{"abstract": "  The presence of extra dimensions generically modify the spacetime geometry of\na rotating black hole, by adding an additional hair, besides the mass $M$ and\nthe angular momentum $J$, known as the `tidal charge' parameter, $\\beta$. In a\nbraneworld scenario with one extra spatial dimension, the extra dimension is\nexpected to manifest itself through -- (a) negative values of $\\beta$, and (b)\nmodified gravitational perturbations. This in turn would affect the\nquasi-normal modes of rotating black holes. We numerically solve the perturbed\ngravitational field equations using the continued fractions method and\ndetermine the quasi-normal mode spectra for the braneworld black hole. We find\nthat increasingly negative values of $\\beta$ correspond to a diminishing\nimaginary part of the quasi-normal mode, or equivalently, an increasing damping\ntime. Using the publicly available data of the properties of the remnant black\nhole in the gravitational wave signal GW150914, we check for consistency\nbetween the predicted values (for a given $\\beta$) of the frequency and damping\ntime of the least-damped $\\ell=2,m=2$ quasi-normal mode and measurements of\nthese quantities using other independent techniques. We find that it is highly\nunlikely for the tidal charge, $\\beta \\lesssim -0.05$, providing a conservative\nlimit on the tidal charge parameter. Implications and future directions are\ndiscussed.\n"}
+{"abstract": "  Magneto-electric multipoles, which are odd under both space-inversion $\\cal\nI$ and time-reversal $\\cal T$ symmetries, are fundamental in understanding and\ncharacterizing magneto-electric materials. However, the detection of these\nmagneto-electric multipoles is often not straightforward as they remain\n\"hidden\" in conventional experiments in part since many magneto-electrics\nexhibit combined $\\cal IT$ symmetry. In the present work, we show that the\nanti-symmetric Compton profile is a unique signature for all the\nmagneto-electric multipoles, since the asymmetric magnetization density of the\nmagneto-electric multipoles couples to space via spin-orbit coupling, resulting\nin an anti-symmetric Compton profile. We develop the key physics of the\nanti-symmetric Compton scattering using symmetry analysis and demonstrate it\nusing explicit first-principles calculations for two well-known representative\nmaterials with magneto-electric multipoles, insulating LiNiPO$_4$ and metallic\nMn$_2$Au. Our work emphasizes the crucial roles of the orientation of the spin\nmoments, the spin-orbit coupling, and the band structure in generating the\nanti-symmetric Compton profile in magneto-electric materials.\n"}
+{"abstract": "  We use the functional renormalization approach for quantum spin systems\ndeveloped by Krieg and Kopietz [Phys. Rev. B $\\mathbf{99}$, 060403(R) (2019)]\nto calculate the spin-spin correlation function $G (\\boldsymbol{k}, \\omega )$\nof quantum Heisenberg magnets at infinite temperature. For small wavevectors\n$\\boldsymbol{k} $ and frequencies $\\omega$ we find that $G ( \\boldsymbol{k},\n\\omega )$ assumes in dimensions $d > 2$ the diffusive form predicted by\nhydrodynamics. In three dimensions our result for the spin-diffusion\ncoefficient ${\\cal{D}}$ is somewhat smaller than previous theoretical\npredictions based on the extrapolation of the short-time expansion, but is\nstill about $30 \\%$ larger than the measured high-temperature value of\n${\\cal{D}}$ in the Heisenberg ferromagnet Rb$_2$CuBr$_4\\cdot$2H$_2$O. In\nreduced dimensions $d \\leq 2$ we find superdiffusion characterized by a\nfrequency-dependent complex spin-diffusion coefficient ${\\cal{D}} ( \\omega )$\nwhich diverges logarithmically in $d=2$, and as a power-law ${\\cal{D}} ( \\omega\n) \\propto \\omega^{-1/3}$ in $d=1$. Our result in one dimension implies scaling\nwith dynamical exponent $z =3/2$, in agreement with recent calculations for\nintegrable spin chains. Our approach is not restricted to the hydrodynamic\nregime and allows us to calculate the dynamic structure factor $S (\n\\boldsymbol{k} , \\omega )$ for all wavevectors. We show how the\nshort-wavelength behavior of $S ( \\boldsymbol{k}, \\omega )$ at high\ntemperatures reflects the relative sign and strength of competing exchange\ninteractions.\n"}
+{"abstract": "  We have improved our previous variational method based constituent quark\nmodel by introducing a complete set of 3-dimensional harmonic oscillator bases\nas the spatial part of the total wave function. To assess the validity of our\napproach, we compared the binding energy, thus calculated with the exact value\nfor the hydrogen model. After fitting to the masses of the ground state\nhadrons, we apply our new method to analyze the doubly-heavy tetraquark states\n$qq'\\bar{Q}\\bar{Q'}$ and compared the result for the binding energies with that\nfrom other works. We also calculated the ground state masses of $T_{sc}\n(ud\\bar{s}\\bar{c})$ and $T_{sb} (ud\\bar{s}\\bar{b})$ with $(I,S) = (0,1),\n(0,2)$. We found that $T_{bb} (ud\\bar{b}\\bar{b})$ and $us\\bar{b}\\bar{b}$, both\nwith $(I,S) = (0,1)$, are stable against the two lowest threshold meson states\nwith binding energies $-145$ MeV and $-42$ MeV, respectively. We further found\nthat $T_{cb} (ud\\bar{c}\\bar{b})$ is near the lowest threshold. The spatial\nsizes for the tetraquarks are also discussed.\n"}
+{"abstract": "  While commodity GPUs provide a continuously growing range of features and\nsophisticated methods for accelerating compute jobs, many state-of-the-art\nsolutions for point cloud rendering still rely on the provided point primitives\n(GL_POINTS, POINTLIST, ...) of graphics APIs for image synthesis. In this\npaper, we present several compute-based point cloud rendering approaches that\noutperform the hardware pipeline by up to an order of magnitude and achieve\nsignificantly better frame times than previous compute-based methods. Beyond\nbasic closest-point rendering, we also introduce a fast, high-quality variant\nto reduce aliasing. We present and evaluate several variants of our proposed\nmethods with different flavors of optimization, in order to ensure their\napplicability and achieve optimal performance on a range of platforms and\narchitectures with varying support for novel GPU hardware features. During our\nexperiments, the observed peak performance was reached rendering 796 million\npoints (12.7GB) at rates of 62 to 64 frames per second (50 billion points per\nsecond, 802GB/s) on an RTX 3090 without the use of level-of-detail structures.\n  We further introduce an optimized vertex order for point clouds to boost the\nefficiency of GL_POINTS by a factor of 5x in cases where hardware rendering is\ncompulsory. We compare different orderings and show that Morton sorted buffers\nare faster for some viewpoints, while shuffled vertex buffers are faster in\nothers. In contrast, combining both approaches by first sorting according to\nMorton-code and shuffling the resulting sequence in batches of 128 points leads\nto a vertex buffer layout with high rendering performance and low sensitivity\nto viewpoint changes.\n"}
+{"abstract": "  The main result of this paper states that for a given countable system of\ndata, there exists a countable iterated function system consisting of Rakotch\ncontractions, such that its attractor is the graph of a fractal interpolation\nfunction corresponding to the given countable system of data.\n"}
+{"abstract": "  We establish the existence and find some qualitative properties of open sets\nthat minimize functionals of the form $\nF(\\lambda_1(\\Omega;\\beta),\\dots,\\lambda_k(\\Omega;\\beta))$ under measure\nconstraint on $\\Omega$, where $\\lambda_i(\\Omega;\\beta)$ designates the $i$-th\neigenvalue of the Laplace operator on $\\Omega$ with Robin boundary conditions\nof parameter $\\beta>0$. Moreover, we show that minimizers of\n$\\lambda_k(\\Omega;\\beta)$ for $k\\geq 2$ verify the conjecture\n$\\lambda_k(\\Omega;\\beta)=\\lambda_{k-1}(\\Omega;\\beta)$ in dimension three and\nmore.\n"}
+{"abstract": "  Let $R$ be a commutative ring with nonzero identity. Let $\\mathcal{I}(R)$ be\nthe set of all ideals of $R$ and let $\\delta : \\mathcal{I}(R)\\longrightarrow\n\\mathcal{I}(R)$ be a function. Then $\\delta$ is called an expansion function of\nideals of $R$ if whenever $L, I, J$ are ideals of R with $J \\subseteq I$, we\nhave $L \\subseteq \\delta( L)$ and $\\delta(J)\\subseteq \\delta(I)$. Let $\\delta$\nbe an expansion function of ideals of $R$. In this paper, we introduce and\ninvestigate a new class of ideals that is closely related to the class of\n$\\delta$-primary ideals. A proper ideal $I$ of $R$ is said to be a\n$1$-absorbing $\\delta$-primary ideal if whenever nonunit elements $a,b,c \\in R\n$ and $abc\\in I$, then $ab \\in I$ or $c\\in \\delta(I).$ Moreover, we give some\nbasic properties of this class of ideals and we study the $1$-absorbing\n$\\delta$-primary ideals of the localization of rings, the direct product of\nrings and the trivial ring extensions.\n"}
+{"abstract": "  The Hall-D Online Skim System (HOSS) was developed to simultaneously solve\ntwo issues for the high intensity GlueX experiment. One was to parallelize the\nwriting of raw data files to disk in order to improve bandwidth. The other was\nto distribute the raw data across multiple compute nodes in order to produce\ncalibration \\textit{skims} of the data online. The highly configurable system\nemploys RDMA, RAM disks, and zeroMQ driven by Python to simultaneously store\nand process the full high intensity GlueX data stream.\n"}
+{"abstract": "  We investigate second harmonic generation (SHG) from hexagonal periodic\narrays of triangular nano-holes of aluminum using a self-consistent methodology\nbased on the hydrodynamics-Maxwell-Bloch approach. It is shown that angular\npolarization patterns of the far-field second harmonic response abide to\nthree-fold symmetry constraints on tensors. When a molecular layer is added to\nthe system and its parameters are adjusted to achieve the strong coupling\nregime between a localized plasmon mode and molecular excitons, Rabi splitting\nis observed from occurrence of both single- and two-photon transition peaks\nwithin the SHG power spectrum. It is argued that the splitting observed for\nboth transitions results from direct transitions between lower and upper\npolaritonic states of the strongly coupled system. This interpretation can be\naccounted by a tailored three-level quantum model, with results in agreement\nwith the unbiased numerical approach. Our results suggest the hybrid states\nformed in strongly coupled systems directly contribute to the nonlinear\ndynamics. This opens new directions in designing THz sources and nonlinear\nfrequency converters.\n"}
+{"abstract": "  In this study I proposed a filtering beliefs method for improving performance\nof Partially Observable Markov Decision Processes(POMDPs), which is a method\nwildly used in autonomous robot and many other domains concerning control\npolicy. My method search and compare every similar belief pair. Because a\nsimilar belief have insignificant influence on control policy, the belief is\nfiltered out for reducing training time. The empirical results show that the\nproposed method outperforms the point-based approximate POMDPs in terms of the\nquality of training results as well as the efficiency of the method.\n"}
+{"abstract": "  Collective strong coupling between a disordered ensemble of $N$ localized\nmolecular vibrations and a resonant optical cavity mode gives rise to 2\npolariton and $N-1\\gg2$ dark modes. Thus, experimental changes in\nthermally-activated reaction kinetics due to polariton formation appear\nentropically unlikely and remain a puzzle. Here we show that the overlooked\ndark modes, while parked at the same energy as bare molecular vibrations, are\nrobustly delocalized across $\\sim$2-3 molecules, yielding enhanced channels of\nvibrational cooling, concomitantly catalyzing or suppressing a chemical\nreaction. As an illustration, we theoretically show a 55% increase in an\nelectron transfer rate due to enhanced product stabilization.\n"}
+{"abstract": "  In this paper, we address the representation of coordinate constructions in\nEnhanced Universal Dependencies (UD), where relevant dependency links are\npropagated from conjunction heads to other conjuncts. English treebanks for\nenhanced UD have been created from gold basic dependencies using a heuristic\nrule-based converter, which propagates only core arguments. With the aim of\ndetermining which set of links should be propagated from a semantic\nperspective, we create a large-scale dataset of manually edited syntax graphs.\nWe identify several systematic errors in the original data, and propose to also\npropagate adjuncts. We observe high inter-annotator agreement for this semantic\nannotation task. Using our new manually verified dataset, we perform the first\nprincipled comparison of rule-based and (partially novel) machine-learning\nbased methods for conjunction propagation for English. We show that learning\npropagation rules is more effective than hand-designing heuristic rules. When\nusing automatic parses, our neural graph-parser based edge predictor\noutperforms the currently predominant pipelinesusing a basic-layer tree parser\nplus converters.\n"}
+{"abstract": "  One of the most common expectations of a quantum theory of gravity is that\nspace-time is uncertain or fluctuating at microscopic scales, making it a\nstochastic medium for particle propagation. Particles traversing this\nspace-time may experience fluctuations in travel times or velocities, together\nreferred to as lightcone fluctuations, with even very small effects potentially\naccumulating into observable signals over large distances. In this work we\npresent a heuristic model of lightcone fluctuations and study the resulting\nmodifications to neutrino propagation, including neutrino decoherence and\narrival time spread. We show the expected scale of such effects due to\n`natural' Planck scale physics and consider how they may be observed in\nneutrino detectors, and compare the potential of neutrinos to $\\gamma$-ray\nastronomy. Using simulations of neutrino mass states propagating in a\nfluctuating environment, we determine an analytic decoherence operator in the\nframework of open quantum systems to quantitatively evaluate neutrino\ndecoherence resulting from lightcone fluctuations, allowing experimental\nconstraints on neutrino decoherence to be connected to Planck scale\nfluctuations in space-time and $\\gamma$-ray results.\n"}
+{"abstract": "  Most of the common applications of Named Entity Recognition (NER) is on\nEnglish and other highly available languages. In this work, we present our\nfindings on Named Entity Recognition for 5 Nigerian Languages (Nigerian\nEnglish, Nigerian Pidgin English, Igbo, Yoruba and Hausa). These languages are\nconsidered low-resourced, and very little openly available Natural Language\nProcessing work has been done in most of them. In this work, individual NER\nmodels were trained and metrics recorded for each of the languages. We also\nworked on a combined model that can handle Named Entity Recognition (NER) for\nany of the five languages. The combined model works well for Named Entity\nRecognition(NER) on each of the languages and with better performance compared\nto individual NER models trained specifically on annotated data for the\nspecific language. The aim of this work is to share our learning on how\ninformation extraction using Named Entity Recognition can be optimized for the\nlisted Nigerian Languages for inclusion, ease of deployment in production and\nreusability of models. Models developed during this project are available on\nGitHub https://git.io/JY0kk and an interactive web app\nhttps://nigner.herokuapp.com/.\n"}
+{"abstract": "  We give relative error coresets for training linear classifiers with a broad\nclass of loss functions, including the logistic loss and hinge loss. Our\nconstruction achieves $(1\\pm \\epsilon)$ relative error with $\\tilde O(d \\cdot\n\\mu_y(X)^2/\\epsilon^2)$ points, where $\\mu_y(X)$ is a natural complexity\nmeasure of the data matrix $X \\in \\mathbb{R}^{n \\times d}$ and label vector $y\n\\in \\{-1,1\\}^n$, introduced in by Munteanu et al. 2018. Our result is based on\nsubsampling data points with probabilities proportional to their $\\ell_1$\n$Lewis$ $weights$. It significantly improves on existing theoretical bounds and\nperforms well in practice, outperforming uniform subsampling along with other\nimportance sampling methods. Our sampling distribution does not depend on the\nlabels, so can be used for active learning. It also does not depend on the\nspecific loss function, so a single coreset can be used in multiple training\nscenarios.\n"}
+{"abstract": "  Inspired by non-abelian vortex anyons in spinor Bose-Einstein condensates, we\nconsider the quantum double $\\mathcal{D}(Q_8)$ anyon model as a platform to\ncarry out a particular instance of Shor's factorization algorithm. We provide\nthe excitation spectrum, the fusion rules, and the braid group representation\nfor this model, and design a circuit architecture that facilitates the\ncomputation. All necessary quantum gates, less one, can be compiled exactly for\nthis hybrid topological quantum computer, and to achieve universality the last\noperation can be implemented in a non-topological fashion. To analyse the\neffect of decoherence on the computation, a noise model based on stochastic\nunitary rotations is considered. The computational potential of this quantum\ndouble anyon model is similar to that of the Majorana fermion based Ising anyon\nmodel, offering a complementary future platform for topological quantum\ncomputation.\n"}
+{"abstract": "  In this paper we prove a positive energy theorem related to fourth-order\ngravitational theories, which is a higher-order analogue of the classical ADM\npositive energy theorem of general relativity. We will also show that, in\nparallel to the corresponding situation in general relativity, this result\nintersects several important problems in geometric analysis. For instance, it\nunderlies positive mass theorems associated to the Paneitz operator, playing a\nsimilar role in the positive $Q$-curvature conformal prescription problem as\nthe Schoen-Yau positive energy theorem does for the Yamabe problem. Several\nother links to $Q$-curvature analysis and rigidity phenomena are established.\n"}
+{"abstract": "  We report on the possibility of studying the proprieties of cosmic diffuse\nbaryons by studying self-gravitating clumps and filaments connected to galaxy\nclusters. While filaments are challenging to detect with X-ray observations,\nthe higher density of clumps makes them visible and a viable tracer to study\nthe thermodynamical proprieties of baryons undergoing accretion along cosmic\nweb filaments onto galaxy clusters. We developed new algorithms to identify\nthese structures and applied them to a set of non-radiative cosmological\nsimulations of galaxy clusters at high resolution. We find that in those\nsimulated clusters, the density and temperature of clumps are independent of\nthe mass of the cluster where they reside. We detected a positive correlation\nbetween the filament temperature and the host cluster mass. The density and\ntemperature of clumps and filaments also tended to correlate. Both the\ntemperature and density decrease moving outward. We observed that clumps are\nhotter, more massive, and more luminous if identified closer to the cluster\ncenter. Especially in the outermost cluster regions (~3*R500,c or beyond),\nX-ray observations might already have the potential to locate cosmic filaments\nbased on the distribution of clumps and to allow one to study the\nthermodynamics of diffuse baryons before they are processed by the intracluster\nmedium.\n"}
+{"abstract": "  Common deployment models for Edge Computing are based on (composable)\nmicroservices that are offloaded to cloudlets. Runtime adaptations-in response\nto varying load, QoS fulfillment, mobility, etc.-are typically based on\ncoarse-grained and costly management operations such as resource re-allocation\nor migration. The services themselves, however, remain non-adaptive, worsening\nthe already limited elasticity of Edge Computing compared to Cloud Computing.\nEdge computing applications often have stringent requirements on the execution\ntime but are flexible regarding the quality of a computation. The potential\nbenefits of exploiting this trade-off remain untapped. This paper introduces\nthe concept of adaptable microservices that provide alternative variants of\nspecific functionalities. We define so-called service variants that differ\nw.r.t. the internal functioning of the service, manifested in different\nalgorithms, parameters, and auxiliary data they use. Such variants allow\nfine-grained trade-offs between the QoS (e.g., a maximum tolerable execution\ntime) and the quality of the computation. We integrate adaptable microservices\ninto an Edge Computing framework, show the practical impact of service\nvariants, and present a strategy for switching variants at runtime.\n"}
+{"abstract": "  In the past decade we have witnessed the failure of traditional polls in\npredicting presidential election outcomes across the world. To understand the\nreasons behind these failures we analyze the raw data of a trusted pollster\nwhich failed to predict, along with the rest of the pollsters, the surprising\n2019 presidential election in Argentina which has led to a major market\ncollapse in that country. Analysis of the raw and re-weighted data from\nlongitudinal surveys performed before and after the elections reveals clear\nbiases (beyond well-known low-response rates) related to mis-representation of\nthe population and, most importantly, to social-desirability biases, i.e., the\ntendency of respondents to hide their intention to vote for controversial\ncandidates. We then propose a longitudinal opinion tracking method based on\nbig-data analytics from social media, machine learning, and network theory that\novercomes the limits of traditional polls. The model achieves accurate results\nin the 2019 Argentina elections predicting the overwhelming victory of the\ncandidate Alberto Fern\\'andez over the president Mauricio Macri; a result that\nnone of the traditional pollsters in the country was able to predict. Beyond\npredicting political elections, the framework we propose is more general and\ncan be used to discover trends in society; for instance, what people think\nabout economics, education or climate change.\n"}
+{"abstract": "  We consider manifestly crossing symmetric dispersion relations for Mellin\namplitudes of scalar four point correlators in conformal field theories (CFTs).\nThis allows us to set up the non-perturbative Polyakov bootstrap for CFTs in\nMellin space on a firm foundation, thereby fixing the contact term ambiguities\nin the crossing symmetric blocks. Our new approach employs certain \"locality\"\nconstraints replacing the requirement of crossing symmetry in the usual\nfixed-$t$ dispersion relation. Using these constraints we show that the sum\nrules based on the two channel dispersion relations and the present dispersion\nrelations are identical. Our framework allows us to connect with the\nconceptually rich picture of the Polyakov blocks being Witten diagrams in\nanti-de Sitter (AdS) space. We also give two sided bounds for Wilson\ncoefficients for effective field theories in AdS space.\n"}
+{"abstract": "  There is observational evidence of the presence of small-amplitude transverse\nmagnetohydrodynamic (MHD) waves with a wide range of frequencies in the threads\nof solar prominences. It is believed that the waves are driven at the\nphotosphere and propagate along the magnetic field lines up to prominences\nsuspended in the corona. The dissipation of MHD wave energy in the partially\nionized prominence plasma is a heating mechanism whose relevance needs to be\nexplored. Here we consider a simple 1D model for a non-uniform thin thread and\ninvestigate the heating associated with dissipation of Alfven waves. The model\nassumes an ad hoc density profile and a uniform pressure, while the temperature\nand ionization degree are self-consistently computed considering either LTE or\nnon-LTE approximations for the hydrogen ionization. A broadband driver for\nAlfven waves is placed at one end of the magnetic field line, representing\nphotospheric excitation. The Alfvenic perturbations along the thread are\nobtained by solving the linearized MHD equations for a partially ionized plasma\nin the single-fluid approximation.We find that wave heating in the partially\nionized part of the thread is significant enough to compensate for energy\nlosses due to radiative cooling. A greater amount of heating is found in the\nLTE case because the ionization degree for core prominence temperatures is\nlower than that in the non-LTE approximation. This results in a greater level\nof dissipation due to ambipolar diffusion in the LTE case. Conversely, in the\nhot coronal part of the model, the plasma is fully ionized and wave heating is\nnegligible. The results of this simple model suggest that MHD wave heating can\nbe relevant for the energy balance in prominences. Further studies based on\nmore elaborate models are required.\n"}
+{"abstract": "  In video-assisted thoracoscopic surgeries, successful procedures of nodule\nresection are highly dependent on the precise estimation of lung deformation\nbetween the inflated lung in the computed tomography (CT) images during\npreoperative planning and the deflated lung in the treatment views during\nsurgery. Lungs in the pneumothorax state during surgery have a large volume\nchange from normal lungs, making it difficult to build a mechanical model. The\npurpose of this study is to develop a deformation estimation method of the 3D\nsurface of a deflated lung from a few partial observations. To estimate\ndeformations for a largely deformed lung, a kernel regression-based solution\nwas introduced. The proposed method used a few landmarks to capture the partial\ndeformation between the 3D surface mesh obtained from preoperative CT and the\nintraoperative anatomical positions. The deformation for each vertex of the\nentire mesh model was estimated per-vertex as a relative position from the\nlandmarks. The landmarks were placed in the anatomical position of the lung's\nouter contour. The method was applied on nine datasets of the left lungs of\nlive Beagle dogs. Contrast-enhanced CT images of the lungs were acquired. The\nproposed method achieved a local positional error of vertices of 2.74 mm,\nHausdorff distance of 6.11 mm, and Dice similarity coefficient of 0.94.\nMoreover, the proposed method could estimate lung deformations from a small\nnumber of training cases and a small observation area. This study contributes\nto the data-driven modeling of pneumothorax deformation of the lung.\n"}
+{"abstract": "  A simplicial complex is a generalization of a graph: a collection of n-ary\nrelationships (instead of binary as the edges of a graph), named simplices. In\nthis paper, we develop a new tool to study the structure of simplicial\ncomplexes: we generalize the graph notion of truss decomposition to complexes,\nand show that this more powerful representation gives rise to different\nproperties compared to the graph-based one. This power, however, comes with\nimportant computational challenges derived from the combinatorial explosion\ncaused by the downward closure property of complexes. Drawing upon ideas from\nitemset mining and similarity search, we design a memory-aware algorithm,\ndubbed STruD, which is able to efficiently compute the truss decomposition of a\nsimplicial complex. STruD adapts its behavior to the amount of available memory\nby storing intermediate data in a compact way. We then devise a variant that\ncomputes directly the n simplices of maximum trussness. By applying STruD to\nseveral datasets, we prove its scalability, and provide an analysis of their\nstructure. Finally, we show that the truss decomposition can be seen as a\nfiltration, and as such it can be used to study the persistent homology of a\ndataset, a method for computing topological features at different spatial\nresolutions, prominent in Topological Data Analysis.\n"}
+{"abstract": "  It is commonly believed that a massive real scalar field $\\phi$ only mediates\nshort-range interactions on the scale of its Compton wavelength via the Yukawa\npotential. However, in the nonperturbative regime of nonlinear self coupling,\n$\\phi$ can also mediate larger scale interactions. Moreover, the classical\npotential, namely, the static configuration of $\\phi$ in the presence of an\nexternal source, is not always unique for given boundary conditions. In this\npaper, a complete set of finite-energy potentials (FEPs) induced by a Gaussian\nsource is identified in one, two, and three spatial dimensions when the\nnonlinearity is of the Mexican-hat type, which is often prescribed to induce\nspontaneous symmetry breaking. In the size-strength parameter space of the\nsource, phase boundaries are mapped out, across which the number of FEPs\ndiffer. Additionally, softer phase transitions are delineated according to\nwhether the potential exhibits a light horizon at which $\\phi$ vanishes. The\nlight horizon is of physical significance when $\\phi$ couples with other\nparticles. For example, when $\\phi$ is the Higgs field, all elementary\nparticles become massless at the light horizon. It is remarkable that white\ndwarfs and neutron stars are potentially in a phase where light horizons exist,\nwhose outer radii are a few times the star sizes. Moreover, suppose elementary\nfermions of mass greater than $\\sim10^3$ GeV exist, then they may also be\nsurrounded by light horizons with radii comparable to the Higgs Compton\nwavelength. Finally, nonperturbative states may also be realized in condensed\nmatter systems, wherein phase transitions are controllable using localized\nsources.\n"}
+{"abstract": "  We show that the Rokhsar-Kivelson (RK) point in quantum dimer models (QDM)\ncan emerge in realistic quantum Ising spin systems. Specifically, we\ninvestigate the $J_1$-$J_2$-$J_3$ transverse field Ising model on the\ntriangular lattice with large-scale quantum Monte Carlo simulations. We find\nthat the multicritical point in the phase diagram corresponds to the RK point\nof the QDM on the honeycomb lattice. We further measure the spectral functions\nand identify three branches of quadratic dispersions. In the phase diagram, we\nalso find a sequence of incommensurate states, which meet the microscopic\ningredients for the 'Cantor deconfinement' scenario. Our study provides a\npromising direction to realise the RK deconfinement in experimental platforms\nsuch as magnetic materials or programmable Rydberg arrays.\n"}
+{"abstract": "  While patents and standards have been identified as essential driving\ncomponents of innovation and market growth, the inclusion of a patent in a\nstandard poses many difficulties. These difficulties arise from the\ncontradicting natures of patents and standards, which makes their combination\nreally challenging, but, also, from the opposing business and market strategies\nof different patent owners involved in the standardisation process. However, a\nvarying set of policies has been adopted to address the issues occurring from\nthe unavoidable inclusion of patents in standards concerning certain industry\nsectors with a constant high degree of innovation, such as telecommunications.\nAs these policies have not always proven adequate enough, constant efforts are\nbeing made to improve and expand them. The intriguing and complicated\nrelationship between patents and standards is finally examined through a review\nof the use cases of well-known standards of the telecommunications sector which\ninclude a growing set of essential patents.\n"}
+{"abstract": "  In this paper we study perturbations of rational Collet-Eckmann maps for\nwhich the Julia set is the whole sphere, and for which the critical set is\nallowed to be slowly recurrent. We show that any such map is a Lebesgue density\npoint of Collet-Eckmann maps in the space of rational maps of the same degree\nat least $2$.\n"}
+{"abstract": "  Typically discussed in the context of optics, caustics are envelopes of\nclassical trajectories (rays) where the density of states diverges, resulting\nin pronounced observable features such as bright points, curves, and extended\nnetworks of patterns. Despite the apparent complexity of such patterns, they\nare based on just a small number of fundamental forms described by the theory\nof catastrophe optics. Here, we demonstrate caustics in the matter waves of an\natom laser, extending catastrophe optics to catastrophe atom optics. We\nshowcase the generic forms of fold and cusp caustics, and exploit internal\nstate manipulation to trace the flow of atoms. Atom optics affords new\nperspectives for fundamental science, metrology, atom interferometry and\nnano-fabrication techniques. Exploring the role of caustics in this context may\nlead to innovation as the generation of caustics is inherently robust, and\nleads to a variety of pronounced and sharply delineated features.\n"}
+{"abstract": "  A novel Lattice QCD (LQCD) method to determine the quark-diquark ($q$-$D$)\ninteraction potential together with the diquark mass ($m_D$) is proposed.\nSimilar to the HAL QCD method, $q$-$D$ potential is determined by demanding it\nto reproduce the $q$-$D$ equal-time Nambu-Bethe-Salpeter (NBS) wave function.\nTo do this, it is necessary to use the masses of the quark and the diquark as\ninputs, which however are not straightforwardly obtained because of the color\nconfinement of QCD. In this work, masses of quark and diquark are determined by\ndemanding that the p-wave spectrums from the two-point correlators be\nreproduced by the potentials for $c$-$\\bar{c}$ and $q$-$D$ sectors determined\nfrom the NBS wave functions. Numerical calculations are performed by using 2+1\nflavor QCD gauge configurations with the pion mass $m_\\pi\\simeq 700$ MeV\ngenerated by PACS-CS collaboration. We apply our method to the $c$-$\\bar c$\nsystem and the charm-diquark system ($\\Lambda_c$ baryon) to obtain the charm\nquark mass, diquark mass and the $c$-$D$ potential. Our preliminary analysis\nleads to the diquark mass $m_D \\simeq 1.127$ GeV which is roughly consistent\nwith a naive estimate based on the constituent quark picture, i.e., $m_{D}\n\\simeq m_{\\rho} \\simeq 1.12$ GeV and $m_{D} \\simeq 2m_N/3 \\simeq 1.06$ GeV.\n"}
+{"abstract": "  Single-molecule magnets (SMMs) exhibiting open hysteresis loops may\npotentially apply to molecule-based information processing and storage.\nHowever, the capacity to retain magnetic memory is always limited by zero-field\nquantum tunneling of magnetization (QTM). Herein, a well-designed dysprosium\nmetallacrown SMM, consisting of an endohedral approximate linear F-Dy-F strong\nanisotropic moiety in a peripheral [15-MCNi-5] metallacrown (MC), is reported\nwith the largest reversal barrier of 1060 cm-1 among d-f SMMs. Rectangle-like\nhysteresis loops are observed with the huge squareness (remanence/saturation\nmagnetization) up to 97% at 2 K. More importantly, zero-field QTM step is\nphenomenologically removed by minimizing the dipole coupling and hyperfine\ninteractions. The results demonstrate for the first time that zero-field QTM\nstep can be eliminated via manipulating the ligand field and vanishing the\nexternal magnetic perturbations, which illuminates a promising blueprint for\ndeveloping high-performance SMMs.\n"}
+{"abstract": "  This study compares the modularity performance of two artificial neural\nnetwork architectures: a Long Short-Term Memory (LSTM) recurrent network, and\nMorphognosis, a neural network based on a hierarchy of spatial and temporal\ncontexts. Mazes are used to measure performance, defined as the ability to\nutilize independently learned mazes to solve mazes composed of them. A maze is\na sequence of rooms connected by doors. The modular task is implemented as\nfollows: at the beginning of the maze, an initial door choice forms a context\nthat must be retained until the end of an intervening maze, where the same door\nmust be chosen again to reach the goal. For testing, the door-association mazes\nand separately trained intervening mazes are presented together for the first\ntime. While both neural networks perform well during training, the testing\nperformance of Morphognosis is significantly better than LSTM on this modular\ntask.\n"}
+{"abstract": "  In this article, we study the dynamics of geodesic flows on Riemannian (not\nnecessarily compact) manifolds with no conjugate points. We prove the Anosov\nClosing Lemma, the local product structure, and the transitivity of the\ngeodesic flows on $\\Omega_1$ under the conditions of bounded asymptote and\nuniform visibility. As an application, we further discuss about some generic\nproperties of the set of invariant probability measures\n"}
+{"abstract": "  In a graph $G$, a vertex dominates itself and its neighbours. A set\n$D\\subseteq V(G)$ is said to be a $k$-tuple dominating set of $G$ if $D$\ndominates every vertex of $G$ at least $k$ times. The minimum cardinality among\nall $k$-tuple dominating sets is the $k$-tuple domination number of $G$. In\nthis paper, we provide new bounds on this parameter. Some of these bounds\ngeneralize other ones that have been given for the case $k=2$. In addition, we\nimprove two well-known lower bounds on the $k$-tuple domination number.\n"}
+{"abstract": "  In this paper, cooperative non-orthogonal multiple access (C-NOMA) is\nconsidered in short packet communications with finite blocklength (FBL) codes.\nThe performance of a decode-and-forward (DF) relaying along with selection\ncombining (SC) and maximum ratio combining (MRC) strategies at the receiver\nside is examined. We explore joint user pairing and resource allocation to\nmaximize fair throughput in a downlink (DL) scenario. In each pair, the user\nwith a stronger channel (strong user) acts as a relay for the other one (weak\nuser), and optimal power and blocklength are allocated to achieve max-min\nthroughput. To this end, first, only one pair is considered, and optimal\nresource allocation is explored. Also, a suboptimal algorithm is suggested,\nwhich converges to a near-optimal solution. Finally, the problem is extended to\na general scenario, and a suboptimal C-NOMA-based user pairing is proposed.\nNumerical results show that the proposed C-NOMA scheme in both SC and MRC\nstrategies significantly improves the users' fair throughput compared to the\nNOMA and OMA. It is also investigated that the proposed pairing scheme based on\nC-NOMA outperforms the Hybrid NOMA/OMA scheme from the average throughput\nperspective, while the fairness index degrades slightly.\n"}
+{"abstract": "  In 2020, the COVID-19 pandemic spreads all over the world. In order to\nalleviate the spread of the epidemic, various blockade policies have been\nimplemented in many areas. In order to formulate a better epidemic prevention\npolicy for urban energy consumption of the commercial tourism cities, this\npaper first analyses the energy characteristics of Macao during the epidemic\nperiod from two aspects, based on the energy consumption data of Macao. On this\nbasis, the power consumption characteristics of commercial tourism cities\nduring the epidemic were analyzed. Then, this paper provides analysis of the\ncharacteristics of the energy consumption in different fields of commercial\ntourism cities from the aspects of hotel, transportation, tourism culture and\npublic utilities. Finally, a detailed analysis of the energy consumption\ncharacteristics of commercial tourism cities represented by Macao during the\nepidemic period is provided, by comparing with some typical countries.\n"}
+{"abstract": "  With the large data set delivered during the second run of the CERN LHC,\ninclusive and differential measurements of the top quark-antiquark production\ncross section at the ATLAS and CMS experiments often reach a precision that is\ncomparable or superior to that of the corresponding theoretical calculations.\nThe results of these measurements can be used to test the validity of\nperturbative quantum chromodynamics (QCD), and to precisely extract the values\nof parameters of the QCD Lagrangian such as the top quark mass\n($m_\\mathrm{t}$). The value of $m_\\mathrm{t}$ can also be measured, with higher\nprecision, by fully or partially reconstructing the invariant mass of the top\nquark decay products. However, the results of these measurements lack a clear\ntheoretical interpretation. In these proceedings, the most recent measurements\nof the top quark mass and cross sections by ATLAS and CMS are presented.\n"}
+{"abstract": "  This work presents an analysis of the efficiency of image augmentations for\nthe face recognition problem from limited data. We considered basic\nmanipulations, generative methods, and their combinations for augmentations.\nOur results show that augmentations, in general, can considerably improve the\nquality of face recognition systems and the combination of generative and basic\napproaches performs better than the other tested techniques.\n"}
+{"abstract": "  Accurately extracting structured content from PDFs is a critical first step\nfor NLP over scientific papers. Recent work has improved extraction accuracy by\nincorporating elementary layout information, e.g., each token's 2D position on\nthe page, into language model pretraining. We introduce new methods that\nexplicitly model VIsual LAyout (VILA) groups, i.e., text lines or text blocks,\nto further improve performance. In our I-VILA approach, we show that simply\ninserting special tokens denoting layout group boundaries into model inputs can\nlead to a 1.9% Macro F1 improvement in token classification. In the H-VILA\napproach, we show that hierarchical encoding of layout-groups can result in\nup-to 47% inference time reduction with less than 0.8% Macro F1 loss. Unlike\nprior layout-aware approaches, our methods do not require expensive additional\npretraining, only fine-tuning, which we show can reduce training cost by up to\n95%. Experiments are conducted on a newly curated evaluation suite, S2-VLUE,\nthat unifies existing automatically-labeled datasets and includes a new dataset\nof manual annotations covering diverse papers from 19 scientific disciplines.\nPre-trained weights, benchmark datasets, and source code are available at\nhttps://github.com/allenai/VILA.\n"}
+{"abstract": "  We consider the time interval ${\\Delta}$ during which the market trade\ntime-series are averaged as the key factor of the consumption-based\nasset-pricing model that causes modification of the basic pricing equation. The\nduration of ${\\Delta}$ determines Taylor series of investor's utility over\ncurrent and future values of consumption. We present consumption at current and\nfuture moments as sums of their mean values and perturbations during ${\\Delta}$\nof the price at current moment t and perturbations of the payoff at day t+1.\nFor linear and quadratic Taylor series approximations of the basic equation we\nobtain new relations on mean price, mean payoff, their volatilities, skewness\nand amount of asset ${\\xi}_{max}$ that delivers max to investor's utility. The\nstochasticity of market trade time-series defines random properties of the\nasset price time-series during ${\\Delta}$. We introduce new market-based price\nprobability measure entirely determined by frequency-based probability measures\nof the market trade value and volume. The conventional frequency-based price\nprobability is a very special case of the market-based price probability\nmeasure when all trade volumes during ${\\Delta}$ equal unit. Prediction of the\nmarket-based price probability at horizon T equals forecast of the market trade\nvalue and volume probabilities at same horizon. The similar Taylor series and\nprobability measures alike to market-based price probability can be used as\napproximations of different versions of asset pricing, financial and economic\nmodels that describe relations between economic and financial variables\naveraged during some time interval ${\\Delta}$.\n"}
+{"abstract": "  Human operators often diagnose industrial machinery via anomalous sounds.\nAutomated acoustic anomaly detection can lead to reliable maintenance of\nmachinery. However, deep learning-driven anomaly detection methods often\nrequire an extensive amount of computational resources which prohibits their\ndeployment in factories. Here we explore a machine-driven design exploration\nstrategy to create OutlierNets, a family of highly compact deep convolutional\nautoencoder network architectures featuring as few as 686 parameters, model\nsizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection\naccuracy matching or exceeding published architectures with as many as 4\nmillion parameters. Furthermore, CPU-accelerated latency experiments show that\nthe OutlierNet architectures can achieve as much as 21x lower latency than\npublished networks.\n"}
+{"abstract": "  In this paper, we prove that the divisor class group of a double cover of the\ncomplex projective space $\\mathbb{P}^n$ is generated by divisorial sheaves\nwhose direct images split into direct sums of two invertible sheaves on\n$\\mathbb{P}^n$. This result shows that any locally free sheaf of rank two on\n$\\mathbb{P}^n$ is generated by direct sums of line bundles on $\\mathbb{P}^n$\nvia some double cover. Moreover, we give a condition for an irreducible divisor\non $\\mathbb{P}^n$ to be a splitting divisor for a double cover whose divisor\nclass group is finitely generated.\n"}
+{"abstract": "  We study the non-parametric estimation of an unknown density f with support\non R+^d based on an i.i.d. sample with multiplicative measurement errors. The\nproposed fully-data driven procedure is based on the estimation of the Mellin\ntransform of the density f and a regularisation of the inverse of the Mellin\ntransform by a spectral cut-off. The upcoming bias-variance trade-off is dealt\nwith by a data-driven anisotropic choice of the cut-off parameter. In order to\ndiscuss the bias term, we consider the Mellin-Sobolev spaces which characterize\nthe regularity of the unknown density f through the decay of its Mellin\ntransform. Additionally, we show minimax-optimality over Mellin-Sobolev spaces\nof the spectral cut-off density estimator.\n"}
+{"abstract": "  In this monograph, we give an account of the relationship between the\nalgebraic structure of finitely generated and countable groups and the\nregularity with which they act on manifolds. We concentrate on the case of\none--dimensional manifolds, culminating with a uniform construction of finitely\ngenerated groups acting with prescribed regularity on the compact interval and\non the circle. We develop the theory of dynamical obstructions to smoothness,\nbeginning with classical results of Denjoy, to more recent results of Kopell,\nand to modern results such as the $abt$--Lemma. We give a classification of the\nright-angled Artin groups that have finite critical regularity and discuss\ntheir exact critical regularities in many cases, and we compute the virtual\ncritical regularity of most mapping class groups of orientable surfaces.\n"}
+{"abstract": "  Conventional saliency prediction models typically learn a deterministic\nmapping from images to the corresponding ground truth saliency maps. In this\npaper, we study the saliency prediction problem from the perspective of\ngenerative models by learning a conditional probability distribution over\nsaliency maps given an image, and treating the prediction as a sampling\nprocess. Specifically, we propose a generative cooperative saliency prediction\nframework based on the generative cooperative networks, where a conditional\nlatent variable model and a conditional energy-based model are jointly trained\nto predict saliency in a cooperative manner. We call our model the SalCoopNets.\nThe latent variable model serves as a fast but coarse predictor to efficiently\nproduce an initial prediction, which is then refined by the iterative Langevin\nrevision of the energy-based model that serves as a fine predictor. Such a\ncoarse-to-fine cooperative saliency prediction strategy offers the best of both\nworlds. Moreover, we generalize our framework to the scenario of weakly\nsupervised saliency prediction, where saliency annotation of training images is\npartially observed, by proposing a cooperative learning while recovering\nstrategy. Lastly, we show that the learned energy function can serve as a\nrefinement module that can refine the results of other pre-trained saliency\nprediction models. Experimental results show that our generative model can\nachieve state-of-the-art performance. Our code is publicly available at:\n\\url{https://github.com/JingZhang617/SalCoopNets}.\n"}
+{"abstract": "  In this paper, we address the problem of training deep neural networks in the\npresence of severe label noise. Our proposed training algorithm ScanMix,\ncombines semantic clustering with semi-supervised learning (SSL) to improve the\nfeature representations and enable an accurate identification of noisy samples,\neven in severe label noise scenarios. To be specific, ScanMix is designed based\non the expectation maximisation (EM) framework, where the E-step estimates the\nvalue of a latent variable to cluster the training images based on their\nappearance representations and classification results, and the M-step optimises\nthe SSL classification and learns effective feature representations via\nsemantic clustering. In our evaluations, we show state-of-the-art results on\nstandard benchmarks for symmetric, asymmetric and semantic label noise on\nCIFAR-10 and CIFAR-100, as well as large scale real label noise on WebVision.\nMost notably, for the benchmarks contaminated with large noise rates (80% and\nabove), our results are up to 27% better than the related work. The code is\navailable at https://github.com/ragavsachdeva/ScanMix.\n"}
+{"abstract": "  Accurate traffic state prediction is the foundation of transportation control\nand guidance. It is very challenging due to the complex spatiotemporal\ndependencies in traffic data. Existing works cannot perform well for multi-step\ntraffic prediction that involves long future time period. The spatiotemporal\ninformation dilution becomes serve when the time gap between input step and\npredicted step is large, especially when traffic data is not sufficient or\nnoisy. To address this issue, we propose a multi-spatial graph convolution\nbased Seq2Seq model. Our main novelties are three aspects: (1) We enrich the\nspatiotemporal information of model inputs by fusing multi-view features (time,\nlocation and traffic states) (2) We build multiple kinds of spatial\ncorrelations based on both prior knowledge and data-driven knowledge to improve\nmodel performance especially in insufficient or noisy data cases. (3) A\nspatiotemporal attention mechanism based on reachability knowledge is novelly\ndesigned to produce high-level features fed into decoder of Seq2Seq directly to\nease information dilution. Our model is evaluated on two real world traffic\ndatasets and achieves better performance than other competitors.\n"}
+{"abstract": "  Studying history involves many difficult tasks. Examples include searching\nfor proper data in a large event space, understanding stories of historical\nevents by time and space, and finding relationships among events that may not\nbe apparent. Instructors who extensively use well-organized and well-argued\nmaterials (e.g., textbooks and online resources) can lead students to a narrow\nperspective in understanding history and prevent spontaneous investigation of\nhistorical events, with the students asking their own questions. In this work,\nwe proposed HisVA, a visual analytics system that allows the efficient\nexploration of historical events from Wikipedia using three views: event, map,\nand resource. HisVA provides an effective event exploration space, where users\ncan investigate relationships among historical events by reviewing and linking\nthem in terms of space and time. To evaluate our system, we present two usage\nscenarios, a user study with a qualitative analysis of user exploration\nstrategies, and %expert feedback with in-class deployment results.\n"}
+{"abstract": "  We pursue a program to confront observations with inhomogeneous extensions of\nthe FLRW metric. The main idea is to test the Copernican principle rather than\nassuming it a priori. We consider the $\\Lambda$CDM model endowed with a\nspherical $\\Lambda$LTB inhomogeneity around us, that is, we assume isotropy and\ntest the hypothesis of homogeneity. We confront the $\\Lambda$LTB model with the\nlatest available data from CMB, BAO, type Ia supernovae, local $H_0$, cosmic\nchronometers, Compton y-distortion and kinetic Sunyaev-Zeldovich effect. We\nfind that these data can constrain tightly this extra inhomogeneity, almost to\nthe cosmic variance level: on scales $\\gtrsim 100$ Mpc structures can have a\nsmall non-Copernican effective contrast of just $\\delta_L \\sim 0.01$.\nFurthermore, the constraints on the standard $\\Lambda$CDM parameters are not\nweakened after marginalizing over the parameters that model the local\nstructure, to which we assign ignorance priors. In other words, dropping the\nCopernican principle assumption does not imply worse constraints on the\ncosmological parameters. This positive result confirms that the present and\nfuture data can be meaningfully analyzed within the framework of inhomogeneous\ncosmology.\n"}
+{"abstract": "  Location prediction is of great importance in location-based applications for\nthe construction of the smart city. To our knowledge, existing models for\nlocation prediction focus on users' preferences on POIs from the perspective of\nthe human side. However, modeling users' interests from the historical\ntrajectory is still limited by the data sparsity. Additionally, most of\nexisting methods predict the next location according to the individual data\nindependently, but the data sparsity makes it difficult to mine explicit\nmobility patterns or capture the casual behavior for each user. To address the\nissues above, we propose a novel Bi-direction Speculation and Dual-level\nAssociation method (BSDA), which considers both users' interests in POIs and\nPOIs' appeal to users. Furthermore, we develop the cross-user and cross-POI\nassociation to alleviate the data sparsity by similar users and POIs to enrich\nthe candidates. Experimental results on two public datasets demonstrate that\nBSDA achieves significant improvements over state-of-the-art methods.\n"}
+{"abstract": "  We develop a new method of online inference for a vector of parameters\nestimated by the Polyak-Ruppert averaging procedure of stochastic gradient\ndescent (SGD) algorithms. We leverage insights from time series regression in\neconometrics and construct asymptotically pivotal statistics via random\nscaling. Our approach is fully operational with online data and is rigorously\nunderpinned by a functional central limit theorem. Our proposed inference\nmethod has a couple of key advantages over the existing methods. First, the\ntest statistic is computed in an online fashion with only SGD iterates and the\ncritical values can be obtained without any resampling methods, thereby\nallowing for efficient implementation suitable for massive online data. Second,\nthere is no need to estimate the asymptotic variance and our inference method\nis shown to be robust to changes in the tuning parameters for SGD algorithms in\nsimulation experiments with synthetic data.\n"}
+{"abstract": "  We develop a mathematical model, based on a system of ordinary differential\nequations, to the upshot of farming alertness in crop pest administration,\nbearing in mind plant biomass, pest, and level of control. Main qualitative\nanalysis of the proposed mathematical model, akin to both pest-free and\ncoexistence equilibrium points and stability analysis, is investigated. We show\nthat all solutions of the model are positive and bounded with initial\nconditions in a certain significant set. The local stability of pest-free and\ncoexistence equilibria is shown using the Routh-Hurwitz criterion. Moreover, we\nprove that when a threshold value is less than one, then the pest-free\nequilibrium is locally asymptotically stable. To get optimum interventions for\ncrop pests, that is, to decrease the number of pests in the crop field, we\napply optimal control theory and find the corresponding optimal controls. We\nestablish existence of optimal controls and characterize them using\nPontryagin's minimum principle. Finally, we make use of numerical simulations\nto illustrate the theoretical analysis of the proposed model, with and without\ncontrol measures.\n"}
+{"abstract": "  Two factors have proven to be very important to the performance of semantic\nsegmentation models: global context and multi-level semantics. However,\ngenerating features that capture both factors always leads to high\ncomputational complexity, which is problematic in real-time scenarios. In this\npaper, we propose a new model, called Attention-Augmented Network (AttaNet), to\ncapture both global context and multilevel semantics while keeping the\nefficiency high. AttaNet consists of two primary modules: Strip Attention\nModule (SAM) and Attention Fusion Module (AFM). Viewing that in challenging\nimages with low segmentation accuracy, there are a significantly larger amount\nof vertical strip areas than horizontal ones, SAM utilizes a striping operation\nto reduce the complexity of encoding global context in the vertical direction\ndrastically while keeping most of contextual information, compared to the\nnon-local approaches. Moreover, AFM follows a cross-level aggregation strategy\nto limit the computation, and adopts an attention strategy to weight the\nimportance of different levels of features at each pixel when fusing them,\nobtaining an efficient multi-level representation. We have conducted extensive\nexperiments on two semantic segmentation benchmarks, and our network achieves\ndifferent levels of speed/accuracy trade-offs on Cityscapes, e.g., 71 FPS/79.9%\nmIoU, 130 FPS/78.5% mIoU, and 180 FPS/70.1% mIoU, and leading performance on\nADE20K as well.\n"}
+{"abstract": "  The strong truncated Hamburger moment problem (STHMP) of degree\n$(-2k_1,2k_2)$ asks to find necessary and sufficient conditions for the\nexistence of a positive Borel measure, supported on $\\mathbb{R}\\setminus\n\\{0\\}$, such that $\\beta_i=\\int x^id\\mu\\; (-2k_1\\leq i\\leq 2k_2)$. Using the\nsolution of the truncated Hamburger moment problem and the properties of Hankel\nmatrices we solve the STHMP. Then, using the equivalence with the STHMP of\ndegree $(-2k,2k)$, we obtain the solution of the 2-dimensional truncated moment\nproblem (TMP) of degree $2k$ with variety $xy=1$, first solved by Curto and\nFialkow. Our addition to their result is the fact previously known only for\n$k=2$, that the existence of a measure is equivalent to the existence of a flat\nextension of the moment matrix. Further on, we solve the STHMP of degree\n$(-2k_1,2k_2)$ with one missing moment in the sequence, i.e., $\\beta_{-2k_1+1}$\nor $\\beta_{2k_2-1}$, which also gives the solution of the TMP with variety\n$x^2y=1$ as a special case, first studied by Fialkow.\n"}
+{"abstract": "  Neural models excel at extracting statistical patterns from large amounts of\ndata, but struggle to learn patterns or reason about language from only a few\nexamples. In this paper, we ask: Can we learn explicit rules that generalize\nwell from only a few examples? We explore this question using program\nsynthesis. We develop a synthesis model to learn phonology rules as programs in\na domain-specific language. We test the ability of our models to generalize\nfrom few training examples using our new dataset of problems from the\nLinguistics Olympiad, a challenging set of tasks that require strong linguistic\nreasoning ability. In addition to being highly sample-efficient, our approach\ngenerates human-readable programs, and allows control over the generalizability\nof the learnt programs.\n"}
+{"abstract": "  This work presents CLIPDraw, an algorithm that synthesizes novel drawings\nbased on natural language input. CLIPDraw does not require any training; rather\na pre-trained CLIP language-image encoder is used as a metric for maximizing\nsimilarity between the given description and a generated drawing. Crucially,\nCLIPDraw operates over vector strokes rather than pixel images, a constraint\nthat biases drawings towards simpler human-recognizable shapes. Results compare\nbetween CLIPDraw and other synthesis-through-optimization methods, as well as\nhighlight various interesting behaviors of CLIPDraw, such as satisfying\nambiguous text in multiple ways, reliably producing drawings in diverse\nartistic styles, and scaling from simple to complex visual representations as\nstroke count is increased. Code for experimenting with the method is available\nat:\nhttps://colab.research.google.com/github/kvfrans/clipdraw/blob/main/clipdraw.ipynb\n"}
+{"abstract": "  We study learning dynamics in distributed production economies such as\nblockchain mining, peer-to-peer file sharing and crowdsourcing. These economies\ncan be modelled as multi-product Cournot competitions or all-pay auctions\n(Tullock contests) when individual firms have market power, or as Fisher\nmarkets with quasi-linear utilities when every firm has negligible influence on\nmarket outcomes. In the former case, we provide a formal proof that Gradient\nAscent (GA) can be Li-Yorke chaotic for a step size as small as $\\Theta(1/n)$,\nwhere $n$ is the number of firms. In stark contrast, for the Fisher market\ncase, we derive a Proportional Response (PR) protocol that converges to market\nequilibrium. The positive results on the convergence of the PR dynamics are\nobtained in full generality, in the sense that they hold for Fisher markets\nwith \\emph{any} quasi-linear utility functions. Conversely, the chaos results\nfor the GA dynamics are established even in the simplest possible setting of\ntwo firms and one good, and they hold for a wide range of price functions with\ndifferent demand elasticities. Our findings suggest that by considering\nmulti-agent interactions from a market rather than a game-theoretic\nperspective, we can formally derive natural learning protocols which are stable\nand converge to effective outcomes rather than being chaotic.\n"}
+{"abstract": "  We consider a chain of spinful fermions with nearest neighbor hopping in the\npresence of a $XY$ antiferromagnetic interaction. The $XY$ term is mapped onto\na Kitaev chain at half-filling such that displays a bosonic zero mode\ntopologically protected and long-range order. As the strength of the hopping\namplitude is changed, the system undergoes a quantum phase transition from the\ntopological non-trivial to the trivial phase. We apply the finite-size scaling\nmethod to determinate the phase diagram of the model.\n"}
+{"abstract": "  Image classification models can depend on multiple different semantic\nattributes of the image. An explanation of the decision of the classifier needs\nto both discover and visualize these properties. Here we present StylEx, a\nmethod for doing this, by training a generative model to specifically explain\nmultiple attributes that underlie classifier decisions. A natural source for\nsuch attributes is the StyleSpace of StyleGAN, which is known to generate\nsemantically meaningful dimensions in the image. However, because standard GAN\ntraining is not dependent on the classifier, it may not represent these\nattributes which are important for the classifier decision, and the dimensions\nof StyleSpace may represent irrelevant attributes. To overcome this, we propose\na training procedure for a StyleGAN, which incorporates the classifier model,\nin order to learn a classifier-specific StyleSpace. Explanatory attributes are\nthen selected from this space. These can be used to visualize the effect of\nchanging multiple attributes per image, thus providing image-specific\nexplanations. We apply StylEx to multiple domains, including animals, leaves,\nfaces and retinal images. For these, we show how an image can be modified in\ndifferent ways to change its classifier output. Our results show that the\nmethod finds attributes that align well with semantic ones, generate meaningful\nimage-specific explanations, and are human-interpretable as measured in\nuser-studies.\n"}
+{"abstract": "  Multiscale transforms have become a key ingredient in many data processing\ntasks. With technological development, we observe a growing demand for methods\nto cope with non-linear data structures such as manifold values. In this paper,\nwe propose a multiscale approach for analyzing manifold-valued data using a\npyramid transform. The transform uses a unique class of downsampling operators\nthat enable a non-interpolating subdivision schemes as upsampling operators. We\ndescribe this construction in detail and present its analytical properties,\nincluding stability and coefficient decay. Next, we numerically demonstrate the\nresults and show the application of our method to denoising and anomaly\ndetection.\n"}
+{"abstract": "  We present first principles calculations of the two-particle excitation\nspectrum of CrI$_3$ using many-body perturbation theory including spin-orbit\ncoupling. Specifically, we solve the Bethe-Salpeter equation, which is\nequivalent to summing up all ladder diagrams with static screening and it is\nshown that excitons as well as magnons can be extracted seamlessly from the\ncalculations. The resulting optical absorption spectrum as well as the magnon\ndispersion agree very well with recent measurements and we extract the\namplitude for optical excitation of magnons resulting from spin-orbit\ninteractions. Importantly, the results do not rely on any assumptions on the\nmicroscopic magnetic interactions such as Dzyaloshinskii-Moriya (DM), Kitaev or\nbiquadratic interactions and we obtain a model independent estimate of the gap\nbetween acoustic and optical magnons of 0.3 meV. In addition, we resolve the\nmagnon wavefunction in terms of band transitions and show that the magnon\ncarries a spin that is significantly smaller than $\\hbar$. This highlights the\nimportance of terms that do not commute with $S^z$ in any Heisenberg model\ndescription.\n"}
+{"abstract": "  Detecting facial forgery images and videos is an increasingly important topic\nin multimedia forensics. As forgery images and videos are usually compressed\ninto different formats such as JPEG and H264 when circulating on the Internet,\nexisting forgery-detection methods trained on uncompressed data often suffer\nfrom significant performance degradation in identifying them. To solve this\nproblem, we propose a novel anti-compression facial forgery detection\nframework, which learns a compression-insensitive embedding feature space\nutilizing both original and compressed forgeries. Specifically, our approach\nconsists of three ideas: (i) extracting compression-insensitive features from\nboth uncompressed and compressed forgeries using an adversarial learning\nstrategy; (ii) learning a robust partition by constructing a metric loss that\ncan reduce the distance of the paired original and compressed images in the\nembedding space; (iii) improving the accuracy of tampered localization with an\nattention-transfer module. Experimental results demonstrate that, the proposed\nmethod is highly effective in handling both compressed and uncompressed facial\nforgery images.\n"}
+{"abstract": "  The aim of the present work is to introduce a lens whose faces are a conical\nsurface and a spherical surface. We illuminate this lens by a plane wavefront\nand its associated refracted wavefronts, light rays and caustic are computed.\nWe find that the caustic has two branches. The first is constituted by two\nsegments of a line, one part of this caustic is real and the other one virtual.\nThe second branch of the caustic is a two-dimensional surface with a\nsingularity of the cusp ridge type. It is important to remark that the two\nbranches of the caustic are disconnected. Because of this property we believe\nthat using this optical element one could generate a scalar optical\naccelerating beam in the region where the caustic is a two-dimensional surface\nof revolution, and at the same time a scalar optical beam with similar\nproperties to the Bessel beam of zero order in the region were the real caustic\nis a segment of a line along the optical axis.\n"}
+{"abstract": "  Luminescent metal-organic frameworks (MOFs) offer a multifunctional platform\nfor creating non-invasive sensors and tuneable optoelectronics. However,\nfluorochromic materials that are photophysically resilient and show high\nsensitivity towards different physical and chemical stimuli are scarce. We\nreport a facile host-guest nanoconfinement strategy to construct a fluorescent\nhybrid material with multiple sensing capabilities. We design and fabricate a\nnew Guest@MOF material: comprising a zeolitic MOF (ZIF-71) as a nanoporous host\nfor encapsulating rhodamine B (RhB dye) guest molecules, resulting in the\nRhB@ZIF-71 system with mechanochromic, thermochromic, and solvatochromic\nsensing response. The fluorochromic sensing properties stem from the\nnanoconfinement effect that ZIF-71 imposes on RhB monomers, yielding the H- or\nJ-type aggregates with tuneable photophysical and photochemical properties. For\nmechanochromism, the external pressure causes an emission red shift in a linear\nfashion, switching RhB guests from H-type to J-type aggregates through a shear\ndeformation. For thermochromism, we demonstrate a linear scaling as a function\nof temperature due to the spatial restriction imposed on J-type aggregates\nincarcerated in ZIF-71 pores. Harnessing the solvatochromism of RhB@ZIF-71, we\nidentified three diverse groups of volatile organic compounds. The multimodal\nsensing response pave the way to smart applications like photonic pressure\nsensors, non-invasive thermometers, and ultrasensitive chemosensors.\n"}
+{"abstract": "  The linear convex log-homotopy has been used in the derivation of particle\nflow filters. One natural question is whether it is beneficial to consider\nother forms of homotopy. We revisit this question by considering a general\nlinear form of log-homotopy for which we derive particle flow filters, validate\nthe distribution of flows, and obtain conditions for the stability of particle\nflows. We then formulate the problem of stiffness mitigation as an optimal\ncontrol problem by minimizing the condition number of the Hessian matrix of the\nposterior density function. The optimal homotopy can be efficiently obtained by\nsolving a one-dimensional second order two-point boundary value problem.\nCompared with traditional matrix analysis based approaches to condition number\nimprovements such as scaling, this novel approach explicitly exploits the\nspecial structure of the stochastic differential equations in particle flow\nfilters. The effectiveness of the proposed approach is demonstrated by a\nnumerical example.\n"}
+{"abstract": "  Let $R$ be a $G$-graded ring. In this article, we introduce two new concepts\non graded rings, namely, weakly graded rings and invertible graded rings, and\nwe discuss the relations between these concepts and several properties of\ngraded rings. Also, we study the concept of weakly crossed products and study\nsome properties defined on weakly crossed product and give the relationship\nbetween this new concept and several properties of graded rings. Moreover, in\nthis article, we give a generalization for the concept of graded essential\nsubmodules, and introduce the concept of graded semi-uniform modules which is a\ngeneralization for the concept of graded uniform modules.\n"}
+{"abstract": "  One of the central problems in machine learning is domain adaptation. Unlike\npast theoretical work, we consider a new model for subpopulation shift in the\ninput or representation space. In this work, we propose a provably effective\nframework for domain adaptation based on label propagation. In our analysis, we\nuse a simple but realistic expansion assumption, proposed in\n\\citet{wei2021theoretical}. Using a teacher classifier trained on the source\ndomain, our algorithm not only propagates to the target domain but also\nimproves upon the teacher. By leveraging existing generalization bounds, we\nalso obtain end-to-end finite-sample guarantees on the entire algorithm. In\naddition, we extend our theoretical framework to a more general setting of\nsource-to-target transfer based on a third unlabeled dataset, which can be\neasily applied in various learning scenarios. Inspired by our theory, we adapt\nconsistency-based semi-supervised learning methods to domain adaptation\nsettings and gain significant improvements.\n"}
+{"abstract": "  The COVID pandemic and the measures which were taken had effect over the\nmental health of persons. The current paper proposes a concept that supports\nthe performance of students by analyzing three ways of distance learning,\nnamely text, text and illustrations, including charts, video. An\nelectroencephalogram headset allows the detection of brainwaves and the\ndeveloped web application enhances the process of distance learning. The\nelectrodes of the headset are placed at contact with the user head and monitor\nthe activity of the left and right frontal regions, along with the temporal\nlobe. Mood, focus, stress, relaxation, engagement, excitement and interest are\ntriggered as numerical values by using the headset. The users provide\ninformation about their daily activities, including learning and evaluation\nprocesses. According to the study, users had the highest long term attention\nwhile using text and illustrations, followed by watching videos. This is caused\nby the fact that the text contained the code for the programs which were\npresented in the video. Also, the users feel comfortable while using the\napplication and they started to pay more attention to the connection between\nstress, health, education and well being. The results triggered by the headset\nhad higher values while students studied for the first time using videos. When\nthey wanted to remember the information, the text and illustrations way of\nlearning was the best option. Based on the study outcomes, the instructional\ndesign can be enhanced. Moreover, the results improved as the students became\nmore equilibrated and confident in themselves. Teachers, professors and parents\nare able to collaborate and enhance training. While studying online under\nlockdown, students have found the proposed solution to be good because their\ninner state influences their productivity while solving problems.\n"}
+{"abstract": "  Three-, four-, and six-fold excitations have significantly extended the\nsubjects of condensed matter physics. There is an urgent need for a realistic\nmaterial that can have coexisting 3-, 4-, and 6-fold excitations. However,\nthese materials are uncommon because these excitations in electronic systems\nare usually broken by spin-orbit coupling (SOC) and normally far from the Fermi\nlevel. Unlike the case in electronic systems, the phonon systems with\nnegligible SOC effect, not constrained by the Pauli exclusion principle,\nprovide a feasible platform to realize these excitations in a wide frequency\nrange. Hence, in this work, we demonstrate by first-principle calculations and\nsymmetry analysis that perfect 3-, 4-, 6-fold excitations appear in the phonon\ndispersion rather than the band structures of Zr3Ni3Sb4, which is a well-known\nindirect-gap semiconductor with an Y3Au3Sb4-type structure. This material\nfeatures 3-fold quadratic contact triple-point phonon, 4-fold Dirac point\nphonon, and 6-fold point phonon. Moreover, these nodal-point phonons are very\nrobust to uniform strain. Two obvious phonon surface arcs of the [001] plane\nare extended in the whole Brillouin zone, which will facilitate their detection\nin future experimental studies. The current work provides an ideal model to\ninvestigate the rich excitations in a single material.\n"}
+{"abstract": "  We show a unified physical picture of single cyclotron electron with\nradiation-reaction, which bridges the classical electron models and quantum\nmechanical self-consistent field theory. On a classical level, we suggest an\nimproved electrodynamical action, which build the classical electron models\ninto a first-principle framework. The link between dynamical defections and\nnon-physical action configurations emerges naturally. On a quantum level, a\nself-consistent description for electron gyro-motion with self-force is\nconstructed in the Schr\\\"odinger-Maxwell theory. We derive a class of\nasymptotic equations. The leading and next-to-leading orders give a good\nanalogue of a classical cyclotron electron, and the limit field theory avoids\nclassical electron induced defections gracefully. Beyond the Hamiltonian\nperturbation theory, we use state-of-the-art geometric simulator to observe\nsingle electron gyro-motions at quantum region. The non-linear and\nnon-perturbative features captured by simulations provide a complete physical\npicture in a very wide range. We show an optimal complementary relation between\nclassical and quantum cyclotron electrons, and find a strange and inexplicable\nelectron chimera state existing at strong non-linear regions, which may be\nobserved in astrophysical environments and strong magnetic experiments.\n"}
+{"abstract": "  We study large uniform random maps with one face whose genus grows linearly\nwith the number of edges, which are a model of discrete hyperbolic geometry. In\nprevious works, several hyperbolic geometric features have been investigated.\nIn the present work, we study the number of short cycles in a uniform\nunicellular map of high genus, and we show that it converges to a Poisson\ndistribution. As a corollary, we obtain the law of the systole of uniform\nunicellular maps in high genus. We also obtain the asymptotic distribution of\nthe vertex degrees in such a map.\n"}
+{"abstract": "  Online property business or known as e-commerce is currently experiencing an\nincrease in home sales. Indonesia's e-commerce property business has positive\ntrending shown by the increasing sales of more than 500% from 2011 to 2015. A\nprediction of the property price is important to help investors or the public\nto have accurate information before buying property. One of the methods for\nprediction is a classification based on several distinctive property industry\nattributes, such as building size, land size, number of rooms, and location.\nToday, data is easily obtained, there are many open data from E-commerce sites.\nE-commerce contains information about homes and other properties advertised to\nsell. People also regularly visit the site to find the right property or to\nsell the property using price information which collectively available as open\ndata. To predict the property sales, this research employed two different\nclassification methods in Data Mining which are Decision Tree and k-NN\nclassification. We compare which model classification is better to predict\nproperty price and their attributes. We use Indonesia's biggest property-based\ne-commerce site Rumah123.com as our open data source, and choose location\nBandung in our experiment. The accuracy result of the decision tree is 75% and\nKNN is 71%, other than that k-NN can explore more data patterns than the\nDecision Tree.\n"}
+{"abstract": "  With neural architecture search methods gaining ground on manually designed\ndeep neural networks -even more rapidly as model sophistication escalates-, the\nresearch trend shifts towards arranging different and often increasingly\ncomplex neural architecture search spaces. In this conjuncture, delineating\nalgorithms which can efficiently explore these search spaces can result in a\nsignificant improvement over currently used methods, which, in general,\nrandomly select the structural variation operator, hoping for a performance\ngain. In this paper, we investigate the effect of different variation operators\nin a complex domain, that of multi-network heterogeneous neural models. These\nmodels have an extensive and complex search space of structures as they require\nmultiple sub-networks within the general model in order to answer to different\noutput types. From that investigation, we extract a set of general guidelines,\nwhose application is not limited to that particular type of model, and are\nuseful to determine the direction in which an architecture optimization method\ncould find the largest improvement. To deduce the set of guidelines, we\ncharacterize both the variation operators, according to their effect on the\ncomplexity and performance of the model; and the models, relying on diverse\nmetrics which estimate the quality of the different parts composing it.\n"}
+{"abstract": "  Purpose: To develop an algorithm for robust partial Fourier (PF)\nreconstruction applicable to diffusion-weighted (DW) images with non-smooth\nphase variations.\n  Methods: Based on an unrolled proximal splitting algorithm, a neural network\narchitecture is derived which alternates between data consistency operations\nand regularization implemented by recurrent convolutions. In order to exploit\ncorrelations, multiple repetitions of the same slice are jointly reconstructed\nunder consideration of permutation-equivariance. The proposed method is trained\non DW liver data of 60 volunteers and evaluated on retrospectively and\nprospectively sub-sampled data of different anatomies and resolutions. In\naddition, the benefits of using a recurrent network over other unrolling\nstrategies is investigated.\n  Results: Conventional PF techniques can be significantly outperformed in\nterms of quantitative measures as well as perceptual image quality. The\nproposed method is able to generalize well to brain data with contrasts and\nresolution not present in the training set. The reduction in echo time (TE)\nassociated with prospective PF-sampling enables DW imaging with higher signal.\nAlso, the TE increase in acquisitions with higher resolution can be compensated\nfor. It can be shown that unrolling by means of a recurrent network produced\nbetter results than using a weight-shared network or a cascade of networks.\n  Conclusion: This work demonstrates that robust PF reconstruction of DW data\nis feasible even at strong PF factors in applications with severe phase\nvariations. Since the proposed method does not rely on smoothness priors of the\nphase but uses learned recurrent convolutions instead, artifacts of\nconventional PF methods can be avoided.\n"}
+{"abstract": "  The increasingly widespread application of AI models motivates increased\ndemand for explanations from a variety of stakeholders. However, this demand is\nambiguous because there are many types of 'explanation' with different\nevaluative criteria. In the spirit of pluralism, I chart a taxonomy of types of\nexplanation and the associated XAI methods that can address them. When we look\nto expose the inner mechanisms of AI models, we develop\nDiagnostic-explanations. When we seek to render model output understandable, we\nproduce Explication-explanations. When we wish to form stable generalizations\nof our models, we produce Expectation-explanations. Finally, when we want to\njustify the usage of a model, we produce Role-explanations that situate models\nwithin their social context. The motivation for such a pluralistic view stems\nfrom a consideration of causes as manipulable relationships and the different\ntypes of explanations as identifying the relevant points in AI systems we can\nintervene upon to affect our desired changes. This paper reduces the ambiguity\nin use of the word 'explanation' in the field of XAI, allowing practitioners\nand stakeholders a useful template for avoiding equivocation and evaluating XAI\nmethods and putative explanations.\n"}
+{"abstract": "  Relation extraction from text is an important task for automatic knowledge\nbase population. In this thesis, we first propose a syntax-focused multi-factor\nattention network model for finding the relation between two entities. Next, we\npropose two joint entity and relation extraction frameworks based on\nencoder-decoder architecture. Finally, we propose a hierarchical entity graph\nconvolutional network for relation extraction across documents.\n"}
+{"abstract": "  We show that polarization singularities, generic for any complex vector field\nbut so far mostly studied for electromagnetic fields, appear naturally in\ninhomogeneous yet monochromatic sound and water-surface (e.g., gravity or\ncapillary) wave fields in fluids or gases. The vector properties of these waves\nare described by the velocity or displacement fields characterizing the local\noscillatory motion of the medium particles. We consider a number of examples\nrevealing C-points of purely circular polarization and polarization M\\\"{o}bius\nstrips (formed by major axes of polarization ellipses) around the C-points in\nsound and gravity wave fields. Our results (i) offer a new readily accessible\nplatform for studies of polarization singularities and topological features of\ncomplex vector wavefields and (ii) can play an important role in characterizing\nvector (e.g., dipole) wave-matter interactions in acoustics and fluid\nmechanics.\n"}
+{"abstract": "  Applying spectral dynamics methods to one typical $N$-body model with a\nbarlens, we dissect the modelled bar into separate components supported by\ncompletely different types of orbits. We identify at least four components: a\nnarrow elongated bar, a boxy bar, and two components contributing to the\nbarlens. We analyse the vertical structure of all components that make up the\nthick part of the bar, which has a boxy/peanut shape (B/P bulge). We show that\nthe `peanut' shape is mainly due to the orbits that assemble the boxy part of\nthe face-on bar. We associate the X-shape with the narrow and elongated bar.\nThe wider part of the barlens with square-like isophotes contributes to the\nboxy shape of the B/P bulge when we observe the galaxy edge-on. However, the\npart of the barlens with rounded isophotes in the face-on view is a rather flat\nstructure in the vertical direction without any significant off-centre\nprotrusions. Thus, for the first time, we demonstrate that the rounded face-on\nbarlens cannot be entirely associated with the B/P bulge.\n"}
+{"abstract": "  As is well-known, the dimension of the space spanned by the non-degenerate\ninvariant symmetric bilinear forms (NISes) on any simple finite-dimensional Lie\nalgebra or Lie superalgebra is equal to at most 1 if the characteristic of the\nalgebraically closed ground field is not 2.\n  We prove that in characteristic 2, the superdimension of the space spanned by\nNISes can be equal to 0, or 1, or $0|1$, or $1|1$; it is equal to $1|1$ if and\nonly if the Lie superalgebra is a queerification (defined in arXiv:1407.1695)\nof a simple classically restricted Lie algebra with a NIS (for examples, mainly\nin characteristic distinct from 2, see arXiv:1806.05505).\n  We give examples of NISes on deformations (with both even and odd parameters)\nof several simple finite-dimensional Lie superalgebras in characteristic 2.\n  We also recall examples of multiple NISes on simple Lie algebras over\nnon-closed fields.\n"}
+{"abstract": "  Analyzing the structure of proteins is a key part of understanding their\nfunctions and thus their role in biology at the molecular level. In addition,\ndesign new proteins in a methodical way is a major engineering challenge. In\nthis work, we introduce a joint geometric-neural networks approach for\ncomparing, deforming and generating 3D protein structures. Viewing protein\nstructures as 3D open curves, we adopt the Square Root Velocity Function (SRVF)\nrepresentation and leverage its suitable geometric properties along with Deep\nResidual Networks (ResNets) for a joint registration and comparison. Our\nResNets handle better large protein deformations while being more\ncomputationally efficient. On top of the mathematical framework, we further\ndesign a Geometric Variational Auto-Encoder (G-VAE), that once trained, maps\noriginal, previously unseen structures, into a low-dimensional (latent)\nhyper-sphere. Motivated by the spherical structure of the pre-shape space, we\nnaturally adopt the von Mises-Fisher (vMF) distribution to model our hidden\nvariables. We test the effectiveness of our models by generating novel protein\nstructures and predicting completions of corrupted protein structures.\nExperimental results show that our method is able to generate plausible\nstructures, different from the structures in the training data.\n"}
+{"abstract": "  Particle showers in dielectric media produce radio signals which are used for\nthe detection of both ultra-high energy cosmic rays and neutrinos with energies\nabove a few PeV. The amplitude, polarization, and spectrum of these short,\nbroadband radio pulses allow us to draw conclusions about the primary particles\nthat caused them, as well as the mechanics of shower development and radio\nemission. However, confidently reconstructing the radio signals can pose a\nchallenge, as they are often obscured by background noise. Information Field\nTheory offers a robust approach to this challenge by using Bayesian inference\nto calculate the most likely radio signal, given the recorded data. In this\npaper, we describe the application of Information Field Theory to radio signals\nfrom particle showers in both air and ice and demonstrate how accurately pulse\nparameters can be obtained from noisy data.\n"}
+{"abstract": "  Wave propagation problems have many applications in physics and engineering,\nand the stochastic effects are important in accurately modeling them due to the\nuncertainty of the media. This paper considers and analyzes a fully discrete\nfinite element method for a class of nonlinear stochastic wave equations, where\nthe diffusion term is globally Lipschitz continuous while the drift term is\nonly assumed to satisfy weaker conditions as in [11]. The novelties of this\npaper are threefold. First, the error estimates cannot not be directly obtained\nif the numerical scheme in primal form is used. The numerical scheme in mixed\nform is introduced and several H\\\"{o}lder continuity results of the strong\nsolution are proved, which are used to establish the error estimates in both\n$L^2$ norm and energy norms. Second, two types of discretization of the\nnonlinear term are proposed to establish the $L^2$ stability and energy\nstability results of the discrete solutions. These two types of discretization\nand proper test functions are designed to overcome the challenges arising from\nthe stochastic scaling in time issues and the nonlinear interaction. These\nstability results play key roles in proving the probability of the set on which\nthe error estimates hold approaches one. Third, higher order moment stability\nresults of the discrete solutions are proved based on an energy argument and\nthe underlying energy decaying property of the method. Numerical experiments\nare also presented to show the stability results of the discrete solutions and\nthe convergence rates in various norms.\n"}
+{"abstract": "  Microcavity-polaritons in two-dimensional lattice geometries have been used\nto study a wide range of interesting physics. Meanwhile, organic materials have\nshown great promise on the road towards polaritonic devices, as the strong\nbinding energy of their Frenkel excitons permits room temperature condensation\nand lasing. Whilst there are theoretical treatments of the condensation\nprocesses in planar organic microcavities, models of lattice geometries are\nlacking. Here, we introduce a model for describing the dynamics of lattices of\nzero-dimensional organic microcavities, where the dominant condensation\nmechanism involves the emission of a vibrational phonon. The vibronic resonance\nprovides a tool for targeted condensation in a particular eigenmode of the\nsystem, which we highlight by examining a dimer and a dimerised chain. For the\ndimer, we observe a double resonance in the condensation efficiency that arises\nfrom tuning the condensate-reservoir detuning in to resonance with either the\nsymmetric or antisymmetric mode. This selective condensation was also exploited\nin the dimerised chain, to condense the system into the topological edge states\nunder homogeneous pumping of all cavities. We also showed an interesting\nsignature of nonreciprocal transport when pumping a single cavity in the chain,\nwhere the direction of propagation depends on the sublattice being pumped.\n"}
+{"abstract": "  The motivation for this thesis was to recast quantum self-testing [MY98,MY04]\nin operational terms. The result is a category-theoretic framework for\ndiscussing the following general question: How do different implementations of\nthe same input-output process compare to each other? In the proposed framework,\nan input-output process is modelled by a causally structured channel in some\nfixed theory, and its implementations are modelled by causally structured\ndilations formalising hidden side-computations. These dilations compare through\na pre-order formalising relative strength of side-computations. Chapter 1\nreviews a mathematical model for physical theories as semicartesian symmetric\nmonoidal categories. Many concrete examples are discussed, in particular\nquantum and classical information theory. The key feature is that the model\nfacilitates the notion of dilations. Chapter 2 is devoted to the study of\ndilations. It introduces a handful of simple yet potent axioms about dilations,\none of which (resembling the Purification Postulate [CDP10]) entails a duality\ntheorem encompassing a large number of classic no-go results for quantum\ntheory. Chapter 3 considers metric structure on physical theories, introducing\nin particular a new metric for quantum channels, the purified diamond distance,\nwhich generalises the purified distance [TCR10,Tom12] and relates to the Bures\ndistance [KSW08a]. Chapter 4 presents a category-theoretic formalism for\ncausality in terms of '(constructible) causal channels' and 'contractions'. It\nsimplifies aspects of the formalisms [CDP09,KU17] and relates to traces in\nmonoidal categories [JSV96]. The formalism allows for the definition of 'causal\ndilations' and the establishment of a non-trivial theory of such dilations.\nChapter 5 realises quantum self-testing from the perspective of chapter 4, thus\npointing towards the first known operational foundation for self-testing.\n"}
+{"abstract": "  Human Activity Recognition (HAR), based on machine and deep learning\nalgorithms is considered one of the most promising technologies to monitor\nprofessional and daily life activities for different categories of people\n(e.g., athletes, elderly, kids, employers) in order to provide a variety of\nservices related, for example to well-being, empowering of technical\nperformances, prevention of risky situation, and educational purposes. However,\nthe analysis of the effectiveness and the efficiency of HAR methodologies\nsuffers from the lack of a standard workflow, which might represent the\nbaseline for the estimation of the quality of the developed pattern recognition\nmodels. This makes the comparison among different approaches a challenging\ntask. In addition, researchers can make mistakes that, when not detected,\ndefinitely affect the achieved results. To mitigate such issues, this paper\nproposes an open-source automatic and highly configurable framework, named\nB-HAR, for the definition, standardization, and development of a baseline\nframework in order to evaluate and compare HAR methodologies. It implements the\nmost popular data processing methods for data preparation and the most commonly\nused machine and deep learning pattern recognition models.\n"}
+{"abstract": "  For a simple graph $G$, denote by $n$, $\\Delta(G)$, and $\\chi'(G)$ its order,\nmaximum degree, and chromatic index, respectively. A connected class 2 graph\n$G$ is edge-chromatic critical if $\\chi'(G-e)<\\Delta(G)+1$ for every edge $e$\nof $G$. Define $G$ to be overfull if $|E(G)|>\\Delta(G) \\lfloor n/2 \\rfloor$.\nClearly, overfull graphs are class 2 and any graph obtained from a regular\ngraph of even order by splitting a vertex is overfull. Let $G$ be an $n$-vertex\nconnected regular class 1 graph with $\\Delta(G) >n/3$. Hilton and Zhao in 1997\nconjectured that if $G^*$ is obtained from $G$ by splitting one vertex of $G$\ninto two vertices, then $G^*$ is edge-chromatic critical, and they verified the\nconjecture for graphs $G$ with $\\Delta(G)\\ge \\frac{n}{2}(\\sqrt{7}-1)\\approx\n0.82n$. The graph $G^*$ is easily verified to be overfull, and so the hardness\nof the conjecture lies in showing that the deletion of every of its edge\ndecreases the chromatic index. Except in 2002, Song showed that the conjecture\nis true for a special class of graphs $G$ with $\\Delta(G)\\ge \\frac{n}{2}$, no\nother progress on this conjecture had been made. In this paper, we confirm the\nconjecture for graphs $G$ with $\\Delta(G) \\ge 0.75n$.\n"}
+{"abstract": "  In this short note we consider RBSDE with Lipschitz drivers and barrier\nprocesses that are optional and right upper semicontinuous. We treat the case\nwhen the barrier can be represented as a decreasing limit of cadlag barriers.\nWe combine well known existence results for cadlag barriers with comparison\narguments for the control process to construct solutions. Finally, we highlight\nthe connection of such RBSDEs with usual cadlag BSDEs.\n"}
+{"abstract": "  The recently commissioned Dark Energy Spectroscopic Instrument (DESI) will\nmeasure the expansion history of the Universe using the Baryon Acoustic\nOscillation technique. The spectra of 35 million galaxies and quasars over\n14000 sqdeg will be measured during the life of the experiment. A new prime\nfocus corrector for the KPNO Mayall telescope delivers light to 5000 fiber\noptic positioners. The fibers in turn feed ten broad-band spectrographs. A\nconsortium of Aix-Marseille University (AMU) and CNRS laboratories (LAM, OHP\nand CPPM) together with LPNHE (CNRS, IN2P3, Sorbonne Universit\\'e and\nUniversit\\'e de Paris) and the WINLIGHT Systems company based in Pertuis\n(France), were in charge of integrating and validating the performance\nrequirements of the ten full spectrographs, equipped with their cryostats,\nshutters and other mechanisms. We present a summary of our activity which\nallowed an efficient validation of the systems in a short-time schedule. We\ndetail the main results. We emphasize the benefits of our approach and also its\nlimitations.\n"}
+{"abstract": "  The inclusive as well as differential cross section of the associated\nproduction of top-antitop quark pairs and a $Z$ boson ($t\\bar{t}Z$) is measured\nin final states with exactly three or four isolated leptons (electrons or\nmuons). For this purpose, the full LHC Run 2 dataset of proton-proton\ncollisions recorded by the ATLAS detector from $2015$ to $2018$, which\ncorresponds to an integrated luminosity of $139\\,\\text{fb}^{-1}$, is used. The\ninclusive production cross section is measured to be\n$\\sigma_{t\\bar{t}Z}=1.05\\pm0.05\\,(\\text{stat.})\\,\\pm0.09\\,(\\text{syst.})\\,\\text{pb}$,\nwhich is in agreement with the most precise Standard Model theoretical\nprediction. Absolute and normalised differential cross section measurements are\nperformed as a function of various kinematic variables in order to probe the\nkinematics of the $t\\bar{t}Z$ system within both parton- and particle-level\nphase spaces.\n"}
+{"abstract": "  Recently, a novel type of a multiscale simulation, called Relative Resolution\n(RelRes), was introduced. In a single system, molecules switch their resolution\nin terms of their relative separation, with near neighbors interacting via\nfine-grained potentials yet far neighbors interacting via coarse-grained\npotentials; notably, these two potentials are analytically parameterized by a\nmultipole approximation. This multiscale approach is consequently able to\ncorrectly retrieve across state space, the structural and thermal, as well as\nstatic and dynamic, behavior of various nonpolar mixtures. Our current work\nfocuses on the practical implementation of RelRes in LAMMPS, specifically for\nthe commonly used Lennard-Jones potential. By examining various correlations\nand properties of several alkane liquids, including complex solutions of\nalternate cooligomers and block copolymers, we confirm the validity of this\nautomated LAMMPS algorithm. Most importantly, we demonstrate that this RelRes\nimplementation gains almost an order of magnitude in computational efficiency,\nas compared with conventional simulations. We thus recommend this novel LAMMPS\nalgorithm for anyone studying systems governed by Lennard-Jones interactions.\n"}
+{"abstract": "  The updated H-band spectral line list (from \\lambda 15,000 - 17,000\\AA)\nadopted by the Apache Point Observatory Galactic Evolution Experiment (APOGEE)\nfor the SDSS IV Data Release 16 (DR16) is presented here. The APOGEE line list\nis a combination of atomic and molecular lines with data from laboratory,\ntheoretical, and astrophysical sources. Oscillator strengths and damping\nconstants are adjusted using high signal-to-noise, high-resolution spectra of\nthe Sun and alpha Boo (Arcturus) as \"standard stars\". Updates to the DR16 line\nlist, when compared to the previous DR14 version, are the inclusion of\nmolecular H_2O and FeH lines, as well as a much larger (by a factor of ~4)\natomic line list, which includes significantly more transitions with hyperfine\nsplitting. More recent references and line lists for the crucial molecules CO\nand OH were used, as well as for C_2 and SiH. In contrast to DR14, DR16\ncontains measurable lines from the heavy neutron-capture elements cerium (as Ce\nII), neodymium (as Nd II), and ytterbium (as Yb II), as well as one line from\nrubidium (as Rb I), that may be detectable in a small fraction of APOGEE red\ngiants.\n"}
+{"abstract": "  In this note we construct a measure $\\mu$ on a $\\sigma$-algebra $\\mathcal{M}$\nof subsets of the positive real axis, $\\mathbb{R}_{>0}$, with the following\nmultiplicative property: \\[\n  \\mu \\left( \\bigcup_j E_j \\right) = \\prod_j \\mu(E_j) \\] for every countable\ncollection $\\{ E_j \\}$ of pairwise disjoint sets of $\\mathcal{M}$. For them, we\napply the Carath\\'eodory's procedure to the triplet $\\left( \\mathbb{R}_{>0},\n\\cdot \\, , \\tau \\right)$, where $\\cdot$ is the product of R and $\\tau$ is the\nusual topology on $\\mathbb{R}_{>0}$. We conclude this note describing the\nconnection between this multiplicative measure $\\mu$ and the Lebesgue measure.\n"}
+{"abstract": "  Agent-Based Models are a powerful class of computational models widely used\nto simulate complex phenomena in many different application areas. However, one\nof the most critical aspects, poorly investigated in the literature, regards an\nimportant step of the model credibility assessment: solution verification. This\nstudy overcomes this limitation by proposing a general verification framework\nfor Agent-Based Models that aims at evaluating the numerical errors associated\nwith the model. A step-by-step procedure, which consists of two main\nverification studies (deterministic and stochastic model verification), is\ndescribed in detail and applied to a specific mission critical scenario: the\nquantification of the numerical approximation error for UISS-TB, an ABM of the\nhuman immune system developed to predict the progression of pulmonary\ntuberculosis. Results provide indications on the possibility to use the\nproposed model verification workflow to systematically identify and quantify\nnumerical approximation errors associated with UISS-TB and, in general, with\nany other ABMs.\n"}
+{"abstract": "  We have realized quantized charge pumping using non-adiabatic single-electron\npumps in dopant-free GaAs two-dimensional electron gases (2DEGs). The\ndopant-free III-V platform allows for ambipolar devices, such as p-i-n\njunctions, that could be combined with such pumps to form electrically-driven\nsingle photon sources. Our pumps operate at up to 0.95 GHz and achieve\nremarkable performance considering the relaxed experimental conditions:\none-gate pumping in zero magnetic field and temperatures up to 5K, driven by a\nsimple RF sine waveform. Fitting to a universal decay cascade model yields\nvalues for the figure of merit $\\delta$ that compare favorably to reported\nmodulation-doped GaAs pumps operating under similar conditions. The devices\nreported here are already suitable for optoelectronics applications, and with\nfurther improvement could offer a route to a current standard that does not\nrequire sub-Kelvin temperatures and high magnetic fields.\n"}
+{"abstract": "  To shift the computational burden from real-time to offline in delay-critical\npower systems applications, recent works entertain the idea of using a deep\nneural network (DNN) to predict the solutions of the AC optimal power flow\n(AC-OPF) once presented load demands. As network topologies may change,\ntraining this DNN in a sample-efficient manner becomes a necessity. To improve\ndata efficiency, this work utilizes the fact OPF data are not simple training\nlabels, but constitute the solutions of a parametric optimization problem. We\nthus advocate training a sensitivity-informed DNN (SI-DNN) to match not only\nthe OPF optimizers, but also their partial derivatives with respect to the OPF\nparameters (loads). It is shown that the required Jacobian matrices do exist\nunder mild conditions, and can be readily computed from the related primal/dual\nsolutions. The proposed SI-DNN is compatible with a broad range of OPF solvers,\nincluding a non-convex quadratically constrained quadratic program (QCQP), its\nsemidefinite program (SDP) relaxation, and MATPOWER; while SI-DNN can be\nseamlessly integrated in other learning-to-OPF schemes. Numerical tests on\nthree benchmark power systems corroborate the advanced generalization and\nconstraint satisfaction capabilities for the OPF solutions predicted by an\nSI-DNN over a conventionally trained DNN, especially in low-data setups.\n"}
+{"abstract": "  Measuring the respiratory signal from a video based on body motion has been\nproposed and recently matured in products for video health monitoring. The core\nalgorithm for this measurement is the estimation of tiny chest/abdominal\nmotions induced by respiration, and the fundamental challenge is motion\nsensitivity. Though prior arts reported on the validation with real human\nsubjects, there is no thorough/rigorous benchmark to quantify the sensitivities\nand boundary conditions of motion-based core respiratory algorithms that\nmeasure sub-pixel displacement between video frames. In this paper, we designed\na setup with a fully-controllable physical phantom to investigate the essence\nof core algorithms, together with a mathematical model incorporating two motion\nestimation strategies and three spatial representations, leading to six\nalgorithmic combinations for respiratory signal extraction. Their promises and\nlimitations are discussed and clarified via the phantom benchmark. The insights\ngained in this paper are intended to improve the understanding and applications\nof camera-based respiration measurement in health monitoring.\n"}
+{"abstract": "  We explore the sensitivity offered by a global network of cosmic ray\ndetectors to a novel, unobserved phenomena: widely separated simultaneous\nextended air showers. Existing localized observatories work independently to\nobserve individual showers, offering insight into the source and nature of\nultra-high energy cosmic rays. However no current observatory is large enough\nto provide sensitivity to anticipated processes such as the GZ effect or\npotential new physics that generate simultaneous air showers separated by\nhundreds to thousands of kilometers. A global network of consumer electronics\n(the CRAYFIS experiment), may provide a novel opportunity for observation of\nsuch phenomena. Two user scenarios are explored. In the first, with maximal\nuser adoption, we find that statistically significant discoveries of\nspatially-separated but coincident showers are possible within a couple years.\nIn the second, more practical adoption model with $10^6$ active devices, we\nfind a worldwide CRAYFIS to be sensitive to novel \"burst\" phenomena where many\nsimultaneous EASs occur at once.\n"}
+{"abstract": "  We report an in-depth study of the reduction of graphene oxide (GO) by\nin-situ thermal transmission electron microscopy (TEM) analysis. In-situ\nheating high-resolution TEM (HRTEM) imaging and electron energy-loss\nspectroscopy (EELS) measurements have been combined to identify the\ntransformations of different oxygen functional groups, the desorption of\nphysisorbed and chemisorbed water and the graphitization as a function of the\ntemperature in the range from 70 up to 1200{\\deg}C. A model for the general\nremoval of water and OFGs is proposed based on different chemical and physical\nparameters that have been monitored. All this unique information provides a\ndetailed roadmap of the thermal behaviour of GO at an extended range of\ntemperature. This is not only of interest to understand the thermal reduction\nprocess of GO but also of critical relevance to the response of GO in\napplications when exposed to thermal effects.\n"}
+{"abstract": "  Multibeam high throughput satellite (MB-HTS) systems will play a key role in\ndelivering broadband services to a large number of users with diverse Quality\nof Service (QoS) requirements. This paper focuses on MB-HTS where the same\nspectrum is re-used by all user links and, in particular, we propose a novel\nuser scheduling design capable to provide guarantees in terms of individual QoS\nrequirements while maximizing the system throughput. This is achieved by\nprecoding to mitigate mutual interference. The combinatorial optimization\nstructure requires an extremely high cost to obtain the global optimum even\nwith a reduced number of users. We, therefore, propose a heuristic algorithm\nyielding a good local solution and tolerable computational complexity,\napplicable for large-scale networks. Numerical results demonstrate the\neffectiveness of our proposed algorithm on scheduling many users with better\nsum throughput than the other benchmarks. Besides, the QoS requirements for all\nscheduled users are guaranteed.\n"}
+{"abstract": "  The problem of saddle-point avoidance for non-convex optimization is quite\nchallenging in large scale distributed learning frameworks, such as Federated\nLearning, especially in the presence of Byzantine workers. The celebrated\ncubic-regularized Newton method of \\cite{nest} is one of the most elegant ways\nto avoid saddle-points in the standard centralized (non-distributed) setup. In\nthis paper, we extend the cubic-regularized Newton method to a distributed\nframework and simultaneously address several practical challenges like\ncommunication bottleneck and Byzantine attacks. Note that the issue of\nsaddle-point avoidance becomes more crucial in the presence of Byzantine\nmachines since rogue machines may create \\emph{fake local minima} near the\nsaddle-points of the loss function, also known as the saddle-point attack.\nBeing a second order algorithm, our iteration complexity is much lower than the\nfirst order counterparts. Furthermore we use compression (or sparsification)\ntechniques like $\\delta$-approximate compression for communication efficiency.\nWe obtain theoretical guarantees for our proposed scheme under several settings\nincluding approximate (sub-sampled) gradients and Hessians. Moreover, we\nvalidate our theoretical findings with experiments using standard datasets and\nseveral types of Byzantine attacks, and obtain an improvement of $25\\%$ with\nrespect to first order methods in iteration complexity.\n"}
+{"abstract": "  Let $(S_t)_{t\\geq 0}$ be the running maximum of a standard Brownian motion\n$(B_t)_{t\\geq 0}$ and $T_m:=\\inf\\{t; \\, mS_t<t\\},\\, m>0$. In this note we\ncalculate the joint distribution of $T_m$ and $B_{T_m}$. The motivation for our\nwork comes from a mathematical model for animal foraging. We also present\nresults for Brownian motion with drift.\n"}
+{"abstract": "  Computer mediated conversations (e.g., videoconferencing) is now the new\nmainstream media. How would credibility be impacted if one could change their\nrace on the fly in these environments? We propose an approach using Deepfakes\nand a supporting GAN architecture to isolate visual features and alter racial\nperception. We then crowd-sourced over 800 survey responses to measure how\ncredibility was influenced by changing the perceived race. We evaluate the\neffect of showing a still image of a Black person versus a still image of a\nWhite person using the same audio clip for each survey. We also test the effect\nof showing either an original video or an altered video where the appearance of\nthe person in the original video is modified to appear more White. We measure\ncredibility as the percent of participant responses who believed the speaker\nwas telling the truth. We found that changing the race of a person in a static\nimage has negligible impact on credibility. However, the same manipulation of\nrace on a video increases credibility significantly (61\\% to 73\\% with p $<$\n0.05). Furthermore, a VADER sentiment analysis over the free response survey\nquestions reveals that more positive sentiment is used to justify the\ncredibility of a White individual in a video.\n"}
+{"abstract": "  We study self-supervised video representation learning, which is a\nchallenging task due to 1) lack of labels for explicit supervision; 2)\nunstructured and noisy visual information. Existing methods mainly use\ncontrastive loss with video clips as the instances and learn visual\nrepresentation by discriminating instances from each other, but they need a\ncareful treatment of negative pairs by either relying on large batch sizes,\nmemory banks, extra modalities or customized mining strategies, which\ninevitably includes noisy data. In this paper, we observe that the consistency\nbetween positive samples is the key to learn robust video representation.\nSpecifically, we propose two tasks to learn the appearance and speed\nconsistency, respectively. The appearance consistency task aims to maximize the\nsimilarity between two clips of the same video with different playback speeds.\nThe speed consistency task aims to maximize the similarity between two clips\nwith the same playback speed but different appearance information. We show that\noptimizing the two tasks jointly consistently improves the performance on\ndownstream tasks, e.g., action recognition and video retrieval. Remarkably, for\naction recognition on the UCF-101 dataset, we achieve 90.8\\% accuracy without\nusing any extra modalities or negative pairs for unsupervised pretraining,\nwhich outperforms the ImageNet supervised pretrained model. Codes and models\nwill be available.\n"}
+{"abstract": "  Modern scientific research has become largely a cooperative activity in the\nInternet age. We build a simulation model to understand the population-level\ncreativity based on the heuristic ant colony algorithm. Each researcher has two\nheuristic parameters characterizing the goodness of his own judgments and his\ntrust on literature. In a population with all kinds of researchers, we find\nthat as the problem scale increases, the contributor distribution significantly\nshifts from the independent regime of relying on one's own judgments to the\ncooperative regime of more closely following the literature. The distribution\nalso changes with the stage of the research problem and the computing power\navailable. Our work provides some preliminary understanding and guidance for\nthe dynamical process of cooperative scientific research in various\ndisciplines.\n"}
+{"abstract": "  Let $K$ be the function field of a curve $C$ over a field $\\mathbb{F}$ of\neither odd or zero characteristic. Following the work by Serre and Mason on\n$\\mathrm{SL}_2$, we study the action of arithmetic subgroups of\n$\\mathrm{SU}(3)$ on its corresponding Bruhat-Tits tree associated to a suitable\ncompletion of $K$. More precisely, we prove that the quotient graph \"looks like\na spider\", in the sense that it is the union of a set of cuspidal rays (the\n\"legs\"), parametrized by an explicit Picard group, that are attached to a\nconnected graph (the \"body\"). We use this description in order to describe\nthese arithmetic subgroups as amalgamated products and study their homology. In\nthe case where $\\mathbb{F}$ is a finite field, we use a result by Bux, K\\\"ohl\nand Witzel in order to prove that the \"body\" is a finite graph, which allows us\nto get even more precise applications.\n"}
+{"abstract": "  This paper investigates the problem of straight-line path following for\nmagnetic helical microswimmers. The control objective is to make the helical\nmicroswimmer to converge to a straight line without violating the step-out\nfrequency constraint. The proposed feedback control solution is based on an\noptimal decision strategy (ODS) that is cast as a trust-region subproblem\n(TRS), i.e., a quadratic program over a sphere. The ODS-based control strategy\nminimizes the difference between the microrobot velocity and an integral\nline-of-sight (ILOS)-based reference vector field while respecting the magnetic\nsaturation constraints and ensuring the absolute continuity of the control\ninput. Due to the embedded integral action in the reference vector field, the\nmicroswimmer will follow the desired straight line by compensating for the\ndrift effect of the environmental disturbances as well as the microswimmer\nweight.\n"}
+{"abstract": "  In \\cite{APSIII} Atiyah, Patodi and Singer introduced spectral flow for\nelliptic operators on odd dimensional compact manifolds. They argued that it\ncould be computed from the Fredholm index of an elliptic operator on a manifold\nof one higher dimension. A general proof of this fact was produced by\nRobbin-Salamon \\cite{RS95}. In \\cite{GLMST}, a start was made on extending\nthese ideas to operators with some essential spectrum as occurs on non-compact\nmanifolds. The new ingredient introduced there was to exploit scattering theory\nfollowing the fundamental paper \\cite{Pu08}. These results do not apply to\ndifferential operators directly, only to pseudo-differential operators on\nmanifolds, due to the restrictive assumption that spectral flow is considered\nbetween an operator and {its perturbation by a relatively trace-class\noperator}. In this paper we extend the main results of these earlier papers to\nspectral flow between an operator and a perturbation satisfying a higher\n$p^{th}$ Schatten class condition for $0\\leq p<\\infty$, thus allowing\ndifferential operators on manifolds of any dimension $d<p+1$. In fact our main\nresult does not assume any ellipticity or Fredholm properties at all and proves\nan operator theoretic trace formula motivated by \\cite{BCPRSW, CGK16}. This\nleads us to introduce a notion of `generalised spectral flow' for such paths\nand to investigate its properties.\n"}
+{"abstract": "  We consider the generalized Good-Boussinesq model in one dimension, with\npower nonlinearity and data in the energy space $H^1\\times L^2$. This model has\nsolitary waves with speeds $-1<c<1$. When $|c|$ approaches 1, Bona and Sachs\nshowed orbital stability of such waves. It is well-known from a work of Liu\nthat for small speeds solitary waves are unstable. In this paper we consider in\nmore detail the long time behavior of zero speed solitary waves, or standing\nwaves. By using virial identities, in the spirit of Kowalczyk, Martel and\nMu\\~noz, we construct and characterize a manifold of even-odd initial data\naround the standing wave for which there is asymptotic stability in the energy\nspace.\n"}
+{"abstract": "  Given a smooth manifold $M$ (with or without boundary), in this paper we\nestablish a global functional calculus (without the standard assumption that\nthe operators are classical pseudo-differential operators) and the G\\r{a}rding\ninequality for global pseudo-differential operators associated with boundary\nvalue problems. The analysis that we follow is free of local coordinate\nsystems. Applications of the G\\r{a}rding inequality to the global solvability\nfor a class of evolution problems are also considered.\n"}
+{"abstract": "  The minimum-time path for intercepting a moving target with a prescribed\nimpact angle is studied in the paper. The candidate paths from Pontryagin's\nmaximum principle are analyzed, so that each candidate is related to a zero of\na real-valued function. It is found that the real-valued functions or their\nfirst-order derivatives can be converted to polynomials of at most fourth\ndegree. As a result, each canidate path can be computed within a constant time\nby embedding a standard polynomial solver into the typical bisection method.\nThe control strategy along the shortest candidate eventually gives rise to the\ntime-optimal guidance law. Finally, the developments of the paper is\nillustrated and verified by three numerical examples.\n"}
+{"abstract": "  NASA's TESS mission is expected to discover hundreds of M dwarf planets.\nHowever, few studies focus on how planets form around low-mass stars. We aim to\nbetter characterize the formation process of M dwarf planets to fill this gap\nand aid in the interpretation of TESS results. We use ten sets of N-body planet\nformation simulations which vary in whether a gas disc is present, initial\nrange of embryo semi-major axes, and initial solid surface density profile.\nEach simulation begins with 147 equal-mass embryos around a 0.2 solar mass star\nand runs for 100 Myr. We find that planets form rapidly, with most collisions\noccurring within the first 1 Myr. The presence of a gas disc reduces the final\nnumber of planets relative to a gas-free environment and causes planets to\nmigrate inward. We find that roughly a quarter of planetary systems experience\ntheir final giant impact inside the gas disc, suggesting that some super-Earths\nmay be able to reaccrete an extended gaseous envelope after their final giant\nimpact, though these may be affected by additional process such as\nphotoevaporation. In addition, we find that the final distribution of planets\ndoes not retain a memory of the slope of the initial surface density profile,\nregardless of whether or not a gas disc is present. Thus, our results suggest\nthat present-day observations are unlikely to provide sufficient information to\naccurately reverse-engineer the initial distribution of solids.\n"}
+{"abstract": "  The understanding of complex quantum many-body systems has been vastly\nboosted by tensor network (TN) methods. Among others, excitation spectrum and\nlong-range interacting systems can be studied using TNs, where one however\nconfronts the intricate summation over an extensive number of tensor diagrams.\nHere, we introduce a set of generating functions, which encode the diagrammatic\nsummations as leading order series expansion coefficients. Combined with\nautomatic differentiation, the generating function allows us to solve the\nproblem of TN diagrammatic summation. We illustrate this scheme by computing\nvariational excited states and dynamical structure factor of a quantum spin\nchain, and further investigating entanglement properties of excited states.\nExtensions to infinite size systems and higher dimension are outlined.\n"}
+{"abstract": "  The dynamic response of a parametric system constituted by a spin precessing\nin a time dependent magnetic field is studied by means of a perturbative\napproach that unveils unexpected features, and is then experimentally\nvalidated. The first-order analysis puts in evidence different regimes: beside\na tailorable low-pass-filter behaviour, a band-pass response with interesting\npotential applications emerges. Extending the analysis to the second\nperturbation order permits to study the response to generically oriented fields\nand to characterize several non-linear features in the behaviour of such kind\nof systems.\n"}
+{"abstract": "  Flexible manufacturing has been the trend in the area of the modern chemical\nprocess nowadays. One of the essential characteristics of flexible\nmanufacturing is to track time-varying target trajectories (e.g. diversity and\nquantity of products). A possible tool to achieve time-varying targets is\ncontraction theory. However, the contraction theory was developed for\ncontinuous time systems and there lacks analysis and synthesis tools for\ndiscrete-time systems. This article develops a systematic approach to\ndiscrete-time contraction analysis and control synthesis using Discrete-time\nControl Contraction Metrics (DCCM) which can be implemented using Sum of Square\n(SOS) programming. The proposed approach is demonstrated by illustrative\nexample.\n"}
+{"abstract": "  We study two interacting identical run and tumble particles (RTP's) in one\ndimension. Each particle is driven by a telegraphic noise, and in some cases,\nalso subjected to a thermal white noise with a corresponding diffusion constant\n$D$. We are interested in the stationary bound state formed by the two RTP's in\nthe presence of a mutual attractive interaction. The distribution of the\nrelative coordinate $y$ indeed reaches a steady state that we characterize in\nterms of the solution of a second-order differential equation. We obtain the\nexplicit formula for the stationary probability $P(y)$ of $y$ for two examples\nof interaction potential $V(y)$. The first one corresponds to $V(y) \\sim |y|$.\nIn this case, for $D=0$ we find that $P(y)$ contains a delta function part at\n$y=0$, signaling a strong clustering effect, together with a smooth exponential\ncomponent. For $D>0$, the delta function part broadens, leading instead to weak\nclustering. The second example is the harmonic attraction $V(y) \\sim y^2$ in\nwhich case, for $D=0$, $P(y)$ is supported on a finite interval. We unveil an\ninteresting relation between this two-RTP model with harmonic attraction and a\nthree-state single RTP model in one dimension, as well as with a four-state\nsingle RTP model in two dimensions. We also provide a general discussion of the\nstationary bound state, including examples where it is not unique, e.g., when\nthe particles cannot cross due to an additional short-range repulsion.\n"}
+{"abstract": "  We construct families of smooth functions\n$H\\colon\\mathbb{R}^{n+1}\\to\\mathbb{R}$ such that the Euclidean $(n+1)$-space is\ncompletely filled by not necessarily round hyperspheres of mean curvature $H$\nat every point.\n"}
+{"abstract": "  We prove that if $A$ is a string algebra then there are not three irreducible\nmorphisms between indecomposable $A$-modules such that its composition belongs\nto $\\Re^{6} \\backslash \\Re^{7}$, whenever the compositions of two of them are\nnot in $\\Re^{3}$. Moreover, for any positive integer $n \\geq 3$, we show that\nthere are $n$ irreducible morphisms such that their composition is in\n$\\Re^{n+4} \\backslash \\Re^{n+5}$.\n"}
+{"abstract": "  Coronary artery disease (CAD) remains the world's leading cause of mortality\nand the disease burden is continually expanding as the population ages.\nRecently, the MR-INFORM randomised trial has demonstrated that the management\nof patients with stable CAD can be guided by stress perfusion cardiovascular\nmagnetic resonance (CMR) imaging and it is non-inferior to the using the\ninvasive reference standard of fractional flow reserve. The benefits of using\nstress perfusion CMR include that it is non-invasive and significantly reduces\nthe number of unnecessary coronary revascularisations. As compared to other\nischaemia tests, it boasts a high spatial resolution and does not expose the\npatient to ionising radiation. However, the main limitation of stress perfusion\nCMR is that the diagnostic accuracy is highly dependent on the level of\ntraining of the operator, resulting in the test only being performed routinely\nin experienced tertiary centres. The clinical translation of stress perfusion\nCMR would be greatly aided by a fully automated, user-independent, quantitative\nevaluation of myocardial blood flow. This thesis presents major steps towards\nthis goal: robust motion correction, automated image processing, reliable\nquantitative modelling, and thorough validation. The motion correction scheme\nmakes use of data decomposition techniques, such as robust principal component\nanalysis, to mitigate the difficulties in image registration caused by the\ndynamic contrast enhancement. The motion corrected image series are input to a\nprocessing pipeline which leverages the recent advances in image processing\nfacilitated by deep learning. The pipeline utilises convolutional neural\nnetworks to perform a series of computer vision tasks including myocardial\nsegmentation and right ventricular insertion point detection...\n"}
+{"abstract": "  Context: In spiral galaxies, star formation tends to trace features of the\nspiral pattern, including arms, spurs, feathers, and branches. However, in our\nown Milky Way, it has been challenging to connect individual star-forming\nregions to their larger Galactic environment owing to our perspective from\nwithin the disk. One feature in nearly all modern models of the Milky Way is\nthe Sagittarius Arm, located inward of the Sun with a pitch angle of ~12 deg.\nAims: We map the 3D locations and velocities of star-forming regions in a\nsegment of the Sagittarius Arm using young stellar objects (YSOs) from the\nSpitzer/IRAC Candidate YSO (SPICY) catalog to compare their distribution to\nmodels of the arm. Methods: Distances and velocities for these objects are\nderived from Gaia EDR3 astrometry and molecular line surveys. We infer\nparallaxes and proper motions for spatially clustered groups of YSOs and\nestimate their radial velocities from the velocities of spatially associated\nmolecular clouds. Results: We identify 25 star-forming regions in the Galactic\nlongitude range l~4.0-18.5 deg arranged in a narrow, ~1 kpc long linear\nstructure with a high pitch angle of $\\psi = 56$ deg and a high aspect ratio of\n~7:1. This structure includes massive star-forming regions such as M8, M16,\nM17, and M20. The motions in the structure are remarkably coherent, with\nvelocities in the direction of Galactic rotation of $240\\pm3$ km/s (slightly\nhigher than average) and slight drifts toward the Galactic center (-4.3 km/s)\nand in the negative Z direction (-2.9 km/s). The rotational shear experienced\nby the structure is 4.6 km/s/kpc. Conclusions: The observed 56 deg pitch angle\nis remarkably high for a segment of the Sagittarius Arm. We discuss possible\ninterpretations of this feature as a substructure within the lower pitch angle\nSagittarius Arm, as a spur, or as an isolated structure.\n"}
+{"abstract": "  Photoacoustic imaging is a new non-destructive medical imaging technology\nbased on photoacoustic effect. It can reflect the difference of light\nabsorption energy by detecting photoacoustic signal. At present, the analysis\nmethods of photoacoustic signals in biological tissues can be divided into two\ncategories, namely, time-domain analysis of signals and frequency-domain\nanalysis of signals. In time domain analysis, the envelope of the received\nphotoacoustic signal is usually used to reconstruct the image. However, due to\nthe influence of various external factors, the time domain signal cannot\naccurately reflect the characteristics of the absorber itself. Here,\nphotoacoustic spectrum analysis was performed by using k-Wave to obtains the\nrelationship between the structure, size, density of the absorber and the\nphotoacoustic spectrum. Firstly, the relationship between the size of absorber\nand the photoacoustic spectrum is studied, and the slope and intercept are used\nto analyze the spectrum. Conversely, the relationship was used to predict the\nsize of the absorber Finally, we used this relationship to predict the size of\nblood vessels.\n"}
+{"abstract": "  Afshani, Barbay and Chan (2017) introduced the notion of instance-optimal\nalgorithm in the order-oblivious setting. An algorithm A is instance-optimal in\nthe order-oblivious setting for a certain class of algorithms A* if the\nfollowing hold:\n  - A takes as input a sequence of objects from some domain;\n  - for any instance $\\sigma$ and any algorithm A' in A*, the runtime of A on\n$\\sigma$ is at most a constant factor removed from the runtime of A' on the\nworst possible permutation of $\\sigma$. If we identify permutations of a\nsequence as representing the same instance, this essentially states that A is\noptimal on every possible input (and not only in the worst case).\n  We design instance-optimal algorithms for the problem of reporting, given a\nbichromatic set of points in the plane S, all pairs consisting of points of\ndifferent color which span an empty axis-aligned rectangle (or reporting all\npoints which appear in such a pair). This problem has applications for\ntraining-set reduction in nearest-neighbour classifiers. It is also related to\nthe problem consisting of finding the decision boundaries of a euclidean\nnearest-neighbour classifier, for which Bremner et al. (2005) gave an optimal\noutput-sensitive algorithm.\n  By showing the existence of an instance-optimal algorithm in the\norder-oblivious setting for this problem we push the methods of Afshani et al.\ncloser to their limits by adapting and extending them to a setting which\nexhibits highly non-local features. Previous problems for which\ninstance-optimal algorithms were proven to exist were based solely on local\nrelationships between points in a set.\n"}
+{"abstract": "  Mobility-as-a-Service (MaaS) is an emerging business model driven by the\nconcept of \"Everything-as-a-Service\" and enabled through mobile internet\ntechnologies. In the context of economic deregulation, a MaaS system consists\nof a typical two-sided market, where travelers and transportation service\nproviders (TSPs) are two groups of agents interacting with each other through a\nMaaS platform. In this study, we propose a modeling and optimization framework\nfor the regulation of two-sided MaaS markets. We consider a name-your-own-price\n(NYOP)-auction mechanism where travelers submit purchase-bids to accommodate\ntheir travel demand via MaaS platform, and TSPs submit sell-bids to supply\nmobility resources for the MaaS platform in exchange for payments. We cast this\nproblem as a single-leader multi-follower game (SLMFG) where the leader is the\nMaaS regulator and two groups of follower problems represent the travelers and\nthe TSPs. The MaaS regulator aims to maximize its profits by optimizing\noperations. In response to the MaaS regulator's decisions, travelers (resp.\nTSPs) adjust their participation level in the MaaS platform to minimize their\ntravel costs (resp. maximize their profits). We analyze cross-group network\neffects in the MaaS market, and formulate SLMFGs without and with network\neffects leading to mixed-integer linear bilevel programming and mixed-integer\nquadratic bilevel programming problems, respectively. We propose customized\nbranch-and-bound algorithms based on strong duality reformulations to solve\nthese SLMFGs. Extensive numerical experiments conducted on large scale\nsimulation instances generated from realistic mobility data highlight that the\nperformance of the proposed algorithms is significantly superior to a\nbenchmarking approach, and provide meaningful managerial insights for the\nregulation of two-sided MaaS markets in practice.\n"}
+{"abstract": "  Continual learning aims to provide intelligent agents that are capable of\nlearning continually a sequence of tasks, building on previously learned\nknowledge. A key challenge in this learning paradigm is catastrophically\nforgetting previously learned tasks when the agent faces a new one. Current\nrehearsal-based methods show their success in mitigating the catastrophic\nforgetting problem by replaying samples from previous tasks during learning a\nnew one. However, these methods are infeasible when the data of previous tasks\nis not accessible. In this work, we propose a new pseudo-rehearsal-based\nmethod, named learning Invariant Representation for Continual Learning (IRCL),\nin which class-invariant representation is disentangled from a conditional\ngenerative model and jointly used with class-specific representation to learn\nthe sequential tasks. Disentangling the shared invariant representation helps\nto learn continually a sequence of tasks, while being more robust to forgetting\nand having better knowledge transfer. We focus on class incremental learning\nwhere there is no knowledge about task identity during inference. We\nempirically evaluate our proposed method on two well-known benchmarks for\ncontinual learning: split MNIST and split Fashion MNIST. The experimental\nresults show that our proposed method outperforms regularization-based methods\nby a big margin and is better than the state-of-the-art pseudo-rehearsal-based\nmethod. Finally, we analyze the role of the shared invariant representation in\nmitigating the forgetting problem especially when the number of replayed\nsamples for each previous task is small.\n"}
+{"abstract": "  Multiple-choice/multiple-response (MCMR) items (i.e., multiple-choice\nquestions for which there may be more than one correct response) can be a\nvaluable tool for assessment. Like traditional multiple-choice/single-response\nquestions, they are easy to grade; but MCMR items may provide more information\nabout student reasoning by probing multiple facets of reasoning in a single\nproblem context. Because MCMR items are infrequently used, best practices for\ntheir implementation are not established. In this paper, we describe the\nadministration of MCMR items on an online, research-based assessment. We\ndiscuss possible differences in performance on MCMR items that may result from\ndifferences in administration method (in-person vs. online). This work is\npresented as a potential first step toward establishing best-practices for the\nadministration of MCMR items on online assessments.\n"}
+{"abstract": "  Graph Convolutional Networks (GCNs) and subsequent variants have been\nproposed to solve tasks on graphs, especially node classification tasks. In the\nliterature, however, most tricks or techniques are either briefly mentioned as\nimplementation details or only visible in source code. In this paper, we first\nsummarize some existing effective tricks used in GCNs mini-batch training.\nBased on this, two novel tricks named GCN_res Framework and Embedding Usage are\nproposed by leveraging residual network and pre-trained embedding to improve\nbaseline's test accuracy in different datasets. Experiments on Open Graph\nBenchmark (OGB) show that, by combining these techniques, the test accuracy of\nvarious GCNs increases by 1.21%~2.84%. We open source our implementation at\nhttps://github.com/ytchx1999/PyG-OGB-Tricks.\n"}
+{"abstract": "  We construct special cycles on the moduli stack of unitary shtukas. We prove\nan identity between (1) the r-th central derivative of non-singular Fourier\ncoefficients of a normalized Siegel--Eisenstein series, and (2) the degree of\nspecial cycles of \"virtual dimension 0\" on the moduli stack of unitary shtukas\nwith r legs. This may be viewed as a function-field analogue of the\nKudla-Rapoport Conjecture, that has the additional feature of encompassing all\nhigher derivatives of the Eisenstein series.\n"}
+{"abstract": "  In this paper, we use homological techniques and a theorem of Thurston to\nstudy homological finiteness of $\\text{BDiff}(M, \\text{rel }\\partial)$ when $M$\nis a reducible $3$-manifold with a non-empty boundary that has distinct\nirreducible factors. Kontsevich conjectured that $\\text{BDiff}(M, \\text{rel\n}\\partial)$ has the homotopy type of a finite CW complex for all $3$-manifolds\nwith non-empty boundary. Hatcher-McCullough proved this conjecture when $M$ is\nirreducible. We show that when $M$ is reducible with non-empty boundary that\nhas distinct irreducible factors, $\\text{BDiff}(M, \\text{rel }\\partial)$ is\nhomology isomorphic to a finite CW-complex.\n"}
+{"abstract": "  Magnetic clouds (MCs) are flux-rope magnetic structures forming a subset of\nsolar coronal mass ejections which have significant space weather impacts. The\ngeoeffectiveness of MCs depends on their properties which evolve during their\ninterplanetary passage. Based on an analysis of observations spanning two solar\ncycles we establish that MCs interacting with the ambient solar wind magnetic\nfield (i.e., heliospheric open flux) lose a substantial amount of their initial\nmagnetic flux via magnetic reconnection, which in some cases, reduce their\ngeoeffectiveness. We find a linear correlation between the eroded flux of MCs\nand solar open flux which is consistent with the scenario that MC erosion is\nmediated via the local heliospheric magnetic field draping around an MC during\nits interplanetary propagation. The solar open flux is governed by the sunspot\ncycle. This work therefore uncovers a hitherto unknown pathway for solar cycle\nmodulation of the properties of MCs.\n"}
+{"abstract": "  Recent developments in surgical robotics have led to new advancements in the\nautomation of surgical sub-tasks such as suturing, soft tissue manipulation,\ntissue tensioning and cutting. However, integration of dynamics to optimize\nthese control policies for the variety of scenes encountered in surgery remains\nunsolved. Towards this effort, we investigate the integration of differentiable\nfluid dynamics to optimizing a suction tool's trajectory to clear the surgical\nfield from blood as fast as possible. The fully differentiable fluid dynamics\nis integrated with a novel suction model for effective model predictive control\nof the tool. The differentiability of the fluid model is crucial because we\nutilize the gradients of the fluid states with respect to the suction tool\nposition to optimize the trajectory. Through a series of experiments, we\ndemonstrate how, by incorporating fluid models, the trajectories generated by\nour method can perform as good as or better than handcrafted human-intuitive\nsuction policies. We also show that our method is adaptable and can work in\ndifferent cavity conditions while using a single handcrafted strategy fails.\n"}
+{"abstract": "  In recent years, network function virtualization and a software defined focus\nhas allowed networks to become flexible and extensible in ways not possible\npreviously. Although network modeling tools such as NS-2, NS-3, and OMNet++\nhave been extended with modules and code to support the absolute latest\nwireless protocols and medium access standards - there has been a growing gap\nin simulation of layers above medium access which recent 5G use cases are\ndesigned to support. In this thesis, I extend research into the topic of\napplication layer modeling in wireless networks, with focus on upcoming\ndeployments of enhanced vehicle services. In particular, I begin in discussing\nthe evolution of vehicle network standards and use cases, along with the most\nrecent initiative `Project 5GCar`; funded by the European Commission. I go\nnarrower, from the macro trend of vehicular network standards evolution, to\ncompare the state of the art in simulation stacks, designed to support\napplication models in vehicle networks. Within this comparison, simulation\nenvironments OVNIS, Veins, iTetris, and VSimRTI are compared for their\ncapabilities. Finally, to measure the qualitative performance of application\nlayer modeling in vehicle networks, I take the cooperative maneuver use case,\npresented under Project 5GCar; to design an autonomous merge algorithm -\ncompleting the steps required to program and model the application in vehicles\nusing the VSimRTI simulation stack.\n"}
+{"abstract": "  With the popularity of mobile devices, mobile applications have become an\nessential part of people's lives. To provide secure mobile application download\nchannels for users, various modern app markets are maintained by different\ncompanies. For example, Google maintains Google Play for Android users, while\nApple maintains App Store for iOS, iPadOS, and MacOS users. Though app markets\nhave come up with strict policies which impose restrictions on developers to\navoid the potential harmful applications, we still have quite limited knowledge\non the process of app vetting and the status of potential harmful apps. To fill\nthis gap, this paper takes the initiative to conduct a large-scale and\nlongitudinal study of removed apps in the iOS app store. Our analysis reveals\nthat although most of the removed apps are low-quality apps, a number of them\nare quite popular. Furthermore, the mis-behaviors of these apps are reflected\non app metadata, which makes it possible to distinguish potential harmful apps.\n"}
+{"abstract": "  In this paper, we propose a novel bidirectional multiscale feature\naggregation (BMFA) network with attentional fusion modules for text-independent\nspeaker verification. The feature maps from different stages of the backbone\nnetwork are iteratively combined and refined in both a bottom-up and top-down\nmanner. Furthermore, instead of simple concatenation or element-wise addition\nof feature maps from different stages, an attentional fusion module is designed\nto compute the fusion weights. Experiments are conducted on the NIST SRE16 and\nVoxCeleb1 datasets. The experimental results demonstrate the effectiveness of\nthe bidirectional aggregation strategy and show that the proposed attentional\nfusion module can further improve the performance.\n"}
+{"abstract": "  This work identifies information-theoretic quantities that are closely\nrelated to the required list size for successive cancellation list (SCL)\ndecoding to implement maximum-likelihood decoding. It also provides an\napproximation for these quantities that can be computed efficiently for very\nlong codes. There is a concentration around the mean of the logarithm of the\nrequired list size for sufficiently large block lengths. We further provide a\nsimple method to estimate the mean via density evolution for the binary erasure\nchannel (BEC). Simulation results for the binary-input additive white Gaussian\nnoise channel as well as the BEC demonstrate the accuracy of the mean estimate.\nA modified Reed-Muller code with dynamic frozen bits performs very close to the\nrandom coding union (RCU) bound down to the block error rate of $10^{-5}$ under\nSCL decoding with list size $L=128$ when the block length is $N=128$. The\nanalysis shows how to modify the design to improve the performance when a more\npractical list size, e.g., $L=32$, is adopted while keeping the performance\nwith $L=128$ unchanged. For the block length of $N=512$, a design performing\nwithin $0.4$ dB from the RCU bound down to the block error rate of $10^{-6}$\nunder an SCL decoder with list size $L=1024$ is provided. The design is\nmodified using the new guidelines so that the performance improves with\npractical list sizes, e.g., $L\\in\\{8,32,128\\}$, outperforming 5G designs.\n"}
+{"abstract": "  Content: Qualitative and ad-hoc descriptions of observations of the\n\"undergraduate\" and \"graduate\" effects. Comparisons between the classical\nmodels of both undergraduate and graduate systems separately lead to the\nconclusion that a quantized model is inevitably required to explain certain\neffects such as \"Spooky Action At A Seminar\" and the FTR (Food Travel Ratio)\namong other select observed phenomena. Two regimes of dynamics in systems are\nexplored as well as interactions of composite systems. An attempt is made to\nrefine the undergraduate/graduate statistical theory in order to explain the\ncommon equilibrium systems known as \"labs\". Finally this work will begin the\ndiscussion on the mysterious ultra-long range \"Advisor\" interaction, which\nseems to contradict the theory of undergraduate/graduate interaction as well as\nspecific graduate/graduate interactions, and graduate/research interactions in\nerratic ways.\n"}
+{"abstract": "  This paper is focused on performing a new method for solving linear and\nnonlinear higher-order boundary value problems (HBVPs). This direct numerical\nmethod based on spectral method. The trial function of this method is the Monic\nChebyshev polynomials (MCPs). This method was relying on derivative of MCPs\nwhich explicit in the series expansion. The advantage of this method is solved\nHBVPs without transforming it to a system of lower-order ordinary differential\nequations (ODEs). This method supported by examples of HBVPs in wide\napplication. The mentioned examples showed that the proposed method is\nefficient and accurate.\n"}
+{"abstract": "  We show that Floquet engineering using bicircular light (BCL) is a versatile\nway to control magnetic symmetries and topology in materials. The electric\nfield of BCL, which is a superposition of two circularly polarized light waves\nwith frequencies that are integer multiples of each other, traces out a rose\npattern in the polarization plane that can be chosen to break selective\nsymmetries, including spatial inversion. Using a realistic low-energy model, we\ntheoretically demonstrate that the three-dimensional Dirac semimetal\nCd$_3$As$_2$ is a promising platform for BCL Floquet engineering. Without\nstrain, BCL irradiation induces a transition to a non-centrosymmetric magnetic\nWeyl semimetal phase with tunable energy separation between the Weyl nodes. In\nthe presence of strain, we predict the emergence of a magnetic topological\ncrystalline insulator with exotic unpinned surface Dirac states that are\nprotected by a combination of twofold rotation and time-reversal $(2')$ and can\nbe controlled by light.\n"}
+{"abstract": "  The Standard Model(SM) vacuum is unstable for the measured values of the top\nYukawa coupling and Higgs mass. Here we study the issue of vacuum stability\nwhen neutrino masses are generated through spontaneous low-scale lepton number\nviolation. In the simplest dynamical inverse seesaw, the SM Higgs has two\nsiblings: a massive CP-even scalar plus a massless Nambu-Goldstone boson,\ncalled majoron. For TeV scale breaking of lepton number, Higgs bosons can have\na sizeable decay into the invisible majorons. We examine the interplay and\ncomplementarity of vacuum stability and perturbativity restrictions, with\ncollider constraints on visible and invisible Higgs boson decay channels. This\nsimple framework may help guiding further studies, for example, at the proposed\nFCC facility.\n"}
+{"abstract": "  Predicting the altered acoustic frames is an effective way of self-supervised\nlearning for speech representation. However, it is challenging to prevent the\npretrained model from overfitting. In this paper, we proposed to introduce two\ndropout regularization methods into the pretraining of transformer encoder: (1)\nattention dropout, (2) layer dropout. Both of the two dropout methods encourage\nthe model to utilize global speech information, and avoid just copying local\nspectrum features when reconstructing the masked frames. We evaluated the\nproposed methods on phoneme classification and speaker recognition tasks. The\nexperiments demonstrate that our dropout approaches achieve competitive\nresults, and improve the performance of classification accuracy on downstream\ntasks.\n"}
+{"abstract": "  The Euclidean path integral is well approximated by instantons. If instantons\nare dynamical, then instantons are necessarily complexified. These fuzzy\ninstantons can have various physical applications. In slow-roll inflation\nmodels, fuzzy instantons can explain the probability distribution of the\ninitial conditions of the universe. Even though the potential shape does not\nsatisfy the slow-roll conditions following the swampland criteria, still the\nfuzzy instantons can explain the origin of the universe. If we extend the\nEuclidean path integral beyond the no-boundary proposal, we may study fuzzy\nEuclidean wormholes that have various physical applications in cosmology and\nblack hole physics. We summarize them and discuss possible future research\ntopics.\n"}
+{"abstract": "  On a Riemannian manifold of dimension $n$ we extend the known analytic\nresults on Yang-Mills connections to the class of connections called\n$\\Omega$-Yang-Mills connections, where $\\Omega$ is a smooth, not necessarily\nclosed, $(n-4)$-form. Special cases include $\\Omega$-anti-self-dual connections\nand Hermitian-Yang-Mills connections over general complex manifolds. By a key\nobservation, a weak compactness result is obtained for moduli space of smooth\n$\\Omega$-Yang-Mills connections with uniformly $L^2$ bounded curvature, and it\ncan be improved in the case of Hermitian-Yang-Mills connections over general\ncomplex manifolds. A removable singularity theorem for singular\n$\\Omega$-Yang-Mills connections on a trivial bundle with small energy\nconcentration is also proven. As an application, it is shown how to compactify\nthe moduli space of smooth Hermitian-Yang-Mills connections on unitary bundles\nover a class of balanced manifolds of Hodge-Riemann type. This class includes\nthe metrics coming from multipolarizations, and in particular, the Kaehler\nmetrics. In the case of multipolarizations on a projective algebraic manifold,\nthe compactification of smooth irreducible Hermitian-Yang-Mills connections\nwith fixed determinant modulo gauge transformations inherits a complex\nstructure from algebro-geometric considerations.\n"}
+{"abstract": "  We propose a method based on cluster expansion to study the low activity/high\ntemperature phase of a continuous particle system confined in a finite volume,\ninteracting through a stable and finite range pair potential with negative\nminimum in presence of non free boundary conditions.\n"}
+{"abstract": "  In spin-orbit-coupled magnetic materials, the usually applied Curie-Weiss law\ncan break down. This is due to potentially sharp temperature-dependence of the\nlocal magnetic moments. We therefore propose a modified Curie-Weiss formula\nsuitable for analysis of experimental susceptibility. We show for octahedrally\ncoordinated materials of $d^5$ filling that the Weiss constant obtained from\nthe improved formula is in excellent agreement with the calculated Weiss\nconstant from microscopic exchange interactions. Reanalyzing the measured\nsusceptibility of several Kitaev candidate materials with the modified formula\nresolves apparent discrepancies between various experiments regarding the\nmagnitude and anisotropies of the underlying magnetic couplings.\n"}
+{"abstract": "  We investigate the formation and control of stationary optical patterns in a\ncold Rydberg atomic gas via double electromagnetically induced transparency. We\nshow that, through the modulational instability of plane-wave state of a laser\nfield with two polarization components, the system undergoes a spontaneous\nsymmetry breaking and hence the emergence of plentiful self-organized spatial\noptical structures, which can be manipulated by the ratio between the cross-\nand self-Kerr nonlinearities, the nonlocality degree of the Kerr\nnonlinearities, and the populations initially prepared in the two atomic ground\nstates. Interestingly, a crossover from mixture to separation in space (optical\nphase separation) of the two polarization components occurs when the ratio\nbetween the cross- and self-Kerr nonlinearities exceeds a critical value. We\nalso show that the system supports nonlocal two-component spatial optical\nsolitons and vortices when the parameters of the system are selected suitably.\nThe rich diversity and active controllability of the self-organized optical\nstructures reported here provide a way for realizing novel optical patterns and\nsolitons and their structural phase transitions based on Rydberg atomic gases.\n"}
+{"abstract": "  The accelerations of tracer and light particles in compressible homogeneous\nisotropic turbulence (CHIT) is investigated by using data from direct numerical\nsimulations (DNS) up to turbulent Mach number $M_t =1$. For tracer particles,\nthe flatness factor of acceleration components, $F_a$, increases gradually for\n$M_t \\in [0.3, 1]$. On the contrary, $F_a$ for light particles develops a\nmaximum around $M_t \\sim 0.6$. The PDF of longitudinal acceleration of tracers\nis increasingly skewed towards the negative value as $M_t$ increases. By\ncontrast, for light particles, the skewness factor of longitudinal\nacceleration, $S_a$, firstly becomes more negative with the increase of $M_t$,\nand then goes back to $0$ when $M_t$ is larger than $0.6$. Similarly,\ndifferences among tracers and light particles appear also in the zero-crossing\ntime of acceleration correlation. It is argued that all these phenomenons are\nintimately linked to the flow structures in compression regions, e.g. close to\nshocklets.\n"}
+{"abstract": "  We propose an algorithm to generate inner and outer polyhedral approximations\nto the upper image of a bounded convex vector optimization problem. It is an\nouter approximation algorithm and is based on solving norm minimizing\nscalarizations. Unlike Pascolleti-Serafini scalarization used in the literature\nfor similar purposes, it does not involve a direction parameter. Therefore, the\nalgorithm is free of direction-biasedness. We also propose a modification of\nthe algorithm by introducing a suitable compact subset of the upper image,\nwhich helps in proving for the first time the finiteness of an algorithm for\nconvex vector optimization. The computational performance of the algorithms is\nillustrated using some of the benchmark test problems, which shows promising\nresults in comparison to a similar algorithm that is based on\nPascoletti-Serafini scalarization.\n"}
+{"abstract": "  In this work we present all the possible solutions for a static cylindrical\nsymmetric spacetime in the Einstein-Aether (EA) theory. As far as we know, this\nis the first work in the literature that considers cylindrically symmetric\nsolutions in the theory of EA. One of these solutions is the generalization in\nEA theory of the Levi-Civita (LC) spacetime in General Relativity (GR) theory.\nWe have shown that this generalized LC solution has unusual geodesic\nproperties, depending on the parameter $c_{14}$ of the aether field. The\ncircular geodesics are the same of the GR theory, no matter the values of\n$c_{14}$. However, the radial and $z$ direction geodesics are allowed only for\ncertain values of $\\sigma$ and $c_{14}$. The $z$ direction geodesics are\nrestricted to an interval of $\\sigma$ different from those predicted by the GR\nand the radial geodesics show that the motion is confined between the origin\nand a maximum radius. The latter is not affected by the aether field but the\nvelocity and acceleration of the test particles are Besides, for\n$0\\leq\\sigma<1/2$, when the cylindrical symmetry is preserved, this spacetime\nis singular at the axis $r=0$, although for $\\sigma>1/2$ exists interval of\n$c_{14}$ where the spacetime is not singular, which is completely different\nfrom that one obtained with the GR theory, where the axis $r=0$ is always\nsingular.\n"}
+{"abstract": "  We theoretically investigate the one-dimensional dynamics of a dark soliton\nin a two-component immiscible mixture of Bose-Einstein condensates with\nrepulsive interactions. We analyze the reflection and transmission of a soliton\nwhen it propagates through the domain wall, and we show that a dark-bright\nsoliton can be dynamically generated by the interaction of the dark soliton\nwith the domain wall, outside the regime of parameters where stationary\nsolutions are known to exist. The dynamics of this dark-bright soliton is\nharmonic like, with a numerical frequency that is in good agreement with the\npredictions of a semi-analytical model.\n"}
+{"abstract": "  Thanks to the widespread availability of large-scale datasets on scholarly\noutputs, science itself has come under the microscope with the aim of capturing\na quantitative understanding of its workings. In this study, we leverage\nwell-established cognitive models coming from anthropology in order to\ncharacterise the personal network of collaborations between scientists, i.e.,\nthe network considered from the standpoint of each individual researcher\n(referred to as ego network), in terms of the cognitive investment they devote\nto the different collaborations. Building upon these models, we study the\ninterplay between the structure of academic collaborations, academic\nperformance, and academic mobility at different career stages. We take into\naccount both purely academic mobility (i.e., the number of affiliation changes)\nand geographical mobility (i.e., physical relocations to different countries).\nFor our investigation, we rely on a dataset comprising the geo-referenced\npublications of a group of 81,500 authors extracted from Scopus, one of the\nbiggest repositories of academic knowledge. Our main finding is that there is a\nclear correlation between the structure of co-authorship ego networks and\nacademic performance indices: the more one publishes and the higher their\nimpact, the larger their collaboration network. However, we observe a capacity\nbound effect, whereby, beyond a certain point, higher performances become\nincreasingly less correlated with large collaboration networks. We also find\nthat international academic migrants are better at growing their networks than\nresearchers that only migrate within the same country, but the latter seem to\nbe better in exploiting their collaboration to achieve higher impact. High\nacademic mobility does not appear to translate into better academic performance\nor larger collaboration networks.\n"}
+{"abstract": "  The negative solution to the famous problem of $36$ officers of Euler implies\nthat there are no two orthogonal Latin squares of order six. We show that the\nproblem has a solution, provided the officers are entangled, and construct\northogonal quantum Latin squares of this size. As a consequence, we find an\nexample of the long-elusive Absolutely Maximally Entangled state AME$(4,6)$ of\nfour subsystems with six levels each, equivalently a $2$-unitary matrix of size\n$36$, which maximizes the entangling power among all bipartite unitary gates of\nthis dimension, or a perfect tensor with four indices, each running from one to\nsix. This special state deserves the appellation golden AME state as the golden\nratio appears prominently in its elements. This result allows us to construct a\npure nonadditive quhex quantum error detection code $(\\!(3,6,2)\\!)_6$, which\nsaturates the Singleton bound and allows one to encode a $6$-level state into a\ntriplet of such states.\n"}
+{"abstract": "  With the multiplication of social media platforms, which offer anonymity,\neasy access and online community formation, and online debate, the issue of\nhate speech detection and tracking becomes a growing challenge to society,\nindividual, policy-makers and researchers. Despite efforts for leveraging\nautomatic techniques for automatic detection and monitoring, their performances\nare still far from satisfactory, which constantly calls for future research on\nthe issue. This paper provides a systematic review of literature in this field,\nwith a focus on natural language processing and deep learning technologies,\nhighlighting the terminology, processing pipeline, core methods employed, with\na focal point on deep learning architecture. From a methodological perspective,\nwe adopt PRISMA guideline of systematic review of the last 10 years literature\nfrom ACM Digital Library and Google Scholar. In the sequel, existing surveys,\nlimitations, and future research directions are extensively discussed.\n"}
+{"abstract": "  The muon $(g-2)_\\mu$ and $b\\to s \\bar \\mu \\mu$ induced $B$ anomalies as hints\nof new physics beyond the standard model (SM) have attracted much attention.\nThese two anomalies indicate that there may exist new interaction specifically\nrelated to muon. A lot of theoretical ideas have been proposed to explain these\nanomalies. Gauged flavor specific $U(1)_{B_q-L_\\mu}$ is among the promising\nones. The new gauge boson $Z'$ from $U(1)_{B_q-L_\\mu}$ interacts with muon and\nprovides necessary ingredient to solve the $(g-2)_\\mu$ anomaly. The $Z'$-quark\ncoupling can generate flavor changing interactions after diagonalization of\nquark mass matrix between weak eigen-state and mass eigen-state basis. We\nrevisit challenges for such models attempting to explain the $(g-2)_\\mu$ and\n$B$ anomalies separately or simultaneously. We find although for\n$U(1)_{B_q-L_\\mu}$ models there is still parameter space to provide solutions\nfor separately explaining the $(g-2)_\\mu$ and $B$ anomalies, there exits no\nparameter space for such models to solve both the anomalies simultaneously,\nafter taking into account existing constraints from $\\tau \\to \\mu \\gamma$,\n$\\tau \\to 3 \\mu$, neutrino trident and $B_s - \\bar B_s$ data. Among them\nleptonic processes restrict $Z^\\prime$ mass to be less than a few hundred MeV\nif required to solve the $(g-2)_\\mu$ anomaly, which causes conflict between\ndata from $B_s - \\bar B_s$, $D^0 - \\bar D^0$ mixing and also hadron decays with\n$Z^\\prime$ in the final states. The effects of $U(1)_Y$ and $U(1)_{B_q-L_\\mu}$\nkinetic mixing on these anomalies are also studied. We find that neither can\nthese effects do much to bring the two anomalies together to be solved\nsimultaneously.\n"}
+{"abstract": "  This paper proposes a rate-profile construction method for\npolarization-adjusted convolutional (PAC) codes of any code length and rate,\nwhich is capable of maintaining trade-off between the error-correction\nperformance and decoding complexity of PAC code. The proposed method can\nimprove the error-correction performance of PAC codes while guaranteeing a low\nmean sequential decoding complexity for signal-to-noise ratio (SNR) values\nbeyond a target SNR value.\n"}
+{"abstract": "  Despite their compelling theoretical properties, Bayesian neural networks\n(BNNs) tend to perform worse than frequentist methods in classification-based\nuncertainty quantification (UQ) tasks such as out-of-distribution (OOD)\ndetection. In this paper, based on empirical findings in prior works, we\nhypothesize that this issue is because even recent Bayesian methods have never\nconsidered OOD data in their training processes, even though this ``OOD\ntraining'' technique is an integral part of state-of-the-art frequentist UQ\nmethods. To validate this, we treat OOD data as a first-class citizen in BNN\ntraining by exploring four different ways of incorporating OOD data into\nBayesian inference. We show in extensive experiments that OOD-trained BNNs are\ncompetitive to recent frequentist baselines. This work thus provides strong\nbaselines for future work in Bayesian UQ.\n"}
+{"abstract": "  We develop the theory of pinchings for non-archimedean analytic spaces. In\nparticular, we show that although pinchings of affinoid spaces do not have to\nbe affinoid, pinchings of Hausdorff analytic spaces always exist in the\ncategory of analytic spaces.\n"}
+{"abstract": "  Despite broad application during labor and delivery, there remains\nconsiderable debate about the value of electronic fetal monitoring (EFM). EFM\nincludes the surveillance of the fetal heart rate (FHR) patterns in conjunction\nwith the maternal uterine contractions providing a wealth of data about fetal\nbehavior and the threat of diminished oxygenation and perfusion. Adverse\noutcomes universally associate a fetal injury with the failure to timely\nrespond to FHR pattern information. Historically, the EFM data, stored\ndigitally, are available only as rasterized pdf images for contemporary or\nhistorical discussion and examination. In reality, however, they are rarely\nreviewed systematically. Using a unique archive of EFM collected over 50 years\nof practice in conjunction with adverse outcomes, we present a deep learning\nframework for training and detection of incipient or past fetal injury. We\nreport 94% accuracy in identifying early, preventable fetal injury intrapartum.\nThis framework is suited for automating an early warning and decision support\nsystem for maintaining fetal well-being during the stresses of labor.\nUltimately, such a system could enable a physician to timely respond during\nlabor and prevent adverse outcomes. When adverse outcomes cannot be avoided,\nthey can provide guidance to the early neuroprotective treatment of the\nnewborn.\n"}
+{"abstract": "  The relational approach to quantum states asserts that the physical\ndescription of quantum systems is always relative to something or someone. In\nrelational quantum mechanics (RQM) it is relative to other quantum systems, in\nthe (neo-)Copenhagen interpretation of quantum theory to measurement contexts,\nand in QBism to the beliefs of the agents. In contrast to the other two\ninterpretations, in RQM any interaction between two quantum systems counts as a\n\"measurement\", and the terms \"observer\" and \"observed system\" apply to\narbitrary systems. We show, in the form of a no-go theorem, that in RQM the\nphysical description of a system relative to an observer cannot represent\nknowledge about the observer in the conventional sense of this term. The\nproblem lies in the ambiguity in the choice of the basis with respect to which\nthe relative states are to be defined in RQM. In interpretations of quantum\ntheory where observations play a fundamental role, the problem does not arise\nbecause the experimental context defines a preferred basis.\n"}
+{"abstract": "  High-dimensional observations are a major challenge in the application of\nmodel-based reinforcement learning (MBRL) to real-world environments. To handle\nhigh-dimensional sensory inputs, existing approaches use representation\nlearning to map high-dimensional observations into a lower-dimensional latent\nspace that is more amenable to dynamics estimation and planning. In this work,\nwe present an information-theoretic approach that employs temporal predictive\ncoding to encode elements in the environment that can be predicted across time.\nSince this approach focuses on encoding temporally-predictable information, we\nimplicitly prioritize the encoding of task-relevant components over nuisance\ninformation within the environment that are provably task-irrelevant. By\nlearning this representation in conjunction with a recurrent state space model,\nwe can then perform planning in latent space. We evaluate our model on a\nchallenging modification of standard DMControl tasks where the background is\nreplaced with natural videos that contain complex but irrelevant information to\nthe planning task. Our experiments show that our model is superior to existing\nmethods in the challenging complex-background setting while remaining\ncompetitive with current state-of-the-art models in the standard setting.\n"}
+{"abstract": "  We connect two developments aiming at extending Voevodsky's theory of motives\nover a field in such a way to encompass non-$\\mathbf{A}^1$-invariant phenomina.\nOne is theory of reciprocity sheaves introduced by Kahn-Saito-Yamazaki. Another\nis theory of the triangulated category\n$\\operatorname{\\mathbf{logDM}}^{\\operatorname{eff}}$ of logarithmic motives\nlaunched by Binda, Park and {\\O}stv{\\ae}r. We prove that the Nisnevich\ncohomology of reciprocity sheaves is representable in\n$\\operatorname{\\mathbf{logDM}}^{\\operatorname{eff}}$.\n"}
+{"abstract": "  We study logarithmic Sobolev inequalities with respect to a heat kernel\nmeasure on finite-dimensional and infinite-dimensional Heisenberg groups. Such\na group is the simplest non-trivial example of a sub-Riemannian manifold. First\nwe consider logarithmic Sobolev inequalities on non-isotropic Heisenberg\ngroups. These inequalities are considered with respect to the hypoelliptic heat\nkernel measure, and we show that the logarithmic Sobolev constants can be\nchosen to be independent of the dimension of the underlying space. In this\nsetting, a natural Laplacian is not an elliptic but a hypoelliptic operator.\nThe argument relies on comparing logarithmic Sobolev constants for the\nthree-dimensional non-isotropic and isotropic Heisenberg groups, and\ntensorization of logarithmic Sobolev inequalities in the sub-Riemannian\nsetting. Furthermore, we apply these results in an infinite-dimensional setting\nand prove a logarithmic Sobolev inequality on an infinite-dimensional\nHeisenberg group modelled on an abstract Wiener space.\n"}
+{"abstract": "  In this work, a two-dimensional time-fractional subdiffusion model is\ndeveloped to investigate the underlying transport phenomena evolving in a\nbinary medium comprised of two sub-domains occupied by homogeneous material. We\nutilise an unstructured mesh control volume method to validate the model\nagainst a derived semi-analytical solution for a class of two-layered problems.\nThis generalised transport model is then used to perform computational\nhomogenisation on various two-dimensional heterogenous porous media. A key\ncontribution of our work is to extend the classical homogenisation theory to\naccommodate the new framework and show that the effective diffusivity tensor\ncan be computed once the cell problems reach steady state at the microscopic\nscale. We verify the theory for binary media via a series of well-known test\nproblems and then investigate media having inclusions that exhibit a molecular\nrelaxation (memory) effect. Finally, we apply the generalised transport model\nto estimate the bound water diffusivity tensor on cellular structures obtained\nfrom environmental scanning electron microscope (ESEM) images for Spruce wood\nand Australian hardwood. A highlight of our work is that the computed\ndiffusivity for the heterogeneous media with molecular relaxation is quite\ndifferent from the classical diffusion cases, being dominated at steady-state\nby the material with memory effects.\n"}
+{"abstract": "  We consider the $\\mathcal{N}=2$ SYM theory with gauge group SU($N$) and a\nmatter content consisting of one multiplet in the symmetric and one in the\nanti-symmetric representation. This conformal theory admits a large-$N$ 't\nHooft expansion and is dual to a particular orientifold of $AdS_{5}\\times\nS^{5}$. We analyze this gauge theory relying on the matrix model provided by\nlocalization a la Pestun. Even though this matrix model has very non-trivial\ninteractions, by exploiting the full Lie algebra approach to the matrix\nintegration, we show that a large class of observables can be expressed in a\nclosed form in terms of an infinite matrix depending on the 't Hooft coupling\n$\\lambda$. These exact expressions can be used to generate the perturbative\nexpansions at high orders in a very efficient way, and also to study\nanalytically the leading behavior at strong coupling. We successfully compare\nthese predictions to a direct Monte Carlo numerical evaluation of the matrix\nintegral and to the Pad\\'e resummations derived from very long perturbative\nseries, that turn out to be extremely stable beyond the convergence disk\n$|\\lambda|<\\pi^2$ of the latter.\n"}
+{"abstract": "  In this work, we propose an overlapped speech detection system trained as a\nthree-class classifier. Unlike conventional systems that perform binary\nclassification as to whether or not a frame contains overlapped speech, the\nproposed approach classifies into three classes: non-speech, single speaker\nspeech, and overlapped speech. By training a network with the more detailed\nlabel definition, the model can learn a better notion on deciding the number of\nspeakers included in a given frame. A convolutional recurrent neural network\narchitecture is explored to benefit from both convolutional layer's capability\nto model local patterns and recurrent layer's ability to model sequential\ninformation. The proposed overlapped speech detection model establishes a\nstate-of-the-art performance with a precision of 0.6648 and a recall of 0.3222\non the DIHARD II evaluation set, showing a 20% increase in recall along with\nhigher precision. In addition, we also introduce a simple approach to utilize\nthe proposed overlapped speech detection model for speaker diarization which\nranked third place in the Track 1 of the DIHARD III challenge.\n"}
+{"abstract": "  Incentives have surprisingly inconsistent effects when it comes to\nencouraging people to behave prosocially. Classical economic theory, according\nto which a specific behavior becomes more prevalent when it is rewarded,\nstruggles to explain why incentives sometimes backfire. More recent theories\ntherefore posit a reputational cost offsetting the benefits of receiving an\nincentive -- yet unexplained effects of incentives remain, for instance across\nincentive types and countries. We propose that social norms can offer an\nexplanation for these inconsistencies. Ultimately, social norms determine the\nreputational costs or benefits resulting from a given behavior, and thus\nvariation in the effect of incentives may reflect variation in norms. We\nimplemented a formal model of prosocial behavior integrating social norms,\nwhich we empirically tested on the real-world prosocial behavior of blood\ndonation. Blood donation is essential for many life-saving medical procedures,\nbut also presents an ideal testing ground for our theory: Various incentive\npolicies for blood donors exist across countries, enabling a comparative\napproach. Our preregistered analyses reveal that social norms can indeed\naccount for the varying effects of financial and time incentives on\nindividual-level blood donation behavior across 28 European countries.\nIncentives are associated with higher levels of prosociality when norms\nregarding the incentive are more positive. The results indicate that social\nnorms play an important role in explaining the relationship between incentives\nand prosocial behavior. More generally, our approach highlights the potential\nof integrating theory from across the economic and behavioral sciences to\ngenerate novel insights, with tangible consequences for policy-making.\n"}
+{"abstract": "  When producing a model to object detection in a specific context, the first\nobstacle is to have a dataset labeling the desired classes. In RoboCup, some\nleagues already have more than one dataset to train and evaluate a model.\nHowever, in the Small Size League (SSL), there is not such dataset available\nyet. This paper presents an open-source dataset to be used as a benchmark for\nreal-time object detection in SSL. This work also presented a pipeline to\ntrain, deploy, and evaluate Convolutional Neural Networks (CNNs) models in a\nlow-power embedded system. This pipeline was used to evaluate the proposed\ndataset with state-of-art optimized models. In this dataset, the MobileNet SSD\nv1 achieves 44.88% AP (68.81% AP50) at 94 Frames Per Second (FPS) while running\non an SSL robot.\n"}
+{"abstract": "  We characterize all random point measures which are in a certain sense stable\nunder the action of branching. Denoting by $\\circledast$ the branching\nconvolution operation introduced by Bertoin and Mallein (2019), and by\n$\\mathcal{Z}$ the law of a random point measure on the real line, we are\ninterested in solutions to the fixed point equation \\[ \\mathcal E = \\mathcal{Z}\n\\circledast \\mathcal E, \\] with $\\mathcal E$ a random point measure\ndistribution. Under suitable assumptions, we characterize all solutions of this\nequation as shifted decorated Poisson point processes with a uniquely defined\nshift.\n"}
+{"abstract": "  In recent years wildfires have caused havoc across the world, especially\naggravated in certain regions, due to climate change. Remote sensing has become\na powerful tool for monitoring fires, as well as for measuring their effects on\nvegetation over the following years. We aim to explain the dynamics of\nwildfires' effects on a vegetation index (previously estimated by causal\ninference through synthetic controls) from pre-wildfire available information\n(mainly proceeding from satellites). For this purpose, we use regression models\nfrom Functional Data Analysis, where wildfire effects are considered functional\nresponses, depending on elapsed time after each wildfire, while pre-wildfire\ninformation acts as scalar covariates. Our main findings show that vegetation\nrecovery after wildfires is a slow process, affected by many pre-wildfire\nconditions, among which the richness and diversity of vegetation is one of the\nbest predictors for the recovery.\n"}
+{"abstract": "  Dynamical models allow us to connect the motion of a set of tracers to the\nunderlying gravitational potential, and thus to the total (luminous and dark)\nmatter distribution. They are particularly useful for understanding the mass\nand spatial distribution of dark matter (DM) in a galaxy. Globular clusters\n(GCs) are an ideal tracer population in dynamical models, since they are bright\nand can be found far out into the halo of galaxies. We aim to test how well\nJeans-Anisotropic-MGE (JAM) models using GCs (positions and line-of-sight\nvelocities) as tracers can constrain the mass and radial distribution of DM\nhalos. For this, we use the E-MOSAICS suite of 25 zoom-in simulations of L*\ngalaxies. We find that the DM halo properties are reasonably well recovered by\nthe JAM models. There is, however, a strong correlation between how well we\nrecover the mass and the radial distribution of the DM and the number of GCs in\nthe galaxy: the constraints get exponentially worse with fewer GCs, and at\nleast 150 GCs are needed in order to guarantee that the JAM model will perform\nwell. We find that while the data quality (uncertainty on the radial\nvelocities) can be important, the number of GCs is the dominant factor in terms\nof the accuracy and precision of the measurements. This work shows promising\nresults for these models to be used in extragalactic systems with a sample of\nmore than 150 GCs.\n"}
+{"abstract": "  We introduce a method for associating a chain complex to a module over a\ncombinatorial category, such that if the complex is exact then the module has a\nrational Hilbert series. We prove homology--vanishing theorems for these\ncomplexes for several combinatorial categories including: the category of\nfinite sets and injections, the opposite of the category of finite sets and\nsurjections, and the category of finite dimensional vector spaces over a finite\nfield and injections.\n  Our main applications are to modules over the opposite of the category of\nfinite sets and surjections, known as $FS^{op}$ modules. We obtain many\nconstraints on the sequence of symmetric group representations underlying a\nfinitely generated $FS^{op}$ module. In particular, we describe its character\nin terms of functions that we call character exponentials. Our results have new\nconsequences for the character of the homology of the moduli space of stable\nmarked curves, and for the equivariant Kazhdan-Luzstig polynomial of the braid\nmatroid.\n"}
+{"abstract": "  We revisit the problem of high-dimensional multiple packing in Euclidean\nspace. Multiple packing is a natural generalization of sphere packing and is\ndefined as follows. Let $ N>0 $ and $ L\\in\\mathbb{Z}_{\\ge2} $. A multiple\npacking is a set $\\mathcal{C}$ of points in $ \\mathbb{R}^n $ such that any\npoint in $ \\mathbb{R}^n $ lies in the intersection of at most $ L-1 $ balls of\nradius $ \\sqrt{nN} $ around points in $ \\mathcal{C} $. We study the multiple\npacking problem for both bounded point sets whose points have norm at most\n$\\sqrt{nP}$ for some constant $P>0$ and unbounded point sets whose points are\nallowed to be anywhere in $ \\mathbb{R}^n $. Given a well-known connection with\ncoding theory, multiple packings can be viewed as the Euclidean analog of\nlist-decodable codes, which are well-studied for finite fields. In this paper,\nwe derive various bounds on the largest possible density of a multiple packing\nin both bounded and unbounded settings. A related notion called average-radius\nmultiple packing is also studied. Some of our lower bounds exactly pin down the\nasymptotics of certain ensembles of average-radius list-decodable codes, e.g.,\n(expurgated) Gaussian codes and (expurgated) Poisson Point Processes. To this\nend, we apply tools from high-dimensional geometry and large deviation theory.\nSome of our lower bounds on the optimal multiple packing density are the best\nknown lower bounds. These bounds are obtained via a proxy known as error\nexponent. The latter quantity is the best exponent of the probability of\nlist-decoding error when the code is corrupted by a Gaussian noise. We\nestablish a curious inequality which relates the error exponent, a quantity of\naverage-case nature, to the list-decoding radius, a quantity of worst-case\nnature. We derive various bounds on the error exponent in both bounded and\nunbounded settings which are of independent interest beyond multiple packing.\n"}
+{"abstract": "  We search for scalar and tensor non-standard interactions using\n(anti)-electron neutrino disappearance in oscillation data. We found a slight\npreference for non-zero CP violation, coming from both tensor and scalar\ninteractions. The preference for CP violation is lead by Daya Bay low-energy\ndata with a significance that reaches $\\sim1.7\\sigma$ in the global analysis\n(and $\\sim2.1\\sigma$ when considering only medium baseline reactors data)\ncompared to the standard neutrino oscillation scenario.\n"}
+{"abstract": "  In the vicinity of the insulator-to-superfluid quantum phase transition in\nits core, a dislocation in a He-4 crystal supports particle-hole excitations\nwith arbitrary small gaps. These exotic analogs of Frenkel interstitial-vacancy\npairs should manifest themselves in various threshold and thermoactivation\neffects. In Worm Algorithm simulations, we reveal the presence of corresponding\nsmall gaps via anomalous thermoactivation behavior of particle number\nfluctuations, which we unambiguously associate with dislocations by\n\"visualization\" techniques. Experimentally, the related threshold and\nthermoactivation dependencies could be observed in the ultrasound absorption.\n"}
+{"abstract": "  The measurement of the silicon lattice parameter by a separate-crystal\ntriple-Laue x-ray interferometer is a key step for the kilogram realisation by\ncounting atoms. Since the measurement accuracy is approaching nine significant\ndigits, a reliable model of the interferometer operation is demanded to\nquantify or exclude systematic errors. This paper investigates both\nanalytically and experimentally the effect of defocus (a difference between the\nsplitter-to-mirror distance on the one hand and the analyser-to-mirror one on\nthe other) on the phase of the interference fringes and the measurement of the\nlattice parameter.\n"}
+{"abstract": "  The ability to combine known skills to create new ones may be crucial in the\nsolution of complex reinforcement learning problems that unfold over extended\nperiods. We argue that a robust way of combining skills is to define and\nmanipulate them in the space of pseudo-rewards (or \"cumulants\"). Based on this\npremise, we propose a framework for combining skills using the formalism of\noptions. We show that every deterministic option can be unambiguously\nrepresented as a cumulant defined in an extended domain. Building on this\ninsight and on previous results on transfer learning, we show how to\napproximate options whose cumulants are linear combinations of the cumulants of\nknown options. This means that, once we have learned options associated with a\nset of cumulants, we can instantaneously synthesise options induced by any\nlinear combination of them, without any learning involved. We describe how this\nframework provides a hierarchical interface to the environment whose abstract\nactions correspond to combinations of basic skills. We demonstrate the\npractical benefits of our approach in a resource management problem and a\nnavigation task involving a quadrupedal simulated robot.\n"}
+{"abstract": "  In this paper, we first determine Bohr's inequality for the class of harmonic\nmappings $f=h+\\overline{g}$ in the unit disk $\\ID$, where either both\n$h(z)=\\sum_{n=0}^{\\infty}a_{pn+m}z^{pn+m}$ and\n$g(z)=\\sum_{n=0}^{\\infty}b_{pn+m}z^{pn+m}$ are analytic and bounded in $\\ID$,\nor satisfies the condition $|g'(z)|\\leq d|h'(z)|$ in $\\ID\\backslash \\{0\\}$ for\nsome $d\\in [0,1]$ and $h$ is bounded. In particular, we obtain Bohr's\ninequality for the class of harmonic $p$-symmetric mappings. Also, we\ninvestigate the Bohr-type inequalities of harmonic mappings with a multiple\nzero at the origin and that most of results are proved to be sharp.\n"}
+{"abstract": "  High-entropy alloys (HEAs) are materials that consist of equimolar or\nnear-equimolar multiple principal components but tend to form single phases,\nwhich is a new research topic in the field of metallurgy, have attracted\nextensive attention in the past decade. The HEAs families contain the\nface-centered-cubic (fcc), body-centered-cubic (bcc), and\nhexagonal-close-packed (hcp)-structured HEAs. On one hand, mechanical\nproperties, e.g. hardness, strength, ductility, fatigue, and elastic moduli,\nare essential for practical applications of HEAs. Scientists have explored in\nthis direction since the advent of HEAs. On the other hand, the pursuit of high\nstrength and good plasticity is the critical research issue of materials.\nHence, strengthening of HEAs is a crucial issue. Recently, many articles are\nfocusing on the strengthening strategies of HEAs[1-14]. In this chapter, we\nreviewed the recent work on the room-temperature elastic properties and\nmechanical behavior of HEAs, including the mechanisms behind the plastic\ndeformation of HEAs at both low and high temperatures. Furthermore, the present\nwork examined the strengthening strategies of HEAs, e.g. strain hardening,\ngrain-boundary strengthening, solid-solution strengthening, and particle\nstrengthening. The fatigue, creep, and fracture properties were briefly\nintroduced. Lastly, the future scientific issues and challenges of HEAs were\ndiscussed.\n"}
+{"abstract": "  We study low-rank parameterizations of weight matrices with embedded spectral\nproperties in the Deep Learning context. The low-rank property leads to\nparameter efficiency and permits taking computational shortcuts when computing\nmappings. Spectral properties are often subject to constraints in optimization\nproblems, leading to better models and stability of optimization. We start by\nlooking at the compact SVD parameterization of weight matrices and identifying\nredundancy sources in the parameterization. We further apply the Tensor Train\n(TT) decomposition to the compact SVD components, and propose a non-redundant\ndifferentiable parameterization of fixed TT-rank tensor manifolds, termed the\nSpectral Tensor Train Parameterization (STTP). We demonstrate the effects of\nneural network compression in the image classification setting and both\ncompression and improved training stability in the generative adversarial\ntraining setting.\n"}
+{"abstract": "  This position paper discusses the relevance of register as a theoretical\nframework for chatbot language design. We present the concept of register and\ndiscuss how using register-specific language influence the user's perceptions\nof the interaction with chatbots. Additionally, we point several research\nopportunities that are important to pursue to establish register as a\nfoundation for advancing chatbot's communication skills.\n"}
+{"abstract": "  Several works have demonstrated the use of variational autoencoders (VAEs)\nfor generating levels in the style of existing games and blending levels across\ndifferent games. Further, quality-diversity (QD) algorithms have also become\npopular for generating varied game content by using evolution to explore a\nsearch space while focusing on both variety and quality. To reap the benefits\nof both these approaches, we present a level generation and game blending\napproach that combines the use of VAEs and QD algorithms. Specifically, we\ntrain VAEs on game levels and run the MAP-Elites QD algorithm using the learned\nlatent space of the VAE as the search space. The latent space captures the\nproperties of the games whose levels we want to generate and blend, while\nMAP-Elites searches this latent space to find a diverse set of levels\noptimizing a given objective such as playability. We test our method using\nmodels for 5 different platformer games as well as a blended domain spanning 3\nof these games. We refer to using MAP-Elites for blending as Blend-Elites. Our\nresults show that MAP-Elites in conjunction with VAEs enables the generation of\na diverse set of playable levels not just for each individual game but also for\nthe blended domain while illuminating game-specific regions of the blended\nlatent space.\n"}
+{"abstract": "  We study the problem of learning Ising models satisfying Dobrushin's\ncondition in the outlier-robust setting where a constant fraction of the\nsamples are adversarially corrupted. Our main result is to provide the first\ncomputationally efficient robust learning algorithm for this problem with\nnear-optimal error guarantees. Our algorithm can be seen as a special case of\nan algorithm for robustly learning a distribution from a general exponential\nfamily. To prove its correctness for Ising models, we establish new\nanti-concentration results for degree-$2$ polynomials of Ising models that may\nbe of independent interest.\n"}
+{"abstract": "  We construct non-K\u007f\\\"ahler Calabi-Yau manifolds of dimension $\\ge$ 4 with\narbitrarily large 2nd Betti numbers by smoothing normal crossing varieties. The\nexamples have K3 fibrations over smooth projective varieties and their\nalgebraic dimensions are of codimension 2.\n"}
+{"abstract": "  Biomedical question-answering (QA) has gained increased attention for its\ncapability to provide users with high-quality information from a vast\nscientific literature. Although an increasing number of biomedical QA datasets\nhas been recently made available, those resources are still rather limited and\nexpensive to produce. Transfer learning via pre-trained language models (LMs)\nhas been shown as a promising approach to leverage existing general-purpose\nknowledge. However, finetuning these large models can be costly and time\nconsuming, often yielding limited benefits when adapting to specific themes of\nspecialised domains, such as the COVID-19 literature. To bootstrap further\ntheir domain adaptation, we propose a simple yet unexplored approach, which we\ncall biomedical entity-aware masking (BEM). We encourage masked language models\nto learn entity-centric knowledge based on the pivotal entities characterizing\nthe domain at hand, and employ those entities to drive the LM fine-tuning. The\nresulting strategy is a downstream process applicable to a wide variety of\nmasked LMs, not requiring additional memory or components in the neural\narchitectures. Experimental results show performance on par with\nstate-of-the-art models on several biomedical QA datasets.\n"}
+{"abstract": "  We propose a novel scene flow estimation approach to capture and infer 3D\nmotions from point clouds. Estimating 3D motions for point clouds is\nchallenging, since a point cloud is unordered and its density is significantly\nnon-uniform. Such unstructured data poses difficulties in matching\ncorresponding points between point clouds, leading to inaccurate flow\nestimation. We propose a novel architecture named Sparse\nConvolution-Transformer Network (SCTN) that equips the sparse convolution with\nthe transformer. Specifically, by leveraging the sparse convolution, SCTN\ntransfers irregular point cloud into locally consistent flow features for\nestimating continuous and consistent motions within an object/local object\npart. We further propose to explicitly learn point relations using a point\ntransformer module, different from exiting methods. We show that the learned\nrelation-based contextual information is rich and helpful for matching\ncorresponding points, benefiting scene flow estimation. In addition, a novel\nloss function is proposed to adaptively encourage flow consistency according to\nfeature similarity. Extensive experiments demonstrate that our proposed\napproach achieves a new state of the art in scene flow estimation. Our approach\nachieves an error of 0.038 and 0.037 (EPE3D) on FlyingThings3D and KITTI Scene\nFlow respectively, which significantly outperforms previous methods by large\nmargins.\n"}
+{"abstract": "  Thanks to the rapid growth in wearable technologies, monitoring complex human\ncontext becomes feasible, paving the way to develop human-in-the-loop IoT\nsystems that naturally evolve to adapt to the human and environment state\nautonomously. Nevertheless, a central challenge in designing such personalized\nIoT applications arises from human variability. Such variability stems from the\nfact that different humans exhibit different behaviors when interacting with\nIoT applications (intra-human variability), the same human may change the\nbehavior over time when interacting with the same IoT application (inter-human\nvariability), and human behavior may be affected by the behaviors of other\npeople in the same environment (multi-human variability). To that end, we\npropose FaiR-IoT, a general reinforcement learning-based framework for adaptive\nand fairness-aware human-in-the-loop IoT applications. In FaiR-IoT, three\nlevels of reinforcement learning agents interact to continuously learn human\npreferences and maximize the system's performance and fairness while taking\ninto account the intra-, inter-, and multi-human variability. We validate the\nproposed framework on two applications, namely (i) Human-in-the-Loop Automotive\nAdvanced Driver Assistance Systems and (ii) Human-in-the-Loop Smart House.\nResults obtained on these two applications validate the generality of FaiR-IoT\nand its ability to provide a personalized experience while enhancing the\nsystem's performance by 40%-60% compared to non-personalized systems and\nenhancing the fairness of the multi-human systems by 1.5 orders of magnitude.\n"}
+{"abstract": "  We consider the properties of a static axially symmetric wormhole described\nby an exact solution of Einstein's field equations and investigate how we can\ndistinguish such a hypothetical object from a black hole. To this aim, we\nexplore the motion of test particles and photons in the wormhole's space-time\nand compare it with the particle dynamics in the well known space-times of\nSchwarzschild and Kerr black holes. We show that precise simultaneous\nmeasurement of test particle motion and photon motion may provide the means to\ndistinguish the wormhole geometry from that of a black hole.\n"}
+{"abstract": "  The performance of image recognition like human pose detection, trained with\nsimulated images would usually get worse due to the divergence between real and\nsimulated data. To make the distribution of a simulated image close to that of\nreal one, there are several works applying GAN-based image-to-image\ntransformation methods, e.g., SimGAN and CycleGAN. However, these methods would\nnot be sensitive enough to the various change in pose and shape of subjects,\nespecially when the training data are imbalanced, e.g., some particular poses\nand shapes are minor in the training data. To overcome this problem, we propose\nto introduce the label information of subjects, e.g., pose and type of objects\nin the training of CycleGAN, and lead it to obtain label-wise transforamtion\nmodels. We evaluate our proposed method called Label-CycleGAN, through\nexperiments on the digit image transformation from SVHN to MNIST and the\nsurveillance camera image transformation from simulated to real images.\n"}
+{"abstract": "  Extending computational harmonic analysis tools from the classical setting of\nregular lattices to the more general setting of graphs and networks is very\nimportant and much research has been done recently. The Generalized Haar-Walsh\nTransform (GHWT) developed by Irion and Saito (2014) is a multiscale transform\nfor signals on graphs, which is a generalization of the classical Haar and\nWalsh-Hadamard Transforms. We propose the extended Generalized Haar-Walsh\nTransform (eGHWT), which is a generalization of the adapted time-frequency\ntilings of Thiele and Villemoes (1996). The eGHWT examines not only the\nefficiency of graph-domain partitions but also that of \"sequency-domain\"\npartitions simultaneously. Consequently, the eGHWT and its associated\nbest-basis selection algorithm for graph signals significantly improve the\nperformance of the previous GHWT with the similar computational cost, $O(N \\log\nN)$, where $N$ is the number of nodes of an input graph. While the GHWT\nbest-basis algorithm seeks the most suitable orthonormal basis for a given task\namong more than $(1.5)^N$ possible orthonormal bases in $\\mathbb{R}^N$, the\neGHWT best-basis algorithm can find a better one by searching through more than\n$0.618\\cdot(1.84)^N$ possible orthonormal bases in $\\mathbb{R}^N$. This article\ndescribes the details of the eGHWT best-basis algorithm and demonstrates its\nsuperiority using several examples including genuine graph signals as well as\nconventional digital images viewed as graph signals. Furthermore, we also show\nhow the eGHWT can be extended to 2D signals and matrix-form data by viewing\nthem as a tensor product of graphs generated from their columns and rows and\ndemonstrate its effectiveness on applications such as image approximation.\n"}
+{"abstract": "  We study a generating function flowing from the one enumerating a set of\npartitions to the one enumerating the corresponding set of noncrossing\npartitions; numerical simulations indicate that its limit in the Adjacency\nrandom matrix model on bipartite Erd\\\"os-Renyi graphs gives a good\napproximation of the spectral distribution for large average degrees. This\nmodel and a Wishart-type random matrix model are described using congruence\nclasses on $k$-divisible partitions.\n  We compute, in the $d\\to \\infty$ limit with $\\frac{Z_a}{d}$ fixed, the\nspectral distribution of an Adjacency and of a Laplacian random block matrix\nmodel, on bipartite Erd\\\"os-Renyi graphs and on bipartite biregular graphs with\ndegrees $Z_1, Z_2$; the former is the approximation previously mentioned; the\nlatter is a mean field approximation of the Hessian of a random bipartite\nbiregular elastic network; it is characterized by an isostatic line and a\ntransition line between the one- and the two-band regions.\n"}
+{"abstract": "  Twenty years ago Breiman (2001) called to our attention a significant\ncultural division in modeling and data analysis between the stochastic data\nmodels and the algorithmic models. Out of his deep concern that the statistical\ncommunity was so deeply and \"almost exclusively\" committed to the former,\nBreiman warned that we were losing our abilities to solve many real-world\nproblems. Breiman was not the first, and certainly not the only statistician,\nto sound the alarm; we may refer to none other than John Tukey who wrote almost\n60 years ago \"data analysis is intrinsically an empirical science.\" However,\nthe bluntness and timeliness of Breiman's article made it uniquely influential.\nIt prepared us for the data science era and encouraged a new generation of\nstatisticians to embrace a more broadly defined discipline. Some might argue\nthat \"The cultural division between these two statistical learning frameworks\nhas been growing at a steady pace in recent years\", to quote Mukhopadhyay and\nWang (2020). In this commentary, we focus on some of the positive changes over\nthe past 20 years and offer an optimistic outlook for our profession.\n"}
+{"abstract": "  Purpose: The purpose of this paper is to explore possible factors impacting\nteam performance in healthcare, by focusing on information exchange within and\nacross hospital's boundaries. Design/methodology/approach: Through a web-survey\nand group interviews, the authors collected data on the communication networks\nof 31 members of four interdisciplinary healthcare teams involved in a system\nredesign initiative within a large US children's hospital. The authors mapped\ntheir internal and external social networks based on management advice,\ntechnical support and knowledge dissemination within and across departments,\nstudying interaction patterns that involved more than 700 actors. The authors\nthen compared team performance and social network metrics such as degree,\ncloseness and betweenness centrality, and computed cross ties and constraint\nlevels for each team. Findings: The results indicate that highly effective\nteams were more inwardly focused and less connected to outside members.\nMoreover, highly recognized teams communicated frequently but, overall, less\nintensely than the others. Originality/value: Mapping knowledge flows and\nbalancing internal focus and outward connectivity of interdisciplinary teams\nmay help healthcare decision makers in their attempt to achieve high value for\npatients, families and employees.\n"}
+{"abstract": "  We may attempt to encapsulate what we know about a physical system by a model\nstructure, $S$. This collection of related models is defined by parametric\nrelationships between system features; say observables (outputs), unobservable\nvariables (states), and applied inputs. Each parameter vector in some parameter\nspace is associated with a completely specified model in $S$. Before choosing a\nmodel in $S$ to predict system behaviour, we must estimate its parameters from\nsystem observations. Inconveniently, multiple models (associated with distinct\nparameter estimates) may approximate data equally well. Yet, if these equally\nvalid alternatives produce dissimilar predictions of unobserved quantities,\nthen we cannot confidently make predictions. Thus, our study may not yield any\nuseful result. We may anticipate the non-uniqueness of parameter estimates\nahead of data collection by testing $S$ for structural global identifiability\n(SGI). Here we will provide an overview of the importance of SGI, some\nessential theory and distinctions, and demonstrate these in testing some\nexamples.\n"}
+{"abstract": "  The transfer matrix is a powerful technique that can be applied to\nstatistical mechanics systems as for example in the calculus of the entropy of\nthe ice model. One interesting way to study such systems is to map it onto a\n3-color problem. In this paper, we explicitly build the transfer matrix for the\n3-color problem in order to calculate the number of possible configurations for\nfinite systems with free, periodic in one direction and toroidal boundary\nconditions (periodic in both directions)\n"}
+{"abstract": "  K. Costello's Pushforward Formula is not correct as stated. We verify it\nholds under an assumption of properness or of a weaker notion we call \"purity.\"\nWe then check many papers that used this formula to make sure it holds in their\ncontext.\n"}
+{"abstract": "  Obtaining coarse-grained models that accurately incorporate finite-size\neffects is an important open challenge in the study of complex, multi-scale\nsystems. We apply Langevin regression, a recently developed method for finding\nstochastic differential equation (SDE) descriptions of realistically-sampled\ntime series data, to understand finite-size effects in the Kuramoto model of\ncoupled oscillators. We find that across the entire bifurcation diagram, the\ndynamics of the Kuramoto order parameter are statistically consistent with an\nSDE whose drift term has the form predicted by the Ott-Antonsen ansatz in the\n$N\\to \\infty$ limit. We find that the diffusion term is nearly independent of\nthe bifurcation parameter, and has a magnitude decaying as $N^{-1/2}$,\nconsistent with the central limit theorem. This shows that the diverging\nfluctuations of the order parameter near the critical point are driven by a\nbifurcation in the underlying drift term, rather than increased stochastic\nforcing.\n"}
+{"abstract": "  Artificial Light pollution (AL) in Turkey and in Turkish observatories\nbetween 2012--2020 have been studied using the archival data of Visible\nInfrared Imaging Radiometer Suite (VIIRS) instrument. The astroGIS database has\nbeen used in processing the data (https://www.astrogis.org)\n\\cite{2020MNRAS.493.1204A}. The total energy released to space from Turkey\nincreased by 80\\% in 2019 with respect to 2012. In the span of the dataset, a\nsteady and continuous increase has been observed throughout all cities of the\ncountry. On the other hand, Dark Sky Park locations, East and Southeast\nAnatolian regions and mostly rural areas around the cities kept their AL level\nconstant. Four demographic parameters have been studied and they were found to\nbe correlated very well with AL: Population ($R \\simeq 0.90$); GDP ($R \\simeq\n0.87$); Total Power Consumption ($R \\simeq 0.66$) and Outdoor Lightening ($R\n\\simeq 0.67$). Contrary to countries acting to prevent AL increases, Turkey\nseems to be at the beginning of an era where AL will arithmetically increase\nthroughout the country and enormous amount of energy will continuously escape\nto space and therefore will be wasted. Therefore, a preventive legislation,\nespecially for invaluable astronomical site locations such as TURAG, TUG, DAG\nand \\c{C}AAM where each is counted as a truly dark site due to their SQM\nvalues, has to be enacted in Turkey, in very near future.\n"}
+{"abstract": "  This paper addresses the question of how to align AI systems with human\nvalues and situates it within a wider body of thought regarding technology and\nvalue. Far from existing in a vacuum, there has long been an interest in the\nability of technology to 'lock-in' different value systems. There has also been\nconsiderable thought about how to align technologies with specific social\nvalues, including through participatory design-processes. In this paper we look\nmore closely at the question of AI value alignment and suggest that the power\nand autonomy of AI systems gives rise to opportunities and challenges in the\ndomain of value that have not been encountered before. Drawing important\ncontinuities between the work of the fairness, accountability, transparency and\nethics community, and work being done by technical AI safety researchers, we\nsuggest that more attention needs to be paid to the question of 'social value\nalignment' - that is, how to align AI systems with the plurality of values\nendorsed by groups of people, especially on the global level.\n"}
+{"abstract": "  We investigate optical response of a linear waveguide quantum electrodynamics\n(QED) system, namely, an optical cavity coupled to a waveguide. Our analysis is\nbased on exact diagonalization of the overall Hamiltonian and is therefore\nrigorous even in the ultrastrong coupling regime of waveguide QED. Owing to the\ncounter-rotating terms in the cavity-waveguide coupling, the motion of cavity\namplitude in the phase space is elliptical in general. Such elliptical motion\nbecomes remarkable in the ultrastrong coupling regime due to the large Lamb\nshift comparable to the bare cavity frequency. We also reveal that such\nelliptical motion does not propagate into the output field and present an\nanalytic form of the reflection coefficient that is asymmetric with respect to\nthe resonance frequency.\n"}
+{"abstract": "  This work analyzes the variation over time of the effect of geographic\ndistance on knowledge flows. The flows are measured through the citations\nexchanged between scientific publications, including and excluding\nself-citations. To calculate geographic distances between citing and cited\npublication, each publication is associated with a \"prevailing\" territory,\naccording to the authors' affiliations. We then apply a gravity model to\naccount for the research size of the territories, in terms of cognitive\nproximity of citing-cited publications. The field of observation is the\n2010-2017 world publications citing the 2010-2012 Italian publications, as\nindexed in the Web of Science. The results show that in domestic knowledge\nflows, geographic proximity remains an influential factor through time,\nalthough with differences among disciplines and trends of attenuating effects.\nFinally, we replicate the analyses of knowledge flows but with the exclusion of\nself-citations: in this manner the effect of geographic proximity seems\nreduced, particularly at the national scale, but the differences (with vs\nwithout self-citations) lessen through time. As shown in previous works, the\neffect of distance on continental flows is modest (imperceptible for\nintercontinental flows), yet here too time has some influence, including\nconcerning exclusion of self-citations.\n"}
+{"abstract": "  In this paper, the scale-invariant version of the mean and variance\nmulti-level Monte Carlo estimate is proposed. The optimization of the\ncomputation cost over the grid levels is done with the help of a novel\nnormalized error based on t-statistic. In this manner, the algorithm\nconvergence is made invariant to the physical scale at which the estimate is\ncomputed. The novel algorithm is tested on the linear elastic example, the\nconstitutive law of which is described by material uncertainty including both\nheterogeneity and anisotropy.\n"}
+{"abstract": "  We revisit the Boltzmann equation governing the spectrum of energetic\nparticles originating from the decay of massive progenitors during the process\nof thermalization. We assume that these decays occur when the background\ntemperature $T$ is much less than the mass $M$ of the progenitor. We pay\nspecial attention to the IR cutoff provided by the thermal bath, and include\nthe suppression resulting from the interference of multiple scattering\nreactions (LPM effect). We solve the resulting integral equation numerically,\nand construct an accurate analytical fit of the solutions.\n"}
+{"abstract": "  We introduce WimPyDD, a modular, object-oriented and customizable Python code\nthat calculates accurate predictions for the expected rates in Weakly\nInteracting Massive Particle (WIMP) direct-detection experiments within the\nframework of Galilean-invariant non-relativistic effective theory in virtually\nany scenario, including inelastic scattering, an arbitrary WIMP spin and a\ngeneric WIMP velocity distribution in the Galactic halo. WimPyDD exploits the\nfactorization of the three main components that enter in the calculation of\ndirect detection signals: i) the Wilson coefficients that encode the dependence\nof the signals on the ultraviolet completion of the effective theory; ii) a\nresponse function that depends on the nuclear physics and on the main features\nof the experimental detector (acceptance, energy resolution, response to\nnuclear recoils); iii) a halo function that depends on the WIMP velocity\ndistribution and that encodes the astrophysical inputs. In WimPyDD these three\ncomponents are calculated and stored separately for later interpolation and\ncombined together only as the last step of the signal evaluation procedure.\nThis makes the phenomenological study of the direct detection scattering rate\nwith WimPyDD transparent and fast also when the parameter space of the WIMP\nmodel has a large dimensionality.\n"}
+{"abstract": "  For genus $g=2i\\geq4$ and the length $g-1$ partition $\\mu =\n(4,2,\\ldots,2,-2,\\ldots,-2)$ of 0, we compute the first coefficients of the\nclass of $\\overline{D}(\\mu)$ in\n$\\mathrm{Pic}_\\mathbb{Q}(\\overline{\\mathcal{R}}_g)$, where $D(\\mu)$ is the\ndivisor consisting of pairs $[C,\\eta]\\in \\mathcal{R}_g$ with $\\eta \\cong\n\\mathcal{O}_C(2x_1+x_2+\\cdots + x_{i-1}-x_i-\\cdots-x_{2i-1})$ for some points\n$x_1,\\ldots, x_{2i-1}$ on $C$. We further provide several enumerative results\nthat will be used for this computation.\n"}
+{"abstract": "  Model-based reinforcement learning is a compelling framework for\ndata-efficient learning of agents that interact with the world. This family of\nalgorithms has many subcomponents that need to be carefully selected and tuned.\nAs a result the entry-bar for researchers to approach the field and to deploy\nit in real-world tasks can be daunting. In this paper, we present MBRL-Lib -- a\nmachine learning library for model-based reinforcement learning in continuous\nstate-action spaces based on PyTorch. MBRL-Lib is designed as a platform for\nboth researchers, to easily develop, debug and compare new algorithms, and\nnon-expert user, to lower the entry-bar of deploying state-of-the-art\nalgorithms. MBRL-Lib is open-source at\nhttps://github.com/facebookresearch/mbrl-lib.\n"}
+{"abstract": "  In this paper we consider the following problem: given $N$ independent\nsamples from an unknown distribution $\\mathcal{P}$ over passwords $pwd_1,pwd_2,\n\\ldots$ can we generate high confidence upper/lower bounds on the guessing\ncurve $\\lambda_G \\doteq \\sum_{i=1}^G p_i$ where $p_i=\\Pr[pwd_i]$ and the\npasswords are ordered such that $p_i \\geq p_{i+1}$. Intuitively, $\\lambda_G$\nrepresents the probability that an attacker who knows the distribution\n$\\mathcal{P}$ can guess a random password $pwd \\leftarrow \\mathcal{P}$ within\n$G$ guesses. Understanding how $\\lambda_G$ increases with the number of guesses\n$G$ can help quantify the damage of a password cracking attack and inform\npassword policies. Despite an abundance of large (breached) password datasets\nupper/lower bounding $\\lambda_G$ remains a challenging problem. We introduce\nseveral statistical techniques to derive tighter upper/lower bounds on the\nguessing curve $\\lambda_G$ which hold with high confidence. We apply our\ntechniques to analyze $9$ large password datasets finding that our new lower\nbounds dramatically improve upon prior work. Our empirical analysis shows that\neven state-of-the-art password cracking models are significantly less guess\nefficient than an attacker who knows the distribution. When $G$ is not too\nlarge we find that our upper/lower bounds on $\\lambda_G$ are both very close to\nthe empirical distribution which justifies the use of the empirical\ndistribution in settings where $G$ is not too large i.e., $G \\ll N$ closely\napproximates $\\lambda_G$. The analysis also highlights regions of the curve\nwhere we can, with high confidence, conclude that the empirical distribution\nsignificantly overestimates $\\lambda_G$. Our new statistical techniques yield\nsubstantially tighter upper/lower bounds on $\\lambda_G$ though there are still\nregions of the curve where the best upper/lower bounds diverge significantly.\n"}
+{"abstract": "  We present a novel algorithm attaining excessively fast, the sought solution\nof linear systems of equations. The algorithm is short in its basic formulation\nand by definition vectorised, while the memory allocation demands trivial,\nbecause for each iteration only one dimension of the given input matrix\n$\\mathbf x$ is utilized. The execution time is very short compared with\nstate-of-the-art methods, exhibiting up to $\\mathcal{O}(10^3)$ speed-up and low\nmemory allocation demands, especially for non-square Systems of Linear\nEquations, with ratio of equations versus features high (tall systems), or low\n(wide systems) accordingly. The accuracy is high and straightforwardly\ncontrolled, and the numerical results highlight the efficiency of the proposed\nalgorithm, in terms of computation time, solution accuracy and memory\nallocations demands. The parallelisation of the algorithm is also presented in\nmulti-threaded and GPU accelerators' setting. The paper also comprises a\ntheoretical proof for the algorithmic convergence. Finally, we extend the\nimplementation of the proposed algorithmic rationale to feature selection\ntasks.\n"}
+{"abstract": "  Probing complex language models has recently revealed several insights into\nlinguistic and semantic patterns found in the learned representations. In this\narticle, we probe BERT specifically to understand and measure the relational\nknowledge it captures in its parametric memory. While probing for linguistic\nunderstanding is commonly applied to all layers of BERT as well as fine-tuned\nmodels, this has not been done for factual knowledge. We utilize existing\nknowledge base completion tasks (LAMA) to probe every layer of pre-trained as\nwell as fine-tuned BERT models(ranking, question answering, NER). Our findings\nshow that knowledge is not just contained in BERT's final layers. Intermediate\nlayers contribute a significant amount (17-60%) to the total knowledge found.\nProbing intermediate layers also reveals how different types of knowledge\nemerge at varying rates. When BERT is fine-tuned, relational knowledge is\nforgotten. The extent of forgetting is impacted by the fine-tuning objective\nand the training data. We found that ranking models forget the least and retain\nmore knowledge in their final layer compared to masked language modeling and\nquestion-answering. However, masked language modeling performed the best at\nacquiring new knowledge from the training data. When it comes to learning\nfacts, we found that capacity and fact density are key factors. We hope this\ninitial work will spur further research into understanding the parametric\nmemory of language models and the effect of training objectives on factual\nknowledge. The code to repeat the experiments is publicly available on GitHub.\n"}
+{"abstract": "  Context: The formation of high-mass star-forming regions from their parental\ngas cloud and the subsequent fragmentation processes lie at the heart of star\nformation research. Aims: We aim to study the dynamical and fragmentation\nproperties at very early evolutionary stages of high-mass star formation.\nMethods: Employing the NOrthern Extended Millimeter Array (NOEMA) and the IRAM\n30m telescope, we observed two young high-mass star-forming regions, ISOSS22478\nand ISOSS23053, in the 1.3mm continuum and spectral line emission at a high\nangular resolution (~0.8''). Results: We resolved 29 cores that are mostly\nlocated along filament-like structures. Depending on the temperature\nassumption, these cores follow a mass-size relation of approximately M~r^2.0,\ncorresponding to constant mean column densities. However, with different\ntemperature assumptions, a steeper mass-size relation up to M~r^3.0, which\nwould be more likely to correspond to constant mean volume densities, cannot be\nruled out. The correlation of the core masses with their nearest neighbor\nseparations is consistent with thermal Jeans fragmentation. We found hardly any\ncore separations at the spatial resolution limit, indicating that the data\nresolve the large-scale fragmentation well. Although the kinematics of the two\nregions appear very different at first sight - multiple velocity components\nalong filaments in ISOSS22478 versus a steep velocity gradient of more than\n50km/s/pc in ISOSS23053 - the findings can be explained within the framework of\na dynamical cloud collapse scenario. Conclusions: While our data are consistent\nwith a dynamical cloud collapse scenario and subsequent thermal Jeans\nfragmentation, the importance of additional environmental properties, such as\nthe magnetization of the gas or external shocks triggering converging gas\nflows, is nonetheless not as well constrained and would require future\ninvestigation.\n"}
+{"abstract": "  In spoken conversations, spontaneous behaviors like filled pause and\nprolongations always happen. Conversational partner tends to align features of\ntheir speech with their interlocutor which is known as entrainment. To produce\nhuman-like conversations, we propose a unified controllable spontaneous\nconversational speech synthesis framework to model the above two phenomena.\nSpecifically, we use explicit labels to represent two typical spontaneous\nbehaviors filled-pause and prolongation in the acoustic model and develop a\nneural network based predictor to predict the occurrences of the two behaviors\nfrom text. We subsequently develop an algorithm based on the predictor to\ncontrol the occurrence frequency of the behaviors, making the synthesized\nspeech vary from less disfluent to more disfluent. To model the speech\nentrainment at acoustic level, we utilize a context acoustic encoder to extract\na global style embedding from the previous speech conditioning on the\nsynthesizing of current speech. Furthermore, since the current and previous\nutterances belong to the different speakers in a conversation, we add a domain\nadversarial training module to eliminate the speaker-related information in the\nacoustic encoder while maintaining the style-related information. Experiments\nshow that our proposed approach can synthesize realistic conversations and\ncontrol the occurrences of the spontaneous behaviors naturally.\n"}
+{"abstract": "  As the electromagnetic spectrum becomes more congested and the environments\nin which we need to operate become more complicated, control over the\nenvironment itself becomes necessary to ensure the integrity of wireless\ncommunication channels. Wavefront shaping with programmable metasurfaces allows\nwave fields to be manipulated in both time and space, providing a method to\ninteract with the environment. When coupled with deep learning, intelligent\nwavefront shaping serves as a catalyst, enabling smart radio environments and\nunlocking applications beyond traditional wireless communication networks. In\nthis paper, we discuss the outlook of intelligent wavefront shaping for wave\npropagation in complex environments and highlight its transformative potential.\n"}
+{"abstract": "  This work explores constituency parsing on automatically recognized\ntranscripts of conversational speech. The neural parser is based on a sentence\nencoder that leverages word vectors contextualized with prosodic features,\njointly learning prosodic feature extraction with parsing. We assess the\nutility of the prosody in parsing on imperfect transcripts, i.e. transcripts\nwith automatic speech recognition (ASR) errors, by applying the parser in an\nN-best reranking framework. In experiments on Switchboard, we obtain 13-15% of\nthe oracle N-best gain relative to parsing the 1-best ASR output, with\ninsignificant impact on word recognition error rate. Prosody provides a\nsignificant part of the gain, and analyses suggest that it leads to more\ngrammatical utterances via recovering function words.\n"}
+{"abstract": "  Quantized transports of fermions are topological phenomena determined by the\nsum of the Chern numbers of all the energy bands below the Fermi energy. For\nbosonic excitations, e.g. phonons and magnons in a crystal, topological\ntransport is dominated by the Chern number of the lowest energy band because\nthe energy distribution of the bosons is limited below the thermal energy.\nHere, we demonstrate the existence of topological transport by bosonic magnons\nin a lattice of magnetic skyrmions - topological defects formed by a\nvortex-like texture of spins. We find a distinct thermal Hall signal when the\nferromagnetic spins in an insulating polar magnet GaV4Se8 form magnetic\nskyrmions. Its origin is identified as the topological thermal Hall effect of\nmagnons in which the trajectories of these magnons are bent by an emergent\nmagnetic field produced by the magnetic skyrmions. Our theoretical simulations\nconfirm that the thermal Hall effect is indeed governed by the Chern number of\nthe lowest energy band of the magnons in a triangular lattice of magnetic\nskyrmions. Our findings lay a foundation for studying topological phenomena of\nother bosonic excitations.\n"}
+{"abstract": "  In this paper, we study the $L(2, 1)$-Labeling of the Mycielski and the\niterated Mycielski of graphs in general. For a graph $G$ and all $t\\geq 1$, we\ngive sharp bounds for $\\lambda(M^t(G))$ the $L(2, 1)$-labeling number of the\n$t$-th iterated Mycielski in terms of the number of iterations $t$, the order\n$n$, the maximum degree $\\bigtriangleup$, and $\\lambda(G)$ the $L(2,\n1)$-labeling number of $G$. For $t=1$, we present necessary and sufficient\nconditions between the $4$-star matching number of the complement graph and\n$\\lambda(M(G))$ the $L(2, 1)$-labeling number of the Mycielski of a graph, with\nsome applications to special graphs. For all $t\\geq 2$, we prove that for any\ngraph $G$ of order $n$, we have $2^{t-1}(n+2)-2\\leq \\lambda(M^t(G))\\leq\n2^{t}(n+1)-2$. Thereafter, we characterize the graphs achieving the upper bound\n$2^t(n+1)-2$, then by using the Marriage Theorem and Tutte's characterization\nof graphs with a perfect $2$-matching, we characterize all graphs without\nisolated vertices achieving the lower bound $2^{t-1}(n+2)-2$. We determine the\n$L(2, 1)$-labeling number for the Mycielski and the iterated Mycielski of some\ngraph classes.\n"}
+{"abstract": "  We prove the Farrell-Jones Conjecture for mapping tori of automorphisms of\nvirtually torsion-free hyperbolic groups. The proof uses recently developed\ngeometric methods for establishing the Farrell-Jones Conjecture by\nBartels-L\\\"{u}ck-Reich, as well as the structure theory of mapping tori by\nDahmani-Krishna.\n"}
+{"abstract": "  We discuss the role of combinators in the development of the modern\nconception of computation over the course of the past century. We describe how\nideas about formalism and mathematical logic led to the introduction of\ncombinators in 1920 as an extension of the discovery of Nand as a basis for\nbasic logic. We then discuss how combinators informed lambda calculus and\nsymbolic computation, and their relationship to the development of practical\ncomputation. We finally describe recent views of combinators in terms of the\ncomputational universe of possible programs, and a recent approach to the\nfundamental theory of physics.\n"}
+{"abstract": "  We suggest an underlying mechanism that governs the growth of a network of\nconcepts, a complex network that reflects the connections between different\nscientific concepts based on their co-occurrences in publications. To this end,\nwe perform empirical analysis of a network of concepts based on the preprints\nin physics submitted to the arXiv.org. We calculate the network characteristics\nand show that they cannot follow as a result of several simple commonly used\nnetwork growth models. In turn, we suggest that a simultaneous account of two\nfactors, i.e., growth by blocks and preferential selection, gives an\nexplanation of empirically observed properties of the concepts network.\nMoreover, the observed structure emerges as a synergistic effect of these both\nfactors: each of them alone does not lead to a satisfactory picture.\n"}
+{"abstract": "  One useful standard method to compute eigenvalues of matrix polynomials ${\\bf\nP}(z) \\in \\mathbb{C}^{n\\times n}[z]$ of degree at most $\\ell$ in $z$ (denoted\nof grade $\\ell$, for short) is to first transform ${\\bf P}(z)$ to an equivalent\nlinear matrix polynomial ${\\bf L}(z)=z{\\bf B}-{\\bf A}$, called a companion\npencil, where ${\\bf A}$ and ${\\bf B}$ are usually of larger dimension than\n${\\bf P}(z)$ but ${\\bf L}(z)$ is now only of grade $1$ in $z$. The eigenvalues\nand eigenvectors of ${\\bf L}(z)$ can be computed numerically by, for instance,\nthe QZ algorithm. The eigenvectors of ${\\bf P}(z)$, including those for\ninfinite eigenvalues, can also be recovered from eigenvectors of ${\\bf L}(z)$\nif ${\\bf L}(z)$ is what is called a \"strong linearization\" of ${\\bf P}(z)$.\n  In this paper we show how to use algorithms for computing the Hermite Normal\nForm of a companion matrix for a scalar polynomial to direct the discovery of\nunimodular matrix polynomial cofactors ${\\bf E}(z)$ and ${\\bf F}(z)$ which, via\nthe equation ${\\bf E}(z){\\bf L}(z){\\bf F}(z) = \\mathrm{diag}( {\\bf P}(z), {\\bf\nI}_n, \\ldots, {\\bf I}_n)$, explicitly show the equivalence of ${\\bf P}(z)$ and\n${\\bf L}(z)$. By this method we give new explicit constructions for several\nlinearizations using different polynomial bases. We contrast these new\nunimodular pairs with those constructed by strict equivalence, some of which\nare also new to this paper. We discuss the limitations of this experimental,\ncomputational discovery method of finding unimodular cofactors.\n"}
+{"abstract": "  We argue that the exact single-particle Green's function ($G$) in quantum\nmany-body theory does not conserve particle number because the single-particle\nbasis is incomplete. We conclude that the exact $G$ is not a probability\namplitude and is not $\\Phi$-derivable in the Kadanoff-Baym sense. This sets up\na number of inconsistencies involving normalization, the definition of $G$,\ninterpretation of the spectral function, and $\\Phi$-derivability. Our result\nsuggests that, in the most general case and in the most literal sense, $G$ is\nnot suitable for computing particle addition/removal spectra.\n"}
+{"abstract": "  In this work we develop an implementation of the Wang--Landau algorithm\n[Phys. Rev. Lett. \\textbf{86}, 2050-2053 (2001)]. This algorithm allows us to\nfind the density of states (DOS), a function that, for a given system,\ndescribes the proportion of states that have a certain energy. The\nimplementation uses the Python language for the algorithm itself, and it can\ntake advantage of any library, such as the powerful LAMMPS library, for the\ncomputation of energy. Therefore, the resulting implementation is simple and\nflexible without sacrificing efficiency. This implementation also considers\nrecent developments in the parallelization of the code for faster computation.\nWe establish the soundness and effectiveness of our implementation by studying\nwell-known systems such as the Ising model, the Lennard--Jones and EAM solids.\nWe have found that our implementation can find the DOS with very good precision\nin a reasonable amount of time. Therefore, we are equipped with a very powerful\nand flexible implementation that can be easily used in order to study more\nrealistic models of matter.\n"}
+{"abstract": "  Machine-learning systems such as self-driving cars or virtual assistants are\ncomposed of a large number of machine-learning models that recognize image\ncontent, transcribe speech, analyze natural language, infer preferences, rank\noptions, etc. Models in these systems are often developed and trained\nindependently, which raises an obvious concern: Can improving a\nmachine-learning model make the overall system worse? We answer this question\naffirmatively by showing that improving a model can deteriorate the performance\nof downstream models, even after those downstream models are retrained. Such\nself-defeating improvements are the result of entanglement between the models\nin the system. We perform an error decomposition of systems with multiple\nmachine-learning models, which sheds light on the types of errors that can lead\nto self-defeating improvements. We also present the results of experiments\nwhich show that self-defeating improvements emerge in a realistic stereo-based\ndetection system for cars and pedestrians.\n"}
+{"abstract": "  In this paper, we propose a modification to the density approach to Stein's\nmethod for intervals for the unit circle $\\mathbb{S}^1$ which is motivated by\nthe differing geometry of $\\mathbb{S}^1$ to Euclidean space. We provide an\nupper bound to the Wasserstein metric for circular distributions and exhibit a\nvariety of different bounds between distributions; particularly, between the\nvon-Mises and wrapped normal distributions, and the wrapped normal and wrapped\nCauchy distributions.\n"}
+{"abstract": "  This contribution acts as an introduction to the requirements of the machine\nprotection system. As the first step, the basics interactions of fast charged\nparticles, neutrons and ${\\gamma}$-rays with matter are summarized. The\narchitecture of a machine protection system based on beam loss detection is\ndescribed. Personal safety issues and personal dosimetry are discussed,\nincluding the concept of radiation shielding.\n"}
+{"abstract": "  We investigate atmospheric responses of modeled hypothetical Earth-like\nplanets in the habitable zone of the M-dwarf AD Leonis to reduced oxygen (O2),\nremoved biomass (dead Earth), varying carbon dioxide (CO2) and surface relative\nhumidity (sRH). Results suggest large O2 differences between the reduced O2 and\ndead scenarios in the lower but not the upper atmosphere. Ozone (O3) and\nnitrous oxide (N2O) also show this behavior. Methane depends on hydroxyl (OH),\nits main sink. Abiotic production of N2O occurs in the upper layers.\nChloromethane (CH3Cl) decreases everywhere on decreasing biomass. Changing CO2\n(from x1 to x100 present atmospheric level (PAL)) and surface relative humidity\n(sRH) (from 0.1 percent to 100 percent) does not influence CH3Cl as much as\nlowering biomass. Therefore, CH3Cl can be considered a good biosignature.\nChanging sRH and CO2 has a greater influence on temperature than O2 and biomass\nalone. Changing the biomass produces ~6 kilometer (km) in effective height (H)\nin transmission compared with changing CO2 and sRH ( about 25km). In\ntransmission O2 is discernible at 0.76 microns for greater than 0.1 PAL. The O3\n9.6 micron band was weak for the low O2 runs and difficult to discern from dead\nEarth, however O3 at 0.3 microns could serve as an indicator to distinguish\nbetween reduced O2 and dead Earth. Spectral features of N2O and CH3Cl\ncorresponded to some km H. CH4 could be detectable tens of parsecs away with\nELT except for the 10-4 and 10-6 PAL O2 scenarios. O2 is barely detectable for\nthe 1 PAL O2 case and unfeasible at lower abundances.\n"}
+{"abstract": "  A prerequisite for the comprehensive understanding of many-body quantum\nsystems is a characterization in terms of their entanglement structure. The\nexperimental detection of entanglement in spatially extended many-body systems\ndescribable by quantum fields still presents a major challenge. We develop a\ngeneral scheme for certifying entanglement and demonstrate it by revealing\nentanglement between distinct subsystems of a spinor Bose-Einstein condensate.\nOur scheme builds on the spatially resolved simultaneous detection of the\nquantum field in two conjugate observables which allows the experimental\nconfirmation of quantum correlations between local as well as non-local\npartitions of the system. The detection of squeezing in Bogoliubov modes in a\nmulti-mode setting illustrates its potential to boost the capabilities of\nquantum simulations to study entanglement in spatially extended many-body\nsystems.\n"}
+{"abstract": "  A search for long-lived particles, which have come to rest within the ATLAS\ndetector, is presented. The subsequent decays of these long-lived particles can\nproduce high-momentum jets, resulting in large out-of-time energy deposits in\nthe ATLAS calorimeters. These decays are detected using data collected during\nperiods in the LHC bunch structure when collisions are absent. The analysed\ndataset is composed of events from proton-proton collisions produced by the\nLarge Hadron Collider at a centre-of-mass energy of $\\sqrt{s}=13$ TeV and\nrecorded by the ATLAS experiment during 2017 and 2018. The dataset used for\nthis search corresponds to a total live time of 579 hours. The results of this\nsearch are used to derive lower limits on the mass of gluino $R$-hadrons,\nassuming a branching fraction $B(\\tilde{g} \\rightarrow q \\bar{q}\n\\tilde{\\chi}_1^0)=100$%, with masses of up to 1.4 TeV excluded for gluino\nlifetimes of $10^{-5}$ to $10^3$ s.\n"}
+{"abstract": "  PySDM is an open-source Python package for simulating the dynamics of\nparticles undergoing condensational and collisional growth, interacting with a\nfluid flow and subject to chemical composition changes. It is intended to serve\nas a building block for process-level as well as computational-fluid-dynamics\nsimulation systems involving representation of a continuous phase (air) and a\ndispersed phase (aerosol), with PySDM being responsible for representation of\nthe dispersed phase. The PySDM package core is a Pythonic high-performance\nimplementation of the Super-Droplet Method (SDM) Monte-Carlo algorithm for\nrepresenting collisional growth, hence the name. PySDM has two alternative\nparallel number-crunching backends available: multi-threaded CPU backend based\non Numba and GPU-resident backend built on top of ThrustRTC. The usage examples\nare built on top of four simple atmospheric cloud modelling frameworks: box,\nadiabatic parcel, single-column and 2D prescribed flow kinematic models. In\naddition, the package ships with tutorial code depicting how PySDM can be used\nfrom Julia and Matlab.\n"}
+{"abstract": "  Holographic complexity in the \"complexity equals action\" approach is\nreconsidered relaxing the requirement of reparameterization invariance of the\naction with the prescription that the action vanish in any vacuum causal\ndiamond. This implies that vacuum anti-de Sitter space plays the role of the\nreference state. Moreover, the complexity of an anti-de Sitter-Schwarzschild\nblack hole becomes intrinsically finite and saturates Lloyd's bound after a\ncritical time. It is also argued that several artifacts, such as the unphysical\nnegative-time interval, can be removed by truly considering the bulk dual of\nthe thermofield double state.\n"}
+{"abstract": "  $n$-Dimensional fuzzy sets are a fuzzy set extension where the membership\nvalues are n-tuples of real numbers in the unit interval [0,1] increasingly\nordered, called n-dimensional intervals. The set of n-dimensional intervals is\ndenoted by $L_n([0,1])$. This paper aims to investigate semi-vector spaces over\na weak semifield and aggregation functions concerning an admissible order on\nthe set of $n$-dimensional intervals and the construction of aggregation\nfunctions on $L_n([0,1])$ based on the operations of the semi-vector spaces. In\nparticular, extensions of the family of OWA and weighted average aggregation\nfunctions are investigated. Finally, we develop a multi-criteria group\ndecision-making method based on n-dimensional aggregation functions with\nrespect to an admissible order and give an illustrative example.\n"}
+{"abstract": "  The analysis of quantitative hemodynamics and luminal pressure may add\nvaluable information to aid treatment strategies and prognosis for aortic\ndissections. This work directly compared in vitro 4D-flow magnetic resonance\nimaging (MRI), catheter-based pressure measurements, and computational fluid\ndynamics that integrated fluid-structure interaction (CFD FSI). Experimental\ndata was acquired with a compliant 3D-printed model of a type-B aortic\ndissection (TBAD) that was embedded into a physiologically tuned flow circuit.\nIn vitro flow and pressure information were used to tune the CFD FSI Windkessel\nboundary conditions. Results showed very good overall agreement of complex flow\npatterns, true to false lumen flow splits, and pressure distribution. This work\ndemonstrates feasibility of a tunable experimental setup that integrates a\npatient-specific compliant model and provides a test bed for exploring critical\nimaging and modeling parameters that ultimately may improve the prognosis for\npatients with aortic dissections.\n"}
+{"abstract": "  The Laplacian matching polynomial of a graph $G$, denoted by\n$\\mathscr{L\\hspace{-0.7mm}M}(G,x)$, is a new graph polynomial whose all roots\nare nonnegative real numbers. In this paper, we investigate the location of\nzeros of the Laplacian matching polynomials. Let $G$ be a connected graph. We\nshow that $0$ is a root of $\\mathscr{L\\hspace{-0.7mm}M}(G, x)$ if and only if\n$G$ is a tree. We prove that the number of distinct positive zeros of\n$\\mathscr{L\\hspace{-0.7mm}M}(G,x)$ is at least equal to the length of the\nlongest path in $G$. It is also established that the zeros of\n$\\mathscr{L\\hspace{-0.7mm}M}(G,x)$ and $\\mathscr{L\\hspace{-0.7mm}M}(G-e,x)$\ninterlace for each edge $e$ of $G$. Using the path-tree of $G$, we present a\nlinear algebraic approach to investigate the largest zero of\n$\\mathscr{L\\hspace{-0.7mm}M}(G,x)$ and particularly to give tight upper and\nlower bounds on it.\n"}
+{"abstract": "  Two-sample tests have been one of the most classical topics in statistics\nwith wide application even in cutting edge applications. There are at least two\nmodes of inference used to justify the two-sample tests. One is usual\nsuperpopulation inference assuming the units are independent and identically\ndistributed (i.i.d.) samples from some superpopulation; the other is finite\npopulation inference that relies on the random assignments of units into\ndifferent groups. When randomization is actually implemented, the latter has\nthe advantage of avoiding distributional assumptions on the outcomes. In this\npaper, we will focus on finite population inference for censored outcomes,\nwhich has been less explored in the literature. Moreover, we allow the\ncensoring time to depend on treatment assignment, under which exact permutation\ninference is unachievable. We find that, surprisingly, the usual logrank test\ncan also be justified by randomization. Specifically, under a Bernoulli\nrandomized experiment with non-informative i.i.d. censoring within each\ntreatment arm, the logrank test is asymptotically valid for testing Fisher's\nnull hypothesis of no treatment effect on any unit. Moreover, the asymptotic\nvalidity of the logrank test does not require any distributional assumption on\nthe potential event times. We further extend the theory to the stratified\nlogrank test, which is useful for randomized blocked designs and when censoring\nmechanisms vary across strata. In sum, the developed theory for the logrank\ntest from finite population inference supplements its classical theory from\nusual superpopulation inference, and helps provide a broader justification for\nthe logrank test.\n"}
+{"abstract": "  Deep models trained on large-scale RGB image datasets have shown tremendous\nsuccess. It is important to apply such deep models to real-world problems.\nHowever, these models suffer from a performance bottleneck under illumination\nchanges. Thermal IR cameras are more robust against such changes, and thus can\nbe very useful for the real-world problems. In order to investigate efficacy of\ncombining feature-rich visible spectrum and thermal image modalities, we\npropose an unsupervised domain adaptation method which does not require\nRGB-to-thermal image pairs. We employ large-scale RGB dataset MS-COCO as source\ndomain and thermal dataset FLIR ADAS as target domain to demonstrate results of\nour method. Although adversarial domain adaptation methods aim to align the\ndistributions of source and target domains, simply aligning the distributions\ncannot guarantee perfect generalization to the target domain. To this end, we\npropose a self-training guided adversarial domain adaptation method to promote\ngeneralization capabilities of adversarial domain adaptation methods. To\nperform self-training, pseudo labels are assigned to the samples on the target\nthermal domain to learn more generalized representations for the target domain.\nExtensive experimental analyses show that our proposed method achieves better\nresults than the state-of-the-art adversarial domain adaptation methods. The\ncode and models are publicly available.\n"}
+{"abstract": "  High-dimensional sparse generalized linear models (GLMs) have emerged in the\nsetting that the number of samples and the dimension of variables are large,\nand even the dimension of variables grows faster than the number of samples.\nFalse discovery rate (FDR) control aims to identify some small number of\nstatistically significantly nonzero results after getting the sparse penalized\nestimation of GLMs. Using the CLIME method for precision matrix estimations, we\nconstruct the debiased-Lasso estimator and prove the asymptotical normality by\nminimax-rate oracle inequalities for sparse GLMs. In practice, it is often\nneeded to accurately judge each regression coefficient's positivity and\nnegativity, which determines whether the predictor variable is positively or\nnegatively related to the response variable conditionally on the rest\nvariables. Using the debiased estimator, we establish multiple testing\nprocedures. Under mild conditions, we show that the proposed debiased\nstatistics can asymptotically control the directional (sign) FDR and\ndirectional false discovery variables at a pre-specified significance level.\nMoreover, it can be shown that our multiple testing procedure can approximately\nachieve a statistical power of 1. We also extend our methods to the two-sample\nproblems and propose the two-sample test statistics. Under suitable conditions,\nwe can asymptotically achieve directional FDR control and directional FDV\ncontrol at the specified significance level for two-sample problems. Some\nnumerical simulations have successfully verified the FDR control effects of our\nproposed testing procedures, which sometimes outperforms the classical knockoff\nmethod.\n"}
+{"abstract": "  We propose a simple, intuitive yet powerful method for human-object\ninteraction (HOI) detection. HOIs are so diverse in spatial distribution in an\nimage that existing CNN-based methods face the following three major drawbacks;\nthey cannot leverage image-wide features due to CNN's locality, they rely on a\nmanually defined location-of-interest for the feature aggregation, which\nsometimes does not cover contextually important regions, and they cannot help\nbut mix up the features for multiple HOI instances if they are located closely.\nTo overcome these drawbacks, we propose a transformer-based feature extractor,\nin which an attention mechanism and query-based detection play key roles. The\nattention mechanism is effective in aggregating contextually important\ninformation image-wide, while the queries, which we design in such a way that\neach query captures at most one human-object pair, can avoid mixing up the\nfeatures from multiple instances. This transformer-based feature extractor\nproduces so effective embeddings that the subsequent detection heads may be\nfairly simple and intuitive. The extensive analysis reveals that the proposed\nmethod successfully extracts contextually important features, and thus\noutperforms existing methods by large margins (5.37 mAP on HICO-DET, and 5.7\nmAP on V-COCO). The source codes are available at\n$\\href{https://github.com/hitachi-rd-cv/qpic}{\\text{this https URL}}$.\n"}
+{"abstract": "  Semantic segmentation of nighttime images plays an equally important role as\nthat of daytime images in autonomous driving, but the former is much more\nchallenging due to poor illuminations and arduous human annotations. In this\npaper, we propose a novel domain adaptation network (DANNet) for nighttime\nsemantic segmentation without using labeled nighttime image data. It employs an\nadversarial training with a labeled daytime dataset and an unlabeled dataset\nthat contains coarsely aligned day-night image pairs. Specifically, for the\nunlabeled day-night image pairs, we use the pixel-level predictions of static\nobject categories on a daytime image as a pseudo supervision to segment its\ncounterpart nighttime image. We further design a re-weighting strategy to\nhandle the inaccuracy caused by misalignment between day-night image pairs and\nwrong predictions of daytime images, as well as boost the prediction accuracy\nof small objects. The proposed DANNet is the first one stage adaptation\nframework for nighttime semantic segmentation, which does not train additional\nday-night image transfer models as a separate pre-processing stage. Extensive\nexperiments on Dark Zurich and Nighttime Driving datasets show that our method\nachieves state-of-the-art performance for nighttime semantic segmentation.\n"}
+{"abstract": "  This is the second paper in the series to study the initial perturbation of\nmean curvature flow. We study the initial perturbation of mean curvature flow,\nwhose first singularity is modeled by an asymptotic conical shrinker. The\nnoncompactness of the limiting shrinker creates essential difficulties.\n  We introduce the Feynman-Kac formula to get precise asymptotic behaviour of\nthe linearized rescaled mean curvature equation along an orbit. We also develop\nthe invariant cone method to the non-compact setting for the local dynamics\nnear the shrinker. As a consequence, we prove that after a generic initial\nperturbation, the perturbed rescaled mean curvature flow avoids the conical\nsingularity.\n"}
+{"abstract": "  We analyse a manuscript star catalogue by Wilhem IV, Landgraf von\nHessen-Kassel, from 1586. From measurements of altitudes and of angles between\nstars, given in the catalogue, we find that the measurement accuracy averages\n26 arcsec for eight fundamental stars, compared to 49 arcsec of the\nmeasurements by Brahe. The computation in converting altitudes to declinations\nand angles between stars to celestial position is very accurate, with errors\nnegligible with respect to the measurement errors. Due to an offset in the\nposition of the vernal equinox the positional error of the catalogue is\nslightly worse than that of Brahe's catalogue, but when correction is made for\nthe offset -- which was known to 17th century astronomers -- the catalogue is\nmore accurate than that of Brahe by a factor two. We provide machine-readable\nTables of the catalogue.\n"}
+{"abstract": "  High-fidelity two-qubit entangling gates are essential building blocks for\nfault-tolerant quantum computers. Over the past decade, tremendous efforts have\nbeen made to develop scalable high-fidelity two-qubit gates with\nsuperconducting quantum circuits. Recently, an easy-to-scale controlled-phase\ngate scheme that utilizes the tunable-coupling architecture with\nfixed-frequency qubits [Phys. Rev. Lett. 125, 240502; Phys. Rev. Lett. 125,\n240503] has been demonstrated with high fidelity and attracted broad interest.\nHowever, in-depth understanding of the underlying mechanism is still missing,\npreventing us from fully exploiting its potential. Here we present a\ncomprehensive theoretical study, explaining the origin of the high-contrast ZZ\ninteraction. Based on improved understanding, we develop a general yet\nconvenient method for shaping an adiabatic pulse in a multilevel system, and\nidentify how to optimize the gate performance from design. Given\nstate-of-the-art coherence properties, we expect the scheme to potentially\nachieve a two-qubit gate error rate near $10^{-5}$, which would drastically\nspeed up the progress towards fault-tolerant quantum computation.\n"}
+{"abstract": "  We present optical photometry and spectroscopy of SN\\,2019stc\n(=ZTF19acbonaa), an unusual Type Ic supernova (SN Ic) at a redshift of\n$z=0.117$. SN\\,2019stc exhibits a broad double-peaked light curve, with the\nfirst peak having an absolute magnitude of $M_r=-20.0$ mag, and the second\npeak, about 80 rest-frame days later, $M_r=-19.2$ mag. The total radiated\nenergy is large, $E_{\\rm rad}\\approx 2.5\\times 10^{50}$ erg. Despite its large\nluminosity, approaching those of Type I superluminous supernovae (SLSNe),\nSN\\,2019stc exhibits a typical SN Ic spectrum, bridging the gap between SLSNe\nand SNe Ic. The spectra indicate the presence of Fe-peak elements, but modeling\nof the first light curve peak with radioactive heating alone leads to an\nunusually high nickel mass fraction of $f_{\\rm Ni}\\approx 31\\%$ ($M_{\\rm\nNi}\\approx 3.2$ M$_\\odot$). Instead, if we model the first peak with a combined\nmagnetar spin-down and radioactive heating model we find a better match with\n$M_{\\rm ej}\\approx 4$ M$_\\odot$, a magnetar spin period of $P_{\\rm spin}\\approx\n7.2$ ms and magnetic field of $B\\approx 10^{14}$ G, and $f_{\\rm Ni}\\lesssim\n0.2$ (consistent with SNe Ic). The prominent second peak cannot be naturally\naccommodated with radioactive heating or magnetar spin-down, but instead can be\nexplained as circumstellar interaction with $\\approx 0.7$ $M_\\odot$ of\nhydrogen-free material located $\\approx 400$ AU from the progenitor. Including\nthe remnant mass leads to a CO core mass prior to explosion of $\\approx 6.5$\nM$_\\odot$. The host galaxy has a metallicity of $\\approx 0.26$ Z$_\\odot$, low\nfor SNe Ic but consistent with SLSNe. Overall, we find that SN\\,2019stc is a\ntransition object between normal SNe Ic and SLSNe.\n"}
+{"abstract": "  Text-to-SQL aims to map natural language questions to SQL queries. The\nsketch-based method combined with execution-guided (EG) decoding strategy has\nshown a strong performance on the WikiSQL benchmark. However, execution-guided\ndecoding relies on database execution, which significantly slows down the\ninference process and is hence unsatisfactory for many real-world applications.\nIn this paper, we present the Schema Dependency guided multi-task Text-to-SQL\nmodel (SDSQL) to guide the network to effectively capture the interactions\nbetween questions and schemas. The proposed model outperforms all existing\nmethods in both the settings with or without EG. We show the schema dependency\nlearning partially cover the benefit from EG and alleviates the need for it.\nSDSQL without EG significantly reduces time consumption during inference,\nsacrificing only a small amount of performance and provides more flexibility\nfor downstream applications.\n"}
+{"abstract": "  Designing a static state-feedback controller subject to structural constraint\nachieving asymptotic stability is a relevant problem with many applications,\nincluding network decentralized control, coordinated control, and sparse\nfeedback design. Leveraging on the Projection Lemma, this work presents a new\nsolution to a class of state-feedback control problems, in which the controller\nis constrained to belong to a given linear space. We show through extensive\ndiscussion and numerical examples that our approach leads to several advantages\nwith respect to existing methods: first, it is computationally efficient;\nsecond, it is less conservative than previous methods, since it relaxes the\nrequirement of restricting the Lyapunov matrix to a block-diagonal form.\n"}
+{"abstract": "  We report on the first complete computation of the mixed QCD$-$electroweak\n(EW) corrections to the neutral-current Drell$-$Yan process. Superseding\npreviously applied approximations, our calculation provides the first result at\nthis order that is valid in the entire range of dilepton invariant masses. The\ntwo-loop virtual contribution is computed by using semi-analytical techniques,\novercoming the technical problems in the evaluation of the relevant master\nintegrals. The cancellation of soft and collinear singularities is achieved by\na formulation of the $q_T$ subtraction formalism valid in presence of charged\nmassive particles in the final state. We present numerical results for the\nfiducial cross section and selected kinematical distributions. At large values\nof the lepton $p_T$ the mixed QCD$-$EW corrections are negative and increase in\nsize, to about $-15\\%$ with respect to the next-to-leading-order QCD result at\n$p_T=500\\,$GeV. Up to dilepton invariant masses of 1 TeV the computed\ncorrections amount to about $-1.5\\%$ with respect to the next-to-leading-order\nQCD result.\n"}
+{"abstract": "  We implement the PaperRank and AuthorRank indices introduced in [Amodio &\nBrugnano, 2014] in the Scopus database, in order to highlight quantitative and\nqualitative information that the bare number of citations and/or the h-index of\nan author are unable to provide. In addition to this, the new indices can be\ncheaply updated in Scopus, since this has a cost comparable to that of updating\nthe number of citations. Some examples are reported to provide insight in their\npotentialities, as well as possible extensions.\n"}
+{"abstract": "  Network intrusion is a well-studied area of cyber security. Current machine\nlearning-based network intrusion detection systems (NIDSs) monitor network data\nand the patterns within those data but at the cost of presenting significant\nissues in terms of privacy violations which may threaten end-user privacy.\nTherefore, to mitigate risk and preserve a balance between security and\nprivacy, it is imperative to protect user privacy with respect to intrusion\ndata. Moreover, cost is a driver of a machine learning-based NIDS because such\nsystems are increasingly being deployed on resource-limited edge devices. To\nsolve these issues, in this paper we propose a NIDS called PCC-LSM-NIDS that is\ncomposed of a Pearson Correlation Coefficient (PCC) based feature selection\nalgorithm and a Least Square Method (LSM) based privacy-preserving algorithm to\nachieve low-cost intrusion detection while providing privacy preservation for\nsensitive data. The proposed PCC-LSM-NIDS is tested on the benchmark intrusion\ndatabase UNSW-NB15, using five popular classifiers. The experimental results\nshow that the proposed PCC-LSM-NIDS offers advantages in terms of less\ncomputational time, while offering an appropriate degree of privacy protection.\n"}
+{"abstract": "  In this article, we study the componentwise linear ideals in the Veronese\nsubrings of $R=K[x_1,\\ldots,x_n]$. If char$(K)=0$, then we give a\ncharacterization for graded ideals in the $c^{th}$ Veronese ring $R^{(c)}$ to\nbe componentwise linear. This characterization is an analogue of that over $R$\ndue to Aramova, Herzog and Hibi in \\cite{ah00} and Conca, Herzog and Hibi in\n\\cite{chh04}.\n"}
+{"abstract": "  Emergent electromagnetism in magnets originates from the strong coupling\nbetween conduction electron spins and those of noncollinear ordered moments and\nthe consequent Berry phase. This offers possibilities to develop new functions\nof quantum transport and optical responses. The emergent inductance in spiral\nmagnets is an example recently proposed and experimentally demonstrated, used\nthe emergent electric field induced by alternating currents. However, the\nmicroscopic theory of this phenomenon is missing, which should reveal the\nfactors to determine the magnitude, sign, frequency dependence, and\nnonlinearity of the inductance L. Here we theoretically study electromagnetic\nresponses of spiral magnets taking into account their collective modes. In\nsharp contrast to the collinear spin-density wave, the system remains metallic\neven in one-dimension, and the canonical conjugate relation of uniform\nmagnetization and phason coordinate plays an essential role, determining the\nproperties of L. This result opens a way to design the emergent inductance of\ndesired properties.\n"}
+{"abstract": "  We study the entanglement properties of non-Hermitian free fermionic models\nwith translation symmetry using the correlation matrix technique. Our results\nshow that the entanglement entropy has a logarithmic correction to the area law\nin both one-dimensional and two-dimensional systems. For any one-dimensional\none-band system, we prove that each Fermi point of the system contributes\nexactly 1/2 to the coefficient c of the logarithmic correction. Moreover, this\nrelation between c and Fermi point is verified for more general one-dimensional\nand two-dimensional cases by numerical calculations and finite-size scaling\nanalysis. In addition, we also study the single-particle and density-density\ncorrelation functions.\n"}
+{"abstract": "  Here are two problems. First, understand the dynamics of a tiling billiard in\na cyclic quadrilateral periodic tiling. Second, describe the topology of\nconnected components of plane sections of a centrally symmetric subsurface $S\n\\subset \\mathbb{T}^3$ of genus $3$. In this note we show that these two\nproblems are related via a helicoidal construction proposed recently by Ivan\nDynnikov. The second problem is a particular case of a classical question\nformulated by Sergei Novikov. The exploration of the relationship between a\nlarge class of tiling billiards (periodic locally foldable tiling billiards)\nand Novikov's problem in higher genus seems promising, as we show in the end of\nthis note.\n"}
+{"abstract": "  Polls are a common way of collecting data, including product reviews and\nfeedback forms. However, few data collectors give upfront privacy guarantees.\nAdditionally, when privacy guarantees are given upfront, they are often vague\nclaims about 'anonymity'. Instead, we propose giving quantifiable privacy\nguarantees through the statistical notion of differential privacy.\nNevertheless, privacy does not come for free. At the heart of differential\nprivacy lies an inherent trade-off between accuracy and privacy that needs to\nbe balanced. Thus, it is vital to properly adjust the accuracy-privacy\ntrade-off before setting out to collect data. Altogether, getting started with\ndifferentially private data collection can be challenging. Ideally, a data\nanalyst should not have to be concerned about all the details of differential\nprivacy, but rather get differential privacy by design. Still, to the best of\nour knowledge, no tools for gathering poll data under differential privacy\nexists.\n  Motivated by the lack of tools to gather poll data under differential\nprivacy, we set out to engineer our own tool. Specifically, to make local\ndifferential privacy accessible for all, in this systems paper we present\nRandori, a set of novel open source tools for differentially private poll data\ncollection. Randori is intended to help data analysts keep their focus on what\ndata their poll is collecting, as opposed to how they should collect it. Our\ntools also allow the data analysts to analytically predict the accuracy of\ntheir poll. Furthermore, we show that differential privacy alone is not enough\nto achieve end-to-end privacy in a server-client setting. Consequently, we also\ninvestigate and mitigate implicit data leaks in Randori.\n"}
+{"abstract": "  Taking Shapiro's cyclic sums $\\sum_{i=1}^n x_i/(x_{i+1}+x_{i+2})$ (assuming\nindex addition mod $n$) as a starting point, we introduce a broader class of\ncyclic sums, called generalized Shapiro-Diananda sums, where the denominators\nare $p$-th order power means of the sets $\\{x_{i+j_1},\\dots,x_{i+j_k}\\}$ with\nfixed distinct integers $j_1,\\dots,j_k$ and $1\\leq i\\leq n$.\n  Generalizing further, we replace the set of arguments of the power mean in\nthe $i$-th denominator by an arbitrary nonempty subset of $\\{1,\\dots,n\\}$\ninterpreted as the set of out-neighbors of the node number $i$ in a directed\ngraph with $n$ nodes. We call such sums graphic power sums since their\nstructure is controlled by directed graphs.\n  The inquiry, as in the well-researched case of Shapiro's sums, concerns the\ngreatest lower bound of the given ``sum'' as a function of positive variables\n$x_1,\\dots,x_n$. We show that the cases of $p=+\\infty$ (max-sums) and\n$p=-\\infty$ (min-sums) are tractable.\n  For the max-sum associated with a given graph the g.l.b. is always an\ninteger; for a strongly connected graph it equals to graph's girth.\n  For the similar min-sum, we could not relate the g.l.b. to a known\ncombinatorial invariant; we only give some estimates and describe a method for\nfinding the g.l.b., which has factorial complexity in $n$.\n  A satisfactory analytical treatment is available for the secondary\nminimization -- when the g.l.b.'s of min-sums for individual graphs are\nmininized over the class of strongly connected graphs with $n$ nodes. The\nresult (depending only on $n$) is found to be asymptotic to $e\\ln n$.\n"}
+{"abstract": "  In this paper, we present a video-based learning framework for animating\npersonalized 3D talking faces from audio. We introduce two training-time data\nnormalizations that significantly improve data sample efficiency. First, we\nisolate and represent faces in a normalized space that decouples 3D geometry,\nhead pose, and texture. This decomposes the prediction problem into regressions\nover the 3D face shape and the corresponding 2D texture atlas. Second, we\nleverage facial symmetry and approximate albedo constancy of skin to isolate\nand remove spatio-temporal lighting variations. Together, these normalizations\nallow simple networks to generate high fidelity lip-sync videos under novel\nambient illumination while training with just a single speaker-specific video.\nFurther, to stabilize temporal dynamics, we introduce an auto-regressive\napproach that conditions the model on its previous visual state. Human ratings\nand objective metrics demonstrate that our method outperforms contemporary\nstate-of-the-art audio-driven video reenactment benchmarks in terms of realism,\nlip-sync and visual quality scores. We illustrate several applications enabled\nby our framework.\n"}
+{"abstract": "  This paper concerned with the effect of generalized uncertainty principle\n(GUP) on the stochastic gravitational wave (SGW) background signal that\nproduced during first order cosmological QCD phase transition in early\nuniverse. A modified formula of entropy is used to calculate the temporal\nevolution of temperature of the universe as a function of the Hubble parameter.\nThe pressure that results from the recent lattice calculations, which provides\nparameterizations of the pressure due to $u,~d,~s$ quarks and gluons, with\ntrace anomaly is used to describe the equation of state around QCD epoch. A\nredshift in the peak frequency of SGW at current epoch is calculated. The\nresults indicate an increase in the frequency peak due to GUP effect, which\nimproves the ability to detect it. Taking into account bubble wall collisions\n(BWC) and turbulent magnetohydrodynamics (MHD) as a source of SGW, a fractional\nenergy density is investigated. It is found that the GUP effect weakens the SGW\nsignal generated during QCD phase transition in comparison to its counterpart\nin the absence of GUP. These results support understanding the cosmological QCD\nphase transition and test the effectiveness of the GUP theory.\n"}
+{"abstract": "  Molecular machine learning bears promise for efficient molecule property\nprediction and drug discovery. However, due to the limited labeled data and the\ngiant chemical space, machine learning models trained via supervised learning\nperform poorly in generalization. This greatly limits the applications of\nmachine learning methods for molecular design and discovery. In this work, we\npresent MolCLR: Molecular Contrastive Learning of Representations via Graph\nNeural Networks (GNNs), a self-supervised learning framework for large\nunlabeled molecule datasets. Specifically, we first build a molecular graph,\nwhere each node represents an atom and each edge represents a chemical bond. A\nGNN is then used to encode the molecule graph. We propose three novel molecule\ngraph augmentations: atom masking, bond deletion, and subgraph removal. A\ncontrastive estimator is utilized to maximize the agreement of different graph\naugmentations from the same molecule. Experiments show that molecule\nrepresentations learned by MolCLR can be transferred to multiple downstream\nmolecular property prediction tasks. Our method thus achieves state-of-the-art\nperformance on many challenging datasets. We also prove the efficiency of our\nproposed molecule graph augmentations on supervised molecular classification\ntasks.\n"}
+{"abstract": "  Developing deep learning models to analyze histology images has been\ncomputationally challenging, as the massive size of the images causes excessive\nstrain on all parts of the computing pipeline. This paper proposes a novel deep\nlearning-based methodology for improving the computational efficiency of\nhistology image classification. The proposed approach is robust when used with\nimages that have reduced input resolution and can be trained effectively with\nlimited labeled data. Pre-trained on the original high-resolution (HR) images,\nour method uses knowledge distillation (KD) to transfer learned knowledge from\na teacher model to a student model trained on the same images at a much lower\nresolution. To address the lack of large-scale labeled histology image\ndatasets, we perform KD in a self-supervised manner. We evaluate our approach\non two histology image datasets associated with celiac disease (CD) and lung\nadenocarcinoma (LUAD). Our results show that a combination of KD and\nself-supervision allows the student model to approach, and in some cases,\nsurpass the classification accuracy of the teacher, while being much more\nefficient. Additionally, we observe an increase in student classification\nperformance as the size of the unlabeled dataset increases, indicating that\nthere is potential to scale further. For the CD data, our model outperforms the\nHR teacher model, while needing 4 times fewer computations. For the LUAD data,\nour student model results at 1.25x magnification are within 3% of the teacher\nmodel at 10x magnification, with a 64 times computational cost reduction.\nMoreover, our CD outcomes benefit from performance scaling with the use of more\nunlabeled data. For 0.625x magnification, using unlabeled data improves\naccuracy by 4% over the baseline. Thus, our method can improve the feasibility\nof deep learning solutions for digital pathology with standard computational\nhardware.\n"}
+{"abstract": "  The digital media, identified as computational propaganda provides a pathway\nfor propaganda to expand its reach without limit. State-backed propaganda aims\nto shape the audiences' cognition toward entities in favor of a certain\npolitical party or authority. Furthermore, it has become part of modern\ninformation warfare used in order to gain an advantage over opponents. Most of\nthe current studies focus on using machine learning, quantitative, and\nqualitative methods to distinguish if a certain piece of information on social\nmedia is propaganda. Mainly conducted on English content, but very little\nresearch addresses Chinese Mandarin content. From propaganda detection, we want\nto go one step further to provide more fine-grained information on propaganda\ntechniques that are applied. In this research, we aim to bridge the information\ngap by providing a multi-labeled propaganda techniques dataset in Mandarin\nbased on a state-backed information operation dataset provided by Twitter. In\naddition to presenting the dataset, we apply a multi-label text classification\nusing fine-tuned BERT. Potentially this could help future research in detecting\nstate-backed propaganda online especially in a cross-lingual context and cross\nplatforms identity consolidation.\n"}
+{"abstract": "  The creativity and emergence of biological and psychological behavior are\nnonlinear. However, that does not necessarily mean only that the measurements\nof the behaviors are curvilinear. Furthermore, the linear model might fail to\nreduce these measurements to a sum of independent random factors, implying\nnonlinear changes over time. The present work reviews some of the concepts\nimplicated in linear changes over time and details the mathematical steps\ninvolved. It introduces multifractality as a mathematical framework helpful in\ndetermining whether and to what degree the measured time series exhibits\nnonlinear changes over time. The mathematical steps include multifractal\nanalysis and surrogate data production for resolving when multifractality\nentails nonlinear changes over time. Ultimately, when measurements fail to fit\nthe structures of the traditional linear model, multifractal modeling gives us\nthe means to make those nonlinear excursions explicit and perhaps permit the\ndevelopment of theory that draws on both linear and nonlinear processes.\n"}
+{"abstract": "  This paper discusses the problem of weakly supervised classification, in\nwhich instances are given weak labels that are produced by some\nlabel-corruption process. The goal is to derive conditions under which loss\nfunctions for weak-label learning are proper and lower-bounded -- two essential\nrequirements for the losses used in class-probability estimation. To this end,\nwe derive a representation theorem for proper losses in supervised learning,\nwhich dualizes the Savage representation. We use this theorem to characterize\nproper weak-label losses and find a condition for them to be lower-bounded.\nFrom these theoretical findings, we derive a novel regularization scheme called\ngeneralized logit squeezing, which makes any proper weak-label loss bounded\nfrom below, without losing properness. Furthermore, we experimentally\ndemonstrate the effectiveness of our proposed approach, as compared to improper\nor unbounded losses. The results highlight the importance of properness and\nlower-boundedness.\n"}
+{"abstract": "  Recent years have witnessed significant progress in person re-identification\n(ReID). However, current ReID approaches still suffer from considerable\nperformance degradation when unseen testing domains exhibit different\ncharacteristics from the source training ones, known as the domain\ngeneralization problem. Given multiple source training domains, previous Domain\nGeneralizable ReID (DG-ReID) methods usually learn all domains together using a\nshared network, which can't learn sufficient knowledge from each domain. In\nthis paper, we propose a novel Multiple Domain Experts Collaborative Learning\n(MECL) framework for better exploiting all training domains, which benefits\nfrom the proposed Domain-Domain Collaborative Learning (DDCL) and\nUniversal-Domain Collaborative Learning (UDCL). DDCL utilizes domain-specific\nexperts for fully exploiting each domain, and prevents experts from\nover-fitting the corresponding domain using a meta-learning strategy. In UDCL,\na universal expert supervises the learning of domain experts and continuously\ngathers knowledge from all domain experts. Note, only the universal expert will\nbe used for inference. Extensive experiments on DG-ReID benchmarks demonstrate\nthe effectiveness of DDCL and UDCL, and show that the whole MECL framework\nsignificantly outperforms state-of-the-arts. Experimental results on\nDG-classification benchmarks also reveal the great potential of applying MECL\nto other DG tasks.\n"}
+{"abstract": "  Transformers have become the powerhouse of natural language processing and\nrecently found use in computer vision tasks. Their effective use of attention\ncan be used in other contexts as well, and in this paper, we propose a\ntransformer-based approach for efficiently solving the complex motion planning\nproblems. Traditional neural network-based motion planning uses convolutional\nnetworks to encode the planning space, but these methods are limited to fixed\nmap sizes, which is often not realistic in the real-world. Our approach first\nidentifies regions on the map using transformers to provide attention to map\nareas likely to include the best path, and then applies local planners to\ngenerate the final collision-free path. We validate our method on a variety of\nrandomly generated environments with different map sizes, demonstrating\nreduction in planning complexity and achieving comparable accuracy to\ntraditional planners.\n"}
+{"abstract": "  Consider the short-time probability distribution $\\mathcal{P}(H,t)$ of the\none-point interface height difference $h(x=0,\\tau=t)-h(x=0,\\tau=0)=H$ of the\nstationary interface $h(x,\\tau)$ described by the Kardar-Parisi-Zhang equation.\nIt was previously shown that the optimal path -- the most probable history of\nthe interface $h(x,\\tau)$ which dominates the upper tail of $\\mathcal{P}(H,t)$\n-- is described by any of \\emph{two} ramp-like structures of $h(x,\\tau)$\ntraveling either to the left, or to the right. These two solutions emerge, at a\ncritical value of $H$, via a spontaneous breaking of the mirror symmetry\n$x\\leftrightarrow -x$ of the optimal path, and this symmetry breaking is\nresponsible for a second-order dynamical phase transition in the system. We\nsimulate the interface configurations numerically by employing a\nlarge-deviation Monte Carlo sampling algorithm in conjunction with the mapping\nbetween the KPZ interface and the directed polymer in a random potential at\nhigh temperature. This allows us to observe the optimal paths, which determine\neach of the two tails of $\\mathcal{P}(H,t)$, down to probability densities as\nsmall as $10^{-500}$. At short times we observe mirror-symmetry-broken\ntraveling optimal paths for the upper tail, and a single mirror-symmetric path\nfor the lower tail, in good quantitative agreement with analytical predictions.\nAt long times, even at moderate values of $H$, where the optimal fluctuation\nmethod is \\emph{not} supposed to apply, we still observe two well-defined\ndominating paths. Each of them violates the mirror symmetry $x\\leftrightarrow\n-x$ and is a mirror image of the other.\n"}
+{"abstract": "  To explore the vulnerability of deep neural networks (DNNs), many attack\nparadigms have been well studied, such as the poisoning-based backdoor attack\nin the training stage and the adversarial attack in the inference stage. In\nthis paper, we study a novel attack paradigm, which modifies model parameters\nin the deployment stage for malicious purposes. Specifically, our goal is to\nmisclassify a specific sample into a target class without any sample\nmodification, while not significantly reduce the prediction accuracy of other\nsamples to ensure the stealthiness. To this end, we formulate this problem as a\nbinary integer programming (BIP), since the parameters are stored as binary\nbits ($i.e.$, 0 and 1) in the memory. By utilizing the latest technique in\ninteger programming, we equivalently reformulate this BIP problem as a\ncontinuous optimization problem, which can be effectively and efficiently\nsolved using the alternating direction method of multipliers (ADMM) method.\nConsequently, the flipped critical bits can be easily determined through\noptimization, rather than using a heuristic strategy. Extensive experiments\ndemonstrate the superiority of our method in attacking DNNs.\n"}
+{"abstract": "  The reliable operation of power grid is supported by energy management\nsystems (EMS) that provide monitoring and control functionalities. Contingency\nanalysis is a critical application of EMS to evaluate the impacts of outages\nand prepare for system failures. However, false data injection attacks (FDIAs)\nhave demonstrated the possibility of compromising sensor measurements and\nfalsifying the estimated power system states. As a result, FDIAs may mislead\nsystem operations and other EMS applications including contingency analysis and\noptimal power flow. In this paper, we assess the effect of FDIAs and\ndemonstrate that such attacks can affect the resulted number of contingencies.\nIn order to mitigate the FDIA impact, we propose CHIMERA, a hybrid\nattack-resilient state estimation approach that integrates model-based and\ndata-driven methods. CHIMERA combines the physical grid information with a Long\nShort Term Memory (LSTM)-based deep learning model by considering a static loss\nof weighted least square errors and a dynamic loss of the difference between\nthe temporal variations of the actual and the estimated active power. Our\nsimulation experiments based on the load data from New York state demonstrate\nthat CHIMERA can effectively mitigate 91.74% of the cases in which FDIAs can\nmaliciously modify the contingencies.\n"}
+{"abstract": "  One objective of a lander mission to Jupiter's icy moon Europa is to detect\nliquid water within 30 km as well as characterizing the subsurface ocean. In\norder to satisfy this objective, water within the ice shell must also be\nidentified. Inductive electromagnetic (EM) methods are optimal for water\ndetection on Europa because even a small fraction of dissolved salts will make\nwater orders of magnitude more electrically conductive than the ice shell.\nCompared to induction studies by the Galileo spacecraft, measurements of\nhigher-frequency ambient EM fields are necessary to resolve the shallower\ndepths of intrashell water. Although these fields have been mostly\ncharacterized by prior missions, their unknown source structures and plasma\nproperties do not allow EM sounding using a single surface magnetometer or the\norbit-to-surface magnetic transfer function, respectively. Instead, broadband\nEM sounding can be accomplished from a single surface station using the\nmagnetotelluric (MT) method, which measures horizontal electric fields as well\nas the three-component magnetic field. We have developed a prototype Europa\nMagnetotelluric Sounder (EMS) to meet the measurement requirements in the\nrelevant thermal, vacuum, and radiation environment. EMS comprises central\nelectronics, a fluxgate magnetometer on a mast, and three ballistically\ndeployed electrodes to measure differences in surface electric potential. In\nthis paper, we describe EMS development and testing as well as providing\nsupporting information on the concept of operations and calculations on water\ndetectability. EMS can uniquely determine the occurrence of intrashell water on\nEuropa, providing important constraints on habitability.\n"}
+{"abstract": "  Laboratory experiments play a key role in deciphering the chemistry of the\ninterstellar medium (ISM) and the role that product complex organic molecules\n(COMs) may play in the origins of life. However, to date, most studies in\nexperimental astrochemistry have made use of reductionist approaches to\nexperimental design in which chemical responses to variations in a single\nparameter are investigated while all other parameters are held constant.\nAlthough such work does afford insight into the chemistry of the ISM, it is\nlikely that several important points, such as the relative importance of an\nexperimental parameter in determining the chemical outcome of a reaction and\nthe interaction between parameters, remain ambiguous. In light of this, we\npropose adopting a new systems astrochemistry framework for experimental\nstudies which draws on current work performed in the field of prebiotic\nchemistry, and present the basic tenants of such an approach in this article.\nThis systems approach would focus on the emergent properties of the chemical\nsystem by performing the simultaneous variation of multiple experimental\nparameters and would allow for the effect of each parameter, as well as their\ninteractions, to be quantified. We anticipate that the application of systems\nscience to laboratory astrochemistry, coupled with developments in hyphenated\nanalytical techniques and data analytics, will uncover significant new data\nhitherto unknown, and will aid in better linking laboratory experiments to\nobservations and modelling work.\n"}
+{"abstract": "  This volume contains a selection of papers presented at LFMTP 2020, the 15th\nInternational Workshop on Logical Frameworks and Meta-Languages: Theory and\nPractice (LFMTP), held the 29-30th of June, 2019, using the Zoom video\nconferencing tool due to COVID restrictions. Officially the workshop was held\nin Paris, France, and it was affiliated with IJCAR 2020, FSCD 2020 and many\nother satellite events.\n  Logical frameworks and meta-languages form a common substrate for\nrepresenting, implementing and reasoning about a wide variety of deductive\nsystems of interest in logic and computer science. Their design, implementation\nand their use in reasoning tasks, ranging from the correctness of software to\nthe properties of formal systems, have been the focus of considerable research\nover the last two decades. This workshop will bring together designers,\nimplementors and practitioners to discuss various aspects impinging on the\nstructure and utility of logical frameworks, including the treatment of\nvariable binding, inductive and co-inductive reasoning techniques and the\nexpressiveness and lucidity of the reasoning process.\n"}
+{"abstract": "  We propose a novel way of studying the gluon number density (the so-called\nWeizs\\\"acker-Williams gluon distribution) using the planned Electron Ion\nCollider. Namely, with the help of the azimuthal correlations between the total\ntransverse momentum of the dijet system and the scattered electron, we examine\nan interplay between the effect of the soft gluon emissions (the Sudakov form\nfactor) and the gluon saturation effects. The kinematic cuts are chosen such\nthat the dijet system is produced in the forward direction in the laboratory\nframe, which provides an upper bound on the probed longitudinal fractions of\nthe hadron momentum carried by scattered gluons. Further cuts enable us to use\nthe factorization formalism that directly involves the unpolarized\nWeizs\\\"acker-Williams gluon distribution. We find this observable to be very\nsensitive to the soft gluon emission and moderately sensitive to the gluon\nsaturation.\n"}
+{"abstract": "  The behaviour of partially ionized hot compressed matter is critical to the\nstudy of planetary interiors as well as for nuclear fusion studies. A recent\nquantum study of carbon in the 10-70 Gbar range and at a temperature of 100 eV\nused $N$-atom density functional theory (DFT) with $N\\sim 32$-64 and molecular\ndynamics (MD). This involves band-structure type electronic calculations and\naveraging over many MD generated ion configurations. The calculated average\nnumber of free electrons per ion, viz., $\\bar{Z}$, was systematically higher\nthan from a standard average atom (AA) quantum calculation. To clarify this\noffset, we examine (a) the effect of the self-interaction (SI) error in such\nestimates (b) the possibility of carbon being a granular plasma containing\nCoulomb crystals. The possibility of `magic-number' bound states is considered.\nThe electrical conductivity, pressure, and the compressibility of the carbon\nsystem are examined. The very low conductivity and the high $\\bar{Z}$ results\nof DFT-MD point to the existence of carbon in a complex non-uniform\nlow-conducting dispersed phase, possibly containing magic-number Coulomb\ncrystals. The NPA estimate of $\\bar{Z}$, conductivity, compressibility, and\npressure reported here pertain to the {\\it uniform liquid}.\n"}
+{"abstract": "  We study the birational geometry of the moduli spaces of hyperelliptic curves\nwith marked points. We complete the Kodaira classification proving that these\nspaces are of Calabi-Yau type when the number of markings is $4g+6$. Further,\nwe provide a full classification of the structure of the pseudo-effective cone\nof divisors, showing the cone is non-polyhedral when the number of markings is\nat least two and polyhedral in the remaining cases.\n"}
+{"abstract": "  In this work, we study the outage probability (OP) at the destination of an\nintelligent reflecting surface (IRS) assisted communication system in a\n$\\kappa-\\mu$ fading environment. A practical system model that takes into\naccount the presence of phase error due to quantization at the IRS when a)\nsource-destination (SD) link is present and b) SD link is absent is considered.\nFirst, an exact expression is derived, and then we derive three simple\napproximations for the OP using the following approaches: (i) uni-variate\ndimension reduction, (ii) moment matching and, (iii) Kullback-Leibler\ndivergence minimization. The resulting expressions for OP are simple to\nevaluate and quite tight even in the tail region. The validity of these\napproximations is demonstrated using extensive Monte Carlo simulations. We also\nstudy the impact of the number of bits available for quantization, the position\nof IRS with respect to the source and destination and the number of IRS\nelements on the OP for systems with and without an SD link.\n"}
+{"abstract": "  Robot interfaces often only use the visual channel. Inspired by Wickens'\nMultiple Resource Theory, we investigated if the addition of audio elements\nwould reduce cognitive workload and improve performance. Specifically, we\ndesigned a search and threat-defusal task (primary) with a memory test task\n(secondary). Eleven participants - predominantly first responders - were\nrecruited to control a robot to clear all threats in a combination of four\nconditions of primary and secondary tasks in visual and auditory channels. We\ndid not find any statistically significant differences in performance or\nworkload across subjects, making it questionable that Multiple Resource Theory\ncould shorten longer-term task completion time and reduce workload. Our results\nsuggest that considering individual differences for splitting interface\nmodalities across multiple channels requires further investigation.\n"}
+{"abstract": "  The latest video coding standard, called versatile video coding (VVC),\nincludes several novel and refined coding tools at different levels of the\ncoding chain. These tools bring significant coding gains with respect to the\nprevious standard, high efficiency video coding (HEVC). However, the encoder\nmay still introduce visible coding artifacts, mainly caused by coding decisions\napplied to adjust the bitrate to the available bandwidth. Hence, pre and\npost-processing techniques are generally added to the coding pipeline to\nimprove the quality of the decoded video. These methods have recently shown\noutstanding results compared to traditional approaches, thanks to the recent\nadvances in deep learning. Generally, multiple neural networks are trained\nindependently to perform different tasks, thus omitting to benefit from the\nredundancy that exists between the models. In this paper, we investigate a\nlearning-based solution as a post-processing step to enhance the decoded VVC\nvideo quality. Our method relies on multitask learning to perform both quality\nenhancement and super-resolution using a single shared network optimized for\nmultiple degradation levels. The proposed solution enables a good performance\nin both mitigating coding artifacts and super-resolution with fewer network\nparameters compared to traditional specialized architectures.\n"}
+{"abstract": "  Context: Software engineering has a problem in that when we empirically\nevaluate competing prediction systems we obtain conflicting results. Objective:\nTo reduce the inconsistency amongst validation study results and provide a more\nformal foundation to interpret results with a particular focus on continuous\nprediction systems. Method: A new framework is proposed for evaluating\ncompeting prediction systems based upon (1) an unbiased statistic, Standardised\nAccuracy, (2) testing the result likelihood relative to the baseline technique\nof random 'predictions', that is guessing, and (3) calculation of effect sizes.\nResults: Previously published empirical evaluations of prediction systems are\nre-examined and the original conclusions shown to be unsafe. Additionally, even\nthe strongest results are shown to have no more than a medium effect size\nrelative to random guessing. Conclusions: Biased accuracy statistics such as\nMMRE are deprecated. By contrast this new empirical validation framework leads\nto meaningful results. Such steps will assist in performing future\nmeta-analyses and in providing more robust and usable recommendations to\npractitioners.\n"}
+{"abstract": "  Chatbots are often designed to mimic social roles attributed to humans.\nHowever, little is known about the impact on user's perceptions of using\nlanguage that fails to conform to the associated social role. Our research\ndraws on sociolinguistic theory to investigate how a chatbot's language choices\ncan adhere to the expected social role the agent performs within a given\ncontext. In doing so, we seek to understand whether chatbots design should\naccount for linguistic register. This research analyzes how register\ndifferences play a role in shaping the user's perception of the human-chatbot\ninteraction. Ultimately, we want to determine whether register-specific\nlanguage influences users' perceptions and experiences with chatbots. We\nproduced parallel corpora of conversations in the tourism domain with similar\ncontent and varying register characteristics and evaluated users' preferences\nof chatbot's linguistic choices in terms of appropriateness, credibility, and\nuser experience. Our results show that register characteristics are strong\npredictors of user's preferences, which points to the needs of designing\nchatbots with register-appropriate language to improve acceptance and users'\nperceptions of chatbot interactions.\n"}
+{"abstract": "  As software systems evolve, their architecture is meant to adapt accordingly\nby following the changes in requirements, the environment, and the\nimplementation. However, in practice, the evolving system often deviates from\nthe architecture, causing severe consequences to system maintenance and\nevolution. This phenomenon of architecture erosion has been studied extensively\nin research, but not yet been examined from the point of view of developers. In\nthis exploratory study, we look into how developers perceive the notion of\narchitecture erosion, its causes and consequences, as well as tools and\npractices to identify and control architecture erosion. To this end, we\nsearched through several popular online developer communities for collecting\ndata of discussions related to architecture erosion. Besides, we identified\ndevelopers involved in these discussions and conducted a survey with 10\nparticipants and held interviews with 4 participants. Our findings show that:\n(1) developers either focus on the structural manifestation of architecture\nerosion or on its effect on run-time qualities, maintenance and evolution; (2)\nalongside technical factors, architecture erosion is caused to a large extent\nby non-technical factors; (3) despite the lack of dedicated tools for detecting\narchitecture erosion, developers usually identify erosion through a number of\nsymptoms; and (4) there are effective measures that can help to alleviate the\nimpact of architecture erosion.\n"}
+{"abstract": "  For a point of the projective space $\\PG(n,q)$, its R\\'edei factor is the\nlinear polynomial in $n+1$ variables, whose coefficients are the point\ncoordinates. The power sum polynomial of a subset $S$ of $\\PG(n,q)$ is the sum\nof the $(q-1)$-th powers of the R\\'edei factors of the points of $S$. The fact\nthat many subsets may share the same power sum polynomial offers a natural\nconnection to discrete tomography. In this paper we deal with the\ntwo-dimensional case and show that the notion of ghost, whose employment\nenables to find all solutions of the tomographic problem, can be rephrased in\nthe finite geometry context, where subsets with null power sum polynomial are\ncalled ghosts as well. In the latter case, one can add ghosts still preserving\nthe power sum polynomial by means of the multiset sum (modulo the field\ncharacteristic). We prove some general results on ghosts in $\\PG(2,q)$ and\ncompute their number in case $q$ is a prime.\n"}
+{"abstract": "  Imaging dynamical processes at interfaces and on the nanoscale is of great\nimportance throughout science and technology. While light-optical imaging\ntechniques often cannot provide the necessary spatial resolution,\nelectron-optical techniques damage the specimen and cause dose-induced\nartefacts. Here, Optical Near-field Electron Microscopy (ONEM) is proposed, an\nimaging technique that combines non-invasive probing with light, with a high\nspatial resolution read-out via electron optics. Close to the specimen, the\noptical near-fields are converted into a spatially varying electron flux using\na planar photocathode. The electron flux is imaged using low energy electron\nmicroscopy, enabling label-free nanometric resolution without the need to scan\na probe across the sample. The specimen is never exposed to damaging electrons.\n"}
+{"abstract": "  With the growth in automated data collection of construction projects, the\nneed for semantic navigation of mobile robots is increasing. In this paper, we\npropose an infrastructure to leverage building-related information for smarter,\nsafer and more precise robot navigation during construction phase. Our use of\nBuilding Information Models (BIM) in robot navigation is twofold: (1) the\nintuitive semantic information enables non-experts to deploy robots and (2) the\nsemantic data exposed to the navigation system allows optimal path planning\n(not necessarily the shortest one). Our Building Information Robotic System\n(BIRS) uses Industry Foundation Classes (IFC) as the interoperable data format\nbetween BIM and the Robotic Operating System (ROS). BIRS generates topological\nand metric maps from BIM for ROS usage. An optimal path planer, integrating\ncritical components for construction assessment is proposed using a cascade\nstrategy (global versus local). The results are validated through series of\nexperiments in construction sites.\n"}
+{"abstract": "  Methods for solving PDEs using neural networks have recently become a very\nimportant topic. We provide an a priori error analysis for such methods which\nis based on the $\\mathcal{K}_1(\\mathbb{D})$-norm of the solution. We show that\nthe resulting constrained optimization problem can be efficiently solved using\na greedy algorithm, which replaces stochastic gradient descent. Following this,\nwe show that the error arising from discretizing the energy integrals is\nbounded both in the deterministic case, i.e. when using numerical quadrature,\nand also in the stochastic case, i.e. when sampling points to approximate the\nintegrals. In the later case, we use a Rademacher complexity analysis, and in\nthe former we use standard numerical quadrature bounds. This extends existing\nresults to methods which use a general dictionary of functions to learn\nsolutions to PDEs and importantly gives a consistent analysis which\nincorporates the optimization, approximation, and generalization aspects of the\nproblem. In addition, the Rademacher complexity analysis is simplified and\ngeneralized, which enables application to a wide range of problems.\n"}
+{"abstract": "  Two prominent methods for integer factorization are those based on general\ninteger sieve and elliptic curve. The general integer sieve method can be\nspecialized to quadratic integer sieve method. In this paper, a probability\nanalysis for the success of these methods is described, under some reasonable\nconditions. The estimates presented are specialized for the elliptic curve\nfactorization. These methods are compared through heuristic estimates. It is\nshown that the elliptic curve method is a probabilistic polynomial time\nalgorithm under the assumption of uniform probability distribution for the\narising group orders and clearly more likely to succeed, faster asymptotically.\n"}
+{"abstract": "  Mapping a search query to a set of relevant categories in the product\ntaxonomy is a significant challenge in e-commerce search for two reasons: 1)\nTraining data exhibits severe class imbalance problem due to biased click\nbehavior, and 2) queries with little customer feedback (e.g., tail queries) are\nnot well-represented in the training set, and cause difficulties for query\nunderstanding. To address these problems, we propose a deep learning model,\nDeepCAT, which learns joint word-category representations to enhance the query\nunderstanding process. We believe learning category interactions helps to\nimprove the performance of category mapping on minority classes, tail and torso\nqueries. DeepCAT contains a novel word-category representation model that\ntrains the category representations based on word-category co-occurrences in\nthe training set. The category representation is then leveraged to introduce a\nnew loss function to estimate the category-category co-occurrences for refining\njoint word-category embeddings. To demonstrate our model's effectiveness on\nminority categories and tail queries, we conduct two sets of experiments. The\nresults show that DeepCAT reaches a 10% improvement on minority classes and a\n7.1% improvement on tail queries over a state-of-the-art label embedding model.\nOur findings suggest a promising direction for improving e-commerce search by\nsemantic modeling of taxonomy hierarchies.\n"}
+{"abstract": "  Eccentric compact binaries pose not only a challenge for gravitational wave\ndetectors, but also provide a probe into the nuclear equation of state if one\nof the objects is a neutron star. At the short pericenter passage, tidal\ninteractions excite f-modes on the star, which in turn emit their own\ngravitational waves. We derive an analytic waveform for these stellar\noscillations within the effective fly-by framework, modeling the emission to\nleading post-Newtonian order. At this order, the f-mode response can be written\nin a Fourier decomposition in terms of orbital harmonics, with the amplitudes\nof each harmonic depending on Hansen coefficients. Re-summing the harmonics of\nthe f-mode results in a simple decaying harmonic oscillator, with the amplitude\nnow determined by a Hansen coefficient of complex harmonic number. We compute\nthe match between the re-summed f-mode and numerical integrations of the tidal\nresponse, and find ${\\cal{M}} > 0.98$ for systems with high orbital\neccentricity $(e > 0.8)$ and low semi-latus rectum $(p < 15M)$ for three\nequations of state. We further compare our model to modes generated from\nsubsequent pericenter passages under the effect of radiation reaction, and\ndevelop an accurate model to time pericenter passages. We show how the timing\nmodel can be used to specify initial conditions to accurately track the f-mode\nexcitation across multiple pericenter passages.\n"}
+{"abstract": "  Rather than obtaining cosmic acceleration with a scalar field potential\n(quintessence) or noncanonical kinetic term (k-essence), we can do it purely\nthrough a modified gravity braiding of the scalar and metric, i.e. the $G_3$\nHorndeski action term. Such \"Horndessence\" allows an exact $\\Lambda$CDM\ncosmological expansion without any cosmological constant, and by requiring\nshift symmetry we can derive the exact form of $G_3$. We find that this route\nof deriving $G_3(X)$ leads to a functional form far from the usual simple\nassumptions such as a power law. Horndessence without any kinetic term or\npotential has the same number of parameters as $\\Lambda$CDM and makes an exact\nprediction for the expansion history ($\\Lambda$CDM) and modified gravity cosmic\ngrowth history; we show the viable gravitational strength $G_{\\rm eff}(a)$ and\ngrowth rate $f\\sigma_8(a)$. The simplest versions of the theory fail soundness\ncriteria, but we learn interesting lessons along the way, in particular about\nrobust parametrization, and indicate possible sound extensions.\n"}
+{"abstract": "  As the field of connectomics has matured, it has expanded from mapping the\nexistence of connections between brain components to measuring the strength of\nconnections. This information is increasingly accessible via methodologies such\nas pairing functional magnetic resonance (MR) imaging and MR tractography in\nthe same human subject, as well as novel methods in non-human animals using\noptogenetics. Systems and network neuroscience have in recent years focused\nextensively on explaining correlation patterns of functional activity in the\nbrain in terms of the degree of connectedness of brain components, the\nso-called functional connectivity-structural connectivity relationship (SC-FC).\nWhat has been surprising has been how low the SC-FC correlations are. Why is it\nthat brain parts that are more well-connected appear not to engender more\ncorrelated activity between them? Several explanations have been proposed but\none possibility has not been considered: perhaps more neural activity doesn't\nimply more functional involvement. This article examines this possibility and\nproposes a new general framework for understanding brain network dynamics based\non the design constraints of large-scale network communication systems. With\nthis new perspective, we may start to answer the article's main question, and\nperhaps others.\n"}
+{"abstract": "  The article introduces an elementary cost and storage reduction method for\nspectral clustering and principal component analysis. The method consists in\nrandomly \"puncturing\" both the data matrix $X\\in\\mathbb{C}^{p\\times n}$ (or\n$\\mathbb{R}^{p\\times n}$) and its corresponding kernel (Gram) matrix $K$\nthrough Bernoulli masks: $S\\in\\{0,1\\}^{p\\times n}$ for $X$ and\n$B\\in\\{0,1\\}^{n\\times n}$ for $K$. The resulting \"two-way punctured\" kernel is\nthus given by $K=\\frac{1}{p}[(X \\odot S)^{\\sf H} (X \\odot S)] \\odot B$. We\ndemonstrate that, for $X$ composed of independent columns drawn from a Gaussian\nmixture model, as $n,p\\to\\infty$ with $p/n\\to c_0\\in(0,\\infty)$, the spectral\nbehavior of $K$ -- its limiting eigenvalue distribution, as well as its\nisolated eigenvalues and eigenvectors -- is fully tractable and exhibits a\nseries of counter-intuitive phenomena. We notably prove, and empirically\nconfirm on GAN-generated image databases, that it is possible to drastically\npuncture the data, thereby providing possibly huge computational and storage\ngains, for a virtually constant (clustering of PCA) performance. This\npreliminary study opens as such the path towards rethinking, from a large\ndimensional standpoint, computational and storage costs in elementary machine\nlearning models.\n"}
+{"abstract": "  Recently, few certified defense methods have been developed to provably\nguarantee the robustness of a text classifier to adversarial synonym\nsubstitutions. However, all existing certified defense methods assume that the\ndefenders are informed of how the adversaries generate synonyms, which is not a\nrealistic scenario. In this paper, we propose a certifiably robust defense\nmethod by randomly masking a certain proportion of the words in an input text,\nin which the above unrealistic assumption is no longer necessary. The proposed\nmethod can defend against not only word substitution-based attacks, but also\ncharacter-level perturbations. We can certify the classifications of over 50%\ntexts to be robust to any perturbation of 5 words on AGNEWS, and 2 words on\nSST2 dataset. The experimental results show that our randomized smoothing\nmethod significantly outperforms recently proposed defense methods across\nmultiple datasets.\n"}
+{"abstract": "  We study fair classification in the presence of an omniscient adversary that,\ngiven an $\\eta$, is allowed to choose an arbitrary $\\eta$-fraction of the\ntraining samples and arbitrarily perturb their protected attributes. The\nmotivation comes from settings in which protected attributes can be incorrect\ndue to strategic misreporting, malicious actors, or errors in imputation; and\nprior approaches that make stochastic or independence assumptions on errors may\nnot satisfy their guarantees in this adversarial setting. Our main contribution\nis an optimization framework to learn fair classifiers in this adversarial\nsetting that comes with provable guarantees on accuracy and fairness. Our\nframework works with multiple and non-binary protected attributes, is designed\nfor the large class of linear-fractional fairness metrics, and can also handle\nperturbations besides protected attributes. We prove near-tightness of our\nframework's guarantees for natural hypothesis classes: no algorithm can have\nsignificantly better accuracy and any algorithm with better fairness must have\nlower accuracy. Empirically, we evaluate the classifiers produced by our\nframework for statistical rate on real-world and synthetic datasets for a\nfamily of adversaries.\n"}
+{"abstract": "  In this paper, we provide a framework of designing the local discontinuous\nGalerkin scheme for integral fractional Laplacian $(-\\Delta)^{s}$ with\n$s\\in(0,1)$ in two dimensions. We theoretically prove and numerically verify\nthe numerical stability and convergence of the scheme with the convergence rate\nno worse than $\\mathcal{O}(h^{k+\\frac{1}{2}})$.\n"}
+{"abstract": "  We aim at estimating the invariant density associated to a stochastic\ndifferential equation with jumps in low dimension, which is for $d=1$ and\n$d=2$. We consider a class of jump diffusion processes whose invariant density\nbelongs to some H\\\"older space. Firstly, in dimension one, we show that the\nkernel density estimator achieves the convergence rate $\\frac{1}{T}$, which is\nthe optimal rate in the absence of jumps. This improves the convergence rate\nobtained in [Amorino, Gloter (2021)], which depends on the Blumenthal-Getoor\nindex for $d=1$ and is equal to $\\frac{\\log T}{T}$ for $d=2$. Secondly, we show\nthat is not possible to find an estimator with faster rates of estimation.\nIndeed, we get some lower bounds with the same rates $\\{\\frac{1}{T},\\frac{\\log\nT}{T}\\}$ in the mono and bi-dimensional cases, respectively. Finally, we obtain\nthe asymptotic normality of the estimator in the one-dimensional case.\n"}
+{"abstract": "  Text recognition is a popular research subject with many associated\nchallenges. Despite the considerable progress made in recent years, the text\nrecognition task itself is still constrained to solve the problem of reading\ncropped line text images and serves as a subtask of optical character\nrecognition (OCR) systems. As a result, the final text recognition result is\nlimited by the performance of the text detector. In this paper, we propose a\nsimple, elegant and effective paradigm called Implicit Feature Alignment (IFA),\nwhich can be easily integrated into current text recognizers, resulting in a\nnovel inference mechanism called IFAinference. This enables an ordinary text\nrecognizer to process multi-line text such that text detection can be\ncompletely freed. Specifically, we integrate IFA into the two most prevailing\ntext recognition streams (attention-based and CTC-based) and propose\nattention-guided dense prediction (ADP) and Extended CTC (ExCTC). Furthermore,\nthe Wasserstein-based Hollow Aggregation Cross-Entropy (WH-ACE) is proposed to\nsuppress negative predictions to assist in training ADP and ExCTC. We\nexperimentally demonstrate that IFA achieves state-of-the-art performance on\nend-to-end document recognition tasks while maintaining the fastest speed, and\nADP and ExCTC complement each other on the perspective of different application\nscenarios. Code will be available at\nhttps://github.com/WangTianwei/Implicit-feature-alignment.\n"}
+{"abstract": "  Linear modal instabilities of flow over finite-span untapered wings have been\ninvestigated numerically at Reynolds number 400, at a range of angles of attack\nand sweep on two wings having aspect ratios 4 and 8. Base flows have been\ngenerated by direct numerical simulation, marching the unsteady incompressible\nthree-dimensional Navier-Stokes equations to a steady state, or using selective\nfrequency damping to obtain stationary linearly unstable flows. Unstable\nthree-dimensional linear global modes of swept wings have been identified for\nthe first time using spectral-element time-stepping solvers. The effect of the\nwing geometry and flow parameters on these modes has been examined in detail.\nAn increase of the angle of attack was found to destabilize the flow, while an\nincrease of the sweep angle had the opposite effect. On unswept wings,\nTriGlobal analysis revealed that the most unstable global mode peaks in the\nmidspan region of the wake; the peak of the mode structure moves towards the\ntip as sweep is increased. Data-driven analysis was then employed to study the\neffects of wing geometry and flow conditions on the nonlinear wake. On unswept\nwings, the dominant mode at low angles of attack is a Kelvin-Helmholtz-like\ninstability, qualitatively analogous with global modes of infinite-span wings\nunder same conditions. At higher angles of attack and moderate sweep angles,\nthe dominant mode is a structure denominated the interaction mode. At high\nsweep angles, this mode evolves into elongated streamwise vortices on higher\naspect ratio wings, while on shorter wings it becomes indistinguishable from\ntip-vortex instability.\n"}
+{"abstract": "  This paper recalls the main achievements of the nTRV experiment which\nmeasured two components of the transverse polarization ($\\sigma_{T_{1}}$,\n$\\sigma_{T_{2}}$) of electrons emitted in the $\\beta$-decay of polarized, free\nneutrons and deduced two correlation coefficients, $R$ and $N$, that are\nsensitive to physics beyond the Standard Model. The value of time-reversal odd\ncoefficient $R$, 0.004$\\pm$0.012$\\pm$0.005, significantly improved limits on\nthe relative strength of imaginary scalar coupling constant in the weak\ninteraction. The value obtained for the time-reversal even correlation\ncoefficient $N$, 0.067$\\pm$0.011$\\pm$0.004, agrees with the Standard Model\nexpectation, providing an important sensitivity test of the electron\npolarimeter. One of the conclusions of this pioneering experiment was that the\ntransverse electron polarization in the neutron $\\beta$-decay is worth more\nsystematic exploring by measurements of yet experimentally not attempted\ncorrelation coefficients such as $H$, $L$, $S$, $U$ and $V$. This article\npresents a brief outlook on that questions.\n"}
+{"abstract": "  For a fixed set $X$ containing $n$ taxon labels, an ordered pair consisting\nof a gene tree topology $G$ and a species tree $S$ bijectively labeled with the\nlabels of $X$ possesses a set of coalescent histories -- mappings from the set\nof internal nodes of $G$ to the set of edges of $S$ describing possible lists\nof edges in $S$ on which the coalescences in $G$ take place. Enumerations of\ncoalescent histories for gene trees and species trees have produced suggestive\nresults regarding the pairs $(G,S)$ that, for a fixed $n$, have the largest\nnumber of coalescent histories. We define a class of 2-cherry binary tree\ntopologies that we term $p$-pseudocaterpillars, examining coalescent histories\nfor non-matching pairs $(G,S)$, in the case in which $S$ has a caterpillar\nshape and $G$ has a $p$-pseudocaterpillar shape. Using a construction that\nassociates coalescent histories for $(G,S)$ with a class of \"roadblocked\"\nmonotonic paths, we identify the $p$-pseudocaterpillar labeled gene tree\ntopology that, for a fixed caterpillar labeled species tree topology, gives\nrise to the largest number of coalescent histories. The shape that maximizes\nthe number of coalescent histories places the \"second\" cherry of the\n$p$-pseudocaterpillar equidistantly from the root of the \"first\" cherry and\nfrom the tree root. A symmetry in the numbers of coalescent histories for\n$p$-pseudocaterpillar gene trees and caterpillar species trees is seen to exist\naround the maximizing value of the parameter $p$. The results provide insight\ninto the factors that influence the number of coalescent histories possible for\na given gene tree and species tree.\n"}
+{"abstract": "  Mesoporous silica nanoparticles have been reported as suitable drug carriers,\nbut their successful delivery to target tissues following systemic\nadministration remains a challenge. In the present work, ultrasound-induced\ninertial cavitation was evaluated as a mechanism to promote their extravasation\nin a flow-through tissue-mimicking agarose phantom. Two different ultrasound\nfrequencies, 0.5 or 1.6 MHz, with pressures in the range 0.5-4 MPa were used to\ndrive cavitation activity which was detected in real time. The optimal\nultrasound conditions identified were employed to deliver dye-loaded\nnanoparticles as a model for drug-loaded nanocarriers, with the level of\nextravasation evaluated by fluorescence microscopy. The same nanoparticles were\nthen co-injected with submicrometric polymeric cavitation nuclei as a means to\npromote cavitation activity and decrease the required in-situ acoustic pressure\nrequired to attain extravasation. The overall cavitation energy and penetration\nof the combination was compared to mesoporous silica nanoparticles alone. The\nresults of the present work suggest that combining mesoporous silica\nnanocarriers and submcrometric cavitation nuclei may help enhance the\nextravasation of the nanocarrier, thus enabling subsequent sustained drug\nrelease to happen from those particles already embedded in the tumour tissue.\n"}
+{"abstract": "  Metal additive manufacturing (AM) by laser powder bed fusion (L-PBF) builds\nupon fundamentals established in the field of laser welding which include the\ninfluence of gas and plume dynamics on weld depth and quality. L-PBF demands a\nthorough investigation of the complex thermophysical phenomena that occur where\nthe laser interacts with the metal powder bed. In particular, melt pool\nturbulence and evaporation are influenced by the ambient gas chemistry and\npressure. This paper presents the design and validation of high pressure laser\nmelting (HPLM) testbed; this accommodates bare metal plate samples as well as\nmanually-coated single powder layers, and operates at up to 300 psig. The open\narchitecture of this testbed allows for full control of all relevant laser\nparameters in addition to ambient gas pressure and gas flow over the build\narea. Representative melt tracks and rasters on bare plate and powder are\nexamined in order to validate system performance, and preliminary analysis\nconcludes that pressure has a significant impact on melt pool aspect ratio. The\nHPLM system thus enables careful study pressure effects on processing of common\nL-PBF materials, and can be applied in the future to materials that are\nchallenging to process under ambient pressure, such as those with high vapor\npressures.\n"}
+{"abstract": "  Pilot studies are often the first step of experimental research. It is\nusually on a smaller scale and the results can inform intervention development,\nstudy feasibility and how the study implementation will play out, if such a\nlarger main study is undertaken. This paper illustrates the relationship\nbetween pilot study sample size and the performance study design of main\nstudies. We present two simple sample size calculation methods to ensure\nadequate study planning for main studies. We use numerical examples and\nsimulations to demonstrate the use and performance of proposed methods.\nPractical heuristic guidelines are provided based on the results.\n"}
+{"abstract": "  This paper proposes a general framework for constructing feedback controllers\nthat drive complex dynamical systems to \"efficient\" steady-state (or slowly\nvarying) operating points. Efficiency is encoded using generalized equations\nwhich can model a broad spectrum of useful objectives, such as optimality or\nequilibria (e.g. Nash, Wardrop, etc.) in noncooperative games. The core idea of\nthe proposed approach is to directly implement iterative solution (or\nequilibrium seeking) algorithms in closed loop with physical systems.\nSufficient conditions for closed-loop stability and robustness are derived;\nthese also serve as the first closed-loop stability results for sampled-data\nfeedback-based optimization. Numerical simulations of smart building automation\nand game-theoretic robotic swarm coordination support the theoretical results.\n"}
+{"abstract": "  Digital twins are meant to bridge the gap between real-world physical systems\nand virtual representations. Both stand-alone and descriptive digital twins\nincorporate 3D geometric models, which are the physical representations of\nobjects in the digital replica. Digital twin applications are required to\nrapidly update internal parameters with the evolution of their physical\ncounterpart. Due to an essential need for having high-quality geometric models\nfor accurate physical representations, the storage and bandwidth requirements\nfor storing 3D model information can quickly exceed the available storage and\nbandwidth capacity. In this work, we demonstrate a novel approach to geometric\nchange detection in the context of a digital twin. We address the issue through\na combined solution of Dynamic Mode Decomposition (DMD) for motion detection,\nYOLOv5 for object detection, and 3D machine learning for pose estimation. DMD\nis applied for background subtraction, enabling detection of moving foreground\nobjects in real-time. The video frames containing detected motion are extracted\nand used as input to the change detection network. The object detection\nalgorithm YOLOv5 is applied to extract the bounding boxes of detected objects\nin the video frames. Furthermore, the rotational pose of each object is\nestimated in a 3D pose estimation network. A series of convolutional neural\nnetworks conducts feature extraction from images and 3D model shapes. Then, the\nnetwork outputs the estimated Euler angles of the camera orientation with\nrespect to the object in the input image. By only storing data associated with\na detected change in pose, we minimize necessary storage and bandwidth\nrequirements while still being able to recreate the 3D scene on demand.\n"}
+{"abstract": "  Thermal gas in the center of galaxy clusters can show substantial motions\nthat generate surface-brightness and temperature discontinuities known as cold\nfronts. The motions may be triggered by minor or off-axis mergers that preserve\nthe cool core of the system. The dynamics of the thermal gas can also generate\nradio emission from the intra-cluster medium (ICM) and impact the evolution of\nclusters' radio sources. We aim to study the central region of Abell 1775, a\nsystem in an ambiguous dynamical state at $z=0.072$ which is known to host an\nextended head-tail radio galaxy, with the goal of investigating the connection\nbetween thermal and nonthermal components in its center. We made use of a deep\n(100 ks) Chandra observation accompanied by LOFAR 144 MHz, GMRT 235 MHz and 610\nMHz, and VLA 1.4 GHz radio data. We find a spiral-like pattern in the X-ray\nsurface brightness that is mirrored in the temperature and pseudo-entropy maps.\nAdditionally, we characterize an arc-shaped cold front in the ICM. We interpret\nthese features in the context of a slingshot gas tail scenario. The structure\nof the head-tail radio galaxy \"breaks\" at the position of the cold front,\nshowing an extension that is detected only at low frequencies, likely due to\nits steep and curved spectrum. We speculate that particle reacceleration is\noccurring in the outer region of this tail, which in total covers a projected\nsize of $\\sim800$ kpc. We also report the discovery of revived fossil plasma\nwith ultra-steep spectrum radio emission in the cluster core together with a\ncentral diffuse radio source that is bounded by the arc-shaped cold front. The\nresults reported in this work demonstrate the interplay between thermal and\nnonthermal components in the cluster center and the presence of ongoing\nparticle reacceleration in the ICM on different scales.\n"}
+{"abstract": "  The most successful tech companies of the world release new software versions\nto production multiple times a day. Thereby, they are able to quickly fix\nemerging bugs and rapidly deliver new features to their customers. This leads\nto short development cycles, minimal lead times and a high customer-centricity.\nShort development cycles are easy to achieve if you start a software project on\na green field. Nevertheless, this does not apply to brown field environments\nwhich are usually found in big corporates such as traditional car\nmanufacturers. For instance, if you want to integrate with real cars you have\nto interface legacy systems with development cycles of up to several months. We\npresent a solution, which worked for one of the world's largest car\nmanufacturer, leveraging in-house core development teams, dynamic stages and\nfeature-toggles to overcome a brown field environment, allow for short\ndevelopment cycles and minimize the lead time.\n"}
+{"abstract": "  Complex oxide functionality, such as ferroelectricity, magnetism or\nsuperconductivity is often achieved in epitaxial thin-film geometries. Oxygen\nvacancies tend to be the dominant type of defect in these materials but a\nfundamental understanding of their stability and electronic structure has so\nfar mostly been established in the bulk or strained bulk, neglecting interfaces\nand surfaces present in a thin-film geometry. We investigate here, via density\nfunctional theory calculations, oxygen vacancies in the model system of a\nSrMnO$_3$ (SMO) thin film grown on a SrTiO$_3$ (STO) (001) substrate.\nStructural and electronic differences compared to bulk SMO result mainly from\nundercoordination at the film surface. The changed crystal field leads to a\ndepletion of subsurface valence-band states and transfer of this charge to\nsurface Mn atoms, both of which strongly affect the defect chemistry in the\nfilm. The result is a strong preference of oxygen vacancies in the surface\nregion compared to deeper layers. Finally, for metastable oxygen vacancies in\nthe substrate, we predict a spatial separation of the defect from its excess\ncharge, the latter being accommodated in the film but close to the substrate\nboundary. These results show that surface and interface effects lead to\nsignificant differences in stability and electronic structure of oxygen\nvacancies in thin-film geometries compared to the (strained) bulk.\n"}
+{"abstract": "  Reliable and secure communication is an important aspect of modern fiber\noptic communication. In this work we consider a multi-mode fiber (MMF) channel\nwiretapped by an eavesdropper. We assume the transmitter knows the legitimate\nchannel, but statistical knowledge of the eavesdropper's channel only. We\npropose a transmission scheme with artificial noise (AN) for such a channel. In\nparticular, we formulate the corresponding optimization problem which aims to\nmaximize the average secrecy rate and develop an algorithm to solve it. We\napply this algorithm to actual measured MMF channels. As real fiber\nmeasurements show, for a 55 mode MMF we can achieve positive average secrecy\nrates with the proper use of AN. Furthermore, the gain compared to standard\nprecoding and power allocation schemes is illustrated.\n"}
+{"abstract": "  Our project task was to create a model that, given a speaker ID, chat\nhistory, and an utterance query, can predict the response utterance in a\nconversation. The model is personalized for each speaker. This task can be a\nuseful tool for building speech bots that talk in a human-like manner in a live\nconversation. Further, we succeeded at using dense-vector encoding clustering\nto be able to retrieve relevant historical dialogue context, a useful strategy\nfor overcoming the input limitations of neural-based models when predictions\nrequire longer-term references from the dialogue history. In this paper, we\nhave implemented a state-of-the-art model using pre-training and fine-tuning\ntechniques built on transformer architecture and multi-headed attention blocks\nfor the Switchboard corpus. We also show how efficient vector clustering\nalgorithms can be used for real-time utterance predictions that require no\ntraining and therefore work on offline and encrypted message histories.\n"}
+{"abstract": "  Robustness against adversarial attack in neural networks is an important\nresearch topic in the machine learning community. We observe one major source\nof vulnerability of neural nets is from overparameterized fully-connected\nlayers. In this paper, we propose a new neighborhood preserving layer which can\nreplace these fully connected layers to improve the network robustness. We\ndemonstrate a novel neural network architecture which can incorporate such\nlayers and also can be trained efficiently. We theoretically prove that our\nmodels are more robust against distortion because they effectively control the\nmagnitude of gradients. Finally, we empirically show that our designed network\narchitecture is more robust against state-of-art gradient descent based\nattacks, such as a PGD attack on the benchmark datasets MNIST and CIFAR10.\n"}
+{"abstract": "  Deep metric learning is a technique used in a variety of discriminative tasks\nto achieve zero-shot, one-shot or few-shot learning. When applied, the system\nlearns an embedding space where a non-parametric approach, such as \\gls{knn},\ncan be used to discriminate features during test time. This work focuses on\ninvestigating to what extent feature information contained within this\nembedding space can be used to carry out sub-discrimination in the feature\nspace. The study shows that within a discrimination embedding, the information\non the salient attributes needed to solve the problem of sub-discrimination is\nsaved within the embedding and that this inherent information can be used to\ncarry out sub-discriminative tasks. To demonstrate this, an embedding designed\ninitially to discriminate faces is used to differentiate several attributes\nsuch as gender, age and skin tone, without any additional training. The study\nis split into two study cases: intra class discrimination where all the\nembeddings took into consideration are from the same identity; and extra class\ndiscrimination where the embeddings represent different identities. After the\nstudy, it is shown that it is possible to infer common attributes to different\nidentities. The system can also perform extra class sub-discrimination with a\nhigh accuracy rate, notably 99.3\\%, 99.3\\% and 94.1\\% for gender, skin tone,\nand age, respectively. Intra class tests show more mixed results with more\nnuanced attributes like emotions not being reliably classified, while more\ndistinct attributes such as thick-framed glasses and beards, achieving 97.2\\%\nand 95.8\\% accuracy, respectively.\n"}
+{"abstract": "  Time-Delay Interferometry (TDI) is the data processing technique that cancels\nthe large laser phase fluctuations affecting the one-way Doppler measurements\nmade by unequal-arm space-based gravitational wave interferometers. By taking\nfinite linear combinations of properly time-shifted Doppler measurements, laser\nphase fluctuations are removed at any time $t$ and gravitational wave signals\ncan be studied at a requisite level of precision. In this article we show the\ndelay operators used in TDI can be represented as matrices acting on arrays\nassociated with the laser noises and Doppler measurements. The matrix\nformulation is nothing but the group theoretic representation (ring\nhomomorphism) of the earlier approach involving time-delay operators and so in\nprinciple is the same. It is shown that the homomorphism is valid generally and\nwe cover all situations of interest. To understand the potential advantages the\nmatrix representation brings, care must be taken by the data analyst to account\nfor the light travel times when linearly relating the one-way Doppler\nmeasurements to the laser noises. This is especially important in view of the\nfuture gravitational wave projects envisaged. We show that the matrix\nformulation of TDI results in the cancellation of the laser noises at an\narbitrary time $t$ by only linearly combining a finite number of samples of the\none-way Doppler data measured at and around time $t$.\n"}
+{"abstract": "  Recent advances in techniques for generating quantum light have stimulated\nresearch on novel spectroscopic measurements using quantum entangled photons.\nOne such spectroscopy technique utilizes non-classical correlations among\nentangled photons to enable measurements with enhanced sensitivity and\nselectivity. Here, we investigate spectroscopic measurement utilizing entangled\nthree photons. In this measurement, time-resolved entangled photon spectroscopy\nwith monochromatic pumping [J. Chem. Phys. 153, 051102 (2020).] is integrated\nwith the frequency-dispersed two-photon counting technique, which suppresses\nundesired accidental photon counts in the detector and thus allows one to\nseparate the weak desired signal. This time-resolved frequency-dispersed\ntwo-photon counting signal, which is a function of two frequencies, is shown to\nprovide the same information as that of coherent two-dimensional optical\nspectra. The spectral distribution of the phase-matching function works as a\nfrequency filter to selectively resolve a specific region of the\ntwo-dimensional spectra, whereas the excited-state dynamics under investigation\nare temporally resolved in the time region longer than the entanglement time.\nThe signal is not subject to Fourier limitations on the joint temporal and\nspectral resolution, and therefore, it is expected to be useful for\ninvestigating complex molecular systems in which multiple electronic states are\npresent within a narrow energy range.\n"}
+{"abstract": "  Stock market returns are typically analyzed using standard regression, yet\nthey reside on irregular domains which is a natural scenario for graph signal\nprocessing. To this end, we consider a market graph as an intuitive way to\nrepresent the relationships between financial assets. Traditional methods for\nestimating asset-return covariance operate under the assumption of statistical\ntime-invariance, and are thus unable to appropriately infer the underlying true\nstructure of the market graph. This work introduces a class of graph spectral\nestimators which cater for the nonstationarity inherent to asset price\nmovements, and serve as a basis to represent the time-varying interactions\nbetween assets through a dynamic spectral market graph. Such an account of the\ntime-varying nature of the asset-return covariance allows us to introduce the\nnotion of dynamic spectral portfolio cuts, whereby the graph is partitioned\ninto time-evolving clusters, allowing for online and robust asset allocation.\nThe advantages of the proposed framework over traditional methods are\ndemonstrated through numerical case studies using real-world price data.\n"}
+{"abstract": "  The formation of drops of a complex fluid, for instance including dissolved\npolymers and/or solid particles, has practical implications in several\nindustrial and biophysical processes. In this Letter, we experimentally\ninvestigate the generation of drops of a viscoelastic suspension, made of\nnon-Brownian spherical particles dispersed in a dilute polymer solution. Using\nhigh-speed imaging, we characterize the different stages of the detachment. Our\nexperiments show that the particles primarily affect the initial Newtonian\nnecking by increasing the fluid viscosity. In the viscoelastic regime,\nparticles do not affect the thinning until the onset of the blistering\ninstability, which they accelerate. We find that the transition from one regime\nto another, which corresponds to the coil-stretch transition of the polymer\nchains, strongly depends on the particle content. Considering that the presence\nof rigid particles increase the deformation of the liquid phase, we propose an\nexpression for the local strain rate in the suspension, which rationalizes our\nexperimental results. This method could enable the precise measurement of local\nstresses in particulate suspensions.\n"}
+{"abstract": "  Transition metal dichalcogenides have emerged as promising materials for\nnano-photonic resonators due to their large refractive index, low absorption\nwithin a large portion of the visible spectrum and compatibility with a wide\nrange of substrates. Here we use these properties to fabricate WS$_2$\ndouble-pillar nano-antennas in a variety of geometries enabled by the\nanisotropy in the crystal structure. Using dark field spectroscopy, we reveal\nmultiple Mie resonances, to which we couple WSe$_2$ monolayer photoluminescence\nand achieve Purcell enhancement and an increased fluorescence by factors up to\n240. We introduce post-fabrication atomic force microscope repositioning and\nrotation of dimer nano-antennas, achieving gaps as small as 10$\\pm$5 nm,\nopening the possibility to a host of potential applications including strong\nPurcell enhancement of single photon emitters and optical trapping, which we\nstudy in simulations. Our findings highlight the advantages of using transition\nmetal dichalcogenides for nano-photonics by exploring new applications enabled\nby their unique properties.\n"}
+{"abstract": "  Variational methods have proven to be excellent tools to approximate ground\nstates of complex many body Hamiltonians. Generic tools like neural networks\nare extremely powerful, but their parameters are not necessarily physically\nmotivated. Thus, an efficient parametrization of the wave-function can become\nchallenging. In this letter we introduce a neural-network based variational\nansatz that retains the flexibility of these generic methods while allowing for\na tunability with respect to the relevant correlations governing the physics of\nthe system. We illustrate the success of this approach on topological,\nlong-range correlated and frustrated models. Additionally, we introduce\ncompatible variational optimization methods for exploration of low-lying\nexcited states without symmetries that preserve the interpretability of the\nansatz.\n"}
+{"abstract": "  Unmanned aerial vehicles (UAVs), as aerial base stations, are a promising\nsolution for providing wireless communications, thanks to their high\nflexibility and autonomy. Moreover, emerging services, such as extended\nreality, require high-capacity communications. To achieve this, millimeter wave\n(mmWave), and recently, terahertz bands have been considered for UAV\ncommunications. However, communication at these high frequencies requires a\nline-of-sight (LoS) to the terminals, which may be located in 3D space and may\nhave extremely limited direct-line-of-view (LoV) due to blocking objects, like\nbuildings and trees. In this paper, we investigate the problem of determining\n3D placement and orientation of UAVs such that users have guaranteed LoS\ncoverage by at least one UAV and the signal-to-noise ratio (SNR) between the\nUAV-user pairs are maximized. We formulate the problem as an integer linear\nprogramming(ILP) problem and prove its NP-hardness. Next, we propose a\nlow-complexity geometry-based greedy algorithm to solve the problem\nefficiently. Our simulation results show that the proposed algorithm (almost)\nalways guarantees LoS coverage to all users in all considered simulation\nsettings.\n"}
+{"abstract": "  This paper studies chemical kinetic systems which decompose into weakly\nreversible complex factorizable (CF) systems. Among power law kinetic systems,\nCF systems (denoted as PL-RDK systems) are those where branching reactions of a\nreactant complex have identical rows in the kinetic order matrix. Mass action\nand generalized mass action systems (GMAS) are well-known examples. Schmitz's\nglobal carbon cycle model is a previously studied non-complex factorizable (NF)\npower law system (denoted as PL-NDK). We derive novel conditions for the\nexistence of weakly reversible CF-decompositions and present an algorithm for\nverifying these conditions. We discuss methods for identifying independent\ndecompositions, i.e., those where the stoichiometric subspaces of the\nsubnetworks form a direct sum, as such decompositions relate positive\nequilibria sets of the subnetworks to that of the whole network. We then use\nthe results to determine the positive equilibria sets of PL-NDK systems which\nadmit an independent weakly reversible decomposition into PL-RDK systems of PLP\ntype, i.e., the positive equilibria are log-parametrized, which is a broad\ngeneralization of a Deficiency Zero Theorem of Fortun et al. (2019).\n"}
+{"abstract": "  The creation of plausible and controllable 3D human motion animations is a\nlong-standing problem that requires a manual intervention of skilled artists.\nCurrent machine learning approaches can semi-automate the process, however,\nthey are limited in a significant way: they can handle only a single trajectory\nof the expected motion that precludes fine-grained control over the output. To\nmitigate that issue, we reformulate the problem of future pose prediction into\npose completion in space and time where multiple trajectories are represented\nas poses with missing joints. We show that such a framework can generalize to\nother neural networks designed for future pose prediction. Once trained in this\nframework, a model is capable of predicting sequences from any number of\ntrajectories. We propose a novel transformer-like architecture, TrajeVAE, that\nbuilds on this idea and provides a versatile framework for 3D human animation.\nWe demonstrate that TrajeVAE offers better accuracy than the trajectory-based\nreference approaches and methods that base their predictions on past poses. We\nalso show that it can predict reasonable future poses even if provided only\nwith an initial pose.\n"}
+{"abstract": "  Non-orthogonal multiple access (NOMA) is a technology enabler for the fifth\ngeneration and beyond networks, which has shown a great flexibility such that\nit can be readily integrated with other wireless technologies. In this paper,\nwe investigate the interplay between NOMA and generalized space shift keying\n(GSSK) in a hybrid NOMA-GSSK (N-GSSK) network. Specifically, we provide a\ncomprehensive analytical framework and propose a novel suboptimal energy-based\nmaximum likelihood (ML) detector for the N-GSSK scheme. The proposed ML decoder\nexploits the energy of the received signals in order to estimate the active\nantenna indices. Its performance is investigated in terms of pairwise error\nprobability, bit error rate union bound, and achievable rate. Finally, we\nestablish the validity of our analysis through Monte-Carlo simulations and\ndemonstrate that N-GSSK outperforms conventional NOMA and GSSK, particularly in\nterms of spectral efficiency.\n"}
+{"abstract": "  This paper considers wave-based imaging through a heterogeneous (random)\nscattering medium. The goal is to estimate the support of the reflectivity\nfunction of a remote scene from measurements of the backscattered wave field.\nThe proposed imaging methodology is based on the coherent interferometric\n(CINT) approach that exploits the local empirical cross correlations of the\nmeasurements of the wave field. The standard CINT images are known to be robust\n(statistically stable) with respect to the random medium, but the stability\ncomes at the expense of a loss of resolution. This paper shows that a two-point\nCINT function contains the information needed to obtain statistically stable\nand high-resolution images. Different methods to build such images are\npresented, theoretically analyzed and compared with the standard imaging\napproaches using numerical simulations. The first method involves a\nphase-retrieval step to extract the reflectivity function from the modulus of\nits Fourier transform. The second method involves the evaluation of the leading\neigenvector of the two-point CINT imaging function seen as the kernel of a\nlinear operator. The third method uses an optimization step to extract the\nreflectivity function from some cross products of its Fourier transform. The\npresentation is for the synthetic aperture radar data acquisition setup, where\na moving sensor probes the scene with signals emitted periodically and records\nthe resulting backscattered wave. The generalization to other imaging setups,\nwith passive or active arrays of sensors, is discussed briefly.\n"}
+{"abstract": "  We analyse a possible connection between CP violations in the quark and\nlepton sectors, parametrised by the CKM and PMNS phases. If one assumes that CP\nbreaking arises from complex Yukawa couplings, both in the quark and lepton\nsectors, the above connection is not possible in general, since Yukawa\ncouplings in the two sectors have independent flavour structures. We show that\nboth the CKM and PMNS phases can instead be generated by a vacuum phase in a\nclass of two Higgs doublet models, and in this case a connection may be\nestablished. This scenario requires the presence of scalar FCNC at tree level,\nboth in the quark and lepton sectors. The appearance of these FCNC is an\nobstacle and a blessing. An obstacle since one has to analyse which models are\nable to conform to the strict experimental limits on FCNC, both in the quark\nand lepton sectors. A blessing, because this class of models is falsifiable\nsince FCNC arise at a level which can be probed experimentally in the near\nfuture, specially in the processes $h\\to e^\\pm\\tau^\\mp$ and $t\\to h c$. The\nconnection between CP violations in CKM and PMNS is explicitely illustrated in\nmodels with Minimal Flavour Violation.\n"}
+{"abstract": "  We study the first chiral homology group of elliptic curves with coefficients\nin vacuum insertions of a conformal vertex algebra V. We find finiteness\nconditions on V guaranteeing that these homologies are finite dimensional,\ngeneralizing the $C_2$-cofinite, or quasi-lisse condition in the degree 0 case.\nWe determine explicitly the flat connections that these homologies acquire\nunder smooth variation of the elliptic curve, as insertions of the conformal\nvector and the Weierstrass $\\zeta$ function. We construct linear functionals\nassociated to self-extensions of V-modules and prove their convergence under\nsaid finiteness conditions. These linear functionals turn out to be degree 1\nanalogs of the n-point functions in the degree 0 case. As a corollary we prove\nthe vanishing of the first chiral homology group of an elliptic curve with\nvalues in several rational vertex algebras, including affine $sl_2$ at\nnon-negative integral level, the (2,2k+1)-minimal models and arbitrary simple\naffine vertex algebras at level 1. Of independent interest, we prove a Fourier\nspace version of the Borcherds formula.\n"}
+{"abstract": "  For $m\\in\\mathbb{R}$ we introduce the symbol classes $S^m$, $m\\in\\mathbb{R}$,\nconsisting of smooth functions $\\sigma$ on $\\mathbb{R}^{2d}$ such that\n$|\\partial^\\alpha \\sigma(z)|\\leq C_\\alpha (1+|z|^2)^{m/2}$,\n$z\\in\\mathbb{R}^{2d}$, and we show that can be characterized by an intersection\nof different types of modulation spaces. In the case $m=0$ we recapture the\nH\\\"{o}rmander class $S^0_{0,0}$ that can be obtained by intersection of\nsuitable Besov spaces as well. Such spaces contain the Shubin classes\n$\\Gamma^m_\\rho$, $0<\\rho\\leq1$, and can be viewed as their limit case $\\rho=0$.\nWe exhibit almost diagonalization properties for the Gabor matrix of\n$\\tau$-pseudodifferential operators with symbols in such classes, extending the\ncharacterization proved by Gr\\\"{o}chenig and Rzeszotnik. Finally, we compute\nthe Gabor matrix of a Born-Jordan operator, which allows to prove new\nboundedness results for such operators.\n"}
+{"abstract": "  In this work we present the results of the survey carried out on one of the\ndeepest X-ray fields observed by the XMM-Newton satellite. The 1.75 Ms Ultra\nNarrow Deep Field (XMM175UNDF) survey is made by 13 observations taken over 2\nyears with a total exposure time of 1.75 Ms (1.372 Ms after flare-filtered) in\na field of $30' \\times 30' $ centered around the blazar 1ES 1553+113. We\nstacked the 13 observations reaching flux limits of $4.03 \\times 10^{-16} $,\n$1.3 \\times 10^{-15}$, and $9.8 \\times 10^{-16}\\, erg\\, s^{-1}\\, cm^{-2}$ in\nthe soft $(0.2 - 2\\, \\mathrm{keV})$, hard $(2 - 12\\, \\mathrm{keV})$, and full\n$(0.2 - 12\\, \\mathrm{keV})$ bands, respectively. Using a conservative threshold\nof Maximum Likelihood significance of $ML \\geq 6$, corresponding to $3\\sigma$,\nwe detected 301 point-sources for which we derived positions, fluxes in\ndifferent bands, and hardness ratios. Thanks to an optical follow-up carried\nout using the 10.4m the Gran Telescopio Canarias (GTC) on the same field in the\n$u'g'r'i'z'$ bands, combined with WISE/2MASS IR data; we identified 244\noptical/IR counterpart candidates for our X-ray sources and estimated their\nX-ray luminosities, redshift distribution, X-ray/optical $-$ X-ray/IR flux\nratios, and absolute magnitudes. Finally, we divided this subsample in 40\nnon-active sources and 204 AGNs, of which 139 are classified as Seyfert\ngalaxies and 41 as Quasars.\n"}
+{"abstract": "  Artificial intelligence (AI) is disrupting the medical field as advances in\nmodern technology allow common household computers to learn anatomical and\npathological features that distinguish between healthy and disease with the\naccuracy of highly specialized, trained physicians. Computer vision AI\napplications use medical imaging, such as lung chest X-Rays (LCXRs), to\nfacilitate diagnoses by providing second-opinions in addition to a physician's\nor radiologist's interpretation. Considering the advent of the current\nCoronavirus disease (COVID-19) pandemic, LCXRs may provide rapid insights to\nindirectly aid in infection containment, however generating a reliably labeled\nimage dataset for a novel disease is not an easy feat, nor is it of highest\npriority when combating a global pandemic. Deep learning techniques such as\nconvolutional neural networks (CNNs) are able to select features that\ndistinguish between healthy and disease states for other lung pathologies; this\nstudy aims to leverage that body of literature in order to apply image\ntransformations that would serve to balance the lack of COVID-19 LCXR data.\nFurthermore, this study utilizes a simple CNN architecture for high-performance\nmulticlass LCXR classification at 94 percent accuracy.\n"}
+{"abstract": "  Tremendous progress has been made in visual representation learning, notably\nwith the recent success of self-supervised contrastive learning methods.\nSupervised contrastive learning has also been shown to outperform its\ncross-entropy counterparts by leveraging labels for choosing where to contrast.\nHowever, there has been little work to explore the transfer capability of\ncontrastive learning to a different domain. In this paper, we conduct a\ncomprehensive study on the transferability of learned representations of\ndifferent contrastive approaches for linear evaluation, full-network transfer,\nand few-shot recognition on 12 downstream datasets from different domains, and\nobject detection tasks on MSCOCO and VOC0712. The results show that the\ncontrastive approaches learn representations that are easily transferable to a\ndifferent downstream task. We further observe that the joint objective of\nself-supervised contrastive loss with cross-entropy/supervised-contrastive loss\nleads to better transferability of these models over their supervised\ncounterparts. Our analysis reveals that the representations learned from the\ncontrastive approaches contain more low/mid-level semantics than cross-entropy\nmodels, which enables them to quickly adapt to a new task. Our codes and models\nwill be publicly available to facilitate future research on transferability of\nvisual representations.\n"}
+{"abstract": "  Due to the pervasiveness of image capturing devices in every-day life, images\nof individuals are routinely captured. Although this has enabled many benefits,\nit also infringes on personal privacy. A promising direction in research on\nobfuscation of facial images has been the work in the k-same family of methods\nwhich employ the concept of k-anonymity from database privacy. However, there\nare a number of deficiencies of k-anonymity that carry over to the k-same\nmethods, detracting from their usefulness in practice. In this paper, we first\noutline several of these deficiencies and discuss their implications in the\ncontext of facial obfuscation. We then develop a framework through which we\nobtain a formal differentially private guarantee for the obfuscation of facial\nimages in generative machine learning models. Our approach provides a provable\nprivacy guarantee that is not susceptible to the outlined deficiencies of\nk-same obfuscation and produces photo-realistic obfuscated output. In addition,\nwe demonstrate through experimental comparisons that our approach can achieve\ncomparable utility to k-same obfuscation in terms of preservation of useful\nfeatures in the images. Furthermore, we propose a method to achieve\ndifferential privacy for any image (i.e., without restriction to facial images)\nthrough the direct modification of pixel intensities. Although the addition of\nnoise to pixel intensities does not provide the high visual quality obtained\nvia generative machine learning models, it offers greater versatility by\neliminating the need for a trained model. We demonstrate that our proposed use\nof the exponential mechanism in this context is able to provide superior visual\nquality to pixel-space obfuscation using the Laplace mechanism.\n"}
+{"abstract": "  We present a modification of the DFS graph search algorithm, suited for\nfinding long induced paths. We use it to give simple proofs of the following\nresults. We show that the induced size-Ramsey number of paths satisfies\n$\\hat{R}_{\\mathrm{ind}}(P_n)\\leq 5\\cdot 10^7n$, thus giving an explicit\nconstant in the linear bound, improving the previous bound with a large\nconstant from a regularity lemma argument by Haxell, Kohayakawa and {\\L}uczak.\nWe also provide a bound for the $k$-color version, showing that\n$\\hat{R}_{\\mathrm{ind}}^k(P_n)=O(k^3\\log^4k)n$. Finally, we present a new short\nproof of the fact that the binomial random graph in the supercritical regime,\n$G(n,\\frac{1+\\varepsilon}{n})$, contains typically an induced path of length\n$\\Theta(\\varepsilon^2) n$.\n"}
+{"abstract": "  The characterization of nanostructured surfaces with sensitivity in the\nsub-nm range is of high importance for the development of current and next\ngeneration integrated electronic circuits. Modern transistor architectures for\ne.g. FinFETs are realized by lithographic fabrication of complex, well ordered\nnanostructures. Recently, a novel characterization technique based on X-ray\nfluorescence measurements in grazing incidence geometry has been proposed for\nsuch applications. This technique uses the X-ray standing wave field, arising\nfrom an interference between incident and the reflected radiation, as a\nnanoscale sensor for the dimensional and compositional parameters of the\nnanostructure. The element sensitivity of the X-ray fluorescence technique\nallows for a reconstruction of the spatial element distribution using a\nfinite-element method. Due to a high computational time, intelligent\noptimization methods employing machine learning algorithms are essential for a\ntimely provision of results. Here, a sampling of the probability distributions\nby Bayesian optimization is not only fast, it also provides an initial estimate\nof the parameter uncertainties and sensitivities. The high sensitivity of the\nmethod requires a precise knowledge of the material parameters in the modeling\nof the dimensional shape provided that some physical properties of the material\nare known or determined beforehand. The unknown optical constants were\nextracted from an unstructured but otherwise identical layer system by means of\nsoft X-ray reflectometry. The spatial distribution profiles of the different\nelements contained in the grating structure were compared to scanning electron\nand atomic force microscopy and the influence of carbon surface contamination\non the modeling results were discussed.\n"}
+{"abstract": "  We perform computer simulations to probe the magnetic hysteresis in a\ntwo-dimensional ($L^{}_x\\times L^{}_y$) assembly of magnetic nanoparticles as a\nfunction of dipolar interaction strength $h^{}_d$, temperature $T$, aspect\nratio $A^{}_r=L^{}_y/L^{}_x$, and the applied alternating magnetic field's\ndirection. In the absence of magnetic interaction ($h^{}_d\\approx0$) and\nthermal fluctuations ($T=0$ K), the hysteresis follows the Stoner and Wohlfarth\nmodel, as expected. For weak dipolar interaction and substantial temperature,\nthe hysteresis has the dominance of superparamagnetic behaviour, irrespective\nof the applied magnetic field's direction and $A^{}_r$. Interestingly, the\nhysteresis curve has all the characteristics of antiferromagnetic interaction\ndominance for $A^{}_r\\leq6$ and considerable dipolar interaction strength\n($h^{}_d>0.2)$, which is independent of applied magnetic direction. When the\nmagnetic field is applied along the system's shorter axis ($x$-direction), a\nnon-hysteresis straight line is observed with large $h^{}_d$. In the case of\nthe magnetic field applied along the long axis of the sample ($y$-direction),\nferromagnetic interaction dominates the hysteresis for large $h^{}_d$ and\n$A^{}_r>6$. Irrespective of $h^{}_d$ and applied magnetic field's direction,\nthe coercive field $\\mu^{}_oH^{}_c$ and remanence $M^{}_r$ are minimal for\n$A^{}_r\\leq6$ and significant temperature. They are found to increase with\n$h^{}_d$ when $A^{}_r$ is enormous. Remarkably, the variation of hysteresis\nloop area $E^{}_H$ as a function of these parameters is the same as that of the\ncoercive field variation. We believe that the concepts presented in this work\nare relevant in various technological applications such as spintronics and\nmagnetic hyperthermia, in which such self-assembled nanoparticle arrays are\nubiquitous.\n"}
+{"abstract": "  We propose an $A_4$ modular invariant flavor model of leptons, in which both\nCP and modular symmetries are broken spontaneously by the vacuum expectation\nvalue of the modulus $\\tau$. The value of the modulus $\\tau$ is restricted by\nthe observed lepton mixing angles and lepton masses for the normal hierarchy of\nneutrino masses at $3\\sigma$ confidence level. The predictive Dirac CP phase\n$\\delta_{CP}$ is in the ranges $[0^\\circ,50^\\circ]$, $[170^\\circ,175^\\circ]$\nand $[280^\\circ,360^\\circ]$ for ${\\rm Re}\\, [\\tau]<0$, and\n$[0^\\circ,80^\\circ]$, $[185^\\circ,190^\\circ]$ and $[310^\\circ,360^\\circ]$ for\n${\\rm Re}\\,[\\tau]>0$ at $3\\sigma$ confidence level. The sum of three neutrino\nmasses is predicted in $[60, 84]$ meV, and the effective mass for the\n$0\\nu\\beta\\beta$ decay is in [0.003, 3] meV. On the other hand, there is no\nallowed region of the modulus $\\tau$ for the inverted hierarchy of neutrino\nmasses at $3\\sigma$ confidence level. The modulus $\\tau$ links the Dirac CP\nphase to the cosmological baryon asymmetry (BAU) via the leptogenesis. Due to\nthe strong wash-out effect, the predictive baryon asymmetry $Y_B$ can be at\nmost the same order of the observed value. Then, the lightest right-handed\nneutrino mass is restricted in the range of $M_1 =[1.5,\\,6.5] \\times 10^{13}$\nGeV. We find the correlation between the predictive $Y_B$ and the Dirac CP\nphase $\\delta_{CP}$. Only two predictive $\\delta_{CP}$ ranges,\n$[5^\\circ,40^\\circ]$ (${\\rm Re}\\,[\\tau]>0$) and $[320^\\circ,355^\\circ]$ (${\\rm\nRe}\\,[\\tau]<0$) are consistent with the BAU.\n"}
+{"abstract": "  This paper addresses the localization of contacts of an unknown grasped rigid\nobject with its environment, i.e., extrinsic to the robot.\n  We explore the key role that distributed tactile sensing plays in localizing\ncontacts external to the robot, in contrast to the role that aggregated\nforce/torque measurements play in localizing contacts on the robot. When in\ncontact with the environment, an object will move in accordance with the\nkinematic and possibly frictional constraints imposed by that contact. Small\nmotions of the object, which are observable with tactile sensors, indirectly\nencode those constraints and the geometry that defines them.\n  We formulate the extrinsic contact sensing problem as a constraint-based\nestimation. The estimation is subject to the kinematic constraints imposed by\nthe tactile measurements of object motion, as well as the kinematic (e.g.,\nnon-penetration) and possibly frictional (e.g., sticking) constraints imposed\nby rigid-body mechanics.\n  We validate the approach in simulation and with real experiments on the case\nstudies of fixed point and line contacts.\n  This paper discusses the theoretical basis for the value of distributed\ntactile sensing in contrast to aggregated force/torque measurements. It also\nprovides an estimation framework for localizing environmental contacts with\npotential impact in contact-rich manipulation scenarios such as assembling or\npacking.\n"}
+{"abstract": "  Convergence-extension is a morphological process involved in the shaping of\ncomplex biological tissues. It is driven by an intricate interplay of cellular\nbehaviours carefully controlled by chemical and mechanical signalling. A key\ncellular process in convergence-extension is intercalation, i.e. the exchange\nof cell neighbours via T1 transitions. Here we construct and analyse a model\nwith positive feedback between recruitment of force-generating molecules and\nmechanical tension in cell junctions. The model characterises an important\nclass of active T1 events, which occur against locally imposed mechanical\nstresses, as a function of a pulling force applied to the tissue boundary,\ncharacteristic time scales of viscoelastic remodelling in cell junctions, and\nkinetics of myosin motors. Furthermore, we measured the time scale associated\nwith T1 events, and used local strain, cell shapes, and polarisation of myosin\nmotors to show the formation of tension chains and alignment of junctions in\nthe direction of intercalation. Using a minimal setup of several active cells\nembedded in an otherwise passive tissue, we found an optimal range of applied\nexternal pulling forces that activate the cell's contractile machinery and\ninduce an active T1 transition. This initiates local convergence-extension\nflows that extend the tissue perpendicular to the direction of the maximum\nprincipal applied stress and provides an important cellular mechanism for\ntissue-scale remodelling, e.g. as observed during primitive streak formation in\navian embryos.\n"}
+{"abstract": "  Nonlinear mechanics of solids is an exciting field that encompasses both\nbeautiful mathematics, such as the emergence of instabilities and the formation\nof complex patterns, as well as multiple applications. Two-dimensional crystals\nand van der Waals (vdW) heterostructures allow revisiting this field on the\natomic level, allowing much finer control over the parameters and offering\natomistic interpretation of experimental observations. In this work, we\nconsider the formation of instabilities consisting of radially-oriented\nwrinkles around mono- and few-layer \"bubbles\" in two-dimensional vdW\nheterostructures. Interestingly, the shape and wavelength of the wrinkles\ndepend not only on the thickness of the two-dimensional crystal forming the\nbubble, but also on the atomistic structure of the interface between the bubble\nand the substrate, which can be controlled by their relative orientation. We\nargue that the periodic nature of these patterns emanates from an energetic\nbalance between the resistance of the top membrane to bending, which favors\nlarge wavelength of wrinkles, and the membrane-substrate vdW attraction, which\nfavors small wrinkle amplitude. Employing the classical \"Winkler foundation\"\nmodel of elasticity theory, we show that the number of radial wrinkles conveys\na valuable relationship between the bending rigidity of the top membrane and\nthe strength of the vdW interaction. Armed with this relationship, we use our\ndata to demonstrate a nontrivial dependence of the bending rigidity on the\nnumber of layers in the top membrane, which shows two different regimes driven\nby slippage between the layers, and a high sensitivity of the vdW force to the\nalignment between the substrate and the membrane.\n"}
+{"abstract": "  Finding a good way to model probability densities is key to probabilistic\ninference. An ideal model should be able to concisely approximate any\nprobability while being also compatible with two main operations:\nmultiplications of two models (product rule) and marginalization with respect\nto a subset of the random variables (sum rule). In this work, we show that a\nrecently proposed class of positive semi-definite (PSD) models for non-negative\nfunctions is particularly suited to this end. In particular, we characterize\nboth approximation and generalization capabilities of PSD models, showing that\nthey enjoy strong theoretical guarantees. Moreover, we show that we can perform\nefficiently both sum and product rule in closed form via matrix operations,\nenjoying the same versatility of mixture models. Our results open the way to\napplications of PSD models to density estimation, decision theory and\ninference.\n"}
+{"abstract": "  The main aim of this study is to introduce a 2-layered Artificial Neural\nNetwork (ANN) for solving the Black-Scholes partial differential equation (PDE)\nof either fractional or ordinary orders. Firstly, a discretization method is\nemployed to change the model into a sequence of Ordinary Differential Equations\n(ODE). Then each of these ODEs is solved with the aid of an ANN. Adam\noptimization is employed as the learning paradigm since it can add the\nforeknowledge of slowing down the process of optimization when getting close to\nthe actual optimum solution. The model also takes advantage of fine tuning for\nspeeding up the process and domain mapping to confront infinite domain issue.\nFinally, the accuracy, speed, and convergence of the method for solving several\ntypes of Black-Scholes model are reported.\n"}
+{"abstract": "  In the consensus halving problem we are given n agents with valuations over\nthe interval $[0,1]$. The goal is to divide the interval into at most $n+1$\npieces (by placing at most n cuts), which may be combined to give a partition\nof $[0,1]$ into two sets valued equally by all agents. The existence of a\nsolution may be established by the Borsuk-Ulam theorem. We consider the task of\ncomputing an approximation of an exact solution of the consensus halving\nproblem, where the valuations are given by distribution functions computed by\nalgebraic circuits. Here approximation refers to computing a point that\n$\\varepsilon$-close to an exact solution, also called strong approximation. We\nshow that this task is polynomial time equivalent to computing an approximation\nto an exact solution of the Borsuk-Ulam search problem defined by a continuous\nfunction that is computed by an algebraic circuit.\n  The Borsuk-Ulam search problem is the defining problem of the complexity\nclass BU. We introduce a new complexity class BBU to also capture an\nalternative formulation of the Borsuk-Ulam theorem from a computational point\nof view. We investigate their relationship and prove several structural results\nfor these classes as well as for the complexity class FIXP.\n"}
+{"abstract": "  We provide a lower value for the volume of a unit vector field tangent to an\nantipodally Euclidean sphere $\\mathbb{S}^2$ depending on the length of an\nellipse determined by the indexes of its singularities.\n"}
+{"abstract": "  It has been suggested that certain antiferromagnetic topological insulators\ncontain axion quasiparticles (AQs), and that such materials could be used to\ndetect axion dark matter (DM). The AQ is a longitudinal antiferromagnetic spin\nfluctuation coupled to the electromagnetic Chern-Simons term, which, in the\npresence of an applied magnetic field, leads to mass mixing between the AQ and\nthe electric field. The electromagnetic boundary conditions and transmission\nand reflection coefficients are computed. A model for including losses into\nthis system is presented, and the resulting linewidth is computed. It is shown\nhow transmission spectroscopy can be used to measure the resonant frequencies\nand damping coefficients of the material, and demonstrate conclusively the\nexistence of the AQ. The dispersion relation and boundary conditions permit\nresonant conversion of axion DM into THz photons in a material volume that is\nindependent of the resonant frequency, which is tuneable via an applied\nmagnetic field. A parameter study for axion DM detection is performed,\ncomputing boost amplitudes and bandwidths using realistic material properties\nincluding loss. The proposal could allow for detection of axion DM in the mass\nrange between 1 and 10 meV using current and near future technology.\n"}
+{"abstract": "  We propose a concentrated thermionic emission solar cell design, which\ndemonstrates a high solar-to-electricity energy conversion efficiency larger\nthan 10\\% under 600 sun, by harnessing the exceptional electrical, thermal and\nradiative properties of the graphene as a collector electrode. By constructing\nan analytical model that explicitly takes into account the non-Richardson\nbehavior of the thermionic emission current from graphene, space charge effect\nin vacuum gap, and the various irreversible energy losses within the\nsubcomponents, we perform a detailed characterization on the conversion\nefficiency limit and electrical power output characteristics of the proposed\nsystem. We systematically model and compare the energy conversion efficiency of\nvarious configurations of graphene-graphene and graphene-diamond and\ndiamond-diamond thermionic emitter, and show that utilizing diamond films as an\nemitter and graphene as a collector offers the highest maximum efficiency, thus\nrevealing the important role of graphene in achieving high-performance\nthermionic emission solar cell. A maximum efficiency of 12.8\\% under 800 sun\nhas been revealed, which is significantly higher than several existing\nsolid-state solar cell designs, such as the solar-driven thermoelectric and\nthermophotovoltaic converters. Our work thus opens up new avenues to advance\nthe efficiency limit of thermionic solar energy conversion and the development\nof next-generation novel-nanomaterial-based solar energy harvesting technology.\n"}
+{"abstract": "  Borexino is a liquid scintillator detector located at the Laboratori\nNazionale del Gran Sasso, Italy with the main goal to measure solar neutrinos.\nThe experiment recently provided the first direct experimental evidence of\nCNO-cycle neutrinos in the Sun, rejecting the no-CNO signal hypothesis with a\nsignificance greater than 5$\\sigma$ at 99\\%C.L. The intrinsic $^{210}$Bi is an\nimportant background for this analysis due to its similar spectral shape to\nthat of CNO neutrinos. $^{210}$Bi can be measured through its daughter\n$^{210}$Po which can be distinguished through an event-by-event basis via pulse\nshape discrimination. However, this required reducing the convective motions in\nthe scintillator that brought additional $^{210}$Po from peripheral sources.\nThis was made possible through the thermal insulation and stabilization\ncampaign performed between 2015 and 2016. This article will explain the\nstrategy and the different methods performed to extract the $^{210}$Bi upper\nlimit in Phase-III (Jul 2016- Feb 2020) of the experiment through the analysis\nof $^{210}$Po in the cleanest region of the detector called the Low Polonium\nField.\n"}
+{"abstract": "  In this paper, we develop a new regularized version of the Factorization\nMethod for positive operators mapping a complex Hilbert Space into it's dual\nspace. The Factorization Method uses Picard's Criteria to define an indicator\nfunction to image an unknown region. In most applications, the data operator is\ncompact which gives that the singular values can tend to zero rapidly which can\ncause numerical instabilities. The regularization of the Factorization Method\npresented here seeks to avoid the numerical instabilities in applying Picard's\nCriteria. This method allows one to image the interior structure of an object\nwith little a priori information in a computationally simple and analytically\nrigorous way. Here we will focus on an application of this method to diffuse\noptical tomography where will prove that this method can be used to recover an\nunknown subregion from the Dirichlet-to-Neumann mapping. Numerical examples\nwill be presented in two dimensions.\n"}
+{"abstract": "  Dialogue policy learning, a subtask that determines the content of system\nresponse generation and then the degree of task completion, is essential for\ntask-oriented dialogue systems. However, the unbalanced distribution of system\nactions in dialogue datasets often causes difficulty in learning to generate\ndesired actions and responses. In this paper, we propose a\nretrieve-and-memorize framework to enhance the learning of system actions.\nSpecially, we first design a neural context-aware retrieval module to retrieve\nmultiple candidate system actions from the training set given a dialogue\ncontext. Then, we propose a memory-augmented multi-decoder network to generate\nthe system actions conditioned on the candidate actions, which allows the\nnetwork to adaptively select key information in the candidate actions and\nignore noises. We conduct experiments on the large-scale multi-domain\ntask-oriented dialogue dataset MultiWOZ 2.0 and MultiWOZ 2.1. Experimental\nresults show that our method achieves competitive performance among several\nstate-of-the-art models in the context-to-response generation task.\n"}
+{"abstract": "  Our observations of TMC-1 with the Yebes 40 m radio telescope in the\n31.0-50.3 GHz range allowed us to detect a group of unidentified lines, showing\na complex line pattern indicative of an open-shell species. {}The observed\nfrequencies of these lines and the similarity of the spectral pattern with that\nof the 2$_{0,2}$-1$_{0,1}$ rotational transition of H$_2$CCN indicate that the\nlines arise from the deuterated cyanomethyl radical, HDCCN. Using Fourier\ntransform microwave spectroscopy experiments combined with electric discharges,\nwe succeeded in producing the radical HDCCN in the laboratory and observed its\n1$_{0,1}$-0$_{0,0}$ and 2$_{0,2}$-1$_{0,1}$ rotational transitions. From our\nobservations and assuming a rotational temperature of 5 K, we derive an\nabundance ratio H$_2$CCN/HDCCN=20$\\pm$4. The high abundance of the deuterated\nform of H$_2$CCN is well accounted for by a standard gas-phase model, in which\ndeuteration is driven by deuteron transfer from the H$_2$D$^+$ molecular ion.\n"}
+{"abstract": "  This is a translation from Latin of 'De novo oscillationum genere', which was\nmotivated by Krafft's accidental observation of a suspended clock setting\nitself in constant motion as a pendulum. This publication, in turn, motivated\nEuler to write 'De novo genere oscillationum', in which Euler derived for the\nfirst time, the differential equation for the (undamped) simple harmonic\noscillator under harmonic excitation.\n"}
+{"abstract": "  Blogs are a source of grey literature which are widely adopted by software\npractitioners for disseminating opinion and experience. Analysing such articles\ncan provide useful insights into the state-of-practice for software engineering\nresearch. However, there are challenges in identifying higher quality content\nfrom the large quantity of articles available. Credibility assessment can help\nin identifying quality content, though there is a lack of existing corpora.\nCredibility is typically measured through a series of conceptual criteria, with\n'argumentation' and 'evidence' being two important criteria.\n  We create a corpus labelled for argumentation and evidence that can aid the\ncredibility community. The corpus consists of articles from the blog of a\nsingle software practitioner and is publicly available.\n  Three annotators label the corpus with a series of conceptual credibility\ncriteria, reaching an agreement of 0.82 (Fleiss' Kappa). We present preliminary\nanalysis of the corpus by using it to investigate the identification of claim\nsentences (one of our ten labels).\n  We train four systems (Bert, KNN, Decision Tree and SVM) using three feature\nsets (Bag of Words, Topic Modelling and InferSent), achieving an F1 score of\n0.64 using InferSent and a Linear SVM.\n  Our preliminary results are promising, indicating that the corpus can help\nfuture studies in detecting the credibility of grey literature. Future research\nwill investigate the degree to which the sentence level annotations can infer\nthe credibility of the overall document.\n"}
+{"abstract": "  ORCID is a scientific infrastructure created to solve the problem of author\nname ambiguity. Over the years ORCID has also become a useful source for\nstudying academic activities reported by researchers. Our objective in this\nresearch was to use ORCID to analyze one of these research activities: the\npublication of datasets. We illustrate how the identification of datasets that\nshared in researchers' ORCID profiles enables the study of the characteristics\nof the researchers who have produced them. To explore the relevance of ORCID to\nstudy data sharing practices we obtained all ORCID profiles reporting at least\none dataset in their \"works\" list, together with information related to the\nindividual researchers producing the datasets. The retrieved data was organized\nand analyzed in a SQL database hosted at CWTS. Our results indicate that\nDataCite is by far the most important data source for providing information\nabout datasets recorded in ORCID. There is also a substantial overlap between\nDataCite records with other repositories (Figshare, Dryad, and Zenodo). The\nanalysis of the distribution of researchers producing datasets shows that the\ntop six countries with more data producers, also have a relatively higher\npercentage of people who have produced datasets out of total researchers with\ndatasets than researchers in the total ORCID. By disciplines, researchers that\nbelong to the areas of Natural Sciences and Medicine and Life Sciences are\nthose with the largest amount of reported datasets. Finally, we observed that\nresearchers who have started their PhD around 2015 published their first\ndataset earlier that those researchers that started their PhD before. The work\nconcludes with some reflections of the possibilities of ORCID as a relevant\nsource for research on data sharing practices.\n"}
+{"abstract": "  The new coronavirus outbreak has been officially declared a global pandemic\nby the World Health Organization. To grapple with the rapid spread of this\nongoing pandemic, most countries have banned indoor and outdoor gatherings and\nordered their residents to stay home. Given the developing situation with\ncoronavirus, mental health is an important challenge in our society today. In\nthis paper, we discuss the investigation of social media postings to detect\nsignals relevant to depression. To this end, we utilize topic modeling features\nand a collection of psycholinguistic and mental-well-being attributes to\ndevelop statistical models to characterize and facilitate representation of the\nmore subtle aspects of depression. Furthermore, we predict whether signals\nrelevant to depression are likely to grow significantly as time moves forward.\nOur best classifier yields F-1 scores as high as 0.8 and surpasses the utilized\nbaseline by a considerable margin, 0.173. In closing, we propose several future\nresearch avenues.\n"}
+{"abstract": "  Educational technologies, and the systems of schooling in which they are\ndeployed, enact particular ideologies about what is important to know and how\nlearners should learn. As artificial intelligence technologies -- in education\nand beyond -- may contribute to inequitable outcomes for marginalized\ncommunities, various approaches have been developed to evaluate and mitigate\nthe harmful impacts of AI. However, we argue in this paper that the dominant\nparadigm of evaluating fairness on the basis of performance disparities in AI\nmodels is inadequate for confronting the systemic inequities that educational\nAI systems (re)produce. We draw on a lens of structural injustice informed by\ncritical theory and Black feminist scholarship to critically interrogate\nseveral widely-studied and widely-adopted categories of educational AI and\nexplore how they are bound up in and reproduce historical legacies of\nstructural injustice and inequity, regardless of the parity of their models'\nperformance. We close with alternative visions for a more equitable future for\neducational AI.\n"}
+{"abstract": "  We prove that there is an action of the cyclic group $\\mathbf{C}_2$ on the\n$10$-dimensional Floyd manifold which turns it into a conjugation manifold. The\nsubmanifold of fixed points is the $5$-dimensional Floyd manifold, whose\ncohomology is isomorphic to that of the large one, scaled down by dividing the\ncohomological degree by a factor two.\n"}
+{"abstract": "  Botnets are one of the online threats with the biggest presence, causing\nbillionaire losses to global economies. Nowadays, the increasing number of\ndevices connected to the Internet makes it necessary to analyze large amounts\nof network traffic data. In this work, we focus on increasing the performance\non botnet traffic classification by selecting those features that further\nincrease the detection rate. For this purpose we use two feature selection\ntechniques, Information Gain and Gini Importance, which led to three\npre-selected subsets of five, six and seven features. Then, we evaluate the\nthree feature subsets along with three models, Decision Tree, Random Forest and\nk-Nearest Neighbors. To test the performance of the three feature vectors and\nthe three models we generate two datasets based on the CTU-13 dataset, namely\nQB-CTU13 and EQB-CTU13. We measure the performance as the macro averaged F1\nscore over the computational time required to classify a sample. The results\nshow that the highest performance is achieved by Decision Trees using a five\nfeature set which obtained a mean F1 score of 85% classifying each sample in an\naverage time of 0.78 microseconds.\n"}
+{"abstract": "  Causal reasoning is the main learning and explanation tool used by humans. AI\nsystems should possess causal reasoning capabilities to be deployed in the real\nworld with trust and reliability. Introducing the ideas of causality to machine\nlearning helps in providing better learning and explainable models.\nExplainability, causal disentanglement are some important aspects of any\nmachine learning model. Causal explanations are required to believe in a\nmodel's decision and causal disentanglement learning is important for transfer\nlearning applications. We exploit the ideas of causality to be used in deep\nlearning models to achieve better and causally explainable models that are\nuseful in fairness, disentangled representation, etc.\n"}
+{"abstract": "  Although it is widely known that Gaussian processes can be conditioned on\nobservations of the gradient, this functionality is of limited use due to the\nprohibitive computational cost of $\\mathcal{O}(N^3 D^3)$ in data points $N$ and\ndimension $D$. The dilemma of gradient observations is that a single one of\nthem comes at the same cost as $D$ independent function evaluations, so the\nlatter are often preferred. Careful scrutiny reveals, however, that derivative\nobservations give rise to highly structured kernel Gram matrices for very\ngeneral classes of kernels (inter alia, stationary kernels). We show that in\nthe low-data regime $N<D$, the Gram matrix can be decomposed in a manner that\nreduces the cost of inference to $\\mathcal{O}(N^2D + (N^2)^3)$ (i.e., linear in\nthe number of dimensions) and, in special cases, to $\\mathcal{O}(N^2D + N^3)$.\nThis reduction in complexity opens up new use-cases for inference with\ngradients especially in the high-dimensional regime, where the\ninformation-to-cost ratio of gradient observations significantly increases. We\ndemonstrate this potential in a variety of tasks relevant for machine learning,\nsuch as optimization and Hamiltonian Monte Carlo with predictive gradients.\n"}
+{"abstract": "  Two-dimensional nanomaterials, such as graphene, have been extensively\nstudied because of their outstanding physical properties. Structure and\ngeometry optimization of nanopores on such materials is beneficial for their\nperformances in real-world engineering applications, like water desalination.\nHowever, the optimization process often involves very large number of\nexperiments or simulations which are expensive and time-consuming. In this\nwork, we propose a graphene nanopore optimization framework via the combination\nof deep reinforcement learning (DRL) and convolutional neural network (CNN) for\nefficient water desalination. The DRL agent controls the growth of nanopore by\ndetermining the atom to be removed at each timestep, while the CNN predicts the\nperformance of nanoporus graphene for water desalination: the water flux and\nion rejection at a certain external pressure. With the synchronous feedback\nfrom CNN-accelerated desalination performance prediction, our DRL agent can\noptimize the nanoporous graphene efficiently in an online manner. Molecular\ndynamics (MD) simulations on promising DRL-designed graphene nanopores show\nthat they have higher water flux while maintaining rival ion rejection rate\ncompared to the normal circular nanopores. Semi-oval shape with rough edges\ngeometry of DRL-designed pores is found to be the key factor for their high\nwater desalination performance. Ultimately, this study shows that DRL can be a\npowerful tool for material design.\n"}
+{"abstract": "  In Generalized Zero-Shot Learning (GZSL), unseen categories (for which no\nvisual data are available at training time) can be predicted by leveraging\ntheir class embeddings (e.g., a list of attributes describing them) together\nwith a complementary pool of seen classes (paired with both visual data and\nclass embeddings). Despite GZSL is arguably challenging, we posit that knowing\nin advance the class embeddings, especially for unseen categories, is an actual\nlimit of the applicability of GZSL towards real-world scenarios. To relax this\nassumption, we propose Open Zero-Shot Learning (OZSL) to extend GZSL towards\nthe open-world settings. We formalize OZSL as the problem of recognizing seen\nand unseen classes (as in GZSL) while also rejecting instances from unknown\ncategories, for which neither visual data nor class embeddings are provided. We\nformalize the OZSL problem introducing evaluation protocols, error metrics and\nbenchmark datasets. We also suggest to tackle the OZSL problem by proposing the\nidea of performing unknown feature generation (instead of only unseen features\ngeneration as done in GZSL). We achieve this by optimizing a generative process\nto sample unknown class embeddings as complementary to the seen and the unseen.\nWe intend these results to be the ground to foster future research, extending\nthe standard closed-world zero-shot learning (GZSL) with the novel open-world\ncounterpart (OZSL).\n"}
+{"abstract": "  Open-domain conversational search assistants aim at answering user questions\nabout open topics in a conversational manner. In this paper we show how the\nTransformer architecture achieves state-of-the-art results in key IR tasks,\nleveraging the creation of conversational assistants that engage in open-domain\nconversational search with single, yet informative, answers. In particular, we\npropose an open-domain abstractive conversational search agent pipeline to\naddress two major challenges: first, conversation context-aware search and\nsecond, abstractive search-answers generation. To address the first challenge,\nthe conversation context is modeled with a query rewriting method that unfolds\nthe context of the conversation up to a specific moment to search for the\ncorrect answers. These answers are then passed to a Transformer-based re-ranker\nto further improve retrieval performance. The second challenge, is tackled with\nrecent Abstractive Transformer architectures to generate a digest of the top\nmost relevant passages. Experiments show that Transformers deliver a solid\nperformance across all tasks in conversational search, outperforming the best\nTREC CAsT 2019 baseline.\n"}
+{"abstract": "  Interpretability methods for image classification assess model\ntrustworthiness by attempting to expose whether the model is systematically\nbiased or attending to the same cues as a human would. Saliency methods for\nfeature attribution dominate the interpretability literature, but these methods\ndo not address semantic concepts such as the textures, colors, or genders of\nobjects within an image. Our proposed Robust Concept Activation Vectors (RCAV)\nquantifies the effects of semantic concepts on individual model predictions and\non model behavior as a whole. RCAV calculates a concept gradient and takes a\ngradient ascent step to assess model sensitivity to the given concept. By\ngeneralizing previous work on concept activation vectors to account for model\nnon-linearity, and by introducing stricter hypothesis testing, we show that\nRCAV yields interpretations which are both more accurate at the image level and\nrobust at the dataset level. RCAV, like saliency methods, supports the\ninterpretation of individual predictions. To evaluate the practical use of\ninterpretability methods as debugging tools, and the scientific use of\ninterpretability methods for identifying inductive biases (e.g. texture over\nshape), we construct two datasets and accompanying metrics for realistic\nbenchmarking of semantic interpretability methods. Our benchmarks expose the\nimportance of counterfactual augmentation and negative controls for quantifying\nthe practical usability of interpretability methods.\n"}
+{"abstract": "  In 1973, Diestel published his seminal paper `Grothendieck spaces and vector\nmeasures' that drew a connection between Grothendieck spaces (Banach spaces for\nwhich weak- and weak*-sequential convergences in the dual space coincide) and\nvector measures. This connection was developed in his book with J. Uhl Jr.\n`Vector measures'. Additionally, Diestel's paper included a section with\nseveral open problems about the structural properties of Grothendieck spaces,\nand only half of them have been solved to this day.\n  The present paper aims at synthetically presenting the state of the art at\nsubjectively selected corners of the theory of Banach spaces with the\nGrothendieck property, describing the main examples of spaces with this\nproperty, recording the solutions to Diestel's problems, providing\ngeneralisations/extensions or new proofs of various results concerning\nGrothendieck spaces, and adding to the list further problems that we believe\nare of relevance and may reinvigorate a better-structured development of the\ntheory.\n"}
+{"abstract": "  Time-dependent wave equations represent an important class of partial\ndifferential equations (PDE) for describing wave propagation phenomena, which\nare often formulated over unbounded domains. Given a compactly supported\ninitial condition, classical numerical methods reduce such problems to bounded\ndomains using artificial boundary condition (ABC). In this work, we present a\nmachine-learning method to solve this type of equations as an alternative to\nABCs. Specifically, the mapping from the initial conditions to the PDE solution\nis represented by a neural network, trained using wave packets that are\nparameterized by their band width and wave numbers. The accuracy is tested for\nboth the second-order wave equation and the Schrodinger equation, including the\nnonlinear Schrodinger equation. We examine the accuracy from both\ninterpolations and extrapolations. For initial conditions lying in the training\nset, the learned map has good interpolation accuracy, due to the approximation\nproperty of deep neural networks. The learned map also exhibits some good\nextrapolation accuracy. We also demonstrate the effectiveness of the method for\nproblems in irregular domains. Overall, the proposed method provides an\ninteresting alternative for finite-time simulation of wave propagation.\n"}
+{"abstract": "  One of the biggest challenges hindering progress in low-resource and\nmultilingual machine translation is the lack of good evaluation benchmarks.\nCurrent evaluation benchmarks either lack good coverage of low-resource\nlanguages, consider only restricted domains, or are low quality because they\nare constructed using semi-automatic procedures. In this work, we introduce the\nFLORES-101 evaluation benchmark, consisting of 3001 sentences extracted from\nEnglish Wikipedia and covering a variety of different topics and domains. These\nsentences have been translated in 101 languages by professional translators\nthrough a carefully controlled process. The resulting dataset enables better\nassessment of model quality on the long tail of low-resource languages,\nincluding the evaluation of many-to-many multilingual translation systems, as\nall translations are multilingually aligned. By publicly releasing such a\nhigh-quality and high-coverage dataset, we hope to foster progress in the\nmachine translation community and beyond.\n"}
+{"abstract": "  We present the EVONANO platform for the evolution of nanomedicines with\napplication to anti-cancer treatments. EVONANO includes a simulator to grow\ntumours, extract representative scenarios, and then simulate nanoparticle\ntransport through these scenarios to predict nanoparticle distribution. The\nnanoparticle designs are optimised using machine learning to efficiently find\nthe most effective anti-cancer treatments. We demonstrate our platform with two\nexamples optimising the properties of nanoparticles and treatment to\nselectively kill cancer cells over a range of tumour environments.\n"}
+{"abstract": "  In this work, the problem of predicting dropout risk in undergraduate studies\nis addressed from a perspective of algorithmic fairness. We develop a machine\nlearning method to predict the risks of university dropout and\nunderperformance. The objective is to understand if such a system can identify\nstudents at risk while avoiding potential discriminatory biases. When modeling\nboth risks, we obtain prediction models with an Area Under the ROC Curve (AUC)\nof 0.77-0.78 based on the data available at the enrollment time, before the\nfirst year of studies starts. This data includes the students' demographics,\nthe high school they attended, and their admission (average) grade. Our models\nare calibrated: they produce estimated probabilities for each risk, not mere\nscores. We analyze if this method leads to discriminatory outcomes for some\nsensitive groups in terms of prediction accuracy (AUC) and error rates\n(Generalized False Positive Rate, GFPR, or Generalized False Negative Rate,\nGFNR). The models exhibit some equity in terms of AUC and GFNR along groups.\nThe similar GFNR means a similar probability of failing to detect risk for\nstudents who drop out. The disparities in GFPR are addressed through a\nmitigation process that does not affect the calibration of the model.\n"}
+{"abstract": "  The dynamics of a molecule in a magnetic field is significantly different\nform its zero-field counterpart. One important difference in the presence of a\nfield is the Lorentz force acting on the nuclei, which can be decomposed as the\nsum of the bare nuclear Lorentz force and a screening force due to the\nelectrons. This screening force is calculated from the Berry curvature and can\nchange the dynamics qualitatively. It is therefore important to include the\ncontributions from the Berry curvature in molecular dynamics simulations in a\nmagnetic field. In this work, we present a scheme for calculating the Berry\ncurvature numerically, by a finite-difference technique, addressing challenges\nrelated to the arbitrary global phase of the wave function. The Berry curvature\nis calculated as a function of bond distance for H$_2$ at the restricted and\nunrestricted Hartree--Fock levels of theory and for CH$^{+}$ as a function of\nthe magnetic field strength at the restricted Hartree--Fock level of theory.\nThe calculations are carried out using basis sets of contracted Gaussian\nfunctions equipped with London phase factors (London orbitals) to ensure\ngauge-origin invariance. In the paper, we also interpret the Berry curvature in\nterms of atomic charges and discuss its convergence in basis sets with and\nwithout London phase factors. Calculation of the Berry curvature allows for its\ninclusion in \\textit{ab initio} molecular dynamics simulations in a magnetic\nfield.\n"}
+{"abstract": "  Modulation of the optical index by means of atomic and material resonances\nprovides a basis for controlling light propagation in natural and artificially\nfabricated materials. In addition, recent advances in the tuning of\nspatiotemporal couplings of ultrashort laser pulses have enabled almost\narbitrary control over the group velocity of light. Here, using femtosecond\ntime-resolved microscopy and numerical calculations, we investigate the\nspatiotemporal dynamics of a surface plasmon polariton wave packet (SPP WP)\nthat interacts with a plasmonic nanocavity. The nanocavity consists of\nmetal-insulator-metal multilayer films that function as subwavelength meta-atom\npossessing tunable discretized eigenmodes. When a chirp-induced femtosecond SPP\nWP is incident on a nanocavity, only the spectral component matching the\nresonance energy is transmitted. This spectral clipping effect is accompanied\nby a spatial shift of the WP. The shift can be adjusted in either the positive\nor negative direction by controlling the resonance energy or the chirp. If this\nspatial shift is regarded as a modulation of the apparent group velocity in the\nnanocavity, the range of modulation includes superluminal, subluminal, and\nnegative group velocities.\n"}
+{"abstract": "  In this paper, we firstly present a dataset (X4K1000FPS) of 4K videos of 1000\nfps with the extreme motion to the research community for video frame\ninterpolation (VFI), and propose an extreme VFI network, called XVFI-Net, that\nfirst handles the VFI for 4K videos with large motion. The XVFI-Net is based on\na recursive multi-scale shared structure that consists of two cascaded modules\nfor bidirectional optical flow learning between two input frames (BiOF-I) and\nfor bidirectional optical flow learning from target to input frames (BiOF-T).\nThe optical flows are stably approximated by a complementary flow reversal\n(CFR) proposed in BiOF-T module. During inference, the BiOF-I module can start\nat any scale of input while the BiOF-T module only operates at the original\ninput scale so that the inference can be accelerated while maintaining highly\naccurate VFI performance. Extensive experimental results show that our XVFI-Net\ncan successfully capture the essential information of objects with extremely\nlarge motions and complex textures while the state-of-the-art methods exhibit\npoor performance. Furthermore, our XVFI-Net framework also performs comparably\non the previous lower resolution benchmark dataset, which shows a robustness of\nour algorithm as well. All source codes, pre-trained models, and proposed\nX4K1000FPS datasets are publicly available at\nhttps://github.com/JihyongOh/XVFI.\n"}
+{"abstract": "  Inward migration of giant planets is predicted by hydrodynamical simulations\nduring the gas phase of the protoplanetary disc. The phenomenon is also invoked\nto explain resonant and near-resonant exoplanetary system structures. The early\ninward migration may also have affected our Solar System and sculpted its\ndifferent minor planet reservoirs. In this study we explore how the early\ninward migration of the giant planets shapes the Kuiper Belt. We test different\nscenarios with only Neptune and Uranus and with all the four giant planets,\nincluding also some models with the subsequent outward planetesimal-driven\nmigration of Neptune after the gas dispersal. We find objects populating mean\nmotion resonances even when Neptune and Uranus do not migrate at all or only\nmigrate inwards. When the planets are fixed, planetesimals stick only\ntemporarily to the mean motion resonances, while inwards migration yields a new\nchannel to populate the resonances without invoking convergent migration. In\nthese cases, however, it is hard to populate mean motion resonances that do not\ncross the planetesimal disc (such as 2:1 and 5:2) and there is a lack of\nresonant KBOs that cross Neptune's orbit. These Neptune crossers are an\nunambiguous signature of the outward migration of Neptune. The starting\nposition and the growth rate of Neptune matters for the contamination of the\nclassical Kuiper belt region from neighbouring regions. The eccentricity and\ninclination space of the hot classicals and the scattered disc region become\nmuch more populated when all the giant planets are included. The 5:2 resonance\nwith Neptune becomes increasingly populated with deeper inward migrations of\nNeptune. The overall inclination distribution, however, is still narrower than\nfrom observations, as is generally the case for Kuiper belt population models.\n"}
+{"abstract": "  The Everett interpretation of quantum mechanics divides naturally into two\nparts: first, the interpretation of the structure of the quantum state, in\nterms of branching, and second, the interpretation of this branching structure\nin terms of probability. This is the second of two reviews of the Everett\ninterpretation, and focuses on probability. Branching processes are identified\nas chance processes, and the squares of branch amplitudes are chances. Since\nbranching is emergent, physical probability is emergent as well.\n"}
+{"abstract": "  Long-distance transfer of quantum information in architectures based on\nquantum dot spin qubits will be necessary for their scalability. One way of\nachieving it is to simply move the electron between two quantum registers.\nPrecise control over the electron shuttling through a chain of tunnel-coupled\nquantum dots is possible when interdot energy detunings are changed\nadiabatically. Deterministic character of shuttling is however endangered by\ncoupling of the transferred electron to thermal reservoirs: sources of\nfluctuations of electric fields, and lattice vibrations. We theoretically\nanalyse how the electron transfer between two quantum dots is affected by\nelectron-phonon scattering, and interaction with sources of $1/f$ and Johnson\ncharge noise in both detuning and tunnel coupling. The electron-phonon\nscattering turns out to be irrelevant in Si quantum dots, while a competition\nbetween the effects of charge noise and Landau-Zener effect leads to an\nexistence of optimal detuning sweep rate, at which probability of leaving the\nelectron behind is minimal. In GaAs quantum dots, on the other hand, coupling\nto phonons is strong enough to make the phonon-assisted processes of interdot\ntransfer dominate over influence of charge noise. The probability of leaving\nthe electron behind depends then monotonically on detuning sweep rate, and\nvalues much smaller than in silicon can be obtained for slow sweeps. However,\nafter taking into account limitations on transfer time imposed by need for\npreservation of electron's spin coherence, minimal probabilities of leaving the\nelectron behind in both GaAs- and Si-based double quantum dots turn out to be\nof the same order of magnitude. Bringing them down below $10^{-3}$ requires\ntemperatures $\\leq \\! 100$ mK and tunnel couplings above $20$ $\\mu$eV.\n"}
+{"abstract": "  Finding shape correspondences can be formulated as an NP-hard quadratic\nassignment problem (QAP) that becomes infeasible for shapes with high sampling\ndensity. A promising research direction is to tackle such quadratic\noptimization problems over binary variables with quantum annealing, which\nallows for some problems a more efficient search in the solution space.\nUnfortunately, enforcing the linear equality constraints in QAPs via a penalty\nsignificantly limits the success probability of such methods on currently\navailable quantum hardware. To address this limitation, this paper proposes\nQ-Match, i.e., a new iterative quantum method for QAPs inspired by the\nalpha-expansion algorithm, which allows solving problems of an order of\nmagnitude larger than current quantum methods. It implicitly enforces the QAP\nconstraints by updating the current estimates in a cyclic fashion. Further,\nQ-Match can be applied iteratively, on a subset of well-chosen correspondences,\nallowing us to scale to real-world problems. Using the latest quantum annealer,\nthe D-Wave Advantage, we evaluate the proposed method on a subset of QAPLIB as\nwell as on isometric shape matching problems from the FAUST dataset.\n"}
+{"abstract": "  Recently, researchers have utilized neural networks to accurately solve\npartial differential equations (PDEs), enabling the mesh-free method for\nscientific computation. Unfortunately, the network performance drops when\nencountering a high nonlinearity domain. To improve the generalizability, we\nintroduce the novel approach of employing multi-task learning techniques, the\nuncertainty-weighting loss and the gradients surgery, in the context of\nlearning PDE solutions. The multi-task scheme exploits the benefits of learning\nshared representations, controlled by cross-stitch modules, between multiple\nrelated PDEs, which are obtainable by varying the PDE parameterization\ncoefficients, to generalize better on the original PDE. Encouraging the network\npay closer attention to the high nonlinearity domain regions that are more\nchallenging to learn, we also propose adversarial training for generating\nsupplementary high-loss samples, similarly distributed to the original training\ndistribution. In the experiments, our proposed methods are found to be\neffective and reduce the error on the unseen data points as compared to the\nprevious approaches in various PDE examples, including high-dimensional\nstochastic PDEs.\n"}
+{"abstract": "  We construct a triangle equivalence between the singularity categories of two\nisolated cyclic quotient singularities of Krull dimensions two and three,\nrespectively. This is the first example of a singular equivalence involving\nconnected commutative algebras of odd and even Krull dimension. In combination\nwith Orlov's localization result, this gives further singular equivalences\nbetween certain quasi-projective varieties of dimensions two and three,\nrespectively.\n"}
+{"abstract": "  Conditional Variance Estimation (CVE) is a novel sufficient dimension\nreduction (SDR) method for additive error regressions with continuous\npredictors and link function. It operates under the assumption that the\npredictors can be replaced by a lower dimensional projection without loss of\ninformation. In contrast to the majority of moment based sufficient dimension\nreduction methods, Conditional Variance Estimation is fully data driven, does\nnot require the restrictive linearity and constant variance conditions, and is\nnot based on inverse regression. CVE is shown to be consistent and its\nobjective function to be uniformly convergent. CVE outperforms the mean average\nvariance estimation, (MAVE), its main competitor, in several simulation\nsettings, remains on par under others, while it always outperforms the usual\ninverse regression based linear SDR methods, such as Sliced Inverse Regression.\n"}
+{"abstract": "  In this pedagogical work we reviewed the mathematical formalism and the\nphysical interpretation, based on statistical mechanics, of the meta-heuristics\ncalled simulated annealing. Moreover, we presented the mathematical formulation\nof the algorithm and why it is capable to yield the optimal solution or a good\napproximated solution of a given problem. Furthermore, we described the\ntravelling salesman problem, showing its interpretation as a Markov Chain and\nhow the simulated annealing can be used to optimize it and we did its\nsimulations for two scenarios. Firstly, for 50 cities distributed around a\ncircle and we found the best solution. Finally, we applied the meta-heuristic\nin a another instance, with 100 nodes random uniformly distributed in a square,\nand one shows that it allows finding a good solution.\n"}
+{"abstract": "  We study a spin 1/2 fermion in a thick braneworld in the context of\nteleparallel $f(T, B)$ gravity. Here, $f(T,B)$ is such that\n$f_1(T,B)=T+k_1B^{n_1}$ and $f_2(T,B)=B+k_2T^{n_2}$, where $n_{1,2}$ and\n$k_{1,2}$ are parameters that control the influence of torsion and the boundary\nterm. We assume Yukawa coupling, where one scalar field is coupled to a Dirac\nspinor field. We show how the $n_{1,2}$ and $k_{1,2}$ parameters control the\nwidth of the massless Kaluza-Klein mode, the breadth of non-normalized massive\nfermionic modes, and the properties of the analogue quantum-potential near the\norigin.\n"}
+{"abstract": "  The cost of annotating training data has traditionally been a bottleneck for\nsupervised learning approaches. The problem is further exacerbated when\nsupervised learning is applied to a number of correlated tasks simultaneously\nsince the amount of labels required scales with the number of tasks. To\nmitigate this concern, we propose an active multitask learning algorithm that\nachieves knowledge transfer between tasks. The approach forms a so-called\ncommittee for each task that jointly makes decisions and directly shares data\nacross similar tasks. Our approach reduces the number of queries needed during\ntraining while maintaining high accuracy on test data. Empirical results on\nbenchmark datasets show significant improvements on both accuracy and number of\nquery requests.\n"}
+{"abstract": "  Quantum imaging with undetected photons (QIUP) is a unique imaging technique\nthat does not require the detection of the light used for illuminating the\nobject. The technique requires a correlated pair of photons. In the existing\nimplementations of QIUP, the imaging is enabled by the momentum correlation\nbetween the twin photons. We investigate the complementary scenario in which\nthe imaging is instead enabled by the position correlation between the two\nphotons. We present a general theory and show that the properties of the images\nobtained in these two cases are significantly distinct.\n"}
+{"abstract": "  Tropical cyclones are one of the most powerful and destructive natural\nphenomena on earth. Tropical storms and heavy rains can cause floods, which\nlead to human lives and economic loss. Devastating winds accompanying cyclones\nheavily affect not only the coastal regions, even distant areas. Our study\nfocuses on the intensity estimation, particularly cyclone grade and maximum\nsustained surface wind speed (MSWS) of a tropical cyclone over the North Indian\nOcean. We use various machine learning algorithms to estimate cyclone grade and\nMSWS. We have used the basin of origin, date, time, latitude, longitude,\nestimated central pressure, and pressure drop as attributes of our models. We\nuse multi-class classification models for the categorical outcome variable,\ncyclone grade, and regression models for MSWS as it is a continuous variable.\nUsing the best track data of 28 years over the North Indian Ocean, we estimate\ngrade with an accuracy of 88% and MSWS with a root mean square error (RMSE) of\n2.3. For higher grade categories (5-7), accuracy improves to an average of\n98.84%. We tested our model with two recent tropical cyclones in the North\nIndian Ocean, Vayu and Fani. For grade, we obtained an accuracy of 93.22% and\n95.23% respectively, while for MSWS, we obtained RMSE of 2.2 and 3.4 and $R^2$\nof 0.99 and 0.99, respectively.\n"}
+{"abstract": "  We find the index of symmetry for all solvable three-dimensional Lie groups\nwith a left-invariant metric. When combined with the work of Reggiani on\nunimodular three-dimensional Lie groups, the index of symmetry is then known\nfor all three-dimensional Lie groups with a left-invariant metric. Similar to\nthe unimodular case, for every solvable three-dimensional Lie algebra there is\nat least one left-invariant metric for which the index of symmetry is positive.\nAlso, the index is never equal to 2.\n"}
+{"abstract": "  We study in this work the convergence of a sequence of asymptotically\ncritical self-exciting systems. In these systems, we use multivariate marked\nHawkes point processes to describe the event arrivals. Under mild assumptions\nwe prove the weak convergence of their rescaled density processes to a\nmulti-type continuous-state branching process with immigration (CBI-process).\nIn addition, we also provide two scaling limits for their shot noise processes\nwith limits being functionals of the multi-type CBI-process. Finally, we\nprovide a Hawkes representation for Crump-Mode-Jagers branching processes and\napply our limit results to study their asymptotic behavior. Specially, we\nobserve an interesting phenomenon, known as state space collapse in queueing\ntheory, in the population structure described by two measure-valued processes:\nage-distribution process and residual-life distribution process. Indeed, under\na convergence condition on the life-length distributions, both the two rescaled\nmeasure-valued processes converge weakly to a common measure-valued diffusion\nprocess, which can be characterized as a lifting map of a multi-type\nCBI-process associated with the limit excess life-length distribution.\n"}
+{"abstract": "  We tackle the problem of learning object detectors without supervision.\nDifferently from weakly-supervised object detection, we do not assume\nimage-level class labels. Instead, we extract a supervisory signal from\naudio-visual data, using the audio component to \"teach\" the object detector.\nWhile this problem is related to sound source localisation, it is considerably\nharder because the detector must classify the objects by type, enumerate each\ninstance of the object, and do so even when the object is silent. We tackle\nthis problem by first designing a self-supervised framework with a contrastive\nobjective that jointly learns to classify and localise objects. Then, without\nusing any supervision, we simply use these self-supervised labels and boxes to\ntrain an image-based object detector. With this, we outperform previous\nunsupervised and weakly-supervised detectors for the task of object detection\nand sound source localization. We also show that we can align this detector to\nground-truth classes with as little as one label per pseudo-class, and show how\nour method can learn to detect generic objects that go beyond instruments, such\nas airplanes and cats.\n"}
+{"abstract": "  We measure the galaxy 2- and 3-point correlation functions at $z=[0.5,0.7]$\nand $z=[0.7, 0.9]$, from the final data release of the VIPERS survey (PDR2). We\nmodel the two statistics including a nonlinear 1-loop model for the 2-point\nfunction and a tree-level model for the 3-point function, and perform a joint\nlikelihood analysis. The entire process and nonlinear corrections are tested\nand validated through the use of the 153 highly realistic VIPERS mock\ncatalogues, showing that they are robust down to scales as small as 10 $h^{-1}\n\\, \\mathrm{Mpc}$. The mocks are also adopted to compute the covariance matrix\nthat we use for the joint 2- and 3-point analysis. Despite the limited\nstatistics of the two (volume-limited) sub-samples analysed, we demonstrate\nthat such a combination successfully breaks the degeneracy existing at 2-point\nlevel between clustering amplitude $\\sigma_8$, linear bias $b_1$ and the linear\ngrowth rate of fluctuations $f$. For the latter, in particular, we measure\n$f(z=0.61)=0.64^{+0.55}_{-0.37}$ and $f(z=0.8)=1.0\\pm1.0$, while the amplitude\nof clustering is found to be $\\sigma_8(z=0.61)=0.50\\pm 0.12$ and\n$\\sigma_8(z=0.8)=0.39^{+0.11}_{-0.13}$. These values are in excellent agreement\nwith the extrapolation of a Planck cosmology.\n"}
+{"abstract": "  We provide a test for the specification of a structural model without\nidentifying assumptions. We show the equivalence of several natural\nformulations of correct specification, which we take as our null hypothesis.\nFrom a natural empirical version of the latter, we derive a Kolmogorov-Smirnov\nstatistic for Choquet capacity functionals, which we use to construct our test.\nWe derive the limiting distribution of our test statistic under the null, and\nshow that our test is consistent against certain classes of alternatives. When\nthe model is given in parametric form, the test can be inverted to yield\nconfidence regions for the identified parameter set. The approach can be\napplied to the estimation of models with sample selection, censored observables\nand to games with multiple equilibria.\n"}
+{"abstract": "  Surprisal theory has provided a unifying framework for understanding many\nphenomena in sentence processing (Hale, 2001; Levy, 2008a), positing that a\nword's conditional probability given all prior context fully determines\nprocessing difficulty. Problematically for this claim, one local statistic,\nword frequency, has also been shown to affect processing, even when conditional\nprobability given context is held constant. Here, we ask whether other local\nstatistics have a role in processing, or whether word frequency is a special\ncase. We present the first clear evidence that more complex local statistics,\nword bigram and trigram probability, also affect processing independently of\nsurprisal. These findings suggest a significant and independent role of local\nstatistics in processing. Further, it motivates research into new\ngeneralizations of surprisal that can also explain why local statistical\ninformation should have an outsized effect.\n"}
+{"abstract": "  Recent advances in multi-fingered robotic grasping have enabled fast\n6-Degrees-Of-Freedom (DOF) single object grasping. Multi-finger grasping in\ncluttered scenes, on the other hand, remains mostly unexplored due to the added\ndifficulty of reasoning over obstacles which greatly increases the\ncomputational time to generate high-quality collision-free grasps. In this work\nwe address such limitations by introducing DDGC, a fast generative multi-finger\ngrasp sampling method that can generate high quality grasps in cluttered scenes\nfrom a single RGB-D image. DDGC is built as a network that encodes scene\ninformation to produce coarse-to-fine collision-free grasp poses and\nconfigurations. We experimentally benchmark DDGC against the\nsimulated-annealing planner in GraspIt! on 1200 simulated cluttered scenes and\n7 real world scenes. The results show that DDGC outperforms the baseline on\nsynthesizing high-quality grasps and removing clutter while being 5 times\nfaster. This, in turn, opens up the door for using multi-finger grasps in\npractical applications which has so far been limited due to the excessive\ncomputation time needed by other methods.\n"}
+{"abstract": "  Opportunistic usage of spectrum owned by licensed (or primary) users is the\ncornerstone on which the Cognitive Radio technology is built. Unlicensed (or\nsecondary) users that thus use the spectrum rely opportunistically on spectrum\nsensing to determine the presence of primary user signal. In such a context, an\nattacker may mimic the behavior of a primary user (PU) to deceive the secondary\nusers (SUs) into believing that a PU signal is present whereas it is not. Such\nan attack is known as the Primary User Emulation Attack (PUEA). A malicious\nuser may launch a PUEA with the intention of grabbing the vacant bands for its\nown transmission. Another reason may be to simply disrupt the functioning of\nthe Cognitive Radio Network (CRN). This work investigates the use of one-class\nclassification for detecting PUEA in an infrastructure-based CRN. We opine that\nsensing data collected at the fusion center mainly for Collaborative Spectrum\nSensing (CSS) can be exploited to characterize a PU signal. The PU signal\nfeatures thus learned can aid in distinguishing a PU signal from a PU signal\nemulation. In particular, we investigate the use of one-class classification\ntechniques, viz., Isolation Forest, Support Vector Machines (SVM), Minimum\nCovariance Determinant(MCD) and Local Outlier Factor(LOF) for detection of PUEA\nattacks. Simulation results support the validity of using one-class\nclassification for detection of PUEA.\n"}
+{"abstract": "  Free-text clinical notes detail all aspects of patient care and have great\npotential to facilitate quality improvement and assurance initiatives as well\nas advance clinical research. However, concerns about patient privacy and\nconfidentiality limit the use of clinical notes for research. As a result, the\ninformation documented in these notes remains unavailable for most researchers.\nDe-identification (de-id), i.e., locating and removing personally identifying\nprotected health information (PHI), is one way of improving access to clinical\nnarratives. However, there are limited off-the-shelf de-identification systems\nable to consistently detect PHI across different data sources and medical\nspecialties. In this abstract, we present the performance of a state-of-the art\nde-id system called NeuroNER1 on a diverse set of notes from University of\nWashington (UW) when the models are trained on data from an external\ninstitution (Partners Healthcare) vs. from the same institution (UW). We\npresent results at the level of PHI and note types.\n"}
+{"abstract": "  The presence of valley states is a significant obstacle to realizing quantum\ninformation technologies in Silicon quantum dots, as leakage into alternate\nvalley states can introduce errors into the computation. We use a perturbative\nanalytical approach to study the dynamics of exchange-coupled quantum dots with\nvalley degrees of freedom. We show that if the valley splitting is large and\nelectrons are not properly initialized to valley eigenstates, then time\nevolution of the system will lead to spin-valley entanglement. Spin-valley\nentanglement will also occur if the valley splitting is small and electrons are\nnot initialized to the same valley state. Additionally, we show that for small\nvalley splitting, spin-valley entanglement does not affect measurement\nprobabilities of two-qubit systems; however, systems with more qubits will be\naffected. This means that two-qubit gate fidelities measured in two-qubit\nsystems may miss the effects of valley degrees of freedom. Our work shows how\nthe existence of valleys may adversely affect multiqubit fidelities even when\nthe system temperature is very low.\n"}
+{"abstract": "  Context: Post-release user feedback plays an integral role in improving\nsoftware quality and informing new features. Given its growing importance,\nfeedback concerning security enhancements is particularly noteworthy. In\nconsidering the rapid uptake of Android we have examined the scale and severity\nof Android security threats as reported by its stakeholders. Objective: We\nsystematically mine Android issue logs to derive insights into stakeholder\nperceptions and experiences in relation to certain Android security issues.\nMethod: We employed contextual analysis techniques to study issues raised\nregarding confidentiality and privacy in the last three major Android releases,\nconsidering covariance of stakeholder comments, and the level of consistency in\nuser preferences and priorities. Results: Confidentiality and privacy concerns\nvaried in severity, and were most prevalent over Jelly Bean releases. Issues\nraised in regard to confidentiality related mostly to access, user credentials\nand permission management, while privacy concerns were mainly expressed about\nphone locking. Community users also expressed divergent preferences for new\nsecurity features, ranging from more relaxed to very strict. Conclusion:\nStrategies that support continuous corrective measures for both old and new\nAndroid releases would likely maintain stakeholder confidence. An approach that\nprovides users with basic default security settings, but with the power to\nconfigure additional security features if desired, would provide the best\nbalance for Android's wide cohort of stakeholders.\n"}
+{"abstract": "  Prophet inequalities and secretary problems have been extensively studied in\nrecent years due to their elegance, connections to online algorithms,\nstochastic optimization, and mechanism design problems in game theoretic\nsettings. Rubinstein and Singla developed a notion of combinatorial prophet\ninequalities in order to generalize the standard prophet inequality setting to\ncombinatorial valuation functions such as submodular and subadditive functions.\nFor non-negative submodular functions they demonstrated a constant factor\nprophet inequality for matroid constraints. Along the way they showed a variant\nof the correlation gap for non-negative submodular functions.\n  In this paper we revisit their notion of correlation gap as well as the\nstandard notion of correlation gap and prove much tighter and cleaner bounds.\nVia these bounds and other insights we obtain substantially improved constant\nfactor combinatorial prophet inequalities for both monotone and non-monotone\nsubmodular functions over any constraint that admits an Online Contention\nResolution Scheme. In addition to improved bounds we describe efficient\npolynomial-time algorithms that achieve these bounds.\n"}
+{"abstract": "  Space-time (ST) wave packets are pulsed optical beams endowed with precise\nspatio-temporal structure by virtue of which they exhibit unique and useful\ncharacteristics, such as propagation invariance and tunable group velocity. We\nstudy in detail here, and in two accompanying papers, the refraction of ST wave\npackets at planar interfaces between non-dispersive, homogeneous, isotropic\ndielectrics. We formulate a law of refraction that determines the change in the\nST wave-packet group velocity across such an interface as a consequence of a\nnewly identified optical refractive invariant that we call 'the spectral\ncurvature'. Because the spectral curvature vanishes in conventional optical\nfields where the spatial and temporal degrees of freedom are separable, these\nphenomena have not been observed to date. We derive the laws of refraction for\nbaseband, X-wave, and sideband ST wave packets that reveal fascinating\nrefractive phenomena, especially for the former class of wave packets. We\npredict theoretically, and confirm experimentally in the accompanying papers,\nrefractive phenomena such as group-velocity invariance (ST wave packets whose\ngroup velocity does not change across the interface), anomalous refraction\n(group-velocity increase in higher-index media), group-velocity inversion\n(change in the sign of the group velocity upon refraction but not its\nmagnitude), and the dependence of the group velocity of the refracted ST wave\npacket on the angle of incidence.\n"}
+{"abstract": "  Selberg's central limit theorem states that the values of $\\log|\\zeta(1/2+i\n\\tau)|$, where $\\tau$ is a uniform random variable on $[T,2T]$, is distributed\nlike a Gaussian random variable of mean $0$ and standard deviation\n$\\sqrt{\\frac{1}{2}\\log \\log T}$. It was conjectured by Radziwi{\\l}{\\l} that\nthis breaks down for values of order $\\log\\log T$, where a multiplicative\ncorrection $C_k$ would be present at level $k\\log\\log T$, $k>0$. This constant\nshould be equal to the leading asymptotic for the $2k^{th}$ moment of $\\zeta$,\nas first conjectured by Keating and Snaith using random matrix theory. In this\npaper, we provide numerical and theoretical evidence for this conjecture. We\npropose that this correction has a significant effect on the distribution of\nthe maximum of $\\log|\\zeta|$ in intervals of size $(\\log T)^\\theta$,\n$\\theta>0$. The precision of the prediction enables the numerical detection of\n$C_k$ even for low $T$'s of order $T=10^8$. A similar correction appears in the\nlarge deviations of the Keating-Snaith central limit theorem for the logarithm\nof the characteristic polynomial of a random unitary matrix, as first proved by\nF\\'eray, M\\'eliot and Nikeghbali.\n"}
+{"abstract": "  We find all analytic SU(2) Yang-Mills solutions on de Sitter space by\nreducing the field equations to Newton's equation for a particle in a\nparticular 3d potential and solving the latter in a special case. In contrast,\nMaxwell's equations on de Sitter space can be solved in generality, by\nseparating them in hysperspherical coordinates. Employing a well-known\nconformal map between (half of) de Sitter space and (the future half of)\nMinkowski space, the Maxwell solutions are mapped to a complete basis of\nrational electromagnetic knot configurations. We discuss some of their\nproperties and illustrate the construction method with two nontrivial examples\ngiven by rational functions of increasing complexity. The material is partly\nbased on [1,2].\n"}
+{"abstract": "  The high-redshift quasar PMN J0909+0354 ($z=3.288$) is known to have a\npc-scale compact jet structure, based on global 5-GHz very long baseline\ninterferometry (VLBI) observations performed in 1992. Its kpc-scale structure\nwas studied with the Karl G. Jansky Very Large Array (VLA) in the radio and the\nChandra space telescope in X-rays. Apart from the north-northwestern jet\ncomponent seen in both the VLA and Chandra images at $2.3''$ separation from\nthe core, there is another X-ray feature at $6.48''$ in the northeastern (NE)\ndirection. To uncover more details and possibly structural changes in the inner\njet, we conducted new observations at 5 GHz using the European VLBI Network\n(EVN) in 2019. These data confirm the northward direction of the one-sided\ninner jet already suspected from the 1992 observations. A compact core and\nmultiple jet components were identified that can be traced up to $\\sim0.25$ kpc\nprojected distance towards the north, while the structure becomes more and more\ndiffuse. A comparison with arcsec-resolution imaging with the VLA shows that\nthe radio jet bends by $\\sim30^\\circ$ between the two scales. The direction of\nthe pc-scale jet as well as the faint optical counterpart found for the\nnewly-detected X-ray point source (NE) favors the nature of the latter as a\nbackground or foreground object in the field of view. However, the extended\n($\\sim160$ kpc) emission around the positions of the quasar core and NE\ndetected by the Wide-field Infrared Survey Explorer (WISE) in the mid-infrared\nmight suggest physical interaction of the two objects.\n"}
+{"abstract": "  A powerful new perspective in the analysis of absolute Galois groups has\nrecently emerged from the study of Galois modules related to classical\nparameterizing spaces of certain Galois extensions. The recurring trend in\nthese decompositions is their stunning simplicity: almost all summands are free\nover some quotient ring. The non-free summands which appear are exceptional not\nonly because they are different in form, but because they play the key role in\ncontrolling arithmetic conditions that allow the remaining summands to be\neasily described. In this way, these exceptional summands are the lynchpin for\na bevy of new properties of absolute Galois groups that have been gleaned from\nthese surprising decompositions.\n  In one such recent decomposition, a remarkable new exceptional summand was\ndiscovered which exhibited interesting properties that have not been seen\nbefore. The exceptional summand is drawn from a particular finite family that\nhas not yet been investigated. The main goal of this paper is to introduce this\nfamily of modules and verify their indecomposability. We believe this module\nwill be of interest to people working in Galois theory, representation theory,\ncombinatorics, and general algebra. The analysis of these modules includes some\ninteresting new tools, including analogs of $p$-adic expansions.\n"}
+{"abstract": "  Model-free deep reinforcement learning has achieved great success in many\ndomains, such as video games, recommendation systems and robotic control tasks.\nIn continuous control tasks, widely used policies with Gaussian distributions\nresults in ineffective exploration of environments and limited performance of\nalgorithms in many cases. In this paper, we propose a density-free off-policy\nalgorithm, Generative Actor-Critic(GAC), using the push-forward model to\nincrease the expressiveness of policies, which also includes an entropy-like\ntechnique, MMD-entropy regularizer, to balance the exploration and\nexploitation. Additionnally, we devise an adaptive mechanism to automatically\nscale this regularizer, which further improves the stability and robustness of\nGAC. The experiment results show that push-forward policies possess desirable\nfeatures, such as multi-modality, which can improve the efficiency of\nexploration and asymptotic performance of algorithms obviously.\n"}
+{"abstract": "  In this note, we derive bounds on the median bias of univariate M-estimators\nunder mild regularity conditions. These requirements are not sufficient to\nimply convergence in distribution of the M-estimators. We also discuss median\nbias of some multivariate M-estimators.\n"}
+{"abstract": "  Based upon the resistor analogy and using the ideal loop gas\napproximation(ILGA) it is shown that only pending loops reduce the modulus of\nan otherwise perfect network made of monodisperse strands and junctions of\nidentical functionality. Thus, the cycle rank of the network with pending\nstructures removed (cyclic and branched) is sufficient to characterize modulus,\nif the resistor analogy can be employed. It is further shown that it is\nimpossible to incorporate finite cycles into a polymer network such that\nindividual network strands are at equilibrium conformations while maintaining\nsimultaneously a force balance at the junctions. Therefore, the resistor\nanalogy provides only an approximation for the phantom modulus of networks\ncontaining finite loops. Improved approaches to phantom modulus can be\nconstructed from considering a force balance at the junctions, which requires\nknowledge of the distribution of cross-link fluctuations in imperfect networks.\nAssuming loops with equilibrium conformations and a force balance at all loop\njunctions, a lower bound estimate for the phantom modulus,\n$G_{\\text{ph}}\\approx\\left(\\xi-c_{\\text{f}}L_{1}\\right)kT/V$ is obtained within\nthe ILGA for end-linked model networks and in the limit of $L_{1}\\ll\\xi$. Here,\n$L_{1}$ is the number of primary (\"pending\") loops, $\\xi$ the cycle rank of the\nnetwork, $k$ the Boltzmann constant, $V$ the volume of the sample, and $T$ the\nabsolute temperature. $c_{\\text{f}}$ is a functionality dependent coefficient\nthat is $\\approx2.56$ for junction functionality $f=3$ and $\\approx3.06$ for\n$f=4$, while it converges quickly towards $\\approx4.2$ in the limit of large\n$f$. Further corrections to phantom modulus beyond finite loops are addressed\nbriefly.\n"}
+{"abstract": "  Genetic association data from national biobanks and large-scale association\nstudies have provided new prospects for understanding the genetic evolution of\ncomplex traits and diseases in humans. In turn, genomes from ancient human\narchaeological remains are now easier than ever to obtain, and provide a direct\nwindow into changes in frequencies of trait-associated alleles in the past.\nThis has generated a new wave of studies aiming to analyse the genetic\ncomponent of traits in historic and prehistoric times using ancient DNA, and to\ndetermine whether any such traits were subject to natural selection. In humans,\nhowever, issues about the portability and robustness of complex trait inference\nacross different populations are particularly concerning when predictions are\nextended to individuals that died thousands of years ago, and for which little,\nif any, phenotypic validation is possible. In this review, we discuss the\nadvantages of incorporating ancient genomes into studies of trait-associated\nvariants, the need for models that can better accommodate ancient genomes into\nquantitative genetic frameworks, and the existing limits to inferences about\ncomplex trait evolution, particularly with respect to past populations.\n"}
+{"abstract": "  We report on large-scale simulations of intrachain exciton dynamics in\npoly(para-phenylene vinylene). Our coarse-grained model describes Frenkel\nexciton coupling to both fast, quantized C-C bond vibrations and slow,\nclassical torsional modes. We also incorporate system-bath interactions. The\ndynamics are simulated using the Time Evolution Block Decimation method, which\navoids the failures of the Ehrenfest approximation to describe decoherence\nprocesses and nonadiabatic interstate conversion. System-bath interactions are\nmodeled using quantum trajectories and Lindblad quantum jump operators. We find\nthat following photoexcitation, the quantum mechanical entanglement of the\nexciton and C-C bond phonons causes exciton-site decoherence. Next, system-bath\ninteractions cause the stochastic collapse of high-energy delocalized excitons\nonto chromophores. Finally, torsional relaxation causes additional\nexciton-density localization. We relate these dynamical processes to the\npredicted fluorescence depolarization and extract the timescales corresponding\nto them.\n"}
+{"abstract": "  High power operation in extreme fast charging significantly increases the\nrisk of internal faults in Electric Vehicle batteries which can lead to\naccelerated battery failure. Early detection of these faults is crucial for\nbattery safety and widespread deployment of fast charging. In this setting, we\npropose a real-time {detection} framework for battery voltage and thermal\nfaults. A major challenge in battery fault detection arises from the effect of\nuncertainties originating from sensor inaccuracies, nominal aging, or\nunmodelled dynamics. Inspired by physics-based learning, we explore a detection\nparadigm that combines physics-based models, model-based detection observers,\nand data-driven learning techniques to address this challenge. Specifically, we\nconstruct the {detection} observers based on an experimentally identified\nelectrochemical-thermal model, and subsequently design the observer tuning\nparameters following Lyapunov's stability theory. Furthermore, we utilize\nGaussian Process Regression technique to learn the model and measurement\nuncertainties which in turn aid the {detection} observers in distinguishing\nfaults and uncertainties. Such uncertainty learning essentially helps\nsuppressing their effects, potentially enabling early detection of faults. We\nperform simulation and experimental case studies on the proposed fault\n{detection} scheme verifying the potential of physics-based learning in early\ndetection of battery faults.\n"}
+{"abstract": "  A large repulsion between particles in a quantum system can lead to their\nlocalization, as it happens for the electrons in Mott insulating materials.\nThis paradigm has recently branched out into a new quantum state, the\norbital-selective Mott insulator, where electrons in some orbitals are\npredicted to localize, while others remain itinerant. We provide a direct\nexperimental realization of this phenomenon, that we extend to a more general\nflavour-selective localization. By using an atom-based quantum simulator, we\nengineer SU(3) Fermi-Hubbard models breaking their symmetry via a tunable\ncoupling between flavours, observing an enhancement of localization and the\nemergence of flavour-dependent correlations. Our realization of\nflavour-selective Mott physics opens the path to the quantum simulation of\nmulticomponent materials, from superconductors to topological insulators.\n"}
+{"abstract": "  Delivering fast responses to retrospective queries on video datasets is\ndifficult due to the large number of frames to consider and the high costs of\nrunning convolutional neural networks (CNNs) on each one. A natural solution is\nto perform a subset of the necessary computations ahead of time, as video is\ningested. However, existing ingest-time systems require knowledge of the\nspecific CNN that will be used in future queries -- a challenging requisite\ngiven the evergrowing space of CNN architectures and training\ndatasets/methodologies.\n  This paper presents Boggart, a retrospective video analytics system that\ndelivers ingest-time speedups in a model-agnostic manner. Our underlying\ninsight is that traditional computer vision (CV) algorithms are capable of\nperforming computations that can be used to accelerate diverse queries with\nwide-ranging CNNs. Building on this, at ingest-time, Boggart carefully employs\na variety of motion tracking algorithms to identify potential objects and their\ntrajectories across frames. Then, at query-time, Boggart uses several novel\ntechniques to collect the smallest sample of CNN results required to meet the\ntarget accuracy: (1) a clustering strategy to efficiently unearth the\ninevitable discrepancies between CV- and CNN-generated outputs, and (2) a set\nof accuracy-preserving propagation techniques to safely extend sampled results\nalong each trajectory. Across many videos, CNNs, and queries Boggart\nconsistently meets accuracy targets while using CNNs sparingly (on 3-54% of\nframes).\n"}
+{"abstract": "  The Transformer architecture deeply changed the natural language processing,\noutperforming all previous state-of-the-art models. However, well-known\nTransformer models like BERT, RoBERTa, and GPT-2 require a huge compute budget\nto create a high quality contextualised representation. In this paper, we study\nseveral efficient pre-training objectives for Transformers-based models. By\ntesting these objectives on different tasks, we determine which of the ELECTRA\nmodel's new features is the most relevant. We confirm that Transformers\npre-training is improved when the input does not contain masked tokens and that\nthe usage of the whole output to compute the loss reduces training time.\nMoreover, inspired by ELECTRA, we study a model composed of two blocks; a\ndiscriminator and a simple generator based on a statistical model with no\nimpact on the computational performances. Besides, we prove that eliminating\nthe MASK token and considering the whole output during the loss computation are\nessential choices to improve performance. Furthermore, we show that it is\npossible to efficiently train BERT-like models using a discriminative approach\nas in ELECTRA but without a complex generator, which is expensive. Finally, we\nshow that ELECTRA benefits heavily from a state-of-the-art hyper-parameters\nsearch.\n"}
+{"abstract": "  Since prior work has identified that cultural differences influence user\ndesign preferences and interaction methods, as well as emphasizing the need to\nreflect on the appropriateness of popular HCI principles, we believe that it is\nequally important to apply this inquiry to digital accessibility and how\naccessibility fits within the design process around the world. Our long-term\nplan is to build upon work in this area by investigating how digital designers\nin different parts of the world consider accessibility and whether current\naccessibility resources (often developed in the west) meet or conflict with\ntheir approach to design.\n"}
+{"abstract": "  Diffusion-weighted magnetic resonance imaging (DW-MRI) can be used to\ncharacterise the microstructure of the nervous tissue, e.g. to delineate brain\nwhite matter connections in a non-invasive manner via fibre tracking. Magnetic\nResonance Imaging (MRI) in high spatial resolution would play an important role\nin visualising such fibre tracts in a superior manner. However, obtaining an\nimage of such resolution comes at the expense of longer scan time. Longer scan\ntime can be associated with the increase of motion artefacts, due to the\npatient's psychological and physical conditions. Single Image Super-Resolution\n(SISR), a technique aimed to obtain high-resolution (HR) details from one\nsingle low-resolution (LR) input image, achieved with Deep Learning, is the\nfocus of this study. Compared to interpolation techniques or sparse-coding\nalgorithms, deep learning extracts prior knowledge from big datasets and\nproduces superior MRI images from the low-resolution counterparts. In this\nresearch, a deep learning based super-resolution technique is proposed and has\nbeen applied for DW-MRI. Images from the IXI dataset have been used as the\nground-truth and were artificially downsampled to simulate the low-resolution\nimages. The proposed method has shown statistically significant improvement\nover the baselines and achieved an SSIM of $0.913\\pm0.045$.\n"}
+{"abstract": "  We develop a model to establish the interconnection between galaxies and\ntheir dark matter halos. We use Principal Component Analysis (PCA) to reduce\nthe dimensionality of both the mass assembly histories of halos/subhalos and\nthe star formation histories of galaxies, and Gradient Boosted Decision Trees\n(GBDT) to transform halo/subhalo properties into galaxy properties. We use two\nsets of hydrodynamic simulations to motivate our model architecture and to\ntrain the transformation. We then apply the two sets of trained models to dark\nmatter only (DMO) simulations to show that the transformation is reliable and\nstatistically accurate. The model trained by a high-resolution hydrodynamic\nsimulation, or by a set of such simulations implementing the same physics of\ngalaxy formation, can thus be applied to large DMO simulations to make \"mock\"\ncopies of the hydrodynamic simulation. The model is both flexible and\ninterpretable, which paves the way for future applications in which we will\nconstrain the model using observations at different redshifts simultaneously\nand explore how galaxies form and evolve in dark matter halos empirically.\n"}
+{"abstract": "  Highly charged liquid droplets are unstable above the critical charge\nsquared-to-volume ratio given by the Rayleigh limit. The instability leads to\nion ejection from jets formed on the droplet's surface. Despite the many\nexperiments that have been performed to capture the jet formation the precise\nfission mechanism has not yet been observed because of its brief transient\nnature. Here, we present the first atomistic simulations that reveal the\nmechanism of Rayleigh fission. We demonstrate that ion ejection takes place\nthrough a drop's deformation from a spherical into a distinct shape that\ncontains a conical protrusion. We assert that the latter state is a free energy\nminimum along an order parameter that measures the degree of droplet\nasphericity. The charged droplet's long-time evolution proceeds by alternating\nbetween the two minima above and below the critical value that are reached\nthrough solvent evaporation and ion ejection, respectively. For the first time,\nthis mechanism allows one to explain the nature of the progeny droplets and the\npercentage of charge lost during fission. We determine that the cone half-angle\nis close to the value predicted from the solution of the electrostatic equation\nfor the dielectric liquid. It is found that the conical deformation is\nindependent of the effect of electrohydrodynamic forces reported in\nexperiments. Contrary to the experimental observations of two diametrically\nopposite jets for droplets suspended in the electric field, we found that a\nsingle jet is formed at the Rayleigh limit. This indicates that super-charged\ndroplet states may have been detected in the experiments.\n"}
+{"abstract": "  We study the behavior of two-flavor dense quark matter under the influence of\nan external magnetic field in the framework of a nonlocal chiral quark model\nwith separable interactions. The nonlocality is incorporated in the model by\nusing a Gaussian form factor. It is found that for low and moderate values of\nmagnetic field there is a decrease of the critical chiral restoration chemical\npotential $\\mu_c$, i.e. an inverse magnetic catalysis effect is observed. For\nlarger values of $eB$ the behavior of $\\mu_c$ becomes more or less flat,\ndepending on the parametrization. Within the considered parametrization range\nwe do not find a significant growth of the critical chemical potential for\nlarge magnetic fields, as occurs in the case of the local NJL model.\n"}
+{"abstract": "  Oxides-on-metals are the well-known \"inverse\" catalysts. Fundamental\nknowledge on the structural transformations that occur in such systems under\noxidizing and reducing conditions is crucial for maximizing their catalytic\nactivity. Sintering of oxide species is one of the effects that may, on one\nhand, lead to catalyst deactivation, but on the other constitutes a powerful\nmeans for fine-tuning their size and structure - which is one of the major\nchallenges in the field. In this Letter, we show that the structure of\nwell-dispersed bilayer iron monoxide (FeO) islands grown on Ru(0001), which\nconstitutes a model \"inverse\" catalyst, can be optimized by thermally-driven\nsintering and phase transition. The structural transformation occurs via the\nOstwald ripening mechanism, as observed in real time using in situ low energy\nelectron microscopy (LEEM). The structure of the islands before and after the\ntransformation is determined using ex situ experimental methods, as well as\ntheoretical calculations.\n"}
+{"abstract": "  We introduce a notion of smooth fields of operators following the notion of\nsmooth fields of Hilbert spaces recently defined by L. Lempert and R.\nSz\\H{o}oke arXiv:1004.4863(2) . Formally, if $\\nabla$ is the connection of a\nsmooth field of Hilbert spaces we show that $\\hat\\nabla=[\\nabla,\\cdot]$ defines\na connection on a suitable space of fields of operators. In order to provide\nexamples we prove that, if $u$ is a suitable constant of motion of\n$h(q,p)=\\|q\\|^2$ (i.e.\\ $\\{u,h\\}=0$), then $\\mathfrak{Op}(u)$ is a smooth field\nof operators over the open interval $(0,\\infty)$, where $\\mathfrak{Op}$ denotes\nthe canonical quantization (Weyl calculus). Moreover, in such case we show that\nwe can compute derivatives using the formula\n$\\hat\\nabla_{X_0}(\\mathfrak{Op}(u))=\\mathfrak{Op}(\\tilde\\nabla_{X_0}(u))$,\nwhere $\\tilde\\nabla$ is a Poisson connection on the Poisson algebra of\nconstants of motion and $X_0=2\\lambda\\frac{\\partial}{\\partial \\lambda}$. We\nalso introduce a notion of smooth field of $C^*$-algebras and we give an\nexample using Hilbert modules theory.\n"}
+{"abstract": "  Existing point-cloud based 3D object detectors use convolution-like operators\nto process information in a local neighbourhood with fixed-weight kernels and\naggregate global context hierarchically. However, non-local neural networks and\nself-attention for 2D vision have shown that explicitly modeling long-range\ninteractions can lead to more robust and competitive models. In this paper, we\npropose two variants of self-attention for contextual modeling in 3D object\ndetection by augmenting convolutional features with self-attention features. We\nfirst incorporate the pairwise self-attention mechanism into the current\nstate-of-the-art BEV, voxel and point-based detectors and show consistent\nimprovement over strong baseline models of up to 1.5 3D AP while simultaneously\nreducing their parameter footprint and computational cost by 15-80% and 30-50%,\nrespectively, on the KITTI validation set. We next propose a self-attention\nvariant that samples a subset of the most representative features by learning\ndeformations over randomly sampled locations. This not only allows us to scale\nexplicit global contextual modeling to larger point-clouds, but also leads to\nmore discriminative and informative feature descriptors. Our method can be\nflexibly applied to most state-of-the-art detectors with increased accuracy and\nparameter and compute efficiency. We show our proposed method improves 3D\nobject detection performance on KITTI, nuScenes and Waymo Open datasets. Code\nis available at https://github.com/AutoVision-cloud/SA-Det3D.\n"}
+{"abstract": "  In current object detection, algorithms require the object to be directly\nvisible in order to be detected. As humans, however, we intuitively use visual\ncues caused by the respective object to already make assumptions about its\nappearance. In the context of driving, such cues can be shadows during the day\nand often light reflections at night. In this paper, we study the problem of\nhow to map this intuitive human behavior to computer vision algorithms to\ndetect oncoming vehicles at night just from the light reflections they cause by\ntheir headlights. For that, we present an extensive open-source dataset\ncontaining 59746 annotated grayscale images out of 346 different scenes in a\nrural environment at night. In these images, all oncoming vehicles, their\ncorresponding light objects (e.g., headlamps), and their respective light\nreflections (e.g., light reflections on guardrails) are labeled. In this\ncontext, we discuss the characteristics of the dataset and the challenges in\nobjectively describing visual cues such as light reflections. We provide\ndifferent metrics for different ways to approach the task and report the\nresults we achieved using state-of-the-art and custom object detection models\nas a first benchmark. With that, we want to bring attention to a new and so far\nneglected field in computer vision research, encourage more researchers to\ntackle the problem, and thereby further close the gap between human performance\nand computer vision systems.\n"}
+{"abstract": "  The dependencies of the B$_{i}$O$_{i}$ defect concentration on doping,\nirradiation fluence and particle type in p-type silicon diodes have been\ninvestigated. We evidenced that large data scattering occurs for fluences above\n$10^{12}$ 1 MeV neutrons/cm$^2$, becoming significant larger for higher\nfluences. We show that the B$_{i}$O$_{i}$ defect is metastable, with two\nconfigurations A and B, of which only A is detected by Deep Level Transient\nSpectroscopy and Thermally Stimulated Currents techniques. The defect's\nelectrical activity is influenced by the inherent variations in ambient and\nprocedural experimental conditions, resulting not only in a large scattering of\nthe results coming from the same type of measurement but making any correlation\nbetween different types of experiments difficult. It is evidenced that the\nvariations in [B$_{i}$O$_{i}^\\mathrm{A}$] are triggered by subjecting the\nsamples to an excess of carriers, by either heating or an inherent short\nexposure to ambient light when manipulating the samples prior to experiments.\nIt causes $\\approx$7h variations in both, the [B$_{i}$O$_{i}^\\mathrm{A}$] and\nin the effective space charge. The analyses of structural damage in a diode\nirradiated with 10$^{19}$ 1 MeV neutrons/cm$^2$ revealed that the Si structure\nremains crystalline and vacancies and interstitials organize in parallel tracks\nnormal to the Si-SiO$_{2}$ interface.\n"}
+{"abstract": "  Learning with an objective to minimize the mismatch with a reference\ndistribution has been shown to be useful for generative modeling and imitation\nlearning. In this paper, we investigate whether one such objective, the\nWasserstein-1 distance between a policy's state visitation distribution and a\ntarget distribution, can be utilized effectively for reinforcement learning\n(RL) tasks. Specifically, this paper focuses on goal-conditioned reinforcement\nlearning where the idealized (unachievable) target distribution has full\nmeasure at the goal. This paper introduces a quasimetric specific to Markov\nDecision Processes (MDPs) and uses this quasimetric to estimate the above\nWasserstein-1 distance. It further shows that the policy that minimizes this\nWasserstein-1 distance is the policy that reaches the goal in as few steps as\npossible. Our approach, termed Adversarial Intrinsic Motivation (AIM),\nestimates this Wasserstein-1 distance through its dual objective and uses it to\ncompute a supplemental reward function. Our experiments show that this reward\nfunction changes smoothly with respect to transitions in the MDP and directs\nthe agent's exploration to find the goal efficiently. Additionally, we combine\nAIM with Hindsight Experience Replay (HER) and show that the resulting\nalgorithm accelerates learning significantly on several simulated robotics\ntasks when compared to other rewards that encourage exploration or accelerate\nlearning.\n"}
+{"abstract": "  The conventional spectral mapping theorem for quantum walks can only be\napplied for walks employing a shift operator whose square is the identity. This\ntheorem gives most of the eigenvalues of the time evolution $U$ by lifting the\neigenvalues of an induced self-adjoint matrix $T$ onto the unit circle on the\ncomplex plane. We acquire a new spectral mapping theorem for the Grover walk\nwith a shift operator whose cube is the identity on finite graphs. Moreover,\ngraphs we can consider for a quantum walk with such a shift operator is\ncharacterized by a triangulation. We call these graphs triangulable graphs in\nthis paper. One of the differences between our spectral mapping theorem and the\nconventional one is that lifting the eigenvalues of $T-1/2$ onto the unit\ncircle gives most of the eigenvalues of $U$.\n"}
+{"abstract": "  A lingering mystery in core-collapse supernova theory is how collective\nneutrino oscillations affect the dynamics. All previously identified flavor\ninstabilities, some of which might make the effects considerable, are\nessentially collisionless phenomena. Here it is shown that collisional\ninstabilities exist as well. They are associated with asymmetries between the\nneutrino and antineutrino interaction rates, are possibly prevalent deep inside\nsupernovae, and pose an unusual instance of decoherent interactions with a\nthermal environment causing the sustained growth of quantum coherence.\n"}
+{"abstract": "  The constraint satisfaction problem (CSP) has important applications in\ncomputer science and AI. In particular, infinite-domain CSPs have been\nintensively used in subareas of AI such as spatio-temporal reasoning. Since\nconstraint satisfaction is a computationally hard problem, much work has been\ndevoted to identifying restricted problems that are efficiently solvable. One\nway of doing this is to restrict the interactions of variables and constraints,\nand a highly successful approach is to bound the treewidth of the underlying\nprimal graph. Bodirsky & Dalmau [J. Comput. System. Sci. 79(1), 2013] and Huang\net al. [Artif. Intell. 195, 2013] proved that CSP$(\\Gamma)$ can be solved in\n$n^{f(w)}$ time (where $n$ is the size of the instance, $w$ is the treewidth of\nthe primal graph and $f$ is a computable function) for certain classes of\nconstraint languages $\\Gamma$. We improve this bound to $f(w) \\cdot n^{O(1)}$,\nwhere the function $f$ only depends on the language $\\Gamma$, for CSPs whose\nbasic relations have the patchwork property. Hence, such problems are\nfixed-parameter tractable and our algorithm is asymptotically faster than the\nprevious ones. Additionally, our approach is not restricted to binary\nconstraints, so it is applicable to a strictly larger class of problems than\nthat of Huang et al. However, there exist natural problems that are covered by\nBodirsky & Dalmau's algorithm but not by ours, and we begin investigating ways\nof generalising our results to larger families of languages. We also analyse\nour algorithm with respect to its running time and show that it is optimal\n(under the Exponential Time Hypothesis) for certain languages such as Allen's\nInterval Algebra.\n"}
+{"abstract": "  The COVID-19 pandemic has resulted in more than 125 million infections and\nmore than 2.7 million casualties. In this paper, we attempt to classify covid\nvs non-covid cough sounds using signal processing and deep learning methods.\nAir turbulence, the vibration of tissues, movement of fluid through airways,\nopening, and closure of glottis are some of the causes for the production of\nthe acoustic sound signals during cough. Does the COVID-19 alter the acoustic\ncharacteristics of breath, cough, and speech sounds produced through the\nrespiratory system? This is an open question waiting for answers. In this\npaper, we incorporated novel data augmentation methods for cough sound\naugmentation and multiple deep neural network architectures and methods along\nwith handcrafted features. Our proposed system gives 14% absolute improvement\nin area under the curve (AUC). The proposed system is developed as part of\nInterspeech 2021 special sessions and challenges viz. diagnosing of COVID-19\nusing acoustics (DiCOVA). Our proposed method secured the 5th position on the\nleaderboard among 29 participants.\n"}
+{"abstract": "  Emerging applications of collaborative autonomy, such as Multi-Target\nTracking, Unknown Map Exploration, and Persistent Surveillance, require robots\nplan paths to navigate an environment while maximizing the information\ncollected via on-board sensors. In this paper, we consider such information\nacquisition tasks but in adversarial environments, where attacks may\ntemporarily disable the robots' sensors. We propose the first receding horizon\nalgorithm, aiming for robust and adaptive multi-robot planning against any\nnumber of attacks, which we call Resilient Active Information acquisitioN\n(RAIN). RAIN calls, in an online fashion, a Robust Trajectory Planning (RTP)\nsubroutine which plans attack-robust control inputs over a look-ahead planning\nhorizon. We quantify RTP's performance by bounding its suboptimality. We base\nour theoretical analysis on notions of curvature introduced in combinatorial\noptimization. We evaluate RAIN in three information acquisition scenarios:\nMulti-Target Tracking, Occupancy Grid Mapping, and Persistent Surveillance. The\nscenarios are simulated in C++ and a Unity-based simulator. In all simulations,\nRAIN runs in real-time, and exhibits superior performance against a\nstate-of-the-art baseline information acquisition algorithm, even in the\npresence of a high number of attacks. We also demonstrate RAIN's robustness and\neffectiveness against varying models of attacks (worst-case and random), as\nwell as, varying replanning rates.\n"}
+{"abstract": "  Machine learning and deep learning have shown great promise in mobile sensing\napplications, including Human Activity Recognition. However, the performance of\nsuch models in real-world settings largely depends on the availability of large\ndatasets that captures diverse behaviors. Recently, studies in computer vision\nand natural language processing have shown that leveraging massive amounts of\nunlabeled data enables performance on par with state-of-the-art supervised\nmodels.\n  In this work, we present SelfHAR, a semi-supervised model that effectively\nlearns to leverage unlabeled mobile sensing datasets to complement small\nlabeled datasets. Our approach combines teacher-student self-training, which\ndistills the knowledge of unlabeled and labeled datasets while allowing for\ndata augmentation, and multi-task self-supervision, which learns robust\nsignal-level representations by predicting distorted versions of the input.\n  We evaluated SelfHAR on various HAR datasets and showed state-of-the-art\nperformance over supervised and previous semi-supervised approaches, with up to\n12% increase in F1 score using the same number of model parameters at\ninference. Furthermore, SelfHAR is data-efficient, reaching similar performance\nusing up to 10 times less labeled data compared to supervised approaches. Our\nwork not only achieves state-of-the-art performance in a diverse set of HAR\ndatasets, but also sheds light on how pre-training tasks may affect downstream\nperformance.\n"}
+{"abstract": "  State-of-the-art approaches to design, develop and optimize software\npacket-processing programs are based on static compilation: the compiler's\ninput is a description of the forwarding plane semantics and the output is a\nbinary that can accommodate any control plane configuration or input traffic.\nIn this paper, we demonstrate that tracking control plane actions and\npacket-level traffic dynamics at run time opens up new opportunities for code\nspecialization. We present Morpheus, a system working alongside static\ncompilers that continuously optimizes the targeted networking code. We\nintroduce a number of new techniques, from static code analysis to adaptive\ncode instrumentation, and we implement a toolbox of domain specific\noptimizations that are not restricted to a specific data plane framework or\nprogramming language. We apply Morpheus to several eBPF and DPDK programs\nincluding Katran, Facebook's production-grade load balancer. We compare\nMorpheus against state-of-the-art optimization frameworks and show that it can\nbring up to 2x throughput improvement, while halving the 99th percentile\nlatency.\n"}
+{"abstract": "  Self-driving technology is expected to revolutionize different sectors and is\nseen as the natural evolution of road vehicles. In the last years, real-world\nvalidation of designed and virtually tested solutions is growing in importance\nsince simulated environments will never fully replicate all the aspects that\ncan affect results in the real world. To this end, this paper presents our\nprototype platform for experimental research on connected and autonomous\ndriving projects. In detail, the paper presents the overall architecture of the\nvehicle focusing both on mechanical aspects related to remote actuation and\nsensors set-up and software aspects by means of a comprehensive description of\nthe main algorithms required for autonomous driving as ego-localization,\nenvironment perception, motion planning, and actuation. Finally, experimental\ntests conducted in an urban-like environment are reported to validate and\nassess the performances of the overall system.\n"}
+{"abstract": "  Multi-Task Learning has emerged as a methodology in which multiple tasks are\njointly learned by a shared learning algorithm, such as a DNN. MTL is based on\nthe assumption that the tasks under consideration are related; therefore it\nexploits shared knowledge for improving performance on each individual task.\nTasks are generally considered to be homogeneous, i.e., to refer to the same\ntype of problem. Moreover, MTL is usually based on ground truth annotations\nwith full, or partial overlap across tasks. In this work, we deal with\nheterogeneous MTL, simultaneously addressing detection, classification &\nregression problems. We explore task-relatedness as a means for co-training, in\na weakly-supervised way, tasks that contain little, or even non-overlapping\nannotations. Task-relatedness is introduced in MTL, either explicitly through\nprior expert knowledge, or through data-driven studies. We propose a novel\ndistribution matching approach, in which knowledge exchange is enabled between\ntasks, via matching of their predictions' distributions. Based on this\napproach, we build FaceBehaviorNet, the first framework for large-scale face\nanalysis, by jointly learning all facial behavior tasks. We develop case\nstudies for: i) continuous affect estimation, action unit detection, basic\nemotion recognition; ii) attribute detection, face identification.\n  We illustrate that co-training via task relatedness alleviates negative\ntransfer. Since FaceBehaviorNet learns features that encapsulate all aspects of\nfacial behavior, we conduct zero-/few-shot learning to perform tasks beyond the\nones that it has been trained for, such as compound emotion recognition. By\nconducting a very large experimental study, utilizing 10 databases, we\nillustrate that our approach outperforms, by large margins, the\nstate-of-the-art in all tasks and in all databases, even in these which have\nnot been used in its training.\n"}
+{"abstract": "  The central challenge for describing the dynamics in open quantum systems is\nthat the Hilbert space of typical environments is too large to be treated\nexactly. In some cases, such as when the environment has a short memory time or\nonly interacts weakly with the system, approximate descriptions of the system\nare possible. Beyond these, numerically exact methods exist, but these are\ntypically restricted to baths with Gaussian correlations, such as\nnon-interacting bosons. Here we present a numerically exact method for\nsimulating open quantum systems with arbitrary environments which consist of a\nset of independent degrees of freedom. Our approach automatically reduces the\nlarge number of environmental degrees of freedom to those which are most\nrelevant. Specifically, we show how the process tensor -- which describes the\neffect of the environment -- can be iteratively constructed and compressed\nusing matrix product state techniques. We demonstrate the power of this method\nby applying it to problems with bosonic, fermionic, and spin environments:\nelectron transport, phonon effects and radiative decay in quantum dots, central\nspin dynamics, anharmonic environments, dispersive coupling to time-dependent\nlossy cavity modes, and superradiance. The versatility and efficiency of our\nautomated compression of environments (ACE) method provides a practical\ngeneral-purpose tool for open quantum systems.\n"}
+{"abstract": "  Early methods in the rapidly developing field of neural architecture search\n(NAS) required fully training thousands of neural networks. To reduce this\nextreme computational cost, dozens of techniques have since been proposed to\npredict the final performance of neural architectures. Despite the success of\nsuch performance prediction methods, it is not well-understood how different\nfamilies of techniques compare to one another, due to the lack of an\nagreed-upon evaluation metric and optimization for different constraints on the\ninitialization time and query time. In this work, we give the first large-scale\nstudy of performance predictors by analyzing 31 techniques ranging from\nlearning curve extrapolation, to weight-sharing, to supervised learning, to\n\"zero-cost\" proxies. We test a number of correlation- and rank-based\nperformance measures in a variety of settings, as well as the ability of each\ntechnique to speed up predictor-based NAS frameworks. Our results act as\nrecommendations for the best predictors to use in different settings, and we\nshow that certain families of predictors can be combined to achieve even better\npredictive power, opening up promising research directions. Our code, featuring\na library of 31 performance predictors, is available at\nhttps://github.com/automl/naslib.\n"}
+{"abstract": "  This document presents in detail the derivation of the formulas that describe\nthe resonance driving terms and the tune spread with amplitude generated by the\nbeam-beam long range interactions and the DC wire compensators in cyclical\nmachines. This analysis make use of the weak-strong approximation.\n"}
+{"abstract": "  For a given Lipschitz domain $\\Omega$, it is a classical result that the\ntrace space of $W^{1,p}(\\Omega)$ is $W^{1-1/p,p}(\\partial\\Omega)$, namely any\n$W^{1,p}(\\Omega)$ function has a well-defined $W^{1-1/p,p}(\\partial\\Omega)$\ntrace on its codimension-1 boundary $\\partial\\Omega$ and any\n$W^{1-1/p,p}(\\partial\\Omega)$ function on $\\partial\\Omega$ can be extended to a\n$W^{1,p}(\\Omega)$ function. Recently, Dyda and Kassmann (2019) characterize the\ntrace space for nonlocal Dirichlet problems involving integrodifferential\noperators with infinite interaction ranges, where the boundary datum is\nprovided on the whole complement of the given domain\n$\\mathbb{R}^d\\backslash\\Omega$. In this work, we study function spaces for\nnonlocal Dirichlet problems with a finite range of nonlocal interactions, which\nnaturally serves a bridging role between the classical local PDE problem and\nthe nonlocal problem with infinite interaction ranges. For these nonlocal\nDirichlet problems, the boundary conditions are normally imposed on a region\nwith finite thickness volume which lies outside of the domain. We introduce a\nfunction space on the volumetric boundary region that serves as a trace space\nfor these nonlocal problems and study the related extension results. Moreover,\nwe discuss the consistency of the new nonlocal trace space with the classical\n$W^{1-1/p,p}(\\partial\\Omega)$ space as the size of nonlocal interaction tends\nto zero. In making this connection, we conduct an investigation on the\nrelations between nonlocal interactions on a larger domain and the induced\ninteractions on its subdomain. The various forms of trace, embedding and\nextension theorems may then be viewed as consequences in different scaling\nlimits.\n"}
+{"abstract": "  Some reinforcement learning methods suffer from high sample complexity\ncausing them to not be practical in real-world situations. $Q$-function reuse,\na transfer learning method, is one way to reduce the sample complexity of\nlearning, potentially improving usefulness of existing algorithms. Prior work\nhas shown the empirical effectiveness of $Q$-function reuse for various\nenvironments when applied to model-free algorithms. To the best of our\nknowledge, there has been no theoretical work showing the regret of\n$Q$-function reuse when applied to the tabular, model-free setting. We aim to\nbridge the gap between theoretical and empirical work in $Q$-function reuse by\nproviding some theoretical insights on the effectiveness of $Q$-function reuse\nwhen applied to the $Q$-learning with UCB-Hoeffding algorithm. Our main\ncontribution is showing that in a specific case if $Q$-function reuse is\napplied to the $Q$-learning with UCB-Hoeffding algorithm it has a regret that\nis independent of the state or action space. We also provide empirical results\nsupporting our theoretical findings.\n"}
+{"abstract": "  We investigate experimentally and theoretically diffusiophoretic separation\nof negatively charged particles in a rectangular channel flow, driven by CO2\ndissolution from one side-wall. Since the negatively charged particles create\nan exclusion zone near the boundary where CO2 is introduced, we model the\nproblem by applying a shear flow approximation in a two-dimensional\nconfiguration. From the form of the equations we define a similarity variable\nto transform the reaction-diffusion equations for CO2 and ions and the\nadvection-diffusion equation for the particle distribution to ordinary\ndifferential equations. The definition of the similarity variable suggests a\ncharacteristic length scale for the particle exclusion zone. We consider\nheight-averaged flow behaviors in rectangular channels to rationalize and\nconnect our experimental observations with the model, by calculating the wall\nshear rate as functions of channel dimensions. Our observations and the\ntheoretical model provide the design parameters such as flow speed, channel\ndimensions and CO2 pressure for the in-flow water cleaning systems.\n"}
+{"abstract": "  Being fundamentally a non-equilibrium process, synchronization comes with\nunavoidable energy costs and has to be maintained under the constraint of\nlimited resources. Such resource constraints are often reflected as a finite\ncoupling budget available in a network to facilitate interaction and\ncommunication. Here, we show that introducing temporal variation in the network\nstructure can lead to efficient synchronization even when stable synchrony is\nimpossible in any static network under the given budget, thereby demonstrating\na fundamental advantage of temporal networks. The temporal networks generated\nby our open-loop design are versatile in the sense of promoting synchronization\nfor systems with vastly different dynamics, including periodic and chaotic\ndynamics in both discrete-time and continuous-time models. Furthermore, we link\nthe dynamic stabilization effect of the changing topology to the curvature of\nthe master stability function, which provides analytical insights into\nsynchronization on temporal networks in general. In particular, our results\nshed light on the effect of network switching rate and explain why certain\ntemporal networks synchronize only for intermediate switching rate.\n"}
+{"abstract": "  In late 2019, a novel coronavirus, the SARS-CoV-2 outbreak was identified in\nWuhan, China and later spread to every corner of the globe. Whilst the number\nof infection-induced deaths in Ghana, West Africa are minimal when compared\nwith the rest of the world, the impact on the local health service is still\nsignificant. Compartmental models are a useful framework for investigating\ntransmission of diseases in societies. To understand how the infection will\nspread and how to limit the outbreak. We have developed a modified SEIR\ncompartmental model with nine compartments (CoVCom9) to describe the dynamics\nof SARS-CoV-2 transmission in Ghana. We have carried out a detailed\nmathematical analysis of the CoVCom9, including the derivation of the basic\nreproduction number, $\\mathcal{R}_{0}$. In particular, we have shown that the\ndisease-free equilibrium is globally asymptotically stable when\n$\\mathcal{R}_{0}<1$ via a candidate Lyapunov function. Using the SARS-CoV-2\nreported data for confirmed-positive cases and deaths from March 13 to August\n10, 2020, we have parametrised the CoVCom9 model. The results of this fit show\ngood agreement with data. We used Latin hypercube sampling-rank correlation\ncoefficient (LHS-PRCC) to investigate the uncertainty and sensitivity of\n$\\mathcal{R}_{0}$ since the results derived are significant in controlling the\nspread of SARS-CoV-2. We estimate that over this five month period, the basic\nreproduction number is given by $\\mathcal{R}_{0} = 3.110$, with the 95\\%\nconfidence interval being $2.042 \\leq \\mathcal{R}_0 \\leq 3.240$, and the mean\nvalue being $\\mathcal{R}_{0}=2.623$. Of the 32 parameters in the model, we find\nthat just six have a significant influence on $\\mathcal{R}_{0}$, these include\nthe rate of testing, where an increasing testing rate contributes to the\nreduction of $\\mathcal{R}_{0}$.\n"}
+{"abstract": "  Unsupervised representation learning achieves promising performances in\npre-training representations for object detectors. However, previous approaches\nare mainly designed for image-level classification, leading to suboptimal\ndetection performance. To bridge the performance gap, this work proposes a\nsimple yet effective representation learning method for object detection, named\npatch re-identification (Re-ID), which can be treated as a contrastive pretext\ntask to learn location-discriminative representation unsupervisedly, possessing\nappealing advantages compared to its counterparts. Firstly, unlike\nfully-supervised person Re-ID that matches a human identity in different camera\nviews, patch Re-ID treats an important patch as a pseudo identity and\ncontrastively learns its correspondence in two different image views, where the\npseudo identity has different translations and transformations, enabling to\nlearn discriminative features for object detection. Secondly, patch Re-ID is\nperformed in Deeply Unsupervised manner to learn multi-level representations,\nappealing to object detection. Thirdly, extensive experiments show that our\nmethod significantly outperforms its counterparts on COCO in all settings, such\nas different training iterations and data percentages. For example, Mask R-CNN\ninitialized with our representation surpasses MoCo v2 and even its\nfully-supervised counterparts in all setups of training iterations (e.g. 2.1\nand 1.1 mAP improvement compared to MoCo v2 in 12k and 90k iterations\nrespectively). Code will be released at https://github.com/dingjiansw101/DUPR.\n"}
+{"abstract": "  We present a comprehensive ultrasound study of the prototypical heavy-fermion\nmaterial CeRhIn$_5$, examining the origin of the enigmatic 30 T transition. For\na field applied at 2$^\\circ$ from the $c$ axis, we observed two sharp anomalies\nin the sound velocity, at $B_m \\approx$ 20 T and $B^* \\approx$ 30 T, in all the\nsymmetry-breaking ultrasound modes at low temperatures. The lower-field anomaly\ncorresponds to the well-known first-order metamagnetic\nincommensurate-to-commensurate transition. The higher-field anomaly takes place\nat 30 T, where an electronic-nematic transition was previously suggested to\noccur. Both anomalies, observed only within the antiferromagnetic state, are of\nsimilar shape, but the corresponding changes of the ultrasound velocity have\nopposite signs. Based on our experimental results, we suggest that a\nfield-induced magnetic transition from a commensurate to another incommensurate\nantiferromagnetic state occurs at $B^*$. With further increasing the field\nangle from the $c$ axis, the anomaly at $B^*$ slowly shifts to higher fields,\nbroadens, and becomes smaller in magnitude. Traced up to 30$^\\circ$ from the\n$c$ axis, it is no longer observed at 40$^\\circ$ below 36 T.\n"}
+{"abstract": "  The Earth's magnetosphere represents a natural plasma laboratory that allows\nus to study the behavior of particle distribution functions in the absence of\nCoulomb collisions, typically described by the Kappa distributions. We have\ninvestigated the properties of these functions for ions and electrons in\ndifferent magnetospheric regions, thereby making it possible to reveal the\n$\\kappa$-parameters for a wide range of plasma beta ($\\beta$) values (from\n$10^{-3}$ to $10^{2}$). This was done using simultaneous ion and electron\nmeasurements from the five Time History of Events and Macroscale Interactions\nduring Substorms (THEMIS) spacecraft spanning the years 2008 to 2018. It was\nfound that for a fixed plasma $\\beta$, the $\\kappa$-index and core energy\n($E_c$) of the distribution can be modeled by the power-law\n$\\kappa=AE_c^\\gamma$ for both species, and the relation between $\\beta$,\n$\\kappa$, and $E_c$ is much more complex than earlier reported: both $A$ and\n$\\gamma$ exhibit systematic dependencies with $\\beta$. Our results indicate\nthat $\\beta \\sim 0.1-0.3$ is a range where the plasma is more dynamic since it\nis influenced by both the magnetic field and temperature fluctuations, which\nsuggests that the transition between magnetically dominated plasmas to\nkinetically dominated plasmas occurs at these values of $\\beta$. For $\\beta > 1\n$, both $A$ and $\\gamma$ take nearly constant values, a feature that is\nespecially notable for the electrons and might be related to their\ndemagnetization. The relation between $\\beta$, $\\kappa$, and $E_c$ that we\npresent is an important result that can be used by theoretical models in the\nfuture.\n"}
+{"abstract": "  Conditionally on the $abc$ conjecture, we generalize previous work of Clark\nand the author to show that a superelliptic curve $C: y^n = f(x)$ of\nsufficiently high genus has infinitely many twists violating the Hasse\nPrinciple if and only if $f(x)$ has no $\\mathbb Q$-rational roots. We also show\nunconditionally that a curve defined by $C: y^{pN}=f(x)$ has infinitely many\ntwists violating the Hasse Principle over any number field $k$ such that $k$\ncontains the $p$th roots of unity and $f(x)$ has no $k$-rational roots.\n"}
+{"abstract": "  The growth rate in the amount of biomedical documents is staggering.\nUnlocking information trapped in these documents can enable researchers and\npractitioners to operate confidently in the information world. Biomedical NER,\nthe task of recognising biomedical names, is usually employed as the first step\nof the NLP pipeline. Standard NER models, based on sequence tagging technique,\nare good at recognising short entity mentions in the generic domain. However,\nthere are several open challenges of applying these models to recognise\nbiomedical names: 1) Biomedical names may contain complex inner structure\n(discontinuity and overlapping) which cannot be recognised using standard\nsequence tagging technique; 2) The training of NER models usually requires\nlarge amount of labelled data, which are difficult to obtain in the biomedical\ndomain; and, 3) Commonly used language representation models are pre-trained on\ngeneric data; a domain shift therefore exists between these models and target\nbiomedical data. To deal with these challenges, we explore several research\ndirections and make the following contributions: 1) we propose a\ntransition-based NER model which can recognise discontinuous mentions; 2) We\ndevelop a cost-effective approach that nominates the suitable pre-training\ndata; and, 3) We design several data augmentation methods for NER. Our\ncontributions have obvious practical implications, especially when new\nbiomedical applications are needed. Our proposed data augmentation methods can\nhelp the NER model achieve decent performance, requiring only a small amount of\nlabelled data. Our investigation regarding selecting pre-training data can\nimprove the model by incorporating language representation models, which are\npre-trained using in-domain data. Finally, our proposed transition-based NER\nmodel can further improve the performance by recognising discontinuous\nmentions.\n"}
+{"abstract": "  Ultrasound (US) images usually contain identifying information outside the\nultrasound fan area and manual annotations placed by the sonographers during\nexams. For those images to be exploitable in a Deep Learning framework, one\nneeds to first delineate the border of the fan which delimits the ultrasound\nfan area and then remove other annotations inside. We propose a parametric\nprobabilistic approach for the first task. We make use of this method to\ngenerate a training data set with segmentation masks of the region of interest\n(ROI) and train a U-Net to perform the same task in a supervised way, thus\nconsiderably reducing computational time of the method, one hundred and sixty\ntimes faster. These images are then processed with existing inpainting methods\nto remove annotations present inside the fan area. To the best of our\nknowledge, this is the first parametric approach to quickly detect the fan in\nan ultrasound image without any other information than the image itself.\n"}
+{"abstract": "  Robotic tasks such as manipulation with visual inputs require image features\nthat capture the physical properties of the scene, e.g., the position and\nconfiguration of objects. Recently, it has been suggested to learn such\nfeatures in an unsupervised manner from simulated, self-supervised, robot\ninteraction; the idea being that high-level physical properties are well\ncaptured by modern physical simulators, and their representation from visual\ninputs may transfer well to the real world. In particular, learning methods\nbased on noise contrastive estimation have shown promising results. To\nrobustify the simulation-to-real transfer, domain randomization (DR) was\nsuggested for learning features that are invariant to irrelevant visual\nproperties such as textures or lighting. In this work, however, we show that a\nnaive application of DR to unsupervised learning based on contrastive\nestimation does not promote invariance, as the loss function maximizes mutual\ninformation between the features and both the relevant and irrelevant visual\nproperties. We propose a simple modification of the contrastive loss to fix\nthis, exploiting the fact that we can control the simulated randomization of\nvisual properties. Our approach learns physical features that are significantly\nmore robust to visual domain variation, as we demonstrate using both rigid and\nnon-rigid objects.\n"}
+{"abstract": "  We show that a partition of the unity (or POVM) on a Hilbert space that is\nalmost orthogonal is close to an orthogonal partition of unity in the same von\nNeumann algebra. This generalizes to infinite dimension and slightly\nstrengthens previous results in matrix algebras by Kempe-Vidick and\nJi-Natarajan-Vidick-Wright-Yuen.\n  We also generalize to infinite dimension a duality result between POVMs and\nminimal majorants of finite subsets in the predual of a von Neumann algebra.\n"}
+{"abstract": "  We investigate entanglement of the Hawking radiation in a dispersive model\nwith subluminal dispersion. In this model, feature of the Hawking radiation is\nrepresented by three mode Bogoliubov transformation connecting the in-vacuum\nstate and the out-state. We obtain the exact form of the tripartite in-vacuum\nstate which encodes structure of multipartite entanglement. Bogoliubov\ncoefficients are computed by numerical calculation of the wave equation with\nsubluminal dispersion and it is found that genuine tripartite entanglement\npersists in whole frequency range up to the cutoff arisen from the subluminal\ndispersion. In the low frequency region, amount of genuine tripartite\nentanglement is small compared to bipartite entanglement between the Hawking\nparticle and its partner mode, and the deviation from the thermal spectrum is\nnegligible. On the other hand, in the high frequency region near the cutoff,\nentanglement is equally shared by two pairs of three modes and the partner\npicture of the Hawking radiation does not hold.\n"}
+{"abstract": "  We investigate scaling and efficiency of the deep neural network multigrid\nmethod (DNN-MG).\n  DNN-MG is a novel neural network-based technique for the simulation of the\nNavier-Stokes equations that combines an adaptive geometric multigrid solver,\ni.e. a highly efficient\n  classical solution scheme, with a recurrent neural network with memory.\n  The neural network replaces in DNN-MG one or multiple finest multigrid layers\nand provides a correction for the classical solve in the next time step.\n  This leads to little degradation in the solution quality while substantially\nreducing the overall computational costs.\n  At the same time, the use of the multigrid solver at the coarse scales allows\nfor a compact network that is easy to train, generalizes well, and allows for\nthe incorporation of physical constraints.\n  Previous work on DNN-MG focused on the overall scheme and how to enforce\ndivergence freedom in the solution.\n  In this work, we investigate how the network size affects training and\nsolution quality and the overall runtime of the computations.\n  Our results demonstrate that larger networks are able to capture the\n  flow behavior better while requiring only little additional training time.\n  At runtime, the use of the neural network correction can even reduce the\ncomputation time compared to a classical multigrid simulation through a faster\nconvergence of the nonlinear solve that is required at every time step.\n"}
+{"abstract": "  Inferring causal individual treatment effect (ITE) from observational data is\na challenging problem whose difficulty is exacerbated by the presence of\ntreatment assignment bias. In this work, we propose a new way to estimate the\nITE using the domain generalization framework of invariant risk minimization\n(IRM). IRM uses data from multiple domains, learns predictors that do not\nexploit spurious domain-dependent factors, and generalizes better to unseen\ndomains. We propose an IRM-based ITE estimator aimed at tackling treatment\nassignment bias when there is little support overlap between the control group\nand the treatment group. We accomplish this by creating diversity: given a\nsingle dataset, we split the data into multiple domains artificially. These\ndiverse domains are then exploited by IRM to more effectively generalize\nregression-based models to data regions that lack support overlap. We show\ngains over classical regression approaches to ITE estimation in settings when\nsupport mismatch is more pronounced.\n"}
+{"abstract": "  Bandit algorithms are increasingly used in real-world sequential\ndecision-making problems. Associated with this is an increased desire to be\nable to use the resulting datasets to answer scientific questions like: Did one\ntype of ad lead to more purchases? In which contexts is a mobile health\nintervention effective? However, classical statistical approaches fail to\nprovide valid confidence intervals when used with data collected with bandit\nalgorithms. Alternative methods have recently been developed for simple models\n(e.g., comparison of means). Yet there is a lack of general methods for\nconducting statistical inference using more complex models on data collected\nwith (contextual) bandit algorithms; for example, current methods cannot be\nused for valid inference on parameters in a logistic regression model for a\nbinary reward. In this work, we develop theory justifying the use of\nM-estimators -- which includes estimators based on empirical risk minimization\nas well as maximum likelihood -- on data collected with adaptive algorithms,\nincluding (contextual) bandit algorithms. Specifically, we show that\nM-estimators, modified with particular adaptive weights, can be used to\nconstruct asymptotically valid confidence regions for a variety of inferential\ntargets.\n"}
+{"abstract": "  Fast outflows and their interaction with slow shells (generally known as the\nfossil circumstellar envelope of asymptotic giant branch stars) play an\nimportant role in the structure and kinematics of protoplanetary and planetary\nnebulae (pPNe, PNe). To properly study their effects within these objects, we\nalso need to observe the intermediate-temperature gas, which is only detectable\nin the far-infrared (FIR) and submillimetre (submm) transitions. We study the\nphysical conditions of the outflows presented in a number of pPNe and PNe, with\na focus on their temperature and excitation states. We carried out\nHerschel/HIFI observations in the submm lines of 12CO in nine pPNe and nine PNe\nand complemented them with low-J CO spectra obtained with the IRAM 30m\ntelescope and taken from the literature. The spectral resolution of HIFI allows\nus to identify and measure the different nebular components in the line\nprofiles. The comparison with large velocity gradient (LVG) model predictions\nwas used to estimate the physical conditions of the warm gas in the nebulae,\nsuch as excitation conditions, temperature, and density. We found high kinetic\ntemperatures for the fast winds of pPNe, typically reaching between 75 K and\n200 K. In contrast, the high-velocity gas in the sampled PNe is colder, with\ncharacteristic temperatures between 25 K and 75 K, and it is found in a lower\nexcitation state. We interpret this correlation of the kinetic temperature and\nexcitation state of fast outflows with the amount of time elapsed since their\nacceleration (probably driven by shocks) as a consequence of the cooling that\noccurred during the pPN phase.\n"}
+{"abstract": "  Image registration plays an important role in medical image analysis.\nConventional optimization based methods provide an accurate estimation due to\nthe iterative process at the cost of expensive computation. Deep learning\nmethods such as learn-to-map are much faster but either iterative or\ncoarse-to-fine approach is required to improve accuracy for handling large\nmotions. In this work, we proposed to learn a registration optimizer via a\nmulti-scale neural ODE model. The inference consists of iterative gradient\nupdates similar to a conventional gradient descent optimizer but in a much\nfaster way, because the neural ODE learns from the training data to adapt the\ngradient efficiently at each iteration. Furthermore, we proposed to learn a\nmodal-independent similarity metric to address image appearance variations\nacross different image contrasts. We performed evaluations through extensive\nexperiments in the context of multi-contrast 3D MR images from both public and\nprivate data sources and demonstrate the superior performance of our proposed\nmethods.\n"}
+{"abstract": "  Till now geometric structures don't play a major role in cryptography.\nGilbert, MacWilliams and Sloane introduced an authentication scheme in the\nprojective plane and showed its perfectness in the sense of Shannon. In\narXiv:2102.10321 we introduced an encryption scheme in the M\\\"obius plane and\nshowed that it fulfills Shannon's requirement of perfectness in first\napproximation and also the requirement of completeness according to Kam and\nDavida. In this paper we will apply a similar approach to define encryption\nschemes in the geometries of the Laguerre plande and the Minkowski plane. We\nwill show that the encryption scheme in the Laguerre geometry meets Shannon's\nrequirement of perfectness sharp and that the encryption scheme in the\nMinkowski geometry meets this requirement in first approximation. The Laguerre\ncipher also fulfills the requirement of completeness according to Kam and\nDavida.\n"}
+{"abstract": "  With the advent of deep learning algorithms, fully automated radiological\nimage analysis is within reach. In spine imaging, several atlas- and\nshape-based as well as deep learning segmentation algorithms have been\nproposed, allowing for subsequent automated analysis of morphology and\npathology. The first Large Scale Vertebrae Segmentation Challenge (VerSe 2019)\nshowed that these perform well on normal anatomy, but fail in variants not\nfrequently present in the training dataset. Building on that experience, we\nreport on the largely increased VerSe 2020 dataset and results from the second\niteration of the VerSe challenge (MICCAI 2020, Lima, Peru). VerSe 2020\ncomprises annotated spine computed tomography (CT) images from 300 subjects\nwith 4142 fully visualized and annotated vertebrae, collected across multiple\ncentres from four different scanner manufacturers, enriched with cases that\nexhibit anatomical variants such as enumeration abnormalities (n=77) and\ntransitional vertebrae (n=161). Metadata includes vertebral labelling\ninformation, voxel-level segmentation masks obtained with a human-machine\nhybrid algorithm and anatomical ratings, to enable the development and\nbenchmarking of robust and accurate segmentation algorithms.\n"}
+{"abstract": "  Increasing the level of automation in pharmaceutical laboratories and\nproduction facilities plays a crucial role in delivering medicine to patients.\nHowever, the particular requirements of this field make it challenging to adapt\ncutting-edge technologies present in other industries. This article provides an\noverview of relevant approaches and how they can be utilized in the\npharmaceutical industry, especially in development laboratories. Recent\nadvancements include the application of flexible mobile manipulators capable of\nhandling complex tasks. However, integrating devices from many different\nvendors into an end-to-end automation system is complicated due to the\ndiversity of interfaces. Therefore, various approaches for standardization are\nconsidered in this article, and a concept is proposed for taking them a step\nfurther. This concept enables a mobile manipulator with a vision system to\n\"learn\" the pose of each device and - utilizing a barcode - fetch interface\ninformation from a universal cloud database. This information includes control\nand communication protocol definitions and a representation of robot actions\nneeded to operate the device. In order to define the movements in relation to\nthe device, devices have to feature - besides the barcode - a fiducial marker\nas standard. The concept will be elaborated following appropriate research\nactivities in follow-up papers.\n"}
+{"abstract": "  It has been a half-decade since the first direct detection of gravitational\nwaves, which signifies the coming of the era of the gravitational-wave\nastronomy and gravitational-wave cosmology. The increasing number of the\ndetected gravitational-wave events has revealed the promising capability of\nconstraining various aspects of cosmology, astronomy, and gravity. Due to the\nlimited space in this review article, we will briefly summarize the recent\nprogress over the past five years, but with a special focus on some of our own\nwork for the Key Project ``Physics associated with the gravitational waves''\nsupported by the National Natural Science Foundation of China. In particular,\n(1) we have presented the mechanism of the gravitational-wave production during\nsome physical processes of the early Universe, such as inflation, preheating\nand phase transition, and the cosmological implications of gravitational-wave\nmeasurements; (2) we have put constraints on the neutron star maximum mass\naccording to GW170817 observations; (3) we have developed a numerical\nrelativity algorithm based on the finite element method and a waveform model\nfor the binary black hole coalescence along an eccentric orbit.\n"}
+{"abstract": "  Despite tremendous efforts, it is very challenging to generate a robust model\nto assist in the accurate quantification assessment of COVID-19 on chest CT\nimages. Due to the nature of blurred boundaries, the supervised segmentation\nmethods usually suffer from annotation biases. To support unbiased lesion\nlocalisation and to minimise the labeling costs, we propose a data-driven\nframework supervised by only image-level labels. The framework can explicitly\nseparate potential lesions from original images, with the help of a generative\nadversarial network and a lesion-specific decoder. Experiments on two COVID-19\ndatasets demonstrate the effectiveness of the proposed framework and its\nsuperior performance to several existing methods.\n"}
+{"abstract": "  We discuss the obstruction to the construction of a multiparticle field\ntheory on a $\\kappa$-Minkowski noncommutative spacetime: the existence of\nmultilocal functions which respect the deformed symmetries of the problem. This\nconstruction is only possible for a light-like version of the commutation\nrelations, if one requires invariance of the tensor product algebra under the\ncoaction of the $\\kappa$-Poincar\\'e group. This necessitates a braided tensor\nproduct. We study the representations of this product, and prove that\n$\\kappa$-Poincar\\'e-invariant N-point functions belong to an Abelian\nsubalgebra, and are therefore commutative. We use this construction to define\nthe 2-point Whightman and Pauli--Jordan functions, which turn out to be\nidentical to the undeformed ones. We finally outline how to construct a free\nscalar $\\kappa$-Poincar\\'e-invariant quantum field theory, and identify some\nopen problems.\n"}
+{"abstract": "  We investigate the special case of diamond relay comprising a Gaussian\nchannel with identical frequency response from the user to the relays and\nfronthaul links with limited rate from the relays to the destination. We use\nthe oblivious compress and forward (CF) with distributed compression and decode\nand forward (DF) where each relay decodes the whole message and sends half of\nits bits to the destination. We derive achievable rate by using time-sharing\nbetween DF and CF. It is proved that optimal CF-DF time sharing is advantageous\nover superposition of CF and DF. The optimal time sharing proportion between DF\nand CF and power and rate allocations are different at each frequency and are\nfully determined.\n"}
+{"abstract": "  Reasoning about information from multiple parts of a passage to derive an\nanswer is an open challenge for reading-comprehension models. In this paper, we\npresent an approach that reasons about complex questions by decomposing them to\nsimpler subquestions that can take advantage of single-span extraction\nreading-comprehension models, and derives the final answer according to\ninstructions in a predefined reasoning template. We focus on subtraction-based\narithmetic questions and evaluate our approach on a subset of the DROP dataset.\nWe show that our approach is competitive with the state-of-the-art while being\ninterpretable and requires little supervision\n"}
+{"abstract": "  Scalar Field Dark Matter (SFDM) comprised of ultralight bosons has attracted\ngreat interest as an alternative to standard, collisionless Cold Dark Matter\n(CDM) because of its novel structure-formation dynamics, described by the\ncoupled Schr\\\"odinger-Poisson equations. In the free-field (\"fuzzy\") limit of\nSFDM (FDM), structure is inhibited below the de Broglie wavelength, but\nresembles CDM on larger scales. Virialized haloes have \"solitonic\" cores of\nradius $\\sim\\lambda_\\text{deB}$, surrounded by CDM-like envelopes. When a\nstrong enough repulsive self-interaction (SI) is also present, structure can be\ninhibited below a second length scale, $\\lambda_\\text{SI}$, with\n$\\lambda_\\text{SI}> \\lambda_\\text{deB}$ -- called the Thomas-Fermi (TF) regime.\nFDM dynamics differs from CDM because of quantum pressure, and SFDM-TF differs\nfurther by adding SI pressure. In the small-$\\lambda_\\text{deB}$ limit,\nhowever, we can model all three by fluid conservation equations for a\ncompressible, $\\gamma=5/3$ ideal gas, with ideal gas pressure sourced by\ninternal velocity dispersion and, for the TF regime, an added SI pressure,\n$P_\\text{SI}\\propto \\rho^2$. We use these fluid equations to simulate halo\nformation from gravitational collapse in 1D, spherical symmetry, demonstrating\nfor the first time that SFDM-TF haloes form with cores the size of\n$R_\\text{TF}$, the radius of an SI-pressure-supported $(n=1)$-polytrope,\nsurrounded by CDM-like envelopes. In comparison with rotation curves of dwarf\ngalaxies in the local Universe, SFDM-TF haloes pass the [\"too-big-to-fail\" +\n\"cusp-core\"]-test if $R_\\text{TF}\\gtrsim 1$ kpc.\n"}
+{"abstract": "  In single-particle entanglement (SPE) two degrees of freedom of a single\nparticle are entangled. SPE is a resource that can be exploited both in quantum\ncommunication protocols and in experimental tests of noncontextuality based on\nthe Kochen-Specker theorem. SPE can be certified via a test of quantum\ncontextuality based on Bell inequalities. Experiments of Bell-like inequality\nviolation by single particle entangled systems may be affected by an analogue\nof the locality loophole in this context, due to the presence of unavoidable\nnon-idealities in the experimental devices which actually produce unwanted\ncorrelations between the two observables that are simultaneously measured. This\nissue is tackled here by quantitatively analyzing the behaviour of realistic\ndevices in SPE experiments with photons. In particular, we show how it is\npossible to provide a semi-device independent randomness certification of\nrealistic quantum random number generators based on Bell inequality violation\nby SPE states of photons. The analysis is further enlarged to encompass, with a\nMarkovian model, memory effects due to dead time, dark counts and afterpulsing\naffecting single photon detectors, in particular when not dealing with\ncoincidence measurements. An unbiased estimator is also proposed for quantum\ntransition probabilities out of the collection of experimental data.\n"}
+{"abstract": "  This study investigates dynamic system-optimal (DSO) and dynamic user\nequilibrium (DUE) traffic assignment of departure/arrival-time choices in a\ncorridor network. The morning commute problems with a many-to-one pattern of\norigin-destination demand and the evening commute problems with a one-to-many\npattern are considered. Specifically, a novel approach to derive closed-form\nsolutions for both DSO and DUE problems is developed. We first derive a\nclosed-form solution to the DSO problem based on the regularities of the cost\nand flow variables at an optimal state. By utilizing this solution, we prove\nthat the queuing delay at a bottleneck in a DUE solution is equal to an optimal\ntoll that eliminates the queue in a DSO solution under certain conditions of a\nschedule delay function. This enables us to derive a closed-form DUE solution\nby using the DSO solution. We also show the theoretical relationship between\nthe DSO and DUE assignment. Numerical examples are provided to illustrate and\nverify the analytical results.\n"}
+{"abstract": "  This paper studies how to learn parameters in diagonal Gaussian mixture\nmodels. The problem can be formulated as computing incomplete symmetric tensor\ndecompositions. We use generating polynomials to compute incomplete symmetric\ntensor decompositions and approximations. Then the tensor approximation method\nis used to learn diagonal Gaussian mixture models. We also do the stability\nanalysis. When the first and third order moments are sufficiently accurate, we\nshow that the obtained parameters for the Gaussian mixture models are also\nhighly accurate. Numerical experiments are also provided.\n"}
+{"abstract": "  High-throughput technologies to collect field data have made observations\npossible at scale in several branches of life sciences. The data collected can\nrange from the molecular level (genotypes) to physiological (phenotypic traits)\nand environmental observations (e.g., weather, soil conditions). These vast\nswathes of data, collectively referred to as phenomics data, represent a\ntreasure trove of key scientific knowledge on the dynamics of the underlying\nbiological system. However, extracting information and insights from these\ncomplex datasets remains a significant challenge owing to their\nmultidimensionality and lack of prior knowledge about their complex structure.\nIn this paper, we present Pheno-Mapper, an interactive toolbox for the\nexploratory analysis and visualization of large-scale phenomics data. Our\napproach uses the mapper framework to perform a topological analysis of the\ndata, and subsequently render visual representations with built-in data\nanalysis and machine learning capabilities. We demonstrate the utility of this\nnew tool on real-world plant (e.g., maize) phenomics datasets. In comparison to\nexisting approaches, the main advantage of Pheno-Mapper is that it provides\nrich, interactive capabilities in the exploratory analysis of phenomics data,\nand it integrates visual analytics with data analysis and machine learning in\nan easily extensible way. In particular, Pheno-Mapper allows the interactive\nselection of subpopulations guided by a topological summary of the data and\napplies data mining and machine learning to these selected subpopulations for\nin-depth exploration.\n"}
+{"abstract": "  Federated learning provides a communication-efficient and privacy-preserving\ntraining process by enabling learning statistical models with massive\nparticipants while keeping their data in local clients. However, standard\nfederated learning techniques that naively minimize an average loss function\nare vulnerable to data corruptions from outliers, systematic mislabeling, or\neven adversaries. In addition, it is often prohibited for service providers to\nverify the quality of data samples due to the increasing concern of user data\nprivacy. In this paper, we address this challenge by proposing Auto-weighted\nRobust Federated Learning (arfl), a novel approach that jointly learns the\nglobal model and the weights of local updates to provide robustness against\ncorrupted data sources. We prove a learning bound on the expected risk with\nrespect to the predictor and the weights of clients, which guides the\ndefinition of the objective for robust federated learning. The weights are\nallocated by comparing the empirical loss of a client with the average loss of\nthe best p clients (p-average), thus we can downweight the clients with\nsignificantly high losses, thereby lower their contributions to the global\nmodel. We show that this approach achieves robustness when the data of\ncorrupted clients is distributed differently from benign ones. To optimize the\nobjective function, we propose a communication-efficient algorithm based on the\nblockwise minimization paradigm. We conduct experiments on multiple benchmark\ndatasets, including CIFAR-10, FEMNIST and Shakespeare, considering different\ndeep neural network models. The results show that our solution is robust\nagainst different scenarios including label shuffling, label flipping and noisy\nfeatures, and outperforms the state-of-the-art methods in most scenarios.\n"}
+{"abstract": "  Since the appearance of Einstein's paper {\\em\"On the Electrodynamics of\nMoving Bodies\"} and the birth of special relativity, it is understood that the\ntheory was basically coded within Maxwell's equations. The celebrated\nmass-energy equivalence relation, $E=mc^2$, is derived by Einstein using\nthought experiments involving the kinematics of the emission of light\n(electromagnetic energy) and the relativity principle. Text book derivations\noften follow paths similar to Einstein's, or the analysis of the kinematics of\nparticle collisions interpreted from the perspective of different inertial\nframes. All the same, in such derivations the direct dynamical link with\nhypothetical fundamental fields describing matter (e.g. Maxwell theory or\nother) is overshadowed by the use of powerful symmetry arguments, kinematics,\nand the relativity principle.\n  Here we show that the formula can be derived directly form the dynamical\nequations of a massless matter model confined in a box (which can be thought of\nas a toy model of a composite particle). The only assumptions in the derivation\nare that the field equations hold and the energy-momentum tensor admits a\nuniversal interpretation in arbitrary coordinate systems. The mass-energy\nequivalence relation follows from the inertia or (taking the equivalence\nprinciple for granted) weight of confined field radiation. The present\nderivation offers an interesting pedagogical perspective on the formula\nproviding a simple toy model on the origin of mass and a natural bridge to the\nfoundations of general relativity.\n"}
+{"abstract": "  We use an enhanced methodology combining specific forms of AI techniques,\nopinion mining and artificial mathematical intelligence (AMI), with public data\non the spread of the coronavirus SARS-CoV-2 and the incidence of COVID-19\ndisease in Colombia during the first three months since the first reported\npositive case. The results obtained, together with conceptual tools coming from\nthe global taxonomy of fundamental cognitive mechanisms emerging in AMI and\nwith suitable contextual information from Colombian public health and\nmainstream social media, allowed us to stating specific preventive guidelines\nfor a better restructuring of initial safe and stable life conditions in\nColombia, and in an extended manner in similar Latin American Countries. More\nspecifically, we describe three major guidelines: 1) regular creative\nvisualization and effective planning, 2) the continuous use of constructive\nlinguistic frameworks, and 3) frequent and moderate use of kinesthetic\nroutines. They should be understood as effective tools from a cognitive and\nbehavioural perspective, rather than from a biological one. Even more, the\nfirst two guidelines should be acknowledged in integral cooperation with the\nthird one regarding the global effect of COVID-19 in human beings as a whole,\nthis includes the mind and body.\n"}
+{"abstract": "  A new model for calculating the Casimir-Lifshitz force per unit length for\ntwo dielectric rods is proposed, based on the Green function method of\nclassical electrodynamics and the Lorentz model for permittivity.\n"}
+{"abstract": "  The \\emph{ab initio} path integral Monte Carlo (PIMC) approach is one of the\nmost successful methods in quantum many-body theory. A particular strength of\nthis method is its straightforward access to imaginary-time correlation\nfunctions (ITCF). For example, the well-known density-density ITCF\n$F(\\mathbf{q},\\tau)$ allows one to estimate the linear response of a given\nsystem for all wave vectors $\\mathbf{q}$ from a single simulation of the\nunperturbed system. Moreover, it constitutes the basis for the reconstruction\nof the dynamic structure factor $S(\\mathbf{q},\\omega)$ -- a key quantity in\nstate-of-the-art scattering experiments. In this work, we present analogous\nrelations between the nonlinear density response in quadratic and cubic order\nof the perturbation strength and generalized ITCFs measuring correlations\nbetween up to four imaginary-time arguments. As a practical demonstration of\nour new approach, we carry out simulations of the warm dense electron gas and\nfind excellent agreement with previous PIMC results that had been obtained with\nsubstantially larger computational effort. In addition, we give a relation\nbetween a cubic ITCF and the triple dynamic structure factor\n$S(\\mathbf{q}_1,\\omega_1;\\mathbf{q}_2,\\omega_2)$, which evokes the enticing\npossibility to study dynamic three-body effects on an \\emph{ab initio} level.\n"}
+{"abstract": "  In this paper, we introduce and prove asymptotic normality for a new\nnonparametric estimator of continuous treatment effects. Specifically, we\nestimate the average dose-response function - the expected value of an outcome\nof interest at a particular level of the treatment level. We utilize tools from\nboth the double debiased machine learning (DML) and the automatic double\nmachine learning (ADML) literatures to construct our estimator. Our estimator\nutilizes a novel debiasing method that leads to nice theoretical stability and\nbalancing properties. In simulations our estimator performs well compared to\ncurrent methods.\n"}
+{"abstract": "  The so-called Froehlich entropy is the entropy variation of a material under\nthe application of an electric field. This quantity can be calculated, under\nsuitable hypotheses, directly from the measured real part of the dielectric\nfunction. Although Froehlich entropy is defined for a biased sample, a\nstraightforward physical interpretation correlates it to the state of order of\nthe considered physical system in absence of field. When Froehlich entropy is\ncalculated from experimental results, its trend is able to give several\ninformation about the evolution in temperature of the explored compound,\nespecially of its phase transition features. We here provide a comprehensive\nreview of the physical systems (dipolar liquids and nematicons, organic\nmolecular crystals, metallic nanoparticles, inorganic disordered\nferroelectrics, etc.) where this approach has been exploited with the aim of\nevaluating their state of order. The variety of compounds where this method has\nbeen applied demonstrates that the estimation of the Froehlich entropy can be\nconsidered a trustworthy tool for carrying out study on the state of order of\ndifferent classes of materials. Indeed Froehlich entropy evaluation can be\nconsidered a fruitful and reliable investigation technique which can be\nexploited alongside more usual experimental approaches.\n"}
+{"abstract": "  Most of the Blockchain permissioned systems employ Byzantine fault-tolerance\n(BFT) consensus protocols to ensure that honest validators agree on the order\nfor appending entries to their ledgers. In this paper, we study the performance\nand the scalability of prominent consensus protocols, namely PBFT, Tendermint,\nHotStuff, and Streamlet, both analytically via load formulas and practically\nvia implementation and evaluation. Under identical conditions, we identify the\nbottlenecks of these consensus protocols and show that these protocols do not\nscale well as the number of validators increases. Our investigation points to\nthe communication complexity as the culprit. Even when there is enough network\nbandwidth, the CPU cost of serialization and deserialization of the messages\nlimits the throughput and increases the latency of the protocols. To alleviate\nthe bottlenecks, the most useful techniques include reducing the communication\ncomplexity, rotating the hotspot of communications, and pipelining across\nconsensus instances.\n"}
+{"abstract": "  Image and video synthesis are closely related areas aiming at generating\ncontent from noise. While rapid progress has been demonstrated in improving\nimage-based models to handle large resolutions, high-quality renderings, and\nwide variations in image content, achieving comparable video generation results\nremains problematic. We present a framework that leverages contemporary image\ngenerators to render high-resolution videos. We frame the video synthesis\nproblem as discovering a trajectory in the latent space of a pre-trained and\nfixed image generator. Not only does such a framework render high-resolution\nvideos, but it also is an order of magnitude more computationally efficient. We\nintroduce a motion generator that discovers the desired trajectory, in which\ncontent and motion are disentangled. With such a representation, our framework\nallows for a broad range of applications, including content and motion\nmanipulation. Furthermore, we introduce a new task, which we call cross-domain\nvideo synthesis, in which the image and motion generators are trained on\ndisjoint datasets belonging to different domains. This allows for generating\nmoving objects for which the desired video data is not available. Extensive\nexperiments on various datasets demonstrate the advantages of our methods over\nexisting video generation techniques. Code will be released at\nhttps://github.com/snap-research/MoCoGAN-HD.\n"}
+{"abstract": "  We construct self-adjoint Laplacians and symmetric Markov semigroups on\npartially hyperbolic attractors and on hyperbolic attractors with\nsingularities, endowed with Gibbs u-measures. If the measure has full support,\nwe can also guarantee the existence of an associated symmetric Hunt diffusion\nprocess. In the special case of partially hyperbolic diffeomorphisms induced by\ngeodesic flows on manifolds of negative sectional curvature the Laplacians we\nconsider are self-adjoint extensions of well-known classical leafwise\nLaplacians.\n"}
+{"abstract": "  This work revisits the production of vorticity at an interface separating two\nimmiscible incompressible fluids. A new decomposition of the vorticity flux is\nproposed in a two-dimensional context which allows to compute explicitly such a\nquantity in terms of surface tension $\\sigma$, viscosity $\\mu$ and gravity $g$.\nThis approach is then applied in the context of gravito-capillary waves. It\nleads to analytical results already known but from a new perspective, provides\nsome quantitative predictions at short time that can be a good test for\nnumerical codes. Finally it is a mean to obtain a qualitative understanding of\ndirect numerical simulation results.\n"}
+{"abstract": "  Majorana fermions exist on the boundaries of two-dimensional topological\nsuperconductors (TSCs) as charge-neutral quasi-particles. The neutrality makes\nthe detection of such states challenging from both experimental and theoretical\npoints of view. Current methods largely rely on transport measurements in which\nMajorana fermions manifest themselves by inducing electron-pair tunneling at\nthe lead-contacting point. Here we show that chiral Majorana fermions in TSCs\ngenerate {enhanced} local optical response. The features of local optical\nconductivity distinguish them not only from trivial superconductors or\ninsulators but also from normal fermion edge states such as those in quantum\nHall systems. Our results provide a new applicable method to detect dispersive\nMajorana fermions and may lead to a novel direction of this research field.\n"}
+{"abstract": "  In an order-driven financial market, the price of a financial asset is\ndiscovered through the interaction of orders - requests to buy or sell at a\nparticular price - that are posted to the public limit order book (LOB).\nTherefore, LOB data is extremely valuable for modelling market dynamics.\nHowever, LOB data is not freely accessible, which poses a challenge to market\nparticipants and researchers wishing to exploit this information. Fortunately,\ntrades and quotes (TAQ) data - orders arriving at the top of the LOB, and\ntrades executing in the market - are more readily available. In this paper, we\npresent the LOB recreation model, a first attempt from a deep learning\nperspective to recreate the top five price levels of the LOB for small-tick\nstocks using only TAQ data. Volumes of orders sitting deep in the LOB are\npredicted by combining outputs from: (1) a history compiler that uses a Gated\nRecurrent Unit (GRU) module to selectively compile prediction relevant quote\nhistory; (2) a market events simulator, which uses an Ordinary Differential\nEquation Recurrent Neural Network (ODE-RNN) to simulate the accumulation of net\norder arrivals; and (3) a weighting scheme to adaptively combine the\npredictions generated by (1) and (2). By the paradigm of transfer learning, the\nsource model trained on one stock can be fine-tuned to enable application to\nother financial assets of the same class with much lower demand on additional\ndata. Comprehensive experiments conducted on two real world intraday LOB\ndatasets demonstrate that the proposed model can efficiently recreate the LOB\nwith high accuracy using only TAQ data as input.\n"}
+{"abstract": "  The definition of the Jones polynomial in the 80's gave rise to a large\nfamily of so-called quantum link invariants, based on quantum groups. These\nquantum invariants are all controlled by the same two-variable invariant (the\nHOMFLY-PT polynomial), which also specializes to the older Alexander\npolynomial. Building upon quantum Schur--Weyl duality and variants of this\nphenomenon, I will explain an algebraic setup that allows for global\ndefinitions of these quantum polynomials, and discuss extensions of these\nquantum objects designed to encompass all of the mentioned invariants,\nincluding the HOMFLY-PT polynomial.\n"}
+{"abstract": "  Entanglement shared among multiple parties presents complex challenges for\nthe characterisation of different types of entanglement. One of the most basic\ninsights is the fact that some mixed states can feature entanglement across\nevery possible cut of a multipartite system, yet can be produced via a mixture\nof partially separable states. To distinguish states that genuinely cannot be\nproduced from mixing partially separable states, the term genuine multipartite\nentanglement was coined. All these considerations originate in a paradigm where\nonly a single copy of the state is distributed and locally acted upon. In\ncontrast, advances in quantum technologies prompt the question of how this\npicture changes when multiple copies of the same state become locally\naccessible. Here we show that multiple copies unlock genuine multipartite\nentanglement from partially separable states, even from undistillable\nensembles, and even more than two copies can be required to observe this\neffect. With these findings, we characterise the notion of partial separability\nin the paradigm of multiple copies and conjecture a strict hierarchy of\nactivatable states and an asymptotic collapse of hierarchy.\n"}
+{"abstract": "  This article describes a volumetric approach for procedural shape modeling\nand a new Procedural Shape Modeling Language (PSML) that facilitates the\nspecification of these models. PSML provides programmers the ability to\ndescribe shapes in terms of their 3D elements where each element may be a\nsemantic group of 3D objects, e.g., a brick wall, or an indivisible object,\ne.g., an individual brick. Modeling shapes in this manner facilitates the\ncreation of models that more closely approximate the organization and structure\nof their real-world counterparts. As such, users may query these models for\nvolumetric information such as the number, position, orientation and volume of\n3D elements which cannot be provided using surface based model-building\ntechniques. PSML also provides a number of new language-specific capabilities\nthat allow for a rich variety of context-sensitive behaviors and\npost-processing functions. These capabilities include an object-oriented\napproach for model design, methods for querying the model for component-based\ninformation and the ability to access model elements and components to perform\nBoolean operations on the model parts. PSML is open-source and includes freely\navailable tutorial videos, demonstration code and an integrated development\nenvironment to support writing PSML programs.\n"}
+{"abstract": "  Temporary school closures caused by the Covid-19 pandemic have posed new\nchallenges for many teachers and students worldwide. Especially the abrupt\nshift to online distance learning posed many obstacles to be overcome and it\nparticularly complicated the implementation of Educational Robotics activities.\nSuch activities usually comprise a variety of different learning artifacts,\nwhich were not accessible to many students during the period of school closure.\nMoreover, online distance learning considerably limits the possibilities for\nstudents to interact with their peers and teachers. In an attempt to address\nthese issues, this work presents the development of an Educational Robotics\nactivity particularly conceived for online distance learning in primary school.\nThe devised activities are based on pen and paper approaches that are\ncomplemented by commonly used social media to facilitate communication and\ncollaboration. They were proposed to 13 students, as a way to continue ER\nactivities in online distance learning over the time period of four weeks.\n"}
+{"abstract": "  Chemical element mapping is an imaging tool that provides essential\ninformation on composite materials and it is crucial for a broad range of\nfields ranging from fundamental science to numerous applications. Methods that\nexploit x-ray fluorescence are very advantageous and are widely used, but\nrequire focusing of the input beam and raster scanning of the sample. Thus the\nmethods are slow and exhibit limited resolution due to focusing challenges. We\ndemonstrate a new focusing free x-ray fluorescence method based ghost imaging\nthat overcomes those limitations. We combine our procedure with compressed\nsensing to reduce the measurement time and the exposure to radiation by more\nthan 80%. Since our method does not require focusing, it opens the possibility\nfor improving the resolution and image quality of chemical element maps with\ntabletop x-ray sources and for extending the applicability of x-ray\nfluorescence detection to new fields such as medical imaging and homeland\nsecurity applications\n"}
+{"abstract": "  Phase change memories (PCM) is an emerging type of non-volatile memory that\nhas shown a strong presence in the data-storage market. This technology has\nrecently attracted significant research interest in the development of non-Von\nNeumann computing architectures such as in-memory and neuromorphic computing.\nResearch in these areas has been primarily motivated by the scalability\npotential of phase change materials and their compatibility with industrial\nnanofabrication processes. In this work, we are presenting our development of\ncrossbar phase change memory arrays through the electrodeposition of GeSbTe\n(GST). We show that GST can be electrodeposited in microfabricated TiN crossbar\narrays using a scalable process. Our phase switching test of the\nelectrodeposited materials have shown that a SET/RESET resistance ratio of 2-3\norders of magnitude is achievable with a switching endurance of around 80\ncycles. These results represent the first phase switching of electrodeposited\nGeSbTe in microfabricated crossbar arrays. Our work paves the way towards\ndeveloping large memory arrays involving electrodeposited materials for passive\nselectors and phase switching devices. It also opens opportunities for\ndeveloping a variety of different electronic devices using electrodeposited\nmaterials.\n"}
+{"abstract": "  Aligning humans' assessment of what a robot can do with its true capability\nis crucial for establishing a common ground between human and robot partners\nwhen they collaborate on a joint task. In this work, we propose an approach to\ncalibrate humans' estimate of a robot's reachable workspace through a small\nnumber of demonstrations before collaboration. We develop a novel motion\nplanning method, REMP (Reachability-Expressive Motion Planning), which jointly\noptimizes the physical cost and the expressiveness of robot motion to reveal\nthe robot's motion capability to a human observer. Our experiments with human\nparticipants demonstrate that a short calibration using REMP can effectively\nbridge the gap between what a non-expert user thinks a robot can reach and the\nground-truth. We show that this calibration procedure not only results in\nbetter user perception, but also promotes more efficient human-robot\ncollaborations in a subsequent joint task.\n"}
+{"abstract": "  The oscillation frequencies observed in Sun-like stars are susceptible to\nbeing shifted by magnetic activity effects. The measured shifts depend on a\ncomplex relationship involving the mode type, the field strength and spatial\ndistribution of activity, as well as the inclination angle of the star.\nEvidence of these shifts is also present in frequency separation ratios which\nare often used when inferring global properties of stars in order to avoid\nsurface effects. However, one assumption when using frequency ratios for this\npurpose is that there are no near-surface perturbations that are\nnon-spherically symmetric. In this work, we studied the impact on inferred\nstellar properties when using frequency ratios that are influenced by\nnon-homogeneous activity distributions. We generate several sets of artificial\noscillation frequencies with various amounts of shift and determine stellar\nproperties using two separate pipelines. We find that for asteroseismic\nobservations of Sun-like targets we can expect magnetic activity to affect mode\nfrequencies which will bias the results from stellar modelling analysis.\nAlthough for most stellar properties this offset should be small, typically\nless than 0.5% in mass, estimates of age and central hydrogen content can have\nan error of up to 5% and 3% respectively. We expect a larger frequency shift\nand therefore larger bias for more active stars. We also warn that for stars\nwith very high or low inclination angles, the response of modes to activity is\nmore easily observable in the separation ratios and hence will incur a larger\nbias.\n"}
+{"abstract": "  We present a Helmholtz Decomposition for many n-dimensional, continuously\ndifferentiable vector fields on unbounded domains that do not decay at\ninfinity. Existing methods are restricted to fields not growing faster than\npolynomially and require solving n-dimensional volume integrals over unbounded\ndomains. With our method only one-dimensional integrals have to be solved to\nderive gradient and rotation potentials.\n  Analytical solutions are obtained for smooth vector fields $f(x)$ whose\ncomponents are separable into a product of two functions: $f_k(x) = u_k(x_k)\n\\cdot v_k(x_{\\neq k})$, where $u_k(x_k)$ depends only on $x_k$ and $v_k(x_{\\neq\nk})$ depends not on $x_k$. Additionally, an integer $\\lambda_k$ must exist such\nthat the $2\\lambda_k$-th integral of one of the functions times the\n$\\lambda_k$-th power of the Laplacian applied to the other function yields a\nproduct that is a multiple of the original product. A similar condition is\nwell-known from repeated partial integration, where the calculation can be\nfinalized if the shifting of derivatives yields a multiple of the original\nintegrand.\n  Also linear combinations of such vector fields can be decomposed. These\nconditions include periodic and exponential functions, combinations of\npolynomials with arbitrary integrable functions, their products and linear\ncombinations, and examples such as Lorenz or R\\\"ossler attractor.\n"}
+{"abstract": "  Recently there has been much interest in the chirality induced spin\nselectivity effect, whereby electron spin polarisation, which is dependent on\nthe molecular chirality, is produced in electrode-molecule electron transfer\nprocesses. Naturally, one might consider if a similar effect can be observed in\nsimple molecular charge transfer reactions, for example in light-induced\nelectron transfer from an electron donor to an electron acceptor. In this work\nwe explore the effect of electron transfer on spins in chiral single radicals\nand chiral radical pairs using Nakajima-Zwanzig theory. In these cases\nchirality, in conjuction with spin-orbit coupling, does not lead to spin\npolarisation, but instead the electron transfer generates quantum coherence\nbetween spins states. In principle, this chirality induced spin coherence could\nmanifest in a range of experiments, and in particular we demonstrate that the\nOOP-ESEEM pulse EPR experiment would be able to detect this effect in oriented\nradical pairs.\n"}
+{"abstract": "  We explore creating automated, personalized feedback in an intelligent\ntutoring system (ITS). Our goal is to pinpoint correct and incorrect concepts\nin student answers in order to achieve better student learning gains. Although\nautomatic methods for providing personalized feedback exist, they do not\nexplicitly inform students about which concepts in their answers are correct or\nincorrect. Our approach involves decomposing students answers using neural\ndiscourse segmentation and classification techniques. This decomposition yields\na relational graph over all discourse units covered by the reference solutions\nand student answers. We use this inferred relational graph structure and a\nneural classifier to match student answers with reference solutions and\ngenerate personalized feedback. Although the process is completely automated\nand data-driven, the personalized feedback generated is highly contextual,\ndomain-aware and effectively targets each student's misconceptions and\nknowledge gaps. We test our method in a dialogue-based ITS and demonstrate that\nour approach results in high-quality feedback and significantly improved\nstudent learning gains.\n"}
+{"abstract": "  Generic scaling laws, such as the Kolmogorov's 5/3-law, are milestone\nachievements of turbulence research in classical fluids. For quantum fluids\nsuch as atomic Bose-Einstein condensates, superfluid helium, and superfluid\nneutron stars, turbulence can also exist in the presence of a chaotic tangle of\nevolving quantized vortex lines. However, due to the lack of suitable\nexperimental tools to directly probe the vortex-tangle motion, so far little is\nknown about possible scaling laws that characterize the velocity correlations\nand trajectory statistics of the vortices in quantum-fluid turbulence (QT).\nAcquiring such knowledge could greatly benefit the development of advanced\nstatistical models of QT. Here we report an experiment where a tangle of\nvortices in superfluid $^4$He are decorated with solidified deuterium tracer\nparticles. Under experimental conditions where these tracers follow the motion\nof the vortices, we observed an apparent superdiffusion of the vortices. Our\nanalysis shows that this superdiffusion is not due to L\\'{e}vy flights, i.e.,\nlong-distance hops that are known to be responsible for superdiffusion of\nrandom walkers. Instead, a previously unknown power-law scaling of the\nvortex-velocity temporal correlation is uncovered as the cause. This finding\nmay motivate future research on hidden scaling laws in QT.\n"}
+{"abstract": "  We study the matter-coupled equations of motion for cosmological NS massless\nfields including all $\\alpha'$ corrections in an O$(d,d)$ duality invariant\napproach, with emphasis on the Kalb-Ramond two-form field $B_{(2)}$ and its\nsource. Solutions for the vacuum and matter case are found and the\ncorresponding Einstein frame cosmologies are discussed. We also show that the\nansatz for $B_{(2)}$ required by the duality invariant framework implies that\nthe two-form is non-isotropic.\n"}
+{"abstract": "  In this work we investigate the nonperturbative decay dynamics of a quantum\nemitter coupled to a composite right/left handed transmission line (CRLH-TL).\nOur theory captures the contributions from the different spectral features of\nthe waveguide, providing an accurate prediction beyond the weak coupling\nregime, and illustrating the multiple possibilities offered by the nontrivial\ndispersion of metamaterial waveguides. We show that the waveguide is\ncharacterized by a band-gap with two asymmetric edges: (i) a mu-near-zero (MNZ)\nband edge, where spontaneous emission is inhibited and an unstable pole is\nsmoothly transformed into a bound state, and (ii) an epsilon-near-zero (ENZ)\nband edge, where the decay rate diverges and unstable and real (bound state)\npoles coexist. In both cases, branch cut singularities contribute with\nfractional decay dynamics whose nature depend on the properties of the\nband-edges.\n"}
+{"abstract": "  Video compression is a basic requirement for consumer and professional video\napplications alike. Video coding standards such as H.264/AVC and H.265/HEVC are\nwidely deployed in the market to enable efficient use of bandwidth and storage\nfor many video applications. To reduce the coding artifacts and improve the\ncompression efficiency, neural network based loop filtering of the\nreconstructed video has been developed in the literature. However, loop\nfiltering is a challenging task due to the variation in video content and\nsampling densities. In this paper, we propose a on-line scaling based\nmulti-density attention network for loop filtering in video compression. The\ncore of our approach lies in several aspects: (a) parallel multi-resolution\nconvolution streams for extracting multi-density features, (b) single attention\nbranch to learn the sample correlations and generate mask maps, (c) a\nchannel-mutual attention procedure to fuse the data from multiple branches, (d)\non-line scaling technique to further optimize the output results of network\naccording to the actual signal. The proposed multi-density attention network\nlearns rich features from multiple sampling densities and performs robustly on\nvideo content of different resolutions. Moreover, the online scaling process\nenhances the signal adaptability of the off-line pre-trained model.\nExperimental results show that 10.18% bit-rate reduction at the same video\nquality can be achieved over the latest Versatile Video Coding (VVC) standard.\nThe objective performance of the proposed algorithm outperforms the\nstate-of-the-art methods and the subjective quality improvement is obvious in\nterms of detail preservation and artifact alleviation.\n"}
+{"abstract": "  The polarization and strain response of ferroelectric materials at fields\nbelow the macroscopic coercive field is of a paramount importance for the\noperation of many electronic devices. The response of real ferroelectric and\nrelated materials is in general complex and difficult to interpret. The reason\nfor this is that many processes in a ferroelectric material contribute to its\nproperties, often concurrently. Examples include motion of ferroelectric and\nferroelastic domains, presence of domains within domains, dynamics of different\ntypes of polar nano-entities, interaction of polar nano-entities (e.g., polar\nnanoregions in relaxors) with the strain and polarization within domains,\nmotion of defects and rearrangement of defect clusters and their interaction\nwith polarization and strain. One signature of these processes is nonlinearity\nof the strain and polarization. Most ferroelectrics exhibit nonlinear response\nat all practical field levels meaning that the apparent material coefficients\ndepend on the amplitude of the driving excitation. In this paper we show that\nan investigation of nonlinear behavior is a sensitive way to study various\nmechanisms operating in dielectric and piezoelectric materials. We review the\nbasic formalism of the nonlinear description of polarization and strain, give a\nphysical interpretation of different terms and illustrate this approach on\nnumerous examples of relaxors, relaxor ferroelectrics, hard and soft\nferroelectrics, and morphotropic phase boundary compositions. An experimental\napproach based on a lock-in technique that is well-suited for such studies is\nalso discussed.\n"}
+{"abstract": "  Efficient devices for light harvesting and photon sensing are fundamental\nbuilding blocks of basic energy science and many essential technologies. Recent\nefforts have turned to biomimicry to design the next generation of\nlight-capturing devices, partially fueled by an appreciation of the fantastic\nefficiency of the initial stages of natural photosynthetic systems at capturing\nphotons. In such systems extended excitonic states are thought to play a\nfundamental functional role, inducing cooperative coherent effects, such as\nsuperabsorption of light and supertransfer of photoexcitations. Inspired by\nthis observation, we design an artificial light-harvesting and photodetection\ndevice that maximally harnesses cooperative effects to enhance efficiency. The\ndesign relies on separating absorption and transfer processes (energetically\nand spatially) in order to overcome the fundamental obstacle to exploiting\ncooperative effects to enhance light capture: the enhanced emission processes\nthat accompany superabsorption. This engineered separation of processes greatly\nimproves the efficiency and the scalability of the system.\n"}
+{"abstract": "  The machine learning publication process is broken, of that there can be no\ndoubt. Many of these flaws are attributed to the current workflow: LaTeX to PDF\nto reviewers to camera ready PDF. This has understandably resulted in the\ndesire for new forms of publications; ones that can increase inclusively,\naccessibility and pedagogical strength. However, this venture fails to address\nthe origins of these inadequacies in the contemporary paper workflow. The\npaper, being the basic unit of academic research, is merely how problems in the\npublication and research ecosystem manifest; but is not itself responsible for\nthem. Not only will simply replacing or augmenting papers with different\nformats not fix existing problems; when used as a band-aid without systemic\nchanges, will likely exacerbate the existing inequities. In this work, we argue\nthat the root cause of hindrances in the accessibility of machine learning\nresearch lies not in the paper workflow but within the misaligned incentives\nbehind the publishing and research processes. We discuss these problems and\nargue that the paper is the optimal workflow. We also highlight some potential\nsolutions for the incentivization problems.\n"}
+{"abstract": "  We are concerned with the focusing $L^2$-critical nonlinear Schr\\\"odinger\nequations in $\\mathbb{R}^d$ for $d=1,2$. The uniqueness is proved for a large\nenergy class of multi-bubble blow-up solutions, which converge to a sum of $K$\npseudo-conformal blow-up solutions particularly with low rate $(T-t)^{0+}$, as\n$t\\to T$, $1\\leq K<\\infty$. Moreover, we also prove the uniqueness in the\nenergy class of multi-solitons which converge to a sum of $K$ solitary waves\nwith convergence rate $(1/t)^{2+}$, as $t\\to \\infty$. The uniqueness class is\nfurther enlarged to contain the multi-solitons with even lower convergence rate\n$(1/t)^{\\frac 12+}$ in the pseudo-conformal space. The proof is mainly based on\nthe pseudo-conformal invariance and the monotonicity properties of several\nfunctionals adapted to the multi-bubble case, the latter is crucial towards the\nupgradation of the convergence to the fast exponential decay rate.\n"}
+{"abstract": "  We discuss an approach to the \\emph{covering} and \\emph{vanishing} theorems\nfor the comparison map from bounded cohomology to singular cohomology, based on\nthe observation that the comparison map is the coassembly map for bounded\ncohomology.\n"}
+{"abstract": "  We define a Mellin amplitude for CFT$_1$ four-point functions. Its analytical\nproperties are inferred from physical requirements on the correlator. We\ndiscuss the analytic continuation that is necessary for a fully nonperturbative\ndefinition of the Mellin transform. The resulting bounded, meromorphic function\nof a single complex variable is used to derive an infinite set of\nnonperturbative sum rules for CFT data of exchanged operators, which we test on\nknown examples. We then consider the perturbative setup produced by quartic\ninteractions with an arbitrary number of derivatives in a bulk AdS$_2$ field\ntheory. With our formalism, we obtain a closed-form expression for the Mellin\ntransform of tree-level contact interactions and for the first correction to\nthe scaling dimension of \"two-particle\" operators exchanged in the generalized\nfree field theory correlator.\n"}
+{"abstract": "  Let $\\mathcal{M}$ be a smooth submanifold of $\\mathbb{R}^n$ equipped with the\nEuclidean (chordal) metric. This note considers the smallest dimension $m$ for\nwhich there exists a bi-Lipschitz function $f: \\mathcal{M} \\mapsto\n\\mathbb{R}^m$ with bi-Lipschitz constants close to one. The main result bounds\nthe embedding dimension $m$ below in terms of the bi-Lipschitz constants of $f$\nand the reach, volume, diameter, and dimension of $\\mathcal{M}$. This new lower\nbound is applied to show that prior upper bounds by Eftekhari and Wakin\n(arXiv:1306.4748) on the minimal low-distortion embedding dimension of such\nmanifolds using random matrices achieve near-optimal dependence on both reach\nand volume. This supports random linear maps as being nearly as efficient as\nthe best possible nonlinear maps at reducing the ambient dimension for manifold\ndata. In the process of proving our main result, we also prove similar results\nconcerning the impossibility of achieving better nonlinear measurement maps\nwith the Restricted Isometry Property (RIP) in compressive sensing\napplications.\n"}
+{"abstract": "  A method of generating differential operators is used to solve the spectral\nproblem for a generalisation of the Sylvester-Kac matrix. As a by-product, we\nfind a linear differential operator with polynomial coefficients of the first\norder that has a finite sequence of polynomial eigenfunctions generalising the\noperator considered by M. Kac. In addition, we explain spectral properties of\ntwo related tridiagonal matrices whose shape differ from our generalisation.\n"}
+{"abstract": "  The milestone improvements brought about by deep representation learning and\npre-training techniques have led to large performance gains across downstream\nNLP, IR and Vision tasks. Multimodal modeling techniques aim to leverage large\nhigh-quality visio-linguistic datasets for learning complementary information\n(across image and text modalities). In this paper, we introduce the\nWikipedia-based Image Text (WIT) Dataset\n(https://github.com/google-research-datasets/wit) to better facilitate\nmultimodal, multilingual learning. WIT is composed of a curated set of 37.6\nmillion entity rich image-text examples with 11.5 million unique images across\n108 Wikipedia languages. Its size enables WIT to be used as a pretraining\ndataset for multimodal models, as we show when applied to downstream tasks such\nas image-text retrieval. WIT has four main and unique advantages. First, WIT is\nthe largest multimodal dataset by the number of image-text examples by 3x (at\nthe time of writing). Second, WIT is massively multilingual (first of its kind)\nwith coverage over 100+ languages (each of which has at least 12K examples) and\nprovides cross-lingual texts for many images. Third, WIT represents a more\ndiverse set of concepts and real world entities relative to what previous\ndatasets cover. Lastly, WIT provides a very challenging real-world test set, as\nwe empirically illustrate using an image-text retrieval task as an example.\n"}
+{"abstract": "  Graphical languages, like quantum circuits or ZX-calculus, have been\nsuccessfully designed to represent (memoryless) quantum computations acting on\na finite number of qubits. Meanwhile, delayed traces have been used as a\ngraphical way to represent finite-memory computations on streams, in a\nclassical setting (cartesian data types). We merge those two approaches and\ndescribe a general construction that extends any graphical language, equipped\nwith a notion of discarding, to a graphical language of finite memory\ncomputations. In order to handle cases like the ZX-calculus, which is complete\nfor post-selected quantum mechanics, we extend the delayed trace formalism\nbeyond the causal case, refining the notion of causality for stream\ntransformers. We design a stream semantics based on stateful morphism sequences\nand, under some assumptions, show universality and completeness results.\nFinally, we investigate the links of our framework with previous works on\ncartesian data types, signal flow graphs, and quantum channels with memories.\n"}
+{"abstract": "  Ransomware is a growing threat that typically operates by either encrypting a\nvictim's files or locking a victim's computer until the victim pays a ransom.\nHowever, it is still challenging to detect such malware timely with existing\ntraditional malware detection techniques. In this paper, we present a novel\nransomware detection system, called \"Peeler\" (Profiling kErnEl -Level Events to\ndetect Ransomware). Peeler deviates from signatures for individual ransomware\nsamples and relies on common and generic characteristics of ransomware depicted\nat the kernel-level. Analyzing diverse ransomware families, we observed\nransomware's inherent behavioral characteristics such as stealth operations\nperformed before the attack, file I/O request patterns, process spawning, and\ncorrelations among kernel-level events. Based on those characteristics, we\ndevelop Peeler that continuously monitors a target system's kernel events and\ndetects ransomware attacks on the system. Our experimental results show that\nPeeler achieves more than 99\\% detection rate with 0.58\\% false-positive rate\nagainst 43 distinct ransomware families, containing samples from both crypto\nand screen-locker types of ransomware. For crypto ransomware, Peeler detects\nthem promptly after only one file is lost (within 115 milliseconds on average).\nPeeler utilizes around 4.9\\% of CPU time with only 9.8 MB memory under the\nnormal workload condition. Our analysis demonstrates that Peeler can\nefficiently detect diverse malware families by monitoring their kernel-level\nevents.\n"}
+{"abstract": "  One of the most important and well-established empirical results in astronomy\nis the Kennicutt-Schmidt (KS) relation between the density of interstellar gas\nand the rate at which that gas forms stars. A tight correlation between these\nquantities has long been measured at galactic scales. More recently, using\nsurveys of YSOs, a KS relationship has been found within molecular clouds\nrelating the surface density of star formation to the surface density of gas;\nhowever, the scaling of these laws varies significantly from cloud to cloud. In\nthis Letter, we use a recently developed, high-accuracy catalog of young\nstellar objects from $\\textit{Spitzer}$ combined with high-dynamic-range gas\ncolumn density maps of twelve nearby ($<$1.5 kpc) molecular clouds from\n$\\textit{Herschel}$ to re-examine the KS relation within individual molecular\nclouds. We find a tight, linear correlation between clouds' star formation rate\nper unit area and their gas surface density normalized by the gas free-fall\ntime. The measured intracloud KS relation, which relates star formation rate to\nthe volume density, extends over more than two orders of magnitude within each\ncloud and is nearly identical in each of the twelve clouds, implying a constant\nstar formation efficiency per free-fall time $\\epsilon_{\\rm ff}\\approx 0.026$.\nThe finding of a universal correlation within individual molecular clouds,\nincluding clouds that contain no massive stars or massive stellar feedback,\nfavors models in which star formation is regulated by local processes such as\nturbulence or stellar feedback such as protostellar outflows, and disfavors\nmodels in which star formation is regulated only by galaxy properties or\nsupernova feedback on galactic scales.\n"}
+{"abstract": "  The paper estimates the need for additional attorney hours annually to\naddress the legal needs of indigent clients throughout the United States in\nmatters that comprise the so-called social determinants of health (SDoH). The\nresult will inform stakeholders such as policy makers and private donors so\nthey can allocate resources appropriately and design programs to close the\ndo-called justice gap. As a pilot study, the scope of the project covers only a\nfew major justice problems related to the social determinants of health\n(standard housing, evictions and foreclosures, guardianships for the\nincapacitated, and victims of domestic violence) because they significantly\nimpact health outcomes and there are data available. Based on our calculations,\nwe estimate that the total number of attorney hours to address only these five\nlegal issues is over 34 million per year. To put that in perspective, the\nAmerican Bar Association estimated that in 2018, the year from which much of\nour data comes, the total number of practicing attorneys in the United States\nwas 1,338,678 (Weiss). Thus, to provide the needed hours, every single\npracticing attorney in the United States would need to contribute about 26\nhours a year. While many lawyers do in fact contribute pro bono hours, they\naddress the full range of legal needs that go well beyond just the five we\nstudied.\n"}
+{"abstract": "  Community structure is an important feature of many networks. One of the most\npopular ways to capture community structure is using a quantitative measure,\nmodularity, which can serve as both a standard benchmark comparing different\ncommunity detection algorithms, and a optimization objective for detecting\ncommunities. Previous works on modularity mainly focus on the approximation\nmethod for modularity maximization to detect communities, or minor\nmodifications to the definition.\n  In this paper, we study modularity from an information-theoretical\nperspective and show that modularity and mutual information in networks are\nessentially the same. The main contribution is that we develop a family of\ngeneralized modularity measure, $f$-Modularity, which includes the original\nmodularity as a special case. At a high level, we show the significance of\ncommunity structure is equivalent to the amount of information contained in the\nnetwork. On the one hand, $f$-Modularity has an information-theoretical\ninterpretation and enjoys the desired properties of mutual information measure.\nOn the other hand, quantifying community structure also provides an approach to\nestimate the mutual information between discrete random samples with a large\nvalue space but given only limited samples. We demonstrate the algorithm for\noptimizing $f$-Modularity in a relatively general case, and validate it through\nexperimental results on simulated networks. We also apply $f$-Modularity to\nreal-world market networks. Our results bridge two important fields, complex\nnetwork and information theory, and also shed light on the design of measures\non community structure in the future.\n"}
+{"abstract": "  Biological evidence shows that animals are capable of evading eminent\ncollision without using depth information, relying solely on looming stimuli.\nIn robotics, collision avoidance among uncooperative vehicles requires\nmeasurement of relative distance to the obstacle. Small, low-cost mobile robots\nand UAVs might be unable to carry distance measuring sensors, like LIDARS and\ndepth cameras. We propose a control framework suitable for a unicycle-like\nvehicle moving in a 2D plane that achieves collision avoidance. The control\nstrategy is inspired by the reaction of invertebrates to approaching obstacles,\nrelying exclusively on line-of-sight (LOS) angle, LOS angle rate, and\ntime-to-collision as feedback. Those quantities can readily be estimated from a\nmonocular camera vision system onboard a mobile robot. The proposed avoidance\nlaw commands the heading angle to circumvent a moving obstacle with unknown\nposition, while the velocity controller is left as a degree of freedom to\naccomplish other mission objectives. Theoretical guarantees are provided to\nshow that minimum separation between the vehicle and the obstacle is attained\nregardless of the exogenous tracking controller.\n"}
+{"abstract": "  Power law distributed fluctuations are known to accompany \\emph{terminal}\nfailure in disordered brittle solids. The associated intermittent scale-free\nbehavior is of interest from the fundamental point of view as it emerges\nuniversally from an intricate interplay of threshold-type nonlinearity,\nquenched disorder, and long-range interactions. We use the simplest mean-field\ndescription of such systems to show that they can be expected to undergo a\ntransition between brittle and quasi-brittle (ductile) responses. While the\nformer is characterized by a power law distribution of avalanches, in the\nlatter, the statistics of avalanches is predominantly Gaussian. The realization\nof a particular regime depends on the variance of disorder and the effective\nrigidity represented by a combination of elastic moduli. We argue that the\nrobust criticality, as in the cases of earthquakes and collapsing porous\nmaterials, indicates the self-tuning of the system towards the boundary\nseparating brittle and ductile regimes.\n"}
+{"abstract": "  In this paper we discuss the possibility to generate and accelerate proton\nnanobeams in fully dielectric laser-driven accelerators (p-DLAs). High gradient\non-chip optical-power dielectric laser accelerators (DLAs) could represent one\nof the most promising way towards future miniaturized particle accelerator. A\nprimary challenge for DLAs are small beam apertures having a size of the order\nof the driving laser wavelength where low charge high-repetition (or also CW)\nultralow emittance nanobeams have to be transported. For electrons beams\ngeneration and acceleration, intense research activities are ongoing, and\nseveral demonstrations have been already obtained by using electrons nanotip\n(or flat photocathode) sources feeding dielectric microstructures. In this\narticle we aim at the possibility to integrate a nanosource for the generation\nof a light ion or proton nano-beams suitable for the subsequent acceleration\ninto sub-relativistic (low-beta) p-DLA stages. Such integration includes the\nidea to use a proton dielectric radiofrequency quadrupole (p-DRFQ) for bridging\nthe gap between the accelerator front-end and the drift-tube and high-beta\nsections. The paper has been prepared as a white book including state-of-art\ntechnologies and new solutions that now put the ambitious frontier of a fully\nnanostructured proton accelerator into reach. Conceptual studies of p-DLAs here\npresented could enable table-top proton nano-beams for several applications:\nproton beam writing, nuclear reaction analysis at sub-micrometer scales, the\nconstruction of miniaturized Proton-Boron Nuclear Fusion based Reactors,\nbiological analysis at the micrometer scale, ion beam analysis at the\nsub-cellular level, mini-beams ion therapy to spare the shallow tissues, proton\nirradiation of transistors, compact proton linac for neutron generation.\n"}
+{"abstract": "  Topological photonics, featured by stable topological edge states resistant\nto perturbations, has been utilized to design robust integrated devices. Here,\nwe present a study exploring the intriguing topological rotated Weyl physics in\na 3D parameter space based on quaternary waveguide arrays on lithium\nniobate-on-insulator (LNOI) chips. Unlike previous works that focus on the\nFermi arc surface states of a single Weyl structure, we can experimentally\nconstruct arbitrary interfaces between two Weyl structures whose orientations\ncan be freely rotated in the synthetic parameter space. This intriguing system\nwas difficult to realize in usual 3D Weyl semimetals due to lattice mismatch.\nWe found whether the interface can host gapless topological interface states\n(TISs) or not, is determined by the relative rotational directions of the two\nWeyl structures. In the experiment, we have probed the local characteristics of\nthe TISs through linear optical transmission and nonlinear second harmonic\ngeneration. Our study introduces a novel path to explore topological photonics\non LNOI chips and various applications in integrated nonlinear and quantum\noptics.\n"}
+{"abstract": "  This paper studies two families of constraints for two-dimensional and\nmultidimensional arrays. The first family requires that a multidimensional\narray will not contain a cube of zeros of some fixed size and the second\nconstraint imposes that there will not be two identical cubes of a given size\nin the array. These constraints are natural extensions of their one-dimensional\ncounterpart that have been rigorously studied recently. For both of these\nconstraint we present conditions of the size of the cube for which the\nasymptotic rate of the set of valid arrays approaches 1 as well as conditions\nfor the redundancy to be at most a single symbol. For the first family we\npresent an efficient encoding algorithm that uses a single symbol to encode\narbitrary information into a valid array and for the second family we present a\nsimilar encoder for the two-dimensional case. The results in the paper are also\nextended to similar constraints where the sub-array is not necessarily a cube,\nbut a box of arbitrary dimensions and only its volume is bounded.\n"}
+{"abstract": "  Supersonic turbulence in the interstellar medium (ISM) is closely linked to\nthe formation of stars, and hence many theories connect the stellar initial\nmass function (IMF) with the turbulent properties of molecular clouds. Here we\ntest three turbulence-based IMF models (by Padoan & Nordlund 2002, Hennebelle &\nChabrier 2008, and Hopkins 2012), which predict the relation between the\nhigh-mass slope ($\\Gamma$) of the IMF, $\\mathrm{d} N/\\mathrm{d} \\log M \\propto\nM^{\\Gamma}$ and the exponent n of the velocity power spectrum of turbulence,\n$E_v(k)\\propto k^{-n} $, where $n\\approx 2$ corresponds to typical ISM\nturbulence. Using hydrodynamic simulations, we drive turbulence with an unusual\nindex of $n\\approx 1$, measure $\\Gamma$, and compare the results with $n\\approx\n2$. We find that reducing $n$ from 2 to 1 primarily changes the high-mass\nregion of the IMF (beyond the median mass), where we measure high-mass slopes\nwithin the 95 per cent confidence interval of $-1.5<\\Gamma<-1$ for $n \\approx\n1$ and $-3.7<\\Gamma<-2.4$ for $n\\approx 2$, respectively. Thus, we find that\n$n=1$ results in a significantly flatter high-mass slope of the IMF, with more\nmassive stars formed than for $n \\approx 2$. We compare these simulations with\nthe predictions of the three IMF theories. We find that while the Padoan &\nNordlund theory matches our simulations with fair accuracy, the other theories\neither fail to reproduce the main qualitative outcome of the simulations or\nrequire some modifications. We conclude that turbulence plays a key role in\nshaping the IMF, with a shallower turbulence power spectrum producing a\nshallower high-mass IMF, and hence more massive stars.\n"}
+{"abstract": "  We establish various analogs of the Kronecker-Weyl equidistribution theorem\nthat can be considered higher-dimensional versions of results established in\nour earlier investigation of the discrete 2-circle problem studied in 1969 by\nVeech. Whereas the Veech problem can be viewed as one of geodesic flow on a\n2-dimensional flat surface, here we study geodesic flow in higher-dimensional\nflat manifolds. This is more challenging, as the overwhelming majority of the\navailable proof techniques for non-integrable flat systems are based on\narguments in dimension 2. For higher dimensions, we need a new approach.\n"}
+{"abstract": "  Dynamic response of the one-dimensional viscoelastic rod of finite length,\nthat has one end fixed and the other subject to prescribed either displacement\nor stress, is analyzed by the analytical means of Laplace transform, yielding\nthe displacement and stress of an arbitrary rod's point as a convolution of the\nboundary forcing and solution kernel. Thermodynamically consistent Burgers\nmodels are adopted as the constitutive equations describing mechanical\nproperties of the rod. Short-time asymptotics implies the finite wave\npropagation speed in the case of the second class models, contrary to the case\nof the first class models. Moreover, Burgers model of the first class yield\nquite classical shapes of displacement and stress time profiles resulting from\nthe boundary forcing assumed as the Heaviside function, while model of the\nsecond class yield responses that resemble to the sequence of excitation and\nrelaxation processes.\n"}
+{"abstract": "  Epitaxial thin films of (Sn$_{x}$Pb$_{1-x}$)$_{1-y}$In$_{y}$Te were\nsuccessfully grown by molecular-beam-epitaxy (MBE) in a broad range of\ncompositions (0 $\\leq$ x $\\leq$ 1, 0 $\\leq$ y $\\leq$ 0.23). We investigated\nelectronic phases of the films by the measurements of electrical transport and\noptical second harmonic generation. In this system, one can control the\ninversion of band gap, the electric polarization that breaks the inversion\nsymmetry, and the Fermi level position by tuning the Pb/Sn ratio and In\ncomposition. A plethora of topological electronic phases are expected to\nemerge, such as topological crystalline insulator, topological semimetal, and\nsuperconductivity. For the samples with large Sn compositions (x > 0.5), hole\ndensity increases with In composition (y), which results in the appearance of\nsuperconductivity. On the other hand, for those with small Sn compositions (x <\n0.5), increase in In composition reduces the hole density and changes the\ncarrier type from p-type to n-type. In a narrow region centered at (x, y) =\n(0.16, 0.07) where the n-type carriers are slightly doped, charge transport\nwith high mobility exceeding 5,000 cm$^{2}$V$^{-1}$s$^{-1}$ shows up,\nrepresenting the possible semimetal states. In those samples, the optical\nsecond harmonic generation measurement shows the breaking of inversion symmetry\nalong the out-of-plane [111] direction, which ensures the presence of polar\nsemimetal state. The thin films of (Sn$_{x}$Pb$_{1-x}$)$_{1-y}$In$_{y}$Te\nmaterials systems with a variety of electronic states would become a promising\nmaterials platform for the exploration of novel quantum phenomena.\n"}
+{"abstract": "  A fundamental theorem of P. Deligne (2002) states that a pre-Tannakian\ncategory over an algebraically closed field of characteristic zero admits a\nfiber functor to the category of supervector spaces (i.e., is the\nrepresentation category of an affine proalgebraic supergroup) if and only if it\nhas moderate growth (i.e., the lengths of tensor powers of an object grow at\nmost exponentially). In this paper we prove a characteristic p version of this\ntheorem. Namely we show that a pre-Tannakian category over an algebraically\nclosed field of characteristic p>0 admits a fiber functor into the Verlinde\ncategory Ver_p (i.e., is the representation category of an affine group scheme\nin Ver_p) if and only if it has moderate growth and is Frobenius exact. This\nimplies that Frobenius exact pre-Tannakian categories of moderate growth admit\na well-behaved notion of Frobenius-Perron dimension.\n  It follows that any semisimple pre-Tannakian category of moderate growth has\na fiber functor to Ver_p (so in particular Deligne's theorem holds on the nose\nfor semisimple pre-Tannakian categories in characteristics 2,3). This settles a\nconjecture of the third author from 2015.\n  In particular, this result applies to semisimplifications of categories of\nmodular representations of finite groups (or, more generally, affine group\nschemes), which gives new applications to classical modular representation\ntheory. For example, it allows us to characterize, for a modular representation\nV, the possible growth rates of the number of indecomposable summands in\nV^{\\otimes n} of dimension prime to p.\n"}
+{"abstract": "  Smart meters are utilized to transmit the consumption information to the\nmetering data management system for observing and management in smart grid\nadvanced metering infrastructure systems. In the meantime, for efficient\nutilization for spectrum, Device-to-Device (D2D) communications underlaying LTE\nnetworks are a promising wireless communication technology for advanced\nmetering infrastructure which supporting a technique for reusing the same radio\nresources (RRs) of LTE networks. Therefore, we examine the utilization of D2D\ncommunication technology for advanced metering infrastructure communications\nunderlaying LTE networks. A novel approach is suggested for provisioning the\nmandatory communication between serving data concentrator and its set of smart\nmeters using this technology. The suggested approach is dependent on two main\nstages. The group of permissible cellular user equipment reuse candidates for\nevery smart meter is calculated with taking the quality of service demands for\ncellular user devices and smart meters into consideration in the first stage.\nThe optimal RR allocation for every smart meter is determined based on\nmaximizing the access rate of smart meters which can be accepted and operated\nin D2D reuse mode in the second stage. Simulation results prove the efficacy of\nthe suggested approach for efficient advanced metering infrastructure\ncommunication underlaying LTE systems with accepting remarkable number of SMs\nand accomplishing outstanding throughput gain.\n"}
+{"abstract": "  We describe the vote package in R, which implements the plurality (or\nfirst-past-the-post), two-round runoff, score, approval and single transferable\nvote (STV) electoral systems, as well as methods for selecting the Condorcet\nwinner and loser. We emphasize the STV system, which we have found to work well\nin practice for multi-winner elections with small electorates, such as\ncommittee and council elections, and the selection of multiple job candidates.\nFor single-winner elections, the STV is also called instant runoff voting\n(IRV), ranked choice voting (RCV), or the alternative vote (AV) system. The\npackage also implements the STV system with equal preferences, for the first\ntime in a software package, to our knowledge. It also implements a new variant\nof STV, in which a minimum number of candidates from a specified group are\nrequired to be elected. We illustrate the package with several real examples.\n"}
+{"abstract": "  Deep learning has achieved notable performance in the denoising task of\nlow-quality medical images and the detection task of lesions, respectively.\nHowever, existing low-quality medical image denoising approaches are\ndisconnected from the detection task of lesions. Intuitively, the quality of\ndenoised images will influence the lesion detection accuracy that in turn can\nbe used to affect the denoising performance. To this end, we propose a\nplay-and-plug medical image denoising framework, namely Lesion-Inspired\nDenoising Network (LIDnet), to collaboratively improve both denoising\nperformance and detection accuracy of denoised medical images. Specifically, we\npropose to insert the feedback of downstream detection task into existing\ndenoising framework by jointly learning a multi-loss objective. Instead of\nusing perceptual loss calculated on the entire feature map, a novel\nregion-of-interest (ROI) perceptual loss induced by the lesion detection task\nis proposed to further connect these two tasks. To achieve better optimization\nfor overall framework, we propose a customized collaborative training strategy\nfor LIDnet. On consideration of clinical usability and imaging characteristics,\nthree low-dose CT images datasets are used to evaluate the effectiveness of the\nproposed LIDnet. Experiments show that, by equipping with LIDnet, both of the\ndenoising and lesion detection performance of baseline methods can be\nsignificantly improved.\n"}
+{"abstract": "  We address the issue of point value reconstructions from cell averages in the\ncontext of third order finite volume schemes, focusing in particular on the\ncells close to the boundaries of the domain. In fact, most techniques known in\nthe literature rely on the creation of ghost cells outside the boundary and on\nsome form of extrapolation from the inside that, taking into account the\nboundary conditions, fills the ghost cells with appropriate values, so that a\nstandard reconstruction can be applied also in boundary cells. In (Naumann,\nKolb, Semplice, 2018), motivated by the difficulty of choosing appropriate\nboundary conditions at the internal nodes of a network, a different technique\nwas explored that avoids the use of ghost cells, but instead employs for the\nboundary cells a different stencil, biased towards the interior of the domain.\n  In this paper, extending that approach, which does not make use of ghost\ncells, we propose a more accurate reconstruction for the one-dimensional case\nand a two-dimensional one for Cartesian grids. In several numerical tests we\ncompare the novel reconstruction with the standard approach using ghost cells.\n"}
+{"abstract": "  We present a theoretical study of the effects of heterostrain and lattice\nrelaxation on the optical conductivity of twisted bilayer graphene near the\nmagic angle, based on the band structures obtained from a continuum model. We\nfind that heterostrain, lattice relaxation and their combination give rise to\nvery distinctive spectroscopic features in the optical conductivity, which can\nbe used to probe and distinguish these effects. From the spectrum at various\nFermi energies, important features in the strain- and relaxation-modified band\nstructure such as the bandgap, bandwidth and van Hove singularities can be\ndirectly measured. The peak associated with the transition between the flat\nbands in the optical conductivity are highly sensitive to the direction of the\nstrain, which can provide direct information on the strain-modified flat bands.\n"}
+{"abstract": "  Regret has been established as a foundational concept in online learning, and\nlikewise has important applications in the analysis of learning dynamics in\ngames. Regret quantifies the difference between a learner's performance against\na baseline in hindsight. It is well-known that regret-minimizing algorithms\nconverge to certain classes of equilibria in games; however, traditional forms\nof regret used in game theory predominantly consider baselines that permit\ndeviations to deterministic actions or strategies. In this paper, we revisit\nour understanding of regret from the perspective of deviations over partitions\nof the full \\emph{mixed} strategy space (i.e., probability distributions over\npure strategies), under the lens of the previously-established $\\Phi$-regret\nframework, which provides a continuum of stronger regret measures. Importantly,\n$\\Phi$-regret enables learning agents to consider deviations from and to mixed\nstrategies, generalizing several existing notions of regret such as external,\ninternal, and swap regret, and thus broadening the insights gained from\nregret-based analysis of learning algorithms. We prove here that the\nwell-studied evolutionary learning algorithm of replicator dynamics (RD)\nseamlessly minimizes the strongest possible form of $\\Phi$-regret in generic $2\n\\times 2$ games, without any modification of the underlying algorithm itself.\nWe subsequently conduct experiments validating our theoretical results in a\nsuite of 144 $2 \\times 2$ games wherein RD exhibits a diverse set of behaviors.\nWe conclude by providing empirical evidence of $\\Phi$-regret minimization by RD\nin some larger games, hinting at further opportunity for $\\Phi$-regret based\nstudy of such algorithms from both a theoretical and empirical perspective.\n"}
+{"abstract": "  For a prime $\\ell$, the McKay conjecture suggests a bijection between the set\nof irreducible characters of a finite group with $\\ell'$-degree and the\ncorresponding set for the normalizer of a Sylow $\\ell$- subgroup. Navarro's\nrefinement suggests that the values of the characters on either side of this\nbijection should also be related, proposing that the bijection commutes with\ncertain Galois automorphisms. Recently, Navarro--Sp\u007faeth--Vallejo have reduced\nthe McKay--Navarro conjecture to certain \"inductive\" conditions on finite\nsimple groups. We prove that these inductive McKay--Navarro (also called the\ninductive Galois--McKay) conditions hold for the prime $\\ell = 2$ for several\ngroups of Lie type, including the untwisted groups without nontrivial graph\nautomorphisms.\n"}
+{"abstract": "  Reinforcement learning (RL) is one of the most active fields of AI research.\nDespite the interest demonstrated by the research community in reinforcement\nlearning, the development methodology still lags behind, with a severe lack of\nstandard APIs to foster the development of RL applications. OpenAI Gym is\nprobably the most used environment to develop RL applications and simulations,\nbut most of the abstractions proposed in such a framework are still assuming a\nsemi-structured methodology. This is particularly relevant for agent-based\nmodels whose purpose is to analyse adaptive behaviour displayed by\nself-learning agents in the simulation. In order to bridge this gap, we present\na workflow and tools for the decoupled development and maintenance of\nmulti-purpose agent-based models and derived single-purpose reinforcement\nlearning environments, enabling the researcher to swap out environments with\nones representing different perspectives or different reward models, all while\nkeeping the underlying domain model intact and separate. The Sim-Env Python\nlibrary generates OpenAI-Gym-compatible reinforcement learning environments\nthat use existing or purposely created domain models as their simulation\nback-ends. Its design emphasizes ease-of-use, modularity and code separation.\n"}
+{"abstract": "  In this article, we introduce a deformation cohomology of Leibniz\nsuperalgebras. Also, we introduce formal deformation theory of Leibniz\nsuperalgebras. Using deformation cohomology we study the formal deformation\ntheory of Leibniz superalgebras.\n"}
+{"abstract": "  We address the problem of recovering the shape and spatially-varying\nreflectance of an object from multi-view images (and their camera poses) of an\nobject illuminated by one unknown lighting condition. This enables the\nrendering of novel views of the object under arbitrary environment lighting and\nediting of the object's material properties. The key to our approach, which we\ncall Neural Radiance Factorization (NeRFactor), is to distill the volumetric\ngeometry of a Neural Radiance Field (NeRF) [Mildenhall et al. 2020]\nrepresentation of the object into a surface representation and then jointly\nrefine the geometry while solving for the spatially-varying reflectance and\nenvironment lighting. Specifically, NeRFactor recovers 3D neural fields of\nsurface normals, light visibility, albedo, and Bidirectional Reflectance\nDistribution Functions (BRDFs) without any supervision, using only a\nre-rendering loss, simple smoothness priors, and a data-driven BRDF prior\nlearned from real-world BRDF measurements. By explicitly modeling light\nvisibility, NeRFactor is able to separate shadows from albedo and synthesize\nrealistic soft or hard shadows under arbitrary lighting conditions. NeRFactor\nis able to recover convincing 3D models for free-viewpoint relighting in this\nchallenging and underconstrained capture setup for both synthetic and real\nscenes. Qualitative and quantitative experiments show that NeRFactor\noutperforms classic and deep learning-based state of the art across various\ntasks. Our videos, code, and data are available at\npeople.csail.mit.edu/xiuming/projects/nerfactor/.\n"}
+{"abstract": "  Software crowdsourcing platforms employ extrinsic rewards such as rating or\nranking systems to motivate workers. Such rating systems are noisy and provide\nlimited knowledge about workers' preferences and performance. To develop better\nunderstanding of worker reliability and trustworthiness in software\ncrowdsourcing, this paper reports an empirical study conducted on more than one\nyear's real-world data from TopCoder, one of the leading software crowdsourcing\nplatforms.\n  To do so, first, we create a bipartite network of active workers based on\ncommon task registrations. Then, we use the Clauset-Newman-Moore graph\nclustering algorithm to identify worker clusters in the network. Finally, we\nconduct an empirical evaluation to measure and analyze workers' behavior per\nidentified community in the platform by workers' rating. More specifically,\nworkers' behavior is analyzed based on their performances in terms of\nreliability, trustworthiness, and success; their preferences in terms of\nefficiency and elasticity; and strategies in terms of comfort, confidence, and\ndeceitfulness. The main result of this study identified four communities of\nactive workers: mixed-ranked, high-ranked, mid-ranked, and low-ranked. This\nstudy shows that the low-ranked community associates with the highest reliable\nworkers with an average reliability of 25%, while the mixed-ranked community\ncontains the most trustworthy workers with average trustworthiness of 16%. Such\nempirical evidence is beneficial to help exploring resourcing options while\nunderstanding the relations among unknown resources to improve task success.\n"}
+{"abstract": "  Particle Filtering (PF) methods are an established class of procedures for\nperforming inference in non-linear state-space models. Resampling is a key\ningredient of PF, necessary to obtain low variance likelihood and states\nestimates. However, traditional resampling methods result in PF-based loss\nfunctions being non-differentiable with respect to model and PF parameters. In\na variational inference context, resampling also yields high variance gradient\nestimates of the PF-based evidence lower bound. By leveraging optimal transport\nideas, we introduce a principled differentiable particle filter and provide\nconvergence results. We demonstrate this novel method on a variety of\napplications.\n"}
+{"abstract": "  We give a sharp lower bound for the Hilbert function in degree $d$ of\nartinian quotients $\\Bbbk[x_1,\\ldots,x_n]/I$ failing the Strong Lefschetz\nproperty, where $I$ is a monomial ideal generated in degree $d \\geq 2$. We also\nprovide sharp lower bounds for other classes of ideals, and connect our result\nto the classification of the Hilbert functions forcing the Strong Lefschetz\nproperty by Zanello and Zylinski.\n"}
+{"abstract": "  Let $n\\in\\omega$. The weak choice principle $\\operatorname{RC}_n$ states that\nfor every infinite set $x$ there is an infinite subset $y\\subseteq x$ with a\nchoice function on $[y]^n:=\\{z\\subseteq y\\mid \\lvert z\\rvert =n\\}$.\n$\\operatorname{C}_n^-$ states that for every infinite family of $n$-element\nsets, there is an infinite subfamily $\\mathcal{G}\\subseteq\\mathcal{F}$ with a\nchoice function. $\\operatorname{LOC}_n^-$ and $\\operatorname{WOC}_n^-$ are the\nsame statement but we assume that the family $\\mathcal{F}$ is linearly\norderable ($\\operatorname{LOC}_n^-$) or well-orderable\n($\\operatorname{WOC}_n^-$).\n  In the first part of this paper we will give a full characterization of when\nthe implication $\\operatorname{RC}_m\\Rightarrow \\operatorname{WOC}_n^-$ with\n$m,n\\in\\omega$ holds in $\\operatorname{ZF}$. We will prove the independence\nresults by using suitable Fraenkel-Mostowski permutation models. In the second\npart of we will show some generalizations. In particular we will show that\n$\\operatorname{RC}_5\\Rightarrow \\operatorname{LOC}_5^-$ and\n$\\operatorname{RC}_6\\Rightarrow \\operatorname{C}_3^-$, answering two open\nquestions from Halbeisen and Tachtsis. Furthermore we will show that\n$\\operatorname{RC}_6\\Rightarrow \\operatorname{C}_9^-$ and\n$\\operatorname{RC}_7\\Rightarrow \\operatorname{LOC}_7^-$.\n"}
+{"abstract": "  The coronavirus is a global event of historical proportions and just a few\nmonths changed the time series properties of the data in ways that make many\npre-covid forecasting models inadequate. It also creates a new problem for\nestimation of economic factors and dynamic causal effects because the\nvariations around the outbreak can be interpreted as outliers, as shifts to the\ndistribution of existing shocks, or as addition of new shocks. I take the\nlatter view and use covid indicators as controls to 'de-covid' the data prior\nto estimation. I find that economic uncertainty remains high at the end of 2020\neven though real economic activity has recovered and covid uncertainty has\nreceded. Dynamic responses of variables to shocks in a VAR similar in magnitude\nand shape to the ones identified before 2020 can be recovered by directly or\nindirectly modeling covid and treating it as exogenous. These responses to\neconomic shocks are distinctly different from those to a covid shock which are\nmuch larger but shorter lived. Disentangling the two types of shocks can be\nimportant in macroeconomic modeling post-covid.\n"}
+{"abstract": "  A multiphysics data analytic platform is established for imaging poroelastic\ninterfaces of finite permeability (e.g., hydraulic fractures) from elastic\nwaveforms and/or acoustic pore pressure measurements. This is accomplished\nthrough recent advances in design of non-iterative sampling methods to inverse\nscattering. The direct problem is formulated via the Biot equations in the\nfrequency domain where a network of discontinuities is illuminated by a set of\ntotal body forces and fluid volumetric sources, while monitoring the induced\n(acoustic and elastic) scattered waves in an arbitrary near-field\nconfiguration. A thin-layer approximation is deployed to capture the rough and\nmultiphase nature of interfaces whose spatially varying hydro-mechanical\nproperties are a priori unknown. In this setting, the well-posedness analysis\nof the forward problem yields the admissibility conditions for the contact\nparameters. In light of which, the poroelastic scattering operator and its\nfirst and second factorizations are introduced and their mathematical\nproperties are carefully examined. It is shown that the non-selfadjoint nature\nof the Biot system leads to an intrinsically asymmetric factorization of the\nscattering operator which may be symmetrized at certain limits. These results\nfurnish a robust framework for systematic design of regularized and convex cost\nfunctionals whose minimizers underpin the multiphysics imaging indicators. The\nproposed solution is synthetically implemented with application to\nspatiotemporal reconstruction of hydraulic fracture networks via deep-well\nmeasurements.\n"}
+{"abstract": "  This paper presents an architecture of a Personal Information Management\nSystem, in which individuals can define the access to their personal data by\nmeans of smart contracts. These smart contracts, running on the Ethereum\nblockchain, implement access control lists and grant immutability, traceability\nand verifiability of the references to personal data, which is stored itself in\na (possibly distributed) file system. A distributed authorization mechanism is\ndevised, where trust from multiple network nodes is necessary to grant the\naccess to the data. To this aim, two possible alternatives are described: a\nSecret Sharing scheme and Threshold Proxy Re-Encryption scheme. The performance\nof these alternatives is experimentally compared in terms of execution time.\nThreshold Proxy Re-Encryption appears to be faster in different scenarios, in\nparticular when increasing message size, number of nodes and the threshold\nvalue, i.e. number of nodes needed to grant the data disclosure.\n"}
+{"abstract": "  The influence maximization (IM) problem aims at finding a subset of seed\nnodes in a social network that maximize the spread of influence. In this study,\nwe focus on a sub-class of IM problems, where whether the nodes are willing to\nbe the seeds when being invited is uncertain, called contingency-aware IM. Such\ncontingency aware IM is critical for applications for non-profit organizations\nin low resource communities (e.g., spreading awareness of disease prevention).\nDespite the initial success, a major practical obstacle in promoting the\nsolutions to more communities is the tremendous runtime of the greedy\nalgorithms and the lack of high performance computing (HPC) for the non-profits\nin the field -- whenever there is a new social network, the non-profits usually\ndo not have the HPCs to recalculate the solutions. Motivated by this and\ninspired by the line of works that use reinforcement learning (RL) to address\ncombinatorial optimization on graphs, we formalize the problem as a Markov\nDecision Process (MDP), and use RL to learn an IM policy over historically seen\nnetworks, and generalize to unseen networks with negligible runtime at test\nphase. To fully exploit the properties of our targeted problem, we propose two\ntechnical innovations that improve the existing methods, including\nstate-abstraction and theoretically grounded reward shaping. Empirical results\nshow that our method achieves influence as high as the state-of-the-art methods\nfor contingency-aware IM, while having negligible runtime at test phase.\n"}
+{"abstract": "  We study how chaos, introduced by a weak perturbation, affects the\nreliability of the output of analog quantum simulation. As a toy model, we\nconsider the Lipkin-Meshkov-Glick (LMG) model. Inspired by the semiclassical\nbehavior of the order parameter in the thermodynamic limit, we propose a\nprotocol to measure the quantum phase transition in the ground state and the\ndynamical quantum phase transition associated with quench dynamics. We show\nthat the presence of a small time-dependent perturbation can render the\ndynamics of the system chaotic. We then show that the estimates of the critical\npoints of these quantum phase transitions, obtained from the quantum simulation\nof its dynamics, are robust to the presence of this chaotic perturbation, while\nother aspects of the system, such as the mean magnetization are fragile, and\ntherefore cannot be reliably extracted from this simulator. This can be\nunderstood in terms of the simulated quantities that depend on the global\nstructure of phase space vs. those that depend on local trajectories.\n"}
+{"abstract": "  Green's general hyperplane restriction theorem gives a sharp upper bound for\nthe Hilbert function of a standard graded algebra over and infinite field K\nmodulo a general linear form. We strengthen Green's result by showing that the\nlinear forms that do not satisfy such estimate belong to a finite union of\nproper linear spaces. As an application we give a method to derive variations\nof the Eakin-Sathaye theorem on reductions. In particular, we recover and\nextend results by O'Carroll on the Eakin-Sathaye theorem for complete and joint\nreductions.\n"}
+{"abstract": "  Oikawa defined an unknotting operation on virtual knots, called a CF-move,\nand gave a classification of 2-component virtual links up to CF-moves by the\nvirtual linking number and his $n$-invariant. In particular, it was proved that\na CF-move characterizes the information contained in the virtual linking number\nfor 2-component odd virtual links. In this paper, we extend this result by\nclassifying odd virtual links and almost odd virtual links with arbitrary\nnumber of components up to CF-moves, using the virtual linking number.\nMoreover, we extend Oikawa's $n$-invariant and introduce two invariants for\n3-component even virtual links. Using these invariants together with the\nvirtual linking number, we classify 3-component even virtual links up to\nCF-moves. As a result, a classification of 3-component virtual links up to\nCF-moves is provided.\n"}
+{"abstract": "  We present a precise measurement of the cross section, proton and $\\rm ^3He$\nanalyzing powers, and spin correlation coefficient $C_{y,y}$ for $p$-$\\rm ^3He$\nelastic scattering near 65 MeV, and a comparison with rigorous four-nucleon\nscattering calculations based on realistic nuclear potentials and a model with\n$\\Delta$-isobar excitation. Clear discrepancies are seen in some of the\nmeasured observables in the regime around the cross section minimum.\nTheoretical predictions using scaling relations between the calculated cross\nsection and the $\\rm ^3 He$ binding energy are not successful in reproducing\nthe data. Large sensitivity to the $NN$ potentials and rather small\n$\\Delta$-isobar effects in the calculated cross section are noticed as\ndifferent features from those in the deuteron-proton elastic scattering. The\nresults obtained above indicate that $p$-$\\rm ^3He$ scattering at intermediate\nenergies is an excellent tool to explore nuclear interactions not accessible by\nthree-nucleon scattering.\n"}
+{"abstract": "  Large language models have shown promising results in zero-shot settings\n(Brown et al.,2020; Radford et al., 2019). For example, they can perform\nmultiple choice tasks simply by conditioning on a question and selecting the\nanswer with the highest probability.\n  However, ranking by string probability can be problematic due to surface form\ncompetition-wherein different surface forms compete for probability mass, even\nif they represent the same underlying concept, e.g. \"computer\" and \"PC.\" Since\nprobability mass is finite, this lowers the probability of the correct answer,\ndue to competition from other strings that are valid answers (but not one of\nthe multiple choice options).\n  We introduce Domain Conditional Pointwise Mutual Information, an alternative\nscoring function that directly compensates for surface form competition by\nsimply reweighing each option according to a term that is proportional to its a\npriori likelihood within the context of the specific zero-shot task. It\nachieves consistent gains in zero-shot performance over both calibrated (Zhao\net al., 2021) and uncalibrated scoring functions on all GPT-2 and GPT-3 models\nover a variety of multiple choice datasets.\n"}
+{"abstract": "  In this paper we report an experiment that verifies an atomic-ensemble\nquantum memory via a measurement-device-independent scheme. A single photon\ngenerated via Rydberg blockade in one atomic ensemble is stored in another\natomic ensemble via electromagnetically induced transparency. After storage for\na long duration, this photon is retrieved and interfered with a second photon\nto perform joint Bell-state measurement (BSM). Quantum state for each photon is\nchosen based on a quantum random number generator respectively in each run. By\nevaluating correlations between the random states and BSM results, we certify\nthat our memory is genuinely entanglement-preserving.\n"}
+{"abstract": "  The recent discovery of superconductivity in doped infinite-layer nickelates\nhas stimulated intensive interest, especially for similarities and differences\ncompared to that in cuprate superconductors. In contrast to cuprates, although\nearlier magnetization measurement reveals a Curie-Weiss-like behavior in\nundoped infinite-layer nickelates, there is no magnetic ordering observed by\nelastic neutron scattering down to liquid helium temperature. Until now, the\nnature of the magnetic ground state in undoped infinite-layer nickelates was\nstill elusive. Here, we perform a nuclear magnetic resonance (NMR) experiment\nthrough 139La nuclei to study the intrinsic spin susceptibility of\ninfinite-layer LaNiO2. First, the signature for magnetic ordering or freezing\nis absent in the 139La NMR spectrum down to 0.24 K, which unambiguously\nconfirms a paramagnetic ground state in LaNiO2. Second, a pseudogap-like\nbehavior instead of Curie-Weiss-like behavior is observed in both the\ntemperature-dependent Knight shift and nuclear spin-lattice relaxation rate\n(1/T1), which is widely observed in both underdoped cuprates and iron-based\nsuperconductors. Furthermore, the scaling behavior between the Knight shift and\n1/T1T has also been discussed. Finally, the present results imply a\nconsiderable exchange interaction in infinite-layer nickelates, which sets a\nstrong constraint for the proposed theoretical models.\n"}
+{"abstract": "  Transition metal (TM) defects in silicon carbide (SiC) are a promising\nplatform in quantum technology, especially because some TM defects emit in the\ntelecom band. We develop a theory for the interaction of an active electron in\nthe $D$-shell of a TM defect in SiC with the TM nuclear spin and derive the\neffective hyperfine tensor within the Kramers doublets formed by the spin-orbit\ncoupling. Based on our theory we discuss the possibility to exchange the\nnuclear and electron states with potential applications for nuclear spin\nmanipulation and long-lived nunclear-spin based quantum memories.\n"}
+{"abstract": "  We consider a Pfaffian system expressing isomonodromy of an irregular system\nof Okubo type, depending on complex deformation parameters u=(u_1,...,u_n),\nwhich are eigenvalues of the leading matrix at the irregular singuilarity. At\nthe same time, we consider a Pfaffian system of non-normalized Schlesinger type\nexpressing isomonodromy of a Fuchsian system, whose poles are the deformation\nparameters u_1,...,u_n. The parameters vary in a polydisc containing a\ncoalescence locus for the eigenvalues of the leading matrix of the irregular\nsystem, corresponding to confluence of the Fuchsian singularities. We construct\nisomonodromic selected and singular vector solutions of the Fuchsian Pfaffian\nsystem together with their isomonodromic connection coefficients, so extending\na result of references [4] and [20] to the isomonodromic case, including\nconfluence of singularities. Then, we introduce an isomonodromic Laplace\ntransform of the selected and singular vector solutions, allowing to obtain\nisomonodromic fundamental solutions for the irregular system, and their Stokes\nmatrices expressed in terms of connection coefficients. These facts, in\naddition to extending [4] and [20] to the isomonodromic case with\ncoalescences/confluences, allow to prove by means of Laplace transform the main\nresult of reference [11], which is the analytic theory of non-generic\nisomonodromic deformations of the irregular system with coalescing eigenvalues.\n"}
+{"abstract": "  Updating observations of a signal due to the delays in the measurement\nprocess is a common problem in signal processing, with prominent examples in a\nwide range of fields. An important example of this problem is the nowcasting of\nCOVID-19 mortality: given a stream of reported counts of daily deaths, can we\ncorrect for the delays in reporting to paint an accurate picture of the\npresent, with uncertainty? Without this correction, raw data will often mislead\nby suggesting an improving situation. We present a flexible approach using a\nlatent Gaussian process that is capable of describing the changing\nauto-correlation structure present in the reporting time-delay surface. This\napproach also yields robust estimates of uncertainty for the estimated\nnowcasted numbers of deaths. We test assumptions in model specification such as\nthe choice of kernel or hyper priors, and evaluate model performance on a\nchallenging real dataset from Brazil. Our experiments show that Gaussian\nprocess nowcasting performs favourably against both comparable methods, and\nagainst a small sample of expert human predictions. Our approach has\nsubstantial practical utility in disease modelling -- by applying our approach\nto COVID-19 mortality data from Brazil, where reporting delays are large, we\ncan make informative predictions on important epidemiological quantities such\nas the current effective reproduction number.\n"}
+{"abstract": "  This paper introduces the unsupervised learning problem of playable video\ngeneration (PVG). In PVG, we aim at allowing a user to control the generated\nvideo by selecting a discrete action at every time step as when playing a video\ngame. The difficulty of the task lies both in learning semantically consistent\nactions and in generating realistic videos conditioned on the user input. We\npropose a novel framework for PVG that is trained in a self-supervised manner\non a large dataset of unlabelled videos. We employ an encoder-decoder\narchitecture where the predicted action labels act as bottleneck. The network\nis constrained to learn a rich action space using, as main driving loss, a\nreconstruction loss on the generated video. We demonstrate the effectiveness of\nthe proposed approach on several datasets with wide environment variety.\nFurther details, code and examples are available on our project page\nwilli-menapace.github.io/playable-video-generation-website.\n"}
+{"abstract": "  We consider the nonlinear Schr\\\"odinger equation (NLS) on a torus of\narbitrary dimension. The equation is studied in presence of an external\npotential field whose time-dependent amplitude is taken as control. Assuming\nthat the potential satisfies a saturation property, we show that the NLS\nequation is approximately controllable between any pair of eigenstates in\narbitrarily small time. The proof is obtained by developing a multiplicative\nversion of a geometric control approach introduced by Agrachev and Sarychev. We\ngive an application of this result to the study of the large time behavior of\nthe NLS equation with random potential. More precisely, we assume that the\namplitude of the potential is a random process whose law is $1$-periodic in\ntime and non-degenerate. Combining the controllability with a stopping time\nargument and the Markov property, we show that the trajectories of the random\nequation are almost surely unbounded in regular Sobolev spaces.\n"}
+{"abstract": "  We present the implementation of a trust-region Newton algorithm ExaTron for\nbound-constrained nonlinear programming problems, fully running on multiple\nGPUs. Without data transfers between CPU and GPU, our implementation has\nachieved the elimination of a major performance bottleneck under a memory-bound\nsituation, particularly when solving many small problems in batch. We discuss\nthe design principles and implementation details for our kernel function and\ncore operations. Different design choices are justified by numerical\nexperiments. By using the application of distributed control of alternating\ncurrent optimal power flow, where a large problem is decomposed into many\nsmaller nonlinear programs using a Lagrangian approach, we demonstrate\ncomputational performance of ExaTron on the Summit supercomputer at Oak\nRidgeNational Laboratory. Our numerical results show the linear scaling with\nrespect to the batch size and the number of GPUs and more than 35 times speedup\non 6 GPUs than on 40 CPUs available on a single node.\n"}
+{"abstract": "  In the present paper, personalized treatment means choosing the best\ntreatment for a patient while taking into account certain relevant personal\ncovariate values. We study the design of trials whose goal is to find the best\ntreatment for a given patient with given covariates. We assume that the\nsubjects in the trial represent a random sample from the population, and\nconsider the allocation, possibly with randomization, of these subjects to the\ndifferent treatment groups in a way that depends on their covariates.\n  We derive approximately optimal allocations, aiming to minimize expected\nregret, assuming that future patients will arrive from the same population as\nthe trial subjects. We find that, for the case of two treatments, an\napproximately optimal allocation design does not depend on the value of the\ncovariates but only on the variances of the responses. In contrast, for the\ncase of three treatments the optimal allocation design does depend on the\ncovariates as we show for specific scenarios. Another finding is that the\noptimal allocation can vary a lot as a function of the sample size, and that\nrandomized allocations are relevant for relatively small samples, and may not\nbe needed for very large studies.\n"}
+{"abstract": "  In present article we consider an axion F(R) gravity model and described with\nthe help of holographic principle the cosmological models of viscous dark fluid\ncoupled with axion matter in a spatially flat Friedmann-Robertson-Walker (FRW)\nuniverse. This description based on generalized infrared-cutoff holographic\ndark energy, proposed by Nojiri and Odintsov. We explored the Little Rip, the\nPseudo Rip, and the power-law bounce cosmological models in terms of the\nparameters of the inhomogeneous equation of the state of viscous dark fluid and\ncalculated the infrared cutoffs analytically. We represented the energy\nconservation equation for the dark fluid from a holographic point of view and\nshowed a correspondence between the cosmology of a viscous fluid and\nholographic cosmology. We analyzed the autonomous dynamic system. In the\nabsence of interaction between fluids, solutions are obtained corresponding to\ntwo cases. In the first case, dark energy is missing and the extension\ndescribes the component of dark matter. The second case corresponds to\ncosmological models with an extension due to dark energy. The solutions\nobtained are investigated for stability. For a cosmological model with the\ninteraction of a special type, the stability of solutions of the dynamic system\nis also investigated.\n"}
+{"abstract": "  Accurately matching visual and textual data in cross-modal retrieval has been\nwidely studied in the multimedia community. To address these challenges posited\nby the heterogeneity gap and the semantic gap, we propose integrating Shannon\ninformation theory and adversarial learning. In terms of the heterogeneity gap,\nwe integrate modality classification and information entropy maximization\nadversarially. For this purpose, a modality classifier (as a discriminator) is\nbuilt to distinguish the text and image modalities according to their different\nstatistical properties. This discriminator uses its output probabilities to\ncompute Shannon information entropy, which measures the uncertainty of the\nmodality classification it performs. Moreover, feature encoders (as a\ngenerator) project uni-modal features into a commonly shared space and attempt\nto fool the discriminator by maximizing its output information entropy. Thus,\nmaximizing information entropy gradually reduces the distribution discrepancy\nof cross-modal features, thereby achieving a domain confusion state where the\ndiscriminator cannot classify two modalities confidently. To reduce the\nsemantic gap, Kullback-Leibler (KL) divergence and bi-directional triplet loss\nare used to associate the intra- and inter-modality similarity between features\nin the shared space. Furthermore, a regularization term based on KL-divergence\nwith temperature scaling is used to calibrate the biased label classifier\ncaused by the data imbalance issue. Extensive experiments with four deep models\non four benchmarks are conducted to demonstrate the effectiveness of the\nproposed approach.\n"}
+{"abstract": "  Owing to its superior visible light absorption and high chemical stability,\nchalcogenide perovskite barium zirconium sulfide has attracted significant\nattention in the past few years as a potential alternative to hybrid halide\nperovskites for optoelectronics. However, the high processing temperatures of\nBaZrS3 thin films at above 1000 C severely limits their potential for device\napplications. Herein, we report the synthesis of BaZrS3 thin films at\ntemperatures as low as 500 C, by changing the chemical reaction pathway. The\nsingle phase BaZrS3 thin film was confirmed by X-ray diffraction and Raman\nspectroscopies. Atomic force microscopy and scanning electron microscopy show\nthat crystalline size and surface roughness were consistently reduced with\ndecreasing annealing temperature. The lower temperatures further eliminate\nsulfur vacancies and carbon contaminations associated with high temperature\nprocessing. The ability to synthesize chalcogenide perovskite thin films at\nlower temperatures removes a major hurdle for their device fabrication. The\nphotodetectors demonstrate fast response and an on/off ratio of 80. The\nfabricated field effect transistors show an ambipolar behavior with electron\nand hole mobilities of 16.8 cm2/Vs and 2.6 cm2/Vs, respectively.\n"}
+{"abstract": "  Switchbacks are widely acknowledged phenomena observed by the Parker Solar\nProbe and appear to occur in patches. Previous studies focused on the\nfluctuations at the magnetic reversals. However, the nature of the fluctuations\ninside the switchbacks remains unknown. Here we utilize the magnetic field data\nand plasma data measured by the Parker Solar Probe in the first four\nencounters. We investigate the fluctuations in the switchback intervals of 100\ns with BR>0 at every instant and compare them to the fluctuations in the\nnonswitchback intervals of 100 s with theta_RB>160o at every instant. We\ncalculate normalized cross-helicity sigma_c, normalized residual energy\nsigma_r, correlation coefficient C_vb between dvA and dv, Alfven ratio rA, and\nthe amplitude of magnetic and kinetic fluctuations. We find that the switchback\nintervals exhibit a distribution of sigma_c similar with the nonswitchback\nintervals. However, the rA of switchback intervals is around 0.35, while the\nnonswitchback intervals have rA around 0.65, indicating the fluctuations in the\nswitchbacks are more magnetically dominated. We also find that the distribution\npattern of pixel average amplitude of both dvA and dv of switchback intervals\nin the C_vb-sigma_r plane show a vertical stripe feature at C_vb>0.8,\nillustrating the possible magnetically dominant magnetic-velocity alignment\nstructure. These results will help us to understand the nature and the\nformation of the switchback turbulence.\n"}
+{"abstract": "  In our past few years' of commercial deployment experiences, we identify\nlocalization as a critical task in autonomous machine applications, and a great\nacceleration target. In this paper, based on the observation that the visual\nfrontend is a major performance and energy consumption bottleneck, we present\nour design and implementation of an energy-efficient hardware architecture for\nORB (Oriented-Fast and Rotated- BRIEF) based localization system on FPGAs. To\nsupport our multi-sensor autonomous machine localization system, we present\nhardware synchronization, frame-multiplexing, and parallelization techniques,\nwhich are integrated in our design. Compared to Nvidia TX1 and Intel i7, our\nFPGA-based implementation achieves 5.6x and 3.4x speedup, as well as 3.0x and\n34.6x power reduction, respectively.\n"}
+{"abstract": "  We consider a solution $u(\\cdot,t)$ to an initial boundary value problem for\ntime-fractional diffusion-wave equation with the order $\\alpha \\in (0,2)\n\\setminus \\{ 1\\}$ where $t$ is a time variable. We first prove that a suitable\nnorm of $u(\\cdot,t)$ is bounded by $\\frac{1}{t^{\\alpha}}$ for $0<\\alpha<1$ and\n$\\frac{1}{t^{\\alpha-1}}$ for $1<\\alpha<2$ for all large $t>0$. Moreover we\ncharacterize initial values in the cases where the decay rates are faster than\nthe above critical exponents. Differently from the classical diffusion equation\n$\\alpha=1$, the decay rate can give some local characterization of initial\nvalues. The proof is based on the eigenfunction expansions of solutions and the\nasymptotic expansions of the Mittag-Leffler functions for large time.\n"}
+{"abstract": "  We study the polarizations of gravitational waves (GWs) in two classes of\nextended gravity theories. First, we formulate the polarizations in linear\nmassive gravity (MG) with generic mass terms of non-Fierz-Pauli type by\nidentifying all the independent variables that obey Klein-Gordon-type\nequations. The dynamical degrees of freedom (dofs) in the generic MG consist of\nspin-2 and spin-0 modes, the former breaking down into two tensor (helicity-2),\ntwo vector (helicity-1) and one scalar (helicity-0) components, while the\nlatter just corresponding to a scalar. We find convenient ways of decomposing\nthe two scalar modes of each spin into distinct linear combinations of the\ntransverse and longitudinal polarizations with coefficients directly expressed\nby the mass parameters, thereby serving as a useful tool in measuring the\nmasses of GWs. Then we analyze the linear perturbations of generic\nhigher-curvature gravity (HCG) whose Lagrangian is an arbitrary polynomial of\nthe Riemann tensor. On a flat background, the linear dynamical dofs in this\ntheory are identified as massless spin-2, massive spin-2, and massive spin-0\nmodes. As its massive part encompasses the identical structure to the generic\nMG, GWs in the generic HCG provide six massive polarizations on top of the\nordinary two massless modes. In parallel to MG, we find convenient\nrepresentations for the scalar-polarization modes directly connected to the\nparameters of HCG. In this analysis, we employ two distinct methods; One takes\nfull advantage of the partial equivalence between the generic HCG and MG at the\nlinear level, whereas the other relies upon a gauge-invariant formalism. We\nconfirm that the two results agree. We also discuss methods to determine the\ntheory parameters by GW-polarization measurements. Our method does not require\nmeasuring the propagation speeds or the details of the waveforms of the GWs.\n[Abridged]\n"}
+{"abstract": "  We study the Lie group structure of asymptotic symmetry groups in General\nRelativity from the viewpoint of infinite-dimensional geometry. To this end, we\nreview the geometric definition of asymptotic simplicity and emptiness due to\nPenrose and the coordinate-wise definition of asymptotic flatness due to Bondi\net al. Then we construct the Lie group structure of the BMS group and discuss\nits Lie theoretic properties. We find that the BMS group is regular in the\nsense of Milnor, but not real analytic. This motivates us to conjecture that it\nis not locally exponential. Finally, we verify the Trotter property as well as\nthe commutator property. As an outlook, we comment on the situation of related\nasymptotic symmetry groups. In particular, the much more involved situation of\nthe Newman--Unti group is highlighted, which will be studied in future work.\n"}
+{"abstract": "  In this study, we have developed a parallel version of the random time\nsimulation algorithm.\n  Firstly, we gave a rigorous basis of the random time description of the\nstochastic process of chemical reaction network time evolution.\n  And then we reviewed the random time simulation algorithm and gave the\nimplementations for the parallel version of next reaction random time\nalgorithm.\n  The discussion of computational complexity suggested a factor of $M$ (which\nis the connection number of the network) folds time consuming reduction for\nrandom time simulation algorithm as compared to other exact stochastic\nsimulation algorithms, such as the Gillespie algorithm.\n  For large-scale system, such like the protein-protein interaction network,\n$M$ is on order of $10^8$.\n  We further demonstrate the power of random time simulation with a GPGPU\nparallel implementation which achieved roughly 100 folds acceleration as\ncompared with CPU implementations.\n  Therefore the stochastic simulation method we developed here can be of great\napplication value for simulating time evolution process of large-scale network.\n"}
+{"abstract": "  Dominating electron-electron scattering enables viscous electron flow\nexhibiting hydrodynamic current density patterns such as Poiseuille profiles or\nvortices. The viscous regime has recently been observed in graphene by\nnon-local transport experiments and mapping of the Poiseuille profile. Here, we\nprobe the current-induced surface potential maps of graphene field effect\ntransistors with moderate mobility using scanning probe microscopy at room\ntemperature. We discover micron-sized large areas appearing close to charge\nneutrality that show current induced electric fields opposing the externally\napplied field. By estimating the local scattering lengths from the gate\ndependence of local in-plane electric fields, we find that electron-electron\nscattering dominates in these areas as expected for viscous flow. Moreover, we\nsuppress the inverted fields by artificially decreasing the electron-disorder\nscattering length via mild ion bombardment. These results imply that viscous\nelectron flow is omnipresent in graphene devices, even at moderate mobility.\n"}
+{"abstract": "  Some of the highest-transition-temperature superconductors across various\nmaterials classes exhibit linear-in-temperature `strange metal' or `Planckian'\nelectrical resistivities in their normal state. It is thus believed by many\nthat this behavior holds the key to unlock the secrets of high-temperature\nsuperconductivity. However, these materials typically display complex phase\ndiagrams governed by various competing energy scales, making an unambiguous\nidentification of the physics at play difficult. Here we use electrical\nresistivity measurements into the micro-Kelvin regime to discover\nsuperconductivity condensing out of an extreme strange metal state -- with\nlinear resistivity over 3.5 orders of magnitude in temperature. We propose that\nthe Cooper pairing is mediated by the modes associated with a recently\nevidenced dynamical charge localization-delocalization transition, a mechanism\nthat may well be pertinent also in other strange metal superconductors.\n"}
+{"abstract": "  We provide the structure of the unit group of $\\f_{p^k}(\\SL(3,2))$, where\n$p\\geq 11$ is a prime and $\\SL(3,2)$ denotes the $3\\times 3$ invertible\nmatrices over $\\f_2$.\n"}
+{"abstract": "  We provide algorithms for isotonic regression minimizing $L_0$ error (Hamming\ndistance). This is also known as monotonic relabeling, and is applicable when\nlabels have a linear ordering but not necessarily a metric. There may be\nexponentially many optimal relabelings, so we look at secondary criteria to\ndetermine which are best. For arbitrary ordinal labels the criterion is\nmaximizing the number of labels which are only changed to an adjacent label\n(and recursively apply this). For real-valued labels we minimize the $L_p$\nerror. For linearly ordered sets we also give algorithms which minimize the sum\nof the $L_p$ and weighted $L_0$ errors, a form of penalized (regularized)\nregression. We also examine $L_0$ isotonic regression on multidimensional\ncoordinate-wise orderings. Previous algorithms took $\\Theta(n^3)$ time, but we\nreduce this to $o(n^{3/2})$.\n"}
+{"abstract": "  This paper presents algorithms for parallelization of inference in hidden\nMarkov models (HMMs). In particular, we propose parallel backward-forward type\nof filtering and smoothing algorithm as well as parallel Viterbi-type\nmaximum-a-posteriori (MAP) algorithm. We define associative elements and\noperators to pose these inference problems as parallel-prefix-sum computations\nin sum-product and max-product algorithms and parallelize them using\nparallel-scan algorithms. The advantage of the proposed algorithms is that they\nare computationally efficient in HMM inference problems with long time\nhorizons. We empirically compare the performance of the proposed methods to\nclassical methods on a highly parallel graphical processing unit (GPU).\n"}
+{"abstract": "  Recommendation algorithms typically build models based on historical\nuser-item interactions (e.g., clicks, likes, or ratings) to provide a\npersonalized ranked list of items. These interactions are often distributed\nunevenly over different groups of items due to varying user preferences.\nHowever, we show that recommendation algorithms can inherit or even amplify\nthis imbalanced distribution, leading to unfair recommendations to item groups.\nConcretely, we formalize the concepts of ranking-based statistical parity and\nequal opportunity as two measures of fairness in personalized ranking\nrecommendation for item groups. Then, we empirically show that one of the most\nwidely adopted algorithms -- Bayesian Personalized Ranking -- produces unfair\nrecommendations, which motivates our effort to propose the novel fairness-aware\npersonalized ranking model. The debiased model is able to improve the two\nproposed fairness metrics while preserving recommendation performance.\nExperiments on three public datasets show strong fairness improvement of the\nproposed model versus state-of-the-art alternatives.\n  This is paper is an extended and reorganized version of our SIGIR\n2020~\\cite{zhu2020measuring} paper. In this paper, we re-frame the studied\nproblem as `item recommendation fairness' in personalized ranking\nrecommendation systems, and provide more details about the training process of\nthe proposed model and details of experiment setup.\n"}
+{"abstract": "  In this paper, we investigate the pointwise time analyticity of three\ndifferential equations. They are the biharmonic heat equation, the heat\nequation with potentials and some nonlinear heat equations with power\nnonlinearity of order $p$. The potentials include all the nonnegative ones. For\nthe first two equations, we prove if $u$ satisfies some growth conditions in\n$(x,t)\\in \\mathrm{M}\\times [0,1]$, then $u$ is analytic in time $(0,1]$. Here\n$\\mathrm{M}$ is $R^d$ or a complete noncompact manifold with Ricci curvature\nbounded from below by a constant. Then we obtain a necessary and sufficient\ncondition such that $u(x,t)$ is analytic in time at $t=0$. Applying this\nmethod, we also obtain a necessary and sufficient condition for the solvability\nof the backward equations, which is ill-posed in general.\n  For the nonlinear heat equation with power nonlinearity of order $p$, we\nprove that a solution is analytic in time $t\\in (0,1]$ if it is bounded in\n$\\mathrm{M}\\times[0,1]$ and $p$ is a positive integer. In addition, we\ninvestigate the case when $p$ is a rational number with a stronger assumption\n$0<C_3 \\leq |u(x,t)| \\leq C_4$. It is also shown that a solution may not be\nanalytic in time if it is allowed to be $0$. As a lemma, we obtain an estimate\nof $\\partial_t^k \\Gamma(x,t;y)$ where $\\Gamma(x,t;y)$ is the heat kernel on a\nmanifold, with an explicit estimation of the coefficients.\n  An interesting point is that a solution may be analytic in time even if it is\nnot smooth in the space variable $x$, implying that the analyticity of space\nand time can be independent. Besides, for general manifolds, space analyticity\nmay not hold since it requires certain bounds on curvature and its derivatives.\n"}
+{"abstract": "  In many operations management problems, we need to make decisions\nsequentially to minimize the cost while satisfying certain constraints. One\nmodeling approach to study such problems is constrained Markov decision process\n(CMDP). When solving the CMDP to derive good operational policies, there are\ntwo key challenges: one is the prohibitively large state space and action\nspace; the other is the hard-to-compute transition kernel. In this work, we\ndevelop a sampling-based primal-dual algorithm to solve CMDPs. Our approach\nalternatively applies regularized policy iteration to improve the policy and\nsubgradient ascent to maintain the constraints. Under mild regularity\nconditions, we show that the algorithm converges at rate $\nO(\\log(T)/\\sqrt{T})$, where T is the number of iterations. When the CMDP has a\nweakly coupled structure, our approach can substantially reduce the dimension\nof the problem through an embedded decomposition. We apply the algorithm to two\nimportant applications with weakly coupled structures: multi-product inventory\nmanagement and multi-class queue scheduling, and show that it generates\ncontrols that outperform state-of-art heuristics.\n"}
+{"abstract": "  We investigate the asymptotic expansion of integrals analogous to Ball's\nintegral \\[\\int_0^\\infty\n\\left(\\frac{\\Gamma(1+\\nu)|J_\\nu(x)|}{(x/2)^\\nu}\\right)^{\\!n}dx\\] for large $n$\nin which the Bessel function $J_\\nu(x)$ is replaced by the modified Bessel\nfunctions $I_\\nu(x)$ and $K_\\nu(x)$ together with appropriate exponential\nfactors $e^{\\mp x}$, respectively.\n  The above integral with $J_\\nu(x)$ replaced by a hyper-Bessel function of the\ntype recently discussed in Aktas {\\it et al.} [The Ramanujan J., 2019] and\ntaken over a finite interval determined by the first positive zero of the\nfunction is also considered for $n\\to\\infty$. We give the leading asymptotic\nbehaviour of the hyper-Bessel function for $x\\to+\\infty$ in an appendix.\nNumerical examples are given to illustrate the accuracy of the various\nexpansions obtained.\n"}
+{"abstract": "  We present a molecular dynamics simulation method for the computation of the\nsolubility of organic crystals in solution. The solubility is calculated based\non the equilibrium free energy difference between the solvated solute and its\ncrystallized state at the crystal surface kink site. In order to efficiently\nsample the growth and dissolution process, we have carried out well-tempered\nMetadynamics simulations with a collective variable that captures the slow\ndegrees of freedom, namely the solute diffusion to and adsorption at the kink\nsite together with the desolvation of the kink site. Simulations were performed\nat different solution concentrations using constant chemical potential\nmolecular dynamics and the solubility was identified at the concentration at\nwhich the free energy values between the grown and dissolved kink states were\nequal. The effectiveness of this method is demonstrated by its success in\nreproducing the experimental trends of solubility of urea and naphthalene in a\nvariety of solvents.\n"}
+{"abstract": "  Waves dissipate energy when they propagate through real medium. Theoretical\nstudy of waves is one of important way to understand the nature of waves in\nmedium with dissipation. The study points out that the theoretical solution to\nthe wave equation describing a disturbance propagating in a dissipative medium\nis not unique, which is determined by the dissipation mechanism of the medium.\nA new general solution is proposed by assuming that the attenuations of\ndisturbance can occur in the time and space domains. The general solution is\nfurther used in case studies. The properties of viscoelastic waves propagating\nin the Kelvin-Voigt medium and electromagnetic waves propagating in conductive\nmedium with the reciprocal attenuation in time and space domains are analyzed.\nThe result shows that the attenuation mechanism has an obvious influence on the\nproperties of waves in the dissipative medium when the wave equations are the\nsame.\n"}
+{"abstract": "  Processing massive application graphs on distributed memory systems requires\nto map the graphs onto the system's processing elements (PEs). This task\nbecomes all the more important when PEs have non-uniform communication costs or\nthe input is highly irregular. Typically, mapping is addressed using\npartitioning, in a two-step approach or an integrated one. Parallel\npartitioning tools do exist; yet, corresponding mapping algorithms or their\npublic implementations all have major sequential parts or other severe scaling\nlimitations. In this paper, we propose a parallel algorithm that maps graphs\nonto the PEs of a hierarchical system. Our solution integrates partitioning and\nmapping; it models the system hierarchy in a concise way as an implicit labeled\ntree. The vertices of the application graph are labeled as well, and these\nvertex labels induce the mapping. The mapping optimization follows the basic\nidea of parallel label propagation, but we tailor the gain computations of\nlabel changes to quickly account for the induced communication costs. Our\nMPI-based code is the first public implementation of a parallel graph mapping\nalgorithm; to this end, we extend the partitioning library ParHIP. To evaluate\nour algorithm's implementation, we perform comparative experiments with complex\nnetworks in the million- and billion-scale range. In general our mapping tool\nshows good scalability on up to a few thousand PEs. Compared to other MPI-based\ncompetitors, our algorithm achieves the best speed to quality trade-off and our\nquality results are even better than non-parallel mapping tools.\n"}
+{"abstract": "  Non-radiative losses to the open-circuit voltage are a primary factor in\nlimiting the power conversion efficiency of organic photovoltaic devices. The\ndominant non-radiative loss is intrinsic to the active layer and can be\ndetermined from the quasi-Fermi level splitting (QFLS) and the radiative\nthermodynamic limit of the photovoltage. Quantification of the QFLS in thin\nfilm devices with low mobility is challenging due to the excitonic nature of\nphotoexcitation and additional sources of nonradiative loss associated with the\ndevice structure. This work outlines an experimental approach based on\nelectro-modulated photoluminescence, which can be used to directly measure the\nintrinsic non-radiative loss to the open-circuit voltage; thereby, quantifying\nthe QFLS. Drift-diffusion simulations are carried out to show that this method\naccurately predicts the QFLS in the bulk of the device regardless of\ndevice-related non-radiative losses. State-of-the-art PM6:Y6-based organic\nsolar cells are used as a model to test the experimental approach, and the QFLS\nis quantified and shown to be independent of device architecture. This work\nprovides a method to quantify the QFLS of organic solar cells under operational\nconditions, fully characterizing the different contributions to the\nnon-radiative losses of the open-circuit voltage. The reported method will be\nuseful in not only characterizing and understanding losses in organic solar\ncells, but also other device platforms such as light-emitting diodes and\nphotodetectors.\n"}
+{"abstract": "  Integrating intelligent reflecting surface (IRS) and Rate-Splitting Multiple\nAccess (RSMA) is an effective solution to improve the spectral/energy\nefficiency in next-generation (beyond 5G (B5G) and 6G) wireless networks. In\nthis paper, we investigate a rate-splitting (RS)-based transmission technique\nfor an IRS-aided communication network involving both near and cell-edge users.\nIn particular, we derive a new architecture called IRS-RS that leverages the\ninterplay between RS and IRS, with an aim to maximize the weighted sum-rate\n(WSR) of users by selecting the reflecting coefficients at the IRS and\ndesigning beamformers at the BS under the constraints of power at the base\nstation (BS), quality of service (QoS) at each user and finite resolution at\nthe IRS. To solve the non-convex WSR maximization problem, we propose an\nalternating algorithm and compare its performance with baseline non-orthogonal\nmultiple access (NOMA) based transmission for an IRS-aided communication\nnetwork for both perfect and imperfect CSIT cases. Through numerical results,\nit is shown that the proposed IRS-RS architecture yields better QoS with\nrespect to the cell-edge users when compared to IRS-NOMA transmission scheme.\n"}
+{"abstract": "  Quantitative MR imaging is increasingly favoured for its richer information\ncontent and standardised measures. However, computing quantitative parameter\nmaps, such as those encoding longitudinal relaxation rate (R1), apparent\ntransverse relaxation rate (R2*) or magnetisation-transfer saturation (MTsat),\ninvolves inverting a highly non-linear function. Many methods for deriving\nparameter maps assume perfect measurements and do not consider how noise is\npropagated through the estimation procedure, resulting in needlessly noisy\nmaps. Instead, we propose a probabilistic generative (forward) model of the\nentire dataset, which is formulated and inverted to jointly recover (log)\nparameter maps with a well-defined probabilistic interpretation (e.g., maximum\nlikelihood or maximum a posteriori). The second order optimisation we propose\nfor model fitting achieves rapid and stable convergence thanks to a novel\napproximate Hessian. We demonstrate the utility of our flexible framework in\nthe context of recovering more accurate maps from data acquired using the\npopular multi-parameter mapping protocol. We also show how to incorporate a\njoint total variation prior to further decrease the noise in the maps, noting\nthat the probabilistic formulation allows the uncertainty on the recovered\nparameter maps to be estimated. Our implementation uses a PyTorch backend and\nbenefits from GPU acceleration. It is available at\nhttps://github.com/balbasty/nitorch.\n"}
+{"abstract": "  The problem is to evaluate a polynomial in several variables and its gradient\nat a power series truncated to some finite degree with multiple double\nprecision arithmetic. To compensate for the cost overhead of multiple double\nprecision and power series arithmetic, data parallel algorithms for general\npurpose graphics processing units are presented. The reverse mode of\nalgorithmic differentiation is organized into a massively parallel computation\nof many convolutions and additions of truncated power series. Experimental\nresults demonstrate that teraflop performance is obtained in deca double\nprecision with power series truncated at degree 152. The algorithms scale well\nfor increasing precision and increasing degrees.\n"}
+{"abstract": "  This paper investigates the motion planning of autonomous dynamical systems\nmodeled by Markov decision processes (MDP) with unknown transition\nprobabilities over continuous state and action spaces. Linear temporal logic\n(LTL) is used to specify high-level tasks over infinite horizon, which can be\nconverted into a limit deterministic generalized B\\\"uchi automaton (LDGBA) with\nseveral accepting sets. The novelty is to design an embedded product MDP\n(EP-MDP) between the LDGBA and the MDP by incorporating a synchronous\ntracking-frontier function to record unvisited accepting sets of the automaton,\nand to facilitate the satisfaction of the accepting conditions. The proposed\nLDGBA-based reward shaping and discounting schemes for the model-free\nreinforcement learning (RL) only depend on the EP-MDP states and can overcome\nthe issues of sparse rewards. Rigorous analysis shows that any RL method that\noptimizes the expected discounted return is guaranteed to find an optimal\npolicy whose traces maximize the satisfaction probability. A modular deep\ndeterministic policy gradient (DDPG) is then developed to generate such\npolicies over continuous state and action spaces. The performance of our\nframework is evaluated via an array of OpenAI gym environments.\n"}
+{"abstract": "  Many software engineering research papers rely on time-based data (e.g.,\ncommit timestamps, issue report creation/update/close dates, release dates).\nLike most real-world data however, time-based data is often dirty. To date,\nthere are no studies that quantify how frequently such data is used by the\nsoftware engineering research community, or investigate sources of and quantify\nhow often such data is dirty. Depending on the research task and method used,\nincluding such dirty data could affect the research results. This paper\npresents the first survey of papers that utilize time-based data, published in\nthe Mining Software Repositories (MSR) conference series. Out of the 690\ntechnical track and data papers published in MSR 2004--2020, we saw at least\n35% of papers utilized time-based data. We then used the Boa and Software\nHeritage infrastructures to help identify and quantify several sources of dirty\ncommit timestamp data. Finally we provide guidelines/best practices for\nresearchers utilizing time-based data from Git repositories.\n"}
+{"abstract": "  A simple expression for the Gamow factor is obtained using a second order\ncurvature corrected tunneling potential. Our results show that it approximates\naccurately the `exact-WKB' transmission coefficient obtained by numerically\nintegrating over the tunneling region to obtain the Gamow factor. The average\ndifference in current density using the respective transmission coefficients is\nabout $1.5 \\%$, across a range of work-functions $\\phi \\in [3-5.5]$eV, Fermi\nenergy ${\\cal{E}}_F$ in [5-10]eV, local electric fields $E_l$ in[3-9]eV and\nradius of curvature $R \\geq 5$nm). An easy-to-use correction factor $\\lambda_P$\nis also provided to approximately map the `exact-WKB' current density to the\n`exact' current density in terms of ${\\cal{E}}_F/\\phi$. The average error on\nusing $\\lambda_P$ is found to be around $3.5\\%$. This is a vast improvement\nover the average error of $15\\%$ when $\\lambda_P = 1$. Finally, an analytical\nexpression for the curvature-corrected current density is obtained using the\nGamow factor. It is found to compare well with the `exact-WKB' current density\neven at small values of local electric field and radius of curvature.\n"}
+{"abstract": "  The Run Length Encoding (RLE) compression method is a long standing simple\nlossless compression scheme which is easy to implement and achieves a good\ncompression on input data which contains repeating consecutive symbols. In its\npure form RLE is not applicable on natural text or other input data with short\nsequences of identical symbols. We present a combination of preprocessing steps\nthat turn arbitrary input data in a byte-wise encoding into a bit-string which\nis highly suitable for RLE compression. The main idea is to first read all most\nsignificant bits of the input byte-string, followed by the second most\nsignificant bit, and so on. We combine this approach by a dynamic byte\nremapping as well as a Burrows-Wheeler-Scott transform on a byte level.\nFinally, we apply a Huffman Encoding on the output of the bit-wise RLE encoding\nto allow for more dynamic lengths of code words encoding runs of the RLE. With\nour technique we can achieve a lossless average compression which is better\nthan the standard RLE compression by a factor of 8 on average.\n"}
+{"abstract": "  A key assumption of top-down human pose estimation approaches is their\nexpectation of having a single person/instance present in the input bounding\nbox. This often leads to failures in crowded scenes with occlusions. We propose\na novel solution to overcome the limitations of this fundamental assumption.\nOur Multi-Instance Pose Network (MIPNet) allows for predicting multiple 2D pose\ninstances within a given bounding box. We introduce a Multi-Instance Modulation\nBlock (MIMB) that can adaptively modulate channel-wise feature responses for\neach instance and is parameter efficient. We demonstrate the efficacy of our\napproach by evaluating on COCO, CrowdPose, and OCHuman datasets. Specifically,\nwe achieve 70.0 AP on CrowdPose and 42.5 AP on OCHuman test sets, a significant\nimprovement of 2.4 AP and 6.5 AP over the prior art, respectively. When using\nground truth bounding boxes for inference, MIPNet achieves an improvement of\n0.7 AP on COCO, 0.9 AP on CrowdPose, and 9.1 AP on OCHuman validation sets\ncompared to HRNet. Interestingly, when fewer, high confidence bounding boxes\nare used, HRNet's performance degrades (by 5 AP) on OCHuman, whereas MIPNet\nmaintains a relatively stable performance (drop of 1 AP) for the same inputs.\n"}
+{"abstract": "  Diversity plays a crucial role in multiple contexts such as team formation,\nrepresentation of minority groups and generally when allocating resources\nfairly. Given a community composed by individuals of different types, we study\nthe problem of partitioning this community such that the global diversity is\npreserved as much as possible in each subgroup. We consider the diversity\nmetric introduced by Simpson in his influential work that, roughly speaking,\ncorresponds to the inverse probability that two individuals are from the same\ntype when taken uniformly at random, with replacement, from the community of\ninterest. We provide a novel perspective by reinterpreting this quantity in\ngeometric terms. We characterize the instances in which the optimal partition\nexactly preserves the global diversity in each subgroup. When this is not\npossible, we provide an efficient polynomial-time algorithm that outputs an\noptimal partition for the problem with two types. Finally, we discuss further\nchallenges and open questions for the problem that considers more than two\ntypes.\n"}
+{"abstract": "  We prove the Extended Delta Conjecture of Haglund, Remmel, and Wilson, a\ncombinatorial formula for $\\Delta _{h_l}\\Delta' _{e_k} e_{n}$, where $\\Delta'\n_{e_k}$ and $\\Delta_{h_l}$ are Macdonald eigenoperators and $e_n$ is an\nelementary symmetric function. We actually prove a stronger identity of\ninfinite series of $GL_m$ characters expressed in terms of LLT series. This is\nachieved through new results in the theory of the Schiffmann algebra and its\naction on the algebra of symmetric functions.\n"}
+{"abstract": "  Finding experts drives successful collaborations and high-quality product\ndevelopment in academic and research domains. To contribute to the expert\nfinding research community, we have developed ExpFinder which is a novel\nensemble model for expert finding by integrating an $N$-gram vector space model\n($n$VSM) and a graph-based model ($\\mu$CO-HITS). This paper provides\ndescriptions of ExpFinder's architecture, key components, functionalities, and\nillustrative examples. ExpFinder is an effective and competitive model for\nexpert finding, significantly outperforming a number of expert finding models.\n"}
+{"abstract": "  Three decades ago, Atick et al. suggested that human frequency sensitivity\nmay emerge from the enhancement required for a more efficient analysis of\nretinal images. Here we reassess the relevance of low-level vision tasks in the\nexplanation of the Contrast Sensitivity Functions (CSFs) in light of (1) the\ncurrent trend of using artificial neural networks for studying vision, and (2)\nthe current knowledge of retinal image representations.\n  As a first contribution, we show that a very popular type of convolutional\nneural networks (CNNs), called autoencoders, may develop human-like CSFs in the\nspatio-temporal and chromatic dimensions when trained to perform some basic\nlow-level vision tasks (like retinal noise and optical blur removal), but not\nothers (like chromatic adaptation) or pure reconstruction after simple\nbottlenecks). As an illustrative example, the best CNN (in the considered set\nof simple architectures for enhancement of the retinal signal) reproduces the\nCSFs with an RMSE error of 11\\% of the maximum sensitivity.\n  As a second contribution, we provide experimental evidence of the fact that,\nfor some functional goals (at low abstraction level), deeper CNNs that are\nbetter in reaching the quantitative goal are actually worse in replicating\nhuman-like phenomena (such as the CSFs). This low-level result (for the\nexplored networks) is not necessarily in contradiction with other works that\nreport advantages of deeper nets in modeling higher-level vision goals.\nHowever, in line with a growing body of literature, our results suggests\nanother word of caution about CNNs in vision science since the use of\nsimplified units or unrealistic architectures in goal optimization may be a\nlimitation for the modeling and understanding of human vision.\n"}
+{"abstract": "  In this work we propose a new algorithm for the computation of statistical\nequilibrium quantities on a cubic lattice when both an energy and a statistical\ntemperature are involved. We demonstrate that the pivot algorithm used in\nsituations such as protein folding works well for a small range of temperatures\nnear the polymeric case, but it fails in other situations. The new algorithm,\nusing localized transformations, seems to perform well for all possible\ntemperature values. Having reliably approximated the values of equilibrium\nenergy, we also propose an efficient way to compute equilibrium entropy for all\ntemperature values. We apply the algorithms in the context of suction or\nsupercritical vortices in a tornadic flow, which are approximated by vortex\nfilaments on a cubic lattice. We confirm that supercritical (smooth,\n\"straight\") vortices have the highest energy and correspond to negative\ntemperatures in this model. The lowest-energy configurations are folded up and\n\"balled up\" to a great extent. The results support A. Chorin's findings that,\nin the context of supercritical vortices in a tornadic flow, when such\nhigh-energy vortices stretch, they need to fold.\n"}
+{"abstract": "  We study the impact of maximal zeros of the Dirac mass matrix on neutrino\nphenomenology and dark matter within the framework of an inverse seesaw ISS\n$(2,3)$. ISS $(2,3)$ is obtained by adding two right handed neutrinos and three\ngauge singlets sterile fermions to the standard model leading to an extra\nsterile state in the keV range. The model is more predictive because of the\npresence of fewer numbers of right handed neutrinos than the conventional\ninverse seesaw. Moreover, texture zeros in the structures of the mass matrices\ninvolved in the model can reduce the free parameters. We extensively study the\neffect of different textures of Dirac mass matrix on sterile neutrino dark\nmatter phenomenology. Our study also includes the implications of these texture\nzero mass matrices on low energy phenomena like neutrinoless double beta decay\n(0$\\nu\\beta\\beta$). The zero textures highly constrain the parameter space of\nthe model. Based on the allowed cosmological ranges of the relic abundance,\ndecay rates, and dark matter mixing with the active neutrinos, we study the\nviability of the different textures within the framework.\n"}
+{"abstract": "  Proteins typically undergo conformational dynamics on the microsecond to\nmillisecond timescale as they perform their function, which is much faster than\nthe time-resolution of cryo-electron microscopy and has thus prevented\nreal-time observations. Here, we propose a novel approach for microsecond\ntime-resolved cryo-electron microscopy that involves rapidly melting a cryo\nspecimen in situ with a laser beam. The sample remains liquid for the duration\nof the laser pulse, offering a tunable time window in which the dynamics of\nembedded particles can be induced in their native liquid environment. After the\nlaser pulse, the sample vitrifies in just a few microseconds, trapping\nparticles in their transient configurations, so that they can subsequently be\ncharacterized with conventional cryo-electron microscopy. We demonstrate that\nour melting and revitrification approach is viable and affords microsecond time\nresolution. As a proof of principle, we study the disassembly of particles\nafter they incur structural damage and trap them in partially unraveled\nconfigurations.\n"}
+{"abstract": "  Independent deeply learned matrix analysis (IDLMA) is one of the\nstate-of-the-art supervised multichannel audio source separation methods. It\nblindly estimates the demixing filters on the basis of source independence,\nusing the source model estimated by the deep neural network (DNN). However,\nsince the ratios of the source to interferer signals vary widely among\ntime-frequency (TF) slots, it is difficult to obtain reliable estimated power\nspectrograms of sources at all TF slots. In this paper, we propose an IDLMA\nextension, empirical Bayesian IDLMA (EB-IDLMA), by introducing a prior\ndistribution of source power spectrograms and treating the source power\nspectrograms as latent random variables. This treatment allows us to implicitly\nconsider the reliability of the estimated source power spectrograms for the\nestimation of demixing filters through the hyperparameters of the prior\ndistribution estimated by the DNN. Experimental evaluations show the\neffectiveness of EB-IDLMA and the importance of introducing the reliability of\nthe estimated source power spectrograms.\n"}
+{"abstract": "  We provide a formal definition for a class of algorithms known as \"particle\nmethods\". Particle methods are used in scientific computing. They include\npopular simulation methods, such as Discrete Element Methods (DEM), Molecular\nDynamics (MD), Particle Strength Exchange (PSE), and Smoothed Particle\nHydrodynamics (SPH), but also particle-based image processing methods,\npoint-based computer graphics, and computational optimization algorithms using\npoint samples. All of these rest on a common concept, which we here formally\ndefine. The presented definition of particle methods makes it possible to\ndistinguish what formally constitutes a particle method, and what not. It also\nenables us to define different sub-classes of particle methods that differ with\nrespect to their computational complexity and power. Our definition is purely\nformal, independent of any application. After stating the definition, we\ntherefore illustrate how several well-known particle methods can be formalized\nin our framework, and we show how the formal definition can be used to\nformulate novel particle methods for non-canonical problems.\n"}
+{"abstract": "  This paper presents a novel Generative Neural Network Architecture for\nmodelling the inverse function of an Artificial Neural Network (ANN) either\ncompletely or partially. Modelling the complete inverse function of an ANN\ninvolves generating the values of all features that corresponds to a desired\noutput. On the other hand, partially modelling the inverse function means\ngenerating the values of a subset of features and fixing the remaining feature\nvalues. The feature set generation is a critical step for artificial neural\nnetworks, useful in several practical applications in engineering and science.\nThe proposed Oracle Guided Generative Neural Network, dubbed as OGGN, is\nflexible to handle a variety of feature generation problems. In general, an ANN\nis able to predict the target values based on given feature vectors. The OGGN\narchitecture enables to generate feature vectors given the predetermined target\nvalues of an ANN. When generated feature vectors are fed to the forward ANN,\nthe target value predicted by ANN will be close to the predetermined target\nvalues. Therefore, the OGGN architecture is able to map, inverse function of\nthe function represented by forward ANN. Besides, there is another important\ncontribution of this work. This paper also introduces a new class of functions,\ndefined as constraint functions. The constraint functions enable a neural\nnetwork to investigate a given local space for a longer period of time. Thus,\nenabling to find a local optimum of the loss function apart from just being\nable to find the global optimum. OGGN can also be adapted to solve a system of\npolynomial equations in many variables. The experiments on synthetic datasets\nvalidate the effectiveness of OGGN on various use cases.\n"}
+{"abstract": "  While artificial intelligence has been applied to control players' decisions\nin board games for over half a century, little attention is given to games with\nno player competition. Pandemic is an exemplar collaborative board game where\nall players coordinate to overcome challenges posed by events occurring during\nthe game's progression. This paper proposes an artificial agent which controls\nall players' actions and balances chances of winning versus risk of losing in\nthis highly stochastic environment. The agent applies a Rolling Horizon\nEvolutionary Algorithm on an abstraction of the game-state that lowers the\nbranching factor and simulates the game's stochasticity. Results show that the\nproposed algorithm can find winning strategies more consistently in different\ngames of varying difficulty. The impact of a number of state evaluation metrics\nis explored, balancing between optimistic strategies that favor winning and\npessimistic strategies that guard against losing.\n"}
+{"abstract": "  We present the first statistical analysis of kinematically-resolved,\nspatially-extended Ly$\\alpha$ emission around $z = 2-3$ galaxies in the Keck\nBaryonic Structure Survey (KBSS) using the Keck Cosmic Web Imager (KCWI). Our\nsample of 59 star-forming galaxies ($z_\\mathrm{med} = 2.29$) comprises the\nsubset with typical KCWI integration times of ~5 hours and with existing\nimaging data from the Hubble Space Telescope and/or adaptive optics-assisted\nintegral field spectroscopy. The high resolution images were used to evaluate\nthe azimuthal dependence of the diffuse Ly$\\alpha$ emission with respect to the\nstellar continuum within projected galactocentric distances of $\\lesssim 30$\nproper kpc. We introduce cylindrically-projected 2D spectra (CP2D) that map the\naveraged Ly$\\alpha$ spectral profile over a specified range of azimuthal angle,\nas a function of impact parameter around galaxies. The averaged CP2D spectrum\nof all galaxies shows clear signatures of Ly$\\alpha$ resonant scattering by\noutflowing gas. We stacked the CP2D spectra of individual galaxies over ranges\nof azimuthal angle with respect to their major axes. The extended Ly$\\alpha$\nemission along the galaxy principal axes are statistically indistinguishable,\nwith residual asymmetry of $\\le$ 2% ($\\sim 2 \\sigma$) of the integrated\nLy$\\alpha$ emission. The symmetry implies that the Ly$\\alpha$ scattering medium\nis dominated by outflows in all directions within 30 kpc. Meanwhile, we find\nthat the blueshifted component of Ly$\\alpha$ emission is marginally stronger\nalong galaxy minor axes for galaxies with relatively weak Ly$\\alpha$ emission.\nWe speculate that this weak directional dependence of Ly$\\alpha$ emission\nbecomes discernible only when the Ly$\\alpha$ escape fraction is low. These\ndiscoveries highlight the need for similar analyses in simulations with\nLy$\\alpha$ radiative transfer modeling.\n"}
+{"abstract": "  Black holes in general relativity are commonly believed to evolve towards a\nSchwarzschild state as they gradually lose angular momentum and electrical\ncharge under Hawking evaporation. However, when Kim and Wen applied quantum\ninformation theory to Hawking evaporation and argued that Hawking particles\nwith maximum mutual information could dominate the emission process, they found\nthat charged black holes tend towards extremality. In view of some evidence\npointing towards extremal black holes being effectively singular, this would\nviolate the cosmic censorship conjecture. In this work, we clarified the\ndifference between the two models -- they pertain to two different regimes. By\ntaking into account the discreteness of the charge-to-mass ratio of finite\nspecies of charged particles, we also obtained a clearer picture of the end\nstate of Hawking evaporation. We found that in agreement with the weak gravity\nconjecture, if there is no particle with charge-to-mass ratio q/m>1, stable\nremnant states are formed, though they are non-extremal. Furthermore, and more\nsurprisingly, we show that extremality can be reached if there exists a\nparticle with q/m<1, even in the presence of other particles with q/m>1. This\nmay explain why there is no charged particle with q/m<1 in the Standard Model\nof particle physics. Cosmic censorship could thus play a bigger role in\nfundamental physics than previously thought.\n"}
+{"abstract": "  We introduce the active audio-visual source separation problem, where an\nagent must move intelligently in order to better isolate the sounds coming from\nan object of interest in its environment. The agent hears multiple audio\nsources simultaneously (e.g., a person speaking down the hall in a noisy\nhousehold) and it must use its eyes and ears to automatically separate out the\nsounds originating from a target object within a limited time budget. Towards\nthis goal, we introduce a reinforcement learning approach that trains movement\npolicies controlling the agent's camera and microphone placement over time,\nguided by the improvement in predicted audio separation quality. We demonstrate\nour approach in scenarios motivated by both augmented reality (system is\nalready co-located with the target object) and mobile robotics (agent begins\narbitrarily far from the target object). Using state-of-the-art realistic\naudio-visual simulations in 3D environments, we demonstrate our model's ability\nto find minimal movement sequences with maximal payoff for audio source\nseparation. Project: http://vision.cs.utexas.edu/projects/move2hear.\n"}
+{"abstract": "  Deep learning based face recognition has achieved significant progress in\nrecent years. Yet, the practical model production and further research of deep\nface recognition are in great need of corresponding public support. For\nexample, the production of face representation network desires a modular\ntraining scheme to consider the proper choice from various candidates of\nstate-of-the-art backbone and training supervision subject to the real-world\nface recognition demand; for performance analysis and comparison, the standard\nand automatic evaluation with a bunch of models on multiple benchmarks will be\na desired tool as well; besides, a public groundwork is welcomed for deploying\nthe face recognition in the shape of holistic pipeline. Furthermore, there are\nsome newly-emerged challenges, such as the masked face recognition caused by\nthe recent world-wide COVID-19 pandemic, which draws increasing attention in\npractical applications. A feasible and elegant solution is to build an\neasy-to-use unified framework to meet the above demands. To this end, we\nintroduce a novel open-source framework, named FaceX-Zoo, which is oriented to\nthe research-development community of face recognition. Resorting to the highly\nmodular and scalable design, FaceX-Zoo provides a training module with various\nsupervisory heads and backbones towards state-of-the-art face recognition, as\nwell as a standardized evaluation module which enables to evaluate the models\nin most of the popular benchmarks just by editing a simple configuration. Also,\na simple yet fully functional face SDK is provided for the validation and\nprimary application of the trained models. Rather than including as many as\npossible of the prior techniques, we enable FaceX-Zoo to easily upgrade and\nextend along with the development of face related domains. The source code and\nmodels are available at https://github.com/JDAI-CV/FaceX-Zoo.\n"}
+{"abstract": "  A report and examination of a Remote Work experience during the Covid-19\npandemic encompassing a 14-hour time difference from the primary work location.\nAdvantages and disadvantages of a globally distributed work experience as\ncompared to an aligned time zone are explored. Logistical aspects of the\narrangement are provided as well as the management support, peer reaction, and\nrelative productivity. Recommendations are also provided on how to improve\nfuture geographically diverse team arrangements.\n  [Keywords: Global Software Development, Offshore Development, Software\nEngineering, IT Management, Remote Work, Remote Office Design, Distributed\nCommunications, Collaboration Technology, Team Management, Research,\nDevelopment, Productivity]\n"}
+{"abstract": "  The effect of protoplanetary differentiation on the fate of life essential\nvolatiles like nitrogen and carbon and its subsequent effect on the dynamics of\nplanetary growth is unknown. Because the dissolution of nitrogen in magma\noceans depends on its partial pressure and oxygen fugacity, it is an ideal\nproxy to track volatile redistribution in protoplanets as a function of their\nsizes and growth zones. Using high pressure and high temperature experiments in\ngraphite undersaturated conditions, here we show that the iron loving character\nof nitrogen is an order of magnitude higher than previous estimates across a\nwide range of oxygen fugacity. The experimental data combined with metal,\nsilicate and atmosphere fractionation models suggest that asteroid sized\nprotoplanets, and planetary embryos that grew from them, were nitrogen\ndepleted. However, protoplanets that grew to planetary embryo size before\nundergoing differentiation had nitrogen rich cores and nitrogen poor silicate\nreservoirs. Bulk silicate reservoirs of large Earth like planets attained\nnitrogen from the cores of latter type of planetary embryos. Therefore, to\nsatisfy the volatile budgets of Earth like planets during the main stage of\ntheir growth, the timescales of planetary embryo accretion had to be shorter\nthan their differentiation timescales, that is, Moon to Mars sized planetary\nembryos grew rapidly within 1 to 2 million years of the Solar System formation.\n"}
+{"abstract": "  Intelligent reflecting/refracting surface (IRS) has recently emerged as a\npromising solution to reconfigure wireless propagation environment for\nenhancing the communication performance. In this paper, we study a new\nIRS-aided high-mobility communication system by employing the intelligent\nrefracting surface with a high-speed vehicle to aid its passenger's\ncommunication with a remote base station (BS). Due to the environment's random\nscattering and vehicle's high mobility, a rapidly time-varying channel is\ntypically resulted between the static BS and fast-moving IRS/user, which\nrenders the channel estimation for IRS with a large number of elements more\nchallenging. In order to reap the high IRS passive beamforming gain with low\nchannel training overhead, we propose a new and efficient transmission protocol\nto achieve both IRS channel estimation and refraction optimization for data\ntransmission. Specifically, by exploiting the quasi-static channel between the\nIRS and user both moving at the same high speed as well as the line-of-sight\n(LoS) dominant channel between the BS and IRS, the user first estimates the LoS\ncomponent of the cascaded BS-IRS-user channel, based on which IRS passive\nrefraction is designed to maximize the corresponding IRS-refracted channel\ngain. Then, the user estimates the resultant IRS-refracted channel as well as\nthe non-IRS-refracted channel for setting an additional common phase shift at\nall IRS refracting elements so as to align these two channels for maximizing\nthe overall channel gain for data transmission. Simulation results show\nsignificant performance improvement of the proposed design as compared to\nvarious benchmark schemes. The proposed on-vehicle IRS system is further\ncompared with a baseline scheme of deploying fixed intelligent reflecting\nsurfaces on the roadside to assist high-speed vehicular communications, which\nachieves significant rate improvement.\n"}
+{"abstract": "  Home automation Internet of Things (IoT) systems have recently become a\ntarget for several types of attacks. In this paper, we present an\nauthentication and key agreement protocol for a home automation network based\non the ZigBee standard, which connects together a central controller and\nseveral end devices. Our scheme performs mutual authentication between end\ndevices and the controller, which is followed by device-to-device\ncommunication. The scheme achieves confidentiality, message integrity,\nanonymity, unlinkability, forward and backward secrecy, and availability. Our\nscheme uses only simple hash and XOR computations and symmetric key encryption,\nand hence is resource-efficient. We show using a detailed security analysis and\nnumerical results that our proposed scheme provides better security and\nanonymity, and is more efficient in terms of computation time, communication\ncost, and storage cost than schemes proposed in prior works.\n"}
+{"abstract": "  The characteristic microstructure of Sm(Co,Fe,Cu,Zr)$_z$ alloys with SmCo$_5$\ncell walls in Sm$_2$Co$_{17}$ cells, all intersected by Zr-rich platelets,\nmakes them some of the best performing high-temperature permanent magnets.\nPlentiful research has been performed to tailor the microstructure at the\nnanoscale, but due to its complexity many questions remain unanswered about the\neffect of the individual phases on the magnetic performance at different\ntemperatures. Here, we explore this mechanism effect for three different\nSm(Co,Fe,Cu,Zr)$_z$ alloys by deploying high-resolution magnetic imaging via\nin-situ transmission electron microscopy and three-dimensional chemical\nanalysis using atom probe tomography. We show that their microstructures differ\nin terms of SmCo$_5$ cell-wall and Z-phase size and density, as well as the Cu\nconcentration in the cell walls, and demonstrate how these features influence\nthe magnetic domain size and density and thus form different magnetic textures.\nMoreover, we illustrate that the dominant coercivity mechanism at room\ntemperature is domain-wall pinning and show that magnets with a denser\ncell-wall network, a steeper Cu gradient across the cell-wall boundary, and\nthinner Z-phase platelets have a higher coercivity. We also show that the\ncoercivity mechanism at high temperatures is domain-wall nucleation at the cell\nwalls. Increasing the Cu concentration inside the cell walls decreases the\ntransition temperature between pinning and nucleation, significantly decreasing\nthe coercivity with increasing temperature. We therefore provide a detailed\nexplanation of how the microstructure on the atomic to nanoscale directly\naffects the magnetic performance and provide detailed guidelines for an\nimproved design of Sm(Co,Fe,Cu,Zr)$_z$ magnets.\n"}
+{"abstract": "  The graphene superlattice equation, a modified sine-Gordon equation, governs\nthe propagation of solitary electromagnetic waves in a graphene superlattice.\nThis equation has kink solutions without explicit analytical expression,\nrequiring the use of quadrature methods. The inelastic collision of kinks and\nantikinks with the same but opposite speed is studied numerically for the first\ntime; after their interaction they escape to infinity when its speed is either\nlarger than a critical value or it is inside a series of resonance windows;\notherwise, they form a breather-like state that slowly decays by radiating\nenergy. Here, the fractal structure of these resonance windows is characterized\nby using a multi-index notation and their main features are compared with the\npredictions of the resonant energy exchange theory showing good agreement. Our\nresults can be interpreted as new evidence in favour of this theory.\n"}
+{"abstract": "  Quantum technology has made tremendous strides over the past two decades with\nremarkable advances in materials engineering, circuit design and dynamic\noperation. In particular, the integration of different quantum modules has\nbenefited from hybrid quantum systems, which provide an important pathway for\nharnessing the different natural advantages of complementary quantum systems\nand for engineering new functionalities. This review focuses on the current\nfrontiers with respect to utilizing magnetic excitatons or magnons for novel\nquantum functionality. Magnons are the fundamental excitations of magnetically\nordered solid-state materials and provide great tunability and flexibility for\ninteracting with various quantum modules for integration in diverse quantum\nsystems. The concomitant rich variety of physics and material selections enable\nexploration of novel quantum phenomena in materials science and engineering. In\naddition, the relative ease of generating strong coupling and forming hybrid\ndynamic systems with other excitations makes hybrid magnonics a unique platform\nfor quantum engineering. We start our discussion with circuit-based hybrid\nmagnonic systems, which are coupled with microwave photons and acoustic\nphonons. Subsequently, we are focusing on the recent progress of magnon-magnon\ncoupling within confined magnetic systems. Next we highlight new opportunities\nfor understanding the interactions between magnons and nitrogen-vacancy centers\nfor quantum sensing and implementing quantum interconnects. Lastly, we focus on\nthe spin excitations and magnon spectra of novel quantum materials investigated\nwith advanced optical characterization.\n"}
+{"abstract": "  We perform micro-rheological experiments with a colloidal bead driven through\na viscoelastic worm-like micellar fluid and observe two distinctive shear\nthinning regimes, each of them displaying a Newtonian-like plateau. The shear\nthinning behavior at larger velocities is in qualitative agreement with\nmacroscopic rheological experiments. The second process, observed at\nWeissenberg numbers as small as a few percent, appears to have no analog in\nmacro rheological findings. A simple model introduced earlier captures the\nobserved behavior, and implies that the two shear thinning processes correspond\nto two different length scales in the fluid. This model also reproduces\noscillations which have been observed in this system previously. While the\nsystem under macro-shear seems to be near equilibrium for shear rates in the\nregime of the intermediate Newtonian-like plateau, the one under micro-shear is\nthus still far from it. The analysis suggests the existence of a length scale\nof a few micrometres, the nature of which remains elusive.\n"}
+{"abstract": "  We present some transversality results for a category of Fr\\'{e}chet\nmanifolds, the so-called $MC^k$-Fr\\'{e}chet manifolds. In this context, we\napply the obtained transversality results to construct the degree of nonlinear\nFredholm mappings by virtue of which we prove a rank theorem, an invariance of\ndomain theorem and a Bursuk-Ulam type theorem.\n"}
+{"abstract": "  In this paper we present a novel class of convolutional codes and investigate\nits use in a variant of the McEliece cryptosystem that possesses several\ninteresting properties, including a reduction of the public key for a given\nsecurity level. In contrast to the classical McEliece cryptosystems, where\nblock codes are used, we propose the use of a convolutional encoder to be part\nof the public key. The secret key is constituted by a Generalized Reed-Solomon\nencoder and two Laurent polynomial matrices that contain large parts that are\ngenerated completely at random. Instead of multiplying on the right the GRS\nencoder by a permutation matrix, it is multiplied by a polynomial matrix with\ncolumns having weight zero or at least two. In this setting the message is a\nsequence of messages instead of a single block message and the errors are added\nrandomly throughout the sequence. We conclude the paper studying its security\nand presenting several examples.\n"}
+{"abstract": "  This paper investigates the problem of computing the equilibrium of\ncompetitive games, which is often modeled as a constrained saddle-point\noptimization problem with probability simplex constraints. Despite recent\nefforts in understanding the last-iterate convergence of extragradient methods\nin the unconstrained setting, the theoretical underpinnings of these methods in\nthe constrained settings, especially those using multiplicative updates, remain\nhighly inadequate, even when the objective function is bilinear. Motivated by\nthe algorithmic role of entropy regularization in single-agent reinforcement\nlearning and game theory, we develop provably efficient extragradient methods\nto find the quantal response equilibrium (QRE) -- which are solutions to\nzero-sum two-player matrix games with entropy regularization -- at a linear\nrate. The proposed algorithms can be implemented in a decentralized manner,\nwhere each player executes symmetric and multiplicative updates iteratively\nusing its own payoff without observing the opponent's actions directly. In\naddition, by controlling the knob of entropy regularization, the proposed\nalgorithms can locate an approximate Nash equilibrium of the unregularized\nmatrix game at a sublinear rate without assuming the Nash equilibrium to be\nunique. Our methods also lead to efficient policy extragradient algorithms for\nsolving (entropy-regularized) zero-sum Markov games at similar rates. All of\nour convergence rates are nearly dimension-free, which are independent of the\nsize of the state and action spaces up to logarithm factors, highlighting the\npositive role of entropy regularization for accelerating convergence.\n"}
+{"abstract": "  SARAS is an ongoing experiment aiming to detect the redshifted global 21-cm\nsignal expected from Cosmic Dawn (CD) and the Epoch of Reionization (EoR).\nStandard cosmological models predict the signal to be present in the redshift\nrange $z \\sim $6--35, corresponding to a frequency range 40--200~MHz, as a\nspectral distortion of amplitude 20--200~mK in the 3~K cosmic microwave\nbackground. Since the signal might span multiple octaves in frequency, and this\nfrequency range is dominated by strong terrestrial Radio Frequency Interference\n(RFI) and astrophysical foregrounds of Galactic and Extragalactic origin that\nare several orders of magnitude greater in brightness temperature, design of a\nradiometer for measurement of this faint signal is a challenging task. It is\ncritical that the instrumental systematics do not result in additive or\nmultiplicative confusing spectral structures in the measured sky spectrum and\nthus preclude detection of the weak 21-cm signal. Here we present the system\ndesign of the SARAS~3 version of the receiver. New features in the evolved\ndesign include Dicke switching, double differencing and optical isolation for\nimproved accuracy in calibration and rejection of additive and multiplicative\nsystematics. We derive and present the measurement equations for the SARAS~3\nreceiver configuration and calibration scheme, and provide results of\nlaboratory tests performed using various precision terminations that qualify\nthe performance of the radiometer receiver for the science goal.\n"}
+{"abstract": "  In 1989, Burnett conjectured that, under appropriate assumptions, the limit\nof highly oscillatory solutions to the Einstein vacuum equations is a solution\nof the Einstein--massless Vlasov system. In a recent breakthrough, Huneau--Luk\n(arXiv:1907.10743) gave a proof of the conjecture in U(1)-symmetry and elliptic\ngauge. They also require control on up to fourth order derivatives of the\nmetric components. In this paper, we give a streamlined proof of a stronger\nresult and, in the spirit of Burnett's original conjecture, we remove the need\nfor control on higher derivatives. Our methods also apply to general wave map\nequations.\n"}
+{"abstract": "  People are often reluctant to sell a house, or shares of stock, below the\nprice at which they originally bought it. While this is generally not\nconsistent with rational utility maximization, it does reflect two strong\nempirical regularities that are central to the behavioral science of human\ndecision-making: a tendency to evaluate outcomes relative to a reference point\ndetermined by context (in this case the original purchase price), and the\nphenomenon of loss aversion in which people are particularly prone to avoid\noutcomes below the reference point. Here we explore the implications of\nreference points and loss aversion in optimal stopping problems, where people\nevaluate a sequence of options in one pass, either accepting the option and\nstopping the search or giving up on the option forever. The best option seen so\nfar sets a reference point that shifts as the search progresses, and a biased\ndecision-maker's utility incurs an additional penalty when they accept a later\noption that is below this reference point.\n  We formulate and study a behaviorally well-motivated version of the optimal\nstopping problem that incorporates these notions of reference dependence and\nloss aversion. We obtain tight bounds on the performance of a biased agent in\nthis model relative to the best option obtainable in retrospect (a type of\nprophet inequality for biased agents), as well as tight bounds on the ratio\nbetween the performance of a biased agent and the performance of a rational\none. We further establish basic monotonicity results, and show an exponential\ngap between the performance of a biased agent in a stopping problem with\nrespect to a worst-case versus a random order. As part of this, we establish\nfundamental differences between optimal stopping problems for rational versus\nbiased agents, and these differences inform our analysis.\n"}
+{"abstract": "  With the advent of Artificial Intelligence (AI)-empowered communications,\nindustry, academia, and standardization organizations are progressing on the\ndefinition of mechanisms and procedures to address the increasing complexity of\nfuture 5G and beyond communications. In this context, the International\nTelecommunication Union (ITU) organized the first AI for 5G Challenge to bring\nindustry and academia together to introduce and solve representative problems\nrelated to the application of Machine Learning (ML) to networks. In this paper,\nwe present the results gathered from Problem Statement~13 (PS-013), organized\nby Universitat Pompeu Fabra (UPF), which primary goal was predicting the\nperformance of next-generation Wireless Local Area Networks (WLANs) applying\nChannel Bonding (CB) techniques. In particular, we overview the ML models\nproposed by participants (including Artificial Neural Networks, Graph Neural\nNetworks, Random Forest regression, and gradient boosting) and analyze their\nperformance on an open dataset generated using the IEEE 802.11ax-oriented\nKomondor network simulator. The accuracy achieved by the proposed methods\ndemonstrates the suitability of ML for predicting the performance of WLANs.\nMoreover, we discuss the importance of abstracting WLAN interactions to achieve\nbetter results, and we argue that there is certainly room for improvement in\nthroughput prediction through ML.\n"}
+{"abstract": "  We present a conjectual hook formula concerning the number of the standard\ntableaux on \"cylindric\" skew diagrams. Our formula can be seen as an extension\nof Naruse's hook formula for skew diagrams. Moreover, we prove our conjecture\nin some special cases.\n"}
+{"abstract": "  We consider the problem of teaching via demonstrations in sequential\ndecision-making settings. In particular, we study how to design a personalized\ncurriculum over demonstrations to speed up the learner's convergence. We\nprovide a unified curriculum strategy for two popular learner models: Maximum\nCausal Entropy Inverse Reinforcement Learning (MaxEnt-IRL) and Cross-Entropy\nBehavioral Cloning (CrossEnt-BC). Our unified strategy induces a ranking over\ndemonstrations based on a notion of difficulty scores computed w.r.t. the\nteacher's optimal policy and the learner's current policy. Compared to the\nstate of the art, our strategy doesn't require access to the learner's internal\ndynamics and still enjoys similar convergence guarantees under mild technical\nconditions. Furthermore, we adapt our curriculum strategy to the setting where\nno teacher agent is present using task-specific difficulty scores. Experiments\non a synthetic car driving environment and navigation-based environments\ndemonstrate the effectiveness of our curriculum strategy.\n"}
+{"abstract": "  A detailed study of the morphology of lenticular galaxies is an important way\nto understand how this type of galaxy formed and evolves over time. Decomposing\na galaxy into its components (disc, bulge, bar, ...) allows recovering the\ncolour gradients present in each system, its star formation history, and its\nassembly history. We use GALFITM to perform a multi-wavelength structural\ndecomposition of the closest lenticular galaxy, NGC 3115, resulting in the\ndescription of its stellar light into several main components: a bulge, a thin\ndisc, a thick disc and also evidence of a bar. We report the finding of central\nbluer stellar populations in the bulge, as compared to the colour of the galaxy\noutskirts, indicating either the presence of an Active Galactic Nucleus (AGN)\nand/or recent star formation activity. From the spectral energy distribution\nresults, we show that the galaxy has a low luminosity AGN component, but even\nexcluding the effect of the nuclear activity, the bulge is still bluer than the\nouter-regions of the galaxy, revealing a recent episode of star formation.\nBased on all of the derived properties, we propose a scenario for the formation\nof NGC 3115 consisting of an initial gas-rich merger, followed by accretions\nand feedback that quench the galaxy, until a recent encounter with the\ncompanion KK084 that reignited the star formation in the bulge, provoked a core\ndisplacement in NGC 3115 and generated spiral-like features. This result is\nconsistent with the two-phase formation scenario, proposed in previous studies\nof this galaxy.\n"}
+{"abstract": "  In 2001, Leo Breiman wrote of a divide between \"data modeling\" and\n\"algorithmic modeling\" cultures. Twenty years later this division feels far\nmore ephemeral, both in terms of assigning individuals to camps, and in terms\nof intellectual boundaries. We argue that this is largely due to the \"data\nmodelers\" incorporating algorithmic methods into their toolbox, particularly\ndriven by recent developments in the statistical understanding of Breiman's own\nRandom Forest methods. While this can be simplistically described as \"Breiman\nwon\", these same developments also expose the limitations of the\nprediction-first philosophy that he espoused, making careful statistical\nanalysis all the more important. This paper outlines these exciting recent\ndevelopments in the random forest literature which, in our view, occurred as a\nresult of a necessary blending of the two ways of thinking Breiman originally\ndescribed. We also ask what areas statistics and statisticians might currently\noverlook.\n"}
+{"abstract": "  Let $\\varphi:\\Sigma_1\\longrightarrow \\mathbb{P}^2$ be a blow up at a point on\n$\\mathbb{P}^2$. Let $C$ be the proper transform of a smooth plane curve of\ndegree $d\\geq 4$ by $\\varphi$, and let $P$ be a point on $C$. Let\n$\\pi:\\tilde{C}\\longrightarrow C$ be a double covering branched along the\nreduced divisor on $C$ obtained as the intersection of $C$ and a reduced\ndivisor in $|-2K_{\\Sigma_1}|$ containing $P$. In this paper, we investigate the\nWeierstrass semigroup $H(\\tilde{P})$ at the ramification point $\\tilde{P}$ of\n$\\pi$ over $P$, in the case where the intersection multiplicity at $\\varphi(P)$\nof $\\varphi(C)$ and the tangent line at $\\varphi(P)$ of $\\varphi(C)$ is $d-1$.\n"}
+{"abstract": "  The origin and nature of ultra-high-energy cosmic rays (UHECRs) remain an\nopen question in astroparticle physics. Motivated by the need for an\nunprecedented aperture for further advancements, the Fluorescence detector\nArray of Single-pixel Telescopes (FAST) is a prospective next-generation,\nground-based UHECR observatory that aims to cover a huge area by deploying a\nlarge array of low-cost fluorescence detectors. The full-scale FAST prototype\nconsists of four 20 cm photomultiplier tubes at the focus of a segmented mirror\n1.6 m in diameter. Over the last five years, three prototypes have been\ninstalled at the Telescope Array Experiment in Utah, USA, and one prototype at\nthe Pierre Auger Observatory in Mendoza, Argentina, commencing remote\nobservation of UHECRs in both hemispheres. We report on the latest results of\nthese FAST prototypes, including telescope calibrations, atmospheric\nmonitoring, ongoing electronics upgrades, development of sophisticated\nreconstruction methods, and UHECR detections.\n"}
+{"abstract": "  We present EgoACO, a deep neural architecture for video action recognition\nthat learns to pool action-context-object descriptors from frame level features\nby leveraging the verb-noun structure of action labels in egocentric video\ndatasets. The core component of EgoACO is class activation pooling (CAP), a\ndifferentiable pooling operation that combines ideas from bilinear pooling for\nfine-grained recognition and from feature learning for discriminative\nlocalization. CAP uses self-attention with a dictionary of learnable weights to\npool from the most relevant feature regions. Through CAP, EgoACO learns to\ndecode object and scene context descriptors from video frame features. For\ntemporal modeling in EgoACO, we design a recurrent version of class activation\npooling termed Long Short-Term Attention (LSTA). LSTA extends convolutional\ngated LSTM with built-in spatial attention and a re-designed output gate.\nAction, object and context descriptors are fused by a multi-head prediction\nthat accounts for the inter-dependencies between noun-verb-action structured\nlabels in egocentric video datasets. EgoACO features built-in visual\nexplanations, helping learning and interpretation. Results on the two largest\negocentric action recognition datasets currently available, EPIC-KITCHENS and\nEGTEA, show that by explicitly decoding action-context-object descriptors,\nEgoACO achieves state-of-the-art recognition performance.\n"}
+{"abstract": "  The Euclid satellite, to be launched by ESA in 2022, will be a major\ninstrument for cosmology for the next decades. \\Euclid\\ is composed of two\ninstruments: the Visible (VIS) instrument and the Near Infrared Spectromete and\nPhotometer (NISP). In this work we estimate the implications of correlated\nreadout noise in the NISP detectors for the final in-flight flux measurements.\nConsidering the multiple accumulated (MACC) readout mode, for which the UTR (Up\nThe Ramp) exposure frames are averaged in groups, we derive an analytical\nexpression for the noise covariance matrix between groups in the presence of\ncorrelated noise. We also characterize the correlated readout noise properties\nin the NISP engineering grade detectors using long dark integrations. For this\npurpose, we assume a $(1/f)^{\\, \\alpha}$-like noise model and fit the model\nparameters to the data, obtaining typical values of $\\sigma =\n19.7^{+1.1}_{-0.8}$ e$^{-} \\rm{Hz}^{-0.5}$, $f_{\\rm{knee}} =\n(5.2^{+1.8}_{-1.3}) \\times 10^{-3} \\, \\rm{Hz}$ and $\\alpha = 1.24\n^{+0.26}_{-0.21}$. Furthermore, via realistic simulations and using a maximum\nlikelihood flux estimator we derive the bias between the input flux and the\nrecovered one. We find that using our analytical expression for the covariance\nmatrix of the correlated readout noise we diminish this bias by up to a factor\nof four with respect to the white noise approximation for the covariance\nmatrix. Finally, we conclude that the final bias on the in-flight NISP flux\nmeasurements should still be negligible even in the white noise approximation,\nwhich is taken as a baseline for the Euclid\\on-board processing\n"}
+{"abstract": "  Based on the quantum master equation approach, the nonlinear electric\nconductivity of graphene is investigated under static electric fields for\nvarious chemical potential shifts. The simulation results show that, as the\nfield strength increases, the effective conductivity is firstly suppressed,\nreflecting the depletion of effective carriers due to the large displacement in\nthe Brillouin zone caused by the strong field. Then, as the field strength\nexceeds $1$~MV/m, the effective conductivity increases, overcoming the carrier\ndepletion via the Landau--Zener tunneling process. Based on the nonlinear\nbehavior of the conductivity, the charge transport induced by few-cycle THz\npulses is studied to elucidate the ultrafast control of electric current in\nmatter.\n"}
+{"abstract": "  In the early 1990's, Billera and Sturmfels introduced the monotone path\npolytope (MPP), a special case of the general theory of fiber polytopes that\nassociates a polytope to a pair $(P,\\varphi)$ of a polytope $P$ and linear\nfunctional $\\varphi$. In that same paper, they showed that MPPs of simplices\nand hyper-cubes are combinatorial cubes and permutahedra respectively. Their\nwork has lead to many developments in combinatorics. Here we investigate the\nmonotone paths for generic orientations of cross-polytopes. We show the face\nlattice of its MPP is isomorphic to the lattice of intervals in the sign poset\nfrom oriented matroid theory. We look at its $f$-vector, its realizations, and\nfacets.\n"}
+{"abstract": "  Liquid metal electrodes are one of the key components of different electrical\nenergy storage technologies. The understanding of transport phenomena in liquid\nelectrodes is mandatory in order to ensure efficient operation. In the present\nstudy we focus our attention on the positive electrode of the Li||Bi liquid\nmetal battery. Starting from a real experimental setup, we numerically\ninvestigate the charge transfer in a molten salt electrolyte and the mass\ntransport in the positive electrode. The two phenomena are tightly coupled,\nbecause the current distribution influences the concentration field in the\npositive electrode. The cell is studied during charging when compositional\nconvection becomes apparent. First results of compositional convection from a\nnon-uniform current distribution are presented, highlighting its capability to\naffect the flow in the positive electrode and the cell performance.\n"}
+{"abstract": "  The data transfer capacity of a communication channel is limited by the\nShannon-Hartley theorem and scales as $\\text{log}_2(1 + \\text{SNR})$ for a\nsingle channel with the power signal-to-noise ratio (SNR). We implement an\narray of atom-optical receivers in a single-input-multi-output (SIMO)\nconfiguration by using spatially distributed probe light beams. The data\ncapacity of the distributed receiver configuration is observed to scale as\n$\\text{log}_2(1 + N\\times\\text{SNR})$ for an array consisting of $N$ receivers.\nOur result is independent on the modulation frequency, and we show that such\nenhancement of the bandwidth cannot be obtained by a single receiver with a\nsimilar level of combined optical power. We investigate both theoretically and\nexperimentally the origins of the single channel capacity limit for our\nimplementation.\n"}
+{"abstract": "  Several large-scale machine learning tasks, such as data summarization, can\nbe approached by maximizing functions that satisfy submodularity. These\noptimization problems often involve complex side constraints, imposed by the\nunderlying application. In this paper, we develop an algorithm with\npoly-logarithmic adaptivity for non-monotone submodular maximization under\ngeneral side constraints. The adaptive complexity of a problem is the minimal\nnumber of sequential rounds required to achieve the objective.\n  Our algorithm is suitable to maximize a non-monotone submodular function\nunder a $p$-system side constraint, and it achieves a $(p +\nO(\\sqrt{p}))$-approximation for this problem, after only poly-logarithmic\nadaptive rounds and polynomial queries to the valuation oracle function.\nFurthermore, our algorithm achieves a $(p + O(1))$-approximation when the given\nside constraint is a $p$-extendible system.\n  This algorithm yields an exponential speed-up, with respect to the\nadaptivity, over any other known constant-factor approximation algorithm for\nthis problem. It also competes with previous known results in terms of the\nquery complexity. We perform various experiments on various real-world\napplications. We find that, in comparison with commonly used heuristics, our\nalgorithm performs better on these instances.\n"}
+{"abstract": "  The effect on dynamo action of an anisotropic electrical conductivity\nconjugated to an anisotropic magnetic permeability is considered. Not only is\nthe dynamo fully axisymmetric, but it requires only a simple differential\nrotation, which twice challenges the well-established dynamo theory. Stability\nanalysis is conducted entirely analytically, leading to an explicit expression\nof the dynamo threshold. The results show a competition between the anisotropy\nof electrical conductivity and that of magnetic permeability, the dynamo effect\nbecoming impossible if the two anisotropies are identical. For isotropic\nelectrical conductivity, Cowling's neutral point argument does imply the\nabsence of an azimuthal component of current density, but does not prevent the\ndynamo effect as long as the magnetic permeability is anisotropic.\n"}
+{"abstract": "  Adaptive Moment Estimation (ADAM) is a very popular training algorithm for\ndeep neural networks and belongs to the family of adaptive gradient descent\noptimizers. However to the best of the authors knowledge no complete\nconvergence analysis exists for ADAM. The contribution of this paper is a\nmethod for the local convergence analysis in batch mode for a deterministic\nfixed training set, which gives necessary conditions for the hyperparameters of\nthe ADAM algorithm. Due to the local nature of the arguments the objective\nfunction can be non-convex but must be at least twice continuously\ndifferentiable. Then we apply this procedure to other adaptive gradient descent\nalgorithms and show for most of them local convergence with hyperparameter\nbounds.\n"}
+{"abstract": "  Plasmons in two-dimensional (2D) materials beyond graphene have recently\ngained much attention. However, the experimental investigation is limited due\nto the lack of suitable materials. Here, we experimentally demonstrate\nlocalized plasmons in a correlated 2D charge-density-wave (CDW) material:\n2H-TaSe2. The plasmon resonance can cover a broad spectral range from the\nterahertz (40 {\\mu}m) to the telecom (1.55 {\\mu}m) region, which is further\ntunable by changing thickness and dielectric environments. The plasmon\ndispersion flattens at large wave vectors, resulted from the universal\nscreening effect of interband transitions. More interestingly, anomalous\ntemperature dependence of plasmon resonances associated with CDW excitations is\nobserved. In the CDW phase, the plasmon peak close to the CDW excitation\nfrequency becomes wider and asymmetric, mimicking two coupled oscillators. Our\nstudy not only reveals the universal role of the intrinsic screening on 2D\nplasmons, but also opens an avenue for tunable plasmons in 2D correlated\nmaterials.\n"}
+{"abstract": "  Boron-doped diamond granular thin films are known to exhibit\nsuperconductivity with an optimal critical temperature of Tc = 7.2K. Here we\nreport the measured complex surface impedance of Boron-doped diamond films in\nthe microwave frequency range using a resonant technique. Experimentally\nmeasured inductance values are in good agreement with estimates obtained from\nthe normal state sheet resistance of the material. The magnetic penetration\ndepth temperature dependence is consistent with that of a fully-gapped s-wave\nsuperconductor. Boron-doped diamond films should find application where high\nkinetic inductance is needed, such as microwave kinetic inductance detectors\nand quantum impedance devices.\n"}
+{"abstract": "  Preferential attachment, homophily and, their consequences such as the glass\nceiling effect have been well-studied in the context of undirected networks.\nHowever, the lack of an intuitive, theoretically tractable model of a directed,\nbi-populated~(i.e.,~containing two groups) network with variable levels of\npreferential attachment, homophily and growth dynamics~(e.g.,~the rate at which\nnew nodes join, whether the new nodes mostly follow existing nodes or the\nexisting nodes follow them, etc.) has largely prevented such consequences from\nbeing explored in the context of directed networks, where they more naturally\noccur due to the asymmetry of links. To this end, we present a rigorous\ntheoretical analysis of the \\emph{Directed Mixed Preferential Attachment} model\nand, use it to analyze the glass ceiling effect in directed networks. More\nspecifically, we derive the closed-form expressions for the power-law exponents\nof the in- and out- degree distributions of each group~(minority and majority)\nand, compare them with each other to obtain insights. In particular, our\nresults yield answers to questions such as: \\emph{when does the minority group\nhave a heavier out-degree (or in-degree) distribution compared to the majority\ngroup? what effect does frequent addition of edges between existing nodes have\non the in- and out- degree distributions of the majority and minority groups?}.\nSuch insights shed light on the interplay between the structure~(i.e., the in-\nand out- degree distributions of the two groups) and dynamics~(characterized\ncollectively by the homophily, preferential attachment, group sizes and growth\ndynamics) of various real-world networks. Finally, we utilize the obtained\nanalytical results to characterize the conditions under which the glass ceiling\neffect emerge in a directed network. Our analytical results are supported by\ndetailed numerical results.\n"}
+{"abstract": "  A set of quadratic forms is simultaneously diagonalizable via congruence\n(SDC) if there exists a basis under which each of the quadratic forms is\ndiagonal. This property appears naturally when analyzing quadratically\nconstrained quadratic programs (QCQPs) and has important implications in this\ncontext. This paper extends the reach of the SDC property by studying two new\nrelated but weaker notions of simultaneous diagonalizability. Specifically, we\nsay that a set of quadratic forms is almost SDC (ASDC) if it is the limit of\nSDC sets and d-restricted SDC (d-RSDC) if it is the restriction of an SDC set\nin up to d-many additional dimensions. Our main contributions are a complete\ncharacterization of the ASDC pairs and the nonsingular ASDC triples, as well as\na sufficient condition for the 1-RSDC property for pairs of quadratic forms.\nSurprisingly, we show that every singular pair is ASDC and that almost every\npair is 1-RSDC.\n  We accompany our theoretical results with preliminary numerical experiments\napplying the RSDC property to QCQPs with a single quadratic constraint.\n"}
+{"abstract": "  In order to increase the resilience of distribution systems against\nhigh-impact low-probability (HILP) events, it is important to prioritize the\ndamaged assets so that the lost loads, especially critical and important loads,\ncan be restored faster. In addition, correctly predicting the number of repair\nteams during critical times contributes to restoring the network to the initial\nresilience level. For this reason, this paper discusses the prioritization of\nelectricity supply lines for evaluating the number of required repair teams. To\nthis end, the economic value of distribution system lines has been considered\nas a criterion representing the sensitivity of the network to hurricanes. The\nmodeling is based on value, in which the load value, failure probability of the\npoles, fragility curve, duration of line repair by the maintenance team, and\nthe topology factor have been considered. This is so that the significance of\nthe demand side, the failure extent and accessibility of the lines, the\nimportance of time, and the network configuration are considered. The results\nprovide a list of line priority for fault resolution, in which the topology\nfactor has a larger effect. The number of repair teams required to restore\ncritical and important loads is determined from this model. This modeling has\nbeen tested on an IEEE 33-bus network. Keywords: Resilience, distribution\nsystems, asset evaluation, restoration prioritization, repair team\n"}
+{"abstract": "  We introduce Z-Sequence, a novel empirical model that utilises photometric\nmeasurements of observed galaxies within a specified search radius to estimate\nthe photometric redshift of galaxy clusters. Z-Sequence itself is composed of a\nmachine learning ensemble based on the k-nearest neighbours algorithm. We\nimplement an automated feature selection strategy that iteratively determines\nappropriate combinations of filters and colours to minimise photometric\nredshift prediction error. We intend for Z-Sequence to be a standalone\ntechnique but it can be combined with cluster finders that do not intrinsically\npredict redshift, such as our own DEEP-CEE. In this proof-of-concept study we\ntrain, fine-tune and test Z-Sequence on publicly available cluster catalogues\nderived from the Sloan Digital Sky Survey. We determine the photometric\nredshift prediction error of Z-Sequence via the median value of $|\\Delta\nz|/(1+z)$ (across a photometric redshift range of $0.05 \\le \\textit{z} \\le\n0.6$) to be $\\sim0.01$ when applying a small search radius. The photometric\nredshift prediction error for test samples increases by 30-50 per cent when the\nsearch radius is enlarged, likely due to line-of-sight interloping galaxies.\nEventually, we aim to apply Z-Sequence to upcoming imaging surveys such as the\nLegacy Survey of Space and Time to provide photometric redshift estimates for\nlarge samples of as yet undiscovered and distant clusters.\n"}
+{"abstract": "  We give almost-linear-time algorithms for constructing sparsifiers with $n\\\npoly(\\log n)$ edges that approximately preserve weighted $(\\ell^{2}_2 +\n\\ell^{p}_p)$ flow or voltage objectives on graphs. For flow objectives, this is\nthe first sparsifier construction for such mixed objectives beyond unit\n$\\ell_p$ weights, and is based on expander decompositions. For voltage\nobjectives, we give the first sparsifier construction for these objectives,\nwhich we build using graph spanners and leverage score sampling. Together with\nthe iterative refinement framework of [Adil et al, SODA 2019], and a new\nmultiplicative-weights based constant-approximation algorithm for\nmixed-objective flows or voltages, we show how to find $(1+2^{-\\text{poly}(\\log\nn)})$ approximations for weighted $\\ell_p$-norm minimizing flows or voltages in\n$p(m^{1+o(1)} + n^{4/3 + o(1)})$ time for $p=\\omega(1),$ which is almost-linear\nfor graphs that are slightly dense ($m \\ge n^{4/3 + o(1)}$).\n"}
+{"abstract": "  This paper proposes an approach to Dense Video Captioning (DVC) without\npairwise event-sentence annotation. First, we adopt the knowledge distilled\nfrom relevant and well solved tasks to generate high-quality event proposals.\nThen we incorporate contrastive loss and cycle-consistency loss typically\napplied to cross-modal retrieval tasks to build semantic matching between the\nproposals and sentences, which are eventually used to train the caption\ngeneration module. In addition, the parameters of matching module are\ninitialized via pre-training based on annotated images to improve the matching\nperformance. Extensive experiments on ActivityNet-Caption dataset reveal the\nsignificance of distillation-based event proposal generation and cross-modal\nretrieval-based semantic matching to weakly supervised DVC, and demonstrate the\nsuperiority of our method to existing state-of-the-art methods.\n"}
+{"abstract": "  We investigate Kapitsa pendulum-like effects in the magnetic moment dynamics\nof a nanomagnet coupled to a Josephson junction under external periodic drive.\nGenerated by the Josephson junction and external drive, the magnetic field\nplays the role of the oscillating force of the suspension point in the Kapitsa\npendulum. The high frequency oscillations change the orientation of magnetic\nmoment. The magnetic field of the quasiparticle current of the Josephson\njunction determines the frequency dependence of the magnetic moment's\norientation. We obtain simple analytical formulas for the stable position of\nmagnetic moment, both under the external periodic drive and without it. The\ninfluence of external periodic drive on the voltage of complete reorientation\nhave been demonstrated.\n"}
+{"abstract": "  Trans-Neptunian Objects (TNOs) in the scattered disk with 50 < a < 100 au are\nthought to cluster near Neptune's n:1 resonances (e.g: 3:1, 4:1, and so on).\nWhile these objects spend lengthy periods of time at large heliocentric\ndistances, if their perihelia remain less than around 40 au, their dynamical\nevolution is still largely coupled to Neptune's. Conversely, around a dozen\nextreme TNOs with a > 250 au and detached perihelia seem to exist in a regime\nwhere they are too distant to be affected by the giant planets, and too close\nfor their dynamics to be governed by external forces. Recent work suggests that\nthe apparent alignment of these orbits in physical space is a signature of\ngravitational shepherding by a distant massive planet. In this paper, we\ninvestigate the evolution of TNOs in each of Neptune's n:1 resonances between\nthe 3:1 and 14:1. We conclude that both resonant and non-resonant objects\nbeyond the 12:1 near ~157 au are removed rather efficiently via perturbations\nfrom the hypothetical Planet Nine. Additionally, we uncover a population of\nsimulated TNOs with a < 100 au, 40 < q < 45 au and low inclinations that\nexperience episodes of resonant interactions with both Neptune and Planet Nine.\nFinally, we simulate the evolution of observed objects with a > 100 au and\nidentify several TNOs that are potentially locked in n:1 resonances with\nNeptune; including the most distant known resonant candidates 2014 JW80 and\n2014 OS394 that appear to be in the 10:1 and 11:1 resonances, respectively. Our\nresults suggest that the detection of similar remote objects might provide a\nuseful constraint on hypotheses invoking the existence of additional distant\nplanets.\n"}
+{"abstract": "  JUNO is a massive liquid scintillator detector with a primary scientific goal\nof determining the neutrino mass ordering by studying the oscillated\nanti-neutrino flux coming from two nuclear power plants at 53 km distance. The\nexpected signal anti-neutrino interaction rate is only 60 counts per day,\ntherefore a careful control of the background sources due to radioactivity is\ncritical. In particular, natural radioactivity present in all materials and in\nthe environment represents a serious issue that could impair the sensitivity of\nthe experiment if appropriate countermeasures were not foreseen. In this paper\nwe discuss the background reduction strategies undertaken by the JUNO\ncollaboration to reduce at minimum the impact of natural radioactivity. We\ndescribe our efforts for an optimized experimental design, a careful material\nscreening and accurate detector production handling, and a constant control of\nthe expected results through a meticulous Monte Carlo simulation program. We\nshow that all these actions should allow us to keep the background count rate\nsafely below the target value of 10 Hz in the default fiducial volume, above an\nenergy threshold of 0.7 MeV.\n"}
+{"abstract": "  We prove an improved spectral large sieve inequality for the family of\n$SL_3(\\mathbb{Z})$ Hecke-Maass cusp forms. The method of proof uses duality and\nits structure reveals unexpected connections to Heath-Brown's large sieve for\ncubic characters.\n"}
+{"abstract": "  Backpropagation image saliency aims at explaining model predictions by\nestimating model-centric importance of individual pixels in the input. However,\nclass-insensitivity of the earlier layers in a network only allows saliency\ncomputation with low resolution activation maps of the deeper layers, resulting\nin compromised image saliency. Remedifying this can lead to sanity failures. We\npropose CAMERAS, a technique to compute high-fidelity backpropagation saliency\nmaps without requiring any external priors and preserving the map sanity. Our\nmethod systematically performs multi-scale accumulation and fusion of the\nactivation maps and backpropagated gradients to compute precise saliency maps.\nFrom accurate image saliency to articulation of relative importance of input\nfeatures for different models, and precise discrimination between model\nperception of visually similar objects, our high-resolution mapping offers\nmultiple novel insights into the black-box deep visual models, which are\npresented in the paper. We also demonstrate the utility of our saliency maps in\nadversarial setup by drastically reducing the norm of attack signals by\nfocusing them on the precise regions identified by our maps. Our method also\ninspires new evaluation metrics and a sanity check for this developing research\ndirection. Code is available here https://github.com/VisMIL/CAMERAS\n"}
+{"abstract": "  Despite advances in feature representation, leveraging geometric relations is\ncrucial for establishing reliable visual correspondences under large variations\nof images. In this work we introduce a Hough transform perspective on\nconvolutional matching and propose an effective geometric matching algorithm,\ndubbed Convolutional Hough Matching (CHM). The method distributes similarities\nof candidate matches over a geometric transformation space and evaluate them in\na convolutional manner. We cast it into a trainable neural layer with a\nsemi-isotropic high-dimensional kernel, which learns non-rigid matching with a\nsmall number of interpretable parameters. To validate the effect, we develop\nthe neural network with CHM layers that perform convolutional matching in the\nspace of translation and scaling. Our method sets a new state of the art on\nstandard benchmarks for semantic visual correspondence, proving its strong\nrobustness to challenging intra-class variations.\n"}
+{"abstract": "  Explaining the behavior of black box machine learning models through human\ninterpretable rules is an important research area. Recent work has focused on\nexplaining model behavior locally i.e. for specific predictions as well as\nglobally across the fields of vision, natural language, reinforcement learning\nand data science. We present a novel model-agnostic approach that derives rules\nto globally explain the behavior of classification models trained on numerical\nand/or categorical data. Our approach builds on top of existing local model\nexplanation methods to extract conditions important for explaining model\nbehavior for specific instances followed by an evolutionary algorithm that\noptimizes an information theory based fitness function to construct rules that\nexplain global model behavior. We show how our approach outperforms existing\napproaches on a variety of datasets. Further, we introduce a parameter to\nevaluate the quality of interpretation under the scenario of distributional\nshift. This parameter evaluates how well the interpretation can predict model\nbehavior for previously unseen data distributions. We show how existing\napproaches for interpreting models globally lack distributional robustness.\nFinally, we show how the quality of the interpretation can be improved under\nthe scenario of distributional shift by adding out of distribution samples to\nthe dataset used to learn the interpretation and thereby, increase robustness.\nAll of the datasets used in our paper are open and publicly available. Our\napproach has been deployed in a leading digital marketing suite of products.\n"}
+{"abstract": "  In this manuscript, working with a binary mechanical system, we examine the\neffect of quantum gravity on the exceptional points of the system. On the one\nside, we find that the exceedingly weak effect of quantum gravity can be sensed\nvia pushing the system towards a second-order exceptional point, where the\nspectra of the non-Hermitian system exhibits non-analytic and even\ndiscontinuous behavior. On the other side, the gravity perturbation will affect\nthe sensitivity of the system to deposition mass. In order to further enhance\nthe sensitivity of the system to quantum gravity, we extend the system to the\nother one which has a higher-order (third-order) exceptional point. Our work\nprovides a feasible way to use exceptional points as a new tool to explore the\neffect of quantum gravity.\n"}
+{"abstract": "  This paper develops a sparsity-inducing version of Bayesian Causal Forests, a\nrecently proposed nonparametric causal regression model that employs Bayesian\nAdditive Regression Trees and is specifically designed to estimate\nheterogeneous treatment effects using observational data. The sparsity-inducing\ncomponent we introduce is motivated by empirical studies where not all the\navailable covariates are relevant, leading to different degrees of sparsity\nunderlying the surfaces of interest in the estimation of individual treatment\neffects. The extended version presented in this work, which we name Shrinkage\nBayesian Causal Forest, is equipped with an additional pair of priors allowing\nthe model to adjust the weight of each covariate through the corresponding\nnumber of splits in the tree ensemble. These priors improve the model's\nadaptability to sparse data generating processes and allow to perform fully\nBayesian feature shrinkage in a framework for treatment effects estimation, and\nthus to uncover the moderating factors driving heterogeneity. In addition, the\nmethod allows prior knowledge about the relevant confounding covariates and the\nrelative magnitude of their impact on the outcome to be incorporated in the\nmodel. We illustrate the performance of our method in simulated studies, in\ncomparison to Bayesian Causal Forest and other state-of-the-art models, to\ndemonstrate how it scales up with an increasing number of covariates and how it\nhandles strongly confounded scenarios. Finally, we also provide an example of\napplication using real-world data.\n"}
+{"abstract": "  3D object detection is a key component of many robotic applications such as\nself-driving vehicles. While many approaches rely on expensive 3D sensors such\nas LiDAR to produce accurate 3D estimates, methods that exploit stereo cameras\nhave recently shown promising results at a lower cost. Existing approaches\ntackle this problem in two steps: first depth estimation from stereo images is\nperformed to produce a pseudo LiDAR point cloud, which is then used as input to\na 3D object detector. However, this approach is suboptimal due to the\nrepresentation mismatch, as the two tasks are optimized in two different metric\nspaces. In this paper we propose a model that unifies these two tasks and\nperforms them in the same metric space. Specifically, we directly construct a\npseudo LiDAR feature volume (PLUME) in 3D space, which is then used to solve\nboth depth estimation and object detection tasks. Our approach achieves\nstate-of-the-art performance with much faster inference times when compared to\nexisting methods on the challenging KITTI benchmark.\n"}
+{"abstract": "  This report documents safety assurance argument templates to support the\ndeployment and operation of autonomous systems that include machine learning\n(ML) components. The document presents example safety argument templates\ncovering: the development of safety requirements, hazard analysis, a safety\nmonitor architecture for an autonomous system including at least one ML\nelement, a component with ML and the adaptation and change of the system over\ntime. The report also presents generic templates for argument defeaters and\nevidence confidence that can be used to strengthen, review, and adapt the\ntemplates as necessary. This report is made available to get feedback on the\napproach and on the templates. This work was sponsored by the UK Dstl under the\nR-cloud framework.\n"}
+{"abstract": "  Integrating robots in complex everyday environments requires a multitude of\nproblems to be solved. One crucial feature among those is to equip robots with\na mechanism for teaching them a new task in an easy and natural way. When\nteaching tasks that involve sequences of different skills, with varying order\nand number of these skills, it is desirable to only demonstrate full task\nexecutions instead of all individual skills. For this purpose, we propose a\nnovel approach that simultaneously learns to segment trajectories into\nreoccurring patterns and the skills to reconstruct these patterns from\nunlabelled demonstrations without further supervision. Moreover, the approach\nlearns a skill conditioning that can be used to understand possible sequences\nof skills, a practical mechanism to be used in, for example,\nhuman-robot-interactions for a more intelligent and adaptive robot behaviour.\nThe Bayesian and variational inference based approach is evaluated on synthetic\nand real human demonstrations with varying complexities and dimensionality,\nshowing the successful learning of segmentations and skill libraries from\nunlabelled data.\n"}
+{"abstract": "  The purpose of this work is to examine the structure of optimal dual fusion\nframes and get more exibility in the use of dual fusion frames for erasures of\nsubspaces. We deal with optimal dual fusion frames with respect to different\ndefinitions of duality and compare the advantages of these approaches. In\naddition, we introduce a new concept so called partial optimal dual which\ninvolves less time and computation for detecting optimal dual for erasures in\nknown locations. Then we study the relationship between local and global\noptimal duals by partial optimal duals which leads to some applicable results.\nIn the sense that, we obtain an overcomplete frame and a family of associated\noptimal duals by a given Riesz fusion basis. We present some examples to\nexhibit the effect of error rate when dual fusion frames are applied in\nreconstruction.\n"}
+{"abstract": "  By controlling quantum fluctuations via the Falk-Bruch inequality we give the\nfirst rigorous argument for the existence of a spin-glass phase in the quantum\nSherrington-Kirkpatrick model with a transverse magnetic field if the\ntemperature and the field are sufficiently low. The argument also applies to\nthe generalization of the model with multi-spin interactions, sometimes dubbed\nas transverse $p$-spin model.\n"}
+{"abstract": "  This paper presents a generic method for generating full facial 3D animation\nfrom speech. Existing approaches to audio-driven facial animation exhibit\nuncanny or static upper face animation, fail to produce accurate and plausible\nco-articulation or rely on person-specific models that limit their scalability.\nTo improve upon existing models, we propose a generic audio-driven facial\nanimation approach that achieves highly realistic motion synthesis results for\nthe entire face. At the core of our approach is a categorical latent space for\nfacial animation that disentangles audio-correlated and audio-uncorrelated\ninformation based on a novel cross-modality loss. Our approach ensures highly\naccurate lip motion, while also synthesizing plausible animation of the parts\nof the face that are uncorrelated to the audio signal, such as eye blinks and\neye brow motion. We demonstrate that our approach outperforms several baselines\nand obtains state-of-the-art quality both qualitatively and quantitatively. A\nperceptual user study demonstrates that our approach is deemed more realistic\nthan the current state-of-the-art in over 75% of cases. We recommend watching\nthe supplemental video before reading the paper:\nhttps://research.fb.com/wp-content/uploads/2021/04/mesh_talk.mp4\n"}
+{"abstract": "  Surfaces inspired by nature and their replication find great interest in\nscience, technology, and medicine due to their unique functional properties.\nThis research aimed to develop an efficient laser milling technology using\nsingle-pulse- and burst-modes of irradiation to replicate bio-inspired\nstructures over large areas at high speed. The ability to form the\ntrapezoidal-riblets inspired by shark skin at high production speeds while\nmaintaining the lowest possible surface roughness was demonstrated.\n"}
+{"abstract": "  Let $\\alpha>0$, $\\beta>\\alpha$, and let $X_1,\\ldots, X_q$ be\n$\\mathscr{C}^{\\alpha}_{\\mathrm{loc}}$ vector fields on a\n$\\mathscr{C}^{\\alpha+1}$ manifold which span the tangent space at every point,\nwhere $\\mathscr{C}^{s}$ denotes the Zygmund-H\\\"older space of order $s$. We\ngive necessary and sufficient conditions for when there is a\n$\\mathscr{C}^{\\beta+1}$ structure on the manifold, compatible with its\n$\\mathscr{C}^{\\alpha+1}$ structure, with respect to which $X_1,\\ldots, X_q$ are\n$\\mathscr{C}^{\\beta}_{\\mathrm{loc}}$. This strengthens previous results of the\nfirst author which dealt with the setting $\\alpha>1$, $\\beta>\\max\\{ \\alpha,\n2\\}$.\n"}
+{"abstract": "  We study scaling behavior of the disorder parameter, defined as the\nexpectation value of a symmetry transformation applied to a finite region, at\nthe deconfined quantum critical point in (2+1)$d$ in the $J$-$Q_3$ model via\nlarge-scale quantum Monte Carlo simulations. We show that the disorder\nparameter for U(1) spin rotation symmetry exhibits perimeter scaling with a\nlogarithmic correction associated with sharp corners of the region, as\ngenerally expected for a conformally-invariant critical point. However, for\nlarge rotation angle the universal coefficient of the logarithmic corner\ncorrection becomes negative, which is not allowed in any unitary conformal\nfield theory. We also extract the current central charge from the small\nrotation angle scaling, whose value is much smaller than that of the free\ntheory.\n"}
+{"abstract": "  We study an inverse problem for the fractional wave equation with a potential\nby the measurement taking on arbitrary subsets of the exterior in the\nspace-time domain. We are interested in the issues of uniqueness and stability\nestimate in the determination of the potential by the exterior\nDirichlet-to-Neumann map. The main tools are the qualitative and quantitative\nunique continuation properties for the fractional Laplacian. For the stability,\nwe also prove that the log type stability estimate is optimal. The log type\nestimate shows the striking difference between the inverse problems for the\nfractional and classical wave equations in the stability issue. The results\nhold for any spatial dimension $n\\in \\N$.\n"}
+{"abstract": "  Wearable data is a rich source of information that can provide deeper\nunderstanding of links between human behaviours and human health. Existing\nmodelling approaches use wearable data summarized at subject level via scalar\nsummaries using regression techniques, temporal (time-of-day) curves using\nfunctional data analysis (FDA), and distributions using distributional data\nanalysis (DDA). We propose to capture temporally local distributional\ninformation in wearable data using subject-specific time-by-distribution (TD)\ndata objects. Specifically, we propose scalar on time-by-distribution\nregression (SOTDR) to model associations between scalar response of interest\nsuch as health outcomes or disease status and TD predictors. We show that TD\ndata objects can be parsimoniously represented via a collection of time-varying\nL-moments that capture distributional changes over the time-of-day. The\nproposed method is applied to the accelerometry study of mild Alzheimer's\ndisease (AD). Mild AD is found to be significantly associated with reduced\nmaximal level of physical activity, particularly during morning hours. It is\nalso demonstrated that TD predictors attain much stronger associations with\nclinical cognitive scales of attention, verbal memory, and executive function\nwhen compared to predictors summarized via scalar total activity counts,\ntemporal functional curves, and quantile functions. Taken together, the present\nresults suggest that the SOTDR analysis provides novel insights into cognitive\nfunction and AD.\n"}
+{"abstract": "  When spin polarised electrons flow through a magnetic texture a transfer\ntorque is generated. We examine the effect of this torque on skyrmions and\nskyrmion bags, skyrmionic structures of arbitrary integer topological degree,\nin thin ferromagnetic films. Using micromagnetic simulations and analysis from\nthe well known Thiele equation we explore the potential for sorting or binning\nskyrmions of varying degrees mechanically. We investigate the applicability of\nthe Thiele equation to problems of this nature and derive a theory of skyrmion\ndeflection ordered by topological degree.\n"}
+{"abstract": "  A key feature of intelligent behaviour is the ability to learn abstract\nstrategies that scale and transfer to unfamiliar problems. An abstract strategy\nsolves every sample from a problem class, no matter its representation or\ncomplexity -- like algorithms in computer science. Neural networks are powerful\nmodels for processing sensory data, discovering hidden patterns, and learning\ncomplex functions, but they struggle to learn such iterative, sequential or\nhierarchical algorithmic strategies. Extending neural networks with external\nmemories has increased their capacities in learning such strategies, but they\nare still prone to data variations, struggle to learn scalable and transferable\nsolutions, and require massive training data. We present the Neural Harvard\nComputer (NHC), a memory-augmented network based architecture, that employs\nabstraction by decoupling algorithmic operations from data manipulations,\nrealized by splitting the information flow and separated modules. This\nabstraction mechanism and evolutionary training enable the learning of robust\nand scalable algorithmic solutions. On a diverse set of 11 algorithms with\nvarying complexities, we show that the NHC reliably learns algorithmic\nsolutions with strong generalization and abstraction: perfect generalization\nand scaling to arbitrary task configurations and complexities far beyond seen\nduring training, and being independent of the data representation and the task\ndomain.\n"}
+{"abstract": "  Understanding the origin of life-essential volatiles like N in the Solar\nSystem and beyond is critical to evaluate the potential habitability of rocky\nplanets. Whether the inner Solar System planets accreted these volatiles from\ntheir inception or had an exogenous delivery from the outer Solar System is,\nhowever, not well understood. Using previously published data of\nnucleosynthetic anomalies of Ni, Mo, W and Ru in iron meteorites along with\ntheir 15N-14N ratios, here we show that the earliest formed protoplanets in the\ninner and outer protoplanetary disk accreted isotopically distinct N. While the\nSun and Jupiter captured N from nebular gas, concomitantly growing protoplanets\nin the inner and outer disk possibly sourced their N from organics and/or dust\n- with each reservoir having a different N isotopic composition. A distinct N\nisotopic signature of the inner Solar System protoplanets coupled with their\nrapid accretion suggests that non-nebular, isotopically processed N was\nubiquitous in their growth zone at 0-0.3 Myr after the formation of CAIs.\nBecause 15N-14N ratio of the bulk silicate Earth falls between that of inner\nand outer Solar System reservoirs, we infer that N in the present-day rocky\nplanets represents a mixture of both inner and outer Solar System material.\n"}
+{"abstract": "  A Hilbert point in $H^p(\\mathbb{T}^d)$, for $d\\geq1$ and $1\\leq p \\leq\n\\infty$, is a nontrivial function $\\varphi$ in $H^p(\\mathbb{T}^d)$ such that\n$\\| \\varphi \\|_{H^p(\\mathbb{T}^d)} \\leq \\|\\varphi + f\\|_{H^p(\\mathbb{T}^d)}$\nwhenever $f$ is in $H^p(\\mathbb{T}^d)$ and orthogonal to $\\varphi$ in the usual\n$L^2$ sense. When $p\\neq 2$, $\\varphi$ is a Hilbert point in $H^p(\\mathbb{T})$\nif and only if $\\varphi$ is a nonzero multiple of an inner function. An inner\nfunction on $\\mathbb{T}^d$ is a Hilbert point in any of the spaces\n$H^p(\\mathbb{T}^d)$, but there are other Hilbert points as well when $d\\geq 2$.\nWe investigate the case of $1$-homogeneous polynomials in depth and obtain as a\nbyproduct a new proof of the sharp Khintchin inequality for Steinhaus variables\nin the range $2<p<\\infty$. We also study briefly the dynamics of a certain\nnonlinear projection operator that characterizes Hilbert points as its fixed\npoints. We exhibit an example of a function $\\varphi$ that is a Hilbert point\nin $H^p(\\mathbb{T}^3)$ for $p=2, 4$, but not for any other $p$; this is\nverified rigorously for $p>4$ but only numerically for $1\\leq p<4$.\n"}
+{"abstract": "  To reduce Operation and Maintenance (O&M) costs on offshore wind farms,\nwherein 80% of the O&M cost relates to deploying personnel, the offshore wind\nsector looks to Robotics and Artificial Intelligence (RAI) for solutions.\nBarriers to Beyond Visual Line of Sight (BVLOS) robotics include operational\nsafety compliance and resilience, inhibiting the commercialization of\nautonomous services offshore. To address safety and resilience challenges we\npropose a Symbiotic System Of Systems Approach (SSOSA), reflecting the\nlifecycle learning and co-evolution with knowledge sharing for mutual gain of\nrobotic platforms and remote human operators. Our novel methodology enables the\nrun-time verification of safety, reliability and resilience during autonomous\nmissions. To achieve this, a Symbiotic Digital Architecture (SDA) was developed\nto synchronize digital models of the robot, environment, infrastructure, and\nintegrate front-end analytics and bidirectional communication for autonomous\nadaptive mission planning and situation reporting to a remote operator. A\nreliability ontology for the deployed robot, based on our holistic\nhierarchical-relational model, supports computationally efficient platform data\nanalysis. We demonstrate an asset inspection mission within a confined space\nthrough Cooperative, Collaborative and Corroborative (C3) governance (internal\nand external symbiosis) via decision-making processes and the associated\nstructures. We create a hyper enabled human interaction capability to analyze\nthe mission status, diagnostics of critical sub-systems within the robot to\nprovide automatic updates to our AI-driven run-time reliability ontology. This\nenables faults to be translated into failure modes for decision-making during\nthe mission.\n"}
+{"abstract": "  The low temperature thermodynamics of correlated 1D fermionic models with\nspin and charge degrees of freedom is obtained by exact diagonalization (ED) of\nsmall systems and followed by density matrix renormalization group (DMRG)\ncalculations that target the lowest hundreds of states $\\{E(N)\\}$ at system\nsize $N$ instead of the ground state. Progressively larger $N$ reaches $T <\n0.05t$ in correlated models with electron transfer $t$ between first neighbors\nand bandwidth $4t$. The size dependence of the many-fermion basis is explicitly\nincluded for arbitrary interactions by scaling the partition function. The\nremaining size dependence is then entirely due to the energy spectrum\n$\\{E(N)\\}$ of the model. The ED/DMRG method is applied to Hubbard and extended\nHubbard models, both gapped and gapless, with $N_e = N$ or $N/2$ electrons and\nis validated against exact results for the magnetic susceptibility $\\chi(T)$\nand entropy $S(T)$ per site. Some limitations of the method are noted. Special\nattention is given to the bond-order-wave phase of the extended Hubbard model\nwith competing interactions and low $T$ thermodynamics sensitive to small gaps.\n"}
+{"abstract": "  Confining dark sectors at the GeV scale can lead to novel collider signatures\nincluding those termed emerging jets with large numbers of displaced vertices.\nThe triggers at the LHC experiments were not designed with this type of new\nphysics in mind, and triggering can be challenging, especially if the mediator\nis relatively light and/or has quantum numbers such that additional jets are\nnot automatically produced in each event. We show that the efficiency and the\ntotal event rate at current triggers can be significantly improved by\nconsidering initial state radiation of the events, with the largest increase in\nrate coming from simulation of two additional jets. We also explore possible\nnew triggers that employ hit counts in different tracker layers as input into a\nmachine learning algorithm. We show that these new triggers can have reasonably\nlow background rates, and that they are sensitive to a wide range of new\nphysics parameters even when trained on a single model.\n"}
+{"abstract": "  Sleep deprivation is a public health concern that significantly impacts one's\nwell-being and performance. Sleep is an intimate experience, and\nstate-of-the-art sleep monitoring solutions are highly-personalized to\nindividual users. With a motivation to expand sleep monitoring at a large-scale\nand contribute sleep data to public health understanding, we present WiSleep, a\nsleep monitoring and analytics platform using smartphone network connections\nthat are passively sensed from WiFi infrastructure. We propose an unsupervised\nensemble model of Bayesian change point detection to predict sleep and wake-up\ntimes. Then, we validate our approach using ground truth from a user study in\ncampus dormitories and a private home. Our results find WiSleep outperforming\nestablished methods for users with irregular sleep patterns while yielding\ncomparable accuracy for regular sleepers with an average 79.5\\% accuracy. This\nis comparable to client-side based methods, albeit utilizing only\ncoarse-grained information. Finally, we show that WiSleep can process data from\n20,000 users on a single commodity server, allowing it to scale to large campus\npopulations with low server requirements.\n"}
+{"abstract": "  A systematic study of the compression creep properties of a\nsingle-crystalline Co-base superalloy (Co-9Al-7.5W-2Ta) was conducted at 950\n{\\deg}C, 975 {\\deg}C and 1000 {\\deg}C to reveal the influence of temperature\nand the resulting diffusion velocity of solutes like Al, W and Ta on the\ndeformation mechanisms. Two creep rate minima are observed at all temperatures\nindicating that the deformation mechanisms causing these minima are quite\nsimilar. Atom-probe tomography analysis reveals elemental segregation to\nstacking faults, which had formed in the $\\gamma\\prime$ phase during creep.\nDensity-functional-theory calculations indicate segregation of W and Ta to the\nstacking fault and an associated considerable reduction of the stacking fault\nenergy. Since solutes diffuse faster at a higher temperature, segregation can\ntake place more quickly. This results in a significantly faster softening of\nthe alloy, since cutting of the $\\gamma\\prime$ precipitate phase by partial\ndislocations is facilitated through segregation already during the early stages\nof creep. This is confirmed by transmission electron microscopy analysis.\nTherefore, not only the smaller precipitate fraction at higher temperatures is\nresponsible for the worse creep properties, but also faster diffusion-assisted\nshearing of the $\\gamma\\prime$ phase by partial dislocations. The understanding\nof these mechanisms will help in future alloy development by offering new\ndesign criteria.\n"}
+{"abstract": "  In the fields of statistics and unsupervised machine learning a fundamental\nand well-studied problem is anomaly detection. Although anomalies are difficult\nto define, many algorithms have been proposed. Underlying the approaches is the\nnebulous understanding that anomalies are rare, unusual or inconsistent with\nthe majority of data. The present work gives a philosophical approach to\nclearly define anomalies and to develop an algorithm for their efficient\ndetection with minimal user intervention. Inspired by the Gestalt School of\nPsychology and the Helmholtz principle of human perception, the idea is to\nassume anomalies are observations that are unexpected to occur with respect to\ncertain groupings made by the majority of the data. Thus, under appropriate\nrandom variable modelling anomalies are directly found in a set of data under a\nuniform and independent random assumption of the distribution of constituent\nelements of the observations; anomalies correspond to those observations where\nthe expectation of occurrence of the elements in a given view is $<1$. Starting\nfrom fundamental principles of human perception an unsupervised anomaly\ndetection algorithm is developed that is simple, real-time and parameter-free.\nExperiments suggest it as the prime choice for univariate data and it shows\npromising performance on the detection of global anomalies in multivariate\ndata.\n"}
+{"abstract": "  Bhargava, Hanke, and Shankar have recently shown that the asymptotic average\n$2$-torsion subgroup size of the family of class groups of monogenized cubic\nfields with positive and negative discriminants is $3/2$ and $2$, respectively.\nIn this paper, we provide strong computational evidence for these asymptotes.\nWe then develop a pair of novel conjectures that predicts, for $p$ prime, the\nasymptotic average $p$-torsion subgroup size in class groups of monogenized\ncubic fields.\n"}
+{"abstract": "  Purpose: We propose a general framework for quantifying predictive\nuncertainties of dose-related quantities and leveraging this information in a\ndose mimicking problem in the context of automated radiation therapy treatment\nplanning.\n  Methods: A three-step pipeline, comprising feature extraction, dose statistic\nprediction and dose mimicking, is employed. In particular, the features are\nproduced by a convolutional variational autoencoder and used as inputs in a\npreviously developed nonparametric Bayesian statistical method, estimating the\nmultivariate predictive distribution of a collection of predefined dose\nstatistics. Specially developed objective functions are then used to construct\na probabilistic dose mimicking problem based on the produced distributions,\ncreating deliverable treatment plans.\n  Results: The numerical experiments are performed using a dataset of 94\nretrospective treatment plans of prostate cancer patients. We show that the\nfeatures extracted by the variational autoencoder capture geometric information\nof substantial relevance to the dose statistic prediction problem and are\nrelated to dose statistics in a more regularized fashion than hand-crafted\nfeatures. The estimated predictive distributions are reasonable and outperforms\na non-input-dependent benchmark method, and the deliverable plans produced by\nthe probabilistic dose mimicking agree better with their clinical counterparts\nthan for a non-probabilistic formulation.\n  Conclusions: We demonstrate that prediction of dose-related quantities may be\nextended to include uncertainty estimation and that such probabilistic\ninformation may be leveraged in a dose mimicking problem. The treatment plans\nproduced by the proposed pipeline resemble their original counterparts well,\nillustrating the merits of a holistic approach to automated planning based on\nprobabilistic modeling.\n"}
+{"abstract": "  Let $T_D$ denote the first exit time of a Brownian motion from a domain $D$\nin ${\\mathbb R}^n$. Given domains $U,W \\subseteq {\\mathbb R}^n$ containing the\norigin, we investigate the cases in which we are more likely to have fast exits\nfrom $U$ than $W$, meaning ${\\bf P}(T_U<t) > {\\bf P}(T_W<t)$ for $t$ small. We\nshow that the primary factor in the probability of fast exits from domains is\nthe proximity of the closest regular part of the boundary to the origin. We\nalso prove a result on the complementary question of longs stays, meaning ${\\bf\nP}(T_U>t) > {\\bf P}(T_W>t)$ for $t$ large. This result, which applies only in\ntwo dimensions, shows that the unit disk has the lowest probability of long\nstays amongst all Schlicht domains.\n"}
+{"abstract": "  The classification problem for simple $\\mathfrak{sl}(2)$-modules leads in a\nnatural way to the study of the category of finite rank torsion free\n$\\mathfrak{sl}(2)$-modules and its subcategory of rational\n$\\mathfrak{sl}(2)$-modules. We prove that the rationalization functor induces\nan identification between the isomorphism classes of simple modules of these\ncategories. This raises the question of what is the precise relationship\nbetween other invariants associated with them. We give a complete solution to\nthis problem for the Grothendieck and Picard groups, obtaining along theway\nseveral new results regarding these categories that are interesting in their\nown right.\n"}
+{"abstract": "  We study the evolutionary and physical properties of evolved O stars in the\nSmall Magellanic Cloud (SMC), with a special focus on their surface abundances\nto investigate the efficiency of rotational mixing as a function of age,\nrotation and global metallicity. We analyse the UV + optical spectra of\nthirteen SMC O-type giants and supergiants, using the stellar atmosphere code\nCMFGEN to derive photospheric and wind properties. We compare the inferred\nproperties to theoretical predictions from evolution models. For a more\ncomprehensive analysis, we interpret the results together with those we\nobtained for O-type dwarfs in a former study. Most dwarfs lie in the early\nphases of the main-sequence. For a given initial mass, giants are farther along\nthe evolutionary tracks, confirming that they are more evolved than dwarfs.\nSupergiants have higher initial masses and are located past the terminal age\nmain-sequence. We find no clear trend of a mass discrepancy, regardless of the\ndiagram that was used to estimate the evolutionary mass. CNO abundances are\nconsistent with nucleosynthesis from the CNO cycle. Comparisons to theoretical\npredictions reveal that the initial mixture is important when the observed\ntrends in the N/C versus N/O diagram are to be reproduced. A trend for stronger\nchemical evolution for more evolved objects is observed. More massive stars,\nare on average, more chemically enriched at a given evolutionary phase,\nqualitatively consistent with evolutionary models. Abundance ratios supports\nthe theoretical prediction that massive stars at low metallicity are more\nchemically processed than their Galactic counterparts. Finally, models\nincluding rotation generally reproduce the surface abundances and rotation\nrates when different initial rotational velocities are considered.\nNevertheless, there are objects for which a stronger braking and/or more\nefficient mixing is required.\n"}
+{"abstract": "  The moir\\'e pattern observed in stacked non-commensurate crystal lattices,\nsuch as hetero-bilayers of transition metal dichalcogenides, produces a\nperiodic modulation of their bandgap. Excitons subjected to this potential\nlandscape exhibit a band structure that gives rise to a quasi-particle dubbed\nmoir\\'e exciton. In the case of MoS$_2$/WSe$_2$ hetero-bilayers, the moir\\'e\ntrapping potential has honeycomb symmetry and, consequently, the moir\\'e\nexciton band structure is the same as that of a Dirac-Weyl fermion, whose mass\ncan be further tuned down to zero with a perpendicularly applied field. Here we\nshow that, analogously to other Dirac-like particles, moir\\'e exciton exhibits\na trembling motion, also known as zitterbewegung, whose long timescales are\ncompatible with current experimental techniques for exciton dynamics. This\npromotes the study of the dynamics of moir\\'e excitons in van der Waals\nheterostructures as an advantageous solid-state platform to probe\nzitterbewegung, broadly tunable by gating and inter-layer twist angle.\n"}
+{"abstract": "  Gluons are strong interaction gauge fields which interact between quarks,\ni.e. constituents of baryons and mesons. Interaction of matters is\nphenomenologically described by gauge theory of strong, electromagnetic, weak\nand gravitational interactions. In electro-weak theory, left handed leptons\n${l}_L$ and neutrino ${\\mathcal \\nu}_L$, right handed leptons ${l}_R$ and left\nhanded quarks $u_L, d_L$ and right handed quarks $u_R, d_R$ follow $SU(2)\\times\nU(1)$ symmetry. Charge of leptons and quarks define hypercharge $Y$, and via\nHiggs mechanism $SU(2)_L\\times U(1)_Y$ symmetry forms $U(1)_{em}$ symmetry. In\norder to describe Hadron dynamics properly, embedding of 4-dimensional space to\n5-dimensional space was tried in lattice simulations, and in light front\nholographic QCD (LFHQCD) approach in which conformally symmetric light-front\ndynamics without ghost are embedded in $AdS_5$, and a parameter that fixes a\nmass scale was chosen from the Principle of Maximum Conformality. Coulomb or\nLandau gauge fixed Faddeev-Popov Yang-Mills field equation is known to have the\nGribov ambiguity, and tunneling between vacua between different topological\nstructures was proposed by van Baal and collaborators. The symmetry of three\ncolors can be assigned three vectors of quaternion ${\\bf H}$, whose\nmultiplication on $2\\times 2$ matrices of Dirac spinors on ${\\bf S}^3$ induces\ntransformations. Instantons or sphalerons whose presence is expected from\nconformal equivalence of ${\\bf S}^3\\times {\\bf R}$ to ${\\bf R}^4$ are reviewed.\nAn extension of the dynamics embedded in complex projective space is also\ndiscussed.\n"}
+{"abstract": "  Controlling the perpendicular magnetic anisotropy (PMA) in thin films has\nreceived considerable attention in recent years due to its technological\nimportance. PMA based devices usually involve heavy-metal\n(oxide)/ferromagnetic-metal bilayers, where, thanks to interfacial spin-orbit\ncoupling (SOC), the in-plane (IP) stability of the magnetization is broken.\nHere we show that in V/MgO/Fe(001) epitaxial junctions with competing in-plane\nand out-of-plane (OOP) magnetic anisotropies, the SOC mediated interaction\nbetween a ferromagnet (FM) and a superconductor (SC) enhances the effective PMA\nbelow the superconducting transition. This produces a partial magnetization\nreorientation without any applied field for all but the largest junctions,\nwhere the IP anisotropy is more robust; for the smallest junctions there is a\nreduction of the field required to induce a complete OOP transition\n($H_\\text{OOP}$) due to the stronger competition between the IP and OOP\nanisotropies. Our results suggest that the degree of effective PMA could be\ncontrolled by the junction lateral size in the presence of superconductivity\nand an applied electric field. We also discuss how the $H_\\text{OOP}$ field\ncould be affected by the interaction between magnetic stray fields and\nsuperconducting vortices. Our experimental findings, supported by numerical\nmodelling of the ferromagnet-superconductor interaction, open pathways to\nactive control of magnetic anisotropy in the emerging dissipation-free\nsuperconducting spin electronics.\n"}
+{"abstract": "  Three-body decay is a rare decay mode observed in a handful of unbound rare\nisotopes. The angular and energy correlations between emitted nucleons are of\nparticular interest, as they provide invaluable information on the interplay\nbetween structure and reaction aspects of the nuclear open quantum system. To\nstudy the mechanism of two-nucleon emission, we developed a time-dependent\napproach that allows us to probe emitted nucleons at long times and large\ndistances. We successfully benchmarked the new method against the Green's\nfunction approach and applied it to low-energy two-proton and two-neutron\ndecays. In particular, we studied the interplay between initial-state\nnucleon-nucleon correlations and final-state interaction. We demonstrated that\nthe time evolution of the two-nucleon wave function is strongly impacted by the\ndiproton/dineutron dynamics and that the correlations between emitted nucleons\nprovide invaluable information on the dinucleon structure in the initial-state.\n"}
+{"abstract": "  Responding rapidly to a patient who is demonstrating signs of imminent\nclinical deterioration is a basic tenet of patient care. This gave rise to a\npatient safety intervention philosophy known as a Rapid Response System (RRS),\nwhereby a patient who meets a pre-determined set of criteria for imminent\nclinical deterioration is immediately assessed and treated, with the goal of\nmitigating the deterioration and preventing intensive care unit (ICU) transfer,\ncardiac arrest, or death. While RRSs have been widely adopted, multiple\nsystematic reviews have failed to find evidence of their effectiveness.\nTypically, RRS criteria are simple, expert (consensus) defined rules that\nidentify significant physiologic abnormalities or are based on clinical\nobservation.\n  If one can find a pattern in the patient's data earlier than the onset of the\nphysiologic derangement manifest in the current criteria, intervention\nstrategies might be more effective. In this paper, we apply machine learning to\nelectronic medical records (EMR) to infer if patients are at risk for clinical\ndeterioration. Our models are more sensitive and offer greater advance\nprediction time compared with existing rule-based methods that are currently\nutilized in hospitals. Our results warrant further testing in the field; if\nsuccessful, hospitals can integrate our approach into their existing IT systems\nand use the alerts generated by the model to prevent ICU transfer, cardiac\narrest, or death, or to reduce the ICU length of stay.\n"}
+{"abstract": "  Flows through porous media can carry suspended and dissolved materials. These\nsediments may deposit inside the pore-space and alter its geometry. In turn,\nthe changing pore structure modifies the preferential flow paths, resulting in\na strong coupling between structural modifications and transport\ncharacteristics. Here, we compare two different models that lead to channel\nobstruction as a result of subsequent deposition. The first model randomly\nobstructs pore-throats across the porous medium, while in the second model the\npore-throat with the highest flow rate is always obstructed first. By\nsubsequently closing pores, we find that the breakdown of the permeability\nfollows a power-law scaling, whose exponent depends on the obstruction model.\nThe pressure jumps that occur during the obstruction process also follow a\npower-law distribution with the same universal scaling exponent as the\navalanche size distribution of invasion percolation, independent of the model.\nThis result suggests that the clogging processes and invasion percolation may\nbelong to the same universality class.\n"}
+{"abstract": "  The search for novel magnetic quantum phases, phenomena and functional\nmaterials has been guided by relativistic magnetic-symmetry groups in coupled\nspin and real space from the dawn of the field in 1950s to the modern era of\ntopological matter. However, the magnetic groups cannot disentangle\nnon-relativistic phases and effects, such as the recently reported\nunconventional spin physics in collinear antiferromagnets from the typically\nweak relativistic spin-orbit coupling phenomena. Here we discover that more\ngeneral spin symmetries in decoupled spin and crystal space categorize\nnon-relativistic collinear magnetism in three phases: conventional ferromagnets\nand antiferromagnets, and a third distinct phase combining zero net\nmagnetization with an alternating spin-momentum locking in energy bands, which\nwe dub \"altermagnetic\". For this third basic magnetic phase, which is omitted\nby the relativistic magnetic groups, we develop a spin-group theory describing\nsix characteristic types of the altermagnetic spin-momentum locking. We\ndemonstrate an extraordinary spin-splitting mechanism in altermagnetic bands\noriginating from a local electric crystal field, which contrasts with the\nconventional magnetic or relativistic splitting by global magnetization or\ninversion asymmetry. Based on first-principles calculations, we identify\naltermagnetic candidates ranging from insulators and metals to a parent crystal\nof cuprate superconductor. Our results underpin emerging research of quantum\nphases and spintronics in high-temperature magnets with light elements,\nvanishing net magnetization, and strong spin-coherence.\n"}
+{"abstract": "  We consider the derivative NLS equation in one spatial dimension, which is\nknown to be completely integrable. We prove that the orbits of $L^2$ bounded\nand equicontinuous sets of initial data remain bounded and equicontinuous, not\nonly under this flow, but under the entire hierarchy. This allows us to remove\nthe small-data restriction from prior conservation laws and global\nwell-posedness results.\n"}
+{"abstract": "  It is shown that if the dual of a Banach space satisfies Property($K^*$) then\n$R(X) \\leq 1 + \\frac{\\displaystyle 1}{\\displaystyle 1 + r_{X^*}(1)} < 2$ where\n$r_{X^*}(c)$ is Opial's modulus for $X^*.$ Thus $X$ has the weak fixed point\nproperty.\n"}
+{"abstract": "  In this paper we propose the PCP-like theorem for sub-linear time\ninapproximability. Abboud et al. have devised the distributed PCP framework for\nproving sub-quadratic time inapproximability. Here we try to go further in this\ndirection. Staring from SETH, we first find a problem denoted as Ext-$k$-SAT,\nwhich can not be computed in linear time, then devise an efficient MA-like\nprotocol for this problem. To use this protocol to prove the sub-linear time\ninapproximability of other problems, we devise a new kind of reduction denoted\nas Ext-reduction, and it is different from existing reduction techniques. We\nalso define two new hardness class, the problems in which can be computed in\nlinear-time, but can not be efficiently approximated in sub-linear time. Some\nproblems are shown to be in the newly defined hardness class.\n"}
+{"abstract": "  With the evolution of the concept of Speaker diarization using LSTM, it is\nrelatively easier to understand the speaker identities for specific segments of\ninput audio stream data than manually tagging the data. With such a concept, it\nis highly desirable to consider the possibility of using the identified speaker\nidentities to aid in recognizing the Speaker States in a conversation. In this\nstudy, the Markov Chains are used to identify and update the Speaker States for\nthe next conversations between the same set of speakers, to enable\nidentification of their states in the most natural and long conversations. The\nmodel is based on several audio samples from natural conversations of three or\ngreater than three speakers in two datasets with overall total error\npercentages for recognized states being lesser than or equal to 12%. The\nfindings imply that the proposed extension to the Speaker diarization is\neffective to predict the states for a conversation.\n"}
+{"abstract": "  High-fidelity parametric gates have been demonstrated with superconducting\nqubits via rf flux modulation of the qubit frequency. The modulation however\nleads to renormalization of the bare qubit-qubit coupling, thereby reducing the\ngate speed. Here, we realize a parametric-resonance gate, which is activated by\nbringing the average frequency of the modulated qubit in resonance with a\nstatic-frequency qubit while approximately retaining the bare qubit-qubit\ncoupling. The activation of parametric-resonance gates does not depend on the\nfrequency of modulation, allowing us to choose the modulation frequencies and\navoid frequency collisions. Moreover, we show that this approach is compatible\nwith tunable coupler architectures, which reduce always-on residual couplings.\nUsing these techniques, we demonstrate iSWAP and CZ gates between two qubits\ncoupled via a tunable coupler with average process fidelities as high as\n$99.3\\%$ and $97.9\\%$, respectively. The flexibility in activating\nparametric-resonance gates combined with a tunable coupler architecture\nprovides a pathway for building large-scale quantum computers.\n"}
+{"abstract": "  In Ni80Fe20/Au/Co/Au deposited on silicon substrate the interaction between\nspin waves and surface acoustic waves is observed by Brillion light scattering\nspectroscopy. We show that by changing the number of receptions in the\nmultilayer we can tune the dispersion of surface acoustic waves (SAWs) not\naffecting significantly fundamentals spin wave (SW) mode. As a result, we can\ncontrol (activate or deactivate) the magnetoelastic interaction between\nfundamental SW mode and SAWs by the thickness of the magnetostrictive\nmultilayer.\n"}
+{"abstract": "  In the last three decades, powerful computer-assisted techniques have been\ndeveloped in order to validate a posteriori numerical solutions of semilinear\nelliptic problems of the form $\\Delta u +f(u,\\nabla u) = 0$. By studying a well\nchosen fixed point problem defined around the numerical solution, these\ntechniques make it possible to prove the existence of a solution in an explicit\n(and usually small) neighborhood the numerical solution. In this work, we\ndevelop a similar approach for a broader class of systems, including nonlinear\ndiffusion terms of the form $\\Delta \\Phi(u)$. In particular, this enables us to\nobtain new results about steady states of a cross-diffusion system from\npopulation dynamics: the (non-triangular) SKT model. We also revisit the idea\nof automatic differentiation in the context of computer-assisted proof, and\npropose an alternative approach based on differential-algebraic equations.\n"}
+{"abstract": "  We prove D.Gaiotto's conjecture about geometric Satake equivalence for\nquantum supergroup\n  $U_q({\\mathfrak{gl}}(N-1|N))$ for generic $q$. The equivalence goes through\nthe category of factorizable\n  sheaves.\n"}
+{"abstract": "  Conferences are a mainstay of most scientific disciplines, where scientists\nof all career stages come together to share cutting-edge ideas and approaches.\nIf you do research, chances are you will attend one or more of these meetings\nin your career. Conferences are a microcosm of their discipline, and while\nconferences offer different perspectives in different disciplines, they all\noffer experiences that range from a casual chat waiting in line for coffee to\nwatching someone present their groundbreaking, hot-off-the-press research. The\nauthors of this piece have attended our fair share of conferences and have\ncollectively mentored hundreds of students in understanding the \"unwritten\nrules\" and pro-tips of conference attendance. As you head to your first\nscientific conference, these rules will help you navigate the conference\nenvironment and make the most of your experience.\n  We have also developed a web portal which contains far more information about\nthese rules, tables of professional societies and conferences in different\ndisciplines, and other resources that may come in handy for first-time\nconference attendees and their mentors. We encourage any reader to use, adapt,\nand contribute to these materials.\n"}
+{"abstract": "  The spectral form factor (SFF), characterizing statistics of energy\neigenvalues, is a key diagnostic of many-body quantum chaos. In addition,\npartial spectral form factors (pSFFs) can be defined which refer to subsystems\nof the many-body system. They provide unique insights into energy eigenstate\nstatistics of many-body systems, as we show in an analysis on the basis of\nrandom matrix theory and of the eigenstate thermalization hypothesis. We\npropose a protocol which allows the measurement of SFF and pSFFs in quantum\nmany-body spin models, within the framework of randomized measurements. Aimed\nto probe dynamical properties of quantum many-body systems, our scheme employs\nstatistical correlations of local random operations which are applied at\ndifferent times in a single experiment. Our protocol provides a unified testbed\nto probe many-body quantum chaotic behavior, thermalization and many-body\nlocalization in closed quantum systems which we illustrate with simulations for\nHamiltonian and Floquet many-body spin-systems.\n"}
+{"abstract": "  Feature attribution is often loosely presented as the process of selecting a\nsubset of relevant features as a rationale of a prediction. Task-dependent by\nnature, precise definitions of \"relevance\" encountered in the literature are\nhowever not always consistent. This lack of clarity stems from the fact that we\nusually do not have access to any notion of ground-truth attribution and from a\nmore general debate on what good interpretations are. In this paper we propose\nto formalise feature selection/attribution based on the concept of relaxed\nfunctional dependence. In particular, we extend our notions to the\ninstance-wise setting and derive necessary properties for candidate selection\nsolutions, while leaving room for task-dependence. By computing ground-truth\nattributions on synthetic datasets, we evaluate many state-of-the-art\nattribution methods and show that, even when optimised, some fail to verify the\nproposed properties and provide wrong solutions.\n"}
+{"abstract": "  Virtualization of computing and networking, IT-OT convergence, cybersecurity\nand AI-based enhancement of autonomy are significantly increasing the\ncomplexity of CPS and CPSoS. New challenges have emerged to demonstrate that\nthese systems are safe and secure. We emphasize the role of control and\nemerging fields therein, like symbolic control or set-based fault-tolerant and\ndecentralized control, to address safety. We have chosen three open\nverification problems we deem central in cost-effective development and\ncertification of safety critical CPSoS. We review some promising threads of\nresearch that could lead in the long term to a scalable and powerful\nverification strategy. Its main components are set-based and invariant-based\ndesign, contracts, adversarial testing, algorithmic geometry of dynamics, and\nprobabilistic estimation derived from compositional massive testing. To explore\nthese orientations in collaborative projects, and to promote them in\ncertification arenas, we propose to continue and upgrade an open innovation\ndrone-based use case that originated from a collaborative research project in\naeronautic certification reformation\n"}
+{"abstract": "  PatchMatch based Multi-view Stereo (MVS) algorithms have achieved great\nsuccess in large-scale scene reconstruction tasks. However, reconstruction of\ntexture-less planes often fails as similarity measurement methods may become\nineffective on these regions. Thus, a new plane hypothesis inference strategy\nis proposed to handle the above issue. The procedure consists of two steps:\nFirst, multiple plane hypotheses are generated using filtered initial depth\nmaps on regions that are not successfully recovered; Second, depth hypotheses\nare selected using Markov Random Field (MRF). The strategy can significantly\nimprove the completeness of reconstruction results with only acceptable\ncomputing time increasing. Besides, a new acceleration scheme similar to\ndilated convolution can speed up the depth map estimating process with only a\nslight influence on the reconstruction. We integrated the above ideas into a\nnew MVS pipeline, Plane Hypothesis Inference Multi-view Stereo (PHI-MVS). The\nresult of PHI-MVS is validated on ETH3D public benchmarks, and it demonstrates\ncompeting performance against the state-of-the-art.\n"}
+{"abstract": "  Despite the close connection between exploration and sample efficiency, most\nstate of the art reinforcement learning algorithms include no considerations\nfor exploration beyond maximizing the entropy of the policy. In this work we\naddress this seeming missed opportunity. We observe that the most common\nformulation of directed exploration in deep RL, known as bonus-based\nexploration (BBE), suffers from bias and slow coverage in the few-sample\nregime. This causes BBE to be actively detrimental to policy learning in many\ncontrol tasks. We show that by decoupling the task policy from the exploration\npolicy, directed exploration can be highly effective for sample-efficient\ncontinuous control. Our method, Decoupled Exploration and Exploitation Policies\n(DEEP), can be combined with any off-policy RL algorithm without modification.\nWhen used in conjunction with soft actor-critic, DEEP incurs no performance\npenalty in densely-rewarding environments. On sparse environments, DEEP gives a\nseveral-fold improvement in data efficiency due to better exploration.\n"}
+{"abstract": "  In this study we present a compressible test-field method (CTFM) for\ncomputing $\\alpha$ effect and turbulent magnetic diffusivity tensors, as well\nas those relevant for mean ponderomotive force and mass source, applied to the\nfull MHD equations. We describe the theoretical background of the method, and\ncompare it to the quasi-kinematic test-field method (QKTFM), and to the\npreviously studied variant working in simplified MHD (SMHD). We present several\ntest cases using velocity and magnetic fields of the Roberts geometry, and also\ncompare with the imposed-field method. We show that in all cases investigated,\nthe CTFM gives results in agreement with the imposed field method. Some\ndeviations between CTFM and SMHD variant exist. As a relevant physical\napplication, we study non-helically forced shear flows, which exhibit\nlarge-scale dynamo (LSD) action, and present a re-analysis of low Reynolds\nnumber, moderate shear systems, where we previously neglected the pressure\ngradient in the momentum equation, and found no coherent shear-current effect.\nAnother key difference is that in the earlier study we used magnetic forcing to\nmimic small-scale dynamo (SSD) action, while here it is self-consistently\ndriven by purely kinetic forcing. We still find no coherent shear-current\neffect, but do recover strong large-scale dynamo (LSD) action, that, according\nto our analysis, is driven through the incoherent effects.\n"}
+{"abstract": "  The vibrational dynamics of a model polymer glass is studied by Molecular\nDynamics simulations. The focus is on the \"soft\" monomers with high\nparticipation to the lower-frequency vibrational modes contributing to the\nthermodynamic anomalies of glasses. To better evidence their role, the\nthreshold to qualify monomers as soft is made severe, allowing for the use of\nsystems with limited size. A marked tendency of soft monomers to form\nquasi-local clusters involving up to 15 monomers is evidenced. Each chain\ncontributes to a cluster up to about three monomers and a single cluster\ninvolves monomer belonging to about 2-3 chains. Clusters with monomers\nbelonging to a single chain are rare. The open and tenuous character of the\nclusters is revealed by their fractal dimension $d_f < 2$. The inertia tensor\nof the soft clusters evidences their strong anisotropy in shape and remarkable\nlinear correlation of the two largest eigenvalues. Owing to the limited size of\nthe system, finite-size effects, as well as dependence of the results on the\nadopted polymer length, cannot be ruled out.\n"}
+{"abstract": "  Finding Hemisystems is a challenging problem and just few examples arising\nfrom the Hermitian surface are known. A recent method to obtain Hemisystems is\nbased on using maximal curves. Along this side of research, we provide new\nexamples of Hemisystems in $PG(3,p^2)$, for each prime of the form $p=1+4a^2$,\nwith $ a $ integer. Last, we use these results to obtain two weight linear\ncodes and strongly regular graphs.\n"}
+{"abstract": "  The ability to realize nonlinear controllers with formal guarantees on\ndynamic robotic systems has the potential to enable more complex robotic\nbehaviors -- yet, realizing these controllers is often practically challenging.\nTo address this challenge, this paper presents the end-to-end realization of\ndynamic bipedal locomotion on an underactuated bipedal robot via hybrid zero\ndynamics and control Lyapunov functions. A compliant model of Cassie is\nrepresented as a hybrid system to set the stage for a trajectory optimization\nframework. With the goal of achieving a variety of walking speeds in all\ndirections, a library of compliant walking motions is compiled and then\nparameterized for efficient use within real-time controllers. Control Lyapunov\nfunctions, which have strong theoretic guarantees, are synthesized to leverage\nthe gait library and coupled with inverse dynamics to obtain optimization-based\ncontrollers framed as quadratic programs. It is proven that this controller\nprovably achieves stable locomotion; this is coupled with a theoretic analysis\ndemonstrating useful properties of the controller for tuning and\nimplementation. The proposed theoretic framework is practically demonstrated on\nthe Cassie robot, wherein 3D walking is achieved through the use of\noptimization-based torque control. The experiments highlight robotic walking at\ndifferent speeds and terrains, illustrating the end-to-end realization of\ntheoretically justified nonlinear controllers on dynamic underactuated robotic\nsystems.\n"}
+{"abstract": "  Quasar luminosity function (QLF) shows the active galactic nucleus (AGN)\ndemography as a result of the combination of the growth and the evolution of\nblack holes, galaxies, and dark matter halos along the cosmic time. The recent\nwide and deep surveys have improved the census of high-redshift quasars, making\nit possible to construct reliable ultraviolet (UV) QLFs at $2\\lesssim\nz\\lesssim6$ down to $M_{1450}=-23$ mag. By parameterizing these up-to-date\nobserved UV QLFs that are the most extensive in both luminosity and survey area\ncoverage at a given redshift, we show that the UV QLF has a universal shape,\nand their evolution can be approximated by a pure density evolution (PDE). In\norder to explain the observed QLF, we construct a model QLF employing the halo\nmass function, a number of empirical scaling relations, and the Eddington ratio\ndistribution. We also include the outshining of AGN over its host galaxy, which\nmade it possible to reproduce a moderately flat shape of the faint end of the\nobserved QLF (slope of $\\sim-1.1$). This model successfully explains the\nobserved PDE behavior of UV QLF at $z>2$, meaning that the QLF evolution at\nhigh redshift can be understood under the framework of halo mass function\nevolution. The importance of the outshining effect in our model also implies\nthat there could be a hidden population of faint AGNs ($M_{1450}\\gtrsim-24$\nmag), which are buried under their host galaxy light.\n"}
+{"abstract": "  Superconducting technology makes it possible to build a high energy $e^+e^-$\nlinear collider with energy recovery (ERLC) and reusable beams. To avoid\nparasitic collisions inside the linacs, a twin (dual) LC is proposed. In this\narticle, I consider the principle scheme of the collider and estimate the\nachievable luminosity, which is limited by collision effects and available\npower. Such a collider can operate in a duty cycle (DC) and in a continuous\n(CW) modes, if sufficient power. With current SC Nb technology ($T=1.8$ K,\n$f_{\\rm RF}=1.3$ GHz, used for ILC) and with power $P= 100$ MW, a luminosity $L\n\\sim 0.33 \\times10^{36}\\,\\rm cm^{-2}s^{-1}$ is possible at the Higgs factory\nwith $2E_0=250$ GeV. Using superconductors operating at 4.5 K with high $Q_0$\nvalues, such as Nb$_3$Sn, and $f_{\\rm RF}=0.65$ GHz, the luminosity can reach\n$L \\sim 1.4 \\times10^{36} \\,\\rm cm^{-2}s^{-1}$ at $2E_0=250$ GeV (with P=100\nMW) and $L \\sim 0.8 \\times 10^{36}\\,\\rm cm^{-2}s^{-1}$ at $2E_0=500$ GeV (with\nP=150 MW), which is almost two orders of magnitude greater than at the ILC,\nwhere the beams are used only once. This technology requires additional efforts\nto obtain the required parameters and reliably operation. Such a collider would\nbe the best machine for precision Higgs studies, including the measurement of\nHiggs self-coupling.\n"}
+{"abstract": "  Automatic learning algorithms for improving the image quality of diagnostic\nB-mode ultrasound (US) images have been gaining popularity in the recent past.\nIn this work, a novel convolutional neural network (CNN) is trained using time\nof flight corrected in-vivo receiver data of plane wave transmit to produce\ncorresponding high-quality minimum variance distortion less response (MVDR)\nbeamformed image. A comprehensive performance comparison in terms of\nqualitative and quantitative measures for fully connected neural network\n(FCNN), the proposed CNN architecture, MVDR and Delay and Sum (DAS) using the\ndataset from Plane wave Imaging Challenge in Ultrasound (PICMUS) is also\nreported in this work. The CNN architecture could leverage the spatial\ninformation and will be more region adaptive during the beamforming process.\nThis is evident from the improvement seen over the baseline FCNN approach and\nconventional MVDR beamformer, both in resolution and contrast with an\nimprovement of 6 dB in CNR using only zero-angle transmission over the\nbaseline. With the observed reduction in the requirement of number of angles to\nproduce similar image metrics would prove advantageous in providing a\npossibility for higher frame rates.\n"}
+{"abstract": "  For many households, investing for retirement is one of the most significant\ndecisions and is fraught with uncertainty. In a classic study in behavioral\neconomics, Benartzi and Thaler (1999) found evidence using bar charts that\ninvestors exhibit myopic loss aversion in retirement decisions: Investors\noverly focus on the potential for short-term losses, leading them to invest\nless in riskier assets and miss out on higher long-term returns. Recently,\nadvances in uncertainty visualizations have shown improvements in\ndecision-making under uncertainty in a variety of tasks. In this paper, we\nconduct a controlled and incentivized crowdsourced experiment replicating\nBenartzi and Thaler (1999) and extending it to measure the effect of different\nuncertainty representations on myopic loss aversion. Consistent with the\noriginal study, we find evidence of myopic loss aversion with bar charts and\nfind that participants make better investment decisions with longer evaluation\nperiods. We also find that common uncertainty representations such as interval\nplots and bar charts achieve the highest mean expected returns while other\nuncertainty visualizations lead to poorer long-term performance and strong\neffects on the equity premium. Qualitative feedback further suggests that\ndifferent uncertainty representations lead to visual reasoning heuristics that\ncan either mitigate or encourage a focus on potential short-term losses. We\ndiscuss implications of our results on using uncertainty visualizations for\nretirement decisions in practice and possible extensions for future work.\n"}
+{"abstract": "  COVID-19 testing, the cornerstone for effective screening and identification\nof COVID-19 cases, remains paramount as an intervention tool to curb the spread\nof COVID-19 both at local and national levels. However, the speed at which the\npandemic struck and the response was rolled out, the widespread impact on\nhealthcare infrastructure, the lack of sufficient preparation within the public\nhealth system, and the complexity of the crisis led to utter confusion among\ntest-takers. Invasion of privacy remains a crucial concern. The user experience\nof test takers remains low. User friction affects user behavior and discourages\nparticipation in testing programs. Test efficacy has been overstated. Test\nresults are poorly understood resulting in inappropriate follow-up\nrecommendations. Herein, we review the current landscape of COVID-19 testing,\nidentify four key challenges, and discuss the consequences of the failure to\naddress these challenges. The current infrastructure around testing and\ninformation propagation is highly privacy-invasive and does not leverage\nscalable digital components. In this work, we discuss challenges complicating\nthe existing covid-19 testing ecosystem and highlight the need to improve the\ntesting experience for the user and reduce privacy invasions. Digital tools\nwill play a critical role in resolving these challenges.\n"}
+{"abstract": "  We prove new results on splitting Brauer classes by genus 1 curves, settling\nin particular the case of degree 7 classes over global fields. Though our\nmethod is cohomological in nature, and proceeds by considering the more\ndifficult problem of splitting $\\mu_N$-gerbes, we use crucial input from the\narithmetic of modular curves and explicit $N$-descent on elliptic curves.\n"}
+{"abstract": "  With the powerful learning ability of deep convolutional networks, deep\nclustering methods can extract the most discriminative information from\nindividual data and produce more satisfactory clustering results. However,\nexisting deep clustering methods usually ignore the relationship between the\ndata. Fortunately, the graph convolutional network can handle such\nrelationship, opening up a new research direction for deep clustering. In this\npaper, we propose a cross-attention based deep clustering framework, named\nCross-Attention Fusion based Enhanced Graph Convolutional Network (CaEGCN),\nwhich contains four main modules: the cross-attention fusion module which\ninnovatively concatenates the Content Auto-encoder module (CAE) relating to the\nindividual data and Graph Convolutional Auto-encoder module (GAE) relating to\nthe relationship between the data in a layer-by-layer manner, and the\nself-supervised model that highlights the discriminative information for\nclustering tasks. While the cross-attention fusion module fuses two kinds of\nheterogeneous representation, the CAE module supplements the content\ninformation for the GAE module, which avoids the over-smoothing problem of GCN.\nIn the GAE module, two novel loss functions are proposed that reconstruct the\ncontent and relationship between the data, respectively. Finally, the\nself-supervised module constrains the distributions of the middle layer\nrepresentations of CAE and GAE to be consistent. Experimental results on\ndifferent types of datasets prove the superiority and robustness of the\nproposed CaEGCN.\n"}
+{"abstract": "  Cosmic-ray muons can be used for the non-destructive imaging of spent nuclear\nfuel in sealed dry storage casks. The scattering data of the muons after\ntraversing provides information on the thereby penetrated materials. Based on\nthese properties, we investigate and discuss the theoretical feasibility of\ndetecting single missing fuel rods in a sealed cask for the first time. We\nperform simulations of a vertically standing generic cask model loaded with\nfuel assemblies from a pressurized water reactor and muon detectors placed\nabove and below the cask. By analysing the scattering angles and applying a\nsignificance ratio based on the Kolmogorov-Smirnov test statistic we conclude\nthat missing rods can be reliably identified in a reasonable measuring time\nperiod depending on their position in the assembly and cask, and on the angular\nacceptance criterion of the primary, incoming muons.\n"}
+{"abstract": "  We are interested in describing the infected size of the SIS Epidemic model\nusing Birth-Death Markov process. The Susceptible-Infected-Susceptible (SIS)\nmodel is defined within a population of constant size $M$; the size is kept\nconstant by replacing each death with a newborn healthy individual. The life\nspan of each individual in the population is modelled by an exponential\ndistribution with parameter $\\alpha$; and the disease spreads within the\npopulation is modelled by a Poisson process with a rate $\\lambda_{I}$.\n$\\lambda_{I}=\\beta I(1-\\frac{I}{M}) $ is similar to the instantaneous rate in\nthe logistic population growth model. The analysis is focused on the disease\noutbreak, where the reproduction number $R=\\frac{\\beta}{\\alpha} $ is greater\nthan one. As methodology, we use both numerical and analytical approaches. The\nanalysis relies on the stationary distribution for Birth and Death Markov\nprocess. The numerical approach creates sample path simulations into order show\nthe infected size dynamics, and the relationship between infected size and $R$.\nAs $M$ becomes large, some stable statistical characteristics of the infected\nsize distribution can be deduced. And the infected size is shown analytically\nto follow a normal distribution with mean $(1-\\frac{1}{R}) M$ and Variance\n$\\frac{M}{R} $.\n"}
+{"abstract": "  Suppose we observe an infinite series of coin flips $X_1,X_2,\\ldots$, and\nwish to sequentially test the null that these binary random variables are\nexchangeable. Nonnegative supermartingales (NSMs) are a workhorse of sequential\ninference, but we prove that they are powerless for this problem. First,\nutilizing a geometric concept called fork-convexity (a sequential analog of\nconvexity), we show that any process that is an NSM under a set of\ndistributions, is also necessarily an NSM under their \"fork-convex hull\".\nSecond, we demonstrate that the fork-convex hull of the exchangeable null\nconsists of all possible laws over binary sequences; this implies that any NSM\nunder exchangeability is necessarily nonincreasing, hence always yields a\npowerless test for any alternative. Since testing arbitrary deviations from\nexchangeability is information theoretically impossible, we focus on Markovian\nalternatives. We combine ideas from universal inference and the method of\nmixtures to derive a \"safe e-process\", which is a nonnegative process with\nexpectation at most one under the null at any stopping time, and is upper\nbounded by a martingale, but is not itself an NSM. This in turn yields a level\n$\\alpha$ sequential test that is consistent; regret bounds from universal\ncoding also demonstrate rate-optimal power. We present ways to extend these\nresults to any finite alphabet and to Markovian alternatives of any order using\na \"double mixture\" approach. We provide an array of simulations, and give\ngeneral approaches based on betting for unstructured or ill-specified\nalternatives. Finally, inspired by Shafer, Vovk, and Ville, we provide\ngame-theoretic interpretations of our e-processes and pathwise results.\n"}
+{"abstract": "  Entity synonyms discovery is crucial for entity-leveraging applications.\nHowever, existing studies suffer from several critical issues: (1) the input\nmentions may be out-of-vocabulary (OOV) and may come from a different semantic\nspace of the entities; (2) the connection between mentions and entities may be\nhidden and cannot be established by surface matching; and (3) some entities\nrarely appear due to the long-tail effect. To tackle these challenges, we\nfacilitate knowledge graphs and propose a novel entity synonyms discovery\nframework, named \\emph{KGSynNet}. Specifically, we pre-train subword embeddings\nfor mentions and entities using a large-scale domain-specific corpus while\nlearning the knowledge embeddings of entities via a joint TransC-TransE model.\nMore importantly, to obtain a comprehensive representation of entities, we\nemploy a specifically designed \\emph{fusion gate} to adaptively absorb the\nentities' knowledge information into their semantic features. We conduct\nextensive experiments to demonstrate the effectiveness of our \\emph{KGSynNet}\nin leveraging the knowledge graph. The experimental results show that the\n\\emph{KGSynNet} improves the state-of-the-art methods by 14.7\\% in terms of\nhits@3 in the offline evaluation and outperforms the BERT model by 8.3\\% in the\npositive feedback rate of an online A/B test on the entity linking module of a\nquestion answering system.\n"}
+{"abstract": "  Silicon detection is a mature technology for registering the passage of\ncharged particles. At the same time it continues to evolve toward increasing\nradiation tolerance as well as precision and adaptability. For these reasons it\nis likely to remain a critical element of detection systems associated with\nextra-terrestrial exploration. Silicon sensor leakage current and depletion\nvoltage depend upon the integrated fluence received by the sensor, and upon its\nthermal history during and after the irradiation process. For minimal\nassumptions on shielding and hence on particle energy spectrum, and using\npublished data on Martian ground temperature, we predict the leakage current\ndensity and the depletion voltage, as a function of time, of silicon sensors\ndeployed continuously on the Mars surface for a duration of up to 28\nEarth-years, for several sensor geometries and a worst-case temperature\nscenario.\n"}
+{"abstract": "  Contemporary business innovation relies increasingly on information and\ncommunications technology (ICT) solutions. As ICT initiatives are generally\nimplemented via projects the management of ICT projects has come under\nincreasing scrutiny. ICT projects continue to fail; as a result, while research\nin ICT project management has indeed increased, many challenges for research\nand practice remain. Many studies have addressed the execution and management\nof ICT projects and the many factors that might relate to project outcomes.\nVery few, however, have considered ICT project initiation and the crucial\ndecisions made at that very early, pre-life cycle stage. The primary intent of\nthis research is therefore to investigate ICT projects with a particular focus\non their initiation. In doing so we wished to understand why ICT projects are\nstarted, and how they are moved from idea or proposal to supported reality. A\ncombination of semi-structured interviews and the repertory grid data\ncollection and analysis method was employed to investigate and validate the\nmotivating factors that influence individual IT Managers' project initiation\ndecisions and the methods they use to transition from idea to enacted project.\nOur results showed that there are indeed multiple underlying reasons for the\ndecisions made at this early stage and that there are some especially common\ndecision drivers. Some were expected, in the sense that they mapped to\nrecommended best practice. For instance, most projects are motivated by a\ndesire to achieve efficiencies or cost savings, and their potential tends to be\nassessed using cost benefit analysis. Other results were more surprising -\ncompetitor pressure was not a common driver for ICT project initiation in our\nanalysis. Unsurprisingly, formal evaluation methods are more frequently used to\nassess project proposals when those projects are larger and higher profile.\n(Abridged)\n"}
+{"abstract": "  How can multiple humans interact with multiple robots? The goal of our\nresearch is to create an effective interface that allows multiple operators to\ncollaboratively control teams of robots in complex tasks. In this paper, we\nfocus on a key aspect that affects our exploration of the design space of\nhuman-robot interfaces -- inter-human communication. More specifically, we\nstudy the impact of direct and indirect communication on several metrics, such\nas awareness, workload, trust, and interface usability. In our experiments, the\nparticipants can engage directly through verbal communication, or indirectly by\nrepresenting their actions and intentions through our interface. We report the\nresults of a user study based on a collective transport task involving 18 human\nsubjects and 9 robots. Our study suggests that combining both direct and\nindirect communication is the best approach for effective multi-human /\nmulti-robot interaction.\n"}
+{"abstract": "  Runaway stars can result from core-collapse supernovae in multiple stellar\nsystems. If the supernova disrupts the system, the companion gets ejected with\nits former orbital velocity. A clear identification of a runaway star can yield\nthe time and place of the explosion as well as orbital parameters of the\npre-supernova binary system. Previous searches have mostly considered O- and\nB-type stars as runaway stars because they are always young in absolute terms\n(not much older than the lifetime of the progenitor) and can be detected up to\nlarger distances. We present here a search for runaway stars of all spectral\ntypes. For late-type stars, a young age can be inferred from the lithium test.\nWe used Gaia data to identify and characterise runaway star candidates in\nnearby supernova remnants, obtained spectra of 39 stars with UVES at the VLT\nand HDS at the Subaru telescope and found a significant amount of lithium in\nthe spectra of six dwarf stars. We present the spectral analysis, including\nmeasurements of radial velocities, atmospheric parameters and lithium\nabundances. Then we estimate the ages of our targets from the\nHertzsprung-Russell diagram and with the lithium test, present a selection of\npromising runaway star candidates and draw constraints on the number of ejected\nrunaway stars compared to model expectations.\n"}
+{"abstract": "  Modern processors can offer hardware primitives that allow a process to run\nin isolation. These primitives implement a trusted execution environment (TEE)\nin which a program can run such that the integrity and confidentiality of its\nexecution are guaranteed. Intel's Software Guard eXtensions (SGX) is an example\nof such primitives and its isolated processes are called \\emph{enclaves}. These\nguarantees, however, can be easily thwarted if the enclave has not been\nproperly designed. Its interface with the untrusted software stack is arguably\nthe largest attack surface that adversaries can exploit; unintended\ninteractions with untrusted code can expose the enclave to memory corruption\nattacks, for instance. In this paper, we propose a notion of an \\emph{orderly}\nenclave which splits its behaviour into several execution phases each of which\nimposes a set of restrictions on accesses to untrusted memory, phase\ntransitions and registers sanitisation. A violation to these restrictions\nindicates an undesired behaviour which could be harnessed to perpetrate attacks\nagainst the enclave. We also introduce \\Analyser{}: a tool that uses symbolic\nexecution to carry out the validation of an enclave against our notion of an\norderly enclave; in this process, it also looks for some typical\nmemory-corruption vulnerabilities. We discuss how our approach can prevent and\nflag enclave vulnerabilities that have been identified in the literature.\nMoreover, we have evaluated how our approach fares in the analysis of some\npractical enclaves. \\Analyser{} was able to identify real vulnerabilities on\nthese enclaves which have been acknowledged and fixed by their maintainers.\n"}
+{"abstract": "  This abstract seeks to introduce the ISSAC community to the DEWCAD project,\nwhich is based at Coventry University and the University of Bath, in the United\nKingdom. The project seeks to push back the Doubly Exponential Wall of\nCylindrical Algebraic Decomposition, through the integration of SAT/SMT\ntechnology, the extension of Lazard projection theory, and the development of\nnew algorithms based on CAD technology but without producing CADs themselves.\nThe project also seeks to develop applications of CAD and will focus on\napplications in the domains of economics and bio-network analysis.\n"}
+{"abstract": "  This paper presents a hierarchical motion planner for planning the\nmanipulation motion to repose long and heavy objects considering external\nsupport surfaces. The planner includes a task level layer and a motion level\nlayer. We formulate the manipulation planning problem at the task level by\nconsidering grasp poses as nodes and object poses for edges. We consider\nregrasping and constrained in-hand slip (drooping) during building graphs and\nfind mixed regrasping and drooping sequences by searching the graph. The\ngenerated sequences autonomously divide the object weight between the arm and\nthe support surface and avoid configuration obstacles. Cartesian planning is\nused at the robot motion level to generate motions between adjacent critical\ngrasp poses of the sequence found by the task level layer. Various experiments\nare carried out to examine the performance of the proposed planner. The results\nshow improved capability of robot arms to manipulate long and heavy objects\nusing the proposed planner. Our contribution is we initially develop a\ngraph-based planning system that reasons both in-hand and regrasp manipulation\nmotion considering external supports. On one hand, the planner integrates\nregrasping and drooping to realize in-hand manipulation with external support.\nOn the other hand, it switches states by releasing and regrasping objects when\nthe object is in stably placed. The search graphs' nodes could be retrieved\nfrom remote cloud servers that provide a large amount of pre-annotated data to\nimplement cyber intelligence.\n"}
+{"abstract": "  Recently, there has been great success in applying deep neural networks on\ngraph structured data. Most work, however, focuses on either node- or\ngraph-level supervised learning, such as node, link or graph classification or\nnode-level unsupervised learning (e.g. node clustering). Despite its wide range\nof possible applications, graph-level unsupervised learning has not received\nmuch attention yet. This might be mainly attributed to the high representation\ncomplexity of graphs, which can be represented by n! equivalent adjacency\nmatrices, where n is the number of nodes. In this work we address this issue by\nproposing a permutation-invariant variational autoencoder for graph structured\ndata. Our proposed model indirectly learns to match the node ordering of input\nand output graph, without imposing a particular node ordering or performing\nexpensive graph matching. We demonstrate the effectiveness of our proposed\nmodel on various graph reconstruction and generation tasks and evaluate the\nexpressive power of extracted representations for downstream graph-level\nclassification and regression.\n"}
+{"abstract": "  The magnetic fields of accretion disks play an important role in studying\ntheir evolution. We may assume that its generation is connected to the dynamo\nmechanism, which is similar with that in the galactic disks. Here, we propose a\nmodel of the magnetic field of the accretion disk that uses the same approaches\nthat have been used for galaxies. It is necessary to obtain the field, which is\nexpected to be less than the equipartition value, and without destroying the\ndisk. To do so, it is necessary to formulate the basic properties of the\nionized medium and to estimate the parameters governing the dynamo. We used the\nno-z approximation that has been developed for thin disks. We also take\ndifferent boundary conditions that can change the value of the field\nsignificantly. We show that the magnetic field strictly depends on the boundary\nconditions. Taking zero conditions and the fixed magnetic field condition on\nthe inner boundary, which are connected to the physical properties of the\naccretion disk, we can avoid solutions that are greater than the equipartition\nfield.\n"}
+{"abstract": "  The use of probe molecules to extract the local dynamical and structural\nproperties of complex dynamical systems is an age-old technique both in\nsimulations and experiments. A lot of important information which is not\nimmediately accessible from the bulk measurements can be accessed via these\nlocal measurements. Still, a detailed understanding of how a probe particle's\ndynamics are affected by the surrounding liquid medium is not very well\nunderstood, especially in the supercooled temperature regime. This work shows\nhow translational dynamics of a rod-like particle immersed in a supercooled\nliquid can give us information on the growth of the correlation length scale\nassociated with dynamical heterogeneity and the multi-body static correlations\nin the medium. A unified scaling theory rationalizes all the observed results\nleading to the development of a novel yet simple method that is accessible in\nexperiments to measure the growth of these important length-scales in molecular\nglass-forming liquids.\n"}
+{"abstract": "  We show within the slave-boson mean field approximation that the\ntwo-dimensional t-J model has an intrinsic instability toward forming a\nquasi-one-dimensional (q-1d) Fermi surface. This q-1d state competes with, and\nis overcome by, the d-wave pairing state for a realistic parameter choice.\nHowever, we find that a small spatial anisotropy in t and J exposes the q-1d\ninstability which has been hidden behind the d-wave pairing state, and brings\nabout the coexistence with the d-wave pairing. We argue that this coexistence\ncan be realized in La_{2-x}Sr_{x}CuO_4 systems\n"}
+{"abstract": "  The role of a new form of dynamic interaction is explored in a network of\ngeneric identical oscillators. The proposed design of dynamic coupling\nfacilitates the onset of a plethora of asymptotic states including synchronous\nstates, amplitude death states, oscillation death states, a mixed state\n(complete synchronized cluster and small amplitude unsynchronized domain), and\nbistable states (coexistence of two attractors). The dynamical transitions from\nthe oscillatory to death state are characterized using an average temporal\ninteraction approximation, which agrees with the numerical results in temporal\ninteraction. A first-order phase transition behavior may change into a\nsecond-order transition in spatial dynamic interaction solely depending on the\nchoice of initial conditions in the bistable regime. However, this possible\nabrupt first-order like transition is completely non-existent in the case of\ntemporal dynamic interaction. Besides the study on periodic Stuart-Landau\nsystems, we present results for paradigmatic chaotic model of R\\\"ossler\noscillators and Mac-arthur ecological model.\n"}
+{"abstract": "  The recent advent of noisy intermediate-scale quantum devices, especially\nnear-term quantum computers, has sparked extensive research efforts concerned\nwith their possible applications. At the forefront of the considered approaches\nare variational methods that use parametrized quantum circuits. The classical\nand quantum Fisher information are firmly rooted in the field of quantum\nsensing and have proven to be versatile tools to study such parametrized\nquantum systems. Their utility in the study of other applications of noisy\nintermediate-scale quantum devices, however, has only been discovered recently.\nHoping to stimulate more such applications, this article aims to further\npopularize classical and quantum Fisher information as useful tools for\nnear-term applications beyond quantum sensing. We start with a tutorial that\nbuilds an intuitive understanding of classical and quantum Fisher information\nand outlines how both quantities can be calculated on near-term devices. We\nalso elucidate their relationship and how they are influenced by noise\nprocesses. Next, we give an overview of the core results of the quantum sensing\nliterature and proceed to a comprehensive review of recent applications in\nvariational quantum algorithms and quantum machine learning.\n"}
+{"abstract": "  The construction of catalogues of a particular type of galaxy can be\ncomplicated by interlopers contaminating the sample. In spectroscopic galaxy\nsurveys this can be due to the misclassification of an emission line; for\nexample in the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) low\nredshift [OII] emitters may make up a few percent of the observed Ly${\\alpha}$\nemitter (LAE) sample. The presence of contaminants affects the measured\ncorrelation functions and power spectra. Previous attempts to deal with this\nusing the cross-correlation function have assumed sources at a fixed redshift,\nor not modelled evolution within the adopted redshift bins. However, in\nspectroscopic surveys like HETDEX, where the contamination fraction is likely\nto be redshift dependent, the observed clustering of misclassified sources will\nappear to evolve strongly due to projection effects, even if their true\nclustering does not. We present a practical method for accounting for the\npresence of contaminants with redshift-dependent contamination fractions and\nprojected clustering. We show using mock catalogues that our method, unlike\nexisting approaches, yields unbiased clustering measurements from the upcoming\nHETDEX survey in scenarios with redshift-dependent contamination fractions\nwithin the redshift bins used. We show our method returns auto-correlation\nfunctions with systematic biases much smaller than the statistical noise for\nsamples with at least as high as 7 per cent contamination. We also present and\ntest a method for fitting for the redshift-dependent interloper fraction using\nthe LAE-[OII] galaxy cross-correlation function, which gives less biased\nresults than assuming a single interloper fraction for the whole sample.\n"}
+{"abstract": "  Stochastic modelling of complex systems plays an essential, yet often\ncomputationally intensive role across the quantitative sciences. Recent\nadvances in quantum information processing have elucidated the potential for\nquantum simulators to exhibit memory advantages for such tasks. Heretofore, the\nfocus has been on lossless memory compression, wherein the advantage is\ntypically in terms of lessening the amount of information tracked by the model,\nwhile -- arguably more practical -- reductions in memory dimension are not\nalways possible. Here we address the case of lossy compression for quantum\nstochastic modelling of continuous-time processes, introducing a method for\ncoarse-graining in quantum state space that drastically reduces the requisite\nmemory dimension for modelling temporal dynamics whilst retaining near-exact\nstatistics. In contrast to classical coarse-graining, this compression is not\nbased on sacrificing temporal resolution, and brings memory-efficient,\nhigh-fidelity stochastic modelling within reach of present quantum\ntechnologies.\n"}
+{"abstract": "  Many distributed optimization algorithms achieve existentially-optimal\nrunning times, meaning that there exists some pathological worst-case topology\non which no algorithm can do better. Still, most networks of interest allow for\nexponentially faster algorithms. This motivates two questions: (1) What network\ntopology parameters determine the complexity of distributed optimization? (2)\nAre there universally-optimal algorithms that are as fast as possible on every\ntopology?\n  We resolve these 25-year-old open problems in the known-topology setting\n(i.e., supported CONGEST) for a wide class of global network optimization\nproblems including MST, $(1+\\varepsilon)$-min cut, various approximate shortest\npaths problems, sub-graph connectivity, etc.\n  In particular, we provide several (equivalent) graph parameters and show they\nare tight universal lower bounds for the above problems, fully characterizing\ntheir inherent complexity. Our results also imply that algorithms based on the\nlow-congestion shortcut framework match the above lower bound, making them\nuniversally optimal if shortcuts are efficiently approximable. We leverage a\nrecent result in hop-constrained oblivious routing to show this is the case if\nthe topology is known -- giving universally-optimal algorithms for all above\nproblems.\n"}
+{"abstract": "  High-fidelity single- and two-qubit gates are essential building blocks for a\nfault-tolerant quantum computer. While there has been much progress in\nsuppressing single-qubit gate errors in superconducting qubit systems,\ntwo-qubit gates still suffer from error rates that are orders of magnitude\nhigher. One limiting factor is the residual ZZ-interaction, which originates\nfrom a coupling between computational states and higher-energy states. While\nthis interaction is usually viewed as a nuisance, here we experimentally\ndemonstrate that it can be exploited to produce a universal set of fast single-\nand two-qubit entangling gates in a coupled transmon qubit system. To implement\narbitrary single-qubit rotations, we design a new protocol called the two-axis\ngate that is based on a three-part composite pulse. It rotates a single qubit\nindependently of the state of the other qubit despite the strong ZZ-coupling.\nWe achieve single-qubit gate fidelities as high as 99.1% from randomized\nbenchmarking measurements. We then demonstrate both a CZ gate and a CNOT gate.\nBecause the system has a strong ZZ-interaction, a CZ gate can be achieved by\nletting the system freely evolve for a gate time $t_g=53.8$ ns. To design the\nCNOT gate, we utilize an analytical microwave pulse shape based on the SWIPHT\nprotocol for realizing fast, low-leakage gates. We obtain fidelities of 94.6%\nand 97.8% for the CNOT and CZ gates respectively from quantum progress\ntomography.\n"}
+{"abstract": "  A model is constructed to describe the thermal-field emission of electrons\nfrom a three-dimensional ($3$D) topological semimetal hosting Dirac/Weyl\nnode(s). The traditional thermal-field electron emission model is generalised\nto accommodate the $3$D non-parabolic energy band structures in the topological\nDirac/Weyl semimetals, such as cadmium arsenide (\\ch{Cd3As2}), sodium\nbismuthide (\\ch{Na3Bi}), tantalum arsenide (\\ch{TaAs}) and tantalum phosphide\n(\\ch{TaP}). Due to the unique Dirac cone band structure, an unusual dual-peak\nfeature is observed in the total energy distribution (TED) spectrum. This\nnon-trivial dual-peak feature, absent from traditional materials, plays a\ncritical role in manipulating the TED spectrum and the magnitude of the\nemission current. At zero temperature limit, a new scaling law for pure field\nemission is derived and it is different from the well-known Fowler-Nordheim\n(FN) law. This model expands the recent understandings of electron emission\nstudied for the Dirac $2$D materials into the $3$D regime, and thus offers a\ntheoretical foundation for the exploration in using topological semimetals as\nnovel electrodes.\n"}
+{"abstract": "  In this paper, we consider a single-cell multi-user orthogonal frequency\ndivision multiple access (OFDMA) network with one unmanned aerial vehicle\n(UAV), which works as an amplify-and-forward relay to improve the\nquality-of-service (QoS) of the user equipments (UEs) in the cell edge. Aiming\nto improve the throughput while guaranteeing the user fairness, we jointly\noptimize the communication mode, subchannel allocation, power allocation, and\nUAV trajectory, which is an NP-hard problem. To design the UAV trajectory and\nresource allocation efficiently, we first decompose the problem into three\nsubproblems, i.e., mode selection and subchannel allocation, trajectory\noptimization, and power allocation, and then solve these subproblems\niteratively. Simulation results show that the proposed algorithm outperforms\nthe random algorithm and the cellular scheme.\n"}
+{"abstract": "  We develop new strategies to build numerical relativity surrogate models for\neccentric binary black hole systems, which are expected to play an increasingly\nimportant role in current and future gravitational-wave detectors. We introduce\na new surrogate waveform model, \\texttt{NRSur2dq1Ecc}, using 47 nonspinning,\nequal-mass waveforms with eccentricities up to $0.2$ when measured at a\nreference time of $5500M$ before merger. This is the first waveform model that\nis directly trained on eccentric numerical relativity simulations and does not\nrequire that the binary circularizes before merger. The model includes the\n$(2,2)$, $(3,2)$, and $(4,4)$ spin-weighted spherical harmonic modes. We also\nbuild a final black hole model, \\texttt{NRSur2dq1EccRemnant}, which models the\nmass, and spin of the remnant black hole. We show that our waveform model can\naccurately predict numerical relativity waveforms with mismatches $\\approx\n10^{-3}$, while the remnant model can recover the final mass and dimensionless\nspin with absolute errors smaller than $\\approx 5 \\times 10^{-4}M$ and $\\approx\n2 \\times10^{-3}$ respectively. We demonstrate that the waveform model can also\nrecover subtle effects like mode-mixing in the ringdown signal without any\nspecial ad-hoc modeling steps. Finally, we show that despite being trained only\non equal-mass binaries, \\texttt{NRSur2dq1Ecc} can be reasonably extended up to\nmass ratio $q\\approx3$ with mismatches $\\simeq 10^{-2}$ for eccentricities\nsmaller than $\\sim 0.05$ as measured at a reference time of $2000M$ before\nmerger. The methods developed here should prove useful in the building of\nfuture eccentric surrogate models over larger regions of the parameter space.\n"}
+{"abstract": "  Research on autonomous driving is advancing dramatically and requires new\ndata and techniques to progress even further. To reflect this pressure, we\npresent an extension of our recent work - the Brno Urban Dataset (BUD). The new\ndata focus on winter conditions in various snow-covered environments and\nfeature additional LiDAR and radar sensors for object detection in front of the\nvehicle. The improvement affects the old data as well. We provide YOLO\ndetection annotations for all old RGB images in the dataset. The detections are\nfurther also transferred by our original algorithm into the infra-red (IR)\nimages, captured by the thermal camera. To our best knowledge, it makes this\ndataset the largest source of machine-annotated thermal images currently\navailable. The dataset is published under MIT license on\nhttps://github.com/Robotics-BUT/Brno-Urban-Dataset.\n"}
+{"abstract": "  Identifying the failure modes of cloud computing systems is a difficult and\ntime-consuming task, due to the growing complexity of such systems, and the\nlarge volume and noisiness of failure data. This paper presents a novel\napproach for analyzing failure data from cloud systems, in order to relieve\nhuman analysts from manually fine-tuning the data for feature engineering. The\napproach leverages Deep Embedded Clustering (DEC), a family of unsupervised\nclustering algorithms based on deep learning, which uses an autoencoder to\noptimize data dimensionality and inter-cluster variance. We applied the\napproach in the context of the OpenStack cloud computing platform, both on the\nraw failure data and in combination with an anomaly detection pre-processing\nalgorithm. The results show that the performance of the proposed approach, in\nterms of purity of clusters, is comparable to, or in some cases even better\nthan manually fine-tuned clustering, thus avoiding the need for deep domain\nknowledge and reducing the effort to perform the analysis. In all cases, the\nproposed approach provides better performance than unsupervised clustering when\nno feature engineering is applied to the data. Moreover, the distribution of\nfailure modes from the proposed approach is closer to the actual frequency of\nthe failure modes.\n"}
+{"abstract": "  We study the error of the number of unimodular lattice points that fall into\na dilated and translated parallelogram. By using an article from Skriganov, we\nsee that this error can be compared to an ergodic sum that involves the\ndiscrete geodesic flow over the space of unimodular lattices. With the right\nnormalization, we show, by using tools from a previous work of Fayad and\nDolgopyat, that a certain point process converges in law towards a Poisson\nprocess and deduce that the ergodic sum converges towards a Cauchy centered law\nwhen the unimodular lattice is distributed according to the normalized Haar\nmeasure. Strong from this experience, we apply the same kind of approach, with\nmore difficulties, to the study of the asymptotic behaviour of the error and\nshow that this error, normalized by $\\log(t)$ with $t$ the factor of dilatation\nof the parallelogram, also converges in law towards a Cauchy centered law when\nthe dilatation parameter tends to infinity and when the lattice and the vector\nof translation are random. In a next article, we will show that, in the case of\na ball in dimension $d$ superior or equal to $2$, the error, normalized by\n$t^{\\frac{d-1}{2}}$ with $t$ the factor of dilatation of the ball, converges in\nlaw when $t \\rightarrow \\infty$ and the limit law admits a moment of order $1$.\n"}
+{"abstract": "  Smart contracts are self-enforcing agreements that are employed to exchange\nassets without the approval of trusted third parties. This feature has\nencouraged various sectors to make use of smart contracts when transacting.\nExperience shows that many deployed contracts are vulnerable to exploitation\ndue to their poor design, which allows attackers to steal valuable assets from\nthe involved parties. Therefore, an assessment approach that allows developers\nto recognise the consequences of deploying vulnerable contracts is needed. In\nthis paper, we propose a debt-aware approach for assessing security design\nvulnerabilities in smart contracts. Our assessment approach involves two main\nsteps: (i) identification of design vulnerabilities using security analysis\ntechniques and (ii) an estimation of the ramifications of the identified\nvulnerabilities leveraging the technical debt metaphor, its principal and\ninterest. We use examples of vulnerable contracts to demonstrate the\napplicability of our approach. The results show that our assessment approach\nincreases the visibility of security design issues. It also allows developers\nto concentrate on resolving smart contract vulnerabilities through technical\ndebt impact analysis and prioritisation. Developers can use our approach to\ninform the design of more secure contracts and for reducing unintentional debts\ncaused by a lack of awareness of security issues.\n"}
+{"abstract": "  Consider the puzzle: given a number, remove $k$ digits such that the\nresulting number is as large as possible. Various techniques were employed to\nderive a linear-time solution to the puzzle: predicate logic was used to\njustify the structure of a greedy algorithm, a dependently-typed proof\nassistant was used to give a constructive proof of the greedy condition, and\nequational reasoning was used to calculate the greedy step as well as the\nfinal, linear-time optimisation.\n"}
+{"abstract": "  In this note, we show how a continuous action of the Morava stabilizer group\n$\\mathbb G_n$ on the Lubin-Tate spectrum $E_n$, satisfying the conclusion\n$E_n^{h\\mathbb G_n}\\simeq L_{K(n)} S$ of the Devinatz-Hopkins Theorem, may be\nobtained by monodromy on the stack of oriented deformations of formal groups in\nthe context of formal spectral algebraic geometry.\n"}
+{"abstract": "  We implement and benchmark the frozen core approximation, a technique\ncommonly adopted in electronic structure theory to reduce the computational\ncost by means of mathematically fixing the chemically inactive core electron\nstates. The accuracy and efficiency of this approach are well controlled by a\nsingle parameter, the number of frozen orbitals. Explicit corrections for the\nfrozen core orbitals and the unfrozen valence orbitals are introduced,\nsafeguarding against seemingly minor numerical deviations from the assumed\northonormality conditions of the basis functions. A speedup of over two-fold\ncan be achieved for the diagonalization step in all-electron density-functional\ntheory simulations containing heavy elements, without any accuracy degradation\nin terms of the electron density, total energy, and atomic forces. This is\ndemonstrated in a benchmark study covering 103 materials across the periodic\ntable, and a large-scale simulation of CsPbBr3 with 2,560 atoms. Our study\nprovides a rigorous benchmark of the precision of the frozen core approximation\n(sub-meV per atom for frozen core orbitals below -200 eV) for a wide range of\ntest cases and for chemical elements ranging from Li to Po. The algorithms\ndiscussed here are implemented in the open-source Electronic Structure\nInfrastructure software package.\n"}
+{"abstract": "  We propose a new viewpoint on Hilbert scales extending them by means of all\nHilbert spaces that are interpolation ones between spaces on the scale. We\nprove that this extension admits an explicit description with the help of\n$\\mathrm{OR}$-varying functions of the operator generating the scale. We also\nshow that this extended Hilbert scale is obtained by the quadratic\ninterpolation (with function parameter) between the above spaces and is closed\nwith respect to the quadratic interpolation between Hilbert spaces. We give\napplications of the extended Hilbert scale to interpolational inequalities,\ngeneralized Sobolev spaces, and spectral expansions induced by abstract and\nelliptic operators.\n"}
+{"abstract": "  In this paper, we introduce a new large-scale face database from KIST,\ndenoted as K-FACE, and describe a novel capturing device specifically designed\nto obtain the data. The K-FACE database contains more than 1 million\nhigh-quality images of 1,000 subjects selected by considering the ratio of\ngender and age groups. It includes a variety of attributes, including 27 poses,\n35 lighting conditions, three expressions, and occlusions by the combination of\nfive types of accessories. As the K-FACE database is systematically constructed\nthrough a hemispherical capturing system with elaborate lighting control and\nmultiple cameras, it is possible to accurately analyze the effects of factors\nthat cause performance degradation, such as poses, lighting changes, and\naccessories. We consider not only the balance of external environmental\nfactors, such as pose and lighting, but also the balance of personal\ncharacteristics such as gender and age group. The gender ratio is the same,\nwhile the age groups of subjects are uniformly distributed from the 20s to 50s\nfor both genders. The K-FACE database can be extensively utilized in various\nvision tasks, such as face recognition, face frontalization, illumination\nnormalization, face age estimation, and three-dimensional face model\ngeneration. We expect systematic diversity and uniformity of the K-FACE\ndatabase to promote these research fields.\n"}
+{"abstract": "  A distinguishing feature of active particles is the nature of the\nnon-equilibrium noise driving their dynamics. Control of these noise properties\nis, therefore, of both fundamental and applied interest. We demonstrate\nemergent tuning of the active noise of a granular self-propelled particle by\nconfining it to a quasi one-dimensional channel. We find that this particle,\nmoving like an active Brownian particle (ABP) in two-dimensions, displays\nrun-and-tumble (RTP) characteristics in confinement. We show that the dynamics\nof the relative orientation co-ordinate of the particle maps to that of a\nBrownian particle in a periodic potential subject to a constant force, in\nanalogy to the dynamics of a molecular motor. This mapping captures the\nessential statistical characteristics of the one-dimensional RTP motion.\nSpecifically, our theoretical analysis is in agreement with the empirical\ndistributions of the relative orientation co-ordinate and the run-times\n(tumble-rates) of the particle. Finally, we explicitly control these emergent\nrun-and-tumble like noise parameters by external driving. Altogether, our work\nillustrates geometry-induced tuning of the active dynamics of self-propelled\nunits thus suggesting an independent route to harness their internal dynamics.\n"}
+{"abstract": "  Two-photon excitation (TPE) proceeds via a \"virtual\" pathway, which depends\non the accessibility of one or more intermediate states, and, in the case of\nnon-centrosymmetric molecules, an additional \"dipole\" pathway involving the\noff-resonance dipole-allowed one-photon transitions and the change in the\npermanent dipole moment between the initial and final states. Here, we control\nthe quantum interference between these two optical excitation pathways by using\nphase-shaped femtosecond laser pulses. We find enhancements by a factor of up\nto 1.75 in the two-photon-excited fluorescence of the photobase FR0-SB in\nmethanol after taking into account the longer pulse duration of the shaped\nlaser pulses. Simulations taking into account the different responses of the\nvirtual and dipole pathways to external fields and the effect of pulse shaping\non two-photon transitions are found to be in good agreement with our\nexperimental measurements. The observed quantum control of TPE in condensed\nphase may lead to enhanced signal at a lower intensity in two-photon\nmicroscopy, multiphoton-excited photoreagents, and novel spectroscopic\ntechniques that are sensitive to the magnitude of the contributions from the\nvirtual and dipole pathways to multiphoton excitations.\n"}
+{"abstract": "  Recently, Abebe et al. (KDD 2018) and Chan et al. (WWW 2019) have considered\nan opinion dynamics optimization problem that is based on a popular model for\nsocial opinion dynamics, in which each agent has some fixed innate opinion, and\na resistance that measures the importance it places on its innate opinion;\nmoreover, the agents influence one another's opinions through an iterative\nprocess. Under certain conditions, this iterative process converges to some\nequilibrium opinion vector. Previous works gave an efficient local search\nalgorithm to solve the unbudgeted variant of the problem, for which the goal is\nto modify the resistance of any number of agents (within some given range) such\nthat the sum of the equilibrium opinions is minimized. On the other hand, it\nwas proved that the $L_0$-budgeted variant is NP-hard, where the $L_0$-budget\nis a restriction given upfront on the number of agents whose resistance may be\nmodified.\n  Inspired by practical situations in which the effort to modify an agent's\nresistance increases with the magnitude of the change, we propose the\n$L_1$-budgeted variant, in which the $L_1$-budget is a restriction on the sum\nof the magnitudes of the changes over all agents' resistance parameters. In\nthis work, we show that the $L_1$-budgeted variant is NP-hard via a reduction\nfrom vertex cover. However, contrary to the $L_0$-budgeted variant, a very\ntechnical argument is needed to show that the optimal solution can be achieved\nby focusing the given $L_1$-budget on as small a number of agents as possible,\nas opposed to spreading the budget over a large number of agents.\n"}
+{"abstract": "  We consider the extremal values of the stationary distribution of sparse\ndirected random graphs with given degree sequences and their relation to the\nextremal values of the in-degree sequence. The graphs are generated by the\ndirected configuration model. Under the assumption of bounded\n$(2+\\eta)$-moments on the in-degrees and of bounded out-degrees, we obtain\ntight comparisons between the maximum value of the stationary distribution and\nthe maximum in-degree. Under the further assumption that the order statistics\nof the in-degrees have a power-law behavior, we show that the extremal values\nof the stationary distribution also have a power-law behavior with the same\nindex. In the same setting, we prove that these results extend to the PageRank\nscores of the random digraph, thus confirming a version of the so-called\npower-law hypothesis. Along the way, we establish several facts about the\nmodel, including the mixing time cutoff and the characterization of the typical\nvalues of the stationary distribution, which were previously obtained under the\nassumption of bounded in-degrees.\n"}
+{"abstract": "  Robust and accurate localization is an essential component for robotic\nnavigation and autonomous driving. The use of cameras for localization with\nhigh definition map (HD Map) provides an affordable localization sensor set.\nExisting methods suffer from pose estimation failure due to error prone data\nassociation or initialization with accurate initial pose requirement. In this\npaper, we propose a cost-effective vehicle localization system with HD map for\nautonomous driving that uses cameras as primary sensors. To this end, we\nformulate vision-based localization as a data association problem that maps\nvisual semantics to landmarks in HD map. Specifically, system initialization is\nfinished in a coarse to fine manner by combining coarse GPS (Global Positioning\nSystem) measurement and fine pose searching. In tracking stage, vehicle pose is\nrefined by implicitly aligning the semantic segmentation result between image\nand landmarks in HD maps with photometric consistency. Finally, vehicle pose is\ncomputed by pose graph optimization in a sliding window fashion. We evaluate\nour method on two datasets and demonstrate that the proposed approach yields\npromising localization results in different driving scenarios. Additionally,\nour approach is suitable for both monocular camera and multi-cameras that\nprovides flexibility and improves robustness for the localization system.\n"}
+{"abstract": "  The transport coefficients for dilute granular gases of inelastic and rough\nhard disks or spheres with constant coefficients of normal ($\\alpha$) and\ntangential ($\\beta$) restitution are obtained in a unified framework as\nfunctions of the number of translational ($d_t$) and rotational ($d_r$) degrees\nof freedom. The derivation is carried out by means of the Chapman--Enskog\nmethod with a Sonine-like approximation in which, in contrast to previous\napproaches, the reference distribution function for angular velocities does not\nneed to be specified. The well-known case of purely smooth $d$-dimensional\nparticles is recovered by setting $d_t=d$ and formally taking the limit $d_r\\to\n0$. In addition, previous results [G. M. Kremer, A. Santos, and V. Garz\\'o,\nPhys. Rev. E 90, 022205 (2014)] for hard spheres are reobtained by taking\n$d_t=d_r=3$, while novel results for hard-disk gases are derived with the\nchoice $d_t=2$, $d_r=1$. The singular quasismooth limit ($\\beta\\to -1$) and the\nconservative Pidduck's gas ($\\alpha=\\beta=1$) are also obtained and discussed.\n"}
+{"abstract": "  While Artificial Neural Networks (ANNs) have yielded impressive results in\nthe realm of simulated intelligent behavior, it is important to remember that\nthey are but sparse approximations of Biological Neural Networks (BNNs). We go\nbeyond comparison of ANNs and BNNs to introduce principles from BNNs that might\nguide the further development of ANNs as embodied neural models. These\nprinciples include representational complexity, complex network\nstructure/energetics, and robust function. We then consider these principles in\nways that might be implemented in the future development of ANNs. In\nconclusion, we consider the utility of this comparison, particularly in terms\nof building more robust and dynamic ANNs. This even includes constructing a\nmorphology and sensory apparatus to create an embodied ANN, which when\ncomplemented with the organizational and functional advantages of BNNs unlocks\nthe adaptive potential of lifelike networks.\n"}
+{"abstract": "  Infrared quasi-stellar objects (IR QSOs) are a rare subpopulation selected\nfrom ultraluminous infrared galaxies (ULIRGs) and have been regarded as\npromising candidates of ULIRG-to-optical QSO transition objects. Here we\npresent NOEMA observations of the CO(1-0) line and 3 mm continuum emission in\nan IR QSO IRAS F07599+6508 at $z=0.1486$, which has many properties in common\nwith Mrk 231. The CO emission is found to be resolved with a major axis of\n$\\sim$6.1 kpc that is larger than the size of $\\sim$4.0 kpc derived for 3 mm\ncontinuum. We identify two faint CO features located at a projected distance of\n$\\sim$11.4 and 19.1 kpc from the galaxy nucleus, respectively, both of which\nare found to have counterparts in the optical and radio bands and may have a\nmerger origin. A systematic velocity gradient is found in the CO main\ncomponent, suggesting that the bulk of molecular gas is likely rotationally\nsupported. Based on the radio-to-millimeter spectral energy distribution and IR\ndata, we estimate that about 30$\\%$ of the flux at 3 mm arises from free-free\nemission and infer a free-free-derived star formation rate of 77 $M_\\odot\\ {\\rm\nyr^{-1}}$, close to the IR estimate corrected for the AGN contribution. We find\na high-velocity CO emission feature at the velocity range of about -1300 to\n-2000 km s$^{-1}$. Additional deep CO observations are needed to confirm the\npresence of a possible very high-velocity CO extension of the OH outflow in\nthis IR QSO.\n"}
+{"abstract": "  We present here the results of broadband spectral analysis of low-mass X-ray\nbinary and a black hole candidate 4U 1957+11. The source was observed nine\ntimes with the Nuclear Spectroscopic Telescope Array (NuSTAR) between 2018\nSeptember and 2019 November. During these observations, the spectral state of\n4U 1957+11 evolved marginally. The disc dominant spectra are well described\nwith a hot, multicolour disc blackbody with disc temperature varying in the\nrange $kT_{\\rm in} \\sim$ 1.35-1.86 keV and a non-thermal component having a\nsteep slope ($\\Gamma =$ 2-3). A broad Fe emission line feature (5-8 keV) was\nobserved in the spectra of all the observations. The relativistic disc model\nwas used to study the effect of distance, inclination, and the black hole mass\non its spin. Simulations indicate a higher spin for smaller distances and lower\nblack hole mass. At smaller distances and higher mass, spin is maximum and\nalmost independent of the distance. An inverse correlation exists between the\nspin and the spectral hardening factor for all the cases. The system prefers a\nmoderate spin of about 0.85 for black hole masses between 4-6 M_sun for a 7 kpc\ndistance.\n"}
+{"abstract": "  Deep learning has demonstrated radiograph screening performances that are\ncomparable or superior to radiologists. However, recent studies show that deep\nmodels for thoracic disease classification usually show degraded performance\nwhen applied to external data. Such phenomena can be categorized into shortcut\nlearning, where the deep models learn unintended decision rules that can fit\nthe identically distributed training and test set but fail to generalize to\nother distributions. A natural way to alleviate this defect is explicitly\nindicating the lesions and focusing the model on learning the intended\nfeatures. In this paper, we conduct extensive retrospective experiments to\ncompare a popular thoracic disease classification model, CheXNet, and a\nthoracic lesion detection model, CheXDet. We first showed that the two models\nachieved similar image-level classification performance on the internal test\nset with no significant differences under many scenarios. Meanwhile, we found\nincorporating external training data even led to performance degradation for\nCheXNet. Then, we compared the models' internal performance on the lesion\nlocalization task and showed that CheXDet achieved significantly better\nperformance than CheXNet even when given 80% less training data. By further\nvisualizing the models' decision-making regions, we revealed that CheXNet\nlearned patterns other than the target lesions, demonstrating its shortcut\nlearning defect. Moreover, CheXDet achieved significantly better external\nperformance than CheXNet on both the image-level classification task and the\nlesion localization task. Our findings suggest improving annotation granularity\nfor training deep learning systems as a promising way to elevate future deep\nlearning-based diagnosis systems for clinical usage.\n"}
+{"abstract": "  Schr\\\"odinger operators with periodic (possibly complex-valued) potentials\nand discrete periodic operators (possibly with complex-valued entries) are\nconsidered, and in both cases the computational spectral problem is\ninvestigated: namely, under what conditions can a \"one-size-fits-all\" algorithm\nfor computing their spectra be devised? It is shown that for periodic banded\nmatrices this can be done, as well as for Schr\\\"odinger operators with periodic\npotentials that are sufficiently smooth. In both cases implementable algorithms\nare provided, along with examples. For certain Schr\\\"odinger operators whose\npotentials may diverge at a single point (but are otherwise well-behaved) it is\nshown that there does not exist such an algorithm, though it is shown that the\ncomputation is possible if one allows for two successive limits.\n"}
+{"abstract": "  Bi-level optimization model is able to capture a wide range of complex\nlearning tasks with practical interest. Due to the witnessed efficiency in\nsolving bi-level programs, gradient-based methods have gained popularity in the\nmachine learning community. In this work, we propose a new gradient-based\nsolution scheme, namely, the Bi-level Value-Function-based Interior-point\nMethod (BVFIM). Following the main idea of the log-barrier interior-point\nscheme, we penalize the regularized value function of the lower level problem\ninto the upper level objective. By further solving a sequence of differentiable\nunconstrained approximation problems, we consequently derive a sequential\nprogramming scheme. The numerical advantage of our scheme relies on the fact\nthat, when gradient methods are applied to solve the approximation problem, we\nsuccessfully avoid computing any expensive Hessian-vector or Jacobian-vector\nproduct. We prove the convergence without requiring any convexity assumption on\neither the upper level or the lower level objective. Experiments demonstrate\nthe efficiency of the proposed BVFIM on non-convex bi-level problems.\n"}
+{"abstract": "  State-of-the-art summarization systems are trained and evaluated on massive\ndatasets scraped from the web. Despite their prevalence, we know very little\nabout the underlying characteristics (data noise, summarization complexity,\netc.) of these datasets, and how these affect system performance and the\nreliability of automatic metrics like ROUGE. In this study, we manually analyze\n600 samples from three popular summarization datasets. Our study is driven by a\nsix-class typology which captures different noise types (missing facts,\nentities) and degrees of summarization difficulty (extractive, abstractive). We\nfollow with a thorough analysis of 27 state-of-the-art summarization models and\n5 popular metrics, and report our key insights: (1) Datasets have distinct data\nquality and complexity distributions, which can be traced back to their\ncollection process. (2) The performance of models and reliability of metrics is\ndependent on sample complexity. (3) Faithful summaries often receive low scores\nbecause of the poor diversity of references. We release the code, annotated\ndata and model outputs.\n"}
+{"abstract": "  We show that combining randomized measurement protocols with importance\nsampling allows for characterizing entanglement in significantly larger quantum\nsystems and in a more efficient way than in previous work. A drastic reduction\nof statistical errors is obtained using classical techniques of\nmachine-learning and tensor networks using partial information on the quantum\nstate. In present experimental settings of engineered many-body quantum systems\nthis effectively doubles the (sub-)system sizes for which entanglement can be\nmeasured. In particular, we show an exponential reduction of the required\nnumber of measurements to estimate the purity of product states and GHZ states.\n"}
+{"abstract": "  We present an infrared photodetector leveraging an all-epitaxial device\narchitecture consisting of a 'designer' plasmonic metal integrated with a\nquantum-engineered detector structure, all in a mature III-V semiconductor\nmaterial system. Incident light is coupled into surface plasmon-polariton modes\nat the detector/'designer' metal interface, and the strong confinement of these\nmodes allows for a sub-diffractive ($\\sim \\lambda_0 / 33$) detector absorber\nlayer thickness, effectively decoupling the detector's absorption efficiency\nand dark current. We demonstrate high-performance detectors operating at\nnon-cryogenic temperatures (T = 195 K), without sacrificing external quantum\nefficiency, and superior to well established and commercially-available\ndetectors. This work provides a practical and scalable plasmonic optoelectronic\ndevice architecture with real world mid-infrared applications.\n"}
+{"abstract": "  We examine the composition of barium stars in the context of mass transfer\nfrom an asymptotic giant branch (AGB) companion. We accrete between 0.01 and\n0.5 M$_\\odot$ of AGB ejecta on to low mass companions of [Fe/H] = -0.25 at the\nages expected for the end of the lives of AGB stars of 2.5, 3 and 4M$_\\odot$.\nIn each case, we form a star of 2.5 M$_\\odot$ which is thought to be a typical\nbarium star mass. We discuss the extent of dilution of accreted material as the\nstar evolves, and describe the impact on the surface abundances. For accretion\nfrom a 2.5\\ms\\ primary, if the secondary's initial mass is 2.45 M$_\\odot$ or\nmore, accretion takes place when the secondary is undergoing core helium\nburning. Using data from the sample of De Castro et al., we attempt to fit the\nobserved properties of 74 barium giants using the models we have computed. We\nfind that all but six of these objects are best fit using ejecta from 2.5\nM$_\\odot$ (32 objects) or 3 M$_\\odot$ (36 objects) AGB stars. Higher accretion\nmasses are typically required when accreting from a lower mass companion. We\nfind accretion masses that are broadly consistent with recent hydrodynamical\nsimulations of wind mass transfer, though the accretion efficiency is toward\nthe upper limit found in these simulations. For the 18 stars with reported\norbital periods, we find no strong correlations between period and accretion\nmass.\n"}
+{"abstract": "  Negative ions have been detected in abundance in recent years by spacecraft\nacross the solar system. These detections were, however, made by instruments\nnot designed for this purpose and, as such, significant uncertainties remain\nregarding the prevalence of these unexpected plasma components. In this\narticle, the phenomenon of photodetachment is examined and experimentally and\ntheoretically derived cross-sections are used to calculate photodetachment\nrates for a range of atomic and molecular negative ions subjected to the solar\nphoton spectrum. These rates are applied to negative ions outflowing from\nEuropa, Enceladus, Titan, Dione and Rhea and their trajectories are traced to\nconstrain source production rates and the extent to which negative ions are\nable to pervade the surrounding space environments. Predictions are also made\nfor further negative ion populations in the outer solar system with Triton used\nas an illustrative example. This study demonstrates how, at increased\nheliocentric distances, negative ions can form stable ambient plasma\npopulations and can be exploited by future missions to the outer solar system.\n"}
+{"abstract": "  The main focus of this work is a novel framework for the joint reconstruction\nand segmentation of parallel MRI (PMRI) brain data. We introduce an image\ndomain deep network for calibrationless recovery of undersampled PMRI data. The\nproposed approach is the deep-learning (DL) based generalization of local\nlow-rank based approaches for uncalibrated PMRI recovery including CLEAR [6].\nSince the image domain approach exploits additional annihilation relations\ncompared to k-space based approaches, we expect it to offer improved\nperformance. To minimize segmentation errors resulting from undersampling\nartifacts, we combined the proposed scheme with a segmentation network and\ntrained it in an end-to-end fashion. In addition to reducing segmentation\nerrors, this approach also offers improved reconstruction performance by\nreducing overfitting; the reconstructed images exhibit reduced blurring and\nsharper edges than independently trained reconstruction network.\n"}
+{"abstract": "  In this paper, we consider a variational formulation for the Dirichlet\nproblem of the wave equation with zero boundary and initial conditions, where\nwe use integration by parts in space and time. To prove unique solvability in a\nsubspace of $H^1(Q$) with $Q$ being the space-time domain, the classical\nassumption is to consider the right-hand side $f$ in $L^2(Q)$. Here, we analyze\na generalized setting of this variational formulation, which allows us to prove\nunique solvability also for $f$ being in the dual space of the test space,\ni.e., the solution operator is an isomorphism between the ansatz space and the\ndual of the test space. This new approach is based on a suitable extension of\nthe ansatz space to include the information of the differential operator of the\nwave equation at the initial time $t=0$. These results are of utmost importance\nfor the formulation and numerical analysis of unconditionally stable space-time\nfinite element methods, and for the numerical analysis of boundary element\nmethods to overcome the well-known norm gap in the analysis of boundary\nintegral operators.\n"}
+{"abstract": "  Let the diameter cover number, $D^t_r(G)$, denote the least integer $d$ such\nthat under any $r$-coloring of the edges of the graph $G$, there exists a\ncollection of $t$ monochromatic subgraphs of diameter at most $d$ such that\nevery vertex of $G$ is contained in at least one of the subgraphs. We explore\nthe diameter cover number with two colors and two subgraphs when $G$ is a\ncomplete multipartite graph with at least three parts. We determine exactly the\nvalue of $D_2^2(G)$ for all complete tripartite graphs $G$, and almost all\ncomplete multipartite graphs with more than three parts.\n"}
+{"abstract": "  The analysis of record-breaking events is of interest in fields such as\nclimatology, hydrology, economy or sports. In connection with the record\noccurrence, we propose three distribution-free statistics for the changepoint\ndetection problem. They are CUSUM-type statistics based on the upper and/or\nlower record indicators which occur in a series. Using a version of the\nfunctional central limit theorem, we show that the CUSUM-type statistics are\nasymptotically Kolmogorov distributed. The main results under the null\nhypothesis are based on series of independent and identically distributed\nrandom variables, but a statistic to deal with series with seasonal component\nand serial correlation is also proposed. A Monte Carlo study of size, power and\nchangepoint estimate has been performed. Finally, the methods are illustrated\nby analyzing the time series of temperatures at Madrid, Spain. The $\\textsf{R}$\npackage $\\texttt{RecordTest}$ publicly available on CRAN implements the\nproposed methods.\n"}
+{"abstract": "  We describe the population-based SEIR (susceptible, exposed, infected,\nremoved) model developed by the Irish Epidemiological Modelling Advisory Group\n(IEMAG), which advises the Irish government on COVID-19 responses. The model\nassumes a time-varying effective contact rate (equivalently, a time-varying\nreproduction number) to model the effect of non-pharmaceutical interventions. A\ncrucial technical challenge in applying such models is their accurate\ncalibration to observed data, e.g., to the daily number of confirmed new cases,\nas the past history of the disease strongly affects predictions of future\nscenarios. We demonstrate an approach based on inversion of the SEIR equations\nin conjunction with statistical modelling and spline-fitting of the data, to\nproduce a robust methodology for calibration of a wide class of models of this\ntype.\n"}
+{"abstract": "  In this paper, we compute normalized Laplacian spectra of central graph of a\nregular graph, central vertex join and central edge join of two regular graphs.\nAlso, we determine their Kemeny's constant and degree Kirchhoff index.\n"}
+{"abstract": "  We present a new adaptive parallel algorithm for the challenging problem of\nmulti-dimensional numerical integration on massively parallel architectures.\nAdaptive algorithms have demonstrated the best performance, but efficient\nmany-core utilization is difficult to achieve because the adaptive work-load\ncan vary greatly across the integration space and is impossible to predict a\npriori. Existing parallel algorithms utilize sequential computations on\nindependent processors, which results in bottlenecks due to the need for data\nredistribution and processor synchronization. Our algorithm employs a\nhigh-throughput approach in which all existing sub-regions are processed and\nsub-divided in parallel. Repeated sub-region classification and filtering\nimproves upon a brute-force approach and allows the algorithm to make efficient\nuse of computation and memory resources. A CUDA implementation shows orders of\nmagnitude speedup over the fastest open-source CPU method and extends the\nachievable accuracy for difficult integrands. Our algorithm typically\noutperforms other existing deterministic parallel methods.\n"}
+{"abstract": "  We show that log-concavity is the weakest power concavity preserved by the\nDirichlet heat flow in $N$-dimensional convex domains, where $N\\ge 2$ (indeed,\nwe prove that starting with a negative power concave initial datum may result\nin losing immediately any reminiscence of concavity). Jointly with what we\nalready know, i.e. that log-concavity is the strongest power concavity\npreserved by the Dirichlet heat flow, we see that log-concavity is indeed the\nonly power concavity preserved by the Dirichlet heat flow.\n"}
+{"abstract": "  The temporal Talbot effect refers to the periodic revivals of a pulse train\npropagating in a dispersive medium, and is a temporal analog of the spatial\nTalbot effect with group-velocity dispersion in time replacing diffraction in\nspace. Because of typically large temporal Talbot lengths, this effect has been\nobserved to date in only single-mode fibers, rather than with freely\npropagating fields in bulk dispersive media. Here we demonstrate for the first\ntime the temporal Talbot effect in free space by employing dispersive\nspace-time wave packets, whose spatio-temporal structure induces group-velocity\ndispersion of controllable magnitude and sign in free space.\n"}
+{"abstract": "  Thermal face imagery, which captures the naturally emitted heat from the\nface, is limited in availability compared to face imagery in the visible\nspectrum. To help address this scarcity of thermal face imagery for research\nand algorithm development, we present the DEVCOM Army Research Laboratory\nVisible-Thermal Face Dataset (ARL-VTF). With over 500,000 images from 395\nsubjects, the ARL-VTF dataset represents, to the best of our knowledge, the\nlargest collection of paired visible and thermal face images to date. The data\nwas captured using a modern long wave infrared (LWIR) camera mounted alongside\na stereo setup of three visible spectrum cameras. Variability in expressions,\npose, and eyewear has been systematically recorded. The dataset has been\ncurated with extensive annotations, metadata, and standardized protocols for\nevaluation. Furthermore, this paper presents extensive benchmark results and\nanalysis on thermal face landmark detection and thermal-to-visible face\nverification by evaluating state-of-the-art models on the ARL-VTF dataset.\n"}
+{"abstract": "  Time series data is a collection of chronological observations which is\ngenerated by several domains such as medical and financial fields. Over the\nyears, different tasks such as classification, forecasting, and clustering have\nbeen proposed to analyze this type of data. Time series data has been also used\nto study the effect of interventions over time. Moreover, in many fields of\nscience, learning the causal structure of dynamic systems and time series data\nis considered an interesting task which plays an important role in scientific\ndiscoveries. Estimating the effect of an intervention and identifying the\ncausal relations from the data can be performed via causal inference. Existing\nsurveys on time series discuss traditional tasks such as classification and\nforecasting or explain the details of the approaches proposed to solve a\nspecific task. In this paper, we focus on two causal inference tasks, i.e.,\ntreatment effect estimation and causal discovery for time series data, and\nprovide a comprehensive review of the approaches in each task. Furthermore, we\ncurate a list of commonly used evaluation metrics and datasets for each task\nand provide in-depth insight. These metrics and datasets can serve as\nbenchmarks for research in the field.\n"}
+{"abstract": "  The $\\pi$-calculus is used as a model for programming languages. Its contexts\nexhibit arbitrary concurrency, making them very discriminating. This may\nprevent validating desirable behavioural equivalences in cases when more\ndisciplined contexts are expected. In this paper we focus on two such common\ndisciplines: sequentiality, meaning that at any time there is a single thread\nof computation, and well-bracketing, meaning that calls to external services\nobey a stack-like discipline. We formalise the disciplines by means of type\nsystems. The main focus of the paper is on studying the consequence of the\ndisciplines on behavioural equivalence. We define and study labelled\nbisimilarities for sequentiality and well-bracketing. These relations are\ncoarser than ordinary bisimilarity. We prove that they are sound for the\nrespective (contextual) barbed equivalence, and also complete under a certain\ntechnical condition. We show the usefulness of our techniques on a number of\nexamples, that have mainly to do with the representation of functions and\nstore.\n"}
+{"abstract": "  An analysis of the projectile motion in stagnant air is presented for an\nevaporating respiratory micro-droplet which has been ejected from the mouth as\nan isolated droplet. It is assumed that the air resistance is a nonlinear\nfunction of the droplet's velocity and that the rate of decrease of the\ndroplet's external surface area depends only on the relative humidity and the\nambient temperature. The droplet's initial content is considered to be 98 wt%\nwater, 1 wt% salt and 1 wt% protein. The change of the average density of the\ndroplet due to water evaporation is determined, up to the instant when the\ndroplet reduces to its nucleus, consisting of salt and dry protein only. The\nnumerical solution of the governing differential equations of droplet's motion\ngives the trajectories of different-sized droplets ejected at different\nvelocities and angles, and under different relative humidities and rates of\nevaporation. The evaporation times are compared with the times for droplets to\nreach the ground after being ejected from a given height. The maximum\nhorizontal and vertical distances reached by the droplet are evaluated in the\npresence of wind and discussed in the context of possible infection spreading.\n"}
+{"abstract": "  We report an on-chip single mode microlaser with low-threshold fabricated on\nErbium doped lithium niobate on insulator (LNOI). The single mode laser\nemission at 1550.5 nm wavelength is generated in a coupled photonic molecule,\nwhich is facilitated by Vernier effect when pumping the photonic molecule at\n970 nm. A threshold pump power as low as 200 uW is demonstrated thanks to the\nhigh quality factor above 10^6. Moreover, the linewidth of the microlaser\nreaches 4 kHz, which is the best result in LNOI microlasers. Such single mode\nmicro-laser lithographically fabricated on chip is highly in demand by photonic\ncommunity.\n"}
+{"abstract": "  Advances in reinforcement learning (RL) have resulted in recent breakthroughs\nin the application of artificial intelligence (AI) across many different\ndomains. An emerging landscape of development environments is making powerful\nRL techniques more accessible for a growing community of researchers. However,\nmost existing frameworks do not directly address the problem of learning in\ncomplex operating environments, such as dense urban settings or defense-related\nscenarios, that incorporate distributed, heterogeneous teams of agents. To help\nenable AI research for this important class of applications, we introduce the\nAI Arena: a scalable framework with flexible abstractions for distributed\nmulti-agent reinforcement learning. The AI Arena extends the OpenAI Gym\ninterface to allow greater flexibility in learning control policies across\nmultiple agents with heterogeneous learning strategies and localized views of\nthe environment. To illustrate the utility of our framework, we present\nexperimental results that demonstrate performance gains due to a distributed\nmulti-agent learning approach over commonly-used RL techniques in several\ndifferent learning environments.\n"}
+{"abstract": "  To study the resolution required for simulating gravitational fragmentation\nwith newly developed Lagrangian hydrodynamic schemes, Meshless Finite Volume\nmethod (MFV) and Meshless Finite Mass method (MFM), we have performed a number\nof simulations of the Jeans test and compared the results with both the\nexpected analytic solution and results from the more standard Lagrangian\napproach: Smoothed Particle Hydrodynamics (SPH). We find that the different\nschemes converge to the analytic solution when the diameter of a fluid element\nis smaller than a quarter of the Jeans wavelength, $\\lambda_\\mathrm{J}$. Among\nthe three schemes, SPH/MFV shows the fastest/slowest convergence to the\nanalytic solution. Unlike the well-known behaviour of Eulerian schemes, none of\nthe Lagrangian schemes investigated displays artificial fragmentation when the\nperturbation wavelength, $\\lambda$, is shorter than $\\lambda_\\mathrm{J}$, even\nat low numerical resolution. For larger wavelengths ($\\lambda >\n\\lambda_\\mathrm{J}$) the growth of the perturbation is delayed when it is not\nwell resolved. Furthermore, with poor resolution, the fragmentation seen with\nthe MFV scheme proceeds very differently compared to the converged solution.\nAll these results suggest that, when unresolved, the ratio of the magnitude of\nhydrodynamic force to that of self-gravity at the sub-resolution scale is the\nlargest/smallest in MFV/SPH, the reasons for which we discussed in detail.\nThese tests are repeated to investigate the effect of kernels of higher-order\nthan the fiducial cubic spline. Our results indicate that the standard\ndeviation of the kernel is a more appropriate definition of the size of a fluid\nelement than its compact support radius.\n"}
+{"abstract": "  There is great interest in using near-term quantum computers to simulate and\nstudy foundational problems in quantum mechanics and quantum information\nscience, such as the scrambling measured by an out-of-time-ordered correlator\n(OTOC). Here we use an IBM Q processor, quantum error mitigation, and weaved\nTrotter simulation to study high-resolution operator spreading in a 4-spin\nIsing model as a function of space, time, and integrability. Reaching 4 spins\nwhile retaining high circuit fidelity is made possible by the use of a\nphysically motivated fixed-node variant of the OTOC, allowing scrambling to be\nestimated without overhead. We find clear signatures of ballistic operator\nspreading in a chaotic regime, as well as operator localization in an\nintegrable regime. The techniques developed and demonstrated here open up the\npossibility of using cloud-based quantum computers to study and visualize\nscrambling phenomena, as well as quantum information dynamics more generally.\n"}
+{"abstract": "  With its high sensitivity, the Pyramid wavefront sensor (PyWFS) is becoming\nan advantageous sensor for astronomical adaptive optics (AO) systems. However,\nthis sensor exhibits significant non-linear behaviours leading to challenging\nAO control issues. In order to mitigate these effects, we propose to use, in\naddition to the classical pyramid sensor, a focal plane image combined with a\nconvolutive description of the sensor to perform a fast tracking of the PyWFS\nnon-linearities, the so-called optical gains (OG). We show that this additional\nfocal plane imaging path only requires a small fraction of the total flux,\nwhile representing a robust solution to estimate the PyWFS OG. Finally, we\ndemonstrate the gain brought by our method with the specific examples of\nbootstrap and Non-Common Path Aberrations (NCPA) handling.\n"}
+{"abstract": "  Many unsupervised domain adaptation (UDA) methods exploit domain adversarial\ntraining to align the features to reduce domain gap, where a feature extractor\nis trained to fool a domain discriminator in order to have aligned feature\ndistributions. The discrimination capability of the domain classifier w.r.t the\nincreasingly aligned feature distributions deteriorates as training goes on,\nthus cannot effectively further drive the training of feature extractor. In\nthis work, we propose an efficient optimization strategy named Re-enforceable\nAdversarial Domain Adaptation (RADA) which aims to re-energize the domain\ndiscriminator during the training by using dynamic domain labels. Particularly,\nwe relabel the well aligned target domain samples as source domain samples on\nthe fly. Such relabeling makes the less separable distributions more separable,\nand thus leads to a more powerful domain classifier w.r.t. the new data\ndistributions, which in turn further drives feature alignment. Extensive\nexperiments on multiple UDA benchmarks demonstrate the effectiveness and\nsuperiority of our RADA.\n"}
+{"abstract": "  The quest for safe high-energy batteries with \"5V-class\" cathodes and lithium\nmetal anodes drives research into solid electrolytes. However, reasons for the\nlarge charge transfer resistances -- the major bottleneck of all-solid-state\nbatteries -- are still debated. In this article, we explore the processes in\nincompressible solid electrolytes between blocking electrodes by theory-based\ncontinuum modeling and numerical simulations. We investigate the experimentally\nobserved wide space-charge-zones in solid electrolytes, which are a possible\ncause for the high interfacial resistances. On time scales relevant for battery\napplications, we reduce our model equations. Analytic and numeric calculations\npredict and study the actual structure of space-charge-layers in solid\nelectrolytes. To illustrate these dynamics and validate our model,\ncomputational results are presented and compared with experimental\nobservations. Analog to semiconductors, we determine the material dependent,\nasymmetric space-charge-layer width in the low temperature limit approximately.\nThis allows us to make an explicit statement about the influence of defect\nconcentrations and dielectric properties on the width of the\nspace-charge-layers in homogeneous solid electrolytes.\n"}
+{"abstract": "  We show that if an equivalence relation $E$ on a Polish space is a countable\nunion of smooth Borel subequivalence relations, then there is either a Borel\nreduction of $E$ to a countable Borel equivalence relation on a Polish space or\na continuous embedding of $\\mathbb{E}_1$ into $E$. We also establish related\nresults concerning countable unions of more general Borel equivalence\nrelations.\n"}
+{"abstract": "  The purpose of this note is to establish convergence of random walks on the\nmoduli space of Abelian differentials on compact Riemann surfaces in two\ndifferent modes: convergence of the $n$-step distributions from almost every\nstarting point in an affine invariant submanifold towards the associated affine\ninvariant measure, and almost sure pathwise equidistribution towards the affine\ninvariant measure on the $SL_2(\\mathbb{R})$-orbit closure of an arbitrary\nstarting point. These are analogues to previous results for random walks on\nhomogeneous spaces.\n"}
+{"abstract": "  We discuss cross-link relations between the $\\pi$ and $\\rho$-meson channels\nemerging from two different descriptions of the QCD vacuum: Instanton physics\nand QCD sum rules with nonlocal condensates (NLC). We derive in both schemes an\nintriguing linear relation between the $\\pi$ and the $\\rho^\\|$-meson\ndistribution amplitudes in terms of their conformal coefficients and work out\nthe specific impact of the scalar NLC in these two channels. Using a simple\nmodel with Gaussian decay of the scalar NLC, we are able to relate it to the\nmoments of the pion non-singlet parton distribution function measurable in\nexperiment -- a highly nontrivial result. The implications for the pion and the\n$\\rho^\\|$-meson DAs entailed by the obtained cross-link relations are outlined\nin terms of two generic scenarios.\n"}
+{"abstract": "  In order for an autonomous robot to efficiently explore an unknown\nenvironment, it must account for uncertainty in sensor measurements, hazard\nassessment, localization, and motion execution. Making decisions for maximal\nreward in a stochastic setting requires value learning and policy construction\nover a belief space, i.e., probability distribution over all possible\nrobot-world states. However, belief space planning in a large spatial\nenvironment over long temporal horizons suffers from severe computational\nchallenges. Moreover, constructed policies must safely adapt to unexpected\nchanges in the belief at runtime. This work proposes a scalable value learning\nframework, PLGRIM (Probabilistic Local and Global Reasoning on Information\nroadMaps), that bridges the gap between (i) local, risk-aware resiliency and\n(ii) global, reward-seeking mission objectives. Leveraging hierarchical belief\nspace planners with information-rich graph structures, PLGRIM addresses\nlarge-scale exploration problems while providing locally near-optimal coverage\nplans. We validate our proposed framework with high-fidelity dynamic\nsimulations in diverse environments and on physical robots in Martian-analog\nlava tubes.\n"}
+{"abstract": "  We present a gauge formulation of the special affine algebra extended to\ninclude an antisymmetric tensorial generator belonging to the tensor\nrepresentation of the special linear group. We then obtain a Maxwell modified\nmetric affine gravity action with a cosmological constant term. We find the\nfield equations of the theory and show that the theory reduces to an\nEinstein-like equation for metric affine gravity with the source added to the\ngravity equations with cosmological constant \\mu contains linear contributions\nfrom the new gauge fields. The reduction of the Maxwell metric affine gravity\nto Riemann-Cartan one is discussed and the shear curvature tensor corresponding\nto the symmetric part of the special linear connection is identified with the\ndark energy. Furthermore, the new gauge fields interpreted as geometrical\ninflaton vector fields which drive accelerated expansion.\n"}
+{"abstract": "  The global spread of COVID-19, the disease caused by the novel coronavirus\nSARS-CoV-2, has cast a significant threat to mankind. As the COVID-19 situation\ncontinues to evolve, predicting localized disease severity is crucial for\nadvanced resource allocation. This paper proposes a method named COURAGE\n(COUnty aggRegation mixup AuGmEntation) to generate a short-term prediction of\n2-week-ahead COVID-19 related deaths for each county in the United States,\nleveraging modern deep learning techniques. Specifically, our method adopts a\nself-attention model from Natural Language Processing, known as the transformer\nmodel, to capture both short-term and long-term dependencies within the time\nseries while enjoying computational efficiency. Our model fully utilizes\npublicly available information of COVID-19 related confirmed cases, deaths,\ncommunity mobility trends and demographic information, and can produce\nstate-level prediction as an aggregation of the corresponding county-level\npredictions. Our numerical experiments demonstrate that our model achieves the\nstate-of-the-art performance among the publicly available benchmark models.\n"}
+{"abstract": "  We reconsider the problem of bounding higher derivative couplings in\nconsistent weakly coupled gravitational theories, starting from general\nassumptions about analyticity and Regge growth of the S-matrix. Higher\nderivative couplings are expected to be of order one in the units of the UV\ncutoff. Our approach justifies this expectation and allows to prove precise\nbounds on the order one coefficients. Our main tool are dispersive sum rules\nfor the S-matrix. We overcome the difficulties presented by the graviton pole\nby measuring couplings at small impact parameter, rather than in the forward\nlimit. We illustrate the method in theories containing a massless scalar\ncoupled to gravity, and in theories with maximal supersymmetry.\n"}
+{"abstract": "  Tactile sensing is important for robots to perceive the world as it captures\nthe texture and hardness of the object in contact and is robust to illumination\nand colour variances. However, due to the limited sensing area and the\nresistance of the fixed surface, current tactile sensors have to tap the\ntactile sensor on target object many times when assessing a large surface,\ni.e., pressing, lifting up and shifting to another region. This process is\nineffective and time consuming. It is also undesirable to drag such sensors as\nthis often damages the sensitive membrane of the sensor or the object. To\naddress these problems, we propose a cylindrical optical tactile sensor named\nTouchRoller that can roll around its center axis. It maintains being in contact\nwith the assessed surface throughout the entire motion, which allows for\nmeasuring the object continuously and effectively. Extensive experiments show\nthat the TouchRoller sensor can cover a textured surface of 8cm*11cm in a short\ntime of 10s, much more effectively than a flat optical tactile sensor (in\n196s). The reconstructed map of the texture from the collected tactile images\nhas a high Structural Similarity Index (SSIM) of 0.31 on average, when compared\nwith the visual texture. In addition, the contacts on the sensor can be\nlocalised with a low localisation error, 2.63mm in the center regions and\n7.66mm on average. The proposed sensor will enable the fast assessment of large\nsurfaces with high-resolution tactile sensing, and also the effective\ncollection of tactile images.\n"}
+{"abstract": "  Local available quantum correlations (LAQC), as defined by Mundarain et al.,\nare analyzed for two subsets of 2-qubit X states. We start by studying X-states\nthat are symmetric under the exchange of subsystems, that is, those with the\nsame non-null local Bloch vector. We also analyze the subset of states that are\nanti-symmetric under subsystem exchange, that is, those that have non-null\nlocal Bloch vectors with an equal magnitude but opposite direction. We present\nvarious examples and compare the obtained results to concurrence as an\nentanglement measure and with quantum discord. We have also included markovian\ndecoherence, with the analysis of amplitude damping decoherence for Werner\nstates. As was previously observed for depolarization and phase damping\ndecoherence, LAQC did not exhibit sudden death behavior for Werner states under\namplitude damping decoherence.\n"}
+{"abstract": "  We demonstrate the low temperature suppression of the platinum (Pt) spin\nNernst angle in bilayers consisting of the antiferromagnetic insulator hematite\n($\\alpha$-Fe$_2$O$_3$) and Pt upon measuring the transverse spin Nernst\nmagnetothermopower (TSNM). We show that the observed signal stems from the\ninterplay between the interfacial spin accumulation in Pt originating from the\nspin Nernst effect and the orientation of the N\\'eel vector of\n$\\alpha$-Fe$_2$O$_3$, rather than its net magnetization. Since the latter is\nnegligible in an antiferromagnet, our device is superior to ferromagnetic\nstructures, allowing to unambiguously distinguish the TSNM from thermally\nexcited magnon transport (TMT), which usually dominates in ferri/ferromagnets\ndue to their non-zero magnetization. Evaluating the temperature dependence of\nthe effect, we observe a vanishing TSNM below ~100 K. We compare these results\nwith theoretical calculations of the temperature dependent spin Nernst\nconductivity and find excellent agreement. This provides evidence for a\nvanishing spin Nernst angle of Pt at low temperatures and the dominance of\nextrinsic contributions to the spin Nernst effect.\n"}
+{"abstract": "  We report on the non-destructive measurement of Young's modulus of thin-film\nsingle crystal beta gallium oxide (beta-Ga2O3) out of its nanoscale mechanical\nstructures by measuring their fundamental mode resonance frequencies. From the\nmeasurements, we extract Young's modulus in (100) plane, EY,(100) =\n261.4+/-20.6 GPa, for beta-Ga2O3 nanoflakes synthesized by low-pressure\nchemical vapor deposition (LPCVD), and Young's modulus in [010] direction,\nEY,[010] = 245.8+/-9.2 GPa, for beta-Ga2O3 nanobelts mechanically cleaved from\nbulk beta-Ga2O3 crystal grown by edge-defined film-fed growth (EFG) method. The\nYoung's moduli extracted directly on nanomechanical resonant device platforms\nare comparable to theoretical values from first-principle calculations and\nexperimentally extracted values from bulk crystal. This study yields important\nquantitative nanomechanical properties of beta-Ga2O3 crystals, and helps pave\nthe way for further engineering of beta-Ga2O3 micro/nanoelectromechanical\nsystems (M/NEMS) and transducers.\n"}
+{"abstract": "  As passenger vehicle technologies have advanced, so have their capabilities\nto avoid obstacles, especially with developments in tires, suspensions,\nsteering, as well as safety technologies like ABS, ESC, and more recently, ADAS\nsystems. However, environments around passenger vehicles have also become more\ncomplex, and dangerous. There have previously been studies that outline driver\ntendencies and performance capabilities when attempting to avoid obstacles\nwhile driving passenger vehicles. Now that autonomous vehicles are being\ndeveloped with obstacle avoidance capabilities, it is important to target\nperformance that meets or exceeds that of human drivers. This manuscript\nhighlights systems that are crucial for an emergency obstacle avoidance\nmaneuver (EOAM) and identifies the state-of-the-art for each of the related\nsystems, while considering the nuances of traveling at highway speeds. Some of\nthe primary EOAM-related systems/areas that are discussed in this review are:\ngeneral path planning methods, system hierarchies, decision-making, trajectory\ngeneration, and trajectory-tracking control methods. After concluding remarks,\nsuggestions for future work which could lead to an ideal EOAM development, are\ndiscussed.\n"}
+{"abstract": "  We use an empirical relation to measure the HI scale height of relatively HI\nrich galaxies using 21-cm observations. The galaxies were selected from the\nBLUEDISK, THINGS and VIVA surveys. We aim to compare the thickness of the HI\nlayer of unusually HI rich with normal spiral galaxies and find any correlation\nbetween the HI scale height with other galaxies properties. We found that on\naverage the unusually HI rich galaxies have similar HI disk thickness to the\ncontrol sample and the galaxies selected from the THINGS and VIVA surveys\nwithin their uncertainties. Our result also show that the average thickness of\nthe neutral hydrogen inside the optical disk is correlated with the atomic gas\nfraction inside the optical disk with a scatter of ~ 0.22 dex. A correlation is\nalso found between the HI scale height with the atomic-to-molecular gas ratio\nwhich indicates the link between star formation and the vertical distribution\nof HI which is consistent with previous studies. This new scaling relation\nbetween the HI scale height and atomic gas fraction will allow us to predict\nthe HI scale height of a large number of galaxies but a larger sample is needed\nto decrease the scatter.\n"}
+{"abstract": "  We define a new measure of causation from a fluctuation-response theorem for\nKullback-Leibler divergences, based on the information-theoretic cost of\nperturbations. This information response has both the invariance properties\nrequired for an information-theoretic measure and the physical interpretation\nof a propagation of perturbations. In linear systems, the information response\nreduces to the transfer entropy, providing a connection between Fisher and\nmutual information.\n"}
+{"abstract": "  The streaming instability (SI) is a mechanism to aerodynamically concentrate\nsolids in protoplanetary disks and trigger the formation of planetesimals. The\nSI produces strong particle clumping if the ratio of solid to gas surface\ndensity -- an effective metallicity -- exceeds a critical value. This critical\nvalue depends on particle sizes and disk conditions such as radial\ndrift-inducing pressure gradients and levels of turbulence. To quantify these\nthresholds, we perform a suite of vertically-stratified SI simulations over a\nrange of dust sizes and metallicities. We find a critical metallicity as low as\n0.4% for the optimum particle sizes and standard radial pressure gradients\n(normalized value of $\\Pi = 0.05$). This sub-Solar metallicity is lower than\nprevious results due to improved numerical methods and computational effort. We\ndiscover a sharp increase in the critical metallicity for small solids, when\nthe dimensionless stopping time (Stokes number) is $\\leq 0.01$. We provide\nsimple fits to the size-dependent SI clumping threshold, including\ngeneralizations to different disk models and levels of turbulence. We also find\nthat linear, unstratified SI growth rates are a surprisingly poor predictor of\nparticle clumping in non-linear, stratified simulations, especially when the\nfinite resolution of simulations is considered. Our results widen the parameter\nspace for the SI to trigger planetesimal formation.\n"}
+{"abstract": "  In this note we give two small results concerning the correlations of the\nSelberg sieve weights. We then use these estimates to derive a new\n(conditional) lower bound on the variance of the primes in short intervals, and\nalso on the so-called `form factor' for the pair correlations of the zeros of\nthe Riemann zeta function. Our bounds ultimately rely on the estimates of\nBettin--Chandee for trilinear Kloosterman fractions.\n"}
+{"abstract": "  The ability to capture complex linguistic structures and long-term\ndependencies among words in the passage is essential for discourse-level\nrelation extraction (DRE) tasks. Graph neural networks (GNNs), one of the\nmethods to encode dependency graphs, have been shown effective in prior works\nfor DRE. However, relatively little attention has been paid to receptive fields\nof GNNs, which can be crucial for cases with extremely long text that requires\ndiscourse understanding. In this work, we leverage the idea of graph pooling\nand propose to use pooling-unpooling framework on DRE tasks. The pooling branch\nreduces the graph size and enables the GNNs to obtain larger receptive fields\nwithin fewer layers; the unpooling branch restores the pooled graph to its\noriginal resolution so that representations for entity mention can be\nextracted. We propose Clause Matching (CM), a novel linguistically inspired\ngraph pooling method for NLP tasks. Experiments on two DRE datasets demonstrate\nthat our models significantly improve over baselines when modeling long-term\ndependencies is required, which shows the effectiveness of the\npooling-unpooling framework and our CM pooling method.\n"}
+{"abstract": "  Feed recommendation models are widely adopted by numerous feed platforms to\nencourage users to explore the contents they are interested in. However, most\nof the current research simply focus on targeting user's preference and lack\nin-depth study of avoiding objectionable contents to be frequently recommended,\nwhich is a common reason that let user detest. To address this issue, we\npropose a Deep Latent Emotion Network (DLEN) model to extract latent\nprobability of a user preferring a feed by modeling multiple targets with\nsemi-supervised learning. With this method, the conflicts of different targets\nare successfully reduced in the training phase, which improves the training\naccuracy of each target effectively. Besides, by adding this latent state of\nuser emotion to multi-target fusion, the model is capable of decreasing the\nprobability to recommend objectionable contents to improve user retention and\nstay time during online testing phase. DLEN is deployed on a real-world\nmulti-task feed recommendation scenario of Tencent QQ-Small-World with a\ndataset containing over a billion samples, and it exhibits a significant\nperformance advantage over the SOTA MTL model in offline evaluation, together\nwith a considerable increase by 3.02% in view-count and 2.63% in user stay-time\nin production. Complementary offline experiments of DLEN model on a public\ndataset also repeat improvements in various scenarios. At present, DLEN model\nhas been successfully deployed in Tencent's feed recommendation system.\n"}
+{"abstract": "  We consider the problem of jointly estimating expectation values of many\nPauli observables, a crucial subroutine in variational quantum algorithms.\nStarting with randomized measurements, we propose an efficient derandomization\nprocedure that iteratively replaces random single-qubit measurements with fixed\nPauli measurements; the resulting deterministic measurement procedure is\nguaranteed to perform at least as well as the randomized one. In particular,\nfor estimating any $L$ low-weight Pauli observables, a deterministic\nmeasurement on only of order $\\log(L)$ copies of a quantum state suffices. In\nsome cases, for example when some of the Pauli observables have a high weight,\nthe derandomized procedure is substantially better than the randomized one.\nSpecifically, numerical experiments highlight the advantages of our\nderandomized protocol over various previous methods for estimating the\nground-state energies of small molecules.\n"}
+{"abstract": "  This study integrates the educators digital competency (DC), as an individual\ncharacteristic construct of the task-technology fit (TTF) theory, to examine a\nbetter fit between Moodle using and teaching task, and to investigate its\neffect on both Moodles utilization and their task performance. For assessing\nour proposed hypotheses, an online survey was conducted with 238 teaching staff\nfrom different departments of universities in Malaysia. Using Structural\nEquation Modelling (SEM), our analysis revealed that all the proposed\ncomponents (i.e., technology literacy, knowledge deepening, presentation\nskills, and professional skills) of digital competency significantly influenced\nthe TTF. The Task-Technology Fit was also found as an influential construct,\nwhich positively and significantly affected both Moodles utilization and\nteachers task performance. Besides, Moodles utilization was confirmed to be a\nsubstantial determinant of the performance impact. In the end, this study\nincluded limitations and future directions based on how the study's\ncontribution can support academics and practitioners for assessing and\nunderstanding what particular components of digital competency impact TTF,\nwhich in turn may influence the systems utilization and performance impact.\n"}
+{"abstract": "  Having recognized gender bias as a major issue affecting current translation\ntechnologies, researchers have primarily attempted to mitigate it by working on\nthe data front. However, whether algorithmic aspects concur to exacerbate\nunwanted outputs remains so far under-investigated. In this work, we bring the\nanalysis on gender bias in automatic translation onto a seemingly neutral yet\ncritical component: word segmentation. Can segmenting methods influence the\nability to translate gender? Do certain segmentation approaches penalize the\nrepresentation of feminine linguistic markings? We address these questions by\ncomparing 5 existing segmentation strategies on the target side of speech\ntranslation systems. Our results on two language pairs (English-Italian/French)\nshow that state-of-the-art sub-word splitting (BPE) comes at the cost of higher\ngender bias. In light of this finding, we propose a combined approach that\npreserves BPE overall translation quality, while leveraging the higher ability\nof character-based segmentation to properly translate gender.\n"}
+{"abstract": "  In this paper we recreate, and improve, the binary classification method for\nparticles proposed in Roe et al. (2005) paper \"Boosted decision trees as an\nalternative to artificial neural networks for particle identification\". Such\nparticles are tau neutrinos, which we will refer to as background, and\nelectronic neutrinos: the signal we are interested in. In the original paper\nthe preferred algorithm is a Boosted decision tree. This is due to its low\neffort tuning and good overall performance at the time. Our choice for\nimplementation is a deep neural network, faster and more promising in\nperformance. We will show how, using modern techniques, we are able to improve\non the original result, both in accuracy and in training time.\n"}
+{"abstract": "  We report extensive numerical simulations of different models of 2D polymer\nrings with internal elasticity. We monitor the dynamical behavior of the rings\nas a function of the packing fraction, to address the effects of particle\ndeformation on the collective response of the system. In particular, we compare\nthree different models: (i) a recently investigated model [Gnan \\& Zaccarelli,\nNat. Phys. 15, 683 (2019)], where an inner hertzian field providing the\ninternal elasticity acts on the monomers of the ring, (ii) the same model where\nthe effect of such a field on the center of mass is balanced by opposite forces\nand (iii) a semi-flexible model where an angular potential between adjacent\nmonomers induces strong particle deformations. By analyzing the dynamics of the\nthree models, we find that, in all cases, there exists a direct link between\nthe system fragility and particle asphericity. Among the three, only the first\nmodel displays anomalous dynamics in the form of a super-diffusive behavior of\nthe mean squared displacement and of a compressed exponential relaxation of the\ndensity auto-correlation function. We show that this is due to the combination\nof internal elasticity and the out-of-equilibrium force self-generated by each\nring, both of which are necessary ingredients to induce such peculiar behavior\noften observed in experiments of colloidal gels. These findings reinforce the\nrole of particle deformation, connected to internal elasticity, in driving the\ndynamical response of dense soft particles.\n"}
+{"abstract": "  We consider a wireless federated learning system where multiple data holder\nedge devices collaborate to train a global model via sharing their parameter\nupdates with an honest-but-curious parameter server. We demonstrate that the\ninherent hardware-induced distortion perturbing the model updates of the edge\ndevices can be exploited as a privacy-preserving mechanism. In particular, we\nmodel the distortion as power-dependent additive Gaussian noise and present a\npower allocation strategy that provides privacy guarantees within the framework\nof differential privacy. We conduct numerical experiments to evaluate the\nperformance of the proposed power allocation scheme under different levels of\nhardware impairments.\n"}
+{"abstract": "  Recent data protection regulations (such as GDPR and CCPA) grant consumers\nvarious rights, including the right to access, modify or delete any personal\ninformation collected about them (and retained) by a service provider. To\nexercise these rights, one must submit a verifiable consumer request proving\nthat collected data indeed pertains to them. This action is relatively\nstraightforward for consumers with active accounts with a service provider at\nthe time of data collection, since they can use standard (e.g., password-based)\nmeans of authentication to validate their requests. However, a major conundrum\narises from the need to support consumers without accounts to exercise their\nrights. To this end, some service providers began requiring these accountless\nconsumers to reveal and prove their identities (e.g., using government-issued\ndocuments, utility bills or credit card numbers) as part of issuing a\nverifiable consumer request. While understandable as a short-term cure, this\napproach is, at the same time, cumbersome and expensive for service providers\nas well as very privacy-invasive for consumers. Consequently, there is a strong\nneed to provide better means of authenticating requests from accountless\nconsumers. To achieve this, we propose VICEROY, a privacy-preserving and\nscalable framework for producing proofs of data ownership, which can be used as\na basis for verifiable consumer requests. Building upon existing web techniques\nand features (e.g., cookies), VICEROY allows accountless consumers to interact\nwith service providers, and later prove -- in a privacy-preserving manner --\nthat they are the same person, with minimal requirements for both parties. We\ndesign and implement VICEROY with the emphasis on security/privacy,\ndeployability and usability. We also thoroughly assess its practicality via\nextensive experiments.\n"}
+{"abstract": "  The advent of social media platforms has been a catalyst for the development\nof digital photography that engendered a boom in vision applications. With this\nmotivation, we introduce a large-scale dataset termed 'Photozilla', which\nincludes over 990k images belonging to 10 different photographic styles. The\ndataset is then used to train 3 classification models to automatically classify\nthe images into the relevant style which resulted in an accuracy of ~96%. With\nthe rapid evolution of digital photography, we have seen new types of\nphotography styles emerging at an exponential rate. On that account, we present\na novel Siamese-based network that uses the trained classification models as\nthe base architecture to adapt and classify unseen styles with only 25 training\nsamples. We report an accuracy of over 68% for identifying 10 other distinct\ntypes of photography styles. This dataset can be found at\nhttps://trisha025.github.io/Photozilla/\n"}
+{"abstract": "  We show that bulk operators lying between the outermost extremal surface and\nthe asymptotic boundary admit a simple boundary reconstruction in the classical\nlimit. This is the converse of the Python's lunch conjecture, which proposes\nthat operators with support between the minimal and outermost (quantum)\nextremal surfaces - e.g. the interior Hawking partners - are highly complex.\nOur procedure for reconstructing this \"simple wedge\" is based on the HKLL\nconstruction, but uses causal bulk propagation of perturbed boundary conditions\non Lorentzian timefolds to expand the causal wedge as far as the outermost\nextremal surface. As a corollary, we establish the Simple Entropy proposal for\nthe holographic dual of the area of a marginally trapped surface as well as a\nsimilar holographic dual for the outermost extremal surface. We find that the\nsimple wedge is dual to a particular coarse-grained CFT state, obtained via\naveraging over all possible Python's lunches. An efficient quantum circuit\nconverts this coarse-grained state into a \"simple state\" that is\nindistinguishable in finite time from a state with a local modular Hamiltonian.\nUnder certain circumstances, the simple state modular Hamiltonian generates an\nexactly local flow; we interpret this result as a holographic dual of black\nhole uniqueness.\n"}
+{"abstract": "  Two recent large data releases for the Atacama Cosmology Telescope (ACT),\ncalled DR4 and DR5, are available for public access. These data include\ntemperature and polarization maps that cover nearly half the sky at arcminute\nresolution in three frequency bands; lensing maps and component-separated maps\ncovering ~ 2,100 deg^2 of sky; derived power spectra and cosmological\nlikelihoods; a catalog of over 4,000 galaxy clusters; and supporting ancillary\nproducts including beam functions and masks. The data and products are\ndescribed in a suite of ACT papers; here we provide a summary. In order to\nfacilitate ease of access to these data we present a set of Jupyter IPython\nnotebooks developed to introduce users to DR4, DR5, and the tools needed to\nanalyze these data. The data products (excluding simulations) and the set of\nnotebooks are publicly available on the NASA Legacy Archive for Microwave\nBackground Data Analysis (LAMBDA); simulation products are available on the\nNational Energy Research Scientific Computing Center (NERSC).\n"}
+{"abstract": "  The nuclear obscurer of Active Galactic Nuclei (AGN) is poorly understood in\nterms of its origin, geometry and dynamics. We investigate whether physically\nmotivated geometries emerging from hydro-radiative simulations can be\ndifferentiated with X-ray reflection spectroscopy. For two new geometries, the\nradiative fountain model of Wada (2012) and a warped disk, we release spectral\nmodels produced with the ray tracing code XARS. We contrast these models with\nspectra of three nearby AGN taken by NuSTAR and Swift/BAT. Along heavily\nobscured sight-lines, the models present different 4-20keV continuum spectra.\nThese can be differentiated by current observations. Spectral fits of the\nCircinus Galaxy favor the warped disk model over the radiative fountain, and\nclumpy or smooth torus models. The necessary reflector (NH>10^25/cm^2) suggests\na hidden population of heavily Compton-thick AGN amongst local galaxies. X-ray\nreflection spectroscopy is a promising pathway to understand the nuclear\nobscurer in AGN.\n"}
+{"abstract": "  In an effort to find an effective interaction which can consistently be used\nfor both the mean-field part and the residual part in beyond mean-field\ntheories, properties of the Skyrme interactions as a residual interaction are\ninvestigated. The time-dependent density-matrix theory (TDDM) is used as a\nbeyond mean-field theory and the ground states of $^{16}$O and $^{40}$Ca are\ncalculated using the five standard parametrizations of the Skyrme interaction\nwhich differs in density and momentum dependence. It is found that the Skyrme\ninteraction which has strong density dependence and weak momentum dependence\ninduces substantial ground-state correlations comparable to the results of\nother theoretical calculations.\n"}
+{"abstract": "  Due to road traffic accidents, 6941 Filipinos died in 2010, and thousands\nmore were wounded or disabled. Head and neck injuries are the main cause of\ndeath, severe injury, and motorcycle users disabilities. Motorcycle users make\nup a large proportion of those on the road who were killed. The main purpose of\nthe study is to develop an MCU Based Motorcycle System for Optimum Road Safety\nwith Anti-theft Capability that will help motorcycle riders to be safe while\ntravelling in national roads. The researchers will be using the prototyping\nmethodology where in a prototype is built according to the initial requirements\ngathered from the motorists themselves. The expected result of the proposed\nmethodology is the system will be utilizing the different function of each\nmodules to ensure that the riders will be able to detect and avoid possible\ndanger while on the road. As a result of different literature in relation to\neach module, the system is expected to provide a new leap to ensure the safety\nof all riders here in the Philippines. Future studies will ensure the\ndevelopment of the system, provide testing and improve the functionality of the\nsystem depending on the test result. Due to the high increase in the number of\ncars and motorcycle travelling on national road, the percentage of accidents\nalso is getting higher. In line with that, the proposed system is expected to\nlessen the percentage of accident by avoiding the possible cause of it.\n"}
+{"abstract": "  We present a microscopic quantum theory of light-matter interaction in\npristine sheets of two-dimensional semiconductors coupled to localized\nelectromagnetic resonators such as optical nanocavities or plasmonic particles.\nThe light-matter interaction breaks the translation symmetry of excitons in the\ntwo-dimensional lattice, and we find that this symmetry-breaking interaction\nleads to the formation of a localized exciton state, which mimics the spatial\ndistribution of the electromagnetic field of the resonator. The localized\nexciton state is in turn coupled to an environment of residual exciton states.\nWe quantify the influence of the environment and find that it is most\npronounced for small lateral confinement length scales of the electromagnetic\nfield in the resonator, and that environmental effects can be neglected if this\nlength scale is sufficiently large. The microscopic theory provides a\nphysically appealing derivation of the coupled oscillator models widely used to\nmodel experiments on these types of systems, in which all observable quantities\nare directly derived from the material parameters and the properties of the\nresonant electromagnetic field. As a consistency check, we show that the theory\nrecovers the results of semiclassical electromagnetic calculations and\nexperimental measurements of the excitonic dielectric response in the linear\nexcitation limit. The theory, however, is not limited to linear response, and\nin general describes nonlinear exciton-exciton interactions in the localized\nexciton state, thereby providing a powerful means of investigating the\nnonlinear optical response of such systems.\n"}
+{"abstract": "  Hidden Markov models (HMMs) and their extensions have proven to be powerful\ntools for classification of observations that stem from systems with temporal\ndependence as they take into account that observations close in time are likely\ngenerated from the same state (i.e.\\ class). When information on the classes of\nthe observations is available in advanced, supervised methods can be applied.\nIn this paper, we provide details for the implementation of four models for\nclassification in a supervised learning context: HMMs, hidden semi-Markov\nmodels (HSMMs), autoregressive-HMMs, and autoregressive-HSMMs. Using\nsimulations, we study the classification performance under various degrees of\nmodel misspecification to characterize when it would be important to extend a\nbasic HMM to an HSMM. As an application of these techniques we use the models\nto classify accelerometer data from Merino sheep to distinguish between four\ndifferent behaviors of interest. In particular in the field of movement\necology, collection of fine-scale animal movement data over time to identify\nbehavioral states has become ubiquitous, necessitating models that can account\nfor the dependence structure in the data. We demonstrate that when the aim is\nto conduct classification, various degrees of model misspecification of the\nproposed model may not impede good classification performance unless there is\nhigh overlap between the state-dependent distributions, that is, unless the\nobservation distributions of the different states are difficult to\ndifferentiate.\n"}
+{"abstract": "  In this study, we propose a novel heuristic two-step algorithm for shared\nridehailing in which users can share their rides with only one more user. The\nalgorithm, which is centrally formulated, starts with matching users and\ncreating a set of passenger pairs in step 1 and is followed by solving an\nassignment problem to assign passenger pairs to the vehicles. To solve the\nproblem of high computational time in dynamic ride-matching problems, we\npropose a distributed system that is based on vehicle to infrastructure (V2I)\nand infrastructure to infrastructure (I2I) communication. To evaluate the\ndistributed system's performance, we compare it with the proposed centralized\nridehailing algorithm. Both centralized and distributed systems are implemented\nin a micro-traffic simulator to assess their performance and their impact on\ntraffic congestion. Downtown Toronto road network was chosen as the study area.\nBased on our obtained results, the service rate of the distributed system was\n91.59% which is close to 95.80% in the centralized system. However, the\ndistributed system yielded much lower computational time compared to\ncentralized. Furthermore, the scalability of the distributed system was shown\nby testing it on a small network and comparing with the entire network.\n"}
+{"abstract": "  Results on the existence, uniqueness and strict comparison for solutions to a\nBSDE driven by a multi-dimensional RCLL martingale are established. The goal is\nto develop a general multi-asset framework encompassing a wide spectrum of\nnonlinear financial models with jumps, including as particular cases the setups\nstudied by Peng and Xu \\cite{PX2009,PX2010} and Dumitrescu et al.\n\\cite{DGQS2018} who dealt with BSDEs driven by a one-dimensional Brownian\nmotion and a purely discontinuous martingale with a single jump.\n"}
+{"abstract": "  The Chow group of zero cycles in the moduli space of stable pointed curves of\ngenus zero is isomorphic to the integer additive group. Let $M$ be monomial in\nthis Chow group. If no two factors of $M$ fulfill a particular quadratic\nrelation, then the monomial can be represented equivalently by a specific tree;\notherwise, $M$ is mapped to zero under the stated isomorphism. Starting from\nthis tree representation, we introduce a graphical algorithm for computing the\ncorresponding integer for $M$ under the aforementioned isomorphism. The\nalgorithm is linear with respect to the size of the tree.\n"}
+{"abstract": "  Various work has suggested that the memorability of an image is consistent\nacross people, and thus can be treated as an intrinsic property of an image.\nUsing computer vision models, we can make specific predictions about what\npeople will remember or forget. While older work has used now-outdated deep\nlearning architectures to predict image memorability, innovations in the field\nhave given us new techniques to apply to this problem. Here, we propose and\nevaluate five alternative deep learning models which exploit developments in\nthe field from the last five years, largely the introduction of residual neural\nnetworks, which are intended to allow the model to use semantic information in\nthe memorability estimation process. These new models were tested against the\nprior state of the art with a combined dataset built to optimize both\nwithin-category and across-category predictions. Our findings suggest that the\nkey prior memorability network had overstated its generalizability and was\noverfit on its training set. Our new models outperform this prior model,\nleading us to conclude that Residual Networks outperform simpler convolutional\nneural networks in memorability regression. We make our new state-of-the-art\nmodel readily available to the research community, allowing memory researchers\nto make predictions about memorability on a wider range of images.\n"}
+{"abstract": "  In this work, we analyze the noncommutative three-dimensional Coulomb\npotential problem. For this purpose, we used the Kustaanheimo-Stiefel mapping\nto write the Schr\\\"odinger equation for Coulomb potential in a solvable way.\nThen, the noncommutative hydrogen-like atoms were treated, and their energy\nlevels were found. In addition, we estimate a bound for the noncommutativity\nparameter.\n"}
+{"abstract": "  The demand for high-performance anomaly detection techniques of IoT data\nbecomes urgent, especially in industry field. The anomaly identification and\nexplanation in time series data is one essential task in IoT data mining. Since\nthat the existing anomaly detection techniques focus on the identification of\nanomalies, the explanation of anomalies is not well-solved. We address the\nanomaly explanation problem for multivariate IoT data and propose a 3-step\nself-contained method in this paper. We formalize and utilize the domain\nknowledge in our method, and identify the anomalies by the violation of\nconstraints. We propose set-cover-based anomaly explanation algorithms to\ndiscover the anomaly events reflected by violation features, and further\ndevelop knowledge update algorithms to improve the original knowledge set.\nExperimental results on real datasets from large-scale IoT systems verify that\nour method computes high-quality explanation solutions of anomalies. Our work\nprovides a guide to navigate the explicable anomaly detection in both IoT fault\ndiagnosis and temporal data cleaning.\n"}
+{"abstract": "  Policy evaluation is an important instrument for the comparison of different\nalgorithms in Reinforcement Learning (RL). Yet even a precise knowledge of the\nvalue function $V^{\\pi}$ corresponding to a policy $\\pi$ does not provide\nreliable information on how far is the policy $\\pi$ from the optimal one. We\npresent a novel model-free upper value iteration procedure $({\\sf UVIP})$ that\nallows us to estimate the suboptimality gap $V^{\\star}(x) - V^{\\pi}(x)$ from\nabove and to construct confidence intervals for $V^\\star$. Our approach relies\non upper bounds to the solution of the Bellman optimality equation via\nmartingale approach. We provide theoretical guarantees for ${\\sf UVIP}$ under\ngeneral assumptions and illustrate its performance on a number of benchmark RL\nproblems.\n"}
+{"abstract": "  We introduce a new class of collider-type observables in conformal field\ntheories which we call generalized event shapes. They are defined as matrix\nelements of light-ray operators that are sensitive to the longitudinal, or\ntime-dependent, structure of the state produced in the collision. Generalized\nevent shapes can be studied using both correlation functions and scattering\namplitudes. They are infrared finite and smoothly transit over to the familiar\nevent shapes. We compute them in planar ${\\cal N}=4$ super-Yang-Mills theory at\nweak and strong coupling, and study their physical properties. We show that at\nstrong coupling both the stringy and quantum-gravitational corrections to the\nenergy-energy correlation exhibit longitudinal broadening that manifests itself\nthrough the presence of long-time tails in the energy flux measured by the\ndetectors.\n"}
+{"abstract": "  Electrical control of magnetism has great potential for low-power spintronics\napplications and the newly discovered two-dimensional van-der-Waals magnetic\nmaterials are promising systems for this type of applications. In fact, it has\nbeen recently shown experimentally that upon electrostatic doping by electrons\nbilayer CrI3 undergoes an antiferromagnetic-ferromagnetic (AFM-FM) phase\ntransition, even in the absence of magnetic field. Doping by holes, on the\nother hand, does not induce the same transition in the experiment, which points\nto an intrinsic asymmetry in the hole and electron doping that limits the\ncontrol of the transition by doping. We here show, based on first-principles\ncalculations, that the asymmetry originates in the relativistic nature of the\nvalence-band-edge states of the pristine bilayer, which inhibits the magnetic\ntransition upon hole doping. Based on this finding, we propose an approach to\nengineer this system so that it displays the AFM-FM transition for both hole\nand electron doping by using moderate uniaxial strain along the soft direction\nof the bilayer.\n"}
+{"abstract": "  We consider the problem of sequentially allocating resources in a censored\nsemi-bandits setup, where the learner allocates resources at each step to the\narms and observes loss. The loss depends on two hidden parameters, one specific\nto the arm but independent of the resource allocation, and the other depends on\nthe allocated resource. More specifically, the loss equals zero for an arm if\nthe resource allocated to it exceeds a constant (but unknown) arm dependent\nthreshold. The goal is to learn a resource allocation that minimizes the\nexpected loss. The problem is challenging because the loss distribution and\nthreshold value of each arm are unknown. We study this setting by establishing\nits `equivalence' to Multiple-Play Multi-Armed Bandits (MP-MAB) and\nCombinatorial Semi-Bandits. Exploiting these equivalences, we derive optimal\nalgorithms for our problem setting using known algorithms for MP-MAB and\nCombinatorial Semi-Bandits. The experiments on synthetically generated data\nvalidate the performance guarantees of the proposed algorithms.\n"}
+{"abstract": "  We prove uniqueness and existence theorems for four-dimensional\nasymptotically flat, Ricci-flat, gravitational instantons with a torus\nsymmetry. In particular, we prove that such instantons are uniquely\ncharacterised by their rod structure, which is data that encodes the fixed\npoint sets of the torus action. Furthermore, we establish that for every\nadmissible rod structure there exists an instanton that is smooth up to\npossible conical singularities at the axes of symmetry. The proofs involve\nadapting the methods that are used to establish black hole uniqueness theorems,\nto a harmonic map formulation of Ricci-flat metrics with torus symmetry, where\nthe target space is directly related to the metric (rather than auxiliary\npotentials). We also give an elementary proof of the nonexistence of\nasymptotically flat toric half-flat instantons. Finally, we derive a general\nset of identities that relate asymptotic invariants such as the mass to the rod\nstructure.\n"}
+{"abstract": "  Relay-assisted free-space optical (FSO) communication systems are exploited\nas a means to mitigate the limiting effects of the turbulence induced\natmospheric scintillation. However, conventional ground relays are stationary,\nand their optimal placement is not always feasible. Due to their mobility and\nflexibility, unmanned aerial vehicles (UAVs) provide new opportunities for FSO\nrelaying systems. In this paper, a hovering UAV-based serial FSO\ndecode-and-forward relaying system is investigated. In the channel modelling\nfor such a system, four types of impairments (i.e., atmospheric loss,\natmospheric turbulence, pointing error, and link interruption due to\nangle-of-arrival fluctuation) are considered. Based on the proposed channel\nmodel, a tractable expression for the probability density function of the total\nchannel gain is obtained. Closed-form expressions of the link outage\nprobability and end-to-end outage probability are derived. Asymptotic outage\nperformance bounds for each link and the overall system are also presented to\nreveal insights into the impacts of different impairments. To improve system\nperformance, we optimize the beam width, field-of-view and UAVs' locations.\nNumerical results show that the derived theoretical expressions are accurate to\nevaluate the outage performance of the system. Moreover, the proposed\noptimization schemes are efficient and can improve performance significantly.\n"}
+{"abstract": "  The problem of anomaly detection in astronomical surveys is becoming\nincreasingly important as data sets grow in size. We present the results of an\nunsupervised anomaly detection method using a Wasserstein generative\nadversarial network (WGAN) on nearly one million optical galaxy images in the\nHyper Suprime-Cam (HSC) survey. The WGAN learns to generate realistic HSC-like\ngalaxies that follow the distribution of the data set; anomalous images are\ndefined based on a poor reconstruction by the generator and outlying features\nlearned by the discriminator. We find that the discriminator is more attuned to\npotentially interesting anomalies compared to the generator, and compared to a\nsimpler autoencoder-based anomaly detection approach, so we use the\ndiscriminator-selected images to construct a high-anomaly sample of\n$\\sim$13,000 objects. We propose a new approach to further characterize these\nanomalous images: we use a convolutional autoencoder to reduce the\ndimensionality of the residual differences between the real and\nWGAN-reconstructed images and perform UMAP clustering on these. We report\ndetected anomalies of interest including galaxy mergers, tidal features, and\nextreme star-forming galaxies. A follow-up spectroscopic analysis of one of\nthese anomalies is detailed in the Appendix; we find that it is an unusual\nsystem most likely to be a metal-poor dwarf galaxy with an extremely blue,\nhigher-metallicity HII region. We have released a catalog with the WGAN anomaly\nscores; the code and catalog are available at\nhttps://github.com/kstoreyf/anomalies-GAN-HSC, and our interactive\nvisualization tool for exploring the clustered data is at\nhttps://weirdgalaxi.es.\n"}
+{"abstract": "  We consider all 16 unary operations that, given a homogeneous binary relation\nR, define a new one by a boolean combination of xRy and yRx. Operations can be\ncomposed, and connected by pointwise-defined logical junctors. We consider the\nusual properties of relations, and allow them to be lifted by prepending an\noperation.\n  We investigate extensional equality between lifted properties (e.g. a\nrelation is connex iff its complement is asymmetric), and give a table to\ndecide this equality. Supported by a counter-example generator and a resolution\ntheorem prover, we investigate all 3-atom implications between lifted\nproperties, and give a sound and complete axiom set for them (containing e.g.\n\"if R's complement is left Euclidean and R is right serial, then R's symmetric\nkernel is left serial\").\n"}
+{"abstract": "  It is conceivable that an RNA virus could use a polysome, that is, a string\nof ribosomes covering the RNA strand, to protect the genetic material from\ndegradation inside a host cell. This paper discusses how such a virus might\noperate, and how its presence might be detected by ribosome profiling. There\nare two possible forms for such a polysomally protected virus, depending upon\nwhether just the forward strand or both the forward and complementary strands\ncan be encased by ribosomes (these will be termed type 1 and type 2,\nrespectively). It is argued that in the type 2 case the viral RNA would evolve\nan ambigrammatic property, whereby the viral genes are free of stop codons in a\nreverse reading frame (with forward and reverse codons aligned). Recent\nobservations of ribosome profiles of ambigrammatic narnavirus sequences are\nconsistent with our predictions for the type 2 case.\n"}
+{"abstract": "  Diversity in patent language is growing and makes finding synonyms for\nconducting patent searches more and more challenging. In addition to that, most\napproaches for dealing with diverse patent language are based on manual search\nand human intuition. In this paper, a word embedding approach using statistical\nanalysis of human labeled data to produce accurate and language independent\nword vectors for technical terms is introduced. This paper focuses on the\nexplanation of the idea behind the statistical analysis and shows first\nqualitative results. The resulting algorithm is a development of the former\nEQMania UG (eqmania.com) and can be tested under eqalice.com until April 2021.\n"}
+{"abstract": "  Silicene is a promising 2D Dirac material as a building block for van der\nWaals heterostructures (vdWHs). Here we investigate the electronic properties\nof hexagonal boron nitride/silicene (BN/Si) vdWHs using first-principles\ncalculations. We calculate the energy band structures of BN/Si/BN\nheterostructures with different rotation angles and find that the electronic\nproperties of silicene are retained and protected robustly by the BN layers. In\nBN/Si/BN/Si/BN heterostructure, we find that the band structure near the Fermi\nenergy is sensitive to the stacking configurations of the silicene layers due\nto interlayer coupling. The coupling is reduced by increasing the number of BN\nlayers between the silicene layers and becomes negligible in BN/Si/(BN)3/Si/BN.\nIn (BN)n/Si superlattices, the band structure undergoes a conversion from Dirac\nlines to Dirac points by increasing the number of BN layers between the\nsilicene layers. Calculations of silicene sandwiched by other 2D materials\nreveal that silicene sandwiched by low-carbon-doped boron nitride or HfO2 is\nsemiconducting.\n"}
+{"abstract": "  Let $\\mathbb{K}$ be a field and $R = \\mathbb{K}[x_1, \\ldots, x_n]$. We obtain\nan improved upper bound for asymptotic resurgence of squarefree monomial ideals\nin $R$. We study the effect on the resurgence when sum, product and\nintersection of ideals are taken. We obtain sharp upper and lower bounds for\nthe resurgence and asymptotic resurgence of cover ideals of finite simple\ngraphs in terms of associated combinatorial invariants. We also explicitly\ncompute the resurgence and asymptotic resurgence of cover ideals of several\nclasses of graphs. We characterize a graph being bipartite in terms of the\nresurgence and asymptotic resurgence of edge and cover ideals. We also compute\nexplicitly the resurgence and asymptotic resurgence of edge ideals of some\nclasses of graphs.\n"}
+{"abstract": "  We introduce a simple but powerful technique to study processes driven by two\nor more reinforcement mechanisms in competition. We apply our method to two\ntypes of models: to non conservative zero range processes on finite graphs, and\nto multi-particle random walks with positive and negative reinforcement on the\nedges. The results hold for a broad class of reinforcement functions, including\nthose with superlinear growth. Our technique consists in a comparison of the\noriginal processes with suitable reference models. To implement the comparison\nwe estimate a Radon-Nikodym derivative on a carefully chosen set of\ntrajectories. Our results describe the almost surely long time behaviour of the\nprocesses. We also prove a phase transition depending on the strength of the\nreinforcement functions.\n"}
+{"abstract": "  Context: Magnetic null points are associated with high-energy coronal\nphenomena such as solar flares and are often sites of reconnection and particle\nacceleration. Dynamic twisting of a magnetic null point can generate a\nKelvin-Helmholtz instability (KHI) within its fan plane and, under continued\ntwisting, can instigate spine-fan reconnection and an associated collapse of\nthe null point.\n  Aim: This article aims to compare the effects of isotropic and anisotropic\nviscosity in simulations of the KHI and collapse in a dynamically twisted\nmagnetic null point.\n  Methods: Simulations were performed using the 3D magnetohydrodynamics code\nLare3d with a custom anisotropic viscosity module. A pair of high resolution\nsimulations was performed, one using isotropic viscosity and another using\nanisotropic viscosity, keeping all other factors identical, and the results\nanalysed in detail. A further parameter study was performed over a range of\nvalues for viscosity and resistivity.\n  Results: Both viscosity models permit the growth of the Kelvin-Helmholtz\ninstability and the eventual collapse of the null point. Over all studied\nparameters, anisotropic viscosity allows a faster growing instability, while\nisotropic viscosity damps the instability to the extent of stabilisation in\nsome cases. Although the viscous heating associated with anisotropic viscosity\nis generally smaller, the ohmic heating dominates and is enhanced by the\ncurrent sheets generated by the instability, leading to a greater overall\nheating rate when using anisotropic viscosity. The collapse of the null point\noccurs significantly sooner when anisotropic viscosity is employed.\n"}
+{"abstract": "  International Classification of Disease (ICD) coding procedure which refers\nto tagging medical notes with diagnosis codes has been shown to be effective\nand crucial to the billing system in medical sector. Currently, ICD codes are\nassigned to a clinical note manually which is likely to cause many errors.\nMoreover, training skilled coders also requires time and human resources.\nTherefore, automating the ICD code determination process is an important task.\nWith the advancement of artificial intelligence theory and computational\nhardware, machine learning approach has emerged as a suitable solution to\nautomate this process. In this project, we apply a transformer-based\narchitecture to capture the interdependence among the tokens of a document and\nthen use a code-wise attention mechanism to learn code-specific representations\nof the entire document. Finally, they are fed to separate dense layers for\ncorresponding code prediction. Furthermore, to handle the imbalance in the code\nfrequency of clinical datasets, we employ a label distribution aware margin\n(LDAM) loss function. The experimental results on the MIMIC-III dataset show\nthat our proposed model outperforms other baselines by a significant margin. In\nparticular, our best setting achieves a micro-AUC score of 0.923 compared to\n0.868 of bidirectional recurrent neural networks. We also show that by using\nthe code-wise attention mechanism, the model can provide more insights about\nits prediction, and thus it can support clinicians to make reliable decisions.\nOur code is available online (https://github.com/biplob1ly/TransICD)\n"}
+{"abstract": "  The aim of this article is to relate the discrete quantum walk on\n$\\mathbb{Z}$ with the continuous Schr\\\"odinger operator on $\\mathbb{R}$ in the\nscattering problem. Each point of $\\mathbb{Z}$ is associated with a barrier of\nthe potential, and the coin operator of the quantum walk is determined by the\ntransfer matrix between bases of WKB solutions on the classically allowed\nregions of both sides of the barrier. This correspondence enables us to\nrepresent each entry of the scattering matrix of the Schr\\\"odinger operator as\na countable sum of probability amplitudes associated with the paths of the\nquantum walker. In particular, the barrier-top scattering corresponds to the\nHadamard walk in the semiclassical limit.\n"}
+{"abstract": "  A viable quantum theory does not allow curvature invariant terms of different\nhigher orders to be accommodated in the gravitational action. We show that\nthere is indeed a conflict between the curvature squared and Gauss-Bonnet\nsquared terms from the point of view of hermiticity. This means one should\nchoose either, in addition to the Einstein-Hilbert term, but never the two\ntogether. We explore early cosmic evolution with Gauss-Bonnet squared term.\n"}
+{"abstract": "  In the scanning transmission electron microscope, fast-scanning and\nframe-averaging are two widely used approaches for reducing electron-beam\ndamage and increasing image signal-noise ratio which require no additional\nspecialised hardware. Unfortunately, for scans with short pixel dwell-times\n(less than 5 ${\\mu}$s), line flyback time represents an increasingly wasteful\noverhead. Although beam exposure during flyback causes damage while yielding no\nuseful information, scan-coil hysteresis means that eliminating it entirely\nleads to unacceptably distorted images. In this work, we reduce this flyback to\nan absolute minimum by calibrating and correcting for this hysteresis in\npostprocessing. Substantial improvements in dose-efficiency can be realised (up\nto 20 %), while crystallographic and spatial fidelity is maintained for\ndisplacement/strain measurement.\n"}
+{"abstract": "  Radiation-dust driven outflows, where radiation pressure on dust grains\naccelerates gas, occur in many astrophysical environments. Almost all previous\nnumerical studies of these systems have assumed that the dust was\nperfectly-coupled to the gas. However, it has recently been shown that the dust\nin these systems is unstable to a large class of 'resonant drag instabilities'\n(RDIs) which de-couple the dust and gas dynamics and could qualitatively change\nthe nonlinear outcome of these outflows. We present the first simulations of\nradiation-dust driven outflows in stratified, inhomogeneous media, including\nexplicit grain dynamics and a realistic spectrum of grain sizes and charge,\nmagnetic fields and Lorentz forces on grains (which dramatically enhance the\nRDIs), Coulomb and Epstein drag forces, and explicit radiation transport\nallowing for different grain absorption and scattering properties. In this\npaper we consider conditions resembling giant molecular clouds (GMCs), HII\nregions, and distributed starbursts, where optical depths are modest ($\\lesssim\n1$), single-scattering effects dominate radiation-dust coupling, Lorentz forces\ndominate over drag on grains, and the fastest-growing RDIs are similar, such as\nmagnetosonic and fast-gyro RDIs. These RDIs generically produce strong\nsize-dependent dust clustering, growing nonlinear on timescales that are much\nshorter than the characteristic times of the outflow. The instabilities produce\nfilamentary and plume-like or 'horsehead' nebular morphologies that are\nremarkably similar to observed dust structures in GMCs and HII regions.\nAdditionally, in some cases they strongly alter the magnetic field structure\nand topology relative to filaments. Despite driving strong micro-scale dust\nclumping which leaves some gas 'behind,' an order-unity fraction of the gas is\nalways efficiently entrained by dust.\n"}
+{"abstract": "  Infectious diseases are a significant threat to human society which was over\nsighted before the incidence of COVID-19, although according to the report of\nthe World Health Organisation (WHO) about 4.2 million people die annually due\nto infectious disease. Due to recent COVID-19 pandemic, more than 2 million\npeople died during 2020 and 96.2 million people got affected by this\ndevastating disease. Recent research shows that applying individual\ninteractions and movements data could help managing the pandemic though\nmodelling the spread of infectious diseases on social contact networks.\nInfectious disease spreading can be explained with the theories and methods of\ndiffusion processes where a dynamic phenomena evolves on networked systems. In\nthe modelling of diffusion process, it is assumed that contagious items spread\nout in the networked system through the inter-node interactions. This resembles\nspreading of infectious virus, e.g. spread of COVID-19, within a population\nthrough individual social interactions. The evolution behaviours of the\ndiffusion process are strongly influenced by the characteristics of the\nunderlying system and the mechanism of the diffusion process itself. Thus,\nspreading of infectious disease can be explained how people interact with each\nother and by the characteristics of the disease itself. This paper presenters\nthe relevant theories and methodologies of diffusion process that can be used\nto model the spread of infectious diseases.\n"}
+{"abstract": "  The nearby elliptical galaxy IC4296 has produced a large (510 kpc)\nlow-luminosity radio source with typical FR I core/jet/lobe morphology. The\nunprecedented combination of brightness sensitivity, dynamic range, and angular\nresolution of a new 1.28 GHz MeerKAT continuum image reveals striking new\nmorphological features which we call threads, ribbons, and rings. The threads\nare faint narrow emission features originating where helical Kelvin-Helmholtz\ninstabilities disrupt the main radio jets. The ribbons are smooth regions\nbetween the jets and the lobes, and they appear to be relics of jets powered by\nearlier activity that have since come into pressure equilibrium. Vortex rings\nin the outer portions of the lobes and their backflows indicate that the\nstraight outer jets and ribbons are inclined by $i = 60 \\pm 5^\\circ$ from the\nline-of-sight, in agreement with photometric, geometric, and gas-dynamical\nestimates of inclination angles near the nucleus.\n"}
+{"abstract": "  Let $A$ be an $n\\times n$ matrix and let $\\vee^k A$ be its $k$-th symmetric\ntensor product. We express the normalized trace of $\\vee^k A$ as an integral of\nthe $k$-th powers of the numerical values of $A$ over the unit sphere\n$\\mathbb{S}^{n}$ of $\\mathbb{C}^{n}$ with respect to the normalized Euclidean\nsurface measure. Equivalently, this expression in turn can be interpreted as an\nintegral representation for the (normalized) complete symmetric polynomials\nover $\\mathbb{C}^n$. As applications, we present a new proof for the MacMahon\nMaster Theorem in enumerative combinatorics. Then, our next application deals\nwith a generalization of the work of Cuttler et al. in \\cite{cuttler}\nconcerning the monotonicity of products of complete symmetric polynomials. In\nthe process, we give a solution to an open problem that was raised by I.\nRoven\\c{t}a and L. E. Temereanca in \\cite{roventa}.\n"}
+{"abstract": "  Communicating the robot state is vital to creating an efficient and\ntrustworthy collaboration between humans and collaborative robots (cobots).\nStandard approaches for Robot-to-human communication face difficulties in\nindustry settings, e.g., because of high noise levels or certain visibility\nrequirements. Therefore, this paper presents zoomorphic gestures based on dog\nbody language as a possible alternative for communicating the state of\nappearance-constrained cobots. For this purpose, we conduct a visual\ncommunication benchmark comparing zoomorphic gestures, abstract gestures, and\nlight displays. We investigate the modalities regarding intuitive\nunderstanding, user experience, and user preference. In a first user study (n =\n93), we evaluate our proposed design guidelines for all visual modalities. A\nsecond user study (n = 214) constituting the benchmark indicates that intuitive\nunderstanding and user experience are highest for both gesture-based\nmodalities. Furthermore, zoomorphic gestures are considerably preferred over\nother modalities. These findings indicate that zoomorphic gestures with their\nplayful nature are especially suitable for novel users and may decrease initial\ninhibitions.\n"}
+{"abstract": "  Measuring and promoting policy diversity is critical for solving games with\nstrong non-transitive dynamics where strategic cycles exist, and there is no\nconsistent winner (e.g., Rock-Paper-Scissors). With that in mind, maintaining a\npool of diverse policies via open-ended learning is an attractive solution,\nwhich can generate auto-curricula to avoid being exploited. However, in\nconventional open-ended learning algorithms, there are no widely accepted\ndefinitions for diversity, making it hard to construct and evaluate the diverse\npolicies. In this work, we summarize previous concepts of diversity and work\ntowards offering a unified measure of diversity in multi-agent open-ended\nlearning to include all elements in Markov games, based on both Behavioral\nDiversity (BD) and Response Diversity (RD). At the trajectory distribution\nlevel, we re-define BD in the state-action space as the discrepancies of\noccupancy measures. For the reward dynamics, we propose RD to characterize\ndiversity through the responses of policies when encountering different\nopponents. We also show that many current diversity measures fall in one of the\ncategories of BD or RD but not both. With this unified diversity measure, we\ndesign the corresponding diversity-promoting objective and population\neffectivity when seeking the best responses in open-ended learning. We validate\nour methods in both relatively simple games like matrix game, non-transitive\nmixture model, and the complex \\textit{Google Research Football} environment.\nThe population found by our methods reveals the lowest exploitability, highest\npopulation effectivity in matrix game and non-transitive mixture model, as well\nas the largest goal difference when interacting with opponents of various\nlevels in \\textit{Google Research Football}.\n"}
+{"abstract": "  We report the feasibility of using magnetoentropic mapping for the rapid\nidentification of magnetic cycloid and skyrmion phases in uniaxial systems,\nbased on the GaV4S8 and GaV4Se8 model skyrmion hosts with easy-axis and\neasy-plane anisotropies respectively. We show that these measurements can be\ninterpreted with the help of a simple numerical model for the spin Hamiltonian\nto yield unambiguous assignments for both single phase regions and phase\nboundaries. In the two lacunar spinel chemistries, we obtain excellent\nagreement between the measured magnetoentropic features and a minimal spin\nHamiltonian built on Heisenberg exchange, single-ion anisotropy, and\nanisotropic Dzyaloshinskii-Moriya interactions. In particular, we identify\ncharacteristic high-entropy behavior in the cycloid phase that serves as a\nprecursor to the formation of skyrmions at elevated temperatures and is a\nreadily-measurable signature of this phase transition. Our results demonstrate\nthat rapid magnetoentropic mapping guided by numerical modeling is an effective\nmeans of understanding the complex magnetic phase diagrams innate to skyrmion\nhosts. One notable exception is the observation of an anomalous,\nlow-temperature high-entropy state in the easy-plane system GaV$_4$Se$_8$,\nwhich is not captured in the numerical model. Possible origins of this state\nare discussed.\n"}
+{"abstract": "  As more and more data being created every day, all of it can help take better\ndecisions with data analysis. It is not different from data generated in\nfinancial markets. Here we examine the process of how the global economy is\naffected by the market sentiment influenced by the micro-blogging data (tweets)\nof American President Donald Trump. The news feed is gathered from The Guardian\nand Bloomberg from the period between December 2016 and October 2019, which are\nused to further identify the potential tweets that influenced the markets as\nmeasured by changes in equity indices.\n"}
+{"abstract": "  A conjecture of Alon, Krivelevich, and Sudakov states that, for any graph\n$F$, there is a constant $c_F > 0$ such that if $G$ is an $F$-free graph of\nmaximum degree $\\Delta$, then $\\chi(G) \\leq c_F \\Delta / \\log\\Delta$. Alon,\nKrivelevich, and Sudakov verified this conjecture for a class of graphs $F$\nthat includes all bipartite graphs. Moreover, it follows from recent work by\nDavies, Kang, Pirot, and Sereni that if $G$ is $K_{t,t}$-free, then $\\chi(G)\n\\leq (t + o(1)) \\Delta / \\log\\Delta$ as $\\Delta \\to \\infty$. We improve this\nbound to $(1+o(1)) \\Delta/\\log \\Delta$, making the constant factor independent\nof $t$. We further extend our result to the DP-coloring setting (also known as\ncorrespondence coloring), introduced by Dvo\\v{r}\\'ak and Postle.\n"}
+{"abstract": "  We propose the first general-purpose gradient-based attack against\ntransformer models. Instead of searching for a single adversarial example, we\nsearch for a distribution of adversarial examples parameterized by a\ncontinuous-valued matrix, hence enabling gradient-based optimization. We\nempirically demonstrate that our white-box attack attains state-of-the-art\nattack performance on a variety of natural language tasks. Furthermore, we show\nthat a powerful black-box transfer attack, enabled by sampling from the\nadversarial distribution, matches or exceeds existing methods, while only\nrequiring hard-label outputs.\n"}
+{"abstract": "  Various fonts give different impressions, such as legible, rough, and\ncomic-text.This paper aims to analyze the correlation between the local shapes,\nor parts, and the impression of fonts. By focusing on local shapes instead of\nthe whole letter shape, we can realize letter-shape independent and more\ngeneral analysis. The analysis is performed by newly combining SIFT and\nDeepSets, to extract an arbitrary number of essential parts from a particular\nfont and aggregate them to infer the font impressions by nonlinear regression.\nOur qualitative and quantitative analyses prove that (1)fonts with similar\nparts have similar impressions, (2)many impressions, such as legible and rough,\nlargely depend on specific parts, (3)several impressions are very irrelevant to\nparts.\n"}
+{"abstract": "  We investigate the focal plane wavefront sensing technique, known as Phase\nDiversity, at the scientific focal plane of a segmented mirror telescope with\nan adaptive optics (AO) system. We specifically consider an optical system\nimaging a point source in the context of (i) an artificial source within the\ntelescope structure and (ii) from AO-corrected images of a bright star. From\nour simulations, we reliably disentangle segmented telescope phasing errors\nfrom non-common path aberrations (NCPA) for both a theoretical source and\non-sky, AO-corrected images where we have simulated the Keck/NIRC2 system. This\nquantification from on-sky images is appealing, as it's sensitive to the\ncumulative wavefront perturbations of the entire optical train; disentanglement\nof phasing errors and NCPA is therefore critical, where any potential\ncorrection to the primary mirror from an estimate must contain minimal NCPA\ncontributions. Our estimates require a one-minute sequence of short-exposure,\nAO-corrected images; by exploiting a slight modification to the AO-loop, we\nfind that 75 defocused images produces reliable estimates. We demonstrate a\ncorrection from our estimates to the primary and deformable mirror results in a\nwavefront error reduction of up to 67% and 65% for phasing errors and NCPA,\nrespectively. If the segment phasing errors on the Keck primary are on the\norder of ~130 nm RMS, we show we can improve the H-band Strehl ratio by up to\n10% by using our algorithm. We conclude our technique works well to estimate\nNCPA alone from on-sky images, suggesting it is a promising method for any\nAO-system.\n"}
+{"abstract": "  In recent years, there has been an explosion of machine learning techniques\nfor turbulence closure modeling, though many rely on augmenting existing\nmodels. While this has proven successful in single-phase flows, it breaks down\nfor multiphase flows, particularly when the system dynamics are controlled by\ntwo-way coupling between the phases. In this work, we propose an approach that\nblends sparse regression and gene expression programming (GEP) to generate\nclosed-form algebraic models from simulation data. Sparse regression is used to\ndetermine a minimum set of functional groups required to capture the physics\nand GEP is used to automate the formulation of the coefficients and\ndependencies on operating conditions. The framework is demonstrated on a\ncanonical gas--solid flow in which two-way coupling generates and sustains\nfluid-phase turbulence.\n"}
+{"abstract": "  Many applications of Visual SLAM, such as augmented reality, virtual reality,\nrobotics or autonomous driving, require versatile, robust and precise\nsolutions, most often with real-time capability. In this work, we describe\nOV$^{2}$SLAM, a fully online algorithm, handling both monocular and stereo\ncamera setups, various map scales and frame-rates ranging from a few Hertz up\nto several hundreds. It combines numerous recent contributions in visual\nlocalization within an efficient multi-threaded architecture. Extensive\ncomparisons with competing algorithms shows the state-of-the-art accuracy and\nreal-time performance of the resulting algorithm. For the benefit of the\ncommunity, we release the source code:\n\\url{https://github.com/ov2slam/ov2slam}.\n"}
+{"abstract": "  Let $(\\Omega, \\mu)$ be a probability space endowed with an ergodic action,\n$\\tau$ of $( {\\mathbb R} ^n, +)$. Let $H(x,p; \\omega)=H_\\omega(x,p)$ be a\nsmooth Hamiltonian on $T^* {\\mathbb R} ^n$ parametrized by $\\omega\\in \\Omega$\nand such that $ H(a+x,p;\\tau_a\\omega)=H(x,p;\\omega)$. We consider for an\ninitial condition $f\\in C^0 ( {\\mathbb R}^n)$, the family of variational\nsolutions of the stochastic Hamilton-Jacobi equations $$\\left\\{ \\begin{aligned}\n\\frac{\\partial u^{ \\varepsilon }}{\\partial t}(t,x;\\omega)+H\\left (\\frac{x}{\n\\varepsilon } , \\frac{\\partial u^\\varepsilon }{\\partial x}(t,x;\\omega);\\omega\n\\right )=0 &\\\\ u^\\varepsilon (0,x;\\omega)=f(x)& \\end{aligned} \\right .$$ Under\nsome coercivity assumptions on $p$ -- but without any convexity assumption --\nwe prove that for a.e. $\\omega \\in \\Omega$ we have $C^0-\\lim\nu^{\\varepsilon}(t,x;\\omega)=v(t,x)$ where $v$ is the variational solution of\nthe homogenized equation $$\\left\\{ \\begin{aligned} \\frac{\\partial v}{\\partial\nt}(x)+{\\overline H}\\left (\\frac{\\partial v }{\\partial x}(x) \\right )=0 &\\\\ v\n(0,x)=f(x)& \\end{aligned} \\right.$$\n"}
+{"abstract": "  Various microorganisms and some mammalian cells are able to swim in viscous\nfluids by performing nonreciprocal body deformations, such as rotating attached\nflagella or by distorting their entire body. In order to perform chemotaxis,\ni.e. to move towards and to stay at high concentrations of nutrients, they\nadapt their swimming gaits in a nontrivial manner. We propose a model how\nmicroswimmers are able to autonomously adapt their shape in order to swim in\none dimension towards high field concentrations using an internal decision\nmaking machinery modeled by an artificial neural network. We present two\nmethods to measure chemical gradients, spatial and temporal sensing, as known\nfor swimming mammalian cells and bacteria, respectively. Using the NEAT genetic\nalgorithm surprisingly simple neural networks evolve which control the shape\ndeformations of the microswimmer and allow them to navigate in static and\ncomplex time-dependent chemical environments. By introducing noisy signal\ntransmission in the neural network the well-known biased run-and-tumble motion\nemerges. Our work demonstrates that the evolution of a simple internal\ndecision-making machinery, which we can fully interpret and is coupled to the\nenvironment, allows navigation in diverse chemical landscapes. These findings\nare of relevance for intracellular biochemical sensing mechanisms of single\ncells, or for the simple nervous system of small multicellular organisms such\nas C. elegans.\n"}
+{"abstract": "  In this paper, novel dissensus algorithms based on the Oja principal\ncomponent analysis (PCA) flow are proposed to model opinion dynamics on the\nunit sphere. The information of the covariance formed by the opinion state of\neach agent is used to achieve a dissensus equilibrium on unsigned graphs. This\ndiffers from most of the existing work where antagonistic interactions\nrepresented by negative weights in signed graphs are used to achieve a\ndissensus equilibrium. The nonlinear algorithm is analyzed under both constant\ncovariance and time-varying covariance leading to different behaviors.\nStability analysis for the unstable consensus and stable dissensus equilibria\nis provided under various conditions. The performance of the algorithm is\nillustrated through a simulation experiment of a multi-agent system.\n"}
+{"abstract": "  The notion of an orthogonality space was recently rediscovered as an\neffective means to characterise the essential properties of quantum logic. The\napproach can be considered as minimalistic; solely the aspect of mutual\nexclusiveness is taken into account. In fact, an orthogonality space is simply\na set endowed with a symmetric and irreflexive binary relation. If the rank is\nat least $4$ and if a certain combinatorial condition holds, these relational\nstructures can be shown to give rise in a unique way to Hermitian spaces. In\nthis paper, we focus on the finite case. In particular, we investigate\northogonality spaces of rank at most $3$.\n"}
+{"abstract": "  In the current era of Noisy Intermediate-Scale Quantum (NISQ) technology, the\npractical use of quantum computers remains inhibited by our inability to aptly\ndecouple qubits from their environment to mitigate computational errors. In\nthis work, we introduce an approach by which knowledge of a qubit's initial\nquantum state and the standard parameters describing its decoherence can be\nleveraged to mitigate the noise present during the execution of a single-qubit\ngate. We benchmark our protocol using cloud-based access to IBM quantum\nprocessors. On ibmq_rome, we demonstrate a reduction of the single-qubit error\nrate by $38\\%$, from $1.6 \\times 10 ^{-3}$ to $1.0 \\times 10 ^{-3}$, provided\nthe initial state of the input qubit is known. On ibmq_bogota, we prove that\nour protocol will never decrease gate fidelity, provided the system's $T_1$ and\n$T_2$ times have not drifted above $100$ times their assumed values. The\nprotocol can be used to reduce quantum state preparation errors, as well as to\nimprove the fidelity of quantum circuits for which some knowledge of the\nqubits' intermediate states can be inferred. This work presents a pathway to\nusing information about noise levels and quantum state distributions to\nsignificantly reduce error rates associated with quantum gates via optimized\ndecomposition into native gates.\n"}
+{"abstract": "  Words are malleable objects, influenced by events that are reflected in\nwritten texts. Situated in the global outbreak of COVID-19, our research aims\nat detecting semantic shifts in social media language triggered by the health\ncrisis. With COVID-19 related big data extracted from Twitter, we train\nseparate word embedding models for different time periods after the outbreak.\nWe employ an alignment-based approach to compare these embeddings with a\ngeneral-purpose Twitter embedding unrelated to COVID-19. We also compare our\ntrained embeddings among them to observe diachronic evolution. Carrying out\ncase studies on a set of words chosen by topic detection, we verify that our\nalignment approach is valid. Finally, we quantify the size of global semantic\nshift by a stability measure based on back-and-forth rotational alignment.\n"}
+{"abstract": "  The increasing cost, energy demand, and environmental issues has led many\nresearchers to find approaches for energy monitoring, and hence energy\nconservation. The emerging technologies of Internet of Things (IoT) and Machine\nLearning (ML) deliver techniques that have the potential to efficiently\nconserve energy and improve the utilization of energy consumption. Smart Home\nEnergy Management Systems (SHEMSs) have the potential to contribute in energy\nconservation through the application of Demand Response (DR) in the residential\nsector. In this paper, we propose appliances Operation Modes Identification\nusing Cycles Clustering (OMICC) which is SHEMS fundamental approach that\nutilizes the sensed residential disaggregated power consumption in supporting\nDR by providing consumers the opportunity to select lighter appliance operation\nmodes. The cycles of the Single Usage Profile (SUP) of an appliance are\nextracted and reformed into features in terms of clusters of cycles. These\nfeatures are then used to identify the operation mode used in every occurrence\nusing K-Nearest Neighbors (KNN). Operation modes identification is considered a\nbasis for many potential smart DR applications within SHEMS towards the\nconsumers or the suppliers\n"}
+{"abstract": "  We focus on cone-shaped nano- and microparticles, which have recently been\nfound to show particularly strong propulsion when they are exposed to a\ntraveling ultrasound wave, and study based on direct acoustofluidic computer\nsimulations how their propulsion depends on the cones' aspect ratio. The\nsimulations reveal that the propulsion velocity and even its sign are very\nsensitive to the aspect ratio, where short particles move forward whereas\nelongated particles move backward. Furthermore, we identify a cone shape that\nallows for a particularly large propulsion speed. Our results contribute to the\nunderstanding of the propulsion of ultrasound-propelled colloidal particles,\nsuggest a method for separation and sorting of nano- and microcones concerning\ntheir aspect ratio, and provide useful guidance for future experiments and\napplications.\n"}
+{"abstract": "  Management of heat during charging and discharging of Li-ion batteries is\ncritical for their safety, reliability, and performance. Understanding the\nthermal conductivity of the materials comprising batteries is crucial for\ncontrolling the temperature and temperature distribution in batteries. This\nwork provides systemic quantitative measurements of the thermal conductivity of\nthree important classes of solid electrolytes (oxides, sulfides, and halides)\nover the temperature range 150-350 K. Studies include the oxides\nLi1.5Al0.5Ge1.5(PO4)3 and Li6.4La3Zr1.4Ta0.6O12, sulfides Li2S-P2S5, Li6PS5Cl,\nand Na3PS4, and halides Li3InCl6 and Li3YCl6. Thermal conductivities of sulfide\nand halide solid electrolytes are in the range 0.45-0.70 W m-1 K-1; thermal\nconductivities of Li6.4La3Zr1.4Ta0.6O12 and Li1.5Al0.5Ge1.5(PO4)3 are 1.4 W m-1\nK-1 and 2.2 W m-1 K-1, respectively. For most of the solid electrolytes studied\nin this work, the thermal conductivity increases with increasing temperature;\ni.e., the thermal conductivity has a glass-like temperature dependence. The\nmeasured room-temperature thermal conductivities agree well with the calculated\nminimum thermal conductivities indicating the phonon mean-free-paths in these\nsolid electrolytes are close to an atomic spacing. We attribute the low,\nglass-like thermal conductivity of the solid electrolytes investigated to the\ncombination of their complex crystal structures and the atomic-scale disorder\ninduced by the materials processing methods that are typically needed to\nproduce high ionic conductivities.\n"}
+{"abstract": "  Normalizing flows are a widely used class of latent-variable generative\nmodels with a tractable likelihood. Affine-coupling (Dinh et al, 2014-16)\nmodels are a particularly common type of normalizing flows, for which the\nJacobian of the latent-to-observable-variable transformation is triangular,\nallowing the likelihood to be computed in linear time. Despite the widespread\nusage of affine couplings, the special structure of the architecture makes\nunderstanding their representational power challenging. The question of\nuniversal approximation was only recently resolved by three parallel papers\n(Huang et al.,2020;Zhang et al.,2020;Koehler et al.,2020) -- who showed\nreasonably regular distributions can be approximated arbitrarily well using\naffine couplings -- albeit with networks with a nearly-singular Jacobian. As\nill-conditioned Jacobians are an obstacle for likelihood-based training, the\nfundamental question remains: which distributions can be approximated using\nwell-conditioned affine coupling flows?\n  In this paper, we show that any log-concave distribution can be approximated\nusing well-conditioned affine-coupling flows. In terms of proof techniques, we\nuncover and leverage deep connections between affine coupling architectures,\nunderdamped Langevin dynamics (a stochastic differential equation often used to\nsample from Gibbs measures) and H\\'enon maps (a structured dynamical system\nthat appears in the study of symplectic diffeomorphisms). Our results also\ninform the practice of training affine couplings: we approximate a padded\nversion of the input distribution with iid Gaussians -- a strategy which\nKoehler et al.(2020) empirically observed to result in better-conditioned\nflows, but had hitherto no theoretical grounding. Our proof can thus be seen as\nproviding theoretical evidence for the benefits of Gaussian padding when\ntraining normalizing flows.\n"}
+{"abstract": "  We study the binary phase separation in active model B, on a two-dimensional\nsubstrate with inhomogeneous activity. The activity was introduced with a\nmaximum value at the center of the box and spread as a Bivariate-Gaussian\ndistribution as we move away from the center. The system was studied for three\ndifferent intensities of the distribution. Towards the boundary of the box,\nactivity is zero or the model is similar to the passive model B. We start from\nthe random homogeneous distribution of density of particles, and the system\nevolves towards a structured distribution of density. With time, density starts\nto phase separately with maximum density at the center of the box and decreases\nas we move away from the center of the box. The width of the density profile at\nthe center increases as a power lawexponent{\\alpha}(t) remains close between\n2/3 to 3/4 up to some moderate time and then decays to zero in the steady\nstate. Hence, our result shows the response of density in an active binary\nsystem with respect to the patterned substrate. It can be used to design\ndevices useful for the trapping and segregation of active particles.\n"}
+{"abstract": "  Periodic space crystals are well established and widely used in physical\nsciences. Time crystals have been increasingly explored more recently, where\ntime is disconnected from space. Periodic relativistic spacetime crystals on\nthe other hand need to account for the mixing of space and time in special\nrelativity through Lorentz transformation, and have been listed only in\n2-dimensions. This work shows that there exists a transformation between the\nconventional Minkowski spacetime (MS) and what is referred to here as\nrenormalized blended spacetime (RBS); they are shown to be equivalent\ndescriptions of relativistic physics in flat spacetime. There are two elements\nto this reformulation of MS, namely, blending and renormalization. One can now\nenumerate the RBS point and space groups in various dimensions with their\nmapping to the well-known space crystal groups. The RBS point group for flat\nisotropic RBS spacetime is identified to be that of cylinders in various\ndimensions. An antisymmetry operation is introduced that can swap between\nspace-like and time-like directions, leading to color spacetime groups. The\nformalism reveals RBS symmetries that are not readily apparent in the\nconventional MS formulation. Mathematica(R) script is provided for plotting the\nMS and RBS geometries discussed in the work.\n"}
+{"abstract": "  Information technology (IT) companies implement multi-dimensional policy\nplans that include procedures, sub-plans, and instructions to outline their\nbusiness scopes, targets, and communications. This work outlined the IT policy\nimplementation plan designed by an imaginary company with a random name called\nNorthcentral Cloud Consulting Firm (NCCF), containing proposed IT policies,\nmilestones and roadmaps, control framework, stakeholder responsibilities,\nknowledge transfer plan, and leadership roles. As NCCF's major customers seek\ndata-driven solutions in cloud computing, the NCCF IT policy plan provides\nvarious data policies, including security and proper usage of machine learning\nservices. The plan offers a detailed roadmap of its financial, geographical,\nand reputational expansion within three years. The IT policy plan also\ncompromises an IT risk management, contingency, and emergency communication\nplan, mainly for protecting data and business continuity. Stakeholder\nresponsibilities are incorporated into the IT policy plan, as NCCF considers\nany engagement with its customers as a collaborative effort in which both\nparties have and share several responsibilities.\n"}
+{"abstract": "  We generalize the Cauchy-Davenport theorem to locally compact groups.\n"}
+{"abstract": "  Characterization of breast parenchyma on dynamic contrast-enhanced magnetic\nresonance imaging (DCE-MRI) is a challenging task owing to the complexity of\nunderlying tissue structures. Current quantitative approaches, including\nradiomics and deep learning models, do not explicitly capture the complex and\nsubtle parenchymal structures, such as fibroglandular tissue. In this paper, we\npropose a novel method to direct a neural network's attention to a dedicated\nset of voxels surrounding biologically relevant tissue structures. By\nextracting multi-dimensional topological structures with high saliency, we\nbuild a topology-derived biomarker, TopoTxR. We demonstrate the efficacy of\nTopoTxR in predicting response to neoadjuvant chemotherapy in breast cancer.\nOur qualitative and quantitative results suggest differential topological\nbehavior of breast tissue on treatment-na\\\"ive imaging, in patients who respond\nfavorably to therapy versus those who do not.\n"}
+{"abstract": "  Optical implementation of artificial neural networks has been attracting\ngreat attention due to its potential in parallel computation at speed of light.\nAlthough all-optical deep neural networks (AODNNs) with a few neurons have been\nexperimentally demonstrated with acceptable errors recently, the feasibility of\nlarge scale AODNNs remains unknown because error might accumulate inevitably\nwith increasing number of neurons and connections. Here, we demonstrate a\nscalable AODNN with programmable linear operations and tunable nonlinear\nactivation functions. We verify its scalability by measuring and analyzing\nerrors propagating from a single neuron to the entire network. The feasibility\nof AODNNs is further confirmed by recognizing handwritten digits and fashions\nrespectively.\n"}
+{"abstract": "  Rumors are often associated with newly emerging events, thus, an ability to\ndeal with unseen rumors is crucial for a rumor veracity classification model.\nPrevious works address this issue by improving the model's generalizability,\nwith an assumption that the model will stay unchanged even after the new\noutbreak of an event. In this work, we propose an alternative solution to\ncontinuously update the model in accordance with the dynamics of rumor domain\ncreations. The biggest technical challenge associated with this new approach is\nthe catastrophic forgetting of previous learnings due to new learnings. We\nadopt continual learning strategies that control the new learnings to avoid\ncatastrophic forgetting and propose an additional strategy that can jointly be\nused to strengthen the forgetting alleviation.\n"}
+{"abstract": "  The purpose of this project is to evaluate three language models named BERT,\nALBERT, and LongFormer on the Question Answering dataset called DuoRC. The\nlanguage model task has two inputs, a question, and a context. The context is a\nparagraph or an entire document while the output is the answer based on the\ncontext. The goal is to perform grid search hyperparameter fine-tuning using\nDuoRC. Pretrained weights of the models are taken from the Huggingface library.\nDifferent sets of hyperparameters are used to fine-tune the models using two\nversions of DuoRC which are the SelfRC and the ParaphraseRC. The results show\nthat the ALBERT (pretrained using the SQuAD1 dataset) has an F1 score of 76.4\nand an accuracy score of 68.52 after fine-tuning on the SelfRC dataset. The\nLongformer model (pretrained using the SQuAD and SelfRC datasets) has an F1\nscore of 52.58 and an accuracy score of 46.60 after fine-tuning on the\nParaphraseRC dataset. The current results outperformed the results from the\nprevious model by DuoRC.\n"}
+{"abstract": "  It is critical yet challenging for deep learning models to properly\ncharacterize uncertainty that is pervasive in real-world environments. Although\na lot of efforts have been made, such as heteroscedastic neural networks\n(HNNs), little work has demonstrated satisfactory practicability due to the\ndifferent levels of compromise on learning efficiency, quality of uncertainty\nestimates, and predictive performance. Moreover, existing HNNs typically fail\nto construct an explicit interaction between the prediction and its associated\nuncertainty. This paper aims to remedy these issues by developing SDE-HNN, a\nnew heteroscedastic neural network equipped with stochastic differential\nequations (SDE) to characterize the interaction between the predictive mean and\nvariance of HNNs for accurate and reliable regression. Theoretically, we show\nthe existence and uniqueness of the solution to the devised neural SDE.\nMoreover, based on the bias-variance trade-off for the optimization in SDE-HNN,\nwe design an enhanced numerical SDE solver to improve the learning stability.\nFinally, to more systematically evaluate the predictive uncertainty, we present\ntwo new diagnostic uncertainty metrics. Experiments on the challenging datasets\nshow that our method significantly outperforms the state-of-the-art baselines\nin terms of both predictive performance and uncertainty quantification,\ndelivering well-calibrated and sharp prediction intervals.\n"}
+{"abstract": "  We verify the inductive blockwise Alperin weight condition in odd\ncharacteristic $\\ell$ for the finite exceptional Chevalley groups $F_4(q)$ for\n$q$ not divisible by $\\ell$.\n"}
+{"abstract": "  Next-generation plasma-based accelerators can push electron beams to GeV\nenergies within centimetre distances. The plasma, excited by a driver pulse, is\nindeed able to sustain huge electric fields that can efficiently accelerate a\ntrailing witness bunch, which was experimentally demonstrated on multiple\noccasions. Thus, the main focus of the current research is being shifted\ntowards achieving a high quality of the beam after the plasma acceleration. In\nthis letter we present beam-driven plasma wakefield acceleration experiment,\nwhere initially preformed high-quality witness beam was accelerated inside the\nplasma and characterized. In this experiment the witness beam quality after the\nacceleration was maintained on high level, with $0.2\\%$ final energy spread and\n$3.8~\\mu m$ resulting normalized transverse emittance after the acceleration.\nIn this article, for the first time to our knowledge, the emittance of the PWFA\nbeam was directly measured.\n"}
+{"abstract": "  Feature importance (FI) estimates are a popular form of explanation, and they\nare commonly created and evaluated by computing the change in model confidence\ncaused by removing certain input features at test time. For example, in the\nstandard Sufficiency metric, only the top-k most important tokens are kept. In\nthis paper, we study several under-explored dimensions of FI explanations,\nproviding conceptual and empirical improvements for this form of explanation.\nFirst, we advance a new argument for why it can be problematic to remove\nfeatures from an input when creating or evaluating explanations: the fact that\nthese counterfactual inputs are out-of-distribution (OOD) to models implies\nthat the resulting explanations are socially misaligned. The crux of the\nproblem is that the model prior and random weight initialization influence the\nexplanations (and explanation metrics) in unintended ways. To resolve this\nissue, we propose a simple alteration to the model training process, which\nresults in more socially aligned explanations and metrics. Second, we compare\namong five approaches for removing features from model inputs. We find that\nsome methods produce more OOD counterfactuals than others, and we make\nrecommendations for selecting a feature-replacement function. Finally, we\nintroduce four search-based methods for identifying FI explanations and compare\nthem to strong baselines, including LIME, Anchors, and Integrated Gradients.\nThrough experiments with six diverse text classification datasets, we find that\nthe only method that consistently outperforms random search is a Parallel Local\nSearch (PLS) that we introduce. Improvements over the second-best method are as\nlarge as 5.4 points for Sufficiency and 17 points for Comprehensiveness. All\nsupporting code for experiments in this paper is publicly available at\nhttps://github.com/peterbhase/ExplanationSearch.\n"}
+{"abstract": "  The discrepancy between the theoretically calculated and experimentally\nmeasured activation gaps in quantum Hall effect has long been a puzzle. We\nrevisit this issue in the context of the $\\nu=1$ quantum Hall state, while also\nincorporating the skyrmion physics. We find that the finite width and the\nLandau level mixing (LLM) effects are not sufficient to explain the observed\nactivation gap. We further show that the presence of charged impurities located\nadjacent to the quantum well can cause a significant reduction in the\nactivation gap, while also causing a suppression of the skyrmion size.\n"}
+{"abstract": "  The class of problems in causal inference which seeks to isolate causal\ncorrelations solely from observational data even without interventions has come\nto the forefront of machine learning, neuroscience and social sciences. As new\nlarge scale quantum systems go online, it opens interesting questions of\nwhether a quantum framework exists on isolating causal correlations without any\ninterventions on a quantum system. We put forth a theoretical framework for\nmerging quantum information science and causal inference by exploiting entropic\nprinciples. At the root of our approach is the proposition that the true causal\ndirection minimizes the entropy of exogenous variables in a non-local hidden\nvariable theory. The proposed framework uses a quantum causal structural\nequation model to build the connection between two fields: entropic causal\ninference and the quantum marginal problem. First, inspired by the definition\nof geometric quantum discord, we fill the gap between classical and quantum\nconditional density matrices to define quantum causal models. Subsequently,\nusing a greedy approach, we develop a scalable algorithm for quantum entropic\ncausal inference unifying classical and quantum causality in a principled way.\nWe apply our proposed algorithm to an experimentally relevant scenario of\nidentifying the subsystem impacted by noise starting from an entangled state.\nThis successful inference on a synthetic quantum dataset can have practical\napplications in identifying originators of malicious activity on future\nmulti-node quantum networks as well as quantum error correction. As quantum\ndatasets and systems grow in complexity, our framework can play a foundational\nrole in bringing observational causal inference from the classical to the\nquantum domain.\n"}
+{"abstract": "  Ablative Rayleigh-Taylor instability growth was investigated to elucidate the\nfundamental physics of thermal conduction suppression in a magnetic field.\nExperiments found that unstable modulation growth is faster in an external\nmagnetic field. This result was reproduced by a magnetohydrodynamic simulation\nbased on a Braginskii model of electron thermal transport. An external magnetic\nfield reduces the electron thermal conduction across the magnetic field lines\nbecause the Larmor radius of the thermal electrons in the field is much shorter\nthan the temperature scale length. Thermal conduction suppression leads to\nspatially nonuniform pressure and reduced thermal ablative stabilization, which\nin turn increases the growth of ablative Rayleigh-Taylor instability.\n"}
+{"abstract": "  X-ray diffraction, thermodynamic measurements, and density-functional\nband-structure calculations are used to study the magnetic behavior of\nBaCuTe$_{2}$O$_{6}$, a member of the $A$CuTe$_2$O$_6$ structural family that\nhosts complex three-dimensional frustrated spin networks with possible\nspin-liquid physics. Temperature-dependent magnetic susceptibility and heat\ncapacity of the Ba compound are well described by the one-dimensional\nspin-$\\frac12$ Heisenberg chain model reminiscent of the Sr analog\nSrCuTe$_2$O$_6$. While the intrachain coupling $J/k_{\\rm B}\\simeq 37$ K is\nreduced compared to 49 K in the Sr compound, the N\\'eel temperature increases\nfrom 5.5 K (Sr) to 6.1 K (Ba). Unlike the Sr compound, BaCuTe$_2$O$_6$\nundergoes only one magnetic transition as a function of temperature and shows\nsignatures of weak spin canting. We elucidate the microscopic difference\nbetween the Sr and Ba compounds and suggest that one of the interchain\ncouplings changes sign as a result of negative pressure caused by the Sr/Ba\nsubstitution. The N\\'eel temperature of BaCuTe$_2$O$_6$ is remarkably\ninsensitive to the magnetic dilution with Zn$^{2+}$ up to the highest reachable\nlevel of about 20%.\n"}
+{"abstract": "  We performed a systematic search for broad-velocity-width molecular features\n(BVFs) in the disk part of our Galaxy by using the CO J = 1-0 survey data\nobtained with the Nobeyama Radio Observatory 45 m telescope. From this search,\n58 BVFs were identified. In comparisons with the infrared and radio continuum\nimages, 36 BVFs appeared to have both infrared and radio continuum\ncounterparts, and 15 of them are described as molecular outflows from young\nstellar objects in the literature. In addition, 21 BVFs have infrared\ncounterparts only, and eight of them are described as molecular outflows in the\nliterature. One BVF (CO 16.134-0.553) does not have any luminous counterpart in\nthe other wavelengths, which suggests that it may be an analog of high-velocity\ncompact clouds in the Galactic center.\n"}
+{"abstract": "  The Fermi-Lab Collaboration has announced the results for the measurement of\nmuon anomalous magnetic moment. Combining with the previous results by the BNL\nexperiment, we have $4.2 \\sigma$ deviation from the Standard Model (SM), which\nstrongly implies the new physics around 1 TeV. To explain the muon anomalous\nmagnetic moment naturally, we analyze the corresponding five Feynman diagrams\nin the Supersymetric SMs (SSMs), and show that the Electroweak Supersymmetry\n(EWSUSY) is definitely needed. We realize the EWSUSY in the Minimal SSM (MSSM)\nwith Genernalized Mininal Supergravity (GmSUGRA). We find large viable\nparameter space, which is consistent with all the current experimental\nconstraints. In particular, the Lightest Supersymmetric Particle (LSP)\nneutralino can be at least as heavy as 550 GeV. Most of the viable parameter\nspace can be probed at the future HL-LHC, while we do need the future HE-LHC to\nprobe some viable parameter space. However, it might still be challenge if\nR-parity is violated.\n"}
+{"abstract": "  Applying the concept of triadic closure to coauthorship networks means that\nscholars are likely to publish a joint paper if they have previously coauthored\nwith the same people. Prior research has identified moderate to high (20% to\n40%) closure rates; suggesting that this mechanism is a reasonable explanation\nfor tie formation between future coauthors. We show how calculating triadic\nclosure based on prior operationalizations of closure, namely Newman's measure\nfor one-mode networks (NCC) and Opsahl's measure for two-mode networks (OCC),\nmay lead to higher amounts of closure as compared to measuring closure over\ntime via a metric that we introduce and test in this paper. Based on empirical\nexperiments using four large-scale, longitudinal datasets, we find a lower\nbound of about 1~3% closure rates and an upper bound of about 4~7%. These\nresults motivate research on new explanatory factors for the formation of\ncoauthorship links.\n"}
+{"abstract": "  The impact of new and highly precise neutron \\b{eta} decay data is reviewed.\nWe focus on recent results from neutron lifetime, \\b{eta} asymmetry, and\nelectron-neutrino correlation experiments. From these results, weak interaction\nparameters are extracted with unprecedented precision, possible also due to\nprogress in effective field theory and lattice QCD. Limits on New Physics\nbeyond the Standard Model derived from neutron decay data are sharper than\nthose derived from high-energy experiments, except for processes involving\nright-handed neutrinos.\n"}
+{"abstract": "  In last 50 years, a significant progress was noticed in medicine,\ncommunications and entertainment. Such advanced development of these fields was\ndirectly related to ability of controlling light. Photonics is exactly about\nthis ability. At the present time, photonics is walking together with a\nfundamental physical concept, optical soliton. Optical solitons are\nshape-preserving laser beams. They are found potentially useful in data\ntransmission, which is very significant nowadays. Hence, research in the field\nof optical solitons is still a vital issue. When optical soliton is perturbed\nin a specific manner, there appear zeros of optical field around the soliton,\nwhich are called optical vortices. In general optical vortices are lines in\nspace. Hence, we might expect them to become knotted. Knotting optical vortices\naround perturbed seems spontaneous and cannot be directly predicted. To explain\nthis phenomenon, a similar system is constructed based on perturbation theory.\nIn this system, however, we have a mathematical problem which yet lacks a full\nunderstanding. We address this problem by introducing concepts from three\ndisciplines: laser physics, knot theory and singular optics. We believe that\nunderstanding the mechanism underlying spontaneous knotting of optical vortices\nwill be a step forward in other systems too, such as quantum turbulence in\nsuperfluids and formation of optical vortices around other types of optical\nsolitons.\n"}
+{"abstract": "  We prove that the moduli stack of Bridgeland semistable holomorphic triples\nover a curve of $g(C)\\geq 1$ with a fixed numerical class and phase is an\nalgebraic stack of finite type over $\\mathbb{C}$ and admits a proper good\nmoduli space. We prove that this also holds for a class of Bridgeland stability\nconditions on the category of holomorphic chains $\\mathcal{T}_{C,n}$.\n  In the process, we construct an explicit geometric realisation of\n$\\mathcal{T}_{C,n}$ and prove the open heart property for noetherian hearts in\nadmissible categories of $D^b(X)$, where $X$ is a smooth projective variety\nover $\\mathbb{C}$, whose orthogonal complements are geometric triangulated\ncategories.\n"}
+{"abstract": "  Contextual bandit is a general framework for online learning in sequential\ndecision-making problems that has found application in a large range of\ndomains, including recommendation system, online advertising, clinical trials\nand many more. A critical aspect of bandit methods is that they require to\nobserve the contexts -- i.e., individual or group-level data -- and the rewards\nin order to solve the sequential problem. The large deployment in industrial\napplications has increased interest in methods that preserve the privacy of the\nusers. In this paper, we introduce a privacy-preserving bandit framework based\non asymmetric encryption. The bandit algorithm only observes encrypted\ninformation (contexts and rewards) and has no ability to decrypt it. Leveraging\nhomomorphic encryption, we show that despite the complexity of the setting, it\nis possible to learn over encrypted data. We introduce an algorithm that\nachieves a $\\widetilde{O}(d\\sqrt{T})$ regret bound in any linear contextual\nbandit problem, while keeping data encrypted.\n"}
+{"abstract": "  We present a novel and cost-effective approach of using a second similarity\ntransformation of the Hamiltonian to include the missing higher-order terms in\nthe second-order approximate coupled cluster singles and doubles (CC2) model.\nThe performance of the newly developed ST-CC2 model has been investigated for\nthe calculation of excitation energies of valence, Rydberg, and charge-transfer\nexcited states. The method shows significant improvement in the excitation\nenergies of Rydberg and charge-transfer excited states as compared to the\nconventional CC2 method while retaining the good performance of the latter for\nthe valence excited state. The method retains the charge-transfer separability\nof the CT excited states, which is a significant advantage over the traditional\nCC2 method. An MBPT2 variant of the new method is also proposed.\n"}
+{"abstract": "  Let $(X,D)$ and $(X',D')$ be two compact Riemann surfaces of genus $g \\geq 4$\nwith the set of marked points $D \\subset X$ and $D' \\subset X'$. Fix a\nparabolic line bundle $L$ with trivial parabolic structure. Let\n$\\mathcal{N}_{\\textnormal{Sp}}(2m,\\alpha,L)$ and\n$\\mathcal{N}'_{\\textnormal{Sp}}(2m,\\alpha,L)$ be the moduli spaces of stable\nsymplectic parabolic Higgs bundles over $X$ and $X'$ respectively, with rank\n$2m$ and fixed parabolic structure $\\alpha$, with the symplectic form taking\nvalues in $L$. We prove that if $\\mathcal{N}_{\\textnormal{Sp}}(2m,\\alpha,L)$ is\nisomorphic to $\\mathcal{N}'_{\\textnormal{Sp}}(2m,\\alpha,L)$, then there exist\nan isomorphism between $X$ and $X'$ sending $D$ to $D'$.\n"}
+{"abstract": "  We study the Quot scheme $\\mathrm{Quot}^l(\\mathscr{E})$ of length $l$\ncoherent sheaf quotients of a locally free sheaf $\\mathscr{E}$ on a smooth\nprojective surface. We conjecture that the singularities of\n$\\mathrm{Quot}^l(\\mathscr{E})$ are rational. For $l=2$ we use the ADHM\ndescription to show that this conjecture holds, and in fact exhibit a modular\nresolution of singularities; this leads us to a sheaf-theoretic generalisation\nof Severi's double point formula. Given a short exact sequence\n$0\\rightarrow\\mathscr{E}^{'}\\rightarrow\\mathscr{E}\\rightarrow\\mathscr{E}^{''}\\rightarrow\n0$, we show that\n$\\mathrm{Quot}^l(\\mathscr{E}^{''})\\subset\\mathrm{Quot}^l(\\mathscr{E})$ is the\nzero scheme of a regular section of the tautological sheaf\n$\\mathscr{E}^{'\\vee[l]}$, which results in a relation between the (ordinary and\nvirtual) fundamental classes of $\\mathrm{Quot}^l(\\mathscr{E}^{''})$ and\n$\\mathrm{Quot}^l(\\mathscr{E})$.\n"}
+{"abstract": "  We propose a simple and effective strategy for data augmentation for\nlow-resource machine reading comprehension (MRC). Our approach first pretrains\nthe answer extraction components of a MRC system on the augmented data that\ncontains approximate context of the correct answers, before training it on the\nexact answer spans. The approximate context helps the QA method components in\nnarrowing the location of the answers. We demonstrate that our simple strategy\nsubstantially improves both document retrieval and answer extraction\nperformance by providing larger context of the answers and additional training\ndata. In particular, our method significantly improves the performance of BERT\nbased retriever (15.12\\%), and answer extractor (4.33\\% F1) on TechQA, a\ncomplex, low-resource MRC task. Further, our data augmentation strategy yields\nsignificant improvements of up to 3.9\\% exact match (EM) and 2.7\\% F1 for\nanswer extraction on PolicyQA, another practical but moderate sized QA dataset\nthat also contains long answer spans.\n"}
+{"abstract": "  In this paper, we investigate thick branes generated by a scalar field in\nmimetic gravity theory. By introducing two auxiliary super-potentials, we\ntransform the second-order field equations of the system into a set of\nfirst-order equations. With this first-order formalism, several types of\nanalytical thick brane solutions are obtained. Then, tensor and scalar\nperturbations are analysed. We find that both kinds of perturbations are\nstable. The effective potentials for the tensor and scalar perturbations are\ndual to each other. The tensor zero mode can be localized on the brane while\nthe scalar zero mode cannot. Thus, the four-dimensional Newtonian potential can\nbe recovered on the brane.\n"}
+{"abstract": "  We study the strong ill-posedness (norm inflation with infinite loss of\nregularity) for the nonlinear wave equation at every initial data in Wiener\namalgam and Fourier amalgam spaces with negative regularity. In particular\nthese spaces contain Fourier-Lebesgue, Sobolev and some modulation spaces. The\nequations are posed on $\\mathbb R^d$ and on torus $\\mathbb T^d$ and involve a\nsmooth power nonlinearity. Our results are sharp with respect to well-posedness\nresults of B\\'enyi and Okoudjou (2009) and Cordero and Nicola (2009) in the\nWiener amalgam and modulation space cases. In particular, we also complement\nnorm inflation result of Christ, Colliander and Tao (2003) and Forlano and\nOkamoto (2020) by establishing infinite loss of regularity in the aforesaid\nspaces.\n"}
+{"abstract": "  In this article, we study several probabilistic properties of polynomials\ndefined over the ring of $p$-adic integers under the Haar measure. First, we\ncalculate the probability that a monic polynomial is separable, generalizing a\nresult of Polak. Second, we introduce the notion of two polynomials being\nstrongly coprime and calculate the probability of two monic polynomials {being}\nstrongly coprime. Finally, we explain how our method can be used to extrapolate\nother probabilistic properties of polynomials over the ring of $p$-adic\nintegers from polynomials defined over the integers modulo powers of $p$.\n"}
+{"abstract": "  Defects are inevitably present in two-dimensional (2D) materials and usually\ngovern their various properties. Here a comprehensive density functional\ntheory-based investigation of 7 kinds of point defects in a recently produced\n{\\gamma} allotrope of 2D phosphorus carbide ({\\gamma}-PC) is conducted. The\ndefects, such as antisites, single C or P, and double C and P and C and C\nvacancies, are found to be stable in {\\gamma}-PC, while the Stone-Wales defect\nis not presented in {\\gamma}-PC due to its transition metal\ndichalcogenides-like structure. The formation energies, stability, and surface\ndensity of the considered defect species as well as their influence on the\nelectronic structure of {\\gamma}-PC is systematically identified. The formation\nof point defects in {\\gamma}-PC is found to be less energetically favourable\nthen in graphene, phosphorene, and MoS2. Meanwhile, defects can significantly\nmodulate the electronic structure of {\\gamma}-PC by inducing hole/electron\ndoping. The predicted scanning tunneling microscopy images suggest that most of\nthe point defects are easy to distinguish from each other and that they can be\neasily recognized in experiments.\n"}
+{"abstract": "  The Hosmer-Lemeshow (HL) test is a commonly used global goodness-of-fit (GOF)\ntest that assesses the quality of the overall fit of a logistic regression\nmodel. In this paper, we give results from simulations showing that the type 1\nerror rate (and hence power) of the HL test decreases as model complexity\ngrows, provided that the sample size remains fixed and binary replicates are\npresent in the data. We demonstrate that the generalized version of the HL test\nby Surjanovic et al. (2020) can offer some protection against this power loss.\nWe conclude with a brief discussion explaining the behaviour of the HL test,\nalong with some guidance on how to choose between the two tests.\n"}
+{"abstract": "  We propose a platform for braiding Majorana non-Abelian anyons based on a\nheterostructure between a $d$-wave high-$T_c$ superconductor and a quantum\nspin-Hall insulator. It has been recently shown that such a setup for a quantum\nspin-Hall insulator leads to a pair of Majorana zero modes at each corner of\nthe sample, and thus can be regarded as a higher-order topological\nsuperconductor. We show that upon applying a Zeeman field in the region, these\nMajorana modes split in space and can be manipulated for braiding processes by\ntuning the field and pairing phase. We show that such a setup can achieve full\nbraiding, exchanging, and arbitrary phase gates (including the $\\pi/8$ magic\ngates) of the Majorana zero modes, all of which are robust and protected by\nsymmetries. As many of the ingredients of our proposed platform have been\nrealized in recent experiments, our results provide a new route toward\nuniversal topological quantum computation.\n"}
+{"abstract": "  Graph signal processing (GSP) provides a powerful framework for analyzing\nsignals arising in a variety of domains. In many applications of GSP, multiple\nnetwork structures are available, each of which captures different aspects of\nthe same underlying phenomenon. To integrate these different data sources,\ngraph alignment techniques attempt to find the best correspondence between\nvertices of two graphs. We consider a generalization of this problem, where\nthere is no natural one-to-one mapping between vertices, but where there is\ncorrespondence between the community structures of each graph. Because we seek\nto learn structure at this higher community level, we refer to this problem as\n\"coarse\" graph alignment. To this end, we propose a novel regularized partial\nleast squares method which both incorporates the observed graph structures and\nimposes sparsity in order to reflect the underlying block community structure.\nWe provide efficient algorithms for our method and demonstrate its\neffectiveness in simulations.\n"}
+{"abstract": "  An equivariant minimal surface in $\\mathbb{CH}^n$ is a minimal map of the\nPoincar\\'{e} disc into $\\mathbb{CH}^n$ which intertwines two actions of the\nfundamental group of a closed surface $\\Sigma$: a Fuchsian representation on\nthe disc and an irreducible action by isometries on $\\mathbb{CH}^n$. The moduli\nspace of these can been studied by relating it to the nilpotent cone in each\nmoduli space of $PU(n,1)$-Higgs bundles over the conformal surface\ncorresponding to the map. By providing a necessary condition for points on this\nnilpotent cone to be smooth this article shows that away from the points\ncorresponding to branched minimal immersions or $\\pm$-holomorphic immersions\nthe moduli space is smooth. The argument is easily adapted to show that for\n$\\mathbb{RH}^n$ the full space of (unbranched) immersions is smooth. For\n$\\mathbb{CH}^2$ we show the connected components of the moduli space of minimal\nimmersions are indexed by the Toledo invariant and the Euler number of the\nnormal bundle of the immersion. This is achieved by studying the limit points\nof the $\\mathbb{C}^\\times$-action on the nilpotent cone. It is shown that the\nlimit points as $t\\to 0$ lead only to branched minimal immersions or\n$\\pm$-holomorphic immersions. In particular, the Euler number of the normal\nbundle can only jump by passing through branched minimal maps.\n"}
+{"abstract": "  Behavior of deep neural networks can be inconsistent between different\nversions. Regressions during model update are a common cause of concern that\noften over-weigh the benefits in accuracy or efficiency gain. This work focuses\non quantifying, reducing and analyzing regression errors in the NLP model\nupdates. Using negative flip rate as regression measure, we show that\nregression has a prevalent presence across tasks in the GLUE benchmark. We\nformulate the regression-free model updates into a constrained optimization\nproblem, and further reduce it into a relaxed form which can be approximately\noptimized through knowledge distillation training method. We empirically\nanalyze how model ensemble reduces regression. Finally, we conduct CheckList\nbehavioral testing to understand the distribution of regressions across\nlinguistic phenomena, and the efficacy of ensemble and distillation methods.\n"}
+{"abstract": "  Within the framework of a Dirac bubble potential model for the C60 fullerene\nshell we investigated the angular time delay in slow-electron elastic\nscattering by C60 as well as average time delay of electrons in this process.\nIt is demonstrated how the angular time delay is connected to the\nEisenbud-Wigner-Smith (EWS) time delay. The angular and energy dependences of\nthese times are investigated. The studies conducted shed light to some extent\non the specific features of these dependencies.\n"}
+{"abstract": "  Koopman mode analysis has provided a framework for analysis of nonlinear\nphenomena across a plethora of fields. Its numerical implementation via Dynamic\nMode Decomposition (DMD) has been extensively deployed and improved upon over\nthe last decade. We address the problems of mean subtraction and DMD mode\nselection in the context of finite dimensional Koopman invariant subspaces.\n  Preprocessing of data by subtraction of the temporal mean of a time series\nhas been a point of contention in companion matrix-based DMD. This stems from\nthe potential of said preprocessing to render DMD equivalent to temporal DFT.\nWe prove that this equivalence is impossible when the order of the DMD-based\nrepresentation of the dynamics exceeds the dimension of the system. Moreover,\nthis parity of DMD and DFT is mostly indicative of an inadequacy of data, in\nthe sense that the number of snapshots taken is not enough to represent the\ntrue dynamics of the system.\n  We then vindicate the practice of pruning DMD eigenvalues based on the norm\nof the respective modes. Once a minimum number of time delays has been taken,\nDMD eigenvalues corresponding to DMD modes with low norm are shown to be\nspurious, and hence must be discarded. When dealing with mean-subtracted data,\nthe above criterion for detecting synthetic eigenvalues can be applied after\nadditional pre-processing. This takes the form of an eigenvalue constraint on\nCompanion DMD, or yet another time delay.\n"}
+{"abstract": "  Digital image processing techniques have wide applications in different\nscientific fields including the medicine. By use of image processing\nalgorithms, physicians have been more successful in diagnosis of different\ndiseases and have achieved much better treatment results. In this paper, we\npropose an automatic method for segmenting the skin lesions and extracting\nfeatures that are associated to them. At this aim, a combination of Speeded-Up\nRobust Features (SURF) and Active Contour Model (ACM), is used. In the\nsuggested method, at first region of skin lesion is segmented from the whole\nskin image, and then some features like the mean, variance, RGB and HSV\nparameters are extracted from the segmented region. Comparing the segmentation\nresults, by use of Otsu thresholding, our proposed method, shows the\nsuperiority of our procedure over the Otsu theresholding method. Segmentation\nof the skin lesion by the proposed method and Otsu thresholding compared the\nresults with physician's manual method. The proposed method for skin lesion\nsegmentation, which is a combination of SURF and ACM, gives the best result.\nFor empirical evaluation of our method, we have applied it on twenty different\nskin lesion images. Obtained results confirm the high performance, speed and\naccuracy of our method.\n"}
+{"abstract": "  The XGIS (X and Gamma Imaging Spectrometer) is one of the three instruments\nonboard the THESEUS mission (ESA M5, currently in Phase-A). Thanks to its wide\nfield of view and good imaging capabilities, it will efficiently detect and\nlocalize gamma-ray bursts and other transients in the 2-150 keV sky, and also\nprovide spectroscopy up to 10 MeV. Its current design has been optimized by\nmeans of scientific simulations based on a Monte Carlo model of the instrument\ncoupled to a state-of-the-art description of the populations of long and short\nGRBs extending to high redshifts. We describe the optimization process that led\nto the current design of the XGIS, based on two identical units with partially\noverlapping fields of view, and discuss the expected performance of the\ninstrument.\n"}
+{"abstract": "  The motion of our solar system relative to the CMB rest frame leads to subtle\ndistortions in the observed CMB sky map due to the aberration effect. Usually\nthe corresponding peculiar velocity is determined from the CMB dipole but\nneglecting intrinsic dipole contributions. Here it is investigated whether\ncertain invariant scalar measures, which are derived from first and second\norder covariant derivatives on the sphere, can detect the distortions caused by\nthe aberration effect at high multipoles. This would in principle allow to\ndisentangle the Doppler from intrinsic dipole contributions providing an\nindependent method for the determination of our peculiar velocity. It is found\nthat the eigenvalues of the Hessian matrix of the temperature field are well\nsuited for that task.\n"}
+{"abstract": "  The multi-access variant of the coded caching problem in the presence of an\nexternal wiretapper is investigated . A multi-access coded caching scheme with\n$K$ users, $K$ caches and $N$ files, where each user has access to $L$\nneighbouring caches in a cyclic wrap-around manner, is proposed, which is\nsecure against the wiretappers. Each transmission in the conventional insecure\nscheme will be now encrypted by a random key. The proposed scheme uses a novel\ntechnique for the key placement in the caches. It is also shown that the\nproposed secure multi-access coded caching scheme is within a constant\nmultiplicative factor from the information-theoretic optimal rate for $L\\geq\n\\frac{K}{2}$ and $N\\geq 2K$.\n"}
+{"abstract": "  Many studies have revealed that confined water chain flipping is closely\nrelated to the spatial size and even quantum effects of the confinement\nenvironment. Here, we show that these are not the only factors that affect the\nflipping process of a confined water chain. First-principles calculations and\nanalyses confirm that quantum tunnelling effects from the water chain itself,\nespecially resonant tunnelling, enhance the hydrogen bond rotation process.\nImportantly, resonant tunnelling can result in tunnelling rotation of hydrogen\nbonds with a probability close to 1 with only 0.597 eV provided energy.\nCompared to sequential tunnelling, resonant tunnelling dominants water chain\nflipping at temperatures up to 20 K higher. Additionally, the ratio of the\nresonant tunnelling probability to the thermal disturbance probability at 200 K\nis at least ten times larger than that of sequential tunnelling, which further\nillustrates the enhancement of hydrogen bond rotation brought about by resonant\ntunnelling.\n"}
+{"abstract": "  We present the results of Atacama Large Millimeter/submillimeter Array (ALMA)\nobservation in $^{12}$CO(1-0) emission at 0.58 $\\times$ 0.52 pc$^2$ resolution\ntoward the brightest HII region N66 of the Small Magellanic Cloud (SMC). The\n$^{12}$CO(1-0) emission toward the north of N66 reveals the clumpy filaments\nwith multiple velocity components. Our analysis shows that a blueshifted\nfilament at a velocity range 154.4-158.6 km s$^{-1}$ interacts with a\nredshifted filament at a velocity 158.0-161.8 km s$^{-1}$. A third velocity\ncomponent in a velocity range 161-165.0 km s$^{-1}$ constitutes hub-filaments.\nAn intermediate-mass young stellar object (YSO) and a young pre-main sequence\nstar cluster have hitherto been reported in the intersection of these\nfilaments. We find a V-shape distribution in the position-velocity diagram at\nthe intersection of two filaments. This indicates the physical association of\nthose filaments due to a cloud-cloud collision. We determine the collision\ntimescale $\\sim$ 0.2 Myr using the relative velocity ($\\sim$ 5.1 km s$^{-1}$)\nand displacement ($\\sim$ 1.1 pc) of those interacting filaments. These results\nsuggest that the event occurred at about 0.2 Myr ago and triggered the star\nformation, possibly an intermediate-mass YSO. We report the first observational\nevidence for a cloud-cloud collision that triggers star formation in N66N of\nthe low metallicity $\\sim$0.2 Z$_{\\odot}$ galaxy, the SMC, with similar\nkinematics as in N159W-South and N159E of the Large Magellanic Cloud.\n"}
+{"abstract": "  The polarization dependence of magnon-photon scattering in an optical\nmicrocavity is reported. Due to the short cavity length, the mode-matching\nconditions found in previously explored, large path-length whispering gallery\nresonators are absent. Nonetheless, for cross-polarized scattering a strong and\nbroadband suppression of one side-band is observed. This arises due to an\ninterference between the Faraday and second-order Cotton-Mouton effects. To\nfully account for the suppression of the cross-polarized scattering, it is\nnecessary to consider the squeezing of magnon modes intrinsic to thin-film\ngeometry. A co-polarized scattering due to Cotton-Mouton effect is also\nobserved. In addition, the magnon modes involved are identified as\nDamon-Eshbach surface modes, whose non-reciprocal propagation could be\nexploited in devices applications. This work experimentally demonstrates the\nimportant role of second order Cotton-Mouton effect for optomagnonic devices.\n"}
+{"abstract": "  We perform a systematic study showing the evolution of the multipoles along\nwith the spectra for a hybrid metal-dielectric nanoantenna, a Si cylinder and\nan Ag disk stacked one on top of another, as its dimensions are varied one by\none. We broaden our analysis to demonstrate the \"magnetic light\" at energies\nabove 1 eV by varying the height of the Ag on the Si cylinder and below 1 eV by\nintroducing insulating spacing between them. We also explore the appearance of\nthe anapole state along with some exceptionally narrow spectral features by\nvarying the radius of the Ag disk.\n"}
+{"abstract": "  Cryo-EM reconstruction algorithms seek to determine a molecule's 3D density\nmap from a series of noisy, unlabeled 2D projection images captured with an\nelectron microscope. Although reconstruction algorithms typically model the 3D\nvolume as a generic function parameterized as a voxel array or neural network,\nthe underlying atomic structure of the protein of interest places well-defined\nphysical constraints on the reconstructed structure. In this work, we exploit\nprior information provided by an atomic model to reconstruct distributions of\n3D structures from a cryo-EM dataset. We propose Cryofold, a generative model\nfor a continuous distribution of 3D volumes based on a coarse-grained model of\nthe protein's atomic structure, with radial basis functions used to model atom\nlocations and their physics-based constraints. Although the reconstruction\nobjective is highly non-convex when formulated in terms of atomic coordinates\n(similar to the protein folding problem), we show that gradient descent-based\nmethods can reconstruct a continuous distribution of atomic structures when\ninitialized from a structure within the underlying distribution. This approach\nis a promising direction for integrating biophysical simulation, learned neural\nmodels, and experimental data for 3D protein structure determination.\n"}
+{"abstract": "  In this paper we will describe two new optimisations implemented in\nMadGraph5_aMC@NLO, both of which are designed to speed-up the computation of\nleading-order processes (for any model). First we implement a new method to\nevaluate the squared matrix element, dubbed helicity recycling, which results\nin factor of two speed-up. Second, we have modified the multi-channel handling\nof the phase-space integrator providing tremendous speed-up for VBF-like\nprocesses (up to thousands times faster).\n"}
+{"abstract": "  The quest for determinism in machine learning has disproportionately focused\non characterizing the impact of noise introduced by algorithmic design choices.\nIn this work, we address a less well understood and studied question: how does\nour choice of tooling introduce randomness to deep neural network training. We\nconduct large scale experiments across different types of hardware,\naccelerators, state of art networks, and open-source datasets, to characterize\nhow tooling choices contribute to the level of non-determinism in a system, the\nimpact of said non-determinism, and the cost of eliminating different sources\nof noise.\n  Our findings are surprising, and suggest that the impact of non-determinism\nin nuanced. While top-line metrics such as top-1 accuracy are not noticeably\nimpacted, model performance on certain parts of the data distribution is far\nmore sensitive to the introduction of randomness. Our results suggest that\ndeterministic tooling is critical for AI safety. However, we also find that the\ncost of ensuring determinism varies dramatically between neural network\narchitectures and hardware types, e.g., with overhead up to $746\\%$, $241\\%$,\nand $196\\%$ on a spectrum of widely used GPU accelerator architectures,\nrelative to non-deterministic training. The source code used in this paper is\navailable at https://github.com/usyd-fsalab/NeuralNetworkRandomness.\n"}
+{"abstract": "  We report the low-temperature magnetothermal properties and spin dynamics of\na mixed pyrochlore, Dy2GaSbO7 through the coordination of measurements of dc\nmagnetization, ac susceptibility, and heat capacity, and CF computation. In\nDy2GaSbO7, the spins freeze at temperature Tice = 3.1 K, corresponding to\ndynamical spin-freezing into a disordered state in compliance with the spin-ice\nrules, but with enhanced zero-point entropy, unlike cannonical dipolar spin-ice\nmaterials, Dy2Ti2O7 and Ho2Ti2O7, due to random disorder and varied chemical\npressure of B-site (GaSb tetrahedra) ions.\n"}
+{"abstract": "  Spin alignments of vector mesons and hyperons in relativistic heavy-ion\ncollisions have been proposed as signals of the global polarization. The STAR\nexperiment first observed the $\\rm \\Lambda$ polarization. Recently, the ALICE\ncollaboration measured the transverse momentum ($p_T$) and the collision\ncentrality dependence of $K^*$ and $\\phi$ spin alignments in Pb-Pb collisions\nat $\\rm \\sqrt{s_{NN}}$ = 2.76 TeV. A large signal is observed in the low $p_T$\nregion of mid-central collisions for $K^*$ while the signal is much smaller for\n$\\phi$, and these have not been understood yet. Since vector mesons have\ndifferent lifetimes and their decay products have different scattering cross\nsections, they suffer from different hadronic effects. In this paper, we study\nthe effect of hadronic interactions on the spin alignment of $K^*$, $\\phi$ and\n$\\rho$ mesons in relativistic heavy-ion collisions with a multi-phase transport\nmodel. We find that hadronic scatterings lead to a deviation of the observed\nspin alignment matrix element $\\rho_{00}$ away from the true value for $\\rho$\nand $K^*$ mesons (with a bigger effect on $\\rho$) while the effect is\nnegligible for the $\\phi$ meson. The effect depends on the kinematic\nacceptance: the observed $\\rho_{00}$ value is lower than the true value when\nthe pseudorapidity ($\\eta$) coverage is small while there is little effect when\nthe $\\eta$ coverage is big. Our study thus provides valuable information to\nunderstand the vector meson spin alignment signals observed in the experiments.\n"}
+{"abstract": "  We introduce the notion of a rank function on a triangulated category\n$\\mathcal{C}$ which generalizes the Sylvester rank function in the case when\n$\\mathcal{C}=\\operatorname{Perf}(A)$ is the perfect derived category of a ring\n$A$. We show that rank functions are closely related to functors into simple\ntriangulated categories and classify Verdier quotients into simple triangulated\ncategories in terms of particular rank functions called localizing. If\n$\\mathcal{C}=\\operatorname{Perf}(A)$ as above, localizing rank functions also\nclassify finite homological epimorphisms from $A$ into differential graded\nskew-fields or, more generally, differential graded Artining rings. To\nestablish these results, we develop the theory of derived localization of\ndifferential graded algebras at thick subcategories of their perfect derived\ncategories. This is a far-reaching generalization of Cohn's matrix localization\nof rings and has independent interest.\n"}
+{"abstract": "  Analysis of the fetal heart rate during pregnancy is essential for monitoring\nthe proper development of the fetus. Current fetal heart monitoring techniques\nlack the accuracy in fetal heart rate monitoring and features acquisition,\nresulting in diagnostic medical issues. The challenge lies in the extraction of\nthe fetal ECG from the mother's ECG during pregnancy. This approach has the\nadvantage of being a reliable and non-invasive technique. For this aim, we\npropose in this paper a wavelet/multi-wavelet method allowing to extract\nperfectly the feta ECG parameters from the abdominal mother ECG. The method is\nessentially due to the exploitation of Clifford wavelets as recent variants in\nthe field. We prove that these wavelets are more efficient and performing\nagainst classical ones. The experimental results are therefore due to two basic\nclasses of wavelets and multi-wavelets. A first-class is the classical Haar\nSchauder, and a second one is due to Clifford valued wavelets and\nmulti-wavelets. These results showed that wavelets/multiwavelets are already\ngood bases for the FECG processing, provided that Clifford ones are the best.\n"}
+{"abstract": "  We consider situations where a user feeds her attributes to a machine\nlearning method that tries to predict her best option based on a random sample\nof other users. The predictor is incentive-compatible if the user has no\nincentive to misreport her covariates. Focusing on the popular Lasso estimation\ntechnique, we borrow tools from high-dimensional statistics to characterize\nsufficient conditions that ensure that Lasso is incentive compatible in large\nsamples. We extend our results to the Conservative Lasso estimator and provide\nnew moment bounds for this generalized weighted version of Lasso. Our results\nshow that incentive compatibility is achieved if the tuning parameter is kept\nabove some threshold. We present simulations that illustrate how this can be\ndone in practice.\n"}
+{"abstract": "  We analyse the emergent cosmological dynamics corresponding to the mean field\nhydrodynamics of quantum gravity condensates, in the tensorial group field\ntheory formalism. We focus in particular on the cosmological effects of\nfundamental interactions, and on the contributions from different quantum\ngeometric modes. The general consequence of such interactions is to produce an\naccelerated expansion of the universe, which can happen both at early times,\nafter the quantum bounce predicted by the model, and at late times. Our main\nresult is that, while this fails to give a compelling inflationary scenario in\nthe early universe, it produces naturally a phantom-like dark energy dynamics\nat late times, compatible with cosmological observations. By recasting the\nemergent cosmological dynamics in terms of an effective equation of state, we\nshow that it can generically cross the phantom divide, purely out of quantum\ngravity effects without the need of any additional phantom matter. Furthermore,\nwe show that the dynamics avoids any Big Rip singularity, approaching instead a\nde Sitter universe asymptotically.\n"}
+{"abstract": "  Power-grid systems constitute one of the most complex man-made spatially\nextended structures. These operate with strict operational bounds to ensure\nsynchrony across the grid. This is particularly relevant for power-grid\nfrequency, which operates strictly at $50\\,$Hz ($60\\,$Hz). Nevertheless, small\nfluctuations around the mean frequency are present at very short time scales\n$<2$ seconds and can exhibit highly complex spatio-temporal behaviour. Here we\napply superstatistical data analysis techniques to measured frequency\nfluctuations in the Nordic Grid. We study the increment statistics and extract\nthe relevant time scales and superstatistical distribution functions from the\ndata. We show that different synchronous recordings of power-grid frequency\nhave very distinct stochastic fluctuations with different types of\nsuperstatistics at different spatial locations, and with transitions from one\nsuperstatistics to another when the time lag of the increment statistics is\nchanged.\n"}
+{"abstract": "  We study the wealth distribution of UK households through a detailed analysis\nof data from wealth surveys and rich lists, and propose a non-linear Kesten\nprocess to model the dynamics of household wealth. The main features of our\nmodel are that we focus on wealth growth and disregard exchange, and that the\nrate of return on wealth is increasing with wealth. The linear case with\nwealth-independent return rate has been well studied, leading to a log-normal\nwealth distribution in the long time limit which is essentially independent of\ninitial conditions. We find through theoretical analysis and simulations that\nthe non-linearity in our model leads to more realistic power-law tails, and can\nexplain an apparent two-tailed structure in the empirical wealth distribution\nof the UK and other countries. Other realistic features of our model include an\nincrease in inequality over time, and a stronger dependence on initial\nconditions compared to linear models.\n"}
+{"abstract": "  An $(n,r)$-arc in $PG(2,q)$ is a set $B$ of points in $PG(2,q)$ such that\neach line in $PG(2,q)$ contains at most $r$ elements of $B$ and such that there\nis at least one line containing exactly $r$ elements of $B$. The value\n$m_r(2,q)$ denotes the maximal number $n$ of points in the projective geometry\n$PG(2,q)$ for which an $(n,r)$-arc exists. By explicitly constructing\n$(n,r)$-arcs using prescribed automorphisms and integer linear programming we\nobtain some improved lower bounds for $m_r(2,q)$: $m_{10}(2,16)\\ge 144$,\n$m_3(2,25)\\ge 39$, $m_{18}(2,25)\\ge 418$, $m_9(2,27)\\ge 201$, $m_{14}(2,29)\\ge\n364$, $m_{25}(2,29)\\ge 697$, $m_{25}(2,31)\\ge 734$. Furthermore, we show by\nsystematically excluding possible automorphisms that putative $(44,5)$-arcs,\n$(90,9)$-arcs in $PG(2,11)$, and $(39,4)$-arcs in $PG(2,13)$ -- in case of\ntheir existence -- are rigid, i.e. they all would only admit the trivial\nautomorphism group of order $1$. In addition, putative $(50,5)$-arcs,\n$(65,6)$-arcs, $(119,10)$-arcs, $(133,11)$-arcs, and $(146,12)$-arcs in\n$PG(2,13)$ would be rigid or would admit a unique automorphism group (up to\nconjugation) of order $2$.\n"}
+{"abstract": "  We show Goodwillie's calculus of functors and $n$-geometric $D^{-}$-stacks\nshare similar features by starting to focus on the convergence of Taylor towers\nfor homotopy functors and the fact that $\\mathbb{R} F(A) \\cong \\text{holim}\n\\mathbb{R} F(A_{\\leq n})$ for geometric stacks, where $\\{ A_{\\leq n} \\}$\nprovides a Postnikov tower of some given $A \\in s \\text{k-Alg}$. From there we\nshow parallel results, such as similar homotopy fibers of connecting maps in\ntowers, as well as polynomial approximations, pointwise approximations and\nreconstruction theorems for towers.\n"}
+{"abstract": "  The detection of gravity modes is expected to give us unprecedented insights\ninto the inner dynamics of the Sun. Within this framework, predicting their\namplitudes is essential to guide future observational strategies and seismic\nstudies. In this work, we predict the amplitude of low-frequency asymptotic\ngravity modes generated by penetrative convection at the top of the radiative\nzone. The result is found to depend critically on the time evolution of the\nplumes inside the generation region. Using a solar model, we compute the GOLF\napparent surface radial velocity of low-degree gravity modes in the frequency\nrange $10~\\mu H_z\\le \\nu \\le 100~\\mu H_z$. In case of a Gaussian plume time\nevolution, gravity modes turn out to be undetectable because of too small\nsurface amplitudes. This holds true despite a wide range of values considered\nfor the parameters of the model. In the other limiting case of an exponential\ntime evolution, plumes are expected to drive gravity modes in a much more\nefficient way because of a much higher temporal coupling between the plumes and\nthe modes than in the Gaussian case. Using reasonable values for the plume\nparameters based on semi-analytical models, the apparent surface velocities in\nthis case turn out to be one order of magnitude smaller than the 22-years GOLF\ndetection threshold and than the previous estimates considering turbulent\npressure as the driving mechanism, with a maximum value of $0.05$ cm s${}^{-1}$\nfor $\\ell =1$ and $\\nu\\approx 100~\\mu H_z$. When accounting for uncertainties\non the plume parameters, the apparent surface velocities in the most favorable\nplausible case become comparable to those predicted with turbulent pressure,\nand the GOLF observation time required for a detection at $ \\nu \\approx100~\\mu\nH_z$ and $\\ell=1$ is reduced to about 50 yrs.\n"}
+{"abstract": "  We study the time evolution of the chirality imbalance $n_5$ and the chiral\nmagnetic effect (CME) under the external parallel electromagnetic fields\nwithout assuming the artificial chiral asymmetric source. We adopt the\ntime-dependent Sauter-type electric and constant magnetic field, and obtain\nanalytical solutions of the Dirac equation for a massive fermion. We use the\npoint-split regularization to calculate the vacuum contribution in the gauge\ninvariant way. As a result, we find that $n_5$ and CME current increase\nsubstantially as the electric field increases, and stay finite after the\nelectric field is switched off. The chirality imbalance and CME current are\nshown to consist of a dominant contribution, which is essentially proportional\nto relativistic velocity, and a small oscillating part. We find a simple\nanalytical relation between $n_5$ and the fermion pair-production rate from the\nvacuum. We also discuss dynamical origin of the chirality imbalance in detail.\n"}
+{"abstract": "  Flat bands provide a natural platform for emergent electronic states beyond\nLandau paradigm. Among those of particular importance are flat Chern bands,\nincluding bands of higher Chern numbers ($C$$>$$1$). We introduce a new\nframework for band flatness through wave functions, and classify the existing\nisolated flat bands in a \"periodic table\" according to tight binding features\nand wave function properties. Our flat band categorization encompasses\nseemingly different classes of flat bands ranging from atomic insulators to\nperfectly flat Chern bands and Landau Levels. The perfectly flat Chern bands\nsatisfy Berry curvature condition $F_{xy} = \\text{Tr} \\, \\mathcal G_{ij}$ which\non the tight-binding level is fulfilled only for infinite-range models. Most of\nthe natural Chern bands fall into category of $C=1$; the complexity of creating\nhigher-$C$ flat bands is beyond the current technology. This is due to the\nbreakdown of the microscopic stability for higher-$C$ flatness, seen\natomistically e.g. in the increase of the hopping range bound as\n$\\propto$$\\sqrt{C} a$. Within our new formalism, we indicate strategies for\nbypassing higher-$C$ constraints and thus dramatically decreasing the\nimplementation complexity.\n"}
+{"abstract": "  In this paper, we develop a theory on the degenerations of Blaschke products\n$\\mathcal{B}_d$ to study the boundaries of hyperbolic components. We give a\ncombinatorial classification of geometrically finite polynomials on the\nboundary of the main hyperbolic component $\\mathcal{H}_d$ containing $z^d$. We\nshow the closure $\\overline{\\mathcal{H}_d}$ is not a topological manifold with\nboundary for $d\\geq 4$ by constructing self-bumps on its boundary.\n"}
+{"abstract": "  Let $\\kappa$ be a positive real number and $m\\in\\mathbb{N}\\cup\\{\\infty\\}$ be\ngiven. Let $p_{\\kappa, m}(n)$ denote the number of partitions of $n$ into the\nparts from the Piatestki-Shapiro sequence $(\\lfloor\n\\ell^{\\kappa}\\rfloor)_{\\ell\\in \\mathbb{N}}$ with at most $m$ times (repetition\nallowed). In this paper we establish asymptotic formulas of Hardy-Ramanujan\ntype for $p_{\\kappa, m}(n)$, by employing a framework of asymptotics of\npartitions established by Roth-Szekeres in 1953, as well as some results on\nequidistribution.\n"}
+{"abstract": "  Major depressive disorder (MDD) is a prevalent psychiatric disorder that is\nassociated with significant healthcare burden worldwide. Phenotyping of MDD can\nhelp early diagnosis and consequently may have significant advantages in\npatient management. In prior research MDD phenotypes have been extracted from\nstructured Electronic Health Records (EHR) or using Electroencephalographic\n(EEG) data with traditional machine learning models to predict MDD phenotypes.\nHowever, MDD phenotypic information is also documented in free-text EHR data,\nsuch as clinical notes. While clinical notes may provide more accurate\nphenotyping information, natural language processing (NLP) algorithms must be\ndeveloped to abstract such information. Recent advancements in NLP resulted in\nstate-of-the-art neural language models, such as Bidirectional Encoder\nRepresentations for Transformers (BERT) model, which is a transformer-based\nmodel that can be pre-trained from a corpus of unsupervised text data and then\nfine-tuned on specific tasks. However, such neural language models have been\nunderutilized in clinical NLP tasks due to the lack of large training datasets.\nIn the literature, researchers have utilized the distant supervision paradigm\nto train machine learning models on clinical text classification tasks to\nmitigate the issue of lacking annotated training data. It is still unknown\nwhether the paradigm is effective for neural language models. In this paper, we\npropose to leverage the neural language models in a distant supervision\nparadigm to identify MDD phenotypes from clinical notes. The experimental\nresults indicate that our proposed approach is effective in identifying MDD\nphenotypes and that the Bio- Clinical BERT, a specific BERT model for clinical\ndata, achieved the best performance in comparison with conventional machine\nlearning models.\n"}
+{"abstract": "  We say that a group is anti-solvable if all of its composition factors are\nnon-abelian. We consider a particular family of anti-solvable finite groups\ncontaining the simple alternating groups for $n\\neq 6$ and all 26 sporadic\nsimple groups. We prove that, if $K$ is a perfect field and $X$ is a\nhomogeneous space of a smooth algebraic $K$-group $G$ with finite geometric\nstabilizers lying in this family, then $X$ is dominated by a $G$-torsor. In\nparticular, if $G=\\mathrm{SL}_n$, all such homogeneous spaces have rational\npoints.\n"}
+{"abstract": "  Several evidences indicate that Lyman Break Galaxies (LBG) in the Epoch of\nReionization (redshift $z>6$) might host massive black holes (MBH). We address\nthis question by using a merger-tree model combined with tight constraints from\nthe 7 Ms Chandra survey, and the known high-$z$ super-MBH population. We find\nthat a typical LBG with $M_{\\rm UV}=-22$ residing in a $M_h\\approx 10^{12}\nM_\\odot$ halo at $z=6$ host a MBH with mass $M_\\bullet \\approx 2\\times 10^8\nM_\\odot$. Depending on the fraction, $f_{\\rm seed}$, of early halos planted\nwith a direct collapse black hole seed ($M_{\\rm seed}=10^5 M_\\odot$), the model\nsuggests two possible scenarios: (a) if $f_{\\rm seed}=1$, MBH in LBGs mostly\ngrow by merging, and must accrete at a low ($\\lambda_E\\simeq 10^{-3}$)\nEddington ratio not to exceed the experimental X-ray luminosity upper bound\n$L_X^* = 10^{42.5} {\\rm erg\\, s}^{-1}$; (b) if $f_{\\rm seed}=0.05$ accretion\ndominates ($\\lambda_E\\simeq 0.22$), and MBH emission in LBGs must be heavily\nobscured. In both scenarios the UV luminosity function is largely dominated by\nstellar emission up to very bright mag, $M_{\\rm UV} > -23$, with BH emission\nplaying a subdominant role. Scenario (a) poses extremely challenging, and\npossibly unphysical, requirements on DCBH formation. Scenario (b) entails\ntestable implications on the physical properties of LBGs involving the FIR\nluminosity, emission lines, and presence of outflows.\n"}
+{"abstract": "  Inferring parameters related to the aggregation pattern of parasites and to\ntheir dispersal propensity is important for predicting their ecological\nconsequences and evolutionary potential. Nonetheless, it is notoriously\ndifficult to infer these parameters from wildlife parasites given the\ndifficulty in tracking these organisms. Molecular-based inferences constitute a\npromising approach that has yet rarely been applied in the wild.Here, we\ncombinedseveral population genetic analyses including sibship reconstruction to\ndocumentthe genetic structure, patterns of sibship aggregation and the\ndispersal dynamics of a non-native parasite of fish, the freshwater copepod\nectoparasiteTracheliastespolycolpus. We collected parasites according to a\nhierarchical sampling design,withthe sampling of all parasites from all host\nindividualscapturedineight sites spread along an upstream-downstream river\ngradient. Individual multilocus genotypes were obtained from 14 microsatellite\nmarkers, and used to assign parasites to full-sib families and to investigate\nthe genetic structure of T.polycolpus among both hosts and sampling sites. The\ndistribution of full-sibs obtainedamong the sampling sites was used to estimate\nindividual dispersal distances within families. Our results showed that T.\npolycolpus sibs tend to be aggregated within sites but not withinhost\nindividuals. We detected important upstream-to-downstream dispersal events of\nT.polycolpusbetween sites (modal distance: 25.4 km; 95% CI [22.9, 27.7]),\nbecoming scarcer as the geographic distance from their family core location\nincreases. Such a dispersal pattern likely contributes to the strong\nisolation-by-distance observed at the river scale. We also detected some\ndownstream-to-upstream dispersal events (modal distance: 2.6 km; 95% CI\n[2.2-23.3]) that likely result from movementsof infected hosts.Within each\nsite, the dispersal of free-living infective larvae among hosts likely\ncontributes to increasing genetic diversity on hosts, possibly fostering the\nevolutionary potential of T. polycolpus.\n"}
+{"abstract": "  In machine learning, classifiers are typically susceptible to noise in the\ntraining data. In this work, we aim at reducing intra-class noise with the help\nof graph filtering to improve the classification performance. Considered graphs\nare obtained by connecting samples of the training set that belong to a same\nclass depending on the similarity of their representation in a latent space. We\nshow that the proposed graph filtering methodology has the effect of\nasymptotically reducing intra-class variance, while maintaining the mean. While\nour approach applies to all classification problems in general, it is\nparticularly useful in few-shot settings, where intra-class noise can have a\nhuge impact due to the small sample selection. Using standardized benchmarks in\nthe field of vision, we empirically demonstrate the ability of the proposed\nmethod to slightly improve state-of-the-art results in both cases of few-shot\nand standard classification.\n"}
+{"abstract": "  Cluster assignment of large and complex images is a crucial but challenging\ntask in pattern recognition and computer vision. In this study, we explore the\npossibility of employing fuzzy clustering in a deep neural network framework.\nThus, we present a novel evolutionary unsupervised learning representation\nmodel with iterative optimization. It implements the deep adaptive fuzzy\nclustering (DAFC) strategy that learns a convolutional neural network\nclassifier from given only unlabeled data samples. DAFC consists of a deep\nfeature quality-verifying model and a fuzzy clustering model, where deep\nfeature representation learning loss function and embedded fuzzy clustering\nwith the weighted adaptive entropy is implemented. We joint fuzzy clustering to\nthe deep reconstruction model, in which fuzzy membership is utilized to\nrepresent a clear structure of deep cluster assignments and jointly optimize\nfor the deep representation learning and clustering. Also, the joint model\nevaluates current clustering performance by inspecting whether the re-sampled\ndata from estimated bottleneck space have consistent clustering properties to\nprogressively improve the deep clustering model. Comprehensive experiments on a\nvariety of datasets show that the proposed method obtains a substantially\nbetter performance for both reconstruction and clustering quality when compared\nto the other state-of-the-art deep clustering methods, as demonstrated with the\nin-depth analysis in the extensive experiments.\n"}
+{"abstract": "  Recent developments in advanced driving assistance systems (ADAS) that rely\non some level of autonomy have led the automobile industry and research\ncommunity to investigate the impact they might have on driving performance.\nHowever, most of the research performed so far is based on simulated\nenvironments. In this study, we investigated the behavior of drivers in a\nvehicle with automated driving system (ADS) capabilities in a real-life driving\nscenario. We analyzed their response to a take over request (TOR) at two\ndifferent driving speeds while being engaged in non-driving-related tasks\n(NDRT). Results from the performed experiments showed that driver reaction time\nto a TOR, gaze behavior and self-reported trust in automation were affected by\nthe type of NDRT being concurrently performed and driver reaction time and gaze\nbehavior additionally depended on the driving or vehicle speed at the time of\nTOR.\n"}
+{"abstract": "  In this paper, we propose a simple sparse approximate inverse for triangular\nmatrices (SAIT). Using the Jacobi iteration method, we obtain an expression of\nthe exact inverse of triangular matrix, which is a finite series. The SAIT is\nconstructed based on this series. We apply the SAIT matrices to iterative\nmethods with ILU preconditioners. The two triangular solvers in the ILU\npreconditioning procedure are replaced by two matrix-vector multiplications,\nwhich can be fine-grained parallelized. We test this method by solving some\nlinear systems and eigenvalue problems with preconditioned iterative methods.\n"}
+{"abstract": "  Here, we analyse the behaviour of the higher order standardised moments of\nfinancial time series when we truncate a large data set into smaller and\nsmaller subsets, referred to below as time windows. We look at the effect of\nthe economic environment on the behaviour of higher order moments in these time\nwindows. We observe two different scaling relations of higher order moments\nwhen the data sub sets' length decreases; one for longer time windows and\nanother for the shorter time windows. These scaling relations drastically\nchange when the time window encompasses a financial crisis. We also observe a\nqualitative change of higher order standardised moments compared to the\ngaussian values in response to a shrinking time window. We extend this analysis\nto incorporate the effects these scaling relations have upon risk. We decompose\nthe return series within these time windows and carry out a Value-at-Risk\ncalculation. In doing so, we observe the manifestation of the scaling relations\nthrough the change in the Value-at-Risk level. Moreover, we model the observed\nscaling laws by analysing the hierarchy of rare events on higher order moments.\n"}
+{"abstract": "  A non-monotonic behavior of the velocity gradient of a test particle\nrevolving around a rapidly rotating black hole in the locally non-rotating\nframe of reference is known as the Aschenbach effect. This effect can serve as\na distinguishing signature of rapidly rotating black holes, being potentially\nuseful for the measurements of the astrophysical black hole spins. This paper\nis the generalization of our previous research to the motion of spinning\nparticles around a rotating black hole with non-zero cosmological constant. We\nshow that both the particle's spin $s$ and the cosmological constant $\\Lambda$\nmodify the critical value of the black hole spin $a_c$, for which the\nAschenbach effect can be observed; $a_c$ can increase or decrease depending on\nthe signs of $s$ and $\\Lambda$. We also found that the particle's spin $s$ can\nmimic the effect of the cosmological constant $\\Lambda$ for a given $a_c$,\ncausing thus a discrepancy in the measurements of $s$, $\\Lambda$ and $a_c$ in\nthe Aschenbach effect.\n"}
+{"abstract": "  The recently discovered so-called $s+is$ superconducting state is a\nnon-topological and non-chiral state in the modern classification scheme. It\ndoes not have topological surface states, and no spontaneous surface currents\nare expected in this class of materials. We present a microscopic study that\ndemonstrates that $s+is$ superconductors can have spontaneous boundary currents\nand spontaneous magnetic fields of a different kind. These arise at\nlower-dimensional boundaries: corners of two-dimensional samples and corners\nand edges of three-dimensional samples.\n"}
+{"abstract": "  Oilfield production optimization is challenging due to subsurface model\ncomplexity and associated non-linearity, large number of control parameters,\nlarge number of production scenarios, and subsurface uncertainties.\nOptimization involves time-consuming reservoir simulation studies to compare\ndifferent production scenarios and settings. This paper presents efficacy of\ntwo hybrid evolutionary optimization approaches for well control optimization\nof a waterflooding operation, and demonstrates their application using Olympus\nbenchmark. A simpler, weighted sum of cumulative fluid (WCF) is used as\nobjective function first, which is then replaced by net present value (NPV) of\ndiscounted cash-flow for comparison. Two popular evolutionary optimization\nalgorithms, genetic algorithm (GA) and particle swarm optimization (PSO), are\nfirst used in standalone mode to solve well control optimization problem. Next,\nboth GA and PSO methods are used with another popular optimization algorithm,\ncovariance matrix adaptation-evolution strategy (CMA-ES), in hybrid mode.\nHybrid optimization run is made by transferring the resulting population from\none algorithm to the next as its starting population for further improvement.\nApproximately four thousand simulation runs are needed for standalone GA and\nPSO methods to converge, while six thousand runs are needed in case of two\nhybrid optimization modes (GA-CMA-ES and PSO-CMA-ES). To reduce turn-around\ntime, commercial cloud computing is used and simulation workload is distributed\nusing parallel programming. GA and PSO algorithms have a good balance between\nexploratory and exploitative properties, thus are able identify regions of\ninterest. CMA-ES algorithm is able to further refine the solution using its\nexcellent exploitative properties. Thus, GA or PSO with CMA-ES in hybrid mode\nyields better optimization result as compared to standalone GA or PSO\nalgorithms.\n"}
+{"abstract": "  We study the reachability problem for continuous one-counter automata, COCA\nfor short. In such automata, transitions are guarded by upper and lower bound\ntests against the counter value. Additionally, the counter updates associated\nwith taking transitions can be (non-deterministically) scaled down by a nonzero\nfactor between zero and one. Our three main results are as follows: (1) We\nprove that the reachability problem for COCA with global upper and lower bound\ntests is in NC2; (2) that, in general, the problem is decidable in polynomial\ntime; and (3) that it is decidable in the polynomial hierarchy for COCA with\nparametric counter updates and bound tests.\n"}
+{"abstract": "  Ten protostellar outflows in the Orion molecular clouds were mapped in the\n$^{12}$CO/$^{13}$CO ${J=6\\rightarrow5}$ and $^{12}$CO ${J=7\\rightarrow6}$\nlines. The maps of these mid-$J$ CO lines have an angular resolution of about\n10$''$ and a typical field size of about 100$''$. Physical parameters of the\nmolecular outflows were derived, including mass transfer rates, kinetic\nluminosities, and outflow forces. The outflow sample was expanded by\nre-analyzing archival data of nearby low-luminosity protostars, to cover a wide\nrange of bolometric luminosities. Outflow parameters derived from other\ntransitions of CO were compared. The mid-$J$ ($J_{\\rm up} \\approx 6$) and\nlow-$J$ ($J_{\\rm up} \\leq 3$) CO line wings trace essentially the same outflow\ncomponent. By contrast, the high-$J$ (up to $J_{\\rm up} \\approx 50$)\nline-emission luminosity of CO shows little correlation with the kinetic\nluminosity from the ${J=6\\rightarrow5}$ line, which suggests that they trace\ndistinct components. The low/mid-$J$ CO line wings trace long-term outflow\nbehaviors while the high-$J$ CO lines are sensitive to short-term activities.\nThe correlations between the outflow parameters and protostellar properties are\npresented, which shows that the strengths of molecular outflows increase with\nbolometric luminosity and envelope mass.\n"}
+{"abstract": "  We continue our study of effective field theory via homotopy transfer of\n$L_\\infty$-algebras, and apply it to tree-level non-Wilsonian effective actions\nof the kind discussed by Sen in which the modes integrated out are comparable\nin mass to the modes that are kept. We focus on the construction of effective\nactions for string states at fixed levels and in particular on the construction\nof weakly constrained double field theory. With these examples in mind, we\ndiscuss closed string theory on toroidal backgrounds and resolve some subtle\nissues involving vertex operators, including the proper form of cocycle factors\nand of the reflector state. This resolves outstanding issues concerning the\nconstruction of covariant closed string field theory on toroidal backgrounds.\nThe weakly constrained double field theory is formally obtained from closed\nstring field theory on a toroidal background by integrating out all but the\ndoubly massless' states and homotopy transfer then gives a prescription for\ndetermining the theory's vertices and symmetries. We also discuss consistent\ntruncation in the context of homotopy transfer.\n"}
+{"abstract": "  Large pre-trained language models have been shown to encode large amounts of\nworld and commonsense knowledge in their parameters, leading to substantial\ninterest in methods for extracting that knowledge. In past work, knowledge was\nextracted by taking manually-authored queries and gathering paraphrases for\nthem using a separate pipeline. In this work, we propose a method for\nautomatically rewriting queries into \"BERTese\", a paraphrase query that is\ndirectly optimized towards better knowledge extraction. To encourage meaningful\nrewrites, we add auxiliary loss functions that encourage the query to\ncorrespond to actual language tokens. We empirically show our approach\noutperforms competing baselines, obviating the need for complex pipelines.\nMoreover, BERTese provides some insight into the type of language that helps\nlanguage models perform knowledge extraction.\n"}
+{"abstract": "  Earth's climate can be understood as a dynamical system that changes due to\nexternal forcing and internal couplings. Essential climate variables, such as\nsurface air temperature, describe this dynamics. Our current interglacial, the\nHolocene (11,700 years ago to today), has been characterized by great stability\nof global mean temperature prior to anthropogenic warming. However, the\nmechanisms and spatiotemporal patterns of fluctuations around this mean, called\ntemperature variability, are poorly understood despite their socio-economic\nrelevance for climate change mitigation and adaptation. Here, we examine\ndiscrepancies between temperature variability from model simulations and\npaleoclimate reconstructions by clarifying the stability of local and global\nsurface air temperature on the timescale of years to centuries. To this end, we\ncontrast power spectral densities (PSD) and their power-law scaling using\nsimulated and observation-based temperature series of the last six thousand\nyears. We further introduce the spectral gain to disentangle the\nexternally-forced and internally-generated variability as a function of\ntimescale. It is based on our estimate of the joint PSD of radiative forcing,\nwhich exhibits a scale break around the period of ten years. We find that local\ntemperature series from paleoclimate reconstructions tend to show unstable\nbehavior on periods of 10 to 200 years, while simulated temperatures almost\nexclusively show stable behavior. Conversely, the PSD and spectral gain of\nglobal mean temperature are consistent across data sets. Our results point to\nthe limitation of climate models to fully represent local temperature\nstatistics over decades to centuries. By highlighting the key characteristics\nof temperature variability, we pave a way to better constrain possible changes\nin temperature variability with global warming and assess future climate risks.\n"}
+{"abstract": "  Maximally recoverable codes are a class of codes which recover from all\npotentially recoverable erasure patterns given the locality constraints of the\ncode. In earlier works, these codes have been studied in the context of codes\nwith locality. The notion of locality has been extended to hierarchical\nlocality, which allows for locality to gradually increase in levels with the\nincrease in the number of erasures. We consider the locality constraints\nimposed by codes with two-level hierarchical locality and define maximally\nrecoverable codes with data-local and local hierarchical locality. We derive\ncertain properties related to their punctured codes and minimum distance. We\ngive a procedure to construct hierarchical data-local MRCs from hierarchical\nlocal MRCs. We provide a construction of hierarchical local MRCs for all\nparameters. We also give constructions of MRC with hierarchical locality for\nsome parameters, whose field size is smaller than that of known constructions\nfor general parameters. We also derive a field size lower bound on MRC with\nhierarchical locality.\n"}
+{"abstract": "  Motivated by the work of T.E. Govindan in [5,8,9], this paper is concerned\nwith a more general semilinear stochastic evolution equation. The difference\nbetween the equations considered in this paper and the previous one is that it\nmakes some changes to the nonlinear function in random integral, which also\ndepends on the probability distribution of stochastic process at that time.\nFirst, this paper considers the existence and uniqueness of mild solutions for\nsuch equations. Furthermore, Trotter-Kato approximation system is introduced\nfor the mild solutions, and the weak convergence of induced probability\nmeasures and zeroth-order approximations are obtained. Then we consider the\nclassical limit theorem about the parameter dependence of this kind of\nequations. Finally, an example of stochastic partial differential equation is\ngiven to illustrate our results.\n"}
+{"abstract": "  3D printing using robots has garnered significant interest in manufacturing\nand construction in recent years. A robot's versatility paired with the design\nfreedom of 3D printing offers promising opportunities for how parts and\nstructures are built in the future. However, 3D printed objects are still\nlimited in size and location due to a lack of vertical mobility of ground\nrobots. These limitations severely restrict the potential of the 3D printing\nprocess. To overcome these limitations, we develop a hexacopter testbed that\ncan print via fused deposition modeling during flight. We discuss the design of\nthis testbed and develop a simple control strategy for initial print tests. By\nsuccessfully performing these initial print tests, we demonstrate the\nfeasibility of this approach and lay the groundwork for printing 3D parts and\nstructures with drones.\n"}
+{"abstract": "  The gapless modes on the edge of four-dimensional (4D) quantum Hall droplets\nare known to be anisotropic: they only propagate in one direction, foliating\nthe 3D boundary into independent 1D conduction channels. This foliation is\nextremely sensitive to the confining potential and generically yields chaotic\nflows. Here we study the quantum correlations and entanglement of such edge\nmodes in 4D droplets confined by harmonic traps, whose boundary is a squashed\nthree-sphere. Commensurable trapping frequencies lead to periodic trajectories\nof electronic guiding centers; the corresponding edge modes propagate\nindependently along $S^1$ fibers, forming a bundle of 1D conformal field\ntheories over a 2D base space. By contrast, incommensurable frequencies produce\nquasi-periodic, ergodic trajectories, each of which covers its invariant torus\ndensely; the corresponding correlation function of edge modes has fractal\nfeatures. This wealth of behaviors highlights the sharp differences between 4D\nHall droplets and their 2D peers; it also exhibits the dependence of 4D edge\nmodes on the choice of trap, suggesting the existence of observable\nbifurcations due to droplet deformations.\n"}
+{"abstract": "  The category of complete differential graded Lie algebras provides nice\nalgebraic models for the rational homotopy types of non-simply connected\nspaces. In particular, there is a realization functor, $\\langle -\\rangle$, of\nany complete differential graded Lie algebra as a simplicial set. In a previous\narticle, we considered the particular case of a complete graded Lie algebra,\n$L_{0}$, concentrated in degree 0 and proved that $\\langle L_{0}\\rangle$ is\nisomorphic to the usual bar construction on the Malcev group associated to\n$L_{0}$.\n  Here we consider the case of a complete differential graded Lie algebra,\n$L=L_{0}\\oplus L_{1}$, concentrated in degrees 0 and 1. We establish that this\ncategory is equivalent to explicit subcategories of crossed modules and Lie\nalgebra crossed modules, extending the equivalence between pronilpotent Lie\nalgebras and Malcev groups. In particular, there is a crossed module\n$\\mathcal{C}(L)$ associated to $L$. We prove that $\\mathcal{C}(L)$ is\nisomorphic to the Whitehead crossed module associated to the simplicial pair\n$(\\langle L\\rangle, \\langle L_{0}\\rangle)$. Our main result is the\nidentification of $\\langle L\\rangle$ with the classifying space of\n$\\mathcal{C}(L)$.\n"}
+{"abstract": "  A systematics of grain boundary (GB) segregation transitions and critical\nphenomena has been derived to expand the classical GB segregation theory. This\nstudy uncovers when GB layering vs. prewetting transitions should occur and how\nthey are related to one another. Moreover, a novel descriptor, normalized\nsegregation strength, is introduced. It can represent several factors that\ncontrol GB segregation, including strain and bond energies for both general and\nsmall-angle GBs, as well as misorientation for small-angle GBs, which had to be\ntreated separately in prior models. In a strong segregation system with a large\nnormalized segregation strength, first-order layering transitions occur at low\ntemperatures and become continuous above GB roughing temperatures. With\nreducing normalized segregation strength, the layering transitions gradually\nmerge and finally lump into prewetting transitions without quantized layer\nnumbers, akin to Cahn's critical-point wetting model. Furthermore, GB\ncomplexion diagrams with universal characters are constructed as the GB\ncounterpart to the classical exemplar of Pelton-Thompson regular-solution\nbinary bulk phase diagrams.\n"}
+{"abstract": "  We consider distributed convex-concave saddle point problems over arbitrary\nconnected undirected networks and propose a decentralized distributed algorithm\nfor their solution. The local functions distributed across the nodes are\nassumed to have global and local groups of variables. For the proposed\nalgorithm we prove non-asymptotic convergence rate estimates with explicit\ndependence on the network characteristics. To supplement the convergence rate\nanalysis, we propose lower bounds for strongly-convex-strongly-concave and\nconvex-concave saddle-point problems over arbitrary connected undirected\nnetworks. We illustrate the considered problem setting by a particular\napplication to distributed calculation of non-regularized Wasserstein\nbarycenters.\n"}
+{"abstract": "  Graph signal processing is a ubiquitous task in many applications such as\nsensor, social, transportation and brain networks, point cloud processing, and\ngraph neural networks. Often, graph signals are corrupted in the sensing\nprocess, thus requiring restoration. In this paper, we propose two graph signal\nrestoration methods based on deep algorithm unrolling (DAU). First, we present\na graph signal denoiser by unrolling iterations of the alternating direction\nmethod of multiplier (ADMM). We then suggest a general restoration method for\nlinear degradation by unrolling iterations of Plug-and-Play ADMM (PnP-ADMM). In\nthe second approach, the unrolled ADMM-based denoiser is incorporated as a\nsubmodule, leading to a nested DAU structure. The parameters in the proposed\ndenoising/restoration methods are trainable in an end-to-end manner. Our\napproach is interpretable and keeps the number of parameters small since we\nonly tune graph-independent regularization parameters. We overcome two main\nchallenges in existing graph signal restoration methods: 1) limited performance\nof convex optimization algorithms due to fixed parameters which are often\ndetermined manually. 2) large number of parameters of graph neural networks\nthat result in difficulty of training. Several experiments for graph signal\ndenoising and interpolation are performed on synthetic and real-world data. The\nproposed methods show performance improvements over several existing techniques\nin terms of root mean squared error in both tasks.\n"}
+{"abstract": "  Recent works show that group equivariance as an inductive bias improves\nneural network performance for both classification and generation. However,\ndesigning group-equivariant neural networks is challenging when the group of\ninterest is large and is unknown. Moreover, inducing equivariance can\nsignificantly reduce the number of independent parameters in a network with\nfixed feature size, affecting its overall performance. We address these\nproblems by proving a new group-theoretic result in the context of equivariant\nneural networks that shows that a network is equivariant to a large group if\nand only if it is equivariant to smaller groups from which it is constructed.\nUsing this result, we design a novel fast group equivariant construction\nalgorithm, and a deep Q-learning-based search algorithm in a reduced search\nspace, yielding what we call autoequivariant networks (AENs). AENs find the\nright balance between equivariance and network size when tested on new\nbenchmark datasets, G-MNIST and G-Fashion-MNIST, obtained via group\ntransformations on MNIST and Fashion-MNIST respectively that we release.\nExtending these results to group convolutional neural networks, where we\noptimize between equivariances, augmentations, and network sizes, we find group\nequivariance to be the most dominating factor in all high-performing GCNNs on\nseveral datasets like CIFAR10, SVHN, RotMNIST, ASL, EMNIST, and KMNIST.\n"}
+{"abstract": "  Transverse momentum $p_T$ spectra of final state particles produced in high\nenergy heavy-ion collision can be divided into two distinct regions based on\nthe difference in the underlying particle production process. We have provided\na unified formalism to explain both low- and high-$p_T$ regime of spectra in a\nconsistent manner. The $p_T$ spectra of final state particles produced at RHIC\nand LHC energies have been analysed using unified formalism to test its\napplicability at different energies, and a good agreement with the data is\nobtained across all energies. Further, the prospect of extracting the elliptic\nflow coefficient directly from the transverse momentum spectra is explored.\n"}
+{"abstract": "  One of the main drawbacks of the powder metallurgy route for High-Entropy\nAlloys (HEAs) is the unavailability of fully pre-alloyed powders in the market.\nUsing commodity powders (commercial Ni, Fe and Co base fully pre-alloyed\npowders, fully available in large quantities and at competitive prices) to\nproduce HEAs presents a completely new and competitive scenario for obtaining\nviable alloys for high-performance applications.\n"}
+{"abstract": "  In this paper, we present our heuristic solutions to the problems of finding\nthe maximum and minimum area polygons with a given set of vertices. Our\nsolutions are based mostly on two simple algorithmic paradigms: greedy method\nand local search. The greedy heuristic starts with a simple polygon and adds\nvertices one by one, according to a weight function. A crucial ingredient to\nobtain good solutions is the choice of an appropriate weight function that\navoids long edges. The local search part consists of moving consecutive\nvertices to another location in the polygonal chain. We also discuss the\ndifferent implementation techniques that are necessary to reduce the running\ntime.\n"}
+{"abstract": "  Continual learning tackles the setting of learning different tasks\nsequentially. Despite the lots of previous solutions, most of them still suffer\nsignificant forgetting or expensive memory cost. In this work, targeted at\nthese problems, we first study the continual learning process through the lens\nof information theory and observe that forgetting of a model stems from the\nloss of \\emph{information gain} on its parameters from the previous tasks when\nlearning a new task. From this viewpoint, we then propose a novel continual\nlearning approach called Bit-Level Information Preserving (BLIP) that preserves\nthe information gain on model parameters through updating the parameters at the\nbit level, which can be conveniently implemented with parameter quantization.\nMore specifically, BLIP first trains a neural network with weight quantization\non the new incoming task and then estimates information gain on each parameter\nprovided by the task data to determine the bits to be frozen to prevent\nforgetting. We conduct extensive experiments ranging from classification tasks\nto reinforcement learning tasks, and the results show that our method produces\nbetter or on par results comparing to previous state-of-the-arts. Indeed, BLIP\nachieves close to zero forgetting while only requiring constant memory\noverheads throughout continual learning.\n"}
+{"abstract": "  Increasing concerns and regulations about data privacy and sparsity\nnecessitate the study of privacy-preserving, decentralized learning methods for\nnatural language processing (NLP) tasks. Federated learning (FL) provides\npromising approaches for a large number of clients (e.g., personal devices or\norganizations) to collaboratively learn a shared global model to benefit all\nclients while allowing users to keep their data locally. Despite interest in\nstudying FL methods for NLP tasks, a systematic comparison and analysis is\nlacking in the literature. Herein, we present the FedNLP, a benchmarking\nframework for evaluating federated learning methods on four different task\nformulations: text classification, sequence tagging, question answering, and\nseq2seq. We propose a universal interface between Transformer-based language\nmodels (e.g., BERT, BART) and FL methods (e.g., FedAvg, FedOPT, etc.) under\nvarious non-IID partitioning strategies. Our extensive experiments with FedNLP\nprovide empirical comparisons between FL methods and helps us better understand\nthe inherent challenges of this direction. The comprehensive analysis points to\nintriguing and exciting future research aimed at developing FL methods for NLP\ntasks.\n"}
+{"abstract": "  The monitoring of rotating machinery is an essential task in today's\nproduction processes. Currently, several machine learning and deep\nlearning-based modules have achieved excellent results in fault detection and\ndiagnosis. Nevertheless, to further increase user adoption and diffusion of\nsuch technologies, users and human experts must be provided with explanations\nand insights by the modules. Another issue is related, in most cases, with the\nunavailability of labeled historical data that makes the use of supervised\nmodels unfeasible. Therefore, a new approach for fault detection and diagnosis\nin rotating machinery is here proposed. The methodology consists of three\nparts: feature extraction, fault detection and fault diagnosis. In the first\npart, the vibration features in the time and frequency domains are extracted.\nSecondly, in the fault detection, the presence of fault is verified in an\nunsupervised manner based on anomaly detection algorithms. The modularity of\nthe methodology allows different algorithms to be implemented. Finally, in\nfault diagnosis, Shapley Additive Explanations (SHAP), a technique to interpret\nblack-box models, is used. Through the feature importance ranking obtained by\nthe model explainability, the fault diagnosis is performed. Two tools for\ndiagnosis are proposed, namely: unsupervised classification and root cause\nanalysis. The effectiveness of the proposed approach is shown on three datasets\ncontaining different mechanical faults in rotating machinery. The study also\npresents a comparison between models used in machine learning explainability:\nSHAP and Local Depth-based Feature Importance for the Isolation Forest (Local-\nDIFFI). Lastly, an analysis of several state-of-art anomaly detection\nalgorithms in rotating machinery is included.\n"}
+{"abstract": "  In the setting of regularly varying time series, a cluster of exceedances is\na short period for which the supremum norm exceeds a high threshold. We propose\nto study a generalization of this notion considering short periods, or blocks,\nwith lp--norm above a high threshold. We derive large deviation principles of\nblocks and apply these results to improve cluster inference. We focus on blocks\nestimators and show they are consistent when we use large empirical quantiles\nfrom the lp --norm of blocks as threshold levels. We derive an adaptive\nthreshold selection method for cluster inference in lp. Our approach focuses on\nthe case p < $\\infty$ rather than the classical one for p = $\\infty$ where the\nbias is more difficult to control.\n"}
+{"abstract": "  In this letter, we show that the wino-Higgsino dark matter (DM) is detectable\nin near future DM direct detection experiments for almost all consistent\nparameter space in the spontaneously broken supergravity (SUGRA) if the muon\ng-2 anomaly is explained by the wino-Higgsino loop diagrams. We also point out\nthat the present and future LHC experiments can exclude or confirm this SUGRA\nexplanation of the observed muon g-2 anomaly.\n"}
+{"abstract": "  With the rapid development of railways, especially high-speed railways, there\nis an increasingly urgent demand for new wireless communication system for\nrailways. Taking the mature 5G technology as an opportunity, 5G-railways (5G-R)\nhave been widely regarded as a solution to meet the diversified demands of\nrailway wireless communications. For the design, deployment and improvement of\n5G-R networks, radio communication scenario classification plays an important\nrole, affecting channel modeling and system performance evaluation. In this\npaper, a standardized radio communication scenario classification, including 18\nscenarios, is proposed for 5G-R. This paper analyzes the differences of 5G-R\nscenarios compared with the traditional cellular networks and GSM-railways,\naccording to 5G-R requirements and the unique physical environment and\npropagation characteristics. The proposed standardized scenario classification\nhelps deepen the research of 5G-R and promote the development and application\nof the existing advanced technologies in railways.\n"}
+{"abstract": "  The evolution of massive stars is influenced by the mass lost to stellar\nwinds over their lifetimes. These winds limit the masses of the stellar\nremnants (such as black holes) that the stars ultimately produce. We use radio\nastrometry to refine the distance to the black hole X-ray binary Cygnus X-1,\nwhich we find to be $2.22^{+0.18}_{-0.17}$ kiloparsecs. When combined with\nprevious optical data, this implies a black hole mass of $21.2\\pm2.2$ solar\nmasses, higher than previous measurements. The formation of such a high-mass\nblack hole in a high-metallicity system constrains wind mass loss from massive\nstars.\n"}
+{"abstract": "  The energy available in Micro Grid (MG) that is powered by solar energy is\ntightly related to the weather conditions in the moment of generation. Very\nshort-term forecast of solar irradiance provides the MG with the capability of\nautomatically controlling the dispatch of energy. We propose to achieve this\nusing a data acquisition systems (DAQ) that simultaneously records sky imaging\nand Global Solar Irradiance (GSI) measurements, with the objective of\nextracting features from clouds and use them to forecast the power produced by\na Photovoltaic (PV) system. The DAQ system is nicknamed as the \\emph{Girasol\nMachine} (Girasol means Sunflower in Spanish). The sky imaging system consists\nof a longwave infrared (IR) camera and a visible (VI) light camera with a\nfisheye lens attached to it. The cameras are installed inside a weatherproof\nenclosure that it is mounted on an outdoor tracker. The tracker updates its pan\nan tilt every second using a solar position algorithm to maintain the Sun in\nthe center of the IR and VI images. A pyranometer is situated on a horizontal\nsupport next to the DAQ system to measure GSI. The dataset, composed of IR\nimages, VI images, GSI measurements, and the Sun's positions, has been tagged\nwith timestamps.\n"}
+{"abstract": "  The $\\mu\\nu$SSM is a highly predictive alternative model to the MSSM. In\nparticular, the electroweak sector of the model can explain the longstanding\ndiscrepancy between the experimental result for the anomalous magnetic moment\nof the muon, $(g-2)_\\mu$, and its Standard Model prediction, while being in\nagreement with all other theoretical and experimental constraints. The recently\npublished MUON G-2 result is within $0.8\\,\\sigma$ in agreement with the older\nBNL result on $(g-2)_\\mu$. The combined result was announced as $a_\\mu^{\\rm\nexp} = (11659206.1 \\pm 4.1) \\times 10^{-10}$, yielding a new deviation from the\nStandard Model prediction of $\\Delta a_\\mu = (25.1 \\pm 5.9) \\times 10^{-10}$,\ncorresponding to $4.2\\,\\sigma$. Using this improved bound we update the\nanalysis in the $\\mu\\nu$SSM as presented in Ref. [1] and set new limits on the\nallowed parameters space of the electroweak sector of the model. We conclude\nthat significant regions of the model can explain the new $(g-2)_\\mu$ data.\n"}
+{"abstract": "  It is well known that the existence of traveling wave solutions (TWS) for\nmany partial differential equations (PDE) is a consequence of the fact that an\nassociated planar ordinary differential equation (ODE) has certain types of\nsolutions defined for all time. In this paper we address the problem of\npersistence of TWS of a given PDE under small perturbations. Our main results\ndeal with the situation where the associated ODE has a center and, as a\nconsequence, the original PDE has a continuum of periodic traveling wave\nsolutions. We prove that the TWS that persist are controlled by the zeroes of\nsome Abelian integrals. We apply our results to several famous PDE, like the\nOstrovsky, Klein-Gordon, sine-Gordon, Korteweg-de Vries, Rosenau-Hyman,\nCamassa-Holm, and Boussinesq equations.\n"}
+{"abstract": "  Dirac points are found to emerge due to the crossing of bands in the\nelectronic structure of bilayer graphene for configurations in which the\nalignment between two hexagonal lattices preserves the parallelism of the\narmchair/zigzag lines between two layers. On the base of electronic\ncalculations using a tight-binding model for the $\\pi$ bands it is shown that\nthe crossing of the energy-band dispersion curves occurs in the vicinity of the\ncorner points of the hexagonal Brillouin zone. Group representation theory\nanalysis confirms the emergence of such Dirac points. It is demonstrated that\nthe band crossings at generic $\\mathbf{k}$ points are guaranteed by the\ncompatibility relations between the symmetries of eigenstates at the\nhigh-symmetry $\\mathbf{k}$ points in the Brillouin zone. The presence of Dirac\npoints governs the geometrical properties of the energy surfaces, and thus the\ntopological structure of the Fermi energy surface and the energy spectrum.\n"}
+{"abstract": "  We give a brief overview of phenomenological developments in the analysis of\nangular observables for exclusive decay modes of $B$ mesons and $\\Lambda_b$\nbaryons, with focus on recent results and some important aspects related to the\ntheoretical background (which mostly concern the treatment of hadronic\nuncertainties).\n"}
+{"abstract": "  Three-dimensional topological insulator (TI) nanowires with quantized surface\nsubband spectra are studied as a main component of Majorana bound states (MBS)\ndevices. However, such wires are known to have large concentration $N \\sim\n10^{19}$ cm$^{-3}$ of Coulomb impurities. It is believed that a MBS device can\nfunction only if the amplitude of long-range fluctuations of the random Coulomb\npotential $\\Gamma$ is smaller than the subband gap $\\Delta$. Here we calculate\n$\\Gamma$ for recently experimentally studied large-dielectric-constant\n(Bi$_{1-x}$Sb$_x$)$_2$Te$_{3}$ wires in a small-dielectric-constant environment\n(no superconductor). We show that provided by such a dielectric-constant\ncontrast, the confinement of electric field of impurities within the wire\nallows more distant impurities to contribute into $\\Gamma$, leading to $\\Gamma\n\\sim 3\\Delta$. We also calculate a TI wire resistance as a function of the\nFermi level and carrier concentration due to scattering on Coulomb and neutral\nimpurities, and do not find observable discrete subband-spectrum related\noscillations at $N \\gtrsim 10^{18}$ cm$^{-3}$.\n"}
+{"abstract": "  We discuss the role of quantum coherence in the energy fluctuations of open\nquantum systems. To this aim, we introduce a protocol, to which we refer to as\nthe end-point-measurement scheme, allowing to define the statistics of energy\nchanges as a function of energy measurements performed only after the evolution\nof the initial state. At the price of an additional uncertainty on the initial\nenergies, this approach prevents the loss of initial quantum coherences and\nenables the estimation of their effects on energy fluctuations. We demonstrate\nour findings by running an experiment on the IBM Quantum Experience\nsuperconducting qubit platform.\n"}
+{"abstract": "  In this work, we introduce the notion of decisional width of a finite\nrelational structure and the notion of decisional width of a regular class of\nfinite structures. Our main result states that given a first-order formula\n{\\psi} over a vocabulary {\\tau}, and a finite automaton F over a suitable\nalphabet B({\\Sigma},w,{\\tau}) representing a width-w regular-decisional class\nof {\\tau}-structures C, one can decide in time f({\\tau},{\\Sigma},{\\psi},w)|F|\nwhether some {\\tau}-structure in C satisfies {\\psi}. Here, f is a function that\ndepends on the parameters {\\tau},{\\Sigma},{\\psi},w, but not on the size of the\nautomaton F representing the class. Therefore, besides implying that the\nfirst-order theory of any given regular-decisional class of finite structures\nis decidable, it also implies that when the parameters {\\tau}, {\\psi}, {\\Sigma}\nand w are fixed, decidability can be achieved in linear time on the size of the\ninput automaton F. Building on the proof of our main result, we show that the\nproblem of counting satisfying assignments for a first-order logic formula in a\ngiven structure A of width w is fixed-parameter tractable with respect to w,\nand can be solved in quadratic time on the length of the input representation\nof A.\n"}
+{"abstract": "  Off-policy evaluation (OPE) leverages data generated by other policies to\nevaluate a target policy. Previous OPE methods mainly focus on precisely\nestimating the true performance of a policy. We observe that in many\napplications, (1) the end goal of OPE is to compare two or multiple candidate\npolicies and choose a good one, which is actually a much simpler task than\nevaluating their true performance; and (2) there are usually multiple policies\nthat have been deployed in real-world systems and thus whose true performance\nis known through serving real users. Inspired by the two observations, in this\nwork, we define a new problem, supervised off-policy ranking (SOPR), which aims\nto rank a set of new/target policies based on supervised learning by leveraging\noff-policy data and policies with known performance. We further propose a\nmethod for supervised off-policy ranking that learns a policy scoring model by\ncorrectly ranking training policies with known performance rather than\nestimating their precise performance. Our method leverages logged states and\npolicies to learn a Transformer based model that maps offline interaction data\nincluding logged states and the actions taken by a target policy on these\nstates to a score. Experiments on different games, datasets, training policy\nsets, and test policy sets show that our method outperforms strong baseline OPE\nmethods in terms of both rank correlation and performance gap between the truly\nbest and the best of the ranked top three policies. Furthermore, our method is\nmore stable than baseline methods.\n"}
+{"abstract": "  We address the problem of class incremental learning, which is a core step\ntowards achieving adaptive vision intelligence. In particular, we consider the\ntask setting of incremental learning with limited memory and aim to achieve\nbetter stability-plasticity trade-off. To this end, we propose a novel\ntwo-stage learning approach that utilizes a dynamically expandable\nrepresentation for more effective incremental concept modeling. Specifically,\nat each incremental step, we freeze the previously learned representation and\naugment it with additional feature dimensions from a new learnable feature\nextractor. This enables us to integrate new visual concepts with retaining\nlearned knowledge. We dynamically expand the representation according to the\ncomplexity of novel concepts by introducing a channel-level mask-based pruning\nstrategy. Moreover, we introduce an auxiliary loss to encourage the model to\nlearn diverse and discriminate features for novel concepts. We conduct\nextensive experiments on the three class incremental learning benchmarks and\nour method consistently outperforms other methods with a large margin.\n"}
+{"abstract": "  This paper presents models and optimization algorithms to jointly optimize\nthe design and control of the transmission of electric vehicles equipped with\none central electric motor (EM). First, considering the required traction power\nto be given, we identify a convex speed-dependent loss model for the EM.\nSecond, we leverage such a model to devise computationally-efficient algorithms\nto determine the energy-optimal design and control strategies for the\ntransmission. In particular, with the objective of minimizing the EM energy\nconsumption on a given drive cycle, we analytically compute the optimal\ngear-ratio trajectory for a continuously variable transmission (CVT) and the\noptimal gear-ratio design for a fixed-gear transmission (FGT) in closed form,\nwhilst efficiently solving the combinatorial joint gear-ratio design and\ncontrol problem for a multiple-gear transmission (MGT), combining convex\nanalysis and dynamic programming in an iterative fashion. Third, we validate\nour models with nonlinear simulations, and benchmark the optimality of our\nmethods with mixed-integer quadratic programming. Finally, we showcase our\nframework in a case-study for a family vehicle, whereby we leverage the\ncomputational efficiency of our methods to jointly optimize the EM size via\nexhaustive search. Our numerical results show that by using a 2-speed MGT, the\nenergy consumption of an electric vehicle can be reduced by 3% and 2.5%\ncompared to an FGT and a CVT, respectively, whilst further increasing the\nnumber of gears may even be detrimental due to the additional weight\n"}
+{"abstract": "  In adaptive sliding mode control methods, an updating gain strategy\nassociated with finite-time convergence to the sliding set is essential to deal\nwith matched bounded perturbations with unknown upper-bound. However, the\nestimation of the finite time of any adaptive design is a complicated task\nsince it depends not only on the upper-bound of unknown perturbation but also\non the size of initial conditions. This brief proposes a uniform adaptive\nreaching phase strategy (ARPS) within a predefined reaching-time. Moreover, as\na case of study, the barrier function approach is extended for perturbed MIMO\nsystems with uncertain control matrix. The usage of proposed ARPS in the MIMO\ncase solves simultaneously two issues: giving a uniform reaching phase with a\npredefined reaching-time and adapting to the perturbation norm while in a\npredefined vicinity of the sliding manifold.\n"}
+{"abstract": "  An independent broadcast on a graph $G$ is a function $f: V \\longrightarrow\n\\{0,\\ldots,{\\rm diam}(G)\\}$ such that $(i)$ $f(v)\\leq e(v)$ for every vertex\n$v\\in V(G)$, where $\\operatorname{diam}(G)$ denotes the diameter of $G$ and\n$e(v)$ the eccentricity of vertex $v$, and $(ii)$ $d(u,v) > \\max \\{f(u),\nf(v)\\}$ for every two distinct vertices $u$ and $v$ with $f(u)f(v)>0$. The\nbroadcast independence number $\\beta_b(G)$ of $G$ is then the maximum value of\n$\\sum_{v \\in V} f(v)$, taken over all independent broadcasts on $G$. We prove\nthat every circulant graph of the form $C(n;1,a)$, $3\\le a\\le\n\\lfloor\\frac{n}{2} \\rfloor$, admits an optimal $2$-bounded independent\nbroadcast, that is, an independent broadcast~$f$ satisfying $f(v)\\le 2$ for\nevery vertex $v$, except when $n=2a+1$, or $n=2a$ and $a$ is even. We then\ndetermine the broadcast independence number of various classes of such\ncirculant graphs, and prove that, for most of these classes, the equality\n$\\beta_b(C(n;1,a)) = \\alpha(C(n;1,a))$ holds, where $\\alpha(C(n;1,a))$ denotes\nthe independence number of $C(n;1,a)$.\n"}
+{"abstract": "  We consider two particles hopping on a chain with a contact interaction\nbetween them. At strong interaction, there is a molecular bound state separated\nby a direct gap from a continuous band of atomic states. Introducing weak\ndisorder in the interaction, the molecular state becomes Anderson localized. At\nstronger disorder, part of the molecular band delocalizes and dissociates due\nto its hybridization to the atomic band. We characterize these different\nregimes by computing the density of states, the inverse participation ratio,\nthe level-spacing statistics and the survival probability of an initially\nlocalized state. The atomic band is best described as that of a rough billiard\nfor a single particle on a square lattice that shows signatures of quantum\nchaos. In addition to typical \"chaotic states\", we find states that are\nlocalized along only one direction. These \"separatrix states\" are more\nlocalized than chaotic states, and similar in this respect to scarred states,\nbut their existence is due to the separatrix iso-energy line in the\ninteraction-free dispersion relation, rather than to unstable periodic orbits.\n"}
+{"abstract": "  We consider normal velocity of smooth sets evolving by the $s-$fractional\ndiffusion. We prove that for small time, the normal velocity of such sets is\nnearly proportional to the mean curvature of the boundary of the initial set\nfor $s\\in [\\frac{1}{2}, 1)$ while, for $s\\in (0, \\frac{1}{2})$, it is nearly\nproportional to the fractional mean curvature of the initial set. Our results\nshow that the motion by (fractional) mean curvature flow can be approximated by\nfractional heat diffusion and by a diffusion by means of harmonic extension of\nsmooth sets.\n"}
+{"abstract": "  This work is devoted to the study of integral $p$-adic Hodge theory in the\ncontext of Artin stacks. For a Hodge-proper stack, using the formalism of\nprismatic cohomology, we establish a version of $p$-adic Hodge theory with the\n\\'etale cohomology of the Raynaud generic fiber as an input. In particular, we\nshow that the corresponding Galois representation is crystalline and that the\nassociated Breuil-Kisin module is given by the prismatic cohomology. An\ninteresting new feature of the stacky setting is that the natural map between\n\\'etale cohomology of the algebraic and the Raynaud generic fibers is often an\nequivalence even outside of the proper case. In particular, we show that this\nholds for global quotients $[X/G]$ where $X$ is a smooth proper scheme and $G$\nis a reductive group. As applications we deduce Totaro's conjectural inequality\nand also set up a theory of $A_{\\mathrm{inf}}$-characteristic classes.\n"}
+{"abstract": "  Hermitian theories play a major role in understanding the physics of most\nphenomena. It has been found only in the past decade that non-Hermiticity\nenables unprecedented effects such as exceptional points, spectral\nsingularities and bulk Fermi arcs. Recent studies further show that\nnon-Hermiticity can fundamentally change the topological band theory, leading\nto the non-Hermitian band topology and non-Hermitian skin effect, as confirmed\nin one-dimensional (1D) systems. However, in higher dimensions, these\nnon-Hermitian effects remain unexplored in experiments. Here, we demonstrate\nthe spin-polarized, higher-order non-Hermitian skin effect in two-dimensional\n(2D) acoustic metamaterials. Using a lattice of coupled whisper-gallery\nacoustic resonators, we realize a spinful 2D higher-order topological insulator\n(HOTI) where the spin-up and spin-down states are emulated by the\nanti-clockwise and clockwise modes, respectively. We find that the\nnon-Hermiticity drives wave localizations toward opposite edge boundaries\ndepending on the spin polarizations. More interestingly, for finite systems\nwith both edge and corner boundaries, the higher-order non-Hermitian skin\neffect leads to wave localizations toward two corner boundaries for the bulk,\nedge and corner states in a spin-dependent manner. We further show that such a\nnon-Hermitian skin effect enables rich wave manipulation through the loss\nconfiguration in each unit-cell. The reported spin-dependent, higher-order\nnon-Hermitian skin effect reveals the interplay between higher-order topology\nand non-Hermiticity, which is further enriched by the spin degrees of freedom.\nThis unveils a new horizon in the study of non-Hermitian physics and the design\nof non-Hermitian metamaterials.\n"}
+{"abstract": "  Recent studies have revealed a security threat to natural language processing\n(NLP) models, called the Backdoor Attack. Victim models can maintain\ncompetitive performance on clean samples while behaving abnormally on samples\nwith a specific trigger word inserted. Previous backdoor attacking methods\nusually assume that attackers have a certain degree of data knowledge, either\nthe dataset which users would use or proxy datasets for a similar task, for\nimplementing the data poisoning procedure. However, in this paper, we find that\nit is possible to hack the model in a data-free way by modifying one single\nword embedding vector, with almost no accuracy sacrificed on clean samples.\nExperimental results on sentiment analysis and sentence-pair classification\ntasks show that our method is more efficient and stealthier. We hope this work\ncan raise the awareness of such a critical security risk hidden in the\nembedding layers of NLP models. Our code is available at\nhttps://github.com/lancopku/Embedding-Poisoning.\n"}
+{"abstract": "  Fast and light-weight methods for animating 3D characters are desirable in\nvarious applications such as computer games. We present a learning-based\napproach to enhance skinning-based animations of 3D characters with vivid\nsecondary motion effects. We design a neural network that encodes each local\npatch of a character simulation mesh where the edges implicitly encode the\ninternal forces between the neighboring vertices. The network emulates the\nordinary differential equations of the character dynamics, predicting new\nvertex positions from the current accelerations, velocities and positions.\nBeing a local method, our network is independent of the mesh topology and\ngeneralizes to arbitrarily shaped 3D character meshes at test time. We further\nrepresent per-vertex constraints and material properties such as stiffness,\nenabling us to easily adjust the dynamics in different parts of the mesh. We\nevaluate our method on various character meshes and complex motion sequences.\nOur method can be over 30 times more efficient than ground-truth physically\nbased simulation, and outperforms alternative solutions that provide fast\napproximations.\n"}
+{"abstract": "  With the ever-increasing complexity of neural language models, practitioners\nhave turned to methods for understanding the predictions of these models. One\nof the most well-adopted approaches for model interpretability is feature-based\ninterpretability, i.e., ranking the features in terms of their impact on model\npredictions. Several prior studies have focused on assessing the fidelity of\nfeature-based interpretability methods, i.e., measuring the impact of dropping\nthe top-ranked features on the model output. However, relatively little work\nhas been conducted on quantifying the robustness of interpretations. In this\nwork, we assess the robustness of interpretations of neural text classifiers,\nspecifically, those based on pretrained Transformer encoders, using two\nrandomization tests. The first compares the interpretations of two models that\nare identical except for their initializations. The second measures whether the\ninterpretations differ between a model with trained parameters and a model with\nrandom parameters. Both tests show surprising deviations from expected\nbehavior, raising questions about the extent of insights that practitioners may\ndraw from interpretations.\n"}
+{"abstract": "  The cybersecurity of smart grids has become one of key problems in developing\nreliable modern power and energy systems. This paper introduces a\nnon-stationary adversarial cost with a variation constraint for smart grids and\nenables us to investigate the problem of optimal smart grid protection against\ncyber attacks in a relatively practical scenario. In particular, a Bayesian\nmulti-node bandit (MNB) model with adversarial costs is constructed and a new\nregret function is defined for this model. An algorithm called Thompson-Hedge\nalgorithm is presented to solve the problem and the superior performance of the\nproposed algorithm is proven in terms of the convergence rate of the regret\nfunction. The applicability of the algorithm to real smart grid scenarios is\nverified and the performance of the algorithm is also demonstrated by numerical\nexamples.\n"}
+{"abstract": "  Recent high-resolution interferometric images of submillimetre galaxies\n(SMGs) reveal fascinatingly complex morphologies. This raises a number of\nquestions: how does the relative orientation of a galaxy affect its observed\nsubmillimetre emission, and does this result in an `orientation bias' in the\nselection and analysis of such galaxies in flux-limited cosmological surveys?\nWe investigate these questions using the Simba cosmological simulation paired\nwith the dust radiative transfer code Powderday. We select eight simulated SMGs\n($S_{850}\\gtrsim2$ mJy) at $z = 2$, and measure the variance of their\n`observed' emission over 50 random orientations. Each galaxy exhibits\nsignificant scatter in its emission close to the peak of the thermal dust\nemission, with variation in flux density of up to $\\sim$50 mJy at the peak.\nThis results in an appreciable dispersion in the inferred dust temperatures and\ninfrared luminosities ($16^{\\mathrm{th}}-84^{\\mathrm{th}}$ percentile ranges of\n5 K and 0.1 dex, respectively) and therefore a fundamental uncertainty in\nderived parameters such as dust mass and star formation rate ($\\sim$30% for the\nlatter using simple calibrations). Using a Monte Carlo simulation we also\nassess the impact of orientation on flux-limited surveys, finding a bias in the\nselection of SMGs towards those with face-on orientations, as well as those at\nlower redshifts. We predict that the orientation bias will affect flux-limited\nsingle-dish surveys, most significantly at THz frequencies, and this bias\nshould be taken into account when placing the results of targeted follow-up\nstudies in a statistical context.\n"}
+{"abstract": "  In this letter, we report a polarization-entangled photon-pair source based\non type-II spontaneous parametric down conversion at telecom O-band in\nperiodically poled silica fiber (PPSF). The photon-pair source exhibits more\nthan 130 nm (~24 THz) emission bandwidth centered at 1306.6 nm. The broad\nemission spectrum results in a short biphoton correlation time and we\nexperimentally demonstrate a Hong-Ou-Mandel interference dip with a full width\nof 26.6 fs at half maximum. Owing to the low birefringence of the PPSF, the\nbiphotons generated from type-II SPDC are polarization-entangled over the\nentire emission bandwidth, with a measured fidelity to a maximally entangled\nstate greater than 95.4%. The biphoton source provides the broadest bandwidth\nentangled biphotons at O-band to our knowledge.\n"}
+{"abstract": "  Using response theory, we calculate the charge-current vortex generated by\nspin pumping at a point-like contact in a system with Rashba spin-orbit\ncoupling. We discuss the spatial profile of the current density for finite\ntemperature and for the zero-temperature limit. The main observation is that\nthe Rashba spin precession leads to a charge current that oscillates as a\nfunction of the distance from the spin-pumping source, which is confirmed by\nnumerical simulations. In our calculations, we consider a Rashba model on a\nsquare lattice, for which we first review the basic properties related to\ncharge and spin transport. In particular, we define the charge- and\nspin-current operators for the tight-binding Hamiltonian as the currents\ncoupled linearly with the U(1) and SU(2) gauge potentials, respectively. By\nanalogy to the continuum model, the spin-orbit-coupling Hamiltonian on the\nlattice is then introduced as the generator of the spin current.\n"}
+{"abstract": "  The first work of Dyson relating to random matrix theory, \"The dynamics of a\ndisordered linear chain\", is reviewed. Contained in this work is an exact\nsolution of a so-called Type I chain in the case of the disorder variables\nbeing given by a gamma distribution. The exact solution exhibits a singularity\nin the density of states about the origin, which has since been shown to be\nuniversal for one-dimensional tight binding models with off diagonal disorder.\nWe discuss this context and also point out some universal features of the weak\ndisorder expansion of the exact solution near the band edge. Further, a link\nbetween the exact solution, and a tridiagonal formalism of anti symmetric\nGaussian $\\beta$-ensembles with $\\beta$ proportional to $1/N$, is made.\n"}
+{"abstract": "  Deep learning is finding its way into high energy physics by replacing\ntraditional Monte Carlo simulations. However, deep learning still requires an\nexcessive amount of computational resources. A promising approach to make deep\nlearning more efficient is to quantize the parameters of the neural networks to\nreduced precision. Reduced precision computing is extensively used in modern\ndeep learning and results to lower execution inference time, smaller memory\nfootprint and less memory bandwidth. In this paper we analyse the effects of\nlow precision inference on a complex deep generative adversarial network model.\nThe use case which we are addressing is calorimeter detector simulations of\nsubatomic particle interactions in accelerator based high energy physics. We\nemploy the novel Intel low precision optimization tool (iLoT) for quantization\nand compare the results to the quantized model from TensorFlow Lite. In the\nperformance benchmark we gain a speed-up of 1.73x on Intel hardware for the\nquantized iLoT model compared to the initial, not quantized, model. With\ndifferent physics-inspired self-developed metrics, we validate that the\nquantized iLoT model shows a lower loss of physical accuracy in comparison to\nthe TensorFlow Lite model.\n"}
+{"abstract": "  In this paper, we investigate the newly developed $f(R,\\mathbf{T}^2)$ theory\n($R$ is the Ricci scalar and $\\mathbf{T}^2=T_{\\alpha\\beta}T^{\\alpha\\beta},~T\n_{\\alpha\\beta}$ demonstrates the energy-momentum tensor) to explore some viable\ncosmological models. For this purpose, we use the Noether symmetry approach in\nthe context of flat Friedmann-Robertson-Walker (FRW) universe. We solve the\nNoether equations of this modified theory for two types of models and obtain\nthe symmetry generators as well as corresponding conserved quantities. We also\nevaluate exact solutions and investigate their physical behavior via different\ncosmological parameters. For the prospective models, the graphical behavior of\nthese parameters indicate consistency with recent observations representing\naccelerated expansion of the universe. In the first case, we take a special\nmodel of this theory and obtain new class of exact solutions with the help of\nconserved quantities. Secondly, we consider minimal and non-minimal coupling\nmodels of $f(R,\\mathbf{T} ^{2})$ gravity. We conclude that conserved quantities\nare very useful to derive the exact solutions that are used to study the cosmic\naccelerated expansion.\n"}
+{"abstract": "  The homological theory of Auslander-Platzeck-Todorov on idempotent ideals\nlaid much of the groundwork for higher Auslander-Reiten theory, providing the\nkey technical lemmas for both higher Auslander correspondence as well as the\nconstruction of higher Nakayama algebras, among other results. Given a\nfinite-dimensional algebra $A$ and idempotent $e$, we expand on a criterion of\nJasso-K\\\"ulshammer in order to determine a correspondence between the\n$2$-precluster-tilting subcategories of $\\mathrm{mod}(A)$ and\n$\\mathrm{mod}(A/\\langle e\\rangle)$. This is then applied in the context of\ngeneralising dimer algebras on surfaces with boundary idempotent.\n"}
+{"abstract": "  The success of deep learning in computer vision has inspired the scientific\ncommunity to explore new analysis methods. Within the field of neuroscience,\nspecifically in electrophysiological neuroimaging, researchers are starting to\nexplore leveraging deep learning to make predictions on their data without\nextensive feature engineering. This paper compares deep learning using\nminimally processed EEG raw data versus deep learning using EEG spectral\nfeatures using two different deep convolutional neural architectures. One of\nthem from Putten et al. (2018) is tailored to process raw data; the other was\nderived from the VGG16 vision network (Simonyan and Zisserman, 2015) which is\ndesigned to process EEG spectral features. We apply them to classify sex on\n24-channel EEG from a large corpus of 1,574 participants. Not only do we\nimprove on state-of-the-art classification performance for this type of\nclassification problem, but we also show that in all cases, raw data\nclassification leads to superior performance as compared to spectral EEG\nfeatures. Interestingly we show that the neural network tailored to process EEG\nspectral features has increased performance when applied to raw data\nclassification. Our approach suggests that the same convolutional networks used\nto process EEG spectral features yield superior performance when applied to EEG\nraw data.\n"}
+{"abstract": "  For infinitesimal learning rates, stochastic gradient descent (SGD) follows\nthe path of gradient flow on the full batch loss function. However moderately\nlarge learning rates can achieve higher test accuracies, and this\ngeneralization benefit is not explained by convergence bounds, since the\nlearning rate which maximizes test accuracy is often larger than the learning\nrate which minimizes training loss. To interpret this phenomenon we prove that\nfor SGD with random shuffling, the mean SGD iterate also stays close to the\npath of gradient flow if the learning rate is small and finite, but on a\nmodified loss. This modified loss is composed of the original loss function and\nan implicit regularizer, which penalizes the norms of the minibatch gradients.\nUnder mild assumptions, when the batch size is small the scale of the implicit\nregularization term is proportional to the ratio of the learning rate to the\nbatch size. We verify empirically that explicitly including the implicit\nregularizer in the loss can enhance the test accuracy when the learning rate is\nsmall.\n"}
+{"abstract": "  A Stick graph G=(A\\cup B, E) is the intersection graph of a set A of\nhorizontal segments and a set B of vertical segments in the plane, whose left\nand respectively bottom endpoints lie on the same ground line with slope -1.\nThese endpoints are respectively called A-origins and B-origins. When a total\norder is provided for the A-origins, the resulting graphs are called A-Stick\ngraphs.\n  In this paper, we propose a characterization of the class of A-Stick graphs\nusing forced pairs, which are pairs of segments in B with the property that\nonly one left-to-right order of their origins is possible on the ground line.\nWe deduce a recognition algorithm for A-Stick graphs running in O(|A|+|B|+|E|)\ntime, thus improving the running time of O(|A|\\cdot |B|) of the best current\nalgorithm. We also introduce the problem of finding, for a Stick graph, a\nrepresentation using segments of minimum total length. The canonical order on\nthe A- and B-origins, output by our recognition algorithm, allows us to obtain\npartial results on this problem.\n"}
+{"abstract": "  It is generally recognized that finite learning rate (LR), in contrast to\ninfinitesimal LR, is important for good generalization in real-life deep nets.\nMost attempted explanations propose approximating finite-LR SGD with Ito\nStochastic Differential Equations (SDEs), but formal justification for this\napproximation (e.g., (Li et al., 2019)) only applies to SGD with tiny LR.\nExperimental verification of the approximation appears computationally\ninfeasible. The current paper clarifies the picture with the following\ncontributions: (a) An efficient simulation algorithm SVAG that provably\nconverges to the conventionally used Ito SDE approximation. (b) A theoretically\nmotivated testable necessary condition for the SDE approximation and its most\nfamous implication, the linear scaling rule (Goyal et al., 2017), to hold. (c)\nExperiments using this simulation to demonstrate that the previously proposed\nSDE approximation can meaningfully capture the training and generalization\nproperties of common deep nets.\n"}
+{"abstract": "  Text classifiers are at the core of many NLP applications and use a variety\nof algorithmic approaches and software. This paper introduces infrastructure\nand methodologies for text classifiers based on large-scale regular\nexpressions. In particular, we describe how Facebook determines if a given\npiece of text - anything from a hashtag to a post - belongs to a narrow topic\nsuch as COVID-19. To fully define a topic and evaluate classifier performance\nwe employ human-guided iterations of keyword discovery, but do not require\nlabeled data. For COVID-19, we build two sets of regular expressions: (1) for\n66 languages, with 99% precision and recall >50%, (2) for the 11 most common\nlanguages, with precision >90% and recall >90%. Regular expressions enable\nlow-latency queries from multiple platforms. Response to challenges like\nCOVID-19 is fast and so are revisions. Comparisons to a DNN classifier show\nexplainable results, higher precision and recall, and less overfitting. Our\nlearnings can be applied to other narrow-topic classifiers.\n"}
+{"abstract": "  This paper is dealing with another multiple person game model under the\nantagonistic duel type setup. The most flexible multiple person duel game is\nanalytically solved and the explicit formulas are solved to determine the time\ndependent duel game model by using the first exceed theory. Unlike conventional\ntwo-person duel game, multiple battle fields are introduced in the paper and\neach battle field becomes shooting ground of pairwise players. This model is\ntargeted for real-world situations especially for selected target shooting\nscenarios. An analogue of the theory in the paper is designed for solving the\nbest shooting time within multiple battle fields. This new proposed model is\nfully mathematically explained to be adapted in various domains including the\nstrategies and operations.\n"}
+{"abstract": "  Topological superconductors host surface Andreev bound states which can be\nclassified as chiral or helical. Chiral superconductors belong to $Z$ symmetry\nclass which breaks time-reversal symmetry (TRS) while helical superconductors\nbelong to $Z_2$ symmetry class which preserves TRS. Chiral and helical\nsuperconductors can be further categorized by the parity-even or odd, of their\norder parameter. In this paper using Hanbury Brown and Twiss (HBT) shot noise\ncorrelations and the non-local conductance, we probe metal/unconventional\nsuperconductor/metal junctions in order to better understand the pairing\nhelical vs chiral in topological superconductors. We also compare these to\nnon-topological superconductors. Topological superconductors are carriers of\nMajorana fermions which are important for topological quantum computation. By\ndistinguishing chiral superconductors, wherein Majorana bound states (MBS)\npersist even in presence of a magnetic field, from helical superconductors\nwhere MBS are fragile, our study will help in the search for stable Majorana\nfermions which will be useful for topological quantum computation.\n"}
+{"abstract": "  Neural Architecture Search (NAS) often trains and evaluates a large number of\narchitectures. Recent predictor-based NAS approaches attempt to alleviate such\nheavy computation costs with two key steps: sampling some\narchitecture-performance pairs and fitting a proxy accuracy predictor. Given\nlimited samples, these predictors, however, are far from accurate to locate top\narchitectures due to the difficulty of fitting the huge search space. This\npaper reflects on a simple yet crucial question: if our final goal is to find\nthe best architecture, do we really need to model the whole space well?. We\npropose a paradigm shift from fitting the whole architecture space using one\nstrong predictor, to progressively fitting a search path towards the\nhigh-performance sub-space through a set of weaker predictors. As a key\nproperty of the weak predictors, their probabilities of sampling better\narchitectures keep increasing. Hence we only sample a few well-performed\narchitectures guided by the previously learned predictor and estimate a new\nbetter weak predictor. This embarrassingly easy framework, dubbed WeakNAS,\nproduces coarse-to-fine iteration to gradually refine the ranking of sampling\nspace. Extensive experiments demonstrate that WeakNAS costs fewer samples to\nfind top-performance architectures on NAS-Bench-101 and NAS-Bench-201. Compared\nto state-of-the-art (SOTA) predictor-based NAS methods, WeakNAS outperforms all\nwith notable margins, e.g., requiring at least 7.5x less samples to find global\noptimal on NAS-Bench-101. WeakNAS can also absorb their ideas to boost\nperformance more. Further, WeakNAS strikes the new SOTA result of 81.3% in the\nImageNet MobileNet Search Space. The code is available at\nhttps://github.com/VITA-Group/WeakNAS.\n"}
+{"abstract": "  We extend a study by Lempp and Hirst of infinite versions of some problems\nfrom finite complexity theory, using an intuitionistic version of reverse\nmathematics and techniques of Weihrauch analysis.\n"}
+{"abstract": "  Twitter has become a major social media platform since its launching in 2006,\nwhile complaints about bot accounts have increased recently. Although extensive\nresearch efforts have been made, the state-of-the-art bot detection methods\nfall short of generalizability and adaptability. Specifically, previous bot\ndetectors leverage only a small fraction of user information and are often\ntrained on datasets that only cover few types of bots. As a result, they fail\nto generalize to real-world scenarios on the Twittersphere where different\ntypes of bots co-exist. Additionally, bots in Twitter are constantly evolving\nto evade detection. Previous efforts, although effective once in their context,\nfail to adapt to new generations of Twitter bots. To address the two challenges\nof Twitter bot detection, we propose SATAR, a self-supervised representation\nlearning framework of Twitter users, and apply it to the task of bot detection.\nIn particular, SATAR generalizes by jointly leveraging the semantics, property\nand neighborhood information of a specific user. Meanwhile, SATAR adapts by\npre-training on a massive number of self-supervised users and fine-tuning on\ndetailed bot detection scenarios. Extensive experiments demonstrate that SATAR\noutperforms competitive baselines on different bot detection datasets of\nvarying information completeness and collection time. SATAR is also proved to\ngeneralize in real-world scenarios and adapt to evolving generations of social\nmedia bots.\n"}
+{"abstract": "  A new approach for image reconstruction in THz computed tomography (THz-CT)\nis presented. Based on a geometrical optics model containing the THz signal\namplitude and phase, a novel algorithm for extracting an average phase from the\nmeasured THz signals is derived. Applying the algorithm results in a\nphase-contrast sinogram, which is further used for image reconstruction. For\nexperimental validation, a fast THz time-domain spectrometer (THz-TDS) in\ntransmission geometry is employed, enabling CT measurements within several\nminutes. Quantitative evaluation of reconstructed 3D printed plastic profiles\nreveals the potential of our approach in non-destructive testing of plastic\nprofiles.\n"}
+{"abstract": "  Automatic analysis of bioacoustic signals is a fundamental tool to evaluate\nthe vitality of our planet. Frogs and bees, for instance, may act like\nbiological sensors providing information about environmental changes. This task\nis fundamental for ecological monitoring still includes many challenges such as\nnonuniform signal length processing, degraded target signal due to\nenvironmental noise, and the scarcity of the labeled samples for training\nmachine learning. To tackle these challenges, we present a bioacoustic signal\nclassifier equipped with a discriminative mechanism to extract useful features\nfor analysis and classification efficiently. The proposed classifier does not\nrequire a large amount of training data and handles nonuniform signal length\nnatively. Unlike current bioacoustic recognition methods, which are\ntask-oriented, the proposed model relies on transforming the input signals into\nvector subspaces generated by applying Singular Spectrum Analysis (SSA). Then,\na subspace is designed to expose discriminative features. The proposed model\nshares end-to-end capabilities, which is desirable in modern machine learning\nsystems. This formulation provides a segmentation-free and noise-tolerant\napproach to represent and classify bioacoustic signals and a highly compact\nsignal descriptor inherited from SSA. The validity of the proposed method is\nverified using three challenging bioacoustic datasets containing anuran, bee,\nand mosquito species. Experimental results on three bioacoustic datasets have\nshown the competitive performance of the proposed method compared to commonly\nemployed methods for bioacoustics signal classification in terms of accuracy.\n"}
+{"abstract": "  In order to deal with the high development time of exact and approximation\nalgorithms for NP-hard combinatorial optimisation problems and the high running\ntime of exact solvers, deep learning techniques have been used in recent years\nas an end-to-end approach to find solutions. However, there are issues of\nrepresentation, generalisation, complex architectures, interpretability of\nmodels for mathematical analysis etc. using deep learning techniques. As a\ncompromise, machine learning can be used to improve the run time performance of\nexact algorithms in a matheuristics framework. In this paper, we use a pruning\nheuristic leveraging machine learning as a pre-processing step followed by an\nexact Integer Programming approach. We apply this approach to sparsify\ninstances of the classical travelling salesman problem. Our approach learns\nwhich edges in the underlying graph are unlikely to belong to an optimal\nsolution and removes them, thus sparsifying the graph and significantly\nreducing the number of decision variables. We use carefully selected features\nderived from linear programming relaxation, cutting planes exploration,\nminimum-weight spanning tree heuristics and various other local and statistical\nanalysis of the graph. Our learning approach requires very little training data\nand is amenable to mathematical analysis. We demonstrate that our approach can\nreliably prune a large fraction of the variables in TSP instances from\nTSPLIB/MATILDA (>85%$) while preserving most of the optimal tour edges. Our\napproach can successfully prune problem instances even if they lie outside the\ntraining distribution, resulting in small optimality gaps between the pruned\nand original problems in most cases. Using our learning technique, we discover\nnovel heuristics for sparsifying TSP instances, that may be of independent\ninterest for variants of the vehicle routing problem.\n"}
+{"abstract": "  Despite significant interest in developing general purpose fact checking\nmodels, it is challenging to construct a large-scale fact verification dataset\nwith realistic claims that would occur in the real world. Existing claims are\neither authored by crowdworkers, thereby introducing subtle biases that are\ndifficult to control for, or manually verified by professional fact checkers,\ncausing them to be expensive and limited in scale. In this paper, we construct\na challenging, realistic, and large-scale fact verification dataset called\nFaVIQ, using information-seeking questions posed by real users who do not know\nhow to answer. The ambiguity in information-seeking questions enables\nautomatically constructing true and false claims that reflect confusions arisen\nfrom users (e.g., the year of the movie being filmed vs. being released). Our\nclaims are verified to be natural, contain little lexical bias, and require a\ncomplete understanding of the evidence for verification. Our experiments show\nthat the state-of-the-art models are far from solving our new task. Moreover,\ntraining on our data helps in professional fact-checking, outperforming models\ntrained on the most widely used dataset FEVER or in-domain data by up to 17%\nabsolute. Altogether, our data will serve as a challenging benchmark for\nnatural language understanding and support future progress in professional fact\nchecking.\n"}
+{"abstract": "  How can we model affect in a general fashion, across dissimilar tasks, and to\nwhich degree are such general representations of affect even possible? To\naddress such questions and enable research towards general affective computing\nthis paper introduces the Affect Game AnnotatIoN (AGAIN) dataset. AGAIN is a\nlarge-scale affective corpus that features over 1,100 in-game videos (with\ncorresponding gameplay data) from nine different games, which are annotated for\narousal from 124 participants in a first-person continuous fashion. Even though\nAGAIN is created for the purpose of investigating the generality of affective\ncomputing across dissimilar tasks, affect modelling can be studied within each\nof its 9 specific interactive games. To the best of our knowledge AGAIN is the\nlargest - over 37 hours of annotated video and game logs - and the most diverse\npublicly available affective dataset based on games as interactive affect\nelicitors.\n"}
+{"abstract": "  We study multielectron bubble phases in the $N=2$ and $N=3$ Landau levels in\na high mobility GaAs/AlGaAs sample. We found that the longitudinal\nmagnetoresistance versus temperature curves in the multielectron bubble region\nexhibit sharp peaks, irrespective of the Landau level index. We associate these\npeaks with an enhanced scattering caused by thermally fluctuating domains of a\nbubble phase and a uniform uncorrelated electron liquid at the onset of the\nbubble phases. Within the $N=3$ Landau level, onset temperatures of\nthree-electron and two-electron bubbles exhibit linear trends with respect to\nthe filling factor; the onset temperatures of three-electron bubbles are\nsystematically higher than those of two-electron bubbles. Furthermore, onset\ntemperatures of the two-electron bubble phases across $N=2$ and $N=3$ Landau\nlevels are similar, but exhibit an offset. This offset and the dominant nature\nof the three-electron bubbles in the $N=3$ Landau level reveals the role of the\nshort-range part of the electron-electron interaction in the formation of the\nbubbles.\n"}
+{"abstract": "  The sizable linear polarization signals produced by the scattering of\nanisotropic radiation in the core of the Ca I 4227 $\\mathrm{\\r{A}}$ line\nconstitute an important observable for probing the inhomogeneous and dynamic\nplasma of the lower solar chromosphere. Here we show the results of a\nthree-dimensional (3D) radiative transfer complete frequency redistribution\n(CRD) investigation of the line's scattering polarization in a\nmagneto-hydrodynamical 3D model of the solar atmosphere. We take into account\nnot only the Hanle effect produced by the model's magnetic field, but also the\nsymmetry breaking caused by the horizontal inhomogeneities and macroscopic\nvelocity gradients. The spatial gradients of the horizontal components of the\nmacroscopic velocities produce very significant forward scattering polarization\nsignals without the need of magnetic fields, while the Hanle effect tends to\ndepolarize them at the locations where the model's magnetic field is stronger\nthan about 5 G. The standard 1.5D approximation is found to be unsuitable for\nunderstanding the line's scattering polarization, but we introduce a novel\nimprovement to this approximation that produces results in qualitative\nagreement with the full 3D results. The instrumental degradation of the\ncalculated polarization signals is also investigated, showing what can we\nexpect to observe with the Visible Spectro-Polarimeter at the upcoming Daniel\nK. Inouye Solar Telescope.\n"}
+{"abstract": "  GW170817 has provided valuable constraints on the equations of state of\nmerging binary neutron stars, which can be considered as the most probable\ncandidate for the source of gravitational waves. On the other hand, these\nnatural laboratories of extreme temperature and density may lead to the\nestimation of some exotic matter like {deconfined quark matter in their cores}.\nIn this paper, we investigate the neutron star matter equation of state (EoS)\nwith the lowest order constrained variational (LOCV) method considering the\nexcluded volume effect (VLOCV) for nucleons {to} compute the tidal\ndeformability of binary neutron star mergers (BNSMs). Within this approach, the\nsize of nucleons makes the EoS {so} stiff that requires a phase transition in\norder to avoid causality violation. Therefore, this phase transition {may lead}\nto the appearance of the third family of compact stars {including} ``twin\nstar'' configurations. {Our EoS models are confronted with observations from\nGW170817, GW190814, GW190425, and also NICER. We find out that regarding all\nthese constraints, the EoS models having the {transition}\npressure$\\approx$30-100 MeV/fm$^{3}$ and the energy density discontinuity\n$\\Delta\\varepsilon$$\\lesssim$300 MeV/fm$^{3}$ are preferable.\n"}
+{"abstract": "  Compactifications of the heterotic string on special T^d/Z_2 orbifolds\nrealize a landscape of string models with 16 supercharges and a gauge group on\nthe left-moving sector of reduced rank d+8. The momenta of untwisted and\ntwisted states span a lattice known as the Mikhailov lattice II_{(d)}, which is\nnot self-dual for d > 1. By using computer algorithms which exploit the\nproperties of lattice embeddings, we perform a systematic exploration of the\nmoduli space for d=1 and 2, and give a list of maximally enhanced points where\nthe U(1)^{d+8} enhances to a rank d+8 non-Abelian gauge group. For d = 1, these\ngroups are simply-laced and simply-connected, and in fact can be obtained from\nthe Dynkin diagram of E_{10}. For d = 2 there are also symplectic and\ndoubly-connected groups. For the latter we find the precise form of their\nfundamental groups from embeddings ofof lattices into the dual of II_{(2)}.\n  Our results easily generalize to d > 2.\n"}
+{"abstract": "  Pulmonary respiratory motion artifacts are common in four-dimensional\ncomputed tomography (4DCT) of lungs and are caused by missing, duplicated, and\nmisaligned image data. This paper presents a geodesic density regression (GDR)\nalgorithm to correct motion artifacts in 4DCT by correcting artifacts in one\nbreathing phase with artifact-free data from corresponding regions of other\nbreathing phases. The GDR algorithm estimates an artifact-free lung template\nimage and a smooth, dense, 4D (space plus time) vector field that deforms the\ntemplate image to each breathing phase to produce an artifact-free 4DCT scan.\nCorrespondences are estimated by accounting for the local tissue density change\nassociated with air entering and leaving the lungs, and using binary artifact\nmasks to exclude regions with artifacts from image regression. The\nartifact-free lung template image is generated by mapping the artifact-free\nregions of each phase volume to a common reference coordinate system using the\nestimated correspondences and then averaging. This procedure generates a fixed\nview of the lung with an improved signal-to-noise ratio. The GDR algorithm was\nevaluated and compared to a state-of-the-art geodesic intensity regression\n(GIR) algorithm using simulated CT time-series and 4DCT scans with clinically\nobserved motion artifacts. The simulation shows that the GDR algorithm has\nachieved significantly more accurate Jacobian images and sharper template\nimages, and is less sensitive to data dropout than the GIR algorithm. We also\ndemonstrate that the GDR algorithm is more effective than the GIR algorithm for\nremoving clinically observed motion artifacts in treatment planning 4DCT scans.\nOur code is freely available at https://github.com/Wei-Shao-Reg/GDR.\n"}
+{"abstract": "  Hydrogenic excited states of a 2D exciton are degenerate. In the presence of\na weak magnetic field, the $S$-states with a zero momentum of the center of\nmass get coupled to the $P$-states with finite momentum of the center of mass.\nThis field-induced coupling leads to a strong modification of the dispersion\nbranches of the exciton spectrum. Namely, the lower branch acquires a shape of\na \"mexican hat\" with a minimum at a finite momentum. At certain magnetic field,\nexciton branches exhibit a linear crossing, similarly to the spectrum of a 2D\nelectron in the presence of spin-orbit coupling. While spin is not involved,\ndegenerate $S$ and $P$ states play the role of the spin projections. Lifting of\ndegeneracy due to diamagnetic shifts and deviation of electron-hole attraction\nfrom purely Coulomb suppresses the linear crossing.\n"}
+{"abstract": "  We give a sufficient condition for a Banach space with which the homogeneous\nextension of a surjective isometry from the unit sphere of it onto another one\nis real-linear. The condition is satisfied by a uniform algebra and a certain\nextremely $C$-regular space of real-valued continuous functions.\n"}
+{"abstract": "  While deep learning is a powerful tool for natural language processing (NLP)\nproblems, successful solutions to these problems rely heavily on large amounts\nof annotated samples. However, manually annotating data is expensive and\ntime-consuming. Active Learning (AL) strategies reduce the need for huge\nvolumes of labeled data by iteratively selecting a small number of examples for\nmanual annotation based on their estimated utility in training the given model.\nIn this paper, we argue that since AL strategies choose examples independently,\nthey may potentially select similar examples, all of which may not contribute\nsignificantly to the learning process. Our proposed approach,\nActive$\\mathbf{^2}$ Learning (A$\\mathbf{^2}$L), actively adapts to the deep\nlearning model being trained to eliminate further such redundant examples\nchosen by an AL strategy. We show that A$\\mathbf{^2}$L is widely applicable by\nusing it in conjunction with several different AL strategies and NLP tasks. We\nempirically demonstrate that the proposed approach is further able to reduce\nthe data requirements of state-of-the-art AL strategies by an absolute\npercentage reduction of $\\approx\\mathbf{3-25\\%}$ on multiple NLP tasks while\nachieving the same performance with no additional computation overhead.\n"}
+{"abstract": "  In this paper, we consider the problem of determining the \\emph{exact} number\nof periodic orbits for polynomial planar flows. This problem is a variant of\nHilbert's 16th problem. Using a natural definition of computability, we show\nthat the problem is noncomputable on the one hand and, on the other hand,\ncomputable uniformly on the set of all structurally stable systems defined on\nthe unit disk. We also prove that there is a family of polynomial planar\nsystems which does not have a computable sharp upper bound on the number of its\nperiodic orbits.\n"}
+{"abstract": "  Neutron-star mergers were recently confirmed as sites of\nrapid-neutron-capture (r-process) nucleosynthesis. However, in Galactic\nchemical evolution models, neutron-star mergers alone cannot reproduce the\nobserved element abundance patterns of extremely metal-poor stars, which\nindicates the existence of other sites of r-process nucleosynthesis. These\nsites may be investigated by studying the element abundance patterns of\nchemically primitive stars in the halo of the Milky Way, because these objects\nretain the nucleosynthetic signatures of the earliest generation of stars. Here\nwe report the element abundance pattern of the extremely metal-poor star SMSS\nJ200322.54-114203.3. We observe a large enhancement in r-process elements, with\nvery low overall metallicity. The element abundance pattern is well matched by\nthe yields of a single 25-solar-mass magnetorotational hypernova. Such a\nhypernova could produce not only the r-process elements, but also light\nelements during stellar evolution, and iron-peak elements during explosive\nnuclear burning. Hypernovae are often associated with long-duration gamma-ray\nbursts in the nearby Universe. This connection indicates that similar\nexplosions of fast-spinning strongly magnetized stars occurred during the\nearliest epochs of star formation in our Galaxy.\n"}
+{"abstract": "  Cartesian fibrations were originally defined by Lurie in the context of\nquasi-categories and are commonly used in $(\\infty,1)$-category theory to study\npresheaves valued in $(\\infty,1)$-categories. In this work we define and study\nfibrations modeling presheaves valued in simplicial spaces and their\nlocalizations. This includes defining a model structure for these fibrations\nand giving effective tools to recognize its fibrations and weak equivalences.\nThis in particular gives us a new method to construct Cartesian fibrations via\ncomplete Segal spaces. In addition to that, it allows us to define and study\nfibrations modeling presheaves of Segal spaces.\n"}
+{"abstract": "  In this paper, we explore the construction of natural language explanations\nfor news claims, with the goal of assisting fact-checking and news evaluation\napplications. We experiment with two methods: (1) an extractive method based on\nBiased TextRank -- a resource-effective unsupervised graph-based algorithm for\ncontent extraction; and (2) an abstractive method based on the GPT-2 language\nmodel. We perform comparative evaluations on two misinformation datasets in the\npolitical and health news domains, and find that the extractive method shows\nthe most promise.\n"}
+{"abstract": "  Group-IV color centers in diamond have attracted significant attention as\nsolid-state spin qubits because of their excellent optical and spin properties.\nAmong these color centers, the tin-vacancy (SnV$^{\\,\\textrm{-}}$) center is of\nparticular interest because its large ground-state splitting enables long spin\ncoherence times at temperatures above 1$\\,$K. However, color centers typically\nsuffer from inhomogeneous broadening, which can be exacerbated by\nnanofabrication-induced strain, hindering the implementation of quantum nodes\nemitting indistinguishable photons. Although strain and Raman tuning have been\ninvestigated as promising techniques to overcome the spectral mismatch between\ndistinct group-IV color centers, other approaches need to be explored to find\nmethods that can offer more localized control without sacrificing emission\nintensity. Here, we study electrical tuning of SnV$^{\\,\\textrm{-}}$ centers in\ndiamond via the direct-current Stark effect. We demonstrate a tuning range\nbeyond 1.7$\\,$GHz. We observe both quadratic and linear dependence on the\napplied electric field. We also confirm that the tuning effect we observe is a\nresult of the applied electric field and is distinct from thermal tuning due to\nJoule heating. Stark tuning is a promising avenue toward overcoming detunings\nbetween emitters and enabling the realization of multiple identical quantum\nnodes.\n"}
+{"abstract": "  The Lorentzian Engle-Pereira-Rovelli-Livine/Freidel-Krasnov (EPRL/FK)\nspinfoam model and the Conrady-Hnybida (CH) timelike-surface extension can be\nexpressed in the integral form $\\int e^S$. This work studies the analytic\ncontinuation of the spinfoam action $S$ to the complexification of the\nintegration domain. Our work extends our knowledge from the real critical\npoints well-studied in the spinfoam large-$j$ asymptotics to general complex\ncritical points of $S$ analytic continued to the complexified domain. The\ncomplex critical points satisfying critical equations of the analytic continued\n$S$. In the large-$j$ regime, the complex critical points give subdominant\ncontributions to the spinfoam amplitude when the real critical points are\npresent. But the contributions from the complex critical points can become\ndominant when the real critical point are absent. Moreover the contributions\nfrom the complex critical points cannot be neglected when the spins $j$ are not\nlarge. In this paper, we classify the complex critical points of the spinfoam\namplitude, and find a subclass of complex critical points that can be\ninterpreted as 4-dimensional simplicial geometries. In particular, we identify\nthe complex critical points corresponding to the Riemannian simplicial\ngeometries although we start with the Lorentzian spinfoam model. The\ncontribution from these complex critical points of Riemannian geometry to the\nspinfoam amplitude give $e^{-S_{Regge}}$ in analogy with the Euclidean path\nintegral, where $S_{Regge}$ is the Riemannian Regge action on simplicial\ncomplex.\n"}
+{"abstract": "  Robot programming methods for industrial robots are time consuming and often\nrequire operators to have knowledge in robotics and programming. To reduce\ncosts associated with reprogramming, various interfaces using augmented reality\nhave recently been proposed to provide users with more intuitive means of\ncontrolling robots in real-time and programming them without having to code.\nHowever, most solutions require the operator to be close to the real robot's\nworkspace which implies either removing it from the production line or shutting\ndown the whole production line due to safety hazards. We propose a novel\naugmented reality interface providing the users with the ability to model a\nvirtual representation of a workspace which can be saved and reused to program\nnew tasks or adapt old ones without having to be co-located with the real\nrobot. Similar to previous interfaces, the operators then have the ability to\nprogram robot tasks or control the robot in real-time by manipulating a virtual\nrobot. We evaluate the intuitiveness and usability of the proposed interface\nwith a user study where 18 participants programmed a robot manipulator for a\ndisassembly task.\n"}
+{"abstract": "  We study the extragradient method for solving vector quasi-equilibrium\nproblems in Banach spaces, which generalizes the extragradient method for\nvector equilibrium problems and scalar quasi-equilibrium problems. We propose a\nregularization procedure which ensures strong convergence of the generated\nsequence to a solution of the vector quasi-equilibrium problem, under standard\nassumptions on the problem without assuming neither any monotonicity assumption\non the vector valued bifunction nor any weak continuity assumption of $f$ in\nits arguments that in the many well-known methods have been used. Also, we show\nthat the boundedness of the generated sequences implies that the solution set\nof the vector quasi-equilibrium problem is nonempty, and prove the strong\nconvergence of the generated sequences to a solution of the problem. Finally,\nwe give some examples of vector quasi-equilibrium problems in several Banach\nspaces to which our main theorem can be applied. We also present some numerical\nexperiments.\n"}
+{"abstract": "  We prove that the genuine $C_{2^n}$-spectrum\n$N_{C_{2}}^{C_{2^n}}MU_{\\mathbb{R}}$ is cofree, for all $n$. Our proof is a\nformal argument using chromatic hypercubes and the Slice Theorem of Hill,\nHopkins, and Ravenel. We show that this gives a new proof of the Segal\nconjecture for $C_2$, independent of Lin's theorem.\n"}
+{"abstract": "  The inferior electrical contact to two-dimensional (2D) materials is a\ncritical challenge for their application in post-silicon very large-scale\nintegrated circuits. Electrical contacts were generally related to their\nresistive effect, quantified as contact resistance. With a systematic\ninvestigation, this work demonstrates a capacitive\nmetal-insulator-semiconductor (MIS) field-effect at the electrical contacts to\n2D materials: the field-effect depletes or accumulates charge carriers,\nredistributes the voltage potential, and give rise to abnormal current\nsaturation and nonlinearity. On the one hand, the current saturation hinders\nthe devices' driving ability, which can be eliminated with carefully engineered\ncontact configurations. On the other hand, by introducing the nonlinearity to\nmonolithic analog artificial neural network circuits, the circuits' perception\nability can be significantly enhanced, as evidenced using a COVID-19 critical\nillness prediction model. This work provides a comprehension of the\nfield-effect at the electrical contacts to 2D materials, which is fundamental\nto the design, simulation, and fabrication of electronics based on 2D material.\n"}
+{"abstract": "  The snowlines of various volatile species in protoplanetary disks are\nassociated with abrupt changes in gas composition and dust physical properties.\nVolatiles may affect dust growth, as they cover grains with icy mantles that\ncan change the fragmentation velocity of the grains. In turn, dust coagulation,\nfragmentation, and drift through the gas disk can contribute to the\nredistribution of volatiles between the ice and gas phases. Here we present the\nhydrodynamic model FEOSAD for protoplanetary disks with two dust populations\nand volatile dynamics. We compute the spatial distributions of major volatile\nmolecules (H$_2$O, CO$_2$, CH$_4$, and CO) in the gas, on small and grown dust,\nand analyze the composition of icy mantles over the initial 0.5 Myr of disk\nevolution. We show that most of ice arrives to the grown dust through\ncoagulation with small grains. Spiral structures and dust rings forming in the\ndisk, as well as photodissociation in the outer regions, lead to the formation\nof complex snowline shapes and multiple snowlines for each volatile species.\nDuring the considered disk evolution, the snowlines shift closer to the star,\nwith their final position being a factor $4-5$ smaller than that at the disk\nformation epoch. We demonstrate that volatiles tend to collect in the vicinity\nof their snowlines, both in the ice and gas phases, leading to the formation of\nthick icy mantles potentially important for dust dynamics. The dust size is\naffected by a lower fragmentation velocity of bare grains in the model with a\nhigher turbulent viscosity.\n"}
+{"abstract": "  False data injection attacks (FDIA) are a main category of cyber-attacks\nthreatening the security of power systems. Contrary to the detection of these\nattacks, less attention has been paid to identifying the attacked units of the\ngrid. To this end, this work jointly studies detecting and localizing the\nstealth FDIA in power grids. Exploiting the inherent graph topology of power\nsystems as well as the spatial correlations of measurement data, this paper\nproposes an approach based on the graph neural network (GNN) to identify the\npresence and location of the FDIA. The proposed approach leverages the\nauto-regressive moving average (ARMA) type graph filters (GFs) which can better\nadapt to sharp changes in the spectral domain due to their rational type filter\ncomposition compared to the polynomial type GFs such as Chebyshev. To the best\nof our knowledge, this is the first work based on GNN that automatically\ndetects and localizes FDIA in power systems. Extensive simulations and\nvisualizations show that the proposed approach outperforms the available\nmethods in both detection and localization of FDIA for different IEEE test\nsystems. Thus, the targeted areas can be identified and preventive actions can\nbe taken before the attack impacts the grid.\n"}
+{"abstract": "  We develop a nonperturbative method through the Hartree factorization to\nexamine the quantum fluctuation effects on the single-field inflationary models\nin a spatially flat FRW cosmological space-time. Apart from the background\nfield equation as well as the Friedmann equation with the corrections of\nquantum field fluctuations, the modified Mukhanov-Sasaki equations for the mode\nfunctions of the quantum scalar field are also derived by introducing the\nnonzero $\\Delta_B$ term. We consider the Universe undergoing the slow roll\n(SR)-ultra slow roll (USR) -slow roll (SR) inflation where in particular the\npresence of the USR inflation triggers the huge growth of $\\Delta_B$ that in\nturn gives the boost effects to the curvature perturbations for the modes that\nleave horizon in the early times of the inflation. However, the cosmic friction\nterm in the mode equation given by the Hubble parameter presumably prohibits\nthe boost effects. Here we propose two representative models to illustrate\nthese two competing terms.\n"}
+{"abstract": "  For quadratic forms in $4$ variables defined over the rational function field\nin one variable over $\\mathbb C(\\!(t)\\!)$, the validity of the local-global\nprinciple for isotropy with respect to different sets of discrete valuations is\nexamined.\n"}
+{"abstract": "  Two different ab initio potential energy surfaces are employed to investigate\nthe efficiency of the rotational excitation channels for the polar molecular\nion HeH$^+$ interacting with He atoms. We further use them to investigate the\nquantum dynamics of both the proton-exchange reaction and the purely rotational\ninelastic collisions over a broad range of temperatures. In current modeling\nstudies, this cation is considered to be one of the possible cooling sources\nunder early universe conditions after the recombination era and has recently\nbeen found to exist in the Interstellar Medium. Results from the present\ncalculations are able to show the large efficiency of the state-changing\nchannels involving rotational states of this cation. In fact, we find them to\nbe similar in size and behaviour to the inelastic and to the reaction rate\ncoefficients obtained in previous studies where H atoms were employed as\nprojectiles. The same rotational excitation processes, occurring when free\nelectrons are the collision partners of this cation, are also compared with the\npresent findings. The relative importance of the reactive, proton-exchange\nchannel and the purely inelastic channels is also analysed and discussed. The\nrotational de-excitation processes are also investigated for the cooling\nkinetics of the present cation under cold trap conditions with He as the buffer\ngas. The implications of the present results for setting up more comprehensive\nnumerical models to describe the chemical evolution networks in different\nenvironments are briefly discussed.\n"}
+{"abstract": "  We study properties of an accretion ring in a steady mass flow from a\ncompanion star to a compact object in an X-ray binary. The accretion ring is a\nplace where matter inflowing from a companion star sojourns for a while to\nbifurcate to accretion and excretion flows due to angular momentum transfer in\nit. The matter in the accretion ring rotates along the Keplerian circular orbit\ndetermined by the intrinsic specific angular momentum of the inflowing matter\nand forms a thick ring-envelope. Two internal flows are expected to appear in\nthe thick envelope. One is a mass spreading flow bifurcating to a thick\naccretion flow and a thick excretion flow, as a result of the angular momentum\ntransfer within the ring-envelope. The other is a cooling flow toward the\nenvelope center governed by radiative cooling under an effect of X-ray\nirradiation. This cooling flow eventually forms a core in the torus, from which\na thin accretion disk and a thin excretion disk spread out as a result of the\nangular momentum transfer there again. Evaluating and comparing the time scales\nfor the two internal flows, the accretion ring is shown to generally originate\na two-layer accretion flow in which a thin accretion disk is sandwiched by a\nthick accretion flow, unless the accretion rate is very low. Properties of the\nthin excretion disk and the thick excretion flow are also investigated. The\nthin excretion disk is expected to terminate at a distance 4 times as large as\nthe accretion ring radius and to form another ring there, unless tidal effects\nfrom the companion star exist. The thick excretion flow is, on the other hand,\nlikely to turn to a super-sonic wind-flow reaching the infinity.\n"}
+{"abstract": "  In applications such as wireless communication, it is important to study the\nstatistical properties of $L_{2}$, the minimal arc distance between a random\npoint (e.g., a cellphone user) uniformly distributed on a sphere to a set of\npre-defined seeds (e.g., wireless towers) on that sphere. In this study, we\nfirst derive the distribution (CDF) and density (PDF) functions of the arc\ndistance between a selected vertex of a spherical triangle to a random point\nuniformly distributed within this triangle. Next, using computational\ntechniques based on spherical Voronoi diagram and triangular partition of\nVoronoi cells, we derive moments of $L_{2}$ and $\\cos L_{2}$. These results are\nverified by extensive Monte Carlo simulations.\n"}
+{"abstract": "  Parker (1972) first proposed that coronal heating was the necessary outcome\nof an energy flux caused by the tangling of coronal magnetic field lines by\nphotospheric flows. In this paper we discuss how this model has been modified\nby subsequent numerical simulations outlining in particular the substantial\ndifferences between the \"nanoflares\" introduced by Parker and \"elementary\nevents\", defined here as small-scale spatially and temporally isolated heating\nevents resulting from the continuous formation and dissipation of field-aligned\ncurrent sheets within a coronal loop. We present numerical simulations of the\ncompressible 3-D MHD equations using the HYPERION code. We use two clustering\nalgorithms to investigate the properties of the simulated elementary events: an\nIDL implementation of a Density-Based Spatial Clustering of Applications with\nNoise (DBSCAN) technique; and our own Physical Distance Clustering (PDC)\nalgorithm. We identify and track elementary heating events in time, both in\ntemperature and in Joule heating space. For every event we characterize\nproperties such as: density, temperature, volume, aspect ratio, length,\nthickness, duration and energy. The energies of the events are in the range\n$10^{18}-10^{21}$ ergs, with durations shorter than 100 seconds. A few events\nlast up to 200 seconds and release energies up to $10^{23}$ ergs. While high\ntemperature are typically located at the flux tube apex, the currents extend\nall the way to the footpoints. Hence a single elementary event cannot at\npresent be detected. The observed emission is due to the superposition of many\nelementary events distributed randomly in space and time within the loop.\n"}
+{"abstract": "  The chemical master equation (CME), which describes the discrete and\nstochastic molecule number dynamics associated with biological processes like\ntranscription, is difficult to solve analytically. It is particularly hard to\nsolve for models involving bursting/gene switching, a biological feature that\ntends to produce heavy-tailed single cell RNA counts distributions. In this\npaper, we present a novel method for computing exact and analytic solutions to\nthe CME in such cases, and use these results to explore approximate solutions\nvalid in different parameter regimes, and to compute observables of interest.\nOur method leverages tools inspired by quantum mechanics, including ladder\noperators and Feynman-like diagrams, and establishes close formal parallels\nbetween the dynamics of bursty transcription, and the dynamics of bosons\ninteracting with a single fermion. We focus on two problems: (i) the chemical\nbirth-death process coupled to a switching gene/the telegraph model, and (ii) a\nmodel of transcription and multistep splicing involving a switching gene and an\narbitrary number of downstream splicing steps. We work out many special cases,\nand exhaustively explore the special functionology associated with these\nproblems. This is Part I in a two-part series of papers; in Part II, we explore\nan alternative solution approach that is more useful for numerically solving\nthese problems, and apply it to parameter inference on simulated RNA counts\ndata.\n"}
+{"abstract": "  Stabbing Planes (also known as Branch and Cut) is a proof system introduced\nvery recently which, informally speaking, extends the DPLL method by branching\non integer linear inequalities instead of single variables. The techniques\nknown so far to prove size and depth lower bounds for Stabbing Planes are\ngeneralizations of those used for the Cutting Planes proof system established\nvia communication complexity arguments. As such they work for the lifted\nversion of combinatorial statements. Rank lower bounds for Cutting Planes are\nalso obtained by geometric arguments called protection lemmas.\n  In this work we introduce two new geometric approaches to prove size/depth\nlower bounds in Stabbing Planes working for any formula: (1) the antichain\nmethod, relying on Sperner's Theorem and (2) the covering method which uses\nresults on essential coverings of the boolean cube by linear polynomials, which\nin turn relies on Alon's combinatorial Nullenstellensatz.\n  We demonstrate their use on classes of combinatorial principles such as the\nPigeonhole principle, the Tseitin contradictions and the Linear Ordering\nPrinciple. By the first method we prove almost linear size lower bounds and\noptimal logarithmic depth lower bounds for the Pigeonhole principle and\nanalogous lower bounds for the Tseitin contradictions over the complete graph\nand for the Linear Ordering Principle. By the covering method we obtain a\nsuperlinear size lower bound and a logarithmic depth lower bound for Stabbing\nPlanes proof of Tseitin contradictions over a grid graph.\n"}
+{"abstract": "  Deep neural networks (DNNs) trained on one set of medical images often\nexperience severe performance drop on unseen test images, due to various domain\ndiscrepancy between the training images (source domain) and the test images\n(target domain), which raises a domain adaptation issue. In clinical settings,\nit is difficult to collect enough annotated target domain data in a short\nperiod. Few-shot domain adaptation, i.e., adapting a trained model with a\nhandful of annotations, is highly practical and useful in this case. In this\npaper, we propose a Polymorphic Transformer (Polyformer), which can be\nincorporated into any DNN backbones for few-shot domain adaptation.\nSpecifically, after the polyformer layer is inserted into a model trained on\nthe source domain, it extracts a set of prototype embeddings, which can be\nviewed as a \"basis\" of the source-domain features. On the target domain, the\npolyformer layer adapts by only updating a projection layer which controls the\ninteractions between image features and the prototype embeddings. All other\nmodel weights (except BatchNorm parameters) are frozen during adaptation. Thus,\nthe chance of overfitting the annotations is greatly reduced, and the model can\nperform robustly on the target domain after being trained on a few annotated\nimages. We demonstrate the effectiveness of Polyformer on two medical\nsegmentation tasks (i.e., optic disc/cup segmentation, and polyp segmentation).\nThe source code of Polyformer is released at\nhttps://github.com/askerlee/segtran.\n"}
+{"abstract": "  The need for reliable systems to determine fingerprint presentation attacks\ngrows with the rising use of the fingerprint for authentication. This work\npresents a new approach to single-class classification for software-based\nfingerprint presentation attach detection. The described method utilizes a\nWasserstein GAN to apply transfer learning to a deep convolutional autoencoder.\nBy doing so, the autoencoder could be pretrained and finetuned on the\nLivDet2021 Dermalog sensor dataset with only 1122 bona fide training samples.\nWithout making use of any presentation attack samples, the model could archive\nan average classification error rate of 16.79%. The Wasserstein GAN implemented\nto pretrain the autoencoders weights can further be used to generate\nrealistic-looking artificial fingerprint patches. Extensive testing of\ndifferent autoencoder architectures and hyperparameters led to coarse\narchitectural guidelines as well as multiple implementations which can be\nutilized for future work.\n"}
+{"abstract": "  In the 2016 experiment by Crossno et al. [Science 351, 1058 (2016)],\nelectronic contribution to the thermal conductivity of graphene was found to\nviolate the well-known Wiedemann-Franz (WF) law for metals. At liquid nitrogen\ntemperatures, the thermal to electrical conductivity ratio of charge-neutral\nsamples was more than 10 times higher than predicted by the WF law, what was\nattributed to interactions between particles leading to collective behavior\ndescribed by hydrodynamics. Here we show, by adapting the handbook derivation\nof the WF law to the case of massless Dirac fermions, that significantly\nenhanced thermal conductivity should appear also in few- or even sub-kelvin\ntemperatures, where the role of interactions can be neglected. The comparison\nwith numerical results obtained within the Landauer-B\\\"uttiker formalism for\nrectangular and disk-shaped (Corbino) devices in ballistic graphene is also\nprovided.\n"}
+{"abstract": "  In this article we construct a compact Riemannian manifold of high dimension\non which the time dependent Euler equations are Turing complete. More\nprecisely, the halting of any Turing machine with a given input is equivalent\nto a certain global solution of the Euler equations entering a certain open set\nin the space of divergence-free vector fields. In particular, this implies the\nundecidability of whether a solution to the Euler equations with an initial\ndatum will reach a certain open set or not in the space of divergence-free\nfields. This result goes one step further in Tao's programme to study the\nblow-up problem for the Euler and Navier-Stokes equations using fluid\ncomputers. As a remarkable spin-off, our method of proof allows us to give a\ncounterexample to a conjecture of Moore dating back to 1998 on the\nnon-existence of analytic maps on compact manifolds that are Turing complete.\n"}
+{"abstract": "  ThumbTrak is a novel wearable input device that recognizes 12 micro-finger\nposes in real-time. Poses are characterized by the thumb touching each of the\n12 phalanges on the hand. It uses a thumb-ring, built with a flexible printed\ncircuit board, which hosts nine proximity sensors. Each sensor measures the\ndistance from the thumb to various parts of the palm or other fingers.\nThumbTrak uses a support-vector-machine (SVM) model to classify finger poses\nbased on distance measurements in real-time. A user study with ten participants\nshowed that ThumbTrak could recognize 12 micro finger poses with an average\naccuracy of 93.6%. We also discuss potential opportunities and challenges in\napplying ThumbTrak in real-world applications.\n"}
+{"abstract": "  We study Jackiw-Teitelboim gravity with dynamical end of the world branes in\nasymptotically nearly AdS$_2$ spacetimes. We quantize this theory in Lorentz\nsignature, and compute the Euclidean path integral summing over topologies\nincluding dynamical branes. The latter will be seen to exactly match with a\nmodification of the SSS matrix model. The resolution of UV divergences in the\ngravitational instantons involving the branes will lead us to understand the\nmatrix model interpretation of the Wilsonian effective theory perspective on\nthe gravitational theory. We complete this modified SSS matrix model\nnonperturbatively by extending the integration contour of eigenvalues into the\ncomplex plane. Furthermore, we give a new interpretation of other phases in\nsuch matrix models. We derive an effective $W(\\Phi)$ dilaton gravity, which\nexhibits similar physics semiclassically. In the limit of a large number of\nflavors of branes, the effective extremal entropy $S_{0,\\text{eff}}$ has the\nform of counting the states of these branes.\n"}
+{"abstract": "  Despite ongoing advancements in quantum computing, businesses are still faced\nwith the problem to decide if they would benefit from investing into this novel\ntechnology for building a business critical application. This uncertainty is\nnot only owing to the limitations in the current state of the technology but\nalso due to the gap between the level at which business applications are\nanalyzed (e.g., using high level semi-formal languages) and the level at which\nquantum computing related information is currently available (e.g., formally\nspecified computational problems, their algorithmic solutions with\ncomputational complexity theoretic analysis) to make informed decisions. To\nfill the discourse gap, in this paper, we present design of an interactive\nadvisor, which augments users while deciding to invest into quantum software\ndevelopment as a plausible future option in their application context. Towards\nthat we apply business process modeling and natural language similarity\nanalysis using text-embeddings to associated business context with\ncomputational problems and formulate constraints in terms of quantum speedup\nand resource requirements to select software development platforms.\n"}
+{"abstract": "  Modern machine learning techniques, such as deep neural networks, are\ntransforming many disciplines ranging from image recognition to language\nunderstanding, by uncovering patterns in big data and making accurate\npredictions. They have also shown promising results for synthesizing new\ndesigns, which is crucial for creating products and enabling innovation.\nGenerative models, including generative adversarial networks (GANs), have\nproven to be effective for design synthesis with applications ranging from\nproduct design to metamaterial design. These automated computational design\nmethods can support human designers, who typically create designs by a\ntime-consuming process of iteratively exploring ideas using experience and\nheuristics. However, there are still challenges remaining in automatically\nsynthesizing `creative' designs. GAN models, however, are not capable of\ngenerating unique designs, a key to innovation and a major gap in AI-based\ndesign automation applications. This paper proposes an automated method, named\nCreativeGAN, for generating novel designs. It does so by identifying components\nthat make a design unique and modifying a GAN model such that it becomes more\nlikely to generate designs with identified unique components. The method\ncombines state-of-art novelty detection, segmentation, novelty localization,\nrewriting, and generative models for creative design synthesis. Using a dataset\nof bicycle designs, we demonstrate that the method can create new bicycle\ndesigns with unique frames and handles, and generalize rare novelties to a\nbroad set of designs. Our automated method requires no human intervention and\ndemonstrates a way to rethink creative design synthesis and exploration.\n"}
+{"abstract": "  Performance degradation of an Automatic Speech Recognition (ASR) system is\ncommonly observed when the test acoustic condition is different from training.\nHence, it is essential to make ASR systems robust against various environmental\ndistortions, such as background noises and reverberations. In a multi-stream\nparadigm, improving robustness takes account of handling a variety of unseen\nsingle-stream conditions and inter-stream dynamics. Previously, a practical\ntwo-stage training strategy was proposed within multi-stream end-to-end ASR,\nwhere Stage-2 formulates the multi-stream model with features from Stage-1\nUniversal Feature Extractor (UFE). In this paper, as an extension, we introduce\na two-stage augmentation scheme focusing on mismatch scenarios: Stage-1\nAugmentation aims to address single-stream input varieties with data\naugmentation techniques; Stage-2 Time Masking applies temporal masks on UFE\nfeatures of randomly selected streams to simulate diverse stream combinations.\nDuring inference, we also present adaptive Connectionist Temporal\nClassification (CTC) fusion with the help of hierarchical attention mechanisms.\nExperiments have been conducted on two datasets, DIRHA and AMI, as a\nmulti-stream scenario. Compared with the previous training strategy,\nsubstantial improvements are reported with relative word error rate reductions\nof 29.7-59.3% across several unseen stream combinations.\n"}
+{"abstract": "  Modern and future observation Space missions face increasingly more demanding\npointing performance requirements. This is accompanied with the development of\nlarger lightweight flexible structures. This paper outlines a methodology for\nmodeling a generic multi-body flexible spacecraft in an end-to-end fashion\nwithin the Two-Inputs Two-Outputs Port framework: from disturbance all the way\nto pointing performance by tacking into account all the uncertainties of each\nsub-system. A particular focus is dedicated to a novel generic model for Solar\nDrive Array Mechanisms and its harmonic disturbances produced by the\nmicro-stepping driver and the gearbox imperfections. The proposed model is\nvalidated with the on-board telemetries of a European spacecraft. Moreover this\npaper shows how the proposed model-based approach can be easily used for\ncontrol design and closed-loop robust performance analysis. Worst-case\nscenarios of mechanical parameters combination are investigated to provide\nrobust performance certificate of the reference study case. A Linear\nParameter-Varying observer, scheduled by the solar array rotor angle, is\nfinally proposed in order to estimate the disturbance torques induced by the\ngearbox imperfections just using the measurements coming from a classical\nattitude control system: a star tracker and a gyrometer.\n"}
+{"abstract": "  With the expansion of scientific research, the number of scientific research\nresults is increasing. How to summarize these data has become an urgent\nproblem. Therefore, knowledge mapping methods come into being, providing a lot\nof management and application functions. However, it is still a problem to\nfully understand the knowledge map, especially in the field of sociology. In\nthis paper, a three-dimensional knowledge map is proposed with time, space and\nnumber based on category and numericity, which concludes all the scientific\nproblems related to numericity interdisciplinary. Compared with the traditional\nway, this map is normative, and puts forward the general production criteria of\nlabeling and digitization. It is also intuitive and readable, on which nature,\nsociety and formal science are expressed in the same picture. The scientific\nmethodologies are summarized, so that the methods with similar logic between\ndifferent disciplines can be used for reference in development. Some social\nsubjects are expressed more vividly than traditional text-based expressions,\nand are compatible with the natural science system. Mathematics also show its\nimportance on the map as formal Science, indicating that it is the key to the\ndevelopment of science. This is not only a preliminary model of a comprehensive\nscientific worldview, but also a preliminary framework for the connection and\ncooperation of various disciplines in the future.\n"}
+{"abstract": "  Tropical ideals are a class of ideals in the tropical polynomial semiring\nthat combinatorially abstracts the possible collections of supports of all\npolynomials in an ideal over a field. We study zero-dimensional tropical ideals\nI with Boolean coefficients in which all underlying matroids are paving\nmatroids, or equivalently, in which all polynomials of minimal support have\nsupport of size deg(I) or deg(I)+1 -- we call them paving tropical ideals. We\nshow that paving tropical ideals of degree d+1 are in bijection with $\\mathbb\nZ^n$-invariant d-partitions of $\\mathbb Z^n$. This implies that\nzero-dimensional tropical ideals of degree 3 with Boolean coefficients are in\nbijection with $\\mathbb Z^n$-invariant 2-partitions of quotient groups of the\nform $\\mathbb Z^n/L$. We provide several applications of these techniques,\nincluding a construction of uncountably many zero-dimensional degree-3 tropical\nideals in one variable with Boolean coefficients, and new examples of\nnon-realizable zero-dimensional tropical ideals.\n"}
+{"abstract": "  An assumption of smooth response to small parameter changes, of statistics or\nlong-time averages of a chaotic system, is generally made in the field of\nsensitivity analysis, and the parametric derivatives of statistical quantities\nare critically used in science and engineering. In this paper, we propose a\nnumerical procedure to assess the differentiability of statistics with respect\nto parameters in chaotic systems. We numerically show that the existence of the\nderivative depends on the Lebesgue-integrability of a certain density gradient\nfunction, which we define as the derivative of logarithmic SRB density along\nthe unstable manifold. We develop a recursive formula for the density gradient\nthat can be efficiently computed along trajectories, and demonstrate its use in\ndetermining the differentiability of statistics. Our numerical procedure is\nillustrated on low-dimensional chaotic systems whose statistics exhibit both\nsmooth and rough regions in parameter space.\n"}
+{"abstract": "  Finite element methods for symmetric linear hyperbolic systems using\nunstructured advancing fronts (satisfying a causality condition) are considered\nin this work. Convergence results and error bounds are obtained for mapped tent\npitching schemes made with standard discontinuous Galerkin discretizations for\nspatial approximation on mapped tents. Techniques to study semidiscretization\non mapped tents, design fully discrete schemes, prove local error bounds, prove\nstability on spacetime fronts, and bound error propagated through unstructured\nlayers are developed.\n"}
+{"abstract": "  Any $C^d$ conservative map $f$ of the $d$-dimensional unit ball $\\mathbb B^d$\ncan be realized by renormalized iteration of a $C^d$ perturbation of identity:\nthere exists a conservative diffeomorphism of $\\mathbb B^d$, arbitrarily close\nto identity in the $C^d$ topology, that has a periodic disc on which the return\ndynamics after a $C^d$ change of coordinates is exactly $f$.\n"}
+{"abstract": "  Humans possess a unique social cognition capability; nonverbal communication\ncan convey rich social information among agents. In contrast, such crucial\nsocial characteristics are mostly missing in the existing scene understanding\nliterature. In this paper, we incorporate different nonverbal communication\ncues (e.g., gaze, human poses, and gestures) to represent, model, learn, and\ninfer agents' mental states from pure visual inputs. Crucially, such a mental\nrepresentation takes the agent's belief into account so that it represents what\nthe true world state is and infers the beliefs in each agent's mental state,\nwhich may differ from the true world states. By aggregating different beliefs\nand true world states, our model essentially forms \"five minds\" during the\ninteractions between two agents. This \"five minds\" model differs from prior\nworks that infer beliefs in an infinite recursion; instead, agents' beliefs are\nconverged into a \"common mind\". Based on this representation, we further devise\na hierarchical energy-based model that jointly tracks and predicts all five\nminds. From this new perspective, a social event is interpreted by a series of\nnonverbal communication and belief dynamics, which transcends the classic\nkeyframe video summary. In the experiments, we demonstrate that using such a\nsocial account provides a better video summary on videos with rich social\ninteractions compared with state-of-the-art keyframe video summary methods.\n"}
+{"abstract": "  We have studied the optical and electrical spectra from an n i p LED as a\nfunction of magnetic field. This sample incorporated three GaAs quantum wells\nin the intrinsic region. This device had excess n type doping and as a result.\nThe quantum wells were populated by a two dimension Landau electron gas. The\nbroad B0 field emission band evolved into a series of discrete features in the\npresence of a magnetic field. These were identified as interband transitions\nbetween the different values of l. Landau levels associated with the sub-bands,\nwith the selection rule. An energy splitting between the two polarised\ncomponents was observed for each Landau level transition. This was found to be\nequal to the sum of the conduction and valence band spin splittings. We used\nthe know value of electron g factor to determine the valence band spin\nsplittings. Our experimental values were compared to the numerically calculated\nvalues and were found to be in reasonable agreement.\n"}
+{"abstract": "  In this paper, we study a class of revenue management problems where the\ndecision maker aims to maximize the total revenue subject to budget constraints\non multiple type of resources over a finite horizon. At each time, a new\norder/customer/bid is revealed with a request of some resource(s) and a reward,\nand the decision maker needs to either accept or reject the order. Upon the\nacceptance of the order, the resource request must be satisfied and the\nassociated revenue (reward) can be collected. We consider a stochastic setting\nwhere all the orders are i.i.d. sampled, i.e., the reward-request pair at each\ntime is drawn from an unknown distribution. The formulation contains many\nclassic applications such as the quantity-based network revenue management\nproblem and the Adwords problem. We consider \\textit{regret} as the performance\nmeasure defined by the gap between the optimal objective value of the\ncertainty-equivalent linear program (LP) and the expected revenue obtained by\nthe online algorithm. We show that under a finite support assumption on the\nunderlying distribution and a nondegeneracy assumption on the underlying LP,\nthe classic LP-based adaptive algorithm achieves a problem-dependent regret\nupper bound that involves several parameters related to the underlying LP but\nis not dependent on the horizon/number of time periods. In this sense, our\nresult improves upon the previous logarithmic regret bound in (Jasin 2015) and\nhighlights that the adaptive algorithm incurs a revenue loss independent on the\nhorizon even without the knowledge of the underlying distribution (Jasin and\nKumar, 2012) and against the strong benchmark of the certainty equivalent LP.\n"}
+{"abstract": "  Henize 2-10 (He 2-10) is a nearby (D = 9 Mpc) starbursting blue compact dwarf\ngalaxy that boasts a high star formation rate and a low luminosity AGN. He 2-10\nis also one of the first galaxies in which embedded superstar clusters (SSCs)\nwere discovered. SSCs are massive, compact star clusters that will impact their\nhost galaxies dramatically when their massive stars evolve. Here, we discuss\nradio, submillimeter, and infrared observations of He 2-10 from 1.87 microns to\n6 cm in high angular resolution (~0.3 arcsec), which allows us to disentangle\nindividual clusters from aggregate complexes as identified at lower resolution.\nThese results indicate the importance of spatial resolution to characterize\nSSCs, as low resolution studies of SSCs average over aggregate complexes that\nmay host SSCs at different stages of evolution. We explore the thermal,\nnon-thermal, and dust emission associated with the clusters along with dense\nmolecular tracers to construct a holistic review of the natal SSCs that have\nyet to dramatically disrupt their parent molecular clouds. We assess the\nproduction rate of ionizing photons, extinction, total mass, and the star\nformation efficiency associated with the clusters. Notably, we find that the\nstar formation efficiency for the some of the natal clusters is high (>70%),\nwhich suggests that these clusters could remain bound even after the gas is\ndispersed from the system from stellar feedback mechanisms. If they remain\nbound, these SSCs could survive to become objects indistinguishable from\nglobular clusters.\n"}
+{"abstract": "  We expose a hidden scaling symmetry of the Navier-Stokes equations in the\nlimit of vanishing viscosity, which stems from dynamical space-time rescaling\naround suitably defined Lagrangian scaling centers. At a dynamical level, the\nhidden symmetry projects solutions which differ up to Galilean invariance and\nglobal temporal scaling onto the same representative flow. At a statistical\nlevel, this projection repairs the scale invariance, which is broken by\nintermittency in the original formulation. Following previous work by the first\nauthor, we here postulate and substantiate with numerics that hidden symmetry\nstatistically holds in the inertial interval of fully developed turbulence. We\nshow that this symmetry accounts for the scale-invariance of a certain class of\nobservables, in particular, the Kolmogorov multipliers.\n"}
+{"abstract": "  We analyze complex multiplication for Jacobians of curves of genus 3, as well\nas the resulting Shimura class groups and their subgroups corresponding to\nGalois conjugation over the reflex field. We combine our results with numerical\nmethods to find CM fields $K$ for which there exist both hyperelliptic and\nnon-hyperelliptic curves whose Jacobian has complex multiplication by\n$\\mathbb{Z}_K$. More precisely, we find all sextic CM fields $K$ in the LMFDB\nfor which (heuristically) Jacobians of both types with CM by $\\mathbb{Z}_K$\nexist. There turn out to be 14 such fields among the 547,156 sextic CM fields\nthat the LMFDB contains. We determine invariants of the corresponding curves,\nand in the simplest case we also give an explicit defining equation.\n"}
+{"abstract": "  Weakly supervised semantic segmentation produces a pixel-level localization\nfrom a classifier, but it is likely to restrict its focus to a small\ndiscriminative region of the target object. AdvCAM is an attribution map of an\nimage that is manipulated to increase the classification score. This\nmanipulation is realized in an anti-adversarial manner, which perturbs the\nimages along pixel gradients in the opposite direction from those used in an\nadversarial attack. It forces regions initially considered not to be\ndiscriminative to become involved in subsequent classifications, and produces\nattribution maps that successively identify more regions of the target object.\nIn addition, we introduce a new regularization procedure that inhibits the\nincorrect attribution of regions unrelated to the target object and limits the\nattributions of the regions that already have high scores. On PASCAL VOC 2012\ntest images, we achieve mIoUs of 68.0 and 76.9 for weakly and semi-supervised\nsemantic segmentation respectively, which represent a new state-of-the-art.\n"}
+{"abstract": "  In recent years the interest in segmentation has been growing, being used in\na wide range of applications such as fraud detection, anomaly detection in\npublic health and intrusion detection. We present an ablation study of\nFgSegNet_v2, analysing its three stages: (i) Encoder, (ii) Feature Pooling\nModule and (iii) Decoder. The result of this study is a proposal of a variation\nof the aforementioned method that surpasses state of the art results. Three\ndatasets are used for testing: CDNet2014, SBI2015 and CityScapes. In CDNet2014\nwe got an overall improvement compared to the state of the art, mainly in the\nLowFrameRate subset. The presented approach is promising as it produces\ncomparable results with the state of the art (SBI2015 and Cityscapes datasets)\nin very different conditions, such as different lighting conditions.\n"}
+{"abstract": "  Despite the recent successes in computer vision, there remain new avenues to\nexplore. In this work, we propose a new dataset to investigate the effect of\nself-occlusion on deep neural networks. With TEOS (The Effect of\nSelf-Occlusion), we propose a 3D blocks world dataset that focuses on the\ngeometric shape of 3D objects and their omnipresent challenge of\nself-occlusion. We designed TEOS to investigate the role of self-occlusion in\nthe context of object classification. Even though remarkable progress has been\nseen in object classification, self-occlusion is a challenge. In the\nreal-world, self-occlusion of 3D objects still presents significant challenges\nfor deep learning approaches. However, humans deal with this by deploying\ncomplex strategies, for instance, by changing the viewpoint or manipulating the\nscene to gather necessary information. With TEOS, we present a dataset of two\ndifficulty levels (L1 and L2 ), containing 36 and 12 objects, respectively. We\nprovide 738 uniformly sampled views of each object, their mask, object and\ncamera position, orientation, amount of self-occlusion, as well as the CAD\nmodel of each object. We present baseline evaluations with five well-known\nclassification deep neural networks and show that TEOS poses a significant\nchallenge for all of them. The dataset, as well as the pre-trained models, are\nmade publicly available for the scientific community under\nhttps://nvision2.data.eecs.yorku.ca/TEOS.\n"}
+{"abstract": "  Mutual interplay between the electronic degrees of freedom in solids, such as\ncharge, spin, orbital, sublattice, and bond degrees of freedom, is a source of\ncross-correlated phenomena with unconventional electronic ordered states. Such\ndegrees of freedom can be described by four types of multipoles (electric,\nmagnetic, magnetic toroidal, and electric toroidal) in a unified way, which\nenable us to tightly connect the microscopic degrees of freedom with\nmacroscopic physical responses in a transparent manner. We complete a\nclassification of the multipoles in all 122 magnetic point groups based on the\ngroup theory. The classification is useful to identify potentially active\nmultipoles not only in ordinary ferromagnetic and antiferromagnetic orderings\nbut also in exotic orderings breaking time-reversal symmetry, e.g., a\nloop-current state. Moreover, the classification gives an insight into the\nmicroscopic origin of the cross-correlated responses and quantum transports. By\nanalyzing response functions up to the second order, we summarize the\nindispensable multipole moments for various responses, such as the linear\nmagnetoelectric, piezoelectric, and elastic responses, and the nonlinear\nconductivity and Nernst coefficient. Our results highly promote a further\ndiscovery of functional multiferroic materials, guided by the bottom-up\nmaterial design based on the symmetry-adapted multipoles.\n"}
+{"abstract": "  We develop a class of algebraic interpretations for many-sorted and\nhigher-order term rewriting systems that takes type information into account.\nSpecifically, base-type terms are mapped to \\emph{tuples} of natural numbers\nand higher-order terms to functions between those tuples. Tuples may carry\ninformation relevant to the type; for instance, a term of type $\\mathsf{nat}$\nmay be associated to a pair $(\\mathsf{cost}, \\mathsf{size})$ representing its\nevaluation cost and size. This class of interpretations results in a more\nfine-grained notion of complexity than runtime or derivational complexity,\nwhich makes it particularly useful to obtain complexity bounds for higher-order\nrewriting systems. We show that rewriting systems compatible with tuple\ninterpretations admit finite bounds on derivation height. Furthermore, we\ndemonstrate how to mechanically construct tuple interpretations and how to\norient $\\beta$ and $\\eta$ reductions within our technique. Finally, we relate\nour method to runtime complexity and prove that specific interpretation shapes\nimply certain runtime complexity bounds.\n"}
+{"abstract": "  This paper explores the extent to which regular expressions (regexes) are\nportable across programming languages. Many languages offer similar regex\nsyntaxes, and it would be natural to assume that regexes can be ported across\nlanguage boundaries. But can regexes be copy/pasted across language boundaries\nwhile retaining their semantic and performance characteristics?\n  In our survey of 158 professional software developers, most indicated that\nthey re-use regexes across language boundaries and about half reported that\nthey believe regexes are a universal language. We experimentally evaluated the\nriskiness of this practice using a novel regex corpus -- 537,806 regexes from\n193,524 projects written in JavaScript, Java, PHP, Python, Ruby, Go, Perl, and\nRust. Using our polyglot regex corpus, we explored the hitherto-unstudied regex\nportability problems: logic errors due to semantic differences, and security\nvulnerabilities due to performance differences.\n  We report that developers' belief in a regex lingua franca is understandable\nbut unfounded. Though most regexes compile across language boundaries, 15%\nexhibit semantic differences across languages and 10% exhibit performance\ndifferences across languages. We explained these differences using regex\ndocumentation, and further illuminate our findings by investigating regex\nengine implementations. Along the way we found bugs in the regex engines of\nJavaScript-V8, Python, Ruby, and Rust, and potential semantic and performance\nregex bugs in thousands of modules.\n"}
+{"abstract": "  Quantum illumination (QI) theoretically promises up to a 6dB error-exponent\nadvantage in target detection over the best classical protocol. The advantage\nis maximised by a regime which includes a very high background, which occurs\nnaturally when one considers microwave operation. Such a regime has well-known\npractical limitations, though it is clear that, theoretically, knowledge of the\nassociated classical benchmark in the microwave is lacking. The requirement of\namplifiers for signal detection necessarily renders the optimal classical\nprotocol here different to that which is traditionally used, and only\napplicable in the optical domain. In this work we outline what is the true\nclassical benchmark for microwave QI using coherent states, providing new\nbounds on the error probability and closed formulae for the receiver operating\ncharacteristic (ROC), for both optimal (based on quantum relative entropy) and\nhomodyne detection schemes. We also propose an alternative source generation\nprocedure based on coherent states which demonstrates potential to reach\nclassically optimal performances achievable in optical applications. We provide\nthe same bounds and measures for the performance of such a source and discuss\nits potential utility in the future of room temperature quantum detection\nschemes in the microwave.\n"}
+{"abstract": "  Modulating image restoration level aims to generate a restored image by\naltering a factor that represents the restoration strength. Previous works\nmainly focused on optimizing the mean squared reconstruction error, which\nbrings high reconstruction accuracy but lacks finer texture details. This paper\npresents a Controllable Unet Generative Adversarial Network (CUGAN) to generate\nhigh-frequency textures in the modulation tasks. CUGAN consists of two modules\n-- base networks and condition networks. The base networks comprise a generator\nand a discriminator. In the generator, we realize the interactive control of\nrestoration levels by tuning the weights of different features from different\nscales in the Unet architecture. Moreover, we adaptively modulate the\nintermediate features in the discriminator according to the severity of\ndegradations. The condition networks accept the condition vector (encoded\ndegradation information) as input, then generate modulation parameters for both\nthe generator and the discriminator. During testing, users can control the\noutput effects by tweaking the condition vector. We also provide a smooth\ntransition between GAN and MSE effects by a simple transition method. Extensive\nexperiments demonstrate that the proposed CUGAN achieves excellent performance\non image restoration modulation tasks.\n"}
+{"abstract": "  Localized patterns in singularly perturbed reaction-diffusion equations\ntypically consist of slow parts -- in which the associated solution follows an\norbit on a slow manifold in a reduced spatial dynamical system -- alternated by\nfast excursions -- in which the solution jumps from one slow manifold to\nanother, or back to the original slow manifold. In this paper we consider the\nexistence and stability of stationary and traveling slow localized patterns\nthat do not exhibit such jumps, i.e. that are completely embedded in a slow\nmanifold of the singularly perturbed spatial dynamical system. These patterns\nhave rarely been considered in the literature, for two reasons: (i) in the\nclassical Gray-Scott/Gierer-Meinhardt type models that dominate the literature,\nthe flow on the slow manifold is typically linear and thus cannot exhibit\nhomoclinic pulse or heteroclinic front solutions; (ii) the slow manifolds\noccurring in the literature are typically `vertical' -- i.e. given by $u \\equiv\nu_0$, where $u$ is the fast variable -- so that the stability problem is\ndetermined by a simple (decoupled) scalar equation. The present research\nconcerns a general system of singularly perturbed reaction-diffusion equations\nand is motivated by several explicit ecosystem models that do give rise to\nnon-vertical normally hyperbolic slow manifolds on which the flow may exhibit\nboth homoclinic and heteroclinic orbits -- that correspond to either stationary\nor traveling localized patterns. The associated spectral stability problems are\nat leading order given by a nonlinear, but scalar, eigenvalue problem with\nSturm-Liouville characteristics and we establish that homoclinic pulse patterns\nare typically unstable, while heteroclinic fronts can either be stable or\nunstable. However, we also show that homoclinic pulse patterns that are\nasymptotically close to a heteroclinic cycle may be stable.\n"}
+{"abstract": "  The Maximum Mean Discrepancy (MMD) has been the state-of-the-art\nnonparametric test for tackling the two-sample problem. Its statistic is given\nby the difference in expectations of the witness function, a real-valued\nfunction defined as a weighted sum of kernel evaluations on a set of basis\npoints. Typically the kernel is optimized on a training set, and hypothesis\ntesting is performed on a separate test set to avoid overfitting (i.e., control\ntype-I error). That is, the test set is used to simultaneously estimate the\nexpectations and define the basis points, while the training set only serves to\nselect the kernel and is discarded. In this work, we argue that this data\nsplitting scheme is overly conservative, and propose to use the training data\nto also define the weights and the basis points for better data efficiency. We\nshow that 1) the new test is consistent and has a well-controlled type-I error;\n2) the optimal witness function is given by a precision-weighted mean in the\nreproducing kernel Hilbert space associated with the kernel, and is closely\nrelated to kernel Fisher discriminant analysis; and 3) the test power of the\nproposed test is comparable or exceeds that of the MMD and other modern tests,\nas verified empirically on challenging synthetic and real problems (e.g., Higgs\ndata).\n"}
+{"abstract": "  Recent empirical work shows that inconsistent results based on choice of\nhyperparameter optimization (HPO) configuration are a widespread problem in ML\nresearch. When comparing two algorithms J and K searching one subspace can\nyield the conclusion that J outperforms K, whereas searching another can entail\nthe opposite. In short, the way we choose hyperparameters can deceive us. We\nprovide a theoretical complement to this prior work, arguing that, to avoid\nsuch deception, the process of drawing conclusions from HPO should be made more\nrigorous. We call this process epistemic hyperparameter optimization (EHPO),\nand put forth a logical framework to capture its semantics and how it can lead\nto inconsistent conclusions about performance. Our framework enables us to\nprove EHPO methods that are guaranteed to be defended against deception, given\nbounded compute time budget t. We demonstrate our framework's utility by\nproving and empirically validating a defended variant of random search.\n"}
+{"abstract": "  Unsupervised domain adaptive object detection aims to adapt detectors from a\nlabelled source domain to an unlabelled target domain. Most existing works take\na two-stage strategy that first generates region proposals and then detects\nobjects of interest, where adversarial learning is widely adopted to mitigate\nthe inter-domain discrepancy in both stages. However, adversarial learning may\nimpair the alignment of well-aligned samples as it merely aligns the global\ndistributions across domains. To address this issue, we design an\nuncertainty-aware domain adaptation network (UaDAN) that introduces conditional\nadversarial learning to align well-aligned and poorly-aligned samples\nseparately in different manners. Specifically, we design an uncertainty metric\nthat assesses the alignment of each sample and adjusts the strength of\nadversarial learning for well-aligned and poorly-aligned samples adaptively. In\naddition, we exploit the uncertainty metric to achieve curriculum learning that\nfirst performs easier image-level alignment and then more difficult\ninstance-level alignment progressively. Extensive experiments over four\nchallenging domain adaptive object detection datasets show that UaDAN achieves\nsuperior performance as compared with state-of-the-art methods.\n"}
+{"abstract": "  This review is dedicated to two-dimensional sigma models with flag manifold\ntarget spaces, which are generalizations of the familiar $CP^{n-1}$ and\nGrassmannian models. They naturally arise in the description of continuum\nlimits of spin chains, and their phase structure is sensitive to the values of\nthe topological angles, which are determined by the representations of spins in\nthe chain. Gapless phases can in certain cases be explained by the presence of\ndiscrete 't Hooft anomalies in the continuum theory. We also discuss integrable\nflag manifold sigma models, which provide a generalization of the theory of\nintegrable models with symmetric target spaces. These models, as well as their\ndeformations, have an alternative equivalent formulation as bosonic Gross-Neveu\nmodels, which proves useful for demonstrating that the deformed geometries are\nsolutions of the renormalization group (Ricci flow) equations, as well as for\nthe analysis of anomalies and for describing potential couplings to fermions.\n"}
+{"abstract": "  Nanomechanical circuits for transverse acoustic waves promise to enable new\napproaches to computing, precision biochemical sensing and many other\napplications. However, progress is hampered by the lack of precise control of\nthe coupling between nanomechanical elements. Here, we demonstrate\nvirtual-phonon coupling between transverse mechanical elements, exploiting\ntunnelling through a zero-mode acoustic barrier. This allows the construction\nof large-scale nanomechanical circuits on a silicon chip, for which we develop\na new scalable fabrication technique. As example applications, we build\nmode-selective acoustic mirrors with controllable reflectivity and demonstrate\nacoustic spatial mode filtering. Our work paves the way towards applications\nsuch as fully nanomechanical computer processors and distributed nanomechanical\nsensors, and to explore the rich landscape of nonlinear nanomechanical\ndynamics.\n"}
+{"abstract": "  In this paper, we analyze the feasibility of applying few-shot learning to\nspeech emotion recognition task (SER). The current speech emotion recognition\nmodels work exceptionally well but fail when then input is multilingual.\nMoreover, when training such models, the models' performance is suitable only\nwhen the training corpus is vast. This availability of a big training corpus is\na significant problem when choosing a language that is not much popular or\nobscure. We attempt to solve this challenge of multilingualism and lack of\navailable data by turning this problem into a few-shot learning problem. We\nsuggest relaxing the assumption that all N classes in an N-way K-shot problem\nbe new and define an N+F way problem where N and F are the number of emotion\nclasses and predefined fixed classes, respectively. We propose this\nmodification to the Model-Agnostic MetaLearning (MAML) algorithm to solve the\nproblem and call this new model F-MAML. This modification performs better than\nthe original MAML and outperforms on EmoFilm dataset.\n"}
+{"abstract": "  Recently, there has been a significant growth of interest in applying\nsoftware engineering techniques for the quality assurance of deep learning (DL)\nsystems. One popular direction is deep learning testing, where adversarial\nexamples (a.k.a.~bugs) of DL systems are found either by fuzzing or guided\nsearch with the help of certain testing metrics. However, recent studies have\nrevealed that the commonly used neuron coverage metrics by existing DL testing\napproaches are not correlated to model robustness. It is also not an effective\nmeasurement on the confidence of the model robustness after testing. In this\nwork, we address this gap by proposing a novel testing framework called\nRobustness-Oriented Testing (RobOT). A key part of RobOT is a quantitative\nmeasurement on 1) the value of each test case in improving model robustness\n(often via retraining), and 2) the convergence quality of the model robustness\nimprovement. RobOT utilizes the proposed metric to automatically generate test\ncases valuable for improving model robustness. The proposed metric is also a\nstrong indicator on how well robustness improvement has converged through\ntesting. Experiments on multiple benchmark datasets confirm the effectiveness\nand efficiency of RobOT in improving DL model robustness, with 67.02% increase\non the adversarial robustness that is 50.65% higher than the state-of-the-art\nwork DeepGini.\n"}
+{"abstract": "  Let $X$ be a compact Riemann surface of genus $g \\geq 3$. We consider the\nmoduli space of holomorphic connections over $X$ and the moduli space of\nlogarithmic connections singular over a finite subset of $X$ with fixed\nresidues. We determine the Chow group of these moduli spaces. We compute the\nglobal sections of the sheaves of differential operators on ample line bundles\nand their symmetric powers over these moduli spaces, and show that they are\nconstant under certain condition. We show the Torelli type theorem for the\nmoduli space of logarithmic connections. We also describe the rational\nconnectedness of these moduli spaces.\n"}
+{"abstract": "  We develop a new approach to $L^{\\infty}$-a priori estimates for degenerate\ncomplex Monge-Amp\\`ere equations on complex manifolds. It only relies on\ncompactness and envelopes properties of quasi-plurisubharmonic functions. Our\nmethod allows one to obtain new and efficient proofs of several fundamental\nresults in K\\\"ahler geometry as we explain in this article. In a sequel we\nshall explain how this approach also applies to the hermitian setting producing\nnew relative a priori bounds, as well as existence results.\n"}
+{"abstract": "  Convolutional Neural Networks (CNNs) use pooling to decrease the size of\nactivation maps. This process is crucial to increase the receptive fields and\nto reduce computational requirements of subsequent convolutions. An important\nfeature of the pooling operation is the minimization of information loss, with\nrespect to the initial activation maps, without a significant impact on the\ncomputation and memory overhead. To meet these requirements, we propose\nSoftPool: a fast and efficient method for exponentially weighted activation\ndownsampling. Through experiments across a range of architectures and pooling\nmethods, we demonstrate that SoftPool can retain more information in the\nreduced activation maps. This refined downsampling leads to improvements in a\nCNN's classification accuracy. Experiments with pooling layer substitutions on\nImageNet1K show an increase in accuracy over both original architectures and\nother pooling methods. We also test SoftPool on video datasets for action\nrecognition. Again, through the direct replacement of pooling layers, we\nobserve consistent performance improvements while computational loads and\nmemory requirements remain limited.\n"}
+{"abstract": "  We investigate coarse-grained models of suspended self-thermophoretic\nmicroswimmers. Upon heating, the Janus spheres, with hemispheres made of\ndifferent materials, induce a heterogeneous local solvent temperature that\ncauses the self-phoretic particle propulsion.\n  Starting from atomistic molecular dynamics simulations, we verify the\ncoarse-grained description of the fluid in terms of a local molecular\ntemperature field, and its role for the particle's thermophoretic\nself-propulsion and hot Brownian motion.\n  The latter is governed by effective nonequilibrium temperatures, which are\nmeasured from simulations by confining the particle position and orientation.\nThey are theoretically shown to remain relevant for any further spatial\ncoarse-graining towards a hydrodynamic description of the entire suspension as\na homogeneous complex fluid.\n"}
+{"abstract": "  We use the Beyond Ultra-deep Frontier Fields and Legacy Observations\n(BUFFALO) strong lensing image catalog of the merging galaxy cluster Abell 370\nto obtain a mass model using the free-form lens inversion algorithm GRALE. The\nimprovement of the strong lensing data quality results in a lens plane rms of\nonly 0.45 arcsec, about a factor of two lower than that of our existing HFF v4\nreconstruction. We attribute the improvement to spectroscopic data and use of\nthe full reprocessed HST mosaics. In our reconstructed mass model, we found\nindications of three distinct mass features in Abell 370: (i) a $\\sim 35$ kpc\noffset between the northern BCG and the nearest mass peak, (ii) a $\\sim 100$\nkpc mass concentration of roughly critical density $\\sim 250$ kpc east of the\nmain cluster, and (iii) a probable filament-like structure passing N-S through\nthe cluster. While (i) is present in some form in most publicly available\nreconstructions spanning the range of modeling techniques: parametric, hybrid,\nand free-form, (ii) and (iii) are recovered by only about half of the\nreconstructions. We tested our hypothesis on the presence of the filament-like\nstructure by creating a synthetic cluster - Irtysh IIIc - mocking the situation\nof a cluster with external mass. We also computed the source plane\nmagnification distributions. Using them we estimated the probabilities of\nmagnifications in the source plane, and scrutinized their redshift dependence.\nFinally, we explored the lensing effects of Abell 370 on the luminosity\nfunctions of sources at $z_s=9.0$, finding it consistent with published\nresults.\n"}
+{"abstract": "  We report the instrumentation of a new positron diffractometer that is\nconnected to the high-intensity positron source NEPOMUC. Crucial elements for\nthe adaption of the positron beam are presented, which include the magnetic\nfield termination, the optional transmission-type remoderator for brightness\nenhancement and the electrostatic system for acceleration and beam optics. The\npositron trajectories of the remoderated and the twofold remoderated beam have\nbeen simulated to optimize the system, i.e. to obtain a coherent beam of small\ndiameter. Within a first beamtime we tuned the system and characterized the\ndirect beam. For the twofold remoderated beam of 10keV energy, we\nexperimentally observe a beam diameter of d < 1.3mm, which agrees well with the\nsimulation.\n"}
+{"abstract": "  Reducing cycle time is a recurrent concern in the field of business process\nmanagement. Depending on the process, various interventions may be triggered to\nreduce the cycle time of a case, for example, using a faster shipping service\nin an order-to-delivery process or giving a phone call to a customer to obtain\nmissing information rather than waiting passively. Each of these interventions\ncomes with a cost. This paper tackles the problem of determining if and when to\ntrigger a time-reducing intervention in a way that maximizes the total net\ngain. The paper proposes a prescriptive process monitoring method that uses\northogonal random forest models to estimate the causal effect of triggering a\ntime-reducing intervention for each ongoing case of a process. Based on this\ncausal effect estimate, the method triggers interventions according to a\nuser-defined policy. The method is evaluated on two real-life logs.\n"}
+{"abstract": "  In recent years, Hyperspectral Imaging (HSI) has become a powerful source for\nreliable data in applications such as remote sensing, agriculture, and\nbiomedicine. However, hyperspectral images are highly data-dense and often\nbenefit from methods to reduce the number of spectral bands while retaining the\nmost useful information for a specific application. We propose a novel band\nselection method to select a reduced set of wavelengths, obtained from an HSI\nsystem in the context of image classification. Our approach consists of two\nmain steps: the first utilizes a filter-based approach to find relevant\nspectral bands based on a collinearity analysis between a band and its\nneighbors. This analysis helps to remove redundant bands and dramatically\nreduces the search space. The second step applies a wrapper-based approach to\nselect bands from the reduced set based on their information entropy values,\nand trains a compact Convolutional Neural Network (CNN) to evaluate the\nperformance of the current selection. We present classification results\nobtained from our method and compare them to other feature selection methods on\ntwo hyperspectral image datasets. Additionally, we use the original\nhyperspectral data cube to simulate the process of using actual filters in a\nmultispectral imager. We show that our method produces more suitable results\nfor a multispectral sensor design.\n"}
+{"abstract": "  In this paper, we present a distributed algorithm for solving convex,\nconstraint-coupled, optimization problems over peer-to-peer networks. We\nconsider a network of processors that aim to cooperatively minimize the sum of\nlocal cost functions, subject to individual constraints and to global coupling\nconstraints. The major assumption of this work is that the cost functions are\nunknown and must be learned online. We propose a fully distributed algorithm,\nbased on a primal decomposition approach, that uses iteratively refined\ndata-driven estimations of the cost functions over the iterations. The\nalgorithm is scalable and maintains private information of agents. We prove\nthat, asymptotically, the distributed algorithm provides the optimal solution\nof the problem even though the true cost functions are never used within the\nalgorithm. The analysis requires an in-depth exploration of the primal\ndecomposition approach and shows that the distributed algorithm can be thought\nof as an epsilon-subgradient method applied to a suitable reformulation of the\noriginal problem. Finally, numerical computations cor- roborate the theoretical\nfindings and show the efficacy of the proposed approach.\n"}
+{"abstract": "  Variational quantum algorithms (VQAs) are proposed to solve relevant\ncomputational problems on near term quantum devices. Popular versions are\nvariational quantum eigensolvers (VQEs) and quantum approximate optimization\nalgorithms (QAOAs) that solve ground state problems from quantum chemistry and\nbinary optimization problems, respectively. They are based on the idea to use a\nclassical computer to train a parameterized quantum circuit. We show that the\ncorresponding classical optimization problems are NP-hard. Moreover, the\nhardness is robust in the sense that for every polynomial time algorithm, there\nexists instances for which the relative error resulting from the classical\noptimization problem can be arbitrarily large, unless P = NP. Even for\nclassically tractable systems, composed of only logarithmically many qubits or\nfree fermions, we show that the optimization is NP-hard. This elucidates that\nthe classical optimization is intrinsically hard and does not merely inherit\nthe hardness from the ground state problem. Our analysis shows that the\ntraining landscape can have many far from optimal persistent local minima. This\nmeans gradient and higher order decent algorithms will generally converge to\nfar from optimal solutions.\n"}
+{"abstract": "  Video action recognition (VAR) is a primary task of video understanding, and\nuntrimmed videos are more common in real-life scenes. Untrimmed videos have\nredundant and diverse clips containing contextual information, so sampling\ndense clips is essential. Recently, some works attempt to train a generic model\nto select the N most representative clips. However, it is difficult to model\nthe complex relations from intra-class clips and inter-class videos within a\nsingle model and fixed selected number, and the entanglement of multiple\nrelations is also hard to explain. Thus, instead of \"only look once\", we argue\n\"divide and conquer\" strategy will be more suitable in untrimmed VAR. Inspired\nby the speed reading mechanism, we propose a simple yet effective clip-level\nsolution based on skim-scan techniques. Specifically, the proposed Skim-Scan\nframework first skims the entire video and drops those uninformative and\nmisleading clips. For the remaining clips, it scans clips with diverse features\ngradually to drop redundant clips but cover essential content. The above\nstrategies can adaptively select the necessary clips according to the\ndifficulty of the different videos. To trade off the computational complexity\nand performance, we observe the similar statistical expression between\nlightweight and heavy networks, thus it supports us to explore the combination\nof them. Comprehensive experiments are performed on ActivityNet and mini-FCVID\ndatasets, and results demonstrate that our solution surpasses the\nstate-of-the-art performance in terms of both accuracy and efficiency.\n"}
+{"abstract": "  Superradiant scattering of linear spin $s=0,\\pm 1,\\pm 2$ fields on Kerr black\nhole background is investigated in the time domain by integrating numerically\nthe homogeneous Teukolsky master equation. The applied numerical setup has\nalready been used in studying long time evolution and tail behavior of\nelectromagnetic and metric perturbations on rotating black hole background\n[arXiv:1905.09082v3]. To have a clear setup the initial data is chosen to be of\nthe compact support, while to optimize superradiance the frequency of the\ninitial data is fine tuned. Our most important finding is that the rate of\nsuperradiance strongly depends on the relative position of the (compact)\nsupport of the initial data and the ergoregion. When they are well-separated\nthen only a modest -- in case of $s=0$ scalar fields negligible --\nsuperradiance occurs, whereas it can get to be amplified significantly whenever\nthe support of the initial data and the ergoregion overlap.\n"}
+{"abstract": "  This paper considers how the motions of a telepresence robot moving\nautonomously affect a person immersed in the robot through a head-mounted\ndisplay. In particular, we explore the preference, comfort, and naturalness of\nelements of piecewise linear paths compared to the same elements on a smooth\npath. In a user study, thirty-six subjects watched panoramic videos of three\ndifferent paths through a simulated museum in virtual reality and responded to\nquestionnaires regarding each path. Preference for a particular path was\ninfluenced the most by comfort, forward speed, and characteristics of the\nturns. Preference was also strongly associated with the users' perceived\nnaturalness, which was primarily determined by the ability to see salient\nobjects, the distance to the walls and objects, as well as the turns.\nParticipants favored the paths that had a one meter per second forward speed\nand rated the path with the least amount of turns as the most comfortable\n"}
+{"abstract": "  This paper proposes a system for automatic surface volume monitoring from\ntime series of SkySat pushframe imagery. A specific challenge of building and\ncomparing large 3D models from SkySat data is to correct inconsistencies\nbetween the camera models associated to the multiple views that are necessary\nto cover the area at a given time, where these camera models are represented as\nRational Polynomial Cameras (RPCs). We address the problem by proposing a\ndate-wise RPC refinement, able to handle dynamic areas covered by sets of\npartially overlapping views. The cameras are refined by means of a rotation\nthat compensates for errors due to inaccurate knowledge of the satellite\nattitude. The refined RPCs are then used to reconstruct multiple consistent\nDigital Surface Models (DSMs) from different stereo pairs at each date. RPC\nrefinement strengthens the consistency between the DSMs of each date, which is\nextremely beneficial to accurately measure volumes in the 3D surface models.\nThe system is tested in a real case scenario, to monitor large coal stockpiles.\nOur volume estimates are validated with measurements collected on site in the\nsame period of time.\n"}
+{"abstract": "  The authors have introduced a novel method for unsupervised anomaly detection\nthat utilises a newly introduced Memory Module in their paper. We validate the\nauthors claim that this helps improve performance by helping the network learn\nprototypical patterns, and uses the learnt memory to reduce the representation\ncapacity of Convolutional Neural Networks. Further, we validate the efficacy of\ntwo losses introduced by the authors, Separateness Loss and Compactness Loss\npresented to increase the discriminative power of the memory items and the\ndeeply learned features. We test the efficacy with the help of t-SNE plots of\nthe memory items.\n"}
+{"abstract": "  The method of using Hopf algebras for calculating Feynman integrals developed\nby Abreu et al. is applied to the two-loop non-planar on-shell diagram with\nmassless propagators and three external mass scales. We show that the existence\nof the method of cut Feynman diagrams comprising of the coproduct, the first\nentry condition and integrability condition that was found to be true for the\nplanar case also holds for the non-planar case; furthermore, the non-planar\nsymbol alphabet is the same as for the planar case. This is one of the main\nresults of this work, and they have been obtained by a systematic analysis of\nthe relevant cuts, using the symbolic manipulation codes HypExp and\nPolyLogTools. The obtained result for the symbol is cross-checked by an\nanalysis of the known two-loop original Feynman integral result. In addition,\nwe also reconstruct the full result from the symbol. This is the other main\nresult in this paper.\n"}
+{"abstract": "  Internet banking (IB) continues to face security concerns arising from\nillegal access to users accounts. Use of personal identification numbers (PIN)\nas a single authentication method for IB users is prone to insecurities such as\nphishing, hacking and shoulder surfing. Fingerprint matching (FPM) as an\nalternative to PIN equally has a downside as fingerprints reside on individual\nmobile devices. A survey we conducted from 170 IB respondents of 5 different\nbanks in Brunei established that majority (65%) of them preferred use of\nbiometric authentication methods. In this work, we propose a two-level\nintegrated authentication mechanism (2L-IAM). At the first level, the user logs\nin to their IB portal using either PIN or FPM. At the second level, user is\nauthenticated by means of face recognition (FR) should they initiate a\ntransaction classified as sensitive. The merits of the introduced 2L-IAM are\n3-fold: - (1) FR guarantees the identity of the rightful user irrespective of\nthe login device; (2) By classifying banking products sensitivity, the\nsensitive transactions are more effectively secured; (3) It is accommodative of\ndifferent users authentication preferences. Adoption of this framework could\nthus improve both users and banks experiences in terms of enhanced security and\nservice delivery respectively.\n"}
+{"abstract": "  Natural convection in porous media is a fundamental process for the long-term\nstorage of CO2 in deep saline aquifers. Typically, details of mass transfer in\nporous media are inferred from the numerical solution of the volume-averaged\nDarcy-Oberbeck-Boussinesq (DOB) equations, even though these equations do not\naccount for the microscopic properties of a porous medium. According to the DOB\nequations, natural convection in a porous medium is uniquely determined by the\nRayleigh number. However, in contrast with experiments, DOB simulations yield a\nlinear scaling of the Sherwood number with the Rayleigh number (Ra) for high\nvalues of Ra (Ra>>1,300). Here, we perform Direct Numerical Simulations (DNS),\nfully resolving the flow field within the pores. We show that the boundary\nlayer thickness is determined by the pore size instead of the Rayleigh number,\nas previously assumed. The mega- and proto- plume sizes increase with the pore\nsize. Our DNS results exhibit a nonlinear scaling of the Sherwood number at\nhigh porosity, and for the same Rayleigh number, higher Sherwood numbers are\npredicted by DNS at lower porosities. It can be concluded that the scaling of\nthe Sherwood number depends on the porosity and the pore-scale parameters,\nwhich is consistent with experimental studies.\n"}
+{"abstract": "  This article investigates a novel electrophoretic molecular communication\nmechanism that utilizes a time-varying electric field, which induces\ntime-varying molecule velocities and in turn improves communication\nperformance. For a sinusoidal field, we specify favorable signal parameters\n(e.g., phase and frequency) that yield excellent communication link\nperformance. We also analytically derive an optimized field function by\nformulating an appropriate cost function and solving the Euler-Lagrange\nequation. In our setup, the field strength is proportional to the molecular\nvelocity; we verify this assumption by solving the Basset-Boussinesq-Oseen\nequation for a given time-varying electric field (forcing function) and\nexamining its implications for practical physical parameterizations of the\nsystem. Our analysis and Monte-Carlo simulation results demonstrate that the\nproposed time-varying approach can significantly increase the number of\ninformation-carrying molecules expected to be observed at the receiver and\nreduce the bit-error probability compared to the constant field benchmark.\n"}
+{"abstract": "  Consider a random vector $\\mathbf{y}=\\mathbf{\\Sigma}^{1/2}\\mathbf{x}$, where\nthe $p$ elements of the vector $\\mathbf{x}$ are i.i.d. real-valued random\nvariables with zero mean and finite fourth moment, and $\\mathbf{\\Sigma}^{1/2}$\nis a deterministic $p\\times p$ matrix such that the spectral norm of the\npopulation correlation matrix $\\mathbf{R}$ of $\\mathbf{y}$ is uniformly\nbounded. In this paper, we find that the log determinant of the sample\ncorrelation matrix $\\hat{\\mathbf{R}}$ based on a sample of size $n$ from the\ndistribution of $\\mathbf{y}$ satisfies a CLT (central limit theorem) for\n$p/n\\to \\gamma\\in (0, 1]$ and $p\\leq n$. Explicit formulas for the asymptotic\nmean and variance are provided. In case the mean of $\\mathbf{y}$ is unknown, we\nshow that after recentering by the empirical mean the obtained CLT holds with a\nshift in the asymptotic mean. This result is of independent interest in both\nlarge dimensional random matrix theory and high-dimensional statistical\nliterature of large sample correlation matrices for non-normal data. At last,\nthe obtained findings are applied for testing of uncorrelatedness of $p$ random\nvariables. Surprisingly, in the null case $\\mathbf{R}=\\mathbf{I}$, the test\nstatistic becomes completely pivotal and the extensive simulations show that\nthe obtained CLT also holds if the moments of order four do not exist at all,\nwhich conjectures a promising and robust test statistic for heavy-tailed\nhigh-dimensional data.\n"}
+{"abstract": "  We investigate the non-equilibrium electronic structure and characteristic\ntime scales in a candidate excitonic insulator, Ta$_2$NiSe$_5$, using time- and\nangle-resolved photoemission spectroscopy with a temporal resolution of 50 fs.\nFollowing a strong photoexcitation, the band gap closes transiently within 100\nfs, i.e., on a time scale faster than the typical lattice vibrational period.\nFurthermore, we find that the characteristic time associated with the rise of\nthe photoemission intensity above the Fermi energy decreases with increasing\nexcitation strength, while the relaxation time of the electron population\ntowards equilibrium shows an opposite behaviour. We argue that these\nexperimental observations can be consistently explained by an excitonic origin\nof the band gap in the material. The excitonic picture is supported by\nmicroscopic calculations based on the non-equilibrium Green's function\nformalism for an interacting two-band system. We interpret the speedup of the\nrise time with fluence in terms of an enhanced scattering probability between\nphoto-excited electrons and excitons, leading to an initially faster decay of\nthe order parameter. We show that the inclusion of electron-phonon coupling at\na semi-classical level changes only the quantitative aspects of the proposed\ndynamics, while the qualitative features remain the same. The experimental\nobservations and microscopic calculations allow us to develop a simple and\nintuitive phenomenological model that captures the main dynamics after\nphotoexcitation in Ta$_2$NiSe$_5$.\n"}
+{"abstract": "  First attempts of prediction of the facial growth (FG) direction were made\nover half of a century ago. Despite numerous attempts and elapsed time, a\nsatisfactory method has not been established yet and the problem still poses a\nchallenge for medical experts. To our knowledge, this paper is the first\nMachine Learning approach to the prediction of FG direction. Conducted data\nanalysis reveals the inherent complexity of the problem and explains the\nreasons of difficulty in FG direction prediction based on 2D X-ray images. To\nperform growth forecasting, we employ a wide range of algorithms, from logistic\nregression, through tree ensembles to neural networks and consider three,\nslightly different, problem formulations. The resulting classification accuracy\nvaries between 71% and 75%.\n"}
+{"abstract": "  In this paper, we study a model of directed polymers in random environment,\nwhere the environment region is a dynamical tube with its width growing\npolynomially along with time. It can be viewed as an interpolation of the\npinning model and the classic directed polymer model. We prove weak coupling\nlimits for the directed polymers on tube in all dimensions, as the inverse\ntemperature vanishes in a suitable rate. Phase transitions among disorder\nirrelevance, marginal relevance and disorder relevance are observed.\n"}
+{"abstract": "  A study of a complete cosmological model based on an asymmetric scalar\ndoublet represented by the classical and phantom scalar Higgs fields is carried\nout. At the same time, the assumption about the nonnegativity of the expansion\nrate of the Universe, which in some cases contradicts the complete system of\nEinstein's equations, was removed. A closed system of dynamic equations\ndescribing the evolution of the cosmological model is formulated, and the\ndependence of the topology of the Einstein - Higgs hypersurface of the\n5-dimensional phase space of the dynamical system that determines the global\nproperties of the cosmological model on the fundamental constants of the model\nis investigated. A qualitative analysis of the dynamical system of the\ncorresponding cosmological model is carried out, asymptotic phase trajectories\nare constructed, and the results of numerical modeling are presented,\nillustrating various types of behavior of the cosmological model.\n"}
+{"abstract": "  Compact cold-atom sensors depend on vacuum technology. One of the major\nlimitations to miniaturizing these sensors are the active pumps -- typically\nion pumps -- required to sustain the low pressure needed for laser cooling.\nAlthough passively pumped chambers have been proposed as a solution to this\nproblem, technical challenges have prevented successful operation at the levels\nneeded for cold-atom experiments. We present the first demonstration of a\nvacuum package successfully independent of ion pumps for more than a week; our\nvacuum package is capable of sustaining a cloud of cold atoms in a\nmagneto-optical trap (MOT) for greater than 200 days using only non-evaporable\ngetters and a rubidium dispenser. Measurements of the MOT lifetime indicate the\npackage maintains a pressure of better than $2\\times10^{-7}$ Torr. This result\nwill significantly impact the development of compact atomic sensors, including\nthose sensitive to magnetic fields, where the absence of an ion pump will be\nadvantageous.\n"}
+{"abstract": "  This paper seeks to provide clues as to why experimental evidence for the\nalignment of slender fibers in semi-dilute suspensions under shear flows does\nnot match theoretical predictions. This paper posits that the hydrodynamic\ninteractions between the different fibers that might be responsible for the\ndeviation from theory, can at least partially be modeled by the coupling\nbetween Jeffery's equation and Stokes' equation. It is proposed that if the\ninitial data is slightly non-uniform, in that the probability distribution of\nthe orientation has small spacial variations, then there is feedback via\nStokes' equation that causes these non-uniformities to grow significantly in\nshort amounts of time, so that the standard uncoupled Jeffery's equation\nbecomes a poor predictor when the volume ratio of fibers to fluid is not\nextremely low. This paper provides numerical evidence, involving spectral\nanalysis of the linearization of the perturbation equation, to support this\ntheory.\n  This paper differs from the published version in that it contains a\ncorrogendum at the end.\n"}
+{"abstract": "  In this paper, we propose a generalization of Continuous Logic ([BBHU08])\nwhere the distances take values in suitable co-quantales (in the way as it was\nproposed in [Fla97]). By assuming suitable conditions (e.g., being\nco-divisible, co-Girard and a V-domain), we provide, as test questions, a proof\nof a version of the Tarski-Vaught test (Proposition 3.35) and Los Theorem\n(Theorem 3.62) in our setting.\n"}
+{"abstract": "  Why is it that semidefinite relaxations have been so successful in numerous\napplications in computer vision and robotics for solving non-convex\noptimization problems involving rotations? In studying the empirical\nperformance we note that there are few failure cases reported in the\nliterature, in particular for estimation problems with a single rotation,\nmotivating us to gain further theoretical understanding.\n  A general framework based on tools from algebraic geometry is introduced for\nanalyzing the power of semidefinite relaxations of problems with quadratic\nobjective functions and rotational constraints. Applications include\nregistration, hand-eye calibration and rotation averaging. We characterize the\nextreme points, and show that there exist failure cases for which the\nrelaxation is not tight, even in the case of a single rotation. We also show\nthat some problem classes are always tight given an appropriate\nparametrization. Our theoretical findings are accompanied with numerical\nsimulations, providing further evidence and understanding of the results.\n"}
+{"abstract": "  We combine the giant equilibration speedup provided by the swap Monte Carlo\nalgorithm to extensive multi-CPU molecular dynamics simulations to explore the\nfirst ten decades of the equilibrium dynamics of supercooled liquids near the\nexperimental glass transition. We demonstrate in particular the emergence of a\npower law in relaxation spectra, in quantitative agreement with the excess\nwings observed experimentally. Detailed analysis of particle motion shows that\nthis power law directly originates from broadly distributed processes\nexperienced by rare, localised regions relaxing much before the bulk. We\nexplain why these wings appear in \"excess\" by constructing an empirical model\nassociating heterogeneous activated dynamics and dynamic facilitation, which\nappear to be the two minimal physical ingredients revealed by our simulations.\n"}
+{"abstract": "  The goal of this article is to extend a theorem of Lurie \\[\n  \\mathsf{Sh}_A (X) = \\mathsf{Fun}(\\mathsf{Exit}_A (X), \\mathsf{S}) \\]\nrepresenting constructible sheaves with values in $ \\mathsf{S} $, the $ \\infty\n$-category of spaces, on a stratified space $ X $ with poset of strata $ A $,\nas functors from the exit paths $ \\infty $-category $ \\mathsf{Exit}_A (X) $ to\n$ \\mathsf{S} $. Lurie's representation theorem works provided $ A $ satisfy the\nascending chain condition. This typically rules out infinite dimensional\nexamples of stratified space.\n  Building on it and with the help of a stratified homotopy invariance theorem\nfrom Haine, we show that when $ X $ is a nice enough $ A $-stratified space and\nwhen $ A $ is itself stratified $ A_{\\leq 0} \\subset A_{\\leq 1} \\subset \\cdots\n\\subset A $ by posets satisfying the ascending chain condition, \\[\n  \\mathsf{Hyp}_A (X) = \\mathsf{Fun}(\\mathsf{Exit}_A(X), \\mathsf{S}) \\] the $\n\\infty $-category of $ A $-constructible hypersheaves on $ X $ is represented\nby functors from the exit paths $ \\infty $-category of $ X $.\n  There are two types of nice stratified spaces on which this extended\nrepresentation theorem applies: conically stratified spaces and spaces that are\nsequential colimits of conically stratified spaces. Examples of application\ninclude the metric and the topological exponentials of a Fr\\'echet manifold,\nlocally countable simplicial complexes and more generally, locally countable\ncylindrically normal CW-complexes.\n"}
+{"abstract": "  We extend the Altmann-Hausen presentation of normal affine algebraic\nC-varieties endowed with effective torus actions to the real setting. In\nparticular, we focus on actions of quasi-split real tori, in which case we\nobtain a simpler presentation.\n"}
+{"abstract": "  We propose a novel formulation of the \"effectiveness problem\" in\ncommunications, put forth by Shannon and Weaver in their seminal work [2], by\nconsidering multiple agents communicating over a noisy channel in order to\nachieve better coordination and cooperation in a multi-agent reinforcement\nlearning (MARL) framework. Specifically, we consider a multi-agent partially\nobservable Markov decision process (MA-POMDP), in which the agents, in addition\nto interacting with the environment can also communicate with each other over a\nnoisy communication channel. The noisy communication channel is considered\nexplicitly as part of the dynamics of the environment and the message each\nagent sends is part of the action that the agent can take. As a result, the\nagents learn not only to collaborate with each other but also to communicate\n\"effectively\" over a noisy channel. This framework generalizes both the\ntraditional communication problem, where the main goal is to convey a message\nreliably over a noisy channel, and the \"learning to communicate\" framework that\nhas received recent attention in the MARL literature, where the underlying\ncommunication channels are assumed to be error-free. We show via examples that\nthe joint policy learned using the proposed framework is superior to that where\nthe communication is considered separately from the underlying MA-POMDP. This\nis a very powerful framework, which has many real world applications, from\nautonomous vehicle planning to drone swarm control, and opens up the rich\ntoolbox of deep reinforcement learning for the design of multi-user\ncommunication systems.\n"}
+{"abstract": "  Robotic manipulation of unknown objects is an important field of research.\nPractical applications occur in many real-world settings where robots need to\ninteract with an unknown environment. We tackle the problem of reactive\ngrasping by proposing a method for unknown object tracking, grasp point\nsampling and dynamic trajectory planning. Our object tracking method combines\nSiamese Networks with an Iterative Closest Point approach for pointcloud\nregistration into a method for 6-DoF unknown object tracking. The method does\nnot require further training and is robust to noise and occlusion. We propose a\nrobotic manipulation system, which is able to grasp a wide variety of formerly\nunseen objects and is robust against object perturbations and inferior grasping\npoints.\n"}
+{"abstract": "  We show that the flow on a Bers slice given by the Weil-Petersson gradient\nvector field of renormalized volume is globally attracting to its fuchsian\nbasepoint.\n"}
+{"abstract": "  There are some shreds of evidence that social opinion polarization leads to\nthe breakup of the relationship, some in the scale of small communities, but\nothers can divide large organizations or even a nation. The legacy methodology\nto answer the root cause of opinion polarization in the society commonly use\nrandom sampling or questionnaire-based approach, which considered expensive in\nterm of time and money. On the contrary, the opportunity to employ big data\napproach using social media data provides a rich source to investigate several\nquestions, such as: how social opinion polarization formed, dynamic social\nnetwork mechanisms during time-windows observation, identification of dominant\nactors and communities. The power of the big data approach lies in the number\nof data analyzed, where the more data involved in the process, the more\naccurate to describe the population condition. Today, computing power is no\nlonger become an obstacle to process large-scale data, thus the observation of\nthe social opinion polarization process is possible.\n"}
+{"abstract": "  Recent applications in machine learning have renewed the interest of the\ncommunity in min-max optimization problems. While gradient-based optimization\nmethods are widely used to solve such problems, there are however many\nscenarios where these techniques are not well-suited, or even not applicable\nwhen the gradient is not accessible. We investigate the use of direct-search\nmethods that belong to a class of derivative-free techniques that only access\nthe objective function through an oracle. In this work, we design a novel\nalgorithm in the context of min-max saddle point games where one sequentially\nupdates the min and the max player. We prove convergence of this algorithm\nunder mild assumptions, where the objective of the max-player satisfies the\nPolyak-\\L{}ojasiewicz (PL) condition, while the min-player is characterized by\na nonconvex objective. Our method only assumes dynamically adjusted accurate\nestimates of the oracle with a fixed probability. To the best of our knowledge,\nour analysis is the first one to address the convergence of a direct-search\nmethod for min-max objectives in a stochastic setting.\n"}
+{"abstract": "  Tensor networks provide an efficient approximation of operations involving\nhigh dimensional tensors and have been extensively used in modelling quantum\nmany-body systems. More recently, supervised learning has been attempted with\ntensor networks, primarily focused on tasks such as image classification. In\nthis work, we propose a novel formulation of tensor networks for supervised\nimage segmentation which allows them to operate on high resolution medical\nimages. We use the matrix product state (MPS) tensor network on non-overlapping\npatches of a given input image to predict the segmentation mask by learning a\npixel-wise linear classification rule in a high dimensional space. The proposed\nmodel is end-to-end trainable using backpropagation. It is implemented as a\nStrided Tensor Network to reduce the parameter complexity. The performance of\nthe proposed method is evaluated on two public medical imaging datasets and\ncompared to relevant baselines. The evaluation shows that the strided tensor\nnetwork yields competitive performance compared to CNN-based models while using\nfewer resources. Additionally, based on the experiments we discuss the\nfeasibility of using fully linear models for segmentation tasks.\n"}
+{"abstract": "  We propose a quantum algorithm based on ridge regression model, which get the\noptimal fitting parameters w and a regularization hyperparameter {\\alpha} by\nanalysing the training dataset. The algorithm consists of two subalgorithms.\nOne is generating predictive value for a new input, the way is to apply the\nphase estimation algorithm to the initial state |Xi and apply the controlled\nrotation to the eigenvalue register. The other is finding an optimal\nregularization hyperparameter {\\alpha} , the way is to apply the phase\nestimation algorithm to the initial state |yi and apply the controlled rotation\nto the eigenvalue register. The second subalgorithm can compute the whole\ntraining dataset in parallel that improve the efficiency. Compared with the\nclassical ridge regression algorithm, our algorithm overcome multicollinearity\nand overfitting. Moreover, it have exponentially faster. What's more, our\nalgorithm can deal with the non-sparse matrices in comparison to some existing\nquantum algorithms and have slightly speedup than the existing quantum\ncounterpart. At present, the quantum algorithm has a wide range of application\nand the proposed algorithm can be used as a subroutine of other quantum\nalgorithms.\n"}
+{"abstract": "  In his study of finite (Vassiliev's) knot invariants,Vogel introduced the\nso-called universal parameters, belonging to the projective plane, which\nparticularly parameterize the simple Lie algebras by the Vogel's table.\nSubsequently a number of quantities, such as some universal knot invariants,\n(quantum) dimensions of simple Lie algebras, etc., have been represented in\nterms of these parameters, i.e. in the universal form. We prove that at the\npoints from the Vogel's table all known universal quantum dimension formulae\nare linearly resolvable, i.e. yield finite answers even if these points are\nsingular, provided one restricts them to the appropriate lines. We show, that\nthe same phenomenon takes place for another three distinguished points in\nVogel's plane - $E_{7\\frac{1}{2}}, X_1,$ and $X_2$. We also examine the same\nformulae on linear resolvability at the remaining 48 distinguished points in\nVogel's plane, which correspond to the so-called $Y$-objects, and discover that\nthere are three points among them, which are regular for all known quantum\ndimension formulae. Two of them happen to be sharing a remarkable similarity\nwith the simple Lie algebras, namely, the universal formulae yield\ninteger-valued outputs (dimensions) at those points in the classical limit.\n"}
+{"abstract": "  An increasingly popular method for solving a constrained combinatorial\noptimisation problem is to first convert it into a quadratic unconstrained\nbinary optimisation (QUBO) problem, and solve it using a standard QUBO solver.\nHowever, this relaxation introduces hyper-parameters that balance the objective\nand penalty terms for the constraints, and their chosen values significantly\nimpact performance. Hence, tuning these parameters is an important problem.\nExisting generic hyper-parameter tuning methods require multiple expensive\ncalls to a QUBO solver, making them impractical for performance critical\napplications when repeated solutions of similar combinatorial optimisation\nproblems are required. In this paper, we propose the QROSS method, in which we\nbuild surrogate models of QUBO solvers via learning from solver data on a\ncollection of instances of a problem. In this way, we are able capture the\ncommon structure of the instances and their interactions with the solver, and\nproduce good choices of penalty parameters with fewer number of calls to the\nQUBO solver. We take the Traveling Salesman Problem (TSP) as a case study,\nwhere we demonstrate that our method can find better solutions with fewer calls\nto QUBO solver compared with conventional hyper-parameter tuning techniques.\nMoreover, with simple adaptation methods, QROSS is shown to generalise well to\nout-of-distribution datasets and different types of QUBO solvers.\n"}
+{"abstract": "  We develop a new machine learning algorithm, Via Machinae, to identify cold\nstellar streams in data from the Gaia telescope. Via Machinae is based on\nANODE, a general method that uses conditional density estimation and sideband\ninterpolation to detect local overdensities in the data in a model agnostic\nway. By applying ANODE to the positions, proper motions, and photometry of\nstars observed by Gaia, Via Machinae obtains a collection of those stars deemed\nmost likely to belong to a stellar stream. We further apply an automated\nline-finding method based on the Hough transform to search for line-like\nfeatures in patches of the sky. In this paper, we describe the Via Machinae\nalgorithm in detail and demonstrate our approach on the prominent stream GD-1.\nThough some parts of the algorithm are tuned to increase sensitivity to cold\nstreams, the Via Machinae technique itself does not rely on astrophysical\nassumptions, such as the potential of the Milky Way or stellar isochrones. This\nflexibility suggests that it may have further applications in identifying other\nanomalous structures within the Gaia dataset, for example debris flow and\nglobular clusters.\n"}
+{"abstract": "  While Landauer's Principle sets a lower bound for the work required for a\ncomputation, that work is recoverable for efficient computations. However,\npractical physical computers, such as modern digital computers or biochemical\nsystems, are subject to constraints that make them inefficient -- irreversibly\ndissipating significant energy. Recent results show that the dissipation in\nsuch systems is bounded by the nonreciprocity of the embedded computation. We\ninvestigate the consequences of this bound for different types of memory,\nshowing that different memory devices are better suited for different\ncomputations. This correspondence comes from the time-reversal symmetries of\nthe memory, which depend on whether information is stored positionally or\nmagnetically. This establishes that the time symmetries of the memory device\nplay an essential roll in determining energetics. The energetic consequences of\ntime symmetries are particularly pronounced in nearly deterministic\ncomputations, where the cost of computing diverges as minus log of the error\nrate. We identify the coefficient of that divergence as the dissipation\ndivergence. We find that the dissipation divergence may be zero for a\ncomputation when implemented in one type of memory while it's maximal when\nimplemented with another. Given flexibility in the type of memory, the\ndissipation divergence is bounded below by the average state compression of the\ncomputation. Moreover, we show how to explicitly construct the memory to\nachieve this minimal dissipation. As a result, we find that logically\nreversible computations are indeed thermodynamically efficient, but logical\nirreversibility comes at a much higher cost than previously anticipated.\n"}
+{"abstract": "  Recognizing relations between entities is a pivotal task of relational\nlearning. Learning relation representations from distantly-labeled datasets is\ndifficult because of the abundant label noise and complicated expressions in\nhuman language. This paper aims to learn predictive, interpretable, and robust\nrelation representations from distantly-labeled data that are effective in\ndifferent settings, including supervised, distantly supervised, and few-shot\nlearning. Instead of solely relying on the supervision from noisy labels, we\npropose to learn prototypes for each relation from contextual information to\nbest explore the intrinsic semantics of relations. Prototypes are\nrepresentations in the feature space abstracting the essential semantics of\nrelations between entities in sentences. We learn prototypes based on\nobjectives with clear geometric interpretation, where the prototypes are unit\nvectors uniformly dispersed in a unit ball, and statement embeddings are\ncentered at the end of their corresponding prototype vectors on the surface of\nthe ball. This approach allows us to learn meaningful, interpretable prototypes\nfor the final classification. Results on several relation learning tasks show\nthat our model significantly outperforms the previous state-of-the-art models.\nWe further demonstrate the robustness of the encoder and the interpretability\nof prototypes with extensive experiments.\n"}
+{"abstract": "  Pre-trained language models like BERT achieve superior performances in\nvarious NLP tasks without explicit consideration of syntactic information.\nMeanwhile, syntactic information has been proved to be crucial for the success\nof NLP applications. However, how to incorporate the syntax trees effectively\nand efficiently into pre-trained Transformers is still unsettled. In this\npaper, we address this problem by proposing a novel framework named\nSyntax-BERT. This framework works in a plug-and-play mode and is applicable to\nan arbitrary pre-trained checkpoint based on Transformer architecture.\nExperiments on various datasets of natural language understanding verify the\neffectiveness of syntax trees and achieve consistent improvement over multiple\npre-trained models, including BERT, RoBERTa, and T5.\n"}
+{"abstract": "  We define and investigate versions of Silver and Mathias forcing with respect\nto lower and upper density. We focus on properness, Axiom A, chain conditions,\npreservation of cardinals and adding Cohen reals. We find rough forcings that\ncollapse 2^\\omega to \\omega, while others are surprisingly gentle. We also\nstudy connections between regularity properties induced by these parametrized\nforcing notions and the Baire property.\n"}
+{"abstract": "  Based on Hrushovski, Palac{\\'i}n and Pillay's example [6], we produce a new\nstructure without the canonical base property, which is interpretable in\nBaudisch's group. Said structure is, in particular, CM-trivial, and thus at the\nlowest possible level of the ample hierarchy.\n"}
+{"abstract": "  In this paper we study quadratic forms which are universal when restricted to\nalmost prime inputs, establishing finiteness theorems akin to the\nConway--Schneeberger 15 theorem.\n"}
+{"abstract": "  Integration of data from multiple omics techniques is becoming increasingly\nimportant in biomedical research. Due to non-uniformity and technical\nlimitations in omics platforms, such integrative analyses on multiple omics,\nwhich we refer to as views, involve learning from incomplete observations with\nvarious view-missing patterns. This is challenging because i) complex\ninteractions within and across observed views need to be properly addressed for\noptimal predictive power and ii) observations with various view-missing\npatterns need to be flexibly integrated. To address such challenges, we propose\na deep variational information bottleneck (IB) approach for incomplete\nmulti-view observations. Our method applies the IB framework on marginal and\njoint representations of the observed views to focus on intra-view and\ninter-view interactions that are relevant for the target. Most importantly, by\nmodeling the joint representations as a product of marginal representations, we\ncan efficiently learn from observed views with various view-missing patterns.\nExperiments on real-world datasets show that our method consistently achieves\ngain from data integration and outperforms state-of-the-art benchmarks.\n"}
+{"abstract": "  Chalcogenide phase change materials enable non-volatile, low-latency\nstorage-class memory. They are also being explored for new forms of computing\nsuch as neuromorphic and in-memory computing. A key challenge, however, is the\ntemporal drift in the electrical resistance of the amorphous states that encode\ndata. Drift, caused by the spontaneous structural relaxation of the newly\nrecreated melt-quenched amorphous phase, has consistently been observed to have\na logarithmic dependence in time. Here, we show that this observation is valid\nonly in a certain observable timescale. Using threshold-switching voltage as\nthe measured variable, based on temperature-dependent and short timescale\nelectrical characterization, we experimentally measure the onset of drift. This\nadditional feature of the structural relaxation dynamics serves as a new\nbenchmark to appraise the different classical models to explain drift.\n"}
+{"abstract": "  A multi-prism lens (MPL) is a refractive x-ray lens with one-dimensional\nfocusing properties. If used as a pre-object collimator in a scanning system\nfor medical x-ray imaging, it reduces the divergence of the radiation and\nimproves on photon economy compared to a slit collimator. Potential advantages\ninclude shorter acquisition times, a reduced tube loading, or improved\nresolution. We present the first images acquired with an MPL in a prototype for\na scanning mammography system. The lens showed a gain of flux of 1.32 compared\nto a slit collimator at equal resolution, or a gain in resolution of 1.31-1.44\nat equal flux. We expect the gain of flux in a clinical set-up with an\noptimized MPL and a custom-made absorption filter to reach 1.67, or 1.45-1.54\ngain in resolution.\n"}
+{"abstract": "  There is a prevailing trend to study urban morphology quantitatively thanks\nto the growing accessibility to various forms of spatial big data, increasing\ncomputing power, and use cases benefiting from such information. The methods\ndeveloped up to now measure urban morphology with numerical indices describing\ndensity, proportion, and mixture, but they do not directly represent\nmorphological features from the human's visual and intuitive perspective. We\ntake the first step to bridge the gap by proposing a deep learning-based\ntechnique to automatically classify road networks into four classes on a visual\nbasis. The method is implemented by generating an image of the street network\n(Colored Road Hierarchy Diagram), which we introduce in this paper, and\nclassifying it using a deep convolutional neural network (ResNet-34). The model\nachieves an overall classification accuracy of 0.875. Nine cities around the\nworld are selected as the study areas with their road networks acquired from\nOpenStreetMap. Latent subgroups among the cities are uncovered through\nclustering on the percentage of each road network category. In the subsequent\npart of the paper, we focus on the usability of such classification: we apply\nour method in a case study of urban vitality prediction. An advanced tree-based\nregression model (LightGBM) is for the first time designated to establish the\nrelationship between morphological indices and vitality indicators. The effect\nof road network classification is found to be small but positively associated\nwith urban vitality. This work expands the toolkit of quantitative urban\nmorphology study with new techniques, supporting further studies in the future.\n"}
+{"abstract": "  The key procedure of haze image translation through adversarial training lies\nin the disentanglement between the feature only involved in haze synthesis,\ni.e.style feature, and the feature representing the invariant semantic content,\ni.e. content feature. Previous methods separate content feature apart by\nutilizing it to classify haze image during the training process. However, in\nthis paper we recognize the incompleteness of the content-style disentanglement\nin such technical routine. The flawed style feature entangled with content\ninformation inevitably leads the ill-rendering of the haze images. To address,\nwe propose a self-supervised style regression via stochastic linear\ninterpolation to reduce the content information in style feature. The ablative\nexperiments demonstrate the disentangling completeness and its superiority in\nlevel-aware haze image synthesis. Moreover, the generated haze data are applied\nin the testing generalization of vehicle detectors. Further study between\nhaze-level and detection performance shows that haze has obvious impact on the\ngeneralization of the vehicle detectors and such performance degrading level is\nlinearly correlated to the haze-level, which, in turn, validates the\neffectiveness of the proposed method.\n"}
+{"abstract": "  Establishing dense correspondences between a pair of images is an important\nand general problem. However, dense flow estimation is often inaccurate in the\ncase of large displacements or homogeneous regions. For most applications and\ndown-stream tasks, such as pose estimation, image manipulation, or 3D\nreconstruction, it is crucial to know when and where to trust the estimated\nmatches.\n  In this work, we aim to estimate a dense flow field relating two images,\ncoupled with a robust pixel-wise confidence map indicating the reliability and\naccuracy of the prediction. We develop a flexible probabilistic approach that\njointly learns the flow prediction and its uncertainty. In particular, we\nparametrize the predictive distribution as a constrained mixture model,\nensuring better modelling of both accurate flow predictions and outliers.\nMoreover, we develop an architecture and training strategy tailored for robust\nand generalizable uncertainty prediction in the context of self-supervised\ntraining. Our approach obtains state-of-the-art results on multiple challenging\ngeometric matching and optical flow datasets. We further validate the\nusefulness of our probabilistic confidence estimation for the task of pose\nestimation. Code and models are available at\nhttps://github.com/PruneTruong/PDCNet.\n"}
+{"abstract": "  In this work, I studied the spectrum of scalar mesons and axial-vector mesons\nvia Dyson-Schwinger equation and Bethe-Salpeter equation approach in the\nrainbow-ladder approximation. An interaction model with a repulsive term added\nto the one used for the pseudoscalar and vector mesons is proposed. My results\nare consistent with the experiment data and other model results, which shows\nthat this interaction model is effective for all the heavy, heavy-light, and\nlight scalar and axial-vector mesons.\n"}
+{"abstract": "  Osteoporosis is a common bone disease that occurs when the creation of new\nbone does not keep up with the loss of old bone, resulting in increased\nfracture risk. Adults over the age of 50 are especially at risk and see their\nquality of life diminished because of limited mobility, which can lead to\nisolation and depression. We are developing a robust screening method capable\nof identifying individuals predisposed to hip fracture to address this clinical\nchallenge. The method uses finite element analysis and relies on segmented\ncomputed tomography (CT) images of the hip. Presently, the segmentation of the\nproximal femur requires manual input, which is a tedious task, prone to human\nerror, and severely limits the practicality of the method in a clinical\ncontext. Here we present a novel approach for segmenting the proximal femur\nthat uses a deep convolutional neural network to produce accurate, automated,\nrobust, and fast segmentations of the femur from CT scans. The network\narchitecture is based on the renowned u-net, which consists of a downsampling\npath to extract increasingly complex features of the input patch and an\nupsampling path to convert the acquired low resolution image into a high\nresolution one. Skipped connections allow us to recover critical spatial\ninformation lost during downsampling. The model was trained on 30 manually\nsegmented CT images and was evaluated on 200 ground truth manual segmentations.\nOur method delivers a mean Dice similarity coefficient (DSC) and 95th\npercentile Hausdorff distance (HD95) of 0.990 and 0.981 mm, respectively.\n"}
+{"abstract": "  In this paper, we introduce an event-driven trading strategy that predicts\nstock movements by detecting corporate events from news articles. Unlike\nexisting models that utilize textual features (e.g., bag-of-words) and\nsentiments to directly make stock predictions, we consider corporate events as\nthe driving force behind stock movements and aim to profit from the temporary\nstock mispricing that may occur when corporate events take place. The core of\nthe proposed strategy is a bi-level event detection model. The low-level event\ndetector identifies events' existences from each token, while the high-level\nevent detector incorporates the entire article's representation and the\nlow-level detected results to discover events at the article-level. We also\ndevelop an elaborately-annotated dataset EDT for corporate event detection and\nnews-based stock prediction benchmark. EDT includes 9721 news articles with\ntoken-level event labels as well as 303893 news articles with minute-level\ntimestamps and comprehensive stock price labels. Experiments on EDT indicate\nthat the proposed strategy outperforms all the baselines in winning rate,\nexcess returns over the market, and the average return on each transaction.\n"}
+{"abstract": "  In this note we give some variations on an extension of Caratheodory's\nnormality criterion due to Grahl and Nevo.\n"}
+{"abstract": "  Dirac and Lov\\'{a}sz independently characterized the $3$-connected graphs\nwith no pair of vertex-disjoint cycles. Equivalently, they characterized all\n$3$-connected graphs with no prism-minors. In this paper, we completely\ncharacterize the $3$-connected graphs with an edge that is contained in the\nunion of no pair of vertex-disjoint cycles. As applications, we answer the\nanalogous questions for edge-disjoint cycles and for $4$-connected graphs and\nwe completely characterize the $3$-connected graphs with no prism-minor using a\nspecified edge.\n"}
+{"abstract": "  We identify a subclass of the regular commutative languages that is closed\nunder the iterated shuffle, or shuffle closure. In particular, it is\nregularity-preserving on this subclass. This subclass contains the commutative\ngroup languages and, for every alphabet $\\Sigma$, the class\n$\\textbf{Com}^+(\\Sigma^*)$ given by the ordered variety $\\textbf{Com}^+$. Then,\nwe state a simple characterization when the iterated shuffle on finite\ncommutative languages gives a regular language again and state partial results\nfor aperiodic commutative languages. We also show that the aperiodic, or\nstar-free, commutative languages and the commutative group languages are closed\nunder projection.\n"}
+{"abstract": "  A comprehensive class of models is proposed that can be used for continuous,\nbinary, ordered categorical and count type responses. The difficulty of items\nis described by difficulty functions, which replace the item difficulty\nparameters that are typically used in item response models. They crucially\ndetermine the response distribution and make the models very flexible with\nregard to the range of distributions that are covered. The model class contains\nseveral widely used models as the binary Rasch model and the graded response\nmodel as special cases, allows for simplifications, and offers a distribution\nfree alternative to count type items. A major strength of the models is that\nthey can be used for mixed item formats, when different types of items are\ncombined to measure abilities or attitudes. It is an immediate consequence of\nthe comprehensive modeling approach that allows that difficulty functions\nautomatically adapt to the response distribution. Basic properties of the model\nclass are shown. Several real data sets are used to illustrate the flexibility\nof the models.\n"}
+{"abstract": "  A new concept for the simultaneous detection of primary and secondary\nscintillation in time projection chambers is proposed. Its core element is a\ntype of very-thick GEM structure supplied with transparent electrodes and\nmachined from a polyethylene naphthalate plate, a natural wavelength-shifter.\nSuch a device has good prospects for scalability and, by virtue of its genuine\noptical properties, it can improve on the light collection efficiency, energy\nthreshold and resolution of conventional micropattern gas detectors. This,\ntogether with the intrinsic radiopurity of its constituting elements, offers\nadvantages for noble gas and liquid based time projection chambers, used for\ndark matter searches and neutrino experiments. Production, optical and\nelectrical characterization, and first measurements performed with the new\ndevice are reported.\n"}
+{"abstract": "  We show that the (positive) zoom complexes, here called tree complexes, with\nfairly natural morphisms, form a dual category to the category of positive\nopetopes with contraction epimorphisms. We also show how this duality can be\nslightly generalized to thicket complexes and opetopic cardinals.\n"}
+{"abstract": "  There are many approaches that use weak-supervision to train networks to\nsegment 2D images. By contrast, existing 3D approaches rely on full-supervision\nof a subset of 2D slices of the 3D image volume. In this paper, we propose an\napproach that is truly weakly-supervised in the sense that we only need to\nprovide a sparse set of 3D point on the surface of target objects, an easy task\nthat can be quickly done. We use the 3D points to deform a 3D template so that\nit roughly matches the target object outlines and we introduce an architecture\nthat exploits the supervision provided by coarse template to train a network to\nfind accurate boundaries.\n  We evaluate the performance of our approach on Computed Tomography (CT),\nMagnetic Resonance Imagery (MRI) and Electron Microscopy (EM) image datasets.\nWe will show that it outperforms a more traditional approach to\nweak-supervision in 3D at a reduced supervision cost.\n"}
+{"abstract": "  In numerical linear algebra, a well-established practice is to choose a norm\nthat exploits the structure of the problem at hand in order to optimize\naccuracy or computational complexity. In numerical polynomial algebra, a single\nnorm (attributed to Weyl) dominates the literature. This article initiates the\nuse of $L_p$ norms for numerical algebraic geometry, with an emphasis on\n$L_{\\infty}$. This classical idea yields strong improvements in the analysis of\nthe number of steps performed by numerous iterative algorithms. In particular,\nwe exhibit three algorithms where, despite the complexity of computing\n$L_{\\infty}$-norm, the use of $L_p$-norms substantially reduces computational\ncomplexity: a subdivision-based algorithm in real algebraic geometry for\ncomputing the homology of semialgebraic sets, a well-known meshing algorithm in\ncomputational geometry, and the computation of zeros of systems of complex\nquadratic polynomials (a particular case of Smale's 17th problem).\n"}
+{"abstract": "  Knowledge distillation refers to a technique of transferring the knowledge\nfrom a large learned model or an ensemble of learned models to a small model.\nThis method relies on access to the original training set, which might not\nalways be available. A possible solution is a data-free adversarial\ndistillation framework, which deploys a generative network to transfer the\nteacher model's knowledge to the student model. However, the data generation\nefficiency is low in the data-free adversarial distillation. We add an\nactivation regularizer and a virtual interpolation method to improve the data\ngeneration efficiency. The activation regularizer enables the students to match\nthe teacher's predictions close to activation boundaries and decision\nboundaries. The virtual interpolation method can generate virtual samples and\nlabels in-between decision boundaries. Our experiments show that our approach\nsurpasses state-of-the-art data-free distillation methods. The student model\ncan achieve 95.42% accuracy on CIFAR-10 and 77.05% accuracy on CIFAR-100\nwithout any original training data. Our model's accuracy is 13.8% higher than\nthe state-of-the-art data-free method on CIFAR-100.\n"}
+{"abstract": "  High eccentricity migration is a possible formation channel for hot Jupiters.\nHowever, in order for it to be consistent with the observed population of\nplanets, tides must circularize the orbits in less than $\\approx$ a Myr. A\npotential mechanism for such rapid circularization is the diffusive growth of\nthe tidally driven planetary f-mode. Such growth occurs if the f-mode's phase\nat pericenter varies chaotically from one pericenter passage to the next.\nPrevious studies focused on the variation of the orbital period due to tidal\nback-reaction on the orbit as the source of chaos. Here we show that nonlinear\nmode interactions can also be an important source. Specifically, we show that\nnonlinear interactions between a parent f-mode and daughter f-/p-modes induce\nan energy-dependent shift in the oscillation frequency of the parent. This\nfrequency shift varies randomly from orbit to orbit because the parent's energy\nvaries. As a result, the parent's phase at pericenter varies randomly, which we\nfind can trigger it to grow diffusively. We show that the phase shift induced\nby nonlinear mode interactions in fact dominates the shift induced by tidal\nback-reaction and significantly lowers the one-kick energy threshold for\ndiffusive growth by about a factor of 5 compared to the linear theory's\nprediction. Nonlinear interactions could thus enhance the formation rate of hot\nJupiters through the high-eccentricity migration channel and potentially\nmitigate the discrepancy between the observed and predicted occurrence rates\nfor close-in gas giants as compared to those further from the star.\n"}
+{"abstract": "  We present an analysis of the environment of six QSO triplets at 1 $\\lesssim$\nz $\\lesssim$ 1.5 by analyzing multiband (r,i,z, or g,r,i) images obtained with\nMegacam at the CFHT telescope, aiming to investigate whether they are\nassociated or not with galaxy protoclusters. This was done by using photometric\nredshifts trained using the high accuracy photometric redshifts of the\nCOSMOS2015 catalogue. To improve the quality of our photometric redshift\nestimation, we included in our analysis near-infrared photometry (3.6 and 4.5\n$\\mu m$) from the unWISE survey available for our fields and the COSMOS survey.\nThis approach allowed us to obtain good photometric redshifts with dispersion,\nas measured with the robust Sigma NMAD statistics of $\\sim$ 0.04 for our six\nfields. Our analysis setup was reproduced on lightcones constructed from the\nMillennium Simulation data and the latest version of the L-GALAXIES\nsemi-analytic model to verify the protocluster detectability in such\nconditions. The density field in a redshift slab containing each triplet was\nthen analyzed with a Gaussian kernel density estimator. We did not find any\nsignificant evidence of the triplets inhabiting dense structures, such as a\nmassive galaxy cluster or protocluster.\n"}
+{"abstract": "  We define a categorical notion of cybernetic system as a dynamical\nrealisation of a generalized open game, along with a coherence condition. We\nshow that this notion captures a wide class of cybernetic systems in\ncomputational neuroscience and statistical machine learning, exposes their\ncompositional structure, and gives an abstract justification for the\nbidirectional structure empirically observed in cortical circuits. Our\nconstruction is built on the observation that Bayesian updates compose\noptically, a fact which we prove along the way, via a fibred category of\nstate-dependent stochastic channels.\n"}
+{"abstract": "  This document describes the details and the motivation behind a new dataset\nwe collected for the semi-supervised recognition challenge~\\cite{semi-aves} at\nthe FGVC7 workshop at CVPR 2020. The dataset contains 1000 species of birds\nsampled from the iNat-2018 dataset for a total of nearly 150k images. From this\ncollection, we sample a subset of classes and their labels, while adding the\nimages from the remaining classes to the unlabeled set of images. The presence\nof out-of-domain data (novel classes), high class-imbalance, and fine-grained\nsimilarity between classes poses significant challenges for existing\nsemi-supervised recognition techniques in the literature. The dataset is\navailable here: \\url{https://github.com/cvl-umass/semi-inat-2020}\n"}
+{"abstract": "  Research managers benchmarking universities against international peers face\nthe problem of affiliation disambiguation. Different databases have taken\nseparate approaches to this problem and discrepancies exist between them.\nBibliometric data sources typically conduct a disambiguation process that\nunifies variant institutional names and those of its sub-units so that\nresearchers can then search all records from that institution using a single\nunified name. This study examined affiliation discrepancies between Scopus, Web\nof Science, Dimensions, and Microsoft Academic for 18 Arab universities over a\nfive-year period. We confirmed that digital object identifiers (DOIs) are\nsuitable for extracting comparable scholarly material across databases and\nquantified the affiliation discrepancies between them. A substantial share of\nrecords assigned to the selected universities in any one database were not\nassigned to the same university in another. The share of discrepancy was higher\nin the larger databases, Dimensions and Microsoft Academic. The smaller, more\nselective databases, Scopus and especially Web of Science tended to agree to a\ngreater degree with affiliations in the other databases. Manual examination of\naffiliation discrepancies showed they were caused by a mixture of missing\naffiliations, unification differences, and assignation of records to the wrong\ninstitution.\n"}
+{"abstract": "  In this manuscript we investigate the analouge of the Brill-Noether problem\nfor smooth curves in the case of normal surface singularities. We determine the\nmaximal possible value of $h^1$ of line bundles without fixed components in the\nPicard group $\\pic^{l'}(\\tX)$ in the following cases: for some special Chern\nclasses $l'$ if $\\tX$ is a resolution of a splice quotient singularity $(X, 0)$\nand for arbitrary Chern classes in the case of weighted homogenous\nsingularities. Motivated by this problem, we define the \\emph{virtual\ncohomology numbers} $h^1_{virt}(l')$ for all Chern classes $l'$ such that\n$h^1_{virt}(0)$ is the canonical normalized Seiberg-Witten invariant and we\ngeneralize the duality formulae of Seiberg-Witten invariants obtained by the\nauthors and A. N\\'emethi in \\cite{LNNdual}, for the virtual cohomology numbers.\n"}
+{"abstract": "  We present new deep, high-resolution, 1.5 GHz observations of the\nprototypical nearby Perseus galaxy cluster from the Karl G. Jansky Very Large\nArray. We isolate for the first time the complete tail of radio emission of the\nbent-jet radio galaxy NGC 1272, which had been previously mistaken to be part\nof the radio mini-halo. The possibility that diffuse radio galaxy emission\ncontributes to mini-halo emission may be a general phenomenon in relaxed\ncool-core clusters, and should be explored. The collimated jets of NGC 1272\ninitially bend to the west, and then transition eastward into faint, 60\nkpc-long extensions with eddy-like structures and filaments. We suggest\ninterpretations for these structures that involve bulk motions of intracluster\ngas, the galaxy's orbit in the cluster including projection effects, and the\npassage of the galaxy through a sloshing cold front. Instabilities and\nturbulence created at the surface of this cold front and in the turbulent wake\nof the infalling host galaxy most likely play a role in the formation of the\nobserved structures. We also discover a series of faint rings, south-east of\nNGC 1272, which are a type of structure that has never been seen before in\ngalaxy clusters.\n"}
+{"abstract": "  Recent years have witnessed the fast development of the emerging topic of\nGraph Learning based Recommender Systems (GLRS). GLRS employ advanced graph\nlearning approaches to model users' preferences and intentions as well as\nitems' characteristics for recommendations. Differently from other RS\napproaches, including content-based filtering and collaborative filtering, GLRS\nare built on graphs where the important objects, e.g., users, items, and\nattributes, are either explicitly or implicitly connected. With the rapid\ndevelopment of graph learning techniques, exploring and exploiting homogeneous\nor heterogeneous relations in graphs are a promising direction for building\nmore effective RS. In this paper, we provide a systematic review of GLRS, by\ndiscussing how they extract important knowledge from graph-based\nrepresentations to improve the accuracy, reliability and explainability of the\nrecommendations. First, we characterize and formalize GLRS, and then summarize\nand categorize the key challenges and main progress in this novel research\narea. Finally, we share some new research directions in this vibrant area.\n"}
+{"abstract": "  The prevalence of location tracking systems has resulted in large volumes of\nspatiotemporal data generated every day. Addressing reachability queries on\nsuch datasets is important for a wide range of applications (surveillance,\npublic health, social networks, etc.) A spatiotemporal reachability query\nidentifies whether a physical item (or information etc.) could have been\ntransferred from the source object $O_S$ to the target object $O_T$ during a\ntime interval $I$ (either directly, or through a chain of intermediate\ntransfers). In previous research on spatiotemporal reachability queries, the\nnumber of such transfers is not limited, and the weight of a piece of\ntransferred information remains the same. This paper introduces novel\nreachability queries, which assume a scenario of information decay. Such\nqueries arise when the value of information that travels through the chain of\nintermediate objects decreases with each transfer. To address such queries\nefficiently over large spatiotemporal datasets, we introduce the RICCdecay\nalgorithm. Further, the decay scenario leads to an important extension: if\nthere are many different sources of information, the aggregate value of\ninformation an object can obtain varies. As a result, we introduce a top-k\nreachability problem, identifying the k objects with the highest accumulated\ninformation. We also present the RICCtopK algorithm that can efficiently\ncompute top-k reachability with transfer decay queries. An experimental\nevaluation shows the efficiency of the proposed algorithms over previous\napproaches.\n"}
+{"abstract": "  We prove a universal approximation property (UAP) for a class of ODENet and a\nclass of ResNet, which are used in many deep learning algorithms. The UAP can\nbe stated as follows. Let $n$ and $m$ be the dimension of input and output\ndata, and assume $m\\leq n$. Then we show that ODENet width $n+m$ with any\nnon-polynomial continuous activation function can approximate any continuous\nfunction on a compact subset on $\\mathbb{R}^n$. We also show that ResNet has\nthe same property as the depth tends to infinity. Furthermore, we derive\nexplicitly the gradient of a loss function with respect to a certain tuning\nvariable. We use this to construct a learning algorithm for ODENet. To\ndemonstrate the usefulness of this algorithm, we apply it to a regression\nproblem, a binary classification, and a multinomial classification in MNIST.\n"}
+{"abstract": "  If C is a closed symmetric monoidal category, the Chu category Chu(C, g) over\nC and an object g of it was defined by Chu, as a *-autonomous category\ngenerated from C. Bishop introduced the category of complemented subsets of a\nset, in order to overcome the problems generated by the use of negation in\nconstructive measure theory. Shulman mentions that Bishop's complemented\nsubsets correspond roughly to the Chu construction. In this paper we explain\nthis correspondence by showing that there is a Chu representation (a full\nembedding) of the category of complemented subsets of a set X into Chu(Set, X x\nX). A Chu representation of the category of Bishop spaces into Chu(Set, R) is\nshown, as the constructive analogue to the standard Chu representation of the\ncategory of topological spaces into Chu(Set, 2). In order to represent the\ncategory of predicates (with objects pairs (X, A), where A is a subset of X,\nand the category of complemented predicates (with objects pairs (X, A), where A\nis a complemented subset of X, we generalise the Chu construction by defining\nthe Chu category over a cartesian closed category C and an endofunctor on C.\nFinally, we introduce the antiparallel Grothendieck construction over a product\ncategory and a contra-variant Set-valued functor on it of which the Chu\nconstruction is a special case, in case C is a locally small, cartesian closed\ncategory.\n"}
+{"abstract": "  Epidemiology models play a key role in understanding and responding to the\nCOVID-19 pandemic. In order to build those models, scientists need to\nunderstand contributing factors and their relative importance. A large strand\nof literature has identified the importance of airflow to mitigate droplets and\nfar-field aerosol transmission risks. However, the specific factors\ncontributing to higher or lower contamination in various settings have not been\nclearly defined and quantified. As part of the MOAI project\n(https://moaiapp.com), we are developing a privacy-preserving test and trace\napp to enable infection cluster investigators to get in touch with patients\nwithout having to know their identity. This approach allows involving users in\nthe fight against the pandemic by contributing additional information in the\nform of anonymous research questionnaires. We first describe how the\nquestionnaire was designed, and the synthetic data was generated based on a\nreview we carried out on the latest available literature. We then present a\nmodel to evaluate the risk exposition of a user for a given setting. We finally\npropose a temporal addition to the model to evaluate the risk exposure over\ntime for a given user.\n"}
+{"abstract": "  Vector Addition Systems and equivalent Petri nets are a well established\nmodels of concurrency. The central algorithmic problem for Vector Addition\nSystems with a long research history is the reachability problem asking whether\nthere exists a run from one given configuration to another. We settle its\ncomplexity to be Ackermann-complete thus closing the problem open for 45 years.\nIn particular we prove that the problem is $\\mathcal{F}_k$-hard for Vector\nAddition Systems with States in dimension $6k$, where $\\mathcal{F}_k$ is the\n$k$-th complexity class from the hierarchy of fast-growing complexity classes.\n"}
+{"abstract": "  In this paper, we present several new structures for the colored HOMFLY-PT\ninvariants of framed links. First, we prove the strong integrality property for\nthe normalized colored HOMFLY-PT invariants by purely using the HOMFLY-PT skein\ntheory developed by H. Morton and his collaborators. By this strong integrality\nproperty, we immediately obtain several symmetric properties for the full\ncolored HOMFLY-PT invariants of links. Then, we apply our results to refine the\nmathematical structures appearing in the Labastida-Mari\\~no-Ooguri-Vafa (LMOV)\nintegrality conjecture for framed links. As another application of the strong\nintegrality, we obtain that the $q=1$ and $a=1$ specializations of the\nnormalized colored HOMFLY-PT invariant are well-defined link polynomials. We\nfind that a conjectural formula for the colored Alexander polynomial which is\nthe $a=1$ specialization of the normalized colored HOMFLY-PT invariant implies\nthat a special case of the LMOV conjecture for frame knot holds.\n"}
+{"abstract": "  In this article we present a novel discrete-time design approach which\nreduces the deteriorating effects of sampling on stability and performance in\ndigitally controlled nonlinear mechanical systems. The method is motivated by\nrecent results for linear systems, where feedback imposes closed-loop behavior\nthat exactly represents the symplectic discretization of a desired target\nsystem. In the nonlinear case, both the second order accurate representation of\nthe sampling process and the definition of the target dynamics stem from the\napplication of the implicit midpoint rule. The implicit nature of the resulting\nstate feedback requires the numerical solution of an in general nonlinear\nsystem of algebraic equations in every sampling interval. For an implementation\nwith pure position feedback, the velocities/momenta have to be approximated in\nthe sampling instants, which gives a clear interpretation of our approach in\nterms of the St\\\"ormer-Verlet integration scheme on a staggered grid. We\npresent discrete-time versions of impedance or energy shaping plus damping\ninjection control as well as computed torque tracking control. Both the\nHamiltonian and the Lagrangian perspective are adopted. Besides a linear\nexample to introduce the concept, the simulations with a planar two link robot\nmodel illustrate the performance and stability gain compared to the discrete\nimplementations of continuous-time control laws. A short analysis of\ncomputation times shows the real-time capability of our method.\n"}
+{"abstract": "  The non-Hermitian skin effect can be realized through asymmetric hopping\nbetween forward and backward directions, where all the modes of the system are\nlocalized at one edge of a finite 1D lattice. However, achieving such an\nasymmetric hopping in optical systems is far from trivial. Here we show\ntheoretically that in a finite chain of 1D exciton-polariton micropillars with\nsymmetric hopping, the inherent non-linearity of the system can exhibit a\nbi-skin effect, where the modes of the system are localized at the two edges of\nthe system. To show the topological origin of such modes, we calculate the\nwinding number.\n"}
+{"abstract": "  Can deep learning solve multiple tasks simultaneously, even when they are\nunrelated and very different? We investigate how the representations of the\nunderlying tasks affect the ability of a single neural network to learn them\njointly. We present theoretical and empirical findings that a single neural\nnetwork is capable of simultaneously learning multiple tasks from a combined\ndata set, for a variety of methods for representing tasks -- for example, when\nthe distinct tasks are encoded by well-separated clusters or decision trees\nover certain task-code attributes. More concretely, we present a novel analysis\nthat shows that families of simple programming-like constructs for the codes\nencoding the tasks are learnable by two-layer neural networks with standard\ntraining. We study more generally how the complexity of learning such combined\ntasks grows with the complexity of the task codes; we find that combining many\ntasks may incur a sample complexity penalty, even though the individual tasks\nare easy to learn. We provide empirical support for the usefulness of the\nlearning bounds by training networks on clusters, decision trees, and SQL-style\naggregation.\n"}
+{"abstract": "  We prove the super-exponential decay of probabilities that there exist $n$\ncrossings of a given quadrilateral in a simple $\\text{CLE}_\\kappa(\\Omega)$,\n$\\frac{8}{3}<\\kappa\\le 4$, as $n$ goes to infinity. Besides being of\nindependent interest, this also provides the missing ingredient in\narXiv:1809.00690 for proving the convergence of probabilities of cylindrical\nevents for the double-dimer loop ensembles to those for the nested\n$\\text{CLE}_4(\\Omega)$.\n"}
+{"abstract": "  We present results from the first deployment of novel, high definition,\ncompact gas Time Projection Chambers (TPCs) with pixel chip readout as part of\nthe BEAST II beam background measurement project at SuperKEKB. The TPCs provide\ndetailed 3D imaging of ionization from neutron-induced nuclear recoils in a\nhelium and carbon dioxide target gas mixture at standard temperature and\npressure. We find excellent electron background rejection, leading to\nbackground-free nuclear recoil measurements above 50 keVee, despite the extreme\nhigh-background environment. We measure an angular resolution better than\n20{\\deg} for recoil tracks longer than 1.7 mm, corresponding to an average\nionization energy of ~100 keVee. We also obtain the 3D vector direction of\nhelium recoils by utilizing charge profile measurements along the recoil axis,\nwith a correct head/tail assignment efficiency of approximately 80%. With this\nperformance, we present comparisons between measured and simulated event rates,\nrecoil energy spectra, and directional distributions originating from beam-gas\nand Touschek beam losses at SuperKEKB. We utilize head/tail recognition to\ndistinguish neutron components travelling with positive radial velocity in the\nBelle II coordinate system from those with opposite directionality. Finally, we\npresent a novel method of discriminating beam-gas interactions from Touschek\nbeam losses that can eliminate the need for dedicated accelerator runs for\nbackground measurements. This method is still statistics-limited. However,\nfuture studies should be able to verify this method, which in turn could lead\nto neutron background analysis runs symbiotic with normal Belle II operation.\nThe capabilities demonstrated here also suggest that high definition recoil\nimaging in gas TPCs is applicable to low energy, low-background experiments,\nsuch as directional dark matter searches.\n"}
+{"abstract": "  Curve reconstruction from unstructured points in a plane is a fundamental\nproblem with many applications that has generated research interest for\ndecades. Involved aspects like handling open, sharp, multiple and non-manifold\noutlines, run-time and provability as well as potential extension to 3D for\nsurface reconstruction have led to many different algorithms. We survey the\nliterature on 2D curve reconstruction and then present an open-sourced\nbenchmark for the experimental study. Our unprecedented evaluation on a\nselected set of planar curve reconstruction algorithms aims to give an overview\nof both quantitative analysis and qualitative aspects for helping users to\nselect the right algorithm for specific problems in the field. Our benchmark\nframework is available online to permit reproducing the results, and easy\nintegration of new algorithms.\n"}
+{"abstract": "  The entropy of $1/16$-th BPS AdS$_5$ black holes can be microscopically\naccounted for by the superconformal index of the $\\mathcal{N}=4$\nsuper-Yang-Mills theory. One way to compute this is through a Cardy-like limit\nof a formula for the index obtained in [1] using the \"$S$-transformation\" of\nthe elliptic $\\Gamma$ function. In this paper, we derive more general\n$SL(3,\\mathbb{Z})$ modular properties of the elliptic $\\Gamma$ function. We\nthen use these properties to obtain a three integer parameter family of\ngeneralized Cardy-like limits of the $\\mathcal{N}=4$ superconformal index. From\nthese limits, we obtain entropy formulae that have a similar form as that of\nthe original AdS$_5$ black hole, up to an overall rescaling of the entropy. We\ninterpret this both on the field theory and the gravitational side. Finally, we\ncomment on how our work suggests a generalization of the Farey tail to four\ndimensions.\n"}
+{"abstract": "  Two-dimensional Dirac semimetals have attracted much attention because of\ntheir linear energy dispersion and non-trivial Berry phase. Graphene-like 2D\nDirac materials are gapless only within certain approximations, e.g., if\nspin-orbit coupling (SOC) is neglected. It has recently been reported that\nmaterials with nonsymmorphic crystal lattice possess symmetry-enforced\nDirac-like band dispersion around certain high-symmetry momenta even in the\npresence of SOC. Here we calculate the optical absorption coefficient of\nnonsymmorphic semimetals, such as $\\alpha$-bismuthene, which hosts two\nanisotropic Dirac cones with different Fermi velocities along $x$ and $y$\ndirections.We find that the optical absorption coefficient depends strongly on\nthe anisotropy factor and the photon polarization. When a magnetic field is\napplied perpendicular to the plane of the material, the absorption coefficient\nalso depends on an internal parameter we termed the mixing angle of the band\nstructure. We further find that an in-plane magnetic field, while leaving the\nsystem gapless, can induce a Van-Hove singularity in the joint density of\nstates: this causes a significant enhancement of the optical absorption at the\nfrequency of the singularity for one direction of polarization but not for the\northogonal one, making the optical properties even more strongly dependent on\npolarization. Due to the anisotropy present in our model, the Dirac cones at\ntwo high-symmetry momenta in the Brillouin zone contribute very differently to\nthe optical absorbance. Consequently, it might be possible to preferentially\npopulate one valley or the other by varying photon polarization and frequency.\nThese results suggest that nonsymmorphic 2D Dirac semimetals are excellent\ncandidate materials for tunable magneto-optic devices.\n"}
+{"abstract": "  Large language models have become increasingly difficult to train because of\nthe growing computation time and cost. In this work, we present SRU++, a\nhighly-efficient architecture that combines fast recurrence and attention for\nsequence modeling. SRU++ exhibits strong modeling capacity and training\nefficiency. On standard language modeling tasks such as Enwik8, Wiki-103 and\nBillion Word datasets, our model obtains better bits-per-character and\nperplexity while using 3x-10x less training cost compared to top-performing\nTransformer models. For instance, our model achieves a state-of-the-art result\non the Enwik8 dataset using 1.6 days of training on an 8-GPU machine. We\nfurther demonstrate that SRU++ requires minimal attention for near\nstate-of-the-art performance. Our results suggest jointly leveraging fast\nrecurrence with little attention as a promising direction for accelerating\nmodel training and inference.\n"}
+{"abstract": "  The multiphase flow of droplets is widespread, both at the industrial and the\nmicroscale, for both biological and non-biological applications alike. But the\nensemble interactions of such systems are inherently nonlinear and complex,\ncompounded by interfacial effects, making it a difficult many-body problem for\ntheory. In comparison, the self-assembly dynamics of solid particles in flow\nhave long been described and successfully exploited in the field of inertial\nmicrofluidics, where particle crystals can be realized from inertial forces and\nhydrodynamic interactions. Here, we report novel self-assembly dynamics of\nliquid drops in confined microfluidic channels that contrast starkly with the\nestablished paradigm of inertial microfluidics: higher inertia leads to better\nspatial ordering. Instead, we find that the conventional straight wall channel\ngeometry not only fails to achieve regular spatial ordering for drops but\nactually exacerbates it with increasing inertia. Conversely, an asymmetric\nserpentine geometry is able to achieve long-range, periodic spatial ordering\nover length scales that are at least 3 orders of magnitude greater than the\ndrop diameter, particularly at low inertia. Experimentally, we are able to\ndecouple droplet generation from ordering, enabling independent variation of\nnumber density, confinement, inertia, and surfactant concentration. We find the\ninertia-dependent emergence of preferred drop separations and show for the\nfirst time that Marangoni effects can influence the longitudinal ordering of\nmultidrop arrays. These results present a largely unexplored direction for\ninertial microfluidics but also show the potential of its unification with\ndroplet microfluidics. In particular, the utility of passively restoring\nuniform drop spacing on-chip is a key requirement for the streamlined\nintegration of incubation and drop-by-drop interrogation capabilities.\n"}
+{"abstract": "  This work is a continuation of [Y. Fittouhi and A. Joseph, Weierstrass\nSections for Parabolic adjoint action in type $A$]. Let $G$ be an irreducible\nsimple algebraic group and $B$ a Borel subgroup of $G$. Let $\\mathfrak n$ be\nthe Lie algebra of the nilradical of $B$. Consider an irreducible subgroup $P$\nof $G$ containing $B$. Let $P'$ be the derived group of $P$. Let $\\mathfrak m$\nbe the Lie algebra of the nilradical of $P$. A theorem of Richardson asserts\nthat the algebra $\\mathbb C[\\mathfrak m]^{P'}$ of $P$ semi-invariants is\nmultiplicity-free. A linear subvariety $e+V$ such that the restriction map\ninduces an isomorphism of $\\mathbb C[\\mathfrak m]^{P'}$ onto $\\mathbb C[e+V]$\nis called a Weierstrass section for the action of $P'$ on $\\mathfrak m$. Here\nin type $A$ such a section is constructed, but in better form than that given\nin Sect. 4, loc cit. Yet the main difference is a complete change of emphasis\nfrom the construction of a Weierstrass section, to its application.\n  Let $\\mathscr N$ be the nilfibre relative to this action. From the\nconstruction of a Weierstrass section $e+V$, it is shown that $e \\in \\mathscr\nN$. Then $P.e$ is contained in a unique irreducible component $\\mathscr C$ of\n$\\mathscr N$. The structure of $e+V$ is used to give a rather explicit\ndescription of $\\mathscr C$ as a $B$ saturation set, that is of the form\n$\\overline{B.\\mathfrak u}$, where $\\mathfrak u$ is a subalgebra of $\\mathfrak\nn$ . This algebra is not necessarily complemented by a subalgebra in $\\mathfrak\nn$ and so $\\overline{B.\\mathfrak u}$ is not necessarily an orbital variety\nclosure (hence Lagrangian) but it can be.\n  It is shown that $\\mathscr C$ need not contain a dense $P$ orbit and this by\na purely theoretical analysis. This occurs for an appropriate parabolic in\n$A_{10}$ and is possibly the simplest example. In this particular case\n$\\mathscr C$ is not an orbital variety closure.\n"}
+{"abstract": "  In 2006, during a meeting of a working group of scientists in La Jolla,\nCalifornia at The Neurosciences Institute (NSI), Gerald Edelman described a\nroadmap towards the creation of a Conscious Artifact. As far as I know, this\nroadmap was not published. However, it did shape my thinking and that of many\nothers in the years since that meeting. This short paper, which is based on my\nnotes taken during the meeting, describes the key steps in this roadmap. I\nbelieve it is as groundbreaking today as it was more than 15 years ago.\n"}
+{"abstract": "  Peer code review has been found to be effective in identifying security\nvulnerabilities. However, despite practicing mandatory code reviews, many Open\nSource Software (OSS) projects still encounter a large number of post-release\nsecurity vulnerabilities, as some security defects escape those. Therefore, a\nproject manager may wonder if there was any weakness or inconsistency during a\ncode review that missed a security vulnerability. Answers to this question may\nhelp a manager pinpointing areas of concern and taking measures to improve the\neffectiveness of his/her project's code reviews in identifying security\ndefects. Therefore, this study aims to identify the factors that differentiate\ncode reviews that successfully identified security defects from those that\nmissed such defects. With this goal, we conduct a case-control study of\nChromium OS project. Using multi-stage semi-automated approaches, we build a\ndataset of 516 code reviews that successfully identified security defects and\n374 code reviews where security defects escaped. The results of our empirical\nstudy suggest that the are significant differences between the categories of\nsecurity defects that are identified and that are missed during code reviews. A\nlogistic regression model fitted on our dataset achieved an AUC score of 0.91\nand has identified nine code review attributes that influence identifications\nof security defects. While time to complete a review, the number of mutual\nreviews between two developers, and if the review is for a bug fix have\npositive impacts on vulnerability identification, opposite effects are observed\nfrom the number of directories under review, the number of total reviews by a\ndeveloper, and the total number of prior commits for the file under review.\n"}
+{"abstract": "  In the non-uniform $k$-center problem, the objective is to cover points in a\nmetric space with specified number of balls of different radii. Chakrabarty,\nGoyal, and Krishnaswamy [ICALP 2016, Trans. on Algs. 2020] (CGK, henceforth)\ngive a constant factor approximation when there are two types of radii. In this\npaper, we give a constant factor approximation for the two radii case in the\npresence of outliers. To achieve this, we need to bypass the technical barrier\nof bad integrality gaps in the CGK approach. We do so using \"the ellipsoid\nmethod inside the ellipsoid method\": use an outer layer of the ellipsoid method\nto reduce to stylized instances and use an inner layer of the ellipsoid method\nto solve these specialized instances. This idea is of independent interest and\ncould be applicable to other problems.\n  Keywords: Approximation, Clustering, Outliers, and Round-or-Cut.\n"}
+{"abstract": "  We consider the combinatorial bandits problem, where at each time step, the\nonline learner selects a size-$k$ subset $s$ from the arms set $\\mathcal{A}$,\nwhere $\\left|\\mathcal{A}\\right| = n$, and observes a stochastic reward of each\narm in the selected set $s$. The goal of the online learner is to minimize the\nregret, induced by not selecting $s^*$ which maximizes the expected total\nreward. Specifically, we focus on a challenging setting where 1) the reward\ndistribution of an arm depends on the set $s$ it is part of, and crucially 2)\nthere is \\textit{no total order} for the arms in $\\mathcal{A}$.\n  In this paper, we formally present a reward model that captures set-dependent\nreward distribution and assumes no total order for arms. Correspondingly, we\npropose an Upper Confidence Bound (UCB) algorithm that maintains UCB for each\nindividual arm and selects the arms with top-$k$ UCB. We develop a novel regret\nanalysis and show an $O\\left(\\frac{k^2 n \\log T}{\\epsilon}\\right)$\ngap-dependent regret bound as well as an $O\\left(k^2\\sqrt{n T \\log T}\\right)$\ngap-independent regret bound. We also provide a lower bound for the proposed\nreward model, which shows our proposed algorithm is near-optimal for any\nconstant $k$. Empirical results on various reward models demonstrate the broad\napplicability of our algorithm.\n"}
+{"abstract": "  Unbalanced optimal power flow refers to a class of optimization problems\nsubject to the steady state physics of three-phase power grids with\nnonnegligible phase unbalance. Significant progress on this problem has been\nmade on the mathematical modelling side of unbalanced OPF, however there is a\nlack of information on implementation aspects as well as data sets for\nbenchmarking. One of the key problems is the lack of definitions of current and\nvoltage bounds across different classes of representations of the power flow\nequations. Therefore, this tutorial-style paper summarizes the structural\nfeatures of the unbalanced (optimal) power problem for three-phase systems. The\nresulting nonlinear complex-value matrix formulations are presented for both\nthe bus injection and branch flow formulation frameworks, which typically\ncannot be implemented as-is in optimization toolboxes. Therefore, this paper\nalso derives the equivalent real-value formulations, and discusses challenges\nrelated to the implementation in optimization modeling toolboxes. The derived\nformulations can be re-used easily for continuous and discrete optimization\nproblems in distribution networks for a variety of operational and planning\nproblems. Finally, bounds are derived for all variables involved, to further\nthe development of benchmarks for unbalanced optimal power flow, where\nconsensus on bound semantics is a pressing need. We believe benchmarks remain a\ncornerstone for the development and validation of scalable and reproducible\noptimization models and tools. The soundness of the derivations is confirmed\nthrough numerical experiments, validated w.r.t. OpenDSS for IEEE test feeders\nwith 3x3 impedance matrices.\n"}
+{"abstract": "  Recently, convolutional neural network (CNN) based image super-resolution\n(SR) methods have achieved significant performance improvement. However, most\nCNN-based methods mainly focus on feed-forward architecture design and neglect\nto explore the feedback mechanism, which usually exists in the human visual\nsystem. In this paper, we propose feedback pyramid attention networks (FPAN) to\nfully exploit the mutual dependencies of features. Specifically, a novel\nfeedback connection structure is developed to enhance low-level feature\nexpression with high-level information. In our method, the output of each layer\nin the first stage is also used as the input of the corresponding layer in the\nnext state to re-update the previous low-level filters. Moreover, we introduce\na pyramid non-local structure to model global contextual information in\ndifferent scales and improve the discriminative representation of the network.\nExtensive experimental results on various datasets demonstrate the superiority\nof our FPAN in comparison with the state-of-the-art SR methods.\n"}
+{"abstract": "  Models pre-trained on multiple languages have shown significant promise for\nimproving speech recognition, particularly for low-resource languages. In this\nwork, we focus on phoneme recognition using Allosaurus, a method for\nmultilingual recognition based on phonetic annotation, which incorporates\nphonological knowledge through a language-dependent allophone layer that\nassociates a universal narrow phone-set with the phonemes that appear in each\nlanguage. To evaluate in a challenging real-world scenario, we curate phone\nrecognition datasets for Bukusu and Saamia, two varieties of the Luhya language\ncluster of western Kenya and eastern Uganda. To our knowledge, these datasets\nare the first of their kind. We carry out similar experiments on the dataset of\nan endangered Tangkhulic language, East Tusom, a Tibeto-Burman language variety\nspoken mostly in India. We explore both zero-shot and few-shot recognition by\nfine-tuning using datasets of varying sizes (10 to 1000 utterances). We find\nthat fine-tuning of Allosaurus, even with just 100 utterances, leads to\nsignificant improvements in phone error rates.\n"}
+{"abstract": "  R-parity conserving supersymmetric models with right-handed (RH) neutrinos\nare very appealing since they could naturally explain neutrino physics and also\nprovide a good dark matter (DM) candidate such as the lightest supersymmetric\nparticle (LSP). In this work we consider the next-to-minimal supersymmetric\nstandard model (NMSSM) plus RH neutrino superfields, with effective Majorana\nmasses dynamically generated at the electroweak scale (EW). We perform a scan\nof the relevant parameter space and study both possible DM candidates: RH\nsneutrino and neutralino. Especially for the case of RH sneutrino DM we analyse\nthe intimate relation between both candidates to obtain the correct amount of\nrelic density. Besides the well-known resonances, annihilations through scalar\nquartic couplings and coannihilation mechanisms with all kind of neutralinos,\nare crucial. Finally, we present the impact of current and future direct and\nindirect detection experiments on both DM candidates.\n"}
+{"abstract": "  We propose a novel approach to summarization based on Bayesian deep learning.\nWe approximate Bayesian summary generation by first extending state-of-the-art\nsummarization models with Monte Carlo dropout and then using them to perform\nmultiple stochastic forward passes. This method allows us to improve\nsummarization performance by simply using the median of multiple stochastic\nsummaries. We show that our variational equivalents of BART and PEGASUS can\noutperform their deterministic counterparts on multiple benchmark datasets. In\naddition, we rely on Bayesian inference to measure the uncertainty of the model\nwhen generating summaries. Having a reliable uncertainty measure, we can\nimprove the experience of the end user by filtering out generated summaries of\nhigh uncertainty. Furthermore, our proposed metric could be used as a criterion\nfor selecting samples for annotation, and can be paired nicely with active\nlearning and human-in-the-loop approaches.\n"}
+{"abstract": "  Retrograde Rossby waves, measured to have significant amplitudes in the Sun,\nlikely have notable implications for various solar phenomena. Rossby waves\ncreate small-amplitude, very-low frequency motions (on the order of the\nrotation rate and lower), which in turn shift the resonant frequencies and\neigenfunctions of the acoustic modes of the Sun. The detection of even\nazimuthal orders Rossby modes using mode coupling presents additional\nchallenges and prior work therefore only focused on odd orders. Here, we\nsuccessfully extend the methodology to measure even azimuthal orders as well.\nWe analyze 4 and 8 years of Helioseismic and Magnetic Imager (HMI) data and\nconsider coupling between different-degree acoustic modes (of separations 1 and\n3 in harmonic degree). The technique uses couplings between different frequency\nbins to capture the temporal variability of the Rossby modes. We observe\nsignificant power close to the theoretical dispersion relation for sectoral\nRossby modes (where the azimuthal order is same as harmonic degree, s = |t|).\nOur results are consistent with prior measurements of Rossby modes with\nazimuthal orders over the range t = 4 to 16 with maximum power occurring at\nmode t = 8. The amplitudes of these modes vary from 1 to 2 m/s. We place an\nupper bound of 0.2 m/s on the sectoral t = 2 mode, which we do not detect in\nour measurements. This effort adds credence to the mode-coupling methodology in\nhelioseismology\n"}
+{"abstract": "  We study lattices acting on $\\textrm{CAT}(0)$ spaces via their commensurated\nsubgroups. To do this we introduce the notions of a graph of lattices and a\ncomplex of lattices giving graph and complex of group splittings of\n$\\textrm{CAT}(0)$ lattices. Using this framework we characterise irreducible\nuniform $(\\textrm{Isom}(\\mathbb{E}^n)\\times T)$-lattices by $C^\\ast$-simplicity\nand the failure of virtual fibring and biautomaticity. We construct\nnon-residually finite uniform lattices acting on arbitrary products of right\nangled buildings and non-biautomatic lattices acting on the product of\n$\\mathbb{E}^n$ and a right-angled building. We investigate the residual\nfiniteness, $L^2$-cohomology, and $C^\\ast$-simplicity of $\\textrm{CAT}(0)$\nlattices more generally. Along the way we prove that many right angled Artin\ngroups with rank $2$ centre are not quasi-isometrically rigid.\n"}
+{"abstract": "  Large annotated lung sound databases are publicly available and might be used\nto train algorithms for diagnosis systems. However, it might be a challenge to\ndevelop a well-performing algorithm for small non-public data, which have only\na few subjects and show differences in recording devices and setup. In this\npaper, we use transfer learning to tackle the mismatch of the recording setup.\nThis allows us to transfer knowledge from one dataset to another dataset for\ncrackle detection in lung sounds. In particular, a single input convolutional\nneural network (CNN) model is pre-trained on a source domain using ICBHI 2017,\nthe largest publicly available database of lung sounds. We use log-mel\nspectrogram features of respiratory cycles of lung sounds. The pre-trained\nnetwork is used to build a multi-input CNN model, which shares the same network\narchitecture for respiratory cycles and their corresponding respiratory phases.\nThe multi-input model is then fine-tuned on the target domain of our\nself-collected lung sound database for classifying crackles and normal lung\nsounds. Our experimental results show significant performance improvements of\n9.84% (absolute) in F-score on the target domain using the multi-input CNN\nmodel based on transfer learning for crackle detection in adventitious lung\nsound classification task.\n"}
+{"abstract": "  Recently, models have been shown to predict the effects of unexpected\nsituations, e.g., would cloudy skies help or hinder plant growth? Given a\ncontext, the goal of such situational reasoning is to elicit the consequences\nof a new situation (st) that arises in that context. We propose a method to\niteratively build a graph of relevant consequences explicitly in a structured\nsituational graph (st-graph) using natural language queries over a finetuned\nlanguage model (M). Across multiple domains, CURIE generates st-graphs that\nhumans find relevant and meaningful in eliciting the consequences of a new\nsituation. We show that st-graphs generated by CURIE improve a situational\nreasoning end task (WIQA-QA) by 3 points on accuracy by simply augmenting their\ninput with our generated situational graphs, especially for a hard subset that\nrequires background knowledge and multi-hop reasoning.\n"}
+{"abstract": "  Different types of malicious activities have been flagged in multiple\npermissionless blockchains such as bitcoin, Ethereum etc. While some malicious\nactivities exploit vulnerabilities in the infrastructure of the blockchain,\nsome target its users through social engineering techniques. To address these\nproblems, we aim at automatically flagging blockchain accounts that originate\nsuch malicious exploitation of accounts of other participants. To that end, we\nidentify a robust supervised machine learning (ML) algorithm that is resistant\nto any bias induced by an over representation of certain malicious activity in\nthe available dataset, as well as is robust against adversarial attacks. We\nfind that most of the malicious activities reported thus far, for example, in\nEthereum blockchain ecosystem, behaves statistically similar. Further, the\npreviously used ML algorithms for identifying malicious accounts show bias\ntowards a particular malicious activity which is over-represented. In the\nsequel, we identify that Neural Networks (NN) holds up the best in the face of\nsuch bias inducing dataset at the same time being robust against certain\nadversarial attacks.\n"}
+{"abstract": "  Oxygen consumption (VO$_2$) provides established clinical and physiological\nindicators of cardiorespiratory function and exercise capacity. However, VO$_2$\nmonitoring is largely limited to specialized laboratory settings, making its\nwidespread monitoring elusive. Here, we investigate temporal prediction of\nVO$_2$ from wearable sensors during cycle ergometer exercise using a temporal\nconvolutional network (TCN). Cardiorespiratory signals were acquired from a\nsmart shirt with integrated textile sensors alongside ground-truth VO$_2$ from\na metabolic system on twenty-two young healthy adults. Participants performed\none ramp-incremental and three pseudorandom binary sequence exercise protocols\nto assess a range of VO$_2$ dynamics. A TCN model was developed using causal\nconvolutions across an effective history length to model the time-dependent\nnature of VO$_2$. Optimal history length was determined through minimum\nvalidation loss across hyperparameter values. The best performing model encoded\n218 s history length (TCN-VO$_2$ A), with 187 s, 97 s, and 76 s yielding less\nthan 3% deviation from the optimal validation loss. TCN-VO$_2$ A showed strong\nprediction accuracy (mean, 95% CI) across all exercise intensities (-22\nml.min$^{-1}$, [-262, 218]), spanning transitions from low-moderate (-23\nml.min$^{-1}$, [-250, 204]), low-high (14 ml.min$^{-1}$, [-252, 280]),\nventilatory threshold-high (-49 ml.min$^{-1}$, [-274, 176]), and maximal (-32\nml.min$^{-1}$, [-261, 197]) exercise. Second-by-second classification of\nphysical activity across 16090 s of predicted VO$_2$ was able to discern\nbetween vigorous, moderate, and light activity with high accuracy (94.1%). This\nsystem enables quantitative aerobic activity monitoring in non-laboratory\nsettings across a range of exercise intensities using wearable sensors for\nmonitoring exercise prescription adherence and personal fitness.\n"}
+{"abstract": "  We introduce the concept of quasi-inverse of quantum and classical channels,\nprove general properties of these inverses and determine them for a large class\nof channels acting in an arbitrary finite dimension. Therefore we extend the\nprevious results of [1] to arbitrary dimensional channels and to the classical\ndomain. We demonstrate how application of the proposed scheme can increase on\nthe average the fidelity between a given random pure state and its image\ntransformed by the quantum channel followed by its quasi-inversion.\n"}
+{"abstract": "  The notion of an Evolutional Deep Neural Network (EDNN) is introduced for the\nsolution of partial differential equations (PDE). The parameters of the network\nare trained to represent the initial state of the system only, and are\nsubsequently updated dynamically, without any further training, to provide an\naccurate prediction of the evolution of the PDE system. In this framework, the\nnetwork parameters are treated as functions with respect to the appropriate\ncoordinate and are numerically updated using the governing equations. By\nmarching the neural network weights in the parameter space, EDNN can predict\nstate-space trajectories that are indefinitely long, which is difficult for\nother neural network approaches. Boundary conditions of the PDEs are treated as\nhard constraints, are embedded into the neural network, and are therefore\nexactly satisfied throughout the entire solution trajectory. Several\napplications including the heat equation, the advection equation, the Burgers\nequation, the Kuramoto Sivashinsky equation and the Navier-Stokes equations are\nsolved to demonstrate the versatility and accuracy of EDNN. The application of\nEDNN to the incompressible Navier-Stokes equation embeds the divergence-free\nconstraint into the network design so that the projection of the momentum\nequation to solenoidal space is implicitly achieved. The numerical results\nverify the accuracy of EDNN solutions relative to analytical and benchmark\nnumerical solutions, both for the transient dynamics and statistics of the\nsystem.\n"}
+{"abstract": "  We reveal that finite-size solid acoustic resonators can support genuine\nbound states in the continuum (BICs) completely localized inside the resonator.\nThe developed theory provides the multipole classification of such BICs in the\nresonators of various shapes. It is shown how breaking of the resonator's\nsymmetry turns BICs into quasi-BICs manifesting themselves in the scattering\nspectra as high-Q Fano resonances. We believe that the revealed novel states\nwill push the performance limits of acoustic devices and will serve as high-Q\nbuilding blocks for acoustic sensors, antennas, and topological acoustic\nstructures.\n"}
+{"abstract": "  The 2-dimensional global rigidity has been shown to be equivalent to\n3-connectedness and redundant rigidity by a combination of two results due to\nJackson and Jord\\'an, and Connelly, respectively. By the characterization, a\ntheorem of Lov\\'asz and Yemini implies that every $6$-connected graph is\nredundantly rigid, and thus globally rigid. The 6-connectedness is best\npossible, since there exist infinitely many 5-connected non-rigid graphs.\nJackson, Servatius and Servatius used the idea of ``essential connectivity''\nand proved that every 4-connected ``essentially 6-connected'' graph is\nredundantly rigid and thus global rigid. Since 3-connectedness is a necessary\ncondition of global rigidity, it is interesting to study 3-connected graphs for\nredundant rigidity and thus globally rigidity. We utilize a different\n``essential connectivity'', and prove that every 3-connected essentially\n9-connected graph is redundantly rigid and thus globally rigid. The essential\n9-connectedness is best possible. Under this essential connectivity, we also\nprove that every 4-connected essentially 6-connected graph is redundantly rigid\nand thus global rigid. Our proofs are based on discharging arguments.\n"}
+{"abstract": "  The question of whether quark matter exists in neutron stars is a long\nstanding one. Generally one finds that a first order phase transition from\nbaryons to quarks softens the equation of state so much that the star would\ncollapse into a black hole. We consider a crossover equation of state, similar\nto the crossover that is found in lattice QCD studies at finite temperature and\nzero or small baryon chemical potentials. We find that with reasonable\nparameters it may be possible to support neutron stars up to about 2.2 solar\nmasses. In that case 1 to 10% of the pressure would be contributed by quark\nmatter in the central core of the highest mass stars.\n"}
+{"abstract": "  The $n$th term of an automatic sequence is the output of a deterministic\nfinite automaton fed with the representation of $n$ in a suitable numeration\nsystem. In this paper, instead of considering automatic sequences built on a\nnumeration system with a regular numeration language, we consider these built\non languages associated with trees having periodic labeled signatures and, in\nparticular, rational base numeration systems. We obtain two main\ncharacterizations of these sequences. The first one is concerned with $r$-block\nsubstitutions where $r$ morphisms are applied periodically. In particular, we\nprovide examples of such sequences that are not morphic. The second\ncharacterization involves the factors, or subtrees of finite height, of the\ntree associated with the numeration system and decorated by the terms of the\nsequence.\n"}
+{"abstract": "  Data science has employed great research efforts in developing advanced\nanalytics, improving data models and cultivating new algorithms. However, not\nmany authors have come across the organizational and socio-technical challenges\nthat arise when executing a data science project: lack of vision and clear\nobjectives, a biased emphasis on technical issues, a low level of maturity for\nad-hoc projects and the ambiguity of roles in data science are among these\nchallenges. Few methodologies have been proposed on the literature that tackle\nthese type of challenges, some of them date back to the mid-1990, and\nconsequently they are not updated to the current paradigm and the latest\ndevelopments in big data and machine learning technologies. In addition, fewer\nmethodologies offer a complete guideline across team, project and data &\ninformation management. In this article we would like to explore the necessity\nof developing a more holistic approach for carrying out data science projects.\nWe first review methodologies that have been presented on the literature to\nwork on data science projects and classify them according to the their focus:\nproject, team, data and information management. Finally, we propose a\nconceptual framework containing general characteristics that a methodology for\nmanaging data science projects with a holistic point of view should have. This\nframework can be used by other researchers as a roadmap for the design of new\ndata science methodologies or the updating of existing ones.\n"}
+{"abstract": "  Can we change the shape of a domain without altering its sizes? By\nintroducing a size-invariant shape transformation, we propose the existence and\nexplore the consequences of a new type of physical effect appearing at the\nquantum scales, which we call here as \"quantum shape effect\". By completely\nseparating the shape effects from size effects, we show that shape alone\nbecomes a control parameter on the thermodynamic state functions of confined\nsystems at nanoscale. We develop an overlapped quantum boundary layer method to\nanalytically predict the quantum shape effects, reducing a thermodynamic\nproblem into a geometric one and revealing the profound link between the\ngeometry and thermodynamics at the quantum scales. Furthermore, we introduce\nthe isoformal, shape preserving, process which opens up the possibility of a\nnew generation of thermodynamic cycles operating at nanoscale with unique\nfeatures. As a whole, this thesis constitutes the proposition and a\ncomprehensive investigation of the theory, construction of the methodology and\nexploration of the applications of quantum shape effects in thermodynamics.\n"}
+{"abstract": "  In this paper, we study a cosmological model inspired in the axionic matter\nwith two canonical scalar fields $\\phi_1$ and $\\phi_2$ interacting through a\nterm added to its potential. Introducing novel dynamical variables, and a\ndimensionless time variable, the resulting dynamical system is studied. The\nmain difficulties arising in the standard dynamical systems approach, where\nexpansion normalized dynamical variables are usually adopted, are due to the\noscillations entering the nonlinear system through the Klein-Gordon (KG)\nequations. This motivates the analysis of the oscillations using methods from\nthe theory of averaging nonlinear dynamical systems. We prove that\ntime-dependent systems, and their corresponding time-averaged versions, have\nthe same late-time dynamics. Then, we study the time-averaged system using\nstandard techniques of dynamical systems. We present numerical simulations as\nevidence of such behavior.\n"}
+{"abstract": "  Continuous fuzzing is an increasingly popular technique for automated quality\nand security assurance. Google maintains OSS-Fuzz: a continuous fuzzing service\nfor open source software. We conduct the first empirical study of OSS-Fuzz,\nanalyzing 23,907 bugs found in 316 projects. We examine the characteristics of\nfuzzer-found faults, the lifecycles of such faults, and the evolution of\nfuzzing campaigns over time. We find that OSS-Fuzz is often effective at\nquickly finding bugs, and developers are often quick to patch them. However,\nflaky bugs, timeouts, and out of memory errors are problematic, people rarely\nfile CVEs for security vulnerabilities, and fuzzing campaigns often exhibit\npunctuated equilibria, where developers might be surprised by large spikes in\nbugs found. Our findings have implications on future fuzzing research and\npractice.\n"}
+{"abstract": "  We investigate the magnetic interactions in triangular rare-earth\ndelafossites materials NaYbO$_2$ and NaYbS$_2$ via first-principles\ncalculations. The calculated Curie-Weiss temperatures are in good agreement\nwith experiments. We perform classical Monte Carlo simulations of the two\ncompounds using the extracted exchange parameters. We find that if only the\nnearest neighbor interactions are considered, the magnetic ground states of\nNaYbO$_2$ and NaYbS$_2$ are a stripe and a planar 120\\degree~ N\\'{e}el state,\nrespectively. The simulated transition temperatures are much higher than the\nlowest experimental temperatures, where no magnetic ordering was observed.\nHowever, we show by adding suitable second neighbor interactions, the {\\it\nclassical} magnetic ground state of NaYbO$_2$ becomes to the $Z_2$ vortex\nphase, and the simulated specific heat $C_v$ are very similar to the\nexperimental observations, with no obvious phase transition down to the\nextremely low temperature.\n"}
+{"abstract": "  The most promising avenue for detecting primordial gravitational waves from\ncosmic inflation is through measurements of degree-scale CMB $B$-mode\npolarisation. This approach must face the challenge posed by gravitational\nlensing of the CMB, which obscures the signal of interest. Fortunately, the\nlensing effects can be partially removed by combining high-resolution $E$-mode\nmeasurements with an estimate of the projected matter distribution. For\nnear-future experiments, the best estimate of the latter will arise from\nco-adding internal reconstructions (derived from the CMB itself) with external\ntracers of the large-scale structure such as the cosmic infrared background\n(CIB). In this work, we characterise how foregrounds impact the delensing\nprocedure when CIB intensity, $I$, is used as the matter tracer. We find that\nhigher-point functions of the CIB and Galactic dust such as $\\langle BEI\n\\rangle_{c}$ and $\\langle EIEI \\rangle_{c}$ can, in principle, bias the power\nspectrum of delensed $B$-modes. After estimating the dust residuals in\ncurrently-available CIB maps and upcoming, foreground-cleaned Simons\nObservatory CMB data, we find, using non-Gaussian dust simulations, that the\nbias to any primordial signal is small compared to statistical errors for\nground-based experiments, but might be significant for space-based experiments\nprobing very large angular scales. However, mitigation techniques based on\nmulti-frequency cleaning appear to be very effective. We also show, by means of\nan analytic model, that the bias arising from the higher-point functions of the\nCIB itself ought to be negligible.\n"}
+{"abstract": "  The Directed Grid Theorem, stating that there is a function $f$ such that a\ndirected graphs of directed treewidth at least $f(k)$ contains a directed grid\nof size at least $k$ as a butterfly minor, after being a conjecture for nearly\n20 years, has been proven in 2015 by Kawarabayashi and Kreutzer. However, the\nfunction $f$ obtained in the proof is very fast growing.\n  In this work, we show that if one relaxes directed grid to bramble of\nconstant congestion, one can obtain a polynomial bound. More precisely, we show\nthat for every $k \\geq 1$ there exists $t = \\mathcal{O}(k^{48} \\log^{13} k)$\nsuch that every directed graph of directed treewidth at least $t$ contains a\nbramble of congestion at most $8$ and size at least $k$.\n"}
+{"abstract": "  This paper presents a framework that allows online\ndynamic-stability-constrained optimal trajectory planning of a mobile\nmanipulator robot working on rough terrain. First, the kinematics model of a\nmobile manipulator robot, and the Zero Moment Point (ZMP) stability measure are\npresented as theoretical background. Then, a sampling-based quasi-static\nplanning algorithm modified for stability guarantee and traction optimization\nin continuous dynamic motion is presented along with a mathematical proof. The\nrobot's quasi-static path is then used as an initial guess to warm-start a\nnonlinear optimal control solver which may otherwise have difficulties finding\na solution to the stability-constrained formulation efficiently. The\nperformance and computational efficiency of the framework are demonstrated\nthrough an application to a simulated timber harvesting mobile manipulator\nmachine working on varying terrain. The results demonstrate feasibility of\nonline trajectory planning on varying terrain while satisfying the dynamic\nstability constraint.\n"}
+{"abstract": "  Using matrix model, Mironov and Morozov recently gave a formula which\nrepresents Kontsevich-Witten tau-function as a linear expansion of Schur\nQ-polynomials. In this paper, we will show directly that the Q-polynomial\nexpansion in this formula satisfies the Virasoro constraints, and consequently\nobtain a proof of this formula without using matrix model. We also give a proof\nfor Alexandrov's conjecture that Kontsevich-Witten tau-function is a\nhypergeometric tau-function of the BKP hierarchy after re-scaling.\n"}
+{"abstract": "  Mid-infrared photometry of the Wolf-Rayet star HD 38030 in the Large\nMagellanic Cloud from the NEOWISE-R mission show it to have undergone a\ndust-formation episode in 2018 and the dust to have cooled in 2019-20. New\nspectroscopy with the MagE spectrograph on the Magellan I Baade Telescope in\n2019 and 2020 show absorption lines attributable to a companion of type near\nO9.7III-IV. We found a significant shift in the radial velocity of the C IV\n5801-12 blend compared with the RVs measured in 1984 and 1993. The results\ncombine to suggest that HD 38030 is a colliding-wind binary having short-lived\ndust formation episodes, like the Galactic systems WR 140 and WR 19, but at\nintervals in excess of 20 yr.\n"}
+{"abstract": "  There is increasing appetite for analysing multiple network data. This is\ndifferent to analysing traditional data sets, where now each observation in the\ndata comprises a network. Recent technological advancements have allowed the\ncollection of this type of data in a range of different applications. This has\ninspired researchers to develop statistical models that most accurately\ndescribe the probabilistic mechanism that generates a network population and\nuse this to make inferences about the underlying structure of the network data.\nOnly a few studies developed to date consider the heterogeneity that can exist\nin a network population. We propose a Mixture of Measurement Error Models for\nidentifying clusters of networks in a network population, with respect to\nsimilarities detected in the connectivity patterns among the networks' nodes.\nExtensive simulation studies show our model performs well in both clustering\nmultiple network data and inferring the model parameters. We further apply our\nmodel on two real world multiple network data sets resulting from the fields of\nComputing (Human Tracking Systems) and Neuroscience.\n"}
+{"abstract": "  NEO is one of the top public chains worldwide. We focus on its backbone\nconsensus protocol, called delegated Byzantine Fault Tolerance (dBFT). The dBFT\nprotocol has been adopted by a variety of blockchain systems such as ONT. dBFT\nclaims to guarantee the security when no more than $f = \\lfloor \\frac{n}{3}\n\\rfloor$ nodes are Byzantine, where $n$ is the total number of consensus\nparticipants. However, we identify attacks to break the claimed security. In\nthis paper, we show our results by providing a security analysis on its dBFT\nprotocol. First, we evaluate NEO's source code and formally present the\nprocedures of dBFT via the state machine replication (SMR) model. Next, we\nprovide a theoretical analysis with two example attacks. These attacks break\nthe security of dBFT with no more than $f$ nodes. Then, we provide\nrecommendations on how to fix the system against the identified attacks. The\nsuggested fixes have been accepted by the NEO official team. Finally, we\nfurther discuss the reasons causing such issues, the relationship with current\npermissioned blockchain systems, and the scope of potential influence.\n"}
+{"abstract": "  Over-parameterized models can perfectly learn various types of data\ndistributions, however, generalization error is usually lower for real data in\ncomparison to artificial data. This suggests that the properties of data\ndistributions have an impact on generalization capability. This work focuses on\nthe search space defined by the input data and assumes that the correlation\nbetween labels of neighboring input values influences generalization. If\ncorrelation is low, the randomness of the input data space is high leading to\nhigh generalization error. We suggest to measure the randomness of an input\ndata space using Maurer's universal. Results for synthetic classification tasks\nand common image classification benchmarks (MNIST, CIFAR10, and Microsoft's\ncats vs. dogs data set) find a high correlation between the randomness of input\ndata spaces and the generalization error of deep neural networks for binary\nclassification problems.\n"}
+{"abstract": "  Background and aim: Most of the Mixed Reality models used in the surgical\ntelepresence are suffering from discrepancies in the boundary area and\nspatial-temporal inconsistency due to the illumination variation in the video\nframes. The aim behind this work is to propose a new solution that helps\nproduce the composite video by merging the augmented video of the surgery site\nand the virtual hand of the remote expertise surgeon. The purpose of the\nproposed solution is to decrease the processing time and enhance the accuracy\nof merged video by decreasing the overlay and visualization error and removing\nocclusion and artefacts. Methodology: The proposed system enhanced the mean\nvalue cloning algorithm that helps to maintain the spatial-temporal consistency\nof the final composite video. The enhanced algorithm includes the 3D mean value\ncoordinates and improvised mean value interpolant in the image cloning process,\nwhich helps to reduce the sawtooth, smudging and discolouration artefacts\naround the blending region. Results: As compared to the state of the art\nsolution, the accuracy in terms of overlay error of the proposed solution is\nimproved from 1.01mm to 0.80mm whereas the accuracy in terms of visualization\nerror is improved from 98.8% to 99.4%. The processing time is reduced to 0.173\nseconds from 0.211 seconds. Conclusion: Our solution helps make the object of\ninterest consistent with the light intensity of the target image by adding the\nspace distance that helps maintain the spatial consistency in the final merged\nvideo.\n"}
+{"abstract": "  In hierarchical structure formation, metal-poor stars in and around the Milky\nWay (MW) originate primarily from mergers of lower-mass galaxies. A common\nexpectation is therefore that metal-poor stars should have isotropic,\ndispersion-dominated orbits that do not correlate strongly with the MW disk.\nHowever, recent observations of stars in the MW show that metal-poor ([Fe/H] <\n-2) stars are preferentially on prograde orbits with respect to the disk. Using\nthe FIRE-2 suite of cosmological zoom-in simulations of MW/M31-mass galaxies,\nwe investigate the prevalence and origin of prograde metal-poor stars. Almost\nall (11 of 12) of our simulations have metal-poor stars on preferentially\nprograde orbits today and throughout most of their history: we thus predict\nthat this is a generic feature of MW/M31-mass galaxies. The typical\nprograde-to-retrograde ratio is ~2:1, which depends weakly on stellar\nmetallicity at Fe/H] < -1. These trends predicted by our simulations agree well\nwith MW observations. Prograde metal-poor stars originate largely from a single\nLMC/SMC-mass gas-rich merger 7 - 12.5 Gyr ago, which deposited existing\nmetal-poor stars and significant gas on an orbital vector that sparked the\nformation of and/or shaped the orientation of a long-lived stellar disk, giving\nrise to a prograde bias for all low-metallicity stars. We find sub-dominant\ncontributions from in-situ stars formed in the host galaxy before this merger,\nand in some cases, additional massive mergers. We find few clear correlations\nbetween any properties of our MW/M31-mass galaxies at z = 0 and the degree of\nthis prograde bias as a result of diverse merger scenarios.\n"}
+{"abstract": "  High precision astrometry aims at source position determination to a very\nsmall fraction of the diffraction image size, in high SNR regime. One of the\nkey limitations to such goal is the optical response variation of the telescope\nover a sizeable FOV, required to ensure bright reference objects to any\nselected target. The issue translates into severe calibration constraints,\nand/or the need for complex telescope and focal plane metrology. We propose an\ninnovative system approach derived from the established TMA telescope concept,\nextended to achieve high filling factor of an annular field of view around the\noptical axis of the telescope. The proposed design is a very compact, 1 m class\ntelescope compatible with modern CCD and CMOS detectors (EFL = 15 m). We\ndescribe the concept implementation guidelines and the optical performance of\nthe current optical design. The diffraction limited FOV exceeds 1.25 square\ndegrees, and the detector occupies the best 0.25 square degree with 66 devices.\n"}
+{"abstract": "  Most modern operating systems have adopted the one-to-one thread model to\nsupport fast execution of threads in both multi-core and single-core systems.\nThis thread model, which maps the kernel-space and user-space threads in a\none-to-one manner, supports quick thread creation and termination in\nhigh-performance server environments. However, the performance of time-critical\nthreads is degraded when multiple threads are being run in low-end CE devices\nwith limited system resources. When a CE device runs many threads to support\ndiverse application functionalities, low-level hardware specifications often\nlead to significant resource contention among the threads trying to obtain\nsystem resources. As a result, the operating system encounters challenges, such\nas excessive thread context switching overhead, execution delay of\ntime-critical threads, and a lack of virtual memory for thread stacks. This\npaper proposes a state-of-the-art Thread Evolution Kit (TEK) that consists of\nthree primary components: a CPU Mediator, Stack Tuner, and Enhanced Thread\nIdentifier. From the experiment, we can see that the proposed scheme\nsignificantly improves user responsiveness (7x faster) under high CPU\ncontention compared to the traditional thread model. Also, TEK solves the\nsegmentation fault problem that frequently occurs when a CE application\nincreases the number of threads during its execution.\n"}
+{"abstract": "  Abundances and partitioning of ices and gases produced by gas-grain chemistry\nare governed by adsorption and desorption on grains. Understanding\nastrophysical observations rely on laboratory measurements of adsorption and\ndesorption rates on dust grains analogs. On flat surfaces, gas adsorption\nprobabilities (or sticking coefficients) have been found close to unity for\nmost gases. Here we report a strong decrease of the sticking coefficients of\nH2O and CO2 on substrates more akin to cosmic dust, such as submicrometer-sized\nparticles of carbon and olivine, bare or covered with ice. This effect results\nfrom the local curvature of the grains, and then extends to larger grains made\nof aggregated small particles, such as fluffy or porous dust in more evolved\nmedia (e.g. circumstellar disks). The main astrophysical implication is that\naccretion rates of gases are reduced accordingly, slowing the growth of cosmic\nices. Furthermore, volatile species that are not adsorbed on a grain at their\nfreeze-out temperature will pertain in the gas phase, which will impact gas-ice\npartitions. We also found that thermal desorption of H2O is not modified by\ngrains size, and thus the snowlines temperature should be independent on the\ndust size distribution.\n"}
+{"abstract": "  Within a new paradigm for communications on the nanoscale, high-frequency\nsurface acoustic waves are becoming effective data carrier and encoder. On-chip\ncommunications require acoustic wave propagation along nano-corrugated surfaces\nwhich strongly scatter traditional Rayleigh waves. Here we propose the delivery\nof information using subsurface acoustic waves with hypersound frequencies ~20\nGHz, which is a nanoscale analogue of subsurface sound waves in the ocean. A\nbunch of subsurface hypersound modes is generated by pulsed optical excitation\nin a multilayer semiconductor structure with a metallic nanograting on top. The\nguided hypersound modes propagate coherently beneath the nanograting, retaining\nthe surface imprinted information, on a distance of more than 50 {\\mu}m which\nessentially exceeds the propagation length of Rayleigh waves. The concept is\nsuitable for interfacing single photon emitters, such as buried quantum dots,\ncarrying coherent spin excitations in magnonic devices, and encoding the\nsignals for optical communications at the nanoscale.\n"}
+{"abstract": "  The emergence of novel differential rotation laws that can reproduce the\nrotational profile of binary neutron star merger remnants has opened the way\nfor the construction of equilibrium models with properties that resemble those\nof remnants in numerical simulations. We construct models of merger remnants,\nusing a recently introduced 4-parameter differential rotation law and three\ntabulated, zero-temperature equations of state. The models have angular momenta\nthat are determined by empirical relations, constructed through numerical\nsimulations. After a systematic exploration of the parameter space of merger\nremnant equilibrium sequences, which includes the determination of turning\npoints along constant angular momentum sequences, we find that a particular\nrotation law can reproduce the threshold mass to prompt collapse to a black\nhole with a relative difference of only $\\sim 1\\%$ with respect to numerical\nsimulations, in all cases considered. Furthermore, our results indicate a\npossible correlation between the compactness of equilibrium models of remnants\nat the threshold mass and the compactness of maximum-mass nonrotating models.\nAnother key prediction of binary neutron star merger simulations is a\nrelatively slowly rotating inner region, where the angular velocity $\\Omega$\n(as measured by an observer at infinity) is mostly due to the frame dragging\nangular velocity $\\omega$. In our investigation of the parameter space of the\nadopted differential rotation law, we naturally find quasi-spherical (Type A)\nremnant models with this property. Our investigation clarifies the impact of\nthe differential rotation law and of the equation of state on key properties of\nbinary neutron star remnants and lays the groundwork for including thermal\neffects in future studies.\n"}
+{"abstract": "  The spherical Fourier-Bessel (SFB) decomposition is a natural choice for the\nradial/angular separation that allows optimal extraction of cosmological\ninformation from large volume galaxy surveys. In this paper we develop a SFB\npower spectrum estimator that allows the measurement of the largest angular and\nradial modes with the next generation of galaxy surveys. The code measures the\npseudo-SFB power spectrum, and takes into account mask, selection function,\npixel window, and shot noise. We show that the local average effect is\nsignificant only in the largest-scale mode, and we provide an analytical\ncovariance matrix. By imposing boundary conditions at the minimum and maximum\nradius encompassing the survey volume, the estimator does not suffer from the\nnumerical instabilities that have proven challenging in the past. The estimator\nis demonstrated on simplified Roman-like, SPHEREx-like, and Euclid-like mask\nand selection functions. For intuition and validation, we also explore the SFB\npower spectrum in the Limber approximation. We release the associated public\ncode written in Julia.\n"}
+{"abstract": "  Modern recording technologies now enable simultaneous recording from large\nnumbers of neurons. This has driven the development of new statistical models\nfor analyzing and interpreting neural population activity. Here we provide a\nbroad overview of recent developments in this area. We compare and contrast\ndifferent approaches, highlight strengths and limitations, and discuss\nbiological and mechanistic insights that these methods provide.\n"}
+{"abstract": "  Spectral functions on Euclidean Jordan algebras arise frequently in convex\nmodels. Despite the success of primal-dual conic interior point solvers, there\nhas been little work on enabling direct support for spectral cones, i.e. proper\nnonsymmetric cones defined from epigraphs and perspectives of spectral\nfunctions. We propose simple logarithmically homogeneous barriers for spectral\ncones and we derive efficient, numerically stable procedures for evaluating\nbarrier oracles such as inverse Hessian operators. For two useful classes of\nspectral cones - the root-determinant cones and the matrix monotone derivative\ncones - we show that the barriers are self-concordant, with nearly optimal\nparameters. We implement these cones and oracles in our open source solver\nHypatia, and we write simple, natural formulations for four applied problems.\nOur computational benchmarks demonstrate that Hypatia often solves the natural\nformulations more efficiently than advanced solvers such as MOSEK 9 solve\nequivalent extended formulations written using only the cones these solvers\nsupport.\n"}
+{"abstract": "  Computing the periods of variable objects is well-known to be computationally\nexpensive. Modern astronomical catalogs contain a significant number of\nobserved objects. Therefore, even if the period ranges for particular classes\nof objects are well-constrained due to expected physical properties, periods\nmust be derived for a tremendous number of objects. In this paper, we propose a\nGPU-accelerated Lomb-Scargle period finding algorithm that computes periods for\nsingle objects or for batches of objects as is necessary in many data\nprocessing pipelines. We demonstrate the performance of several optimizations,\nincluding comparing the use of shared and global memory GPU kernels and using\nmultiple CUDA streams to copy periodogram data from the GPU to the host. Also,\nwe quantify the difference between 32-bit and 64-bit floating point precision\non two classes of GPUs, and show that the performance degradation of using\n64-bit over 32-bit is greater on the CPU than a GPU designed for scientific\ncomputing. We find that the GPU algorithm achieves superior performance over\nthe baseline parallel CPU implementation, achieving a speedup of up to\n174.53$\\times$. The Vera C. Rubin Observatory will carry out the Legacy Survey\nof Space and Time (LSST). We perform an analysis that shows we can derive the\nrotation periods of batches of Solar System objects at LSST scale in near\nreal-time, which will be employed in a future LSST event broker. All source\ncode has been made publicly available.\n"}
+{"abstract": "  We present photometric and spectroscopic observations of the nearby Type Ia\nSN 2019yvq, from its discovery $\\sim$1 day after explosion to $\\sim$100 days\nafter its peak brightness. This SN exhibits several unusual features, most\nnotably an extremely bright UV excess seen within $\\sim$5 days of its\nexplosion. As seen in Swift UV data, this early excess outshines its \"peak\"\nbrightness, making this object more extreme than other SNe with early UV/blue\nexcesses (e.g. iPTF14atg and SN 2017cbv). In addition, it was underluminous\n($M_B=-18.4$), relatively quickly declining ($\\Delta m_{15}(B)=1.35$), and\nshows red colors past its early blue bump. Unusual (although not unprecedented)\nspectral features include extremely broad-lined and high-velocity Si\nabsorption. Despite obvious differences in peak spectra, we classify SN 2019yvq\nas a transitional member of the 02es-like subclass due to its similarities in\nseveral respects (e.g. color, peak luminosity, peak Ti, nebular [Ca II]). We\nmodel this dataset with a variety of published models, including SN ejecta -\ncompanion shock interaction and sub-Chandrasekhar mass WD double detonation\nmodels. Radio constraints from the VLA place an upper limit of $(4.5 - 20)\n\\times 10^{-8}$ M$_{\\odot}$/yr on the mass-loss rate from a symbiotic\nprogenitor, which does not exclude a red giant or main sequence companion.\nUltimately we find that no one model can accurately replicate all aspects of\nthe dataset, and further we find that the ubiquity of early excesses in\n02es-like SNe Ia requires a progenitor system that is capable of producing\nisotropic UV flux, ruling out some models for this class of objects.\n"}
+{"abstract": "  The literature on ranking from ordinal data is vast, and there are several\nways to aggregate overall preferences from pairwise comparisons between\nobjects. In particular, it is well known that any Nash equilibrium of the zero\nsum game induced by the preference matrix defines a natural solution concept\n(winning distribution over objects) known as a von Neumann winner. Many\nreal-world problems, however, are inevitably multi-criteria, with different\npairwise preferences governing the different criteria. In this work, we\ngeneralize the notion of a von Neumann winner to the multi-criteria setting by\ntaking inspiration from Blackwell's approachability. Our framework allows for\nnon-linear aggregation of preferences across criteria, and generalizes the\nlinearization-based approach from multi-objective optimization.\n  From a theoretical standpoint, we show that the Blackwell winner of a\nmulti-criteria problem instance can be computed as the solution to a convex\noptimization problem. Furthermore, given random samples of pairwise\ncomparisons, we show that a simple plug-in estimator achieves near-optimal\nminimax sample complexity. Finally, we showcase the practical utility of our\nframework in a user study on autonomous driving, where we find that the\nBlackwell winner outperforms the von Neumann winner for the overall\npreferences.\n"}
+{"abstract": "  Marine microorganisms must cope with complex flow patterns and even\nturbulence as they navigate the ocean. To survive they must avoid predation and\nfind efficient energy sources. A major difficulty in analysing possible\nsurvival strategies is that the time series of environmental cues in non-linear\nflow is complex, and that it depends on the decisions taken by the organism.\nOne way of determining and evaluating optimal strategies is reinforcement\nlearning. In a proof-of-principle study, Colabrese et al. [Phys. Rev. Lett.\n(2017)] used this method to find out how a micro-swimmer in a vortex flow can\nnavigate towards the surface as quickly as possible, given a fixed swimming\nspeed. The swimmer measured its instantaneous swimming direction and the local\nflow vorticity in the laboratory frame, and reacted to these cues by swimming\neither left, right, up, or down. However, usually a motile microorganism\nmeasures the local flow rather than global information, and it can only react\nin relation to the local flow, because in general it cannot access global\ninformation (such as up or down in the laboratory frame). Here we analyse\noptimal strategies with local signals and actions that do not refer to the\nlaboratory frame. We demonstrate that symmetry-breaking is required in order to\nlearn vertical migration in a meaningful way. Using reinforcement learning we\nanalyse the emerging strategies for different sets of environmental cues that\nmicroorganisms are known to measure.\n"}
+{"abstract": "  We present the second public data release (DR) of the VISTA EXtension to\nAuxiliary Surveys (VEXAS), where we classify objects into stars, galaxies and\nquasars based on an ensemble of machine learning algorithms. The aim of VEXAS\nis to build the widest multi-wavelength catalogue, providing reference\nmagnitudes, colours and morphological information for a large number of\nscientific uses. We apply an ensemble of 32 different machine learning models,\nbased on three different algorithms and on different magnitude sets, training\nsamples and classification problems on the three VEXAS DR1 optical+infrared\n(IR) tables. The tables were created in DR1 cross-matching VISTA near-IR data\nwith WISE far-IR data and with optical magnitudes from the Dark Energy Survey\n(VEXAS-DESW), the Sky Mapper Survey (VEXAS-SMW), and the PanSTARRS (VEXAS-PSW).\nWe assemble a large table of spectroscopically confirmed objects (415 628\nunique objects), based on the combination of 6 different spectroscopic surveys\nthat we use for training. We develop feature imputation to classify also\nobjects for which magnitudes in one or more bands are missing. We classify in\ntotal ~90 million objects in the Southern Hemisphere. Among these,~62.9M\n(~52.6M) are classified as 'high confidence' ('secure') stars, ~920k (~750k) as\n'high confidence' ('secure') quasars and ~34.8M (~34.1M) as 'high confidence'\n('secure') galaxies, with probabilities $p_{\\rm class}\\ge 0.7$ ($p_{\\rm\nclass}\\ge 0.9$). The density of high-confidence extragalactic objects varies\nstrongly with the survey depth: at $p_{\\rm class}\\ge 0.7$, there are\n111/deg$^2$ quasars in the VEXAS-DESW footprint and 103/deg$^2$ in the\nVEXAS-PSW footprint, while only 10.7/deg$^2$ in the VEXAS-SM footprint.\nImproved depth in the midIR and coverage in the optical and nearIR are needed\nfor the SM footprint that is not already covered by DESW and PSW.\n"}
+{"abstract": "  In the last years we have witnessed the fields of geosciences and remote\nsensing and artificial intelligence to become closer. Thanks to both the\nmassive availability of observational data, improved simulations, and\nalgorithmic advances, these disciplines have found common objectives and\nchallenges to advance the modeling and understanding of the Earth system.\nDespite such great opportunities, we also observed a worrying tendency to\nremain in disciplinary comfort zones applying recent advances from artificial\nintelligence on well resolved remote sensing problems. Here we take a position\non research directions where we think the interface between these fields will\nhave the most impact and become potential game changers. In our declared agenda\nfor AI on Earth sciences, we aim to inspire researchers, especially the younger\ngenerations, to tackle these challenges for a real advance of remote sensing\nand the geosciences.\n"}
+{"abstract": "  Recently, the BESIII Collaboration reported a resonance $Y(2040)$ with\n$M=2034\\pm13\\pm9$ MeV and $\\Gamma=234\\pm30\\pm25$ MeV in the process of\n$e^+e^-\\rightarrow\\omega\\pi^0$. In addition, new measurements with much higher\nprecision for the $\\rho(1900)$ and $\\rho(2150)$ states are obtained by the\nBESIII and BABAR Collaborations. In this work, we perform a systematic study on\nthe mass spectrum of the excited $\\rho$ resonances using the modified\nGodfrey-Isgur model, and the strong decays of $Y(2040)$, $\\rho(1900)$, and\n$\\rho(2150)$ within the $^3P_0$ model.We find that $Y(2040)$, $\\rho(1900)$, and\n$\\rho(2150)$ can be interpreted as the $\\rho(2^3D_1)$, $\\rho(3^3S_1)$, and\n$\\rho(4^3S_1)$ states, respectively. Meanwhile, the mass and strong decays of\nthe $\\rho(3^3D_1)$ state are predicted as well, which could be helpful to\nsearch for this state in future.\n"}
+{"abstract": "  We compare the spectrum of the stochastic gravitational wave background\nproduced in several models of cosmic strings with the common-spectrum process\nrecently reported by NANOGrav. We discuss theoretical uncertainties in\ncomputing such a background, and show that despite such uncertainties, cosmic\nstrings remain a good explanation for the potential signal, but the\nconsequences for cosmic string parameters depend on the model. Superstrings\ncould also explain the signal, but only in a restricted parameter space where\ntheir network behavior is effectively identical to that of ordinary cosmic\nstrings.\n"}
+{"abstract": "  Power system N-x contingency analysis has inherent challenges due to its\ncombinatorial characteristic where outages grow exponentially with the increase\nof x and N. To address these challenges, this paper proposes a method that\nutilizes Line Outage Distribution Factors (LODFs) and group betweenness\ncentrality to identify subsets of critical branches. The proposed LODF metrics\nare used to select the high-impact branches. Based on each selected branch, the\napproach constructs the subgraph with parameters of distance and search level,\nwhile using branches' LODF metrics as the weights. A key innovation of this\nwork is the use of the distance and search level parameters, which allow the\nsubgraph to identify the most coupled critical elements that may be far away\nfrom a selected branch. The proposed approach is validated using the 200- and\n500-bus test cases, and results show that the proposed approach can identify\nmultiple N-x contingencies that cause violations.\n"}
+{"abstract": "  Neural shape representations have recently shown to be effective in shape\nanalysis and reconstruction tasks. Existing neural network methods require\npoint coordinates and corresponding normal vectors to learn the implicit level\nsets of the shape. Normal vectors are often not provided as raw data,\ntherefore, approximation and reorientation are required as pre-processing\nstages, both of which can introduce noise. In this paper, we propose a\ndivergence guided shape representation learning approach that does not require\nnormal vectors as input. We show that incorporating a soft constraint on the\ndivergence of the distance function favours smooth solutions that reliably\norients gradients to match the unknown normal at each point, in some cases even\nbetter than approaches that use ground truth normal vectors directly.\nAdditionally, we introduce a novel geometric initialization method for\nsinusoidal shape representation networks that further improves convergence to\nthe desired solution. We evaluate the effectiveness of our approach on the task\nof surface reconstruction and show state-of-the-art performance compared to\nother unoriented methods and on-par performance compared to oriented methods.\n"}
+{"abstract": "  We study how parallelism can speed up quantum simulation. A parallel quantum\nalgorithm is proposed for simulating the dynamics of a large class of\nHamiltonians with good sparse structures, called uniform-structured\nHamiltonians, including various Hamiltonians of practical interest like local\nHamiltonians and Pauli sums. Given the oracle access to the target sparse\nHamiltonian, in both query and gate complexity, the running time of our\nparallel quantum simulation algorithm measured by the quantum circuit depth has\na doubly (poly-)logarithmic dependence $\\operatorname{polylog}\\log(1/\\epsilon)$\non the simulation precision $\\epsilon$. This presents an exponential\nimprovement over the dependence $\\operatorname{polylog}(1/\\epsilon)$ of\nprevious optimal sparse Hamiltonian simulation algorithm without parallelism.\nTo obtain this result, we introduce a novel notion of parallel quantum walk,\nbased on Childs' quantum walk. The target evolution unitary is approximated by\na truncated Taylor series, which is obtained by combining these quantum walks\nin a parallel way. A lower bound $\\Omega(\\log \\log (1/\\epsilon))$ is\nestablished, showing that the $\\epsilon$-dependence of the gate depth achieved\nin this work cannot be significantly improved.\n  Our algorithm is applied to simulating three physical models: the Heisenberg\nmodel, the Sachdev-Ye-Kitaev model and a quantum chemistry model in second\nquantization. By explicitly calculating the gate complexity for implementing\nthe oracles, we show that on all these models, the total gate depth of our\nalgorithm has a $\\operatorname{polylog}\\log(1/\\epsilon)$ dependence in the\nparallel setting.\n"}
+{"abstract": "  The recursively-constructed family of Mandelbrot matrices $M_n$ for $n=1$,\n$2$, $\\ldots$ have nonnegative entries (indeed just $0$ and $1$, so each $M_n$\ncan be called a binary matrix) and have eigenvalues whose negatives $-\\lambda =\nc$ give periodic orbits under the Mandelbrot iteration, namely $z_k =\nz_{k-1}^2+c$ with $z_0=0$, and are thus contained in the Mandelbrot set. By the\nPerron--Frobenius theorem, the matrices $M_n$ have a dominant real positive\neigenvalue, which we call $\\rho_n$. This article examines the eigenvector\nbelonging to that dominant eigenvalue and its fractal-like structure, and\nsimilarly examines (with less success) the dominant singular vectors of $M_n$\nfrom the singular value decomposition.\n"}
+{"abstract": "  Time Reversal Symmetry (TRS) broken topological phases provide gapless\nsurface states protected by topology, regardless of additional internal\nsymmetries, spin or valley degrees of freedom. Despite the numerous\ndemonstrations of 2D topological phases, few examples of 3D topological systems\nwith TRS breaking exist. In this article, we devise a general strategy to\ndesign 3D Chern insulating (3D CI) cubic photonic crystals in a weakly TRS\nbroken environment with orientable and arbitrarly large Chern vectors. The\ndesigns display topologically protected chiral and unidirectional surface\nstates with disjoint equifrequency loops. The resulting crystals present the\nfollowing novel characteristics: First, by increasing the Chern number,\nmultiple surface states channels can be supported. Second, the Chern vector can\nbe oriented along any direction simply changing the magnetization axis, opening\nup larger 3D CI/3D CI interfacing possibilities as compared to 2D. Third, by\nlowering the TRS breaking requirements, the system is ideal for realistic\nphotonic applications where the magnetic response is weak.\n"}
+{"abstract": "  This paper investigates the capability of long-baseline experiments, which\nare making use of neutrinos that are coming from Japan Proton Accelerator\nResearch Complex (J-PARC), in establishing the unitarity of active-neutrino\nmixing by ruling out the non-unitary mixing scheme as a function of true values\nof CP-violating phase $\\delta_{\\mathrm{CP}}$. It is found that T2HK can\nestablish unitarity of active neutrino mixing at above 2$\\sigma$ C.L.\nirrespective of neutrino mass hierarchy and true value of $\\delta_{CP}$, if\nnon-unitary (NU) parameter $\\alpha_{21}$ is of the order of $10^{-2}$. Further,\nthis paper is also discuss the bound on NU parameter in 21 sector and\nsensitivity limit of these experiments in determining NU parameter. It is found\nthat the bounds on $\\left(\\alpha_{21}/2\\right)$ are 0.028, 0.0026, 0.005 at\n2$\\sigma$ C.L. respectively for T2K, T2HK, and T2HKK. Moreover, it is also\nfound that the sensitivity limit of T2HK on NU parameter is far better than\nthat of both T2HKK and T2K.\n"}
+{"abstract": "  Two-dimensional (2D) semiconductors have attracted tremendous interests as\nnatural passivation and atomically thin channels that could facilitate\ncontinued transistor scaling. However, air-stable 2D semiconductors with high\nperformance were quite elusive. Recently, extremely air-stable MoSi2N4\nmonolayer had been successfully fabricated [Hong et al., Science 369, 670\n(2020)]. To further reveal its potential applications in the sub-5 nm MOSFETs,\nthere is an urgent need to develop integrated circuits. Here we report\nfirst-principles quantum transport simulations on the performance limits of n-\nand p-type sub-5 nm monolayer (ML) MoSi2N4 metal-oxide-semiconductor FETs\n(MOSFETs). We find that the on-state current in the MoSi2N4 MOSFETs can be\neffectively manipulated by the length of gate and underlap (UL), as well as the\ndoping concentration. Very strikingly, we also find that the n-type devices the\noptimized on-state current can reach up to 1390 and 1025 uA/um for the high\nperformance (HP) and low power (LP) applications, respectively, both of which\nsatisfy the International Technology Roadmap for Semiconductors (ITRS)\nrequirements. Whereas, the optimized on-state current can meet the LP\napplication (348 uA/um) for the p-type devices. Finally, we find that the\nMoSi2N4 MOSFETs have ultra-low subthreshold swing and power delay product,\nwhich have potential to realize the high speed and low power consumption\ndevices. Our results show that MoSi2N4 is an ideal 2D channel material for\nfuture competitive ultrascaled devices.\n"}
+{"abstract": "  Beyond assigning the correct class, an activity recognition model should also\nbe able to determine, how certain it is in its predictions. We present the\nfirst study of how welthe confidence values of modern action recognition\narchitectures indeed reflect the probability of the correct outcome and propose\na learning-based approach for improving it. First, we extend two popular action\nrecognition datasets with a reliability benchmark in form of the expected\ncalibration error and reliability diagrams. Since our evaluation highlights\nthat confidence values of standard action recognition architectures do not\nrepresent the uncertainty well, we introduce a new approach which learns to\ntransform the model output into realistic confidence estimates through an\nadditional calibration network. The main idea of our Calibrated Action\nRecognition with Input Guidance (CARING) model is to learn an optimal scaling\nparameter depending on the video representation. We compare our model with the\nnative action recognition networks and the temperature scaling approach - a\nwide spread calibration method utilized in image classification. While\ntemperature scaling alone drastically improves the reliability of the\nconfidence values, our CARING method consistently leads to the best uncertainty\nestimates in all benchmark settings.\n"}
+{"abstract": "  The study of the structural behaviour of pure and multi-component lipids at\nvarious temperatures and the interaction of these multi-component lipids with\npharmaceutically important drugs carry huge importance. Here, we investigated\nthe phase behaviour of the pure PSPC\n(1-palmitoyl-2-stearoyl-sn-glycero-3-phosphocholine), and multicomponent PSPC\nand\nDSPE-PEG2000(1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N[amino(polyethylene\nglycol)-2000]) membranes at seven different temperatures ranging from 280 K to\n360 K, and calculated their structural properties. We observe a transition from\nthe gel phase to the liquid crystalline phase between 320 K and 330 K in\nagreement with experimental reports for pure PSPC. PSPC remained in the tilted\ngel phase L\\b{eta}$'$ at 320 K and 310 K, entered the 'mixed ordered' domain\nwith a partially interdigitated region at 300 K, and finally formed the sub gel\nphase at 280 K. We studied the self-assembly for the multicomponent PSPC and\nDSPE-PEG2000 membranes and found the coexistence of ordered and disordered\nphases at 320 K. In comparison to the pure PSPC, for multicomponent system,\nthis transition was gradual, and a complete liquid crystalline to gel phase\ntransformation occurred between 320 K and 310 K. We further studied the\ninteraction of Paclitaxel with pure PSPC and PEGylated multicomponent lipid\nbilayers using umbrella sampling technique and observed PEG promotes the\ninteraction of Paclitaxel with the later one in comparison to the former. Above\nthe bilayer transition temperature, Paclitaxel interacts more with the bilayer\nand enters inside the bilayer easily for both systems. Understanding of\nstructural and interaction behaviour of the PEGylated multicomponent lipid\nbilayers with Paclitaxel will help explore Paclitaxel based drug applications\nin the future.\n"}
+{"abstract": "  For most of the anchor-based detectors, Intersection over Union(IoU) is\nwidely utilized to assign targets for the anchors during training. However, IoU\npays insufficient attention to the closeness of the anchor's center to the\ntruth box's center. This results in two problems: (1) only one anchor is\nassigned to most of the slender objects which leads to insufficient supervision\ninformation for the slender objects during training and the performance on the\nslender objects is hurt; (2) IoU can not accurately represent the alignment\ndegree between the receptive field of the feature at the anchor's center and\nthe object. Thus during training, some features whose receptive field aligns\nbetter with objects are missing while some features whose receptive field\naligns worse with objects are adopted. This hurts the localization accuracy of\nmodels. To solve these problems, we firstly design Gaussian Guided IoU(GGIoU)\nwhich focuses more attention on the closeness of the anchor's center to the\ntruth box's center. Then we propose GGIoU-balanced learning method including\nGGIoU-guided assignment strategy and GGIoU-balanced localization loss. The\nmethod can assign multiple anchors for each slender object and bias the\ntraining process to the features well-aligned with objects. Extensive\nexperiments on the popular benchmarks such as PASCAL VOC and MS COCO\ndemonstrate GGIoU-balanced learning can solve the above problems and\nsubstantially improve the performance of the object detection model, especially\nin the localization accuracy.\n"}
+{"abstract": "  We propose a new framework -- Square Root Principal Component Pursuit -- for\nlow-rank matrix recovery from observations corrupted with noise and outliers.\nInspired by the square root Lasso, this new formulation does not require prior\nknowledge of the noise level. We show that a single, universal choice of the\nregularization parameter suffices to achieve reconstruction error proportional\nto the (a priori unknown) noise level. In comparison, previous formulations\nsuch as stable PCP rely on noise-dependent parameters to achieve similar\nperformance, and are therefore challenging to deploy in applications where the\nnoise level is unknown. We validate the effectiveness of our new method through\nexperiments on simulated and real datasets. Our simulations corroborate the\nclaim that a universal choice of the regularization parameter yields near\noptimal performance across a range of noise levels, indicating that the\nproposed method outperforms the (somewhat loose) bound proved here.\n"}
+{"abstract": "  Ordinal embedding aims at finding a low dimensional representation of objects\nfrom a set of constraints of the form \"item $j$ is closer to item $i$ than item\n$k$\". Typically, each object is mapped onto a point vector in a low dimensional\nmetric space. We argue that mapping to a density instead of a point vector\nprovides some interesting advantages, including an inherent reflection of the\nuncertainty about the representation itself and its relative location in the\nspace. Indeed, in this paper, we propose to embed each object as a Gaussian\ndistribution. We investigate the ability of these embeddings to capture the\nunderlying structure of the data while satisfying the constraints, and explore\nproperties of the representation. Experiments on synthetic and real-world\ndatasets showcase the advantages of our approach. In addition, we illustrate\nthe merit of modelling uncertainty, which enriches the visual perception of the\nmapped objects in the space.\n"}
+{"abstract": "  Graph Neural Networks (GNNs) are the first choice for learning algorithms on\ngraph data. GNNs promise to integrate (i) node features as well as (ii) edge\ninformation in an end-to-end learning algorithm. How does this promise work out\npractically? In this paper, we study to what extend GNNs are necessary to solve\nprominent graph classification problems. We find that for graph classification,\na GNN is not more than the sum of its parts. We also find that, unlike\nfeatures, predictions with an edge-only model do not always transfer to GNNs.\n"}
+{"abstract": "  Moving Object Detection (MOD) is a crucial task for the Autonomous Driving\npipeline. MOD is usually handled via 2-stream convolutional architectures that\nincorporates both appearance and motion cues, without considering the\ninter-relations between the spatial or motion features. In this paper, we\ntackle this problem through multi-head attention mechanisms, both across the\nspatial and motion streams. We propose MODETR; a Moving Object DEtection\nTRansformer network, comprised of multi-stream transformer encoders for both\nspatial and motion modalities, and an object transformer decoder that produces\nthe moving objects bounding boxes using set predictions. The whole architecture\nis trained end-to-end using bi-partite loss. Several methods of incorporating\nmotion cues with the Transformer model are explored, including two-stream RGB\nand Optical Flow (OF) methods, and multi-stream architectures that take\nadvantage of sequence information. To incorporate the temporal information, we\npropose a new Temporal Positional Encoding (TPE) approach to extend the Spatial\nPositional Encoding(SPE) in DETR. We explore two architectural choices for\nthat, balancing between speed and time. To evaluate the our network, we perform\nthe MOD task on the KITTI MOD [6] data set. Results show significant 5% mAP of\nthe Transformer network for MOD over the state-of-the art methods. Moreover,\nthe proposed TPE encoding provides 10% mAP improvement over the SPE baseline.\n"}
+{"abstract": "  We present the results of direct simulation of the expansionof a\ntwo-component ultracold plasmafor various numbers of particles, densities, and\nelectron temperatures. A description of the expansionprocess common to all\nplasma parameters is given. After the escape of fast electrons from the\nplasmacloud, the excess positive charge is localized at the outer boundary, in\na narrow layer. This layerhas a characteristic front shape with a sharp drop in\nthe charge concentration. The charged layerretains the remaining electrons\nduring the entire expansion process. As the plasma expands, thespeed of\nmovement of the charged layer becomes constant and significantly exceeds the\nsonic speedof ions. In addition, the dependence of the radial velocity of ions\non the radius acquires a self-similarcharacter long before the final stage of\nexpansion. On the basis of the calculation results, equationsand self-similar\nsolutions are obtained. General dependences on plasma parameters are\ndetermined,which are compared with experimental data.\n"}
+{"abstract": "  This work addresses hand mesh recovery from a single RGB image. In contrast\nto most of the existing approaches where the parametric hand models are\nemployed as the prior, we show that the hand mesh can be learned directly from\nthe input image. We propose a new type of GAN called Im2Mesh GAN to learn the\nmesh through end-to-end adversarial training. By interpreting the mesh as a\ngraph, our model is able to capture the topological relationship among the mesh\nvertices. We also introduce a 3D surface descriptor into the GAN architecture\nto further capture the 3D features associated. We experiment two approaches\nwhere one can reap the benefits of coupled groundtruth data availability of\nimages and the corresponding meshes, while the other combats the more\nchallenging problem of mesh estimations without the corresponding groundtruth.\nThrough extensive evaluations we demonstrate that the proposed method\noutperforms the state-of-the-art.\n"}
+{"abstract": "  We present 87 candidates for RR Lyrae variable stars in binary systems, based\non our new search using the light-travel time effect (LTTE) and observed -\ncalculated ($O-C$) diagrams in the Galactic bulge time-series photometry of the\nOptical Gravitational Lensing Experiment. Out of these, 61 are new candidates,\nwhile 26 have been announced previously. Furthermore, 12 stars considered as\nbinary candidates in earlier works are discarded from the list, either as they\nwere found to have $O-C$ diagrams incompatible with the LTTE or because their\nlong-term periodicity is definitely caused by the Blazhko effect. This sample\nof RR Lyrae binary candidates allows us to draw the first firm conclusions\nabout the population of such objects: no candidate has an orbital period below\n1000 days, while their occurrence rate steadily increases with increasing\nperiod, and peaks between 3000 and 4000 days; however, the decrease in the\nnumber of stars toward even longer periods is probably the result of\nobservational biases. The eccentricities show a very significant concentration\nbetween 0.25 and 0.3, with a quarter of candidates found in this single bin,\noverlaid on an otherwise flat distribution between 0.05 and 0.6. Only six stars\nhave higher inferred eccentricities above 0.6. Lastly, the distribution of the\nmass functions is highly peculiar, exhibiting strong trimodality. We interpret\nthese modes as the presence of three distinct groups of companions, with\ntypical inferred masses of $\\sim0.6$, $\\sim0.2$, and\n$\\sim0.067\\,\\mathrm{M}_\\odot$, which can be associated with populations of\nwhite dwarf and main sequence, red dwarf, and brown dwarf companions,\nrespectively.\n"}
+{"abstract": "  In this review, we discuss critical dynamics of simple nonequilibrium models\non large connectomes, obtained by diffusion MRI, representing the white matter\nof the human brain. In the first chapter, we overview graph theoretical and\ntopological analysis of these networks, pointing out that universality allows\nselecting a representative network, the KKI-18, which has been used for\ndynamical simulation. The critical and sub-critical behaviour of simple, two-\nor three-state threshold models is discussed with special emphasis on\nrare-region effects leading to robust Griffiths Phases (GP). Numerical results\nof synchronization phenomena, studied by the Kuramoto model, are also shown,\nleading to a continuous analog of the GP, termed frustrated synchronization.\nThe models presented here exhibit dynamical scaling behaviour with exponents in\nagreement with brain experimental data if local homeostasis is provided.\n"}
+{"abstract": "  In the whole space $R^d$, $d\\ge 2$, we study homogenization of a divergence\nform elliptic fourth-order operator $A_\\varepsilon$ with measurable\n$\\varepsilon$-periodic coefficients, where $\\varepsilon$ is a small parameter.\nFor the resolvent $(A_\\varepsilon+1)^{-1}$, acting as an operator from $L^2$ to\n$H^2$, we find an approximation with remainder term of order $O(\\varepsilon^2)$\nas $\\varepsilon$ tends to $0$. Relying on this result, we construct the\nresolvent approximation with remainder of order $O(\\varepsilon^3)$ in the\noperator $L^2$-norm. We employ two-scale expansions that involve smoothing.\n"}
+{"abstract": "  In this paper, we consider a wireless multihop device-to-device (D2D) based\nmobile edge computing (MEC) system, where the destination wireless device (WD)\nis scheduled to compute nomographic functions. Under the MapReduce framework\nand motivated by reducing communication resource overhead, we propose a new\nmulti-level over-the-air (OTA) aggregation scheme for the destination WD to\ncollect the individual partially aggregated intermediate values (IVAs) for\nreduction from multiple source WDs in the data shuffling phase. For OTA\naggregation per level, the source WDs employ a channel inverse structure\nmultiplied by their individual transmit coefficients in transmission over the\nsame time frequency resource blocks, and the destination WD finally uses a\nreceive filtering factor to construct the aggregated IVA. Under this setup, we\ndevelop a unified transceiver design framework that minimizes the mean squared\nerror (MSE) of the aggregated IVA at the destination WD subject to the source\nWDs' individual power constraints, by jointly optimizing the source WDs'\nindividual transmit coefficients and the destination WD's receive filtering\nfactor. First, based on the primal decomposition method, we derive the\nclosed-form solution under the special case of a common transmit coefficient.\nIt shows that all the source WDs' common transmit is determined by the minimal\ntransmit power budget among the source WDs. Next, for the general case, we\ntransform the original problem into a quadratic fractional programming problem,\nand then develop a low-complexity algorithm to obtain the (near-) optimal\nsolution by leveraging Dinkelbach's algorithm along with the Gaussian\nrandomization method.\n"}
+{"abstract": "  In this paper we develop a concrete and fully implementable approach to the\noptimization of functionally generated portfolios in stochastic portfolio\ntheory. The main idea is to optimize over a family of rank-based portfolios\nparameterized by an exponentially concave function on the unit interval. This\nchoice can be motivated by the long term stability of the capital distribution\nobserved in large equity markets, and allows us to circumvent the curse of\ndimensionality. The resulting optimization problem, which is convex, allows for\nvarious regularizations and constraints to be imposed on the generating\nfunction. We prove an existence and uniqueness result for our optimization\nproblem and provide a stability estimate in terms of a Wasserstein metric of\nthe input measure. Then, we formulate a discretization which can be implemented\nnumerically using available software packages and analyze its approximation\nerror. Finally, we present empirical examples using CRSP data from the US stock\nmarket, including the performance of the portfolios allowing for dividends,\ndefaults, and transaction costs.\n"}
+{"abstract": "  Contrastive learning is a family of self-supervised methods where a model is\ntrained to solve a classification task constructed from unlabeled data. It has\nrecently emerged as one of the leading learning paradigms in the absence of\nlabels across many different domains (e.g. brain imaging, text, images).\nHowever, theoretical understanding of many aspects of training, both\nstatistical and algorithmic, remain fairly elusive.\n  In this work, we study the setting of time series -- more precisely, when we\nget data from a strong-mixing continuous-time stochastic process. We show that\na properly constructed contrastive learning task can be used to estimate the\ntransition kernel for small-to-mid-range intervals in the diffusion case.\nMoreover, we give sample complexity bounds for solving this task and\nquantitatively characterize what the value of the contrastive loss implies for\ndistributional closeness of the learned kernel. As a byproduct, we illuminate\nthe appropriate settings for the contrastive distribution, as well as other\nhyperparameters in this setup.\n"}
+{"abstract": "  In this paper we propose the first deep unsupervised approach in human body\nreconstruction to estimate body surface from a sparse set of landmarks, so\ncalled DeepMurf. We apply a denoising autoencoder to estimate missing\nlandmarks. Then we apply an attention model to estimate body joints from\nlandmarks. Finally, a cascading network is applied to regress parameters of a\nstatistical generative model that reconstructs body. Our set of proposed loss\nfunctions allows us to train the network in an unsupervised way. Results on\nfour public datasets show that our approach accurately reconstructs the human\nbody from real world mocap data.\n"}
+{"abstract": "  Two-dimensional (2D) Van Hove singularities (VHSs) associated with the saddle\npoints or extrema of the energy dispersion usually show logarithmic divergences\nin the density of states (DOS). However, recent studies find that the VHSs\noriginating from higher-order saddle-points have faster-than-logarithmic\ndivergences, which can amplify electron correlation effects and create exotic\nstates such as supermetals in 2D materials. Here we report the existence of\n'high-order' VHSs in the cuprates and related high-Tc superconductors and show\nthat the anomalous divergences in their spectra are driven by the electronic\ndimensionality of the system being lower than the dimensionality of the\nlattice. The order of VHS is found to correlate with the superconducting Tc\nsuch that materials with higher order VHSs display higher Tc's. We further show\nthat the presence of the normal and higher-order VHSs in the electronic\nspectrum can provide a straightforward marker for identifying the propensity of\na material toward correlated phases such as excitonic insulators or\nsupermetals. Our study opens up a new materials playground for exploring the\ninterplay between high-order VHSs, superconducting transition temperatures and\nelectron correlation effects in the cuprates and related high-Tc\nsuperconductors.\n"}
+{"abstract": "  Higher rank symmetry and higher moment conservation have been drawn\nconsiderable attention from, e.g., subdiffusive transport to fracton\ntopological order. In this paper, we perform a one-loop renormalization group\n(RG) analysis and show how these phenomena emerge at low energies. We consider\na $d$-dimensional model of interacting bosons of d components. At\nhigher-rank-symmetric points with conserved angular moments, the $a$-th bosons\nhave kinetic energy only along the $x^a$ direction. Therefore, the symmetric\npoints look highly anisotropic and fine-tuned. By studying RG in a wide\nvicinity of the symmetric points, we find that symmetry-disallowed kinetic\nterms tend to be irrelevant within the perturbative regime, which potentially\nleads to emergent higher-rank symmetry and higher-moment conservation at the\ndeep infrared limit. While non-perturbative analysis is called for in the\nfuture, by regarding higher-rank symmetry as an emergent phenomenon, the RG\nanalysis presented in this paper holds alternative promise for realizing\nhigher-rank symmetry and higher-moment conservation in experimentally\nachievable systems.\n"}
+{"abstract": "  Simulation models are widely used in practice to facilitate decision-making\nin a complex, dynamic and stochastic environment. But they are computationally\nexpensive to execute and optimize, due to lack of analytical tractability.\nSimulation optimization is concerned with developing efficient sampling schemes\n-- subject to a computational budget -- to solve such optimization problems. To\nmitigate the computational burden, surrogates are often constructed using\nsimulation outputs to approximate the response surface of the simulation model.\nIn this tutorial, we provide an up-to-date overview of surrogate-based methods\nfor simulation optimization with continuous decision variables. Typical\nsurrogates, including linear basis function models and Gaussian processes, are\nintroduced. Surrogates can be used either as a local approximation or a global\napproximation. Depending on the choice, one may develop algorithms that\nconverge to either a local optimum or a global optimum. Representative examples\nare presented for each category. Recent advances in large-scale computation for\nGaussian processes are also discussed.\n"}
+{"abstract": "  In this paper we study algebras of modular forms on unitary groups of\nsignature $(n,1)$. We give a necessary and sufficient condition for an algebra\nof unitary modular forms to be free in terms of the modular Jacobian. As a\ncorollary we obtain a criterion that guarantees in many cases that, if $L$ is\nan even lattice with complex multiplication and the ring of modular forms for\nits orthogonal group is a polynomial algebra, then the ring of modular forms\nfor its unitary group is also a polynomial algebra. We prove that a number of\nrings of unitary modular forms are freely generated by applying these criteria\nto Hermitian lattices over the rings of integers of $\\mathbb{Q}(\\sqrt{d})$ for\n$d=-1,-2,-3$. As a byproduct, our modular groups provide many explicit examples\nof finite-covolume reflection groups acting on complex hyperbolic space.\n"}
+{"abstract": "  In bidisperse particle mixtures varying in size or density alone, large\nparticles rise (driven by percolation) and heavy particles sink (driven by\nbuoyancy). When the two particle species differ from each other in both size\nand density, the two segregation mechanisms either enhance (large/light and\nsmall/heavy) or oppose (large/heavy and small/light) each other. In the latter\ncase, an equilibrium condition exists in which the two segregation mechanisms\nbalance and the particles no longer segregate. This leads to a methodology to\ndesign non-segregating particle mixtures by specifying particle size ratio,\ndensity ratio, and mixture concentration to achieve the equilibrium condition.\nUsing DEM simulations of quasi-2D bounded heap flow, we show that segregation\nis significantly reduced for particle mixtures near the equilibrium condition.\nIn addition, the rise-sink transition for a range of particle size and density\nratios matches the combined size and density segregation model predictions.\n"}
+{"abstract": "  Age estimation is an essential challenge in computer vision. With the\nadvances of convolutional neural networks, the performance of age estimation\nhas been dramatically improved. Existing approaches usually treat age\nestimation as a classification problem. However, the age labels are ambiguous,\nthus make the classification task difficult. In this paper, we propose a simple\nyet effective approach for age estimation, which improves the performance\ncompared to classification-based methods. The method combines four\nclassification losses and one regression loss representing different class\ngranularities together, and we name it as Age-Granularity-Net. We validate the\nAge-Granularity-Net framework on the CVPR Chalearn 2016 dataset, and extensive\nexperiments show that the proposed approach can reduce the prediction error\ncompared to any individual loss. The source code link is\nhttps://github.com/yipersevere/age-estimation.\n"}
+{"abstract": "  In the analysis and control of discrete-time linear systems, the spectral\nradius of the system state matrix plays an essential role. Usually, it is\nassumed that the system matrices are known, from which the spectral radius can\nbe directly computed. Instead, we consider the setting where the linear system\nis affected by process noises, and one has only finitely many samples of system\ninput and state measurements. We provide two methods for estimating the\nspectral radius and derive error bounds that hold with high probability.\nMoreover, we show how to use the derived results to test stabilizability for\nnetworked control systems (NCSs) with lossy channels when only finitely many\nsamples of system input, state, and the packet drop sequence are available.\n"}
+{"abstract": "  We propose a new model of Early Dark Energy (EDE) as a solution to the Hubble\ntension in cosmology, the apparent discrepancy between local measurements of\nthe Hubble constant $H_0\\simeq 74$ km s$^{-1}$ Mpc$^{-1}$ and $H_0\\simeq 67$ km\ns$^{-1}$ Mpc$^{-1}$ inferred from the Cosmic Microwave Background (CMB). In\nChain EDE, the Universe undergoes a series of first order phase transitions,\nstarting at a high energy vacuum in a potential, and tunneling down through a\nchain of every lower energy metastable minima. As in all EDE models, the\ncontribution of the vacuum energy to the total energy density of the universe\nis initially negligible, but reaches $\\sim 10\\%$ around matter-radiation\nequality, before cosmological data require it to redshift away quickly -- at\nleast as fast as radiation. We indeed obtain this required behavior with a\nseries of $N$ tunneling events, and show that for $N>600$ the phase transitions\nare rapid enough to allow fast percolation and thereby avoid large scale\nanisotropies in the CMB. We construct a specific example of Chain EDE featuring\na scalar field in a quasiperiodic potential (a tilted cosine), which is\nubiquitous in axion physics and, therefore, carries strong theoretical\nmotivation. Interestingly, the energy difference between vacua can be roughly\nthe size of today's Dark Energy (meV scale). Therefore, the end result of Chain\nEDE could provide a natural explanation of Dark Energy, if the tunneling\nbecomes extremely slow in the final step before the field reaches zero (or\nnegative) energy. We discuss a simple mechanism which can stop the scalar field\nin the desired minimum. Thus Chain EDE offers the exciting prospect to explain\nEDE and Dark Energy by the same scalar field.\n"}
+{"abstract": "  The development of silicon anodes to replace conventional graphite in efforts\nto increase energy densities of lithium-ion batteries has been largely impeded\nby poor interfacial stability against liquid electrolytes. Here, stable\noperation of 99.9 weight% micro-Si (uSi) anode is enabled by utilizing the\ninterface passivating properties of sulfide based solid-electrolytes. Bulk to\nsurface characterization, as well as quantification of interfacial components\nshowed that such an approach eliminates continuous interfacial growth and\nirreversible lithium losses. In uSi || layered-oxide full cells, high current\ndensities at room temperature (5 mA cm 2), wide operating temperature\n(-20{\\deg}C to 80{\\deg}C) and high loadings (>11 mAh cm-2) were demonstrated\nfor both charge and discharge operations. The promising battery performance can\nbe attributed to both the desirable interfacial property between uSi and\nsulfide electrolytes, as well as the unique chemo-mechanical behavior of the\nLi-Si alloys.\n"}
+{"abstract": "  Great achievements in last five years, such as record-efficient\namorphous/crystalline silicon heterojunction (SHJ) solar cells and cutting-edge\nperovskite/SHJ tandem solar cells, place hydrogenated amorphous silicon\n(a-Si:H) at the forefront of emerging photovoltaics. Due to the extremely low\ndoping efficiency of trivalent boron (B) in amorphous tetravalent silicon,\nlight harvesting of aforementioned devices are limited by their fill factors\n(FF), which is a direct metric of the charge carrier transport. It is\nchallenging but crucial to develop highly conductive doped a-Si:H for\nminimizing the FF losses. Here we report intensive light soaking can\nefficiently boost the dark conductance of B-doped a-Si:H \"thin\" films, which is\nan abnormal Staebler-Wronski effect. By implementing this abnormal effect to\nSHJ solar cells, we achieve a certificated power conversion efficiency (PCE) of\n25.18% (26.05% on designated area) with FF of 85.42% on a 244.63-cm2 wafer.\nThis PCE is one of the highest reported values for total-area \"top/rear\"\ncontact silicon solar cells. The FF reaches 98.30 per cent of its\nShockley-Queisser limit.\n"}
+{"abstract": "  In this work, we analyse the properties of the Maupertuis' action as a tool\nto reveal the phase space structure for Hamiltonian systems. We construct a\nscalar field with the action's values along the trajectories in the phase\nspace. The different behaviour of the trajectories around important phase space\nobjects like unstable periodic orbits, their stable and unstable manifolds, and\nKAM islands generate characteristic patterns on the scalar field constructed\nwith the action. Using these different patterns is possible to identify the\nskeleton of the phase space and understand the dynamics. Also, we present a\nsimple argument based on the conservation of the energy and the behaviour of\nthe trajectories to understand the values of their actions. In order to show\nhow this tool reveals the phase space structures and its effectiveness, we\ncompare the scalar field constructed with the actions with Poincare maps for\nthe same set of initial conditions in the phase space of an open Hamiltonian\nsystem with 2 degrees of freedom.\n"}
+{"abstract": "  Imperfect labels limit the quality of predictions learned by deep neural\nnetworks. This is particularly relevant in medical image segmentation, where\nreference annotations are difficult to collect and vary significantly even\nacross expert annotators. Prior work on mitigating label noise focused on\nsimple models of mostly uniform noise. In this work, we explore biased and\nunbiased errors artificially introduced to brain tumour annotations on MRI\ndata. We found that supervised and semi-supervised segmentation methods are\nrobust or fairly robust to unbiased errors but sensitive to biased errors. It\nis therefore important to identify the sorts of errors expected in medical\nimage labels and especially mitigate the biased errors.\n"}
+{"abstract": "  In this paper, we consider the motion of incompressible magnetohydrodynamics\n(MHD) with resistivity in a domain bounded by a free surface. The free boundary\nproblem for MHD is an important problem not only for mathematical fluid\ndynamics but also some application to the field of engineering In fact, when a\nthermonuclear reaction is caused artificially, a high-temperature plasma is\nsometimes subjected to a magnetic field and held in the air, and the boundary\nof the fluid at this time is a free one. In this paper, an electromagnetic\nfield generated by some currents in an external domain keeps an MHD flow in a\nbounded domain. On the free surface, free boundary conditions for MHD flow and\ntransmission conditions for electromagnetic fields are imposed. We proved the\nlocal well-posedness in the general setting of domains from a mathematical\npoint of view. The solutions are obtained in the maximal regularity class, and\nin particular, the regularity class of velocity fields is one more higher than\nthe regularity class of magnetic fields. To prove our main result, we use Lp-Lq\nmaximal regularity theorem for the Stokes equations with free boundary\nconditions and for the magnetic field equations with transmission conditions.\n"}
+{"abstract": "  Interface of transition metal dichalcogenide (TMDC) and high-k dielectric\ntransition metal oxides (TMO) had triggerred umpteen discourses due to the\nindubitable impact of TMO in reducing the contact resistances and restraining\nthe Fermi-level pinning for the metal-TMDC contacts. In the present work, we\nfocus on the unresolved tumults of large-area TMDC/TMO interfaces, grown by\nadopting different techniques. Here, on a pulsed laser deposited (PLD) MoS2\nthin film, a layer of TiO2 is grown by using both atomic layer deposition (ALD)\nand PLD. These two different techniques emanate TiO2 layers with different\ncrystalline properties, thicknesses and interfacial morphologies, subsequently\ninfluencing the electronic and optical properties of the interfaces. In\naddition, they manifest a boost in the extent of p-type doping with increasing\nthickness of TiO2, as emerged after analyzing the core-level shifts of the\nX-ray photoelectron spectra (XPS). Density functional analysis of the\nMoS2/Anatase-TiO2 interfaces, for pristine and in presence of a wide range of\ninterfacial defects, could explain the interdependence of doping and the\nterminating atomic-surface of TiO2 on MoS2. The optical properties of the\ninterface, encompassing the photoluminescence, transient absorption and z-scan\ntwo-photon absorption indicate the presence of defect-induced localized mid-gap\nlevels in MoS2/TiO2 (PLD), resulting quenched exciton signals. On the contrary,\nthe relatively defect-free interface in MoS2/TiO2 (ALD) demonstrates a clear\npresence of both A and B excitons of MoS2. From the investigation of optical\nproperties, we indicate that MoS2/TiO2 (PLD) interface may act as a promising\nsaturable absorber. Moreover, MoS2/TiO2 (PLD) interface had resulted a better\nphoto-transport. A potential application of MoS2/TiO2 (PLD) is demonstrated by\nthe fabrication of a p-type photo-transistor with the ionic-gel top gate.\n"}
+{"abstract": "  Massive stars die an explosive death as a core-collapse supernova (CCSN). The\nexact physical processes that cause the collapsing star to rebound into an\nexplosion are not well-understood, and the key in resolving this issue may lie\nin the measurement of the shape of CCSNe ejecta. Spectropolarimetry is the only\nway to perform this measurement for CCSNe outside of the Milky Way and\nMagellanic Clouds. We present an infrared (IR) spectropolarimetric detection of\na CCSN, enabled by the new highly sensitive WIRC+Pol instrument at Palomar\nObservatory, that can observe CCSNe (M = -17 mags) out to 20 Mpc to ~0.1%\npolarimetric precision. IR spectropolarimetry is less affected than optical by\ndust scattering in the circumstellar and interstellar media, thereby providing\na more unbiased probe of the intrinsic geometry of the SN ejecta. SN 2018hna, a\nSN 1987A-like explosion, shows 2.0+-0.3% continuum polarization in the J band\noriented at ~160 degree on-sky at 182 d after the explosion. Assuming prolate\ngeometry like in SN 1987A, we infer an ejecta axis ratio of <0.48 with the axis\nof symmetry pointing at 70 degree position angle. The axis ratio is similar to\nthat of SN 1987A suggesting that they may share intrinsic geometry and\ninclination angle. Our data do not rule out oblate ejecta. We also observe one\nother core-collapse and two thermonuclear SNe in the J band. SN 2020oi, a\nstripped-envelope Type Ic SN in Messier 100 has p = 0.37+-0.09% at peak light,\nindicative of either a 10% asymmetry or host interstellar polarization. The SNe\nIa, 2019ein and 2020ue have p < 0.33% and < 1.08% near peak light, indicative\nof asymmetries of less than 10% and 20%, respectively.\n"}
+{"abstract": "  We study large uniform random maps with one face whose genus grows linearly\nwith the number of edges. They can be seen as a model of discrete hyperbolic\ngeometry. In the past, several of these hyperbolic geometric features have been\ndiscovered, such as their local limit or their logarithmic diameter. In this\nwork, we show that with high probability such a map contains a very large\ninduced subgraph that is an expander.\n"}
+{"abstract": "  We propose and explore a physical mechanism for the stabilization of the\ncomplex spatiotemporal dynamics in arrays (bars) of broad area laser diodes\ntaking advantage of the symmetry breaking in non-Hermitian potentials. We show\nthat such stabilization can be achieved by specific pump and index profiles\nleading to a PT-symmetric coupling between nearest neighboring lasers within\nthe semiconductor bar. A numerical analysis is performed using a complete\n(2+1)-dimensional space-temporal model, including transverse and longitudinal\nspatial degrees of freedom and temporal evolution of the electric field and\ncarriers. We show regimes of temporal stabilization and light emission spatial\nredistribution and enhancement. We also consider a simplified (1+1)-dimensional\nmodel for an array of lasers holding the proposed non-Hermitian coupling with a\nglobal axisymmetric geometry. We numerically demonstrate a two-fold benefit:\nthe control over the temporal dynamics over the EELs bar and the field\nconcentration on the central lasers leading to a brighter output beam,\nfacilitating a direct coupling to an optical fiber.\n"}
+{"abstract": "  A particular case of the fundamental Sz.-Nagy--Foias functional model for a\ncontraction states that a pure contraction always dilates to a pure isometry.\nWe are interested in the similar question for pairs, more precisely: does a\npair of commuting pure contractions always dilate to a pair of commuting pure\nisometries? The purpose of this article is to identify pairs of commuting pure\ncontractions for which the above question has an affirmative answer. Our method\nis based on an explicit structure of isometric dilation for pure pairs of\ncommuting contractions obtained in a recent work by Das, Sarkar and the author.\n"}
+{"abstract": "  Spin systems such as silicon or nitrogen vacancy centers in diamond, quantum\ndots and quantum dot molecules coupled to optical cavities appear as key\nelements for creating quantum networks as not only constituting the nodes of\nthe network, but also assisting the creation of photonic networks. Here we\nstudy deterministic preparation of arbitrary size $W$ states with spin systems.\nWe present an efficient operation on three qubits, two being the logical qubits\nand one being the ancillary qubit, where no interaction between the logical\nqubits are required. The proposed operation can create a $W$-type\nEinstein-Podolsky-Rosen (EPR) pair from two separable qubits, and expand that\nEPR pair or an arbitrary size $W$ state by one, creating a $W$-like state.\nTaking this operation as the fundamental building block, we show how to create\na large scale $W$ state out of separable qubits, or double the size of a $W$\nstate. Based on this operation and focusing on nitrogen vacancy (NV) centers in\ndiamond as an exemplary spin system, we propose a setup for preparing $W$\nstates of circularly polarized photons, assisted by a single spin qubit, where\nno photon-photon interactions are required. Next, we propose a setup for\npreparing $W$ states of spin qubits of spatially separated systems, assisted by\na single photon. We also analyze the effects of possible imperfections in\nimplementing the gates on the fidelity of the generated $W$ states. In our\nsetups, neither post-measurement, nor post-processing on the states of spin or\nphotonic qubit is required. Our setups can be implemented with current\ntechnology, and we anticipate that they contribute to quantum science and\ntechnologies.\n"}
+{"abstract": "  Route choice models in public transport have been discussed for a long time.\nThe main factor why a passenger chooses a specific path is usually based on its\nlength or travel time. However, also the ticket price that passengers have to\npay may influence their decision since passengers prefer cheaper paths over\nmore expensive ones. In this paper, we deal with the cheapest ticket problem\nwhich asks for a cheapest ticket to travel between a pair of stations. The\ncomplexity and the algorithmic approach to solve this problem depend crucially\non the underlying fare structure, e.g., it is easy if the ticket prices are\nproportional to the distance traveled (as in distance tariff fare structures),\nbut may become NP-complete in zone tariff fare structures. We hence discuss the\ncheapest ticket problem for different variations of distance- and zone-based\nfare structures. We start by modeling the respective fare structure\nmathematically, identify its main properties, and finally provide a polynomial\nalgorithm, or prove NP-completeness of the cheapest ticket problem. We also\nprovide general results on the combination of two fare structures, which is\noften observed in practice.\n"}
+{"abstract": "  Ensuring a faithful interaction with data and its representation for\nhumanities can and should depend on expert-constructed ground truths.\n"}
+{"abstract": "  Celebrated theorems of Roth and of Matou\\v{s}ek and Spencer together show\nthat the discrepancy of arithmetic progressions in the first $n$ positive\nintegers is $\\Theta(n^{1/4})$. We study the analogous problem in the\n$\\mathbb{Z}_n$ setting. We asymptotically determine the logarithm of the\ndiscrepancy of arithmetic progressions in $\\mathbb{Z}_n$ for all positive\ninteger $n$. We further determine up to a constant factor the discrepancy of\narithmetic progressions in $\\mathbb{Z}_n$ for many $n$. For example, if $n=p^k$\nis a prime power, then the discrepancy of arithmetic progressions in\n$\\mathbb{Z}_n$ is $\\Theta(n^{1/3+r_k/(6k)})$, where $r_k \\in \\{0,1,2\\}$ is the\nremainder when $k$ is divided by $3$. This solves a problem of Hebbinghaus and\nSrivastav.\n"}
+{"abstract": "  We study a nonlocal parametric problem driven by the fractional Laplacian\noperator combined with a Kirchhoff-type coefficient and involving a critical\nnonlinearity term in the sense of Sobolev embeddings. Our approach is of\nvariational and topological nature. The obtained results can be viewed as a\nnontrivial extension to the nonlocal setting of some recent contributions\nalready present in the literature.\n"}
+{"abstract": "  Canonical quantization covers a broad class of classical systems, but that\ndoes not include all the problems of interest. Affine quantization has the\nbenefit of providing a successful quantization of many important problems\nincluding the quantization of half-harmonic oscillators [1] nonrenormalizable\nscalar fields, such as $(\\varphi^{12})_3$ [2], and $(\\varphi^4)_4$ [3], as well\nas the quantum theory of Einstein's general relativity [4]. The features that\ndistinguish affine quantization are emphasized, especially, that affine\nquantization differs from canonical quantization only by the choice of\nclassical variables promoted to quantum operators. Coherent states are used to\nensure proper quantizations are physically correct. While quantization of\nnonrenormalizable covariant scalars and gravity are difficult, we focus on\nappropriate ultralocal scalars and gravity which are fully soluble while, in\nthat case, implying that affine quantization is the proper procedure to ensure\nthe validity of affine quantizations for nonrenormalizable covariant scalar\nfields and Einstein's gravity.\n"}
+{"abstract": "  There is a growing interest in developing computer vision methods that can\nlearn from limited supervision. In this paper, we consider the problem of\nlearning to predict camera viewpoints, where obtaining ground-truth annotations\nare expensive and require special equipment, from a limited number of labeled\nimages. We propose a semi-supervised viewpoint estimation method that can learn\nto infer viewpoint information from unlabeled image pairs, where two images\ndiffer by a viewpoint change. In particular our method learns to synthesize the\nsecond image by combining the appearance from the first one and viewpoint from\nthe second one. We demonstrate that our method significantly improves the\nsupervised techniques, especially in the low-label regime and outperforms the\nstate-of-the-art semi-supervised methods.\n"}
+{"abstract": "  A number of auditory models have been developed using diverging approaches,\neither physiological or perceptual, but they share comparable stages of signal\nprocessing, as they are inspired by the same constitutive parts of the auditory\nsystem. We compare eight monaural models that are openly accessible in the\nAuditory Modelling Toolbox. We discuss the considerations required to make the\nmodel outputs comparable to each other, as well as the results for the\nfollowing model processing stages or their equivalents: outer and middle ear,\ncochlear filter bank, inner hair cell, auditory nerve synapse, cochlear\nnucleus, and inferior colliculus. The discussion includes some practical\nconsiderations related to the use of monaural stages in binaural frameworks.\n"}
+{"abstract": "  Bayesian inference provides a uniquely rigorous approach to obtain principled\njustification for uncertainty in predictions, yet it is difficult to articulate\nsuitably general prior belief in the machine learning context, where\ncomputational architectures are pure abstractions subject to frequent\nmodifications by practitioners attempting to improve results. Parsimonious\ninference is an information-theoretic formulation of inference over arbitrary\narchitectures that formalizes Occam's Razor; we prefer simple and sufficient\nexplanations. Our universal hyperprior assigns plausibility to prior\ndescriptions, encoded as sequences of symbols, by expanding on the core\nrelationships between program length, Kolmogorov complexity, and Solomonoff's\nalgorithmic probability. We then cast learning as information minimization over\nour composite change in belief when an architecture is specified, training data\nare observed, and model parameters are inferred. By distinguishing model\ncomplexity from prediction information, our framework also quantifies the\nphenomenon of memorization.\n  Although our theory is general, it is most critical when datasets are\nlimited, e.g. small or skewed. We develop novel algorithms for polynomial\nregression and random forests that are suitable for such data, as demonstrated\nby our experiments. Our approaches combine efficient encodings with prudent\nsampling strategies to construct predictive ensembles without cross-validation,\nthus addressing a fundamental challenge in how to efficiently obtain\npredictions from data.\n"}
+{"abstract": "  Electronic degrees of freedom and their coupling to lattice vibrations in\nsemiconductors can be strongly modified by doping. Accordingly, the addition of\nsurplus charge carriers to chirality-mixed carbon nanotube samples has\npreviously been found to give rise to a Drude-type plasmon feature as well as\nFanotype antiresonances in the far- to mid-infrared spectral range (FIR/MIR).\nHere we investigate the FIR/MIR response of redox-chemically doped\nsemiconducting (6,5) carbon nanotubes (s-SWNTs). We find that, contrary to\nexpectations, the Drude-type plasmon shifts to lower wavenumbers with\nincreasing doping level. By means of Monte-Carlo simulations of the optical\nresponse, we attribute this behavior to the confinement of excess charge\ncarriers at low doping levels and their progressive delocalization when\napproaching degenerate doping. The coupling of vibrational modes to intraband\nexcitations in the doped s-SWNTs can be probed via a double resonance process\nsimilar to that responsible for the Raman D-band. The resulting Fano\nantiresonances shed new light onto the character and coupling of electronic and\nvibrational degrees of freedom in these one-dimensional semiconductors.\n"}
+{"abstract": "  We study dense packings of a large number of congruent non-overlapping\ncircles inside a square by looking for configurations which maximize the\npacking density, defined as the ratio between the area occupied by the disks\nand the area of the square container. The search for these configurations is\ncarried out with the help of two algorithms that we have devised: a first\nalgorithm is in charge of obtaining sufficiently dense configurations starting\nfrom a random guess, while a second algorithm improves the configurations\nobtained in the first stage. The algorithms can be used sequentially or\nindependently. The performance of these algorithms is assessed by carrying out\nnumerical tests for configurations with a large number of circles.\n"}
+{"abstract": "  In this paper, we propose HOME, a framework tackling the motion forecasting\nproblem with an image output representing the probability distribution of the\nagent's future location. This method allows for a simple architecture with\nclassic convolution networks coupled with attention mechanism for agent\ninteractions, and outputs an unconstrained 2D top-view representation of the\nagent's possible future. Based on this output, we design two methods to sample\na finite set of agent's future locations. These methods allow us to control the\noptimization trade-off between miss rate and final displacement error for\nmultiple modalities without having to retrain any part of the model. We apply\nour method to the Argoverse Motion Forecasting Benchmark and achieve 1st place\non the online leaderboard.\n"}
+{"abstract": "  Engineering materials to include nanoscale porosity or other nanoscale\nstructures has become a well-established strategy for enhancing the\nthermoelectric performance of dielectrics. However, the approach is only\nconsidered beneficial for materials where the intrinsic phonon mean-free path\nis much longer than that of the charge carriers. As such, the approach would\nnot be expected to provide significant performance gains in polycrystalline\nsemiconducting alloys, such as SixGe1-x, where mass disorder and grains provide\nstrong phonon scattering. In this manuscript, we demonstrate that the addition\nof nanoscale porosity to even ultrafine-grained Si0.8Ge0.2 may be worthwhile.\nThe semiclassical Boltzmann transport equation was used to model electrical and\nphonon transport in polycrystalline Si0.8Ge0.2 containing prismatic pores\nperpendicular to the transport current. The models are free of tuning\nparameters and were validated against experimental data. The models reveal that\na combination of pores and grain boundaries suppresses phonon conductivity to a\nmagnitude comparable with the electronic thermal conductivity. In this regime,\nZT can be further enhanced by reducing carrier concentration to the electrical\nand electronic thermal conductivity and simultaneously increasing thermopower.\nAlthough increases in ZT are modest, the optimal carrier concentration is\nsignificantly lowered, meaning semiconductors need not be so strongly\nsupersaturated with dopants.\n"}
+{"abstract": "  To plan safe and comfortable trajectories for automated vehicles on highways,\naccurate predictions of traffic situations are needed. So far, a lot of\nresearch effort has been spent on detecting lane change maneuvers rather than\non estimating the point in time a lane change actually happens. In practice,\nhowever, this temporal information might be even more useful. This paper deals\nwith the development of a system that accurately predicts the time to the next\nlane change of surrounding vehicles on highways using long short-term\nmemory-based recurrent neural networks. An extensive evaluation based on a\nlarge real-world data set shows that our approach is able to make reliable\npredictions, even in the most challenging situations, with a root mean squared\nerror around 0.7 seconds. Already 3.5 seconds prior to lane changes the\npredictions become highly accurate, showing a median error of less than 0.25\nseconds. In summary, this article forms a fundamental step towards downstreamed\nhighly accurate position predictions.\n"}
+{"abstract": "  Can we make undergraduate engineering education easier and more fun? This\nresearch aims to see if we can answer the ambitious question! The digital\ngame-based learning (DGBL) has been found to increase the efficacy of learning\nwhen applied in engineering classes thanks to its ability to make students feel\neasy and fun. However, the state-of-the-art DGBL schemes still observe\nchallenges in various aspects including cost, efficacy, readiness of\ninstructors and students, etc. Motivated from the challenges, this research\nproposes to design a DGBL platform that features visualized and systematic\nviews. Specifically, we identify the blockchain applied to wireless\ncommunications and networking systems as a key ecosystem that we capitalize the\nbenefit of the proposed platform.. As such, in this paper, we lay out a\ncomprehensive DGBL pedagogy that includes (i) creation of relevant assignment\nactivities and class materials in a relevant course and (ii) evaluation of the\npedagogical efficacy. In a long-term view, a successful delivery of this\nresearch will increase the confidence of undergraduate engineering students on\nthe \"in-concert\" dynamics of various factors determining the performance of a\nblockchain system built on a wireless network.\n"}
+{"abstract": "  We reveal intrinsic topological vector potentials underlying the nonlinear\nwaves governed by one-dimensional nonlinear Schr\\\"{o}dinger equations by\ninvestigating the Berry connection of the linearized Bogoliubov-de-Gennes (BdG)\nequations in an extended complex coordinate space. Surprisingly, we find that\nthe density zeros of these nonlinear waves exactly correspond to the degenerate\npoints of the BdG energy spectra and can constitute monopole fields with a\nquantized magnetic flux of elementary $\\pi$. Such a vector potential consisting\nof paired monopoles with opposite charges can completely capture the essential\ncharacteristics of nonlinear wave evolution. As an application, we investigate\nrogue waves and explain their exotic property of ``appearing from nowhere and\ndisappearing without a trace'' by means of a monopole collision mechanism. The\nmaximum amplification ratio and multiple phase steps of a high-order rogue wave\nare found to be closely related to the number of monopoles. Important\nimplications of the intrinsic topological vector potentials are discussed.\n"}
+{"abstract": "  Chaotic quantum many-body dynamics typically lead to relaxation of local\nobservables. In this process, known as quantum thermalization, a subregion\nreaches a thermal state due to quantum correlations with the remainder of the\nsystem, which acts as an intrinsic bath. While the bath is generally assumed to\nbe unobserved, modern quantum science experiments have the ability to track\nboth subsystem and bath at a microscopic level. Here, by utilizing this\nability, we discover that measurement results associated with small subsystems\nexhibit universal random statistics following chaotic quantum many-body\ndynamics, a phenomenon beyond the standard paradigm of quantum thermalization.\nWe explain these observations with an ensemble of pure states, defined via\ncorrelations with the bath, that dynamically acquires a close to random\ndistribution. Such random ensembles play an important role in quantum\ninformation science, associated with quantum supremacy tests and device\nverification, but typically require highly-engineered, time-dependent control\nfor their preparation. In contrast, our approach uncovers random ensembles\nnaturally emerging from evolution with a time-independent Hamiltonian. As an\napplication of this emergent randomness, we develop a benchmarking protocol\nwhich estimates the many-body fidelity during generic chaotic evolution and\ndemonstrate it using our Rydberg quantum simulator. Our work has wide ranging\nimplications for the understanding of quantum many-body chaos and\nthermalization in terms of emergent randomness and at the same time paves the\nway for applications of this concept in a much wider context.\n"}
+{"abstract": "  We introduce a domain decomposition-based nonlinear preconditioned iteration\nfor solving nonlinear, nonsmooth elliptic optimal control problems, with a\nnonlinear reaction term, $L^1$ regularization and box constraints on the\ncontrol function. The method is obtained by applying semismooth Newton to the\nfixed-point equation of the parallel optimized Schwarz iteration. As a proof of\nconcept, numerical experiments are performed on two subdomains, as well as on a\nmulti-subdomain test case. The results show that it is possible to obtain\nsubstantial improvements in robustness and efficiency with the new method,\nrelative to semismooth Newton applied directly to the full optimization\nproblem, provided appropriate Robin parameters and a good continuation strategy\nare chosen.\n"}
+{"abstract": "  State-of-the-art temporal action detectors to date are based on two-stream\ninput including RGB frames and optical flow. Although combining RGB frames and\noptical flow boosts performance significantly, optical flow is a hand-designed\nrepresentation which not only requires heavy computation, but also makes it\nmethodologically unsatisfactory that two-stream methods are often not learned\nend-to-end jointly with the flow. In this paper, we argue that optical flow is\ndispensable in high-accuracy temporal action detection and image level data\naugmentation (ILDA) is the key solution to avoid performance degradation when\noptical flow is removed. To evaluate the effectiveness of ILDA, we design a\nsimple yet efficient one-stage temporal action detector based on single RGB\nstream named DaoTAD. Our results show that when trained with ILDA, DaoTAD has\ncomparable accuracy with all existing state-of-the-art two-stream detectors\nwhile surpassing the inference speed of previous methods by a large margin and\nthe inference speed is astounding 6668 fps on GeForce GTX 1080 Ti. Code is\navailable at \\url{https://github.com/Media-Smart/vedatad}.\n"}
+{"abstract": "  Approximate Bayesian inference methods that scale to very large datasets are\ncrucial in leveraging probabilistic models for real-world time series. Sparse\nMarkovian Gaussian processes combine the use of inducing variables with\nefficient Kalman filter-like recursions, resulting in algorithms whose\ncomputational and memory requirements scale linearly in the number of inducing\npoints, whilst also enabling parallel parameter updates and stochastic\noptimisation. Under this paradigm, we derive a general site-based approach to\napproximate inference, whereby we approximate the non-Gaussian likelihood with\nlocal Gaussian terms, called sites. Our approach results in a suite of novel\nsparse extensions to algorithms from both the machine learning and signal\nprocessing literature, including variational inference, expectation\npropagation, and the classical nonlinear Kalman smoothers. The derived methods\nare suited to large time series, and we also demonstrate their applicability to\nspatio-temporal data, where the model has separate inducing points in both time\nand space.\n"}
+{"abstract": "  This article describes research on claim verification carried out using a\nmultiple GAN-based model. The proposed model consists of three pairs of\ngenerators and discriminators. The generator and discriminator pairs are\nresponsible for generating synthetic data for supported and refuted claims and\nclaim labels. A theoretical discussion about the proposed model is provided to\nvalidate the equilibrium state of the model. The proposed model is applied to\nthe FEVER dataset, and a pre-trained language model is used for the input text\ndata. The synthetically generated data helps to gain information which helps\nthe model to perform better than state of the art models and other standard\nclassifiers.\n"}
+{"abstract": "  In this paper, we prove Sp\\\"ath's Character Triple Conjecture for\n$p$-solvable groups. This is a conjecture proposed by Sp\\\"ath during the\nreduction process of Dade's Projective Conjecture to quasisimple groups. In\naddition, as suggested by Isaacs and Navarro, we take into account the\n$p$-residue of characters.\n"}
+{"abstract": "  We construct a gauge theory based in the supergroup $G=SU(2,2|2)$ that\ngeneralizes MacDowell-Mansouri supergravity. This is done introducing an\nextended notion of Hodge operator in the form of an outer automorphism of\n$su(2,2|2)$-valued 2-form tensors. The model closely resembles a Yang-Mills\ntheory -- including the action principle, equations of motion and gauge\ntransformations -- which avoids the use of the otherwise complicated component\nformalism. The theory enjoys $H=SO(3,1)\\times \\mathbb{R} \\times U(1)\\times\nSU(2)$ off-shell symmetry whilst the broken symmetries $G/H$, translation-type\nsymmetries and supersymmetry, can be recovered on surface of integrability\nconditions of the equations of motion, for which it suffices the\nRarita-Schwinger equation and torsion-like constraints to hold. Using the\n\\textit{matter ansatz} -- projecting the $1 \\otimes 1/2$ reducible\nrepresentation into the spin-$1/2$ irreducible sector -- we obtain (chiral)\nfermion models with gauge and gravity interactions.\n"}
+{"abstract": "  Subspace methods are essential to high-resolution environment sensing in the\nemerging unmanned systems, if further combined with the millimeter-wave\n(mm-Wave) massive multi-input multi-output (MIMO) technique. The estimation of\nsignal/noise subspace, as one critical step, is yet computationally complex and\npresents a particular challenge when developing high-resolution yet\nlow-complexity automotive radars. In this work, we develop a fast\nrandomized-MUSIC (R-MUSIC) algorithm, which exploits the random matrix\nsketching to estimate the signal subspace via approximated computation. Our new\napproach substantially reduces the time complexity in acquiring a high-quality\nsignal subspace. Moreover, the accuracy of R-MUSIC suffers no degradation\nunlike others low-complexity counterparts, i.e. the high-resolution angle of\narrival (AoA) estimation is attained. Numerical simulations are provided to\nvalidate the performance of our R-MUSIC method. As shown, it resolves the\nlong-standing contradiction in complexity and accuracy of MIMO radar signal\nprocessing, which hence have great potentials in real-time super-resolution\nautomotive sensing.\n"}
+{"abstract": "  The amount of data, manpower and capital required to understand, evaluate and\nagree on a group of symptoms for the elementary prognosis of pandemic diseases\nis enormous. In this paper, we present FedPandemic, a novel noise\nimplementation algorithm integrated with cross-device Federated learning for\nElementary symptom prognosis during a pandemic, taking COVID-19 as a case\nstudy. Our results display consistency and enhance robustness in recovering the\ncommon symptoms displayed by the disease, paving a faster and cheaper path\ntowards symptom retrieval while also preserving the privacy of patient's\nsymptoms via Federated learning.\n"}
+{"abstract": "  In this paper we analyze some interesting features of the thermodynamics of\nthe rotating BTZ black hole from the Carath\\'{e}odory axiomatic postulate, for\nwhich, we exploit the appropriate Pfaffian form. The allowed adiabatic\ntransformations are then obtained by solving the corresponding Cauchy problem,\nand are studied accordingly. Furthermore, we discuss the implications of our\napproach, regarding the the second and third laws of black hole thermodynamics.\nIn particular, the merging of two extremal black holes is studied in detail.\n"}
+{"abstract": "  Preregistration refers to the practice of specifying what you are going to\ndo, and what you expect to find in your study, before carrying out the study.\nThis practice is increasingly common in medicine and psychology, but is rarely\ndiscussed in NLP. This paper discusses preregistration in more detail, explores\nhow NLP researchers could preregister their work, and presents several\npreregistration questions for different kinds of studies. Finally, we argue in\nfavour of registered reports, which could provide firmer grounds for slow\nscience in NLP research. The goal of this paper is to elicit a discussion in\nthe NLP community, which we hope to synthesise into a general NLP\npreregistration form in future research.\n"}
+{"abstract": "  Lecture-notes: introducing and discussing basic kp concepts related to bands\ninteraction at an elementary level to beginners\n"}
+{"abstract": "  In this paper, we consider the evolution of spacelike graphic hypersurfaces\ndefined over a convex piece of hyperbolic plane $\\mathscr{H}^{n}(1)$, of center\nat origin and radius $1$, in the $(n+1)$-dimensional Lorentz-Minkowski space\n$\\mathbb{R}^{n+1}_{1}$ along an anisotropic inverse mean curvature flow with\nthe vanishing Neumann boundary condition, and prove that this flow exists for\nall the time. Moreover, we can show that, after suitable rescaling, the\nevolving spacelike graphic hypersurfaces converge smoothly to a piece of\nhyperbolic plane of center at origin and prescribed radius, which actually\ncorresponds to a constant function defined over the piece of $\\mathscr{H}^{n}\n(1)$, as time tends to infinity. Clearly, this conclusion is an extension of\nour previous work [2].\n"}
+{"abstract": "  Multi-armed Bandit (MAB) algorithms identify the best arm among multiple arms\nvia exploration-exploitation trade-off without prior knowledge of arm\nstatistics. Their usefulness in wireless radio, IoT, and robotics demand\ndeployment on edge devices, and hence, a mapping on system-on-chip (SoC) is\ndesired. Theoretically, the Bayesian approach-based Thompson Sampling (TS)\nalgorithm offers better performance than the frequentist approach-based Upper\nConfidence Bound (UCB) algorithm. However, TS is not synthesizable due to Beta\nfunction. We address this problem by approximating it via a pseudo-random\nnumber generator-based approach and efficiently realize the TS algorithm on\nZynq SoC. In practice, the type of arms distribution (e.g., Bernoulli,\nGaussian, etc.) is unknown and hence, a single algorithm may not be optimal. We\npropose a reconfigurable and intelligent MAB (RI-MAB) framework. Here,\nintelligence enables the identification of appropriate MAB algorithms for a\ngiven environment, and reconfigurability allows on-the-fly switching between\nalgorithms on the SoC. This eliminates the need for parallel implementation of\nalgorithms resulting in huge savings in resources and power consumption. We\nanalyze the functional correctness, area, power, and execution time of the\nproposed and existing architectures for various arm distributions, word-length,\nand hardware-software co-design approaches. We demonstrate the superiority of\nthe RI-MAB over TS and UCB only architectures.\n"}
+{"abstract": "  Accurate and precise terrain estimation is a difficult problem for robot\nlocomotion in real-world environments. Thus, it is useful to have systems that\ndo not depend on accurate estimation to the point of fragility. In this paper,\nwe explore the limits of such an approach by investigating the problem of\ntraversing stair-like terrain without any external perception or terrain models\non a bipedal robot. For such blind bipedal platforms, the problem appears\ndifficult (even for humans) due to the surprise elevation changes. Our main\ncontribution is to show that sim-to-real reinforcement learning (RL) can\nachieve robust locomotion over stair-like terrain on the bipedal robot Cassie\nusing only proprioceptive feedback. Importantly, this only requires modifying\nan existing flat-terrain training RL framework to include stair-like terrain\nrandomization, without any changes in reward function. To our knowledge, this\nis the first controller for a bipedal, human-scale robot capable of reliably\ntraversing a variety of real-world stairs and other stair-like disturbances\nusing only proprioception.\n"}
+{"abstract": "  Several aspects influence corrosive processes in RC structures, such as\nenvironmental conditions, structural geometry, and mechanical properties. Since\nthese aspects present large randomnesses, probabilistic models allow a more\naccurate description of the corrosive phenomena. On the other hand, the\ndefinition of limit states, applied in the reliability assessment, requires a\nproper mechanical model. In this context, this study proposes an accurate\nmethodology for the mechanical-probabilistic modelling of RC structures\nsubjected to reinforcements' corrosion. To this purpose, an improved damage\napproach is proposed to define the limit states for the probabilistic\nmodelling, considering three main degradation phenomena: concrete cracking,\nrebar yielding, and rebar corrosion caused either by chlorides or carbonation\nprocess. The stochastic analysis is evaluated by the Monte Carlo simulation\nmethod due to the computational efficiency of the LDMC. The proposed\nmechanical-probabilistic methodology is implemented in a computational\nframework and applied to the analysis of a simply supported RC beam, and a 2D\nRC frame. Curves illustrate the probability of failure over a service life of\n50 years. Moreover, the proposed model allows drawing the probability of\nfailure map and then identify the critical failure path for progressive\ncollapse analysis. Collapse path changes caused by the corrosion phenomena are\nobserved.\n"}
+{"abstract": "  Recently, the importance of weather parameters and location information to\nbetter understand the context of the communication of children with profound\nintellectual and multiple disabilities (PIMD) or severe motor and intellectual\ndisorders (SMID) has been proposed. However, an investigation on whether these\ndata can be used to classify their behavior for system optimization aimed for\npredicting their behavior for independent communication and mobility has not\nbeen done. Thus, this study investigates whether recalibrating the datasets\nincluding either minor or major behavior categories or both, combining location\nand weather data and feature selection method training (Boruta) would allow\nmore accurate classification of behavior discriminated to binary and multiclass\nclassification outcomes using eXtreme Gradient Boosting (XGB), support vector\nmachine (SVM), random forest (RF), and neural network (NN) classifiers.\nMultiple single-subject face-to-face and video-recorded sessions were conducted\namong 20 purposively sampled 8 to 10 -year old children diagnosed with\nPIMD/SMID or severe or profound intellectual disabilities and their caregivers.\n"}
+{"abstract": "  There are two puzzles surrounding the Pleiades, or Seven Sisters. First, why\nare the mythological stories surrounding them, typically involving seven young\ngirls being chased by a man associated with the constellation Orion, so similar\nin vastly separated cultures, such as the Australian Aboriginal cultures and\nGreek mythology? Second, why do most cultures call them \"Seven Sisters\" even\nthough most people with good eyesight see only six stars? Here we show that\nboth these puzzles may be explained by a combination of the great antiquity of\nthe stories combined with the proper motion of the stars, and that these\nstories may predate the departure of most modern humans out of Africa around\n100,000 BC.\n"}
+{"abstract": "  The method of covariant perturbation theory allowed for the computation of\nthe kernel of the evolution equation on a spin Riemannian manifold. The\nproposed axiomatic definition of the covariant effective action introduces the\nuniversal scale parameter, with the length square dimensionality, into a\ndimensionless mathematical theory. It is shown that this geometrical result has\na physical meaning of the action of field theory, including gravity. Two lowest\ntensor order terms of this functional are independent of a spin group and\nlocal. They reproduce the action of general relativity with the cosmological\nconstant. The current value of the universal scale can be determined with the\nmeasured Hubble constant. This scale parameter considered as a physical\nvariable can let us build cosmological theories axiomatically.\n"}
+{"abstract": "  In this work, we provide an asymptotic analysis of the solutions to an\nelliptic integro-differential equation. This equation describes the\nevolutionary equilibria of a phenotypically structured population, subject to\nselection, mutation, and both local and non-local dispersion in a spatially\nheterogeneous, possibly patchy, environment. Considering small effects of\nmutations, we provide an asymptotic description of the equilibria of the\nphenotypic density. This asymptotic description involves a Hamilton-Jacobi\nequation with constraint coupled with an eigenvalue problem. Based on such\nanalysis, we characterize some qualitative properties of the phenotypic density\nat equilibrium depending on the heterogeneity of the environment. In\nparticular, we show that when the heterogeneity of the environment is low, the\npopulation concentrates around a single phenotypic trait leading to a unimodal\nphenotypic distribution. On the contrary, a strong fragmentation of the\nenvironment leads to multi-modal phenotypic distributions.\n"}
+{"abstract": "  General equilibrium macroeconomic models are a core tool used by policymakers\nto understand a nation's economy. They represent the economy as a collection of\nforward-looking actors whose behaviours combine, possibly with stochastic\neffects, to determine global variables (such as prices) in a dynamic\nequilibrium. However, standard semi-analytical techniques for solving these\nmodels make it difficult to include the important effects of heterogeneous\neconomic actors. The COVID-19 pandemic has further highlighted the importance\nof heterogeneity, for example in age and sector of employment, in macroeconomic\noutcomes and the need for models that can more easily incorporate it. We use\ntechniques from reinforcement learning to solve such models incorporating\nheterogeneous agents in a way that is simple, extensible, and computationally\nefficient. We demonstrate the method's accuracy and stability on a toy problem\nfor which there is a known analytical solution, its versatility by solving a\ngeneral equilibrium problem that includes global stochasticity, and its\nflexibility by solving a combined macroeconomic and epidemiological model to\nexplore the economic and health implications of a pandemic. The latter\nsuccessfully captures plausible economic behaviours induced by differential\nhealth risks by age.\n"}
+{"abstract": "  In a new Weyl 2-spinor approach to Non abelian Gauge Theories, starting with\nthe local U(1) Gauge group of a previous work, we study now the SU(3) case\ncorresponding to quarks (antiquarks) interacting with color fields. The\nprincipal difference with the conventional approach is that particle-field\ninteractions are not described by means of potentials but by the field strength\nmagnitudes. Some analytical expressions showing similarities with\nelectrodynamics are obtained. Classical equations that describe the behavior of\nquarks under gluon fields might be in principle applied to the quark-gluon\nplasma phase existing during the first instants of the Universe.\n"}
+{"abstract": "  This paper studies the nonlinear stability of capillary-gravity waves\npropagating along the interface dividing two immiscible fluid layers of finite\ndepth. The motion in both regions is governed by the incompressible and\nirrotational Euler equations, with the density of each fluid being constant but\ndistinct. A diverse collection of small-amplitude solitary wave solutions for\nthis system have been constructed by several authors in the case of strong\nsurface tension (as measured by the Bond number) and slightly subcritical\nFroude number. We prove that all of these waves are (conditionally) orbitally\nstable in the natural energy space. Moreover, the trivial solution is shown to\nbe conditionally stable when the Bond and Froude numbers lie in a certain\nunbounded parameter region. For the near critical surface tension regime, we\nprove that one can infer conditional orbital stability or orbital instability\nof small-amplitude traveling waves solutions to the full Euler system from\nconsiderations of a dispersive PDE model equation.\n  These results are obtained by reformulating the problem as an\ninfinite-dimensional Hamiltonian system, then applying a version of the\nGrillakis--Shatah--Strauss method recently introduced by Varholm, Wahl\\'en, and\nWalsh. A key part of the analysis consists of computing the spectrum of the\nlinearized augmented Hamiltonian at a shear flow or small-amplitude wave. For\nthis, we generalize an idea used by Mielke to treat capillary-gravity water\nwaves beneath vacuum.\n"}
+{"abstract": "  The Continuous Spontaneous Localisation (CSL) theory in the cosmological\ncontext is subject to uncertainties related to the choice of the collapse\noperator. In this paper, we constrain its form based on generic arguments. We\nshow that, if the collapse operator is even in the field variables, it is\nunable to induce the collapse of the wavefunction. Instead, if it is odd, we\nfind that only linear operators are such that the outcomes are distributed\naccording to Gaussian statistics, as required by measurements of the cosmic\nmicrowave background. We discuss implications of these results for previously\nproposed collapse operators. We conclude that the cosmological CSL collapse\noperator should be linear in the field variables.\n"}
+{"abstract": "  An adaptive variational quantum imaginary time evolution (AVQITE) approach is\nintroduced that yields efficient representations of ground states for\ninteracting Hamiltonians on near-term quantum computers. It is based on\nMcLachlan's variational principle applied to imaginary time evolution of\nvariational wave functions. The variational parameters evolve deterministically\naccording to equations of motions that minimize the difference to the exact\nimaginary time evolution, which is quantified by the McLachlan distance. Rather\nthan working with a fixed variational ansatz, where the McLachlan distance is\nconstrained by the quality of the ansatz, the AVQITE method iteratively expands\nthe ansatz along the dynamical path to keep the McLachlan distance below a\nchosen threshold. This ensures the state is able to follow the quantum\nimaginary time evolution path in the system Hilbert space rather than in a\nrestricted variational manifold set by a predefined fixed ansatz. AVQITE is\nused to prepare ground states of H$_4$, H$_2$O and BeH$_2$ molecules, where it\nyields compact variational ans\\\"atze and ground state energies within chemical\naccuracy. Polynomial scaling of circuit depth with system size is demonstrated\nthrough a set of AVQITE calculations of quantum spin models. Finally, it is\nshown that quantum Lanczos calculations can also be naturally performed\nalongside AVQITE without additional quantum resource costs.\n"}
+{"abstract": "  Estimating the features of noise is the first step in a chain of protocols\nthat will someday lead to fault tolerant quantum computers. The randomized\nbenchmarking (RB) protocol is designed with this exact mindset, estimating the\naverage strength of noise in a quantum processor with relative ease in\npractice. However, RB, along with most other benchmarking and characterization\nmethods, is limited in scope because it assumes that the noise is temporally\nuncorrelated (Markovian), which is increasingly evident not to be the case.\nHere, we combine the RB protocol with a recent framework describing\nnon-Markovian quantum phenomena to derive a general analytical expression of\nthe average sequence fidelity (ASF) for non-Markovian RB with the Clifford\ngroup. We show that one can identify non-Markovian features of the noise\ndirectly from the ASF through its deviations from the Markovian case, proposing\na set of methods to collectively estimate these deviations, non-Markovian\nmemory time-scales, and diagnose (in)coherence of non-Markovian noise in an RB\nexperiment. Finally, we demonstrate the efficacy of our proposal by means of\nseveral proof-of-principle examples. Our methods are directly implementable and\npave the pathway to better understanding correlated noise in quantum\nprocessors.\n"}
+{"abstract": "  We write down the Algebraic Bethe Ansatz for string theory on\n$\\mbox{AdS}_3\\times\\mbox{S}^3\\times\\mbox{T}^4$ and\n$\\mbox{AdS}_3\\times\\mbox{S}^3\\times\\mbox{K3}$ in its orbifold limits. We use it\nto determine the wave-functions of protected closed strings in these\nbackgrounds and prove that their energies are protected to all orders in\n$\\alpha'$. We further apply the ABA to find the wave functions of protected\nstates of $\\mbox{AdS}_3\\times\\mbox{S}^3\\times\\mbox{S}^3\\times \\mbox{S}^1$ and\nits $\\mathbf{Z}_2$ orbifold. Our findings match with protected spectrum\ncalculations from supergravity, $\\mbox{Sym}^N$ orbifolds and apply to the\ncomplete moduli space of these theories, excluding orbifold blow-up modes for\nwhich further analysis is necessary.\n"}
+{"abstract": "  Low temperature optical spectroscopy in applied magnetic fields provides\nclear evidence of magnetoelastic coupling in the spin ice material Ho2Ti2O7. In\nIR measurements, we observe field dependent features around 61, 72 and 78 meV,\nenergies corresponding to crystal electronic field doublets. Calculating the\nelectronic band structure based on the crystal field Hamiltonian allows\ndetermination of crystal field energies, values for the crystal field\nparameters, and confirmation that the observed features in IR are consistent\nwith magnetic-dipole-allowed transitions between 5I8 CEF levels. Additionally,\nwe identify a weak field-dependent feature near one of the CEF doublets, which\nwe associate with a vibronic bound state that was previously observed by others\nin inelastic neutron measurements.\n"}
+{"abstract": "  Recent work suggests that convolutional neural networks of different\narchitectures learn to classify images in the same order. To understand this\nphenomenon, we revisit the over-parametrized deep linear network model. Our\nasymptotic analysis, assuming that the hidden layers are wide enough, reveals\nthat the convergence rate of this model's parameters is exponentially faster\nalong directions corresponding to the larger principal components of the data,\nat a rate governed by the singular values. We term this convergence pattern the\nPrincipal Components bias (PC-bias). We show how the PC-bias streamlines the\norder of learning of both linear and non-linear networks, more prominently at\nearlier stages of learning. We then compare our results to the simplicity bias,\nshowing that both biases can be seen independently, and affect the order of\nlearning in different ways. Finally, we discuss how the PC-bias may explain\nsome benefits of early stopping and its connection to PCA, and why deep\nnetworks converge more slowly when given random labels.\n"}
+{"abstract": "  This report gives the reasoning for the setting of the target queue delay\nparameter in the reference Linux implementation of PI$^2$ Active Queue\nManagement (AQM)\n"}
+{"abstract": "  \"Transcription bottlenecks\", created by a shortage of effective human\ntranscribers are one of the main challenges to endangered language (EL)\ndocumentation. Automatic speech recognition (ASR) has been suggested as a tool\nto overcome such bottlenecks. Following this suggestion, we investigated the\neffectiveness for EL documentation of end-to-end ASR, which unlike Hidden\nMarkov Model ASR systems, eschews linguistic resources but is instead more\ndependent on large-data settings. We open source a Yolox\\'ochitl Mixtec EL\ncorpus. First, we review our method in building an end-to-end ASR system in a\nway that would be reproducible by the ASR community. We then propose a novice\ntranscription correction task and demonstrate how ASR systems and novice\ntranscribers can work together to improve EL documentation. We believe this\ncombinatory methodology would mitigate the transcription bottleneck and\ntranscriber shortage that hinders EL documentation.\n"}
+{"abstract": "  Since being launched in 2014, Alexa, Amazon's versatile cloud-based voice\nservice, is now active in over 100 million households worldwide. Alexa's\nuser-friendly, personalized vocal experience offers customers a more natural\nway of interacting with cutting-edge technology by allowing the ability to\ndirectly dictate commands to the assistant. Now in the present year, the Alexa\nservice is more accessible than ever, available on hundreds of millions of\ndevices from not only Amazon but third-party device manufacturers.\nUnfortunately, that success has also been the source of concern and\ncontroversy. The success of Alexa is based on its effortless usability, but in\nturn, that has led to a lack of sufficient security. This paper surveys various\nattacks against Amazon Alexa ecosystem including attacks against the frontend\nvoice capturing and the cloud backend voice command recognition and processing.\nOverall, we have identified six attack surfaces covering the lifecycle of Alexa\nvoice interaction that spans several stages including voice data collection,\ntransmission, processing and storage. We also discuss the potential mitigation\nsolutions for each attack surface to better improve Alexa or other voice\nassistants in terms of security and privacy.\n"}
+{"abstract": "  In a recent paper, Madritsch and Tichy established Diophantine inequalities\nfor the fractional parts of polynomial-like functions. In particular, for\n$f(x)=x^k+x^c$ where $k$ is a positive integer and $c>1$ is a non-integer, and\nany fixed $\\xi\\in [0,1]$ they obtained \\[\\min_{2\\leq p\\leq X} \\Vert \\xi \\lfloor\nf(p)\\rfloor \\Vert\\ll_{k,c,\\epsilon} X^{-\\rho_1(c,k)+\\epsilon}\\] for\n$\\rho_1(c,k)>0$ explicitly given. In the present note, we improve upon their\nresults in the case $c>k$ and $c>4$.\n"}
+{"abstract": "  In this paper, I reply to the recent article by Jeffery and Verheijen (2020)\n'A new soil health policy paradigm: Pay for practice not performance!'. While\nexpressing support for their call for a more pronounced role of soil protection\nin agri-environmental policy, I critically discuss the two main elements of\ntheir specific proposal: its emphasis of the concept of soil health and the\nrecommendation to use action-based payments as the main policy instrument. I\nargue for using soil functions as a more established concept (and thus more\nadequate for policy purposes), which is also informationally richer than soil\nhealth. Furthermore, I provide a more differentiated discussion of the relative\nadvantages and disadvantages of result-based and action-based payments, while\naddressing the specific criticisms towards the former that Jeffery and\nVerheijen voice. Also, I suggest an alternative approach (a hybrid model-based\nscheme) that addresses the limitations of both Jeffery and Verheijen's own\nproposal and the valid criticisms they direct at result-based payments.\n"}
+{"abstract": "  White matter fiber clustering (WMFC) enables parcellation of white matter\ntractography for applications such as disease classification and anatomical\ntract segmentation. However, the lack of ground truth and the ambiguity of\nfiber data (the points along a fiber can equivalently be represented in forward\nor reverse order) pose challenges to this task. We propose a novel WMFC\nframework based on unsupervised deep learning. We solve the unsupervised\nclustering problem as a self-supervised learning task. Specifically, we use a\nconvolutional neural network to learn embeddings of input fibers, using\npairwise fiber distances as pseudo annotations. This enables WMFC that is\ninsensitive to fiber point ordering. In addition, anatomical coherence of fiber\nclusters is improved by incorporating brain anatomical segmentation data. The\nproposed framework enables outlier removal in a natural way by rejecting fibers\nwith low cluster assignment probability. We train and evaluate our method using\n200 datasets from the Human Connectome Project. Results demonstrate superior\nperformance and efficiency of the proposed approach.\n"}
+{"abstract": "  Controllable strong interactions between a nanocavity and a single emitter is\nimportant to manipulating optical emission in a nanophotonic systems but\nchallenging to achieve. Here a three-dimensional DNA origami, named as DNA rack\n(DR) is proposed and demonstrated to deterministically and precisely assemble\nsingle emitters within ultra-small plasmonic nanocavities formed by closely\ncoupled gold nanorods (AuNRs). The DR uniquely possesses a saddle shape with\ntwo tubular grooves that geometrically allows a snug fit and linearly align two\nAuNRs with a bending angle <10{\\deg}. It also includes a spacer at the saddle\npoint to maintain the gap between AuNRs as small as 2-3 nm, forming a\nnanocavity estimated to be 20 nm3 and an experimentally measured Q factor of\n7.3. A DNA docking strand is designed at the spacer to position a single\nfluorescent emitter at nanometer accuracy within the cavity. Using Cy5 as a\nmodel emitter, a ~30-fold fluorescence enhancement and a significantly reduced\nemission lifetime (from 1.6 ns to 670 ps) were experimentally verified,\nconfirming significant emitter-cavity interactions. This DR-templated assembly\nmethod is capable of fitting AuNRs of variable length-to-width aspect ratios to\nform anisotropic nanocavities and deterministically incorporating different\nsingle emitters, thus enabling flexible design of both cavity resonance and\nemission wavelengths to tailor light-matter interactions at nanometer scale.\n"}
+{"abstract": "  In machine learning we often encounter structured output prediction problems\n(SOPPs), i.e. problems where the output space admits a rich internal structure.\nApplication domains where SOPPs naturally occur include natural language\nprocessing, speech recognition, and computer vision. Typical SOPPs have an\nextremely large label set, which grows exponentially as a function of the size\nof the output. Existing generalization analysis implies generalization bounds\nwith at least a square-root dependency on the cardinality $d$ of the label set,\nwhich can be vacuous in practice. In this paper, we significantly improve the\nstate of the art by developing novel high-probability bounds with a logarithmic\ndependency on $d$. Moreover, we leverage the lens of algorithmic stability to\ndevelop generalization bounds in expectation without any dependency on $d$. Our\nresults therefore build a solid theoretical foundation for learning in\nlarge-scale SOPPs. Furthermore, we extend our results to learning with weakly\ndependent data.\n"}
+{"abstract": "  Background: The neutron skin thickness $R_{\\rm skin}^{\\rm PV}$ of PREX-II is\npresented in Phys. Rev. Lett. {\\bf 126}, 172502 (2021). The reaction cross\nsection $\\sigma_R$ is useful to determine the matter radius $R_m$ and $R_{\\rm\nskin}$. For proton scattering, the reaction cross section $\\sigma_R$ are\navailable for $E_{\\rm in} > 400$ MeV.\n  Method and results: We determine $R_n^{\\rm exp}=5.727 \\pm 0.071$ fm and\n$R_m^{\\rm exp}=5.617 \\pm 0.044$ fm from $R_p^{\\rm exp}$ = 5.444 fm and $R_{\\rm\nskin}^{\\rm PV}$. The $R_p^{\\rm GHFB}$ calculated with D1S-GHFB with the angular\nmomentum projection (AMP). agrees with $R_p^{\\rm exp}$. The neutron density\ncalculated with GHFB+AMP is scaled so as to $R_n^{\\rm scaling}=5.727$ fm. The\nLove-Franey $t$-matrix model with the scaled densities reproduces the data on\n$\\sigma_R$.\n  Aim: Our aim is to find the $\\sigma_R$ of proton scattering consistent with\n$R_{\\rm skin}^{\\rm PV}$.\n  Conclusion: The $\\sigma_R$ of proton scattering consistent with $R_{\\rm\nskin}^{\\rm PV}$ are $\\sigma_R^{\\rm exp}$ at $E_{\\rm in} = 534.1, 549, 806$ MeV.\n"}
+{"abstract": "  Deep Learning networks have established themselves as providing state of art\nperformance for semantic segmentation. These techniques are widely applied\nspecifically to medical detection, segmentation and classification. The advent\nof the U-Net based architecture has become particularly popular for this\napplication. In this paper we present the Dense Recurrent Residual\nConvolutional Neural Network(Dense R2U CNN) which is a synthesis of Recurrent\nCNN, Residual Network and Dense Convolutional Network based on the U-Net model\narchitecture. The residual unit helps training deeper network, while the dense\nrecurrent layers enhances feature propagation needed for segmentation. The\nproposed model tested on the benchmark Lung Lesion dataset showed better\nperformance on segmentation tasks than its equivalent models.\n"}
+{"abstract": "  In this paper, we present a novel implicit glyph shape representation, which\nmodels glyphs as shape primitives enclosed by quadratic curves, and naturally\nenables generating glyph images at arbitrary high resolutions. Experiments on\nfont reconstruction and interpolation tasks verified that this structured\nimplicit representation is suitable for describing both structure and style\nfeatures of glyphs. Furthermore, based on the proposed representation, we\ndesign a simple yet effective disentangled network for the challenging one-shot\nfont style transfer problem, and achieve the best results comparing to\nstate-of-the-art alternatives in both quantitative and qualitative comparisons.\nBenefit from this representation, our generated glyphs have the potential to be\nconverted to vector fonts through post-processing, reducing the gap between\nrasterized images and vector graphics. We hope this work can provide a powerful\ntool for 2D shape analysis and synthesis, and inspire further exploitation in\nimplicit representations for 2D shape modeling.\n"}
+{"abstract": "  In this paper we present a new approach to tackle complex routing problems\nwith an improved state representation that utilizes the model complexity better\nthan previous methods. We enable this by training from temporal differences.\nSpecifically Q-Learning is employed. We show that our approach achieves\nstate-of-the-art performance for autoregressive policies that sequentially\ninsert nodes to construct solutions on the CVRP. Additionally, we are the first\nto tackle the MDVRP with machine learning methods and demonstrate that this\nproblem type greatly benefits from our approach over other ML methods.\n"}
+{"abstract": "  We demonstrate the occurrence of nodal non-Hermitian (NH) phases featuring\nexceptional degeneracies in chiral-symmetric disordered quantum wires, where NH\nphysics naturally arises from the self-energy in a disorder-averaged Green's\nfunction description. Notably, we find that at least two nodal points in the\nclean Hermitian system are required for exceptional points to be effectively\nstabilized upon adding disorder. We identify and study experimental signatures\nof our theoretical findings both in the spectral functions and in mesoscopic\nquantum transport properties of the considered systems. Our results are\nquantitatively corroborated and exemplified by numerically exact simulations on\na microscopic lattice model. The proposed setting provides a conceptually\nminimal framework for the realization and study of topological NH phases in\nquantum many-body systems.\n"}
+{"abstract": "  We adapt to global fields of positive characteristic the contents of the book\nby Labesse and Waldspurger on the Twisted Trace Formula after the Friday\nMorning Seminar.\n"}
+{"abstract": "  Atmospheric turbulence generally limits free-space optical (FSO)\ncommunications, and this problem is severely exacerbated when implementing\nhighly sensitive and spectrally efficient coherent detection. Specifically,\nturbulence induces power coupling from the transmitted Gaussian mode to\nhigher-order Laguerre-Gaussian (LG) modes, resulting in a significant decrease\nof the power that mixes with a single-mode local oscillator (LO). Instead, we\ntransmit a frequency-offset Gaussian pilot tone along with the data signal,\nsuch that both experience similar turbulence and modal power coupling.\nSubsequently, the photodetector (PD) optoelectronically mixes all corresponding\npairs of the beams' modes. During mixing, a conjugate of the turbulence\nexperienced by the pilot tone is automatically generated and compensates the\nturbulence experienced by the data, and nearly all orders of the same\ncorresponding modes efficiently mix. We demonstrate a 12-Gbit/s\n16-quadrature-amplitude-modulation (16-QAM) polarization-multiplexed (PolM) FSO\nlink that exhibits resilience to emulated turbulence. Experimental results for\nturbulence D/r_0~5.5 show up to ~20 dB reduction in the mixing power loss over\na conventional coherent receiver. Therefore, our approach automatically\nrecovers nearly all the captured data power to enable high-performance coherent\nFSO systems.\n"}
+{"abstract": "  Users install many apps on their smartphones, raising issues related to\ninformation overload for users and resource management for devices. Moreover,\nthe recent increase in the use of personal assistants has made mobile devices\neven more pervasive in users' lives. This paper addresses two research problems\nthat are vital for developing effective personal mobile assistants: target apps\nselection and recommendation. The former is the key component of a unified\nmobile search system: a system that addresses the users' information needs for\nall the apps installed on their devices with a unified mode of access. The\nlatter, instead, predicts the next apps that the users would want to launch.\nHere we focus on context-aware models to leverage the rich contextual\ninformation available to mobile devices. We design an in situ study to collect\nthousands of mobile queries enriched with mobile sensor data (now publicly\navailable for research purposes). With the aid of this dataset, we study the\nuser behavior in the context of these tasks and propose a family of\ncontext-aware neural models that take into account the sequential, temporal,\nand personal behavior of users. We study several state-of-the-art models and\nshow that the proposed models significantly outperform the baselines.\n"}
+{"abstract": "  Airborne transmission is now believed to be the primary way that COVID-19\nspreads. We study the airborne transmission risk associated with holding\nin-person classes on university campuses. We utilize a model for airborne\ntransmission risk in an enclosed room that considers the air change rate for\nthe room, mask efficiency, initial infection probability of the occupants, and\nalso the activity level of the occupants. We introduce, and use for our\nevaluations, a metric $R_0^{eff}$ that represents the ratio of new infections\nthat occur over a week due to classroom interactions to the number of infected\nindividuals at the beginning of the week. This can be seen as a surrogate for\nthe well-known $R_0$ reproductive number metric, but limited in scope to\nclassroom interactions and calculated on a weekly basis. The simulations take\ninto account the possibility of repeated in-classroom interactions between\nstudents throughout the week. We presented model predictions were generated\nusing Fall 2019 and Fall 2020 course registration data at a large US\nuniversity, allowing us to evaluate the difference in transmission risk between\nin-person and hybrid programs. We quantify the impact of parameters such as\nreduced occupancy levels and mask efficacy. Our simulations indicate that\nuniversal mask usage results in an approximately $3.6\\times$ reduction in new\ninfections through classroom interactions. Moving 90% of the classes online\nleads to about $18\\times$ reduction in new cases. Reducing class occupancy to\n20%, by having hybrid classes, results in an approximately $2.15-2.3\\times$\nfurther reduction in new infections.\n"}
+{"abstract": "  High speed microfluidic jets can be generated by a thermocavitation process:\nfrom the evaporation of the liquid inside a microfluidic channel, a rapidly\nexpanding bubble is formed and generates a jet through a flow focusing effect.\nHere, we study the impact and traversing of such jets on a pendant liquid\ndroplet. Upon impact, an expanding cavity is created, and, above a critical\nimpact velocity, the jet traverses the entire droplet. We predict the critical\ntraversing velocity (i) from a simple energy balance and (ii) by comparing the\nYoung-Laplace and dynamic pressures in the cavity that is created during\nimpact. We contrast the model predictions against experiments, in which we vary\nthe liquid properties of the pendant droplet and find good agreement. In\naddition, we asses how surfactants and viscoelastic effects influence the\ncritical impact velocity. Our results are relevant for the study of needle-free\ninjections, where jets of similar velocities and dimensions are being used.\nGiven the simplicity of our system we can systematically vary the target\nproperties and unravel their effect on the impact dynamics, a true challenge\nwhen injecting into real skin.\n"}
+{"abstract": "  Defending computer networks from cyber attack requires coordinating actions\nacross multiple nodes based on imperfect indicators of compromise while\nminimizing disruptions to network operations. Advanced attacks can progress\nwith few observable signals over several months before execution. The resulting\nsequential decision problem has large observation and action spaces and a long\ntime-horizon, making it difficult to solve with existing methods. In this work,\nwe present techniques to scale deep reinforcement learning to solve the cyber\nsecurity orchestration problem for large industrial control networks. We\npropose a novel attention-based neural architecture with size complexity that\nis invariant to the size of the network under protection. A pre-training\ncurriculum is presented to overcome early exploration difficulty. Experiments\nshow in that the proposed approaches greatly improve both the learning sample\ncomplexity and converged policy performance over baseline methods in\nsimulation.\n"}
+{"abstract": "  This study was aimed to consider the NG-group that consisting of\ntransformations on a nonempty set A has no bijection as its element. In\naddition, it tried to find the maximal order of these groups. It found the\norder of NG-group not greater than n. Our results proved by showing that any\nkind of NG-group in the theorem be isomorphic to a permutation group on a\nquotient set of A with respect to an equivalence relation on A.\n"}
+{"abstract": "  Logical reasoning tasks over symbols, such as learning arithmetic operations\nand computer program evaluations, have become challenges to deep learning. In\nparticular, even state-of-the-art neural networks fail to achieve\n\\textit{out-of-distribution} (OOD) generalization of symbolic reasoning tasks,\nwhereas humans can easily extend learned symbolic rules. To resolve this\ndifficulty, we propose a neural sequence-to-grid (seq2grid) module, an input\npreprocessor that automatically segments and aligns an input sequence into a\ngrid. As our module outputs a grid via a novel differentiable mapping, any\nneural network structure taking a grid input, such as ResNet or TextCNN, can be\njointly trained with our module in an end-to-end fashion. Extensive experiments\nshow that neural networks having our module as an input preprocessor achieve\nOOD generalization on various arithmetic and algorithmic problems including\nnumber sequence prediction problems, algebraic word problems, and computer\nprogram evaluation problems while other state-of-the-art sequence transduction\nmodels cannot. Moreover, we verify that our module enhances TextCNN to solve\nthe bAbI QA tasks without external memory.\n"}
+{"abstract": "  Based on the continuous interpretation of deep learning cast as an optimal\ncontrol problem, this paper investigates the benefits of employing B-spline\nbasis functions to parameterize neural network controls across the layers.\nRather than equipping each layer of a discretized ODE-network with a set of\ntrainable weights, we choose a fixed number of B-spline basis functions whose\ncoefficients are the trainable parameters of the neural network. Decoupling the\ntrainable parameters from the layers of the neural network enables us to\ninvestigate and adapt the accuracy of the network propagation separated from\nthe optimization learning problem. We numerically show that the spline-based\nneural network increases robustness of the learning problem towards\nhyperparameters due to increased stability and accuracy of the network\npropagation. Further, training on B-spline coefficients rather than layer\nweights directly enables a reduction in the number of trainable parameters.\n"}
+{"abstract": "  We are concerned here with an exact solution to the governing equations for\ngeophysical fluid dynamics in spherical coordinates which incorporates\ndiscontinuous fluid stratification. This solution represents a steady,\npurely--azimuthal equatorial flow with an associated free-surface and an\ninterface separating two fluid regions, each of which having its own continuous\ndistribution of density. However, the two density functions do not match along\nthe interface. Following the derivation of the solution we demonstrate that\nthere is a well-defined relationship between the imposed pressure at the\nfree-surface and the resulting distortion of the surface's shape. Moreover,\nimposing the continuity of the pressure along the interface generates an\nequation that describes (implicitly) the shape of the interface. Interestingly,\nit turns out that the interface defining function has infinite regularity.\n"}
+{"abstract": "  Let $\\mathcal{T}^*$ be the semi-group maximal function associated to the\nSchr\\\"odinger operator $-\\Delta+V(x)$ with $V$ satisfying an appropriate\nreverse H\\\"{o}lder inequality. In this paper, we show that the commutator of\n$\\mathcal{T}^*$ is a compact operator on $L^p(w)$ for $1<p<\\infty$ if $b\\in\n\\text{CMO}_\\theta(\\rho)(\\mathbb{R}^n)$ and $w\\in\nA_p^{\\rho,\\theta}(\\mathbb{R}^n)$. Here $\\text{ CMO}_\\theta(\\rho)(\\mathbb{R}^n)$\ndenotes the closure of $\\mathcal{C}_c^\\infty(\\mathbb{R}^n)$ in the\n$\\text{BMO}_\\theta(\\rho)(\\mathbb{R}^n)$ (which is larger than the classical\n$\\text{BMO}(\\mathbb{R}^n)$ space) topology. The space where $b$ belongs and the\nweighs class $w$ belongs are more larger than the usual\n$\\text{CMO}(\\mathbb{R}^n)$ space and the Muckenhoupt $A_p$ weights class,\nrespectively.\n"}
+{"abstract": "  We present a context classification pipeline to allow a robot to change its\nnavigation strategy based on the observed social scenario. Socially-Aware\nNavigation considers social behavior in order to improve navigation around\npeople. Most of the existing research uses different techniques to incorporate\nsocial norms into robot path planning for a single context. Methods that work\nfor hallway behavior might not work for approaching people, and so on. We\ndeveloped a high-level decision-making subsystem, a model-based context\nclassifier, and a multi-objective optimization-based local planner to achieve\nsocially-aware trajectories for autonomously sensed contexts. Using a context\nclassification system, the robot can select social objectives that are later\nused by Pareto Concavity Elimination Transformation (PaCcET) based local\nplanner to generate safe, comfortable, and socially-appropriate trajectories\nfor its environment. This was tested and validated in multiple environments on\na Pioneer mobile robot platform; results show that the robot was able to select\nand account for social objectives related to navigation autonomously.\n"}
+{"abstract": "  We study learning algorithms for the classical Markovian bandit problem with\ndiscount. We explain how to adapt PSRL [24] and UCRL2 [2] to exploit the\nproblem structure. These variants are called MB-PSRL and MB-UCRL2. While the\nregret bound and runtime of vanilla implementations of PSRL and UCRL2 are\nexponential in the number of bandits, we show that the episodic regret of\nMB-PSRL and MB-UCRL2 is $\\tilde O(S\\sqrt{nK})$ where $K$ is the number of\nepisodes, n is the number of bandits and S is the number of states of each\nbandit (the exact bound in $S$, $n$ and $K$ is given in the paper). Up to a\nfactor $\\sqrt S$, this matches the lower bound of $\\Omega(\\sqrt{SnK}$) that we\nalso derive in the paper. MB-PSRL is also computationally efficient: its\nruntime is linear in the number of bandits. We further show that this linear\nruntime cannot be achieved by adapting classical non-Bayesian algorithms such\nas UCRL2 or UCBVI to Markovian bandit problems. Finally, we perform numerical\nexperiments that confirm that MB-PSRL outperforms other existing algorithms in\npractice, both in terms of regret and of computation time.\n"}
+{"abstract": "  Whilst there are perhaps only a few scientific methods, there seem to be\nalmost as many artistic methods as there are artists. Artistic processes appear\nto inhabit the highest order of open-endedness. To begin to understand some of\nthe processes of art making it is helpful to try to automate them even\npartially. In this paper, a novel algorithm for producing generative art is\ndescribed which allows a user to input a text string, and which in a creative\nresponse to this string, outputs an image which interprets that string. It does\nso by evolving images using a hierarchical neural Lindenmeyer system, and\nevaluating these images along the way using an image text dual encoder trained\non billions of images and their associated text from the internet. In doing so\nwe have access to and control over an instance of an artistic process, allowing\nanalysis of which aspects of the artistic process become the task of the\nalgorithm, and which elements remain the responsibility of the artist.\n"}
+{"abstract": "  We have explored an extended seesaw model accommodating a keV sterile\nneutrino adopting $U(1)_{B-L}$ symmetry. This model provides a natural platform\nfor achieving resonant leptogenesis to account for the observed baryon\nasymmetry of the Universe. The required lepton asymmetry is sourced by the CP\nviolating decay of the lightest heavy right handed neutrino to Standard Model\nleptons and Higgs. The presence of the light sterile neutrino in the model\nbrings out an enhancement in the final lepton asymmetry through an additional\nself-energy contribution. Adopting a proper treatment for all the washout\nprocesses this framework strictly favours a strong washout regime thereby\nprotecting the low energy neutrino mass parameters in agreement with the\npresent neutrino and cosmology data. This framework of extended seesaw scheme\noffers the source of matter-antimatter asymmetry without any severe fine tuning\nof the Yukawa couplings governing the tiny neutrino mass. We also comment on\nthe half-life period for the neutrino less double beta decay process in the\nbackground of having a keV sterile neutrino satisfying all the constraints\nwhich guide the explanation for the observed baryon asymmetry of the Universe.\n"}
+{"abstract": "  Understanding the implicit bias of training algorithms is of crucial\nimportance in order to explain the success of overparametrised neural networks.\nIn this paper, we study the dynamics of stochastic gradient descent over\ndiagonal linear networks through its continuous time version, namely stochastic\ngradient flow. We explicitly characterise the solution chosen by the stochastic\nflow and prove that it always enjoys better generalisation properties than that\nof gradient flow. Quite surprisingly, we show that the convergence speed of the\ntraining loss controls the magnitude of the biasing effect: the slower the\nconvergence, the better the bias. To fully complete our analysis, we provide\nconvergence guarantees for the dynamics. We also give experimental results\nwhich support our theoretical claims. Our findings highlight the fact that\nstructured noise can induce better generalisation and they help explain the\ngreater performances observed in practice of stochastic gradient descent over\ngradient descent.\n"}
+{"abstract": "  This paper proposes a novel transmission policy for an intelligent reflecting\nsurface (IRS) assisted wireless powered sensor network (WPSN). An IRS is\ndeployed to enhance the performance of wireless energy transfer (WET) and\nwireless information transfer (WIT) by intelligently adjusting phase shifts of\neach reflecting elements. To achieve its self-sustainability, the IRS needs to\ncollect energy from energy station to support its control circuit operation.\nOur proposed policy for the considered WPSN is called IRS assisted\nharvest-then-transmit time switching, which is able to schedule the\ntransmission time slots by switching between energy collection and energy\nreflection modes. We study the achievable sum throughput of the proposed\ntransmission policy and investigate a joint design of the transmission time\nslots, the power allocation, as well as the discrete phase shifts of the WET\nand WIT. This formulates the problem as a mixed-integer non-linear program,\nwhich is NP-hard and non-convex. We first relax it to one with continuous phase\nshifts, and then propose a two-step approach and decompose the original problem\ninto two sub-problems. We solve the first sub-problem with respect to the phase\nshifts of the WIT in terms of closed-form expression. For the second\nsub-problem, we consider a special case without the circuit power of each\nsensor node, the Lagrange dual method and the KKT conditions are applied to\nderive the optimal closed-form transmission time slots, power allocation, and\nphase shift of the WET. Then we generalise the case with the circuit power of\neach sensor node, which can be solved via employing a semi-definite programming\nrelaxation. The optimal discrete phase shifts can be obtained by quantizing the\ncontinuous values. Numerical results demonstrate the effectiveness of the\nproposed policy and validate the beneficial role of the IRS in comparison to\nthe benchmark schemes.\n"}
+{"abstract": "  Convolutional neural networks (CNNs) have made great breakthroughs in 2D\ncomputer vision. However, their irregular structure makes it hard to harness\nthe potential of CNNs directly on meshes. A subdivision surface provides a\nhierarchical multi-resolution structure, in which each face in a closed\n2-manifold triangle mesh is exactly adjacent to three faces. Motivated by these\ntwo observations, this paper presents SubdivNet, an innovative and versatile\nCNN framework for 3D triangle meshes with Loop subdivision sequence\nconnectivity. Making an analogy between mesh faces and pixels in a 2D image\nallows us to present a mesh convolution operator to aggregate local features\nfrom nearby faces. By exploiting face neighborhoods, this convolution can\nsupport standard 2D convolutional network concepts, e.g. variable kernel size,\nstride, and dilation. Based on the multi-resolution hierarchy, we make use of\npooling layers which uniformly merge four faces into one and an upsampling\nmethod which splits one face into four. Thereby, many popular 2D CNN\narchitectures can be easily adapted to process 3D meshes. Meshes with arbitrary\nconnectivity can be remeshed to have Loop subdivision sequence connectivity via\nself-parameterization, making SubdivNet a general approach. Extensive\nevaluation and various applications demonstrate SubdivNet's effectiveness and\nefficiency.\n"}
+{"abstract": "  Safe navigation of autonomous agents in human centric environments requires\nthe ability to understand and predict motion of neighboring pedestrians.\nHowever, predicting pedestrian intent is a complex problem. Pedestrian motion\nis governed by complex social navigation norms, is dependent on neighbors'\ntrajectories, and is multimodal in nature. In this work, we propose SCAN, a\nSpatial Context Attentive Network that can jointly predict socially-acceptable\nmultiple future trajectories for all pedestrians in a scene. SCAN encodes the\ninfluence of spatially close neighbors using a novel spatial attention\nmechanism in a manner that relies on fewer assumptions, is parameter efficient,\nand is more interpretable compared to state-of-the-art spatial attention\napproaches. Through experiments on several datasets we demonstrate that our\napproach can also quantitatively outperform state of the art trajectory\nprediction methods in terms of accuracy of predicted intent.\n"}
+{"abstract": "  For any given neural network architecture a permutation of weights and biases\nresults in the same functional network. This implies that optimization\nalgorithms used to `train' or `learn' the network are faced with a very large\nnumber (in the millions even for small networks) of equivalent optimal\nsolutions in the parameter space. To the best of our knowledge, this\nobservation is absent in the literature. In order to narrow down the parameter\nsearch space, a novel technique is introduced in order to fix the bias vector\nconfigurations to be monotonically increasing. This is achieved by augmenting a\ntypical learning problem with inequality constraints on the bias vectors in\neach layer. A Moreau-Yosida regularization based algorithm is proposed to\nhandle these inequality constraints and a theoretical convergence of the this\nalgorithm is established. Applications of the proposed approach to standard\ntrigonometric functions and more challenging stiff ordinary differential\nequations arising in chemically reacting ows clearly illustrate the benefits of\nthe proposed approach.\n"}
+{"abstract": "  The sensitivity of Density Functional Theory plus Dynamical Mean Field Theory\ncalculations to different constructions of the correlated orbitals is\ninvestigated via a detailed comparison of results obtained for the quantum\nmaterial NdNiO$_2$ using different Wannier and projector methods to define the\ncorrelation problem. Using the same interaction parameters we find that the\ndifferent methods produce different results for the orbital and band basis mass\nenhancements and for the orbital occupancies, with differing implications\nregarding the importance of multiorbital effects and charge transfer physics.\nUsing interaction parameters derived from cRPA enhances the difference in\nresults. For the isostructural cuprate CaCuO$_2$, the different methods give\nquantitatively different mass enhancements but still result in the same\nqualitative physics.\n"}
+{"abstract": "  We present a bit encoding scheme for a highly efficient and scalable\nrepresentation of bosonic Fock number states in the restricted Boltzmann\nmachine neural network architecture. In contrast to common density matrix\nimplementations, the complexity of the neural network scales only with the\nnumber of bit-encoded neurons rather than the maximum boson number. Crucially,\nin the high occupation regime its information compression efficiency is shown\nto surpass even maximally optimized density matrix implementations, where a\nprojector method is used to access the sparsest Hilbert space representation\navailable.\n"}
+{"abstract": "  In this paper we study model reduction of linear and bilinear quadratic\nstochastic control problems with parameter uncertainties. Specifically, we\nconsider slow-fast systems with unknown diffusion coefficient and study the\nconvergence of the slow process in the limit of infinite scale separation. The\naim of our work is two-fold: Firstly, we want to propose a general framework\nfor averaging and homogenisation of multiscale systems with parametric\nuncertainties in the drift or in the diffusion coefficient. Secondly, we want\nto use this framework to quantify the uncertainty in the reduced system by\nderiving a limit equation that represents a worst-case scenario for any given\n(possibly path-dependent) quantity of interest. We do so by reformulating the\nslow-fast system as an optimal control problem in which the unknown parameter\nplays the role of a control variable that can take values in a closed bounded\nset. For systems with unknown diffusion coefficient, the underlying stochastic\ncontrol problem admits an interpretation in terms of a stochastic differential\nequation driven by a G-Brownian motion. We prove convergence of the slow\nprocess with respect to the nonlinear expectation on the probability space\ninduced by the G-Brownian motion. The idea here is to formulate the nonlinear\ndynamic programming equation of the underlying control problem as a\nforward-backward stochastic differential equation in the G-Brownian motion\nframework (in brief: G-FBSDE), for which convergence can be proved by standard\nmeans. We illustrate the theoretical findings with two simple numerical\nexamples, exploiting the connection between fully nonlinear dynamic programming\nequations and second-order BSDE (2BSDE): a linear quadratic Gaussian regulator\nproblem and a bilinear multiplicative triad that is a standard benchmark system\nin turbulence and climate modelling.\n"}
+{"abstract": "  A direct measurement protocol allows reconstructing specific elements of the\ndensity matrix of a quantum state without using quantum state tomography.\nHowever, the direct measurement protocols to date are primarily based on weak\nor strong measurements with an ancillary pointer, which interacts with the\ninvestigated system to extract information about the specified elements. Here,\nwe present a direct measurement scheme based on phase-shifting operations which\ndo not need ancillary pointers. In this method, estimates of at most six\nexpectation values of projective observables suffice to determine any specific\nelement of an unknown quantum density matrix. A concrete quantum circuit to\nimplement this direct measurement protocol for multiqubit states is provided,\nwhich is composed of just single-qubit gates and two multiqubit\ncontrolled-phase gates. This scheme is also extended for the direct measurement\nof the density matrix of continuous-variable quantum states. Our method can be\nused in quantum information applications where only partial information about\nthe quantum state needs to be extracted, for example, problems such as\nentanglement witnessing, fidelity estimation of quantum systems, and quantum\ncoherence estimation.\n"}
+{"abstract": "  We study experimentally the dynamics of long waves among turbulent bending\nwaves in a thin elastic plate set into vibration by a monochromatic forcing at\na frequency $f_0$. This frequency is chosen large compared with the\ncharacteristic frequencies of bending waves. As a consequence, a range of\nconservative scales, without energy flux in average, exists for frequencies\n$f<f_0$. Within this range, we report a flat power density spectrum for the\northogonal velocity, corresponding to energy equipartition between modes. Thus,\nthe average energy per mode $\\beta^{-1}$ -- analogous to a temperature -- fully\ncharacterizes the large-scale turbulent wave field. We present an expression\nfor $\\beta$ as a function of the forcing frequency and amplitude, and of the\nplate characteristics.\n"}
+{"abstract": "  Computer modeling of human decision making is of large importance for, e.g.,\nsustainable transport, urban development, and online recommendation systems. In\nthis paper we present a model for predicting the behavior of an individual\nduring a binary game under different amounts of risk, gain, and time pressure.\nThe model is based on Quantum Decision Theory (QDT), which has been shown to\nenable modeling of the irrational and subjective aspects of the decision\nmaking, not accounted for by the classical Cumulative Prospect Theory (CPT).\nExperiments on two different datasets show that our QDT-based approach\noutperforms both a CPT-based approach and data driven approaches such as\nfeed-forward neural networks and random forests.\n"}
+{"abstract": "  Entanglement plays a prominent role in the study of condensed matter\nmany-body systems: Entanglement measures not only quantify the possible use of\nthese systems in quantum information protocols, but also shed light on their\nphysics. However, exact analytical results remain scarce, especially for\nsystems out of equilibrium. In this work we examine a paradigmatic\none-dimensional fermionic system that consists of a uniform tight-binding chain\nwith an arbitrary scattering region near its center, which is subject to a DC\nbias voltage at zero temperature. The system is thus held in a current-carrying\nnonequilibrium steady state, which can nevertheless be described by a pure\nquantum state. Using a generalization of the Fisher-Hartwig conjecture, we\npresent an exact calculation of the bipartite entanglement entropy of a\nsubsystem with its complement, and show that the scaling of entanglement with\nthe length of the subsystem is highly unusual, containing both a volume-law\nlinear term and a logarithmic term. The linear term is related to imperfect\ntransmission due to scattering, and provides a generalization of the\nLevitov-Lesovik full counting statistics formula. The logarithmic term arises\nfrom the Fermi discontinuities in the distribution function. Our analysis also\nproduces an exact expression for the particle-number-resolved entanglement. We\nfind that although to leading order entanglement equipartition applies, the\nfirst term breaking it grows with the size of the subsystem, a novel behavior\nnot observed in previously studied systems. We apply our general results to a\nconcrete model of a tight-binding chain with a single impurity site, and show\nthat the analytical expressions are in good agreement with numerical\ncalculations. The analytical results are further generalized to accommodate the\ncase of multiple scattering regions.\n"}
+{"abstract": "  For any natural $k \\leq n$, the rational cohomology ring of the space of\ncontinuous maps $S^{2k-1} \\to S^{2n-1}$ (respectively, $S^{4k-1} \\to S^{4n-1}$)\nequivariant under the Hopf action of the circle (respectively, of the group\n$S^3$ of unit quaternions) is naturally isomorphic to that of the Stiefel\nmanifold $V_k({\\mathbb C}^n)$ (respectively, $V_k({\\mathbb H}^n)$).\n"}
+{"abstract": "  We present convergence analysis of operator learning in [Chen and Chen 1995]\nand [Lu et al. 2020], where continuous operators are approximated by a sum of\nproducts of branch and trunk networks. In this work, we consider the rates of\nlearning solution operators from both linear and nonlinear advection-diffusion\nequations with or without reaction. We find that the convergence rates depend\non the architecture of branch networks as well as the smoothness of inputs and\noutputs of solution operators.\n"}
+{"abstract": "  This paper investigates the problem of regulating in real time a linear\ndynamical system to the solution trajectory of a time-varying constrained\nconvex optimization problem. The proposed feedback controller is based on an\nadaptation of the saddle-flow dynamics, modified to take into account\nprojections on constraint sets and output-feedback from the plant. We derive\nsufficient conditions on the tunable parameters of the controller (inherently\nrelated to the time-scale separation between plant and controller dynamics) to\nguarantee exponential and input-to-state stability of the closed-loop system.\nThe analysis is tailored to the case of time-varying strongly convex cost\nfunctions and polytopic output constraints. The theoretical results are further\nvalidated in a ramp metering control problem in a network of traffic highways.\n"}
+{"abstract": "  Entropic regularization of policies in Reinforcement Learning (RL) is a\ncommonly used heuristic to ensure that the learned policy explores the\nstate-space sufficiently before overfitting to a local optimal policy. The\nprimary motivation for using entropy is for exploration and disambiguating\noptimal policies; however, the theoretical effects are not entirely understood.\nIn this work, we study the more general regularized RL objective and using\nFenchel duality; we derive the dual problem which takes the form of an\nadversarial reward problem. In particular, we find that the optimal policy\nfound by a regularized objective is precisely an optimal policy of a\nreinforcement learning problem under a worst-case adversarial reward. Our\nresult allows us to reinterpret the popular entropic regularization scheme as a\nform of robustification. Furthermore, due to the generality of our results, we\napply to other existing regularization schemes. Our results thus give insights\ninto the effects of regularization of policies and deepen our understanding of\nexploration through robust rewards at large.\n"}
+{"abstract": "  Source code can be parsed into the abstract syntax tree (AST) based on\ndefined syntax rules. However, in pre-training, little work has considered the\nincorporation of tree structure into the learning process. In this paper, we\npresent TreeBERT, a tree-based pre-trained model for improving programming\nlanguage-oriented generation tasks. To utilize tree structure, TreeBERT\nrepresents the AST corresponding to the code as a set of composition paths and\nintroduces node position embedding. The model is trained by tree masked\nlanguage modeling (TMLM) and node order prediction (NOP) with a hybrid\nobjective. TMLM uses a novel masking strategy designed according to the tree's\ncharacteristics to help the model understand the AST and infer the missing\nsemantics of the AST. With NOP, TreeBERT extracts the syntactical structure by\nlearning the order constraints of nodes in AST. We pre-trained TreeBERT on\ndatasets covering multiple programming languages. On code summarization and\ncode documentation tasks, TreeBERT outperforms other pre-trained models and\nstate-of-the-art models designed for these tasks. Furthermore, TreeBERT\nperforms well when transferred to the pre-trained unseen programming language.\n"}
+{"abstract": "  We show the coexistence of chaotic behaviors (positive metric entropy) and\nelliptic behaviors (intregrable KAM island) among analytic, symplectic\ndiffeomorphism of any closed surface. In particilar this solves a problem by F.\nPrzytycki (1982).\n"}
+{"abstract": "  We introduce a reweighting technique which allows for a continuous sampling\nof temperatures in a single simulation and employ it to compute the temperature\ndependence of the QCD topological susceptibility at high temperatures. The\nmethod determines the ratio of susceptibility between any two temperatures\nwithin the explored temperature range. We find that the results from the method\nagree with our previous determination and that it is competitive with but not\nbetter than existing methods of determining the temperature derivative of the\nsusceptibility. The method may also be useful in exploring the temperature\ndependence of other thermodynamical observables in QCD in a continuous way.\n"}
+{"abstract": "  We study the behavior of the quasinormal modes (QNMs) of massless and massive\nlinear waves on Schwarzschild-de Sitter black holes as the black hole mass\ntends to 0. Via uniform estimates for a degenerating family of ODEs, we show\nthat in bounded subsets of the complex plane and for fixed angular momenta, the\nQNMs converge to those of the static model of de Sitter space. Detailed\nnumerics illustrate our results and suggest a number of open problems.\n"}
+{"abstract": "  This paper outlines a perspective on the future of AI, discussing directions\nfor machines models of human-like intelligence. We explain how developmental\nand evolutionary theories of human cognition should further inform artificial\nintelligence. We emphasize the role of ecological niches in sculpting\nintelligent behavior, and in particular that human intelligence was\nfundamentally shaped to adapt to a constantly changing socio-cultural\nenvironment. We argue that a major limit of current work in AI is that it is\nmissing this perspective, both theoretically and experimentally. Finally, we\ndiscuss the promising approach of developmental artificial intelligence,\nmodeling infant development through multi-scale interaction between\nintrinsically motivated learning, embodiment and a fastly changing\nsocio-cultural environment. This paper takes the form of an interview of\nPierre-Yves Oudeyer by Mandred Eppe, organized within the context of a KI -\nK{\\\"{u}}nstliche Intelligenz special issue in developmental robotics.\n"}
+{"abstract": "  The most serious threat to ecosystems is the global climate change fueled by\nthe uncontrolled increase in carbon emissions. In this project, we use mean\nfield control and mean field game models to analyze and inform the decisions of\nelectricity producers on how much renewable sources of production ought to be\nused in the presence of a carbon tax. The trade-off between higher revenues\nfrom production and the negative externality of carbon emissions is quantified\nfor each producer who needs to balance in real time reliance on reliable but\npolluting (fossil fuel) thermal power stations versus investing in and\ndepending upon clean production from uncertain wind and solar technologies. We\ncompare the impacts of these decisions in two different scenarios: 1) the\nproducers are competitive and hopefully reach a Nash Equilibrium; 2) they\ncooperate and reach a Social Optimum. We first prove that both problems have a\nunique solution using forward-backward systems of stochastic differential\nequations. We then illustrate with numerical experiments the producers'\nbehavior in each scenario. We further introduce and analyze the impact of a\nregulator in control of the carbon tax policy, and we study the resulting\nStackelberg equilibrium with the field of producers.\n"}
+{"abstract": "  Rich functionalities of transition-metal oxides and their interfaces bear an\nenormous technological potential. Its realization in practical devices\nrequires, however, a significant improvement of yet relatively low electron\nmobility in oxide materials. Recently, a mobility boost of about two orders of\nmagnitude has been demonstrated at the spinel/perovskite {\\gamma}-Al2O3/SrTiO3\ninterface compared to the paradigm perovskite/perovskite LaAlO3/SrTiO3. We\nexplore the fundamental physics behind this phenomenon from direct measurements\nof the momentum-resolved electronic structure of this interface using resonant\nsoft-X-ray angle-resolved photoemission. We find an anomaly in orbital ordering\nof the mobile electrons in {\\gamma}-Al2O3/SrTiO3 which depopulates electron\nstates in the top STO layer. This rearrangement of the mobile electron system\npushes the electron density away from the interface that reduces its overlap\nwith the interfacial defects and weakens the electron-phonon interaction, both\neffects contributing to the mobility boost. A crystal-field analysis shows that\nthe band order alters owing to the symmetry breaking between the spinel\n{\\gamma}-Al2O3 and perovskite SrTiO3. The band-order engineering exploiting the\nfundamental symmetry properties emerges as another route to boost the\nperformance of oxide devices.\n"}
+{"abstract": "  In this paper, some of the distance sensor, including Kinect, Hokuyo\nUTM-30LX, and RPLidar were observed experimentally. Strengths and weaknesses of\neach sensor were reviewed so that it can be used as a reference for selecting a\nsuitable sensor for any particular application. A software application has been\ndeveloped in C programming language as a platform for gathering information for\nall tested sensors. According to the experiment results, it showed that Hokuyo\nUTM-30LX results in random normally distributed error on measuring distance\nwith average error 21.94 mm and variance 32.11. On the other hand, error\nmeasurement resulted by Kinect and RPLidar strongly depended on measured\ndistance of the object from the sensors, while measurement error resulted by\nKinect had a negative correlation with the measured distance and the error\nresulted by RPLidar sensor had a positive correlation with the measured\ndistance. The performance of these three sensors for detecting a transparent\nobject shows that the Kinect sensors can detect the transparent object on its\neffective range measurement, Hokuyo UTM-30LX can detect the transparent object\nin the distance more than equal to 200 mm, and the RPLidar sensor cannot detect\nthe transparent object at all tested distance. Lastly, the experiment shows\nthat the Hokuyo UTM-30LX has the fastest processing time significantly, and the\nRPLidar has the slowest processing time significantly, while the processing\ntime of Kinect sensor was in between. These processing times were not\nsignificantly affected by various tested distance measurement.\n"}
+{"abstract": "  Open source software projects usually acknowledge contributions with text\nfiles, websites, and other idiosyncratic methods. These data sources are hard\nto mine, which is why contributorship is most frequently measured through\nchanges to repositories, such as commits, pushes, or patches. Recently, some\nopen source projects have taken to recording contributor actions with\nstandardized systems; this opens up a unique opportunity to understand how\ncommunity-generated notions of contributorship map onto codebases as the\nmeasure of contribution. Here, we characterize contributor acknowledgment\nmodels in open source by analyzing thousands of projects that use a model\ncalled All Contributors to acknowledge diverse contributions like outreach,\nfinance, infrastructure, and community management. We analyze the life cycle of\nprojects through this model's lens and contrast its representation of\ncontributorship with the picture given by other methods of acknowledgment,\nincluding GitHub's top committers indicator and contributions derived from\nactions taken on the platform. We find that community-generated systems of\ncontribution acknowledgment make work like idea generation or bug finding more\nvisible, which generates a more extensive picture of collaboration. Further, we\nfind that models requiring explicit attribution lead to more clearly defined\nboundaries around what is and what is not a contribution.\n"}
+{"abstract": "  Organic semiconductors are indispensable for today's display technologies in\nform of organic light emitting diodes (OLEDs) and further optoelectronic\napplications. However, organic materials do not reach the same charge carrier\nmobility as inorganic semiconductors, limiting the efficiency of devices. To\nfind or even design new organic semiconductors with higher charge carrier\nmobility, computational approaches, in particular multiscale models, are\nbecoming increasingly important. However, such models are computationally very\ncostly, especially when large systems and long time scales are required, which\nis the case to compute static and dynamic energy disorder, i.e. dominant factor\nto determine charge transport. Here we overcome this drawback by integrating\nmachine learning models into multiscale simulations. This allows us to obtain\nunprecedented insight into relevant microscopic materials properties, in\nparticular static and dynamic disorder contributions for a series of\napplication-relevant molecules. We find that static disorder and thus the\ndistribution of shallow traps is highly asymmetrical for many materials,\nimpacting widely considered Gaussian disorder models. We furthermore analyse\ncharacteristic energy level fluctuation times and compare them to typical\nhopping rates to evaluate the importance of dynamic disorder for charge\ntransport. We hope that our findings will significantly improve the accuracy of\ncomputational methods used to predict application relevant materials properties\nof organic semiconductors, and thus make these methods applicable for virtual\nmaterials design.\n"}
+{"abstract": "  This paper presents a lightweight, open-source and high-performance python\npackage for solving peridynamics problems in solid mechanics. The development\nof this solver is motivated by the need for fast analysis tools to achieve the\nlarge number of simulations required for `outer-loop' applications, including\nsensitivity analysis, uncertainty quantification and optimisation. Our python\nsoftware toolbox utilises the heterogeneous nature of OpenCL so that it can be\nexecuted on any platform with CPU or GPU cores. We illustrate the package use\nthrough a range of industrially motivated examples, which should enable other\nresearchers to build on and extend the solver for use in their own\napplications. Step improvements in execution speed and functionality over\nexisting techniques are presented. A comparison between this solver and an\nexisting OpenCL implementation in the literature is presented, tested on\nbenchmarks with hundreds of thousands to tens of millions of nodes. We\ndemonstrate the scalability of the solver on the GeForce RTX 2080 TiGPU from\nNVIDIA, and the memory-bound limitations are analysed. In all test cases, the\nimplementation is between 1.4 and 10.0 times faster than a similar existing GPU\nimplementation in the literature. In particular, this improvement has been\nachieved by utilising local memory on the GPU.\n"}
+{"abstract": "  In this paper, we study tractability of $L_2$-approximation of one-periodic\nfunctions from weighted Korobov spaces in the worst-case setting. The\nconsidered weights are of product form. For the algorithms we allow information\nfrom the class $\\Lambda^{{\\rm all}}$ consisting of all continuous linear\nfunctionals and from the class $\\Lambda^{{\\rm std}}$, which only consists of\nfunction evaluations.\n  We provide necessary and sufficient conditions on the weights of the function\nspace for quasi-polynomial tractability, uniform weak tractability, weak\ntractability and $(\\sigma,\\tau)$-weak tractability. Together with the already\nknown results for strong polynomial and polynomial tractability, our findings\nprovide a complete picture of the weight conditions for all current standard\nnotions of tractability.\n"}
+{"abstract": "  The paper proposes a new text recognition network for scene-text images. Many\nstate-of-the-art methods employ the attention mechanism either in the text\nencoder or decoder for the text alignment. Although the encoder-based attention\nyields promising results, these schemes inherit noticeable limitations. They\nperform the feature extraction (FE) and visual attention (VA) sequentially,\nwhich bounds the attention mechanism to rely only on the FE final single-scale\noutput. Moreover, the utilization of the attention process is limited by only\napplying it directly to the single scale feature-maps. To address these issues,\nwe propose a new multi-scale and encoder-based attention network for text\nrecognition that performs the multi-scale FE and VA in parallel. The\nmulti-scale channels also undergo regular fusion with each other to develop the\ncoordinated knowledge together. Quantitative evaluation and robustness analysis\non the standard benchmarks demonstrate that the proposed network outperforms\nthe state-of-the-art in most cases.\n"}
+{"abstract": "  The emerging technique of mid-infrared optical coherence tomography (MIR-OCT)\ntakes advantage of the reduced scattering of MIR light in various materials and\ndevices, enabling tomographic imaging at deeper penetration depths. Because of\nchallenges in MIR detection technology, the image acquisition time is however\nsignificantly longer than for tomographic imaging methods in the\nvisible/near-infrared. Here we demonstrate an alternative approach to MIR\ntomography with high-speed imaging capabilities. Through femtosecond\nnon-degenerate two-photon absorption of MIR light in a conventional Si-based\nCCD camera, we achieve wide-field, high-definition tomographic imaging with\nchemical selectivity of structured materials and biological samples in mere\nseconds.\n"}
+{"abstract": "  A coarse-grained bead-spring-dashpot chain model with the dashpots\nrepresenting the presence of internal friction, is solved exactly numerically,\nfor the case of chains with more than two beads. Using a decoupling procedure\nto remove the explicit coupling of a bead's velocity with that of its nearest\nneighbors, the governing set of stochastic differential equations are solved\nwith Brownian dynamics simulations to obtain material functions in oscillatory\nand steady simple shear flow. Simulation results for the real and imaginary\ncomponents of the complex viscosity have been compared with the results of\npreviously derived semi-analytical approximations, and the difference in the\npredictions are seen to diminish with an increase in the number of beads in the\nchain. The inclusion of internal friction results in a non-monotonous variation\nof the viscosity with shear rate, with the occurrence of continuous\nshear-thickening following an initial shear-thinning regime. The onset of\nshear-thickening in the first normal stress coefficient is pushed to lower\nshear rates with an increase in the internal friction parameter.\n"}
+{"abstract": "  Organic color centers in single-walled carbon nanotubes have demonstrated\nexceptional ability to generate single photons at room temperature in the\ntelecom range. Combining the color centers with pristine air-suspended tubes\nwould be desirable for improved performance, but all current synthetic methods\noccur in solution which makes them incompatible. Here we demonstrate formation\nof color centers in air-suspended nanotubes using vapor-phase reaction.\nFunctionalization is directly verified on the same nanotubes by\nphotoluminescence spectroscopy, with unambiguous statistics from more than a\nfew thousand individual nanotubes. The color centers show a strong\ndiameter-dependent emission intensity, which can be explained with a\ntheoretical model for chemical reactivity taking into account strain along the\ntube curvature. We are also able to estimate the defect density by comparing\nthe experiments with simulations based on a one-dimensional diffusion equation,\nwhereas the analysis of diameter dependent peak energies gives insight to the\nnature of the dopant states. Time-resolved measurements show a longer lifetime\nfor color center emission compared to E$_{11}$ exciton states. Our results\nhighlight the influence of the tube structure on vapor-phase reactivity and\nemission properties, providing guidelines for development of high-performance\nnear-infrared quantum light sources.\n"}
+{"abstract": "  Volatility clustering is an important characteristic that has a significant\neffect on the behavior of stock markets. However, designing robust models for\naccurate prediction of future volatilities of stock prices is a very\nchallenging research problem. We present several volatility models based on\ngeneralized autoregressive conditional heteroscedasticity (GARCH) framework for\nmodeling the volatility of ten stocks listed in the national stock exchange\n(NSE) of India. The stocks are selected from the auto sector and the banking\nsector of the Indian economy, and they have a significant impact on the\nsectoral index of their respective sectors in the NSE. The historical stock\nprice records from Jan 1, 2010, to Apr 30, 2021, are scraped from the Yahoo\nFinance website using the DataReader API of the Pandas module in the Python\nprogramming language. The GARCH modules are built and fine-tuned on the\ntraining data and then tested on the out-of-sample data to evaluate the\nperformance of the models. The analysis of the results shows that asymmetric\nGARCH models yield more accurate forecasts on the future volatility of stocks.\n"}
+{"abstract": "  In this work, we develop a combined convolutional neural networks (CNNs) and\nfinite element method (FEM) to examine the effective thermal properties of\ncomposite phase change materials (CPCMs) consisting of paraffin and copper\nfoam. In this approach, first the CPCM microstructures are modeled using FEM\nand next the image dataset with corresponding thermal properties is created.\nThe image dataset is subsequently used to train and test the CNN performance,\nwhich is then compared with the performance of a popular network architecture\nfor image classification tasks. The predicted thermal properties are employed\nto define the properties of the CPCM material of a battery pack. The heat\ngeneration and electrochemical response of a Li-ion cell during the\ncharging/discharging is simulated by applying Newman battery model. Thermal\nmanagement is achieved by the latent heat of paraffin, with copper foam for\nenhancing the thermal conductivity. The multiscale model is finally developed\nusing FEM to investigate the effectiveness of the thermal management of the\nbattery pack. In these models the thermal properties estimated by the FEM and\nthe CNN are employed to define the CPCM materials properties of a battery pack.\nOur results confirm that the model developed on the basis of a CNN can evaluate\nthe effectiveness of the battery packs thermal management system with an\nexcellent accuracy in comparison with the original FEM models.\n"}
+{"abstract": "  The Einasto profile has been successful in describing the density profiles of\ndark matter haloes in $\\Lambda$CDM N-body simulations. It has also been able to\ndescribe multiple components in the surface brightness profiles of galaxies.\nHowever, analytically projecting it to calculate quantities under projection is\nchallenging. In this paper, we will see the development of a highly accurate\nanalytical approximation for the mass (or counts) enclosed in an infinitely\nlong cylindrical column for Einasto profiles--also known as the projected mass\n(or counts)--using a novel methodology. We will then develop a self-consistent\nhigh-accuracy model for the surface density from the expression for the\nprojected mass. Both models are quite accurate for a broad family of functions,\nwith a shape parameter $\\alpha$ varying by a factor of 100 in the range $0.05\n\\lesssim \\alpha \\lesssim 5.0$, with fractional errors $\\sim 10^{-6}$ for\n$\\alpha \\lesssim 0.4$. Profiles with $\\alpha \\lesssim 0.4$ have been shown to\nfit the density profiles of dark matter haloes in N-body simulations as well as\nthe luminosity profiles of the outer components of massive galaxies. Since the\nprojected mass and the surface density are used in gravitational lensing, I\nwill illustrate how these models facilitate (for the first time) analytical\ncomputation of several quantities of interest in lensing due to Einasto\nprofiles. The models, however, are not limited to lensing and apply to similar\nquantities under projection, such as the projected luminosity, the projected\n(columnar) number counts and the projected density or the surface brightness.\n"}
+{"abstract": "  We extend the well-known characterizations of convergence in the spaces $l_p$\n($1\\le p<\\infty$) of $p$-summable sequence and $c_0$ of vanishing sequences to\na general characterization of convergence in a Banach space with a Schauder\nbasis and obtain as instant corollaries characterizations of convergence in an\ninfinite-dimensional separable Hilbert space and the space $c$ of convergent\nsequences.\n"}
+{"abstract": "  In this paper, we study the Ricci flow on a closed manifold and finite time\ninterval $[0,T)~(T < \\infty)$ on which certain integral curvature energies are\nfinite. We prove that in dimension four, such flow converges to a smooth\nRiemannian manifold except for finitely many orbifold singularities. We also\nshow that in higher dimensions, the same assertions hold for a closed Ricci\nflow satisfying another conditions of integral curvature bounds. Moreover, we\nshow that such flows can be extended over $T$ by an orbifold Ricci flow.\n"}
+{"abstract": "  By studying the commuting graphs of conjugacy classes of the sequence of\nHeisenberg groups $H_{2n+1}(p)$ and their limit $H_\\infty(p)$ we find\npseudo-random behavior (and the random graph in the limiting case). This makes\na nice case study for transfer of information between finite and infinite\nobjects. Some of this behavior transfers to the problem of understanding what\nmakes understanding the character theory of the uni-upper-triangular group (mod\np) \"wild\". Our investigations in this paper may be seen as a meditation on the\nquestion: is randomness simple or is it complicated?\n"}
+{"abstract": "  In this paper, we present a novel algorithm named synchronous integral\nQ-learning, which is based on synchronous policy iteration, to solve the\ncontinuous-time infinite horizon optimal control problems of input-affine\nsystem dynamics. The integral reinforcement is measured as an excitation signal\nin this method to estimate the solution to the Hamilton-Jacobi-Bellman\nequation. Moreover, the proposed method is completely model-free, i.e. no a\npriori knowledge of the system is required. Using policy iteration, the actor\nand critic neural networks can simultaneously approximate the optimal value\nfunction and policy. The persistence of excitation condition is required to\nguarantee the convergence of the two networks. Unlike in traditional policy\niteration algorithms, the restriction of the initial admissible policy is\nrelaxed in this method. The effectiveness of the proposed algorithm is verified\nthrough numerical simulations.\n"}
+{"abstract": "  The concept of porous numbers is presented. A number $k$ which is not a\nmultiple of 10 is called {\\it porous} if every number $m$ with sum of digits =\n$k$ and $k$ a divisor of both $m$ and digit reversal of $m$ has a zero in its\ndigits. It is proved that 11, 37, 74, 101 and 121 are the only porous numbers\nsmaller than 1000.\n"}
+{"abstract": "  We provide a detailed analysis of the Gelfand integral on Fr\\'echet spaces,\nshowing among other things a Vitali theorem, dominated convergence and a Fubini\nresult. Furthermore, the Gelfand integral commutes with linear operators. The\nSkorohod integral is conveniently expressed in terms of a Gelfand integral on\nHida distribution space, which forms our prime motivation and example. We\nextend several results of Skorohod integrals to a general class of pathwise\nGelfand integrals. For example, we provide generalizations of the\nHida-Malliavin derivative and extend the integration-by-parts formula in\nMalliavin Calculus. A Fubini-result is also shown, based on the commutative\nproperty of Gelfand integrals with linear operators. Finally, our studies give\nthe motivation for two existing definitions of stochastic Volterra integration\nin Hida space.\n"}
+{"abstract": "  Environmental concerns are the chief drive for more innovative recycling\ntechniques for end-of-life polymeric products. One attractive option is taking\nadvantage of C and H content of polymeric waste in steelmaking industry. In\nthis work, we examined the interaction of two high production polymers, i.e.,\npolyurethane and polysulfide with molten iron using ab initio molecular\ndynamics simulation. We demonstrate that both polymers can be used as\ncarburizers for molten iron. Additionally, we found that light weight H$_2$ and\nCH$_x$ molecules were released as by-products of the polymer-molten iron\ninteraction. The outcomes of this study will have applications in the\ncarburization of molten iron during ladle metallurgy and waste plastic\ninjection in electric arc furnace.\n"}
+{"abstract": "  Analysis of human sketches in deep learning has advanced immensely through\nthe use of waypoint-sequences rather than raster-graphic representations. We\nfurther aim to model sketches as a sequence of low-dimensional parametric\ncurves. To this end, we propose an inverse graphics framework capable of\napproximating a raster or waypoint based stroke encoded as a point-cloud with a\nvariable-degree B\\'ezier curve. Building on this module, we present\nCloud2Curve, a generative model for scalable high-resolution vector sketches\nthat can be trained end-to-end using point-cloud data alone. As a consequence,\nour model is also capable of deterministic vectorization which can map novel\nraster or waypoint based sketches to their corresponding high-resolution\nscalable B\\'ezier equivalent. We evaluate the generation and vectorization\ncapabilities of our model on Quick, Draw! and K-MNIST datasets.\n"}
+{"abstract": "  The idea of coded caching was introduced by Maddah-Ali and Niesen who\ndemonstrated the advantages of coding in caching problems. To capture the\nessence of the problem, they introduced the $(N, K)$ canonical cache network in\nwhich $K$ users with independent caches of size $M$ request files from a server\nthat has $N$ files. Among other results, the caching scheme and lower bounds\nproposed by them led to a characterization of the exact rate memory tradeoff\nwhen $M\\geq \\frac{N}{K}(K-1)$. These lower bounds along with the caching scheme\nproposed by Chen et al. led to a characterization of the exact rate memory\ntradeoff when $M\\leq \\frac{1}{K}$. In this paper we focus on small caches where\n$M\\in \\left[0,\\frac{N}{K}\\right]$ and derive new lower bounds. For the case\nwhen $\\big\\lceil\\frac{K+1}{2}\\big\\rceil\\leq N \\leq K$ and $M\\in\n\\big[\\frac{1}{K},\\frac{N}{K(N-1)}\\big]$, our lower bounds demonstrate that the\ncaching scheme introduced by G{\\'o}mez-Vilardeb{\\'o} is optimal and thus extend\nthe characterization of the exact rate memory tradeoff. For the case $1\\leq\nN\\leq \\big\\lceil\\frac{K+1}{2}\\big\\rceil$, we show that the new lower bounds\nimprove upon the previously known lower bounds.\n"}
+{"abstract": "  Let $M$ denote the Merimovich's model in which for each infinite cardinal\n$\\lambda, 2^\\lambda=\\lambda^{+3}$. We show that in $M$ the following hold:\n  (1) Shelah's strong hypothesis fails at all singular cardinals, indeed,\n$\\forall \\lambda (\\lambda$ is a singular cardinal $\\Rightarrow\npp(\\lambda)=\\lambda^{+3}).$\n  (2) For each singular cardinal $\\lambda$ there is an inner model $N$ of $M$\nsuch that $M$ and $N$ have the same bounded subsets of $\\lambda,$ $\\lambda$ is\na singular cardinal in $N$, $(\\lambda^{+i})^N=(\\lambda^{+i})^M$, for $i=1,2,3,$\nand $N \\models 2^\\lambda=\\lambda^{+}$. Thus it is possible to add many new\nfresh subsets to $\\lambda$ without adding any new bounded subsets to $\\lambda$.\n"}
+{"abstract": "  The machinery of quantum mechanics is fully capable of describing a single\nrealistic world. Here we discuss the converse: in spite of appearances, and\nindeed numerous claims to the contrary, any quantum mechanical model can be\nmimicked, up to any finite accuracy, by a completely classical system of\nequations. An implication of this observation is that Bell's theorem is not\napplicable in the cases considered. This is explained by scrutinising Bell's\nassumptions concerning causality, retrocausality, statistical (in-)dependence,\nand his fear of `conspiracy' (there is no conspiracy in the language used to\ndescribe the deterministic models). The most crucial mechanism for the counter\nintuitive Bell/CHSH violation is the fact that, regardless the settings chosen\nby Alice and Bob, the initial state of the system should be a realistic one.\nThe potential importance of our construction in model building is discussed.\n"}
+{"abstract": "  Crossing symmetry asserts that particles are indistinguishable from\nanti-particles traveling back in time. In quantum field theory, this statement\ntranslates to the long-standing conjecture that probabilities for observing the\ntwo scenarios in a scattering experiment are described by one and the same\nfunction. Why could we expect it to be true? In this work we examine this\nquestion in a simplified setup and take steps towards illuminating a possible\nphysical interpretation of crossing symmetry. To be more concrete, we consider\nplanar scattering amplitudes involving any number of particles with arbitrary\nspins and masses to all loop orders in perturbation theory. We show that by\ndeformations of the external momenta one can smoothly interpolate between pairs\nof crossing channels without encountering singularities or violating mass-shell\nconditions and momentum conservation. The analytic continuation can be realized\nusing two types of moves. The first one makes use of an $i\\varepsilon$\nprescription for avoiding singularities near the physical kinematics and allows\nus to adjust the momenta of the external particles relative to one another\nwithin their lightcones. The second, more violent, step involves a rotation of\nsubsets of particle momenta via their complexified lightcones from the future\nto the past and vice versa. We show that any singularity along such a\ndeformation would have to correspond to two beams of particles scattering off\neach other. For planar Feynman diagrams, these kinds of singularities are\nabsent because of the particular flow of energies through their propagators. We\nprescribe a five-step sequence of such moves that combined together proves\ncrossing symmetry for planar scattering amplitudes in perturbation theory,\npaving a way towards settling this question for more general scattering\nprocesses in quantum field theories.\n"}
+{"abstract": "  The Search for Extraterrestrial Intelligence (SETI) is a research activity\nthat started in the late 1950s, predating the arrival of \"Big History\" and\n\"Astrobiology\" by several decades. Many elements first developed as part of the\noriginal SETI narrative are now incorporated in both of these emergent fields.\nHowever, SETI still offers the widest possible perspective, since the topic\nnaturally leads us to consider not only the future development of our own\nsociety but also the forward trajectories (and past histories) of many other\nintelligent extraterrestrial forms. In this paper, I present a provocative view\nof Big History, its rapid convergent focus on our own planet and society, its\noversimplified and incomplete view of events in cosmic history, and its limited\nappreciation of how poorly we understand some aspects of the physical world.\nAstrophysicists are also not spared - in particular those who wish to\nunderstand the nature of the universe in \"splendid isolation\", only looking\noutwards and upwards. SETI can help re-expand all of our horizons but the\ndiscovery of extraterrestrial intelligence may also require its own\npractitioners to abandon preconceptions of what constitutes intelligent,\nsentient, thinking minds.\n"}
+{"abstract": "  As the only thiol-bearing amino acid, cysteine (Cys) residues in proteins\nhave the reactive thiol side chain, which is susceptible to a series of\npost-translational modifications (PTMs). These PTMs participate in a wide range\nof biological activities including the alteration of enzymatic reactions,\nprotein-protein interactions and protein stability. Here we summarize the\nadvance of cysteine PTM identification technologies and the features of the\nvarious kinds of the PTMs. We also discuss in silico approaches for the\nprediction of the different types of cysteine modified sites, giving directions\nfor future study.\n"}
+{"abstract": "  We demonstrate two simple theorems about squeezing induced by bilinear\nspin-spin interactions that conserve spin parity -- including a vast majority\nof quantum spin models implemented by state-of-the-art quantum simulators. In\nparticular we show that squeezing captures the first form of quantum\ncorrelations which are produced: 1) at equilibrium, by adiabatically turning on\nthe spin-spin interactions starting from a factorized state aligned with an\nexternal, arbitrary field; 2) away from equilibrium, by evolving unitarily the\nsame state with the interacting Hamiltonian.\n"}
+{"abstract": "  We report fractional revival phenomena in an ultracold matter wave inside a\nring waveguide. The specific fractional revival times are precisely identified\nand corresponding spatial density patterns are depicted. Thorough analyses of\nthe autocorrelation function and quantum carpet provide clear evidence of their\noccurrence. The exhibited theoretical model is in exact conformity of our\nnumerical results. We also investigate the stability of the condensate and a\nvariation of revival time with the diameter of the ring.\n"}
+{"abstract": "  We present PhySG, an end-to-end inverse rendering pipeline that includes a\nfully differentiable renderer and can reconstruct geometry, materials, and\nillumination from scratch from a set of RGB input images. Our framework\nrepresents specular BRDFs and environmental illumination using mixtures of\nspherical Gaussians, and represents geometry as a signed distance function\nparameterized as a Multi-Layer Perceptron. The use of spherical Gaussians\nallows us to efficiently solve for approximate light transport, and our method\nworks on scenes with challenging non-Lambertian reflectance captured under\nnatural, static illumination. We demonstrate, with both synthetic and real\ndata, that our reconstructions not only enable rendering of novel viewpoints,\nbut also physics-based appearance editing of materials and illumination.\n"}
+{"abstract": "  Explicit stabilized integrators are an efficient alternative to implicit or\nsemi-implicit methods to avoid the severe timestep restriction faced by\nstandard explicit integrators applied to stiff diffusion problems. In this\npaper, we provide a fully discrete strong convergence analysis of a family of\nexplicit stabilized methods coupled with finite element methods for a class of\nparabolic semilinear deterministic and stochastic partial differential\nequations. Numerical experiments including the semilinear stochastic heat\nequation with space-time white noise confirm the theoretical findings.\n"}
+{"abstract": "  We assess the value of machine learning as an accelerator for the\nparameterisation schemes of operational weather forecasting systems,\nspecifically the parameterisation of non-orographic gravity wave drag.\nEmulators of this scheme can be trained to produce stable and accurate results\nup to seasonal forecasting timescales. Generally, more complex networks produce\nmore accurate emulators. By training on an increased complexity version of the\nexisting parameterisation scheme we build emulators that produce more accurate\nforecasts. {For medium range forecasting we find evidence our emulators are\nmore accurate} than the version of the parametrisation scheme that is used for\noperational predictions. Using the current operational CPU hardware our\nemulators have a similar computational cost to the existing scheme, but are\nheavily limited by data movement. On GPU hardware our emulators perform ten\ntimes faster than the existing scheme on a CPU.\n"}
+{"abstract": "  We show how uncertainty in the causal structure of field theory is\nessentially inevitable when one includes quantum gravity. This includes the\nfact that lightcones are ill-defined in such a theory. This effect is small in\nthe effective field theory regime, where it it independent of the UV completion\nof the theory, but grows with energy and represents an unknown uncertainty for\na generic UV completion. We include details of the causality uncertainty which\narises in a particular UV completion, i.e. quadratic gravity. We describe how\nthe mechanisms uncovered in the effective field theory treatment, and some of\nthose in quadratic gravity, could be common features of quantum gravity.\n"}
+{"abstract": "  The Lie algebra generated by supertranslation and superrotation vector fields\nat null infinity, known as the extended BMS (eBMS) algebra is expected to be a\nsymmetry algebra of the quantum gravity S matrix. However, the algebra of\ncommutators of the quantized eBMS charges has been a thorny issue in the\nliterature. On the one hand, recent developments in celestial holography point\ntowards a symmetry algebra which is a closed Lie algebra with no central\nextension or anomaly, and on the other hand, work of Distler, Flauger and Horn\nhas shown that when these charges are quantized at null infinity, the\ncommutator of a supertranslation and a superrotation charge does not close into\na supertranslation but gets deformed by a 2 cocycle term, which is consistent\nwith the original proposal of Barnich and Troessaert.\n  In this paper, we revisit this issue in light of recent developments in the\nclassical understanding of superrotation charges. We show that, for extended\nBMS symmetries, a phase space at null infinity is an extension of hitherto\nconsidered phase spaces which also includes a mode associated to the spin\nmemory and its conjugate partner. We also show that for holomorphic vector\nfields on the celestial plane, quantization of the eBMS charges in the new\nphase space leads to an algebra which closes without a 2 cocycle. The\ndegenerate vacua are labelled by the soft news and a Schwarzian mode which\ncorresponds to deformations of the celestial metric by superrotations. The\nclosed eBMS quantum algebra may also lead to a convergence between two\nmanifestations of asymptotic symmetries, one via asymptotic quantization at\nnull infinity and the other through celestial holography.\n"}
+{"abstract": "  In this paper, a novel unsupervised low-rank representation model, i.e.,\nAuto-weighted Low-Rank Representation (ALRR), is proposed to construct a more\nfavorable similarity graph (SG) for clustering. In particular, ALRR enhances\nthe discriminability of SG by capturing the multi-subspace structure and\nextracting the salient features simultaneously. Specifically, an auto-weighted\npenalty is introduced to learn a similarity graph by highlighting the effective\nfeatures, and meanwhile, overshadowing the disturbed features. Consequently,\nALRR obtains a similarity graph that can preserve the intrinsic geometrical\nstructures within the data by enforcing a smaller similarity on two dissimilar\nsamples. Moreover, we employ a block-diagonal regularizer to guarantee the\nlearned graph contains $k$ diagonal blocks. This can facilitate a more\ndiscriminative representation learning for clustering tasks. Extensive\nexperimental results on synthetic and real databases demonstrate the\nsuperiority of ALRR over other state-of-the-art methods with a margin of\n1.8\\%$\\sim$10.8\\%.\n"}
+{"abstract": "  We prove convergence of the 2- and 4-point fermionic observables of the\nFK-Ising model on simply connected domains discretised by a planar isoradial\nlattice in massive (near-critical) scaling limit. The former is alternatively\nknown as a (fermionic) martingale observable (MO) for the massive interface,\nand in particular encapsulates boundary visit probabilties of the interface.\nThe latter encodes connection probabilities in the 4-point alternating\n(generalised Dobrushin) boundary condition, whose exact convergence is then\nfurther analysed to yield crossing estimates for general boundary conditions.\nNotably, we obtain a massive version of the so-called Russo- Seymour-Welsh\n(RSW) type estimates on isoradial lattice. These observables satisfy a massive\nversion of s-holomorphicity [Smi10], and we develop robust techniques to\nexploit this condition which do not require any regularity assumption of the\ndomain or a particular direction of perturbation. Since many other\nnear-critical observables satisfy the same relation (cf. [BeDu12, CIM21,\nPar19]), these strategies are of direct use in the analysis of massive models\nin broader setting.\n"}
+{"abstract": "  Driven by the unprecedented high throughput and low latency requirements in\nnext-generation wireless networks, this paper introduces an artificial\nintelligence (AI) enabled framework in which unmanned aerial vehicles (UAVs)\nuse non-orthogonal multiple access (NOMA) and mobile edge computing (MEC)\ntechniques to service terrestrial mobile users (MUs). The proposed framework\nenables the terrestrial MUs to offload their computational tasks\nsimultaneously, intelligently, and flexibly, thus enhancing their connectivity\nas well as reducing their transmission latency and their energy consumption. To\nthis end, the fundamentals of this framework are first introduced. Then, a\nnumber of communication and AI techniques are proposed to improve the quality\nof experiences of terrestrial MUs. To this end, federated learning and\nreinforcement learning are introduced for intelligent task offloading and\ncomputing resource allocation. For each learning technique, motivations,\nchallenges, and representative results are introduced. Finally, several key\ntechnical challenges and open research issues of the proposed framework are\nsummarized.\n"}
+{"abstract": "  Let $f$ be a transcendental entire function. It was shown in a previous paper\nthat the holomorphic flow $\\dot z = f(z)$ always has infinitely many\ntrajectories tending to infinity in finite time. It will be proved here that\nsuch trajectories are in a certain sense rare, although an example will be\ngiven to show that there can be uncountably many. In contrast, for the\nclassical antiholomorphic flow $\\dot z = \\bar f(z)$, such trajectories need not\nexist at all, although they must if $f$ belongs to the Eremenko-Lyubich class\n$\\mathcal{B}$. It is also shown that for transcendental entire $f$ in\n$\\mathcal{B}$ there exists a path tending to infinity on which $f$ and all its\nderivatives tend to infinity, thus affirming a conjecture of Rubel for this\nclass.\n"}
+{"abstract": "  Deep Neural Networks (DNNs) are a critical component for self-driving\nvehicles. They achieve impressive performance by reaping information from high\namounts of labeled data. Yet, the full complexity of the real world cannot be\nencapsulated in the training data, no matter how big the dataset, and DNNs can\nhardly generalize to unseen conditions. Robustness to various image\ncorruptions, caused by changing weather conditions or sensor degradation and\naging, is crucial for safety when such vehicles are deployed in the real world.\nWe address this problem through a novel type of layer, dubbed StyleLess, which\nenables DNNs to learn robust and informative features that can cope with\nvarying external conditions. We propose multiple variations of this layer that\ncan be integrated in most of the architectures and trained jointly with the\nmain task. We validate our contribution on typical autonomous-driving tasks\n(detection, semantic segmentation), showing that in most cases, this approach\nimproves predictive performance on unseen conditions (fog, rain), while\npreserving performance on seen conditions and objects.\n"}
+{"abstract": "  The ice-rich dwarf planet Ceres is the largest object in the main asteroid\nbelt and is thought to have a brine or mud layer at a depth of tens of\nkilometers. Furthermore, recent surface deposits of brine-sourced material\nimply shallow feeder structures such as sills or dikes. Inductive sounding of\nCeres can be performed using the solar wind as a source, as was done for the\nMoon during Apollo. However, the magnetotelluric method -- measuring both\nelectric and magnetic fields at the surface -- is not sensitive to plasma\neffects that were experienced for Apollo, which used an orbit-to-surface\nmagnetic transfer function. The highly conductive brine targets are readily\nseparable from the resistive ice and rock interior, such that the depth to deep\nand shallow brines can be assessed simultaneously. The instrumentation will be\ntested on the Moon in 2023 and is ready for implementation on a Ceres landed\nmission.\n"}
+{"abstract": "  We study the number of planes for four dimensional projective hypersurfaces\nwhich has so-called inductive structure. We also determine transcendental\nlattices for cubic fourfolds of this type.\n"}
+{"abstract": "  The delivery of Medical Countermeasures(MCMs) for mass prophylaxis in the\ncase of a bio-terrorist attack is an active research topic that has interested\nthe research community over the past decades. The objective of this study is to\ndesign an efficient algorithm for the Receive Reload and Store Problem(RSS) in\nwhich we aim to find feasible routes to deliver MCMs to a target population\nconsidering time, physical, and human resources, and capacity limitations. For\ndoing this, we adapt the p-median problem to the POD-based emergency response\nplanning procedures and propose an efficient algorithm solution to perform the\np-median in reasonable computational time. We present RE-PLAN, the Response\nPLan Analyzer system that contains some RSS solutions developed at The Center\nfor Computational Epidemiology and Response Analysis (CeCERA) at the University\nof North Texas. Finally, we analyze a study case where we show how the\ncomputational performance of the algorithm can impact the process of decision\nmaking and emergency planning in the short and long terms.\n"}
+{"abstract": "  This short note provides a new and simple proof of the convergence rate for\nPeng's law of large numbers under sublinear expectations, which improves the\ncorresponding results in Song [15] and Fang et al. [3].\n"}
+{"abstract": "  Invented some 65 years ago in a seminal paper by Marguerite Straus-Frank and\nPhilip Wolfe, the Frank-Wolfe method recently enjoys a remarkable revival,\nfuelled by the need of fast and reliable first-order optimization methods in\nData Science and other relevant application areas. This review tries to explain\nthe success of this approach by illustrating versatility and applicability in a\nwide range of contexts, combined with an account on recent progress in\nvariants, both improving on the speed and efficiency of this surprisingly\nsimple principle of first-order optimization.\n"}
+{"abstract": "  We present the first determination of the $x$-dependent pion gluon\ndistribution from lattice QCD using the pseudo-PDF approach. We use lattice\nensembles with 2+1+1 flavors of highly improved staggered quarks (HISQ),\ngenerated by MILC Collaboration, at two lattice spacings $a\\approx 0.12$ and\n0.15~fm and three pion masses $M_\\pi\\approx 220$, 310 and 690 MeV. We use\nclover fermions for the valence action and momentum smearing to achieve pion\nboost momentum up to 2.29 GeV. We find that the dependence of the pion gluon\nparton distribution on lattice spacing and pion mass is mild. We compare our\nresults from the lightest pion mass ensemble with the determination by JAM and\nxFitter global fits.\n"}
+{"abstract": "  Light scattering due to the interaction of photons and acoustic waves present\nin a dilute inert gas is analyzed through the use of irreversible\nthermodynamics. The dispersion relation, which governs the dynamics of the\ndensity fluctuation of the gas allows the establishment of a simple criterion\nfor the corresponding Rayleigh-Brillouin spectrum to be observed. The criterion\nhere proposed allows a clear physical interpretation and suggests\ngeneralizations for other interesting physical scenarios.\n"}
+{"abstract": "  We first announce our recent result on adjunction and inversion of\nadjunction. Then we clarify the relationship between our inversion of\nadjunction and Hacon's inversion of adjunction for log canonical centers of\narbitrary codimension.\n"}
+{"abstract": "  We examine the properties of the low-redshift circumgalactic medium (CGM)\naround star-forming and quenched galaxies in the Simba cosmological\nhydrodynamic simulations, focusing on comparing HI and metal line absorption to\nobservations from the COS-Halos and COS-Dwarfs surveys. Halo baryon fractions\nare generally $\\lesssim 50\\%$ of the cosmic fraction due to stellar feedback at\nlow masses, and jet-mode AGN feedback at high masses. Baryons and metals in the\nCGM of quenched galaxies are $\\gtrsim 90\\%$ hot gas, while the CGM of\nstar-forming galaxies is more multi-phase. Hot CGM gas has low metallicity,\nwhile warm and cool CGM gas have metallicity close to that of galactic gas.\nEquivalent widths, covering fractions and total path absorption of HI and\nselected metal lines (MgII, SiIII, CIV and OVI) around a matched sample of\nSimba star-forming galaxies are mostly consistent with COS-Halos and COS-Dwarfs\nobservations to $\\lesssim 0.4$~dex, depending on ion and assumed ionising\nbackground. Around matched quenched galaxies, absorption in all ions is lower,\nwith HI absorption significantly under-predicted. Metal-line absorption is\nsensitive to choice of photo-ionising background; assuming recent backgrounds,\nSimba matches OVI but under-predicts low ions, while an older background\nmatches low ions but under-predicts OVI. Simba reproduces the observed\ndichotomy of OVI absorption around star forming and quenched galaxies. CGM\nmetals primarily come from stellar feedback, while jet-mode AGN feedback\nreduces absorption particularly for lower ions.\n"}
+{"abstract": "  Fix an odd prime $p$. If $r$ is a positive integer and $f$ a polynomial with\ncoefficients in $\\mathbb{F}_{p^r}$, let $P_{p,r}(f)$ be the proportion of\n$\\mathbb{P}^1(\\mathbb{F}_{p^r})$ that is periodic with respect to $f$. We show\nthat as $r$ increases, the expected value of $P_{p,r}(f)$, as $f$ ranges over\nquadratic polynomials, is less than $22/(\\log{\\log{p^r}})$. This result follows\nfrom a uniformity theorem on specializations of dynamical systems of rational\nfunctions over residually finite Dedekind domains. The specialization theorem\ngeneralizes previous work by Juul et al. that holds for rings of integers of\nnumber fields. Moreover, under stronger hypotheses, we effectivize this\nuniformity theorem by using the machinery of heights over general global\nfields; this version of the theorem generalizes previous work of Juul on\npolynomial dynamical systems over rings of integers of number fields. From\nthese theorems we derive effective bounds on image sizes and periodic point\nproportions of families of rational functions over finite fields.\n"}
+{"abstract": "  Consider a message processing system whose objective is to produce the most\ncurrent information as measured by the quantity known as \"age of information\".\nWe have argued in previous papers that if we are allowed to design the message\nprocessing policy ad libitum, we should keep a small buffer and operate\naccording to a LIFO policy. In this small note we provide an analysis for the\nAoI of the P_m system which uses a buffer of size m, a single server, operating\nwithout service preemption and in a LIFO manner for stored messages. Analytical\nexpressions for the mean (or even distribution) of the AoI in steady-state are\npossible but with the aid computer algebra. We explain the the analysis for\nm=3.\n"}
+{"abstract": "  It is well known that sets of $p$-capacity zero are removable for bounded\n$p$-harmonic functions, but on metric spaces there are examples of removable\nsets of positive capacity. In this paper, we show that this can happen even on\nunweighted $\\mathbf{R}^n$ when $n > p$, although only in very special cases. A\ncomplete characterization of removable singularities for bounded\n$\\mathcal{A}$-harmonic functions on weighted $\\mathbf{R}^n$, $n \\ge 1$, is also\ngiven, where the weight is $p$-admissible. The same characterization is also\nshown to hold for bounded quasiharmonic functions on weighted $\\mathbf{R}^n$,\n$n \\ge 2$, as well as on unweighted $\\mathbf{R}$. For bounded\n$\\mathcal{A}$-superharmonic functions and bounded quasisuperharmonic functions\non weighted $\\mathbf{R}^n$, $n \\ge 2$, we show that relatively closed sets are\nremovable if and only if they have zero capacity.\n"}
+{"abstract": "  Human Activity Recognition (HAR) is one of the key applications of health\nmonitoring that requires continuous use of wearable devices to track daily\nactivities. This paper proposes an Adaptive CNN for energy-efficient HAR (AHAR)\nsuitable for low-power edge devices. Unlike traditional early exit architecture\nthat makes the exit decision based on classification confidence, AHAR proposes\na novel adaptive architecture that uses an output block predictor to select a\nportion of the baseline architecture to use during the inference phase.\nExperimental results show that traditional early exit architectures suffer from\nperformance loss whereas our adaptive architecture provides similar or better\nperformance as the baseline one while being energy-efficient. We validate our\nmethodology in classifying locomotion activities from two datasets- Opportunity\nand w-HAR. Compared to the fog/cloud computing approaches for the Opportunity\ndataset, our baseline and adaptive architecture shows a comparable weighted F1\nscore of 91.79%, and 91.57%, respectively. For the w-HAR dataset, our baseline\nand adaptive architecture outperforms the state-of-the-art works with a\nweighted F1 score of 97.55%, and 97.64%, respectively. Evaluation on real\nhardware shows that our baseline architecture is significantly energy-efficient\n(422.38x less) and memory-efficient (14.29x less) compared to the works on the\nOpportunity dataset. For the w-HAR dataset, our baseline architecture requires\n2.04x less energy and 2.18x less memory compared to the state-of-the-art work.\nMoreover, experimental results show that our adaptive architecture is 12.32%\n(Opportunity) and 11.14% (w-HAR) energy-efficient than our baseline while\nproviding similar (Opportunity) or better (w-HAR) performance with no\nsignificant memory overhead.\n"}
+{"abstract": "  Many Imitation and Reinforcement Learning approaches rely on the availability\nof expert-generated demonstrations for learning policies or value functions\nfrom data. Obtaining a reliable distribution of trajectories from motion\nplanners is non-trivial, since it must broadly cover the space of states likely\nto be encountered during execution while also satisfying task-based\nconstraints. We propose a sampling strategy based on variational inference to\ngenerate distributions of feasible, low-cost trajectories for high-dof motion\nplanning tasks. This includes a distributed, particle-based motion planning\nalgorithm which leverages a structured graphical representations for inference\nover multi-modal posterior distributions. We also make explicit connections to\nboth approximate inference for trajectory optimization and entropy-regularized\nreinforcement learning.\n"}
+{"abstract": "  This paper is devoted to studying the two-weight extrapolation theory of\nRubio de Francia. We start to establish endpoint extrapolation results\nincluding $A_1$, $A_p$ and $A_\\infty$ extrapolation in the context of Banach\nfunction spaces and modular spaces. Furthermore, we present extrapolation for\ncommutators in weighted Banach function spaces. Beyond that, we give several\napplications which can be easily obtained using extrapolation. First, we get\nlocal decay estimates for various operators and commutators. Second,\nCoifman-Fefferman inequalities are established and can be used to show some\nknown sharp $A_1$ inequalities. Third, Muckenhoupt-Wheeden and Sawyer\nconjectures are also presented for many operators, which go beyond\nCalder\\'{o}n-Zygmund operators. Finally, we obtain two-weight inequalities for\nLittlewood-Paley operators and Fourier integral operators on weighted Banach\nfunction spaces.\n"}
+{"abstract": "  An instrumentation unit that measures heat flow along the test column of\nsolid metal samples is described. The study also describes the design of a\ndatalogger using Arduino Mega Microcontroller. The designed instrumentation\nunit incorporates current and voltage sensing unit, ten thermocouple sensors\nand amplifiers, microSD card shield, two LCDs, four microcontrolled switching\nrelays and a 12 V DC water pump motor. The heat flow parameters measured by the\ninstrumentation unit includes temperature gradient, heater current and heater\nvoltage. The designed datalogger logs the measured value of temperature,\ncurrent and voltage at a time of ten minutes. An area of application for this\nstudy is in the development of a device for measurement of thermal conductivity\nof solid metals. It is an improvement over existing heat flow measurement\ntechniques.\n"}
+{"abstract": "  Quantum machine learning is an emerging field at the intersection of machine\nlearning and quantum computing. Classical cross entropy plays a central role in\nmachine learning. We define its quantum generalization, the quantum cross\nentropy, prove its lower bounds, and investigate its relation to quantum\nfidelity. In the classical case, minimizing cross entropy is equivalent to\nmaximizing likelihood. In the quantum case, when the quantum cross entropy is\nconstructed from quantum data undisturbed by quantum measurements, this\nrelation holds. Classical cross entropy is equal to negative log-likelihood.\nWhen we obtain quantum cross entropy through empirical density matrix based on\nmeasurement outcomes, the quantum cross entropy is lower-bounded by negative\nlog-likelihood. These two different scenarios illustrate the information loss\nwhen making quantum measurements. We conclude that to achieve the goal of full\nquantum machine learning, it is crucial to utilize the deferred measurement\nprinciple.\n"}
+{"abstract": "  Using dominant eigenvectors for background modeling (usually known as\nEigenbackground) is a common technique in the literature. However, its results\nsuffer from noticeable artifacts. Thus have been many attempts to reduce the\nartifacts by making some improvements/enhancement in the Eigenbackground\nalgorithm.\n  In this paper, we show the main problem of the Eigenbackground is in its own\ncore and in fact, it is not a good idea to use strongest eigenvectors for\nmodeling the background. Instead, we propose an alternative solution by\nexploiting the weakest eigenvectors (which are usually thrown away and treated\nas garbage data) for background modeling. MATLAB codes are available at\n\\url{https://github.com/mamintoosi/Eigenbackground-Revisited}\n"}
+{"abstract": "  This work presents a new way of exploiting non-uniform file popularity in\ncoded caching networks. Focusing on a fully-connected fully-interfering\nwireless setting with multiple cache-enabled transmitters and receivers, we\nshow how non-uniform file popularity can be used very efficiently to accelerate\nthe impact of transmitter-side data redundancy on receiver-side coded caching.\nThis approach is motivated by the recent discovery that, under any realistic\nfile-size constraint, having content appear in multiple transmitters can in\nfact dramatically boost the speed-up factor attributed to coded caching.\n  We formulate an optimization problem that exploits file popularity to\noptimize the placement of files at the transmitters. We then provide a proof\nthat reduces significantly the variable search space, and propose a new search\nalgorithm that solves the problem at hand. We also prove an analytical\nperformance upper bound, which is in fact met by our algorithm in the regime of\nmany receivers. Our work reflects the benefits of allocating higher cache\nredundancy to more popular files, but also reflects a law of diminishing\nreturns where for example very popular files may in fact benefit from minimum\nredundancy. In the end, this work reveals that in the context of coded caching,\nemploying multiple transmitters can be a catalyst in fully exploiting file\npopularity, as it avoids various asymmetry complications that appear when file\npopularity is used to alter the receiver-side cache placement.\n"}
+{"abstract": "  In financial investing, universal portfolios are a means of constructing\nportfolios which guarantee a certain level of performance relative to a\nbaseline, while making no statistical assumptions about the future market data.\nThey fall under the broad category of regret minimization algorithms. This\ndocument covers an introduction and survey to universal portfolio techniques,\ncovering some of the basic concepts and proofs in the area. Topics include:\nConstant Rebalanced Portfolios, Cover's Algorithm, Incorporating Transaction\nCosts, Efficient Computation of Portfolios, Including Side Information, and\nFollow The Leader Algorithm.\n"}
+{"abstract": "  We show that every convergent power series with monomial extended Jacobian\nideal is right equivalent to a Thom-Sebastiani polynomial. This solves a\nproblem posed by Hauser and Schicho. On the combinatorial side, we introduce a\nnotion of Jacobian semigroup ideal involving a transversal matroid. For any\nsuch ideal we construct a defining Thom-Sebastiani polynomial. On the analytic\nside, we show that power series with a quasihomogeneous extended Jacobian ideal\nare strongly Euler homogeneous. Due to a Mather-Yau-type theorem, such power\nseries are determined by their Jacobian ideal up to right equivalence.\n"}
+{"abstract": "  In this extended abstract, we report on ongoing work towards an approximate\nmultimodal optimization algorithm with asymptotic guarantees. Multimodal\noptimization is the problem of finding all local optimal solutions (modes) to a\npath optimization problem. This is important to compress path databases, as\ncontingencies for replanning and as source of symbolic representations.\nFollowing ideas from Morse theory, we define modes as paths invariant under\noptimization of a cost functional. We develop a multi-mode estimation algorithm\nwhich approximately finds all modes of a given motion optimization problem and\nasymptotically converges. This is made possible by integrating sparse roadmaps\nwith an existing single-mode optimization algorithm. Initial evaluation results\nshow the multi-mode estimation algorithm as a promising direction to study path\nspaces from a topological point of view.\n"}
+{"abstract": "  For nearly a century since Ising model was proposed in 1925, it is agreed\nthat there is no phase transition with temperature in the one-dimensional based\non no global spontaneous magnetization in whole temperature region. In this\npaper, the exact calculation of local spontaneous magnetization shows that a\ndiffuse phase transition with temperature occurs in one-dimensional Ising\nmodel. In addition, although diffuse phase transition phenomenon is common in\nthe systems of heterogeneous-components and grains etc., there is no accurate\nprediction of corresponding theoretical models so far, so the present works lay\nthe theoretical foundation of this kind of phase transition.\n"}
+{"abstract": "  Many polytopes arising in polyhedral combinatorics are linear projections of\nhigher-dimensional polytopes with significantly fewer facets. Such lifts may\nyield compressed representations of polytopes, which are typically used to\nconstruct small-size linear programs. Motivated by algorithmic implications for\nthe closest vector problem, we study lifts of Voronoi cells of lattices.\n  We construct an explicit $d$-dimensional lattice such that every lift of the\nrespective Voronoi cell has $2^{\\Omega(d / \\log d)}$ facets. On the positive\nside, we show that Voronoi cells of $d$-dimensional root lattices and their\ndual lattices have lifts with $O(d)$ and $O(d \\log d)$ facets, respectively. We\nobtain similar results for spectrahedral lifts.\n"}
+{"abstract": "  Given input-output pairs of an elliptic partial differential equation (PDE)\nin three dimensions, we derive the first theoretically-rigorous scheme for\nlearning the associated Green's function $G$. By exploiting the hierarchical\nlow-rank structure of $G$, we show that one can construct an approximant to $G$\nthat converges almost surely and achieves an expected relative error of\n$\\epsilon$ using at most\n$\\mathcal{O}(\\epsilon^{-6}\\log^4(1/\\epsilon)/\\Gamma_\\epsilon)$ input-output\ntraining pairs, for any $0<\\epsilon<1$. The quantity $0<\\Gamma_\\epsilon\\leq 1$\ncharacterizes the quality of the training dataset. Along the way, we extend the\nrandomized singular value decomposition algorithm for learning matrices to\nHilbert--Schmidt operators and characterize the quality of covariance kernels\nfor PDE learning.\n"}
+{"abstract": "  We present a calculation of the decays\n$\\eta^{(')}\\to\\pi^+\\pi^-\\gamma^{(\\ast)}$ at the one-loop level up to and\nincluding next-to-next-to-leading order (NNLO) in large-$N_c$ chiral\nperturbation theory. The numerical evaluation of the results is performed\nsuccessively at LO, NLO, and NNLO, fitting the relevant low-energy constants to\nthe available experimental data. We discuss the widths and decay spectra of\n$\\eta^{(')}\\to\\pi^+\\pi^-\\gamma$ as well as $\\eta^{(')}\\to\\pi^+\\pi^-l^+l^-$,\nwith $l=e,\\ \\mu$.\n"}
+{"abstract": "  The physical conditions leading the sunspot penumbra decay are poorly\nunderstood so far. We investigate the photospheric magnetic and velocity\nproperties of a sunspot penumbra during the decay phase to advance the current\nknowledge of the conditions leading to this process. A penumbral decay was\nobserved with the CRISP instrument at the Swedish 1m Solar Telescope on 2016\nSeptember 4 and 5 in active region NOAA 12585. During these days, full-Stokes\nspectropolarimetric scans along the Fe I 630 nm line pair were acquired over\nmore than one hour. We inverted these observations with the VFISV code in order\nto obtain the evolution of the magnetic and velocity properties. We complement\nthe study with data from instruments onboard the Solar Dynamics Observatory and\nHinode space missions. The studied penumbra disappears progressively in both\ntime and space. The magnetic flux evolution seems to be linked to the presence\nof Moving Magnetic Features (MMFs). Decreasing Stokes V signals are observed.\nEvershed flows and horizontal fields were detected even after the disappearance\nof the penumbral sector. The analyzed penumbral decay seems to result from the\ninteraction between opposite polarity fields in type III MMFs and penumbra,\nwhile the presence of overlying canopies rules the evolution in the different\npenumbral sectors.\n"}
+{"abstract": "  We present sensitive ALMA observations of TWA 3, a nearby, young ($\\sim$10\nMyr) hierarchical system composed of three pre-main sequence M3--M4.5 stars.\nFor the first time, we detected ${}^{12}$CO and ${}^{13}$CO $J$=2-1 emission\nfrom the circumbinary protoplanetary disk around TWA 3A. We jointly fit the\nprotoplanetary disk velocity field, stellar astrometric positions, and stellar\nradial velocities to infer the architecture of the system. The Aa and Ab stars\n($0.29\\pm0.01\\,M_\\odot$ and $0.24\\pm0.01\\,M_\\odot$, respectively) comprising\nthe tight ($P=35$ days) eccentric ($e=0.63\\pm0.01$) spectroscopic binary are\ncoplanar with their circumbinary disk (misalignment $< 6^{\\circ}$ with 68%\nconfidence), similar to other short-period binary systems. From models of the\nspectral energy distribution, we found the inner radius of the circumbinary\ndisk ($r_\\mathrm{inner} = 0.50 - 0.75$ au) to be consistent with theoretical\npredictions of dynamical truncation $r_\\mathrm{cav}/a_\\mathrm{inner} \\approx\n3$. The outer orbit of the tertiary star B ($0.40\\pm0.28\\,M_\\odot$, $a\\sim65\n\\pm 18$ au, $e=0.3\\pm0.2$) is not as well constrained as the inner orbit,\nhowever, orbits coplanar with the A system are still preferred (misalignment $\n< 20^{\\circ}$). To better understand the influence of the B orbit on the TWA 3A\ncircumbinary disk, we performed SPH simulations of the system and found that\nthe outer edge of the gas disk ($r_\\mathrm{outer}=8.5\\pm0.2$ au) is most\nconsistent with truncation from a coplanar, circular or moderately eccentric\norbit, supporting the preference from the joint orbital fit.\n"}
+{"abstract": "  Multimedia content transmission heavily taxes network resources and puts a\nsignificant burden on wireless systems in terms of capacity and energy\nconsumption. In this context, device-to-device (D2D) paradigm has a utilitarian\nvalue to alleviate the network burden by utilizing short-range transmissions\nwith less energy cost. For the realization of proper D2D networking, caching in\ndevices is essential. Additionally, caching needs to operate efficiently with\nthe aim of realizing network-wide energy improvements. To this end, we study\nmultimedia caching in cellular D2D networks and propose an energy prioritized\nD2D caching (EPDC) algorithm in this work. We also investigate the optimal\ncaching policy in that system setting. The impact of device capacity and D2D\ntransmission range on energy consumption is studied by focusing on different\noperation modes such as D2D and base station transmissions. According to the\nsimulation results, EPDC algorithm has substantial energy-efficiency gains over\nthe commonly utilized Least Recently Used (LRU) algorithm and content-based\ncaching algorithms PDC and SXO. For larger D2D transmission ranges, this\nimprovement becomes more evident.\n"}
+{"abstract": "  We demonstrate a compact two-chip terahertz-emitting vertical-external-cavity\nsurface-emitting laser (TECSEL) source, which provides 1-THz output based on\nintracavity frequency conversion of dual-wavelength emission in a\nperiodically-poled lithium niobate crystal. The type-I frequency conversion\nscheme at room temperature highly benefits from the power-scaling possibilities\nin a multichip cavity with intracavity powers in excess of 500 W.\n"}
+{"abstract": "  Location-aware networks will introduce innovative services and applications\nfor modern convenience, applied ocean sciences, and public safety. In this\npaper, we establish a hybrid method for model-based and data-driven inference.\nWe consider a cooperative localization (CL) scenario where the mobile agents in\na wireless network aim to localize themselves by performing pairwise\nobservations with other agents and by exchanging location information. A\ntraditional method for distributed CL in large agent networks is belief\npropagation (BP) which is completely model-based and is known to suffer from\nproviding inconsistent (overconfident) estimates. The proposed approach\naddresses these limitations by complementing BP with learned information\nprovided by a graph neural network (GNN). We demonstrate numerically that our\nmethod can improve estimation accuracy and avoid overconfident beliefs, while\nits computational complexity remains comparable to BP. Notably, more consistent\nbeliefs are obtained by not explicitly addressing overconfidence in the loss\nfunction used for training of the GNN.\n"}
+{"abstract": "  Calculations of the Fourier transform of a constant quantity over an area or\nvolume defined by polygons (connected vertices) are often useful in modeling\nwave scattering, or in fourier-space filtering of real-space vector-based\nvolumes and area projections. If the system is discretized onto a regular\narray, Fast Fourier techniques can speed up the resulting calculations but if\nhigh spatial resolution is required the initial step of discretization can\nlimit performance; at other times the discretized methods result in\nunacceptable artifacts in the resulting transform. An alternative approach is\nto calculate the full Fourier integral transform of a polygonal area as a sum\nover the vertices, which has previously been derived in the literature using\nthe divergence theorem to reduce the problem from a 3-dimensional to line\nintegrals over the perimeter of the polygon surface elements, and converted to\na sum over the straight segments of that contour. We demonstrate a software\nimplementation of this algorithm and show that it can provide accurate\napproximations of the Fourier transform of real shapes with faster convergence\nthan a block-based (voxel) discretization.\n"}
+{"abstract": "  Since the discovery of hypervelocity stars in 2005, it has been widely\nbelieved that only the disruption of a binary system by a supermassive black\nhole at the Galactic center (GC), that is, the so-called Hills mechanism, is\ncapable of accelerating stars to beyond the Galactic escape velocity. In the\nmeantime, however, driven by the Gaia space mission, there is mounting evidence\nthat many of the most extreme high-velocity early-type stars at high Galactic\nlatitudes do originate in the Galactic disk and not in the GC. Moreover, the\nejection velocities of these extreme disk-runaway stars exceed the predicted\nlimits of the classical scenarios for the production of runaway stars. Based on\nproper motions from the Gaia early data release 3 and on recent and new\nspectrophotometric distances, we studied the kinematics of 30 such extreme\ndisk-runaway stars, allowing us to deduce their spatial origins in and their\nejection velocities from the Galactic disk with unprecedented precision. Only\nthree stars in the sample have past trajectories that are consistent with an\norigin in the GC, most notably S5-HVS1, which is the most extreme object in the\nsample by far. All other program stars are shown to be disk runaways with\nejection velocities that sharply contrast at least with classical ejection\nscenarios. They include HVS5 and HVS6, which are both gravitationally unbound\nto the Milky Way. While most stars originate from within a galactocentric\nradius of 15kpc, which corresponds to the observed extent of the spiral arms, a\ngroup of five stars stems from radii of about 21-29kpc. This indicates a\npossible link to outer Galactic rings and a potential origin from infalling\nsatellite galaxies.\n"}
+{"abstract": "  Nonnegative matrix factorization (NMF) based topic modeling methods do not\nrely on model- or data-assumptions much. However, they are usually formulated\nas difficult optimization problems, which may suffer from bad local minima and\nhigh computational complexity. In this paper, we propose a deep NMF (DNMF)\ntopic modeling framework to alleviate the aforementioned problems. It first\napplies an unsupervised deep learning method to learn latent hierarchical\nstructures of documents, under the assumption that if we could learn a good\nrepresentation of documents by, e.g. a deep model, then the topic word\ndiscovery problem can be boosted. Then, it takes the output of the deep model\nto constrain a topic-document distribution for the discovery of the\ndiscriminant topic words, which not only improves the efficacy but also reduces\nthe computational complexity over conventional unsupervised NMF methods. We\nconstrain the topic-document distribution in three ways, which takes the\nadvantages of the three major sub-categories of NMF -- basic NMF, structured\nNMF, and constrained NMF respectively. To overcome the weaknesses of deep\nneural networks in unsupervised topic modeling, we adopt a non-neural-network\ndeep model -- multilayer bootstrap network. To our knowledge, this is the first\ntime that a deep NMF model is used for unsupervised topic modeling. We have\ncompared the proposed method with a number of representative references\ncovering major branches of topic modeling on a variety of real-world text\ncorpora. Experimental results illustrate the effectiveness of the proposed\nmethod under various evaluation metrics.\n"}
+{"abstract": "  In this paper we consider the inverse problem of determining on a compact\nRiemannian manifold the metric tensor in the wave equation with Dirichlet data\nfrom measured Neumann boundary observations. This information is enclosed in\nthe dynamical Dirichlet-to-Neumann map associated to the wave equation. We\nprove in dimension $n\\geq 2$ that the knowledge of the Dirichlet-to-Neumann map\nfor the wave equation uniquely determines the metric tensor and we establish\nlogarithm-type stability.\n"}
+{"abstract": "  Carpooling has the potential to transform itself into a mass transportation\nmode by abandoning its adherence to deterministic passenger-driver matching for\ndoor-to-door journeys, and by adopting instead stochastic matching on a network\nof fixed meeting points. Stochastic matching is where a passenger sends out a\ncarpooling request at a meeting point, and then waits for the arrival of a\nself-selected driver who is already travelling to the requested meeting point.\nCrucially there is no centrally dispatched driver. Moreover, the carpooling is\nassured only between the meeting points, so the onus is on the passengers to\ntravel to/from them by their own means. Thus the success of a stochastic\ncarpooling service relies on the convergence, with minimal perturbation to\ntheir existing travel patterns, to the meeting points which are highly\nfrequented by both passengers and drivers. Due to the innovative nature of\nstochastic carpooling, existing off-the-shelf workflows are largely\ninsufficient for this purpose. To fill the gap in the market, we introduce a\nnovel workflow, comprising of a combination of data science and GIS (Geographic\nInformation Systems), to analyse driver GPS traces. We implement it for an\noperational stochastic carpooling service in south-eastern France, and we\ndemonstrate that relaxing door-to-door matching reduces passenger waiting\ntimes. Our workflow provides additional key operational indicators, namely the\ndriver flow maps, the driver flow temporal profiles and the driver\nparticipation rates.\n"}
+{"abstract": "  In a recent study, we developed a method to model the impact of photometric\nredshift uncertainty on the two-point correlation function (2PCF). In this\nmethod, we can obtain both the intrinsic clustering strength and the\nphotometric redshift errors simultaneously by fitting the projected 2PCF with\ntwo integration depths along the line-of-sight. Here we apply this method to\nthe DESI Legacy Imaging Surveys Data Release 8 (LS DR8), the largest galaxy\nsample currently available. We separate galaxies into 20 samples in 8 redshift\nbins from $z=0.1$ to $z=1.0$, and a few $\\rm z$-band absolute magnitude bins,\nwith $M_{\\rm z} \\le -20$. These galaxies are further separated into red and\nblue sub-samples according to their $M^{0.5}_{\\rm r}-M^{0.5}_{\\rm z}$ colors.\nWe measure the projected 2PCFs for all these galaxy (sub-)samples, and fit them\nusing our photometric redshift 2PCF model. We find that the photometric\nredshift errors are smaller in red sub-samples than the overall population. On\nthe other hand, there might be some systematic photometric redshift errors in\nthe blue sub-samples, so that some of the sub-samples show significantly\nenhanced 2PCF at large scales. Therefore, focusing only on the red and all\n(sub-)samples, we find that the biases of galaxies in these (sub-)samples show\nclear color, redshift and luminosity dependencies, in that red brighter\ngalaxies at higher redshift are more biased than their bluer and low redshift\ncounterparts. Apart from the best fit set of parameters, $\\sigma_{z}$ and $b$,\nfrom this state-of-the-art photometric redshift survey, we obtain high\nprecision intrinsic clustering measurements for these 40 red and all galaxy\n(sub-)samples. These measurements on large and small scales hold important\ninformation regarding the cosmology and galaxy formation, which will be used in\nour subsequent probes in this series.\n"}
+{"abstract": "  We estimate the net information exchange between adjacent quantum subsystems\nholographically living on the boundary of $AdS$ spacetime. The information\nexchange is a real time phenomenon and only after long time interval it may get\nsaturated. Normally we prepare systems for small time intervals and measure the\ninformation exchange over finite interval only. We find that the information\nflow between entangled subsystems gets reduced if systems are in excited state\nwhereas the ground state allows maximum information flow at any given time.\nEspecially for $CFT_2$ we exactly show that a rise in the entropy is\ndetrimental to the information exchange by a quantum dot and vice-versa. We\nnext observe that there is a reduction in circuit (CV) complexity too in the\npresence of excitations for small times.\n"}
+{"abstract": "  The development of an automatic way to extract user opinions about products,\nmovies, and foods from online social network (OSN) interactions is among the\nmain interests of sentiment analysis and opinion mining studies. Existing\napproaches in the sentiment analysis domain mostly do not discriminate the\nsentences of different types of users, even though some users are always\nnegative and some are always positive. Thus, finding a way to identify these\ntwo types of user is significant because their attitudes can change the\nanalysis of user reviews of businesses and products. Due to the complexity of\nnatural language processing, pure text mining methods may lead to\nmisunderstandings about the exact nature of the sentiments expressed in review\ntext. In this study, we propose a neural network classifier to predict the\npresence of biased users on the basis of users' psychological behaviors. The\nidentification of the psychological behaviors of users allows us to find overly\npositive and overly negative users and to categorize these users' attitudes\nregardless of the content of their review texts. The experiment result\nindicates that the biased users can be predicted based on user behavior at an\naccuracy rate of 89%, 67% and 81% for three different datasets.\n"}
+{"abstract": "  We investigate the structural, electronic and magnetic properties of the full\nHeusler compounds Mn$_2$YSn (Y = Mo, Nb, Zr) by first-principles density\nfunctional theory using the generalized gradient approximation. It is found\nthat the calculated lattice constants are in good agreement with the\ntheoretical values. We observe that the Cu$_2$MnAl-type structure is more\nstable than the Hg$_2$CuTi type. The calculated total magnetic moments of\nMn$_2$NbSn and Mn$_2$ZrSn are 1 $\\mu_{\\text{B}}$ and 2 $\\mu_{\\text{B}}$ at the\nequilibrium lattice constant of 6.18 \\AA and 6.31 \\AA, respectively, for the\nCu$_2$MnAl-type structure. Mn$_2$MoSn have a metallic character in both\nHg$_2$CuTi and Cu$_2$MnAl type structures. The total spin magnetic moment obeys\nthe Slater-Pauling rule. Half-metal exhibits 100% spin polarization at the\nFermi level. Thus, these alloys are promising magnetic candidates in spintronic\ndevices.\n"}
+{"abstract": "  A (positive definite and integral) quadratic form is called {\\it an\nisolation} of a quadratic form $f$ if it represents all subforms of $f$ except\nfor $f$ itself. The minimum rank of isolations of a quadratic form $f$ is\ndenoted, if it exists, by $\\text{Iso}(f)$. In this article, we show that\n$\\text{Iso}(I_2)=5$ and $\\text{Iso}(I_3)=6$, where $I_n=x_1^2+\\dots+x_n^2$ is\nthe sum of $n$ squares for any positive integer $n$. After proving that there\nalways exists an isolation of $I_n$ for any positive integer $n$, we provide\nsome explicit lower and upper bounds for $\\text{Iso}(I_n)$. In particular, we\nshow that $\\text{Iso}(I_n) \\in \\Omega(n^{\\frac32-\\epsilon})$ for any\n$\\epsilon>0$.\n"}
+{"abstract": "  This article produces a complete list of all maximal subgroups of the finite\nsimple groups of type $F_4$, $E_6$ and twisted $E_6$ over all finite fields.\nAlong the way, we determine the collection of Lie primitive almost simple\nsubgroups of the corresponding algebraic groups. We give the stabilizers under\nthe actions of outer automorphisms, from which one can obtain complete\ninformation about the maximal subgroups of all almost simple groups with socle\none of these groups. We also provide a new maximal subgroup of ${}^2\\!F_4(8)$,\ncorrecting the maximal subgroups for that group from the list of Malle. This\nprovides the first new exceptional groups of Lie type to have their maximal\nsubgroups enumerated for three decades.\n  The techniques are a mixture of algebraic groups, representation theory,\ncomputational algebra, and use of the trilinear form on the $27$-dimensional\nminimal module for $E_6$. We provide a collection of auxiliary Magma files that\nprove the author's computational claims, yielding existence and the number of\nconjugacy classes of all maximal subgroups mentioned in the text.\n"}
+{"abstract": "  Magnetic nulls are ubiquitous in space plasmas, and are of interest as sites\nof localized energy dissipation or magnetic reconnection. As such, a number of\nmethods have been proposed for detecting nulls in both simulation data and in\nsitu spacecraft data from Earth's magnetosphere. The same methods can be\napplied to detect stagnation points in flow fields. In this paper we describe a\nsystematic comparison of different methods for finding magnetic nulls. The\nPoincare index method, the first-order Taylor expansion (FOTE) method, and the\ntrilinear method are considered. We define a magnetic field containing fourteen\nmagnetic nulls whose positions and types are known to arbitrary precision.\nFurthermore, we applied the selected techniques in order to find and classify\nthose nulls. Two situations are considered: one in which the magnetic field is\ndiscretized on a rectangular grid, and the second in which the magnetic field\nis discretized along synthetic `spacecraft trajectories' within the domain. At\npresent, FOTE and trilinear are the most reliable methods for finding nulls in\nthe spacecraft data and in numerical simulations on Cartesian grids,\nrespectively. The Poincare index method is suitable for simulations on both\ntetrahedral and hexahedral meshes. The proposed magnetic field configuration\ncan be used for grading and benchmarking the new and existing tools for finding\nmagnetic nulls and flow stagnation points.\n"}
+{"abstract": "  Absolute camera pose regressors estimate the position and orientation of a\ncamera from the captured image alone. Typically, a convolutional backbone with\na multi-layer perceptron head is trained with images and pose labels to embed a\nsingle reference scene at a time. Recently, this scheme was extended for\nlearning multiple scenes by replacing the MLP head with a set of fully\nconnected layers. In this work, we propose to learn multi-scene absolute camera\npose regression with Transformers, where encoders are used to aggregate\nactivation maps with self-attention and decoders transform latent features and\nscenes encoding into candidate pose predictions. This mechanism allows our\nmodel to focus on general features that are informative for localization while\nembedding multiple scenes in parallel. We evaluate our method on commonly\nbenchmarked indoor and outdoor datasets and show that it surpasses both\nmulti-scene and state-of-the-art single-scene absolute pose regressors. We make\nour code publicly available from\nhttps://github.com/yolish/multi-scene-pose-transformer.\n"}
+{"abstract": "  We reconsider the evaluation of OOD detection methods for image recognition.\nAlthough many studies have been conducted so far to build better OOD detection\nmethods, most of them follow Hendrycks and Gimpel's work for the method of\nexperimental evaluation. While the unified evaluation method is necessary for a\nfair comparison, there is a question of if its choice of tasks and datasets\nreflect real-world applications and if the evaluation results can generalize to\nother OOD detection application scenarios. In this paper, we experimentally\nevaluate the performance of representative OOD detection methods for three\nscenarios, i.e., irrelevant input detection, novel class detection, and domain\nshift detection, on various datasets and classification tasks. The results show\nthat differences in scenarios and datasets alter the relative performance among\nthe methods. Our results can also be used as a guide for practitioners for the\nselection of OOD detection methods.\n"}
+{"abstract": "  We introduce new foundations for relative topos theory based on stacks. One\nof the central results in our theory is an adjunction between the category of\ntoposes over the topos of sheaves on a given site $({\\mathcal{C}}, J)$ and that\nof ${\\mathcal{C}}$-indexed categories. This represents a wide generalization of\nthe classical adjunction between presheaves on a topological space and bundles\nover it, and allows one to interpret several constructions on sheaves and\nstacks in a geometrical way; in particular, it leads to fibrational\ndescriptions of direct and inverse images of sheaves and stacks, as well as to\na geometric understanding of the sheafification process. It also naturally\nallows one to regard any Grothendieck topos as a 'petit' topos associated with\na 'gros' topos, thereby providing an answer to a problem posed by Grothendieck\nin the seventies. Another key ingredient in our theory is a notion of relative\nsite, which allows one to represent arbitrary geometric morphisms towards a\nfixed base topos of sheaves on a site as structure morphisms induced by\nrelative sites over that site.\n"}
+{"abstract": "  The main aim of decision support systems is to find solutions that satisfy\nuser requirements. Often, this leads to predictability of those solutions, in\nthe sense that having the input data and the model, an adversary or enemy can\npredict to a great extent the solution produced by your decision support\nsystem. Such predictability can be undesirable, for example, in military or\nsecurity timetabling, or applications that require anonymity. In this paper, we\ndiscuss the notion of solution predictability and introduce potential\nmechanisms to intentionally avoid it.\n"}
+{"abstract": "  Learning feature representation from discriminative local regions plays a key\nrole in fine-grained visual classification. Employing attention mechanisms to\nextract part features has become a trend. However, there are two major\nlimitations in these methods: First, they often focus on the most salient part\nwhile neglecting other inconspicuous but distinguishable parts. Second, they\ntreat different part features in isolation while neglecting their\nrelationships. To handle these limitations, we propose to locate multiple\ndifferent distinguishable parts and explore their relationships in an explicit\nway. In this pursuit, we introduce two lightweight modules that can be easily\nplugged into existing convolutional neural networks. On one hand, we introduce\na feature boosting and suppression module that boosts the most salient part of\nfeature maps to obtain a part-specific representation and suppresses it to\nforce the following network to mine other potential parts. On the other hand,\nwe introduce a feature diversification module that learns semantically\ncomplementary information from the correlated part-specific representations.\nOur method does not need bounding boxes/part annotations and can be trained\nend-to-end. Extensive experimental results show that our method achieves\nstate-of-the-art performances on several benchmark fine-grained datasets.\nSource code is available at https://github.com/chaomaer/FBSD.\n"}
+{"abstract": "  The production of hydrogen fuels, via water splitting, is of practical\nrelevance for meeting global energy needs and mitigating the environmental\nconsequences of fossil-fuel-based transportation. Water photoelectrolysis has\nbeen proposed as a viable approach for generating hydrogen, provided that\nstable and inexpensive photocatalysts with conversion efficiencies over 10% can\nbe discovered, synthesized at scale, and successfully deployed (Pinaud et al.,\nEnergy Environ. Sci., 2013, 6, 1983). While a number of first-principles\nstudies have focused on the data-driven discovery of photocatalysts, in the\nabsence of systematic experimental validation, the success rate of these\npredictions may be limited. We address this problem by developing a screening\nprocedure with co-validation between experiment and theory to expedite the\nsynthesis, characterization, and testing of the computationally predicted, most\ndesirable materials. Starting with 70,150 compounds in the Materials Project\ndatabase, the proposed protocol yielded 71 candidate photocatalysts, 11 of\nwhich were synthesized as single-phase materials. Experiments confirmed\nhydrogen generation and favorable band alignment for 6 of the 11 compounds,\nwith the most promising ones belonging to the families of alkali and\nalkaline-earth indates and orthoplumbates. This study shows the accuracy of a\nnonempirical, Hubbard-corrected density-functional theory method to predict\nband gaps and band offsets at a fraction of the computational cost of hybrid\nfunctionals, and outlines an effective strategy to identify photocatalysts for\nsolar hydrogen generation.\n"}
+{"abstract": "  The main goal of this paper is to introduce the notion of twisted partial\naction of groupoids. We generalize the theorem about the existence of an\nenveloping action, also known as the globalization theorem, and show that the\ncrossed products of the twisted partial action and of its associated twisted\nglobal action are Morita equivalent. Finally, we generalize the concepts of\npartial projective representation and partial Schur multiplier for a groupoid,\nand we show the interaction between groupoid partial projective representions\nand groupoid twisted partial actions.\n"}
+{"abstract": "  We study the generalization of deep learning models in relation to the convex\nhull of their training sets. A trained image classifier basically partitions\nits domain via decision boundaries and assigns a class to each of those\npartitions. The location of decision boundaries inside the convex hull of\ntraining set can be investigated in relation to the training samples. However,\nour analysis shows that in standard image classification datasets, all testing\nimages are considerably outside that convex hull, in the pixel space, in the\nwavelet space, and in the internal representations learned by deep networks.\nTherefore, the performance of a trained model partially depends on how its\ndecision boundaries are extended outside the convex hull of its training data.\nFrom this perspective which is not studied before, over-parameterization of\ndeep learning models may be considered a necessity for shaping the extension of\ndecision boundaries. At the same time, over-parameterization should be\naccompanied by a specific training regime, in order to yield a model that not\nonly fits the training set, but also its decision boundaries extend desirably\noutside the convex hull. To illustrate this, we investigate the decision\nboundaries of a neural network, with various degrees of parameters, inside and\noutside the convex hull of its training set. Moreover, we use a polynomial\ndecision boundary to study the necessity of over-parameterization and the\ninfluence of training regime in shaping its extensions outside the convex hull\nof training set.\n"}
+{"abstract": "  Much of the information about the magnetic field in the Milky Way and other\ngalaxies comes from measurements which are path integrals, such as Faraday\nrotation and the polarization of synchrotron radiation of cosmic ray electrons.\nThe measurement made at the radio telescope results from the contributions of\nvolume elements along a long line of sight. The inferred magnetic field is\ntherefore some sort of average over a long line segment. A magnetic field\nmeasurement at a given spatial location is of much more physical significance.\nIn this paper, we point out that HII regions fortuitously offer such a\n``point'' measurement, albeit of one component of the magnetic field, and\naveraged over the sightline through the HII region. However, the line of sight\n(LOS) through an HII region is much smaller (e.g. 30 - 50 pc) than one through\nthe entire Galactic disk, and thus constitutes a ``pseudo-local'' measurement.\nWe use published HII region Faraday rotation measurements to provide a new\nconstraint on the magnitude of magnetohydrodynamic (MHD) turbulence in the\nGalaxy, as well as to raise intriguing speculations about the modification of\nthe Galactic field during the star formation process.\n"}
+{"abstract": "  In this paper we study a cross-diffusion system whose coefficient matrix is\nnon-symmetric and degenerate. The system arises in the study of tissue growth\nwith autophagy. The existence of a weak solution is established. We also\ninvestigate the limiting behavior of solutions as the pressure gets stiff. The\nso-called incompressible limit is a free boundary problem of Hele-Shaw type.\nOur key new discovery is that the usual energy estimate still holds as long as\nthe time variable stays away from $0$.\n"}
+{"abstract": "  Compression and efficient storage of neural network (NN) parameters is\ncritical for applications that run on resource-constrained devices. Although NN\nmodel compression has made significant progress, there has been considerably\nless investigation in the actual physical storage of NN parameters.\nConventionally, model compression and physical storage are decoupled, as\ndigital storage media with error correcting codes (ECCs) provide robust\nerror-free storage. This decoupled approach is inefficient, as it forces the\nstorage to treat each bit of the compressed model equally, and to dedicate the\nsame amount of resources to each bit. We propose a radically different approach\nthat: (i) employs analog memories to maximize the capacity of each memory cell,\nand (ii) jointly optimizes model compression and physical storage to maximize\nmemory utility. We investigate the challenges of analog storage by studying\nmodel storage on phase change memory (PCM) arrays and develop a variety of\nrobust coding strategies for NN model storage. We demonstrate the efficacy of\nour approach on MNIST, CIFAR-10 and ImageNet datasets for both existing and\nnovel compression methods. Compared to conventional error-free digital storage,\nour method has the potential to reduce the memory size by one order of\nmagnitude, without significantly compromising the stored model's accuracy.\n"}
+{"abstract": "  We propose a B tree representation storing $n$ keys, each of $k$ bits, in\neither (a) $nk + O(nk / \\lg n)$ bits or (b) $nk + O(nk \\lg \\lg n/ \\lg n)$ bits\nof space supporting all B tree operations in either (a) $O(\\lg n )$ time or (b)\n$O(\\lg n / \\lg \\lg n)$ time, respectively. We can augment each node with an\naggregate value such as the minimum value within its subtree, and maintain\nthese aggregate values within the same space and time complexities. Finally, we\ngive the sparse suffix tree as an application, and present a linear-time\nalgorithm computing the sparse longest common prefix array from the suffix AVL\ntree of Irving et al. [JDA'2003].\n"}
+{"abstract": "  We analyze the robustness of the exponential stability of\ninfinite-dimensional sampled-data systems with unbounded control operators. The\nunbounded perturbations we consider are the so-called Desch-Schappacher\nperturbations, which arises, e.g., from the boundary perturbations of systems\ndescribed by partial differential equations. As the main result, we show that\nthe exponential stability of the sampled-data systems is preserved under all\nDesch-Schappacher perturbations sufficiently small in a certain sense.\n"}
+{"abstract": "  A recent focus of quantum spin liquid (QSL) studies is how\ndisorder/randomness in a QSL candidate affects its true magnetic ground state.\nThe ultimate question is whether the QSL survives disorder or the disorder\nleads to a \"spin-liquid-like\" state, such as the proposed random-singlet (RS)\nstate. Since disorder is a standard feature of most QSL candidates, this\nquestion represents a major challenge for QSL candidates. YbMgGaO$_4$, a\ntriangular lattice antiferromagnet with effective spin-1/2 Yb$^{3+}$ ions, is\nan ideal system to address this question, since it shows no long-range magnetic\nordering with Mg/Ga site disorder. Despite the intensive study, it remains\nunresolved as to whether YbMgGaO$_4$ is a QSL or in the RS state. Here, through\nultralow-temperature thermal conductivity and magnetic torque measurements,\nplus specific heat and DC magnetization data, we observed a residual\n$\\kappa_0/T$ term and series of quantum spin state transitions in the zero\ntemperature limit for YbMgGaO$_4$. These observations strongly suggest that a\nQSL state with itinerant excitations and quantum spin fluctuations survives\ndisorder in YbMgGaO$_4$.\n"}
+{"abstract": "  Monte Carlo (MC) dropout is a simple and efficient ensembling method that can\nimprove the accuracy and confidence calibration of high-capacity deep neural\nnetwork models. However, MC dropout is not as effective as more\ncompute-intensive methods such as deep ensembles. This performance gap can be\nattributed to the relatively poor quality of individual models in the MC\ndropout ensemble and their lack of diversity. These issues can in turn be\ntraced back to the coupled training and substantial parameter sharing of the\ndropout models. Motivated by this perspective, we propose a strategy to compute\nan ensemble of subnetworks, each corresponding to a non-overlapping dropout\nmask computed via a pruning strategy and trained independently. We show that\nthe proposed subnetwork ensembling method can perform as well as standard deep\nensembles in both accuracy and uncertainty estimates, yet with a computational\nefficiency similar to MC dropout. Lastly, using several computer vision\ndatasets like CIFAR10/100, CUB200, and Tiny-Imagenet, we experimentally\ndemonstrate that subnetwork ensembling also consistently outperforms recently\nproposed approaches that efficiently ensemble neural networks.\n"}
+{"abstract": "  Face recognition approaches often rely on equal image resolution for\nverification faces on two images. However, in practical applications, those\nimage resolutions are usually not in the same range due to different image\ncapture mechanisms or sources. In this work, we first analyze the impact of\nimage resolutions on the face verification performance with a state-of-the-art\nface recognition model. For images, synthetically reduced to $5\\, \\times 5\\,\n\\mathrm{px}$ resolution, the verification performance drops from $99.23\\%$\nincreasingly down to almost $55\\%$. Especially, for cross-resolution image\npairs (one high- and one low-resolution image), the verification accuracy\ndecreases even further. We investigate this behavior more in-depth by looking\nat the feature distances for every 2-image test pair. To tackle this problem,\nwe propose the following two methods: 1) Train a state-of-the-art\nface-recognition model straightforward with $50\\%$ low-resolution images\ndirectly within each batch. \\\\ 2) Train a siamese-network structure and adding\na cosine distance feature loss between high- and low-resolution features. Both\nmethods show an improvement for cross-resolution scenarios and can increase the\naccuracy at very low resolution to approximately $70\\%$. However, a\ndisadvantage is that a specific model needs to be trained for every\nresolution-pair ...\n"}
+{"abstract": "  To further understand the underlying mechanism of various reinforcement\nlearning (RL) algorithms and also to better use the optimization theory to make\nfurther progress in RL, many researchers begin to revisit the linear-quadratic\nregulator (LQR) problem, whose setting is simple and yet captures the\ncharacteristics of RL. Inspired by this, this work is concerned with the\nmodel-free design of stochastic LQR controller for linear systems subject to\nGaussian noises, from the perspective of both RL and primal-dual optimization.\nFrom the RL perspective, we first develop a new model-free off-policy policy\niteration (MF-OPPI) algorithm, in which the sampled data is repeatedly used for\nupdating the policy to alleviate the data-hungry problem to some extent. We\nthen provide a rigorous analysis for algorithm convergence by showing that the\ninvolved iterations are equivalent to the iterations in the classical policy\niteration (PI) algorithm. From the perspective of optimization, we first\nreformulate the stochastic LQR problem at hand as a constrained non-convex\noptimization problem, which is shown to have strong duality. Then, to solve\nthis non-convex optimization problem, we propose a model-based primal-dual\n(MB-PD) algorithm based on the properties of the resulting Karush-Kuhn-Tucker\n(KKT) conditions. We also give a model-free implementation for the MB-PD\nalgorithm by solving a transformed dual feasibility condition. More\nimportantly, we show that the dual and primal update steps in the MB-PD\nalgorithm can be interpreted as the policy evaluation and policy improvement\nsteps in the PI algorithm, respectively. Finally, we provide one simulation\nexample to show the performance of the proposed algorithms.\n"}
+{"abstract": "  We accelerate deep reinforcement learning-based training in visually complex\n3D environments by two orders of magnitude over prior work, realizing\nend-to-end training speeds of over 19,000 frames of experience per second on a\nsingle GPU and up to 72,000 frames per second on a single eight-GPU machine.\nThe key idea of our approach is to design a 3D renderer and embodied navigation\nsimulator around the principle of \"batch simulation\": accepting and executing\nlarge batches of requests simultaneously. Beyond exposing large amounts of work\nat once, batch simulation allows implementations to amortize in-memory storage\nof scene assets, rendering work, data loading, and synchronization costs across\nmany simulation requests, dramatically improving the number of simulated agents\nper GPU and overall simulation throughput. To balance DNN inference and\ntraining costs with faster simulation, we also build a computationally\nefficient policy DNN that maintains high task performance, and modify training\nalgorithms to maintain sample efficiency when training with large mini-batches.\nBy combining batch simulation and DNN performance optimizations, we demonstrate\nthat PointGoal navigation agents can be trained in complex 3D environments on a\nsingle GPU in 1.5 days to 97% of the accuracy of agents trained on a prior\nstate-of-the-art system using a 64-GPU cluster over three days. We provide\nopen-source reference implementations of our batch 3D renderer and simulator to\nfacilitate incorporation of these ideas into RL systems.\n"}
+{"abstract": "  We demonstrate dynamical topological phase transitions in evolving\nSu-Schrieffer-Heeger (SSH) lattices made of interacting soliton arrays, which\nare entirely driven by nonlinearity and thereby exemplify emergent nonlinear\ntopological phenomena. The phase transitions occur from topologically\ntrivial-to-nontrivial phase in periodic succession with crossovers from\ntopologically nontrivial-to-trivial regime. The signature of phase transition\nis gap-closing and re-opening point, where two extended states are pulled from\nthe bands into the gap to become localized topological edge states. Crossovers\noccur via decoupling of the edge states from the bulk of the lattice.\n"}
+{"abstract": "  Conditional quantile estimation is a key statistical learning challenge\nmotivated by the need to quantify uncertainty in predictions or to model a\ndiverse population without being overly reductive. As such, many models have\nbeen developed for this problem. Adopting a meta viewpoint, we propose a\ngeneral framework (inspired by neural network optimization) for aggregating any\nnumber of conditional quantile models in order to boost predictive accuracy. We\nconsider weighted ensembling strategies of increasing flexibility where the\nweights may vary over individual models, quantile levels, and feature values.\nAn appeal of our approach is its portability: we ensure that estimated\nquantiles at adjacent levels do not cross by applying simple transformations\nthrough which gradients can be backpropagated, and this allows us to leverage\nthe modern deep learning toolkit for building quantile ensembles. Our\nexperiments confirm that ensembling can lead to big gains in accuracy, even\nwhen the constituent models are themselves powerful and flexible.\n"}
+{"abstract": "  Internal oscillations in switched antenna transmitters cause undesirable\nfluctuations of the stored energy in the system, reducing the effectiveness of\ntime-varying broadbanding methods, such as energy-synchronous direct antenna\nmodulation. To mitigate these parasitic oscillations, a modified direct antenna\nmodulation system with an auxiliary DC source is introduced to stabilize energy\nstorage on the antenna. A detailed circuit model for a direct antenna\nmodulation system is used to identify the origin of the oscillations and to\njustify the selection of the DC source. Measured phase shift keyed waveforms\ntransmitted by the modified system show significant increases in signal\nfidelity, including a 10-20 dB reduction in error vector magnitude compared to\na time-invariant system. Comparison to an equivalent, scalable time-invariant\nantenna suggests that the switched transmitter behaves as though it has 2-3\ntimes lower radiation Q-factor and 20% higher radiation efficiency.\n"}
+{"abstract": "  We define a product of pairs of double complex spaces $C^n_m(V, \\mathcal{W},\n\\mathcal{F})$ for gradig-restricted vertex algebra cohomology of codimension\none foliation on a complex curve. We introduce a vertex algebra counterpart of\nthe classical %product-type cohomological class using the orthogonality\nconditions on elements of double complex spaces with respect to the product we\nintroduced.\n"}
+{"abstract": "  Using the theory of Dirichlet forms we construct a large class of continuous\nsemimartingales on an open domain $E \\subset \\mathbb{R}^d$, which are governed\nby rank-based, in addition to name-based, characteristics. Using the results of\nBaur et al. [Potential Analysis, 38(4):1233-1258,2013] we obtain a strong\nFeller property for this class of diffusions. As a consequence we are able to\nestablish the nonexistence of triple collisions and obtain a simplified formula\nfor the dynamics of its rank process. We also establish conditions under which\nthe process is ergodic. Our main motivation is Stochastic Portfolio Theory\n(SPT), where rank-based diffusions of this type are used to model financial\nmarkets. We show that three main classes of models studied in SPT -- Atlas\nmodels, generalized volatility-stabilized models and polynomial models -- are\nspecial cases of our framework.\n"}
+{"abstract": "  Multi-label text classification (MLTC) is an attractive and challenging task\nin natural language processing (NLP). Compared with single-label text\nclassification, MLTC has a wider range of applications in practice. In this\npaper, we propose a heterogeneous graph convolutional network model to solve\nthe MLTC problem by modeling tokens and labels as nodes in a heterogeneous\ngraph. In this way, we are able to take into account multiple relationships\nincluding token-level relationships. Besides, the model allows a good\nexplainability as the token-label edges are exposed. We evaluate our method on\nthree real-world datasets and the experimental results show that it achieves\nsignificant improvements and outperforms state-of-the-art comparison methods.\n"}
+{"abstract": "  Recent single-channel speech enhancement methods based on deep neural\nnetworks (DNNs) have achieved remarkable results, but there are still\ngeneralization problems in real scenes. Like other data-driven methods,\nDNN-based speech enhancement models produce significant performance degradation\non untrained data. In this study, we make full use of the contribution of\nmulti-target joint learning to the model generalization capability, and propose\na lightweight and low-computing dilated convolutional network (DCN) model for a\nmore robust speech denoising task. Our goal is to integrate the masking target,\nthe mapping target, and the parameters of the traditional speech enhancement\nestimator into a DCN model to maximize their complementary advantages. To do\nthis, we build a multi-stage learning framework to deal with multiple targets\nin stages to achieve their joint learning, namely `MT-in-MS'. Our experimental\nresults show that compared with the state-of-the-art time domain and\ntime-frequency domain models, this proposed low-cost DCN model can achieve\nbetter generalization performance in speaker, noise, and channel mismatch\ncases.\n"}
+{"abstract": "  The importance of the chromosphere in the mass and energy transport within\nthe solar atmosphere is now widely recognised. This review discusses the\nphysics of magnetohydrodynamic (MHD) waves and instabilities in large-scale\nchromospheric structures as well as in magnetic flux tubes. We highlight a\nnumber of key observational aspects that have helped our understanding of the\nrole of the solar chromosphere in various dynamic processes and wave phenomena,\nand the heating scenario of the solar chromosphere is also discussed. The\nreview focuses on the physics of waves and invokes the basics of plasma\ninstabilities in the context of this important layer of the solar atmosphere.\nPotential implications, future trends and outstanding questions are also\ndelineated.\n"}
+{"abstract": "  Integrated optical amplifiers and light sources are of great significance for\nphotonic integrated circuits (PICs) and have attracted many research interests.\nDoping rare-earth ions in materials as a solution to realize efficient optical\namplifiers and lasing has been investigated a lot. We investigate the\nerbium-doped lithium niobate on insulator (LNOI). Here, spiral waveguide\namplifiers were fabricated on a 1-mol\\% erbium-doped LNOI by CMOS-compatible\ntechnique. We demonstrated a maximum internal net gain of 8.3 dB at 1530 nm\nindicating a net gain per unit length of 15.6 dB/cm with a compact spiral\nwaveguide of 5.3 mm length and $ \\sim $0.06 mm$ ^{2} $ footprint. The\nerbium-doped integrated lithium niobate spiral waveguide amplifiers would pave\nthe way in the PICs of the lithium niobate platform, especially in achieving\nefficient integration of active and passive devices on a lithium niobate thin\nfilm, which will make full use of its excellent physical properties such as\nremarkable photoacoustic, electro-optic, and piezoelectric characteristics.\n"}
+{"abstract": "  This paper aims at studying a generalized Camassa-Holm equation under random\nperturbation. We establish a local well-posedness result in the sense of\nHadamard, i.e., existence, uniqueness and continuous dependence on initial\ndata, as well as blow-up criteria for pathwise solutions in the Sobolev spaces\n$H^s$ with $s>3/2$ for two cases, i.e., $x\\in\\R$ and $x\\in\\T=(\\R/2\\pi\\Z)$. The\nanalysis on continuous dependence on initial data for nonlinear stochastic\npartial differential equations has gained less attention in the literature so\nfar. In this work, we first show that the solution map is continuous. Then we\nintroduce a notion of stability of exiting times. We provide an example showing\nthat one cannot improve the stability of the exiting time and simultaneously\nimprove the continuity of the dependence on initial data. Finally, we analyze\nthe regularization effect of nonlinear noise in preventing blow-up. Precisely,\nwe demonstrate that global existence holds true almost surely provided that the\nnoise is strong enough.\n"}
+{"abstract": "  Novel dynamical phases that violate ergodicity have been a subject of\nextensive research in recent years. A periodically driven system is naively\nexpected to lose all memory of its initial state due to thermalization, yet\nthis can be avoided in the presence of many-body localization. A discrete time\ncrystal represents a driven system whose local observables spontaneously break\ntime translation symmetry and retain memory of the initial state indefinitely.\nHere we report the observation of a discrete time crystal on a chain consisting\nof 57 superconducting qubits on a state-of-the-art quantum computer. We probe\nrandom initial states and compare the cases of vanishing and finite disorder to\ndistinguish many-body localization from pre-thermal dynamics. We further report\nresults on the dynamical phase transition between the discrete time crystal and\na thermal regime, which is observed via critical fluctuations in the system's\nsub-harmonic frequency response and a significant speed-up of spin\ndepolarisation.\n"}
+{"abstract": "  \"Deep Learning\"/\"Deep Neural Nets\" is a technological marvel that is now\nincreasingly deployed at the cutting-edge of artificial intelligence tasks.\nThis dramatic success of deep learning in the last few years has been hinged on\nan enormous amount of heuristics and it has turned out to be a serious\nmathematical challenge to be able to rigorously explain them. In this thesis,\nsubmitted to the Department of Applied Mathematics and Statistics, Johns\nHopkins University we take several steps towards building strong theoretical\nfoundations for these new paradigms of deep-learning. In chapter 2 we show new\ncircuit complexity theorems for deep neural functions and prove classification\ntheorems about these function spaces which in turn lead to exact algorithms for\nempirical risk minimization for depth 2 ReLU nets. We also motivate a measure\nof complexity of neural functions to constructively establish the existence of\nhigh-complexity neural functions. In chapter 3 we give the first algorithm\nwhich can train a ReLU gate in the realizable setting in linear time in an\nalmost distribution free set up. In chapter 4 we give rigorous proofs towards\nexplaining the phenomenon of autoencoders being able to do sparse-coding. In\nchapter 5 we give the first-of-its-kind proofs of convergence for stochastic\nand deterministic versions of the widely used adaptive gradient deep-learning\nalgorithms, RMSProp and ADAM. This chapter also includes a detailed empirical\nstudy on autoencoders of the hyper-parameter values at which modern algorithms\nhave a significant advantage over classical acceleration based methods. In the\nlast chapter 6 we give new and improved PAC-Bayesian bounds for the risk of\nstochastic neural nets. This chapter also includes an experimental\ninvestigation revealing new geometric properties of the paths in weight space\nthat are traced out by the net during the training.\n"}
+{"abstract": "  We give a complexity dichotomy for the Quantified Constraint Satisfaction\nProblem QCSP(H) when H is a reflexive tournament. It is well-known that\nreflexive tournaments can be split into a sequence of strongly connected\ncomponents H_1,...,H_n so that there exists an edge from every vertex of H_i to\nevery vertex of H_j if and only if i<j. We prove that if H has both its initial\nand final strongly connected component (possibly equal) of size 1, then QCSP(H)\nis in NL and otherwise QCSP(H) is NP-hard.\n"}
+{"abstract": "  The boundaries of quantum materials can host a variety of exotic effects such\nas topologically robust edge states or anyonic quasiparticles. Here, we show\nthat fermionic systems such as graphene that admit a low energy Dirac\ndescription can exhibit counterintuitive relativistic effects at their\nboundaries. As an example, we consider carbon nanotubes and demonstrate that\nrelativistic bulk spinor states can have non zero charge density on the\nboundaries, in contrast to the sinusoidal distribution of non-relativistic wave\nfunctions that are necessarily zero at the boundaries. This unusual property of\nrelativistic spinors is complementary to the linear energy dispersion relation\nexhibited by Dirac materials and can influence their coupling to leads,\ntransport properties or their response to external fields.\n"}
+{"abstract": "  Most modern asset transfer systems use consensus to maintain a totally\nordered chain of transactions. It was recently shown that consensus is not\nalways necessary for implementing asset transfer. More efficient, asynchronous\nsolutions can be built using reliable broadcast instead of consensus. This\napproach has been originally used in the closed (permissioned) setting. In this\npaper, we extend it to the open (permissionless) environment. We present\nPastro, a permissionless and asynchronous asset-transfer implementation, in\nwhich quorum systems, traditionally used in reliable broadcast, are replaced\nwith a weighted Proof-of-Stake mechanism. Pastro tolerates a dynamic adversary\nthat is able to adaptively corrupt participants based on the assets owned by\nthem.\n"}
+{"abstract": "  We present analysis of XMM-Newton Optical Monitor observations in the\nnear-ultraviolet of HD 189733, covering twenty primary transits of its hot\nJupiter planet. The transit is clearly detected with both the UVW2 and UVM2\nfilters, and our fits to the data reveal transit depths in agreement with that\nobserved optically. The measured depths correspond to radii of\n$1.059^{+0.046}_{-0.050}$ and $0.94^{+0.15}_{-0.17}$ times the\noptically-measured radius (1.187 R$_{\\rm J}$ at 4950 \\r{A}) in the UVW2 and\nUVM2 bandpasses, respectively. We also find no statistically significant\nvariation in the transit depth across the 8 year baseline of the observations.\nWe rule out extended broadband absorption towards or beyond the Roche lobe at\nthe wavelengths investigated, although observations with higher spectral\nresolution are required to determine if absorption out to those distances from\nthe planet is present in individual near-UV lines.\n"}
+{"abstract": "  Hawking has predicted that the formation of a black hole by gravitational\ncollapse causes quantum particle creation and the spectrum of the particles is\nalmost thermal. This phenomenon is called the Hawking radiation. Recently, it\nhas been predicted that the particle creation may drastically change from the\nHawking radiation to a strong double burst if the gravitational collapse\nsuddenly stops just before the formation of the event horizon. By contrast with\nthe Hawking radiation, the burst may be so strong that it can be of\nobservational interest even for collapsing objects with astrophysical mass\nscales. However, the burst phenomenon has been predicted through the studies of\nidealized models in which a spherical hollow shell begins to collapse, but\nstops shrinking and eventually settles down to a static \"star\". Therefore, one\nmight guess that it could be particular to the hollow shell model. In this\npaper, we study the particle creation due to the gravitational collapse of a\nspherical object whose interior is filled with matter. In this model, we obtain\nsimilar results to those in the case of the hollow shell model. This implies\nthat the double burst is a robust property of particle creation by the sudden\nbraking of gravitational collapse.\n"}
+{"abstract": "  Compositional disorder induces myriad captivating phenomena in perovskites.\nTarget-driven discovery of perovskite solid solutions has been a great\nchallenge due to the analytical complexity introduced by disorder. Here, we\ndemonstrate that an unsupervised deep learning strategy can find fingerprints\nof disordered materials that embed perovskite formability and underlying\ncrystal structure information by learning only from the chemical composition,\nmanifested in (A1-xA'x)BO3 and A(B1-xB'x)O3 formulae. This phenomenon can be\ncapitalized to predict the crystal symmetry of experimental compositions,\noutperforming several supervised machine learning (ML) algorithms. The educated\nnature of material fingerprints has led to the conception of analogical\nmaterials discovery that facilitates inverse exploration of promising\nperovskites based on similarity investigation with known materials. The search\nspace of unstudied perovskites is screened from ~600,000 feasible compounds\nusing experimental data powered ML models and automated web mining tools at a\n94% success rate. This concept further provides insights on possible phase\ntransitions and computational modelling of complex compositions. The proposed\nquantitative analysis of materials analogies is expected to bridge the gap\nbetween the existing materials literature and the undiscovered terrain.\n"}
+{"abstract": "  Recently the LARS and LAMB optimizers have been proposed for training neural\nnetworks faster using large batch sizes. LARS and LAMB add layer-wise\nnormalization to the update rules of Heavy-ball momentum and Adam,\nrespectively, and have become popular in prominent benchmarks and deep learning\nlibraries. However, without fair comparisons to standard optimizers, it remains\nan open question whether LARS and LAMB have any benefit over traditional,\ngeneric algorithms. In this work we demonstrate that standard optimization\nalgorithms such as Nesterov momentum and Adam can match or exceed the results\nof LARS and LAMB at large batch sizes. Our results establish new, stronger\nbaselines for future comparisons at these batch sizes and shed light on the\ndifficulties of comparing optimizers for neural network training more\ngenerally.\n"}
+{"abstract": "  Datacenter power demand has been continuously growing and is the key driver\nof its cost. An accurate mapping of compute resources (CPU, RAM, etc.) and\nhardware types (servers, accelerators, etc.) to power consumption has emerged\nas a critical requirement for major Web and cloud service providers. With the\nglobal growth in datacenter capacity and associated power consumption, such\nmodels are essential for important decisions around datacenter design and\noperation. In this paper, we discuss two classes of statistical power models\ndesigned and validated to be accurate, simple, interpretable and applicable to\nall hardware configurations and workloads across hyperscale datacenters of\nGoogle fleet. To the best of our knowledge, this is the largest scale power\nmodeling study of this kind, in both the scope of diverse datacenter planning\nand real-time management use cases, as well as the variety of hardware\nconfigurations and workload types used for modeling and validation. We\ndemonstrate that the proposed statistical modeling techniques, while simple and\nscalable, predict power with less than 5% Mean Absolute Percent Error (MAPE)\nfor more than 95% diverse Power Distribution Units (more than 2000) using only\n4 features. This performance matches the reported accuracy of the previous\nstarted-of-the-art methods, while using significantly less features and\ncovering a wider range of use cases.\n"}
+{"abstract": "  Several neural network approaches for solving differential equations employ\ntrial solutions with a feedforward neural network. There are different means to\nincorporate the trial solution in the construction, for instance one may\ninclude them directly in the cost function. Used within the corresponding\nneural network, the trial solutions define the so-called neural form. Such\nneural forms represent general, flexible tools by which one may solve various\ndifferential equations. In this article we consider time-dependent initial\nvalue problems, which require to set up the neural form framework adequately.\nThe neural forms presented up to now in the literature for such a setting can\nbe considered as first order polynomials. In this work we propose to extend the\npolynomial order of the neural forms. The novel collocation-type construction\nincludes several feedforward neural networks, one for each order. Additionally,\nwe propose the fragmentation of the computational domain into subdomains. The\nneural forms are solved on each subdomain, whereas the interfacing grid points\noverlap in order to provide initial values over the whole fragmentation. We\nillustrate in experiments that the combination of collocation neural forms of\nhigher order and the domain fragmentation allows to solve initial value\nproblems over large domains with high accuracy and reliability.\n"}
+{"abstract": "  The variational quantum Monte Carlo (VQMC) method received significant\nattention in the recent past because of its ability to overcome the curse of\ndimensionality inherent in many-body quantum systems. Close parallels exist\nbetween VQMC and the emerging hybrid quantum-classical computational paradigm\nof variational quantum algorithms. VQMC overcomes the curse of dimensionality\nby performing alternating steps of Monte Carlo sampling from a parametrized\nquantum state followed by gradient-based optimization. While VQMC has been\napplied to solve high-dimensional problems, it is known to be difficult to\nparallelize, primarily owing to the Markov Chain Monte Carlo (MCMC) sampling\nstep. In this work, we explore the scalability of VQMC when autoregressive\nmodels, with exact sampling, are used in place of MCMC. This approach can\nexploit distributed-memory, shared-memory and/or GPU parallelism in the\nsampling task without any bottlenecks. In particular, we demonstrate the\nGPU-scalability of VQMC for solving up to ten-thousand dimensional\ncombinatorial optimization problems.\n"}
+{"abstract": "  Neural networks are sensitive to hyper-parameter and architecture choices.\nAutomated Machine Learning (AutoML) is a promising paradigm for automating\nthese choices. Current ML software libraries, however, are quite limited in\nhandling the dynamic interactions among the components of AutoML. For example,\nefficientNAS algorithms, such as ENAS and DARTS, typically require an\nimplementation coupling between the search space and search algorithm, the two\nkey components in AutoML. Furthermore, implementing a complex search flow, such\nas searching architectures within a loop of searching hardware configurations,\nis difficult. To summarize, changing the search space, search algorithm, or\nsearch flow in current ML libraries usually requires a significant change in\nthe program logic. In this paper, we introduce a new way of programming AutoML\nbased on symbolic programming. Under this paradigm, ML programs are mutable,\nthus can be manipulated easily by another program. As a result, AutoML can be\nreformulated as an automated process of symbolic manipulation. With this\nformulation, we decouple the triangle of the search algorithm, the search space\nand the child program. This decoupling makes it easy to change the search space\nand search algorithm (without and with weight sharing), as well as to add\nsearch capabilities to existing code and implement complex search flows. We\nthen introduce PyGlove, a new Python library that implements this paradigm.\nThrough case studies on ImageNet and NAS-Bench-101, we show that with PyGlove\nusers can easily convert a static program into a search space, quickly iterate\non the search spaces and search algorithms, and craft complex search flows to\nachieve better results.\n"}
+{"abstract": "  We identify a quantum critical point with fractal symmetry whose effective\ntheory eludes the renormalization group framework. We consider the Newman-Moore\nmodel with three-body interaction subjected to an external transverse field,\nwhich exhibits a Kramers-Wannier type self-duality and a fractal $Z_2$ symmetry\nwith Ising charge conserved on a fractal subset of sites, i.e., on Sierpinski\ngaskets. Using large-scale quantum Monte Carlo simulations, we identify a\ncontinuous quantum phase transition between a phase with spontaneous fractal\nsymmetry breaking and a paramagnetic phase. This phase transition is\ncharacterized by the emergence of a fractal scaling dimension $d=\\ln(3)/\\ln(2)$\nat the quantum critical point, where the power-law exponent of the correlation\nfunction is related to the fractal dimension of the Sierpinski triangle. We\ndevelop a field theory to elucidate such quantum criticality and denote the\nfractal scaling as a subsequence of UV-IR mixing, where the low energy modes at\nthe critical point are manipulated by short-wavelength physics due to the\nfractal symmetry.\n"}
+{"abstract": "  The recent discovery of magnetic topological insulators has opened new\navenues to explore exotic states of matter that can emerge from the interplay\nbetween topological electronic states and magnetic degrees of freedom, be it\nordered or strongly fluctuating. Motivated by the effects that the dynamics of\nthe magnetic moments can have on the topological surface states, we investigate\nthe magnetic fluctuations across the\n(MnBi$_{\\text{2}}$Te$_{\\text{4}}$)(Bi$_{\\text{2}}$Te$_{\\text{3}}$)$_{\\text{n}}$\nfamily. Our paramagnetic electron spin resonance experiments reveal contrasting\nMn spin dynamics in different compounds, which manifests in a strongly\nanisotropic Mn spin relaxation in MnBi$_{\\text{2}}$Te$_{\\text{4}}$ while being\nalmost isotropic in MnBi$_{\\text{4}}$Te$_{\\text{7}}$. Our density-functional\ncalculations explain these striking observations in terms of the sensitivity of\nthe local electronic structure to the Mn spin-orientation, and indicate that\nthe anisotropy of the magnetic fluctuations can be controlled by the carrier\ndensity, which may directly affect the electronic topological surface states.\n"}
+{"abstract": "  We demonstrate fast and ultrasensitive charge detection with a\ncavity-embedded Cooper pair transistor (cCPT) via dispersive readout of its\nJosephson inductance. We report a minimum charge sensitivity of $14$ $\\mu\ne/\\sqrt{\\mathrm{Hz}}$ with a detection bandwidth on the order of $1$ MHz using\n$16$ attowatts of power, corresponding to the single-photon level of the\ncavity. In addition, our measured sensitivities are within a factor of $5$ of\nthe quantum limit for this device. The single-photon-level sensitivity of the\ncCPT is comparable to that of the rf-SET, which typically operates using\npicowatts of power corresponding to hundreds of thousands of photons in its\ntank circuit. Our results support the feasibility of using the cCPT to mediate\nan optomechanical interaction that reaches the single-photon strong coupling\nregime.\n"}
+{"abstract": "  In this paper, we generalize the problem of single-index model to the context\nof continual learning in which a learner is challenged with a sequence of tasks\none by one and the dataset of each task is revealed in an online fashion. We\npropose a randomized strategy that is able to learn a common single-index\n(meta-parameter) for all tasks and a specific link function for each task. The\ncommon single-index allows to transfer the information gained from the previous\ntasks to a new one. We provide a rigorous theoretical analysis of our proposed\nstrategy by proving some regret bounds under different assumption on the loss\nfunction.\n"}
+{"abstract": "  This work is the first to propose the concept of a semantic numeration system\n(SNS) as a certain class of context-based numeration methods. The development\nof the SNS concept required the introduction of fundamentally new concepts such\nas a cardinal abstract entity, a cardinal semantic operator, a cardinal\nabstract object, a numeration space. The main attention is paid to the key\nelements of semantic numeration systems - cardinal semantic operators. A\nclassification of semantic numeration systems is given.\n"}
+{"abstract": "  The preparation of low-entropy starting conditions is a key requirement for\nmany experiments involving neutral atoms. Here, we propose a method to\nautonomously assemble arbitrary spatial configurations of atoms within arrays\nof optical tweezers or lattice sites, enabled by a combination of tunneling and\nground-state laser cooling. In contrast to previous methods, our protocol does\nnot rely on either imaging or evaporative cooling. This circumvents limitations\nassociated with imaging fidelity and loss, especially in systems with small\nspatial scales, while providing a substantial improvement in speed relative to\nevaporative approaches. These features may make it well-suited for preparing\narbitrary initial conditions for Bose-Hubbard or Rydberg interacting systems.\n"}
+{"abstract": "  Abstract syntax tree (AST) mapping algorithms are widely used to analyze\nchanges in source code. Despite the foundational role of AST mapping\nalgorithms, little effort has been made to evaluate the accuracy of AST mapping\nalgorithms, i.e., the extent to which an algorihtm captures the evolution of\ncode. We observe that a program element often has only one best-mapped program\nelement. Based on this observation, we propose a hierarchical approach to\nautomatically compare the similarity of mapped statements and tokens by\ndifferent algorithms. By performing the comparison, we determine if each of the\ncompared algorithms generates inaccurate mappings for a statement or its\ntokens. We invite 12 external experts to determine if three commonly used AST\nmapping algorithms generate accurate mappings for a statement and its tokens\nfor 200 statements. Based on the experts' feedback,we observe that our approach\nachieves a precision of 0.98--1.00 and a recall of 0.65--0.75. Furthermore, we\nconduct a large-scale study with a dataset of ten Java projects, containing a\ntotal of 263,165 file revisions. Our approach determines that GumTree, MTDiff\nand IJM generate inaccurate mappings for 20%--29%, 25%--36% and 21%--30% of the\nfile revisions, respectively. Our experimental results show that state-of-art\nAST mapping agorithms still need improvements.\n"}
+{"abstract": "  We compute the area of a generic d-sphere in a Snyder geometry.\n"}
+{"abstract": "  In this letter we generalize Spekkens' notion of measurement\nnon-contextuality (NC). We show that any non-contextual ontological model based\non this notion of contextuality fails to explain the statistics of outcomes of\na single carefully constructed POVM executed sequentially on a quantum system.\nThe POVM essentially forms a single measurement device. The context of\nmeasurement arises from the different configurations in which the device can be\nused. We develop an inequality from the non-contextual ontic model, and\nconstruct corresponding quantum situations where the measurement outcomes from\nthe device violate this NC inequality. Our work brings out the hitherto\nunexplored implications of contextuality for a single measurement device, and\npaves the way for further study of consequences of contextuality for sequential\nmeasurements.\n"}
+{"abstract": "  Travel speed is an intrinsic feature of transport, and enlarging the speed is\nconsidered as beneficial. The benefit of a speed increase is generally assessed\nas the value of the saved travel time. However, this approach conflicts with\nthe observation that time spent on travelling is rather constant and might not\nbe affected by speed changes. The paper aims to define the benefits of a speed\nincrease and addresses two research questions. First, how will a speed increase\nin person transport work out, which factors are affected? Second, is the value\nof time a good proxy for the value of speed? Based on studies on time spending\nand research on the association between speed and land use, we argue that human\nwealth could be the main affected factor by speed changes, rather than time or\naccess. Then the value of time is not a good proxy for the value of speed: the\nbenefits of a wealth increase are negatively correlated with prosperity\nfollowing the law of diminishing marginal utility, while the calculated\nbenefits of saved travel time prove to be positively correlated. The inadequacy\nof the value of time is explained by some shortcomings with respect to the\nwillingness to pay that is generally used for assessing the value of time:\npeople do not predict correctly the personal benefits that will be gained from\na decision, and they neglect the social impacts.\n"}
+{"abstract": "  Superconductivity allows to construct and operate magnets at field values\nbeyond 2 Tesla, the practical limitation of normal-conducting magnets\nexploiting ferro-magnetism. The field of superconducting magnets is dominated\nby the field generated in the coil. The stored energy and the electromagnetic\nforces generated by the coil are the main challenges to be overcome in the\ndesign of these magnets. For further reading you may consult the following\nbooks: [1], [2], [3], [4] or the proceedings of two specialized CAS courses:\n[5] and [6].\n"}
+{"abstract": "  The convex body isoperimetric conjecture in the plane asserts that the least\nperimeter to enclose given area inside a unit disk is greater than inside any\nother convex set of area $\\pi$. In this note we confirm two cases of the\nconjecture: domains symmetric to both coordinate axes and perturbations of unit\ndisk.\n"}
+{"abstract": "  Most recent video super-resolution (SR) methods either adopt an iterative\nmanner to deal with low-resolution (LR) frames from a temporally sliding\nwindow, or leverage the previously estimated SR output to help reconstruct the\ncurrent frame recurrently. A few studies try to combine these two structures to\nform a hybrid framework but have failed to give full play to it. In this paper,\nwe propose an omniscient framework to not only utilize the preceding SR output,\nbut also leverage the SR outputs from the present and future. The omniscient\nframework is more generic because the iterative, recurrent and hybrid\nframeworks can be regarded as its special cases. The proposed omniscient\nframework enables a generator to behave better than its counterparts under\nother frameworks. Abundant experiments on public datasets show that our method\nis superior to the state-of-the-art methods in objective metrics, subjective\nvisual effects and complexity. Our code will be made public.\n"}
+{"abstract": "  In this paper, we discuss the application of the Generalized Finite Element\nMethod (GFEM) to approximate the solutions of quasilinear elliptic equations\nwith multiple interfaces in one dimensional space. The problem is characterized\nby spatial discontinuity of the elliptic coefficient that depends on the\nunknown solution. It is known that unless the partition of the domain matches\nthe discontinuity configuration, accuracy of standard finite element techniques\nsignificantly deteriorates and standard refinement of the partition may not\nsuffice. The GFEM is a viable alternative to overcome this predicament. It is\nbased on the construction of certain enrichment functions supplied to the\nstandard space that capture effects of the discontinuity. This approach is\ncalled stable (SGFEM) if it maintains an optimal rate of convergence and the\nconditioning of GFEM is not worse than that of the standard FEM. A convergence\nanalysis is derived and performance of the method is illustrated by several\nnumerical examples. Furthermore, it is known that typical global formulations\nsuch as FEMs do not enjoy the numerical local conservation property that is\ncrucial in many conservation law-based applications. To remedy this issue, a\nLagrange multiplier technique is adopted to enforce the local conservation. A\nnumerical example is given to demonstrate the performance of proposed\ntechnique.\n"}
+{"abstract": "  We present a protocol for the fully automated construction of quantum\nmechanical-(QM)-classical hybrid models by extending our previously reported\napproach on self-parametrizing system-focused atomistic models (SFAM) J. Chem.\nTheory Comput. 2020, 16, 1646]. In this QM/SFAM approach, the size and\ncomposition of the QM region is evaluated in an automated manner based on first\nprinciples so that the hybrid model describes the atomic forces in the center\nof the QM region accurately. This entails the automated construction and\nevaluation of differently sized QM regions with a bearable computational\noverhead that needs to be paid for automated validation procedures. Applying\nSFAM for the classical part of the model eliminates any dependence on\npre-existing parameters due to its system-focused quantum mechanically derived\nparametrization. Hence, QM/SFAM is capable of delivering a high fidelity and\ncomplete automation. Furthermore, since SFAM parameters are generated for the\nwhole system, our ansatz allows for a convenient re-definition of the QM region\nduring a molecular exploration. For this purpose, a local re-parametrization\nscheme is introduced, which efficiently generates additional classical\nparameters on the fly when new covalent bonds are formed (or broken) and moved\nto the classical region.\n"}
+{"abstract": "  We extend the notion of the Nieh-Yan invariant to generic metric-affine\ngeometries, where both torsion and nonmetricity are taken into account.\nNotably, we show that the properties of projective invariance and topologicity\ncan be independently accommodated by a suitable choice of the parameters\nfeaturing this new Nieh-Yan term. We then consider a special class of modified\ntheories of gravity able to promote the Immirzi parameter to a dynamical scalar\nfield coupled to the Nieh-Yan form, and we discuss in more detail the dynamics\nof the effective scalar tensor theory stemming from such a revised theoretical\nframework. We focus, in particular, on cosmological Bianchi I models and we\nderive classical solutions where the initial singularity is safely removed in\nfavor of a big-bounce, which is ultimately driven by the non-minimal coupling\nwith the Immirzi field. These solutions, moreover, turn out to be characterized\nby finite time singularities, but we show that such critical points do not\nspoil the geodesic completeness and wave regularity of these space-times.\n"}
+{"abstract": "  This paper explores a family of generalized sweeping preconditionners for\nHelmholtz problems with non-overlapping checkerboard partition of the\ncomputational domain. The domain decomposition procedure relies on high-order\ntransmission conditions and cross-point treatments, which cannot scale without\nan efficient preconditioning technique when the number of subdomains increases.\nWith the proposed approach, existing sweeping preconditioners, such as the\nsymmetric Gauss-Seidel and parallel double sweep preconditioners, can be\napplied to checkerboard partitions with different sweeping directions (e.g.\nhorizontal and diagonal). Several directions can be combined thanks to the\nflexible version of GMRES, allowing for the rapid transfer of information in\nthe different zones of the computational domain, then accelerating the\nconvergence of the final iterative solution procedure. Several two-dimensional\nfinite element results are proposed to study and to compare the sweeping\npreconditioners, and to illustrate the performance on cases of increasing\ncomplexity.\n"}
+{"abstract": "  We study the global strong solutions to the compressible Navier-Stokes system\nwith potential temperature transport in $\\mathbb{R}^n.$ Different from the\nNavier-Stokes-Fourier system, the pressure is a nonlinear function of the\ndensity and the potential temperature, we can not exploit the special\nquasi-diagonalization structure of this system to capture any dissipation of\nthe density. Some new idea and delicate analysis involved in high or low\nfrequency decomposition in the Besov spaces have to be made to close the energy\nestimates.\n"}
+{"abstract": "  This paper presents the system that we propose for the Reliable Intelligence\nIndentification on Vietnamese Social Network Sites (ReINTEL) task of the\nVietnamese Language and Speech Processing 2020 (VLSP 2020) Shared Task. In this\ntask, the VLSP 2020 provides a dataset with approximately 6,000 trainning\nnews/posts annotated with reliable or unreliable labels, and a test set\nconsists of 2,000 examples without labels. In this paper, we conduct\nexperiments on different transfer learning models, which are bert4news and\nPhoBERT fine-tuned to predict whether the news is reliable or not. In our\nexperiments, we achieve the AUC score of 94.52% on the private test set from\nReINTEL's organizers.\n"}
+{"abstract": "  This paper presents an efficient multi-scale vision Transformer, called ResT,\nthat capably served as a general-purpose backbone for image recognition. Unlike\nexisting Transformer methods, which employ standard Transformer blocks to\ntackle raw images with a fixed resolution, our ResT have several advantages:\n(1) A memory-efficient multi-head self-attention is built, which compresses the\nmemory by a simple depth-wise convolution, and projects the interaction across\nthe attention-heads dimension while keeping the diversity ability of\nmulti-heads; (2) Position encoding is constructed as spatial attention, which\nis more flexible and can tackle with input images of arbitrary size without\ninterpolation or fine-tune; (3) Instead of the straightforward tokenization at\nthe beginning of each stage, we design the patch embedding as a stack of\noverlapping convolution operation with stride on the 2D-reshaped token map. We\ncomprehensively validate ResT on image classification and downstream tasks.\nExperimental results show that the proposed ResT can outperform the recently\nstate-of-the-art backbones by a large margin, demonstrating the potential of\nResT as strong backbones. The code and models will be made publicly available\nat https://github.com/wofmanaf/ResT.\n"}
+{"abstract": "  In linear regression, SLOPE is a new convex analysis method that generalizes\nthe Lasso via the sorted L1 penalty: larger fitted coefficients are penalized\nmore heavily. This magnitude-dependent regularization requires an input of\npenalty sequence $\\lambda$, instead of a scalar penalty as in the Lasso case,\nthus making the design extremely expensive in computation. In this paper, we\npropose two efficient algorithms to design the possibly high-dimensional SLOPE\npenalty, in order to minimize the mean squared error. For Gaussian data\nmatrices, we propose a first order Projected Gradient Descent (PGD) under the\nApproximate Message Passing regime. For general data matrices, we present a\nzero-th order Coordinate Descent (CD) to design a sub-class of SLOPE, referred\nto as the k-level SLOPE. Our CD allows a useful trade-off between the accuracy\nand the computation speed. We demonstrate the performance of SLOPE with our\ndesigns via extensive experiments on synthetic data and real-world datasets.\n"}
+{"abstract": "  Generating diverse populations of high quality solutions has gained interest\nas a promising extension to the traditional optimization tasks. We contribute\nto this line of research by studying evolutionary diversity optimization for\ntwo of the most prominent permutation problems, namely the Traveling\nSalesperson Problem (TSP) and Quadratic Assignment Problem (QAP). We explore\nthe worst-case performance of a simple mutation-only evolutionary algorithm\nwith different mutation operators, using an established diversity measure.\nTheoretical results show most mutation operators for both problems ensure\nproduction of maximally diverse populations of sufficiently small size within\ncubic expected run-time. We perform experiments on QAPLIB instances in\nunconstrained and constrained settings, and reveal much more optimistic\npractical performances. Our results should serve as a baseline for future\nstudies.\n"}
+{"abstract": "  Two-dimensional molecular crystals have been beyond the reach of systematic\ninvestigation because of the lack or instability of their well-defined forms.\nHere, we demonstrate drastically enhanced photostability and Davydov splitting\nin single and few-layer tetracene (Tc) crystals sandwiched between inorganic 2D\ncrystals of graphene or hexagonal BN. Molecular orientation and long-range\norder mapped with polarized wide-field photoluminescence imaging and optical\nsecond-harmonic generation revealed high crystallinity of the 2D Tc and its\ndistinctive orientational registry with the 2D inorganic crystals, which were\nalso verified with first-principles calculations. The reduced dielectric\nscreening in 2D space was manifested by enlarged Davydov splitting and\nattenuated vibronic sidebands in the excitonic absorption and emission of\nmonolayer Tc crystals. Photostable 2D molecular crystals and their size effects\nwill lead to novel photophysical principles and photonic applications.\n"}
+{"abstract": "  A new paradigm for data-driven, model-agnostic new physics searches at\ncolliders is emerging, and aims to leverage recent breakthroughs in anomaly\ndetection and machine learning. In order to develop and benchmark new anomaly\ndetection methods within this framework, it is essential to have standard\ndatasets. To this end, we have created the LHC Olympics 2020, a community\nchallenge accompanied by a set of simulated collider events. Participants in\nthese Olympics have developed their methods using an R&D dataset and then\ntested them on black boxes: datasets with an unknown anomaly (or not). This\npaper will review the LHC Olympics 2020 challenge, including an overview of the\ncompetition, a description of methods deployed in the competition, lessons\nlearned from the experience, and implications for data analyses with future\ndatasets as well as future colliders.\n"}
+{"abstract": "  In the present paper, we develop the algorithmic correspondence theory for\nhybrid logic with binder. We define the class of Sahlqvist inequalities, each\ninequality of which is shown to have a first-order frame correspondent\neffectively computable by an algorithm ALBA.\n"}
+{"abstract": "  In protoplanetary discs and planetary atmospheres, dust grains coagulate to\nform fractal dust aggregates. The geometric cross-section of these aggregates\nis a crucial parameter characterizing aerodynamical friction, collision rates,\nand opacities. However, numerical measurements of the cross-section are often\ntime-consuming as aggregates exhibit complex shapes. In this study, we derive a\nnovel analytic expression for geometric cross-sections of fractal aggregates.\nIf an aggregate consists of $N$ monomers of radius $R_0$, its geometric\ncross-section $G$ is expressed as \\begin{equation} \\frac{G}{N\\pi\nR_0^2}=\\frac{A}{1+(N-1)\\tilde{\\sigma}}, \\nonumber\n  \\end{equation} where $\\tilde{\\sigma}$ is an overlapping efficiency, and $A$\nis a numerical factor connecting the analytic expression to the small\nnon-fractal cluster limit. The overlapping efficiency depends on the fractal\ndimension, fractal prefactor, and $N$ of the aggregate, and its analytic\nexpression is derived as well. The analytic expressions successfully reproduce\nnumerically measured cross-sections of aggregates. We also find that our\nexpressions are compatible with the mean-field light scattering theory of\naggregates in the geometrical optics limit. The analytic expressions greatly\nsimplify an otherwise tedious calculation and will be useful in model\ncalculations of fractal grain growth in protoplanetary discs and planetary\natmospheres.\n"}
+{"abstract": "  The complexity of comprehensive ocean models poses an important question for\nparameterisations: is there a minimum set of equations that should be\nparameterised, on the one hand, to reduce the development to a minimum, and, on\nthe other hand, to ensure an accurate representation of large-scale flow\npatterns? This work seeks to answer this to assist modern parameterisations be\nmore selective in their targets. For this, we considered a North Atlantic\nconfiguration of the MIT general circulation model and studied contributions of\ndifferent model equations to the accuracy of representation of the Gulf Stream\nat low resolution. Our results suggest that it is enough to parameterise only\nthe tracer equations for temperature and salinity and leave the other equations\nin the hydrodynamic part, as well as the atmospheric model unmodified.\n"}
+{"abstract": "  The increasing complexity of configuring cellular networks suggests that\nmachine learning (ML) can effectively improve 5G technologies. Deep learning\nhas proven successful in ML tasks such as speech processing and computational\nvision, with a performance that scales with the amount of available data. The\nlack of large datasets inhibits the flourish of deep learning applications in\nwireless communications. This paper presents a methodology that combines a\nvehicle traffic simulator with a raytracing simulator, to generate channel\nrealizations representing 5G scenarios with mobility of both transceivers and\nobjects. The paper then describes a specific dataset for investigating\nbeams-election techniques on vehicle-to-infrastructure using millimeter waves.\nExperiments using deep learning in classification, regression and reinforcement\nlearning problems illustrate the use of datasets generated with the proposed\nmethodology\n"}
+{"abstract": "  Video deraining is an important task in computer vision as the unwanted rain\nhampers the visibility of videos and deteriorates the robustness of most\noutdoor vision systems. Despite the significant success which has been achieved\nfor video deraining recently, two major challenges remain: 1) how to exploit\nthe vast information among continuous frames to extract powerful\nspatio-temporal features across both the spatial and temporal domains, and 2)\nhow to restore high-quality derained videos with a high-speed approach. In this\npaper, we present a new end-to-end video deraining framework, named Enhanced\nSpatio-Temporal Interaction Network (ESTINet), which considerably boosts\ncurrent state-of-the-art video deraining quality and speed. The ESTINet takes\nthe advantage of deep residual networks and convolutional long short-term\nmemory, which can capture the spatial features and temporal correlations among\ncontinuing frames at the cost of very little computational source. Extensive\nexperiments on three public datasets show that the proposed ESTINet can achieve\nfaster speed than the competitors, while maintaining better performance than\nthe state-of-the-art methods.\n"}
+{"abstract": "  Deep learning has recently been applied to optical music recognition (OMR).\nHowever, currently OMR processing from various sheet music images still lacks\nprecision to be widely applicable. Here, we present an MMdA (Measure-based\nMultimodal deep learning (DL)-driven Assembly) method allowing for end-to-end\nOMR processing from various images including inclined photo images. Using this\nmethod, measures are extracted by a deep learning model, aligned, and resized\nto be used for inference of given musical symbol components by using multiple\ndeep learning models in sequence or in parallel. Use of each standardized\nmeasure enables efficient training of the models and accurate adjustment of\nfive staff lines in each measure. Multiple musical symbol component category\nmodels with a small number of feature types can represent a diverse set of\nnotes and other musical symbols including chords. This MMdA method provides a\nsolution to end-to-end OMR processing with precision.\n"}
+{"abstract": "  Strategies based on Explainable Artificial Intelligence - XAI have emerged in\ncomputing to promote a better understanding of predictions made by black box\nmodels. Most XAI measures used today explain these types of models, generating\nattribute rankings aimed at explaining the model, that is, the analysis of\nAttribute Importance of Model. There is no consensus on which XAI measure\ngenerates an overall explainability rank. For this reason, several proposals\nfor tools have emerged (Ciu, Dalex, Eli5, Lofo, Shap and Skater). An\nexperimental benchmark of explainable AI techniques capable of producing global\nexplainability ranks based on tabular data related to different problems and\nensemble models are presented herein. Seeking to answer questions such as \"Are\nthe explanations generated by the different measures the same, similar or\ndifferent?\" and \"How does data complexity play along model explainability?\" The\nresults from the construction of 82 computational models and 592 ranks shed\nsome light on the other side of the problem of explainability: dataset\ncomplexity!\n"}
+{"abstract": "  We prove that any positive solution of the Yamabe equation on an\nasymptotically flat $n$-dimensional manifold of flatness order at least\n$\\frac{n-2}{2}$ and $n\\le 24$ must converge at infinity either to a fundamental\nsolution of the Laplace operator on the Euclidean space or to a radial Fowler\nsolution defined on the entire Euclidean space. The flatness order\n$\\frac{n-2}{2}$ is the minimal flatness order required to define ADM mass in\ngeneral relativity; the dimension $24$ is the dividing dimension of the\nvalidity of compactness of solutions to the Yamabe problem. We also prove such\nalternatives for bounded solutions when $n>24$.\n  We prove these results by establishing appropriate asymptotic behavior near\nan isolated singularity of solutions to the Yamabe equation when the metric has\na flatness order of at least $\\frac{n-2}{2}$ at the singularity and $n<24$,\nalso when $n>24$ and the solution grows no faster than the fundamental solution\nof the flat metric Laplacian at the singularity. These results extend earlier\nresults of L. Caffarelli, B. Gidas and J. Spruck, also of N. Korevaar, R.\nMazzeo, F. Pacard and R. Schoen, when the metric is conformally flat, and work\nof C.C. Chen and C. S. Lin when the scalar curvature is a non-constant function\nwith appropriate flatness at the singular point, also work of F. Marques when\nthe metric is not necessarily conformally flat but smooth, and the dimension of\nthe manifold is three, four, or five, as well as recent similar results by the\nsecond and third authors in dimension six.\n"}
+{"abstract": "  Recently, based on the phase-field modeling, it was predicted that Hf1-xZrxO2\n(HZO) exhibits the morphotropic phase boundary (MPB) in its compositional phase\ndiagram. Here, we investigate the effect of structural changes between\ntetragonal (t) and orthorhombic (o) phases on the ferroelectric and dielectric\nproperties of HZO films to probe the existence of MPB region. The structural\nanalysis show that by adjusting the ozone dosage during the atomic layer\ndeposition process and annealing conditions, different ratios of t- to o-phases\n(f_(t/o) ) were achieved which consequently affect the ferroelectric and\ndielectric properties of the samples. Polarization versus electric field\nmeasurements show a remarkable increase in ferroelectric characteristics (Pr\nand Ec) of the sample that contains the minimum t-phase fraction (f_(t/o)~\n0.04). This sample shows the lowest dielectric constant compared to the other\nsamples which is due to the formation of ferroelectric o-phase. The sample that\ncontains the maximum f_(t/o)~ 0.41 demonstrates the highest dielectric\nresponse. By adjusting the f_(t/o), a large dielectric constant of ~ 55 is\nachieved. Our study reveals a direct relation between f_(t/o) and dielectric\nconstant of HZO thin films which can be understood by considering the density\nof MPB region.\n"}
+{"abstract": "  When two Neutron Stars collide a multi-band electromagnetic emission, known\nas Kilonova (KN), follows being powered by the radioactive decay of ejecta\nproducts. In this contribution we discuss how the equation of state of dense\nmatter, impacts the mass and velocity in the KN ejecta and thus its light\ncurve. Using this information encoded in the stellar mass-radius relationship,\nwe ellaborate on how the future experimental observations in photon channels,\nin addition to complementary multimessenger probes, could provide a new and\nmore detailed insight into the equation of state of nuclear matter.\n"}
+{"abstract": "  We consider mappings of domains of Riemannian manifolds that admit branch\npoints and satisfy a certain condition regarding the distortion of the modulus\nof families of paths. We have established logarithmic estimates of distance\ndistortion under such mappings. A separate study relates to the situation when\nthe mappings are defined in metric spaces, and one of them is the Loewner\nspace. We also studied the question of equicontinuity of the families of the\nindicated mappings in the closure of the domain. In addition, we have\nestablished the possibility of continuous extension of these mappings to an\nisolated point of the boundary.\n"}
+{"abstract": "  Ultrarelativistic collisions between heavy nuclei briefly generate the\nquark-gluon plasma (QGP), a new state of matter characterized by deconfined\npartons last seen microseconds after the Big Bang. The properties of the QGP\nare of intense interest, and a large community has developed over several\ndecades, to produce, measure and understand this primordial plasma. The plasma\nis now recognized to be a strongly-coupled fluid with remarkable properties,\nand hydrodynamics is commonly used to quantify and model the system. An\nimportant feature of any fluid is its vorticity, related to the local angular\nmomentum density; however, this degree of freedom has received relatively\nlittle attention because no experimental signals of vorticity had been\ndetected. Thanks to recent high-statistics datasets from experiments with\nprecision tracking and complete kinemetic coverage at collider energies,\nhyperon spin polarization measurements have begun to uncover the vorticity of\nthe QGP created at the Relativistic Heavy Ion Collider. The injection of this\nnew degree of freedom into a relatively mature field of research represents an\nenormous opportunity to generate new insights into the physics of the QGP. The\ncommunity has responded with enthusiasm, and this book (to be published as a\nvolume of Lecture Notes in Physics series by Springer) represents some of the\ndiverse lines of inquiry into aspects of strongly interacting matter under\nrotation.\n"}
+{"abstract": "  Electrons confined in silicon quantum dots exhibit orbital, spin, and valley\ndegrees of freedom. The valley degree of freedom originates from the bulk\nbandstructure of silicon, which has six degenerate electronic minima. The\ndegeneracy can be lifted in silicon quantum wells due to strain and electronic\nconfinement, but the \"valley splitting\" of the two lowest lying valleys is\nknown to be sensitive to atomic-scale disorder. Large valley splittings are\ndesirable to have a well-defined spin qubit. In addition, an understanding of\nthe inter-valley tunnel coupling that couples different valleys in adjacent\nquantum dots is extremely important, as the resulting gaps in the energy level\ndiagram may affect the fidelity of charge and spin transfer protocols in\nsilicon quantum dot arrays. Here we use microwave spectroscopy to probe spatial\nvariations in the valley splitting, and the intra- and inter-valley tunnel\ncouplings ($t_{ij}$ and $t'_{ij}$) that couple dots $i$ and $j$ in a triple\nquantum dot (TQD). We uncover large spatial variations in the ratio of\ninter-valley to intra-valley tunnel couplings $t_{12}'/t_{12}=0.90$ and\n$t_{23}'/t_{23}=0.56$. By tuning the interdot tunnel barrier we also show that\n$t'_{ij}$ scales linearly with $t_{ij}$, as expected from theory. The results\nindicate strong interactions between different valley states on neighboring\ndots, which we attribute to local inhomogeneities in the silicon quantum well.\n"}
+{"abstract": "  Progressively applying Gaussian noise transforms complex data distributions\nto approximately Gaussian. Reversing this dynamic defines a generative model.\nWhen the forward noising process is given by a Stochastic Differential Equation\n(SDE), Song et al. (2021) demonstrate how the time inhomogeneous drift of the\nassociated reverse-time SDE may be estimated using score-matching. A limitation\nof this approach is that the forward-time SDE must be run for a sufficiently\nlong time for the final distribution to be approximately Gaussian. In contrast,\nsolving the Schr\\\"odinger Bridge problem (SB), i.e. an entropy-regularized\noptimal transport problem on path spaces, yields diffusions which generate\nsamples from the data distribution in finite time. We present Diffusion SB\n(DSB), an original approximation of the Iterative Proportional Fitting (IPF)\nprocedure to solve the SB problem, and provide theoretical analysis along with\ngenerative modeling experiments. The first DSB iteration recovers the\nmethodology proposed by Song et al. (2021), with the flexibility of using\nshorter time intervals, as subsequent DSB iterations reduce the discrepancy\nbetween the final-time marginal of the forward (resp. backward) SDE with\nrespect to the prior (resp. data) distribution. Beyond generative modeling, DSB\noffers a widely applicable computational optimal transport tool as the\ncontinuous state-space analogue of the popular Sinkhorn algorithm (Cuturi,\n2013).\n"}
+{"abstract": "  Motivated by recent nanofluidics experiments, we use Brownian dynamics and\nMonte Carlo simulations to study the conformation, organization and dynamics of\ntwo polymer chains confined to a single box-like cavity. The polymers are\nmodeled as flexible hard-sphere chains, and the box has a square cross-section\nof side length $L$ and a height that is small enough to compress the polymers\nin that dimension. For sufficiently large $L$, the system behaviour approaches\nthat of an isolated polymer in a slit. However, the combined effects of\ncrowding and confinement on the polymer organization, conformation and\nequilibrium dynamics become significant when $L/R_{{\\rm g},xy}^*\\lesssim 5$,\nwhere $R_{{\\rm g},xy}^*$ is the transverse radius of gyration for a slit\ngeometry. In this regime, the centre-of-mass probability distribution in the\ntransverse plane exhibits a depletion zone near the centre of the cavity\n(except at very small $L$) and a 4-fold symmetry with quasi-discrete positions.\nReduction in polymer size with decreasing $L$ arises principally from\nconfinement rather than inter-polymer crowding. By contrast, polymer diffusion\nand internal motion are strongly affected by inter-polymer crowding. The two\npolymers tend to occupy opposite positions relative to the box centre, about\nwhich they diffuse relatively freely. Qualitatively, this static and dynamical\nbehaviour differs significantly from that previously observed for confinement\nof two polymers to a narrow channel. The simulation results for a suitably\nchosen box width are qualitatively consistent with results from a recent\nexperimental study of two $\\lambda$-DNA chains confined to a nanofluidic\ncavity.\n"}
+{"abstract": "  For a quantum harmonic oscillator an explicit expression that describes the\nenergy distribution as a coordinate function is obtained. The presence of the\nenergy function poles is shown for the quantum system in domains where the\nWigner function has negative values.\n"}
+{"abstract": "  In this paper, some sufficient conditions for the differentiability of the\n$n$-variable real-valued function are obtained, which are given based on the\ndifferentiability of the $n-1$-variable real-valued function and are weaker\nthan classical conditions.\n"}
+{"abstract": "  We introduce the Australia Telescope Compact Array (ATCA) rapid-response mode\nby presenting the first successful trigger on the short-duration gamma-ray\nburst (GRB) 181123B. Early-time radio observations of short GRBs may provide\nvital insights into the radio afterglow properties of Advanced LIGO- and\nVirgo-detected gravitational wave events, which will in turn inform follow-up\nstrategies to search for counterparts within their large positional\nuncertainties. The ATCA was on target within 12.6 hr post-burst, when the\nsource had risen above the horizon. While no radio afterglow was detected\nduring the 8.3 hr observation, we obtained force-fitted flux densities of $7\n\\pm 12$ and $15 \\pm 11~\\mu$Jy at 5.5 and 9 GHz, respectively. Afterglow\nmodelling of GRB 181123B showed that the addition of the ATCA force-fitted\nradio flux densities to the Swift X-ray Telescope detections provided more\nstringent constraints on the fraction of thermal energy in the electrons\n(log$\\epsilon_e = -0.75^{+0.39}_{-0.40}$ rather than log$\\epsilon_e =\n-1.13^{+0.82}_{-1.2}$ derived without the inclusion of the ATCA values), which\nis consistent with the range of typical $\\epsilon_e$ derived from GRB afterglow\nmodelling. This allowed us to predict that the forward shock may have peaked in\nthe radio band $\\sim10$ days post-burst, producing detectable radio emission\n$\\gtrsim3-4$ days post-burst. Overall, we demonstrate the potential for\nextremely rapid radio follow-up of transients and the importance of triggered\nradio observations for constraining GRB blast wave properties, regardless of\nwhether there is a detection, via the inclusion of force-fitted radio flux\ndensities in afterglow modelling efforts.\n"}
+{"abstract": "  The excited-state structure of atomic nuclei can modify nuclear processes in\nstellar environments. In this work, we study the influence of nuclear\nexcitations on Urca cooling (repeated back-and-forth beta decay and electron\ncapture in a pair of nuclear isotopes) in the crust and ocean of neutron stars.\nWe provide for the first time an expression for Urca process neutrino\nluminosity which accounts for excited states of both members of an Urca pair.\nWe use our new formula with state-of-the-art nuclear structure inputs to\ncompute neutrino luminosities of candidate Urca cooling pairs. Our nuclear\ninputs consist of the latest experimental data supplemented with calculations\nusing the projected shell model. We show that, in contrast with previous\nresults that only consider the ground states of both nuclei in the pair, our\ncalculated neutrino luminosities for different Urca pairs vary sensitively with\nthe environment temperature and can be radically different from those obtained\nin the one transition approximation.\n"}
+{"abstract": "  We study the problem of stochastic bandits with adversarial corruptions in\nthe cooperative multi-agent setting, where $V$ agents interact with a common\n$K$-armed bandit problem, and each pair of agents can communicate with each\nother to expedite the learning process. In the problem, the rewards are\nindependently sampled from distributions across all agents and rounds, but they\nmay be corrupted by an adversary. Our goal is to minimize both the overall\nregret and communication cost across all agents. We first show that an additive\nterm of corruption is unavoidable for any algorithm in this problem. Then, we\npropose a new algorithm that is agnostic to the level of corruption. Our\nalgorithm not only achieves near-optimal regret in the stochastic setting, but\nalso obtains a regret with an additive term of corruption in the corrupted\nsetting, while maintaining efficient communication. The algorithm is also\napplicable for the single-agent corruption problem, and achieves a high\nprobability regret that removes the multiplicative dependence of $K$ on\ncorruption level. Our result of the single-agent case resolves an open question\nfrom Gupta et al. [2019].\n"}
+{"abstract": "  Mixtures of lipids and cholesterol are commonly used as model systems for\nstudying the formation of liquid-ordered ($L_o$) domains in heterogeneous\nbiological membranes. The simplest model system exhibiting coexistence between\n$L_o$ domains and a liquid-disordered ($L_d$) matrix is that of a binary\nmixture of saturated lipids like DPPC and cholesterol (Chol). DPPC/Chol\nmixtures have been investigated for decades both experimentally, theoretically,\nand recently also by means of atomistic simulations. Here, we present a minimal\nlattice model that captures the correct behavior of this mixture across\nmultiple scales. On the macroscopic scales, we present simulation results of\nmixtures of thousands of lipids and Chol molecules which show excellent\nagreement with the phase diagram of the system. The simulations are conducted\non timescales of hundreds of microseconds and show the morphologies and\ndynamics of the domains. On the molecular scales, the simulations reveal local\nstructures similar to those recently seen in atomistic simulations, including\nthe formation of gel-like nano-domains ($\\sim$ 1-10 nm) within larger Chol-rich\n$L_o$ domains ($\\sim$ 10-100 nm). The observed multi-scale behavior is related\nto the tendency of Chol to induce ordering of acyl chains on the one hand, and\ndisrupt their packing with each other, on the other hand.\n"}
+{"abstract": "  We approach the problem of hotel recognition with deep metric learning. We\noverview the existing approaches and propose a modification to Contrastive loss\ncalled Contrastive-Triplet loss. We construct a robust pipeline for\nbenchmarking metric learning models and perform experiments on Hotels-50K and\nCUB200 datasets. Contrastive-Triplet loss is shown to achieve better retrieval\non Hotels-50k. We open-source our code.\n"}
+{"abstract": "  Style transfer aims to render the content of a given image in the\ngraphical/artistic style of another image. The fundamental concept underlying\nNeuralStyle Transfer (NST) is to interpret style as a distribution in the\nfeature space of a Convolutional Neural Network, such that a desired style can\nbe achieved by matching its feature distribution. We show that most current\nimplementations of that concept have important theoretical and practical\nlimitations, as they only partially align the feature distributions. We propose\na novel approach that matches the distributions more precisely, thus\nreproducing the desired style more faithfully, while still being\ncomputationally efficient. Specifically, we adapt the dual form of Central\nMoment Discrepancy (CMD), as recently proposed for domain adaptation, to\nminimize the difference between the target style and the feature distribution\nof the output image. The dual interpretation of this metric explicitly matches\nall higher-order centralized moments and is therefore a natural extension of\nexisting NST methods that only take into account the first and second moments.\nOur experiments confirm that the strong theoretical properties also translate\nto visually better style transfer, and better disentangle style from semantic\nimage content.\n"}
+{"abstract": "  We investigate the ringdown waveform and reflectivity of a Lifshitz scalar\nfield around a fixed Schwarzschild black hole. The radial wave equation is\nmodified due to the Lorentz breaking terms, which leads to a diversity of\nringdown waveforms. Also, it turns out that Lifshitz waves scattered by the\nSchwarzschild black hole exhibits superradiance. The Lorentz breaking terms\nlead to superluminal propagation and high-frequency modes can enter and leave\nthe interior of the Killing horizon where negativity of energy is not\nprohibited. This allows the Lifshitz waves to carry out additional positive\nenergy to infinity while leaving negative energy inside the Killing horizon,\nsimilar to the Penrose process in the ergosphere of a Kerr spacetime. Another\ninteresting phenomenon is emergence of long-lived quasinormal modes, associated\nwith roton-type dispersion relations. These effects drastically modify the\ngreybody factor of a microscopic black hole, whose Hawking temperature is\ncomparable with or higher than the Lifshitz energy scale.\n"}
+{"abstract": "  In this paper, we prove a Poincar\\'e inequality for any volume-constraint\nlocal energy-minimizing sets, provided its singular set is of Hausdorff\ndimension at most $n-3$. With this inequality, we prove that the only\nvolume-constraint local energy-minimizing sets in the Euclidean unit ball,\nwhose singular set is closed and of Hausdorff dimension at most $n-3$, are\ntotally geodesic balls or spherical caps intersecting the unit sphere with\nconstant contact angle; for stable sets in a wedge-shaped domain or in a half\nspace, provided the same condition of the singular set, must be spherical. In\nparticular, they are smooth.\n"}
+{"abstract": "  The superiority of variational quantum algorithms (VQAs) such as quantum\nneural networks (QNNs) and variational quantum eigen-solvers (VQEs) heavily\ndepends on the expressivity of the employed ansatze. Namely, a simple ansatze\nis insufficient to capture the optimal solution, while an intricate ansatze\nleads to the hardness of the trainability. Despite its fundamental importance,\nan effective strategy of measuring the expressivity of VQAs remains largely\nunknown. Here, we exploit an advanced tool in statistical learning theory,\ni.e., covering number, to study the expressivity of VQAs. In particular, we\nfirst exhibit how the expressivity of VQAs with an arbitrary ansatze is upper\nbounded by the number of quantum gates and the measurement observable. We next\nexplore the expressivity of VQAs on near-term quantum chips, where the system\nnoise is considered. We observe an exponential decay of the expressivity with\nincreasing circuit depth. We also utilize the achieved expressivity to analyze\nthe generalization of QNNs and the accuracy of VQE. We numerically verify our\ntheory employing VQAs with different levels of expressivity. Our work opens the\navenue for quantitative understanding of the expressivity of VQAs.\n"}
+{"abstract": "  Checkpoint/restart (C/R) provides fault-tolerant computing capability,\nenables long running applications, and provides scheduling flexibility for\ncomputing centers to support diverse workloads with different priority. It is\ntherefore vital to get transparent C/R capability working at NERSC. MANA, by\nGarg et. al., is a transparent checkpointing tool that has been selected due to\nits MPI-agnostic and network-agnostic approach. However, originally written as\na proof-of-concept code, MANA was not ready to use with NERSC's diverse\nproduction workloads, which are dominated by MPI and hybrid MPI+OpenMP\napplications. In this talk, we present ongoing work at NERSC to enable MANA for\nNERSC's production workloads, including fixing bugs that were exposed by the\ntop applications at NERSC, adding new features to address system changes,\nevaluating C/R overhead at scale, etc. The lessons learned from making MANA\nproduction-ready for HPC applications will be useful for C/R tool developers,\nsupercomputing centers and HPC end-users alike.\n"}
+{"abstract": "  In this paper, we first establish a version of multidimensional analogues of\nthe refined Bohr's inequality. Then we establish two versions of\nmultidimensional analogues of improved Bohr's inequality with initial\ncoefficient being zero. Finally we establish two versions of multidimensional\nanalogues of improved Bohr's inequality with the initial coefficient being\nreplaced by absolute value of the function, and to prove that most of the\nresults are sharp.\n"}
+{"abstract": "  Cancer registries collect multisource data and provide valuable information\nthat can lead to unique research opportunities. In the Netherlands, a registry\nand model-based approach (MBA) are used for the selection of patients that are\neligible for proton therapy. We collected baseline characteristics including\ndemographic, clinical, tumour and treatment information. These data were\ntransformed into a machine readable format using the FAIR (Findable,\nAccessible, Interoperable, Reusable) data principles and resulted in a\nknowledge graph with baseline characteristics of proton therapy patients. With\nthis approach, we enable the possibility of linking external data sources and\noptimal flexibility to easily adapt the data structure of the existing\nknowledge graph to the needs of the clinic.\n"}
+{"abstract": "  There has recently been significant interest in training reinforcement\nlearning (RL) agents in vision-based environments. This poses many challenges,\nsuch as high dimensionality and the potential for observational overfitting\nthrough spurious correlations. A promising approach to solve both of these\nproblems is an attention bottleneck, which provides a simple and effective\nframework for learning high performing policies, even in the presence of\ndistractions. However, due to poor scalability of attention architectures,\nthese methods cannot be applied beyond low resolution visual inputs, using\nlarge patches (thus small attention matrices). In this paper we make use of new\nefficient attention algorithms, recently shown to be highly effective for\nTransformers, and demonstrate that these techniques can be successfully adopted\nfor the RL setting. This allows our attention-based controllers to scale to\nlarger visual inputs, and facilitate the use of smaller patches, even\nindividual pixels, improving generalization. We show this on a range of tasks\nfrom the Distracting Control Suite to vision-based quadruped robots locomotion.\nWe provide rigorous theoretical analysis of the proposed algorithm.\n"}
+{"abstract": "  Privacy preservation remains a key challenge in data mining and Natural\nLanguage Understanding (NLU). Previous research shows that the input text or\neven text embeddings can leak private information. This concern motivates our\nresearch on effective privacy preservation approaches for pretrained Language\nModels (LMs). We investigate the privacy and utility implications of applying\ndx-privacy, a variant of Local Differential Privacy, to BERT fine-tuning in NLU\napplications. More importantly, we further propose privacy-adaptive LM\npretraining methods and show that our approach can boost the utility of BERT\ndramatically while retaining the same level of privacy protection. We also\nquantify the level of privacy preservation and provide guidance on privacy\nconfiguration. Our experiments and findings lay the groundwork for future\nexplorations of privacy-preserving NLU with pretrained LMs.\n"}
+{"abstract": "  Classical main-sequence chemically peculiar stars show light variability that\noriginates in surface abundance spots. In the spots, the flux redistribution\ndue to line (bound-bound) and bound-free transitions is modulated by stellar\nrotation and leads to light variability. White dwarfs and hot subdwarfs may\nalso have surface abundance spots either owing to the elemental diffusion or as\na result of accretion of debris. We model the light variability of typical\nwhite dwarfs and hot subdwarfs that results from putative surface abundance\nspots. We show that the spots with radiatively supported iron overabundance may\ncause observable light variability of hot white dwarfs and subdwarfs. Accretion\nof debris material may lead to detectable light variability in warm white\ndwarfs. We apply our model to the helium star HD 144941 and conclude that the\nspot model is able to explain most of observed light variations of this star.\n"}
+{"abstract": "  We propose a new model, DocHopper, that iteratively attends to different\nparts of long, hierarchically structured documents to answer complex questions.\nSimilar to multi-hop question-answering (QA) systems, at each step, DocHopper\nuses a query $q$ to attend to information from a document, combines this\n``retrieved'' information with $q$ to produce the next query. However, in\ncontrast to most previous multi-hop QA systems, DocHopper is able to\n``retrieve'' either short passages or long sections of the document, thus\nemulating a multi-step process of ``navigating'' through a long document to\nanswer a question. To enable this novel behavior, DocHopper does not combine\ndocument information with $q$ by concatenating text to the text of $q$, but by\ncombining a compact neural representation of $q$ with a compact neural\nrepresentation of a hierarchical part of the document, which can potentially be\nquite large. We experiment with DocHopper on four different QA tasks that\nrequire reading long and complex documents to answer multi-hop questions, and\nshow that DocHopper achieves state-of-the-art results on three of the datasets.\nAdditionally, DocHopper is efficient at inference time, being 3--10 times\nfaster than the baselines.\n"}
+{"abstract": "  Streaming 360{\\deg} video demands high bandwidth and low latency, and poses\nsignificant challenges to Internet Service Providers (ISPs) and Mobile Network\nOperators (MNOs). The identification of 360{\\deg} video traffic can therefore\nbenefits fixed and mobile carriers to optimize their network and provide better\nQuality of Experience (QoE) to the user. However, end-to-end encryption of\nnetwork traffic has obstructed identifying those 360{\\deg} videos from regular\nvideos. As a solution this paper presents 360NorVic, a near-realtime and\noffline Machine Learning (ML) classification engine to distinguish 360{\\deg}\nvideos from regular videos when streamed from mobile devices. We collect packet\nand flow level data for over 800 video traces from YouTube & Facebook\naccounting for 200 unique videos under varying streaming conditions. Our\nresults show that for near-realtime and offline classification at packet level,\naverage accuracy exceeds 95%, and that for flow level, 360NorVic achieves more\nthan 92% average accuracy. Finally, we pilot our solution in the commercial\nnetwork of a large MNO showing the feasibility and effectiveness of 360NorVic\nin production settings.\n"}
+{"abstract": "  It is a well-known fact that the non-pluripolar self-products of a closed\npositive (1,1)-current in a big nef cohomology class on a compact Kahler\nmanifold are not of full mass in the presence of positive Lelong numbers of the\ncurrent in consideration. In this paper, we give a quantitative version of the\nlast property. Our proof involves a generalization of Demailly's comparison of\nLelong numbers to the setting of density currents, a reversed\nAlexandrov-Fenchel inequality, and the notion of relative non-pluripolar\nproducts.\n"}
+{"abstract": "  We present a novel redirected walking controller based on alignment that\nallows the user to explore large and complex virtual environments, while\nminimizing the number of collisions with obstacles in the physical environment.\nOur alignment-based redirection controller, ARC, steers the user such that\ntheir proximity to obstacles in the physical environment matches the proximity\nto obstacles in the virtual environment as closely as possible. To quantify a\ncontroller's performance in complex environments, we introduce a new metric,\nComplexity Ratio (CR), to measure the relative environment complexity and\ncharacterize the difference in navigational complexity between the physical and\nvirtual environments. Through extensive simulation-based experiments, we show\nthat ARC significantly outperforms current state-of-the-art controllers in its\nability to steer the user on a collision-free path. We also show through\nquantitative and qualitative measures of performance that our controller is\nrobust in complex environments with many obstacles. Our method is applicable to\narbitrary environments and operates without any user input or parameter\ntweaking, aside from the layout of the environments. We have implemented our\nalgorithm on the Oculus Quest head-mounted display and evaluated its\nperformance in environments with varying complexity. Our project website is\navailable at https://gamma.umd.edu/arc/.\n"}
+{"abstract": "  I introduce a modification of continuous matrix product states (CMPS) that\nmakes them adapted to relativistic quantum field theories (QFT). These\nrelativistic CMPS can be used to solve genuine 1+1 dimensional QFT without UV\ncutoff and directly in the thermodynamic limit. The main idea is to work\ndirectly in the basis that diagonalizes the free part of the model considered,\nwhich allows to fit its short distance behavior exactly. This makes\ncomputations slightly less trivial than with standard CMPS. However, they\nremain feasible and I present all the steps needed for the optimization. The\nasymptotic cost as a function of the bond dimension remains the same as for\nstandard CMPS. I illustrate the method on the self-interacting scalar field,\na.k.a. the $\\phi^4_2$ model. Aside from providing unequaled precision in the\ncontinuum, the numerical results obtained are truly variational, and thus\nprovide rigorous energy upper bounds.\n"}
+{"abstract": "  Relatively dominated representations give a common generalization of\ngeometrically finiteness in rank one on the one hand, and the Anosov condition\nwhich serves as a higher-rank analogue of convex cocompactness on the other.\nThis note proves three results about these representations.\n  Firstly, we remove the quadratic gaps assumption involved in the original\ndefinition. Secondly, we give a characterization using eigenvalue gaps,\nproviding a relative analogue of a result of Kassel--Potrie for Anosov\nrepresentations. Thirdly, we formulate characterizations in terms of singular\nvalue or eigenvalue gaps combined with limit maps, in the spirit of\nGu\\'eritaud--Guichard--Kassel--Wienhard for Anosov representations, and use\nthem to show that inclusion representations of certain groups playing weak\nping-pong and positive representations in the sense of Fock--Goncharov are\nrelatively dominated.\n"}
+{"abstract": "  I summarize recent developments in electroweak precision physics and global\nfits. Expectations for future measurements, both at lower energies and the\nenergy frontier, are also discussed.\n"}
+{"abstract": "  Metal halide perovskite semiconductors have outstanding optoelectronic\nproperties. Although these perovskites are defect-tolerant electronically,\ndefects hamper their long-term stability and cause degradation. Density\nfunctional theory (DFT) calculations are an important tool to unravel the\nmicroscopic structures of defects, but results suffer from the different\napproximations used in the DFT functionals. In the case of metal halide\nperovskites, qualitatively different results have been reported with different\nfunctionals, either predicting vacancy or interstitial point defects to be most\ndominant. Here, we conduct a comprehensive comparison of a wide range of\nfunctionals for calculating the equilibrium defect formation energies and\nconcentrations of point defects in the archetype metal halide perovskite,\nMAPbI$_3$. We find that it is essential to include long-range Van der Waals\ninteractions in the functional, and that it is vital to self-consistently\noptimize structure and volume of all compounds involved in the defect\nformation. For calculating equilibrium formation energies of point defects in\nMAPbI$_3$ and similar metal halide perovskites, we argue that the exact values\nof the chemical potentials of the species involved, or of the intrinsic Fermi\nlevel, are not important. In contrast to the simple Schottky or Frenkel\npictures, we find that the dominant defects are MA and I interstitials, and Pb\nvacancies.\n"}
+{"abstract": "  We cross-match the Alma catalog of OB stars with Gaia DR2 astrometry and\nphotometry as a first step towards producing a clean sample of massive stars in\nthe solar neighbourhood with a high degree of completeness. We analyze the\nresulting colour-absolute magnitude diagram to divide our sample into\ncategories and compare extinction estimates from two sources, finding problems\nwith both of them. The distances obtained with three different priors are found\nto have few differences among them, indicating that Gaia DR2 distances are\nrobust. An analysis of the 3-D distribution of massive stars in the solar\nneighbourhood is presented. We show that a kinematically distinct structure we\ndub the Cepheus spur extends from the Orion-Cygnus spiral arm towards the\nPerseus arm and is located above the Galactic mid-plane, likely being related\nto the recently discovered Radcliffe wave. We propose that this corrugation\npattern in the Galactic disk may be responsible for the recent enhanced star\nformation at its crests and troughs. We also discuss our plans to extend this\nwork in the immediate future.\n"}
+{"abstract": "  A pandemic caused by a new coronavirus (COVID-19) has spread worldwide,\ninducing an epidemic still active in Argentina. In this chapter, we present a\ncase study using an SEIR (Susceptible-Exposed-Infected-Recovered) diffusion\nmodel of fractional order in time to analyze the evolution of the epidemic in\nBuenos Aires and neighboring areas (Regi\\'on Metropolitana de Buenos Aires,\n(RMBA)) comprising about 15 million inhabitants. In the SEIR model, individuals\nare divided into four classes, namely, susceptible (S), exposed (E), infected\n(I) and recovered (R). The SEIR model of fractional order allows for the\nincorporation of memory, with hereditary properties of the system, being a\ngeneralization of the classic SEIR first-order system, where such effects are\nignored. Furthermore, the fractional model provides one additional parameter to\nobtain a better fit of the data. The parameters of the model are calibrated by\nusing as data the number of casualties officially reported. Since infinite\nsolutions honour the data, we show a set of cases with different values of the\nlockdown parameters, fatality rate, and incubation and infectious periods. The\ndifferent reproduction ratios R0 and infection fatality rates (IFR) so obtained\nindicate the results may differ from recent reported values, constituting\npossible alternative solutions. A comparison with results obtained with the\nclassic SEIR model is also included. The analysis allows us to study how\nisolation and social distancing measures affect the time evolution of the\nepidemic.\n"}
+{"abstract": "  Lazy search algorithms have been developed to efficiently solve planning\nproblems in domains where the computational effort is dominated by the cost of\nedge evaluation. The existing algorithms operate by intelligently balancing\ncomputational effort between searching the graph and evaluating edges. However,\nthey are designed to run as a single process and do not leverage the\nmultithreading capability of modern processors. In this work, we propose a\nmassively parallelized, bounded suboptimal, lazy search algorithm (MPLP) that\nharnesses modern multi-core processors. In MPLP, searching of the graph and\nedge evaluations are performed completely asynchronously in parallel, leading\nto a drastic improvement in planning time. We validate the proposed algorithm\nin two different planning domains: 1) motion planning for 3D humanoid\nnavigation and 2) task and motion planning for a robotic assembly task. We show\nthat MPLP outperforms the state of the art lazy search as well as parallel\nsearch algorithms.\n"}
+{"abstract": "  We introduce polynomial couplings, a generalization of probabilistic\ncouplings, to develop an algorithm for the computation of equivalence relations\nwhich can be interpreted as a lifting of probabilistic bisimulation to\npolynomial differential equations, a ubiquitous model of dynamical systems\nacross science and engineering. The algorithm enjoys polynomial time complexity\nand complements classical partition-refinement approaches because: (a) it\nimplements a local exploration of the system, possibly yielding equivalences\nthat do not necessarily involve the inspection of the whole system of\ndifferential equations; (b) it can be enhanced by up-to techniques; and (c) it\nallows the specification of pairs which ought not to be included in the output.\nUsing a prototype, these advantages are demonstrated on case studies from\nsystems biology for applications to model reduction and comparison. Notably, we\nreport four orders of magnitude smaller runtimes than partition-refinement\napproaches when disproving equivalences between Markov chains.\n"}
+{"abstract": "  Chalcogenide perovskites have emerged as a new class of electronic materials,\nbut fundamental properties and applications of chalcogenide perovskites remain\nlimited by the lack of high quality epitaxial thin films. We report epitaxial\nthin film growth of BaZrS3, a prototypical chalcogenide, by pulsed laser\ndeposition. X-ray diffraction studies show that the films are strongly textured\nout of plane and have a clear in-plane epitaxial relationship with the\nsubstrate. Electron microscopy studies confirm the presence of epitaxy for the\nfirst few layers of the film at the interface, even though away from the\ninterface the films are polycrystalline with a large number of extended defects\nsuggesting the potential for further improvement in growth. X-Ray reflectivity\nand atomic force microscopy show smooth film surfaces and interfaces between\nthe substrate and the film. The films show strong light absorption near the\nband edge and photoluminescence in the visible region. The photodetector\ndevices show fast and efficient photo response with the highest ON/OFF ratio\nreported for BaZrS3 films thus far. Our study opens up opportunities to realize\nepitaxial thin films, heterostructures, and superlattices of chalcogenide\nperovskites to probe fundamental physical phenomena and the resultant\nelectronic and photonic device technologies.\n"}
+{"abstract": "  Using a high degree of parallelism is essential to perform data assimilation\nefficiently. The state formulation of the incremental weak constraint\nfour-dimensional variational data assimilation method allows parallel\ncalculations in the time dimension. In this approach, the solution is\napproximated by minimising a series of quadratic cost functions using the\nconjugate gradient method. To use this method in practice, effective\npreconditioning strategies that maintain the potential for parallel\ncalculations are needed. We examine approximations to the control variable\ntransform (CVT) technique when the latter is beneficial. The new strategy\nemploys a randomised singular value decomposition and retains the potential for\nparallelism in the time domain. Numerical results for the Lorenz 96 model show\nthat this approach accelerates the minimisation in the first few iterations,\nwith better results when CVT performs well.\n"}
+{"abstract": "  We carry out a multi-probe self-consistency test of the flat $\\Lambda$CDM\nmodel with the aim of exploring potential causes of the reported tensions\nbetween high- and low-redshift cosmological observations. We divide the model\ninto two theory regimes determined by the smooth background (geometry) and the\nevolution of matter density fluctuations (growth), each governed by an\nindependent set of Lambda Cold Dark Matter ($\\Lambda$CDM) cosmological\nparameters. This extended model is constrained by a combination of weak\ngravitational lensing measurements from the Kilo-Degree Survey, galaxy\nclustering signatures extracted from Sloan Digital Sky Survey campaigns and the\nSix-Degree Field Galaxy Survey, and the angular baryon acoustic scale and the\nprimordial scalar fluctuation power spectrum measured in $\\textit{Planck}$\ncosmic microwave background (CMB) data. We find strong consistency between the\ngeometry and growth parameters, and with the posterior of standard $\\Lambda$CDM\nanalysis. Tension in the amplitude of matter density fluctuations as measured\nby the parameter $S_8$ persists at around 3$\\sigma$, with a $1.5\\,\\%$\nconstraint of $S_8 = 0.776_{-0.008}^{+0.016}$ for the combined probes. We also\nobserve a less significant preference (at least $2\\sigma$) for higher values of\nthe Hubble constant, $H_0 = 70.5^{+0.7}_{-1.5}\\,{\\rm km\\, s^{-1} Mpc^{-1}}$, as\nwell as for lower values of the total matter density parameter $\\Omega_{\\rm{m}}\n= 0.289^{+0.007}_{-0.005}$ compared to the full $\\textit{Planck}$ analysis.\nIncluding the subset of the CMB information in the probe combination enhances\nthese differences rather than alleviate them, which we link to the discrepancy\nbetween low and high multipoles in $\\textit{Planck}$ data.\n"}
+{"abstract": "  In the previous four decades, a two-coil mutual inductance (MI) technique has\nbeen widely employed in characterizing magnetic penetration depth, $\\lambda$,\nof superconducting films. However, the conventional methods used to obtain\n$\\lambda$ are not applicable to small-sized films with common shapes, which\nlimits the application of the MI technique in superconductivity research. Here,\nwe first employed the fast wavelet collocation (FWC) method to a two-coil\nsystem and then proposed the possibility of directly obtaining the absolute\n$\\lambda$ of polygonal superconducting films with arbitrary sizes. To verify\nits accuracy and advantages, we extracted the $\\lambda$ values of square NbN\nfilms with different sizes using the FWC and conventional flux leakage\nsubtraction (FLS) methods. Notably, the FLS method fails for a $5\\times 5 \\,\n\\rm mm^2$ film, which is attributed to the significant current peak at the film\nedge. In contrast, the absolute $\\lambda$ extracted using the FWC method was\nindependent of the film size. Finally, we established the applicability of the\nFWC method to large coil spacings, which may pave the way for integrating\nhigh-accuracy $\\lambda$ measurements with the ionic liquid gating technique.\n"}
+{"abstract": "  Automated adversary emulation is becoming an indispensable tool of network\nsecurity operators in testing and evaluating their cyber defenses. At the same\ntime, it has exposed how quickly adversaries can propagate through the network.\nWhile research has greatly progressed on quality decoy generation to fool human\nadversaries, we may need different strategies to slow computer agents. In this\npaper, we show that decoy generation can slow an automated agent's decision\nprocess, but that the degree to which it is inhibited is greatly dependent on\nthe types of objects used. This points to the need to explicitly evaluate decoy\ngeneration and placement strategies against fast moving, automated adversaries.\n"}
+{"abstract": "  A version of Connes Integration Formula which provides concrete asymptotics\nof the eigenvalues is given. This radically extending the class of\nquantum-integrable functions on compact Riemannian manifolds.\n"}
+{"abstract": "  Batteries have been identified as one most likely small UAS (sUAS) components\nto fail in flight. sUAS safety will therefore be improved with redundant or\nbackup batteries. This paper presents a prognostics-informed Markov Decision\nProcess (MDP) model for managing multi-battery reconfiguration for sUAS\nmissions. Typical lithium polymer (Lipo) battery properties are experimentally\ncharacterized and used in Monte Carlo simulations to establish battery dynamics\nin sUAS flights of varying duration. Case studies illustrate the trade off\nbetween multi-battery system increased complexity/weight and resilience to\nnon-ideal battery performance.\n"}
+{"abstract": "  In this work, we introduce a novel, end-to-end trainable CNN-based\narchitecture to deliver high quality results for grasp detection suitable for a\nparallel-plate gripper, and semantic segmentation. Utilizing this, we propose a\nnovel refinement module that takes advantage of previously calculated grasp\ndetection and semantic segmentation and further increases grasp detection\naccuracy. Our proposed network delivers state-of-the-art accuracy on two\npopular grasp dataset, namely Cornell and Jacquard. As additional contribution,\nwe provide a novel dataset extension for the OCID dataset, making it possible\nto evaluate grasp detection in highly challenging scenes. Using this dataset,\nwe show that semantic segmentation can additionally be used to assign grasp\ncandidates to object classes, which can be used to pick specific objects in the\nscene.\n"}
+{"abstract": "  Increasing number in global COVID-19 cases demands for mathematical model to\nanalyze the interaction between the virus dynamics and the response of innate\nand adaptive immunity. Here, based on the assumption of a weak and delayed\nresponse of the innate and adaptive immunity in SARS-CoV-2 infection, we\nconstructed a mathematical model to describe the dynamic processes of immune\nsystem. Integrating theoretical results with clinical COVID-19 patients' data,\nwe classified the COVID-19 development processes into three typical modes of\nimmune responses, correlated with the clinical classification of mild &\nmoderate, severe and critical patients. We found that the immune efficacy (the\nability of host to clear virus and kill infected cells) and the lymphocyte\nsupply (the abundance and pool of na\\\"ive T and B cell) play important roles in\nthe dynamic process and determine the clinical outcome, especially for the\nsevere and critical patients. Furthermore, we put forward possible treatment\nstrategies for the three typical modes of immune response. We hope our results\ncan help to understand the dynamical mechanism of the immune response against\nSARS-CoV-2 infection, and to be useful for the treatment strategies and vaccine\ndesign.\n"}
+{"abstract": "  We study the interplay between Mott physics, driven by Coulomb repulsion U,\nand Hund physics, driven by Hund's coupling J, for a minimal model for Hund\nmetals, the orbital-symmetric three-band Hubbard-Hund model (3HHM) for a\nlattice filling of 1/3. Hund-correlated metals are characterized by\nspin-orbital separation (SOS), a Hund's-rule-induced two-stage Kondo-type\nscreening process, in which spin screening occurs at much lower energy scales\nthan orbital screening. By contrast, in Mott-correlated metals, lying close to\nthe phase boundary of a metal-insulator transition, the SOS window becomes\nnegligibly small and the Hubbard bands are well separated. Using dynamical\nmean-field theory and the numerical renormalization group as real-frequency\nimpurity solver, we identify numerous fingerprints distinguishing Hundness from\nMottness in the temperature dependence of various physical quantities. These\ninclude ARPES-type spectra, the local self-energy, static local orbital and\nspin susceptibilities, resistivity, thermopower, and lattice and impurity\nentropies. Our detailed description of the behavior of these quantities within\nthe context of a simple model Hamiltonian will be helpful for distinguishing\nHundness from Mottness in experimental and theoretical studies of real\nmaterials.\n"}
+{"abstract": "  The classical Cram\\'er-Lundberg risk process models the ruin probability of\nan insurance company experiencing an incoming cash flow - the premium income,\nand an outgoing cash flow - the claims. From a system's viewpoint, the web of\ninsurance agents and risk objects can be represented by a bipartite network. In\nsuch a bipartite network setting, it has been shown that joint ruin of a group\nof agents may be avoided even if individual agents would experience ruin in the\nclassical Cram\\'er-Lundberg model. This paper describes and examines a phase\ntransition phenomenon for these ruin probabilities.\n"}
+{"abstract": "  In recent years, the use of lithium-ion batteries has greatly expanded into\nproducts from many industrial sectors, e.g. cars, power tools or medical\ndevices. An early prediction and robust understanding of battery faults could\ntherefore greatly increase product quality in those fields. While current\napproaches for data-driven fault prediction provide good results on the exact\nprocesses they were trained on, they often lack the ability to flexibly adapt\nto changes, e.g. in operational or environmental parameters. Continual learning\npromises such flexibility, allowing for an automatic adaption of previously\nlearnt knowledge to new tasks. Therefore, this article discusses different\ncontinual learning approaches from the group of regularization strategies,\nwhich are implemented, evaluated and compared based on a real battery wear\ndataset. Online elastic weight consolidation delivers the best results, but, as\nwith all examined approaches, its performance appears to be strongly dependent\non task characteristics and task sequence.\n"}
+{"abstract": "  Deployed into an open world, object detectors are prone to open-set errors,\nfalse positive detections of object classes not present in the training\ndataset. We propose GMM-Det, a real-time method for extracting epistemic\nuncertainty from object detectors to identify and reject open-set errors.\nGMM-Det trains the detector to produce a structured logit space that is\nmodelled with class-specific Gaussian Mixture Models. At test time, open-set\nerrors are identified by their low log-probability under all Gaussian Mixture\nModels. We test two common detector architectures, Faster R-CNN and RetinaNet,\nacross three varied datasets spanning robotics and computer vision. Our results\nshow that GMM-Det consistently outperforms existing uncertainty techniques for\nidentifying and rejecting open-set detections, especially at the low-error-rate\noperating point required for safety-critical applications. GMM-Det maintains\nobject detection performance, and introduces only minimal computational\noverhead. We also introduce a methodology for converting existing object\ndetection datasets into specific open-set datasets to evaluate open-set\nperformance in object detection.\n"}
+{"abstract": "  The hair and beauty industry is a fast-growing industry. This led to the\ndevelopment of various applications, such as virtual hair dyeing or hairstyle\ntransfer, to satisfy the customer's needs. Although several hairstyle datasets\nare available for these applications, they often consist of a relatively small\nnumber of images with low resolution, thus limiting their performance on\nhigh-quality hair editing. In response, we introduce a novel large-scale Korean\nhairstyle dataset, K-hairstyle, containing 500,000 high-resolution images. In\naddition, K-hairstyle includes various hair attributes annotated by Korean\nexpert hairstylists as well as hair segmentation masks. We validate the\neffectiveness of our dataset via several applications, such as hair dyeing,\nhairstyle transfer, and hairstyle classification. K-hairstyle is publicly\navailable at https://psh01087.github.io/K-Hairstyle/.\n"}
+{"abstract": "  Understanding the role of time-varying pollution mixtures on human health is\ncritical as people are simultaneously exposed to multiple pollutants during\ntheir lives. For vulnerable sub-populations who have well-defined exposure\nperiods (e.g., pregnant women), questions regarding critical windows of\nexposure to these mixtures are important for mitigating harm. We extend\nCritical Window Variable Selection (CWVS) to the multipollutant setting by\nintroducing CWVS for Mixtures (CWVSmix), a hierarchical Bayesian method that\ncombines smoothed variable selection and temporally correlated weight\nparameters to (i) identify critical windows of exposure to mixtures of\ntime-varying pollutants, (ii) estimate the time-varying relative importance of\neach individual pollutant and their first order interactions within the\nmixture, and (iii) quantify the impact of the mixtures on health. Through\nsimulation, we show that CWVSmix offers the best balance of performance in each\nof these categories in comparison to competing methods. Using these approaches,\nwe investigate the impact of exposure to multiple ambient air pollutants on the\nrisk of stillbirth in New Jersey, 2005-2014. We find consistent elevated risk\nin gestational weeks 2, 16-17, and 20 for non-Hispanic Black mothers, with\npollution mixtures dominated by ammonium (weeks 2, 17, 20), nitrate (weeks 2,\n17), nitrogen oxides (weeks 2, 16), PM2.5 (week 2), and sulfate (week 20). The\nmethod is available in the R package CWVSmix.\n"}
+{"abstract": "  In this letter, we propose a blockwise phase rotation-aided analog transmit\nbeamforming (BPR-ATB) scheme to improve the spectral efficiency and the\nbit-error-rate (BER) performance in millimeter wave (mmWave) communication\nsystems. Due to the phase angle optimization issues of the conventional analog\nbeamforming, we design the BPR-ATB for reducing the rotated beamspace of the\nequivalent channel and improving the minimum Euclidean distance. To verify the\neffectiveness of the proposed BPR-ATB scheme, we employ an Alamouti coding\ntechnique at the transmitter and evaluate the bit-error-rate performance for\nmmWave multiple-input and single-output systems. The simulation results show\nthat the proposed BPR-ATB scheme outperforms the conventional discrete Fourier\ntransform-based ATB scheme.\n"}
+{"abstract": "  We identify the large-$N$ scaling of the metrological quantum gain offered by\nover-squeezed spin states that are accessible by one-axis-twisting, as a\nfunction of the preparation time. We further determine how the scaling is\nmodified by relevant decoherence processes and predict a discontinuous change\nof the quantum gain at a critical preparation time that depends on the noise.\nOur analytical results provide recipes for optimal and feasible implementations\nof quantum enhancements with non-Gaussian spin states in existing experiments,\nwell beyond the reach of spin squeezing.\n"}
+{"abstract": "  Graph self-supervised learning has gained increasing attention due to its\ncapacity to learn expressive node representations. Many pretext tasks, or loss\nfunctions have been designed from distinct perspectives. However, we observe\nthat different pretext tasks affect downstream tasks differently cross\ndatasets, which suggests that searching pretext tasks is crucial for graph\nself-supervised learning. Different from existing works focusing on designing\nsingle pretext tasks, this work aims to investigate how to automatically\nleverage multiple pretext tasks effectively. Nevertheless, evaluating\nrepresentations derived from multiple pretext tasks without direct access to\nground truth labels makes this problem challenging. To address this obstacle,\nwe make use of a key principle of many real-world graphs, i.e., homophily, or\nthe principle that ``like attracts like,'' as the guidance to effectively\nsearch various self-supervised pretext tasks. We provide theoretical\nunderstanding and empirical evidence to justify the flexibility of homophily in\nthis search task. Then we propose the AutoSSL framework which can automatically\nsearch over combinations of various self-supervised tasks. By evaluating the\nframework on 7 real-world datasets, our experimental results show that AutoSSL\ncan significantly boost the performance on downstream tasks including node\nclustering and node classification compared with training under individual\ntasks. Code will be released at https://github.com/ChandlerBang/AutoSSL.\n"}
+{"abstract": "  We investigate a new algebraic structure which always gives rise to a\nset-theoretic solution of the Yang-Baxter equation. Specifically, a skew (left)\ninverse semi-brace is a non-empty set $S$ endowed with two binary operations\n$+$ and $\\circ$ such that both $(S,+)$ and $(S, \\circ)$ are inverse semigroups\nand they hold\n  \\begin{align*}\n  a \\circ \\left(b+c\\right) = a\\circ b - a +a\\circ c \\qquad \\text{and} \\qquad\n  a\\circ a^- = - a + a,\n  \\end{align*}\n  for all $a,b,c \\in S$, where $-a$ and $a^-$ are the inverses of $a$ with\nrespect to $+$ and $\\circ$, respectively. In particular, such structures\ninclude that of skew braces and form a subclass of inverse semi-braces. Any\nsolution $r$ associated to an arbitrary skew inverse semi-brace $S$ has a\nbehavior close to bijectivity, namely $r$ is a completely regular element in\nthe full transformation semigroup on $S\\times S$. In addition, we provide some\nmethods to construct skew inverse semi-braces.\n"}
+{"abstract": "  We study several fairness notions in allocating indivisible chores (i.e.,\nitems with non-positive values) to agents who have additive and submodular cost\nfunctions. The fairness criteria we are concern with are envy-free up to any\nitem (EFX), envy-free up to one item (EF1), maximin share (MMS), and pairwise\nmaximin share (PMMS), which are proposed as relaxations of envy-freeness in the\nsetting of additive cost functions. For allocations under each fairness\ncriterion, we establish their approximation guarantee for other fairness\ncriteria. Under the additive setting, our results show strong connections\nbetween these fairness criteria and, at the same time, reveal intrinsic\ndifferences between goods allocation and chores allocation. However, such\nstrong relationships cannot be inherited by the submodular setting, under which\nPMMS and MMS are no longer relaxations of envy-freeness and, even worse, few\nnon-trivial guarantees exist. We also investigate efficiency loss under these\nfairness constraints and establish their prices of fairness.\n"}
+{"abstract": "  It is not hard to see that the need for clean water is growing by considering\nthe decrease of the water sources day by day in the world. Potable fresh water\nis also used for irrigation, so it should be planned to decrease freshwater\nwastage. With the development of technology and the availability of cheaper and\nmore effective solutions, the efficiency of irrigation increased and the water\nloss can be reduced. In particular, Internet of things (IoT) devices has begun\nto be used in all areas. We can easily and precisely collect temperature,\nhumidity and mineral values from the irrigation field with the IoT devices and\nsensors. Most of the operations and decisions about irrigation are carried out\nby people. For people, it is hard to have all the real-time data such as\ntemperature, moisture and mineral levels in the decision-making process and\nmake decisions by considering them. People usually make decisions with their\nexperience. In this study, a wide range of information from the irrigation\nfield was obtained by using IoT devices and sensors. Data collected from IoT\ndevices and sensors sent via communication channels and stored on MongoDB. With\nthe help of Weka software, the data was normalized and the normalized data was\nused as a learning set. As a result of the examinations, a decision tree (J48)\nalgorithm with the highest accuracy was chosen and an artificial intelligence\nmodel was created. Decisions are used to manage operations such as starting,\nmaintaining and stopping the irrigation. The accuracy of the decisions was\nevaluated and the irrigation system was tested with the results. There are\noptions to manage, view the system remotely and manually and also see the\nsystem s decisions with the created mobile application.\n"}
+{"abstract": "  Randomized Smoothing (RS) is a promising method for obtaining robustness\ncertificates by evaluating a base model under noise. In this work, we: (i)\ntheoretically motivate why ensembles are a particularly suitable choice as base\nmodels for RS, and (ii) empirically confirm this choice, obtaining\nstate-of-the-art results in multiple settings. The key insight of our work is\nthat the reduced variance of ensembles over the perturbations introduced in RS\nleads to significantly more consistent classifications for a given input. This,\nin turn, leads to substantially increased certifiable radii for samples close\nto the decision boundary. Additionally, we introduce key optimizations which\nenable an up to 55-fold decrease in sample complexity of RS, thus drastically\nreducing its computational overhead. Experimentally, we show that ensembles of\nonly 3 to 10 classifiers consistently improve on their strongest constituting\nmodel with respect to their average certified radius (ACR) by 5% to 21% on both\nCIFAR10 and ImageNet, achieving a new state-of-the-art ACR of 0.86 and 1.11,\nrespectively. We release all code and models required to reproduce our results\nupon publication.\n"}
+{"abstract": "  The possibility of suddenly ionized molecules undergoing extremely fast\nelectron hole dynamics prior to significant structural change was first\nrecognized more than 20 years ago and termed charge migration. The accurate\nprobing of ultrafast electron hole dynamics requires measurements that have\nboth sufficient temporal resolution and can detect the localization of a\nspecific hole within the molecule. We report an investigation of the dynamics\nof inner valence hole states in isopropanol where we use an x-ray pump/x-ray\nprobe experiment, with site and state-specific probing of a transient hole\nstate localized near the oxygen atom in the molecule, together with an ab\ninitio theoretical treatment. We record the signature of transient hole\ndynamics and make the first observation of dynamics driven by frustrated\nAuger-Meitner transitions. We verify that the hole lifetime is consistent with\nour theoretical prediction. This state-specific measurement paves the way to\nwidespread application for observations of transient hole dynamics localized in\nspace and time in molecules and thus to charge transfer phenomena that are\nfundamental in chemical and material physics.\n"}
+{"abstract": "  A significant fraction of baryons in galaxies are in the form of diffuse gas\nof the circumgalactic medium (CGM). One critical component of the multi-phases\nof CGM, the so-called \"coronal\" warm-hot phase gas ($\\rm 10^{5}-10^{6}$ K)\ntraced by O VI 1031.93, 1037.62 \\r{A} resonance lines, has rarely been detected\nin emission from galaxy halos other than Milky Way. Here we report four\nadditional detections of O VI emission gas in the halos of nearby edge-on\ngalaxies, NGC 4631 and NGC 891, using archival Far Ultraviolet Spectroscopic\nExplorer data and an updated data pipeline. We find the most intense O VI\nemission to be from fields forming a vertical line near the center of NGC 4631,\ndespite the close proximity to the disk of two other fields. The detected O VI\nemission surface brightness are about 1.1$\\pm 0.3$ $\\times$ $10^{-18}$ to\n3.9$\\pm0.8$ $\\times$ $10^{-18}$ ergs s$^{-1}$ cm$^{-2}$ arcsec$^{-2}$. The\nspatial distribution of the five 30\" $\\times$ 30\" O VI detection fields in NGC\n4631 can be interpreted as the existence of filamentary structures of more\nintense O VI emission superimposed within a diffuse and faint O VI halo in\nstar-forming galaxies. Volume-filled O VI emission mapping is greatly needed to\ndetermine the structure and prevalence of warm-hot gas and the role it plays in\nthe cycling of gas between the galaxy disk and the halo. Finally, we present\nthe sensitivity of future funded and proposed UV missions (LUVOIR-A, LUVOIR-B,\nCETUS, and Aspera) to the detection of diffuse and faint O VI emission in\nnearby galaxy halos.\n"}
+{"abstract": "  Data augmentation is commonly used to help build a robust speaker\nverification system, especially in limited-resource case. However, conventional\ndata augmentation methods usually focus on the diversity of acoustic\nenvironment, leaving the lexicon variation neglected. For text dependent\nspeaker verification tasks, it's well-known that preparing training data with\nthe target transcript is the most effectual approach to build a well-performing\nsystem, however collecting such data is time-consuming and expensive. In this\nwork, we propose a unit selection synthesis based data augmentation method to\nleverage the abundant text-independent data resources. In this approach\ntext-independent speeches of each speaker are firstly broke up to speech\nsegments each contains one phone unit. Then segments that contain phonetics in\nthe target transcript are selected to produce a speech with the target\ntranscript by concatenating them in turn. Experiments are carried out on the\nAISHELL Speaker Verification Challenge 2019 database, the results and analysis\nshows that our proposed method can boost the system performance significantly.\n"}
+{"abstract": "  Low-light image enhancement (LLIE) aims at improving the perception or\ninterpretability of an image captured in an environment with poor illumination.\nRecent advances in this area are dominated by deep learning-based solutions,\nwhere many learning strategies, network structures, loss functions, training\ndata, etc. have been employed. In this paper, we provide a comprehensive survey\nto cover various aspects ranging from algorithm taxonomy to open issues. To\nexamine the generalization of existing methods, we propose a low-light image\nand video dataset, in which the images and videos are taken by different mobile\nphones' cameras under diverse illumination conditions. Besides, for the first\ntime, we provide a unified online platform that covers many popular LLIE\nmethods, of which the results can be produced through a user-friendly web\ninterface. In addition to qualitative and quantitative evaluation of existing\nmethods on publicly available and our proposed datasets, we also validate their\nperformance in face detection in the dark.This survey together with the\nproposed dataset and online platform could serve as a reference source for\nfuture study and promote the development of this research field. The proposed\nplatform and dataset as well as the collected methods, datasets, and evaluation\nmetrics are publicly available and will be regularly updated.\n"}
+{"abstract": "  The efficiency of the transport of angular momentum and chemical elements\ninside intermediate-mass stars lacks proper calibration, thereby introducing\nuncertainties on a star's evolutionary pathway. Improvements require better\nestimation of stellar masses, evolutionary stages, and internal mixing\nproperties. We aim to develop a neural network approach for asteroseismic\nmodelling and test its capacity to provide stellar masses, ages, and\novershooting parameter for a sample of 37 $\\gamma$ Doradus stars. Here, our\ngoal is to perform the parameter estimation from modelling of individual\nperiods measured for dipole modes with consecutive radial order. We have\ntrained neural networks to predict theoretical pulsation periods of high-order\ngravity modes as well as the luminosity, effective temperature, and surface\ngravity for a given mass, age, overshooting parameter, diffusive envelope\nmixing, metallicity, and near-core rotation frequency. We have applied our\nneural networks for Computing Pulsation Periods and Photospheric Observables,\nC-3PO, to our sample and compute grids of stellar pulsation models for the\nestimated parameters. We present the near-core rotation rates (from literature)\nas a function of the inferred stellar age and critical rotation rate. We assess\nthe rotation rates of the sample near the start of the main sequence assuming\nrigid rotation. Furthermore, we measure the extent of the core overshoot region\nand find no correlation with mass, age, or rotation. The neural network\napproach developed in this study allows for the derivation of stellar\nproperties dominant for stellar evolution -- such as mass, age, and extent of\ncore-boundary mixing. It also opens a path for future estimation of mixing\nprofiles throughout the radiative envelope, with the aim to infer those\nprofiles for large samples of $\\gamma$ Doradus stars.\n"}
+{"abstract": "  Security is one of the main issues in Internet of Things (IoT). Encryption\nplays a curtail role in making these systems secure. Substitution Box (S-Box)\nhas an effective impact in block encryption methods. Due to the restricted\nresource capacities of IoT nodes, providing a lightweight S-Box is a\nchallenging problem. This paper presents a key-dependent S-Box using\nHyperelliptic curve. The proposed S-Box is analytically evaluated using\nperformance criteria including bijection, nonlinearity, strict avalanche\neffect, and algebraic degree. The evaluation results endorse that the offered\nS-Box production algorithm is considerably an effective way to generate\ncryptographic strong S-Box.\n"}
+{"abstract": "  We establish Gagliardo-Nirenberg-Sobolev type inequalities on nonlocal\nSobolev spaces driven by $p$-L\\'evy integrable functions, by imposing some\nappropriate growth conditions on the associated critical function. This gives\nrise to the embedding of the local and the nonlocal Sobolev spaces into Orlicz\ntype spaces. The Gagliardo-Nirenberg-Sobolev type inequalities, as in the\nclassical context, turn out to have some reciprocity with Poincar\\'e and\nPoincar\\'e-Sobolev type inequalities. The classical fractional Sobolev\ninequality is also derived as a direct consequence.\n"}
+{"abstract": "  We revisit the dependence of covering factor (CF) of dust torus on physical\nproperties of active galactic nuclei (AGNs) by taking into account an AGN polar\ndust emission. The CF is converted from a ratio of infrared (IR) luminosity\ncontributed from AGN dust torus ($L_{\\rm IR}^{\\rm torus}$) and AGN bolometric\nluminosity ($L_{\\rm bol}$), by assuming a non-linear relation between\nluminosity ratio and intrinsic CF. We select 37,181 type 1 quasars at $z < 0.7$\nfrom the Sloan Digital Sky Survey Data Release 16 quasar catalog. Their $L_{\\rm\nbol}$, black hole mass ($M_{\\rm BH}$), and Eddington ratio ($\\lambda_{\\rm\nEdd}$) are derived by spectral fitting with QSFit. We conduct spectral energy\ndistribution decomposition by using X-CIGALE with clumpy torus and polar dust\nmodel to estimate $L_{\\rm IR}^{\\rm torus}$ without being affected by the\ncontribution of stellar and AGN polar dust to IR emission. For 5720 quasars\nwhose physical quantities are securely determined, we perform a correlation\nanalysis on CF and (i) $L_{\\rm bol}$, (ii) $M_{\\rm BH}$, and (iii)\n$\\lambda_{\\rm Edd}$. As a result, anti-correlations for CF-$L_{\\rm bol}$,\nCF-$M_{\\rm BH}$, and CF-$\\lambda_{\\rm Edd}$ are confirmed. We find that\nincorporating the AGN polar dust emission makes those anti-correlations\nstronger which are compared to those without considering it. This indicates\nthat polar dust wind provably driven by AGN radiative pressure is one of the\nkey components to regulate obscuring material of AGNs.\n"}
+{"abstract": "  The last decade has seen a significant increase of interest in deep learning\nresearch, with many public successes that have demonstrated its potential. As\nsuch, these systems are now being incorporated into commercial products. With\nthis comes an additional challenge: how can we build AI systems that solve\ntasks where there is not a crisp, well-defined specification? While multiple\nsolutions have been proposed, in this competition we focus on one in\nparticular: learning from human feedback. Rather than training AI systems using\na predefined reward function or using a labeled dataset with a predefined set\nof categories, we instead train the AI system using a learning signal derived\nfrom some form of human feedback, which can evolve over time as the\nunderstanding of the task changes, or as the capabilities of the AI system\nimprove.\n  The MineRL BASALT competition aims to spur forward research on this important\nclass of techniques. We design a suite of four tasks in Minecraft for which we\nexpect it will be hard to write down hardcoded reward functions. These tasks\nare defined by a paragraph of natural language: for example, \"create a\nwaterfall and take a scenic picture of it\", with additional clarifying details.\nParticipants must train a separate agent for each task, using any method they\nwant. Agents are then evaluated by humans who have read the task description.\nTo help participants get started, we provide a dataset of human demonstrations\non each of the four tasks, as well as an imitation learning baseline that\nleverages these demonstrations.\n  Our hope is that this competition will improve our ability to build AI\nsystems that do what their designers intend them to do, even when the intent\ncannot be easily formalized. Besides allowing AI to solve more tasks, this can\nalso enable more effective regulation of AI systems, as well as making progress\non the value alignment problem.\n"}
+{"abstract": "  In the Landau levels of a two-dimensional electron system or when flat bands\nare present, e.g. in twisted van der Waals bilayers, strong electron-electron\ninteraction gives rise to quantum Hall ferromagnetism with spontaneously broken\nsymmetries in the spin and isospin sectors. Quantum Hall ferromagnets support a\nrich variety of low-energy collective excitations that are instrumental to\nunderstand the nature of the magnetic ground states and are also potentially\nuseful as carriers of quantum information. Probing such collective excitations,\nespecially their dispersion {\\omega}(k), has been experimentally challenging\ndue to small sample size and measurement constraints. In this work, we\ndemonstrate an all-electrical approach that integrates a Fabry-P\\'erot cavity\nwith non-equilibrium transport to achieve the excitation, wave vector selection\nand detection of spin waves in graphene heterostructures. Our experiments\nreveal gapless, linearly dispersed spin wave excitations in the E = 0 Landau\nlevel of bilayer graphene, thus providing direct experimental evidence for a\npredicted canted antiferromagnetic order. We show that the gapless spin wave\nmode propagates with a high group velocity of several tens of km/s and\nmaintains phase coherence over a distance of many micrometers. Its dependence\non the magnetic field and temperature agree well with the hydrodynamic theory\nof spin waves. These results lay the foundation for the quest of spin\nsuperfluidity in this high-quality material. The resonant cavity technique we\ndeveloped offers a powerful and timely method to explore the collective\nexcitation of many spin and isospin-ordered many-body ground states in van der\nWaals heterostructures and open the possibility of engineering magnonic\ndevices.\n"}
+{"abstract": "  In this note we introduce a notion of free cofibrations of permutative\ncategories. We show that each cofibration of permutative categories is a\nretract of a free cofibration.\n"}
+{"abstract": "  Until the beginning of 2021, lung cancer is known to be the most common\ncancer in the world. The disease is common due to factors such as occupational\nexposure, smoking and environmental pollution. The early diagnosis and\ntreatment of the disease is of great importance as well as the prevention of\nthe causes that cause the disease. The study was planned to create a web\ninterface that works with machine learning algorithms to predict prognosis\nusing lung cancer clinical and gene expression in the GDC data portal.\n"}
+{"abstract": "  This article describes techniques employed in the production of a synthetic\ndataset of driver telematics emulated from a similar real insurance dataset.\nThe synthetic dataset generated has 100,000 policies that included observations\nabout driver's claims experience together with associated classical risk\nvariables and telematics-related variables. This work is aimed to produce a\nresource that can be used to advance models to assess risks for usage-based\ninsurance. It follows a three-stage process using machine learning algorithms.\nThe first stage is simulating values for the number of claims as multiple\nbinary classifications applying feedforward neural networks. The second stage\nis simulating values for aggregated amount of claims as regression using\nfeedforward neural networks, with number of claims included in the set of\nfeature variables. In the final stage, a synthetic portfolio of the space of\nfeature variables is generated applying an extended $\\texttt{SMOTE}$ algorithm.\nThe resulting dataset is evaluated by comparing the synthetic and real datasets\nwhen Poisson and gamma regression models are fitted to the respective data.\nOther visualization and data summarization produce remarkable similar\nstatistics between the two datasets. We hope that researchers interested in\nobtaining telematics datasets to calibrate models or learning algorithms will\nfind our work valuable.\n"}
+{"abstract": "  We consider graphical $n$-person games with perfect information that have no\nNash equilibria in pure stationary strategies. Solving these games in mixed\nstrategies, we introduce probabilistic distributions in all non-terminal\npositions. The corresponding plays can be analyzed under two different basic\nassumptions: Markov's and a priori realizations. The former one guarantees\nexistence of a uniformly best response of each player in every situation.\nNevertheless, Nash equilibrium may fail to exist even in mixed strategies. The\nclassical Nash theorem is not applicable, since Markov's realizations may\nresult in the limit distributions and effective payoff functions that are not\ncontinuous. The a priori realization does not share many nice properties of the\nMarkov one (for example, existence of the uniformly best response) but in\nreturn, Nash's theorem is applicable. We illustrate both realizations in\ndetails by two examples with $2$ and $3$ players and also provide some general\nresults.\n"}
+{"abstract": "  In this work, we introduce a novel data-driven model-reference control design\napproach for unknown linear systems with fully measurable state. The proposed\ncontrol action is composed by a static feedback term and a reference tracking\nblock, which are shaped from data to reproduce the desired behavior in\nclosed-loop. By focusing on the case where the reference model and the plant\nshare the same order, we propose an optimal design procedure with Lyapunov\nstability guarantees, tailored to handle state measurements with additive\nnoise. Two simulation examples are finally illustrated to show the potential of\nthe proposed strategy as compared to the state of the art approaches.\n"}
+{"abstract": "  Lattice-based cryptography is one of the leading proposals for post-quantum\ncryptography. The Shortest Vector Problem (SVP) is arguably the most important\nproblem for the cryptanalysis of lattice-based cryptography, and many\nlattice-based schemes have security claims based on its hardness. The best\nquantum algorithm for the SVP is due to Laarhoven [Laa16 PhD] and runs in\n(heuristic) time $2^{0.2653d + o(d)}$. In this article, we present an\nimprovement over Laarhoven's result and present an algorithm that has a\n(heuristic) running time of $2^{0.2570 d + o(d)}$ where $d$ is the lattice\ndimension. We also present time-memory trade-offs where we quantify the amount\nof quantum memory and quantum random access memory of our algorithm. The core\nidea is to replace Grover's algorithm used in [Laa16 PhD] in a key part of the\nsieving algorithm by a quantum random walk in which we add a layer of local\nsensitive filtering.\n"}
+{"abstract": "  This paper proposes a method for OOD detection. Questioning the premise of\nprevious studies that ID and OOD samples are separated distinctly, we consider\nsamples lying in the intermediate of the two and use them for training a\nnetwork. We generate such samples using multiple image transformations that\ncorrupt inputs in various ways and with different severity levels. We estimate\nwhere the generated samples by a single image transformation lie between ID and\nOOD using a network trained on clean ID samples. To be specific, we make the\nnetwork classify the generated samples and calculate their mean classification\naccuracy, using which we create a soft target label for them. We train the same\nnetwork from scratch using the original ID samples and the generated samples\nwith the soft labels created for them. We detect OOD samples by thresholding\nthe entropy of the predicted softmax probability. The experimental results show\nthat our method outperforms the previous state-of-the-art in the standard\nbenchmark tests. We also analyze the effect of the number and particular\ncombinations of image corrupting transformations on the performance.\n"}
+{"abstract": "  The angular-dependent magnetoresistance (AMR) of the ab plane is measured on\nthe single crystals of FeSe1-xSx (x = 0, 0.07, 0.13 and 1) and FeSe1-yTey (y =\n0.06, 0.61 and 1) at various temperatures under fields up to 9 T. A pronounced\ntwofold-anisotropic carrier-scattering effect is identified by AMR, and\nattributed to a magnetic-field-induced spin nematicity that emerges from the\ntetragonal normal-state regime below a characteristic temperature Tsn. This\nmagnetically polarized spin nematicity is found to be ubiquitous in the\nisoelectronic FeSe1-xSx and FeSe1-yTey systems, no matter whether the sample\nshows an electronic nematic order at Ts < Tsn, or an antiferromagnetic order at\nTN < Tsn, or neither order. Importantly, we find that the isoelectronic\nsubstitution with sulfur does not suppress but even enhances the characteristic\nTsn of the induced spin nematicity in FeSe1-xSx samples. This contrasts sharply\nwith their rapidly suppressed Ts, the transition temperature of the spontaneous\nelectronic nematicity. Furthermore, we find that the superconductivity is\nsignificantly suppressed with the enhancement of the induced spin nematicity in\nboth FeSe1-xSx and FeSe1-yTey samples.\n"}
+{"abstract": "  Huge computational costs brought by convolution and batch normalization (BN)\nhave caused great challenges for the online training and corresponding\napplications of deep neural networks (DNNs), especially in resource-limited\ndevices. Existing works only focus on the convolution or BN acceleration and no\nsolution can alleviate both problems with satisfactory performance. Online\ntraining has gradually become a trend in resource-limited devices like mobile\nphones while there is still no complete technical scheme with acceptable model\nperformance, processing speed, and computational cost. In this research, an\nefficient online-training quantization framework termed EOQ is proposed by\ncombining Fixup initialization and a novel quantization scheme for DNN model\ncompression and acceleration. Based on the proposed framework, we have\nsuccessfully realized full 8-bit integer network training and removed BN in\nlarge-scale DNNs. Especially, weight updates are quantized to 8-bit integers\nfor the first time. Theoretical analyses of EOQ utilizing Fixup initialization\nfor removing BN have been further given using a novel Block Dynamical Isometry\ntheory with weaker assumptions. Benefiting from rational quantization\nstrategies and the absence of BN, the full 8-bit networks based on EOQ can\nachieve state-of-the-art accuracy and immense advantages in computational cost\nand processing speed. What is more, the design of deep learning chips can be\nprofoundly simplified for the absence of unfriendly square root operations in\nBN. Beyond this, EOQ has been evidenced to be more advantageous in small-batch\nonline training with fewer batch samples. In summary, the EOQ framework is\nspecially designed for reducing the high cost of convolution and BN in network\ntraining, demonstrating a broad application prospect of online training in\nresource-limited devices.\n"}
+{"abstract": "  Machine learning (ML) has been well applied to studying equilibrium phase\ntransition models, by accurately predicating critical thresholds and some\ncritical exponents. Difficulty will be raised, however, for integrating ML into\nnon-equilibrium phase transitions. The extra dimension in a given\nnon-equilibrium system, namely time, can greatly slow down the procedure\ntowards the steady state. In this paper we find that by using some simple\ntechniques of ML, non-steady state configurations of directed percolation (DP)\nsuffice to capture its essential critical behaviors in both (1+1) and (2+1)\ndimensions. With the supervised learning method, the framework of our binary\nclassification neural networks can identify the phase transition threshold, as\nwell as the spatial and temporal correlation exponents. The characteristic time\n$t_{c}$, specifying the transition from active phases to absorbing ones, is\nalso a major product of the learning. Moreover, we employ the convolutional\nautoencoder, an unsupervised learning technique, to extract dimensionality\nreduction representations and cluster configurations of (1+1) bond DP. It is\nquite appealing that such a method can yield a reasonable estimation of the\ncritical point.\n"}
+{"abstract": "  We propose an inferential approach for maximum likelihood estimation of the\nhidden Markov models for continuous responses. We extend to the case of\nlongitudinal observations the finite mixture model of multivariate Gaussian\ndistributions with Missing At Random (MAR) outcomes, also accounting for\npossible dropout. The resulting hidden Markov model accounts for different\ntypes of missing pattern: (i) partially missing outcomes at a given time\noccasion; (ii) completely missing outcomes at a given time occasion\n(intermittent pattern); (iii) dropout before the end of the period of\nobservation (monotone pattern). The MAR assumption is formulated to deal with\nthe first two types of missingness, while to account for informative dropout we\nassume an extra absorbing state. Maximum likelihood estimation of the model\nparameters is based on an extended Expectation-Maximization algorithm relying\non suitable recursions. The proposal is illustrated by a Monte Carlo simulation\nstudy and an application based on historical data on primary biliary\ncholangitis.\n"}
+{"abstract": "  We design, in a most simple way, Fabry-Perot cavities with longitudinal\nchiral modes by sandwiching between two smooth metallic silver mirrors a layer\nof polystyrene made planar chiral by torsional shear stress. We demonstrate\nthat the helicity-preserving features of our cavities stem from a spin-orbit\ncoupling mechanism seeded inside the cavities by the specific chiroptical\nfeatures of planar chirality. Planar chirality gives rise to an extrinsic\nsource of three-dimensional chirality under oblique illumination that endows\nthe cavities with enantiomorphic signatures measured experimentally and\nsimulated with excellent agreement. The simplicity of our scheme is\nparticularly promising in the context of chiral cavity QED and polaritonic\nasymmetric chemistry.\n"}
+{"abstract": "  Composite quantum compounds (CQCs) have become an important avenue for the\ninvestigation of inter-correlation between two distinct phenomenon in physics.\nTopological superconductors, axion insulators etc. are few such CQCs which have\nrecently drawn tremendous attention in the community. Topological nontriviality\nand Rashba spin physics are two different quantum phenomena but can be\nintertwined within a CQC platform. In this letter, we present a general\nsymmetry based mechanism, supported by \\textit{ab-initio} calculations to\nachieve intertwined giant Rashba splitting and topological non-trivial states\nsimultaneously in a single crystalline system. Such co-existent properties can\nfurther be tuned to achieve other rich phenomenon. We have achieved Rashba\nsplitting energy ($\\Delta E$) and Rashba coefficient ($\\alpha$) values as large\nas 161 meV and 4.87 eV$\\r{A}$ respectively in conjunction with Weyl semimetal\nphase in KSnSb$_{0.625}$Bi$_{0.375}$. Interestingly, these values are even\nlarger than the values reported for widely studied topologically trivial Rashba\nsemiconductor BiTeI. The advantage of our present analysis is that one can\nachieve various topological phases without compromising the Rashba parameters,\nwithin this CQC platform.\n"}
+{"abstract": "  In this short paper we report on student programming competition results by\nstudents from the Computer Science Department (COSC) of Okanagan College (OC)\nand discuss the achieved results from an educational point of view. We found\nthat some freshmen and sophomore students in diploma and degree programs are\nvery capable and eager to be involved in applied research projects as early as\nthe second semester, and into local and international programming competitions\nas well. Our observation is based on the last 2 educational years, beginning\n2015 when we introduced programming competitions to COSC students. Students\nreported that participation in competitions give them motivation to effectively\nlearn in their programming courses, inspire them to learn deeper and more\nthoroughly, and help them achieve better results in their classes.\n"}
+{"abstract": "  To derive the properties of the dense cores in the galactic star-forming\ncomplex NGC6357 and to investigate the effects of an intense far-UV radiation\nfield on their properties, we mapped the region at 450 and 850 micron, and in\nthe CO(3-2) line with the JCMT. We also made use of the Herschel Hi-GAL data at\n70 and 160 micron. We used Gaussclumps to retrieve 686 compact cores embedded\nin the diffuse sub-mm emission and constructed their SED from 70 to 850 micron,\nfrom which we derived mass and temperature. The estimated mass completeness\nlimit is ~5Mo. We divided the observed area in an 'active' region, exposed to\nthe far-UV radiation from the more massive members of three star clusters (411\ncores), and a 'quiescent' region, less affected by far-UV radiation (275\ncores). We also attempted to select a sample of pre-stellar cores based on\ncross-correlation with 70 micron emission and red WISE point sources. Most of\nthe cores above the mass completeness limit are likely to be gravitationally\nbound. The fraction of gas in dense cores is very low, 1.4%. We found a\nmass-size relation log(M/Mo) ~ (2.0-2.4) x log (D/arcsec), depending on the\nprecise selection of the sample. The temperature distributions in the two\nsub-regions are clearly different, peaking at ~25K in the quiescent region and\nat ~35K in the active region. The core mass functions are different as well, at\na 2sigma level, consistent with a Salpeter IMF in the quiescent region and\nflatter than that in the active region. The dense cores lying close to the HII\nregions are consistent with pre-existing cores being gradually engulfed by a\nPDR and photoevaporating. We attribute the different global properties of dense\ncores in the two sub-regions to the influence of the far-UV radiation field.\n"}
+{"abstract": "  Monitoring complex systems results in massive multivariate time series data,\nand anomaly detection of these data is very important to maintain the normal\noperation of the systems. Despite the recent emergence of a large number of\nanomaly detection algorithms for multivariate time series, most of them ignore\nthe correlation modeling among multivariate, which can often lead to poor\nanomaly detection results. In this work, we propose a novel anomaly detection\nmodel for multivariate time series with \\underline{HI}gh-order\n\\underline{F}eature \\underline{I}nteractions (HIFI). More specifically, HIFI\nbuilds multivariate feature interaction graph automatically and uses the graph\nconvolutional neural network to achieve high-order feature interactions, in\nwhich the long-term temporal dependencies are modeled by attention mechanisms\nand a variational encoding technique is utilized to improve the model\nperformance and robustness. Extensive experiments on three publicly available\ndatasets demonstrate the superiority of our framework compared with\nstate-of-the-art approaches.\n"}
+{"abstract": "  Stratifying factors, like age and gender, can modify the effect of treatments\nand exposures on risk of a studied outcome. Several effect measures, including\nthe relative risk, hazard ratio, odds ratio, and risk difference, can be used\nto measure this modification. It is known that choice of effect measure may\ndetermine the presence and direction of effect-measure modification. We show\nthat considering the opposite outcome -- for example, recovery instead of death\n-- may similarly influence effect-measure modification. In fact, if the\nrelative risk for the studied outcome and the relative risk for the opposite\noutcome agree about the direction of effect-measure modification, then so will\nthe two cumulative hazard ratios, the risk difference, and the odds ratio. When\nrisks are randomly sampled from the uniform (0,1) distribution, the probability\nof this happening is 5/6. Disagreement is probable enough that researchers\nconsidering one relative risk should also consider the other and further\ndiscussion if they disagree. (If possible, researchers should also report\nestimated risks.) We provide examples through case studies on HCV, COVID-19,\nand bankruptcy following melanoma treatment.\n"}
+{"abstract": "  Binary population synthesis (BPS) employs prescriptions to predict final\nfates, explosion or implosion, and remnant masses based on one or two stellar\nparameters at the evolutionary cutoff imposed by the code, usually at or near\ncentral carbon ignition. In doing this, BPS disregards the integral role\nlate-stage evolution plays in determining the final fate, remnant type, and\nremnant mass within the neutrino-driven explosion paradigm. To highlight\ndifferences between a popular prescription which relies only on the core and\nfinal stellar mass and emerging methods which rely on a star's presupernova\ncore structure, we generate a series of compact object distributions using\nthree different methods for a sample population of single and binary stars\ncomputed in BPASS. The first method estimates remnant mass based on a star's\ncarbon-oxygen (CO) core mass and final total mass. The second method uses the\npresupernova core structure based on the bare CO-core models of \\citet{Pat20}\ncombined with a parameterized explosion criterion to first determine final fate\nand remnant type, then remnant mass. The third method associates presupernova\nhelium-core masses with remnant masses determined from public explosion models\nwhich rely implicitly on core structure. We find that the core-/final\nmass-based prescription favors lower mass remnants, including a large\npopulation of mass gap black holes, and predicts neutron star masses which span\na wide range, whereas the structure-based prescriptions favor slightly higher\nmass remnants, mass gap black holes only as low as 3.5 \\Msun, and predict\nneutron star mass distributions which cluster in a narrow range.\n"}
+{"abstract": "  We estimate the solar peculiar velocities and Oort constants using a sample\nof 5,627 A-type stars with $d<0.6\\,\\rm kpc$ and $|z|<0.1\\,\\rm kpc$, selected\nfrom the LAMOST surveys. The radial and tangential velocities of these A-type\nstars are fitted by using a non-axisymmetric model. The best-fitting result\nyields the solar peculiar velocities $(U_\\odot,V_\\odot,W_\\odot)=(11.69\\pm0.68,\n10.16\\pm0.51, 7.67\\pm0.10)\\,\\rm km\\,s^{-1}$ and Oort constants\n$A=16.31\\pm0.89\\,\\rm km\\,s^{-1}\\,kpc^{-1}$, $B=-11.99\\pm0.79\\,\\rm\nkm\\,s^{-1}\\,kpc^{-1}$, $C=-3.10\\pm0.48\\,\\rm km\\,s^{-1}\\,kpc^{-1}$,\n$K=-1.25\\pm1.04\\,\\rm km\\,s^{-1}\\,kpc^{-1}$, respectively. $|K+C|>4\\,\\rm\nkm\\,s^{-1}\\,kpc^{-1}$ means that there is a radial velocity gradient in the\nextended local disk, implying the local disk is in a non-asymmetric potential.\nUsing the derived Oort constants, we derive the local angular velocity\n$\\Omega\\,{\\approx}\\,A-B=28.30\\pm1.19\\,\\rm km\\,s^{-1}\\,kpc^{-1}$. By using\nA-type star sample of different volumes, we further try to evaluate the impacts\nof the ridge pattern in $R$-$V_{\\phi}$ plane on constraining the solar motions\nand Oort constants. As the volume becomes larger toward the anti-center\ndirection, the values of $A$ and $B$ become larger (implying a steeper slope of\nthe local rotation curve) and the value of $V_\\odot$ becomes smaller probably\ncaused by the ridge structure and its signal increasing with distance.\n"}
+{"abstract": "  We propose a two-stage approach Spec PC-CP to identify change points in\nmultivariate time series. In the first stage, we obtain a low-dimensional\nsummary of the high-dimensional time series by Spectral Principal Component\nAnalysis (Spec-PCA). In the second stage, we apply cumulative sum-type test on\nthe Spectral PCA component using a binary segmentation algorithm. Compared with\nexisting approaches, the proposed method is able to capture the lead-lag\nrelationship in time series. Our simulations demonstrate that the Spec PC-CP\nmethod performs significantly better than competing methods for detecting\nchange points in high-dimensional time series. The results on epileptic seizure\nEEG data and stock data also indicate that our new method can efficiently\n{detect} change points corresponding to the onset of the underlying events.\n"}
+{"abstract": "  We review the issue of steady spherically symmetric accretion onto a\nrenormalization group improved Schwarzschild space-time which is solution to an\nasymptotically safe theory (AS) containing high-derivative terms. We use a\nHamiltonian dynamical system approach for the analysis of the accretion of four\ntypes of isothermal test fluids: ultra-stiff fluid, ultra-relativistic fluid,\nradiation fluid, and sub-relativistic fluid. An important outcome of our study\nis that, contrary to what claimed in a recent work, there exist physical\nsolutions for the accretion of an ultra-relativistic fluid in AS which include\nsubsonic, supersonic and transonic regimes. We also study quantum corrections\nto the known stability of the accretion in general relativity (GR). To this end\nwe use a perturbative procedure based on the continuity equation with the mass\naccretion rate being the perturbed quantity. Two classes of perturbations are\nstudied: standing waves and travelling waves. We find that quantum gravity\neffects either enhance or diminish the stability of the accretion depending on\nthe type of test fluid and depending on the radial distance to the central\nobject.\n"}
+{"abstract": "  Einstein-Gauss-Bonnet theory is a string-generated gravity theory when\napproaching the low energy limit. By introducing the higher order curvature\nterms, this theory is supposed to help to solve the black hole singularity\nproblem. In this work, we investigate the evaporation of the static spherically\nsymmetric neutral AdS black holes in Einstein-Gauss-Bonnet gravity in various\nspacetime dimensions with both positive and negative couping constant $\\alpha$.\nBy summarizing the asymptotic behavior of the evaporation process, we find the\nlifetime of the black holes is dimensional dependent. For $\\alpha>0$, in\n$D\\geqslant6$ cases, the black holes will be completely evaporated in a finite\ntime, which resembles the Schwarzschild-AdS case in Einstein gravity. While in\n$D=4,5$ cases, the black hole lifetime is always infinite, which means the\nblack hole becomes a remnant in the late time. Remarkably, the cases of\n$\\alpha>0, D=4,5$ will solve the terminal temperature divergent problem of the\nSchwarzschild-AdS case. For $\\alpha<0$, in all dimensions, the black hole will\nalways spend a finite time to a minimal mass corresponding to the smallest\nhorizon radius $r_{min}=\\sqrt{2|\\alpha|}$ which coincide with an additional\nsingularity. This implies that there may exist constraint conditions to the\nchoice of coupling constant.\n"}
+{"abstract": "  Close-in planets evolve under extreme conditions, raising questions about\ntheir origins and current nature. Two predominant mechanisms are orbital\nmigration, which brings them close to their star, and atmospheric escape under\nthe resulting increased irradiation. Yet, their relative roles remain unclear\nbecause we lack models that couple the two mechanisms with high precision on\nsecular timescales. To address this need, we developed the JADE code, which\nsimulates the secular atmospheric and dynamical evolution of a planet around\nits star, and can include the perturbation induced by a distant third body. On\nthe dynamical side, the 3D evolution of the orbit is modeled under stellar and\nplanetary tidal forces, a relativistic correction, and the action of the\ndistant perturber. On the atmospheric side, the vertical structure of the\natmosphere is integrated over time based on its thermodynamical properties,\ninner heating, and the evolving stellar irradiation, which results, in\nparticular, in photo-evaporation. The JADE code is benchmarked on GJ436 b,\nprototype of evaporating giants on eccentric, misaligned orbits at the edge of\nthe hot Neptunes desert. We confirm that its orbital architecture is well\nexplained by Kozai migration and unveil a strong interplay between its\natmospheric and orbital evolution. During the resonance phase, the atmosphere\npulsates in tune with the Kozai cycles, which leads to stronger tides and an\nearlier migration. This triggers a strong evaporation several Gyr after the\nplanet formed, refining the paradigm that mass loss is dominant in the early\nage of close-in planets. This suggests that the edge of the desert could be\nformed of warm Neptunes whose evaporation was delayed by migration. It\nstrengthens the importance of coupling atmospheric and dynamical evolution over\nsecular timescales, which the JADE code will allow simulating for a wide range\nof systems.\n"}
+{"abstract": "  This paper investigates the performance of different general-game-playing\nheuristics for games in the Ludii general game system. Based on these results,\nwe train several regression learning models to predict the performance of these\nheuristics based on each game's description file. We also provide a condensed\nanalysis of the games available in Ludii, and the different ludemes that define\nthem.\n"}
+{"abstract": "  This paper studies the identification of causal effects of a continuous\ntreatment using a new difference-in-difference strategy. Our approach allows\nfor endogeneity of the treatment, and employs repeated cross-sections. It\nrequires an exogenous change over time which affects the treatment in a\nheterogeneous way, stationarity of the distribution of unobservables and a rank\ninvariance condition on the time trend. On the other hand, we do not impose any\nfunctional form restrictions or an additive time trend, and we are invariant to\nthe scaling of the dependent variable. Under our conditions, the time trend can\nbe identified using a control group, as in the binary difference-in-differences\nliterature. In our scenario, however, this control group is defined by the\ndata. We then identify average and quantile treatment effect parameters. We\ndevelop corresponding nonparametric estimators and study their asymptotic\nproperties. Finally, we apply our results to the effect of disposable income on\nconsumption.\n"}
+{"abstract": "  The peak power improvement and running safety of petawatt (PW) lasers are\nlimited by laser-induced damage of optical components with limited sizes and\ndamage thresholds. Diffraction gratings in pulse compressors have been the\nshortest stave of PW lasers up to now, as to manufacture a high quality\nmeter-sized grating remains particularly challenging. Here, the asymmetric\nfour-grating compressor (AFGC) with asymmetric configuration is proposed for PW\nlasers to increase the maximum bearable output pulse energy and running safety\nwithout neither additional optical component nor extra control in comparison to\na traditional Treacy four-grating compressor (TFGC) with symmetric\nconfiguration. In AFGC, suitable spatial dispersion can be introduced in the\noutput laser beam which is able to decrease the laser spatial intensity\nmodulation (LSIM) of the output beam on the final grating. The introduced\nspatial dispersion can be automatically compensated at the focal plane by using\nthe spatiotemporal focusing technique. Based on this simple AFGC design, not\nonly the damage risk of the final grating can be reduced, but also the maximum\noutput pulse energy can be improved by about 1.8 times theoretically. As an\nexample, 100 PW output power can be achieved theoretically by using the AFGC\nwith an input beam size of 550*700 mm2.\n"}
+{"abstract": "  Let $L(m,n)$ denote Young's lattice consisting of all partitions whose Young\ndiagrams are contained in the $m\\times n$ rectangle. It is a well-known result\nthat the poset $L(m,n)$ is rank symmetric, rank unimodal, and Sperner. A direct\nproof of this result by finding an explicit order matching of $L(m,n)$ is an\noutstanding open problem. In this paper, we present an explicit order matching\n$\\varphi$ for $L(3,n)$ by several different approaches, and give chain tableau\nversion of $\\varphi$ that is very helpful in finding patterns. It is surprise\nthat the greedy algorithm and a recursive knead process also give the same\norder matching. Our methods extend for $L(4,n)$.\n"}
+{"abstract": "  Federated Learning (FL) is a paradigm that aims to support loosely connected\nclients in learning a global model collaboratively with the help of a\ncentralized server. The most popular FL algorithm is Federated Averaging\n(FedAvg), which is based on taking weighted average of the client models, with\nthe weights determined largely based on dataset sizes at the clients. In this\npaper, we propose a new approach, termed Federated Node Selection (FedNS), for\nthe server's global model aggregation in the FL setting. FedNS filters and\nre-weights the clients' models at the node/kernel level, hence leading to a\npotentially better global model by fusing the best components of the clients.\nUsing collaborative image classification as an example, we show with\nexperiments from multiple datasets and networks that FedNS can consistently\nachieve improved performance over FedAvg.\n"}
+{"abstract": "  Probabilistic image segmentation encodes varying prediction confidence and\ninherent ambiguity in the segmentation problem. While different probabilistic\nsegmentation models are designed to capture different aspects of segmentation\nuncertainty and ambiguity, these modelling differences are rarely discussed in\nthe context of applications of uncertainty. We consider two common use cases of\nsegmentation uncertainty, namely assessment of segmentation quality and active\nlearning. We consider four established strategies for probabilistic\nsegmentation, discuss their modelling capabilities, and investigate their\nperformance in these two tasks. We find that for all models and both tasks,\nreturned uncertainty correlates positively with segmentation error, but does\nnot prove to be useful for active learning.\n"}
+{"abstract": "  We present quantum algorithms for routing concentration assignments on full\ncapacity fat-and-slim concentrators, bounded fat-and-slim concentrators, and\nregular fat-and-slim concentrators. Classically, the concentration assignment\ntakes $O(n)$ time on all these concentrators, where $n$ is the number of\ninputs. Powered by Grover's quantum search algorithm, our algorithms take\n$O(\\sqrt{nc}\\ln{c})$ time, where $c$ is the capacity of the concentrator. Thus,\nour quantum algorithms are asymptotically faster than their classical\ncounterparts, when $c\\ln^2{c}=o(n)$.In general, $c = n^\\mu,$ satisfies\n$c\\ln^2{c}=o(n),$ implying a time complexity of $O(n^{0.5(1+ \\mu )} \\ln n),$\nfor any $\\mu, 0 < \\mu < 1.$\n"}
+{"abstract": "  Electronic structure of one of the nickelates (LaNiO$_{2}$) and one of the\ncuprates (CaCuO$_{2}$) is studied with three self consistent GW-based methods:\nscGW, sc(GW+Vertex), and quasiparticle self-consistent GW. Low energy features\nobtained in our study are in many respects similar to the features reported in\nprevious DFT+DMFT studies. Consistent with the DFT+DMFT conclusion, we find\nLaNiO$_{2}$ as more correlated than CaCuO$_{2}$. However, correlation effects\nincluded in our study change the DFT Fermi surface near the $\\Gamma$ point\ndifferently than it was reported in the DMFT studies. Features which are a few\nelectron-volts away from the Fermi level are broader in our calculations than\nin the DFT+DMFT which reflects the differences between the DFT and the GW\nmethods. Our results are in qualitative agreement with previous G0W0 results,\nbut the self-consistency brings in the quantitative differences. Generally,\ncorrelation effects are found to be sufficiently weak in both materials which\nallows one to use totally ab-initio diagrammatic approaches like sc(GW+Vertex)\nand to avoid the methods with adjustable parameters (DFT+U or DFT+DMFT).\nHowever, the possibility of some strong correlations at low energy which cannot\nbe captured by perturbative methods cannot be completely excluded. For\ninstance, differences in the Fermi surface should be resolved: experimental\nstudies are necessary.\n"}
+{"abstract": "  We study the effect of non-universal $Z'$ boson on the rare leptonic decay\nmodes of $B_s^*$ mesons, mediated by $b arrow sll$ quark transition. Rare B\ndecays are sensitive to various new physics operators. As B mesons are\ncomposite particles, such decay modes of the excited states are ideal for\nprobing new physics beyond the standard model. We have constrained our model\nparameters from $B_(s,d)-$ {B bar(s,d)}$ mixing data to predict the lepton\npolarization asymmetry of $B_(s,d)^*$ arrow ${\\mu}^+ {\\mu}^-$ channel. It is\nfound that this asymmetry deviates from its SM prediction along with the\n$Z'$effect\n"}
+{"abstract": "  Weyl semimetals exhibit exotic transport responses, among which, recently\nGoos-Haenchen (GH) and Imbert-Fedorov (IF) effects have received a revived\nattention, which are, otherwise, well-studied phenomena in optical systems and\ncertain electronic systems. Besides the usual parametric dependence of the\nshifts inherited from the underlying Hamiltonian to describe the Weyl system\nand/or that induced by external controls, the IF shift further carries a\ntopological identity -- it depends on the chirality of the Weyl cones. A\nrealistic system of Weyl semimetal naturally accommodates surface potentials\ninduced by impurities present on its surface that could pose impediments to\nobserve clean transport signatures predicted in theoretical models. Classifying\nthese potentials, we study their effects on GH and IF shifts to provide useful\nguidance to future experiments that are tuned to the objective of\ncharacterizing Weyl semimetals and for a possible realisation of novel devices\nbased on these phenomena. A transfer matrix-based approach is invoked to study\nthe profile of Weyl wavefunctions across the interface which is hosting the\nimpurity potentials, revealing that such potentials can lead to several\ndiscerning effects which, in certain cases, extend even to nullifying the IF\nshift completely and giving rise to phenomenon like valley inversion.\n"}
+{"abstract": "  The rapid growth of ride-hailing platforms has created a highly competitive\nmarket where businesses struggle to make profits, demanding the need for better\noperational strategies. However, real-world experiments are risky and expensive\nfor these platforms as they deal with millions of users daily. Thus, a need\narises for a simulated environment where they can predict users' reactions to\nchanges in the platform-specific parameters such as trip fares and incentives.\nBuilding such a simulation is challenging, as these platforms exist within\ndynamic environments where thousands of users regularly interact with one\nanother. This paper presents a framework to mimic and predict user,\nspecifically driver, behaviors in ride-hailing services. We use a data-driven\nhybrid reinforcement learning and imitation learning approach for this. First,\nthe agent utilizes behavioral cloning to mimic driver behavior using a\nreal-world data set. Next, reinforcement learning is applied on top of the\npre-trained agents in a simulated environment, to allow them to adapt to\nchanges in the platform. Our framework provides an ideal playground for\nride-hailing platforms to experiment with platform-specific parameters to\npredict drivers' behavioral patterns.\n"}
+{"abstract": "  While multifidelity modeling provides a cost-effective way to conduct\nuncertainty quantification with computationally expensive models, much greater\nefficiency can be achieved by adaptively deciding the number of required\nhigh-fidelity (HF) simulations, depending on the type and complexity of the\nproblem and the desired accuracy in the results. We propose a framework for\nactive learning with multifidelity modeling emphasizing the efficient\nestimation of rare events. Our framework works by fusing a low-fidelity (LF)\nprediction with an HF-inferred correction, filtering the corrected LF\nprediction to decide whether to call the high-fidelity model, and for enhanced\nsubsequent accuracy, adapting the correction for the LF prediction after every\nHF model call. The framework does not make any assumptions as to the LF model\ntype or its correlations with the HF model. In addition, for improved\nrobustness when estimating smaller failure probabilities, we propose using\ndynamic active learning functions that decide when to call the HF model. We\ndemonstrate our framework using several academic case studies and two finite\nelement (FE) model case studies: estimating Navier-Stokes velocities using the\nStokes approximation and estimating stresses in a transversely isotropic model\nsubjected to displacements via a coarsely meshed isotropic model. Across these\ncase studies, not only did the proposed framework estimate the failure\nprobabilities accurately, but compared with either Monte Carlo or a standard\nvariance reduction method, it also required only a small fraction of the calls\nto the HF model.\n"}
+{"abstract": "  Wire-feed laser additive manufacturing is an emerging fabrication technique\ncapable of highly automated large-scale volume production that can reduce both\nmaterial waste and overall cost while improving product lead times. Quality\nassurance is necessary for implementation into critical structural\napplications. However, the large number of process variables along with the\ncost associated with traditional trial and error methods makes this difficult.\nThis study investigates a comprehensive quality framework based on learning\nfrom experimental data that will enable improved quality control along with\nconsistent microstructural features of the part. Specifically, a comprehensive\nexperimental data across multiple process variables and output characteristics\nin terms of overall bead quality, geometric shape (i.g. bead height, width,\nfusion zone depth, etc.), and microstructural features are collected. The\npredicted process-geometry-microstructure relations are then captured by virtue\nof data-driven machine learning models. The properties of printed beads are\nvisualized based on an extensive range of processing space within a\n3-dimensional contour map. The insights and impacts of process variables on\nbead morphology, geometric and microstructural features are comprehensively\ninvestigated for quality improvement during manufacturing processes.\n"}
+{"abstract": "  Data-free compression raises a new challenge because the original training\ndataset for a pre-trained model to be compressed is not available due to\nprivacy or transmission issues. Thus, a common approach is to compute a\nreconstructed training dataset before compression. The current reconstruction\nmethods compute the reconstructed training dataset with a generator by\nexploiting information from the pre-trained model. However, current\nreconstruction methods focus on extracting more information from the\npre-trained model but do not leverage network engineering. This work is the\nfirst to consider network engineering as an approach to design the\nreconstruction method. Specifically, we propose the AutoReCon method, which is\na neural architecture search-based reconstruction method. In the proposed\nAutoReCon method, the generator architecture is designed automatically given\nthe pre-trained model for reconstruction. Experimental results show that using\ngenerators discovered by the AutoRecon method always improve the performance of\ndata-free compression.\n"}
+{"abstract": "  Jets and outflows are thought to play important roles in regulating star\nformation and disk evolution. HD 163296 is a well-studied Herbig Ae star that\nhosts proto-planet candidates, a protoplanetary disk, a protostellar jet, and a\nmolecular outflow, which makes it an excellent laboratory for studying jets. We\naim to characterize the jet at the inner regions and check if there are large\ndifferences with the features at large separations. A secondary objective is to\ndemonstrate the performance of Multi Unit Spectroscopic Explorer (MUSE) in\nhigh-contrast imaging of extended line emission. MUSE in the narrow field mode\n(NFM) can provide observations at optical wavelengths with high spatial\n($\\sim$75 mas) and medium spectral ($R\\sim$2500) resolution. With the\nhigh-resolution spectral differential imaging (HRSDI) technique, we can\ncharacterize the kinematic structures and physical conditions of jets down to\n100 mas. We detect multiple atomic lines in two new knots, B3 and A4, at\ndistances of <4\" from the host star with MUSE. The derived $\\dot{M}_{\\rm jet} /\n\\dot{M}_{\\rm acc}$ is about 0.08 and 0.06 for knots B3 and A4, respectively.\nThe observed [Ca II]/[S II] ratios indicate that there is no sign of dust\ngrains at distances of <4\". Assuming the knot A4 traces the streamline, we set\nan upper limit of 2.2 au on the size of the launching region. Although MUSE has\nthe ability to detect the velocity shifts caused by high- and low-velocity\ncomponents, we found no significant evidence of velocity decrease transverse to\nthe jet direction. Our work demonstrates the capability of using MUSE NFM\nobservations for the detailed study of stellar jets in the optical down to\n100~mas. The derived $\\dot{M}_{\\rm jet} / \\dot{M}_{\\rm acc}$, no dust grain,\nand jet radius at the star support the magneto-centrifugal models as a\nlaunching mechanism for the jet.\n"}
+{"abstract": "  This paper presents the projections on the anomalous neutral triple gauge\ncouplings ($aNTGC$) via $pp \\rightarrow ZZ$ production in the 2$\\ell$2$\\nu$\nfinal state at a 100 TeV proton-proton collider, \\verb\"FCC-hh\". %100 TeV center\nof mass energy of The realistic \\verb\"FCC-hh\" detector environments and its\neffects taken into account in the analysis. The study is carried out in the\nmode where one Z boson decays into a pair of same-flavor, opposite-sign leptons\n(electrons or muons) and the other one decays to the two neutrinos. The new\nbounds on the charge-parity (CP)-conserving couplings $C_{\\widetilde{B}W} /\n\\Lambda^{4}$ and CP-violating couplings $C_{WW} / \\Lambda^{4}$, $C_{BW} /\n\\Lambda^{4}$ and $C_{BB} / \\Lambda^{4}$ achived at 95\\% Confidence Level (C.L.)\nusing the transverse momentum of the dilepton system ($p_{T}^{\\ell \\ell}$) are\n$[-\\, 0.042, \\,\\, +\\,0.042]$, $[-\\,0.050, \\,\\, +\\,0.050]$, $[-\\,0.050, \\,\\,\n+\\,0.050]$, and $[-\\,0.048, \\,\\, +\\,0.048]$ in units of TeV$^{-4}$,\nrespectively.\n"}
+{"abstract": "  We consider the problem to learn a concept or a query in the presence of an\nontology formulated in the description logic ELr, in Angluin's framework of\nactive learning that allows the learning algorithm to interactively query an\noracle (such as a domain expert). We show that the following can be learned in\npolynomial time: (1) EL-concepts, (2) symmetry-free ELI-concepts, and (3)\nconjunctive queries (CQs) that are chordal, symmetry-free, and of bounded\narity. In all cases, the learner can pose to the oracle membership queries\nbased on ABoxes and equivalence queries that ask whether a given concept/query\nfrom the considered class is equivalent to the target. The restriction to\nbounded arity in (3) can be removed when we admit unrestricted CQs in\nequivalence queries. We also show that EL-concepts are not polynomial query\nlearnable in the presence of ELI-ontologies.\n"}
+{"abstract": "  We study the complexity of answer counting for ontology-mediated queries and\nfor querying under constraints, considering conjunctive queries and unions\nthereof (UCQs) as the query language and guarded TGDs as the ontology and\nconstraint language, respectively. Our main result is a classification\naccording to whether answer counting is fixed-parameter tractable (FPT),\nW[1]-equivalent, #W[1]-equivalent, #W[2]-hard, or #A[2]-equivalent, lifting a\nrecent classification for UCQs without ontologies and constraints due to Dell\net al. The classification pertains to various structural measures, namely\ntreewidth, contract treewidth, starsize, and linked matching number.\n  Our results rest on the assumption that the arity of relation symbols is\nbounded by a constant and, in the case of ontology-mediated querying, that all\nsymbols from the ontology and query can occur in the data (so-called full data\nschema).\n  We also study the meta-problems for the mentioned structural measures, that\nis, to decide whether a given ontology-mediated query or constraint-query\nspecification is equivalent to one for which the structural measure is bounded.\n"}
+{"abstract": "  In this paper, We present our approach for IEEEBigMM 2020, Grand Challenge\n(BMGC), Identifying senti-ments from tweets related to the MeToo movement. The\nmodelis based on an ensemble of Convolutional Neural Network,Bidirectional LSTM\nand a DNN for final classification. Thispaper is aimed at providing a detailed\nanalysis of the modeland the results obtained. We have ranked 5th out of 10\nteamswith a score of 0.51491\n"}
+{"abstract": "  We study the sequential resource allocation problem where a decision maker\nrepeatedly allocates budgets between resources. Motivating examples include\nallocating limited computing time or wireless spectrum bands to multiple users\n(i.e., resources). At each timestep, the decision maker should distribute its\navailable budgets among different resources to maximize the expected reward, or\nequivalently to minimize the cumulative regret. In doing so, the decision maker\nshould learn the value of the resources allocated for each user from feedback\non each user's received reward. For example, users may send messages of\ndifferent urgency over wireless spectrum bands; the reward generated by\nallocating spectrum to a user then depends on the message's urgency. We assume\neach user's reward follows a random process that is initially unknown. We\ndesign combinatorial multi-armed bandit algorithms to solve this problem with\ndiscrete or continuous budgets. We prove the proposed algorithms achieve\nlogarithmic regrets under semi-bandit feedback.\n"}
+{"abstract": "  New optical sensors with a segmented photosensitive area are being developed\nfor the next generation of neutrino telescopes at the South Pole. In addition\nto increasing sensitivity to high-energy astrophysical neutrinos, we show that\nthis will also lead to a significant improvement in sensitivity to MeV\nneutrinos, such as those produced in core-collapse supernovae (CCSN). These\nlow-energy neutrinos can provide a detailed picture of the events after stellar\ncore collapse, testing our understanding of these violent explosions. We\npresent studies on the event-based detection of MeV neutrinos with a segmented\nsensor and, for the first time, the potential of a corresponding detector in\nthe deep ice at the South Pole for the detection of extra-galactic CCSN. We\nfind that exploiting temporal coincidences between signals in different\nphotocathode segments, a $27\\ \\mathrm{M}_{\\odot}$ progenitor mass CCSN can be\ndetected up to a distance of 341 kpc with a false detection rate of $0.01$\nyear$^{-1}$ with a detector consisting of 10000 sensors. Increasing the number\nof sensors to 20000 and reducing the optical background by a factor of 70\nexpands the range such that a CCSN detection rate of $0.1$ per year is\nachieved, while keeping the false detection rate at $0.01$ year$^{-1}$.\n"}
+{"abstract": "  We address the problem of decoding video file fragments when the necessary\nencoding parameters are missing. With this objective, we propose a method that\nautomatically generates H.264 video headers containing these parameters and\nextracts coded pictures in the partially available compressed video data. To\naccomplish this, we examined a very large corpus of videos to learn patterns of\nencoding settings commonly used by encoders and created a parameter dictionary.\nFurther, to facilitate a more efficient search our method identifies\ncharacteristics of a coded bitstream to discriminate the entropy coding mode.\nIt also utilizes the application logs created by the decoder to identify\ncorrect parameter values. Evaluation of the effectiveness of the proposed\nmethod on more than 55K videos with diverse provenance shows that it can\ngenerate valid headers on average in 11.3 decoding trials per video. This\nresult represents an improvement by more than a factor of 10 over the\nconventional approach of video header stitching to recover video file\nfragments.\n"}
+{"abstract": "  During the recent Coronavirus disease 2019 (COVID-19) outbreak, the\nmicroblogging service Twitter has been widely used to share opinions and\nreactions to events. Italy was one of the first European countries to be\nseverely affected by the outbreak and to establish lockdown and stay-at-home\norders, potentially leading to country reputation damage. We resort to\nsentiment analysis to investigate changes in opinions about Italy reported on\nTwitter before and after the COVID-19 outbreak. Using different lexicons-based\nmethods, we find a breakpoint corresponding to the date of the first\nestablished case of COVID-19 in Italy that causes a relevant change in\nsentiment scores used as proxy of the country reputation. Next, we demonstrate\nthat sentiment scores about Italy are strongly associated with the levels of\nthe FTSE-MIB index, the Italian Stock Exchange main index, as they serve as\nearly detection signals of changes in the values of FTSE-MIB. Finally, we make\na content-based classification of tweets into positive and negative and use two\nmachine learning classifiers to validate the assigned polarity of tweets posted\nbefore and after the outbreak.\n"}
+{"abstract": "  We extend on ideas from standard thermodynamics to show that temperature can\nbe assigned to a general nonequilibrium quantum system. By choosing a\nphysically motivated complete set of observables and expanding the system state\nthereupon, one can read a set of relevant, independent thermodynamic variables\nwhich include internal energy. This expansion allows us to read a\nnonequilibrium temperature as the partial derivative of the von Neumann entropy\nwith respect to internal energy. We show that this definition of temperature is\none of a set of thermodynamics parameters unambiguously describing the system\nstate. It has appealing features such as positivity for passive states and\nconsistency with the standard temperature for thermal states. By attributing\ntemperature to correlations in a bipartite system, we obtain a universal\nrelation which connects the temperatures of subsystems, total system as a\nwhole, and correlation. All these temperatures can be different even when the\ncomposite system is in a well-defined Gibbsian thermal state.\n"}
+{"abstract": "  Development of the methods to exploit nuclear hyperpolarization and search\nfor molecules whose nuclear spins can be efficiently hyperpolarized is an\nactive area in nuclear magnetic resonance. Of particular interest are those\nmolecules that have long nuclear relaxation times, making them to be suitable\ncandidates as contrast agents in magnetic resonance imaging. In this work, we\npresent a detailed study of SABRE SHEATH (Signal Amplification By Reversible\nExchange in Shield Enabled Alignment Transfer to Heteronuclei) experiments of\n15N,15N' azobenzene. In SABRE SHEATH experiments nuclear spins of the target\nare hyperpolarized by transfer of spin polarization from parahydrogen at\nultralow fields during a reversible chemical process. The studied system is\ncomplicated, and we are concerned only about a subset of the data, presenting\ndetails for the molecules that experience fast chemical exchange at the\ncatalytic complex and thus are involved in polarizing the free azobenzene.\nAzobenzene exists in two isomers trans- and cis-. We show that all nuclear\nspins in cis-azobenzene can be efficiently hyperpolarized by SABRE at suitable\nmagnetic fields. Enhancement factors (relative to 9.4 T) reach several\nthousands of times for 15N spins and a few tens of times for the 1H spins.\nThere are two approaches to observe either hyperpolarized magnetization of\n15N/1H spins or hyperpolarized singlet order of the 15N spin pair. We compare\nthese approaches and present the field dependencies of SABRE experiments for\nthem. No hyperpolarization of trans 15N,15N' azobenzene was observed. The\nresults presented here will be useful for further experiments where\nhyperpolarized cis-15N,15N' azobenzene is switched by light to trans 15N,15N'\nazobenzene for storing the produced hyperpolarization in the long-lived spin\nstate of the 15N pair of trans-15N,15N' azobenzene.\n"}
+{"abstract": "  The use of deep learning-based techniques for approximating secure encoding\nfunctions has attracted considerable interest in wireless communications due to\nimpressive results obtained for general coding and decoding tasks for wireless\ncommunication systems. Of particular importance is the development of\nmodel-free techniques that work without knowledge about the underlying channel.\nSuch techniques utilize for example generative adversarial networks to estimate\nand model the conditional channel distribution, mutual information estimation\nas a reward function, or reinforcement learning. In this paper, the approach of\nreinforcement learning is studied and, in particular, the policy gradient\nmethod for a model-free approach of neural network-based secure encoding is\ninvestigated. Previously developed techniques for enforcing a certain co-set\nstructure on the encoding process can be combined with recent reinforcement\nlearning approaches. This new approach is evaluated by extensive simulations,\nand it is demonstrated that the resulting decoding performance of an\neavesdropper is capped at a certain error level.\n"}
+{"abstract": "  We give an overview about some elementary properties of Hoggatt matrices,\nwhich are generalizations of Pascal triangle, and study q-analogs and Fibonacci\nanalogs and derive a common generalization.\n"}
+{"abstract": "  Deep learning approaches show unprecedented results for speckle reduction in\nSAR amplitude images. The wide availability of multi-temporal stacks of SAR\nimages can improve even further the quality of denoising. In this paper, we\npropose a flexible yet efficient way to integrate temporal information into a\ndeep neural network for speckle suppression. Archives provide access to long\ntime-series of SAR images, from which multi-temporal averages can be computed\nwith virtually no remaining speckle fluctuations. The proposed method combines\nthis multi-temporal average and the image at a given date in the form of a\nratio image and uses a state-of-the-art neural network to remove the speckle in\nthis ratio image. This simple strategy is shown to offer a noticeable\nimprovement compared to filtering the original image without knowledge of the\nmulti-temporal average.\n"}
+{"abstract": "  We study the neural network (NN) compression problem, viewing the tension\nbetween the compression ratio and NN performance through the lens of\nrate-distortion theory. We choose a distortion metric that reflects the effect\nof NN compression on the model output and then derive the tradeoff between rate\n(compression ratio) and distortion. In addition to characterizing theoretical\nlimits of NN compression, this formulation shows that \\emph{pruning},\nimplicitly or explicitly, must be a part of a good compression algorithm. This\nobservation bridges a gap between parts of the literature pertaining to NN and\ndata compression, respectively, providing insight into the empirical success of\npruning for NN compression. Finally, we propose a novel pruning strategy\nderived from our information-theoretic formulation and show that it outperforms\nthe relevant baselines on CIFAR-10 and ImageNet datasets.\n"}
+{"abstract": "  If dark matter (DM) interacts with the Standard Model (SM) via the kinetic\nmixing (KM) portal, it necessitates the existence of portal matter (PM)\nparticles which carry both dark and SM quantum numbers that will appear in\nvacuum polarization-like loop graphs. In addition to the familiar $\\sim\ne\\epsilon Q$ strength, QED-like interaction for the dark photon (DP), in some\nsetups different loop graphs of these PM states can also induce other coupling\nstructures for the SM fermions that may come to dominate in at least some\nregions of parameter space regions and which can take the form of `dark'\nmoments, \\eg, magnetic dipole-type interactions in the IR, associated with a\nlarge mass scale, $\\Lambda$. In this paper, motivated by a simple toy model, we\nperform a phenomenological investigation of a possible loop-induced dark\nmagnetic dipole moment for SM fermions, in particular, for the electron. We\nshow that at the phenomenological level such a scenario can not only be made\ncompatible with existing experimental constraints for a significant range of\ncorrelated values for $\\Lambda$ and the dark $U(1)_D$ gauge coupling, $g_D$,\nbut can also lead to quantitatively different signatures once the DP is\ndiscovered. In this setup, assuming complex scalar DM to satisfy CMB\nconstraints, parameter space regions where the DP decays invisibly are found to\nbe somewhat preferred if PM mass limits from direct searches at the LHC and our\ntoy model setup are all taken seriously. High precision searches for, or\nmeasurements of, the $e^+e^- \\to \\gamma +{\\rm DP}$ process at Belle II are\nshown to provide some of the strongest future constraints on this scenario.\n"}
+{"abstract": "  This work considers new entropy-based proofs of some known, or otherwise\nrefined, combinatorial bounds for bipartite graphs. These include upper bounds\non the number of the independent sets, lower bounds on the minimal number of\ncolors in constrained edge coloring, and lower bounds on the number of walks of\na given length in bipartite graphs. The proofs of these combinatorial results\nrely on basic properties of the Shannon entropy.\n"}
+{"abstract": "  To enable personalized cancer treatment, machine learning models have been\ndeveloped to predict drug response as a function of tumor and drug features.\nHowever, most algorithm development efforts have relied on cross validation\nwithin a single study to assess model accuracy. While an essential first step,\ncross validation within a biological data set typically provides an overly\noptimistic estimate of the prediction performance on independent test sets. To\nprovide a more rigorous assessment of model generalizability between different\nstudies, we use machine learning to analyze five publicly available cell\nline-based data sets: NCI60, CTRP, GDSC, CCLE and gCSI. Based on observed\nexperimental variability across studies, we explore estimates of prediction\nupper bounds. We report performance results of a variety of machine learning\nmodels, with a multitasking deep neural network achieving the best cross-study\ngeneralizability. By multiple measures, models trained on CTRP yield the most\naccurate predictions on the remaining testing data, and gCSI is the most\npredictable among the cell line data sets included in this study. With these\nexperiments and further simulations on partial data, two lessons emerge: (1)\ndifferences in viability assays can limit model generalizability across\nstudies, and (2) drug diversity, more than tumor diversity, is crucial for\nraising model generalizability in preclinical screening.\n"}
+{"abstract": "  Fine-grained indoor localization has attracted attention recently because of\nthe rapidly growing demand for indoor location-based services (ILBS).\nSpecifically, massive (large-scale) multiple-input and multiple-output (MIMO)\nsystems have received increasing attention due to high angular resolution. This\npaper presents an indoor localization testbed based on a massive MIMO\northogonal frequency-division multiplexing (OFDM) system, which supports\nphysical-layer channel measurements. Instead of exploiting channel state\ninformation (CSI) directly for localization, we focus on positioning from the\nperspective of multipath components (MPCs), which are extracted from the CSI\nthrough the space-alternating generalized expectation-maximization (SAGE)\nalgorithm. On top of the available MPCs, we propose a generalized\nfingerprinting system based on different single-metric and hybrid-metric\nschemes. We evaluate the impact of the varying antenna topologies, the size of\nthe training set, the number of antennas, and the effective signal-to-noise\nratio (SNR). The experimental results show that the proposed fingerprinting\nmethod can achieve centimeter-level positioning accuracy with a relatively\nsmall training set. Specifically, the distributed uniform linear array obtains\nthe highest accuracy with about 1.63-2.5-cm mean absolute errors resulting from\nthe high spatial resolution.\n"}
+{"abstract": "  Fake News Detection is an essential problem in the field of Natural Language\nProcessing. The benefits of an effective solution in this area are manifold for\nthe goodwill of society. On a surface level, it broadly matches with the\ngeneral problem of text classification. Researchers have proposed various\napproaches to tackle fake news using simple as well as some complex techniques.\nIn this paper, we try to make a comparison between the present Deep Learning\ntechniques by representing the news instances in some vector space using a\ncombination of common mathematical operations with available vector space\nrepresentations. We do a number of experiments using various combinations and\npermutations. Finally, we conclude with a sound analysis of the results and\nevaluate the reasons for such results.\n"}
+{"abstract": "  6G wireless networks will require the flexibility to accommodate an extremely\ndiverse set of service types. This necessitates the use of mixed numerologies\nto accommodate different quality of service (QoS) requirements. Non-orthogonal\nmultiple access (NOMA) techniques can potentially be used to accommodate users\nwith different numerologies while also gaining the performance benefits\nassociated with NOMA. To achieve the full performance benefits of a mixed\nnumerology NOMA (MN-NOMA) system, resource allocation among the users is\nparamount. However, the coexistence of mixed numerologies changes the nature of\nthe interference that each user experiences. This means that techniques used in\nsingle-numerology NOMA (SN-NOMA) are no longer sufficient. In light of this, we\napproach the problem of optimizing subcarrier and power allocation for\nmaximizing the spectral efficiency of MN-NOMA while considering a minimum rate\nconstraint for each user. In this letter, we propose a two-stage sub-optimal\napproach to solve the problem. We present numerical results which show the\nsuperiority of our proposed method over existing benchmark schemes in both\nspectral efficiency and fairness.\n"}
+{"abstract": "  We address the issue of safety in reinforcement learning. We pose the problem\nin an episodic framework of a constrained Markov decision process. Existing\nresults have shown that it is possible to achieve a reward regret of\n$\\tilde{\\mathcal{O}}(\\sqrt{K})$ while allowing an\n$\\tilde{\\mathcal{O}}(\\sqrt{K})$ constraint violation in $K$ episodes. A\ncritical question that arises is whether it is possible to keep the constraint\nviolation even smaller. We show that when a strictly safe policy is known, then\none can confine the system to zero constraint violation with arbitrarily high\nprobability while keeping the reward regret of order\n$\\tilde{\\mathcal{O}}(\\sqrt{K})$. The algorithm which does so employs the\nprinciple of optimistic pessimism in the face of uncertainty to achieve safe\nexploration. When no strictly safe policy is known, though one is known to\nexist, then it is possible to restrict the system to bounded constraint\nviolation with arbitrarily high probability. This is shown to be realized by a\nprimal-dual algorithm with an optimistic primal estimate and a pessimistic dual\nupdate.\n"}
+{"abstract": "  In this early draft, we provide an overview on similarities and differences\nin the implementation of a paper card-based vaccine credential system and an\napp-based vaccine credential system. A vaccine credential's primary goal is to\nregulate entry and ensure safety of individuals within densely packed public\nlocations and workspaces. This is critical for containing the rapid spread of\nCovid-19 in densely packed public locations since a single individual can\ninfect a large majority of people in a crowd. A vaccine credential can also\nprovide information such as an individual's Covid-19 vaccination history and\nadverse symptom reaction history to judge their potential impact on the overall\nhealth of individuals within densely packed public locations and workspaces.\nAfter completing the comparisons, we believe a card-based implementation will\nbenefit regions with less socioeconomic mobility, limited resources, and\nstagnant administrations. An app-based implementation on the other hand will\nbenefit regions with equitable internet access and lower technological divide.\nWe also believe an interoperable system of both credential systems will work\nbest for regions with enormous working-class populations and dense housing\nclusters.\n"}
+{"abstract": "  We study discontinuous interval maps generated by the action of erasing block\nsubstitutions on the binary expansion. After establishing some general\nproperties of these maps, we categorize erasing block substitutions in a\nhierarchy of classes displaying progressively stronger erasing character. We\ninvestigate how this affects the dynamics of the corresponding interval maps,\nshowing that the richest dynamical behavior (Devaney and Li-Yorke chaos,\ninfinite topological entropy) is achieved at a precise step in this hierarchy,\nwhich we name completely erasing substitutions. KEYWORDS: Topological dynamics,\nErasing substitutions; Devaney chaos; Li-Yorke chaos; Topological entropy.\n"}
+{"abstract": "  We use differentiable programming and gradient descent to find unitary\nmatrices that can be used in the period finding algorithm to extract period\ninformation from the state of a quantum computer post application of the\noracle. The standard procedure is to use the inverse quantum Fourier transform.\nOur findings suggest that that this is not the only unitary matrix appropriate\nfor the period finding algorithm, There exist several unitary matrices that can\naffect out the same transformation and they are significantly different from\neach other as well. These unitary matrices can be learned by an algorithm.\nNeural networks can be applied to differentiate such unitary matrices from\nrandomly generated ones indicating that these unitaries do have characteristic\nfeatures that cannot otherwise be discerned easily.\n"}
+{"abstract": "  In this paper, we consider the problem of privacy preservation in the average\nconsensus problem when communication among nodes is quantized. More\nspecifically, we consider a setting where some nodes in the network are curious\nbut not malicious and they try to identify the initial states of other nodes\nbased on the data they receive during their operation (without interfering in\nthe computation in any other way), while some nodes in the network want to\nensure that their initial states cannot be inferred exactly by the curious\nnodes. We propose two privacy-preserving event-triggered quantized average\nconsensus algorithms that can be followed by any node wishing to maintain its\nprivacy and not reveal the initial state it contributes to the average\ncomputation. Every node in the network (including the curious nodes) is allowed\nto execute a privacy-preserving algorithm or its underlying average consensus\nalgorithm. Under certain topological conditions, both algorithms allow the\nnodes who adopt privacypreserving protocols to preserve the privacy of their\ninitial quantized states and at the same time to obtain, after a finite number\nof steps, the exact average of the initial states.\n"}
+{"abstract": "  The price of solar panels is decreasing rapidly and governments around the\nworld are investing in solar power plants to increase the portion of solar\nenergy in the smart grid. The price of electricity produced by solar power\nplants is much cheaper than conventional thermal power plants and innovation in\nsolar cells is further increasing the divide. Consequently, setting up solar\npower plants will decrease electricity spot prices on average. However, the\ntopology of the power grid must be considered while setting up solar power\nplants so that full benefits of cheap solar power can be gained by all\nconsumers. In this paper we will calculate day-ahead spot prices using\nconventional generators. Then one of the generators will be replaced by a\nphotovoltaic generator and effects on day-ahead market will be analyzed. Total\ncost of electricity generated at each hour in a day using conventional power\nplants and solar power plants will be compared. The effects of line congestion\non the benefits of solar power to end customers will be analyzed by comparing\nnodal prices. The optimization problems related to generator scheduling and\nday-ahead markets will be solved using Dynamic Programming, Mixed Integer\nLinear Programming (MILP) and Karush-Kuhn-Tucker (KKT) method for convex\noptimization.\n"}
+{"abstract": "  Cross correlation analyses of high resolution spectroscopic data have\nrecently shown great success in directly detecting planetary signals and\nenabling the characterization of their atmospheres. One such technique aims to\nobserve a system at multiple epochs and combine the measured planetary radial\nvelocities from each epoch into a measurement of the planetary Keplerian\norbital velocity $K_p$, constituting a direct detection of the planetary\nsignal. Recent work has shown that in few-epoch ($\\sim$5) data sets, unintended\nstructure can arise at a high level, obscuring the planetary detection. In this\nwork, we look to simulations to examine whether there are ways to reduce this\nstructured noise in few-epoch data sets by careful planning of observations.\nThe choice of observation date allows observers to select the primary (stellar)\nvelocity - through a set systemic velocity and chosen barycentric velocity -\nand the planetary orbital phase, and so we focus on the effects of these two\nparameters. We find that epochs taken when the primary velocity is near zero,\nand the stellar lines remain relatively fixed to the telluric rest-frame,\ngreatly reduce the level of structured noise and allow for much stronger\nplanetary detections, on average more than twice the significance of detections\nmade with epochs using randomly selected primary velocities. Following these\nresults, we recommend that observers looking to build up high-resolution\nmulti-epoch data sets target nights when their system has a near-zero primary\nvelocity.\n"}
+{"abstract": "  We present CREST, an approach for causal reasoning in simulation to learn the\nrelevant state space for a robot manipulation policy. Our approach conducts\ninterventions using internal models, which are simulations with approximate\ndynamics and simplified assumptions. These interventions elicit the structure\nbetween the state and action spaces, enabling construction of neural network\npolicies with only relevant states as input. These policies are pretrained\nusing the internal model with domain randomization over the relevant states.\nThe policy network weights are then transferred to the target domain (e.g., the\nreal world) for fine tuning. We perform extensive policy transfer experiments\nin simulation for two representative manipulation tasks: block stacking and\ncrate opening. Our policies are shown to be more robust to domain shifts, more\nsample efficient to learn, and scale to more complex settings with larger state\nspaces. We also show improved zero-shot sim-to-real transfer of our policies\nfor the block stacking task.\n"}
+{"abstract": "  Muttalib-Borodin determinants are generalizations of Hankel determinants and\ndepend on a parameter $\\theta>0$. In this paper, we obtain large $n$\nasymptotics for $n \\times n$ Muttalib-Borodin determinants whose weight\npossesses an arbitrary number of Fisher-Hartwig singularities. As a corollary,\nwe obtain asymptotics for the expectation and variance of the real and\nimaginary parts of the logarithm of the characteristic polynomial, several\ncentral limit theorems, and some global bulk rigidity upper bounds. Our results\nare valid for all $\\theta > 0$.\n"}
+{"abstract": "  It is known that a trained Restricted Boltzmann Machine (RBM) on the binary\nMonte Carlo Ising spin configurations, generates a series of iterative\nreconstructed spin configurations which spontaneously flow and stabilize to the\ncritical point of physical system. Here we construct a variety of Neural\nNetwork (NN) flows using the RBM and (variational) autoencoders, to study the\nq-state Potts and clock models on the square lattice for q = 2, 3, 4. The NN\nare trained on Monte Carlo spin configurations at various temperatures. We find\nthat the trained NN flow does develop a stable point that coincides with\ncritical point of the q-state spin models. The behavior of the NN flow is\nnontrivial and generative, since the training is unsupervised and without any\nprior knowledge about the critical point and the Hamiltonian of the underlying\nspin model. Moreover, we find that the convergence of the flow is independent\nof the types of NNs and spin models, hinting a universal behavior. Our results\nstrengthen the potential applicability of the notion of the NN flow in studying\nvarious states of matter and offer additional evidence on the connection with\nthe Renormalization Group flow.\n"}
+{"abstract": "  An Euler tour in a hypergraph is a closed walk that traverses each edge of\nthe hypergraph exactly once, while an Euler family is a family of closed walks\nthat jointly traverse each edge exactly once and cannot be concatenated. In\nthis paper, we show how the problem of existence of an Euler tour (family) in a\nhypergraph $H$ can be reduced to the analogous problem in some smaller\nhypergraphs that are derived from $H$ using an edge cut of $H$. In the process,\nnew techniques of edge cut assignments and collapsed hypergraphs are\nintroduced. Moreover, we describe algorithms based on these characterizations\nthat determine whether or not a hypergraph admits an Euler tour (family), and\ncan also construct an Euler tour (family) if it exists.\n"}
+{"abstract": "  Permissioned blockchains employ Byzantine fault-tolerant (BFT) state machine\nreplication (SMR) to reach agreement on an ever-growing, linearly ordered log\nof transactions. A new paradigm, combined with decades of research in BFT SMR\nand blockchain (namely chained-BFT, or cBFT), has emerged for directly\nconstructing blockchain protocols. Chained-BFT protocols have a unifying\npropose-vote scheme instead of multiple different voting phases with a set of\nvoting and commit rules to guarantee safety and liveness. However, distinct\nvoting and commit rules impose varying impacts on performance under different\nworkloads, network conditions, and Byzantine attacks. Therefore, a fair\ncomparison of the proposed protocols poses a challenge that has not yet been\naddressed by existing work.\n  We fill this gap by studying a family of cBFT protocols with a two-pronged\nsystematic approach. First, we present an evaluation framework, Bamboo, for\nquick prototyping of cBFT protocols and that includes helpful benchmarking\nfacilities. To validate Bamboo, we introduce an analytic model using queuing\ntheory which also offers a back-of-the-envelope guide for dissecting these\nprotocols. We build multiple cBFT protocols using Bamboo and we are the first\nto fairly compare three representatives (i.e., HotStuff, two-chain HotStuff,\nand Streamlet). We evaluated these protocols under various parameters and\nscenarios, including two Byzantine attacks that have not been widely discussed\nin the literature. Our findings reveal interesting trade-offs (e.g.,\nresponsiveness vs. forking-resilience) between different cBFT protocols and\ntheir design choices, which provide developers and researchers with insights\ninto the design and implementation of this protocol family.\n"}
+{"abstract": "  By a ring groupoid we mean an animated ring whose i-th homotopy groups are\nzero for all i>1.\n  In this expository note we give an elementary treatment of the (2,1)-category\nof ring groupoids (i.e., without referring to general animated rings and\nwithout using n-categories for n>2).\n  The note is motivated by the fact that ring stacks play a central role in the\nBhatt-Lurie approach to prismatic cohomology.\n"}
+{"abstract": "  Cortical pyramidal neurons have a complex dendritic anatomy, whose function\nis an active research field. In particular, the segregation between its soma\nand the apical dendritic tree is believed to play an active role in processing\nfeed-forward sensory information and top-down or feedback signals. In this\nwork, we use a simple two-compartment model accounting for the nonlinear\ninteractions between basal and apical input streams and show that standard\nunsupervised Hebbian learning rules in the basal compartment allow the neuron\nto align the feed-forward basal input with the top-down target signal received\nby the apical compartment. We show that this learning process, termed\ncoincidence detection, is robust against strong distractions in the basal input\nspace and demonstrate its effectiveness in a linear classification task.\n"}
+{"abstract": "  Despite intense interest and considerable effort, the current generation of\nneural networks suffers a significant loss of accuracy under most practically\nrelevant privacy training regimes. One particularly challenging class of neural\nnetworks are the wide ones, such as those deployed for NLP typeahead prediction\nor recommender systems. Observing that these models share something in\ncommon--an embedding layer that reduces the dimensionality of the input--we\nfocus on developing a general approach towards training these models that takes\nadvantage of the sparsity of the gradients. More abstractly, we address the\nproblem of differentially private empirical risk minimization (ERM) for models\nthat admit sparse gradients. We demonstrate that for non-convex ERM problems,\nthe loss is logarithmically dependent on the number of parameters, in contrast\nwith polynomial dependence for the general case. Following the same intuition,\nwe propose a novel algorithm for privately training neural networks. Finally,\nwe provide an empirical study of a DP wide neural network on a real-world\ndataset, which has been rarely explored in the previous work.\n"}
+{"abstract": "  The continuous convergence of machine learning algorithms, 5G and beyond\n(5G+) wireless communications, and artificial intelligence (AI) hardware\nimplementation hastens the birth of federated learning (FL) over 5G+ mobile\ndevices, which pushes AI functions to mobile devices and initiates a new era of\non-device AI applications. Despite the remarkable progress made in FL, huge\nenergy consumption is one of the most significant obstacles restricting the\ndevelopment of FL over battery-constrained 5G+ mobile devices. To address this\nissue, in this paper, we investigate how to develop energy efficient FL over\n5G+ mobile devices by making a trade-off between energy consumption for\n\"working\" (i.e., local computing) and that for \"talking\" (i.e., wireless\ncommunications) in order to boost the overall energy efficiency. Specifically,\nwe first examine energy consumption models for graphics processing unit (GPU)\ncomputation and wireless transmissions. Then, we overview the state of the art\nof integrating FL procedure with energy-efficient learning techniques (e.g.,\ngradient sparsification, weight quantization, pruning, etc.). Finally, we\npresent several potential future research directions for FL over 5G+ mobile\ndevices from the perspective of energy efficiency.\n"}
+{"abstract": "  In networks of nonlinear oscillators, symmetries place hard constraints on\nthe system that can be exploited to predict universal dynamical features and\nsteady-states, providing a rare generic organizing principle for\nfar-from-equilibrium systems. However, the robustness of this class of theories\nto symmetry-disrupting imperfections is untested. Here, we develop a model\nexperimental reaction-diffusion network of chemical oscillators to test\napplications of this theory in the context of self-organizing systems relevant\nto biology and soft robotics. The network is a ring of 4 identical\nmicroreactors containing the oscillatory Belousov-Zhabotinsky reaction coupled\nto nearest neighbors via diffusion. Assuming perfect symmetry, theory predicts\n4 categories of stable spatiotemporal phase-locked periodic states and 4\ncategories of invariant manifolds that guide and structure transitions between\nphase-locked states. In our experiments, we observed the predicted\nsymmetry-derived synchronous clustered transients that occur when the dynamical\ntrajectories coincide with invariant manifolds. However, we observe only 3 of\nthe 4 phase-locked states that are predicted for the idealized homogeneous\nsystem. Quantitative agreement between experiment and numerical simulations is\nfound by accounting for the small amount of experimentally determined\nheterogeneity. This work demonstrates that a surprising degree of the network's\ndynamics are constrained by symmetry in spite of the breakdown of the\nassumption of homogeneity and raises the question of why heterogeneity\ndestabilizes some symmetry predicted states, but not others.\n"}
+{"abstract": "  Open set recognition (OSR), aiming to simultaneously classify the seen\nclasses and identify the unseen classes as 'unknown', is essential for reliable\nmachine learning.The key challenge of OSR is how to reduce the empirical\nclassification risk on the labeled known data and the open space risk on the\npotential unknown data simultaneously. To handle the challenge, we formulate\nthe open space risk problem from the perspective of multi-class integration,\nand model the unexploited extra-class space with a novel concept Reciprocal\nPoint. Follow this, a novel learning framework, termed Adversarial Reciprocal\nPoint Learning (ARPL), is proposed to minimize the overlap of known\ndistribution and unknown distributions without loss of known classification\naccuracy. Specifically, each reciprocal point is learned by the extra-class\nspace with the corresponding known category, and the confrontation among\nmultiple known categories are employed to reduce the empirical classification\nrisk. Then, an adversarial margin constraint is proposed to reduce the open\nspace risk by limiting the latent open space constructed by reciprocal points.\nTo further estimate the unknown distribution from open space, an instantiated\nadversarial enhancement method is designed to generate diverse and confusing\ntraining samples, based on the adversarial mechanism between the reciprocal\npoints and known classes. This can effectively enhance the model\ndistinguishability to the unknown classes. Extensive experimental results on\nvarious benchmark datasets indicate that the proposed method is significantly\nsuperior to other existing approaches and achieves state-of-the-art\nperformance.\n"}
+{"abstract": "  Norms represent behavioural aspects that are encouraged by a social group of\nagents or the majority of agents in a system. Normative systems enable\ncoordinating synthesised norms of heterogeneous agents in complex multi-agent\nsystems autonomously. In real applications, agents have multiple objectives\nthat may contradict each other or contradict the synthesised norms. Therefore,\nagents need a mechanism to understand the impact of a suggested norm on their\nobjectives and decide whether or not to adopt it. To address these challenges,\na utility based norm synthesis (UNS) model is proposed which allows the agents\nto coordinate their behaviour while achieving their conflicting objectives. UNS\nproposes a utility-based case-based reasoning technique, using case-based\nreasoning for run-time norm synthesising in a centralised approach, and a\nutility function derived from the objectives of the system and its operating\nagents to decide whether or not to adopt a norm. The model is evaluated using a\ntwo intersecting roads scenario and the results show its efficacy to optimise\nmultiple objectives while adopting synthesised norms.\n"}
+{"abstract": "  We use spectral method to prove a joint equidistribution of primitive\nrational points and the same along expanding horocycle orbits in the products\nof the circle and the unit cotangent bundle of the modular surface. This result\nexplicates the error bound in a recent work of Einsiedler, Luethi, and Shah\n\\cite[Theorem $1.1$]{ELS}. The error is sharp upon the best known progress\ntowards the Ramanujan conjecture at the finite places for the modular surface.\n"}
+{"abstract": "  This thesis focuses on modifications on Einstein's theory of General\nRelativity, which could explain the current problems in gravitation and\ncosmology. More specifically, modifications of the affine structure of the\nspacetime, which is the structure that tells us how to perform derivatives, are\nstudied. These theories appear when imposing that the system must be invariant\nunder the action of the local Poincar\\'e group. Within these theories, their\nstability, the conditions for the appearance of singularities, the movement of\nfermions, and if it is possible to find different Black Hole solutions than in\nEinstein's theory, is explored. Moreover, a non-local extension of the\nmentioned modification is proposed, which can make the theory free of\ninstabilities and singularities at the linear limit.\n"}
+{"abstract": "  In this work, dynamics of chiral domain walls in long and narrow magnetic\nheterostructures based on non-centrosymmetric chiral magnets with bulk\nDzyaloshinskii-Moriya interactions (DMI) and perpendicular magnetic anisotropy\nis systematically investigated. The driving forces can be out-of-plane magnetic\nfields and in-plane currents, correspondingly both steady and precessional\nflows are considered. Their dividing points (the Walker critical field and\ncurrent density) are obtained as functions of bulk DMI strength\n($D_{\\mathrm{b}}$) and the ratio ($\\kappa$) of total (crystalline plus shape)\nanisotropy in the hard axis over that in the easy one. When far beyond Walker\nbreakdown, the dependence curve of wall velocity on external in-plane bias\nfield takes parabolic shape around the compensation point where the total\nin-plane field disappears. The center shift is determined by $D_{\\mathrm{b}}$,\n$\\kappa$, and the wall's topological charge, thus can be used to measure the\nbulk DMI strength in chiral magnets.\n"}
+{"abstract": "  Given a graph $G = (V,E)$, a subgraph $H$ is an \\emph{additive $+\\beta$\nspanner} if $\\dist_H(u,v) \\le \\dist_G(u,v) + \\beta$ for all $u, v \\in V$. A\n\\emph{pairwise spanner} is a spanner for which the above inequality only must\nhold for specific pairs $P \\subseteq V \\times V$ given on input, and when the\npairs have the structure $P = S \\times S$ for some subset $S \\subseteq V$, it\nis specifically called a \\emph{subsetwise spanner}. Spanners in unweighted\ngraphs have been studied extensively in the literature, but have only recently\nbeen generalized to weighted graphs.\n  In this paper, we consider a multi-level version of the subsetwise spanner in\nweighted graphs, where the vertices in $S$ possess varying level, priority, or\nquality of service (QoS) requirements, and the goal is to compute a nested\nsequence of spanners with the minimum number of total edges. We first\ngeneralize the $+2$ subsetwise spanner of [Pettie 2008, Cygan et al., 2013] to\nthe weighted setting. We experimentally measure the performance of this and\nseveral other algorithms for weighted additive spanners, both in terms of\nruntime and sparsity of output spanner, when applied at each level of the\nmulti-level problem. Spanner sparsity is compared to the sparsest possible\nspanner satisfying the given error budget, obtained using an integer\nprogramming formulation of the problem. We run our experiments with respect to\ninput graphs generated by several different random graph generators:\nErd\\H{o}s--R\\'{e}nyi, Watts--Strogatz, Barab\\'{a}si--Albert, and random\ngeometric models. By analyzing our experimental results we developed a new\ntechnique of changing an initialization parameter value that provides better\nperformance in practice.\n"}
+{"abstract": "  Many of these challenges are won by neural network models created by\nfull-time artificial intelligence scientists. Due to this origin, they have a\nblack-box character that makes their use and application less clear to learning\nscientists. We describe our experience with competition from the perspective of\neducational data mining, a field founded in the learning sciences and connected\nwith roots in psychology and statistics. We describe our efforts from the\nperspectives of learning scientists and the challenges to our methods, some\nreal and some imagined. We also discuss some basic results in the Kaggle system\nand our thoughts on how those results may have been improved. Finally, we\ndescribe how learner model predictions are used to make pedagogical decisions\nfor students. Their practical use entails a) model predictions and b) a\ndecision rule (based on the predictions). We point out how increased model\naccuracy can be of limited practical utility, especially when paired with\nsimple decision rules and argue instead for the need to further investigate\noptimal decision rules.\n"}
+{"abstract": "  Several examples are given illustrating the (presumably rather general) fact\nthat bosonic Hamiltonians that are supersymmetrizable automatically possess\nLax-pairs, and square-roots.\n"}
+{"abstract": "  The learning rate (LR) schedule is one of the most important hyper-parameters\nneeding careful tuning in training DNNs. However, it is also one of the least\nautomated parts of machine learning systems and usually costs significant\nmanual effort and computing. Though there are pre-defined LR schedules and\noptimizers with adaptive LR, they introduce new hyperparameters that need to be\ntuned separately for different tasks/datasets. In this paper, we consider the\nquestion: Can we automatically tune the LR over the course of training without\nhuman involvement? We propose an efficient method, AutoLRS, which automatically\noptimizes the LR for each training stage by modeling training dynamics. AutoLRS\naims to find an LR applied to every $\\tau$ steps that minimizes the resulted\nvalidation loss. We solve this black-box optimization on the fly by Bayesian\noptimization (BO). However, collecting training instances for BO requires a\nsystem to evaluate each LR queried by BO's acquisition function for $\\tau$\nsteps, which is prohibitively expensive in practice. Instead, we apply each\ncandidate LR for only $\\tau'\\ll\\tau$ steps and train an exponential model to\npredict the validation loss after $\\tau$ steps. This mutual-training process\nbetween BO and the loss-prediction model allows us to limit the training steps\ninvested in the BO search. We demonstrate the advantages and the generality of\nAutoLRS through extensive experiments of training DNNs for tasks from diverse\ndomains using different optimizers. The LR schedules auto-generated by AutoLRS\nlead to a speedup of $1.22\\times$, $1.43\\times$, and $1.5\\times$ when training\nResNet-50, Transformer, and BERT, respectively, compared to the LR schedules in\ntheir original papers, and an average speedup of $1.31\\times$ over\nstate-of-the-art heavily-tuned LR schedules.\n"}
+{"abstract": "  A classical paradox in high-mass star formation is that powerful radiation\npressure can halt accretion, preventing further growth of a central star. Disk\naccretion has been proposed to solve this problem, but the disks and the\naccretion process in high-mass star formation are poorly understood. We\nexecuted high-resolution ($R$=35,000-70,000) iSHELL spectroscopy in $K$-band\nfor eleven high-mass protostars. Br-$\\gamma$ emission was observed toward eight\nsources, and the line profiles for most of these sources are similar to those\nof low-mass PMS stars. Using an empirical relationship between the Br-$\\gamma$\nand accretion luminosities, we tentatively estimate disk accretion rates\nranging from $\\lesssim$10$^{-8}$ and $\\sim$10$^{-4}$ $M_\\odot$ yr$^{-1}$. These\nlow-mass-accretion rates suggest that high-mass protostars gain more mass via\nepisodic accretion as proposed for low-mass protostars. Given the detection\nlimits, CO overtone emission ($v$=2-0 and 3-1), likely associated with the\ninner disk region ($r \\ll 100$ au), was found towards two sources. This\nlow-detection rate compared with Br-$\\gamma$ emission is consistent with\nprevious observations. Ten out of the eleven sources show absorption at the\n$v$=0-2 ${\\rm R(7)-R(14)}$ CO R-branch. Most of them are either blueshifted or\nredshifted, indicating that the absorption is associated with an outflow or an\ninflow with a velocity of up to $\\sim50$ km s$^{-1}$. Our analysis indicates\nthat the absorption layer is well thermalized (and therefore $n_{\\mathrm H_2}\n\\gtrsim 10^6$ cm$^{-3}$) at a single temperature of typically 100-200 K, and\nlocated within 200-600 au of the star.\n"}
+{"abstract": "  Evaluating predictive models is a crucial task in predictive analytics. This\nprocess is especially challenging with time series data where the observations\nshow temporal dependencies. Several studies have analysed how different\nperformance estimation methods compare with each other for approximating the\ntrue loss incurred by a given forecasting model. However, these studies do not\naddress how the estimators behave for model selection: the ability to select\nthe best solution among a set of alternatives. We address this issue and\ncompare a set of estimation methods for model selection in time series\nforecasting tasks. We attempt to answer two main questions: (i) how often is\nthe best possible model selected by the estimators; and (ii) what is the\nperformance loss when it does not. We empirically found that the accuracy of\nthe estimators for selecting the best solution is low, and the overall\nforecasting performance loss associated with the model selection process ranges\nfrom 1.2% to 2.3%. We also discovered that some factors, such as the sample\nsize, are important in the relative performance of the estimators.\n"}
+{"abstract": "  This paper considers the problem of multi-agent distributed linear regression\nin the presence of system noises. In this problem, the system comprises\nmultiple agents wherein each agent locally observes a set of data points, and\nthe agents' goal is to compute a linear model that best fits the collective\ndata points observed by all the agents. We consider a server-based distributed\narchitecture where the agents interact with a common server to solve the\nproblem; however, the server cannot access the agents' data points. We consider\na practical scenario wherein the system either has observation noise, i.e., the\ndata points observed by the agents are corrupted, or has process noise, i.e.,\nthe computations performed by the server and the agents are corrupted. In\nnoise-free systems, the recently proposed distributed linear regression\nalgorithm, named the Iteratively Pre-conditioned Gradient-descent (IPG) method,\nhas been claimed to converge faster than related methods. In this paper, we\nstudy the robustness of the IPG method, against both the observation noise and\nthe process noise. We empirically show that the robustness of the IPG method\ncompares favorably to the state-of-the-art algorithms.\n"}
+{"abstract": "  In network analysis, many community detection algorithms have been developed,\nhowever, their implementation leaves unaddressed the question of the\nstatistical validation of the results. Here we present robin(ROBustness In\nNetwork), an R package to assess the robustness of the community structure of a\nnetwork found by one or more methods to give indications about their\nreliability. The procedure initially detects if the community structure found\nby a set of algorithms is statistically significant and then compares two\nselected detection algorithms on the same graph to choose the one that better\nfits the network of interest. We demonstrate the use of our package on the\nAmerican College Football benchmark dataset.\n"}
+{"abstract": "  Record linkage is aimed at the accurate and efficient identification of\nrecords that represent the same entity within or across disparate databases. It\nis a fundamental task in data integration and increasingly required for\naccurate decision making in application domains ranging from health analytics\nto national security. Traditional record linkage techniques calculate string\nsimilarities between quasi-identifying (QID) values, such as the names and\naddresses of people. Errors, variations, and missing QID values can however\nlead to low linkage quality because the similarities between records cannot be\ncalculated accurately. To overcome this challenge, we propose a novel technique\nthat can accurately link records even when QID values contain errors or\nvariations, or are missing. We first generate attribute signatures\n(concatenated QID values) using an Apriori based selection of suitable QID\nattributes, and then relational signatures that encapsulate relationship\ninformation between records. Combined, these signatures can uniquely identify\nindividual records and facilitate fast and high quality linking of very large\ndatabases through accurate similarity calculations between records. We evaluate\nthe linkage quality and scalability of our approach using large real-world\ndatabases, showing that it can achieve high linkage quality even when the\ndatabases being linked contain substantial amounts of missing values and\nerrors.\n"}
+{"abstract": "  MUSE-based emission-line maps of the spiral galaxy NGC 4030 reveal the\nexistence of unresolved sources with forbidden line emission enhanced with\nrespect to those seen in its own HII regions. This study reports our efforts to\ndetect and isolate these objects and identify their nature. Candidates are\nfirst detected as unresolved sources on an image of the second principal\ncomponent of the Hb, [OIII]5007, Ha, [NII]6584, [SII]6716, 6731 emission-line\ndata cube, where they stand out clearly against both the dominant HII region\npopulation and the widespread diffuse emission. The intrinsic emission is then\nextracted accounting for the highly inhomogeneous emission-line \"background\"\nthroughout the field of view. Collisional to recombination line ratios like\n[SII]/Ha, [NII]/Ha, and [OI]/Ha tend to increase when the background emission\nis corrected for. We find that many (but not all) sources detected with the\nprincipal component analysis have properties compatible with supernova remnants\n(SNRs). Applying a combined [SII]/Ha and [NII]/Ha classification criterion\nleads to a list of 59 sources with SNR-like emission lines. Many of them\nexhibit conspicuous spectral signatures of SNRs around 7300 Angs, and a\nstacking analysis shows that these features are also present, except weaker, in\nother cases. At nearly 30 Mpc, these are the most distant SNRs detected by\noptical means to date. We further report the serendipitous discovery of a\nluminous (M_V ~ -12.5), blue, and variable source, possibly associated with a\nsupernova impostor.\n"}
+{"abstract": "  We consider multiverses as time-amalgamated multiply warped products of\nLorentzian (Einstein) manifolds. We define the Local Multiverse as\ntimely-connected component of our physical (3+1)-spacetime. It is a collection\nof ``parallel universes\" with (mutually) synchronized timelines. Metaphysical\nconsiderations suggest that the Local Multiverse could be an extremely complex\nagglomeration with, at least, several hundred parallel universes in the Solar\nneighbourhood (and many thousands in galaxy bulks). In this paper we study a\nsimplified time-almagamated globally hyperbolic model. Our picture implies the\nmultiversality of elementary particles which are, actually, transcosmic\n(super)strings with multiple endpoints on parallel universes considered as\nD-branes.\n"}
+{"abstract": "  We investigate how light polarization affects the motion of photo-responsive\nalgae, \\textit{Euglena gracilis}. In a uniformly polarized field, cells swim\napproximately perpendicular to the polarization direction and form a nematic\nstate with zero mean velocity. When light polarization varies spatially, cell\nmotion is modulated by local polarization. In such light fields, cells exhibit\ncomplex spatial distribution and motion patterns which are controlled by\ntopological properties of the underlying fields; we further show that ordered\ncell swimming can generate directed transporting fluid flow. Experimental\nresults are quantitatively reproduced by an active Brownian particle model in\nwhich particle motion direction is nematically coupled to local light\npolarization.\n"}
+{"abstract": "  Recently few-shot object detection is widely adopted to deal with\ndata-limited situations. While most previous works merely focus on the\nperformance on few-shot categories, we claim that detecting all classes is\ncrucial as test samples may contain any instances in realistic applications,\nwhich requires the few-shot detector to learn new concepts without forgetting.\nThrough analysis on transfer learning based methods, some neglected but\nbeneficial properties are utilized to design a simple yet effective few-shot\ndetector, Retentive R-CNN. It consists of Bias-Balanced RPN to debias the\npretrained RPN and Re-detector to find few-shot class objects without\nforgetting previous knowledge. Extensive experiments on few-shot detection\nbenchmarks show that Retentive R-CNN significantly outperforms state-of-the-art\nmethods on overall performance among all settings as it can achieve competitive\nresults on few-shot classes and does not degrade the base class performance at\nall. Our approach has demonstrated that the long desired never-forgetting\nlearner is available in object detection.\n"}
+{"abstract": "  With the advent of advancements in deep learning approaches, such as deep\nconvolution neural network, residual neural network, adversarial network; U-Net\narchitectures are most widely utilized in biomedical image segmentation to\naddress the automation in identification and detection of the target regions or\nsub-regions. In recent studies, U-Net based approaches have illustrated\nstate-of-the-art performance in different applications for the development of\ncomputer-aided diagnosis systems for early diagnosis and treatment of diseases\nsuch as brain tumor, lung cancer, alzheimer, breast cancer, etc., using various\nmodalities. This article contributes in presenting the success of these\napproaches by describing the U-Net framework, followed by the comprehensive\nanalysis of the U-Net variants by performing 1) inter-modality, and 2)\nintra-modality categorization to establish better insights into the associated\nchallenges and solutions. Besides, this article also highlights the\ncontribution of U-Net based frameworks in the ongoing pandemic, severe acute\nrespiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19.\nFinally, the strengths and similarities of these U-Net variants are analysed\nalong with the challenges involved in biomedical image segmentation to uncover\npromising future research directions in this area.\n"}
+{"abstract": "  Excitons in a semiconductor monolayer form a collective resonance that can\nreflect resonant light with extraordinarily high efficiency. Here, we\ninvestigate the nonlinear optical properties of such atomistically thin mirrors\nand show that finite-range interactions between excitons can lead to the\ngeneration of highly non-classical light. We describe two scenarios, in which\noptical nonlinearities arise either from direct photon coupling to excitons in\nexcited Rydberg states or from resonant two-photon excitation of Rydberg\nexcitons with finite-range interactions. The latter case yields conditions of\nelectromagnetically induced transparency and thereby provides an efficient\nmechanism for single-photon switching between high transmission and reflectance\nof the monolayer, with a tunable dynamical timescale of the emerging\nphoton-photon interactions. Remarkably, it turns out that the resulting high\ndegree of photon correlations remains virtually unaffected by Rydberg-state\ndecoherence, in excess of non-radiative decoherence observed for ground-state\nexcitons in two-dimensional semiconductors. This robustness to imperfections\nsuggests a promising new approach to quantum photonics at the level of\nindividual photons.\n"}
+{"abstract": "  This is the first of a two part paper investigating the geometry of the\nintegrator reach sets, and the applications thereof. In this Part I, we\nestablish that this compact convex set is semialgebraic, translated zonoid, and\nnot a spectrahedron. We derive the parametric as well as the implicit\nrepresentation of the boundary of this reach set. We also deduce the closed\nform formula for the volume and diameter of this set, and discuss their scaling\nwith state dimension and time. We point out that these results may be utilized\nin benchmarking the performance of the reach set over-approximation algorithms.\n"}
+{"abstract": "  Storm water detention ponds are used to manage the discharge of rainfall\nrunoff from urban areas to nearby streams. Their purpose is to reduce the\nhydraulic impact and sediment loads of the receiving waters. Detention ponds\nare currently designed based on static controls: the output flow of a pond is\ncapped at a fixed value. This is not optimal with respect to the current\ninfrastructure capacity and for some detention ponds it might even violate\ncurrent regulations set by the European Water Framework Directive. We apply\nformal methods to synthesize (i.e., derive automatically) a safe and optimal\nactive controller. We model the storm water detention pond, including the urban\ncatchment area and the rain forecasts, as a hybrid Markov decision process.\nSubsequently, we use the tool Uppaal Stratego to synthesize a control strategy\nminimizing the cost related to pollution (optimality) while guaranteeing no\nemergency overflow of the detention pond (safety). Simulation results for an\nexisting pond show that Uppaal Stratego can learn optimal strategies that\nprevent emergency overflows, where the current static control is not always\nable to prevent it. At the same time, our approach can improve sedimentation\nduring low rain periods.\n"}
+{"abstract": "  It has been proposed that paramagnetic materials exhibit a unique\nthermoelectric effect near the ferromagnetic transition point due to spin\nfluctuations. This phenomenon is often referred to as paramagnon drag. We\ncalculate the contribution of this paramagnon drag to the Seebeck coefficient\nmicroscopically on the basis of the linear response theory. This leed to a\ngeneral formula for the contribution to the Seebeck coefficient due to the\nparamagnon drag, and we clarify the conditions in which the Seebeck coefficient\nenhances near the ferromagnetic transition point for a single-band and\nisotropic system. Moreover, we calculate the Seebeck coefficients for a band\n$\\varepsilon \\propto k^n$ and a mixture of free-electron-like and flat bands.\n"}
+{"abstract": "  While there have been lots of work studying frequent subgraph mining, very\nrare publications have discussed frequent subnet mining from more complicated\ndata structures such as Petri nets. This paper studies frequent subnets mining\nfrom a single large Petri net. We follow the idea of transforming a Petri net\nin net graph form and to mine frequent sub-net graphs to avoid high complexity.\nTechnically, we take a minimal traversal approach to produce a canonical label\nof the big net graph. We adapted the maximal independent embedding set approach\nto the net graph representation and proposed an incremental pattern growth\n(independent embedding set reduction) way for discovering frequent sub-net\ngraphs from the single large net graph, which are finally transformed back to\nfrequent subnets. Extensive performance studies made on a single large Petri\nnet, which contains 10K events, 40K conditions and 30 K arcs, showed that our\napproach is correct and the complexity is reasonable.\n"}
+{"abstract": "  This article presents the data used to evaluate the performance of\nevolutionary clustering algorithm star (ECA*) compared to five traditional and\nmodern clustering algorithms. Two experimental methods are employed to examine\nthe performance of ECA* against genetic algorithm for clustering++\n(GENCLUST++), learning vector quantisation (LVQ) , expectation maximisation\n(EM) , K-means++ (KM++) and K-means (KM). These algorithms are applied to 32\nheterogenous and multi-featured datasets to determine which one performs well\non the three tests. For one, ther paper examines the efficiency of ECA* in\ncontradiction of its corresponding algorithms using clustering evaluation\nmeasures. These validation criteria are objective function and cluster quality\nmeasures. For another, it suggests a performance rating framework to measurethe\nthe performance sensitivity of these algorithms on varos dataset features\n(cluster dimensionality, number of clusters, cluster overlap, cluster shape and\ncluster structure). The contributions of these experiments are two-folds: (i)\nECA* exceeds its counterpart aloriths in ability to find out the right cluster\nnumber; (ii) ECA* is less sensitive towards dataset features compared to its\ncompetitive techniques. Nonetheless, the results of the experiments performed\ndemonstrate some limitations in the ECA*: (i) ECA* is not fully applied based\non the premise that no prior knowledge exists; (ii) Adapting and utilising ECA*\non several real applications has not been achieved yet.\n"}
+{"abstract": "  Government employees in Brazil are granted tenure after three years on the\njob. Firing a tenured government employee is all but impossible, so tenure is a\nbig employee benefit. But exactly how big is it? In other words: how much money\nis tenure worth to a government employee in Brazil? No one has ever attempted\nto answer that question. I do that in this paper. I use a modified version of\nthe Sharpe ratio to estimate what the risk-adjusted salaries of government\nworkers should be. The difference between actual salary and risk-adjusted\nsalary gives us an estimate of how much tenure is worth to each employee. I\nfind that in the 2005-2019 period the monthly value of tenure was 3980 reais to\nthe median federal government employee, 1971 reais to the median state\ngovernment employee, and 500 reais to the median municipal government employee.\n"}
+{"abstract": "  In this work, we study the partial sums of independent and identically\ndistributed random variables with the number of terms following a fractional\nPoisson (FP) distribution. The FP sum contains the Poisson and geometric\nsummations as particular cases. We show that the weak limit of the FP\nsummation, when properly normalized, is a mixture between the normal and\nMittag-Leffler distributions, which we call by Normal-Mittag-Leffler (NML) law.\nA parameter estimation procedure for the NML distribution is developed and the\nassociated asymptotic distribution is derived. Simulations are performed to\ncheck the performance of the proposed estimators under finite samples. An\nempirical illustration on the daily log-returns of the Brazilian stock exchange\nindex (IBOVESPA) shows that the NML distribution captures better the tails than\nsome of its competitors. Related problems such as a mixed Poisson\nrepresentation for the FP law and the weak convergence for the\nConway-Maxwell-Poisson random sum are also addressed.\n"}
+{"abstract": "  Second-order nonlinear optical processes are used to convert light from one\nwavelength to another and to generate quantum entanglement. Creating chip-scale\ndevices to more efficiently realize and control these interactions greatly\nincreases the reach of photonics. Optical crystals and guided wave devices made\nfrom lithium niobate and potassium titanyl phosphate are typically used to\nrealize second-order processes but face significant drawbacks in scalability,\npower, and tailorability when compared to emerging integrated photonic systems.\nSilicon or silicon nitride integrated photonic circuits enhance and control the\nthird-order optical nonlinearity by confining light in dispersion-engineered\nwaveguides and resonators. An analogous platform for second-order nonlinear\noptics remains an outstanding challenge in photonics. It would enable stronger\ninteractions at lower power and reduce the number of competing nonlinear\nprocesses that emerge. Here we demonstrate efficient frequency doubling and\nparametric oscillation in a thin-film lithium niobate photonic circuit. Our\ndevice combines recent progress on periodically poled thin-film lithium niobate\nwaveguidesand low-loss microresonators. Here we realize efficient >10%\nsecond-harmonic generation and parametric oscillation with microwatts of\noptical power using a periodically-poled thin-film lithium niobate\nmicroresonator. The operating regimes of this system are controlled using the\nrelative detuning of the intracavity resonances. During nondegenerate\noscillation, the emission wavelength is tuned over terahertz by varying the\npump frequency by 100's of megahertz. We observe highly-enhanced effective\nthird-order nonlinearities caused by cascaded second-order processes resulting\nin parametric oscillation. These resonant second-order nonlinear circuits will\nform a crucial part of the emerging nonlinear and quantum photonics platforms.\n"}
+{"abstract": "  The advent of wide-field sky surveys has led to the growth of transient and\nvariable source discoveries. The data deluge produced by these surveys has\nnecessitated the use of machine learning (ML) and deep learning (DL) algorithms\nto sift through the vast incoming data stream. A problem that arises in\nreal-world applications of learning algorithms for classification is imbalanced\ndata, where a class of objects within the data is underrepresented, leading to\na bias for over-represented classes in the ML and DL classifiers. We present a\nrecurrent neural network (RNN) classifier that takes in photometric time-series\ndata and additional contextual information (such as distance to nearby galaxies\nand on-sky position) to produce real-time classification of objects observed by\nthe Gravitational-wave Optical Transient Observer (GOTO), and use an\nalgorithm-level approach for handling imbalance with a focal loss function. The\nclassifier is able to achieve an Area Under the Curve (AUC) score of 0.972 when\nusing all available photometric observations to classify variable stars,\nsupernovae, and active galactic nuclei. The RNN architecture allows us to\nclassify incomplete light curves, and measure how performance improves as more\nobservations are included. We also investigate the role that contextual\ninformation plays in producing reliable object classification.\n"}
+{"abstract": "  We demonstrated broadband highly efficient optical nonlinear processes in\non-chip integrated lithium niobate (LN) microdisk resonators. The Q factors of\nthe micro-resonators fabricated by femtosecond laser writing and\nchemo-mechanical polishing are reliably above 10^8, approaching the intrinsic\nmaterial absorption limit of LN. Broadband nonlinear processes, including\noptical parametric oscillation (OPO), second harmonic generation (SHG), third\nharmonic generation, and fourth harmonic generation, were observed with\nultrahigh efficiencies in the same LN microdisk without introducing domain\ninversion, thanks to the natural quasi phase-matching and the dense spectral\nmodes of the X-cut LN microdisk with millimeter diameter. The threshold of OPO\nand the absolute conversion efficiency of SHG are 19.6 microwatt and 66%, both\nsurpass the state-of-the-art values among on-chip LN micro-resonators\ndemonstrated so far. The broadband and highly efficient nonlinear frequency\nconversions achieved with the ultrahigh-Q LN microdisk resonators promise\nhigh-density integration of nonlinear photonic devices such as frequency\nconvertors and entangled photon sources.\n"}
+{"abstract": "  Under disturbances, superconductors may experience sudden, most undesirable\nphase transitions (quench) from superconducting to normal conducting state.\nQuench may lead to damage or even to catastrophic conductor failure. A\nsuperconductor is stable if it does not quench. Exact determination of\nsuperconductor transient, resistive states (flux flow, Ohmic) thus is mandatory\nto safely avoid quench. This request sharp comparison of local, transient\nconductor temperature and current transport density with local values of\ncritical superconductor temperature, TCrit, and critical current density,\nJCrit, and the latter is a strong function of temperature. Numerical, Finite\nElement simulations reported previously and in this paper have provided the\nrequested, transient temperature distribution; under disturbances, this\ndistribution may strongly be non-uniform. But what happens if the other\nvariable, TCrit, might not uniquely be defined? A multi-physics model\n(fractional parentage, Pauli selection rule, time of flight-concept with a\nmediating Boson, the Yukawa model and the uncertainty principle) provides\nrelaxation time at which TCrit, as the thermodynamic, equilibrium state,\nfinally would be obtained. But relaxation time diverges the closer the electron\nsystem approaches the phase transition, which questions existence of uniquely\ndefined TCrit within finite process time. In this paper, focus is on multiple\ninternal heat transfer (solid conduction and, in thin films, radiation), a\nsuggested operator method to solve the incompleteness problem of radiative\ntransfer, and time dependence of the order parameter obtained from the\nquantum-mechanical model. Traditional stability models cannot provide this\ninformation.\n"}
+{"abstract": "  In this paper, an original reduction algorithm for solving simultaneous\nmultivariate polynomial equations is presented. The algorithm is exponential in\ncomplexity, but the well-known algorithms, such as the extended Euclidean\nalgorithm and Buchberger's algorithm, are superexponential. The\nsuperexponential complexity of the well-known algorithms is due to their not\nbeing \"minimal\" in a certain sense. Buchberger's algorithm produces a Groebner\nbasis. The proposed original reduction algorithm achieves the required task,\nvia computation of determinants of parametric Sylvester matrices, and produces\na Rabin basis, which is shown to be minimal, when two multivariate polynomials\nare reduced at a time. The minimality of Rabin basis allows us to prove\nexponential lower bounds for the space complexity of an algebraic proof of\ncertification, for a specific computational problem in the computational\ncomplexity class PSPACE, showing that the complexity classes PSPACE and P\ncannot be the same. By the same reasoning, it follows that Co-NP is not the\nsame as either of the complexity classes NP and P, and that the polynomial time\nhierarchy does not collapse. It is also shown that the class BPP of languages\ndecidable by bounded error probabilistic algorithms with (probabilistic)\npolynomial time proofs for the membership of input words is not the same as any\none of the complexity classes P, NP and Co-NP. It follows again, from the\ndiscussions, that the complexity classes NP and P are not the same, by\nrelativization of BPP, with respect to P and NP.\n"}
+{"abstract": "  The generating functions for the gauge theory observables are often\nrepresented in terms of the unitary matrix integrals. In this work, the\nperturbative and non-perturbative aspects of the generic multi-critical unitary\nmatrix models are studied by adopting the integrable operator formalism, and\nthe multi-critical generalization of the Tracy--Widom distribution in the\ncontext of random partitions. We obtain the universal results for the\nmulti-critical model in the weak and strong coupling phases. The free energy of\nthe instanton sector in the weak coupling regime, and the genus expansion of\nthe free energy in the strong coupling regime are explicitly computed and the\nuniversal multi-critical phase structure of the model is explored. Finally, we\napply our results in concrete examples of supersymmetric indices of gauge\ntheories in the large $N$ limit.\n"}
+{"abstract": "  Non-equilibrium phase separating systems with reactions can break\ntime-reversal symmetry (TRS) in two distinct ways. Firstly, the conservative\nand non-conservative sectors of the dynamics can be governed by incompatible\nfree energies; when both sectors are present, this is the leading-order TRS\nviolation, captured in its simplest form by 'Model AB'. Second, the diffusive\ndynamics can break TRS in its own right. This happens only at higher order in\nthe gradient expansion (but is the leading behaviour without reactions present)\nand is captured by 'Active Model B+' (AMB+). Each of the two mechanisms can\nlead to microphase separation, by quite different routes. Here we introduce\nModel AB+, for which both mechanisms are simultaneously present, and show that\nfor slow reaction rates the system can undergo a new type of hierarchical\nmicrophase separation, whereby a continuous phase of fluid 1 contains large\ndroplets of fluid 2 within which small droplets of fluid 1 are continuously\ncreated and then absorbed into the surrounding fluid-1 phase. In this state of\n'bubbly microphase separation' the small-scale 1-in-2 droplets arise by the\nconservative diffusive dynamics with the larger scale 2-in-1 structure governed\nby the nonconservative reactions.\n"}
+{"abstract": "  Halo assembly bias is the secondary dependence of the clustering of\ndark-matter haloes on their assembly histories at fixed halo mass. This\nestablished dependence is expected to manifest itself on the clustering of the\ngalaxy population, a potential effect commonly known as galaxy assembly bias.\nUsing the IllustrisTNG300 magnetohydrodynamical simulation, we analyse the\ndependence of the properties and clustering of galaxies on the shape of the\nspecific mass accretion history of their hosting haloes (sMAH). We first show\nthat several halo and galaxy properties strongly correlate with the slope of\nthe sMAH ($\\beta$) at fixed halo mass. Namely, haloes with increasingly steeper\n$\\beta$ increment their halo masses faster at early times, and their hosted\ngalaxies present larger stellar-to-halo mass ratios, lose their gas faster,\nreach the peak of their star formation histories at higher redshift, and become\nquenched earlier. We also demonstrate that $\\beta$ is more directly connected\nto these key galaxy formation properties than other broadly employed halo\nproxies, such as formation time. Finally, we measure the secondary dependence\nof galaxy clustering on $\\beta$ at fixed halo mass as a function of redshift.\nBy tracing back the evolution of individual haloes, we show that the amplitude\nof the galaxy assembly bias signal for the progenitors of $z=0$ galaxies\nincreases with redshift, reaching a factor of 2 at $z = 1$ for haloes of\n$M_\\mathrm{halo}=10^{11.5}-10^{12}$ $h^{-1}\\mathrm{M}_\\odot$. The measurement\nof the evolution of assembly bias along the merger tree provides a new\ntheoretical perspective to the study of secondary bias. Our findings, which\nshow a tight relationship between halo accretion and both the clustering and\nthe observational properties of the galaxy population, have also important\nimplications for the generation of mock catalogues for upcoming cosmological\nsurveys.\n"}
+{"abstract": "  Visual place recognition (VPR) is the problem of recognising a previously\nvisited location using visual information. Many attempts to improve the\nperformance of VPR methods have been made in the literature. One approach that\nhas received attention recently is the multi-process fusion where different VPR\nmethods run in parallel and their outputs are combined in an effort to achieve\nbetter performance. The multi-process fusion, however, does not have a\nwell-defined criterion for selecting and combining different VPR methods from a\nwide range of available options. To the best of our knowledge, this paper\ninvestigates the complementarity of state-of-the-art VPR methods systematically\nfor the first time and identifies those combinations which can result in better\nperformance. The paper presents a well-defined framework which acts as a sanity\ncheck to find the complementarity between two techniques by utilising a\nMcNemar's test-like approach. The framework allows estimation of upper and\nlower complementarity bounds for the VPR techniques to be combined, along with\nan estimate of maximum VPR performance that may be achieved. Based on this\nframework, results are presented for eight state-of-the-art VPR methods on ten\nwidely-used VPR datasets showing the potential of different combinations of\ntechniques for achieving better performance.\n"}
+{"abstract": "  In this work, we consider the Yukawa-type and power-type non-Newtonian\ncorrections, which induce amplification of gravitational interaction on\nsubmillimeter scales, and analytically calculate deviations produced by the\natomic gravitational field on the energy levels of hydrogen-like ions.\nAnalyzing ionic transitions between Rydberg states, we derive prospective\nconstraints for non-Newtonian corrections. It is shown that the results also\nprovide stronger constraints, due to the high accuracy for Rydberg transition\nmeasures into optical spectrum frequency range, than the current empirical\nbounds following from Casimir force measurements.\n"}
+{"abstract": "  Online political advertising is a central aspect of modern election\ncampaigning for influencing public opinion. Computational analysis of political\nads is of utmost importance in political science to understand the\ncharacteristics of digital campaigning. It is also important in computational\nlinguistics to study features of political discourse and communication on a\nlarge scale. In this work, we present the first computational study on online\npolitical ads with the aim to (1) infer the political ideology of an ad\nsponsor; and (2) identify whether the sponsor is an official political party or\na third-party organization. We develop two new large datasets for the two tasks\nconsisting of ads from the U.S.. Evaluation results show that our approach that\ncombines textual and visual information from pre-trained neural models\noutperforms a state-of-the-art method for generic commercial ad classification.\nFinally, we provide an in-depth analysis of the limitations of our\nbest-performing models and linguistic analysis to study the characteristics of\npolitical ads discourse.\n"}
+{"abstract": "  Investigating the grain boundary energies of pure fcc metals and their\nsurface energies obtained from ab initio modeling, we introduce a robust method\nto estimate the grain boundary energies for complex multicomponent alloys. The\ninput parameter is the surface energy of the alloy, which can easily be\naccessed by modern ab initio calculations based on density functional theory.\nThe method is demonstrated in the case of paramagnetic Fe-Cr-Ni alloys for\nwhich reliable grain boundary data is available.\n"}
+{"abstract": "  A space-time framework is applied to simulate dense granular flow. Two\ndifferent numerical experiments are performed: a column collapse and a dam\nbreak on an inclined plane. The experiments are modeled as two-phase flows. The\ndense granular material is represented by a constitutive model, the\n$\\mu$($I$)-rheology, that is based on the Coulomb's friction law, such that the\nnormal stress applied by the pressure is related to the tangential stress. The\nmodel represents a complex shear thinning viscoplastic material behavior. The\ninterface between the dense granular material and the surrounding light fluid\nis captured with a level set function. Due to discontinuities close to the the\ninterface, the mesh requires a sufficient resolution. The space-time approach\nallows unstructured meshes in time and, therefore a well refined mesh in the\ntemporal direction around the interface. In this study, results and performance\nof a flat and a simplex space time discretization are verified and analyzed.\n"}
+{"abstract": "  Positron emission tomography, like many other tomographic imaging modalities,\nrelies on an image reconstruction step to produce cross-sectional images from\nprojection data. Detection and localization of the back-to-back annihilation\nphotons produced by positron-electron annihilation defines the trajectories of\nthese photons, which when combined with tomographic reconstruction algorithms,\npermits recovery of the distribution of positron-emitting radionuclides. Here\nwe produce cross-sectional images directly from the detected coincident\nannihilation photons, without using a reconstruction algorithm. Ultra-fast\nradiation detectors with a resolving time averaging 32 picoseconds measured the\ndifference in arrival time of pairs of annihilation photons, localizing the\nannihilation site to 4.8 mm. This is sufficient to directly generate an image\nwithout reconstruction and without the geometric and sampling constraints that\nnormally present for tomographic imaging systems.\n"}
+{"abstract": "  Neural abstractive summarization has been studied in many pieces of\nliterature and achieves great success with the aid of large corpora. However,\nwhen encountering novel tasks, one may not always benefit from transfer\nlearning due to the domain shifting problem, and overfitting could happen\nwithout adequate labeled examples. Furthermore, the annotations of abstractive\nsummarization are costly, which often demand domain knowledge to ensure the\nground-truth quality. Thus, there are growing appeals for Low-Resource\nAbstractive Summarization, which aims to leverage past experience to improve\nthe performance with limited labeled examples of target corpus. In this paper,\nwe propose to utilize two knowledge-rich sources to tackle this problem, which\nare large pre-trained models and diverse existing corpora. The former can\nprovide the primary ability to tackle summarization tasks; the latter can help\ndiscover common syntactic or semantic information to improve the generalization\nability. We conduct extensive experiments on various summarization corpora with\ndifferent writing styles and forms. The results demonstrate that our approach\nachieves the state-of-the-art on 6 corpora in low-resource scenarios, with only\n0.7% of trainable parameters compared to previous work.\n"}
+{"abstract": "  Recent work in iterative voting has defined the difference in social welfare\nbetween the truthful winner and worst-case equilibrium winner, due to repeated\nstrategic manipulations, known as the additive dynamic price of anarchy\n(ADPoA). While all iterative plurality winners have been shown to differ from\ntruth by at most one initial vote, it is less understood how agents' welfare\nchanges in equilibrium. To this end, we differentiate agents' utility from\ntheir iteration mechanism and determine iterative plurality's ADPoA in the\nworst- and average-case. We first negatively demonstrate that the worst-case\nADPoA is linear in the number of agents. In expectation, rather, equilibrium\nwinners have a constant order welfare advantage over the truthful winner. Our\npositive results illustrate the prospect for social welfare to increase due to\nstrategic manipulation.\n"}
+{"abstract": "  Bid optimization for online advertising from single advertiser's perspective\nhas been thoroughly investigated in both academic research and industrial\npractice. However, existing work typically assume competitors do not change\ntheir bids, i.e., the wining price is fixed, leading to poor performance of the\nderived solution. Although a few studies use multi-agent reinforcement learning\nto set up a cooperative game, they still suffer the following drawbacks: (1)\nThey fail to avoid collusion solutions where all the advertisers involved in an\nauction collude to bid an extremely low price on purpose. (2) Previous works\ncannot well handle the underlying complex bidding environment, leading to poor\nmodel convergence. This problem could be amplified when handling multiple\nobjectives of advertisers which are practical demands but not considered by\nprevious work. In this paper, we propose a novel multi-objective cooperative\nbid optimization formulation called Multi-Agent Cooperative bidding Games\n(MACG). MACG sets up a carefully designed multi-objective optimization\nframework where different objectives of advertisers are incorporated. A global\nobjective to maximize the overall profit of all advertisements is added in\norder to encourage better cooperation and also to protect self-bidding\nadvertisers. To avoid collusion, we also introduce an extra platform revenue\nconstraint. We analyze the optimal functional form of the bidding formula\ntheoretically and design a policy network accordingly to generate auction-level\nbids. Then we design an efficient multi-agent evolutionary strategy for model\noptimization. Offline experiments and online A/B tests conducted on the Taobao\nplatform indicate both single advertiser's objective and global profit have\nbeen significantly improved compared to state-of-art methods.\n"}
+{"abstract": "  We consider a composite Higgs model based on the $SU(6)/Sp(6)$ coset, where\nan $U(1)$ subgroup of $Sp(6)$ is identified as the flavor symmetry. A complex\nscalar field $s$, which is a pseudo-Nambu-Goldstone boson of the broken\nsymmetry, carries a flavor charge and plays the role of a flavon field. The\n$U(1)_F$ flavor symmetry is then broken by a VEV of the flavon field, which\nleads to a small parameter and generates the mass hierarchy between the top and\nbottom quarks. A light flavon below the TeV scale can be naturally introduced,\nwhich provides a fully testable model for the origin of flavor hierarchy. A\nlight flavon also leads to substantial flavor changing neutral currents, which\nare strongly constrained by the flavor precision tests. The direct search of\nadditional scalar bosons can also be conducted in HL-LHC and future hadron\ncolliders.\n"}
+{"abstract": "  We study a dynamic ordered logit model for panel data with fixed effects. We\nestablish the validity of a set of moment conditions that are free of the fixed\neffects and that can be computed using four or more periods of data. We\nestablish sufficient conditions for these moment conditions to identify the\nregression coefficients, the autoregressive parameters, and the threshold\nparameters. The parameters can be estimated using the generalized method of\nmoments. We document the performance of this estimator using Monte Carlo\nsimulations and an empirical illustration to self-reported health status using\nthe British Household Panel Survey.\n"}
+{"abstract": "  We present a new conceptual radiation detector, the Architected Multimaterial\nScintillator System, that utilizes a scintillator composed of multiple\nmaterials arranged in architected structures to enable new capabilities. By\nstructuring differently-dyed materials, the wavelength of the scintillation\nlight encodes additional information in radiation measurements. These\nstructures can be realized through additive manufacture (3D-printing). Two\nclasses of this concept are described and modelled using Monte Carlo\nsimulations to evaluate their performance. The first detector design uses dye\nmicrostructures to encode particle tracking information, allowing for\ndirectional neutron detection and gamma/neutron discrimination. The second\ndesign uses a dye gradient to indicate the position of radiation along the\ngradient. The simulation results indicate this new concept in radiation\ndetection can achieve strong performance in a variety of capabilities including\nparticle identification, directionality and spectroscopy measurements, and\nparticle position reconstruction.\n"}
+{"abstract": "  Accurate prediction of laminar-turbulent transition is a critical element of\ncomputational fluid dynamics simulations for aerodynamic design across multiple\nflow regimes. Traditional methods of transition prediction cannot be easily\nextended to flow configurations where the transition process depends on a large\nset of parameters. In comparison, neural network methods allow higher\ndimensional input features to be considered without compromising the efficiency\nand accuracy of the traditional data driven models. Neural network methods\nproposed earlier follow a cumbersome methodology of predicting instability\ngrowth rates over a broad range of frequencies, which are then processed to\nobtain the N-factor envelope, and then, the transition location based on the\ncorrelating N-factor. This paper presents an end-to-end transition model based\non a recurrent neural network, which sequentially processes the mean\nboundary-layer profiles along the surface of the aerodynamic body to directly\npredict the N-factor envelope and the transition locations over a\ntwo-dimensional airfoil. The proposed transition model has been developed and\nassessed using a large database of 53 airfoils over a wide range of chord\nReynolds numbers and angles of attack. The sequence-to-sequence transduction\nmodel proposed herein provides a more direct approach for accurate predictions\nof the transition location than the earlier neural network methods, which\npredict the local amplification rate of a single instability mode at a fixed\nlocation along the airfoil. The large universe of airfoils encountered in\nvarious applications causes additional difficulties. As such, we provide\nfurther insights on selecting training datasets from large amounts of available\ndata.\n"}
+{"abstract": "  Acoustic waves at microwave frequencies have been widely used in signal\nprocessing applications. They are also emerging as a promising approach for\ncommunication and manipulation of quantum information. However, dynamic control\nof acoustic waves in a low-loss and scalable manner remains an outstanding\nchallenge, which hinders the development of phononic integrated circuits. Here\nwe demonstrate electrical control of traveling acoustic waves on an integrated\nlithium niobate platform at both room and cryogenic temperatures. We\nelectrically tune the material elasticity to modulate the phase and amplitude\nof the acoustic waves and demonstrate an acoustic frequency shifter by\nserrodyne phase modulation. Furthermore, we show reconfigurable nonreciprocal\nmodulation by tailoring the phase matching between acoustic and quasi-traveling\nelectric fields. Our scalable electro-acoustic platform comprises the\nfundamental elements for arbitrary acoustic signal processing and manipulation\nof phononic quantum information.\n"}
+{"abstract": "  The observation of gravitational waves from LIGO and Virgo detectors inferred\nthe mergers rates to be $23.9^{+14.9}_{-8.6}$ Gpc$^{-3}$ yr$^{-1}$ for binary\nblack holes and $320^{+490}_{-240}$ Gpc$^{-3}$ yr$^{-1}$ for binary neutron\nstars. These rates suggest that there is a significant chance that two or more\nof these signals will overlap with each other during their lifetime in the\nsensitivity-band of future gravitational-wave detectors such as the Cosmic\nExplorer and Einstein Telescope. The detection pipelines provide the\ncoalescence time of each signal with an accuracy $\\mathcal{O}(10\\,\\rm ms)$. We\nshow that using the information of the coalescence time, it is possible to\ncorrectly infer the properties of these \"overlapping signals\" with the current\ndata-analysis infrastructure. Studying different configurations of the signals,\nwe conclude that the inference is robust provided that the two signals are not\ncoalescing within less than $\\sim 1-2\\,\\mathrm{s}$. Signals whose coalescence\nepochs lie within $\\sim 0.5\\,\\rm s$ of each other suffer from significant\nbiases in parameter inference, and new strategies and algorithms are required\nto overcome such biases.\n"}
+{"abstract": "  We present a faster symbolic algorithm for the following central problem in\nprobabilistic verification: Compute the maximal end-component (MEC)\ndecomposition of Markov decision processes (MDPs). This problem generalizes the\nSCC decomposition problem of graphs and closed recurrent sets of Markov chains.\nThe model of symbolic algorithms is widely used in formal verification and\nmodel-checking, where access to the input model is restricted to only symbolic\noperations (e.g., basic set operations and computation of one-step\nneighborhood). For an input MDP with $n$ vertices and $m$ edges, the classical\nsymbolic algorithm from the 1990s for the MEC decomposition requires $O(n^2)$\nsymbolic operations and $O(1)$ symbolic space. The only other symbolic\nalgorithm for the MEC decomposition requires $O(n \\sqrt{m})$ symbolic\noperations and $O(\\sqrt{m})$ symbolic space. A main open question is whether\nthe worst-case $O(n^2)$ bound for symbolic operations can be beaten. We present\na symbolic algorithm that requires $\\widetilde{O}(n^{1.5})$ symbolic operations\nand $\\widetilde{O}(\\sqrt{n})$ symbolic space. Moreover, the parametrization of\nour algorithm provides a trade-off between symbolic operations and symbolic\nspace: for all $0<\\epsilon \\leq 1/2$ the symbolic algorithm requires\n$\\widetilde{O}(n^{2-\\epsilon})$ symbolic operations and\n$\\widetilde{O}(n^{\\epsilon})$ symbolic space ($\\widetilde{O}$ hides\npoly-logarithmic factors).\n  Using our techniques we present faster algorithms for computing the\nalmost-sure winning regions of $\\omega$-regular objectives for MDPs. We\nconsider the canonical parity objectives for $\\omega$-regular objectives, and\nfor parity objectives with $d$-priorities we present an algorithm that computes\nthe almost-sure winning region with $\\widetilde{O}(n^{2-\\epsilon})$ symbolic\noperations and $\\widetilde{O}(n^{\\epsilon})$ symbolic space, for all $0 <\n\\epsilon \\leq 1/2$.\n"}
+{"abstract": "  The counting of the dimension of the space of $U(N) \\times U(N) \\times U(N)$\npolynomial invariants of a complex $3$-index tensor as a function of degree $n$\nis known in terms of a sum of squares of Kronecker coefficients. For $n \\le N$,\nthe formula can be expressed in terms of a sum of symmetry factors of\npartitions of $n$ denoted $Z_3(n)$. We derive the large $n$ all-orders\nasymptotic formula for $ Z_3(n)$ making contact with high order results\npreviously obtained numerically. The derivation relies on the dominance in the\nsum, of partitions with many parts of length $1$. The dominance of other small\nparts in restricted partition sums leads to related asymptotic results. The\nresult for the $3$-index tensor observables gives the large $n$ asymptotic\nexpansion for the counting of bipartite ribbon graphs with $n$ edges, and for\nthe dimension of the associated Kronecker permutation centralizer algebra. We\nexplain how the different terms in the asymptotics are associated with\nprobability distributions over ribbon graphs. The large $n$ dominance of small\nparts also leads to conjectured formulae for the asymptotics of invariants for\ngeneral $d$-index tensors. The coefficients of $ 1/n$ in these expansions\ninvolve Stirling numbers of the second kind along with restricted partition\nsums.\n"}
+{"abstract": "  Text generation has received a lot of attention in computational\nargumentation research as of recent. A particularly challenging task is the\ngeneration of counter-arguments. So far, approaches primarily focus on\nrebutting a given conclusion, yet other ways to counter an argument exist. In\nthis work, we go beyond previous research by exploring argument undermining,\nthat is, countering an argument by attacking one of its premises. We\nhypothesize that identifying the argument's weak premises is key to effective\ncountering. Accordingly, we propose a pipeline approach that first assesses the\npremises' strength and then generates a counter-argument targeting the weak\nones. On the one hand, both manual and automatic evaluation proves the\nimportance of identifying weak premises in counter-argument generation. On the\nother hand, when considering correctness and content richness, human annotators\nfavored our approach over state-of-the-art counter-argument generation.\n"}
+{"abstract": "  We introduce a novel method for reconstructing the projected matter\ndistributions of galaxy clusters with weak-lensing (WL) data based on\nconvolutional neural network (CNN). Training datasets are generated with\nray-tracing through cosmological simulations. We control the noise level of the\ngalaxy shear catalog such that it mimics the typical properties of the existing\nground-based WL observations of galaxy clusters. We find that the mass\nreconstruction by our multi-layered CNN with the architecture of alternating\nconvolution and trans-convolution filters significantly outperforms the\ntraditional reconstruction methods. The CNN method provides better\npixel-to-pixel correlations with the truth, restores more accurate positions of\nthe mass peaks, and more efficiently suppresses artifacts near the field edges.\nIn addition, the CNN mass reconstruction lifts the mass-sheet degeneracy when\napplied to our projected cluster mass estimation from sufficiently large\nfields. This implies that this CNN algorithm can be used to measure cluster\nmasses in a model-independent way for future wide-field WL surveys.\n"}
+{"abstract": "  For homogeneous bilinear control systems, the control sets are characterized\nusing a Lie algebra rank condition for the induced systems on projective space.\nThis is based on a classical Diophantine approximation result. For affine\ncontrol systems, the control sets around the equilibria for constant controls\nare characterized with particular attention to the question when the control\nsets are unbounded.\n"}
+{"abstract": "  A sudden loss of vacuum can be catastrophic for particle accelerators. In\nsuch an event, air leaks into the liquid-helium-cooled accelerator beamline\ntube and condenses on its inner surface, causing rapid boiling of the helium\nand dangerous pressure build-up. Understanding the coupled heat and mass\ntransfer processes is important for the design of beamline cryogenic systems.\nOur past experimental study on nitrogen gas propagating in a copper tube cooled\nby normal liquid helium (He I) has revealed a nearly exponential slowing down\nof the gas front. A theoretical model that accounts for the interplay of the\ngas dynamics and the condensation was developed, which successfully reproduced\nvarious key observations. However, since many accelerator beamlines are\nactually cooled by the superfluid phase of helium (He II) in which the heat\ntransfer is via a non-classical thermal-counterflow mode, we need to extend our\nwork to the He II cooled tube. This paper reports our systematic measurements\nusing He II and the numerical simulations based on a modified model that\naccounts for the He II heat-transfer characteristics. By tuning the He II peak\nheat-flux parameter in our model, we have reproduced the observed gas dynamics\nin all experimental runs. The fine-tuned model is then utilized to reliably\nevaluate the heat deposition in He II. This work not only advances our\nunderstanding of condensing gas dynamics but also has practical implications to\nthe design codes for beamline safety.\n"}
+{"abstract": "  We present unconditionally energy stable Runge-Kutta (RK) discontinuous\nGalerkin (DG) schemes for solving a class of fourth order gradient flows. Our\nalgorithm is geared toward arbitrarily high order approximations in both space\nand time, while energy dissipation remains preserved without imposing any\nrestriction on time steps and meshes. We achieve this in two steps. First,\ntaking advantage of the penalty free DG method introduced by Liu and Yin [J\nSci. Comput. 77:467--501, 2018] for spatial discretization, we reformulate an\nextended linearized ODE system by the energy quadratization (EQ) approach.\nSecond, we apply an s-stage algebraically stable RK method for temporal\ndiscretization. The resulting fully discrete DG schemes are linear and\nunconditionally energy stable. In addition, we introduce a\nprediction-correction procedure to improve both the accuracy and stability of\nthe scheme. We illustrate the effectiveness of the proposed schemes by\nnumerical tests with benchmark problems.\n"}
+{"abstract": "  Iron selenide (FeSe) - the structurally simplest iron-based superconductor,\nhas attracted tremendous interest in the past years. While the transition\ntemperature (Tc) of bulk FeSe is $\\sim$ 8 K, it can be significantly enhanced\nto 40 - 50 K by various ways of electron doping. However, the underlying\nphysics for such great enhancement of Tc and so the Cooper pairing mechanism\nstill remain puzzles. Here, we report a systematic study of the\nsuperconducting- and normal-state properties of FeSe films via ionic liquid\ngating. With fine tuning, Tc evolves continuously from below 10 K to above 40\nK; in situ two-coil mutual inductance measurements unambiguously confirm the\ngating is a uniform bulk effect. Close to Tc, the normal-state resistivity\nshows a linear dependence on temperature and the linearity extends to lower\ntemperatures with the superconductivity suppressed by high magnetic fields. At\nhigh fields, the normal-state magnetoresistance exhibits a linear-in-field\ndependence and obeys a simple scaling relation between applied field and\ntemperature. Consistent behaviors are observed for different-Tc states\nthroughout the gating process, suggesting the pairing mechanism very likely\nremains the same from low- to high-Tc state. Importantly, the coefficient of\nthe linear-in-temperature resistivity is positively correlated with Tc,\nsimilarly to the observations in cuprates, Bechgaard salts and iron pnictide\nsuperconductors. Our study points to a short-range antiferromagnetic exchange\ninteraction mediated pairing mechanism in FeSe.\n"}
+{"abstract": "  Nowadays, with the vigorous expansion and development of gaming video\nstreaming techniques and services, the expectation of users, especially the\nmobile phone users, for higher quality of experience is also growing swiftly.\nAs most of the existing research focuses on traditional video streaming, there\nis a clear lack of both subjective study and objective quality models that are\ntailored for quality assessment of mobile gaming content. To this end, in this\nstudy, we first present a brand new Tencent Gaming Video dataset containing\n1293 mobile gaming sequences encoded with three different codecs. Second, we\npropose an objective quality framework, namely Efficient hard-RAnk Quality\nEstimator (ERAQUE), that is equipped with (1) a novel hard pairwise ranking\nloss, which forces the model to put more emphasis on differentiating similar\npairs; (2) an adapted model distillation strategy, which could be utilized to\ncompress the proposed model efficiently without causing significant performance\ndrop. Extensive experiments demonstrate the efficiency and robustness of our\nmodel.\n"}
+{"abstract": "  In this paper, we introduce a novel visual representation learning which\nrelies on a handful of adaptively learned tokens, and which is applicable to\nboth image and video understanding tasks. Instead of relying on hand-designed\nsplitting strategies to obtain visual tokens and processing a large number of\ndensely sampled patches for attention, our approach learns to mine important\ntokens in visual data. This results in efficiently and effectively finding a\nfew important visual tokens and enables modeling of pairwise attention between\nsuch tokens, over a longer temporal horizon for videos, or the spatial content\nin images. Our experiments demonstrate strong performance on several\nchallenging benchmarks for both image and video recognition tasks. Importantly,\ndue to our tokens being adaptive, we accomplish competitive results at\nsignificantly reduced compute amount. We obtain comparable results to the\nstate-of-the-arts on ImageNet while being computationally more efficient. We\nestablish new state-of-the-arts on multiple video datasets, including\nKinetics-400, Kinetics-600, Charades, and AViD.\n  The code is available at:\nhttps://github.com/google-research/scenic/tree/main/scenic/projects/token_learner\n"}
+{"abstract": "  Multi-target prediction (MTP) serves as an umbrella term for machine learning\ntasks that concern the simultaneous prediction of multiple target variables.\nClassical instantiations are multi-label classification, multivariate\nregression, multi-task learning, dyadic prediction, zero-shot learning, network\ninference, and matrix completion. Despite the significant similarities, all\nthese domains have evolved separately into distinct research areas over the\nlast two decades. This led to the development of a plethora of\nhighly-engineered methods, and created a substantially-high entrance barrier\nfor machine learning practitioners that are not experts in the field. In this\nwork we present a generic deep learning methodology that can be used for a wide\nrange of multi-target prediction problems. We introduce a flexible multi-branch\nneural network architecture, partially configured via a questionnaire that\nhelps end-users to select a suitable MTP problem setting for their needs.\nExperimental results for a wide range of domains illustrate that the proposed\nmethodology manifests a competitive performance compared to methods from\nspecific MTP domains.\n"}
+{"abstract": "  Extreme-scale cosmological simulations have been widely used by today's\nresearchers and scientists on leadership supercomputers. A new generation of\nerror-bounded lossy compressors has been used in workflows to reduce storage\nrequirements and minimize the impact of throughput limitations while saving\nlarge snapshots of high-fidelity data for post-hoc analysis. In this paper, we\npropose to adaptively provide compression configurations to compute partitions\nof cosmological simulations with newly designed post-analysis aware\nrate-quality modeling. The contribution is fourfold: (1) We propose a novel\nadaptive approach to select feasible error bounds for different partitions,\nshowing the possibility and efficiency of adaptively configuring lossy\ncompression for each partition individually. (2) We build models to estimate\nthe overall loss of post-analysis result due to lossy compression and to\nestimate compression ratio, based on the property of each partition. (3) We\ndevelop an efficient optimization guideline to determine the best-fit\nconfiguration of error bounds combination in order to maximize the compression\nratio under acceptable post-analysis quality loss. (4) Our approach introduces\nnegligible overheads for feature extraction and error-bound optimization for\neach partition, enabling post-analysis-aware in situ lossy compression for\ncosmological simulations. Experiments show that our proposed models are highly\naccurate and reliable. Our fine-grained adaptive configuration approach\nimproves the compression ratio of up to 73% on the tested datasets with the\nsame post-analysis distortion with only 1% performance overhead.\n"}
+{"abstract": "  Sequence-to-sequence (seq2seq) models are competitive with hybrid models for\nautomatic speech recognition (ASR) tasks when large amounts of training data\nare available. However, data sparsity and domain adaptation are more\nproblematic for seq2seq models than their hybrid counterparts. We examine\ncorpora of five languages from the IARPA MATERIAL program where the transcribed\ndata is conversational telephone speech (CTS) and evaluation data is broadcast\nnews (BN). We show that there is a sizable initial gap in such a data condition\nbetween hybrid and seq2seq models, and the hybrid model is able to further\nimprove through the use of additional language model (LM) data. We use an\nadditional set of untranscribed data primarily in the BN domain for\nsemisupervised training. In semisupervised training, a seed model trained on\ntranscribed data generates hypothesized transcripts for unlabeled\ndomain-matched data for further training. By using a hybrid model with an\nexpanded language model for pseudotranscription, we are able to improve our\nseq2seq model from an average word error rate (WER) of 66.7% across all five\nlanguages to 29.0% WER. While this puts the seq2seq model at a competitive\noperating point, hybrid models are still able to use additional LM data to\nmaintain an advantage.\n"}
+{"abstract": "  The anthology of spoken languages today is inundated with textual\ninformation, necessitating the development of automatic summarization models.\nIn this manuscript, we propose an extractor-paraphraser based abstractive\nsummarization system that exploits semantic overlap as opposed to its\npredecessors that focus more on syntactic information overlap. Our model\noutperforms the state-of-the-art baselines in terms of ROUGE, METEOR and word\nmover similarity (WMS), establishing the superiority of the proposed system via\nextensive ablation experiments. We have also challenged the summarization\ncapabilities of the state of the art Pointer Generator Network (PGN), and\nthrough thorough experimentation, shown that PGN is more of a paraphraser,\ncontrary to the prevailing notion of a summarizer; illustrating it's\nincapability to accumulate information across multiple sentences.\n"}
+{"abstract": "  We consider the problem of computing reachable sets directly from noisy data\nwithout a given system model. Several reachability algorithms are presented,\nand their accuracy is shown to depend on the underlying system generating the\ndata. First, an algorithm for computing over-approximated reachable sets based\non matrix zonotopes is proposed for linear systems. Constrained matrix\nzonotopes are introduced to provide less conservative reachable sets at the\ncost of increased computational expenses and utilized to incorporate prior\nknowledge about the unknown system model. Then we extend the approach to\npolynomial systems and under the assumption of Lipschitz continuity to\nnonlinear systems. Theoretical guarantees are given for these algorithms in\nthat they give a proper over-approximative reachable set containing the true\nreachable set. Multiple numerical examples show the applicability of the\nintroduced algorithms, and accuracy comparisons are made between algorithms.\n"}
+{"abstract": "  Epitaxially-grown quantum well and quantum dot solar cells suffer from weak\nlight absorption, strongly limiting their performance. Light trapping based on\noptical resonances is particularly relevant for such devices to increase light\nabsorption and thereby current generation. Compared to homogeneous media, the\nposition of the quantum layers within the device is an additional parameter\nthat can strongly influence resonant absorption. However, this effect has so\nfar received little attention from the photovoltaic community. In this work, we\ndevelop a theoretical framework to evaluate and optimize resonant light\nabsorption in a thin slab with multiple quantum layers. Using numerical\nsimulations, we show that the position of the layers can make the difference\nbetween strong absorption enhancement and completely suppressed absorption, and\nthat an optimal position leads to an absorption enhancement twice larger than\naverage. We confirm these results experimentally by measuring the absorption\nenhancement from photoluminescence spectra in InAs/GaAs quantum dot samples.\nOverall, this work provides an additional degree of freedom to substantially\nimprove absorption, encouraging the development of quantum wells and quantum\ndots-based devices such as intermediate-band solar cells.\n"}
+{"abstract": "  In this paper we develop the stability rules for NASICON structured\nmaterials, as an example of compounds with complex bond topology and\ncomposition. By applying machine learning to the ab-initio computed phase\nstability of 3881 potential NASICONs we can extract a simple two-dimensional\ndescriptor that is extremely good at separating stable from unstable NASICONS.\nThis machine-learned \"tolerance factor\" contains information on the Na content,\nthe radii and electronegativities of the elements, and the Madelung energy. We\ntest the predictive capability of this approach by selecting six predicted\nNASICON compositions. Five out of the six resulted in a phase pure NASICON\nwhile the sixth composition led to a NASICON that coexisted with other phases,\nvalidating the efficacy of this approach. This work not only provide tools to\nunderstand synthetic accessibility of NASICON-type materials, but also\ndemonstrate an efficient paradigm for discovering new materials with complicate\ncomposition and atomic structure.\n"}
+{"abstract": "  Humans often communicate by using imprecise language, suggesting that fuzzy\nconcepts with unclear boundaries are prevalent in language use. In this paper,\nwe test the extent to which models trained to capture the distributional\nstatistics of language show correspondence to fuzzy-membership patterns. Using\nthe task of natural language inference, we test a recent state of the art model\non the classical case of temperature, by examining its mapping of temperature\ndata to fuzzy-perceptions such as \"cool\", \"hot\", etc. We find the model to show\npatterns that are similar to classical fuzzy-set theoretic formulations of\nlinguistic hedges, albeit with a substantial amount of noise, suggesting that\nmodels trained solely on language show promise in encoding fuzziness.\n"}
+{"abstract": "  Elements with low First Ionization Potential (FIP) are known to be three to\nfour times more abundant in active region loops of the solar corona than in the\nphotosphere. There have been observations suggesting that this observed \"FIP\nbias\" may be different in other parts of the solar corona and such observations\nare thus important in understanding the underlying mechanism. The Solar X-ray\nMonitor (XSM) on board the Chandrayaan-2 mission carried out spectroscopic\nobservations of the Sun in soft X-rays during the 2019-20 solar minimum,\nconsidered to be the quietest solar minimum of the past century. These\nobservations provided a unique opportunity to study soft X-ray spectra of the\nquiescent solar corona in the absence of any active regions. By modelling high\nresolution broadband X-ray spectra from XSM, we estimate the temperature and\nemission measure during periods of possibly the lowest solar X-ray intensity.\nWe find that the derived parameters remain nearly constant over time with a\ntemperature around 2 MK, suggesting the emission is dominated by X-ray Bright\nPoints (XBPs). We also obtain the abundances of Mg, Al, and Si relative to H,\nand find that the FIP bias is ~2, lower than the values observed in active\nregions.\n"}
+{"abstract": "  Datasets displaying temporal dependencies abound in science and engineering\napplications, with Markov models representing a simplified and popular view of\nthe temporal dependence structure. In this paper, we consider Bayesian settings\nthat place prior distributions over the parameters of the transition kernel of\na Markov model, and seeks to characterize the resulting, typically intractable,\nposterior distributions. We present a PAC-Bayesian analysis of variational\nBayes (VB) approximations to tempered Bayesian posterior distributions,\nbounding the model risk of the VB approximations. Tempered posteriors are known\nto be robust to model misspecification, and their variational approximations do\nnot suffer the usual problems of over confident approximations. Our results tie\nthe risk bounds to the mixing and ergodic properties of the Markov data\ngenerating model. We illustrate the PAC-Bayes bounds through a number of\nexample Markov models, and also consider the situation where the Markov model\nis misspecified.\n"}
+{"abstract": "  We present an approach for Task-Motion Planning (TMP) using Iterative\nDeepened AND/OR Graph Networks (TMP-IDAN) that uses an AND/OR graph network\nbased novel abstraction for compactly representing the task-level states and\nactions. While retrieving a target object from clutter, the number of object\nre-arrangements required to grasp the target is not known ahead of time. To\naddress this challenge, in contrast to traditional AND/OR graph-based planners,\nwe grow the AND/OR graph online until the target grasp is feasible and thereby\nobtain a network of AND/OR graphs. The AND/OR graph network allows faster\ncomputations than traditional task planners. We validate our approach and\nevaluate its capabilities using a Baxter robot and a state-of-the-art robotics\nsimulator in several challenging non-trivial cluttered table-top scenarios. The\nexperiments show that our approach is readily scalable to increasing number of\nobjects and different degrees of clutter.\n"}
+{"abstract": "  In this paper we consider PDE's problems involving the anisotropic Laplacian\noperator, with Robin boundary conditions. By means of Talenti techniques,\nwidely used in the last decades, we prove a comparison result between the\nsolutions of the above-mentioned problems and the solutions of the symmetrized\nones. As a consequence of these results, a Bossel-Daners type inequality can be\nshown in dimension 2.\n"}
+{"abstract": "  In a recent Letter [Phys. Rev. Lett. 126, 063902 (2021)], M. A. Carroll et\nal. derived a model to analytically determine regimes of thermal, collective\nanti-bunching, and laser emission for emitters in a cavity. According to their\nmodel, nanolasers exhibit a distinct threshold at which the coherent laser\nfield emerges from a bifurcation at a finite pump rate. The amplitude of this\ncoherent field increases with a further increase in the pump rate. Such a\nbehavior contrasts the usual view of the transition to lasing in single-mode\nhigh-$\\beta$ nanolasers, according to which strong spontaneous emission into\nthe lasing mode results in a smooth transformation from the thermal state to\nthe coherent state as the pump rate increases. Here, we demonstrate that the\nauthors have ignored important terms in the equations of their model, which\ncaused the bifurcation and the ideally monochromatic field to emerge at a\nfinite pump rate.\n"}
+{"abstract": "  In this paper, we propose a clearing model for prices in a financial markets\ndue to margin calls on short sold assets. In doing so, we construct an explicit\nformulation for the prices that would result immediately following asset\npurchases and a margin call. The key result of this work is the determination\nof a threshold short interest ratio which, if exceeded, results in the\ndiscontinuity of the clearing prices due to a feedback loop.\n"}
+{"abstract": "  A central question in resource theory is whether one can construct a set of\nmonotones that completely characterise the allowed transitions dictated by a\nset of free operations. A similar question is whether two distinct sets of free\noperations generate the same class of transitions. These questions are part of\nthe more general problem of whether it is possible to pass from one\ncharacterisation of a resource theory to another. In the present letter we\nprove that in the context of quantum resource theories this class of problems\nis undecidable in general. This is done by proving the undecidability of the\nmembership problem for CPTP maps, which subsumes all the other results.\n"}
+{"abstract": "  The Covid-19 pandemic caused disruptive effects for individuals, firms, and\nsocieties. In this paper, we offer insights on the major issues and challenges\nfirms are facing in the Covid-19 pandemic, as well as their concerns for\nCorporate Social Responsibility (CSR) themes. To do so, we investigate large\nItalian firms' discussion on Twitter in the first nine months of the pandemic.\nWe downloaded all Twitter posts from 1st of March, 2020, to 17th of November,\n2020 by the accounts of the largest Italian firms, i.e. those with 250 or more\nemployees. We then built the bipartite network of accounts and hashtags and,\nusing an entropy-based null model as a benchmark, we projected the information\ncontained in the network into the accounts layers, identifying a network of\naccounts in which a link indicates a non trivial similarity in terms of their\nusage of hashtags. We find that the conversation is focused around 13\ncommunities, 10 of which include Covid-19 themes. The core of the network is\nformed of 5 communities, which deal with environmental sustainability, digital\ninnovation and safety. Firms' ownership type does not seem to influence the\nconversation. 10 communities out of 13 mention hashtags related to CSR, with\nthe environmental and social dimensions as the prevalent ones. Interestingly\nenough, the social dimension seems more relevant in the communities dealing\nwith digital innovation and safety. However, the relevance of CSR hashtags is\nvery small at the single message level, but with some peculiarities arising in\nspecific communities. Overall, our paper highlights the role of network methods\non Twitter data as a tool which can support managers and policy makers to\ndesign their strategies and decision making, capturing firms' emerging issues\nand relevant themes.\n"}
+{"abstract": "  Recommender systems (RSs) are becoming an inseparable part of our everyday\nlives. They help us find our favorite items to purchase, our friends on social\nnetworks, and our favorite movies to watch. Traditionally, the recommendation\nproblem was considered as a simple classification or prediction problem;\nhowever, the sequential nature of the recommendation problem has been shown.\nAccordingly, it can be formulated as a Markov decision process (MDP) and\nreinforcement learning (RL) methods can be employed to solve it. In fact,\nrecent advances in combining deep learning with traditional RL methods, i.e.\ndeep reinforcement learning (DRL), has made it possible to apply RL to the\nrecommendation problem with massive state and action spaces. In this paper, a\nsurvey on reinforcement learning based recommender systems (RLRSs) is\npresented. We first recognize the fact that algorithms developed for RLRSs can\nbe generally classified into RL- and DRL-based methods. Then, we present these\nRL- and DRL-based methods in a classified manner based on the specific RL\nalgorithm, e.g., Q-learning, SARSA, and REINFORCE, that is used to optimize the\nrecommendation policy. Furthermore, some tables are presented that contain\ndetailed information about the MDP formulation of these methods, as well as\nabout their evaluation schemes. Finally, we discuss important aspects and\nchallenges that can be addressed in the future.\n"}
+{"abstract": "  We show that a class of compounds with $I$4/$mcm$ crystalline symmetry hosts\nthree-dimensional semi-Dirac fermions. Unlike the known two-dimensional\nsemi-Dirac points, the degeneracy of these three-dimensional semi-Dirac points\nis not lifted by spin-orbit coupling due to the protection by a nonsymmorphic\nsymmetry -- screw rotation in the $a-b$ plane and a translation along the $c$\naxis. This crystalline symmetry is found in tetragonal perovskite oxides,\nrealizable in thin films by epitaxial strain that results in\na$^0$a$^0$c$^-$-type octahedral rotation. Interestingly, with broken\ntime-reversal symmetry, two pairs of Weyl points emerge from the semi-Dirac\npoints within the Brillouin zone, and an additional lattice distortion leads to\nenhanced intrinsic anomalous Hall effect. The ability to tune the Berry phase\nby epitaxial strain can be useful in novel oxide-based electronic devices.\n"}
+{"abstract": "  Hydrogels hold promise in agriculture as reservoirs of water in dry soil,\npotentially alleviating the burden of irrigation. However, confinement in soil\ncan markedly reduce the ability of hydrogels to absorb water and swell,\nlimiting their widespread adoption. Unfortunately, the underlying reason\nremains unknown. By directly visualizing the swelling of hydrogels confined in\nthree-dimensional granular media, we demonstrate that the extent of hydrogel\nswelling is determined by the competition between the force exerted by the\nhydrogel due to osmotic swelling and the confining force transmitted by the\nsurrounding grains. Furthermore, the medium can itself be restructured by\nhydrogel swelling, as set by the balance between the osmotic swelling force,\nthe confining force, and intergrain friction. Together, our results provide\nquantitative principles to predict how hydrogels behave in confinement,\npotentially improving their use in agriculture as well as informing other\napplications such as oil recovery, construction, mechanobiology, and\nfiltration.\n"}
+{"abstract": "  We present a method for a real time visualization and automatic processing\nfor detection and classification of untouched cancer cells in blood during\nstain free imaging flow cytometry using digital holographic microscopy and\nmachine learning in throughput of 15 cells per second. As a preliminary model\nfor circulating tumor cells in blood, we automatically classified primary and\nmetastatic colon cancer cells, where the two types of cancer cells were\nisolated from the same individual, as well as four types of blood cells. We\nused low-coherence off-axis interferometric phase microscopy and a microfluidic\nchannel to quantitatively image cells during flow. The acquired images were\nprocessed and classified based on their morphology and quantitative phase\nfeatures during the cell flow. We achieved high accuracy of 92.56 percent for\ndistinguishing between the cells, paving the way for future automatic\nlabel-free cancer cell classification in blood samples.\n"}
+{"abstract": "  Self-attention mechanism recently achieves impressive advancement in Natural\nLanguage Processing (NLP) and Image Processing domains. And its permutation\ninvariance property makes it ideally suitable for point cloud processing.\nInspired by this remarkable success, we propose an end-to-end architecture,\ndubbed Cross-Level Cross-Scale Cross-Attention Network (CLCSCANet), for point\ncloud representation learning. First, a point-wise feature pyramid module is\nintroduced to hierarchically extract features from different scales or\nresolutions. Then a cross-level cross-attention is designed to model long-range\ninter-level and intra-level dependencies. Finally, we develop a cross-scale\ncross-attention module to capture interactions between-and-within scales for\nrepresentation enhancement. Compared with state-of-the-art approaches, our\nnetwork can obtain competitive performance on challenging 3D object\nclassification, point cloud segmentation tasks via comprehensive experimental\nevaluation.\n"}
+{"abstract": "  Space-filling designs are important in computer experiments, which are\ncritical for building a cheap surrogate model that adequately approximates an\nexpensive computer code. Many design construction techniques in the existing\nliterature are only applicable for rectangular bounded space, but in real world\napplications, the input space can often be non-rectangular because of\nconstraints on the input variables. One solution to generate designs in a\nconstrained space is to first generate uniformly distributed samples in the\nfeasible region, and then use them as the candidate set to construct the\ndesigns. Sequentially Constrained Monte Carlo (SCMC) is the state-of-the-art\ntechnique for candidate generation, but it still requires large number of\nconstraint evaluations, which is problematic especially when the constraints\nare expensive to evaluate. Thus, to reduce constraint evaluations and improve\nefficiency, we propose the Constrained Minimum Energy Design (CoMinED) that\nutilizes recent advances in deterministic sampling methods. Extensive\nsimulation results on 15 benchmark problems with dimensions ranging from 2 to\n13 are provided for demonstrating the improved performance of CoMinED over the\nexisting methods.\n"}
+{"abstract": "  In this report, we summarize the set partition enumeration problems and\nthoroughly explain the algorithms used to solve them. These algorithms iterate\nthrough the partitions in lexicographic order and are easy to understand and\nimplement in modern high-level programming languages, without recursive\nstructures and jump logic. We show that they require linear space in respect to\nthe set cardinality and advance the enumeration in constant amortized time. The\nmethods discussed in this document are not novel. Our goal is to demonstrate\nthe process of enumerating set partitions and highlight the ideas behind it.\nThis work is an aid for learners approaching this enumeration problem and\nprogrammers undertaking the task of implementing it.\n"}
+{"abstract": "  In this paper we prove new bounds for sums of convex or concave functions.\nSpecifically, we prove that for all $A,B \\subseteq \\mathbb R$ finite sets, and\nfor all $f,g$ convex or concave functions, we have\n  $$|A + B|^{38}|f(A) + g(B)|^{38} \\gtrsim |A|^{49}|B|^{49}.$$\n  This result can be used to obtain bounds on a number of two-variable\nexpanders of interest, as well as to the asymmetric sum-product problem. We\nalso adjust our technique to also prove the three-variable expansion result\n  \\[\n  |AB+A|\\gtrsim |A|^{\\frac32 +\\frac3{170}}\\,.\n  \\]\n  Our methods follow a series of recent developments in the sum-product\nliterature, presenting a unified picture. Of particular interest is an\nadaptation of a regularisation technique of Xue, that enables us to find\npositive proportion subsets with certain desirable properties.\n"}
+{"abstract": "  The Group Quantization formalism is a scheme for constructing a functional\nspace that is an irreducible infinite dimensional representation of the Lie\nalgebra belonging to a dynamical symmetry group. We apply this formalism to the\nconstruction of functional space and operators for quadratic potentials --\ngaussian pricing kernels in finance. We describe the Black-Scholes theory, the\nHo-Lee interest rate model and the Euclidean repulsive and attractive\noscillators. The symmetry group used in this work has the structure of a\nprincipal bundle with base (dynamical) group a semi-direct extension of the\nHeisenberg-Weyl group by SL(2,R), and structure group (fiber) the positive real\nline.\n"}
+{"abstract": "  Recent works leveraging Graph Neural Networks to approach graph matching\ntasks have shown promising results. Recent progress in learning discrete\ndistributions poses new opportunities for learning graph matching models. In\nthis work, we propose a new model, Stochastic Iterative Graph MAtching (SIGMA),\nto address the graph matching problem. Our model defines a distribution of\nmatchings for a graph pair so the model can explore a wide range of possible\nmatchings. We further introduce a novel multi-step matching procedure, which\nlearns how to refine a graph pair's matching results incrementally. The model\nalso includes dummy nodes so that the model does not have to find matchings for\nnodes without correspondence. We fit this model to data via scalable stochastic\noptimization. We conduct extensive experiments across synthetic graph datasets\nas well as biochemistry and computer vision applications. Across all tasks, our\nresults show that SIGMA can produce significantly improved graph matching\nresults compared to state-of-the-art models. Ablation studies verify that each\nof our components (stochastic training, iterative matching, and dummy nodes)\noffers noticeable improvement.\n"}
+{"abstract": "  This is a contribution to the program of dynamical approach to mean curvature\nflow initiated by Colding and Minicozzi. In this paper, we prove two main\ntheorems. The first one is local in nature and the second one is global. In\nthis first result, we pursue the stream of ideas of \\cite{CM3} and get a slight\nrefinement of their results. We apply the invariant manifold theory from\nhyperbolic dynamics to study the dynamics close to a closed shrinker that is\nnot a sphere. In the second theorem, we show that if a hypersurface under the\nrescaled mean curvature flow converges to a closed shrinker that is not a\nsphere, then a generic perturbation on initial data would make the flow leave a\nsmall neighborhood of the shrinker and never come back.\n"}
+{"abstract": "  For treatment effects - one of the core issues in modern econometric analysis\n- prediction and estimation are two sides of the same coin. As it turns out,\nmachine learning methods are the tool for generalized prediction models.\nCombined with econometric theory, they allow us to estimate not only the\naverage but a personalized treatment effect - the conditional average treatment\neffect (CATE). In this tutorial, we give an overview of novel methods, explain\nthem in detail, and apply them via Quantlets in real data applications. We\nstudy the effect that microcredit availability has on the amount of money\nborrowed and if 401(k) pension plan eligibility has an impact on net financial\nassets, as two empirical examples. The presented toolbox of methods contains\nmeta-learners, like the Doubly-Robust, R-, T- and X-learner, and methods that\nare specially designed to estimate the CATE like the causal BART and the\ngeneralized random forest. In both, the microcredit and 401(k) example, we find\na positive treatment effect for all observations but conflicting evidence of\ntreatment effect heterogeneity. An additional simulation study, where the true\ntreatment effect is known, allows us to compare the different methods and to\nobserve patterns and similarities.\n"}
+{"abstract": "  Intense light-matter interactions and unique structural and electrical\nproperties make Van der Waals heterostructures composed by Graphene (Gr) and\nmonolayer transition metal dichalcogenides (TMD) promising building blocks for\ntunnelling transistors, flexible electronics, as well as optoelectronic\ndevices, including photodetectors, photovoltaics and QLEDs, bright and\nnarrow-line emitters using minimal amounts of active absorber material. The\nperformance of such devices is critically ruled by interlayer interactions\nwhich are still poorly understood in many respects. Specifically, two classes\nof coupling mechanisms have been proposed: charge transfer (CT) and energy\ntransfer (ET), but their relative efficiency and the underlying physics is an\nopen question. Here, building on a time resolved Raman scattering experiment,\nwe determine the electronic temperature profile of Gr in response to TMD\nphoto-excitation, tracking the picosecond dynamics of the G and 2D bands.\nCompelling evidence for a dominant role ET process accomplished within a\ncharacteristic time of ~ 3 ps is provided. Our results suggest the existence of\nan intermediate process between the observed picosecond ET and the generation\nof a net charge underlying the slower electric signals detected in\noptoelectronic applications.\n"}
+{"abstract": "  We introduce LTrack, a new tracking attack on LTE that allows an attacker to\nstealthily extract user devices' locations and permanent identifiers (IMSI). To\nremain stealthy, the localization of devices in LTrack is fully passive,\nrelying on our new uplink/downlink sniffer. Our sniffer records both the times\nof arrival of LTE messages and the contents of the Timing Advance Commands,\nbased on which LTrack calculates locations. LTrack is the first to show the\nfeasibility of a passive localization in LTE through implementation on\nsoftware-defined radio.\n  Passive localization attacks reveal a user's location traces but can at best\nlink these traces to a device's pseudonymous temporary identifier (TMSI),\nmaking tracking in dense areas or over a long time-period challenging. LTrack\novercomes this challenge by introducing and implementing a new type of IMSI\nCatcher named IMSI Extractor. It extracts a device's IMSI and binds it to its\ncurrent TMSI. Instead of relying on fake base stations like existing IMSI\nCatchers, which are detectable due to their continuous transmission, IMSI\nExtractor relies on our uplink/downlink sniffer enhanced with surgical message\novershadowing. This makes our IMSI Extractor the stealthiest IMSI Catcher to\ndate.\n  We evaluate LTrack through a series of experiments and show that in\nline-of-sight conditions, the attacker can estimate the location of a phone\nwith less than 6m error in 90% of the cases. We successfully tested our IMSI\nExtractor against a set of 17 modern smartphones connected to our\nindustry-grade LTE testbed. We further validated our uplink/downlink sniffer\nand IMSI Extractor in a test facility of an operator.\n"}
+{"abstract": "  We perform a short review of the history of quantum mechanics, with a focus\non the historical problems with describing ionization theoretically in the\ncontext of quantum mechanics. The essentials of the theory of resonances are\npresented. The exterior complex scaling method for obtaining resonance\nparameters within the context of the Schrodinger equation is detailed. We\nexplain how this is implemented for a numerical solution using a finite element\nmethod for the scaled variable. Results for the resonance parameters of a\none-dimensional hydrogen model in an external direct current (DC) electric\nfield are presented as proof of the independence of the theory from the scaling\nangle. We apply the theory to the real hydrogen atom in a DC field and present\nresults which agree with literature values. The resonance parameters for singly\nionized helium are also presented. Using a model potential energy for the water\nmolecule, we solve for the energy eigenvalues. We then solve for the resonance\nparameters of the water molecule in a DC field and compare to literature\nresults. Our widths for the valence orbital are shown to agree well with the\nso-called \"coupled-cluster singles and doubles with perturbative triples\nexcitations\" method.\n"}
+{"abstract": "  We consider the problem of online scheduling on a single machine in order to\nminimize weighted flow time. The existing algorithms for this problem (STOC\n'01, SODA '03, FOCS '18) all require exact knowledge of the processing time of\neach job. This assumption is crucial, as even a slight perturbation of the\nprocessing time would lead to polynomial competitive ratio. However, this\nassumption very rarely holds in real-life scenarios.\n  In this paper, we present the first algorithm for weighted flow time which do\nnot require exact knowledge of the processing times of jobs. Specifically, we\nintroduce the Scheduling with Predicted Processing Time (SPPT) problem, where\nthe algorithm is given a prediction for the processing time of each job,\ninstead of its real processing time. For the case of a constant factor\ndistortion between the predictions and the real processing time, our algorithms\nmatch all the best known competitiveness bounds for weighted flow time --\nnamely $O(\\log P), O(\\log D)$ and $O(\\log W)$, where $P,D,W$ are the maximum\nratios of processing times, densities, and weights, respectively. For larger\nerrors, the competitiveness of our algorithms degrades gracefully.\n"}
+{"abstract": "  This paper presents a new ridesharing simulation platform that accounts for\ndynamic driver supply and passenger demand, and complex interactions between\ndrivers and passengers. The proposed simulation platform explicitly considers\ndriver and passenger acceptance/rejection on the matching options, and\ncancellation before/after being matched. New simulation events, procedures and\nmodules have been developed to handle these realistic interactions. The\ncapabilities of the simulation platform are illustrated using numerical\nexperiments. The experiments confirm the importance of considering supply and\ndemand interactions and provide new insights to ridesharing operations. Results\nshow that increase of driver supply does not always increase matching option\naccept rate, and larger matching window could have negative impacts on overall\nridesharing success rate. These results emphasize the importance of a careful\nplanning of a ridesharing system.\n"}
+{"abstract": "  We investigate a model of interacting Dirac fermions in $2+1$ dimensions with\n$M$ flavors and $N$ colors having the $\\mathrm{U}(M)\\times \\mathrm{SU}(N)$\nsymmetry. In the large-$N$ limit, we find that the $\\mathrm{U}(M)$ symmetry is\nspontaneously broken in a variety of ways. In the vacuum, when the\nparity-breaking flavor-singlet mass is varied, the ground state undergoes a\nsequence of $M$ first-order phase transitions, experiencing $M+1$ phases\ncharacterized by symmetry breaking $\\mathrm{U}(M)\\to \\mathrm{U}(M-k)\\times\n\\mathrm{U}(k)$ with $k\\in\\{0,1,2,\\cdots,M\\}$, bearing a close resemblance to\nthe vacuum structure of three-dimensional QCD. At finite temperature and\nchemical potential, a rich phase diagram with first and second-order phase\ntransitions and tricritical points is observed. Also exotic phases with\nspontaneous symmetry breaking of the form as $\\mathrm{U}(3)\\to\n\\mathrm{U}(1)^3$, $\\mathrm{U}(4)\\to \\mathrm{U}(2)\\times \\mathrm{U}(1)^2$, and\n$\\mathrm{U}(5)\\to \\mathrm{U}(2)^2\\times \\mathrm{U}(1)$ exist. For a large\nflavor-singlet mass, the increase of the chemical potential $\\mu$ brings about\n$M$ consecutive first-order transitions that separate the low-$\\mu$ phase\ndiagram with vanishing fermion density from the high-$\\mu$ region with a high\nfermion density.\n"}
+{"abstract": "  In contrast to conventional pipeline Spoken Language Understanding (SLU)\nwhich consists of automatic speech recognition (ASR) and natural language\nunderstanding (NLU), end-to-end SLU infers the semantic meaning directly from\nspeech and overcomes the error propagation caused by ASR. End-to-end slot\nfilling (SF) from speech is an essential component of end-to-end SLU, and is\nusually regarded as a sequence-to-sequence generation problem, heavily relied\non the performance of language model of ASR. However, it is hard to generate a\ncorrect slot when the slot is out-of-vovabulary (OOV) in training data,\nespecially when a slot is an anti-linguistic entity without grammatical rule.\nInspired by object detection in computer vision that is to detect the object\nfrom an image, we consider SF as the task of slot detection from speech. In\nthis paper, we formulate the SF task as a matching task and propose an\nend-to-end knowledge-based SF model, named Speech-to-Slot (Speech2Slot), to\nleverage knowledge to detect the boundary of a slot from the speech. We also\nrelease a large-scale dataset of Chinese speech for slot filling, containing\nmore than 830,000 samples. The experiments show that our approach is markedly\nsuperior to the conventional pipeline SLU approach, and outperforms the\nstate-of-the-art end-to-end SF approach with 12.51% accuracy improvement.\n"}
+{"abstract": "  While the techniques in optimal control theory are often model-based, the\npolicy optimization (PO) approach can directly optimize the performance metric\nof interest without explicit dynamical models, and is an essential approach for\nreinforcement learning problems. However, it usually leads to a non-convex\noptimization problem in most cases, where there is little theoretical\nunderstanding on its performance. In this paper, we focus on the\nrisk-constrained Linear Quadratic Regulator (LQR) problem with noisy input via\nthe PO approach, which results in a challenging non-convex problem. To this\nend, we first build on our earlier result that the optimal policy has an affine\nstructure to show that the associated Lagrangian function is locally gradient\ndominated with respect to the policy, based on which we establish strong\nduality. Then, we design policy gradient primal-dual methods with global\nconvergence guarantees to find an optimal policy-multiplier pair in both\nmodel-based and sample-based settings. Finally, we use samples of system\ntrajectories in simulations to validate our policy gradient primal-dual\nmethods.\n"}
+{"abstract": "  The connectivity and resource-constrained nature of single-board devices\nopens up to cybersecurity concerns affecting Internet of Things (IoT)\nscenarios. One of the most important is the presence of evil IoT twins. Evil\nIoT twins are malicious devices, with identical hardware and software\nconfigurations to authorized ones, that can provoke sensitive information\nleakages, data poisoning, or privilege escalation in IoT scenarios. Combining\nbehavioral fingerprinting and Machine/Deep Learning (ML/DL) techniques is a\npromising solution to identify evil IoT twins by detecting minor performance\ndifferences generated by imperfections in manufacturing. However, existing\nsolutions are not suitable for single-board devices because they do not\nconsider their hardware and software limitations, underestimate critical\naspects such as fingerprint stability, and do not explore the potential of\nML/DL techniques. To improve it, this work proposes an ML/DL-oriented\nmethodology that uses behavioral fingerprinting to identify identical\nsingle-board devices. The methodology leverages the different built-in\ncomponents of the system, comparing their internal behavior with each other to\ndetect variations that occurred in manufacturing processes. The validation has\nbeen performed in a real environment composed of identical Raspberry Pi 4 Model\nB and Raspberry Pi 3 Model B+ devices, obtaining a 92.6\\% average accuracy with\na Random Forest model and achieving the identification for all devices by\nsetting a 50\\% threshold in the evaluation process. Finally, a discussion\ncompares the proposed solution with related work and provides important lessons\nlearned and limitations.\n"}
+{"abstract": "  The advent of deep learning has led to the prevalence of deep neural network\narchitectures for monaural music source separation, with end-to-end approaches\nthat operate directly on the waveform level increasingly receiving research\nattention. Among these approaches, transformation of the input mixture to a\nlearned latent space, and multiplicative application of a soft mask to the\nlatent mixture, achieves the best performance, but is prone to the introduction\nof artifacts to the source estimate. To alleviate this problem, in this paper\nwe propose a hybrid time-domain approach, termed the HTMD-Net, combining a\nlightweight masking component and a denoising module, based on skip\nconnections, in order to refine the source estimated by the masking procedure.\nEvaluation of our approach in the task of monaural singing voice separation in\nthe musdb18 dataset indicates that our proposed method achieves competitive\nperformance compared to methods based purely on masking when trained under the\nsame conditions, especially regarding the behavior during silent segments,\nwhile achieving higher computational efficiency.\n"}
+{"abstract": "  Cache advertisements reduce the access cost by allowing users to skip the\ncache when it does not contain their datum. Such advertisements are used in\nmultiple networked domains such as 5G networks, wide area networks, and\ninformation-centric networking. The selection of an advertisement strategy\nexposes a trade-off between the access cost and bandwidth consumption. Still,\nexisting works mostly apply a trial-and-error approach for selecting the best\nstrategy, as the rigorous foundations required for optimizing such decisions is\nlacking. Our work shows that the desired advertisement policy depends on\nnumerous parameters such as the cache policy, the workload, the cache size, and\nthe available bandwidth. In particular, we show that there is no ideal single\nconfiguration. Therefore, we design an adaptive, self-adjusting algorithm that\nperiodically selects an advertisement policy. Our algorithm does not require\nany prior information about the cache policy, cache size, or workload, and does\nnot require any apriori configuration. Through extensive simulations, using\nseveral state-of-the-art cache policies, and real workloads, we show that our\napproach attains a similar cost to that of the best static configuration (which\nis only identified in retrospect) in each case.\n"}
+{"abstract": "  Lck (lymphocyte-specific protein tyrosine kinase) is an enzyme which plays a\nnumber of important roles in the function of immune cells. It belongs to the\nSrc family of kinases which are known to undergo autophosphorylation. It turns\nout that this leads to a remarkable variety of dynamical behaviour which can\noccur during their activation. We prove that in the presence of\nautophosphorylation one phenomenon, bistability, already occurs in a\nmathematical model for a protein with a single phosphorylation site. We further\nshow that a certain model of Lck exhibits oscillations. Finally we discuss the\nrelations of these results to models in the literature which involve Lck and\ndescribe specific biological processes, such as the early stages of T cell\nactivation and the stimulation of T cell responses resulting from the\nsuppression of PD-1 signalling which is important in immune checkpoint therapy\nfor cancer.\n"}
+{"abstract": "  Recent work has found that adversarially-robust deep networks used for image\nclassification are more interpretable: their feature attributions tend to be\nsharper, and are more concentrated on the objects associated with the image's\nground-truth class. We show that smooth decision boundaries play an important\nrole in this enhanced interpretability, as the model's input gradients around\ndata points will more closely align with boundaries' normal vectors when they\nare smooth. Thus, because robust models have smoother boundaries, the results\nof gradient-based attribution methods, like Integrated Gradients and DeepLift,\nwill capture more accurate information about nearby decision boundaries. This\nunderstanding of robust interpretability leads to our second contribution:\n\\emph{boundary attributions}, which aggregate information about the normal\nvectors of local decision boundaries to explain a classification outcome. We\nshow that by leveraging the key factors underpinning robust interpretability,\nboundary attributions produce sharper, more concentrated visual explanations --\neven on non-robust models. Any example implementation can be found at\n\\url{https://github.com/zifanw/boundary}.\n"}
+{"abstract": "  Crustal deformation due to the 2016 earthquake sequence in Kumamoto, Japan,\nthat culminated in a preceding earthquake of magnitude M6.5 and a subsequent\nM7.3 earthquake 28 hours later, caused stress perturbation on and around the\ncausative Futagawa-Hinagu fault zone. Monitoring changes in seismicity pattern\nalong this zone plays a role in understanding the process before and after\nmajor earthquakes. For this purpose, stress-dependent laws in statistical\nseismology can be used: the Gutenberg-Richter frequency-size law and the\nOmori-Utsu aftershock-decay law. We review the results obtained by using these\nlaws in previous studies to show a zone of high stress near the eventual\nepicenters of the M6.5 and M7.3 earthquakes before the start of the Kumamoto\nsequence, and after it, showing a decreasing trend in stress along the\nFutagawa-Hinagu fault zone. Detailed analysis suggests aseismic slips along the\ncausative fault zone. The aseismic preslip locally reduced stress just prior to\nthe M7.3 earthquake near its epicenter. Recently, a system was proposed by\nGulia and Wiemer (2019) that utilizes the Gutenberg-Richter frequency-size law\nto judge, immediately after a large earthquake, whether it was the mainshock or\na foreshock to a future event. Based on the reviewed results and our new\nresults, further research that takes into account the spatial variation of\nfrequency-size distribution, allowing the exploration of the possibility of a\nlocal preslip of a future nearby earthquake, is needed to improve this system.\n"}
+{"abstract": "  Magnitude pruning is a common, effective technique to identify sparse\nsubnetworks at little cost to accuracy. In this work, we ask whether a\nparticular architecture's accuracy-sparsity tradeoff can be improved by\ncombining pruning information across multiple runs of training. From a shared\nResNet-20 initialization, we train several network copies (\\emph{siblings}) to\ncompletion using different SGD data orders on CIFAR-10. While the siblings'\npruning masks are naively not much more similar than chance, starting sibling\ntraining after a few epochs of shared pretraining significantly increases\npruning overlap. We then choose a subnetwork by either (1) taking all weights\nthat survive pruning in any sibling (mask union), or (2) taking only the\nweights that survive pruning across all siblings (mask intersection). The\nresulting subnetwork is retrained. Strikingly, we find that union and\nintersection masks perform very similarly. Both methods match the\naccuracy-sparsity tradeoffs of the one-shot magnitude pruning baseline, even\nwhen we combine masks from up to $k = 10$ siblings.\n"}
+{"abstract": "  While checkpointing is typically combined with a restart of the whole\napplication, localized recovery permits all but the affected processes to\ncontinue. In task-based cluster programming, for instance, the application can\nthen be finished on the intact nodes, and the lost tasks be reassigned.\n  This extended abstract suggests to adapt a checkpointing and localized\nrecovery technique that has originally been developed for independent tasks to\nnested fork-join programs. We consider a Cilk-like work stealing scheme with\nwork-first policy in a distributed memory setting, and describe the required\nalgorithmic changes. The original technique has checkpointing overheads below\n1% and neglectable costs for recovery, we expect the new algorithm to achieve a\nsimilar performance.\n"}
+{"abstract": "  Sentence embedding models aim to provide general purpose embeddings for\nsentences. Most of the models studied in this paper claim to perform well on\nSTS tasks - but they do not report on their suitability for clustering. This\npaper looks at four recent sentence embedding models (Universal Sentence\nEncoder (Cer et al., 2018), Sentence-BERT (Reimers and Gurevych, 2019), LASER\n(Artetxe and Schwenk, 2019), and DeCLUTR (Giorgi et al., 2020)). It gives a\nbrief overview of the ideas behind their implementations. It then investigates\nhow well topic classes in two text classification datasets (Amazon Reviews (Ni\net al., 2019) and News Category Dataset (Misra, 2018)) map to clusters in their\ncorresponding sentence embedding space. While the performance of the resulting\nclassification model is far from perfect, it is better than random. This is\ninteresting because the classification model has been constructed in an\nunsupervised way. The topic classes in these real life topic classification\ndatasets can be partly reconstructed by clustering the corresponding sentence\nembeddings.\n"}
+{"abstract": "  In this study, we analytically formulated the path integral representation of\nthe conditional probabilities for non-Markovian kinetic processes in terms of\nthe free energy of the thermodynamic system. We carry out analytically the\ntime-fractional kinetic equations for these processes. Thus, in a simple way,\nwe generalize path integral solutions of the Markovian to the non-Markovian\ncases. We conclude that these pedagogical results can be applied to some\nphysical problems such as the deformed ion channels, internet networks and\nnon-equilibrium phase transition problems.\n"}
+{"abstract": "  Reconstruction of a quantum state is of prime importance for\nquantum-information science. Specifically, means of efficient determination of\na state of atoms of room-temperature vapor may enable applications in quantum\ncomputations and cryptography. To step toward such applications, here we\npresent a method of reconstruction of a collective density matrix of an atomic\nensemble, consisting of atoms with an $F=1$ ground state. Such a long-lived\nstate is often encountered in real systems (e.g., potassium, sodium, rubidium)\nand hence may be practically utilized. Our theoretical treatment enables\nderivation of explicit formulas relating optical signals (polarization rotation\nand ellipticity change) with specific density-matrix elements. The analysis are\nsupported with numerical simulations, which allows to evaluate fidelity and\nrobustness of the algorithm. The tests show that our algorithm allows to obtain\nthe fidelity exceeded 0.95 even at noisy environment and/or significant atomic\nmanipulation imperfections.\n"}
+{"abstract": "  Our first experience of dimension typically comes in the intuitive Euclidean\nsense: a line is one dimensional, a plane is two-dimensional, and a volume is\nthree-dimensional. However, following the work of Mandelbrot \\cite{mandelbrot},\nsystems with a fractional dimension, ``fractals'', now play an important role\nin science. The novelty of encountering fractional dimension, and the intrinsic\nbeauty of many fractals, have a strong appeal to students and provide a\npowerful teaching tool. I describe here a low-cost and convenient experimental\nmethod for observing fractal dimension, by measuring the power-law scaling of\nthe resistance of a fractal network of resistors. The experiments are quick to\nperform, and the students enjoy both the construction of the network and the\ncollaboration required to create the largest networks. Learning outcomes\ninclude analysis of resistor networks beyond the elementary series and parallel\ncombinations, scaling laws, and an introduction to fractional dimension.\n"}
+{"abstract": "  First described in 2014, Roli's cube $\\mathcal{R}$ is a chiral $4$-polytope,\nfaithfully realized in Euclidean $4$-space (a situation earlier thought to be\nimpossible). Here we describe $\\mathcal{R}$ in a new way, determine its minimal\nregular cover, and reveal connections to the M\\\"{o}bius-Kantor configuration.\n"}
+{"abstract": "  A level set topology optimization approach that uses an auxiliary density\nfield to nucleate holes during the optimization process and achieves minimum\nfeature size control in optimized designs is explored. The level set field\ndetermines the solid-void interface, and the density field describes the\ndistribution of a fictitious porous material using the solid isotropic material\nwith penalization. These fields are governed by two sets of independent\noptimization variables which are initially coupled using a penalty for hole\nnucleation. The strength of the density field penalization and projection are\ngradually increased through the optimization process to promote a 0-1 density\ndistribution. This treatment of the density field combined with a second\npenalty that regulates the evolution of the density field in the void phase,\nmitigate the appearance of small design features. The minimum feature size of\noptimized designs is controlled by the radius of the linear filter applied to\nthe density optimization variables. The structural response is predicted by the\nextended finite element method, the sensitivities by the adjoint method, and\nthe optimization variables are updated by a gradient-based optimization\nalgorithm. Numerical examples investigate the robustness of this approach with\nrespect to algorithmic parameters and mesh refinement. The results show the\napplicability of the combined density level set topology optimization approach\nfor both optimal hole nucleation and for minimum feature size control in 2D and\n3D. This comes, however, at the cost of a more advanced problem formulation and\nadditional computational cost due to an increased number of optimization\nvariables.\n"}
+{"abstract": "  The Hamiltonian shape invariant of a domain $X \\subset \\mathbb R^4$, as a\nsubset of $\\mathbb R^2$, describes the product Lagrangian tori which may be\nembedded in $X$. We provide necessary and sufficient conditions to determine\nwhether or not a path in the shape invariant can lift, that is, be realized as\na smooth family of embedded Lagrangian tori, when $X$ is a basic\n$4$-dimensional toric domain such as a ball $B^4(R)$, an ellipsoid $E(a,b)$\nwith $\\frac{b}{a} \\in {\\mathbb N}_{\\geq 2}$, or a polydisk $P(c,d)$. As\napplications, via the path lifting, we can detect knotted embeddings of product\nLagrangian tori in many toric $X$. We also obtain novel obstructions to\nsymplectic embeddings between domains that are more general than toric concave\nor toric convex.\n"}
+{"abstract": "  The interfacial band structures of multilayer systems play a crucial role for\nthe ultrafast charge and spin carrier dynamics at interfaces. Here, we study\nthe energy- and momentum-dependent quasiparticle lifetimes of excited states of\na lead monolayer film on Ag(111) prior and after the adsorption of a monolayer\nof 3,4,9,10-perylene-tetracarboxylic-dianhydride (PTCDA). Using time-resolved\ntwo-photon momentum microscopy, we show that the electron dynamics of the bare\nPb/Ag(111) bilayer system is dominated by isotropic intraband scattering\nprocesses within the quantum well state as well as interband scattering\nprocesses from the QWS into the Pb sideband. After the adsorption of PTCDA on\nthe Pb monolayer, the interband scattering is suppressed and the electron\ndynamics is solely determined by intraband or inelastic scattering processes.\nOur findings hence uncover a new possibility to selectively tune and control\nscattering processes of quantum well systems by the adsorption of organic\nmolecules.\n"}
+{"abstract": "  Near-term quantum hardware can support two-qubit operations only on the\nqubits that can interact with each other. Therefore, to execute an arbitrary\nquantum circuit on the hardware, compilers have to first perform the task of\nqubit routing, i.e., to transform the quantum circuit either by inserting\nadditional SWAP gates or by reversing existing CNOT gates to satisfy the\nconnectivity constraints of the target topology. We propose a procedure for\nqubit routing that is architecture agnostic and that outperforms other\navailable routing implementations on various circuit benchmarks. The depth of\nthe transformed quantum circuits is minimised by utilizing the Monte Carlo tree\nsearch to perform qubit routing, aided by a Graph neural network that evaluates\nthe value function and action probabilities for each state.\n"}
+{"abstract": "  We extend the model-free data-driven paradigm for rate-independent fracture\nmechanics proposed in Carrara et al. (2020), Data-driven Fracture Mechanics,\nComp. Meth. App. Mech. Eng., 372 to rate-dependent fracture and sub-critical\nfatigue. The problem is formulated by combining the balance governing equations\nstemming from variational principles with a set of data points that encodes the\nfracture constitutive behavior of the material. The solution is found as the\ndata point that best satisfies the meta-stability condition as given by the\nvariational procedure and following a distance minimization approach based on\nclosest-point-projection. The approach is tested on different setups adopting\ndifferent types of rate-dependent fracture and fatigue models affected or not\nby white noise.\n"}
+{"abstract": "  Several proposed models for dark matter posit the existence of\nself-interaction processes that can impact the shape of dark matter halos,\nmaking them more spherical than the ellipsoidal halos of collisionless dark\nmatter. One method of probing the halo shapes, and thus the strength of the\ndark matter self-interaction, is by measuring the shape of the X-ray gas that\ntraces the gravitational potential in relaxed elliptical galaxies. In this work\nwe identify a sample of 11 relaxed, isolated elliptical galaxies and measure\nthe ellipticity of the gravitating matter using X-ray images from the\nXMM-Newton and Chandra telescopes. We explore a variety of different mass\nconfigurations and find that the dark matter halos of these galaxies have\nellipticities around $\\epsilon\\approx 0.2-0.5$. While we find non-negligible\nscatter in the ellipticity distribution, our results are consistent with some\ndegree of self-interaction at the scale of $\\sigma/m \\sim 1$ cm$^2$/g, yet they\nalso remain compatible with a cold dark matter scenario. We additionally\ndemonstrate how our results can be used to directly constrain specific dark\nmatter models and discuss implications for current and future simulations of\nself-interacting dark matter models.\n"}
+{"abstract": "  The traditional design and development of metallic alloys has taken a\nhill-climbing approach to date, with incremental advances. Throughout the last\ncentury, aluminium (Al) alloy design has been essentially empirical and\niterative, based on lessons learned from in service use and human experience.\nIncremental alloy development is costly, slow, and doesn't fully harness the\ndata that exists in the field of Al-alloy metallurgy. In the present work, an\nattempt has been made to utilise a data science approach to develop a machine\nlearning (ML) model for Al-alloy design. An objective-optimisation process has\nalso been developed, to exploit the ML model, for user experience and practical\napplication. A successful model was developed and presented herein, along with\nthe open-access software.\n"}
+{"abstract": "  Coulomb interactions play an essential role in atomically-thin materials. On\none hand, they are strong and long-ranged in layered systems due to the lack of\nenvironmental screening. On the other hand, they can be efficiently tuned by\nmeans of surrounding dielectric materials. Thus all physical properties which\ndecisively depend on the exact structure of the electronic interactions can be\nin principle efficiently controlled and manipulated from the outside via\nCoulomb engineering. Here, we show how this concept can be used to create\nfundamentally new plasmonic waveguides in metallic layered materials. We\ndiscuss in detail how dielectrically structured environments can be utilized to\nnon-invasively confine plasmonic excitations in an otherwise homogeneous\nmetallic 2D system by modification of its many-body interactions. We define\noptimal energy ranges for this mechanism and demonstrate plasmonic confinement\nwithin several nanometers. In contrast to conventional functionalization\nmechanisms, this scheme relies on a purely many-body concept and does not\ninvolve any direct modifications to the active material itself.\n"}
+{"abstract": "  In this paper, we study the Erd\\H{o}s-Falconer distance problem in five\ndimensions for sets of Cartesian product structures. More precisely, we show\nthat for $A\\subset \\mathbb{F}_p$ with $|A|\\gg p^{\\frac{13}{22}}$, then\n$\\Delta(A^5)=\\mathbb{F}_p$. When $|A-A|\\sim |A|$, we obtain stronger statements\nas follows:\n  If $|A|\\gg p^{\\frac{13}{22}}$, then $(A-A)^2+A^2+A^2+A^2+A^2=\\mathbb{F}_p.$\n  If $|A|\\gg p^{\\frac{4}{7}}$, then\n$(A-A)^2+(A-A)^2+A^2+A^2+A^2+A^2=\\mathbb{F}_p.$\n  We also prove that if $p^{4/7}\\ll |A-A|=K|A|\\le p^{5/8}$, then \\[|A^2+A^2|\\gg\n\\min \\left\\lbrace \\frac{p}{K^4},\n\\frac{|A|^{8/3}}{K^{7/3}p^{2/3}}\\right\\rbrace.\\] As a consequence,\n$|A^2+A^2|\\gg p$ when $|A|\\gg p^{5/8}$ and $K\\sim 1$, where $A^2=\\{x^2\\colon\nx\\in A\\}$.\n"}
+{"abstract": "  A total coloring of a graph $G = (V, E)$ is an assignment of colors to\nvertices and edges such that neither two adjacent vertices nor two incident\nedges get the same color, and, for each edge, the end-points and the edge\nitself receive different colors. Any valid total coloring induces a partition\nof the elements of $G$ into total matchings, which are defined as subsets of\nvertices and edges that can take the same color. In this paper, we propose\nInteger Linear Programming models for both the Total Coloring and the Total\nMatching problems, and we study the strength of the corresponding Linear\nProgramming relaxations. The total coloring is formulated as the problem of\nfinding the minimum number of total matchings that cover all the graph\nelements, and we prove that this relaxation is tighter than a natural\nassignment model. This covering formulation can be solved by a column\ngeneration algorithm, where the pricing subproblem corresponds to the Weighted\nTotal Matching Problem. Hence, we study the Total Matching Polytope. We\nintroduce two families of nontrivial valid inequalities: congruent-2k3 cycle\ninequalities based on the parity of the vertex set induced by the cycle, and\nclique inequalities induced by complete subgraphs of even order. We prove that\ncongruent-2k3 cycle inequalities are facet-defining only when k = 4, while the\neven cliques are always facet-defining. Since the separation problem of the\nclique inequalities of even order is NP-hard, we get a polyhedral proof of the\nNP-hardness of the Weighted Total Matching Problem.\n"}
+{"abstract": "  Recent approaches to data-to-text generation have adopted the very successful\nencoder-decoder architecture or variants thereof. These models generate text\nwhich is fluent (but often imprecise) and perform quite poorly at selecting\nappropriate content and ordering it coherently. To overcome some of these\nissues, we propose a neural model with a macro planning stage followed by a\ngeneration stage reminiscent of traditional methods which embrace separate\nmodules for planning and surface realization. Macro plans represent high level\norganization of important content such as entities, events and their\ninteractions; they are learnt from data and given as input to the generator.\nExtensive experiments on two data-to-text benchmarks (RotoWire and MLB) show\nthat our approach outperforms competitive baselines in terms of automatic and\nhuman evaluation.\n"}
+{"abstract": "  We establish the Krylov Safonov Harnack inequalities and Holder estimates for\nfully nonlinear nonlocal operators of non-divergence form on Riemannian\nmanifolds with nonnegative sectional curvatures. To this end, we first define\nthe nonlocal Pucci operators on manifolds that give rise to the concept of\nnon-divergence form operators. We then provide the uniform regularity results\nfor these operators which recover the classical results for second order local\noperators as limits.\n"}
+{"abstract": "  Understanding physical properties of quantum emitters strongly interacting\nwith quantized electromagnetic modes, both discrete and continuous spectra, is\none of the primary goals in the emergent field of waveguide quantum\nelectrodynamics (QED). When the light-matter coupling strength is comparable to\nor even exceeds energies of elementary excitations, conventional approaches\nbased on perturbative treatment of light-matter interactions, two-level\ndescription of matter excitations, and photon-number truncation are no longer\nsufficient. Here we study in and out of equilibrium properties of waveguide QED\nin such nonperturbative regimes on the basis of a comprehensive and rigorous\ntheoretical approach using an asymptotic decoupling unitary transformation. We\nuncover several surprising features ranging from symmetry-protected many-body\nbound states in the continuum to strong renormalization of the effective mass\nand potential; the latter may explain recent experiments demonstrating\ncavity-induced changes in chemical reactivity as well as enhancements of\nferromagnetism or superconductivity. To illustrate our general results with\nconcrete examples, we use our formalism to study a model of coupled cavity\narrays, which is relevant to experiments in superconducting qubits interacting\nwith microwave resonators or atoms coupled to photonic crystals. We examine the\nrelation between our results and delocalization-localization transition in the\nspin-boson model; notably, we point out that one can find a quantum phase\ntransition akin to the superradiant transition in multi-emitter waveguide QED\nsystems with superlinear photonic dispersion. We also discuss applications of\nour results to other problems in different fields, including quantum optics,\ncondensed matter physics, and quantum chemistry.\n"}
+{"abstract": "  Twisted bilayer graphene (TBG) realizes a highly tunable, strongly\ninteracting system featuring superconductivity and various correlated\ninsulating states. We establish gate-defined wires in TBG with\nproximity-induced spin-orbit coupling as $(i)$ a tool for revealing the nature\nof correlated insulators and $(ii)$ a platform for Majorana-based topological\nqubits. In particular, we show that the band structure of a gate-defined wire\nimmersed in an `inter-valley coherent' correlated insulator inherits\nelectrically detectable fingerprints of symmetry breaking native to the latter.\nSurrounding the wire by a superconducting TBG region on one side and an\ninter-valley coherent correlated insulator on the other further enables the\nformation of Majorana zero modes--possibly even at zero magnetic field\ndepending on the precise symmetry-breaking order present. Our proposal not only\nintroduces a highly gate-tunable topological qubit medium relying on internally\ngenerated proximity effects, but can also shed light on the Cooper-pairing\nmechanism in TBG.\n"}
+{"abstract": "  We study the concept of frame in tensor product of n-Hilbert spaces as tensor\nproduct of n-Hilbert spaces is again a n-Hilbert space. We generalize some of\nthe known results about bases to frames in this new Hilbert space. A\nrelationship between frame and bounded linear operator in tensor product of\nn-Hilbert spaces is studied. Finally, the dual frame in tensor product of\nn-Hilbert spaces is discussed.\n"}
+{"abstract": "  Using a holographic model, we study quantum field theories with a layer of\none CFT surrounded by another CFT, on either a periodic or an infinite\ndirection. We study the vacuum energy density in each CFT as a function of the\ncentral charges, the thickness of the layer(s), and the properties of the\ninterfaces between the CFTs. The dual spacetimes in the holographic model\ninclude two regions separated by a dynamical interface with some tension. For\ntwo or more spatial dimensions, we find that a layer of CFT with more degrees\nof freedom than the surrounding one can have an anomalously large negative\nvacuum energy density for certain types of interfaces. The negative energy\ndensity (or null-energy density in the direction perpendicular to the\ninterface) becomes arbitrarily large for fixed layer width when the tension of\nthe bulk interface approaches a lower critical value. We argue that in cases\nwhere we have large negative energy density, we also have an anomalously high\ntransition temperature to the high-temperature thermal state.\n"}
+{"abstract": "  Spectral methods include a family of algorithms related to the eigenvectors\nof certain data-generated matrices. In this work, we are interested in studying\nthe geometric landscape of the eigendecomposition problem in various spectral\nmethods. In particular, we first extend known results regarding the landscape\nat critical points to larger regions near the critical points in a special case\nof finding the leading eigenvector of a symmetric matrix. For a more general\neigendecomposition problem, inspired by recent findings on the connection\nbetween the landscapes of empirical risk and population risk, we then build a\nnovel connection between the landscape of an eigendecomposition problem that\nuses random measurements and the one that uses the true data matrix. We also\napply our theory to a variety of low-rank matrix optimization problems and\nconduct a series of simulations to illustrate our theoretical findings.\n"}
+{"abstract": "  Control over plasmonic colors on the nanoscale is of great interest for\nhigh-resolution display, imaging, and information encryption applications.\nHowever, so far, very limited schemes have been attempted for dynamic plasmonic\ncolor generation. In this paper, we demonstrate a scanning plasmonic color\ngeneration scheme, in which subwavelength plasmonic pixels can be laterally\nswitched on/off through directional hydrogenation/dehydrogenation of a\nmagnesium screen. We show several dynamic plasmonic color displays with\ndifferent scanning functions by varying the number and geometries of the\npalladium gates, where hydrogen enters the scanning screens. In particular, we\nemploy the scanning effects to create a dynamic plasmonic quick response code.\nThe information cannot be decrypted by varying the polarization states of light\nor by accessing the physical features. Rather, it can only be read out using\nhydrogen as a decoding key. Our work advances the established design concepts\nfor plasmonic color printing and provides insights into the development of\noptical information storage and anticounterfeiting features.\n"}
+{"abstract": "  Large-radius excitons in polar crystals are considered. It is shown that\ntranslation invariant description of excitons interacting with a phonon field\nleads to a nonzero contribution of phonons into the exciton ground state energy\nonly in the case of weak or intermediate electron-phonon coupling. A conclusion\nis made that self-trapped excitons cannot exist in the limit of strong\ncoupling. Peculiarities of the absorption and emission spectra of translation\ninvariant excitons in a phonon field are discussed. Conditions when the\nhydrogen-like exciton model remains valid in the case of electron-phonon\ninteraction are found.\n"}
+{"abstract": "  The single ion properties of the zig-zag chain compound SrTm$_2$O$_4$ have\nbeen investigated using heat capacity, magnetic susceptibility, magnetization,\ninelastic neutron scattering, and polarized muon spectroscopy. Two crystal\nfield models are employed to estimate the single ion properties; a Density\nFunction Theory based model and an effective charge model based on the\nHutchings point charge model. The latter describes our experimental results\nwell. This model estimates an easy-axis anisotropy for one of the Tm$^{3+}$\nsites and an easy-plane anisotropy for the second site. It also predicts a\nmixed ground state with dominating $J = 0$ characteristics for both sites.\nAdditionally, muon spin rotation/relaxation ($\\mu^+$SR) spectra reveal\noscillations, typically a sign of long-range magnetic order. However, the\ntemperature dependence of the precession frequency and the relaxation rates\nindicate that the system is in an extended critical regime and the observed\nrelaxation is actually dynamic.\n"}
+{"abstract": "  Expanding laser plasmas, produced by high energy laser radiation, possess\nboth high thermal and magnetic field energy density. Characterization of such\nplasma is challenging but needed for understanding of its physical behaviour.\nAmong all standard experimental techniques for plasma diagnostics, classical\ninterferometry is one of the most convenient, informative and accurate.\nAttempts to extract more information from each single laser shot have led to\ndevelopment of complex interferometry, which under certain processing allows to\nreconstruct both plasma electron density and magnetic field strength\ndistributions using just one data object. However, the increase in complexity\nand universality of the diagnostics requires a more detailed analysis of the\nextracted values and their accuracy.This work focuses on axisymmetric\ninteraction geometry. We present general analysis, starting from the basic\nprinciples and trace main error sources, finally ending up with plasma density\nand magnetic field distributions with the derived error bars. Use of synthetic\nprofiles makes the analysis universal, though we present an example with real\nexperimental data in the Appendix.\n"}
+{"abstract": "  In this paper, we consider the portfolio optimization problem in a financial\nmarket under a general utility function. Empirical results suggest that if a\nsignificant market fluctuation occurs, invested wealth tends to have a notable\nchange from its current value. We consider an incomplete stochastic volatility\nmarket model, that is driven by both a Brownian motion and a jump process. At\nfirst, we obtain a closed-form formula for an approximation to the optimal\nportfolio in a small-time horizon. This is obtained by finding the associated\nHamilton-Jacobi-Bellman integro-differential equation and then approximating\nthe value function by constructing appropriate super-solution and sub-solution.\nIt is shown that the true value function can be obtained by sandwiching the\nconstructed super-solution and sub-solution. We also prove the accuracy of the\napproximation formulas. Finally, we provide a procedure for generating a\nclose-to-optimal portfolio for a finite time horizon.\n"}
+{"abstract": "  Multi-state survival analysis considers several potential events of interest\nalong a disease pathway. Such analyses are crucial to model complex patient\ntrajectories and are increasingly being used in epidemiological and health\neconomic settings. Multi-state models often make the Markov assumption, whereby\nan individual's future trajectory is dependent only upon their present state,\nnot their past. In reality, there may be transitional dependence upon either\nprevious events and/or more than one timescale, for example time since entry to\nthe current or previous state(s). The aim of this study was to develop an\nillness-death Weibull model allowing for multiple timescales to impact the\nfuture risk of death. Following this, we evaluated the performance of the\nmultiple timescale model against a Markov illness-death model in a set of\nplausible simulation scenarios when the Markov assumption was violated. Guided\nby a study in breast cancer, data were simulated from Weibull baseline\ndistributions, with hazard functions dependent on single and multiple\ntimescales. Markov and non-Markov models were fitted to account for/ignore the\nunderlying data structure. Ignoring the presence of multiple timescales led to\nbias in underlying transition rates between states and associated covariate\neffects, while transition probabilities and lengths of stay were fairly\nrobustly estimated. Further work may be needed to evaluate different estimands\nor more complex multi-state models. Software implementations in Stata are also\ndescribed for simulating and estimating multiple timescale multi-state models.\n"}
+{"abstract": "  The numerical solution of differential equations can be formulated as an\ninference problem to which formal statistical approaches can be applied.\nHowever, nonlinear partial differential equations (PDEs) pose substantial\nchallenges from an inferential perspective, most notably the absence of\nexplicit conditioning formula. This paper extends earlier work on linear PDEs\nto a general class of initial value problems specified by nonlinear PDEs,\nmotivated by problems for which evaluations of the right-hand-side, initial\nconditions, or boundary conditions of the PDE have a high computational cost.\nThe proposed method can be viewed as exact Bayesian inference under an\napproximate likelihood, which is based on discretisation of the nonlinear\ndifferential operator. Proof-of-concept experimental results demonstrate that\nmeaningful probabilistic uncertainty quantification for the unknown solution of\nthe PDE can be performed, while controlling the number of times the\nright-hand-side, initial and boundary conditions are evaluated. A suitable\nprior model for the solution of the PDE is identified using novel theoretical\nanalysis of the sample path properties of Mat\\'{e}rn processes, which may be of\nindependent interest.\n"}
+{"abstract": "  The asset trading volume on blockchain-based exchanges (DEX) increased\nsubstantially since the advent of Automated Market Makers (AMM). Yet, AMMs and\ntheir forks compete on the same blockchain, incurring unnecessary network and\nblock-space overhead, by attracting sandwich attackers and arbitrage\ncompetitions. Moreover, conceptually speaking, a blockchain is one database,\nand we find little reason to partition this database into multiple competing\nexchanges, which then necessarily require price synchronization through\narbitrage.\n  This paper shows that DEX arbitrage and trade routing among similar AMMs can\nbe performed efficiently and atomically on-chain within smart contracts. These\ninsights lead us to create a new AMM design, an Automated Arbitrage Market\nMaker, short A2MM DEX. A2MM aims to unite multiple AMMs to reduce overheads,\ncosts and increase blockchain security. With respect to Miner Extractable Value\n(MEV), A2MM serves as a decentralized design for users to atomically collect\nMEV, mitigating the dangers of centralized MEV relay services.\n  We show that A2MM offers essential security benefits. First, A2MM strengthens\nthe blockchain consensus security by mitigating the competitive exploitation of\nMEV, therefore reducing the risks of consensus forks. A2MM reduces the network\nlayer overhead of competitive transactions, improves network propagation,\nleading to less stale blocks and better blockchain security. Through trade\nrouting, A2MM reduces the predatory risks of sandwich attacks by taking\nadvantage of the minimum profitable victim input. A2MM also offers financial\nbenefits to traders. Failed swap transactions from competitive trading occupy\nvaluable block space, implying an upward pressure on transaction fees. Our\nevaluations shows that A2MM frees up 32.8% block-space of AMM-related\ntransactions. In expectation, A2MM's revenue allows to reduce swap fees by 90%.\n"}
+{"abstract": "  Some prokaryotes possess CRISPR-Cas systems that provide adaptive immunity to\nviruses guided by DNA segments called spacers acquired from invading phage.\nHowever, the patchy incidence and limited memory breadth of CRISPR-Cas systems\nsuggest that their fitness benefits are offset by costs. Here, we propose that\ncross-reactive CRISPR targeting can lead to heterologous autoimmunity, whereby\nforeign spacers guide self-targeting in a spacer-length dependent fashion.\nBalancing antiviral defense against autoimmunity predicts a scaling relation\nbetween spacer length and CRISPR repertoire size. We find evidence for this\nscaling through comparative analysis of sequenced prokaryotic genomes, and show\nthat this association also holds at the level of CRISPR types. In contrast, the\nscaling is absent in strains with nonfunctional CRISPR loci. Finally, we\ndemonstrate that stochastic spacer loss can explain variations around the\nscaling relation, even between strains of the same species. Our results suggest\nthat heterologous autoimmunity is a selective factor shaping the evolution of\nCRISPR-Cas systems.\n"}
+{"abstract": "  We consider computations over networks with multiple broadcast channels that\nintersect at a single party. Each broadcast link suffers from random bit-flip\nnoise that affects the receivers independently. We design interactive coding\nschemes that successfully perform any computation over these noisy networks and\nstrive to reduce their communication overhead with respect to the original\n(noiseless) computation.\n  A simple variant of a coding scheme by Rajagopalan and Schulman (STOC 1994)\nshows that any (noiseless) protocol of $R$ rounds can be reliably simulated in\n$O(R\\log n)$ rounds over a network with $n=n_1n_2+1$ parties in which a single\nparty is connected to $n_2$ noisy broadcast channels, each of which connects\n$n_1$ distinct parties. We design new coding schemes with improved overheads.\nOur approach divides the network into four regimes according to the\nrelationship between $n_1$ and $n_2$. We employ a two-layer coding where the\ninner code protects each broadcast channel and is tailored to the specific\nconditions of the regime in consideration. The outer layer protects the\ncomputation in the network and is generally based on the scheme of Rajagopalan\nand Schulman, adapted to the case of broadcast channels. The overhead we obtain\nranges from $O(\\log\\log n_2)$ to $O(\\log n_2 \\frac{\\log\\log n_1}{\\log n_1})$\nand beats the trivial $O(\\log n)$ overhead in all four regimes.\n"}
+{"abstract": "  EZ Cnc, or EPIC 212182292, is a non-Blazhko RRab variable star located in the\nfield of K2 Campaign 16. Its atmospheric parameters ($T_{\\rm eff}$, $\\log{g}$,\n[M/H]) and radial velocities are measured from the 55 high-quality LAMOST\nmedium-resolution spectra. The fundamental frequency of pulsation is derived as\n$f=1.8323(17)$ d$^{-1}$ from the K2 light curves. The amplitude ratios $R_{21}\n= 0.5115(15), 0.490(8)$, $R_{31} = 0.3249(20), 0.279(7)$ and Fourier phase\ndifferences $\\varphi_{21}=2.7550(20), 2.764(16)$, $\\varphi_{31}=5.7194(25),\n5.719(31)$ are determined from the Fourier decomposition of K2 light curve and\nLAMOST radial velocity curve, respectively. Through the constraints of the\nparameters, four optimal models are obtained in a time-dependent turbulent\nconvection model survey for EPIC 212182292. The parameters of EPIC 212182292\nare derived as $M=0.48\\pm0.03$ M$_{\\odot}$, $L = 42\\pm2$ L$_{\\odot}$, $T_{\\rm\neff}=6846\\pm50$ K, $\\log{g}=2.79\\pm0.01$ dex, and $Z = 0.006\\pm0.002$,\nrespectively. The precisely determined parameters for RRab variable stars like\nEPIC 212180092 might help to better understand the period-luminosity\nrelationship of RR Lyrae stars.\n"}
+{"abstract": "  The AGM postulates by Alchourr\\'{o}n, G\\\"{a}rdenfors, and Makinson continue\nto represent a cornerstone in research related to belief change. We generalize\nthe approach of Katsuno and Mendelzon (KM) for characterizing AGM base revision\nfrom propositional logic to the setting of (multiple) base revision in\narbitrary monotonic logics. Our core result is a representation theorem using\nthe assignment of total - yet not transitive - \"preference\" relations to belief\nbases. We also provide a characterization of all logics for which our result\ncan be strengthened to preorder assignments (as in KM's original work).\n"}
+{"abstract": "  Realizing complete observability in the three-phase distribution system\nremains a challenge that hinders the implementation of classic state estimation\nalgorithms. In this paper, a new method, called the pruned physics-aware neural\nnetwork (P2N2), is developed to improve the voltage estimation accuracy in the\ndistribution system. The method relies on the physical grid topology, which is\nused to design the connections between different hidden layers of a neural\nnetwork model. To verify the proposed method, a numerical simulation based on\none-year smart meter data of load consumptions for three-phase power flow is\ndeveloped to generate the measurement and voltage state data. The IEEE 123-node\nsystem is selected as the test network to benchmark the proposed algorithm\nagainst the classic weighted least squares (WLS). Numerical results show that\nP2N2 outperforms WLS in terms of data redundancy and estimation accuracy.\n"}
+{"abstract": "  We show that one can enumerate the vertices of the convex hull of integer\npoints in polytopes whose constraint matrices have bounded and nonzero\nsubdeterminants, in time polynomial in the dimension and encoding size of the\npolytope. This extends a previous result by Artmann et al. who showed that\ninteger linear optimization in such polytopes can be done in polynomial time.\n"}
+{"abstract": "  We study the structure of galactic halos within a scalar dark matter model,\nendowed with a repulsive quartic self-interaction, capable of undergoing the\nsuperfluid phase transition in high-density regions. We demonstrate that the\nthermalized cores are prone to fragmentation into superfluid droplets due to\nthe Jeans instability. Furthermore, since cores of astrophysical size may be\ngenerated only when most of the particles comprising the halo reside in a\nhighly degenerate phase-space, the well-known bound on the dark matter\nself-interaction cross section inferred from the collision of clusters needs to\nbe revised, accounting for the enhancement of the interaction rate due to\ndegeneracy. As a result, generation of kpc-size superfluid solitons, within the\nparameter subspace consistent with the Bullet Cluster bound, requires dark\nmatter particles to be ultra-light.\n"}
+{"abstract": "  The selection of a single molecular handedness, or homochirality across all\nliving matter, is a mystery in the origin of life. Frank's seminal model showed\nin the fifties how chiral symmetry breaking can occur in non-equilibrium\nchemical networks. However, an important shortcoming in this classic model is\nthat it considers a small number of species, while there is no reason for the\nprebiotic system, in which homochirality first appeared, to have had such a\nsimple composition. Furthermore, this model does not provide information on\nwhat could have been the size of the molecules involved in this homochiral\nprebiotic system. Here, we show that large molecular systems are likely to\nundergo a phase transition towards a homochiral state, as a consequence of the\nfact that they contain a large number of chiral species. Using chemoinformatics\ntools, we quantify how abundant are chiral species in the chemical universe of\nall possible molecules of a given length. Then, we propose that Frank's model\nshould be extended to include a large number of species, in order to possess\nthe transition towards homochirality as confirmed by numerical simulations.\nFinally, using random matrix theory, we prove that large non-equilibrium\nreaction networks possess a generic and robust phase transition towards a\nhomochiral state.\n"}
+{"abstract": "  In machine learning for fluid mechanics, fully-connected neural network (FNN)\nonly uses the local features for modelling, while the convolutional neural\nnetwork (CNN) cannot be applied to data on structured/unstructured mesh. In\norder to overcome the limitations of FNN and CNN, the unstructured\nconvolutional neural network (UCNN) is proposed, which aggregates and\neffectively exploits the features of neighbour nodes through the weight\nfunction. Adjoint vector modelling is taken as the task to study the\nperformance of UCNN. The mapping function from flow-field features to adjoint\nvector is constructed through efficient parallel implementation on GPU. The\nmodelling capability of UCNN is compared with that of FNN on validation set and\nin aerodynamic shape optimization at test case. The influence of mesh changing\non the modelling capability of UCNN is further studied. The results indicate\nthat UCNN is more accurate in modelling process.\n"}
+{"abstract": "  Although single-image super-resolution (SISR) methods have achieved great\nsuccess on single degradation, they still suffer performance drop with multiple\ndegrading effects in real scenarios. Recently, some blind and non-blind models\nfor multiple degradations have been explored. However, those methods usually\ndegrade significantly for distribution shifts between the training and test\ndata. Towards this end, we propose a conditional meta-network framework (named\nCMDSR) for the first time, which helps SR framework learn how to adapt to\nchanges in input distribution. We extract degradation prior at task-level with\nthe proposed ConditionNet, which will be used to adapt the parameters of the\nbasic SR network (BaseNet). Specifically, the ConditionNet of our framework\nfirst learns the degradation prior from a support set, which is composed of a\nseries of degraded image patches from the same task. Then the adaptive BaseNet\nrapidly shifts its parameters according to the conditional features. Moreover,\nin order to better extract degradation prior, we propose a task contrastive\nloss to decrease the inner-task distance and increase the cross-task distance\nbetween task-level features. Without predefining degradation maps, our blind\nframework can conduct one single parameter update to yield considerable SR\nresults. Extensive experiments demonstrate the effectiveness of CMDSR over\nvarious blind, even non-blind methods. The flexible BaseNet structure also\nreveals that CMDSR can be a general framework for large series of SISR models.\n"}
+{"abstract": "  Deep neural networks (DNNs) could be very useful in blockchain applications\nsuch as DeFi and NFT trading. However, training / running large-scale DNNs as\npart of a smart contract is infeasible on today's blockchain platforms, due to\ntwo fundamental design issues of these platforms. First, blockchains nowadays\ntypically require that each node maintain the complete world state at any time,\nmeaning that the node must execute all transactions in every block. This is\nprohibitively expensive for computationally intensive smart contracts involving\nDNNs. Second, existing blockchain platforms expect smart contract transactions\nto have deterministic, reproducible results and effects. In contrast, DNNs are\nusually trained / run lock-free on massively parallel computing devices such as\nGPUs, TPUs and / or computing clusters, which often do not yield deterministic\nresults.\n  This paper proposes novel platform designs, collectively called A New Hope\n(ANH), that address the above issues. The main ideas are (i)\ncomputing-intensive smart contract transactions are only executed by nodes who\nneed their results, or by specialized serviced providers, and (ii) a\nnon-deterministic smart contract transaction leads to uncertain results, which\ncan still be validated, though at a relatively high cost; specifically for\nDNNs, the validation cost can often be reduced by verifying properties of the\nresults instead of their exact values. In addition, we discuss various\nimplications of ANH, including its effects on token fungibility, sharding,\nprivate transactions, and the fundamental meaning of a smart contract.\n"}
+{"abstract": "  Let $P$ be a crossing-free polygon and $\\mathcal C$ a set of shortcuts, where\neach shortcut is a directed straight-line segment connecting two vertices of\n$P$. A shortcut hull of $P$ is another crossing-free polygon that encloses $P$\nand whose oriented boundary is composed of elements from $\\mathcal C$. Shortcut\nhulls find their application in geo-related problems such as the simplification\nof contour lines. We aim at a shortcut hull that linearly balances the enclosed\narea and perimeter. If no holes in the shortcut hull are allowed, the problem\nadmits a straight-forward solution via shortest paths. For the more challenging\ncase that the shortcut hull may contain holes, we present a polynomial-time\nalgorithm that is based on computing a constrained, weighted triangulation of\nthe input polygon's exterior. We use this problem as a starting point for\ninvestigating further variants, e.g., restricting the number of edges or bends.\nWe demonstrate that shortcut hulls can be used for drawing the rough extent of\npoint sets as well as for the schematization of polygons.\n"}
+{"abstract": "  Low-precision arithmetic trains deep learning models using less energy, less\nmemory and less time. However, we pay a price for the savings: lower precision\nmay yield larger round-off error and hence larger prediction error. As\napplications proliferate, users must choose which precision to use to train a\nnew model, and chip manufacturers must decide which precisions to manufacture.\nWe view these precision choices as a hyperparameter tuning problem, and borrow\nideas from meta-learning to learn the tradeoff between memory and error. In\nthis paper, we introduce Pareto Estimation to Pick the Perfect Precision\n(PEPPP). We use matrix factorization to find non-dominated configurations (the\nPareto frontier) with a limited number of network evaluations. For any given\nmemory budget, the precision that minimizes error is a point on this frontier.\nPractitioners can use the frontier to trade memory for error and choose the\nbest precision for their goals.\n"}
+{"abstract": "  The asymmetric dark matter (ADM) scenario solves the baryon-dark matter\ncoincidence problem when the dark matter (DM) mass is of $\\mathcal{O}(1)$GeV.\nComposite ADM models based on QCD-like strong dynamics are particularly\nmotivated since the strong dynamics naturally provides the DM mass of\n$\\mathcal{O}(1)$GeV and the large annihilation cross-section simultaneously. In\nthose models, the sub-GeV dark photon often plays an essential role in\ntransferring the excessive entropy in the dark sector into the visible sector,\ni.e., the Standard Model sector. This paper constructs a chiral composite ADM\nmodel where the $U(1)_D$ gauge symmetry is embedded into the chiral flavor\nsymmetry. Due to the dynamical breaking of the chiral flavor symmetry, the\nmodel naturally provides the masses of the dark photon and the dark pions in\nthe sub-GeV range, both of which play crucial roles for a successful ADM model.\n"}
+{"abstract": "  In the present paper we present the potential of Explainable Artificial\nIntelligence methods for decision-support in medical image analysis scenarios.\nWith three types of explainable methods applied to the same medical image data\nset our aim was to improve the comprehensibility of the decisions provided by\nthe Convolutional Neural Network (CNN). The visual explanations were provided\non in-vivo gastral images obtained from a Video capsule endoscopy (VCE), with\nthe goal of increasing the health professionals' trust in the black box\npredictions. We implemented two post-hoc interpretable machine learning methods\nLIME and SHAP and the alternative explanation approach CIU, centered on the\nContextual Value and Utility (CIU). The produced explanations were evaluated\nusing human evaluation. We conducted three user studies based on the\nexplanations provided by LIME, SHAP and CIU. Users from different non-medical\nbackgrounds carried out a series of tests in the web-based survey setting and\nstated their experience and understanding of the given explanations. Three user\ngroups (n=20, 20, 20) with three distinct forms of explanations were\nquantitatively analyzed. We have found that, as hypothesized, the CIU\nexplainable method performed better than both LIME and SHAP methods in terms of\nincreasing support for human decision-making as well as being more transparent\nand thus understandable to users. Additionally, CIU outperformed LIME and SHAP\nby generating explanations more rapidly. Our findings suggest that there are\nnotable differences in human decision-making between various explanation\nsupport settings. In line with that, we present three potential explainable\nmethods that can with future improvements in implementation be generalized on\ndifferent medical data sets and can provide great decision-support for medical\nexperts.\n"}
+{"abstract": "  Astronomers use to derive MOdified Newtonian Dynamics (MOND) rotation curves\nusing the simple algebraic rule of calculating the acceleration as equal to the\nNewtonian acceleration ($a$) divided by some factor $\\mu (a)$. However, there\nare velocity differences between this simple rule and the calculation derived\nfrom more sophisticated MOND versions such as AQUAL or QMOND, created to expand\nMOND heuristic law and preserve the conservation of momentum, angular momentum,\nand energy, and follow the weak equivalence principle. Here we provide recipes\nbased on Milgrom's proposal to calculate semianalytically (without numerical\nsimulations) MOND rotation curves for any density distribution based on AQUAL,\napplying it to different models of thin disks. The application of this\nformalism is equivalent to the creation of a fictitious phantom mass whose\nfield may be used in a Newtonian way to calculate iteratively the MOND\naccelerations. In most cases, the differences between the application of the\nsimple algebraic rule and the AQUAL-MOND calculations are small, $\\lesssim 5$%.\nHowever, the error of the algebraic solution is larger than 5% when more than\nhalf of the mass is in the MONDian regime (where Newtonian and MOND rotation\nspeeds differ by more than 10%), reaching in some cases $>70$% discrepance,\nsuch as in Maclaurin disks, representative of galaxies for which the rotational\nvelocity rises to the edge of the disk as is seen in irregular galaxies. The\nslope of the rotation speed in the dependence with the radius or the vertical\ndistance of the plane is also significantly changed.\n"}
+{"abstract": "  Spin-orbit effects, inherent to electrons confined in quantum dots at a\nsilicon heterointerface, provide a means to control electron spin qubits\nwithout the added complexity of on-chip, nanofabricated micromagnets or nearby\ncoplanar striplines. Here, we demonstrate a novel singlet-triplet qubit\noperating mode that can drive qubit evolution at frequencies in excess of 200\nMHz. This approach offers a means to electrically turn on and off fast control,\nwhile providing high logic gate orthogonality and long qubit dephasing times.\nWe utilize this operational mode for dynamical decoupling experiments to probe\nthe charge noise power spectrum in a silicon metal-oxide-semiconductor double\nquantum dot. In addition, we assess qubit frequency drift over longer\ntimescales to capture low-frequency noise. We present the charge noise power\nspectral density up to 3 MHz, which exhibits a $1/f^{\\alpha}$ dependence\nconsistent with $\\alpha \\sim 0.7$, over 9 orders of magnitude in noise\nfrequency.\n"}
+{"abstract": "  Piezoelectric fluoropolymers convert mechanical energy to electricity and are\nideal for sustainably providing power to electronic devices. To convert\nmechanical energy, a net polarization must be induced in the fluoropolymer,\nwhich is currently achieved via an energy intensive electrical poling process.\nEliminating this process will enable the low-energy production of efficient\nenergy harvesters. Here, by combining molecular dynamics simulations,\npiezoresponse force microscopy, and electrodynamic measurements, we reveal a\nhitherto unseen polarization locking phenomena of poly(vinylidene\nfluoride-$\\mathit{co}$-trifluoroethylene) (PVDF-TrFE) perpendicular to the\nbasal plane of two-dimensional (2D) Ti$_{3}$C$_{2}$T$_{\\mathit{x}}$ MXene\nnanosheets. This polarization locking, driven by strong electrostatic\ninteractions enabled exceptional energy harvesting performance, with a measured\npiezoelectric charge coefficient, $\\mathit{d_{33}}$, of -52.0 picocoulombs per\nnewton, significantly higher than electrically poled PVDF-TrFE (approximately\n-38 picocoulombs per newton). This study provides a new fundamental and low\nenergy input mechanism of poling fluoropolymers, which enables new levels of\nperformance in electromechanical technologies.\n"}
+{"abstract": "  We analyze the effect of interphase boundary anisotropy on the dynamics of\nlamellar eutectic solidification fronts, in the limit that the lamellar spacing\nvaries slowly along the envelope of the front. In the isotropic case, it is\nknown that the spacing obeys a diffusion equation, which can be obtained\ntheoretically by making two assumptions: (i) the lamellae always grow normal to\nthe large-scale envelope of the front, and (ii) the Jackson-Hunt law that links\nlamellar spacing and front temperature remains locally valid. For anisotropic\nboundaries, we replace hypothesis (i) by the symmetric pattern approximation,\nwhich has recently been found to yield good predictions for lamellar growth\ndirection in presence of interphase anisotropy. We obtain a generalized\nJackson-Hunt law for tilted lamellae, and an evolution equation for the\nenvelope of the front. The latter contains a propagative term if the initial\nlamellar array is tilted with respect to the direction of the temperature\ngradient. However, the propagation velocity of the propagative wave modes are\nfound to be small, so that the dynamics of the front can be reasonably\ndescribed by a diffusion equation with a diffusion coefficient that is modified\nwith respect to the isotropic case.\n"}
+{"abstract": "  We investigate the feasibility of sample average approximation (SAA) for\ngeneral stochastic optimization problems, including two-stage stochastic\nprogramming without the relatively complete recourse assumption. Instead of\nanalyzing problems with specific structures, we utilize results from the\nVapnik-Chervonenkis (VC) dimension and Probably Approximately Correct learning\nto provide a general framework that offers explicit feasibility bounds for SAA\nsolutions under minimal structural or distributional assumption. We show that,\nas long as the hypothesis class formed by the feasbible region has a finite VC\ndimension, the infeasibility of SAA solutions decreases exponentially with\ncomputable rates and explicitly identifiable accompanying constants. We\ndemonstrate how our bounds apply more generally and competitively compared to\nexisting results.\n"}
+{"abstract": "  In this paper, explicit stable integrators based on symplectic and contact\ngeometries are proposed for a non-autonomous ordinarily differential equation\n(ODE) found in improving convergence rate of Nesterov's accelerated gradient\nmethod. Symplectic geometry is known to be suitable for describing Hamiltonian\nmechanics, and contact geometry is known as an odd-dimensional counterpart of\nsymplectic geometry. Moreover, a procedure, called symplectization, is a known\nway to construct a symplectic manifold from a contact manifold, yielding\nHamiltonian systems from contact ones. It is found in this paper that a\npreviously investigated non-autonomous ODE can be written as a contact\nHamiltonian system. Then, by symplectization of a non-autonomous contact\nHamiltonian vector field expressing the non-autonomous ODE, novel symplectic\nintegrators are derived. Because the proposed symplectic integrators preserve\nhidden symplectic and contact structures in the ODE, they should be more stable\nthan the Runge-Kutta method. Numerical experiments demonstrate that, as\nexpected, the second-order symplectic integrator is stable and high convergence\nrates are achieved.\n"}
+{"abstract": "  In this paper we study quasilinear elliptic equations driven by the double\nphase operator along with a reaction that has a singular and a parametric\nsuperlinear term and with a nonlinear Neumann boundary condition of critical\ngrowth. Based on a new equivalent norm for Musielak-Orlicz Sobolev spaces and\nthe Nehari manifold along with the fibering method we prove the existence of at\nleast two weak solutions provided the parameter is sufficiently small.\n"}
+{"abstract": "  We propose a novel method for drift estimation of multiscale diffusion\nprocesses when a sequence of discrete observations is given. For the Langevin\ndynamics in a two-scale potential, our approach relies on the eigenvalues and\nthe eigenfunctions of the homogenized dynamics. Our first estimator is derived\nfrom a martingale estimating function of the generator of the homogenized\ndiffusion process. However, the unbiasedness of the estimator depends on the\nrate with which the observations are sampled. We therefore introduce a second\nestimator which relies also on filtering the data and we prove that it is\nasymptotically unbiased independently of the sampling rate. A series of\nnumerical experiments illustrate the reliability and efficiency of our\ndifferent estimators.\n"}
+{"abstract": "  Photochemically induced dynamic nuclear polarization (photo-CIDNP) is a\nmethod to hyperpolarize nuclear spins using light. In most cases, CIDNP\nexperiments are performed at a high magnetic field and the sample is irradiated\nby the light inside a nuclear magnetic resonance (NMR) spectrometer. Here we\ndemonstrate photo-CIDNP hyperpolarization generated in the Earth magnetic field\nand under zero- to ultralow field (ZULF) conditions. Irradiating a sample for\nseveral seconds with inexpensive light-emitting diodes produces a strong\nhyperpolarization of 1H and 13C nuclear spins enhancing the NMR signals several\nhundred times. The hyperpolarized spin states at the Earth field and in ZULF\nare different. In the latter case, the state corresponds to the singlet order\nbetween scalar-coupled 1H-13C nuclear spins. This state has a longer lifetime\nthan the state hyperpolarized at Earth field. The method is simple and\ncost-efficient and should be applicable to many molecular systems known to\nexhibit photo-CIDNP, including amino acids and nucleotides.\n"}
+{"abstract": "  In this letter, we propose a joint active device detection and channel\nestimation framework based on factor graphs for asynchronous uplink grant-free\nmassive multiple-antenna systems. We then develop the message-scheduling GAMP\n(MSGAMP) algorithm to perform joint active device detection and channel\nestimation. In MSGAMP we apply scheduling techniques based on the residual\nbelief propagation (RBP) and the activity user detection (AUD) in which\nmessages are generated using the latest available information. MSGAMP-type\nschemes show a good performance in terms of activity error rate and normalized\nmean squared error, requiring a smaller number of iterations for convergence\nand lower complexity than state-of-the-art techniques.\n"}
+{"abstract": "  This work investigates the use of pulse stretching inverters for monitoring\nthe variation of flux and Linear Energy Transfer (LET) of energetic particles.\nThe basic particle detector consists of two cascaded pulse stretching\n(skew-sized) inverters designed in CMOS technology, and the required sensing\narea is obtained by connecting multiple two-inverter pulse stretching cells in\nparallel, and employing the required number of parallel arrays. The particle\nstrikes are detected in terms of the Single Event Transients (SETs), and the\ndetector provides the information on the SET count rate and SET pulse width\nvariation, from which the particle flux and LET can be determined. The main\nadvantage of the proposed solution is the possibility to sense the LET\nvariations using purely digital processing logic. The SPICE simulations done on\nIHP 130 nm bulk CMOS technology have shown that the SET pulse width at the\noutput of detector changes by 550 ps over the LET range from 1 to 100\nMeVcm2mg-1. The proposed solution is intended to operate as an on-chip particle\ndetector within the self-adaptive multiprocessing systems.\n"}
+{"abstract": "  In this paper, we study the $k$-th moment of central values of the family of\nquadratic Dirichlet $L$-functions of moduli $8p$, with $p$ ranging over odd\nprimes. Assuming the truth of the generlized Riemann hypothesis, we establish\nsharp upper and lower bounds for the $k$-th power moment of these $L$-values\nfor all real $k \\geq 0$.\n"}
+{"abstract": "  The effect of the interaction of mesons in the final state is additionally\nconsidered within the description of $\\tau \\to \\pi \\eta(\\eta') \\nu_{\\tau}$\ndecays. This interaction is taken into account at the level of production of\nintermediate pions. One of them, in turn, might be transited into $\\eta$ or\n$\\eta'$ mesons. Our results do not exceed the experimentally established\nbranching fractions, and they are in agreement with the results of other\ntheoretical studies.\n"}
+{"abstract": "  Two polarization modes of gravitational wave are derived from the general\nrelativity which are plus and cross modes. However, the alternative theories of\ngravity can yield the gravitational wave with up to six polarizations.\nSearching for the polarizations beyond plus and cross is an important test of\ngeneral relativity. In principle, one space-borne detector, like LISA, could\nmeasure the gravitational wave polarizations from a long time observation with\nits orbital motion. With the comparable sensitivities, the joint LISA and TAIJI\nmissions will improve the observations on the polarization predictions of\ntheories beyond general relativity. In this work, a class of parameterized\npost-Einsteinian waveform is employed to describe the alternative\npolarizations, and six parameterized post-Einsteinian parameters quantifying\nfrom general relativity waveform are examined by using the LISA-TAIJI network.\nOur results show that the measurements on amplitudes of alternative\npolarizations from joint LISA-TAIJI observation could be improved by more than\n10 times compared to LISA single mission in an optimal scenario.\n"}
+{"abstract": "  We analyze the decay of spin waves into Stoner excitations in magnetic Weyl\nsemimetals. The lifetime of a mode is found to have a universal dependence on\nits frequency and momentum, and on a few parameters that characterize the\nrelativistic Weyl spectrum. At the same time, Gilbert damping by Weyl electrons\nis absent. The decay rate of spin waves is calculated perturbatively using the\ns-d model of itinerant Weyl or Dirac electrons coupled to local moments. We\nshow that many details of the Weyl spectrum, such as the momentum-space\nlocations, dispersions and sizes of the Weyl Fermi pockets, can be deduced\nindirectly by probing the spin waves of local moments using inelastic neutron\nscattering.\n"}
+{"abstract": "  This letter is a proof of concept for quantum power flow (QPF) algorithms\nwhich underpin various unprecedentedly efficient power system analytics\nexploiting quantum computing. Our contributions are three-fold: 1) Establish a\nquantum-state-based fast decoupled model empowered by Hermitian and constant\nJacobian matrices; 2) Devise an enhanced Harrow-Hassidim-Lloyd (HHL) algorithm\nto solve the fast decoupled QPF; 3) Further improve the HHL efficiency by\nparameterizing quantum phase estimation and reciprocal rotation only at the\nbeginning stage. Promising test results validate the accuracy and efficacy of\nQPF and demonstrate QPF's enormous potential in the era of quantum computing.\n"}
+{"abstract": "  This paper deals with the design of resilient Cooperative Adaptive Cruise\nControl (CACC) for homogeneous vehicle platoons in which communication is\nvulnerable to Denial-of-Service (DOS) attacks. We consider DOS attacks as\nconsecutive packet dropouts. We present a controller tuning procedure based on\nlinear matrix inequalities (LMI) that maximizes the resiliency to DOS attacks,\nwhile guaranteeing performance and string stability. The design procedure\nreturns controller gains and gives a lower bound on the maximum allowable\nnumber of successive packet dropouts. A numerical example is employed to\nillustrate the effectiveness of the proposed approach.\n"}
+{"abstract": "  We present on-going work of evaluating the, to our knowledge, first large\ngenerative language model trained to converse in Swedish, using data from the\nonline discussion forum Flashback. We conduct a human evaluation pilot study\nthat indicates the model is often able to respond to conversations in both a\nhuman-like and informative manner, on a diverse set of topics. While data from\nonline forums can be useful to build conversational systems, we reflect on the\nnegative consequences that incautious application might have, and the need for\ntaking active measures to safeguard against them.\n"}
+{"abstract": "  The physical and mathematical properties of the non-linearly coupled\nblack-hole-orbiting-ring system are studied analytically to second order in the\ndimensionless angular velocity $M_{\\text{ir}}\\omega_{\\text{H}}$ of the\nblack-hole horizon (here $M_{\\text{ir}}$ is the irreducible mass of the slowly\nrotating central black hole). In particular, we determine analytically, to\nfirst order in the dimensionless ring-to-black-hole mass ratio\n$m/M_{\\text{ir}}$, the shift $\\Delta\\Omega_{\\text{mb}}/\\Omega_{\\text{mb}}$ in\nthe orbital frequency of the {\\it marginally bound} circular geodesic that\ncharacterizes the composed curved spacetime. Interestingly, our analytical\nresults for the frequency shift $\\Delta\\Omega_{\\text{mb}}$ in the composed\nblack-hole-orbiting-ring toy model agree qualitatively with the recently\npublished numerical results for the corresponding frequency shift in the\nphysically related (and mathematically much more complex)\nblack-hole-orbiting-particle system. In particular, the present analysis\nprovides evidence that, at order $O(m/M_{\\text{ir}})$, the recently observed\npositive shift in the angular frequency of the marginally bound circular orbit\nis directly related to the physically intriguing phenomenon of dragging of\ninertial frames by orbiting masses in general relativity.\n"}
+{"abstract": "  We discuss gluon-gluon mechanisms for production of mesons with hidden\nstrangeness, such as $\\eta'$ and $\\phi$ meson, in proton-proton collisions at\nlarge energies. The $g^* g^* \\to \\eta'$ and $g^* g^* \\to \\phi g$ mechanisms are\nconsidered only and the corresponding cross sections are calculated in the\n$k_t$-factorization approach. The $F_{\\gamma^* \\gamma^* \\to \\eta'}$ and $F_{g^*\ng^* \\to \\eta'}$ form factors are calculated from quark-antiquark $\\eta'$\nlight-cone wave function including quark/antiquark transverse momenta. The\nresult for two-photon transition form factor demonstrates that higher twists\nmay survive even to large photon virtualities. The result is compared with the\nresult of a recent leading-twist NLO analysis which uses phenomenological\ndistribution amplitudes fitted to exclusive production of $\\eta'$ in $e^+ e^-\n\\to e^+ e^- \\eta'$ reaction. We calculate transverse momentum distributions of\nboth $\\eta'$ and $\\phi$ mesons in proton-proton collisions for RHIC and LHC\nenergies. The results are compared to experimental data whenever available. The\nresults of the Lund string model are shown for comparison for $\\eta'$\nproduction at LHC energies. It seems that the two-gluon fusion is not the\ndominant mechanism for both $\\phi$ and $\\eta'$ production, although the\nsituation for $\\eta'$, especially at larger energies, is less clear due to lack\nof experimental data.\n"}
+{"abstract": "  Most subdwarf B (sdB) + Helium white dwarf (He WD) binaries are believed to\nbe formed from a particular channel. In this channel, the He WDs are produced\nfirst from red giants (RGs) with degenerate cores via stable mass transfer and\nsdB stars are produced from RGs with degenerate cores via common envelope (CE)\nejection. They are important for the studies of CE evolution, binary evolution,\nand binary population synthesis. However, the relation between WD mass and\norbital period of sdB + He WD binaries has not been specifically studied. In\nthis paper, we first use a semi-analytic method to follow their formation and\nfind a WD mass and orbital period relation. Then we use a detailed stellar\nevolution code to model their formation from main-sequence binaries. We find a\nsimilar relation between the WD mass and orbital period, which is in broad\nagreement with observations. For most sdB + He WD systems, if the WD mass\n(orbital period) can be determined, the orbital period (WD mass) can be\ninferred with this relation and then the inclination angle can be constrained\nwith the binary mass function. In addition, we can also use this relation to\nconstrain the CE ejection efficiency and find that a relative large CE ejection\nefficiency is favoured. If both the WD and sdB star masses can be determined,\nthe critical mass ratios of dynamically unstable mass transfer for RG binaries\ncan also be constrained.\n"}
+{"abstract": "  Recurrent meta reinforcement learning (meta-RL) agents are agents that employ\na recurrent neural network (RNN) for the purpose of \"learning a learning\nalgorithm\". After being trained on a pre-specified task distribution, the\nlearned weights of the agent's RNN are said to implement an efficient learning\nalgorithm through their activity dynamics, which allows the agent to quickly\nsolve new tasks sampled from the same distribution. However, due to the\nblack-box nature of these agents, the way in which they work is not yet fully\nunderstood. In this study, we shed light on the internal working mechanisms of\nthese agents by reformulating the meta-RL problem using the Partially\nObservable Markov Decision Process (POMDP) framework. We hypothesize that the\nlearned activity dynamics is acting as belief states for such agents. Several\nillustrative experiments suggest that this hypothesis is true, and that\nrecurrent meta-RL agents can be viewed as agents that learn to act optimally in\npartially observable environments consisting of multiple related tasks. This\nview helps in understanding their failure cases and some interesting\nmodel-based results reported in the literature.\n"}
+{"abstract": "  This work presents proximally optimal predictive control algorithm, which is\nessentially a model-based lateral controller for steered autonomous vehicles\nthat selects an optimal steering command within the neighborhood of previous\nsteering angle based on the predicted vehicle location. The proposed algorithm\nwas formulated with an aim of overcoming the limitations associated with the\nexisting control laws for autonomous steering - namely PID, Pure-Pursuit and\nStanley controllers. Particularly, our approach was aimed at bridging the gap\nbetween tracking efficiency and computational cost, thereby ensuring effective\npath tracking in real-time. The effectiveness of our approach was investigated\nthrough a series of dynamic simulation experiments pertaining to autonomous\npath tracking, employing an adaptive control law for longitudinal motion\ncontrol of the vehicle. We measured the latency of the proposed algorithm in\norder to comment on its real-time factor and validated our approach by\ncomparing it against the established control laws in terms of both crosstrack\nand heading errors recorded throughout the respective path tracking\nsimulations.\n"}
+{"abstract": "  Marginalized importance sampling (MIS), which measures the density ratio\nbetween the state-action occupancy of a target policy and that of a sampling\ndistribution, is a promising approach for off-policy evaluation. However,\ncurrent state-of-the-art MIS methods rely on complex optimization tricks and\nsucceed mostly on simple toy problems. We bridge the gap between MIS and deep\nreinforcement learning by observing that the density ratio can be computed from\nthe successor representation of the target policy. The successor representation\ncan be trained through deep reinforcement learning methodology and decouples\nthe reward optimization from the dynamics of the environment, making the\nresulting algorithm stable and applicable to high-dimensional domains. We\nevaluate the empirical performance of our approach on a variety of challenging\nAtari and MuJoCo environments.\n"}
+{"abstract": "  An important issue in dealing with Deep Convolutional Neural Networks (DCNN)\nis the 'black box problem', which represents the unknowns about internal\ninformation representation and processing, especially in the middle and higher\nlayers. In this study, we adopted a systems neuroscience methodology to measure\nthe visual feature selectivity and visualize the spatial receptive field of the\nunits in VGG16. Orientation and spatial frequency tunings of each unit were\nmeasured using sinusoidal grating stimuli. The image category selectivity of\neach unit was also measured using natural image stimuli. The spatial structures\nof the receptive fields of all convolutional units were estimated by\nactivation-weighted average (AWA) and activation-weighted covariance (AWC)\nanalyses. In the middle layers (convolutional layers in block3 and block4), AWC\nanalysis successfully reconstructed the receptive field that predicted the\nvisual feature selectivity of the unit. Those results suggested the possibility\nthat analyzing the reconstructed receptive field structure can be used to\ninterpret the functional significance of the units and layers of a DCNN.\n"}
+{"abstract": "  Safety assurance is a critical yet challenging aspect when developing\nself-driving technologies. Hamilton-Jacobi backward-reachability analysis is a\nformal verification tool for verifying the safety of dynamic systems in the\npresence of disturbances. However, the standard approach is too conservative to\nbe applied to self-driving applications due to its worst-case assumption on\nhumans' behaviors (i.e., guard against worst-case outcomes). In this work, we\nintegrate a learning-based prediction algorithm and a game-theoretic human\nbehavioral model to online update the conservativeness of backward-reachability\nanalysis. We evaluate our approach using real driving data. The results show\nthat, with reasonable assumptions on human behaviors, our approach can\neffectively reduce the conservativeness of the standard approach without\nsacrificing its safety verification ability.\n"}
+{"abstract": "  This paper describes an approach to solving the next destination city\nrecommendation problem for a travel reservation system. We propose a two stages\napproach: a heuristic approach for candidates selection and an attention neural\nnetwork model for candidates re-ranking. Our method was inspired by listwise\nlearning-to-rank methods and recent developments in natural language processing\nand the transformer architecture in particular. We used this approach to solve\nthe Booking.com recommendations challenge Our team achieved 5th place on the\nchallenge using this method, with 0.555 accuracy@4 value on the closed part of\nthe dataset.\n"}
+{"abstract": "  The simulation of time evolution of large quantum systems is a classically\nchallenging and in general intractable task, making it a promising application\nfor quantum computation. A Trotter-Suzuki approximation yields an\nimplementation thereof, where a higher approximation accuracy can be traded for\nan increased gate count. In this work, we introduce a variational algorithm\nwhich uses solutions of classical optimizations to predict efficient quantum\ncircuits for time evolution of translationally invariant quantum systems. Our\nstrategy can improve upon the Trotter-Suzuki accuracy by several orders of\nmagnitude. It translates into a reduction in gate count and hence gain in\noverall fidelity at the same algorithmic accuracy. This is important in\nNISQ-applications where the fidelity of the output state decays exponentially\nwith the number of gates. The performance advantage of our classical assisted\nstrategy can be extended to open boundaries with translational symmetry in the\nbulk. We can extrapolate our method to beyond classically simulatable system\nsizes, maintaining its total fidelity advantage over a Trotter-Suzuki\napproximation making it an interesting candidate for beyond classical time\nevolution.\n"}
+{"abstract": "  Robust tube-based model predictive control (MPC) methods address constraint\nsatisfaction by leveraging an a priori determined tube controller in the\nprediction to tighten the constraints. This paper presents a system level\ntube-MPC (SLTMPC) method derived from the system level parameterization (SLP),\nwhich allows optimization over the tube controller online when solving the MPC\nproblem, which can significantly reduce conservativeness. We derive the SLTMPC\nmethod by establishing an equivalence relation between a class of robust MPC\nmethods and the SLP. Finally, we show that the SLTMPC formulation naturally\narises from an extended SLP formulation and show its merits in a numerical\nexample.\n"}
+{"abstract": "  Generating metaphors is a challenging task as it requires a proper\nunderstanding of abstract concepts, making connections between unrelated\nconcepts, and deviating from the literal meaning. In this paper, we aim to\ngenerate a metaphoric sentence given a literal expression by replacing relevant\nverbs. Based on a theoretically-grounded connection between metaphors and\nsymbols, we propose a method to automatically construct a parallel corpus by\ntransforming a large number of metaphorical sentences from the Gutenberg Poetry\ncorpus (Jacobs, 2018) to their literal counterpart using recent advances in\nmasked language modeling coupled with commonsense inference. For the generation\ntask, we incorporate a metaphor discriminator to guide the decoding of a\nsequence to sequence model fine-tuned on our parallel data to generate\nhigh-quality metaphors. Human evaluation on an independent test set of literal\nstatements shows that our best model generates metaphors better than three\nwell-crafted baselines 66% of the time on average. A task-based evaluation\nshows that human-written poems enhanced with metaphors proposed by our model\nare preferred 68% of the time compared to poems without metaphors.\n"}
+{"abstract": "  There is a longstanding discrepancy between the observed Galactic classical\nnova rate of $\\sim 10$ yr$^{-1}$ and the predicted rate from Galactic models of\n$\\sim 30$--50 yr$^{-1}$. One explanation for this discrepancy is that many\nnovae are hidden by interstellar extinction, but the degree to which dust can\nobscure novae is poorly constrained. We use newly available all-sky\nthree-dimensional dust maps to compare the brightness and spatial distribution\nof known novae to that predicted from relatively simple models in which novae\ntrace Galactic stellar mass. We find that only half ($\\sim 48$\\%) of novae are\nexpected to be easily detectable ($g \\lesssim 15$) with current all-sky optical\nsurveys such as the All-Sky Automated Survey for Supernovae (ASAS-SN). This\nfraction is much lower than previously estimated, showing that dust does\nsubstantially affect nova detection in the optical. By comparing complementary\nsurvey results from ASAS-SN, OGLE-IV, and the Palomar Gattini IR-survey in the\ncontext of our modeling, we find a tentative Galactic nova rate of $\\sim 40$\nyr$^{-1}$, though this could decrease to as low as $\\sim 30$ yr$^{-1}$\ndepending on the assumed distribution of novae within the Galaxy. These\npreliminary estimates will be improved in future work through more\nsophisticated modeling of nova detection in ASAS-SN and other surveys.\n"}
+{"abstract": "  We propose a generalized CUR (GCUR) decomposition for matrix pairs $(A, B)$.\nGiven matrices $A$ and $B$ with the same number of columns, such a\ndecomposition provides low-rank approximations of both matrices simultaneously,\nin terms of some of their rows and columns. We obtain the indices for selecting\nthe subset of rows and columns of the original matrices using the discrete\nempirical interpolation method (DEIM) on the generalized singular vectors. When\n$B$ is square and nonsingular, there are close connections between the GCUR of\n$(A, B)$ and the DEIM-induced CUR of $AB^{-1}$. When $B$ is the identity, the\nGCUR decomposition of $A$ coincides with the DEIM-induced CUR decomposition of\n$A$. We also show a similar connection between the GCUR of $(A, B)$ and the CUR\nof $AB^+$ for a nonsquare but full-rank matrix $B$, where $B^+$ denotes the\nMoore--Penrose pseudoinverse of $B$. While a CUR decomposition acts on one data\nset, a GCUR factorization jointly decomposes two data sets. The algorithm may\nbe suitable for applications where one is interested in extracting the most\ndiscriminative features from one data set relative to another data set. In\nnumerical experiments, we demonstrate the advantages of the new method over the\nstandard CUR approximation; for recovering data perturbed with colored noise\nand subgroup discovery.\n"}
+{"abstract": "  Automatic medical image segmentation has made great progress benefit from the\ndevelopment of deep learning. However, most existing methods are based on\nconvolutional neural networks (CNNs), which fail to build long-range\ndependencies and global context connections due to the limitation of receptive\nfield in convolution operation. Inspired by the success of Transformer in\nmodeling the long-range contextual information, some researchers have expended\nconsiderable efforts in designing the robust variants of Transformer-based\nU-Net. Moreover, the patch division used in vision transformers usually ignores\nthe pixel-level intrinsic structural features inside each patch. To alleviate\nthese problems, we propose a novel deep medical image segmentation framework\ncalled Dual Swin Transformer U-Net (DS-TransUNet), which might be the first\nattempt to concurrently incorporate the advantages of hierarchical Swin\nTransformer into both encoder and decoder of the standard U-shaped architecture\nto enhance the semantic segmentation quality of varying medical images. Unlike\nmany prior Transformer-based solutions, the proposed DS-TransUNet first adopts\ndual-scale encoder subnetworks based on Swin Transformer to extract the coarse\nand fine-grained feature representations of different semantic scales. As the\ncore component for our DS-TransUNet, a well-designed Transformer Interactive\nFusion (TIF) module is proposed to effectively establish global dependencies\nbetween features of different scales through the self-attention mechanism.\nFurthermore, we also introduce the Swin Transformer block into decoder to\nfurther explore the long-range contextual information during the up-sampling\nprocess. Extensive experiments across four typical tasks for medical image\nsegmentation demonstrate the effectiveness of DS-TransUNet, and show that our\napproach significantly outperforms the state-of-the-art methods.\n"}
+{"abstract": "  Accurate constraints on curvature provide a powerful probe of inflation.\nHowever, curvature constraints based on specific assumptions of dark energy may\nlead to unreliable conclusions when used to test inflation models. To avoid\nthis, it is important to obtain constraints that are independent on assumptions\nfor dark energy. In this paper, we investigate such constraints on curvature\nfrom the geometrical probe constructed from galaxy-lensing cross-correlations.\nWe study comprehensively the cross-correlations of galaxy with magnification,\nmeasured from type Ia supernovae's brightnesses (\"$g\\kappa^{\\rm SN}$\"), with\nshear (\"$g\\kappa^{\\rm g}$\"), and with CMB lensing (\"$g\\kappa^{\\rm CMB}$\"). We\nfind for the LSST and Stage IV CMB surveys, \"$g\\kappa^{\\rm SN}$\" ,\n\"$g\\kappa^{\\rm g}$\" and \"$g\\kappa^{\\rm CMB}$\" can be detected with\nsignal-to-noise ratio $S/N=104,\\ 2291,\\ 1842$ respectively. When combined with\nsupernovae Hubble diagram (\"SN\") to constrain curvature, we find galaxy-lensing\ncross-correlation becomes increasingly important with more degrees of freedom\nallowed in dark energy. Without any priors, we obtain error on $\\Omega_K$ of\n$0.723$ from \"SN + $g\\kappa^{\\rm SN}$\", $0.0417$ from \"SN + $g\\kappa^{\\rm g}$\",\nand $0.04$ from \"SN + $g\\kappa^{\\rm g}$ + $g\\kappa^{\\rm CMB}$\" for the LSST and\nStage IV CMB surveys. The last one is more competitive than a Stage IV BAO\nsurvey (\"BAO\"). When galaxy-lensing cross-correlations are added to the\ncombined probe of \"SN + BAO + CMB\", where \"CMB\" stands for Planck measurement\nfor the CMB acoustic scale, we obtain constraint on $\\Omega_K$ of $0.0013$,\nwhich is a factor of 7 improvement from \"SN + BAO + CMB\". We study improvements\nin these results from increasing the high redshift extension of supernovae.\n"}
+{"abstract": "  Improving the use of Type Ia supernovae (SNIa) as standard candles requires a\nbetter approach to incorporate the relationship between SNIa and the properties\nof their host galaxies. Using a spectroscopically-confirmed sample of\n$\\sim$1600 SNIa, we develop the first empirical model of underlying populations\nfor SNIa light-curve properties that includes their dependence on host-galaxy\nstellar mass. These populations are important inputs to simulations that are\nused to model selection effects and correct distance biases within the BEAMS\nwith Bias Correction (BBC) framework. Here we improve BBC to also account for\nSNIa-host correlations, and we validate this technique on simulated data\nsamples. We recover the input relationship between SNIa luminosity and\nhost-galaxy stellar mass (the mass step, $\\gamma$) to within 0.004 mags, which\nis a factor of 5 improvement over the previous method that results in a\n$\\gamma$-bias of ${\\sim}0.02$. We adapt BBC for a novel dust-based model of\nintrinsic brightness variations, which results in a greatly reduced mass step\nfor data ($\\gamma = 0.017 \\pm 0.008$), and for simulations ($\\gamma =0.006 \\pm\n0.007$). Analysing simulated SNIa, the biases on the dark energy\nequation-of-state, $w$, vary from $\\Delta w = 0.006(5)$ to $0.010(5)$ with our\nnew BBC method; these biases are significantly smaller than the $0.02(5)$\n$w$-bias using previous BBC methods that ignore SNIa-host correlations.\n"}
+{"abstract": "  Sagittarius A* (Sgr A*) -- the supermassive black hole (SMBH) in the center\nof our galaxy -- has been observed in most of the electromagnetic spectrum,\nfrom radio to X-rays. Diffuse gamma-ray emission has been observed around Sgr\nA* and a gamma-ray point source has been detected coinciding with the SMBH's\nposition, although there is no definitive association between the two. In this\nwork, we have used ~11 years of Fermi Large Area Telescope (LAT) observations\nof the point source 4FGL J1745.6-2859 and performed a detailed imaging analysis\nacross four energy bands. Our goal is to elucidate the nature of the gamma-ray\nemission at the Galactic Center (GC) and whether it is associated with the\nSMBH. We find that the centroid of the emission approaches Sgr A*'s location as\nthe energy increases. Assuming that the gamma-ray point source is located at\nthe GC, we estimate a luminosity of 2.61E36 erg/s in the 100 MeV to 500 GeV\nenergy range. This is consistent with Sgr A*'s bolometric luminosity. Based on\nthe point source properties, we ruled out several potential candidates for its\nnature and favor a cosmic ray origin either from protons, electrons or both,\naccelerated by -- or in the vicinity of -- the SMBH. Our results indicate that\nthe point source at the GC is indeed the gamma-ray counterpart of Sgr A* in the\nGeV range.\n"}
+{"abstract": "  We study high energy neutrino emission from relativistic jets launched by a\nblack hole (BH) spiraling-in inside the envelope of a red supergiant (RSG), and\nfind that such common envelope jets supernovae (CEJSNe) are a potential source\nfor the ~10^15 eV neutrinos detected by IceCube. We first use the stellar\nevolution code MESA to mimic the effect of the jets on the RSG envelope, and\nfind that the jets substantially inflate the envelope. We then study the\npropagation of jets inside the extended RSG envelope and find that in most\ncases the jets do not penetrate the envelope but are rather stalled. We show\nthat such jets can accelerate cosmic rays to high enough energies to produce\nhigh energy neutrinos. While the neutrinos stream out freely, the gamma-rays\nthat accompany the neutrino production remain trapped inside the optically\nthick envelope. This explains the lack of observational association between\nhigh energy neutrinos and gamma-rays. We crudely estimate the diffuse neutrino\nspectrum from a CEJSN and find that CEJSNe with BH companions might have a\nsubstantial contribution to the high energy neutrinos flux detected by IceCube.\n"}
+{"abstract": "  In this paper we introduce and develop the concept of Interval-Weighted\nNetworks (IWN), a novel approach in Social Network Analysis, where the edge\nweights are represented by closed intervals composed with precise information,\ncomprehending intrinsic variability. We extend IWN for both Newman's modularity\nand modularity gain and the Louvain algorithm (LA), considering a tabular\nrepresentation of networks by contingency tables. We apply our methodology in a\nreal-world commuter network in mainland Portugal between the twenty three NUTS\n3 regions. The optimal partition of regions is developed and compared using two\nnew different approaches, designated as ``Classic Louvain'' (CL) and ``Hybrid\nLouvain'' (HL), which allow taking into account the variability observed in the\noriginal network, thereby minimizing the loss of information present in the raw\ndata. Our findings suggest the division of the twenty three Portuguese regions\nin three main communities. However, we find different geographical partitions\naccording to the community detection methodology used. This analysis can be\nuseful in many real-world applications, since it takes into account that the\nweights may vary within the ranges, rather than being constant.\n"}
+{"abstract": "  Let $g(z)=\\int_0^zp(t)\\exp(q(t))\\,dt+c$ where $p,q$ are polynomials and\n$c\\in\\mathbb{C}$, and let $f$ be the function from Newton's method for $g$. We\nshow that under suitable assumptions the Julia set of $f$ has Lebesgue measure\nzero. Together with a theorem by Bergweiler, our result implies that $f^n(z)$\nconverges to zeros of $g$ almost everywhere in $\\mathbb{C}$ if this is the case\nfor each zero of $g''$. In order to prove our result, we establish general\nconditions ensuring that Julia sets have Lebesgue measure zero.\n"}
+{"abstract": "  Left-wing authoritarianism remains far less understood than right-wing\nauthoritarianism. We contribute to the literature on the former, which\ntypically relies on surveys, using a new social media analytics approach. We\nuse a list of 60 terms to provide an exploratory sketch of the outlines of a\npolitical ideology (tribal equalitarianism) with origins in 19th and 20th\ncentury social philosophy. We then use analyses of the English Corpus of Google\nBooks (over 8 million books) and scraped unique tweets from Twitter (n =\n202,852) to conduct a series of investigations to discern the extent to which\nthis ideology is cohesive amongst the public, reveals signatures of\nauthoritarianism and has been growing in popularity. Though exploratory, our\nresults provide some evidence of left-wing authoritarianism in two forms (1) a\nuniquely conservative moral signature amongst ostensible liberals using\nmeasures from Moral Foundations Theory and (2) a substantial prevalence of\nanger, relative to anxiety or sadness. In general, results indicate that this\nworldview is growing in popularity, is increasingly cohesive, and shows\nsignatures of authoritarianism.\n"}
+{"abstract": "  The phenomenon of adversarial examples in deep learning models has caused\nsubstantial concern over their reliability. While many deep neural networks\nhave shown impressive performance in terms of predictive accuracy, it has been\nshown that in many instances an imperceptible perturbation can falsely flip the\nnetwork's prediction. Most research has then focused on developing defenses\nagainst adversarial attacks or learning under a worst-case adversarial loss. In\nthis work, we take a step back and aim to provide a framework for determining\nwhether a model's label change under small perturbation is justified (and when\nit is not). We carefully argue that adversarial robustness should be defined as\na locally adaptive measure complying with the underlying distribution. We then\nsuggest a definition for an adaptive robust loss, derive an empirical version\nof it, and develop a resulting data-augmentation framework. We prove that our\nadaptive data-augmentation maintains consistency of 1-nearest neighbor\nclassification under deterministic labels and provide illustrative empirical\nevaluations.\n"}
+{"abstract": "  Sleep disorders are very widespread in the world population and suffer from a\ngeneralized underdiagnosis, given the complexity of their diagnostic methods.\nTherefore, there is an increasing interest in developing simpler screening\nmethods. A pulse oximeter is an ideal device for sleep disorder screenings\nsince it is a portable, low-cost and accessible technology. This device can\nprovide an estimation of the heart rate (HR), which can be useful to obtain\ninformation regarding the sleep stage. In this work, we developed a network\narchitecture with the aim of classifying the sleep stage in awake or asleep\nusing only HR signals from a pulse oximeter. The proposed architecture has two\nfundamental parts. The first part has the objective of obtaining a\nrepresentation of the HR by using temporal convolutional networks. Then, the\nobtained representation is used to feed the second part, which is based on\ntransformers, a model built solely with attention mechanisms. Transformers are\nable to model the sequence, learning the transition rules between sleep stages.\nThe performance of the proposed method was evaluated on Sleep Heart Health\nStudy dataset, composed of 5000 healthy and pathological subjects. The dataset\nwas split into three subsets: 2500 for training, $1250$ for validating, and\n1250 for testing. The overall accuracy, specificity, sensibility, and Cohen's\nKappa coefficient were 90.0%, 94.9%, 78.1%, and 0.73.\n"}
+{"abstract": "  Subshifts of deterministic substitutions are ubiquitous objects in dynamical\nsystems. Two of their most appealing features are that they have low complexity\n(zero topological entropy) and are uniquely ergodic. Random substitutions are a\ngeneralisation of deterministic substitutions where the substituted image of a\nletter is determined by a Markov process. In stark contrast to their\ndeterministic counterparts, subshifts of random substitution often have\npositive topological entropy, and support uncountably many ergodic measures,\nknown as frequency measures. Here, we develop techniques for computing and\nstudying the entropy of these frequency measures. As an application of our\nresults, we obtain closed form formulas for the entropy of frequency measures\nfor a wide range of random substitution subshifts and show that in many cases\nthere exists a frequency measure of maximal entropy. Further, for a class of\nrandom substitution subshifts, we prove that this measure is the unique measure\nof maximal entropy.\n"}
+{"abstract": "  Assuming positive entropy we prove a measure rigidity theorem for higher rank\nactions on tori and solenoids by commuting automorphisms. We also apply this\nresult to obtain a complete classification of disjointness and measurable\nfactors for these actions.\n"}
+{"abstract": "  Recently, collaborative learning proposed by Song and Chai has achieved\nremarkable improvements in image classification tasks by simultaneously\ntraining multiple classifier heads. However, huge memory footprints required by\nsuch multi-head structures may hinder the training of large-capacity baseline\nmodels. The natural question is how to achieve collaborative learning within a\nsingle network without duplicating any modules. In this paper, we propose four\nways of collaborative learning among different parts of a single network with\nnegligible engineering efforts. To improve the robustness of the network, we\nleverage the consistency of the output layer and intermediate layers for\ntraining under the collaborative learning framework. Besides, the similarity of\nintermediate representation and convolution kernel is also introduced to reduce\nthe reduce redundant in a neural network. Compared to the method of Song and\nChai, our framework further considers the collaboration inside a single model\nand takes smaller overhead. Extensive experiments on Cifar-10, Cifar-100,\nImageNet32 and STL-10 corroborate the effectiveness of these four ways\nseparately while combining them leads to further improvements. In particular,\ntest errors on the STL-10 dataset are decreased by $9.28\\%$ and $5.45\\%$ for\nResNet-18 and VGG-16 respectively. Moreover, our method is proven to be robust\nto label noise with experiments on Cifar-10 dataset. For example, our method\nhas $3.53\\%$ higher performance under $50\\%$ noise ratio setting.\n"}
+{"abstract": "  We present a systematic investigation of the role and importance of excitonic\neffects on the optical properties of transitions metal oxide perovskites. A\nrepresentative set of fourteen compounds has been selected, including 3$d$\n(SrTiO$_3$, LaScO$_3$, LaTiO$_3$, LaVO$_3$, LaCrO$_3$, LaMnO$_3$, LaFeO$_3$ and\nSrMnO$_3$), 4$d$ (SrZrO$_3$, SrTcO$_3$ and Ca$_2$RuO$_4$) and 5$d$ (SrHfO$_3$,\nKTaO$_3$ and NaOsO$_3$) perovskites, covering a band gap ranging from 0.1 eV to\n6.1 eV and exhibiting different electronic, structural and magnetic properties.\nOptical conductivities and optical transitions including electron-hole\ninteractions are calculated through the solution of the Bethe-Salpeter equation\n(BSE) with quasi-particle energies evaluated by single-shot $G_0W_0$\napproximation. The exciton binding energies are computed by means of a\nmodel-BSE (mBSE), carefully benchmarked against the full BSE method, in order\nto obtain well-converged results in terms of k-point sampling. The predicted\nresults are compared with available measured data, with an overall satisfactory\nagreement between theory and experiment.\n"}
+{"abstract": "  Data efficiency is a key challenge for deep reinforcement learning. We\naddress this problem by using unlabeled data to pretrain an encoder which is\nthen finetuned on a small amount of task-specific data. To encourage learning\nrepresentations which capture diverse aspects of the underlying MDP, we employ\na combination of latent dynamics modelling and unsupervised goal-conditioned\nRL. When limited to 100k steps of interaction on Atari games (equivalent to two\nhours of human experience), our approach significantly surpasses prior work\ncombining offline representation pretraining with task-specific finetuning, and\ncompares favourably with other pretraining methods that require orders of\nmagnitude more data. Our approach shows particular promise when combined with\nlarger models as well as more diverse, task-aligned observational data --\napproaching human-level performance and data-efficiency on Atari in our best\nsetting. We provide code associated with this work at\nhttps://github.com/mila-iqia/SGI.\n"}
+{"abstract": "  Standard losses for training deep segmentation networks could be seen as\nindividual classifications of pixels, instead of supervising the global shape\nof the predicted segmentations. While effective, they require exact knowledge\nof the label of each pixel in an image.\n  This study investigates how effective global geometric shape descriptors\ncould be, when used on their own as segmentation losses for training deep\nnetworks. Not only interesting theoretically, there exist deeper motivations to\nposing segmentation problems as a reconstruction of shape descriptors:\nAnnotations to obtain approximations of low-order shape moments could be much\nless cumbersome than their full-mask counterparts, and anatomical priors could\nbe readily encoded into invariant shape descriptions, which might alleviate the\nannotation burden. Also, and most importantly, we hypothesize that, given a\ntask, certain shape descriptions might be invariant across image acquisition\nprotocols/modalities and subject populations, which might open interesting\nresearch avenues for generalization in medical image segmentation.\n  We introduce and formulate a few shape descriptors in the context of deep\nsegmentation, and evaluate their potential as standalone losses on two\ndifferent challenging tasks. Inspired by recent works in constrained\noptimization for deep networks, we propose a way to use those descriptors to\nsupervise segmentation, without any pixel-level label. Very surprisingly, as\nlittle as 4 descriptors values per class can approach the performance of a\nsegmentation mask with 65k individual discrete labels. We also found that shape\ndescriptors can be a valid way to encode anatomical priors about the task,\nenabling to leverage expert knowledge without additional annotations. Our\nimplementation is publicly available and can be easily extended to other tasks\nand descriptors: https://github.com/hkervadec/shape_descriptors\n"}
+{"abstract": "  This letter analyzes a class of information freshness metrics for large IoT\nsystems in which terminals employ slotted ALOHA to access a common channel.\nConsidering a Gilbert- Elliot channel model, information freshness is evaluated\nthrough a penalty function that follows a power law of the time elapsed since\nthe last received update, in contrast with the linear growth of age of\ninformation. By means of a signal flow graph analysis of Markov processes, we\nprovide exact closed form expressions for the average penalty and for the peak\npenalty violation probability.\n"}
+{"abstract": "  We review the current status of the research on effective nonlocal NJL-like\nchiral quark models with separable interactions, focusing on the application of\nthis approach to the description of the properties of hadronic and quark matter\nunder extreme conditions. The analysis includes the predictions for various\nhadron properties in vacuum, as well as the study of the features of\ndeconfinement and chiral restoration phase transitions for systems at finite\ntemperature and/or density. We also address other related subjects, such as the\nstudy of phase transitions for imaginary chemical potentials, the possible\nexistence of inhomogeneous phase regions, the presence of color\nsuperconductivity, the effects produced by strong external magnetic fields, and\nthe application to the description of compact stellar objects.\n"}
+{"abstract": "  We propose the concept of a spontaneous many-body Floquet state. This is a\nstate that, in the absence of external periodic driving, still self-oscillates\nlike in the presence of a Floquet Hamiltonian, this behavior being\nspontaneously induced by many-body interactions. Furthermore, we prove that it\nis also a time crystal, presenting long-range time-periodic order. However, its\ntime crystalline behavior is very different to that of conventional Floquet\ndiscrete time crystals: here, there is no external periodic driving, and the\nnature of the spontaneous symmetry breaking is continuous instead of discrete.\nWe also demonstrate that a spontaneous many-body Floquet state can be\nimplemented in a one-dimensional flowing atom condensate, resulting from a\ndynamical phase transition and stable against quantum fluctuations, and propose\nrealistic experimental scenarios for its observation. The realization of a\nspontaneous many-body Floquet state would then not only provide a novel form of\nordered quantum matter, but also a continuous time crystal.\n"}
+{"abstract": "  Recent studies of warp kinematics using Gaia DR2 data have produced\ndetections of warp precession for the first time, which greatly exceeds\ntheoretical predictions of models. However, this detection assumes a warp model\nderived for a young population (few tens of megayears) to fit velocities of an\naverage older stellar population of the thin disk (several gigayears) in\nGaia-DR2 observations, which may lead to unaccounted systematic errors. Here,\nwe recalculate the warp precession with the same approach and Gaia DR2\nkinematic data, but using different warp parameters based on the fit of star\ncounts of the Gaia DR2 sample, which has a much lower warp amplitude than the\nyoung population. When we take into account this variation of the warp\namplitude with the age of the population, we find that there is no need for\nprecession. We find the value of warp precession $\\beta = 4^{+6}_{-4}$ km\ns$^{-1}$ kpc$^{-1}$, which does not exclude nonprecessing warp.\n"}
+{"abstract": "  In this letter, we use the exactly solvable Sachdev-Ye-Kitaev model to\naddress the issue of entropy dynamics when an interacting quantum system is\ncoupled to a non-Markovian environment. We find that at the initial stage, the\nentropy always increases linearly matching the Markovian result. When the\nsystem thermalizes with the environment at a sufficiently long time, if the\nenvironment temperature is low and the coupling between system and environment\nis weak, then the total thermal entropy is low and the entanglement between\nsystem and environment is also weak, which yields a small system entropy in the\nlong-time steady state. This manifestation of non-Markovian effects of the\nenvironment forces the entropy to decrease in the later stage, which yields the\nPage curve for the entropy dynamics. We argue that this physical scenario\nrevealed by the exact solution of the Sachdev-Ye-Kitaev model is universally\napplicable for general chaotic quantum many-body systems and can be verified\nexperimentally in near future.\n"}
+{"abstract": "  Digital payment innovation is currently increasingly needed by the community,\nespecially in making non-cash payment transactions. The purpose of this\nresearch is to know and measure the ease and usefulness of e-wallet digital\nwallet services, especially in the GoPay application. The population in this\nstudy are users of the Go-Pay service on the GO-JEK platform. The sample of\nthis study consisted of 124 respondents from distributing questionnaires in\nDepok, West Java using a modified Technology Acceptance Model (TAM) based on\nexisting references. The data processing in this research uses Importance\nPerformance Analysis (IPA) analysis. The results show that based on the gap\nanalysis, it is found that in general Go-Pay users are not satisfied with the\ncurrent service quality. Based on the IPA analysis, the priority scale of\nE-Wallet Go-Pay quality improvement can be mapped, where quadrant I is the\nhighest priority scale according to the user's perspective: [1], [4], [5], and\n[6]. These three items must be upgraded immediately by the manager to meet user\nexpectations. Areas that become the achievements or advantages of the GoPay\nE-Wallet that must be maintained are in quadrant II, namely: [2] and [3]. From\nthis explanation, it can be concluded that in general the E-Wallet GoPay\nService must be improved to improve its service performance.\n"}
+{"abstract": "  We recursively extend the Lov\\'asz theta number to geometric hypergraphs on\nthe unit sphere and on Euclidean space, obtaining an upper bound for the\nindependence ratio of these hypergraphs. As an application we reprove a result\nin Euclidean Ramsey theory in the measurable setting, namely that every\n$k$-simplex is exponentially Ramsey, and we improve existing bounds for the\nbase of the exponential.\n"}
+{"abstract": "  We interpret the correlation between local star-forming galaxy positions and\nultra-high-energy cosmic ray (UHECR) directions, recently detected by the\nPierre Auger Observatory (PAO), in terms of physical parameters: the local\ndensity of sources and the magnetic fields governing the UHECR propagation. We\ninclude a Galactic magnetic field model on top of a random extragalactic\nmagnetic field description to determine the level of UHECR deflections expected\nfrom an ensemble of source positions. Besides deflections in magnetic fields,\nwe also take into account energy losses with background photon fields as well\nas spectrum and composition measurements by the PAO. We find consistency\nbetween the PAO anisotropy measurement and the local star-forming galaxy\ndensity for large extragalactic magnetic field strengths with $B > 0.2 \\ \\rm\nnG$ (for a coherence length of $1 \\ \\rm Mpc$) at the $5\\sigma$ confidence\nlevel. Larger source densities lead to more isotropic background and\nconsequently allow for weaker extragalactic magnetic fields. However, the\nacceleration of UHECR by such abundant sources is more challenging to motivate.\nToo large source densities and extragalactic magnetic field strengths, on the\nother hand, are also disfavored as that decreases the expected level of\nanisotropy. This leads to upper limits of $B < 22 \\ \\rm nG$ and $\\rho_0 < 8.4\n\\cdot 10^{-2} \\ \\rm Mpc^{-3}$ at the 90\\% confidence level.\n"}
+{"abstract": "  The penalization method is a popular technique to provide particle swarm\noptimizers with the ability to handle constraints. The downside is the need of\npenalization coefficients whose settings are problem-specific. While adaptive\ncoefficients can be found in the literature, a different adaptive scheme is\nproposed in this paper, where coefficients are kept constant. A pseudo-adaptive\nrelaxation of the tolerances for constraint violations while penalizing only\nviolations beyond such tolerances results in a pseudo-adaptive penalization. A\nparticle swarm optimizer is tested on a suite of benchmark problems for three\ntypes of tolerance relaxation: no relaxation; self-tuned initial relaxation\nwith deterministic decrease; and self-tuned initial relaxation with\npseudo-adaptive decrease. Other authors' results are offered as frames of\nreference.\n"}
+{"abstract": "  There is an ongoing debate in the NLP community whether modern language\nmodels contain linguistic knowledge, recovered through so-called probes. In\nthis paper, we study whether linguistic knowledge is a necessary condition for\nthe good performance of modern language models, which we call the\n\\textit{rediscovery hypothesis}. In the first place, we show that language\nmodels that are significantly compressed but perform well on their pretraining\nobjectives retain good scores when probed for linguistic structures. This\nresult supports the rediscovery hypothesis and leads to the second contribution\nof our paper: an information-theoretic framework that relates language modeling\nobjectives with linguistic information. This framework also provides a metric\nto measure the impact of linguistic information on the word prediction task. We\nreinforce our analytical results with various experiments, both on synthetic\nand on real NLP tasks in English.\n"}
+{"abstract": "  Digital privacy notices aim to provide users with information to make\ninformed decisions. They are, however, fraught with difficulties. Instead, I\npropose that data privacy decisions can be understood as an expression of user\nvalues. To optimize this value expression, I further propose the creation of a\nvalue-centered privacy assistant (VcPA). Here, I preliminary explore how a VcPA\ncould enhance user value expression by utilizing three user scenarios in the\ncontext of considering whether or not to download an environmental application,\nthe OpenLitterMap app. These scenarios are conceptually constructed from\nestablished privacy user groups - the privacy fundamentalists; the privacy\npragmatists; and the privacy unconcerned. I conclude that the VcPA best\nfacilitates user value expression of the privacy fundamentalists. In contrast,\nthe value expression of the privacy pragmatists and the privacy unconcerned\ncould be enhanced or hindered depending on the context and their internal\nstates. Possible implications for optimal VcPA design are also discussed.\nFollowing this initial conceptual exploration of VcPAs, further empirical\nresearch will be required to demonstrate the effectiveness of the VcPA system\nin real-world settings.\n"}
+{"abstract": "  The mass media play at least five basic functions which include news\ndissemination, surveillance of the environment, correlation of the components\nof the society, entertainment and transmission of social heritage. Sometimes,\ndisruptions and impairments do occur in the performance of these roles and some\nof these basic functions become dysfunctions, which turn the media into\npurveyor of negative values. The present study investigates how popular the\nNigerian TV reality show, Big Brother Naija BBN, is perceived by its viewers.\nThree hundred heavy viewers of the program were surveyed from Lagos and Ede,\nSouth-West Nigeria, and their opinions and attitudes were sought regarding, why\nthey like or dislike the program; the gratifications that those who like the\nprogram derive and whether the BBN, as media content, is generally functional\nor dysfunctional to the society. Sixty six per cent 66 33.7 of respondents like\nthe program because it entertains. Half of the respondents, 99 50.5 dislike\nimmoral aspects of the program. The viewers affirm that the eviction part of\nthe program was their highest form of gratification. Most respondents, despite\npublic outcry against the program, consider the program to be functional.\nFindings reinforce the postulation that TV viewers are not passive consumers of\nmedia contents.\n"}
+{"abstract": "  The initial majority identification task is a fundamental test problem in\ncellular automaton research. To pass the test, an automaton must evolve to a\nuniform configuration consisting of the state that was in the majority for any\ninitial configuration, employing only its internal, local dynamics. It is known\nthat no two-state automaton can perform the majority task perfectly. Thus, it\nis a matter of continuing interest to identify and analyze new automata with\nimproved majority identification capability. Here, we show that a coarsened\nversion of one of the best majority identifiers can out-perform its parent\nautomaton while simultaneously reducing the associated computational costs.\n"}
+{"abstract": "  We present a novel method for relation extraction (RE) from a single\nsentence, mapping the sentence and two given entities to a canonical fact in a\nknowledge graph (KG). Especially in this presumed sentential RE setting, the\ncontext of a single sentence is often sparse. This paper introduces the KGPool\nmethod to address this sparsity, dynamically expanding the context with\nadditional facts from the KG. It learns the representation of these facts\n(entity alias, entity descriptions, etc.) using neural methods, supplementing\nthe sentential context. Unlike existing methods that statically use all\nexpanded facts, KGPool conditions this expansion on the sentence. We study the\nefficacy of KGPool by evaluating it with different neural models and KGs\n(Wikidata and NYT Freebase). Our experimental evaluation on standard datasets\nshows that by feeding the KGPool representation into a Graph Neural Network,\nthe overall method is significantly more accurate than state-of-the-art\nmethods.\n"}
+{"abstract": "  Glasses show vibrational properties that are markedly different to those of\ncrystals which are known as phonons. For example, excess low-frequency modes\n(the so-called boson peak), vibrational localization, and strong scattering of\nphonons have been the most discussed topics, and a theoretical understanding of\nthese phenomena is challenging. To address this problem, computational\nsimulations are a powerful tool, which have been employed by many previous\nworks. In this chapter, we describe simulation methods for studying the\nvibrational properties of glasses (and any solid-state materials). We first\npresent a method for studying vibrational eigenmodes. Since vibrational motions\nof particles are excited along eigenmodes, the eigenmodes are fundamental to\ndescriptions of vibrational properties. The eigenmodes in glasses are\nnon-phonon modes in general, and some of them are even localized in space. We\nnext present a method of analysing phonon transport, which is also crucial for\nunderstanding vibrational properties. Since phonons are not eigenmodes in\nglasses, they are decomposed into several different, non-phonon eigenmodes. As\na result, phonons in glasses are strongly scattered. In addition, we describe\nhow to analyse the elastic response. The elastic response of glasses is also\nanomalous with respect to that of crystals. Finally, we briefly introduce\nrecent advances that have been achieved by means of large-scale computational\nsimulations.\n"}
+{"abstract": "  Target speech separation is the process of filtering a certain speaker's\nvoice out of speech mixtures according to the additional speaker identity\ninformation provided. Recent works have made considerable improvement by\nprocessing signals in the time domain directly. The majority of them take fully\noverlapped speech mixtures for training. However, since most real-life\nconversations occur randomly and are sparsely overlapped, we argue that\ntraining with different overlap ratio data benefits. To do so, an unavoidable\nproblem is that the popularly used SI-SNR loss has no definition for silent\nsources. This paper proposes the weighted SI-SNR loss, together with the joint\nlearning of target speech separation and personal VAD. The weighted SI-SNR loss\nimposes a weight factor that is proportional to the target speaker's duration\nand returns zero when the target speaker is absent. Meanwhile, the personal VAD\ngenerates masks and sets non-target speech to silence. Experiments show that\nour proposed method outperforms the baseline by 1.73 dB in terms of SDR on\nfully overlapped speech, as well as by 4.17 dB and 0.9 dB on sparsely\noverlapped speech of clean and noisy conditions. Besides, with slight\ndegradation in performance, our model could reduce the time costs in inference.\n"}
+{"abstract": "  We consider the sensing of scalar valued fields with specific spatial\ndependence using a network of sensors, e.g. multiple atoms located at different\npositions within a trap. We show how to harness the spatial correlations to\nsense only a specific signal, and be insensitive to others at different\npositions or with unequal spatial dependence by constructing a decoherence-free\nsubspace for noise sources at fixed, known positions. This can be extended to\nnoise sources lying on certain surfaces, where we encounter a connection to\nmirror charges and equipotential surfaces in classical electrostatics. For\ngeneral situations, we introduce the notion of an approximate decoherence-free\nsubspace, where noise for all sources within some volume is significantly\nsuppressed, at the cost of reducing the signal strength in a controlled way. We\nshow that one can use this approach to maintain Heisenberg-scaling over long\ntimes and for a large number of sensors, despite the presence of multiple noise\nsources in large volumes. We introduce an efficient formalism to construct\ninternal states and sensor configurations, and apply it to several examples to\ndemonstrate the usefulness and wide applicability of our approach.\n"}
+{"abstract": "  This paper explores the challenge of teaching a machine how to\nreverse-engineer the grid-marked surfaces used to represent data in 3D surface\nplots of two-variable functions. These are common in scientific and economic\npublications; and humans can often interpret them with ease, quickly gleaning\ngeneral shape and curvature information from the simple collection of curves.\nWhile machines have no such visual intuition, they do have the potential to\naccurately extract the more detailed quantitative data that guided the\nsurface's construction. We approach this problem by synthesizing a new dataset\nof 3D grid-marked surfaces (SurfaceGrid) and training a deep neural net to\nestimate their shape. Our algorithm successfully recovers shape information\nfrom synthetic 3D surface plots that have had axes and shading information\nremoved, been rendered with a variety of grid types, and viewed from a range of\nviewpoints.\n"}
+{"abstract": "  The light field (LF) reconstruction is mainly confronted with two challenges,\nlarge disparity and the non-Lambertian effect. Typical approaches either\naddress the large disparity challenge using depth estimation followed by view\nsynthesis or eschew explicit depth information to enable non-Lambertian\nrendering, but rarely solve both challenges in a unified framework. In this\npaper, we revisit the classic LF rendering framework to address both challenges\nby incorporating it with advanced deep learning techniques. First, we\nanalytically show that the essential issue behind the large disparity and\nnon-Lambertian challenges is the aliasing problem. Classic LF rendering\napproaches typically mitigate the aliasing with a reconstruction filter in the\nFourier domain, which is, however, intractable to implement within a deep\nlearning pipeline. Instead, we introduce an alternative framework to perform\nanti-aliasing reconstruction in the image domain and analytically show\ncomparable efficacy on the aliasing issue. To explore the full potential, we\nthen embed the anti-aliasing framework into a deep neural network through the\ndesign of an integrated architecture and trainable parameters. The network is\ntrained through end-to-end optimization using a peculiar training set,\nincluding regular LFs and unstructured LFs. The proposed deep learning pipeline\nshows a substantial superiority in solving both the large disparity and the\nnon-Lambertian challenges compared with other state-of-the-art approaches. In\naddition to the view interpolation for an LF, we also show that the proposed\npipeline also benefits light field view extrapolation.\n"}
+{"abstract": "  Municipal solid waste management is a major challenge for nowadays urban\nsocieties, because it accounts for a large proportion of public budget and,\nwhen mishandled, it can lead to environmental and social problems. This work\nfocuses on the problem of locating waste bins in an urban area, which is\nconsidered to have a strong influence in the overall efficiency of the reverse\nlogistic chain. This article contributes with an exact multiobjective approach\nto solve the waste bin location in which the optimization criteria that are\nconsidered are: the accessibility to the system (as quality of service\nmeasure), the investment cost, and the required frequency of waste removal from\nthe bins (as a proxy of the posterior routing costs). In this approach,\ndifferent methods to obtain the objectives ideal and nadir values over the\nPareto front are proposed and compared. Then, a family of heuristic methods\nbased on the PageRank algorithm is proposed which aims to optimize the\naccessibility to the system, the amount of collected waste and the installation\ncost. The experimental evaluation was performed on real-world scenarios of the\ncities of Montevideo, Uruguay, and Bah\\'ia Blanca, Argentina. The obtained\nresults show the competitiveness of the proposed approaches for constructing a\nset of candidate solutions that considers the different trade-offs between the\noptimization criteria.\n"}
+{"abstract": "  Social dynamics models may present discontinuities in the right-hand side of\nthe dynamics for multiple reasons, including topology changes and quantization.\nSeveral concepts of generalized solutions for discontinuous equations are\navailable in the literature and are useful to analyze these models. In this\nchapter, we study Caratheodory and Krasovsky generalized solutions for\ndiscontinuous models of opinion dynamics with state dependent interactions. We\nconsider two definitions of \"bounded confidence\" interactions, which we\nrespectively call metric and topological: in the former, individuals interact\nif their opinions are closer than a threshold; in the latter, individuals\ninteract with a fixed number of nearest neighbors. We compare the dynamics\nproduced by the two kinds of interactions, in terms of existence, uniqueness\nand asymptotic behavior of different types of solutions.\n"}
+{"abstract": "  In this paper, we introduce the first Challenge on Multi-modal Aerial View\nObject Classification (MAVOC) in conjunction with the NTIRE 2021 workshop at\nCVPR. This challenge is composed of two different tracks using EO andSAR\nimagery. Both EO and SAR sensors possess different advantages and drawbacks.\nThe purpose of this competition is to analyze how to use both sets of sensory\ninformation in complementary ways. We discuss the top methods submitted for\nthis competition and evaluate their results on our blind test set. Our\nchallenge results show significant improvement of more than 15% accuracy from\nour current baselines for each track of the competition\n"}
+{"abstract": "  Road intersections are widely recognized as a lead cause for accidents and\ntraffic delays. In a future scenario with a significant adoption of Cooperative\nAutonomous Vehicles, solutions based on fully automatic, signage-less\nIntersection Control would become viable. Such a solution, however, requires\ncommunication between vehicles and, possibly, the infrastructure over wireless\nnetworks. This increases the attack surface available to a malicious actor,\nwhich could lead to dangerous situations. In this paper, we address the safety\nof Intersection Control algorithms, and design a Sliding-Mode-Observer based\nsolution capable of detecting and estimating false data injection attacks\naffecting vehicles' communication. With respect to previous literature, a novel\ndetection logic with improved detection performances is presented. Simulation\nresults are provided to show the effectiveness of the proposed approach.\n"}
+{"abstract": "  Getting machines to generate text perceived as creative is a long-pursued\ngoal. A growing body of research directs this goal towards augmenting the\ncreative writing abilities of human authors. In this paper, we pursue this\nobjective by analyzing how observing examples of automatically generated text\ninfluences writing. In particular, we examine a task referred to as sentence\ninfilling, which involves transforming a list of words into a complete\nsentence. We emphasize \"storiability\" as a desirable feature of the resulting\nsentences, where \"storiable\" sentences are those that suggest a story a reader\nwould be curious to hear about. Both humans and an automated system (based on a\nneural language model) performed this sentence infilling task. In one setting,\npeople wrote sentences on their own; in a different setting, people observed\nthe sentences produced by the model while writing their own sentences. Readers\nthen assigned storiability preferences to the resulting sentences in a\nsubsequent evaluation. We find that human-authored sentences were judged as\nmore storiable when authors observed the generated examples, and that\nstoriability increased as authors derived more semantic content from the\nexamples. This result gives evidence of an \"inspiration through observation\"\nparadigm for human-computer collaborative writing, through which human writing\ncan be enhanced by text generation models without directly copying their\noutput.\n"}
+{"abstract": "  Fire stations are among the most crucial emergency facilities in urban\nemergency control system in terms of their quick response to fires and other\nemergencies. Location plannings for fire stations have a significant influence\non their effectiveness and capability of emergency responses trading off with\nthe cost of constructions. To obtain efficient and practical siting plans for\nfire stations, various major requirements including effectiveness maximization,\ndistance constraint and workload limitation are required to be considered in\nlocation models, especially for multi-level fire facility systems with macro\nand micro fire stations having specific aims and construction requirements.\nThis paper proposes a novel hierarchical optimization approach taking all the\nmajor requirements for location planning into consideration and bonds\nfunctional connections between different levels of fire stations at the same\ntime. A single-objective and a multi-objective optimization model are\nestablished to solve the location siting problems for macro and micro fire\nstations simultaneously. Genetic algorithm with elitist reservation and\nPareto-based multi-objective evolutionary algorithm are adopted to solve the\nproblem with NP-hard nature. The proposed hierarchical location model is\nfurther performed in a case study of Futian District in Shenzhen, and the\nsiting result justifies the effectiveness and practicality of our novel\napproach.\n"}
+{"abstract": "  While the utility of well-chosen abstractions for understanding and\npredicting the behaviour of complex systems is well appreciated, precisely what\nan abstraction $\\textit{is}$ has so far has largely eluded mathematical\nformalization. In this paper, we aim to set out a mathematical theory of\nabstraction. We provide a precise characterisation of what an abstraction is\nand, perhaps more importantly, suggest how abstractions can be learnt directly\nfrom data both for static datasets and for dynamical systems. We define an\nabstraction to be a small set of `summaries' of a system which can be used to\nanswer a set of queries about the system or its behaviour. The difference\nbetween the ground truth behaviour of the system on the queries and the\nbehaviour of the system predicted only by the abstraction provides a measure of\nthe `leakiness' of the abstraction which can be used as a loss function to\ndirectly learn abstractions from data. Our approach can be considered a\ngeneralization of classical statistics where we are not interested in\nreconstructing `the data' in full, but are instead only concerned with\nanswering a set of arbitrary queries about the data. While highly theoretical,\nour results have deep implications for statistical inference and machine\nlearning and could be used to develop explicit methods for learning precise\nkinds of abstractions directly from data.\n"}
+{"abstract": "  Gradient descent optimization algorithms are the standard ingredients that\nare used to train artificial neural networks (ANNs). Even though a huge number\nof numerical simulations indicate that gradient descent optimization methods do\nindeed convergence in the training of ANNs, until today there is no rigorous\ntheoretical analysis which proves (or disproves) this conjecture. In\nparticular, even in the case of the most basic variant of gradient descent\noptimization algorithms, the plain vanilla gradient descent method, it remains\nan open problem to prove or disprove the conjecture that gradient descent\nconverges in the training of ANNs. In this article we solve this problem in the\nspecial situation where the target function under consideration is a constant\nfunction. More specifically, in the case of constant target functions we prove\nin the training of rectified fully-connected feedforward ANNs with one-hidden\nlayer that the risk function of the gradient descent method does indeed\nconverge to zero. Our mathematical analysis strongly exploits the property that\nthe rectifier function is the activation function used in the considered ANNs.\nA key contribution of this work is to explicitly specify a Lyapunov function\nfor the gradient flow system of the ANN parameters. This Lyapunov function is\nthe central tool in our convergence proof of the gradient descent method.\n"}
+{"abstract": "  Recent work has demonstrated real-time mapping and reconstruction from dense\nperception, while motion planning based on distance fields has been shown to\nachieve fast, collision-free motion synthesis with good convergence properties.\nHowever, demonstration of a fully integrated system that can safely re-plan in\nunknown environments, in the presence of static and dynamic obstacles, has\nremained an open challenge. In this work, we first study the impact that signed\nand unsigned distance fields have on optimisation convergence, and the\nresultant error cost in trajectory optimisation problems in 2D path planning,\narm manipulator motion planning, and whole-body loco-manipulation planning. We\nfurther analyse the performance of three state-of-the-art approaches to\ngenerating distance fields (Voxblox, Fiesta, and GPU-Voxels) for use in\nreal-time environment reconstruction. Finally, we use our findings to construct\na practical hybrid mapping and motion planning system which uses GPU-Voxels and\nGPMP2 to perform receding-horizon whole-body motion planning that can smoothly\navoid moving obstacles in 3D space using live sensor data. Our results are\nvalidated in simulation and on a real-world Toyota Human Support Robot (HSR).\n"}
+{"abstract": "  Cluster states are useful in many quantum information processing\napplications. In particular, universal measurement-based quantum computation\n(MBQC) utilizes 2D cluster states, and topologically fault-tolerant MBQC\nrequires cluster states with three or higher dimensions. This work proposes a\nprotocol to deterministically generate multidimensional photonic cluster states\nusing a single atom-cavity system and time-delay feedback. The dimensionality\nof the cluster state increases linearly with the number of time-delay feedback.\nWe firstly give a diagrammatic derivation of the tensor network states, which\nis valuable in simulating matrix product states and projected entangled pair\nstates generated from sequential photons. Our method also provides a simple way\nto bridge and analyze the experimental imperfections and the logical errors of\nthe generated states. In this method, we analyze the generated cluster states\nunder realistic experimental conditions and address both one-qubit and\ntwo-qubit errors. Through numerical simulation, we observe an optimal\natom-cavity cooperativity for the fidelity of the generated states, which is\nsurprising given the prevailing assumption that higher cooperativity systems\nare inherently better for photonic applications.\n"}
+{"abstract": "  In this work, we use the framework of effective field theory to couple\nEinstein's gravity to quantum electrodynamics (QED) and determine the\ngravitational corrections to the two-loop beta function of the electric charge\nin arbitrary electrodynamics (Lorentz-like) and arbitrary (de Donder like)\ngravitational gauges. Our results indicate that gravitational corrections do\nnot alter the running behavior of the electric charge; on the contrary, we\nobserve that it gives a positive contribution to the beta function, making the\nelectric charge grow even faster.\n"}
+{"abstract": "  Stochastic thermodynamics allows us to define heat and work for microscopic\nsystems far from thermodynamic equilibrium, based on observations of their\nstochastic dynamics. However, a complete account of the energetics necessitates\nthat all relevant nonequilibrium degrees of freedom are resolved, which is not\nfeasible in many experimental situations. A simple approach is to map the\nvisible dynamics onto a Markov model, which produces a lower-bound estimate of\nthe entropy production. The bound, however, can be quite loose, especially when\nthe visible dynamics only have small or vanishing observable currents. An\nalternative approach is presented that uses all observable data to find an\nunderlying hidden Markov model responsible for generating the observed\nnon-Markovian dynamics. For masked Markovian kinetic networks, one obtains the\ntightest possible lower bound on entropy production of the full dynamics that\nis compatible with the observable data. The formalism is illustrated with a\nsimple example system.\n"}
+{"abstract": "  We review recent progress in elucidating the relationship between high-energy\nradiation and the interstellar medium (ISM) in young supernova remnants (SNRs)\nwith ages of $\\sim$2000 yr, focusing in particular on RX J1713.7$-$3946 and RCW\n86. Both SNRs emit strong nonthermal X-rays and TeV $\\gamma$-rays, and they\ncontain clumpy distributions of interstellar gas that includes both atomic and\nmolecular hydrogen. We find that shock-cloud interactions provide a viable\nexplanation for the spatial correlation between the X-rays and ISM. In these\ninteractions, the supernova shocks hit the typically pc-scale dense cores,\ngenerating a highly turbulent velocity field that amplifies the magnetic field\nup to 0.1-1 mG. This amplification leads to enhanced nonthermal synchrotron\nemission around the clumps, whereas the cosmic-ray electrons do not penetrate\nthe clumps. Accordingly, the nonthermal X-rays exhibit a spatial distribution\nsimilar to that of the ISM on the pc scale, while they are anticorrelated at\nsub-pc scales. These results predict that hadronic $\\gamma$-rays can be emitted\nfrom the dense cores, resulting in a spatial correspondence between the\n$\\gamma$-rays and the ISM. The current pc-scale resolution of $\\gamma$-ray\nobservations is too low to resolve this correspondence. Future $\\gamma$-ray\nobservations with the Cherenkov Telescope Array will be able to resolve the\nsub-pc-scale $\\gamma$-ray distribution and provide clues to the origin of these\ncosmic $\\gamma$-rays.\n"}
+{"abstract": "  There is a lack of scalable quantitative measures of reactivity for\nfunctional groups in organic chemistry. Measuring reactivity experimentally is\ncostly and time-consuming and does not scale to the astronomical size of\nchemical space. In previous quantum chemistry studies, we have introduced\nMethyl Cation Affinities (MCA*) and Methyl Anion Affinities (MAA*), using a\nsolvation model, as quantitative measures of reactivity for organic functional\ngroups over the broadest range. Although MCA* and MAA* offer good estimates of\nreactivity parameters, their calculation through Density Functional Theory\n(DFT) simulations is time-consuming. To circumvent this problem, we first use\nDFT to calculate MCA* and MAA* for more than 2,400 organic molecules thereby\nestablishing a large dataset of chemical reactivity scores. We then design deep\nlearning methods to predict the reactivity of molecular structures and train\nthem using this curated dataset in combination with different representations\nof molecular structures. Using ten-fold cross-validation, we show that graph\nattention neural networks applied to informative input fingerprints produce the\nmost accurate estimates of reactivity, achieving over 91% test accuracy for\npredicting the MCA* plus-minus 3.0 or MAA* plus-minus 3.0, over 50 orders of\nmagnitude. Finally, we demonstrate the application of these reactivity scores\nto two tasks: (1) chemical reaction prediction; (2) combinatorial generation of\nreaction mechanisms. The curated dataset of MCA* and MAA* scores is available\nthrough the ChemDB chemoinformatics web portal at www.cdb.ics.uci.edu.\n"}
+{"abstract": "  Twisted homogeneous coordinate rings are natural invariants associated to a\nprojective variety X with an automorphism f. We study the Gelfand-Kirillov\ndimensions of these noncommutative algebras from the perspective of complex\ndynamics, by noticing that when X is a smooth complex projective variety, they\nessentially coincide with the polynomial logarithmic volume growth Plov(f) of\n(X,f). We formulate some basic dynamical properties about these invariants and\nstudy explicit examples. Our main results are new upper bounds and lower bounds\nof these invariants, in terms of the dimension of X (as well as other\nrefinements). As an application, we completely determine Plov(f) when dim X =\n3.\n"}
+{"abstract": "  In the present article, we develop a general framework for the description of\nan $N$-sequential state discrimination, where each of $N$ receivers always\nobtains a conclusive result. For this new state discrimination scenario, we\nderive two mutually equivalent general representations of the success\nprobability and prove that if one of two states, pure or mixed, is prepared by\na sender, then the optimal success probability is given by the Helstrom bound\nfor any number $N$ of sequential receivers. Furthermore, we specify receivers'\nindirect measurements resulting in the optimal $N$-sequential conclusive state\ndiscrimination protocol. The developed framework is true for any number $N$ of\nsequential receivers, any number of arbitrary quantum states, pure or mixed, to\nbe discriminated, and all types of receivers' quantum measurements. The new\ngeneral results derived within the developed framework are important both from\nthe theoretical point of view and for a successful multipartite quantum\ncommunication even in the presence of a quantum noise.\n"}
+{"abstract": "  We discuss soft-gluon resummation for the associated production of a single\ntop quark and a Higgs boson ($tqH$ production) in single-particle-inclusive\n(1PI) kinematics. We present analytical results for the higher-order\ncorrections and numerical results for the cross sections at LHC energies. We\ncalculate approximate NNLO total rates, including scale dependence and\nuncertainties from parton distributions, as well as top-quark\ntransverse-momentum and rapidity distributions. In all cases we find that the\nsoft-gluon corrections are dominant and provide important contributions to the\ntotal and differential cross sections.\n"}
+{"abstract": "  Aluminum monochloride (AlCl) has been proposed as an excellent candidate for\nlaser cooling. Here we present absorption spectroscopy measurements on the\n$A^1\\Pi \\leftarrow X^1\\Sigma^+$ transition in AlCl inside a cryogenic helium\nbuffer-gas beam cell. The high resolution absorption data enables a rigorous,\nquantitative comparison with our high-level ab initio calculations of the\nelectronic and rovibronic energies, providing a comprehensive picture of the\nAlCl quantum structure. The combination of high resolution spectral data and\ntheory permits the evaluation of spectroscopic constants and associated\nproperties, like equilibrium bond length, with an order of magnitude higher\nprecision. Based on the measured molecular equilibrium constants of the\n$A^1\\Pi$ state, we estimate a Franck-Condon factor of the $A^1\\Pi \\leftarrow\nX^1\\Sigma^+$ of 99.88%, which confirms that AlCl is amenable to laser cooling.\n"}
+{"abstract": "  Rich and poor galaxy clusters have the same measured halo metallicity,\n0.35-0.4 $Z_\\odot$, despite being an order of magnitude apart in stellar\nfraction, $M_*/M_{gas}$. The measured ICM metallicity in rich clusters cannot\nbe explained by the visible stellar population as stars make up only 5% of the\ntotal gas mass. The independence of metallicity from $M_*/M_{gas}$ suggests an\nexternal and universal source of metals such as an Early Enrichment Population\n(EEP). Galaxy cluster RX J1416.4+2315 has the lowest stellar fraction known,\n$M_*/M_{gas}=0.015\\pm0.003$, and here we improve the halo metallicity\ndetermination using archival $\\textit{Chandra}$ and $\\textit{XMM Newton}$\nobservations. We determine the ICM metallicity of RXJ to be $0.336\\pm0.058$\n$Z_\\odot$ within $0.3<R/R_{500}<0.75$, excluding the central galaxy. We combine\nthis measurement with other clusters with a wider range of $M_*/M_{gas}$\nresulting in the fit of $Z_{tot}=(0.37\\pm0.01)+(0.13\\pm 0.17)(M_*/M_{gas})$.\nThis fit is largely independent of $M_*/M_{gas}$, and shows that for a low\n$M_*/M_{gas}$ system the observed stellar population can make only 10-20% of\nthe total metals. We quantify the Fe contribution of the EEP further by\nadopting a standard Fe yield for visible stellar population, and find that\n$Z_{EEP}=(0.36\\pm0.01)-(1.44\\pm0.17)(M_*/M_{gas})$. To account for the observed\nFe mass, a supernova rate of $6.6\\pm3.0$ SNe yr$^{-1}$ (Type Ia) and\n$5.0\\pm2.5$ SNe yr$^{-1}$ (core collapse) is required over the redshift range\n$3<z<10$ for a single galaxy cluster with mass $\\sim3\\times10^{14}$ $M_\\odot$\nat z=0. These supernovae might be visible in JWST observation of high redshift\nclusters and protoclusters.\n"}
+{"abstract": "  The Peaks of Eternal Light (PELs), that are largely unshaded regions mostly\nat the lunar south pole, have been suggested as a source of solar power for\nmining the water and other volatiles in the nearby permanently dark regions. As\nmining is a power-intensive activity, it is interesting to estimate the maximum\nsolar power that could be generated at the PELs. Here we use average percentage\nillumination maps for a range of heights above the local topography from 2 m to\n2 km to determine the total power available as a function of time of lunar day.\nOvershadowing of highly illuminated areas by towers placed in sunward locations\n(at a given time of day) limits the total power to much smaller values than the\nhighly illuminated area would suggest. We find that for near-term realizable\ntowers (up to 20 m), the upper limit to the time-averaged power available is\n~55 MW at >70% illumination, and ~6 MW at >90% illumination. For the more\ndistant future a maximum time-averaged power of order 21000 MW at >70%\nillumination could be realizable for towers up to 2 km in height, and ~5270 MW,\nrespectively, at 90% illumination. Towers 1 km high provide about a factor 2.7\ntimes less power. The variation with lunar time of day ranges from a factor of\n1.1 to ~ 3.\n"}
+{"abstract": "  Examining effectiveness of institutional scientific coalitions can inform\nfuture policies. This is a study on the structure of scientific collaborations\nin three cities in central Germany. Since 1995, the three universities of this\nregion have formed and maintained a coalition which led to the establishment of\nan interdisciplinary center in 2012, i.e., German Center for Integrative\nBiodiversity Research (iDiv). We investigate whether the impact of the former\ncoalition is evident in the region's structure of scientific collaborations and\nthe scientific output of the new center. Using publications data from\n1996-2018, we build co-authorship networks and identify the most cohesive\ncommunities in terms of collaboration, and compare them with communities\nidentified based on publications presented as the scientific outcome of the\ncoalition and new center on their website. Our results show that despite the\nhighly cohesive structure of collaborations presented on the coalition website,\nthere is still much potential to be realized. The newly established center has\nbridged the member institutions but not to a particularly strong level. We see\nthat geographical proximity, collaboration policies, funding, and\norganizational structure alone do not ensure prosperous scientific\ncollaboration structures. When new center's scientific output is compared with\nits regional context, observed trends become less conspicuous. Nevertheless,\nthe level of success the coalition achieved could inform policy makers\nregarding other regions' scientific development plans.\n"}
+{"abstract": "  The purpose of this paper is two-fold: we present some matrix inequalities of\nlog-majorization type for eigenvalues indexed by a sequence; we then apply our\nmain theorem to generalize and improve the Hua-Marcus' inequalities. Our\nresults are stronger and more general than the existing ones.\n"}
+{"abstract": "  We study the dynamics of periodically-kicked many-body systems away from the\nhigh-frequency regime, and discuss a family of Floquet systems where the notion\nof prethermalization can be naturally extended to intermediate and low driving\nfrequencies. We investigate numerically the dynamics of both integrable and\nnonintegrable systems, and report on the formation of a long-lived prethermal\nplateau, akin to the high-frequency limit, where the system thermalizes with\nrespect to an effective Hamiltonian captured by the inverse-frequency expansion\n(IFE). Unlike the high-frequency regime, we find that the relevant heating\ntimes are model dependent: we analyze the stability of the prethermal plateau\nto small perturbations in the drive period, and show that, in a spin chain\nwhose IFE is intractable, the plateau duration is insensitive to the\nperturbation strength, in contrast to a chain where the IFE admits the\nresummation of an entire subseries. Infinitesimal perturbations are enough to\nrestore the ergodic properties of the system, and decrease residual finite-size\neffects. Although the regime where the Floquet system leaves the prethermal\nplateau and starts heating up to infinite temperature is not captured by the\nIFE, we provide evidence that the evolved subsystem is described well by a\nthermal state w.r.t.~the IFE Hamiltonian, with a gradually changing\ntemperature, in accord with the Eigenstate Thermalization Hypothesis.\n"}
+{"abstract": "  6G and beyond networks tend towards fully intelligent and adaptive design in\norder to provide better operational agility in maintaining universal wireless\naccess and supporting a wide range of services and use cases while dealing with\nnetwork complexity efficiently. Such enhanced network agility will require\ndeveloping a self-evolving capability in designing both the network\narchitecture and resource management to intelligently utilize resources, reduce\noperational costs, and achieve the coveted quality of service (QoS). To enable\nthis capability, the necessity of considering an integrated vertical\nheterogeneous network (VHetNet) architecture appears to be inevitable due to\nits high inherent agility. Moreover, employing an intelligent framework is\nanother crucial requirement for self-evolving networks to deal with real-time\nnetwork optimization problems. Hence, in this work, to provide a better insight\non network architecture design in support of self-evolving networks, we\nhighlight the merits of integrated VHetNet architecture while proposing an\nintelligent framework for self-evolving integrated vertical heterogeneous\nnetworks (SEI-VHetNets). The impact of the challenges associated with\nSEI-VHetNet architecture, on network management is also studied considering a\ngeneralized network model. Furthermore, the current literature on network\nmanagement of integrated VHetNets along with the recent advancements in\nartificial intelligence (AI)/machine learning (ML) solutions are discussed.\nAccordingly, the core challenges of integrating AI/ML in SEI-VHetNets are\nidentified. Finally, the potential future research directions for advancing the\nautonomous and self-evolving capabilities of SEI-VHetNets are discussed.\n"}
+{"abstract": "  We report on finite bias spectroscopy measurements of the two-electron\nspectrum in a gate defined bilayer graphene (BLG) quantum dot for varying\nmagnetic fields. The spin and valley degree of freedom in BLG give rise to\nmultiplets of 6 orbital symmetric and 10 orbital anti-symmetric states. We find\nthat orbital symmetric states are lower in energy and separated by $\\approx 0.4\n- 0.8$ meV from orbital anti-symmetric states. The symmetric multiplet exhibits\nan additional energy splitting of its 6 states of $\\approx 0.15 - 0.5$ meV due\nto lattice scale interactions. The experimental observations are supported by\ntheoretical calculations, which allow to determine that inter-valley scattering\nand 'current-current' interaction constants are of the same magnitude in BLG.\n"}
+{"abstract": "  When an electron passes through a chiral molecule there is a high probability\nfor a correlation between the momentum and spin of the charge, thus leading to\nspin polarized current. This phenomenon is known as the chiral induced spin\nselectivity (CISS) effect. One of the most surprising experimental results\nrecently demonstrated is that magnetization reversal in a ferromagnet (FM) with\nperpendicular anisotropy can be realized solely by chemisorbing a chiral\nmolecular monolayer without applying any current or external magnetic field.\nThis result raises the currently open question of whether this effect is due to\nthe bonding event, held by the ferromagnet, or a long timescale effect\nstabilized by exchange interactions. In this work we have performed vectorial\nmagnetic field measurements of the magnetization reorientation of a\nferromagnetic layer exhibiting perpendicular anisotropy due to CISS using\nnitrogen-vacancy centers in diamond, and followed the time dynamics of this\neffect. In parallel, we have measured the molecular monolayer tilt angle in\norder to find a correlation between the time dependence of the magnetization\nre-orientation and the change of the tilt angle of the molecular monolayer. We\nhave identified that changes in the magnetization direction correspond to\nchanges of the molecular monolayer tilt angle, providing evidence for a\nlong-timescale characteristic of the induced magnetization reorientation. This\nsuggests that the CISS effect has an effect over long-timescales which we\nattribute to exchange interactions. These results offer significant insights\ninto the fundamental processes underlying the CISS effect, contributing to the\nimplementation of CISS in state-of-the-art applications such as spintronic and\nmagnetic memory devices.\n"}
+{"abstract": "  We describe a solvable model of a quantum transition in a single band model\ninvolving a change in the size of the electron Fermi surface without any\nsymmetry breaking. In a model with electron density $1-p$, we find a 'large'\nFermi surface state with the conventional Luttinger volume $1-p$ of electrons\nfor $p>p_c$, and a first order transition to a 'small' Fermi surface state with\na non-Luttinger volume $p$ of holes for $p<p_c$. As required by extended\nLuttinger theorems, the small Fermi surface state also has fractionalized\nspinon excitations. The model has electrons with strong local interactions in a\nsingle band; after a canonical transformation, the interactions are transferred\nto a coupling to two layers of ancilla qubits, as proposed by Zhang and Sachdev\n(Phys. Rev. Research ${\\bf 2}$, 023172 (2020)). Solvability is achieved by\nemploying random exchange interactions within the ancilla layers, and taking\nthe large $M$ limit with SU($M$) spin symmetry, as in the Sachdev-Ye-Kitaev\nmodels. The local electron spectral function of the small Fermi surface phase\ndisplays a particle-hole asymmetric pseudogap, and maps onto the spectral\nfunction of a lightly doped Kondo insulator of a Kondo-Heisenberg lattice\nmodel. We discuss connections to the physics of the hole-doped cuprates: the\nasymmetric pseudogap observed in STM, and the sudden change from incoherent to\ncoherent anti-nodal spectra observed recently in photoemission. A holographic\nanalogy to wormhole transitions between multiple black holes is briefly noted.\n"}
+{"abstract": "  The compliance of IoT platforms to quality is paramount to achieve users\nsatisfaction. Currently, we do not have a comprehensive set of guidelines to\nappraise and select the most suitable IoT platform architectures that meet\nrelevant criteria. This paper is a tentative response to this critical\nknowledge gap where we adopted the design science research approach to develop\na novel evaluation framework. Our research, on the one hand, stimulates an\nunbiased competition among IoT platform providers and, on the other hand,\nestablishes a solid foundation for IoT platform consumers to make informed\ndecisions in this multiplicity. The application of the framework is illustrated\nin example scenarios. Moreover, lessons learned from applying design science\nresearch are shared.\n"}
+{"abstract": "  With the open-source revolution, source codes are now more easily accessible\nthan ever. This has, however, made it easier for malicious users and\ninstitutions to copy the code without giving regards to the license, or credit\nto the original author. Therefore, source code author identification is a\ncritical task with paramount importance. In this paper, we propose ICodeNet - a\nhierarchical neural network that can be used for source code file-level tasks.\nThe ICodeNet processes source code in image format and is employed for the task\nof per file author identification. The ICodeNet consists of an ImageNet trained\nVGG encoder followed by a shallow neural network. The shallow network is based\neither on CNN or LSTM. Different variations of models are evaluated on a source\ncode author classification dataset. We have also compared our image-based\nhierarchical neural network model with simple image-based CNN architecture and\ntext-based CNN and LSTM models to highlight its novelty and efficiency.\n"}
+{"abstract": "  With the continuous increase of the spectrum and antennas, endogenous sensing\nis now possible in the fifth generation and future wireless communication\nsystems. However, sensing is a highly complex task for a heterogeneous\ncommunication network with massive connections. Seeking multi-domain\ncooperation is necessary. In this article, we present an integrated sensing and\ncommunication (ISAC) system that performs active, passive, and interactive\nsensing in different stages of communication through hardware and software. We\nalso propose different methods about how multi-user and multi-frequency band\ncooperate to further enhance the ISAC system's performance. Finally, we\nelaborate on the advantages of multi-domain cooperation from the physical layer\nto the network layer for the ISAC system.\n"}
+{"abstract": "  We consider large-scale industrial computer model calibration, combining\nmulti-output simulation with limited physical observation, involved in the\ndevelopment of a honeycomb seal. Toward that end, we adopt a localized sampling\nand emulation strategy called \"on-site surrogates (OSSs)\", designed to cope\nwith the amalgamated challenges of high-dimensional inputs, large-scale\nsimulation campaigns, and nonstationary response surfaces. In previous\napplications, OSSs were one-at-a-time affairs for multiple outputs. We\ndemonstrate that this leads to dissonance in calibration efforts for a common\nparameter set across outputs for the honeycomb. Instead, a conceptually\nstraightforward, but implementationally intricate, principal-components\nrepresentation, adapted from ordinary Gaussian process surrogate modeling to\nthe OSS setting, can resolve this tension. With a two-pronged -\noptimization-based and fully Bayesian - approach, we show how pooled\ninformation across outputs can reduce uncertainty and enhance (statistical and\ncomputational) efficiency in calibrated parameters for the honeycomb relative\nto the previous, \"data-poor\" univariate analog.\n"}
+{"abstract": "  This article is devoted to the study of a nonlinear and nonlocal parabolic\nequation introduced by Stefan Steinerberger to study the roots of polynomials\nunder differentiation; it also appeared in a work by Dimitri Shlyakhtenko and\nTerence Tao on free convolution. Rafael Granero-Belinch\\'on obtained a global\nwell-posedness result for positive initial data small enough in a Wiener space,\nand recently Alexander Kiselev and Changhui Tan proved a global well-posedness\nresult for any positive initial data in the Sobolev space $H^s(\\mathbb{S})$\nwith $s>3/2$. In this paper, we consider the Cauchy problem in the critical\nspace $H^{1/2}(\\mathbb{S})$. Two interesting new features, at this level of\nregularity, are that the equation can be written in the form $$ \\partial_t\nu+V\\partial_x u+\\gamma \\Lambda u=0, $$ where $\\gamma$ is non-negative but not\nbounded from below and $V/\\sqrt{\\gamma}$ is not bounded. Therefore, the\nequation is only weakly parabolic. We prove that nevertheless the Cauchy\nproblem is well posed locally in time and that the solutions are smooth for\npositive times. Combining this with the results of Kiselev and Tan, this gives\na global well-posedness result for any positive initial data in\n$H^{1/2}(\\mathbb{S})$. Our proof relies on sharp commutator estimates and\nintroduces a strategy to prove a local well-posedness result in a situation\nwhere the lifespan depends on the profile of the initial data and not only on\nits norm.\n"}
+{"abstract": "  This paper considers optimization of smooth nonconvex functionals in smooth\ninfinite dimensional spaces. A H\\\"older gradient descent algorithm is first\nproposed for finding approximate first-order points of regularized polynomial\nfunctionals. This method is then applied to analyze the evaluation complexity\nof an adaptive regularization method which searches for approximate first-order\npoints of functionals with $\\beta$-H\\\"older continuous derivatives. It is shown\nthat finding an $\\epsilon$-approximate first-order point requires at most\n$O(\\epsilon^{-\\frac{p+\\beta}{p+\\beta-1}})$ evaluations of the functional and\nits first $p$ derivatives.\n"}
+{"abstract": "  In this article a recognition principle for $\\infty$-loop pairs of spaces of\nconnective commutative algebra spectra over connective commutative ring spectra\nis proved. This is done by generalizing the classical recognition principle for\nconnective commutative ring spectra using relative operads. The machinery of\nidempotent quasiadjunctions is used to handle the model theoretical aspects of\nthe proof.\n"}
+{"abstract": "  Building on the recent computation of the cohomology rings of smooth toric\nvarieties and partial quotients of moment-angle complexes, we investigate the\nnaturality properties of the resulting isomorphism between the cohomology of\nsuch a space and the torsion product involving the Stanley-Reisner ring. If 2\nis invertible in the chosen coefficient ring, then the isomorphism is natural\nwith respect to toric morphisms, which for partial quotients are defined in\nanalogy with toric varieties. In general there are deformation terms that we\ndescribe explicitly.\n"}
+{"abstract": "  Intelligent reflecting surfaces (IRS) can improve the physical layer security\n(PLS) by providing a controllable wireless environment. In this paper, we\npropose a novel PLS technique with the help of IRS implemented by an\nintelligent mirror array for the visible light communication (VLC) system.\nFirst, for the IRS aided VLC system containing an access point (AP), a\nlegitimate user and an eavesdropper, the IRS channel gain and a lower bound of\nthe achievable secrecy rate are derived. Further, to enhance the IRS channel\ngain of the legitimate user while restricting the IRS channel gain of the\neavesdropper, we formulate an achievable secrecy rate maximization problem for\nthe proposed IRS-aided PLS technique to find the optimal orientations of\nmirrors. Since the sensitivity of mirrors' orientations on the IRS channel gain\nmakes the optimization problem hard to solve, we transform the original problem\ninto a reflected spot position optimization problem and solve it by a particle\nswarm optimization (PSO) algorithm. Our simulation results show that secrecy\nperformance can be significantly improved by adding an IRS in a VLC system.\n"}
+{"abstract": "  We consider infinite matrices obtained by restricting Hardy integral kernels\nto natural numbers. For a suitable class of Hardy kernels we describe the\nabsolutely continuous spectrum, the essential spectrum and the asymptotic\nspectral density of these matrices.\n"}
+{"abstract": "  We study the problem of finding the best approximate circuit that is the\nclosest (in some pertinent metric) to a target circuit, and which satisfies a\nnumber of hardware constraints, like gate alphabet and connectivity. We look at\nthe problem in the CNOT+rotation gate set from a mathematical programming\nstandpoint, offering contributions both in terms of understanding the\nmathematics of the problem and its efficient solution. Among the results that\nwe present, we are able to derive a 14-CNOT 4-qubit Toffoli decomposition from\nscratch, and show that the Quantum Shannon Decomposition can be compressed by a\nfactor of two without practical loss of fidelity.\n"}
+{"abstract": "  Augmented Reality (AR) enriches a user's real environment by adding spatially\naligned virtual objects (3D models, 2D textures, textual annotations, etc) by\nmeans of special display technologies. These are either worn on the body or\nplaced in the working environment. From a technical point of view, AR faces\nthree major challenges: (1) to generate a high quality rendering, (2) to\nprecisely register (in position and orientation) the virtual objects (VOs) with\nthe real environment, and (3) to do so in interactive real-time (Regenbrecht,\nWagner, and Baratoff, 2002). The goal is to create the impression that the VOs\nare part of the real environment. Therefore, and similar to definitions of\nvirtual reality (Steuer, 1992), it makes sense to define AR from a\npsychological point of view: Augmented Reality conveys the impression that VOs\nare present in the real environment. In order to evaluate how well this goal is\nreached, a psychological measurement of this type of presence is necessary. In\nthe following, we will describe technological features of AR systems that make\na special questionnaire version necessary, describe our approach to the\nquestionnaire development, and the data collection strategy. Finally we will\npresent first results of the application of the questionnaire in a recent study\nwith 385 participants.\n"}
+{"abstract": "  High precision measurements of the polarized electron beam-spin asymmetry in\nsemi-inclusive deep inelastic scattering (SIDIS) from the proton have been\nperformed using a 10.6 GeV incident electron beam and the CLAS12 spectrometer\nat Jefferson Lab. We report here the first multidimensional study of single\n$\\pi^+$ SIDIS data over a large kinematic range in $z$, $x_B$, $P_T$ and\nvirtualities $Q^2$ ranging from 1 GeV$^2$ up to 7 GeV$^2$. In particular, the\nstructure function ratio $F_{LU}^{\\sin\\phi}/F_{UU}$ has been determined, where\n$F_{LU}^{\\sin\\phi}$ is a twist-3 quantity that can reveal novel properties of\nquark-gluon correlations within the nucleon. The impact of the data on the\nevolving understanding of the underlying reaction mechanisms and their\nkinematic variation is explored using theoretical models for the different\ncontributing transverse momentum dependent parton distribution functions.\n"}
+{"abstract": "  Bloom Filter is a probabilistic data structure for the membership query, and\nit has been intensely experimented in various fields to reduce memory\nconsumption and enhance a system's performance. Bloom Filter is classified into\ntwo key categories: counting Bloom Filter (CBF), and non-counting Bloom Filter.\nCBF has a higher false positive probability than standard Bloom Filter (SBF),\ni.e., CBF uses a higher memory footprint than SBF. But CBF can address the\nissue of the false negative probability. Notably, SBF is also false negative\nfree, but it cannot support delete operations like CBF. To address these\nissues, we present a novel counting Bloom Filter based on SBF and 2D Bloom\nFilter, called countBF. countBF uses a modified murmur hash function to enhance\nits various requirements, which is experimentally evaluated. Our experimental\nresults show that countBF uses $1.96\\times$ and $7.85\\times$ less memory than\nSBF and CBF respectively, while preserving lower false positive probability and\nexecution time than both SBF and CBF. The overall accuracy of countBF is\n$99.999921$, and it proves the superiority of countBF over SBF and CBF. Also,\nwe compare with other state-of-the-art counting Bloom Filters.\n"}
+{"abstract": "  We have systematically constructed the general structure of the gauge boson\nself-energy and the effective propagator in presence of a nontrivial background\nlike hot magnetized material medium. Based on this as well as the general\nstructure of fermion propagator in weakly magnetized medium we have calculated\npressure of deconfined QCD matter within HTL approximation.\n"}
+{"abstract": "  Using fixed point characterization, we develop a new goodness of fit test for\nuniform distribution. We also discuss how the right censored observations can\nbe incorporated in the proposed test procedure. We study the asymptotic\nproperties of the proposed test statistics. A Monte Carlo simulation is carried\nout to evaluate the finite sample performance of the tests. We illustrate the\ntest procedures using real data sets.\n"}
+{"abstract": "  The spectral efficiency of wireless networks can be made nearly infinitely\nlarge by deploying many antennas, but the deployment of very many antennas\nrequires new topologies beyond the compact and discrete antenna arrays used by\nconventional base stations. In this paper, we consider the large intelligent\nsurface scenario where small antennas are deployed on a large and dense\ntwo-dimensional grid. Building on the heritage of MIMO, we first analyze the\nbeamwidth and sidelobes when transmitting from large intelligent surfaces. We\ncompare different precoding schemes and determine how to optimize the transmit\npower with respect to different utility functions.\n"}
+{"abstract": "  Stars form in cold dense cores showing subsonic velocity dispersions. The\nparental molecular clouds display higher temperatures and supersonic velocity\ndispersions. The transition from core to cloud has been observed in velocity\ndispersion, but temperature and abundance variations are unknown. We aim to\nstudy the transition from cores to ambient cloud in temperature and velocity\ndispersion using a single tracer.\n  We use NH3 (1,1) and (2,2) maps in L1688 from the Green Bank Ammonia Survey,\nsmoothed to 1', and determine the physical properties from fits. We identify\nthe coherent cores and study the changes in temperature and velocity dispersion\nfrom cores to the surrounding cloud. We obtain a kinetic temperature map\ntracing the extended cloud, improving from previous maps tracing mostly the\ncores. The cloud is 4-6 K warmer than the cores, and shows a larger velocity\ndispersion (diff. = 0.15-0.25 km/s). Comparing to Herschel-based measurements,\nwe find that cores show kinetic temperature $\\approx$1.8 K lower than the dust\ntemperature; while the gas temperature is higher than the dust temperature in\nthe cloud. We find an average p-NH3 fractional abundance (with respect to H2)\nof $(4.2\\pm0.2) \\times 10^{-9}$ towards the coherent cores, and $(1.4\\pm0.1)\n\\times 10^{-9}$ outside the core boundaries. Using stacked spectra, we detect\ntwo components, one narrow and one broad, towards cores and their\nneighbourhoods. We find the turbulence in the narrow component to be correlated\nto the size of the structure (Pearson-r=0.54). With these unresolved regional\nmeasurements, we obtain a turbulence-size relation of ${\\sigma}_{v,NT}\\propto\nr^{0.5}$, similar to previous findings using multiple tracers.\n  We discover that the subsonic component extends up to 0.15 pc beyond the\ntypical coherent boundaries, unveiling larger extents of the coherent cores and\nshowing gradual transition to coherence over ~0.2 pc.\n"}
+{"abstract": "  We propose a Goodness of Causal Fit (GCF) measure which depends on Pearl \"do\"\ninterventions. This is different from a measure of Goodness of Fit (GF), which\ndoes not use interventions. Given a DAG set ${\\cal G}$, to find a good $G\\in\n{\\cal G}$, we propose plotting $GCF(G)$ versus $GF(G)$ for all $G\\in {\\cal G}$,\nand finding a graph $G\\in {\\cal G}$ with a large amount of both types of\ngoodness.\n"}
+{"abstract": "  Speech sound disorders are a common communication impairment in childhood.\nBecause speech disorders can negatively affect the lives and the development of\nchildren, clinical intervention is often recommended. To help with diagnosis\nand treatment, clinicians use instrumented methods such as spectrograms or\nultrasound tongue imaging to analyse speech articulations. Analysis with these\nmethods can be laborious for clinicians, therefore there is growing interest in\nits automation. In this paper, we investigate the contribution of ultrasound\ntongue imaging for the automatic detection of speech articulation errors. Our\nsystems are trained on typically developing child speech and augmented with a\ndatabase of adult speech using audio and ultrasound. Evaluation on typically\ndeveloping speech indicates that pre-training on adult speech and jointly using\nultrasound and audio gives the best results with an accuracy of 86.9%. To\nevaluate on disordered speech, we collect pronunciation scores from experienced\nspeech and language therapists, focusing on cases of velar fronting and gliding\nof /r/. The scores show good inter-annotator agreement for velar fronting, but\nnot for gliding errors. For automatic velar fronting error detection, the best\nresults are obtained when jointly using ultrasound and audio. The best system\ncorrectly detects 86.6% of the errors identified by experienced clinicians. Out\nof all the segments identified as errors by the best system, 73.2% match errors\nidentified by clinicians. Results on automatic gliding detection are harder to\ninterpret due to poor inter-annotator agreement, but appear promising. Overall\nfindings suggest that automatic detection of speech articulation errors has\npotential to be integrated into ultrasound intervention software for\nautomatically quantifying progress during speech therapy.\n"}
+{"abstract": "  Using concepts from integral geometry, we propose a definition for a local\ncoarse-grained curvature tensor that is well-defined on polygonal surfaces.\nThis coarse-grained curvature tensor shows fast convergence to the curvature\ntensor of smooth surfaces, capturing with accuracy not only the principal\ncurvatures but also the principal directions of curvature. Thanks to the\nadditivity of the integrated curvature tensor, coarse-graining procedures can\nbe implemented to compute it over arbitrary patches of polygons. When computed\nfor a closed surface, the integrated curvature tensor is identical to a rank-2\nMinkowski tensor. We also provide an algorithm to extend an existing C++\npackage, that can be used to compute efficiently local curvature tensors on\ntriangulated surfaces.\n"}
+{"abstract": "  We present the results of a near-infrared (NIR) monitoring program carried\nout between 1999 and 2005 to determine the variability properties of the\nArecibo sample of OH/IR stars. The sample consists of 385 IRAS-selected\nAsymptotic Giant Branch (AGB) candidates, for which their O-rich chemistry has\nbeen proven by the detection of 1612 MHz OH maser emission. The monitoring data\nwas complemented by data collected from public optical and NIR surveys. We\nfitted the light curves obtained in the optical and NIR bands with a model\nusing an asymmetric cosine function, and derived a period for 345 sources (90%\nof the sample). Based on their variability properties, most of the Arecibo\nsources are classified as long-period large-amplitude variable stars (LPLAV),\n4% as (candidate) post-AGB stars, and 3% remain unclassified although they are\nlikely post-AGB stars or highly obscured AGB stars. The period distribution of\nthe LPLAVs peaks at 400 days, with periods between 300 and 800 days for most of\nthe sources, and has a long tail up to 2100 days. Typically, the amplitudes are\nbetween 1 and 3 mag in the NIR and between 2 and 6 mag in the optical. We find\ncorrelations between periods and amplitudes, with larger amplitudes associated\nto longer periods, as well as between the period and the infrared colours, with\nthe longer periods linked to the redder sources. Among the post-AGB stars, the\nlight curve of IRAS 19566+3423 was exceptional, showing a large systematic\nincrease in K-band brightness over 7 years.\n"}
+{"abstract": "  The effect of Ca and Zn in solid solution on the critical resolved shear\nstress (CRSS) of <a> basal slip, tensile twinning and <c+a> pyramidal slip in\nMg alloys has been measured through compression tests on single crystal\nmicropillars with different orientations. The solute atoms increased the CRSS\nfor basal slip to ~ 13.5 MPa, while the CRSS for pyramidal slip was lower than\n85 MPa, reducing significantly the plastic anisotropy in comparison with pure\nMg. Moreover, the CRSSs for twin nucleation and growth were very similar (~ 37\nMPa) and the large value of the CRSS for twin growth hindered the growth of\ntwins during thermo-mechanical processing. Finally, evidence of <a> prismatic\nslip and cross-slip between basal and prismatic dislocations was found. It is\nconcluded that the reduction of plastic anisotropy, the activation of different\nslip systems and cross-slip and the weak basal texture promoted by the large\nCRSS for twin growth are responsible for the improved ductility and formability\nof Mg-Ca-Zn alloys.\n"}
+{"abstract": "  We propose a particle-based method to simulate thin-film fluid that jointly\nfacilitates aggressive surface deformation and vigorous tangential flows. We\nbuild our dynamics model from the surface tension driven Navier-Stokes equation\nwith the dimensionality reduced using the asymptotic lubrication theory and\ncustomize a set of differential operators based on the weakly compressible\nSmoothed Particle Hydrodynamics (SPH) for evolving pointset surfaces. The key\ninsight is that the compressible nature of SPH, which is unfavorable in its\ntypical usage, is helpful in our application to co-evolve the thickness,\ncalculate the surface tension, and enforce the fluid incompressibility on a\nthin film. In this way, we are able to two-way couple the surface deformation\nwith the in-plane flows in a physically based manner. We can simulate complex\nvortical swirls, fingering effects due to Rayleigh-Taylor instability,\ncapillary waves, Newton's interference fringes, and the Marangoni effect on\nliberally deforming surfaces by presenting both realistic visual results and\nnumerical validations. The particle-based nature of our system also enables it\nto conveniently handle topology changes and codimension transitions, allowing\nus to marry the thin-film simulation with a wide gamut of 3D phenomena, such as\npinch-off of unstable catenoids, dripping under gravity, merging of droplets,\nas well as bubble rupture.\n"}
+{"abstract": "  Chromatin moves dynamically inside the cell nucleus, and its motion is often\ncorrelated with gene functions such as DNA recombination and transcription. A\nrecent study has shown that during early embryogenesis of the nematode,\nCaenorhabiditis elegans, the chromatin motion markedly decreases. However, the\nunderlying mechanism for this transition has yet to be elucidated. We\nsystematically investigated the impact of nuclear size to demonstrate that it\nis indeed a decisive factor in chromatin mobility. To this end, we established\na method to quantify chromatin motion inside the nucleus, while excluding the\ncontribution of the movement of the nucleus itself, which allowed us to extract\nthe intrinsic mean-squared displacement (iMSD) of individual chromosomal loci\nin moving nuclei from the correlated motion of two loci. We show that a simple\ntheoretical description, which takes into account the topological constraints\nof chromatin polymers, can quantitatively describe the relationship between the\nnucleus size and the chromatin motion in vivo. Our results emphasize a\nregulatory role of nuclear size in restricting chromatin motion, and a generic\npolymer physics model plays a guiding role in capturing this essential feature.\n"}
+{"abstract": "  Non-linear model predictive control (nMPC) is a powerful approach to control\ncomplex robots (such as humanoids, quadrupeds, or unmanned aerial manipulators\n(UAMs)) as it brings important advantages over other existing techniques. The\nfull-body dynamics, along with the prediction capability of the optimal control\nproblem (OCP) solved at the core of the controller, allows to actuate the robot\nin line with its dynamics. This fact enhances the robot capabilities and\nallows, e.g., to perform intricate maneuvers at high dynamics while optimizing\nthe amount of energy used. Despite the many similarities between humanoids or\nquadrupeds and UAMs, full-body torque-level nMPC has rarely been applied to\nUAMs.\n  This paper provides a thorough description of how to use such techniques in\nthe field of aerial manipulation. We give a detailed explanation of the\ndifferent parts involved in the OCP, from the UAM dynamical model to the\nresiduals in the cost function. We develop and compare three different nMPC\ncontrollers: Weighted MPC, Rail MPC, and Carrot MPC, which differ on the\nstructure of their OCPs and on how these are updated at every time step. To\nvalidate the proposed framework, we present a wide variety of simulated case\nstudies. First, we evaluate the trajectory generation problem, i.e., optimal\ncontrol problems solved offline, involving different kinds of motions (e.g.,\naggressive maneuvers or contact locomotion) for different types of UAMs. Then,\nwe assess the performance of the three nMPC controllers, i.e., closed-loop\ncontrollers solved online, through a variety of realistic simulations. For the\nbenefit of the community, we have made available the source code related to\nthis work.\n"}
+{"abstract": "  Modular Decomposition focuses on repeatedly identifying a module M (a\ncollection of vertices that shares exactly the same neighbourhood outside of M)\nand collapsing it into a single vertex. This notion of exactitude of\nneighbourhood is very strict, especially when dealing with real world graphs.\nWe study new ways to relax this exactitude condition. However, generalizing\nmodular decomposition is far from obvious. Most of the previous proposals lose\nalgebraic properties of modules and thus most of the nice algorithmic\nconsequences. We introduce the notion of an ({\\alpha}, {\\beta})-module, a\nrelaxation that allows a bounded number of errors in each node and maintains\nsome of the algebraic structure. It leads to a new combinatorial decomposition\nwith interesting properties. Among the main results in this work, we show that\nminimal ({\\alpha}, {\\beta})-modules can be computed in polynomial time, and\nthat every graph admits an ({\\alpha},{\\beta})-modular decomposition tree, thus\ngeneralizing Gallai's Theorem (which corresponds to the case for {\\alpha} =\n{\\beta} = 0). Unfortunately we give evidence that computing such a\ndecomposition tree can be difficult.\n"}
+{"abstract": "  We study static traversable wormholes obtained by Morris and Thorne in\ngeneral relativity (GR) in the framework of a modified theory of gravity. The\nmodified gravitational action $f(R,T)$ is a function of the Ricci scalar ($R$)\nand of the trace of the energy momentum tensor ($T$). For a modified gravity\n$f(R,T)=R+\\alpha R^{2}+\\lambda T$, where $\\alpha$ and $\\lambda$ are constants,\nwe obtain wormhole solutions (WH) with normal matter for a relevant shape\nfunctions. The energy conditions are checked at the throat and away from the\nthroat of the WH. The coupling parameters $\\alpha $ and $\\lambda$ in the\ngravitational action play an important role to accommodate the matter\ncomposition. For a given $\\lambda$, WH solutions are found in the presence of\nexotic matter at the throat for $\\alpha <0$. It is shown that WH exists in the\nmodified gravity without exotic matter when $\\alpha >0$. We consider two\ndifferent shape functions to investigate the existence of WH in the presence of\nexotic or normal matter. A class of WH solutions exist with anisotropic fluid\nprovided $\\lambda \\neq - 8 \\pi$. In flat asymptotic regions on both sides of\nthe throat with anisotropic source and $\\lambda= - 8 \\pi$, it does not permit\nWH as per no go theorem. As $\\lambda \\rightarrow 0$ in the gravitational\naction, all the energy conditions are obeyed with the hybrid shape function\nindicating existence of WH with normal matter.\n"}
+{"abstract": "  Cutting plane methods play a significant role in modern solvers for tackling\nmixed-integer programming (MIP) problems. Proper selection of cuts would remove\ninfeasible solutions in the early stage, thus largely reducing the\ncomputational burden without hurting the solution accuracy. However, the major\ncut selection approaches heavily rely on heuristics, which strongly depend on\nthe specific problem at hand and thus limit their generalization capability. In\nthis paper, we propose a data-driven and generalizable cut selection approach,\nnamed Cut Ranking, in the settings of multiple instance learning. To measure\nthe quality of the candidate cuts, a scoring function, which takes the\ninstance-specific cut features as inputs, is trained and applied in cut ranking\nand selection. In order to evaluate our method, we conduct extensive\nexperiments on both synthetic datasets and real-world datasets. Compared with\ncommonly used heuristics for cut selection, the learning-based policy has shown\nto be more effective, and is capable of generalizing over multiple problems\nwith different properties. Cut Ranking has been deployed in an industrial\nsolver for large-scale MIPs. In the online A/B testing of the product planning\nproblems with more than $10^7$ variables and constraints daily, Cut Ranking has\nachieved the average speedup ratio of 12.42% over the production solver without\nany accuracy loss of solution.\n"}
+{"abstract": "  Democratization of machine learning requires architectures that automatically\nadapt to new problems. Neural Differential Equations (NDEs) have emerged as a\npopular modeling framework by removing the need for ML practitioners to choose\nthe number of layers in a recurrent model. While we can control the\ncomputational cost by choosing the number of layers in standard architectures,\nin NDEs the number of neural network evaluations for a forward pass can depend\non the number of steps of the adaptive ODE solver. But, can we force the NDE to\nlearn the version with the least steps while not increasing the training cost?\nCurrent strategies to overcome slow prediction require high order automatic\ndifferentiation, leading to significantly higher training time. We describe a\nnovel regularization method that uses the internal cost heuristics of adaptive\ndifferential equation solvers combined with discrete adjoint sensitivities to\nguide the training process towards learning NDEs that are easier to solve. This\napproach opens up the blackbox numerical analysis behind the differential\nequation solver's algorithm and directly uses its local error estimates and\nstiffness heuristics as cheap and accurate cost estimates. We incorporate our\nmethod without any change in the underlying NDE framework and show that our\nmethod extends beyond Ordinary Differential Equations to accommodate Neural\nStochastic Differential Equations. We demonstrate how our approach can halve\nthe prediction time and, unlike other methods which can increase the training\ntime by an order of magnitude, we demonstrate similar reduction in training\ntimes. Together this showcases how the knowledge embedded within\nstate-of-the-art equation solvers can be used to enhance machine learning.\n"}
+{"abstract": "  Complex numbers are widely used in both classical and quantum physics, and\nare indispensable components for describing quantum systems and their dynamical\nbehavior. Recently, the resource theory of imaginarity has been introduced,\nallowing for a systematic study of complex numbers in quantum mechanics and\nquantum information theory. In this work we develop theoretical methods for the\nresource theory of imaginarity, motivated by recent progress within theories of\nentanglement and coherence. We investigate imaginarity quantification, focusing\non the geometric imaginarity and the robustness of imaginarity, and apply these\ntools to the state conversion problem in imaginarity theory. Moreover, we\nanalyze the complexity of real and general operations in optical experiments,\nfocusing on the number of unfixed wave plates for their implementation. We also\ndiscuss the role of imaginarity for local state discrimination, proving that\nany pair of real orthogonal pure states can be discriminated via local real\noperations and classical communication. Our study reveals the significance of\ncomplex numbers in quantum physics, and proves that imaginarity is a resource\nin optical experiments.\n"}
+{"abstract": "  The distributions of the $k$-th largest level at the soft edge scaling limit\nof Gaussian ensembles are some of the most important distributions in random\nmatrix theory, and their numerical evaluation is a subject of great practical\nimportance. One numerical method for evaluating the distributions uses the fact\nthat they can be represented as Fredholm determinants involving the so-called\nAiry integral operator. When the spectrum of the integral operator is computed\nby discretizing it directly, the eigenvalues are known to at most absolute\nprecision. Remarkably, the Airy integral operator is an example of a so-called\nbispectral operator, which admits a commuting differential operator that shares\nthe same eigenfunctions. In this manuscript, we develop an efficient numerical\nalgorithm for evaluating the eigendecomposition of the Airy integral operator\nto full relative precision, using the eigendecomposition of the commuting\ndifferential operator. This allows us to rapidly evaluate the distributions of\nthe $k$-th largest level to full relative precision rapidly everywhere except\nin the left tail, where they are computed to absolute precision. In addition,\nwe characterize the eigenfunctions of the Airy integral operator, and describe\ntheir extremal properties in relation to an uncertainty principle involving the\nAiry transform. We observe that the Airy integral operator is fairly universal,\nand we describe a separate application to Airy beams in optics. Using the\neigenfunctions, we compute a finite-energy Airy beam that is optimal, in the\nsense that the beam is both maximally concentrated, and maximally\nnon-diffracting and self-accelerating.\n"}
+{"abstract": "  We explore the use of large pretrained language models as few-shot semantic\nparsers. The goal in semantic parsing is to generate a structured meaning\nrepresentation given a natural language input. However, language models are\ntrained to generate natural language. To bridge the gap, we use language models\nto paraphrase inputs into a controlled sublanguage resembling English that can\nbe automatically mapped to a target meaning representation. Our results\ndemonstrate that with only a small amount of data and very little code to\nconvert into English-like representations, our blueprint for rapidly\nbootstrapping semantic parsers leads to surprisingly effective performance on\nmultiple community tasks, greatly exceeding baseline methods also trained on\nthe same limited data.\n"}
+{"abstract": "  We study the self-assembly on a spherical surface of a model for a binary\nmixture of amphiphilic dimers in the presence of guest particles via Monte\nCarlo (MC) computer simulation. All particles have a hard core, but one monomer\nof the dimer also interacts with the guest particle by means of a short-range\nattractive potential. We observe the formation of aggregates of various shape\nas a function of the composition of the mixture and of the size of guest\nparticles. Our MC simulations are a further step towards a microscopic\nunderstanding of experiments on colloidal aggregation over curved surfaces,\nsuch as oil droplets.\n"}
+{"abstract": "  Character rigging is universally needed in computer graphics but notoriously\nlaborious. We present a new method, HeterSkinNet, aiming to fully automate such\nprocesses and significantly boost productivity. Given a character mesh and\nskeleton as input, our method builds a heterogeneous graph that treats the mesh\nvertices and the skeletal bones as nodes of different types and uses graph\nconvolutions to learn their relationships. To tackle the graph heterogeneity,\nwe propose a new graph network convolution operator that transfers information\nbetween heterogeneous nodes. The convolution is based on a new distance\nHollowDist that quantifies the relations between mesh vertices and bones. We\nshow that HeterSkinNet is robust for production characters by providing the\nability to incorporate meshes and skeletons with arbitrary topologies and\nmorphologies (e.g., out-of-body bones, disconnected mesh components, etc.).\nThrough exhaustive comparisons, we show that HeterSkinNet outperforms\nstate-of-the-art methods by large margins in terms of rigging accuracy and\nnaturalness. HeterSkinNet provides a solution for effective and robust\ncharacter rigging.\n"}
+{"abstract": "  A classical question in economics is whether complex, randomized selling\nprotocols can improve a firm's revenue beyond that of simple,\ntake-it-or-leave-it posted prices. In 1981, Myerson answered this question with\nan emphatic ``No'' for a monopolist selling a single good. By contrast, there\nis no crisp answer for multiple goods, and a major focus of Bayesian mechanism\ndesign since then has been to understand the power of randomized lotteries over\ndeterministic pricing in different settings.\n  In this paper, we ask the same question for assortment optimization, where\ngoods have exogenously-fixed prices, and the decision is a set of substitute\ngoods to offer. To formalize this question, we introduce a Bayesian mechanism\ndesign problem with fixed prices and ordinal preferences, in which assortments\ncorrespond to deterministic mechanisms. Meanwhile, randomized mechanisms\ncorrespond to lotteries whose payment is restricted to equal the fixed price of\nthe randomly-allocated good. This models the ``fixed-price lotteries'' trending\nin designer fashion, a significant departure from traditional lotteries based\non price discounts.\n  We first show that for general ordinal distributions, lotteries under this\nrestriction can still earn greater revenue than any deterministic assortment.\nWe then derive a natural sufficient condition on the distribution which ensures\nthe optimality of assortments. Importantly, this sufficient condition captures\ncommonly-used distributions in the assortment optimization literature,\nincluding Multi-Nomial Logit, Markov Chain, Tversky's Elimination by Aspects,\nand mixtures with Independent Demand Models. The takeaway is that unless a firm\nhas a sophisticated model for consumer choice, fixed-price lotteries are no\nbetter than assortments.\n"}
+{"abstract": "  In this paper we consider the system with $n$ identical elements and one\nrepairing device. In each time moment only one element works while the rest\nelements stay in reserve. The distribution of element repairing period is\nexponential, the distribution of element working period is general. We obtain\nthe asymptotic distribution of the system lifetime under condition of the\nelement's fast repair.\n"}
+{"abstract": "  We study Schubert polynomials using geometry of infinite-dimensional flag\nvarieties and degeneracy loci. Applications include Graham-positivity of\ncoefficients appearing in equivariant coproduct formulas and expansions of\nback-stable and enriched Schubert polynomials. We also construct embeddings of\naffine and type C flag varieties, study the corresponding pullback maps on\n(equivariant) cohomology rings, and give a presentation of the integral\nequivariant cohomology of the (type A) affine Grassmannian.\n"}
+{"abstract": "  Recently, we have been witnessing an increasing use of machine learning\nmethods in self-adaptive systems. Machine learning methods offer a variety of\nuse cases for supporting self-adaptation, e.g., to keep runtime models up to\ndate, reduce large adaptation spaces, or update adaptation rules. Yet, since\nmachine learning methods apply in essence statistical methods, they may have an\nimpact on the decisions made by a self-adaptive system. Given the wide use of\nformal approaches to provide guarantees for the decisions made by self-adaptive\nsystems, it is important to investigate the impact of applying machine learning\nmethods when such approaches are used. In this paper, we study one particular\ninstance that combines linear regression to reduce the adaptation space of a\nself-adaptive system with statistical model checking to analyze the resulting\nadaptation options. We use computational learning theory to determine a\ntheoretical bound on the impact of the machine learning method on the\npredictions made by the verifier. We illustrate and evaluate the theoretical\nresult using a scenario of the DeltaIoT artifact. To conclude, we look at\nopportunities for future research in this area.\n"}
+{"abstract": "  Optimal sensor placement (SP) usually minimizes an impact measure, such as\nthe amount of contaminated water or the number of inhabitants affected before\ndetection. The common choice is to minimize the minimum detection time (MDT)\naveraged over a set of contamination events, with contaminant injected at a\ndifferent location. Given a SP, propagation is simulated through a hydraulic\nsoftware model of the network to obtain spatio-temporal concentrations and the\naverage MDT. Searching for an optimal SP is NP-hard: even for mid-size\nnetworks, efficient search methods are required, among which evolutionary\napproaches are often used. A bi-objective formalization is proposed: minimizing\nthe average MDT and its standard deviation, that is the risk to detect some\ncontamination event too late than the average MDT. We propose a data structure\n(sort of spatio-temporal heatmap) collecting simulation outcomes for every SP\nand particularly suitable for evolutionary optimization. Indeed, the proposed\ndata structure enabled a convergence analysis of a population-based algorithm,\nleading to the identification of indicators for detecting problem-specific\nconverge issues which could be generalized to other similar problems. We used\nPymoo, a recent Python framework flexible enough to incorporate our problem\nspecific termination criterion. Results on a benchmark and a real-world network\nare presented.\n"}
+{"abstract": "  Managing a pandemic requires continuous dissemination of infectious disease\nsurveillance data. Legislation permits sharing de-identified patient data;\nhowever, current de-identification approaches are time-consuming and do not\nflex with changes in infection rates or population demographics over time. In\nthis paper, we introduce a framework to dynamically adapt de-identification for\nnear-real time sharing of patient-level surveillance data. The framework\nleverages a simulation mechanism, capable of being applied to any geographic\nlevel, to forecast and manage disclosure risks. We use data from Johns Hopkins\nUniversity and the Centers for Disease Control and Prevention to demonstrate\nthe framework's effectiveness in maintaining the privacy risk below a threshold\nbased on public health standards for COVID-19 county-level case data from\nAugust 2020 to April 2021. Across all US counties, the framework's approach\nmeets the threshold for 95.2% of daily data releases, while a policy based on\ncurrent de-identification techniques meets the threshold for only 24.6%.\n"}
+{"abstract": "  We consider a quasilinear degenerate parabolic equation driven by the\northotropic $p-$Laplacian. We prove that local weak solutions are locally\nLipschitz continuous in the spatial variable, uniformly in time.\n"}
+{"abstract": "  Because many of our X-ray telescopes are optimized towards observing faint\nsources, observations of bright sources like X-ray binaries in outburst are\noften affected by instrumental biases. These effects include dead time and\nphoton pile-up, which can dramatically change the statistical inference of\nphysical parameters from these observations. While dead time is difficult to\ntake into account in a statistically consistent manner, simulating dead\ntime-affected data is often straightforward. This structure makes the issue of\ninferring physical properties from dead time-affected observations fall into a\nclass of problems common across many scientific disciplines. There is a growing\nnumber of methods to address them under the name of Simulation-Based Inference\n(SBI), aided by new developments in density estimation and statistical machine\nlearning. In this paper, we introduce SBI as a principled way to infer\nvariability properties from dead time-affected light curves. We use Sequential\nNeural Posterior Estimation to estimate the posterior probability for\nvariability properties. We show that this method can recover variability\nparameters on simulated data even when dead time is variable, and present\nresults of an application of this approach to NuSTAR observations of the\ngalactic black hole X-ray binary GRS 1915+105.\n"}
+{"abstract": "  Low-complexity beamformer design with practical constraints is an attractive\nresearch area for hybrid analog/digital systems in mm-wave massive\nmultiple-input multiple-output (MIMO). This paper investigates\ninterference-aware pre-beamformer (analog beamformer) design for joint spatial\ndivision and multiplexing (JSDM) which is a user-grouping based two-stage\nbeamforming method. Single-carrier frequency domain equalization (SC-FDE) is\nemployed in uplink frequency-selective channels. First, unconstrained slowly\nchanging statistical analog beamformer of each group, namely, generalized\neigenbeamformer (GEB) which has strong interference suppression capability is\ndesigned where the mutual information in reduced dimension is maximized. Then,\nconstant-modulus constrained approximations of unconstrained beamformer are\nobtained by utilizing alternating minimization algorithms for fully connected\narrays and fixed subarrays. In addition, a dynamic subarray algorithm is\nproposed where the connections between radio frequency (RF) chains and antennas\nare changed with changing channel statistics. Convergence of the proposed\nalternating minimization-based algorithms are provided along with their\ncomplexity analysis. It is observed that additional complexity of proposed\nalgorithms is insignificant for the overall system design. Although most of the\ninterference is suppressed with the help of proposed constrained beamformers,\nthere may be some residual interference after the analog beamforming stage.\nTherefore, linear minimum mean square error (LMMSE) type digital beamformers,\nwhich take the residual interference in reduced dimension into account, are\nproposed instead of zero-forcing (ZF) type. Simulation results verify the\nsuperiority of the proposed interference-aware constrained design over existing\napproaches in terms of beampattern, spectral efficiency, outage capacity and\nchannel estimation accuracy.\n"}
+{"abstract": "  We estimate Weyl sums over the integers with sum of binary digits either\nfixed or restricted by some congruence condition. In our proofs we use ideas\nthat go back to a paper by Banks, Conflitti and the first author (2002).\nMoreover, we apply the \"main conjecture\" on the Vinogradov mean value theorem\nwhich has been established by Bourgain, Demeter and Guth (2016) as well as by\nWooley (2016, 2019). We use our result to give an estimate of the discrepancy\nof point sets that are defined by the values of polynomials at arguments having\nthe sum of binary digits restricted in different ways.\n"}
+{"abstract": "  We consider a dynamic Colonel Blotto game (CBG) in which one of the players\nis the learner and has limited troops (budget) to allocate over a finite time\nhorizon. At each stage, the learner strategically determines the budget\ndistribution among the battlefields based on past observations. The other\nplayer is the adversary, who chooses its budget allocation strategies randomly\nfrom some fixed unknown distribution. The learner's objective is to minimize\nits regret, which is the difference between the payoff of the best mixed\nstrategy and the realized payoff by following a learning algorithm. The dynamic\nCBG is analyzed under the framework of combinatorial bandit and bandit with\nknapsacks. We first convert the dynamic CBG with budget constraint to a path\nplanning problem on a graph. We then devise an efficient dynamic policy for the\nlearner that uses a combinatorial bandit algorithm Edge on the path planning\ngraph as a subroutine for another algorithm LagrangeBwK. It is shown that under\nthe proposed policy, the learner's regret is bounded with high probability by a\nterm sublinear in time horizon $T$ and polynomial with respect to other\nparameters.\n"}
+{"abstract": "  In this paper, we consider a problem of counting rational points near\nself-similar sets. Let $n\\geq 1$ be an integer. We shall show that for some\nself-similar measures on $\\mathbb{R}^n$, the set of rational points\n$\\mathbb{Q}^n$ is 'equidistributed' in a sense that will be introduced in this\npaper. This implies that an inhomogeneous Khinchine convergence type result can\nbe proved for those measures. In particular, for $n=1$ and large enough\nintegers $p,$ the above holds for the middle-$p$th Cantor measure, i.e. the\nnatural Hausdorff measure on the set of numbers whose base $p$ expansions do\nnot have digit $[(p-1)/2].$ Furthermore, we partially proved a conjecture of\nBugeaud and Durand for the middle-$p$th Cantor set and this also answers a\nquestion posed by Levesley, Salp and Velani. Our method includes a fine\nanalysis of the Fourier coefficients of self-similar measures together with an\nErd\\H{o}s-Kahane type argument. We will also provide a numerical argument to\nshow that $p>10^7$ is sufficient for the above conclusions. In fact, $p\\geq 15$\nis already enough for most of the above conclusions.\n"}
+{"abstract": "  Recently, significant progress has been made in audio source separation by\nthe application of deep learning techniques. Current methods that combine both\naudio and visual information use 2D representations such as images to guide the\nseparation process. However, in order to (re)-create acoustically correct\nscenes for 3D virtual/augmented reality applications from recordings of real\nmusic ensembles, detailed information about each sound source in the 3D\nenvironment is required. This demand, together with the proliferation of 3D\nvisual acquisition systems like LiDAR or rgb-depth cameras, stimulates the\ncreation of models that can guide the audio separation using 3D visual\ninformation. This paper proposes a multi-modal deep learning model to perform\nmusic source separation conditioned on 3D point clouds of music performance\nrecordings. This model extracts visual features using 3D sparse convolutions,\nwhile audio features are extracted using dense convolutions. A fusion module\ncombines the extracted features to finally perform the audio source separation.\nIt is shown, that the presented model can distinguish the musical instruments\nfrom a single 3D point cloud frame, and perform source separation qualitatively\nsimilar to a reference case, where manually assigned instrument labels are\nprovided.\n"}
+{"abstract": "  Motivation: Protein-ligand affinity prediction is an important part of\nstructure-based drug design. It includes molecular docking and affinity\nprediction. Although molecular dynamics can predict affinity with high accuracy\nat present, it is not suitable for large-scale virtual screening. The existing\naffinity prediction and evaluation functions based on deep learning mostly rely\non experimentally-determined conformations. Results: We build a predictive\nmodel of protein-ligand affinity through the ResNet neural network with added\nattention mechanism. The resulting ResAtom-Score model achieves Pearson's\ncorrelation coefficient R = 0.833 on the CASF-2016 benchmark test set. At the\nsame time, we evaluated the performance of a variety of existing scoring\nfunctions in combination with ResAtom-Score in the absence of\nexperimentally-determined conformations. The results show that the use of\n{\\Delta}VinaRF20 in combination with ResAtom-Score can achieve affinity\nprediction close to scoring functions in the presence of\nexperimentally-determined conformations. These results suggest that ResAtom\nsystem may be used for in silico screening of small molecule ligands with\ntarget proteins in the future. Availability: https://github.com/wyji001/ResAtom\n"}
+{"abstract": "  We consider a planar convex body $C$ and we prove several analogs of Roth's\ntheorem on irregularities of distribution. When $\\partial C$ is $\\mathcal{C}%\n^{2}$ regardless of curvature, we prove that for every set $\\mathcal{P}_{N}$ of\n$N$ points in $\\mathbb{T}^{2}$ we have the sharp bound \\[\n\\int_{0}^{1}\\int_{\\mathbb{T}^{2}}\\left\\vert \\mathrm{card}\\left(\n\\mathcal{P}_{N}\\mathcal{\\cap}\\left( \\lambda C+t\\right) \\right) -\\lambda\n^{2}N\\left\\vert C\\right\\vert \\right\\vert ^{2}~dtd\\lambda\\geqslant cN^{1/2}\\;.\n\\] When $\\partial C$ is only piecewise $\\mathcal{C}^{2}$ and is not a polygon\nwe prove the sharp bound% \\[ \\int_{0}^{1}\\int_{\\mathbb{T}^{2}}\\left\\vert\n\\mathrm{card}\\left( \\mathcal{P}_{N}\\mathcal{\\cap}\\left( \\lambda C+t\\right)\n\\right) -\\lambda ^{2}N\\left\\vert C\\right\\vert \\right\\vert\n^{2}~dtd\\lambda\\geqslant cN^{2/5}. \\] We also give a whole range of\nintermediate sharp results between $N^{2/5}$ and $N^{1/2}$. Our proofs depend\non a lemma of Cassels-Montgomery, on ad hoc constructions of finite point sets,\nand on a geometric type estimate for the average decay of the Fourier transform\nof the characteristic function of $C$.\n"}
+{"abstract": "  In this contribution a numerical model is developed to study the time\ndynamics of photoluminescence emitted by Tb3+ doped multimode\nchalcogenide-selenide glass fibers pumped by laser light at approximately 2\nmicrons. The model consists of a set of partial differential equations (PDEs),\nwhich describe the temporal and spatial evolution of the photon density and\nlevel populations within the fiber. In order to solve numerically the PDEs a\nMethod of Lines is applied. The modeling parameters are extracted from\nmeasurements and from data available in the literature. The numerical results\nobtained support experimental observations. In particular, the developed model\nreproduces the discrepancies that are observed between the photoluminescence\ndecay curves obtained from different points along the fiber. The numerical\nanalysis is also used to explain the source of these discrepancies.\n"}
+{"abstract": "  We show how to compute annihilators and associated varieties of simple\nHarish-Chandra modules for Sp(p,q) with trivial infinitesimal character from a\npair of domino tableaux attached to a parameter for such a module.\n"}
+{"abstract": "  LUX-ZEPLIN (LZ) is a dark matter detector expected to obtain world-leading\nsensitivity to weakly interacting massive particles (WIMPs) interacting via\nnuclear recoils with a ~7-tonne xenon target mass. This manuscript presents\nsensitivity projections to several low-energy signals of the complementary\nelectron recoil signal type: 1) an effective neutrino magnetic moment and 2) an\neffective neutrino millicharge, both for pp-chain solar neutrinos, 3) an axion\nflux generated by the Sun, 4) axion-like particles forming the galactic dark\nmatter, 5) hidden photons, 6) mirror dark matter, and 7) leptophilic dark\nmatter. World-leading sensitivities are expected in each case, a result of the\nlarge 5.6t 1000d exposure and low expected rate of electron recoil backgrounds\nin the $<$100keV energy regime. A consistent signal generation, background\nmodel and profile-likelihood analysis framework is used throughout.\n"}
+{"abstract": "  An accurate force field is the key to the success of all molecular mechanics\nsimulations on organic polymers and biomolecules. Accuracy beyond density\nfunctional theory is often needed to describe the intermolecular interactions,\nwhile most correlated wavefunction (CW) methods are prohibitively expensive for\nlarge molecules. Therefore, it posts a great challenge to develop an extendible\nab initio force field for large flexible organic molecules at CW level of\naccuracy. In this work, we face this challenge by combining the physics-driven\nnonbonding potential with a data-driven subgraph neural network bonding model\n(named sGNN). Tests on polyethylene glycol polymer chains show that our\nstrategy is highly accurate and robust for molecules of different sizes.\nTherefore, we can develop the force field from small molecular fragments (with\nsizes easily accessible to CW methods) and safely transfer it to large\npolymers, thus opening a new path to the next-generation organic force fields.\n"}
+{"abstract": "  This paper is concerned with a class of approximate non-linear\ntransformations that compress solutions of the (generalized) sinh-Gordon\nequation into parametrically small regions in two-dimensional spacetime. Given\nthe sinh-Gordon field near a time-slice, a long Nambu-Goto string can be\nconstructed in three-dimensional anti-de Sitter space. The string is then\napproximated to arbitrary accuracy by a slightly smoothed piecewise linear\nstring of N segments. The corresponding sinh-Gordon field has a comb-like\nstructure and its size is controlled by the amount of smoothing applied to the\nsegmented string. In a (singular) large-N limit, the transformation commutes\nwith time evolution. As an example, a static cosh-Gordon solution is discussed\nin detail. The corresponding smooth and segmented string solutions are obtained\nand the compressed cosh-Gordon potential is investigated.\n"}
+{"abstract": "  Market by order (MBO) data - a detailed feed of individual trade instructions\nfor a given stock on an exchange - is arguably one of the most granular sources\nof microstructure information. While limit order books (LOBs) are implicitly\nderived from it, MBO data is largely neglected by current academic literature\nwhich focuses primarily on LOB modelling. In this paper, we demonstrate the\nutility of MBO data for forecasting high-frequency price movements, providing\nan orthogonal source of information to LOB snapshots and expanding the universe\nof alpha discovery. We provide the first predictive analysis on MBO data by\ncarefully introducing the data structure and presenting a specific\nnormalisation scheme to consider level information in order books and to allow\nmodel training with multiple instruments. Through forecasting experiments using\ndeep neural networks, we show that while MBO-driven and LOB-driven models\nindividually provide similar performance, ensembles of the two can lead to\nimprovements in forecasting accuracy - indicating that MBO data is additive to\nLOB-based features.\n"}
+{"abstract": "  We study the groups of automorphisms of rational algebraic surfaces that\nadmit a relatively minimal pencil of curves of arithmetic genus one over an\nalgebraically closed field of arbitrary characteristic. In particular, we\nclassify such surfaces that admit non-trivial automorphisms that act trivially\non the Picard group. As an application, we classify classical Enriques surfaces\nin characteristic $2$ that admit non-trivial numerically trivial automorphisms.\n"}
+{"abstract": "  We have realized different honeycomb lattices for microwave photons in the 4\nto 8 GHz band using superconducting spiral resonators. Each lattice comprises a\nfew hundred sites. Two designs have been studied, one leading to two bands\ntouching at the Dirac points and one where a gap opens at the Dirac points.\nUsing a scanning laser technique to image the eigenmodes of this new type of\nphotonic lattices, we are able to reconstruct their band structure. The\nmeasured bands are in excellent agreement with ab initio models that combine\nnumerical simulations of the electromagnetic properties of the spiral resonator\nand analytical calculations.\n"}
+{"abstract": "  In this paper we present our submission for the EACL 2021-Shared Task on\nOffensive Language Identification in Dravidian languages. Our final system is\nan ensemble of mBERT and XLM-RoBERTa models which leverage task-adaptive\npre-training of multilingual BERT models with a masked language modeling\nobjective. Our system was ranked 1st for Kannada, 2nd for Malayalam and 3rd for\nTamil.\n"}
+{"abstract": "  We introduce Feynman-like rules to compute quivers for two loops and higher\nfor the coloured planar $\\phi^3$ theory for winding number zero. We demonstrate\nthis for a few cases.\n"}
+{"abstract": "  Using the programming language Haskell, we introduce an implementation of\npropositional calculus, number theory, and a simple imperative language that\ncan evaluate arithmetic and boolean expressions. Finally, we provide an\nimplementation of Hoare's logic which will allow us to deduce facts about\nprograms without the need for a full evaluation.\n"}
+{"abstract": "  We show that several new classes of groups are measure strongly treeable. In\nparticular, finitely generated groups admitting planar Cayley graphs,\nelementarily free groups, and the group of isometries of the hyperbolic plane\nand all its closed subgroups. In higher dimensions, we also prove a dichotomy\nthat the fundamental group of a closed aspherical 3-manifold is either amenable\nor has strong ergodic dimension 2. Our main technical tool is a method for\nfinding measurable treeings of Borel planar graphs by constructing one-ended\nspanning subforests in their planar dual. Our techniques for constructing\none-ended spanning subforests also give a complete classification of the\nlocally finite pmp graphs which admit Borel a.e. one-ended spanning subforests.\n"}
+{"abstract": "  Recent years have witnessed the rapid development of service-oriented\ncomputing technologies. The boom of Web services increases the selection burden\nof software developers in developing service-based systems (such as mashups).\nHow to recommend suitable follow-up component services to develop new mashups\nhas become a fundamental problem in service-oriented software engineering. Most\nof the existing service recommendation approaches are designed for mashup\ndevelopment in the single-round recommendation scenario. It is hard for them to\nupdate recommendation results in time according to developers' requirements and\nbehaviors (e.g., instant service selection). To address this issue, we propose\na deep-learning-based interactive service recommendation framework named DLISR,\nwhich aims to capture the interactions among the target mashup, selected\nservices, and the next service to recommend. Moreover, an attention mechanism\nis employed in DLISR to weigh selected services when recommending the next\nservice. We also design two separate models for learning interactions from the\nperspectives of content information and historical invocation information,\nrespectively, as well as a hybrid model called HISR. Experiments on a\nreal-world dataset indicate that HISR outperforms several state-of-the-art\nservice recommendation methods in the online interactive scenario for\ndeveloping new mashups iteratively.\n"}
+{"abstract": "  A to-date unsolved and highly limiting safety concern for Ultra High-Field\n(UHF) magnetic resonance imaging (MRI) is the deposition of radiofrequency (RF)\npower in the body, quantified by the specific absorption rate (SAR), leading to\ndangerous tissue heating/damage in the form of local SAR hotspots that cannot\ncurrently be measured/monitored, thereby severely limiting the applicability of\nthe technology for clinical practice and in regulatory approval. The goal of\nthis study has been to show proof of concept of an artificial intelligence (AI)\nbased exam-integrated real-time MRI safety prediction software (MRSaiFE)\nfacilitating the safe generation of 3T and 7T images by means of accurate local\nSAR-monitoring at sub-W/kg levels. We trained the software with a small\ndatabase of image as a feasibility study and achieved successful proof of\nconcept for both field strengths. SAR patterns were predicted with a residual\nroot mean squared error (RSME) of <11% along with a structural similarity\n(SSIM) level of >84% for both field strengths (3T and 7T).\n"}
+{"abstract": "  The motion of charged particles can be interfaced with electric circuitry via\nthe current induced in nearby pick-up electrodes. Here we show how the\nrotational and translational dynamics of levitated objects with arbitrary\ncharge distributions can be coupled to a circuit and how the latter acts back\non the particle motion. The ensuing cooling rates in series and parallel RLC\ncircuits are determined, demonstrating that quadrupole ion traps are well\nsuited for implementing all-electric cooling. We derive the effective\nmacromotion potential for general trap geometries and demonstrate numerically\nhow consecutive rotational and translational resistive cooling of a microscale\nparticle can be achieved in linear Paul traps.\n"}
+{"abstract": "  A great issue of discussion of an infectious disease is its basic\nreproduction number R0, which provides an estimation of the contagiousness of\nthe disease. When R0 > 1, the disease spread will potentially lead to an\noutbreak, such that of the ongoing COVID-19 pandemics. During the evolution of\nan outbreak, various non-pharmaceutical interventions are employed, the impact\nof which is frequently assessed by the reduction that they introduce to the\neffective reproduction number Rt; reduction below 1 is an indication of\neventual dying out of the disease spread. Motivated by the fact that R0\nessentially expresses the stability of the disease-free equilibrium, in this\nwork, Rt was examined in the view of timescale analysis. It was shown that\nduring the evolution of the COVID-19 outbreak in Italy, when various\ninterventions were in place, Rt had a clear relation with the explosive\ntimescale characterizing the dynamics of the outbreak. In particular, it is\nshown that the existence of an explosive timescale during the progression of\nthe epidemics implies Rt > 1, while its absence implies Rt < 1. In addition, as\nthis timescale converges/diverges with the immediately slowest one, Rt\napproaches to/withdraws from its threshold value 1. These results suggest that\ntimescale analysis can be utilized for the assessment of the impact of various\ninterventions, since it reflects the insight provided by the effective\nreproduction number, without being hindered by the selection of the population\nmodel, nor the parameter estimation process followed for model calibration.\n"}
+{"abstract": "  We report the results of magnetic, heat-capacity, electrical and\nmagnetoresistance measurements on Ho4RhAl and Er4RhAl, characterized by 3 sites\nfor rare-earths (R). Antiferromagnetic ordering sets in at (T_N =) about 8.8\nand 4.0 K respectively. While Ho compound appears to enter into a complex\nspin-glass phase at T less thanT_N (at nearly 5 K), spin-glass component\nappears to set in essentially almost at T_N for the Er case. The loss of the\nspin-disorder contribution in the magnetically ordered state is not pronounced,\nmimicking that in Gd2PdSi3, a compound which now attracts interest in the area\nof topological Hall effect and magnetic skyrmions, indicating complex Fermi\nsurface. There is a minimum in the temperature dependence of electrical\nresistivity in the case of only Ho above T_N, but significant negative\nmagnetoresistance is observed over a wide temperature range in the paramagnetic\nstate increasing with decreasing temperature for both the cases. This finding\nestablishes that these compounds belong to a select group of intermetallics in\nwhich spin-disorder contribution apparently increases gradually as one\napproaches respective TN with decreasing temperature. This could be an\nexperimental signature for the effect due to classical spin-liquid above T_N.\nIn view of these properties analogous to those of Gd2PdSi3, it is of interest\nto investigate these 4:1:1 compounds further to understand possible\nunconventional roles of itinerant electrons, not only in the magnetically\nordered state but also in the paramagnetic state, predicted by some theories in\nrecent years for which this Gd compound is considered to be a classic\nexample.Besides, magnetoresistance and isothermal entropy change\n(magnetocaloric effect) track each other.\n"}
+{"abstract": "  Invariant inclusive single particle/jet cross sections in p-p collisions can\nbe factorized in terms of two separable pT dependences, a [pT - sqrt(s)] sector\nand an [xR - pT - sqrt(s)] sector, where xR = E/Emax is the radial scaling\nvariable. Here, we extend our earlier work by analyzing more extensive data to\nexplore various s-dependent attributes and other systematics of inclusive jet,\nphoton and single particle inclusive reactions. Approximate power laws in\nsqrt(s), pT and xR are found. Physical arguments are given which relate\nobservations to the underlying physics of parton - parton hard scattering and\nthe parton distribution functions in the proton. We show that the A(sqrt(s),\npT)-function, introduced in our earlier publication to describe the\npT-dependence of the inclusive cross section, is directly related to the\nunderlying hard parton-parton scattering for jet production with little\ninfluence from soft physics. In addition to the A-function, we introduce\nanother function, the F(sqrt(s), xR)-function, which obeys radial scaling for\ninclusive jets that offers another test of the underlying parton physics. An\napplication to heavy ion physics is given where we use our variables to\ndetermine the transparency of cold nuclear matter to penetrating heavy mesons\nthrough the lead nucleus.\n"}
+{"abstract": "  We construct discontinuous point of the Lyapunov exponent of quasiperiodic\nSchr\\\"odinger cocycles in the Gevrey space $G^{s}$ with $s>2$. In contrast, the\nLyapunov exponent has been proved to be continuous in the Gevrey space $G^{s}$\nwith $s<2$ \\cite{klein,cgyz}. This shows that $G^2$ is the transition space for\nthe continuity of the Lyapunov exponent.\n"}
+{"abstract": "  Bi-periodic patterns of waves that propagate in the x direction with\namplitude variation in the y direction are generated in a laboratory. The\namplitude variation in the y direction is studied within the framework of the\nvector (vNLSE) and scalar (sNLSE) nonlinear Schrodinger equations using the\nuniform-amplitude, Stokes-like solution of the vNLSE and the Jacobi elliptic\nsine function solution of the sNLSE. The wavetrains are generated using the\nStokes-like solution of vNLSE; however, a comparison of both predictions shows\nthat while they both do a reasonably good job of predicting the observed\namplitude variation in y, the comparison with the elliptic function solution of\nthe sNLSE has significantly less error. Additionally, for agreement with the\nvNLSE solution, a third harmonic in y term from a Stokes-type expansion of\ninteracting, symmetric wavetrains must be included. There is no evidence of\ninstability growth in the x-direction, consistent with the work of Segur and\ncolleagues, who showed that dissipation stabilizes the modulational\ninstability. There is some extra amplitude variation in y, which is examined\nvia a qualitative stability calculation that allows symmetry breaking in that\ndirection.\n"}
+{"abstract": "  In this paper, we introduce the notion of the complete joint Jacobi\npolynomial of two linear codes of length $n$ over $\\mathbb{F}_q$ and\n$\\mathbb{Z}_k$. We give the MacWilliams type identity for the complete joint\nJacobi polynomials of codes. We also introduce the concepts of the average\nJacobi polynomial and the average complete joint Jacobi polynomial over\n$\\mathbb{F}_q$ and $\\mathbb{Z}_k$. We give a representation of the average of\nthe complete joint Jacobi polynomials of two linear codes of length $n$ over\n$\\mathbb{F}_q$ and $\\mathbb{Z}_k$ in terms of the compositions of $n$ and its\ndistribution in the codes. Further we present a generalization of the\nrepresentation for the average of the $(g+1)$-fold complete joint Jacobi\npolynomials of codes over $\\mathbb{F}_{q}$ and $\\mathbb{Z}_{k}$. Finally, we\ngive the notion of the average Jacobi intersection number of two codes.\n"}
+{"abstract": "  In this paper, the underlying energy flow in rocking dynamics of rigid blocks\nis utilized to elucidate their response details and overturning conditions.\nBased merely on the law of the conservation of energy, the exact criteria for a\nrigid block to overturn in the free vibration regime are established for the\nfirst time in the context of both nonlinear and linear theory. That is, if the\ninitial conditions of the free oscillation regime fall within the prescribed\nstable libration region, then it is a priori determined that the block will not\noverturn and vice versa. The analytical solution to the nonlinear free rocking\nproblem is also derived, utilizing integration techniques originating from the\nsolution of the nonlinear pendulum. Additionally, pulse-type overturning\nspectra are constructed easily by numerical integration of the equation of\nmotion only during the pulse duration. If the block does not topple during the\nforced regime, the angle and angular velocity at the end of the excitations are\nemployed to assess immediately the safety or unsafety of the block using the\nexact overturning nonlinear criterion. Finally, new insights into the stability\nregions of the overturning spectra are illuminated and explained in detail\nusing a color mapping.\n"}
+{"abstract": "  The solar corona is a highly-structured plasma which can reach temperatures\nof more than ~2 MK. At low frequencies (decimetric and metric wavelengths),\nscattering and refraction of electromagnetic waves are thought to considerably\nincrease the imaged radio source sizes (up to a few arcminutes). However,\nexactly how source size relates to scattering due to turbulence is still\nsubject to investigation. The theoretical predictions relating source\nbroadening to propagation effects have not been fully confirmed by observations\ndue to the rarity of high spatial resolution observations of the solar corona\nat low frequencies. Here, the LOw Frequency ARray (LOFAR) was used to observe\nthe solar corona at 120-180 MHz using baselines of up to ~3.5 km (corresponding\nto a resolution of ~1-2') during the partial solar eclipse of 2015 March 20. A\nlunar de-occultation technique was used to achieve higher spatial resolution\n(~0.6') than that attainable via standard interferometric imaging (~2.4'). This\nprovides a means of studying the contribution of scattering to apparent source\nsize broadening. It was found that the de-occultation technique reveals a more\nstructured quiet corona that is not resolved from standard imaging, implying\nscattering may be overestimated in this region when using standard imaging\ntechniques. However, an active region source was measured to be ~4' using both\nde-occultation and standard imaging. This may be explained by the increased\nscattering of radio waves by turbulent density fluctuations in active regions,\nwhich is more severe than in the quiet Sun.\n"}
+{"abstract": "  Bound states in the continuum (BICs) in a periodic structure sandwiched\nbetween two homogeneous media have interesting properties and useful\napplications in photonics. The topological nature of BICs was previously\nrevealed based on a topological charge related to the far-field polarization\nvector of the surrounding resonant states. Recently, it was established that\nwhen a symmetry-protected BIC (with a nonzero topological charge) is destroyed\nby a generic symmetry-breaking perturbation, a pair of circularly polarized\nresonant states (CPSs) emerge and the net topological charge is conserved. A\nperiodic structure can also support propagating BICs with a nonzero wavevector.\nThese BICs are not protected by symmetry in the sense of symmetry mismatch, but\nthey need symmetry for their robust existence. Based on a highly accurate\ncomputational method for a periodic array of slightly noncircular cylinders, we\nshow that a propagating BIC is typically destroyed by a structural perturbation\nthat breaks only the in-plane inversion symmetry, and when this happens, a pair\nof CPSs of opposite handedness emerge so that the net topological charge is\nconserved. We also study the generation and annihilation of CPSs when a\nstructural parameter is varied. It is shown that two CPSs with opposite\ntopological charge and same handedness, connected to two BICs or in a\ncontinuous branch from one BIC, may collapse and become a CPS with a zero\ncharge. Our study clarifies the important connection between symmetry and\ntopological charge conservation.\n"}
+{"abstract": "  In the recording studio, producers of Electronic Dance Music (EDM) spend more\ntime creating, shaping, mixing and mastering sounds, than with compositional\naspects or arrangement. They tune the sound by close listening and by\nleveraging audio metering and audio analysis tools, until they successfully\ncreat the desired sound aesthetics. DJs of EDM tend to play sets of songs that\nmeet their sound ideal. We therefore suggest using audio metering and\nmonitoring tools from the recording studio to analyze EDM, instead of relying\non conventional low-level audio features. We test our novel set of features by\na simple classification task. We attribute songs to DJs who would play the\nspecific song. This new set of features and the focus on DJ sets is targeted at\nEDM as it takes the producer and DJ culture into account. With simple\ndimensionality reduction and machine learning these features enable us to\nattribute a song to a DJ with an accuracy of 63%. The features from the audio\nmetering and monitoring tools in the recording studio could serve for many\napplications in Music Information Retrieval, such as genre, style and era\nclassification and music recommendation for both DJs and consumers of\nelectronic dance music.\n"}
+{"abstract": "  The Non-Fungible Token (NFT) market is mushrooming in recent years. The\nconcept of NFT originally comes from a token standard of Ethereum, aiming to\ndistinguish each token with distinguishable signs. This type of token can be\nbound with virtual/digital properties as their unique identifications. With\nNFTs, all marked properties can be freely traded with customized values\naccording to their ages, rarity, liquidity, etc. It has greatly stimulated the\nprosperity of the decentralized application (DApp) market. At the time of\nwriting (May 2021), the total money used on completed NFT sales has reached\n$34,530,649.86$ USD. The thousandfold return on its increasing market draws\nhuge attention worldwide. However, the development of the NFT ecosystem is\nstill in its early stage, and the technologies of NFTs are pre-mature.\nNewcomers may get lost in their frenetic evolution due to the lack of\nsystematic summaries. In this technical report, we explore the NFT ecosystems\nin several aspects. We start with an overview of state-of-the-art NFT\nsolutions, then provide their technical components, protocols, standards, and\ndesired proprieties. Afterward, we give a security evolution, with discussions\non the perspectives of their design models, opportunities, and challenges. To\nthe best of our knowledge, this is the first systematic study on the current\nNFT ecosystems.\n"}
+{"abstract": "  It is generally taken for granted that our Universe is free of antimatter\nobjects and domains. This certitude has recently been challenged by the\npossible detection of anti-helium nuclei by AMS-02. Should the observation be\nconfirmed, the existence of nearby antistars would make a plausible hypothesis\nto explain the origin of the antinuclei. In this paper we use the 10-years\nFermi Large Area Telescope (LAT) gamma-ray source catalog to set constraints on\nthe abundance of antistars around the Sun. We identify in the catalog 14\nantistar candidates not associated with any objects belonging to established\ngamma-ray source classes and with a spectrum compatible with baryon-antibaryon\nannihilation. We use them along with an estimate of the LAT sensitivity to\nantistars to set upper limits on the local antistar fraction $f_{\\bar{\\ast}}$\nwith respect to normal stars. We provide parametric limits as a function of the\nclosest antistar mass, velocity, and surrounding matter density. We also employ\na novel Monte~Carlo method to set limits for a few hypotheses about the\nantistar population. For a population with properties equivalent to those of\nregular stars concentrated in the Galactic disk we obtain $f_{\\bar{\\ast}} < 2.5\n\\times 10^{-6}$ at 95\\% confidence level, which is 20 times more constraining\nthan limits previously available. For a primordial population of antistars\ndistributed in the Galactic halo we obtain new local upper limits which\ndecrease as a function of antistar mass $M$ from $f_{\\bar{\\ast}} < 0.2$ at 95\\%\nconfidence level for $M = 1 \\; M_\\odot$ to $f_{\\bar{\\ast}} < 1.6 \\times\n10^{-4}$ at 95\\% confidence level for $M = 10 \\; M_\\odot$. By combining these\nlimits with existing microlensing constraints for lighter objects in the\nMagellanic clouds, we infer that a primordial population of halo antistars must\nhave a density lower than $\\mathcal{O}(10^{-5}\\;\\text{pc}^{-3})$ to\n$\\mathcal{O}(10^{-2}\\;\\text{pc}^{-3})$ depending on their masses. Our limits\ncan constrain models for the origin and propagation of antinuclei in cosmic\nrays.\n"}
+{"abstract": "  Entangled states like two-mode squeezed vacuum states are known to give\nquantum advantage in the illumination protocol, a method to detect a weakly\nreflecting target submerged in a thermal background. We use non-Gaussian\nphoton-added and subtracted states as probes for the single-shot quantum\nillumination both in the presence and absence of noise. Based on the difference\nbetween the Chernoff bounds obtained with the coherent state and the\nnon-Gaussian state having equal signal strengths, whose positive values are\nreferred to as a quantum advantage in illumination, we classify the performance\nof non-Gaussian states, when photons are added (subtracted) in (from) a single\nmode or in (from) both the modes. We highlight the hierarchy among Gaussian and\nnon-Gaussian states obtained via this method, which is compatible with\ncorrelations per unit signal strength. Interestingly, such hierarchy is\ndifferent when comparisons are made only using the Chernoff bounds. The entire\nanalysis is performed in presence of different noisy apparatus like faulty\ntwin-beam generator, imperfect photon addition or subtraction as well as with\nnoisy non-Gaussian probe states.\n"}
+{"abstract": "  We show that the equation of motion of scalar-tensor theory acquires\nthermodynamic identity when projected on a generic null surface. The relevant\nprojection is given by $E_{ab}l^ak^b$, where $E_{ab} =8\\pi T_{ab}^{(m)}$\nrepresents the equation motion for gravitational field in presence of external\nmatter, $l^a$ is the generator of the null surface and $k^a$ is the\ncorresponding auxiliary null vector. Our analysis is done completely in a\ncovariant way. Therefore all the thermodynamic quantities are in covariant form\nand hence can be used for any specific form of metric adapted to a null\nsurface. We show this both in Einstein and Jordan frames and find that these\ntwo frames provide equivalent thermodynamic quantities. This is consistent with\nthe previous findings for a Killing horizon. Also, a concrete proof of the\nzeroth law in scalar-tensor theory is provided when the null surface is defined\nby a Killing vector.\n"}
+{"abstract": "  We consider the Dirac operator on asymptotically static Lorentzian manifolds\nwith an odd-dimensional compact Cauchy surface. We prove that if\nAtiyah-Patodi-Singer boundary conditions are imposed at infinite times then the\nDirac operator is Fredholm. This generalizes a theorem due to B\\\"ar-Strohmaier\nin the case of finite times, and we also show that the corresponding index\nformula extends to the infinite setting. Furthermore, we demonstrate the\nexistence of a Fredholm inverse which is at the same time a Feynman parametrix\nin the sense of Duistermaat-H\\\"ormander. The proof combines methods from\ntime-dependent scattering theory with a variant of Egorov's theorem for\npseudo-differential hyperbolic systems.\n"}
+{"abstract": "  Multi-degree Tchebycheffian splines are splines with pieces drawn from\nextended (complete) Tchebycheff spaces, which may differ from interval to\ninterval, and possibly of different dimensions. These are a natural extension\nof multi-degree polynomial splines. Under quite mild assumptions, they can be\nrepresented in terms of a so-called MDTB-spline basis; such basis possesses all\nthe characterizing properties of the classical polynomial B-spline basis. We\npresent a practical framework to compute MDTB-splines, and provide an\nobject-oriented implementation in Matlab. The implementation supports the\nconstruction, differentiation, and visualization of MDTB-splines whose pieces\nbelong to Tchebycheff spaces that are null-spaces of constant-coefficient\nlinear differential operators. The construction relies on an extraction\noperator that maps local Tchebycheffian Bernstein functions to the MDTB-spline\nbasis of interest.\n"}
+{"abstract": "  A covariant formulation for the Newton-Hooke particle is presented by\nfollowing an algorithm developed by us \\cite{BMM1, BMM2, BMM3}. It naturally\nleads to a coupling with the Newton-Cartan geometry. From this result we\nprovide an understanding of gravitation in a Newtonian geometric background.\nUsing Dirac's constrained analysis a canonical formulation for the Newton-Hooke\ncovariant action is done in both gauge independent and gauge fixed approaches.\nWhile the former helps in identifying the various symmetries of the model, the\nlatter is able to define the physical variables. From this analysis a path to\ncanonical quantisation is traced and the Schroedinger equation is derived which\nis shown to satisfy various consistency checks. Some consequences of this\nequation are briefly mentioned.\n"}
+{"abstract": "  We trace a systematic and consistent method to precisely numerate the\nmagnitude range for various structural and isospin compositional properties of\nthe neutron star. Incompressibility, symmetry energy, slope parameter and\ncurvature of a neutron star are investigated using the relativistic energy\ndensity functional within the framework of coherent density fluctuation model.\nThe analytical expression for the energy density functional of the neutron star\nmatter is motivated from the Br$\\ddot{u}$ckner functional and acquired by the\npolynomial fitting of the saturation curves for three different relativistic\nmean-field parameter sets (NL3, G3 and IU-FSU). The modified functional is\nfolded with the neutron star's density-dependent weight function to calculate\nthe numerical values for incompressibility and symmetry energy using the\ncoherent density fluctuation model. NL3 parameter set, being the stiffest\nequation of state, endue us with a higher magnitude of all the properties\ncompared to the other two parameter sets.\n"}
+{"abstract": "  With the improvement of computer performance and the development of\nGPU-accelerated technology, trading with machine learning algorithms has\nattracted the attention of many researchers and practitioners. In this\nresearch, we propose a novel portfolio management strategy based on the\nframework of Deep Deterministic Policy Gradient, a policy-based reinforcement\nlearning framework, and compare its performance to that of other trading\nstrategies. In our framework, two Long Short-Term Memory neural networks and\ntwo fully connected neural networks are constructed. We also investigate the\nperformance of our strategy with and without transaction costs. Experimentally,\nwe choose eight US stocks consisting of four low-volatility stocks and four\nhigh-volatility stocks. We compare the compound annual return rate of our\nstrategy against seven other strategies, e.g., Uniform Buy and Hold,\nExponential Gradient and Universal Portfolios. In our case, the compound annual\nreturn rate is 14.12%, outperforming all other strategies. Furthermore, in\nterms of Sharpe Ratio (0.5988), our strategy is nearly 33% higher than that of\nthe second-best performing strategy.\n"}
+{"abstract": "  The recent COVID-19 pandemic has resulted in high fatality rates, especially\nfor patients who suffer from underlying health issues. One of the more serious\nsymptoms exhibited from patients suffering from an acute COVID-19 infection is\nbreathing difficulties and shortness of breath, which is largely due to the\nexcessive fluid (cellular leakage and cytokine storm) and mucoid debris that\nhave filled lung alveoli, and reduced the surfactant tension resulting in heavy\nand stiff lungs. In this paper, we propose the use of micro-bubbles filled with\nexosomes that can be released upon exposure to ultrasound signals as possible\nrescue therapy in deteriorating COVID-19 patients. Recent studies have shown\nthat exosomes can be used to repair and treat lung damage for patients who have\nsuffered from viral infection. We have conducted simulations to show the\nefficacy of the ultrasound signals that will penetrate through layers of\ntissues reaching the alveoli that contain the micro-bubbles. Our results have\nshown that ultrasound signals with low frequencies are required to oscillate\nand rupture the polymer-based micro-bubbles. Our proposed system can be used\nfor patients who require immediate rescue treatments for lung damage, as well\nas for recovered patients who may suffer from viral relapse infection, where\nthe micro-bubbles will remain dormant for a temporary therapeutic window until\nthey are exposed to the ultrasound signals.\n"}
+{"abstract": "  In this work, we explore joint energy-based model (EBM) training during the\nfinetuning of pretrained text encoders (e.g., Roberta) for natural language\nunderstanding (NLU) tasks. Our experiments show that EBM training can help the\nmodel reach a better calibration that is competitive to strong baselines, with\nlittle or no loss in accuracy. We discuss three variants of energy functions\n(namely scalar, hidden, and sharp-hidden) that can be defined on top of a text\nencoder, and compare them in experiments. Due to the discreteness of text data,\nwe adopt noise contrastive estimation (NCE) to train the energy-based model. To\nmake NCE training more effective, we train an auto-regressive noise model with\nthe masked language model (MLM) objective.\n"}
+{"abstract": "  The Leding-Logarithmic (LL) gluon Sudakov formfactor is derived from\nrapidity-ordered BFKL evolution with longitudinal-momentum conservation. This\nderivation further clarifies the relation between High-Energy and\nTMD-factorizations and can be extended beyond LL-approximation as well.\n"}
+{"abstract": "  The 3D building modelling is important in urban planning and related domains\nthat draw upon the content of 3D models of urban scenes. Such 3D models can be\nused to visualize city images at multiple scales from individual buildings to\nentire cities prior to and after a change has occurred. This ability is of\ngreat importance in day-to-day work and special projects undertaken by\nplanners, geo-designers, and architects. In this research, we implemented a\nnovel approach to 3D building models for such matter, which included the\nintegration of geographic information systems (GIS) and 3D Computer Graphics\n(3DCG) components that generate 3D house models from building footprints\n(polygons), and the automated generation of simple and complex roof geometries\nfor rapid roof area damage reporting. These polygons (footprints) are usually\northogonal. A complicated orthogonal polygon can be partitioned into a set of\nrectangles. The proposed GIS and 3DCG integrated system partitions orthogonal\nbuilding polygons into a set of rectangles and places rectangular roofs and\nbox-shaped building bodies on these rectangles. Since technicians are drawing\nthese polygons manually with digitizers, depending on aerial photos, not all\nbuilding polygons are precisely orthogonal. But, when placing a set of boxes as\nbuilding bodies for creating the buildings, there may be gaps or overlaps\nbetween these boxes if building polygons are not precisely orthogonal. In our\nproposal, after approximately orthogonal building polygons are partitioned and\nrectified into a set of mutually orthogonal rectangles, each rectangle knows\nwhich rectangle is adjacent to and which edge of the rectangle is adjacent to,\nwhich will avoid unwanted intersection of windows and doors when building\nbodies combined.\n"}
+{"abstract": "  In this paper we present an inexact proximal point method for variational\ninequality problem on Hadamard manifolds and study its convergence properties.\nThe proposed algorithm is inexact in two sense. First, each proximal subproblem\nis approximated by using the enlargement of the vector field in consideration\nand then the next iterated is obtained by solving this subproblem allowing a\nsuitable error tolerance. As an application, we obtain an inexact proximal\npoint method for constrained optimization problems, equilibrium problems and\nnonlinear optimization problems on Hadamard manifolds.\n"}
+{"abstract": "  The HyperTh{\\'e}sau and Bibracte num{\\'e}rique projects have given rise to a\ncollective effort centred on the use of vocabulary as a means of ensuring the\ninteroperability of archaeological data throughout its life cycle. To this end,\nthe use of the standardised form of the thesaurus -- via the Opentheso platform\n-- provides a tool that is already adapted to the Linked Data. Nevertheless,\nits use quickly raised the question of the different paradigms presiding over\nthe elaboration of a specific vocabulary by each (group of) scientist(s). The\nISO 25964 standard -- designed for the management and interoperability of\nindexing languages -- is flexible enough to permit the comparison and linking\nof different scientific or documentary ``points of view''. Their coherence\nthrough interoperability alignments nevertheless requires to interface\ndifferent semantic granularities: search reporting, the description of raw\ndata, a gateway or ''pivot'' between the two, by using a regulated cooperation\nmethodology. The challenges that remain to be met on this path do not prevent\nthe thesaurus tool from already being a suitable support for a complete\n''human-to-machine-to-human'' interoperability, developed within the framework\nof the Bibracte Ville Ouverte project and exemplified through a research on the\nceramics of that archaeological site.\n"}
+{"abstract": "  Causal bandit is a nascent learning model where an agent sequentially\nexperiments in a causal network of variables, in order to identify the\nreward-maximizing intervention. Despite the model's wide applicability,\nexisting analytical results are largely restricted to a parallel bandit version\nwhere all variables are mutually independent. We introduce in this work the\nhierarchical causal bandit model as a viable path towards understanding general\ncausal bandits with dependent variables. The core idea is to incorporate a\ncontextual variable that captures the interaction among all variables with\ndirect effects. Using this hierarchical framework, we derive sharp insights on\nalgorithmic design in causal bandits with dependent arms and obtain nearly\nmatching regret bounds in the case of a binary context.\n"}
+{"abstract": "  In a Cohen-Macaulay local ring $(A, \\mathfrak{m})$, we study the Hilbert\nfunction of an integrally closed $\\mathfrak{m}$-primary ideal $I$ whose\nreduction number is three. With a mild assumption we give an inequality\n$\\ell_A(A/I) \\ge \\mathrm{e}_0(I) - \\mathrm{e}_1(I) + \\dfrac{\\mathrm{e}_2(I) +\n\\ell_A(I^2/QI)}{2}$, where $\\mathrm{e}_i(I)$ denotes the $i$th Hilbert\ncoefficients and $Q$ denotes a minimal reduction of $I$. The inequality is\nlocated between inequalities of Itoh and Elias-Valla. Furthermore our\ninequality becomes an equality if and only if the depth of the associated\ngraded ring of $I$ is larger than or equal to $\\dim A-1$. We also study the\nCohen-Macaulayness of the associated graded rings of determinantal rings.\n"}
+{"abstract": "  Long duration gamma-ray bursts (GRBs) are among the least understood\nastrophysical transients powering the high-energy universe. To date, various\nmechanisms have been proposed to explain the observed electromagnetic GRB\nemission. In this work, we show that, although different jet models may be\nequally successful in fitting the observed electromagnetic spectral energy\ndistributions, the neutrino production strongly depends on the adopted emission\nand dissipation model. To this purpose, we compute the neutrino production for\na benchmark high-luminosity GRB in the internal shock model, including a\ndissipative photosphere as well as three emission components, in the jet model\ninvoking internal-collision-induced magnetic reconnection and turbulence\n(ICMART), in the case of a magnetic jet with gradual dissipation, and in a jet\nwith dominant proton synchrotron radiation. We find that the expected neutrino\nfluence can vary up to three orders of magnitude in amplitude and peak at\nenergies ranging from $10^4$ to $10^8$ GeV. For our benchmark input parameters,\nnone of the explored GRB models is excluded by the targeted searches carried\nout by the IceCube and ANTARES Collaborations. However, our work highlights the\npotential of high-energy neutrinos of pinpointing the underlying GRB emission\nmechanism and the importance of relying on different jet models for unbiased\nstacking searches.\n"}
+{"abstract": "  How to learn a good predictor on data with missing values? Most efforts focus\non first imputing as well as possible and second learning on the completed data\nto predict the outcome. Yet, this widespread practice has no theoretical\ngrounding. Here we show that for almost all imputation functions, an\nimpute-then-regress procedure with a powerful learner is Bayes optimal. This\nresult holds for all missing-values mechanisms, in contrast with the classic\nstatistical results that require missing-at-random settings to use imputation\nin probabilistic modeling. Moreover, it implies that perfect conditional\nimputation is not needed for good prediction asymptotically. In fact, we show\nthat on perfectly imputed data the best regression function will generally be\ndiscontinuous, which makes it hard to learn. Crafting instead the imputation so\nas to leave the regression function unchanged simply shifts the problem to\nlearning discontinuous imputations. Rather, we suggest that it is easier to\nlearn imputation and regression jointly. We propose such a procedure, adapting\nNeuMiss, a neural network capturing the conditional links across observed and\nunobserved variables whatever the missing-value pattern. Experiments confirm\nthat joint imputation and regression through NeuMiss is better than various two\nstep procedures in our experiments with finite number of samples.\n"}
+{"abstract": "  Drivers heterogeneity and the broad range of vehicle characteristics are\nconsidered primarily responsible for the stochasticity observed in road traffic\ndynamics. Assessing the differences in driving style and incorporating\nindividual driving behaviour in microsimulation has attracted significant\nattention lately. The first topic is studied extensively in the literature. The\nsecond one, on the contrary, remains an open issue. The present study proposes\na methodology to characterise driving style in the free-flow regime and to\nincorporate drivers heterogeneity within a microsimulation framework. The\nmethodology uses explicit and simplified modelling of the vehicle powertrain to\nseparate the drivers behavior from the vehicle characteristics. Results show\nthat inter and intra-driver heterogeneity can be captured by log-normal\ndistributions of well-designed metric.Drivers are classified into three\ndifferent groups (dynamic, ordinary and timid drivers).\n"}
+{"abstract": "  We show that any finite affinely independent set can be isometrically\nembedded into a regular polygonal torus, that is, a finite product of regular\npolygons. As a consequence, with a straightforward application of\nK\\v{r}\\'{i}\\v{z}'s theorem, we get an alternative proof of the fact that all\nfinite affinely independent sets are Ramsey, a result which was originally\nproved by Frankl and R\\\"{o}dl.\n"}
+{"abstract": "  Knowledge of quantum mechanical systems is becoming more important for many\nscience and engineering students who are looking to join the emerging quantum\nworkforce. To better prepare a wide range of students for these careers, we\nmust seek to develop new tools to enhance our education in quantum topics. We\npresent initial studies on the use of one of these such tools, Quantum\nComposer, a 1D quantum simulation and visualization tool developed for\neducation and research purposes. In particular, we conducted five think-aloud\ninterviews with students who worked through an exercise using Quantum Composer\nthat focused on the statics and dynamics of quantum states in single- and\ndouble-harmonic well systems. Our results show that Quantum Composer helps\nstudents to obtain the correct answers to the questions posed, but additional\nsupport is needed to facilitate the development of student reasoning behind\nthese answers. In addition, we find that students explore familiar and\nunfamiliar problems in similar ways, indicating that Quantum Composer is a\nuseful tool for exploring systems that students have not seen before.\n"}
+{"abstract": "  Regression has attracted immense interest lately due to its effectiveness in\ntasks like predicting values. And Regression is of widespread use in multiple\nfields such as Economics, Finance, Business, Biology and so on. While\nconsiderable studies have proposed some impressive models, few of them have\nprovided a whole picture regarding how and to what extent Regression has\ndeveloped. With the aim of aiding beginners in understanding the relationships\namong different Regression algorithms, this paper characterizes a broad and\nthoughtful selection of recent regression algorithms, providing an organized\nand comprehensive overview of existing work and models utilized frequently. In\nthis paper, the relationship between Regression and Deep Learning is also\ndiscussed and a conclusion can be drawn that Deep Learning can be more powerful\nas an combination with Regression models in the future.\n"}
+{"abstract": "  Two-dimensional systems with Rashba spin-orbit coupling are not Galilean\ninvariant and therefore electron--electron collisions in them may affect the\ncurrent. However when taken alone, they cannot ensure a nonzero dc resistivity,\nso their effects are masked by impurity scattering. Here we calculate the\nrelated finite-frequency response and show that the electron--electron\nscattering in clean Rashba conductors decreases the Drude weight while\nresulting in a finite dissipative component of the response outside of the\nDrude peak.\n"}
+{"abstract": "  Single-crystal (SC) NMR spectroscopy is a solid-state NMR method that has\nbeen used since the early days of NMR to study the magnitude and orientation of\ntensorial nuclear spin interactions in solids. This review first presents the\nfield of SC NMR instrumentation, then provides a survey of software for\nanalysis of SC NMR data, and finally it highlights selected applications of SC\nNMR in various fields of research. The aim of the last part is not to provide a\ncomplete review of all SC NMR literature but to provide examples that\ndemonstrate interesting applications of SC NMR.\n"}
+{"abstract": "  In semi-supervised graph-based binary classifier learning, a subset of known\nlabels $\\hat{x}_i$ are used to infer unknown labels, assuming that the label\nsignal $x$ is smooth with respect to a similarity graph specified by a\nLaplacian matrix. When restricting labels $x_i$ to binary values, the problem\nis NP-hard. While a conventional semi-definite programming (SDP) relaxation can\nbe solved in polynomial time using, for example, the alternating direction\nmethod of multipliers (ADMM), the complexity of iteratively projecting a\ncandidate matrix $M$ onto the positive semi-definite (PSD) cone ($M \\succeq 0$)\nremains high. In this paper, leveraging a recent linear algebraic theory called\nGershgorin disc perfect alignment (GDPA), we propose a fast projection-free\nmethod by solving a sequence of linear programs (LP) instead. Specifically, we\nfirst recast the SDP relaxation to its SDP dual, where a feasible solution $H\n\\succeq 0$ can be interpreted as a Laplacian matrix corresponding to a balanced\nsigned graph sans the last node. To achieve graph balance, we split the last\nnode into two that respectively contain the original positive and negative\nedges, resulting in a new Laplacian $\\bar{H}$. We repose the SDP dual for\nsolution $\\bar{H}$, then replace the PSD cone constraint $\\bar{H} \\succeq 0$\nwith linear constraints derived from GDPA -- sufficient conditions to ensure\n$\\bar{H}$ is PSD -- so that the optimization becomes an LP per iteration.\nFinally, we extract predicted labels from our converged LP solution $\\bar{H}$.\nExperiments show that our algorithm enjoyed a $40\\times$ speedup on average\nover the next fastest scheme while retaining comparable label prediction\nperformance.\n"}
+{"abstract": "  2-local derivation is a generalized derivation for a Lie algebra, which plays\nan important role to the study of local properties of the structure of the Lie\nalgebra. In this paper, we prove that every 2-local derivation on the twisted\nHeisenberg-Virasoro algebra is a derivation.\n"}
+{"abstract": "  We use a continuum quark+diquark approach to the nucleon bound-state problem\nin relativistic quantum field theory to deliver parameter-free predictions for\nthe nucleon axial and induced pseudoscalar form factors, $G_A$ and $G_P$, and\nunify them with the pseudoscalar form factor $G_5$ or, equivalently, the\npion-nucleon form factor $G_{\\pi NN}$. We explain how partial conservation of\nthe axial-vector current and the associated Goldberger-Treiman relation are\nsatisfied once all necessary couplings of the external current to the building\nblocks of the nucleon are constructed consistently; in particular, we fully\nresolve the seagull couplings to the diquark-quark vertices associated with the\naxial-vector and pseudoscalar currents. Among the results we describe, the\nfollowing are worth highlighting. A dipole form factor defined by an axial\ncharge $g_A=G_A(0)=1.25(3)$ and a mass-scale $M_A = 1.23(3) m_N$, where $m_N$\nis the nucleon mass, can accurately describe the pointwise behavior of $G_A$.\nConcerning $G_P$, we obtain the pseudoscalar charge $g_p^\\ast = 8.80(23)$, and\nfind that the pion pole dominance approach delivers a reliable estimate of the\ndirectly computed result. Our computed value of the pion-nucleon coupling\nconstant, $g_{\\pi NN}/m_N =14.02(33)/{\\rm GeV}$ is consistent with a\nRoy--Steiner-equation analysis of pion-nucleon scattering. We also observe a\nmarked suppression of the size of the $d$-quark component relative to that of\nthe $u$-quark in the ratio $g_A^d/g_A^u=-0.16(2)$, which highlights the\npresence of strong diquark correlations inside the nucleon -- both scalar and\naxial-vector, with the scalar diquark being dominant.\n"}
+{"abstract": "  Modern embedded and cyber-physical systems require every day more\nperformance, power efficiency and flexibility, to execute several profiles and\nfunctionalities targeting the ever growing adaptivity needs and preserving\nexecution efficiency. Such requirements pushed designers towards the adoption\nof heterogeneous and reconfigurable substrates, which development and\nmanagement is not that straightforward. Despite acceleration and flexibility\nare desirable in many domains, the barrier of hardware deployment and operation\nis still there since specific advanced expertise and skills are needed. Related\nchallenges are effectively tackled by leveraging on automation strategies that\nin some cases, as in the proposed work, exploit model-based approaches. This\npaper is focused on the Multi-Dataflow Composer (MDC) tool, that intends to\nsolve issues related to design, optimization and operation of coarse-grain\nreconfigurable hardware accelerators and their easy adoption in modern\nheterogeneous substrates. MDC latest features and improvements are introduced\nin detail and have been assessed on the so far unexplored robotics application\nfield. A multi-profile trajectory generator for a robotic arm is implemented\nover a Xilinx FPGA board to show in which cases coarse-grain reconfiguration\ncan be applied and which can be the parameters and trade-offs MDC will allow\nusers to play with.\n"}
+{"abstract": "  I present results for soft anomalous dimensions through three loops for\nseveral processes involving the production of top quarks. In particular, I\ndiscuss single-top and top-pair production. I also present some numerical\nresults for double-differential distributions in $t{\\bar t}$ production through\napproximate N$^3$LO.\n"}
+{"abstract": "  Background. Studies have shown that the conventional left ventricular\nmechanical dyssynchrony (LVMD) parameters have their own statistical\nlimitations. The purpose of this study is to extract new LVMD parameters from\nthe phase analysis of gated SPECT MPI by deep learning to help CRT patient\nselection. Methods. One hundred and three patients who underwent rest gated\nSPECT MPI were enrolled in this study. CRT response was defined as a decrease\nin left ventricular end-systolic volume (LVESV) >= 15% at 6 +- 1 month follow\nup. Autoencoder (AE), an unsupervised deep learning method, was trained by the\nraw LV systolic phase polar maps to extract new LVMD parameters, called\nAE-based LVMD parameters. Correlation analysis was used to explain the\nrelationships between new parameters with conventional LVMD parameters.\nUnivariate and multivariate analyses were used to establish a multivariate\nmodel for predicting CRT response. Results. Complete data were obtained in 102\npatients, 44.1% of them were classified as CRT responders. AE-based LVMD\nparameter was significant in the univariate (OR 1.24, 95% CI 1.07 - 1.44, P =\n0.006) and multivariate analyses (OR 1.03, 95% CI 1.01 - 1.06, P = 0.006).\nMoreover, it had incremental value over PSD (AUC 0.72 vs. 0.63, LH 8.06, P =\n0.005) and PBW (AUC 0.72 vs. 0.64, LH 7.87, P = 0.005), combined with\nsignificant clinic characteristics, including LVEF and gender. Conclusions. The\nnew LVMD parameters extracted by autoencoder from the baseline gated SPECT MPI\nhas the potential to improve the prediction of CRT response.\n"}
+{"abstract": "  Identifying what broken symmetries are present in the cuprates has become a\nmajor area of research. Many authors have reported evidence for so-called \"$Q\n\\sim 0$\" order that involves broken inversion, mirror, chiral, or time-reversal\nsymmetry that is uniform in space. Not all these observations are well\nunderstood and new experimental probes are needed. Here we use resonant soft\nx-ray scattering (RSXS) to search for $Q \\sim 0$ order in\nBi$_{2.1}$Sr$_{1.9}$CaCu$_2$O$_{8+x}$ (Bi-2212) by measuring the region of a\nforbidden Bragg peak, $(0,0,3)$, which is normally extinguished by symmetry but\nmay become allowed on resonance if valence band order is present. Using\ncircularly polarized light, we found that this reflection becomes allowed on\nthe Cu $L_3$ resonance for temperatures $T_c < T < T^\\ast$, though remains\nabsent in linear polarization and at other temperatures. This observation\nsuggests the existence of spatially uniform valence band order near the\npseudogap temperature. In addition, we observed periodic oscillations in the\nspecular reflectivity from the sample surface that resemble thin film\ninterference fringes, though no known film is present. These fringes are highly\nresonant, appear in all polarizations, and exhibit a period that depends on the\nlocation where the beam strikes the sample surface. We speculate that these\nfringes arise from interaction between some intrinsic valence band instability\nand extrinsic structural surface morphologies of the material. Our study\nsupports the existence of some kind of $Q \\sim 0$ broken symmetry state in\nBi-2212 at intermediate temperatures, and calls for further study using a\nmicrofocused beam that could disentangle microscopic effects from macroscopic\nheterogeneities.\n"}
+{"abstract": "  We reveal the importance of ongoing in-situ star formation in the Brightest\nCluster Galaxy in the massive cool-core CLASH cluster MACS 1931.8-2635 at\nz=0.35. Using a multi-wavelength approach, we assess the stellar and warm\nionized medium components, spatially resolved by the VLT-MUSE spectroscopy, and\nlink them to the molecular gas by incorporating sub-mm ALMA observations. We\nmeasure the fluxes of strong emission lines, allowing us to determine the\nphysical conditions of the warm ionized gas. The ionized gas flux brightness\npeak corresponds to the location of the supermassive black hole and the system\nshows a diffuse warm ionized gas tail extending 30 kpc in N-E direction. The\nionized and molecular gas are co-spatial and co-moving, with the gaseous\ncomponent in the tail falling inward, providing fuel for star formation and\naccretion-powered nuclear activity. The gas is ionized by a mix of star\nformation and other energetic processes which give rise to LINER-like emission,\nwith active galactic nuclei emission dominant only in the BCG core. We measure\na star formation rate of 97 Msun/yr, with its peak at the BCG core. However,\nstar formation accounts for only 50-60% of the energetics needed to ionize the\nwarm gas. In situ star formation generated by thermally unstable intracluster\nmedium cooling and/or dry mergers dominate the stellar mass growth at z<0.5 and\nthese mechanisms account for the build-up of 20% of the mass of the system. Our\nmeasurements reveal that the most central regions of the BCG contain the lowest\ngas phase oxygen abundance, whereas the tail exhibits slightly more elevated\nvalues. The galaxy is a dispersion dominated system, typical for massive,\nelliptical galaxies. The gas and stellar kinematics are decoupled, with the\ngaseous velocity fields being more closely related to the bulk motions of the\nintracluster medium.\n"}
+{"abstract": "  We uncover a form of quantum contextuality that connects maximal\ncontextuality to boson indistinguihability in a similar way maximal nonlocality\nwith respect to the Clauser-Horne-Shimony-Holt Bell inequality is connected to\nmaximal entanglement. Unlike previous forms of photonic contextuality, this\nform cannot be simulated with classical light, as it relies on\nindistinguishability and higher-order interference. Ideal measurements on the\nbosonic system can be performed by means of dispersive coupling with an\nancillary qubit. This allows us delaying at will the ending of each measurement\nand targeting high-dimensional contextual correlations, which are features\nwhich cannot be achieved with existing platforms.\n"}
+{"abstract": "  In this study, we develop a probabilistic approach to map the parametric\nuncertainty to the output state uncertainty in first-order hyperbolic\nconservation laws. We analyze this problem for nonlinear immiscible two-phase\ntransport in heterogeneous porous media in the presence of a stochastic\nvelocity field. The uncertainty in the velocity field can arise from the\nincomplete description of either porosity field, injection flux, or both. The\nuncertainty in the total-velocity field leads to the spatiotemporal uncertainty\nin the saturation field. Given information about the spatial/temporal\nstatistics of the correlated heterogeneity, we leverage method of distributions\nto derive deterministic equations that govern the evolution of single-point CDF\nof saturation. Unlike Buckley Leverett equation, the equation for the raw CDF\nfunction is linear in space and time. Hereby, we give routes to circumventing\nthe computational cost of Monte Carlo scheme while obtaining the full\nstatistical description of saturation. We conduct a set of numerical\nexperiments and compare statistics of saturation computed with the method of\ndistributions, against those obtained using the statistical moment equations\napproach and kernel density estimation post-processing of high-resolution Monte\nCarlo simulations. This comparison demonstrates that the CDF equations remain\naccurate over a wide range of statistical properties, i.e. standard deviation\nand correlation length of the underlying random fields, while the corresponding\nlow-order statistical moment equations significantly deviate from Monte Carlo\nresults, unless for very small values of standard deviation and correlation\nlength.\n"}
+{"abstract": "  Data poisoning has been proposed as a compelling defense against facial\nrecognition models trained on Web-scraped pictures. By perturbing the images\nthey post online, users can fool models into misclassifying future\n(unperturbed) pictures. We demonstrate that this strategy provides a false\nsense of security, as it ignores an inherent asymmetry between the parties:\nusers' pictures are perturbed once and for all before being published (at which\npoint they are scraped) and must thereafter fool all future models -- including\nmodels trained adaptively against the users' past attacks, or models that use\ntechnologies discovered after the attack. We evaluate two systems for poisoning\nattacks against large-scale facial recognition, Fawkes (500,000+ downloads) and\nLowKey. We demonstrate how an \"oblivious\" model trainer can simply wait for\nfuture developments in computer vision to nullify the protection of pictures\ncollected in the past. We further show that an adversary with black-box access\nto the attack can (i) train a robust model that resists the perturbations of\ncollected pictures and (ii) detect poisoned pictures uploaded online. We\ncaution that facial recognition poisoning will not admit an \"arms race\" between\nattackers and defenders. Once perturbed pictures are scraped, the attack cannot\nbe changed so any future successful defense irrevocably undermines users'\nprivacy.\n"}
+{"abstract": "  The complex unfolding of the US opioid epidemic in the last 20 years has been\nthe subject of a large body of medical and pharmacological research, and it has\nsparked a multidisciplinary discussion on how to implement interventions and\npolicies to effectively control its impact on public health. This study\nleverages Reddit as the primary data source to investigate the opioid crisis.\nWe aimed to find a large cohort of Reddit users interested in discussing the\nuse of opioids, trace the temporal evolution of their interest, and extensively\ncharacterize patterns of the nonmedical consumption of opioids, with a focus on\nroutes of administration and drug tampering. We used a semiautomatic\ninformation retrieval algorithm to identify subreddits discussing nonmedical\nopioid consumption, finding over 86,000 Reddit users potentially involved in\nfirsthand opioid usage. We developed a methodology based on word embedding to\nselect alternative colloquial and nonmedical terms referring to opioid\nsubstances, routes of administration, and drug-tampering methods. We modeled\nthe preferences of adoption of substances and routes of administration,\nestimating their prevalence and temporal unfolding, observing relevant trends\nsuch as the surge in synthetic opioids like fentanyl and an increasing interest\nin rectal administration. Ultimately, through the evaluation of odds ratios\nbased on co-mentions, we measured the strength of association between opioid\nsubstances, routes of administration, and drug tampering, finding evidence of\nunderstudied abusive behaviors like chewing fentanyl patches and dissolving\nbuprenorphine sublingually. We believe that our approach may provide a novel\nperspective for a more comprehensive understanding of nonmedical abuse of\nopioids substances and inform the prevention, treatment, and control of the\npublic health effects.\n"}
+{"abstract": "  We study the QCD Adler function in the energy region $\\approx 0.7-2.5$ GeV,\nin which the non-perturbative effects become dominant. Our analysis is a\nrenormalon-based evaluation using transseries within the resurgence of the\nRenormalization-Group-Equation and does not require the\nOperator-Product-Expansion.\n"}
+{"abstract": "  We present a proposal to relate the de Sitter Conjecture (dSC) to the\nCovariant Entropy Bound (CEB). By assuming an early phase of accelerated\nexpansion where the CEB is satisfied, we take into account a contribution from\nextra-dimensions to the four-dimensional entropy which restricts the values of\nthe usual slow-roll parameters. We show in this context that the dSC\ninequalities follow from the CEB -- including their mutual exclusion -- in both\nsingle and multi-field inflationary scenarios. We also observe that the order\none constants, c and c' in the conjecture are given in terms of physical\nquantities such as the change in entropy over time, the Hubble constant and the\ndynamics of the effective scalar fields. Finally, we give a simple example to\nillustrate a possible contribution to the four-dimensional entropy from a flux\nstring scenario.\n"}
+{"abstract": "  The Hall effect arises when time reversal symmetry is broken by either\nintrinsic magnetism or an external magnetic field. The latter contribution\ndominates in non-magnetic materials, in which the angular dependence of the\nHall effect is typically a smooth cosine function because only the out-of-plane\nprojection of the field generates the in-plane transverse motion of electrons.\nHere, we report the observation of an abrupt switching of the Hall effect by\nfield rotation in a non-magnetic material, ZrTe5. The angular dependence of the\nHall resistivity approaches a signum function, persisting down to an extremely\nlow field of 0.03 T. By varying the carrier density of ZrTe5 over three orders\nof magnitude, we show that this singular behavior is due to the anomalous Hall\neffect generated by the ultra-dilute massive Dirac carriers in the quantum\nlimit of Pauli paramagnetism when the Zeeman energy exceeds the Fermi energy.\nOur results elucidate the origin of the anomalous Hall effect in ZrTe5, arising\nowing to the spin-polarized massive Dirac electrons rather than the separation\nof Weyl nodes.\n"}
+{"abstract": "  The objective of the present paper is to study 4-dimensional almost pseudo\nRicci symmetric perfect fluid spacetimes $(APRS)_4$. We show that a\nRobertson-Walker spacetime is $(APRS)_4$ and vice versa under certain condition\nimposed on its scale factor. Some popular toy models of $F(R)$-gravity are also\nstudied under the current setting and various energy conditions are\ninvestigated.\n"}
+{"abstract": "  The recently discovered family of AV$_3$Sb$_5$ (A: K, Rb Cs) kagome metals\npossess a unique combination of nontrivial band topology, superconducting\nground states, and signatures of electron correlations manifest via competing\ncharge density wave order. Little is understood regarding the nature of the\ncharge density wave (CDW) instability inherent to these compounds and the\npotential correlation with the accompanying onset of a large anomalous Hall\nresponse. To understand the impact of the CDW order on the electronic structure\nin these systems, we present quantum oscillation measurements on single\ncrystals of CsV$_3$Sb$_5$. Our data provides direct evidence that the CDW\ninvokes a substantial reconstruction of the Fermi surface pockets associated\nwith the vanadium orbitals and the kagome lattice framework. In conjunction\nwith density functional theory modeling, we are able to identify split\noscillation frequencies originating from reconstructed pockets built from\nvanadium orbitals and Dirac-like bands. Complementary diffraction measurements\nare further able to demonstrate that the CDW instability has a correlated\nphasing between neighboring V$_3$Sb$_5$ planes. These results provide critical\ninsights into the underlying CDW instability in AV$_3$Sb$_5$ kagome metals and\nsupport minimal models of CDW order arising from within the vanadium-based\nkagome lattice.\n"}
+{"abstract": "  Detectors based upon the noble elements, especially liquid xenon as well as\nliquid argon, as both single- and dual-phase types, require reconstruction of\nthe energies of interacting particles, both in the field of direct detection of\ndark matter (Weakly Interacting Massive Particles or WIMPs, axions, etc.) and\nin neutrino physics. Experimentalists, as well as theorists who\nreanalyze/reinterpret experimental data, have used a few different techniques\nover the past few decades. In this paper, we review techniques based on solely\nthe primary scintillation channel, the ionization or secondary channel\navailable at non-zero drift electric fields, and combined techniques that\ninclude a simple linear combination and weighted averages, with a brief\ndiscussion of the applications of profile likelihood, maximum likelihood, and\nmachine learning. Comparing results for electron recoils (beta and gamma\ninteractions) and nuclear recoils (primarily from neutrons) from the Noble\nElement Simulation Technique (NEST) simulation to available data, we confirm\nthat combining all available information generates higher-precision means,\nlower widths (energy resolution), and more symmetric shapes (approximately\nGaussian) especially at keV-scale energies, with the symmetry even greater when\nthresholding is addressed. Near thresholds, bias from upward fluctuations\nmatters. For MeV-GeV scales, if only one channel is utilized, an\nionization-only-based energy scale outperforms scintillation; channel\ncombination remains beneficial. We discuss here what major collaborations use.\n"}
+{"abstract": "  We introduce Habitat 2.0 (H2.0), a simulation platform for training virtual\nrobots in interactive 3D environments and complex physics-enabled scenarios. We\nmake comprehensive contributions to all levels of the embodied AI stack - data,\nsimulation, and benchmark tasks. Specifically, we present: (i) ReplicaCAD: an\nartist-authored, annotated, reconfigurable 3D dataset of apartments (matching\nreal spaces) with articulated objects (e.g. cabinets and drawers that can\nopen/close); (ii) H2.0: a high-performance physics-enabled 3D simulator with\nspeeds exceeding 25,000 simulation steps per second (850x real-time) on an\n8-GPU node, representing 100x speed-ups over prior work; and, (iii) Home\nAssistant Benchmark (HAB): a suite of common tasks for assistive robots (tidy\nthe house, prepare groceries, set the table) that test a range of mobile\nmanipulation capabilities. These large-scale engineering contributions allow us\nto systematically compare deep reinforcement learning (RL) at scale and\nclassical sense-plan-act (SPA) pipelines in long-horizon structured tasks, with\nan emphasis on generalization to new objects, receptacles, and layouts. We find\nthat (1) flat RL policies struggle on HAB compared to hierarchical ones; (2) a\nhierarchy with independent skills suffers from 'hand-off problems', and (3) SPA\npipelines are more brittle than RL policies.\n"}
+{"abstract": "  The Tunka Radio Extension (Tunka-Rex) is a digital antenna array (63 antennas\ndistributed over 1km^2) co-located with the TAIGA observatory in Eastern\nSiberia. Tunka-Rex measures radio emission of air-showers induced by ultra-high\nenergy cosmic rays in the frequency band of 30-80 MHz. Air-shower signal is a\nshort (tens of nanoseconds) broadband pulse. Using time positions and\namplitudes of these pulses, we reconstruct parameters of air showers and\nprimary cosmic rays. The amplitudes of low-energy event (E<10^17 eV) cannot be\nused for successful reconstruction due to the domination of background. To\nlower the energy threshold of the detection and increase the efficiency, we use\nautoencoder neural network which removes noise from the measured data. This\nwork describes our approach to denoising raw data and further reconstruction of\nair-shower parameters. We also present results of the low-energy events\nreconstruction with autoencoder.\n"}
+{"abstract": "  We prove that iterating projections onto convex subsets of Hadamard spaces\ncan behave in a more complicated way than in Hilbert spaces, resolving a\nproblem formulated by Miroslav Ba\\v{c}\\'ak.\n"}
+{"abstract": "  We study vote delegation with \"well-behaving\" and \"misbehaving\" agents and\ncompare it with conventional voting. Typical examples for vote delegation are\nvalidation or governance tasks on blockchains. There is a majority of\nwell-behaving agents, but they may abstain or delegate their vote to other\nagents since voting is costly. Misbehaving agents always vote. We compare\nconventional voting allowing for abstention with vote delegation. Preferences\nof voters are private information and a positive outcome is achieved if\nwell-behaving agents win. We illustrate that vote delegation leads to quite\ndifferent outcomes than conventional voting with abstention. In particular, we\nobtain three insights: First, if the number of misbehaving voters, denoted by f\n, is high, both voting methods fail to deliver a positive outcome. Second, if f\ntakes an intermediate value, conventional voting delivers a positive outcome,\nwhile vote delegation fails with probability one. Third, if f is low,\ndelegation delivers a positive outcome with higher probability than\nconventional voting. Finally, our results characterize worst-case outcomes that\ncan happen in a liquid democracy.\n"}
+{"abstract": "  In this paper, we extend a framework that we developed earlier for\ncoordination of connected and automated vehicles (CAVs) at a signal-free\nintersection to incorporate uncertainty. Using the possibly noisy observations\nof actual time trajectories and leveraging Gaussian process regression, we\nlearn the bounded confidence intervals for deviations from the nominal\ntrajectories of CAVs online. Incorporating these confidence intervals, we\nreformulate the trajectory planning as a robust coordination problem, the\nsolution of which guarantees that constraints in the system are satisfied in\nthe presence of bounded deviations from the nominal trajectories. We\ndemonstrate the effectiveness of our extended framework through a numerical\nsimulation.\n"}
+{"abstract": "  A typical part of learning to play the piano is the progression through a\nseries of practice units that focus on individual dimensions of the skill, such\nas hand coordination, correct posture, or correct timing. Ideally, a focus on a\nparticular practice method should be made in a way to maximize the learner's\nprogress in learning to play the piano. Because we each learn differently, and\nbecause there are many choices for possible piano practice tasks and methods,\nthe set of practice tasks should be dynamically adapted to the human learner.\nHowever, having a human teacher guide individual practice is not always\nfeasible since it is time consuming, expensive, and not always available.\nInstead, we suggest to optimize in the space of practice methods, the so-called\npractice modes. The proposed optimization process takes into account the skills\nof the individual learner and their history of learning. In this work we\npresent a modeling framework to guide the human learner through the learning\nprocess by choosing practice modes that have the highest expected utility\n(i.e., improvement in piano playing skill). To this end, we propose a human\nlearner utility model based on a Gaussian process, and exemplify the model\ntraining and its application for practice scaffolding on an example of\nsimulated human learners.\n"}
+{"abstract": "  Model-informed precision dosing (MIPD) is a quantitative dosing framework\nthat combines prior knowledge on the drug-disease-patient system with patient\ndata from therapeutic drug/ biomarker monitoring (TDM) to support\nindividualized dosing in ongoing treatment.Structural models and prior\nparameter distributions used in MIPD approaches typically build on prior\nclinical trials that involve only a limited number of patients selected\naccording to some exclusion/inclusion criteria. Compared to the prior clinical\ntrial population, the patient population in clinical practice can be expected\nto include also altered behavior and/or increased interindividual variability,\nthe extent of which, however, is typically unknown. Here, we address the\nquestion of how to adapt and refine models on the level of the model parameters\nto better reflect this real-world diversity. We propose an approach for\ncontinued learning across patients during MIPD using a sequential hierarchical\nBayesian framework. The approach builds on two stages to separate the update of\nthe individual patient parameters from updating the population parameters.\nConsequently, it enables continued learning across hospitals or study centers,\nsince only summary patient data (on the level of model parameters) need to be\nshared, but no individual TDM data. We illustrate this continued learning\napproach with neutrophil-guided dosing of paclitaxel. The present study\nconstitutes an important step towards building confidence in MIPD and\neventually establishing MIPD increasingly in everyday therapeutic use.\n"}
+{"abstract": "  In recent years, chatbots have been empowered to engage in social\nconversations with humans and have the potential to elicit people to disclose\ntheir personal experiences, opinions, and emotions. However, how and to what\nextent people respond to chabots' self-disclosure remain less known. In this\nwork, we designed a social chatbot with three self-disclosure levels that\nconducted small talks and provided relevant recommendations to people. 372\nMTurk participants were randomized to one of the four groups with different\nself-disclosure levels to converse with the chatbot on two topics, movies, and\nCOVID-19. We found that people's self-disclosure level was strongly reciprocal\nto a chatbot's self-disclosure level. Chatbots' self-disclosure also positively\nimpacted engagement and users' perception of the bot and led to a more\neffective recommendation such that participants enjoyed and agreed more with\nthe recommendations.\n"}
+{"abstract": "  In this paper, we propose an RNN-Transducer model for recognizing Japanese\nand Chinese offline handwritten text line images. As far as we know, it is the\nfirst approach that adopts the RNN-Transducer model for offline handwritten\ntext recognition. The proposed model consists of three main components: a\nvisual feature encoder that extracts visual features from an input image by CNN\nand then encodes the visual features by BLSTM; a linguistic context encoder\nthat extracts and encodes linguistic features from the input image by embedded\nlayers and LSTM; and a joint decoder that combines and then decodes the visual\nfeatures and the linguistic features into the final label sequence by fully\nconnected and softmax layers. The proposed model takes advantage of both visual\nand linguistic information from the input image. In the experiments, we\nevaluated the performance of the proposed model on the two datasets: Kuzushiji\nand SCUT-EPT. Experimental results show that the proposed model achieves\nstate-of-the-art performance on all datasets.\n"}
+{"abstract": "  Large-scale quantum devices provide insights beyond the reach of classical\nsimulations. However, for a reliable and verifiable quantum simulation, the\nbuilding blocks of the quantum device require exquisite benchmarking. This\nbenchmarking of large scale dynamical quantum systems represents a major\nchallenge due to lack of efficient tools for their simulation. Here, we present\na scalable algorithm based on neural networks for Hamiltonian tomography in\nout-of-equilibrium quantum systems. We illustrate our approach using a model\nfor a forefront quantum simulation platform: ultracold atoms in optical\nlattices. Specifically, we show that our algorithm is able to reconstruct the\nHamiltonian of an arbitrary size quasi-1D bosonic system using an accessible\namount of experimental measurements. We are able to significantly increase the\npreviously known parameter precision.\n"}
+{"abstract": "  In this work we investigate the Wigner localization of two interacting\nelectrons at very low density in two and three dimensions using the exact\ndiagonalization of the many-body Hamiltonian. We use our recently developed\nmethod based on Clifford periodic boundary conditions with a renormalized\ndistance in the Coulomb potential. To accurately represent the electronic wave\nfunction we use a regular distribution in space of gaussian-type orbitals and\nwe take advantage of the translational symmetry of the system to efficiently\ncalculate the electronic wave function. We are thus able to accurately describe\nthe wave function up to very low density. We validate our approach by comparing\nour results to a semi-classical model that becomes exact in the low-density\nlimit. With our approach we are able to observe the Wigner localization without\nambiguity.\n"}
+{"abstract": "  Several novel imaging and non-destructive testing technologies are based on\nreconstructing the spatially dependent coefficient in an elliptic partial\ndifferential equation from measurements of its solution(s). In practical\napplications, the unknown coefficient is often assumed to be piecewise constant\non a given pixel partition (corresponding to the desired resolution), and only\nfinitely many measurements can be made. This leads to the problem of inverting\na finite-dimensional non-linear forward operator $\\mathcal F:\\ \\mathcal\nD(\\mathcal F)\\subseteq \\mathbb R^n\\to \\mathbb R^m$, where evaluating $\\mathcal\nF$ requires one or several PDE solutions.\n  Numerical inversion methods require the implementation of this forward\noperator and its Jacobian. We show how to efficiently implement both using a\nstandard FEM package and prove convergence of the FEM approximations against\ntheir true-solution counterparts. We present simple example codes for Comsol\nwith the Matlab Livelink package, and numerically demonstrate the challenges\nthat arise from non-uniqueness, non-linearity and instability issues. We also\ndiscuss monotonicity and convexity properties of the forward operator that\narise for symmetric measurement settings.\n"}
+{"abstract": "  Substantial improvements have been made in machine reading comprehension,\nwhere the machine answers questions based on a given context. Current\nstate-of-the-art models even surpass human performance on several benchmarks.\nHowever, their abilities in the cross-lingual scenario are still to be\nexplored. Previous works have revealed the abilities of pre-trained\nmultilingual models for zero-shot cross-lingual reading comprehension. In this\npaper, we further utilized unlabeled data to improve the performance. The model\nis first supervised-trained on source language corpus, and then self-trained\nwith unlabeled target language data. The experiment results showed improvements\nfor all languages, and we also analyzed how self-training benefits\ncross-lingual reading comprehension in qualitative aspects.\n"}
+{"abstract": "  In order to improve the energy efficiency in district heating networks and\nthe comfort of their customers, these networks need to overcome the problem of\nunfair heat distribution under heat deficits. This paper introduces a new\nstrategy to achieve this thermal fairness objective: it is low-cost in terms of\ncommunication and computation. The proposed approach is illustrated on a\nsimulation example.\n"}
+{"abstract": "  This paper presents an emotion-regularized conditional variational\nautoencoder (Emo-CVAE) model for generating emotional conversation responses.\nIn conventional CVAE-based emotional response generation, emotion labels are\nsimply used as additional conditions in prior, posterior and decoder networks.\nConsidering that emotion styles are naturally entangled with semantic contents\nin the language space, the Emo-CVAE model utilizes emotion labels to regularize\nthe CVAE latent space by introducing an extra emotion prediction network. In\nthe training stage, the estimated latent variables are required to predict the\nemotion labels and token sequences of the input responses simultaneously.\nExperimental results show that our Emo-CVAE model can learn a more informative\nand structured latent space than a conventional CVAE model and output responses\nwith better content and emotion performance than baseline CVAE and\nsequence-to-sequence (Seq2Seq) models.\n"}
+{"abstract": "  In this correspondence, we study the physical layer security in a stochastic\nunmanned aerial vehicles (UAVs) network from a network-wide perspective, where\nthe locations of UAVs are modeled as a Mat$\\acute{\\text{e}}$rn hard-core point\nprocess (MHCPP) to characterize the minimum safety distance between UAVs, and\nthe locations of users and eavesdroppers are modeled as a Poisson cluster\nprocess and a Poisson point process, respectively. UAVs adopt zero-forcing\nprecoding to serve multiple ground users and emit artificial noise to combat\neavesdropping. We derive the approximations for the coverage probability and\nsecrecy probability of a typical user, with which we derive the secrecy\nthroughput of the whole network. Numerical results show the analytical results\ncan well approximate the simulation results. Impacts of parameters on the\nsecrecy performance are shown.\n"}
+{"abstract": "  Distributed implementations are crucial in speeding up large scale machine\nlearning applications. Distributed gradient descent (GD) is widely employed to\nparallelize the learning task by distributing the dataset across multiple\nworkers. A significant performance bottleneck for the per-iteration completion\ntime in distributed synchronous GD is $straggling$ workers. Coded distributed\ncomputation techniques have been introduced recently to mitigate stragglers and\nto speed up GD iterations by assigning redundant computations to workers. In\nthis paper, we consider gradient coding (GC), and propose a novel dynamic GC\nscheme, which assigns redundant data to workers to acquire the flexibility to\ndynamically choose from among a set of possible codes depending on the past\nstraggling behavior. In particular, we consider GC with clustering, and\nregulate the number of stragglers in each cluster by dynamically forming the\nclusters at each iteration; hence, the proposed scheme is called $GC$ $with$\n$dynamic$ $clustering$ (GC-DC). Under a time-correlated straggling behavior,\nGC-DC gains from adapting to the straggling behavior over time such that, at\neach iteration, GC-DC aims at distributing the stragglers across clusters as\nuniformly as possible based on the past straggler behavior. For both\nhomogeneous and heterogeneous worker models, we numerically show that GC-DC\nprovides significant improvements in the average per-iteration completion time\nwithout an increase in the communication load compared to the original GC\nscheme.\n"}
+{"abstract": "  The choice of negative examples is important in noise contrastive estimation.\nRecent works find that hard negatives -- highest-scoring incorrect examples\nunder the model -- are effective in practice, but they are used without a\nformal justification. We develop analytical tools to understand the role of\nhard negatives. Specifically, we view the contrastive loss as a biased\nestimator of the gradient of the cross-entropy loss, and show both\ntheoretically and empirically that setting the negative distribution to be the\nmodel distribution results in bias reduction. We also derive a general form of\nthe score function that unifies various architectures used in text retrieval.\nBy combining hard negatives with appropriate score functions, we obtain strong\nresults on the challenging task of zero-shot entity linking.\n"}
+{"abstract": "  We demonstrate the design of a matterwave interferometer to measure\nacceleration in one dimension with high precision. The system we base this on\nconsists of ultracold atoms in an optical lattice potential created by\ninterfering laser beams. Our approach uses reinforcement learning, a branch of\nmachine learning, that generates the protocols needed to realize lattice-based\nanalogs of optical components including a beam splitter, a mirror, and a\nrecombiner. The performance of these components is evaluated by comparison with\ntheir optical analogs. The interferometer's sensitivity to acceleration is\nquantitatively evaluated using a Bayesian statistical approach. We find the\nsensitivity to surpass that of standard Bragg interferometry, demonstrating the\nfuture potential for this design methodology.\n"}
+{"abstract": "  Simultaneous translation, which starts translating each sentence after\nreceiving only a few words in source sentence, has a vital role in many\nscenarios. Although the previous prefix-to-prefix framework is considered\nsuitable for simultaneous translation and achieves good performance, it still\nhas two inevitable drawbacks: the high computational resource costs caused by\nthe need to train a separate model for each latency $k$ and the insufficient\nability to encode information because each target token can only attend to a\nspecific source prefix. We propose a novel framework that adopts a simple but\neffective decoding strategy which is designed for full-sentence models. Within\nthis framework, training a single full-sentence model can achieve arbitrary\ngiven latency and save computational resources. Besides, with the competence of\nthe full-sentence model to encode the whole sentence, our decoding strategy can\nenhance the information maintained in the decoded states in real time.\nExperimental results show that our method achieves better translation quality\nthan baselines on 4 directions: Zh$\\rightarrow$En, En$\\rightarrow$Ro and\nEn$\\leftrightarrow$De.\n"}
+{"abstract": "  Explainable machine learning (ML) has gained traction in recent years due to\nthe increasing adoption of ML-based systems in many sectors. Algorithmic\nRecourses (ARs) provide \"what if\" feedback of the form \"if an input datapoint\nwere x' instead of x, then an ML-based system's output would be y' instead of\ny.\" ARs are attractive due to their actionable feedback, amenability to\nexisting legal frameworks, and fidelity to the underlying ML model. Yet,\ncurrent AR approaches are single shot -- that is, they assume x can change to\nx' in a single time period. We propose a novel stochastic-control-based\napproach that generates sequential ARs, that is, ARs that allow x to move\nstochastically and sequentially across intermediate states to a final state x'.\nOur approach is model agnostic and black box. Furthermore, the calculation of\nARs is amortized such that once trained, it applies to multiple datapoints\nwithout the need for re-optimization. In addition to these primary\ncharacteristics, our approach admits optional desiderata such as adherence to\nthe data manifold, respect for causal relations, and sparsity -- identified by\npast research as desirable properties of ARs. We evaluate our approach using\nthree real-world datasets and show successful generation of sequential ARs that\nrespect other recourse desiderata.\n"}
+{"abstract": "  We study the effects of including a nonzero positron-to-electron fraction in\nemitting plasma on the polarized SEDs and sub-millimeter images of jet and\naccretion flow models for near-horizon emission from M87* and Sgr A*. For M87*,\nwe consider a semi-analytic fit to the force-free plasma regions of a general\nrelativistic magnetohydrodynamic jet simulation which we populate with\npower-law leptons with a constant electron-to-magnetic pressure ratio. For Sgr\nA*, we consider a standard self-similar radiatively inefficient accretion flow\nwhere the emission is predominantly from thermal leptons with a small fraction\nin a power-law tail. In both models, we fix the positron-to-electron ratio\nthroughout the emission region. We generate polarized images and spectra from\nour models using the general-relativistic ray tracing and radiative transfer\nfrom GRTRANS. We find that a substantial positron fraction reduces the circular\npolarization fraction at infrared and higher frequencies. However, in\nsub-millimeter images higher positron fractions increase polarization fractions\ndue to strong effects of Faraday conversion. We find a M87* jet model that best\nmatches the available broadband total intensity and 230 GHz polarization data\nis a sub-equipartition, with positron fraction of $\\simeq$ 10%. We show that\njet models with significant positron fractions do not satisfy the polarimetric\nconstraints at 230 GHz from the Event Horizon Telescope (EHT). Sgr A* models\nshow similar trends in their polarization fractions with increasing pair\nfraction. Both models suggest that resolved, polarized EHT images are useful to\nconstrain the presence of pairs at 230 GHz emitting regions of M87* and Sgr A*.\n"}
+{"abstract": "  Humans and other intelligent animals evolved highly sophisticated perception\nsystems that combine multiple sensory modalities. On the other hand,\nstate-of-the-art artificial agents rely mostly on visual inputs or structured\nlow-dimensional observations provided by instrumented environments. Learning to\nact based on combined visual and auditory inputs is still a new topic of\nresearch that has not been explored beyond simple scenarios. To facilitate\nprogress in this area we introduce a new version of VizDoom simulator to create\na highly efficient learning environment that provides raw audio observations.\nWe study the performance of different model architectures in a series of tasks\nthat require the agent to recognize sounds and execute instructions given in\nnatural language. Finally, we train our agent to play the full game of Doom and\nfind that it can consistently defeat a traditional vision-based adversary. We\nare currently in the process of merging the augmented simulator with the main\nViZDoom code repository. Video demonstrations and experiment code can be found\nat https://sites.google.com/view/sound-rl.\n"}
+{"abstract": "  In no-reference 360-degree image quality assessment (NR 360IQA), graph\nconvolutional networks (GCNs), which model interactions between viewports\nthrough graphs, have achieved impressive performance. However, prevailing\nGCN-based NR 360IQA methods suffer from three main limitations. First, they\nonly use high-level features of the distorted image to regress the quality\nscore, while the human visual system (HVS) scores the image based on\nhierarchical features. Second, they simplify complex high-order interactions\nbetween viewports in a pairwise fashion through graphs. Third, in the graph\nconstruction, they only consider spatial locations of viewports, ignoring its\ncontent characteristics. Accordingly, to address these issues, we propose an\nadaptive hypergraph convolutional network for NR 360IQA, denoted as AHGCN.\nSpecifically, we first design a multi-level viewport descriptor for extracting\nhierarchical representations from viewports. Then, we model interactions\nbetween viewports through hypergraphs, where each hyperedge connects two or\nmore viewports. In the hypergraph construction, we build a location-based\nhyperedge and a content-based hyperedge for each viewport. Experimental results\non two public 360IQA databases demonstrate that our proposed approach has a\nclear advantage over state-of-the-art full-reference and no-reference IQA\nmodels.\n"}
+{"abstract": "  We have grown single crystals of Na$_2$BaNi(PO$_4$)$_2$, a new spin-1\nequilateral triangular lattice antiferromagnet (ETLAF), and performed magnetic\nsusceptibility, specific heat and thermal conductivity measurements at ultralow\ntemperatures. The main results are (i) at zero magnetic field,\nNa$_2$BaNi(PO$_4$)$_2$ exhibits a magnetic ordering at 430 mK with a weak\nferromagnetic moment along the $c$ axis. This suggests a canted 120$^\\circ$\nspin structure, which is in a plane including the crystallographic $c$ axis due\nto the existence of an easy-axis anisotropy and ferromagnetically stacked along\nthe $c$ axis; (ii) with increasing field along the $c$ axis, a 1/3\nmagnetization plateau is observed which means the canted 120$^\\circ$ spin\nstructure is transformed to a up up down (UUD) spin structure. With even higher\nfields, the UUD phase further evolves to possible V and V' phases; (iii) with\nincreasing field along the $a$ axis, the canted 120$^\\circ$ spin structure is\npossibly transformed to a umbrella phase and a V phase. Therefore,\nNa$_2$BaNi(PO$_4$)$_2$ is a rare example of spin-1 ETLAF with single\ncrystalline form to exhibit easy-axis spin anisotropy and series of quantum\nspin state transitions.\n"}
+{"abstract": "  As the curation of data for machine learning becomes increasingly automated,\ndataset tampering is a mounting threat. Backdoor attackers tamper with training\ndata to embed a vulnerability in models that are trained on that data. This\nvulnerability is then activated at inference time by placing a \"trigger\" into\nthe model's input. Typical backdoor attacks insert the trigger directly into\nthe training data, although the presence of such an attack may be visible upon\ninspection. In contrast, the Hidden Trigger Backdoor Attack achieves poisoning\nwithout placing a trigger into the training data at all. However, this hidden\ntrigger attack is ineffective at poisoning neural networks trained from\nscratch. We develop a new hidden trigger attack, Sleeper Agent, which employs\ngradient matching, data selection, and target model re-training during the\ncrafting process. Sleeper Agent is the first hidden trigger backdoor attack to\nbe effective against neural networks trained from scratch. We demonstrate its\neffectiveness on ImageNet and in black-box settings. Our implementation code\ncan be found at https://github.com/hsouri/Sleeper-Agent.\n"}
+{"abstract": "  In this letter, we investigate the hybrid beamforming based on deep\nreinforcement learning (DRL) for millimeter Wave (mmWave) multi-user (MU)\nmultiple-input-single-output (MISO) system. A multi-agent DRL method is\nproposed to solve the exploration efficiency problem in DRL. In the proposed\nmethod, prioritized replay buffer and more informative reward are applied to\naccelerate the convergence. Simulation results show that the proposed\narchitecture achieves higher spectral efficiency and less time consumption than\nthe benchmarks, thus is more suitable for practical applications.\n"}
+{"abstract": "  The geometric structure of S-matrix encapsulated by the \"Amplituhedron\nprogram\" has begun to reveal itself even in non-supersymmetric quantum field\ntheories. Starting with the seminal work of Arkani-Hamed, Bai, He and Yan it is\nnow understood that for a wide class of scalar quantum field theories,\ntree-level amplitudes are canonical forms associated to polytopes known as\naccordiohedra. Similarly the higher loop scalar integrands are canonical forms\nassociated to so called type-D cluster polytopes for cubic interactions or\nrecently discovered class of polytopes termed pseudo-accordiohedron for higher\norder scalar interactions.\n  In this paper, we continue to probe the universality of these structures for\na wider class of scalar quantum field theories. More in detail, we discover new\nrealisations of the associahedron in planar kinematic space whose canonical\nforms generate (colour-ordered) tree-level S matrix of external massless\nparticles with $n-4$ massless poles and one massive pole at $m^{2}$. The\nresulting amplitudes are associated to $\\lambda_{1}\\, \\phi_{1}^{3}\\, +\\,\n\\lambda_{2}\\, \\phi_{1}^{2}\\phi_{2}$ potential where $\\phi_{1}$ and $\\phi_{2}$\nare massless and massive scalar fields with bi-adjoint colour indices\nrespectively. We also show how in the \"decoupling limit\" (where $m \\rightarrow\n\\infty, \\lambda_{2} \\rightarrow \\infty$ such that $g := \\frac{\\lambda_{2}}{m} =\n\\textrm{finite}$) these associahedra project onto a specific class of\naccordiohedron which are known to be positive geometries of amplitudes\ngenerated by $\\lambda \\phi_{1}^{3} + g \\phi_{1}^{4}$.\n"}
+{"abstract": "  Much recent research has been dedicated to improving the efficiency of\ntraining and inference for image classification. This effort has commonly\nfocused on explicitly improving theoretical efficiency, often measured as\nImageNet validation accuracy per FLOP. These theoretical savings have, however,\nproven challenging to achieve in practice, particularly on high-performance\ntraining accelerators.\n  In this work, we focus on improving the practical efficiency of the\nstate-of-the-art EfficientNet models on a new class of accelerator, the\nGraphcore IPU. We do this by extending this family of models in the following\nways: (i) generalising depthwise convolutions to group convolutions; (ii)\nadding proxy-normalized activations to match batch normalization performance\nwith batch-independent statistics; (iii) reducing compute by lowering the\ntraining resolution and inexpensively fine-tuning at higher resolution. We find\nthat these three methods improve the practical efficiency for both training and\ninference. Code available at\nhttps://github.com/graphcore/graphcore-research/tree/main/Making_EfficientNet_More_Efficient .\n"}
+{"abstract": "  We describe a quantum mechanical measurement as a variational principle\nincluding interaction between the system under measurement and the measurement\napparatus. Augmenting the action with a nonlocal term (a double integration\nover the duration of the interaction) results in a theory capable of describing\nboth the measurement process (agreement between system state and pointer state)\nand the collapse of both systems into a single eigenstate (or superposition of\ndegenerate eigenstates) of the relevant operator. In the absence of the\ninteraction, a superposition of states is stable, and the theory agrees with\nthe predictions of standard quantum theory. Because the theory is nonlocal, the\nresulting wave equation is an integrodifferential equation (IDE). We\ndemonstrate these ideas using a simple Lagrangian for both systems, as proof of\nprinciple. The variational principle is time-symmetric and retrocausal, so the\nsolution for the measurement process is determined by boundary conditions at\nboth initial and final times; the initial condition is determined by the\nexperimental preparation and the final condition is the natural boundary\ncondition of variational calculus. We hypothesize that one or more hidden\nvariables (not ruled out by Bell's Theorem, due both to the retrocausality and\nthe nonlocality of the theory) influence the outcome of the measurement, and\nthat distributions of the hidden variables that arise plausibly in a typical\nensemble of experimental realizations give rise to outcome frequencies\nconsistent with Born's rule. We outline steps in a theoretical validation of\nthe hypothesis. We discuss the role of both initial and final conditions to\ndetermine a solution at intermediate times, the mechanism by which a system\nresponds to measurement, time symmetry of the new theory, causality concerns,\nand issues surrounding solution of the IDE.\n"}
+{"abstract": "  We introduce a new class $\\mathcal{G}$ of bipartite plane graphs and prove\nthat each graph in $\\mathcal{G}$ admits a proper square contact representation.\nA contact between two squares is \\emph{proper} if they intersect in a line\nsegment of positive length. The class $\\mathcal{G}$ is the family of\nquadrangulations obtained from the 4-cycle $C_4$ by successively inserting a\nsingle vertex or a 4-cycle of vertices into a face.\n  For every graph $G\\in \\mathcal{G}$, we construct a proper square contact\nrepresentation. The key parameter of the recursive construction is the aspect\nratio of the rectangle bounded by the four outer squares. We show that this\naspect ratio may continuously vary in an interval $I_G$. The interval $I_G$\ncannot be replaced by a fixed aspect ratio, however, as we show, the feasible\ninterval $I_G$ may be an arbitrarily small neighborhood of any positive real.\n"}
+{"abstract": "  In this paper, we present a new ellipsoid-type algorithm for solving\nnonsmooth problems with convex structure. Examples of such problems include\nnonsmooth convex minimization problems, convex-concave saddle-point problems\nand variational inequalities with monotone operator. Our algorithm can be seen\nas a combination of the standard Subgradient and Ellipsoid methods. However, in\ncontrast to the latter one, the proposed method has a reasonable convergence\nrate even when the dimensionality of the problem is sufficiently large. For\ngenerating accuracy certificates in our algorithm, we propose an efficient\ntechnique, which ameliorates the previously known recipes.\n"}
+{"abstract": "  We consider an energy system with $n$ consumers who are linked by a Demand\nSide Management (DSM) contract, i.e. they agreed to diminish, at random times,\ntheir aggregated power consumption by a predefined volume during a predefined\nduration. Their failure to deliver the service is penalised via the difference\nbetween the sum of the $n$ power consumptions and the contracted target. We are\nled to analyse a non-zero sum stochastic game with $n$ players, where the\ninteraction takes place through a cost which involves a delay induced by the\nduration included in the DSM contract. When $n \\to \\infty$, we obtain a\nMean-Field Game (MFG) with random jump time penalty and interaction on the\ncontrol. We prove a stochastic maximum principle in this context, which allows\nto compare the MFG solution to the optimal strategy of a central planner. In a\nlinear quadratic setting we obtain an semi-explicit solution through a system\nof decoupled forward-backward stochastic differential equations with jumps,\ninvolving a Riccati Backward SDE with jumps. We show that it provides an\napproximate Nash equilibrium for the original $n$-player game for $n$ large.\nFinally, we propose a numerical algorithm to compute the MFG equilibrium and\npresent several numerical experiments.\n"}
+{"abstract": "  Reproducing Kernel Hilbert Space (RKHS) is the common mathematical platform\nfor various kernel methods in machine learning. The purpose of kernel learning\nis to learn an appropriate RKHS according to different machine learning\nscenarios and training samples. Because RKHS is uniquely generated by the\nkernel function, kernel learning can be regarded as kernel function learning.\nThis paper proposes a Domain Adaptive Learning method based on Sample-Dependent\nand Learnable Kernels (SDLK-DAL). The first contribution of our work is to\npropose a sample-dependent and learnable Positive Definite Quadratic Kernel\nfunction (PDQK) framework. Unlike learning the exponential parameter of\nGaussian kernel function or the coefficient of kernel combinations, the\nproposed PDQK is a positive definite quadratic function, in which the symmetric\npositive semi-definite matrix is the learnable part in machine learning\napplications. The second contribution lies on that we apply PDQK to Domain\nAdaptive Learning (DAL). Our approach learns the PDQK through minimizing the\nmean discrepancy between the data of source domain and target domain and then\ntransforms the data into an optimized RKHS generated by PDQK. We conduct a\nseries of experiments that the RKHS determined by PDQK replaces those in\nseveral state-of-the-art DAL algorithms, and our approach achieves better\nperformance.\n"}
+{"abstract": "  There are still several unanswered fundamental questions concerning our\nplanet and in particular, about the deep Earth, from where we lack direct\nsamples. Today, due to the progress in neutrino-detection techniques, a new and\nunique tool to study our planet exists: geoneutrinos, antineutrinos from the\ndecays of long-lived radioactive elements inside the Earth. In 2020, the\nBorexino experiment published its updated geoneutrino measurement. Thanks to\nthe increase in acquired data and the improved analysis techniques in an\nenlarged fiducial volume, the final precision has significantly improved,\ncorresponding to a total geoneutrino signal of 47.0$^{+8.4}_{-7.7}\\,({\\rm\nstat)}^{+2.4}_{-1.9}\\,({\\rm sys})$ TNU. The null-hypothesis of the geoneutrino\nsignal from the Earth's mantle was excluded at a 99% C.L. for the first time,\nwhile exploiting the knowledge of the local crust around the detector. The\narticle will introduce the field and describe the key elements of the updated\nanalysis. Geological interpretation and significance of the new result will be\ndiscussed in terms of the corresponding radiogenic heat and the limits to a\nhypothetical georeactor.\n"}
+{"abstract": "  This work presents an analysis of the Duckworth-Lewis-Stern (DLS) method for\nOne Day International (ODI) cricket matches. The accuracy of the DLS method is\ncompared against various supervised learning algorithms for result prediction.\nThe result of a cricket match is predicted during the second inning. The paper\nalso optimized DLS resource table which is used in the Duckworth-Lewis (D/L)\nformula to increase its predictive power. Finally, an Unpredictability Index is\ndeveloped that ranks different cricket playing nations according to how\nunpredictable they are while playing an ODI match.\n"}
+{"abstract": "  Plasmonic, as a rapidly growing research field, provides new pathways to\nguide and modulate highly confined light in the microwave to the optical range\nof frequencies. We demonstrate a plasmonic slot waveguide, at the nanometer\nscale, based on high transition temperature superconductor BSCCO, to\nfacilitates the manifestation of the chip-scale millimeter waves to terahertz\nintegrated circuitry operating at cryogenic temperatures. We investigate the\neffect of geometrical parameters on the modal characteristics of the BSCCO\nplasmonic slot waveguide between 100 GHz and 500 GHz. In addition, we\ninvestigate the thermal sensing of the modal characteristics of the nanoscale\nsuperconducting slot waveguide and show that at a lower frequency, the\nfundamental mode of the waveguide has a larger propagation length, a lower\neffective refractive index, and a strongly localized modal energy. Moreover, we\nfind that our device offers a larger SPP propagation length and higher field\nconfinement than the gold plasmonic waveguides at broad temperature ranges\nbelow BSCCO Tc. The proposed device can open up a new route towards realizing\ncryogenic low-loss photonic integrated circuitry at the nanoscale.\n"}
+{"abstract": "  In this work, we develop a fast hierarchical solver for solving large, sparse\nleast squares problems. We build upon the algorithm, spaQR (sparsified QR),\nthat was developed by the authors to solve large sparse linear systems. Our\nalgorithm is built on top of a Nested Dissection based multifrontal QR\napproach. We use low-rank approximations on the frontal matrices to sparsify\nthe vertex separators at every level in the elimination tree. Using a two-step\nsparsification scheme, we reduce the number of columns and maintain the ratio\nof rows to columns in each front without introducing any additional fill-in.\nWith this improvised scheme, we show that the runtime of the algorithm scales\nas $\\mathcal{O}(M \\log N)$ and uses $\\mathcal{O}(M)$ memory to store the\nfactorization. This is achieved at the expense of a small and controllable\napproximation error. The end result is an approximate factorization of the\nmatrix stored as a sequence of sparse orthogonal and upper-triangular factors\nand hence easy to apply/solve with a vector. Finally, we compare the\nperformance of the spaQR algorithm in solving sparse least squares problems\nwith direct multifrontal QR and CGLS iterative method with a standard diagonal\npreconditioner.\n"}
+{"abstract": "  Two-dimensional electron and hole gases separated by a few nm from each other\nare produced in p-i-n diodes based upon MBE-grown GaAs/AlGaAs heterostructures.\nAt such interlayer distances, the exciton formation and possibly Bose-Einstein\ncondensation (BEC) is expected. We measure the capacitance between the layers\nand find it to oscillate as a function of the bias voltage. The peak values\nexceed the geometric capacitance by up to a factor of two. The surprisingly\nregular periods of the oscillations are of the order of 10 to 30 mV and scale\nlinearly with the inverse of the thickness, between 60 and 150 nm, of the GaAs\nlayer placed between the barrier and the p-doped AlGaAs. The oscillations are\nrelated to the resonant electron tunneling into high energy levels of this GaAs\nlayer acting as a quantum well. We suggest that long lifetimes of the electrons\nin these levels are the origin of the large peak values of the capacitance. The\npossible relation of the capacitance oscillations with BEC is discussed.\n"}
+{"abstract": "  Significance: Histopathological analysis of tissues is an essential tool for\ngrading, staging, diagnosing and resecting cancers and other malignancies.\nCurrent histopathological techniques require substantial sample processing\nprior to staining with hematoxylin and eosin (H&E) dyes, to highlight nuclear\nand cellular morphology. Sample preparation and staining is resource-intensive\nand introduces potential for variability during sample preparation.\n  Aim: We present a novel method for direct label-free histopathological\nassessment of formalin fixed paraffin embedded tissue blocks and thin tissue\nsections using a dual contrast photoacoustic remote sensing (PARS) microscopy\nsystem.\n  Approach: To emulate the nuclear and cellular contrast of H&E staining, we\nleverage unique properties of the PARS system. Here the ultraviolet excitation\nof the PAARS microscope takes advantage of DNA's unique optical absorption to\nprovide nuclear contrast analogous to hematoxylin staining of cell nucelli.\nConcurrently, the optical scattering contrast of the PARS detection system is\nleveraged to provide bulk tissue contrast analogous to eosin staining of cell\nmembranes.\n  Results: We demonstrate the efficacy of this technique by imaging human\nbreast tissue and human skin tissues in formalin fixed paraffin embedded tissue\nblocks and frozen sections respectively. Salient nuclear and extra-nuclear\nfeatures such as cancerous cells, glands and ducts, adipocytes, and stromal\nstructures such as collagen.\n  Conclusions. The presented dual contrast PARS microscope enables label-free\nvisualizations of tissue with contrast and quality analogous to the current\ngold standard for histopathological analysis. Thus, the proposed system is well\npositioned to augment existing histopathological workflows, providing\nhistological imaging directly on unstained tissue blocks and sections.\n"}
+{"abstract": "  This paper presents a new evolutionary approach, EvoSplit, for the\ndistribution of multi-label data sets into disjoint subsets for supervised\nmachine learning. Currently, data set providers either divide a data set\nrandomly or using iterative stratification, a method that aims to maintain the\nlabel (or label pair) distribution of the original data set into the different\nsubsets. Following the same aim, this paper first introduces a single-objective\nevolutionary approach that tries to obtain a split that maximizes the\nsimilarity between those distributions independently. Second, a new\nmulti-objective evolutionary algorithm is presented to maximize the similarity\nconsidering simultaneously both distributions (labels and label pairs). Both\napproaches are validated using well-known multi-label data sets as well as\nlarge image data sets currently used in computer vision and machine learning\napplications. EvoSplit improves the splitting of a data set in comparison to\nthe iterative stratification following different measures: Label Distribution,\nLabel Pair Distribution, Examples Distribution, folds and fold-label pairs with\nzero positive examples.\n"}
+{"abstract": "  Alzheimer's disease (AD) is an irreversible, progressive neuro degenerative\ndisorder that slowly destroys memory and thinking skills and eventually, the\nability to carry out the simplest tasks. In this paper, a deep neural network\nbased prediction of AD from magnetic resonance images (MRI) is proposed. The\nstate of the art image classification networks like VGG, residual networks\n(ResNet) etc. with transfer learning shows promising results. Performance of\npre-trained versions of these networks are improved by transfer learning.\nResNet based architecture with large number of layers is found to give the best\nresult in terms of predicting different stages of the disease. The experiments\nare conducted on Kaggle dataset.\n"}
+{"abstract": "  Current radio frequency (RF) sensors at the Edge lack the computational\nresources to support practical, in-situ training for intelligent spectrum\nmonitoring, and sensor data classification in general. We propose a solution\nvia Deep Delay Loop Reservoir Computing (DLR), a processing architecture that\nsupports general machine learning algorithms on compact mobile devices by\nleveraging delay-loop reservoir computing in combination with innovative\nelectrooptical hardware. With both digital and photonic realizations of our\ndesign of the loops, DLR delivers reductions in form factor, hardware\ncomplexity and latency, compared to the State-of-the-Art (SoA). The main impact\nof the reservoir is to project the input data into a higher dimensional space\nof reservoir state vectors in order to linearly separate the input classes.\nOnce the classes are well separated, traditionally complex, power-hungry\nclassification models are no longer needed for the learning process. Yet, even\nwith simple classifiers based on Ridge regression (RR), the complexity grows at\nleast quadratically with the input size. Hence, the hardware reduction required\nfor training on compact devices is in contradiction with the large dimension of\nstate vectors. DLR employs a RR-based classifier to exceed the SoA accuracy,\nwhile further reducing power consumption by leveraging the architecture of\nparallel (split) loops. We present DLR architectures composed of multiple\nsmaller loops whose state vectors are linearly combined to create a lower\ndimensional input into Ridge regression. We demonstrate the advantages of using\nDLR for two distinct applications: RF Specific Emitter Identification (SEI) for\nIoT authentication, and wireless protocol recognition for IoT situational\nawareness.\n"}
+{"abstract": "  The paper has two relatively distinct but connected goals; the first is to\ndefine the notion of Pad\\'e\\ approximation of Weyl-Stiltjes transforms on an\narbitrary compact Riemann surface of higher genus. The data consists of a\ncontour in the Riemann surface and a measure on it, together with the\nadditional datum of a local coordinate near a point and a divisor of degree\n$g$. The denominators of the resulting Pad\\'e--like approximation also satisfy\nan orthogonality relation and are sections of appropriate line bundles. A\nRiemann--Hilbert problem for a square matrix of rank two is shown to\ncharacterize these orthogonal sections, in a similar fashion to the ordinary\northogonal polynomial case.\n  The second part extends this idea to explore its connection to integrable\nsystems. The same data can be used to define a pairing between two sequences of\nline bundles. The locus in the deformation space where the pairing becomes\ndegenerate for fixed degree coincides with the zeros of a \"tau\" function. We\nshow how this tau function satisfies the Kadomtsev--Petviashvili hierarchy with\nrespect to either deformation parameters, and a certain modification of the\n2--Toda hierarchy when considering the whole sequence of tau functions. We also\nshow how this construction is related to the Krichever construction of\nalgebro--geometric solutions.\n"}
+{"abstract": "  An unexpected explanation for neutrino mass, Dark Matter (DM) and Dark Energy\n(DE) from genuine Quantum Chromodynamics (QCD) of the Standard Model (SM) is\nproposed here, while the strong CP problem is resolved without any need to\naccount for fundamental axions. We suggest that the neutrino sector can be in a\ndouble phase in the Universe: i) relativistic neutrinos, belonging to the SM;\nii) non-relativistic condensate of Majorana neutrinos. The condensate of\nneutrinos can provide an attractive alternative candidate for the DM, being in\na cold coherent state. We will explain how neutrinos, combining into Cooper\npairs, can form collective low-energy degrees of freedom, hence providing a\nstrongly motivated candidate for the QCD (composite) axion.\n"}
+{"abstract": "  With the rapid adoption of Internet technologies, digital footprints have\nbecome ubiquitous and versatile to revolutionise the financial industry in\ndigital transformation. This paper takes initiatives to investigate a new\nparadigm of the unified credit assessment with the use of federated artificial\nintelligence. We conceptualised digital human representation which consists of\nsocial, contextual, financial and technological dimensions to assess the\ncommercial creditworthiness and social reputation of both banked and unbanked\nindividuals. A federated artificial intelligence platform is proposed with a\ncomprehensive set of system design for efficient and effective credit scoring.\nThe study considerably contributes to the cumulative development of financial\nintelligence and social computing. It also provides a number of implications\nfor academic bodies, practitioners, and developers of financial technologies.\n"}
+{"abstract": "  Operational forecasting centers are investing in decadal (1-10 year) forecast\nsystems to support long-term decision making for a more climate-resilient\nsociety. One method that has previously been employed is the Dynamic Mode\nDecomposition (DMD) algorithm - also known as the Linear Inverse Model - which\nfits linear dynamical models to data. While the DMD usually approximates\nnon-linear terms in the true dynamics as a linear system with random noise, we\ninvestigate an extension to the DMD that explicitly represents the non-linear\nterms as a neural network. Our weight initialization allows the network to\nproduce sensible results before training and then improve the prediction after\ntraining as data becomes available. In this short paper, we evaluate the\nproposed architecture for simulating global sea surface temperatures and\ncompare the results with the standard DMD and seasonal forecasts produced by\nthe state-of-the-art dynamical model, CFSv2.\n"}
+{"abstract": "  The augmented Iwasawa algebra of a p-adic Lie group is a generalisation of\nthe Iwasawa algebra of a compact p-adic Lie group. We prove that a\nsplit-semisimple group over a p-adic field has a coherent augmented Iwasawa\nalgebra if and only if its root system is of rank one. We also characterise\nwhen certain solvable p-adic Lie groups have a coherent augmented Iwasawa\nalgebra.\n"}
+{"abstract": "  An information cascade is a circumstance where agents make decisions in a\nsequential fashion by following other agents. Bikhchandani et al., predict that\nonce a cascade starts it continues, even if it is wrong, until agents receive\nan external input such as public information. In an information cascade, even\nif an agent has its own personal choice, it is always overridden by observation\nof previous agents' actions. This could mean agents end up in a situation where\nthey may act without valuing their own information. As information cascades can\nhave serious social consequences, it is important to have a good understanding\nof what causes them. We present a detailed Bayesian model of the information\ngained by agents when observing the choices of other agents and their own\nprivate information. Compared to prior work, we remove the high impact of the\nfirst observed agent's action by incorporating a prior probability distribution\nover the information of unobserved agents and investigate an alternative model\nof choice to that considered in prior work: weighted random choice. Our results\nshow that, in contrast to Bikhchandani's results, cascades will not necessarily\noccur and adding prior agents' information will delay the effects of cascades.\n"}
+{"abstract": "  The modern Very Long Baseline Interferometry (VLBI) relativistic delay model,\nas documented in the IERS Conventions refers to the time epoch when the signal\npasses one of two stations of an interferometer baseline (selected arbitrarily\nfrom the pair of stations and called the 'reference station', or 'station 1').\nThis model consists of the previous correlation procedure used before the year\n2002. However, since 2002 a new correlation procedure that produces the VLBI\ngroup delays referring to the time epoch of signal passage at the geocenter has\nbeen used. A corresponding correction to the conventional VLBI model delay has\nto be introduced. However, this correction has not been thoroughly presented in\npeer reviewed journals, and different approaches are used at the correlators to\ncalculate the final group delays officially published in the IVS database. This\nmay cause an inconsistency up to 6 ps for ground-based VLBI experiments between\nthe group delay obtained by the correlator and the geometrical model delay from\nthe IERS Conventions used in data analysis software. Moreover, a miscalculation\nof the signal arrival moment to the 'reference station' could result a larger\nmodelling error (up to 50 ps). The paper presents the justification of the\ncorrection due to transition between two epochs elaborated from the Lorentz\ntransformation, and the approach to model the uncertainty of the calculation of\nthe signal arrival moment. The both changes are particularly essential for\nupcoming broadband technology geodetic VLBI observations.\n"}
+{"abstract": "  Imputing missing values is an important preprocessing step in data analysis,\nbut the literature offers little guidance on how to choose between different\nimputation models. This letter suggests adopting the imputation model that\ngenerates a density of imputed values most similar to those of the observed\nvalues for an incomplete variable after balancing all other covariates. We\nrecommend stable balancing weights as a practical approach to balance\ncovariates whose distribution is expected to differ if the values are not\nmissing completely at random. After balancing, discrepancy statistics can be\nused to compare the density of imputed and observed values. We illustrate the\napplication of the suggested approach using simulated and real-world survey\ndata from the American National Election Study, comparing popular imputation\napproaches including random forests, hot-deck, predictive mean matching, and\nmultivariate normal imputation. An R package implementing the suggested\napproach accompanies this letter.\n"}
+{"abstract": "  Radio halos are synchrotron diffuse sources at the centre of a fraction of\ngalaxy clusters. The study of large samples of clusters with adequate radio and\nX-ray data is necessary to investigate the origin of radio halos and their\nconnection with the cluster dynamics and formation history. The aim of this\npaper is to compile a well-selected sample of galaxy clusters with deep radio\nobservations to perform an unbiased statistical study of the properties of\nradio halos. We selected 75 clusters with M > = 6e14 Msun at z=0.08-0.33 from\nthe Planck Sunyaev-Zel'dovich catalogue. Clusters without suitable radio data\nwere observed with the Giant Metrewave Radio Telescope (GMRT) and/or the Jansky\nVery Large Array (JVLA) to complete the information about the possible presence\nof diffuse emission. We used archival Chandra X-ray data to derive information\non the clusters' dynamical states. This observational campaign led to the\ndetection of several cluster-scale diffuse radio sources and candidates that\ndeserve future follow-up observations. Here we summarise their properties and\nadd information resulting from our new observations. For the clusters where we\ndid not detect any hint of diffuse emission, we derived new upper limits to\ntheir diffuse flux. We have built the largest mass-selected (> 80 per cent\ncomplete in mass) sample of galaxy clusters with deep radio observations\navailable to date. The statistical analysis of the sample, which includes the\nconnection between radio halos and cluster mergers, the radio power-mass\ncorrelation, and the occurrence of radio halos as a function of the cluster\nmass, will be presented in paper II.\n"}
+{"abstract": "  Travel restrictions have often been used as a measure to combat the spread of\ndisease -- in particular, they have been extensively applied in 2020 against\ncoronavirus disease 2019 (COVID-19). How to best restrict travel, however, is\nunclear. Most studies and policies simply constrain the distance r individuals\nmay travel from their home or neighbourhood. However, the epidemic risk is\nrelated not only to distance travelled, but also to frequency of contacts,\nwhich is proxied by the frequency f with which individuals revisit locations\nover a given reference period. Inspired by recent literature that uncovers a\nclear universality pattern on how r and f interact in routine human mobility,\nthis paper addresses the following question: does this universal relation\nbetween r and f carry over to epidemic spreading, so that the risk associated\nwith human movement can be modeled by a single, unifying variable r * f? To\nanswer this question, we use two large-scale datasets of individual human\nmobility to simulate disease spread. Results show that a universal relation\nbetween r and f indeed exists in the context of epidemic spread: in both of the\ndatasets, the final size and the spatial distribution of the infected\npopulation depends on the product r * f more directly than on the individual\nvalues of r and f. The important implication here is that restricting r (where\nyou can go), but not f (how frequently), could be unproductive: high frequency\ntrips to nearby locations can be as dangerous for disease spread as low\nfrequency trips to distant locations. This counter-intuitive discovery could\nexplain the modest effectiveness of distance-based travel restrictions and\ncould inform future policies on COVID-19 and other epidemics.\n"}
+{"abstract": "  The conventionally independent power, water, and heating networks are\nbecoming more tightly connected, which motivates their joint optimal energy\nscheduling to improve the overall efficiency of an integrated energy system.\nHowever, such a joint optimization is known as a challenging problem with\ncomplex network constraints and couplings of electric, hydraulic, and thermal\nmodels that are nonlinear and nonconvex. We formulate an optimal\npower-water-heat flow (OPWHF) problem and develop a computationally efficient\nheuristic to solve it. The proposed heuristic decomposes OPWHF into\nsubproblems, which are iteratively solved via convex relaxation and\nconvex-concave procedure. Simulation results validate that the proposed\nframework can improve operational flexibility and social welfare of the\nintegrated system, wherein the water and heating networks respond as virtual\nenergy storage to time-varying energy prices and solar photovoltaic generation.\nMoreover, we perform sensitivity analysis to compare two modes of heating\nnetwork control: by flow rate and by temperature. Our results reveal that the\nlatter is more effective for heating networks with a wider space of pipeline\nparameters.\n"}
+{"abstract": "  There are several proofs now for the stability of Toom's example of a\ntwo-dimensional stable cellular automaton and its application to fault-tolerant\ncomputation. Simon and Berman simplified and strengthened Toom's original\nproof: the present report is a simplified exposition of their proof.\n"}
+{"abstract": "  We study the problem of recovering a collection of $n$ numbers from the\nevaluation of $m$ power sums. This yields a system of polynomial equations,\nwhich can be underconstrained ($m < n$), square ($m = n$), or overconstrained\n($m > n$). Fibers and images of power sum maps are explored in all three\nregimes, and in settings that range from complex and projective to real and\npositive. This involves surprising deviations from the B\\'ezout bound, and the\nrecovery of vectors from length measurements by $p$-norms.\n"}
+{"abstract": "  Malicious bots make up about a quarter of all traffic on the web, and degrade\nthe performance of personalization and recommendation algorithms that operate\non e-commerce sites. Positive-Unlabeled learning (PU learning) provides the\nability to train a binary classifier using only positive (P) and unlabeled (U)\ninstances. The unlabeled data comprises of both positive and negative classes.\nIt is possible to find labels for strict subsets of non-malicious actors, e.g.,\nthe assumption that only humans purchase during web sessions, or clear\nCAPTCHAs. However, finding signals of malicious behavior is almost impossible\ndue to the ever-evolving and adversarial nature of bots. Such a set-up\nnaturally lends itself to PU learning. Unfortunately, standard PU learning\napproaches assume that the labeled set of positives are a random sample of all\npositives, this is unlikely to hold in practice. In this work, we propose two\nmodifications to PU learning that make it more robust to violations of the\nselected-completely-at-random assumption, leading to a system that can filter\nout malicious bots. In one public and one proprietary dataset, we show that\nproposed approaches are better at identifying humans in web data than standard\nPU learning methods.\n"}
+{"abstract": "  Optimization algorithms such as projected Newton's method, FISTA, mirror\ndescent and its variants enjoy near-optimal regret bounds and convergence\nrates, but suffer from a computational bottleneck of computing \"projections''\nin potentially each iteration (e.g., $O(T^{1/2})$ regret of online mirror\ndescent). On the other hand, conditional gradient variants solve a linear\noptimization in each iteration, but result in suboptimal rates (e.g.,\n$O(T^{3/4})$ regret of online Frank-Wolfe). Motivated by this trade-off in\nruntime v/s convergence rates, we consider iterative projections of close-by\npoints over widely-prevalent submodular base polytopes $B(f)$. We develop a\ntoolkit to speed up the computation of projections using both discrete and\ncontinuous perspectives. We subsequently adapt the away-step Frank-Wolfe\nalgorithm to use this information and enable early termination. For the special\ncase of cardinality based submodular polytopes, we improve the runtime of\ncomputing certain Bregman projections by a factor of $\\Omega(n/\\log(n))$. Our\ntheoretical results show orders of magnitude reduction in runtime in\npreliminary computational experiments.\n"}
+{"abstract": "  Human Activity Recognition (HAR) is considered a valuable research topic in\nthe last few decades. Different types of machine learning models are used for\nthis purpose, and this is a part of analyzing human behavior through machines.\nIt is not a trivial task to analyze the data from wearable sensors for complex\nand high dimensions. Nowadays, researchers mostly use smartphones or smart home\nsensors to capture these data. In our paper, we analyze these data using\nmachine learning models to recognize human activities, which are now widely\nused for many purposes such as physical and mental health monitoring. We apply\ndifferent machine learning models and compare performances. We use Logistic\nRegression (LR) as the benchmark model for its simplicity and excellent\nperformance on a dataset, and to compare, we take Decision Tree (DT), Support\nVector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN).\nAdditionally, we select the best set of parameters for each model by grid\nsearch. We use the HAR dataset from the UCI Machine Learning Repository as a\nstandard dataset to train and test the models. Throughout the analysis, we can\nsee that the Support Vector Machine performed (average accuracy 96.33%) far\nbetter than the other methods. We also prove that the results are statistically\nsignificant by employing statistical significance test methods.\n"}
+{"abstract": "  Numerical solutions to the equation for advection are determined using\ndifferent finite-difference approximations and physics-informed neural networks\n(PINNs) under conditions that allow an analytical solution. Their accuracy is\nexamined by comparing them to the analytical solution. We used a machine\nlearning framework like PyTorch to implement PINNs. PINNs approach allows\ntraining neural networks while respecting the PDEs as a strong constraint in\nthe optimization as apposed to making them part of the loss function. In\nstandard small-scale circulation simulations, it is shown that the conventional\napproach incorporates a pseudo diffusive effect that is almost as large as the\neffect of the turbulent diffusion model; hence the numerical solution is\nrendered inconsistent with the PDEs. This oscillation causes inaccuracy and\ncomputational uncertainty. Of all the schemes tested, only the PINNs\napproximation accurately predicted the outcome. We assume that the PINNs\napproach can transform the physics simulation area by allowing real-time\nphysics simulation and geometry optimization without costly and time-consuming\nsimulations on large supercomputers.\n"}
+{"abstract": "  Distributional regression is extended to Gaussian response vectors of\ndimension greater than two by parameterizing the covariance matrix $\\Sigma$ of\nthe response distribution using the entries of its Cholesky decomposition. The\nmore common variance-correlation parameterization limits such regressions to\nbivariate responses -- higher dimensions require complicated constraints among\nthe correlations to ensure positive definite $\\Sigma$ and a well-defined\nprobability density function. In contrast, Cholesky-based parameterizations\nensure positive definiteness for all distributional dimensions no matter what\nvalues the parameters take, enabling estimation and regularization as for other\ndistributional regression models. In cases where components of the response\nvector are assumed to be conditionally independent beyond a certain lag $r$,\nmodel complexity can be further reduced by setting Cholesky parameters beyond\nthis lag to zero a priori. Cholesky-based multivariate Gaussian regression is\nfirst illustrated and assessed on artificial data and subsequently applied to a\nreal-world 10-dimensional weather forecasting problem. There the regression is\nused to obtain reliable joint probabilities of temperature across ten future\ntimes, leveraging temporal correlations over the prediction period to obtain\nmore precise and meteorologically consistent probabilistic forecasts.\n"}
+{"abstract": "  We introduce and motivate generative modeling as a central task for machine\nlearning and provide a critical view of the algorithms which have been proposed\nfor solving this task. We overview how generative modeling can be defined\nmathematically as trying to make an estimating distribution the same as an\nunknown ground truth distribution. This can then be quantified in terms of the\nvalue of a statistical divergence between the two distributions. We outline the\nmaximum likelihood approach and how it can be interpreted as minimizing\nKL-divergence. We explore a number of approaches in the maximum likelihood\nfamily, while discussing their limitations. Finally, we explore the alternative\nadversarial approach which involves studying the differences between an\nestimating distribution and a real data distribution. We discuss how this\napproach can give rise to new divergences and methods that are necessary to\nmake adversarial learning successful. We also discuss new evaluation metrics\nwhich are required by the adversarial approach.\n"}
+{"abstract": "  Complementary to the development of highly three-dimensional (3D) integrated\ncircuits in the continuation of Moore's law, there has been a growing interest\nin new 3D deformation strategies to improve device performance. To continue\nthis search for new 3D deformation techniques, it is essential to explore\nbeforehand - using computational predictive methods - which strain tensor leads\nto the desired properties. In this work, we study germanium (Ge) under an\nisotropic 3D strain on the basis of first-principle methods. The transport and\noptical properties are studied by a fully ab initio Boltzmann transport\nequation and many-body Bethe-Salpeter equation (BSE) approach, respectively.\nOur findings show that a direct band gap in Ge could be realized with only\n0.34% triaxial tensile strain (negative pressure) and without the challenges\nassociated with Sn doping. At the same time a significant increase in\nrefractive index and carrier mobility - particularly for electrons - is\nobserved. These results demonstrate that there is a huge potential in exploring\nthe 3D deformation space for semiconductors - and potentially many other\nmaterials - in order to optimize their properties.\n"}
+{"abstract": "  We describe a modulation-domain loss function for deep-learning-based speech\nenhancement systems. Learnable spectro-temporal receptive fields (STRFs) were\nadapted to optimize for a speaker identification task. The learned STRFs were\nthen used to calculate a weighted mean-squared error (MSE) in the modulation\ndomain for training a speech enhancement system. Experiments showed that adding\nthe modulation-domain MSE to the MSE in the spectro-temporal domain\nsubstantially improved the objective prediction of speech quality and\nintelligibility for real-time speech enhancement systems without incurring\nadditional computation during inference.\n"}
+{"abstract": "  The radio environment of the Moon at low frequencies, particularly in lunar\npolar regions and the permanently shadowed regions (PSR) found there, is\nrelatively poorly explored and may contain some novel features. In addition,\nthese areas of the Moon, shielded from the natural and artificial emissions of\nthe Earth, and the natural radio emissions of the Sun and the other planets,\nare near-ideal locations for radio astronomy observations in the last\nunexplored region of the electromagnetic frequency spectrum. We are developing\na low-mass RadioLuna radio science precursor mission to deploy an\ninterferometric array on the lunar surface using Space Initiatives \"Mote\"\npenetrometers. The current RadioLuna default mission would be an array of 10 -\n12 penetrators deployed on the floor of Shackleton crater in the PSR 4 km below\nthe crater rim, where it would be shielded from all terrestial, solar and\nplanetary interference. At the present level of understanding of the low\nfrequency lunar radio environment there will be a tight coupling between\nadvances in technology and advances in science, and RadioLuna can be expected\nto lead to improvements in both lunar radio science and lunar communication\ntechniques.\n"}
+{"abstract": "  We consider an optimal investment and risk control problem for an insurer\nunder the mean-variance (MV) criterion. By introducing a deterministic\nauxiliary process defined forward in time, we formulate an alternative\ntime-consistent problem related to the original MV problem, and obtain the\noptimal strategy and the value function to the new problem in closed-form. We\ncompare our formulation and optimal strategy to those under the precommitment\nand game-theoretic framework. Numerical studies show that, when the financial\nmarket is negatively correlated with the risk process, optimal investment may\ninvolve short selling the risky asset and, if that happens, a less risk averse\ninsurer short sells more risky asset.\n"}
+{"abstract": "  Social norms characterize collective and acceptable group conducts in human\nsociety. Furthermore, some social norms emerge from interactions of agents or\nhumans. To achieve agent autonomy and make norm satisfaction explainable, we\ninclude emotions into the normative reasoning process, which evaluate whether\nto comply or violate a norm. Specifically, before selecting an action to\nexecute, an agent observes the environment and infer the state and consequences\nwith its internal states after norm satisfaction or violation of a social norm.\nBoth norm satisfaction and violation provoke further emotions, and the\nsubsequent emotions affect norm enforcement. This paper investigates how\nmodeling emotions affect the emergence and robustness of social norms via\nsocial simulation experiments. We find that an ability in agents to consider\nemotional responses to the outcomes of norm satisfaction and violation (1)\npromote norm compliance; and (2) improve societal welfare.\n"}
+{"abstract": "  Light field imaging introduced the capability to refocus an image after\ncapturing. Currently there are two popular methods for refocusing,\nshift-and-sum and Fourier slice methods. Neither of these two methods can\nrefocus the light field in real-time without any pre-processing. In this paper\nwe introduce a machine learning based refocusing technique that is capable of\nextracting 16 refocused images with refocusing parameters of\n\\alpha=0.125,0.250,0.375,...,2.0 in real-time. We have trained our network,\nwhich is called RefNet, in two experiments. Once using the Fourier slice method\nas the training -- i.e., \"ground truth\" -- data and another using the\nshift-and-sum method as the training data. We showed that in both cases, not\nonly is the RefNet method at least 134x faster than previous approaches, but\nalso the color prediction of RefNet is superior to both Fourier slice and\nshift-and-sum methods while having similar depth of field and focus distance\nperformance.\n"}
+{"abstract": "  The analysis of environmental mixtures is of growing importance in\nenvironmental epidemiology, and one of the key goals in such analyses is to\nidentify exposures and their interactions that are associated with adverse\nhealth outcomes. Typical approaches utilize flexible regression models combined\nwith variable selection to identify important exposures and estimate a\npotentially nonlinear relationship with the outcome of interest. Despite this\nsurge in interest, no approaches to date can identify exposures and\ninteractions while controlling any form of error rates with respect to exposure\nselection. We propose two novel approaches to estimating the health effects of\nenvironmental mixtures that simultaneously 1) Estimate and provide valid\ninference for the overall mixture effect, and 2) identify important exposures\nand interactions while controlling the false discovery rate. We show that this\ncan lead to substantial power gains to detect weak effects of environmental\nexposures. We apply our approaches to a study of persistent organic pollutants\nand find that our approach is able to identify more interactions than existing\napproaches.\n"}
+{"abstract": "  Digital investigations of stealthy attacks on Android devices pose particular\nchallenges to incident responders. Whereas consequential late detection demands\naccurate and comprehensive forensic timelines to reconstruct all malicious\nactivities, reduced forensic footprints with minimal malware involvement, such\nas when Living-Off-the-Land (LOtL) tactics are adopted, leave investigators\nlittle evidence to work with. Volatile memory forensics can be an effective\napproach since app execution of any form is always bound to leave a trail of\nevidence in memory, even if perhaps ephemeral. Just-in-Time Memory Forensics\n(JIT-MF) is a recently proposed technique that describes a framework to process\nmemory forensics on existing stock Android devices, without compromising their\nsecurity by requiring them to be rooted. Within this framework, JIT-MF drivers\nare designed to promptly dump in-memory evidence related to app usage or\nmisuse. In this work, we primarily introduce a conceptualized presentation of\nJIT-MF drivers. Subsequently, through a series of case studies involving the\nhijacking of widely-used messaging apps, we show that when the target apps are\nforensically enhanced with JIT-MF drivers, investigators can generate richer\nforensic timelines to support their investigation, which are on average 26%\ncloser to ground truth.\n"}
+{"abstract": "  Here we develop an original approach to investigate the grand canonical\npartition function of the multicomponent mixtures of Boltzmann particles with\nhard-core interaction in finite and even small systems of the volumes above 20\nfm$^3$. The derived expressions of the induced surface tension equation of\nstate are analyzed in details. It is shown that the metastable states, which\ncan emerge in the finite systems with realistic interaction, appear at very\nhigh pressures at which the hadron resonance gas, most probably, is not\napplicable at all. It is shown how and under what conditions the obtained\nresults for finite systems can be generalized to include into a formalism the\nequation for curvature tension. The applicability range of the obtained\nequations of induced surface and curvature tensions for finite systems is\ndiscussed and their close relations to the equations of the morphological\nthermodynamics are established. The hadron resonance gas model on the basis of\nthe obtained advanced equation of state is worked out. Also, this model is\napplied to analyze the chemical freeze-out of hadrons and light nuclei with the\nnumber of (anti-)baryons not exceeding 4, including the most problematic ratios\nof hyper-triton and its antiparticle. Their multiplicities were measured by the\nALICE Collaboration in the central lead-lead collisions at the center-of-mass\nenergy $\\sqrt{s_{\\rm NN}} =$ 2.76 TeV.\n"}
+{"abstract": "  The Juno orbiter continues to collect data on Jupiter's gravity field with\nunprecedented precision since 2016, recently reporting a non-hydrostatic\ncomponent in the tidal response of the planet. At the mid-mission perijove 17,\nJuno registered a Love number $k_2=0.565\\pm0.006$ that is $-4\\pm1\\%$\n($1\\sigma$) from the theoretical hydrostatic $k_2^{(hs)}=0.590$. Here we assess\nwhether the aforementioned departure of tides from hydrostatic equilibrium\nrepresents the neglected gravitational contribution of dynamical tides. We\nemploy perturbation theory and simple tidal models to calculate a fractional\ndynamical correction $\\Delta k_2$ to the well-known hydrostatic $k_2$.\nExploiting the analytical simplicity of a toy uniform-density model, we show\nhow the Coriolis acceleration motivates the negative sign in the $\\Delta k_2$\nobserved by Juno. By simplifying Jupiter's interior into a core-less,\nfully-convective, and chemically-homogeneous body, we calculate $\\Delta k_2$ in\na model following an $n=1$ polytrope equation of state. Our numerical results\nfor the $n=1$ polytrope qualitatively follow the behaviour of the\nuniform-density model, mostly because the main component of the tidal flow is\nsimilar in each case. Our results indicate that the gravitational effect of the\nIo-induced dynamical tide leads to $\\Delta k_2=-4\\pm1\\%$, in agreement with the\nnon-hydrostatic component reported by Juno. Consequently, our results suggest\nthat Juno obtained the first unambiguous detection of the gravitational effect\nof dynamical tides in a gas giant planet. These results facilitate a future\ninterpretation of Juno tidal gravity data with the purpose of elucidating the\nexistence of a dilute core in Jupiter.\n"}
+{"abstract": "  Constraint-based environments such as configuration systems, recommender\nsystems, and scheduling systems support users in different decision making\nscenarios. These environments exploit a knowledge base for determining\nsolutions of interest for the user. The development and maintenance of such\nknowledge bases is an extremely time-consuming and error-prone task. Users\noften specify constraints which do not reflect the real-world. For example,\nredundant constraints are specified which often increase both, the effort for\ncalculating a solution and efforts related to knowledge base development and\nmaintenance. In this paper we present a new algorithm (CoreDiag) which can be\nexploited for the determination of minimal cores (minimal non-redundant\nconstraint sets). The algorithm is especially useful for distributed knowledge\nengineering scenarios where the degree of redundancy can become high. In order\nto show the applicability of our approach, we present an empirical study\nconducted with commercial configuration knowledge bases.\n"}
+{"abstract": "  Stellar-mass black holes can become embedded within the gaseous disks of\nactive galactic nuclei (AGNs). Afterwards, their interactions are mediated by\ntheir gaseous surroundings. In this work, we study the evolution of\nstellar-mass binary black holes (BBHs) embedded within AGN disks using a\ncombination of three-dimensional hydrodynamic simulations and analytic methods,\nfocusing on environments in which the AGN disk scale height $H$ is $\\gtrsim$\nthe BBH sphere of influence. We model the local surroundings of the embedded\nBBHs using a wind tunnel formalism and characterize different accretion regimes\nbased on the local properties of the disk, which range from wind-dominated to\nquasi-spherical. We use our simulations to develop prescriptions for mass\naccretion and drag for embedded BBHs. We use these prescriptions, along with\nAGN disk models that can represent the Toomre-unstable outer regions of AGN\ndisks, to study the long-term evolution of the BBHs as they migrate through the\ndisk. We find that BBHs typically merge within $\\lesssim 5-30\\,{\\rm Myr}$,\nincreasing their mass significantly in the process, allowing BBHs to enter (or\ncross) the pair-instability supernova mass gap. The rate at which gas is\nsupplied to these BBHs often exceeds the Eddington limit, sometimes by several\norders of magnitude. We conclude that most embedded BBHs will merge before\nmigrating significantly in the disk. Depending on the conditions of the ambient\ngas and the distance to the system, LISA can detect the transition between the\ngas-dominated and gravitational wave dominated regime for inspiraling BBHs that\nare formed sufficiently close to the AGN ($\\lesssim$ 0.1 pc). We also discuss\npossible electromagnetic signatures during and following the inspiral, finding\nthat it is generally unlikely but not inconceivable for the bolometric\nluminosity of the BBH to exceed that of the host AGN.\n"}
+{"abstract": "  Multi-label classification tasks such as OCR and multi-object recognition are\na major focus of the growing machine learning as a service industry. While many\nmulti-label prediction APIs are available, it is challenging for users to\ndecide which API to use for their own data and budget, due to the heterogeneity\nin those APIs' price and performance. Recent work shows how to select from\nsingle-label prediction APIs. However the computation complexity of the\nprevious approach is exponential in the number of labels and hence is not\nsuitable for settings like OCR. In this work, we propose FrugalMCT, a\nprincipled framework that adaptively selects the APIs to use for different data\nin an online fashion while respecting user's budget. The API selection problem\nis cast as an integer linear program, which we show has a special structure\nthat we leverage to develop an efficient online API selector with strong\nperformance guarantees. We conduct systematic experiments using ML APIs from\nGoogle, Microsoft, Amazon, IBM, Tencent and other providers for tasks including\nmulti-label image classification, scene text recognition and named entity\nrecognition. Across diverse tasks, FrugalMCT can achieve over 90% cost\nreduction while matching the accuracy of the best single API, or up to 8%\nbetter accuracy while matching the best API's cost.\n"}
+{"abstract": "  Radio frequency (RF) wireless energy transfer (WET) is a key technology that\nmay allow seamlessly powering future massive low-energy Internet of Things\n(IoT) networks. To enable efficient massive WET, channel state information\n(CSI)-limited/free multi-antenna transmit schemes have been recently proposed\nin the literature. The idea is to reduce/null the energy costs to be paid by\nenergy harvesting (EH) IoT nodes from participating in large-scale\ntime/power-consuming CSI training, but still enable some transmit spatial\ngains. In this paper, we take another step forward by proposing a novel\nCSI-free rotary antenna beamforming (RAB) WET scheme that outperforms all\nstate-of-the-art CSI-free schemes in a scenario where a power beacon (PB)\nequipped with a uniform linear array (ULA) powers a large set of surrounding EH\nIoT devices. RAB uses a properly designed CSI-free beamformer combined with a\ncontinuous or periodic rotation of the ULA at the PB to provide average EH\ngains that scale as $0.85\\sqrt{M}$, where $M$ is the number of PB's antenna\nelements. Moreover, a rotation-specific power control mechanism was proposed to\ni) fairly optimize the WET process if devices' positioning information is\navailable, and/or ii) to avoid hazards to human health in terms of specific\nabsorption rate (SAR). We show that RAB performance even approaches quickly (or\nsurpasses, for scenarios with sufficiently large number of EH devices, or when\nusing the proposed power control) the performance of a traditional full-CSI\nbased transmit scheme, and it is also less sensitive to SAR constraints.\nFinally, we discuss important practicalities related to RAB such as its\nrobustness against non line-of-sight conditions compared to other CSI-free WET\nschemes, and its generalizability to scenarios where the PB uses other than a\nULA topology.\n"}
+{"abstract": "  Understanding inter-site mutual mode interaction in coupled physical systems\nis essential to comprehend large compound systems as this local interaction\ndetermines the successive multiple inter-site energy transfer efficiency. We\ndemonstrate that only the non-Hermitian coupling can correctly account for the\nlight transfer between two coupled optical cavities. We also reveal that the\nnon-Hermitian coupling effect becomes much crucial as the system dimension gets\nsmaller. Our results provide an important insight to deal with general\ncoupled-devices in the quantum regime.\n"}
+{"abstract": "  Galaxy clusters are filled with hot, diffuse X-ray emitting plasma, with a\nstochastically tangled magnetic field whose energy is close to equipartition\nwith the energy of the turbulent motions \\cite{zweibel1997, Vacca}. In the\ncluster cores, the temperatures remain anomalously high compared to what might\nbe expected considering that the radiative cooling time is short relative to\nthe Hubble time \\cite{cowie1977,fabian1994}. While feedback from the central\nactive galactic nuclei (AGN) \\cite{fabian2012,birzan2012,churazov2000} is\nbelieved to provide most of the heating, there has been a long debate as to\nwhether conduction of heat from the bulk to the core can help the core to reach\nthe observed temperatures \\cite{narayan2001,ruszkowski2002,kunz2011}, given the\npresence of tangled magnetic fields. Interestingly, evidence of very sharp\ntemperature gradients in structures like cold fronts implies a high degree of\nsuppression of thermal conduction \\cite{markevitch2007}. To address the problem\nof thermal conduction in a magnetized and turbulent plasma, we have created a\nreplica of such a system in a laser laboratory experiment. Our data show a\nreduction of local heat transport by two orders of magnitude or more, leading\nto strong temperature variations on small spatial scales, as is seen in cluster\nplasmas \\cite{markevitch2003}.\n"}
+{"abstract": "  Humans are increasingly interacting with machines through language, sometimes\nin contexts where the user may not know they are talking to a machine (like\nover the phone or a text chatbot). We aim to understand how system designers\nand researchers might allow their systems to confirm its non-human identity. We\ncollect over 2,500 phrasings related to the intent of ``Are you a robot?\". This\nis paired with over 2,500 adversarially selected utterances where only\nconfirming the system is non-human would be insufficient or disfluent. We\ncompare classifiers to recognize the intent and discuss the precision/recall\nand model complexity tradeoffs. Such classifiers could be integrated into\ndialog systems to avoid undesired deception. We then explore how both a\ngenerative research model (Blender) as well as two deployed systems (Amazon\nAlexa, Google Assistant) handle this intent, finding that systems often fail to\nconfirm their non-human identity. Finally, we try to understand what a good\nresponse to the intent would be, and conduct a user study to compare the\nimportant aspects when responding to this intent.\n"}
+{"abstract": "  Unlike theoretical distributed learning (DL), DL over wireless edge networks\nfaces the inherent dynamics/uncertainty of wireless connections and edge nodes,\nmaking DL less efficient or even inapplicable under the highly dynamic wireless\nedge networks (e.g., using mmW interfaces). This article addresses these\nproblems by leveraging recent advances in coded computing and the deep dueling\nneural network architecture. By introducing coded structures/redundancy, a\ndistributed learning task can be completed without waiting for straggling\nnodes. Unlike conventional coded computing that only optimizes the code\nstructure, coded distributed learning over the wireless edge also requires to\noptimize the selection/scheduling of wireless edge nodes with heterogeneous\nconnections, computing capability, and straggling effects. However, even\nneglecting the aforementioned dynamics/uncertainty, the resulting joint\noptimization of coding and scheduling to minimize the distributed learning time\nturns out to be NP-hard. To tackle this and to account for the dynamics and\nuncertainty of wireless connections and edge nodes, we reformulate the problem\nas a Markov Decision Process and then design a novel deep reinforcement\nlearning algorithm that employs the deep dueling neural network architecture to\nfind the jointly optimal coding scheme and the best set of edge nodes for\ndifferent learning tasks without explicit information about the wireless\nenvironment and edge nodes' straggling parameters. Simulations show that the\nproposed framework reduces the average learning delay in wireless edge\ncomputing up to 66% compared with other DL approaches. The jointly optimal\nframework in this article is also applicable to any distributed learning scheme\nwith heterogeneous and uncertain computing nodes.\n"}
+{"abstract": "  Whereas low-temperature ferroelectrics have a well understood ordered spatial\ndipole arrangement, the fate of these dipoles in paraelectric phases remains\npoorly understood. Using density functional theory (DFT), we find that unlike\nthe case in conventional non-polar ABO$_3$ compounds illustrated here for cubic\nBaZrO$_3$, the origin of the distribution of the B site off-centering in cubic\nparaelectric such as BaTiO$_3$ is an intrinsic, energy stabilizing symmetry\nbreaking. Minimizing the internal energy E of a constrained cubic phase already\nreveals the formation of a distribution of intrinsic local displacements that\n(i) mimic the symmetries of the low temperature phases, while (ii) being the\nprecursors of what finite temperature DFT Molecular Dynamics finds as thermal\nmotifs. The implications of such symmetry breaking on the microscopic\nstructures and anomalous properties in these kinds of PE materials are\ndiscussed.\n"}
+{"abstract": "  The latest advances in computer-assisted precision medicine are making it\nfeasible to move from population-wide models that are useful to discover\naggregate patterns that hold for group-based analysis to patient-specific\nmodels that can drive patient-specific decisions with regard to treatment\nchoices, and predictions of outcomes of treatment. Body Composition is\nrecognized as an important driver and risk factor for a wide variety of\ndiseases, as well as a predictor of individual patient-specific clinical\noutcomes to treatment choices or surgical interventions. 3D CT images are\nroutinely acquired in the oncological worklows and deliver accurate rendering\nof internal anatomy and therefore can be used opportunistically to assess the\namount of skeletal muscle and adipose tissue compartments. Powerful tools of\nartificial intelligence such as deep learning are making it feasible now to\nsegment the entire 3D image and generate accurate measurements of all internal\nanatomy. These will enable the overcoming of the severe bottleneck that existed\npreviously, namely, the need for manual segmentation, which was prohibitive to\nscale to the hundreds of 2D axial slices that made up a 3D volumetric image.\nAutomated tools such as presented here will now enable harvesting whole-body\nmeasurements from 3D CT or MRI images, leading to a new era of discovery of the\ndrivers of various diseases based on individual tissue, organ volume, shape,\nand functional status. These measurements were hitherto unavailable thereby\nlimiting the field to a very small and limited subset. These discoveries and\nthe potential to perform individual image segmentation with high speed and\naccuracy are likely to lead to the incorporation of these 3D measures into\nindividual specific treatment planning models related to nutrition, aging,\nchemotoxicity, surgery and survival after the onset of a major disease such as\ncancer.\n"}
+{"abstract": "  We introduce a class of equivalences, which we call generalized semi-infinite\nHecke equivalences, between certain categories of representations of graded\nassociative algebras which appear in the setting of semi-infinite cohomology\nfor associative algebras and categories of representations of related algebras\nof Hecke type which we call semi-infinite Hecke algebras. As an application we\nobtain an equivalence between a category of representations of a non-twisted\naffine Lie algebra $\\widehat{\\mathfrak g}$ of level $-2h^\\vee-k$, where\n$h^\\vee$ is the dual Coxeter number of the underlying semisimple Lie algebra\n$\\mathfrak g$ and $k\\in \\mathbb{C}$, and the category of finitely generated\nrepresentations of the W-algebra associated to $\\widehat{\\mathfrak g}$ of level\n$k$. When $k=-h^\\vee$ this yields an equivalence between a category of\nrepresentations of $\\widehat{\\mathfrak g}$ of central charge $-h^\\vee$ and the\ncategory ${\\rm Coh}({\\rm Op}_{^LG}(D^\\times))$ of coherent sheaves on the space\n${\\rm Op}_{^LG}(D^\\times)$ of $^LG$-opers on the punctured disc $D^\\times$,\nwhere $^LG$ is the Langlands dual group to the algebraic group of adjoint type\nwith Lie algebra $\\mathfrak g$. This can be regarded as a version of the local\ngeometric Langlands correspondence. The above mentioned equivalences generalize\nto the case of affine Lie algebras the Skryabin equivalence between the\ncategories of generalized Gelfand-Graev representations of $\\mathfrak g$ and\nthe categories of representations of the corresponding finitely generated\nW-algebras, and Kostant's results on the classification of Whittaker modules\nover $\\mathfrak g$.\n"}
+{"abstract": "  Fish diseases in aquaculture constitute a significant hazard to nutriment\nsecurity. Identification of infected fishes in aquaculture remains challenging\nto find out at the early stage due to the dearth of necessary infrastructure.\nThe identification of infected fish timely is an obligatory step to thwart from\nspreading disease. In this work, we want to find out the salmon fish disease in\naquaculture, as salmon aquaculture is the fastest-growing food production\nsystem globally, accounting for 70 percent (2.5 million tons) of the market. In\nthe alliance of flawless image processing and machine learning mechanism, we\nidentify the infected fishes caused by the various pathogen. This work divides\ninto two portions. In the rudimentary portion, image pre-processing and\nsegmentation have been applied to reduce noise and exaggerate the image,\nrespectively. In the second portion, we extract the involved features to\nclassify the diseases with the help of the Support Vector Machine (SVM)\nalgorithm of machine learning with a kernel function. The processed images of\nthe first portion have passed through this (SVM) model. Then we harmonize a\ncomprehensive experiment with the proposed combination of techniques on the\nsalmon fish image dataset used to examine the fish disease. We have conveyed\nthis work on a novel dataset compromising with and without image augmentation.\nThe results have bought a judgment of our applied SVM performs notably with\n91.42 and 94.12 percent of accuracy, respectively, with and without\naugmentation.\n"}
+{"abstract": "  We calculate the neutrino signal from Population III supermassive star\ncollapse using a neutrino transfer code originally developed for core collapse\nsupernovae and massive star collapse. Using this code, we are able to\ninvestigate the supermassive star mass range thought to undergo neutrino\ntrapping ($\\sim 10^4$ M$_\\odot$), a mass range which has been neglected by\nprevious works because of the difficulty of neutrino transfer. For models in\nthis mass range, we observe a neutrino-sphere with a large radius and low\ndensity compared to typical massive star neutrino-spheres. We calculate the\nneutrino light-curve emitted from this neutrino-sphere. The resulting neutrino\nluminosity is significantly lower than the results of a previous analytical\nmodel. We briefly discuss the possibility of detecting a neutrino burst from a\nsupermassive star or the neutrino background from many supermassive stars and\nconclude that the former is unlikely with current technology, unless the SMS\ncollapse is located as close as 1 Mpc, while the latter is also unlikely even\nunder very generous assumptions. However, the supermassive star neutrino\nbackground is still of interest as it may serve as a source of noise in\nproposed dark matter direct detection experiments.\n"}
+{"abstract": "  In the present work, a novel class of hybrid elements is proposed to\nalleviate the locking anomaly in non-uniform rational B-spline (NURBS)-based\nisogeometric analysis (IGA) using a two-field Hellinger-Reissner variational\nprinciple. The proposed hybrid elements are derived by adopting the independent\ninterpolation schemes for displacement and stress field. The key highlight of\nthe present study is the choice and evaluation of higher-order terms for the\nstress interpolation function to provide a locking-free solution. Furthermore,\nthe present study demonstrates the efficacy of the proposed elements with the\ntreatment of several two-dimensional linear-elastic benchmark problems\nalongside the conventional single-field IGA, Lagrangian-based finite element\nanalysis (FEA), and hybrid FEA formulation. It is shown that the proposed class\nof hybrid elements performs effectively for analyzing the nearly incompressible\nproblem domains that are severely affected by volumetric locking along with the\nthin plate and shell problems where the shear and membrane locking is dominant.\nA better coarse mesh accuracy of the proposed method in comparison with the\nconventional formulation is demonstrated through various numerical examples.\nMoreover, the formulation is not restricted to the locking-dominated problem\ndomains but can also be implemented to solve the problems of general form\nwithout any special treatment. Thus, the proposed method is robust, most\nefficient, and highly effective against different types of locking.\n"}
+{"abstract": "  21-cm radio signal has emerged as an important probe in investigating the\ndark age of the Universe (recombination to reionization). In the current\nanalysis, we explore the combined effects of primordial black holes (PBH),\ncooling off of the baryonic matter due to dark matter (DM) - baryon collisions\nand interaction of dark matter - dark energy (DE) fluid on the 21-cm brightness\ntemperature. The variation of brightness temperature shows remarkable\ndependence on DM mass ($m_{\\chi}$) and the dark matter - baryon scattering\ncross-section ($\\sigma_0$). Bounds in $m_{\\chi}$ - $\\sigma_0$ parameter space\nare obtained for different possible PBH masses and for different interacting\ndark energy (IDE) models. These bounds are estimated based on the observed\nexcess ($-500^{+200}_{-500}$ mK) of 21-cm brightness temperature by EDGES\nexperiment. Eventually, bounds on PBH mass is also obtained for different\nvalues of dark matter mass and for different IDE model coupling parameters. The\ncompatibility of the constraints of the IDE models, in the estimated bounds are\nalso addressed.\n"}
+{"abstract": "  The $N$-qubit Greenberger-Horne-Zeilinger (GHZ) states are the maximally\nentangled states of $N$ qubits, which have had many important applications in\nquantum information processing, such as quantum key distribution and quantum\nsecret sharing. Thus how to distinguish the GHZ states from other quantum\nstates becomes a significant problem. In this work, by presenting a family of\nthe generalized Clauser-Horne-Shimony-Holt (CHSH) inequality, we show that the\n$N$-qubit GHZ states can be indeed identified by the maximal violations of the\ngeneralized CHSH inequality under some specific measurement settings. The\ngeneralized CHSH inequality is simple and contains only four correlation\nfunctions for any $N$-qubit system, thus has the merit of facilitating\nexperimental verification. Furthermore, we present a quantum phenomenon of\nrobust violations of the generalized CHSH inequality, in which the maximal\nviolation of Bell's inequality can be robust under some specific noises adding\nto the $N$-qubit GHZ states.\n"}
+{"abstract": "  We develop an effective version of the Chabauty--Kim method which gives\nexplicit upper bounds on the number of $S$-integral points on a hyperbolic\ncurve in terms of dimensions of certain Bloch--Kato Selmer groups. Using this,\nwe give a new \"motivic\" proof that the number of solutions to the $S$-unit\nequation is bounded uniformly in terms of $\\#S$.\n"}
+{"abstract": "  The tractability of certain CSPs for dense or sparse instances is known from\nthe 90s. Recently, the densification and the sparsification of CSPs were\nformulated as computational tasks and the systematical study of their\ncomputational complexity was initiated.\n  We approach this problem by introducing the densification operator, i.e. the\nclosure operator that, given an instance of a CSP, outputs all constraints that\nare satisfied by all of its solutions. According to the Galois theory of\nclosure operators, any such operator is related to a certain implicational\nsystem (or, a functional dependency) $\\Sigma$. We are specifically interested\nin those classes of fixed-template CSPs, parameterized by constraint languages\n$\\Gamma$, for which the size of an implicational system $\\Sigma$ is a\npolynomial in the number of variables $n$. We show that in the Boolean case,\n$\\Sigma$ is of polynomial size if and only if $\\Gamma$ is of bounded width. For\nsuch languages, $\\Sigma$ can be computed in log-space or in a logarithmic time\nwith a polynomial number of processors. Given an implicational system $\\Sigma$,\nthe densification task is equivalent to the computation of the closure of input\nconstraints. The sparsification task is equivalent to the computation of the\nminimal key. This leads to ${\\mathcal O}({\\rm poly}(n)\\cdot N^2)$-algorithm for\nthe sparsification task where $N$ is the number of non-redundant\nsparsifications of an original CSP.\n  Finally, we give a complete classification of constraint languages over the\nBoolean domain for which the densification problem is tractable.\n"}
+{"abstract": "  As the COVID-19 ravaging through the globe, accurate forecasts of the disease\nspread is crucial for situational awareness, resource allocation, and public\nhealth decision-making. Alternative to the traditional disease surveillance\ndata collected by the United States (US) Centers for Disease Control and\nPrevention (CDC), big data from Internet such as online search volumes has been\npreviously shown to contain valuable information for tracking infectious\ndisease dynamics. In this study, we evaluate the feasibility of using Internet\nsearch volume of relevant queries to track and predict COVID-19 pandemic. We\nfound strong association between COVID-19 death trend and the search volume of\nsymptom-related queries such as \"loss of taste\". Then, we further develop an\ninfluenza-tracking model to predict future 4-week COVID-19 deaths on the US\nnational level, by combining search volume information with COVID-19 time\nseries information. Encouraged by the 20% error reduction on national level\ncomparing to the baseline time series model, we additionally build state-level\nCOVID-19 deaths models, leveraging the cross-state cross-resolution spatial\ntemporal framework that pools information from search volume and COVID-19\nreports across states, regions and the nation. These variants of ARGOX are then\naggregated in a winner-takes-all ensemble fashion to produce the final\nstate-level 4-week forecasts. Numerical experiments demonstrate that our method\nsteadily outperforms time series baseline models, and achieves the\nstate-of-the-art performance among the publicly available benchmark models.\nOverall, we show that disease dynamics and relevant public search behaviors\nco-evolve during the COVID-19 pandemic, and capturing their dependencies while\nleveraging historical cases/deaths as well as spatial-temporal cross-region\ninformation will enable stable and accurate US national and state-level\nforecasts.\n"}
+{"abstract": "  Exploiting the inner-shot and inter-shot dependencies is essential for\nkey-shot based video summarization. Current approaches mainly devote to\nmodeling the video as a frame sequence by recurrent neural networks. However,\none potential limitation of the sequence models is that they focus on capturing\nlocal neighborhood dependencies while the high-order dependencies in long\ndistance are not fully exploited. In general, the frames in each shot record a\ncertain activity and vary smoothly over time, but the multi-hop relationships\noccur frequently among shots. In this case, both the local and global\ndependencies are important for understanding the video content. Motivated by\nthis point, we propose a Reconstructive Sequence-Graph Network (RSGN) to encode\nthe frames and shots as sequence and graph hierarchically, where the\nframe-level dependencies are encoded by Long Short-Term Memory (LSTM), and the\nshot-level dependencies are captured by the Graph Convolutional Network (GCN).\nThen, the videos are summarized by exploiting both the local and global\ndependencies among shots. Besides, a reconstructor is developed to reward the\nsummary generator, so that the generator can be optimized in an unsupervised\nmanner, which can avert the lack of annotated data in video summarization.\nFurthermore, under the guidance of reconstruction loss, the predicted summary\ncan better preserve the main video content and shot-level dependencies.\nPractically, the experimental results on three popular datasets i.e., SumMe,\nTVsum and VTW) have demonstrated the superiority of our proposed approach to\nthe summarization task.\n"}
+{"abstract": "  Recently discovered photonic higher-order topological insulators enable\nunprecedented flexibility in the robust localization of light in structures of\ndifferent dimensionality. While the potential of the two-dimensional systems is\ncurrently under active investigation, only a few studies explore the physics of\nthe three-dimensional higher-order topological insulators. Here we propose a\nthree-dimensional structure with cubic symmetry exhibiting vanishing bulk\npolarization but nonzero corner charge and hosting a zero-dimensional corner\nstate mediated by the long-range interactions. We trace the evolution of the\ncorner state with the next-nearest-neighbor coupling strength and prove the\ntopological origin of the corner mode calculating the associated topological\ninvariants. Our results thus reveal the potential of long-range couplings for\nthe formation of three-dimensional higher-order topological phases.\n"}
+{"abstract": "  World has been introduced with a new way of living by inventing this advanced\ntechnology Internet of Things (IOT). By using this technology all the\nreal-world objects can be made to interconnect and communicate with each other\nwhich can be done by diagnosing, sensing and networking over the internet to\nfulfill some objectives. Due to advancements that have taken place in sensors,\nwireless communication systems and cloud computing has brought steps to invent\nthis innovative technology. This paper deals with the elements, applications\nand the challenges faced by Internet of Things. This paper mainly concentrates\non the current scenario of IoT\n"}
+{"abstract": "  We compare the two basic ordinal patterns, i.e., the original and amplitude\npermutations, used to characterize vector structures. The original permutation\nconsists of the indexes of reorganized values in the original vector. By\ncontrast, the amplitude permutation comprises the positions of values in the\nreordered vector, and it directly reflects the temporal structure. To\naccurately convey the structural characteristics of vectors, we modify indexes\nof equal values in permutations to be the same as, for example, the smallest or\nlargest indexes in each group of equalities. Overall, we clarify the\nrelationship between the original and amplitude permutations. And the results\nhave implications for time- and amplitude-symmetric vectors and will lead to\nfurther theoretical and experimental studies.\n"}
+{"abstract": "  We analyse the high-quality Hertzsprung-Russell diagram (HRD) derived from\nGaia data release 2 for the Solar Neighbourhood. We start building an\nalmost-complete sample within 200 pc and for |b|>25 deg, so as to limit the\nimpact of known errors and artefacts in the Gaia catalog. Particular effort is\nthen put into improving the modelling of population of binaries, which produce\ntwo marked features in the HRD: the sequence of near-equal mass binaries along\nthe lower main sequence, and the isolated group of hot subdwarfs. We describe a\nnew tool, BinaPSE, to follow the evolution of interacting binaries in a way\nthat improves the consistency with PARSEC evolutionary tracks for single stars.\nBinaPSE is implemented into the TRILEGAL code for the generation of \"partial\nmodels\" for both single and binary stellar populations, taking into account the\npresence of resolved and unresolved binaries. We then fit the Gaia HRD via MCMC\nmethods that search for the star formation history (SFH) and initial binary\nfraction (by mass) that maximise the likelihood. The main results are (i) the\nbinary fraction derived from the lower main sequence is close to 0.4, while\ntwice larger values are favoured when the upper part of the HRD is fitted; (ii)\npresent models predict the observed numbers of hot subdwarfs to within a factor\nof 2; (iii) irrespective of the prescription for the binaries, the star\nformation rate peaks at values 1.5e-4 Msun/yr at ages slightly above 2 Gyr, and\nthen decreases to 0.8e-4 Msun/yr at very old ages.\n"}
+{"abstract": "  Unsupervised representation learning of words from large multilingual corpora\nis useful for downstream tasks such as word sense disambiguation, semantic text\nsimilarity, and information retrieval. The representation precision of\nlog-bilinear fastText models is mostly due to their use of subword information.\nIn previous work, the optimization of fastText's subword sizes has not been\nfully explored, and non-English fastText models were trained using subword\nsizes optimized for English and German word analogy tasks. In our work, we find\nthe optimal subword sizes on the English, German, Czech, Italian, Spanish,\nFrench, Hindi, Turkish, and Russian word analogy tasks. We then propose a\nsimple n-gram coverage model and we show that it predicts better-than-default\nsubword sizes on the Spanish, French, Hindi, Turkish, and Russian word analogy\ntasks. We show that the optimization of fastText's subword sizes matters and\nresults in a 14% improvement on the Czech word analogy task. We also show that\nexpensive parameter optimization can be replaced by a simple n-gram coverage\nmodel that consistently improves the accuracy of fastText models on the word\nanalogy tasks by up to 3% compared to the default subword sizes, and that it is\nwithin 1% accuracy of the optimal subword sizes.\n"}
+{"abstract": "  Rotated object detection is a challenging issue of computer vision field.\nLoss of spatial information and confusion of parametric order have been the\nbottleneck for rotated detection accuracy. In this paper, we propose an\norientation-sensitive keypoint based rotated detector OSKDet. We adopt a set of\nkeypoints to characterize the target and predict the keypoint heatmap on ROI to\nform a rotated target. By proposing the orientation-sensitive heatmap, OSKDet\ncould learn the shape and direction of rotated target implicitly and has\nstronger modeling capabilities for target representation, which improves the\nlocalization accuracy and acquires high quality detection results. To extract\nhighly effective features at border areas, we design a rotation-aware\ndeformable convolution module. Furthermore, we explore a new keypoint reorder\nalgorithm and feature fusion module based on the angle distribution to\neliminate the confusion of keypoint order. Experimental results on several\npublic benchmarks show the state-of-the-art performance of OSKDet.\nSpecifically, we achieve an AP of 77.81% on DOTA, 89.91% on HRSC2016, and\n97.18% on UCAS-AOD, respectively.\n"}
+{"abstract": "  In large studies involving multi protocol Magnetic Resonance Imaging (MRI),\nit can occur to miss one or more sub-modalities for a given patient owing to\npoor quality (e.g. imaging artifacts), failed acquisitions, or hallway\ninterrupted imaging examinations. In some cases, certain protocols are\nunavailable due to limited scan time or to retrospectively harmonise the\nimaging protocols of two independent studies. Missing image modalities pose a\nchallenge to segmentation frameworks as complementary information contributed\nby the missing scans is then lost. In this paper, we propose a novel model,\nMulti-modal Gaussian Process Prior Variational Autoencoder (MGP-VAE), to impute\none or more missing sub-modalities for a patient scan. MGP-VAE can leverage the\nGaussian Process (GP) prior on the Variational Autoencoder (VAE) to utilize the\nsubjects/patients and sub-modalities correlations. Instead of designing one\nnetwork for each possible subset of present sub-modalities or using frameworks\nto mix feature maps, missing data can be generated from a single model based on\nall the available samples. We show the applicability of MGP-VAE on brain tumor\nsegmentation where either, two, or three of four sub-modalities may be missing.\nOur experiments against competitive segmentation baselines with missing\nsub-modality on BraTS'19 dataset indicate the effectiveness of the MGP-VAE\nmodel for segmentation tasks.\n"}
+{"abstract": "  We compute the ghost spectral function in Yang-Mills theory by solving the\ncorresponding Dyson-Schwinger equation for a given input gluon spectral\nfunction. The results encompass both scaling and decoupling solutions for the\ngluon propagator input. The resulting ghost spectral function displays a\nparticle peak at vanishing momentum and a negative scattering spectrum, whose\ninfrared and ultraviolet tails are obtained analytically. The ghost dressing\nfunction is computed in the entire complex plane, and its salient features are\nidentified and discussed.\n"}
+{"abstract": "  State estimation of multi-modal hybrid systems is an important problem with\nmany applications in the field robotics. However, incorporating discrete modes\nin the estimation process is hampered by a potentially combinatorial growth in\ncomputation. In this paper we present a novel incremental multi-hypothesis\nsmoother based on eliminating a hybrid factor graph into a multi-hypothesis\nBayes tree, which represents possible discrete state sequence hypotheses.\nFollowing iSAM, we enable incremental inference by conditioning the past on the\nfuture but we add to that the capability of maintaining multiple discrete mode\nhistories, exploiting the temporal structure of the problem to obtain a\nsimplified representation that unifies the multiple hypothesis tree with the\nBayes tree. In the results section we demonstrate the generality of the\nalgorithm with examples in three problem domains: lane change detection (1D),\naircraft maneuver detection (2D), and contact detection in legged robots (3D).\n"}
+{"abstract": "  The knowledge of the ages of stars hosting exoplanets allows us to obtain an\noverview on the evolution of exoplanets and understand the mechanisms affecting\ntheir life. The measurement of the ages of stars in the Galaxy is usually\naffected by large uncertainties. An exception are the stellar clusters: for\ntheir coeval members, born from the same molecular cloud, ages can be measured\nwith extreme accuracy. In this context, the project PATHOS is providing\ncandidate exoplanets orbiting members of stellar clusters and associations\nthrough the analysis of high-precision light curves obtained with cutting-edge\ntools. In this work, we exploited the data collected during the second year of\nthe TESS mission. We extracted, analysed, and modelled the light curves of\n$\\sim 90000$ stars in open clusters located in the northern ecliptic hemisphere\nin order to find candidate exoplanets. We measured the frequencies of candidate\nexoplanets in open clusters for different orbital periods and planetary radii,\ntaking into account the detection efficiency of our pipeline and the false\npositive probabilities of our candidates. We analysed the Age--$R_{\\rm P}$\ndistribution of candidate and confirmed exoplanets with periods $<100$ days and\nwell constrained ages. While no peculiar trends are observed for Jupiter-size\nand (super-)Earth-size planets, we found that objects with $4\\,R_{\\rm Earth}\n\\lesssim R_{\\rm P} \\lesssim 13\\,R_{\\rm Earth}$ are concentrated at ages\n$\\lesssim 200$ Myr; different scenarios (atmospheric losses, migration, etc.)\nare considered to explain the observed age-$R_{\\rm P}$ distribution.\n"}
+{"abstract": "  In this work, we propose an automatic mesh generation algorithm, FlowMesher,\nwhich can be used to generate unstructured meshes for mesh domains in any shape\nwith minimum (or even no) user intervention. The approach can generate\nhigh-quality simplex meshes directly from scanned images in OBJ format in 2D\nand 3D or just from a line drawing in 2-D. Mesh grading can be easily\ncontrolled also. The FlowMesher is robust and easy to be implemented and is\nuseful for a variety of applications including surgical simulators.\n  The core idea of the FlowMesher is that a mesh domain is considered as an\n\"airtight container\" into which fluid particles are \"injected\" at one or\nmultiple selected interior points. The particles repel each other and occupy\nthe whole domain somewhat like blowing up a balloon. When the container is full\nof fluid particles and the flow is stopped, a Delaunay triangulation algorithm\nis employed to link the fluid particles together to generate an unstructured\nmesh (which is then optimized using a combination of automated mesh smoothing\nand element removal in 3D). The performance of the FlowMesher is demonstrated\nby generating meshes for several 2D and 3D mesh domains including a scanned\nimage of a bone.\n"}
+{"abstract": "  We are interested in solving the congruences $f^3+g^3+1\\equiv 0\\pmod{fg}$ and\n$f^4-4g^2+4\\equiv 0\\pmod{fg}$ in polynomials $f, g$ with rational coefficients.\nMoreover, we present results of computations of all integer points on certain\none parametric curves of genus 1 and 3, related to cubic and quartic fields,\nrespectively.\n"}
+{"abstract": "  The earliest phases of star formation are characterised by intense mass\naccretion from the circumstellar disk to the central star. One group of young\nstellar objects, the FU Orionis-type stars exhibit accretion rate peaks\naccompanied by bright eruptions. The occurrence of these outbursts might solve\nthe luminosity problem of protostars, play a key role in accumulating the final\nstar mass, and have a significant effect on the parameters of the envelope and\nthe disk. In the framework of the Structured Accretion Disks ERC project, we\nare conducting a systematic investigation of these sources with millimeter\ninterferometry to examine whether they represent normal young stars in\nexceptional times or they are unusual objects. Our results show that FU\nOrionis-type stars can be similar to both Class I and Class II systems and may\nbe in a special evolutionary phase between the two classes with their\ninfall-driven episodic eruptions being the main driving force of the\ntransition.\n"}
+{"abstract": "  The cosmological constant if considered as a fundamental constant, provides\nan information treatment for gravitation problems, both cosmological and of\nblack holes. The efficiency of that approach is shown via gedanken experiments\nfor the information behavior of the horizons for Schwarzschild-de Sitter and\nKerr-de Sitter metrics. A notion of entropy regarding any observer and in all\npossible non-extreme black hole solutions is suggested, linked also to\nBekenstein bound. The suggested information approach forbids the existence of\nnaked singularities.\n"}
+{"abstract": "  This paper is an attempt to study the thermodynamics of the structure\nformation in the large scale universe in the non local gravity using Boltzmann\nstatistics and the Tsallis statistics. The partition function is obtained in\nboth the approaches and the corresponding thermodynamics properties are\nevaluated. The important thing about the paper is that we surprisingly get the\ndivergence free integrals and thus stress upon the fact that the nonlocal\ngravity is the singularity free model of gravity.\n"}
+{"abstract": "  The main point of the paper is to take the explicit motivic Chabauty-Kim\nmethod developed in papers of Dan-Cohen--Wewers and Dan-Cohen and the author\nand make it work for non-rational curves. In particular, we calculate the\nabstract form of an element of the Chabauty-Kim ideal for\n$\\mathbb{Z}[1/\\ell]$-points on a punctured elliptic curve, and lay some\ngroundwork for certain kinds of higher genus curves. For this purpose, we\ndevelop an \"explicit Tannakian Chabauty-Kim method\" using\n$\\mathbb{Q}_{p}$-Tannakian categories of Galois representations in place of\n$\\mathbb{Q}$-linear motives. In future work, we intend to use this method to\nexplicitly apply the Chabauty-Kim method to a curve of positive genus in a\nsituation where Quadratic Chabauty does not apply.\n"}
+{"abstract": "  Source code (Context) and its parsed abstract syntax tree (AST; Structure)\nare two complementary representations of the same computer program.\nTraditionally, designers of machine learning models have relied predominantly\neither on Structure or Context. We propose a new model, which jointly learns on\nContext and Structure of source code. In contrast to previous approaches, our\nmodel uses only language-agnostic features, i.e., source code and features that\ncan be computed directly from the AST. Besides obtaining state-of-the-art on\nmonolingual code summarization on all five programming languages considered in\nthis work, we propose the first multilingual code summarization model. We show\nthat jointly training on non-parallel data from multiple programming languages\nimproves results on all individual languages, where the strongest gains are on\nlow-resource languages. Remarkably, multilingual training only from Context\ndoes not lead to the same improvements, highlighting the benefits of combining\nStructure and Context for representation learning on code.\n"}
+{"abstract": "  The purpose of this work is to introduce and characterize the Bounded\nAcceleration Shortest Path (BASP) problem, a generalization of the Shortest\nPath (SP) problem. This problem is associated to a graph: the nodes represent\npositions of a mobile vehicle and the arcs are associated to pre-assigned\ngeometric paths that connect these positions. BASP consists in finding the\nminimum-time path between two nodes. Differently from SP, we require that the\nvehicle satisfy bounds on maximum and minimum acceleration and speed, that\ndepend on the vehicle position on the currently traveled arc. We prove that\nBASP is NP-hard and define solution algorithm that achieves polynomial\ntime-complexity under some additional hypotheses on problem data.\n"}
+{"abstract": "  We studied the $e^+e^- \\to h \\gamma $ process at the International Linear\nCollider (ILC) at $\\sqrt{s}=250$ GeV, based on the full detector simulation of\nthe International Large Detector (ILD). This process is loop-induced in the\nStandard Model (SM) and is sensitive to new physics which alters $h \\gamma\n\\gamma$ or $h \\gamma Z$ coupling. We performed the analysis by employing the\nleading signal channels with $h \\to b \\bar{b}$ and $h \\to WW^*$ and including\nfull SM background processes. The results are obtained for two scenarios of\nbeam polarisations each with an integrated luminosity of 900 fb$^{-1}$. We\nfound the expected significance of the SM signal is 0.40$\\sigma$ for\n$P(e^-,e^+)=(-0.8,+0.3)$ (the left-handed polarisation), and 0.06$\\sigma$ for\n$P(e^-,e^+)=(+0.8,-0.3)$ (the right-handed polarisation). The bounds on new\nphysics effects are reported as the 95% C.L. upper limit for the cross-section\nof $e^+e^- \\to h \\gamma$: $\\sigma_{h\\gamma}^L <$ 1.8 fb and $\\sigma_{h\\gamma}^R\n<$ 0.5 fb respectively for left- and right-handed polarisations. The\nconstraints on effective $h\\gamma Z$ couplings are to be further studied.\n"}
+{"abstract": "  We present a finite-energy electroweak-monopole solution obtained by\nconsidering non-linear extensions of the hypercharge sector of the Electroweak\nTheory, based on logarithmic and exponential versions of electrodynamics. We\nfind constraints for a class of non-linear extensions and also work out an\nestimate for the monopole mass in this scenario. We finally derive a lower\nbound for the energy of the monopole and discuss the simpler case of a Dirac\nmagnetic charge.\n"}
+{"abstract": "  NASA Astrophysics Division funds development of cutting-edge technology to\nenable its missions to achieve ambitious and groundbreaking science goals.\nThese technology development efforts are managed by the Physics of the Cosmos,\nCosmic Origins, and Exoplanet Exploration Programs. The NASA Strategic\nAstrophysics Technology Program (SAT) was established in 2009 as a new\ntechnology maturation program to fill the gap in the Technology Readiness Level\nrange from 3 to 6. Since program inception, 100 SAT grants have been openly\ncompeted and awarded, along with dozens of direct-funded projects, leading to a\nhost of technologies advancing their Technology Readiness Levels and/or being\ninfused into space and suborbital missions and ground-based projects. We\npresent the portfolio distribution in terms of specific technology areas\naddressed, including optics, detectors, coatings, corona graphs, star shades,\nlasers, electronics, and cooling subsystems. We show an analysis of the rate of\nTechnology Readiness Level advances, infusion success stories, and other\nbenefits such as training the future astrophysics workforce, including students\nand postdoctoral fellows hired by projects. Finally, we present the\nAstrophysics Division current strategic technology maturation priorities for\ninvestment, enabling a range of future strategic astrophysics missions.\n"}
+{"abstract": "  In this paper the seven-decade historical dataset of the Hungarian power\nsystem is used to perform vulnerability assessment applying a complex network\napproach.\n"}
+{"abstract": "  Each orthogonal group O(n) has a nontrivial GL(1)-extension, which we call\nGPin(n). The identity component of GPin(n) is the more familiar GSpin(n), the\ngeneral Spin group. We prove that the restriction to GPin(n-1) of an\nirreducible admissible representation of GPin(n) over a nonarchimedean local\nfield of characteristic zero is multiplicity free and also prove the analogous\ntheorem for GSpin(n). The case for GPin(n) is the analogue of the theorem for\nO(n) proven by Aizenbud, Gourevitch, Rallis and Schiffmann, and the case for\nGSpin(n) the one for SO(n) by Waldspurger.\n"}
+{"abstract": "  I first met Louis Nirenberg in person in 1972 when I became a Courant\nInstructor. He was already a celebrated mathematician and a suave sophisticated\nNew Yorker, even though he was born in Hamilton, Canada and grew up in\nMontreal. In this informal style paper I will describe some of his famous\npapers, some of our joint work and other work he inspired. I will concentrate\non some of Louis' work inspired by geometric problems beginning around 1974,\nespecially the method of moving planes and implicit fully nonlinear elliptic\nequations. I have also sprinkled throughout some comments on his character and\npersonality that I believe contributed to his great success.\n"}
+{"abstract": "  Phytoparasitic nematodes (or phytonematodes) are causing severe damage to\ncrops and generating large-scale economic losses worldwide. In soybean crops,\nannual losses are estimated at 10.6% of world production. Besides, identifying\nthese species through microscopic analysis by an expert with taxonomy knowledge\nis often laborious, time-consuming, and susceptible to failure. In this\nperspective, robust and automatic approaches are necessary for identifying\nphytonematodes capable of providing correct diagnoses for the classification of\nspecies and subsidizing the taking of all control and prevention measures. This\nwork presents a new public data set called NemaDataset containing 3,063\nmicroscopic images from five nematode species with the most significant damage\nrelevance for the soybean crop. Additionally, we propose a new Convolutional\nNeural Network (CNN) model defined as NemaNet and a comparative assessment with\nthirteen popular models of CNNs, all of them representing the state of the art\nclassification and recognition. The general average calculated for each model,\non a from-scratch training, the NemaNet model reached 96.99% accuracy, while\nthe best evaluation fold reached 98.03%. In training with transfer learning,\nthe average accuracy reached 98.88\\%. The best evaluation fold reached 99.34%\nand achieve an overall accuracy improvement over 6.83% and 4.1%, for\nfrom-scratch and transfer learning training, respectively, when compared to\nother popular models.\n"}
+{"abstract": "  The segmentation of emails into functional zones (also dubbed email zoning)\nis a relevant preprocessing step for most NLP tasks that deal with emails.\nHowever, despite the multilingual character of emails and their applications,\nprevious literature regarding email zoning corpora and systems was developed\nessentially for English.\n  In this paper, we analyse the existing email zoning corpora and propose a new\nmultilingual benchmark composed of 625 emails in Portuguese, Spanish and\nFrench. Moreover, we introduce OKAPI, the first multilingual email segmentation\nmodel based on a language agnostic sentence encoder. Besides generalizing well\nfor unseen languages, our model is competitive with current English benchmarks,\nand reached new state-of-the-art performances for domain adaptation tasks in\nEnglish.\n"}
+{"abstract": "  Size distribution of sunspots provides key information about the generation\nand emergence processes of the solar magnetic field. Previous studies on the\nsize distribution have primarily focused on either the whole group or\nindividual spot areas. In this paper, we investigate the organization of spot\nareas within sunspot groups. In particular, we analyze the ratio, $\\rm{R}$, of\nthe area of the biggest spot ($\\rm{A_{big\\_spot}}$) inside a group, to the\ntotal area of that group ($\\rm{A_{group}}$). We use sunspot observations from\nKislovodsk, Pulkovo and Debrecen observatories, together covering solar cycles\n17 to 24. We find that at the time when the group area reaches its maximum, the\nsingle biggest spot in a group typically occupies about 60% of the group area.\nFor half of all groups, $\\rm R$ lies in the range between roughly 50% and 70%.\nWe also find R to change with the group area, $\\rm{A_{group}}$, such that\n$\\rm{R}$ reaches a maximum of about 0.65 for groups with $\\rm{A_{group}}\\approx\n200\\mu$Hem and then remains at about 0.6 for lager groups. Our findings imply a\nscale invariant emergence pattern, providing an observational constraint on the\nemergence process. Furthermore, extrapolation of our results to larger sunspot\ngroups may have a bearing on the giant unresolved starspot features found in\nDoppler images of highly active sun-like stars. Our results suggest that such\ngiant features are composed of multiple spots, with the largest spot occupying\nroughly 55--75% of the total group area (i.e. of the area of the giant\nstarspots seen in Doppler images).\n"}
+{"abstract": "  After the appearance of the no-cloning theorem, approximate quantum cloning\nmachines (QCMs) have become one of the most well-studied subject in quantum\ninformation theory. Among several measures to quantify the performance of a\nQCM, single-qudit fidelity and global fidelity have been most widely used. In\nthis paper we compute the optimal global fidelity for phase-covariant cloning\nmachines via semi-definite programming optimization, thereby completing a\nremaining gap in the previous results on QCMs. We also consider optimal\nsimulations of the cloning and transpose cloning map, both by a direct\noptimization and by a composition of component-wise optimal QCMs. For the\ncloning map the composition method is sub-optimal whereas for the transpose\ncloning map the method is asymptotically optimal.\n"}
+{"abstract": "  We construct soliton solution of a variable coefficients nonlinear\nSchr\\\"odinger equation in the presence of parity reflection - time reversal\n$(\\mathcal{PT})-$ symmetric Rosen-Morse potential using similarity\ntransformation technique. We transform the variable coefficients nonlinear\nSchr\\\"odinger equation into the nonlinear Schr\\\"odinger equation with\n$\\mathcal{PT}-$symmetric potential with certain integrability conditions. We\ninvestigate in-detail the features of the obtained soliton solutions with two\ndifferent forms of dispersion parameters. Further, we analyze the nonlinear\ntunneling effect of soliton profiles by considering two different forms of\nnonlinear barrier/well and dispersion barrier/well. Our results show that the\nsoliton can tunnel through nonlinear barrier/well and dispersion barrier/well\nwith enlarged and suppressed amplitudes depending on the sign of the height.\nOur theoretical findings are experimentally realizable and might help to model\nthe optical devices.\n"}
+{"abstract": "  Grant-free non-orthogonal multiple access (GF-NOMA) is a potential technique\nto support massive Ultra-Reliable and Low-Latency Communication (mURLLC)\nservice. However, the dynamic resource configuration in GF-NOMA systems is\nchallenging due to random traffics and collisions, that are unknown at the base\nstation (BS). Meanwhile, joint consideration of the latency and reliability\nrequirements makes the resource configuration of GF-NOMA for mURLLC more\ncomplex. To address this problem, we develop a general learning framework for\nsignature-based GF-NOMA in mURLLC service taking into account the multiple\naccess signature collision, the UE detection, as well as the data decoding\nprocedures for the K-repetition GF and the Proactive GF schemes. The goal of\nour learning framework is to maximize the long-term average number of\nsuccessfully served users (UEs) under the latency constraint. We first perform\na real-time repetition value configuration based on a double deep Q-Network\n(DDQN) and then propose a Cooperative Multi-Agent learning technique based on\nthe DQN (CMA-DQN) to optimize the configuration of both the repetition values\nand the contention-transmission unit (CTU) numbers. Our results show that the\nnumber of successfully served UEs under the same latency constraint in our\nproposed learning framework is up to ten times for the K-repetition scheme, and\ntwo times for the Proactive scheme, more than that with fixed repetition values\nand CTU numbers. In addition, the superior performance of CMA-DQN over the\nconventional load estimation-based approach (LE-URC) demonstrates its\ncapability in dynamically configuring in long term. Importantly, our general\nlearning framework can be used to optimize the resource configuration problems\nin all the signature-based GF-NOMA schemes.\n"}
+{"abstract": "  This paper outlines an approach for IEEE to take leadership for digital\nprivacy to align many existing IEEE Societies and efforts in the areas of\ncomputer systems & applications security, organizational & global\narchitectures, policy-supporting legislation, originating new standards,\nintegrating compliance into technologies, and helping design decision-board\ninfrastructures for governance bodies. Much of the current emphasis on evolving\nprivacy technologies centers on big corporate enterprises and institutions,\ncausing the industry to support corporate assets protection mainly. Fostering\ntechnology to empower individual privacy-enabling tools has lagged, and\npersonal privacy has diminished because corporate big data applications have\nmade sizable investments into exploiting private data. As one of the largest\nindividual-member-based organizations, IEEE is urged to develop a collaborative\napproach for digital privacy with privacy-enabling technologies to benefit its\nmembers. The recommendations outlined define a prospective course that could\nresult in future global individualized privacy capabilities which employ a\ncombination of synergistic technologies such as distributed ledgers,\ndifferential privacy, homomorphic encryption, secure distributed multi-party\ncomputation, zero-trust architectures, proof-of-origin of data, software, or\nother techniques. Such an effort would involve community engagement and\noutreach, academic peer-review events, the establishment of governance bodies,\ncoordination & expansion of existing standards, and the development of\npublicly-accessible prototypes. Collaboration with other IEEE-sponsored efforts\nfor transactive energy systems, confidentiality and security of healthcare\nrecords and devices, and other IEEE-funded projects will help magnify digital\nprivacy investments already in progress in these applications of emerging\ntechnologies.\n"}
+{"abstract": "  Cardiovascular disease is a major threat to health and one of the primary\ncauses of death globally. The 12-lead ECG is a cheap and commonly accessible\ntool to identify cardiac abnormalities. Early and accurate diagnosis will allow\nearly treatment and intervention to prevent severe complications of\ncardiovascular disease. In the PhysioNet/Computing in Cardiology Challenge\n2020, our objective is to develop an algorithm that automatically identifies 27\nECG abnormalities from 12-lead ECG recordings.\n"}
+{"abstract": "  With the rapid innovations in technology, wireless internet-connected devices\nare more ubiquitous than ever and can be found in virtually every aspect of\nboth our personal and professional lives. In this paper, we propose a\ncomprehensive literature review that focuses on various network components that\ncreate connectivity among different devices, specifically Wireless Sensor\nNetworks (WSNs), Radio-Frequency Identification (RFID) tags, Internet of Things\n(IoT) devices, and how these devices helped usher in the 4th Industrial\nRevolution, or Industry 4.0. This paper focuses on the protocols, architecture,\nuses, security concerns, and solutions used in these network technologies, as\nwell as their differences and similarities.\n"}
+{"abstract": "  Being capable of sensing and behavioral adaptation in line with their\nchanging environments, cognitive cyber-physical systems (CCPSs) are the new\nform of applications in future wireless networks. With the advancement of the\nmachine learning algorithms, the transmission scheme providing the best\nperformance can be utilized to sustain a reliable network of CCPS agents\nequipped with self-decision mechanisms, where the interactions between each\nagent are modeled in terms of service quality, security, and cost dimensions.\nIn this work, first, we provide network utility as a reliability metric, which\nis a weighted sum of the individual utility values of the CCPS agents. The\nindividual utilities are calculated by mixing the quality of service (QoS),\nsecurity, and cost dimensions with the proportions determined by the\nindividualized user requirements. By changing the proportions, the CCPS network\ncan be tuned for different applications of next-generation wireless networks.\nThen, we propose a secure transmission policy selection (STPS) mechanism that\nmaximizes the network utility by using the Markov-decision process (MDP). In\nSTPS, the CCPS network jointly selects the best performing physical layer\nsecurity policy and the parameters of the selected secure transmission policy\nto adapt to the changing environmental effects. The proposed STPS is realized\nby reinforcement learning (RL), considering its real-time decision mechanism\nwhere agents can decide automatically the best utility providing policy in an\naltering environment.\n"}
+{"abstract": "  Maximum bound principle (MBP) is an important property for a large class of\nsemilinear parabolic equations, in the sense that the time-dependent solution\nof the equation with appropriate initial and boundary conditions and nonlinear\noperator preserves for all time a uniform pointwise bound in absolute value. It\nhas been a challenging problem on how to design unconditionally MBP-preserving\nhigh-order accurate time-stepping schemes for these equations. In this paper,\nwe combine the integrating factor Runge-Kutta (IFRK) method with the linear\nstabilization technique to develop a stabilized IFRK (sIFRK) method, and\nsuccessfully derive sufficient conditions for the proposed method to preserve\nMBP unconditionally in the discrete setting. We then elaborate some sIFRK\nschemes with up to the third-order accuracy, which are proven to be\nunconditionally MBP-preserving by verifying these conditions. In addition, it\nis shown that many classic strong stability-preserving sIFRK schemes do not\nsatisfy these conditions except the first-order one. Extensive numerical\nexperiments are also carried out to demonstrate the performance of the proposed\nmethod.\n"}
+{"abstract": "  Polygonal meshes are ubiquitous, but have only played a relatively minor role\nin the deep learning revolution. State-of-the-art neural generative models for\n3D shapes learn implicit functions and generate meshes via expensive\niso-surfacing. We overcome these challenges by employing a classical spatial\ndata structure from computer graphics, Binary Space Partitioning (BSP), to\nfacilitate 3D learning. The core operation of BSP involves recursive\nsubdivision of 3D space to obtain convex sets. By exploiting this property, we\ndevise BSP-Net, a network that learns to represent a 3D shape via convex\ndecomposition without supervision. The network is trained to reconstruct a\nshape using a set of convexes obtained from a BSP-tree built over a set of\nplanes, where the planes and convexes are both defined by learned network\nweights. BSP-Net directly outputs polygonal meshes from the inferred convexes.\nThe generated meshes are watertight, compact (i.e., low-poly), and well suited\nto represent sharp geometry. We show that the reconstruction quality by BSP-Net\nis competitive with those from state-of-the-art methods while using much fewer\nprimitives. We also explore variations to BSP-Net including using a more\ngeneric decoder for reconstruction, more general primitives than planes, as\nwell as training a generative model with variational auto-encoders. Code is\navailable at https://github.com/czq142857/BSP-NET-original.\n"}
+{"abstract": "  Euler wrote several papers on Astronomy, most of them in Latin. This is a\ncommented translation of E304 'Considerationes de motu corporum coelestium'\n(Considerations on the motion of celestial bodies). In this publication, Euler\nessentially focuses on the solution of two particular motions of a three-body\nproblem consisting of Sun, Earth and Moon. The first motion, represents a\nhypothetical situation of these three celestial bodies in perpetual alignment\nin syzygy - the three-body problem on a straight line -, for which the\nparameter that controls the distances among the planets was found to be given\nby a quintic function. The conclusion was that if the Moon were four times more\ndistant from the Earth (either in conjunction or in opposition), a motion of\nthis kind would have been possible to exist, such that the Moon would appear\nalways connected to the Sun. The second motion considered by Euler was Moon\nlibration, when these planets are aligned in regular syzygy. Here, perhaps for\nthe first time, Euler introduces an archaic form of a Fourier sine series\nexpansion to describe the Moon's wagging motion. However, as Euler himself\nrecognizes, the calculations turned out very tedious, and led him to greatly\nsimplify his model in order to obtain some numerical values for the phenomenon.\n"}
+{"abstract": "  The ambitious energy target to achieve climate-neutrality in the European\nUnion (EU) energy system raises the feasibility question of using only\nrenewables across all energy sectors. As one of the EU's leading industrialized\ncountries, Germany has adopted several climate-action plans for the realistic\nimplementation and maximum utilization of renewable energies in its energy\nsystem. The literature review shows a clear gap in comprehensive techniques\ndescribing an open modeling approach for analyzing fully renewable and\nsector-coupled energy systems. This paper outlines a method for analyzing the\n100% renewable-based and sector-coupled energy system's feasibility in Germany.\nBased on the open energy modeling framework, an hourly optimization tool\n'OSeEM-DE' is developed to investigate the German energy system. The model\nresults show that a 100% renewable-based and sector-coupled system for\nelectricity and building heat is feasible in Germany. The investment capacities\nand component costs depend on the parametric variations of the developed\nscenarios. The annual investment costs vary between 17.6 and 26.6 bn Euro/yr\nfor volatile generators and between 23.7 and 28.8 bn Euro/yr for heat\ngenerators. The model suggests an investment of a minimum of 2.7-3.9 bn Euro/yr\nfor electricity and heat storage. Comparison of OSeEM-DE results with recent\nstudies validates the percentage-wise energy mix composition and the calculated\nLevelized Cost of Electricity (LCOE) values from the model. Sensitivity\nanalyses indicate that storage and grid expansion maximize the system's\nflexibility and decrease the investment cost. The study concludes by showing\nhow the tool can analyze different energy systems in the EU context.\n"}
+{"abstract": "  We consider linear codes over a finite field $\\mathbb{F}_q$, for odd $q$,\nderived from determinantal varieties, obtained from symmetric matrices of\nbounded ranks. A formula for the weight of a codeword is derived. Using this\nformula, we have computed the minimum distance for the codes corresponding to\nmatrices upperbounded by any fixed, even rank. A conjecture is proposed for the\ncases where the upper bound is odd. At the end of the article, tables for the\nweights of these codes, for spaces of symmetric matrices up to order $5$, are\ngiven.\n"}
+{"abstract": "  Elementary cellular automata (ECA) present iconic examples of complex\nsystems. Though described only by one-dimensional strings of binary cells\nevolving according to nearest-neighbour update rules, certain ECA rules\nmanifest complex dynamics capable of universal computation. Yet, the\nclassification of precisely which rules exhibit complex behaviour remains a\nsignificant challenge. Here we approach this question using tools from quantum\nstochastic modelling, where quantum statistical memory -- the memory required\nto model a stochastic process using a class of quantum machines -- can be used\nto quantify the structure of a stochastic process. By viewing ECA rules as\ntransformations of stochastic patterns, we ask: Does an ECA generate structure\nas quantified by the quantum statistical memory, and if so, how quickly? We\nillustrate how the growth of this measure over time correctly distinguishes\nsimple ECA from complex counterparts. Moreover, it provides a more refined\nmeans for quantitatively identifying complex ECAs -- providing a spectrum on\nwhich we can rank the complexity of ECA by the rate in which they generate\nstructure.\n"}
+{"abstract": "  Conventional supervisory control theory assumes full synchronization between\nthe supervisor and the plant. This assumption is violated in a networked-based\ncommunication setting due to the presence of delays, and this may result in\nincorrect behavior of a supervisor obtained from conventional supervisory\ncontrol theory. This paper presents a technique to synthesize a networked\nsupervisor handling communication delays. For this purpose, first, a networked\nsupervisory control framework is provided, where the supervisor interacts with\nthe plant through control and observation channels, both of which introduce\ndelays. The control channel is FIFO, but the observation channel is assumed to\nbe non-FIFO so that the observation of events may not necessarily be received\nby the supervisor in the same order as they occurred in the plant. It is\nassumed that a global clock exists in the networked control system, and so the\ncommunication delays are represented in terms of time. Based on the proposed\nframework, a networked plant automaton is achieved, which models the behavior\nof the plant under the effects of communication delays and disordered\nobservations. Based on the networked plant, the networked supervisor is\nsynthesized, which is guaranteed to be (timed networked) controllable,\nnonblocking, time-lock free, (timed networked) maximally permissive, and\nsatisfies control requirements for the plant.\n"}
+{"abstract": "  We consider the problem of computing the k-means centers for a large\nhigh-dimensional dataset in the context of edge-based machine learning, where\ndata sources offload machine learning computation to nearby edge servers.\nk-Means computation is fundamental to many data analytics, and the capability\nof computing provably accurate k-means centers by leveraging the computation\npower of the edge servers, at a low communication and computation cost to the\ndata sources, will greatly improve the performance of these analytics. We\npropose to let the data sources send small summaries, generated by joint\ndimensionality reduction (DR) and cardinality reduction (CR), to support\napproximate k-means computation at reduced complexity and communication cost.\nBy analyzing the complexity, the communication cost, and the approximation\nerror of k-means algorithms based on state-of-the-art DR/CR methods, we show\nthat: (i) it is possible to achieve a near-optimal approximation at a\nnear-linear complexity and a constant or logarithmic communication cost, (ii)\nthe order of applying DR and CR significantly affects the complexity and the\ncommunication cost, and (iii) combining DR/CR methods with a properly\nconfigured quantizer can further reduce the communication cost without\ncompromising the other performance metrics. Our findings are validated through\nexperiments based on real datasets.\n"}
+{"abstract": "  The current practice in land cover/land use change analysis relies heavily on\nthe individually classified maps of the multitemporal data set. Due to varying\nacquisition conditions (e.g., illumination, sensors, seasonal differences), the\nclassification maps yielded are often inconsistent through time for robust\nstatistical analysis. 3D geometric features have been shown to be stable for\nassessing differences across the temporal data set. Therefore, in this article\nwe investigate he use of a multitemporal orthophoto and digital surface model\nderived from satellite data for spatiotemporal classification. Our approach\nconsists of two major steps: generating per-class probability distribution maps\nusing the random-forest classifier with limited training samples, and making\nspatiotemporal inferences using an iterative 3D spatiotemporal filter operating\non per-class probability maps. Our experimental results demonstrate that the\nproposed methods can consistently improve the individual classification results\nby 2%-6% and thus can be an important postclassification refinement approach.\n"}
+{"abstract": "  In this work, we provide consistent compactifications of Einstein-Maxwell and\nEinstein-Maxwell-Lovelock theories on direct product spacetimes of the form\n$\\mathcal{M}_D=\\mathcal{M}_d\\times\\mathcal{K}^{p}$, where $\\mathcal{K}^p$ is a\nEuclidean internal manifold of constant curvature. For these compactifications\nto take place, it is required the distribution of a precise flux of $p$-forms\nover the internal manifold. The dynamic of the $p$-forms are demanded to be\ncontrolled by two types of interaction. First, by specific couplings with the\ncurvature tensor and, second, by a suitable interaction with the\nelectromagnetic field of the $d$-dimensional brane, the latter being dictated\nby a modification of the recently proposed theory of Quasitopological\nElectromagnetism. The field equations of the corresponding compactified\ntheories, which are of second order, are solved and general homogenous charged\nblack p-branes are constructed. We explicitly provide homogenous\nReissner-Nordstr\\\"om black strings and black p-branes in Einstein-Maxwell\ntheory and homogenous charged Boulware-Deser black p-branes for quadratic and\ncubic Maxwell-Lovelock gravities.\n"}
+{"abstract": "  Given a triangle, what is the equation of the line which bisects its area and\nhas a given slope? The set of all lines bisecting the area of a triangle has\nbeen elegantly determined as a certain 'deltoid' envelope and this gives an\nindirect method of solution. We find that vector algebra allows the equation to\nbe written down rather directly and neatly.\n"}
+{"abstract": "  In this paper, we report the first extended GeV $\\gamma$-ray emission, at a\nsignificant level of $\\sim$ 8.13$\\sigma$, from the region of the supernova\nremnant (SNR) SNR G317.3-0.2 by analyzing $\\sim$ 12.2 years of Fermi Large Area\nTelescope (Fermi-LAT) Pass 8 data in the work. The best-fit position of the new\n$\\gamma$-ray source matches that of the 843 MHz radio energy band of SNR\nG317.3-0.2, and there is no significant variability of the photon flux of the\ncorresponding light curve (LC) in the data for the 12.2 year period; therefore,\nby excluding other known $\\gamma$-ray sources or candidates within a 2$\\sigma$\nerror radius from the best-fit position of SNR G317.3-0.2, we suggest that the\n$\\gamma$-ray source is likely to be a GeV counterpart of SNR G317.3-0.2.\n"}
+{"abstract": "  Existing classification-based face recognition methods have achieved\nremarkable progress, introducing large margin into hypersphere manifold to\nlearn discriminative facial representations. However, the feature distribution\nis ignored. Poor feature distribution will wipe out the performance improvement\nbrought about by margin scheme. Recent studies focus on the unbalanced\ninter-class distribution and form a equidistributed feature representations by\npenalizing the angle between identity and its nearest neighbor. But the problem\nis more than that, we also found the anisotropy of intra-class distribution. In\nthis paper, we propose the `gradient-enhancing term' that concentrates on the\ndistribution characteristics within the class. This method, named IntraLoss,\nexplicitly performs gradient enhancement in the anisotropic region so that the\nintra-class distribution continues to shrink, resulting in isotropic and more\ncompact intra-class distribution and further margin between identities. The\nexperimental results on LFW, YTF and CFP-FP show that our outperforms\nstate-of-the-art methods by gradient enhancement, demonstrating the superiority\nof our method. In addition, our method has intuitive geometric interpretation\nand can be easily combined with existing methods to solve the previously\nignored problems.\n"}
+{"abstract": "  Any modern network inference paradigm must incorporate multiple aspects of\nnetwork structure, including information that is often encoded both in vertices\nand in edges. Methodology for handling vertex attributes has been developed for\na number of network models, but comparable techniques for edge-related\nattributes remain largely unavailable. We address this gap in the literature by\nextending the latent position random graph model to the line graph of a random\ngraph, which is formed by creating a vertex for each edge in the original\nrandom graph, and connecting each pair of edges incident to a common vertex in\nthe original graph. We prove concentration inequalities for the spectrum of a\nline graph, and then establish that although naive spectral decompositions can\nfail to extract necessary signal for edge clustering, there exist\nsignal-preserving singular subspaces of the line graph that can be recovered\nthrough a carefully-chosen projection. Moreover, we can consistently estimate\nedge latent positions in a random line graph, even though such graphs are of a\nrandom size, typically have high rank, and possess no spectral gap. Our results\nalso demonstrate that the line graph of a stochastic block model exhibits\nunderlying block structure, and we synthesize and test our methods in\nsimulations for cluster recovery and edge covariate inference in stochastic\nblock model graphs.\n"}
+{"abstract": "  We propose a multi-task, probabilistic approach to facilitate distantly\nsupervised relation extraction by bringing closer the representations of\nsentences that contain the same Knowledge Base pairs. To achieve this, we bias\nthe latent space of sentences via a Variational Autoencoder (VAE) that is\ntrained jointly with a relation classifier. The latent code guides the pair\nrepresentations and influences sentence reconstruction. Experimental results on\ntwo datasets created via distant supervision indicate that multi-task learning\nresults in performance benefits. Additional exploration of employing Knowledge\nBase priors into the VAE reveals that the sentence space can be shifted towards\nthat of the Knowledge Base, offering interpretability and further improving\nresults.\n"}
+{"abstract": "  We study quantitative aspects and concentration phenomena for ground states\nof the following nonlocal Schr\\\"odinger equation $\n  (-\\Delta)^s u+V(x)u= u^{2_s^*-1-\\varepsilon} \\ \\\n  \\text{in}\\ \\ \\mathbb{R}^N, $ where $\\varepsilon>0$, $s\\in (0,1)$,\n$2^*_s:=\\frac{2N}{N-2s}$, $N>4s$. We show that the ground state\n$u_{\\varepsilon}$ blows up and precisely with the following rate\n$\\|u_{\\varepsilon}\\|_{L^\\infty(\\mathbb{R}^N)}\\sim\n\\varepsilon^{-\\frac{N-2s}{4s}}$, as $\\epsilon\\rightarrow 0^+$. We also localize\nthe concentration points and, in the case of radial potentials $V$, we prove\nlocal uniqueness of sequences of ground states which exhibit a concentrating\nbehavior.\n"}
+{"abstract": "  We present the first data release (DR1) of the VST Early-type GAlaxy Survey\n(VEGAS). This is a deep multi-band (u'g'r'i') imaging survey, carried out with\nthe ESO VLT Survey Telescope (VST). To date, using about 90% of the total\nobserving time, VEGAS has already collected 43 targets (groups and clusters of\ngalaxies) covering a total area on the sky of about 95 square degrees. Taking\nadvantage of the wide (1 deg^2) field-of-view of OmegaCAM@VST, the long\nintegration time and the wide variety of targets, VEGAS has proven to be a gold\nmine to explore the structure of galaxies down to the faintest surface\nbrightness levels of about 27-30 mag/arcsec^2 in the SDSS g' band, for the\ndense clusters of galaxies and for the unexplored poor groups of galaxies.\nBased on the analysed data, VEGAS allowed us to i) study the galaxy outskirts,\ndetect the intra-cluster light and low-surface brightness features in the\nintra-cluster/group space, ii) trace the mass assembly in galaxies, by\nestimating the accreted mass fraction in the stellar halos and provide results\nthat can be directly compared with the predictions of galaxy formation models,\niii) trace the spatial distribution of candidate globular clusters, and iv)\ndetect the ultra-diffuse galaxies. With the DR1, we provide the reduced VST\nmosaics of 10 targets, which have been presented in the VEGAS publications. The\ndata products are available via the ESO Science Portal (see\nhttp://www.eso.org/sci/observing/phase3/news.html#VEGAS-DR1).\n"}
+{"abstract": "  We study the statistical behavior of multimoded optical systems under\nequilibrium conditions. We investigate the role of variations of the system\nparameters in the thermodynamic description and derive, an optical analogue of\nthe first law of thermodynamics, a generic expression for the work done to the\nsystem, and an optical Gibbs-Duhem equation. To demonstrate these effects, we\nfocus in the case of two-dimensional photonic lattices. We study the conditions\nunder which the entropy in such waveguide arrays can be considered as\nextensive. In this respect, small deviations from the extensive character of\nthe entropy give rise to stress and strain terms. We examine how the\nconservation laws in such array configurations are affected by variations in\nthe system parameters, and furthermore, we analyze the respective thermodynamic\nprocesses (isentropic and Joule-type expansions).\n"}
+{"abstract": "  In this article, the mechanism of the unexpected high fluidity in SiOx\nnanowire under modest irradiation was proposed, the high fluidity is attributed\nto the long lifetime of irradiation-induced holes, which arise from formation\nof small polarons. The holes created in irradiation could have a long lifetime,\nand localized in space, such missing of bonding electron could suppress the\nenergy barrier(athermal activation effect) for a Pachner move of the network.\nThe atomic level dynamics of the system is proposed by interaction of phonon\nand local configuration, the activation effect was then studied with passing\nrate of corresponding stochastic dynamic equation, calculation shows an\nexponential dependent of the time-lapse of Pachner move to lifetime of the\nactivation, furthermore, connection between the local configuration time and\nviscosity of the fluid indicates a strong sensitivity of viscosity to lifetime\nof the athermal activation, such mechanism would give an effective\ninterpretation to the unexpected high fluidity together with the passivation\neffect of the conductor on the material.\n"}
+{"abstract": "  We present the analytical singular value decomposition of the stoichiometry\nmatrix for a spatially discrete reaction-diffusion system on a one dimensional\ndomain. The domain has two subregions which share a single common boundary.\nEach of the subregions is further partitioned into a finite number of\ncompartments. Chemical reactions can occur within a compartment, whereas\ndiffusion is represented as movement between adjacent compartments. Inspired by\nbiology, we study both 1) the case where the reactions on each side of the\nboundary are different and only certain species diffuse across the boundary as\nwell as 2) the case with spatially homogenous reactions and diffusion. We write\nthe stoichiometry matrix for these two classes of systems using a Kronecker\nproduct formulation. For the first scenario, we apply linear perturbation\ntheory to derive an approximate singular value decomposition in the limit as\ndiffusion becomes much faster than reactions. For the second scenario, we\nderive an exact analytical singular value decomposition for all relative\ndiffusion and reaction time scales. By writing the stoichiometry matrix using\nKronecker products, we show that the singular vectors and values can also be\nwritten concisely using Kronecker products.\n  Ultimately, we find that the singular value decomposition of the\nreaction-diffusion stoichiometry matrix depends on the singular value\ndecompositions of smaller matrices. These smaller matrices represent modified\nversions of the reaction-only stoichiometry matrices and the analytically known\ndiffusion-only stoichiometry matrix. Our results provide a mathematical\nframework that can be used to study complex biochemical systems with metabolic\ncompartments. MATLAB code for calculating the SVD equations is available at\n\\url{www.github.com/MathBioCU/ReacDiffStoicSVD}.\n"}
+{"abstract": "  This paper sketches a new approach using Fuzzy Cognitive Maps (FCMs) to\noperably map and simulate digital transformation in architecture and urban\nplanning. Today these processes are poorly understood. Many current studies on\ndigital transformation are only treating questions of economic efficiency.\nSustainability and social impact only play a minor role. Decisive definitions,\nconcepts and terms stay unclear. Therefore this paper develops an open\nexperimental testbed for sustainable and innovative environments (ETSIE) for\nthree different digital transformation scenarios using FCMs. A traditional\ngrowth-oriented scenario, a COVID-19 scenario and an innovative and sustainable\nCOVID-19 scenario are modeled and tested. All three scenarios have the same\nnumber of components, connections and the same driver components. Only the\ninitial state vectors are different and the internal correlations are weighted\ndifferently. This allows for comparing all three scenarios on an equal basis.\nThe mental modeler software is used (Gray et al. 2013). This paper presents one\nof the first applications of FCMs in the context of digital transformation. It\nis shown, that the traditional growth-oriented scenario is structurally very\nsimilar to the current COVID-19 scenario. The current pandemic is able to\naccelerate digital transformation to a certain extent. But the pandemic does\nnot guarantee for a distinct sustainable and innovative future development.\nOnly by changing the initial state vectors and the weights of the connections\nan innovative and sustainable turnaround in a third scenario becomes possible.\n"}
+{"abstract": "  When constructing a Bayesian Machine Learning model, we might be faced with\nmultiple different prior distributions and thus are required to properly\nconsider them in a sensible manner in our model. While this situation is\nreasonably well explored for classical Bayesian Statistics, it appears useful\nto develop a corresponding method for complex Machine Learning problems. Given\ntheir underlying Bayesian framework and their widespread popularity, Gaussian\nProcesses are a good candidate to tackle this task. We therefore extend the\nidea of Mixture models for Gaussian Process regression in order to work with\nmultiple prior beliefs at once - both a analytical regression formula and a\nSparse Variational approach are considered. In addition, we consider the usage\nof our approach to additionally account for the problem of prior\nmisspecification in functional regression problems.\n"}
+{"abstract": "  We present closed-form expressions of unrefined instanton partition functions\nfor gauge groups of type $BCD$ as sums over Young diagrams. For\n$\\mathrm{SO}(n)$ gauge groups, we provide a fivebrane web picture of our\nformula based on the vertex-operator formalism of the topological vertex with a\nnew type called O-vertex for an O5-plane.\n"}
+{"abstract": "  An adversarial patch can arbitrarily manipulate image pixels within a\nrestricted region to induce model misclassification. The threat of this\nlocalized attack has gained significant attention because the adversary can\nmount a physically-realizable attack by attaching patches to the victim object.\nRecent provably robust defenses generally follow the PatchGuard framework by\nusing CNNs with small receptive fields and secure feature aggregation for\nrobust model predictions. In this paper, we extend PatchGuard to PatchGuard++\nfor provably detecting the adversarial patch attack to boost both provable\nrobust accuracy and clean accuracy. In PatchGuard++, we first use a CNN with\nsmall receptive fields for feature extraction so that the number of features\ncorrupted by the adversarial patch is bounded. Next, we apply masks in the\nfeature space and evaluate predictions on all possible masked feature maps.\nFinally, we extract a pattern from all masked predictions to catch the\nadversarial patch attack. We evaluate PatchGuard++ on ImageNette (a 10-class\nsubset of ImageNet), ImageNet, and CIFAR-10 and demonstrate that PatchGuard++\nsignificantly improves the provable robustness and clean performance.\n"}
+{"abstract": "  An emerging optimisation problem from the real-world applications, named the\nmulti-point dynamic aggregation (MPDA) problem, has become one of the active\nresearch topics of the multi-robot system. This paper focuses on a\nmulti-objective MPDA problem which is to design an execution plan of the robots\nto minimise the number of robots and the maximal completion time of all the\ntasks. The strongly-coupled relationships among robots and tasks, the\nredundancy of the MPDA encoding, and the variable-size decision space of the\nMO-MPDA problem posed extra challenges for addressing the problem effectively.\nTo address the above issues, we develop a hybrid decomposition-based\nmulti-objective evolutionary algorithm (HDMOEA) using $ \\varepsilon\n$-constraint method. It selects the maximal completion time of all tasks as the\nmain objective, and converted the other objective into constraints. HDMOEA\ndecomposes a MO-MPDA problem into a series of scalar constrained optimization\nsubproblems by assigning each subproblem with an upper bound robot number. All\nthe subproblems are optimized simultaneously with the transferring knowledge\nfrom other subproblems. Besides, we develop a hybrid population initialisation\nmechanism to enhance the quality of initial solutions, and a reproduction\nmechanism to transmit effective information and tackle the encoding redundancy.\nExperimental results show that the proposed HDMOEA method significantly\noutperforms the state-of-the-art methods in terms of several most-used metrics.\n"}
+{"abstract": "  This paper studies random fields on the unit sphere. Traditionally, isotropic\nGaussian random fields are considered as the underlying statistical model of\nthe cosmic microwave background (CMB) data. This paper discusses the\ngeneralized multifractional Brownian motion and its pointwise H\\\"older exponent\non the sphere. The multifractional approach is used to investigate the CMB data\nfrom the Planck mission. These data consist of CMB radiation measurements at\nnarrow angles of the sky sphere. The obtained results suggest that the\nestimated H\\\"older exponents for different CMB regions do change from location\nto location. Therefore, CMB data are multifractional. Then the developed\nmethodology is used to suggest two approaches for detecting regions with\nanomalies in cleaned CMB maps.\n"}
+{"abstract": "  This paper proposes a novel voice conversion (VC) method based on\nnon-autoregressive sequence-to-sequence (NAR-S2S) models. Inspired by the great\nsuccess of NAR-S2S models such as FastSpeech in text-to-speech (TTS), we extend\nthe FastSpeech2 model for the VC problem. We introduce the\nconvolution-augmented Transformer (Conformer) instead of the Transformer,\nmaking it possible to capture both local and global context information from\nthe input sequence. Furthermore, we extend variance predictors to variance\nconverters to explicitly convert the source speaker's prosody components such\nas pitch and energy into the target speaker. The experimental evaluation with\nthe Japanese speaker dataset, which consists of male and female speakers of\n1,000 utterances, demonstrates that the proposed model enables us to perform\nmore stable, faster, and better conversion than autoregressive S2S (AR-S2S)\nmodels such as Tacotron2 and Transformer.\n"}
+{"abstract": "  The specific heat $C$ of the cuprate superconductors La$_{2-x}$Sr$_x$CuO$_4$\nand Bi$_{2+y}$Sr$_{2-x-y}$La$_x$CuO$_{6+\\delta}$ was measured at low\ntemperature (down to $0.5~{\\rm K}$), for dopings $p$ close to $p^\\star$, the\ncritical doping for the onset of the pseudogap phase. A magnetic field up to\n$35~{\\rm T}$ was applied to suppress superconductivity, giving direct access to\nthe normal state at low temperature, and enabling a determination of $C_e$, the\nelectronic contribution to the normal-state specific heat, at $T \\to 0$. In\nLa$_{2-x}$Sr$_x$CuO$_4$ at $x=p = 0.22$, $0.24$ and $0.25$, $C_e / T =\n15-16~{\\rm mJmol}^{-1}{\\rm K}^{-2}$ at $T = 2~{\\rm K}$, values that are twice\nas large as those measured at higher doping ($p > 0.3$) and lower doping ($p <\n0.15$). This confirms the presence of a broad peak in the doping dependence of\n$C_e$ at $p^\\star\\simeq 0.19$, as previously reported for samples in which\nsuperconductivity was destroyed by Zn impurities. Moreover, at those three\ndopings, we find a logarithmic growth as $T \\to 0$, such that $C_e / T \\sim\n{\\rm B}\\ln(T_0/T)$. The peak vs $p$ and the logarithmic dependence vs $T$ are\nthe two typical thermodynamic signatures of quantum criticality. In the very\ndifferent cuprate Bi$_{2+y}$Sr$_{2-x-y}$La$_x$CuO$_{6+\\delta}$, we again find\nthat $C_e / T \\sim {\\rm B}\\ln(T_0/T$) at $p \\simeq p^\\star$, strong evidence\nthat this $\\ln(1/T)$ dependence - first discovered in the cuprates\nLa$_{1.8-x}$Eu$_{0.2}$Sr$_x$CuO$_4$ and La$_{1.6-x}$Nd$_{0.4}$Sr$_x$CuO$_4$ -\nis a universal property of the pseudogap critical point. All four materials\ndisplay similar values of the $\\rm B$ coefficient, indicating that they all\nbelong to the same universality class.\n"}
+{"abstract": "  In facing with the explosive Internet traffic growth, optical transport\nnetworks based on WDM technologies forming the core part of Internet\ninfrastructure carrying multi-Tb/s has to be re-considered from both designing,\nplanning, operation and management perspectives to attain greater efficiency.\nThanks to the convergence of significant advances in optical transmission\ntechnologies, and photonic switching, transparent (all-optical) architecture\nhas come into practice, paving the way for eliminating the over-utilization of\ncostly optical-electrical-optical (O-E-O) interfaces and hence, yielding\nremarkable savings of cost and energy consumption compared to opaque\narchitecture. Traditional designs for transparent optical networks based on\nsingle-objective optimization model aiming at optimizing solely a single\nperformance metric appears to be insufficient to capture the nuances of\npractical designs while conventional multi-objective approach tends to reach\n(non-) optimal solutions. Different from existing works, we present a new\nframework for multi-objective WDM network designs capturing several goals on\none hand and on the other hand, achieving optimal solutions. Moreover, our\nproposal exploits the characteristics of each constituent objectives to lay the\nfoundation for setting up weight coefficient so that the order of optimization\nis guaranteed. Equally important, our proposal is pragmatic in the sense that\nthe complexity of the optimization model remains the same as the\nsingle-objective model while the quality of solution has been greatly improved.\nWe have extensively tested realistic optical core networks topologies, that is,\nCOST239 and NSFNET, with various network traffic conditions and it turns out\nthat our design brings about a saving of wavelength link usage up to roughly\n$28\\%$ in the most favorable cases while $14\\%$ is expected for the least\nfavorable cases.\n"}
+{"abstract": "  In this paper, we outline a way to deploy a privacy-preserving protocol for\nmultiparty Randomized Controlled Trials on the scale of 500 million rows of\ndata and more than a billion gates. Randomized Controlled Trials (RCTs) are\nwidely used to improve business and policy decisions in various sectors such as\nhealthcare, education, criminology, and marketing. A Randomized Controlled\nTrial is a scientifically rigorous method to measure the effectiveness of a\ntreatment. This is accomplished by randomly allocating subjects to two or more\ngroups, treating them differently, and then comparing the outcomes across\ngroups. In many scenarios, multiple parties hold different parts of the data\nfor conducting and analyzing RCTs. Given privacy requirements and expectations\nof each of these parties, it is often challenging to have a centralized store\nof data to conduct and analyze RCTs.\n  We accomplish this by a three-stage solution. The first stage uses the\nPrivate Secret Share Set Intersection (PS$^3$I) solution to create a joined set\nand establish secret shares without revealing membership, while discarding\nindividuals who were placed into more than one group. The second stage runs\nmultiple instances of a general purpose MPC over a sharded database to\naggregate statistics about each experimental group while discarding individuals\nwho took an action before they received treatment. The third stage adds\ndistributed and calibrated Differential Privacy (DP) noise to the aggregate\nstatistics and uncertainty measures, providing formal two-sided privacy\nguarantees.\n  We also evaluate the performance of multiple open source general purpose MPC\nlibraries for this task. We additionally demonstrate how we have used this to\ncreate a working ads effectiveness measurement product capable of measuring\nhundreds of millions of individuals per experiment.\n"}
+{"abstract": "  Deep learning models in large-scale machine learning systems are often\ncontinuously trained with enormous data from production environments. The sheer\nvolume of streaming training data poses a significant challenge to real-time\ntraining subsystems and ad-hoc sampling is the standard practice. Our key\ninsight is that these deployed ML systems continuously perform forward passes\non data instances during inference, but ad-hoc sampling does not take advantage\nof this substantial computational effort. Therefore, we propose to record a\nconstant amount of information per instance from these forward passes. The\nextra information measurably improves the selection of which data instances\nshould participate in forward and backward passes. A novel optimization\nframework is proposed to analyze this problem and we provide an efficient\napproximation algorithm under the framework of Mini-batch gradient descent as a\npractical solution. We also demonstrate the effectiveness of our framework and\nalgorithm on several large-scale classification and regression tasks, when\ncompared with competitive baselines widely used in industry.\n"}
+{"abstract": "  Bound states in the continuum (BICs) enable unique features in tailoring\nlight-matter interaction on nanoscale. These radiationless localized states\ndrive theoretically infinite quality factors and lifetimes for modern\nnanophotonics, making room for a variety of emerging applications. Here we use\nthe peculiar properties possessed by the so-called $\\mathcal{PT}$-symmetric\noptical structures to propose the novel mechanism for the quasi-BIC\nmanifestation governed by the $\\mathcal{PT}$-symmetry breaking. In particular,\nwe study regularities of the spontaneous $\\mathcal{PT}$-symmetry breaking in\ntrilayer structures with the outer loss and gain layers consisting of materials\nwith permittivity close to zero. We reveal singular points on the curves\nseparating $\\mathcal{PT}$-symmetric and broken-$\\mathcal{PT}$-symmetry states\nin the parametric space of the light frequency and the angle of incidence.\nThese singularities remarkably coincide with the BIC positions at the frequency\nof volume plasmon excitation, where the dielectric permittivity vanishes. The\nloss and gain value acts as an asymmetry parameter that disturbs conditions of\nthe ideal BIC inducing the quasi-BIC. Fascinating properties of these\nquasi-BICs having ultrahigh quality factors and almost perfect transmission can\nbe utilized in sensing, nonlinear optics, and other applications.\n"}
+{"abstract": "  The massive shock of the COVID-19 pandemic is already showing its negative\neffects on economies around the world, unprecedented in recent history.\nCOVID-19 infections and containment measures have caused a general slowdown in\nresearch and new knowledge production. Because of the link between R&D spending\nand economic growth, it is to be expected then that a slowdown in research\nactivities will slow in turn the global recovery from the pandemic. Many recent\nstudies also claim an uneven impact on scientific production across gender. In\nthis paper, we investigate the phenomenon across countries, analysing preprint\ndepositions. Differently from other works, that compare the number of preprint\ndepositions before and after the pandemic outbreak, we analyse the depositions\ntrends across geographical areas, and contrast after-pandemic depositions with\nexpected ones. Differently from common belief and initial evidence, in few\ncountries female scientists increased their scientific output while males\nplunged.\n"}
+{"abstract": "  In this paper, we resort to the TensorFlow framework to investigate the\nbenefits of applying data vectorization and fitness caching methods to domain\nevaluation in Genetic Programming. For this purpose, an independent engine was\ndeveloped, TensorGP, along with a testing suite to extract comparative timing\nresults across different architectures and amongst both iterative and\nvectorized approaches. Our performance benchmarks demonstrate that by\nexploiting the TensorFlow eager execution model, performance gains of up to two\norders of magnitude can be achieved on a parallel approach running on dedicated\nhardware when compared to a standard iterative approach.\n"}
+{"abstract": "  Imaging the vibrations of nanomechanical resonators means measuring their\nflexural mode shapes from the dependence of their frequency response on\nin-plane position. Applied to two-dimensional resonators, this technique\nprovides a wealth of information on the mechanical properties of\natomically-thin membranes. We present a simple and robust system to image the\nvibrations of few layer graphene (FLG) resonators at room temperature and in\nvacuum with an in-plane displacement precision of $\\approx0.20$ $\\mu$m. It\nconsists of a sturdy vacuum enclosure mounted on a three-axis micropositioning\nstage and designed for free space optical measurements of vibrations. The\nsystem is equipped with ultra-flexible radio frequency waveguides to\nelectrically actuate resonators. With it we characterize the lowest frequency\nmode of a FLG resonator by measuring its frequency response as a function of\nposition on the membrane. The resonator is suspended over a nanofabricated\nlocal gate electrode acting both as a mirror and as a capacitor plate to\nactuate vibrations at radio frequencies. From these measurements, we estimate\nthe ratio of thermal expansion coefficient to thermal conductivity of the\nmembrane, and we measure the effective mass of the lowest frequency mode. We\ncomplement our study with a globally gated resonator and image its first three\nvibration modes. There, we find that folds in the membrane locally suppress\nvibrations.\n"}
+{"abstract": "  Graph Neural Networks (GNNs) are deep learning methods which provide the\ncurrent state of the art performance in node classification tasks. GNNs often\nassume homophily -- neighboring nodes having similar features and labels--, and\ntherefore may not be at their full potential when dealing with non-homophilic\ngraphs. In this work, we focus on addressing this limitation and enable Graph\nAttention Networks (GAT), a commonly used variant of GNNs, to explore the\nstructural information within each graph locality. Inspired by the positional\nencoding in the Transformers, we propose a framework, termed Graph Attentional\nNetworks with Positional Embeddings (GAT-POS), to enhance GATs with positional\nembeddings which capture structural and positional information of the nodes in\nthe graph. In this framework, the positional embeddings are learned by a model\npredictive of the graph context, plugged into an enhanced GAT architecture,\nwhich is able to leverage both the positional and content information of each\nnode. The model is trained jointly to optimize for the task of node\nclassification as well as the task of predicting graph context. Experimental\nresults show that GAT-POS reaches remarkable improvement compared to strong GNN\nbaselines and recent structural embedding enhanced GNNs on non-homophilic\ngraphs.\n"}
+{"abstract": "  Image-based age estimation aims to predict a person's age from facial images.\nIt is used in a variety of real-world applications. Although end-to-end deep\nmodels have achieved impressive results for age estimation on benchmark\ndatasets, their performance in-the-wild still leaves much room for improvement\ndue to the challenges caused by large variations in head pose, facial\nexpressions, and occlusions. To address this issue, we propose a simple yet\neffective method to explicitly incorporate facial semantics into age\nestimation, so that the model would learn to correctly focus on the most\ninformative facial components from unaligned facial images regardless of head\npose and non-rigid deformation. To this end, we design a face parsing-based\nnetwork to learn semantic information at different scales and a novel face\nparsing attention module to leverage these semantic features for age\nestimation. To evaluate our method on in-the-wild data, we also introduce a new\nchallenging large-scale benchmark called IMDB-Clean. This dataset is created by\nsemi-automatically cleaning the noisy IMDB-WIKI dataset using a constrained\nclustering method. Through comprehensive experiment on IMDB-Clean and other\nbenchmark datasets, under both intra-dataset and cross-dataset evaluation\nprotocols, we show that our method consistently outperforms all existing age\nestimation methods and achieves a new state-of-the-art performance. To the best\nof our knowledge, our work presents the first attempt of leveraging face\nparsing attention to achieve semantic-aware age estimation, which may be\ninspiring to other high level facial analysis tasks.\n"}
+{"abstract": "  We introduce several geometric notions, including thick-thin decompositions\nand the width of a homology class, to the theory of persistent homology. These\nideas provide geometric interpretations of persistence diagrams. Indeed, we\ngive quantitative and geometric descriptions of the ``size'' or ``persistence''\nof a homology class. As a case study, we analyze the power filtration on\nunweighted graphs, and provide explicit bounds for the life spans of homology\nclasses in persistence diagrams in all dimensions.\n"}
+{"abstract": "  Training of Artificial Neural Networks is a complex task of great importance\nin supervised learning problems. Evolutionary Algorithms are widely used as\nglobal optimization techniques and these approaches have been used for\nArtificial Neural Networks to perform various tasks. An optimization algorithm,\ncalled Group Search Optimizer (GSO), was proposed and inspired by the search\nbehaviour of animals. In this article we present two new hybrid approaches:\nCGSO-Hk-WD and CGSO-Sk-WD. Cooperative GSOs are based on the divide-and-conquer\nparadigm, employing cooperative behaviour between GSO groups to improve the\nperformance of the standard GSO. We also apply the weight decay strategy (WD,\nacronym for Weight Decay) to increase the generalizability of the networks. The\nresults show that cooperative GSOs are able to achieve better performance than\ntraditional GSO for classification problems in benchmark datasets such as\nCancer, Diabetes, Ecoli and Glass datasets.\n"}
+{"abstract": "  The transport of virus-laden aerosols from a host to a susceptible person is\ngoverned by complex turbulent airflow, and physics related to breathing,\ncoughing and sneezing, mechanical and passive ventilation, thermal buoyancy\neffects, surface deposition, masks, and air filtration. In this paper, we study\nthe infection risk via airborne transmission on an urban bus using unsteady\nReynolds-Averaged Navier--Stokes equations and a passive-scalar model of the\nvirus-laden aerosol concentration. Results from these simulations are directly\ncompared to the widely-used well mixed model, and show significant differences\nin the concentration field and number of inhaled particles. Specifically, in\nthe limit of low mechanical ventilation rate, the well-mixed model will\nover-predict concentration far from the infected passenger, and substantially\nunderpredict concentration near the infected passenger. The results reported\nherein also apply to other enclosed spaces.\n"}
+{"abstract": "  Recently, a special kind of graph, i.e., supernet, which allows two nodes\nconnected by multi-choice edges, has exhibited its power in neural architecture\nsearch (NAS) by searching for better architectures for computer vision (CV) and\nnatural language processing (NLP) tasks. In this paper, we discover that the\ndesign of such discrete architectures also appears in many other important\nlearning tasks, e.g., logical chain inference in knowledge graphs (KGs) and\nmeta-path discovery in heterogeneous information networks (HINs). Thus, we are\nmotivated to generalize the supernet search problem on a broader horizon.\nHowever, none of the existing works are effective since the supernet topology\nis highly task-dependent and diverse. To address this issue, we propose to\ntensorize the supernet, i.e., unify the subgraph search problems by a tensor\nformulation and encode the topology inside the supernet by a tensor network. We\nfurther propose an efficient algorithm that admits both stochastic and\ndeterministic objectives to solve the search problem. Finally, we perform\nextensive experiments on diverse learning tasks, i.e., architecture design for\nCV, logic inference for KG, and meta-path discovery for HIN. Empirical results\ndemonstrate that our method leads to better performance and architectures.\n"}
+{"abstract": "  In this letter, we have carried out an independent validation of the Gaia\nEDR3 photometry using about 10,000 Landolt standard stars from Clem & Landolt\n(2013). Using a machine learning technique, the UBVRI magnitudes are converted\ninto the Gaia magnitudes and colors and then compared to those in the EDR3,\nwith the effect of metallicity incorporated. Our result confirms the\nsignificant improvements in the calibration process of the Gaia EDR3. Yet\nmodest trends up to 10 mmag with G magnitude are found for all the magnitudes\nand colors for the 10 < G < 19 mag range, particularly for the bright and faint\nends. With the aid of synthetic magnitudes computed on the CALSPEC spectra with\nthe Gaia EDR3 passbands, absolute corrections are further obtained, paving the\nway for optimal usage of the Gaia EDR3 photometry in high accuracy\ninvestigations.\n"}
+{"abstract": "  Clustering algorithms partition a dataset into groups of similar points. The\nclustering problem is very general, and different partitions of the same\ndataset could be considered correct and useful. To fully understand such data,\nit must be considered at a variety of scales, ranging from coarse to fine. We\nintroduce the Multiscale Environment for Learning by Diffusion (MELD) data\nmodel, which is a family of clusterings parameterized by nonlinear diffusion on\nthe dataset. We show that the MELD data model precisely captures latent\nmultiscale structure in data and facilitates its analysis. To efficiently learn\nthe multiscale structure observed in many real datasets, we introduce the\nMultiscale Learning by Unsupervised Nonlinear Diffusion (M-LUND) clustering\nalgorithm, which is derived from a diffusion process at a range of temporal\nscales. We provide theoretical guarantees for the algorithm's performance and\nestablish its computational efficiency. Finally, we show that the M-LUND\nclustering algorithm detects the latent structure in a range of synthetic and\nreal datasets.\n"}
+{"abstract": "  The topological evolution of classic eigenmodes including\nHermite-Laguerre-Gaussian and (helical) InceGaussian modes is exploited to\nconstruct coherent state modes, which unifies the representations of\ntravelingwave (TW) and standing-wave (SW) ray-wave structured light for the\nfirst time and realizes the TW-SW unified ray-wave geometric beam with topology\nof raytrajectories splitting effect, breaking the boundary of TW and SW\nstructured light. We experimentally generate these new modes with high purity\nand dynamic control by digital holography method, revealing potential\napplications in optical manipulation and communication.\n"}
+{"abstract": "  In this paper we introduce the characteristic gluing problem for the Einstein\nvacuum equations. We present a codimension-$10$ gluing construction for\ncharacteristic initial data which are close to the Minkowski data and we show\nthat the $10$-dimensional obstruction space consists of gauge-invariant charges\nwhich are conserved by the linearized null constraint equations. By relating\nthese $10$ charges to the ADM energy, linear momentum, angular momentum and the\ncenter-of-mass we prove that asymptotically flat data can be characteristically\nglued (including the $10$ charges) to the data of a suitably chosen Kerr\nspacetime, obtaining as a corollary an alternative proof of the Corvino--Schoen\nspacelike gluing construction. Moreover, we derive a localized version of our\nconstruction where the given data restricted on an angular sector is\ncharacteristically glued to the Minkowski data restricted on another angular\nsector. As a corollary we obtain an alternative proof of the Carlotto-Schoen\nlocalized spacelike gluing construction. Our method yields no loss of decay in\nthe transition region, resolving an open problem. We also discuss a number of\nother applications.\n"}
+{"abstract": "  We study communication systems over band-limited Additive White Gaussian\nNoise (AWGN) channels in which the transmitter output is constrained to be\nsymmetric binary (bi-polar). In this work we improve the original\nOzarov-Wyner-Ziv (OWZ) lower bound on capacity by introducing new achievability\nschemes with two advantages over the studied OWZ scheme which is based on\npeak-power constrained pulse-amplitude modulation. Our schemes achieve a\nmoderately improved information rate and do so with much less sign transitions\nof the binary signal. The gap between the known upper-bound based on spectral\nconstrains of bi-polar signals and our achievable lower bound is reduced to\n0.93 bits per Nyquist interval at high SNR.\n"}
+{"abstract": "  Scenarios requiring humans to choose from multiple seemingly optimal actions\nare commonplace, however standard imitation learning often fails to capture\nthis behavior. Instead, an over-reliance on replicating expert actions induces\ninflexible and unstable policies, leading to poor generalizability in an\napplication. To address the problem, this paper presents the first imitation\nlearning framework that incorporates Bayesian variational inference for\nlearning flexible non-parametric multi-action policies, while simultaneously\nrobustifying the policies against sources of error, by introducing and\noptimizing disturbances to create a richer demonstration dataset. This\ncombinatorial approach forces the policy to adapt to challenging situations,\nenabling stable multi-action policies to be learned efficiently. The\neffectiveness of our proposed method is evaluated through simulations and\nreal-robot experiments for a table-sweep task using the UR3 6-DOF robotic arm.\nResults show that, through improved flexibility and robustness, the learning\nperformance and control safety are better than comparison methods.\n"}
+{"abstract": "  In Jiang et al. (2020), we reported a possible bright flash (hereafter\nGN-z11-flash) from a galaxy GN-z11 at z ~ 11. Recently, Steinhardt et al.\n(2021; arXiv:2101.12738) found 27 images with transient signals in Keck MOSFIRE\narchival data and claimed that GN-z11-flash was more likely from a moving\nobject in our Solar system. We show that the Steinhardt et al.'s definition of\nthe chance probability and their methodology of finding GN-z11-flash-like\ntransients are problematic in several aspects. In particular, none of their\ntransients is analogous to GN-z11-flash, and none of them is positionally\ncoincident with a known object in their imaging data. In Jiang et al., we\nperformed a comprehensive analysis of the origin of GN-z11-flash and ruled out,\nto the best of our knowledge, the possibility of known man-made objects or\nmoving objects in the Solar system, based on all available information and our\ncurrent understanding of these objects. Steinhardt et al. did not use such\ninformation and did not analyse the GN-z11-flash event itself. The majority of\ntheir transients are apparently low-Earth orbit satellites or aircrafts.\nTherefore, their analysis can neither prove nor disprove our results. Finally,\nwe present a method to estimate the chance probability of finding\nGN-z11-flash-like transients in archival data. Based on this method and the\narchival data used by Steinhardt et al., we obtain a loose upper limit of the\nprobability that actually support the original results of Jiang et al. (2020).\n"}
+{"abstract": "  Characterization of the molecular properties of surfaces under ambient or\nchemically reactive conditions is a fundamental scientific challenge. Moreover,\nmany traditional analytical techniques used for probing surfaces often lack\ndynamic or molecular selectivity, which limits their applicability for\nmechanistic and kinetic studies under realistic chemical conditions. Nuclear\nmagnetic resonance spectroscopy (NMR) is a widely used technique and would be\nideal for probing interfaces due to the molecular information it provides\nnoninvasively. However, it lacks the sensitivity to probe the small number of\nspins at surfaces. Here, we use nitrogen vacancy (NV) centers in diamond as\nquantum sensors to optically detect nuclear magnetic resonance signals from\nchemically modified aluminum oxide surfaces, prepared with atomic layer\ndeposition (ALD). With the surface NV-NMR technique, we are able to monitor in\nreal-time the formation kinetics of a self assembled monolayer (SAM) based on\nphosphonate anchoring chemistry to the surface. This demonstrates the\ncapability of quantum sensors as a new surface-sensitive tool with\nsub-monolayer sensitivity for in-situ NMR analysis with the additional\nadvantage of a strongly reduced technical complexity.\n"}
+{"abstract": "  This monograph is centred at the intersection of three mathematical topics,\nthat are theoretical in nature, yet with motivations and relevance deep rooted\nin applications: the linear inverse problems on abstract, in general\ninfinite-dimensional Hilbert space; the notion of Krylov subspace associated to\nan inverse problem, i.e., the cyclic subspace built upon the datum of the\ninverse problem by repeated application of the linear operator; the possibility\nto solve the inverse problem by means of Krylov subspace methods, namely\nprojection methods where the finite-dimensional truncation is made with respect\nto the Krylov subspace and the approximants converge to an exact solution to\nthe inverse problem.\n"}
+{"abstract": "  In 2019, Eliud Chipgoke ran a sub-two hour marathon in an unofficial race\nwearing last-generation shoes. The legitimacy of this feat was naturally\nquestioned due to unusual racing conditions and suspicions of technological\ndoping. In this work, we assess the likelihood of a sub-two hour marathon in an\nofficial race, and the potential influence of Vaporfly shoes, by studying the\nevolution of running top performances from 2001 to 2019 for distances ranging\nfrom 10k to marathon. The analysis is performed using extreme value theory, a\nfield of statistics dealing with analysis of records. We find a significant\nevidence of technological doping with a 12% increase of the probability that a\nnew world record for marathon-man discipline is set in 2021. However, results\nsuggest that achieving a sub-two hour marathon in an official event in 2021 is\nstill very unlikely, and exceeds 10% probability only by 2025.\n"}
+{"abstract": "  We perform a detailed photometric and astrometric analysis of stars in the\nJet stream using data from the first data release of the DECam Local Volume\nExploration Survey (DELVE) DR1 and \\emph{Gaia} EDR3. We discover that the\nstream extends over $\\sim 29 ^{\\circ}$ on the sky (increasing the known length\nby $18^{\\circ}$), which is comparable to the kinematically cold Phoenix, ATLAS,\nand GD-1 streams. Using blue horizontal branch stars, we resolve a distance\ngradient along the Jet stream of 0.2 kpc/deg, with distances ranging from\n$D_\\odot \\sim 27-34$ kpc. We use natural splines to simultaneously fit the\nstream track, width, and intensity to quantitatively characterize density\nvariations in the Jet stream, including a large gap, and identify substructure\noff the main track of the stream. Furthermore, we report the first measurement\nof the proper motion of the Jet stream and find that it is well-aligned with\nthe stream track suggesting the stream has likely not been significantly\nperturbed perpendicular to the line of sight. Finally, we fit the stream with a\ndynamical model and find that the stream is on a retrograde orbit, and is well\nfit by a gravitational potential including the Milky Way and Large Magellanic\nCloud. These results indicate the Jet stream is an excellent candidate for\nfuture studies with deeper photometry, astrometry, and spectroscopy to study\nthe potential of the Milky Way and probe perturbations from baryonic and dark\nmatter substructure.\n"}
+{"abstract": "  The atomic cluster expansion is a general polynomial expansion of the atomic\nenergy in multi-atom basis functions. Here we implement the atomic cluster\nexpansion in the performant C++ code \\verb+PACE+ that is suitable for use in\nlarge scale atomistic simulations. We briefly review the atomic cluster\nexpansion and give detailed expressions for energies and forces as well as\nefficient algorithms for their evaluation. We demonstrate that the atomic\ncluster expansion as implemented in \\verb+PACE+ shifts a previously established\nPareto front for machine learning interatomic potentials towards faster and\nmore accurate calculations. Moreover, general purpose parameterizations are\npresented for copper and silicon and evaluated in detail. We show that the new\nCu and Si potentials significantly improve on the best available potentials for\nhighly accurate large-scale atomistic simulations.\n"}
+{"abstract": "  We study quasi-locally complete locally convex spaces and generalize this\nconcept to quasi-locally Baire locally convex spaces. It is shown that an\ninductive limit of strictly webbed spaces is regular if it is quasi-locally\nBaire. This extends Qiu's theorem on regularity. Additionally, if each step is\nstrictly webbed and quasi-locally Baire, then the inductive limit is\nquasi-locally Baire if it is regular. Relevant examples are provided.\n"}
+{"abstract": "  For a connected finite poset $P$, let $E(P)$ be the poset induced by the\nextremal points of $P$. We show that the fixed point property of $E(P)$ implies\nthe fixed point property of $P$. On the other hand, we show that a homomorphism\n$f : E(P) \\rightarrow Q$ can be extended to $P$ if $Q$ is a flat poset not\ncontaining a 4-crown. We conclude that every retract-crown of $E(P)$ with more\nthan four points is a retract-crown of $P$, too. We see that for $P$ having the\nfixed point property but $E(P)$ not, every edge of every crown in $E(P)$ must\nbelong to a so-called improper 4-crown, with additional specifications if $P$\nhas height two. The results provide several sufficient and necessary conditions\nfor $P$ having the fixed point property, and these conditions refer to objects\nsimpler than $P$.\n"}
+{"abstract": "  Establishing the origin of the water D/H ratio in the Solar System is central\nto our understanding of the chemical trail of water during the star and planet\nformation process. Recent modeling suggests that comparisons of the D$_2$O/HDO\nand HDO/H$_2$O ratios are a powerful way to trace the chemical evolution of\nwater and, in particular, determine whether the D/H ratio is inherited from the\nmolecular cloud or established locally. We seek to determine the D$_2$O column\ndensity and derive the D$_2$O/HDO ratios in the warm region toward the low-mass\nClass 0 sources B335 and L483. The results are compared with astrochemical\nmodels and previous observations to determine their implications for the\nchemical evolution of water. We present ALMA observations of the D$_2$O\ntransition at 316.8 GHz toward B335 and L483 at $<$0.5\" ($<$ 100 au)\nresolution, probing the inner warm envelope gas. The column densities of\nD$_2$O, HDO, and H$_{2}^{18}$O are determined by synthetic spectrum modeling\nand direct Gaussian fitting, under the assumption of a single excitation\ntemperature and similar spatial extent for the three water isotopologs. D$_2$O\nis detected toward both sources in the inner warm envelope. The derived\nD$_2$O/HDO ratios is $(1.0\\pm0.2)\\times10^{-2}$ for L483 and\n$(1.4\\pm0.1)\\times10^{-2}$ for B335. The high D$_2$O/HDO ratios are a strong\nindication of chemical inheritance of water from the prestellar phase down to\nthe inner warm envelope. This implies that the local cloud conditions in the\nprestellar phase, such as temperatures and timescales, determine the water\nchemistry at later stages and could provide a source of chemical\ndifferentiation in young systems. In addition, the observed D$_2$O/H$_2$O\nratios support an observed dichotomy in the deuterium fractionation of water\ntoward isolated and clustered protostars, namely, a higher D/H ratio toward\nisolated sources\n"}
+{"abstract": "  The goal of this work is to find the simplest UV completion of Accidental\nComposite Dark Matter Models (ACDM) that can dynamically generate an asymmetry\nfor the DM candidate, the lightest \\textit{dark baryon} (DCb), and\nsimultaneously annihilate the symmetric component. In this framework the DCb is\na bound state of a confining $\\text{SU}(N)_{\\text{DC}}$ gauge group, and can\ninteract weakly with the visible sector. The constituents of the DCb can\npossess non-trivial charges under the Standard Model gauge group. The\ngeneration of asymmetry for such candidate is a two-flavor variation of the\n\\emph{out-of-equilibrium} decay of a heavy scalar, with mass $M_\\phi\\gtrsim\n10^{10}$ GeV. Below the scale of the scalars, the models recover accidental\nstability, or long-livedness, of the DM candidate. The symmetric component is\nannihilated by residual confined interactions provided that the mass of the DCb\n$m_{\\text{DCb}} \\lesssim 75$ TeV. We implement the mechanism of asymmetry\ngeneration, or a variation of it, in all the original ACDM models, managing to\ngenerate the correct asymmetry for DCb of masses in this range. For some of the\nmodels found, the stability of the DM candidate is not spoiled even considering\ngeneric GUT completions or asymmetry generation mechanisms in the visible\nsector.\n"}
+{"abstract": "  A few years ago Avetissian {\\it et al.} \\cite{Avetissian2014,Avetissian2015}\ndiscovered that the exponential growth rate of the stimulated annihilation\nphotons from a singlet positronium Bose-Einstein condensate should be\nproportional to the square root of the positronium number density, not to the\nnumber density itself. In order to elucidate this surprising result obtained\nvia a field-theoretical analysis, we point out that the basic physics involved\nis the same as that of resonant subharmonic transitions between two quantum\noscillators. Using this model, we show that nonlinearities of the type\ndiscovered by Avetissian {\\it et al.} are not unique to positronium and in fact\nwill be encountered in a wide range of systems that can be modeled as\nnonlinearly coupled quantum oscillators.\n"}
+{"abstract": "  This paper proposes an approach to addresses the control challenges posed by\na fault-induced uncertainty in both the dynamics and control input\neffectiveness of a class of hierarchical nonlinear systems in which the\nhigh-level dynamics is nonlinearly coupled with a multi-agent low-level\ndynamics. The high-level dynamics has a multiplicative uncertainty in the\ncontrol input effectiveness and is subjected to an exogenous disturbance input.\nOn the other hand, the low-level system is subjected to actuator faults causing\na time-varying multiplicative uncertainty in the dynamical model and associated\ncontrol effectiveness. Moreover, the nonlinear coupling between the high-level\nand the low-level dynamics makes the problem even more challenging. To address\nthis problem, an online parameter estimation algorithm is designed, coupled\nwith an adaptive splitting mechanism which automatically distributes the\ncontrol action among low level multi-agent systems. A nonlinear\n$\\mathcal{L}_2$-gain-based controller, and then a state-feedback controller are\ndesigned in the high-level, and the low-level, respectively, to recover the\nsystem from faults with high performance in the transient response, and reject\nthe exogenous disturbance. The resulting analysis guarantees a robust tracking\nof the high-level reference command signal.\n"}
+{"abstract": "  Modern policy gradient algorithms such as Proximal Policy Optimization (PPO)\nrely on an arsenal of heuristics, including loss clipping and gradient\nclipping, to ensure successful learning. These heuristics are reminiscent of\ntechniques from robust statistics, commonly used for estimation in outlier-rich\n(``heavy-tailed'') regimes. In this paper, we present a detailed empirical\nstudy to characterize the heavy-tailed nature of the gradients of the PPO\nsurrogate reward function. We demonstrate that the gradients, especially for\nthe actor network, exhibit pronounced heavy-tailedness and that it increases as\nthe agent's policy diverges from the behavioral policy (i.e., as the agent goes\nfurther off policy). Further examination implicates the likelihood ratios and\nadvantages in the surrogate reward as the main sources of the observed\nheavy-tailedness. We then highlight issues arising due to the heavy-tailed\nnature of the gradients. In this light, we study the effects of the standard\nPPO clipping heuristics, demonstrating that these tricks primarily serve to\noffset heavy-tailedness in gradients. Thus motivated, we propose incorporating\nGMOM, a high-dimensional robust estimator, into PPO as a substitute for three\nclipping tricks. Despite requiring less hyperparameter tuning, our method\nmatches the performance of PPO (with all heuristics enabled) on a battery of\nMuJoCo continuous control tasks.\n"}
+{"abstract": "  With increasing availability of high dimensional, multi-source data, the\nidentification of joint and data specific patterns of variability has become a\nsubject of interest in many research areas. Several matrix decomposition\nmethods have been formulated for this purpose, for example JIVE (Joint and\nIndividual Variation Explained), and its angle based variation, aJIVE. Although\nthe effect of data contamination on the estimated joint and individual\ncomponents has not been considered in the literature, gross errors and outliers\nin the data can cause instability in such methods, and lead to incorrect\nestimation of joint and individual variance components. We focus on the aJIVE\nfactorization method and provide a thorough analysis of the effect outliers on\nthe resulting variation decomposition. After showing that such effect is not\nnegligible when all data-sources are contaminated, we propose a robust\nextension of aJIVE (RaJIVE) that integrates a robust formulation of the\nsingular value decomposition into the aJIVE approach. The proposed RaJIVE is\nshown to provide correct decompositions even in the presence of outliers and\nimproves the performance of aJIVE. We use extensive simulation studies with\ndifferent levels of data contamination to compare the two methods. Finally, we\ndescribe an application of RaJIVE to a multi-omics breast cancer dataset from\nThe Cancer Genome Atlas. We provide the R package RaJIVE with a ready-to-use\nimplementation of the methods and documentation of code and examples.\n"}
+{"abstract": "  Firth-type logistic regression has become a standard approach for the\nanalysis of binary outcomes with small samples. Whereas it reduces the bias in\nmaximum likelihood estimates of coefficients, bias towards 1/2 is introduced in\nthe predicted probabilities. The stronger the imbalance of the outcome, the\nmore severe is the bias in the predicted probabilities. We propose two simple\nmodifications of Firth-type logistic regression resulting in unbiased predicted\nprobabilities. The first corrects the predicted probabilities by a post-hoc\nadjustment of the intercept. The other is based on an alternative formulation\nof Firth-types estimation as an iterative data augmentation procedure. Our\nsuggested modification consists in introducing an indicator variable which\ndistinguishes between original and pseudo observations in the augmented data.\nIn a comprehensive simulation study these approaches are compared to other\nattempts to improve predictions based on Firth-type penalization and to other\npublished penalization strategies intended for routine use. For instance, we\nconsider a recently suggested compromise between maximum likelihood and\nFirth-type logistic regression. Simulation results are scrutinized both with\nregard to prediction and regression coefficients. Finally, the methods\nconsidered are illustrated and compared for a study on arterial closure devices\nin minimally invasive cardiac surgery.\n"}
+{"abstract": "  Current deep learning techniques for style transfer would not be optimal for\ndesign support since their \"one-shot\" transfer does not fit exploratory design\nprocesses. To overcome this gap, we propose parametric transcription, which\ntranscribes an end-to-end style transfer effect into parameter values of\nspecific transformations available in an existing content editing tool. With\nthis approach, users can imitate the style of a reference sample in the tool\nthat they are familiar with and thus can easily continue further exploration by\nmanipulating the parameters. To enable this, we introduce a framework that\nutilizes an existing pretrained model for style transfer to calculate a\nperceptual style distance to the reference sample and uses black-box\noptimization to find the parameters that minimize this distance. Our\nexperiments with various third-party tools, such as Instagram and Blender, show\nthat our framework can effectively leverage deep learning techniques for\ncomputational design support.\n"}
+{"abstract": "  We give an overview of the practice and science of call center workforce\nplanning, where we evaluate the commonly used methods by their quality and the\ntheory by its applicability. As such this paper is useful for developers and\nconsultants interested in the background and advanced methodology of workforce\nmanagement, and for researchers interested in practically relevant science.\n"}
+{"abstract": "  Computing Education Research (CER) is critical for supporting the increasing\nnumber of students who need to learn computing skills. To systematically\nadvance knowledge, publications must be clear enough to support replications,\nmeta-analyses, and theory-building. The goal of this study is to characterize\nthe reporting of empiricism in CER literature by identifying whether\npublications include information to support replications, meta-analyses, and\ntheory building. The research questions are: RQ1) What percentage of papers in\nCER venues have empirical evaluation? RQ2) What are the characteristics of the\nempirical evaluation? RQ3) Do the papers with empirical evaluation follow\nreporting norms (both for inclusion and for labeling of key information)? We\nconducted an SLR of 427 papers published during 2014 and 2015 in five CER\nvenues: SIGCSE TS, ICER, ITiCSE, TOCE, and CSE. We developed and applied the\nCER Empiricism Assessment Rubric. Over 80% of papers had some form of empirical\nevaluation. Quantitative evaluation methods were the most frequent. Papers most\nfrequently reported results on interventions around pedagogical techniques,\ncurriculum, community, or tools. There was a split in papers that had some type\nof comparison between an intervention and some other data set or baseline. Many\npapers lacked properly reported research objectives, goals, research questions,\nor hypotheses, description of participants, study design, data collection, and\nthreats to validity. CER authors are contributing empirical results to the\nliterature; however, not all norms for reporting are met. We encourage authors\nto provide clear, labeled details about their work so readers can use the\nmethodologies and results for replications and meta-analyses. As our community\ngrows, our reporting of CER should mature to help establish computing education\ntheory to support the next generation of computing learners.\n"}
+{"abstract": "  The spatial discretization of the single-cone Dirac Hamiltonian on the\nsurface of a topological insulator or superconductor needs a special\n\"staggered\" grid, to avoid the appearance of a spurious second cone in the\nBrillouin zone. We adapt the Stacey discretization from lattice gauge theory to\nproduce a generalized eigenvalue problem, of the form ${\\mathcal H}\\psi=E\n{\\mathcal P}\\psi$, with Hermitian tight-binding operators ${\\mathcal H}$,\n${\\mathcal P}$, a locally conserved particle current, and preserved chiral and\nsymplectic symmetries. This permits the study of the spectral statistics of\nDirac fermions in each of the four symmetry classes A, AII, AIII, and D.\n"}
+{"abstract": "  We propose a construction with which to resolve the black hole singularity\nand enable an anisotropic cosmology to emerge from the inside of the hole. The\nmodel relies on the addition of an S-brane to the effective action which\ndescribes the geometry of space-time. This space-like defect is located inside\nof the horizon on a surface where the Weyl curvature reaches a limiting value.\nWe study how metric fluctuations evolve from the outside of the black hole to\nthe beginning of the cosmological phase to the future of the S-brane. Our setup\naddresses i) the black hole singularity problem, ii) the cosmological\nsingularity problem and iii) the information loss paradox since the outgoing\nHawking radiation is entangled with the state inside the black hole which\nbecomes the new universe.\n"}
+{"abstract": "  We prove large (and moderate) deviations for a class of linear combinations\nof spacings generated by i.i.d. exponentially distributed random variables. We\nallow a wide class of coefficients which can be expressed in terms of\ncontinuous functions defined on [0, 1] which satisfy some suitable conditions.\nIn this way we generalize some recent results by Giuliano et al. (2015) which\nconcern the empirical cumulative entropies defined in Di Crescenzo and\nLongobardi (2009a).\n"}
+{"abstract": "  Let $F$ be a finite unramified extension of $\\mathbb Q\\_p$ and $\\bar\\rho$ be\nan absolutely irreducible mod~$p$ $2$-dimensional representation of the\nabsolute Galois group of $F$. Let $t$ be a tame inertial type of $F$. We\nconjecture that the deformation space parametrizing the potentially\nBarsotti--Tate liftings of $\\bar\\rho$ having type $t$ depends only on the Kisin\nvariety attached to the situation, enriched with its canonical embedding into\n$(\\mathbb P^1)^f$ and its shape stratification. We give evidences towards this\nconjecture by proving that the Kisin variety determines the cardinality of the\nset of common Serre weights $D(t,\\bar\\rho) = D(t) \\cap D(\\bar\\rho)$. Besides,\nwe prove that this dependance is nondecreasing (the smaller is the Kisin\nvariety, the smaller is the number of common Serre weights) and compatible with\nproducts (if the Kisin variety splits as a product, so does the number of\nweights).\n"}
+{"abstract": "  Direct optimization of IR metrics has often been adopted as an approach to\ndevise and develop ranking-based recommender systems. Most methods following\nthis approach aim at optimizing the same metric being used for evaluation,\nunder the assumption that this will lead to the best performance. A number of\nstudies of this practice bring this assumption, however, into question. In this\npaper, we dig deeper into this issue in order to learn more about the effects\nof the choice of the metric to optimize on the performance of a ranking-based\nrecommender system. We present an extensive experimental study conducted on\ndifferent datasets in both pairwise and listwise learning-to-rank scenarios, to\ncompare the relative merit of four popular IR metrics, namely RR, AP, nDCG and\nRBP, when used for optimization and assessment of recommender systems in\nvarious combinations. For the first three, we follow the practice of loss\nfunction formulation available in literature. For the fourth one, we propose\nnovel loss functions inspired by RBP for both the pairwise and listwise\nscenario. Our results confirm that the best performance is indeed not\nnecessarily achieved when optimizing the same metric being used for evaluation.\nIn fact, we find that RBP-inspired losses perform at least as well as other\nmetrics in a consistent way, and offer clear benefits in several cases.\nInteresting to see is that RBP-inspired losses, while improving the\nrecommendation performance for all uses, may lead to an individual performance\ngain that is correlated with the activity level of a user in interacting with\nitems. The more active the users, the more they benefit. Overall, our results\nchallenge the assumption behind the current research practice of optimizing and\nevaluating the same metric, and point to RBP-based optimization instead as a\npromising alternative when learning to rank in the recommendation context.\n"}
+{"abstract": "  Advances in unmanned aerial vehicle (UAV) design have opened up applications\nas varied as surveillance, firefighting, cellular networks, and delivery\napplications. Additionally, due to decreases in cost, systems employing fleets\nof UAVs have become popular. The uniqueness of UAVs in systems creates a novel\nset of trajectory or path planning and coordination problems. Environments\ninclude many more points of interest (POIs) than UAVs, with obstacles and\nno-fly zones. We introduce REPlanner, a novel multi-agent reinforcement\nlearning algorithm inspired by economic transactions to distribute tasks\nbetween UAVs. This system revolves around an economic theory, in particular an\nauction mechanism where UAVs trade assigned POIs. We formulate the path\nplanning problem as a multi-agent economic game, where agents can cooperate and\ncompete for resources. We then translate the problem into a Partially\nObservable Markov decision process (POMDP), which is solved using a\nreinforcement learning (RL) model deployed on each agent. As the system\ncomputes task distributions via UAV cooperation, it is highly resilient to any\nchange in the swarm size. Our proposed network and economic game architecture\ncan effectively coordinate the swarm as an emergent phenomenon while\nmaintaining the swarm's operation. Evaluation results prove that REPlanner\nefficiently outperforms conventional RL-based trajectory search.\n"}
+{"abstract": "  Strain is ubiquitous in solid-state materials, but despite its fundamental\nimportance and technological relevance, leveraging externally applied strain to\ngain control over material properties is still in its infancy. In particular,\nstrain control over the diverse phase transitions and topological states in\ntwo-dimensional (2D) transition metal dichalcogenides (TMDs) remains an open\nchallenge. Here, we exploit uniaxial strain to stabilize the long-debated\nstructural ground state of the 2D topological semimetal IrTe$_2$, which is\nhidden in unstrained samples. Combined angle-resolved photoemission\nspectroscopy (ARPES) and scanning tunneling microscopy (STM) data reveal the\nstrain-stabilized phase has a 6x1 periodicity and undergoes a Lifshitz\ntransition, granting unprecedented spectroscopic access to previously\ninaccessible type-II topological Dirac states that dominate the modified\ninter-layer hopping. Supported by density functional theory (DFT) calculations,\nwe show that strain induces a charge transfer strongly weakening the\ninter-layer Te bonds and thus reshaping the energetic landscape of the system\nin favor of the 6x1 phase. Our results highlight the potential to exploit\nstrain-engineered properties in layered materials, particularly in the context\nof tuning inter-layer behavior.\n"}
+{"abstract": "  We present a novel control-theoretic understanding of online optimization and\nlearning in games, via the notion of passivity. Passivity is a fundamental\nconcept in control theory, which abstracts energy conservation and dissipation\nin physical systems. It has become a standard tool in analysis of general\nfeedback systems, to which game dynamics belong. Our starting point is to show\nthat all continuous-time Follow-the-Regularized-Leader (FTRL) dynamics, which\ninclude the well-known Replicator Dynamic, are lossless, i.e. it is passive\nwith no energy dissipation. Interestingly, we prove that passivity implies\nbounded regret, connecting two fundamental primitives of control theory and\nonline optimization.\n  The observation of energy conservation in FTRL inspires us to present a\nfamily of lossless learning dynamics, each of which has an underlying energy\nfunction with a simple gradient structure. This family is closed under convex\ncombination; as an immediate corollary, any convex combination of FTRL dynamics\nis lossless and thus has bounded regret. This allows us to extend the framework\nof Fox and Shamma [Games, 2013] to prove not just global asymptotic stability\nresults for game dynamics, but Poincar\\'e recurrence results as well.\nIntuitively, when a lossless game (e.g. graphical constant-sum game) is coupled\nwith lossless learning dynamics, their feedback interconnection is also\nlossless, which results in a pendulum-like energy-preserving recurrent\nbehavior, generalizing the results of Piliouras and Shamma [SODA, 2014] and\nMertikopoulos, Papadimitriou and Piliouras [SODA, 2018].\n"}
+{"abstract": "  Neural text generation models are typically trained by maximizing\nlog-likelihood with the sequence cross entropy loss, which encourages an exact\ntoken-by-token match between a target sequence with a generated sequence. Such\ntraining objective is sub-optimal when the target sequence is not perfect,\ne.g., when the target sequence is corrupted with noises, or when only weak\nsequence supervision is available. To address this challenge, we propose a\nnovel Edit-Invariant Sequence Loss (EISL), which computes the matching loss of\na target n-gram with all n-grams in the generated sequence. Drawing\ninspirations from the classical convolutional networks (ConvNets) which capture\nshift-invariance in image modeling, EISL is designed to be robust to the shift\nof n-grams to tolerate various noises and edits in the target sequences.\nMoreover, the EISL computation is essentially a convolution operation with\ntarget n-grams as kernels, which is easy to implement and efficient to compute\nwith existing libraries. To demonstrate the effectiveness of EISL, we conduct\nexperiments on a wide range of tasks, including machine translation with noisy\ntarget sequences, unsupervised text style transfer with only weak training\nsignals, and non-autoregressive generation with non-predefined generation\norder. Experimental results show our method significantly outperforms the\ncommon cross-entropy loss and other strong baselines on all the tasks.\n"}
+{"abstract": "  Aims: We aim to investigate the consequences of a fast massive stellar\nremnant - a black hole (BH) or a neutron star (NS) - encountering a planetary\nsystem. Methods: We modelled a close encounter between the actual Solar System\n(SS) and a $2\\,M_\\odot$ NS and a $10\\,M_\\odot$ BH, using a few-body symplectic\nintegrator. We used a range of impact parameters, orbital phases at the start\nof the simulation derived from the current SS orbital parameters, encounter\nvelocities, and incidence angles relative to the plane of the SS. Results: We\ngive the distribution of possible outcomes, such as when the SS remains bound,\nwhen it suffers a partial or complete disruption, and in which cases the\nintruder is able to capture one or more planets, yielding planetary systems\naround a BH or a NS. We also show examples of the long-term stability of the\ncaptured planetary systems.\n"}
+{"abstract": "  The purpose of this project was to derive a reliable estimate of the\nfrequency of occurrence of the 30 phonemes - plus consonant geminated\ncounterparts - of the Italian language, based on four selected written texts.\nSince no comparable dataset was found in previous literature, the present\nanalysis may serve as a reference in future studies. Four textual sources were\nconsidered: Come si fa una tesi di laurea: le materie umanistiche by Umberto\nEco, I promessi sposi by Alessandro Manzoni, a recent article in Corriere della\nSera (a popular daily Italian newspaper), and In altre parole by Jhumpa Lahiri.\nThe sources were chosen to represent varied genres, subject matter, time\nperiods, and writing styles. Results of the analysis, which also included an\nanalysis of variance, showed that, for all four sources, the frequencies of\noccurrence reached relatively stable values after about 6,000 phonemes (approx.\n1,250 words), varying by <0.025%. Estimated frequencies are provided for each\nsingle source and as an average across sources.\n"}
+{"abstract": "  In this work we consider the problem of repeated hyperparameter and neural\narchitecture search (HNAS). We propose an extension of Successive Halving that\nis able to leverage information gained in previous HNAS problems with the goal\nof saving computational resources. We empirically demonstrate that our solution\nis able to drastically decrease costs while maintaining accuracy and being\nrobust to negative transfer. Our method is significantly simpler than competing\ntransfer learning approaches, setting a new baseline for transfer learning in\nHNAS.\n"}
+{"abstract": "  The cascaded decay in a four-level quantum emitter is a well established\nmechanism to generate polarization entangled photon pairs, the building blocks\nof many applications in quantum technologies. The four most prominent maximally\nentangled photon pair states are the Bell states. In a typical experiment based\non an undriven emitter only one type of Bell state entanglement can be observed\nin a given polarization basis. Other types of Bell state entanglement in the\nsame basis can be created by continuously driving the system by an external\nlaser. In this work we propose a protocol for time-dependent entanglement\nswitching in a four-level quantum emitter--cavity system that can be operated\nby changing the external driving strength. By selecting different two-photon\nresonances between the laser-dressed states, we can actively switch back and\nforth between the different types of Bell state entanglement in the same basis\nas well as between entangled and nonentangled photon pairs. This remarkable\nfeature demonstrates the possibility to achieve a controlled, time-dependent\nmanipulation of the entanglement type that could be used in many innovative\napplications.\n"}
+{"abstract": "  An overlapped continuous model framework, for the Helmholtz wave propagation\nproblem in unbounded regions comprising bounded heterogeneous media, was\nrecently introduced and analyzed by the authors ({\\tt J. Comput. Phys., {\\bf\n403}, 109052, 2020}). The continuous Helmholtz system incorporates a radiation\ncondition (RC) and our equivalent hybrid framework facilitates application of\nwidely used finite element methods (FEM) and boundary element methods (BEM),\nand the resulting discrete systems retain the RC exactly. The FEM and BEM\ndiscretizations, respectively, applied to the designed interior heterogeneous\nand exterior homogeneous media Helmholtz systems include the FEM and BEM\nsolutions matching in artificial interface domains, and allow for computations\nof the exact ansatz based far-fields. In this article we present rigorous\nnumerical analysis of a discrete two-dimensional FEM-BEM overlapped coupling\nimplementation of the algorithm. We also demonstrate the efficiency of our\ndiscrete FEM-BEM framework and analysis using numerical experiments, including\napplications to non-convex heterogeneous multiple particle Janus\nconfigurations. Simulations of the far-field induced differential scattering\ncross sections (DSCS) of heterogeneous configurations and orientation-averaged\n(OA) counterparts are important for several applications, including inverse\nwave problems. Our robust FEM-BEM framework facilities computations of such\nquantities of interest, without boundedness or homogeneity or shape\nrestrictions on the wave propagation model.\n"}
+{"abstract": "  We present a calculation of the $cos (2 \\phi_h)$ asymmetry in $J/\\psi$\nproduction in electron-proton collision at the future electron-ion collider\n(EIC), a useful channel to probe the gluon TMDs. We calculate the asymmetry at\nnext-to-leading order (NLO) in $\\alpha_s$ in the framework of generalized\nfactorization. The dominating sub-process is $\\gamma^* +g \\rightarrow\nJ/\\psi+g$. The production of $J/\\psi$ is calculated in the non-relativistic\nQCD(NRQCD) framework with the inclusion of both color singlet and color octet\ncontributions. Numerical estimates of the $cos(2\\phi_h)$ asymmetry are given in\nthe kinematical region to be accessed by the future EIC. The asymmetry depends\non the parameterization of the gluon TMDs, as well as on the long distance\nmatrix elements (LDMEs). We use both Gaussian-type parameterization and\nMcLerran-Venugopalan model for the TMDs in the kinematical region of small-$x$,\nwhere the gluons play a dominant role. We obtain sizable asymmetry, which may\nbe useful to probe the ratio of the linearly polarized and the unpolarized\ngluon distribution in the proton.\n"}
+{"abstract": "  This paper provides a scheme for generating maximally entangled qubit states\nin the anti-Jaynes-Cummings interaction mechanism, so called entangled\nanti-polariton qubit states. We demonstrate that in an initial vacuum-field,\nRabi oscillations in a cavity mode in the anti-Jaynes-Cummings interaction\nprocess, occur in the reverse sense relative to the Jaynes-Cummings interaction\nprocess and that time evolution of entanglement in the anti-Jaynes-Cummings\ninteraction process takes the same form as in the Jaynes-Cummings interaction\nprocess. With the generated anti-polariton qubit state as one of the initial\nqubits, we present quantum teleportation of an atomic quantum state by applying\nentanglement swapping protocol achieving an impressive maximal teleportation\nfidelity~$F_\\rho=1$.\n"}
+{"abstract": "  We characterize the tail behavior of the distribution of the PageRank of a\nuniformly chosen vertex in a directed preferential attachment graph and show\nthat it decays as a power law with an explicit exponent that is described in\nterms of the model parameters. Interestingly, this power law is heavier than\nthe tail of the limiting in-degree distribution, which goes against the\ncommonly accepted {\\em power law hypothesis}. This deviation from the power law\nhypothesis points at the structural differences between the inbound\nneighborhoods of typical vertices in a preferential attachment graph versus\nthose in static random graph models where the power law hypothesis has been\nproven to hold (e.g., directed configuration models and inhomogeneous random\ndigraphs). In addition to characterizing the PageRank distribution of a typical\nvertex, we also characterize the explicit growth rate of the PageRank of the\noldest vertex as the network size grows.\n"}
+{"abstract": "  In this paper, we propose a novel conditional convolution network, named\nlocation-variable convolution, to model the dependencies of the waveform\nsequence. Different from the use of unified convolution kernels in WaveNet to\ncapture the dependencies of arbitrary waveform, the location-variable\nconvolution uses convolution kernels with different coefficients to perform\nconvolution operations on different waveform intervals, where the coefficients\nof kernels is predicted according to conditioning acoustic features, such as\nMel-spectrograms. Based on location-variable convolutions, we design LVCNet for\nwaveform generation, and apply it in Parallel WaveGAN to design more efficient\nvocoder. Experiments on the LJSpeech dataset show that our proposed model\nachieves a four-fold increase in synthesis speed compared to the original\nParallel WaveGAN without any degradation in sound quality, which verifies the\neffectiveness of location-variable convolutions.\n"}
+{"abstract": "  Click-through rate (CTR) estimation plays as a core function module in\nvarious personalized online services, including online advertising, recommender\nsystems, and web search etc. From 2015, the success of deep learning started to\nbenefit CTR estimation performance and now deep CTR models have been widely\napplied in many industrial platforms. In this survey, we provide a\ncomprehensive review of deep learning models for CTR estimation tasks. First,\nwe take a review of the transfer from shallow to deep CTR models and explain\nwhy going deep is a necessary trend of development. Second, we concentrate on\nexplicit feature interaction learning modules of deep CTR models. Then, as an\nimportant perspective on large platforms with abundant user histories, deep\nbehavior models are discussed. Moreover, the recently emerged automated methods\nfor deep CTR architecture design are presented. Finally, we summarize the\nsurvey and discuss the future prospects of this field.\n"}
+{"abstract": "  The ground-state properties of two-component repulsive Fermi gases in two\ndimensions are investigated by means of fixed-node diffusion Monte Carlo\nsimulations. The energy per particle is determined as a function of the\nintercomponent interaction strength and of the population imbalance. The regime\nof universality in terms of the s-wave scattering length is identified by\ncomparing results for hard-disk and for soft-disk potentials. In the large\nimbalance regime, the equation of state turns out to be well described by a\nLandau-Pomeranchuk functional for two-dimensional polarons. To fully\ncharacterize this expansion, we determine the polarons' effective mass and\ntheir coupling parameter, complementing previous studies on their chemical\npotential. Furthermore, we extract the magnetic susceptibility from\nlow-imbalance data, finding only small deviations from the mean-field\nprediction. While the mean-field theory predicts a direct transition from a\nparamagnetic to a fully ferromagnetic phase, our diffusion Monte Carlo results\nsuggest that the partially ferromagnetic phase is stable in a narrow interval\nof the interaction parameter. This finding calls for further analyses on the\neffects due to the fixed-node constraint.\n"}
+{"abstract": "  Exact Hausdorff dimensions are computed for singular continuous components of\nthe spectral measures of a class of Schr\\\"odinger operators in bounded\nintervals.\n"}
+{"abstract": "  We computed the orbital evolution and the emitted gravitational radiation of\nthe supermassive black hole binary OJ 287. Here we used the initial data\nprovided by the outburst structure of the system. We considered the spin-spin,\nspin-orbit, and the next-to-next leading order fourth post-Newtonian (4PN)\ncorrections in our analysis. In this way, we could make an accurate examination\nof unstable orbits ($3M < r <6M$) of the secondary black hole. We tested the\n4PN terms by analyzing the total and radiated energy, compared the\npost-Newtonian parameters and the separation of the two black holes for 3PN and\n4PN. In conclusion, the 4PN corrections provided a significantly more accurate\ntool for analyzing unstable orbits than earlier 3PN terms. Furthermore, in this\npaper, we show the gravitational waves emitted by OJ 287 during its complete\norbital evolution including unstable orbits.\n"}
+{"abstract": "  This paper proposes an attentional network for the task of Continuous Sign\nLanguage Recognition. The proposed approach exploits co-independent streams of\ndata to model the sign language modalities. These different channels of\ninformation can share a complex temporal structure between each other. For that\nreason, we apply attention to synchronize and help capture entangled\ndependencies between the different sign language components. Even though Sign\nLanguage is multi-channel, handshapes represent the central entities in sign\ninterpretation. Seeing handshapes in their correct context defines the meaning\nof a sign. Taking that into account, we utilize the attention mechanism to\nefficiently aggregate the hand features with their appropriate spatio-temporal\ncontext for better sign recognition. We found that by doing so the model is\nable to identify the essential Sign Language components that revolve around the\ndominant hand and the face areas. We test our model on the benchmark dataset\nRWTH-PHOENIX-Weather 2014, yielding competitive results.\n"}
+{"abstract": "  Pathogens like the SARS-CoV-2 are transmitted not only through violent\nexpiratory events like coughing, but also through routine activities like\nbreathing/speaking/singing. We perform direct numerical simulations of the\nturbulent transport of potentially infectious aerosols in short conversations.\nIt is shown that a two-way conversation significantly reduces the aerosol\nexposure compared to a relative monologue by one person and relative silence of\nthe other. This is because the interaction of the jets ejected from the mouth\nof each speaker produce a \"canceling\" effect. Unequal conversation is shown to\nsignificantly increase the risk of infection to the person who talks less.\nInterestingly, a small height difference is worse for infection spread, due to\nreduced interference between the two speech jets, than two faces at the same\nlevel! For small axial separation, speech jets show large oscillations and\nreach the other person intermittently. We suggest a range of lateral\nseparations between two people to minimize transmission risk. A realistic\nestimate of the infection probability is provided by including exposure through\neyes and mouth, in addition to the more common method of using inhaled virions\nalone. We expect that our results will provide useful inputs to epidemiological\nmodels and to disease management.\n"}
+{"abstract": "  An important parameter for COVID-19 is the case fatality rate (CFR). It has\nbeen applied to wide applications, including the measure of the severity of the\ninfection, the estimation of the number of infected cases, risk assessment etc.\nHowever, there remains a lack of understanding on several aspects of CFR,\nincluding population factors that are important to CFR, the apparent\ndiscrepancy of CFRs in different countries, and how the age effect comes into\nplay. We analyze the CFRs at two different time snapshots, July 6 and Dec 28,\n2020, with one during the first wave and the other a second wave of the\nCOVID-19 pandemic. We consider two important population covariates, age and GDP\nas a proxy for the quality and abundance of public health. Extensive\nexploratory data analysis leads to some interesting findings. First, there is a\nclear exponential age effect among different age groups, and, more importantly,\nthe exponential index is almost invariant across countries and time in the\npandemic. Second, the roles played by the age and GDP are a little surprising:\nduring the first wave, age is a more significant factor than GDP, while their\nroles have switched during the second wave of the pandemic, which may be\npartially explained by the delay in time for the quality and abundance of\npublic health and medical research to factor in.\n"}
+{"abstract": "  We investigate coincidences of the (one-variable) Jones polynomial amongst\nrational knots, what we call `Jones rational coincidences'. We provide moves on\nthe continued fraction expansion of the associated rational which we prove do\nnot change the Jones polynomial and conjecture (based on experimental evidence\nfrom all rational knots with determinant $<900$) that these moves are\nsufficient to generate all Jones rational coincidences. These coincidences are\ngenerically not mutants, as is verified by checking the HOMFLYPT polynomial. In\nthe process we give a new formula for the Jones polynomial of a rational knot\nbased on a continued fraction expansion of the associated rational, which has\nsignificantly fewer terms than other formulae known to us. The paper is based\non the second author's Ph.D. thesis and gives an essentially self-contained\naccount.\n"}
+{"abstract": "  We study the maximum capture problem in facility location under random\nutility models, i.e., the problem of seeking to locate new facilities in a\ncompetitive market such that the captured user demand is maximized, assuming\nthat each customer chooses among all available facilities according to a random\nutility maximization model. We employ the generalized extreme value (GEV)\nfamily of discrete choice models and show that the objective function in this\ncontext is monotonic and submodular. This finding implies that a simple greed\nheuristic can always guarantee an (1-1/e) approximation solution. We further\ndevelop a new algorithm combining a greedy heuristic, a gradient-based local\nsearch and an exchanging procedure to efficiently solve the problem. We conduct\nexperiments using instances of difference sizes and under different discrete\nchoice models, and we show that our approach significantly outperforms prior\napproaches in terms of both returned objective value and CPU time. Our\nalgorithm and theoretical findings can be applied to the maximum capture\nproblems under various random utility models in the literature, including the\npopular multinomial logit, nested logit, cross nested logit, and the mixed\nlogit models.\n"}
+{"abstract": "  We present the theoretical framework to efficiently solve the Jeans equations\nfor multi-component axisymmetric stellar systems, focusing on the scaling of\nall quantities entering them. The models may include an arbitrary number of\nstellar distributions, a dark matter halo, and a central supermassive black\nhole; each stellar distribution is implicitly described by a two- or\nthree-integral distribution function, and the stellar components can have\ndifferent structural (density profile, flattening, mass, scale-length),\ndynamical (rotation, velocity dispersion anisotropy), and population (age,\nmetallicity, initial mass function, mass-to-light ratio) properties. In order\nto determine the ordered rotational velocity and the azimuthal velocity\ndispersion fields of each component, we introduce a decomposition that can be\nused when the commonly adopted Satoh decomposition cannot be applied. The\nscheme developed is particularly suitable for a numerical implementation; we\ndescribe its realisation within our code JASMINE2, optimised to maximally\nexploit the scalings allowed by the Poisson and the Jeans equations, also in\nthe post-processing procedures. As applications, we illustrate the building of\nthree multi-component galaxy models with two distinct stellar populations, a\ncentral black hole, and a dark matter halo; we also study the solution of the\nJeans equations for an exponential thick disc, and for its multi-component\nrepresentation as the superposition of three Miyamoto-Nagai discs. A useful\ngeneral formula for the numerical evaluation of the gravitational potential of\nfactorised thick discs is finally given.\n"}
+{"abstract": "  These are the guidelines for the application of SNACS (Semantic Network of\nAdposition and Case Supersenses; Schneider et al. 2018) to Modern Standard\nHindi of Delhi. SNACS is an inventory of 50 supersenses (semantic labels) for\nlabelling the use of adpositions and case markers with respect to both\nlexical-semantic function and relation to the underlying context. The English\nguidelines (Schneider et al., 2020) were used as a model for this document.\n  Besides the case system, Hindi has an extremely rich adpositional system\nbuilt on the oblique genitive, with productive incorporation of loanwords even\nin present-day Hinglish.\n  This document is aligned with version 2.5 of the English guidelines.\n"}
+{"abstract": "  This paper introduces our solution for the Track2 in AI City Challenge 2021\n(AICITY21). The Track2 is a vehicle re-identification (ReID) task with both the\nreal-world data and synthetic data. We mainly focus on four points, i.e.\ntraining data, unsupervised domain-adaptive (UDA) training, post-processing,\nmodel ensembling in this challenge. (1) Both cropping training data and using\nsynthetic data can help the model learn more discriminative features. (2) Since\nthere is a new scenario in the test set that dose not appear in the training\nset, UDA methods perform well in the challenge. (3) Post-processing techniques\nincluding re-ranking, image-to-track retrieval, inter-camera fusion, etc,\nsignificantly improve final performance. (4) We ensemble CNN-based models and\ntransformer-based models which provide different representation diversity. With\naforementioned techniques, our method finally achieves 0.7445 mAP score,\nyielding the first place in the competition. Codes are available at\nhttps://github.com/michuanhaohao/AICITY2021_Track2_DMT.\n"}
+{"abstract": "  It is well-known that Microsoft Word/Excel compatible documents or PDF files\ncan contain malicious content. LaTeX files are unfortunately no exception\neither. LaTeX users often include third-party codes through sources or packages\n(.sty or .cls files). But those packages can execute malicious commands on the\nusers' system, in order to capture sensitive information or to perform denial\nof service attacks. Checkoway et al. [3] were the first to warn LaTeX users of\nthese threats. Collaborative cloud-based LaTeX editors and services compiling\nLaTeX sources are particularly concerned. In this paper, we have created a\nLaTeX package that collects system data and hides them inside the PDF file\nproduced by the target. Then, we have measured what can be recovered by hackers\nusing malicious LaTeX file on online services, and which measures those\nservices have enforced to thwart the threats. Services defend themselves using\nsandbox or commands restrictions. Commands restrictions are more difficult to\nsetup and we found one service (PMLatex) which is too permissive.\n"}
+{"abstract": "  Background: COVID-19 is impacting people worldwide and is currently a leading\ncause of death in many countries. This study sought to redefine the Healthy\nPeople 2030 SDoH taxonomy to accommodate the COVID-19 pandemic. Furthermore, we\naim to provide a blueprint and implement a prototype for the Urban Population\nHealth Observatory (UPHO), a web-based platform that integrates classified\ngroup-level SDoH indicators to individual- and aggregate-level population\nhealth data. The process of building the UPHO involves collecting and\nintegrating data from several sources, classifying the collected data into\ndrivers and outcomes, incorporating data science techniques for calculating\nmeasurable indicators from the raw variables, and studying the extent to which\ninterventions are identified or developed to mitigate drivers that lead to the\nundesired outcomes. We generated and classified the indicators of social\ndeterminants of health, which are linked to COVID-19. To display the\nfunctionalities of the UPHO platform, we presented a prototype design to\ndemonstrate its features. We provided a use case scenario for 4 different\nusers. UPHO serves as an apparatus for implementing effective interventions and\ncan be adopted as a global platform for chronic and infectious diseases. The\nUPHO surveillance platform provides a novel approach and novel insights into\nimmediate and long-term health policy responses to the COVID-19 pandemic and\nother future public health crises. The UPHO assists public health organizations\nand policymakers in their efforts in reducing health disparities, achieving\nhealth equity, and improving urban population health.\n"}
+{"abstract": "  We study Ramsey expansions of certain homogeneous 3-hypertournaments. We show\nthat they exhibit an interesting behaviour and, in one case, they seem not to\nsubmit to current gold-standard methods for obtaining Ramsey expansions. This\nmakes these examples very interesting from the point of view of structural\nRamsey theory as there is a large demand for novel examples.\n"}
+{"abstract": "  A low-complexity convolutional neural network estimator which learns the\nminimum mean squared error channel estimator for single-antenna users was\nrecently proposed. We generalize the architecture to the estimation of MIMO\nchannels with multiple-antenna users and incorporate complexity-reducing\nassumptions based on the channel model. Learning is used in this context to\ncombat the mismatch between the assumptions and real scenarios where the\nassumptions may not hold. We derive a high-level description of the estimator\nfor arbitrary choices of the pilot sequence. It turns out that the proposed\nestimator has the implicit structure of a two-layered convolutional neural\nnetwork, where the derived quantities can be relaxed to learnable parameters.\nWe show that by using discrete Fourier transform based pilots the number of\nlearnable network parameters decreases significantly and the online run time of\nthe estimator is reduced considerably, where we can achieve linearithmic order\nof complexity in the number of antennas. Numerical results demonstrate\nperformance gains compared to state-of-the-art algorithms from the field of\ncompressive sensing or covariance estimation of the same or even higher\ncomputational complexity. The simulation code is available online.\n"}
+{"abstract": "  We present a fully lock-free variant of the recent Montage system for\npersistent data structures. Our variant, nbMontage, adds persistence to almost\nany nonblocking concurrent structure without introducing significant overhead\nor blocking of any kind. Like its predecessor, nbMontage is buffered durably\nlinearizable: it guarantees that the state recovered in the wake of a crash\nwill represent a consistent prefix of pre-crash execution. Unlike its\npredecessor, nbMontage ensures wait-free progress of the persistence frontier,\nthereby bounding the number of recent updates that may be lost on a crash, and\nallowing a thread to force an update of the frontier (i.e., to perform a sync\noperation) without the risk of blocking. As an extra benefit, the helping\nmechanism employed by our wait-free sync significantly reduces its latency.\n  Performance results for nonblocking queues, skip lists, trees, and hash\ntables rival custom data structures in the literature -- dramatically faster\nthan achieved with prior general-purpose systems, and generally within 50% of\nequivalent non-persistent structures placed in DRAM.\n"}
+{"abstract": "  We present the analysis of XMM-Newton observations of two X-ray luminous cool\ncore clusters, RXCJ1504.1-0248 and Abell 1664. The Reflection Grating\nSpectrometer reveals a radiative cooling rate of $180\\pm 40\\, \\rm\nM_{\\odot}\\rm\\,yr^{-1}$ and $34\\pm 6\\, \\rm M_{\\odot}\\rm\\,yr^{-1}$ in\nRXCJ1504.1-0248 and Abell 1664 for gas above 0.7 keV, respectively. These\ncooling rates are higher than the star formation rates observed in the\nclusters, and support simultaneous star formation and molecular gas mass growth\non a timescale of 3$\\times 10^8$ yr or longer. At these rates, the energy of\nthe X-ray cooling gas is inadequate to power the observed UV/optical\nline-emitting nebulae, which suggests additional strong heating. No significant\nresidual cooling is detected below 0.7 keV in RXCJ1504.1-0248. By\nsimultaneously fitting the first and second order spectra, we place an upper\nlimit on turbulent velocity of 300 km$\\rm s^{-1}$ at 90 per cent confidence\nlevel for the soft X-ray emitting gas in both clusters. The turbulent energy\ndensity is considered to be less than 8.9 and 27 per cent of the thermal energy\ndensity in RXCJ1504.1-0248 and Abell 1664, respectively. This means it is\ninsufficient for AGN heating to fully propagate throughout the cool core via\nturbulence. We find the cool X-ray component of Abell 1664 ($\\sim$0.8 keV) is\nblueshifted from the systemic velocity by 750$^{+800}_{-280}$ km$\\rm s^{-1}$.\nThis is consistent with one component of the molecular gas in the core and\nsuggests a similar dynamical structure for the two phases. We find that an\nintrinsic absorption model allows the cooling rate to increase to $520\\pm 30\\,\n\\rm M_{\\odot}\\rm\\,yr^{-1}$ in RXCJ1504.1-0248.\n"}
+{"abstract": "  Co-Salient Object Detection (CoSOD) aims at simulating the human visual\nsystem to discover the common and salient objects from a group of relevant\nimages. Recent methods typically develop sophisticated deep learning based\nmodels have greatly improved the performance of CoSOD task. But there are still\ntwo major drawbacks that need to be further addressed, 1) sub-optimal\ninter-image relationship modeling; 2) lacking consideration of inter-image\nseparability. In this paper, we propose the Co-Salient Object Detection\nTransformer (CoSformer) network to capture both salient and common visual\npatterns from multiple images. By leveraging Transformer architecture, the\nproposed method address the influence of the input orders and greatly improve\nthe stability of the CoSOD task. We also introduce a novel concept of\ninter-image separability. We construct a contrast learning scheme to modeling\nthe inter-image separability and learn more discriminative embedding space to\ndistinguish true common objects from noisy objects. Extensive experiments on\nthree challenging benchmarks, i.e., CoCA, CoSOD3k, and Cosal2015, demonstrate\nthat our CoSformer outperforms cutting-edge models and achieves the new\nstate-of-the-art. We hope that CoSformer can motivate future research for more\nvisual co-analysis tasks.\n"}
+{"abstract": "  Less than one percent of the discovered small solar system objects have\nhighly inclined orbits ($i>60^{\\circ}$), and revolve around the Sun on\nnear-polar or retrograde orbits. The origin and evolutionary history of these\nobjects are not yet clear. In this work we study the surface properties and\norbital dynamics of selected high-inclination objects. BVRI photometric\nobservations were performed in 2019-2020 using the 2.0m telescope at the\nTerskol Observatory and the 2.6m telescope at the Crimean Astrophysical\nObservatory. Additionally, we searched for high-inclination objects in the\nSloan Digital Sky Survey and Pan-STARRS. The dynamics of the selected objects\nwas studied using numerical simulations. We obtained new photometric\nobservations of six high-inclination objects. All of the objects have similar\n$B-V$, $V-R$, $R-I$ colours, which are close to those of moderately red TNOs\nand grey Centaurs. The photometric data that were extracted from the all-sky\nsurveys also correspond to moderately red surfaces of high-inclination objects.\nNo signs of ultra-red material on the surface of high-inclination asteroids\nwere found, which supports the results of previous works. The comet C/2018 DO4\n(Lemmon) revealed some complex morphology with structures that could be\nassociated with particles that were ejected from the cometary nucleus. For\nobjects 2013 LU28, 2015 KZ120, and 2020 EP we estimated future and past\nlifetimes on their orbits. It appears that the orbits of considered objects are\nstrongly chaotic, and with the available accuracy of the orbital elements no\nreliable predictions can be made about their distant past or future. The\nlifetimes of high-inclination objects turned out to be highly non-sensitive to\nthe precision of the orbital elements, and to the Yarkovsky orbital drift.\n"}
+{"abstract": "  This paper presents a layer-2 micropayment pool design which supports\nhigh-throughput blockchain off-chain payment, designed specifically for the use\ncase of rewarding users who share redundant bandwidth to assist video stream\ndelivery. We analyze the validity and effectiveness of the proposed\nmicropayment pool. Our analysis results demonstrate that the proposed\nmicropayment pool design is better suited for the bandwidth reward use case\ncompared to existing off-chain payment channel solution.\n"}
+{"abstract": "  We give infinitely many knots in $S^3$ that are not smoothly H-slice (that\nis, bounding a null-homologous disk) in many $4$-manifolds but they are\ntopologically H-slice. In particular, we give such knots in punctured elliptic\nsurfaces $E(2n)$. In addition, we give obstructions to codimension-0 embedding\nof weak symplectic fillings with $b_3=0$ into closed symplectic 4-manifolds\nwith $b_1=0$ and $b_2^+\\equiv 3 \\operatorname{mod} 4$. We also show that any\nweakly symplectically fillable $3$-manifold bounds a $4$-manifold with at least\ntwo smooth structures. All of these results follow from our main result that\ngives an adjunction inequality for embedded surfaces in certain $4$-manifolds\nwith contact boundary under a non-vanishing assumption on Bauer-Furuta type\ninvariants.\n"}
+{"abstract": "  Green and Tao famously proved in a 2008 paper that there are arithmetic\nprogressions of prime numbers of arbitrary lengths. Soon after, analogous\nstatements were proved by Tao for the ring of Gaussian integers and by L\\^e for\nthe polynomial rings over finite fields. In 2020 this was extented to orders of\narbitrary number fields by Kai-Mimura-Munemasa-Seki-Yoshino. We settle the case\nof the coordinate rings of affine curves over finite fields. The main\ncontribution of this paper is subtle choice of a polynomial subring of the\ngiven ring which plays the role of $\\mathbb Z$ in the number field case. This\nis enabled by the Riemann-Roch formula.\n"}
+{"abstract": "  Facial action unit recognition has many applications from market research to\npsychotherapy and from image captioning to entertainment. Despite its recent\nprogress, deployment of these models has been impeded due to their limited\ngeneralization to unseen people and demographics. This work conducts an\nin-depth analysis of performance across several dimensions: individuals(40\nsubjects), genders (male and female), skin types (darker and lighter), and\ndatabases (BP4D and DISFA). To help suppress the variance in data, we use the\nnotion of self-supervised denoising autoencoders to design a method for deep\nface normalization(DeepFN) that transfers facial expressions of different\npeople onto a common facial template which is then used to train and evaluate\nfacial action recognition models. We show that person-independent models yield\nsignificantly lower performance (55% average F1 and accuracy across 40\nsubjects) than person-dependent models (60.3%), leading to a generalization gap\nof 5.3%. However, normalizing the data with the newly introduced DeepFN\nsignificantly increased the performance of person-independent models (59.6%),\neffectively reducing the gap. Similarly, we observed generalization gaps when\nconsidering gender (2.4%), skin type (5.3%), and dataset (9.4%), which were\nsignificantly reduced with the use of DeepFN. These findings represent an\nimportant step towards the creation of more generalizable facial action unit\nrecognition systems.\n"}
+{"abstract": "  To avoid serious diabetic complications, people with type 1 diabetes must\nkeep their blood glucose levels (BGLs) as close to normal as possible. Insulin\ndosages and carbohydrate consumption are important considerations in managing\nBGLs. Since the 1960s, models have been developed to forecast blood glucose\nlevels based on the history of BGLs, insulin dosages, carbohydrate intake, and\nother physiological and lifestyle factors. Such predictions can be used to\nalert people of impending unsafe BGLs or to control insulin flow in an\nartificial pancreas. In past work, we have introduced an LSTM-based approach to\nblood glucose level prediction aimed at \"what if\" scenarios, in which people\ncould enter foods they might eat or insulin amounts they might take and then\nsee the effect on future BGLs. In this work, we invert the \"what-if\" scenario\nand introduce a similar architecture based on chaining two LSTMs that can be\ntrained to make either insulin or carbohydrate recommendations aimed at\nreaching a desired BG level in the future. Leveraging a recent state-of-the-art\nmodel for time series forecasting, we then derive a novel architecture for the\nsame recommendation task, in which the two LSTM chain is used as a repeating\nblock inside a deep residual architecture. Experimental evaluations using real\npatient data from the OhioT1DM dataset show that the new integrated\narchitecture compares favorably with the previous LSTM-based approach,\nsubstantially outperforming the baselines. The promising results suggest that\nthis novel approach could potentially be of practical use to people with type 1\ndiabetes for self-management of BGLs.\n"}
+{"abstract": "  In this work we discuss extensions of the pioneering analysis by\nDzyaloshinskii and Larkin of correlation functions for one-dimensional Fermi\nsystems, focusing on the effects of quasiparticle relaxation enabled by a\nnonlinear dispersion. Throughout the work we employ both, the weakly\ninteracting Fermi gas picture and nonlinear Luttinger liquid theory to describe\nattenuation of excitations and explore the fermion-boson duality between both\napproaches. Special attention is devoted to the role of spin-exchange\nprocesses, effects of interaction screening, and integrability. Thermalization\nrates for electron- and hole-like quasiparticles, as well as the decay rate of\ncollective plasmon excitations and the momentum space mobility of spin\nexcitations are calculated for various temperature regimes. The phenomenon of\nspin-charge drag is considered and the corresponding momentum transfer rate is\ndetermined. We further discuss how momentum relaxation due to several competing\nmechanisms, viz. triple electron collisions, electron-phonon scattering, and\nlong-range inhomogeneities affect transport properties, and highlight energy\ntransfer facilitated by plasmons from the perspective of the inhomogeneous\nLuttinger liquid model. Finally, we derive the full matrix of thermoelectric\ncoefficients at the quantum critical point of the first conductance plateau\ntransition, and address magnetoconductance in ballistic semiconductor nanowires\nwith strong Rashba spin-orbit coupling.\n"}
+{"abstract": "  We derive equivalent linear and dynamic programs for infinite horizon\nrisk-sensitive control for minimization of the asymptotic growth rate of the\ncumulative cost.\n"}
+{"abstract": "  Breiman organizes \"Statistical modeling: The two cultures\" around a simple\nvisual. Data, to the far right, are compelled into a \"black box\" with an arrow\nand then catapulted left by a second arrow, having been transformed into an\noutput. Breiman then posits two interpretations of this visual as encapsulating\na distinction between two cultures in statistics. The divide, he argues is\nabout what happens in the \"black box.\" In this comment, I argue for a broader\nperspective on statistics and, in doing so, elevate questions from \"before\" and\n\"after\" the box as fruitful areas for statistical innovation and practice.\n"}
+{"abstract": "  The presence of metamorphism in the protein's native state is not yet fully\nunderstood. In an attempt to throw light on this issue here we present an\nassessment, in terms of the amide hydrogen exchange protection factor, that\naims to determine the likely existence of structural fluctuations in the\nnative-state consistent with both the upper bound marginal stability of\nproteins and the metamorphism presence. The preliminary results enable us to\nconclude that the native-state metamorphism is, indeed, more probable than\nthought.\n"}
+{"abstract": "  Quantum metrology is the use of genuinely quantum properties such as\nentanglement as a resource to outperform classical sensing strategies.\nTypically, entanglement is created by implementing gate operations or inducing\nmany-body interactions. However, existing sensing schemes with these approaches\nrequire accurate control of the probe system such as switching on and off the\ninteraction among qubits, which can be challenging for practical applications.\nHere, we propose an entanglement-enhanced sensing scheme with an always-on\nnearest-neighbor interaction between qubits. We adopt the transverse field\nIsing chain as the probe system, making use of the so-called quantum domino\ndynamics for the generation of the entangled states. In addition to the\nadvantage that our scheme can be implemented without controlling the\ninteractions, it only requires initialization of the system, projective\nmeasurements on a single qubit, and control of the uniform magnetic fields. We\ncan achieve an improved sensitivity beyond the standard quantum limit even\nunder the effect of realistic decoherence.\n"}
+{"abstract": "  Medical imaging is widely used in cancer diagnosis and treatment, and\nartificial intelligence (AI) has achieved tremendous success in various tasks\nof medical image analysis. This paper reviews AI-based tumor subregion analysis\nin medical imaging. We summarize the latest AI-based methods for tumor\nsubregion analysis and their applications. Specifically, we categorize the\nAI-based methods by training strategy: supervised and unsupervised. A detailed\nreview of each category is presented, highlighting important contributions and\nachievements. Specific challenges and potential AI applications in tumor\nsubregion analysis are discussed.\n"}
+{"abstract": "  The possible conditions for hadron-quark phase transition in hybrid star\ncores are investigated in the present work. For the hadronic matter part the\neffective chiral model is adopted. Exotic baryonic degrees like hyperons and\nthe delta baryons are also taken into account. As $\\Delta$s posses Breit-Wigner\nmass distribution ($1232 \\pm 120$ MeV), the hadronic equation of state is\nobtained by varying the mass of the delta baryons in this range. For the quark\nphase the MIT bag model is chosen with repulsive effects of the unpaired\nquarks. Phase transition is achieved using Gibbs construction and the gross\nproperties of the resultant hybrid star are calculated in both static and\nrotating conditions and compared with the various constraints on them from\ndifferent observational and empirical perspectives. The work presents a\nthorough study of the phase transition properties like the critical density of\nappearance of quarks, the density range for the persistence of the mixed phase\nand the population of different hadrons and quarks in hybrid star matter. The\nhybrid star properties, calculated in both static and rotating conditions, are\nfound to be consistent with the bounds on them from different perspectives.\n"}
+{"abstract": "  Intrusive uncertainty quantification methods for hyperbolic problems exhibit\nspurious oscillations at shocks, which leads to a significant reduction of the\noverall approximation quality. Furthermore, a challenging task is to preserve\nhyperbolicity of the gPC moment system. An intrusive method which guarantees\nhyperbolicity is the intrusive polynomial moment (IPM) method, which performs\nthe gPC expansion on the entropy variables. The method, while still being\nsubject to oscillations, requires solving a convex optimization problem in\nevery spatial cell and every time step. The aim of this work is to mitigate\noscillations in the IPM solution by applying filters. Filters reduce\noscillations by damping high order gPC coefficients. Naive filtering, however,\nmay lead to unrealizable moments, which means that the IPM optimization problem\ndoes not have a solution and the method breaks down. In this paper, we propose\nand analyze two separate strategies to guarantee the existence of a solution to\nthe IPM problem. First, we propose a filter which maintains realizability by\nbeing constructed from an underlying Fokker-Planck equation. Second, we\nregularize the IPM optimization problem to be able to cope with non-realizable\ngPC coefficients. Consequently, standard filters can be applied to the\nregularized IPM method. We demonstrate numerical results for the two strategies\nby investigating the Euler equations with uncertain shock structures in one-\nand two-dimensional spatial settings. We are able to show a significant\nreduction of spurious oscillations by the proposed filters.\n"}
+{"abstract": "  In the present paper we show that it is possible to obtain the well known\nPauli group $P=\\langle X,Y,Z \\ | \\ X^2=Y^2=Z^2=1, (YZ)^4=(ZX)^4=(XY)^4=1\n\\rangle $ of order $16$ as an appropriate quotient group of two distinct spaces\nof orbits of the three dimensional sphere $S^3$. The first of these spaces of\norbits is realized via an action of the quaternion group $Q_8$ on $S^3$; the\nsecond one via an action of the cyclic group of order four $\\mathbb{Z}(4)$ on\n$S^3$. We deduce a result of decomposition of $P$ of topological nature and\nthen we find, in connection with the theory of pseudo-fermions, a possible\nphysical interpretation of this decomposition.\n"}
+{"abstract": "  Combined diverse two-dimensional (2D) materials for semiconductor interfaces\nare attractive for electrically controllable carrier confinement to enable\nexcellent electrostatic control. We investigated the transport characteristic\nin heterointerface of multilayer molybdenum disulfide and hexagonal boron\nnitride (MoS$_2$/h-BN) to reveal that the charge transfer switching (CTS) is\nhighly dependent on both the local gate constriction and bias in the channel.\nNotably, the CTS is shown to be controlled at a molecular level through\nelectrotunable gated constriction. The resulting significant change in\nconductance due to exposing 100 parts-per-billion of nitrogen dioxide gas led\nto a high on/off ratio of 10 2 for completely switching off the channel thus,\nacting as a molecular switch. First-principle calculations further explained\nthe mechanism of molecular CTS in the device. The molecular tunability of CTS\nhas not been previously reported in any of the van der Waals semiconductor\ninterfaces. Our finding opens avenues to exploit various atomically thin\nheterostructures for the mesoscopic transport phenomena towards molecular\nswitching operation at room temperature.\n"}
+{"abstract": "  In stochastic optimal control, change of measure arguments have been crucial\nfor stochastic analysis. Such an approach is often called static reduction in\ndynamic team theory and has been an effective method for establishing existence\nand approximation results for optimal policies. These arguments have also been\napplied to the study of dynamic games. In this paper, we demonstrate the\nlimitations of such an approach for a wide class of stochastic dynamic games,\nwhere additionally, unlike the team setting considered in Part I, informational\ndependence of equilibrium behavior is significantly more complicated. We\nidentify three types of static reductions: (i) those that are\npolicy-independent (as those introduced by Witsenhausen for teams), (ii) those\nthat are policy-dependent (as those introduced by Ho and Chu for partially\nnested dynamic teams), and (iii) a third type that we will refer to as static\nmeasurements with control-sharing reduction as in Part I (where the\nmeasurements are static although control actions are shared according to the\npartially nested information structure). For the first type, we show that there\nis a bijection between Nash equilibrium policies under the original information\nstructure and their policy-independent static reductions. However, for the\nsecond type, we show that there is generally no isomorphism between Nash\nequilibrium solutions under the original information structure and their\npolicy-dependent static reductions. Sufficient conditions are presented to\nestablish such an isomorphism relationship between Nash equilibria of dynamic\nnon-zero-sum games and their policy-dependent static reductions. For zero-sum\ngames, these sufficient conditions are relaxed and stronger results are\nestablished. We also study three classes of multi-stage games, where we\nestablish connections between closed-loop, open-loop, and control-sharing Nash\nand saddle point equilibria.\n"}
+{"abstract": "  The discovery of ferroelectric HfO2 in thin films and more recently in bulk\nis an important breakthrough because of its silicon-compatibility and\nunexpectedly persistent polarization at low dimensions, but the origin of its\nferroelectricity is still under debate. The stabilization of the metastable\npolar orthorhombic phase was often considered as the cumulative result of\nvarious extrinsic factors such as stress, grain boundary, and oxygen vacancies\nas well as phase transition kinetics during the annealing process. We propose a\nnovel mechanism to stabilize the polar orthorhombic phase over the nonpolar\nmonoclinic phase that is the bulk ground state. Our first-principles\ncalculations demonstrate that the doubly positively charged oxygen vacancy, an\noverlooked defect but commonly presented in binary oxides, is critical for the\nstabilization of ferroelectric phase. The charge state of oxygen vacancy serves\nas a new degree of freedom to control the thermodynamic stability of competing\nphases of wide-band-gap oxides.\n"}
+{"abstract": "  Let T d (n) be the set of d-ary rooted trees with n internal nodes. We give a\nmethod to construct a sequence (t n , n $\\ge$ 0) where, for any n $\\ge$ 1, t n\nhas the uniform distribution in T d (n), and t n is constructed from t n--1 by\nthe addition of a new node, and a rearrangement of the structure of t n--1.\nThis method is inspired by R{\\'e}my's algorithm which does this job in the\nbinary case, but it is different from it. This provides a method for the random\ngeneration of a uniform d-ary tree in T d (n) with a cost linear in n.\n"}
+{"abstract": "  Vaccine hesitancy and misinformation on social media has increased concerns\nabout COVID-19 vaccine uptake required to achieve herd immunity and overcome\nthe pandemic. However anti-science and political misinformation and\nconspiracies have been rampant throughout the pandemic. For COVID-19 vaccines,\nwe investigate misinformation and conspiracy campaigns and their characteristic\nbehaviours. We identify whether coordinated efforts are used to promote\nmisinformation in vaccine related discussions, and find accounts coordinately\npromoting a `Great Reset' conspiracy group promoting vaccine related\nmisinformation and strong anti-vaccine and anti-social messages such as boycott\nvaccine passports, no lock-downs and masks. We characterize other\nmisinformation communities from the information diffusion structure, and study\nthe large anti-vaccine misinformation community and smaller anti-vaccine\ncommunities, including a far-right anti-vaccine conspiracy group. In comparison\nwith the mainstream and health news, left-leaning group, which are more\npro-vaccine, the right-leaning group is influenced more by the anti-vaccine and\nfar-right misinformation/conspiracy communities. The misinformation communities\nare more vocal either specific to the vaccine discussion or political\ndiscussion, and we find other differences in the characteristic behaviours of\ndifferent communities. Lastly, we investigate misinformation narratives and\ntactics of information distortion that can increase vaccine hesitancy, using\ntopic modeling and comparison with reported vaccine side-effects (VAERS)\nfinding rarer side-effects are more frequently discussed on social media.\n"}
+{"abstract": "  Revealing the mechanisms shaping the architecture of planetary systems is\ncrucial for our understanding of their formation and evolution. In this\ncontext, it has been recently proposed that stellar clustering might be the key\nin shaping the orbital architecture of exoplanets. The main goal of this work\nis to explore the factors that shape the orbits of planets. We used a\nhomogeneous sample of relatively young FGK dwarf stars with RV detected planets\nand tested the hypothesis that their association to phase space\n(position-velocity) over-densities ('cluster' stars) and under-densities\n('field' stars) impacts the orbital periods of planets. When controlling for\nthe host star properties, on a sample of 52 planets orbiting around 'cluster'\nstars and 15 planets orbiting around 'field' star, we found no significant\ndifference in the period distribution of planets orbiting these two populations\nof stars. By considering an extended sample of 73 planets orbiting around\n'cluster' stars and 25 planets orbiting 'field' stars, a significant different\nin the planetary period distributions emerged. However, the hosts associated to\nstellar under-densities appeared to be significantly older than their 'cluster'\ncounterparts. This did not allow us to conclude whether the planetary\narchitecture is related to age, environment, or both. We further studied a\nsample of planets orbiting 'cluster' stars to study the mechanism responsible\nfor the shaping of orbits of planets in similar environments. We could not\nidentify a parameter that can unambiguously be responsible for the orbital\narchitecture of massive planets, perhaps, indicating the complexity of the\nissue. Conclusions. Increased number of planets in clusters and in over-density\nenvironments will help to build large and unbiased samples which will then\nallow to better understand the dominant processes shaping the orbits of\nplanets.\n"}
+{"abstract": "  Determining physically admissible boundary conditions for higher moments in\nan extended continuum model is recognised as a major obstacle. Boundary\nconditions for the regularised 26-moment (R26) equations obtained using\nMaxwell's accommodation model do exist in the literature; however, we show in\nthis article that these boundary conditions violate the second law of\nthermodynamics and the Onsager reciprocity relations for certain boundary value\nproblems, and, hence, are not physically admissible. We further prove that the\nlinearised R26 (LR26) equations possess a proper $H$-theorem (second-law\ninequality) by determining a quadratic form without cross-product terms for the\nentropy density. The establishment of the $H$-theorem for the LR26 equations in\nturn leads to a complete set of boundary conditions that are physically\nadmissible for all processes and comply with the Onsager reciprocity relations.\nAs an application, the problem of a slow rarefied gas flow past a spherical\ndroplet with and without evaporation is considered and solved analytically. The\nresults are compared with the numerical solution of the linearised Boltzmann\nequation, experimental results from the literature and/or other macroscopic\ntheories to show that the LR26 theory with the physically admissible boundary\nconditions provides an excellent prediction up to Knudsen number $\\,\\lesssim 1$\nand, consequently, provides transpicuous insights into intriguing effects, such\nas thermal polarisation. In particular, the analytic results for the drag force\nobtained in the present work are in an excellent agreement with experimental\nresults even for very large values of the Knudsen number.\n"}
+{"abstract": "  Versatile video coding (VVC) is the next generation video coding standard\ndeveloped by the joint video experts team (JVET) and released in July 2020. VVC\nintroduces several new coding tools providing a significant coding gain over\nthe high efficiency video coding (HEVC) standard. It is well known that\nincreasing the coding efficiency adds more dependencies in the video bitstream\nmaking format-compliant encryption with the standard more challenging. In this\npaper we tackle the problem of selective encryption of the VVC standard in\nformat-compliant and constant bitrate. These two constraints ensure that the\nencrypted bitstream can be decoded by any VVC decoder while the bitrate remains\nunchanged by the encryption. The selective encryption of all possible VVC\nsyntax elements is investigated. A new algorithm is proposed to encrypt in\nformat-compliant and constant bitrate the transform coefficients (TCs) together\nwith other syntax elements at the level of the entropy encoder. The proposed\nsolution was integrated and assessed under the VVC reference software model\nversion 6.0. Experimental results showed that the encryption drastically\ndecreases the video quality while the encryption is robust against several\ntypes of attacks. The encryption space is estimated in the range of 15% to 26%\nof the bitstream size resulting in a lightweight encryption process. The web\npage of this work is available at https://gugautie.github.io/sevvc/.\n"}
+{"abstract": "  Age of Information (AoI) has emerged as an important quality-of-service\nmeasure for applications that prioritize delivery of the freshest information,\ne.g., virtual or augmented reality over mobile devices and wireless sensor\nnetworks used in the control of cyber-physical systems. We derive the Laplace\ntransform of the stationary AoI for the M/GI/1/2 system with a \"dynamic\"\nservice preemption and pushout policy depending on the existing service time of\nthe in-service message. Thus, our system generalizes both the static M/GI/1/2\nqueue-pushout system without service preemption and the M/GI/1/1 bufferless\nsystem with service preemption - two systems considered to provide very good\nAoI performance. Based on our analysis, for a service-time distribution that is\na mixture of deterministic and exponential, we numerically show that the\ndynamic policy has lower mean AoI than that of these two static policies and\nalso that of the well studied M/GI/1/1 blocking system.\n"}
+{"abstract": "  We report on recent results that show that the pair correlation function of\nsystems with exponentially decaying interactions can fail to exhibit\nOrnstein-Zernike asymptotics at all sufficiently high temperatures and all\nsufficiently small densities. This turns out to be related to a lack of\nanalyticity of the correlation length as a function of temperature and/or\ndensity and even occurs for one-dimensional systems.\n"}
+{"abstract": "  Let $X$ be a complex smooth quasi-projective surface acted upon by a finite\ngroup $G$ such that the quotient $X/G$ has singularities only of ADE type. We\nobtain an explicit expression for the generating series of the Euler\ncharacteristics of the zero-dimensional components in the moduli space of\nzero-dimensional subschemes on $X$ invariant under the action of $G$. We show\nthat this generating series (up to a suitable rational power of the formal\nvariable) is a holomorphic modular form.\n"}
+{"abstract": "  We analyse high-frequency realised volatility dynamics and spillovers in the\nbitcoin market, focusing on two pairs: bitcoin against the US dollar (the main\nfiat-crypto pair) and trading bitcoin against tether (the main crypto-crypto\npair). We find that the tether-margined perpetual contract on Binance is\nclearly the main source of volatility, continuously transmitting strong flows\nto all other instruments and receiving only a little volatility. Moreover, we\nfind that (i) during US trading hours, traders pay more attention and are more\nreactive to prevailing market conditions when updating their expectations and\n(ii) the crypto market exhibits a higher interconnectedness when traditional\nWestern stock markets are open. Our results highlight that regulators should\nnot only consider spot exchanges offering bitcoin-fiat trading but also the\ntether-margined derivatives products available on most unregulated exchanges,\nmost importantly Binance.\n"}
+{"abstract": "  Microgrids (MG) are anticipated to be important players in the future smart\ngrid. For proper operation of MGs an Energy Management System (EMS) is\nessential. The EMS of an MG could be rather complicated when renewable energy\nresources (RER), energy storage system (ESS) and demand side management (DSM)\nneed to be orchestrated. Furthermore, these systems may belong to different\nentities and competition may exist between them. Nash equilibrium is most\ncommonly used for coordination of such entities however the convergence and\nexistence of Nash equilibrium can not always be guaranteed. To this end, we use\nthe correlated equilibrium to coordinate agents, whose convergence can be\nguaranteed. In this paper, we build an energy trading model based on mid-market\nrate, and propose a correlated Q-learning (CEQ) algorithm to maximize the\nrevenue of each agent. Our results show that CEQ is able to balance the revenue\nof agents without harming total benefit. In addition, compared with Q-learning\nwithout correlation, CEQ could save 19.3% cost for the DSM agent and 44.2% more\nbenefits for the ESS agent.\n"}
+{"abstract": "  Uncertainty quantification in forecasting represents a topic of great\nimportance in statistics, especially when dealing with complex data\ncharacterized by non-trivial dependence structure. Pushed by novel works\nconcerning distribution-free prediction, we propose a scalable procedure that\noutputs closed-form simultaneous prediction bands for multivariate functional\nresponse variables in a time series setting, which is able to guarantee\nperformance bounds in terms of unconditional coverage and asymptotic exactness,\nboth under some conditions. After evaluating its performance on synthetic data,\nthe method is used to build multivariate prediction bands for daily demand and\noffer curves in the Italian gas market. The prediction framework thus obtained\nallows traders to directly evaluate the impact of their own offers/bids on the\nmarket, providing an intriguing tool for the business practice.\n"}
+{"abstract": "  Multiplex tissue immunostaining is a technology of growing relevance as it\ncan capture in situ the complex interactions existing between the elements of\nthe tumor microenvironment. The existence and availability of large, annotated\nimage datasets is key for the objective development and benchmarking of\nbioimage analysis algorithms. Manual annotation of multiplex images, is\nhowever, laborious, often impracticable. In this paper, we present Synplex, a\nsimulation system able to generate multiplex immunostained in situ tissue\nimages based on user-defined parameters. This includes the specification of\nstructural attributes, such as the number of cell phenotypes, the number and\nlevel of expression of cellular markers, or the cell morphology. Synplex\nconsists of three sequential modules, each being responsible for a separate\ntask: modeling of cellular neighborhoods, modeling of cell phenotypes, and\nsynthesis of realistic cell/tissue textures. Synplex flexibility and accuracy\nare demonstrated qualitatively and quantitatively by generating synthetic\ntissues that simulate disease paradigms found in the real scenarios. Synplex is\npublicly available for scientific purposes, and we believe it will become a\nvaluable tool for the training and/or validation of multiplex image analysis\nalgorithms.\n"}
+{"abstract": "  Accurate channel parameter estimation is challenging for wideband\nmillimeter-wave (mmWave) large-scale hybrid arrays, due to beam squint and much\nfewer radio frequency (RF) chains than antennas. This paper presents a novel\njoint delay and angle estimation approach for wideband mmWave fully-connected\nhybrid uniform cylindrical arrays. We first design a new hybrid beamformer to\nreduce the dimension of received signals on the horizontal plane by exploiting\nthe convergence of the Bessel function, and to reduce the active beams in the\nvertical direction through preselection. The important recurrence relationship\nof the received signals needed for subspace-based angle and delay estimation is\npreserved, even with substantially fewer RF chains than antennas. Then, linear\ninterpolation is generalized to reconstruct the received signals of the hybrid\nbeamformer, so that the signals can be coherently combined across the whole\nband to suppress the beam squint. As a result, efficient subspace-based\nalgorithm algorithms can be developed to estimate the angles and delays of\nmultipath components. The estimated delays and angles are further matched and\ncorrectly associated with different paths in the presence of non-negligible\nnoises, by putting forth perturbation operations. Simulations show that the\nproposed approach can approach the Cram\\'{e}r-Rao lower bound (CRLB) of the\nestimation with a significantly lower computational complexity than existing\ntechniques.\n"}
+{"abstract": "  Invariance and equivariance to the rotation group have been widely discussed\nin the 3D deep learning community for pointclouds. Yet most proposed methods\neither use complex mathematical tools that may limit their accessibility, or\nare tied to specific input data types and network architectures. In this paper,\nwe introduce a general framework built on top of what we call Vector Neuron\nrepresentations for creating SO(3)-equivariant neural networks for pointcloud\nprocessing. Extending neurons from 1D scalars to 3D vectors, our vector neurons\nenable a simple mapping of SO(3) actions to latent spaces thereby providing a\nframework for building equivariance in common neural operations -- including\nlinear layers, non-linearities, pooling, and normalizations. Due to their\nsimplicity, vector neurons are versatile and, as we demonstrate, can be\nincorporated into diverse network architecture backbones, allowing them to\nprocess geometry inputs in arbitrary poses. Despite its simplicity, our method\nperforms comparably well in accuracy and generalization with other more complex\nand specialized state-of-the-art methods on classification and segmentation\ntasks. We also show for the first time a rotation equivariant reconstruction\nnetwork.\n"}
+{"abstract": "  The nature of circumstellar envelopes (CSE) around Cepheids is still a matter\nof debate. The physical origin of their infrared (IR) excess could be either a\nshell of ionized gas, or a dust envelope, or both. This study aims at\nconstraining the geometry and the IR excess of the environment of the\nlong-period Cepheid $\\ell$ Car (P=35.5 days) at mid-IR wavelengths to\nunderstand its physical nature. We first use photometric observations in\nvarious bands and Spitzer Space Telescope spectroscopy to constrain the IR\nexcess of $\\ell$ Car. Then, we analyze the VLTI/MATISSE measurements at a\nspecific phase of observation, in order to determine the flux contribution, the\nsize and shape of the environment of the star in the L band. We finally test\nthe hypothesis of a shell of ionized gas in order to model the IR excess. We\nreport the first detection in the L band of a centro-symmetric extended\nemission around l Car, of about 1.7$R_\\star$ in FWHM, producing an excess of\nabout 7.0\\% in this band. In the N band, there is no clear evidence for dust\nemission from VLTI/MATISSE correlated flux and Spitzer data. On the other side,\nthe modeled shell of ionized gas implies a more compact CSE\n($1.13\\pm0.02\\,R_\\star$) and fainter (IR excess of 1\\% in the L band). We\nprovide new evidences for a compact CSE of $\\ell$ Car and we demonstrate the\ncapabilities of VLTI/MATISSE for determining common properties of CSEs. While\nthe compact CSE of $\\ell$ Car is probably of gaseous nature, the tested model\nof a shell of ionized gas is not able to simultaneously reproduce the IR excess\nand the interferometric observations. Further Galactic Cepheids observations\nwith VLTI/MATISSE are necessary for determining the properties of CSEs, which\nmay also depend on both the pulsation period and the evolutionary state of the\nstars.\n"}
+{"abstract": "  Detecting out-of-distribution (OOD) samples plays a key role in open-world\nand safety-critical applications such as autonomous systems and healthcare.\nRecently, self-supervised representation learning techniques (via contrastive\nlearning and pretext learning) have shown effective in improving OOD detection.\nHowever, one major issue with such approaches is the choice of shifting\ntransformations and pretext tasks which depends on the in-domain distribution.\nIn this paper, we propose a simple framework that leverages a shifting\ntransformation learning setting for learning multiple shifted representations\nof the training set for improved OOD detection. To address the problem of\nselecting optimal shifting transformation and pretext tasks, we propose a\nsimple mechanism for automatically selecting the transformations and modulating\ntheir effect on representation learning without requiring any OOD training\nsamples. In extensive experiments, we show that our simple framework\noutperforms state-of-the-art OOD detection models on several image datasets. We\nalso characterize the criteria for a desirable OOD detector for real-world\napplications and demonstrate the efficacy of our proposed technique against\nstate-of-the-art OOD detection techniques.\n"}
+{"abstract": "  The recent many-fold increase in the size of deep neural networks makes\nefficient distributed training challenging. Many proposals exploit the\ncompressibility of the gradients and propose lossy compression techniques to\nspeed up the communication stage of distributed training. Nevertheless,\ncompression comes at the cost of reduced model quality and extra computation\noverhead. In this work, we design an efficient compressor with minimal\noverhead. Noting the sparsity of the gradients, we propose to model the\ngradients as random variables distributed according to some sparsity-inducing\ndistributions (SIDs). We empirically validate our assumption by studying the\nstatistical characteristics of the evolution of gradient vectors over the\ntraining process. We then propose Sparsity-Inducing Distribution-based\nCompression (SIDCo), a threshold-based sparsification scheme that enjoys\nsimilar threshold estimation quality to deep gradient compression (DGC) while\nbeing faster by imposing lower compression overhead. Our extensive evaluation\nof popular machine learning benchmarks involving both recurrent neural network\n(RNN) and convolution neural network (CNN) models shows that SIDCo speeds up\ntraining by up to 41:7%, 7:6%, and 1:9% compared to the no-compression\nbaseline, Topk, and DGC compressors, respectively.\n"}
+{"abstract": "  Structured illumination microscopy (SIM) has emerged as an essential\ntechnique for 3D and live-cell super-resolution imaging. However, to date,\nthere has not been a dedicated workshop or journal issue covering the various\naspects of SIM, from bespoke hardware and software development and the use of\ncommercial instruments to biological applications. This special issue aims to\nrecap recent developments as well as outline future trends. In addition to SIM,\nwe cover related topics such as complementary super-resolution microscopy\ntechniques, computational imaging, visualisation and image processing methods.\n"}
+{"abstract": "  We investigate the equilibrium and radial stability of spherically symmetric\nrelativistic stars, considering a polytropic equation of state (EoS), within\nthe framework of $f(R,T)$ gravity with a conservative energy-momentum tensor.\nBoth modified stellar structure equations and Chandrasekhar's pulsation\nequations are derived for the $f(R,T)= R+ h(T)$ gravity model, where the\nfunction $h(T)$ assumes a specific form in order to safeguard the conservation\nequation for the energy-momentum tensor. The neutron star properties, such as\nradius, mass, binding energy and oscillation spectrum are studied in detail.\nOur results show that a cusp -- which signals the appearance of instability --\nis formed when the binding energy is plotted as a function of the compact star\nproper mass. We find that the squared frequency of the fundamental vibration\nmode passes through zero at the central-density value corresponding to such a\ncusp where the binding energy is a minimum.\n"}
+{"abstract": "  Customer service is a setting that calls for empathy in live human agent\nresponses. Recent advances have demonstrated how open-domain chatbots can be\ntrained to demonstrate empathy when responding to live human utterances. We\nshow that a blended skills chatbot model that responds to customer queries is\nmore likely to resemble actual human agent response if it is trained to\nrecognize emotion and exhibit appropriate empathy, than a model without such\ntraining. For our analysis, we leverage a Twitter customer service dataset\ncontaining several million customer<->agent dialog examples in customer service\ncontexts from 20 well-known brands.\n"}
+{"abstract": "  The Fermi constant ($G_F$) is extremely well measured through the muon\nlifetime, defining one of the key fundamental parameters in the Standard Model\n(SM). Therefore, to search for physics beyond the SM (BSM) via $G_F$, the\nconstraining power is determined by the precision of the second-best\nindependent determination of $G_F$. The best alternative extractions of $G_F$\nproceed either via the global electroweak (EW) fit or from superallowed $\\beta$\ndecays in combination with the Cabibbo angle measured in kaon, $\\tau$, or $D$\ndecays. Both variants display some tension with $G_F$ from muon decay, albeit\nin opposite directions, reflecting the known tensions within the EW fit and\nhints for the apparent violation of CKM unitarity, respectively. We investigate\nhow BSM physics could bring the three determinations of $G_F$ into agreement\nusing SM effective field theory and comment on future perspectives.\n"}
+{"abstract": "  Supervised neural network training has led to significant progress on\nsingle-channel sound separation. This approach relies on ground truth isolated\nsources, which precludes scaling to widely available mixture data and limits\nprogress on open-domain tasks. The recent mixture invariant training (MixIT)\nmethod enables training on in-the-wild data; however, it suffers from two\noutstanding problems. First, it produces models which tend to over-separate,\nproducing more output sources than are present in the input. Second, the\nexponential computational complexity of the MixIT loss limits the number of\nfeasible output sources. In this paper we address both issues. To combat\nover-separation we introduce new losses: sparsity losses that favor fewer\noutput sources and a covariance loss that discourages correlated outputs. We\nalso experiment with a semantic classification loss by predicting weak class\nlabels for each mixture. To handle larger numbers of sources, we introduce an\nefficient approximation using a fast least-squares solution, projected onto the\nMixIT constraint set. Our experiments show that the proposed losses curtail\nover-separation and improve overall performance. The best performance is\nachieved using larger numbers of output sources, enabled by our efficient MixIT\nloss, combined with sparsity losses to prevent over-separation. On the FUSS\ntest set, we achieve over 13 dB in multi-source SI-SNR improvement, while\nboosting single-source reconstruction SI-SNR by over 17 dB.\n"}
+{"abstract": "  We perform a systematic study on the interactions of the\n$\\Sigma_c^{(*)}D^{(*)}$ systems within the framework of chiral effective field\ntheory. We introduce the contact term, one-pion-exchange and two-pion-exchange\ncontributions to describe the short-, long-, and intermediate-range\ninteractions. The low energy constants of the $\\Sigma_c^{(*)}D^{(*)}$ systems\nare estimated from the $N\\bar{N}$ scattering data by introducing a quark level\nLagrangian. With three solutions of LECs, all the $\\Sigma_c^{(*)}D^{(*)}$\nsystems with isospin $I=1/2$ can form bound states, in which different inputs\nof LECs may lead to distinguishable mass spectra. In addition, we also\ninvestigate the interactions of the charmed-bottom\n$\\Sigma_c^{(*)}\\bar{B}^{(*)}$, $\\Sigma_b^{(*)}D^{(*)}$, and\n$\\Sigma_b^{(*)}\\bar{B}^{(*)}$ systems. Among the obtained bound states, the\nbindings become deeper when the reduced masses of the corresponding systems are\nheavier.\n"}
+{"abstract": "  Computer systems are so complex, so they are usually designed and analyzed in\nterms of layers of abstraction. Complexity is still a challenge facing logical\nreasoning tools that are used to find software design flaws and implementation\nbugs. Abstraction is also a common technique for scaling those tools to more\ncomplex systems. However, the abstractions used in the design phase of systems\nare in many cases different from those used for assurance. In this paper we\nargue that different software quality assurance techniques operate on different\naspects of software systems. To facilitate assurance, and for a smooth\nintegration of assurance tools into the Software Development Lifecycle (SDLC),\nwe present a 4-dimensional meta-architecture that separates computational,\ncoordination, and stateful software artifacts early on in the design stage. We\nenumerate some of the design and assurance challenges that can be addressed by\nthis meta-architecture, and demonstrate it on the high-level design of a simple\nfile system.\n"}
+{"abstract": "  The FE$^2$ method is a very flexible but computationally expensive tool for\nmultiscale simulations. In conventional implementations, the microscopic\ndisplacements are iteratively solved for within each macroscopic iteration\nloop, although the macroscopic strains imposed as boundary conditions at the\nmicro-scale only represent estimates. In order to reduce the number of\nexpensive micro-scale iterations, the present contribution presents a\nmonolithic FE$^2$ scheme, for which the displacements at the micro-scale and at\nthe macro-scale are solved for in a common Newton-Raphson loop. In this case,\nthe linear system of equations within each iteration is solved by static\ncondensation, so that only very limited modifications to the conventional,\nstaggered scheme are necessary. The proposed monolithic FE$^2$ algorithm is\nimplemented into the commercial FE code Abaqus. Benchmark examples demonstrate\nthat the monolithic scheme saves up to ~60% of computational costs.\n"}
+{"abstract": "  Khovanov and Sazdanovic recently introduced symmetric monoidal categories\nparameterized by rational functions and given by quotients of categories of\ntwo-dimensional cobordisms. These categories generalize Deligne's interpolation\ncategories of representations of symmetric groups. In this paper, we classify\nindecomposable objects and identify the associated graded Grothendieck rings of\nKhovanov-Sazdanovic's categories through sums of representation categories over\ncrossed products of polynomial rings over a general field. To obtain these\nresults, we introduce associated graded categories for Krull-Schmidt categories\nwith filtrations as a categorification of the associated graded Grothendieck\nring, and study field extensions and Galois descent for Krull-Schmidt\ncategories.\n"}
+{"abstract": "  Predicting 3D human pose from a single monoscopic video can be highly\nchallenging due to factors such as low resolution, motion blur and occlusion,\nin addition to the fundamental ambiguity in estimating 3D from 2D. Approaches\nthat directly regress the 3D pose from independent images can be particularly\nsusceptible to these factors and result in jitter, noise and/or inconsistencies\nin skeletal estimation. Much of which can be overcome if the temporal evolution\nof the scene and skeleton are taken into account. However, rather than tracking\nbody parts and trying to temporally smooth them, we propose a novel transformer\nbased network that can learn a distribution over both pose and motion in an\nunsupervised fashion. We call our approach Skeletor. Skeletor overcomes\ninaccuracies in detection and corrects partial or entire skeleton corruption.\nSkeletor uses strong priors learn from on 25 million frames to correct skeleton\nsequences smoothly and consistently. Skeletor can achieve this as it implicitly\nlearns the spatio-temporal context of human motion via a transformer based\nneural network. Extensive experiments show that Skeletor achieves improved\nperformance on 3D human pose estimation and further provides benefits for\ndownstream tasks such as sign language translation.\n"}
+{"abstract": "  We report a systematic magnetotransport study of superconducting\ninfinite-layer nickelate thin films Nd$_{1-x}$Sr$_x$NiO$_2$ with $0.15 \\leq x\n\\leq 0.225$. By suppressing superconductivity with out-of-plane magnetic fields\nup to 37.5 T, we find that the normal state resistivity of\nNd$_{1-x}$Sr$_x$NiO$_2$ is characterized by a crossover from a metallic\n$T^2$-behavior to an insulating log(1/$T$)-behavior for all $x$ except $x =\n0.225$, at which the resistivity is predominantly metallic. The\nlog(1/$T$)-behavior is found to be robust against magnetic fields, inconsistent\nwith scenarios involving localization or Kondo scattering, and points to an\nanomalous insulating state possibly driven by strong correlations. In the\nmetallic state, we find no evidence for non-Fermi-liquid behavior arising from\nproximity to a putative quantum critical point located inside the\nsuperconducting dome.\n"}
+{"abstract": "  Time series exploration and mining has many applications across several\nindustrial and scientific domains. In this paper, we consider the problem of\ndetecting locally similar pairs and groups, called bundles, over co-evolving\ntime series. These are pairs or groups of subsequences whose values do not\ndiffer by more than {\\epsilon} for at least delta consecutive timestamps, thus\nindicating common local patterns and trends. We first present a baseline\nalgorithm that performs a sweep line scan across all timestamps to identify\nmatches. Then, we propose a filter-verification technique that only examines\ncandidate matches at judiciously chosen checkpoints across time. Specifically,\nwe introduce two block scanning algorithms for discovering local pairs and\nbundles respectively, which leverage the potential of checkpoints to\naggressively prune the search space. We experimentally evaluate our methods\nagainst real-world and synthetic datasets, demonstrating a speed-up in\nexecution time by an order of magnitude over the baseline. This paper has been\npublished in the 16th International Symposium on Spatial and Temporal Databases\n(SSTD19).\n"}
+{"abstract": "  In a stochastic process, where noise is always present, the\nfluctuation-dissipation theorem (FDT) becomes one of the most important tools\nin statistical mechanics and, consequently, it appears everywhere. Its major\nutility is to provide a simple response to study certain processes in solids\nand fluids. However, in many situations we are not talking about a FDT, but\nabout the noise intensity. For example, noise has enormous importance in\ndiffusion and growth phenomena. Although we have an explicit FDT for diffusion\nphenomena, we do not have one for growth processes where we have a noise\nintensity. We show that there is a hidden FDT for the growth phenomenon,\nsimilar to the diffusive one. Moreover, we show that growth with correlated\nnoise presents as well a similar form of FDT. We also call attention to the\nhierarchy within the theorems of statistical mechanics and how this explains\nthe violation of the FDT in some phenomena.\n"}
+{"abstract": "  In this paper a hypergraph will be identified with the family of its edges. A\nhypergraph $\\mathcal E$ possesses property $C(k,{\\rho})$ iff $|\\bigcap \\mathcal\nE'|<{\\rho}$ for each $\\mathcal E'\\in {[\\mathcal E]}^{k}$. A vertex set\n$Y\\subset \\bigcup\\mathcal E$ is a \"vertex cover\" of $\\mathcal E$ iff $E\\cap\nY\\ne \\emptyset$ for each $E\\in \\mathcal E$. A vertex cover $Y$ is \"minimal\" iff\nno proper subset of $Y$ is vertex cover. If $A$ is a set and $S$ is a set of\ncardinals, write $$ {[A]}^{S}=\\{B\\subset A: |B|\\in S\\}. $$ If ${\\lambda}$ and\n${\\rho}$ are cardinals, $S$ is a set of cardinals, $k\\in {\\omega}$, then we\nwrite $$\\mathbf M({{\\lambda}},{S},{k},{{\\mu}})\\to \\mathbf{MinVC} $$ iff every\nhypergraph $\\mathcal E\\subset {[{\\lambda}]}^{S}$ possessing property\n$C({k},{{\\rho}})$ has a minimal vertex cover. If $S=\\{\\kappa\\}$, then we simply\nwrite $\\mathbf M({{\\lambda}},{\\kappa},{k},{{\\mu}})\\to \\mathbf{MinVC}$ for\n$\\mathbf M({{\\lambda}},\\{{\\kappa}\\},{k},{{\\mu}})\\to \\mathbf{MinVC}$ A set $S$\nof cardinals is \"nowhere stationary\" iff $S\\cap {\\alpha}$ is not stationary in\n${\\alpha}$ for any ordinal ${\\alpha}$ with $cf({\\alpha})>{\\omega}$. Countable\nsets of cardinals, and sets of successor cardinals are nowhere stationary. In\nthis paper we prove:\n  (1) $\\mathbf M({{\\lambda}},{S},{2},{k})\\to \\mathbf{MinVC}$ for each nowhere\nstationary set $S$ of cardinals and ${\\omega}\\le {\\lambda}$,\n  (2) $\\mathbf M({{\\lambda}},{{\\kappa}} ,{2},{{\\rho}})\\to \\mathbf{MinVC}$\nprovided ${\\rho}<\\beth_{\\omega}\\le {\\kappa}\\le {\\lambda}$,\n  (3) $\\mathbf M({{\\lambda}},{{\\omega}},{r},{k})\\to \\mathbf{MinVC}$ provided\n${\\omega}\\le {\\lambda}$ and $k,r\\in {\\omega}$,\n  (4) $\\mathbf M({{\\lambda}},{{\\omega}_1},{3},{k})\\to \\mathbf{MinVC}$ provided\n${\\omega}_1\\le {\\lambda}$ and $k\\in {\\omega}$.\n"}
+{"abstract": "  The paper proposes and develops new globally convergent algorithms of the\ngeneralized damped Newton type for solving important classes of nonsmooth\noptimization problems. These algorithms are based on the theory and\ncalculations of second-order subdifferentials of nonsmooth functions with\nemploying the machinery of second-order variational analysis and generalized\ndifferentiation. First we develop a globally superlinearly convergent damped\nNewton-type algorithm for the class of continuously differentiable functions\nwith Lipschitzian gradients, which are nonsmooth of second order. Then we\ndesign such a globally convergent algorithm to solve a class of nonsmooth\nconvex composite problems with extended-real-valued cost functions, which\ntypically arise in machine learning and statistics. Finally, the obtained\nalgorithmic developments and justifications are applied to solving a major\nclass of Lasso problems with detailed numerical implementations. We present the\nresults of numerical experiments and compare the performance of our main\nalgorithm applied to Lasso problems with those achieved by other first-order\nand second-order methods.\n"}
+{"abstract": "  We employ a typical genetic circuit model to explore how noise can influence\nthe dynamic structure. With the increase of a key interactive parameter, the\nmodel will deterministically go through two bifurcations and three dynamic\nstructure regions. We find that a new quasi-periodic component, which is not\nallowed by deterministic dynamics, will be generated by noise inducing in the\nfirst two regions, and this quasi-period will be more and more stable along\nwith the noise increasing. Especially, in the second region, the quasi-period\nwill compete with a stable limit cycle and perform a new transient rhythm.\nFurthermore, we ascertain the entropy production rate (EPR) and the heat\ndissipation rate (HDR), and discover a minimal value with elucidating\ntheoretically. In the end, we unveil the mechanism of the quasi-periods\nforming, and show a practical biological instance. We expect that this work is\nhelpful to solve some biological or ecological problems, such as genetic origin\nof periodical cicadas and population dynamics with fluctuation.\n"}
+{"abstract": "  We study a stochastic behavior of aerosols by the non-equilibrium statistical\nmechanics approach using the analytical approach of the Langevin equation. We\nfirstly show that superdiffusion can possibly occur right after the emission,\nwhich may be attributed to the physical mechanism of the outbreak of the\nCOVID-19 pandemic. We also provide clear evidence of the least required\ndistance to prevent infections occurred by aerosols. In particular, the\nrequired distance to prevent aerosol infections is derived to be about 42 m\nwhen we assume Cauchy distribution as an initial velocity distribution. This\nfact implies that due to superdiffusion the aerosol infection can occur even\nfar away from a long distance as compared to the previous considerations.\n"}
+{"abstract": "  Chemical transformations, such as ion exchange, are commonly employed to\nmodify nanocrystal compositions. Yet the mechanisms of these transformations,\nwhich often operate far from equilibrium and entail mixing diverse chemical\nspecies, remain poorly understood. Here, we explore an idealized model for ion\nexchange in which a chemical potential drives compositional defects to\naccumulate at a crystal's surface. These impurities subsequently diffuse\ninward. We find that the nature of interactions between sites in a\ncompositionally impure crystal strongly impacts exchange trajectories. In\nparticular, elastic deformations which accompany lattice-mismatched species\npromote spatially modulated patterns in the composition. These same patterns\ncan be produced at equilibrium in core/shell nanocrystals, whose structure\nmimics transient motifs observed in nonequilibrium trajectories. Moreover, the\ncore of such nanocrystals undergoes a phase transition - from modulated to\nunstructured - as the thickness or stiffness of the shell is decreased. Our\nresults help explain the varied patterns observed in heterostructured\nnanocrystals produced by ion exchange and suggest principles for the rational\ndesign of compositionally-patterned nanomaterials.\n"}
+{"abstract": "  Novel sparse reconstruction algorithms are proposed for beamspace channel\nestimation in massive multiple-input multiple-output systems. The proposed\nalgorithms minimize a least-squares objective having a nonconvex regularizer.\nThis regularizer removes the penalties on a few large-magnitude elements from\nthe conventional l1-norm regularizer, and thus it only forces penalties on the\nremaining elements that are expected to be zeros. Accurate and fast\nreconstructions can be achieved by performing gradient projection updates\nwithin the framework of difference of convex functions (DC) programming. A\ndouble-loop algorithm and a single-loop algorithm are proposed via different DC\ndecompositions, and these two algorithms have distinct computation complexities\nand convergence rates. Then, an extension algorithm is further proposed by\ndesigning the step sizes of the single-loop algorithm. The extension algorithm\nhas a faster convergence rate and can achieve approximately the same level of\naccuracy as the proposed double-loop algorithm. Numerical results show\nsignificant advantages of the proposed algorithms over existing reconstruction\nalgorithms in terms of reconstruction accuracies and runtimes. Compared to the\nbenchmark channel estimation techniques, the proposed algorithms also achieve\nsmaller mean squared error and higher achievable spectral efficiency.\n"}
+{"abstract": "  We present ObSuRF, a method which turns a single image of a scene into a 3D\nmodel represented as a set of Neural Radiance Fields (NeRFs), with each NeRF\ncorresponding to a different object. A single forward pass of an encoder\nnetwork outputs a set of latent vectors describing the objects in the scene.\nThese vectors are used independently to condition a NeRF decoder, defining the\ngeometry and appearance of each object. We make learning more computationally\nefficient by deriving a novel loss, which allows training NeRFs on RGB-D inputs\nwithout explicit ray marching. After confirming that the model performs equal\nor better than state of the art on three 2D image segmentation benchmarks, we\napply it to two multi-object 3D datasets: A multiview version of CLEVR, and a\nnovel dataset in which scenes are populated by ShapeNet models. We find that\nafter training ObSuRF on RGB-D views of training scenes, it is capable of not\nonly recovering the 3D geometry of a scene depicted in a single input image,\nbut also to segment it into objects, despite receiving no supervision in that\nregard.\n"}
+{"abstract": "  Non-orthogonal multiple access (NOMA) and massive multiple-input\nmultiple-output (MIMO) systems are highly efficient. Massive MIMO systems are\ninherently resistant to passive attackers (eavesdroppers), thanks to\ntransmissions directed to the desired users. However, active attackers can\ntransmit a combination of legitimate user pilot signals during the channel\nestimation phase. This way they can mislead the base station (BS) to rotate the\ntransmission in their direction, and allow them to eavesdrop during the\ndownlink data transmission phase. In this paper, we analyse this vulnerability\nin an improved system model and stronger adversary assumptions, and investigate\nhow physical layer security can mitigate such attacks and ensure secure\n(confidential) communication. We derive the secrecy outage probability (SOP)\nand a lower bound on the ergodic secrecy capacity, using stochastic geometry\ntools when the number of antennas in the BSs tends to infinity. We adapt the\nresult to evaluate the secrecy performance in massive orthogonal multiple\naccess (OMA). We find that appropriate power allocation allows NOMA to\noutperform OMA in terms of ergodic secrecy rate and SOP.\n"}
+{"abstract": "  Coupled quantum Hall edge channels show intriguing non-trivial modes, for\nexample, charge and neutral modes at Landau level filling factors 2 and 2/3. We\npropose an appropriate and effective model with Coulomb interaction and\ndisorder-induced tunneling characterized by coupling capacitances and tunneling\nconductances, respectively. This model explains how the transport eigenmodes,\nwithin the interaction- and disorder-dominated regimes, change with the\ncoupling capacitance, tunneling conductance, and measurement frequency. We\npropose frequency- and time-domain transport experiments, from which eigenmodes\ncan be determined using this model.\n"}
+{"abstract": "  Communities of practice in science communication can make important\ncontributions to public engagement with science but are under-researched. In\nthis article, we look at the perspectives of a community of practice in\nastronomy communication regarding (relations with) their public(s). Most\nparticipants in this study consider that public(s) have several deficits and\nvulnerabilities. Moreover, practitioners have little to no contact with (and\ntherefore make no use of) academic research on science communication. We argue\nthat collaboration between science communication researchers and practitioners\ncould benefit the science-public relationship and that communities of practice\nmay be critical to that purpose.\n"}
+{"abstract": "  In this paper we study CP violation in photon self-interactions at low\nenergy. These interactions, mediated by the effective operator $FFF\\tilde{F}$,\nwhere ($\\tilde F$) $F$ is the (dual) electromagnetic field strength, have yet\nto be directly probed experimentally. Possible sources for such interactions\nare weakly coupled light scalars with both scalar and pseudoscalar couplings to\nphotons (for instance, complex Higgs-portal scalars or the relaxion), or new\nlight fermions coupled to photons via dipole operators. We propose a method to\nisolate the CP-violating contribution to the photon self-interactions using\nSuperconducting Radio-Frequency cavities and vacuum birefringence experiments.\nIn addition, we consider several theoretical and experimental indirect bounds\non the scale of new physics associated with the above effective operator, and\npresent projections for the sensitivity of the proposed experiments to this\nscale. We also discuss the implications of these bounds on the CP-violating\ncouplings of new light particles coupled to photons.\n"}
+{"abstract": "  Flaming phenomena represent the divergence in the strength of user dynamics\nas created by user interactions in online social networks (OSNs). Although it\nhas been known that flaming phenomena occur when the Laplacian matrix of the\nOSN has non-real eigenvalues, it was recently shown that flaming phenomena may\noccur even if all the eigenvalues are real numbers. This effect appears only in\nthe situation that some eigenvalues are degenerate, and a special unitary\ntransformation is applied to the equations representing user dynamics; whether\nactual OSNs satisfy this condition has not been fully discussed. In this paper,\nwe clarify that the user dynamics caused by the degeneration of eigenvalue 0 is\none specific example of the above condition. We also investigate the mechanism\nand characteristics of flaming phenomena generated by degenerated eigenvalues.\nFurthermore, we demonstrate through numerical simulations that the degeneration\nof eigenvalues can cause divergence.\n"}
+{"abstract": "  Spatio-temporal forecasting is challenging attributing to the high\nnonlinearity in temporal dynamics as well as complex location-characterized\npatterns in spatial domains, especially in fields like weather forecasting.\nGraph convolutions are usually used for modeling the spatial dependency in\nmeteorology to handle the irregular distribution of sensors' spatial location.\nIn this work, a novel graph-based convolution for imitating the meteorological\nflows is proposed to capture the local spatial patterns. Based on the\nassumption of smoothness of location-characterized patterns, we propose\nconditional local convolution whose shared kernel on nodes' local space is\napproximated by feedforward networks, with local representations of coordinate\nobtained by horizon maps into cylindrical-tangent space as its input. The\nestablished united standard of local coordinate system preserves the\norientation on geography. We further propose the distance and orientation\nscaling terms to reduce the impacts of irregular spatial distribution. The\nconvolution is embedded in a Recurrent Neural Network architecture to model the\ntemporal dynamics, leading to the Conditional Local Convolution Recurrent\nNetwork (CLCRN). Our model is evaluated on real-world weather benchmark\ndatasets, achieving state-of-the-art performance with obvious improvements. We\nconduct further analysis on local pattern visualization, model's framework\nchoice, advantages of horizon maps and etc.\n"}
+{"abstract": "  We develop a version of the Bass-Serre theory for Lie algebras (over a field\n$k$) via a homological approach. We define the notion of fundamental Lie\nalgebra of a graph of Lie algebras and show that this construction yields\nMayer-Vietoris sequences. We extend some well known results in group theory to\n$\\mathbb{N}$-graded Lie algebras: for example, we show that one relator\n$\\mathbb{N}$-graded Lie algebras are iterated HNN extensions with free bases\nwhich can be used for cohomology computations and apply the Mayer-Vietoris\nsequence to give some results about coherence of Lie algebras.\n"}
+{"abstract": "  We consider the problem of secure distributed matrix multiplication. Coded\ncomputation has been shown to be an effective solution in distributed matrix\nmultiplication, both providing privacy against workers and boosting the\ncomputation speed by efficiently mitigating stragglers. In this work, we\npresent a non-direct secure extension of the recently introduced bivariate\npolynomial codes. Bivariate polynomial codes have been shown to be able to\nfurther speed up distributed matrix multiplication by exploiting the partial\nwork done by the stragglers rather than completely ignoring them while reducing\nthe upload communication cost and/or the workers' storage's capacity needs. We\nshow that, especially for upload communication or storage constrained settings,\nthe proposed approach reduces the average computation time of secure\ndistributed matrix multiplication compared to its competitors in the\nliterature.\n"}
+{"abstract": "  Autonomous vehicles rely heavily upon their perception subsystems to see the\nenvironment in which they operate. Unfortunately, the effect of variable\nweather conditions presents a significant challenge to object detection\nalgorithms, and thus it is imperative to test the vehicle extensively in all\nconditions which it may experience. However, development of robust autonomous\nvehicle subsystems requires repeatable, controlled testing - while real weather\nis unpredictable and cannot be scheduled. Real-world testing in adverse\nconditions is an expensive and time-consuming task, often requiring access to\nspecialist facilities. Simulation is commonly relied upon as a substitute, with\nincreasingly visually realistic representations of the real-world being\ndeveloped. In the context of the complete autonomous vehicle control pipeline,\nsubsystems downstream of perception need to be tested with accurate recreations\nof the perception system output, rather than focusing on subjective visual\nrealism of the input - whether in simulation or the real world. This study\ndevelops the untapped potential of a lightweight weather augmentation method in\nan autonomous racing vehicle - focusing not on visual accuracy, but rather the\neffect upon perception subsystem performance in real time. With minimal\nadjustment, the prototype developed in this study can replicate the effects of\nwater droplets on the camera lens, and fading light conditions. This approach\nintroduces a latency of less than 8 ms using compute hardware well suited to\nbeing carried in the vehicle - rendering it ideal for real-time implementation\nthat can be run during experiments in simulation, and augmented reality testing\nin the real world.\n"}
+{"abstract": "  The incoherent approximation (IA) is often used for calculating the\none-phonon inelastic neutron scattering cross section for arbitrary solids. It\nis valid for thermal neutrons but for slow neutrons it requires a correction,\nwhich is significant for isotopes that are strong coherent scatterers. In this\narticle, we present the extension of the Placzek--Van Hove corrections for slow\nneutrons in the limit of low temperatures using the example of solid\n\\emph{ortho}-deuterium (sD$_2$). Our approach yields realistic one-phonon\nup-scattering cross sections for sD$_2$ and shows the IA to be a factor of 2 to\n5 too high for ultracold neutron (UCN) up-scattering in sD$_2$. Our\ncalculations are compared with previously published Monte Carlo calculations of\nthe one-phonon cross section based on the dynamic structure function\n$S(q,\\omega)$ of polycrystalline \\emph{ortho}-deuterium and are found to be\nconsistent with them. Furthermore, we provide the means for easily replicable\ncalculations of the one-phonon up-scattering cross sections of solid\n\\emph{ortho}-deuterium for slow neutrons. These should from now on be used in\ncalculations and simulations of UCN scattering in sD$_2$.\n"}
+{"abstract": "  In this paper, we show that the bulk reconstruction in the AdS/CFT\ncorrespondence is rather simple and has an intuitive picture, by showing that\nthe HKLL bulk reconstruction formula can be simplified. We also reconstruct the\nwave packets in the bulk theory from the CFT primary operators. With these wave\npackets, we discuss the causality and duality constraints and find our picture\nis only the consistent one. Our picture of the bulk reconstruction can be\napplied to the asymptotic AdS spacetime.\n"}
+{"abstract": "  To perform precision medicine in realtime at home, a device capable of long\ndistance continuously monitoring target biomolecules in unprocessed blood under\ndynamic situations is essential. In this study, an integrative buffer free\nwireless device is developed to measure drug concentrations in patients blood\nin real time for remote clinical healthcare. To demonstrate its capability, the\ndrug molecules (i.e., small molecule drug doxorubicin, DOX) are continuously\nmeasured in the unprocessed whole blood of live animals (e.g., rats). The\ndynamic changes of drug concentrations with sub minute temporal resolution are\nrecorded for an extended period of time (8 hours). As an advance in remote\ndiagnosis, this device would benefit the public by enabling long-distance\nprecision medicine to prevent pandemics in advance.\n"}
+{"abstract": "  We analyze the modulation characteristics of the uncooled terahertz (THz) and\ninfrared (IR) detectors using the variation of the density and effective\ntemperature of the two-dimensional electron-hole plasma in uniform graphene\nlayers (GLs) and perforated graphene layers (PGLs) due to the absorption of THz\nand IR radiation. The performance of the photodetectors (both the\nGL-photoresistor and the PGL-based barrier photodiodes) are compared. Their\ncharacteristics are also compared with the GL reverse-biased photodiodes. The\nobtained results allow to evaluate the ultimate modulation frequencies of these\nphotodetectors and can be used for their optimization.\n"}
+{"abstract": "  The cross sections of the process $e^{+}e^{-} \\to K_{S}^{0}K_{L}^{0}$ are\nmeasured at fifteen center-of-mass energies $\\sqrt{s}$ from $2.00$ to\n$3.08~{\\rm GeV}$ with the BESIII detector at the Beijing Electron Positron\nCollider (BEPCII). The results are found to be consistent with those obtained\nby BaBar. A resonant structure around $2.2~{\\rm GeV}$ is observed, with a mass\nand width of $2273.7 \\pm 5.7 \\pm 19.3~{\\rm MeV}/c^2$ and $86 \\pm 44 \\pm 51~{\\rm\nMeV}$, respectively, where the first uncertainties are statistical and the\nsecond ones are systematic. The product of its radiative width\n($\\Gamma_{e^+e^-}$) with its branching fraction to $K_{S}^{0}K_{L}^{0}$\n($Br_{K_{S}^{0}K_{L}^{0}}$) is $0.9 \\pm 0.6 \\pm 0.7~{\\rm eV}$.\n"}
+{"abstract": "  The pion electromagnetic form factor has been calculated for the first time\nfrom the solutions of the Bethe-Salpeter equation, obtained directly in\nMinkowski space by using the Nakanishi integral representation of the\nBethe-Salpeter amplitude. The one-gluon exchange kernel contains as inputs the\nquark and gluon masses, as well as a scale parameter featuring the extended\nquark-gluon vertex. The range of variability of these parameters is suggested\nby lattice calculations. After presenting a very successful comparison with the\nexisting data in the whole range of the momentum transfer, we show how\ninserting the light-front (LF) formalism in our approach allows to achieve\nfurther interesting results, like the evaluation of the LF valence form factor\nand a first investigation of the end-points effects.\n"}
+{"abstract": "  Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term,\nsentiment and opinion term triplets from sentences and tries to provide a\ncomplete solution for aspect-based sentiment analysis (ABSA). However, some\ntriplets extracted by ASTE are confusing, since the sentiment in a triplet\nextracted by ASTE is the sentiment that the sentence expresses toward the\naspect term rather than the sentiment of the aspect term and opinion term pair.\nIn this paper, we introduce a more fine-grained Aspect-Sentiment-Opinion\nTriplet Extraction (ASOTE) Task. ASOTE also extracts aspect term, sentiment and\nopinion term triplets. However, the sentiment in a triplet extracted by ASOTE\nis the sentiment of the aspect term and opinion term pair. We build four\ndatasets for ASOTE based on several popular ABSA benchmarks. We propose a\nPosition-aware BERT-based Framework (PBF) to address this task. PBF first\nextracts aspect terms from sentences. For each extracted aspect term, PBF first\ngenerates aspect term-specific sentence representations considering both the\nmeaning and the position of the aspect term, then extracts associated opinion\nterms and predicts the sentiments of the aspect term and opinion term pairs\nbased on the sentence representations. Experimental results on the four\ndatasets show the effectiveness of PBF.\n"}
+{"abstract": "  A new method for the classification of respiratory diseases is presented. The\nmethod is based on a novel class of features, extracted from pulmonary sounds,\nby parameterizing their spectrograms that are represented as surfaces, and by\nutilizing geometrical distortions defined with reference to these surfaces.\nThis method yields a set of highly descriptive features for the analysis of\npulmonary sound recordings. Furthermore, by combining these features with\nMel-frequency cepstral coefficients, we introduce a powerful model for the\nautomatic diagnosis of common respiratory pathologies. Compared with baseline\nmethods, our model achieves superior results for binary and multi-class\nclassifications of common respiratory diseases. Our new approach to the\nclassification of one-dimensional signals is applicable to other signals in the\ncontext of their representations in combined spaces or manifolds.\n"}
+{"abstract": "  We establish upper bounds for the joint moments of the $2k^{\\text{th}}$ power\nof the Riemann zeta function with the $2h^{\\text{th}}$ power of its derivative\nfor $0 \\leq h \\leq 1$ and $1 \\leq k \\leq 2$. These bounds are expected to be\nsharp based upon predictions from random matrix theory.\n"}
+{"abstract": "  Depending on the application, radiological diagnoses can be associated with\nhigh inter- and intra-rater variabilities. Most computer-aided diagnosis (CAD)\nsolutions treat such data as incontrovertible, exposing learning algorithms to\nconsiderable and possibly contradictory label noise and biases. Thus, managing\nsubjectivity in labels is a fundamental problem in medical imaging analysis. To\naddress this challenge, we introduce auto-decoded deep latent embeddings\n(ADDLE), which explicitly models the tendencies of each rater using an\nauto-decoder framework. After a simple linear transformation, the latent\nvariables can be injected into any backbone at any and multiple points,\nallowing the model to account for rater-specific effects on the diagnosis.\nImportantly, ADDLE does not expect multiple raters per image in training,\nmeaning it can readily learn from data mined from hospital archives. Moreover,\nthe complexity of training ADDLE does not increase as more raters are added.\nDuring inference each rater can be simulated and a 'mean' or 'greedy' virtual\nrating can be produced. We test ADDLE on the problem of liver steatosis\ndiagnosis from 2D ultrasound (US) by collecting 46 084 studies along with\nclinical US diagnoses originating from 65 different raters. We evaluated\ndiagnostic performance using a separate dataset with gold-standard biopsy\ndiagnoses. ADDLE can improve the partial areas under the curve (AUCs) for\ndiagnosing severe steatosis by 10.5% over standard classifiers while\noutperforming other annotator-noise approaches, including those requiring 65\ntimes the parameters.\n"}
+{"abstract": "  Dynamical backaction resulting from radiation pressure forces in\noptomechanical systems has proven to be a versatile tool for manipulating\nmechanical vibrations. Notably, dynamical backaction has resulted in the\ncooling of a mechanical resonator to its ground-state, driving phonon lasing,\nthe generation of entangled states, and observation of the optical-spring\neffect. In certain magnetic materials, mechanical vibrations can interact with\nmagnetic excitations (magnons) via the magnetostrictive interaction, resulting\nin an analogous magnon-induced dynamical backaction. In this article, we\ndirectly observe the impact of magnon-induced dynamical backaction on a\nspherical magnetic sample's mechanical vibrations. Moreover, dynamical\nbackaction effects play a crucial role in many recent theoretical proposals;\nthus, our work provides the foundation for future experimental work pursuing\nmany of these theoretical proposals.\n"}
+{"abstract": "  We study a function $\\mathcal{L}_{\\Gamma}$ which quantifies the LEF (local\nembeddability into finite groups) property for a finitely generated group\n$\\Gamma$. We compute this \"LEF growth\" function in some examples, including\ncertain wreath products. We compare LEF growth with the analogous quantitative\nversion of residual finiteness, and exhibit a family of finitely generated\nresidually finite groups which nevertheless admit many more local embeddings\ninto finite groups than they do finite quotients. Along the way, we give a new\nproof that B.H. Neumann's continuous family of $2$-generated groups contains no\nfinitely presented group, a result originally due to Baumslag and Miller. We\ncompare $\\mathcal{L}_{\\Gamma}$ with quantitative versions of soficity and other\nmetric approximation properties of groups. Finally, we show that there exists a\n\"universal\" function which is an upper bound on the LEF growth of any group on\na given number of generators, and that (for non-cyclic groups) any such\nfunction is non-computable.\n"}
+{"abstract": "  In this research, we have two serum SELDI (surface-enhanced laser desorption\nand ionization) mass spectra (MS) datasets to be used to select features\namongst them to identify proteomic cancerous serums from normal serums.\nFeatures selection techniques have been applied and classification techniques\nhave been applied as well. Amongst the features selection techniques we have\nchosen to evaluate the performance of PCA (Principal Component Analysis ) and\nGA (Genetic algorithm), and amongst the classification techniques we have\nchosen the LDA (Linear Discriminant Analysis) and Neural networks so as to\nevaluate the ability of the selected features in identifying the cancerous\npatterns. Results were obtained for two combinations of features selection\ntechniques and classification techniques, the first one was PCA+(t-test)\ntechnique for features selection and LDA for accuracy tracking yielded an\naccuracy of 93.0233 % , the other one was genetic algorithm and neural network\nyielded an accuracy of 100%. So, we conclude that GA is more efficient for\nfeatures selection and hence for cancerous patterns detection than PCA\ntechnique.\n"}
+{"abstract": "  We calculate the fully differential medium-induced radiative spectrum at\nnext-to-leading order (NLO) accuracy within the Improved Opacity Expansion\n(IOE) framework. This scheme allows us to gain analytical control of the\nradiative spectrum at low and high gluon frequencies simultaneously. The high\nfrequency regime can be obtained in the standard opacity expansion framework in\nwhich the resulting power series diverges at the characteristic frequency\n$\\omega_c\\sim \\hat q L^2$. In the IOE, all orders in opacity are resumed\nsystematically below $\\omega_c$ yielding an asymptotic series controlled by\nlogarithmically suppressed remainders down to the thermal scale $T \\ll\n\\omega_c$, while matching the opacity expansion at high frequency. Furthermore,\nwe demonstrate that the IOE at NLO accuracy reproduces the characteristic\nCoulomb tail of the single hard scattering contribution as well as the Gaussian\ndistribution resulting from multiple soft momentum exchanges. Finally, we\ncompare our analytic scheme with a recent numerical solution, that includes a\nfull resummation of multiple scatterings, for LHC-inspired medium parameters.\nWe find a very good agreement both at low and high frequencies showcasing the\nperformance of the IOE which provides for the first time accurate analytic\nformulas for radiative energy loss in the relevant perturbative kinematic\nregimes for dense media.\n"}
+{"abstract": "  We propose and investigate a scheme based on Markovian feedback control that\nallows for the preparation of single targeted eigenstates of a system of\nbosonic atoms in a one-dimensional optical lattice with high fidelity. It can\nbe used for in-situ cooling the interacting system without particle loss, both\nfor weak and strong interactions, and for experimentally preparing and probing\nindividual excited eigenstates. For that purpose the system is assumed to be\nprobed weakly via homodyne detection of photons that are scattered\noff-resonantly by the atoms from a structured probe beam into a cavity mode. By\napplying an inertial force to the system that is proportional to the measured\nsignal, the system is then guided into a pure target state. The scheme is found\nto be robust against reduced measurement efficiencies.\n"}
+{"abstract": "  We prove concentration inequalities for functions of independent random\nvariables {under} sub-gaussian and sub-exponential conditions. The utility of\nthe inequalities is demonstrated by an extension of the now classical method of\nRademacher complexities to Lipschitz function classes and unbounded\nsub-exponential distribution.\n"}
+{"abstract": "  We investigate feedback forms for linear time-invariant systems described by\ndifferential-algebraic equations. Feedback forms are representatives of certain\nequivalence classes. For example state space transformations, invertible\ntransformations from the left, and proportional state feedback constitute an\nequivalence relation. The representative of such an equivalence class, which we\ncall proportional feedback form for the above example, allows to read off\nrelevant system theoretic properties. Our main contribution is to derive a\nquasi proportional feedback form. This form is advantageous since it provides\nsome geometric insight and is simple to compute, but still allows to read off\nthe relevant structural properties of the control system. We also derive a\nquasi proportional and derivative feedback form. Similar advantages hold.\n"}
+{"abstract": "  We present a novel stochastic approach to binary optimization for optimal\nexperimental design (OED) for Bayesian inverse problems governed by\nmathematical models such as partial differential equations. The OED utility\nfunction, namely, the regularized optimality criterion, is cast into a\nstochastic objective function in the form of an expectation over a multivariate\nBernoulli distribution. The probabilistic objective is then solved by using a\nstochastic optimization routine to find an optimal observational policy. The\nproposed approach is analyzed from an optimization perspective and also from a\nmachine learning perspective with correspondence to policy gradient\nreinforcement learning. The approach is demonstrated numerically by using an\nidealized two-dimensional Bayesian linear inverse problem, and validated by\nextensive numerical experiments carried out for sensor placement in a parameter\nidentification setup.\n"}
+{"abstract": "  The trend of deploying digital systems in numerous industries has induced a\nhike in recording digital information. The health sector has observed an\nextensive adoption of digital devices and systems that generate large volumes\nof personal medical records. Electronic health records contain valuable\ninformation for retrospective and prospective analysis that is often not\nentirely exploited because of the dense information storage. The crude purpose\nof condensing health records is to select the information that holds most\ncharacteristics of the original documents based on reported disease. These\nsummaries may boost diagnosis and extend a doctor's time with the patient\nduring a high workload situation like the COVID-19 pandemic. In this paper, we\npropose applying a multi-head attention-based mechanism to perform extractive\nsummarization of meaningful phrases in clinical notes. This method finds major\nsentences for a summary by correlating tokens, segments, and positional\nembeddings. The model outputs attention scores that are statistically\ntransformed to extract key phrases and can be used to projection on the\nheat-mapping tool for visual and human use.\n"}
+{"abstract": "  This paper proposes a method for representation learning of multimodal data\nusing contrastive losses. A traditional approach is to contrast different\nmodalities to learn the information shared between them. However, that approach\ncould fail to learn the complementary synergies between modalities that might\nbe useful for downstream tasks. Another approach is to concatenate all the\nmodalities into a tuple and then contrast positive and negative tuple\ncorrespondences. However, that approach could consider only the stronger\nmodalities while ignoring the weaker ones. To address these issues, we propose\na novel contrastive learning objective, TupleInfoNCE. It contrasts tuples based\nnot only on positive and negative correspondences but also by composing new\nnegative tuples using modalities describing different scenes. Training with\nthese additional negatives encourages the learning model to examine the\ncorrespondences among modalities in the same tuple, ensuring that weak\nmodalities are not ignored. We provide a theoretical justification based on\nmutual information for why this approach works, and we propose a sample\noptimization algorithm to generate positive and negative samples to maximize\ntraining efficacy. We find that TupleInfoNCE significantly outperforms the\nprevious state of the arts on three different downstream tasks.\n"}
+{"abstract": "  Topological Coding consists of two different kinds of mathematics:\ntopological structure and mathematical relation. The colorings and labelings of\ngraph theory are main techniques in topological coding applied in asymmetric\nencryption system. Topsnut-gpws (also, colored graphs) have the following\nadvantages: (1) Run fast in communication networks because they are saved in\ncomputer by popular matrices rather than pictures. (2) Produce easily\ntext-based (number-based) strings for encrypt files. (3) Diversity of\nasymmetric ciphers, one public-key corresponds to more private-keys, or more\npublic-keys correspond more private-keys. (4) Irreversibility, Topsnut-gpws can\ngenerate quickly text-based (number-based) strings with bytes as long as\ndesired, but these strings can not reconstruct the original Topsnut-gpws. (5)\nComputational security, since there are many non-polynomial (NP-complete,\nNP-hard) algorithms in creating Topsnut-gpws. (6) Provable security, since\nthere are many mathematical conjectures (open problems) in graph labelings and\ngraph colorings. We are committed to create more kinds of new Topsnut-gpws to\napproximate practical applications and antiquantum computation, and try to use\nalgebraic method and Topsnut-gpws to establish graphic group, graphic lattice,\ngraph homomorphism etc.\n"}
+{"abstract": "  Adversarial attacks have shown the vulnerability of machine learning models,\nhowever, it is non-trivial to conduct textual adversarial attacks on natural\nlanguage processing tasks due to the discreteness of data. Most previous\napproaches conduct attacks with the atomic \\textit{replacement} operation,\nwhich usually leads to fixed-length adversarial examples and therefore limits\nthe exploration on the decision space. In this paper, we propose\nvariable-length textual adversarial attacks~(VL-Attack) and integrate three\natomic operations, namely \\textit{insertion}, \\textit{deletion} and\n\\textit{replacement}, into a unified framework, by introducing and manipulating\na special \\textit{blank} token while attacking. In this way, our approach is\nable to more comprehensively find adversarial examples around the decision\nboundary and effectively conduct adversarial attacks. Specifically, our method\ndrops the accuracy of IMDB classification by $96\\%$ with only editing $1.3\\%$\ntokens while attacking a pre-trained BERT model. In addition, fine-tuning the\nvictim model with generated adversarial samples can improve the robustness of\nthe model without hurting the performance, especially for length-sensitive\nmodels. On the task of non-autoregressive machine translation, our method can\nachieve $33.18$ BLEU score on IWSLT14 German-English translation, achieving an\nimprovement of $1.47$ over the baseline model.\n"}
+{"abstract": "  Separating raypaths in a multipath shallow-water environment is a challenge\nproblem due to the interferences between them and colored noise existing in\nocean environment, especially for two raypaths arrive close to each other.\nThus, in this paper, a three dimensional (3D) higher-order raypath separation\nin an array to array configuration is proposed. Performance tests using\nsimulation data in a multipath environment, real data obtained in an ultrasonic\nwaveguide and ocean shallow-water data, respectively, illustrate that the\nproposed algorithm achieves a higher resolution and a stronger robustness\ncomparing to the existing algorithms.\n"}
+{"abstract": "  Machine unlearning refers to mechanisms that can remove the influence of a\nsubset of training data upon request from a trained model without incurring the\ncost of re-training from scratch. This paper develops a unified PAC-Bayesian\nframework for machine unlearning that recovers the two recent design principles\n- variational unlearning (Nguyen et.al., 2020) and forgetting Lagrangian\n(Golatkar et.al., 2020) - as information risk minimization problems\n(Zhang,2006). Accordingly, both criteria can be interpreted as PAC-Bayesian\nupper bounds on the test loss of the unlearned model that take the form of free\nenergy metrics.\n"}
+{"abstract": "  Dimension reduction techniques are often used when the high-dimensional\ntensor has relatively low intrinsic rank compared to the ambient dimension of\nthe tensor. The CANDECOMP/PARAFAC (CP) tensor completion is a widely used\napproach to find a low-rank approximation for a given tensor. In the tensor\nmodel, an $\\ell_1$ regularized optimization problem was formulated with an\nappropriate choice of the regularization parameter. The choice of the\nregularization parameter is important in the approximation accuracy. However,\nthe emergence of the large amount of data poses onerous computational burden\nfor computing the regularization parameter via classical approaches such as the\nweighted generalized cross validation (WGCV), the unbiased predictive risk\nestimator, and the discrepancy principle. In order to improve the efficiency of\nchoosing the regularization parameter and leverage the accuracy of the CP\ntensor, we propose a new algorithm for tensor completion by embedding the\nflexible hybrid method into the framework of the CP tensor. The main benefits\nof this method include incorporating regularization automatically and\nefficiently, improved reconstruction and algorithmic robustness. Numerical\nexamples from image reconstruction and model order reduction demonstrate the\nperformance of the propose algorithm.\n"}
+{"abstract": "  Attention-based end-to-end automatic speech recognition (ASR) systems have\nrecently demonstrated state-of-the-art results for numerous tasks. However, the\napplication of self-attention and attention-based encoder-decoder models\nremains challenging for streaming ASR, where each word must be recognized\nshortly after it was spoken. In this work, we present the dual\ncausal/non-causal self-attention (DCN) architecture, which in contrast to\nrestricted self-attention prevents the overall context to grow beyond the\nlook-ahead of a single layer when used in a deep architecture. DCN is compared\nto chunk-based and restricted self-attention using streaming transformer and\nconformer architectures, showing improved ASR performance over restricted\nself-attention and competitive ASR results compared to chunk-based\nself-attention, while providing the advantage of frame-synchronous processing.\nCombined with triggered attention, the proposed streaming end-to-end ASR\nsystems obtained state-of-the-art results on the LibriSpeech, HKUST, and\nSwitchboard ASR tasks.\n"}
+{"abstract": "  Data-driven optimization has found many successful applications in the real\nworld and received increased attention in the field of evolutionary\noptimization. Most existing algorithms assume that the data used for\noptimization is always available on a central server for construction of\nsurrogates. This assumption, however, may fail to hold when the data must be\ncollected in a distributed way and is subject to privacy restrictions. This\npaper aims to propose a federated data-driven evolutionary\nmulti-/many-objective optimization algorithm. To this end, we leverage\nfederated learning for surrogate construction so that multiple clients\ncollaboratively train a radial-basis-function-network as the global surrogate.\nThen a new federated acquisition function is proposed for the central server to\napproximate the objective values using the global surrogate and estimate the\nuncertainty level of the approximated objective values based on the local\nmodels. The performance of the proposed algorithm is verified on a series of\nmulti/many-objective benchmark problems by comparing it with two\nstate-of-the-art surrogate-assisted multi-objective evolutionary algorithms.\n"}
+{"abstract": "  Shocks form the basis of our understanding for the density and velocity\nstatistics of supersonic turbulent flows, such as those found in the cool\ninterstellar medium (ISM). The variance of the density field,\n$\\sigma^2_{\\rho/\\rho_0}$, is of particular interest for molecular clouds (MCs),\nthe birthplaces of stars in the Universe. The density variance may be used to\ninfer underlying physical processes in an MC, and parameterises the star\nformation (SF) rate of a cloud. However, models for $\\sigma^2_{\\rho/\\rho_0}$\nall share a common feature -- the variance is assumed to be isotropic. This\nassumption does not hold when a trans/sub-Alfv\\'enic mean magnetic field,\n$\\vec{B}_0$, is present in the cloud, which observations suggest is relevant\nfor some MCs. We develop an anisotropic model for $\\sigma_{\\rho/\\rho_0}^2$,\nusing contributions from hydrodynamical and fast magnetosonic shocks that\npropagate orthogonal to each other. Our model predicts an upper bound for\n$\\sigma_{\\rho/\\rho_0}^2$ in the high Mach number $(\\mathcal{M})$ limit as\nsmall-scale density fluctuations become suppressed by the strong $\\vec{B}_0$.\nThe model reduces to the isotropic $\\sigma_{\\rho/\\rho_0}^2-\\mathcal{M}$\nrelation in the hydrodynamical limit. To validate our model, we calculate\n$\\sigma_{\\rho/\\rho_0}^2$ from 12~high-resolution, three-dimensional,\nsupersonic, sub-Alfv\\'enic magnetohydrodynamical (MHD) turbulence simulations\nand find good agreement with our theory. We discuss how the two MHD shocks may\nbe the bimodally oriented over-densities observed in some MCs and the\nimplications for SF theory in the presence of a sub-Alfv\\'enic $\\vec{B}_0$. By\ncreating an anisotropic, supersonic density fluctuation model, this study paves\nthe way for SF theory in the highly anisotropic regime of interstellar\nturbulence.\n"}
+{"abstract": "  Explainable Artificial Intelligence (XAI) is a rising field in AI. It aims to\nproduce a demonstrative factor of trust, which for human subjects is achieved\nthrough communicative means, which Machine Learning (ML) algorithms cannot\nsolely produce, illustrating the necessity of an extra layer producing support\nto the model output. When approaching the medical field, we can see challenges\narise when dealing with the involvement of human-subjects, the ideology behind\ntrusting a machine to tend towards the livelihood of a human poses an ethical\nconundrum - leaving trust as the basis of the human-expert in acceptance to the\nmachines decision. The aim of this paper is to apply XAI methods to demonstrate\nthe usability of explainable architectures as a tertiary layer for the medical\ndomain supporting ML predictions and human-expert opinion, XAI methods produce\nvisualization of the feature contribution towards a given models output on both\na local and global level. The work in this paper uses XAI to determine feature\nimportance towards high-dimensional data-driven questions to inform\ndomain-experts of identifiable trends with a comparison of model-agnostic\nmethods in application to ML algorithms. The performance metrics for a\nglass-box method is also provided as a comparison against black-box capability\nfor tabular data. Future work will aim to produce a user-study using metrics to\nevaluate human-expert usability and opinion of the given models.\n"}
+{"abstract": "  Individual mobility is driven by demand for activities with diverse\nspatiotemporal patterns, but existing methods for mobility prediction often\noverlook the underlying activity patterns. To address this issue, this study\ndevelops an activity-based modeling framework for individual mobility\nprediction. Specifically, an input-output hidden Markov model (IOHMM) framework\nis proposed to simultaneously predict the (continuous) time and (discrete)\nlocation of an individual's next trip using transit smart card data. The\nprediction task can be transformed into predicting the hidden activity duration\nand end location. Based on a case study of Hong Kong's metro system, we show\nthat the proposed model can achieve similar prediction performance as the\nstate-of-the-art long short-term memory (LSTM) model. Unlike LSTM, the proposed\nIOHMM model can also be used to analyze hidden activity patterns, which\nprovides meaningful behavioral interpretation for why an individual makes a\ncertain trip. Therefore, the activity-based prediction framework offers a way\nto preserve the predictive power of advanced machine learning methods while\nenhancing our ability to generate insightful behavioral explanations, which is\nuseful for enhancing situational awareness in user-centric transportation\napplications such as personalized traveler information.\n"}
+{"abstract": "  $\\alpha$-cluster structures above double shell closures are among the\ncornerstones for nuclear $\\alpha$-cluster physics. Semi-microscopic cluster\nmodels (SMCMs) are important theoretical models to study their properties. A\ncrucial ingredient of SMCM is the effective potential between the alpha cluster\nand the doubly magic nucleus. We derive new double-folding potentials between\n$\\alpha$ clusters and doubly magic nuclei from soft local chiral\nnucleon-nucleon potentials given by chiral effective field theory ($\\chi$EFT)\nat the next-to-next-to-leading order. The $\\alpha$-cluster structures in\n${}^{8}\\text{Be}$, ${}^{20}\\text{Ne}$, ${}^{44,52}\\text{Ti}$, and\n${}^{212}\\text{Po}$ are explored to validate these new double-folding\npotentials. The $\\alpha$ decay of ${}^{104}\\text{Te}$ is also studied in the\nlight of recent experimental results. Our study shows that double-folding\npotentials from $\\chi$EFT are the new reliable effective potentials for the\nSMCM approach to $\\alpha$-cluster structures above double shell closures, with\nboth conceptual and phenomenological merits.\n"}
+{"abstract": "  We investigate stabilizers of finite sets of rational points in Cantor space\nfor the Higman-Thompson groups $V_{n,r}$. We prove that the pointwise\nstabilizer is an iterated ascending HNN extension of $V_{n,q}$ for any $q\\geq\n1$. We also prove that the commutator subgroup of the pointwise stabilizer is\nsimple, and we compute the abelianization. Finally, for each $n$ we classify\nsuch pointwise stabilizers up to isomorphism.\n"}
+{"abstract": "  Animal cells are active, contractile objects. While bioassays address the\nmolecular characterization of cell contractility, the mechanical\ncharacterization of the active forces in cells remains challenging. Here by\nconfronting theoretical analysis, finite element modeling and experiments, we\nanalyze the relevance of existing mechanical approaches and show that their\ncomplementarity allows us to characterize both the resistive and the active\ncomponents of the intracellular stresses that build up following cell adhesion.\nBy highlighting their linear correlation, we show that these mechanical\napproaches allow us to localize the stress generators in the cells, give an\nupper bound to their size, and demonstrate that they are mechanically linked to\nthe substrate at a very local scale.\n"}
+{"abstract": "  Parents - particularly moms - increasingly consult social media for support\nwhen taking decisions about their young children, and likely also when advising\nother family members such as elderly relatives. Minimizing malignant online\ninfluences is therefore crucial to securing their assent for policies ranging\nfrom vaccinations, masks and social distancing against the pandemic, to\nhousehold best practices against climate change, to acceptance of future 5G\ntowers nearby. Here we show how a strengthening of bonds across online\ncommunities during the pandemic, has led to non-Covid-19 conspiracy theories\n(e.g. fluoride, chemtrails, 5G) attaining heightened access to mainstream\nparent communities. Alternative health communities act as the critical conduits\nbetween conspiracy theorists and parents, and make the narratives more\npalatable to the latter. We demonstrate experimentally that these\ninter-community bonds can perpetually generate new misinformation, irrespective\nof any changes in factual information. Our findings show explicitly why\nFacebook's current policies have failed to stop the mainstreaming of\nnon-Covid-19 and Covid-19 conspiracy theories and misinformation, and why\ntargeting the largest communities will not work. A simple yet exactly solvable\nand empirically grounded mathematical model, shows how modest tailoring of\nmainstream communities' couplings could prevent them from tipping against\nestablishment guidance. Our conclusions should also apply to other social media\nplatforms and topics.\n"}
+{"abstract": "  In two dimensional isotropic scale invariant theories, the time scaling of\nthe entanglement entropy of a segment is fixed via the conformal symmetry. We\nconsider scale invariance in a more general sense and show that in integrable\ntheories that the scale invariance is anisotropic between time and space,\nparametrized by $z$, most of the entanglement is carried by the slow modes for\n$z>1$. At early times entanglement grows linearly due to the contribution of\nthe fast modes, before smoothly entering a slow mode regime where it grows\nforever with $t^{\\frac{1}{1-z}}$. The slow mode regime admits a logarithmic\nenhancement in bosonic theories. We check our analytical results against\nnumerical simulations in corresponding fermionic and bosonic lattice models\nfinding extremely good agreement. We show that in these non-relativistic\ntheories that the slow modes are dominant, local quantum information is\nuniversally scrambled in a stronger way compared to their relativistic\ncounterparts.\n"}
+{"abstract": "  Preserving customers' expectations and understanding factors affecting their\npurchasing decisions would significantly affect designing effective marketing\nand advertising strategies. However, constantly and swiftly changing the\ncustomers' interests and consumption behaviors, make it inevitable to utilize\nthe sophisticated tools and approaches based on advanced technologies. Among\nthem, neuromarketing by measuring the customers' physiological and neural\nsignals, studying the consumers' cognitive, and affective responses to\nmarketing stimulus, provides deep insight into the customers' motivations,\npreferences, and decisions. Recently, the Internet of Everything (IoE) has\nbrought many new opportunities to the industry and has attracted the attention\nof many researchers in recent years. The main objective of this paper is to\naddress how the Internet of Everything (IoE) would empower neuromarketing\ntechniques. In particular, applications of IoE gadgets and devices in eleven\ngroups of neuromarketing techniques are discussed to present numerous solutions\nthat would help meet this goal. Moreover, we present an in-depth understanding\nof current research issues as well as emerging trends.\n"}
+{"abstract": "  The intense emission of 511 keV photons from the Galactic center and within\nterrestrial thunderstorms is attributed to the formation of parapositronia'\nclouds. Unbound electron-positron pairs and positronia can be created by strong\nelectromagnetic fields produced in interactions of electrically charged\nobjects, in particular, in collisions of heavy nuclei. Kinematics of this\nprocess favours abundant creation of the unbound electron-positron pairs with\nvery small masses and the confined parapositronia states which decay directly\nto two 511 keV quanta. Therefore we propose to consider interactions of\nelectromagnetic fields of colliding heavy ions as a source of low-mass pairs\nwhich can transform to 511 keV quanta. Intensity of their creation is enlarged\nby the factor Z$^4$ (Z is the electric charge of a heavy ion) compared to\nprotons with Z=1. These processes are especially important at very high\nenergies of nuclear collisions because their cross sections increase\nproportionally to cube of the logarithm of energy and can even exceed the cross\nsections of strong interactions which may not increase faster than the squared\nlogarithm of energy. Moreover, production of extremely low-mass $e^+e^-$-pairs\nin ultraperipheral nuclear collisions is strongly enhanced due to the\nSommerfeld-Gamow-Sakharov (SGS) factor which accounts for mutual Coulomb\nattraction of non-relativistic electrons to positrons in case of low\npair-masses. This attraction may lead to their annihilation and, therefore, to\nthe increased intensity of 511 keV photons. It is proposed to confront the\nobtained results to forthcoming experimental data at NICA collider.\n"}
+{"abstract": "  Most prior works on deep learning-based wireless device classification using\nradio frequency (RF) data apply off-the-shelf deep neural network (DNN) models,\nwhich were matured mainly for domains like vision and language. However,\nwireless RF data possesses unique characteristics that differentiate it from\nthese other domains. For instance, RF data encompasses intermingled time and\nfrequency features that are dictated by the underlying hardware and protocol\nconfigurations. In addition, wireless RF communication signals exhibit\ncyclostationarity due to repeated patterns (PHY pilots, frame prefixes, etc.)\nthat these signals inherently contain. In this paper, we begin by explaining\nand showing the unsuitability as well as limitations of existing DNN feature\ndesign approaches currently proposed to be used for wireless device\nclassification. We then present novel feature design approaches that exploit\nthe distinct structures of the RF communication signals and the spectrum\nemissions caused by transmitter hardware impairments to custom-make DNN models\nsuitable for classifying wireless devices using RF signal data. Our proposed\nDNN feature designs substantially improve classification robustness in terms of\nscalability, accuracy, signature anti-cloning, and insensitivity to environment\nperturbations. We end the paper by presenting other feature design strategies\nthat have great potentials for providing further performance improvements of\nthe DNN-based wireless device classification, and discuss the open research\nchallenges related to these proposed strategies.\n"}
+{"abstract": "  Rowhammer attacks that corrupt level-1 page tables to gain kernel privilege\nare the most detrimental to system security and hard to mitigate. However,\nrecently proposed software-only mitigations are not effective against such\nkernel privilege escalation attacks. In this paper, we propose an effective and\npractical software-only defense, called SoftTRR, to protect page tables from\nall existing rowhammer attacks on x86. The key idea of SoftTRR is to refresh\nthe rows occupied by page tables when a suspicious rowhammer activity is\ndetected. SoftTRR is motivated by DRAM-chip-based target row refresh (ChipTRR)\nbut eliminates its main security limitation (i.e., ChipTRR tracks a limited\nnumber of rows and thus can be bypassed by many-sided hammer). Specifically,\nSoftTRR protects an unlimited number of page tables by tracking memory accesses\nto the rows that are in close proximity to page-table rows and refreshing the\npage-table rows once the tracked access count exceeds a pre-defined threshold.\nWe implement a prototype of SoftTRR as a loadable kernel module, and evaluate\nits security effectiveness, performance overhead, and memory consumption. The\nexperimental results show that SoftTRR protects page tables from real-world\nrowhammer attacks and incurs small performance overhead as well as memory cost.\n"}
+{"abstract": "  This paper presents a clustering algorithm that is an extension of the\nCategory Trees algorithm. Category Trees is a clustering method that creates\ntree structures that branch on category type and not feature. The development\nin this paper is to consider a secondary order of clustering that is not the\ncategory to which the data row belongs, but the tree, representing a single\nclassifier, that it is eventually clustered with. Each tree branches to store\nsubsets of other categories, but the rows in those subsets may also be related.\nThis paper is therefore concerned with looking at that second level of\nclustering between the other category subsets, to try to determine if there is\nany consistency over it. It is argued that Principal Components may be a\nrelated and reciprocal type of structure, and there is an even bigger question\nabout the relation between exemplars and principal components, in general. The\ntheory is demonstrated using the Portugal Forest Fires dataset as a case study.\nThe Category Trees are then combined with other Self-Organising algorithms from\nthe author and it is suggested that they all belong to the same family type,\nwhich is an Entropy-style of classifier.\n"}
+{"abstract": "  Model-based planning and prospection are widely studied in both cognitive\nneuroscience and artificial intelligence (AI), but from different perspectives\n- and with different desiderata in mind (biological realism versus scalability)\nthat are difficult to reconcile. Here, we introduce a novel method to plan in\nlarge POMDPs - Active Tree Search - that combines the normative character and\nbiological realism of a leading planning theory in neuroscience (Active\nInference) and the scalability of Monte-Carlo methods in AI. This unification\nis beneficial for both approaches. On the one hand, using Monte-Carlo planning\npermits scaling up the biologically grounded approach of Active Inference to\nlarge-scale problems. On the other hand, the theory of Active Inference\nprovides a principled solution to the balance of exploration and exploitation,\nwhich is often addressed heuristically in Monte-Carlo methods. Our simulations\nshow that Active Tree Search successfully navigates binary trees that are\nchallenging for sampling-based methods, problems that require adaptive\nexploration, and the large POMDP problem Rocksample. Furthermore, we illustrate\nhow Active Tree Search can be used to simulate neurophysiological responses\n(e.g., in the hippocampus and prefrontal cortex) of humans and other animals\nthat contain large planning problems. These simulations show that Active Tree\nSearch is a principled realisation of neuroscientific and AI theories of\nplanning, which offers both biological realism and scalability.\n"}
+{"abstract": "  In this paper we provide further studies of the Fenchel duality theory in the\ngeneral frame work of locally convex topological vector (LCTV) spaces. We prove\nthe validity of the Fenchel strong duality under some qualification conditions\nvia generalized relative interiors imposed on the epigraphs and the domains of\nthe functions involved. Our results directly generalize the classical\nFenchel-Rockafellar theorem on strong duality from finite dimensions to LCTV\nspaces.\n"}
+{"abstract": "  Optimization of the intermetallic layer thickness and the suppression of\ninterfacial defects are key elements to improve the load bearing capacity of\ndissimilar joints. However, till date we do not have a systematic tool to\ninvestigate the dissimilar joints and the intermetallic properties produced by\na welding condition. Friction Melt Bonding (FMB) is a recently developed\ntechnique for joining dissimilar metals that also does not exempt to these\nchallenges. The FMB of DP980 and Al6061-T6 is investigated using a new physical\nsimulation tool, based on Gleeble thermo-mechanical simulator, to understand\nthe effect of individual parameter on the intermetallic formation. The proposed\nmethod demonstrates its capability in reproducing the intermetallic\ncharacteristics, including the thickness of intermetallic bonding layer, the\nmorphology and texture of its constituents (Fe2Al5 and Fe4Al13), as well as\ntheir nanohardness and reduced modulus. The advantages of physical simulation\ntool can enable novel developing routes for the development of dissimilar metal\njoining processes and facilitate to reach the requiring load bearing capacity\nof the joints.\n"}
+{"abstract": "  In this paper we discuss a sequential algorithm for the computation of a\nminimum-time speed profile over a given path, under velocity, acceleration and\njerk constraints. Such a problem arises in industrial contexts such as\nautomated warehouses, where LGVs need to perform assigned tasks as fast as\npossible in order to increase productivity. It can be reformulated as an\noptimization problem with a convex objective function, linear velocity and\nacceleration constraints, and non-convex jerk constraints, which, thus,\nrepresent the main source of difficulty. While existing non-linear programming\n(NLP) solvers can be employed for the solution of this problem, it turns out\nthat the performance and robustness of such solvers can be enhanced by the\nsequential line-search algorithm proposed in this paper. At each iteration a\nfeasible direction, with respect to the current feasible solution, is computed,\nand a step along such direction is taken in order to compute the next iterate.\nThe computation of the feasible direction is based on the solution of a\nlinearized version of the problem, and the solution of the linearized problem,\nthrough an approach which strongly exploits its special structure, represents\nthe main contribution of this work. The efficiency of the proposed approach\nwith respect to existing NLP solvers is proved through different computational\nexperiments.\n"}
+{"abstract": "  Under structural conditions which are almost optimal, we derive a\nquantitative version of boundary estimate then prove existence of solutions to\nDirichlet problem for a class of fully nonlinear elliptic equations on\nHermitian manifolds.\n"}
+{"abstract": "  The development of Graph Neural Networks (GNNs) has led to great progress in\nmachine learning on graph-structured data. These networks operate via diffusing\ninformation across the graph nodes while capturing the structure of the graph.\nRecently there has also seen tremendous progress in quantum computing\ntechniques. In this work, we explore applications of multi-particle quantum\nwalks on diffusing information across graphs. Our model is based on learning\nthe operators that govern the dynamics of quantum random walkers on graphs. We\ndemonstrate the effectiveness of our method on classification and regression\ntasks.\n"}
+{"abstract": "  Backstepping based controller and observer models were designed for higher\norder linear and nonlinear Schr\\\"odinger equations on a finite interval in Part\nI of this study where the controller was assumed to be acting from the left\nendpoint of the medium. In this companion paper, we further the analysis by\nconsidering boundary controller(s) acting at the right endpoint of the domain.\nIt turns out that the problem is more challenging in this scenario as the\nassociated boundary value problem for the backstepping kernel becomes\noverdetermined and lacks a smooth solution. The latter is essential to switch\nback and forth between the original plant and the so called target system. To\novercome this difficulty we rely on the strategy of using an imperfect kernel,\nnamely one of the boundary conditions in kernel PDE model is disregarded. The\ndrawback is that one loses rapid stabilization in comparison with the left\nendpoint controllability. Nevertheless, the exponential decay of the $L^2$-norm\nwith a certain rate still holds. The observer design is associated with new\nchallenges from the point of view of wellposedness and one has to prove\nsmoothing properties for an associated initial boundary value problem with\ninhomogeneous boundary data. This problem is solved by using Laplace transform\nin time. However, the Bromwich integral that inverts the transformed solution\nis associated with certain analyticity issues which are treated through a\nsubtle analysis. Numerical algorithms and simulations verifying the theoretical\nresults are given.\n"}
+{"abstract": "  After a short revision of the historical milestones on normal numbers, we\nintroduce the Borel numbers as the reals admitting a probability function on\ntheir different bases representations. In this setting, we provide two\nprobabilistic characterizations of normality based on the stochastic\nindependence of their digits. Finally, we define the pseudonormality condition,\nwhich is satisfied by normal numbers and may be evaluated in a finite number of\nsteps.\n"}
+{"abstract": "  Various forms of exact laws governing magnetohydrodynamic (MHD) turbulence\nhave been derived either in the incompressibility limit, or for isothermal\ncompressible flows. Here we propose a more general method that allows us to\nobtain such laws for any turbulent isentropic flow (i.e., constant entropy). We\ndemonstrate that the known MHD exact laws (incompressible and isothermal) and\nthe new (polytropic) one can be obtained as specific cases of the general law\nwhen the corresponding closure equation is stated. We also recover all known\nexact laws of hydrodynamic (HD) turbulence (incompressible, isothermal and\npolytropic) from this law in the limit ${\\bf B}=0$. We furthermore show that\nthe difference between the two forms (isothermal and polytropic) of the MHD\nexact laws of interest in this work resides in some of the source terms and in\nthe explicit form of the flux term that depends on internal energy. Finally, we\napply these two forms to Parker Solar Probe (PSP) data taken in the inner\nheliosphere to highlight how the different closure equations affect the energy\ncascade rate estimates.\n"}
+{"abstract": "  Blockchain like Bitcoin and Ethereum suffer from scalability issues. Sharding\nis one of the most promising and leading solutions to scale blockchain. The\nbasic idea behind sharding is to divide the blockchain network into multiple\ncommittees, where each processing a separate set of transactions, rather than\nthe entire network processes all transactions. In this paper, we propose a\nprobabilistic approach to analyze the security of sharding-based blockchain\nprotocols. Based on this approach, we investigate the threat of Sybil attacks\nin these protocols. The key contribution of our paper is a tractable\nprobabilistic approach to accurately compute the failure probability that at\nleast one committee fails and ultimately compute the probability of a\nsuccessful attack. To show the effectiveness of our approach, we conduct a\nnumerical and comparative analysis of the proposed approach with existing\napproaches.\n"}
+{"abstract": "  By Liouville's theorem, in dimensions 3 or more conformal transformations\nform a finite-dimensional group, an apparent drastic departure from the\n2-dimensional case. We propose a derived enhancement of the conformal Lie\nalgebra which is an infinite-dimensional dg-Lie algebra incorporating not only\nsymmetries but also deformations of the conformal structure. Our approach is\nbased on (derived) deformation theory of the ambitwistor space of complex\nnull-geodesics.\n"}
+{"abstract": "  We construct a family of growing finite bounded degree rooted graphs, $G_n$,\nin which the mixing time for simple random walk, starting at the root, is order\n$\\log |G_n|$. Yet after a quasi - isometry, the ratio of $|G_n|$ over the\nmixing time grows arbitrarily slow to infinity.\n"}
+{"abstract": "  We are producing high statistics 250 GeV common MC samples for the ILD\nphysics study using the latest generator, simulation, and reconstruction\npackages. In the production, we utilize ILCDirac distributed computing\nenvironment. The estimated resource requirements, the current status and\nprospects of the production are presented.\n"}
+{"abstract": "  A scheme is presented to catalyze nuclear fusion by the excitation of the\nlocalized surface plasmon resonance (LSPR) of an Au nanobipyramid (NBP) by\nultrafast (femtosecond) laser pulses. The effect utilizes the exceptionally\nhigh electric field enhancement provided by the Au NBP LSPR, localized to nm^3\nvolumes and fs time scales, to produce a ponderomotive force that in turn\nprovides for screening energies near the Au NBP tip. Discrete-dipole\napproximation (DDA) simulations are presented to support the proposed scheme.\nCalculations made with conservative parameters suggest that the effect should\nbe observable in a laboratory setting using commercially available ultrafast\nlasers.\n"}
+{"abstract": "  Real time monitoring using in situ sensors is becoming a common approach for\nmeasuring water quality within watersheds. High frequency measurements produce\nbig data sets that present opportunities to conduct new analyses for improved\nunderstanding of water quality dynamics and more effective management of rivers\nand streams. Of primary importance is enhancing knowledge of the relationships\nbetween nitrate, one of the most reactive forms of inorganic nitrogen in the\naquatic environment, and other water quality variables. We analysed high\nfrequency water quality data from in situ sensors deployed in three sites from\ndifferent watersheds and climate zones within the National Ecological\nObservatory Network, USA. We used generalised additive mixed models to explain\nthe nonlinear relationships at each site between nitrate concentration and\nconductivity, turbidity, dissolved oxygen, water temperature, and elevation.\nTemporal auto correlation was modelled with an auto regressive moving average\nmodel and we examined the relative importance of the explanatory variables.\nTotal deviance explained by the models was high for all sites. Although\nvariable importance and the smooth regression parameters differed among sites,\nthe models explaining the most variation in nitrate contained the same\nexplanatory variables. This study demonstrates that building a model for\nnitrate using the same set of explanatory water quality variables is\nachievable, even for sites with vastly different environmental and climatic\ncharacteristics. Applying such models will assist managers to select cost\neffective water quality variables to monitor when the goals are to gain a\nspatially and temporally in depth understanding of nitrate dynamics and adapt\nmanagement plans accordingly.\n"}
+{"abstract": "  With the advent of neuromorphic hardware, spiking neural networks can be a\ngood energy-efficient alternative to artificial neural networks. However, the\nuse of spiking neural networks to perform computer vision tasks remains\nlimited, mainly focusing on simple tasks such as digit recognition. It remains\nhard to deal with more complex tasks (e.g. segmentation, object detection) due\nto the small number of works on deep spiking neural networks for these tasks.\nThe objective of this paper is to make the first step towards modern computer\nvision with supervised spiking neural networks. We propose a deep convolutional\nspiking neural network for the localization of a single object in a grayscale\nimage. We propose a network based on DECOLLE, a spiking model that enables\nlocal surrogate gradient-based learning. The encouraging results reported on\nOxford-IIIT-Pet validates the exploitation of spiking neural networks with a\nsupervised learning approach for more elaborate vision tasks in the future.\n"}
+{"abstract": "  We discuss how to use correlations between different physical observables to\nimprove recently obtained thermodynamics bounds, notably the\nfluctuation-response inequality (FRI) and the thermodynamic uncertainty\nrelation (TUR). We show that increasing the number of measured observables will\nalways produce a tighter bound. This tighter bound becomes particularly useful\nif one of the observables is a conserved quantity, whose expectation is\ninvariant under a given perturbation of the system. For the case of the TUR, we\nshow that this applies to any function of the state of the system. We\ndemonstrate our finding on a model of the F1-ATPase molecular motor, where we\nobtain a bound that is more than a factor of 2 tighter than the one given by\nthe TUR.\n"}
+{"abstract": "  We explore the tunneling transport properties of a quantum dot embedded in an\noptical microcavity and coupled to a semiconductor-superconductor\none-dimensional nanowire (Majorana nanowire) hosting Majorana zero modes (MZMs)\nat their edges. Conductance profiles reveal that strong light-matter coupling\ncan be employed to distinguish between the cases of highly nonlocal MZMs,\noverlapped MZMs and quasi-MZMs. Moreover, we show that it is possible to access\nthe degree of Majorana nonlocality (topological quality factor) by changing the\ndot spectrum through photon-induced transitions tuned by an external pump\napplied to the microcavity.\n"}
+{"abstract": "  Bi-2212 is the only high-field, high-temperature superconductor (HTS) capable\nof reaching a critical current density $J_{\\text{c}}$(16 T, 4.2 K) of 6500\n$\\mathrm{A\\cdot mm^{-2}}$ in the highly desirable round wire (RW) form.\nHowever, state-of-the-art Bi-2212 conductors still have a critical current\ndensity ($J_{\\text{c}}$) to depairing current density ($J_{\\text{d}}$) ratio\naround 20 to 30 times lower than that of state-of-the-art $\\mathrm{Nb-Ti}$ or\nREBCO. Previously, we have shown that recent improvements in Bi-2212 RW\n$J_{\\text{c}}$ are due to improved connectivity associated with optimization of\nthe heat treatment process, and most recently due to a transition to a finer\nand more uniform powder manufactured by Engi-Mat. One quantitative measure of\nconnectivity may be the critical current ($I_{\\text{c}}$) distribution, since\nthe local $I_{\\text{c}}$ in a wire can vary along the length due to variable\nvortex-microstructure interactions and to factors such as filament shape\nvariations, grain-to-grain connectivity variations and blocking secondary phase\ndistributions. Here we compare $\\sim$ 0.1 m length $I_{\\text{c}}$ distributions\nof Bi-2212 RWs with recent state-of-the-art very high-$J_{\\text{c}}$ Engi-Mat\npowder and lower $J_{\\text{c}}$ and older Nexans granulate powder. We do find\nthat the $I_{\\text{c}}$ spread for Bi-2212 wires is about twice the relative\nstandard of high-$J_{\\text{c}}$ $\\mathrm{Nb-Ti}$ well below $H_{\\text{irr}}$.\nWe do not yet see any obvious contribution of the Bi-2212 anisotropy to the\n$I_{\\text{c}}$ distribution and are rather encouraged that these Bi-2212 round\nwires show relative $I_{\\text{c}}$ distributions not too far from\nhigh-$J_{\\text{c}}$ $\\mathrm{Nb-Ti}$ wires.\n"}
+{"abstract": "  The three Laser Interferometer Space Antenna (LISA) spacecraft are going to\nbe placed in a triangular formation in an Earth-trailing or Earth-leading\norbit. They will be launched together on a single rocket and transferred to\nthat science orbit using Solar Electric Propulsion. Since the transfer $\\Delta\nv$ depends on the chosen science orbit, both transfer and science orbit have\nbeen optimised together. For a thrust level of 90 mN, an allocation of 1092 m/s\nper spacecraft is sufficient for an all-year launch in 2034. For every launch\nmonth a dedicated science orbit is designed with a corner angle variation of\nclose to $60^\\circ \\pm 1.0^\\circ$ and an arm length rate of maximum 10 m/s.\nMoreover, a detailed navigation analysis of the science orbit insertion and the\nimpact on insertion errors on the constellation stability has been conducted.\nThe analysis shows that Range/Doppler measurements together with a series of\ncorrection manoeuvres at the beginning of the science orbit phase can reduce\ninsertion dispersions to a level where corner angle variations remain at about\n$60^\\circ \\pm 1.1^\\circ$ at $99\\%$ C.L.. However, the situation can become\nsignificantly worse if the self-gravity accelerations acting during the science\norbit phase are not sufficiently characterised prior to science orbit\ninsertion.\n"}
+{"abstract": "  We propose a new single photon source based on the principle of active\nmultiplexing of heralded single photons which, unlike previously reported\narchitecture, requires a limited amount of physical resources. We discuss both\nits feasibility and the purity and indistinguishability of single photons as\nfunction of the key parameters of a possible implementation.\n"}
+{"abstract": "  We present a new approach for the mechanically consistent modelling and\nsimulation of fluid-structure interactions with contact. The fundamental idea\nconsists of combining a relaxed contact formulation with the modelling of\nseepage through a porous layer of co-dimension 1 during contact. For the\nlatter, a Darcy model is considered in a thin porous layer attached to a solid\nboundary in the limit of infinitesimal thickness. In combination with a\nrelaxation of the contact conditions the computational model is both\nmechanically consistent and simple to implement. We analyse the approach in\ndetailed numerical studies with both thick- and thin-walled solids, within a\nfully Eulerian and an immersed approach for the fluid-structure interaction and\nusing fitted and unfitted finite element discretisations.\n"}
+{"abstract": "  Therapeutics machine learning is an emerging field with incredible\nopportunities for innovatiaon and impact. However, advancement in this field\nrequires formulation of meaningful learning tasks and careful curation of\ndatasets. Here, we introduce Therapeutics Data Commons (TDC), the first\nunifying platform to systematically access and evaluate machine learning across\nthe entire range of therapeutics. To date, TDC includes 66 AI-ready datasets\nspread across 22 learning tasks and spanning the discovery and development of\nsafe and effective medicines. TDC also provides an ecosystem of tools and\ncommunity resources, including 33 data functions and types of meaningful data\nsplits, 23 strategies for systematic model evaluation, 17 molecule generation\noracles, and 29 public leaderboards. All resources are integrated and\naccessible via an open Python library. We carry out extensive experiments on\nselected datasets, demonstrating that even the strongest algorithms fall short\nof solving key therapeutics challenges, including real dataset distributional\nshifts, multi-scale modeling of heterogeneous data, and robust generalization\nto novel data points. We envision that TDC can facilitate algorithmic and\nscientific advances and considerably accelerate machine-learning model\ndevelopment, validation and transition into biomedical and clinical\nimplementation. TDC is an open-science initiative available at\nhttps://tdcommons.ai.\n"}
+{"abstract": "  Bayesian hierarchical mixture clustering (BHMC) is an interesting model that\nimproves on the traditional Bayesian hierarchical clustering approaches.\nRegarding the parent-to-node diffusion in the generative process, BHMC replaces\nthe conventional Gaussian-to-Gaussian (G2G) kernels with a Hierarchical\nDirichlet Process Mixture Model (HDPMM). However, the drawback of the BHMC lies\nin that it might obtain comparatively high nodal variance in the higher levels\n(i.e., those closer to the root node). This can be interpreted as that the\nseparation between the nodes, in particular those in the higher levels, might\nbe weak. Attempting to overcome this drawback, we consider a recent inferential\nframework named posterior regularization, which facilitates a simple manner to\nimpose extra constraints on a Bayesian model to address some weakness of the\noriginal model. Hence, to enhance the separation of clusters, we apply\nposterior regularization to impose max-margin constraints on the nodes at every\nlevel of the hierarchy. In this paper, we illustrate how the framework\nintegrates with the BHMC and achieves the desired improvements over the\noriginal model.\n"}
+{"abstract": "  We investigated daily COVID-19 cases and deaths in the 337 lower tier local\nauthority regions in England and Wales to better understand how the disease\npropagated over a 15-month period. Population density scaling models revealed\nresidual variance and skewness to be sensitive indicators of the dynamics of\npropagation. Lockdowns and schools reopening triggered increased variance\nindicative of outbreaks with local impact and country scale heterogeneity.\nUniversity reopening and December holidays triggered reduced variance\nindicative of country scale homogenisation which reached a minimum in\nmid-January 2021. Homogeneous propagation was associated with better\ncorrespondence with normally distributed residuals while heterogeneous\npropagation was more consistent with skewed models. Skewness varied from\nstrongly negative to strongly positive revealing an unappreciated feature of\ncommunity propagation. Hot spots and super-spreading events are well understood\ndescriptors of regional disease dynamics that would be expected to be\nassociated with positively skewed distributions. Positively skewed behaviour\nwas observed; however, negative skewness indicative of cold-spots and\nsuper-isolation dominated for approximately 8 months during the period of\nstudy. In contrast, death metrics showed near constant behaviour in scaling,\nvariance, and skewness metrics over the full period with rural regions\npreferentially affected, an observation consistent with regional age\ndemographics in England and Wales. Regional positions relative to density\nscaling laws were remarkably persistent after the first 5-9 days of the\navailable data set. The determinants of this persistent behaviour probably\nprecede the pandemic and remain unchanged.\n"}
+{"abstract": "  Non-linearity of a Boolean function indicates how far it is from any linear\nfunction. Despite there being several strong results about identifying a linear\nfunction and distinguishing one from a sufficiently non-linear function, we\nfound a surprising lack of work on computing the non-linearity of a function.\nThe non-linearity is related to the Walsh coefficient with the largest absolute\nvalue; however, the naive attempt of picking the maximum after constructing a\nWalsh spectrum requires $\\Theta(2^n)$ queries to an $n$-bit function. We\nimprove the scenario by designing highly efficient quantum and randomised\nalgorithms to approximate the non-linearity allowing additive error, denoted\n$\\lambda$, with query complexities that depend polynomially on $\\lambda$. We\nprove lower bounds to show that these are not very far from the optimal ones.\nThe number of queries made by our randomised algorithm is linear in $n$,\nalready an exponential improvement, and the number of queries made by our\nquantum algorithm is surprisingly independent of $n$. Our randomised algorithm\nuses a Goldreich-Levin style of navigating all Walsh coefficients and our\nquantum algorithm uses a clever combination of Deutsch-Jozsa, amplitude\namplification and amplitude estimation to improve upon the existing quantum\nversions of the Goldreich-Levin technique.\n"}
+{"abstract": "  Whether the number of beta-steps in the lambda-calculus can be taken as a\nreasonable time cost model (that is, polynomially related to the one of Turing\nmachines) is a delicate problem, which depends on the notion of evaluation\nstrategy. Since the nineties, it is known that weak (that is, out of\nabstractions) call-by-value evaluation is a reasonable strategy while L\\'evy's\noptimal parallel strategy, which is strong (that is, it reduces everywhere), is\nnot. The strong case turned out to be subtler than the weak one. In 2014\nAccattoli and Dal Lago have shown that strong call-by-name is reasonable, by\nintroducing a new form of useful sharing and, later, an abstract machine with\nan overhead quadratic in the number of beta-steps.\n  Here we show that also strong call-by-value evaluation is reasonable for\ntime, via a new abstract machine realizing useful sharing and having a linear\noverhead. Moreover, our machine uses a new mix of sharing techniques, adding on\ntop of useful sharing a form of implosive sharing, which on some terms brings\nan exponential speed-up. We give examples of families that the machine executes\nin time logarithmic in the number of beta-steps.\n"}
+{"abstract": "  In this article, we construct a Stratonovich solution for the stochastic wave\nequation in spatial dimension $d \\leq 2$, with time-independent noise and\nlinear term $\\sigma(u)=u$ multiplying the noise. The noise is spatially\nhomogeneous and its spectral measure satisfies an integrability condition which\nis stronger than Dalang's condition. We give a probabilistic representation for\nthis solution, similar to the Feynman-Kac-type formula given in Dalang, Mueller\nand Tribe (2008) for the solution of the stochastic wave equation with\nspatially homogeneous Gaussian noise, that is white in time. We also give the\nchaos expansion of the Stratonovich solution and we compare it with the chaos\nexpansion of the Skorohod solution from Balan, Chen and Chen (2020).\n"}
+{"abstract": "  Bifurcating Markov chains (BMC) are Markov chains indexed by a full binary\ntree representing the evolution of a trait along a population where each\nindividual has two children. We provide a central limit theorem for additive\nfunctionals of BMC under $L^2$-ergodic conditions with three different regimes.\nThis completes the pointwise approach developed in a previous work. As\napplication, we study the elementary case of symmetric bifurcating\nautoregressive process, which justify the non-trivial hypothesis considered on\nthe kernel transition of the BMC. We illustrate in this example the phase\ntransition observed in the fluctuations.\n"}
+{"abstract": "  Bassino et al. 2010 and Regnier et al. 1998 showed the generating functions\nof the distributions of the number of the occurrences of words (distributions\nof words for short) in finite string in the form of rational functions. However\nthe coefficients of the expansion of the rational functions are complicated and\nwe do not have a simple formula of the exact distributions of words from\nrational functions. In this paper we study the finite dimensional generating\nfunctions of the distribution of nonoverlapping words for each fixed sample\nsize and show the explicit formula of the distributions of words for Bernoulli\nmodel. We demonstrate that 1) the tests based on the distributions of words\nreject the random number generator in BSD Library with p-value almost zero and\n2) computation of the distributions of words in the human DNA size strings.\n"}
+{"abstract": "  The optimized effective potential (OEP) method presents an unambiguous way to\nconstruct the Kohn-Sham potential corresponding to a given diagrammatic\napproximation for the exchange-correlation functional. The OEP from the\nrandom-phase approximation (RPA) has played an important role ever since the\nconception of the OEP formalism. However, the solution of the OEP equation is\ncomputationally fairly expensive and has to be done in a self-consistent way.\nSo far, large scale solid state applications have therefore been performed only\nusing the quasiparticle approximation (QPA), neglecting certain dynamical\nscreening effects. We obtain the exact RPA-OEP for 15 semiconductors and\ninsulators by direct solution of the linearized Sham-Schl\\\"uter equation. We\ninvestigate the accuracy of the QPA on Kohn-Sham band gaps and dielectric\nconstants, and comment on the issue of self-consistency.\n"}
+{"abstract": "  Information sharing is vital in resisting cyberattacks, and the volume and\nseverity of these attacks is increasing very rapidly. Therefore responders must\ntriage incoming warnings in deciding how to act. This study asked a very\nspecific question: \"how can the addition of confidence information to alerts\nand warnings improve overall resistance to cyberattacks.\" We sought, in\nparticular, to identify current practices, and if possible, to identify some\n\"best practices.\" The research involved literature review and interviews with\nsubject matter experts at every level from system administrators to persons who\ndevelop broad principles of policy. An innovative Modified Online Delphi Panel\ntechnique was used to elicit judgments and recommendations from experts who\nwere able to speak with each other and vote anonymously to rank proposed\npractices.\n"}
+{"abstract": "  The immersed boundary (IB) method is a non-body conforming approach to\nfluid-structure interaction (FSI) that uses an Eulerian description of the\nmomentum, viscosity, and incompressibility of a coupled fluid-structure system\nand a Lagrangian description of the deformations, stresses, and resultant\nforces of the immersed structure. Integral transforms with Dirac delta function\nkernels couple Eulerian and Lagrangian variables. In practice, discretizations\nof these integral transforms use regularized delta function kernels, and\nalthough a number of different types of regularized delta functions have been\nproposed, there has been limited prior work to investigate the impact of the\nchoice of kernel function on the accuracy of the methodology. This work\nsystematically studies the effect of the choice of regularized delta function\nin several fluid-structure interaction benchmark tests using the immersed\nfinite element/difference (IFED) method, which is an extension of the IB method\nthat uses finite element structural discretizations combined with a Cartesian\ngrid finite difference method for the incompressible Navier-Stokes equations.\nFurther, many IB-type methods evaluate the delta functions at the nodes of the\nstructural mesh, and this requires the Lagrangian mesh to be relatively fine\ncompared to the background Eulerian grid to avoid leaks. The IFED formulation\noffers the possibility to avoid leaks with relatively coarse structural meshes\nby evaluating the delta function on a denser collection of interaction points.\nThis study investigates the effect of varying the relative mesh widths of the\nLagrangian and Eulerian discretizations. Although this study is done within the\ncontext of the IFED method, the effect of different kernels could be important\nnot just for this method, but also for other IB-type methods more generally.\n"}
+{"abstract": "  This paper presents a novel method for embedding transfer, a task of\ntransferring knowledge of a learned embedding model to another. Our method\nexploits pairwise similarities between samples in the source embedding space as\nthe knowledge, and transfers them through a loss used for learning target\nembedding models. To this end, we design a new loss called relaxed contrastive\nloss, which employs the pairwise similarities as relaxed labels for\ninter-sample relations. Our loss provides a rich supervisory signal beyond\nclass equivalence, enables more important pairs to contribute more to training,\nand imposes no restriction on manifolds of target embedding spaces. Experiments\non metric learning benchmarks demonstrate that our method largely improves\nperformance, or reduces sizes and output dimensions of target models\neffectively. We further show that it can be also used to enhance quality of\nself-supervised representation and performance of classification models. In all\nthe experiments, our method clearly outperforms existing embedding transfer\ntechniques.\n"}
+{"abstract": "  In the present study, Kolmogorov flow represents the stationary sinusoidal\nsolution $(\\sin y,0)$ to a two-dimensional spatially periodic Navier-Stokes\nsystem, driven by an external force. This system admits the additional\nnon-stationary solution $(\\sin y,0)+e^{-\\nu t} (\\sin y,0)$, which tends\nexponentially to the Kolmogorov flow at the minimum decay rate determined by\nthe viscosity $\\nu$. Enhanced damping or enhanced dissipation of the problem is\nobtained by presenting higher decay rate for the difference between a solution\nand the non-stationary basic solution. Moreover, for the understanding of the\nmetastability problem in an explicit manner, a variety of exact solutions are\npresented to show enhanced and unenhanced dampings.\n"}
+{"abstract": "  The new measurement of the muon's anomalous magnetic moment released by the\nMuon g-2 experiment at Fermilab sets strong constraints on the properties of\nmany new particles. Using an effective field theory approach to the\ninteractions of higher-spin fields, we evaluate the contribution of an\nelectrically neutral and colour singlet spin-3/2 fermion to $(g-2)_\\mu$ and\nderive the corresponding constraints on its mass and couplings. These\nconstraints are then compared with the ones on spin-1/2 fermions, such as the\nvector-like leptons that are predicted by various extensions of the Standard\nModel, the excited leptons which appear in composite models, as well as the\ncharginos and neutralinos of supersymmetric theories. Unlike these new spin-1/2\nfermions, the spin-3/2 particles generate only small contributions to the muon\nanomalous magnetic moment.\n"}
+{"abstract": "  In this paper, we propose a novel four-stage data augmentation approach to\nResNet-Conformer based acoustic modeling for sound event localization and\ndetection (SELD). First, we explore two spatial augmentation techniques, namely\naudio channel swapping (ACS) and multi-channel simulation (MCS), to deal with\ndata sparsity in SELD. ACS and MDS focus on augmenting the limited training\ndata with expanding direction of arrival (DOA) representations such that the\nacoustic models trained with the augmented data are robust to localization\nvariations of acoustic sources. Next, time-domain mixing (TDM) and\ntime-frequency masking (TFM) are also investigated to deal with overlapping\nsound events and data diversity. Finally, ACS, MCS, TDM and TFM are combined in\na step-by-step manner to form an effective four-stage data augmentation scheme.\nTested on the Detection and Classification of Acoustic Scenes and Events\n(DCASE) 2020 data sets, our proposed augmentation approach greatly improves the\nsystem performance, ranking our submitted system in the first place in the SELD\ntask of DCASE 2020 Challenge. Furthermore, we employ a ResNet-Conformer\narchitecture to model both global and local context dependencies of an audio\nsequence to yield further gains over those architectures used in the DCASE 2020\nSELD evaluations.\n"}
+{"abstract": "  The derivative nonlinear Schrodinger (DNLS) equation is the canonical model\nfor dynamics of nonlinear waves in plasma physics and optics. We study exact\nsolutions describing rogue waves on the background of periodic standing waves\nin the DNLS equation. We show that the space-time localization of a rogue wave\nis only possible if the periodic standing wave is modulationally unstable. If\nthe periodic standing wave is modulationally stable, the rogue wave solutions\ndegenerate into algebraic solitons propagating along the background and\ninteracting with the periodic standing waves. Maximal amplitudes of rogue waves\nare found analytically and confirmed numerically.\n"}
+{"abstract": "  A major challenge for physically unconstrained gaze estimation is acquiring\ntraining data with 3D gaze annotations for in-the-wild and outdoor scenarios.\nIn contrast, videos of human interactions in unconstrained environments are\nabundantly available and can be much more easily annotated with frame-level\nactivity labels. In this work, we tackle the previously unexplored problem of\nweakly-supervised gaze estimation from videos of human interactions. We\nleverage the insight that strong gaze-related geometric constraints exist when\npeople perform the activity of \"looking at each other\" (LAEO). To acquire\nviable 3D gaze supervision from LAEO labels, we propose a training algorithm\nalong with several novel loss functions especially designed for the task. With\nweak supervision from two large scale CMU-Panoptic and AVA-LAEO activity\ndatasets, we show significant improvements in (a) the accuracy of\nsemi-supervised gaze estimation and (b) cross-domain generalization on the\nstate-of-the-art physically unconstrained in-the-wild Gaze360 gaze estimation\nbenchmark. We open source our code at\nhttps://github.com/NVlabs/weakly-supervised-gaze.\n"}
+{"abstract": "  With the approval of vaccines for the coronavirus disease by many countries\nworldwide, most developed nations have begun, and developing nations are\ngearing up for the vaccination process. This has created an urgent need to\nprovide a solution to optimally distribute the available vaccines once they are\nreceived by the authorities. In this paper, we propose a clustering-based\nsolution to select optimal distribution centers and a Constraint Satisfaction\nProblem framework to optimally distribute the vaccines taking into\nconsideration two factors namely priority and distance. We demonstrate the\nefficiency of the proposed models using real-world data obtained from the\ndistrict of Chennai, India. The model provides the decision making authorities\nwith optimal distribution centers across the district and the optimal\nallocation of individuals across these distribution centers with the\nflexibility to accommodate a wide range of demographics.\n"}
+{"abstract": "  Modern wake word detection systems usually rely on neural networks for\nacoustic modeling. Transformers has recently shown superior performance over\nLSTM and convolutional networks in various sequence modeling tasks with their\nbetter temporal modeling power. However it is not clear whether this advantage\nstill holds for short-range temporal modeling like wake word detection.\nBesides, the vanilla Transformer is not directly applicable to the task due to\nits non-streaming nature and the quadratic time and space complexity. In this\npaper we explore the performance of several variants of chunk-wise streaming\nTransformers tailored for wake word detection in a recently proposed LF-MMI\nsystem, including looking-ahead to the next chunk, gradient stopping, different\npositional embedding methods and adding same-layer dependency between chunks.\nOur experiments on the Mobvoi wake word dataset demonstrate that our proposed\nTransformer model outperforms the baseline convolution network by 25% on\naverage in false rejection rate at the same false alarm rate with a comparable\nmodel size, while still maintaining linear complexity w.r.t. the sequence\nlength.\n"}
+{"abstract": "  There is increasing interest in developing personalized Task-oriented\nDialogue Systems (TDSs). Previous work on personalized TDSs often assumes that\ncomplete user profiles are available for most or even all users. This is\nunrealistic because (1) not everyone is willing to expose their profiles due to\nprivacy concerns; and (2) rich user profiles may involve a large number of\nattributes (e.g., gender, age, tastes, . . .). In this paper, we study\npersonalized TDSs without assuming that user profiles are complete. We propose\na Cooperative Memory Network (CoMemNN) that has a novel mechanism to gradually\nenrich user profiles as dialogues progress and to simultaneously improve\nresponse selection based on the enriched profiles. CoMemNN consists of two core\nmodules: User Profile Enrichment (UPE) and Dialogue Response Selection (DRS).\nThe former enriches incomplete user profiles by utilizing collaborative\ninformation from neighbor users as well as current dialogues. The latter uses\nthe enriched profiles to update the current user query so as to encode more\nuseful information, based on which a personalized response to a user request is\nselected.\n  We conduct extensive experiments on the personalized bAbI dialogue benchmark\ndatasets. We find that CoMemNN is able to enrich user profiles effectively,\nwhich results in an improvement of 3.06% in terms of response selection\naccuracy compared to state-of-the-art methods. We also test the robustness of\nCoMemNN against incompleteness of user profiles by randomly discarding\nattribute values from user profiles. Even when discarding 50% of the attribute\nvalues, CoMemNN is able to match the performance of the best performing\nbaseline without discarding user profiles, showing the robustness of CoMemNN.\n"}
+{"abstract": "  This work proposes a scheme that allows learning complex multi-agent\nbehaviors in a sample efficient manner, applied to 2v2 soccer. The problem is\nformulated as a Markov game, and solved using deep reinforcement learning. We\npropose a basic multi-agent extension of TD3 for learning the policy of each\nplayer, in a decentralized manner. To ease learning, the task of 2v2 soccer is\ndivided in three stages: 1v0, 1v1 and 2v2. The process of learning in\nmulti-agent stages (1v1 and 2v2) uses agents trained on a previous stage as\nfixed opponents. In addition, we propose using experience sharing, a method\nthat shares experience from a fixed opponent, trained in a previous stage, for\ntraining the agent currently learning, and a form of frame-skipping, to raise\nperformance significantly. Our results show that high quality soccer play can\nbe obtained with our approach in just under 40M interactions. A summarized\nvideo of the resulting game play can be found in https://youtu.be/f25l1j1U9RM.\n"}
+{"abstract": "  This article is a survey of our recent work on the connections between\nKoba-Nielsen amplitudes and local zeta functions (in the sense of Gel'fand,\nWeil, Igusa, Sato, Bernstein, Denef, Loeser, etc.). Our research program is\nmotivated by the fact that the p-adic strings seem to be related in some\ninteresting ways with ordinary strings. For instance, connections through the\nadelic relations and through the limit when p tends to 1. Gerasimov and\nShatashvili studied the limit p tends to 1 of the p-adic effective action\nintroduced by Brekke, Freund, Olson and Witten. They showed that this limit\ngives rise to a boundary string field theory, which was previously proposed by\nWitten in the context of background independent string theory. Explicit\ncomputations in the cases of 4 and 5 points show that the Feynman amplitudes at\nthe tree level of the Gerasimov-Shatashvili Lagrangian are related with the\nlimit p tends to 1 of the p-adic Koba-Nielsen amplitudes. At a mathematical\nlevel, this phenomenon is deeply connected with the topological zeta functions\nintroduced by Denef and Loeser. A Koba-Nielsen amplitude is just a new type of\nlocal zeta function, which can be studied by using embedded resolution of\nsingularities. In this way, one shows the existence of a meromorphic\ncontinuations for the Koba-Nielsen amplitudes as functions of the kinematic\nparameters. The Koba-Nielsen local zeta functions are algebraic-geometric\nintegrals that can be defined over arbitrary local fields (for instance R, C,\nQ_{p}, F_{p}((T)), and it is completely natural to expect connections between\nthese objects. The limit p tends to one of the Koba-Nielsen amplitudes give\nrise to a new amplitudes which we have called Denef-Loeser amplitudes. Along\nthe article, we have emphasized the explicit calculations in the cases of 4 and\n5 points.\n"}
+{"abstract": "  Let $\\mathfrak{g}$ be a hyperbolic Kac-Moody algebra of rank $2$, and let\n$\\lambda$ be an arbitrary integral weight. We denote by $\\mathbb{B}(\\lambda)$\nthe crystal of all Lakshmibai-Seshadri paths of shape $\\lambda$. Let\n$V(\\lambda)$ be the extremal weight module of extremal weight $\\lambda$\ngenerated by the (cyclic) extremal weight vector $v_\\lambda$ of weight\n$\\lambda$, and let $\\mathcal{B}(\\lambda)$ be the crystal basis of $V(\\lambda)$\nwith $u_\\lambda \\in \\mathcal{B}(\\lambda)$ the element corresponding to\n$v_\\lambda$. We prove that the connected component $\\mathcal{B}_0(\\lambda)$ of\n$\\mathcal{B}(\\lambda)$ containing $u_\\lambda$ is isomorphic, as a crystal, to\nthe connected component $\\mathbb{B}_0(\\lambda)$ of $\\mathbb{B}(\\lambda)$\ncontaining the straight line $\\pi_\\lambda$. Furthermore, we prove that if\n$\\lambda$ satisfies a special condition, then the crystal basis\n$\\mathcal{B}(\\lambda)$ is isomorphic, as a crystal, to the crystal\n$\\mathbb{B}(\\lambda)$. As an application of these results, we obtain an\nalgorithm for computing the number of elements of weight $\\mu$ in\n$\\mathcal{B}(\\Lambda_1-\\Lambda_2)$, where $\\Lambda_1, \\Lambda_2$ are the\nfundamental weights, in the case that $\\mathfrak{g}$ is symmetric.\n"}
+{"abstract": "  The reading of arbitrarily-shaped text has received increasing research\nattention. However, existing text spotters are mostly built on two-stage\nframeworks or character-based methods, which suffer from either Non-Maximum\nSuppression (NMS), Region-of-Interest (RoI) operations, or character-level\nannotations. In this paper, to address the above problems, we propose a novel\nfully convolutional Point Gathering Network (PGNet) for reading\narbitrarily-shaped text in real-time. The PGNet is a single-shot text spotter,\nwhere the pixel-level character classification map is learned with proposed\nPG-CTC loss avoiding the usage of character-level annotations. With a PG-CTC\ndecoder, we gather high-level character classification vectors from\ntwo-dimensional space and decode them into text symbols without NMS and RoI\noperations involved, which guarantees high efficiency. Additionally, reasoning\nthe relations between each character and its neighbors, a graph refinement\nmodule (GRM) is proposed to optimize the coarse recognition and improve the\nend-to-end performance. Experiments prove that the proposed method achieves\ncompetitive accuracy, meanwhile significantly improving the running speed. In\nparticular, in Total-Text, it runs at 46.7 FPS, surpassing the previous\nspotters with a large margin.\n"}
+{"abstract": "  Within the context of social balance theory, much attention has been paid to\nthe attainment and stability of unipolar or bipolar societies. However,\nmultipolar societies are commonplace in the real world, despite the fact that\nthe mechanism of their emergence is much less explored. Here, we investigate\nthe evolution of a society of interacting agents with friendly (positive) and\nenmity (negative) relations into a final stable multipolar state. Triads are\nassigned energy according to the degree of tension they impose on the network.\nAgents update their connections in order to decrease the total energy (tension)\nof the system, on average. Our approach is to consider a variable energy\n$\\epsilon\\in[0,1]$ for triads which are entirely made of negative relations. We\nshow that the final state of the system depends on the initial density of the\nfriendly links $\\rho_0$. For initial densities greater than an $\\epsilon$\ndependent threshold $\\rho^c_0(\\epsilon)$ unipolar (paradise) state is reached.\nHowever, for $\\rho_0 \\leq \\rho^c_0(\\epsilon)$ multi-polar and bipolar states\ncan emerge. We observe that the number of stable final poles increases with\ndecreasing $\\epsilon$ where the first transition from bipolar to multipolar\nsociety occurs at $\\epsilon^*\\approx 0.67$. We end the paper by providing a\nmean-field calculation that provides an estimate for the critical ($\\epsilon$\ndependent) initial positive link density, which is consistent with our\nsimulations.\n"}
+{"abstract": "  Recently, several deep learning models have been proposed for 3D human pose\nestimation. Nevertheless, most of these approaches only focus on the\nsingle-person case or estimate 3D pose of a few people at high resolution.\nFurthermore, many applications such as autonomous driving or crowd analysis\nrequire pose estimation of a large number of people possibly at low-resolution.\nIn this work, we present PandaNet (Pose estimAtioN and Dectection Anchor-based\nNetwork), a new single-shot, anchor-based and multi-person 3D pose estimation\napproach. The proposed model performs bounding box detection and, for each\ndetected person, 2D and 3D pose regression into a single forward pass. It does\nnot need any post-processing to regroup joints since the network predicts a\nfull 3D pose for each bounding box and allows the pose estimation of a possibly\nlarge number of people at low resolution. To manage people overlapping, we\nintroduce a Pose-Aware Anchor Selection strategy. Moreover, as imbalance exists\nbetween different people sizes in the image, and joints coordinates have\ndifferent uncertainties depending on these sizes, we propose a method to\nautomatically optimize weights associated to different people scales and joints\nfor efficient training. PandaNet surpasses previous single-shot methods on\nseveral challenging datasets: a multi-person urban virtual but very realistic\ndataset (JTA Dataset), and two real world 3D multi-person datasets (CMU\nPanoptic and MuPoTS-3D).\n"}
+{"abstract": "  Conductance of the edge modes as well as conductance across the\nco-propagating edge modes around the \\nu = 4/3, 5/3 and 2 quantum Hall states\nare measured by individually exciting the modes. Temperature dependent\nequilibration rates of the outer unity conductance edge mode are presented for\ndifferent filling fractions. We find that the equilibration rate of the outer\nunity conductance mode at \\nu = 2 is higher and more temperature sensitive\ncompared to the mode at fractional filling 5/3 and 4/3. At lowest temperature,\nequilibration length of the outer unity conductance mode tends to saturate with\nlowering filling fraction \\nu by increasing magnetic field B. We speculate this\nsaturating nature of equilibration length is arising from an interplay of\nCoulomb correlation and spin orthogonality.\n"}
+{"abstract": "  Manual labelling of training examples is common practice in supervised\nlearning. When the labelling task is of non-trivial difficulty, the supplied\nlabels may not be equal to the ground-truth labels, and label noise is\nintroduced into the training dataset. If the manual annotation is carried out\nby multiple experts, the same training example can be given different class\nassignments by different experts, which is indicative of label noise. In the\nframework of model-based classification, a simple, but key observation is that\nwhen the manual labels are sampled using the posterior probabilities of class\nmembership, the noisy labels are as valuable as the ground-truth labels in\nterms of statistical information. A relaxation of this process is a random\neffects model for imperfect labelling by a group that uses approximate\nposterior probabilities of class membership. The relative efficiency of\nlogistic regression using the noisy labels compared to logistic regression\nusing the ground-truth labels can then be derived. The main finding is that\nlogistic regression can be robust to label noise when label noise and\nclassification difficulty are positively correlated. In particular, when\nclassification difficulty is the only source of label errors, multiple sets of\nnoisy labels can supply more information for the estimation of a classification\nrule compared to the single set of ground-truth labels.\n"}
+{"abstract": "  A broad range of dynamical systems involve multi-body interactions, or group\ninteractions, which may not be encoded in traditional graphical structures. In\nthis work, we focus on a canonical example from opinion dynamics, the Majority\nRule, and investigate the possibility to represent and analyse the system by\nmeans of hypergraphs. We explore the formation of consensus and restrict our\nattention to interaction groups of size $3$, in order to simplify our analysis\nfrom a combinatorial perspective. We propose different types of hypergraph\nmodels, incorporating modular structure or degree heterogeneity, and recast the\ndynamics in terms of Fokker-Planck equations, which allows us to predict the\ntransient dynamics toward consensus. Numerical simulations show a very good\nagreement between the stochastic dynamics and theoretical predictions for large\npopulation sizes.\n"}
+{"abstract": "  Grauert showed that it is possible to construct complete K\\\"{a}hler metrics\non the complement of complex analytic sets in a domain of holomorphy. In this\nnote, we study the holomorphic sectional curvatures of such metrics on the\ncomplement of a principal divisor in $\\mathbb{C}^n$, $n \\ge 1$. In addition, we\nalso study how this metric and its holomorphic sectional curvature behaves when\nthe corresponding principal divisors vary continuously.\n"}
+{"abstract": "  Let $G$ be an almost simple sporadic group and let $H$ be a soluble subgroup\nof $G$. In this paper we prove that there exists $x,y \\in G$ such that $H \\cap\nH^x \\cap H^y=1$, which is equivalent to the bound $b(G,H) \\leqslant 3$ with\nrespect to the base size of $G$ on the set of cosets of $H$. This bound is best\npossible. In this setting, our main result establishes a strong form of a more\ngeneral conjecture of Vdovin on the intersection of conjugate soluble subgroups\nof finite groups. The proof uses a combination of computational and\nprobabilistic methods.\n"}
+{"abstract": "  Linear forms in logarithms have an important role in the theory of\nDiophantine equations. In this article, we prove explicit $p$-adic lower bounds\nfor linear forms in $p$-adic logarithms of rational numbers using Pad\\'e\napproximations of the second kind.\n"}
+{"abstract": "  Although the idea that there is a maximum force in nature seems untenable, we\nexplore whether this concept can make sense in the restricted context of black\nholes. We discuss uniformly accelerated and cosmological black holes and we\nfind that, although a maximum force acting on these black holes can in\nprinciple be introduced, this concept is rather tautological.\n"}
+{"abstract": "  We consider the usual causal structure $(I^+,J^+)$ on a spacetime, and a\nnumber of alternatives based on Minguzzi's $D^+$ and Sorkin and Woolgar's\n$K^+$, in the case where the spacetime metric is continuous, but not\nnecessarily smooth. We compare the different causal structures based on three\nkey properties, namely the validity of the push-up lemma, the openness of\nchronological futures, and the existence of limit causal curves. Recall that if\nthe spacetime metric is smooth, $(I^+,J^+)$ satisfies all three properties, but\nthat in the continuous case, the push-up lemma fails. Among the proposed\nalternative causal structures, there is one that satisfies push-up and open\nfutures, and one that has open futures and limit curves. Furthermore, we show\nthat spacetimes with continuous metrics do not, in general, admit a causal\nstructure satisfying all three properties at once.\n"}
+{"abstract": "  A reputable social media or review account can be a good cover for spamming\nactivities. It has become prevalent that spammers buy/sell such accounts openly\non the Web. We call these sold/bought accounts the changed-hands (CH) accounts.\nThey are hard to detect by existing spam detection algorithms as their spamming\nactivities are under the disguise of clean histories. In this paper, we first\npropose the problem of detecting CH accounts, and then design an effective\ndetection algorithm which exploits changes in content and writing styles of\nindividual accounts, and a proposed novel feature selection method that works\nat a fine-grained level within each individual account. The proposed method not\nonly determines if an account has changed hands, but also pinpoints the change\npoint. Experimental results with online review accounts demonstrate the high\neffectiveness of our approach.\n"}
+{"abstract": "  Aims. The connection between initial disc conditions and final orbital and\nphysical properties of planets is not well-understood. In this paper, we\nnumerically study the formation of planetary systems via pebble accretion and\ninvestigate the effects of disc properties such as masses, dissipation\ntimescales, and metallicities on planet formation outcomes. Methods. We\nimproved the N-body code SyMBA that was modified by taking account of new\nplanet-disc interaction models and type II migration. We adopted the 'two-alpha\ndisc' model to mimic the effects of both the standard disc turbulence and the\nmass accretion driven by the magnetic disc wind. Results. We successfully\nreproduced the overall distribution trends of semi-major axes, eccentricities,\nand planetary masses of extrasolar giant planets. We find that, when planet\nformation happens fast enough, giant planets are fully grown (Jupiter mass or\nhigher) and are distributed widely across the disc. On the other hand, when\nplanet formation is limited by the disc's dissipation, discs generally form\nlow-mass cold Jupiters (CJs). Our simulations also naturally explain why hot\nJupiters (HJs) tend to be alone and how the observed eccentricity-metallicity\ntrends arise. The low-metallicity discs tend to form nearly circular and\ncoplanar HJs in situ, because planet formation is slower than high-metallicity\ndiscs, and thus protoplanetary cores migrate significantly before gas\naccretion. The high-metallicity discs, on the other hand, generate HJs in situ\nor via tidal circularisation of eccentric orbits. Both pathways usually involve\ndynamical instabilities, and thus HJs tend to have broader eccentricity and\ninclination distributions. When giant planets with very wide orbits\n('super-cold Jupiters') are formed, we find that they often belong to\nmetal-rich stars, have eccentric orbits, and tend to have (~80%) companions\ninterior to their orbits.\n"}
+{"abstract": "  Uncertainty quantification for deep neural networks has recently evolved\nthrough many techniques. In this work, we revisit Laplace approximation, a\nclassical approach for posterior approximation that is computationally\nattractive. However, instead of computing the curvature matrix, we show that,\nunder some regularity conditions, the Laplace approximation can be easily\nconstructed using the gradient second moment. This quantity is already\nestimated by many exponential moving average variants of Adagrad such as Adam\nand RMSprop, but is traditionally discarded after training. We show that our\nmethod (L2M) does not require changes in models or optimization, can be\nimplemented in a few lines of code to yield reasonable results, and it does not\nrequire any extra computational steps besides what is already being computed by\noptimizers, without introducing any new hyperparameter. We hope our method can\nopen new research directions on using quantities already computed by optimizers\nfor uncertainty estimation in deep neural networks.\n"}
+{"abstract": "  Background: Conferences bring scientists together and provide one of the most\ntimely means for disseminating new ideas and cutting-edge works.The importance\nof conferences in scientific areas is testified by quantitative indicators. In\nComputer Science, for instance, almost two out of three papers published on\nScopus are conference papers. Objective/Purpose: The main goal of this paper is\nto investigate a novel research question: is there any correlation between the\nimpact of a scientific conference and the venue where it took place? Approach:\nIn order to measure the impact of conferences we conducted a large scale\nanalysis on the bibliographic data extracted from 3,838 Computer Science\nconference series and over 2.5 million papers spanning more than 30 years of\nresearch. To quantify the \"touristicity\" of a venue we exploited some\nindicators such as the size of the Wikipedia page for the city hosting the\nvenue and other indexes from reports of the World Economic Forum.\nResults/Findings: We found out that the two aspects are related, and the\ncorrelation with conference impact is stronger when considering country-wide\ntouristic indicators, such as the Travel&Tourism Competitiveness Index.\nMore-over the almost linear correlation with the Tourist Service Infrastructure\nindex attests the specific importance of tourist/accommodation facilities in a\ngiven country. Conclusions: This is the first attempt to focus on the\nrelationship of venue characteristics to conference papers. The results open up\nnew possibilities, such as allowing conference organizers and authors to\nestimate in advance the impact of conferences, thus supporting them in their\ndecisions.\n"}
+{"abstract": "  In some Internet of Things (IoT) applications, multi-path propagation is a\nmain constraint of communication channel. Recently, the chaotic baseband\nwireless communication system (CBWCS) is promising to eliminate the\ninter-symbol interference (ISI) caused by multipath propagation. However, the\ncurrent technique is only capable of removing the partial effect of ISI, due to\nonly past decoded bits are available for the suboptimal decoding threshold\ncalculation. However, the future transmitting bits also contribute to the\nthreshold. The unavailable future information bits needed by the optimal\ndecoding threshold are an obstacle to further improve the bit error rate (BER)\nperformance. Different from the previous method using echo state network (ESN)\nto predict one future information bit, the proposed method in this paper\npredicts the optimal threshold directly using ESN. The proposed ESN-based\nthreshold prediction method simplifies the symbol decoding operation by\nremoving the threshold calculation from the transmitting symbols and channel\ninformation, which achieves better BER performance as compared to the previous\nmethod. The reason for this superior result lies in two folds, first, the\nproposed ESN is capable of using more future symbols information conveyed by\nthe ESN input to get more accurate threshold; second, the proposed method here\ndoes not need to estimate the channel information using Least Square method,\nwhich avoids the extra error caused by inaccurate channel information\nestimation. By this way, the calculation complexity is decreased as compared to\nthe previous method. Simulation results and experiment based on a wireless\nopen-access research platform under a practical wireless channel, show the\neffectiveness and superiority of the proposed method.\n"}
+{"abstract": "  In many decision-making tasks, some specific actions are limited in their\nfrequency or total amounts, such as \"fire\" in the gunfight game and \"buy/sell\"\nin the stock trading. We name such actions as \"sparse action\". Sparse action\noften plays a crucial role in achieving good performance. However, their\nQ-values, estimated by \\emph{classical Bellman update}, usually suffer from a\nlarge estimation error due to the sparsity of their samples. The \\emph{greedy}\npolicy could be greatly misled by the biased Q-function and takes sparse action\naggressively, which leads to a huge sub-optimality. This paper constructs a\nreference distribution that assigns a low probability to sparse action and\nproposes a regularized objective with an explicit constraint to the reference\ndistribution. Furthermore, we derive a regularized Bellman operator and a\nregularized optimal policy that can slow down the propagation of error and\nguide the agent to take sparse action more carefully. The experiment results\ndemonstrate that our method achieves state-of-the-art performance on typical\nsparse action tasks.\n"}
+{"abstract": "  This paper is build around the stationary anisotropic Stokes and\nNavier-Stokes systems with an $L^\\infty$-tensor coefficient satisfying an\nellipticity condition in terms of symmetric matrices in ${\\mathbb R}^{n\\times\nn}$ with zero matrix traces. We analyze, in $L^2$-based Sobolev spaces, the\nnon-homogeneous boundary value problems of Dirichlet-transmission type for the\nanisotropic Stokes and Navier-Stokes systems in a compressible framework in a\nbounded Lipschitz domain with a Lipschitz interface in ${\\mathbb R}^n$, $n\\ge\n2$ ($n=2,3$ for the nonlinear problems). The transversal interface intersects\nthe boundary of the Lipschitz domain. First, we use a mixed variational\napproach to prove well-posedness results for the linear anisotropic Stokes\nsystem. Then we show the existence of a weak solution for the nonlinear\nanisotropic Navier-Stokes system by implementing the Leray-Schauder fixed point\ntheorem and using various results and estimates from the linear case, as well\nas the Leray-Hopf and some other norm inequalities. Explicit conditions for\nuniqueness of solutions to the nonlinear problems are also provided.\n"}
+{"abstract": "  In this work, the branching ratio $\\mathcal{B}[B\\to K\\chi_{c0}(2P)]$ is\nextracted for the first time through the fit fractions in the newly measured\n$B\\to KD\\bar{D}$ process by LHCb. With the rescattering mechanism, this\nextracted branching ratio is reproduced well. Our study further enforces the\nrole of the newly measured $B\\to KD\\bar{D}$ process to establish the\n$\\chi_{c0}(2P)$ charmonium state, which is a crucial step when constructing\ncharmonium family.\n"}
+{"abstract": "  QED cascades in a strong electromagnetic field of optical range and arbitrary\nconfiguration are considered. A general expression for short-time dependence of\nthe key electron quantum dynamical parameter is derived, allowing to generalize\nthe effective threshold condition of QED cascade onset. The generalized theory\nis applied to selfsustained cascades in a single focused laser pulse. According\nto numerical simulations, if a GeV electron bunch is used as a seed, an\nordinary cascade can be converted into the selfsustained one. As an\napplication, it would be also possible to produce this way bright collimated\nphoton beams with up to GeV photon energies.\n"}
+{"abstract": "  For a separable Hamiltonian, there are two fundamental, time-symmetric,\nsecond-order velocity-Verlet (VV) and position-Verlet (PV) symplectic\nintegrators. Similarly, there are two VV and PV version of exact energy\nconserving algorithms for solving magnetic field trajectories. For a constant\nmagnetic field, both algorithms can be further modified so that their\ntrajectories are exactly on the gyro-circle. The magnetic PV integrator then\nbecomes the well known Boris solver, while VV yields a second, previously\nunknown, Boris-type algorithm. Remarkably, the required on-orbit modification\nis a reparametrization of the time step, reminiscent of the Ge-Marsden theorem.\n"}
+{"abstract": "  In this paper, we study the generalization properties of Model-Agnostic\nMeta-Learning (MAML) algorithms for supervised learning problems. We focus on\nthe setting in which we train the MAML model over $m$ tasks, each with $n$ data\npoints, and characterize its generalization error from two points of view:\nFirst, we assume the new task at test time is one of the training tasks, and we\nshow that, for strongly convex objective functions, the expected excess\npopulation loss is bounded by ${\\mathcal{O}}(1/mn)$. Second, we consider the\nMAML algorithm's generalization to an unseen task and show that the resulting\ngeneralization error depends on the total variation distance between the\nunderlying distributions of the new task and the tasks observed during the\ntraining process. Our proof techniques rely on the connections between\nalgorithmic stability and generalization bounds of algorithms. In particular,\nwe propose a new definition of stability for meta-learning algorithms, which\nallows us to capture the role of both the number of tasks $m$ and number of\nsamples per task $n$ on the generalization error of MAML.\n"}
+{"abstract": "  This paper intends to apply the Hidden Markov Model into stock market and and\nmake predictions. Moreover, four different methods of improvement, which are\nGMM-HMM, XGB-HMM, GMM-HMM+LSTM and XGB-HMM+LSTM, will be discussed later with\nthe results of experiment respectively. After that we will analyze the pros and\ncons of different models. And finally, one of the best will be used into stock\nmarket for timing strategy.\n"}
+{"abstract": "  We decompose the monthly aa-index of cycles 10-23 using principal component\nanalysis (PCA). We show that the first component (PC1) is related to solar\ncycle, and accounts for 41.5 % of the variance of the data. The second\ncomponent (PC2) is related to 22-year Hale cycle, and explains 23.6% of the\nvariance of the data. The PC1 time series of aa cycles 10-23 has only one peak\nin its power spectrum at the period 10.95 years, which is the average solar\ncycle period for the interval SC10-SC23. The PC2 time series of the same cycles\nhas a clear peak at period 21.90 (Hale cycle) and a smaller peak at 3/4 of that\nperiod. We also study the principal component of sunspot numbers (SSN) for\ncycles 10-23, and compare mutual behavior of the PC2 components of aa-index and\nSSN PCA analyses. We note that they are in the same phase in all other cycles\nthan Solar Cycles 15 and 20. The aa cycle 20 also differs from other even aa\ncycles in its shape, especially in anomalously high peaks during its descending\nphase. Even though there is a coherence in the PC2 time series phases of\naa-index and sunspot number, this effect is too small to cause all of the\ndifference of the shape of even and odd aa cycles. We estimate that 30% of the\nshape of the PC2 component of aa-index is due to sunspot number and the rest to\nother recurrent events in Sun/solar-wind. The first maximum of aa-data (typical\nto odd cycles), during sunspot maximum, has been shown to be related to coronal\nmass ejections (CME), while the second maximum (typical to even cycles) in the\ndeclining phase of cycle, is probably related to high-speed streams (HSS). The\nlast events increase the activity level such that the minimum between even-odd\ncycle-pair is always higher than the minimum between succeeding odd-even cycle\npair.\n"}
+{"abstract": "  We present the model of an ultrasensitive mid-infrared (mid-IR) photodetector\noperating in the mid-wavelength infrared (MWIR) and long-wavelength infrared\n(LWIR) domains consisting of a hybrid heterostructure made of nanopatterned\ngraphene (NPG) and vanadium dioxide (VO$_2$) which exhibits a large\nresponsivity of $R\\sim 10^4$ V/W, a detectivity exceeding $D^*\\sim 10^{10}$ J,\nand a sensitivity in terms of noise-equivalent power $\\mathrm{NEP}\\sim 100$\nfW/$\\sqrt{\\rm Hz}$ close to room temperature by taking advantage of the phase\nchange of a thin VO$_2$ film. Our proposed photodetector can reach an\nabsorption of nearly 100\\% in monolayer graphene due to localized surface\nplasmons (LSPs) around the patterned circular holes. The geometry of the\nnanopattern and an electrostatic gate potential can be used to tune the\nabsorption peak in the mid-IR regime between 3 and 12 $\\mu$m. After the photon\nabsorption by the NPG sheet and the resulting phase change of VO$_2$ from\ninsulating to metallic phase the applied bias voltage $V_b$ triggers a current\nthrough the VO$_2$ sheet, which can be detected electronically in about 1 ms,\nshorter than the detection times of current VO$_2$ bolometers. Our envisioned\nmid-IR photodetector reaches detectivities of cryogenically cooled HgCdTe\nphotodetectors and sensitivities larger than VO$_2$ microbolometers while\noperating close to room temperature.\n"}
+{"abstract": "  In this article, we construct a class of explicit, smooth and spherically\nsymmetric solutions to the asymptotically flat vacuum constraint equations\nwhich have ADM mass of arbitrary sign ($- \\infty$, negative, zero, positive).\nAs a direct consequence, there exist asymptotically flat vacuum initial data\nsets whose metrics are exactly negative mass Schwarzschild outside a given\nball. We emphasize that our result does not contradict the spacetime positive\nenergy theorem proven by Eichmair, instead it shows that the decay rate at\ninfinity of the symmetric $(0,2)$-tensor $k$ stated in the theorem is sharp.\nThe key argument we use in the article is classical, based on the conformal\nmethod, in which the conformal equations are equivalently transformed into a\nsingle nonlinear equation of functions of one variable.\n"}
+{"abstract": "  Transformer-based language models benefit from conditioning on contexts of\nhundreds to thousands of previous tokens. What aspects of these contexts\ncontribute to accurate model prediction? We describe a series of experiments\nthat measure usable information by selectively ablating lexical and structural\ninformation in transformer language models trained on English Wikipedia. In\nboth mid- and long-range contexts, we find that several extremely destructive\ncontext manipulations -- including shuffling word order within sentences and\ndeleting all words other than nouns -- remove less than 15% of the usable\ninformation. Our results suggest that long contexts, but not their detailed\nsyntactic and propositional content, are important for the low perplexity of\ncurrent transformer language models.\n"}
+{"abstract": "  Semantic segmentation has a wide array of applications ranging from\nmedical-image analysis, scene understanding, autonomous driving and robotic\nnavigation. This work deals with medical image segmentation and in particular\nwith accurate polyp detection and segmentation during colonoscopy examinations.\nSeveral convolutional neural network architectures have been proposed to\neffectively deal with this task and with the problem of segmenting objects at\ndifferent scale input. The basic architecture in image segmentation consists of\nan encoder and a decoder: the first uses convolutional filters to extract\nfeatures from the image, the second is responsible for generating the final\noutput. In this work, we compare some variant of the DeepLab architecture\nobtained by varying the decoder backbone. We compare several decoder\narchitectures, including ResNet, Xception, EfficentNet, MobileNet and we\nperturb their layers by substituting ReLU activation layers with other\nfunctions. The resulting methods are used to create deep ensembles which are\nshown to be very effective. Our experimental evaluations show that our best\nensemble produces good segmentation results by achieving high evaluation scores\nwith a dice coefficient of 0.884, and a mean Intersection over Union (mIoU) of\n0.818 for the Kvasir-SEG dataset. To improve reproducibility and research\nefficiency the MATLAB source code used for this research is available at\nGitHub: https://github.com/LorisNanni.\n"}
+{"abstract": "  We investigate the gravitational wave spectrum resulted from the cosmological\nfirst-order phase transition. We compare two models; one is a scalar field\nmodel without gravitation, while the other is a scalar field model with\ngravitation. Based on the sensitivity curves of the LISA space-based\ninterferometer on the stochastic gravitational-wave background, we compare the\ndifference between the gravitational wave spectra of the former and the latter\ncases resulted from the bubble collision process. Especially, we calculated the\nspeed of the bubble wall before collision for the two models numerically. We\nshow that the difference between the amplitudes of those spectra can clearly\ndistinguish between the two models. We expect that the LISA with Signal to\nNoise Ratio =10 could observe the spectrum as the fast first-order phase\ntransition.\n"}
+{"abstract": "  Deep learning models for automatic readability assessment generally discard\nlinguistic features traditionally used in machine learning models for the task.\nWe propose to incorporate linguistic features into neural network models by\nlearning syntactic dense embeddings based on linguistic features. To cope with\nthe relationships between the features, we form a correlation graph among\nfeatures and use it to learn their embeddings so that similar features will be\nrepresented by similar embeddings. Experiments with six data sets of two\nproficiency levels demonstrate that our proposed methodology can complement\nBERT-only model to achieve significantly better performances for automatic\nreadability assessment.\n"}
+{"abstract": "  Recapturing attack can be employed as a simple but effective anti-forensic\ntool for digital document images. Inspired by the document inspection process\nthat compares a questioned document against a reference sample, we proposed a\ndocument recapture detection scheme by employing Siamese network to compare and\nextract distinct features in a recapture document image. The proposed algorithm\ntakes advantages of both metric learning and image forensic techniques. Instead\nof adopting Euclidean distance-based loss function, we integrate the forensic\nsimilarity function with a triplet loss and a normalized softmax loss. After\ntraining with the proposed triplet selection strategy, the resulting feature\nembedding clusters the genuine samples near the reference while pushes the\nrecaptured samples apart. In the experiment, we consider practical domain\ngeneralization problems, such as the variations in printing/imaging devices,\nsubstrates, recapturing channels, and document types. To evaluate the\nrobustness of different approaches, we benchmark some popular off-the-shelf\nmachine learning-based approaches, a state-of-the-art document image detection\nscheme, and the proposed schemes with different network backbones under various\nexperimental protocols. Experimental results show that the proposed schemes\nwith different network backbones have consistently outperformed the\nstate-of-the-art approaches under different experimental settings.\nSpecifically, under the most challenging scenario in our experiment, i.e.,\nevaluation across different types of documents that produced by different\ndevices, we have achieved less than 5.00% APCER (Attack Presentation\nClassification Error Rate) and 5.56% BPCER (Bona Fide Presentation\nClassification Error Rate) by the proposed network with ResNeXt101 backbone at\n5% BPCER decision threshold.\n"}
+{"abstract": "  Pixelwise annotation of image sequences can be very tedious for humans.\nInteractive video object segmentation aims to utilize automatic methods to\nspeed up the process and reduce the workload of the annotators. Most\ncontemporary approaches rely on deep convolutional networks to collect and\nprocess information from human annotations throughout the video. However, such\nnetworks contain millions of parameters and need huge amounts of labeled\ntraining data to avoid overfitting. Beyond that, label propagation is usually\nexecuted as a series of frame-by-frame inference steps, which is difficult to\nbe parallelized and is thus time consuming. In this paper we present a graph\nneural network based approach for tackling the problem of interactive video\nobject segmentation. Our network operates on superpixel-graphs which allow us\nto reduce the dimensionality of the problem by several magnitudes. We show,\nthat our network possessing only a few thousand parameters is able to achieve\nstate-of-the-art performance, while inference remains fast and can be trained\nquickly with very little data.\n"}
+{"abstract": "  Recommendation algorithms have been pointed out as one of the major culprits\nof misinformation spreading in the digital sphere. However, it is still unclear\nhow these algorithms really propagate misinformation, e.g., it has not been\nshown which particular recommendation approaches are more prone to suggest\nmisinforming items, or which internal parameters of the algorithms could be\ninfluencing more on their misinformation propagation capacity.\n  Motivated by this fact, in this paper we present an analysis of the effect of\nsome of the most popular recommendation algorithms on the spread of\nmisinformation in Twitter. A set of guidelines on how to adapt these algorithms\nis provided based on such analysis and a comprehensive review of the research\nliterature. A dataset is also generated and released to the scientific\ncommunity to stimulate discussions on the future design and development of\nrecommendation algorithms to counter misinformation. The dataset includes\neditorially labelled news items and claims regarding their misinformation\nnature.\n"}
+{"abstract": "  Let $X$ be a Banach space and $Y \\subseteq X$ be a closed subspace. We prove\nthat if the quotient $X/Y$ is weakly Lindel\\\"{o}f determined or weak Asplund,\nthen for every $w^*$-convergent sequence $(y_n^*)_{n\\in \\mathbb N}$ in $Y^*$\nthere exist a subsequence $(y_{n_k}^*)_{k\\in \\mathbb N}$ and a $w^*$-convergent\nsequence $(x_k^*)_{k\\in \\mathbb N}$ in $X^*$ such that $x_k^*|_Y=y_{n_k}^*$ for\nall $k\\in \\mathbb N$. As an application we obtain that $Y$ is Grothendieck\nwhenever $X$ is Grothendieck and $X/Y$ is reflexive, which answers a question\nraised by Gonz\\'{a}lez and Kania.\n"}
+{"abstract": "  Deformable object manipulation (DOM) is an emerging research problem in\nrobotics. The ability to manipulate deformable objects endows robots with\nhigher autonomy and promises new applications in the industrial, services, and\nhealthcare sectors. However, compared to rigid object manipulation, the\nmanipulation of deformable objects is considerably more complex, and is still\nan open research problem. Addressing DOM challenges demand breakthroughs in\nalmost all aspects of robotics, namely hardware design, sensing, (deformation)\nmodeling, planning, and control. In this article, we review recent advances and\nhighlight the main challenges when considering deformation in each sub-field. A\nparticular focus of our paper lies in the discussions of these challenges and\nproposing future directions of research.\n"}
+{"abstract": "  In-situ etching using Ga flux in an ultra-high vacuum environment like MBE is\nintroduced as a method to make high aspect ratio 3 dimensional structures in\n$\\beta$-Ga2O3. Etching of $\\beta$-Ga2O3 due to excess Ga adatoms on the\nepilayer surface had been viewed as non-ideal for epitaxial growth especially\nsince it results in plateauing and lowering of growth rate. In this study, we\nuse this well-known reaction from epitaxial growth to intentionally etch\n$\\beta$-Ga2O3. We demonstrate etch rate ranging from 2.9 nm/min to 30 nm/min\nwith the highest reported etch rate being only limited by the highest Ga flux\nused. Patterned in-situ etching is also demonstrated and used to study the\neffect of fin orientation on the sidewall profiles and dopant (Si) segregation\non the etched surface. Using in-situ Ga etching, we also demonstrate 150 nm\nwide fins and 200 nm wide nano pillars with high aspect ratio. This new etching\nmethod could enable future development of highly scaled vertical and lateral 3D\ndevices in $\\beta$-Ga2O3.\n"}
+{"abstract": "  Tensors, or multidimensional arrays, are data structures that can naturally\nrepresent visual data of multiple dimensions. Inherently able to efficiently\ncapture structured, latent semantic spaces and high-order interactions, tensors\nhave a long history of applications in a wide span of computer vision problems.\nWith the advent of the deep learning paradigm shift in computer vision, tensors\nhave become even more fundamental. Indeed, essential ingredients in modern deep\nlearning architectures, such as convolutions and attention mechanisms, can\nreadily be considered as tensor mappings. In effect, tensor methods are\nincreasingly finding significant applications in deep learning, including the\ndesign of memory and compute efficient network architectures, improving\nrobustness to random noise and adversarial attacks, and aiding the theoretical\nunderstanding of deep networks.\n  This article provides an in-depth and practical review of tensors and tensor\nmethods in the context of representation learning and deep learning, with a\nparticular focus on visual data analysis and computer vision applications.\nConcretely, besides fundamental work in tensor-based visual data analysis\nmethods, we focus on recent developments that have brought on a gradual\nincrease of tensor methods, especially in deep learning architectures, and\ntheir implications in computer vision applications. To further enable the\nnewcomer to grasp such concepts quickly, we provide companion Python notebooks,\ncovering key aspects of the paper and implementing them, step-by-step with\nTensorLy.\n"}
+{"abstract": "  We show that the nonlinear stochastic dynamics of a\nmeasurement-feedback-based coherent Ising machine (MFB-CIM) in the presence of\nquantum noise can be exploited to sample degenerate ground and low-energy spin\nconfigurations of the Ising model. We formulate a general discrete-time\nGaussian-state model of the MFB-CIM which faithfully captures the nonlinear\ndynamics present at and above system threshold. This model overcomes the\nlimitations of both mean-field models, which neglect quantum noise, and\ncontinuous-time models, which assume long photon lifetimes. Numerical\nsimulations of our model show that when the MFB-CIM is operated in a\nquantum-noise-dominated regime with short photon lifetimes (i.e., low cavity\nfinesse), homodyne monitoring of the system can efficiently produce samples of\nlow-energy Ising spin configurations, requiring many fewer roundtrips to sample\nthan suggested by established high-finesse, continuous-time models. We find\nthat sampling performance is robust to, or even improved by, turning off or\naltogether reversing the sign of the parametric drive, but performance is\ncritically reduced in the absence of optical nonlinearity. For the class of\nMAX-CUT problems with binary-signed edge weights, the number of roundtrips\nsufficient to fully sample all spin configurations up to the first-excited\nIsing energy, including all degeneracies, scales as $1.08^N$. At a problem size\nof $N = 100$ with a few dozen (median of 20) such desired configurations per\ninstance, we have found median sufficient sampling times of $6\\times10^6$\nroundtrips; in an experimental implementation of an MFB-CIM with a 10 GHz\nrepetition rate, this corresponds to a wall-clock sampling time of 0.6 ms.\n"}
+{"abstract": "  We present an implementation of the $GW$ space-time approach that allows\ncubic-scaling all-electron calculations with standard Gaussian basis sets\nwithout exploiting any localization nor sparsity considerations. The\nindependent-electron susceptibility is constructed in a time representation\nover a non-uniform distribution of real-space locations $\\lbrace {\\bf r}_k\n\\rbrace$ optimized within a separable resolution-of-the-identity framework to\nreproduce standard Coulomb-fitting calculations with meV accuracy. The\ncompactness of the obtained $\\lbrace {\\bf r}_k \\rbrace$ distribution leads to a\ncrossover with the standard Coulomb-fitting scheme for system sizes below a few\nhundred electrons. The needed analytic continuation follows a recent approach\nthat requires the continuation of the screened Coulomb potential rather than\nthe much more structured self-energy. The present scheme is benchmarked over\nlarge molecular sets and scaling properties are demonstrated on a family of\ndefected hexagonal boron-nitride flakes containing up to 6000 electrons.\n"}
+{"abstract": "  Theoretical models and experimental observations suggest that gamma-ray\nbursts (GRB) and high-energy neutrino bursts travelling through the\ninterstellar space may reach the Earth at different speeds. We propose and\nstudy in details the mechanism i), which always exists, where GRB are slowed\ndown due to the dispersion of light in the interstellar medium. In addition to\nthe standard media such as electrons and photons as CMB, we consider the medium\nwith invisible axions. The amount of GRB delays in different media are\ncalculated in details utilizing a novel technique in QFT by using the hitherto\nknown or estimated densities of particles in the space without introducing any\narbitrary parameter. Previously, the GRB delays have been interpreted as a sign\nof Lorentz invariance violation by modifying the dispersion relation of Special\nRelativity, which relates the energy, the momentum and the mass of a particle,\nbased on different mechanisms ii), such as a stringy spacetime foam, coming\nfrom a quantum gravity effect and using an adjustable parameter. Obviously, all\nthe above-mentioned mechanisms i) and ii) are induced (seeming) Lorentz\ninvariance violations but not an intrinsic (genuine) one. The amount of GRB\ndelay due to the two aforementioned interpretations can be distinguished by\nobserving the time of arrival of light with different frequencies. Namely,\ndispersion of light i) predicts that the higher energy GRB arrive the Earth\nearlier, while in the other interpretations ii), they arrive later. We notice\nthat the needed amount for delay due to the dispersion of light shall have the\npotential power to shed additional light on the microstructure of interstellar\nmedia with respect to the densities of constituent particles and the origins of\ntheir sources. Finally, we indicate the ways to detect the intrinsic Lorentz\ninvariance violation and to interpret them theoretically.\n"}
+{"abstract": "  Long-term deployment of a fleet of mobile robots requires reliable and secure\ntwo-way communication channels between individual robots and remote human\noperators for supervision and tasking. Existing open-source solutions to this\nproblem degrade in performance in challenging real-world situations such as\nintermittent and low-bandwidth connectivity, do not provide security control\noptions, and can be computationally expensive on hardware-constrained mobile\nrobot platforms. In this paper, we present Robofleet, a lightweight open-source\nsystem which provides inter-robot communication, remote monitoring, and remote\ntasking for a fleet of ROS-enabled service-mobile robots that is designed with\nthe practical goals of resilience to network variance and security control in\nmind.\n  Robofleet supports multi-user, multi-robot communication via a central\nserver. This architecture deduplicates network traffic between robots,\nsignificantly reducing overall network load when compared with native ROS\ncommunication. This server also functions as a single entrypoint into the\nsystem, enabling security control and user authentication. Individual robots\nrun the lightweight Robofleet client, which is responsible for exchanging\nmessages with the Robofleet server. It automatically adapts to adverse network\nconditions through backpressure monitoring as well as topic-level priority\ncontrol, ensuring that safety-critical messages are successfully transmitted.\nFinally, the system includes a web-based visualization tool that can be run on\nany internet-connected, browser-enabled device to monitor and control the\nfleet.\n  We compare Robofleet to existing methods of robotic communication, and\ndemonstrate that it provides superior resilience to network variance while\nmaintaining performance that exceeds that of widely-used systems.\n"}
+{"abstract": "  Video frame interpolation can up-convert the frame rate and enhance the video\nquality. In recent years, although the interpolation performance has achieved\ngreat success, image blur usually occurs at the object boundaries owing to the\nlarge motion. It has been a long-standing problem, and has not been addressed\nyet. In this paper, we propose to reduce the image blur and get the clear shape\nof objects by preserving the edges in the interpolated frames. To this end, the\nproposed Edge-Aware Network (EA-Net) integrates the edge information into the\nframe interpolation task. It follows an end-to-end architecture and can be\nseparated into two stages, \\emph{i.e.}, edge-guided flow estimation and\nedge-protected frame synthesis. Specifically, in the flow estimation stage,\nthree edge-aware mechanisms are developed to emphasize the frame edges in\nestimating flow maps, so that the edge-maps are taken as the auxiliary\ninformation to provide more guidance to boost the flow accuracy. In the frame\nsynthesis stage, the flow refinement module is designed to refine the flow map,\nand the attention module is carried out to adaptively focus on the\nbidirectional flow maps when synthesizing the intermediate frames. Furthermore,\nthe frame and edge discriminators are adopted to conduct the adversarial\ntraining strategy, so as to enhance the reality and clarity of synthesized\nframes. Experiments on three benchmarks, including Vimeo90k, UCF101 for\nsingle-frame interpolation and Adobe240-fps for multi-frame interpolation, have\ndemonstrated the superiority of the proposed EA-Net for the video frame\ninterpolation task.\n"}
+{"abstract": "  With the increase in the availability of speech from varied domains, it is\nimperative to use such out-of-domain data to improve existing speech systems.\nDomain adaptation is a prominent pre-processing approach for this. We\ninvestigate it for adapt microphone speech to the telephone domain.\nSpecifically, we explore CycleGAN-based unpaired translation of microphone data\nto improve the x-vector/speaker embedding network for Telephony Speaker\nVerification. We first demonstrate the efficacy of this on real challenging\ndata and then, to improve further, we modify the CycleGAN formulation to make\nthe adaptation task-specific. We modify CycleGAN's identity loss,\ncycle-consistency loss, and adversarial loss to operate in the deep feature\nspace. Deep features of a signal are extracted from an auxiliary (speaker\nembedding) network and, hence, preserves speaker identity. Our 3D\nconvolution-based Deep Feature Discriminators (DFD) show relative improvements\nof 5-10% in terms of equal error rate. To dive deeper, we study a challenging\nscenario of pooling (adapted) microphone and telephone data with data\naugmentations and telephone codecs. Finally, we highlight the sensitivity of\nCycleGAN hyper-parameters and introduce a parameter called probability of\nadaptation.\n"}
+{"abstract": "  An implementation of a complex solver for the solution of the response\nequations required to compute the complex response functions of damped response\ntheory is presented for the resolution-of-identity (RI) coupled-cluster singles\nand approximate doubles CC2 method. The implementation uses a partitioned\nformulation that avoids the storage of double excitation amplitudes to make it\napplicable to large molecules. The solver is the keystone element for the\ndevelopment of the damped coupled-cluster response formalism for linear and\nnonlinear effects in resonant frequency regions at the RI-CC2 level of theory.\nIllustrative results are reported for the one-photon absorption cross section\nof C60, the electronic circular dichroism of $n$-helicenes ($n$ = 5, 6, 7), and\nthe $C_6$ dispersion coefficients of a set of selected organic molecules and\nfullerenes.\n"}
+{"abstract": "  The Earth is constantly hit by energetic particles originating from galactic\nsources. The flux of these particles is altered by the magnetized solar wind in\nthe heliosphere and the Earth's magnetic field. For this reason, the ability of\na particle to approach a spacecraft in LEO depends on its energy and the\nposition of the spacecraft within the Earth' magnetosphere. Moreover, there are\nsome areas (radiation belts) where the particles are trapped for a long time,\nand therefore the flux of energetic particles is particularly high.\nOccasionally, SEP contribute to the energetic particle flux too. DOSTEL is one\nof the instruments aboard the \\ac{ISS} that monitors the radiation field within\nthe European module Columbus. Because being installed inside the \\ac{ISS},\nparticles produced by the interaction between the \"primary\" radiation and the\nISS materials are also measured. To describe the observations in such a complex\nradiation field, we follow the method by Caballero-Lopez and Moraal (2012) in\norder to compute the so-called yield function using precise measurements of the\nproton and Helium energy spectra obtained by AMS and the systematic variation\nof the DOSTEL measurements within the Earth's magnetosphere\n"}
+{"abstract": "  Residual networks (ResNets) employ skip connections in their networks --\nreusing activations from previous layers -- to improve training convergence,\nbut these skip connections create challenges for hardware implementations of\nResNets. The hardware must either wait for skip connections to be processed\nbefore processing more incoming data or buffer them elsewhere. Without skip\nconnections, ResNets would be more hardware-efficient. Thus, we present the\nteacher-student learning method to gradually prune away all of a ResNet's skip\nconnections, constructing a network we call NonResNet. We show that when\nimplemented for FPGAs, NonResNet decreases ResNet's BRAM utilization by 9% and\nLUT utilization by 3% and increases throughput by 5%.\n"}
+{"abstract": "  Directional excitation of guidance modes is central to many applications\nranging from light harvesting, optical information processing to quantum\noptical technology. Of paramount interest, especially, the active control of\nnear-field directionality provides a new paradigm for the real-time on-chip\nmanipulation of light. Here we find that for a given dipolar source, its\nnear-field directionality can be toggled efficiently via tailoring the\npolarization of surface waves that are excited, for example, via tuning the\nchemical potential of graphene in a graphene-metasurface waveguide. This\nfinding enables a feasible scheme for the active near-field directionality.\nCounterintuitively, we reveal that this scheme can transform a circular\nelectric/magnetic dipole into a Huygens dipole in the near-field coupling.\nMoreover, for Janus dipoles, this scheme enables us to actively flip their\nnear-field coupling and non-coupling faces.\n"}
+{"abstract": "  Electron spins in silicon quantum dots are promising qubits due to their long\ncoherence times, scalable fabrication, and potential for all-electrical\ncontrol. However, charge noise in the host semiconductor presents a major\nobstacle to achieving high-fidelity single- and two-qubit gates in these\ndevices. In this work, we measure the charge-noise spectrum of a Si/SiGe\nsinglet-triplet qubit over more than 13 decades in frequency using a\ncombination of methods, including dynamically-decoupled exchange oscillations\nwith up to 512 $\\pi$ pulses during the qubit evolution. The charge noise is\ncolored across the entire frequency range of our measurements, although the\nspectral exponent changes with frequency. Moreover, the charge-noise spectrum\ninferred from conductance measurements of a proximal sensor quantum dot agrees\nwith that inferred from coherent oscillations of the singlet-triplet qubit,\nsuggesting that simple transport measurements can accurately characterize the\ncharge noise over a wide frequency range in Si/SiGe quantum dots.\n"}
+{"abstract": "  To get the energy spectrum distribution and cross-sections of emitted light\ncharged particles and explore the nuclear reaction, a experiment of 80.5 MeV/u\n12C beam bombarding on C, W, Cu, Au, Pb targets has been carried out at\nInstitute of Modern Physics, Chinese Academy of Science. 30, 60 and 120 degree\nrelative to the incident beam have been detected using three sets of telescope\ndetectors. The results indicate that there is a tendency that heavier targets\nhave larger double differential cross-sections and the emitted fragments are\nmore likely to go forward. Besides, the decrease of cross-sections of fragments\nproducing with the increasing emitted angles may follow some kind of pattern.\n"}
+{"abstract": "  Using methods from nonstandard analysis, we define a nonstandard Minkowski\ndimension which exists for all bounded sets and which has the property that\n$\\dim(A\\times B) = \\dim(A) + \\dim(B)$.That is, our new dimension is\nproduct-summable. To illustrate our theorem we generalize an example of\nFalconer's to show that the standard upper Minkowski dimension, as well as the\nHausdorff dimension, are not product-summable. We also include a method for\ncreating sets of arbitrary rational dimension.\n"}
+{"abstract": "  In this paper, we have presented the big rip and pseudo rip cosmological\nmodels in an extended theory of gravity. The matter field is considered to be\nthat of perfect fluid. The geometrical parameters are adjusted in such a manner\nthat it matches the prescriptions given by cosmological observations, to be\nspecific to the range of Hubble tension $(H_{0})$. The models favor phantom\nbehavior. The violation of strong energy conditions are shown in both the\nmodels, as it has become essential in an extended gravity. The representative\nvalues of the coupling parameter are significant on the evolution of the\nuniverse.\n"}
+{"abstract": "  Charged particle optics, the description of particle trajectories in the\nvicinity of some optical axis, describe the imaging properties of particle\noptics devices. Here, we present a complete and compact description of charged\nparticle optics employing perturbative expansion of Hamiltonian mechanics. The\nderived framework allows the straightforward computation of transversal and\nlongitudinal (chromatic) properties of static and dynamic optical devices with\nstraight and curved optical axes. It furthermore gives rise to geometric\nintegration schemes preserving the symplectic phase space structure and\npertaining Lagrange invariants, which may be employed to derive analytic\napproximations of aberration coefficients and efficient numerical trajectory\nsolvers.\n"}
+{"abstract": "  We develop generalized approach to obtaining Edgeworth expansions for\n$t$-statistics of an arbitrary order using computer algebra and combinatorial\nalgorithms. To incorporate various versions of mean-based statistics, we\nintroduce Adjusted Edgeworth expansions that allow polynomials in the terms to\ndepend on a sample size in a specific way and prove their validity. Provided\nresults up to 5th order include one and two-sample ordinary $t$-statistics with\nbiased and unbiased variance estimators, Welch $t$-test, and moderated\n$t$-statistics based on empirical Bayes method, as well as general results for\nany statistic with available moments of the sampling distribution. These\nresults are included in a software package that aims to reach a broad community\nof researchers and serve to improve inference in a wide variety of analytical\nprocedures; practical considerations of using such expansions are discussed.\n"}
+{"abstract": "  The distribution and kinematics of the circumstellar medium (CSM) around a\nsupernova remnant (SNR) tell us useful information about the explosion of its\nnatal supernova (SN). Kepler's SNR, the remnant of SN1604, is widely regarded\nto be of Type Ia origin. Its shock is moving through a dense, asymmetric CSM.\nThe presence of this dense gas suggests that its parent progenitor system\nconsisted of a white dwarf and an asymptotic giant branch (AGB) star. In this\npaper, we analyze a new and long observation with the reflection grating\nspectrometers (RGS) on board the XMM-Newton satellite, spatially resolving the\nremnant emission in the cross-dispersion direction. We find that the CSM\ncomponent is blue-shifted with velocities in the general range 0-500 km/s. We\nalso derive information on the central bar structure and find that the\nnorthwest half is blue-shifted, while the southeast half is red-shifted. Our\nresult is consistent with a picture proposed by previous studies, in which a\n\"runaway\" AGB star moved to the north-northwest and toward us in the line of\nsight, although it is acceptable for both single-degenerate and core-degenerate\nscenarios for the progenitor system.\n"}
+{"abstract": "  Although deep models have greatly improved the accuracy and robustness of\nimage segmentation, obtaining segmentation results with highly accurate\nboundaries and fine structures is still a challenging problem. In this paper,\nwe propose a simple yet powerful Boundary-Aware Segmentation Network (BASNet),\nwhich comprises a predict-refine architecture and a hybrid loss, for highly\naccurate image segmentation. The predict-refine architecture consists of a\ndensely supervised encoder-decoder network and a residual refinement module,\nwhich are respectively used to predict and refine a segmentation probability\nmap. The hybrid loss is a combination of the binary cross entropy, structural\nsimilarity and intersection-over-union losses, which guide the network to learn\nthree-level (ie, pixel-, patch- and map- level) hierarchy representations. We\nevaluate our BASNet on two reverse tasks including salient object segmentation,\ncamouflaged object segmentation, showing that it achieves very competitive\nperformance with sharp segmentation boundaries. Importantly, BASNet runs at\nover 70 fps on a single GPU which benefits many potential real applications.\nBased on BASNet, we further developed two (close to) commercial applications:\nAR COPY & PASTE, in which BASNet is integrated with augmented reality for\n\"COPYING\" and \"PASTING\" real-world objects, and OBJECT CUT, which is a\nweb-based tool for automatic object background removal. Both applications have\nalready drawn huge amount of attention and have important real-world impacts.\nThe code and two applications will be publicly available at:\nhttps://github.com/NathanUA/BASNet.\n"}
+{"abstract": "  The radiative transfer equation is a fundamental equation in transport theory\nand applications, which is a 5-dimensional PDE in the stationary one-velocity\ncase, leading to great difficulties in numerical simulation. To tackle this\nbottleneck, we first use the discrete ordinate technique to discretize the\nscattering term, an integral with respect to the angular variables, resulting\nin a semi-discrete hyperbolic system. Then, we make the spatial discretization\nby means of the discontinuous Galerkin (DG) method combined with the sparse\ngrid method. The final linear system is solved by the block Gauss-Seidal\niteration method. The computational complexity and error analysis are developed\nin detail, which show the new method is more efficient than the original\ndiscrete ordinate DG method. A series of numerical results are performed to\nvalidate the convergence behavior and effectiveness of the proposed method.\n"}
+{"abstract": "  Liquids realize a highly complex state of matter in which strong competing\nkinetic and interaction effects come to life. As such, liquids are, generally,\nmore challenging to understand than either gases or solids. In weakly\ninteracting gases, the kinetic effects dominate. By contrast, low temperature\nsolids typically feature far smaller fluctuations about their ground state.\nNotwithstanding their complexity, with the exception of quantum fluids (e.g.,\nsuperfluid Helium) and supercooled liquids (including glasses), various aspects\nof common liquid dynamics such as their dynamic viscosity are often assumed to\nbe given by rather simple, Arrhenius-type, activated forms with nearly constant\n(i.e., temperature independent) energy barriers. In this work, we analyze\nexperimentally measured viscosities of numerous liquids far above their\nequilibrium melting temperature to see how well this assumption fares. We find\nmarked deviations from simple activated dynamics. Even far above their\nequilibrium melting temperatures, as the temperature drops, the viscosity of\nthese liquids increases more strongly than predicted by activated dynamics\ndominated by a single uniform energy barrier. For metallic fluids, the scale of\nthe prefactor in the Arrhenius form for the viscosity is consistent with that\ngiven by the product $nh$ with $n$ the number density and $h$ Planck's\nconstant. More generally, in all fluids (whether metallic or non-metallic),\n$nh$ constitutes a lower bound on the viscosity. We find that a scaling of the\ntemperature axis (complementing that of the viscosity) leads to a partial\ncollapse of the temperature dependent viscosities of different fluids; such a\nscaling allows for a functional dependence of the viscosity on temperature that\nincludes yet is far more general than activated Arrhenius form alone. We\nspeculate on relations between non-Arrhenius dynamics and thermodynamic\nobservables.\n"}
+{"abstract": "  How do active galactic nuclei with low optical luminosities produce powerful\nradio emission? Recent studies of active galactic nuclei with moderate radio\nand low optical luminosities (Fanaroff & Riley class I, FR I) searching for\nbroad nuclear emission lines in polarized light, as predicted by some active\ngalactic nucleus unification models, have found heterogeneous results. These\nmodels typically consist of a central engine surrounded by a torus of discrete\ndusty clouds. These clouds would absorb and scatter optical emission, blocking\nbroad nuclear emission lines, and reradiate in mid-infrared. Some scattered\nbroad-line emission may be observable, depending on geometry, which would be\npolarized. We present a wide-band infrared spectroscopic analysis of 10 nearby\nFR I radio galaxies to determine whether there is significant emission from a\ndusty obscuring structure. We used Markov Chain Monte Carlo algorithms to\ndecompose Spitzer/IRS spectra of our sample. We constrained the wide-band\nbehavior of our models with photometry from the Two Micron All Sky Survey,\nSpitzer/IRAC, Spitzer/MIPS, and Herschel/SPIRE. We find that one galaxy is best\nfit by a clumpy torus and three others show some thermal mid-infrared\ncomponent. This suggests that in those three there is likely some obscuring\ndust structure that is inconsistent with our torus models and there must be\nsome source of photons heating the dust. We conclude that 40% of our FR I radio\ngalaxies show evidence of obscuring dusty material, possibly some other form of\nhidden broad-line nucleus, but only 10% favor the clumpy torus model\nspecifically.\n"}
+{"abstract": "  Here we report the performance of a discrete modulated CVQKD protocol based\non eight and sixteen state optimal APK constellations with pure loss quantum\nchannel transmission. Our constellations use the same amplitudes and state\nprobability as in an optimal four-state unidimensional constellation model and\napplies amplitude/phase modulation on the quadratures of coherent states. We\ncompare the obtained results with the performance of conventional $m$-PSK and\n$m$-APK constellations. The results show that the proposed constellations\napplied to CVQKD protocol outperforms the conventional PSK and APK based\nprotocols in both secret key rate and link distance. In special, our protocol\nis able to accomplish viable SKR's in low transmittance regime, $\\tau<0.2$.\n"}
+{"abstract": "  We propose policy-gradient algorithms for solving the problem of control in a\nrisk-sensitive reinforcement learning (RL) context. The objective of our\nalgorithms is to maximize the distorted risk measure (DRM) of the cumulative\nreward in an episodic Markov decision process (MDP). We derive a variant of the\npolicy gradient theorem that caters to the DRM objective. Using this theorem in\nconjunction with a likelihood ratio (LR) based gradient estimation scheme, we\npropose policy gradient algorithms for optimizing DRM in both on-policy and\noff-policy RL settings. We derive non-asymptotic bounds that establish the\nconvergence of our algorithms to an approximate stationary point of the DRM\nobjective.\n"}
+{"abstract": "  The discovery of a large putative impact crater buried beneath Hiawatha\nGlacier along the margin of the northwestern Greenland Ice Sheet has\nreinvigorated interest into the nature of large impacts into thick ice masses.\nThis circular structure is relatively shallow and exhibits a small central\nuplift, whereas a peak-ring morphology is expected. This discrepancy may be due\nto long-term and ongoing subglacial erosion but may also be explained by a\nrelatively recent impact through the Greenland Ice Sheet, which is expected to\nalter the final crater morphology. Here we model crater formation using\nhydrocode simulations, varying pre-impact ice thickness and impactor\ncomposition over crystalline target rock. We find that an ice-sheet thickness\nof 1.5 or 2 km results in a crater morphology that is consistent with the\npresent morphology of this structure. Further, an ice sheet that thick\nsubstantially inhibits ejection of rocky material, which might explain the\nabsence of rocky ejecta in most existing Greenland deep ice cores if the impact\noccurred during the late Pleistocene. From the present morphology of the\nputative Hiawatha impact crater alone, we cannot distinguish between an older\ncrater formed by a pre-Pleistocene impact into ice-free bedrock or a younger,\nPleistocene impact into locally thick ice, but based on our modeling we\nconclude that latter scenario is possible.\n"}
+{"abstract": "  Domain adaptation (DA) approaches address domain shift and enable networks to\nbe applied to different scenarios. Although various image DA approaches have\nbeen proposed in recent years, there is limited research towards video DA. This\nis partly due to the complexity in adapting the different modalities of\nfeatures in videos, which includes the correlation features extracted as\nlong-term dependencies of pixels across spatiotemporal dimensions. The\ncorrelation features are highly associated with action classes and proven their\neffectiveness in accurate video feature extraction through the supervised\naction recognition task. Yet correlation features of the same action would\ndiffer across domains due to domain shift. Therefore we propose a novel\nAdversarial Correlation Adaptation Network (ACAN) to align action videos by\naligning pixel correlations. ACAN aims to minimize the distribution of\ncorrelation information, termed as Pixel Correlation Discrepancy (PCD).\nAdditionally, video DA research is also limited by the lack of cross-domain\nvideo datasets with larger domain shifts. We, therefore, introduce a novel\nHMDB-ARID dataset with a larger domain shift caused by a larger statistical\ndifference between domains. This dataset is built in an effort to leverage\ncurrent datasets for dark video classification. Empirical results demonstrate\nthe state-of-the-art performance of our proposed ACAN for both existing and the\nnew video DA datasets.\n"}
+{"abstract": "  Increasing scale and heterogeneity in data centers have led to the\ndevelopment of federated clusters such as KubeFed, Hydra, and Pigeon, that\nfederate individual data center clusters. In our work, we introduce Megha, a\nnovel decentralized resource management framework for such federated clusters.\nMegha employs flexible logical partitioning of clusters to distribute its\nscheduling load, ensuring that the requirements of the workload are satisfied\nwith very low scheduling overheads. It uses a distributed global scheduler that\ndoes not rely on a centralized data store but, instead, works with eventual\nconsistency, unlike other schedulers that use a tiered architecture or rely on\ncentralized databases. Our experiments with Megha show that it can schedule\ntasks taking into account fairness and placement constraints with low resource\nallocation times - in the order of tens of milliseconds.\n"}
+{"abstract": "  This paper investigates the reconfigurable reflecting surface (RIS)-aided\nmultiple-input-single-output (MISO) systems with imperfect channel state\ninformation (CSI), where RIS-related channels are modeled by Rician fading.\nConsidering the overhead and complexity in practical systems, we employ the\nlow-complexity maximum ratio combining (MRC) beamforming at the base station\n(BS), and configure the phase shifts of the RIS based on long-term statistical\nCSI. Specifically, we first estimate the overall channel matrix based on the\nlinear minimum mean square error (LMMSE) estimator, and evaluate the\nperformance of MSE and normalized MSE (NMSE). Then, with the estimated channel,\nwe derive the closed-form expressions of the ergodic rate. The derived\nexpressions show that with Rician RIS-related channels, the rate can maintain\nat a non-zero value when the transmit power is scaled down proportionally to\n$1/M$ or $1/N^2$, where $M$ and $N$ are the number of antennas and reflecting\nelements, respectively. However, if all the RIS-related channels are fully\nRayleigh, the transmit power of each user can only be scaled down\nproportionally to $1/\\sqrt{M}$ or $1/N$. Finally, numerical results verify the\npromising benefits from the RIS to traditional MISO systems.\n"}
+{"abstract": "  Despite -- or maybe because of -- their astonishing capacity to fit data,\nneural networks are believed to have difficulties extrapolating beyond training\ndata distribution. This work shows that, for extrapolations based on finite\ntransformation groups, a model's inability to extrapolate is unrelated to its\ncapacity. Rather, the shortcoming is inherited from a learning hypothesis:\nExamples not explicitly observed with infinitely many training examples have\nunderspecified outcomes in the learner's model. In order to endow neural\nnetworks with the ability to extrapolate over group transformations, we\nintroduce a learning framework counterfactually-guided by the learning\nhypothesis that any group invariance to (known) transformation groups is\nmandatory even without evidence, unless the learner deems it inconsistent with\nthe training data. Unlike existing invariance-driven methods for\n(counterfactual) extrapolations, this framework allows extrapolations from a\nsingle environment. Finally, we introduce sequence and image extrapolation\ntasks that validate our framework and showcase the shortcomings of traditional\napproaches.\n"}
+{"abstract": "  The active Brownian particle (ABP) model describes a swimmer, synthetic or\nliving, whose direction of swimming is a Brownian motion. The swimming is due\nto a propulsion force, and the fluctuations are typically thermal in origin. We\npresent a 2D model where the fluctuations arise from nonthermal noise in a\npropelling force acting at a single point, such as that due to a flagellum. We\ntake the overdamped limit and find several modifications to the traditional ABP\nmodel. Since the fluctuating force causes a fluctuating torque, the diffusion\ntensor describing the process has a coupling between translational and\nrotational degrees of freedom. An anisotropic particle also exhibits a\nnoise-induced induced drift. We show that these effects have measurable\nconsequences for the long-time diffusivity of active particles, in particular\nadding a contribution that is independent of where the force acts.\n"}
+{"abstract": "  Purpose: To determine whether evaporation, tangential flow, or a combination\nof the two cause tear film breakup in a variety of instances; to estimate\nrelated breakup parameters that cannot be measured in breakup during subject\ntrials; and to validate our procedure against previous work. Methods: Five\nordinary differential equation models for tear film thinning were designed that\nmodel evaporation, osmosis, and various types of flow. Eight tear film breakup\ninstances of five healthy subjects that were identified in fluorescence images\nin previous work were fit with these five models. The fitting procedure used a\nnonlinear least squares optimization that minimized the difference of the\ncomputed theoretical fluorescent intensity from the models and the experimental\nfluorescent intensity from the images. The optimization was conducted over the\nevaporation rate and up to three flow rate parameters. The smallest norm of the\ndifference was determined to correspond to the model that best explained the\ntear film dynamics. Results: All of the breakup instances were best fit by\nmodels with time-dependent flow. Our optimal parameter values and thinning rate\nand fluid flow profiles compare well with previous partial differential\nequation model results in most instances. Conclusion: Our fitting procedure\nsuggests that the combination of the Marangoni effect and evaporation cause\nmost of the breakup instances. Comparison with results from previous work\nsuggests that the simplified models can capture the essential tear film\ndynamics in most cases, thereby validating this procedure as one that could be\nused on many other instances.\n"}
+{"abstract": "  Reinforcement learning (RL) can be used to create a decision-making agent for\nautonomous driving. However, previous approaches provide only black-box\nsolutions, which do not offer information on how confident the agent is about\nits decisions. An estimate of both the aleatoric and epistemic uncertainty of\nthe agent's decisions is fundamental for real-world applications of autonomous\ndriving. Therefore, this paper introduces the Ensemble Quantile Networks (EQN)\nmethod, which combines distributional RL with an ensemble approach, to obtain a\ncomplete uncertainty estimate. The distribution over returns is estimated by\nlearning its quantile function implicitly, which gives the aleatoric\nuncertainty, whereas an ensemble of agents is trained on bootstrapped data to\nprovide a Bayesian estimation of the epistemic uncertainty. A criterion for\nclassifying which decisions that have an unacceptable uncertainty is also\nintroduced. The results show that the EQN method can balance risk and time\nefficiency in different occluded intersection scenarios, by considering the\nestimated aleatoric uncertainty. Furthermore, it is shown that the trained\nagent can use the epistemic uncertainty information to identify situations that\nthe agent has not been trained for and thereby avoid making unfounded,\npotentially dangerous, decisions outside of the training distribution.\n"}
+{"abstract": "  Deep learning based image recognition systems have been widely deployed on\nmobile devices in today's world. In recent studies, however, deep learning\nmodels are shown vulnerable to adversarial examples. One variant of adversarial\nexamples, called adversarial patch, draws researchers' attention due to its\nstrong attack abilities. Though adversarial patches achieve high attack success\nrates, they are easily being detected because of the visual inconsistency\nbetween the patches and the original images. Besides, it usually requires a\nlarge amount of data for adversarial patch generation in the literature, which\nis computationally expensive and time-consuming. To tackle these challenges, we\npropose an approach to generate inconspicuous adversarial patches with one\nsingle image. In our approach, we first decide the patch locations basing on\nthe perceptual sensitivity of victim models, then produce adversarial patches\nin a coarse-to-fine way by utilizing multiple-scale generators and\ndiscriminators. The patches are encouraged to be consistent with the background\nimages with adversarial training while preserving strong attack abilities. Our\napproach shows the strong attack abilities in white-box settings and the\nexcellent transferability in black-box settings through extensive experiments\non various models with different architectures and training methods. Compared\nto other adversarial patches, our adversarial patches hold the most negligible\nrisks to be detected and can evade human observations, which is supported by\nthe illustrations of saliency maps and results of user evaluations. Lastly, we\nshow that our adversarial patches can be applied in the physical world.\n"}
+{"abstract": "  The high-order cumulants and factorial cumulants of conserved charges are\nsuggested to study the critical dynamics in heavy ion collisions. In this\npaper, using parametric representation of the 3-dimensional Ising model, the\nsign distribution on the phase diagram and temperature dependence of the\ncumulants and factorial cumulants is studied and compared. In the vicinity of\nthe critical point, the cumulants and factorial cumulants can not be\ndistinguished. Far away from the critical point, sign changes occur in the\nfactorial cumulants comparing with the same order cumulants. The cause of these\nsign changes is analysed. They may be used to measure the distance to the\ncritical point.\n"}
+{"abstract": "  In many choice problems, the interaction between several distinct variables\ndetermines the payoff of each alternative. I propose and axiomatize a model of\na decision maker who recognizes that she may not accurately perceive the\ncorrelation between these variables, and who takes this into account when\nmaking her decision. She chooses as if she calculates each alternative's\nexpected outcome under multiple possible correlation structures, and then\nevaluates it according to the worst expected outcome.\n"}
+{"abstract": "  Quantum error correction is an indispensable ingredient for scalable quantum\ncomputing. In this Perspective we discuss a particular class of quantum codes\ncalled low-density parity-check (LDPC) quantum codes. The codes we discuss are\nalternatives to the surface code, which is the currently leading candidate to\nimplement quantum fault-tolerance. We introduce the zoo of LDPC quantum codes\nand discuss their potential for making quantum computers robust against noise.\nIn particular, we explain recent advances in the theory of LDPC quantum codes\nrelated to certain product constructions and discuss open problems in the\nfield.\n"}
+{"abstract": "  This paper introduces scattering transform for speech emotion recognition\n(SER). Scattering transform generates feature representations which remain\nstable to deformations and shifting in time and frequency without much loss of\ninformation. In speech, the emotion cues are spread across time and localised\nin frequency. The time and frequency invariance characteristic of scattering\ncoefficients provides a representation robust against emotion irrelevant\nvariations e.g., different speakers, language, gender etc. while preserving the\nvariations caused by emotion cues. Hence, such a representation captures the\nemotion information more efficiently from speech. We perform experiments to\ncompare scattering coefficients with standard mel-frequency cepstral\ncoefficients (MFCCs) over different databases. It is observed that frequency\nscattering performs better than time-domain scattering and MFCCs. We also\ninvestigate layer-wise scattering coefficients to analyse the importance of\ntime shift and deformation stable scalogram and modulation spectrum\ncoefficients for SER. We observe that layer-wise coefficients taken\nindependently also perform better than MFCCs.\n"}
+{"abstract": "  Radiographs are used as the most important imaging tool for identifying spine\nanomalies in clinical practice. The evaluation of spinal bone lesions, however,\nis a challenging task for radiologists. This work aims at developing and\nevaluating a deep learning-based framework, named VinDr-SpineXR, for the\nclassification and localization of abnormalities from spine X-rays. First, we\nbuild a large dataset, comprising 10,468 spine X-ray images from 5,000 studies,\neach of which is manually annotated by an experienced radiologist with bounding\nboxes around abnormal findings in 13 categories. Using this dataset, we then\ntrain a deep learning classifier to determine whether a spine scan is abnormal\nand a detector to localize 7 crucial findings amongst the total 13. The\nVinDr-SpineXR is evaluated on a test set of 2,078 images from 1,000 studies,\nwhich is kept separate from the training set. It demonstrates an area under the\nreceiver operating characteristic curve (AUROC) of 88.61% (95% CI 87.19%,\n90.02%) for the image-level classification task and a mean average precision\n(mAP@0.5) of 33.56% for the lesion-level localization task. These results serve\nas a proof of concept and set a baseline for future research in this direction.\nTo encourage advances, the dataset, codes, and trained deep learning models are\nmade publicly available.\n"}
+{"abstract": "  We report the results of combined analyses of X-ray and optical data of two\ngalaxy clusters, CL 0024$+$1654 and RX J0152.7$-$1357 at redshift $z = 0.395$\nand $z = 0.830$, respectively, which offer a holistic physical description of\nthe two clusters. Our X-ray analysis yields temperature and density profile of\nthe gas in the intra-cluster medium (ICM). Using optical photometric and\nspectroscopic data, complemented with mass distribution from gravitational\nlensing study, we investigate any possible correlation between the physical\nproperties of the galaxy members, i.e., their color, morphology, and star\nformation rate (SFR) with their environments. We quantify the properties of the\nenvironment of each galaxy by galaxy number density, ICM temperature, and mass\ndensity. Although our result shows that the two clusters exhibit a weaker\ncorrelation compared to relaxed clusters, it still confirms the significant\neffect of the ICM on the SFRs in the galaxies. The closer relation between\ngalaxy physical property and the condition of its immediate environment found\nin this work indicates the locality of galaxy evolution, even within a larger\nbound system such as cluster. Various physical mechanisms are suggested to\nexplain the relation between the properties of galaxies and their environment.\n"}
+{"abstract": "  Can we teach a robot to recognize and make predictions for activities that it\nhas never seen before? We tackle this problem by learning models for video from\ntext. This paper presents a hierarchical model that generalizes instructional\nknowledge from large-scale text-corpora and transfers the knowledge to video.\nGiven a portion of an instructional video, our model recognizes and predicts\ncoherent and plausible actions multiple steps into the future, all in rich\nnatural language. To demonstrate the capabilities of our model, we introduce\nthe \\emph{Tasty Videos Dataset V2}, a collection of 4022 recipes for zero-shot\nlearning, recognition and anticipation. Extensive experiments with various\nevaluation metrics demonstrate the potential of our method for generalization,\ngiven limited video data for training models.\n"}
+{"abstract": "  This is the second paper in a series in which we develop machine learning\n(ML) moment closure models for the radiative transfer equation (RTE). In our\nprevious work \\cite{huang2021gradient}, we proposed an approach to directly\nlearn the gradient of the unclosed high order moment, which performs much\nbetter than learning the moment itself and the conventional $P_N$ closure.\nHowever, the ML moment closure model in \\cite{huang2021gradient} is not able to\nguarantee hyperbolicity and long time stability. We propose in this paper a\nmethod to enforce the global hyperbolicity of the ML closure model. The main\nidea is to seek a symmetrizer (a symmetric positive definite matrix) for the\nclosure system, and derive constraints such that the system is globally\nsymmetrizable hyperbolic. It is shown that the new ML closure system inherits\nthe dissipativeness of the RTE and preserves the correct diffusion limit as the\nKnunsden number goes to zero. Several benchmark tests including the Gaussian\nsource problem and the two-material problem show the good accuracy, long time\nstability and generalizability of our globally hyperbolic ML closure model.\n"}
+{"abstract": "  We show that cascaded diffusion models are capable of generating high\nfidelity images on the class-conditional ImageNet generation benchmark, without\nany assistance from auxiliary image classifiers to boost sample quality. A\ncascaded diffusion model comprises a pipeline of multiple diffusion models that\ngenerate images of increasing resolution, beginning with a standard diffusion\nmodel at the lowest resolution, followed by one or more super-resolution\ndiffusion models that successively upsample the image and add higher resolution\ndetails. We find that the sample quality of a cascading pipeline relies\ncrucially on conditioning augmentation, our proposed method of data\naugmentation of the lower resolution conditioning inputs to the\nsuper-resolution models. Our experiments show that conditioning augmentation\nprevents compounding error during sampling in a cascaded model, helping us to\ntrain cascading pipelines achieving FID scores of 1.48 at 64x64, 3.52 at\n128x128 and 4.88 at 256x256 resolutions, outperforming BigGAN-deep, and\nclassification accuracy scores of 63.02% (top-1) and 84.06% (top-5) at 256x256,\noutperforming VQ-VAE-2.\n"}
+{"abstract": "  I examine global dynamics in a monetary model with overlapping generations of\nfinite-horizon agents and a binding lower bound on nominal interest rates. Debt\ntargeting rules exacerbate the possibility of self-fulfilling liquidity traps,\nfor agents expect austerity following deflationary slumps. Conversely, activist\nbut sustainable fiscal policy regimes - implementing intertemporally balanced\ntax cuts and/or transfer increases in response to disinflationary trajectories\n- are capable of escaping liquidity traps and embarking inflation into a\nglobally stable path that converges to the target. Should fiscal stimulus of\nlast resort be overly aggressive, however, spiral dynamics around the\nliquidity-trap steady state exist, causing global indeterminacy.\n"}
+{"abstract": "  Multiple instances of Zika virus epidemic have been reported around the world\nin the last two decades, turning the related illness into an international\nconcern. In this context the use of mathematical models for epidemics is of\ngreat importance, since they are useful tools to study the underlying outbreak\nnumbers and allow one to test the effectiveness of different strategies used to\ncombat the associated diseases. This work deals with the development and\ncalibration of an epidemic model to describe the 2016 outbreak of Zika virus in\nBrazil. A system of 8 differential equations with 8 parameters is employed to\nmodel the evolution of the infection through two populations. Nominal values\nfor the model parameters are estimated from the literature. An inverse problem\nis formulated and solved by comparing the system response to real data from the\noutbreak. The calibrated results presents realistic parameters and returns\nreasonable descriptions, with the curve shape similar to the outbreak evolution\nand peak value close to the highest number of infected people during 2016.\nConsiderations about the lack of data for some initial conditions are also made\nthrough an analysis over the response behavior according to their change in\nvalue.\n"}
+{"abstract": "  Sparse manifold learning algorithms combine techniques in manifold learning\nand sparse optimization to learn features that could be utilized for downstream\ntasks. The standard setting of compressive sensing can not be immediately\napplied to this setup. Due to the intrinsic geometric structure of data,\ndictionary atoms might be redundant and do not satisfy the restricted isometry\nproperty or coherence condition. In addition, manifold learning emphasizes\nlearning local geometry which is not reflected in a standard $\\ell_1$\nminimization problem. We propose weighted $\\ell_0$ and weighted $\\ell_1$\nmetrics that encourage representation via neighborhood atoms suited for\ndictionary based manifold learning. Assuming that the data is generated from\nDelaunay triangulation, we show the equivalence of weighted $\\ell_1$ and\nweighted $\\ell_0$. We discuss an optimization program that learns the\ndictionaries and sparse coefficients and demonstrate the utility of our\nregularization on synthetic and real datasets.\n"}
+{"abstract": "  As the population grows, the need for a quality level of medical services\ngrows correspondingly, so does the demand for information technology in\nmedicine. The concept of \"Smart Healthcare\" offers many approaches aimed at\nsolving the acute problems faced by modern healthcare. In this paper, we review\nthe main problems of modern healthcare, analyze existing approaches and\ntechnologies in the areas of digital twins, the Internet of Things and mobile\nmedicine, determine their effectiveness in solving the set problems, consider\nthe technologies that are used to monitor and treat patients and propose the\nconcept of the Smart Healthcare platform.\n"}
+{"abstract": "  Visible-Infrared person re-identification (VI-ReID) aims to match\ncross-modality pedestrian images, breaking through the limitation of\nsingle-modality person ReID in dark environment. In order to mitigate the\nimpact of large modality discrepancy, existing works manually design various\ntwo-stream architectures to separately learn modality-specific and\nmodality-sharable representations. Such a manual design routine, however,\nhighly depends on massive experiments and empirical practice, which is time\nconsuming and labor intensive. In this paper, we systematically study the\nmanually designed architectures, and identify that appropriately separating\nBatch Normalization (BN) layers is the key to bring a great boost towards\ncross-modality matching. Based on this observation, the essential objective is\nto find the optimal separation scheme for each BN layer. To this end, we\npropose a novel method, named Cross-Modality Neural Architecture Search\n(CM-NAS). It consists of a BN-oriented search space in which the standard\noptimization can be fulfilled subject to the cross-modality task. Equipped with\nthe searched architecture, our method outperforms state-of-the-art counterparts\nin both two benchmarks, improving the Rank-1/mAP by 6.70%/6.13% on SYSU-MM01\nand by 12.17%/11.23% on RegDB. Code is released at\nhttps://github.com/JDAI-CV/CM-NAS.\n"}
+{"abstract": "  Retinal degenerative diseases lead to the blindness of millions of people\naround the world. In case of age-related macular degeneration (AMD), the\natrophy of retinal pigment epithelium (RPE) precedes neural dystrophy. But as\ncrucial as understanding both healthy and pathological RPE cell physiology is\nfor those diseases, no current technique allows subcellular in vivo or in vitro\nlive observation of this critical cell layer. To fill this gap, we propose\ndynamic full-field OCT (D-FFOCT) as a candidate for live observation of in\nvitro RPE phenotype. In this way, we monitored primary porcine and human stem\ncell-derived RPE cells in stress model conditions by performing scratch assays.\nIn this study, we quantified wound healing parameters on the stressed RPE, and\nobserved different cell phenotypes, displayed by the D-FFOCT signal. In order\nto decipher the subcellular contributions to these dynamic profiles, we\nperformed immunohistochemistry to identify which organelles generate the signal\nand found mitochondria to be the main contributor to D-FFOCT contrast.\nAltogether, D-FFOCT appears to be an innovative method to follow degenerative\ndisease evolution and could be an appreciated method in the future for live\npatient diagnostics and to direct treatment choice.\n"}
+{"abstract": "  Novelty detection in discrete sequences is a challenging task, since\ndeviations from the process generating the normal data are often small or\nintentionally hidden. Novelties can be detected by modeling normal sequences\nand measuring the deviations of a new sequence from the model predictions.\nHowever, in many applications data is generated by several distinct processes\nso that models trained on all the data tend to over-generalize and novelties\nremain undetected. We propose to approach this challenge through decomposition:\nby clustering the data we break down the problem, obtaining simpler modeling\ntask in each cluster which can be modeled more accurately. However, this comes\nat a trade-off, since the amount of training data per cluster is reduced. This\nis a particular problem for discrete sequences where state-of-the-art models\nare data-hungry. The success of this approach thus depends on the quality of\nthe clustering, i.e., whether the individual learning problems are sufficiently\nsimpler than the joint problem. While clustering discrete sequences\nautomatically is a challenging and domain-specific task, it is often easy for\nhuman domain experts, given the right tools. In this paper, we adapt a\nstate-of-the-art visual analytics tool for discrete sequence clustering to\nobtain informed clusters from domain experts and use LSTMs to model each\ncluster individually. Our extensive empirical evaluation indicates that this\ninformed clustering outperforms automatic ones and that our approach\noutperforms state-of-the-art novelty detection methods for discrete sequences\nin three real-world application scenarios. In particular, decomposition\noutperforms a global model despite less training data on each individual\ncluster.\n"}
+{"abstract": "  In a graph G, a dissociation set is a subset of vertices which induces a\nsubgraph with vertex degree at most 1. Finding a dissociation set of maximum\ncardinality in a graph is NP-hard even for bipartite graphs and is called the\nmaximum dissociation set problem. The complexity of maximum dissociation set\nproblem in various subclasses of graphs has been extensively studied in the\nliterature. In this paper, we study the maximum dissociation problem from\ndifferent perspectives and characterize the vertices belonging to all maximum\ndissociation sets, and to no maximum dissociation set of a tree. We present a\nlinear time recognition algorithm which can determine whether a given vertex in\na tree is contained in all (or no) maximum dissociation sets of the tree. Thus\nfor a tree with n vertices, we can find all vertices belonging to all (or no)\nmaximum dissociation sets of the tree in O(n^2) time.\n"}
+{"abstract": "  Generating droplets from a continuous stream of fluid requires precise tuning\nof a device to find optimized control parameter conditions. It is analytically\nintractable to compute the necessary control parameter values of a\ndroplet-generating device that produces optimized droplets. Furthermore, as the\nlength scale of the fluid flow changes, the formation physics and optimized\nconditions that induce flow decomposition into droplets also change. Hence, a\nsingle proportional integral derivative controller is too inflexible to\noptimize devices of different length scales or different control parameters,\nwhile classification machine learning techniques take days to train and require\nmillions of droplet images. Therefore, the question is posed, can a single\nmethod be created that universally optimizes multiple length-scale droplets\nusing only a few data points and is faster than previous approaches? In this\npaper, a Bayesian optimization and computer vision feedback loop is designed to\nquickly and reliably discover the control parameter values that generate\noptimized droplets within different length-scale devices. This method is\ndemonstrated to converge on optimum parameter values using 60 images in only\n2.3 hours, 30x faster than previous approaches. Model implementation is\ndemonstrated for two different length-scale devices: a milliscale inkjet device\nand a micofluidics device.\n"}
+{"abstract": "  We investigate analytic solutions to the problem of finding driving\nstrategies which minimize total tractive energy consumption subject to\nconstraints that ensure safe separation for a fleet of trains travelling on the\nsame track in the same direction and minimize line occupancy time span for the\nfleet. It is known that a fleet of trains can be safely separated if\nintermediate signal times are prescribed by inequality constraints for each\ntrain at each signal location. The optimal driving strategy for each train is\nthen uniquely defined by an optimal driving speed on each actively timed\nsegment. The problem of finding optimal intermediate times has been solved for\na fleet of two trains but for more than two trains the problem rapidly becomes\nintractable as the number of trains and signals increases. The main difficulty\nis in distinguishing between active and inactive constraints. This is\nexacerbated by the fact that an inactive constraint with one set of prescribed\ntimes may become active if the set of prescribed times changes. The curse of\ndimensionality means it is not feasible to check every different combination of\ndesignated active and inactive constraints and to optimize the corresponding\nprescribed times. Nevertheless we can formulate a substantial optimization\nproblem with intermediate signal times prescribed by active equality\nconstraints for all trains in the fleet. We find necessary and sufficient\nconditions on the optimal times and demonstrate viable solution algorithms to\nfind the associated optimal speed profiles for each train. Compared to the\nschedule required for safe separation when each train uses the classic\nsingle-train optimal strategy our method provides a substantial reduction in\nthe line occupancy time span with only a minimal increase in journey costs.\n"}
+{"abstract": "  Extracting detailed 3D information of objects from video data is an important\ngoal for holistic scene understanding. While recent methods have shown\nimpressive results when reconstructing meshes of objects from a single image,\nresults often remain ambiguous as part of the object is unobserved. Moreover,\nexisting image-based datasets for mesh reconstruction don't permit to study\nmodels which integrate temporal information. To alleviate both concerns we\npresent SAIL-VOS 3D: a synthetic video dataset with frame-by-frame mesh\nannotations which extends SAIL-VOS. We also develop first baselines for\nreconstruction of 3D meshes from video data via temporal models. We demonstrate\nefficacy of the proposed baseline on SAIL-VOS 3D and Pix3D, showing that\ntemporal information improves reconstruction quality. Resources and additional\ninformation are available at http://sailvos.web.illinois.edu.\n"}
+{"abstract": "  We proposes a flexible person generation framework called Dressing in Order\n(DiOr), which supports 2D pose transfer, virtual try-on, and several fashion\nediting tasks. The key to DiOr is a novel recurrent generation pipeline to\nsequentially put garments on a person, so that trying on the same garments in\ndifferent orders will result in different looks. Our system can produce\ndressing effects not achievable by existing work, including different\ninteractions of garments (e.g., wearing a top tucked into the bottom or over\nit), as well as layering of multiple garments of the same type (e.g., jacket\nover shirt over t-shirt). DiOr explicitly encodes the shape and texture of each\ngarment, enabling these elements to be edited separately. Joint training on\npose transfer and inpainting helps with detail preservation and coherence of\ngenerated garments. Extensive evaluations show that DiOr outperforms other\nrecent methods like ADGAN in terms of output quality, and handles a wide range\nof editing functions for which there is no direct supervision.\n"}
+{"abstract": "  We present a unified framework for Petri nets and various variants, such as\npre-nets and Kock's whole-grain Petri nets. Our framework is based on a less\nwell-studied notion that we call $\\Sigma$-nets, which allow finer control over\nwhether tokens are treated using the collective or individual token philosophy.\nWe describe three forms of execution semantics in which pre-nets generate\nstrict monoidal categories, $\\Sigma$-nets (including whole-grain Petri nets)\ngenerate symmetric strict monoidal categories, and Petri nets generate\ncommutative monoidal categories, all by left adjoint functors. We also\nconstruct adjunctions relating these categories of nets to each other, in\nparticular showing that all kinds of net can be embedded in the unifying\ncategory of $\\Sigma$-nets, in a way that commutes coherently with their\nexecution semantics.\n"}
+{"abstract": "  The global formality of Dolgushev depends on the choice of a torsion-free\ncovariant derivative. We prove that the globalized formalities with respect to\ntwo different covariant derivatives are homotopic. More explicitly, we derive\nthe statement by proving a more general homotopy equivalence between\n$L_\\infty$-morphisms that are twisted with gauge equivalent Maurer-Cartan\nelements.\n"}
+{"abstract": "  Drive towards improved performance of machine learning models has led to the\ncreation of complex features representing a database of condensed matter\nsystems. The complex features, however, do not offer an intuitive explanation\non which physical attributes do improve the performance. The effect of the\ndatabase on the performance of the trained model is often neglected. In this\nwork we seek to understand in depth the effect that the choice of features and\nthe properties of the database have on a machine learning application. In our\nexperiments, we consider the complex phase space of carbon as a test case, for\nwhich we use a set of simple, human understandable and cheaply computable\nfeatures for the aim of predicting the total energy of the crystal structure.\nOur study shows that (i) the performance of the machine learning model varies\ndepending on the set of features and the database, (ii) is not transferable to\nevery structure in the phase space and (iii) depends on how well structures are\nrepresented in the database.\n"}
+{"abstract": "  In modelling time series data coming from different sources, frequencies can\neasily vary since some variable can be measured at higher frequencies, others,\nat lower frequencies. Given data measured over spatial units and at varying\nfrequencies, we postulated a semiparametric spatiotemporal model. This\noptimizes utilization of information from variables measured at higher\nfrequency by estimating its nonparametric effect on the response through the\nbackfitting algorithm in and additive modelling framework. Simulation studies\nsupport the optimality of the model over simple generalized additive model with\naggregation of high frequency predictors to match the dependent variable\nmeasured at lower frequency. With quarterly corn production and the dependent\nvariable, the model is fitted with predictors coming from remotely-sensed data\n(vegetation and precipitation indices), predictive ability is better compared\nto two benchmark generalized additive models.\n"}
+{"abstract": "  We present a continuous-wave, 810 nm laser with watt-level powers. Our system\nis based on difference-frequency generation of 532 nm and 1550 nm fiber lasers\nin a single pass through periodically poled lithium niobate (PPLN). We measure\nthe broadband spectral noise and residual intensity noise to be compatible with\noff-resonant dipole trapping of ultracold atoms. Given the large bandwidth of\nthe fiber amplifiers, the output can be optimized for a range of wavelengths,\nincluding the strontium clock-magic wavelength of 813 nm. Furthermore, with the\nexploration of more appropriate nonlinear crystals, we believe there is a path\ntoward scaling this proof-of-principle design to many watts of power, and that\nthis approach could provide a robust, rack-mountable trapping-laser for future\nuse in strontium-based optical clocks.\n"}
+{"abstract": "  A Robotic Mobile Fulfillment System is a robotised parts-to-picker system\nthat is particularly well-suited for e-commerce warehousing. One distinguishing\nfeature of this type of warehouse is its high storage modularity. Numerous\nrobots are moving shelves simultaneously, and the shelves can be returned to\nany open location after the picking operation is completed. This work focuses\non the real-time storage allocation problem to minimise the travel time of the\nrobots. An efficient -- but computationally costly -- Monte Carlo Tree Search\nmethod is used offline to generate high-quality experience. This experience can\nbe learned by a neural network with a proper coordinates-based features\nrepresentation. The obtained neural network is used as an action predictor in\nseveral new storage policies, either as-is or in rollout and supervised tree\nsearch strategies. Resulting performance levels depend on the computing time\navailable at a decision step and are consistently better compared to real-time\ndecision rules from the literature.\n"}
+{"abstract": "  We present a comprehensive framework for egocentric interaction recognition\nusing markerless 3D annotations of two hands manipulating objects. To this end,\nwe propose a method to create a unified dataset for egocentric 3D interaction\nrecognition. Our method produces annotations of the 3D pose of two hands and\nthe 6D pose of the manipulated objects, along with their interaction labels for\neach frame. Our dataset, called H2O (2 Hands and Objects), provides\nsynchronized multi-view RGB-D images, interaction labels, object classes,\nground-truth 3D poses for left & right hands, 6D object poses, ground-truth\ncamera poses, object meshes and scene point clouds. To the best of our\nknowledge, this is the first benchmark that enables the study of first-person\nactions with the use of the pose of both left and right hands manipulating\nobjects and presents an unprecedented level of detail for egocentric 3D\ninteraction recognition. We further propose the method to predict interaction\nclasses by estimating the 3D pose of two hands and the 6D pose of the\nmanipulated objects, jointly from RGB images. Our method models both inter- and\nintra-dependencies between both hands and objects by learning the topology of a\ngraph convolutional network that predicts interactions. We show that our method\nfacilitated by this dataset establishes a strong baseline for joint hand-object\npose estimation and achieves state-of-the-art accuracy for first person\ninteraction recognition.\n"}
+{"abstract": "  In this paper we analyse the efficiency, precision, and accuracy of computing\nelastic nucleon-nucleon (NN) scattering amplitudes with the wave-packet\ncontinuum discretisation method (WPCD). This method provides approximate\nscattering solutions at multiple scattering energies simultaneously. We\ntherefore utilise a graphics processing unit (GPU) to explore the benefits of\nthis inherent parallelism. From a theoretical perspective, the WPCD method\npromises a speedup compared to a standard matrix-inversion method. We use the\nchiral NNLO$_{\\rm opt}$ interaction to demonstrate that WPCD enables efficient\ncomputation of NN scattering amplitudes provided one can tolerate an averaged\nmethod error of $~1-5$ mb in the total cross section. For scattering energies\n$\\gtrsim 40$ MeV, in the laboratory frame of reference, we find a much smaller\nmethod error of $\\lesssim 1$ mb. By increasing the number of wave-packets we\ncan further reduce the overall method error. However, the parallel leverage of\nthe WPCD method will be offset by the increased size of the resulting\ndiscretisation mesh. In practice, the GPU implementation is only useful for\nmatrices that fit in the fast on-chip shared memory. Nevertheless, we find that\nWPCD is a promising method for computationally efficient, statistical analyses\nof EFT nuclear interactions, where we can utilise Bayesian inference methods to\nincorporate relevant uncertainties.\n"}
+{"abstract": "  We study the holographic entanglement entropy at the Hawking-Page phase\ntransition point for the BTZ black hole in the gauge/gravity setup and derive a\nfirst law of entanglement thermodynamics for the boundary field theory. We find\nthat when the Hawking-Page phase transition takes place, the Page curve will be\nmodified and then all the information of the black hole will come out at the\ncritical temperature. Furthermore, the influence of the first-order phase\ntransition of black holes on the Page curve is universal. These results mean\nthat black holes information does not have to escape by the radiation, and the\nfirst-order phase transition of black holes can also cause information to\nescape.\n"}
+{"abstract": "  State-of-the-art process discovery methods construct free-choice process\nmodels from event logs. Consequently, the constructed models do not take into\naccount indirect dependencies between events. Whenever the input behaviour is\nnot free-choice, these methods fail to provide a precise model. In this paper,\nwe propose a novel approach for enhancing free-choice process models by adding\nnon-free-choice constructs discovered a-posteriori via region-based techniques.\nThis allows us to benefit from the performance of existing process discovery\nmethods and the accuracy of the employed fundamental synthesis techniques. We\nprove that the proposed approach preserves fitness with respect to the event\nlog while improving the precision when indirect dependencies exist. The\napproach has been implemented and tested on both synthetic and real-life\ndatasets. The results show its effectiveness in repairing models discovered\nfrom event logs.\n"}
+{"abstract": "  Does having visual priors (e.g. the ability to detect objects) facilitate\nlearning to perform vision-based manipulation (e.g. picking up objects)? We\nstudy this problem under the framework of transfer learning, where the model is\nfirst trained on a passive vision task, and adapted to perform an active\nmanipulation task. We find that pre-training on vision tasks significantly\nimproves generalization and sample efficiency for learning to manipulate\nobjects. However, realizing these gains requires careful selection of which\nparts of the model to transfer. Our key insight is that outputs of standard\nvision models highly correlate with affordance maps commonly used in\nmanipulation. Therefore, we explore directly transferring model parameters from\nvision networks to affordance prediction networks, and show that this can\nresult in successful zero-shot adaptation, where a robot can pick up certain\nobjects with zero robotic experience. With just a small amount of robotic\nexperience, we can further fine-tune the affordance model to achieve better\nresults. With just 10 minutes of suction experience or 1 hour of grasping\nexperience, our method achieves ~80% success rate at picking up novel objects.\n"}
+{"abstract": "  Properties of plasmonic materials are associated with surface plasmons - the\nelectromagnetic excitations coupled to coherent electron charge density\noscillations on a metal/dielectric interface. Although decay of such\noscillations cannot be avoided, there are prospects for controlling plasmon\ndamping dynamics. In spherical metal nanoparticles (MNPs) the basic properties\nof Localized Surface Plasmons (LSPs) can be controlled with their radius. The\npresent paper handles the link between the size-dependent description of LSP\nproperties derived from the dispersion relation based on Maxwell's equations\nand the quantum picture in which MNPs are treated as \"quasi-particles\". Such\npicture, based on the reduced density-matrix of quantum open systems ruled by\nthe master equation in the Lindblad form, enables to distinguish between\ndamping processes of populations and coherences of multipolar plasmon\noscillatory states and to establish the intrinsic relations between the rates\nof these processes, independently of the size of MNP. The impact of the\nradiative and the nonradiative energy dissipation channels is discussed.\n"}
+{"abstract": "  The NMR measurements performed on a single orthorhombic crystal of\nsuperconducting ferromagnet UCoGe (Y.Ihara et al, Phys. Rev. Lett. v.105,\n206403 (2010)) demonstrate strongly anisotropic magnetic properties of this\nmaterial. The presented calculations allow to establish the dependence of\nlongitudinal spin-lattice relaxation rate from temperature and magnetic field.\nThe value 1/T_1T in field perpendicular to spontaneous magnetisation directed\nalong c-axis has maximum in vicinity of Curie temperature whereas it does not\nreveal similar behaviour in field parallel to the direction of spontaneous\nmagnetisation. Also there was shown that the longitudinal spin-lattice\nrelaxation rate is strongly field dependent when the field directed in\nb-crystallographic direction but field independent if magnetic field is\noriented along a-axis.\n"}
+{"abstract": "  Cubic boron nitride (cBN) is an ultra-wide bandgap, super-hard material with\npotential for extreme-temperature and -pressure applications. A\nproof-of-principle p-n junction using cBN was demonstrated almost three decades\nago. However, to date, there remain two unresolved challenges that prevent its\npractical use in technologies: (i) it is difficult to produce high-quality cBN\nfilms and (ii) it is difficult to controllably n- and p-dope its matrix. In\nthis theoretical work, we study the reasons for doping-limitations, which is an\nacute issue in realizing cBN-based electronics. In particular, we find that\ndifferent unintentionally-present intrinsic and extrinsic defects act as\ncompensating defects and/or introduce trap states. In turn, the presence of\nthese defects and their complexes affect the incorporation, as well as the\nelectronic structure properties, of shallow dopants [silicon and beryllium],\nwhich are introduced intentionally to n- and p-dope cBN. Our analysis of\ndoping-limitations provides a path towards finding solutions for controllably\nn- and p-doping cBN.\n"}
+{"abstract": "  As is well known, pulsars are extremely stable rotators. However, although\nslowly, they spin down thanks to brake mechanisms, which are in fact still\nsubject of intense investigation in the literature. Since pulsars are usually\nmodelled as highly magnetized neutron stars that emit beams of electromagnetic\nradiation out of their magnetic poles, it is reasonable to consider that the\nspindown has to do with a magnetic brake. Although an interesting and simple\nidea, a pure magnetic brake is not able to adequately account for the spindown\nrate. Thus, many alternative spindown mechanisms appear in the literature,\namong them the pulsar wind model, where a wind of particles coming from the\npulsar itself can carry part of its rotational kinetic energy. Such a spindown\nmechanism depends critically on three parameters, namely, the dipole magnetic\nfield $B$, the angle between the magnetic and rotation axes $(\\phi)$, and the\ndensity of primary particles $(\\zeta)$ of the pulsar's magnetosphere.\nDifferently from a series of articles in this subject, we consider for the\nfirst time in the literature a statistical modelling which includes a\ncombination of a dipole magnetic and wind brakes. As a result, we are able to\nconstrain the above referred parameters in particular for Crab and Vela\npulsars.\n"}
+{"abstract": "  Van der Waals materials such as thin films of transition-metal\ndichalcogenides (TMDCs) manifest strongly bound exciton states in the visible\nspectrum at ambient conditions that provide an ideal platform for\nexciton-photon couplings. Utilizing semiconducting TMDCs in the form of\nmultilayer structure combined with metals can increase significantly the\nlight-matter interaction. In this way, the interaction between excitons and\nsurface-plasmon polaritons emerge as a platform for transferring the\nelectromagnetic energy at confined modal volumes. Here, we theoretically\ninvestigate how moving electrons can be used as a probe of hybrid\nexciton-plasmon polaritons of gold-WSe2 multilayers within the context of\nelectron energy-loss spectroscopy (EELS) and cathodoluminescence (CL)\nspectroscopy. Interestingly, and in contrast to WSe2 slab waveguides where\nquasi-propagating photonic modes interact with only exciton A, in gold-WSe2\nmultilayer, exciton A and exciton B can both strongly interact with surface\nplasmon polaritons. Hence, we observe CL emission suppression at excitonic or\nplasmonic peaks, which reveals the energy transfer between excitons and\nplasmons in the form of nonradiating guided waves. Our work provides a\nsystematic study for deeper understanding of the effect of the configuration\nand the thickness of layers on the photonic and plasmonic modes and hence on\nthe strength of the coupling between excitons and surface-plasmon polaritons.\nOur findings pay the way for designing efficient photodetectors, sensitive\nsensors, and light emitting devices based on metal/semiconducting hybrid\nmaterials.\n"}
+{"abstract": "  For the paradigm of the quantum double-slit experiment (DSE), we apply\nOckham's Razor to interpret quantum observations and to evaluate terminology\noften associated with wave-particle duality. One finds that the Correspondence\nPrinciple (CP), combined with classical wave DSEs, e.g., Young [1804], is\nsufficient to predict the observed quantum particle and wave phenomena. The\nempirical approach of Ockham infers that observed individual quanta are whole\nparticles only; an individual quantum has no observed wavelike character. The\nobserved, so-called wave nature of quanta emerges only in the limit of large\nnumbers of particle observation events. That is, the \"measurement problem\" is\nno problem at all; \"particle\" and \"wave\" derive from separate and different\naspects of the observations. Such artificial constructs as wave function\ncollapse are irrelevant to the observation of individual quanta, each of which\nis a \"particle\" from emission to measurement. The histogram of detected events\nis a collective property identical to a classical interference pattern in the\nlimit of large numbers, as the CP decrees. For a specific individual quantum,\nthe mystery of what happens in the region between emission and detection has so\nfar been inaccessible to direct observation in coincidence with detection of\nthe same quantum on the DSE screen and is therefore inessential to interpreting\nthe extant database. To analyze actual data and predict future DSEs, therefore,\nthe Correspondence Principle is sufficient. True scientific progress can only\nfollow from new, relevant experiments and not via mathematics or imagination.\n"}
+{"abstract": "  The Fermi-LAT DR1 and DR2 4FGL catalogues feature more than 5000 gamma-ray\nsources of which about one fourth are not associated with already known\nobjects, and approximately one third are associated with blazars of uncertain\nnature. We perform a three-category classification of the 4FGL DR1 and DR2\nsources independently, using an ensemble of Artificial Neural Networks (ANNs)\nto characterise them based on the likelihood of being a Pulsar (PSR), a BL Lac\ntype blazar (BLL) or a Flat Spectrum Radio Quasar (FSRQ). We identify candidate\nPSR, BLL and FSRQ among the unassociated sources with approximate equipartition\namong the three categories and select ten classification outliers as\npotentially interesting for follow up studies.\n"}
+{"abstract": "  Let $\\mathcal{A}$ be a connected cochain DG algebra such that its underlying\ngraded algebra $\\mathcal{A}^{\\#}$ is the graded skew polynomial algebra\n$$k\\langle x_1,x_2, x_3\\rangle/\\left(\\begin{array}{ccc} x_1x_2+x_2x_1\\\\\nx_2x_3+x_3x_2\\\\ x_3x_1+x_1x_3 \\end {array}\\right), |x_1|=|x_2|=|x_3|=1.$$ From\n\\cite{MWZ} or \\cite{MWYZ}, one sees that the differential\n$\\partial_{\\mathcal{A}}$ is determined by \\begin{align*} \\left(\n\\begin{array}{c} \\partial_{\\mathcal{A}}(x_1) \\partial_{\\mathcal{A}}(x_2)\n\\partial_{\\mathcal{A}}(x_3) \\end{array} \\right)=M\\left( \\begin{array}{c} x_1^2\nx_2^2 x_3^2 \\end{array} \\right), \\end{align*} for some $M\\in M_3(k)$. For the\ncase $1\\le r(M)\\le 3$, we compute $H(\\mathcal{A})$ case by case. The\ncomputational results in this paper give substantial support for \\cite{MWZ},\nwhere the various homological properties of such DG algebras are systematically\nstudied. We find some examples, which indicate that the cohomology graded\nalgebra of a Koszul Calabi-Yau DG algebra may be not left (right) Gorenstein.\n"}
+{"abstract": "  Given an integer $g \\geq 0$ and a weight vector $w \\in \\mathbb{Q}^n \\cap (0,\n1]^n$ satisfying $2g - 2 + \\sum w_i > 0$, let $\\Delta_{g, w}$ denote the moduli\nspace of $n$-marked, $w$-stable tropical curves of genus $g$ and volume one. We\ncalculate the automorphism group $\\mathrm{Aut}(\\Delta_{g, w})$ for $g \\geq 1$\nand arbitrary $w$, and we calculate the group $\\mathrm{Aut}(\\Delta_{0, w})$\nwhen $w$ is heavy/light. In both of these cases, we show that\n$\\mathrm{Aut}(\\Delta_{g, w}) \\cong \\mathrm{Aut}(K_w)$, where $K_w$ is the\nabstract simplicial complex on $\\{1, \\ldots, n\\}$ whose faces are subsets with\n$w$-weight at most $1$. We show that these groups are precisely the finite\ndirect products of symmetric groups. The space $\\Delta_{g, w}$ may also be\nidentified with the dual complex of the divisor of singular curves in the\nalgebraic Hassett space $\\overline{\\mathcal{M}}_{g, w}$. Following the work of\nMassarenti and Mella on the biregular automorphism group\n$\\mathrm{Aut}(\\overline{\\mathcal{M}}_{g, w})$, we show that\n$\\mathrm{Aut}(\\Delta_{g, w})$ is naturally identified with the subgroup of\nautomorphisms which preserve the divisor of singular curves.\n"}
+{"abstract": "  We present the latest results of an ongoing multiplicity survey of exoplanet\nhosts, which was initiated at the Astrophysical Institute and University\nObservatory Jena, using data from the second data release of the ESA-Gaia\nmission. In this study the multiplicity of 289 targets was investigated, all\nlocated within a distance of about 500 pc from the Sun. In total, 41 binary,\nand 5 hierarchical triple star systems with exoplanets were detected in the\ncourse of this project, yielding a multiplicity rate of the exoplanet hosts of\nabout 16 %. A total of 61 companions (47 stars, a white dwarf, and 13 brown\ndwarfs) were detected around the targets, whose equidistance and common proper\nmotion with the exoplanet hosts were proven with their precise Gaia DR2\nastrometry, which also agrees with the gravitational stability of most of these\nsystems. The detected companions exhibit masses from about 0.016 up to 1.66\nM$_\\odot$ and projected separations in the range between about 52 and 9555 au.\n"}
+{"abstract": "  Popular wisdom amongst theoretical physicists says that spacetime is probably\nnot a fundamental entity of nature, but rather an emergent one. While most\narguments to support that view arise from very speculative ideas (usually\nrelated to attempts to understand gravity at the quantum level), the argument\ncan also be made by only invoking standard physics. In this manuscript, I shall\nargue that a novel theory of relativity -- which simply follows the three basic\nprinciples that Einstein invoked as the building blocks of his theory of\ngeneral relativity -- might change the deal.\n"}
+{"abstract": "  The propagation of dust-ion-acoustic shock waves (DIASHWs) in a\nthree-component magnetized plasma having inertialess super-thermal electrons,\ninertial warm positive ions and negative dust grains has been investigated. A\nBurgers' equation is derived by employing the reductive perturbation method.\nUnder consideration of inertial warm positive ions and negative dust grains,\nboth positive and negative shock structures are numerically observed in the\npresence of super-thermal electrons. The effects of oblique angle ($\\delta$),\nspectral index ($\\kappa$), kinematic viscosity ($\\eta$), number density and\ncharge state of the plasma species on the formation of the DIASHWs are\nexamined. It is found that the positive and negative shock wave potentials\nincrease with the oblique angle. It is also observed that the magnitude of the\namplitude of positive and negative shock waves is not affected by the variation\nof the kinematic viscosity of plasma species but the steepness of the positive\nand negative shock waves decreases with kinematic viscosity of plasma species.\nThe implications of our findings in space and laboratory plasmas are briefly\ndiscussed.\n"}
+{"abstract": "  One of the most intriguing problems of light transport in solids is the\nlocalization that has been observed in various disordered photonic\nstructures1-11. The light localization in defect-free icosahedral quasicrystals\nhas recently been predicted theoretically without experimental verification10.\nHere we report on the fabrication of submicron-size dielectric icosahedral\nquasicrystals and demonstrate the results of detailed studies of the photonic\nproperties of these structures. Here, we present the first direct experimental\nobservation of intrinsic light localization in defect-free quasicrystals. This\nresult was obtained in time-resolved measurements at different laser\nwavelengths in the visible. We linked localization with the aperiodicity of the\nicosahedral structure, which led to uncompensated scattering of light from an\nindividual structural element over the entire sphere, providing multiple\nscattering inside the sample and, as a result, the intrinsic localization of\nlight.\n"}
+{"abstract": "  The numerical solution of partial differential equations by discretization\ntechniques is ubiquitous in computational physics. In this work we benchmark\nthis approach in the quantum realm by solving the heat equation for a square\nplate subject to fixed temperatures at the edges and random heat sources and\nsinks within the domain. The hybrid classical-quantum approach consists in the\nsolution on a quantum computer of the coupled linear system of equations that\nresult from the discretization step. Owing to the limitations in the number of\nqubits and their connectivity, we use the Gauss-Seidel method to divide the\nfull system of linear equations into subsystems, which are solved iteratively\nin block fashion. Each of the linear subsystems were solved using 2000Q and\nAdvantage quantum computers developed by D-Wave Systems Inc. By comparing\nclassical numerical and quantum solutions, we observe that the errors and chain\nbreak fraction are, on average, greater on the 2000Q system. Unlike the\nclassical Gauss-Seidel method, the errors of the quantum solutions level off\nafter a few iterations of our algorithm. This is partly a result of the span of\nthe real number line available from the mapping of the chosen size of the set\nof qubit states. We verified this by using techniques to progressively shrink\nthe range mapped by the set of qubit states at each iteration (increasing\nfloating-point accuracy). As a result, no leveling off is observed. However, an\nincrease in qubits does not translate to an overall lower error. This is\nbelieved to be indicative of the increasing length of chains required for the\nmapping to real numbers and the ensuing limitations of hardware.\n"}
+{"abstract": "  We establish a sufficient condition for a continuous map, acting on a compact\nmetric space, to have a Baire residual set of points exhibiting historic\nbehavior (also known as irregular points). This criterion applies, for\ninstance, to a minimal and non-uniquely ergodic map; to maps preserving two\ndistinct probability measures with full support; to non-trivial homoclinic\nclasses; to some non-uniformly expanding maps; and to partially hyperbolic\ndiffeomorphisms with two periodic points whose stable manifolds are dense,\nincluding Ma\\~n\\'e and Shub examples of robustly transitive diffeomorphisms.\nThis way, our unifying approach recovers a collection of known deep theorems on\nthe genericity of the irregular set, for both additive and sub-additive\npotentials, and also provides a number of new applications.\n"}
+{"abstract": "  Machine learning models are often deployed in different settings than they\nwere trained and validated on, posing a challenge to practitioners who wish to\npredict how well the deployed model will perform on a target distribution. If\nan unlabeled sample from the target distribution is available, along with a\nlabeled sample from a possibly different source distribution, standard\napproaches such as importance weighting can be applied to estimate performance\non the target. However, importance weighting struggles when the source and\ntarget distributions have non-overlapping support or are high-dimensional.\nTaking inspiration from fields such as epidemiology and polling, we develop\nMandoline, a new evaluation framework that mitigates these issues. Our key\ninsight is that practitioners may have prior knowledge about the ways in which\nthe distribution shifts, which we can use to better guide the importance\nweighting procedure. Specifically, users write simple \"slicing functions\" -\nnoisy, potentially correlated binary functions intended to capture possible\naxes of distribution shift - to compute reweighted performance estimates. We\nfurther describe a density ratio estimation framework for the slices and show\nhow its estimation error scales with slice quality and dataset size. Empirical\nvalidation on NLP and vision tasks shows that \\name can estimate performance on\nthe target distribution up to $3\\times$ more accurately compared to standard\nbaselines.\n"}
+{"abstract": "  Blazars are a subclass of active galactic nuclei (AGN) that have a\nrelativistic jet with a small viewing angle towards the observer. Recent\nresults based on hadronic scenarios have motivated an ongoing discussion of how\na blazar can produce high energy neutrinos during a flaring state and which\nscenario can successfully describe the observed gamma-ray behaviour. Markarian\n421 is one of the closest and brightest objects in the extragalactic gamma-ray\nsky and showed flaring activity over a 14-day period in March 2010. In this\nwork, we describe the performed analysis of \\textit{Fermi}-LAT data from the\nsource focused on the MeV range (100 MeV - 1 GeV), and study the possibility of\na contribution coming from the $p\\gamma$ interactions between protons and MeV\nSSC target photons to fit the very high energy (VHE) gamma-ray emission. The\nfit results were compared with two leptonic models (one-zone and two-zone)\nusing the Akaike Information Criteria (AIC) test, which evaluates\ngoodness-of-fit alongside the simplicity of the model. In all cases the\nphotohadronic model was favoured as a better fit description in comparison to\nthe one-zone leptonic model, and with respect to the two-zone model in the\nmajority of cases. Our results show the potential of a photohadronic\ncontribution to a lepto-hadronic origin of gamma-ray flux of blazars. Future\ngamma-ray observations above tens of TeV and below 100 MeV in energy will be\ncrucial to test and discriminate between models.\n"}
+{"abstract": "  Nematode worms are one of most abundant metazoan groups on the earth,\noccupying diverse ecological niches. Accurate recognition or identification of\nnematodes are of great importance for pest control, soil ecology,\nbio-geography, habitat conservation and against climate changes. Computer\nvision and image processing have witnessed a few successes in species\nrecognition of nematodes; however, it is still in great demand. In this paper,\nwe identify two main bottlenecks: (1) the lack of a publicly available imaging\ndataset for diverse species of nematodes (especially the species only found in\nnatural environment) which requires considerable human resources in field work\nand experts in taxonomy, and (2) the lack of a standard benchmark of\nstate-of-the-art deep learning techniques on this dataset which demands the\ndiscipline background in computer science. With these in mind, we propose an\nimage dataset consisting of diverse nematodes (both laboratory cultured and\nnaturally isolated), which, to our knowledge, is the first time in the\ncommunity. We further set up a species recognition benchmark by employing\nstate-of-the-art deep learning networks on this dataset. We discuss the\nexperimental results, compare the recognition accuracy of different networks,\nand show the challenges of our dataset. We make our dataset publicly available\nat: https://github.com/xuequanlu/I-Nema\n"}
+{"abstract": "  Deep learning typically requires large data sets and much compute power for\neach new problem that is learned. Meta-learning can be used to learn a good\nprior that facilitates quick learning, thereby relaxing these requirements so\nthat new tasks can be learned quicker; two popular approaches are MAML and the\nmeta-learner LSTM. In this work, we compare the two and formally show that the\nmeta-learner LSTM subsumes MAML. Combining this insight with recent empirical\nfindings, we construct a new algorithm (dubbed TURTLE) which is simpler than\nthe meta-learner LSTM yet more expressive than MAML. TURTLE outperforms both\ntechniques at few-shot sine wave regression and image classification on\nminiImageNet and CUB without any additional hyperparameter tuning, at a\ncomputational cost that is comparable with second-order MAML. The key to\nTURTLE's success lies in the use of second-order gradients, which also\nsignificantly increases the performance of the meta-learner LSTM by 1-6%\naccuracy.\n"}
+{"abstract": "  Omnichannel retailing, a new form of distribution system, seamlessly\nintegrates the Internet and physical stores. This study considers the pricing\nand fulfillment strategies of a retailer that has two sales channels: online\nand one physical store. The retailer offers consumers three purchasing options:\ndelivery from the fulfillment center, buy online and pick up in-store (BOPS),\nand purchasing at the store. Consumers choose one of these options to maximize\ntheir utility, dividing them into several segments. Given the retailer can\ninduce consumers to the profitable segment by adjusting the online and store\nprices, our analysis shows that it has three optimal strategies: (1) The\nretailer excludes consumers far from the physical store from the market and\nlets the others choose BOPS or purchasing at the store. (2) It lets consumers\nfar from the physical store choose delivery from the fulfillment center and the\nothers choose BOPS or purchasing at the store. (3) It lets all consumers choose\ndelivery from the fulfillment center. Finally, we present simple dynamic\nsimulations that considers how the retailer's optimal strategy changes as\nconsumers' subjective probability of believing the product is in stock\ndecreases. The results show that the retailer should offer BOPS in later\nperiods of the selling season to maximize its profit as the subjective\nprobability decreases.\n"}
+{"abstract": "  We prove convergence of renormalized correlations of primary fields, i. e.,\nspins, disorders, fermions and energy densities, in the scaling limit of the\ncritical Ising model in arbitrary finitely connected domains, with fixed (plus\nor minus) or free boundary conditions, or mixture thereof. We describe the\nlimits of correlations in terms of solutions of Riemann boundary value\nproblems, and prove their conformal covariance. Moreover, we prove fusion\nrules, or operator product expansions, which describe asymptotics of the\nscaling limits of the correlations as some of the points collide together. We\ngive explicit formulae for correlations in the case of simply-connected and\ndoubly-connected domains. Our presentation is self-contained, and the proofs\nare simplified as compared to the previous work where particular cases are\ntreated.\n"}
+{"abstract": "  Question answering (QA) is an important use case on voice assistants. A\npopular approach to QA is extractive reading comprehension (RC) which finds an\nanswer span in a text passage. However, extractive answers are often unnatural\nin a conversational context which results in suboptimal user experience. In\nthis work, we investigate conversational answer generation for QA. We propose\nAnswerBART, an end-to-end generative RC model which combines answer generation\nfrom multiple passages with passage ranking and answerability. Moreover, a\nhurdle in applying generative RC are hallucinations where the answer is\nfactually inconsistent with the passage text. We leverage recent work from\nsummarization to evaluate factuality. Experiments show that AnswerBART\nsignificantly improves over previous best published results on MS MARCO 2.1\nNLGEN by 2.5 ROUGE-L and NarrativeQA by 9.4 ROUGE-L.\n"}
+{"abstract": "  In this paper, we analyze the interplay between the use of offensive language\nand mental health. We acquired publicly available datasets created for\noffensive language identification and depression detection and we train\ncomputational models to compare the use of offensive language in social media\nposts written by groups of individuals with and without self-reported\ndepression diagnosis. We also look at samples written by groups of individuals\nwhose posts show signs of depression according to recent related studies. Our\nanalysis indicates that offensive language is more frequently used in the\nsamples written by individuals with self-reported depression as well as\nindividuals showing signs of depression. The results discussed here open new\navenues in research in politeness/offensiveness and mental health.\n"}
+{"abstract": "  Letter to the Editor about how the diet soda and Mentos reaction can be\nproduced by the direct immersion of a plastic soda bottle in liquid nitrogen.\n"}
+{"abstract": "  We introduce a framework to justify hydrodynamic limits of the\nVlasov-Navier-Stokes system. We specifically study high friction regimes, which\ntake into account the fact that particles of the dispersed phase are light\n(resp. small) compared to the fluid part, and lead to the derivation of\nTransport-Navier-Stokes (resp. Inhomogeneous Navier-Stokes) systems.\n"}
+{"abstract": "  We present a procedure for averaging one-parameter random unitary groups and\nrandom self-adjoint groups. Central to this is a generalization of the notion\nof weak convergence of a sequence of measures and the corresponding\ngeneralization of the concept of convergence in distribution. The convergence\nis established in determination of the sequence of compositions of independent\nrandom transformations. When sequences of compositions of independent random\ntransformations of the shift by the Euclidean vector in space, the results\nobtained coincide with the central limit theorem for the sums independent\nrandom vectors. The results are applied to the dynamics of quantum systems\narising random quantization of the classical Hamiltonian system.\n"}
+{"abstract": "  Image quality is the basis of image communication and understanding tasks.\nDue to the blur and noise effects caused by imaging, transmission and other\nprocesses, the image quality is degraded. Blind image restoration is widely\nused to improve image quality, where the main goal is to faithfully estimate\nthe blur kernel and the latent sharp image. In this study, based on\nexperimental observation and research, an adaptively sparse regularized\nminimization method is originally proposed. The high-order gradients combine\nwith low-order ones to form a hybrid regularization term, and an adaptive\noperator derived from the image entropy is introduced to maintain a good\nconvergence. Extensive experiments were conducted on different blur kernels and\nimages. Compared with existing state-of-the-art blind deblurring methods, our\nmethod demonstrates superiority on the recovery accuracy.\n"}
+{"abstract": "  Machine learning is a powerful method for modeling in different fields such\nas education. Its capability to accurately predict students' success makes it\nan ideal tool for decision-making tasks related to higher education. The\naccuracy of machine learning models depends on selecting the proper\nhyper-parameters. However, it is not an easy task because it requires time and\nexpertise to tune the hyper-parameters to fit the machine learning model. In\nthis paper, we examine the effectiveness of automated hyper-parameter tuning\ntechniques to the realm of students' success. Therefore, we develop two\nautomated Hyper-Parameter Optimization methods, namely grid search and random\nsearch, to assess and improve a previous study's performance. The experiment\nresults show that applying random search and grid search on machine learning\nalgorithms improves accuracy. We empirically show automated methods'\nsuperiority on real-world educational data (MIDFIELD) for tuning HPs of\nconventional machine learning classifiers. This work emphasizes the\neffectiveness of automated hyper-parameter optimization while applying machine\nlearning in the education field to aid faculties, directors', or non-expert\nusers' decisions to improve students' success.\n"}
+{"abstract": "  We consider the inhomogeneous biharmonic nonlinear Schr\\\"odinger equation $$\ni u_t +\\Delta^2 u+\\lambda|x|^{-b}|u|^\\alpha u = 0, $$ where $\\lambda=\\pm 1$ and\n$\\alpha$, $b>0$. In the subctritical case, we improve the global well-posedness\nresult obtained in \\cite{GUZPAS} for dimensions $N=5,6,7$ in the Sobolev space\n$H^2(\\mathbb{R}^N)$. The fundamental tools to establish our results are the\nstandard Strichartz estimates related to the linear problem and the\nHardy-Littlewood inequality. Results concerning the energy-critical case, that\nis, $\\alpha=\\frac{8-2b}{N-4}$ are also reported. More precisely, we show\nwell-posedness and a stability result with initial data in the critical space\n$\\dot{H}^2$.\n"}
+{"abstract": "  As a generalization of the traditional connectivity, the g-component edge\nconnectivity c{\\lambda}g(G) of a non-complete graph G is the minimum number of\nedges to be deleted from the graph G such that the resulting graph has at least\ng components. Hypercube-like networks (HL-networks for short) are obtained by\nmanipulating some pairs of edges in hypercubes, which contain several famous\ninterconnection networks such as twisted cubes, Mobius cubes, crossed cubes,\nlocally twisted cubes. In this paper, we determine the (g + 1)-component edge\nconnectivity of the n-dimensional HL-networks.\n"}
+{"abstract": "  Denoising diffusion probabilistic models (DDPMs) (Ho et al. 2020) have shown\nimpressive results on image and waveform generation in continuous state spaces.\nHere, we introduce Discrete Denoising Diffusion Probabilistic Models (D3PMs),\ndiffusion-like generative models for discrete data that generalize the\nmultinomial diffusion model of Hoogeboom et al. 2021, by going beyond\ncorruption processes with uniform transition probabilities. This includes\ncorruption with transition matrices that mimic Gaussian kernels in continuous\nspace, matrices based on nearest neighbors in embedding space, and matrices\nthat introduce absorbing states. The third allows us to draw a connection\nbetween diffusion models and autoregressive and mask-based generative models.\nWe show that the choice of transition matrix is an important design decision\nthat leads to improved results in image and text domains. We also introduce a\nnew loss function that combines the variational lower bound with an auxiliary\ncross entropy loss. For text, this model class achieves strong results on\ncharacter-level text generation while scaling to large vocabularies on LM1B. On\nthe image dataset CIFAR-10, our models approach the sample quality and exceed\nthe log-likelihood of the continuous-space DDPM model.\n"}
+{"abstract": "  Magnetoelastic measurements in the piezomagnetic antiferromagnet UO$_{2}$\nwere performed via the fiber Bragg grating method in magnetic fields up to\n$150\\,\\mathrm{T}$ generated by a single-turn coil setup. We show that in short\ntimescales, order of a few micro seconds, pulsed-magnetic fields excite\nmechanical resonances at temperatures ranging from $10\\,\\mathrm{K}$ to\n$300\\,\\mathrm{K}$, in the paramagnetic as well as within the robust\nantiferromagnetic state of the material. These resonances, which are barely\nattenuated within the 100 ms observations, are attributed to the strong\nmagnetoelastic coupling in UO$_{2}$ combined with the high crystallographic\nquality of the single crystal samples. They compare well with mechanical\nresonances obtained by a resonant ultrasound technique and superimpose on the\nknown non-monotonic magnetostriction background. A clear phase-shift of $\\pi$\nin the lattice oscillations is, unexpectedly, observed in the antiferromagnetic\nstate when the magnetic field overcomes the piezomagnetic switch-field $H_c\n\\simeq -18\\,\\mathrm{T}$. We further present simulations and a theoretical\nargument to explain the observed phenomena.\n"}
+{"abstract": "  Difference schemes are considered for dynamical systems $ \\dot x = f (x) $\nwith a quadratic right-hand side, which have $t$-symmetry and are reversible.\nReversibility is interpreted in the sense that the Cremona transformation is\nperformed at each step in the calculations using a difference scheme. The\ninheritance of periodicity and the Painlev\\'e property by the approximate\nsolution is investigated. In the computer algebra system Sage, such values are\nfound for the step $ \\Delta t $, for which the approximate solution is a\nsequence of points with the period $ n \\in \\mathbb N $. Examples are given and\nhypotheses about the structure of the sets of initial data generating sequences\nwith the period $ n $ are formulated.\n"}
+{"abstract": "  We apply generative adversarial convolutional neural networks to the problem\nof style transfer to underdrawings and ghost-images in x-rays of fine art\npaintings with a special focus on enhancing their spatial resolution. We build\nupon a neural architecture developed for the related problem of synthesizing\nhigh-resolution photo-realistic image from semantic label maps. Our neural\narchitecture achieves high resolution through a hierarchy of generators and\ndiscriminator sub-networks, working throughout a range of spatial resolutions.\nThis coarse-to-fine generator architecture can increase the effective\nresolution by a factor of eight in each spatial direction, or an overall\nincrease in number of pixels by a factor of 64. We also show that even just a\nfew examples of human-generated image segmentations can greatly improve --\nqualitatively and quantitatively -- the generated images. We demonstrate our\nmethod on works such as Leonardo's Madonna of the carnation and the\nunderdrawing in his Virgin of the rocks, which pose several special problems in\nstyle transfer, including the paucity of representative works from which to\nlearn and transfer style information.\n"}
+{"abstract": "  In this work, we develop a pair of rate-diverse encoder and decoder for a\ntwo-user Gaussian multiple access channel (GMAC). The proposed scheme enables\nthe users to transmit with the same codeword length but different coding rates\nunder diverse user channel conditions. First, we propose the row-combining (RC)\nmethod and row-extending (RE) method to design practical low-density\nparity-check (LDPC) channel codes for rate-diverse GMAC. Second, we develop an\niterative rate-diverse joint user messages decoding (RDJD) algorithm for GMAC,\nwhere all user messages are decoded with a single parity-check matrix. In\ncontrast to the conventional network-coded multiple access (NCMA) and\ncompute-forward multiple access (CFMA) schemes that first recover a linear\ncombination of the transmitted codewords and then decode both user messages,\nthis work can decode both the user messages simultaneously. Extrinsic\ninformation transfer (EXIT) chart analysis and simulation results indicate that\nRDJD can achieve gains up to 1.0 dB over NCMA and CFMA in the two-user GMAC. In\nparticular, we show that there exists an optimal rate allocation for the two\nusers to achieve the best decoding performance given the channel conditions and\nsum rate.\n"}
+{"abstract": "  Finding the shortest path distance between an arbitrary pair of vertices is a\nfundamental problem in graph theory. A tremendous amount of research has been\nsuccessfully attempted on this problem, most of which is limited to static\ngraphs. Due to the dynamic nature of real-world networks, there is a pressing\nneed to address this problem for dynamic networks undergoing changes. In this\npaper, we propose an \\emph{online incremental} method to efficiently answer\ndistance queries over very large dynamic graphs. Our proposed method\nincorporates incremental update operations, i.e. edge and vertex additions,\ninto a highly scalable framework of answering distance queries. We\ntheoretically prove the correctness of our method and the preservation of\nlabelling minimality. We have also conducted extensive experiments on 12 large\nreal-world networks to empirically verify the efficiency, scalability, and\nrobustness of our method.\n"}
+{"abstract": "  The sense of touch is fundamental in several manipulation tasks, but rarely\nused in robot manipulation. In this work we tackle the problem of learning rich\ntouch features from cross-modal self-supervision. We evaluate them identifying\nobjects and their properties in a few-shot classification setting. Two new\ndatasets are introduced using a simulated anthropomorphic robotic hand equipped\nwith tactile sensors on both synthetic and daily life objects. Several\nself-supervised learning methods are benchmarked on these datasets, by\nevaluating few-shot classification on unseen objects and poses. Our experiments\nindicate that cross-modal self-supervision effectively improves touch\nrepresentation, and in turn has great potential to enhance robot manipulation\nskills.\n"}
+{"abstract": "  This paper evaluates and assesses the risk associated with capital allocation\nin cryptocurrencies (CCs). In this regard, we take a basket of 27 CCs and the\nCC index EWCI$^-$ into account. After considering a series of statistical tests\nwe find the stable distribution (SDI) to be the most appropriate to model the\nbody of CCs returns. However, as we find the SDI to possess less favorable\nproperties in the tail area for high quantiles, the generalized Pareto\ndistribution is adapted for a more precise risk assessment. We use a\ncombination of both distributions to calculate the Value at Risk and the\nConditional Value at Risk, indicating two subgroups of CCs with differing risk\ncharacteristics.\n"}
+{"abstract": "  In statistical network analysis it is common to observe so called interaction\ndata. Such data is characterized by the actors who form the vertices of a\nnetwork. These are able to interact with each other along the edges of the\nnetwork. One usually assumes that the edges in the network are randomly formed\nand dissolved over the observation horizon. In addition covariates are observed\nand the interest is to model the impact of the covariates on the interactions.\nIn this paper we develop a framework to test if a non-parametric form of the\nbaseline intensity allows for more flexibility than a baseline which is\nparametrically dependent on system-wide covariates (i.e. covariates which take\nthe same value for all individuals, e.g. time). This allows to test if certain\nseasonality effects can be explained by simple covariates like the time. The\nprocedure is applied to modeling the baseline intensity in a bike-sharing\nnetwork by using weather and time information.\n"}
+{"abstract": "  We have performed a quantum mechanical analysis of travelling-wave parametric\namplifiers (TWPAs) in order to investigate five experimental phenomena related\nto their operations, namely the effect of impedance mismatch, the presence of\nupper idler modes, the presence of quantum and thermal noise, the generation of\nsqueezed states, and the preservation of pre-squeezed states during\namplification. Our analysis uses momentum operators to describe the spatial\nevolution of quantised modes along a TWPA. We calculate the restriction placed\non pump amplitude as well as amplifier gain as a result of impedance mismatch\nbetween a TWPA and its external system. We apply our analysis to upper idler\nmodes and demonstrate that they will result in suppressed gain. We show that an\nideal TWPA is indeed quantum-limited - i.e. it introduces a half-quantum of\nzero-point fluctuation which is the minimum possible noise contribution for a\nphase-preserving linear amplifier. We analyse the thermal noise associated with\na TWPA by considering the effect of distributed sources along an amplifier\ntransmission line. Our analysis predicts a doubling of thermal noise in the\nhigh gain limit as a result of wave-mixing between signal and idler modes. We\nstudy the operation of a TWPA in the presence of a DC bias current, and have\nshown that highly squeezed states can in principle be generated. However,\namplifying a pre-squeezed state using a non-degenerate TWPA generally reduces\nthe squeezing advantage.\n"}
+{"abstract": "  The plug-and-play priors (PnP) and regularization by denoising (RED) methods\nhave become widely used for solving inverse problems by leveraging pre-trained\ndeep denoisers as image priors. While the empirical imaging performance and the\ntheoretical convergence properties of these algorithms have been widely\ninvestigated, their recovery properties have not previously been theoretically\nanalyzed. We address this gap by showing how to establish theoretical recovery\nguarantees for PnP/RED by assuming that the solution of these methods lies near\nthe fixed-points of a deep neural network. We also present numerical results\ncomparing the recovery performance of PnP/RED in compressive sensing against\nthat of recent compressive sensing algorithms based on generative models. Our\nnumerical results suggest that PnP with a pre-trained artifact removal network\nprovides significantly better results compared to the existing state-of-the-art\nmethods.\n"}
+{"abstract": "  We consider inference from non-random samples in data-rich settings where\nhigh-dimensional auxiliary information is available both in the sample and the\ntarget population, with survey inference being a special case. We propose a\nregularized prediction approach that predicts the outcomes in the population\nusing a large number of auxiliary variables such that the ignorability\nassumption is reasonable while the Bayesian framework is straightforward for\nquantification of uncertainty. Besides the auxiliary variables, inspired by\nLittle & An (2004), we also extend the approach by estimating the propensity\nscore for a unit to be included in the sample and also including it as a\npredictor in the machine learning models. We show through simulation studies\nthat the regularized predictions using soft Bayesian additive regression trees\nyield valid inference for the population means and coverage rates close to the\nnominal levels. We demonstrate the application of the proposed methods using\ntwo different real data applications, one in a survey and one in an\nepidemiology study.\n"}
+{"abstract": "  An approach to adjusting the conduction band population for tuning the\nmagnetic and magnetocaloric response of EuO1-{\\delta} thin films through\ncontrol of oxygen vacancies ({\\delta} = 0, 0.025, and 0.09) is presented. The\nfilms each showed a paramagnetic to ferromagnetic transition around 65 K, with\nan additional magnetic ordering transition at higher temperatures in the oxygen\ndeficient samples. All transitions are observed to be of second order. A\nmaximum magnetic entropy change of 6.4 J/kg K over a field change of 2 T with a\nrefrigerant capacity of 223 J/kg was found in the sample with {\\delta} = 0, and\nin all cases the refrigerant capacities of the thin films under study were\nfound to exceed that reported for bulk EuO. Adjusting the oxygen content was\nshown to produce table-like magnetocaloric effects, desirable for ideal\nEricsson-cycle magnetic refrigeration. These films are thus excellent\ncandidates for small-scale magnetic cooling technology in the liquid nitrogen\ntemperature range.\n"}
+{"abstract": "  Adversarial training (AT) in order to achieve adversarial robustness wrt\nsingle $l_p$-threat models has been discussed extensively. However, for\nsafety-critical systems adversarial robustness should be achieved wrt all\n$l_p$-threat models simultaneously. In this paper we develop a simple and\nefficient training scheme to achieve adversarial robustness against the union\nof $l_p$-threat models. Our novel $l_1+l_\\infty$-AT scheme is based on\ngeometric considerations of the different $l_p$-balls and costs as much as\nnormal adversarial training against a single $l_p$-threat model. Moreover, we\nshow that using our $l_1+l_\\infty$-AT scheme one can fine-tune with just 3\nepochs any $l_p$-robust model (for $p \\in \\{1,2,\\infty\\}$) and achieve multiple\nnorm adversarial robustness. In this way we boost the previous state-of-the-art\nreported for multiple-norm robustness by more than $6\\%$ on CIFAR-10 and report\nup to our knowledge the first ImageNet models with multiple norm robustness.\nMoreover, we study the general transfer of adversarial robustness between\ndifferent threat models and in this way boost the previous SOTA\n$l_1$-robustness on CIFAR-10 by almost $10\\%$.\n"}
+{"abstract": "  We prove existence and uniqueness of global in time weak solutions for the\nStokes system for compressible fluids with a general, non-monotone pressure. We\nconstruct the solution at the level of Lagrangian formulation and then define\nthe transformation to the original Eulerian coordinates. For nonnegative and\nbounded initial density the solution is also nonnegative for all $t$ and\nbelongs to $L^\\infty([0,\\infty)\\times\\mathbb{T}^d)$. A key point of our\nconsiderations is the uniqueness of such transformation. Since the velocity\nmight not be Lipschitz continuous, we develop a method which relies on the\nresults of Crippa \\& De Lellis, concerning regular Lagriangian flows. The\nuniqueness is obtained thanks to the application of a certain weighted flow and\ndetail analysis based on the properties of the $BMO$ space.\n"}
+{"abstract": "  The possibility of using mixed crystals highly doped with rare earth ions\n(REIs) as physical systems for creating fast quantum computers with a sampling\ntime of nanoseconds is discussed. The electronic 4f states of rare earth ions\nwith small values of the diagonal elements of the Judd-Ofelt matrix U(2) are\nproposed as optical frequency qubit levels. CNOT and other conditional gate\noperations are performed by exciting the rare earth ion into the 4f state with\na large diagonal element of U(2), causing a Stark blockade. It is found that\nthe main interaction responsible for this blockade is the quadrupole-quadrupole\ninteraction. The large inhomogeneous broadening of the frequencies of the\nelectronic transitions in mixed crystals and the weak interaction of 4f\nelectrons with phonons make it possible to achieve a high computation rate and\na long decoherence time of the qubits. An ensemble of closest REIs is described\nthat can act as an OQC instance; the frequencies of the corresponding qubits\ncan be found using the spectral hole burning method.\n"}
+{"abstract": "  A growing number of empirical software engineering researchers suggest that a\ncomplementary focus on theory is required if the discipline is to mature. A\nfirst step in theory-building involves the establishment of suitable\ntheoretical constructs. For researchers studying software projects, the lack of\na theoretical construct for context is problematic for both experimentation and\neffort estimation. For experiments, insufficiently understood contextual\nfactors confound results, and for estimation, unstated contextual factors\naffect estimation reliability. We have earlier proposed a framework that we\nsuggest may be suitable as a construct for context i.e. represents a minimal,\nspanning set for the space of software contexts. The framework has six\ndimensions, described as Who, Where, What, When, How and Why. In this paper, we\nreport the outcomes of a pilot study to test its suitability by categorising\ncontextual factors from the software engineering literature into the framework.\nWe found that one of the dimensions, Why, does not represent context, but\nrather is associated with objectives. We also identified some factors that do\nnot clearly fit into the framework and require further investigation. Our\ncontributions are the pursuing of a theoretical approach to understanding\nsoftware context, the initial establishment and evaluation of a construct for\ncontext and the exposure of a lack of clarity of meaning in many 'contexts'\ncurrently applied as factors for estimating project outcomes.\n"}
+{"abstract": "  Recent years have witnessed tremendous interest in deep learning on\ngraph-structured data. Due to the high cost of collecting labeled\ngraph-structured data, domain adaptation is important to supervised graph\nlearning tasks with limited samples. However, current graph domain adaptation\nmethods are generally adopted from traditional domain adaptation tasks, and the\nproperties of graph-structured data are not well utilized. For example, the\nobserved social networks on different platforms are controlled not only by the\ndifferent crowd or communities but also by the domain-specific policies and the\nbackground noise. Based on these properties in graph-structured data, we first\nassume that the graph-structured data generation process is controlled by three\nindependent types of latent variables, i.e., the semantic latent variables, the\ndomain latent variables, and the random latent variables. Based on this\nassumption, we propose a disentanglement-based unsupervised domain adaptation\nmethod for the graph-structured data, which applies variational graph\nauto-encoders to recover these latent variables and disentangles them via three\nsupervised learning modules. Extensive experimental results on two real-world\ndatasets in the graph classification task reveal that our method not only\nsignificantly outperforms the traditional domain adaptation methods and the\ndisentangled-based domain adaptation methods but also outperforms the\nstate-of-the-art graph domain adaptation algorithms.\n"}
+{"abstract": "  The first binary neutron star merger has already been detected in\ngravitational waves. The signal was accompanied by an electromagnetic\ncounterpart including a kilonova component powered by the decay of radioactive\nnuclei, as well as a short $\\gamma$-ray burst. In order to understand the\nradioactively-powered signal, it is necessary to simulate the outflows and\ntheir nucleosynthesis from the post-merger disk. Simulating the disk and\npredicting the composition of the outflows requires general relativistic\nmagnetohydrodynamical (GRMHD) simulations that include a realistic,\nfinite-temperature equation of state (EOS) and self-consistently calculating\nthe impact of neutrinos. In this work, we detail the implementation of a\nfinite-temperature EOS and the treatment of neutrinos in the GRMHD code\nHARM3D+NUC, based on HARM3D. We include formal tests of both the\nfinite-temperature EOS and the neutrino leakage scheme. We further test the\ncode by showing that, given conditions similar to those of published remnant\ndisks following neutron star mergers, it reproduces both recombination of free\nnucleons to a neutron-rich composition and excitation of a thermal wind.\n"}
+{"abstract": "  With the growing capabilities of intelligent systems, the integration of\nrobots in our everyday life is increasing. However, when interacting in such\ncomplex human environments, the occasional failure of robotic systems is\ninevitable. The field of explainable AI has sought to make complex-decision\nmaking systems more interpretable but most existing techniques target domain\nexperts. On the contrary, in many failure cases, robots will require recovery\nassistance from non-expert users. In this work, we introduce a new type of\nexplanation, that explains the cause of an unexpected failure during an agent's\nplan execution to non-experts. In order for error explanations to be\nmeaningful, we investigate what types of information within a set of\nhand-scripted explanations are most helpful to non-experts for failure and\nsolution identification. Additionally, we investigate how such explanations can\nbe autonomously generated, extending an existing encoder-decoder model, and\ngeneralized across environments. We investigate such questions in the context\nof a robot performing a pick-and-place manipulation task in the home\nenvironment. Our results show that explanations capturing the context of a\nfailure and history of past actions, are the most effective for failure and\nsolution identification among non-experts. Furthermore, through a second user\nevaluation, we verify that our model-generated explanations can generalize to\nan unseen office environment, and are just as effective as the hand-scripted\nexplanations.\n"}
+{"abstract": "  This demo presents the implementation and usage details of GASSERT, the first\ntool to automatically improve assertion oracles. Assertion oracles are\nexecutable boolean expressions placed inside the program that should pass\n(return true) for all correct executions and fail (return false) for all\nincorrect executions. Because designing perfect assertion oracles is difficult,\nassertions are prone to both false positives (the assertion fails but should\npass) and false negatives (the assertion passes but should fail). Given a Java\nmethod containing an assertion oracle to improve, GASSERT returns an improved\nassertion with fewer false positives and false negatives than the initial\nassertion. Internally, GASSERT implements a novel co-evolutionary algorithm\nthat explores the space of possible assertions guided by two fitness functions\nthat reward assertions with fewer false positives, fewer false negatives, and\nsmaller size.\n"}
+{"abstract": "  We consider the zero-average Gaussian free field on a certain class of finite\n$d$-regular graphs for fixed $d\\ge 3$. This class includes $d$-regular\nexpanders of large girth and typical realisations of random $d$-regular graphs.\nWe show that the level set of the zero-average Gaussian free field above level\n$h$ has a giant component in the whole supercritical phase, that is for all\n$h<h_\\star$, with probability tending to one as the size of the graphs tends to\ninfinity. In addition, we show that this component is unique. This\nsignificantly improves the result of [AC20b], where it was shown that a linear\nfraction of vertices is in mesoscopic components if $h<h_\\star$.\n"}
+{"abstract": "  We present a new version of the Ogre open source Python package with the\ncapability to perform structure prediction of epitaxial inorganic interfaces by\nlattice and surface matching. In the lattice matching step a scan over\ncombinations of substrate and film Miller indices is performed to identify the\ndomain-matched interfaces with the lowest mismatch. Subsequently, surface\nmatching is conducted by Bayesian optimization to find the optimal interfacial\ndistance and in-plane registry between the substrate and film. For the\nobjective function, a geometric score function is proposed, based on the\noverlap and empty space between atomic spheres at the interface. The score\nfunction reproduces the results of density functional theory (DFT) at a\nfraction of the computational cost. The optimized interfaces are pre-ranked\nusing a score function based on the similarity of the atomic environment at the\ninterface to the bulk environment. Final ranking of the top candidate\nstructures is performed with DFT. Ogre streamlines DFT calculations of\ninterface energies and electronic properties by automating the construction of\ninterface models. The application of Ogre is demonstrated for two interfaces of\ninterest for quantum computing and spintronics, Al/InAs and Fe/InSb.\n"}
+{"abstract": "  In this note we estimate herd immunity levels for the Covid-19 epidemic using\na standard SEIR system that models the disease dynamics in homogeneous\npopulations. The results obtained are indicative of values between 80% and 90%\nfor unprotected, fully susceptible populations. Basic protective measures such\nas hand hygiene and mask wearing may be effective to lower herd immunity levels\ndown to values between 50% and 60% of the total population.\n"}
+{"abstract": "  Gauge field theory with rank-one field $T_{\\mu}$ is a quantum field theory\nthat describes the interaction of elementary spin-1 particles, of which being\nmassless to preserve gauge symmetry. In this paper, we give a generalized,\nextended study of abelian gauge field theory under successive rotor model in\ngeneral $D$-dimensional flat spacetime for spin-1 particles in the context of\nhigher order derivatives. We establish a theorem that $n$ rotor contributes to\nthe $\\Box^n T^{\\mu}$ fields in the integration-by-parts formalism of the\naction. This corresponds to the transformation of gauge field $T^{\\mu}\n\\rightarrow \\Box^n T^{\\mu}$ and gauge field strength $G_{\\mu\\nu}\\rightarrow\n\\Box^n G_{\\mu\\nu} $ in the action. The $n=0$ case restores back to the standard\nabelian gauge field theory. The equation of motion and Noether's conserved\ncurrent of the theory are also studied.\n"}
+{"abstract": "  We study an abelian $U(1)_{X}$ extension to the Standard Model group\nconsisting of an extended scalar sector of two doublets and one singlet plus\nthree additional exotic quarks and two exotic leptons through non-universal\ninteraction of the new $X$ quantum number and a $\\mathbb{Z}_{2}$ parity. In\nthis model, the lightest fermions are massless at tree-level so effective\noperators up to dimension seven are considered to fill all zeros in mass\nmatrices, providing an upper bound for the $\\Lambda$ energy scale from the\nelectron, up, down and strange quark masses. We obtain that the model can\nexplain the $B$ meson anomaly while it provides an upper bound for $v_{\\chi}$\nof $8.4$ TeV which translates in an upper bound for the new $Z_{2}^{\\mu}$ gauge\nboson of $9.9$ TeV. Finally, muon $g-2$ contributions are also studied, where\nwe found that positive contributions coming from charged $W^{+}$ bosons and\nexotic Majorana neutrinos while contributions coming from heavy charged and\nneutral scalar bosons, are negative. In particular, the allowed regions\naccording to the experimental muon $g-2$ parameter, shows that as the exotic\nneutrino mass increases, heavy scalars and the exotic lepton masses increase as\nwell.\n"}
+{"abstract": "  The forthcoming space-based gravitational wave observatory LISA will open a\nnew window for the measurement of galactic binaries, which will deliver\nunprecedented information about these systems. However, the detection of\ngalactic binary gravitational wave signals is challenged by the presence of\ngaps in the data. Whether being planned or not, gapped data dramatically reduce\nour ability to detect faint signals and the risk of misdetection soars.\nInspired by advances in signal processing, we introduce a \\nonparametric\ninpainting algorithm based on the sparse representation of the galactic binary\nsignal in the Fourier domain. In contrast to traditional inpainting approaches,\nnoise statistics are known theoretically on ungapped measurements only. This\ncalls for the joint recovery of both the ungapped noise and the galactic binary\nsignal. We thoroughly show that sparse inpainting yields an accurate estimation\nof the gravitational imprint of the galactic binaries. Additionally, we\nhighlight that the proposed algorithm produces a statistically consistent\nungapped noise estimate. We further evaluate the performances of the proposed\ninpainting methods to recover the gravitational wave signal on a simple example\ninvolving verification galactic binaries recently proposed in LISA data\nchallenges.\n"}
+{"abstract": "  Impact induced attrition processes are, beyond being essential models of\nindustrial ore processing, broadly regarded as the key to decipher the\nprovenance of sedimentary particles. A detailed understanding of single impact\nphenomena of solid bodies has been obtained in physics and engineering,\nhowever, the description of gradual mass reduction and shape evolution in\nimpact sequences relies on approximate mathematical models of mean field type,\nformulated as curvature-driven partial differential equations. Here we\nestablish the first link between microscopic, particle-based material models\nand the mean field theory for these processes. Based on realistic computer\nsimulations of particle-wall collision sequences, we first identify the\nwell-known damage and fragmentation energy phases, then we show that the former\nis split into the abrasion phase with infinite sample lifetime, analogous to\nSternberg's Law, at finite asymptotic mass and the cleavage phase with finite\nsample lifetime, decreasing as a power law of the impact velocity, analogous to\nBasquin's Law. We demonstrate that only in the abrasion phase does shape\nevolution emerging in microscopic material models reproduce with startling\naccuracy the spatio-temporal patterns predicted by macroscopic mean field\napproaches. Our results substantially extend the phase diagram of impact\nphenomena and set the boundaries of the applicability of geometric mean field\ntheories for geological shape evolution. Additionally, the scaling laws\nobtained can be exploited for quantitative predictions of evolution histories.\n"}
+{"abstract": "  This paper presents a novel approach of representing dynamic visual scenes\nwith static maps generated from video/image streams. Such representation allows\neasy visual assessment of motion in dynamic environments. These maps are 2D\nmatrices calculated recursively, in a pixel-wise manner, that is based on the\nrecently introduced concept of Eccentricity data analysis. Eccentricity works\nas a metric of a discrepancy between a particular pixel of an image and its\nnormality model, calculated in terms of mean and variance of past readings of\nthe same spatial region of the image. While Eccentricity maps carry temporal\ninformation about the scene, actual images do not need to be stored nor\nprocessed in batches. Rather, all the calculations are done recursively, based\non a small amount of statistical information stored in memory, thus resulting\nin a very computationally efficient (processor- and memory-wise) method. The\nlist of potential applications includes video-based activity recognition,\nintent recognition, object tracking, video description, and so on.\n"}
+{"abstract": "  Experimental studies of cell motility in culture have shown that under\nadequate conditions these living organisms possess the ability to organize\nthemselves into complex structures. Such structures may exhibit a synergy that\ngreatly increases their survival rate and facilitate growth or spreading to\ndifferent tissues. These properties are even more significant for cancer cells\nand related pathologies. Theoretical studies supported by experimental evidence\nhave also shown that adhesion plays a significant role in cellular\norganization. Here we show that the directional persistence observed in\npolarized displacements permits the formation of stable cell aggregates in the\nabsence of adhesion, even in low-density regimes. We introduce a discrete\nstochastic model for the dispersal of polarized cells with exclusion and derive\nthe hydrodynamic limit. We demonstrate that the persistence coupled with the\ncell-cell exclusion hinders the cellular motility around other cells, leading\nto a non-linear diffusion which facilitates their capture into larger\naggregates.\n"}
+{"abstract": "  We establish existence and uniqueness results for initial-boundary value\nproblems for transport equations in one space dimension with nearly\nincompressible velocity fields, under the sole assumption that the fields are\nbounded. In the case where the velocity field is either nonnegative or\nnonpositive, one can rely on similar techniques as in the case of the Cauchy\nproblem. Conversely, in the general case we introduce a new and more\ntechnically demanding construction, which heuristically speaking relies on a\n\"lagrangian formulation\" of the problem, albeit in a highly irregular setting.\nWe also establish stability of the solution in weak and strong topologies, and\npropagation of the $BV$ regularity. In the case of either nonnegative or\nnonpositive velocity fields we also establish a $BV$-in-time regularity result,\nand we exhibit a counterexample showing that the result is false in the case of\nsign-changing vector fields. To conclude, we establish a trace renormalization\nproperty.\n"}
+{"abstract": "  Deep Neural Networks (DNNs) are known to be strong predictors, but their\nprediction strategies can rarely be understood. With recent advances in\nExplainable Artificial Intelligence, approaches are available to explore the\nreasoning behind those complex models' predictions. One class of approaches are\npost-hoc attribution methods, among which Layer-wise Relevance Propagation\n(LRP) shows high performance. However, the attempt at understanding a DNN's\nreasoning often stops at the attributions obtained for individual samples in\ninput space, leaving the potential for deeper quantitative analyses untouched.\nAs a manual analysis without the right tools is often unnecessarily labor\nintensive, we introduce three software packages targeted at scientists to\nexplore model reasoning using attribution approaches and beyond: (1) Zennit - a\nhighly customizable and intuitive attribution framework implementing LRP and\nrelated approaches in PyTorch, (2) CoRelAy - a framework to easily and quickly\nconstruct quantitative analysis pipelines for dataset-wide analyses of\nexplanations, and (3) ViRelAy - a web-application to interactively explore\ndata, attributions, and analysis results.\n"}
+{"abstract": "  In robot navigation, generalizing quickly to unseen environments is\nessential. Hierarchical methods inspired by human navigation have been\nproposed, typically consisting of a high-level landmark proposer and a\nlow-level controller. However, these methods either require precise high-level\ninformation to be given in advance or need to construct such guidance from\nextensive interaction with the environment. In this work, we propose an\napproach that leverages a rough 2-D map of the environment to navigate in novel\nenvironments without requiring further learning. In particular, we introduce a\ndynamic topological map that can be initialized from the rough 2-D map along\nwith a high-level planning approach for proposing reachable 2-D map patches of\nthe intermediate landmarks between the start and goal locations. To use\nproposed 2-D patches, we train a deep generative model to generate intermediate\nlandmarks in observation space which are used as subgoals by low-level\ngoal-conditioned reinforcement learning. Importantly, because the low-level\ncontroller is only trained with local behaviors (e.g. go across the\nintersection, turn left at a corner) on existing environments, this framework\nallows us to generalize to novel environments given only a rough 2-D map,\nwithout requiring further learning. Experimental results demonstrate the\neffectiveness of the proposed framework in both seen and novel environments.\n"}
+{"abstract": "  We derive an approximate but explicit formula for the Mean First Passage Time\nof a random walker between a source and a target node of a directed and\nweighted network. The formula does not require any matrix inversion, and it\ntakes as only input the transition probabilities into the target node. It is\nderived from the calculation of the average resolvent of a deformed ensemble of\nrandom sub-stochastic matrices $H=\\langle H\\rangle +\\delta H$, with $\\langle\nH\\rangle$ rank-$1$ and non-negative. The accuracy of the formula depends on the\nspectral gap of the reduced transition matrix, and it is tested numerically on\nseveral instances of (weighted) networks away from the high sparsity regime,\nwith an excellent agreement.\n"}
+{"abstract": "  It is important to build a rigorous verification and validation (V&V) process\nto evaluate the safety of highly automated vehicles (HAVs) before their wide\ndeployment on public roads. In this paper, we propose an interaction-aware\nframework for HAV safety evaluation which is suitable for some\nhighly-interactive driving scenarios including highway merging, roundabout\nentering, etc. Contrary to existing approaches where the primary other vehicle\n(POV) takes predetermined maneuvers, we model the POV as a game-theoretic\nagent. To capture a wide variety of interactions between the POV and the\nvehicle under test (VUT), we characterize the interactive behavior using\nlevel-k game theory and social value orientation and train a diverse set of\nPOVs using reinforcement learning. Moreover, we propose an adaptive test case\nsampling scheme based on the Gaussian process regression technique to generate\ncustomized and diverse challenging cases. The highway merging is used as the\nexample scenario. We found the proposed method is able to capture a wide range\nof POV behaviors and achieve better coverage of the failure modes of the VUT\ncompared with other evaluation approaches.\n"}
+{"abstract": "  In this paper, we study the nonlinear Schr\\\"odinger equation with focusing\npoint nonlinearity in dimension one. First, we establish a scattering criterion\nfor the equation based on Kenig-Merle's compactness-rigidity argument. Then we\nprove the energy scattering below and above the mass-energy threshold. We also\ndescribe the dynamics of solutions with data at the ground state threshold.\nFinally, we prove a blow-up criteria for the equation with initial data with\narbitrarily large energy.\n"}
+{"abstract": "  Several active organisms in nature tend to reside as a community in a viscous\nfluid medium. We analyze the variation of swimming characteristics of an active\nswimmer present in a dilute and disperse suspension, modeled as an effective\nBrinkman medium. This idealized representation of a collection of active\nswimmers allows one to distinguish the impact of the interior domain available\nto an individual swimmer as well as the contribution of its neighbors. Darcy's\nlaw along with the analytical solution enables the effective resistivity to be\npredicted as a function of the volume fraction which is in close agreement with\nthe well-known Carman-Kozeny equation. This facilitates the successive analysis\nof the propulsion speed, power dissipation, and swimming efficiency of the\ntargeted swimmer as a function of the volume fraction which is decisive in\nnutrient transport and uptake or reproduction in a collective environment. A\nstress-jump condition is also imposed across a cell to indicate a mean\neffective force due to the nearby swimmers. At suitable values of this\nstress-jump coefficient, the relative increase of the migration velocity and\nswimming efficiency is noticeably higher at an optimum occupancy.\n"}
+{"abstract": "  A full-fledged data exploration system must combine different access\nmodalities with a powerful concept of guiding the user in the exploration\nprocess, by being reactive and anticipative both for data discovery and for\ndata linking. Such systems are a real opportunity for our community to cater to\nusers with different domain and data science expertise. We introduce INODE --\nan end-to-end data exploration system -- that leverages, on the one hand,\nMachine Learning and, on the other hand, semantics for the purpose of Data\nManagement (DM). Our vision is to develop a classic unified, comprehensive\nplatform that provides extensive access to open datasets, and we demonstrate it\nin three significant use cases in the fields of Cancer Biomarker Reearch,\nResearch and Innovation Policy Making, and Astrophysics. INODE offers\nsustainable services in (a) data modeling and linking, (b) integrated query\nprocessing using natural language, (c) guidance, and (d) data exploration\nthrough visualization, thus facilitating the user in discovering new insights.\nWe demonstrate that our system is uniquely accessible to a wide range of users\nfrom larger scientific communities to the public. Finally, we briefly\nillustrate how this work paves the way for new research opportunities in DM.\n"}
+{"abstract": "  Deep neural network (DNN) usually learns the target function from low to high\nfrequency, which is called frequency principle or spectral bias. This frequency\nprinciple sheds light on a high-frequency curse of DNNs -- difficult to learn\nhigh-frequency information. Inspired by the frequency principle, a series of\nworks are devoted to develop algorithms for overcoming the high-frequency\ncurse. A natural question arises: what is the upper limit of the decaying rate\nw.r.t. frequency when one trains a DNN? In this work, our theory, confirmed by\nnumerical experiments, suggests that there is a critical decaying rate w.r.t.\nfrequency in DNN training. Below the upper limit of the decaying rate, the DNN\ninterpolates the training data by a function with a certain regularity.\nHowever, above the upper limit, the DNN interpolates the training data by a\ntrivial function, i.e., a function is only non-zero at training data points.\nOur results indicate a better way to overcome the high-frequency curse is to\ndesign a proper pre-condition approach to shift high-frequency information to\nlow-frequency one, which coincides with several previous developed algorithms\nfor fast learning high-frequency information. More importantly, this work\nrigorously proves that the high-frequency curse is an intrinsic difficulty of\nDNNs.\n"}
+{"abstract": "  We report observations of the flickering variability of the dwarf nova RX And\nin five bands (UBVRI) on two nights. On 25 October 2019 the brightness of the\nstar was $B\\approx 13.9$ mag, the amplitude of the flickering was 0.47 mag, and\nwe estimate for the flickering source temperature $T_{fl} = 10700 \\pm 400$ K,\nand radius $R_{fl} =0.046 \\pm 0.004$ $R_\\odot$. On 2 January 2020, the star was\nabout 3 magnitudes brighter ($B \\approx 10.7$), the amplitude of the flickering\nwas significantly lower (0.07 mag) and we derive for the flickering source\n$T_{fl} = 9600 \\pm 700$ K, and radius $R_{fl} = 0.098 \\pm 0.009$ $R_\\odot$. The\nresults indicate that 3 magnitudes brightening of the star doubled the radius\nof the flickering source.\n  The data are available upon request from the authors.\n"}
+{"abstract": "  In this paper, we present the results of a numerical study of air-water\nturbulent bubbly flow in a periodic vertical square duct. The study is\nconducted using a novel numerical technique which leverages Volume of Fluid\nmethod for interface capturing and Sharp Surface Force method for accurate\nrepresentation of the surface tension forces. A three-dimensional geometry\nconstruction method is employed during solution of interface equation which\ngives absolute conservation of mass and sharp interface between gas and liquid\nphases. The entire algorithm has been implemented on a data parallel mode on\nmultiple graphics processing units (GPU) taking advantage of the large number\nof available cores.\n  We have studied the dynamics of a swarm of spherical bubbles co-flowing with\nthe upward turbulent flow and compared results with an unladen turbulent flow.\nThe frictional Reynolds number of the unladen $Re_{\\tau}$ is 360, which is\nsufficient to sustain a turbulent flow. We observe the turbulence-driven\nsecondary flows in the mean flow, with complex instantaneous turbulent vortical\nstructures. The interaction of these secondary flows with the upwards rising\nbubbles is very complex and leads to significant changes in the instantaneous\nand time-averaged flow field.\n  We present the results of mean void fraction distribution, mean velocities,\nlongitudinal and transverse turbulence intensities along the wall, corner\nbisector, and wall bisector. A peak in the void fraction distribution near the\nwalls is observed representing the migration of bubbles to a preferred section\nof the duct. The effects of turbulence-driven secondary flows and instantaneous\nlarge eddies on preferential concentration of the bubbles are discussed. The\ndispersed bubbles are seen to break the long elongated turbulent structures\ncommonly observed in the unladen turbulent flow.\n"}
+{"abstract": "  Multiple instance learning (MIL) is the preferred approach for whole slide\nimage classification. However, most MIL approaches do not exploit the\ninterdependencies of tiles extracted from a whole slide image, which could\nprovide valuable cues for classification. This paper presents a novel MIL\napproach that exploits the spatial relationship of tiles for classifying whole\nslide images. To do so, a sparse map is built from tiles embeddings, and is\nthen classified by a sparse-input CNN. It obtained state-of-the-art performance\nover popular MIL approaches on the classification of cancer subtype involving\n10000 whole slide images. Our results suggest that the proposed approach might\n(i) improve the representation learning of instances and (ii) exploit the\ncontext of instance embeddings to enhance the classification performance. The\ncode of this work is open-source at {github censored for review}.\n"}
+{"abstract": "  The degree of porosity in interstellar dust-grain material is poorly defined,\nalthough recent work has suggested that the grains could be highly porous.\nAside from influencing the optical properties of the dust, porosity has the\npotential to affect the chemistry occurring on dust-grain surfaces, via\nincreased surface area, enhanced local binding energies, and the possibility of\ntrapping of molecules within the pores as ice mantles build up on the grains.\nThrough computational kinetics simulations, we investigate how interstellar\ngrain-surface chemistry and ice composition are affected by the porosity of the\nunderlying dust-grain material. Using a simple routine, idealized\nthree-dimensional dust-grains are constructed, atom by atom, with varying\ndegrees of porosity. Diffusive chemistry is then simulated on these surfaces\nusing the off-lattice microscopic Monte Carlo chemical kinetics model, MIMICK,\nassuming physical conditions appropriate to dark interstellar clouds. On the\nporous grain surface, the build-up of ice mantles, mostly composed of water,\nleads to the covering over of the pores, leaving empty pockets. Once the pores\nare completely covered, the chemical and structural behavior is similar to\nnon-porous grains of the same size. The most prominent chemical effect of the\npresence of grain porosity is the trapping of molecular hydrogen, formed on the\ngrain surfaces, within the ices and voids inside the grain pores. Trapping of\nH2 in this way may indicate that other volatiles, such as inert gases not\nincluded in these models, could be trapped within dust-grain porous structures\nwhen ices begin to form.\n"}
+{"abstract": "  Differential evolution (DE) is an effective global evolutionary optimization\nalgorithm using to solve global optimization problems mainly in a continuous\ndomain. In this field, researchers pay more attention to improving the\ncapability of DE to find better global solutions, however, the computational\nperformance of DE is also a very interesting aspect especially when the problem\nscale is quite large. Firstly, this paper analyzes the design of parallel\ncomputation of DE which can easily be executed in Math Kernel Library (MKL) and\nCompute Unified Device Architecture (CUDA). Then the essence of the exponential\ncrossover operator is described and we point out that it cannot be used for\nbetter parallel computation. Later, we propose a new exponential crossover\noperator (NEC) that can be executed parallelly with MKL/CUDA. Next, the\nextended experiments show that the new crossover operator can speed up DE\ngreatly. In the end, we test the new parallel DE structure, illustrating that\nthe former is much faster.\n"}
+{"abstract": "  We study the Ehresmann--Schauenburg bialgebroid of a noncommutative principal\nbundle as a quantization of the gauge groupoid of a classical principal bundle.\nWe show that the gauge group of the noncommutative bundle is isomorphic to the\ngroup of bisections of the bialgebroid, and we give a crossed module structure\nfor the bisections and the automorphisms of the bialgebroid. Examples\nillustrating these constructions include: Galois objects of Taft algebras, a\nmonopole bundle over a quantum spheres and a not faithfully flat Hopf--Galois\nextension of commutative algebras. The latter two examples have in fact a\nstructure of Hopf algebroid for a suitable invertible antipode.\n"}
+{"abstract": "  In arXiv1312.7267, the first non-trivial example of a Poisson manifold of\nstrong compact type is given. The construction uses the theory of K3 surfaces\nand results in a Poisson manifold with leaf space $S^1$. We modify the\nconstruction to obtain a new class of examples. Specifically, we obtain for\neach strongly integral affine 2-torus a Poisson manifold of strong compact type\nwith said torus as leaf space.\n"}
+{"abstract": "  Identifying phase transitions is one of the key challenges in quantum\nmany-body physics. Recently, machine learning methods have been shown to be an\nalternative way of localising phase boundaries also from noisy and imperfect\ndata and without the knowledge of the order parameter. Here we apply different\nunsupervised machine learning techniques including anomaly detection and\ninfluence functions to experimental data from ultracold atoms. In this way we\nobtain the topological phase diagram of the Haldane model in a completely\nunbiased fashion. We show that the methods can successfully be applied to\nexperimental data at finite temperature and to data of Floquet systems, when\npostprocessing the data to a single micromotion phase. Our work provides a\nbenchmark for unsupervised detection of new exotic phases in complex many-body\nsystems.\n"}
+{"abstract": "  Defining an efficient training set is one of the most delicate phases for the\nsuccess of remote sensing image classification routines. The complexity of the\nproblem, the limited temporal and financial resources, as well as the high\nintraclass variance can make an algorithm fail if it is trained with a\nsuboptimal dataset. Active learning aims at building efficient training sets by\niteratively improving the model performance through sampling. A user-defined\nheuristic ranks the unlabeled pixels according to a function of the uncertainty\nof their class membership and then the user is asked to provide labels for the\nmost uncertain pixels. This paper reviews and tests the main families of active\nlearning algorithms: committee, large margin and posterior probability-based.\nFor each of them, the most recent advances in the remote sensing community are\ndiscussed and some heuristics are detailed and tested. Several challenging\nremote sensing scenarios are considered, including very high spatial resolution\nand hyperspectral image classification. Finally, guidelines for choosing the\ngood architecture are provided for new and/or unexperienced user.\n"}
+{"abstract": "  We study the modular symmetry on magnetized toroidal orbifolds with\nScherk-Schwarz phases. In particular, we investigate finite modular flavor\ngroups for three-generation modes on magnetized orbifolds. The three-generation\nmodes can be the three-dimensional irreducible representations of covering\ngroups and central extended groups of $\\Gamma_N$ for $N=3,4,5,7,8,16$, that is,\ncovering groups of $\\Delta(6(N/2)^2)$ for $N=$ even and central extensions of\n$PSL(2,\\mathbb{Z}_{N})$ for $N=$odd with Scherk-Schwarz phases. We also study\nanomaly behaviors.\n"}
+{"abstract": "  In this paper, we describe a new algorithm to compute the extreme\neigenvalue/eigenvector pairs of a symmetric matrix. The proposed algorithm can\nbe viewed as an extension of the Jacobi transformation method for symmetric\nmatrix diagonalization to the case where we want to compute just a few\neigenvalues/eigenvectors. The method is also particularly well suited for the\ncomputation of sparse eigenspaces. We show the effectiveness of the method for\nsparse low-rank approximations and show applications to random symmetric\nmatrices, graph Fourier transforms, and with the sparse principal component\nanalysis in image classification experiments.\n"}
+{"abstract": "  Space weather is a collective term for different solar or space phenomena\nthat can detrimentally affect technology. However, current understanding of\nspace weather hazards is still relatively embryonic in comparison to\nterrestrial natural hazards such as hurricanes or earthquakes. Indeed, certain\ntypes of space weather such as large Coronal Mass Ejections (CMEs) are an\narchetypal example of a low probability, high severity hazard. Few major\nevents, short time-series data and a lack of consensus regarding the potential\nimpacts on critical infrastructure have hampered the economic impact assessment\nof space weather. Yet, space weather has the potential to disrupt a wide range\nof Critical National Infrastructure (CNI) systems including electricity\ntransmission, satellite communications and positioning, aviation and rail\ntransportation. Recently there has been growing interest in these potential\neconomic and societal impacts. Estimates range from millions of dollars of\nequipment damage from the Quebec 1989 event, to some analysts reporting\nbillions of lost dollars in the wider economy from potential future disaster\nscenarios. Hence, this provides motivation for this article which tracks the\norigin and development of the socio-economic evaluation of space weather, from\n1989 to 2017, and articulates future research directions for the field.\n"}
+{"abstract": "  Humans approach driving in a holistic fashion which entails, in particular,\nunderstanding road events and their evolution. Injecting these capabilities in\nan autonomous vehicle has thus the potential to take situational awareness and\ndecision making closer to human-level performance. To this purpose, we\nintroduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to\nour knowledge the first of its kind. ROAD is designed to test an autonomous\nvehicle's ability to detect road events, defined as triplets composed by a\nmoving agent, the action(s) it performs and the corresponding scene locations.\nROAD comprises 22 videos, originally from the Oxford RobotCar Dataset,\nannotated with bounding boxes showing the location in the image plane of each\nroad event. We also provide as baseline a new incremental algorithm for online\nroad event awareness, based on inflating RetinaNet along time, which achieves a\nmean average precision of 16.8% and 6.1% for frame-level and video-level event\ndetection, respectively, at 50% overlap. Though promising, these figures\nhighlight the challenges faced by situation awareness in autonomous driving.\nFinally, ROAD allows scholars to investigate exciting tasks such as complex\n(road) activity detection, future road event anticipation and the modelling of\nsentient road agents in terms of mental states. Dataset can be obtained from\nhttps://github.com/gurkirt/road-dataset and baseline code from\nhttps://github.com/gurkirt/3D-RetinaNet.\n"}
+{"abstract": "  In this paper we study numerically solving optimal control problems with\nbang-bang control functions. We present a formal Lagrangian approach for\nsolving the optimal control problem, and address difficulties encountered when\nnumerically solving the state and adjoint equations by using the immersed\ninterface method. We note that our numerical approach does not approximate the\ndiscontinuous control function with smooth functions, instead we solve the true\nbang-bang optimal control problem. Our approach for solving the optimal control\nproblem uses an adjoint-based gradient. We use the gradient in our first-order\ntrust-region method to generate a local minimizing control. We present detailed\nnumerical results to demonstrate the effectiveness of our method.\n"}
+{"abstract": "  A synergistic approach for optimizing devices, circuits, and neural network\narchitectures was used to abate junction-temperature-change-induced performance\ndegradation of a Fe-FinFET-based artificial neural network. We demonstrated\nthat the digital nature of the binarized neural network, with the \"0\" state\nprogrammed deep in the subthreshold and the \"1\" state in strong inversion, is\ncrucial for robust DNN inference. The performance of a purely software-based\nbinary neural network (BNN), with 96.1% accuracy for Modified National\nInstitute of Standards and Technology (MNIST) handwritten digit recognition,\nwas used as a baseline. The Fe-FinFET-based BNN (including device-to-device\nvariation at 300 K) achieved 95.7% inference accuracy on the MNIST dataset.\nAlthough substantial inference accuracy degradation with temperature change was\nobserved in a nonbinary neural network, the BNN with optimized Fe-FinFETs as\nsynaptic devices had excellent resistance to temperature change effects and\nmaintained a minimum inference accuracy of 95.2% within a temperature range of\n-233K to 398K after gate stack and bias optimization. However, reprogramming to\nadjust device conductance was necessary for temperatures higher than 398K.\n"}
+{"abstract": "  Electroanatomical maps are a key tool in the diagnosis and treatment of\natrial fibrillation. Current approaches focus on the activation times recorded.\nHowever, more information can be extracted from the available data. The fibers\nin cardiac tissue conduct the electrical wave faster, and their direction could\nbe inferred from activation times. In this work, we employ a recently developed\napproach, called physics informed neural networks, to learn the fiber\norientations from electroanatomical maps, taking into account the physics of\nthe electrical wave propagation. In particular, we train the neural network to\nweakly satisfy the anisotropic eikonal equation and to predict the measured\nactivation times. We use a local basis for the anisotropic conductivity tensor,\nwhich encodes the fiber orientation. The methodology is tested both in a\nsynthetic example and for patient data. Our approach shows good agreement in\nboth cases, with an RMSE of 2.2ms on the in-silico data and outperforming a\nstate of the art method on the patient data. The results show a first step\ntowards learning the fiber orientations from electroanatomical maps with\nphysics-informed neural networks.\n"}
+{"abstract": "  We propose an approach to the semantics of package management which relates\nit to general event structures, well-known mathematical objects used in the\nsemantics of concurrent, nondeterministic systems. In this approach, the data\nof a package repository is treated as a declarative specification of a\nnondeterministic, concurrent program. We introduce a process calculus\ncorresponding to this data, and investigate its operational and categorical\nsemantics. Our hope is this lays the basis for further formal study of package\nmanagement in which the weight of existing tools can be brought to bear.\n"}
+{"abstract": "  The quantum states of the Kapitza pendulum are described within the effective\npotential obtained by the method of averaging over the fast oscillations. An\nanalytical estimate of the energy spectrum of stabilized states is given using\napproximate model potential. For the lowest states of an inverted pendulum, the\nspectrum is repeduced by the energies of a harmonic oscillator with\nperturbation theory corrections. Tunneling effect contribution to the energies\nof resonance states in the double-well effective potential is estimated. The\nresults of numerical simulations of vibrational and rotational spectra of the\nKapitza pendulum by the semiclassical method and by the Numerov algorithm are\ncompared.\n"}
+{"abstract": "  We consider a harmonic oscillator under periodic driving and coupled to two\nharmonic-oscillator heat baths at different temperatures. We use the\nthermofield transformation with chain mapping for this setup, which allows us\nto study the unitary evolution of the system and the baths up to a time when\nthe periodic steady state emerges in the system. We characterize this periodic\nsteady state, and we show that, by tuning the system and the bath parameters,\none can turn this system from an engine to an accelerator or even to a heater.\nThe possibility to study the unitary evolution of the system and baths also\nallows us to evaluate the steady correlations that build between the system and\nthe baths, and correlations that grow between the baths.\n"}
+{"abstract": "  In quantum mechanics, each particle is described by a complex valued wave\nfunction characterized by amplitude and phase. When many particles interact\neach other, cooperative phenomena give rise to a quantum many-body state with a\nspecific quantum coherence. What is the interplay between particle's phase\ncoherence and many-body quantum coherence? Over the years, such question has\nbeen object of profound analysis in quantum physics. Here, we demonstrate how\nstate of the art quantum technology allows to explore the question operatively.\nTo this end, we study the time-dependent interference formed by releasing an\ninteracting degenerate Fermi-gas from a specific matter-wave circuit in an\neffective magnetic field. Particle phase coherence, indicated by the\nfirst-order correlator, and many-body quantum coherence, indicated by the noise\ncorrelator, are displayed as distinct features of the interferogram. Particle\nphase coherence produces spiral interference of the Fermi orbitals at\nintermediate times. Many-body quantum coherence emerges at long times. The\ncoupling between particles coherence and many-body coherence is reflected in a\nspecific dependence of the interference pattern on the effective magnetic\nfield.\n"}
+{"abstract": "  Probabilistic time series forecasting involves estimating the distribution of\nfuture based on its history, which is essential for risk management in\ndownstream decision-making. We propose a deep state space model for\nprobabilistic time series forecasting whereby the non-linear emission model and\ntransition model are parameterized by networks and the dependency is modeled by\nrecurrent neural nets. We take the automatic relevance determination (ARD) view\nand devise a network to exploit the exogenous variables in addition to time\nseries. In particular, our ARD network can incorporate the uncertainty of the\nexogenous variables and eventually helps identify useful exogenous variables\nand suppress those irrelevant for forecasting. The distribution of multi-step\nahead forecasts are approximated by Monte Carlo simulation. We show in\nexperiments that our model produces accurate and sharp probabilistic forecasts.\nThe estimated uncertainty of our forecasting also realistically increases over\ntime, in a spontaneous manner.\n"}
+{"abstract": "  Let $\\{c_k\\}$ be a nonincreasing sequence of positive numbers (more general\nclasses of sequences are also considered), and $\\alpha>0$ be not an integer. We\nfind necessary and sufficient conditions for the uniform convergence of the\nseries $\\sum_k c_k\\sin k^{\\alpha}x$ and $\\sum_k c_k\\cos k^{\\alpha}x$ on the\nreal line and its bounded subsets.\n"}
+{"abstract": "  In this paper we deal with positive singular solutions to semilinear elliptic\nproblems involving a first order term and a singular nonlinearity. Exploiting a\nfine adaptation of the well-known moving plane method of Alexandrov-Serrin and\na careful choice of the cutoff functions, we deduce symmetry and monotonicity\nproperties of the solutions.\n"}
+{"abstract": "  This paper aims to address few-shot semantic segmentation. While existing\nprototype-based methods have achieved considerable success, they suffer from\nuncertainty and ambiguity caused by limited labelled examples. In this work, we\npropose attentional prototype inference (API), a probabilistic latent variable\nframework for few-shot semantic segmentation. We define a global latent\nvariable to represent the prototype of each object category, which we model as\na probabilistic distribution. The probabilistic modeling of the prototype\nenhances the model's generalization ability by handling the inherent\nuncertainty caused by limited data and intra-class variations of objects. To\nfurther enhance the model, we introduce a local latent variable to represent\nthe attention map of each query image, which enables the model to attend to\nforeground objects while suppressing background. The optimization of the\nproposed model is formulated as a variational Bayesian inference problem, which\nis established by amortized inference networks.We conduct extensive experiments\non three benchmarks, where our proposal obtains at least competitive and often\nbetter performance than state-of-the-art methods. We also provide comprehensive\nanalyses and ablation studies to gain insight into the effectiveness of our\nmethod for few-shot semantic segmentation.\n"}
+{"abstract": "  Rare information on photodisintegration reactions of nuclei with mass numbers\n$A \\approx 160$ at astrophysical conditions impedes our understanding of the\norigin of $p$-nuclei. Experimental determination of the key ($p,\\gamma$) cross\nsections has been playing an important role to verify nuclear reaction models\nand to provide rates of relevant ($\\gamma,p$) reactions in $\\gamma$-process. In\nthis paper we report the first cross section measurements of\n$^{160}$Dy($p,\\gamma$)$^{161}$Ho and $^{161}$Dy($p,n$)$^{161}$Ho in the beam\nenergy range of 3.4 - 7.0 MeV, partially covering the Gamow window. Such\ndeterminations are possible by using two targets with various isotopic\nfractions. The cross section data can put a strong constraint on the nuclear\nlevel densities and gamma strength functions for $A \\approx$ 160 in the\nHauser-Feshbach statistical model. Furthermore, we find the best parameters for\nTALYS that reproduce the A $\\thicksim$ 160 data available,\n$^{160}$Dy($p,\\gamma$)$^{161}$Ho and $^{162}$Er($p,\\gamma$)$^{163}$Tm, and\nrecommend the constrained $^{161}$Ho($\\gamma,p$)$^{160}$Dy reaction rates over\na wide temperature range for $\\gamma$-process network calculations. Although\nthe determined $^{161}$Ho($\\gamma$, p) stellar reaction rates at the\ntemperature of 1 to 2 GK can differ by up to one order of magnitude from the\nNON-SMOKER predictions, it has a minor effect on the yields of $^{160}$Dy and\naccordingly the $p$-nuclei, $^{156,158}$Dy. A sensitivity study confirms that\nthe cross section of $^{160}$Dy($p$, $\\gamma$)$^{161}$Ho is measured precisely\nenough to predict yields of $p$-nuclei in the $\\gamma$-process.\n"}
+{"abstract": "  We consider the adjacency matrix $A$ of the Erd{\\H o}s--R\\'enyi graph on $N$\nvertices with edge probability $d/N$. For $(\\log \\log N)^4 \\ll d \\lesssim \\log\nN$, we prove that the eigenvalues near the spectral edge form asymptotically a\nPoisson process and the associated eigenvectors are exponentially localized. As\na corollary, at the critical scale $d \\asymp \\log N$, the limiting distribution\nof the largest nontrivial eigenvalue does not match with any previously known\ndistribution. Together with [arXiv:2005.14180], our result establishes the\ncoexistence of a fully delocalized phase and a fully localized phase in the\nspectrum of $A$. The proof relies on a three-scale rigidity argument, which\ncharacterizes the fluctuations of the eigenvalues in terms of the fluctuations\nof sizes of spheres of radius 1 and 2 around vertices of large degree.\n"}
+{"abstract": "  Carrinheiros are collectors of recyclable materials that use human-powered\nvehicles. Carrinheiro's collection routes can be tiring depending on the paths\nchosen. Therefore, this work proposes an algorithm for suggesting customizable\nroutes based on three edge costing policies: Less Work Policy, Less Impedance\nPolicy, and Short Distance Policy. This work used the tools osmnx and networkx\nto construct graphs, geographic data from Open Street Map, and elevations from\nTopodata. The simulations performed in Simulation of Urban MObility (SUMO)\ndemonstrated that the proposed algorithm could minimize the power applied to\npush the vehicle, the distance, and the travel time, according to the policy\nused.\n"}
+{"abstract": "  The paper is concerned with the finite-time stabilization of a hybrid PDE-ODE\nsystem describing the motion of an overhead crane with a flexible cable. The\ndynamics of the flexible cable is described by the wave equation with a\nvariable coefficient which is an affine function of the curvilinear abscissa\nalong the cable. Using several changes of variables, a backstepping\ntransformation, and a finite-time stable second-order ODE for the dynamics of a\nconveniently chosen variable, we prove that a global finite-time stabilization\noccurs for the full system constituted of the platform and the cable. The\nkernel equations and the finite-time stable ODE are numerically solved in order\nto compute the nonlinear feedback law, and numerical simulations validating our\nfinite-time stabilization approach are presented.\n"}
+{"abstract": "  A recent line of research focuses on the study of the stochastic multi-armed\nbandits problem (MAB), in the case where temporal correlations of specific\nstructure are imposed between the player's actions and the reward distributions\nof the arms (Kleinberg and Immorlica [FOCS18], Basu et al. [NeurIPS19]). As\nopposed to the standard MAB setting, where the optimal solution in hindsight\ncan be trivially characterized, these correlations lead to (sub-)optimal\nsolutions that exhibit interesting dynamical patterns -- a phenomenon that\nyields new challenges both from an algorithmic as well as a learning\nperspective. In this work, we extend the above direction to a combinatorial\nbandit setting and study a variant of stochastic MAB, where arms are subject to\nmatroid constraints and each arm becomes unavailable (blocked) for a fixed\nnumber of rounds after each play. A natural common generalization of the\nstate-of-the-art for blocking bandits, and that for matroid bandits, yields a\n$(1-\\frac{1}{e})$-approximation for partition matroids, yet it only guarantees\na $\\frac{1}{2}$-approximation for general matroids. In this paper we develop\nnew algorithmic ideas that allow us to obtain a polynomial-time $(1 -\n\\frac{1}{e})$-approximation algorithm (asymptotically and in expectation) for\nany matroid, and thus to control the $(1-\\frac{1}{e})$-approximate regret. A\nkey ingredient is the technique of correlated (interleaved) scheduling. Along\nthe way, we discover an interesting connection to a variant of Submodular\nWelfare Maximization, for which we provide (asymptotically) matching upper and\nlower approximability bounds.\n"}
+{"abstract": "  Tomographic reconstruction recovers an unknown image given its projections\nfrom different angles. State-of-the-art methods addressing this problem assume\nthe angles associated with the projections are known a-priori. Given this\nknowledge, the reconstruction process is straightforward as it can be\nformulated as a convex problem. Here, we tackle a more challenging setting: 1)\nthe projection angles are unknown, 2) they are drawn from an unknown\nprobability distribution. In this set-up our goal is to recover the image and\nthe projection angle distribution using an unsupervised adversarial learning\napproach. For this purpose, we formulate the problem as a distribution matching\nbetween the real projection lines and the generated ones from the estimated\nimage and projection distribution. This is then solved by reaching the\nequilibrium in a min-max game between a generator and a discriminator. Our\nnovel contribution is to recover the unknown projection distribution and the\nimage simultaneously using adversarial learning. To accommodate this, we use\nGumbel-softmax approximation of samples from categorical distribution to\napproximate the generator's loss as a function of the unknown image and the\nprojection distribution. Our approach can be generalized to different inverse\nproblems. Our simulation results reveal the ability of our method in\nsuccessfully recovering the image and the projection distribution in various\nsettings.\n"}
+{"abstract": "  Floquet theory has spawned many exciting possibilities for electronic\nstructure control with light with enormous potential for future applications.\nThe experimental realization in solids, however, largely remains pending. In\nparticular, the influence of scattering on the formation of Floquet-Bloch\nstates remains poorly understood. Here we combine time- and angle-resolved\nphotoemission spectroscopy with time-dependent density functional theory and a\ntwo-level model with relaxation to investigate the survival of Floquet-Bloch\nstates in the presence of scattering. We find that Floquet-Bloch states will be\ndestroyed if scattering -- activated by electronic excitations -- prevents the\nBloch electrons from following the driving field coherently. The two-level\nmodel also shows that Floquet-Bloch states reappear at high field intensities\nwhere energy exchange with the driving field dominates over energy dissipation\nto the bath. Our results clearly indicate the importance of long scattering\ntimes combined with strong driving fields for the successful realization of\nvarious Floquet phenomena.\n"}
+{"abstract": "  Deep kernel learning (DKL) and related techniques aim to combine the\nrepresentational power of neural networks with the reliable uncertainty\nestimates of Gaussian processes. One crucial aspect of these models is an\nexpectation that, because they are treated as Gaussian process models optimized\nusing the marginal likelihood, they are protected from overfitting. However, we\nidentify situations where this is not the case. We explore this behavior,\nexplain its origins and consider how it applies to real datasets. Through\ncareful experimentation on the UCI, CIFAR-10, and the UTKFace datasets, we find\nthat the overfitting from overparameterized maximum marginal likelihood, in\nwhich the model is \"somewhat Bayesian\", can in certain scenarios be worse than\nthat from not being Bayesian at all. We explain how and when DKL can still be\nsuccessful by investigating optimization dynamics. We also find that failures\nof DKL can be rectified by a fully Bayesian treatment, which leads to the\ndesired performance improvements over standard neural networks and Gaussian\nprocesses.\n"}
+{"abstract": "  Quantum mass functions (QMFs), which are tightly related to decoherence\nfunctionals, were introduced by Loeliger and Vontobel [IEEE Trans. Inf. Theory,\n2017, 2020] as a generalization of probability mass functions toward modeling\nquantum information processing setups in terms of factor graphs.\n  Simple quantum mass functions (SQMFs) are a special class of QMFs that do not\nexplicitly model classical random variables. Nevertheless, classical random\nvariables appear implicitly in an SQMF if some marginals of the SQMF satisfy\nsome conditions; variables of the SQMF corresponding to these \"emerging\" random\nvariables are called classicable variables. Of particular interest are jointly\nclassicable variables.\n  In this paper we initiate the characterization of the set of marginals given\nby the collection of jointly classicable variables of a graphical model and\ncompare them with other concepts associated with graphical models like the sets\nof realizable marginals and the local marginal polytope.\n  In order to further characterize this set of marginals given by the\ncollection of jointly classicable variables, we generalize the CHSH inequality\nbased on the Pearson correlation coefficients, and thereby prove a conjecture\nproposed by Pozsgay et al. A crucial feature of this inequality is its\nnonlinearity, which poses difficulties in the proof.\n"}
+{"abstract": "  With the increase of research in self-adaptive systems, there is a need to\nbetter understand the way research contributions are evaluated. Such insights\nwill support researchers to better compare new findings when developing new\nknowledge for the community. However, so far there is no clear overview of how\nevaluations are performed in self-adaptive systems. To address this gap, we\nconduct a mapping study. The study focuses on experimental evaluations\npublished in the last decade at the prime venue of research in software\nengineering for self-adaptive systems -- the International Symposium on\nSoftware Engineering for Adaptive and Self-Managing Systems (SEAMS). Results\npoint out that specifics of self-adaptive systems require special attention in\nthe experimental process, including the distinction of the managing system\n(i.e., the target of evaluation) and the managed system, the presence of\nuncertainties that affect the system behavior and hence need to be taken into\naccount in data analysis, and the potential of managed systems to be reused\nacross experiments, beyond replications. To conclude, we offer a set of\nsuggestions derived from our study that can be used as input to enhance future\nexperiments in self-adaptive systems.\n"}
+{"abstract": "  When managing wide-area networks, network architects must decide how to\nbalance multiple conflicting metrics, and ensure fair allocations to competing\ntraffic while prioritizing critical traffic. The state of practice poses\nchallenges since architects must precisely encode their (somewhat fuzzy) intent\ninto formal optimization models using abstract notions such as utility\nfunctions, and ad-hoc manually tuned knobs. In this paper, we present the first\neffort to synthesize network designs with indeterminate objectives using an\ninteractive program-synthesis-based approach. We make three contributions.\nFirst, we present a novel framework in which a user's design objective, and the\nsynthesis of a program (network design) that optimizes that objective are done\nin tandem. Second, we develop a novel algorithm for our framework in which a\nvoting-guided learner makes two kinds of queries (Propose and Compare) to the\nuser, with the aim of minimizing the number of queries. We present theoretical\nanalysis of the convergence rate of the algorithm. Third, we implemented\nNet10Q, a system based on our approach, and demonstrate its effectiveness on\nfour real-world network case studies using black-box oracles and simulation\nexperiments, as well as a pilot user study comprising network researchers and\npractitioners. Both theoretical and experimental results show the promise of\nour approach.\n"}
+{"abstract": "  Uniform asymptotic expansions are derived for Whittaker's confluent\nhypergeometric functions $M_{\\kappa,\\mu}(z)$ and $W_{\\kappa,\\mu}(z)$, as well\nas the numerically satisfactory companion function $W_{-\\kappa,\\mu}(ze^{-\\pi\ni})$. The expansions are uniformly valid for $\\mu \\rightarrow \\infty$, $0 \\leq\n\\kappa/\\mu \\leq 1-\\delta <1$, and for $0 \\leq \\arg(z) \\leq \\pi$. By using\nappropriate connection and analytic continuation formulas these expansions can\nbe extended to all unbounded nonzero complex $z$. The approximations come from\nrecent asymptotic expansions involving elementary functions and Airy functions,\nand explicit error bounds are either provided or available.\n"}
+{"abstract": "  Carbon nanotubes tend to collapse when their diameters exceed a certain\nthreshold, or when a sufficiently large external pressure is applied on their\nwalls. Their radial stability of tubes has been studied in each of these cases,\nhowever a general theory able to predict collapse is still lacking. Here, we\npropose a simple model predicting stability limits as a function of the tube\ndiameter, the number of walls and the pressure. The model is supported by\natomistic simulations, experiments, and is used to plot collapse phase\ndiagrams. We have identified the most stable carbon nanotube, which can support\na maximum pressure of 18 GPa before collapsing. The latter was identified as a\nmultiwall tube with an internal tube diameter of 12nm and 30 walls. This\nmaximum pressure is lowered depending on the internal tube diameter and the\nnumber of walls. We then identify a tube diameter domain in which the radial\nmechanical stability can be treated as equivalent to macroscopic tubes, known\nto be described by the canonical L\\'evy-Carrier law. This multiscale behavior\nis shown to be in good agreement with experiments based on O-ring gaskets\ncollapse, proposed as a simple macroscopic parallel to nanotubes in this\ndomain.\n"}
+{"abstract": "  The formation and evolution of light elements in the Universe act as\nimportant cosmological constraints. It has long been assumed that the oldest\nstars of the Galaxy display the primordial Li abundance in their outer layers,\nalthough studies of stellar physics have proven that this abundance must have\ndecreased with time. The primordial Li abundance deduced from the observations\nof the cosmic background is larger than the maximum observed in these stars.\nRecent observations have given evidence of a large Li abundance dispersion in\nvery metal-poor stars. Many of these stars are carbon-rich, the carbon-enhanced\nmetal-poor (CEMP) stars. We address the general question of the observed\nabundances in metal-poor stars and we focus our study on the case of CEMP-s\nstars. We study how the accretion of the wind of stellar companions, especially\nasymptotic giant branch stars, modifies the element abundances of metal-poor\nstars and, in particular Li, taking into account the stellar structure and the\nhydrodynamic processes that take place after accretion. We compare the results\nwith the observations of Li and heavier elements in these old stars on the main\nsequence. We use the Montr\\'eal/Montpellier stellar evolution code, which\nincludes atomic diffusion and thermohaline convection, to compute the internal\nstructure of the proto-CEMP-s stars and their evolution, from [Fe/H]=-2.31 down\nto [Fe/H]=-5.45. We study a number of cases that vary according to the masses\nof the stars, their ages, metallicities, and the distances to their respective\ncompanions. We show that the observations of Li dispersion that is associated\n(or not) with carbon enrichment are well accounted for in terms of accretion on\nto the metal-poor stars of the winds of stellar companions, with accreted\nmasses smaller than those considered in previous studies. The derived\nprimordial value is in accordance with the cosmological results.\n"}
+{"abstract": "  Promising access to high-speed quantum networks relies on the creation of\nhigh-dimensional entangled memories that provide quantum communication with\nhigher capacity of noisy quantum channels, thereby reducing the transmission\ntime of information. Yet, the distribution of multidimensional entanglement\nbetween remote memory nodes is still faintly investigated. We propose an\nexperimentally feasible protocol of deterministic generation of\nhigh-dimensional entanglement between distant multi-level atoms confined in\nhigh-finesse optical cavities and driven by laser pulses. Three-dimensional\nentanglement is generated deterministically between remote atoms by triggering\na two-photon wavepacket, which mediates a superposition of states chosen by the\nlaser pulse parameters. The efficient transfer of atomic states between remote\nnodes allows the construction of a three-dimensional quantum repeater, where\nthe successful creation of entanglement can be verified by a simple method for\nreliable measurement of atomic ground Zeeman states.\n"}
+{"abstract": "  The rapid spreading of SARS-CoV-2 and its dramatic consequences, are forcing\npolicymakers to take strict measures in order to keep the population safe. At\nthe same time, societal and economical interactions are to be safeguarded. A\nwide spectrum of containment measures have been hence devised and implemented,\nin different countries and at different stages of the pandemic evolution.\nMobility towards workplace or retails, public transit usage and permanence in\nresidential areas constitute reliable tools to indirectly photograph the actual\ngrade of the imposed containment protocols. In this paper, taking Italy as an\nexample, we will develop and test a deep learning model which can forecast\nvarious spreading scenarios based on different mobility indices, at a regional\nlevel. We will show that containment measures contribute to \"flatten the curve\"\nand quantify the minimum time frame necessary for the imposed restrictions to\nresult in a perceptible impact, depending on their associated grade.\n"}
+{"abstract": "  Graph Neural Networks (GNNs) are a wide class of connectionist models for\ngraph processing. They perform an iterative message passing operation on each\nnode and its neighbors, to solve classification/ clustering tasks -- on some\nnodes or on the whole graph -- collecting all such messages, regardless of\ntheir order. Despite the differences among the various models belonging to this\nclass, most of them adopt the same computation scheme, based on a local\naggregation mechanism and, intuitively, the local computation framework is\nmainly responsible for the expressive power of GNNs. In this paper, we prove\nthat the Weisfeiler--Lehman test induces an equivalence relationship on the\ngraph nodes that exactly corresponds to the unfolding equivalence, defined on\nthe original GNN model. Therefore, the results on the expressive power of the\noriginal GNNs can be extended to general GNNs which, under mild conditions, can\nbe proved capable of approximating, in probability and up to any precision, any\nfunction on graphs that respects the unfolding equivalence.\n"}
+{"abstract": "  We study embeddings of groups of Lie type $H$ in characteristic $p$ into\nexceptional algebraic groups $\\mathbf G$ of the same characteristic. We exclude\nthe case where $H$ is of type $\\mathrm{PSL}_2$. A subgroup of $\\mathbf G$ is\n\\emph{Lie primitive} if it is not contained in any proper, positive-dimensional\nsubgroup of $\\mathbf G$.\n  With a few possible exceptions, we prove that there are no Lie primitive\nsubgroups $H$ in $\\mathbf G$, with the conditions on $H$ and $\\mathbf G$ given\nabove. The exceptions are for $H$ one of $\\mathrm{PSL}_3(3)$,\n$\\mathrm{PSU}_3(3)$, $\\mathrm{PSL}_3(4)$, $\\mathrm{PSU}_3(4)$,\n$\\mathrm{PSU}_3(8)$, $\\mathrm{PSU}_4(2)$, $\\mathrm{PSp}_4(2)'$ and\n${}^2\\!B_2(8)$, and $\\mathbf G$ of type $E_8$. No examples are known of such\nLie primitive embeddings.\n  We prove a slightly stronger result, including stability under automorphisms\nof $\\mathbf G$. This has the consequence that, with the same exceptions, any\nalmost simple group with socle $H$, that is maximal inside an almost simple\nexceptional group of Lie type $F_4$, $E_6$, ${}^2\\!E_6$, $E_7$ and $E_8$, is\nthe fixed points under the Frobenius map of a corresponding maximal closed\nsubgroup inside the algebraic group.\n  The proof uses a combination of representation-theoretic, algebraic\ngroup-theoretic, and computational means.\n"}
+{"abstract": "  It is crucial to distinguish mislabeled samples for dealing with noisy\nlabels. Previous methods such as Coteaching and JoCoR introduce two different\nnetworks to select clean samples out of the noisy ones and only use these clean\nones to train the deep models. Different from these methods which require to\ntrain two networks simultaneously, we propose a simple and effective method to\nidentify clean samples only using one single network. We discover that the\nclean samples prefer to reach consistent predictions for the original images\nand the transformed images while noisy samples usually suffer from inconsistent\npredictions. Motivated by this observation, we introduce to constrain the\ntransform consistency between the original images and the transformed images\nfor network training, and then select small-loss samples to update the\nparameters of the network. Furthermore, in order to mitigate the negative\ninfluence of noisy labels, we design a classification loss by using the\noff-line hard labels and on-line soft labels to provide more reliable\nsupervisions for training a robust model. We conduct comprehensive experiments\non CIFAR-10, CIFAR-100 and Clothing1M datasets. Compared with the baselines, we\nachieve the state-of-the-art performance. Especially, in most cases, our\nproposed method outperforms the baselines by a large margin.\n"}
+{"abstract": "  Set theoretical paradoxes have a common root -- lack of understanding of why\nsome multitudes are not sets. Why some multitudes of objects of thought cannot\nthemselves be objects of thought? Moreover, it is a logical truth that such\nmultitudes do exist. However we do not understand this logical truth so well as\nwe understand, for example, the logical truth $\\forall x \\ x=x$. In this paper,\nwe formulate a logical truth which we call the productivity principle. Bertrand\nRusell was the first one to formulate this principle, but in a restricted form\nand with a different purpose. The principle explicates a logical mechanism that\nlies behind paradoxical multitudes and is understandable as well as any simple\nlogical truth. However, it does not explain the concept of set. It only sets\nlogical bounds of the concept within the framework of the classical two-valued\n$\\in$ - language. The principle behaves as a logical regulator of any theory we\nformulate to explain and describe sets. It provides tools to identify\nparadoxical classes inside the theory. We show how the known paradoxical\nclasses follow from the productivity principle and how the principle gives us a\nuniform way to generate new paradoxical classes. In the case of $ZFC$ set\ntheory, the productivity principle shows that the limitation of size principles\nare of a restrictive nature and that they do not explain which classes are\nsets. The productivity principle, as a logical regulator, can have a definite\nheuristic role in the development of a consistent set theory. We sketch such a\ntheory -- the cumulative cardinal theory of sets.\n"}
+{"abstract": "  In this work, we present a per-instant pose optimization method that can\ngenerate configurations that achieve specified pose or motion objectives as\nbest as possible over a sequence of solutions, while also simultaneously\navoiding collisions with static or dynamic obstacles in the environment. We\ncast our method as a multi-objective, non-linear constrained optimization-based\nIK problem where each term in the objective function encodes a particular pose\nobjective. We demonstrate how to effectively incorporate environment collision\navoidance as a single term in this multi-objective, optimization-based IK\nstructure, and provide solutions for how to spatially represent and organize\nexternal environments such that data can be efficiently passed to a real-time,\nperformance-critical optimization loop. We demonstrate the effectiveness of our\nmethod by comparing it to various state-of-the-art methods in a testbed of\nsimulation experiments and discuss the implications of our work based on our\nresults.\n"}
+{"abstract": "  This paper presents maplet, an open-source R package for the creation of\nhighly customizable, fully reproducible statistical pipelines for omics data\nanalysis, with a special focus on metabolomics-based methods. It builds on the\nSummarizedExperiment data structure to create a centralized pipeline framework\nfor storing data, analysis steps, results, and visualizations. maplet's key\ndesign feature is its modularity, which offers several advantages, such as\nensuring code quality through the individual maintenance of functions and\npromoting collaborative development by removing technical barriers to code\ncontribution. With over 90 functions, the package includes a wide range of\nfunctionalities, covering many widely used statistical approaches and data\nvisualization techniques.\n"}
+{"abstract": "  Singularity of the potential function makes quantum tunneling problem\nmathematically underdetermined. To circumvent the difficulties it introduced in\nphysics, a potential singularity cutoff is often used, followed by a reverse\nlimit transition, or is a suitable self-adjoint extension of the Hamiltonian\nalong the entire coordinate axis made. However, both of them somehow affect the\nsingular nature of the problem, and so I discuss here how quantum tunneling\nwill behave if the original singular nature of the Schrodinger equation left\nuntuched. To do this, I use the property of the probability density current\nthat the singularities are mutually destroyed in it. It is found that the\nmildly singular potential has a finite, but unusual tunneling transparency, in\nparticular, a non-zero value at zero energy of incident particle. The tunneling\nof one dimensional Coulomb potential exhibits infinitely fast and complete\noscillation at the zero energy boundary and a suppresion to zero in the\nhigh-energy limit. In the more singular region, the tunneling becomes\nforbidden, theby repeating the well-known result of the regularized\ncounterparts.\n"}
+{"abstract": "  Deep learning researchers and practitioners usually leverage GPUs to help\ntrain their deep neural networks (DNNs) faster. However, choosing which GPU to\nuse is challenging both because (i) there are many options, and (ii) users\ngrapple with competing concerns: maximizing compute performance while\nminimizing costs. In this work, we present a new practical technique to help\nusers make informed and cost-efficient GPU selections: make performance\npredictions with the help of a GPU that the user already has. Our technique\nexploits the observation that, because DNN training consists of repetitive\ncompute steps, predicting the execution time of a single iteration is usually\nenough to characterize the performance of an entire training process. We make\npredictions by scaling the execution time of each operation in a training\niteration from one GPU to another using either (i) wave scaling, a technique\nbased on a GPU's execution model, or (ii) pre-trained multilayer perceptrons.\nWe implement our technique into a Python library called Habitat and find that\nit makes accurate iteration execution time predictions (with an average error\nof 11.8%) on ResNet-50, Inception v3, the Transformer, GNMT, and DCGAN across\nsix different GPU architectures. Habitat supports PyTorch, is easy to use, and\nis open source.\n"}
+{"abstract": "  Machine learning models fit complex algorithms to arbitrarily large datasets.\nThese algorithms are well-known to be high on performance and low on\ninterpretability. We use interactive visualization of slices of predictor space\nto address the interpretability deficit; in effect opening up the black-box of\nmachine learning algorithms, for the purpose of interrogating, explaining,\nvalidating and comparing model fits. Slices are specified directly through\ninteraction, or using various touring algorithms designed to visit\nhigh-occupancy sections or regions where the model fits have interesting\nproperties. The methods presented here are implemented in the R package\n\\pkg{condvis2}.\n"}
+{"abstract": "  Bound states in superconductors are expected to exhibit a spatially resolved\nelectron-hole asymmetry which is the hallmark of their quantum nature. This\nasymmetry manifests as oscillations at the Fermi wavelength, which is usually\ntiny and thus washed out by thermal broadening or by scattering at defects.\nHere we demonstrate theoretically and confirm experimentally that, when coupled\nto magnetic impurities, bound states in a vortex core exhibit an emergent axial\nelectron-hole asymmetry on a much longer scale, set by the coherence length. We\nstudy vortices in 2H-NbSe$_2$ and in 2H-NbSe$_{1.8}$S_{0.2}$ with magnetic\nimpurities, characterizing these with detailed Hubbard-corrected density\nfunctional calculations. We find that the induced electron-hole imbalance\ndepends on the band character of the superconducting material. Our results open\ninteresting prospects for the study of coupled superconducting bound states.\n"}
+{"abstract": "  In this paper, we study the problem of evaluating the addition of elements to\na set. This problem is difficult, because it can, in the general case, not be\nreduced to unconditional preferences between the choices. Therefore, we model\npreferences based on the context of the decision. We discuss and compare two\ndifferent Siamese network architectures for this task: a twin network that\ncompares the two sets resulting after the addition, and a triplet network that\nmodels the contribution of each candidate to the existing set. We evaluate the\ntwo settings on a real-world task; learning human card preferences for deck\nbuilding in the collectible card game Magic: The Gathering. We show that the\ntriplet approach achieves a better result than the twin network and that both\noutperform previous results on this task.\n"}
+{"abstract": "  We present a new ground-based visible transmission spectrum of the\nhigh-gravity, hot Jupiter HAT-P-23b, obtained as part of the ACCESS project. We\nderive the spectrum from five transits observed between 2016 and 2018, with\ncombined wavelength coverage between 5200 {\\AA} - 9269 {\\AA} in 200 {\\AA} bins,\nand with a median precision of 247 ppm per bin. HAT-P-23b's relatively high\nsurface gravity (g ~ 30 m/s^2), combined with updated stellar and planetary\nparameters from Gaia DR2, gives a 5-scale-height signal of 384 ppm for a\nhydrogen-dominated atmosphere. Bayesian models favor a clear atmosphere for the\nplanet with the tentative presence of TiO, after simultaneously modeling\nstellar contamination, using spots parameter constraints from photometry. If\nconfirmed, HAT-P-23b would be the first example of a high-gravity gas giant\nwith a clear atmosphere observed in transmission at optical/NIR wavelengths;\ntherefore, we recommend expanding observations to the UV and IR to confirm our\nresults and further characterize this planet. This result demonstrates how\ncombining transmission spectroscopy of exoplanet atmospheres with long-term\nphotometric monitoring of the host stars can help disentangle the exoplanet and\nstellar activity signals.\n"}
+{"abstract": "  A central question in the underdoped cuprates pertains to the nature of the\npseudogap ground state. A conventional metallic ground state of the pseudogap\nregion has been argued to host quantum oscillations upon destruction of the\nsuperconducting order parameter by modest magnetic fields. Here we use low\napplied measurement currents and millikelvin temperatures on ultra-pure single\ncrystals of underdoped YBa$_2$Cu$_3$O$_{6+x}$ to unearth an unconventional\nquantum vortex matter ground state characterized by vanishing electrical\nresistivity, magnetic hysteresis, and non-ohmic electrical transport\ncharacteristics beyond the highest laboratory accessible static fields. A new\nmodel of the pseudogap ground state is now required to explain quantum\noscillations that are hosted by the bulk quantum vortex matter state without\nexperiencing sizeable additional damping in the presence of a large maximum\nsuperconducting gap; possibilities include a pair density wave.\n"}
+{"abstract": "  Two-dimensional electron gases (2DEGs) on the SrTiO3 (STO) surface or in\nSTO-based heterostructures have exhibited many intriguing phenomena, which are\nstrongly dependent on the 2DEG-carrier density. We report that the tunability\nof the 2DEG-carrier density is significantly enhanced by adding a monolayer\nLaTiO3 (LTO) onto the STO. Ultraviolet (UV) irradiation induced maximum carrier\ndensity of the 2DEG in LTO/STO is increased by a factor of ~4 times, compared\nto that of the bare STO. By oxygen gas exposure, it becomes 10 times smaller\nthan that of the bare STO. This enhanced tunability is attributed to the\ndrastic surface property change of a polar LTO layer by UV irradiation and O2\nexposure. This indicates that the 2DEG controllability in LTO/STO is more\nreliable than that on the bare STO driven by defects, such an oxygen vacancy.\n"}
+{"abstract": "  The rigidity propeties of higher rank diagonalizable actions is a major theme\nin homogenous dynamics, with origins in work of Cassels and Swinnerton-Dyer in\nthe 1950s and Furstenberg. We survey both results and conjectures regarding\nsuch actions, with emphasize on the applications of these results towards\nunderstanding the distribution of integer points on varieties, quantum unique\nergodicity, and Diophantine approximations.\n"}
+{"abstract": "  We show that a signature of the quantum nature of gravity is the quantum\nmechanical squeezing of the differential motion of two identical masses with\nrespect to their common mode. This is because the gravitational interaction\ndepends solely on the relative position of the two masses. In principle, this\nsqueezing is equivalent to quantum entanglement between the masses. In\npractice, detecting the squeezing is more feasible than detecting the\nentanglement. To that end, we propose an optical interferometric scheme to\nfalsify hypothetical models of gravity.\n"}
+{"abstract": "  As the COVID-19 epidemic began to worsen in the first months of 2020,\nstringent lockdown policies were implemented in numerous cities throughout the\nworld to control human transmission and mitigate its spread. Although\ntransportation density reduction inside the city was felt subjectively, there\nhas thus far been no objective and quantitative study of its variation to\nreflect the intracity population flows and their corresponding relationship\nwith lockdown policy stringency from the view of remote sensing images with the\nhigh resolution under 1m. Accordingly, we here provide a quantitative\ninvestigation of the transportation density reduction before and after lockdown\nwas implemented in six epicenter cities (Wuhan, Milan, Madrid, Paris, New York,\nand London) around the world during the COVID-19 epidemic, which is\naccomplished by extracting vehicles from the multi-temporal high-resolution\nremote sensing images. A novel vehicle detection model combining unsupervised\nvehicle candidate extraction and deep learning identification was specifically\nproposed for the images with the resolution of 0.5m. Our results indicate that\ntransportation densities were reduced by an average of approximately 50% (and\nas much as 75.96%) in these six cities following lockdown. The influences on\ntransportation density reduction rates are also highly correlated with policy\nstringency, with an R^2 value exceeding 0.83. Even within a specific city, the\ntransportation density changes differed and tended to be distributed in\naccordance with the city's land-use patterns. Considering that public\ntransportation was mostly reduced or even forbidden, our results indicate that\ncity lockdown policies are effective at limiting human transmission within\ncities.\n"}
+{"abstract": "  In this paper, a diffuse-interface lattice Boltzmann method (DI-LBM) is\ndeveloped for fluid-particle interaction problems. In this method, the sharp\ninterface between the fluid and solid is replaced by a thin but nonzero\nthickness transition region named diffuse interface, where the physical\nvariables varies continuously. In order to describe the diffuse interface, we\nintroduce a smooth function, which is similar to the order parameter in\nphase-field model or the volume fraction of solid phase in the partially\nsaturated lattice Boltzmann method (PS-LBM). In addition, to depict the\nfluid-particle interaction more accurately, a modified force term is also\nproposed and included in the evolution equation of the DI-LBM. Some classical\nproblems are used to test the DI-LBM, and the results are in good agreement\nwith some available theoretical and numerical works. Finally, it is also found\nthat the DI-LBM is more efficient and accurate than the PS-LBM with the\nsuperposition model.\n"}
+{"abstract": "  We present SoftDICE, which achieves state-of-the-art performance for\nimitation learning. SoftDICE fixes several key problems in ValueDICE, an\noff-policy distribution matching approach for sample-efficient imitation\nlearning. Specifically, the objective of ValueDICE contains logarithms and\nexponentials of expectations, for which the mini-batch gradient estimate is\nalways biased. Second, ValueDICE regularizes the objective with replay buffer\nsamples when expert demonstrations are limited in number, which however changes\nthe original distribution matching problem. Third, the re-parametrization trick\nused to derive the off-policy objective relies on an implicit assumption that\nrarely holds in training. We leverage a novel formulation of distribution\nmatching and consider an entropy-regularized off-policy objective, which yields\na completely offline algorithm called SoftDICE. Our empirical results show that\nSoftDICE recovers the expert policy with only one demonstration trajectory and\nno further on-policy/off-policy samples. SoftDICE also stably outperforms\nValueDICE and other baselines in terms of sample efficiency on Mujoco benchmark\ntasks.\n"}
+{"abstract": "  We develop a framework for comparing data manifolds, aimed, in particular,\ntowards the evaluation of deep generative models. We describe a novel tool,\nCross-Barcode(P,Q), that, given a pair of distributions in a high-dimensional\nspace, tracks multiscale topology spacial discrepancies between manifolds on\nwhich the distributions are concentrated. Based on the Cross-Barcode, we\nintroduce the Manifold Topology Divergence score (MTop-Divergence) and apply it\nto assess the performance of deep generative models in various domains: images,\n3D-shapes, time-series, and on different datasets: MNIST, Fashion MNIST, SVHN,\nCIFAR10, FFHQ, chest X-ray images, market stock data, ShapeNet. We demonstrate\nthat the MTop-Divergence accurately detects various degrees of mode-dropping,\nintra-mode collapse, mode invention, and image disturbance. Our algorithm\nscales well (essentially linearly) with the increase of the dimension of the\nambient high-dimensional space. It is one of the first TDA-based practical\nmethodologies that can be applied universally to datasets of different sizes\nand dimensions, including the ones on which the most recent GANs in the visual\ndomain are trained. The proposed method is domain agnostic and does not rely on\npre-trained networks.\n"}
+{"abstract": "  With the standardization of 5G, commercial millimeter wave (mmWave)\ncommunications has become a reality despite all the concerns about the\nunfavorable propagation characteristics of these frequencies. Even though the\n5G systems are still being rolled out, it is argued that their gigabits per\nsecond rates may fall short in supporting many emerging applications, such as\n3D gaming and extended reality. Such applications will require several hundreds\nof gigabits per second to several terabits per second data rates with low\nlatency and high reliability, which are expected to be the design goals of the\nnext generation 6G communications systems. Given the potential of terahertz\n(THz) communications systems to provide such data rates over short distances,\nthey are widely regarded to be the next frontier for the wireless\ncommunications research. The primary goal of this chapter is to equip readers\nwith sufficient background about the mmWave and THz bands so that they are able\nto both appreciate the necessity of using these bands for commercial\ncommunications in the current wireless landscape and to reason the key design\nconsiderations for the communications systems operating in these bands. Towards\nthis goal, this chapter provides a unified treatment of these bands with\nparticular emphasis on their propagation characteristics, channel models,\ndesign and implementation considerations, and potential applications to 6G\nwireless. A brief summary of the current standardization activities related to\nthe use of these bands for commercial communications applications is also\nprovided.\n"}
+{"abstract": "  Traditional supervised learning with deep neural networks requires a\ntremendous amount of labelled data to converge to a good solution. For 3D\nmedical images, it is often impractical to build a large homogeneous annotated\ndataset for a specific pathology. Self-supervised methods offer a new way to\nlearn a representation of the images in an unsupervised manner with a neural\nnetwork. In particular, contrastive learning has shown great promises by\n(almost) matching the performance of fully-supervised CNN on vision tasks.\nNonetheless, this method does not take advantage of available meta-data, such\nas participant's age, viewed as prior knowledge. Here, we propose to leverage\ncontinuous proxy metadata, in the contrastive learning framework, by\nintroducing a new loss called y-Aware InfoNCE loss. Specifically, we improve\nthe positive sampling during pre-training by adding more positive examples with\nsimilar proxy meta-data with the anchor, assuming they share similar\ndiscriminative semantic features.With our method, a 3D CNN model pre-trained on\n$10^4$ multi-site healthy brain MRI scans can extract relevant features for\nthree classification tasks: schizophrenia, bipolar diagnosis and Alzheimer's\ndetection. When fine-tuned, it also outperforms 3D CNN trained from scratch on\nthese tasks, as well as state-of-the-art self-supervised methods. Our code is\nmade publicly available here.\n"}
+{"abstract": "  The generative adversarial network (GAN) is a well-known model for learning\nhigh-dimensional distributions, but the mechanism for its generalization\nability is not understood. In particular, GAN is vulnerable to the memorization\nphenomenon, the eventual convergence to the empirical distribution. We consider\na simplified GAN model with the generator replaced by a density, and analyze\nhow the discriminator contributes to generalization. We show that with early\nstopping, the generalization error measured by Wasserstein metric escapes from\nthe curse of dimensionality, despite that in the long term, memorization is\ninevitable. In addition, we present a hardness of learning result for WGAN.\n"}
+{"abstract": "  Physical-layer based key generation schemes exploit the channel reciprocity\nfor secret key extraction, which can achieve information-theoretic secrecy\nagainst eavesdroppers. Such methods, although practical, have been shown to be\nvulnerable against man-in-the-middle (MitM) attacks, where an active adversary,\nMallory, can influence and infer part of the secret key generated between Alice\nand Bob by injecting her own packet upon observing highly correlated\nchannel/RSS measurements from Alice and Bob. As all the channels remain stable\nwithin the channel coherence time, Mallory's injected packets cause Alice and\nBob to measure similar RSS, which allows Mallory to successfully predict the\nderived key bits. To defend against such a MitM attack, we propose to utilize a\nreconfigurable antenna at one of the legitimate transceivers to proactively\nrandomize the channel state across different channel probing rounds. The\nrandomization of the antenna mode at every probing round breaks the temporal\ncorrelation of the channels from the adversary to the legitimate devices, while\npreserving the reciprocity of the channel between the latter. This prevents key\ninjection from the adversary without affecting Alice and Bob's ability to\nmeasure common randomness. We theoretically analyze the security of the\nprotocol and conduct extensive simulations and real-world experiments to\nevaluate its performance. Our results show that our approach eliminates the\nadvantage of an active MitM attack by driving down the probability of\nsuccessfully guessing bits of the secret key to a random guess.\n"}
+{"abstract": "  We have investigated tidal forces and geodesic deviation motion in the\n4D-Einstein-Gauss-Bonnet spacetime. Our results show that tidal force and\ngeodesic deviation motion depend sharply on the sign of Gauss-Bonnet coupling\nconstant. Comparing with Schwarzschild spacetime, the strength of tidal force\nbecomes stronger for the negative Gauss-Bonnet coupling constant, but is weaker\nfor the positive one. Moreover, tidal force behaves like those in the\nSchwarzschild spacetime as the coupling constant is negative, and like those in\nReissner-Nordstr\\\"{o}m black hole as the constant is positive. We also present\nthe change of geodesic deviation vector with Gauss-Bonnet coupling constant\nunder two kinds of initial conditions.\n"}
+{"abstract": "  Inspired by two basic mechanisms in animal visual systems, we introduce a\nfeature transform technique that imposes invariance properties in the training\nof deep neural networks. The resulting algorithm requires less parameter\ntuning, trains well with an initial learning rate 1.0, and easily generalizes\nto different tasks. We enforce scale invariance with local statistics in the\ndata to align similar samples at diverse scales. To accelerate convergence, we\nenforce a GL(n)-invariance property with global statistics extracted from a\nbatch such that the gradient descent solution should remain invariant under\nbasis change. Profiling analysis shows our proposed modifications takes 5% of\nthe computations of the underlying convolution layer. Tested on convolutional\nnetworks and transformer networks, our proposed technique requires fewer\niterations to train, surpasses all baselines by a large margin, seamlessly\nworks on both small and large batch size training, and applies to different\ncomputer vision and language tasks.\n"}
+{"abstract": "  In this paper, we study the issue of automatic singer identification (SID) in\npopular music recordings, which aims to recognize who sang a given piece of\nsong. The main challenge for this investigation lies in the fact that a\nsinger's singing voice changes and intertwines with the signal of background\naccompaniment in time domain. To handle this challenge, we propose the KNN-Net\nfor SID, which is a deep neural network model with the goal of learning local\ntimbre feature representation from the mixture of singer voice and background\nmusic. Unlike other deep neural networks using the softmax layer as the output\nlayer, we instead utilize the KNN as a more interpretable layer to output\ntarget singer labels. Moreover, attention mechanism is first introduced to\nhighlight crucial timbre features for SID. Experiments on the existing artist20\ndataset show that the proposed approach outperforms the state-of-the-art method\nby 4%. We also create singer32 and singer60 datasets consisting of Chinese pop\nmusic to evaluate the reliability of the proposed method. The more extensive\nexperiments additionally indicate that our proposed model achieves a\nsignificant performance improvement compared to the state-of-the-art methods.\n"}
+{"abstract": "  The physical scenario responsible for gamma-ray flaring activity and its\nlocation for Flat Spectrum Radio Quasars is still debated. The study of the\nstatistical distribution of waiting-times between flares (the time intervals\nbetween consecutive activity peaks) can give information on the distribution of\nflaring times, and constrain the physical mechanism responsible for gamma-ray\nemission. We adopt here a Scan-Statistic driven clustering method (iSRS) to\nrecognize flaring states within the FERMI-LAT data, and identify the time of\nactivity-peaks. Results: Flares waiting times can be described with a\npoissonian process, consisting of a set of overlapping bursts of flares, with\nan average burst duration of about 0.6 year, and average rate of 1.3/y . For\nwaiting times below 1d host-frame we found a statistically-relevant second\npopulation, the fast-component, mainly from CTA 102 data. The period of\nconspicuous detection of the fast component for CTA 102 coincides with the\ncrossing-time of the superluminal K1 feature with the C1 stationary feature in\nradio reported in Jorstad et al. (2017); Casadio et al. (2019). Conclusions: To\nreconcile the mechanism proposed in Jorstad et al. (2017); Casadio et al.\n(2019) with the bursting-activity, we have to assume that plasma streams with a\ntypical length of about 2pc (in the stream reference-frame) reach the\nrecollimation-shock. Otherwise, the distribution of waiting-times can be\ninterpreted as originating from relativistic-plasma moving along the jet for a\ndeprojected length of about 30-50pc (assuming a Lorentz-factor=10), that\nsporadically produces gamma-ray flares. In magnetic-reconnection scenario,\nreconnection events or plasma injection to the reconnection-sites should be\nintermittent. Individual plasmoids can be resolved in a few favourable cases\nonly (Christie et al., 2019); they could be responsible for the fast component.\n"}
+{"abstract": "  We study stochastic convex optimization with heavy-tailed data under the\nconstraint of differential privacy (DP). Most prior work on this problem is\nrestricted to the case where the loss function is Lipschitz. Instead, as\nintroduced by Wang, Xiao, Devadas, and Xu \\cite{WangXDX20}, we study general\nconvex loss functions with the assumption that the distribution of gradients\nhas bounded $k$-th moments. We provide improved upper bounds on the excess\npopulation risk under concentrated DP for convex and strongly convex loss\nfunctions. Along the way, we derive new algorithms for private mean estimation\nof heavy-tailed distributions, under both pure and concentrated DP. Finally, we\nprove nearly-matching lower bounds for private stochastic convex optimization\nwith strongly convex losses and mean estimation, showing new separations\nbetween pure and concentrated DP.\n"}
+{"abstract": "  There have been a number of corner detection methods proposed for event\ncameras in the last years, since event-driven computer vision has become more\naccessible. Current state-of-the-art have either unsatisfactory accuracy or\nreal-time performance when considered for practical use, for example when a\ncamera is randomly moved in an unconstrained environment. In this paper, we\npresent yet another method to perform corner detection, dubbed look-up\nevent-Harris (luvHarris), that employs the Harris algorithm for high accuracy\nbut manages an improved event throughput. Our method has two major\ncontributions, 1. a novel \"threshold ordinal event-surface\" that removes\ncertain tuning parameters and is well suited for Harris operations, and 2. an\nimplementation of the Harris algorithm such that the computational load per\nevent is minimised and computational heavy convolutions are performed only\n\"as-fast-as-possible\", i.e. only as computational resources are available. The\nresult is a practical, real-time, and robust corner detector that runs more\nthan 2.6x the speed of current state-of-the-art; a necessity when using\nhigh-resolution event-camera in real-time. We explain the considerations taken\nfor the approach, compare the algorithm to current state-of-the-art in terms of\ncomputational performance and detection accuracy, and discuss the validity of\nthe proposed approach for event cameras.\n"}
+{"abstract": "  Metasurfaces are planar structures that can manipulate the amplitude, phase\nand polarization (APP) of light at subwavelength scale. Although various\nfunctionalities have been proposed based on metasurface, a most general optical\ncontrol, i.e., independent complex-amplitude (amplitude and phase) control of\narbitrary two orthogonal states of polarizations, has not yet been realized.\nSuch level of optical control can not only cover the various functionalities\nrealized previously, but also enable new functionalities that are not feasible\nbefore. Here, we propose a single-layer dielectric metasurface to realize this\ngoal and experimentally demonstrate several advanced functionalities, such as\ntwo independent full-color printing images under arbitrary elliptically\northogonal polarizations and dual sets of printing-hologram integrations. Our\nwork opens the way for a wide range of applications in advanced image display,\ninformation encoding, and polarization optics.\n"}
+{"abstract": "  The key challenge of unsupervised vehicle re-identification (Re-ID) is\nlearning discriminative features from unlabelled vehicle images. Numerous\nmethods using domain adaptation have achieved outstanding performance, but\nthose methods still need a labelled dataset as a source domain. This paper\naddresses an unsupervised vehicle Re-ID method, which no need any types of a\nlabelled dataset, through a Self-supervised Metric Learning (SSML) based on a\nfeature dictionary. Our method initially extracts features from vehicle images\nand stores them in a dictionary. Thereafter, based on the dictionary, the\nproposed method conducts dictionary-based positive label mining (DPLM) to\nsearch for positive labels. Pair-wise similarity, relative-rank consistency,\nand adjacent feature distribution similarity are jointly considered to find\nimages that may belong to the same vehicle of a given probe image. The results\nof DPLM are applied to dictionary-based triplet loss (DTL) to improve the\ndiscriminativeness of learnt features and to refine the quality of the results\nof DPLM progressively. The iterative process with DPLM and DTL boosts the\nperformance of unsupervised vehicle Re-ID. Experimental results demonstrate the\neffectiveness of the proposed method by producing promising vehicle Re-ID\nperformance without a pre-labelled dataset. The source code for this paper is\npublicly available on `https://github.com/andreYoo/VeRI_SSML_FD.git'.\n"}
+{"abstract": "  We report that the RS CVn-type star GT Mus (HR 4492, HD 101379 + HD 101380)\nwas the most active star in the X-ray sky in the last decade in terms of the\nscale of recurrent energetic flares. We detected 11 flares from GT Mus in 8 yr\nof observations with Monitor of All-sky X-ray Image (MAXI) from 2009 August to\n2017 August. The detected flare peak luminosities were 1-4 $\\times$ 10$^{33}$\nerg s$^{-1}$ in the 2.0-20.0 keV band for its distance of 109.6 pc. Our timing\nanalysis showed long durations ($\\tau_{\\rm r} + \\tau_{\\rm d}$) of 2-6 days with\nlong decay times ($\\tau_{\\rm d}$) of 1-4 days. The released energies during the\ndecay phases of the flares in the 0.1-100 keV band ranged 1-11 $\\times$\n10$^{38}$ erg, which are at the upper end of the observed stellar flare. The\nreleased energies during whole duration time ranged 2-13 $\\times$ 10$^{38}$ erg\nin the same band. We carried out X-ray follow-up observations for one of the 11\nflares with Neutron star Interior Composition Explorer (NICER) on 2017 July 18\nand found that the flare cooled quasi-statically. On the basis of a\nquasi-static cooling model, the flare loop length is derived to be 4 $\\times$\n10$^{12}$ cm (or 60 R$_{\\odot}$). The electron density is derived to be 1\n$\\times$ 10$^{10}$ cm$^{-3}$, which is consistent with the typical value of\nsolar and stellar flares (10$^{10-13}$ cm$^{-3}$). The ratio of the cooling\ntimescales between radiative cooling ($\\tau_{\\rm rad}$) and conductive cooling\n($\\tau_{\\rm cond}$) is estimated to be $\\tau_{\\rm rad}$ $\\sim$ 0.1$\\tau_{\\rm\ncond}$ from the temperature; thus radiative cooling was dominant in this flare.\n"}
+{"abstract": "  Gluon dressing of the light quarks within hadrons is very strong and\nextremely important in that it dynamically generates most of the observable\nmass through the breaking of chiral symmetry. The quark and gluon parton\ndensities, $q(x)$ and $g(x)$, are necessarily interrelated since any gluon\nemission and absorption process, especially dressing of a quark, contributes to\n$g(x)$ and modifies $q(x)$. Guided by long-established results for the\nparton-in-parton distributions from a strict 1-loop perturbative analysis of a\nquark target, we extend the non-perturbative QCD approach based on the\nRainbow-Ladder truncation of the Dyson-Schwinger equations to describe the\ninterrelated valence $q_{\\rm v}(x)$ and the dressing-gluon $g(x)$ for a hadron\nat its intrinsic model scale. We employ the pion description from previous DSE\nwork that accounted for the gluon-in-quark effect and introduce a simple model\nof the nucleon for exploratory purposes. We find typically \\mbox{$\\langle x\n\\rangle_g \\sim 0.20$} for both pion and nucleon at the model scale, and the\nvalence quark helicity contributes 52\\% of nucleon spin. We deduce both $q_{\\rm\nv}(x)$ and $g(x)$ from 30 calculated Mellin moments, and after adopting\nexisting data analysis results for $q_{\\rm sea}(x)$, we find that NLO scale\nevolution produces $g(x)$ in good agreement with existing data analysis results\nfor the pion at 1.3 GeV and the nucleon at 5 GeV$^2$. At the scale 2 GeV\ntypical of lattice-QCD calculations, we obtain \\mbox{$\\langle x \\rangle_g^{\\rm\nN} = 0.42$} in good agreement with 0.38 from the average of recent lattice-QCD\ncalculations.\n"}
+{"abstract": "  We derive a multi-species BGK model with velocity-dependent collision\nfrequency for a non-reactive, multi-component gas mixture. The model is derived\nby minimizing a weighted entropy under the constraint that the number of\nparticles of each species, total momentum, and total energy are conserved. We\nprove that this minimization problem admits a unique solution for very general\ncollision frequencies. Moreover, we prove that the model satisfies an H-Theorem\nand characterize the form of equilibrium.\n"}
+{"abstract": "  In this article, an analytical model is proposed for the study of\nTransverse-electric (TE) surface plasmon polaritons (SPPs) in nonlinear\nmulti-layer graphene-based waveguides. Each graphene sheet has been located\nbetween two different Kerr-type layers. As special cases of the general,\nproposed structure, two new nonlinear graphene-based waveguides are introduced\nand investigated in this paper. The obtained results show that the propagation\nproperties of these exemplary structures are adjustable via chemical potential\nand nonlinear coefficients. A large value of the effective index, i.e. neff=82\nis obtained for the chemical potential of 0.15 ev and the nonlinear ratio of\n0.8 for the second structure at the frequency of 61 THz. The presented study\nsuggests a novel platform in graphene plasmonics, which can be used for the\ndesign of innovative THz devices.\n"}
+{"abstract": "  The photoluminescence intermittency (blinking) of quantum dots is interesting\nbecause it is an easily-measured quantum process whose transition statistics\ncannot be explained by Fermi's Golden Rule. Commonly, the transition statistics\nare power-law distributed, implying that quantum dots possess at least trivial\nmemories. By investigating the temporal correlations in the blinking data, we\ndemonstrate with high statistical confidence that quantum dot blinking data has\nnon-trivial memory, which we define to be statistical complexity greater than\none. We show that this memory cannot be discovered using the transition\ndistribution. We show by simulation that this memory does not arise from\nstandard data manipulations. Finally, we conclude that at least three physical\nmechanisms can explain the measured non-trivial memory: 1) Storage of state\ninformation in the chemical structure of a quantum dot; 2) The existence of\nmore than two intensity levels in a quantum dot; and 3) The overlap in the\nintensity distributions of the quantum dot states, which arises from\nfundamental photon statistics.\n"}
+{"abstract": "  The fifth generation (5G) mobile networks with enhanced connectivity and\npositioning capabilities play an increasingly important role in the development\nof automated vehicle-to-everything (V2X) and other advanced industrial Internet\nof Things (IoT) systems. In this article, we address the prospects of 5G New\nRadio (NR) sidelink based V2X networks and their applicability for increasing\nthe situational awareness, in terms of continuous tracking of moving connected\nmachines and vehicles, in industrial systems. For increased system flexibility\nand fast deployments, we assume that the locations of the so-called anchor\nnodes are unknown, and describe an extended Kalman filter-based joint\npositioning and tracking framework in which the locations of both the anchor\nnodes and the target nodes can be estimated simultaneously. We assess and\ndemonstrate the achievable 3D positioning and tracking performance in the\ncontext of a realistic industrial warehouse facility, through extensive\nray-tracing based evaluations at the 26 GHz NR band. Our findings show that\nwhen both angle-based and time-based measurements are utilized, reaching sub-1\nmeter accuracy is realistic and that the system is also relatively robust\nagainst different node geometries. Finally, several research challenges towards\nachieving robust, high-performance and cost-efficient positioning solutions are\noutlined and discussed, identifying various potential directions for future\nwork.\n"}
+{"abstract": "  The spread of infectious epidemics is often accelerated by super-spreader\nevents. Understanding their effect is important, particularly in the context of\nstandard epidemiological models, which require estimates for parameters such as\n$R_0$. In this letter, we show that the effective value of $R_0$ in\nsuper-spreader situations is significantly large, of the order of hundreds,\nsuggesting a delta-function-like behavior during the event. Use of a well-mixed\nroom model supports these findings. They elucidate infection kinetic modeling\nin enclosed environments, which differ from the standard SIR model, and provide\nexpressions for $R_0$ in terms of physical and operational parameters. The\noverall impact of super-spreader events can be significant, depending on the\nstate of the epidemic and how the infections generated by the event\nsubsequently spread in the community.\n"}
+{"abstract": "  A natural class of conformally invariant ways for discovering the loops of a\nconformal loop ensemble $\\text{CLE}_4$ is given by a certain family of\n$\\text{SLE}_4^{\\langle\\mu\\rangle}(-2)$ exploration processes for real $\\mu$.\nSuch an exploration consists of one simple continuous path called the trunk of\nthe exploration that discovers $\\text{CLE}_4$ loops along the way. The\nparameter $\\mu$ appears in the Loewner chain description of the path that\ntraces the trunk and all CLE loops encountered by the trunk in chronological\norder. These explorations can also be interpreted in terms of level lines of a\nGaussian free field. It has been shown by Miller, Sheffield and Werner that the\ntrunk of such an exploration is an $\\text{SLE}_4(\\rho,-2-\\rho)$ process for\nsome (unknown) value of $\\rho \\in (-2, 0)$. The main result of the present\npaper is to establish the relation between $\\mu$ and $\\rho$, more specifically\nto show that $\\mu = -\\pi\\cot(\\pi\\rho/2)$. The crux of the paper is to show how\nexplorations of $\\text{CLE}_4$ can be approximated by explorations of\n$\\text{CLE}_\\kappa$ for $\\kappa \\uparrow 4$, which then makes it possible to\nuse recent results by Miller, Sheffield and Werner about the trunks of\n$\\text{CLE}_\\kappa$ explorations for $\\kappa < 4$.\n"}
+{"abstract": "  Privacy dashboards and transparency tools help users review and manage the\ndata collected about them online. Since 2016, Google has offered such a tool,\nMy Activity, which allows users to review and delete their activity data from\nGoogle services. We conducted an online survey with $n = 153$ participants to\nunderstand if Google's My Activity, as an example of a privacy transparency\ntool, increases or decreases end-users' concerns and benefits regarding data\ncollection. While most participants were aware of Google's data collection, the\nvolume and detail was surprising, but after exposure to My Activity,\nparticipants were significantly more likely to be both less concerned about\ndata collection and to view data collection more beneficially. Only $25\\,\\%$\nindicated that they would change any settings in the My Activity service or\nchange any behaviors. This suggests that privacy transparency tools are quite\nbeneficial for online services as they garner trust with their users and\nimprove their perceptions without necessarily changing users' behaviors. At the\nsame time, though, it remains unclear if such transparency tools actually\nimprove end user privacy by sufficiently assisting or motivating users to\nchange or review data collection settings.\n"}
+{"abstract": "  We study the Hall conductivity of a two-dimensional electron gas under an\ninhomogeneous magnetic field $B(x)$. First, we prove using the quantum kinetic\ntheory that an odd magnetic field can lead to a purely nonlinear Hall response.\nSecond, considering a real-space magnetic dipole consisting of a sign-changing\nmagnetic field and based on numerical semiclassical dynamics, we unveil a\nparametric resonance involving the cyclotron ratio and a characteristic width\nof $B(x)$, which can greatly enhance the Hall response. Different from previous\nmechanisms that rely on the bulk Berry curvature dipole, here, the effect\nlargely stems from boundary states associated with the real-space magnetic\ndipole. Our findings pave a new way to engineer current rectification and\nhigher harmonic generation in two-dimensional materials having or not crystal\ninversion symmetry.\n"}
+{"abstract": "  Bacterial swarms display intriguing dynamical states like active turbulence.\nUsing a hydrodynamic model we now show that such dense active suspensions\nmanifest super-diffusion, via L\\'evy walks, which masquerades as a crossover\nfrom ballistic to diffusive scaling in measurements of\nmean-squared-displacements, and is tied to the emergence of hitherto undetected\noscillatory streaks in the flow. Thus, while laying the theoretical framework\nof an emergent advantageous strategy in the collective behaviour of\nmicroorganisms, our study underlines the essential differences between active\nand inertial turbulence.\n"}
+{"abstract": "  In this paper, a new family of rotationally symmetric planar graphs is\ndescribed based on an edge coalescence of planar chorded cycles. Their local\nfractional metric dimension is established for those ones arisen from chorded\ncycles of order up to six. Their asymptotic behaviour enables us to ensure the\nexistence of new families of rotationally symmetric planar graphs with either\nconstant or bounded local fractional dimension.\n"}
+{"abstract": "  We consider the problem of predicting the future path of a pedestrian using\nits motion history and the motion history of the surrounding pedestrians,\ncalled social information. Since the seminal paper on Social-LSTM,\ndeep-learning has become the main tool used to model the impact of social\ninteractions on a pedestrian's motion. The demonstration that these models can\nlearn social interactions relies on an ablative study of these models. The\nmodels are compared with and without their social interactions module on two\nstandard metrics, the Average Displacement Error and Final Displacement Error.\nYet, these complex models were recently outperformed by a simple\nconstant-velocity approach. This questions if they actually allow to model\nsocial interactions as well as the validity of the proof. In this paper, we\nfocus on the deep-learning models with a soft-attention mechanism for social\ninteraction modeling and study whether they use social information at\nprediction time. We conduct two experiments across four state-of-the-art\napproaches on the ETH and UCY datasets, which were also used in previous work.\nFirst, the models are trained by replacing the social information with random\nnoise and compared to model trained with actual social information. Second, we\nuse a gating mechanism along with a $L_0$ penalty, allowing models to shut down\ntheir inner components. The models consistently learn to prune their\nsoft-attention mechanism. For both experiments, neither the course of the\nconvergence nor the prediction performance were altered. This demonstrates that\nthe soft-attention mechanism and therefore the social information are ignored\nby the models.\n"}
+{"abstract": "  Click-through rate (CTR) prediction plays a critical role in recommender\nsystems and online advertising. The data used in these applications are\nmulti-field categorical data, where each feature belongs to one field. Field\ninformation is proved to be important and there are several works considering\nfields in their models. In this paper, we proposed a novel approach to model\nthe field information effectively and efficiently. The proposed approach is a\ndirect improvement of FwFM, and is named as Field-matrixed Factorization\nMachines (FmFM, or $FM^2$). We also proposed a new explanation of FM and FwFM\nwithin the FmFM framework, and compared it with the FFM. Besides pruning the\ncross terms, our model supports field-specific variable dimensions of embedding\nvectors, which acts as soft pruning. We also proposed an efficient way to\nminimize the dimension while keeping the model performance. The FmFM model can\nalso be optimized further by caching the intermediate vectors, and it only\ntakes thousands of floating-point operations (FLOPs) to make a prediction. Our\nexperiment results show that it can out-perform the FFM, which is more complex.\nThe FmFM model's performance is also comparable to DNN models which require\nmuch more FLOPs in runtime.\n"}
+{"abstract": "  We prove that for every $n\\geq 3$ the sharp upper bound for the dimension of\nthe symmetry groups of homogeneous, 2-nondegenerate, $(2n+1)$-dimensional CR\nmanifolds of hypersurface type with a $1$-dimensional Levi kernel is equal to\n$n^2+7$. This supports Beloshapka's conjecture stating that hypersurface models\nwith a maximal finite dimensional group of symmetries for a given dimension of\nthe underlying manifold are Levi nondegenerate.\n"}
+{"abstract": "  In this work, we propose extreme compression techniques like binarization,\nternarization for Neural Decoders such as TurboAE. These methods reduce memory\nand computation by a factor of 64 with a performance better than the quantized\n(with 1-bit or 2-bits) Neural Decoders. However, because of the limited\nrepresentation capability of the Binary and Ternary networks, the performance\nis not as good as the real-valued decoder. To fill this gap, we further propose\nto ensemble 4 such weak performers to deploy in the edge to achieve a\nperformance similar to the real-valued network. These ensemble decoders give 16\nand 64 times saving in memory and computation respectively and help to achieve\nperformance similar to real-valued TurboAE.\n"}
+{"abstract": "  Disordered solids respond to quasistatic shear with intermittent avalanches\nof plastic activity, an example of the crackling noise observed in many\nnonequilibrium critical systems. The temporal power spectrum of activity within\ndisordered solids consists of three distinct domains: a novel power-law rise\nwith frequency at low frequencies indicating anticorrelation, white-noise at\nintermediate frequencies, and a power-law decay at high frequencies. As the\nstrain rate increases, the white-noise regime shrinks and ultimately disappears\nas the finite strain rate restricts the maximum size of an avalanche. A new\nstrain-rate- and system-size-dependent scaling theory is derived for power\nspectra in both the quasistatic and finite-strain-rate regimes. This theory is\nvalidated using data from overdamped two- and three-dimensional molecular\ndynamics simulations. We identify important exponents in the yielding\ntransition including the dynamic exponent $z$ which relates the size of an\navalanche to its duration, the fractal dimension of avalanches, and the\nexponent characterizing the divergence in correlations with strain rate.\nResults are related to temporal correlations within a single avalanche and\nbetween multiple avalanches.\n"}
+{"abstract": "  Classifying hand-written digits and letters has taken a big leap with the\nintroduction of ConvNets. However, on very constrained hardware the time\nnecessary to train such models would be high. Our main contribution is twofold.\nFirst, we extensively test an end-to-end vanilla neural network (MLP) approach\nin pure numpy without any pre-processing or feature extraction done beforehand.\nSecond, we show that basic data mining operations can significantly improve the\nperformance of the models in terms of computational time, without sacrificing\nmuch accuracy. We illustrate our claims on a simpler variant of the Extended\nMNIST dataset, called Balanced EMNIST dataset. Our experiments show that,\nwithout any data mining, we get increased generalization performance when using\nmore hidden layers and regularization techniques, the best model achieving\n84.83% accuracy on a test dataset. Using dimensionality reduction done by PCA\nwe were able to increase that figure to 85.08% with only 10% of the original\nfeature space, reducing the memory size needed by 64%. Finally, adding methods\nto remove possibly harmful training samples like deviation from the mean helped\nus to still achieve over 84% test accuracy but with only 32.8% of the original\nmemory size for the training set. This compares favorably to the majority of\nliterature results obtained through similar architectures. Although this\napproach gets outshined by state-of-the-art models, it does scale to some\n(AlexNet, VGGNet) trained on 50% of the same dataset.\n"}
+{"abstract": "  In this work, we have systematically studied the role of point defects in the\nrecombination time of monolayer MoS$_2$ using time-dependent ab initio\nnon-adiabatic molecular dynamics simulations. Various types of point defects,\nsuch as S vacancy, S interstitial, Mo vacancy and Mo interstitial have been\nconsidered. We show that defects strongly accelerate the electron-hole\nrecombination, especially interstitial S atoms do that by 3 orders of magnitude\nhigher compared to pristine MoS$_2$. Mo defects (both vacancy and interstitial)\nintroduce a multitude of de-excitation pathways via various defect levels in\nthe energy gap. The results of this study provide some fundamental\nunderstanding of photoinduced de-excitation dynamics in presence of defects in\nhighly technologically relevant 2D MoS$_2$.\n"}
+{"abstract": "  Let G be a simple non-compact linear connected Lie group and H be a closed\nnon-compact semisimple subgroup. We are interested in finding classes of\nhomogeneous spaces G/H admitting proper actions of discrete non virtually\nabelian subgroups of G. We develop an algorithm for finding such homogeneous\nspaces. As a testing example we obtain a list of all non-compact homogeneous\nspaces G/H admitting proper action of a discrete and non virtually abelian\nsubgroup of G in the case when G has rank at most 8, and H is a maximal proper\nsemisimple subgroup.\n"}
+{"abstract": "  We compare spectra of the zonal harmonics of the large-scale magnetic field\nof the Sun using observation results and solar dynamo models. The main solar\nactivity cycle as recorded in these tracers is a much more complicated\nphenomenon than the eigen solution of solar dynamo equations with the growth\nsaturated by a back reaction of the dynamo-driven magnetic field on solar\nhydrodynamics. The nominal 11(22)-year cycle as recorded in each mode has a\nspecific phase shift varying from cycle to cycle; the actual length of the\ncycle varies from one cycle to another and from tracer to tracer. Both the\nobservation and the dynamo model show an exceptional role of the axisymmetric\n$\\ell_{5}$ mode. Its origin seems to be readily connected with the formation\nand evolution of sunspots on the solar surface. The results of observations and\ndynamo models show a good agreement for the low $\\ell_{1}$ and $\\ell_{3}$\nmodes. The results for these modes do not differ significantly for the\naxisymmetric and nonaxisymmetric models. Our findings support the idea that the\nsources of the solar dynamo arise as a result of both the distributed dynamo\nprocesses in the bulk of the convection zone and the surface magnetic activity.\n"}
+{"abstract": "  The OPC UA protocol is an upcoming de-facto standard for building Industry\n4.0 processes in Europe, and one of the few industrial protocols that promises\nsecurity features to prevent attackers from manipulating and damaging critical\ninfrastructures. Despite the importance of the protocol, challenges in the\nadoption of OPC UA's security features by product vendors, libraries\nimplementing the standard, and end-users were not investigated so far.\n  In this work, we systematically investigate 48 publicly available artifacts\nconsisting of products and libraries for OPC UA and show that 38 out of the 48\nartifacts have one (or more) security issues. In particular, we show that 7 OPC\nUA artifacts do not support the security features of the protocol at all. In\naddition, 31 artifacts that partially feature OPC UA security rely on\nincomplete libraries and come with misleading instructions. Consequently,\nrelying on those products and libraries will result in vulnerable\nimplementations of OPC UA security features. To verify our analysis, we design,\nimplement, and demonstrate attacks in which the attacker can steal credentials\nexchanged between victims, eavesdrop on process information, manipulate the\nphysical process through sensor values and actuator commands, and prevent the\ndetection of anomalies.\n"}
+{"abstract": "  Flowing granular materials often abruptly arrest if not driven by sufficient\napplied stresses. Such abrupt cessation of motion can be economically expensive\nin industrial materials handling and processing, and is significantly\nconsequential in intermittent geophysical phenomena such as landslides and\nearthquakes. Using discrete element simulations, we calculate a flow-arrest\nstate diagram for granular materials under the conditions of constant applied\npressure and shear stress, which are also most relevant in practice. Here the\nmaterial can dilate or compact, and flow or arrest, in response to the applied\nstress. Our simulations highlight that under external stress, the intrinsic\nresponse of granular materials is characterized by uniquely-defined\nnonequilibrium steady states of flow or arrest, which are highly sensitive to\ninterparticle friction. While the flowing states can be equivalently\ncharacterized by volume fraction, coordination number or internal stress ratio,\nto characterize the states of shear arrest, one needs to also consider the\nstructural anisotropy in the contact network. We highlight the role of dilation\nin the flow-arrest transition, and discuss our findings in the context of shear\njamming and discontinuous shear thickening.\n"}
+{"abstract": "  Social networks represent nowadays in many contexts the main source of\ninformation transmission and the way opinions and actions are influenced. For\ninstance, generic advertisements are way less powerful than suggestions from\nour contacts. However, this process hugely depends on the influence of people\nwho disseminate these suggestions. Therefore modern marketing often involves\npaying some targeted users, or influencers, for advertising products or ideas.\nFinding the set of nodes in a social network that lead to the highest\ninformation spread -- the so-called Influence Maximization (IM) problem -- is\ntherefore a pressing question and as such it has recently attracted a great\nresearch interest. In particular, several approaches based on Evolutionary\nAlgorithms (EAs) have been proposed, although they are known to scale poorly\nwith the graph size. In this paper, we tackle this limitation in two ways.\nFirstly, we use approximate fitness functions to speed up the EA. Secondly, we\ninclude into the EA various graph-aware mechanisms, such as smart\ninitialization, custom mutations and node filtering, to facilitate the EA\nconvergence. Our experiments show that the proposed modifications allow to\nobtain a relevant runtime gain and also improve, in some cases, the spread\nresults.\n"}
+{"abstract": "  This contribution gives an extensive study on spectra of mixed graphs via its\nHermitian adjacency matrix of the second kind { ($N$-matrix for short)}\nintroduced by Mohar \\cite{0001}. This matrix is indexed by the vertices of the\nmixed graph, and the entry corresponding to an arc from $u$ to $v$ is equal to\nthe sixth root of unity $\\omega=\\frac{1+{\\bf i}\\sqrt{3}}{2}$ (and its symmetric\nentry is $\\bar{\\omega}=\\frac{1-{\\bf i}\\sqrt{3}}{2}$); the entry corresponding\nto an undirected edge is equal to 1, and 0 otherwise. The main results of this\npaper include the following: {equivalent} conditions for a mixed graph that\nshares the same spectrum of its $N$-matrix with its underlying graph are given.\nA sharp upper bound on the spectral radius is established and the corresponding\nextremal mixed graphs are identified. Operations which are called two-way and\nthree-way switchings are discussed--they give rise to some cospectral mixed\ngraphs. We extract all the mixed graphs whose rank of its $N$-matrix is $2$\n(resp. 3). Furthermore, we show that {if $M_G$ is a connected mixed graph with\nrank $2,$ then $M_G$ is switching equivalent to each connected mixed graph to\nwhich it is cospectral}. However, this does not hold for some connected mixed\ngraphs with rank $3$. We identify all mixed graphs whose eigenvalues of its\n$N$-matrix lie in the range $(-\\alpha,\\, \\alpha)$ for\n$\\alpha\\in\\left\\{\\sqrt{2},\\,\\sqrt{3},\\,2\\right\\}$.\n"}
+{"abstract": "  In this paper, we describe the automorphism group of semidirect product of\ntwo groups that fixes the non-normal subgroup of it. We have computed these\nautomorphisms for the non-abelian metacyclic $p$-group and non-abelian\n$p$-groups $(p\\ge 5)$ of order $p^{4}$, where $p$ is a prime.\n"}
+{"abstract": "  We consider the problem of coded computing, where a computational task is\nperformed in a distributed fashion in the presence of adversarial workers. We\npropose techniques to break the adversarial toleration threshold barrier\npreviously known in coded computing. More specifically, we leverage\nlist-decoding techniques for folded Reed-Solomon codes and propose novel\nalgorithms to recover the correct codeword using side information. In the coded\ncomputing setting, we show how the master node can perform certain carefully\ndesigned extra computations to obtain the side information. The workload of\ncomputing this side information is negligible compared to the computations done\nby each worker. This side information is then utilized to prune the output of\nthe list decoder and uniquely recover the true outcome. We further propose\nfolded Lagrange coded computing (FLCC) to incorporate the developed techniques\ninto a specific coded computing setting. Our results show that FLCC outperforms\nLCC by breaking the barrier on the number of adversaries that can be tolerated.\nIn particular, the corresponding threshold in FLCC is improved by a factor of\ntwo asymptotically compared to that of LCC.\n"}
+{"abstract": "  Reconstruction of turbulent flow based on data assimilation methods is of\nsignificant importance for improving the estimation of flow characteristics by\nincorporating limited observations. Existing works mainly focus on using only\none observation data source, e.g., velocity, wall pressure, lift or drag force,\nto reconstruct the flow. In practical applications observations are disparate\ndata sources that often vary in dimension and quality. Simultaneously\nincorporating these disparate data is worth investigation to improve the flow\nreconstruction. In this work, we investigate the disparate data assimilation\nwith ensemble methods to enhance the reconstruction of turbulent mean flows.\nSpecifically, a regularized ensemble Kalman method is employed to incorporate\nthe observation of velocity and different sources of wall quantities (e.g.,\nwall shear stress, wall pressure distribution, lift and drag force). Three\nnumerical examples are used to demonstrate the capability of the proposed\nframework for assimilating disparate observation data. The first two cases,\ni.e., a one-dimensional planar channel flow and a two-dimensional transitional\nflow over plate, are used to incorporate both the sparse velocity and wall\nfriction. In the third case of the flow over periodic hills, the wall pressure\ndistribution and the lift and drag force are regarded as observation in\naddition to velocity, to recover the flow fields. The results demonstrate the\nmerits of incorporating various disparate data sources to improve the accuracy\nof the flow-field estimation. The ensemble-based method can assimilate\ndisparate data non-intrusively and robustly without requiring significant\nchanges to the model simulation codes. The method demonstrated here opens up\npossibilities for assimilating realistic experimental data, which are often\ndisparate.\n"}
+{"abstract": "  We define a new family of commuting operators $F_k$ in Khovanov-Rozansky link\nhomology, similar to the action of tautological classes in cohomology of\ncharacter varieties. We prove that $F_2$ satisfies ``hard Lefshetz property\"\nand hence exhibits the symmetry in Khovanov-Rozansky homology conjectured by\nDunfield, Gukov and Rasmussen.\n"}
+{"abstract": "  This paper provides details of the massless three-loop three-point integrals\ncalculation at the symmetric point. Our work aimed to extend known two-loop\nresults for such integrals to the three-loop level. Obtained results can find\ntheir application in regularization-invariant symmetric point\nmomentum-subtraction (RI/SMOM) scheme QCD calculations of renormalization group\nfunctions and various composite operator matrix elements. To calculate\nintegrals, we solve differential equations for auxiliary integrals by\ntransforming the system to the $\\varepsilon$-form. Calculated integrals are\nexpressed through the basis of functions with uniform transcendental weight. We\nprovide expansion up to the transcendental weight six for the basis functions\nin terms of harmonic polylogarithms with six-root of unity argument.\n"}
+{"abstract": "  We investigate a scenario inspired by natural supersymmetry, where neutrino\ndata is explained within a low-scale seesaw scenario. For this the Minimal\nSupersymmetric Standard Model is extended by adding light right-handed\nneutrinos and their superpartners, the R-sneutrinos. Moreover, we consider the\nlightest electroweakinos to be higgsino-like.\n  We demonstrate that a prospective international $e^+ e^-$ linear collider\nwith a center of mass energy of 1 TeV will be able to discover sleptons in\nscenarios which can be difficult for current colliders. Moreover, we also show\nthat a measurement of the spectrum will be possible within 1-3 per-cent\naccuracy.\n"}
+{"abstract": "  Sampling-based methods such as Rapidly-exploring Random Trees (RRTs) have\nbeen widely used for generating motion paths for autonomous mobile systems. In\nthis work, we extend time-based RRTs with Control Barrier Functions (CBFs) to\ngenerate, safe motion plans in dynamic environments with many pedestrians. Our\nframework is based upon a human motion prediction model which is well suited\nfor indoor narrow environments. We demonstrate our approach on a high-fidelity\nmodel of the Toyota Human Support Robot navigating in narrow corridors. We show\nin three scenarios that our proposed online method can navigate safely in the\npresence of moving agents with unknown dynamics.\n"}
+{"abstract": "  High Bandwidth Memory (HBM) provides massive aggregated memory bandwidth by\nexposing multiple memory channels to the processing units. To achieve high\nperformance, an accelerator built on top of an FPGA configured with HBM (i.e.,\nFPGA-HBM platform) needs to scale its performance according to the available\nmemory channels. In this paper, we propose an accelerator for BFS\n(Breadth-First Search) algorithm, named as ScalaBFS, that builds multiple\nprocessing elements to sufficiently exploit the high bandwidth of HBM to\nimprove efficiency. We implement the prototype system of ScalaBFS and conduct\nBFS in both real-world and synthetic scale-free graphs on Xilinx Alveo U280\nFPGA card real hardware. The experimental results show that ScalaBFS scales its\nperformance almost linearly according to the available memory pseudo channels\n(PCs) from the HBM2 subsystem of U280. By fully using the 32 PCs and building\n64 processing elements (PEs) on U280, ScalaBFS achieves a performance up to\n19.7 GTEPS (Giga Traversed Edges Per Second). When conducting BFS in sparse\nreal-world graphs, ScalaBFS achieves equivalent GTEPS to Gunrock running on the\nstate-of-art Nvidia V100 GPU that features 64-PC HBM2 (twice memory bandwidth\nthan U280).\n"}
+{"abstract": "  Face is one of the predominant means of person recognition. In the process of\nageing, human face is prone to many factors such as time, attributes, weather\nand other subject specific variations. The impact of these factors were not\nwell studied in the literature of face aging. In this paper, we propose a novel\nholistic model in this regard viz., ``Face Age progression With Attribute\nManipulation (FAWAM)\", i.e. generating face images at different ages while\nsimultaneously varying attributes and other subject specific characteristics.\nWe address the task in a bottom-up manner, as two submodules i.e. face age\nprogression and face attribute manipulation. For face aging, we use an\nattribute-conscious face aging model with a pyramidal generative adversarial\nnetwork that can model age-specific facial changes while maintaining intrinsic\nsubject specific characteristics. For facial attribute manipulation, the age\nprocessed facial image is manipulated with desired attributes while preserving\nother details unchanged, leveraging an attribute generative adversarial network\narchitecture. We conduct extensive analysis in standard large scale datasets\nand our model achieves significant performance both quantitatively and\nqualitatively.\n"}
+{"abstract": "  In settings ranging from weather forecasts to political prognostications to\nfinancial projections, probability estimates of future binary outcomes often\nevolve over time. For example, the estimated likelihood of rain on a specific\nday changes by the hour as new information becomes available. Given a\ncollection of such probability paths, we introduce a Bayesian framework --\nwhich we call the Gaussian latent information martingale, or GLIM -- for\nmodeling the structure of dynamic predictions over time. Suppose, for example,\nthat the likelihood of rain in a week is 50 %, and consider two hypothetical\nscenarios. In the first, one expects the forecast to be equally likely to\nbecome either 25 % or 75 % tomorrow; in the second, one expects the forecast to\nstay constant for the next several days. A time-sensitive decision-maker might\nselect a course of action immediately in the latter scenario, but may postpone\ntheir decision in the former, knowing that new information is imminent. We\nmodel these trajectories by assuming predictions update according to a latent\nprocess of information flow, which is inferred from historical data. In\ncontrast to general methods for time series analysis, this approach preserves\nimportant properties of probability paths such as the martingale structure and\nappropriate amount of volatility and better quantifies future uncertainties\naround probability paths. We show that GLIM outperforms three popular baseline\nmethods, producing better estimated posterior probability path distributions\nmeasured by three different metrics. By elucidating the dynamic structure of\npredictions over time, we hope to help individuals make more informed choices.\n"}
+{"abstract": "  We report different growth modes and corresponding magnetic properties of\nthin EuSe films grown by molecular beam epitaxy on BaF2, Pb1-xEuxSe, GaAs, and\nBi2Se3 substrates. We show that EuSe growth predominantly in (001) orientation\non GaAs(111) and Bi2Se3, but along (111) crystallographic direction on BaF2\n(111) and Pb1-xEuxSe (111). High-resolution transmission electron microscopy\nmeasurements reveal an abrupt and highly crystalline interface for both (001)\nand (111) EuSe films. In agreement with previous studies, ordered magnetic\nphases include antiferromagnetic, ferrimagnetic, and ferromagnetic phases. In\ncontrast to previous studies, we found strong hysteresis for the\nantiferromagnetic-ferrimagnetic transition. An ability to grow epitaxial films\nof EuSe on Bi2Se3 and of Bi2Se3 on EuSe enables further investigation of\ninterfacial exchange interactions between various phases of an insulating\nmetamagnetic material and a topological insulator.\n"}
+{"abstract": "  We argue that the Standard Model quiver can be embedded into compact\nCalabi-Yau geometries through orientifolded D3-branes at del Pezzo\nsingularities $\\mathrm{dP}_n$ with $n\\geq 5$ in a framework including moduli\nstabilisation. To illustrate our approach, we explicitly construct a local\n$\\mathrm{dP}_{5}$ model via a combination of Higgsing and orientifolding. This\nprocedure reduces the original $\\mathrm{dP}_{5}$ quiver gauge theory to the\nLeft-Right symmetric model with three families of quarks and leptons as well as\na Higgs sector to further break the symmetries to the Standard Model gauge\ngroup. We embed this local model in a globally consistent Calabi-Yau flux\ncompactification with tadpole and Freed-Witten anomaly cancellations. The model\nfeatures closed string moduli stabilisation with a de Sitter minimum from\nT-branes, supersymmetry broken by the K\\\"ahler moduli, and the MSSM as the low\nenergy spectrum. We further discuss phenomenological and cosmological\nimplications of this construction.\n"}
+{"abstract": "  We describe an analytical theory investigating the regime of validity of the\nFermi liquid theory in interacting, via the long-range Coulomb coupling,\ntwo-dimensional Fermi systems comparing it with with the corresponding 3D\nsystems. We find that the 2D Fermi liquid theory and 2D quasiparticles are\nrobust up to high energies and temperatures of the order of Fermi energy above\nthe Fermi surface, very similar to the corresponding three-dimensional\nsituation. We calculate the phase diagram in the frequency-temperature space\nseparating the collisionless ballistic regime and the collision-dominated\nhydrodynamic regime for 2D and 3D interacting electron systems. We also provide\nthe temperature corrections up to third order for the renormalized effective\nmass, and comment on the validity of 2D Wiedemann-Franz law and 2D\nKadawoki-Woods relation.\n"}
+{"abstract": "  Deepfake is the manipulated video made with a generative deep learning\ntechnique such as Generative Adversarial Networks (GANs) or Auto Encoder that\nanyone can utilize. Recently, with the increase of Deepfake videos, some\nclassifiers consisting of the convolutional neural network that can distinguish\nfake videos as well as deepfake datasets have been actively created. However,\nthe previous studies based on the CNN structure have the problem of not only\noverfitting, but also considerable misjudging fake video as real ones. In this\npaper, we propose a Vision Transformer model with distillation methodology for\ndetecting fake videos. We design that a CNN features and patch-based\npositioning model learns to interact with all positions to find the artifact\nregion for solving false negative problem. Through comparative analysis on\nDeepfake Detection (DFDC) Dataset, we verify that the proposed scheme with\npatch embedding as input outperforms the state-of-the-art using the combined\nCNN features. Without ensemble technique, our model obtains 0.978 of AUC and\n91.9 of f1 score, while previous SOTA model yields 0.972 of AUC and 90.6 of f1\nscore on the same condition.\n"}
+{"abstract": "  The quantum ergotropy quantifies the maximal amount of work that can be\nextracted from a quantum state without changing its entropy. Given that the\nergotropy can be expressed as the difference of quantum and classical relative\nentropies of the quantum state with respect to the thermal state, we define the\nclassical ergotropy, which quantifies how much work can be extracted from\ndistributions that are inhomogeneous on the energy surfaces. A unified approach\nto treat both quantum as well as classical scenarios is provided by geometric\nquantum mechanics, for which we define the geometric relative entropy. The\nanalysis is concluded with an application of the conceptual insight to\nconditional thermal states, and the correspondingly tightened maximum work\ntheorem.\n"}
+{"abstract": "  Climate change is a pressing issue that is currently affecting and will\naffect every part of our lives. It's becoming incredibly vital we, as a\nsociety, address the climate crisis as a universal effort, including those in\nthe Computer Vision (CV) community. In this work, we analyze the total cost of\nCO2 emissions by breaking it into (1) the architecture creation cost and (2)\nthe life-time evaluation cost. We show that over time, these costs are\nnon-negligible and are having a direct impact on our future. Importantly, we\nconduct an ethical analysis of how the CV-community is unintentionally\noverlooking its own ethical AI principles by emitting this level of CO2. To\naddress these concerns, we propose adding \"enforcement\" as a pillar of ethical\nAI and provide some recommendations for how architecture designers and broader\nCV community can curb the climate crisis.\n"}
+{"abstract": "  In computational psycholinguistics, various language models have been\nevaluated against human reading behavior (e.g., eye movement) to build\nhuman-like computational models. However, most previous efforts have focused\nalmost exclusively on English, despite the recent trend towards linguistic\nuniversal within the general community. In order to fill the gap, this paper\ninvestigates whether the established results in computational psycholinguistics\ncan be generalized across languages. Specifically, we re-examine an established\ngeneralization -- the lower perplexity a language model has, the more\nhuman-like the language model is -- in Japanese with typologically different\nstructures from English. Our experiments demonstrate that this established\ngeneralization exhibits a surprising lack of universality; namely, lower\nperplexity is not always human-like. Moreover, this discrepancy between English\nand Japanese is further explored from the perspective of (non-)uniform\ninformation density. Overall, our results suggest that a cross-lingual\nevaluation will be necessary to construct human-like computational models.\n"}
+{"abstract": "  Attacks on Industrial Control Systems (ICS) can lead to significant physical\ndamage. While offline safety and security assessments can provide insight into\nvulnerable system components, they may not account for stealthy attacks\ndesigned to evade anomaly detectors during long operational transients. In this\npaper, we propose a predictive online monitoring approach to check the safety\nof the system under potential stealthy attacks. Specifically, we adapt previous\nresults in reachability analysis for attack impact assessment to provide an\nefficient algorithm for online safety monitoring for Linear Time-Invariant\n(LTI) systems. The proposed approach relies on an offline computation of\nsymbolic reachable sets in terms of the estimated physical state of the system.\nThese sets are then instantiated online, and safety checks are performed by\nleveraging ideas from ellipsoidal calculus. We illustrate and evaluate our\napproach using the Tennessee-Eastman process. We also compare our approach with\nthe baseline monitoring approaches proposed in previous work and assess its\nefficiency and scalability. Our evaluation results demonstrate that our\napproach can predict in a timely manner if a false data injection attack will\nbe able to cause damage, while remaining undetected. Thus, our approach can be\nused to provide operators with real-time early warnings about stealthy attacks.\n"}
+{"abstract": "  Adobe's Portable Document Format (PDF) is a popular way of distributing\nview-only documents with a rich visual markup. This presents a challenge to NLP\npractitioners who wish to use the information contained within PDF documents\nfor training models or data analysis, because annotating these documents is\ndifficult. In this paper, we present PDF Annotation with Labels and Structure\n(PAWLS), a new annotation tool designed specifically for the PDF document\nformat. PAWLS is particularly suited for mixed-mode annotation and scenarios in\nwhich annotators require extended context to annotate accurately. PAWLS\nsupports span-based textual annotation, N-ary relations and freeform,\nnon-textual bounding boxes, all of which can be exported in convenient formats\nfor training multi-modal machine learning models. A read-only PAWLS server is\navailable at https://pawls.apps.allenai.org/ and the source code is available\nat https://github.com/allenai/pawls.\n"}
+{"abstract": "  The Hamilton-Waterloo problem is a problem of graph factorization. The\nHamilton-Waterloo problem HWP$(H;m,n;\\alpha,\\beta)$ asks for a\n$2$-factorization of $H$ containing $\\alpha$ $C_m$-factors and $\\beta$\n$C_n$-factors. In this paper, we almost completely solve the Hamilton-Waterloo\nproblem on wreath product graph $C_m \\wr K_{16}$ with $C_{16}$-factors and\n$C_m$-factors for an odd integer $m$.\n"}
+{"abstract": "  We perform a Bayesian analysis of the maximum mass $M_{\\rm TOV}$ of neutron\nstars with a quark core, incorporating the observational data from tidal\ndeformability of the GW170817 binary neutron star merger as detected by\nLIGO/Virgo and the mass and radius of PSR J0030+0451 as detected by \\nicer. The\nanalysis is performed under the assumption that the hadron-quark phase\ntransition is of first order, where the low-density hadronic matter described\nin a unified manner by the soft QMF or the stiff DD2 equation of state (EOS)\ntransforms into a high-density phase of quark matter modeled by the generic\n\"Constant-sound-speed\" (CSS) parameterization. The mass distribution measured\nfor the $2.14 \\,{\\rm M}_{\\odot}$ pulsar, MSP J0740+6620, is used as the lower\nlimit on $M_{\\rm TOV}$. We find the most probable values of the hybrid star\nmaximum mass are $M_{\\rm TOV}=2.36^{+0.49}_{-0.26}\\,{\\rm M}_{\\odot}$\n($2.39^{+0.47}_{-0.28}\\,{\\rm M}_{\\odot}$) for QMF (DD2), with an absolute upper\nbound around $2.85\\,{\\rm M}_{\\odot}$, to the $90\\%$ posterior credible level.\nSuch results appear robust with respect to the uncertainties in the hadronic\nEOS. We also discuss astrophysical implications of this result, especially on\nthe post-merger product of GW170817, short gamma-ray bursts, and other likely\nbinary neutron star mergers.\n"}
+{"abstract": "  Analogue Hamiltonian simulation is a promising application of quantum\ncomputing in the NISQ era. Recent work has demonstrated the existence of\nuniversal Hamiltonians -- simple spin lattice models that can simulate any\nother quantum many body system. Until now proofs of universality have relied on\nexplicit constructions, which are shown to be able to replicate the physics of\nsome arbitrary target Hamiltonian. In this work, we derive necessary and\nsufficient conditions for a family of Hamiltonians to be universal. The proof\nrelies on complexity theoretic concepts -- leveraging the ability to encode\nquantum computation into the ground states of Hamiltonians. This offers a far\nsimpler route to proving universality, and provides insight into the\nrelationship between complexity and universality.\n"}
+{"abstract": "  Classical federated learning approaches incur significant performance\ndegradation in the presence of non-IID client data. A possible direction to\naddress this issue is forming clusters of clients with roughly IID data. Most\nsolutions following this direction are iterative and relatively slow, also\nprone to convergence issues in discovering underlying cluster formations. We\nintroduce federated learning with taskonomy (FLT) that generalizes this\ndirection by learning the task-relatedness between clients for more efficient\nfederated aggregation of heterogeneous data. In a one-off process, the server\nprovides the clients with a pretrained (and fine-tunable) encoder to compress\ntheir data into a latent representation, and transmit the signature of their\ndata back to the server. The server then learns the task-relatedness among\nclients via manifold learning, and performs a generalization of federated\naveraging. FLT can flexibly handle a generic client relatedness graph, when\nthere are no explicit clusters of clients, as well as efficiently decompose it\ninto (disjoint) clusters for clustered federated learning. We demonstrate that\nFLT not only outperforms the existing state-of-the-art baselines in non-IID\nscenarios but also offers improved fairness across clients.\n"}
+{"abstract": "  Chordal and factor-width decomposition methods for semidefinite programming\nand polynomial optimization have recently enabled the analysis and control of\nlarge-scale linear systems and medium-scale nonlinear systems. Chordal\ndecomposition exploits the sparsity of semidefinite matrices in a semidefinite\nprogram (SDP), in order to formulate an equivalent SDP with smaller\nsemidefinite constraints that can be solved more efficiently. Factor-width\ndecompositions, instead, relax or strengthen SDPs with dense semidefinite\nmatrices into more tractable problems, trading feasibility or optimality for\nlower computational complexity. This article reviews recent advances in\nlarge-scale semidefinite and polynomial optimization enabled by these two types\nof decomposition, highlighting connections and differences between them. We\nalso demonstrate that chordal and factor-width decompositions allow for\nsignificant computational savings on a range of classical problems from control\ntheory, and on more recent problems from machine learning. Finally, we outline\npossible directions for future research that have the potential to facilitate\nthe efficient optimization-based study of increasingly complex large-scale\ndynamical systems.\n"}
+{"abstract": "  This paper examines the impact of coronavirus (COVID-19) on stock market\nvolatility (SMV) in Pakistan by controlling the effect of exchange rate,\ninterest rate and government/central bank interventions to combat the pandemic.\nWe used the vector autoregressive (VAR) model over a sample period ranging from\nFebruary 25, 2020 to December 7, 2020. We find that a shock to total daily\ncoronavirus cases in Pakistan lead to a significant increase in SMV. This\nresult is aligned with a vast literature on pandemics and investors uncertainty\nand remains robust to several robustness checks applied in our analysis.\n"}
+{"abstract": "  A finite metric space is called here distance degree regular if its distance\ndegree sequence is the same for every vertex. A notion of designs in such\nspaces is introduced that generalizes that of designs in $Q$-polynomial\ndistance-regular graphs. An approximation of their cumulative distribution\nfunction, based on the notion of Christoffel function in approximation theory\nis given. As an application we derive limit laws on the weight distributions of\nbinary orthogonal arrays of strength going to infinity. An analogous result for\ncombinatorial designs of strength going to infinity is given.\n"}
+{"abstract": "  Despite the intricacies involved in designing a computer as a teampartner, we\ncan observe patterns in team behavior which allow us to describe at a general\nlevel how AI systems are to collaborate with humans. Whereas most work on\nhuman-machine teaming has focused on physical agents (e.g. robotic systems),\nour aim is to study how humans can collaborate with information agents. We\npropose some appropriate team design patterns, and test them using our\nCollaborative Intelligence Analysis (CIA) tool.\n"}
+{"abstract": "  The design of antibacterial-releasing coatings or wrapping materials with\ncontrolled drug release capability is a promising strategy to minimise risks of\ninfection and medical device failure in vivo. Collagen fibres have been\nemployed as medical device building block, although they still fail to display\ncontrolled release capability, competitive wet-state mechanical properties, and\nretained triple helix organisation. We investigated this challenge by pursuing\na multiscale design approach integrating drug encapsulation, in-situ covalent\ncrosslinking and fibre spinning. By selecting ciprofloxacin (Cip) as a typical\nantibacterial drug, wet spinning was selected as a triple helix-friendly route\ntowards Cip-encapsulated collagen fibres; whilst in situ crosslinking of\nfibre-forming triple helices with 1,3 phenylenediacetic acid (Ph) was\nhypothesised to yield Ph-Cip {\\pi}-{\\pi} stacking aromatic interactions and\nenable controlled drug release. Higher tensile modulus and strength were\nmeasured in Ph crosslinked fibres compared to state-of-the-art carbodiimide\ncrosslinked controls. Cip-encapsulated Ph-crosslinked fibres revealed decreased\nelongation at break and significantly-enhanced drug retention in vitro with\nrespect to Cip-free variants and carbodiimide-crosslinked controls,\nrespectively. This multiscale manufacturing strategy provides new insight\naiming at wet spun collagen triple helices with nanoscale-regulated tensile\nproperties and drug release capability.\n"}
+{"abstract": "  Template-based discriminative trackers are currently the dominant tracking\nmethods due to their robustness and accuracy, and the Siamese-network-based\nmethods that depend on cross-correlation operation between features extracted\nfrom template and search images show the state-of-the-art tracking performance.\nHowever, general cross-correlation operation can only obtain relationship\nbetween local patches in two feature maps. In this paper, we propose a novel\ntracker network based on a powerful attention mechanism called Transformer\nencoder-decoder architecture to gain global and rich contextual\ninterdependencies. In this new architecture, features of the template image is\nprocessed by a self-attention module in the encoder part to learn strong\ncontext information, which is then sent to the decoder part to compute\ncross-attention with the search image features processed by another\nself-attention module. In addition, we design the classification and regression\nheads using the output of Transformer to localize target based on\nshape-agnostic anchor. We extensively evaluate our tracker TrTr, on VOT2018,\nVOT2019, OTB-100, UAV, NfS, TrackingNet, and LaSOT benchmarks and our method\nperforms favorably against state-of-the-art algorithms. Training code and\npretrained models are available at https://github.com/tongtybj/TrTr.\n"}
+{"abstract": "  Recent developments in Banach space theory provided unexpected examples of\nunital Banach algebras that are isomorphic to Calkin algebras of Banach spaces,\nhowever no example of a unital Banach algebra that cannot be realised as\na~Calkin algebra has been found so far. This naturally led to the question of\npossible limitations of such assignments. In the present note we provide\nexamples of unital Banach algebras meeting the necessary density condition for\nbeing the Calkin algebra of a separable Banach space that are not isomorphic to\nCalkin algebras of such spaces, nonetheless. The examples may be found of the\nform $C(X)$ for a compact space $X$, $\\ell_1(G)$ for some torsion-free Abelian\ngroup, and a~simple, unital AF $C^*$-algebra. Extensions to higher densities\nare also presented.\n"}
+{"abstract": "  In this paper, we study a Target-Attacker-Defender (TAD) differential game\ninvolving one attacker, one target and multiple defenders. We consider two\nvariations where (a) the attacker and the target have unlimited observation\nrange and the defenders are visibility constrained (b) only the attacker has\nunlimited observation range and the remaining players are visibility\nconstrained. We model the players' interactions as a dynamic game with\nasymmetric information. Here, the visibility constraints of the players induce\na visibility network which encapsulates the visibility information during the\nevolution of the game. Based on this observation, we introduce network adapted\nfeedback or implementable strategies for visibility constrained players. Using\ninverse game theory approach we obtain network adapted feedback Nash\nequilibrium strategies. We introduce a consistency criterion for selecting a\nsubset (or refinement) of network adapted feedback Nash strategies, and provide\nan optimization based approach for computing them. Finally, we illustrate our\nresults with numerical experiments.\n"}
+{"abstract": "  In order to better understand a complex theory like quantum mechanics, it is\nsometimes useful to take a step back and create alternative theories, with more\nintuitive foundations, and examine which features of quantum mechanics can be\nreproduced by such a foil theory. A prominent example is Spekkens' toy theory,\nwhich is based off a simple premise: \"What if we took a common classical theory\nand added the uncertainty principle as a postulate?\" In other words, the theory\nimposes an epistemic restriction on our knowledge about a physical system: only\nhalf of the variables can ever be known to an observer. Like good science\nfiction, from this simple principle a rich behaviour emerges, most notoriously\nwhen we compose several systems. The toy theory emulates some aspects of\nquantum non-locality, although crucially it is still a non-contextual model. In\nthis pedagogical review we consolidate different approaches to Spekkens' toy\ntheory, including the stabilizer formalism and the generalization to arbitrary\ndimensions, completing them with new results where necessary. In particular, we\nintroduce a general characterization of measurements, superpositions and\nentanglement in the toy theory.\n"}
+{"abstract": "  In this paper, we completely determine the irreducible characters of the four\nfamilies of Suzuki $p$-groups. This corrects some errors in the paper [8].\n"}
+{"abstract": "  Punctuation prediction for automatic speech recognition (ASR) output\ntranscripts plays a crucial role for improving the readability of the ASR\ntranscripts and for improving the performance of downstream natural language\nprocessing applications. However, achieving good performance on punctuation\nprediction often requires large amounts of labeled speech transcripts, which is\nexpensive and laborious. In this paper, we propose a Discriminative\nSelf-Training approach with weighted loss and discriminative label smoothing to\nexploit unlabeled speech transcripts. Experimental results on the English\nIWSLT2011 benchmark test set and an internal Chinese spoken language dataset\ndemonstrate that the proposed approach achieves significant improvement on\npunctuation prediction accuracy over strong baselines including BERT, RoBERTa,\nand ELECTRA models. The proposed Discriminative Self-Training approach\noutperforms the vanilla self-training approach. We establish a new\nstate-of-the-art (SOTA) on the IWSLT2011 test set, outperforming the current\nSOTA model by 1.3% absolute gain on F$_1$.\n"}
+{"abstract": "  Pattern formation in the classical and fractional Schnakenberg equations is\nstudied to understand the nonlocal effects of anomalous diffusion. Starting\nwith linear stability analysis, we find that if the activator and inhibitor\nhave the same diffusion power, the Turing instability space depends only on the\nratio of diffusion coefficients $\\kappa_1/\\kappa_2$. However, the smaller\ndiffusive powers might introduce larger unstable wave numbers with wider band,\nimplying that the patterns may be more chaotic in the fractional cases. We then\napply a weakly nonlinear analysis to predict the parameter regimes for spot,\nstripe, and mixed patterns in the Turing space. Our numerical simulations\nconfirm the analytical results and demonstrate the differences of normal and\nanomalous diffusion on pattern formation. We find that in the presence of\nsuperdiffusion the patterns exhibit multiscale structures. The smaller the\ndiffusion powers, the larger the unstable wave numbers and the smaller the\npattern scales.\n"}
+{"abstract": "  Magnetic structures are known to possess magnon excitations confined to their\nsurfaces and interfaces, but these spatially localized modes are often not\nresolved in spectroscopy experiments. We develop a theory to calculate the\nconfined magnon spectra and its associated spin scattering function, which is\nthe physical observable in neutron and electron scattering, and a proxy for\nphoton spectroscopy based on X-ray, Raman and THz sources. We show that extra\nanisotropy at the surface or interface plays a key role in magnon confinement.\nWe obtain analytical expressions for the confinement length scale, and show\nthat it is qualitatively similar for ferromagnets and antiferromagnets in\ndimension d>=2. For d=1 we find remarkable differences between ferromagnetic\nand antiferromagnetic models. The theory indicates the presence of several\nconfined magnon resonances in addition to the usual magnons thought to explain\nthe excitations of magnetic nanostructures. Detecting these modes may elucidate\nthe impact of the interface on spin anisotropy and magnetic order.\n"}
+{"abstract": "  We propose a numerical method for approximating integro-differential\nequations arising in age-of-infection epidemic models. The method is based on a\nnon-standard finite differences approximation of the integral term appearing in\nthe equation. The study of convergence properties and the analysis of the\nqualitative behavior of the numerical solution show that it preserves all the\nbasic properties of the continuous model with no restrictive conditions on the\nstep-length $h$ of integration and that it recovers the continuous dynamic as\n$h$ tends to zero.\n"}
+{"abstract": "  GAGG:Ce (Cerium-doped Gadolinium Aluminium Gallium Garnet) is a promising new\nscintillator crystal. A wide array of interesting features, such as high light\noutput, fast decay times, almost non-existent intrinsic background and\nrobustness, make GAGG:Ce an interesting candidate as a component of new\nspace-based gamma-ray detectors. As a consequence of its novelty, literature on\nGAGG:Ce is still lacking on points crucial to its applicability in space\nmissions. In particular, GAGG:Ce is characterized by unusually high and\nlong-lasting delayed luminescence. This afterglow emission can be stimulated by\nthe interactions between the scintillator and the particles of the near-Earth\nradiation environment. By contributing to the noise, it will impact the\ndetector performance to some degree. In this manuscript we summarize the\nresults of an irradiation campaign of GAGG:Ce crystals with protons, conducted\nin the framework of the HERMES-TP/SP (High Energy Rapid Modular Ensemble of\nSatellites - Technological and Scientific Pathfinder) mission. A GAGG:Ce sample\nwas irradiated with 70 MeV protons, at doses equivalent to those expected in\nequatorial and sun-synchronous Low-Earth orbits over orbital periods spanning 6\nmonths to 10 years, time lapses representative of satellite lifetimes. We\nintroduce a new model of GAGG:Ce afterglow emission able to fully capture our\nobservations. Results are applied to the HERMES-TP/SP scenario, aiming at an\nupper-bound estimate of the detector performance degradation due to the\nafterglow emission expected from the interaction between the scintillator and\nthe near-Earth radiation environment.\n"}
+{"abstract": "  We study video crowd counting, which is to estimate the number of objects\n(people in this paper) in all the frames of a video sequence. Previous work on\ncrowd counting is mostly on still images. There has been little work on how to\nproperly extract and take advantage of the spatial-temporal correlation between\nneighboring frames in both short and long ranges to achieve high estimation\naccuracy for a video sequence. In this work, we propose Monet, a novel and\nhighly accurate motion-guided non-local spatial-temporal network for video\ncrowd counting. Monet first takes people flow (motion information) as guidance\nto coarsely segment the regions of pixels where a person may be. Given these\nregions, Monet then uses a non-local spatial-temporal network to extract\nspatial-temporally both short and long-range contextual information. The whole\nnetwork is finally trained end-to-end with a fused loss to generate a\nhigh-quality density map. Noting the scarcity and low quality (in terms of\nresolution and scene diversity) of the publicly available video crowd datasets,\nwe have collected and built a large-scale video crowd counting datasets,\nVidCrowd, to contribute to the community. VidCrowd contains 9,000 frames of\nhigh resolution (2560 x 1440), with 1,150,239 head annotations captured in\ndifferent scenes, crowd density and lighting in two cities. We have conducted\nextensive experiments on the challenging VideoCrowd and two public video crowd\ncounting datasets: UCSD and Mall. Our approach achieves substantially better\nperformance in terms of MAE and MSE as compared with other state-of-the-art\napproaches.\n"}
+{"abstract": "  In this paper, we set out to systematically design a minimal active RoT for\ntiny low-end MCU-s. We begin with the following questions: (1) What functions\nand hardware support are required to guarantee actions in the presence of\nmalware?, (2) How to implement this efficiently?, and (3) What security\nbenefits stem from such an active RoT architecture? We then design, implement,\nformally verify, and evaluate GAROTA: Generalized Active Root-Of-Trust\nArchitecture. We believe that GAROTA is the first clean-slate design of an\nactive RoT for low-end MCU-s. We show how GAROTA guarantees that even a fully\nsoftware-compromised low-end MCU performs a desired action. We demonstrate its\npracticality by implementing GAROTA in the context of three types of\napplications where actions are triggered by: sensing hardware, network events\nand timers. We also formally specify and verify GAROTA functionality and\nproperties.\n"}
+{"abstract": "  Warp drives in Einstein's general theory of relativity provide a unique\nmechanism for manned interstellar travel. It is well-known that the classical\nsuperluminal soliton spacetimes require negative energy densities, likely\nsourced by quantum processes of the uncertainty principle. It has even been\nclaimed by few that negative energy densities are a requirement of superluminal\nmotion. However, recent studies suggest this may not be the case. A general\ndecomposition of the defining variables and the corresponding decomposition of\nthe Eulerian energy are studied. A geometrical interpretation of the Eulerian\nenergy is found, shedding new light on superluminal solitons generated by\nrealistic energy distributions. With this new interpretation, it becomes a\nrelatively simple matter to generate solitonic configurations, within a certain\nsubclass, that respect the positive energy constraint. Using this newfound\ninterpretation, a superluminal solitonic spacetime is presented that possesses\npositive semi-definite energy. A modest numerical analysis is carried out on a\nset of example configurations, finding total energy requirements four orders of\nmagnitude smaller than the solar mass. Extraordinarily, the example\nconfigurations are generated by purely positive energy densities, a tremendous\nimprovement on the classical configurations. The geometrical interpretation of\nthe Eulerian energy thus opens new doors to generating realistic warp fields\nfor laboratory study and potential future manned interstellar travel.\n"}
+{"abstract": "  Trail camera imagery has increasingly gained popularity amongst biologists\nfor conservation and ecological research. Minimal human interference required\nto operate camera traps allows capturing unbiased species activities. Several\nstudies - based on human and wildlife interactions, migratory patterns of\nvarious species, risk of extinction in endangered populations - are limited by\nthe lack of rich data and the time-consuming nature of manually annotating\ntrail camera imagery. We introduce a challenging wildlife camera trap\nclassification dataset collected from two different locations in Southwestern\nFlorida, consisting of 104,495 images featuring visually similar species,\nvarying illumination conditions, skewed class distribution, and including\nsamples of endangered species, i.e. Florida panthers. Experimental evaluations\nwith ResNet-50 architecture indicate that this image classification-based\ndataset can further push the advancements in wildlife statistical modeling. We\nwill make the dataset publicly available.\n"}
+{"abstract": "  In complex dynamical systems, the detection of coupling and its direction\nfrom observed time series is a challenging task. We study coupling in coupled\nDuffing oscillator systems in regular and chaotic dynamical regimes. By\nobserving the conditional mutual information (CMI) based on the Shannon\nentropy, we successfully infer the direction of coupling for different system\nregimes. Moreover, we show that in the weak coupling limit the values of CMI\ncan be used to infer the coupling parameters by computing the derivative of the\nconditional mutual information with respect to the coupling strength, called\nthe information susceptibility. The complete numerical implementation is\navailable at https://repo.ijs.si/mbresar/duffing-cmi.\n"}
+{"abstract": "  A complete recipe of measure-preserving diffusions in Euclidean space was\nrecently derived unifying several MCMC algorithms into a single framework. In\nthis paper, we develop a geometric theory that improves and generalises this\nconstruction to any manifold. We thereby demonstrate that the completeness\nresult is a direct consequence of the topology of the underlying manifold and\nthe geometry induced by the target measure $P$; there is no need to introduce\nother structures such as a Riemannian metric, local coordinates, or a reference\nmeasure. Instead, our framework relies on the intrinsic geometry of $P$ and in\nparticular its canonical derivative, the deRham rotationnel, which allows us to\nparametrise the Fokker--Planck currents of measure-preserving diffusions using\npotentials. The geometric formalism can easily incorporate constraints and\nsymmetries, and deliver new important insights, for example, a new complete\nrecipe of Langevin-like diffusions that are suited to the construction of\nsamplers. We also analyse the reversibility and dissipative properties of the\ndiffusions, the associated deterministic flow on the space of measures, and the\ngeometry of Langevin processes. Our article connects ideas from various\nliterature and frames the theory of measure-preserving diffusions in its\nappropriate mathematical context.\n"}
+{"abstract": "  One of the major prerequisites for any deep learning approach is the\navailability of large-scale training data. When dealing with scanned document\nimages in real world scenarios, the principal information of its content is\nstored in the layout itself. In this work, we have proposed an automated deep\ngenerative model using Graph Neural Networks (GNNs) to generate synthetic data\nwith highly variable and plausible document layouts that can be used to train\ndocument interpretation systems, in this case, specially in digital mailroom\napplications. It is also the first graph-based approach for document layout\ngeneration task experimented on administrative document images, in this case,\ninvoices.\n"}
+{"abstract": "  In this second part of a series work, we further develop the theory of higher\nfusion categories, including center functors, centralizers and group theoretic\nhigher fusion categories. Along the way we prove several conjectures on modular\nextensions and the representation categories of finite higher groups.\n"}
+{"abstract": "  Post-selection, the power of discarding all runs of a computation in which an\nundesirable event occurs, is an influential concept introduced to the field of\nquantum complexity theory by Aaronson (Proceedings of the Royal Society A,\n2005). In the present paper, we initiate the study of post-selection for\nspace-bounded quantum complexity classes. Our main result shows the identity\n$\\sf PostBQL=PL$, i.e., the class of problems that can be solved by a\nbounded-error (polynomial-time) logarithmic-space quantum algorithm with\npost-selection ($\\sf PostBQL$) is equal to the class of problems that can be\nsolved by unbounded-error logarithmic-space classical algorithms ($\\sf PL$).\nThis result gives a space-bounded version of the well-known result $\\sf\nPostBQP=PP$ proved by Aaronson for polynomial-time quantum computation. As a\nby-product, we also show that $\\sf PL$ coincides with the class of problems\nthat can be solved by bounded-error logarithmic-space quantum algorithms that\nhave no time bound.\n"}
+{"abstract": "  Artificial intelligence (AI) systems have become increasingly common and the\ntrend will continue. Examples of AI systems include autonomous vehicles (AV),\ncomputer vision, natural language processing, and AI medical experts. To allow\nfor safe and effective deployment of AI systems, the reliability of such\nsystems needs to be assessed. Traditionally, reliability assessment is based on\nreliability test data and the subsequent statistical modeling and analysis. The\navailability of reliability data for AI systems, however, is limited because\nsuch data are typically sensitive and proprietary. The California Department of\nMotor Vehicles (DMV) oversees and regulates an AV testing program, in which\nmany AV manufacturers are conducting AV road tests. Manufacturers participating\nin the program are required to report recurrent disengagement events to\nCalifornia DMV. This information is being made available to the public. In this\npaper, we use recurrent disengagement events as a representation of the\nreliability of the AI system in AV, and propose a statistical framework for\nmodeling and analyzing the recurrent events data from AV driving tests. We use\ntraditional parametric models in software reliability and propose a new\nnonparametric model based on monotonic splines to describe the event process.\nWe develop inference procedures for selecting the best models, quantifying\nuncertainty, and testing heterogeneity in the event process. We then analyze\nthe recurrent events data from four AV manufacturers, and make inferences on\nthe reliability of the AI systems in AV. We also describe how the proposed\nanalysis can be applied to assess the reliability of other AI systems.\n"}
+{"abstract": "  We continue our program of improving the complexity of so-called Boltzmann\nsampling algorithms, for the exact sampling of combinatorial structures, and\nreach average linear-time complexity, i.e. optimality up to a multiplicative\nconstant. Here we solve this problem for irreducible context-free structures, a\nbroad family of structures to which the celebrated Drmota--Lalley--Woods\nTheorem applies. Our algorithm is a rejection algorithm. The main idea is to\nsingle out some degrees of freedom, i.e. write $p(x)=p_1(y) p_2(x|y)$, which\nallows to introduce a rejection factor at the level of the $y$ object, that is\nalmost surely of order $1$.\n"}
+{"abstract": "  Monocular 3D object detection is of great significance for autonomous driving\nbut remains challenging. The core challenge is to predict the distance of\nobjects in the absence of explicit depth information. Unlike regressing the\ndistance as a single variable in most existing methods, we propose a novel\ngeometry-based distance decomposition to recover the distance by its factors.\nThe decomposition factors the distance of objects into the most representative\nand stable variables, i.e. the physical height and the projected visual height\nin the image plane. Moreover, the decomposition maintains the self-consistency\nbetween the two heights, leading to robust distance prediction when both\npredicted heights are inaccurate. The decomposition also enables us to trace\nthe causes of the distance uncertainty for different scenarios. Such\ndecomposition makes the distance prediction interpretable, accurate, and\nrobust. Our method directly predicts 3D bounding boxes from RGB images with a\ncompact architecture, making the training and inference simple and efficient.\nThe experimental results show that our method achieves the state-of-the-art\nperformance on the monocular 3D Object Detection and Birds Eye View tasks of\nthe KITTI dataset, and can generalize to images with different camera\nintrinsics.\n"}
+{"abstract": "  The translation of an operator is defined by using conjugation with\ntime-frequency shifts. Thus, one can define $\\Lambda$-shift-invariant subspaces\nof Hilbert-Schmidt operators, finitely generated, with respect to a lattice\n$\\Lambda$ in $\\mathbb{R}^{2d}$. These spaces can be seen as a generalization of\nclassical shift-invariant subspaces of square integrable functions. Obtaining\nsampling results for these subspaces appears as a natural question that can be\nmotivated by the problem of channel estimation in wireless communications.\nThese sampling results are obtained in the light of the frame theory in a\nseparable Hilbert space.\n"}
+{"abstract": "  A molecular Mott insulator $\\kappa$-(ET)$_2$B(CN)$_4$ [ET =\nbis(ethylenedithio)tetrathiafulvalene] with a distorted triangular lattice\nexhibits a quantum disordered state with gapped spin excitation in the ground\nstate. $^{13}$C nuclear magnetic resonance, magnetization, and magnetic torque\nmeasurements reveal that magnetic field suppresses valence bond order and\ninduces long-range magnetic order above a critical field $\\sim 8$ T. The\nnuclear spin-lattice relaxation rate $1/T_1$ shows persistent evolution of\nantiferromagnetic correlation above the transition temperature, highlighting a\nquantum spin liquid state with fractional excitations. The field-induced\ntransition as observed in the spin-Peierls phase suggests that the valence bond\norder transition is driven through renormalized one-dimensionality and\nspin-lattice coupling.\n"}
+{"abstract": "  We investigate the problem of controller synthesis for hyperproperties\nspecified in the temporal logic HyperLTL. Hyperproperties are system properties\nthat relate multiple execution traces. Hyperproperties can elegantly express\ninformation-flow policies like noninterference and observational determinism.\nThe controller synthesis problem is to automatically design a controller for a\nplant that ensures satisfaction of a given specification in the presence of the\nenvironment or adversarial actions. We show that the controller synthesis\nproblem is decidable for HyperLTL specifications and finite-state plants. We\nprovide a rigorous complexity analysis for different fragments of HyperLTL and\ndifferent system types: tree-shaped, acyclic, and general graphs.\n"}
+{"abstract": "  A major challenge in both neuroscience and machine learning is the\ndevelopment of useful tools for understanding complex information processing\nsystems. One such tool is probes, i.e., supervised models that relate features\nof interest to activation patterns arising in biological or artificial neural\nnetworks. Neuroscience has paved the way in using such models through numerous\nstudies conducted in recent decades. In this work, we draw insights from\nneuroscience to help guide probing research in machine learning. We highlight\ntwo important design choices for probes $-$ direction and expressivity $-$ and\nrelate these choices to research goals. We argue that specific research goals\nplay a paramount role when designing a probe and encourage future probing\nstudies to be explicit in stating these goals.\n"}
+{"abstract": "  In this paper, we propose a machine learning (ML) method to learn how to\nsolve a generic constrained continuous optimization problem. To the best of our\nknowledge, the generic methods that learn to optimize, focus on unconstrained\noptimization problems and those dealing with constrained problems are not\neasy-to-generalize. This approach is quite useful in optimization tasks where\nthe problem's parameters constantly change and require resolving the\noptimization task per parameter update. In such problems, the computational\ncomplexity of optimization algorithms such as gradient descent or interior\npoint method preclude near-optimal designs in real-time applications. In this\npaper, we propose an unsupervised deep learning (DL) solution for solving\nconstrained optimization problems in real-time by relegating the main\ncomputation load to offline training phase. This paper's main contribution is\nproposing a method for enforcing the equality and inequality constraints to the\nDL-generated solutions for generic optimization tasks.\n"}
+{"abstract": "  Adhesions are an important cause of chronic pain following abdominal surgery.\nRecent developments in abdominal cine-MRI have enabled the non-invasive\ndiagnosis of adhesions. Adhesions are identified on cine-MRI by the absence of\nsliding motion during movement. Diagnosis and mapping of adhesions improves the\nmanagement of patients with pain. Detection of abdominal adhesions on cine-MRI\nis challenging from both a radiological and deep learning perspective. We focus\non classifying presence or absence of adhesions in sagittal abdominal cine-MRI\nseries. We experimented with spatio-temporal deep learning architectures\ncentered around a ConvGRU architecture. A hybrid architecture comprising a\nResNet followed by a ConvGRU model allows to classify a whole time-series.\nCompared to a stand-alone ResNet with a two time-point (inspiration/expiration)\ninput, we show an increase in classification performance (AUROC) from 0.74 to\n0.83 ($p<0.05$). Our full temporal classification approach adds only a small\namount (5%) of parameters to the entire architecture, which may be useful for\nother medical imaging problems with a temporal dimension.\n"}
+{"abstract": "  The search for feebly-interacting new-physics particles in the MeV-GeV mass\nrange often involves high-intensity beams dumped into thick heavy targets. The\nchallenge of evaluating the expected backgrounds for these searches from first\nprinciples is limited by the CPU time needed to generate the shower induced by\nthe primary beam. We present a Monte Carlo biasing method allowing a three\norders of magnitude increase in the efficiency for the simulation of the muon\nproduction in a 400 GeV$/c$ proton beam-dump setup. At the same time, this\nbiasing method is maintaining nearly every feature of a simulation from first\nprinciples.\n"}
+{"abstract": "  We propose a text classification tool based on support vector machines for\nthe assessment of organizational leadership styles, as appearing to Twitter\nusers. We collected Twitter data over 51 days, related to the first 30 Italian\norganizations in the 2015 ranking of Forbes Global 2000-out of which we\nselected the five with the most relevant volumes of tweets. We analyzed the\ncommunication of the company leaders, together with the dialogue among the\nstakeholders of each company, to understand the association with perceived\nleadership styles and dimensions. To assess leadership profiles, we referred to\nthe 10-factor model developed by Barchiesi and La Bella in 2007. We maintain\nthe distinctiveness of the approach we propose, as it allows a rapid assessment\nof the perceived leadership capabilities of an enterprise, as they emerge from\nits social media interactions. It can also be used to show how companies\nrespond and manage their communication when specific events take place, and to\nassess their stakeholder's reactions.\n"}
+{"abstract": "  The linear and nonlinear theories of electron-acoustic waves (EAWs) are\nstudied in a partially degenerate quantum plasma with two-temperature electrons\nand stationary ions. The initial equilibrium of electrons is assumed to be\ngiven by the Fermi-Dirac distribution at finite temperature. By employing the\nmulti-scale asymptotic expansion technique to the one-dimensional Wigner-Moyal\nand Poisson equations, it is shown that the effects of multi-plasmon resonances\nlead to a modified complex Korteweg-de Vries (KdV) equation with a new nonlocal\nnonlinearity. Besides giving rise to a nonlocal nonlinear term, the\nwave-particle resonance also modifies the local nonlinear coupling coefficient\nof the KdV equation. The latter is shown to conserve the number of particles,\nhowever, the wave energy decays with time. A careful analysis shows that the\ntwo-plasmon resonance is the dominant mechanism for nonlinear Landau damping of\nEAWs. An approximate soliton solution of the KdV equation is also obtained, and\nit is shown that the nonlinear Landau damping causes the wave amplitude to\ndecay slowly with time compared to the classical theory.\n"}
+{"abstract": "  Gene expression datasets consist of thousand of genes with relatively small\nsamplesizes (i.e. are large-$p$-small-$n$). Moreover, dependencies of various\norders co-exist in the datasets. In the Undirected probabilistic Graphical\nModel (UGM) framework the Glasso algorithm has been proposed to deal with high\ndimensional micro-array datasets forcing sparsity. Also, modifications of the\ndefault Glasso algorithm are developed to overcome the problem of complex\ninteraction structure. In this work we advocate the use of a simple score-based\nHill Climbing algorithm (HC) that learns Gaussian Bayesian Networks (BNs)\nleaning on Directed Acyclic Graphs (DAGs). We compare HC with Glasso and its\nmodifications in the UGM framework on their capability to reconstruct GRNs from\nmicro-array data belonging to the Escherichia Coli genome. We benefit from the\nanalytical properties of the Joint Probability Density (JPD) function on which\nboth directed and undirected PGMs build to convert DAGs to UGMs.\n  We conclude that dependencies in complex data are learned best by the HC\nalgorithm, presenting them most accurately and efficiently, simultaneously\nmodelling strong local and weaker but significant global connections coexisting\nin the gene expression dataset. The HC algorithm adapts intrinsically to the\ncomplex dependency structure of the dataset, without forcing a specific\nstructure in advance. On the contrary, Glasso and modifications model\nunnecessary dependencies at the expense of the probabilistic information in the\nnetwork and of a structural bias in the JPD function that can only be relieved\nincluding many parameters.\n"}
+{"abstract": "  We study the anisotropic Kondo line defects in products of chiral $SU(2)$ WZW\nmodels. We propose an ODE/IM correspondence for the anisotropic Kondo problems\nby considering the four-dimensional Chern Simons theory in the trigonometric\ncase. We verify the claim both by explicit perturbative calculations in the\nultraviolet and by exact WKB analysis in the infrared. In doing so, we derived\nthe infrared dynamics of a large class of anisotropic line defects.\n"}
+{"abstract": "  This work is concerned with the development of an adaptive numerical method\nfor semilinear heat flow models featuring a general (possibly) nonlinear\nreaction term that may cause the solution to blow up in finite time. The fully\ndiscrete scheme consists of a high order discontinuous Galerkin (dG) time\nstepping method and a conforming finite element discretisation (cG) in space.\nThe proposed adaptive procedure is based on rigorously devised conditional a\nposteriori error bounds in the $L^{\\infty}(L^{\\infty})$ norm. Numerical\nexperiments complement the theoretical results.\n"}
+{"abstract": "  Let $F$ be a local field of characteristic $p>0$. By adapting methods of\nScholze, we give a new proof of the local Langlands correspondence for $\\GL_n$\nover $F$. More specifically, we construct $\\ell$-adic Galois representations\nassociated with many discrete automorphic representations over global function\nfields, which we use to construct a map\n$\\DeclareMathOperator{\\rec}{rec}\\pi\\mapsto\\rec(\\pi)$ from isomorphism classes\nof irreducible smooth representations of $\\GL_n(F)$ to isomorphism classes of\n$n$-dimensional semisimple continuous representations of $W_F$. Our map $\\rec$\nis characterized in terms of a local compatibility condition on traces of a\ncertain test function $f_{\\tau,h}$, and we prove that $\\rec$ equals the usual\nlocal Langlands correspondence (after forgetting the monodromy operator).\n"}
+{"abstract": "  The prediction capability of recurrent-type neural networks is investigated\nfor real-time short-term prediction (nowcasting) of ship motions in high sea\nstate. Specifically, the performance of recurrent neural networks, long-short\nterm memory, and gated recurrent units models are assessed and compared using a\ndata set coming from computational fluid dynamics simulations of a\nself-propelled destroyer-type vessel in stern-quartering sea state 7. Time\nseries of incident wave, ship motions, rudder angle, as well as immersion\nprobes, are used as variables for a nowcasting problem. The objective is to\nobtain about 20 s ahead prediction. Overall, the three methods provide\npromising and comparable results.\n"}
+{"abstract": "  We introduce a dispersive regularization of the compressible Euler equations\nin Lagrangian coordinates, in the one-dimensional torus. We assume a Van der\nWaals pressure law, which presents both hyperbolic and elliptic zones. The\ndispersive regularization is of Schroedinger type. In particular, the\nregularized system is complex-valued. It has a conservation law, which, for\nreal unknowns, is identical to the energy of the unregularized physical system.\nThe regularized system supports high-frequency solutions, with an existence\ntime or an amplitude which depend strongly on the pressure law.\n"}
+{"abstract": "  Reinforcement learning (RL) has gained increasing interest since the\ndemonstration it was able to reach human performance on video game benchmarks\nusing deep Q-learning (DQN). The current consensus for training neural networks\non such complex environments is to rely on gradient-based optimization.\nAlthough alternative Bayesian deep learning methods exist, most of them still\nrely on gradient-based optimization, and they typically do not scale on\nbenchmarks such as the Atari game environment. Moreover none of these\napproaches allow performing the analytical inference for the weights and biases\ndefining the neural network. In this paper, we present how we can adapt the\ntemporal difference Q-learning framework to make it compatible with the\ntractable approximate Gaussian inference (TAGI), which allows learning the\nparameters of a neural network using a closed-form analytical method.\nThroughout the experiments with on- and off-policy reinforcement learning\napproaches, we demonstrate that TAGI can reach a performance comparable to\nbackpropagation-trained networks while using fewer hyperparameters, and without\nrelying on gradient-based optimization.\n"}
+{"abstract": "  We present new SOFIA [CII] and ALMA CO(J=1-0) observations of the nearby\nasymmetric barred spiral galaxy NGC 7479. The data, which cover the whole bar\nof the galaxy and the counter-arms visible in the radio continuum, are analyzed\nin conjunction with a wealth of existing visible, infrared, radio, and X-ray\ndata. As in most normal galaxies, the [CII] emission is generally consistent\nwith emission from cooling gas excited by photoelectric heating in\nphoto-dissociation regions. However, anomalously high [CII]/CO ratios are seen\nat the two ends of the counter-arms. Both ends show shell-like structures,\npossibly bubbles, in H-alpha emission. In addition, the southern end has [CII]\nto infrared emission ratios inconsistent with normal star formation. Because\nthere is little HI emission at this location, the [CII] emission probably\noriginates in warm shocked molecular gas heated by the interaction of the radio\njet forming the counter-arms with the interstellar medium in the galaxy. At two\nother locations, the high [CII]/CO ratios provide evidence for the existence of\npatches of CO-dark molecular gas. The [CII] and CO observations also reveal\nresolved velocity components along the bar. In particular, the CO emission can\nbe separated into two components associated to gas along the leading edge of\nthe bar and gas trailing the bar. The trailing gas component that amounts to\napproximately 40% of the gas around the bar region may be related to a minor\nmerger.\n"}
+{"abstract": "  This paper summarizes the progress in developing a rugged, low-cost,\nautomated ground cone robot network capable of traffic delineation at\nlane-level precision. A holonomic omnidirectional base with a traffic\ndelineator was developed to allow flexibility in initialization. RTK GPS was\nutilized to reduce minimum position error to 2 centimeters. Due to recent\ndevelopments, the cost of the platform is now less than $1,600. To minimize the\neffects of GPS-denied environments, wheel encoders and an Extended Kalman\nFilter were implemented to maintain lane-level accuracy during operation and a\nmaximum error of 1.97 meters through 50 meters with little to no GPS signal.\nFuture work includes increasing the operational speed of the platforms,\nincorporating lanelet information for path planning, and cross-platform\nestimation.\n"}
+{"abstract": "  This paper introduces the Diabetic Foot Ulcers dataset (DFUC2021) for\nanalysis of pathology, focusing on infection and ischaemia. We describe the\ndata preparation of DFUC2021 for ground truth annotation, data curation and\ndata analysis. The final release of DFUC2021 consists of 15,683 DFU patches,\nwith 5,955 training, 5,734 for testing and 3,994 unlabeled DFU patches. The\nground truth labels are four classes, i.e. control, infection, ischaemia and\nboth conditions. We curate the dataset using image hashing techniques and\nanalyse the separability using UMAP projection. We benchmark the performance of\nfive key backbones of deep learning, i.e. VGG16, ResNet101, InceptionV3,\nDenseNet121 and EfficientNet on DFUC2021. We report the optimised results of\nthese key backbones with different strategies. Based on our observations, we\nconclude that EfficientNetB0 with data augmentation and transfer learning\nprovided the best results for multi-class (4-class) classification with\nmacro-average Precision, Recall and F1-score of 0.57, 0.62 and 0.55,\nrespectively. In ischaemia and infection recognition, when trained on\none-versus-all, EfficientNetB0 achieved comparable results with the state of\nthe art. Finally, we interpret the results with statistical analysis and\nGrad-CAM visualisation.\n"}
+{"abstract": "  In a world abundant with diverse data arising from complex acquisition\ntechniques, there is a growing need for new data analysis methods. In this\npaper we focus on high-dimensional data that are organized into several\nhierarchical datasets. We assume that each dataset consists of complex samples,\nand every sample has a distinct irregular structure modeled by a graph. The\nmain novelty in this work lies in the combination of two complementing powerful\ndata-analytic approaches: topological data analysis (TDA) and geometric\nmanifold learning. Geometry primarily contains local information, while\ntopology inherently provides global descriptors. Based on this combination, we\npresent a method for building an informative representation of hierarchical\ndatasets. At the finer (sample) level, we devise a new metric between samples\nbased on manifold learning that facilitates quantitative structural analysis.\nAt the coarser (dataset) level, we employ TDA to extract qualitative structural\ninformation from the datasets. We showcase the applicability and advantages of\nour method on simulated data and on a corpus of hyper-spectral images. We show\nthat an ensemble of hyper-spectral images exhibits a hierarchical structure\nthat fits well the considered setting. In addition, we show that our new method\ngives rise to superior classification results compared to state-of-the-art\nmethods.\n"}
+{"abstract": "  Recently, there are unprecedented data growth originating from different\nonline platforms which contribute to big data in terms of volume, velocity,\nvariety and veracity (4Vs). Given this nature of big data which is\nunstructured, performing analytics to extract meaningful information is\ncurrently a great challenge to big data analytics. Collecting and analyzing\nunstructured textual data allows decision makers to study the escalation of\ncomments/posts on our social media platforms. Hence, there is need for\nautomatic big data analysis to overcome the noise and the non-reliability of\nthese unstructured dataset from the digital media platforms. However, current\nmachine learning algorithms used are performance driven focusing on the\nclassification/prediction accuracy based on known properties learned from the\ntraining samples. With the learning task in a large dataset, most machine\nlearning models are known to require high computational cost which eventually\nleads to computational complexity. In this work, two supervised machine\nlearning algorithms are combined with text mining techniques to produce a\nhybrid model which consists of Na\\\"ive Bayes and support vector machines (SVM).\nThis is to increase the efficiency and accuracy of the results obtained and\nalso to reduce the computational cost and complexity. The system also provides\nan open platform where a group of persons with a common interest can share\ntheir comments/messages and these comments classified automatically as legal or\nillegal. This improves the quality of conversation among users. The hybrid\nmodel was developed using WEKA tools and Java programming language. The result\nshows that the hybrid model gave 96.76% accuracy as against the 61.45% and\n69.21% of the Na\\\"ive Bayes and SVM models respectively.\n"}
+{"abstract": "  This paper introduces two decomposition-based methods for two-block\nmixed-integer linear programs (MILPs), which break the original problem into a\nsequence of smaller MILP subproblems. The first method is based on the\nl1-augmented Lagrangian. The second method is based on the alternating\ndirection method of multipliers. When the original problem has a block-angular\nstructure, the subproblems of the first block have low dimensions and can be\nsolved in parallel. We add reverse-norm cuts and augmented Lagrangian cuts to\nthe subproblems of the second block. For both methods, we show asymptotic\nconvergence to globally optimal solutions and present iteration upper bounds.\nNumerical comparisons with recent decomposition methods demonstrate the\nexactness and efficiency of our proposed methods.\n"}
+{"abstract": "  The mean vertical heat transport $\\langle wT \\rangle$ in convection between\nisothermal plates driven by uniform internal heating is investigated by means\nof rigorous bounds. These are obtained as a function of the Rayleigh number $R$\nby constructing feasible solutions to a convex variational problem, derived\nusing a formulation of the classical background method in terms of quadratic\nauxiliary functions. When the fluid's temperature relative to the boundaries is\nallowed to be positive or negative, numerical solution of the variational\nproblem shows that best previous bound $\\langle wT \\rangle \\leq 1/2$ can only\nbe improved up to finite $R$. Indeed, we demonstrate analytically that $\n\\langle wT \\rangle \\leq 2^{-21/5} R^{1/5}$ and therefore prove that $\\langle\nwT\\rangle< 1/2$ for $R < 65\\,536$. However, if the minimum principle for\ntemperature is invoked, which asserts that internal temperature is at least as\nlarge as the temperature of the isothermal boundaries, then numerically\noptimised bounds are strictly smaller than $1/2$ until at least $R =3.4\\times\n10^{5}$. While the computational results suggest that the best bound on\n$\\langle wT\\rangle$ approaches $1/2$ asymptotically from below as $R\\rightarrow\n\\infty$, we prove that typical analytical constructions cannot be used to prove\nthis conjecture.\n"}
+{"abstract": "  We present a multi-phase design parameterization to obtain optimized\nheterogeneous lattice structures. The 3D domain is discretized into a cubical\ngrid wherein each cube has eight distinct unit cell types or phases. When all\nphases are present, the domain resembles a densely connected ground structure.\nThe cross-section area of beam segments in lattice units, modelled using\nTimoshenko beam theory, are the design variables. All beam segments in a\nparticular lattice phase have the same area of cross-section to keep the number\nof design variables low. The optimization problem is formulated for stiff\nstructures and is solved using the optimality criteria algorithm. We present a\ncase study to show the superiority of topology-optimized heterogeneous\nstructures over uniform lattices of a single phase. In order to interpret the\nphase composition, we perform four basic load tests on single phases, namely,\ntension or compression, shear, bending, and torsion. The phases are ranked\nbased on the stiffness corresponding to individual loading conditions. The\nresults show that in the optimized structure, the local internal load\nconfiguration drives the selection of phases. We also note that micropolar\nelasticity captures the bulk behaviour of heterogeneous lattice structures, and\nhelps not only to interpret the optimality of phases but also to improve the\ncomputational efficiency of the proposed optimization technique.\n"}
+{"abstract": "  This chapter contains an exposition of the sheaf-theoretic framework for\ncontextuality emphasising resource-theoretic aspects, as well as some original\nresults on this topic. In particular, we consider functions that transform\nempirical models on a scenario S to empirical models on another scenario T, and\ncharacterise those that are induced by classical procedures between S and T\ncorresponding to 'free' operations in the (non-adaptive) resource theory of\ncontextuality. We proceed by expressing such functions as empirical models\nthemselves, on a new scenario built from S and T. Our characterisation then\nboils down to the non-contextuality of these models. We also show that this\nconstruction on scenarios provides a closed structure in the category of\nmeasurement scenarios.\n"}
+{"abstract": "  The fast and accessible verification of nonclassical resources is an\nindispensable step towards a broad utilization of continuous-variable quantum\ntechnologies. Here, we use machine learning methods for the identification of\nnonclassicality of quantum states of light by processing experimental data\nobtained via homodyne detection. For this purpose, we train an artificial\nneural network to classify classical and nonclassical states from their\nquadrature-measurement distributions. We demonstrate that the network is able\nto correctly identify classical and nonclassical features from real\nexperimental quadrature data for different states of light. Furthermore, we\nshow that nonclassicality of some states that were not used in the training\nphase is also recognized. Circumventing the requirement of the large sample\nsizes needed to perform homodyne tomography, our approach presents a promising\nalternative for the identification of nonclassicality for small sample sizes,\nindicating applicability for fast sorting or direct monitoring of experimental\ndata.\n"}
+{"abstract": "  Language identification of social media text has been an interesting problem\nof study in recent years. Social media messages are predominantly in code mixed\nin non-English speaking states. Prior knowledge by pre-training contextual\nembeddings have shown state of the art results for a range of downstream tasks.\nRecently, models such as BERT have shown that using a large amount of unlabeled\ndata, the pretrained language models are even more beneficial for learning\ncommon language representations. Extensive experiments exploiting transfer\nlearning and fine-tuning BERT models to identify language on Twitter are\npresented in this paper. The work utilizes a data collection of\nHindi-English-Urdu codemixed text for language pre-training and Hindi-English\ncodemixed for subsequent word-level language classification. The results show\nthat the representations pre-trained over codemixed data produce better results\nby their monolingual counterpart.\n"}
+{"abstract": "  We study the two-dimensional multiphase Muskat problem describing the motion\nof three immiscible fluids with equal viscosities in a vertical homogeneous\nporous medium identified with $\\mathbb{R}^2$ under the effect of gravity. We\nfirst formulate the governing equations as a strongly coupled evolution problem\nfor the functions that parameterize the sharp interfaces between the fluids.\nAfterwards we prove that the problem is of parabolic type and establish its\nwell-posedness together with two parabolic smoothing properties. For solutions\nthat are not global we exclude, in a certain regime, that the interfaces come\ninto contact along a curve segment.\n"}
+{"abstract": "  The void ellipticity distribution today can be well explained by the tidal\nfield. Going a step further from the overall distribution, we investigate\nindividuality on the tidal response of void shape in non-linear dynamical\nevolution. We perform an N-body simulation and trace individual voids using\nparticle ID. The voids are defined based on Voronoi tessellation and watershed\nalgorithm, using public code VIDE. A positive correlation is found between the\ntime variation of void ellipticity and tidal field around a void if the void\nmaintains its constituent particles. Such voids tend to have smaller mass\ndensities. Conversely, not a few voids significantly deform by particle\nexchange, rather than the tidal field. Those voids may prevent us from\ncorrectly proving a quadrupole field of gravity out of a void shape.\n"}
+{"abstract": "  Adaptive gradient methods have shown excellent performances for solving many\nmachine learning problems. Although multiple adaptive gradient methods were\nrecently studied, they mainly focus on either empirical or theoretical aspects\nand also only work for specific problems by using some specific adaptive\nlearning rates. Thus, it is desired to design a universal framework for\npractical algorithms of adaptive gradients with theoretical guarantee to solve\ngeneral problems. To fill this gap, we propose a faster and universal framework\nof adaptive gradients (i.e., SUPER-ADAM) by introducing a universal adaptive\nmatrix that includes most existing adaptive gradient forms. Moreover, our\nframework can flexibly integrate the momentum and variance reduced techniques.\nIn particular, our novel framework provides the convergence analysis support\nfor adaptive gradient methods under the nonconvex setting. In theoretical\nanalysis, we prove that our SUPER-ADAM algorithm can achieve the best known\ngradient (i.e., stochastic first-order oracle (SFO)) complexity of\n$\\tilde{O}(\\epsilon^{-3})$ for finding an $\\epsilon$-stationary point of\nnonconvex optimization, which matches the lower bound for stochastic smooth\nnonconvex optimization. In numerical experiments, we employ various deep\nlearning tasks to validate that our algorithm consistently outperforms the\nexisting adaptive algorithms. Code is available at\nhttps://github.com/LIJUNYI95/SuperAdam\n"}
+{"abstract": "  The possibility of shifting sound energy from lower to higher frequency bands\nis investigated. The system configuration considered is a segmented structure\nhaving non-linear stiffness characteristics. It is proposed here that such a\nfrequency-shifting mechanism could complement metamaterial concepts for\nmass-efficient sound barriers. The acoustical behavior of the material system\nwas studied through a representative two-dimensional model consisting of a\nsegmented plate with a contact interface. Multiple harmonic peaks were observed\nin response to a purely single frequency excitation, and the strength of the\nresponse was found to depend on the degree of non-linearity introduced. The\nlower and closer an excitation frequency was to the characteristic resonance\nfrequencies of the base system, the stronger was the predicted higher harmonic\nresponse. The broadband sound transmission loss of these systems has also been\ncalculated and the low frequency sound transmission loss was found to increase\nas the level of the broadband incident sound field increased. The present\nfindings support the feasibility of designing material systems that transfer\nenergy from lower frequency bands, where a sound barrier is less efficient, to\nhigher bands where energy is more readily dissipated.\n"}
+{"abstract": "  The free energy of glasses cannot be estimated using thermodynamic\nintegration, as glasses are intrinsically not in equilibrium. We present\nnumerical simulations showing that, in contrast, plausible free-energy\nestimates of a Kob-Andersen glass can be obtained using the Jarzynski relation.\nUsing the Jarzynski relation, we also compute the chemical potential difference\nof the two components of this system, and find that, in the glassy regime, the\nJarzynski estimate matches well with the extrapolated value of the supercooled\nliquid. Our findings are of broader interest as they show that the Jarzynski\nmethod can be used under conditions where the thermodynamic integration\napproach, which is normally more accurate, breaks down completely. Systems\nwhere such an approach might be useful are gels and jammed, glassy structures\nformed by compression.\n"}
+{"abstract": "  Nonlinear dynamical systems such as Lorenz63 equations are known to be\nchaotic in nature and sensitive to initial conditions. As a result, a small\nperturbation in the initial conditions results in deviation in state trajectory\nafter a few time steps. The algorithms and computational resources needed to\naccurately identify the system states vary depending on whether the solution is\nin transition region or not. We refer to the transition and non-transition\nregions as unstable and stable regions respectively. We label a system state to\nbe stable if it's immediate past and future states reside in the same regime.\nHowever, at a given time step we don't have the prior knowledge about whether\nsystem is in stable or unstable region. In this paper, we develop and train a\nfeed forward (multi-layer perceptron) Neural Network to classify the system\nstates of a Lorenz system as stable and unstable. We pose this task as a\nsupervised learning problem where we train the neural network on Lorenz system\nwhich have states labeled as stable or unstable. We then test the ability of\nthe neural network models to identify the stable and unstable states on a\ndifferent Lorenz system that is generated using different initial conditions.\nWe also evaluate the classification performance in the mismatched case i.e.,\nwhen the initial conditions for training and validation data are sampled from\ndifferent intervals. We show that certain normalization schemes can greatly\nimprove the performance of neural networks in especially these mismatched\nscenarios. The classification framework developed in the paper can be a\npreprocessor for a larger context of sequential decision making framework where\nthe decision making is performed based on observed stable or unstable states.\n"}
+{"abstract": "  The advent of long-range magnetic order in non-centrosymmetric compounds has\nstimulated interest in the possibility of exotic spin transport phenomena and\ntopologically protected spin textures for applications in next-generation\nspintronics. This work reports a novel wurtzite-structure polar magnetic metal,\nidentified as AA'-stacked (Fe0.5Co0.5)5-xGeTe2, which exhibits a Neel-type\nskyrmion lattice as well as a Rashba-Edelstein effect at room temperature.\nAtomic resolution imaging of the structure reveals a structural transition as a\nfunction of Co-substitution, leading to the polar phase at 50% Co. This\ndiscovery reveals an unprecedented layered polar magnetic system for\ninvestigating intriguing spin topologies and ushers in a promising new\nframework for spintronics.\n"}
+{"abstract": "  The most direct approach for characterizing the quantum dynamics of a\nstrongly-interacting system is to measure the time-evolution of its full\nmany-body state. Despite the conceptual simplicity of this approach, it quickly\nbecomes intractable as the system size grows. An alternate framework is to\nthink of the many-body dynamics as generating noise, which can be measured by\nthe decoherence of a probe qubit. Our work centers on the following question:\nWhat can the decoherence dynamics of such a probe tell us about the many-body\nsystem? In particular, we utilize optically addressable probe spins to\nexperimentally characterize both static and dynamical properties of\nstrongly-interacting magnetic dipoles. Our experimental platform consists of\ntwo types of spin defects in diamond: nitrogen-vacancy (NV) color centers\n(probe spins) and substitutional nitrogen impurities (many-body system). We\ndemonstrate that signatures of the many-body system's dimensionality, dynamics,\nand disorder are naturally encoded in the functional form of the NV's\ndecoherence profile. Leveraging these insights, we directly characterize the\ntwo-dimensional nature of a nitrogen delta-doped diamond sample. In addition,\nwe explore two distinct facets of the many-body dynamics: First, we address a\npersistent debate about the microscopic nature of spin dynamics in\nstrongly-interacting dipolar systems. Second, we demonstrate direct control\nover the spectral properties of the many-body system, including its correlation\ntime. Our work opens the door to new directions in both quantum sensing and\nsimulation.\n"}
+{"abstract": "  Joint access point (AP) association and physical carrier sensing (PCS)\nthreshold selection has the potential to improve the performance in high\ndensity wireless LANs (WLANs) under high contention, interference and\nself-interference (SI) limited transmissions. Using tools from stochastic\ngeometry, user and AP locations are independent realizations of spatial point\nprocesses. Considering the inherent effects of the channel access protocol, the\nspatial density of throughput (SDT), which depends on channel access\nprobability and coverage rate, is derived as the performance objective.\nLeveraging spatial statistics of the network, a throughput-utility maximization\nproblem is formulated to seek AP association and PCS threshold selection\npolicies that jointly maximize SDT. The AP association and the PCS threshold\nselection policies are derived analytically while an algorithm is proposed for\nnumerical solution. Under simulated scenarios involving full-duplex (FD) nodes,\noptimizing AP association yields performance gains for low to high node density\nin large-scale wireless networks. Considering PCS threshold selection\noptimization jointly with AP association is shown to improve performance by\neffectively separating concurrent transmissions in space. It is shown that AP\nassociation in FD WLANs groups users into minimal contention domains and PCS\nthreshold optimization reduces the interference domain of user groups for\nadditional performance gains.\n"}
+{"abstract": "  Ablowitz and Ladik discovered a discretization which preserves the\nintegrability of the nonlinear Schroedinger equation in one dimension. We\ncompute the generalized free energy of this model and determine the GGE\naveraged fields and currents. They are linked to the mean-field circular\nunitary matrix ensemble (CUE). The resulting hydrodynamic equations follow the\npattern already known from other integrable many-body systems. Studied is also\nthe discretized modified Korteweg-de-Vries equation which turns out to be\nrelated to the beta Jacobi log gas.\n"}
+{"abstract": "  In analogy to the theory of nilpotent orbit in finite-dimensional semisimple\nLie algebras, it is known that the principal $\\mathfrak{sl}_2$ subalgebras can\nbe constructed in hyperbolic Kac-Moody Lie algebras. We obtained a series of\n$\\mathfrak{sl}_2$ subalgebras in rank 2 symmetric hyperbolic Kac-Moody Lie\nalgebras by extending the aforementioned construction. We present this result\nand also discuss $\\mathfrak{sl}_2$ modules obtained by the action of the\n$\\mathfrak{sl}_2$ subalgebras on the original Lie algebras.\n"}
+{"abstract": "  Compared with cheap addition operation, multiplication operation is of much\nhigher computation complexity. The widely-used convolutions in deep neural\nnetworks are exactly cross-correlation to measure the similarity between input\nfeature and convolution filters, which involves massive multiplications between\nfloat values. In this paper, we present adder networks (AdderNets) to trade\nthese massive multiplications in deep neural networks, especially convolutional\nneural networks (CNNs), for much cheaper additions to reduce computation costs.\nIn AdderNets, we take the $\\ell_1$-norm distance between filters and input\nfeature as the output response. We first develop a theoretical foundation for\nAdderNets, by showing that both the single hidden layer AdderNet and the\nwidth-bounded deep AdderNet with ReLU activation functions are universal\nfunction approximators. An approximation bound for AdderNets with a single\nhidden layer is also presented. We further analyze the influence of this new\nsimilarity measure on the optimization of neural network and develop a special\ntraining scheme for AdderNets. Based on the gradient magnitude, an adaptive\nlearning rate strategy is proposed to enhance the training procedure of\nAdderNets. AdderNets can achieve a 75.7% Top-1 accuracy and a 92.3% Top-5\naccuracy using ResNet-50 on the ImageNet dataset without any multiplication in\nthe convolutional layer.\n"}
+{"abstract": "  We present a new finite-sample analysis of M-estimators of locations in\n$\\mathbb{R}^d$ using the tool of the influence function. In particular, we show\nthat the deviations of an M-estimator can be controlled thanks to its influence\nfunction (or its score function) and then, we use concentration inequality on\nM-estimators to investigate the robust estimation of the mean in high dimension\nin a corrupted setting (adversarial corruption setting) for bounded and\nunbounded score functions. For a sample of size $n$ and covariance matrix\n$\\Sigma$, we attain the minimax speed\n$\\sqrt{Tr(\\Sigma)/n}+\\sqrt{\\|\\Sigma\\|_{op}\\log(1/\\delta)/n}$ with probability\nlarger than $1-\\delta$ in a heavy-tailed setting. One of the major advantages\nof our approach compared to others recently proposed is that our estimator is\ntractable and fast to compute even in very high dimension with a complexity of\n$O(nd\\log(Tr(\\Sigma)))$ where $n$ is the sample size and $\\Sigma$ is the\ncovariance matrix of the inliers. In practice, the code that we make available\nfor this article proves to be very fast.\n"}
+{"abstract": "  We experimentally generate cylindrically polarized wavepackets with\ntransverse orbital angular momentum, demonstrating the coexistence of\nspatiotemporal optical vortex with spatial polarization singularity. The\nresults in this paper extend the study of spatiotemporal wavepackets to a\nbroader scope, paving the way for its applications in various areas such as\nlight-matter interaction, optical tweezers, spatiotemporal spin-orbit angular\nmomentum coupling, etc.\n"}
+{"abstract": "  We have previously reported ferromagnetism evinced by a large hysteretic\nanomalous Hall effect in twisted bilayer graphene (tBLG). Subsequent\nmeasurements of a quantized Hall resistance and small longitudinal resistance\nconfirmed that this magnetic state is a Chern insulator. Here we report that,\nwhen tilting the sample in an external magnetic field, the ferromagnetism is\nhighly anisotropic. Because spin-orbit coupling is negligible in graphene such\nanisotropy is unlikely to come from spin, but rather favors theories in which\nthe ferromagnetism is orbital. We know of no other case in which ferromagnetism\nhas a purely orbital origin. For an applied in-plane field larger than $5\\\n\\mathrm{T}$, the out-of-plane magnetization is destroyed, suggesting a\ntransition to a new phase.\n"}
+{"abstract": "  Glioblastoma (GBM) is a highly morbid and lethal disease with poor prognosis.\nTheir emergent properties such as cellular heterogeneity, therapy resistance,\nand self-renewal are largely attributed to the interactions between a subset of\ntheir population known as glioblastoma-derived stem cells (GSCs) and their\nmicroenvironment. Identifying causal patterns in the developmental trajectories\nbetween GSCs and the mature, well-differentiated GBM phenotypes remains a\nchallenging problem in oncology. The paper presents a blueprint of complex\nsystems approaches to infer attractor dynamics from the single-cell gene\nexpression datasets of pediatric GBM and adult GSCs. These algorithms include\nWaddington landscape reconstruction, Generative Adversarial Networks, and\nfractal dimension analysis. Here I show, a Rossler-like strange attractor with\na fractal dimension of roughly 1.7 emerged in the GAN-reconstructed patterns of\nall twelve patients. The findings suggest a strange attractor may be driving\nthe complex dynamics and adaptive behaviors of GBM in signaling state-space.\n"}
+{"abstract": "  The jet-medium interaction in high energy heavy ion collisions is an\nimportant phenomena to characterize the hot and dense medium produced in such\ncollisions. The study of medium-induced modifications to the substructure of\ninclusive charged jets indicates a redistribution of energy inside the jet cone\nand provides insight into the energy loss mechanisms of jets in the medium. We\ninvestigate the in-medium modification to two jet shape observables i.e., the\ndifferential jet shape ($\\rho$(r)) and the angularity (g) in the most central\n$Pb-Pb$ collisions at $\\sqrt{s_{NN}} ~=~ 2.76 $ TeV using two commonly used\nevent generators i.e., JEWEL (recoil OFF) and EPOS-3 in the jet-p$_T$ range of\n20-40 GeV/c. JEWEL with recoil OFF has been used primarily as a reference\nsystem as that has been found to explain the global jet observables\nsatisfactorily but lacks in jet-shape variables at higher jet-radii. EPOS-3\nthat explains the bulk properties in such collisions quite well takes into\naccount a hydrodynamically evolving bulk matter, jets and hard-soft\ninteractions. A comparison between the results from these models shows that\nwhile JEWEL (recoil OFF) does not explain the distribution of lost energy at\nhigher radii with respect to the jet-axis, EPOS-3 explains the effect quite\nwell. However, in EPOS-3, the partonic energy loss mechanism and secondary\nhard-soft interactions during hadronization and hadronic cascade phase are\ndifferent from the conventional jet energy loss models. The current study can,\ntherefore, provide important new insights on mechanisms regarding the modeling\nof the medium and hard-soft interactions in heavy ion collisions.\n"}
+{"abstract": "  We consider the first Dirichlet eigenvalue problem for a mixed local/nonlocal\nelliptic operator and we establish a quantitative Faber-Krahn inequality. More\nprecisely, we show that balls minimize the first eigenvalue among sets of given\nvolume and we provide a stability result for sets that almost attain the\nminimum.\n"}
+{"abstract": "  We investigated progestin and corticosteroid activation of the progesterone\nreceptor (PR) from elephant shark (Callorhinchus milii), a cartilaginous fish\nbelonging to the oldest group of jawed vertebrates. Comparison with human PR\nexperiments provides insights into the evolution of steroid activation of human\nPR. At 1 nM steroid, elephant shark PR is activated by progesterone,\n17-hydroxy-progesterone, 20beta-hydroxy-progesterone, 11-deoxycorticosterone\n(21-hydroxyprogesterone) and 11-deoxycortisol. At 1 nM steroid, human PR is\nactivated only by progesterone and11-deoxycorticosterone indicating increased\nspecificity for progestins and corticosteroids during the evolution of human\nPR. RU486, an important clinical antagonist of human PR, did not inhibit\nprogesterone activation of elephant shark PR. Cys-528 in elephant shark PR\ncorresponds to Gly-722 in human PR, which is essential for RU486 inhibition of\nhuman PR. Confirming the importance of this site on elephant shark PR, RU486\ninhibited progesterone activation of the Cys528Gly mutant PR. There also was a\ndecline in activation of elephant shark Cys528Gly PR by 11-deoxycortisol,\n17-hydroxy-progesterone and 20beta-hydroxy-progesterone and an increase in\nactivation of human Gly722Cys PR by 11-deoxycortisol and decreased activation\nby corticosterone. One or more of these changes may have selected for the\nmutation corresponding to human glycine-722 PR that first evolved in platypus\nPR, a basal mammal.\n"}
+{"abstract": "  Bayesian optimization (BO) conventionally relies on handcrafted acquisition\nfunctions (AFs) to sequentially determine the sample points. However, it has\nbeen widely observed in practice that the best-performing AF in terms of regret\ncan vary significantly under different types of black-box functions. It has\nremained a challenge to design one AF that can attain the best performance over\na wide variety of black-box functions. This paper aims to attack this challenge\nthrough the perspective of reinforced few-shot AF learning (FSAF).\nSpecifically, we first connect the notion of AFs with Q-functions and view a\ndeep Q-network (DQN) as a surrogate differentiable AF. While it serves as a\nnatural idea to combine DQN and an existing few-shot learning method, we\nidentify that such a direct combination does not perform well due to severe\noverfitting, which is particularly critical in BO due to the need of a\nversatile sampling policy. To address this, we present a Bayesian variant of\nDQN with the following three features: (i) It learns a distribution of\nQ-networks as AFs based on the Kullback-Leibler regularization framework. This\ninherently provides the uncertainty required in sampling for BO and mitigates\noverfitting. (ii) For the prior of the Bayesian DQN, we propose to use a demo\npolicy induced by an off-the-shelf AF for better training stability. (iii) On\nthe meta-level, we leverage the meta-loss of Bayesian model-agnostic\nmeta-learning, which serves as a natural companion to the proposed FSAF.\nMoreover, with the proper design of the Q-networks, FSAF is general-purpose in\nthat it is agnostic to the dimension and the cardinality of the input domain.\nThrough extensive experiments, we demonstrate that the FSAF achieves comparable\nor better regrets than the state-of-the-art benchmarks on a wide variety of\nsynthetic and real-world test functions.\n"}
+{"abstract": "  In the context of the quest for a holographic formulation of quantum gravity,\nwe investigate the basic boundary theory structure for loop quantum gravity. In\n3+1 space-time dimensions, the boundary theory lives on the 2+1-dimensional\ntime-like boundary and is supposed to describe the time evolution of the edge\nmodes living on the 2-dimensional boundary of space, i.e. the space-time\ncorner. Focusing on \"electric\" excitations -- quanta of area -- living on the\ncorner, we formulate their dynamics in terms of classical spinor variables and\nwe show that the coupling constants of a polynomial Hamiltonian can be\nunderstood as the components of a background boundary 2+1-metric. This leads to\na deeper conjecture of a correspondence between boundary Hamiltonian and\nboundary metric states. We further show that one can reformulate the quanta of\narea data in terms of a SL(2,C) connection, transporting the spinors on the\nboundary surface and whose SU(2) component would define \"magnetic\" excitations\n(tangential Ashtekar-Barbero connection), thereby opening the door to writing\nthe loop quantum gravity boundary dynamics as a 2+1-dimensional SL(2,C) gauge\ntheory.\n"}
+{"abstract": "  Federated learning (FL), invented by Google in 2016, has become a hot\nresearch trend. However, enabling FL in wireless networks has to overcome the\nlimited battery challenge of mobile users. In this regard, we propose to apply\nunmanned aerial vehicle (UAV)-empowered wireless power transfer to enable\nsustainable FL-based wireless networks. The objective is to maximize the UAV\ntransmit power efficiency, via a joint optimization of transmission time and\nbandwidth allocation, power control, and the UAV placement. Directly solving\nthe formulated problem is challenging, due to the coupling of variables. Hence,\nwe leverage the decomposition technique and a successive convex approximation\napproach to develop an efficient algorithm, namely UAV for sustainable FL\n(UAV-SFL). Finally, simulations illustrate the potential of our proposed\nUAV-SFL approach in providing a sustainable solution for FL-based wireless\nnetworks, and in reducing the UAV transmit power by 32.95%, 63.18%, and 78.81%\ncompared with the benchmarks.\n"}
+{"abstract": "  Galaxy mergers are central to our understanding of galaxy formation,\nespecially within the context of hierarchical models. Besides having a large\nimpact on the star formation history, mergers are also able to influence gas\nmotions at the centre of galaxies and trigger an Active Galactic Nucleus (AGN).\nIn this paper, we present a case study of the Seyfert galaxy NGC 2992, which\ntogether with NGC 2993 forms the early-stage merger system Arp 245. Using\nGemini Multi-Object Spectrograph (GMOS) integral field unit (IFU) data from the\ninner 1.1 kpc of the galaxy we were able to spatially resolve the stellar\npopulations, the ionisation mechanism and kinematics of ionised gas. From full\nspectral synthesis, we found that the stellar population is primarily composed\nby old metal-rich stars (t $\\geq$ 1.4 Gyr, $Z \\geq 2.0$\\zsun), with a\ncontribution of at most 30 per cent of the light from a young and metal-poor\npopulation (t $\\leq$ 100 Myr, $Z \\leq 1.0$\\zsun). We detect \\halpha and \\hbeta\nemission from the Broad Line Region (BLR) with a Full Width at Half Maximum\n(FWHM) of $\\sim$ 2000\\kms. The Narrow Line Region (NLR) kinematics presents two\nmain components: one from gas orbiting the galaxy disk and a blueshifted\n(velocity $\\approx$ -200\\kms) outflow, possibly correlated with the radio\nemission, with mass outflow rate of $\\sim$ 2 M$_{\\odot}$ yr$^{-1}$ and a\nkinematic power of $\\sim$ 2 $\\times 10^{40}$ erg s$^{-1}$ (\\Eout/\\Lbol\n$\\approx$ 0.2 per cent). We also show even though the main ionisation mechanism\nis the AGN radiation, ionisation by young stars and shocks may also contribute\nto the emission line ratios presented in the innermost region of the galaxy.\n"}
+{"abstract": "  The accurate computation of the covariance matrix of fitted model parameters\nis a somewhat neglected task in Statistics. Algorithms are given for computing\naccurate covariance matrices derived from computing the Hessian matrix by\nnumerical differentiation, and also for the covariance matrix of the posterior\ndistribution of model parameters. Evaluations on two datasets where the Hessian\ncould be computed analytically show that the numerical differentiation\nalgorithm is very accurate.\n"}
+{"abstract": "  We present a general technique for constructing Graph Neural Networks (GNNs)\ncapable of using multi-relational domain knowledge. The technique is based on\nmode-directed inverse entailment (MDIE) developed in Inductive Logic\nProgramming (ILP). Given a data instance $e$ and background knowledge $B$, MDIE\nidentifies a most-specific logical formula $\\bot_B(e)$ that contains all the\nrelational information in $B$ that is related to $e$. We represent $\\bot_B(e)$\nby a \"bottom-graph\" that can be converted into a form suitable for GNN\nimplementations. This transformation allows a principled way of incorporating\ngeneric background knowledge into GNNs: we use the term `BotGNN' for this form\nof graph neural networks. For several GNN variants, using real-world datasets\nwith substantial background knowledge, we show that BotGNNs perform\nsignificantly better than both GNNs without background knowledge and a recently\nproposed simplified technique for including domain knowledge into GNNs. We also\nprovide experimental evidence comparing BotGNNs favourably to multi-layer\nperceptrons (MLPs) that use features representing a \"propositionalised\" form of\nthe background knowledge; and BotGNNs to a standard ILP based on the use of\nmost-specific clauses. Taken together, these results point to BotGNNs as\ncapable of combining the computational efficacy of GNNs with the\nrepresentational versatility of ILP.\n"}
+{"abstract": "  We show that the degrees of rational endomorphisms of very general complex\nFano and Calabi-Yau hypersurfaces satisfy certain congruence conditions by\nspecializing to characteristic p. As a corollary we show that very general\nn-dimensional hypersurfaces of degree $d\\ge 5\\lceil (n+3)/6\\rceil$ are not\nbirational to elliptic fibrations. A key part of the argument is to resolve\nsingularities of general p-cyclic covers in mixed characteristic p.\n"}
+{"abstract": "  Successor-style representations have many advantages for reinforcement\nlearning: for example, they can help an agent generalize from past experience\nto new goals, and they have been proposed as explanations of behavioral and\nneural data from human and animal learners. They also form a natural bridge\nbetween model-based and model-free RL methods: like the former they make\npredictions about future experiences, and like the latter they allow efficient\nprediction of total discounted rewards. However, successor-style\nrepresentations are not optimized to generalize across policies: typically, we\nmaintain a limited-length list of policies, and share information among them by\nrepresentation learning or GPI. Successor-style representations also typically\nmake no provision for gathering information or reasoning about latent\nvariables. To address these limitations, we bring together ideas from\npredictive state representations, belief space value iteration, successor\nfeatures, and convex analysis: we develop a new, general successor-style\nrepresentation, together with a Bellman equation that connects multiple sources\nof information within this representation, including different latent states,\npolicies, and reward functions. The new representation is highly expressive:\nfor example, it lets us efficiently read off an optimal policy for a new reward\nfunction, or a policy that imitates a new demonstration. For this paper, we\nfocus on exact computation of the new representation in small, known\nenvironments, since even this restricted setting offers plenty of interesting\nquestions. Our implementation does not scale to large, unknown environments --\nnor would we expect it to, since it generalizes POMDP value iteration, which is\ndifficult to scale. However, we believe that future work will allow us to\nextend our ideas to approximate reasoning in large, unknown environments.\n"}
+{"abstract": "  Heterogeneous computing systems provide high performance and energy\nefficiency. However, to optimally utilize such systems, solutions that\ndistribute the work across host CPUs and accelerating devices are needed. In\nthis paper, we present a performance and energy aware approach that combines AI\nplanning heuristics for parameter space exploration with a machine learning\nmodel for performance and energy evaluation to determine a near-optimal system\nconfiguration. For data-parallel applications our approach determines a\nnear-optimal host-device distribution of work, number of processing units\nrequired and the corresponding scheduling strategy. We evaluate our approach\nfor various heterogeneous systems accelerated with GPU or the Intel Xeon Phi.\nThe experimental results demonstrate that our approach finds a near-optimal\nsystem configuration by evaluating only about 7% of reasonable configurations.\nFurthermore, the performance per Joule estimation of system configurations\nusing our machine learning model is more than 1000x faster compared to the\nsystem evaluation by program execution.\n"}
+{"abstract": "  Passionate employees are essential for organisational success as they foster\nhigher performance and exhibit lower turnover or absenteeism. While a large\nbody of research has investigated the consequences of passion, we know only\nlittle about its antecedents. Integrating trait interaction theory with trait\nactivation theory, this paper examines how personality traits, i.e.\nconscientiousness, agreeableness, and neuroticism impact passion at work across\ndifferent job situations. Passion has been conceptualized as a two-dimensional\nconstruct, consisting of harmonious work passion (HWP) and obsessive work\npassion (OWP). Our study is based on a sample of N = 824 participants from the\nmyPersonality project. We find a positive relationship between neuroticism and\nOWP in enterprising environments. Further, we find a three-way interaction\nbetween conscientiousness, agreeableness, and enterprising environment in\npredicting OWP. Our findings imply that the impact of personality\nconfigurations on different forms of passion is contingent on the job\nenvironment. Moreover, in line with self-regulation theory, the results reveal\nagreeableness as a \"cool influencer\" and neuroticism as a \"hot influencer\" of\nthe relationship between conscientiousness and work passion. We derive\npractical implications for organisations on how to foster work passion,\nparticularly HWP, in organisations.\n"}
+{"abstract": "  A long-standing open problem in the semantics of programming languages\nsupporting probabilistic choice is to find a commutative monad for probability\non the category DCPO. In this paper we present three such monads and a general\nconstruction for finding even more. We show how to use these monads to provide\na sound and adequate denotational semantics for the Probabilistic FixPoint\nCalculus (PFPC) -- a call-by-value simply-typed lambda calculus with\nmixed-variance recursive types, term recursion and probabilistic choice. We\nalso show that in the special case where we consider continuous dcpo's, then\nall three monads coincide with the valuations monad of Jones and we fully\ncharacterise the induced Eilenberg-Moore categories by showing that they are\nall isomorphic to the category of continuous Kegelspitzen of Keimel and\nPlotkin.\n"}
+{"abstract": "  We propose a new approach to text semantic analysis and general corpus\nanalysis using, as termed in this article, a \"bi-gram graph\" representation of\na corpus. The different attributes derived from graph theory are measured and\nanalyzed as unique insights or against other corpus graphs. We observe a vast\ndomain of tools and algorithms that can be developed on top of the graph\nrepresentation; creating such a graph proves to be computationally cheap, and\nmuch of the heavy lifting is achieved via basic graph calculations.\nFurthermore, we showcase the different use-cases for the bi-gram graphs and how\nscalable it proves to be when dealing with large datasets.\n"}
+{"abstract": "  Amino acids are the essential keys in chemistry that contribute to the study\nof the formation of life. The complex organic molecule glycine\n(NH$_{2}$CH$_{2}$COOH) is the simplest amino acid that has been investigated in\nthe interstellar medium for a long period to search for a potential connection\nbetween the Universe and the origin of life. In the last forty years, several\nattempts have failed to detect the interstellar glycine in the hot molecular\ncores and star-forming regions. We report the possible detection of the\nrotational emission lines of interstellar glycine with conformer I and II in\nthe hot molecular core G10.47+0.03 between the frequency range of $\\nu$ =\n158.6$-$160.4 GHz with Atacama Large Millimeter/Submillimeter Array (ALMA)\nobservation. Under the Local Thermodynamic Equilibrium (LTE) condition, we\napply the rotational diagram method to estimate the column density ($N$) and\nrotational temperature ($T_{rot}$) of the detected amino acid glycine. Using\nrotational diagram, we find the column density of glycine\n$N$(NH$_{2}$CH$_{2}$COOH) = 2.8$\\times$10$^{18}$ cm$^{-2}$ with rotational\ntemperature $T_{rot}$ = 115.9 K. We also apply the Levenberg$-$Marquardt\nalgorithm to extract the line parameters of detected emission lines of glycine.\n"}
+{"abstract": "  Reinforcement learning is generally difficult for partially observable Markov\ndecision processes (POMDPs), which occurs when the agent's observation is\npartial or noisy. To seek good performance in POMDPs, one strategy is to endow\nthe agent with a finite memory, whose update is governed by the policy.\nHowever, policy optimization is non-convex in that case and can lead to poor\ntraining performance for random initialization. The performance can be\nempirically improved by constraining the memory architecture, then sacrificing\noptimality to facilitate training. Here we study this trade-off in a\ntwo-hypothesis testing problem, akin to the two-arm bandit problem. We compare\ntwo extreme cases: (i) the random access memory where any transitions between\n$M$ memory states are allowed and (ii) a fixed memory where the agent can\naccess its last $m$ actions and rewards. For (i), the probability $q$ to play\nthe worst arm is known to be exponentially small in $M$ for the optimal policy.\nOur main result is to show that similar performance can be reached for (ii) as\nwell, despite the simplicity of the memory architecture: using a conjecture on\nGray-ordered binary necklaces, we find policies for which $q$ is exponentially\nsmall in $2^m$, i.e. $q\\sim\\alpha^{2^m}$ with $\\alpha < 1$. In addition, we\nobserve empirically that training from random initialization leads to very poor\nresults for (i), and significantly better results for (ii) thanks to the\nconstraints on the memory architecture.\n"}
+{"abstract": "  The paper investigates the effects of the credit market development on the\nlabor mobility between the informal and formal labor sectors. In the case of\nRussia, due to the absence of a credit score system, a formal lender may set a\ncredit limit based on the verified amount of income. To get a loan, an informal\nworker must first formalize his or her income (switch to a formal job), and\nthen apply for a loan. To show this mechanism, the RLMS data was utilized, and\nthe empirical method is the dynamic multinomial logit model of employment. The\nempirical results show that a relaxation of credit constraints increases the\nprobability of transition from an informal to a formal job, and improved CMA\n(by one standard deviation) increases the chances of informal sector workers to\nformalize by 5.4 ppt. These results are robust in different specifications of\nthe model. Policy simulations show strong support for a reduction in informal\nemployment in response to better CMA in credit-constrained communities.\n"}
+{"abstract": "  We prove the existence of ballistic transport for a Schr\\\"odinger operator\nwith a generic quasi-periodic potential in any dimension $d>1$.\n"}
+{"abstract": "  Two novel decoupling and matching networks (DMNs) in microstrip technology\nfor three-element uniform circular arrays (UCAs) are investigated and compared\nto a more conventional DMN approach with simple neutralization lines. The array\nelements are coaxially-fed quarter-wavelength monopole antennas over a finite\ngroundplane. Three-element arrays are considered since UCAs with an odd number\nof elements are able to provide an almost constant maximum array factor over\nthe whole azimuthal angular range. The new designs are explained from a\ntheoretical point of view and their implementations are compared to four- and\nthree-elements UCAs without DMN in terms of decoupling and matching bandwidth\nas well as beamforming capabilities. In addition to excellent decoupling and\nmatching below -16 dB, a broader bandwidth is obtained by the two DMNs. The\nreasons for the enhanced bandwidth are similar in both cases: By introducing\nseveral circuit elements offering additional degrees of freedom, matching of\nthe monopole input impedances at different frequencies becomes feasible. One of\nthe presented designs offers a larger bandwidth, while the other design is able\nto provide a better total efficiency. Scattering parameters, radiation\npatterns, beamforming capabilities, and enhanced gain are all verified by\nmeasurements over the operating bandwidth.\n"}
+{"abstract": "  Deep residual network architectures have been shown to achieve superior\naccuracy over classical feed-forward networks, yet their success is still not\nfully understood. Focusing on massively over-parameterized, fully connected\nresidual networks with ReLU activation through their respective neural tangent\nkernels (ResNTK), we provide here a spectral analysis of these kernels.\nSpecifically, we show that, much like NTK for fully connected networks\n(FC-NTK), for input distributed uniformly on the hypersphere\n$\\mathbb{S}^{d-1}$, the eigenfunctions of ResNTK are the spherical harmonics\nand the eigenvalues decay polynomially with frequency $k$ as $k^{-d}$. These in\nturn imply that the set of functions in their Reproducing Kernel Hilbert Space\nare identical to those of FC-NTK, and consequently also to those of the Laplace\nkernel. We further show, by drawing on the analogy to the Laplace kernel, that\ndepending on the choice of a hyper-parameter that balances between the skip and\nresidual connections ResNTK can either become spiky with depth, as with FC-NTK,\nor maintain a stable shape.\n"}
+{"abstract": "  This work examines the discrete-time networked SIR\n(susceptible-infected-recovered) epidemic model, where the infection and\nrecovery parameters may be time-varying. We provide a sufficient condition for\nthe SIR model to converge to the set of healthy states exponentially. We\npropose a stochastic framework to estimate the system states from observed\ntesting data and provide an analytic expression for the error of the estimation\nalgorithm. Employing the estimated and the true system states, we provide two\nnovel eradication strategies that guarantee at least exponential convergence to\nthe set of healthy states. We illustrate the results via simulations over\nnorthern Indiana, USA.\n"}
+{"abstract": "  Photonic brain-inspired platforms are emerging as novel analog computing\ndevices, enabling fast and energy-efficient operations for machine learning.\nThese artificial neural networks generally require tailored optical elements,\nsuch as integrated photonic circuits, engineered diffractive layers,\nnanophotonic materials, or time-delay schemes, which are challenging to train\nor stabilize. Here we present a neuromorphic photonic scheme - photonic extreme\nlearning machines - that can be implemented simply by using an optical encoder\nand coherent wave propagation in free space. We realize the concept through\nspatial light modulation of a laser beam, with the far field that acts as\nfeature mapping space. We experimentally demonstrated learning from data on\nvarious classification and regression tasks, achieving accuracies comparable to\ndigital extreme learning machines. Our findings point out an optical machine\nlearning device that is easy-to-train, energetically efficient, scalable and\nfabrication-constraint free. The scheme can be generalized to a plethora of\nphotonic systems, opening the route to real-time neuromorphic processing of\noptical data.\n"}
+{"abstract": "  We study Bridgeland moduli spaces of semistable objects of $(-1)$-classes and\n$(-4)$-classes in the Kuznetsov components on index one prime Fano threefold\n$X_{4d+2}$ of degree $4d+2$ and index two prime Fano threefold $Y_d$ of degree\n$d$ for $d=3,4,5$. For every Serre-invariant stability condition on the\nKuznetsov components, we show that the moduli spaces of stable objects of\n$(-1)$-classes on $X_{4d+2}$ and $Y_d$ are isomorphic. We show that moduli\nspaces of stable objects of $(-1)$-classes on $X_{14}$ are realized by Fano\nsurface $\\mathcal{C}(X)$ of conics, moduli spaces of semistable sheaves\n$M_X(2,1,6)$ and $M_X(2,-1,6)$ and the correspondent moduli spaces on cubic\nthreefold $Y_3$ are realized by moduli spaces of stable vector bundles\n$M^b_Y(2,1,2)$ and $M^b_Y(2,-1,2)$. We show that moduli spaces of semistable\nobjects of $(-4)$-classes on $Y_{d}$ are isomorphic to the moduli spaces of\ninstanton sheaves $M^{inst}_Y$ when $d\\neq 1,2$, and show that there're open\nimmersions of $M^{inst}_Y$ into moduli spaces of semistable objects of\n$(-4)$-classes when $d=1,2$. Finally, when $d=3,4,5$ we show that these moduli\nspaces are all isomorphic to $M^{ss}_X(2,0,4)$.\n"}
+{"abstract": "  We compute curvatures of a family of tractable metrics on Stiefel manifolds,\nintroduced recently by H{\\\"u}per, Markina and Silva Leite, which includes the\nwell-known embedded and canonical metrics on Stiefel manifolds as special\ncases. The metrics could be identified with the Cheeger deformation metrics. We\nidentify parameter values in the family to make a Stiefel manifold an Einstein\nmanifold and show Stiefel manifolds always carry an Einstein metric. We analyze\nthe sectional curvature range and identify the parameter range where the\nmanifold has non-negative sectional curvature. We provide the exact sectional\ncurvature range when the number of columns in a Stiefel matrix is $2$, and a\nconjectural range for other cases. We derive the formulas from two approaches,\none from a global curvature formula derived in our recent work, another using\ncurvature formulas for left-invariant metrics. The second approach leads to\ncurvature formulas for Cheeger deformation metrics on normal homogeneous\nspaces.\n"}
+{"abstract": "  Deep learning techniques have achieved remarkable performance in wide-ranging\ntasks. However, when trained on privacy-sensitive datasets, the model\nparameters may expose private information in training data. Prior attempts for\ndifferentially private training, although offering rigorous privacy guarantees,\nlead to much lower model performance than the non-private ones. Besides,\ndifferent runs of the same training algorithm produce models with large\nperformance variance. To address these issues, we propose DPlis--Differentially\nPrivate Learning wIth Smoothing. The core idea of DPlis is to construct a\nsmooth loss function that favors noise-resilient models lying in large flat\nregions of the loss landscape. We provide theoretical justification for the\nutility improvements of DPlis. Extensive experiments also demonstrate that\nDPlis can effectively boost model quality and training stability under a given\nprivacy budget.\n"}
+{"abstract": "  Deep-Neural-Network (DNN) based speaker verification sys-tems use the angular\nsoftmax loss with margin penalties toenhance the intra-class compactness of\nspeaker embeddings,which achieved remarkable performance. In this paper, we\npro-pose a novel angular loss function called adaptive margin cir-cle loss for\nspeaker verification. The stage-based margin andchunk-based margin are applied\nto improve the angular discrim-ination of circle loss on the training set. The\nanalysis on gradi-ents shows that, compared with the previous angular loss\nlikeAdditive Margin Softmax(Am-Softmax), circle loss has flexi-ble optimization\nand definite convergence status. Experimentsare carried out on the Voxceleb and\nSITW. By applying adap-tive margin circle loss, our best system achieves\n1.31%EER onVoxceleb1 and 2.13% on SITW core-core.\n"}
+{"abstract": "  Levitated systems are desirable due to reduced clamping losses and reduced\nthermal contact. These advantageous properties have been exploited in\noptomechanics to achieve ultra-strong coupling between the mechanical mode and\nthe electromagnetic mode. Such schemes provide an opportunity for the quantum\nmanipulation of a macroscopic system. In this letter, we report the first\nsuccessful experiments with a levitated millimeter-scale neodymium magnet\nwithin a centimeter-scale superconducting aluminum coaxial quarter-wave stub\ncavity. The magnet levitated near the top of the stub, where the electric field\nis concentrated, perturbs the electric field distribution allowing for small\nperturbations in the magnet's position to be detected through shifts in the\nresonance frequency. Resonance spectra are collected via a vector network\nanalyzer (VNA) between temperatures of 5 K and 50 mK revealing movement of the\nmagnet inside of the cavity. Room temperature measurements and finite element\ncalculations are done to calculate the shift in frequency for various positions\nof the magnet, and an experimentally measured 100 MHz upshift when\ntransitioning into a superconducting state confirms levitation with remanences\nup to 140 times stronger than the critical field of the aluminum. We achieve\nlevitation heights of 1 - 1.8 mm. We investigate the dependence of levitation\nheight and levitation temperature on the strength of the magnet and,\nsurprisingly, we observe that the levitation temperature and height both\nincrease with permanent magnet strength. Our work describes a novel macroscopic\nmechanical system capable of sensing and transducing forces, thus allowing for\nthe coupling of disparate classical and quantum systems.\n"}
+{"abstract": "  Performing inference in graphs is a common task within several machine\nlearning problems, e.g., image segmentation, community detection, among others.\nFor a given undirected connected graph, we tackle the statistical problem of\nexactly recovering an unknown ground-truth binary labeling of the nodes from a\nsingle corrupted observation of each edge. Such problem can be formulated as a\nquadratic combinatorial optimization problem over the boolean hypercube, where\nit has been shown before that one can (with high probability and in polynomial\ntime) exactly recover the ground-truth labeling of graphs that have an\nisoperimetric number that grows with respect to the number of nodes (e.g.,\ncomplete graphs, regular expanders). In this work, we apply a powerful\nhierarchy of relaxations, known as the sum-of-squares (SoS) hierarchy, to the\ncombinatorial problem. Motivated by empirical evidence on the improvement in\nexact recoverability, we center our attention on the degree-4 SoS relaxation\nand set out to understand the origin of such improvement from a graph\ntheoretical perspective. We show that the solution of the dual of the relaxed\nproblem is related to finding edge weights of the Johnson and Kneser graphs,\nwhere the weights fulfill the SoS constraints and intuitively allow the input\ngraph to increase its algebraic connectivity. Finally, as byproduct of our\nanalysis, we derive a novel Cheeger-type lower bound for the algebraic\nconnectivity of graphs with signed edge weights.\n"}
+{"abstract": "  We develop a stochastic mean-field theory to describe active frequency\nmeasurements of pulsed superradiant emission, studied in recent experiments\nwith strontium-87 atoms trapped in an optical lattice inside an optical cavity\n[M. Norcia, et al., Phys. Rev. X 8, 21036 (2018)]. Our theory reveals the\nintriguing dynamics of atomic ensembles with multiple transition frequencies,\nand it reproduces the superradiant beats signal, noisy power spectra, and\nfrequency uncertainty in remarkable agreement with the experiments. Moreover,\nby reducing the number of atoms, elongating the superradiant pulses and\nshortening the experimental duty cycle, we predict a short-term frequency\nuncertainty $9\\times10^{-16} \\sqrt{\\tau/s}$, which makes active frequency\nmeasurements with superradiant transitions comparable with the record\nperformance of current frequency standards [M. Schioppo, et al., Nat.\nPhotonics, 11, 48 (2017)]. Our theory combines cavity-quantum electrodynamics\nand quantum measurement theory, and it can be readily applied to explore\nconditional quantum dynamics and describe frequency measurements for other\nprocesses such as steady-state superradiance and superradiant Raman lasing.\n"}
+{"abstract": "  In real-world scenarios, many factors may harm face recognition performance,\ne.g., large pose, bad illumination,low resolution, blur and noise. To address\nthese challenges, previous efforts usually first restore the low-quality faces\nto high-quality ones and then perform face recognition. However, most of these\nmethods are stage-wise, which is sub-optimal and deviates from the reality. In\nthis paper, we address all these challenges jointly for unconstrained face\nrecognition. We propose an Multi-Degradation Face Restoration (MDFR) model to\nrestore frontalized high-quality faces from the given low-quality ones under\narbitrary facial poses, with three distinct novelties. First, MDFR is a\nwell-designed encoder-decoder architecture which extracts feature\nrepresentation from an input face image with arbitrary low-quality factors and\nrestores it to a high-quality counterpart. Second, MDFR introduces a pose\nresidual learning strategy along with a 3D-based Pose Normalization Module\n(PNM), which can perceive the pose gap between the input initial pose and its\nreal-frontal pose to guide the face frontalization. Finally, MDFR can generate\nfrontalized high-quality face images by a single unified network, showing a\nstrong capability of preserving face identity. Qualitative and quantitative\nexperiments on both controlled and in-the-wild benchmarks demonstrate the\nsuperiority of MDFR over state-of-the-art methods on both face frontalization\nand face restoration.\n"}
+{"abstract": "  At present, the Synthetic Aperture Radar (SAR) image classification method\nbased on convolution neural network (CNN) has faced some problems such as poor\nnoise resistance and generalization ability. Spiking neural network (SNN) is\none of the core components of brain-like intelligence and has good application\nprospects. This article constructs a complete SAR image classifier based on\nunsupervised and supervised learning of SNN by using spike sequences with\ncomplex spatio-temporal information. We firstly expound the spiking neuron\nmodel, the receptive field of SNN, and the construction of spike sequence. Then\nwe put forward an unsupervised learning algorithm based on STDP and a\nsupervised learning algorithm based on gradient descent. The average\nclassification accuracy of single layer and bilayer unsupervised learning SNN\nin three categories images on MSTAR dataset is 80.8\\% and 85.1\\%, respectively.\nFurthermore, the convergent output spike sequences of unsupervised learning can\nbe used as teaching signals. Based on the TensorFlow framework, a single layer\nsupervised learning SNN is built from the bottom, and the classification\naccuracy reaches 90.05\\%. By comparing noise resistance and model parameters\nbetween SNNs and CNNs, the effectiveness and outstanding advantages of SNN are\nverified. Code to reproduce our experiments is available at\n\\url{https://github.com/Jiankun-chen/Supervised-SNN-with-GD}.\n"}
+{"abstract": "  We extend the Levi-Civita (L-C) and Kustaanheimo-Stiefel (K-S) regularization\nmethods that maps the classical system where a particle moves under the\ncombined influence of $\\frac{1}{r}$ and $r^2$ potentials to a harmonic\noscillator with inverted sextic potential and interactions to corresponding\nquantum mechanical counterparts, both in 2 and 3 dimensions. Using the\nperturbative solutions of the Schr\\\"odinger equation of the later systems, we\nderive the eigen spectrum of the Hydrogen atom in presence of an additional\nharmonic potential. We have also obtained the mapping of a particle moving in\nthe shifted harmonic potential to H-atom using Bohlin-Sundman transformation,\nfor quantum regime. Exploiting this equivalence, the solution to the\nSchr\\\"odinger equation of the former is obtained from the solutions of the\nlater.\n"}
+{"abstract": "  We study compatibility of the Standard Model of particle physics and General\nRelativity by means of gravitational positivity bounds, which provide a\nnecessary condition for a low-energy gravitational theory to be UV completable\nwithin the weakly coupled regime of gravity. In particular, we identify the\ncutoff scale of the Standard Model coupled to gravity by studying consistency\nof light-by-light scattering. While the precise value depends on details of the\nPomeron effects in QCD, the cutoff scale reads $10^{16}$GeV if the\nsingle-Pomeron exchange picture works well up to this scale. We also\ndemonstrate that the cutoff scale is lowered to $10^{13}$GeV if we consider the\nelectroweak theory without the QCD sector.\n"}
+{"abstract": "  The relations between the specific angular momenta ($j$) and masses ($M$) of\ngalaxies are often used as a benchmark in analytic models and hydrodynamical\nsimulations as they are considered to be amongst the most fundamental scaling\nrelations. Using accurate measurements of the stellar ($j_\\ast$), gas ($j_{\\rm\ngas}$), and baryonic ($j_{\\rm bar}$) specific angular momenta for a large\nsample of disc galaxies, we report the discovery of tight correlations between\n$j$, $M$, and the cold gas fraction of the interstellar medium ($f_{\\rm gas}$).\nAt fixed $f_{\\rm gas}$, galaxies follow parallel power laws in 2D $(j,M)$\nspaces, with gas-rich galaxies having a larger $j_\\ast$ and $j_{\\rm bar}$ (but\na lower $j_{\\rm gas}$) than gas-poor ones. The slopes of the relations have a\nvalue around 0.7. These new relations are amongst the tightest known scaling\nlaws for galaxies. In particular, the baryonic relation ($j_{\\rm bar}-M_{\\rm\nbar}-f_{\\rm gas}$), arguably the most fundamental of the three, is followed not\nonly by typical discs but also by galaxies with extreme properties, such as\nsize and gas content, and by galaxies previously claimed to be outliers of the\nstandard 2D $j-M$ relations. The stellar relation ($j_{\\ast}-M_{\\ast}-f_{\\rm\ngas}$) may be connected to the known $j_\\ast-M_\\ast-$bulge fraction relation;\nhowever, we argue that the $j_{\\rm bar}-M_{\\rm bar}-f_{\\rm gas}$ relation can\noriginate from the radial variation in the star formation efficiency in\ngalaxies, although it is not explained by current disc instability models.\n"}
+{"abstract": "  Photoacoustic (PA) computed tomography (PACT) shows great potentials in\nvarious preclinical and clinical applications. A great number of measurements\nare the premise that obtains a high-quality image, which implies a low imaging\nrate or a high system cost. The artifacts or sidelobes could pollute the image\nif we decrease the number of measured channels or limit the detected view. In\nthis paper, a novel compressed sensing method for PACT using an untrained\nneural network is proposed, which decreases half number of the measured\nchannels and recoveries enough details. This method uses a neural network to\nreconstruct without the requirement for any additional learning based on the\ndeep image prior. The model can reconstruct the image only using a few\ndetections with gradient descent. Our method can cooperate with other existing\nregularization, and further improve the quality. In addition, we introduce a\nshape prior to easily converge the model to the image. We verify the\nfeasibility of untrained network based compressed sensing in PA image\nreconstruction, and compare this method with a conventional method using total\nvariation minimization. The experimental results show that our proposed method\noutperforms 32.72% (SSIM) with the traditional compressed sensing method in the\nsame regularization. It could dramatically reduce the requirement for the\nnumber of transducers, by sparsely sampling the raw PA data, and improve the\nquality of PA image significantly.\n"}
+{"abstract": "  Sarcasm detection is the task of identifying irony containing utterances in\nsentiment-bearing text. However, the figurative and creative nature of sarcasm\nposes a great challenge for affective computing systems performing sentiment\nanalysis. This article compiles and reviews the salient work in the literature\nof automatic sarcasm detection. Thus far, three main paradigm shifts have\noccurred in the way researchers have approached this task: 1) semi-supervised\npattern extraction to identify implicit sentiment, 2) use of hashtag-based\nsupervision, and 3) incorporation of context beyond target text. In this\narticle, we provide a comprehensive review of the datasets, approaches, trends,\nand issues in sarcasm and irony detection.\n"}
+{"abstract": "  In this talk, we present a way to improve the accuracy of theoretical\npredictions for Higgs boson production cross sections at the LHC using the\nmeasurements of lepton angular distributions. In this regards, we exploit the\nsensitivity of the lepton angular coefficient associated with the longitudinal\nZ-boson polarization to the parton density function (PDF) for gluons resolved\nfrom the incoming protons, in order to constrain the Higgs boson cross section\nfrom gluon fusion processes. We find that high-statistics determinations of the\nlongitudinally polarized angular coefficient at the LHC Run 3 and\nhigh-luminosity HL-LHC improve the PDF systematics of the Higgs boson cross\nsection predictions by 50% over a broad range of Higgs boson rapidities. This\nstudy has been conducted using the open-source fitting framework xFitter. This\ntalk refers to the following paper: arXiv:2012.10298[hep-ph]\n"}
+{"abstract": "  Moving mirrors have been known as tractable setups modeling Hawking radiation\nfrom black holes. In this paper, motivated by recent developments regarding the\nblack hole information problem, we present extensive studies of moving mirrors\nin conformal field theories by employing both field theoretic as well as\nholographic methods. Reviewing first the usual field theoretic formulation of\nmoving mirrors, we construct their gravity dual by resorting to the AdS/BCFT\nconstruction. Based on our holographic formulation, we then calculate the time\nevolution of entanglement entropy in various moving mirror models. In doing so,\nwe mainly focus on three different setups: escaping mirror, which models\nconstant Hawking radiation emanating from an eternal black hole; kink mirror,\nwhich models an evaporating black hole formed from collapse; and the double\nescaping mirror, which models two constantly radiating eternal black holes. In\nparticular, by computing the holographic entanglement entropy, we show that the\nkink mirror gives rise to an ideal Page curve. We also find that an interesting\nphase transition arises in the case of the double escaping mirror. Furthermore,\nwe argue and provide evidence for an interpretation of moving mirrors in terms\nof two dimensional Liouville gravity. We also discuss the connection between\nquantum energy conditions and the time evolution of holographic entanglement\nentropy in moving mirror models.\n"}
+{"abstract": "  Ensuring group fairness among groups of individuals in our society is\ndesirable and crucial for many application domains. A social planner's typical\nmedium of achieving group fair outcomes is through solving an optimization\nproblem under a given objective for a particular domain. When the input is\nprovided by strategic agents, the planner is facing a difficult situation of\nachieving fair outcomes while ensuring agent truthfulness without using\nincentive payment. To address this challenge, we consider the approximate\nmechanism design without money paradigm with group-fair objectives. We first\nconsider the group-fair facility location problems where agents are divided\ninto groups. The agents are located on a real line, modeling agents' private\nideal preferences/points for the facility's location. Our aim is to locate a\nfacility to approximately minimize the costs of groups of agents to the\nfacility fairly while eliciting the agents' private locations truthfully. We\nconsider various group-fair objectives and show that many objectives have an\nunbounded approximation ratio. We then consider the objectives of minimizing\nthe maximum total group cost and the average group cost. For the first\nobjective, we show that the approximation ratio of the median mechanism depends\non the number of groups and provide a new group-based mechanism with an\napproximation ratio of 3. For the second objective, the median mechanism\nobtains a ratio of 3, and we propose a randomized mechanism that obtains a\nbetter approximation ratio. We also provide lower bounds for both objectives.\nWe then study the notion of intergroup and intragroup fairness that measures\nfairness between groups and within each group. We consider various objectives\nand provide mechanisms with tight approximation ratios.\n"}
+{"abstract": "  Robot navigation in crowded public spaces is a complex task that requires\naddressing a variety of engineering and human factors challenges. These\nchallenges have motivated a great amount of research resulting in important\ndevelopments for the fields of robotics and human-robot interaction over the\npast three decades. Despite the significant progress and the massive recent\ninterest, we observe a number of significant remaining challenges that prohibit\nthe seamless deployment of autonomous robots in public pedestrian environments.\nIn this survey article, we organize existing challenges into a set of\ncategories related to broader open problems in motion planning, behavior\ndesign, and evaluation methodologies. Within these categories, we review past\nwork, and offer directions for future research. Our work builds upon and\nextends earlier survey efforts by a) taking a critical perspective and\ndiagnosing fundamental limitations of adopted practices in the field and b)\noffering constructive feedback and ideas that we aspire will drive research in\nthe field over the coming decade.\n"}
+{"abstract": "  We applied Deep Learning algorithm known as Generative Adversarial Networks\n(GANs) to perform solar image-to-image translation. That is, from Solar\nDynamics Observatory (SDO)/Helioseismic and Magnetic Imager(HMI) line of sight\nmagnetogram images to SDO/Atmospheric Imaging Assembly(AIA) 0304-{\\AA} images.\nThe Ultraviolet(UV)/Extreme Ultraviolet(EUV) observations like the\nSDO/AIA0304-{\\AA} images were only made available to scientists in the late\n1990s even though the magenetic field observations like the SDO/HMI have been\navailable since the 1970s. Therefore by leveraging Deep Learning algorithms\nlike GANs we can give scientists access to complete datasets for analysis. For\ngenerating high resolution solar images we use the Pix2PixHD and Pix2Pix\nalgorithms. The Pix2PixHD algorithm was specifically designed for high\nresolution image generation tasks, and the Pix2Pix algorithm is by far the most\nwidely used image to image translation algorithm. For training and testing we\nused the data for the year 2012, 2013 and 2014. The results show that our deep\nlearning models are capable of generating high resolution(1024 x 1024 pixels)\nAIA0304 images from HMI magnetograms. Specifically, the pixel-to-pixel Pearson\nCorrelation Coefficient of the images generated by Pix2PixHD and original\nimages is as high as 0.99. The number is 0.962 if Pix2Pix is used to generate\nimages. The results we get for our Pix2PixHD model is better than the results\nobtained by previous works done by others to generate AIA0304 images. Thus, we\ncan use these models to generate AIA0304 images when the AIA0304 data is not\navailable which can be used for understanding space weather and giving\nresearchers the capability to predict solar events such as Solar Flares and\nCoronal Mass Ejections. As far as we know, our work is the first attempt to\nleverage Pix2PixHD algorithm for SDO/HMI to SDO/AIA0304 image-to-image\ntranslation.\n"}
+{"abstract": "  Digital memcomputing machines (DMMs) are a novel, non-Turing class of\nmachines designed to solve combinatorial optimization problems. They can be\nphysically realized with continuous-time, non-quantum dynamical systems with\nmemory (time non-locality), whose ordinary differential equations (ODEs) can be\nnumerically integrated on modern computers. Solutions of many hard problems\nhave been reported by numerically integrating the ODEs of DMMs, showing\nsubstantial advantages over state-of-the-art solvers. To investigate the\nreasons behind the robustness and effectiveness of this method, we employ three\nexplicit integration schemes (forward Euler, trapezoid and Runge-Kutta 4th\norder) with a constant time step, to solve 3-SAT instances with planted\nsolutions. We show that, (i) even if most of the trajectories in the phase\nspace are destroyed by numerical noise, the solution can still be achieved;\n(ii) the forward Euler method, although having the largest numerical error,\nsolves the instances in the least amount of function evaluations; and (iii)\nwhen increasing the integration time step, the system undergoes a\n\"solvable-unsolvable transition\" at a critical threshold, which needs to decay\nat most as a power law with the problem size, to control the numerical errors.\nTo explain these results, we model the dynamical behavior of DMMs as directed\npercolation of the state trajectory in the phase space in the presence of\nnoise. This viewpoint clarifies the reasons behind their numerical robustness\nand provides an analytical understanding of the unsolvable-solvable transition.\nThese results land further support to the usefulness of DMMs in the solution of\nhard combinatorial optimization problems.\n"}
+{"abstract": "  The explosive growth of image data facilitates the fast development of image\nprocessing and computer vision methods for emerging visual applications,\nmeanwhile introducing novel distortions to the processed images. This poses a\ngrand challenge to existing blind image quality assessment (BIQA) models,\nfailing to continually adapt to such subpopulation shift. Recent work suggests\ntraining BIQA methods on the combination of all available human-rated IQA\ndatasets. However, this type of approach is not scalable to a large number of\ndatasets, and is cumbersome to incorporate a newly created dataset as well. In\nthis paper, we formulate continual learning for BIQA, where a model learns\ncontinually from a stream of IQA datasets, building on what was learned from\npreviously seen data. We first identify five desiderata in the new setting with\na measure to quantify the plasticity-stability trade-off. We then propose a\nsimple yet effective method for learning BIQA models continually. Specifically,\nbased on a shared backbone network, we add a prediction head for a new dataset,\nand enforce a regularizer to allow all prediction heads to evolve with new data\nwhile being resistant to catastrophic forgetting of old data. We compute the\nquality score by an adaptive weighted summation of estimates from all\nprediction heads. Extensive experiments demonstrate the promise of the proposed\ncontinual learning method in comparison to standard training techniques for\nBIQA.\n"}
+{"abstract": "  Self-supervised learning (SSL), which can automatically generate ground-truth\nsamples from raw data, holds vast potential to improve recommender systems.\nMost existing SSL-based methods perturb the raw data graph with uniform\nnode/edge dropout to generate new data views and then conduct the\nself-discrimination based contrastive learning over different views to learn\ngeneralizable representations. Under this scheme, only a bijective mapping is\nbuilt between nodes in two different views, which means that the\nself-supervision signals from other nodes are being neglected. Due to the\nwidely observed homophily in recommender systems, we argue that the supervisory\nsignals from other nodes are also highly likely to benefit the representation\nlearning for recommendation. To capture these signals, a general socially-aware\nSSL framework that integrates tri-training is proposed in this paper.\nTechnically, our framework first augments the user data views with the user\nsocial information. And then under the regime of tri-training for multi-view\nencoding, the framework builds three graph encoders (one for recommendation)\nupon the augmented views and iteratively improves each encoder with\nself-supervision signals from other users, generated by the other two encoders.\nSince the tri-training operates on the augmented views of the same data sources\nfor self-supervision signals, we name it self-supervised tri-training.\nExtensive experiments on multiple real-world datasets consistently validate the\neffectiveness of the self-supervised tri-training framework for improving\nrecommendation. The code is released at https://github.com/Coder-Yu/QRec.\n"}
+{"abstract": "  Annotation is an effective reading strategy people often undertake while\ninteracting with digital text. It involves highlighting pieces of text and\nmaking notes about them. Annotating while reading in a desktop environment is\nconsidered trivial but, in a mobile setting where people read while\nhand-holding devices, the task of highlighting and typing notes on a mobile\ndisplay is challenging. In this paper, we introduce GAVIN, a gaze-assisted\nvoice note-taking application, which enables readers to seamlessly take voice\nnotes on digital documents by implicitly anchoring them to text passages. We\nfirst conducted a contextual enquiry focusing on participants' note-taking\npractices on digital documents. Using these findings, we propose a method which\nleverages eye-tracking and machine learning techniques to annotate voice notes\nwith reference text passages. To evaluate our approach, we recruited 32\nparticipants performing voice note-taking. Following, we trained a classifier\non the data collected to predict text passage where participants made voice\nnotes. Lastly, we employed the classifier to built GAVIN and conducted a user\nstudy to demonstrate the feasibility of the system. This research demonstrates\nthe feasibility of using gaze as a resource for implicit anchoring of voice\nnotes, enabling the design of systems that allow users to record voice notes\nwith minimal effort and high accuracy.\n"}
+{"abstract": "  This paper studies the end-to-end construction of an NLP Knowledge Graph (KG)\nfrom scientific papers. We focus on extracting four types of relations:\nevaluatedOn between tasks and datasets, evaluatedBy between tasks and\nevaluation metrics, as well as coreferent and related relations between the\nsame type of entities. For instance, F1-score is coreferent with F-measure. We\nintroduce novel methods for each of these relation types and apply our final\nframework (SciNLP-KG) to 30,000 NLP papers from ACL Anthology to build a\nlarge-scale KG, which can facilitate automatically constructing scientific\nleaderboards for the NLP community. The results of our experiments indicate\nthat the resulting KG contains high-quality information.\n"}
+{"abstract": "  3D deep learning has been increasingly more popular for a variety of tasks\nincluding many safety-critical applications. However, recently several works\nraise the security issues of 3D deep models. Although most of them consider\nadversarial attacks, we identify that backdoor attack is indeed a more serious\nthreat to 3D deep learning systems but remains unexplored. We present the\nbackdoor attacks in 3D point cloud with a unified framework that exploits the\nunique properties of 3D data and networks. In particular, we design two attack\napproaches on point cloud: the poison-label backdoor attack (PointPBA) and the\nclean-label backdoor attack (PointCBA). The first one is straightforward and\neffective in practice, while the latter is more sophisticated assuming there\nare certain data inspections. The attack algorithms are mainly motivated and\ndeveloped by 1) the recent discovery of 3D adversarial samples suggesting the\nvulnerability of deep models under spatial transformation; 2) the proposed\nfeature disentanglement technique that manipulates the feature of the data\nthrough optimization methods and its potential to embed a new task. Extensive\nexperiments show the efficacy of the PointPBA with over 95% success rate across\nvarious 3D datasets and models, and the more stealthy PointCBA with around 50%\nsuccess rate. Our proposed backdoor attack in 3D point cloud is expected to\nperform as a baseline for improving the robustness of 3D deep models.\n"}
+{"abstract": "  Equipping robots with the ability to infer human intent is a vital\nprecondition for effective collaboration. Most computational approaches towards\nthis objective employ probabilistic reasoning to recover a distribution of\n\"intent\" conditioned on the robot's perceived sensory state. However, these\napproaches typically assume task-specific notions of human intent (e.g.\nlabelled goals) are known a priori. To overcome this constraint, we propose the\nDisentangled Sequence Clustering Variational Autoencoder (DiSCVAE), a\nclustering framework that can be used to learn such a distribution of intent in\nan unsupervised manner. The DiSCVAE leverages recent advances in unsupervised\nlearning to derive a disentangled latent representation of sequential data,\nseparating time-varying local features from time-invariant global aspects.\nThough unlike previous frameworks for disentanglement, the proposed variant\nalso infers a discrete variable to form a latent mixture model and enable\nclustering of global sequence concepts, e.g. intentions from observed human\nbehaviour. To evaluate the DiSCVAE, we first validate its capacity to discover\nclasses from unlabelled sequences using video datasets of bouncing digits and\n2D animations. We then report results from a real-world human-robot interaction\nexperiment conducted on a robotic wheelchair. Our findings glean insights into\nhow the inferred discrete variable coincides with human intent and thus serves\nto improve assistance in collaborative settings, such as shared control.\n"}
+{"abstract": "  Bell inequalities are relevant for many problems in quantum information\nscience, but finding them for many particles is computationally hard. Recently,\na computationally feasible method called cone-projection technique has been\ndeveloped to find all optimal Bell inequalities under some constraints, which\nmay be given by some symmetry or other linear conditions. In this paper we\nextend this work in several directions. We use the method to generalize the\nI4422 inequality to three particles and a so-called GYNI inequality to four\nparticles. Additionally, we find Bell inequalities for three particles that\ngeneralize the I3322 inequality and the CHSH inequality at the same time. We\ndiscuss the obtained inequalities in some detail and characterize their\nviolation in quantum mechanics.\n"}
+{"abstract": "  We outline two dishonest strategies that can be cheaply executed on the\nEthereum 2.0 beacon chain, even by validators holding less than one-third of\nthe total stake: malicious chain reorganizations (\"reorgs\") and finality\ndelays. In a malicious reorg, an attacker withholds their blocks and\nattestations before releasing them at an opportune time in order to force a\nchain reorganization, which they can take advantage of by double-spending or\nfront-running transactions. To execute a finality delay an attacker uses\ndelayed block releases and withholding of attestations to increase the mean and\nvariance of the time it takes blocks to become finalized. This impacts the\nefficiency and predictability of the system. We provide a probabilistic and\ncost analysis for each of these attacks, considering a validator with 30% of\nthe total stake.\n"}
+{"abstract": "  Mechanical instabilities can be exploited to design innovative structures,\nable to change their shape in the presence of external stimuli. In this work,\nwe derive a mathematical model of an elastic beam subjected to an axial force\nand constrained to smoothly slide along a rigid support, where the distance\nbetween the rod midline and the constraint is fixed and finite. Using both\ntheoretical and computational techniques, we characterize the bifurcations of\nsuch a mechanical system, in which the axial force and the natural curvature of\nthe beam are used as control parameters. We show that, in the presence of a\nstraight support, the rod can deform into shapes exhibiting helices and\nperversions, namely transition zones connecting together two helices with\nopposite chirality. The mathematical predictions of the proposed model are also\ncompared with some experiments, showing a good quantitative agreement. In\nparticular, we find that the buckled configurations may exhibit multiple\nperversions and we propose a possible explanation for this phenomenon based on\nthe energy landscape of the mechanical system.\n"}
+{"abstract": "  In supervised learning, obtaining a large set of fully-labeled training data\nis expensive. We show that we do not always need full label information on\nevery single training example to train a competent classifier. Specifically,\ninspired by the principle of sufficiency in statistics, we present a statistic\n(a summary) of the fully-labeled training set that captures almost all the\nrelevant information for classification but at the same time is easier to\nobtain directly. We call this statistic \"sufficiently-labeled data\" and prove\nits sufficiency and efficiency for finding the optimal hidden representations,\non which competent classifier heads can be trained using as few as a single\nrandomly-chosen fully-labeled example per class. Sufficiently-labeled data can\nbe obtained from annotators directly without collecting the fully-labeled data\nfirst. And we prove that it is easier to directly obtain sufficiently-labeled\ndata than obtaining fully-labeled data. Furthermore, sufficiently-labeled data\nnaturally preserves user privacy by storing relative, instead of absolute,\ninformation. Extensive experimental results are provided to support our theory.\n"}
+{"abstract": "  We prove that a random group, in Gromov's density model with $d<1/16$,\nsatisfies a universal sentence $\\sigma$ (in the language of groups) if and only\nif $\\sigma$ is true in a nonabelian free group.\n"}
+{"abstract": "  PKS\\,1351$-$018 at a redshift of $z=3.71$ is one of the most luminous, steady\nsynchrotron sources with a luminosity $> 10^{47}$\\,erg~s$^{-1}$. The\nsynchrotron luminosity does not seem to vary by more than $\\sim 25\\%$ over 35\nyears. In order to appreciate this remarkable behavior, if it were at $z=0.5$,\nit would have a flux density at 15 GHz in a range of $110 - 137$\\,Jy over 11\nyrs. In spite of this steady behavior, two strong $\\gamma$-ray flares $\\lesssim\n10^{49}$\\,erg~s$^{-1}$ were detected in 2011 and 2016. There is blazar-like\nbehavior coexisting with the steady behavior. This study is aimed at\nelucidating the dual nature of this source. We find that the radio source is\nextremely compact with a bright core and a steep spectrum secondary component,\n12\\,mas away, that appears to be constant in position and flux density in six\nepochs from 1995 to 2018. We estimate that a jet with a time averaged power of\n$(5.2 \\pm 3.2) \\times 10^{45}$\\,erg~s$^{-1}$ terminates in this lobe that is\nadvancing $\\gtrsim 0.9 c$ at a deprojected distance of $1-3$\\,kpc from the\ncentral engine. This is the rare case of a young ($\\sim 6000$\\,yr), very\npowerful radio source that is viewed a few degrees from the jet axis. We find\nevidence of a high velocity (4000\\,km~s$^{-1}$), high ionization wind emanating\nform a luminous quasar. The young radio jet appears to experience modest\nbending as it navigates through the intense quasar environment.\n"}
+{"abstract": "  Inverse reinforcement learning attempts to reconstruct the reward function in\na Markov decision problem, using observations of agent actions. As already\nobserved in Russell [1998] the problem is ill-posed, and the reward function is\nnot identifiable, even under the presence of perfect information about optimal\nbehavior. We provide a resolution to this non-identifiability for problems with\nentropy regularization. For a given environment, we fully characterize the\nreward functions leading to a given policy and demonstrate that, given\ndemonstrations of actions for the same reward under two distinct discount\nfactors, or under sufficiently different environments, the unobserved reward\ncan be recovered up to a constant. We also give general necessary and\nsufficient conditions for reconstruction of time-homogeneous rewards on finite\nhorizons, and for action-independent rewards, generalizing recent results of\nKim et al. [2021] and Fu et al. [2018].\n"}
+{"abstract": "  This work presents a microscale approach for simulating the dielectrophoresis\n(DEP) assembly of polarizable particles under an external electric field. The\nmodel is shown to capture interesting dynamical and topological features, such\nas the formation of chains of particles and their incipient aggregation into\nhierarchical structures. A quantitative characterization in terms of the number\nand size of these structures is also discussed. This computational model could\nrepresent a viable numerical tool to study the mechanical properties of\nparticle-based hierarchical materials and suggest new strategies for enhancing\ntheir design and manufacture.\n"}
+{"abstract": "  While reinforcement learning algorithms provide automated acquisition of\noptimal policies, practical application of such methods requires a number of\ndesign decisions, such as manually designing reward functions that not only\ndefine the task, but also provide sufficient shaping to accomplish it. In this\npaper, we discuss a new perspective on reinforcement learning, recasting it as\nthe problem of inferring actions that achieve desired outcomes, rather than a\nproblem of maximizing rewards. To solve the resulting outcome-directed\ninference problem, we establish a novel variational inference formulation that\nallows us to derive a well-shaped reward function which can be learned directly\nfrom environment interactions. From the corresponding variational objective, we\nalso derive a new probabilistic Bellman backup operator reminiscent of the\nstandard Bellman backup operator and use it to develop an off-policy algorithm\nto solve goal-directed tasks. We empirically demonstrate that this method\neliminates the need to design reward functions and leads to effective\ngoal-directed behaviors.\n"}
+{"abstract": "  The recent star formation histories (SFHs) of post-starburst galaxies have\nbeen determined almost exclusively from detailed modeling of their composite\nstar light. This has provided important but limited information on the number,\nstrength, and duration of bursts of star formation. In this work, we present a\ndirect and independent measure of the recent SFH of S12 (plate-mjd-fiber for\nSDSS 623-52051-207; designated EAS12 in Smercina et al. 2018) from its star\ncluster population. We detect clusters from high resolution, $UBR$ optical\nobservations from HST, and compare their luminosities and colors with stellar\npopulation models to estimate the ages and masses of the clusters. No clusters\nyounger than $\\sim$70 Myr are found, indicating star formation shut off at this\ntime. Clusters formed $\\sim$120 Myr ago reach masses up to $\\sim\n\\mbox{few}\\times10^7~M_{\\odot}$, several times higher than similar age\ncounterparts formed in actively merging galaxies like the Antennae and NGC\n3256. We develop a new calibration based on known properties for 8 nearby\ngalaxies to estimate the star formation rate (SFR) of a galaxy from the mass of\nthe most massive cluster, $M_{\\rm max}$. The cluster population indicates that\nS12 experienced an extremely intense but short-lived burst $\\sim$120 Myr ago,\nwith an estimated peak of $500^{+500}_{-250}~M_{\\odot}~\\mbox{yr}^{-1}$ and\nduration of $50\\pm25$ Myr, one of the highest SFRs estimated for any galaxy in\nthe nearby universe. Prior to the recent, intense burst, S12 was forming stars\nat a moderate rate of $\\sim 3{-}5~M_{\\odot}~\\mbox{yr}^{-1}$, typical of spiral\ngalaxies. However, the system also experienced an earlier burst approximately\n$1{-}3$ Gyr ago. While fairly uncertain, we estimate that the SFR during this\nearlier burst was $\\sim20{-}30~M_{\\odot}~\\mbox{yr}^{-1}$, similar to the\ncurrent SFR in the Antennae and NGC 3256.\n"}
+{"abstract": "  We present an experimentally practical method to reveal\nEinstein-Podolsky-Rosen steering in non-Gaussian spin states by exploiting a\nconnection to quantum metrology. Our criterion is based on the quantum Fisher\ninformation, and uses bounds derived from generalized spin-squeezing parameters\nthat involve measurements of higher-order moments. This leads us to introduce\nthe concept of conditional spin-squeezing parameters, which quantify the\nmetrological advantage provided by conditional states, as well as detect the\npresence of an EPR paradox.\n"}
+{"abstract": "  Abnormality detection is essential to the performance of safety-critical and\nlatency-constrained systems. However, as systems are becoming increasingly\ncomplicated with a large quantity of heterogeneous data, conventional\nstatistical change point detection methods are becoming less effective and\nefficient. Although Deep Learning (DL) and Deep Neural Networks (DNNs) are\nincreasingly employed to handle heterogeneous data, they still lack theoretic\nassurable performance and explainability. This paper integrates zero-bias DNN\nand Quickest Event Detection algorithms to provide a holistic framework for\nquick and reliable detection of both abnormalities and time-dependent abnormal\nevents in the Internet of Things (IoT). We first use the zero-bias dense layer\nto increase the explainability of DNN. We provide a solution to convert\nzero-bias DNN classifiers into performance assured binary abnormality\ndetectors. Using the converted abnormality detector, we then present a\nsequential quickest detection scheme that provides the theoretically assured\nlowest abnormal event detection delay under false alarm constraints. Finally,\nwe demonstrate the effectiveness of the framework using both massive signal\nrecords from real-world aviation communication systems and simulated data. Code\nand data of our work is available at\n\\url{https://github.com/pcwhy/AbnormalityDetectionInZbDNN}\n"}
+{"abstract": "  We revisit the nonlinear stability of the critical invasion front in the\nGinzburg-Landau equation. Our main result shows that the amplitude of localized\nperturbations decays with rate $t^{-3/2}$, while the phase decays diffusively.\nWe thereby refine earlier work of Bricmont and Kupiainen as well as Eckmann and\nWayne, who separately established nonlinear stability but with slower decay\nrates. On a technical level, we rely on sharp linear estimates obtained through\nanalysis of the resolvent near the essential spectrum via a far-field/core\ndecomposition which is well suited to accurately describing the dynamics of\nseparate neutrally stable modes arising from far-field behavior on the left and\nright.\n"}
+{"abstract": "  In this article, we present a semantics-level adaption of the Optional\nStopping Theorem, sketch an expected-cost analysis as its application, and\nsurvey different variants of the Optional Stopping Theorem that have been used\nin static analysis of probabilistic programs.\n"}
+{"abstract": "  Neural networks are very popular in many areas, but great computing\ncomplexity makes it hard to run neural networks on devices with limited\nresources. To address this problem, quantization methods are used to reduce\nmodel size and computation cost, making it possible to use neural networks on\nembedded platforms or mobile devices.\n  In this paper, an integer-only-quantization scheme is introduced. This scheme\nuses one layer that combines shift-based batch normalization and uniform\nquantization to implement 4-bit integer-only inference. Without big integer\nmultiplication(which is used in previous integer-only-quantization methods),\nthis scheme can achieve good power and latency efficiency, and is especially\nsuitable to be deployed on co-designed hardware platforms. Tests have proved\nthat this scheme works very well for easy tasks. And for tough tasks,\nperformance loss can be tolerated for its inference efficiency. Our work is\navailable on github: https://github.com/hguq/IntegerNet.\n"}
+{"abstract": "  A search for the charged lepton flavor violating decay $J/\\psi\\to\ne^{\\pm}\\tau^{\\mp}$ with $\\tau^{\\mp} \\to \\pi^{\\mp}\\pi^0\\nu_{\\tau}$ is performed\nwith about $10$ billion $J/ \\psi$ events collected with the BESIII detector at\nthe BEPCII. No significant signal is observed, and an upper limit is set on the\nbranching fraction $\\mathcal{B}(J/\\psi\\to e^{\\pm}\\tau^{\\mp})<7.5\\times10^{-8}$\nat the 90$\\%$ confidence level. This improves the previously published limit by\ntwo orders of magnitude.\n"}
+{"abstract": "  Batch normalization (BN) is a key facilitator and considered essential for\nstate-of-the-art binary neural networks (BNN). However, the BN layer is costly\nto calculate and is typically implemented with non-binary parameters, leaving a\nhurdle for the efficient implementation of BNN training. It also introduces\nundesirable dependence between samples within each batch. Inspired by the\nlatest advance on Batch Normalization Free (BN-Free) training, we extend their\nframework to training BNNs, and for the first time demonstrate that BNs can be\ncompleted removed from BNN training and inference regimes. By plugging in and\ncustomizing techniques including adaptive gradient clipping, scale weight\nstandardization, and specialized bottleneck block, a BN-free BNN is capable of\nmaintaining competitive accuracy compared to its BN-based counterpart.\nExtensive experiments validate the effectiveness of our proposal across diverse\nBNN backbones and datasets. For example, after removing BNs from the\nstate-of-the-art ReActNets, it can still be trained with our proposed\nmethodology to achieve 92.08%, 68.34%, and 68.0% accuracy on CIFAR-10,\nCIFAR-100, and ImageNet respectively, with marginal performance drop\n(0.23%~0.44% on CIFAR and 1.40% on ImageNet). Codes and pre-trained models are\navailable at: https://github.com/VITA-Group/BNN_NoBN.\n"}
+{"abstract": "  Electronic nematicity is often found in unconventional superconductors,\nsuggesting its relevance for electronic pairing. In the strongly hole-doped\niron-based superconductors, the symmetry channel and strength of the nematic\nfluctuations, as well as the possible presence of long-range nematic order,\nremain controversial. Here, we address these questions using transport\nmeasurements under elastic strain. By decomposing the strain response into the\nappropriate symmetry channels, we demonstrate the emergence of a giant in-plane\nsymmetric contribution, associated with the growth of both strong electronic\ncorrelations and the sensitivity of these correlations to strain. We find\nweakened remnants of the nematic fluctuations that are present at optimal\ndoping, but no change in the symmetry channel of nematic fluctuations with hole\ndoping. Furthermore, we find no evidence for a nematic-ordered state in the\nAFe$_2$As$_2$(A = K, Rb, Cs) superconductors. These results revise the current\nunderstanding of nematicity in hole-doped iron-based superconductors.\n"}
+{"abstract": "  The scientific and technological exploration of artificially designed\nthree-dimensional magnetic nanostructures opens the path to exciting novel\nphysical phenomena, originating from the increased complexity in spin textures,\ntopology, and frustration in three dimensions. Theory predicts that the\nequilibrium magnetic ground state of two-dimensional systems which reflects the\ncompetition between symmetric (Heisenberg) and antisymmetric\n(Dzyaloshinskii-Moriya interaction (DMI)) exchange interaction is significantly\nmodified on curved surfaces when the radius of local curvature becomes\ncomparable to fundamental magnetic length scales. Here, we present an\nexperimental study of the spin texture in an 8 nm thin magnetic multilayer with\ngrowth-induced in-plane anisotropy and DMI deposited onto the curved surface of\na 1.8 {\\mu}m long non-magnetic carbon nanowire with a 67 nm radius. Using\nmagnetic soft x-ray tomography the three-dimensional spin configuration in this\nnanotube was retrieved with about 30nm spatial resolution. The transition\nbetween two vortex configurations on the two ends of the nanotube with opposite\ncirculation occurs through a domain wall that is aligned at an inclined angle\nrelative to the wire axis. Three-dimensional micromagnetic simulations support\nthe experimental observations and represent a visualization of the\ncurvature-mediated DMI. They also allow a quantitative estimate of the DMI\nvalue for the magnetic multilayered nanotube.\n"}
+{"abstract": "  We present a model compound with a spin-1/2 spatially anisotropic frustrated\nsquare lattice, in which three antiferromagnetic interactions and one\nferromagnetic interaction are competing. We observe an unconventional gradual\nincrease in the low-temperature magnetization curve reminiscent of the quantum\ncritical behavior between gapped and gapless phases. In addition, the specific\nheat and electron spin resonance signals indicate one-dimensional\ncharacteristics. These results demonstrate quantum critical behavior associated\nwith one dimensionalization caused by frustrated interactions in the spin-1/2\nspatially anisotropic square lattice.\n"}
+{"abstract": "  We propose a fast algorithm for the probabilistic solution of boundary value\nproblems (BVPs), which are ordinary differential equations subject to boundary\nconditions. In contrast to previous work, we introduce a Gauss--Markov prior\nand tailor it specifically to BVPs, which allows computing a posterior\ndistribution over the solution in linear time, at a quality and cost comparable\nto that of well-established, non-probabilistic methods. Our model further\ndelivers uncertainty quantification, mesh refinement, and hyperparameter\nadaptation. We demonstrate how these practical considerations positively impact\nthe efficiency of the scheme. Altogether, this results in a practically usable\nprobabilistic BVP solver that is (in contrast to non-probabilistic algorithms)\nnatively compatible with other parts of the statistical modelling tool-chain.\n"}
+{"abstract": "  Hexagonal boron nitride (h-BN) has long been recognized as an ideal substrate\nfor electronic devices due to its dangling-bond-free surface, insulating nature\nand thermal/chemical stability. Therefore, to analyse the lattice structure and\norientation of h-BN crystals becomes important. Here, the stacking order and\nwrinkles of h-BN are investigated by transmission electron microscopy (TEM). It\nis experimentally confirmed that the layers in the h-BN flakes are arranged in\nthe AA' stacking. The wrinkles in a form of threefold network throughout the\nh-BN crystal are oriented along the armchair direction, and their formation\nmechanism was further explored by molecular dynamics simulations. Our findings\nprovide a deep insight about the microstructure of h-BN and shed light on the\nstructural design/electronic modulations of two-dimensional crystals.\n"}
+{"abstract": "  The aim of this note is to prove that almost-minimizers of the perimeter are\nReifenberg flat, for a very weak notion of minimality. The main observation is\nthat smallness of the excess at some scale implies smallness of the excess at\nall smaller scales.\n"}
+{"abstract": "  In this paper, we study a non-stationary stochastic bandit problem, which\ngeneralizes the switching bandit problem. On top of the switching bandit\nproblem (\\textbf{Case a}), we are interested in three concrete examples:\n(\\textbf{b}) the means of the arms are local polynomials, (\\textbf{c}) the\nmeans of the arms are locally smooth, and (\\textbf{d}) the gaps of the arms\nhave a bounded number of inflexion points and where the highest arm mean cannot\nvary too much in a short range. These three settings are very different, but\nhave in common the following: (i) the number of similarly-sized level sets of\nthe logarithm of the gaps can be controlled, and (ii) the highest mean has a\nlimited number of abrupt changes, and otherwise has limited variations. We\npropose a single algorithm in this general setting, that in particular solves\nin an efficient and unified way the four problems (a)-(d) mentioned.\n"}
+{"abstract": "  This paper deals with the first analysis of the neutron and gamma time series\nmeasured with organic scintillators from plutonium samples by using information\nmeasures. Fast neutron detection with organic scintillators has been widely\nused for various nuclear applications and homeland security. One of the\nsignificant attributes of special nuclear materials (SNM) is the high\nmultiplicity events in a short period of time. The time distributions of\nneutron and gamma-rays events for the plutonium metal plates designed as fuel\nplates for the Idaho National Laboratory (INL) Zero Power were measured with\nthe Fast Neutron Multiplicity Counter (FNMC) consisting of 8 EJ-309 liquid\nscintillators and 8 stilbene detectors. Since the neutron correlated counts\nwithin the coincidence window of 40 ns are related to 240Pu effective mass of\nplutonium metal plates we investigated the randomness of the measured neutron\nand gamma-rays events. To access such information, we resort to complexity\nmeasures in the hope of being able to connect complexity values with the\nreliability of detection. That was done through (i) application of Kolmogorov\ncomplexity (KC) and its derivatives [Kolmogorov spectrum and its highest value\n(KCM) and running complexity (RKC)] and (ii) establishing the breaking point\nafter which there exists a sharp drop in the running Kolmogorov complexity of\nthe neutron and gamma-rays time series. It was found that the complexity of all\nthe time series detected from the sample with 5, 9, 11, 13, and 15 plutonium\nplates had the high almost identical values of KC while the sample with 3\nplates had by one-third smaller KC values than all the others. These\ncalculations were supplemented by the Lypaunov exponents for a time series and\nthe NIST tests. The low KC values can be addressed to the different sources of\nuncertainties in measuring procedure with the sample consisting of three\nplates.\n"}
+{"abstract": "  Direct laser acceleration (DLA) of electrons in a plasma of near critical\nelectron density (NCD) and associated synchrotron-like radiation are discussed\nfor moderate relativistic laser intensity (the normalized laser amplitude $a_0$\n$\\leq$ 4.3) and ps-long pulse. This regime is typical for kJ PW-class laser\nfacilities designed for high energy density research. Currently, in experiments\nat the PHELX laser it was demonstrated that interaction of 10$^{19}$ W/cm$^{2}$\nsub-ps laser pulse with sub-mm long NCD plasma results in generation of\nhigh-current well-directed super-ponderomotive electrons with effective\ntemperature that is 10$\\times$ higher than the ponderomotive potential [O.\nRosmej et al., PPCF 62, 115024 (2020)]. Three-dimensional Particle-In-Cell\nsimulations provided a good agreement with the measured electron energy\ndistribution and were used in the current work to study synchrotron radiation\nof the DLA accelerated electrons. The resulting x-ray spectrum with a critical\nenergy of 5 keV reveals an ultra-high photon number of 7$\\times$10$^{11}$ in\nthe 1-30 keV photon energy range at the focused laser energy of 20 J. Numerical\nsimulations of a betatron x-ray phasecontrast imaging based on the DLA process\nfor the parameters of a PHELIX laser is presented. The results are of interest\nfor applications in high energy density (HED) experiments, which require a\npicosecond x-ray pulse and a high photon flux.\n"}
+{"abstract": "  A fraction of the XMM-Newton/EPIC FOV is obscured by the dysfunctional (i.e.\nbad) pixels. The fraction varies between different EPIC instruments in a given\nobservation. These complications affect the analysis of extended X-ray sources\nobserved with XMM-Newton/EPIC and the consequent scientific interpretation of\nthe results. For example, the accuracy of the widely used cosmological probe of\nthe gas mass of clusters of galaxies depends on the accuracy of the procedure\nof removing the obscuration effect from the measured flux. The Science Analysis\nSoftware (SAS) includes an option for recovering the lost fraction of the flux\nmeasured by a primary instrument by utilising a supplementary image of the same\nsource. The correction may be accurate if the supplementary image is minimally\nobscured at the locations of the bad pixels of the primary instrument. This can\nbe achieved e.g. by using the observation-based MOS2 image for correcting the\npn flux, or by using a synthetic model image. By utilising a sample of 27\ngalaxy cluster observations we evaluated the accuracy of the recovery method\nbased on observed images, as implemented in SAS 18.0.0. We found that the\naccuracy of the recovered total flux in the 0.5-7.0 keV band in the full\ngeometric area within the central r = 6 arcmin is better than 0.1% on average\nwhile in some individual cases the recovered flux may be uncertain by ~1%.\n"}
+{"abstract": "  Quantum ESPRESSO is an open-source distribution of computer codes for\nquantum-mechanical materials modeling, based on density-functional theory,\npseudopotentials, and plane waves, and renowned for its performance on a wide\nrange of hardware architectures, from laptops to massively parallel computers,\nas well as for the breadth of its applications. In this paper we present a\nmotivation and brief review of the ongoing effort to port Quantum ESPRESSO onto\nheterogeneous architectures based on hardware accelerators, which will overcome\nthe energy constraints that are currently hindering the way towards exascale\ncomputing.\n"}
+{"abstract": "  We present the general construction of the $U$-projector (the homomorphism of\nthe algebra into its field of $U$-invariants identical on the subalgebra of\n$U$-invariants). It is shown how to apply $U$-projector to find the systems of\nfree generators of the fields of $U$-invariants for representations of\nreductive groups.\n"}
+{"abstract": "  Social and information networks are gaining huge popularity recently due to\ntheir various applications. Knowledge representation through graphs in the form\nof nodes and edges should preserve as many characteristics of the original data\nas possible. Some of the interesting and useful applications on these graphs\nare graph classification, node classification, link prediction, etc. The Graph\nNeural Networks have evolved over the last few years. Graph Neural Networks\n(GNNs) are efficient ways to get insight into large and dynamic graph datasets\ncapturing relationships among billions of entities also known as knowledge\ngraphs.\n  In this paper, we discuss the graph convolutional neural networks graph\nautoencoders and spatio-temporal graph neural networks. The representations of\nthe graph in lower dimensions can be learned using these methods. The\nrepresentations in lower dimensions can be used further for downstream machine\nlearning tasks.\n"}
+{"abstract": "  We present a parameter estimation method for nonlinear mixed effect models\nbased on ordinary differential equations (NLME-ODEs). The method presented here\naims at regularizing the estimation problem in presence of model\nmisspecifications, practical identifiability issues and unknown initial\nconditions. For doing so, we define our estimator as the minimizer of a cost\nfunction which incorporates a possible gap between the assumed model at the\npopulation level and the specific individual dynamic. The cost function\ncomputation leads to formulate and solve optimal control problems at the\nsubject level. This control theory approach allows to bypass the need to know\nor estimate initial conditions for each subject and it regularizes the\nestimation problem in presence of poorly identifiable parameters. Comparing to\nmaximum likelihood, we show on simulation examples that our method improves\nestimation accuracy in possibly partially observed systems with unknown initial\nconditions or poorly identifiable parameters with or without model error. We\nconclude this work with a real application on antibody concentration data after\nvaccination against Ebola virus coming from phase 1 trials. We use the\nestimated model discrepancy at the subject level to analyze the presence of\nmodel misspecification.\n"}
+{"abstract": "  Natural language inference requires reasoning about contradictions,\nnegations, and their commonsense implications. Given a simple premise (e.g.,\n\"I'm mad at you\"), humans can reason about the varying shades of contradictory\nstatements ranging from straightforward negations (\"I'm not mad at you\") to\ncommonsense contradictions (\"I'm happy\"). Moreover, these negated or\ncontradictory statements shift the commonsense implications of the original\npremise in nontrivial ways. For example, while \"I'm mad\" implies \"I'm unhappy\nabout something,\" negating the premise (i.e., \"I'm not mad\") does not\nnecessarily negate the corresponding commonsense implications.\n  In this paper, we present the first comprehensive study focusing on\ncommonsense implications of negated statements and contradictions. We introduce\nANION1, a new commonsense knowledge graph with 624K if-then rules focusing on\nnegated and contradictory events. We then present joint generative and\ndiscriminative inference models for this new resource, providing novel\nempirical insights on how logical negations and commonsense contradictions\nreshape the commonsense implications of their original premises.\n"}
+{"abstract": "  This paper investigates whether the framework of fractional quantum mechanics\ncan broaden our perspective of black hole thermodynamics. Concretely, we employ\na {\\it space-fractional} derivative \\cite{Rie} as our main tool. Moreover, we\nrestrict our analysis to the case of a Schwarzschild configuration. From a\nsubsequently modified Wheeler-DeWitt equation, we retrieve the corresponding\nexpressions for specific observables. Namely, the black hole mass spectrum,\n$M$, its temperature $T$, and entropy, $S$. We find that these bear\nconsequential alterations conveyed through a fractional parameter, $\\alpha$. In\nparticular, the standard results are recovered in the specific limit\n$\\alpha=2$. Furthermore, we elaborate how generalizations of the entropy-area\nrelation suggested by Tsallis and Cirto \\cite{Tsallis} and Barrow \\cite{Barrow}\nacquire a complementary interpretation in terms of a fractional point of view.\nA thorough discussion of our results is presented.\n"}
+{"abstract": "  Graph-based multi-view clustering aiming to obtain a partition of data across\nmultiple views, has received considerable attention in recent years. Although\ngreat efforts have been made for graph-based multi-view clustering, it remains\na challenge to fuse characteristics from various views to learn a common\nrepresentation for clustering. In this paper, we propose a novel Consistent\nMultiple Graph Embedding Clustering framework(CMGEC). Specifically, a multiple\ngraph auto-encoder(M-GAE) is designed to flexibly encode the complementary\ninformation of multi-view data using a multi-graph attention fusion encoder. To\nguide the learned common representation maintaining the similarity of the\nneighboring characteristics in each view, a Multi-view Mutual Information\nMaximization module(MMIM) is introduced. Furthermore, a graph fusion\nnetwork(GFN) is devised to explore the relationship among graphs from different\nviews and provide a common consensus graph needed in M-GAE. By jointly training\nthese models, the common latent representation can be obtained which encodes\nmore complementary information from multiple views and depicts data more\ncomprehensively. Experiments on three types of multi-view datasets demonstrate\nCMGEC outperforms the state-of-the-art clustering methods.\n"}
+{"abstract": "  This paper describes an AI agent that plays the popular first-person-shooter\n(FPS) video game `Counter-Strike; Global Offensive' (CSGO) from pixel input.\nThe agent, a deep neural network, matches the performance of the medium\ndifficulty built-in AI on the deathmatch game mode, whilst adopting a humanlike\nplay style. Unlike much prior work in games, no API is available for CSGO, so\nalgorithms must train and run in real-time. This limits the quantity of\non-policy data that can be generated, precluding many reinforcement learning\nalgorithms. Our solution uses behavioural cloning - training on a large noisy\ndataset scraped from human play on online servers (4 million frames, comparable\nin size to ImageNet), and a smaller dataset of high-quality expert\ndemonstrations. This scale is an order of magnitude larger than prior work on\nimitation learning in FPS games.\n"}
+{"abstract": "  With superior optical properties, high flexibility in engineering its\nmaterial properties, and strong capability for large-scale on-chip integration,\ngraphene oxide (GO) is an attractive solution for on-chip integration of\ntwo-dimensional (2D) materials to implement functional integrated photonic\ndevices capable of new features. Over the past decade, integrated GO photonics,\nrepresenting an innovative merging of integrated photonic devices and thin GO\nfilms, has experienced significant development, leading to a surge in many\napplications covering almost every field of optical sciences. This paper\nreviews the recent advances in this emerging field, providing an overview of\nthe optical properties of GO as well as methods for the on-chip integration of\nGO. The main achievements made in GO hybrid integrated photonic devices for\ndiverse applications are summarized. The open challenges as well as the\npotential for future improvement are also discussed.\n"}
+{"abstract": "  Many self-propelled objects are large enough to exhibit inertial effects but\nstill suffer from environmental fluctuations. The corresponding basic equations\nof motion are governed by active Langevin dynamics which involve inertia,\nfriction and stochastic noise for both the translational and orientational\ndegrees of freedom coupled via the self-propulsion along the particle\norientation. In this paper, we generalize the active Langevin model to\ntime-dependent parameters and explicitly discuss the effect of time-dependent\ninertia for achiral and chiral particles. Realizations of this situation are\nmanifold ranging from minirockets which are self-propelled by burning their own\nmass, dust particles in plasma which lose mass by evaporating material to\nwalkers with expiring activity. Here we present analytical solutions for\nseveral dynamical correlation functions such as the mean-square displacement\nand the orientational and velocity autocorrelation functions. If the parameters\nexhibit a slow power-law in time, we obtain anomalous superdiffusion with a\nnon-trivial dynamical exponent. Finally we constitute the \"Langevin rocket\"\nmodel by including orientational fluctuations in the traditional Tsiolkovsky\nrocket equation. We calculate the mean reach of the Langevin rocket and discuss\ndifferent mass ejection strategies to maximize it. Our results can be tested in\nexperiments on macroscopic robotic or living particles or in self-propelled\nmesoscopic objects moving in media of low viscosity such as complex plasma.\n"}
+{"abstract": "  Can a machine learn Machine Learning? This work trains a machine learning\nmodel to solve machine learning problems from a University undergraduate level\ncourse. We generate a new training set of questions and answers consisting of\ncourse exercises, homework, and quiz questions from MIT's 6.036 Introduction to\nMachine Learning course and train a machine learning model to answer these\nquestions. Our system demonstrates an overall accuracy of 96% for open-response\nquestions and 97% for multiple-choice questions, compared with MIT students'\naverage of 93%, achieving grade A performance in the course, all in real-time.\nQuestions cover all 12 topics taught in the course, excluding coding questions\nor questions with images. Topics include: (i) basic machine learning\nprinciples; (ii) perceptrons; (iii) feature extraction and selection; (iv)\nlogistic regression; (v) regression; (vi) neural networks; (vii) advanced\nneural networks; (viii) convolutional neural networks; (ix) recurrent neural\nnetworks; (x) state machines and MDPs; (xi) reinforcement learning; and (xii)\ndecision trees. Our system uses Transformer models within an encoder-decoder\narchitecture with graph and tree representations. An important aspect of our\napproach is a data-augmentation scheme for generating new example problems. We\nalso train a machine learning model to generate problem hints. Thus, our system\nautomatically generates new questions across topics, answers both open-response\nquestions and multiple-choice questions, classifies problems, and generates\nproblem hints, pushing the envelope of AI for STEM education.\n"}
+{"abstract": "  The diffraction spectrum of an aperiodic solid is related to the group of\neigenvalues of the dynamical system associated with the solid. Those\neigenvalues with continuous eigenfunctions constitute the topological Bragg\nspectrum. We relate the topological Bragg spectrum to the gap-labelling group,\nwhich is the group of possible gap labels for the spectrum of a Schr\\\"odinger\noperator describing the electronic motion in the solid.\n"}
+{"abstract": "  In this paper, we propose MixSpeech, a simple yet effective data augmentation\nmethod based on mixup for automatic speech recognition (ASR). MixSpeech trains\nan ASR model by taking a weighted combination of two different speech features\n(e.g., mel-spectrograms or MFCC) as the input, and recognizing both text\nsequences, where the two recognition losses use the same combination weight. We\napply MixSpeech on two popular end-to-end speech recognition models including\nLAS (Listen, Attend and Spell) and Transformer, and conduct experiments on\nseveral low-resource datasets including TIMIT, WSJ, and HKUST. Experimental\nresults show that MixSpeech achieves better accuracy than the baseline models\nwithout data augmentation, and outperforms a strong data augmentation method\nSpecAugment on these recognition tasks. Specifically, MixSpeech outperforms\nSpecAugment with a relative PER improvement of 10.6$\\%$ on TIMIT dataset, and\nachieves a strong WER of 4.7$\\%$ on WSJ dataset.\n"}
+{"abstract": "  This paper considers the balanced hypergraph partitioning problem, which asks\nfor partitioning the vertices into $k$ disjoint blocks of bounded size while\nminimizing an objective function over the hyperedges. Here, we consider the\nmost commonly used connectivity metric. We describe our open source hypergraph\npartitioner KaHyPar which is based on the successful multi-level approach --\ndriving it to the extreme of one level for (almost) every vertex. Using\ncarefully designed data structures and dynamic update techniques, this approach\noffers a very good time-quality tradeoff. We present two preprocessing\ntechniques -- pin sparsification using locality sensitive hashing and community\ndetection based on the Louvain algorithm. The community structure is used to\nguide the coarsening process that incrementally contracts vertices.\nPortfolio-based partitioning of the contracted hypergraph already achieves good\ninitial solutions. While reversing the contractions, a combination of\nhighly-localized direct $k$-way local search and flow-based techniques that\ntake a more global view, refine the partition to achieve high quality.\nOptionally, a memetic algorithm evolves a pool of solution candidates to obtain\neven higher quality.\n  We evaluate KaHyPar on a large set of instances from a wide range of\napplication domains. With respect to quality, KaHyPar outperforms all\npreviously considered systems that can handle large hypergraphs such as hMETIS,\nPaToH, Mondriaan, or Zoltan. KaHyPar is also faster than most of these systems\nexcept for PaToH which represents a different speed-quality tradeoff. The\nresults even extend to the special case of graph partitioning, where\nspecialized systems such as KaHIP should have an advantage.\n"}
+{"abstract": "  While classic control theory offers state of the art solutions in many\nproblem scenarios, it is often desired to improve beyond the structure of such\nsolutions and surpass their limitations. To this end, residual policy learning\n(RPL) offers a formulation to improve existing controllers with reinforcement\nlearning (RL) by learning an additive \"residual\" to the output of a given\ncontroller. However, the applicability of such an approach highly depends on\nthe structure of the controller. Often, internal feedback signals of the\ncontroller limit an RL algorithm to adequately change the policy and, hence,\nlearn the task. We propose a new formulation that addresses these limitations\nby also modifying the feedback signals to the controller with an RL policy and\nshow superior performance of our approach on a contact-rich peg-insertion task\nunder position and orientation uncertainty. In addition, we use a recent\nCartesian impedance control architecture as the control framework which can be\navailable to us as a black-box while assuming no knowledge about its\ninput/output structure, and show the difficulties of standard RPL. Furthermore,\nwe introduce an adaptive curriculum for the given task to gradually increase\nthe task difficulty in terms of position and orientation uncertainty. A video\nshowing the results can be found at https://youtu.be/SAZm_Krze7U .\n"}
+{"abstract": "  Video super-resolution (VSR) technology excels in reconstructing low-quality\nvideo, avoiding unpleasant blur effect caused by interpolation-based\nalgorithms. However, vast computation complexity and memory occupation hampers\nthe edge of deplorability and the runtime inference in real-life applications,\nespecially for large-scale VSR task. This paper explores the possibility of\nreal-time VSR system and designs an efficient and generic VSR network, termed\nEGVSR. The proposed EGVSR is based on spatio-temporal adversarial learning for\ntemporal coherence. In order to pursue faster VSR processing ability up to 4K\nresolution, this paper tries to choose lightweight network structure and\nefficient upsampling method to reduce the computation required by EGVSR network\nunder the guarantee of high visual quality. Besides, we implement the batch\nnormalization computation fusion, convolutional acceleration algorithm and\nother neural network acceleration techniques on the actual hardware platform to\noptimize the inference process of EGVSR network. Finally, our EGVSR achieves\nthe real-time processing capacity of 4K@29.61FPS. Compared with TecoGAN, the\nmost advanced VSR network at present, we achieve 85.04% reduction of\ncomputation density and 7.92x performance speedups. In terms of visual quality,\nthe proposed EGVSR tops the list of most metrics (such as LPIPS, tOF, tLP,\netc.) on the public test dataset Vid4 and surpasses other state-of-the-art\nmethods in overall performance score. The source code of this project can be\nfound on https://github.com/Thmen/EGVSR.\n"}
+{"abstract": "  Visual Sensor Networks can be used in a variety of perception applications\nsuch as infrastructure support for autonomous driving in complex road segments.\nThe pose of the sensors in such networks directly determines the coverage of\nthe environment and objects therein, which impacts the performance of\napplications such as object detection and tracking. Existing sensor pose\noptimisation methods in the literature either maximise the coverage of ground\nsurfaces, or consider the visibility of the target objects as binary variables,\nwhich cannot represent various degrees of visibility. Such formulations cannot\nguarantee the visibility of the target objects as they fail to consider\nocclusions. This paper proposes two novel sensor pose optimisation methods,\nbased on gradient-ascent and Integer Programming techniques, which maximise the\nvisibility of multiple target objects in cluttered environments. Both methods\nconsider a realistic visibility model based on a rendering engine that provides\npixel-level visibility information about the target objects. The proposed\nmethods are evaluated in a complex environment and compared to existing methods\nin the literature. The evaluation results indicate that explicitly modelling\nthe visibility of target objects is critical to avoid occlusions in cluttered\nenvironments. Furthermore, both methods significantly outperform existing\nmethods in terms of object visibility.\n"}
+{"abstract": "  Interactions between users and videos are the major data source of performing\nvideo recommendation. Despite lots of existing recommendation methods, user\nbehaviors on videos, which imply the complex relations between users and\nvideos, are still far from being fully explored. In the paper, we present a\nmodel named Sagittarius. Sagittarius adopts a graph convolutional neural\nnetwork to capture the influence between users and videos. In particular,\nSagittarius differentiates between different user behaviors by weighting and\nfuses the semantics of user behaviors into the embeddings of users and videos.\nMoreover, Sagittarius combines multiple optimization objectives to learn user\nand video embeddings and then achieves the video recommendation by the learned\nuser and video embeddings. The experimental results on multiple datasets show\nthat Sagittarius outperforms several state-of-the-art models in terms of\nrecall, unique recall and NDCG.\n"}
+{"abstract": "  We present {\\tt RandomPoints}, a package in \\emph{Macaulay2} designed mainly\nto identify rational and geometric points in a variety over a finite field. We\nprovide tools to estimate the dimension of a variety. We also present methods\nto obtain non-vanishing minors of a given size in a given matrix, by evaluating\nthe matrix at a point.\n"}
+{"abstract": "  This work presents a physics based compact model for SiC power MOSFETs that\naccurately describes the I-V characteristics up to large voltages and currents.\nCharge-based formulations accounting for the different physics of SiC power\nMOSFETs are presented. The formulations account for the effect of the large\nSiC/SiO2 interface traps density characteristic of SiC MOSFETs and its\ndependence with temperature. The modeling of interface charge density is found\nto be necessary to describe the electrostatics of SiC power MOSFETs when\noperating at simultaneous high current and high voltage regions. The proposed\ncompact model accurately fits the measurement data extracted of a 160 milli\nohms, 1200V SiC power MOSFET in the complete IV plane from drain-voltage $V_d$\n= 5mV up to 800 V and current ranges from few mA to 30 A.\n"}
+{"abstract": "  In this paper, we revisit the possibilities of Ba\\~nados-Silk-West (BSW)\neffect in Kerr-Newman-Taub-NUT (KNTN) spacetime for two neutral particles\nmoving over the equatorial plane and constant $\\theta$ in Boyer-Lindquist\ncoordinate. Contrary to a previous study on this topic, we found that BSW\neffect for two particles confined to move over the equatorial plane is not\npossible. Numerical calculations shows that BSW effect in constant $\\theta$\ngeodesics is possible under certain circumstances.\n"}
+{"abstract": "  The data distribution commonly evolves over time leading to problems such as\nconcept drift that often decrease classifier performance. We seek to predict\nunseen data (and their labels) allowing us to tackle challenges due to a\nnon-constant data distribution in a \\emph{proactive} manner rather than\ndetecting and reacting to already existing changes that might already have led\nto errors. To this end, we learn a domain transformer in an unsupervised manner\nthat allows generating data of unseen domains. Our approach first matches\nindependently learned latent representations of two given domains obtained from\nan auto-encoder using a Cycle-GAN. In turn, a transformation of the original\nsamples can be learned that can be applied iteratively to extrapolate to unseen\ndomains. Our evaluation on CNNs on image data confirms the usefulness of the\napproach. It also achieves very good results on the well-known problem of\nunsupervised domain adaption, where labels but not samples have to be\npredicted.\n"}
+{"abstract": "  Using an extended Dicke model, we study the effect of the spin-orbit coupling\nbetween the singlet and the triplet molecular excitons in organic\nmicrocavities. We exactly solve the model in the strong coupling regime in the\nsingle excitation space. We calculate the polaritonic states and the optical\nabsorption spectrum of the system and explore the spin-orbit induced splitting\nof the lower polariton into two branches. The effect of this splitting in the\nultra strong coupling regime on the superradiant phase transition is explored\nusing a variational approach. We find that the spin-orbit coupling pushes the\nsystem towards the superradiant phase and reduces the critical matter-light\ncoupling required for it.\n"}
+{"abstract": "  How far can we go with textual representations for understanding pictures? In\nimage understanding, it is essential to use concise but detailed image\nrepresentations. Deep visual features extracted by vision models, such as\nFaster R-CNN, are prevailing used in multiple tasks, and especially in visual\nquestion answering (VQA). However, conventional deep visual features may\nstruggle to convey all the details in an image as we humans do. Meanwhile, with\nrecent language models' progress, descriptive text may be an alternative to\nthis problem. This paper delves into the effectiveness of textual\nrepresentations for image understanding in the specific context of VQA. We\npropose to take description-question pairs as input, instead of deep visual\nfeatures, and fed them into a language-only Transformer model, simplifying the\nprocess and the computational cost. We also experiment with data augmentation\ntechniques to increase the diversity in the training set and avoid learning\nstatistical bias. Extensive evaluations have shown that textual representations\nrequire only about a hundred words to compete with deep visual features on both\nVQA 2.0 and VQA-CP v2.\n"}
+{"abstract": "  This paper investigates the well-posedness of a type of state constraint\nergodic Mean Field Game system in a bounded domain in which the\nHamilton-Jacobi-Bellman equation is paired with an infinite Dirichlet boundary\ncondition. In this setting, the infinite boundary condition prevents the\nunderlying stochastic trajectories from reaching the boundary, resulting in a\nstate constraint solution. We consider a class of superlinear power-like\nHamiltonians and establish existence and uniqueness results under appropriate\nassumptions on the coupling. We treat both non-local and local couplings, and\nestablish the uniqueness of the system by exploiting the decay of the density\nnear the boundary.\n"}
+{"abstract": "  Minimax problems, such as generative adversarial network, adversarial\ntraining, and fair training, are widely solved by a multi-step gradient descent\nascent (MGDA) method in practice. However, its convergence guarantee is\nlimited. In this paper, inspired by the primal-dual hybrid gradient method, we\npropose a new semi-anchoring (SA) technique for the MGDA method. This makes the\nMGDA method find a stationary point of a structured nonconvex-nonconcave\ncomposite minimax problem; its saddle-subdifferential operator satisfies the\nweak Minty variational inequality condition. The resulting method, named\nSA-MGDA, is built upon a Bregman proximal point method. We further develop its\nbacktracking line-search version, and its non-Euclidean version for smooth\nadaptable functions. Numerical experiments, including a fair classification\ntraining, are provided.\n"}
+{"abstract": "  We present an IoT-based intelligent bed sensor system that collects and\nanalyses respiration-associated signals for unobtrusive monitoring in the home,\nhospitals and care units. A contactless device is used, which contains four\nload sensors mounted under the bed and one data processing unit (data logger).\nVarious machine learning methods are applied to the data streamed from the data\nlogger to detect the Respiratory Rate (RR). We have implemented Support Vector\nMachine (SVM) and also Neural Network (NN)-based pattern recognition methods,\nwhich are combined with either peak detection or Hilbert transform for robust\nRR calculation. Experimental results show that our methods could effectively\nextract RR using the data collected by contactless bed sensors. The proposed\nmethods are robust to outliers and noise, which are caused by body movements.\nThe monitoring system provides a flexible and scalable way for continuous and\nremote monitoring of sleep, movement and weight using the embedded sensors.\n"}
+{"abstract": "  Recently proposed automatic pathological speech classification techniques use\nunsupervised auto-encoders to obtain a high-level abstract representation of\nspeech. Since these representations are learned based on reconstructing the\ninput, there is no guarantee that they are robust to pathology-unrelated cues\nsuch as speaker identity information. Further, these representations are not\nnecessarily discriminative for pathology detection. In this paper, we exploit\nsupervised auto-encoders to extract robust and discriminative speech\nrepresentations for Parkinson's disease classification. To reduce the influence\nof speaker variabilities unrelated to pathology, we propose to obtain speaker\nidentity-invariant representations by adversarial training of an auto-encoder\nand a speaker identification task. To obtain a discriminative representation,\nwe propose to jointly train an auto-encoder and a pathological speech\nclassifier. Experimental results on a Spanish database show that the proposed\nsupervised representation learning methods yield more robust and discriminative\nrepresentations for automatically classifying Parkinson's disease speech,\noutperforming the baseline unsupervised representation learning system.\n"}
+{"abstract": "  In wireless communication, accurate channel state information (CSI) is of\npivotal importance. In practice, due to processing and feedback delays,\nestimated CSI can be outdated, which can severely deteriorate the performance\nof the communication system. Besides, to feedback estimated CSI, a strong\ncompression of the CSI, evaluated at the user equipment (UE), is performed to\nreduce the over-the-air (OTA) overhead. Such compression strongly reduces the\nprecision of the estimated CSI, which ultimately impacts the performance of\nmultiple-input multiple-output (MIMO) precoding. Motivated by such issues, we\npresent a novel scalable idea of reporting CSI in wireless networks, which is\napplicable to both time-division duplex (TDD) and frequency-division duplex\n(FDD) systems. In particular, the novel approach introduces the use of a\nchannel predictor function, e.g., Kalman filter (KF), at both ends of the\ncommunication system to predict CSI. Simulation-based results demonstrate that\nthe novel approach reduces not only the channel mean-squared-error (MSE) but\nalso the OTA overhead to feedback the estimated CSI when there is immense\nvariation in the mobile radio channel. Besides, in the immobile radio channel,\nfeedback can be eliminated, which brings the benefit of further reducing the\nOTA overhead. Additionally, the proposed method provides a significant\nsignal-to-noise ratio (SNR) gain in both the channel conditions, i.e., highly\nmobile and immobile.\n"}
+{"abstract": "  Each of the individual factors of the Drake Equation is considered. Each in\nturn is either abandoned or redefined and finally reduced to a single new\nfactor, fd, the fraction of technological life that is detectable by any means.\nHowever, neither the Drake Equation, nor its replacement, can actually solve\nfor N. Only a vibrant SETI program and, ultimately, contact with an alien\ncivilization might result in the determination of N.\n"}
+{"abstract": "  This paper studies the stability and $\\mathcal{H}_{\\infty}$ performance\nanalysis problem for linear networked and quantized control systems with both\ncommunication delays random packet losses. To deal with the network-induced\nuncertainties and random packet dropouts, a novel discrete-time stochastic\nsystem model is developed for continuous-time networked control systems, and\nfurther overapproximated via a polytopic system with norm-bounded uncertainty.\nBased on the overapproximated system model, sufficient conditions are\nestablished for linear networked and quantized control systems in different\ncases to guarantee input-to-state stability and $\\mathcal{H}_{\\infty}$\nperformance with respect to the network-induced errors. Finally, a numerical\nexample is presented to illustrated the developed results.\n"}
+{"abstract": "  We provide out-of-sample certificates on the controlled invariance property\nof a given set with respect to a class of black-box linear systems.\nSpecifically, we consider linear time-invariant models whose state space\nmatrices are known only to belong to a certain family due to a possibly inexact\nquantification of some parameters. By exploiting a set of realizations of those\nundetermined parameters, verifying the controlled invariance property of the\ngiven set amounts to a linear program, whose feasibility allows us to establish\nan a-posteriori probabilistic certificate on the controlled invariance property\nof such a set with respect to the nominal linear time-invariant dynamics. The\nproposed framework is applied to the control of a networked multi-agent system\nwith unknown weighted graph.\n"}
+{"abstract": "  We present TES, a new n-body integration code for the accurate and rapid\npropagation of planetary systems in the presence of close encounters. TES\nbuilds upon the classic Encke method and integrates only the perturbations to\nKeplerian trajectories to reduce both the error and runtime of simulations.\nVariable step size is used throughout to enable close encounters to be\nprecisely handled. A suite of numerical improvements are presented that\ntogether makes TES optimal in terms of energy error. Lower runtimes are found\nin all test problems considered when compared to direct integration using\nias15. TES is freely available.\n"}
+{"abstract": "  Semantic parsing maps natural language (NL) utterances into logical forms\n(LFs), which underpins many advanced NLP problems. Semantic parsers gain\nperformance boosts with deep neural networks, but inherit vulnerabilities\nagainst adversarial examples. In this paper, we provide the empirical study on\nthe robustness of semantic parsers in the presence of adversarial attacks.\nFormally, adversaries of semantic parsing are considered to be the perturbed\nutterance-LF pairs, whose utterances have exactly the same meanings as the\noriginal ones. A scalable methodology is proposed to construct robustness test\nsets based on existing benchmark corpora. Our results answered five research\nquestions in measuring the sate-of-the-art parsers' performance on robustness\ntest sets, and evaluating the effect of data augmentation.\n"}
+{"abstract": "  In this paper, we address the multi-robot collaborative perception problem,\nspecifically in the context of multi-view infilling for distributed semantic\nsegmentation. This setting entails several real-world challenges, especially\nthose relating to unregistered multi-agent image data. Solutions must\neffectively leverage multiple, non-static, and intermittently-overlapping RGB\nperspectives. To this end, we propose the Multi-Agent Infilling Network: an\nextensible neural architecture that can be deployed (in a distributed manner)\nto each agent in a robotic swarm. Specifically, each robot is in charge of\nlocally encoding and decoding visual information, and an extensible neural\nmechanism allows for an uncertainty-aware and context-based exchange of\nintermediate features. We demonstrate improved performance on a realistic\nmulti-robot AirSim dataset.\n"}
+{"abstract": "  In autonomous driving, goal-based multi-trajectory prediction methods are\nproved to be effective recently, where they first score goal candidates, then\nselect a final set of goals, and finally complete trajectories based on the\nselected goals. However, these methods usually involve goal predictions based\non sparse predefined anchors. In this work, we propose an anchor-free model,\nnamed DenseTNT, which performs dense goal probability estimation for trajectory\nprediction. Our model achieves state-of-the-art performance, and ranks 1st on\nthe Waymo Open Dataset Motion Prediction Challenge. Project page is at\nhttps://github.com/Tsinghua-MARS-Lab/DenseTNT.\n"}
+{"abstract": "  This work provides new results for the analysis of random sequences in terms\nof $\\ell_p$-compressibility. The results characterize the degree in which a\nrandom sequence can be approximated by its best $k$-sparse version under\ndifferent rates of significant coefficients (compressibility analysis). In\nparticular, the notion of strong $\\ell_p$-characterization is introduced to\ndenote a random sequence that has a well-defined asymptotic limit (sample-wise)\nof its best $k$-term approximation error when a fixed rate of significant\ncoefficients is considered (fixed-rate analysis). The main theorem of this work\nshows that the rich family of asymptotically mean stationary (AMS) processes\nhas a strong $\\ell_p$-characterization. Furthermore, we present results that\ncharacterize and analyze the $\\ell_p$-approximation error function for this\nfamily of processes. Adding ergodicity in the analysis of AMS processes, we\nintroduce a theorem demonstrating that the approximation error function is\nconstant and determined in closed-form by the stationary mean of the process.\nOur results and analyses contribute to the theory and understanding of\ndiscrete-time sparse processes and, on the technical side, confirm how\ninstrumental the point-wise ergodic theorem is to determine the compressibility\nexpression of discrete-time processes even when stationarity and ergodicity\nassumptions are relaxed.\n"}
+{"abstract": "  We develop a Van der Waerden type theorem in an axiomatic setting of graded\nlattices and show that this axiomatic formulation can be applied to various\nlattices, for instance the set partition and the Boolean lattices. We derive\nthe Hales-Jewett theorem as a corollary.\n"}
+{"abstract": "  Unmanned aerial vehicles (UAVs) are becoming largely ubiquitous with an\nincreasing demand for aerial data. Accurate navigation and localization,\nrequired for precise data collection in many industrial applications, often\nrelies on RTK GNSS. These systems, able of centimeter-level accuracy, require a\nsetup and calibration process and are relatively expensive. This paper\naddresses the problem of accurate positioning and navigation of UAVs through\ncooperative localization. Inexpensive ultra-wideband (UWB) transceivers\ninstalled on both the UAV and a support ground robot enable centimeter-level\nrelative positioning. With fast deployment and wide setup flexibility, the\nproposed system is able to accommodate different environments and can also be\nutilized in GNSS-denied environments. Through extensive simulations and test\nfields, we evaluate the accuracy of the system and compare it to GNSS in urban\nenvironments where multipath transmission degrades accuracy. For completeness,\nwe include visual-inertial odometry in the experiments and compare the\nperformance with the UWB-based cooperative localization.\n"}
+{"abstract": "  We propose a simple statistical-physics-inspired model for the effect of\nintrinsic fluctuations on supply and demand in markets. The model consists of\nagents that trade in two types of goods of which the total number is separately\nconserved. The relative preference of an individual agent for the two types of\ngoods is determined by a utility that is identical for all agents. Market\nsupply and demand curves are computed and compared for various motivated\nchoices of the distribution of goods over the agents. In particular, we compare\nthe \"mean-field\" case, in which all agents have the same number of goods and\nthat is akin to the economics textbook case, to the case of Boltzmann-Gibbs\ndistributed goods, in which agents have a fluctuating number of goods. We find\nthat the resulting equilibrium prices are not equal for these two approaches,\nespecially when a large fraction of the agents can neither buy nor sell.\n"}
+{"abstract": "  We study the evolution of states of an infinite system of point particles\ndwelling in a locally compact Polish space $X$. Each particle produces at\nrandom a finite `cloud' of offsprings distributed over $X$ according to some\nlaw, and disappears afterwards. The system's states are probability measures on\nan appropriate space of locally finite counting measures on $X$. Their\nevolution is obtained by solving the corresponding Fokker-Planck equation. We\nprove that this equation has a unique solution and discuss some of its\nproperties. Our pivotal idea of dealing with infinite systems consists in\npassing to tempered counting measures by imposing appropriate restrictions on\nthe branching. In this approach, we first solve a nonlinear evolution equation\nin the space of bounded continuous functions on $X$ -- so called log-Laplace\nequation. Next we solve the Kolmogorov equation which is then used to solve the\nFokker-Planck equation and thus describe the evolution in question.\n"}
+{"abstract": "  Document-level MT models are still far from satisfactory. Existing work\nextend translation unit from single sentence to multiple sentences. However,\nstudy shows that when we further enlarge the translation unit to a whole\ndocument, supervised training of Transformer can fail. In this paper, we find\nsuch failure is not caused by overfitting, but by sticking around local minima\nduring training. Our analysis shows that the increased complexity of\ntarget-to-source attention is a reason for the failure. As a solution, we\npropose G-Transformer, introducing locality assumption as an inductive bias\ninto Transformer, reducing the hypothesis space of the attention from target to\nsource. Experiments show that G-Transformer converges faster and more stably\nthan Transformer, achieving new state-of-the-art BLEU scores for both\nnon-pretraining and pre-training settings on three benchmark datasets.\n"}
+{"abstract": "  Given the abundance and ease of access of personal data today, individual\nprivacy has become of paramount importance, particularly in the healthcare\ndomain. In this work, we aim to utilise patient data extracted from multiple\nhospital data centres to train a machine learning model without sacrificing\npatient privacy. We develop a scheduling algorithm in conjunction with a\nstudent-teacher algorithm that is deployed in a federated manner. This allows a\ncentral model to learn from batches of data at each federal node. The teacher\nacts between data centres to update the main task (student) algorithm using the\ndata that is stored in the various data centres. We show that the scheduler,\ntrained using meta-gradients, can effectively organise training and as a result\ntrain a machine learning model on a diverse dataset without needing explicit\naccess to the patient data. We achieve state-of-the-art performance and show\nhow our method overcomes some of the problems faced in the federated learning\nsuch as node poisoning. We further show how the scheduler can be used as a\nmechanism for transfer learning, allowing different teachers to work together\nin training a student for state-of-the-art performance.\n"}
+{"abstract": "  We consider mean field game systems in time-horizon $(0,T)$, where the\nindividual cost functional depends locally on the density distribution of the\nagents, and the Hamiltonian is locally uniformly convex. We show that, even if\nthe coupling cost functions are mildly non-monotone, then the system is still\nwell posed due to the effect of individual noise. The rate of anti-monotonicity\n(i.e. the aggregation rate of the cost functions) which can be afforded depends\non the intensity of the diffusion and on global bounds of solutions. We give\napplications to either the case of globally Lipschitz Hamiltonians or the case\nof quadratic Hamiltonians and couplings having mild growth.\n  Under similar conditions, we investigate the long time behavior of solutions\nand we give a complete description of the ergodic and long term properties of\nthe system. In particular we prove: (i) the turnpike property of solutions in\nthe finite (long) horizon $(0,T)$, (ii) the convergence of the system in\n$(0,T)$ towards the system in $(0,\\infty)$, (iii) the vanishing discount limit\nof the infinite horizon problem and the long time convergence towards the\nergodic stationary solution.\n  This way we extend previous results which were known only for the case of\nmonotone and smoothing couplings; our approach is self-contained and does not\nneed the use of the linearized system or of the master equation.\n"}
+{"abstract": "  We propose the Lie-algebraic interpretation of poly-analytic functions in\n$L_2(\\C,d\\mu)$, with the Gaussian measure $d\\mu$, based on a flag structure\nformed by the representation spaces of the $\\mathfrak{sl}(2)$-algebra realized\nby differential operators in $z$ and $\\bar z$. Following the pattern of the\none-dimensional situation, we define poly-Fock spaces in $d$ complex variables\nin a Lie-algebraic way, as the invariant spaces for the action of generators of\na certain Lie algebra. In addition to the basic case of the algebra\n$\\mathfrak{sl}(d+1)$, we consider also the family of algebras\n$\\mathfrak{sl}(m_1+1) \\otimes \\ldots \\otimes \\mathfrak{sl}(m_n+1)$ for tuples\n$\\mathbf{m} = (m_1,m_2,\\ldots,m_n)$ of positive integers whose sum is equal to\n$d$.\n"}
+{"abstract": "  Deep reinforcement learning (DRL) is a promising way to achieve human-like\nautonomous driving. However, the low sample efficiency and difficulty of\ndesigning reward functions for DRL would hinder its applications in practice.\nIn light of this, this paper proposes a novel framework to incorporate human\nprior knowledge in DRL, in order to improve the sample efficiency and save the\neffort of designing sophisticated reward functions. Our framework consists of\nthree ingredients, namely expert demonstration, policy derivation, and\nreinforcement learning. In the expert demonstration step, a human expert\ndemonstrates their execution of the task, and their behaviors are stored as\nstate-action pairs. In the policy derivation step, the imitative expert policy\nis derived using behavioral cloning and uncertainty estimation relying on the\ndemonstration data. In the reinforcement learning step, the imitative expert\npolicy is utilized to guide the learning of the DRL agent by regularizing the\nKL divergence between the DRL agent's policy and the imitative expert policy.\nTo validate the proposed method in autonomous driving applications, two\nsimulated urban driving scenarios (unprotected left turn and roundabout) are\ndesigned. The strengths of our proposed method are manifested by the training\nresults as our method can not only achieve the best performance but also\nsignificantly improve the sample efficiency in comparison with the baseline\nalgorithms (particularly 60\\% improvement compared to soft actor-critic). In\ntesting conditions, the agent trained by our method obtains the highest success\nrate and shows diverse and human-like driving behaviors as demonstrated by the\nhuman expert.\n"}
+{"abstract": "  Each p-ring class field K(f) modulo a p-admissible conductor f over a\nquadratic base field K with p-ring class rank r(f) mod f is classified\naccording to Galois cohomology and differential principal factorization type of\nall members of its associated heterogeneous multiplet\nM(K(f))=[(N(c,i))_{1<=i<=m(c)}]_{c|f} of dihedral fields N(c,i) with various\nconductors c|f having p-multiplicities m(c) over K such that sum_{c|f}\nm(c)=(p^r(f)-1)/(p-1). The advanced viewpoint of classifying the entire\ncollection M(K(f)), instead of its individual members separately, admits\nconsiderably deeper insight into the class field theoretic structure of ring\nclass fields, and the actual construction of the multiplet M(K(f)) is enabled\nby exploiting the routines for abelian extensions in the computational algebra\nsystem Magma.\n"}
+{"abstract": "  We consider the problem of decentralized optimization over time-varying\ndirected networks. The network nodes can access only their local objectives,\nand aim to collaboratively minimize a global function by exchanging messages\nwith their neighbors. Leveraging sparsification, gradient tracking and\nvariance-reduction, we propose a novel communication-efficient decentralized\noptimization scheme that is suitable for resource-constrained time-varying\ndirected networks. We prove that in the case of smooth and strongly-convex\nobjective functions, the proposed scheme achieves an accelerated linear\nconvergence rate. To our knowledge, this is the first decentralized\noptimization framework for time-varying directed networks that achieves such a\nconvergence rate and applies to settings requiring sparsified communication.\nExperimental results on both synthetic and real datasets verify the theoretical\nresults and demonstrate efficacy of the proposed scheme.\n"}
+{"abstract": "  A set $R \\subseteq V(G)$ is a resolving set of a graph $G$ if for all\ndistinct vertices $v,u \\in V(G)$ there exists an element $r \\in R$ such that\n$d(r,v) \\neq d(r,u)$. The metric dimension $\\dim(G)$ of the graph $G$ is the\nminimum cardinality of a resolving set of $G$. A resolving set with cardinality\n$\\dim(G)$ is called a metric basis of $G$. We consider vertices that are in all\nmetric bases, and we call them basis forced vertices. We give several\nstructural properties of sparse and dense graphs where basis forced vertices\nare present. In particular, we give bounds for the maximum number of edges in a\ngraph containing basis forced vertices. Our bound is optimal whenever the\nnumber of basis forced vertices is even. Moreover, we provide a method of\nconstructing fairly sparse graphs with basis forced vertices. We also study\nvertices which are in no metric basis in connection to cut-vertices and\npendants. Furthermore, we show that deciding whether a vertex is in all metric\nbases is co-NP-hard, and deciding whether a vertex is in no metric basis is\nNP-hard.\n"}
+{"abstract": "  We consider an observational learning model with exogenous public payoff\nshock. We show that confounded learning doesn't arise for almost all private\nsignals and almost all shocks, even if players have sufficiently divergent\npreferences.\n"}
+{"abstract": "  Recent work in data-driven control has revived behavioral theory to perform a\nvariety of complex control tasks, by directly plugging libraries of past\ninput-output trajectories into optimal control problems. Despite recent\nadvances, two key aspects remain unclear when the data are corrupted by noise:\nhow can safety be guaranteed, and to what extent is the control performance\naffected? In this work, we provide a quantitative answer to these questions. In\nparticular, we formulate a robustly safe version of the recently introduced\nBehavioral Input-Output Parametrization (BIOP) for the optimal predictive\ncontrol of unknown constrained systems. The proposed framework has three main\nadvantages: 1) it allows one to safely operate the system while explicitly\nquantifying, as a function of the noise level corrupting the data, how much the\nperformance degrades, 2) it can be used in combination with state-of-the-art\nimpulse response estimators, and finally, being a data-driven approach, 3) the\nstate-space parameters and the initial state need not be specified for\ncontroller synthesis. We corroborate our results through numerical experiments.\n"}
+{"abstract": "  Cognitive diagnosis models (CDMs) are a popular tool for assessing students'\nmastery of sets of skills. Given a set of $K$ skills tested on an assessment,\nstudents are classified into one of $2^K$ latent skill set profiles that\nrepresent whether they have mastered each skill or not. Traditional approaches\nto estimating these profiles are computationally intensive and become\ninfeasible on large datasets. Instead, proxy skill estimates can be generated\nfrom the observed responses and then clustered, and these clusters can be\nassigned to different profiles. Building on previous work, we consider how to\noptimally perform this clustering when not all $2^K$ profiles are possible,\ne.g. because of hierarchical relationships among the skills, and when not all\npossible profiles are present in the population. We compare hierarchical\nclustering and several k-means variants, including semisupervised clustering\nusing simulated student responses. The empty k-means algorithm paired with a\nnovel method for generating starting centers yields the best overall\nperformance.\n"}
+{"abstract": "  Let $F$ be a totally real field and let $E/F$ be a CM quadratic extension. We\nconstruct a $p$-adic $L$-function attached to Hida families for the group\n$GL_{2/F} \\times GL_{1/E}$. It interpolates critical Rankin--Selberg $L$-values\nat all classical points corresponding to representations $\\pi\\boxtimes \\chi$\nwith the weights of $\\chi$ smaller than the weights of $\\pi$. This confirms a\nconjecture of the author, making related results on the $p$-adic Beilinson\nconjecture and its Iwasawa-theoretic variants unconditional.\n  Our $p$-adic $L$-function agrees with a previous result of Hida when $E/F$\nsplits above $p$, and it is new otherwise. We build it as a ratio of families\nof global and local Waldspurger zeta integrals, the latter constructed using\nthe local Langlands correspondence in families.\n  In an appendix of possibly independent recreational interest, we give a\nreality-TV-inspired proof of an identity concerning double factorials.\n"}
+{"abstract": "  Local Group satellite galaxies show a wide diversity of star formation\nhistories (SFHs) whose origin is yet to be fully understood. Using\nhydrodynamical simulations from the Constrained Local UniversE project, we\nstudy the SFHs of satellites of Milky Way-like galaxies in a cosmological\ncontext: while in the majority of the cases the accretion onto their host\ngalaxy causes the satellites to lose their gas, with a subsequent suppression\nin SF, in about 25$\\%$ of our sample we observe a clear enhancement of SF after\ninfall. Peaks in SF clearly correlate with the satellite pericentric passage\naround its host and, in one case, with a satellite-satellite interaction. We\nidentify two key ingredients that result in enhanced SF after infall: galaxies\nmust enter the host's virial radius with a reservoir of cold gas $M_{\\rm\ngas,inf}/M_{\\rm vir,inf}\\gtrsim 10^{-2}$ and with a minimum pericentric\ndistance $\\gtrsim$10 kpc (mean distance $\\sim$50 kpc for the full sample), in\norder to form new stars due to compression of cold gas at pericentric passage.\nOn the other hand, satellites that infall with little gas or whose pericentric\ndistance is too small, have their gas ram-pressure stripped and subsequent SF\nquenched. The pericentric passage of satellites likewise correlates with SF\npeaks in their hosts, suggesting that this mechanism induces bursts of SF in\nsatellites and central galaxies alike, in agreement with recent studies of our\nGalaxy's SFH. Our findings can explain the recently reported multiple stellar\npopulations observed in dwarf galaxies such as Carina and Fornax, and should be\ntaken into account in semi-analytic models of galaxy formation and satellite\nquenching.\n"}
+{"abstract": "  We use multiplicative calculus and obtain necessary and sufficient conditions\nunder which convergence of sequences in $\\mathbb{R}^+$ follows from convergence\nof weighted geometric means. Besides, we introduce some new types of\nconvergence for sequences of intuitionistic fuzzy numbers(IFNs) and apply the\nobtained conditions to achieve the convergence of sequences of IFNs.\n"}
+{"abstract": "  The [OIII] 88 $\\mu$m fine structure emission line has been detected into the\nEpoch of Reionization (EoR) from star-forming galaxies at redshifts $6 < z\n\\lesssim 9$ with ALMA. These measurements provide valuable information\nregarding the properties of the interstellar medium (ISM) in the highest\nredshift galaxies discovered thus far. The [OIII] 88 $\\mu$m line observations\nleave, however, a degeneracy between the gas density and metallicity in these\nsystems. Here we quantify the prospects for breaking this degeneracy using\nfuture ALMA observations of the [\\oiii] 52 $\\mu$m line. Among the current set\nof ten [OIII] 88 $\\mu$m emitters at $6 < z \\lesssim 9$, we forecast 52 $\\mu$m\ndetections (at 6-$\\sigma$) in SXDF-NB1006-2, B14-6566, J0217-0208, and\nJ1211-0118 within on-source observing times of 2-10 hours, provided their gas\ndensities are larger than about $n_{\\mathrm{H}} \\gtrsim 10^2-10^3$ cm$^{-3}$.\nOther targets generally require much longer integration times for a 6-$\\sigma$\ndetection. Either successful detections of the 52 $\\mu$m line, or reliable\nupper limits, will lead to significantly tighter constraints on ISM parameters.\nThe forecasted improvements are as large as $\\sim 3$ dex in gas density and\n$\\sim 1$ dex in metallicity for some regions of parameter space. We suggest\nSXDF-NB1006-2 as a promising first target for 52 $\\mu$m line measurements. We\ndiscuss how such measurements will help in understanding the mass metallicity\nrelationship during the EoR.\n"}
+{"abstract": "  The dynamics of social relations and the possibility of reaching the state of\nstructural balance (Heider balance) are discussed for various topologies of the\nnetworks of interacting actors under the influence of the temperature modeling\nthe social noise level. For that purpose, two main types of lattices are\nconsidered. The first is created by removing some links from a regular\ntriangular lattice to produce a diluted triangular lattice or adding more links\nto create an enhanced triangular lattice. The second is the classical random\ngraph. In both those cases, the full range of possible graph densities is\ndiscussed, limited by the extreme cases of networks which consist of a small\nnumber of separated triads and fully connected networks. The performed\nsimulations indicate that for random lattices a transition between the balanced\nand imbalanced states is always possible and occurs at the critical temperature\nwhich depends on the graph density. On the other hand, it is shown that this\ntransition and existence of the balanced state are not possible for networks\nbased on the diluted triangular lattices if the average node degree is too\nclose to the value characterizing the regular triangular lattice. The critical\ntemperatures are size-independent only for the diluted triangular lattices and\ndepend on the size of the system for the enhanced triangular lattices and\nrandom graphs.\n"}
+{"abstract": "  Directing a jet of humid air to impinge on a surface that is cooled below the\ndew point results in micro-sized water droplets. Lord Rayleigh discussed the\nphenomenon called such behavior Breath Figures (BF). Historically, utilizing\ndew as a water source was investigated by several scientists dating back to\nAristotle. However, due to the degrading effects of air as a non-condensable\ngas (NCG) such efforts are limited to small scale water production systems.\nRecently, the concept of BF has been utilized extensively in the generation of\nmicro-scale polymer patterns as a self-assembly process. However, the\ngeneration of BF on surfaces while being impinged by a humid air jet has not\nbeen quantified. In this work, we illustrate that a BF spot generated on a\ncooled surface is a manifestation of a recovery concentration. The concept is\nanalogous to the concept of adiabatic-wall temperature defined for heat\ntransfer applications. Upon closer examination of the vapor concentration\ndistribution on a cooled impinged surface, we found that the distribution\nexhibits distinct regimes depending on the radial location from the center of\nthe impingement region. The first regime is confined within the impingement\nregion, whereas the second regime lies beyond this radial location including\nthe wall jet region. Scaling analysis as well as numerical solution of the\nformer regime shows that the maximum concentration on the surface is equivalent\nto its counterpart of a free unbounded jet with similar geometrical conditions.\nAdditionally, the scaling analysis of the latter regime reveals that the jet\nspeed and standoff distance are not important in determining the recovery\nconcentration. However, the recovery concentration is found to vary\nmonotonically with the radial location. Our conclusions are of great importance\nin optimizing jet impingement where condensation phase change is prevalent.\n"}
+{"abstract": "  In this paper, we derive a general formula for the quantized fractional\ncorner charge in two-dimensional C_n-symmetric higher-order topological\ninsulators. We assume that the electronic states can be described by the\nWannier functions and that the edges are charge neutral, but we do not assume\nvanishing bulk electric polarization. We expand the scope of the corner charge\nformula obtained in previous works by considering more general surface\nconditions, such as surfaces with higher Miller index and surfaces with surface\nreconstruction. Our theory is applicable even when the electronic states are\nlargely modulated near system boundaries. It also applies to insulators with\nnon-vanishing bulk polarization, and we find that in such cases the value of\nthe corner charge depends on the surface termination even for the same bulk\ncrystal with C_3 or C_4 symmetry, via a difference in the Wyckoff position of\nthe center of the C_n-symmetric crystal.\n"}
+{"abstract": "  Given a graph $G$, a function $c:V(G)\\longrightarrow \\{1,\\ldots,k\\}$ with the\nproperty that $c(u)=c(v)=i$ implies that the distance between $u$ and $v$ is\ngreater than $i$, is called a $k$-packing coloring of $G$. The smallest integer\n$k$ for which there exists a $k$-packing coloring of $G$ is called the packing\nchromatic number of $G$, and is denoted by $\\chi_\\rho$. Packing chromatic\nvertex-critical graphs are the graphs $G$ for which $\\chi_\\rho(G-x) <\n\\chi_\\rho(G)$ holds for every vertex $x$ of $G$. A graph $G$ is called a\npacking chromatic critical graph if for every proper subgraph $H$ of $G$,\n$\\chi_\\rho(H) < \\chi_\\rho(G)$. Both of the mentioned variations of critical\ngraphs with respect to the packing chromatic number have already been studied.\nAll packing chromatic (vertex-)critical graphs $G$ with $\\chi_\\rho(G)=3$ were\ncharacterized, while there were known only partial results for graphs $G$ with\n$\\chi_\\rho(G)=4$. In this paper, we provide characterizations of all packing\nchromatic vertex-critical graphs $G$ with $\\chi_\\rho(G)=4$ and all packing\nchromatic critical graphs $G$ with $\\chi_\\rho(G)=4$.\n"}
+{"abstract": "  We present polarimetric observations of the Keyhole Nebula in the Carina\nNebula Complex carried out using the Stratospheric Observatory for Infrared\nAstronomy. The Keyhole Nebula located to the west of $\\eta$ Carinae is believed\nto be disturbed by the stellar winds from the star. We observed the Keyhole\nNebula at 89 $\\mu$m wavelength with the HAWC+ instrument. The observations\ncover the entire Keyhole Nebula spanning 8$'$ by 5$'$ with central position RA\n= 10:44:43 and Dec = -59:38:04. The typical uncertainty of polarization\nmeasurement is less than 0.5\\% in the region with intensity above 5,500 MJy\nsr$^{-1}$. The polarization has a mean of 2.4\\% with a standard deviation of\n1.6\\% in the region above this intensity, similar to values in other high--mass\nstar--forming regions. The magnetic field orientation in the bar--shaped\nstructure is similar to the large--scale magnetic field orientation. On the\nother hand, the magnetic field direction in the loop is not aligned with the\nlarge--scale magnetic fields but has tight alignment with the loop itself.\nAnalysis of the magnetic field angles and the gas turbulence suggests that the\nfield strength is $\\sim$70 $\\mu$G in the loop. A simple comparison of the\nmagnetic field tension to the ram pressure of $\\eta$ Carinae's stellar wind\nsuggests that the magnetic fields in the Keyhole Nebula are not strong enough\nto maintain the current structure against the impact of the stellar wind, and\nthat the role of the magnetic field in resisting stellar feedback in the\nKeyhole Nebula is limited.\n"}
+{"abstract": "  In this work, we study the relation of the eikonal quasinormal modes (EQNMs)\nand the unstable fundamental photon orbits (UFPOs) in the Kerr-Newman\nspacetime. We find that in the eikonal limit the gravitational and\nelectromagnetic perturbations of the Kerr-Newman black hole are naturally\ndecoupled, and a single one-dimensional Schr\\\"odinger-like equation encoding\nthe QNM spectrum can be derived. We then show that the decoupled Teukolsky\nmaster equation and the Klein-Gordon equation for the massless scalar field in\nthe Kerr-Newman spacetime are of the same form in the eikonal limit. As a\ndirect consequence, taking into account of the boundary conditions for EQNMs we\nshow an exact correspondence between EQNMs and UFPOs, that is, EQNM/UFPO\ncorrespondence. More precisely, similar to the Kerr case, the real part of\nEQNM's frequency is a linear combination of the precessional and (polar)\norbital frequencies, while the imaginary part of the frequency is proportional\nto the Lyapunov exponent of the UFPO.\n"}
+{"abstract": "  We report on a windowless, high-density, gas flow target at Jefferson Lab\nthat was used to measure $r_p$, the root-mean-square charge radius of the\nproton. To our knowledge, this is the first such system used in a fixed-target\nexperiment at a (non-storage ring) electron accelerator. The target achieved\nits design goal of an areal density of 2$\\times$10$^{18}$ atoms/cm$^2$, with\nthe gas uniformly distributed over the 4 cm length of the cell and less than 1%\nresidual gas outside the cell. This design eliminated scattering from the end\ncaps of the target cell, a problem endemic to previous measurements of the\nproton charge radius in electron scattering experiments, and permitted a\nprecise, model-independent extraction of $r_p$ by reaching unprecedentedly low\nvalues of $Q^2$, the square of the electron's transfer of four-momentum to the\nproton.\n"}
+{"abstract": "  The undecidability of basic decision problems for general FIFO machines such\nas reachability and unboundedness is well-known. In this paper, we provide an\nunderapproximation for the general model by considering only runs that are\ninput-bounded (i.e.\\ the sequence of messages sent through a particular channel\nbelongs to a given bounded language). We prove, by reducing this model to a\ncounter machine with restricted zero tests, that the rational-reachability\nproblem (and by extension, control-state reachability, unboundedness, deadlock,\netc.) is decidable. This class of machines subsumes input-letter-bounded\nmachines, flat machines, linear FIFO nets, and monogeneous machines, for which\nsome of these problems were already shown to be decidable. These theoretical\nresults can form the foundations to build a tool to verify general FIFO\nmachines based on the analysis of input-bounded machines.\n"}
+{"abstract": "  Let $S$ be an unramified regular local ring of mixed characteristic two and\n$R$ the integral closure of $S$ in a biquadratic extension of its quotient\nfield obtained by adjoining roots of sufficiently general square free elements\n$f,g\\in S$. Let $S^2$ denote the subring of $S$ obtained by lifting to $S$ the\nimage of the Frobenius map on $S/2S$. When at least one of $f,g\\in S^2$, we\ncharacterize the Cohen-Macaulayness of $R$ and show that $R$ admits a\nbirational small Cohen-Macaulay module. It is noted that $R$ is not\nautomatically Cohen-Macaulay in case $f,g\\in S^2$ or if $f,g\\notin S^2$.\n"}
+{"abstract": "  Given a nonnegative, smooth potential $V: \\mathbb{R}^k \\to \\mathbb{R}$ ($k\n\\geq 2$) with multiple zeros, we say that a curve $\\mathfrak{q}: \\mathbb{R} \\to\n\\mathbb{R}^k$ is a connecting orbit if it solves the autonomous system of\nordinary differential equations \\begin{equation} \\mathfrak{q}''=\n\\nabla_{\\mathbf{u}} V(\\mathfrak{q}) , \\hspace{2mm} \\mbox{ in } \\mathbb{R}\n\\end{equation} and tends to a zero of $V$ at $\\pm \\infty$. Broadly, our goal is\nto study the existence of connecting orbits for the problem above using\nvariational methods. Despite the rich previous literature concerning the\nexistence of connecting orbits for other types of second order systems, to our\nknowledge only connecting orbits which minimize the associated energy\nfunctional in a suitable function space were proven to exist for autonomous\nmulti-well potentials. The contribution of this paper is to provide, for a\nclass of such potentials, some existence results regarding non-minimizing\nconnecting orbits. Our results are closely related to the ones in the same\nspirit obtained by J. Bisgard in his PhD thesis (University of\nWisconsin-Madison, 2005), where non-autonomous periodic multi-well potentials\n(ultimately excluding autonomous potentials) are considered. Our approach is\nbased on several refined versions of the classical Mountain Pass Lemma.\n"}
+{"abstract": "  We investigate the problem of determining the shape of a rotating celestial\nobject - e.g., a comet or an asteroid - under its own gravitational field. More\nspecifically, we consider an object symmetric with respect to one axis - such\nas a dumbbell - that rotates around a second axis perpendicular to the symmetry\naxis. We assume that the object can be modeled as an incompressible fluid of\nconstant mass density, which is regarded as a first approximation of an\naggregate of particles.\n  In the literature, the gravitational field of a body is often described as a\nmultipolar expansion involving spherical coordinates (Kaula, 1966). In this\nwork we describe the shape in terms of cylindrical coordinates, which are most\nnaturally adapted to the symmetry of the body, and we express the gravitational\npotential generated by the rotating body as a simple formula in terms of\nelliptic integrals. An equilibrium shape occurs when the gravitational\npotential energy and the rotational kinetic energy at the surface of the body\nbalance each other out. Such an equilibrium shape can be derived as a solution\nof an optimization problem, which can be found via the variational method. We\ngive an example where we apply this method to a two-parameter family of\ndumbbell shapes, and find approximate numerical solutions to the corresponding\noptimization problem.\n"}
+{"abstract": "  Recent experiments have elucidated that novel nonequilibrium states inherent\nin the so-called hydrodynamic regime are realized in ultrapure metals with\nsufficiently strong momentum-conserving scattering. In this letter, we\nformulate a theory of electron hydrodynamics with broken inversion symmetry\nunder magnetic fields and find that novel terms emerge in hydrodynamic\nequations which play a crucial role for the realization of the nonreciprocal\nresponses. Specifically, we clarify that there exist a novel type of\nnonreciprocal collective modes dubbed nonreciprocal surface magnetoplasmons\narising from an interplay between magnetic fields and the orbital magnetic\nmoment. We reveal that these nonreciprocal collective modes indeed give rise to\nthe nonreciprocity in magneto-optical responses such as the reflectivity. The\nphysics discussed here will bridge the two important notions of\nmagnetoplasmonics and nonreciprocity in electron hydrodynamic materials with\ninversion symmetry breaking and magnetic fields.\n"}
+{"abstract": "  A remarkable approach for grasping the relevant statistical features of real\nnetworks with the help of random graphs is offered by hyperbolic models,\ncentred around the idea of placing nodes in a low-dimensional hyperbolic space,\nand connecting node pairs with a probability depending on the hyperbolic\ndistance. It is widely appreciated that these models can generate random graphs\nthat are small-world, highly clustered and scale-free at the same time; thus,\nreproducing the most fundamental common features of real networks. In the\npresent work, we focus on a less well-known property of the\npopularity-similarity optimisation (PSO) model and the\n$\\mathbb{S}^1/\\mathbb{H}^2$ model from this model family, namely that the\nnetworks generated by these approaches also contain communities for a wide\nrange of the parameters, which was certainly not an intention at the design of\nthe models. We extracted the communities from the studied networks using\nwell-established community finding methods such as Louvain, Infomap and label\npropagation. The observed high modularity values indicate that the community\nstructure can become very pronounced under certain conditions. In addition, the\nmodules found by the different algorithms show good consistency, implying that\nthese are indeed relevant and apparent structural units. Since the appearance\nof communities is rather common in networks representing real systems as well,\nthis feature of hyperbolic models makes them even more suitable for describing\nreal networks than thought before.\n"}
+{"abstract": "  In this paper, we investigate the state estimation problem over multiple\nMarkovian packet drop channels. In this problem setup, a remote estimator\nreceives measurement data transmitted from multiple sensors over individual\nchannels. By the method of Markovian jump linear systems, an optimal stationary\nestimator that minimizes the error variance in the steady state is obtained,\nbased on the mean-square (MS) stabilizing solution to the coupled algebraic\nRiccati equations. An explicit necessary and sufficient condition is derived\nfor the existence of the MS stabilizing solution, which coincides with that of\nthe standard Kalman filter. More importantly, we provide a sufficient condition\nunder which the MS detectability with multiple Markovian packet drop channels\ncan be decoupled, and propose a locally optimal stationary estimator but\ncomputationally more tractable. Analytic sufficient and necessary MS\ndetectability conditions are presented for the decoupled subsystems\nsubsequently. Finally, numerical simulations are conducted to illustrate the\nresults on the MS stabilizing solution, the MS detectability, and the\nperformance of the optimal and locally optimal stationary estimators.\n"}
+{"abstract": "  While Shapley Values (SV) are one of the gold standard for interpreting\nmachine learning models, we show that they are still poorly understood, in\nparticular in the presence of categorical variables or of variables of low\nimportance. For instance, we show that the popular practice that consists in\nsumming the SV of dummy variables is false as it provides wrong estimates of\nall the SV in the model and implies spurious interpretations. Based on the\nidentification of null and active coalitions, and a coalitional version of the\nSV, we provide a correct computation and inference of important variables.\nMoreover, a Python library (All the experiments and simulations can be\nreproduced with the publicly available library Active Coalition of Variables,\nhttps://www.github.com/salimamoukou/acv00) that computes reliably conditional\nexpectations and SV for tree-based models, is implemented and compared with\nstate-of-the-art algorithms on toy models and real data sets.\n"}
+{"abstract": "  In this paper, it is shown that for a minimal system $(X,T)$ and $d,k\\in\n\\mathbb{N}$, if $(x,x_i)$ is regionally proximal of order $d$ for $1\\leq i\\leq\nk$, then $(x,x_1,\\ldots,x_k)$ is $(k+1)$-regionally proximal of order $d$.\n  Meanwhile, we introduce the notion of $\\mathrm{IN}^{[d]}$-pair: for a\ndynamical system $(X,T)$ and $d\\in \\mathbb{N}$, a pair $(x_0,x_1)\\in X\\times X$\nis called an $\\mathrm{IN}^{[d]}$-pair if for any $k\\in \\mathbb{N}$ and any\nneighborhoods $U_0 ,U_1 $ of $x_0$ and $x_1$ respectively, there exist integers\n$p_j^{(i)},1\\leq i\\leq k,$ $1\\leq j\\leq d$ such that $$ \\bigcup_{i=1}^k\\{\np_1^{(i)}\\epsilon(1)+\\ldots+p_d^{(i)} \\epsilon(d):\\epsilon(j)\\in \\{0,1\\},1\\leq\nj\\leq d\\}\\backslash \\{0\\}\\subset \\mathrm{Ind}(U_0,U_1), $$ where\n$\\mathrm{Ind}(U_0,U_1)$ denotes the collection of all independence sets for\n$(U_0,U_1)$. It turns out that for a minimal system, if it dose not contain any\nnontrivial $\\mathrm{IN}^{[d]}$-pair, then it is an almost one-to-one extension\nof its maximal factor of order $d$.\n"}
+{"abstract": "  Mortality forecasting plays a pivotal role in insurance and financial risk\nmanagement of life insurers, pension funds, and social securities. Mortality\ndata is usually high-dimensional in nature and favors factor model approaches\nto modelling and forecasting. This paper introduces a new forecast-driven\nhierarchical factor model (FHFM) customized for mortality forecasting. Compared\nto existing models, which only capture the cross-sectional variation or\ntime-serial dependence in the dimension reduction step, the new model captures\nboth features efficiently under a hierarchical structure, and provides insights\ninto the understanding of dynamic variation of mortality patterns over time. By\ncomparing with static PCA utilized in Lee and Carter 1992, dynamic PCA\nintroduced in Lam et al. 2011, as well as other existing mortality modelling\nmethods, we find that this approach provides both better estimation results and\nsuperior out-of-sample forecasting performance. Simulation studies further\nillustrate the advantages of the proposed model based on different data\nstructures. Finally, empirical studies using the US mortality data demonstrate\nthe implications and significance of this new model in life expectancy\nforecasting and life annuities pricing.\n"}
+{"abstract": "  We present a novel unsupervised framework for instance-level image-to-image\ntranslation. Although recent advances have been made by incorporating\nadditional object annotations, existing methods often fail to handle images\nwith multiple disparate objects. The main cause is that, during inference, they\napply a global style to the whole image and do not consider the large style\ndiscrepancy between instance and background, or within instances. To address\nthis problem, we propose a class-aware memory network that explicitly reasons\nabout local style variations. A key-values memory structure, with a set of\nread/update operations, is introduced to record class-wise style variations and\naccess them without requiring an object detector at the test time. The key\nstores a domain-agnostic content representation for allocating memory items,\nwhile the values encode domain-specific style representations. We also present\na feature contrastive loss to boost the discriminative power of memory items.\nWe show that by incorporating our memory, we can transfer class-aware and\naccurate style representations across domains. Experimental results demonstrate\nthat our model outperforms recent instance-level methods and achieves\nstate-of-the-art performance.\n"}
+{"abstract": "  This paper proposes a novel ternary hash encoding for learning to hash\nmethods, which provides a principled more efficient coding scheme with\nperformances better than those of the state-of-the-art binary hashing\ncounterparts. Two kinds of axiomatic ternary logic, Kleene logic and\n{\\L}ukasiewicz logic are adopted to calculate the Ternary Hamming Distance\n(THD) for both the learning/encoding and testing/querying phases. Our work\ndemonstrates that, with an efficient implementation of ternary logic on\nstandard binary machines, the proposed ternary hashing is compared favorably to\nthe binary hashing methods with consistent improvements of retrieval mean\naverage precision (mAP) ranging from 1\\% to 5.9\\% as shown in CIFAR10, NUS-WIDE\nand ImageNet100 datasets.\n"}
+{"abstract": "  We demonstrate an approach to obtaining near quantum limited far-field\nimaging resolution of thermal, incoherent sources with arbitrary distributions.\nOur method assumes no prior knowledge of the source distribution, but rather\nuses an adaptive approach to imaging via spatial-mode demultiplexing that\niteratively updates both the form of the spatial imaging modes and the estimate\nof the source distribution. The optimal imaging modes are determined by\nminimizing the estimated Cram\\'er-Rao bound over the manifold of all possible\nsets of orthogonal modes. We have observed through Monte Carlo simulations that\nthe manifold-optimized spatial mode demultiplexing measurement consistently\noutperforms standard imaging techniques in the resolution and precision of\nsource reconstructions, and approaches the quantum limits as set by the quantum\nCram\\'er-Rao bound. The adaptive framework presented here allows for a\nconsistent approach to achieving near quantum-limited imaging resolution of\narbitrarily distributed thermal sources.\n"}
+{"abstract": "  In the last decade, the growing influence of open source software has\nnecessitated the need to reduce the abstraction levels in hardware design. Open\nsource hardware significantly reduces the development time, increasing the\nprobability of first-pass success and enable developers to optimize software\nsolutions based on hardware features, thereby reducing the design costs. The\nrecent introduction of open source Process Development Kit (OpenPDK) by\nSkywater technologies in June 2020 has eliminated the barriers to\nApplication-Specific Integrated Circuit (ASIC) design, which is otherwise\nconsidered expensive and not easily accessible. The OpenPDK is the first\nconcrete step towards achieving the goal of open source circuit blocks that can\nbe imported to reuse and modify in ASIC design. With process technologies\nscaling down for better performance, the need for entirely digital designs,\nwhich can be synthesized in any standard Automatic Place-and-Route (APR) tool,\nhas increased considerably, for mapping physical design to the new process\ntechnology. This work presents the first open source all-digital\nSerializer/Deserializer (SerDes) for multi-GHz serial links designed using\nSkywater OpenPDK 130nm process node. To ensure that the design is fully\nsynthesizable, the SerDes uses CMOS inverter-based drivers at the Tx, while the\nRx front end comprises a resistive feedback inverter as a sensing element,\nfollowed by sampling elements. A fully digital oversampling CDR at the Rx\nrecovers the Tx clock for proper decoding of data bits. The physical design\nflow utilizes OpenLANE, which is an end-to-end tool for generating GDS from\nRTL. Virtuoso has been used for extracting parasitics for post-layout\nsimulations, which exhibit the SerDes functionality at 2 Gbps for 34 dB channel\nloss while consuming 438 mW power. The GDS and netlist files of the SerDes are\nuploaded in a GitHub repository for public access.\n"}
+{"abstract": "  We prove Lojasiewicz inequalities for the harmonic map energy for maps from\nsurfaces of positive genus into general target manifolds which are close to\nsimple bubble trees. Our results are not restricted to particular targets but\napply whenever the underlying bubble is not branched, or more generally\nattached at a point which is not a branch point, and has integrable Jacobi\nfields.\n"}
+{"abstract": "  Vision-and-language navigation (VLN) is a multimodal task where an agent\nfollows natural language instructions and navigates in visual environments.\nMultiple setups have been proposed, and researchers apply new model\narchitectures or training techniques to boost navigation performance. However,\nrecent studies witness a slow-down in the performance improvements in both\nindoor and outdoor VLN tasks, and the agents' inner mechanisms for making\nnavigation decisions remain unclear. To the best of our knowledge, the way the\nagents perceive the multimodal input is under-studied and clearly needs\ninvestigations. In this work, we conduct a series of diagnostic experiments to\nunveil agents' focus during navigation. Results show that indoor navigation\nagents refer to both object tokens and direction tokens in the instruction when\nmaking decisions. In contrast, outdoor navigation agents heavily rely on\ndirection tokens and have a poor understanding of the object tokens.\nFurthermore, instead of merely staring at surrounding objects, indoor\nnavigation agents can set their sights on objects further from the current\nviewpoint. When it comes to vision-and-language alignments, many models claim\nthat they are able to align object tokens with certain visual targets, but we\ncast doubt on the reliability of such alignments.\n"}
+{"abstract": "  In this paper, we consider a wireless network consisting of a base station\n(BS) that is serving multiple real-time traffic streams forwarding information\nupdates to their destinations in order to sustain the freshness of information\nfor time-critical applications. Since the wireless channels may be unreliable\ndue to the impurities of the propagation environments, such as deep fading,\nblockages, etc., we integrate a reconfigurable intelligent surface (RIS) to the\nwireless system in order to mitigate the propagation-induced impairments,\nenhance the quality of the wireless links, and ensure that the required\nfreshness of information is achieved for these real-time applications. For this\nnetwork set-up, we investigate the joint optimization of the traffic streams\nscheduling and the RIS phase-shift matrix with the goal of minimizing the\nlong-term average Age of Information (AoI). The formulated optimization problem\nis a mixed-integer non-convex optimization problem, which is difficult to\nsolve. To circumvent the high-coupled optimization variables, we decompose the\noriginal problem into two sub-problems, namely, a traffic stream scheduling\nsub-problem and a RIS phase-shift matrix sub-problem. Then, we resort to deep\nreinforcement learning to obtain the traffic stream scheduling solution and\nsemi-definite relaxation (SDR) to determine the optimal RIS configuration.\nFinally, we show that our approach evaluates the combined impact of scheduling\npolicy and RIS configuration on the long-term average AoI through extensive\nsimulations, where we demonstrate its superiority against other baseline\nschemes.\n"}
+{"abstract": "  Quantum magnonics is an emerging research field, with great potential for\napplications in magnon based hybrid systems and quantum information processing.\nQuantum correlation, such as entanglement, is a central resource in many\nquantum information protocols that naturally comes about in any study toward\nquantum technologies. This applies also to quantum magnonics. Here, we\ninvestigate antiferromagnets in which sublattices with ferromagnetic\ninteractions can have two different magnon modes, and we show how this may lead\nto experimentally detectable bipartite continuous variable magnon-magnon\nentanglement. The entanglement can be fully characterized via a single\nsqueezing parameter, or, equivalently, entanglement parameter. The clear\nrelation between the entanglement parameter and the Einstein, Podolsky, and\nRosen (EPR) function of the ground state opens up for experimental observation\nof magnon-magnon continuous variable entanglement and EPR non-locality. We\npropose a practical experimental realization to detect the EPR function of the\nground state, in a setting that relies on magnon-photon interaction in a\nmicrowave cavity.\n"}
+{"abstract": "  Domain adaptation (DA) aims at transferring knowledge from a labeled source\ndomain to an unlabeled target domain. Though many DA theories and algorithms\nhave been proposed, most of them are tailored into classification settings and\nmay fail in regression tasks, especially in the practical keypoint detection\ntask. To tackle this difficult but significant task, we present a method of\nregressive domain adaptation (RegDA) for unsupervised keypoint detection.\nInspired by the latest theoretical work, we first utilize an adversarial\nregressor to maximize the disparity on the target domain and train a feature\ngenerator to minimize this disparity. However, due to the high dimension of the\noutput space, this regressor fails to detect samples that deviate from the\nsupport of the source. To overcome this problem, we propose two important\nideas. First, based on our observation that the probability density of the\noutput space is sparse, we introduce a spatial probability distribution to\ndescribe this sparsity and then use it to guide the learning of the adversarial\nregressor. Second, to alleviate the optimization difficulty in the\nhigh-dimensional space, we innovatively convert the minimax game in the\nadversarial training to the minimization of two opposite goals. Extensive\nexperiments show that our method brings large improvement by 8% to 11% in terms\nof PCK on different datasets.\n"}
+{"abstract": "  Computing devices are vital to all areas of modern life and permeate every\naspect of our society. The ubiquity of computing and our reliance on it has\nbeen accelerated and amplified by the COVID-19 pandemic. From education to work\nenvironments to healthcare to defense to entertainment - it is hard to imagine\na segment of modern life that is not touched by computing. The security of\ncomputers, systems, and applications has been an active area of research in\ncomputer science for decades. However, with the confluence of both the scale of\ninterconnected systems and increased adoption of artificial intelligence, there\nare many research challenges the community must face so that our society can\ncontinue to benefit and risks are minimized, not multiplied. Those challenges\nrange from security and trust of the information ecosystem to adversarial\nartificial intelligence and machine learning.\n  Along with basic research challenges, more often than not, securing a system\nhappens after the design or even deployment, meaning the security community is\nroutinely playing catch-up and attempting to patch vulnerabilities that could\nbe exploited any minute. While security measures such as encryption and\nauthentication have been widely adopted, questions of security tend to be\nsecondary to application capability. There needs to be a sea-change in the way\nwe approach this critically important aspect of the problem: new incentives and\neducation are at the core of this change. Now is the time to refocus research\ncommunity efforts on developing interconnected technologies with security\n\"baked in by design\" and creating an ecosystem that ensures adoption of\npromising research developments. To realize this vision, two additional\nelements of the ecosystem are necessary - proper incentive structures for\nadoption and an educated citizenry that is well versed in vulnerabilities and\nrisks.\n"}
+{"abstract": "  The Perseus-Pisces supercluster is known as one of the largest structures in\nthe nearby Universe that has been charted by the galaxy and galaxy cluster\ndistributions. For the latter mostly clusters from the Abell catalogue have\nbeen used. Here we take a new approach to a quantitative characterisation of\nthe Perseus-Pisces supercluster using a statistically complete sample of X-ray\nluminous galaxy groups and clusters from our CLASSIX galaxy cluster redshift\nsurvey. We used a friends-of-friends technique to construct the supercluster\nmembership. We also studied the structure of the Southern Great Wall, which\nmerges with the Perseus-Pisces supercluster with a slightly increased\nfriends-of-friends linking length. In this work we discuss the geometric\nstructure of the superclusters, compare the X-ray luminosity distribution of\nthe members with that of the surroundings, and provide an estimate of the\nsupercluster mass. These results establish Perseus-Pisces as the largest\nsuperstructure in the Universe at redshifts z <= 0.03. With the new data this\nsupercluster extends through the zone of avoidance, which has also been\nindicated by some studies of the galaxy distribution by means of HI\nobservations. We investigated whether the shapes of the member groups and\nclusters in X-rays are aligned with the major axis of the supercluster. We find\nno evidence for a pronounced alignment, except for the ellipticities of Perseus\nand AWM7, which are aligned with the separation vector of the two systems and\nweakly with the supercluster.\n"}
+{"abstract": "  In this work, we investigate the diffusive optical tomography (DOT) problem\nin the case that limited boundary measurements are available. Motivated by the\ndirect sampling method (DSM), we develop a deep direct sampling method (DDSM)\nto recover the inhomogeneous inclusions buried in a homogeneous background. In\nthis method, we design a convolutional neural network (CNN) to approximate the\nindex functional that mimics the underling mathematical structure. The benefits\nof the proposed DDSM include fast and easy implementation, capability of\nincorporating multiple measurements to attain high-quality reconstruction, and\nadvanced robustness against the noise. Numerical experiments show that the\nreconstruction accuracy is improved without degrading the efficiency,\ndemonstrating its potential for solving the real-world DOT problems.\n"}
+{"abstract": "  The impact of machine learning models on healthcare will depend on the degree\nof trust that healthcare professionals place in the predictions made by these\nmodels. In this paper, we present a method to provide people with clinical\nexpertise with domain-relevant evidence about why a prediction should be\ntrusted. We first design a probabilistic model that relates meaningful latent\nconcepts to prediction targets and observed data. Inference of latent variables\nin this model corresponds to both making a prediction and providing supporting\nevidence for that prediction. We present a two-step process to efficiently\napproximate inference: (i) estimating model parameters using variational\nlearning, and (ii) approximating maximum a posteriori estimation of latent\nvariables in the model using a neural network, trained with an objective\nderived from the probabilistic model. We demonstrate the method on the task of\npredicting mortality risk for patients with cardiovascular disease.\nSpecifically, using electrocardiogram and tabular data as input, we show that\nour approach provides appropriate domain-relevant supporting evidence for\naccurate predictions.\n"}
+{"abstract": "  This paper is preoccupied with the following question: given a (possibly\nopaque) learning system, how can we understand whether its behaviour adheres to\ngovernance constraints? The answer can be quite simple: we just need to \"ask\"\nthe system about it. We propose to construct an investigator agent to query a\nlearning agent -- the suspect agent -- to investigate its adherence to a given\nethical policy in the context of an information-seeking dialogue, modeled in\nformal argumentation settings. This formal dialogue framework is the main\ncontribution of this paper. Through it, we break down compliance checking\nmechanisms into three modular components, each of which can be tailored to\nvarious needs in a vast amount of ways: an investigator agent, a suspect agent,\nand an acceptance protocol determining whether the responses of the suspect\nagent comply with the policy. This acceptance protocol presents a fundamentally\ndifferent approach to aggregation: rather than using quantitative methods to\ndeal with the non-determinism of a learning system, we leverage the use of\nargumentation semantics to investigate the notion of properties holding\nconsistently. Overall, we argue that the introduced formal dialogue framework\nopens many avenues both in the area of compliance checking and in the analysis\nof properties of opaque systems.\n"}
+{"abstract": "  Non-Gaussian quantum states have been deterministically prepared and\nautonomously stabilized in single- and two-mode circuit quantum electrodynamics\narchitectures via engineered dissipation. However, it is currently unknown how\nto scale up this technique to multi-mode non-Gaussian systems. Here, we upgrade\ndissipation engineering to collective (normal) modes of nonlinear resonator\narrays and show how to stabilize multi-mode Schrodinger cat states. These\nstates are multi-photon and multi-mode quantum superpositions of coherent\nstates in a single normal mode delocalized over an arbitrary number of\ncavities. We consider tailored dissipative coupling between resonators that are\nparametrically driven and feature an on-site nonlinearity, which is either a\nKerr-type nonlinearity or an engineered two-photon loss. For both types of\nnonlinearity, we find the same exact closed-form solutions for the\ntwo-dimensional steady-state manifold spanned by superpositions of multi-mode\nSchrodinger cat states. We further show that, in the Zeno limit of strong\ndissipative coupling, the even parity multi-mode cat state can be\ndeterministically prepared from the vacuum. Remarkably, engineered two-photon\nloss gives rise to a fast relaxation towards the steady state, protecting the\nstate preparation against decoherence due to intrinsic single-photon losses and\nimperfections in tailored dissipative coupling, which sets in at longer times.\nThe relaxation time is independent of system size making the state preparation\nscalable. Multi-mode cat states are naturally endowed with a noise bias that\nincreases exponentially with system size and can thus be exploited for enhanced\nrobust encoding of quantum information.\n"}
+{"abstract": "  Using a sample of 96,201 primary red clump (RC) stars selected from the\nLAMOST and Gaia surveys, we investigate the stellar structure of the Galactic\ndisk. The sample stars show two separated sequences of high-[{\\alpha}/Fe] and\nlow-[{\\alpha}/Fe] in the [{\\alpha}/Fe]-[Fe/H] plane. We divide the sample stars\ninto five mono-abundance populations (MAPs) with different ranges of\n[{\\alpha}/Fe] and [Fe/H], named as the high-[{\\alpha}/Fe], high-[{\\alpha}/Fe] &\nhigh-[Fe/H], low-[Fe/H], solar, high-[Fe/H] MAPs respectively. We present the\nstellar number density distributions in the R R Z plane, and the scale heights\nand scale lengths of the individual MAPs by fitting their vertical and radial\ndensity profiles. The vertical profiles, the variation trend of scale height\nwith the Galactocentric radius, indicate that there is a clear disk flare in\nthe outer disk both for the low-[{\\alpha}/Fe] and the high-[{\\alpha}/Fe] MAPs.\nWhile the radial surface density profiles show a peak radius of 7 kpc and 8 kpc\nfor the high-[{\\alpha}/Fe] and low-[{\\alpha}/Fe] MAPs, respectively. We also\ninvestigate the correlation between the mean rotation velocity and metallicity\nof the individual MAPs, and find that the mean rotation velocities are well\nseparated and show different trends between the high-[{\\alpha}/Fe] and the\nlow-[{\\alpha}/Fe] MAPs. At last, we discuss the character of the\nhigh-[{\\alpha}/Fe] & high-[Fe/H] MAP and find that it is more similar to the\nhigh-[{\\alpha}/Fe] MAP either in the radial and vertical density profiles or in\nthe rotation velocity.\n"}
+{"abstract": "  We studied $\\alpha$ cluster states in $^{26}$Mg via the\n$^{22}$Ne($^{6}$Li,$d\\gamma$)$^{26}$Mg reaction in inverse kinematics at an\nenergy of $7$ MeV/nucleon. States between $E_x$ = 4 - 12 MeV in $^{26}$Mg were\npopulated and relative $\\alpha$ spectroscopic factors were determined. Some of\nthese states correspond to resonances in the Gamow window of the\n$^{22}$Ne($\\alpha$,n)$^{25}$Mg reaction, which is one of the main neutron\nsources in the astrophysical $s$-process. We show that $\\alpha$-cluster\nstrength of the states analyzed in this work have critical impact on s-process\nabundances. Using our new $^{22}$Ne($\\alpha$,n)$^{25}$Mg and\n$^{22}$Ne($\\alpha$,$\\gamma$)$^{26}$Mg reaction rates, we performed new\ns-process calculations for massive stars and Asymptotic Giant Branch stars and\ncompared the resulting yields with the yields obtained using other\n$^{22}$Ne+$\\alpha$ rates from the literature. We observe an impact on the\ns-process abundances up to a factor of three for intermediate-mass AGB stars\nand up to a factor of ten for massive stars. Additionally, states in $^{25}$Mg\nat $E_x$ $<$ 5 MeV are identified via the $^{22}$Ne($^{6}$Li,$t$)$^{25}$Mg\nreaction for the first time. We present the ($^6$Li, $t$) spectroscopic factors\nof these states and note similarities to the $(d,p$) reaction in terms of\nreaction selectivity.\n"}
+{"abstract": "  Climate models encapsulate our best understanding of the Earth system,\nallowing research to be conducted on its future under alternative assumptions\nof how human-driven climate forces are going to evolve. An important\napplication of climate models is to provide metrics of mean and extreme climate\nchanges, particularly under these alternative future scenarios, as these\nquantities drive the impacts of climate on society and natural systems. Because\nof the need to explore a wide range of alternative scenarios and other sources\nof uncertainties in a computationally efficient manner, climate models can only\ntake us so far, as they require significant computational resources, especially\nwhen attempting to characterize extreme events, which are rare and thus demand\nlong and numerous simulations in order to accurately represent their changing\nstatistics. Here we use deep learning in a proof of concept that lays the\nfoundation for emulating global climate model output for different scenarios.\nWe train two \"loosely conditioned\" Generative Adversarial Networks (GANs) that\nemulate daily precipitation output from a fully coupled Earth system model: one\nGAN modeling Fall-Winter behavior and the other Spring-Summer. Our GANs are\ntrained to produce spatiotemporal samples: 32 days of precipitation over a\n64x128 regular grid discretizing the globe. We evaluate the generator with a\nset of related performance metrics based upon KL divergence, and find the\ngenerated samples to be nearly as well matched to the test data as the\nvalidation data is to test. We also find the generated samples to accurately\nestimate the mean number of dry days and mean longest dry spell in the 32 day\nsamples. Our trained GANs can rapidly generate numerous realizations at a\nvastly reduced computational expense, compared to large ensembles of climate\nmodels, which greatly aids in estimating the statistics of extreme events.\n"}
+{"abstract": "  The notion of long-memory is considered in the case of multivariate time\nseries, not necessarily Gaussian nor stationary. The long-memory\ncharacteristics are defined by the long-memory parameters describing the\nautocorrelation structure of each process and the long-run covariance measuring\nthe coupling between time series. A phase term is present in the model to widen\nthe classes of models. We introduce a representation of the time series by\nquasi-analytic wavelets for inference in this setting. We first show that the\ncovariance of the wavelet coefficients provides an adequate estimator of the\ncovariance structure of the processes, including the phase term. Consistent\nestimators are then proposed which is based on a Whittle approximation.\nSimulations highlight a satisfactory behavior of the estimation on finite\nsamples on some linear time series and on multivariate fractional Brownian\nmotions. An application on a real dataset in neuroscience is displayed, where\nlong-memory and brain connectivity are inferred.\n"}
+{"abstract": "  Gamma-ray bursts (GRBs) are known to be highly collimated events, and are\nmostly detectable when they are seen on-axis or very nearly on-axis. However,\nGRBs can be seen from off-axis angles, and the recent detection of a short GRB\nassociated to a gravitational wave event has conclusively shown such a\nscenario. The observer viewing angle plays an important role in the observable\nspectral shape and the energetic of such events. We present a numerical model\nwhich is based on the single-pulse approximation with emission from a top-hat\njet and has been developed to investigate the effects of the observer viewing\nangle. We assume a conical jet parametrized by a radius $R_{\\rm jet}$,\nhalf-opening angle $\\theta_{\\rm jet}$, a comoving-frame emissivity law and an\nobserver viewing angle $\\theta_{\\rm obs}$, and then study the effects for the\nconditions $\\theta_{\\rm obs} < \\theta_{\\rm jet}$ and $\\theta_{\\rm obs}\n>\\theta_{\\rm jet}$. We present results considering a smoothly broken power-law\nemissivity law in jet comoving frame, albeit the model implementation easily\nallows to consider other emissivity laws. We find that the relation $E^{\\rm\ni}_{\\rm p} \\propto E_{\\rm iso}^{0.5}$ (Amati relation) is naturally obtained\nfrom pure relativistic kinematic when $\\Gamma \\gtrsim 10$ and $\\theta_{\\rm obs}\n< \\theta_{\\rm jet}$; on the contrary, when $\\theta_{\\rm obs} > \\theta_{\\rm\njet}$ it results $E^{\\rm i}_{\\rm p} \\propto E_{\\rm iso}^{0.25}$. Using data\nfrom literature for a class of well-know sub-energetic GRBs, we show that their\nposition in the $E^{\\rm i}_{\\rm p}-E_{\\rm iso}$ plane is consistent with event\nobserved off-axis. The presented model is developed as a module to be\nintegrated in spectral fitting software package XSPEC and can be used by the\nscientific community.\n"}
+{"abstract": "  Gas flow and heat transfer in confined geometries at micro and nano scales\ndiffer considerably from those at macro-scales, mainly due to nonequilibrium\neffects such as velocity slip and temperature jump. The nonequilibrium effects\nenhance with a decrease in the characteristic length-scale of the fluid flow or\nthe gas density, leading to the failure of the standard Navier-Stokes-Fourier\n(NSF) equations in predicting thermal and fluid flow fields. The direct\nsimulation Monte-Carlo (DSMC) method is employed in the present work to\ninvestigate pressure-driven nitrogen flow in divergent microchannels with\nvarious divergence angles and isothermal walls. The thermal fields obtained\nfrom numerical simulations are analysed for different inlet-to-outlet pressure\nratios (1.5 $\\leq \\Pi \\leq$ 2.5), tangential momentum accommodation\ncoefficients and Knudsen numbers (0.05 $\\leq \\mathrm{Kn} \\leq$ 12.5), covering\nslip to free-molecular rarefaction regimes. The thermal field in the\nmicrochannel is predicted, heat-lines are visualised, and the physics of heat\ntransfer in the microchannel is discussed. Due to the rarefaction effects, the\ndirection of heat flow is largely opposite to that of the mass flow. However,\nthe interplay between thermal and pressure gradients, which are affected by\ngeometrical configurations of the microchannel and applied boundary conditions,\ndetermines the net heat flow direction. Additionally, the occurrence of thermal\nseparation and cold-to-hot heat transfer (also known as anti-Fourier heat\ntransfer) in divergent microchannels is explained.\n"}
+{"abstract": "  By adopting a divide-and-conquer strategy, subsystem-DFT (sDFT) can\ndramatically reduce the computational cost of large-scale electronic structure\ncalculations. The key ingredients of sDFT are the nonadditive kinetic energy\nand exchange-correlation functionals which dominate it's accuracy. Even though,\nsemilocal nonadditive functionals find a broad range of applications, their\naccuracy is somewhat limited especially for those systems where achieving\nbalance between exchange-correlation interactions on one side and nonadditive\nkinetic energy on the other is crucial. In eQE 2.0, we improve dramatically the\naccuracy of sDFT simulations by (1) implementing nonlocal nonadditive kinetic\nenergy functionals based on the LMGP family of functionals; (2) adapting\nQuantum ESPRESSO's implementation of rVV10 and vdW-DF nonlocal\nexchange-correlation functionals to be employed in sDFT simulations; (3)\nimplementing \"deorbitalized\" meta GGA functionals (e.g., SCAN-L). We carefully\nassess the performance of the newly implemented tools on the S22-5 test set.\neQE 2.0 delivers excellent interaction energies compared to conventional\nKohn-Sham DFT and CCSD(T). The improved performance does not come at a loss of\ncomputational efficiency. We show that eQE 2.0 with nonlocal nonadditive\nfunctionals retains the same linear scaling behavior achieved in eQE 1.0 with\nsemilocal nonadditive functionals.\n"}
+{"abstract": "  Typical ASR systems segment the input audio into utterances using purely\nacoustic information, which may not resemble the sentence-like units that are\nexpected by conventional machine translation (MT) systems for Spoken Language\nTranslation. In this work, we propose a model for correcting the acoustic\nsegmentation of ASR models for low-resource languages to improve performance on\ndownstream tasks. We propose the use of subtitles as a proxy dataset for\ncorrecting ASR acoustic segmentation, creating synthetic acoustic utterances by\nmodeling common error modes. We train a neural tagging model for correcting ASR\nacoustic segmentation and show that it improves downstream performance on MT\nand audio-document cross-language information retrieval (CLIR).\n"}
+{"abstract": "  W prove a dimension-free estimate for the $L^2(\\mathbb{R}^d)$ norm of the\nmaximal truncated Riesz transform in terms of the $L^2(\\mathbb{R}^d)$ norm of\nthe Riesz transform. Consequently, the vector of maximal truncated Riesz\ntransforms has a dimension-free estimate on $L^2(\\mathbb{R}^d)$. We also show\nthat the maximal function of the vector of truncated Riesz transforms has a\ndimension-free estimate on all $L^p(\\mathbb{R}^d)$ spaces, $1<p<\\infty.$\n"}
+{"abstract": "  Main visual techniques used to obtain information from speckle patterns are\nFujii method, generalized difference, weighted generalized difference, mean\nwindowed difference, structural function (SF), modified SF, etc. In this work,\na comparative analysis of major visual techniques for natural gum sample is\ncarried out. Obtained results conclusively establish SF based method as an\noptimum tool for visual inspection of dynamic speckle data.\n"}
+{"abstract": "  Pixelizations of Platonic solids such as the cube and icosahedron have been\nwidely used to represent spherical data, from climate records to Cosmic\nMicrowave Background maps. Platonic solids have well-known global symmetries.\nOnce we pixelize each face of the solid, each face also possesses its own local\nsymmetries in the form of Euclidean isometries. One way to combine these\nsymmetries is through a hierarchy. However, this approach does not adequately\nmodel the interplay between the two levels of symmetry transformations. We show\nhow to model this interplay using ideas from group theory, identify the\nequivariant linear maps, and introduce equivariant padding that respects these\nsymmetries. Deep networks that use these maps as their building blocks\ngeneralize gauge equivariant CNNs on pixelized spheres. These deep networks\nachieve state-of-the-art results on semantic segmentation for climate data and\nomnidirectional image processing. Code is available at https://git.io/JGiZA.\n"}
+{"abstract": "  GRETA, the Gamma-Ray Energy Tracking Array, is an array of highly-segmented\nHPGe detectors designed to track gamma-rays emitted in beam-physics\nexperiments. Its high detection efficiency and state-of-the-art position\nresolution make it well-suited for imaging applications. In this paper, we use\nsimulated imaging data to illustrate how imaging can be applied to nuclear\nlifetime measurments. This approach can offer multiple benefits over\ntraditional lifetime techniques such as RDM.\n"}
+{"abstract": "  One of the most striking and curious features of the small Kuiper Belt Object\n(KB), Arrokoth, explored by New Horizons, is the bright, annular neck it\nexhibits at the junction between its two lobes. Here we summarize past reported\nfindings regarding the properties of this feature and report new findings\nregarding its dimensions, reflectivity and color, shape profile, and its lack\nof identifiable craters. We conclude by enumerating possible origin scenarios\nfor this unusual feature. New results include a new measurement of the observed\nneck area of 8+/-1.5 km2, a total neck surface area of 32 km2, a 12.5:1 ratio\nof neck circumference to height, a normal reflectance histogram of the observed\nneck, and the fact that no significant (i.e., >2 sigma) color units were\nidentified, meaning the neck's color is generally spatially uniform at the 1.5\nkm/pixel scale of the best color images. Although several origin hypotheses for\nthe bright material in the neck are briefly discussed, none can be conclusively\ndemonstrated to be the actual origin mechanism at this time; some future tests\nare identified.\n"}
+{"abstract": "  While deep learning has obtained state-of-the-art results in many\napplications, the adaptation of neural network architectures to incorporate new\noutput features remains a challenge, as neural networks are commonly trained to\nproduce a fixed output dimension. This issue is particularly severe in online\nlearning settings, where new output features, such as items in a recommender\nsystem, are added continually with few or no associated observations. As such,\nmethods for adapting neural networks to novel features which are both time and\ndata-efficient are desired. To address this, we propose the Contextual\nHyperNetwork (CHN), an auxiliary model which generates parameters for extending\nthe base model to a new feature, by utilizing both existing data as well as any\nobservations and/or metadata associated with the new feature. At prediction\ntime, the CHN requires only a single forward pass through a neural network,\nyielding a significant speed-up when compared to re-training and fine-tuning\napproaches.\n  To assess the performance of CHNs, we use a CHN to augment a partial\nvariational autoencoder (P-VAE), a deep generative model which can impute the\nvalues of missing features in sparsely-observed data. We show that this system\nobtains improved few-shot learning performance for novel features over existing\nimputation and meta-learning baselines across recommender systems, e-learning,\nand healthcare tasks.\n"}
+{"abstract": "  The standard problem setting in Dec-POMDPs is self-play, where the goal is to\nfind a set of policies that play optimally together. Policies learned through\nself-play may adopt arbitrary conventions and implicitly rely on multi-step\nreasoning based on fragile assumptions about other agents' actions and thus\nfail when paired with humans or independently trained agents at test time. To\naddress this, we present off-belief learning (OBL). At each timestep OBL agents\nfollow a policy $\\pi_1$ that is optimized assuming past actions were taken by a\ngiven, fixed policy ($\\pi_0$), but assuming that future actions will be taken\nby $\\pi_1$. When $\\pi_0$ is uniform random, OBL converges to an optimal policy\nthat does not rely on inferences based on other agents' behavior (an optimal\ngrounded policy). OBL can be iterated in a hierarchy, where the optimal policy\nfrom one level becomes the input to the next, thereby introducing multi-level\ncognitive reasoning in a controlled manner. Unlike existing approaches, which\nmay converge to any equilibrium policy, OBL converges to a unique policy,\nmaking it suitable for zero-shot coordination (ZSC). OBL can be scaled to\nhigh-dimensional settings with a fictitious transition mechanism and shows\nstrong performance in both a toy-setting and the benchmark human-AI & ZSC\nproblem Hanabi.\n"}
+{"abstract": "  High precision studies of Beyond-Standard-Model physics through\naccelerator-based neutrino oscillation experiments require a very accurate\ndescription of neutrino-nucleus cross sections in a broad energy region, going\nfrom quasielastic scattering up to deep inelastic scattering. In this work we\nfocus on the following processes: quasielastic scattering,\ntwo-particle-two-hole excitations, and the excitation of the first (Delta) and\nsecond (Roper) resonances of the nucleon. The nuclear model is fully\nrelativistic and includes both one- and two-body currents. We compare our\nresults with recent T2K and MicroBooNE data on carbon and argon targets, and\npresent predictions for DUNE kinematics.\n"}
+{"abstract": "  Chemical bonding in 2D layered materials and van der Waals solids is central\nto understanding and harnessing their unique electronic, magnetic, optical,\nthermal and superconducting properties. Here we report the discovery of\nspontaneous, bidirectional, bilayer twisting (twist angle ~ 4.5{\\deg}) in the\nmetallic kagom\\'e MgCo6Ge6 at T = 100(2) K via X-ray diffraction measure-ments,\nenabled by the preparation of single crystals by the Laser Bridgman method.\nDespite the appearance of static twisting on cooling from T ~ 300 K to 100 K,\nno evidence for a phase transition was found in physical properties\nmeasurements. Combined with the presence of an Einstein phonon mode\ncontribution in the specific heat, this implies that the twisting exists at all\ntemperatures but is thermally fluctuating at room temperature. Crystal Orbital\nHamilton Population analysis demonstrates that the cooperative twisting between\nlayers stabilizes the Co-kagom\\'e network when coupled to strongly bonded and\nrigid (Ge2) dimers that connect adjacent layers. Further modelling of the\ndisplacive disorder in the crystal structure shows the presence of second,\nMg-deficient, stacking sequence. This alternative stacking sequence also\nexhibits inter-layer twisting, but with a different pattern, consistent with\nthe change in electron count due to removal of Mg. Magnetization, resistivity,\nand low-temperature specific heat measurements are all consistent with a Pauli\nparamagnetic, strongly correlated metal. Our results provide crucial insight\ninto how chemical concepts lead to interesting electronic structures and\nbehaviors in layered materials.\n"}
+{"abstract": "  We examine the impact of a faster expanding Universe on the phenomenology of\nscalar dark matter (DM) associated with $SU(2)_L$ multiplets. Earlier works\nwith radiation dominated Universe have reported the presence of desert region\nfor both inert $SU(2)_L$ doublet and triplet DM candidates where the DM is\nunder abundant. We find that the existence of a faster expanding component\nbefore BBN can revive a substantial part of the desert parameter space\nconsistent with relic density requirements and other direct and indirect search\nbounds. We also review the possible collider search prospects of the newly\nobtained parameter space and predict that such region might be probed at the\nfuture colliders with improved sensitivity via a disappearing/stable charged\ntrack.\n"}
+{"abstract": "  Object orientation has become the predominant paradigm for conceptual\nmodeling (e.g., UML), where the notions of class and object form the primitive\nbuilding blocks of thought. Classes act as templates for objects that have\nattributes and methods (actions). The modeled systems are not even necessarily\nsoftware systems: They can be human and artificial systems of many different\nkinds (e.g., teaching and learning systems). The UML class diagram is described\nas a central component of model-driven software development. It is the most\ncommon diagram in object-oriented models and used to model the static design\nview of a system. Objects both carry data and execute actions. According to\nsome authorities in modeling, a certain degree of difficulty exists in\nunderstanding the semantics of these notions in UML class diagrams. Some\nresearchers claim class diagrams have limited use for conceptual analysis and\nthat they are best used for logical design. Performing conceptual analysis\nshould not concern the ways facts are grouped into structures. Whether a fact\nwill end up in the design as an attribute is not a conceptual issue. UML leads\nto drilling down into physical design details (e.g., private/public attributes,\nencapsulated operations, and navigating direction of an association). This\npaper is a venture to further the understanding of object-orientated concepts\nas exemplified in UML with the aim of developing a broad comprehension of\nconceptual modeling fundamentals. Thinging machine (TM) modeling is a new\nmodeling language employed in such an undertaking. TM modeling interlaces\nstructure (components) and actionality where actions infiltrate the attributes\nas much as the classes. Although space limitations affect some aspects of the\nclass diagram, the concluding assessment of this study reveals the class\ndescription is a kind of shorthand for a richer sematic TM construct.\n"}
+{"abstract": "  The present study reports on flow past airfoils (stationary and moving) using\nsharp interface immersed-boundary approach. Non-boundary conforming approach\nlike immersed-boundary method offers a viable alternative over traditional\nboundary conforming approach by allowing us to model flow past arbitrarily\ncomplex shapes, by eliminating the need to re-grid the flow domain as the body\nexhibits motion. We present flow past a NACA 0012 airfoil at stationary\nconditions as well as exhibiting pitching motion. Evolution of vortex dynamics\nand wake structures are presented to show that the developed sharp interface\nimmersed-boundary approach captures the flow physics of dynamic stall\naccurately. Moving body problems involving immersed-boundary approach usually\nencounter the issues of spurious oscillations and mass conservation. This is\nhandled through a field extension strategy based on ghost cell approach, which\nallows for extrapolating the flow field value onto the ghost nodes, ensuring\nsmooth temporal transition as the immersed surface moves through time. The\nresults presented here show excellent agreement with the experimental results\nfound in the literature.\n"}
+{"abstract": "  The temperatures of red supergiants (RSGs) are expected to depend on\nmetallicity (Z) in such a way that lower-Z RSGs are warmer. In this work, we\ninvestigate the Z-dependence of the Hayashi limit by analysing RSGs in the\nlow-Z galaxy Wolf-Lundmark-Mellote (WLM), and compare with the RSGs in the\nhigher-Z environments of the Small Magellanic Cloud (SMC) and Large Magellanic\nCloud (LMC). We determine the effective temperature ($T_{\\textrm{eff}}$) of\neach star by fitting their spectral energy distributions, as observed by\nVLT+SHOOTER, with MARCS model atmospheres. We find average temperatures of\n$T_{\\textrm{eff}_{\\textrm{WLM}}}=4400\\pm202$ K,\n$T_{\\textrm{eff}_{\\textrm{SMC}}}=4130\\pm103$ K, and\n$T_{\\textrm{eff}_{\\textrm{LMC}}}=4140\\pm148$ K. From population synthesis\nanalysis, we find that although the Geneva evolutionary models reproduce this\ntrend qualitatively, the RSGs in these models are systematically too cool. We\nspeculate that our results can be explained by the inapplicability of the\nstandard solar mixing length to RSGs.\n"}
+{"abstract": "  Networks with a scale-free degree distribution are widely thought to promote\ncooperation in various games. Herein, by studying the well-known prisoner's\ndilemma game, we demonstrate that this need not necessarily be true. For the\nvery same degree sequence and degree distribution, we present a variety of\npossible behaviour. We reassess the perceived importance of hubs in a network\ntowards the maintenance of cooperation. We also reevaluate the dependence of\ncooperation on network clustering and assortativity.\n"}
+{"abstract": "  Aims: The giant HII region 30 Doradus (30 Dor) located in the eastern part of\nthe Large Magellanic Cloud is one of the most active star-forming regions in\nthe Local Group. Studies of HI data have revealed two large gas structures\nwhich must have collided with each other in the region around 30 Dor. In X-rays\nthere is extended emission ($\\sim 1$ kpc) south of 30 Dor called the X-ray\nspur, which appears to be anticorrelated with the HI gas. We study the\nproperties of the hot interstellar medium (ISM) in the X-ray spur and\ninvestigate its origin including related interactions in the ISM. Methods: We\nanalyzed new and archival XMM-Newton data of the X-ray spur and its\nsurroundings to determine the properties of the hot diffuse plasma. We created\ndetailed plasma property maps by utilizing the Voronoi tessellation algorithm.\nWe also studied HI and CO data, as well as optical line emission data of\nH$\\alpha$ and [SII], and compared them to the results of the X-ray spectral\nanalysis. Results: We find evidence of two hot plasma components with\ntemperatures of $kT_1 \\sim 0.2$ keV and $kT_2 \\sim 0.5-0.9$ keV, with the\nhotter component being much more pronounced near 30 Dor and the X-ray spur. In\n30 Dor, the plasma has most likely been heated by massive stellar winds and\nsupernova remnants. In the X-ray spur, we find no evidence of heating by stars.\nInstead, the X-ray spur must have been compressed and heated by the collision\nof the HI gas.\n"}
+{"abstract": "  We show the equivalences of several notions of entropy, like a version of the\ntopological entropy of the geodesic flow and the Minkowski dimension of the\nboundary, in metric spaces with convex geodesic bicombings satisfying a uniform\npacking condition. Similar estimates will be given in case of closed subsets of\nthe boundary of Gromov-hyperbolic metric spaces with convex geodesic\nbicombings.\n"}
+{"abstract": "  Currently, deep learning methods have been widely applied in and thus\npromoted the development of different fields. In the financial accounting\nfield, the rapid increase in the number of financial tickets dramatically\nincreases labor costs; hence, using a deep learning method to relieve the\npressure on accounting is necessary. At present, a few works have applied deep\nlearning methods to financial ticket recognition. However, first, their\napproaches only cover a few types of tickets. In addition, the precision and\nspeed of their recognition models cannot meet the requirements of practical\nfinancial accounting systems. Moreover, none of the methods provides a detailed\nanalysis of both the types and content of tickets. Therefore, this paper first\nanalyzes the different features of 482 kinds of financial tickets, divides all\nkinds of financial tickets into three categories and proposes different\nrecognition patterns for each category. These recognition patterns can meet\nalmost all types of financial ticket recognition needs. Second, regarding the\nfixed format types of financial tickets (accounting for 68.27\\% of the total\ntypes of tickets), we propose a simple yet efficient network named the\nFinancial Ticket Faster Detection network (FTFDNet) based on a Faster RCNN.\nFurthermore, according to the characteristics of the financial ticket text, in\norder to obtain higher recognition accuracy, the loss function, Region Proposal\nNetwork (RPN), and Non-Maximum Suppression (NMS) are improved to make FTFDNet\nfocus more on text. Finally, we perform a comparison with the best ticket\nrecognition model from the ICDAR2019 invoice competition. The experimental\nresults illustrate that FTFDNet increases the processing speed by 50\\% while\nmaintaining similar precision.\n"}
+{"abstract": "  There have been recent calls for research on the human side of software\nengineering and its impact on various factors such as productivity, developer\nhappiness and project success. An analysis of which challenges in software\nengineering teams are most frequent is still missing.\n  We aim to provide a starting point for a theory about relevant human\nchallenges and their causes in software engineering. We establish a reusable\nset of challenges and start out by investigating the effect of team\nvirtualization. Virtual teams often use digital communication and consist of\nmembers with different nationalities.\n  We designed a survey instrument and asked respondents to assess the frequency\nand criticality of a set of challenges, separated in context \"within teams\" as\nwell as \"between teams and clients\", compiled from previous empiric work, blog\nposts and pilot survey feedback. For the team challenges, we asked if\nmitigation measures were already in place. Respondents were also asked to\nprovide information about their team setup. The survey also measured Schwartz\nhuman values. Finally, respondents were asked if there were additional\nchallenges at their workplace.\n  We report on the results obtained from 192 respondents. We present a set of\nchallenges that takes the survey feedback into account and introduce two\ncategories of challenges; \"interpersonal\" and \"intrapersonal\". We found no\nevidence for links between human values and challenges. We found some\nsignificant links between the number of distinct nationalities in a team and\ncertain challenges, with less frequent and critical challenges occurring if 2-3\ndifferent nationalities were present compared to a team having members of just\none nationality or more than three. A higher degree of virtualization seems to\nincrease the frequency of some human challenges.\n"}
+{"abstract": "  The present paper is devoted to the investigation of the long time dynamics\nfor a double free boundary system with nonlocal diffusions, which models the\ninfectious diseases transmitted via digestive system such as fecal-oral\ndiseases, cholera, hand-foot and mouth, etc...We start by proving the existence\nand uniqueness of the Cauchy problem, which is not a trivial step due to\npresence of couple dispersals and new types of nonlinear reaction terms. Next,\nwe provide simple conditions on comparing the basic reproduction numbers\n$\\CR_0$ and $\\CR^*$ with some certain numbers to characterize the global\ndynamics, as $t\\to\\infty$. We further obtain the sharp criteria for the\nspreading and vanishing in term of the initial data. This is also called the\nvanishing-spreading phenomena. The couple dispersals yield significant obstacle\nthat we cannot employ the approach of Zhao, Zhang, Li, Du \\cite{WWZ1} and Du-Ni\n\\cite{DN}. To overcome this, we must prove the existence and the variational\ncharacterization for the principal eigenvalue of a linear system with nonlocal\ndispersals, then use it to obtain the right limits as the dispersal rates and\ndomain tend to zero or infinity. The maximum principle and sliding method for\nthe nonlocal operator are ingeniously employed to achieve the desired results.\n"}
+{"abstract": "  In the moduli space $\\mathcal{R}_g$ of double \\'etale covers of curves of a\nfixed genus $g$, the locus formed by covers of curves with a semicanonical\npencil consists of two irreducible divisors $\\mathcal T^e_g$ and $\\mathcal\nT^o_g$. We study the Prym map on these divisors, which shows significant\ndifferences between them and has a rich geometry in the cases of low genus. In\nparticular, the analysis of $\\mathcal T^o_5$ has enumerative consequences for\nlines on cubic threefolds.\n"}
+{"abstract": "  This paper presents a distributed multi-robot printing method which utilizes\nan optimization approach to decompose and allocate a printing task to a group\nof mobile robots. The motivation for this problem is to minimize the printing\ntime of the robots by using an appropriate task decomposition algorithm. We\npresent one such algorithm which decomposes an image into rasterized geodesic\ncells before allocating them to the robots for printing. In addition to this,\nwe also present the design of a numerically controlled holonomic robot capable\nof spraying ink on smooth surfaces. Further, we use this robot to\nexperimentally verify the results of this paper.\n"}
+{"abstract": "  A formal approach to rephrase nonlinear filtering of stochastic differential\nequations is the Kushner setting in applied mathematics and dynamical systems.\nThanks to the ability of the Carleman linearization, the nonlinear stochastic\ndifferential equation can be equivalently expressed as a finite system of\nbilinear stochastic differential equations with the augmented state under the\nfinite closure. Interestingly, the novelty of this paper is to embed the\nCarleman linearization into a stochastic evolution of the Markov process. To\nillustrate the Carleman linearization of the Markov process, this paper embeds\nthe Carleman linearization into a nonlinear swing stochastic differential\nequation. Furthermore, we achieve the nonlinear swing equation filtering in the\nCarleman setting. Filtering in the Carleman setting has simplified algorithmic\nprocedure. The concerning augmented state accounts for the nonlinearity as well\nas stochasticity. We show that filtering of the nonlinear stochastic swing\nequation in the Carleman framework is more refined as well as sharper in\ncontrast to benchmark nonlinear EKF. This paper suggests the usefulness of the\nCarleman embedding into the stochastic differential equation to filter the\nconcerning nonlinear stochastic differential system. This paper will be of\ninterest to nonlinear stochastic dynamists exploring and unfolding\nlinearization embedding techniques to their research.\n"}
+{"abstract": "  A hallmark of fluid turbulence theory is the universal power law scaling of\nthe velocity difference statistics between two points in space in the inertial\nrange between the large energy injection scale and the small energy dissipation\nscale. Even at the highest Reynolds numbers available, laboratory and natural\nflows such universal power laws have not been convincingly demonstrated. Here\nwe show for the decaying active grid turbulence of the Max Planck Variable\nDensity Turbulence Tunnel that the velocity difference statistics at high\nReynolds numbers do not exhibit a power law, but have a universal functional\nform independent of the Reynolds number. We separate this functional form from\nthe power law exponent and discuss potential consequences for turbulence\nmodelling.\n"}
+{"abstract": "  Density-functional theory within a Berry-phase formulation of the dynamical\npolarization is used to determine the second-order susceptibility $\\chi^{(2)}$\nof lithium niobate (LiNbO$_3$). Defect trapped polarons and bipolarons are\nfound to strongly enhance the nonlinear susceptibility of the material, in\nparticular if localized at Nb$_\\mathrm{V}$-V$_{\\mathrm{Li}}$ defect pairs. This\nis essentially a consequence of the polaronic excitation resulting in\nrelaxation-induced gap states. The occupation of these levels leads to strongly\nenhanced $\\chi^{(2)}$ coefficients and allows for the spatial and transient\nmodification of the second-harmonic generation of macroscopic samples.\n"}
+{"abstract": "  We study the problem of identifying the best arm in a stochastic multi-armed\nbandit game. Given a set of $n$ arms indexed from $1$ to $n$, each arm $i$ is\nassociated with an unknown reward distribution supported on $[0,1]$ with mean\n$\\theta_i$ and variance $\\sigma_i^2$. Assume $\\theta_1 > \\theta_2 \\geq \\cdots\n\\geq\\theta_n$. We propose an adaptive algorithm which explores the gaps and\nvariances of the rewards of the arms and makes future decisions based on the\ngathered information using a novel approach called \\textit{grouped median\nelimination}. The proposed algorithm guarantees to output the best arm with\nprobability $(1-\\delta)$ and uses at most $O \\left(\\sum_{i = 1}^n\n\\left(\\frac{\\sigma_i^2}{\\Delta_i^2} + \\frac{1}{\\Delta_i}\\right)(\\ln \\delta^{-1}\n+ \\ln \\ln \\Delta_i^{-1})\\right)$ samples, where $\\Delta_i$ ($i \\geq 2$) denotes\nthe reward gap between arm $i$ and the best arm and we define $\\Delta_1 =\n\\Delta_2$. This achieves a significant advantage over the variance-independent\nalgorithms in some favorable scenarios and is the first result that removes the\nextra $\\ln n$ factor on the best arm compared with the state-of-the-art. We\nfurther show that $\\Omega \\left( \\sum_{i = 1}^n \\left(\n\\frac{\\sigma_i^2}{\\Delta_i^2} + \\frac{1}{\\Delta_i} \\right) \\ln \\delta^{-1}\n\\right)$ samples are necessary for an algorithm to achieve the same goal,\nthereby illustrating that our algorithm is optimal up to doubly logarithmic\nterms.\n"}
+{"abstract": "  Multi-messenger observations of binary neutron star mergers offer a promising\npath towards resolution of the Hubble constant ($H_0$) tension, provided their\nconstraints are shown to be free from systematics such as the Malmquist bias.\nIn the traditional Bayesian framework, accounting for selection effects in the\nlikelihood requires calculation of the expected number (or fraction) of\ndetections as a function of the parameters describing the population and\ncosmology; a potentially costly and/or inaccurate process. This calculation\ncan, however, be bypassed completely by performing the inference in a framework\nin which the likelihood is never explicitly calculated, but instead fit using\nforward simulations of the data, which naturally include the selection. This is\nLikelihood-Free Inference (LFI). Here, we use density-estimation LFI, coupled\nto neural-network-based data compression, to infer $H_0$ from mock catalogues\nof binary neutron star mergers, given noisy redshift, distance and peculiar\nvelocity estimates for each object. We demonstrate that LFI yields\nstatistically unbiased estimates of $H_0$ in the presence of selection effects,\nwith precision matching that of sampling the full Bayesian hierarchical model.\nMarginalizing over the bias increases the $H_0$ uncertainty by only $6\\%$ for\ntraining sets consisting of $O(10^4)$ populations. The resulting LFI framework\nis applicable to population-level inference problems with selection effects\nacross astrophysics.\n"}
+{"abstract": "  Polymeric materials are prized for their formability, low density, and\nfunctional versatility. However, the refractive indices of common polymers fall\nin a relatively narrow range between 1.4 and 1.6. Here, we demonstrate that\nloading commercially-available polymers with large concentrations of a\nplant-based pigment can effectively enhance their refractive index.For\npolystyrene loaded with 67w/w\\% $\\beta$-carotene, we achieve a peak value of\n2.2 near the absorption edge at $531~\\mathrm{nm}$, while maintaining values\nabove 1.75 across longer wavelengths of the visible spectrum. Despite high\npigment loadings, this blend maintains the thermoforming ability of\npolystyrene, and $\\beta$-carotene remains molecularly dispersed. Similar\nresults are demonstrated for the plant-derived polymer ethyl cellulose. Since\nthe refractive index enhancement is intimately connected to the introduction of\nstrong absorption, it is best suited to applications where light travels short\ndistances through the material, such as reflectors and nanophotonic systems.We\nexperimentally demonstrate enhanced reflectance from films, as large as\nseven-fold for ethyl cellulose at selected wavelengths. Theoretical\ncalculations that highlight that this simple strategy can significantly\nincrease light scattering by nanoparticles and enhance the performance of Bragg\nreflectors.\n"}
+{"abstract": "  We improve and refine a method for certifying that the values' sizes computed\nby an imperative program will be bounded by polynomials in the program's\ninputs' sizes. Our work ''tames'' the non-determinism of the original analysis,\nand offers an innovative way of completing the analysis when a non-polynomial\ngrowth is found. We furthermore enrich the analyzed language by adding function\ndefinitions and calls, allowing to compose the analysis of different libraries\nand offering generally more modularity. The implementation of our improved\nmethod, discussed in a tool paper\n(https://hal.archives-ouvertes.fr/hal-03269121), also required to reason about\nthe efficiency of some of the needed operations on the matrices produced by the\nanalysis. It is our hope that this work will enable and facilitate static\nanalysis of source code to guarantee its correctness with respect to resource\nusages.\n"}
+{"abstract": "  Multi-vehicle coordinated decision making and control can improve traffic\nefficiency while guaranteeing driving safety. Formation control is a typical\nmulti-vehicle coordination method in the multi-lane scenario. Among the\nexisting formation control methods, the formation switching process is\npredefined and the collision-free behavior of vehicles has not been considered\nand explained in detail. This paper proposes a formation control method for\nmultiple Connected and Automated Vehicles (CAVs) on multi-lane roads. Firstly,\na bi-level planning framework is proposed to switch the structure of the\nformation in different scenarios smoothly and effectively. Secondly, the\nrelative coordinate system is established and the conflict-free relative paths\nare planned in the upper level. Then, multi-stage trajectory planning and\ntracking are modeled and solved as an optimal control problem with path\nconstraints in the lower level. Next, case study is conducted to verify the\nfunction of the proposed method in different scenarios. Finally, simulation in\nthe lane-drop bottleneck scenario is carried out under different traffic volume\nand numerical results indicate that the proposed method can improve both\ntraffic efficiency and fuel economy at high traffic volume.\n"}
+{"abstract": "  Fully Bayesian approaches to sequential decision-making assume that problem\nparameters are generated from a known prior, while in practice, such\ninformation is often lacking, and needs to be estimated through learning. This\nproblem is exacerbated in decision-making setups with partial information,\nwhere using a misspecified prior may lead to poor exploration and inferior\nperformance. In this work we prove, in the context of stochastic linear bandits\nand Gaussian priors, that as long as the prior estimate is sufficiently close\nto the true prior, the performance of an algorithm that uses the misspecified\nprior is close to that of the algorithm that uses the true prior. Next, we\naddress the task of learning the prior through metalearning, where a learner\nupdates its estimate of the prior across multiple task instances in order to\nimprove performance on future tasks. The estimated prior is then updated within\neach task based on incoming observations, while actions are selected in order\nto maximize expected reward. In this work we apply this scheme within a linear\nbandit setting, and provide algorithms and regret bounds, demonstrating its\neffectiveness, as compared to an algorithm that knows the correct prior. Our\nresults hold for a broad class of algorithms, including, for example, Thompson\nSampling and Information Directed Sampling.\n"}
+{"abstract": "  Oscillons are long-lived, slowly radiating solutions of nonlinear classical\nrelativistic field theories. Recently it was discovered that in one spatial\ndimension their decay may proceed in \"staccato\" bursts. Here we perform a\nsystematic numerical study to demonstrate that although this behaviour is not\nconfined to one spatial dimension, it quickly becomes unobservable when the\ndimension of space is increased, at least for the class of potentials\nconsidered here. To complete the picture we also present explicit results on\nthe dimension dependence of the collapse instability observed for\nthree-dimensional oscillons.\n"}
+{"abstract": "  In this work we describe semiclassical states in graphene under a constant\nperpendicular magnetic field by constructing coherent states in the\nBarut-Girardello sense. Since we want to keep track of the angular momentum,\nthe use of the symmetric gauge and polar coordinates seemed the most logical\nchoice. Different classes of coherent states are obtained by means of the\nunderlying algebra system, which consists of the direct sum of two\nHeisenberg-Weyl algebras. The most interesting cases are a kind of partial\ncoherent states and the coherent states with a well-defined total angular\nmomentum.\n"}
+{"abstract": "  Click logs are valuable resources for a variety of information retrieval (IR)\ntasks. This includes query understanding/analysis, as well as learning\neffective IR models particularly when the models require large amounts of\ntraining data. We release a large-scale domain-specific dataset of click logs,\nobtained from user interactions of the Trip Database health web search engine.\nOur click log dataset comprises approximately 5.2 million user interactions\ncollected between 2013 and 2020. We use this dataset to create a standard IR\nevaluation benchmark -- TripClick -- with around 700,000 unique free-text\nqueries and 1.3 million pairs of query-document relevance signals, whose\nrelevance is estimated by two click-through models. As such, the collection is\none of the few datasets offering the necessary data richness and scale to train\nneural IR models with a large amount of parameters, and notably the first in\nthe health domain. Using TripClick, we conduct experiments to evaluate a\nvariety of IR models, showing the benefits of exploiting this data to train\nneural architectures. In particular, the evaluation results show that the best\nperforming neural IR model significantly improves the performance by a large\nmargin relative to classical IR models, especially for more frequent queries.\n"}
+{"abstract": "  Artificial Intelligence has been a growth catalyst to our society and is\ncosidered across all idustries as a fundamental technology. However, its\ndevelopment has been limited to the signal processing domain that relies on the\ngenerated and collected data from other sensors. In recent research, concepts\nof Digital Artificial Intelligence and Physicial Artifical Intelligence have\nemerged and this can be considered a big step in the theoretical development of\nArtifical Intelligence. In this paper we explore the concept of Physicial\nArtifical Intelligence and propose two subdomains: Integrated Physicial\nArtifical Intelligence and Distributed Physicial Artifical Intelligence. The\npaper will also examine the trend and governance of Physicial Artifical\nIntelligence.\n"}
+{"abstract": "  We report detection of PSR B0656$+$14 with the Gran Telescopio Canarias in\nnarrow optical $F657$, $F754$, $F802$, and $F902$ and near-infrared $JHK_s$\nbands. The pulsar detection in the $K_s$ band extends its spectrum to 2.2\n$\\mu$m and confirms its flux increase towards the infrared. We also present a\nthorough analysis of the optical spectrum obtained by us with the VLT. For a\nconsistency check, we revised the pulsar near-infrared and narrow-band\nphotometry obtained with the \\textit{HST}. We find no narrow spectral lines in\nthe optical spectrum. We compile available near-infrared-optical-UV and\narchival 0.3-20keV X-ray data and perform a self-consistent analysis of the\nrotation phase-integrated spectrum of the pulsar using unified spectral models.\nThe spectrum is best fitted by the four-component model including two\nblackbodies, describing the thermal emission from the neutron star surface and\nits hot polar cap, the broken power-law, originating from the pulsar\nmagnetosphere, and an absorption line near $\\sim$0.5 keV detected previously.\nThe fit provides better constraints on the model parameters than using only a\nsingle spectral domain. The derived surface temperature is\n$T_{NS}^{\\infty}=7.9(3)\\times10^5$K. The intrinsic radius (7.8-9.9 km) of the\nemitting region is smaller than a typical neutron star radius (13km) and\nsuggests a nonuniform temperature distribution over the star surface. In\ncontrast, the derived radius of the hot polar cap is about twice as large as\nthe `canonical' one. The spectrum of the nonthermal emission steepens from the\noptical to X-rays and has a break near 0.1 keV. The X-ray data suggest the\npresence of another absorption line near 0.3keV.\n"}
+{"abstract": "  One of the primary reasons behind the difficulty in observing the Unruh\neffect is that for achievable acceleration scales the finite temperature\neffects are significant only for the low frequency modes of the field. Since\nthe density of field modes falls for small frequencies in free space, the field\nmodes which are relevant for the thermal effects would be less in number to\nmake an observably significant effect. In this work, we investigate the\nresponse of a Unruh-DeWitt detector coupled to a massless scalar field which is\nconfined in a long cylindrical cavity. The density of field modes inside such a\ncavity shows a {\\it resonance structure} i.e. it rises abruptly for some\nspecific cavity configurations. We show that an accelerating detector inside\nthe cavity exhibits a non-trivial excitation and de-excitation rates for {\\it\nsmall} accelerations around such resonance points. If the cavity parameters are\nadjusted to lie in a neighborhood of such resonance points, the (small)\nacceleration-induced emission rate can be made much larger than the already\nobservable inertial emission rate. We comment on the possibilities of employing\nthis detector-field-cavity system in the experimental realization of Unruh\neffect, and argue that the necessity of extremely high acceleration can be\ntraded off in favor of precision in cavity manufacturing for realizing\nnon-inertial field theoretic effects in laboratory settings.\n"}
+{"abstract": "  Obtaining high resolution images from low resolution data with clipped noise\nis algorithmically challenging due to the ill-posed nature of the problem. So\nfar such problems have hardly been tackled, and the few existing approaches use\nsimplistic regularisers. We show the usefulness of two adaptive regularisers\nbased on anisotropic diffusion ideas: Apart from evaluating the classical\nedge-enhancing anisotropic diffusion regulariser, we introduce a novel\nnon-local one with one-sided differences and superior performance. It is termed\nsector diffusion. We combine it with all six variants of the classical\nsuper-resolution observational model that arise from permutations of its three\noperators for warping, blurring, and downsampling. Surprisingly, the evaluation\nin a practically relevant noisy scenario produces a different ranking than the\none in the noise-free setting in our previous work (SSVM 2017).\n"}
+{"abstract": "  Data analysis in modern science using extensive experimental and\nobservational facilities, such as a gravitational wave detector, is essential\nin the search for novel scientific discoveries. Accordingly, various techniques\nand mathematical principles have been designed and developed to date. A\nrecently proposed approximate correlation method based on the information\ntheory is widely adopted in science and engineering. Although the maximal\ninformation coefficient (MIC) method remains in the phase of improving its\nalgorithm, it is particularly beneficial in identifying the correlations of\nmultiple noise sources in gravitational-wave detectors including non-linear\neffects. This study investigates various prospects for determining MIC\nparameters to improve the reliability of handling multi-channel time-series\ndata, reduce high computing costs, and propose a novel method of determining\noptimized parameter sets for identifying noise correlations in gravitational\nwave data.\n"}
+{"abstract": "  The nearby young open cluster eta Chamaeleontis has been observed by\neROSITA/SRG during its CalPV phase for 150 ks. The eROSITA data were taken in\nthe field-scan mode, an observing mode of Spectrum-Roentgen-Gamma (SRG) that\nfollows a rectangular grid-like pattern, here covering a 5x5 deg field with an\nexposure depth of about 5 ks. We study the known members in X-rays and search\nfor potential new members of the anticipated dispersed low-mass cluster\npopulation. Detected sources were identified by cross-matching X-ray sources\nwith Gaia and 2MASS, and young stars were identified by their X-ray activity,\nthe position in the color-magnitude diagram, and by their astrometric and\nkinematic properties. X-ray-luminosities, light curves, and spectra of cluster\nmembers were obtained and compared with previous X-ray data. Literature results\nof other member searches were used to verify our new member candidates in the\nobserved field. We determine X-ray properties of virtually all known eta Cha\nmembers and identify five additional stellar systems that show basically\nidentical characteristics, but are more dispersed. Four of them were previously\nproposed as potential members; this status is supported by our X-ray study.\nBased on their spatial distribution, further members are expected beyond the\nsky region we surveyed. The identified stellar systems very likely belong to\nthe ejected halo population, which brings the total number of eta Cha cluster\nmembers to at least 23.\n"}
+{"abstract": "  This paper surveys the field of multiagent deep reinforcement learning. The\ncombination of deep neural networks with reinforcement learning has gained\nincreased traction in recent years and is slowly shifting the focus from\nsingle-agent to multiagent environments. Dealing with multiple agents is\ninherently more complex as (a) the future rewards depend on the joint actions\nof multiple players and (b) the computational complexity of functions\nincreases. We present the most common multiagent problem representations and\ntheir main challenges, and identify five research areas that address one or\nmore of these challenges: centralised training and decentralised execution,\nopponent modelling, communication, efficient coordination, and reward shaping.\nWe find that many computational studies rely on unrealistic assumptions or are\nnot generalisable to other settings; they struggle to overcome the curse of\ndimensionality or nonstationarity. Approaches from psychology and sociology\ncapture promising relevant behaviours such as communication and coordination.\nWe suggest that, for multiagent reinforcement learning to be successful, future\nresearch addresses these challenges with an interdisciplinary approach to open\nup new possibilities for more human-oriented solutions in multiagent\nreinforcement learning.\n"}
+{"abstract": "  We show that the resistivity plateau of SmB$_6$ at low temperature, typically\ntaken as a hallmark of its conducting surface state, can systematically be\ninfluenced by different surface treatments. We investigate the effect of\ninflicting an increasing number of hand-made scratches and microscopically\ndefined focused ion beam-cut trenches on the surfaces of flux-grown\nSm$_{1-x}$Gd$_x$B$_6$ with $x =$ 0, 0.0002. Both treatments increase the\nresistance of the low-temperature plateau, whereas the bulk resistance at\nhigher temperature largely remains unaffected. Notably, the temperature at\nwhich the resistance deviates from the thermally activated behavior decreases\nwith cumulative surface damage. These features are more pronounced for the\nfocused ion beam treated samples, with the difference likely being related to\nthe absence of microscopic defects like subsurface cracks. Therefore, our\nmethod presents a systematic way of controlling the surface conductance.\n"}
+{"abstract": "  We consider optimal intervention in the Elliott-Golub-Jackson network model\nand show that it can be transformed into an influence maximization problem,\ninterpreted as the reverse of a default cascade. Our analysis of the optimal\nintervention problem extends well-established targeting results to the economic\nnetwork setting, which requires additional theoretical steps. We prove several\nresults about optimal intervention: it is NP-hard and additionally hard to\napproximate to a constant factor in polynomial time. In turn, we show that\nrandomizing failure thresholds leads to a version of the problem which is\nmonotone submodular, for which existing powerful approximations in polynomial\ntime can be applied. In addition to optimal intervention, we also show\npractical consequences of our analysis to other economic network problems: (1)\nit is computationally hard to calculate expected values in the economic\nnetwork, and (2) influence maximization algorithms can enable efficient\nimportance sampling and stress testing of large failure scenarios. We\nillustrate our results on a network of firms connected through input-output\nlinkages inferred from the World Input Output Database.\n"}
+{"abstract": "  Feedback design is an important aspect of person-following robots for older\nadults. This paper presents a user-centred design approach to ensure the design\nis focused on the needs and preferences of the users. A sequence of user\nstudies with a total of 35 older adults (aged 62 years and older) was conducted\nto explore their preferences regarding feedback parameters for a socially\nassistive person-following robot. The preferred level of robot transparency and\nthe desired content for the feedback was first explored. This was followed by\nan assessment of the preferred mode and timing of feedback. The chosen feedback\nparameters were then implemented and evaluated in a final experiment to\nevaluate the effectiveness of the design. Results revealed that older adults\npreferred to receive only basic status information. They preferred voice\nfeedback overtone, and at a continuous rate to keep them constantly aware of\nthe state and actions of the robot. The outcome of the study is a further step\ntowards feedback design guidelines that could improve interaction quality for\nperson-following robots for older adults.\n"}
+{"abstract": "  It is well-known fact that there exists 1-1 correspondence between so-called\ndouble (or flou) sets and intuitionistic sets (also known as orthopairs). At\nfirst glance, these two concepts seem to be irreconcilable. However, one must\nremember that algebraic operations in these two classes are also defined\ndifferently. Hence, the expected compatibility is possible. Contrary to this\napproach, we combine standard definition of double set with operations which\nare typical for intuitionistic sets. We show certain advantages and limitations\nof this viewpoint. Moreover, we suggest an interpretation of our sets and\noperations in terms of logic, data clustering and multi-criteria decision\nmaking. As a result, we obtain a structure of discussion between several\nparticipants who propose their \"necessary\" and \"allowable\" requirements or\npropositions.\n"}
+{"abstract": "  Recently, Intelligent Transportation Systems are leveraging the power of\nincreased sensory coverage and computing power to deliver data-intensive\nsolutions achieving higher levels of performance than traditional systems.\nWithin Traffic Signal Control (TSC), this has allowed the emergence of Machine\nLearning (ML) based systems. Among this group, Reinforcement Learning (RL)\napproaches have performed particularly well. Given the lack of industry\nstandards in ML for TSC, literature exploring RL often lacks comparison against\ncommercially available systems and straightforward formulations of how the\nagents operate. Here we attempt to bridge that gap. We propose three different\narchitectures for TSC RL agents and compare them against the currently used\ncommercial systems MOVA, SurTrac and Cyclic controllers and provide pseudo-code\nfor them. The agents use variations of Deep Q-Learning and Actor Critic, using\nstates and rewards based on queue lengths. Their performance is compared in\nacross different map scenarios with variable demand, assessing them in terms of\nthe global delay and average queue length. We find that the RL-based systems\ncan significantly and consistently achieve lower delays when compared with\nexisting commercial systems.\n"}
+{"abstract": "  An initial-boundary value problem for the $n$-dimensional wave equation is\nconsidered. A three-level explicit in time and conditionally stable 4th-order\ncompact scheme constructed recently for $n=2$ and the square mesh is\ngeneralized to the case of any $n\\geq 1$ and the rectangular uniform mesh.\nAnother approach to approximate the solution at the first time level (not\nexploiting high-order derivatives of the initial functions) is suggested. New\nstability bounds in the mesh energy norms and the discrete energy conservation\nlaws are given, and the 4th order error bound is rigorously proved.\nGeneralizations to the cases of the non-uniform meshes in space and time as\nwell as of the wave equation with variable coefficients are suggested.\n"}
+{"abstract": "  We study the evolution of cosmological perturbations in dark-matter models\nwith elastic and velocity-independent self interactions. Such interactions are\nimprinted in the matter-power spectrum as dark acoustic oscillations, which can\nbe experimentally explored to determine the strength of the self scatterings.\nModels with self interactions have similarities to warm dark matter, as they\nlead to suppression of power on small scales when the dark-matter velocity\ndispersion is sizable. Nonetheless, both the physical origin and the extent of\nthe suppression differ for self-interacting dark matter from conventional warm\ndark matter, with a dark sound horizon controlling the reduction of power in\nthe former case, and a free-streaming length in the latter. We thoroughly\nanalyze these differences by performing computations of the linear power\nspectrum using a newly developed Boltzmann code. We find that while current\nLyman-$\\alpha$ data disfavor conventional warm dark matter with a mass less\nthan 5.3 keV, when self interactions are included at their maximal value\nconsistent with bounds from the Bullet Cluster, the limits are relaxed to 4.4\nkeV. Finally, we make use of our analysis to set novel bounds on light scalar\nsinglet dark matter.\n"}
+{"abstract": "  This paper introduces ROmodel, an open source Python package extending the\nmodeling capabilities of the algebraic modeling language Pyomo to robust\noptimization problems. ROmodel helps practitioners transition from\ndeterministic to robust optimization through modeling objects which allow\nformulating robust models in close analogy to their mathematical formulation.\nROmodel contains a library of commonly used uncertainty sets which can be\ngenerated using their matrix representations, but it also allows users to\ndefine custom uncertainty sets using Pyomo constraints. ROmodel supports\nadjustable variables via linear decision rules. The resulting models can be\nsolved using ROmodels solvers which implement both the robust reformulation and\ncutting plane approach. ROmodel is a platform to implement and compare custom\nuncertainty sets and reformulations. We demonstrate ROmodel's capabilities by\napplying it to six case studies. We implement custom uncertainty sets based on\n(warped) Gaussian processes to show how ROmodel can integrate data-driven\nmodels with optimization.\n"}
+{"abstract": "  Word vector embeddings have been shown to contain and amplify biases in data\nthey are extracted from. Consequently, many techniques have been proposed to\nidentify, mitigate, and attenuate these biases in word representations. In this\npaper, we utilize interactive visualization to increase the interpretability\nand accessibility of a collection of state-of-the-art debiasing techniques. To\naid this, we present Visualization of Embedding Representations for deBiasing\nsystem (\"VERB\"), an open-source web-based visualization tool that helps the\nusers gain a technical understanding and visual intuition of the inner workings\nof debiasing techniques, with a focus on their geometric properties. In\nparticular, VERB offers easy-to-follow use cases in exploring the effects of\nthese debiasing techniques on the geometry of high-dimensional word vectors. To\nhelp understand how various debiasing techniques change the underlying\ngeometry, VERB decomposes each technique into interpretable sequences of\nprimitive transformations and highlights their effect on the word vectors using\ndimensionality reduction and interactive visual exploration. VERB is designed\nto target natural language processing (NLP) practitioners who are designing\ndecision-making systems on top of word embeddings, and also researchers working\nwith fairness and ethics of machine learning systems in NLP. It can also serve\nas a visual medium for education, which helps an NLP novice to understand and\nmitigate biases in word embeddings.\n"}
+{"abstract": "  We develop a quantum theory of plasmon polaritons in chains of metallic\nnanoparticles, describing both near- and far-field interparticle distances, by\nincluding plasmon-photon Umklapp processes. Taking into account the retardation\neffects of the long-range dipole-dipole interaction between the nanoparticles,\nwhich are induced by the coupling of the plasmonic degrees of freedom to the\nphotonic continuum, we reveal the polaritonic nature of the normal modes of the\nsystem. We compute the dispersion relation and radiative linewidth, as well as\nthe group velocities of the eigenmodes, and compare our numerical results to\nclassical electrodynamic calculations within the point-dipole approximation.\nInterestingly, the group velocities of the polaritonic excitations present an\nalmost periodic sign change and are found to be highly tunable by modifying the\nspacing between the nanoparticles. We show that, away from the intersection of\nthe plasmonic eigenfrequencies with the free photon dispersion, an analytical\nperturbative treatment of the light-matter interaction is in excellent\nagreement with our fully retarded numerical calculations. We further study\nquantitatively the hybridization of light and matter excitations, through an\nanalysis of Hopfield's coefficients. Finally, we consider the limit of\ninfinitely spaced nanoparticles and discuss some recent results on single\nnanoparticles that can be found in the literature.\n"}
+{"abstract": "  When it comes to location-based services (LBS), user privacy protection can\nbe in conflict with security of both users and trips. While LBS providers could\nadopt privacy preservation mechanisms to obfuscate customer data, the accuracy\nof vehicle location data and trajectories is crucial for detecting anomalies,\nespecially when machine learning methods are adopted by LBS. This paper aims to\ntackle this dilemma by evaluating the tradeoff between location privacy and\nsecurity in LBS. In particular, we investigate the impact of applying location\ndata privacy-preservation techniques on the performance of two detectors,\nnamely a Density-based spatial clustering of applications with noise (DBSCAN),\nand a Recurrent Neural Network (RNN). The experimental results suggest that, by\napplying privacy on location data, DBSCAN is more sensitive to Laplace noise\nthan RNN, although they achieve similar detection accuracy on the trip data\nwithout privacy preservation. Further experiments reveal that DBSCAN is not\nscalable to large size datasets containing millions of trips, because of the\nlarge number of computations needed for clustering trips. On the other hand,\nDBSCAN only requires less than 10 percent of the data used by RNN to achieve\nsimilar performance when applied to vehicle data without obfuscation,\ndemonstrating that clustering-based methods can be easily applied to small\ndatasets. Based on the results, we recommend usage scenarios of the two types\nof trajectory anomaly detectors when applying privacy preservation, by taking\ninto account customers' need for privacy, the size of the available vehicle\ntrip data, and real-time constraints of the LBS application.\n"}
+{"abstract": "  We perform a new Monte Carlo QCD analysis of pion parton distribution\nfunctions, including, for the first time, transverse momentum dependent\npion-nucleus Drell-Yan cross sections together with $p_{\\rm T}$-integrated\nDrell-Yan and leading neutron electroproduction data from HERA. We assess the\nsensitivity of the Monte Carlo fits to kinematic cuts, factorization scale, and\nparametrization choice, and we discuss the impact of the various datasets on\nthe pion's quark and gluon distributions. This study provides the necessary\nstep towards the simultaneous analysis of collinear and transverse momentum\ndependent pion distributions and the determination of the pion's\nthree-dimensional structure.\n"}
+{"abstract": "  The Ly-$\\alpha$ line of $^{14}$Si with the energy 3.55-keV is formed in the\nmagnetic field $6\\cdot 10^{12}$ G. In close binary stellar systems with a red\ngiant and a neutron star in super-strong magnetic fields recombination laser\nradiation to the Landau ground level may appear from hydrogen-like ions. If the\n3.55-keV Ly-$\\alpha$ silicon laser realizes, then the detection of this line\nfrom any distances in the Universe on $z\\leq100$ is possible and this line may\nbe the X-ray candle because of a huge abundance of silicon in these binary\nsystems. Energies for a Ly-$\\alpha$ line of hydrogen-like $^{14}$Si in magnetic\nfields $4\\cdot10^{12}-10^{13}$ G are calculated. This line is the unique event\nbecause we see this radiation from perpendicular direction to the magnetic\ncolumn in near-wall layers. The laser radiation to the ground level in the\nrange of (1-20) keV may arise from other hydrogen-like ions. In the presence of\na laser, line narrowing is inevitable.\n"}
+{"abstract": "  The Reduced Density Matrix Functional Theory (RDMFT) is a remarkable tool for\nstudying properties of ground states of strongly interacting quantum many body\nsystems. As it gives access to the one-particle reduced density matrix of the\nground state, it provides a perfectly tailored approach to studying the\nBose-Einstein condensation or systems of strongly correlated electrons. In\nparticular, for homogeneous Bose-Einstein condensates as well as for the\nBose-Hubbard dimer it has been recently shown that the relevant density\nfunctional exhibits a repulsive gradient (called the Bose-Einstein condensation\nforce) which diverges when the fraction of non-condensed bosons tends to zero.\nIn this paper, we show that the existence of the Bose-Einstein condensation\nforce is completely universal for any type of pair-interaction and also in the\nnon-homogeneous gases. To this end, we construct a universal family of\nvariational trial states which allows us to suitably approximate the relevant\ndensity functional in a finite region around the set of the completely\ncondensed states. We also show the existence of an analogous repulsive gradient\nin the fermionic RDMFT for the $N$-fermion singlet sector in the vicinity of\nthe set of the Hartree-Fock states. Finally, we show that our approximate\nfunctional may perform well in electron transfer calculations involving low\nnumbers of electrons. This is demonstrated numerically in the Fermi-Hubbard\nmodel in the strongly correlated limit where some other approximate functionals\nare known to fail.\n"}
+{"abstract": "  We prove moment inequalities for exponential sums with respect to singular\nmeasures, whose Fourier decay matches those of curved hypersurfaces. Our\nemphasis will be on proving estimates that are sharp with respect to the scale\nparameter $N$, apart from $N^\\epsilon$ losses. In a few instances, we manage to\nremove these losses.\n"}
+{"abstract": "  We investigate the problem of approximating the matrix function $f(A)$ by\n$r(A)$, with $f$ a Markov function, $r$ a rational interpolant of $f$, and $A$\na symmetric Toeplitz matrix. In a first step, we obtain a new upper bound for\nthe relative interpolation error $1-r/f$ on the spectral interval of $A$. By\nminimizing this upper bound over all interpolation points, we obtain a new,\nsimple and sharp a priori bound for the relative interpolation error. We then\nconsider three different approaches of representing and computing the rational\ninterpolant $r$. Theoretical and numerical evidence is given that any of these\nmethods for a scalar argument allows to achieve high precision, even in the\npresence of finite precision arithmetic. We finally investigate the problem of\nefficiently evaluating $r(A)$, where it turns out that the relative error for a\nmatrix argument is only small if we use a partial fraction decomposition for\n$r$ following Antoulas and Mayo. An important role is played by a new stopping\ncriterion which ensures to automatically find the degree of $r$ leading to a\nsmall error, even in presence of finite precision arithmetic.\n"}
+{"abstract": "  Microscopic-level understanding of the separation mechanism for\ntwo-dimensional (2D) membranes is an active area of research due to potential\nimplications of this class of membranes for various technological processes.\nHelium (He) purification from the natural resources is of particular interest\ndue to the shortfall in its production. In this work, we applied the ring\npolymer molecular dynamics (RPMD) method to graphdiyne (Gr2) and graphtriyne\n(Gr3) 2D membranes having variable pore sizes for the separation of He\nisotopes. We found that the transmission rate through Gr3 is many orders of\nmagnitude greater than Gr2. The selectivity of either isotope at low\ntemperatures is a consequence of a delicate balance between the zero-point\nenergy effect and tunneling of $^4$He and $^3$He. RPMD provides an efficient\napproach for studying the separation of He isotopes, taking into account\nquantum effects of light nuclei motions at low temperatures, which classical\nmethods fail to capture.\n"}
+{"abstract": "  We discuss the $SU(5)$ grand unified extension of flavour models with\nmultiple modular symmetries. The proposed model involves two modular $S_4$\ngroups, one acting in the charged fermion sector, associated with a modulus\nfield value $\\tau_T$ with residual $Z_3^T$ symmetry, and one acting in the\nright-handed neutrino sector, associated with another modulus field value\n$\\tau_{SU}$ with residual $Z_2^{SU}$ symmetry. Quark and lepton mass\nhierarchies are naturally generated with the help of weightons, which are SM\nsinglet fields, where their non-zero modular weights play the role of\nFroggatt-Nielsen charges. The model predicts TM$_1$ lepton mixing, and\nneutrinoless double beta decay at rates close to the sensitivity of current and\nfuture experiments, for both normal and inverted orderings, with suppressed\ncorrections from charged lepton mixing due to the triangular form of its Yukawa\nmatrix.\n"}
+{"abstract": "  We present a novel method for approximating the probability density function\n(PDF) of the first-passage times in the Ratcliff diffusion decision model\n(DDM). We implemented this approximation method in $\\texttt{C++}$ using the\n$\\texttt{R}$ package $\\texttt{Rcpp}$ to utilize the faster $\\texttt{C++}$\nlanguage while maintaining the $\\texttt{R}$ language interface. In addition to\nour novel approximation method, we also compiled all known approximation\nmethods for the DDM density function (with fixed and variable drift rate),\nincluding previously unused combinations of techniques found in the relevant\nliterature. We ported these approximation methods to $\\texttt{C++}$ and\noptimized them to run in this new language. Given an acceptable error tolerance\nin the value of the PDF approximation, we benchmarked all of these\napproximation methods to compare their speed against each other and also\nagainst commonly used $\\texttt{R}$ functions from the literature. The results\nof these tests show that our novel approximation method is not only orders of\nmagnitude faster than the current standards, but it is also faster than all of\nthe other approximation methods available even after translation and\noptimization to the faster $\\texttt{C++}$ language. All of these approximation\nmethods are bundled in the $\\texttt{fddm}$ package for the $\\texttt{R}$\nstatistical computing language; this package is available via CRAN, and the\nsource code is available on GitHub.\n"}
+{"abstract": "  We construct an anticyclotomic Euler system for the Rankin-Selberg\nconvolution of two modular forms, using $p$-adic families of generalized\nGross-Kudla-Schoen diagonal cycles. As applications of this construction, we\nprove new cases of the Bloch-Kato conjecture in analytic rank zero (and results\ntowards new cases in analytic rank one), and a divisibility towards an Iwasawa\nmain conjecture.\n"}
+{"abstract": "  Recently, program autotuning has become very popular especially in embedded\nsystems, when we have limited resources such as computing power and memory\nwhere these systems run generally time-critical applications. Compiler\noptimization space gradually expands with the renewed compiler options and\ninclusion of new architectures. These advancements bring autotuning even more\nimportant position. In this paper, we introduced Flag Optimization with Genetic\nAlgorithm (FOGA) as an autotuning solution for GCC flag optimization. FOGA has\ntwo main advantages over the other autotuning approaches: the first one is the\nhyperparameter tuning of the genetic algorithm (GA), the second one is the\nmaximum iteration parameter to stop when no further improvement occurs. We\ndemonstrated remarkable speedup in the execution time of C++ source codes with\nthe help of optimization flags provided by FOGA when compared to the state of\nthe art framework OpenTuner.\n"}
+{"abstract": "  This paper introduces new scan statistics for multivariate functional data\nindexed in space. The new methods are derivated from a MANOVA test statistic\nfor functional data, an adaptation of the Hotelling T2-test statistic, and a\nmultivariate extension of the Wilcoxon rank-sum test statistic. In a simulation\nstudy, the latter two methods present very good performances and the adaptation\nof the functional MANOVA also shows good performances for a normal\ndistribution. Our methods detect more accurate spatial clusters than an\nexisting nonparametric functional scan statistic. Lastly we applied the methods\non multivariate functional data to search for spatial clusters of abnormal\ndaily concentrations of air pollutants in the north of France in May and June\n2020.\n"}
+{"abstract": "  Automated decision support can accelerate tedious tasks as users can focus\ntheir attention where it is needed most. However, a key concern is whether\nusers overly trust or cede agency to automation. In this paper, we investigate\nthe effects of introducing automation to annotating clinical texts--a\nmulti-step, error-prone task of identifying clinical concepts (e.g.,\nprocedures) in medical notes, and mapping them to labels in a large ontology.\nWe consider two forms of decision aid: recommending which labels to map\nconcepts to, and pre-populating annotation suggestions. Through laboratory\nstudies, we find that 18 clinicians generally build intuition of when to rely\non automation and when to exercise their own judgement. However, when presented\nwith fully pre-populated suggestions, these expert users exhibit less agency:\naccepting improper mentions, and taking less initiative in creating additional\nannotations. Our findings inform how systems and algorithms should be designed\nto mitigate the observed issues.\n"}
+{"abstract": "  There exists a large body of previous work using reduced two-dimensional\nmodels of the SOL, which model fluctuations in the drift-plane but approximate\nparallel transport with effective loss terms. Full size three-dimensional\nsimulations of SOL turbulence in experimental geometries are now possible, but\nare far more computationally expensive than 2D models. We therefore use a\nflux-tube geometry model of the scrape-off layer to compare the results of 2D\nsimulations to 3D simulations with a similar setup, looking for systematic\ndifferences. Overall there is good agreement in the basic radial profiles,\nprobability distribution functions, and power spectra of fluctuations. However,\nthe average temperature is over-predicted in 2D relative to 3D, and we explain\nthe difference in terms of the effect of geometrical simplifications of devices\nat low power. Varying geometric parameters, we find that supersonic flow in the\ndivertor leg, which occurs because our simulations do not include neutrals and\nso represent low-recycling conditions, means that the divertor leg length only\nhas a weak effect on the output. Finally, we examine the effect of altering the\nmagnitude of source and sink terms in 2D, concluding that they cannot easily be\nused to recreate both the density and temperature profiles observed in 3D\nsimultaneously.\n"}
+{"abstract": "  In this work, we examine an intelligent reflecting surface (IRS) assisted\ndownlink non-orthogonal multiple access (NOMA) scenario with the aim of\nmaximizing the sum rate of users. The optimization problem at the IRS is quite\ncomplicated, and non-convex, since it requires the tuning of the phase shift\nreflection matrix. Driven by the rising deployment of deep reinforcement\nlearning (DRL) techniques that are capable of coping with solving non-convex\noptimization problems, we employ DRL to predict and optimally tune the IRS\nphase shift matrices. Simulation results reveal that IRS assisted NOMA based on\nour utilized DRL scheme achieves high sum rate compared to OMA based one, and\nas the transmit power increases, the capability of serving more users\nincreases. Furthermore, results show that imperfect successive interference\ncancellation (SIC) has a deleterious impact on the data rate of users\nperforming SIC. As the imperfection increases by ten times, the rate decreases\nby more than 10%.\n"}
+{"abstract": "  Metasurfaces are a key photonic platform to manipulate classical light using\nsub-wavelength structures with designer optical response. Static metasurfaces\nhave recently entered the realm of quantum photonics, showing their ability to\ntailor nonclassical states of light. We introduce the concept of space-time\nquantum metasurfaces for dynamical control of quantum light. We provide\nillustrative examples of the impact of spatio-temporally modulated metasurfaces\nin quantum photonics, including the creation of frequency-spin-path\nhyperentanglement on a single photon and the realization of space-time\nasymmetry at the deepest level of the quantum vacuum. Photonic platforms based\non the space-time quantum metasurface concept have the potential to enable\nnovel functionalities, such as on-demand entanglement generation for quantum\ncommunications, nonreciprocal photon propagation for free-space quantum\nisolation, and reconfigurable quantum imaging and sensing.\n"}
+{"abstract": "  There has been a paradigm-shift in urban logistic services in the last years;\ndemand for real-time, instant mobility and delivery services grows. This poses\nnew challenges to logistic service providers as the underlying stochastic\ndynamic vehicle routing problems (SDVRPs) require anticipatory real-time\nrouting actions. Searching the combinatorial action space for efficient routing\nactions is by itself a complex task of mixed-integer programming (MIP)\nwell-known by the operations research community. This complexity is now\nmultiplied by the challenge of evaluating such actions with respect to their\neffectiveness given future dynamism and uncertainty, a potentially ideal case\nfor reinforcement learning (RL) well-known by the computer science community.\nFor solving SDVRPs, joint work of both communities is needed, but as we show,\nessentially non-existing. Both communities focus on their individual strengths\nleaving potential for improvement. Our survey paper highlights this potential\nin research originating from both communities. We point out current obstacles\nin SDVRPs and guide towards joint approaches to overcome them.\n"}
+{"abstract": "  We introduce a framework for universal algebra in categories of relational\nstructures given by finitary relational signatures and finitary or infinitary\nHorn theories, with the arity $\\lambda$ of a Horn theory understood as a strict\nupper bound on the number of premisses in its axioms; key examples include\npartial orders ($\\lambda=\\omega$) or metric spaces ($\\lambda=\\omega_1$). We\nestablish a bijective correspondence between $\\lambda$-accessible enriched\nmonads on the given category of relational structures and a notion of\n$\\lambda$-ary algebraic theories (i.e. with operations of arity $<\\lambda$),\nwith the syntax of algebraic theories induced by the relational signature (e.g.\ninequations or equations-up-to-$\\epsilon$). We provide a generic sound and\ncomplete derivation system for such relational algebraic theories, thus in\nparticular recovering (extensions of) recent systems of this type for monads on\npartial orders and metric spaces by instantiation. In particular, we present an\n$\\omega_1$-ary algebraic theory of metric completion. The theory-to-monad\ndirection of our correspondence remains true for the case of $\\kappa$-ary\nalgebraic theories and $\\kappa$-accessible monads for $\\kappa<\\lambda$, e.g.\nfor finitary theories over metric spaces.\n"}
+{"abstract": "  The use of Shiny in research publications is investigated. From the\nappearance of this popular web application framework for R through to 2018, it\nhas been utilised in many diverse research areas. While it can be shown that\nthe complexity of Shiny applications is limited by the background architecture,\nand real security concerns exist for novice app developers, the collaborative\nbenefits are worth attention from the wider research community. Shiny\nsimplifies the use of complex methodologies for users of different\nspecialities, at the level of proficiency appropriate for the end user. This\nenables a diverse community of users to interact efficiently, utilising\ncutting-edge methodologies. The literature reviewed demonstrates that complex\nmethodologies can be put into practice without the necessity for investment in\nprofessional training. It would appear that Shiny opens up concurrent benefits\nin communication between those who analyse data and those in other disciplines,\nthereby potentially enriching research through this technology.\n"}
+{"abstract": "  In this paper, we present a novel method to constrain invexity on Neural\nNetworks (NN). Invex functions ensure every stationary point is global minima.\nHence, gradient descent commenced from any point will lead to the global\nminima. Another advantage of invexity on NN is to divide data space locally\ninto two connected sets with a highly non-linear decision boundary by simply\nthresholding the output. To this end, we formulate a universal invex function\napproximator and employ it to enforce invexity in NN. We call it Input Invex\nNeural Networks (II-NN). We first fit data with a known invex function,\nfollowed by modification with a NN, compare the direction of the gradient and\npenalize the direction of gradient on NN if it contradicts with the direction\nof reference invex function. In order to penalize the direction of the gradient\nwe perform Gradient Clipped Gradient Penalty (GC-GP). We applied our method to\nthe existing NNs for both image classification and regression tasks. From the\nextensive empirical and qualitative experiments, we observe that our method\ngives the performance similar to ordinary NN yet having invexity. Our method\noutperforms linear NN and Input Convex Neural Network (ICNN) with a large\nmargin. We publish our code and implementation details at github.\n"}
+{"abstract": "  We prove a transversality \"lifting property\" for compactified configuration\nspaces as an application of the multijet transversality theorem: given a\nsubmanifold of configurations of points on an embedding of a compact manifold\n$M$ in Euclidean space, we can find a dense set of smooth embeddings of $M$ for\nwhich the corresponding configuration space of points is transverse to any\nsubmanifold of the configuration space of points in Euclidean space, as long as\nthe two submanifolds of compactified configuration space are boundary-disjoint.\nWe use this setup to provide an attractive proof of the square-peg problem:\nthere is a dense family of smoothly embedded circles in the plane where each\nsimple closed curve has an odd number of inscribed squares, and there is a\ndense family of smoothly embedded circles in $\\R^n$ where each simple closed\ncurve has an odd number of inscribed square-like quadrilaterals.\n"}
+{"abstract": "  A Digital Twin (DT) is a digital representation of a physical object used to\nsimulate it before it is built or to predict failures after the object is\ndeployed. In this article, we introduce our approach, which applies the concept\nof a Cyber Digital Twin (CDT) to automotive software for the purpose of\nsecurity analysis. In our approach, automotive firmware is transformed into a\nCDT, which contains automatically extracted, security-relevant information from\nthe firmware. Based on the CDT, we evaluate security requirements through\nautomated analysis and requirements verification using policy enforcement\nchecks and vulnerabilities detection. The evaluation of a CDT is conducted\ncontinuously integrating new checks derived from new security requirements and\nfrom newly disclosed vulnerabilities. We applied our approach to about 100\nautomotive firmwares. In average, about 600 publicly disclosed vulnerabilities\nand 80 unknown weaknesses were detected per firmware in the pre-production\nphase. Therefore, the use of a CDT enables efficient continuous verification of\nsecurity requirements.\n"}
+{"abstract": "  Word embedding learning methods require a large number of occurrences of a\nword to accurately learn its embedding. However, out-of-vocabulary (OOV) words\nwhich do not appear in the training corpus emerge frequently in the smaller\ndownstream data. Recent work formulated OOV embedding learning as a few-shot\nregression problem and demonstrated that meta-learning can improve results\nobtained. However, the algorithm used, model-agnostic meta-learning (MAML) is\nknown to be unstable and perform worse when a large number of gradient steps\nare used for parameter updates. In this work, we propose the use of Leap, a\nmeta-learning algorithm which leverages the entire trajectory of the learning\nprocess instead of just the beginning and the end points, and thus ameliorates\nthese two issues. In our experiments on a benchmark OOV embedding learning\ndataset and in an extrinsic evaluation, Leap performs comparably or better than\nMAML. We go on to examine which contexts are most beneficial to learn an OOV\nembedding from, and propose that the choice of contexts may matter more than\nthe meta-learning employed.\n"}
+{"abstract": "  We propose a conjugate logic that can capture the behavior of quantum and\nquantum-like systems. The proposal is similar to the more generic concept of\nepistemic logic: it encodes knowledge or perhaps more correctly, predictions\nabout outcomes of future observations on some systems. For a quantum system,\nthese predictions are statements about future outcomes of measurements\nperformed on specific degrees of freedom of the system. The proposed logic will\ninclude propositions and their relations including connectives, but importantly\nalso transformations between propositions on conjugate degrees of freedom of\nthe systems. A key point is the addition of a transformation that allows to\nconvert propositions about single systems into propositions about correlations\nbetween systems. We will see that subtle choices of the properties of the\ntransformations lead to drastically different underlying mathematical models;\none choice gives stabilizer quantum mechanics, while another choice gives\nSpekkens' toy theory. This points to a crucial basic property of quantum and\nquantum-like systems that can be handled within the present conjugate logic by\nadjusting the mentioned choice. It also enables a discussion on what behaviors\nare properly quantum or only quantum-like, relating to that choice and how it\nmanifests in the system under scrutiny.\n"}
+{"abstract": "  We examine the double copy structure of anyons in gauge theory and gravity.\nUsing on-shell amplitude techniques, we construct little group covariant\nspinor-helicity variables describing massive particles with spin, which\ntogether with locality and unitarity enables us to derive the long-range\ntree-level scattering amplitudes involving anyons. We discover that classical\ngauge theory anyon solutions double copy to their gravitational counterparts in\na non-trivial manner. Interestingly, we show that the massless double copy\ncaptures the topological structure of curved spacetime in three dimensions by\nintroducing a non-trivial mixing of the topological graviton and the dilaton.\nFinally, we show that the celebrated Aharonov-Bohm phase can be derived\ndirectly from the constructed on-shell amplitude and that it too enjoys a\nsimple double copy to its gravitational counterpart.\n"}
+{"abstract": "  Human interpretability of deep neural networks' decisions is crucial,\nespecially in domains where these directly affect human lives. Counterfactual\nexplanations of already trained neural networks can be generated by perturbing\ninput features and attributing importance according to the change in the\nclassifier's outcome after perturbation. Perturbation can be done by replacing\nfeatures using heuristic or generative in-filling methods. The choice of\nin-filling function significantly impacts the number of artifacts, i.e.,\nfalse-positive attributions. Heuristic methods result in false-positive\nartifacts because the image after the perturbation is far from the original\ndata distribution. Generative in-filling methods reduce artifacts by producing\nin-filling values that respect the original data distribution. However, current\ngenerative in-filling methods may also increase false-negatives due to the high\ncorrelation of in-filling values with the original data. In this paper, we\npropose to alleviate this by generating in-fillings with the\nstatistically-grounded Knockoffs framework, which was developed by Barber and\nCand\\`es in 2015 as a tool for variable selection with controllable false\ndiscovery rate. Knockoffs are statistically null-variables as decorrelated as\npossible from the original data, which can be swapped with the originals\nwithout changing the underlying data distribution. A comparison of different\nin-filling methods indicates that in-filling with knockoffs can reveal\nexplanations in a more causal sense while still maintaining the compactness of\nthe explanations.\n"}
+{"abstract": "  This study investigates simple games. A fundamental research question in this\nfield is to determine necessary and sufficient conditions for a simple game to\nbe a weighted majority game. Taylor and Zwicker (1992) showed that a simple\ngame is non-weighted if and only if there exists a trading transform of finite\nsize. They also provided an upper bound on the size of such a trading\ntransform, if it exists. Gvozdeva and Slinko (2011) improved that upper bound;\ntheir proof employed a property of linear inequalities demonstrated by Muroga\n(1971).In this study, we provide a new proof of the existence of a trading\ntransform when a given simple game is non-weighted. Our proof employs Farkas'\nlemma (1894), and yields an improved upper bound on the size of a trading\ntransform.\n  We also discuss an integer-weight representation of a weighted simple game,\nimproving the bounds obtained by Muroga (1971). We show that our bound on the\nquota is tight when the number of players is less than or equal to five, based\non the computational results obtained by Kurz (2012).\n  Furthermore, we discuss the problem of finding an integer-weight\nrepresentation under the assumption that we have minimal winning coalitions and\nmaximal losing coalitions.In particular, we show a performance of a rounding\nmethod.\n  Lastly, we address roughly weighted simple games. Gvozdeva and Slinko (2011)\nshowed that a given simple game is not roughly weighted if and only if there\nexists a potent certificate of non-weightedness. We give an upper bound on the\nlength of a potent certificate of non-weightedness. We also discuss an\ninteger-weight representation of a roughly weighted simple game.\n"}
+{"abstract": "  We propose a multivariate extension of a well-known characterization by S.\nKusuoka of regular and coherent risk measures as maximal correlation\nfunctionals. This involves an extension of the notion of comonotonicity to\nrandom vectors through generalized quantile functions. Moreover, we propose to\nreplace the current law invariance, subadditivity and comonotonicity axioms by\nan equivalent property we call strong coherence and that we argue has more\nnatural economic interpretation. Finally, we reformulate the computation of\nregular and coherent risk measures as an optimal transportation problem, for\nwhich we provide an algorithm and implementation.\n"}
+{"abstract": "  Crofton formulas on simply-connected Riemannian space forms allow to compute\nthe volumes, or more generally the Lipschitz-Killing curvature integrals of a\nsubmanifold with corners, by integrating the Euler characteristic of its\nintersection with all geodesic submanifolds. We develop a framework of Crofton\nformulas with distributions replacing measures, which has in its core Alesker's\nRadon transform on valuations. We then apply this framework, and our recent\nHadwiger-type classification, to compute explicit Crofton formulas for all\nisometry-invariant valuations on all pseudospheres, pseudo-Euclidean and\npseudohyperbolic spaces. We find that, in essence, a single measure which\ndepends analytically on the metric, gives rise to all those Crofton formulas\nthrough its distributional boundary values at parts of the boundary\ncorresponding to the different indefinite signatures. In particular, the\nCrofton formulas we obtain are formally independent of signature.\n"}
+{"abstract": "  When data is collected in an adaptive manner, even simple methods like\nordinary least squares can exhibit non-normal asymptotic behavior. As an\nundesirable consequence, hypothesis tests and confidence intervals based on\nasymptotic normality can lead to erroneous results. We propose an online\ndebiasing estimator to correct these distributional anomalies in least squares\nestimation. Our proposed method takes advantage of the covariance structure\npresent in the dataset and provides sharper estimates in directions for which\nmore information has accrued. We establish an asymptotic normality property for\nour proposed online debiasing estimator under mild conditions on the data\ncollection process, and provide asymptotically exact confidence intervals. We\nadditionally prove a minimax lower bound for the adaptive linear regression\nproblem, thereby providing a baseline by which to compare estimators. There are\nvarious conditions under which our proposed estimator achieves the minimax\nlower bound up to logarithmic factors. We demonstrate the usefulness of our\ntheory via applications to multi-armed bandit, autoregressive time series\nestimation, and active learning with exploration.\n"}
+{"abstract": "  A microscopic calculation is presented for the spin-transfer torques (STT)\nand damping torques in metallic antiferromagnets (AF). It is found that the\nsign of the STT is opposite to that in ferromagnets because of the AF transport\ncharacter, and the current-to-STT conversion factor is enhanced near the AF gap\nedge. The dissipative torque parameter $\\beta_n$ and the damping parameter\n$\\alpha_n$ for the N\\'eel vector arise from spin relaxation of electrons.\nPhysical consequences are demonstrated for the AF domain wall motion using\ncollective coordinates, and some similarities to the ferromagnetic case are\npointed out such as intrinsic pinning and the specialty of $\\alpha_n =\n\\beta_n$. A recent experiment on a ferrimagnetic GdFeCo near its\nangular-momentum compensation temperature is discussed.\n"}
+{"abstract": "  This paper investigates the stability properties of discrete-time multilinear\ndynamical systems via tensor spectral theory. In particular, if the dynamic\ntensor of a multilinear dynamical system is orthogonally decomposable (odeco),\nwe can construct its explicit solution by exploiting tensor Z-eigenvalues and\nZ-eigenvectors. Based on the form of the explicit solution, we illustrate that\nthe Z-eigenvalues of the dynamic tensor play a significant role in the\nstability analysis, offering necessary and sufficient conditions. In addition,\nby utilizing the upper bounds of Z-spectral radii, we are able to determine the\nasymptotic stability of the multilinear dynamical system efficiently.\nFurthermore, we extend the stability results to the multilinear dynamical\nsystems with non-odeco dynamic tensors by exploiting tensor singular values. We\ndemonstrate our results via numerical examples.\n"}
+{"abstract": "  The rapid spread of COVID-19 has already affected human lives throughout the\nglobe. Governments of different countries have taken various measures, but how\nthey affected people lives is not clear. In this study, a rule-based and a\nmachine-learning based models are applied to answer the above question using\npublic tweets from Japan, USA, UK, and Australia. Two polarity timeseries\n(meanPol and pnRatio) and two events, namely \"lockdown or emergency (LED)\" and\n\"the economic support package (ESP)\", are considered in this study. Statistical\ntesting on the sub-series around LED and ESP events showed their positive\nimpacts to the people of (UK and Australia) and (USA and UK), respectively\nunlike Japanese people that showed opposite effects. Manual validation with the\nrelevant tweets showed an agreement with the statistical results. A case study\nwith Japanese tweets using supervised logistic regression classifies tweets\ninto heath-worry, economy-worry and other classes with 83.11% accuracy.\nPredicted tweets around events re-confirm the statistical outcomes.\n"}
+{"abstract": "  We study the influence of the in-medium mass difference between boson and\nantiboson on their spectra. The in-medium mass difference may lead to a\ndifference between the transverse momentum spectra of boson and antiboson. This\neffect increases with the increasing in-medium mass difference between boson\nand antiboson. The difference between the transverse momentum spectra of boson\nand antiboson increases with the increasing expanding velocity of the source\nand decreases with the increasing transverse momentum in large transverse mass\nregion (mT > 1:6 GeV). The interactions between the hadron and the medium may\nincrease with the increasing temperature of the medium and the higher\nfreeze-out temperature may lead to a larger mass difference between boson and\nantiboson, and may give rise to a larger difference between the transverse\nmomentum spectra of boson and antiboson for higher freeze-out temperature.\n"}
+{"abstract": "  The use of iteration and piecewise functions allows analytic expression of\nthe trajectories of an R\\\"ossler-like attractor, avoiding infinite series\nsolution. It seems possible to extend this approach to other attractors, even\nif the differential equations are not known, but the experimental data have\nbeen collected.\n"}
+{"abstract": "  For geometrically finite Kleinian surface groups, Bonahon and Otal proved the\nexistence part, and partly the uniqueness part of the bending lamination\nconjecture. In this paper, we generalise the existence part to general Kleinian\nsurface groups including geometrically infinite ones. We furthermore prove the\ncompactness of the set of Kleinian surface groups realising an arbitrarily\nfixed data of bending laminations and ending laminations. Our proof is\nindependent of that of Bonahon and Otal.\n"}
+{"abstract": "  The rise of intelligent assistant systems like Siri and Alexa have led to the\nemergence of Conversational Search, a research track of Information Retrieval\n(IR) that involves interactive and iterative information-seeking user-system\ndialog. Recently released OR-QuAC and TCAsT19 datasets narrow their research\nfocus on the retrieval aspect of conversational search i.e. fetching the\nrelevant documents (passages) from a large collection using the conversational\nsearch history. Currently proposed models for these datasets incorporate\nhistory in retrieval by appending the last N turns to the current question\nbefore encoding. We propose to use another history selection approach that\ndynamically selects and weighs history turns using the attention mechanism for\nquestion embedding. The novelty of our approach lies in experimenting with soft\nattention-based history selection approach in an open-retrieval setting.\n"}
+{"abstract": "  This paper presents an extension to an existing instruction set architecture,\nwhich gains considerable reduction in power consumption. The reduction in power\nconsumption is achieved through coding of the most commonly executed\ninstructions in a short format done by the compiler based on a profile of\nprevious executions. This leads to fewer accesses to the instruction cache and\nthat more instructions can fit in the cache. As a secondary effect, this turned\nout to be very beneficial in terms of power. Another major advantage, which is\nthe main concern of this paper is the reduction in the number of instruction\nfetch cycles which will also contribute significantly towards reduction in\npower consumption. The work involves implementing the new processor\narchitecture in ASIC and estimation of power-consumption compared to the normal\narchitecture.\n"}
+{"abstract": "  By analyzing the analogies between the effective system of $N$ spins\ndescribed by the Ising Hamiltonian and the phenomenon of the self--gravity in\nmixed particle systems, we show that cooled ions held in a segmented ion trap\nand exposed to a magnetic field gradient can simulate the proposed mechanism of\nmutual interaction in mixed neutrino system. We show that with trapped ions one\ncan reproduce the expected corrections to the flavor transitions and the $CPT$\nviolation induced by gravity on flavor fields, which may have played an\nimportant role in the early stages of the universe. The results presented are\nexperimentally testable. They indicate that ions confined in microtraps can\nrepresent a new tool to test fundamental phenomena of nature.\n"}
+{"abstract": "  Deep Neural Networks lead the state of the art of computer vision tasks.\nDespite this, Neural Networks are brittle in that small changes in the input\ncan drastically affect their prediction outcome and confidence. Consequently\nand naturally, research in this area mainly focus on adversarial attacks and\ndefenses. In this paper, we take an alternative stance and introduce the\nconcept of Assistive Signals, which are optimized to improve a model's\nconfidence score regardless if it's under attack or not. We analyse some\ninteresting properties of these assistive perturbations and extend the idea to\noptimize assistive signals in the 3D space for real-life scenarios simulating\ndifferent lighting conditions and viewing angles. Experimental evaluations show\nthat the assistive signals generated by our optimization method increase the\naccuracy and confidence of deep models more than those generated by\nconventional methods that work in the 2D space. In addition, our Assistive\nSignals illustrate the intrinsic bias of ML models towards certain patterns in\nreal-life objects. We discuss how we can exploit these insights to re-think, or\navoid, some patterns that might contribute to, or degrade, the detectability of\nobjects in the real-world.\n"}
+{"abstract": "  Considering the growing demand and popularity of Do-It-Yourself (DIY)\nnetworks, low-cost devices managed by the people, for the people and the ease\nof deployment of localised/decentralised Internet services, it is convenient\nfor such networks to have an efficient low-cost monitoring platform to ensure\navailability, responsiveness and users' Quality of Experience (QoE). This is\nespecially relevant for the developing world where Community Networks (CN) are\nincreasing in popularity and complexity. In this letter, we discuss Octopus: an\narchitecture for stream network monitoring.\n"}
+{"abstract": "  Learning an unknown $n$-qubit quantum state $\\rho$ is a fundamental challenge\nin quantum computing. Information-theoretically, it is known that tomography\nrequires exponential in $n$ many copies of $\\rho$ to estimate it up to trace\ndistance. Motivated by computational learning theory, Aaronson et al.\nintroduced many (weaker) learning models: the PAC model of learning states\n(Proceedings of Royal Society A'07), shadow tomography (STOC'18) for learning\n\"shadows\" of a state, a model that also requires learners to be differentially\nprivate (STOC'19) and the online model of learning states (NeurIPS'18). In\nthese models it was shown that an unknown state can be learned \"approximately\"\nusing linear-in-$n$ many copies of rho. But is there any relationship between\nthese models? In this paper we prove a sequence of (information-theoretic)\nimplications from differentially-private PAC learning, to communication\ncomplexity, to online learning and then to quantum stability.\n  Our main result generalizes the recent work of Bun, Livni and Moran (Journal\nof the ACM'21) who showed that finite Littlestone dimension (of Boolean-valued\nconcept classes) implies PAC learnability in the (approximate) differentially\nprivate (DP) setting. We first consider their work in the real-valued setting\nand further extend their techniques to the setting of learning quantum states.\nKey to our results is our generic quantum online learner, Robust Standard\nOptimal Algorithm (RSOA), which is robust to adversarial imprecision. We then\nshow information-theoretic implications between DP learning quantum states in\nthe PAC model, learnability of quantum states in the one-way communication\nmodel, online learning of quantum states, quantum stability (which is our\nconceptual contribution), various combinatorial parameters and give further\napplications to gentle shadow tomography and noisy quantum state learning.\n"}
+{"abstract": "  Vaccination strategy is crucial in fighting against the COVID-19 pandemic.\nSince the supply is limited, contact network-based interventions can be most\npowerful to set an optimal strategy by identifying high-risk individuals or\ncommunities. However, due to the high dimension, only partial and noisy network\ninformation can be available in practice, especially for dynamical systems\nwhere the contact networks are highly time-variant. Furthermore, numerous\nmutations of SARS-CoV-2 impact considerably the current infectious probability,\nrequiring real-time network updating algorithms. In this study, we propose a\nsequential network updating approach based on data assimilation techniques to\ncombine different sources of temporal information. We then prioritise the\nindividuals with high-degree or high-centrality, obtained from the assimilated\nnetworks, for vaccination. The assimilation-based approach is compared with the\nstandard method (based on partially observed networks) and a random selection\nstrategy in terms of vaccination effectiveness in a SIR model. The numerical\ncomparison is first carried out using real-world face-to-face dynamical\nnetworks collected in a high school, following by sequential multi-layer\nnetworks, generated relying on the Barabasi-Albert model emulating the\ndepartment of Computing at Imperial College London in the UK as an example.\n"}
+{"abstract": "  Monitoring the responses of plants to environmental changes is essential for\nplant biodiversity research. This, however, is currently still being done\nmanually by botanists in the field. This work is very laborious, and the data\nobtained is, though following a standardized method to estimate plant coverage,\nusually subjective and has a coarse temporal resolution. To remedy these\ncaveats, we investigate approaches using convolutional neural networks (CNNs)\nto automatically extract the relevant data from images, focusing on plant\ncommunity composition and species coverages of 9 herbaceous plant species. To\nthis end, we investigate several standard CNN architectures and different\npretraining methods. We find that we outperform our previous approach at higher\nimage resolutions using a custom CNN with a mean absolute error of 5.16%. In\naddition to these investigations, we also conduct an error analysis based on\nthe temporal aspect of the plant cover images. This analysis gives insight into\nwhere problems for automatic approaches lie, like occlusion and likely\nmisclassifications caused by temporal changes.\n"}
+{"abstract": "  We consider the dynamics of a two-dimensional incompressible perfect fluid on\na M\\\"obius strip embedded in $\\mathbb{R}^3$. The vorticity-streamfunction\nformulation of the Euler equations is derived from an exterior-calculus form of\nthe momentum equation. The non-orientability of the M\\\"obius strip and the\ndistinction between forms and pseudo-forms this introduces lead to unusual\nproperties: a boundary condition is provided by the conservation of circulation\nalong the single boundary of the strip, and there is no integral conservation\nfor the vorticity or for any odd function thereof. A finite-difference\nnumerical implementation is used to illustrate the M\\\"obius-strip realisation\nof familiar phenomena: translation of vortices along boundaries, shear\ninstability, and decaying turbulence.\n"}
+{"abstract": "  We propose a novel algorithm named Expert Q-learning. Expert Q-learning was\ninspired by Dueling Q-learning and aimed at incorporating the ideas from\nsemi-supervised learning into reinforcement learning through splitting Q-values\ninto state values and action advantages. Different from Generative Adversarial\nImitation Learning and Deep Q-Learning from Demonstrations, the offline expert\nwe have used only predicts the value of a state from {-1, 0, 1}, indicating\nwhether this is a bad, neutral or good state. An expert network was designed in\naddition to the Q-network, which updates each time following the regular\noffline minibatch update whenever the expert example buffer is not empty. The\nQ-network plays the role of the advantage function only during the update. Our\nalgorithm also keeps asynchronous copies of the Q-network and expert network,\npredicting the target values using the same manner as of Double Q-learning.\n  We compared on the game of Othello our algorithm with the state-of-the-art\nQ-learning algorithm, which was a combination of Double Q-learning and Dueling\nQ-learning. The results showed that Expert Q-learning was indeed useful and\nmore resistant to the overestimation bias of Q-learning. The baseline\nQ-learning algorithm exhibited unstable and suboptimal behavior, especially\nwhen playing against a stochastic player, whereas Expert Q-learning\ndemonstrated more robust performance with higher scores. Expert Q-learning\nwithout using examples has also gained better results than the baseline\nalgorithm when trained and tested against a fixed player. On the other hand,\nExpert Q-learning without examples cannot win against the baseline Q-learning\nalgorithm in direct game competitions despite the fact that it has also shown\nthe strength of reducing the overestimation bias.\n"}
+{"abstract": "  This article deals with the most recent developments in the field of\nexoplanetary science connecting the interior of the planets with their\nhabitability. In this issue, I have specified the importance of interior\ndynamics and briefly reviewed some of the main factors by which interior of a\nplanet can effect the habitability of extra-solar planets.\n"}
+{"abstract": "  Two qubit density matrices which are of X-shape, are a natural generalization\nof Bell Diagonal States recently simulated on the IBM quantum device. We\npropose a quantum circuit for simulation of a general X-state on the same\nquantum device and study its properties for several values of the extended\nparameter space. We also show by specific measurements that their X-shape is\napproximately robust against noisy quantum gates. To further physically\nmotivate this study, we invoke the two-spin Heisenberg XYZ system and show that\nfor a wide class of initial states, it leads to dynamical density matrices\nwhich are X-states. Due to the symmetries of this Hamiltonian, we show that by\nonly two qubits, one can simulate the dynamics of this system on the IBM\nquantum computer.\n"}
+{"abstract": "  Classes of two-nucleon ($2N$) contact interactions are developed in\nconfiguration space at leading order (LO), next-to-leading order (NLO), and\nnext-to-next-to-next-to-leading order (N3LO) by fitting the experimental\nsinglet $np$ scattering length and deuteron binding energy at LO, and $np$ and\n$pp$ scattering data in the laboratory-energy range 0--15 MeV at NLO and 0--25\nMeV at N3LO. These interactions are regularized by including two Gaussian\ncutoffs, one for $T\\,$=$\\,0$ and the other for $T\\,$=$\\,1$ channels. The\ncutoffs are taken to vary in the ranges $R_0\\,$=$(1.5$--2.3) fm and\n$R_1\\,$=$(1.5$--3.0) fm. The 780 (1,100) data points up to 15 (25) MeV energy,\nprimarily differential cross sections, are fitted by the NLO (N3LO) models with\na $\\chi^2$/datum about 1.7 or less (well below 1.5), when harder cutoff values\nare adopted. As a first application, we report results for the binding energies\nof nuclei with mass numbers $A\\,$=$\\,3$--6 and 16 obtained with selected LO and\nNLO $2N$ models both by themselves as well as in combination with a LO\nthree-nucleon ($3N$) contact interaction. The latter is characterized by a\nsingle low-energy constant that is fixed to reproduce the experimental $^3$H\nbinding energy. The inclusion of the $3N$ interaction largely removes the\nsensitivity to cutoff variations in the few-nucleon systems and leads to\npredictions for the $^3$He and $^4$He binding energies that cluster around 7.8\nMeV and 30 MeV, respectively. However, in $^{16}$O this cutoff sensitivity\nremains rather strong. Finally, predictions at LO only are also reported for\nmedium-mass nuclei with $A\\,$=$\\,40$, 48, and 90.\n"}
+{"abstract": "  The article reviews the theory of open quantum system from a perspective of\nthe non-Hermiticity that emerges from the environment with an infinite number\nof degrees of freedom. The non-Hermiticity produces resonant states with\ncomplex eigenvalues, resulting in peak structures in scattering amplitudes and\ntransport coefficients. After introducing the definition of resonant states\nwith complex eigenvalues, we answer typical questions regarding the\nnon-Hermiticity of open quantum systems. What is the physical meaning of the\ncomplex eigenmomenta and eigenenergies? How and why do the resonant states\nbreak the time-reversal symmetry that the system observes? Can we make the\nprobabilistic interpretation of the resonant states with diverging wave\nfunctions? What is the physical meaning of the divergence of the wave\nfunctions? We also present an alternative way of finding resonant states,\nnamely the Feshbach formalism, in which we eliminate the infinite number of the\nenvironmental degrees of freedom. In this formalism, we attribute the\nnon-Hermiticity to the introduction of the retarded and advanced Green's\nfunctions.\n"}
+{"abstract": "  The binary neutron star merger gravitational-wave signal GW170817 was\nfollowed by three electromagnetic counterparts, including a kilonova arising\nfrom the radioactivity of freshly synthesized $r$-process elements in ejecta\nfrom the merger. Finding kilonovae after gravitational-wave triggers is crucial\nfor (i) the search for further counterparts, such as the afterglow, (ii)\nprobing the diversity of kilonovae and their dependence on the system's\ninclination angle, and (iii) building a sample for multi-messenger cosmology.\nDuring the third observing run of the gravitational-wave interferometer\nnetwork, no kilonova counterpart was found. We aim to predict the expected\npopulation of detectable kilonova signals for the upcoming O4 and O5 observing\nruns of the LIGO-Virgo-KAGRA instruments. Using a simplified criterion for\ngravitational-wave detection and a simple GW170817-calibrated model for the\nkilonova peak magnitude, we determine the rate of kilonovae in reach of\nfollow-up campaigns and their distributions in magnitude for various bands. We\nbriefly consider the case of GW190425, the only binary neutron star merger\nconfirmed since GW170817, and obtain constraints on its inclination angle from\nthe non-detection of its kilonova, assuming the source was below the follow-up\nthresholds. We also show that non-gravitational-wave-triggered kilonovae can be\na numerous class of sources in future surveys and briefly discuss associations\nwith short bright gamma-ray bursts. We finally discuss the detection of the\njetted outflow afterglow in addition to the kilonova.\n"}
+{"abstract": "  Narrow-band imaging surveys allow the study of the spectral characteristics\nof galaxies without the need of performing their spectroscopic follow-up. In\nthis work, we forward-model the Physics of the Accelerating Universe Survey\n(PAUS) narrow-band data. The aim is to improve the constraints on the spectral\ncoefficients used to create the galaxy spectral energy distributions (SED) of\nthe galaxy population model in Tortorelli et al. 2020. In that work, the model\nparameters were inferred from the Canada-France-Hawaii Telescope Legacy Survey\n(CFHTLS) data using Approximate Bayesian Computation (ABC). This led to\nstringent constraints on the B-band galaxy luminosity function parameters, but\nleft the spectral coefficients only broadly constrained. To address that, we\nperform an ABC inference using CFHTLS and PAUS data. This is the first time our\napproach combining forward-modelling and ABC is applied simultaneously to\nmultiple datasets. We test the results of the ABC inference by comparing the\nnarrow-band magnitudes of the observed and simulated galaxies using Principal\nComponent Analysis, finding a very good agreement. Furthermore, we prove the\nscientific potential of the constrained galaxy population model to provide\nrealistic stellar population properties by measuring them with the SED fitting\ncode \\textsc{CIGALE}. We use CFHTLS broad-band and PAUS narrow-band photometry\nfor a flux-limited ($\\mathrm{i}<22.5$) sample of galaxies up to redshift\n$\\mathrm{z \\sim 0.8}$. We find that properties like stellar masses,\nstar-formation rates, mass-weighted stellar ages and metallicities are in\nagreement within errors between observations and simulations. Overall, this\nwork shows the ability of our galaxy population model to correctly\nforward-model a complex dataset such as PAUS and the ability to reproduce the\ndiversity of galaxy properties at the redshift range spanned by CFHTLS and\nPAUS.\n"}
+{"abstract": "  We report the detection of multiple faint radio sources, that we identify as\nAGN-jets, within CLJ1449+0856 at z=2 using 3 GHz VLA observations. We study the\neffects of radio-jet based kinetic feedback at high redshifts, which has been\nfound to be crucial in low redshift clusters to explain the observed\nthermodynamic properties of their ICM. We investigate this interaction at an\nepoch featuring high levels of AGN activity and a transitional phase of ICM in\nregards to the likelihood of residual cold-gas accretion. We measure a total\nflux of $\\rm 30.6 \\pm 3.3~\\mu Jy$ from the 6 detected jets. Their power\ncontribution is estimated to be $1.2 ~(\\pm 0.6)~ \\times 10^{44} ~\\rm ergs~\ns^{-1}$, although this value could be up to $4.7 ~ \\times 10^{44} ~\\rm ergs~\ns^{-1}$. This is a factor $\\sim 0.25 - 1.0$ of the previously estimated\ninstantaneous energy injection into the ICM of CLJ1449+0856 from AGN outflows\nand star formation, that have already been found to be sufficient in globally\noffsetting the cooling flows in the cluster core. In line with the already\ndetected abundance of star formation, this mode of feedback being distributed\nover multiple sites, contrary to a single central source observed at low\nredshifts, points to accretion of gas into the cluster centre. This also\nsuggests a 'steady state' of the cluster featuring non cool-core like\nbehaviour. Finally, we also examine the TIR-radio luminosity ratio for the\nknown sample of galaxies within the cluster core and find that dense\nenvironments do not have any serious consequence on the compliance of galaxies\nto the IR-radio correlation.\n"}
+{"abstract": "  In this work, we use the concept of quaternion time and demonstrate that it\ncan be applied for description of four-dimensional space-time intervals. We\ndemonstrate that the quaternion time interval together with the finite speed of\nlight propagation allow for a simple intuitive understanding of the time\ninterval measurement during arbitrary relative motion between a signal source\nand observer. We derive a quaternion form of Lorentz time dilation and show\nthat the norm corresponds to the traditional expression of the Lorentz\ntransformation and represents the measured value of time intervals, making the\nnew theory inseparable from the theory of measurement. We determine that the\nspace-time interval in the observer reference frame is given by a conjugate\nquaternion expression, which is essential for proper definition of the\nquaternion derivatives in the observer reference frame. Then, we apply\nquaternion differentiation to an arbitrary potential, which leads to\ngeneralized Lorentz force. The second quaternion derivative of the potential\nleads to expressions similar to generalized Maxwell equations. Finally, we\napply the resulting formalism to electromagnetic and gravitational interactions\nand show that the new expressions are similar to the traditional expressions,\nwith the exception of additional terms, related to scalar fields, that need\nfurther study and experimental verification. Therefore, the new mathematical\napproach based on Hamilton's quaternions may serve as a useful foundation of\nthe unified theory of space-time and matter.\n"}
+{"abstract": "  This work investigates the importance of verification and validation (V&V) to\nachieve predictive scale-resolving simulations (SRS) of turbulence, i.e.,\ncomputations capable of resolving a fraction of the turbulent flow scales.\nToward this end, we present a novel but simple V&V strategy based on grid and\nphysical resolution refinement studies that can be used even when the exact\ninitial flow conditions are unknown, or reference data is unavailable. This is\nparticularly relevant for transient and transitional flow problems, as well as\nfor the improvement of turbulence models. We start by presenting a literature\nsurvey of results obtained with distinct SRS models for flows past circular\ncylinders. It confirms the importance of V&V by illustrating a large\nvariability of results, which is independent of the selected mathematical model\nand Reynolds number. The proposed V&V strategy is then used on three\nrepresentative problems of practical interest. The results illustrate that it\nis possible to conduct reliable verification and validation exercises with SRS\nmodels, and evidence the importance of V&V to predictive SRS of turbulence. The\ndata also confirm the advantages of the proposed V\\&V strategy.\n"}
+{"abstract": "  As narrow optical bandgap materials, semiconducting single-walled carbon\nnanotubes (SWCNTs) are rarely regarded as charge donors in photoinduced\ncharge-transfer (PCT) reactions. However, the unique band structure and unusual\nexciton dynamics of SWCNTs add more possibilities to the classical PCT\nmechanism.In this work, we demonstrate PCT from photoexcited semiconducting\n(6,5) SWCNTs to a wide-bandgap wrapping\npoly-[(9,9-dioctylfluorenyl-2,7-diyl)-alt-(6,6')-(2,2'-bipyridine)] (PFO-BPy)\nvia femtosecond transient absorption spectroscopy. By monitoring the spectral\ndynamics of the SWCNT polaron, we show that charge transfer from photoexcited\nSWCNTs to PFO-BPy can be driven not only by the energetically favorable E33\ntransition but also by the energetically unfavorable E22 excitation under high\npump fluence. This unusual PCT from narrow-bandgap SWCNTs toward a wide-bandgap\npolymer originates from the up-converted high-energy excitonic state (E33 or\nhigher) that is promoted by the Auger recombination of excitons and charge\ncarriers in SWCNTs. These insights provide new pathways for charge separation\nin SWCNT-based photodetectors and photovoltaic cells.\n"}
+{"abstract": "  In this paper, we investigate whether overdensity formation via streaming\ninstability is consistent with recent multi-wavelength ALMA observations in the\nLupus star forming region. We simulate the local action of streaming\ninstability in 2D using the code ATHENA, and examine the radiative properties\nat mm wavelengths of the resulting clumpy dust distribution by focusing on two\nobservable quantities: the optically thick fraction $ff$ (in ALMA band 6) and\nthe spectral index $\\alpha$ (in bands 3-7). By comparing the simulated\ndistribution in the $ff-\\alpha$ plane before and after the action of streaming\ninstability, we observe that clump formation causes $ff$ to drop, because of\nthe suppression of emission from grains that end up in optically thick clumps.\n$\\alpha$, instead, can either increase or decline after the action of streaming\ninstability; we use a simple toy model to demonstrate that this behaviour\ndepends on the sizes of the grains whose emission is suppressed by being\nincorporated in optically thick clumps. In particular, the sign of evolution of\n$\\alpha$ depends on whether grains near the opacity maximum at a few tenths of\na mm end up in clumps. By comparing the simulation distributions before/after\nclump formation to the data distribution, we note that the action of streaming\ninstability drives simulations towards the area of the plane where the data are\nlocated. We furthermore demonstrate that this behaviour is replicated in\nintegrated disc models provided that the instability is operative over a region\nof the disc that contributes significantly to the total mm flux.\n"}
+{"abstract": "  Platelet products are both expensive and have very short shelf lives. As\nusage rates for platelets are highly variable, the effective management of\nplatelet demand and supply is very important yet challenging. The primary goal\nof this paper is to present an efficient forecasting model for platelet demand\nat Canadian Blood Services (CBS). To accomplish this goal, four different\ndemand forecasting methods, ARIMA (Auto Regressive Moving Average), Prophet,\nlasso regression (least absolute shrinkage and selection operator) and LSTM\n(Long Short-Term Memory) networks are utilized and evaluated. We use a large\nclinical dataset for a centralized blood distribution centre for four hospitals\nin Hamilton, Ontario, spanning from 2010 to 2018 and consisting of daily\nplatelet transfusions along with information such as the product\nspecifications, the recipients' characteristics, and the recipients' laboratory\ntest results. This study is the first to utilize different methods from\nstatistical time series models to data-driven regression and a machine learning\ntechnique for platelet transfusion using clinical predictors and with different\namounts of data. We find that the multivariate approaches have the highest\naccuracy in general, however, if sufficient data are available, a simpler time\nseries approach such as ARIMA appears to be sufficient. We also comment on the\napproach to choose clinical indicators (inputs) for the multivariate models.\n"}
+{"abstract": "  We present a revised characterisation of the previously discovered transiting\nplanet systems HATS-34 and HATS-46. We make use of the newly available\nspace-based light curves from the NASA TESS mission and high-precision parallax\nand absolute photometry measurements from the ESA Gaia mission to determine the\nmass and radius of the planets and host stars with dramatically increased\nprecision and accuracy compared to published values, with the uncertainties in\nsome parameters reduced by as much as a factor of seven. Using an isochrone\nbased fit, for HATS-34 we measure a revised host star mass and radius of\n$0.952_{-0.02}^{+0.04}M_S$ and of $0.9381\\pm0.0080R_S$, respectively, and a\nrevised mass and radius for the transiting planet of $0.951\\pm0.050 M_J$ and\n$1.282 \\pm0.064 R_J$ respectively. Similarly, for HATS-46 we measure a revised\nmass and radius for the host star of $0.869\\pm0.023M_S$, and $0.894\\pm0.010\nR_S$, respectively, and a revised mass and radius for the planet of $0.158\n\\pm0.042 M_J$, and $0.951 \\pm 0.029 R_J$, respectively. The uncertainties that\nwe determine on the stellar and planetary masses and radii are also\nsubstantially lower than re-determinations that incorporate the Gaia results\nwithout performing a full re-analysis of the light curves and other\nobservational data. We argue that, in light of Gaia and TESS, a full\nre-analysis of previously discovered transiting planets is warranted.\n"}
+{"abstract": "  We derive a novel framework called Bi-Directional Grid Constrained (BGC)\nstochastic processes in which the further an Ito diffusion drifts away from the\norigin, then the further it will be constrained. By making suitable\nmodifications to the Law of Iterated Logarithm (LIL), we derive a novel theorem\nabout the upper and lower bounds for BGC processes and their hidden barrier. To\nvisualize the theorem, we run many simulations of the Ito diffusions for a\nrepresentative expression for lambda(X, t), both before and after BGC and\nuncover some interesting results with applications into finance and many other\nareas.\n"}
+{"abstract": "  We present fully differential predictions for the production cross section of\na Higgs boson via the gluon fusion mechanism at next-to-next-to-next-to leading\norder (N$^3$LO) in QCD perturbation theory. To perform our calculation we apply\nthe Projection-to-Born method for the first time to the calculation of the\nnon-factorising production of a colorless final state at the LHC at N$^3$LO. We\npredict differential distributions for the two photon final state produced by\nthe decay of the Higgs boson and apply fiducial cuts on the photon rapidities\nand momenta. The N$^3$LO corrections to these differential distributions have\ncomplex features and are in part larger than the inclusive N$^3$LO corrections\nto the production cross section. Overall, we observe that the inclusion of the\nN$^3$LO QCD corrections significantly reduces the perturbative uncertainties\nand leads to a stabilisation of the perturbative expansion.\n"}
+{"abstract": "  In this contribution I present results achieved recently in the field of the\nOT forecast that push further the limit of the accuracy of the OT forecasts and\nopen to new perspectives in this field.\n"}
+{"abstract": "  Nonradiative processes limit optoelectronic functionality of nanocrystals and\ncurb their device performance. Nevertheless, the dynamic structural origins of\nnonradiative relaxations in nanocrystals are not understood. Here, femtosecond\nelectron diffraction measurements corroborated by atomistic simulations uncover\ntransient lattice deformations accompanying radiationless electronic processes\nin semiconductor nanocrystals. Investigation of the excitation energy\ndependence shows that hot carriers created by a photon energy considerably\nlarger than the bandgap induce structural distortions at nanocrystal surfaces\non few picosecond timescales associated with the localization of trapped holes.\nOn the other hand, carriers created by a photon energy close to the bandgap\nresult in transient lattice heating that occurs on a much longer 200 ps\ntimescale, governed by an Auger heating mechanism. Elucidation of the\nstructural deformations associated with the surface trapping of hot holes\nprovides atomic-scale insights into the mechanisms deteriorating optoelectronic\nperformance and a pathway towards minimizing these losses in nanocrystal\ndevices.\n"}
+{"abstract": "  The 2020 NBA playoffs were played inside of a bubble in Disney World because\nof the COVID-19 pandemic. This meant that there were no fans in attendance,\ngames played on neutral courts and no traveling for teams, which in theory\nremoves home-court advantage from the games. This setting has attracted much\ndiscussion as analysts and fans debated the possible effects it may have on the\noutcome of games. Home-court advantage has historically played an influential\nrole in NBA playoff series outcomes. The 2020 playoff provided a unique\nopportunity to study the effects of the bubble and home-court advantage by\ncomparing the 2020 season with the seasons in the past. While many factors\ncontribute to the outcome of games, points scored is the deciding factor of who\nwins games, so scoring is the primary focus of this study. The specific\nmeasures of interest are team scoring totals and team shooting percentage on\ntwo-pointers, three-pointers, and free throws. Comparing these measures for\nhome teams and away teams in 2020 vs. 2017-2019 shows that the 2020 playoffs\nfavored away teams more than usual, particularly with two point shooting and\ntotal scoring.\n"}
+{"abstract": "  Word segmentation and part-of-speech tagging are two critical preliminary\nsteps for downstream tasks in Vietnamese natural language processing. In\nreality, people tend to consider also the phrase boundary when performing word\nsegmentation and part of speech tagging rather than solely process word by word\nfrom left to right. In this paper, we implement this idea to improve word\nsegmentation and part of speech tagging the Vietnamese language by employing a\nsimplified constituency parser. Our neural model for joint word segmentation\nand part-of-speech tagging has the architecture of the syllable-based CRF\nconstituency parser. To reduce the complexity of parsing, we replace all\nconstituent labels with a single label indicating for phrases. This model can\nbe augmented with predicted word boundary and part-of-speech tags by other\ntools. Because Vietnamese and Chinese have some similar linguistic phenomena,\nwe evaluated the proposed model and its augmented versions on three Vietnamese\nbenchmark datasets and six Chinese benchmark datasets. Our experimental results\nshow that the proposed model achieves higher performances than previous works\nfor both languages.\n"}
+{"abstract": "  It has been recently reported that classical systems have speed limit for\nstate evolution, although such a concept of speed limit had been considered to\nbe unique to quantum systems. Owing to the speed limit for classical system,\nthe lower bound for calculation time of simulated annealing with desired\ncalculation accuracy can be derived. However, such a lower bound does not work\nas a criterion for completion of calculation in a practical time. In this\npaper, we derive an inequality for classical system analogous to the quantum\nadiabatic theorem that gives calculation time for an accuracy-guaranteed\nfluctuation-exploiting computation. The trade-off relation between calculation\ntime and accuracy is given in the form tolerable in practical use.\n"}
+{"abstract": "  Cancer is responsible for millions of deaths worldwide every year. Although\nsignificant progress has been achieved in cancer medicine, many issues remain\nto be addressed for improving cancer therapy. Appropriate cancer patient\nstratification is the prerequisite for selecting appropriate treatment plan, as\ncancer patients are of known heterogeneous genetic make-ups and phenotypic\ndifferences. In this study, built upon deep phenotypic characterizations\nextractable from Mayo Clinic electronic health records (EHRs) and genetic test\nreports for a collection of cancer patients, we developed a system leveraging a\njoint of phenotypic and genetic features for cancer patient subgrouping.\n  The workflow is roughly divided into three parts: feature preprocessing,\ncancer patient classification, and cancer patient clustering based. In feature\npreprocessing step, we performed filtering, retaining the most relevant\nfeatures. In cancer patient classification, we utilized joint categorical\nfeatures to build a patient-feature matrix and applied nine different machine\nlearning models, Random Forests (RF), Decision Tree (DT), Support Vector\nMachine (SVM), Naive Bayes (NB), Logistic Regression (LR), Multilayer\nPerceptron (MLP), Gradient Boosting (GB), Convolutional Neural Network (CNN),\nand Feedforward Neural Network (FNN), for classification purposes. Finally, in\nthe cancer patient clustering step, we leveraged joint embeddings features and\npatient-feature associations to build an undirected feature graph and then\ntrained the cancer feature node embeddings.\n"}
+{"abstract": "  Geolocated time series, i.e., time series associated with certain locations,\nabound in many modern applications. In this paper, we consider hybrid queries\nfor retrieving geolocated time series based on filters that combine spatial\ndistance and time series similarity. For the latter, unlike existing work, we\nallow filtering based on local similarity, which is computed based on\nsubsequences rather than the entire length of each series, thus allowing the\ndiscovery of more fine-grained trends and patterns. To efficiently support such\nqueries, we first leverage the state-of-the-art BTSR-tree index, which utilizes\nbounds over both the locations and the shapes of time series to prune the\nsearch space. Moreover, we propose optimizations that check at specific\ntimestamps to identify candidate time series that may exceed the required local\nsimilarity threshold. To further increase pruning power, we introduce the\nSBTSR-tree index, an extension to BTSR-tree, which additionally segments the\ntime series temporally, allowing the construction of tighter bounds. Our\nexperimental results on several real-world datasets demonstrate that SBTSR-tree\ncan provide answers much faster for all examined query types. This paper has\nbeen published in the 27th International Conference on Advances in Geographic\nInformation Systems (ACM SIGSPATIAL 2019).\n"}
+{"abstract": "  The secondary component of GW190814 with a mass of 2.50-2.67 $M_{\\odot}$ may\nbe the lightest black hole or the heaviest neutron star ever observed in a\nbinary compact object system. To explore the possible equation of state (EOS),\nwhich can support such massive neutron star, we apply the relativistic\nmean-field model with a density-dependent isovector coupling constant to\ndescribe the neutron-star matter. The acceptable EOS should satisfy some\nconstraints: the EOS model can provide a satisfactory description of the\nnuclei; the maximum mass $M_\\textrm{TOV}$ is above 2.6 $M_{\\odot}$; the tidal\ndeformability of a canonical 1.4 $M_{\\odot}$ neutron star $\\Lambda_{1.4}$\nshould lie in the constrained range from GW170817. In this paper, we find that\nthe nuclear symmetry energy and its density dependence play a crucial role in\ndetermining the EOS of neutron-star matter. The constraints from the mass of\n2.6 $M_{\\odot}$ and the tidal deformability $\\Lambda_{1.4}=616_{-158}^{+273}$\n(based on the assumption that GW190814 is a neutron star-black hole binary) can\nbe satisfied as the slope of symmetry energy $L \\leq 50$ MeV. Even including\nthe constraint of $\\Lambda_{1.4}=190_{-120}^{+390}$ from GW170817 which\nsuppresses the EOS stiffness at low density, the possibility that the secondary\ncomponent of GW190814 is a massive neutron star cannot be excluded in this\nstudy.\n"}
+{"abstract": "  Existing video stabilization methods often generate visible distortion or\nrequire aggressive cropping of frame boundaries, resulting in smaller field of\nviews. In this work, we present a frame synthesis algorithm to achieve\nfull-frame video stabilization. We first estimate dense warp fields from\nneighboring frames and then synthesize the stabilized frame by fusing the\nwarped contents. Our core technical novelty lies in the learning-based\nhybrid-space fusion that alleviates artifacts caused by optical flow inaccuracy\nand fast-moving objects. We validate the effectiveness of our method on the\nNUS, selfie, and DeepStab video datasets. Extensive experiment results\ndemonstrate the merits of our approach over prior video stabilization methods.\n"}
+{"abstract": "  In this paper we publish the largest identity-annotated Holstein-Friesian\ncattle dataset Cows2021 and a first self-supervision framework for video\nidentification of individual animals. The dataset contains 10,402 RGB images\nwith labels for localisation and identity as well as 301 videos from the same\nherd. The data shows top-down in-barn imagery, which captures the breed's\nindividually distinctive black and white coat pattern. Motivated by the\nlabelling burden involved in constructing visual cattle identification systems,\nwe propose exploiting the temporal coat pattern appearance across videos as a\nself-supervision signal for animal identity learning. Using an\nindividual-agnostic cattle detector that yields oriented bounding-boxes,\nrotation-normalised tracklets of individuals are formed via\ntracking-by-detection and enriched via augmentations. This produces a\n`positive' sample set per tracklet, which is paired against a `negative' set\nsampled from random cattle of other videos. Frame-triplet contrastive learning\nis then employed to construct a metric latent space. The fitting of a Gaussian\nMixture Model to this space yields a cattle identity classifier. Results show\nan accuracy of Top-1 57.0% and Top-4: 76.9% and an Adjusted Rand Index: 0.53\ncompared to the ground truth. Whilst supervised training surpasses this\nbenchmark by a large margin, we conclude that self-supervision can nevertheless\nplay a highly effective role in speeding up labelling efforts when initially\nconstructing supervision information. We provide all data and full source code\nalongside an analysis and evaluation of the system.\n"}
+{"abstract": "  Unsupervised person re-identification (re-ID) aims at closing the performance\ngap to supervised methods. These methods build reliable relationship between\ndata points while learning representations. However, we empirically show that\nthe reason why they are successful is not only their label generation\nmechanisms, but also their unexplored designs. By studying two unsupervised\nperson re-ID methods in a cross-method way, we point out a hard negative\nproblem is handled implicitly by their designs of data augmentations and PK\nsampler respectively. In this paper, we find another simple solution for the\nproblem, i.e., taking more positives during training, by which we generate\npseudo-labels and update models in an iterative manner. Based on our findings,\nwe propose a contrastive learning method without a memory back for unsupervised\nperson re-ID. Our method works well on benchmark datasets and outperforms the\nstate-of-the-art methods. Code will be made available.\n"}
+{"abstract": "  Generative Adversarial Networks (GANs) have recently attracted considerable\nattention in the AI community due to its ability to generate high-quality data\nof significant statistical resemblance to real data. Fundamentally, GAN is a\ngame between two neural networks trained in an adversarial manner to reach a\nzero-sum Nash equilibrium profile. Despite the improvement accomplished in GANs\nin the last few years, several issues remain to be solved. This paper reviews\nthe literature on the game theoretic aspects of GANs and addresses how game\ntheory models can address specific challenges of generative model and improve\nthe GAN's performance. We first present some preliminaries, including the basic\nGAN model and some game theory background. We then present taxonomy to classify\nstate-of-the-art solutions into three main categories: modified game models,\nmodified architectures, and modified learning methods. The classification is\nbased on modifications made to the basic GAN model by proposed game-theoretic\napproaches in the literature. We then explore the objectives of each category\nand discuss recent works in each category. Finally, we discuss the remaining\nchallenges in this field and present future research directions.\n"}
+{"abstract": "  The impact of individual scientists is commonly quantified using\ncitation-based measures. The most common such measure is the h-index. A\nscientist's h-index affects hiring, promotion, and funding decisions, and thus\nshapes the progress of science. Here we report a large-scale study of\nscientometric measures, analyzing millions of articles and hundreds of millions\nof citations across four scientific fields and two data platforms. We find that\nthe correlation of the h-index with awards that indicate recognition by the\nscientific community has substantially declined. These trends are associated\nwith changing authorship patterns. We show that these declines can be mitigated\nby fractional allocation of citations among authors, which has been discussed\nin the literature but not implemented at scale. We find that a fractional\nanalogue of the h-index outperforms other measures as a correlate and predictor\nof scientific awards. Our results suggest that the use of the h-index in\nranking scientists should be reconsidered, and that fractional allocation\nmeasures such as h-frac provide more robust alternatives. An interactive\nvisualization of our work can be found at https://h-frac.org\n"}
+{"abstract": "  Unsupervised domain adaptation (UDA) aims to learn models for a target domain\nof unlabeled data by transferring knowledge from a labeled source domain. In\nthe traditional UDA setting, labeled source data are assumed to be available\nfor adaptation. Due to increasing concerns for data privacy, source-free UDA is\nhighly appreciated as a new UDA setting, where only a trained source model is\nassumed to be available, while labeled source data remain private. However,\ntrained source models may also be unavailable in practice since source models\nmay have commercial values and exposing source models brings risks to the\nsource domain, e.g., problems of model misuse and white-box attacks. In this\nwork, we study a subtly different setting, named Black-Box Unsupervised Domain\nAdaptation (B$^2$UDA), where only the application programming interface of\nsource model is accessible to the target domain; in other words, the source\nmodel itself is kept as a black-box one. To tackle B$^2$UDA, we propose a\nsimple yet effective method, termed Iterative Learning with Noisy Labels\n(IterLNL). With black-box models as tools of noisy labeling, IterLNL conducts\nnoisy labeling and learning with noisy labels (LNL), iteratively. To facilitate\nthe implementation of LNL in B$^2$UDA, we estimate the noise rate from model\npredictions of unlabeled target data and propose category-wise sampling to\ntackle the unbalanced label noise among categories. Experiments on benchmark\ndatasets show the efficacy of IterLNL. Given neither source data nor source\nmodels, IterLNL performs comparably with traditional UDA methods that make full\nuse of labeled source data.\n"}
+{"abstract": "  For machine learning systems to be reliable, we must understand their\nperformance in unseen, out-of-distribution environments. In this paper, we\nempirically show that out-of-distribution performance is strongly correlated\nwith in-distribution performance for a wide range of models and distribution\nshifts. Specifically, we demonstrate strong correlations between\nin-distribution and out-of-distribution performance on variants of CIFAR-10 &\nImageNet, a synthetic pose estimation task derived from YCB objects, satellite\nimagery classification in FMoW-WILDS, and wildlife classification in\niWildCam-WILDS. The strong correlations hold across model architectures,\nhyperparameters, training set size, and training duration, and are more precise\nthan what is expected from existing domain adaptation theory. To complete the\npicture, we also investigate cases where the correlation is weaker, for\ninstance some synthetic distribution shifts from CIFAR-10-C and the tissue\nclassification dataset Camelyon17-WILDS. Finally, we provide a candidate theory\nbased on a Gaussian data model that shows how changes in the data covariance\narising from distribution shift can affect the observed correlations.\n"}
+{"abstract": "  The China Spallation Neutron Source (CSNS) operates in pulsed mode and has a\nhigh neutron flux. This provides opportunities for energy resolved neutron\nimaging by using the TOF (Time Of Flight) approach. An Energy resolved neutron\nimaging instrument (ERNI) is being built at the CSNS but significant challenges\nfor the detector persist because it simultaneously requires a spatial\nresolution of less than 100 {\\mu}m, as well as a microsecond-scale timing\nresolution. This study constructs a prototype of an energy resolved neutron\nimaging detector based on the fast optical camera, TPX3Cam coupled with an\nimage intensifier. To evaluate its performance, a series of proof of principle\nexperiments were performed in the BL20 at the CSNS to measure the spatial\nresolution and the neutron wavelength spectrum, and perform neutron imaging\nwith sliced wavelengths and Bragg edge imaging of the steel sample. A spatial\nresolution of 57 {\\mu}m was obtained for neutron imaging by using the\ncentroiding algorithm, the timing resolution was on the microsecond scale and\nthe measured wavelength spectrum was identical to that measured by a beam\nmonitor. In addition, any wavelengths can be selected for the neutron imaging\nof the given object, and the detector can be used for Bragg edge imaging. The\nresults show that our detector has good performances and can satisfy the\nrequirements of ERNI for detectors at the CSNS\n"}
+{"abstract": "  The main result of the paper is the first polynomial-time algorithm for\nranking bracelets. The time-complexity of the algorithm is O(k^2 n^4), where k\nis the size of the alphabet and n is the length of the considered bracelets.\nThe key part of the algorithm is to compute the rank of any word with respect\nto the set of bracelets by finding three other ranks: the rank over all\nnecklaces, the rank over palindromic necklaces, and the rank over enclosing\napalindromic necklaces. The last two concepts are introduced in this paper.\nThese ranks are key components to our algorithm in order to decompose the\nproblem into parts. Additionally, this ranking procedure is used to build a\npolynomial-time unranking algorithm.\n"}
+{"abstract": "  In this paper, we propose a progressive margin loss (PML) approach for\nunconstrained facial age classification. Conventional methods make strong\nassumption on that each class owns adequate instances to outline its data\ndistribution, likely leading to bias prediction where the training samples are\nsparse across age classes. Instead, our PML aims to adaptively refine the age\nlabel pattern by enforcing a couple of margins, which fully takes in the\nin-between discrepancy of the intra-class variance, inter-class variance and\nclass center. Our PML typically incorporates with the ordinal margin and the\nvariational margin, simultaneously plugging in the globally-tuned deep neural\nnetwork paradigm. More specifically, the ordinal margin learns to exploit the\ncorrelated relationship of the real-world age labels. Accordingly, the\nvariational margin is leveraged to minimize the influence of head classes that\nmisleads the prediction of tailed samples. Moreover, our optimization carefully\nseeks a series of indicator curricula to achieve robust and efficient model\ntraining. Extensive experimental results on three face aging datasets\ndemonstrate that our PML achieves compelling performance compared to state of\nthe arts. Code will be made publicly.\n"}
+{"abstract": "  Graph states are a central resource in measurement-based quantum information\nprocessing. In the photonic qubit architecture based on\nGottesman-Kitaev-Preskill (GKP) encoding, the generation of high-fidelity graph\nstates composed of realistic, finite-energy approximate GKP-encoded qubits thus\nconstitutes a key task. We consider the finite-energy approximation of GKP\nqubit states given by a coherent superposition of shifted finite-squeezed\nvacuum states, where the displacements are Gaussian distributed. We present an\nexact description of graph states composed of such approximate GKP qubits as a\ncoherent superposition of a Gaussian ensemble of randomly displaced ideal\nGKP-qubit graph states. We determine the transformation rules for the\ncovariance matrix and the mean displacement vector of the Gaussian distribution\nof the ensemble under tools such as GKP-Steane error correction and fusion\noperations that can be used to grow large, high-fidelity GKP-qubit graph\nstates. The former captures the noise in the graph state due to the\nfinite-energy approximation of GKP qubits, while the latter relates to the\npossible absolute displacement errors on the individual qubits due to the\nhomodyne measurements that are a part of these tools. The rules thus help in\npinning down an exact coherent error model for graph states generated from\ntruly finite-energy GKP qubits, which can shed light on their error correction\nproperties.\n"}
+{"abstract": "  Rapid solidification experiments on thin film aluminum samples reveal the\npresence of lattice orientation gradients within crystallizing grains. To study\nthis phenomenon, a single-component phase-field crystal (PFC) model that\ncaptures the properties of solid, liquid, and vapor phases is proposed to model\npure aluminium quantitatively. A coarse-grained amplitude representation of\nthis model is used to simulate solidification in samples approaching micrometer\nscales. The simulations reproduce the experimentally observed orientation\ngradients within crystallizing grains when grown at experimentally relevant\nrapid quenches. We propose a causal connection between formation of defects and\norientation gradients.\n"}
+{"abstract": "  It is well known that active galactic nuclei (AGN) show various forms of\ninteraction with their host galaxy, in a number of phenomena generally called\nAGN feedback. In particular, the relativistic plasma jets launched by a\nfraction of AGN can strongly affect their environment. We present here a study\nof the [O III] $\\lambda\\lambda$4959,5007 lines in a diverse sample of early\nevolution stage AGN -- specifically narrow-line Seyfert 1 galaxies. Radio\nimaging observations of all of the sources enable a division to jetted and\nnon-jetted sources, and exploiting this we show that the ionized gas properties\nare significantly influenced by the presence of the jets, as we often find the\n[O III] lines (blue-)shifted with respect to their restframe wavelength. We\nalso show how the radio morphology and the radio spectral index do not seem to\nplay a role in the origin of the [O III] shifts, thus suggesting that the\nsource inclination is not relevant to the lines displacement. We do not find a\nstrong relation between the [O III] line properties and the bolometric\nluminosity, suggesting that within our sample radiatively driven outflows do\nnot seem to have a significant contribution to the [O III] line kinematics. We\nfinally suggest that [O~ III] shifts may be a good proxy to identify the\npresence of relativistic jets. Additional studies, especially with\nintegral-field spectroscopy, will provide a deeper insight into the relation\nbetween jets and their environment in early evolution stage AGN.\n"}
+{"abstract": "  This paper presents a novel storage aware routing (STAR) protocol designed to\nprovide a general networking solution over a broad range of wired and wireless\nusage scenarios. STAR enables routing policies which adapt seamlessly from a\nwell-connected wired network to a disconnected wireless network. STAR uses a\n2-Dimensional routing metric composed of a short and a long term route cost and\nstorage availability on downstream routers to make store or forward routing\ndecisions. Temporary in-network storage is preferred over forwarding along a\npath that is slower than average and opportunistic transmission is encouraged\nwhen faster than average routes become available. Results from ns2 based\nsimulations show that STAR achieves $40-50\\%$ higher throughput compared to\nOLSR in mobile vehicular and DTN scenarios and does $12-20\\%$ better than OLSR\nin the static mesh case. Experimental evaluation of STAR on the ORBIT testbed\nvalidates the protocol implementation, and demonstrates significant performance\nimprovements with 25\\% higher peak throughput compared to OLSR in a wireless\nmesh network.\n"}
+{"abstract": "  Several early transition metal dipnictides have been found to host\ntopological semimetal states and exhibit large magnetoresistance. In this\nstudy, we use angle-resolved photoemission spectroscopy (ARPES) and\nmagneto-transport to study the electronic properties of a new transition metal\ndipnictide ZrP$_2$. We find that ZrP$_2$ exhibits an extremely large and\nunsaturated magnetoresistance of up to 40,000 % at 2 K, which originates from\nan almost perfect electron-hole compensation. Our band structure calculations\nfurther show that ZrP$_2$ hosts a topological nodal loop in proximity to the\nFermi level. Based on the ARPES measurements, we confirm the results of our\ncalculations and determine the surface band structure. Our study establishes\nZrP$_2$ as a new platform to investigate near-perfect electron-hole\ncompensation and its interplay with topological band structures.\n"}
+{"abstract": "  We present a kinetic version of the Watanabe-Strogatz(WS) transform for\nvector models in this paper. From the generalized WS-transform, we obtain the\ncross-ratio type constant of motion functionals for kinetic vector models under\nsuitable conditions. We present the sufficient and necessary conditions for the\nexistence of the suggested constant of motion functional. As an application of\nthe constant of motion functional, we provide the instability of bipolar states\nof the kinetic swarm sphere model. We also provide the WS-transform and\nconstant of motion functional for non-identical kinetic vector models.\n"}
+{"abstract": "  THESEUS is a medium size space mission of the European Space Agency,\ncurrently under evaluation for a possible launch in 2032. Its main objectives\nare to investigate the early Universe through the observation of gamma-ray\nbursts and to study the gravitational waves electromagnetic counterparts and\nneutrino events. On the other hand, its instruments, which include a wide field\nof view X-ray (0.3-5 keV) telescope based on lobster-eye focusing optics and a\ngamma-ray spectrometer with imaging capabilities in the 2-150 keV range, are\nalso ideal for carrying out unprecedented studies in time domain astrophysics.\nIn addition, the presence onboard of a 70 cm near infrared telescope will allow\nsimultaneous multi-wavelegth studies. Here we present the THESEUS capabilities\nfor studying the time variability of different classes of sources in parallel\nto, and without affecting, the gamma-ray bursts hunt.\n"}
+{"abstract": "  The paper considers a boundary value problem for the high-order\nLavrent'ev-Bitsadze equation. Necessary and sufficient conditions for the\nuniqueness of the solution are found. When substantiating the existence, the\nproblem of \"small denominators\" arises. Sufficient conditions for the\nseparability of the \"small denominator\" from zero are found. An example of a\nboundary value problem not solvable by the Fourier method is given, in the\ncases rectangle with integer side measurements.\n"}
+{"abstract": "  In this work we have performed a detailed study of chiral nuclear forces at\nN4LO approximation applied to selected channels of the neutron-proton ($n p$)\nscattering. The idea is to analyse the different contributions to the\nnucleon-nucleon ($NN$) potential by separating the part coming from the\nexchange of pions and the one coming from the contact interactions. We consider\ntwo state-of-the-art chiral interactions at N4LO which are constructed using\ndifferent regularization procedures: the non-local Idaho-Salamanca force and\nthe semi-local interaction from the Bochum group. In order to compare the two\ntypes of regularization we consider both interactions with a 500 MeV cutoff and\nto analyse the cutoff dependence we select the Bochum potential with three\ndifferent cutoff values: 500, 450 and 400 MeV. Our results show that the\nbalance between pion exchanges and contact interactions depends strongly on the\nregularization procedure. The non-local angle-independent regularization of\nboth components of the interaction implemented in the Idaho-Salamanca potential\nmake the contact terms to be present at large distances while the local\nregularization of the pion exchanges in the Bochum potential restricts the\ncontact interactions to small distances. Also, the value of the cutoff affects\nthe strength of the potential but the interplay between pion exchanges and\ncontact terms remains qualitatively the same.\n"}
+{"abstract": "  In the clinical diagnosis and treatment of brain tumors, manual image reading\nconsumes a lot of energy and time. In recent years, the automatic tumor\nclassification technology based on deep learning has entered people's field of\nvision. Brain tumors can be divided into primary and secondary intracranial\ntumors according to their source. However, to our best knowledge, most existing\nresearch on brain tumors are limited to primary intracranial tumor images and\ncannot classify the source of the tumor. In order to solve the task of tumor\nsource type classification, we analyze the existing technology and propose an\nattention guided deep convolution neural network (CNN) model. Meanwhile, the\nmethod proposed in this paper also effectively improves the accuracy of\nclassifying the presence or absence of tumor. For the brain MR dataset, our\nmethod can achieve the average accuracy of 99.18% under ten-fold\ncross-validation for identifying the presence or absence of tumor, and 83.38%\nfor classifying the source of tumor. Experimental results show that our method\nis consistent with the method of medical experts. It can assist doctors in\nachieving efficient clinical diagnosis of brain tumors.\n"}
+{"abstract": "  Background: Positron Emission Tomography (PET) with 18F-fluorodeoxyglucose\n(18F-FDG) reveals metabolic abnormalities in Parkinson's disease (PD) at a\nsystemic level. Previous metabolic connectome studies derived from groups of\npatients have failed to identify the individual neurophysiological details. We\naim to establish an individual metabolic connectome method to characterize the\naberrant connectivity patterns and topological alterations of the\nindividual-level brain metabolic connectome and their diagnostic value in PD.\nMethods: The 18F-FDG PET data of 49 PD patients and 49 healthy controls (HCs)\nwere recruited. Each individual's metabolic brain network was ascertained using\nthe proposed Jensen-Shannon Divergence Similarity Estimation (JSSE) method. The\nintergroup difference of the individual's metabolic brain network and its\nglobal and local graph metrics were analyzed to investigate the metabolic\nconnectome's alterations. The identification of the PD from HC individuals was\nused by the multiple kernel support vector machine (MK-SVM) to combine the\ninformation from connection and topological metrics. The validation was\nconducted using the nest leave-one-out cross-validation strategy to confirm the\nperformance of the methods. Results: The proposed JSSE metabolic connectome\nmethod showed the most involved metabolic motor networks were PUT-PCG, THA-PCG,\nand SMA pathways in PD, which was similar to the typical group-level method,\nand yielded another detailed individual pathological connectivity in ACG-PCL,\nDCG-PHG and ACG pathways. These aberrant functional network measures exhibited\nan ideal classification performance in the identifying of PD individuals from\nHC individuals at an accuracy of up to 91.84%.\n"}
+{"abstract": "  Geothermal heat, as renewable energy, shows great advantage with respect to\nits environmental impact due to its significantly lower CO2 emissions than\nconventional fossil fuel. Open and closed-loop geothermal heat pumps, which\nutilize shallow geothermal systems, are an efficient technology for cooling and\nheating buildings, especially in urban areas. Integrated use of geothermal\nenergy technologies for district heating, cooling, and thermal energy storage\ncan be applied to optimize the subsurface for communities to provide them with\nmultiple sustainable energy and community resilience benefits. The utilization\nof the subsurface resources may lead to a variation in the underground\nenvironment, which might further impact local environmental conditions.\nHowever, very few simulators can handle such a highly complex set of coupled\ncomputations on a regional or city scale. We have developed high-performance\ncomputing (HPC) based hydrothermal finite element (FE) simulator that can\nsimulate the subsurface and its hydrothermal conditions at a scale of tens of\nkm. The HPC simulator enables us to investigate the subsurface thermal and\nhydrologic response to the built underground environment (such as basements and\nsubways) at the community scale. In this study, a coupled hydrothermal\nsimulator is developed based on the open-source finite element library deal.II.\nThe HPC simulator was validated by comparing the results of a benchmark case\nstudy against COMSOL Multiphysics, in which Aquifer Thermal Energy Storage\n(ATES) is modeled and a process of heat injection into ATES is simulated. The\nuse of an energy pile system at the Treasure Island redevelopment site (San\nFrancisco, CA, USA) was selected as a case study to demonstrate the HPC\ncapability of the developed simulator. The simulator is capable of modeling\nmultiple city-scale geothermal scenarios in a reasonable amount of time.\n"}
+{"abstract": "  We investigate the integrability of an anisotropic universe with matter and\ncosmological constant formulated as Bianchi IX models. The presence of the\ncosmological constant causes the existence of a critical point in the finite\npart of the phase space. The separatrix associated to this Einstein's static\nuniverse is entirely contained in an invariant isotropic plane forgetting the\nsingularity at the origin. This invariant plane of isotropy is an integrable\nsub-space of the Taub type. In this paper we analyse the differential Galois\ngroup of the second order variational equations to this plane in order to apply\nthe integrability theorem of the second author with Ramis and Sim\\' o. The main\nresult is that the model is non-integrable by meromorphic functions.\n"}
+{"abstract": "  It is well-known that nonlinearity may lead to localization effects and\ncoupling of internally resonant modes. However, research focused primarily on\nconservative systems commonly assumes that the near-resonant forced response\nclosely follows the autonomous dynamics. Our results for even a simple system\nof two coupled oscillators with a cubic spring clearly contradict this common\nbelief. We demonstrate analytically and numerically global effects of a weak\nlocal damping source in a harmonically forced nonlinear system under condition\nof 1:3 internal resonance: The global motion becomes asynchronous, i.e., mode\ncomplexity is introduced with a non-trivial phase difference between the modal\noscillations. In particular, we show that a maximum mode complexity with a\nphase difference of $90^\\circ$ is attained in a multi-harmonic sense. This\ncorresponds to a transition from generalized standing to traveling waves in the\nsystem's modal space. We further demonstrate that the localization is crucially\naffected by the system's damping. Finally, we propose an extension of the\ndefinition of mode complexity and mode localization to nonlinear quasi-periodic\nmotions, and illustrate their application to a quasi-periodic regime in the\nforced response.\n"}
+{"abstract": "  We consider the problem of regularization by noise for the three dimensional\nmagnetohydrodynamical (3D MHD) equations. It is shown that, in a suitable\nscaling limit, multiplicative noise of transport type gives rise to bounds on\nthe vorticity fields of the fluid velocity and magnetic fields. As a result, if\nthe noise intensity is big enough, then the stochastic 3D MHD equations admit a\npathwise unique global solution for large initial data, with high probability.\n"}
+{"abstract": "  In this article, the goal is to find the shortest presentation of a matrix $A\n\\in PSL(2,\\mathbb{Z})$ in terms of the so-called continuant matrices which are\nmost known for their role in continued fraction theory. In chapter 7 of\narXiv:1811.01229, Morier-G\\'enoud and Ovsienko investigate this problem with\nthe restriction that all coefficients of the continuant matrices are positive.\nNow, the goal is to determine the shortest presentation allowing all integer\ncoefficients. To determine this minimal presentation, a few characteristic\ntransformations will be introduced. It will also be investigated under which\nconditions such a minimal presentation becomes unique. The results are also\ngeneralized on conjugacy classes in $PSL(2,\\mathbb{Z})$.\n  If you wish to contact the author or you have some questions related to this\nwork, feel free to write an email to christian.streib@online.de.\n"}
+{"abstract": "  In this letter, we consider an underlay cognitive radio network assisted by\ndual-hop decode-and-forward (DF) relaying. For a general multi-user network, we\nadopt a max-min fairness relay selection scheme and analyse the outage\nprobability when the channels are subject to independent and non-identical\nNakagami-m fading. The relay network operates within the constraint imposed on\nthe peak interference power tolerable by the primary receiver. We then analyse\nthe asymptotic outage probability performance and illustrate the existence of\ni) the full-diversity order when the interference level at the primary user\nincreases proportionally with the relay transmit power; and ii) an outage floor\nwhen the transmit powers of the relays are restricted by the primary receiver.\nWe also analyse the outage probability with imperfect channel state information\n(CSI) and the average throughput over Rayleigh fading channels. Illustrative\nanalytical results are accurately validated by numerical simulations.\n"}
+{"abstract": "  Beam-driven collinear wakefield accelerators (CWAs) that operate by using\nslow-wave structures or plasmas hold great promise toward reducing the size of\ncontemporary accelerators. Sustainable acceleration of charged particles to\nhigh energies in the CWA relies on using field-generating relativistic electron\nbunches with a highly asymmetric peak current profile and a large energy chirp.\nA new approach to obtaining such bunches has been proposed and illustrated with\nthe accelerator design supported by particle tracking simulations. It has been\nshown that the required particle distribution in the longitudinal phase space\ncan be obtained without collimators, giving CWAs an opportunity for employment\nin applications requiring a high repetition rate of operation.\n"}
+{"abstract": "  We study how Betti numbers of ideals in a local ring change under small\nperturbations. Given $p\\in\\mathbb N$ and given an ideal $I$ of a Noetherian\nlocal ring $(R,\\mathfrak m)$, our main result states that there exists $N>0$\nsuch that if $J$ is an ideal with $I\\equiv J\\bmod \\mathfrak m^N$ and with the\nsame Hilbert function as $I$, then the Betti numbers $\\beta_i^R(R/I)$ and\n$\\beta_i^R(R/J)$ coincide for $0\\le i\\le p$. Moreover, we present several cases\nin which an ideal $J$ such that $I \\equiv J \\bmod \\mathfrak m^N$ is forced to\nhave the same Hilbert function as $I$, and therefore the same Betti numbers.\n"}
+{"abstract": "  With there being many technical and ethical issues with artificial\nintelligence (AI) that involve marginalized communities, there is a growing\ninterest for design methods used with marginalized people that may be\ntransferable to the design of AI technologies. Participatory design (PD) is a\ndesign method that is often used with marginalized communities for the design\nof social development, policy, IT and other matters and solutions. However,\nthere are issues with the current PD, raising concerns when it is applied to\nthe design of technologies, including AI technologies. This paper argues for\nthe use of PD for the design of AI technologies, and introduces and proposes a\nnew PD, which we call agile participatory design, that not only can could be\nused for the design of AI and data-driven technologies, but also overcomes\nissues surrounding current PD and its use in the design of such technologies.\n"}
+{"abstract": "  We present a unified description of extremal metrics for the Laplace and\nSteklov eigenvalues on manifolds of arbitrary dimension using the notion of\n$n$-harmonic maps. Our approach extends the well-known results linking extremal\nmetrics for eigenvalues on surfaces with minimal immersions and harmonic maps.\nIn the process, we uncover two previously unknown features of the Steklov\neigenvalues. First, we show that in higher dimensions there is a unique\nnormalization involving both the volume of the boundary and of the manifold\nitself, which leads to meaningful extremal eigenvalue problems. Second, we\nobserve that the critical points of the eigenvalue functionals in a fixed\nconformal class have a natural geometric interpretation provided one considers\nthe Steklov problem with a density. As an example, we construct a family of\nfree boundary harmonic annuli in the three-dimensional ball and conjecture that\nthey correspond to metrics maximizing the first Steklov eigenvalue in their\nrespective conformal classes.\n"}
+{"abstract": "  Topological quantum dots (TQDs) are 3D topological insulator nanoparticles\nwith radius below 100 nm, which display symmetry-protected surface states with\ndiscretized energies. We propose a scheme which harnesses these energy levels\nin a closed lasing scheme, in which a single TQD, when pumped with incoherent\nTHz light, lases from its surface states in the THz regime. The time scales\nassociated with the system are unusually slow, and lasing occurs with a very\nlow threshold. Due to the low threshold, we predict that blackbody radiation at\nroom-temperature provides enough photons to pump the system, providing a route\nto room-temperature THz lasing with blackbody radiation as the pump.\n"}
+{"abstract": "  In this article we extend results of Kakutani, Adler--Flatto, Smilansky and\nothers on the classical $\\alpha$-Kakutani equidistribution result for sequences\narising from finite partitions of the interval. In particular, we describe a\ngeneralization of the equidistribution result to infinite partitions. In\naddition, we give discrepancy estimates, extending results of Drmota--Infusino.\n"}
+{"abstract": "  Since the outbreak of COVID-19 policy makers have been relying upon\nnon-pharmacological interventions to control the outbreak. With air pollution\nas a potential transmission vector there is need to include it in intervention\nstrategies. We propose a U-net driven quantile regression model to predict\n$PM_{2.5}$ air pollution based on easily obtainable satellite imagery. We\ndemonstrate that our approach can reconstruct $PM_{2.5}$ concentrations on\nground-truth data and predict reasonable $PM_{2.5}$ values with their spatial\ndistribution, even for locations where pollution data is unavailable. Such\npredictions of $PM_{2.5}$ characteristics could crucially advise public policy\nstrategies geared to reduce the transmission of and lethality of COVID-19.\n"}
+{"abstract": "  For flow-enhanced crystallization in fiber spinning, the viscoelastic\ntwo-phase fiber models by Doufas et al. (J. Non-Newton. Fluid Mech., 2000) and\nShrikhande et al. (J. Appl. Polym. Sci., 2006) are state of the art. However,\nthe boundary conditions associated to the onset of crystallization are still\nunder discussion, as their choice might cause artificial boundary layers and\nnumerical difficulties. In this paper we show that the model class of ordinary\ndifferential equations is singularly perturbed in a small parameter belonging\nto the semi-crystalline relaxation time and derive asymptotically justified\nboundary conditions. Their effect on the overall solution behavior is\nrestricted to a small region near the onset of crystallization. But their\nimpact on the performance of the numerical solvers is huge, since artificial\nlayering, ambiguities and parameter tunings are avoided. The numerics becomes\nfast and robust and opens the field for simulation-based process design and\nmaterial optimization.\n"}
+{"abstract": "  Geosystems are geological formations altered by humans activities such as\nfossil energy exploration, waste disposal, geologic carbon sequestration, and\nrenewable energy generation. Geosystems also represent a critical link in the\nglobal water-energy nexus, providing both the source and buffering mechanisms\nfor enabling societal adaptation to climate variability and change. The\nresponsible use and exploration of geosystems are thus critical to the\ngeosystem governance, which in turn depends on the efficient monitoring, risk\nassessment, and decision support tools for practical implementation. Fast\nadvances in machine learning (ML) algorithms and novel sensing technologies in\nrecent years have presented new opportunities for the subsurface research\ncommunity to improve the efficacy and transparency of geosystem governance.\nAlthough recent studies have shown the great promise of scientific ML (SciML)\nmodels, questions remain on how to best leverage ML in the management of\ngeosystems, which are typified by multiscality, high-dimensionality, and data\nresolution inhomogeneity. This survey will provide a systematic review of the\nrecent development and applications of domain-aware SciML in geosystem\nresearches, with an emphasis on how the accuracy, interpretability,\nscalability, defensibility, and generalization skill of ML approaches can be\nimproved to better serve the geoscientific community.\n"}
+{"abstract": "  A general approach for building a smart assistant that guides a user from a\nforecast generated by a machine learning model through a sequence of\ndecision-making steps is presented. We develop a methodology to build such a\nsystem. The system is demonstrated on a demand forecasting use case in\nmanufacturing. The methodology can be extended to several use cases in\nmanufacturing. The system provides means for knowledge acquisition, gathering\ndata from users. We envision active learning can be used to get data labels\nwhere labeled data is scarce.\n"}
+{"abstract": "  The high computational cost of evaluating atomic interactions recently\nmotivated the development of computationally inexpensive kinetic models, which\ncan be parametrized from MD simulations of complex chemistry of thousands of\nspecies or other processes and accelerate the prediction of the chemical\nevolution by up to four order of magnitude. Such models go beyond the commonly\nemployed potential energy surface fitting methods in that they are aimed purely\nat describing kinetic effects. So far, such kinetic models utilize molecular\ndescriptions of reactions and have been constrained to only reproduce molecules\npreviously observed in MD simulations. Therefore, these descriptions fail to\npredict the reactivity of unobserved molecules, for example in the case of\nlarge molecules or solids. Here we propose a new approach for the extraction of\nreaction mechanisms and reaction rates from MD simulations, namely the use of\natomic-level features. Using the complex chemical network of hydrocarbon\npyrolysis as example, it is demonstrated that kinetic models built using atomic\nfeatures are able to explore chemical reaction pathways never observed in the\nMD simulations used to parametrize them. Atomic-level features are shown to\nconstruct reaction mechanisms and estimate reaction rates of unknown molecular\nspecies from elementary atomic events. Through comparisons of the model ability\nto extrapolate to longer simulation timescales and different chemical\ncompositions than the ones used for parameterization, it is demonstrated that\nkinetic models employing atomic features retain the same level of accuracy and\ntransferability as the use of features based on molecular species, while being\nmore compact and parametrized with less data. We also find that atomic features\ncan better describe the formation of large molecules enabling the simultaneous\ndescription of small molecules and condensed phases.\n"}
+{"abstract": "  In this paper, we study non sense-preserving harmonic mappings\n$f=h+\\overline{g}$ in $\\mathbb{D}$ when its analytic part $h$ is convex and\ninjective in $\\mathbb{D}$ and obtain radius of injectivety.\n"}
+{"abstract": "  Bulk superconductivity in a topological semimetal is a first step towards\nrealizing topological superconductors, which can host Majorana fermions\nallowing us to achieve quantum computing. Here, we report superconductivity and\ncompensation of electrons and holes in single crystals of the nodal-line\nsemimetal CaSb$_2$. We characterize the superconducting state and find that\nCooper pairs have moderate-weak coupling, and the superconducting transition in\nspecific heat down to 0.22 K deviates from that of a BCS superconductor. The\nnon-saturating magnetoresistance and electron-hole compensation at low\ntemperature are consistent with density functional theory (DFT) calculations\nshowing nodal-line features. Furthermore, we observe de Haas-van Alphen (dHvA)\noscillations consistent with a small Fermi surface in the semimetallic state of\nCaSb$_2$. Our DFT calculations show that the two electron bands crossing the\nFermi level are associated with Sb1 zig-zag chains, while the hole band is\nassociated with Sb2 zig-zag chains. The Sb1 zig-zag chains form a distorted\nsquare net, which may relate the $M$Sb$_2$ family to the well known $M$SbTe\nsquare net semimetals. Realization of superconductivity and a compensated\nsemimetal state in single crystals of CaSb$_2$ establishes the diantimonide\nfamily as a candidate class of materials for achieving topological\nsuperconductivity.\n"}
+{"abstract": "  The problem of finding an ancestral acyclic directed mixed graph (ADMG) that\nrepresents the causal relationships between a set of variables is an important\narea of research on causal inference. Most existing score-based structure\nlearning methods focus on learning directed acyclic graph (DAG) models without\nlatent variables. A number of score-based methods have recently been proposed\nfor the ADMG learning, yet they are heuristic in nature and do not guarantee an\noptimal solution. We propose a novel exact score-based method that solves an\ninteger programming (IP) formulation and returns a score-maximizing ancestral\nADMG for a set of continuous variables that follow a multivariate Gaussian\ndistribution. We generalize the state-of-the-art IP model for DAG learning\nproblems and derive new classes of valid inequalities to formulate an IP model\nfor ADMG learning. Empirically, our model can be solved efficiently for\nmedium-sized problems and achieves better accuracy than state-of-the-art\nscore-based methods as well as benchmark constraint-based methods.\n"}
+{"abstract": "  Steerable needles are capable of accurately targeting difficult-to-reach\nclinical sites in the body. By bending around sensitive anatomical structures,\nsteerable needles have the potential to reduce the invasiveness of many medical\nprocedures. However, inserting these needles with curved trajectories increases\nthe risk of tissue shearing due to large forces being exerted on the\nsurrounding tissue by the needle's shaft. Such shearing can cause significant\ndamage to surrounding tissue, potentially worsening patient outcomes. In this\nwork, we derive a tissue and needle force model based on a Cosserat string\nformulation, which describes the normal forces and frictional forces along the\nshaft as a function of the planned needle path, friction parameters, and tip\npiercing force. We then incorporate this force model as a cost function in an\nasymptotically near-optimal motion planner and demonstrate the ability to plan\nmotions that consider the tissue normal forces from the needle shaft during\nplanning in a simulated steering environment and a simulated lung tumor biopsy\nscenario. By planning motions for the needle that aim to minimize the tissue\nnormal force explicitly, our method plans needle paths that reduce the risk of\ntissue shearing while still reaching desired targets in the body.\n"}
+{"abstract": "  Contaminants such as dust, dirt and moisture adhering to the camera lens can\ngreatly affect the quality and clarity of the resulting image or video. In this\npaper, we propose a video restoration method to automatically remove these\ncontaminants and produce a clean video. Our approach first seeks to detect\nattention maps that indicate the regions that need to be restored. In order to\nleverage the corresponding clean pixels from adjacent frames, we propose a flow\ncompletion module to hallucinate the flow of the background scene to the\nattention regions degraded by the contaminants. Guided by the attention maps\nand completed flows, we propose a recurrent technique to restore the input\nframe by fetching clean pixels from adjacent frames. Finally, a multi-frame\nprocessing stage is used to further process the entire video sequence in order\nto enforce temporal consistency. The entire network is trained on a synthetic\ndataset that approximates the physical lighting properties of contaminant\nartifacts. This new dataset and our novel framework lead to our method that is\nable to address different contaminants and outperforms competitive restoration\napproaches both qualitatively and quantitatively.\n"}
+{"abstract": "  Radio halos are diffuse synchrotron sources observed in dynamically unrelaxed\ngalaxy clusters. Current observations and models suggest that halos trace\nturbulent regions in the intra-cluster medium where mildly relativistic\nparticles are re-accelerated during cluster mergers. Due to the higher\nluminosities and detection rates with increasing cluster mass, radio halos have\nbeen mainly observed in massive systems ($M_{500} \\gtrsim 5 \\times10^{14}$\nM$_\\odot$). Here, we report the discovery of a radio halo with a largest linear\nscale of $\\simeq$750 kpc in PSZ2G145.92-12.53 ($z=0.03$) using LOFAR\nobservations at 120$-$168 MHz. With a mass of $M_{500} = (1.9\\pm0.2) \\times\n10^{14}$ M$_\\odot$ and a radio power at 150 MHz of $P_{150} = (3.5 \\pm 0.7)\n\\times 10^{23}$ W/Hz, this is the least powerful radio halo in the least\nmassive cluster discovered to date. Additionally, we discover a radio relic\nwith a mildly convex morphology at $\\sim$1.7 Mpc from the cluster center. Our\nresults demonstrate that LOFAR has the potential to detect radio halos even in\nlow-mass clusters, where the expectation to form them is very low ($\\sim$5%)\nbased on turbulent re-acceleration models. Together with the observation of\nlarge samples of clusters, this opens the possibility to constrain the low end\nof the power-mass relation of radio halos.\n"}
+{"abstract": "  The goal of this note is to generalize Isoperimetric Inequality for random\ngroups to the class of non-planar diagrams of bounded number of faces.\n"}
+{"abstract": "  We propose a $SU(5) \\times U(1)_X \\times U(1)_{PQ}$ model, where $U(1)_X$ is\nthe generalization of the $B-L$ (baryon minus lepton number) gauge symmetry and\n$U(1)_{PQ}$ is the global Peccei-Quinn (PQ) symmetry. There are four fermions\nfamilies in $\\bf{{\\overline 5}} + \\bf{10}$ representations of $SU(5)$, a mirror\nfamily in $\\bf{5}+\\bf{{\\overline {10}}}$ representations, and three $SU(5)$\nsinglet Majorana fermions. The $U(1)_X$ related anomalies all cancel in the\npresence of the Majorana neutrinos. The $SU(5)$ symmetry is broken at $M_{GUT}\n\\simeq (6-9)\\times 10^{15}$ GeV and the proton lifetime $\\tau_p$ is estimated\nto be well within the expected sensitivity of the future Hyper-Kamiokande\nexperiment, $\\tau_p \\lesssim 1.3 \\times 10^{35}$ years. The $SU(5)$ breaking\nalso triggers the breaking of the PQ symmetry, resulting in axion dark matter\n(DM), with the axion decay constant $f_a$ of order $M_{GUT}$ or somewhat\nlarger. The CASPEr experiment can search for such an axion DM candidate. The\nHubble parameter during inflation must be low, $H_{inf} \\lesssim 10^9 $ GeV, in\norder to successfully resolve the axion domain wall, axion DM isocurvature and\n$SU(5)$ monopole problems. With the identification of the $U(1)_X$ breaking\nHiggs field with the inflaton field, we implement inflection-point inflation,\nwhich is capable of realizing the desired value for $H_{inf}$. The vectorlike\nfermions in the model are essential for achieving successful unification of the\nSM gauge couplings as well as the phenomenological viability of both axion DM\nand inflation scenario.\n"}
+{"abstract": "  Coherent driving has established itself as a powerful tool for guiding a\nmany-body quantum system into a desirable, coherent non-equilibrium state. A\nthermodynamically large system will, however, almost always saturate to a\nfeatureless infinite temperature state under continuous driving and so the\noptical manipulation of many-body systems is considered feasible only if a\ntransient, prethermal regime exists, where heating is suppressed. Here we show\nthat, counterintuitively, in a broad class of lattices Floquet heating can\nactually be an advantageous effect. Specifically, we prove that the maximum\nentropy steady states which form upon driving the ground state of the Hubbard\nmodel on unbalanced bi-partite lattices possess uniform off-diagonal long-range\norder which remains finite even in the thermodynamic limit. This creation of a\n`hot' condensate can occur on \\textit{any} driven unbalanced lattice and\nprovides an understanding of how heating can, at the macroscopic level, expose\nand alter the order in a quantum system. We discuss implications for recent\nexperiments observing emergent superconductivity in photoexcited materials.\n"}
+{"abstract": "  The categorical models of the differential lambda-calculus are additive\ncategories because of the Leibniz rule which requires the summation of two\nexpressions. This means that, as far as the differential lambda-calculus and\ndifferential linear logic are concerned, these models feature finite\nnondeterminism and indeed these languages are essentially non-deterministic. We\nintroduce a categorical framework for differentiation which does not require\nadditivity and is compatible with deterministic models such as coherence spaces\nand probabilistic models such as probabilistic coherence spaces. Based on this\nsemantics we sketch the syntax of a deterministic version of the differential\nlambdacalculus.\n"}
+{"abstract": "  This paper is based on a three year project during which we studied\nattackers' behavior, reading military planning literature, and thinking on how\nwould we do the same things they do, and what problems would we, as attackers,\nface. This research is still ongoing, but while participating in applications\nfor other projects and talking to cyber security experts we constantly face the\nsame issues, namely attackers' behavior is not well understood, and\nconsequently, there are a number of misconceptions floating around that are\nsimply not true, or are only partially true. This is actually expected as\nsomeone who casually follows news about incidents easily gets impression that\nattackers and attacks are everywhere and every one is under attack. Our goal in\nthis paper is to debunk these myths, to show what attackers really can and can\nnot, what dilemmas they face, what we don't know about attackers and attacks,\netc. The conclusion is that, while attackers do have upper hand, they don't\nhave absolute advantage, i.e. they also operate in an uncertain environment.\nKnowing this, means that defenses could be well established.\n"}
+{"abstract": "  During the development of NoSQL-backed software, the data model evolves\nnaturally alongside the application code. Especially in agile development, new\napplication releases are deployed frequently causing schema changes.\nEventually, decisions have to be made regarding the migration of versioned\nlegacy data which is persisted in the cloud-hosted production database. We\nsolve this schema evolution problem and present the results of near-exhaustive\ncalculations by means of which software project stakeholders can manage the\noperative costs for data model evolution and adapt their software release\nstrategy accordingly in order to comply with service-level agreements regarding\nthe competing metrics of migration costs and latency. We clarify conclusively\nhow data model evolution in NoSQL databases impacts the metrics while taking\nall relevant characteristics of migration scenarios into account. As\ncalculating all possible combinatorics in the search space of migration\nscenarios would by far exceed computational means, we used a probabilistic\nMonte Carlo method of repeated sampling, serving as a well-established means to\nbring the complexity of data model evolution under control. Our experiments\nshow the qualitative and quantitative impact on the performance of migration\nstrategies with respect to intensity and distribution of data entity accesses,\nthe kinds of schema changes, and the characteristics of the underlying data\nmodel.\n"}
+{"abstract": "  Model misspecification constitutes a major obstacle to reliable inference in\nmany inverse problems. Inverse problems in seismology, for example, are\nparticularly affected by misspecification of wave propagation velocities. In\nthis paper, we focus on a specific seismic inverse problem - full-waveform\nmoment tensor inversion - and develop a Bayesian framework that seeks\nrobustness to velocity misspecification. A novel element of our framework is\nthe use of transport-Lagrangian (TL) distances between observed and model\npredicted waveforms to specify a loss function, and the use of this loss to\ndefine a generalized belief update via a Gibbs posterior. The TL distance\nnaturally disregards certain features of the data that are more sensitive to\nmodel misspecification, and therefore produces less biased or dispersed\nposterior distributions in this setting. To make the latter notion precise, we\nuse several diagnostics to assess the quality of inference and uncertainty\nquantification, i.e., continuous rank probability scores and rank histograms.\nWe interpret these diagnostics in the Bayesian setting and compare the results\nto those obtained using more typical Gaussian noise models and squared-error\nloss, under various scenarios of misspecification. Finally, we discuss\npotential generalizability of the proposed framework to a broader class of\ninverse problems affected by model misspecification.\n"}
+{"abstract": "  Double-pushout rewriting is an established categorical approach to the\nrule-based transformation of graphs and graph-like objects. One of its standard\nresults is the construction of concurrent rules and the Concurrency Theorem\npertaining to it: The sequential application of two rules can equivalently be\nreplaced by the application of a concurrent rule and vice versa. We extend and\ngeneralize this result by introducing generalized concurrent rules (GCRs).\nTheir distinguishing property is that they allow identifying and preserving\nelements that are deleted by their first underlying rule and created by the\nsecond one. We position this new kind of composition of rules among the\nexisting ones and obtain a Generalized Concurrency Theorem for it. We conduct\nour work in the same generic framework in which the Concurrency Theorem has\nbeen presented, namely double-pushout rewriting in M-adhesive categories via\nrules equipped with application conditions.\n"}
+{"abstract": "  The modern field of quantum communication thrives on promise to deliver\nefficient and unconditionally secure ways to exchange information by exploiting\nquantum laws of physics. Here, quantum teleportation stands out as an exemplary\nprotocol allowing for the disembodied and safe transfer of unknown quantum\nstates using quantum entanglement and classical communication as resources. The\nexperimental feasibility of quantum teleportation with propagating waves,\nrelevant to communication scenarios, has been demonstrated in various physical\nsettings. However, an analogous implementation of quantum teleportation in the\nmicrowave domain was missing so far. At the same time, recent breakthroughs in\nquantum computation with superconducting circuits have triggered a demand for\nquantum communication between spatially separated superconducting processors\noperated at microwave frequencies. Here, we demonstrate a realization of\ndeterministic quantum teleportation of coherent microwave states by exploiting\ntwo-mode squeezing and analog feedforward over macroscopic distances $d =\n42\\,$cm. We achieve teleportation fidelities $F = 0.689 \\pm 0.004$ exceeding\nthe no-cloning $F_\\mathrm{nc} = 2/3$ threshold for coherent states with an\naverage photon number of up to $n_\\mathrm{d} = 1.1$. Our results provide a key\ningredient for the teleportation-based quantum gate for modular quantum\ncomputing with superconducting circuits and establish a solid foundation for\nfuture microwave quantum local area networks.\n"}
+{"abstract": "  Experimental demonstration of complex robotic behaviors relies heavily on\nfinding the correct controller gains. This painstaking process is often\ncompleted by a domain expert, requiring deep knowledge of the relationship\nbetween parameter values and the resulting behavior of the system. Even when\nsuch knowledge is possessed, it can take significant effort to navigate the\nnonintuitive landscape of possible parameter combinations. In this work, we\nexplore the extent to which preference-based learning can be used to optimize\ncontroller gains online by repeatedly querying the user for their preferences.\nThis general methodology is applied to two variants of control Lyapunov\nfunction based nonlinear controllers framed as quadratic programs, which have\nnice theoretic properties but are challenging to realize in practice. These\ncontrollers are successfully demonstrated both on the planar underactuated\nbiped, AMBER, and on the 3D underactuated biped, Cassie. We experimentally\nevaluate the performance of the learned controllers and show that the proposed\nmethod is repeatably able to learn gains that yield stable and robust\nlocomotion.\n"}
+{"abstract": "  In this paper we study the degeneration behavior of the norm of the Riemann\n$\\theta$-function in a family of principally polarized abelian varieties over\nthe punctured complex unit disc in terms of the associated polarized real\ntorus. As an application, we obtain the degeneration behavior of the\nZhang--Kawazumi invariant $\\varphi(M_t)$ of a family of Riemann surfaces $M_t$\nin terms of Zhang's invariant $\\varphi(\\Gamma)$ of the associated metrized\nreduction graph $\\Gamma$. This allows us to deduce a uniform lower bound for\nthe essential minimum of the N\\'eron-Tate height on the tautological cycles of\nany Jacobian variety over a number field.\n"}
+{"abstract": "  We introduce optimal energy shaping as an enhancement of classical\npassivity-based control methods. A promising feature of passivity theory,\nalongside stability, has traditionally been claimed to be intuitive performance\ntuning along the execution of a given task. However, a systematic approach to\nadjust performance within a passive control framework has yet to be developed,\nas each method relies on few and problem-specific practical insights. Here, we\ncast the classic energy-shaping control design process in an optimal control\nframework; once a task-dependent performance metric is defined, an optimal\nsolution is systematically obtained through an iterative procedure relying on\nneural networks and gradient-based optimization. The proposed method is\nvalidated on state-regulation tasks.\n"}
+{"abstract": "  When a molecule dissociates, the exact Kohn-Sham (KS) and Pauli potentials\nmay form step structures. Reproducing these steps correctly is central for the\ndescription of dissociation and charge-transfer processes in density functional\ntheory (DFT): The steps align the KS eigenvalues of the dissociating subsystems\nrelative to each other and determine where electrons localize. While the step\nheight can be calculated from the asymptotic behavior of the KS orbitals, this\nprovides limited insight into what causes the steps. We give an explanation of\nthe steps with an exact mapping of the many-electron problem to a one-electron\nproblem, the exact electron factorizaton (EEF). The potentials appearing in the\nEEF have a clear physical meaning that translates to the DFT potentials by\nreplacing the interacting many-electron system with the KS system. With a\nsimple model of a diatomic, we illustrate that the steps are a consequence of\nspatial electron entanglement and are the result of a charge transfer. From\nthis mechanism, the step height can immediately be deduced. Moreover, two\nmethods to approximately reproduce the potentials during dissociation suggest\nthemselves. One is based on the states of the dissociated system, while the\nother one is based on an analogy to the Born-Oppenheimer treatment of a\nmolecule. The latter method also shows that the steps connect adiabatic\npotential energy surfaces. The view of DFT from the EEF thus provides a better\nunderstanding of how many-electron effects are encoded in a one-electron theory\nand how they can be modeled.\n"}
+{"abstract": "  The ability to plan into the future while utilizing only raw high-dimensional\nobservations, such as images, can provide autonomous agents with broad\ncapabilities. Visual model-based reinforcement learning (RL) methods that plan\nfuture actions directly have shown impressive results on tasks that require\nonly short-horizon reasoning, however, these methods struggle on temporally\nextended tasks. We argue that it is easier to solve long-horizon tasks by\nplanning sequences of states rather than just actions, as the effects of\nactions greatly compound over time and are harder to optimize. To achieve this,\nwe draw on the idea of collocation, which has shown good results on\nlong-horizon tasks in optimal control literature, and adapt it to the\nimage-based setting by utilizing learned latent state space models. The\nresulting latent collocation method (LatCo) optimizes trajectories of latent\nstates, which improves over previously proposed shooting methods for visual\nmodel-based RL on tasks with sparse rewards and long-term goals. Videos and\ncode at https://orybkin.github.io/latco/.\n"}
+{"abstract": "  Arxiv is acting weird and throwing error: \"Bad character(s) in field\nAbstract.\" for no reason. Please refer to the manuscript.\n"}
+{"abstract": "  Controllable synthesis of defect-free graphene is crucial for applications\nsince the properties of graphene are highly sensitive to any deviations from\nthe crystalline lattice. We focus here on the emerging use of liquid Cu\ncatalysts, which has high potential for fast and efficient industrial-scale\nproduction of high-quality graphene. The interface between graphene and liquid\nCu is studied using force field and ab initio molecular dynamics, revealing a\ncomplete or partial embedding of finite-sized flakes. By analyzing flakes of\ndifferent sizes we find that the size-dependence of the embedding can be\nrationalized based on the energy cost of embedding versus bending the graphene\nflake. The embedding itself is driven by the formation of covalent bonds\nbetween the under-coordinated edge C atoms and the liquid Cu surface, which is\naccompanied by a significant charge transfer. In contrast, the central flake\natoms are located around or slightly above 3 {\\AA} from the liquid Cu surface\nand exhibit weak vdW-bonding and much lower charge transfer. The structural and\nelectronic properties of the embedded state revealed in our work provides the\natomic-scale information needed to develop effective models to explain the\nspecial growth observed in experiments where various interesting phenomena such\nas flake self-assembly and rotational alignment, high growth speeds and low\ndefect densities in the final graphene product have been observed.\n"}
+{"abstract": "  Sparse regression algorithms have been proposed as the appropriate framework\nto model the governing equations of a system from data, without needing prior\nknowledge of the underlying physics. In this work, we use sparse regression to\nbuild an accurate and explainable model of the stellar mass of central galaxies\ngiven properties of their host dark matter (DM) halo. Our data set comprises\n9,521 central galaxies from the EAGLE hydrodynamic simulation. By matching the\nhost halos to a DM-only simulation, we collect the halo mass and specific\nangular momentum at present time and for their main progenitors in 10 redshift\nbins from $z=0$ to $z=4$. The principal component of our governing equation is\na third-order polynomial of the host halo mass, which models the stellar-mass\nhalo-mass relation. The scatter about this relation is driven by the halo mass\nevolution and is captured by second and third-order correlations of the halo\nmass evolution with the present halo mass. An advantage of sparse regression\napproaches is that unnecessary terms are removed. Although we include\ninformation on halo specific angular momentum, these parameters are discarded\nby our methodology. This suggests that halo angular momentum has little\nconnection to galaxy formation efficiency. Our model has a root mean square\nerror (RMSE) of $0.167 \\log_{10}(M^*/M_\\odot)$, and accurately reproduces both\nthe stellar mass function and central galaxy correlation function of EAGLE. The\nmethodology appears to be an encouraging approach for populating the halos of\nDM-only simulations with galaxies, and we discuss the next steps that are\nrequired.\n"}
+{"abstract": "  Imaging individual vacancies in solids and revealing their interactions with\nsolute atoms remains one of the frontiers in microscopy and microanalysis. Here\nwe study a creep-deformed binary Ni-2 at.% Ta alloy. Atom probe tomography\nreveals a random distribution of Ta. Field ion microscopy, with contrast\ninterpretation supported by density-functional theory and time-of-flight mass\nspectrometry, evidences a positive correlation of tantalum with vacancies. Our\nresults support solute-vacancy binding, which explains improvement in creep\nresistance of Ta-containing Ni-based superalloys and helps guide future\nmaterial design strategies.\n"}
+{"abstract": "  The vulnerability of deep neural networks to adversarial examples, which are\ncrafted maliciously by modifying the inputs with imperceptible perturbations to\nmisled the network produce incorrect outputs, reveals the lack of robustness\nand poses security concerns. Previous works study the adversarial robustness of\nimage classifiers on image level and use all the pixel information in an image\nindiscriminately, lacking of exploration of regions with different semantic\nmeanings in the pixel space of an image. In this work, we fill this gap and\nexplore the pixel space of the adversarial image by proposing an algorithm to\nlooking for possible perturbations pixel by pixel in different regions of the\nsegmented image. The extensive experimental results on CIFAR-10 and ImageNet\nverify that searching for the modified pixel in only some pixels of an image\ncan successfully launch the one-pixel adversarial attacks without requiring all\nthe pixels of the entire image, and there exist multiple vulnerable points\nscattered in different regions of an image. We also demonstrate that the\nadversarial robustness of different regions on the image varies with the amount\nof semantic information contained.\n"}
+{"abstract": "  In author name disambiguation, author forenames are used to decide which name\ninstances are disambiguated together and how much they are likely to refer to\nthe same author. Despite such a crucial role of forenames, their effect on the\nperformances of heuristic (string matching) and algorithmic disambiguation is\nnot well understood. This study assesses the contributions of forenames in\nauthor name disambiguation using multiple labeled datasets under varying ratios\nand lengths of full forenames, reflecting real-world scenarios in which an\nauthor is represented by forename variants (synonym) and some authors share the\nsame forenames (homonym). Results show that increasing the ratios of full\nforenames improves substantially the performances of both heuristic and\nmachine-learning-based disambiguation. Performance gains by algorithmic\ndisambiguation are pronounced when many forenames are initialized or homonym is\nprevalent. As the ratios of full forenames increase, however, they become\nmarginal compared to the performances by string matching. Using a small portion\nof forename strings does not reduce much the performances of both heuristic and\nalgorithmic disambiguation compared to using full-length strings. These\nfindings provide practical suggestions such as restoring initialized forenames\ninto a full-string format via record linkage for improved disambiguation\nperformances.\n"}
+{"abstract": "  Cardiac tagging magnetic resonance imaging (t-MRI) is the gold standard for\nregional myocardium deformation and cardiac strain estimation. However, this\ntechnique has not been widely used in clinical diagnosis, as a result of the\ndifficulty of motion tracking encountered with t-MRI images. In this paper, we\npropose a novel deep learning-based fully unsupervised method for in vivo\nmotion tracking on t-MRI images. We first estimate the motion field (INF)\nbetween any two consecutive t-MRI frames by a bi-directional generative\ndiffeomorphic registration neural network. Using this result, we then estimate\nthe Lagrangian motion field between the reference frame and any other frame\nthrough a differentiable composition layer. By utilizing temporal information\nto perform reasonable estimations on spatio-temporal motion fields, this novel\nmethod provides a useful solution for motion tracking and image registration in\ndynamic medical imaging. Our method has been validated on a representative\nclinical t-MRI dataset; the experimental results show that our method is\nsuperior to conventional motion tracking methods in terms of landmark tracking\naccuracy and inference efficiency.\n"}
+{"abstract": "  Ices are an important constituent of protoplanetary disks. New observational\nfacilities, notably the James Webb Space Telescope (JWST), will greatly enhance\nour view of disk ices by measuring their infrared spectral features. We present\na suite of models to complement these upcoming observations. Our models use a\nkinetics-based gas-grain chemical evolution code to simulate the distribution\nof ices in a disk, followed by a radiative transfer code using a subset of key\nice species to simulate the observations. We present models reflecting both\nmolecular inheritance and chemical reset initial conditions. We find that\nnear-to-mid-IR absorption features of H$_2$O, CO$_2$, and CH$_3$OH are readily\nobservable in disk-integrated spectra of highly inclined disks while CO,\nNH$_3$, and CH$_4$ ice do not show prominent features. CH$_3$OH ice has low\nabundance and is not observable in the reset model, making this species an\nexcellent diagnostic of initial chemical conditions. CO$_2$ ice features\nexhibit the greatest change over disk lifetime, decreasing and increasing for\nthe inheritance and reset models, respectively. Spatially resolved spectra of\nedge-on disks, possible with JWST's integral field unit observing modes, are\nideal for constraining the vertical distribution of ices and may be able to\nisolate features from ices closer to the midplane (e.g., CO) given sufficient\nsensitivity. Spatially-resolved spectra of face-on disks can trace\nscattered-light features from H$_2$O, CO$_2$, and CH$_3$OH, plus CO and CH$_4$\nfrom the outermost regions. We additionally simulate far-IR H$_2$O ice emission\nfeatures and find they are strongest for disks viewed face-on.\n"}
+{"abstract": "  We study the multi-agent safe control problem where agents should avoid\ncollisions to static obstacles and collisions with each other while reaching\ntheir goals. Our core idea is to learn the multi-agent control policy jointly\nwith learning the control barrier functions as safety certificates. We propose\na novel joint-learning framework that can be implemented in a decentralized\nfashion, with generalization guarantees for certain function classes. Such a\ndecentralized framework can adapt to an arbitrarily large number of agents.\nBuilding upon this framework, we further improve the scalability by\nincorporating neural network architectures that are invariant to the quantity\nand permutation of neighboring agents. In addition, we propose a new\nspontaneous policy refinement method to further enforce the certificate\ncondition during testing. We provide extensive experiments to demonstrate that\nour method significantly outperforms other leading multi-agent control\napproaches in terms of maintaining safety and completing original tasks. Our\napproach also shows exceptional generalization capability in that the control\npolicy can be trained with 8 agents in one scenario, while being used on other\nscenarios with up to 1024 agents in complex multi-agent environments and\ndynamics.\n"}
+{"abstract": "  We study, in the framework of the entanglement harvesting protocol, the\nentanglement harvesting of both a pair of inertial and uniformly accelerated\ndetectors locally interacting with vacuum massless scalar fields subjected to a\nperfectly reflecting plane boundary. We find that the presence of the boundary\ngenerally degrades the harvested entanglement when two detectors are very close\nto the boundary. However, when the distance between detectors and the boundary\nbecomes comparable to the interaction duration parameter, the amount of the\nharvested entanglement approaches a peak, which even goes beyond that without a\nboundary. Remarkably, the parameter space of the detectors' separation and the\nmagnitude of acceleration that allows\n  entanglement harvesting to occur is enlarged due to the presence of the\nboundary. In this sense, the boundary plays a double-edged role on entanglement\nharvesting,\n  degrading in general the harvested entanglement while enlarging the\nentanglement harvesting-achievable parameter space.\n  A comparison of three different acceleration scenarios of the detectors with\nrespect to the boundary, i.e., parallel, anti-parallel and mutually\nperpendicular acceleration, shows that the phenomenon of entanglement\nharvesting crucially depends on the acceleration, the separation between two\ndetectors and the detectors' distance from the boundary.\n"}
+{"abstract": "  We propose a change-point detection method for large scale multiple testing\nproblems with data having clustered signals. Unlike the classic change-point\nsetup, the signals can vary in size within a cluster. The clustering structure\non the signals enables us to effectively delineate the boundaries between\nsignal and non-signal segments. New test statistics are proposed for\nobservations from one and/or multiple realizations. Their asymptotic\ndistributions are derived. We also study the associated variance estimation\nproblem. We allow the variances to be heteroscedastic in the multiple\nrealization case, which substantially expands the applicability of the proposed\nmethod. Simulation studies demonstrate that the proposed approach has a\nfavorable performance. Our procedure is applied to {an array based Comparative\nGenomic Hybridization (aCGH)} dataset.\n"}
+{"abstract": "  This letter explores covariance matching-based adaptive robust cubature\nKalman filter (CMRACKF). In this method, the innovation sequence is used to\ndetermine the covariance matrix of measurement noise that can overcome the\nlimitation of conventional CKF. In the proposed algorithm, weights are\nadaptively adjusted and used for updating the measurement noise covariance\nmatrices online. It can also enhance the adaptive capability of the ACKF. The\nsimulation results are illustrated to evaluate the performance of the proposed\nalgorithm.\n"}
+{"abstract": "  Synthetic Aperture Sonar (SAS) surveys produce imagery with large regions of\ntransition between seabed types. Due to these regions, it is difficult to label\nand segment the imagery and, furthermore, challenging to score the image\nsegmentations appropriately. While there are many approaches to quantify\nperformance in standard crisp segmentation schemes, drawing hard boundaries in\nremote sensing imagery where gradients and regions of uncertainty exist is\ninappropriate. These cases warrant weak labels and an associated appropriate\nscoring approach. In this paper, a labeling approach and associated modified\nversion of the Rand index for weakly-labeled data is introduced to address\nthese issues. Results are evaluated with the new index and compared to\ntraditional segmentation evaluation methods. Experimental results on a SAS data\nset containing must-link and cannot-link labels show that our Weakly-Labeled\nRand index scores segmentations appropriately in reference to qualitative\nperformance and is more suitable than traditional quantitative metrics for\nscoring weakly-labeled data.\n"}
+{"abstract": "  A recent article (Khair and Kabarowski; Phys. Rev. Fluids 5, 033702) has\nstudied the cross-streamline migration of electrophoretic particles in\nunbounded shear flows with weak inertia or viscoelasticity. That work compares\ntheir results with those reported in two of our previous studies (Choudhary et\nal.: J. Fluid Mech. 874; J. Fluid Mech. 898) and reports a disagreement in the\nderived analytical expressions. In this comment, we resolve this discrepancy.\nFor viscoelastic flows, we show that Khair and Kabarowski have not accounted\nfor a leading order surface integral of polymeric stress in their calculation\nof first-order viscoelastic lift. When this integral is included, the resulting\nmigration velocity matches exactly with that reported in our work (J. Fluid\nMech. 898). This qualitatively changes the migration direction that is reported\nby Khair and Kabarowski for viscoelastic flows. For inertial flows, we clarify\nthat Khair and Kabarowski find the coefficient of lift to be 1.75$\\pi$,\ncompared to 2.35$ \\pi $ in our previous work (J. Fluid Mech. 874). We show that\nthis difference occurs because Khair and Kabarowski accurately include the\neffect of rapidly decaying $ \\sim O(1/r^{4}) $ velocity field (a correction to\nthe stresslet field $ \\sim 1/r^{2} $), which was neglected in our previous work\n(J. Fluid Mech. 874).\n"}
+{"abstract": "  Let $\\Lambda$ be a row-finite and source-free higher rank graph with finitely\nmany vertices. In this paper, we define the Higman-Thompson like group $\\Lht$\nof the graph C*-algebra $\\mathcal{O}_\\Lambda$ to be a special subgroup of the\nunitary group in $\\O_\\Lambda$. It is shown that $\\Lht$ is closely related to\nthe topological full groups of the groupoid associated with $\\Lambda$. Some\nproperties of $\\Lht$ are also investigated. We show that its commutator group\n$\\DLht$ is simple and that $\\DLht$ has only one nontrivial uniformly recurrent\nsubgroup if $\\Lambda$ is aperiodic and strongly connected. Furthermore, if\n$\\Lambda$ is single-vertex, then we prove that $\\Lht$ is C*-simple and also\nprovide an explicit description on the stabilizer uniformly recurrent subgroup\nof $\\Lht$ under a natural action on the infinite path space of $\\Lambda$.\n"}
+{"abstract": "  In this note, we present some ideas for describing the distributions of the\nrunning maximum/minimum, first passage times and telegraphic meanders. Explicit\nformulae for joint distribution of the extrema, the number of velocity switches\nand the terminal position are derived using coupled integral equations\ntechnique.\n"}
+{"abstract": "  Computational models of managerial search often build on backward-looking\nsearch based on hill-climbing algorithms. Regardless of its prevalence, there\nis some evidence that this family of algorithms does not universally represent\nmanagers' search behavior. Against this background, the paper proposes an\nalternative algorithm that captures key elements of Simon's concept of\nsatisficing which received considerable support in behavioral experiments. The\npaper contrasts the satisficing-based algorithm to two variants of\nhill-climbing search in an agent-based model of a simple decision-making\norganization. The model builds on the framework of NK fitness landscapes which\nallows controlling for the complexity of the decision problem to be solved. The\nresults suggest that the model's behavior may remarkably differ depending on\nwhether satisficing or hill-climbing serves as an algorithmic representation\nfor decision-makers' search. Moreover, with the satisficing algorithm, results\nindicate oscillating aspiration levels, even to the negative, and intense - and\npotentially destabilizing - search activities when intra-organizational\ncomplexity increases. Findings may shed some new light on prior computational\nmodels of decision-making in organizations and point to avenues for future\nresearch.\n"}
+{"abstract": "  In this paper, a lifelong learning problem is studied for an Internet of\nThings (IoT) system. In the considered model, each IoT device aims to balance\nits information freshness and energy consumption tradeoff by controlling its\ncomputational resource allocation at each time slot under dynamic environments.\nAn unmanned aerial vehicle (UAV) is deployed as a flying base station so as to\nenable the IoT devices to adapt to novel environments. To this end, a new\nlifelong reinforcement learning algorithm, used by the UAV, is proposed in\norder to adapt the operation of the devices at each visit by the UAV. By using\nthe experience from previously visited devices and environments, the UAV can\nhelp devices adapt faster to future states of their environment. To do so, a\nknowledge base shared by all devices is maintained at the UAV. Simulation\nresults show that the proposed algorithm can converge $25\\%$ to $50\\%$ faster\nthan a policy gradient baseline algorithm that optimizes each device's decision\nmaking problem in isolation.\n"}
+{"abstract": "  Large-scale atomistic computer simulations of materials rely on interatomic\npotentials providing computationally efficient predictions of energy and\nNewtonian forces. Traditional potentials have served in this capacity for over\nthree decades. Recently, a new class of potentials has emerged, which is based\non a radically different philosophy. The new potentials are constructed using\nmachine-learning (ML) methods and a massive reference database generated by\nquantum-mechanical calculations. While the traditional potentials are derived\nfrom physical insights into the nature of chemical bonding, the ML potentials\nutilize a high-dimensional mathematical regression to interpolate between the\nreference energies. We review the current status of the interatomic potential\nfield, comparing the strengths and weaknesses of the traditional and ML\npotentials. A third class of potentials is introduced, in which an ML model is\ncoupled with a physics-based potential to improve the transferability to\nunknown atomic environments. The discussion is focused on potentials intended\nfor materials science applications. Possible future directions in this field\nare outlined.\n"}
+{"abstract": "  The contribution of this paper includes two aspects. First, we study the\nlower bound complexity for the minimax optimization problem whose objective\nfunction is the average of $n$ individual smooth component functions. We\nconsider Proximal Incremental First-order (PIFO) algorithms which have access\nto gradient and proximal oracle for each individual component. We develop a\nnovel approach for constructing adversarial problems, which partitions the\ntridiagonal matrix of classical examples into $n$ groups. This construction is\nfriendly to the analysis of incremental gradient and proximal oracle. With this\napproach, we demonstrate the lower bounds of first-order algorithms for finding\nan $\\varepsilon$-suboptimal point and an $\\varepsilon$-stationary point in\ndifferent settings. Second, we also derive the lower bounds of minimization\noptimization with PIFO algorithms from our approach, which can cover the\nresults in \\citep{woodworth2016tight} and improve the results in\n\\citep{zhou2019lower}.\n"}
+{"abstract": "  We consider Steklov eigenvalues of three-dimensional, nearly-spherical\ndomains. In previous work, we have shown that the Steklov eigenvalues are\nanalytic functions of the domain perturbation parameter. Here, we compute the\nfirst-order term of the asymptotic expansion, which can explicitly be written\nin terms of the Wigner 3-jsymbols. We analyze the asymptotic expansion and\nprove the isoperimetric result that, if l is a square integer, the\nvolume-normalized l-th Steklov eigenvalue is stationary for a ball.\n"}
+{"abstract": "  We consider Schr\\\"odinger operators with periodic potentials on periodic\ndiscrete graphs. Their spectrum consists of a finite number of bands. We obtain\ntwo-sided estimates of the total bandwidth for the Schr\\\"odinger operators in\nterms of geometric parameters of the graph and the potentials. In particular,\nwe show that these estimates are sharp. It means that these estimates become\nidentities for specific graphs and potentials. The proof is based on the\nFloquet theory and trace formulas for fiber operators. The traces are expressed\nas finite Fourier series of the quasimomentum with coefficients depending on\nthe potentials and cycles of the quotient graph from some specific cycle sets.\nIn order to obtain our results we estimate these Fourier coefficients in terms\nof geometric parameters of the graph and the potentials.\n"}
+{"abstract": "  Learning from imprecise labels such as \"animal\" or \"bird\", but making precise\npredictions like \"snow bunting\" at test time is an important capability when\nexpertly labeled training data is scarce. Contributions by volunteers or\nresults of web crawling lack precision in this manner, but are still valuable.\nAnd crucially, these weakly labeled examples are available in larger quantities\nfor lower cost than high-quality bespoke training data.\n  CHILLAX, a recently proposed method to tackle this task, leverages a\nhierarchical classifier to learn from imprecise labels. However, it has two\nmajor limitations. First, it is not capable of learning from effectively\nunlabeled examples at the root of the hierarchy, e.g. \"object\". Second, an\nextrapolation of annotations to precise labels is only performed at test time,\nwhere confident extrapolations could be already used as training data.\n  In this work, we extend CHILLAX with a self-supervised scheme using\nconstrained extrapolation to generate pseudo-labels. This addresses the second\nconcern, which in turn solves the first problem, enabling an even weaker\nsupervision requirement than CHILLAX. We evaluate our approach empirically and\nshow that our method allows for a consistent accuracy improvement of 0.84 to\n1.19 percent points over CHILLAX and is suitable as a drop-in replacement\nwithout any negative consequences such as longer training times.\n"}
+{"abstract": "  Integrability of the XXZ model induces an extensive number of conserved\nquantities. In this paper we give a closed form expression for the series of\nlocal conserved charges of the XXZ model on a closed chain with or without a\ntwist. We prove that each element of the series commutes with the Hamiltonian.\n"}
+{"abstract": "  The success of deep reinforcement learning (DRL) is due to the power of\nlearning a representation that is suitable for the underlying exploration and\nexploitation task. However, existing provable reinforcement learning algorithms\nwith linear function approximation often assume the feature representation is\nknown and fixed. In order to understand how representation learning can improve\nthe efficiency of RL, we study representation learning for a class of low-rank\nMarkov Decision Processes (MDPs) where the transition kernel can be represented\nin a bilinear form. We propose a provably efficient algorithm called ReLEX that\ncan simultaneously learn the representation and perform exploration. We show\nthat ReLEX always performs no worse than a state-of-the-art algorithm without\nrepresentation learning, and will be strictly better in terms of sample\nefficiency if the function class of representations enjoys a certain mild\n\"coverage'' property over the whole state-action space.\n"}
+{"abstract": "  Binary polynomial optimization is equivalent to the problem of minimizing a\nlinear function over the intersection of the multilinear set with a polyhedron.\nMany families of valid inequalities for the multilinear set are available in\nthe literature, though giving a polyhedral characterization of the convex hull\nis not tractable in general as binary polynomial optimization is NP-hard. In\nthis paper, we study the cardinality constrained multilinear set in the special\ncase when the number of monomials is exactly two. We give an extended\nformulation, with two more auxiliary variables and exponentially many\ninequalities, of the convex hull of solutions of the standard linearization of\nthis problem. We also show that the separation problem can be solved\nefficiently.\n"}
+{"abstract": "  We study upward planar straight-line drawings that use only a constant number\nof slopes. In particular, we are interested in whether a given directed graph\nwith maximum in- and outdegree at most $k$ admits such a drawing with $k$\nslopes. We show that this is in general NP-hard to decide for outerplanar\ngraphs ($k = 3$) and planar graphs ($k \\ge 3$). On the positive side, for\ncactus graphs deciding and constructing a drawing can be done in polynomial\ntime. Furthermore, we can determine the minimum number of slopes required for a\ngiven tree in linear time and compute the corresponding drawing efficiently.\n"}
+{"abstract": "  This paper studies classical weight modules over the $\\imath$quantum group\n$\\mathbf{U}^{\\imath}$ of type AI. We introduce the notion of based\n$\\mathbf{U}^{\\imath}$-modules by generalizing the notion of based modules over\nthe quantum groups. We prove that each finite-dimensional irreducible classical\nweight $\\mathbf{U}^{\\imath}$-module with integer highest weight is a based\n$\\mathbf{U}^{\\imath}$-module. As a byproduct, a new combinatorial formula for\nthe branching rule from $\\mathfrak{sl}_n$ to $\\mathfrak{so}_n$ is obtained.\n"}
+{"abstract": "  The Simons Observatory (SO) will be a cosmic microwave background (CMB)\nsurvey experiment with three small-aperture telescopes and one large-aperture\ntelescope, which will observe from the Atacama Desert in Chile. In total, SO\nwill field over 60,000 transition-edge sensor (TES) bolometers in six spectral\nbands centered between 27 and 280 GHz in order to achieve the sensitivity\nnecessary to measure or constrain numerous cosmological quantities, as outlined\nin The Simons Observatory Collaboration et al. (2019). These telescopes require\n33 highly transparent, large aperture, refracting optics. To this end, we\ndeveloped mechanically robust, highly efficient, metamaterial anti-reflection\n(AR) coatings with octave bandwidth coverage for silicon optics up to 46 cm in\ndiameter for the 22-55, 75-165, and 190-310 GHz bands. We detail the design,\nthe manufacturing approach to fabricate the SO lenses, their performance, and\npossible extensions of metamaterial AR coatings to optical elements made of\nharder materials such as alumina.\n"}
+{"abstract": "  Starlight subtraction algorithms based on the method of Karhunen-Lo\\`eve\neigenimages have proved invaluable to exoplanet direct imaging. However, they\nscale poorly in runtime when paired with differential imaging techniques. In\nsuch observations, reference frames and frames to be starlight-subtracted are\ndrawn from the same set of data, requiring a new subset of references (and\neigenimages) for each frame processed to avoid self-subtraction of the signal\nof interest. The data rates of extreme adaptive optics instruments are such\nthat the only way to make this computationally feasible has been to downsample\nthe data. We develop a technique that updates a pre-computed singular value\ndecomposition of the full dataset to remove frames (i.e. a \"downdate\") without\na full recomputation, yielding the modified eigenimages. This not only enables\nanalysis of much larger data volumes in the same amount of time, but also\nexhibits near-linear scaling in runtime as the number of observations\nincreases. We apply this technique to archival data and investigate its scaling\nbehavior for very large numbers of frames $N$. The resulting algorithm provides\nspeed improvements of $2.6\\times$ (for 200 eigenimages at $N = 300$) to $140\n\\times$ (at $N = 10^4$) with the advantage only increasing as $N$ grows. This\nalgorithm has allowed us to substantially accelerate KLIP even for modest $N$,\nand will let us quickly explore how KLIP parameters affect exoplanet\ncharacterization in large $N$ datasets.\n"}
+{"abstract": "  We suggest a possible origin of noncollinear magnetic textures in\nferromagnets (FMs) with the $D_{3h}$ point group symmetry. Suggested mechanism\nis different from the Dzyaloshinskii-Moriya interaction (DMI). Considered\nsymmetry class is important because a large fraction of all single-layer\nintrinsic FMs should belong to it. In particular, so does a monolayer\nFe$_3$GeTe$_2$. At the same time, DMI vanishes identically in materials\ndescribed by this point group, in the continuous limit. We use symmetry\nanalysis to derive all possible contributions to the free energy density that\nare of the fourth order with respect to the unit vector $\\boldsymbol n$ of the\nlocal magnetization direction and linear with respect to spatial derivatives of\n$\\boldsymbol n$. There are precisely seven such contributions. However, up to\nboundary terms, only two of them can be considered as independent. Moreover,\nfor a two-dimensional system, one of these two necessarily vanishes! We\ninvestigate whether the remaining contribution can serve as a source of spin\nspirals and skyrmions recently observed in Fe$_3$GeTe$_2$. We also address a\npossible stabilization of bimerons by the same contribution.\n"}
+{"abstract": "  We study certain toric Gorenstein varieties with isolated singularities which\nare the quotient spaces of generic unimodular representation spaces by the\none-dimensional torus, or the product of the one-dimensional torus with a\nfinite abelian group. Based on the works of \\v{S}penko and Van den Bergh\n[Invent. Math. 210 (2017), no. 1, 3-67] and Mori and Ueyama [Adv. Math. 297\n(2016), 54-92], we show that the singularity categories of these varieties\nadmit tilting objects, and hence are triangle equivalent to the derived\ncategories of some finite dimensional algebras.\n"}
+{"abstract": "  Uncertainty and confidence have been shown to be useful metrics in a wide\nvariety of techniques proposed for deep learning testing, including test data\nselection and system supervision.We present uncertainty-wizard, a tool that\nallows to quantify such uncertainty and confidence in artificial neural\nnetworks. It is built on top of the industry-leading tf.keras deep learning API\nand it provides a near-transparent and easy to understand interface. At the\nsame time, it includes major performance optimizations that we benchmarked on\ntwo different machines and different configurations.\n"}
+{"abstract": "  Quantized neural networks (NN) are the common standard to efficiently deploy\ndeep learning models on tiny hardware platforms. However, we notice that\nquantized NNs are as vulnerable to adversarial attacks as the full-precision\nmodels. With the proliferation of neural networks on small devices that we\ncarry or surround us, there is a need for efficient models without sacrificing\ntrust in the prediction in presence of malign perturbations. Current mitigation\napproaches often need adversarial training or are bypassed when the strength of\nadversarial examples is increased.\n  In this work, we investigate how a probabilistic framework would assist in\novercoming the aforementioned limitations for quantized deep learning models.\nWe explore Stochastic-Shield: a flexible defense mechanism that leverages input\nfiltering and a probabilistic deep learning approach materialized via Monte\nCarlo Dropout. We show that it is possible to jointly achieve efficiency and\nrobustness by accurately enabling each module without the burden of\nre-retraining or ad hoc fine-tuning.\n"}
+{"abstract": "  Modern vehicles can be thought of as complex distributed embedded systems\nthat run a variety of automotive applications with real-time constraints.\nRecent advances in the automotive industry towards greater autonomy are driving\nvehicles to be increasingly connected with various external systems (e.g.,\nroadside beacons, other vehicles), which makes emerging vehicles highly\nvulnerable to cyber-attacks. Additionally, the increased complexity of\nautomotive applications and the in-vehicle networks results in poor attack\nvisibility, which makes detecting such attacks particularly challenging in\nautomotive systems. In this work, we present a novel anomaly detection\nframework called LATTE to detect cyber-attacks in Controller Area Network (CAN)\nbased networks within automotive platforms. Our proposed LATTE framework uses a\nstacked Long Short Term Memory (LSTM) predictor network with novel attention\nmechanisms to learn the normal operating behavior at design time. Subsequently,\na novel detection scheme (also trained at design time) is used to detect\nvarious cyber-attacks (as anomalies) at runtime. We evaluate our proposed LATTE\nframework under different automotive attack scenarios and present a detailed\ncomparison with the best-known prior works in this area, to demonstrate the\npotential of our approach.\n"}
+{"abstract": "  In this paper, we prove precise late-time asymptotics for solutions to the\nwave equation supported on angular frequencies greater or equal to $\\ell$ on\nthe domain of outer communications of subextremal Reissner-Nordstr\\\"om\nspacetimes up to and including the event horizon. Our asymptotics yield, in\nparticular, sharp upper and lower decay rates which are consistent with Price's\nlaw on such backgrounds. We present a theory for inverting the time operator\nand derive an explicit representation of the leading-order asymptotic\ncoefficient in terms of the Newman-Penrose charges at null infinity associated\nwith the time integrals. Our method is based on purely physical space\ntechniques. For each angular frequency $\\ell$ we establish a sharp hierarchy of\n$r$-weighted radially commuted estimates with length $2\\ell+5$. We complement\nthis hierarchy with a novel hierarchy of weighted elliptic estimates of length\n$\\ell+1$.\n"}
+{"abstract": "  Various attempts have been made in the literature at describing the origin\nand the physical mechanisms behind flaring events in blazars with radiative\nemission models, but detailed properties of multi-wavelength (MWL) light curves\nstill remain difficult to reproduce. We have developed a versatile radiative\ncode, based on a time-dependent treatment of particle acceleration, escape and\nradiative cooling, allowing us to test different scenarios to connect the\ncontinuous low-state emission self-consistently with that during flaring\nstates. We consider flares as weak perturbations of the quiescent state and\napply this description to the February 2010 MWL flare of Mrk 421, the brightest\nVery High Energy (VHE) flare ever detected from this archetypal blazar,\nfocusing on interpretations with a minimum number of free parameters. A general\ncriterion is obtained, which disfavours a one-zone model connecting low and\nhigh state under our assumptions. A two-zone model combining physically\nconnected acceleration and emission regions yields a satisfactory\ninterpretation of the available time-dependent MWL light curves and spectra of\nMrk 421, although certain details remain difficult to reproduce. The two-zone\nscenario finally proposed for the complex quiescent and flaring VHE emitting\nregion involves both Fermi-I and Fermi-II acceleration mechanisms, respectively\nat the origin of the quiescent and flaring emission.\n"}
+{"abstract": "  We present self-energy corrected tight-binging(TB) parameters in the basis of\nthe directed hybridised atomic orbitals constructed from first principles, for\nnano-diamonds as well as bulk diamond and zinc blende structures made of\nelements of group 13, 14 and 15 in the 2p, 3p and 4p blocks. With increasing\nprincipal quantum number of frontier orbitals, the lowering of self-energy\ncorrections(SEC) to the band-gap and consequently to the dominant inter-atomic\nTB parameters, is much faster in bulk than in nano-diamonds and hence not\ntransferable from bulks to nano-structures. However, TB parameters transfered\nfrom smaller nano-diamonds to much larger ones exclusively through mapping of\nneighbourhoods of atoms not limited to nearest neighbours, are found to render\nHOMO-LUMO gaps of the larger nano-diamonds with few hundreds of atoms in good\nagreement with their explicitly computed values at the DFT as well as DFT+G0W0\nlevels. TB parameters and their SEC are found to vary significantly from 2p to\n3p block but negligibly from 3p to 4p, while varying rather slowly within each\nblock, implying the possibility of transfer of SEC across block with increasing\nprincipal quantum number. The demonstrated easy transferability of self-energy\ncorrected TB parameters in the hybrid orbital basis thus promises\ncomputationally inexpensive estimation of quasi-particle electronic structure\nof large finite systems with thousands of atoms.\n"}
+{"abstract": "  Clusters and nanodroplets hold the promise of enhancing high-order nonlinear\noptical effects due to their high local density. However, only moderate\nenhancement has been demonstrated to date. Here, we report the observation of\nenergetic electrons generated by above-threshold ionization (ATI) of helium\n(He) nanodroplets which are resonantly excited by ultrashort extreme\nultraviolet (XUV) free-electron laser pulses and subsequently ionized by\nnear-infrared (NIR) or near-ultraviolet (UV) pulses. The electron emission due\nto high-order ATI is enhanced by several orders of magnitude compared to He\natoms. The crucial dependence of the ATI intensities with the number of\nexcitations in the droplets suggests a local collective enhancement effect.\n"}
+{"abstract": "  The inhomogeneous Muskat problem models the dynamics of an interface between\ntwo fluids of differing characteristics inside a non-uniform porous medium. We\nconsider the case of a porous media with a permeability jump across a\nhorizontal boundary away from an interface between two fluids of different\nviscosities and densities. For initial data of explicit medium size, depending\non the characteristics of the fluids and porous media, we will prove the global\nexistence and uniqueness of a solution which is instantly analytic and decays\nin time to the flat interface.\n"}
+{"abstract": "  As the major target of many vaccines and neutralizing antibodies against\nSARS-CoV-2, the spike (S) protein is observed to mutate over time. In this\npaper, we present statistical approaches to tackle some challenges associated\nwith the analysis of S-protein data. We build a Bayesian hierarchical model to\nstudy the temporal and spatial evolution of S-protein sequences, after grouping\nthe sequences into representative clusters. We then apply sampling methods to\ninvestigate possible changes to the S-protein's 3-D structure as a result of\ncommonly observed mutations. While the increasing spread of D614G variants has\nbeen noted in other research, our results also show that the co-occurring\nmutations of D614G together with S477N or A222V may spread even more rapidly,\nas quantified by our model estimates.\n"}
+{"abstract": "  A temporal constraint language is a set of relations that are first-order\ndefinable over (Q;<). We show that several temporal constraint languages whose\nconstraint satisfaction problem is maximally tractable are also maximally\ntractable for the more expressive quantified constraint satisfaction problem.\nThese constraint languages are defined in terms of preservation under certain\nbinary polymorphisms. We also present syntactic characterizations of the\nrelations in these languages.\n"}
+{"abstract": "  In recent times, as a result of COVID-19 pandemic, higher institutions in\nNigeria have been shutdown and the leadership of Academic Staff Union of\nUniversity (ASUU) said that Nigerian universities cannot afford to mount Online\nlearning platforms let alone conduct such learning system in Nigeria due to\nlack of infrastructure, capacity and skill sets in the face of COVID-19\npandemic. In the light of this, this research undertook an online survey using\nUniversity of Nigeria, Nsukka (UNN) as a case study to know which type of\nonline learning system ASUU leadership is talking about - Asynchronous or\nSynchronous? How did ASUU come about their facts? Did ASUU base their assertion\non facts, if YES, what are the benchmarks? Therefore, this research project is\nfocused on providing benchmarks to assess if a Nigerian University has what it\ntakes to run a synchronous Online Learning. It includes Infrastructure needed\n(Hardware, Software, Network connectivity), Skill sets from staff (Computer\nliteracy level). In a bid to do this, an online survey was administered to the\nstaff of Centre for Distance and E-learning of UNN and out of the 40 members of\nthat section of the University, we had 32 respondents. The survey seeks to find\nwhether UNN has the requisite infrastructure and the skill sets to mount\nsynchronous online learning. The available results of the study reveal that UNN\nis deficit in both the requisite infrastructure and Skills sets to mount\nsynchronous online learning.\n"}
+{"abstract": "  Acquisition and analysis of time-tagged events is a ubiquitous tool in\nscientific and industrial applications. With increasing time resolution, number\nof input channels, and acquired events, the amount of data can be overwhelming\nfor standard processing techniques. We developed the Extensible Time-tag\nAnalyzer (ETA), a powerful and versatile, yet easy to use software to\nefficiently analyze and display time-tagged data. Our tool allows for flexible\nextraction of correlation from time-tagged data beyond start-stop measurements\nthat were traditionally used. A combination of state diagrams and simple code\nsnippets allows for analysis of arbitrary complexity while keeping\ncomputational efficiency high.\n"}
+{"abstract": "  In this paper, a novel model-free wide-area damping control (WADC) method is\nproposed, which can achieve full decoupling of modes and damp multiple critical\ninter-area oscillations simultaneously using grid-connected voltage source\nconverters (VSCs). The proposed method is purely measurement based and requires\nno knowledge of the network topology and the dynamic model parameters. Hence,\nthe designed controller using VSCs can update the control signals online as the\nsystem operating condition varies. Numerical studies in the modified IEEE\n68-bus system with grid-connected VSCs show that the proposed method can\nestimate the system dynamic model accurately and can damp inter-area\noscillations effectively under different working conditions and network\ntopologies.\n"}
+{"abstract": "  Closed quantum systems exhibit different dynamical regimes, like Many-Body\nLocalization or thermalization, which determine the mechanisms of spread and\nprocessing of information. Here we address the impact of these dynamical phases\nin quantum reservoir computing, an unconventional computing paradigm recently\nextended into the quantum regime that exploits dynamical systems to solve\nnonlinear and temporal tasks. We establish that the thermal phase is naturally\nadapted to the requirements of quantum reservoir computing and report an\nincreased performance at the thermalization transition for the studied tasks.\nUncovering the underlying physical mechanisms behind optimal information\nprocessing capabilities of spin networks is essential for future experimental\nimplementations and provides a new perspective on dynamical phases.\n"}
+{"abstract": "  The red giant branch (RGB) of globular clusters (GCs) is home to some exotic\nstars, which may provide clues on the formation of multiple stellar populations\nin GCs. It is well known that binary interactions are responsible for many\nexotic stars. Thus, it is important to understand what fraction of stars on the\nRGB of GCs is the result of binary interactions. In this paper, we performed a\nbinary population synthesis study to track the number of\npost-binary-interaction (post-BI) stars that appear on the RGB, with particular\nemphasis on the evolved blue straggler stars (E-BSSs). Assuming an initial\nbinary fraction of nearly 50%, we find that about half of the objects on the\nRGB (called giants) underwent the binary interactions, and that E-BSSs account\nfor around 10% of the giants in our standard simulation. We also compare the\nproperties of post-BI giants that evolved from different channels. We find that\nthe initial orbital period and mass ratio distributions significantly affect\nthe fraction of post-BI giants. Our results imply that the non-standard stars\nfrom binary interactions provide a non-negligible contribution to the RGB stars\nin GCs, which should be considered in future investigations of the origin of\nmultiple stellar populations.\n"}
+{"abstract": "  The accuracy of photometric redshifts (photo-zs) particularly affects the\nresults of the analyses of galaxy clustering with photometrically-selected\ngalaxies (GCph) and weak lensing. In the next decade, space missions like\nEuclid will collect photometric measurements for millions of galaxies. These\ndata should be complemented with upcoming ground-based observations to derive\nprecise and accurate photo-zs. In this paper, we explore how the tomographic\nredshift binning and depth of ground-based observations will affect the\ncosmological constraints expected from Euclid. We focus on GCph and extend the\nstudy to include galaxy-galaxy lensing (GGL). We add a layer of complexity to\nthe analysis by simulating several realistic photo-z distributions based on the\nEuclid Consortium Flagship simulation and using a machine learning photo-z\nalgorithm. We use the Fisher matrix formalism and these galaxy samples to study\nthe cosmological constraining power as a function of redshift binning, survey\ndepth, and photo-z accuracy. We find that bins with equal width in redshift\nprovide a higher Figure of Merit (FoM) than equipopulated bins and that\nincreasing the number of redshift bins from 10 to 13 improves the FoM by 35%\nand 15% for GCph and its combination with GGL, respectively. For GCph, an\nincrease of the survey depth provides a higher FoM. But the addition of faint\ngalaxies beyond the limit of the spectroscopic training data decreases the FoM\ndue to the spurious photo-zs. When combining both probes, the number density of\nthe sample, which is set by the survey depth, is the main factor driving the\nvariations in the FoM. We conclude that there is more information that can be\nextracted beyond the nominal 10 tomographic redshift bins of Euclid and that we\nshould be cautious when adding faint galaxies into our sample, since they can\ndegrade the cosmological constraints.\n"}
+{"abstract": "  The heterogeneous edge-cloud computing paradigm can provide a more optimal\ndirection to deploy scientific workflows than traditional distributed computing\nor cloud computing environments. Due to the different sizes of scientific\ndatasets and some of these datasets must keep private, it is still a difficult\nproblem to finding an data placement strategy that can minimize data\ntransmission as well as placement cost. To address this issue, this paper\ncombines advantages of both edge and cloud computing to construct a data\nplacement model, which can balance data transfer time and data placement cost\nusing intelligent computation. The most difficult research challenge the model\nsolved is to consider many constrain in this hybrid computing environments,\nwhich including shared datasets within individual and among multiple workflows\nacross various geographical regions. According to the constructed model, the\nstudy propose a new data placement strategy named DE-DPSO-DPS, which using a\ndiscrete particle swarm optimization algorithm with differential evolution\n(DE-DPSO-DPA) to distribute these scientific datasets. The strategy also not\nonly consider the characteristics such as the number and storage capacity of\nedge micro-datacenters, the bandwidth between different datacenters and the\nproportion of private datasets, but also analysis the performance of algorithm\nduring the workflows execution. Comprehensive experiments are designed in\nsimulated heterogeneous edge-cloud computing environments demonstrate that the\ndata placement strategy can effectively reduce the data transmission time and\nplacement cost as compared to traditional strategies for data-sharing\nscientific workflows.\n"}
+{"abstract": "  This paper investigates requirements on a networked dynamic system (NDS) such\nthat its subsystem interactions can be solely determined from experiment data\nor reconstructed from its overall model. The NDS is constituted from several\nsubsystems whose dynamics are described through a descriptor form. Except\nregularity on each subsystem and the whole NDS, no other restrictions are put\non either subsystem dynamics or subsystem interactions. A matrix rank based\nnecessary and sufficient condition is derived for the global identifiability of\nsubsystem interactions, which leads to several conclusions about NDS structure\nidentifiability when there is some a priori information. This matrix also gives\nan explicit description for the set of subsystem interactions that can not be\ndistinguished from experiment data only. In addition, under a well-posedness\nassumption, a necessary and sufficient condition is obtained for the\nreconstructibility of subsystem interactions from an NDS descriptor form model.\nThis condition can be verified with each subsystem separately and is therefore\nattractive in the analysis and synthesis of a large-scale NDS. Simulation\nresults show that rather than increases monotonically with the distance of\nsubsystem interactions to the undifferentiable set, the magnitude of the\nexternal output differences between two NDSs with distinct subsystem\ninteractions increases much more rapidly when one of them is close to be\nunstable. In addition, directions of probing signals are also very important in\ndistinguishing external outputs of distinctive NDSs.These findings are expected\nto be helpful in identification experiment designs, etc.\n"}
+{"abstract": "  This article deals with the investigation of perfect fluid $GRW$ spacetimes.\nIt is shown that in a $GRW$ perfect fluid spacetime, the Weyl tensor is\ndivergence free and in dimension 4, a perfect fluid $GRW$ spacetime is a $RW$\nspacetime. Moreover, we prove that if a $GRW$ perfect fluid spacetime admits a\nsecond order symmetric parallel tensor, then either the state equation of the\nperfect fluid $GRW$ spacetime is characterized by $p=\\frac{3-n}{n-1}\\mu$ , or\nthe tensor is a constant multiple of the metric tensor. We also characterize\nthe perfect fluid $GRW$ spacetimes whose Lorentzian metrics are Ricci soliton,\ngradient Ricci soliton, gradient Yamabe solitons and gradient $m$-quasi\nEinstein solitons, respectively.\n"}
+{"abstract": "  We present a new method for using the observed starlight polarization and\npolarized submm emission to constrain the shapes and porosities of interstellar\ngrains. We present the modified picket fence approximation (MPFA), and verify\nthat it is sufficiently accurate for modeling starlight polarization. We\nintroduce the starlight polarization integral $\\Pi_{\\rm obs}$ as a measure of\noverall strength of the observed polarization of starlight, and the starlight\npolarization efficiency integral $\\Phi$ to characterize the effectiveness of\ndifferent grain types for producing polarization of starlight. The starlight\npolarization integral $\\Pi_{\\rm obs}$ determines the mass-weighted alignment\n$\\langle f_{\\rm align}\\rangle$ of the grains. Approximating the aligned grains\nin the interstellar medium as spheroids, we use $\\Pi_{\\rm obs}/\\Phi$ to show\nthat the observed starlight polarization constrains the grains to have a\nminimum degree of asphericity. For porosity ${\\cal P}=0$, the minimum axial\nratio is $\\sim$1.4 for oblate spheroids, or $\\sim$1.8 for prolate spheroids. If\nthe grains are porous, more extreme axial ratios are required. The same grains\nthat produce the starlight polarization are able to provide the observed\npolarized emission at submm wavelengths, but with further limits on shape and\nporosity. Porosities ${\\cal P}>0.75$ are ruled out. If interstellar grains can\nbe approximated by astrodust spheroids, we predict the ratio of 10$\\mu{\\rm m}$\npolarization to starlight polarization $p_V$: $p(10\\mu{\\rm\nm})/p_V=0.222\\pm0.026$. For Cyg OB2-12 we predict $p(10\\mu{\\rm\nm})=(2.1\\pm0.3)\\%$, which should be observable.\n"}
+{"abstract": "  These notes introduce a handful of core ideas from quantum information\nscience that figure prominently in modern research on quantum gravity. The\ncentral concept that forms the base of these notes is that of a quantum\nchannel; that is, the most general physically-reasonable map between quantum\nstates and between operators on Hilbert space. After reviewing some\nfundamentals, we will study channels and their properties, and then go on to\nformulate quantum error correction in terms of quantum channels. Along the way,\nwe will see how a handful of problems in high energy physics, such as the black\nhole information problem and bulk reconstruction in AdS/CFT, can be cast in the\ninformation-theoretic language being set up.\n"}
+{"abstract": "  We present fully general relativistic simulations of the quasi-circular\ninspiral and merger of charged, non-spinning, binary black holes with\ncharge-to-mass ratio $\\lambda \\le 0.3$. We discuss the key features that\nenabled long term and stable evolutions of these binaries. We also present a\nformalism for computing the angular momentum carried away by electromagnetic\nwaves, and the electromagnetic contribution to black-hole horizon properties.\nWe implement our formalism and present the results for the first time in\nnumerical-relativity simulations. In addition, we compare our full non-linear\nsolutions with existing approximate models for the inspiral and ringdown\nphases. We show that Newtonian models based on the quadrupole approximation\nhave errors of 20 % - 100 % in key gauge-invariant quantities. On the other\nhand, for the systems considered, we find that estimates of the remnant black\nhole spin based on the motion of test particles in Kerr-Newman spacetimes agree\nwith our non-linear calculations to within a few percent. Finally, we discuss\nthe prospects for detecting black hole charge by future gravitational-wave\ndetectors using either the inspiral-merger-ringdown signal or the ringdown\nsignal alone.\n"}
+{"abstract": "  We introduce a top-down approach to discourse parsing that is conceptually\nsimpler than its predecessors (Kobayashi et al., 2020; Zhang et al., 2020). By\nframing the task as a sequence labelling problem where the goal is to\niteratively segment a document into individual discourse units, we are able to\neliminate the decoder and reduce the search space for splitting points. We\nexplore both traditional recurrent models and modern pre-trained transformer\nmodels for the task, and additionally introduce a novel dynamic oracle for\ntop-down parsing. Based on the Full metric, our proposed LSTM model sets a new\nstate-of-the-art for RST parsing.\n"}
+{"abstract": "  Recent promises of generative deep learning lately brought interest to its\npotential uses in neural engineering. In this paper we firstly review recently\nemerging studies on generating artificial electroencephalography (EEG) signals\nwith deep neural networks. Subsequently, we present our feasibility experiments\non generating condition-specific multichannel EEG signals using conditional\nvariational autoencoders. By manipulating real resting-state EEG epochs, we\npresent an approach to synthetically generate time-series multichannel signals\nthat show spectro-temporal EEG patterns which are expected to be observed\nduring distinct motor imagery conditions.\n"}
+{"abstract": "  Flow structure stability of a steady radial thermocapillary flow from the\nlocal heat source in cylindrical geometry has been studied numerically. The up\nboundary of the liquid was partially covered by the stationary film of an\ninsoluble surfactant, pushed to the wall of the cavity. The calculations are\nfulfilled on the base of interfacial hydrodynamics equations by using\nmathematical package Comsol Multiphysics. It is shown that the effect of a\nradial flow symmetry breakdown is appeared as a result of convective\ninstability, so that the initial poloidal rotation of the liquid transforms\ninto the azimuthal plane. The system of inclined steady volumetric vortices is\nformed under the film of surfactant. The inclination angle of the plane of\nrotation in dependence on the intensity of radial thermocapillary flow is\nstudied. It is shown that the azimuthal vorticity can be regulated by the power\nof point-heat source and by free surface area. The increase of heating\nintensity leads to the growth of the azimuthal vorticity. Thus, the initially\nradially symmetric toroidal flow is divided into two parts with the origination\nof vortices in the azimuthal plane under the film\n"}
+{"abstract": "  We investigate lower bounds for the variance in arithmetic progressions of\ncertain multiplicative functions \"close\" to $1$. Specifically, we consider\n$\\alpha_N$-fold divisor functions, when $\\alpha_N$ is a sequence of positive\nreal numbers approaching $1$ in a suitable way or $\\alpha_N=1$, and the\nindicator of $y$-smooth numbers, for suitably large parameters $y$. As a\ncorollary, we will strengthen a previous author's result on the first subject\nand obtain matching lower bounds to some Barban-Davenport-Halberstam type\ntheorems for $y$-smooth numbers. Incidentally, we will also find a lower bound\nfor the variance in arithmetic progressions of the prime factors counting\nfunctions $\\omega(n)$ and $\\Omega(n)$.\n"}
+{"abstract": "  We study the link between Wick, anti-Wick and analytic kernel operators on\nthe Bargmann transform side. We find classes of kernels, e.g. $\\wideparen\n{\\mathcal B} _s$, whose corresponding operators agree with the sets of linear\nand continuous operators on $\\mathcal A _s$, the images of Pilipovi{\\'c} under\nthe Bargmann transform. We show that in several situations, the sets of Wick,\nanti-Wick and kernel operators with symbols and kernels in $\\wideparen\n{\\mathcal B} _s$ agree. We also show some ring, module and composition\nproperties for $\\wideparen {\\mathcal B} _s$, and similarly for other spaces\nrelated to $\\wideparen {\\mathcal B} _s$.\n"}
+{"abstract": "  Clinical trials are an instrument for making informed decisions based on\nevidence from well-designed experiments. Here we consider adaptive designs\nmainly from the perspective of multi-arm Phase II clinical trials, in which one\nor more experimental treatments are compared to a control. Treatment allocation\nof individual trial participants is assumed to take place according to a fixed\nblock randomization, albeit with an important twist: The performance of each\ntreatment arm is assessed after every measured outcome, in terms of the\nposterior distribution of a corresponding model parameter. Different treatments\narms are then compared to each other, according to pre-defined criteria and\nusing the joint posterior as the basis for such assessment. If a treatment is\nfound to be sufficiently clearly inferior to the currently best candidate, it\ncan be closed off either temporarily or permanently from further participant\naccrual. The latter possibility provides a method for adaptive treatment\nselection, including early stopping of the trial. The main development in the\npaper is in terms of binary outcomes, but some extensions, notably for handling\ntime-to-event data, are discussed as well. The presentation is to a large\nextent comparative and expository.\n"}
+{"abstract": "  We describe an automated analysis method to quantify the detailed growth\ndynamics of a population of bacilliform bacteria. We propose an innovative\napproach to frame-sequence tracking of deformable-cell motion by the automated\nminimization of a new, specific cost functional. This minimization is\nimplemented by dedicated Boltzmann machines (stochastic recurrent neural\nnetworks). Automated detection of cell divisions is handled similarly by\nsuccessive minimizations of two cost functions, alternating the identification\nof children pairs and parent identification. We validate this automatic cell\ntracking algorithm using recordings of simulated cell colonies that closely\nmimic the growth dynamics of \\emph{E. coli} in microfluidic traps. On a batch\nof 1100 image frames, cell registration accuracies per frame ranged from 94.5\\%\nto 100\\%, with a high average. Our initial tests using experimental image\nsequences of \\emph{E. coli} colonies also yield convincing results, with a\nregistration accuracy ranging from 90\\% to 100\\%.\n"}
+{"abstract": "  In this paper, the 2-adic complexity of a class of balanced Whiteman\ngeneralized cyclotomic sequences of period $pq$ is considered. Through\ncalculating the determinant of the circulant matrix constructed by one of these\nsequences, we derive a lower bound on the 2-adic complexity of the\ncorresponding sequence, which further expands our previous work (Zhao C, Sun Y\nand Yan T. Study on 2-adic complexity of a class of balanced generalized\ncyclotomic sequences. Journal of Cryptologic Research,6(4):455-462, 2019). The\nresult shows that the 2-adic complexity of this class of sequences is large\nenough to resist the attack of the rational approximation algorithm(RAA) for\nfeedback with carry shift registers(FCSRs), i.e., it is in fact lower bounded\nby $pq-p-q-1$, which is far larger than one half of the period of the\nsequences. Particularly, the 2-adic complexity is maximal if suitable\nparameters are chosen.\n"}
+{"abstract": "  A lot of online marketing campaigns aim to promote user interaction. The\naverage treatment effect (ATE) of campaign strategies need to be monitored\nthroughout the campaign. A/B testing is usually conducted for such needs,\nwhereas the existence of user interaction can introduce interference to normal\nA/B testing. With the help of link prediction, we design a network A/B testing\nmethod LinkLouvain to minimize graph interference and it gives an accurate and\nsound estimate of the campaign's ATE. In this paper, we analyze the network A/B\ntesting problem under a real-world online marketing campaign, describe our\nproposed LinkLouvain method, and evaluate it on real-world data. Our method\nachieves significant performance compared with others and is deployed in the\nonline marketing campaign.\n"}
+{"abstract": "  We present a tool-supported approach for the synthesis, verification and\nvalidation of the control software responsible for the safety of the\nhuman-robot interaction in manufacturing processes that use collaborative\nrobots. In human-robot collaboration, software-based safety controllers are\nused to improve operational safety, e.g., by triggering shutdown mechanisms or\nemergency stops to avoid accidents. Complex robotic tasks and increasingly\nclose human-robot interaction pose new challenges to controller developers and\ncertification authorities. Key among these challenges is the need to assure the\ncorrectness of safety controllers under explicit (and preferably weak)\nassumptions. Our controller synthesis, verification and validation approach is\ninformed by the process, risk analysis, and relevant safety regulations for the\ntarget application. Controllers are selected from a design space of feasible\ncontrollers according to a set of optimality criteria, are formally verified\nagainst correctness criteria, and are translated into executable code and\nvalidated in a digital twin. The resulting controller can detect the occurrence\nof hazards, move the process into a safe state, and, in certain circumstances,\nreturn the process to an operational state from which it can resume its\noriginal task. We show the effectiveness of our software engineering approach\nthrough a case study involving the development of a safety controller for a\nmanufacturing work cell equipped with a collaborative robot.\n"}
+{"abstract": "  Machine Learning (ML) algorithms are susceptible to adversarial attacks and\ndeception both during training and deployment. Automatic reverse engineering of\nthe toolchains behind these adversarial machine learning attacks will aid in\nrecovering the tools and processes used in these attacks. In this paper, we\npresent two techniques that support automated identification and attribution of\nadversarial ML attack toolchains using Co-occurrence Pixel statistics and\nLaplacian Residuals. Our experiments show that the proposed techniques can\nidentify parameters used to generate adversarial samples. To the best of our\nknowledge, this is the first approach to attribute gradient based adversarial\nattacks and estimate their parameters. Source code and data is available at:\nhttps://github.com/michael-goebel/ei_red\n"}
+{"abstract": "  The evolution of social media platforms have empowered everyone to access\ninformation easily. Social media users can easily share information with the\nrest of the world. This may sometimes encourage spread of fake news, which can\nresult in undesirable consequences. In this work, we train models which can\nidentify health news related to COVID-19 pandemic as real or fake. Our models\nachieve a high F1-score of 98.64%. Our models achieve second place on the\nleaderboard, tailing the first position with a very narrow margin 0.05% points.\n"}
+{"abstract": "  We analyze the morphological evolution of open clusters and provide shape\nparameters for 265 open clusters. The results show that the overall shape of\nsample clusters becomes more elliptical as they grow older, while their core\nremains circular or slightly trend to circularize. There is a negative\ncorrelation of the ellipticities with the number of members of the sample\nclusters. A significant negative correlation between the overall ellipticities\nand masses is also detected for the sample clusters with log(age/year) $\\geq$\n8, suggesting that the overall shapes of the clusters are possibly influenced\nby the number of members and masses, in addition to the external forces and the\nsurrounding environment. For most young sample clusters, the radial\nstratification degree of the short axis direction is greater than that of the\nlong, implying that the radial stratification degree in the two directions\nwithin the young sample cluster may be unevenly affected by an internal\nevolutionary process. Older sample clusters exhibit lower stratification in the\ntangential direction, which possibly means those clusters may continue to\nsurvive for a long time at a low level of stratification. Our analysis shows\nthat the overall shape of the sample clusters may be more susceptible to the\ninfluence of Galactic tides toward the Galactic center than the shear forces\nembedded in Galactic differential rotation. By analyzing the distribution of\nthe ages and number of members of star clusters, we suggest that NGC 6791 may\noriginate from superclusters.\n"}
+{"abstract": "  Based on the current measurement of the neutron distribution radius ($R_n$)\nof $^{208}$Pb through the PREX-2 data, we re-visited the recently developed G3\nand IOPB-I force parameter by fine-tuning some of the specific couplings within\nthe relativistic mean-field model. The $\\omega-\\rho-$mesons coupling\n$\\Lambda_{\\omega}$ and the $\\rho-$meson coupling $g_{\\rho}$ are refitted to\nreproduce the experimental neutron radius of $^{208}$Pb without compromising\nthe bulk properties of finite nuclei and infinite nuclear matter observables.\nThe modified parameter sets are applied to calculate the gross properties of\nfinite nuclei for a few double closed-shell nuclei and further used to obtain\nthe various infinite nuclear matter observables at saturation. In addition to\nthese, the force parameters are adopted to calculate the properties of high\nisospin asymmetry dense system such as neutron star matter and tested for the\nvalidation for the constraint from GW170817 binary neutron star merger events.\nThe tuned forces are predicting relatively good results for finite and infinite\nnuclear matter systems and the current limitation on neutron radius from\nPREX-2. A systematic analysis using these two refitted parameter sets over the\nnuclear chat will be communicated shortly.\n"}
+{"abstract": "  We consider the KZ equations over $\\mathbb C$ in the case, when the\nhypergeometric solutions are hyperelliptic integrals of genus $g$. Then the\nspace of solutions is a $2g$-dimensional complex vector space. We also consider\nthe same equations modulo $p^s$, where $p$ is an odd prime and $s$ is a\npositive integer, and over the field $\\mathbb Q_p$ of $p$-adic numbers. We\nconstruct polynomial solutions of the KZ equations modulo $p^s$ and study the\nspace $\\mathcal M_{p^s}$ of all constructed solutions. We show that the\n$p$-adic limit of $\\mathcal M_{p^s}$ as $s\\to\\infty$ gives us a $g$-dimensional\nvector space of solutions of the KZ equations over $\\mathbb Q_p$. The solutions\nover $\\mathbb Q_p$ are power series at a certain asymptotic zone of the KZ\nequations.\n  In the appendix written jointly with Steven Sperber we consider all\nasymptotic zones of the KZ equations in the case $g=1$ of elliptic integrals.\nThe $p$-adic limit of $\\mathcal M_{p^s}$ as $s\\to \\infty$ gives us a\none-dimensional space of solutions over $\\mathbb Q_p$ at every asymptotic zone.\nWe apply Dwork's theory and show that our germs of solutions over $\\mathbb Q_p$\ndefined at different asymptotic zones analytically continue into a single\nglobal invariant line subbundle of the associated KZ connection. Notice that\nthe corresponding KZ connection over $\\mathbb C$ does not have proper\nnontrivial invariant subbundles, and therefore our invariant line subbundle is\na new feature of the KZ equations over $\\mathbb Q_p$. We describe the Frobenius\ntransformations of solutions of the KZ equations for $g =1$ and then recover\nthe unit roots of the zeta functions of the elliptic curves defined by the\nequations $y^2= \\beta \\,x(x-1)(x-\\alpha)$ over the finite field $\\mathbb F_p$.\nHere $\\alpha,\\beta\\in\\mathbb F_p^\\times, \\alpha \\ne 1$.\n"}
+{"abstract": "  This work introduces World-GAN, the first method to perform data-driven\nProcedural Content Generation via Machine Learning in Minecraft from a single\nexample. Based on a 3D Generative Adversarial Network (GAN) architecture, we\nare able to create arbitrarily sized world snippets from a given sample. We\nevaluate our approach on creations from the community as well as structures\ngenerated with the Minecraft World Generator. Our method is motivated by the\ndense representations used in Natural Language Processing (NLP) introduced with\nword2vec [1]. The proposed block2vec representations make World-GAN independent\nfrom the number of different blocks, which can vary a lot in Minecraft, and\nenable the generation of larger levels. Finally, we demonstrate that changing\nthis new representation space allows us to change the generated style of an\nalready trained generator. World-GAN enables its users to generate Minecraft\nworlds based on parts of their creations.\n"}
+{"abstract": "  Software systems are designed according to guidelines and constraints defined\nby business rules. Some of these constraints define the allowable or required\nvalues for data handled by the systems. These data constraints usually\noriginate from the problem domain (e.g., regulations), and developers must\nwrite code that enforces them. Understanding how data constraints are\nimplemented is essential for testing, debugging, and software change.\nUnfortunately, there are no widely-accepted guidelines or best practices on how\nto implement data constraints.\n  This paper presents an empirical study that investigates how data constraints\nare implemented in Java. We study the implementation of 187 data constraints\nextracted from the documentation of eight real-world Java software systems.\nFirst, we perform a qualitative analysis of the textual description of data\nconstraints and identify four data constraint types. Second, we manually\nidentify the implementations of these data constraints and reveal that they can\nbe grouped into 30 implementation patterns. The analysis of these\nimplementation patterns indicates that developers prefer a handful of patterns\nwhen implementing data constraints and deviations from these patterns are\nassociated with unusual implementation decisions or code smells. Third, we\ndevelop a tool-assisted protocol that allows us to identify 256 additional\ntrace links for the data constraints implemented using the 13 most common\npatterns. We find that almost half of these data constraints have multiple\nenforcing statements, which are code clones of different types.\n"}
+{"abstract": "  We propose a unifying framework for the matrix-based formulation and analysis\nof discontinuous Galerkin (DG) and flux reconstruction (FR) methods for\nconservation laws on general unstructured grids. Within such an algebraic\nframework, the multidimensional summation-by-parts (SBP) property is used to\nestablish the discrete equivalence of strong and weak formulations, as well as\nthe conservation and energy stability properties of a broad class of DG and FR\nschemes. Specifically, the analysis enables the extension of the equivalence\nbetween the strong and weak forms of the discontinuous Galerkin collocation\nspectral-element method demonstrated by Kopriva and Gassner (J Sci Comput\n44:136-155, 2010) to more general nodal and modal DG formulations, as well as\nto the Vincent-Castonguay-Jameson-Huynh (VCJH) family of FR methods. Moreover,\nnew algebraic proofs of conservation and energy stability for DG and VCJH\nschemes with respect to suitable quadrature rules and discrete norms are\npresented, in which the SBP property serves as a unifying mechanism for\nestablishing such results. Numerical experiments are provided for the\ntwo-dimensional linear advection and Euler equations, highlighting the design\nchoices afforded for methods within the proposed framework and corroborating\nthe theoretical analysis.\n"}
+{"abstract": "  Mild cognitive impairment (MCI) conversion prediction, i.e., identifying MCI\npatients of high risks converting to Alzheimer's disease (AD), is essential for\npreventing or slowing the progression of AD. Although previous studies have\nshown that the fusion of multi-modal data can effectively improve the\nprediction accuracy, their applications are largely restricted by the limited\navailability or high cost of multi-modal data. Building an effective prediction\nmodel using only magnetic resonance imaging (MRI) remains a challenging\nresearch topic. In this work, we propose a multi-modal multi-instance\ndistillation scheme, which aims to distill the knowledge learned from\nmulti-modal data to an MRI-based network for MCI conversion prediction. In\ncontrast to existing distillation algorithms, the proposed multi-instance\nprobabilities demonstrate a superior capability of representing the complicated\natrophy distributions, and can guide the MRI-based network to better explore\nthe input MRI. To our best knowledge, this is the first study that attempts to\nimprove an MRI-based prediction model by leveraging extra supervision distilled\nfrom multi-modal information. Experiments demonstrate the advantage of our\nframework, suggesting its potentials in the data-limited clinical settings.\n"}
+{"abstract": "  Discrete abstractions have become a standard approach to assist control\nsynthesis under complex specifications. Most techniques for the construction of\na discrete abstraction for a continuous-time system require time-space\ndiscretization of the concrete system, which constitutes property satisfaction\nfor the continuous-time system non-trivial. In this work, we aim at relaxing\nthis requirement by introducing a control interface. Firstly, we connect the\ncontinuous-time uncertain concrete system with its discrete deterministic\nstate-space abstraction with a control interface. Then, a novel stability\nnotion called $\\eta$-approximate controlled globally practically stable, and a\nnew simulation relation called robust approximate simulation relation are\nproposed. It is shown that the uncertain concrete system, under the condition\nthat there exists an admissible control interface such that the augmented\nsystem (composed of the concrete system and its abstraction) can be made\n$\\eta$-approximate controlled globally practically stable, robustly\napproximately simulates its discrete abstraction. The effectiveness of the\nproposed results is illustrated by two simulation examples.\n"}
+{"abstract": "  Multi-frequency Electrical Impedance Tomography (mfEIT) is an emerging\nbiomedical imaging modality to reveal frequency-dependent conductivity\ndistributions in biomedical applications. Conventional model-based image\nreconstruction methods suffer from low spatial resolution, unconstrained\nfrequency correlation and high computational cost. Deep learning has been\nextensively applied in solving the EIT inverse problem in biomedical and\nindustrial process imaging. However, most existing learning-based approaches\ndeal with the single-frequency setup, which is inefficient and ineffective when\nextended to address the multi-frequency setup. In this paper, we present a\nMultiple Measurement Vector (MMV) model based learning algorithm named MMV-Net\nto solve the mfEIT image reconstruction problem. MMV-Net takes into account the\ncorrelations between mfEIT images and unfolds the update steps of the\nAlternating Direction Method of Multipliers (ADMM) for the MMV problem. The\nnon-linear shrinkage operator associated with the weighted l2_1 regularization\nterm is generalized with a cascade of a Spatial Self-Attention module and a\nConvolutional Long Short-Term Memory (ConvLSTM) module to capture intra- and\ninter-frequency dependencies. The proposed MMVNet was validated on our\nEdinburgh mfEIT Dataset and a series of comprehensive experiments. All\nreconstructed results show superior image quality, convergence performance and\nnoise robustness against the state of the art.\n"}
+{"abstract": "  Master equations describing open quantum dynamics are typically first order\ndifferential equations. When such dynamics brings the trajectories in state\nspace of more than one initial state to the same point at finite instants in\ntime, the generator of the corresponding master equation becomes singular. The\nfirst-order, time-local, homogeneous master equations then fail to describe the\ndynamics beyond the singular point. Retaining time-locality in the master\nequation necessitates a reformulation in terms of higher-order differential\nequations. We formulate a method to eliminate the divergent behavior of the\ngenerator by using a combination of higher-order derivatives of the generator\nwith suitable weights and illustrate it with several examples. We also present\na detailed study of the central spin model and we propose the average rate of\ninformation inflow in non-Markovian processes as a quantity that captures a\ndifferent aspect of non-Markovian dynamics.\n"}
+{"abstract": "  A radiative transfer model was used to reproduce several millions of OMEGA\n(Observatoire pour la Min\\'eralogie, l'Eau, les Glaces et l'Activit\\'e) spectra\nrepresentative of igneous terrains of Mars. This task provided the modal\ncomposition and grain sizes at a planetary scale. The lithology can be\nsummarized in five mineral maps at km-scale. We found that the low albedo\nequatorial regions of the Martian surface (from 60{\\deg}S to 30{\\deg}N) are\nglobally dominated by plagioclase with average abundance ~50 vol% and pyroxenes\nwith total averaged abundance close to 40 vol%. An evolution of the\nLCP/(LCP+HCP) ratio is observed with time at the global scale, suggesting an\nevolution of the degree of partial melting throughout the Martian eras. Olivine\nand Martian dust are minor components of the modelled terrains. The olivine\ndistribution is quite different from the other minerals because it is found on\nlocalized areas with abundance reaching 20 vol%. A statistical approach, to\nclassify the pixels of the abundances maps, using k-means clustering,\nhighlighted seven distinct mineral assemblages on the surface. This\nclassification illustrates that diverse mineralogical units are found in the\nNoachian and Hesperian terrains, which suggests the presence of various and\ncomplex magmatic processes at a global scale during the two oldest eras. The\nchemical composition was derived from the modal composition maps. The\nOMEGA-derived chemical composition is quite consistent with several distinctive\ngeochemical characteristics previously considered as fingerprints of the\nMartian surface. A major discrepancy is in regards to the Fe content that is\nsignificantly smaller than soil and rock analyses from GRS and in situ\nmeasurements. The discrepancy could be partly explained by the assumptions used\nfor the spectral modelling or could also indicate surface alteration rinds.\n"}
+{"abstract": "  Panoptic segmentation unifies semantic segmentation and instance segmentation\nwhich has been attracting increasing attention in recent years. However, most\nexisting research was conducted under a supervised learning setup whereas\nunsupervised domain adaptive panoptic segmentation which is critical in\ndifferent tasks and applications is largely neglected. We design a domain\nadaptive panoptic segmentation network that exploits inter-style consistency\nand inter-task regularization for optimal domain adaptive panoptic\nsegmentation. The inter-style consistency leverages geometric invariance across\nthe same image of the different styles which fabricates certain\nself-supervisions to guide the network to learn domain-invariant features. The\ninter-task regularization exploits the complementary nature of instance\nsegmentation and semantic segmentation and uses it as a constraint for better\nfeature alignment across domains. Extensive experiments over multiple domain\nadaptive panoptic segmentation tasks (e.g., synthetic-to-real and real-to-real)\nshow that our proposed network achieves superior segmentation performance as\ncompared with the state-of-the-art.\n"}
+{"abstract": "  At the Large Hadron Collider (LHC), both the ATLAS and CMS Collaborations\nhave been searching for light charged Higgs bosons via top (anti)quark\nproduction and decays channels, like $pp\\to t \\bar{t}$ with one top (anti)quark\ndecaying into a charged Higgs boson and a $b$ (anti)quark, when the decay is\nkinematically open (i.e., when $m_{H^\\pm}\\lesssim m_t$). In this paper, we\npropose new searches at the LHC involving light charged Higgs bosons via their\npair production channels like $pp\\to H^\\pm h/A$ and $pp\\to H^+ H^-$ in the\n2-Higgs Doublet Model (2HDM) Type-I and -X scenarios. By focusing on the case\nwhere the heavy $H$ state plays the role of the Standard Model (SM)-like Higgs\nboson with a mass near 125 GeV, we study the aforementioned Higgs boson pair\nproduction channels and investigate their bosonic decays, such as $H^\\pm \\to\nW^{\\pm } h$ and/or $H^\\pm \\to W^{\\pm } A$. We demonstrate that for a light\ncharged Higgs boson state, with $m_{H^\\pm}\\lesssim m_t$, at the LHC, such\ndi-Higgs production and decay channels can give rise to signatures with event\nrates much larger than those emerging from $pp\\to t\\bar{t}\\to t\\bar{b} H^-$ +\nc.c.. We specifically study $h/A\\to b\\bar b$ and $\\tau^+\\tau^-$ decays. We,\ntherefore, claim that the discussed combination of new production and decay\nmodes can result in an alternative discovery channel for charged Higgs bosons\nlighter than the top (anti)quark at the LHC within the above two 2HDM Types.\nFinally, in order to motivate experimentalists in ATLAS and CMS to search for\nsuch signatures, we propose 16 Benchmark Points (BPs) which are compatible with\nboth theoretical and experimental constraints.\n"}
+{"abstract": "  Quantum key distribution (QKD) is a promising technique for secure\ncommunication based on quantum mechanical principles. To improve the secure key\nrate of a QKD system, most studies on reconciliation primarily focused on\nimproving the efficiency. With the increasing performance of QKD systems, the\nresearch priority has shifted to the improvement of both throughput and\nefficiency. In this paper, we propose a high performance solution of Cascade\nreconciliation, including a high-throughput-oriented framework and an\nintegrated-optimization-oriented scheme. Benefiting from the fully utilizing\ncomputation and storage resources, effectively dealing with communication\ndelays, the integrated-optimization-oriented parameters setting, etc., an\nexcellent overall performance was achieved. Experimental results showed that,\nthe throughput of up to 570Mbps with an efficiency of 1.038 was achieved,\nwhich, to our knowledge, was more than four times faster than any throughput\npreviously demonstrated. Furthermore, throughputs on real data sets were\ncapable of reaching up to 86Mbps even on embedded platforms. Additionally, our\nsolution offers good adaptability to the fluctuating communication delay and\nquantum bit error rate (QBER). Based on our study, low performance (i.e. low\npower-consumption and cost-effective) CPU platforms will be sufficient for\nreconciliation in the existing and near-term QKD systems.\n"}
+{"abstract": "  In this study we follow up our recent paper (Monteiro et al. 2020) and\npresent a homogeneous sample of fundamental parameters of open clusters in our\nGalaxy, entirely based on Gaia DR2 data. We used published membership\nprobability of the stars derived from Gaia DR2 data and applied our isochrone\nfitting code, updated as in Monteiro et al. (2020), to GB and GR Gaia DR2 data\nfor member stars. In doing this we take into account the nominal errors in the\ndata and derive distance, age, and extinction of each cluster. This work\ntherefore provides parameters for 1743 open clusters and, as a byproduct, a\nlist of likely not physical or dubious open clusters is provided as well for\nfuture investigations. Furthermore, it was possible to estimate the mean radial\nvelocity of 831 clusters (198 of which are new and unpublished so far) using\nstellar radial velocities from Gaia DR2 catalog. By comparing the open cluster\ndistances obtained from isochrone fitting with those obtained from a maximum\nlikelihood estimate of individual member parallaxes, we found a systematic\noffset of $(-0.05\\pm0.04)$mas.\n"}
+{"abstract": "  Patients with long-standing diabetes often fall prey to Diabetic Retinopathy\n(DR) resulting in changes in the retina of the human eye, which may lead to\nloss of vision in extreme cases. The aim of this study is two-fold: (a) create\ndeep learning models that were trained to grade degraded retinal fundus images\nand (b) to create a browser-based application that will aid in diagnostic\nprocedures by highlighting the key features of the fundus image. In this\nresearch work, we have emulated the images plagued by distortions by degrading\nthe images based on multiple different combinations of Light Transmission\nDisturbance, Image Blurring and insertion of Retinal Artifacts. InceptionV3,\nResNet-50 and InceptionResNetV2 were trained and used to classify retinal\nfundus images based on their severity level and then further used in the\ncreation of a browser-based application, which implements the Integration\nGradient (IG) Attribution Mask on the input image and demonstrates the\npredictions made by the model and the probability associated with each class.\n"}
+{"abstract": "  Due to its proximity to Earth, Jupiter of the Solar System serves as a unique\ncase study for gas-giant exoplanets. In the current study, we perform fits of\nab initio, reflective, semi-infinite, homogeneous model atmospheres to 61 phase\ncurves from 0.40 to 1.00 $\\mu$m, obtained from the Cassini spacecraft, within a\nBayesian framework. We reproduce the previous finding that atmospheric models\nusing classic reflection laws (Lambertian, Rayleigh, single Henyey-Greenstein)\nprovide poor fits to the data. Using the double Henyey-Greenstein reflection\nlaw, we extract posterior distributions of the single-scattering albedo and\nscattering asymmetry factors and tabulate their median values and\nuncertainties. We infer that the aerosols in the Jovian atmosphere are large,\nirregular, polydisperse particles that produce strong forward scattering\ntogether with a narrow backscattering lobe. The near-unity values of the\nsingle-scattering albedos imply that multiple scattering of radiation is an\nimportant effect. We speculate that the observed narrow backscattering lobe is\ncaused by coherent backscattering of radiation, which is usually associated\nwith Solar System bodies with solid surfaces and regolith. Our findings\ndemonstrate that precise, multi-wavelength phase curves encode valuable\ninformation on the fundamental properties of cloud/haze particles. The method\ndescribed in this Letter enables single-scattering albedos and scattering\nasymmetry factors to be retrieved from James Webb Space Telescope phase curves\nof exoplanets.\n"}
+{"abstract": "  Automatic code summarization frees software developers from the heavy burden\nof manual commenting and benefits software development and maintenance.\nAbstract Syntax Tree (AST), which depicts the source code's syntactic\nstructure, has been incorporated to guide the generation of code summaries.\nHowever, existing AST based methods suffer from the difficulty of training and\ngenerate inadequate code summaries. In this paper, we present the Block-wise\nAbstract Syntax Tree Splitting method (BASTS for short), which fully utilizes\nthe rich tree-form syntax structure in ASTs, for improving code summarization.\nBASTS splits the code of a method based on the blocks in the dominator tree of\nthe Control Flow Graph, and generates a split AST for each code split. Each\nsplit AST is then modeled by a Tree-LSTM using a pre-training strategy to\ncapture local non-linear syntax encoding. The learned syntax encoding is\ncombined with code encoding, and fed into Transformer to generate high-quality\ncode summaries. Comprehensive experiments on benchmarks have demonstrated that\nBASTS significantly outperforms state-of-the-art approaches in terms of various\nevaluation metrics. To facilitate reproducibility, our implementation is\navailable at https://github.com/XMUDM/BASTS.\n"}
+{"abstract": "  In order to face food insecurity as a global phenomenon, it is essential to\nrely on measurement tools that guarantee comparability across countries.\nAlthough the official indicators adopted by the United Nations in the context\nof the Sustainable Development Goals (SDGs) and based on the Food Insecurity\nExperience Scale (FIES) already embeds cross-country comparability, other\nexperiential scales of food insecurity currently employ national thresholds and\nissues of comparability thus arise. In this work we address comparability of\nfood insecurity experience-based scales by presenting two different studies.\nThe first one involves the FIES and three national scales (ELCSA, EMSA and\nEBIA) currently included in national surveys in Guatemala, Ecuador, Mexico and\nBrazil. The second study concerns the adult and children versions of these\nnational scales. Different methods from the equating practice of the\neducational testing field are explored: classical and based on the Item\nResponse Theory (IRT).\n"}
+{"abstract": "  Unsupervised learning methods have recently shown their competitiveness\nagainst supervised training. Typically, these methods use a single objective to\ntrain the entire network. But one distinct advantage of unsupervised over\nsupervised learning is that the former possesses more variety and freedom in\ndesigning the objective. In this work, we explore new dimensions of\nunsupervised learning by proposing the Progressive Stage-wise Learning (PSL)\nframework. For a given unsupervised task, we design multilevel tasks and define\ndifferent learning stages for the deep network. Early learning stages are\nforced to focus on lowlevel tasks while late stages are guided to extract\ndeeper information through harder tasks. We discover that by progressive\nstage-wise learning, unsupervised feature representation can be effectively\nenhanced. Our extensive experiments show that PSL consistently improves results\nfor the leading unsupervised learning methods.\n"}
+{"abstract": "  Recent advancements in radiation detection and computer vision have enabled\nsmall unmanned aerial systems (sUASs) to produce 3D nuclear radiation maps in\nreal-time. Currently these state-of-the-art systems still require two\noperators: one to pilot the sUAS and another operator to monitor the detected\nradiation. In this work we present a system that integrates real-time 3D\nradiation visualization with semi-autonomous sUAS control. Our Virtual Reality\ninterface enables a single operator to define trajectories using waypoints to\nabstract complex flight control and utilize the semi-autonomous maneuvering\ncapabilities of the sUAS. The interface also displays a fused radiation\nvisualization and environment map, thereby enabling simultaneous remote\noperation and radiation monitoring by a single operator. This serves as the\nbasis for development of a single system that deploys and autonomously controls\nfleets of sUASs.\n"}
+{"abstract": "  We consider the mimetic tachyon model in the Lagrange multiplier approach. We\nstudy both the linear and non-linear perturbations and find the perturbation\nand non-gaussianity parameters in this setup. By adopting two types of the\nscale factor as the power-law ($a=a_{0}\\,t^{n}$) and intermediate\n($a=a_{0}\\exp(bt^{\\beta})$) scale factors, we perform a numerical analysis on\nthe model which is based on Planck2018 TT, TE, EE+lowE+lensing +BAO +BK14 and\nPlanck2018 TTT, EEE, TTE and EET data sets. We show that the mimetic tachyon\nmodel with both the power-law and intermediate scale factors, in some ranges of\nits parameter space is instabilities-free and observationally viable. The\npower-law mimetic tachyon model with $26.3<n<33.0$ and the intermediate mimetic\ntachyon model with $0.116<\\beta<0.130$ are consistent with observational data\nand free of the ghost and gradient instabilities.\n"}
+{"abstract": "  We obtain the energy eigenvalues and radial wave functions of the\nD-Dimensional Dirac equation in the case of spin symmetry for Woods-Saxon\npotential in minimal length formalism. The radial part of the D-Dimensional\nDirac equation is solved by applied the supersymmetric quantum mechanics method\nusing the Pekeris approximation to deal with the centrifugal term. The behavior\nof bound-state energy eigenvalues versus dimension and also quantum number is\ndiscussed for various minimal length parameters.\n"}
+{"abstract": "  Web search queries can be ambiguous: is \"source of the nile\" meant to find\ninformation on the actual river or on a board game of that name? We tackle this\nproblem by deriving entity-based query interpretations: given some query, the\ntask is to derive all reasonable ways of linking suitable parts of the query to\nsemantically compatible entities in a background knowledge base. Our suggested\napproach focuses on effectiveness but also on efficiency since web search\nresponse times should not exceed some hundreds of milliseconds. In our\napproach, we use query segmentation as a pre-processing step that finds\npromising segment-based \"interpretation skeletons\". The individual segments\nfrom these skeletons are then linked to entities from a knowledge base and the\nreasonable combinations are ranked in a final step. An experimental comparison\non a combined corpus of all existing query entity linking datasets shows our\napproach to have a better interpretation accuracy at a better run time than the\npreviously most effective methods.\n"}
+{"abstract": "  The NIH 3D Print Exchange is a public and open source repository for\nprimarily 3D printable medical device designs with contributions from\nexpert-amateur makers, engineers from industry and academia, and clinicians. In\nresponse to the COVID-19 pandemic, a collection was formed to foster\nsubmissions of low-cost, local manufacture of personal protective equipment\n(Personal Protective Equipment (PPE)). We systematically evaluated the 623\nsubmissions in this collection to understand: what makers contributed, how they\nwere made, who made them, and key characteristics of their designs. Our\nanalysis reveals an immediate design convergence to derivatives of a few\ninitial designs affiliated with NIH partners (e.g., universities, the Veteran's\nHealth Administration, America Makes) and major for-profit groups (e.g.,\nPrusa). The NIH worked to review safe and effective designs but was quickly\noverloaded by derivative works. We found that the vast majority were never\nreviewed (81.3%) while 10.4% of those reviewed were deemed safe for clinical\n(5.6%) or community use (4.8%). Our work contributes insights into: the\noutcomes of distributed, community-based, medical making; features the\ncommunity accepted as \"safe\" making; and how platforms can support regulated\nmaker activities in high-risk domains (e.g., healthcare).\n"}
+{"abstract": "  Nonequilibrium topological matter has been a fruitful topic of both\ntheoretical and experimental interest. A great variety of exotic topological\nphases unavailable in static systems may emerge under nonequilibrium\nsituations, often challenging our physical intuitions. How to locate the\nborders between different nonequilibrium topological phases is an important\nissue to facilitate topological characterization and further understand phase\ntransition behaviors. In this work, we develop an unsupervised machine-learning\nprotocol to distinguish between different Floquet (periodically driven)\ntopological phases, by incorporating the system's dynamics within one driving\nperiod, adiabatic deformation in the time dimension, plus the system's symmetry\nall into our machine learning algorithm. Results from two rich case studies\nindicate that machine learning is able to reliably reveal intricate topological\nphase boundaries and can hence be a powerful tool to discover novel topological\nmatter afforded by the time dimension.\n"}
+{"abstract": "  The recent discovery of room-temperature superconductivity (RTSC) at\npressures of several megabars has led to intensive efforts to probe the origin\nof superconducting (SC) electron pairs. Although the signatures of the SC phase\ntransition have been well established, few reports of the SC properties of\nRTSCs have been published because of the diamond anvil cell (DAC) environments.\nHere, we report the first direct evidence of two SC gaps in Y metal via\npoint-contact spectroscopy (PCS) in DAC environments, where a sharp peak at the\nzero-bias voltage in the differential conductance is overlaid with a broad peak\nowing to Andreev reflection. Analysis based on the Blonder-Tinkham-Klapwijk\n(BTK) model reveals the existence of two SC gaps: the larger gap is 3.63 meV\nand the smaller gap is 0.46 meV. The temperature dependence of the two SC gaps\nis well explained by the BCS theory, indicating that two-band superconductivity\nis realized in Y metal. The successful application of PCS to Y in DAC\nenvironments is expected to guide future research on the SC gap in megabar\nhigh-Tc superconductors.\n"}
+{"abstract": "  Given an integral commutative residuated lattice L=(L,\\vee,\\wedge), its full\ntwist-product (L^2,\\sqcup,\\sqcap) can be endowed with two binary operations\n\\odot and \\Rightarrow introduced formerly by M. Busaniche and R. Cignoli as\nwell as by C. Tsinakis and A. M. Wille such that it becomes a commutative\nresiduated lattice. For every a in L we define a certain subset P_a(L) of L^2.\nWe characterize when P_a(L) is a sublattice of the full twist-product\n(L^2,\\sqcup,\\sqcap). In this case P_a(L) together with some natural antitone\ninvolution ' becomes a pseudo-Kleene lattice. If L is distributive then\n(P_a(L),\\sqcup,\\sqcap,') becomes a Kleene lattice. We present sufficient\nconditions for P_a(L) being a subalgebra of\n(L^2,\\sqcup,\\sqcap,\\odot,\\Rightarrow) and thus for \\odot and \\Rightarrow being\na pair of adjoint operations on P_a(L). Finally, we introduce another pair\n\\odot and \\Rightarrow of adjoint operations on the full twist-product of a\nbounded commutative residuated lattice such that the resulting algebra is a\nbounded commutative residuated lattice satisfying the double negation law and\nwe investigate when P_a(L) is closed under these new operations \\odot and\n\\Rightarrow.\n"}
+{"abstract": "  Measurement for qubits plays a key role in quantum computation. Current\nmethods for classifying states of single qubit in a superconducting multi-qubit\nsystem produce fidelities lower than expected due to the existence of\ncrosstalk, especially in case of frequency crowding. Here, We make the digital\nsignal processing (DSP) system used in measurement into a shallow neural\nnetwork and train it to be an optimal classifier to reduce the impact of\ncrosstalk. The experiment result shows the crosstalk-induced readout error\ndeceased by 100% after a 3-second optimization applied on the 6-qubit\nsuperconducting quantum chip.\n"}
+{"abstract": "  Using a recently introduced tensor network method, we study the density of\nstates of the lattice Schwinger model, a standard testbench for lattice gauge\ntheory numerical techniques, but also the object of recent experimental quantum\nsimulations. We identify regimes of parameters where the spectrum appears to be\nsymmetric and displays the expected continuum properties even for finite\nlattice spacing and number of sites. However, we find that for moderate system\nsizes and lattice spacing of $ga\\sim O(1)$, the spectral density can exhibit\nvery different properties with a highly asymmetric form. We also explore how\nthe method can be exploited to extract thermodynamic quantities.\n"}
+{"abstract": "  The trend of space commercialization is changing the decision-making process\nfor future space exploration architectures, and there is a growing need for a\nnew decision-making framework that explicitly considers the interactions\nbetween the mission coordinator (i.e., government) and the commercial players.\nIn response to this challenge, this paper develops a framework for space\nexploration and logistics decision making that considers the incentive\nmechanism to stimulate commercial participation in future space infrastructure\ndevelopment and deployment. By extending the state-of-the-art space logistics\ndesign formulations from the game-theoretic perspective, we first analyze the\nrelationship between the mission coordinator and commercial players and then\nderive the formulation for the optimal architecture design and incentive\nmechanism in three different scenarios. To demonstrate and evaluate the\neffectiveness of the proposed framework, we conduct a case study on lunar\nhabitat infrastructure design and deployment. Results show how total mission\ndemands and in-situ resource utilization (ISRU) system performances after\ndeployment may impact the cooperation among stakeholders. As an outcome of this\nstudy, we derive an incentive-based decision-making framework that can benefit\nboth the mission coordinator and the commercial players from commercialization,\nleading to a mutually beneficial space exploration between the government and\nthe industry.\n"}
+{"abstract": "  In this work we present the design of a new test geometry inspired by the\nTapered Double Cantilever Beam (TDCB) specimen that is shown to provide an\nimproved characterization of the fracture properties of brittle solids. First,\nwe show that our new design results in an exponential increase of the specimen\ncompliance with crack length, leading to an extremely stable crack growth\nduring the test. We determine an analytical description of this behavior, which\nprovides a simple procedure to extract the fracture energy without depending on\nfinite element calculations. Validation tests are done on\npolymethylmethacrylate (PMMA) specimens. We use both finite element simulations\nand our analytical model to interpret the data. We find a very good agreement\nbetween the toughness determined by both methods. The stable nature of crack\ngrowth in our improved TDCB specimens results in a precise control of the crack\nspeed. This feature is employed to go one step further and characterize the\nvariations of toughness with crack speed. We propose an original optimization\nprocedure for the determination of the material parameters characterizing the\nkinetic law describing the toughness rate dependency. Overall, the approach\nproposed together with the newly designed test geometry offer unprecedented\npossibilities for the full and accurate characterization of the fracture\nbehavior of brittle materials such as rocks, sandstone, mortar etc.\n"}
+{"abstract": "  In the present work, two machine learning based constitutive models for\nfinite deformations are proposed. Using input convex neural networks, the\nmodels are hyperelastic, anisotropic and fulfill the polyconvexity condition,\nwhich implies ellipticity and thus ensures material stability. The first\nconstitutive model is based on a set of polyconvex, anisotropic and objective\ninvariants. The second approach is formulated in terms of the deformation\ngradient, its cofactor and determinant, uses group symmetrization to fulfill\nthe material symmetry condition, and data augmentation to fulfill objectivity\napproximately. The extension of the dataset for the data augmentation approach\nis based on mechanical considerations and does not require additional\nexperimental or simulation data. The models are calibrated with highly\nchallenging simulation data of cubic lattice metamaterials, including finite\ndeformations and lattice instabilities. A moderate amount of calibration data\nis used, based on deformations which are commonly applied in experimental\ninvestigations. While the invariant-based model shows drawbacks for several\ndeformation modes, the model based on the deformation gradient alone is able to\nreproduce and predict the effective material behavior very well and exhibits\nexcellent generalization capabilities. In addition, the models are calibrated\nwith transversely isotropic data, generated with an analytical polyconvex\npotential. For this case, both models show excellent results, demonstrating the\nstraightforward applicability of the polyconvex neural network constitutive\nmodels to other symmetry groups.\n"}
+{"abstract": "  In this work, we present a web-based annotation and querying tool Sangrahaka.\nIt annotates entities and relationships from text corpora and constructs a\nknowledge graph (KG). The KG is queried using templatized natural language\nqueries. The application is language and corpus agnostic, but can be tuned for\nspecial needs of a specific language or a corpus. A customized version of the\nframework has been used in two annotation tasks. The application is available\nfor download and installation. Besides having a user-friendly interface, it is\nfast, supports customization, and is fault tolerant on both client and server\nside. The code is available at https://github.com/hrishikeshrt/sangrahaka and\nthe presentation with a demo is available at https://youtu.be/nw9GFLVZMMo.\n"}
+{"abstract": "  Coarse-to-fine models and cascade segmentation architectures are widely\nadopted to solve the problem of large scale variations in medical image\nsegmentation. However, those methods have two primary limitations: the\nfirst-stage segmentation becomes a performance bottleneck; the lack of overall\ndifferentiability makes the training process of two stages asynchronous and\ninconsistent. In this paper, we propose a differentiable two-stage network\narchitecture to tackle these problems. In the first stage, a localization\nnetwork (L-Net) locates Regions of Interest (RoIs) in a detection fashion; in\nthe second stage, a segmentation network (S-Net) performs fine segmentation on\nthe recalibrated RoIs; a RoI recalibration module between L-Net and S-Net\neliminating the inconsistencies. Experimental results on the public dataset\nshow that our method outperforms state-of-the-art coarse-to-fine models with\nnegligible computation overheads.\n"}
+{"abstract": "  P-time event graphs (P-TEGs) are specific timed discrete-event systems, in\nwhich the timing of events is constrained by intervals. An important problem is\nto check, for all natural numbers $d$, the existence of consistent $d$-periodic\ntrajectories for a given P-TEG. In graph theory, the\nProportional-Inverse-Constant-Non-positive Circuit weight Problem (PIC-NCP)\nconsists in finding all the values of a parameter such that a particular\nparametric weighted directed graph does not contain circuits with positive\nweight. In a related paper, we have proposed a strongly polynomial algorithm\nthat solves the PIC-NCP in lower worst-case complexity compared to other\nalgorithms reported in literature. In the present paper, we show that the first\nproblem can be formulated as an instance of the second; consequently, we prove\nthat the same algorithm can be used to find $d$-periodic trajectories in\nP-TEGs. Moreover, exploiting the connection between the PIC-NCP and max-plus\nalgebra we prove that, given a P-TEG, the existence of a consistent 1-periodic\ntrajectory of a certain period is a necessary and sufficient condition for the\nexistence of a consistent $d$-periodic trajectory of the same period, for any\nvalue of $d$.\n"}
+{"abstract": "  A weighted point-availability time-dependent network is a list of temporal\nedges, where each temporal edge has an appearing time value, a travel time\nvalue, and a cost value. In this paper we consider the single source Pareto\nproblem in weighted point-availability time-dependent networks, which consists\nof computing, for any destination d, all Pareto optimal pairs (t, c), where t\nand c are the arrival time and the cost of a path from s to d, respectively (a\npair (t, c) is Pareto optimal if there is no path with arrival time smaller\nthan t and cost no worse than c or arrival time no greater than t and better\ncost). We design and analyse a general algorithm for solving this problem,\nwhose time complexity is O(M log P), where M is the number of temporal edges\nand P is the maximum number of Pareto optimal pairs for each node of the\nnetwork. This complexity significantly improves the time complexity of the\npreviously known solution. Our algorithm can be used to solve several different\nminimum cost path problems in weighted point-availability time-dependent\nnetworks with a vast variety of cost definitions, and it can be easily modified\nin order to deal with the single destination Pareto problem. All our results\napply to directed networks, but they can be easily adapted to undirected\nnetworks with no edges with zero travel time.\n"}
+{"abstract": "  Skin effect, experimentally discovered in one dimension, describes the\nphysical phenomenon that on an open chain, an extensive number of eigenstates\nof a non-Hermitian hamiltonian are localized at the end(s) of the chain. Here\nin two and higher dimensions, we establish a theorem that the skin effect\nexists, if and only if periodic-boundary spectrum of the hamiltonian covers a\nfinite area on the complex plane. This theorem establishes the universality of\nthe effect, because the above condition is satisfied in almost every generic\nnon-Hermitian hamiltonian, and, unlike in one dimension, is compatible with all\nspatial symmetries. We propose two new types of skin effect in two and higher\ndimensions: the corner-skin effect where all eigenstates are localized at one\ncorner of the system, and the geometry-dependent-skin effect where skin modes\ndisappear for systems of a particular shape, but appear on generic polygons. An\nimmediate corollary of our theorem is that any non-Hermitian system having\nexceptional points (lines) in two (three) dimensions exhibits skin effect,\nmaking this phenomenon accessible to experiments in photonic crystals, Weyl\nsemimetals, and Kondo insulators.\n"}
+{"abstract": "  This work develops and demonstrates the integration of the SCAMP-5d vision\nsystem into the CoppeliaSim robot simulator, creating a semi-simulated\nenvironment. By configuring a camera in the simulator and setting up\ncommunication with the SCAMP python host through remote API, sensor images from\nthe simulator can be transferred to the SCAMP vision sensor, where on-sensor\nimage processing such as CNN inference can be performed. SCAMP output is then\nfed back into CoppeliaSim. This proposed platform integration enables rapid\nprototyping validations of SCAMP algorithms for robotic systems. We demonstrate\na car localisation and tracking task using this proposed semi-simulated\nplatform, with a CNN inference on SCAMP to command the motion of a robot. We\nmade this platform available online.\n"}
+{"abstract": "  The formation mechanism of the W50/SS433 complex has long been a mystery. We\npropose a new scenario in which the SS433 jets themselves form the W50/SS433\nsystem. We carry out magnetohydrodynamics simulations of two-side jet\npropagation using the public code CANS+. As found in previous jet studies, when\nthe propagating jet is lighter than the surrounding medium, the shocked plasma\nflows back from the jet tip to the core. We find that the morphology of light\njets is spheroidal at early times, and afterward, the shell and wings are\ndeveloped by the broadening spherical cocoon. The morphology strongly depends\non the density ratio of the injected jet to the surrounding medium. Meanwhile,\nthe ratio of the lengths of the two-side jets depends only on the density\nprofile of the surrounding medium. We also find that most of the jet kinetic\nenergy is dissipated at the oblique shock formed by the interaction between the\nbackflow and beam flow, rather than at the jet terminal shock. The position of\nthe oblique shock is spatially consistent with the X-ray and TeV gamma-ray\nhotspots of W50.\n"}
+{"abstract": "  Soft-elasticity in monodomain liquid crystal elastomers (LCEs) is promising\nfor impact-absorbing applications where strain energy is ideally absorbed at\nconstant stress. Conventionally, compressive and impact studies on LCEs have\nnot been performed given the notorious difficulty synthesizing sufficiently\nlarge monodomain devices. Here we demonstrate 3D printing bulk ($>cm^3$)\nmonodomain LCE devices using direct ink writing and study their compressive\nsoft-elasticity over 8 decades of strain rate. At quasi-static rates, the\nmonodomain soft-elastic LCE dissipated 45% of strain energy while comparator\nmaterials dissipated less than 20%. At strain rates up to $3000~s^{-1}$, our\nsoft-elastic monodomain LCE consistently performed closest to an ideal-impact\nabsorber. Drop testing reveals soft-elasticity as a likely mechanism for\neffectively reducing the severity of impacts -- with soft elastic LCEs offering\na Gadd Severity Index 40% lower than a comparable isotropic elastomer. Lastly,\nwe demonstrate tailoring deformation and buckling behavior in monodomain LCEs\nvia the printed director orientation.\n"}
+{"abstract": "  Scalar field cosmologies with a generalized harmonic potential and a matter\nfluid with a barotropic Equation of State (EoS) with barotropic index $\\gamma$\nfor the Locally Rotationally Symmetric (LRS) Bianchi I and flat\nFriedmann-Lema\\^itre-Robertson-Walker (FLRW) metrics are investigated. Methods\nfrom the theory of averaging of nonlinear dynamical systems are used to prove\nthat time-dependent systems and their corresponding time-averaged versions have\nthe same late-time dynamics. Therefore, the simplest time-averaged system\ndetermines the future asymptotic behavior. Depending on the values of $\\gamma$,\nthe late-time attractors of physical interests are flat quintessence dominated\nFLRW universe and Einstein-de Sitter solution. With this approach, the\noscillations entering the system through the Klein-Gordon (KG) equation can be\ncontrolled and smoothed out as the Hubble parameter $H$ - acting as\ntime-dependent perturbation parameter - tends monotonically to zero. Numerical\nsimulations are presented as evidence of such behavior.\n"}
+{"abstract": "  Let $F_n(k)$ be the generalized Fibonacci number defined by (with $F_i(k)$\nabbreviated to $F_i$): $F_n = F_{n-1} + F_{n-2} + \\dots + F_{n-k}$, for $n \\geq\nk$, and the initial values $(F_0,F_1,...,F_{k-1})$. Let $B_n(k,j)$ be $F_n(k)$\nwith initial values given by $F_j = 1$ and, for $i<j$ and $j<i<k$, $F_i = 0$.\nThis paper shows that any $F_n(k)$ can be expressed as the sum of $B_n(k,j)$s.\nThis paper also expresses $B_n(k,j)$ and $F_n(k)$ as finite sums, derives some\nproperties and evaluates their 2-adic order for a range of values of $k, j$ and\n$n$ and those of $B_n(3,j)$ and $B_n(4,j)$ for most values of $j$ and $n$.\n"}
+{"abstract": "  Photometric measurements of the contact binary system CK Aqr are presented.\n49 new times of minimum were obtained between 2005 and 2020. Along with already\npublished observations, this allows the derivation of an improved ephemeris.\nThe resulting O-C diagram shows oscillations which are interpreted as the light\ntime travel effect due to a third component, with a period of 8.2 years.\n"}
+{"abstract": "  This paper has proposed a novel approach to classify the subjects' smoking\nbehavior by extracting relevant regions from a given image using deep learning.\nAfter the classification, we have proposed a conditional detection module based\non Yolo-v3, which improves model's performance and reduces its complexity. As\nper the best of our knowledge, we are the first to work on this dataset. This\ndataset contains a total of 2,400 images that include smokers and non-smokers\nequally in various environmental settings. We have evaluated the proposed\napproach's performance using quantitative and qualitative measures, which\nconfirms its effectiveness in challenging situations. The proposed approach has\nachieved a classification accuracy of 96.74% on this dataset.\n"}
+{"abstract": "  Recent accelerated MRI reconstruction models have used Deep Neural Networks\n(DNNs) to reconstruct relatively high-quality images from highly undersampled\nk-space data, enabling much faster MRI scanning. However, these techniques\nsometimes struggle to reconstruct sharp images that preserve fine detail while\nmaintaining a natural appearance. In this work, we enhance the image quality by\nusing a Conditional Wasserstein Generative Adversarial Network combined with a\nnovel Adaptive Gradient Balancing (AGB) technique that automates the process of\ncombining the adversarial and pixel-wise terms and streamlines hyperparameter\ntuning. In addition, we introduce a Densely Connected Iterative Network, which\nis an undersampled MRI reconstruction network that utilizes dense connections.\nIn MRI, our method minimizes artifacts, while maintaining a high-quality\nreconstruction that produces sharper images than other techniques. To\ndemonstrate the general nature of our method, it is further evaluated on a\nbattery of image-to-image translation experiments, demonstrating an ability to\nrecover from sub-optimal weighting in multi-term adversarial training.\n"}
+{"abstract": "  We present a detailed study of the dynamics associated with the ghost sector\nof quenched QCD in the Landau gauge, where the relevant dynamical equations are\nsupplemented with key inputs originating from large-volume lattice simulations.\nIn particular, we solve the coupled system of Schwinger-Dyson equations that\ngoverns the evolution of the ghost dressing function and the ghost-gluon\nvertex, using as input for the gluon propagator lattice data that have been\ncured from volume and discretization artifacts. In addition, we explore the\nsoft gluon limit of the same system, employing recent lattice data for the\nthree-gluon vertex that enters in one of the diagrams defining the\nSchwinger-Dyson equation of the ghost-gluon vertex. The results obtained from\nthe numerical treatment of these equations are in excellent agreement with\nlattice data for the ghost dressing function, once the latter have undergone\nthe appropriate scale-setting and artifact elimination refinements. Moreover,\nthe coincidence observed between the ghost-gluon vertex in general kinematics\nand in the soft gluon limit reveals an outstanding consistency of physical\nconcepts and computational schemes.\n"}
+{"abstract": "  The technology of network slicing, as the most characteristic feature of the\nfifth generation (5G) wireless networks, manages the resources and network\nfunctions in heterogeneous and logically isolated slices on the top of a shared\nphysical infrastructure, where every slice can be independently customized to\nfulfill the specific requirements of its devoted service type. It enables a new\nparadigm of multi-tenancy networking, where the network slices can be leased by\nthe mobile network operator (MNO) to tenants in form of public cloud computing\nservice, known as Slice-asa- Service (SlaaS). Similar to classical cloud\ncomputing scenarios, SlaaS benefits from overbooking its resources to numerous\ntenants, taking advantage of the resource elasticity and diversity, at a price\nof risking overloading network resources and violating the service-level\nagreements (SLAs), which stipulate the quality of service (QoS) that shall be\nguaranteed to the network slices. Thus, it becomes a critical challenge to the\nMNOs, accurately estimating the resource overload risk - especially under the\nsophisticated network dynamics - for monitoring and enhancing the reliability\nof SlaaS business.\n"}
+{"abstract": "  This paper applies neural network models to forecast inflation. The use of a\nparticular recurrent neural network, the long-short term memory model, or LSTM,\nthat summarizes macroeconomic information into common components is a major\ncontribution of the paper. Results from an exercise with US data indicate that\nthe estimated neural nets usually present better forecasting performance than\nstandard benchmarks, especially at long horizons. The LSTM in particular is\nfound to outperform the traditional feed-forward network at long horizons,\nsuggesting an advantage of the recurrent model in capturing the long-term trend\nof inflation. This finding can be rationalized by the so called long memory of\nthe LSTM that incorporates relatively old information in the forecast as long\nas accuracy is improved, while economizing in the number of estimated\nparameters. Interestingly, the neural nets containing macroeconomic information\ncapture well the features of inflation during and after the Great Recession,\npossibly indicating a role for nonlinearities and macro information in this\nepisode. The estimated common components used in the forecast seem able to\ncapture the business cycle dynamics, as well as information on prices.\n"}
+{"abstract": "  The article proposes a new type of AI system that does not give solutions\ndirectly but rather points toward it, friendly prompting the user with\nquestions and adjusting messages. Models of AI human collaboration can be\ndeduced from the classic literary example of interaction between Mr. Holmes and\nDr. Watson from the stories by Conan Doyle, where the highly qualified expert\nMr. Holmes answers questions posed by Dr. Watson. Here Mr. Holmes, with his\nrule-based calculations, logic, and memory management, apparently plays the\nrole of an AI system, and Dr. Watson is the user. Looking into the same\nHolmes-Watson interaction, we find and promote another model in which the AI\nbehaves like Dr. Watson, who, by asking questions and acting in a particular\nway, helps Holmes (the AI user) make the right decisions. We call the systems\nbased on this principle \"Dr. Watson-type systems.\" The article describes the\nproperties of such systems and introduces two particular: Patient Management\nSystem for intensive care physicians and Data Error Prevention System.\n"}
+{"abstract": "  Network models describe the brain as sets of nodes and edges that represent\nits distributed organization. So far, most discoveries in network neuroscience\nhave prioritized insights that highlight distinct groupings and specialized\nfunctional contributions of network nodes. Importantly, these functional\ncontributions are determined and expressed by the web of their\ninterrelationships, formed by network edges. Here, we underscore the important\ncontributions made by brain network edges for understanding distributed brain\norganization. Different types of edges represent different types of\nrelationships, including connectivity and similarity among nodes. Adopting a\nspecific definition of edges can fundamentally alter how we analyze and\ninterpret a brain network. Furthermore, edges can associate into collectives\nand higher-order arrangements, describe time series, and form edge communities\nthat provide insights into brain network topology complementary to the\ntraditional node-centric perspective. Focusing on the edges, and the\nhigher-order or dynamic information they can provide, discloses previously\nunderappreciated aspects of structural and functional network organization.\n"}
+{"abstract": "  The classical Raychaudhuri equation predicts the formation of conjugate\npoints for a congruence of geodesics, in a finite proper time. This in\nconjunction with the Hawking-Penrose singularity theorems predicts the\nincompleteness of geodesics and thereby the singular nature of practically all\nspacetimes. We compute the generic corrections to the Raychaudhuri equation in\nthe interior of a Schwarzschild black hole, arising from modifications to the\nalgebra inspired by the generalized uncertainty principle (GUP) theories. Then\nwe study four specific models of GUP, compute their effective dynamics as well\nas their expansion and its rate of change using the Raychaudhuri equation. We\nshow that the modification from GUP in two of these models, where such\nmodifications are dependent of the configuration variables, lead to finite\nKretchmann scalar, expansion and its rate, hence implying the resolution of the\nsingularity. However, the other two models for which the modifications depend\non the momenta still retain their singularities even in the effective regime.\n"}
+{"abstract": "  We present benchmark calculations of the Anderson lattice model based on the\nrecently-developed \"ghost Gutzwiller approximation\". Our analysis shows that,\nin some parameters regimes, the predictions of the standard Gutzwiller\napproximation can be incorrect by orders of magnitude for this model. We show\nthat this is caused by the inability of this method to describe simultaneously\nthe Mott physics and the hybridization between correlated and itinerant degrees\nof freedom (whose interplay often governs the metal-insulator transition in\nreal materials). Finally, we show that the ghost Gutzwiller approximation\nsolves this problem, providing us with results in remarkable agreement with\ndynamical mean field theory throughout the entire phase diagram, while being\nmuch less computationally demanding. We provide an analytical explanation of\nthese findings and discuss their implications within the context of ab-initio\ncomputation of strongly-correlated matter.\n"}
+{"abstract": "  We study theoretically the nonlinear optical response of Tomonaga-Luttinger\nspin liquid in the context of terahertz (THz) two-dimensional coherent\nspectroscopy (2DCS). Using the gapless phase of the XXZ-type spin chain as an\nexample, we show that its third-order nonlinear magnetic susceptibilities\n$\\chi^{(3)}_{+--+}$ and $\\chi^{(3)}_{-++-}$ exhibit photon echo, where $\\pm$\nrefers to the left/right-hand circular polarization with respect to the $S^z$\naxis. The photon echo arises from a ``lensing'' phenomenon in which the wave\npackets of fractional excitations move apart and then come back toward each\nother, amounting to a refocusing of the excitations' world lines.\nRenormalization group irrelevant corrections to the fixed point Hamiltonian\nresult in dispersion and/or damping of the wave packets, which can be\nsensitively detected by lensing and consequently the photon echo. Our results\nthus unveil the strength of THz-2DCS in probing the dynamical properties of the\ncollective excitations in a prototypical gapless many-body system.\n"}
+{"abstract": "  Let $f(x)\\in\\mathbb{Z}[x]$ be a nonconstant polynomial. Let $n, k$ and $c$ be\nintegers such that $n\\ge 1$ and $k\\ge 2$. An integer $a$ is called an\n$f$-exunit in the ring $\\mathbb{Z}_n$ of residue classes modulo $n$ if\n$\\gcd(f(a),n)=1$. In this paper, we use the principle of cross-classification\nto derive an explicit formula for the number ${\\mathcal N}_{k,f,c}(n)$ of\nsolutions $(x_1,...,x_k)$ of the congruence $x_1+...+x_k\\equiv c\\pmod n$ with\nall $x_i$ being $f$-exunits in the ring $\\mathbb{Z}_n$. This extends a recent\nresult of Anand {\\it et al.} [On a question of $f$-exunits in\n$\\mathbb{Z}/{n\\mathbb{Z}}$, {\\it Arch. Math. (Basel)} {\\bf 116} (2021),\n403-409]. We derive a more explicit formula for ${\\mathcal N}_{k,f,c}(n)$ when\n$f(x)$ is linear or quadratic.\n"}
+{"abstract": "  By means of high-pressure resistivity measurements on single crystals, we\ninvestigate the charge transport properties of Cu$_x$PdTe$_2$, notable for the\ncombination of topological type-II Dirac semimetallic properties with\nsuperconductivity up to $T_c = 2.5$ K. In both cases of pristine ($x = 0$) and\nintercalated ($x=0.05$) samples, we find an unconventional $T^4$ power law\nbehavior of the low-temperature resistivity visible up to $\\sim$40 K and\nremarkably stable under pressure up to 8.2 GPa. This observation is explained\nby the low carrier density $n$, which strongly reduces the $k$-region available\nfor electron-phonon scattering, as previously reported in other low-$n$\ntwo-dimensional systems, such as multilayer graphene and semiconductor\nheterostructures. Our data analysis complemented by specific heat measurements\nand supported by previous quantum oscillation studies and \\textit{ab initio}\ncalculations suggests a scenario of one-band charge transport. Within this\nscenario, our analysis yields a large value of transport electron-phonon\ncoupling constant $\\lambda_{tr} = 1.2$ at ambient pressure that appears to be\nstrongly enhanced by pressure assuming a constant effective mass.\n"}
+{"abstract": "  The physics of Cosmic ray (CR) transport remains a key uncertainty in\nassessing whether CRs can produce galaxy-scale outflows consistent with\nobservations. In this paper, we elucidate the physics of CR-driven galactic\nwinds for CR transport dominated by diffusion. A companion paper considers CR\nstreaming. We use analytic estimates validated by time-dependent\nspherically-symmetric simulations to derive expressions for the mass-loss rate,\nmomentum flux, and speed of CR-driven galactic winds, suitable for\ncosmological-scale or semi-analytic models of galaxy formation. For CR\ndiffusion coefficients $\\kappa \\gtrsim r_0 c_i$ where $r_0$ is the base radius\nof the wind and $c_i$ is the isothermal gas sound speed, the asymptotic wind\nenergy flux is comparable to that supplied to CRs, and the outflow rapidly\naccelerates to supersonic speeds. By contrast, for $\\kappa \\lesssim r_0 c_i$,\nCR-driven winds accelerate more slowly and lose most of their energy to\ngravity, a CR analogue of photon-tired stellar winds. Given CR diffusion\ncoefficients estimated using Fermi gamma-ray observations of pion decay, we\npredict mass-loss rates in CR-driven galactic winds of order the star formation\nrate for dwarf and disc galaxies. The dwarf galaxy mass-loss rates are small\ncompared to the mass-loadings needed to reconcile the stellar and dark matter\nhalo mass functions. For nuclear starbursts (e.g., M82, Arp 220), CR diffusion\nand pion losses suppress the CR pressure in the galaxy and the strength of\nCR-driven winds. We discuss the implications of our results for interpreting\nobservations of galactic winds and for the role of CRs in galaxy formation.\n"}
+{"abstract": "  Inspired by the classical Riemannian systolic inequality of Gromov we present\na combinatorial analogue providing a lower bound on the number of vertices of a\nsimplicial complex in terms of its edge-path systole. Similarly to the\nRiemannian case, where the inequality holds under a topological assumption of\n\"essentiality'', our proofs rely on a combinatorial analogue of that\nassumption. Under a stronger assumption, expressed in terms of cohomology\ncup-length, we improve our results quantitatively. We also illustrate our\nmethods in the continuous setting, generalizing and improving quantitatively\nthe Minkowski principle of Balacheff and Karam; a corollary of this result is\nthe extension of the Guth--Nakamura cup-length systolic bound from manifolds to\ncomplexes.\n"}
+{"abstract": "  Recently, intriguing physical properties have been unraveled in anisotropic\nlayered semiconductors with the in-plane anisotropy often originates directly\nfrom the low crystallographic symmetry. However, little has been known about\nthe systems where the size effect dominates the anisotropy of electronic band\nstructures. Here, applying both experiment and theory, we show that the\nanisotropic energy bands of monoclinic gallium telluride (GaTe) are determined\nby a strong bulk-surface interaction rather than geometric factors. Bulk\nelectronic states are found to be the major contribution to the highest valence\nband, whose anisotropy is yet immune to surface doping by potassium atoms.\nFurther analysis indicates the weakened bulk-surface interaction gives rise to\nan inverse anisotropy of hole effective masses and the strong interlayer\ncoupling induces a direct-indirect-direct band gap transition at transfer from\nmono- to few-layer GaTe. Our results thus pave the way to future applications\nof anisotropic layered semiconductors in nanoelectronics and optoelectronics.\n"}
+{"abstract": "  It is commonly accepted that the periods of superhumps can be satisfactorily\nexplained within a model involving apsidal motion of the accretion disk\nprovided the frequency of the apsidal motion in addition to the dynamical term\nincludes also the pressure effects. Using a larger sample of systems with\nreliable mass ratios it is shown, however, that this view is not true and the\nmodel requires further modifications.\n"}
+{"abstract": "  Clouds are expected to form in a wide range of conditions in the atmosphere\nof exoplanets given the large range of possible condensible species. However\nthis diversity might lead to very different small-scale dynamics depending on\nradiative transfer in various thermal conditions: we aim at providing some\ninsights into these dynamical regimes. We perform an analytical linear\nstability analysis of a compositional discontinuity with a heating source term\nthat depends on composition. We also perform idealized two-dimensional (2D)\nsimulations of an opacity discontinuity in a stratified medium with the code\nARK. We use a two-stream grey model for radiative transfer and explore the\nbrown-dwarf and earth-like regimes. We reveal the existence of a Radiative\nRayleigh-Taylor Instability (RRTI hereafter, a particular case of diabatic\nRayleigh-Taylor instability) when an opacity discontinuity is present in a\nstratified medium. This instability is similar in nature to diabatic convection\nand relies only on buoyancy with radiative transfer heating and cooling. When\nthe temperature is decreasing with height in the atmosphere, a lower-opacity\nmedium on top of a higher-opacity medium is dynamically unstable while a\nhigher-opacity medium on top of a lower-opacity medium is stable. This\nstability/instability behavior is reversed if the temperature is increasing\nwith height. The existence of the RRTI could have important implications for\nthe stability of the cloud cover of a wide range of planetary atmospheres. In\nour solar system, it could help explain the formation of mammatus cloud in\nEarth atmospheres and the existence of Venus cloud deck. Likewise, it suggests\nthat stable and large scale cloud covers could be ubiquitous in strongly\nirradiated exoplanets but might be more patchy in low-irradiated or isolated\nobjects like brown dwarfs and directly imaged exoplanets.\n"}
+{"abstract": "  Speech quality assessment has been a critical issue in speech processing for\ndecades. Existing automatic evaluations usually require clean references or\nparallel ground truth data, which is infeasible when the amount of data soars.\nSubjective tests, on the other hand, do not need any additional clean or\nparallel data and correlates better to human perception. However, such a test\nis expensive and time-consuming because crowd work is necessary. It thus\nbecomes highly desired to develop an automatic evaluation approach that\ncorrelates well with human perception while not requiring ground truth data. In\nthis paper, we use self-supervised pre-trained models for MOS prediction. We\nshow their representations can distinguish between clean and noisy audios.\nThen, we fine-tune these pre-trained models followed by simple linear layers in\nan end-to-end manner. The experiment results showed that our framework\noutperforms the two previous state-of-the-art models by a significant\nimprovement on Voice Conversion Challenge 2018 and achieves comparable or\nsuperior performance on Voice Conversion Challenge 2016. We also conducted an\nablation study to further investigate how each module benefits the task. The\nexperiment results are implemented and reproducible with publicly available\ntoolkits.\n"}
+{"abstract": "  As one of the most important estimators in classical statistics, the\nuniformly minimum variance unbiased estimator (UMVUE) has been adopted for\npoint estimation in many statistical studies, especially for small sample\nproblems. Moving beyond typical settings in the exponential distribution\nfamily, it is usually challenging to prove the existence and further construct\nsuch UMVUE in finite samples. For example in the ongoing Adaptive COVID-19\nTreatment Trial (ACTT), it is hard to characterize the complete sufficient\nstatistics of the underlying treatment effect due to pre-planned modifications\nto design aspects based on accumulated unblinded data. As an alternative\nsolution, we propose a Deep Neural Networks (DNN) guided ensemble learning\nframework to construct an improved estimator from existing ones. We show that\nour estimator is consistent and asymptotically reaches the minimal variance\nwithin the class of linearly combined estimators. Simulation studies are\nfurther performed to demonstrate that our proposed estimator has considerable\nfinite-sample efficiency gain. In the ACTT on COVID-19 as an important\napplication, our method essentially contributes to a more ethical and efficient\nadaptive clinical trial with fewer patients enrolled.\n"}
+{"abstract": "  We provide a deterministic CONGEST algorithm to constant factor approximate\nthe minimum dominating set on graphs of bounded arboricity in $O(\\log n)$\nrounds. This improves over the well-known randomized algorithm of Lenzen and\nWattenhofer[DISC2010] by making it a deterministic algorithm.\n"}
+{"abstract": "  Cellular appendages conferring motility, such as flagella or cilia, are known\nto synchronise their periodic beats. The origin of synchronisation is a\ncombination of long-range hydrodynamic interactions with physical mechanisms\nallowing the phases of these biological oscillators to evolve. Two of such\nmechanisms have been identified by previous work, the elastic compliance of the\nperiodic orbit or oscillations driven by phase-dependent biological forcing. To\nhelp uncover the physical mechanism for hydrodynamic synchronisation most\nessential overall in biology, we theoretically investigate the effect of strong\nconfinement on the effectiveness of hydrodynamic synchronisation. We use\nminimal models where appendages are modelled as rigid spheres forced to move\nalong circular trajectories near a rigid surface. Strong confinement is\nmodelled by adding a second nearby surface, parallel to the first one, where\nthe distance between the surfaces is much smaller than the typical distance\nbetween the cilia. We calculate separately the impact of confinement on the\nsynchronisation dynamics of the elastic compliance and the force modulation\nmechanisms and compare our results to the case with no confinement. Applying\nour results to the biologically-relevant situation of nodal cilia, we show that\nforce modulation is a mechanism that leads to phase-locked states under strong\nconfinement that are very similar to those without confinement as a difference\nwith the elastic compliance mechanism. Our results point therefore to the\nrobustness of force modulation for synchronisation, an important feature for\nbiological dynamics that suggests it could be the most essential physical\nmechanism overall in arrays of nodal cilia. We further examine the distinct\nsituation of primary cilia and show in that case that the difference in\nrobustness of the mechanisms is not as pronounced but still favours the force\nmodulation.\n"}
+{"abstract": "  We prove dispersive estimate for the elastic wave equation by which we extend\nthe known Strichartz estimates for the classical wave equation to those for the\nelastic wave equation. In particular, the endpoint Strichartz estimates are\ndeduced. For the purpose we diagonalize the symbols of the Lam\\'e operator and\nits semigroup, which also gives an alternative and simpler proofs of the\nprevious results on perturbed elastic wave equations. Furthermore, we obtain\nuniform Sobolev inequalities for the elastic wave operator.\n"}
+{"abstract": "  In this paper we study circular orbits around higher dimensional rotating\nMyers-Perry and pure Gauss-Bonnet (GB) black holes. It turns out that for the\nformer there occurs no potential well to harbour bound and thereby stable\ncircular orbits. The only circular orbits that could occur are all unstable and\ntheir radius is bounded from the below by that of the photon circular orbit. On\nthe other hand bound and stable circular orbits do exist in pure GB/Lovelock\nrotating black holes (the metric is though not an exact solution of pure\nLovelock vacuum equation but it satisfies the equation in the leading order and\nhas all the desired properties) in dimensions, $2N+2 \\leq D \\leq 4N$ (for $N=2$\npure GB in $D = 6, 7, 8$) where $N$ is the degree of Lovelock polynomial. Thus\nbound and stable circular orbits could exist around higher dimensional rotating\nblack holes only for pure GB/Lovelock gravity. This property is a nice\ndiscriminator between Myers-Perry and pure GB/Lovelock rotating black holes.\n"}
+{"abstract": "  We propose the use of the Earth as a transducer for ultralight dark-matter\ndetection. In particular we point out a novel signal of kinetically mixed\ndark-photon dark matter: a monochromatic oscillating magnetic field generated\nat the surface of the Earth. Similar to the signal in a laboratory experiment\nin a shielded box (or cavity), this signal arises because the lower atmosphere\nis a low-conductivity air gap sandwiched between the highly conductive interior\nof the Earth below and ionosphere or interplanetary medium above. At low masses\n(frequencies) the signal in a laboratory detector is usually suppressed by the\nsize of the detector multiplied by the dark-matter mass. Crucially, in our case\nthe suppression is by the radius of the Earth, and not by the (much smaller)\nheight of the atmosphere. We compute the size and global vectorial pattern of\nour magnetic field signal, which enables sensitive searches for this signal\nusing unshielded magnetometers dispersed over the surface of the Earth. In\nprinciple, the signal we compute exists for any dark photon in the mass range\n$10^{-21} \\text{eV}\\lesssim m_{A'} \\lesssim 3\\times 10^{-14} \\text{eV}$. We\nsummarize the results of our companion paper [arXiv:2108.08852], in which we\ndetail such a search using a publicly available dataset from the SuperMAG\nCollaboration: we report no robust signal candidates and so place constraints\nin the (more limited) dark-photon dark-matter mass range $2\\times 10^{-18}\n\\text{eV} \\lesssim m_{A'} \\lesssim 7\\times 10^{-17} \\text{eV}$ (corresponding\nto frequencies $6\\times 10^{-4} \\text{Hz}\\lesssim f \\lesssim 2\\times 10^{-2}\n\\text{Hz}$). These constraints are complementary to existing astrophysical\nbounds. Future searches for this signal may improve the sensitivity over a wide\nrange of ultralight dark-matter candidates and masses.\n"}
+{"abstract": "  This article introduces a novel approach to the classification of categorical\ntime series under the supervised learning paradigm. To construct meaningful\nfeatures for categorical time series classification, we consider two relevant\nquantities: the spectral envelope and its corresponding set of optimal\nscalings. These quantities characterize oscillatory patterns in a categorical\ntime series as the largest possible power at each frequency, or spectral\nenvelope, obtained by assigning numerical values, or scalings, to categories\nthat optimally emphasize oscillations at each frequency. Our procedure combines\nthese two quantities to produce an interpretable and parsimonious feature-based\nclassifier that can be used to accurately determine group membership for\ncategorical time series. Classification consistency of the proposed method is\ninvestigated, and simulation studies are used to demonstrate accuracy in\nclassifying categorical time series with various underlying group structures.\nFinally, we use the proposed method to explore key differences in oscillatory\npatterns of sleep stage time series for patients with different sleep disorders\nand accurately classify patients accordingly.\n"}
+{"abstract": "  We briefly explain the structure of the\nHartree-Fock-Bardeen-Cooper-Schrieffer (HFBCS) and Quasi-particle Random Phase\nApproximation (QRPA) code hfbcs-qrpa, highlighting only the differences with\nthe code skyrme_rpa that has been previously published [G. Col\\`o, L. Cao, N.\nVan Giai, and L. Capelli, Comp. Phys. Comm. 184, 142 (2013)]. The code deals\nwith open shell spherical systems and non-spin flip, non-charge-exchange and\nnatural parity $(-1)^l$ excitations.\n"}
+{"abstract": "  Estimating the contribution from axial-vector intermediate states to hadronic\nlight-by-light scattering requires input on their transition form factors\n(TFFs). Due to the Landau-Yang theorem, any experiment sensitive to these TFFs\nneeds to involve at least one virtual photon, which complicates their\nmeasurement. Phenomenologically, the situation is best for the $f_1(1285)$\nresonance, for which information is available from $e^+e^-\\to e^+e^- f_1$,\n$f_1\\to 4\\pi$, $f_1\\to \\rho \\gamma$, $f_1\\to \\phi \\gamma$, and $f_1\\to e^+e^-$.\nWe provide a comprehensive analysis of the $f_1$ TFFs in the framework of\nvector meson dominance, including short-distance constraints, to determine to\nwhich extent the three independent TFFs can be constrained from the available\nexperimental input, a prerequisite for improved calculations of the\naxial-vector contribution to hadronic light-by-light scattering. In particular,\nwe focus on the process $f_1\\to e^+e^-$, evidence for which has been reported\nrecently by SND for the first time, and discuss the impact that future improved\nmeasurements will have on the determination of the $f_1$ TFFs.\n"}
+{"abstract": "  We calculate the two-magnon Raman scattering spectra in antiferromagnetic\nphases of several frustrated spin models defined on the honeycomb lattice.\nThese include the N\\'{e}el antiferromagnetic phase of a $J_1$-$J_2$-$J_3$ model\nand the stripe phase of the Heisenberg-Kitaev model. We show that both the\nmagnetic frustration and the anisotropy of interactions may significantly\naffect the Raman spectra. We further discuss the implications of our results to\nthe magnetic excitations of the iron-based compound BaFe$_2$Se$_2$O and show\nhow the magnetic interactions can be extracted from fit to the Raman spectrum.\n"}
+{"abstract": "  The existence of a positive solution to a class of Choquard equations with\npotential going at a positive limit at infinity possibly from above or\noscillating is proved. Our results include the physical case and do not require\nany symmetry assumptions on the potential.\n"}
+{"abstract": "  Correlators of Wilson-line operators in non-abelian gauge theories are known\nto exponentiate, and their logarithms can be organised in terms of collections\nof Feynman diagrams called webs. In [1] we introduced the concept of Cweb, or\ncorrelator web, which is a set of skeleton diagrams built with connected gluon\ncorrelators, and we computed the mixing matrices for all Cwebs connecting four\nor five Wilson lines at four loops. Here we complete the evaluation of\nfour-loop mixing matrices, presenting the results for all Cwebs connecting two\nand three Wilson lines. We observe that the conjuctured column sum rule is\nobeyed by all the mixing matrices that appear at four-loops. We also show how\nlow-dimensional mixing matrices can be uniquely determined from their known\ncombinatorial properties, and provide some all-order results for selected\nclasses of mixing matrices. Our results complete the required colour building\nblocks for the calculation of the soft anomalous dimension matrix at four-loop\norder.\n"}
+{"abstract": "  Recommender systems are gaining increasing and critical impacts on human and\nsociety since a growing number of users use them for information seeking and\ndecision making. Therefore, it is crucial to address the potential unfairness\nproblems in recommendations. Just like users have personalized preferences on\nitems, users' demands for fairness are also personalized in many scenarios.\nTherefore, it is important to provide personalized fair recommendations for\nusers to satisfy their personalized fairness demands. Besides, previous works\non fair recommendation mainly focus on association-based fairness. However, it\nis important to advance from associative fairness notions to causal fairness\nnotions for assessing fairness more properly in recommender systems. Based on\nthe above considerations, this paper focuses on achieving personalized\ncounterfactual fairness for users in recommender systems. To this end, we\nintroduce a framework for achieving counterfactually fair recommendations\nthrough adversary learning by generating feature-independent user embeddings\nfor recommendation. The framework allows recommender systems to achieve\npersonalized fairness for users while also covering non-personalized\nsituations. Experiments on two real-world datasets with shallow and deep\nrecommendation algorithms show that our method can generate fairer\nrecommendations for users with a desirable recommendation performance.\n"}
+{"abstract": "  The fast-growing techniques of measuring and fusing multi-modal biomedical\nsignals enable advanced motor intent decoding schemes of lowerlimb\nexoskeletons, meeting the increasing demand for rehabilitative or assistive\napplications of take-home healthcare. Challenges of exoskeletons motor intent\ndecoding schemes remain in making a continuous prediction to compensate for the\nhysteretic response caused by mechanical transmission. In this paper, we solve\nthis problem by proposing an ahead of time continuous prediction of lower limb\nkinematics, with the prediction of knee angles during level walking as a case\nstudy. Firstly, an end-to-end kinematics prediction network(KinPreNet),\nconsisting of a feature extractor and an angle predictor, is proposed and\nexperimentally compared with features and methods traditionally used in\nahead-of-time prediction of gait phases. Secondly, inspired by the\nelectromechanical delay(EMD), we further explore our algorithm's capability of\ncompensating response delay of mechanical transmission by validating the\nperformance of the different sections of prediction time. And we experimentally\nreveal the time boundary of compensating the hysteretic response. Thirdly, a\ncomparison of employing EMG signals or not is performed to reveal the EMG and\nkinematic signals collaborated contributions to the continuous prediction.\nDuring the experiments, EMG signals of nine muscles and knee angles calculated\nfrom inertial measurement unit (IMU) signals are recorded from ten healthy\nsubjects. To the best of our knowledge, this is the first study of continuously\npredicting lower-limb kinematics in an ahead-of-time manner based on the\nelectromechanical delay (EMD).\n"}
+{"abstract": "  Of the five exceptional groups, $\\mathrm{E}_6$ is considered the most\nattractive for unification due to the following reasons: (i) it contains both\n$\\mathrm{Spin} (10) \\times \\mathrm{U}(1)$ and $\\mathrm{SU} (3) \\times\n\\mathrm{SU}(3) \\times \\mathrm{SU}(3)$ as maximal subgroups, each of which admit\nembeddings of the Standard Model; (ii) uniquely among the exceptional groups,\nit admits complex representations; in particular, its 27 dimensional\nfundamental representation accommodates one generation of left-handed fermions\nunder the usual charge assignments; (iii) all of its representations are\nanomaly-free. In this master's thesis, written in the spirit of Baez and\nHuerta's \"The Algebra of Grand Unified Theories\", we rigorously show how an\n$\\mathrm{E}_6$ grand unified theory is mathematically constructed. Our modest\ncontribution to the literature includes an explicit check that that\n$\\mathbb{Z}_4$ kernel of the homomorphism $\\mathrm{Spin} (10) \\times\n\\mathrm{U}(1) \\to \\mathrm{E}_6$ acts trivially on every fermion; we also\nformulate symmetry breaking, in particular the symmetry breaking of the exotic\n$\\mathrm{E}_6$ fermions under $\\mathrm{Spin} (10) \\to \\mathrm{SU}(5)$, using a\ndifferent approach than the usual Dynkin diagrams: we explicitly embedded\n$\\mathfrak{su}(5) \\hookrightarrow \\mathfrak{so}(10) \\cong \\mathfrak{spin} (10)$\nand solve the related eigenvalue problem. Phenomenological aspects of grand\nunified theories are also discussed.\n"}
+{"abstract": "  The rational Chow ring of the moduli space $\\mathcal{M}_g$ of curves of genus\n$g$ is known for $g \\leq 6$. Here, we determine the rational Chow rings of\n$\\mathcal{M}_7, \\mathcal{M}_8,$ and $\\mathcal{M}_9$ by showing they are\ntautological. One key ingredient is intersection theory on Hurwitz spaces of\ndegree $4$ and $5$ covers of $\\mathbb{P}^1$, as developed by the authors in\n[1]. The main focus of this paper is a detailed geometric analysis of special\ntetragonal and pentagonal covers whose associated vector bundles on\n$\\mathbb{P}^1$ are so unbalanced that they fail to lie in the large open subset\nconsidered in [1]. In genus $9$, we use work of Mukai [23] to present the locus\nof hexagonal curves as a global quotient stack, and, using equivariant\nintersection theory, we show its Chow ring is generated by restrictions of\ntautological classes.\n"}
+{"abstract": "  Phosphine is now well established as a biosignature, which has risen to\nprominence with its recent tentative detection on Venus. To follow up this\ndiscovery and related future exoplanet biosignature detections, it is important\nto spectroscopically detect the presence of phosphorus-bearing atmospheric\nmolecules that could be involved in the chemical networks producing, destroying\nor reacting with phosphine. We start by enumerating phosphorus-bearing\nmolecules (P-molecules) that could potentially be detected spectroscopically in\nplanetary atmospheres and collecting all available spectral data. Gaseous\nP-molecules are rare, with speciation information scarce. Very few molecules\nhave high accuracy spectral data from experiment or theory; instead, the best\navailable data is from the RASCALL approach and obtained using functional group\ntheory. Here, we present a high-throughput approach utilising established\ncomputational quantum chemistry methods (CQC) to produce a database of\napproximate infrared spectra for 958 P-molecules. These data are of interest\nfor astronomy and astrochemistry (importantly identifying potential ambiguities\nin molecular assignments), improving RASCALL's underlying data, big data\nspectral analysis and future machine learning applications. However, this data\nwill probably not be sufficiently accurate for secure experimental detections\nof specific molecules within complex gaseous mixtures in laboratory or\nastronomy settings.\n"}
+{"abstract": "  The success of software crowdsourcing depends on active and trustworthy pool\nof worker supply. The uncertainty of crowd workers' behaviors makes it\nchallenging to predict workers' success and plan accordingly. In a competitive\ncrowdsourcing marketplace, competition for success over shared tasks adds\nanother layer of uncertainty in crowd workers' decision-making process.\nPreliminary analysis on software worker behaviors reveals an alarming task\ndropping rate of 82.9%. These factors lead to the need for an automated\nrecommendation system for CSD workers to improve the visibility and\npredictability of their success in the competition. To that end, this paper\nproposes a collaborative recommendation system for crowd workers. The proposed\nrecommendation system method uses five input metrics based on workers'\ncollaboration history in the pool, workers' preferences in taking tasks in\nterms of monetary prize and duration, workers' specialty, and workers'\nproficiency. The proposed method then recommends the most suitable tasks for a\nworker to compete on based on workers' probability of success in the task.\nExperimental results on 260 active crowd workers demonstrate that just\nfollowing the top three success probabilities of task recommendations, workers\ncan achieve success up to 86%\n"}
+{"abstract": "  The traveltime of compressional (P) and shear (S) waves have proven essential\nin many applications of earthquake and exploration seismology. An accurate and\nefficient traveltime computation for P and S waves is crucial for the success\nof these applications. However, solutions to the Eikonal equation with a\ncomplex phase velocity field in anisotropic media is challenging. The Eikonal\nequation is a first-order, hyperbolic, nonlinear partial differential equation\n(PDE) that represents the high-frequency asymptotic approximation of the wave\nequation. The fast marching and sweeping methods are commonly used due to their\nefficiency in numercally solving Eikonal equation. However, these methods\nsuffer from numerical inaccuracy in anisotropic media with sharp heterogeneity,\nirregular surface topography and complex phase velocity fields. This study\npresents a new method to solving the Eikonal equation by employing the\nperidynamic differential operator (PDDO). The PDDO provides the nonlocal form\nof the Eikonal equation by introducing an internal length parameter (horizon)\nand a weight function with directional nonlocality. The operator is immune to\ndiscontinuities in the form sharp changes in field or model variables and\ninvokes the direction of traveltime in a consistent manner. The weight function\ncontrols the degree of association among points within the horizon. Solutions\nare constructed in a consistent manner without upwind assumptions through\nsimple discretization. The capability of this approach is demonstrated by\nconsidering different types of Eikonal equations on complex velocity models in\nanisotropic media. The examples demonstrate its unconditional numerical\nstability and results compare well with the reference solutions.\n"}
+{"abstract": "  In this paper we study affine hypersurfaces with non-degenerate second\nfundamental form of arbitrary signature additionally equipped with an almost\nsymplectic structure $\\omega$.\n  We prove that if $R^p\\omega=0$ or $\\nabla^p\\omega =0$ for some positive\ninteger $p$ then the rank of the shape operator is at most one. The results\nprovide complete classification of affine hypersurfaces with higher order\nparallel almost symplectic forms and are generalization of recently obtained\nresults for Lorentzian affine hypersurfaces.\n"}
+{"abstract": "  Let K be a field of characteristic zero. We prove that images of a linear\nK-derivation and a linear K-E-derivation of the ring K[x 1 ,x 2 ,x 3 ] of\npolynomial in three variables over K are Mathieu-Zhao subspaces, which affirms\nthe LFED conjecture for linear K-derivations and linear K-E-derivations of K[x\n1 ,x 2 ,x 3 ].\n"}
+{"abstract": "  Outflows driven by active galactic nuclei (AGN) are expected to have a\nsignificant impact on the host galaxy evolution, but it is still debated how\nthey are accelerated and propagate on galaxy-wide scales. This work addresses\nthese questions by studying the link between X-ray, nuclear ultra-fast outflows\n(UFOs) and extended ionised outflows, for the first time in two quasars close\nto the peak of AGN activity ($z\\sim2$), where AGN feedback is expected to be\nmore effective. As targets, we selected two multiple-lensed quasars at\n$z\\sim1.5$, HS 0810+2554 and SDSS J1353+1138, known to host UFOs and observed\nwith the near-IR integral field spectrometer SINFONI at the VLT. We performed a\nkinematical analysis of the [O III]$\\lambda$5007 optical emission line, in\norder to trace the presence of ionised outflows. We detected spatially resolved\nionised outflows in both galaxies, extended more than 8 kpc and moving up to\n$v>2000$ km/s. We derived mass outflow rates of $\\sim$12 M$_{sun}$/yr and\n$\\sim$2 M$_{sun}$/yr for HS 0810+2554 and SDSS J1353+1138. Comparing with the\nco-hosted UFO energetics, the ionised outflow energetics in HS 0810+2554 is\nbroadly consistent with a momentum-driven regime of wind propagation, while in\nSDSS J1353+1138 it differs by a factor of $\\sim$100 from theoretical\npredictions, requiring either a massive molecular outflow or a high variability\nof the AGN activity to account for such a discrepancy. By additionally\nconsidering our results with those from the small sample of well-studied\nobjects (all local but one), with both UFO and extended\n(ionised/atomic/molecular) outflow detections, we found that in 10 out of 12\ngalaxies the large-scale outflow energetics is consistent with the theoretical\npredictions of either a momentum- or an energy-driven scenario. This suggests\nthat such models explain relatively well the acceleration mechanism of\nAGN-driven winds on large scales.\n"}
+{"abstract": "  Topic modeling algorithms traditionally model topics as list of weighted\nterms. These topic models can be used effectively to classify texts or to\nsupport text mining tasks such as text summarization or fact extraction. The\ngeneral procedure relies on statistical analysis of term frequencies. The focus\nof this work is on the implementation of the knowledge-based topic modelling\nservices in a KNIME workflow. A brief description and evaluation of the\nDBPedia-based enrichment approach and the comparative evaluation of enriched\ntopic models will be outlined based on our previous work. DBpedia-Spotlight is\nused to identify entities in the input text and information from DBpedia is\nused to extend these entities. We provide a workflow developed in KNIME\nimplementing this approach and perform a result comparison of topic modeling\nsupported by knowledge base information to traditional LDA. This topic modeling\napproach allows semantic interpretation both by algorithms and by humans.\n"}
+{"abstract": "  This paper introduces an improved image processing method usable in\ncapacitive imaging applications. Standard capacitive imaging tends to prefer\namplitude-based images over the use of phase due to better signal-to-noise\nratios. The new approach exploits the best features of both types of\ninformation by combining them to form clearer images, hence improving both\ndefect detection and characterization in non-destructive evaluation. The\nmethodology is demonstrated and optimized using a benchmark sample. Additional\nexperiments on glass fibre composite sample illustrate the advantages of the\ntechnique.\n"}
+{"abstract": "  Hybrid quantum systems involving cold atoms and microwave resonators can\nenable cavity-mediated infinite-range interactions between atomic spin systems\nand realize atomic quantum memories and transducers for microwave to optical\nconversion. To achieve strong coupling of atoms to on-chip microwave\nresonators, it was suggested to use atomic Rydberg states with strong electric\ndipole transitions. Here we report on the realization of coherent coupling of a\nRydberg transition of ultracold atoms trapped on an integrated superconducting\natom chip to the microwave field of an on-chip coplanar waveguide resonator. We\nobserve and characterize the cavity driven Rabi oscillations between a pair of\nRydberg states of atoms in an inhomogeneous electric field near the chip\nsurface. Our studies demonstrate the feasibility, but also reveal the\nchallenges, of coherent state manipulation of Rydberg atoms interacting with\nsuperconducting circuits.\n"}
+{"abstract": "  In this paper, a novel framework for anomaly estimation is proposed. The\nbasic idea behind our method is to reduce the data into a two-dimensional space\nand then rank each data point in the reduced space. We attempt to estimate the\ndegree of anomaly in both spatial and density domains. Specifically, we\ntransform the data points into a density space and measure the distances in\ndensity domain between each point and its k-Nearest Neighbors in spatial\ndomain. Then, an anomaly coordinate system is built by collecting two\nunilateral anomalies from k-nearest neighbors of each point. Further more, we\nintroduce two schemes to model their correlation and combine them to get the\nfinal anomaly score. Experiments performed on the synthetic and real world\ndatasets demonstrate that the proposed method performs well and achieve highest\naverage performance. We also show that the proposed method can provide\nvisualization and classification of the anomalies in a simple manner. Due to\nthe complexity of the anomaly, none of the existing methods can perform best on\nall benchmark datasets. Our method takes into account both the spatial domain\nand the density domain and can be adapted to different datasets by adjusting a\nfew parameters manually.\n"}
+{"abstract": "  Internet is one of the important inventions and a large number of persons are\nits users. These persons use this for different purposes. There are different\nsocial media platforms that are accessible to these users. Any user can make a\npost or spread the news through the online platforms. These platforms do not\nverify the users or their posts. So some of the users try to spread fake news\nthrough these platforms. These news can be propaganda against an individual,\nsociety, organization or political party. A human being is unable to detect all\nthese fake news. So there is a need for machine learning classifiers that can\ndetect these fake news automatically. Use of machine learning classifiers for\ndetecting fake news is described in this systematic literature review.\n"}
+{"abstract": "  In this paper, we propose a weakly supervised multilingual representation\nlearning framework, called cross-lingual self-training (XLST). XLST is able to\nutilize a small amount of annotated data from high-resource languages to\nimprove the representation learning on multilingual un-annotated data.\nSpecifically, XLST uses a supervised trained model to produce initial\nrepresentations and another model to learn from them, by maximizing the\nsimilarity between output embeddings of these two models. Furthermore, the\nmoving average mechanism and multi-view data augmentation are employed, which\nare experimentally shown to be crucial to XLST. Comprehensive experiments have\nbeen conducted on the CommonVoice corpus to evaluate the effectiveness of XLST.\nResults on 5 downstream low-resource ASR tasks shows that our multilingual\npretrained model achieves relatively 18.6% PER reduction over the\nstate-of-the-art self-supervised method, with leveraging additional 100 hours\nof annotated English data.\n"}
+{"abstract": "  We propose the first method to adaptively modify the duration of a given\nspeech signal. Our approach uses a Bayesian framework to define a latent\nattention map that links frames of the input and target utterances. We train a\nmasked convolutional encoder-decoder network to produce this attention map via\na stochastic version of the mean absolute error loss function; our model also\npredicts the length of the target speech signal using the encoder embeddings.\nThe predicted length determines the number of steps for the decoder operation.\nDuring inference, we generate the attention map as a proxy for the similarity\nmatrix between the given input speech and an unknown target speech signal.\nUsing this similarity matrix, we compute a warping path of alignment between\nthe two signals. Our experiments demonstrate that this adaptive framework\nproduces similar results to dynamic time warping, which relies on a known\ntarget signal, on both voice conversion and emotion conversion tasks. We also\nshow that our technique results in a high quality of generated speech that is\non par with state-of-the-art vocoders.\n"}
+{"abstract": "  We derive the 3D N=1 superpotential for the closed string sector of type IIB\nsupergravity on toroidal O5 orientifolds with co-calibrated G2 structure and RR\nbackground flux. We find that such compactifications can provide full closed\nstring moduli stabilization on supersymmetric AdS$_3$ vacua, and once we\ninclude brane-supersymmetry-breaking we also find indication for the existence\nof classical 3D de Sitter solutions. The latter however are rather difficult to\nreconcile with the shape moduli stabilization and flux quantization. We also\ndiscuss the possibility of achieving scale separation in AdS$_3$ and dS$_3$\nvacua, but such effects seems to be hindered by the geometric flux\nquantization.\n"}
+{"abstract": "  In large-scale feature models, feature modeling and configuration processes\nare highly expected to be done by a group of stakeholders. In this context,\nrecommendation techniques can increase the efficiency of feature-model design\nand find optimal configurations for groups of stakeholders. Existing studies\nshow plenty of issues concerning feature model navigation support, group\nmembers' satisfaction, and conflict resolution. This study proposes group\nrecommendation techniques for feature modeling and configuration on the basis\nof addressing the mentioned issues.\n"}
+{"abstract": "  Early prediction of patients at risk of clinical deterioration can help\nphysicians intervene and alter their clinical course towards better outcomes.\nIn addition to the accuracy requirement, early warning systems must make the\npredictions early enough to give physicians enough time to intervene.\nInterpretability is also one of the challenges when building such systems since\nbeing able to justify the reasoning behind model decisions is desirable in\nclinical practice. In this work, we built an interpretable model for the early\nprediction of various adverse clinical events indicative of clinical\ndeterioration. The model is evaluated on two datasets and four clinical events.\nThe first dataset is collected in a predominantly COVID-19 positive population\nat Stony Brook Hospital. The second dataset is the MIMIC III dataset. The model\nwas trained to provide early warning scores for ventilation, ICU transfer, and\nmortality prediction tasks on the Stony Brook Hospital dataset and to predict\nmortality and the need for vasopressors on the MIMIC III dataset. Our model\nfirst separates each feature into multiple ranges and then uses logistic\nregression with lasso penalization to select the subset of ranges for each\nfeature. The model training is completely automated and doesn't require expert\nknowledge like other early warning scores. We compare our model to the Modified\nEarly Warning Score (MEWS) and quick SOFA (qSOFA), commonly used in hospitals.\nWe show that our model outperforms these models in the area under the receiver\noperating characteristic curve (AUROC) while having a similar or better median\ndetection time on all clinical events, even when using fewer features. Unlike\nMEWS and qSOFA, our model can be entirely automated without requiring any\nmanually recorded features. We also show that discretization improves model\nperformance by comparing our model to a baseline logistic regression model.\n"}
+{"abstract": "  Online advertising fuels the (seemingly) free internet. However, although\nusers can access most of the web services free of charge, they pay a heavy\ncoston their privacy. They are forced to trust third parties and\nintermediaries, who not only collect behavioral data but also absorb great\namounts of ad revenues. Consequently, more and more users opt out from\nadvertising by resorting to ad blockers, thus costing publishers millions of\ndollars in lost ad revenues. Albeit there are various privacy-preserving\nadvertising proposals (e.g.,Adnostic, Privad, Brave Ads) from both academia and\nindustry, they all rely on centralized management that users have to blindly\ntrust without being able to audit, while they also fail to guarantee the\nintegrity of the per-formance analytics they provide to advertisers.\n  In this paper, we design and deploy THEMIS, a novel, decentralized and\nprivacy-by-design ad platform that requires zero trust by users. THEMIS (i)\nprovides auditability to its participants, (ii) rewards users for viewing ads,\nand (iii) allows advertisers to verify the performance and billing reports of\ntheir ad campaigns. By leveraging smart contracts and zero-knowledge schemes,\nwe implement a prototype of THEMIS and early performance evaluation results\nshow that it can scale linearly on a multi sidechain setup while it supports\nmore than 51M users on a single-sidechain.\n"}
+{"abstract": "  The separability of the Hamilton-Jacobi equation has a well-known connection\nto the existence of Killing vectors and rank-two Killing tensors. This paper\ncombines this connection with the detailed knowledge of the compactification\nmetrics of consistent truncations on spheres. The fact that both the inverse\nmetric of such compactifications, as well as the rank-two Killing tensors can\nbe written in terms of bilinears of Killing vectors on the underlying \"round\nmetric,\" enables us to perform a detailed analyses of the separability of the\nHamilton-Jacobi equation for consistent truncations. We introduce the idea of a\nseparating isometry and show that when a consistent truncation, without\nreduction gauge vectors, has such an isometry, then the Hamilton-Jacobi\nequation is always separable. When gauge vectors are present, the gauge group\nis required to be an abelian subgroup of the separating isometry to not impede\nseparability. We classify the separating isometries for consistent truncations\non spheres, $S^n$, for $n=2, \\dots, 7$, and exhibit all the corresponding\nKilling tensors. These results may be of practical use in both identifying when\nsupergravity solutions belong to consistent truncations and generating\nseparable solutions amenable to scalar probe calculations. Finally, while our\nprimary focus is the Hamilton-Jacobi equation, we also make some remarks about\nseparability of the wave equation.\n"}
+{"abstract": "  Li-ion batteries with nickel-manganese-cobalt (NMC) cathode and graphite\nanode are popularly used in portable electronic devices and electric vehicles.\nCalendar loss of the lithium ion battery is a dominating factor in battery\ndegradation. However, few modeling work was reported on studying the calendar\ncapacity loss of NMC-graphite Li-ion batteries. In this work, an\nelectrochemical model for NMC-graphite Li-ion battery was developed to\ninvestigate its calendar loss behavior. Various factors affecting the calendar\nloss of the NMC-graphite batteries were systematically investigated, with the\nresults validated with experimental data of a Sanyo 18,650 cylindrical cell. It\nwas found that at 25 o C working temperature and 100% SOC, the capacity drops\n6.4% of its original capacity after 10 months. Also, when the anode particle\nsize decreases from 26.2 {\\mu}m to 6.55 {\\mu}m, the capacity drop ratio is over\n22% after 10 months under the same operation condition. Our simulation results\ndemonstrate that a smaller SOC, a lower cell working temperature and a larger\nparticle size could prolong the battery life during the storage period. This\nmodeling work can help better understand the calendar loss behavior of\nNMC-graphite Li-ion batteries, and serve as a robust reference for the battery\nperformance optimization in future.\n"}
+{"abstract": "  Clustering in high-dimensions poses many statistical challenges. While\ntraditional distance-based clustering methods are computationally feasible,\nthey lack probabilistic interpretation and rely on heuristics for estimation of\nthe number of clusters. On the other hand, probabilistic model-based clustering\ntechniques often fail to scale and devising algorithms that are able to\neffectively explore the posterior space is an open problem. Based on recent\ndevelopments in Bayesian distance-based clustering, we propose a hybrid\nsolution that entails defining a likelihood on pairwise distances between\nobservations. The novelty of the approach consists in including both cohesion\nand repulsion terms in the likelihood, which allows for cluster\nidentifiability. This implies that clusters are composed of objects which have\nsmall \"dissimilarities\" among themselves (cohesion) and similar dissimilarities\nto observations in other clusters (repulsion). We show how this modelling\nstrategy has interesting connection with existing proposals in the literature\nas well as a decision-theoretic interpretation. The proposed method is\ncomputationally efficient and applicable to a wide variety of scenarios. We\ndemonstrate the approach in a simulation study and an application in digital\nnumismatics.\n"}
+{"abstract": "  In this talk, we have reviewed the recent development on the time evolution\nof lepton family number carried by Majorana neutrinos \\cite{Adam:2021qiq}. This\narticle focuses on the subtle points of the derivation of the lepton family\nnumbers and their time evolution. We also show how the time evolution is\nsensitive to $m_{ee}$ and $m_{e\\mu}$ components of the effective Majorana mass\nmatrix by applying the formula to the two family case. The dependence on the\nMajorana phase is clarified and the implication on CNB (cosmic neutrino\nbackground) is also discussed.\n"}
+{"abstract": "  Calculating polarizabilities of large clusters with first-principles\ntechniques is challenging because of the unfavorable scaling of computational\ncost with cluster size. To address this challenge, we demonstrate that\npolarizabilities of large hydrogenated silicon clusters containing thousands of\natoms can be efficiently calculated with machine learning methods.\nSpecifically, we construct machine learning models based on the smooth overlap\nof atomic positions (SOAP) descriptor and train the models using a database of\ncalculated random-phase approximation polarizabilities for clusters containing\nup to 110 silicon atoms. We first demonstrate the ability of the machine\nlearning models to fit the data and then assess their ability to predict\ncluster polarizabilities using k-fold cross validation. Finally, we study the\nmachine learning predictions for clusters that are too large for explicit\nfirst-principles calculations and find that they accurately describe the\ndependence of the polarizabilities on the ratio of hydrogen to silicon atoms\nand also predict a bulk limit that is in good agreement with previous studies.\n"}
+{"abstract": "  In this paper, we introduce the notion of binomial edge ideals of a clutter\nand obtain results similar to those obtained for graphs by Rauf \\& Rinaldo in\n\\cite{raufrin}. We also answer a question posed in their paper.\n"}
+{"abstract": "  We examine the impact of baryonic physics on the halo distribution in\nhydrodynamic simulations (Illustris, IllustrisTNG, and EAGLE), particularly\nwith regards to how it differs from that in dark matter only (DMO) simulations.\nWe find that, in general, DMO simulations produce halo mass functions (HMF)\nthat are shifted to higher halo masses than their hydrodynamic counterparts,\ndue to the lack of baryonic physics. However, the exact nature of this mass\nshift is a complex function of mass, halo definition, redshift, and\nlarger-scale environment, and it also depends on the specifics of the baryonic\nphysics implemented in the simulation. We present fitting formulae for the\ncorrections one would need to apply to each DMO halo catalogue in order to\nreproduce the HMF found in its hydrodynamic counterpart. We provide these\nformulae for all three simulations, for five different halo definitions at\nredshifts 0, 1, and 2. Additionally, we explore the dependence on environment\nof this HMF discrepancy, and find that, in most cases, halos in low density\nenvironments are slightly more impacted by baryonic physics than halos in high\ndensity environments. We thus also provide environment-dependent mass\ncorrection formulae that can reproduce the conditional, as well as global, HMF.\nWe show that our mass corrections also repair the large-scale clustering of\nhalos, though the environment-dependent corrections are required to achieve an\naccuracy better than 2%. Finally, we examine the impact of baryonic physics on\nthe halo mass - concentration relation, and find that its slope in hydrodynamic\nsimulations is consistent with that in DMO simulations. Ultimately, we\nrecommend that any future work relying on DMO halo catalogues incorporate our\nmass corrections to test the robustness of their results to baryonic effects.\n"}
+{"abstract": "  The mid-depth ocean stratification was fitted by Munk (1966) to an\nexponential profile and shown to be consistent with a vertical\nadvective-diffusive balance. However, tracer release experiments show that\nvertical diffusivity in the mid-depth ocean is an order of magnitude too small\nto explain the observed 1 km exponential scale. Alternative mechanisms\nsuggested that the overturning is mostly adiabatic, that interior diapycnal\nupwelling is negligible, and that nearly all mid-depth water upwells\nadiabatically in the Southern Ocean (SO). In this picture, SO eddies and wind\nset isopycnal slopes in the SO and therefore determine a non-vanishing\nmid-depth interior stratification even in the adiabatic limit. The effect of SO\neddies on SO isopycnal slopes can be understood via either a marginal\ncriticality condition or via a near-vanishing residual overturning conditions\nin the adiabatic limit. We examine the role of SO eddies vs interior mixing in\nsetting the mid-depth stratification by using eddy-permitting numerical\nsimulations, in which we artificially change the diapycnal mixing only away\nfrom the SO. We find that SO isopycnal slopes change in response to changes of\nthe interior diapycnal mixing even when the wind forcing is constant,\nconsistent with previous studies. However, in the limit of small interior\nmixing, the interior stratification is far from exponential, suggesting that SO\nprocesses alone do not lead to the observed stratification. The results suggest\nthat while SO eddies contribute to the non-vanishing mid-depth interior\nstratification, the exponential shape of the stratification must also involve\ninterior diapycnal mixing. Both SO eddies and interior diapycnal mixing are\ntherefore important in determining the interior mid-depth exponential\nstratification.\n"}
+{"abstract": "  The convolutional neural networks (CNNs) trained on ILSVRC12 ImageNet were\nthe backbone of various applications as a generic classifier, a feature\nextractor or a base model for transfer learning. This paper describes automated\nheuristics based on model consensus, explainability and confident learning to\ncorrect labeling mistakes and remove ambiguous images from this dataset. After\nmaking these changes on the training and validation sets, the ImageNet-Clean\nimproves the model performance by 2-2.4 % for SqueezeNet and EfficientNet-B0\nmodels. The results support the importance of larger image corpora and\nsemi-supervised learning, but the original datasets must be fixed to avoid\ntransmitting their mistakes and biases to the student learner. Further\ncontributions describe the training impacts of widescreen input resolutions in\nportrait and landscape orientations. The trained models and scripts are\npublished on Github (https://github.com/kecsap/imagenet-clean) to clean up\nImageNet and ImageNetV2 datasets for reproducible research.\n"}
+{"abstract": "  All-sky monitors can measure the fluxes of astrophysical sources by measuring\nthe changes in observed counts as the source is occulted by the Earth. Such\nmeasurements have typically been carried out by all-sky monitors like\n$\\textit{CGRO}$-BATSE and $\\textit{Fermi}$-GBM. We demonstrate for the first\ntime the application of this technique to measure fluxes of sources using a\ncollimated instrument: the Cadmium Zinc Telluride detector on\n$\\textit{AstroSat}$. Reliable flux measurements are obtained for the Crab\nnebula and pulsar, and for Cyg X-1 by carefully selecting the best occultation\ndata sets. We demonstrate that CZTI can obtain such measurements for hard\nsources with intensities $\\gtrsim1$Crab.\n"}
+{"abstract": "  Speaker extraction algorithm emulates human's ability of selective attention\nto extract the target speaker's speech from a multi-talker scenario. It\nrequires an auxiliary stimulus to form the top-down attention towards the\ntarget speaker. It has been well studied to use a reference speech as the\nauxiliary stimulus. Visual cues also serve as an informative reference for\nhuman listening. They are particularly useful in the presence of acoustic noise\nand interference speakers. We believe that the temporal synchronization between\nspeech and its accompanying lip motion is a direct and dominant audio-visual\ncue. In this work, we aim to emulate human's ability of visual attention for\nspeaker extraction based on speech-lip synchronization. We propose a\nself-supervised pre-training strategy, to exploit the speech-lip\nsynchronization in a multi-talker scenario. We transfer the knowledge from the\npre-trained model to a speaker extraction network. We show that the proposed\nspeaker extraction network outperforms various competitive baselines in terms\nof signal quality and perceptual evaluation, achieving state-of-the-art\nperformance.\n"}
+{"abstract": "  High gain parametric amplifier with a single-pass pulsed pump is known to\ngenerate broadband twin photon fields that are entangled in amplitude and phase\nbut have complicated spectral correlation. Fortunately, they can be decomposed\ninto independent temporal modes. But the common treatment of parametric\ninteraction Hamiltonian does not consider the issue of time ordering problem of\ninteraction Hamiltonian and thus leads to incorrect conclusion that the mode\nstructure and the temporal mode functions do not change as the gain increases.\nIn this paper, we use an approach that is usually employed for treating\nnonlinear interferometers and avoids the time ordering issue. This allows us to\nderive an evolution equation in differential-integral form. Numerical solutions\nfor high gain situation indicate a gain-dependent mode structure that has its\nmode distributions changed and mode functions broadened as the gain increases.\n"}
+{"abstract": "  This work addresses friction-induced modal interactions in jointed\nstructures, and their effects on the passive mitigation of vibrations by means\nof friction damping. Under the condition of (nearly) commensurable natural\nfrequencies, the nonlinear character of friction can cause so-called nonlinear\nmodal interactions. If harmonic forcing near the natural frequency of a\nspecific mode is applied, for instance, another mode may be excited due to\nnonlinear energy transfer and thus contribute considerably to the vibration\nresponse. We investigate how this phenomenon affects the performance of\nfriction damping. To this end, we study the steady-state, periodic forced\nvibrations of a system of two beams connected via a local mechanical friction\njoint. The system can be tuned to continuously adjust the ratio between the\nfirst two natural frequencies in the range around the $1:3$ internal resonance,\nin order to trigger or suppress the emergence of modal interactions. Due to the\nre-distribution of the vibration energy, the vibration level can in fact be\nreduced in certain situations. However, in other situations, the multi-harmonic\ncharacter of the vibration has detrimental effects on the effective damping\nprovided by the friction joint. The resulting response level can be\nsignificantly larger than in the absence of modal interactions. Moreover, it is\nshown that the vibration behavior is highly sensitive in the neighborhood of\ninternal resonances. It is thus concluded that the condition of internal\nresonance should be avoided in the design of friction-damped systems.\n"}
+{"abstract": "  Full insight into the dynamics of a coupled quantum system depends on the\nability to follow the effect of a local excitation in real-time. Here, we trace\nthe coherent evolution of a pair of coupled atomic spins by means of scanning\ntunneling microscopy. We use a pump-probe scheme to detect the local\nmagnetization following a current-induced excitation performed on one of the\nspins. Making use of magnetic interaction with the probe tip, we are able to\ntune the relative precession of the spins. We show that only if their Larmor\nfrequencies match, the two spins can entangle, causing the excitation to be\nswapped back and forth. These results provide insight into the locality of\nelectron-spin scattering, and set the stage for controlled migration of a\nquantum state through an extended spin lattice.\n"}
+{"abstract": "  When observations must come from incompatible devices and cannot be produced\nby compatible devices? This question motivates two integer valued\nquantifications of incompatibility, called incompatibility dimension and\ncompatibility dimension. The first one quantifies how many states are minimally\nneeded to detect incompatibility if the test states are chosen carefully,\nwhereas the second one quantifies how many states one may have to use if they\nare randomly chosen. With concrete examples we show that these quantities have\nunexpected behaviour with respect to noise.\n"}
+{"abstract": "  Lifelong Learning (LL) refers to the ability to continually learn and solve\nnew problems with incremental available information over time while retaining\nprevious knowledge. Much attention has been given lately to Supervised Lifelong\nLearning (SLL) with a stream of labelled data. In contrast, we focus on\nresolving challenges in Unsupervised Lifelong Learning (ULL) with streaming\nunlabelled data when the data distribution and the unknown class labels evolve\nover time. Bayesian framework is natural to incorporate past knowledge and\nsequentially update the belief with new data. We develop a fully Bayesian\ninference framework for ULL with a novel end-to-end Deep Bayesian Unsupervised\nLifelong Learning (DBULL) algorithm, which can progressively discover new\nclusters without forgetting the past with unlabelled data while learning latent\nrepresentations. To efficiently maintain past knowledge, we develop a novel\nknowledge preservation mechanism via sufficient statistics of the latent\nrepresentation for raw data. To detect the potential new clusters on the fly,\nwe develop an automatic cluster discovery and redundancy removal strategy in\nour inference inspired by Nonparametric Bayesian statistics techniques. We\ndemonstrate the effectiveness of our approach using image and text corpora\nbenchmark datasets in both LL and batch settings.\n"}
+{"abstract": "  For each of the simple Lie algebras $\\mathfrak{g}=A_l$, $D_l$ or $E_6$, we\nshow that the all-genera one-point FJRW invariants of $\\mathfrak{g}$-type,\nafter multiplication by suitable products of Pochhammer symbols, are the\ncoefficients of an algebraic generating function and hence are integral.\nMoreover, we find that the all-genera invariants themselves coincide with the\ncoefficients of the unique calibration of the Frobenius manifold of\n$\\mathfrak{g}$-type evaluated at a special point. For the $A_4$ (5-spin) case\nwe also find two other normalizations of the sequence that are again integral\nand of at most exponential growth, and hence conjecturally are the Taylor\ncoefficients of some period functions.\n"}
+{"abstract": "  Game theory deals with strategic interactions among players and evolutionary\ngame dynamics tracks the fate of the players' populations under selection. In\nthis paper, we consider the replicator equation for two-player-two-strategy\ngames involving cooperators and defectors. We modify the equation to include\nthe effect of mutation and also delay that corresponds either to the delayed\ninformation about the population state or in realizing the effect of\ninteraction among players. By focusing on the four exhaustive classes of\nsymmetrical games -- the Stag Hunt game, the Snowdrift game, the Prisoners'\nDilemma game, and the Harmony game -- we analytically and numerically analyze\ndelayed replicator-mutator equation to find the explicit condition for the Hopf\nbifurcation bringing forth stable limit cycle. The existence of the\nasymptotically stable limit cycle imply the coexistence of the cooperators and\nthe defectors; and in the games, where defection is a stable Nash strategy, a\nstable limit cycle does provide a mechanism for evolution of cooperation. We\nfind that while mutation alone can never lead to oscillatory cooperation state\nin two-player-two-strategy games, the delay can change the scenario. On the\nother hand, there are situations when delay alone cannot lead to the Hopf\nbifurcation in the absence of mutation in the selection dynamics.\n"}
+{"abstract": "  Through an exact analysis, we show the existence of Mpemba effect in an\nanisotropically driven inelastic Maxwell gas, a simplified model for granular\ngases, in two dimensions. Mpemba effect refers to the couterintuitive\nphenomenon of a hotter system relaxing to the steady state faster than a cooler\nsystem, when both are quenched to the same lower temperature. The Mpemba effect\nhas been illustrated in earlier studies on isotropically driven granular gases,\nbut its existence requires non-stationary initial states, limiting experimental\nrealisation. In this paper, we demonstrate the existence of the Mpemba effect\nin anisotropically driven granular gases even when the initial states are\nnon-equilibrium steady states. The precise conditions for the Mpemba effect,\nits inverse, and the stronger version, where the hotter system cools\nexponentially faster are derived.\n"}
+{"abstract": "  For a Weyl group $W$ of rank $r$, the $W$-Catalan number is the number of\nantichains of the poset of positive roots, and the $W$-Narayana numbers refine\nthe $W$-Catalan number by keeping track of the cardinalities of these\nantichains. The $W$-Narayana numbers are symmetric, i.e., the number of\nantichains of cardinality $k$ is the same as the number of cardinality $r-k$.\nHowever, this symmetry is far from obvious. Panyushev posed the problem of\ndefining an involution on root poset antichains that exhibits the symmetry of\nthe $W$-Narayana numbers.\n  Rowmotion and rowvacuation are two related operators, defined as compositions\nof \"toggles,\" that give a dihedral action on the set of antichains of any\nranked poset. Rowmotion acting on root posets has been the subject of a\nsignificant amount of research in the recent past. We prove that for the root\nposets of classical types, rowvacuation is Panyushev's desired involution.\n"}
+{"abstract": "  In the research field of big data, one of important issues is how to recover\nthe sequentially changing sets of true features when the data sets arrive\nsequentially. The paper presents a general framework for online updating\nvariable selection and parameter estimation in generalized linear models with\nstreaming datasets. This is a type of online updating penalty likelihoods with\ndifferentiable or non-differentiable penalty function. The online updating\ncoordinate descent algorithm is proposed to solve the online updating\noptimization problem. Moreover, a tuning parameter selection is suggested in an\nonline updating way. The selection and estimation consistencies, and the oracle\nproperty are established, theoretically. Our methods are further examined and\nillustrated by various numerical examples from both simulation experiments and\na real data analysis.\n"}
+{"abstract": "  This work deals with the problem of designing observers for the estimation of\na single function of the states for discrete-time nonlinear systems. Necessary\nand sufficient conditions for the existence of lower order functional observers\nwith linear dynamics and linear output map are derived. The results provide a\ndirect generalization to Luenberger's linear theory of functional observers.\nThe design methodology is tested on a non-isothermal CSTR case study.\n"}
+{"abstract": "  We use machine learning methods for an automated classification of\nexperimental XPCS two-time correlation functions from an arrested liquid-liquid\nphase separation of a protein solution. We couple simulations based on the\nCahn-Hilliard equation with a glass transition scenario and classify the\nmeasured correlation maps automatically according to quench depth and critical\nconcentration at a glass/gel transition. We introduce routines and\nmethodologies using an autoencoder network and a differential evolution based\nalgorithm for classification of the measured correlation functions. The here\npresented method is a first step towards handling large amounts of dynamic data\nmeasured at high brilliance synchrotron and X-ray free-electron laser sources\nfacilitating fast comparison to phase field models of phase separation.\n"}
+{"abstract": "  We introduce a general theory on stationary approximations for locally\nstationary continuous-time processes. Based on the stationary approximation, we\nuse $\\theta$-weak dependence to establish laws of large numbers and central\nlimit type results under different observation schemes. Hereditary properties\nfor a large class of finite and infinite memory transformations show the\nflexibility of the developed theory. Sufficient conditions for the existence of\nstationary approximations for time-varying L\\'evy-driven state space models are\nderived and compared to existing results. We conclude with comprehensive\nresults on the asymptotic behavior of the first and second order localized\nsample moments of time-varying L\\'evy-driven state space models.\n"}
+{"abstract": "  We experimentally investigate the suitability of a multi-path waveguide\ninterferometer with mechanical shutters for performing a test for hypercomplex\nquantum mechanics. Probing the interferometer with coherent light we\nsystematically analyse the influence of experimental imperfections that could\nlead to a false-positive test result. In particular, we analyse the effects of\ndetector nonlinearity, input-power and phase fluctuations on different\ntimescales, closed-state transmissivity of shutters and crosstalk between\ndifferent interferometer paths. In our experiment, a seemingly small shutter\ntransmissivity in the order of about $2 \\times 10^{-4}$ is the main source of\nsystematic error, which suggests that this is a key imperfection to monitor and\nmitigate in future experiments.\n"}
+{"abstract": "  For a large class of transitive non-hyperbolic systems, we construct\nnonhyperbolic ergodic measures with entropy arbitrarily close to its maximal\npossible value. The systems we consider are partially hyperbolic with\none-dimension central direction for which there are positive entropy ergodic\nmeasures whose central Lyapunov exponent is negative, zero, or positive. We\nconstruct ergodic measures with zero central Lyapunov exponent whose entropy is\npositive and arbitrarily close to the topological entropy of the set of points\nwith central Lyapunov exponent zero. This provides a restricted variational\nprinciple for nonhyperbolic (zero exponent) ergodic measures.\n  The result is applied to the setting of $\\mathrm{SL}(2,\\mathbb R)$ matrix\ncocycles and provides a counterpart to Furstenberg's classical result: for an\nopen and dense subset of elliptic $\\mathrm{SL}(2,\\mathbb R)$ cocycles we\nconstruct ergodic measures with upper Lyapunov exponent zero and with metric\nentropy arbitrarily close to the topological entropy of the set of infinite\nmatrix products with subexponential growth of the norm.\n"}
+{"abstract": "  In this paper, we present the design of a new chaotic map circuit with a 65nm\nCMOS process. This chaotic map circuit uses a dynamic parameter-control\ntopology and generates a wide chaotic range. We propose two designs of dynamic\nparameter-controlled chaotic map (DPCCM)-based pseudo-random number generators\n(PRNG). The randomness of the generated sequence is verified using three\ndifferent statistical tests, namely, NIST SP 800-22 test, FIPS PUB 140-2 test,\nand Diehard test. Our first design offers a throughput of 200 MS/s with an\non-chip area of 0.024mm2 and a power consumption of 2.33mW. The throughput of\nour second design is 300 MS/s with an area consumption of 0.132mm2 and power\nconsumption of 2.14mW. The wider chaotic range and lower-overhead, offered by\nour designs, can be highly suitable for various applications such as, logic\nobfuscation, chaos-based cryptography, re-configurable random number\ngeneration,and hard-ware security for resource-constrained edge devices like\nIoT.\n"}
+{"abstract": "  Recent advances in experiment and theory suggest that superfluid $^3$He under\nplanar confinement may form a pair-density wave (PDW) whereby superfluid and\ncrystalline orders coexist. While a natural candidate for this phase is a\nunidirectional stripe phase predicted by Vorontsov and Sauls in 2007, recent\nnuclear magnetic resonance measurements of the superfluid order parameter\nrather suggest a two-dimensional PDW with noncollinear wavevectors, of possibly\nsquare or hexagonal symmetry. In this work, we present a general mechanism by\nwhich a PDW with the symmetry of a triangular lattice can be stabilized, based\non a superfluid generalization of Landau's theory of the liquid-solid\ntransition. A soft-mode instability at finite wavevector within the\ntranslationally invariant planar-distorted B phase triggers a transition from\nuniform superfluid to PDW that is first order due to a cubic term generally\npresent in the PDW free-energy functional. This cubic term also lifts the\ndegeneracy of possible PDW states in favor of those for which wavevectors add\nto zero in triangles, which in two dimensions uniquely selects the triangular\nlattice.\n"}
+{"abstract": "  Email threat is a serious issue for enterprise security, which consists of\nvarious malicious scenarios, such as phishing, fraud, blackmail and\nmalvertisement. Traditional anti-spam gateway commonly requires to maintain a\ngreylist to filter out unexpected emails based on suspicious vocabularies\nexisted in the mail subject and content. However, the signature-based approach\ncannot effectively discover novel and unknown suspicious emails that utilize\nvarious hot topics at present, such as COVID-19 and US election. To address the\nproblem, in this paper, we present Holmes, an efficient and lightweight\nsemantic based engine for anomalous email detection. Holmes can convert each\nevent log of email to a sentence through word embedding then extract\ninteresting items among them by novelty detection. Based on our observations,\nwe claim that, in an enterprise environment, there is a stable relation between\nsenders and receivers, but suspicious emails are commonly from unusual sources,\nwhich can be detected through the rareness selection. We evaluate the\nperformance of Holmes in a real-world enterprise environment, in which it sends\nand receives around 5,000 emails each day. As a result, Holmes can achieve a\nhigh detection rate (output around 200 suspicious emails per day) and maintain\na low false alarm rate for anomaly detection.\n"}
+{"abstract": "  The bottom quark forward-backward asymmetry ($A_{FB}^b$) data at LEP exhibits\na long-standing discrepancy with the standard model prediction. We propose a\nnovel method to probe the $Zb\\bar{b}$ interactions through $gg\\to Zh$\nproduction at the LHC, which is sensitive to the axial-vector component of the\n$Zb\\bar{b}$ couplings. We demonstrate that the $Zh$ data collected at the 13\nTeV LHC can already resolve the apparent degeneracy of the anomalous\n$Zb\\bar{b}$ couplings implied by the LEP precision electroweak measurements,\nwith a strong dependence on the observed distribution of the $Z$ boson\ntransverse momentum. We also show the potential of the HL-LHC to either verify\nor exclude the anomalous $Zb\\bar{b}$ couplings observed at LEP through\nmeasuring the $Zh$ production rate at the HL-LHC, and this conclusion is not\nsensitive to possible new physics contribution induced by top quark or Higgs\nboson anomalous couplings in the loop.\n"}
+{"abstract": "  In this paper we study pricing of American put options on the Black and\nScholes market with a stochastic interest rate and finite-time maturity. We\nprove that the option value is a $C^1$ function of the initial time, interest\nrate and stock price. By means of Ito calculus we rigorously derive the option\nvalue's early exercise premium formula and the associated hedging portfolio. We\nprove the existence of an optimal exercise boundary splitting the state space\ninto continuation and stopping region. The boundary has a parametrisation as a\njointly continuous function of time and stock price, and it is the unique\nsolution to an integral equation which we compute numerically. Our results hold\nfor a large class of interest rate models including CIR and Vasicek models. We\nshow a numerical study of the option price and the optimal exercise boundary\nfor Vasicek model.\n"}
+{"abstract": "  In this paper, we propose a unified approach for solving structure-preserving\neigenvalue embedding problem (SEEP) for quadratic regular matrix polynomials\nwith symmetry structures. First, we determine perturbations of a quadratic\nmatrix polynomial, unstructured or structured, such that the perturbed\npolynomials reproduce a desired invariant pair while maintaining the invariance\nof another invariant pair of the unperturbed polynomial. If the latter is\nunknown, it is referred to as no spillover perturbation. Then we use these\nresults for solving the SEEP for structured quadratic matrix polynomials that\ninclude: symmetric, Hermitian, $\\star$-even and $\\star$-odd quadratic matrix\npolynomials. Finally, we show that the obtained analytical expressions of\nperturbations can realize existing results for structured polynomials that\narise in real-world applications, as special cases. The obtained results are\nsupported with numerical examples.\n"}
+{"abstract": "  This study aims to construct a theoretical formulation of a nonequilibrium\nprocess for a system of Bose--Einstein condensate associated with a spontaneous\nsymmetry breakdown. For this, Thermo Field Dynamics is used. We then describe\nthe most general forms of a 4x4 transformation and an unperturbed Hamiltonian.\nAfter calculating the $4\\times 4$ self-energy and identifying its on-shell, we\nimpose the renormalization condition in which the entire on-shell self-energy\nshould vanish. This condition provides a sufficient number of independent\nequations to determine all of the parameters in an unperturbed Hamiltonian,\namong which the quantum transport equations for normal and anomalous number\ndistributions are included.\n"}
+{"abstract": "  IMSI catchers have been a long standing and serious privacy problem in pre-5G\nmobile networks. To tackle this, 3GPP introduced the Subscription Concealed\nIdentifier (SUCI) and other countermeasures in 5G. In this paper, we analyze\nthe new SUCI mechanism and discover that it provides very poor anonymity when\nused with the variable length Network Specific Identifiers (NSI), which are\npart of the 5G standard. When applied to real-world name length data, we see\nthat SUCI only provides 1-anonymity, meaning that individual subscribers can\neasily be identified and tracked. We strongly recommend 3GPP and GSMA to\nstandardize and recommend the use of a padding mechanism for SUCI before\nvariable length identifiers get more commonly used. We further show that the\npadding schemes, commonly used for network traffic, are not optimal for padding\nof identifiers based on real names. We propose a new improved padding scheme\nthat achieves much less message expansion for a given $k$-anonymity.\n"}
+{"abstract": "  Automatically identifying harmful content in video is an important task with\na wide range of applications. However, due to the difficulty of collecting\nhigh-quality labels as well as demanding computational requirements, the task\nhas not yet had a fully general approach. Typically, only small subsets of the\nproblem are considered, such as identifying violent content. In cases where the\ngeneral problem is tackled, approximations and simplifications are made to deal\nwith the lack of labels and computational complexity. In this work, we identify\nand tackle some of the main obstacles. First, we create an open dataset of 3589\nvideo clips from film trailers and annotated by professionals in the field.\nSecond, we perform an analysis of our constructed dataset, investigating among\nother things the relation between clip and trailer level annotations. Lastly,\nwe train audiovisual models on our dataset and conduct an in-depth study on our\nmodeling choices. We find that results greatly improve by combining the visual\nand audio modality and that pre-training on large-scale video recognition\ndatasets as well as class balanced sampling further improves performance.\nFurther details of our dataset is available at this webpage:\nhttps://vidharm.github.io/.\n"}
+{"abstract": "  In this paper, we study the trade-off between the transmission cost and the\ncontrol performance of the multi-loop networked control system subject to\nnetwork-induced delay. Within the linear-quadratic-Gaussian (LQG) framework,\nthe joint design of control policy and networking strategy is decomposed into\nseparation optimization problems. Based on the trade-off analysis, a scalable,\ndelay-dependent Value-of-Information (VoI) based scheduling policy is\nconstructed to quantify the value of transmitting the data packet, and enables\nthe decision-makers embedded in subsystems to determine the transmission\npolicy. The proposed scalable VoI inherits the task criticality of the previous\nVoI metric meanwhile is sensitive to the system parameters such as information\nfreshness and network delays. The VoI-based scheduling policy is proved to\noutperform the periodical triggering policy and existing Age-of-Information\n(AoI) based policy for network control system under transmission delay. The\neffectiveness of the constructed VoI with arbitrary network delay is validated\nthrough numerical simulations.\n"}
+{"abstract": "  The electronic properties of $\\pi$-conjugated two-dimensional (2D) polymers\nnear the Fermi level are determined by structural topology and chemical\ncomposition. Thus, tight-binding (TB) calculations of the corresponding\nfundamental network can be used to explore the parameter space to find\nconfigurations with intriguing properties before designing the the atomistic 2D\npolymer network. The vertex-transitive \\textbf{fes} lattice, which is also\ncalled square-octagon lattice, is rich in interesting topological features\nincluding Dirac points and flat bands. Herein, we study its electronic and\ntopological properties within the TB framework using representative parameters\nfor chemical systems. Secondly, we demonstrate that the rational implementation\nof band structure features obtained from TB calculations into 2D polymers is\nfeasible with a family of 2D polymers possessing \\textbf{fes} structure. A\none-to-one band structure correspondence between fundamental network and 2D\npolymers is found. Moreover, changing the relative length of linkers connecting\nthe triangulene units in the 2D polymers reflect tuning of hopping parameters\nin the TB model. These perturbations allow to open sizeable local band gaps at\nvarious positions in the Brillouin zone. From analysis of Berry curvature flux,\nnone of the polymers exhibits a large topologically non-trivial band gap.\nHowever, we find a particular configuration of semimetallic characteristics\nwith separate electron and hole pockets, which possess very low effective\nmasses both for electrons (as small as $m^*_\\mathrm{e} = 0.05$) and holes (as\nsmall as $m^*_\\mathrm{h} = 0.01$).\n"}
+{"abstract": "  We investigate the generation of entanglement through a quantum dot molecule\nunder the influence of vibrational phonon modes in a bias voltage junction. The\nmolecular quantum dot system is realized by coupled quantum dots inside a\nsuspended carbon nanotube. We consider the dynamical entanglement as a function\nof bias voltage and temperature by taking into account the electron-phonon\ninteraction. In order to generate the robust entanglement between quantum dots\nand preserve it to reach the maximal achievable amount steadily, we introduce\nan asymmetric coupling protocol and apply the easy tunable bias voltage-driven\nfield. For an oscillating bias voltage, the time-varying entanglement can\nperiodically reach the maximum revival. In thermal entanglement dynamics, the\nphenomena of thermal entanglement degradation and thermal entanglement revival\nare observed which are intensively affected by the strength of phonon\ndecoherence. The revival of entanglement shows a larger value for a higher\nphonon coupling.\n"}
+{"abstract": "  We consider the fully nonlinear equation with variable-exponent double phase\ntype degeneracies $$ \\big[|Du|^{p(x)}+a(x)|Du|^{q(x)}\\big]F(D^2u)=f(x). $$\nUnder some appropriate assumptions, by making use of geometric tangential\nmethods and combing a refined improvement-of-flatness approach with compactness\nand scaling techniques we obtain the sharp local $C^{1,\\alpha}$ regularity of\nviscosity solutions to such equations.\n"}
+{"abstract": "  We construct an example of a smooth spatial quartic surface that contains 800\nirreducible conics.\n"}
+{"abstract": "  Physics-informed neural networks (PINNs) impose known physical laws into the\nlearning of deep neural networks, making sure they respect the physics of the\nprocess while decreasing the demand of labeled data. For systems represented by\nOrdinary Differential Equations (ODEs), the conventional PINN has a continuous\ntime input variable and outputs the solution of the corresponding ODE. In their\noriginal form, PINNs do not allow control inputs neither can they simulate for\nlong-range intervals without serious degradation in their predictions. In this\ncontext, this work presents a new framework called Physics-Informed Neural Nets\nfor Control (PINC), which proposes a novel PINN-based architecture that is\namenable to \\emph{control} problems and able to simulate for longer-range time\nhorizons that are not fixed beforehand. The framework has new inputs to account\nfor the initial state of the system and the control action. In PINC, the\nresponse over the complete time horizon is split such that each smaller\ninterval constitutes a solution of the ODE conditioned on the fixed values of\ninitial state and control action for that interval. The whole response is\nformed by feeding back the predictions of the terminal state as the initial\nstate for the next interval. This proposal enables the optimal control of\ndynamic systems, integrating a priori knowledge from experts and data collected\nfrom plants into control applications. We showcase our proposal in the control\nof two nonlinear dynamic systems: the Van der Pol oscillator and the four-tank\nsystem.\n"}
+{"abstract": "  Forecasting graph-based time-dependent data has many practical applications.\nThis task is challenging as models need not only to capture spatial dependency\nand temporal dependency within the data, but also to leverage useful auxiliary\ninformation for accurate predictions. In this paper, we analyze limitations of\nstate-of-the-art models on dealing with temporal dependency. To address this\nlimitation, we propose GSA-Forecaster, a new deep learning model for\nforecasting graph-based time-dependent data. GSA-Forecaster leverages graph\nsequence attention (GSA), a new attention mechanism proposed in this paper, for\neffectively capturing temporal dependency. GSA-Forecaster embeds the graph\nstructure of the data into its architecture to address spatial dependency.\nGSA-Forecaster also accounts for auxiliary information to further improve\npredictions. We evaluate GSA-Forecaster with large-scale real-world graph-based\ntime-dependent data and demonstrate its effectiveness over state-of-the-art\nmodels with 6.7% RMSE and 5.8% MAPE reduction.\n"}
+{"abstract": "  Random Forest (RFs) are among the most widely used Machine Learning (ML)\nclassifiers. Even though RFs are not interpretable, there are no dedicated\nnon-heuristic approaches for computing explanations of RFs. Moreover, there is\nrecent work on polynomial algorithms for explaining ML models, including naive\nBayes classifiers. Hence, one question is whether finding explanations of RFs\ncan be solved in polynomial time. This paper answers this question negatively,\nby proving that computing one PI-explanation of an RF is D^P-complete.\nFurthermore, the paper proposes a propositional encoding for computing\nexplanations of RFs, thus enabling finding PI-explanations with a SAT solver.\nThis contrasts with earlier work on explaining boosted trees (BTs) and neural\nnetworks (NNs), which requires encodings based on SMT/MILP. Experimental\nresults, obtained on a wide range of publicly available datasets, demontrate\nthat the proposed SAT-based approach scales to RFs of sizes common in practical\napplications. Perhaps more importantly, the experimental results demonstrate\nthat, for the vast majority of examples considered, the SAT-based approach\nproposed in this paper significantly outperforms existing heuristic approaches.\n"}
+{"abstract": "  Substrates have strong effects on optoelectronic properties of\ntwo-dimensional (2D) materials, which have emerged as promising platforms for\nexotic physical phenomena and outstanding applications. To reliably interpret\nexperimental results and predict such effects at 2D interfaces, theoretical\nmethods accurately describing electron correlation and electron-hole\ninteraction such as first-principles many-body perturbation theory are\nnecessary. In our previous work [Phys. Rev. B 102, 205113(2020)], we developed\nthe reciprocal-space linear interpolation method that can take into account the\neffects of substrate screening for arbitrarily lattice-mismatched interfaces at\nthe GW level of approximation. In this work, we apply this method to examine\nthe substrate effect on excitonic excitation and recombination of 2D materials\nby solving the Bethe-Salpeter equation. We predict the nonrigid shift of 1s and\n2s excitonic peaks due to substrate screening, in excellent agreements with\nexperiments. We then reveal its underlying physical mechanism through 2D\nhydrogen model and the linear relation between quasiparticle gaps and exciton\nbinding energies when varying the substrate screening. At the end, we calculate\nthe exciton radiative lifetime of monolayer hexagonal boron nitride with\nvarious substrates at zero and room temperature, as well as the one of WS2\nwhere we obtain good agreement with experimental lifetime. Our work answers\nimportant questions of substrate effects on excitonic properties of 2D\ninterfaces.\n"}
+{"abstract": "  AC transmission lines with lengths greater than 80km cannot be used to their\nmaximum capacity due to limits in voltage drops and transient stability. This\ninefficient way of using conductors in a transmission line can be overcome if\nthe electrical frequency at which energy is transmitted is reduced. This is why\nthis work focuses on the comparison of the Modular Multilevel Matrix Converter\n(MMMC) and the Back-to-Back Modular Multilevel Converter (BTB-MMC), topologies\nthat have shown qualities as frequency converters. For comparison, an\nanalytical model of each topology is used to relate design considerations to\ntheir operational variables. Among the aspects to be compared are, the\nrequirements of the semiconductors, the required reactive components, operating\nlosses and fault tolerance. Detailed design equations, EMTP simulations, and\ncomparison table are presented.\n"}
+{"abstract": "  Among the Markov chains breaking detailed-balance that have been proposed in\nthe field of Monte-Carlo sampling in order to accelerate the convergence\ntowards the steady state with respect to the detailed-balance dynamics, the\nidea of 'Lifting' consists in duplicating the configuration space into two\ncopies $\\sigma=\\pm$ and in imposing directed flows in each copy in order to\nexplore the configuration space more efficiently. The skew-detailed-balance\nLifted-Markov-chain introduced by K. S. Turitsyn, M. Chertkov and M. Vucelja\n[Physica D Nonlinear Phenomena 240 , 410 (2011)] is revisited for the\nCurie-Weiss mean-field ferromagnetic model, where the dynamics for the\nmagnetization is closed. The large deviations at various levels for empirical\ntime-averaged observables are analyzed and compared with their detailed-balance\ncounterparts, both for the discrete extensive magnetization $M$ and for the\ncontinuous intensive magnetization $m=\\frac{M}{N}$ for large system-size $N$.\n"}
+{"abstract": "  We propose a new fast fully unsupervised method to discover semantic\npatterns. Our algorithm is able to hierarchically find visual categories and\nproduce a segmentation mask where previous methods fail. Through the modeling\nof what is a visual pattern in an image, we introduce the notion of \"semantic\nlevels\" and devise a conceptual framework along with measures and a dedicated\nbenchmark dataset for future comparisons. Our algorithm is composed by two\nphases. A filtering phase, which selects semantical hotsposts by means of an\naccumulator space, then a clustering phase which propagates the semantic\nproperties of the hotspots on a superpixels basis. We provide both qualitative\nand quantitative experimental validation, achieving optimal results in terms of\nrobustness to noise and semantic consistency. We also made code and dataset\npublicly available.\n"}
+{"abstract": "  Discrete Ginzburg-Landau (DGL) equations with non-local nonlinearities have\nbeen established as significant inherently discrete models in numerous physical\ncontexts, similar to their counterparts with local nonlinear terms. We study\ntwo prototypical examples of non-local and local DGLs on the one-dimensional\ninfinite lattice. For the non-local DGL, we identify distinct scenarios for the\nasymptotic behavior of the globally existing in time solutions depending on\ncertain parametric regimes. One of these scenarios is associated with a\nrestricted compact attractor according to J. K. Hale's definition. We also\nprove the closeness of the solutions of the two models in the sense of a\n\"continuous dependence on their initial data\" in the $l^2$ metric under general\nconditions on the intrinsic linear gain or loss incorporated in the model. As a\nconsequence of the closeness results, in the dissipative regime we establish\nthe congruence of the attractors possessed by the semiflows of the non-local\nand of the local model respectively, for initial conditions in a suitable\ndomain of attraction defined by the non-local system.\n"}
+{"abstract": "  Standard approaches to decision-making under uncertainty focus on sequential\nexploration of the space of decisions. However, \\textit{simultaneously}\nproposing a batch of decisions, which leverages available resources for\nparallel experimentation, has the potential to rapidly accelerate exploration.\nWe present a family of (parallel) contextual linear bandit algorithms, whose\nregret is nearly identical to their perfectly sequential counterparts -- given\naccess to the same total number of oracle queries -- up to a lower-order\n\"burn-in\" term that is dependent on the context-set geometry. We provide\nmatching information-theoretic lower bounds on parallel regret performance to\nestablish our algorithms are asymptotically optimal in the time horizon.\nFinally, we also present an empirical evaluation of these parallel algorithms\nin several domains, including materials discovery and biological sequence\ndesign problems, to demonstrate the utility of parallelized bandits in\npractical settings.\n"}
+{"abstract": "  Magnons enable transferring a magnetic moment or spin over macroscopic\ndistance. In quantum Hall ferromagnet, it has been predicted in the early 90s\nthat spin and charges are entangled, meaning that any change of the spin\ntexture modifies the charge distribution. As a direct consequence of this\nentanglement, magnons carry an electric dipole moment. Here we report the first\nevidence of the existence of this electric dipole moment in a graphene quantum\nHall ferromagnet using a Mach-Zehnder interferometer as a quantum sensor. By\npropagating towards the interferometer through an insulating bulk, the magnon\nelectric dipole moment modifies the Aharonov-Bohm flux through the\ninterferometer, changing both its phase and its visibility. In particular, we\nrelate the phase shift to the sign of this electric dipole moment, and the\nexponential loss of visibility to the flux of emitted magnons. Finally, we\nprobe the emission energy threshold of the magnons close to filling factor v=1.\nApproaching v=0, we observe that the emission energy threshold diminishes\ntowards zero, which might be linked to the existence of gapless mode in the\ncanted-antiferromagnetic (CAF) phase at v=0. The detection and manipulation of\nmagnons based on their electric dipole open the field for a new type of\ncoherent magnon quantum circuits that will be electrostatically controlled.\n"}
+{"abstract": "  We propose a novel approach for modelling bat motion dynamics and use it to\npredict roost locations using data from static acoustic detectors.\nSpecifically, radio tracking studies of Greater Horseshoe bats demonstrate that\nbat movement can be split into two phases: dispersion and return. Dispersion is\neasily understood and can be modelled as simple random motion. The return phase\nis much more complex, as it requires intelligent directed motion and results in\nall agents returning home in a stereotypical manner. Critically, combining\nreaction-diffusion theory and domain shrinking we deterministically and\nstochastically model a ``leap-frogging'' motion, which fits favourably with the\nobserved tracking data.\n"}
+{"abstract": "  We propose self-adaptive training -- a unified training algorithm that\ndynamically calibrates and enhances training processes by model predictions\nwithout incurring an extra computational cost -- to advance both supervised and\nself-supervised learning of deep neural networks. We analyze the training\ndynamics of deep networks on training data that are corrupted by, e.g., random\nnoise and adversarial examples. Our analysis shows that model predictions are\nable to magnify useful underlying information in data and this phenomenon\noccurs broadly even in the absence of any label information, highlighting that\nmodel predictions could substantially benefit the training processes:\nself-adaptive training improves the generalization of deep networks under noise\nand enhances the self-supervised representation learning. The analysis also\nsheds light on understanding deep learning, e.g., a potential explanation of\nthe recently-discovered double-descent phenomenon in empirical risk\nminimization and the collapsing issue of the state-of-the-art self-supervised\nlearning algorithms. Experiments on the CIFAR, STL, and ImageNet datasets\nverify the effectiveness of our approach in three applications: classification\nwith label noise, selective classification, and linear evaluation. To\nfacilitate future research, the code has been made publicly available at\nhttps://github.com/LayneH/self-adaptive-training.\n"}
+{"abstract": "  In this paper, we study the task of source-free domain adaptation (SFDA),\nwhere the source data are not available during target adaptation. Previous\nworks on SFDA mainly focus on aligning the cross-domain distributions. However,\nthey ignore the generalization ability of the pretrained source model, which\nlargely influences the initial target outputs that are vital to the target\nadaptation stage. To address this, we make the interesting observation that the\nmodel accuracy is highly correlated with whether or not attention is focused on\nthe objects in an image. To this end, we propose a generic and effective\nframework based on Transformer, named TransDA, for learning a generalized model\nfor SFDA. Specifically, we apply the Transformer as the attention module and\ninject it into a convolutional network. By doing so, the model is encouraged to\nturn attention towards the object regions, which can effectively improve the\nmodel's generalization ability on the target domains. Moreover, a novel\nself-supervised knowledge distillation approach is proposed to adapt the\nTransformer with target pseudo-labels, thus further encouraging the network to\nfocus on the object regions. Experiments on three domain adaptation tasks,\nincluding closed-set, partial-set, and open-set adaption, demonstrate that\nTransDA can greatly improve the adaptation accuracy and produce\nstate-of-the-art results. The source code and trained models are available at\nhttps://github.com/ygjwd12345/TransDA.\n"}
+{"abstract": "  We study the effects of an attractive interaction between the boron (B) and\nthe nitrogen (N) atoms doped in a bilayer graphene (BLG), BC$_{14}$N, on the\nelectronic, the thermal and the optical properties for two different types of a\ndoping process: First, both the B and the N atoms are doped in the same layer\nwhile the other layer is undoped. Second, the B and N atoms are doped in both\nlayers. An attractive interaction between the B and N atoms does not influence\nthe interlayer interaction in the first case, while it does in the second case.\nWe find that the strong B-N attractive interaction in one layer induces\nmetallic behavior due to the crossing of the valence band and the Fermi energy,\nwhile the strong attractive interaction between both layers induces a\nsemiconductor property arising from the emergence a bandgap. We therefore\nconfirm that the metallic-like BLG is not a good material for thermal devices\nbecause it has a low figure of merit, while we notice that the\nsemiconductor-like BLG has a high Seebeck coefficient and figure of merit as\nwell as a low thermal conductivity. The strong attractive interaction of the\nB-N atoms between the layers gives rise to a prominent peak to appear in\ndielectric function, the excitation and the absorption spectra in the low\nenergy, visible range, while a very weak peak is seen in the case of a strong\nattractive interaction between the B and N doped in one layer. Controlling the\nB and N atomic configurations in the BLG may help to improve the material for\nuse in both thermoelectric and optoelectronic devices.\n"}
+{"abstract": "  We consider a class of exponentials in the Weyl-Heisenberg algebra with\nexponents of type at most linear in coordinates and arbitrary functions of\nmomenta. They are expressed in terms of normal ordering where coordinates stand\nto the left from momenta. Exponents appearing in normal ordered form satisfy\ndifferential equations with boundary conditions that could be solved\nperturbatively order by order. Two propositions are presented for the\nWeyl-Heisenberg algebra in 2 dimensions and their generalizations in higher\ndimensions. These results can be applied to arbitrary noncommutative spaces for\nconstruction of star products, coproducts of momenta and twist operators. They\ncan also be related to the BCH formula.\n"}
+{"abstract": "  Spin waves in magnetic microresonators are at the core of modern magnonics.\nHere we demonstrate a new method of tunable excitation of different spin wave\nmodes in magnetic microdisks by using a train of laser pulses coming at a\nrepetition rate higher than the decay rate of spin precession. The microdisks\nare etched in a transparent bismuth iron garnet film and the light pulses\ninfluence the spins nonthermally through the inverse Faraday effect. The high\nrepetition rate of the laser stimulus of 10 GHz establishes an interplay\nbetween the spin wave resonances in the frequency and momentum domains. As a\nresult, the excitation efficiency of different spin modes can be tuned by a\nsmall variation of the external magnetic field. An additional degree of freedom\nis provided by scanning the laser spot within the microdisk area. This makes\nthe proposed method for spin wave excitation advantageous for the forthcoming\napplication of magnonics for telecommunication and quantum technologies.\n"}
+{"abstract": "  Finding the best mathematical equation to deal with the different challenges\nfound in complex scenarios requires a thorough understanding of the scenario\nand a trial and error process carried out by experts. In recent years, most\nstate-of-the-art equation discovery methods have been widely applied in\nmodeling and identification systems. However, equation discovery approaches can\nbe very useful in computer vision, particularly in the field of feature\nextraction. In this paper, we focus on recent AI advances to present a novel\nframework for automatically discovering equations from scratch with little\nhuman intervention to deal with the different challenges encountered in\nreal-world scenarios. In addition, our proposal can reduce human bias by\nproposing a search space design through generative network instead of\nhand-designed. As a proof of concept, the equations discovered by our framework\nare used to distinguish moving objects from the background in video sequences.\nExperimental results show the potential of the proposed approach and its\neffectiveness in discovering the best equation in video sequences. The code and\ndata are available at:\nhttps://github.com/carolinepacheco/equation-discovery-scene-analysis\n"}
+{"abstract": "  This paper studies sequential search models that (1) incorporate unobserved\nproduct quality, which can be correlated with endogenous observable\ncharacteristics (such as price) and endogenous search cost variables (such as\nproduct rankings in online search intermediaries); and (2) do not require\nresearchers to know the true distribution of the match value between consumers\nand products. A likelihood approach to estimate such models gives biased\nresults. Therefore, I propose a new estimator -- pairwise maximum rank (PMR)\nestimator -- for both preference and search cost parameters. I show that the\nPMR estimator is consistent using only data on consumers' search order among\none pair of products rather than data on consumers' full consideration set or\nfinal purchase. Additionally, we can use the PMR estimator to test for the true\nmatch value distribution in the data. In the empirical application, I apply the\nPMR estimator to quantify the effect of rankings in Expedia hotel search using\ntwo samples of the data set, to which consumers are randomly assigned. I find\nthe position effect to be \\$0.11-\\$0.36, and the effect estimated using the\nsample with randomly generated rankings is close to the effect estimated using\nthe sample with endogenous rankings. Moreover, I find that the true match value\ndistribution in the data is unlikely to be N(0,1). Likelihood estimation\nignoring endogeneity gives an upward bias of at least \\$1.17; misspecification\nof match value distribution as N(0,1) gives an upward bias of at least \\$2.99.\n"}
+{"abstract": "  A left brace is a triple $(\\mathcal{B},+,\\cdot)$, where $(\\mathcal{B},+)$ is\nan abelian group, $(\\mathcal{B},\\cdot)$ is a group, and there is a\nleft-distributivity-like axiom that relates between the two operations in\n$\\mathcal{B}$. In analogy with a left brace, we define a left\n$\\mathscr{M}$-brace to be a triple $(\\mathcal{B},+,\\cdot)$, where\n$(\\mathcal{B},+)$ is a commutative monoid, $(\\mathcal{B},\\cdot)$ is a monoid,\nand the axiom of left distributivity holds. A lcm-monoid $M$ is a\nleft-cancellative monoid such that $1$ is the unique invertible element in $M$,\nand every pair of elements in $M$ admit a lcm with respect to\nleft-divisibility. The class of lcm-monoids contains the Gaussian,\nquasi-Garside and Garside monoids. We show that every lcm-monoid induces a left\n$\\mathscr{M}$-brace. Furthermore, we show that every Gaussian group induces a\npartial left brace.\n"}
+{"abstract": "  Modern software systems, such as Spark, are usually written in multiple\nprogramming languages (PLs). Besides benefiting from code reuse, such systems\ncan also take advantages of specific PLs to implement certain features, to meet\nvarious quality needs, and to improve development efficiency. In this context,\na change to such systems may need to modify source files written in different\nPLs. We define a multi-programming-language commit (MPLC) in a version control\nsystem (e.g., Git) as a commit that involves modified source files written in\ntwo or more PLs. To our knowledge, the phenomenon of MPLCs in software\ndevelopment has not been explored yet. In light of the potential impact of\nMPLCs on development difficulty and software quality, we performed an empirical\nstudy to understand the state of MPLCs, their change complexity, as well as\ntheir impact on open time of issues and bug proneness of source files in\nreal-life software projects. By exploring the MPLCs in 20 non-trivial Apache\nprojects with 205,994 commits, we obtained the following findings: (1) 9% of\nthe commits from all the projects are MPLCs, and the proportion of MPLCs in 80%\nof the projects goes to a relatively stable level; (2) more than 90% of the\nMPLCs from all the projects involve source files written in two PLs; (3) the\nchange complexity of MPLCs is significantly higher than that of non-MPLCs in\nall projects; (4) issues fixed in MPLCs take significantly longer to be\nresolved than issues fixed in non-MPLCs in 80% of the projects; and (5) source\nfiles that have been modified in MPLCs tend to be more bug-prone than source\nfiles that have never been modified in MPLCs. These findings provide\npractitioners with useful insights on the architecture design and quality\nmanagement of software systems written in multiple PLs.\n"}
+{"abstract": "  Deep anomaly detection methods learn representations that separate between\nnormal and anomalous samples. Very effective representations are obtained when\npowerful externally trained feature extractors (e.g. ResNets pre-trained on\nImageNet) are fine-tuned on the training data which consists of normal samples\nand no anomalies. However, this is a difficult task that can suffer from\ncatastrophic collapse, i.e. it is prone to learning trivial and non-specific\nfeatures. In this paper, we propose a new loss function which can overcome\nfailure modes of both center-loss and contrastive-loss methods. Furthermore, we\ncombine it with a confidence-invariant angular center loss, which replaces the\nEuclidean distance used in previous work, that was sensitive to prediction\nconfidence. Our improvements yield a new anomaly detection approach, based on\n$\\textit{Mean-Shifted Contrastive Loss}$, which is both more accurate and less\nsensitive to catastrophic collapse than previous methods. Our method achieves\nstate-of-the-art anomaly detection performance on multiple benchmarks including\n$97.5\\%$ ROC-AUC on the CIFAR-10 dataset.\n"}
+{"abstract": "  Bundles of filaments are subject to geometric frustration: certain\ndeformations (e.g. bending while twisted) require longitudinal variations in\nspacing between filaments. While bundles are common -- from protein fibers to\nyarns -- the mechanical consequences of longitudinal frustration are unknown.\nWe derive a geometrically-nonlinear formalism for bundle mechanics, using a\ngauge-like symmetry under reptations along filament backbones. We relate force\nbalance to orientational geometry and assess the elastic cost of frustration in\ntwisted toroidal bundles.\n"}
+{"abstract": "  Reaching the 2030 targets for the EU primary energy use (PE) and CO2eq\nemissions (CE) requires an accurate assessment of how different technologies\nperform on these two fronts. In this regard, the focus in academia is\nincreasingly shifting from traditional technologies to electricity consuming\nalternatives. Calculating and comparing their performance with respect to\ntraditional technologies requires conversion factors (CFs) like a primary\nenergy factor and a CO2eq intensity. These reflect the PE and CE associated\nwith each unit of electricity consumed. Previous work has shown that the\ncalculation and use of CFs is a contentious and multifaceted issue. However,\nthis has mostly remained a theoretical discussion. A stock-taking of how CFs\nare actually calculated and used in academic literature has so far been\nmissing, impeding insight into what the contemporary trends and challenges are.\nTherefore, we structurally review 65 publications across six methodological\naspects. We find that 72% of the publications consider only a single country,\n86% apply a purely retrospective perspective, 54% apply a yearly temporal\nresolution, 65% apply a purely operational (instead of a life-cycle)\nperspective, 91% make use of average (rather than marginal) CFs, and 75% ignore\nelectricity imports from surrounding countries. We conclude that there is a\nstrong need in the literature for a publicly available, transparently\ncalculated dataset of CFs, which avoids the shortcomings found in the\nliterature. This would enable more accurate and transparent PE and CE\ncalculations, and support the development of new building energy performance\nassessment methods and smart grid algorithms.\n"}
+{"abstract": "  In a typical Internet-of-Things setting that involves scientific\napplications, a target computation can be evaluated in many different ways\ndepending on the split of computations among various devices. On the one hand,\ndifferent implementations (or algorithms)--equivalent from a mathematical\nperspective--might exhibit significant difference in terms of performance. On\nthe other hand, some of the implementations are likely to show similar\nperformance characteristics. In this paper, we focus on analyzing the\nperformance of a given set of algorithms by clustering them into performance\nclasses. To this end, we use a measurement-based approach to evaluate and score\nalgorithms based on pair-wise comparisons; we refer to this approach\nas\"Relative performance analysis\". Each comparison yields one of three\noutcomes: one algorithm can be \"better\", \"worse\", or \"equivalent\" to another;\nthose algorithms evaluating to have equivalent performance are merged into the\nsame performance class. We show that our clustering methodology facilitates\nalgorithm selection with respect to more than one metric; for instance, from\nthe subset of equivalently fast algorithms, one could then select an algorithm\nthat consumes the least energy on a certain device.\n"}
+{"abstract": "  The planar problem of a viscous laminar flow around elliptical cylinders\nunder angle of attack is considered. From the solution of the laminar boundary\nlayer equations using the Loytsyansky local similarity method, the shear stress\nat the ellipse boundary and the separation point is found. From the separation\npoints velocities equality, the circulation is found. A complete solution to\nthe problem of the velocity and pressure field outside the boundary layer is\nalso constructed. The coefficients of lift and resistance are found depending\non the angle of attack and the ellipse axes ratio. The theoretical results are\ncompared with the available experimental data and direct numerical solutions of\nthe Navier-Stokes equations.\n"}
+{"abstract": "  The dichromatic number $\\vec{\\chi}(D)$ of a digraph $D$ is the smallest $k$\nfor which it admits a $k$-coloring where every color class induces an acyclic\nsubgraph. Inspired by Hadwiger's conjecture for undirected graphs, several\ngroups of authors have recently studied the containment of directed graph\nminors in digraphs with given dichromatic number. In this short note we improve\nseveral of the existing bounds and prove almost linear bounds by reducing the\nproblem to a recent result of Postle on Hadwiger's conjecture.\n"}
+{"abstract": "  Modeling the time evolution of discrete sets of items (e.g., genetic\nmutations) is a fundamental problem in many biomedical applications. We\napproach this problem through the lens of continuous-time Markov chains, and\nshow that the resulting learning task is generally underspecified in the usual\nsetting of cross-sectional data. We explore a perhaps surprising remedy:\nincluding a number of additional independent items can help determine time\norder, and hence resolve underspecification. This is in sharp contrast to the\ncommon practice of limiting the analysis to a small subset of relevant items,\nwhich is followed largely due to poor scaling of existing methods. To put our\ntheoretical insight into practice, we develop an approximate likelihood\nmaximization method for learning continuous-time Markov chains, which can scale\nto hundreds of items and is orders of magnitude faster than previous methods.\nWe demonstrate the effectiveness of our approach on synthetic and real cancer\ndata.\n"}
+{"abstract": "  Purpose: Computational Fluid Dynamics (CFD) simulations are performed to\ninvestigate the impact of adding a grid to a two-inlet dry powder inhaler\n(DPI). The purpose of the paper is to show the importance of the correct choice\nof closure model and modeling approach, as well as to perform validation\nagainst particle dispersion data obtained from in-vitro studies and flow\nvelocity data obtained from particle image velocimetry (PIV) experiments.\nMethods: CFD simulations are performed using the Ansys Fluent 2020R1 software\npackage. Two RANS turbulence models (realisable $k - \\epsilon$ and $k - \\omega$\nSST) and the Stress Blended Eddy Simulation (SBES) models are considered.\nLagrangian particle tracking for both carrier and fine particles is also\nperformed. Results: Excellent comparison with the PIV data is found for the\nSBES approach and the particle tracking data are consistent with the dispersion\nresults, given the simplicity of the assumptions made. Conclusions: This work\nshows the importance of selecting the correct turbulence modelling approach and\nboundary conditions to obtain good agreement with PIV data for the flow-field\nexiting the device. With this validated, the model can be used with much higher\nconfidence to explore the fluid and particle dynamics within the device.\n"}
+{"abstract": "  When primed with only a handful of training samples, very large pretrained\nlanguage models such as GPT-3, have shown competitive results when compared to\nfully-supervised fine-tuned large pretrained language models. We demonstrate\nthat the order in which the samples are provided can be the difference between\nnear state-of-the-art and random guess performance: Essentially some\npermutations are \"fantastic\" and some not. We analyse this phenomenon in\ndetail, establishing that: it is present across model sizes (even for the\nlargest current models), it is not related to a specific subset of samples, and\nthat a given good permutation for one model is not transferable to another.\nWhile one could use a development set to determine which permutations are\nperformant, this would deviate from the few-shot setting as it requires\nadditional annotated data. Instead, we use the generative nature of the\nlanguage models to construct an artificial development set and based on entropy\nstatistics of the candidate permutations from this set we identify performant\nprompts. Our method improves upon GPT-family models by on average 13% relative\nacross eleven different established text classification tasks.\n"}
+{"abstract": "  Sequentially obtained dataset usually exhibits different behavior at\ndifferent data resolutions/scales. Instead of inferring from data at each scale\nindividually, it is often more informative to interpret the data as an ensemble\nof time series from different scales. This naturally motivated us to propose a\nnew concept referred to as the scale-based inference. The basic idea is that\nmore accurate prediction can be made by exploiting scale information of a time\nseries. We first propose a nonparametric predictor based on $k$-nearest\nneighbors with an optimally chosen $k$ for a single time series. Based on that,\nwe focus on a specific but important type of scale information, the\nresolution/sampling rate of time series data. We then propose an algorithm to\nsequentially predict time series using past data at various resolutions. We\nprove that asymptotically the algorithm produces the mean prediction error that\nis no larger than the best possible algorithm at any single resolution, under\nsome optimally chosen parameters. Finally, we establish the general\nformulations for scale inference, and provide further motivating examples.\nExperiments on both synthetic and real data illustrate the potential\napplicability of our approaches to a wide range of time series models.\n"}
+{"abstract": "  The $\\text{t}\\bar{\\text{t}}\\text{H}(\\text{b}\\bar{\\text{b}})$ process is an\nessential channel to reveal the Higgs properties but has an irreducible\nbackground from the $\\text{t}\\bar{\\text{t}}\\text{b}\\bar{\\text{b}}$ process,\nwhich produces a top quark pair in association with a b quark pair. Therefore,\nunderstanding the $\\text{t}\\bar{\\text{t}}\\text{b}\\bar{\\text{b}}$ process is\ncrucial for improving the sensitivity of a search for the\n$\\text{t}\\bar{\\text{t}}\\text{H}(\\text{b}\\bar{\\text{b}})$ process. To this end,\nwhen measuring the differential cross-section of the\n$\\text{t}\\bar{\\text{t}}\\text{b}\\bar{\\text{b}}$ process, we need to distinguish\nthe b-jets originated from top quark decays, and additional b-jets originated\nfrom gluon splitting. Since there are no simple identification rules, we adopt\ndeep learning methods to learn from data to identify the additional b-jets from\nthe $\\text{t}\\bar{\\text{t}}\\text{b}\\bar{\\text{b}}$ events. Specifically, by\nexploiting the special structure of the\n$\\text{t}\\bar{\\text{t}}\\text{b}\\bar{\\text{b}}$ event data, we propose several\nloss functions that can be minimized to directly increase the matching\nefficiency, the accuracy of identifying additional b-jets. We discuss the\ndifference between our method and another deep learning-based approach based on\nbinary classification arXiv:1910.14535 using synthetic data. We then verify\nthat additional b-jets can be identified more accurately by increasing matching\nefficiency directly rather than the binary classification accuracy, using\nsimulated $\\text{t}\\bar{\\text{t}}\\text{b}\\bar{\\text{b}}$ event data in the\nlepton+jets channel from pp collision at $\\sqrt{s}$ = 13 TeV.\n"}
+{"abstract": "  Exposure mappings facilitate investigations of complex causal effects when\nunits interact in experiments. Current methods assume that the exposures are\ncorrectly specified, but such an assumption cannot be verified, and its\nvalidity is often questionable. This paper describes conditions under which one\ncan draw inferences about exposure effects when the exposures are misspecified.\nThe main result is a proof of consistency under mild conditions on the errors\nintroduced by the misspecification. The rate of convergence is determined by\nthe dependence between units' specification errors, and consistency is achieved\neven if the errors are large as long as they are sufficiently weakly dependent.\nIn other words, exposure effects can be precisely estimated also under\nmisspecification as long as the units' exposures are not misspecified in the\nsame way. The limiting distribution of the estimator is discussed. Asymptotic\nnormality is achieved under stronger conditions than those needed for\nconsistency. Similar conditions also facilitate conservative variance\nestimation.\n"}
+{"abstract": "  In this work, we determine the full expression of the local truncation error\nof hyperbolic partial differential equations (PDEs) on a uniform mesh. If we\nare employing a stable numerical scheme and the global solution error is of the\nsame order of accuracy as the global truncation error, we make the following\nobservations in the asymptotic regime, where the truncation error is dominated\nby the powers of $\\Delta x$ and $\\Delta t$ rather than their coefficients.\nAssuming that we reach the asymptotic regime before the machine precision error\ntakes over, (a) the order of convergence of stable numerical solutions of\nhyperbolic PDEs at constant ratio of $\\Delta t$ to $\\Delta x$ is governed by\nthe minimum of the orders of the spatial and temporal discretizations, and (b)\nconvergence cannot even be guaranteed under only spatial or temporal\nrefinement. We have tested our theory against numerical methods employing\nMethod of Lines and not against ones that treat space and time together, and we\nhave not taken into consideration the reduction in the spatial and temporal\norders of accuracy resulting from slope-limiting monotonicity-preserving\nstrategies commonly applied to finite volume methods. Otherwise, our theory\napplies to any hyperbolic PDE, be it linear or non-linear, and employing finite\ndifference, finite volume, or finite element discretization in space, and\nadvanced in time with a predictor-corrector, multistep, or a deferred\ncorrection method. If the PDE is reduced to an ordinary differential equation\n(ODE) by specifying the spatial gradients of the dependent variable and the\ncoefficients and the source terms to be zero, then the standard local\ntruncation error of the ODE is recovered. We perform the analysis with generic\nand specific hyperbolic PDEs using the symbolic algebra package SymPy, and\nconduct a number of numerical experiments to demonstrate our theoretical\nfindings.\n"}
+{"abstract": "  We introduce a definition of the fractional Laplacian $(-\\Delta)^{s(\\cdot)}$\nwith spatially variable order $s:\\Omega\\to [0,1]$ and study the solvability of\nthe associated Poisson problem on a bounded domain $\\Omega$. The initial\nmotivation arises from the extension results of Caffarelli and Silvestre, and\nStinga and Torrea; however the analytical tools and approaches developed here\nare new. For instance, in some cases we allow the variable order $s(\\cdot)$ to\nattain the values $0$ and $1$ leading to a framework on weighted Sobolev spaces\nwith non-Muckenhoupt weights. Initially, and under minimal assumptions, the\noperator $(-\\Delta)^{s(\\cdot)}$ is identified as the Lagrange multiplier\ncorresponding to an optimization problem; and its domain is determined as a\nquotient space of weighted Sobolev spaces. The well-posedness of the associated\nPoisson problem is then obtained for data in the dual of this quotient space.\nSubsequently, two trace regularity results are established, allowing to\npartially characterize functions in the aforementioned quotient space whenever\na Poincar\\'e type inequality is available. Precise examples are provided where\nsuch inequality holds, and in this case the domain of the operator\n$(-\\Delta)^{s(\\cdot)}$ is identified with a subset of a weighted Sobolev space\nwith spatially variant smoothness $s(\\cdot)$. The latter further allows to\nprove the well-posedness of the Poisson problem assuming functional regularity\nof the data.\n"}
+{"abstract": "  Following the collapse of their cores, some of the massive binary stars that\npopulate our Universe are expected to form merging binaries composed of black\nholes and neutron stars. Gravitational-wave observations of the resulting\ncompact binaries can reveal precious details on the inner workings of the\nsupernova mechanism and the subsequent formation of compact objects. Within the\nframework of the population-synthesis code MOBSE, we present the implementation\nof a new supernova model that relies on the compactness of the collapsing star.\nThe model has two free parameters, namely the compactness threshold that\nseparates the formation of black holes and that of neutron stars, and the\nfraction of the envelope that falls back onto the newly formed black holes. We\ncompare this model extensively against other prescriptions that are commonly\nused in binary population synthesis. We find that the cleanest signatures of\nthe role of the pre-supernova stellar compactness are (i) the relative\nformation rates of the different kinds of compact binaries, which mainly depend\non the compactness threshold parameter, and (ii) the location of the upper edge\nof the mass gap between the lightest black holes and the heaviest neutron\nstars, which mainly depends on the fallback fraction.\n"}
+{"abstract": "  We consider quantum stochastic processes and discuss a level 2.5 large\ndeviation formalism providing an explicit and complete characterisation of\nfluctuations of time-averaged quantities, in the large-time limit. We analyse\ntwo classes of quantum stochastic dynamics, within this framework. The first\nclass consists of the quantum jump trajectories related to photon detection;\nthe second is quantum state diffusion related to homodyne detection. For both\nprocesses, we present the level 2.5 functional starting from the corresponding\nquantum stochastic Schr\\\"odinger equation and we discuss connections of these\nfunctionals to optimal control theory.\n"}
+{"abstract": "  Context: Prior research has established that a small proportion of\nindividuals dominate team communication during global software development. It\nis not known, however, how these members' contributions affect their teams'\nknowledge diffusion process, or whether their personality profiles are\nresponsible for their dominant presence. Objective: We set out to address this\ngap through the study of repository artifacts. Method: Artifacts from ten teams\nwere mined from the IBM Rational Jazz repository. We employed social network\nanalysis (SNA) to group practitioners into two clusters, Top Members and\nOthers, based on the numbers of messages they communicated and their engagement\nin task changes. SNA metrics (density, in-degree and closeness) were then used\nto study practitioners' importance in knowledge diffusion. Thereafter, we\nperformed psycholinguistic analysis on practitioners' messages using linguistic\ndimensions that had been previously correlated with the Big Five personality\nprofiles. Results: For our sample of 146 practitioners we found that Top\nMembers occupied critical roles in knowledge diffusion, and demonstrated more\nopenness to experience than the Others. Additionally, all personality profiles\nwere represented during teamwork, although openness to experience,\nagreeableness and extroversion were particularly evident. However, no specific\npersonality predicted members' involvement in knowledge diffusion. Conclusion:\nTask assignment that promotes highly connected team communication networks may\nmitigate tacit knowledge loss in global software teams. Additionally, while\nmembers expressing openness to experience are likely to be particularly driven\nto perform, this is not entirely responsible for a global team's success.\n"}
+{"abstract": "  Since the first coronavirus case was identified in the U.S. on Jan. 21, more\nthan 1 million people in the U.S. have confirmed cases of COVID-19. This\ninfectious respiratory disease has spread rapidly across more than 3000\ncounties and 50 states in the U.S. and have exhibited evolutionary clustering\nand complex triggering patterns. It is essential to understand the complex\nspacetime intertwined propagation of this disease so that accurate prediction\nor smart external intervention can be carried out. In this paper, we model the\npropagation of the COVID-19 as spatio-temporal point processes and propose a\ngenerative and intensity-free model to track the spread of the disease. We\nfurther adopt a generative adversarial imitation learning framework to learn\nthe model parameters. In comparison with the traditional likelihood-based\nlearning methods, this imitation learning framework does not need to prespecify\nan intensity function, which alleviates the model-misspecification. Moreover,\nthe adversarial learning procedure bypasses the difficult-to-evaluate integral\ninvolved in the likelihood evaluation, which makes the model inference more\nscalable with the data and variables. We showcase the dynamic learning\nperformance on the COVID-19 confirmed cases in the U.S. and evaluate the social\ndistancing policy based on the learned generative model.\n"}
+{"abstract": "  We report direct visualization of gigahertz-frequency Lamb waves propagation\nin aluminum nitride phononic circuits by transmission-mode microwave impedance\nmicroscopy (TMIM). Consistent with the finite-element modeling, the acoustic\neigenmodes in both a horn-shaped coupler and a sub-wavelength waveguide are\nrevealed in the TMIM images. Using fast Fourier transform filtering, we\nquantitatively analyze the acoustic loss of individual Lamb modes along the\nwaveguide and the power coupling coefficient between the waveguide and the\nparabolic couplers. Our work provides insightful information on the\npropagation, mode conversion, and attenuation of acoustic waves in\npiezoelectric nanostructures, which is highly desirable for designing and\noptimizing phononic devices for microwave signal processing and quantum\ninformation transduction.\n"}
+{"abstract": "  In two-dimensional random waves, phase singularities are point-like\ndislocations with a behavior reminiscent of interacting particles. This --\nqualitative -- consideration, stems from the spatial arrangement of these\nentities, which finds its hallmark in a pair correlation reminiscent of\nliquid-like system. Starting from their pair correlation function, we derive an\neffective pair-interaction for phase singularities in random waves by using a\nreverse Monte Carlo method. This study initiates a new approach for the\ntreatment of singularities in random waves and can be generalized to\ntopological defects in any system.\n"}
+{"abstract": "  The astrophysical sites where r-process elements are synthesized remain\nmysterious: it is clear that neutron star mergers (kilonovae (KNe)) contribute,\nand some classes of core-collapse supernovae (SNe) are also likely sources of\nat least the lighter r-process species. The discovery of 60Fe on the Earth and\nMoon implies that one or more astrophysical explosions have occurred near the\nEarth within the last few million years, probably SNe. Intriguingly, 244Pu has\nnow been detected, mostly overlapping with 60Fe pulse. However, the 244Pu flux\nmay extend to before 12 Myr ago, pointing to a different origin. Motivated by\nthese observations and difficulties for r-process nucleosynthesis in SN models,\nwe propose that ejecta from a KN enriched the giant molecular cloud that gave\nrise to the Local Bubble, where the Sun resides. Accelerator mass spectrometry\n(AMS) measurements of 244Pu and searches for other live isotopes could probe\nthe origins of the r-process and the history of the solar neighborhood,\nincluding triggers for mass extinctions, e.g., that at the end of the Devonian\nepoch, motivating the calculations of the abundances of live r-process\nradioisotopes produced in SNe and KNe that we present here. Given the presence\nof 244Pu, other r-process species such as 93Zr, 107Pd, 129I, 135Cs, 182Hf,\n236U, 237Np and 247Cm should be present. Their abundances and well-resolved\ntime histories could distinguish between the SN and KN scenarios, and we\ndiscuss prospects for their detection in deep-ocean deposits and the lunar\nregolith. We show that AMS 129I measurements in Fe-Mn crusts already constrain\na possible nearby KN scenario.\n"}
+{"abstract": "  An alias table is a data structure that allows for efficiently drawing\nweighted random samples in constant time and can be constructed in linear time.\nThe PSA algorithm by H\\\"ubschle-Schneider and Sanders is able to construct\nalias tables in parallel on the CPU. In this report, we transfer the PSA\nalgorithm to the GPU. Our construction algorithm achieves a speedup of 17 on a\nconsumer GPU in comparison to the PSA method on a 16-core high-end desktop CPU.\nFor sampling, we achieve an up to 24 times higher throughput. Both operations\nalso require several times less energy than on the CPU. Adaptations helping to\nachieve this include changing memory access patterns to do coalesced access.\nWhere this is not possible, we first copy data to the faster shared memory\nusing coalesced access. We also enhance a generalization of binary search\nenabling to search for a range of items in parallel. Besides naive sampling, we\nalso give improved batched sampling algorithms.\n"}
+{"abstract": "  The paper presents a discrete variation of the Frechet distance between\nclosed curves, which can be seen as an approximation of the continuous measure.\nA rather straightforward approach to compute the discrete Frechet distance\nbetween two closed sequences of m and n points using binary search takes O(mn\nlog mn) time. We present an algorithm that takes O(mn log* mn) time, where log*\nis the iterated logarithm.\n"}
+{"abstract": "  In reinforcement learning, experience replay stores past samples for further\nreuse. Prioritized sampling is a promising technique to better utilize these\nsamples. Previous criteria of prioritization include TD error, recentness and\ncorrective feedback, which are mostly heuristically designed. In this work, we\nstart from the regret minimization objective, and obtain an optimal\nprioritization strategy for Bellman update that can directly maximize the\nreturn of the policy. The theory suggests that data with higher hindsight TD\nerror, better on-policiness and more accurate Q value should be assigned with\nhigher weights during sampling. Thus most previous criteria only consider this\nstrategy partially. We not only provide theoretical justifications for previous\ncriteria, but also propose two new methods to compute the prioritization\nweight, namely ReMERN and ReMERT. ReMERN learns an error network, while ReMERT\nexploits the temporal ordering of states. Both methods outperform previous\nprioritized sampling algorithms in challenging RL benchmarks, including MuJoCo,\nAtari and Meta-World.\n"}
+{"abstract": "  Neural module networks (NMN) have achieved success in image-grounded tasks\nsuch as Visual Question Answering (VQA) on synthetic images. However, very\nlimited work on NMN has been studied in the video-grounded language tasks.\nThese tasks extend the complexity of traditional visual tasks with the\nadditional visual temporal variance. Motivated by recent NMN approaches on\nimage-grounded tasks, we introduce Video-grounded Neural Module Network (VGNMN)\nto model the information retrieval process in video-grounded language tasks as\na pipeline of neural modules. VGNMN first decomposes all language components to\nexplicitly resolve any entity references and detect corresponding action-based\ninputs from the question. The detected entities and actions are used as\nparameters to instantiate neural module networks and extract visual cues from\nthe video. Our experiments show that VGNMN can achieve promising performance on\ntwo video-grounded language tasks: video QA and video-grounded dialogues.\n"}
+{"abstract": "  We study the following rainbow version of subgraph containment problems in a\nfamily of (hyper)graphs, which generalizes the classical subgraph containment\nproblems in a single host graph. For a collection $\\textbf{G}=\\{G_1,\nG_2,\\ldots, G_{m}\\}$ of not necessarily distinct graphs on the same vertex set\n$[n]$, a (sub)graph $H$ on $[n]$ is rainbow if $E(H)\\subseteq \\bigcup_{i\\in\n[m]}E(G_i)$ and $|E(H)\\cap E(G_i)|\\le 1$ for $i\\in[m]$. Note that if\n$|E(H)|=m$, then a rainbow $H$ consists of exactly one edge from each $G_i$.\n  Our main results are on rainbow clique-factors in (hyper)graph systems with\nminimum $d$-degree conditions. In particular,\n  (1) we obtain a rainbow analogue of an asymptotical version of the\nHajnal--Szemer\\'{e}di theorem, namely, if $t\\mid n$ and\n$\\delta(G_i)\\geq(1-\\frac{1}{t}+\\varepsilon)n$ for each\n$i\\in[\\frac{n}{t}\\binom{t}{2}]$, then $\\textbf{G}$ contains a rainbow\n$K_t$-factor;\n  (2) we prove that for $1\\le d\\le k-1$, essentially a minimum $d$-degree\ncondition forcing a perfect matching in a $k$-graph also forces rainbow perfect\nmatchings in $k$-graph systems.\n  The degree assumptions in both results are asymptotically best possible\n(although the minimum $d$-degree condition forcing a perfect matching in a\n$k$-graph is in general unknown). For (1) we also discuss two directed versions\nand a multipartite version. Finally, to establish these results, we in fact\nprovide a general framework to attack this type of problems, which reduces it\nto subproblems with finitely many colors.\n"}
+{"abstract": "  It has been recently established by the first and third author that on\nuniformly rectifiable sets the Green function is almost affine in the weak\nsense, and moreover, in some scenarios such Green function estimates are\nequivalent to the uniform rectifiability of a set. The present paper tackles a\nstrong analogue of these results, starting with the \"flagship\" degenerate\noperators on sets with lower dimensional boundaries.\n  We consider the elliptic operators $L_{\\beta,\\gamma} =- {\\rm div}\nD^{d+1+\\gamma-n} \\nabla$ associated to a domain $\\Omega \\subset \\mathbb R^n$\nwith a uniformly rectifiable boundary $\\Gamma$ of dimension $d < n-1$, the now\nusual distance to the boundary $D = D_\\beta$ given by $D_\\beta(X)^{-\\beta} =\n\\int_{\\Gamma} |X-y|^{-d-\\beta} d\\sigma(y)$ for $X \\in \\Omega$, where $\\beta >0$\nand $\\gamma \\in (-1,1)$. In this paper we show that the Green function $G$ for\n$L_{\\beta,\\gamma}$, with pole at infinity, is well approximated by multiples of\n$D^{1-\\gamma}$, in the sense that the function $\\big| D\\nabla\\big(\\ln\\big(\n\\frac{G}{D^{1-\\gamma}} \\big)\\big)\\big|^2$ satisfies a Carleson measure estimate\non $\\Omega$. We underline that the strong and the weak results are different in\nnature and, of course, at the level of the proofs: the latter extensively used\ncompactness arguments, while the present paper relies on some intricate\nintegration by parts and the properties of the \"magical\" distance function from\na previous work from the first author, the third author, and Max Engelstein.\n"}
+{"abstract": "  We generalize the concept of a norm on a vector space to one of a norm on a\ncategory. This provides a unified perspective on many specific matters in many\ndifferent areas of mathematics like set theory, functional analysis, measure\ntheory, topology, and metric space theory. We will especially address the two\nlast areas in which the monotone-light factorization and, respectively, the\nGromov-Hausdorff distance will naturally appear. In our formalization a\nSchr\\\"oder-Bernstein property becomes an axiom of a norm which constitutes\ninteresting properties of the categories in question. The proposed concept\nprovides a convenient framework for metrizations.\n"}
+{"abstract": "  The quantum alcove model associated to a dominant weight plays an important\nrole in many branches of mathematics, such as combinatorial representation\ntheory, the theory of Macdonald polynomials, and Schubert calculus. For a\ndominant weight, it is proved by Lenart-Lubovsky that the quantum alcove model\ndoes not depend on the choice of a reduced alcove path, which is a shortest\npath of alcoves from the fundamental one to its translation by the given\ndominant weight. This is established through quantum Yang-Baxter moves, which\nbiject the objects of the model associated with two such alcove paths, and can\nbe viewed as a generalization of jeu de taquin slides to arbitrary root\nsystems. The purpose of this paper is to give a generalization of quantum\nYang-Baxter moves to the quantum alcove model corresponding to an arbitrary\nweight, which was used to express a general Chevalley formula in the\nequivariant $K$-group of semi-infinite flag manifolds. The generalized quantum\nYang-Baxter moves give rise to a \"sijection\" (bijection between signed sets),\nand are shown to preserve certain important statistics, including weights and\nheights. As an application, we prove that the generating function of these\nstatistics does not depend on the choice of a reduced alcove path. Also, we\nobtain an identity for the graded characters of Demazure submodules of\nlevel-zero extremal weight modules over a quantum affine algebra, which can be\nthought of as a representation-theoretic analogue of the mentioned Chevalley\nformula. Other applications and some open problems involving \"signed crystals\"\nare discussed.\n"}
+{"abstract": "  The explosive growth of easily-accessible unlabeled data has lead to growing\ninterest in active learning, a paradigm in which data-hungry learning\nalgorithms adaptively select informative examples in order to lower\nprohibitively expensive labeling costs. Unfortunately, in standard worst-case\nmodels of learning, the active setting often provides no improvement over\nnon-adaptive algorithms. To combat this, a series of recent works have\nconsidered a model in which the learner may ask enriched queries beyond labels.\nWhile such models have seen success in drastically lowering label costs, they\ntend to come at the expense of requiring large amounts of memory. In this work,\nwe study what families of classifiers can be learned in bounded memory. To this\nend, we introduce a novel streaming-variant of enriched-query active learning\nalong with a natural combinatorial parameter called lossless sample compression\nthat is sufficient for learning not only with bounded memory, but in a\nquery-optimal and computationally efficient manner as well. Finally, we give\nthree fundamental examples of classifier families with small, easy to compute\nlossless compression schemes when given access to basic enriched queries:\naxis-aligned rectangles, decision trees, and halfspaces in two dimensions.\n"}
+{"abstract": "  The exact exchange-correlation (xc) kernel $f_\\mathrm{xc}(x,x',\\omega)$ of\nlinear response time-dependent density functional theory is computed over a\nwide range of frequencies, for three canonical one-dimensional finite systems.\nMethods used to ensure the numerical robustness of $f_\\mathrm{xc}$ are set out.\nThe frequency dependence of $f_\\text{xc}$ is found to be due largely to its\nanalytic structure, i.e. its singularities at certain frequencies, which are\nrequired in order to capture particular transitions, including those of double\nexcitation character. However, within the frequency range of the first few\ninteracting excitations, $f_\\mathrm{xc}$ is approximately frequency\nindependent, meaning the exact adiabatic approximation $f_\\mathrm{xc}(\\omega =\n0)$ remedies the failings of the local density approximation and random phase\napproximation for these lowest transitions. The key differences between the\nexact $f_\\mathrm{xc}$ and its common approximations are analyzed, and cannot be\neliminated by exploiting the limited gauge freedom in $f_\\mathrm{xc}$. The\noptical spectrum benefits from using as accurate as possible an $f_\\mathrm{xc}$\nand ground-state xc potential, while maintaining exact compatibility between\nthe two is of less importance.\n"}
+{"abstract": "  Recently, self-supervised learning methods have achieved remarkable success\nin visual pre-training task. By simply pulling the different augmented views of\neach image together or other novel mechanisms, they can learn much unsupervised\nknowledge and significantly improve the transfer performance of pre-training\nmodels. However, these works still cannot avoid the representation collapse\nproblem, i.e., they only focus on limited regions or the extracted features on\ntotally different regions inside each image are nearly the same. Generally,\nthis problem makes the pre-training models cannot sufficiently describe the\nmulti-grained information inside images, which further limits the upper bound\nof their transfer performance. To alleviate this issue, this paper introduces a\nsimple but effective mechanism, called Exploring the Diversity and Invariance\nin Yourself E-DIY. By simply pushing the most different regions inside each\naugmented view away, E-DIY can preserve the diversity of extracted region-level\nfeatures. By pulling the most similar regions from different augmented views of\nthe same image together, E-DIY can ensure the robustness of region-level\nfeatures. Benefited from the above diversity and invariance exploring\nmechanism, E-DIY maximally extracts the multi-grained visual information inside\neach image. Extensive experiments on downstream tasks demonstrate the\nsuperiority of our proposed approach, e.g., there are 2.1% improvements\ncompared with the strong baseline BYOL on COCO while fine-tuning Mask R-CNN\nwith the R50-C4 backbone and 1X learning schedule.\n"}
+{"abstract": "  Reinforcement learning control of an underground loader is investigated in\nsimulated environment, using a multi-agent deep neural network approach. At the\nstart of each loading cycle, one agent selects the dig position from a depth\ncamera image of the pile of fragmented rock. A second agent is responsible for\ncontinuous control of the vehicle, with the goal of filling the bucket at the\nselected loading point, while avoiding collisions, getting stuck, or losing\nground traction. It relies on motion and force sensors, as well as on camera\nand lidar. Using a soft actor-critic algorithm the agents learn policies for\nefficient bucket filling over many subsequent loading cycles, with clear\nability to adapt to the changing environment. The best results, on average 75%\nof the max capacity, are obtained when including a penalty for energy usage in\nthe reward.\n"}
+{"abstract": "  The widespread adoption of Deep Neural Networks (DNNs) in important domains\nraises questions about the trustworthiness of DNN outputs. Even a highly\naccurate DNN will make mistakes some of the time, and in settings like\nself-driving vehicles these mistakes must be quickly detected and properly\ndealt with in deployment. Just as our community has developed effective\ntechniques and mechanisms to monitor and check programmed components, we\nbelieve it is now necessary to do the same for DNNs. In this paper we present\nDNN self-checking as a process by which internal DNN layer features are used to\ncheck DNN predictions. We detail SelfChecker, a self-checking system that\nmonitors DNN outputs and triggers an alarm if the internal layer features of\nthe model are inconsistent with the final prediction. SelfChecker also provides\nadvice in the form of an alternative prediction. We evaluated SelfChecker on\nfour popular image datasets and three DNN models and found that SelfChecker\ntriggers correct alarms on 60.56% of wrong DNN predictions, and false alarms on\n2.04% of correct DNN predictions. This is a substantial improvement over prior\nwork (SELFORACLE, DISSECTOR, and ConfidNet). In experiments with self-driving\ncar scenarios, SelfChecker triggers more correct alarms than SELFORACLE for two\nDNN models (DAVE-2 and Chauffeur) with comparable false alarms. Our\nimplementation is available as open source.\n"}
+{"abstract": "  Artificial organisms are computer programs that self-replicate, mutate,\ncompete and evolve. How do these lifelike information-processing behaviours\ncould arise in diverse far-from-equilibrium physical systems remains an open\nquestion. Here, I devise a toy model where the onset of self-replication of a\nquantum artificial organism (a chain of lambda systems) is owing to\nsingle-photon pulses added to a zero-temperature environment. The model results\nin a replication probability that is proportional to the absorbed work from the\nphoton, in agreement with the theory of dissipative adaptation. Unexpectedly,\nspontaneous mutations are unavoidable in this model, due to rare but finite\nabsorption of off-resonant photons. These results hint at self-replication as a\npossible link between dissipative adaptation and open-ended evolution.\n"}
+{"abstract": "  ProtoDUNE-DP is a 6x6x6 m3 liquid argon time-projection-chamber operated at\nthe CERN Neutrino Platform in 2019-2020 as a prototype of the Dual Phase\nconcept for the DUNE Far Detector. The Photon Detection System (PDS) is based\non 36 8-inch photo-multiplier tubes (PMTs) and allows triggering on the\nscintillation light signals produced by cosmic rays and other charged particles\ntraversing the detector. The acquisition and calibration software specifically\ndeveloped for the ProtoDUNE-DP PDS is described in this paper. This software\ncontrols the high-voltage power supplies, the calibration system, and the PDS\nDAQ. It has been developed with Qt Creator, and features different operation\nmodes, and a graphical user interface. This software has already been validated\nand used during the ProtoDUNE-DP operation.\n"}
+{"abstract": "  There is sustained and widespread interest in understanding the limit, if\nany, to the human lifespan. Apart from its intrinsic and biological interest,\nchanges in survival in old age have implications for the sustainability of\nsocial security systems. A central question is whether the endpoint of the\nunderlying lifetime distribution is finite. Recent analyses of data on the\noldest human lifetimes have led to competing claims about survival and to some\ncontroversy, due in part to incorrect statistical analysis. This paper\ndiscusses the particularities of such data, outlines correct ways of handling\nthem and presents suitable models and methods for their analysis. We provide a\ncritical assessment of some earlier work and illustrate the ideas through\nreanalysis of semi-supercentenarian lifetime data. Our analysis suggests that\nremaining life-length after age 109 is exponentially distributed, and that any\nupper limit lies well beyond the highest lifetime yet reliably recorded. Lower\nlimits to 95% confidence intervals for the human lifespan are around 130 years,\nand point estimates typically indicate no upper limit at all.\n"}
+{"abstract": "  Data augmentation has been shown to effectively improve the performance of\nmultimodal machine learning models. This paper introduces a generative model\nfor data augmentation by leveraging the correlations among multiple modalities.\nDifferent from conventional data augmentation approaches that apply low-level\noperations with deterministic heuristics, our method learns a generator that\ngenerates samples of the target modality conditioned on observed modalities in\nthe variational auto-encoder framework. Additionally, the proposed model is\nable to quantify the confidence of augmented data by its generative\nprobability, and can be jointly optimised with a downstream task. Experiments\non Visual Question Answering as downstream task demonstrate the effectiveness\nof the proposed generative model, which is able to improve strong UpDn-based\nmodels to achieve state-of-the-art performance.\n"}
+{"abstract": "  We present an approach to measure the Milky Way (MW) potential using the\nangular accelerations of stars in aggregate as measured by astrometric surveys\nlike Gaia. Accelerations directly probe the gradient of the MW potential, as\nopposed to indirect methods using e.g. stellar velocities. We show that\nend-of-mission Gaia stellar acceleration data may be used to measure the\npotential of the MW disk at approximately 3$\\sigma$ significance and, if recent\nmeasurements of the solar acceleration are included, the local dark matter\ndensity at ~2$\\sigma$ significance. Since the significance of detection scales\nsteeply as $t^{5/2}$ for observing time $t$, future surveys that include\nangular accelerations in the astrometric solutions may be combined with Gaia to\nprecisely measure the local dark matter density and shape of the density\nprofile.\n"}
+{"abstract": "  Recently it was shown that a certain class of phylogenetic networks, called\nlevel-$2$ networks, cannot be reconstructed from their associated distance\nmatrices. In this paper, we show that they can be reconstructed from their\ninduced shortest and longest distance matrices. That is, if two level-$2$\nnetworks induce the same shortest and longest distance matrices, then they must\nbe isomorphic. We further show that level-$2$ networks are reconstructible from\ntheir shortest distance matrices if and only if they do not contain a subgraph\nfrom a family of graphs. A generator of a network is the graph obtained by\ndeleting all pendant subtrees and suppressing degree-$2$ vertices. We also show\nthat networks with a leaf on every generator side is reconstructible from their\ninduced shortest distance matrix, regardless of level.\n"}
+{"abstract": "  AstroSat is India's first dedicated multi-wavelength space observatory\nlaunched by the Indian Space Research Organisation (ISRO) on 28 September 2015.\nAfter launch, the AstroSat Science Support Cell (ASSC) was set up as a joint\nventure of ISRO and the Inter-University Centre for Astronomy and Astrophysics\n(IUCAA) with the primary purpose of facilitating the use of AstroSat, both for\nmaking observing proposals and for utilising archival data. The ASSC organises\nmeetings, workshops and webinars to train users in these activities, runs a\nhelp desk to address user queries, provides utility tools and disseminates\nanalysis software through a consolidated web portal. It also maintains the\nAstroSat Proposal Processing System (APPS) which is deployed at ISSDC, a\nsoftware platform central to the workflow management of AstroSat operations.\nThis paper illustrates the various aspects of ASSC functionality.\n"}
+{"abstract": "  As the data scale grows, deep recognition models often suffer from\nlong-tailed data distributions due to the heavy imbalanced sample number across\ncategories. Indeed, real-world data usually exhibit some similarity relation\namong different categories (e.g., pigeons and sparrows), called category\nsimilarity in this work. It is doubly difficult when the imbalance occurs\nbetween such categories with similar appearances. However, existing solutions\nmainly focus on the sample number to re-balance data distribution. In this\nwork, we systematically investigate the essence of the long-tailed problem from\na unified perspective. Specifically, we demonstrate that long-tailed\nrecognition suffers from both sample number and category similarity.\nIntuitively, using a toy example, we first show that sample number is not the\nunique influence factor for performance dropping of long-tailed recognition.\nTheoretically, we demonstrate that (1) category similarity, as an inevitable\nfactor, would also influence the model learning under long-tailed distribution\nvia similar samples, (2) using more discriminative representation methods\n(e.g., self-supervised learning) for similarity reduction, the classifier bias\ncan be further alleviated with greatly improved performance. Extensive\nexperiments on several long-tailed datasets verify the rationality of our\ntheoretical analysis, and show that based on existing state-of-the-arts\n(SOTAs), the performance could be further improved by similarity reduction. Our\ninvestigations highlight the essence behind the long-tailed problem, and claim\nseveral feasible directions for future work.\n"}
+{"abstract": "  The existence of a constant density of two-level systems (TLS) was proposed\nas the basis of some intriguing universal aspects of glasses at ultra-low\ntemperatures. Here we ask whether their existence is necessary for explaining\nthe universal density of states quasi-localized modes (QLM) in glasses at\nultra-low temperatures. A careful examination of the QLM that exist in a\ngeneric atomistic model of a glass former reveals at least two types of them,\neach exhibiting a different density of states, one depending on the frequency\nas $\\omega^3$ and the other as $\\omega^4$. The properties of the glassy energy\nlandscape that is responsible for the two types of modes is examined here,\nexplaining the analytic feature responsible for the creations of (at least) two\nfamilies of QLM's. Although adjacent wells certainly exist in the complex\nenergy landscape of glasses, doubt is cast on the relevance of TLS for the\nuniversal density of QLM's.\n"}
+{"abstract": "  The optical polarization plane of some blazars occasionally exhibits smooth\nhundred degree long rotations. Multiple theoretical models have been proposed\nto explain the nature of such events. A deterministic origin of these\nrotations, however, remains uncertain. We aim to find repeating patterns of\nflares in gamma-ray light curves of blazars, which accompany optical\npolarization plane rotations. Such patterns have been predicted to occur by one\nof the models explaining this phenomenon. For the blazar 3C 279, where multiple\npolarization plane rotations have been reported in the literature, we obtain\nthe Fermi-LAT gamma-ray light curve and analyze its intervals adjacent to\npolarization plane rotations. We find a complex characteristic pattern of\nflares in the gamma-ray light curve that is repeated during periods adjacent to\nthree large amplitude EVPA rotation events in 3C 279. We discover a \"hidden\nEVPA rotation\", which can only be seen in the relative Stokes parameters plane\nand that occurred simultaneously with the fourth repetition of the pattern.\nThis finding strongly favors the hypothesis of emission features propagating in\nthe jet as the reason of optical polarization plane rotations. Furthermore, it\nis compatible with the hypothesis of a sheath in the jet comprised of more\nslowly propagating emission features.\n"}
+{"abstract": "  Low-scale baryogenesis could be discovered at $B$-factories and the LHC. In\nthe $B$-Mesogenesis paradigm [G. Elor, M. Escudero, and A. E. Nelson, PRD 99,\n035031 (2019), arXiv:1810.00880], the CP violating oscillations and subsequent\ndecays of $B$ mesons in the early Universe simultaneously explain the origin of\nthe baryonic and the dark matter of the Universe. This mechanism for baryo- and\ndark matter genesis from $B$ mesons gives rise to distinctive signals at\ncollider experiments, which we scrutinize in this paper. We study CP violating\nobservables in the $B^0_q-\\bar{B}_q^0$ system, discuss current and expected\nsensitivities for the exotic decays of $B$ mesons into a visible baryon and\nmissing energy, and explore the implications of direct searches for a TeV-scale\ncolored scalar at the LHC and in meson-mixing observables. Remarkably, we\nconclude that a combination of measurements at BaBar, Belle, Belle II, LHCb,\nATLAS and CMS can fully test $B$-Mesogenesis.\n"}
+{"abstract": "  Resolvent analysis is performed to identify the origin of two-dimensional\ntransonic buffet over an airfoil. The base flow for the resolvent analysis is\nthe time-averaged flow over a NACA 0012 airfoil at a chord-based Reynolds\nnumber of 2000 and a free-stream Mach number of 0.85. We reveal that the\nmechanism of buffet is buried underneath the global low-Reynolds-number flow\nphysics. At this low Reynolds number, the dominant flow feature is the von\nKarman shedding. However, we show that with the appropriate forcing input,\nbuffet can appear even at a Reynolds number that is much lower than what is\ntraditionally associated with transonic buffet. The source of buffet is\nidentified to be at the shock foot from the windowed resolvent analysis, which\nis validated by companion simulations using sustained forcing inputs based on\nresolvent modes. We also comment on the role of perturbations in the vicinity\nof the trailing edge. The present study not only provides insights on the\norigin of buffet but also serves a building block for low-Reynolds-number\ncompressible aerodynamics in light of the growing interests in Martian flights.\n"}
+{"abstract": "  In this paper, we consider the stabilization of wave equations with moving\nboundary. First, we show the solution behaviour of wave equation with Neumann\nboundary conditions, that is, the energy of wave equation with mixed boundary\nconditions may decrease, increase or conserve depending on the different range\nof parameter. Second, we prove the wellposedness and stabilization for the wave\nequation with time delay and moving boundary.\n"}
+{"abstract": "  This paper establishes a central limit theorem under the assumption that\nconditional variances can vary in a largely unstructured history-dependent way\nacross experiments subject only to the restriction that they lie in a fixed\ninterval. Limits take a novel and tractable form, and are expressed in terms of\noscillating Brownian motion. A second contribution is application of this\nresult to a class of multi-armed bandit problems where the decision-maker is\nloss averse.\n"}
+{"abstract": "  We develop a flow renormalization approach for periodically-driven quantum\nsystems, which reveals prethermal dynamical regimes and associated timescales\nvia direct correspondence between real time and flow time behavior. In this\nformalism, the dynamical problem is recast in terms of coupling constants of\nthe theory flowing towards an attractive fixed point that represents the\nthermal Floquet Hamiltonian at long times, while narrowly avoiding a series of\nunstable fixed points which determine distinct prethermal regimes at\nintermediate times. We study a class of relevant perturbations that trigger the\nonset of heating and thermalization, and demonstrate that the renormalization\nflow has an elegant representation in terms of a flow of matrix product\noperators. Our results permit microscopic calculations of the emergence of\ndistinct dynamical regimes directly in the thermodynamic limit in an efficient\nmanner, establishing a new computational tool for driven non-equilibrium\nsystems.\n"}
+{"abstract": "  We re-examine the XUV luminosity evolution of TRAPPIST-1 utilizing new\nobservational constraints (XUV and bolometric luminosity) from multi-epoch\nX-ray/UV photometry. Following the formalism presented in Fleming et al.\n(2020), we infer that TRAPPIST-1 maintained a saturated XUV luminosity,\nrelative to the bolometric luminosity, of $\\log_{10}$(L$_{\\rm XUV}$/L$_{\\rm\nbol}$) $= -3.03_{-0.23}^{+0.25}$ at early times for a period of $t_{sat} =\n3.14_{-1.46}^{+2.22}$ Gyr. After the saturation phase, we find L$_{\\rm XUV}$\ndecayed over time by an exponential rate of $\\beta_{\\rm XUV} =\n-1.17_{-0.28}^{+0.27}$. Compared to our inferred age of the system, ${\\rm age}\n= 7.96_{-1.87}^{+1.78}$ Gyr, our result for $t_{sat}$ suggests that there is\nonly a $\\sim4\\%$~chance that TRAPPIST-1 still remains in the saturated phase\ntoday, which is significantly lower than the previous estimate of 40\\%. Despite\nthis reduction in $t_{sat}$, our results remain consistent in the conclusion\nthat the TRAPPIST-1 planets likely received an extreme amount XUV energy -- an\nestimated integrated XUV energy of $\\sim10^{30}-10^{32}$ erg over the star's\nlifetime.\n"}
+{"abstract": "  This paper studies task adaptive pre-trained model selection, an\nunderexplored problem of assessing pre-trained models for the target task and\nselect best ones from the model zoo \\emph{without fine-tuning}. A few pilot\nworks addressed the problem in transferring supervised pre-trained models to\nclassification tasks, but they cannot handle emerging unsupervised pre-trained\nmodels or regression tasks. In pursuit of a practical assessment method, we\npropose to estimate the maximum value of label evidence given features\nextracted by pre-trained models. Unlike the maximum likelihood, the maximum\nevidence is \\emph{immune to over-fitting}, while its expensive computation can\nbe dramatically reduced by our carefully designed algorithm. The Logarithm of\nMaximum Evidence (LogME) can be used to assess pre-trained models for transfer\nlearning: a pre-trained model with a high LogME value is likely to have good\ntransfer performance. LogME is \\emph{fast, accurate, and general},\ncharacterizing itself as the first practical method for assessing pre-trained\nmodels. Compared with brute-force fine-tuning, LogME brings at most\n$3000\\times$ speedup in wall-clock time and requires only $1\\%$ memory\nfootprint. It outperforms prior methods by a large margin in their setting and\nis applicable to new settings. It is general enough for diverse pre-trained\nmodels (supervised pre-trained and unsupervised pre-trained), downstream tasks\n(classification and regression), and modalities (vision and language). Code is\navailable at this repository:\n\\href{https://github.com/thuml/LogME}{https://github.com/thuml/LogME}.\n"}
+{"abstract": "  Fe$_{1+x}$Te is a two dimensional van der Waals antiferromagnet that becomes\nsuperconducting on anion substitution on the Te site. The parent phase of\nFe$_{1+x}$Te is sensitive to the amount of interstitial iron situated between\nthe iron-tellurium layers displaying collinear magnetic order coexisting with\nlow temperature metallic resistivity for small concentrations of interstitial\niron $x$ and helical magnetic order for large values of $x$. While this phase\ndiagram has been established through scattering [see for example E. E.\nRodriguez $\\textit{et al.}$ Phys. Rev. B ${\\bf{84}}$, 064403 (2011) and S.\nR\\\"ossler $\\textit{et al.}$ Phys. Rev. B ${\\bf{84}}$, 174506 (2011)], recent\nscanning tunnelling microscopy measurements [C. Trainer $\\textit{et al.}$ Sci.\nAdv. ${\\bf{5}}$, eaav3478 (2019)] have observed a different magnetic structure\nfor small interstitial iron concentrations $x$ with a significant canting of\nthe magnetic moments along the crystallographic $c$ axis of $\\theta$=28 $\\pm$\n3$^{\\circ}$. In this paper, we revisit the magnetic structure of Fe$_{1.09}$Te\nusing spherical neutron polarimetry and scanning tunnelling microscopy to\nsearch for this canting in the bulk phase and compare surface and bulk\nmagnetism. The results show that the bulk magnetic structure of Fe$_{1.09}$Te\nis consistent with collinear in-plane order ($\\theta=0$ with an error of $\\sim$\n5$^{\\circ}$). Comparison with scanning tunnelling microscopy on a series of\nFe$_{1+x}$Te samples reveals that the surface exhibits a magnetic surface\nreconstruction with a canting angle of the spins of $\\theta=29.8^{\\circ}$. We\nsuggest that this is a consequence of structural relaxation of the surface\nlayer resulting in an out-of-plane magnetocrystalline anisotropy. The magnetism\nin Fe$_{1+x}$Te displays different properties at the surface when the symmetry\nconstraints of the bulk are removed.\n"}
+{"abstract": "  Inspired by the stringy quintessence F-term problem we highlight a generic\ncontribution to the effective moduli masses that arises due to kinetic mixings\nbetween the moduli and the quintessence sector. We then proceed to discuss few\nsupergravity toy models that accommodate such effect, and point out possible\nshortcomings. Interestingly, in the standard 2-derivative supergravity action\nthere is no term to mediate the supersymmetry breaking from the kinetic\nquintessence sector to the gaugini and generate Majorana masses. Therefore we\nalso propose a 2-derivative supersymmetric invariant that plays exactly this\nrole.\n"}
+{"abstract": "  Extracting patterns and useful information from Natural Language datasets is\na challenging task, especially when dealing with data written in a language\ndifferent from English, like Italian. Machine and Deep Learning, together with\nNatural Language Processing (NLP) techniques have widely spread and improved\nlately, providing a plethora of useful methods to address both Supervised and\nUnsupervised problems on textual information. We propose RECKONition, a\nNLP-based system for Industrial Accidents at Work Prevention. RECKONition,\nwhich is meant to provide Natural Language Understanding, Clustering and\nInference, is the result of a joint partnership with the Italian National\nInstitute for Insurance against Accidents at Work (INAIL). The obtained results\nshowed the ability to process textual data written in Italian describing\nindustrial accidents dynamics and consequences.\n"}
+{"abstract": "  We investigate the claim that the Utah congressional districts enacted in\n2011 represent an unfair Republican gerrymander, and we evaluate the most\ncommon measures of partisan fairness and gerrymandering in the context of\nUtah's congressional districts. We do this by generating large ensembles of\nalternative redistricting plans using Markov chain Monte Carlo methods. We also\npropose a new metric of partisan fairness in Utah, namely, the Republican vote\nshare in the least-Republican district. This metric only makes sense in\nsettings with at most one competitive district, but it is very effective for\nquantifying gerrymandering and for evaluating other metrics used in such\nsettings.\n"}
+{"abstract": "  In the spring of 2017 an ER-2 aircraft campaign was undertaken over\ncontinental United States to observe energetic radiation from thunderstorms and\nlightning. The payload consisted of a suite of instruments designed to detect\noptical signals, electric fields, and gamma rays from lightning. Starting from\nGeorgia, USA, 16 flights were performed, for a total of about 70 flight hours\nat a cruise altitude of 20 km. Of these, 45 flight hours were over thunderstorm\nregions. An analysis of two gamma ray glow events that were observed over\nColorado at 21:47 UT on 8May 2017 is presented.We explore the charge structure\nof the cloud system, as well as possible mechanisms that can produce the gamma\nray glows. The thundercloud system we passed during the gamma ray glow\nobservation had strong convection in the core of the cloud system. Electric\nfield measurements combined with radar and radio measurements suggest an\ninverted charge structure, with an upper negative charge layer and a lower\npositive charge layer. Based on modeling results, we were not able to\nunambiguously determine the production mechanism. Possible mechanisms are\neither an enhancement of cosmic background locally (above or below 20 km) by an\nelectric field below the local threshold or an enhancement of the cosmic\nbackground inside the cloud but then with normal polarity and an electric field\nwell above the Relativistic Runaway Electron Avalanche threshold\n"}
+{"abstract": "  After providing a brief genealogy of our recently proposed model for High-Tc\ncuprates, we investigate the details of the microscopic mechanism that produces\nan attractive interaction between neighboring holes. We show that a peculiar\narrangement of the $p_x$ and $p_y$ oxygen orbitals makes the mutual magnetic\ninteraction of the holes with the localized copper ions to produce a net\nattractive interaction between themselves, which is responsible for the\nemergence of a superconducting phase. We also study the connection existing\nbetween the proposed pseudogap order parameter and the spectral density. We\nshow that the occurrence of two sharp peaks in the latter, between which the\ndensity of states suffers a depletion is a direct consequence of the d-wave\ncharacter of the pseudogap order parameter dependence on $\\mathbf{k}$, which\nbreaks the 90$^\\circ$-rotation symmetry of the oxygen lattices. The peak\nseparation in the spectral density works effectively as an overall pseudogap\norder parameter for the cuprates. We explicitly calculate the spectral density\nin the strange metal and pseudogap phases of Bi2212, at different temperatures,\nand show that our results compare very well with the experimental data.\n"}
+{"abstract": "  We study the flat-band ferromagnetic phase of the Haldane-Hubbard model on a\nhoneycomb lattice within a bosonization scheme for flat-band Chern insulators,\nfocusing on the calculation of the spin-wave excitation spectrum. We consider\nthe Haldane-Hubbard model with the noninteracting lower bands in a nearly-flat\nband limit, previously determined for the spinless model, and at 1/4-filling of\nits corresponding noninteracting limit. Within the bosonization scheme, the\nHaldane-Hubbard model is mapped into an effective interacting boson model,\nwhose quadratic term allows us to determine the spin-wave spectrum at the\nharmonic approximation. We show that the excitation spectrum has two branches\nwith a Goldstone mode and Dirac points at center and at the K and K' points of\nthe first Brillouin zone, respectively. We also consider the effects on the\nspin-wave spectrum due to an energy offset in the on-site Hubbard repulsion\nenergies and due to the presence of an staggered on-site energy term, both\nquantities associated with the two triangular sublattices. In both cases, we\nfind that an energy gap opens at the K and K' points. Moreover, we also find\nsome evidences for an instability of the flat-band ferromagnetic phase in the\npresence of the staggered on-site energy term. We provide some additional\nresults for the square lattice topological Hubbard model previous studied\nwithin the bosonization formalism and comment on the differences between the\nbosonization scheme implementation for the correlated Chern insulators on both\nsquare and honeycomb lattices.\n"}
+{"abstract": "  The Sun is the primary source of energy for the Earth. The small changes in\ntotal solar irradiance (TSI) can affect our climate in the longer timescale. In\nthe evolutionary timescale, the TSI varies by a large amount and hence its\ninfluence on the Earth's mean surface temperature (T$_{s}$) also increases\nsignificantly. We develop a mass-loss dependent analytical model of TSI in the\nevolutionary timescale and evaluated its influence on the T$_{s}$. We\ndetermined the numerical solution of TSI for the next 8.23 Gyrs to be used as\nan input to evaluate the T$_{s}$ which formulated based on a zero-dimensional\nenergy balance model. We used the present-day albedo and bulk atmospheric\nemissivity of the Earth and Mars as initial and final boundary conditions,\nrespectively. We found that the TSI increases by 10\\% in 1.42 Gyr, by 40\\% in\nabout 3.4 Gyrs, and by 120\\% in about 5.229 Gyrs from now, while the T$_{s}$\nshows an insignificant change in 1.644 Gyrs and increases to 298.86 K in about\n3.4 Gyrs. The T$_{s}$ attains the peak value of 2319.2 K as the Sun evolves to\nthe red giant and emits the enormous TSI of 7.93$\\times10^{6} Wm^{-2}$ in 7.676\nGys. At this temperature Earth likely evolves to be a liquid planet. In our\nfinding, the absorbed and emitted flux equally increases and approaches the\nsurface flux in the main sequence, and they are nearly equal beyond the main\nsequence, while the flux absorbed by the cloud shows opposite trend.\n"}
+{"abstract": "  We introduce a new two-sided stable matching problem that describes the\nsummer internship matching practice of an Australian university. The model is a\ncase between two models of Kamada and Kojima on matchings with distributional\nconstraints. We study three solution concepts, the strong and weak stability\nconcepts proposed by Kamada and Kojima, and a new one in between the two,\ncalled cutoff stability. Kamada and Kojima showed that a strongly stable\nmatching may not exist in their most restricted model with disjoint regional\nquotas. Our first result is that checking its existence is NP-hard. We then\nshow that a cutoff stable matching exists not just for the summer internship\nproblem but also for the general matching model with arbitrary heredity\nconstraints. We present an algorithm to compute a cutoff stable matching and\nshow that it runs in polynomial time in our special case of summer internship\nmodel. However, we also show that finding a maximum size cutoff stable matching\nis NP-hard, but we provide a Mixed Integer Linear Program formulation for this\noptimisation problem.\n"}
+{"abstract": "  Convolutional neural networks have allowed remarkable advances in single\nimage super-resolution (SISR) over the last decade. Among recent advances in\nSISR, attention mechanisms are crucial for high-performance SR models. However,\nthe attention mechanism remains unclear on why and how it works in SISR. In\nthis work, we attempt to quantify and visualize attention mechanisms in SISR\nand show that not all attention modules are equally beneficial. We then propose\nattention in attention network (A$^2$N) for more efficient and accurate SISR.\nSpecifically, A$^2$N consists of a non-attention branch and a coupling\nattention branch. A dynamic attention module is proposed to generate weights\nfor these two branches to suppress unwanted attention adjustments dynamically,\nwhere the weights change adaptively according to the input features. This\nallows attention modules to specialize to beneficial examples without otherwise\npenalties and thus greatly improve the capacity of the attention network with\nfew parameters overhead. Experimental results demonstrate that our final model\nA$^2$N could achieve superior trade-off performances comparing with\nstate-of-the-art networks of similar sizes. Codes are available at\nhttps://github.com/haoyuc/A2N.\n"}
+{"abstract": "  Recent advances in self-attention and pure multi-layer perceptrons (MLP)\nmodels for vision have shown great potential in achieving promising performance\nwith fewer inductive biases. These models are generally based on learning\ninteraction among spatial locations from raw data. The complexity of\nself-attention and MLP grows quadratically as the image size increases, which\nmakes these models hard to scale up when high-resolution features are required.\nIn this paper, we present the Global Filter Network (GFNet), a conceptually\nsimple yet computationally efficient architecture, that learns long-term\nspatial dependencies in the frequency domain with log-linear complexity. Our\narchitecture replaces the self-attention layer in vision transformers with\nthree key operations: a 2D discrete Fourier transform, an element-wise\nmultiplication between frequency-domain features and learnable global filters,\nand a 2D inverse Fourier transform. We exhibit favorable accuracy/complexity\ntrade-offs of our models on both ImageNet and downstream tasks. Our results\ndemonstrate that GFNet can be a very competitive alternative to\ntransformer-style models and CNNs in efficiency, generalization ability and\nrobustness. Code is available at https://github.com/raoyongming/GFNet\n"}
+{"abstract": "  A uniformly accelerated detector in an inertial vacuum undergoes an\nunavoidable dissipation, and the final steady-state becomes thermal. However,\nto attain such a mixed state, there is no bound for the acceleration of the\nsingle atomic detector. Here we show that the scenario is entirely different\nfor two atoms with the same energy levels. There exists a critical limit of the\nacceleration for two atomic detectors, below which the purity of a particular\ninitial state can be preserved. We observe that the generator of the\ndissipative dynamics (Lindbladian) is invariant under a weak symmetry\ntransformation at this limit. Hence one of the eigenstates of the symmetry\noperator is unchanged during the evolution. This kind of state is called a\nquantum dark state, which is essentially a decoherence-free subspace. As a\nconsequence, the system becomes localized, and it can skip the Unruh\nthermalization. Beyond the critical limit, the symmetry is explicitly broken.\nTherefore our results suggest that the system goes through a first-order\ndissipative phase transition from a localized to a thermal phase.\n"}
+{"abstract": "  A method is proposed with which the locations of the roots of the monic\nsymbolic quintic polynomial $x^5 + a_4 x^4 + a_3 x^3 + a_2 x^2 + a_1 x + a_0$\ncan be determined using the roots of two resolvent quadratic polynomials:\n$q_1(x) = x^2 + a_4 x + a_3$ and $q_2(x) = a_2 x^2 + a_1 x + a_0$, whose\ncoefficients are exactly those of the quintic polynomial. The different cases\ndepend on the coefficients of $q_1(x)$ and $q_2(x)$ and on some specific\nrelationships between them. The method is illustrated with the full analysis of\none of the possible cases. Some of the roots of the symbolic quintic equation\nfor this case have their isolation intervals determined and, as this cannot be\ndone for all roots with the help of quadratic equations only, finite intervals\ncontaining 1 or 3 roots, or 0 or 2 roots, or, rarely, 0, or 2, or 4 roots of\nthe quintic are identified. Knowing the stationary points of the quintic\npolynomial, lifts the latter indeterminacy and allows one to find the isolation\ninterval of each of the roots of the quintic. Separately, using the complete\nroot classification of the quintic, one can also lift this indeterminacy. The\nmethod also allows to see how variation of the individual coefficients of the\nquintic affect its roots. No root finding iterations or any numerical\napproximations are used and no equations of degree higher than 2 are solved.\n"}
+{"abstract": "  A potential Mars Sample Return (MSR) architecture is being jointly studied by\nNASA and ESA. As currently envisioned, the MSR campaign consists of a series of\n3 missions: sample cache, fetch and return to Earth. In this paper, we focus on\nthe fetch part of the MSR, and more specifically the problem of autonomously\ndetecting and localizing sample tubes deposited on the Martian surface. Towards\nthis end, we study two machine-vision based approaches: First, a\ngeometry-driven approach based on template matching that uses hard-coded\nfilters and a 3D shape model of the tube; and second, a data-driven approach\nbased on convolutional neural networks (CNNs) and learned features.\nFurthermore, we present a large benchmark dataset of sample-tube images,\ncollected in representative outdoor environments and annotated with ground\ntruth segmentation masks and locations. The dataset was acquired systematically\nacross different terrain, illumination conditions and dust-coverage; and\nbenchmarking was performed to study the feasibility of each approach, their\nrelative strengths and weaknesses, and robustness in the presence of adverse\nenvironmental conditions.\n"}
+{"abstract": "  Existing methods for skeleton-based action recognition mainly focus on\nimproving the recognition accuracy, whereas the efficiency of the model is\nrarely considered. Recently, there are some works trying to speed up the\nskeleton modeling by designing light-weight modules. However, in addition to\nthe model size, the amount of the data involved in the calculation is also an\nimportant factor for the running speed, especially for the skeleton data where\nmost of the joints are redundant or non-informative to identify a specific\nskeleton. Besides, previous works usually employ one fix-sized model for all\nthe samples regardless of the difficulty of recognition, which wastes\ncomputations for easy samples. To address these limitations, a novel approach,\ncalled AdaSGN, is proposed in this paper, which can reduce the computational\ncost of the inference process by adaptively controlling the input number of the\njoints of the skeleton on-the-fly. Moreover, it can also adaptively select the\noptimal model size for each sample to achieve a better trade-off between\naccuracy and efficiency. We conduct extensive experiments on three challenging\ndatasets, namely, NTU-60, NTU-120 and SHREC, to verify the superiority of the\nproposed approach, where AdaSGN achieves comparable or even higher performance\nwith much lower GFLOPs compared with the baseline method.\n"}
+{"abstract": "  Transition probability densities are fundamental to option pricing. Advancing\nrecent work in deep learning, we develop novel transition density function\ngenerators through solving backward Kolmogorov equations in parametric space\nfor cumulative probability functions, using neural networks to obtain accurate\napproximations of transition probability densities, creating ultra-fast\ntransition density function generators offline that can be trained for any\nunderlying. These are 'single solve' , so they do not require recalculation\nwhen parameters are changed (e.g. recalibration of volatility) and are portable\nto other option pricing setups as well as to less powerful computers, where\nthey can be accessed as quickly as closed-form solutions. We demonstrate the\nrange of application for one-dimensional cases, exemplified by the\nBlack-Scholes-Merton model, two-dimensional cases, exemplified by the Heston\nprocess, and finally for a modified Heston model with time-dependent parameters\nthat has no closed-form solution.\n"}
+{"abstract": "  Voting rules based on evaluation inputs rather than preference orders have\nbeen recently proposed, like majority judgement, range voting or approval\nvoting. Traditionally, probabilistic analysis of voting rules supposes the use\nof simulation models to generate preferences data, like the Impartial Culture\n(IC) or Impartial and Anonymous Culture (IAC) models. But these simulation\nmodels are not suitable for the analysis of evaluation-based voting rules as\nthey generate preference orders instead of the needed evaluations. We propose\nin this paper several simulation models for generating evaluation-based voting\ninputs. These models, inspired by classical ones, are defined, tested and\ncompared for recommendation purpose.\n"}
+{"abstract": "  Vision transformers (ViTs) process input images as sequences of patches via\nself-attention; a radically different architecture than convolutional neural\nnetworks (CNNs). This makes it interesting to study the adversarial feature\nspace of ViT models and their transferability. In particular, we observe that\nadversarial patterns found via conventional adversarial attacks show very low\nblack-box transferability even for large ViT models. However, we show that this\nphenomenon is only due to the sub-optimal attack procedures that do not\nleverage the true representation potential of ViTs. A deep ViT is composed of\nmultiple blocks, with a consistent architecture comprising of self-attention\nand feed-forward layers, where each block is capable of independently producing\na class token. Formulating an attack using only the last class token\n(conventional approach) does not directly leverage the discriminative\ninformation stored in the earlier tokens, leading to poor adversarial\ntransferability of ViTs. Using the compositional nature of ViT models, we\nenhance the transferability of existing attacks by introducing two novel\nstrategies specific to the architecture of ViT models. (i) Self-Ensemble: We\npropose a method to find multiple discriminative pathways by dissecting a\nsingle ViT model into an ensemble of networks. This allows explicitly utilizing\nclass-specific information at each ViT block. (ii) Token Refinement: We then\npropose to refine the tokens to further enhance the discriminative capacity at\neach block of ViT. Our token refinement systematically combines the class\ntokens with structural information preserved within the patch tokens. An\nadversarial attack, when applied to such refined tokens within the ensemble of\nclassifiers found in a single vision transformer, has significantly higher\ntransferability.\n"}
+{"abstract": "  In this paper we provide an expansion formula for Hawkes processes which\ninvolves the addition of jumps at deterministic times to the Hawkes process in\nthe spirit of the well-known integration by parts formula (or more precisely\nthe Mecke formula) for Poisson functional. Our approach allows us to provide an\nexpansion of the premium of a class of cyber insurance derivatives (such as\nreinsurance contracts including generalized Stop-Loss contracts) or risk\nmanagement instruments (like Expected Shortfall) in terms of so-called shifted\nHawkes processes. From the actuarial point of view, these processes can be seen\nas \"stressed\" scenarios. Our expansion formula for Hawkes processes enables us\nto provide lower and upper bounds on the premium (or the risk evaluation) of\nsuch cyber contracts and to quantify the surplus of premium compared to the\nstandard modeling with a homogenous Poisson process.\n"}
+{"abstract": "  Models based on attention mechanisms have shown unprecedented speech\nrecognition performance. However, they are computationally expensive and\nunnecessarily complex for keyword spotting, a task targeted to small-footprint\ndevices. This work explores the application of Lambda networks, an alternative\nframework for capturing long-range interactions without attention, for the\nkeyword spotting task. We propose a novel \\textit{ResNet}-based model by\nswapping the residual blocks by temporal Lambda layers. Furthermore, the\nproposed architecture is built upon uni-dimensional temporal convolutions that\nfurther reduce its complexity. The presented model does not only reach\nstate-of-the-art accuracies on the Google Speech Commands dataset, but it is\n85% and 65% lighter than its Transformer-based (KWT) and convolutional (Res15)\ncounterparts while being up to 100 times faster. To the best of our knowledge,\nthis is the first attempt to explore the Lambda framework within the speech\ndomain and therefore, we unravel further research of new interfaces based on\nthis architecture.\n"}
+{"abstract": "  In this letter, we consider the uplink of a cell-free Massive multiple-input\nmultiple-output (MIMO) network where each user is decoded by a subset of access\npoints (APs). An additional step is introduced in the cell-free Massive MIMO\nprocessing: each AP in the uplink locally implements soft MIMO detection and\nthen shares the resulting bit log-likelihoods on the front-haul link. The\ndecoding of the data is performed at the central processing unit (CPU),\ncollecting the data from the APs. The non-linear processing at the APs consists\nof the approximate computation of the posterior density for each received data\nbit, exploiting only local channel state information. The proposed method\noffers good performance in terms of frame-error-rate and considerably lower\ncomplexity than the optimal maximum-likelihood demodulator.\n"}
+{"abstract": "  Paper presents a new view of seismic risk and tectonic stress reduction in\nthe earthquake focus\n"}
+{"abstract": "  The behaviors of deep neural networks (DNNs) are notoriously resistant to\nhuman interpretations. In this paper, we propose Hypergradient Data Relevance\nAnalysis, or HYDRA, which interprets the predictions made by DNNs as effects of\ntheir training data. Existing approaches generally estimate data contributions\naround the final model parameters and ignore how the training data shape the\noptimization trajectory. By unrolling the hypergradient of test loss w.r.t. the\nweights of training data, HYDRA assesses the contribution of training data\ntoward test data points throughout the training trajectory. In order to\naccelerate computation, we remove the Hessian from the calculation and prove\nthat, under moderate conditions, the approximation error is bounded.\nCorroborating this theoretical claim, empirical results indicate the error is\nindeed small. In addition, we quantitatively demonstrate that HYDRA outperforms\ninfluence functions in accurately estimating data contribution and detecting\nnoisy data labels. The source code is available at\nhttps://github.com/cyyever/aaai_hydra_8686.\n"}
+{"abstract": "  While commercial mid-air gesture recognition systems have existed for at\nleast a decade, they have not become a widespread method of interacting with\nmachines. This is primarily due to the fact that these systems require rigid,\ndramatic gestures to be performed for accurate recognition that can be\nfatiguing and unnatural. The global pandemic has seen a resurgence of interest\nin touchless interfaces, so new methods that allow for natural mid-air gestural\ninteractions are even more important. To address the limitations of recognition\nsystems, we propose a neuromorphic gesture analysis system which naturally\ndeclutters the background and analyzes gestures at high temporal resolution.\nOur novel model consists of an event-based guided Variational Autoencoder (VAE)\nwhich encodes event-based data sensed by a Dynamic Vision Sensor (DVS) into a\nlatent space representation suitable to analyze and compute the similarity of\nmid-air gesture data. Our results show that the features learned by the VAE\nprovides a similarity measure capable of clustering and pseudo labeling of new\ngestures. Furthermore, we argue that the resulting event-based encoder and\npseudo-labeling system are suitable for implementation in neuromorphic hardware\nfor online adaptation and learning of natural mid-air gestures.\n"}
+{"abstract": "  This paper is part of an ongoing endeavor to bring the theory of fair\ndivision closer to practice by handling requirements from real-life\napplications. We focus on two requirements originating from the division of\nland estates: (1) each agent should receive a plot of a usable geometric shape,\nand (2) plots of different agents must be physically separated. With these\nrequirements, the classic fairness notion of proportionality is impractical,\nsince it may be impossible to attain any multiplicative approximation of it. In\ncontrast, the ordinal maximin share approximation, introduced by Budish in\n2011, provides meaningful fairness guarantees. We prove upper and lower bounds\non achievable maximin share guarantees when the usable shapes are squares, fat\nrectangles, or arbitrary axes-aligned rectangles, and explore the algorithmic\nand query complexity of finding fair partitions in this setting.\n"}
+{"abstract": "  We generalize Wentzel-Kramers-Brillouin (WKB) semi-classical equations for\npseudospin-1 $\\alpha-\\mathcal{T}_3$ materials with arbitrary hopping parameter\n$0 < \\alpha < 1$, which includes the dice lattice and graphene as two limiting\ncases. In conjunction with a series-expansion method in powers of Planck\nconstant $\\hbar$, we acquired and solved a system of recurrent differential\nequations for semi-classical electron wave functions in $\\alpha-\\mathcal{T}_3$.\nMaking use of these obtained wave functions, we analyzed the physics-related\nmechanism and quantified the transmission of pseudospin-1 Dirac electrons\nacross non-rectangular potential barriers in $\\alpha-\\mathcal{T}_3$ materials\nwith both zero and finite band gaps. Our studies reveal several unique\nfeatures, including the way in which the electron transmission depends on the\nenergy gap, the slope of the potential barrier profile and the transverse\nmomentum of incoming electrons. Specifically, we have found a strong dependence\nof the obtained transmission amplitude on the geometry-phase $\\phi = \\tan^{-1}\n\\alpha$ of $\\alpha-\\mathcal{T}_3$ lattices. We believe our current findings can\nbe applied to Dirac cone-based tunneling transistors in ultrafast analog RF\ndevices, as well as to tunneling-current control by a potential barrier through\na one-dimensional array of scatters.\n"}
+{"abstract": "  The reconstruction of a smooth field onto a fixed grid is a necessary step\nfor direct comparisons to various real-world observations. Projecting SPH data\nonto a fixed grid becomes challenging in adaptive environments, where some\nparticles may have smoothing lengths far below the grid size, whilst others are\nresolved by thousands of pixels. In this paper we show how the common approach\nof treating particles below the grid size as Monte Carlo tracers of the field\nleads to significant reconstruction errors, and despite good convergence\nproperties is unacceptable for use in synthetic observations in astrophysics.\nWe propose a new method, where particles smaller than the grid size are\n`blitted' onto the grid using a high-resolution pre-calculated kernel, and\nthose close to the grid size are subsampled, that allows for converged\npredictions for projected quantities at all grid sizes.\n"}
+{"abstract": "  End-to-end neural network models achieve improved performance on various\nautomatic speech recognition (ASR) tasks. However, these models perform poorly\non edge hardware due to large memory and computation requirements. While\nquantizing model weights and/or activations to low-precision can be a promising\nsolution, previous research on quantizing ASR models is limited. In particular,\nthe previous approaches use floating-point arithmetic during inference and thus\nthey cannot fully exploit efficient integer processing units. Moreover, they\nrequire training/validation data during quantization, which may not be\navailable due to security/privacy concerns. To address these limitations, we\npropose an integer-only, zero shot quantization scheme for ASR models. In\nparticular, we generate synthetic data whose runtime statistics resemble the\nreal data, and we use it to calibrate models during quantization. We apply our\nmethod to quantize QuartzNet, Jasper, and Conformer and show negligible WER\nchange as compared to the full-precision baseline models, even without using\nany training data. Moreover, we achieve up to 2.35x speedup on a T4 GPU and 4x\ncompression rate, with a modest WER degradation of <1% with INT8 quantization.\n"}
+{"abstract": "  A normal variety $X$ is called $H$-spherical for the action of the complex\nreductive group $H$ if it contains a dense orbit of some Borel subgroup of $H$.\nWe resolve a conjecture of Hodges--Yong by showing that their spherical\npermutations are characterized by permutation pattern avoidance. Together with\nresults of Gao--Hodges--Yong this implies that the sphericality of a Schubert\nvariety $X_w$ with respect to the largest possible Levi subgroup is\ncharacterized by this same pattern avoidance condition.\n"}
+{"abstract": "  Graphs are topological spaces that include broader objects than discretized\nmanifolds, making them interesting playgrounds for the study of quantum phases\nnot realized by symmetry breaking. In particular they are known to support\nanyons of an even richer variety than the two-dimensional space. We explore\nthis possibility by building a class of frustration-free and gapped\nHamiltonians based on discrete abelian gauge groups. The resulting models have\na ground state degeneracy that can be either a topological invariant, an\nextensive quantity or a mixture of the two. For two basis of the degenerate\nground states which are complementary in quantum theory, the entanglement\nentropy is exactly computed. The result for one basis has a constant global\nterm, known as the topological entanglement entropy, implying long-range\nentanglement. On the other hand, the topological entanglement entropy vanishes\nin the result for the other basis. Comparisons are made with similar\noccurrences in the toric code. We analyze excitations and identify anyon-like\nexcitations that account for the topological entanglement entropy. An analogy\nbetween the ground states of this system and the $\\theta$-vacuum for a $U(1)$\ngauge theory on a circle is also drawn.\n"}
+{"abstract": "  A novel space-time parallel index modulation scheme is proposed in this paper\nfor conveying extra digital information with the aid of space and time indices.\nIn this proposed scheme, time-indexing is cleverly performed in parallel\nspatial modulation (PSM) schemes using transmit antenna grouping, and referred\nto as time-indexed parallel spatial modulation (TI-PSM). In this scheme,\ntransmit antennas are divided into groups to adopt TI-SM scheme in large-scale\nmultiple input multiple output (MIMO) systems with inter-channel interference\n(ICI) avoidance. This scheme is attractive due to both the high data rate and\ngood performance improvement that can be achieved. The bit error rate (BER)\nperformance of the TI-PSM scheme is evaluated and compared to that of the\nconventional schemes. Simulation results demonstrated that a significant\nimprovement is achieved by the TI-PSM scheme as compared to the TI-SM and PSM\nschemes for the same achieved rate. It is also shown that TI-SM and TI-PSM\nschemes provide higher performance drop with channel estimation errors (CEEs)\nthan the conventional SM and PSM systems. Therefore, due to the high\nperformance improvements achieved in TI-PSM scheme, it can be effectively\nregarded as a promising solution for various 5G and beyond wireless networks.\n"}
+{"abstract": "  With advancements in technology, personal computing devices are better\nadapted for and further integrated into people's lives and homes. The\nintegration of technology into society also results in an increasing desire to\ncontrol who and what has access to sensitive information, especially for\nvulnerable people including children and the elderly. With blockchain coming in\nto the picture as a technology that can revolutionise the world, it is now\npossible to have an immutable audit trail of locational data over time. By\ncontrolling the process through inexpensive equipment in the home, it is\npossible to control whom has access to such personal data. This paper presents\na blockchain based family security system for tracking the location of\nconsenting family members' smart phones. The locations of the family members'\nsmart phones are logged and stored in a private blockchain which can be\naccessed through a node installed in the family home on a computer. The data\nfor the whereabouts of family members stays within the family unit and does not\ngo to any third party. The system is implemented in a small scale (one miner\nand two other nodes) and the technical feasibility is discussed along with the\nlimitations of the system. Further research will cover the integration of the\nsystem into a smart home environment, and ethical implementations of tracking,\nespecially of vulnerable people, using the immutability of blockchain.\n"}
+{"abstract": "  Over the past decades, numerous practical applications of machine learning\ntechniques have shown the potential of data-driven approaches in a large number\nof computing fields. Machine learning is increasingly included in computing\ncurricula in higher education, and a quickly growing number of initiatives are\nexpanding it in K-12 computing education, too. As machine learning enters K-12\ncomputing education, understanding how intuition and agency in the context of\nsuch systems is developed becomes a key research area. But as schools and\nteachers are already struggling with integrating traditional computational\nthinking and traditional artificial intelligence into school curricula,\nunderstanding the challenges behind teaching machine learning in K-12 is an\neven more daunting challenge for computing education research. Despite the\ncentral position of machine learning in the field of modern computing, the\ncomputing education research body of literature contains remarkably few studies\nof how people learn to train, test, improve, and deploy machine learning\nsystems. This is especially true of the K-12 curriculum space. This article\ncharts the emerging trajectories in educational practice, theory, and\ntechnology related to teaching machine learning in K-12 education. The article\nsituates the existing work in the context of computing education in general,\nand describes some differences that K-12 computing educators should take into\naccount when facing this challenge. The article focuses on key aspects of the\nparadigm shift that will be required in order to successfully integrate machine\nlearning into the broader K-12 computing curricula. A crucial step is\nabandoning the belief that rule-based \"traditional\" programming is a central\naspect and building block in developing next generation computational thinking.\n"}
+{"abstract": "  Cylindrical algebraic decomposition (CAD) plays an important role in the\nfield of real algebraic geometry and many other areas. As is well-known, the\nchoice of variable ordering while computing CAD has a great effect on the time\nand memory use of the computation as well as the number of sample points\ncomputed. In this paper, we indicate that typical CAD algorithms, if executed\nwith respect to a special kind of variable orderings (called \"the perfect\nelimination orderings\"), naturally preserve chordality, which is an important\nproperty on sparsity of variables. Experimentation suggests that if the\nassociated graph of the polynomial system in question is chordal (\\emph{resp.},\nis nearly chordal), then a perfect elimination ordering of the associated graph\n(\\emph{resp.}, of a minimal chordal completion of the associated graph) can be\na good variable ordering for the CAD computation. That is, by using the perfect\nelimination orderings, the CAD computation may produce a much smaller full set\nof projection polynomials than by using other naive variable orderings. More\nimportantly, for the complexity analysis of the CAD computation via a perfect\nelimination ordering, a so-called $(m,d)$-property of the full set of\nprojection polynomials obtained via such an ordering is given, through which\nthe \"size\" of this set is characterized. This property indicates that when the\ncorresponding perfect elimination tree has a lower height, the full set of\nprojection polynomials also tends to have a smaller \"size\". This is well\nconsistent with the experimental results, hence the perfect elimination\norderings with lower elimination tree height are further recommended to be used\nin the CAD projection.\n"}
+{"abstract": "  Traditional machine learning is centralized in the cloud (data centers).\nRecently, the security concern and the availability of abundant data and\ncomputation resources in wireless networks are pushing the deployment of\nlearning algorithms towards the network edge. This has led to the emergence of\na fast growing area, called federated learning (FL), which integrates two\noriginally decoupled areas: wireless communication and machine learning. In\nthis paper, we provide a comprehensive study on the applications of FL for\nsixth generation (6G) wireless networks. First, we discuss the key requirements\nin applying FL for wireless communications. Then, we focus on the motivating\napplication of FL for wireless communications. We identify the main problems,\nchallenges, and provide a comprehensive treatment of implementing FL techniques\nfor wireless communications.\n"}
+{"abstract": "  The use of local detectors and descriptors in typical computer vision\npipelines work well until variations in viewpoint and appearance change become\nextreme. Past research in this area has typically focused on one of two\napproaches to this challenge: the use of projections into spaces more suitable\nfor feature matching under extreme viewpoint changes, and attempting to learn\nfeatures that are inherently more robust to viewpoint change. In this paper, we\npresent a novel framework that combines learning of invariant descriptors\nthrough data augmentation and orthographic viewpoint projection. We propose\nrotation-robust local descriptors, learnt through training data augmentation\nbased on rotation homographies, and a correspondence ensemble technique that\ncombines vanilla feature correspondences with those obtained through\nrotation-robust features. Using a range of benchmark datasets as well as\ncontributing a new bespoke dataset for this research domain, we evaluate the\neffectiveness of the proposed approach on key tasks including pose estimation\nand visual place recognition. Our system outperforms a range of baseline and\nstate-of-the-art techniques, including enabling higher levels of place\nrecognition precision across opposing place viewpoints and achieves\npractically-useful performance levels even under extreme viewpoint changes.\n"}
+{"abstract": "  We study the widely used hierarchical agglomerative clustering (HAC)\nalgorithm on edge-weighted graphs. We define an algorithmic framework for\nhierarchical agglomerative graph clustering that provides the first efficient\n$\\tilde{O}(m)$ time exact algorithms for classic linkage measures, such as\ncomplete- and WPGMA-linkage, as well as other measures. Furthermore, for\naverage-linkage, arguably the most popular variant of HAC, we provide an\nalgorithm that runs in $\\tilde{O}(n\\sqrt{m})$ time. For this variant, this is\nthe first exact algorithm that runs in subquadratic time, as long as\n$m=n^{2-\\epsilon}$ for some constant $\\epsilon > 0$. We complement this result\nwith a simple $\\epsilon$-close approximation algorithm for average-linkage in\nour framework that runs in $\\tilde{O}(m)$ time. As an application of our\nalgorithms, we consider clustering points in a metric space by first using\n$k$-NN to generate a graph from the point set, and then running our algorithms\non the resulting weighted graph. We validate the performance of our algorithms\non publicly available datasets, and show that our approach can speed up\nclustering of point datasets by a factor of 20.7--76.5x.\n"}
+{"abstract": "  Machine learning practitioners invest significant manual and computational\nresources in finding suitable learning rates for optimization algorithms. We\nprovide a probabilistic motivation, in terms of Gaussian inference, for popular\nstochastic first-order methods. As an important special case, it recovers the\nPolyak step with a general metric. The inference allows us to relate the\nlearning rate to a dimensionless quantity that can be automatically adapted\nduring training by a control algorithm. The resulting meta-algorithm is shown\nto adapt learning rates in a robust manner across a large range of initial\nvalues when applied to deep learning benchmark problems.\n"}
+{"abstract": "  With the advancement of the Internet of Things (IoT) and communication\nplatform, large scale sensor deployment can be easily implemented in an urban\ncity to collect various information. To date, there are only a handful of\nresearch studies about understanding the usage of urban public spaces.\nLeveraging IoT, various sensors have been deployed in an urban residential area\nto monitor and study public space utilization patterns. In this paper, we\npropose a data processing system to generate space-centric insights about the\nutilization of an urban residential region of multiple points of interest\n(PoIs) that consists of 190,000m$^2$ real estate. We identify the activeness of\neach PoI based on the spectral clustering, and then study their corresponding\nstatic features, which are composed of transportation, commercial facilities,\npopulation density, along with other characteristics. Through the heuristic\nfeatures inferring, the residential density and commercial facilities are the\nmost significant factors affecting public place utilization.\n"}
+{"abstract": "  Information retrieval (IR) systems traditionally aim to maximize metrics\nbuilt on rankings, such as precision or NDCG. However, the\nnon-differentiability of the ranking operation prevents direct optimization of\nsuch metrics in state-of-the-art neural IR models, which rely entirely on the\nability to compute meaningful gradients. To address this shortcoming, we\npropose SmoothI, a smooth approximation of rank indicators that serves as a\nbasic building block to devise differentiable approximations of IR metrics. We\nfurther provide theoretical guarantees on SmoothI and derived approximations,\nshowing in particular that the approximation errors decrease exponentially with\nan inverse temperature-like hyperparameter that controls the quality of the\napproximations. Extensive experiments conducted on four standard\nlearning-to-rank datasets validate the efficacy of the listwise losses based on\nSmoothI, in comparison to previously proposed ones. Additional experiments with\na vanilla BERT ranking model on a text-based IR task also confirm the benefits\nof our listwise approach.\n"}
+{"abstract": "  As an effective tool for two-dimensional data analysis, two-dimensional\ncanonical correlation analysis (2DCCA) is not only capable of preserving the\nintrinsic structural information of original two-dimensional (2D) data, but\nalso reduces the computational complexity effectively. However, due to the\nunsupervised nature, 2DCCA is incapable of extracting sufficient discriminatory\nrepresentations, resulting in an unsatisfying performance. In this letter, we\npropose a complete discriminative tensor representation learning (CDTRL) method\nbased on linear correlation analysis for analyzing 2D signals (e.g. images).\nThis letter shows that the introduction of the complete discriminatory tensor\nrepresentation strategy provides an effective vehicle for revealing, and\nextracting the discriminant representations across the 2D data sets, leading to\nimproved results. Experimental results show that the proposed CDTRL outperforms\nstate-of-the-art methods on the evaluated data sets.\n"}
+{"abstract": "  A stationary free boundary problem modeling a three-dimensional electrostatic\nMEMS device is investigated. The device is made of a rigid ground plate and an\nelastic top plate which is hinged at its boundary, the plates being held at\ndifferent voltages. The model couples a nonlocal fourth-order equation for the\ndeformation of the top plate to a Laplace equation for the electrostatic\npotential in the free domain between the two plates. The strength of the\ncoupling is tuned by a parameter $\\lambda$ which is proportional to the square\nof the applied voltage difference. Existence of a stable stationary solution is\nestablished for small values of $\\lambda$. Nonexistence of stationary solutions\nis obtained when $\\lambda$ is large enough.\n"}
+{"abstract": "  Secret Sharing Schemes (SSS) are methods for distributing a secret among a\nset of participants. One of the first Secret Sharing Schemes was proposed by M.\nMignotte, based on the Chinese remainder theorem over the ring of integers. In\nthis article we extend the Mignotte's scheme to the ring of Gaussian Integers\nand study some of its properties. While doing this we aim to solve a gap in a\nprevious construction of such extension. In addition we show that any access\nstructure can be made through a SSS over $ \\mathbb{Z}[i]$.\n"}
+{"abstract": "  We investigate fermions with Lifshitz scaling symmetry and study their\nentanglement entropy in 1+1 dimensions as a function of the scaling exponent\n$z$. Remarkably, in the ground state the entanglement entropy vanishes for even\nvalues of $z$, whereas for odd values it is independent of $z$ and equal to the\nrelativistic case with $z=1$. We show this using the correlation method on the\nlattice, and also using a holographic cMERA approach. The entanglement entropy\nin a thermal state is a more detailed function of $z$ and $T$ which we plot\nusing the lattice correlation method. The dependence on the even- or oddness of\n$z$ still shows for small temperatures, but is washed out for large\ntemperatures or large values of $z$.\n"}
+{"abstract": "  Quantum many-body systems are characterized by patterns of correlations that\ndefine highly-non trivial manifolds when interpreted as data structures.\nPhysical properties of phases and phase transitions are typically retrieved via\nsimple correlation functions, that are related to observable response\nfunctions. Recent experiments have demonstrated capabilities to fully\ncharacterize quantum many-body systems via wave-function snapshots, opening new\npossibilities to analyze quantum phenomena. Here, we introduce a method to data\nmine the correlation structure of quantum partition functions via their path\nintegral (or equivalently, stochastic series expansion) manifold. We\ncharacterize path-integral manifolds generated via state-of-the-art Quantum\nMonte Carlo methods utilizing the intrinsic dimension (ID) and the variance of\ndistances from nearest neighbors (NN): the former is related to dataset\ncomplexity, while the latter is able to diagnose connectivity features of\npoints in configuration space. We show how these properties feature universal\npatterns in the vicinity of quantum criticality, that reveal how data\nstructures {\\it simplify} systematically at quantum phase transitions. This is\nfurther reflected by the fact that both ID and variance of NN-distances exhibit\nuniversal scaling behavior in the vicinity of second-order and\nBerezinskii-Kosterlitz-Thouless critical points. Finally, we show how\nnon-Abelian symmetries dramatically influence quantum data sets, due to the\nnature of (non-commuting) conserved charges in the quantum case. Complementary\nto neural network representations, our approach represents a first, elementary\nstep towards a systematic characterization of path integral manifolds before\nany dimensional reduction is taken, that is informative about universal\nbehavior and complexity, and can find immediate application to both experiments\nand Monte Carlo simulations.\n"}
+{"abstract": "  We prove a new computable version of Hall's Harem theorem. We apply it to\nnon-amenable computable coarse spaces.\n"}
+{"abstract": "  We introduce an automated tool for deploying ultra low-latency, low-power\ndeep neural networks with convolutional layers on FPGAs. By extending the\nhls4ml library, we demonstrate an inference latency of $5\\,\\mu$s using\nconvolutional architectures, targeting microsecond latency applications like\nthose at the CERN Large Hadron Collider. Considering benchmark models trained\non the Street View House Numbers Dataset, we demonstrate various methods for\nmodel compression in order to fit the computational constraints of a typical\nFPGA device used in trigger and data acquisition systems of particle detectors.\nIn particular, we discuss pruning and quantization-aware training, and\ndemonstrate how resource utilization can be significantly reduced with little\nto no loss in model accuracy. We show that the FPGA critical resource\nconsumption can be reduced by 97% with zero loss in model accuracy, and by 99%\nwhen tolerating a 6% accuracy degradation.\n"}
+{"abstract": "  Depositing magnetic insulators on graphene has been a promising route to\nintroduce magnetism via exchange proximity interaction in graphene for future\nspintronics applications. Molecule-based magnets may offer unique opportunities\nbecause of their synthesis versatility. Here, we investigated the magnetic\nproximity effect of epitaxial iron phthalocyanine (FePc) molecules on\nhigh-quality monolayer and bilayer graphene devices on hexagonal boron nitride\nsubstrate by probing the local and non-local transport. Although the FePc\nmolecules introduce large hole doping effects combined with mobility\ndegradation, the magnetic proximity gives rise to a canted antiferromagnetic\nstate under a magnetic field in the monolayer graphene. On bilayer graphene and\nFePc heterostructure devices, the non-local transport reveals a pronounced\nZeeman spin-Hall effect. Further analysis of the scattering mechanism in the\nbilayer shows a dominated long-range scattering. Our findings in\ngraphene/organic magnetic insulator heterostructure provide a new insight for\nthe use of molecule-based magnets in two-dimensional spintronic devices.\n"}
+{"abstract": "  We make precise the analogy between Goodwillie's calculus of functors in\nhomotopy theory and the differential calculus of smooth manifolds by\nintroducing a higher-categorical framework of which both theories are examples.\nThat framework is an extension to infinity-categories of the tangent categories\nof Cockett and Cruttwell (introduced originally by Rosick\\'y). A tangent\nstructure on an infinity-category X consists of an endofunctor on X, which\nplays the role of the tangent bundle construction, together with various\nnatural transformations that mimic structure possessed by the ordinary tangent\nbundles of smooth manifolds and that satisfy similar conditions.\n  The tangent bundle functor in Goodwillie calculus is Lurie's tangent bundle\nfor infinity-categories, introduced to generalize the cotangent complexes of\nAndr\\'e, Quillen and Illusie. We show that Lurie's construction admits the\nadditional structure maps and satisfies the conditions needed to form a tangent\ninfinity-category, which we refer to as the Goodwillie tangent structure on the\ninfinity-category of infinity-categories.\n  Cockett and Cruttwell (and others) have started to develop various aspects of\ndifferential geometry in the abstract context of tangent categories, and we\nbegin to apply those ideas to Goodwillie calculus. For example, we show that\nthe role of Euclidean spaces in the calculus of manifolds is played in\nGoodwillie calculus by the stable infinity-categories. We also show that\nGoodwillie's n-excisive functors are the direct analogues of n-jets of smooth\nmaps between manifolds; to state that connection precisely, we develop a notion\nof tangent (infinity, 2)-category and show that Goodwillie calculus is best\nunderstood in that context.\n"}
+{"abstract": "  Preparation and measurement of physical systems are the operational building\nblocks of any physical experiment, and to describe them is the first purpose of\nany physical theory. It is remarkable that, in some situations, even when only\npreparation and measurement devices of a single system are present and they are\nuncharacterized, it is possible to distinguish between the behaviours of\nquantum and classical systems relying only on observational data. Certifying\nthe physical origin of measurement statistics in the prepare and measure\nscenario is of primal importance for developing quantum networks, distributing\nquantum keys and certifying randomness, to mention a few applications, but,\nsurprisingly, no general methods to do so are known. We progress on this\nproblem by crafting a general, sufficient condition to certify that a given set\nof preparations can only generate classical statistics, for any number of\ngeneralized measurements. As an application, we employ the method to\ndemonstrate non-classicality activation in the prepare and measure scenario,\nalso considering its application in random access codes. Following that, we\nadapt our method to certify, again through a sufficient condition, whether a\ngiven set of measurements can never give rise to non-classical behaviors,\nirrespective of what preparations they may act upon. This, in turn, allows us\nto find a large set of incompatible measurements that cannot be used to\ndemonstrate non-classicality, thus showing incompatibility is not sufficient\nfor non-classicality in the prepare and measure scenario.\n"}
+{"abstract": "  Random operators constitute fundamental building blocks of models of complex\nsystems yet are far from fully understood. Here, we explain an asymmetry\nemerging upon repeating identical isotropic (uniformly random) operations.\nSpecifically, in two dimensions, repeating an isotropic rotation twice maps a\ngiven point on the two-dimensional unit sphere (the unit circle) uniformly at\nrandom to any point on the unit sphere, reflecting a statistical symmetry as\nexpected. In contrast, in three and higher dimensions, a point is mapped more\noften closer to the original point than a uniform distribution predicts.\nCuriously, in the limit of the dimension $d \\rightarrow \\infty$, a symmetric\ndistribution is approached again. We intuitively explain the emergence of this\nasymmetry and why it disappears in higher dimensions by disentangling isotropic\nrotations into a sequence of partial actions. The asymmetry emerges in two\nqualitatively different forms and for a wide range of general random operations\nrelevant in complex systems modeling, including repeated continuous and\ndiscrete rotations, roto-reflections and general orthogonal transformations\n"}
+{"abstract": "  Powerful deep learning algorithms open an opportunity for solving non-image\nMachine Learning (ML) problems by transforming these problems to into the image\nrecognition problems. The CPC-R algorithm presented in this chapter converts\nnon-image data into images by visualizing non-image data. Then deep learning\nCNN algorithms solve the learning problems on these images. The design of the\nCPC-R algorithm allows preserving all high-dimensional information in 2-D\nimages. The use of pair values mapping instead of single value mapping used in\nthe alternative approaches allows encoding each n-D point with 2 times fewer\nvisual elements. The attributes of an n-D point are divided into pairs of its\nvalues and each pair is visualized as 2-D points in the same 2-D Cartesian\ncoordinates. Next, grey scale or color intensity values are assigned to each\npair to encode the order of pairs. This is resulted in the heatmap image. The\ncomputational experiments with CPC-R are conducted for different CNN\narchitectures, and methods to optimize the CPC-R images showing that the\ncombined CPC-R and deep learning CNN algorithms are able to solve non-image ML\nproblems reaching high accuracy on the benchmark datasets. This chapter expands\nour prior work by adding more experiments to test accuracy of classification,\nexploring saliency and informativeness of discovered features to test their\ninterpretability, and generalizing the approach.\n"}
+{"abstract": "  We study the spectrum of certain two-particle operators in the supergravity\nregime of the D1-D5 system, focussing on the four-point correlators of tensor\nmultiplets on $AdS_3\\times S^3$ at tree level. In Mellin space, these are\nnicely determined by a single amplitude, which makes manifest the large $p$\nlimit, the connection with the flat space S-matrix, and the six dimensional\nconformal symmetry. We compute the $(1,1)\\times \\overline{(1,1)}$\nsuperconformal blocks for the two-dimensional $\\mathcal{N}=(4,4)$ conformal\ntheory at the boundary, and then we obtain a formula for the anomalous\ndimensions of the operators exchanged in the symmetric and anti-symmetric\nflavor channels. These anomalous dimensions solve an unmixing problem which is\nanalogous to the one in $AdS_5\\times S^5$, with interesting modifications.\nAlong the way we show how the $(1,1)\\times \\overline{(1,1)}$ superconformal\nblocks relate to those in $\\mathcal{N}=4$ SYM in four dimensions, and we\nprovide new intuition on the known data for $AdS_5\\times S^5$.\n"}
+{"abstract": "  Primordial black holes as a viable candidate of cold dark matter are\nclustering in the large-scale structures of the Universe. The stochastic\ngravitational-wave background, arising from an incoherent superposition of\nunresolved gravitational waves from primordial black hole binaries, is expected\nto display anisotropies across the sky. In this work, we obtain the angular\npower spectrum for such anisotropies for the first time and the difference\nbetween it and the one from astrophysical black hole binaries. When multi-band\nmeasurements are employed, the angular power spectrum can be a smoking gun for\nthe existence of primordial black holes, in particular those of stellar masses\nthat otherwise are challenging to be distinguished from the astrophysical black\nholes.\n"}
+{"abstract": "  Although writing code seems trivial at times, problems arise when humans\nmisinterpret what the code actually does. One of the potential causes are\n\"atoms of confusion\", the smallest possible patterns of misinterpretable source\ncode. Previous research has investigated the impact of atoms of confusion in C\ncode. Results show that developers make significantly more mistakes in code\nwhere atoms are present. In this paper, we replicate the work of Gopstein et\nal. to the Java language. After deriving a set of atoms of confusion for Java,\nwe perform a two-phase experiment with 132 computer science students (i.e.,\nnovice developers). Our results show that participants are 2.7 up to 56 times\nmore likely to make mistakes in code snippets affected by 7 out of the 14\nstudied atoms of confusion, and when faced with both versions of the code\nsnippets, participants perceived the version affected by the atom of confusion\nto be more confusing and/or less readable in 10 out of the 14 studied atoms of\nconfusion.\n"}
+{"abstract": "  Answering a question of Dan Haran and generalizing some results of\nAschbacher-Guralnick and Suzuki, we prove that given a set of primes pi, any\nfinite group can be generated by a pi-subgroup and a pi'-subgroup. This gives a\nfree product description of a countably generated free profinite group.\n"}
+{"abstract": "  Butt rot (BR) damages associated with Norway spruce (Picea abies [L.] Karst.)\naccount for considerable economic losses in timber production across the\nnorthern hemisphere. While information on BR damages is critical for optimal\ndecision-making in forest management, the maps of BR damages are typically\nlacking in forest information systems. We predicted timber volume damaged by BR\nat the stand-level in Norway using harvester information of 186,026 stems\n(clear-cuts), remotely sensed, and environmental data (e.g. climate and terrain\ncharacteristics). We utilized random forest (RF) models with two sets of\npredictor variables: (1) predictor variables available after harvest\n(theoretical case) and (2) predictor variables available prior to harvest\n(mapping case). We found that forest attributes characterizing the maturity of\nforest, such as remote sensing-based height, harvested timber volume and\nquadratic mean diameter at breast height, were among the most important\npredictor variables. Remotely sensed predictor variables obtained from airborne\nlaser scanning data and Sentinel-2 imagery were more important than the\nenvironmental variables. The theoretical case with a leave-stand-out\ncross-validation achieved an RMSE of 11.4 $m^3ha^{-1}$ (pseudo $R^2$: 0.66)\nwhereas the mapping case resulted in a pseudo $R^2$ of 0.60. When the spatially\ndistinct k-means clusters of harvested forest stands were used as units in the\ncross-validation, the RMSE value and pseudo $R^2$ associated with the mapping\ncase were 15.6 $m^3ha^{-1}$ and 0.37, respectively. This indicates that the\nknowledge about the BR status of spatially close stands is of high importance\nfor obtaining satisfactory error rates in the mapping of BR damages.\n"}
+{"abstract": "  We report that Ly$\\alpha$-emitting galaxies (LAEs) may not faithfully trace\nthe cosmic web of neutral hydrogen (HI), but their distribution is likely\nbiased depending on the viewing direction. We calculate the cross-correlation\n(CCF) between galaxies and Ly$\\alpha$ forest transmission fluctuations on the\nnear and far sides of the galaxies separately, for three galaxy samples at\n$z\\sim2$: LAEs, [OIII] emitters (O3Es), and continuum-selected galaxies. We\nfind that only LAEs have anisotropic CCFs, with the near side one showing lower\nsignals up to $r=3-4~h^{-1}$ comoving Mpc. This means that the average HI\ndensity on the near side of LAEs is lower than that on the far-side by a factor\nof $2.1$ under the Fluctuating Gunn-Peterson Approximation. Mock LAEs created\nby assigning Ly$\\alpha$ equivalent width ($EW_\\text{Ly$\\alpha$}^\\text{obs}$)\nvalues to O3Es with an empirical relation also show similar, anisotropic CCFs\nif we use only objects with higher $EW_\\text{Ly$\\alpha$}^\\text{obs}$ than a\ncertain threshold. These results indicate that galaxies on the far side of a\ndense region are more difficult to be detected (\"hidden\") in Ly$\\alpha$ because\nLy$\\alpha$ emission toward us is absorbed by dense neutral hydrogen. If the\nsame region is viewed from a different direction, a different set of LAEs will\nbe selected as if galaxies are playing hide-and-seek using HI gas. Care is\nneeded when using LAEs to search for overdensities.\n"}
+{"abstract": "  For all 4d $\\mathcal{N} = 4$ SYM theories with simple gauge groups $G$, we\nshow that the residues of the integrands in the $\\mathcal{N} = 4$ Schur\nindices, which are related to Gukov-Witten type surface defects in the\ntheories, equal the vacuum characters of rank$G$ copies of $bc \\beta \\gamma$\nsystems that provide the free field realization of associated $\\mathcal{N} = 4$\nVOAs. This result predicts that these residues, as module characters, are\nadditional solutions to the flavored modular differential equations satisfied\nby the original Schur index. The prediction is verified in the $G = SU(2)$\ncase, where an additional logarithmic solution is constructed.\n"}
+{"abstract": "  We present an up-to-date complete model-independent global fit to $b\\to\ns\\ell^+\\ell^-$ observables that confirms patterns of New Physics able to\nexplain the data. We include the recent LHCb measurements of $R_K$, $R_{K_S}$,\n$R_{K^{*+}}$, $B_s \\to \\phi\\mu^+\\mu^-$ and $B_s\\to\\mu\\mu$ in our analysis,\nwhich now includes 254 observables. This updates our previous analyses and\nstrengthens their two main outcomes. First, the presence of right-handed\ncouplings encoded in the Wilson coefficients $\\mathcal{C}_{9'\\mu}$ and\n$\\mathcal{C}_{10'\\mu}$ remains a viable possibility. Second, a lepton flavour\nuniversality violating (LFUV) left-handed lepton coupling\n($\\mathcal{C}_{9\\mu}^{\\rm V}=-\\mathcal{C}_{10\\mu}^{\\rm V}$), often preferred\nfrom the model building point of view, accommodates the data better if\nlepton-flavour universal New Physics is allowed, in particular in\n$\\mathcal{C}_{9}^{\\rm U}$. We observe that the LFUV observable $Q_5$ offers a\nvery interesting possibility to separate both types of scenarios.\n"}
+{"abstract": "  The new paradigm of human-robot collaboration has led to the creation of\nshared work environments in which humans and robots work in close contact with\neach other. Consequently, the safety regulations have been updated addressing\nthese new scenarios. The mere application of these regulations may lead to a\nvery inefficient behavior of the robot. In order to preserve safety for the\nhuman operators and allow the robot to reach a desired configuration in a safe\nand efficient way, a two layers architecture for trajectory planning and\nscaling is proposed. The first layer calculates the nominal trajectory and\ncontinuously adapts it based on the human behavior. The second layer, which\nexplicitly considers the safety regulations, scales the robot velocity and\nrequests for a new trajectory if the robot speed drops. The proposed\narchitecture is experimentally validated on a Pilz PRBT manipulator.\n"}
+{"abstract": "  Microarray studies, in order to identify genes associated with an outcome of\ninterest, usually produce noisy measurements for a large number of gene\nexpression features from a small number of subjects. One common approach to\nanalyzing such high-dimensional data is to use linear errors-in-variables\nmodels; however, current methods for fitting such models are computationally\nexpensive. In this paper, we present two efficient screening procedures, namely\ncorrected penalized marginal screening and corrected sure independence\nscreening, to reduce the number of variables for final model building. Both\nscreening procedures are based on fitting corrected marginal regression models\nrelating the outcome to each contaminated covariate separately, which can be\ncomputed efficiently even with a large number of features. Under mild\nconditions, we show that these procedures achieve screening consistency and\nreduce the number of features considerably, even when the number of covariates\ngrows exponentially with the sample size. Additionally, if the true covariates\nare weakly correlated, corrected penalized marginal screening can achieve full\nvariable selection consistency. Through simulation studies and an analysis of\ngene expression data for bone mineral density of Norwegian women, we\ndemonstrate that the two new screening procedures make estimation of linear\nerrors-in-variables models computationally scalable in high dimensional\nsettings, and improve finite sample estimation and selection performance\ncompared with estimators that do not employ a screening stage.\n"}
+{"abstract": "  GRB 180720B, observed by {\\it Fermi}-GBM, with redshift $z=0.653$, isotropic\nenergy $E_{\\rm iso}=5.92\\times 10^{53}$ erg, and X-ray afterglow observed by\nthe XRT onboard the Neil Gehrels Swift satellite, is here classified as a\nBinary-driven Hypernova I (BdHN I). BdHN I are long GRBs with a binary\nprogenitor composed of a carbon-oxygen core (CO$_{\\rm core}$) and a neutron\nstar (NS) companion with orbital period $\\sim 5$ min. The gravitational\ncollapse of the CO$_{\\rm core}$ generates a supernova (SN) and a new NS\n($\\nu$NS) at its center. The SN hypercritical accretion onto the companion NS\ntriggers its gravitational collapse forming a black hole (BH). An\nelectrodynamical process near the BH horizon leads to the long-lasting GeV\nemission with power-law luminosity $L_{\\rm GeV}\\propto t^{-1.19\\pm0.04}$,\npowered by the BH rotational energy. Correspondingly, we determine the BH mass\nand spin. The $\\nu$NS via its pulsar-like emission and fallback accretion\ninjects energy into the magnetized SN ejecta generating synchrotron radiation.\nThis explains the \\textit{long-lasting} X-ray afterglow with power-law\nluminosity $L_X \\propto t^{-1.48\\pm 0.32}$, energized by the $\\nu$NS rotational\nenergy. We apply this to GRB 180720B determining the $\\nu$NS magnetic field and\nspin. We also analyze the GRB 180720B emission observed by the High-Energy\nStereoscopic System (H.E.S.S.), at $100$-$440$ GeV energies, $10.1$-$12.1$ h\nafter the Fermi-GBM trigger. We propose that this \\textit{short-term} radiation\nof energy $2.4\\times 10^{50}$ erg and duration $\\sim 10^3$ s, is powered by a\n\"glitch\" event that suddenly injects relativistic electrons into the $\\nu$NS\nmagnetosphere during its slowing down phase.\n"}
+{"abstract": "  It is demonstrated that a constant magnetic moment does not emit\nelecto-magnetic radiation while moving in an arbitrary field\n"}
+{"abstract": "  We present Fourier-space based methods to calculate the electronic structure\nof wurtzite quantum dot systems with continuous alloy profiles. We incorporate\nspatially varying elastic and dielectric constants in strain and piezoelectric\npotential calculations. A method to incorporate smooth alloy profiles in all\naspects of the calculations is presented. We demonstrate our methodology for\nthe case of a 1D InGaN quantum dot array and show the importance of including\nthese spatially varying parameters in the modeling of devices. We demonstrate\nthat convergence of the lowest bound state energies is to good approximation\ndetermined by the largest wave vector used in constructing the states. We also\npresent a novel approach of coupling strain into the\n$\\mathbf{k}\\cdot\\mathbf{p}$ Hamiltonian, greatly reducing the computational\ncost of generating the Hamiltonian.\n"}
+{"abstract": "  Image-to-image (I2I) translation methods based on generative adversarial\nnetworks (GANs) typically suffer from overfitting when limited training data is\navailable. In this work, we propose a data augmentation method (ReMix) to\ntackle this issue. We interpolate training samples at the feature level and\npropose a novel content loss based on the perceptual relations among samples.\nThe generator learns to translate the in-between samples rather than memorizing\nthe training set, and thereby forces the discriminator to generalize. The\nproposed approach effectively reduces the ambiguity of generation and renders\ncontent-preserving results. The ReMix method can be easily incorporated into\nexisting GAN models with minor modifications. Experimental results on numerous\ntasks demonstrate that GAN models equipped with the ReMix method achieve\nsignificant improvements.\n"}
+{"abstract": "  Artificial neural networks have achieved great success in many fields ranging\nfrom image recognition to video understanding. However, its high requirements\nfor computing and memory resources have limited further development on\nprocessing big data with high dimensions. In recent years, advances in quantum\ncomputing show that building neural networks on quantum processors is a\npotential solution to this problem. In this paper, we propose a novel neural\nnetwork model named Quantum Convolutional Neural Network (QCNN), aiming at\nutilizing the computing power of quantum systems to accelerate classical\nmachine learning tasks. The designed QCNN is based on implementable quantum\ncircuits and has a similar structure as classical convolutional neural\nnetworks. Numerical simulation results on the MNIST dataset demonstrate the\neffectiveness of our model.\n"}
+{"abstract": "  In this paper, we demonstrate the generation of high-performance entangled\nphoton-pairs in different degrees of freedom from a single piece of fiber\npigtailed periodically poled LiNbO$_3$ (PPLN) waveguide. We utilize cascaded\nsecond-order nonlinear optical processes, i.e. second-harmonic generation (SHG)\nand spontaneous parametric down conversion (SPDC), to generate photon-pairs.\nPreviously, the performance of the photon pairs is contaminated by Raman noise\nphotons from the fiber pigtails. Here by integrating the PPLN waveguide with\nnoise rejecting filters, we obtain a coincidence-to-accidental ratio (CAR)\nhigher than 52,600 with photon-pair generation and detection rate of 52.3 kHz\nand 3.5 kHz, respectively. Energy-time, frequency-bin and time-bin entanglement\nis prepared by coherently superposing correlated two-photon states in these\ndegrees of freedom, respectively. The energy-time entangled two-photon states\nachieve the maximum value of CHSH-Bell inequality of S=2.708$\\pm$0.024 with a\ntwo-photon interference visibility of 95.74$\\pm$0.86%. The frequency-bin\nentangled two-photon states achieve fidelity of 97.56$\\pm$1.79% with a spatial\nquantum beating visibility of 96.85$\\pm$2.46%. The time-bin entangled\ntwo-photon states achieve the maximum value of CHSH-Bell inequality of\nS=2.595$\\pm$0.037 and quantum tomographic fidelity of 89.07$\\pm$4.35%. Our\nresults provide a potential candidate for quantum light source in quantum\nphotonics.\n"}
+{"abstract": "  Quantum coherence, like entanglement, is a fundamental resource in quantum\ninformation. In recent years, remarkable progress has been made in formulating\nresource theory of coherence from a broader perspective. The notions of\nblock-coherence and POVM-based coherence have been established. Certain\nchallenges, however, remain to be addressed. It is difficult to define\nincoherent operations directly, without requiring incoherent states, which\nproves a major obstacle in establishing the resource theory of dynamical\ncoherence. In this paper, we overcome this limitation by introducing an\nalternate definition of incoherent operations, induced via coherence measures,\nand quantify dynamical coherence based on this definition. Finally, we apply\nour proposed definition to quantify POVM-based dynamical coherence.\n"}
+{"abstract": "  In causal bandit problems, the action set consists of interventions on\nvariables of a causal graph. Several researchers have recently studied such\nbandit problems and pointed out their practical applications. However, all\nexisting works rely on a restrictive and impractical assumption that the\nlearner is given full knowledge of the causal graph structure upfront. In this\npaper, we develop novel causal bandit algorithms without knowing the causal\ngraph. Our algorithms work well for causal trees, causal forests and a general\nclass of causal graphs. The regret guarantees of our algorithms greatly improve\nupon those of standard multi-armed bandit (MAB) algorithms under mild\nconditions. Lastly, we prove our mild conditions are necessary: without them\none cannot do better than standard MAB algorithms.\n"}
+{"abstract": "  Two-dimensional, resonant scanners have been utilized in a large variety of\nimaging modules due to their compact form, low power consumption, large angular\nrange, and high speed. However, resonant scanners have problems with\nnon-optimal and inflexible scanning patterns and inherent phase uncertainty,\nwhich limit practical applications. Here we propose methods for optimized\ndesign and control of the scanning trajectory of two-dimensional resonant\nscanners under various physical constraints, including high frame-rate and\nlimited actuation amplitude. First, we propose an analytical design rule for\nuniform spatial sampling. We demonstrate theoretically and experimentally that\nby including non-repeating scanning patterns, the proposed designs outperform\nprevious designs in terms of scanning range and fill factor. Second, we show\nthat we can create flexible scanning patterns that allow focusing on\nuser-defined Regions-of-Interest (RoI) by modulation of the scanning\nparameters. The scanning parameters are found by an optimization algorithm. In\nsimulations, we demonstrate the benefits of these designs with standard metrics\nand higher-level computer vision tasks (LiDAR odometry and 3D object\ndetection). Finally, we experimentally implement and verify both unmodulated\nand modulated scanning modes using a two-dimensional, resonant MEMS scanner.\nCentral to the implementations is high bandwidth monitoring of the phase of the\nangular scans in both dimensions. This task is carried out with a\nposition-sensitive photodetector combined with high-bandwidth electronics,\nenabling fast spatial sampling at ~ 100Hz frame-rate.\n"}
+{"abstract": "  We give a simple, low resource method to produce order embeddings from\nontologies. Such embeddings map words to vectors so that order relations on the\nwords, such as hypernymy/hyponymy, are represented in a direct way. Our method\nuses sketching techniques, in particular countsketch, for dimensionality\nreduction. We also study methods to merge ontologies, in particular those in\nmedical domains, so that order relations are preserved. We give computational\nresults for medical ontologies and for wordnet, showing that our merging\ntechniques are effective and our embedding yields an accurate representation in\nboth generic and specialised domains.\n"}
+{"abstract": "  With information technology entering new fields and levels of deployment,\ne.g., in areas of energy, mobility, and production, network security monitoring\nneeds to be able to cope with those environments and their evolution. However,\nstate-of-the-art Network Security Monitors (NSMs) typically lack the necessary\nflexibility to handle the diversity of the packet-oriented layers below the\nabstraction of TCP/IP connections. In this work, we advance the software\narchitecture of a network security monitor to facilitate the flexible\nintegration of lower-layer protocol dissectors while maintaining required\nperformance levels. We proceed in three steps: First, we identify the\nchallenges for modular packet-level analysis, present a refined NSM\narchitecture to address them and specify requirements for its implementation.\nSecond, we evaluate the performance of data structures to be used for protocol\ndispatching, implement the proposed design into the popular open-source NSM\nZeek and assess its impact on the monitor performance. Our experiments show\nthat hash-based data structures for dispatching introduce a significant\noverhead while array-based approaches qualify for practical application.\nFinally, we demonstrate the benefits of the proposed architecture and\nimplementation by migrating Zeek's previously hard-coded stack of link and\ninternet layer protocols to the new interface. Furthermore, we implement\ndissectors for non-IP based industrial communication protocols and leverage\nthem to realize attack detection strategies from recent applied research. We\nintegrate the proposed architecture into the Zeek open-source project and\npublish the implementation to support the scientific community as well as\npractitioners, promoting the transfer of research into practice.\n"}
+{"abstract": "  We present an experimental study of the magnetic structure and dynamics of\ntwo frustrated hyperkagome compounds, Gd3Ga5O12 and Gd3Al5O12. It has\npreviously been shown that Gd3Ga5O12 exhibits long-range correlations of\nmultipolar directors, that are formed from antiferromagnetic spins on loops of\nten ions. Using neutron diffraction and Reverse Monte Carlo simulations we\nprove the existence of similar magnetic correlations in Gd3Al5O12, showing the\nubiquity of these complex structures in frustrated hyperkagome materials.\n  Using inelastic neutron scattering we shed further light on the director\nstate and the associated low lying magnetic excitations. In addition we have\nmeasured quasielastic dynamics that show evidence of spin diffusion. Finally,\nwe present AC susceptibility measurements on both Gd3Ga5O12 and Gd3Al5O12,\nrevealing a large difference in the low frequency dynamics between the two\notherwise similar compounds.\n"}
+{"abstract": "  We revisit the formulation of non-relativistic (NR) string theory and its\ntarget space geometry. We obtain a new formulation in which the geometry\ncontains a two-form field that couples to the tension current and that\ntransforms under string Galilei boosts. This parallels the Newton-Cartan\none-form that couples to the mass current of a non-relativistic point particle.\nWe show how this formulation of the NR string arises both from an infinite\nspeed of light limit and a null reduction of the relativistic closed bosonic\nstring. In both cases, the two-form originates from a combination of metric\nquantities and the Kalb-Ramond field. The target space geometry of the NR\nstring is seen to arise from the gauging of a new algebra that is obtained by\nan Inonu-Wigner contraction of the Poincar\\'e algebra extended by the\nsymmetries of the Kalb-Ramond field. In this new formulation, there are no\nsuperfluous target space fields that can be removed by fixing a St\\\"uckelberg\nsymmetry. Classically, there are no foliation/torsion constraints imposed on\nthe target space geometry.\n"}
+{"abstract": "  Application of the radial velocity (RV) technique in the near infrared is\nvaluable because of the diminished impact of stellar activity at longer\nwavelengths, making it particularly advantageous for the study of late-type\nstars but also for solar-type objects. In this paper, we present the IGRINS RV\nopen source python pipeline for computing infrared RV measurements from reduced\nspectra taken with IGRINS, a R ~ 45,000 spectrograph with simultaneous coverage\nof the H band (1.49--1.80 $\\mu$m) and K band (1.96--2.46 $\\mu$m). Using a\nmodified forward modeling technique, we construct high resolution telluric\ntemplates from A0 standard observations on a nightly basis to provide a source\nof common-path wavelength calibration while mitigating the need to mask or\ncorrect for telluric absorption. Telluric standard observations are also used\nto model the variations in instrumental resolution across the detector,\nincluding a yearlong period when the K band was defocused. Without any\nadditional instrument hardware, such as a gas cell or laser frequency comb, we\nare able to achieve precisions of 26.8 $\\rm m\\,s^{-1}$ in the K band and 31.1\n$\\rm m\\,s^{-1}$ in the H band for narrow-line hosts. These precisions are\nempirically determined by a monitoring campaign of two RV standard stars as\nwell as the successful retrieval of planet-induced RV signals for both HD\n189733 and $\\tau$ Boo A; furthermore, our results affirm the presence of the\nRossiter-McLaughlin effect for HD 189733. The IGRINS RV pipeline extends\nanother important science capability to IGRINS, with publicly available\nsoftware designed for widespread use.\n"}
+{"abstract": "  The paper introduces the concept of complex frequency. The imaginary part of\nthe complex frequency is the variation with respect of a synchronous reference\nof the local bus frequency as commonly defined in power system studies. The\nreal part is defined based on the variation of the voltage magnitude. The\nlatter term is crucial for the correct interpretation and analysis of the\nvariation of the frequency at each bus of the network. The paper also develops\na set of differential equations that describe the link between complex powers\nand complex frequencies at network buses in transient conditions. No\nsimplifications are assumed except for constant elements of the network\nadmittance matrix. A variety of analytical and numerical examples show the\napplications and potentials of the proposed concept.\n"}
+{"abstract": "  Within the context of autonomous driving, safety-related metrics for deep\nneural networks have been widely studied for image classification and object\ndetection. In this paper, we further consider safety-aware correctness and\nrobustness metrics specialized for semantic segmentation. The novelty of our\nproposal is to move beyond pixel-level metrics: Given two images with each\nhaving N pixels being class-flipped, the designed metrics should, depending on\nthe clustering of pixels being class-flipped or the location of occurrence,\nreflect a different level of safety criticality. The result evaluated on an\nautonomous driving dataset demonstrates the validity and practicality of our\nproposed methodology.\n"}
+{"abstract": "  We present a statistical mechanical description of randomly packed spherical\nparticles, where the average coordination number is treated as a macroscopic\nthermodynamic variable. The overall packing entropy is shown to have two\ncontributions: geometric, reflecting statistical weights of individual\nconfigurations, and topological, which corresponds to the number of\ntopologically distinct states. Both of them are computed in the thermodynamic\nlimit for isostatic packings in 2D and 3D, and the result is further expanded\nto the case of \"floppy\" particle clusters. The theory is directly applicable to\nsticky colloids, and in addition, generalizes concepts of granular and glassy\nconfigurational entropies for the case of non-jammed systems.\n"}
+{"abstract": "  Encoding textural content remains a challenge for current standardised video\ncodecs. It is therefore beneficial to understand video textures in terms of\nboth their spatio-temporal characteristics and their encoding statistics in\norder to optimize encoding performance. In this paper, we analyse the\nspatio-temporal features and statistics of video textures, explore the\nrate-quality performance of different texture types and investigate models to\nmathematically describe them. For all considered theoretical models, we employ\nmachine-learning regression to predict the rate-quality curves based solely on\nselected spatio-temporal features extracted from uncompressed content. All\nexperiments were performed on homogeneous video textures to ensure validity of\nthe observations. The results of the regression indicate that using an\nexponential model we can more accurately predict the expected rate-quality\ncurve (with a mean Bj{\\o}ntegaard Delta rate of 0.46% over the considered\ndataset) while maintaining a low relative complexity. This is expected to be\nadopted by in the loop processes for faster encoding decisions such as\nrate-distortion optimisation, adaptive quantization, partitioning, etc.\n"}
+{"abstract": "  We develop a notion of formal groups in the filtered setting and describe a\nduality relating these to a specified class of filtered Hopf algebras. We then\nstudy a deformation to the normal cone construction in the setting of derived\nalgebraic geometry. Applied to the unit section of a formal group\n$\\widehat{\\mathbb{G}}$, this provides a $\\mathbb{G}_m$-equivariant degeneration\nof $\\widehat{\\mathbb{G}}$ to its tangent Lie algebra. We prove a unicity result\non complete filtrations, which, in particular, identifies the resulting\nfiltration on the coordinate algebra of this deformation with the adic\nfiltration on the coordinate algebra of $\\widehat{\\mathbb{G}}$. We use this in\na special case, together with the aforementioned notion of Cartier duality, to\nrecover the filtration on the filtered circle of [MRT19]. Finally, we\ninvestigate some properties of $\\widehat{\\mathbb{G}}$-Hochschild homology set\nout in loc. cit., and describe \"lifts\" of these invariants to the setting of\nspectral algebraic geometry.\n"}
+{"abstract": "  The origin of the Chicxulub impactor, which is attributed as the cause of the\nK/T mass extinction event, is an unsolved puzzle. The background impact rates\nof main-belt asteroids and long-period comets have been previously dismissed as\nbeing too low to explain the Chicxulub impact event. Here, we show that a\nfraction of long-period comets are tidally disrupted after passing close to the\nSun, each producing a collection of smaller fragments that cross the orbit of\nEarth. This population could increase the impact rate of long-period comets\ncapable of producing Chicxulub impact events by an order of magnitude. This new\nrate would be consistent with the age of the Chicxulub impact crater, thereby\nproviding a satisfactory explanation for the origin of the impactor. Our\nhypothesis explains the composition of the largest confirmed impact crater in\nEarth's history as well as the largest one within the last million years. It\npredicts a larger proportion of impactors with carbonaceous chondritic\ncompositions than would be expected from meteorite falls of main-belt\nasteroids.\n"}
+{"abstract": "  Within SU(2) Yang-Mills theory with a source of the non-Abelian gauge field\nin the form of a classical spinor field, we study the dependence of the mass\ngap on the coupling constant between the gauge and nonlinear spinor fields. It\nis shown that the total dimensionless energy of the monopole interacting with\nthe nonlinear spinor fields depends only on the dimensionless coupling\nconstant.\n"}
+{"abstract": "  This review summarizes recent progress in investigating polymer systems by\nusing Differential dynamic microscopy (DDM), a rapidly emerging approach that\ntransforms a commercial microscope by combining real-space information with the\npowerful capabilities of conventional light scattering analysis. DDM analysis\nof a single microscope movie gives access to the sample dynamics in a wide\nrange of scattering wave-vectors, enabling contemporary polymer science\nexperiments that would be difficult or impossible with standard light\nscattering techniques. Examples of application include the characterization of\npolymer solutions and networks, of polymer based colloidal systems, of\nbiopolymers, and of cellular motility in polymeric fluids. Further applications\nof DDM to a variety of polymer systems are suggested to be just behind the\ncorner and it is thus likely that DDM will become a tool of choice of the\nmodern experimental polymer scientists.\n"}
+{"abstract": "  Charge manipulation and fabrication of stable domain patterns in\nferroelectric materials by scanning probe microscopy open up broad avenues for\nthe development of tunable electronics. Harnessing the polarization energy and\nelectrostatic forces with specific geometry of the system enables producing the\nnanoscale domains by-design. Along with that, domain engineering requires\nmastery of underlying physical mechanisms that govern the domain formation.\nHere, we present a theoretical description of the domain formation by a\nscanning probe microscopy tip in a ferroelectric film with strong in-plane\nanisotropy of polarization. We demonstrate that local charge injection produces\nwedge-shaped domains that propagate along the anisotropy axis, whereas the\ntip-written lines of charge generate a comb-like domain structure. The results\nof our calculations agree with earlier experimental observations and allow for\nthe optimization of the targeted domain structures.\n"}
+{"abstract": "  We use the Dirac operator technique to establish sharp distance estimates for\ncompact spin manifolds under lower bounds on the scalar curvature in the\ninterior and on the mean curvature of the boundary. In the situations we\nconsider, we thereby give refined answers to questions on metric inequalities\nrecently proposed by Gromov. This includes optimal estimates for Riemannian\nbands and for the long neck problem. In the case of bands over manifolds of\nnon-vanishing $\\widehat{\\mathrm{A}}$-genus, we establish a rigidity result\nstating that any band attaining the predicted upper bound is isometric to a\nparticular warped product over some spin manifold admitting a parallel spinor.\nFurthermore, we establish scalar- and mean curvature extremality results for\ncertain log-concave warped products. The latter includes annuli in all\nsimply-connected space forms. On a technical level, our proofs are based on new\nspectral estimates for the Dirac operator augmented by a Lipschitz potential\ntogether with local boundary conditions.\n"}
+{"abstract": "  Several methods have been explored for automating parts of Systematic Mapping\n(SM) and Systematic Review (SR) methodologies. Challenges typically evolve\naround the gaps in semantic understanding of text, as well as lack of domain\nand background knowledge necessary to bridge that gap. In this paper we\ninvestigate possible ways of automating parts of the SM/SR process, i.e. that\nof extracting keywords and key-phrases from scientific documents using\nunsupervised methods, which are then used as a basis to construct the\ncorresponding Classification Scheme using semantic key-phrase clustering\ntechniques. Specifically, we explore the effect of ensemble scores measure in\nkey-phrase extraction, we explore semantic network based word embedding in\nembedding representation of phrase semantics and finally we also explore how\nclustering can be used to group related key-phrases. The evaluation is\nconducted on a dataset of publications pertaining the domain of \"Explainable\nAI\" which we constructed using standard publicly available digital libraries\nand sets of indexing terms (keywords). Results shows that: ensemble ranking\nscore does improve the key-phrase extraction performance. Semantic-network\nbased word embedding based on the ConceptNet Semantic Network has similar\nperformance with contextualized word embedding, however the former are\ncomputationally more efficient. Finally Semantic key-phrase clustering at\nterm-level can group similar terms together that can be suitable for\nclassification scheme.\n"}
+{"abstract": "  Various fonts give us various impressions, which are often represented by\nwords. This paper proposes Impressions2Font (Imp2Font) that generates font\nimages with specific impressions. Imp2Font is an extended version of\nconditional generative adversarial networks (GANs). More precisely, Imp2Font\naccepts an arbitrary number of impression words as the condition to generate\nthe font images. These impression words are converted into a soft-constraint\nvector by an impression embedding module built on a word embedding technique.\nQualitative and quantitative evaluations prove that Imp2Font generates font\nimages with higher quality than comparative methods by providing multiple\nimpression words or even unlearned words.\n"}
+{"abstract": "  Context. The high number of super-Earth and Earth-like planets in the\nhabitable zone (HZ) detected around M-dwarf stars in the last years has\nrevealed these stellar objects to be the key for planetary radial velocity (RV)\nsearches. Aims. Using the HARPS-N spectrograph within The HArps-n red Dwarf\nExoplanet Survey (HADES) we reach the precision needed to detect small planets\nwith a few Earth masses using the RV technique. Methods. We obtained 138\nHARPS-N RV measurements between 2013 May and 2020 September of GJ 720 A,\nclassified as an M0.5V star located at a distance of 15.56 pc. To characterize\nthe stellar variability and to discern the periodic variation due to the\nKeplerian signals from those related to stellar activity, the HARPS-N\nspectroscopic activity indicators and the simultaneous photometric observations\nwere analyzed. The combined analysis of HARPS-N RVs and activity indicators let\nus to address the nature of the periodic signals. The final model and the\norbital planetary parameters were obtained by fitting simultaneously the\nstellar variability and the Keplerian signal using a Gaussian process\nregression and following a Bayesian criterion. Results. The HARPS-N RV periodic\nsignals around 40 d and 100 d have counterparts at the same frequencies in\nHARPS-N activity indicators and photometric light curves. Then we attribute\nthese periodicities to stellar activity the former period being likely\nassociated with the stellar rotation. GJ 720 A shows the most significant\nsignal at 19.466$\\pm$0.005 d with no counterparts in any stellar activity\nindices. We hence ascribe this RV signal, having a semiamplitude of\n4.72$\\pm$0.27 m/s , to the presence of a sub-Neptune mass planet. The planet GJ\n720 Ab has a minimum mass of 13.64$\\pm$0.79 M$_{\\oplus}$, it is in circular\norbit at 0.119$\\pm$0.002 AU from its parent star, and lies inside the inner\nboundary of the HZ around its parent star.\n"}
+{"abstract": "  We provide a new family of $K_k$-free pseudorandom graphs with edge density\n$\\Theta(n^{-1/(k-1)})$, matching a recent construction due to Bishnoi, Ihringer\nand Pepe. As in the former result, the idea is to use large subgraphs of\npolarity graphs, which are defined over a finite field $\\mathbb{F}_q$. While\ntheir construction required $q$ to be odd, we will give the first construction\nwith $q$ even.\n"}
+{"abstract": "  In this article, we consider a closed rank one Riemannian manifold $M$\nwithout focal points. Let $P(t)$ be the set of free-homotopy classes containing\na closed geodesic on $M$ with length at most $t$, and $\\# P(t)$ its\ncardinality. We obtain the following Margulis-type asymptotic estimates:\n\\[\\lim_{t\\to \\infty}\\#P(t)/\\frac{e^{ht}}{ht}=1\\] where $h$ is the topological\nentropy of the geodesic flow. In the appendix, we also show that the unique\nmeasure of maximal entropy of the geodesic flow has the Bernoulli property.\n"}
+{"abstract": "  Many intellectual endeavors require mathematical problem solving, but this\nskill remains beyond the capabilities of computers. To measure this ability in\nmachine learning models, we introduce MATH, a new dataset of 12,500 challenging\ncompetition mathematics problems. Each problem in MATH has a full step-by-step\nsolution which can be used to teach models to generate answer derivations and\nexplanations. To facilitate future research and increase accuracy on MATH, we\nalso contribute a large auxiliary pretraining dataset which helps teach models\nthe fundamentals of mathematics. Even though we are able to increase accuracy\non MATH, our results show that accuracy remains relatively low, even with\nenormous Transformer models. Moreover, we find that simply increasing budgets\nand model parameter counts will be impractical for achieving strong\nmathematical reasoning if scaling trends continue. While scaling Transformers\nis automatically solving most other text-based tasks, scaling is not currently\nsolving MATH. To have more traction on mathematical problem solving we will\nlikely need new algorithmic advancements from the broader research community.\n"}
+{"abstract": "  It is well-known that machine learning models are vulnerable to small but\ncleverly-designed adversarial perturbations that can cause misclassification.\nWhile there has been major progress in designing attacks and defenses for\nvarious adversarial settings, many fundamental and theoretical problems are yet\nto be resolved. In this paper, we consider classification in the presence of\n$\\ell_0$-bounded adversarial perturbations, a.k.a. sparse attacks. This setting\nis significantly different from other $\\ell_p$-adversarial settings, with\n$p\\geq 1$, as the $\\ell_0$-ball is non-convex and highly non-smooth. Under the\nassumption that data is distributed according to the Gaussian mixture model,\nour goal is to characterize the optimal robust classifier and the corresponding\nrobust classification error as well as a variety of trade-offs between\nrobustness, accuracy, and the adversary's budget. To this end, we develop a\nnovel classification algorithm called FilTrun that has two main modules:\nFiltration and Truncation. The key idea of our method is to first filter out\nthe non-robust coordinates of the input and then apply a carefully-designed\ntruncated inner product for classification. By analyzing the performance of\nFilTrun, we derive an upper bound on the optimal robust classification error.\nWe also find a lower bound by designing a specific adversarial strategy that\nenables us to derive the corresponding robust classifier and its achieved\nerror. For the case that the covariance matrix of the Gaussian mixtures is\ndiagonal, we show that as the input's dimension gets large, the upper and lower\nbounds converge; i.e. we characterize the asymptotically-optimal robust\nclassifier. Throughout, we discuss several examples that illustrate interesting\nbehaviors such as the existence of a phase transition for adversary's budget\ndetermining whether the effect of adversarial perturbation can be fully\nneutralized.\n"}
+{"abstract": "  Transients powered by interaction with the circumstellar medium (CSM) are\noften observed in wavelengths other than optical, and multi-wavelength\nmodelling can be important when inferring the properties of the explosion and\nCSM, or for distinguishing from other powering mechanisms. We develop a model\ncalculating time dependent emission spectrum of interaction-powered transients.\nWe solve energy equations of electron-proton plasma in the shocked SN ejecta\nand CSM and a radiation transfer equation out to the outer edge of the CSM,\nincorporating the collisional relaxation and the comptonization of the\nbremsstrahlung radiation. We compare our model to observations of Type IIn\nsupernovae covering frequency ranges from optical to X-rays. For SN 2010jl the\nobserved optical and X-ray light curves can be consistently explained if clumpy\nor asymmetric structure in the CSM is assumed, in agreement with previous\nstudies. For SN 2014C our model successfully reproduces the X-ray\nbremsstrahlung component and the emergence of H$\\alpha$ emission at 400 days\nafter explosion. Finally we find a parameter space where the supernova is\nextremely X-ray bright, reaching $10^{43}$-$10^{44}\\ {\\rm erg\\ s^{-1}}$ for up\nto $100$ days. Such X-ray transients are likely detectable with all-sky surveys\nby e.g. eROSITA.\n"}
+{"abstract": "  Data from NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite is essential\nto many carbon management strategies. A retrieval algorithm is used to estimate\nCO2 concentration using the radiance data measured by OCO-2. However, due to\nfactors such as cloud cover and cosmic rays, the spatial coverage of the\nretrieval algorithm is limited in some areas of critical importance for carbon\ncycle science. Mixed land/water pixels along the coastline are also not used in\nthe retrieval processing due to the lack of valid ancillary variables including\nland fraction. We propose an approach to model spatial spectral data to solve\nthese two problems by radiance imputation and land fraction estimation. The\nspectral observations are modeled as spatially indexed functional data with\nfootprint-specific parameters and are reduced to much lower dimensions by\nfunctional principal component analysis. The principal component scores are\nmodeled as random fields to account for the spatial dependence, and the missing\nspectral observations are imputed by kriging the principal component scores.\nThe proposed method is shown to impute spectral radiance with high accuracy for\nobservations over the Pacific Ocean. An unmixing approach based on this model\nprovides much more accurate land fraction estimates in our validation study\nalong Greece coastlines.\n"}
+{"abstract": "  Formal design of embedded and cyber-physical systems relies on mathematical\nmodeling. In this paper, we consider the model class of hybrid automata whose\ndynamics are defined by affine differential equations. Given a set of\ntime-series data, we present an algorithmic approach to synthesize a hybrid\nautomaton exhibiting behavior that is close to the data, up to a specified\nprecision, and changes in synchrony with the data. A fundamental problem in our\nsynthesis algorithm is to check membership of a time series in a hybrid\nautomaton. Our solution integrates reachability and optimization techniques for\naffine dynamical systems to obtain both a sufficient and a necessary condition\nfor membership, combined in a refinement framework. The algorithm processes one\ntime series at a time and hence can be interrupted, provide an intermediate\nresult, and be resumed. We report experimental results demonstrating the\napplicability of our synthesis approach.\n"}
+{"abstract": "  We report on the first steady-state simulations of strong plasma shocks with\nfully kinetic ions and electrons, independently confirmed by two fully kinetic\ncodes (an Eulerian continuum and a Lagrangian particle-in-cell). While kinetic\nelectrons do not fundamentally change the shock structure as compared with\nfluid electrons, we find an appreciable rearrangement of the preheat layer,\nassociated with nonlocal electron heat transport effects. The electron heat\nflux profile qualitatively agrees between kinetic and fluid electron models,\nsuggesting a certain level of \"stiffness\", though substantial nonlocality is\nobserved in the kinetic heat flux. We also find good agreement with nonlocal\nelectron heat-flux closures proposed in the literature. Finally, in contrast to\nthe classical hydrodynamic picture, we find a significant collapse in the\n\"precursor\" electric-field shock at the preheat layer edge, which correlates\nwith the electron-temperature gradient relaxation.\n"}
+{"abstract": "  Discriminative analysis in neuroimaging by means of deep/machine learning\ntechniques is usually tested with validation techniques, whereas the associated\nstatistical significance remains largely under-developed due to their\ncomputational complexity. In this work, a non-parametric framework is proposed\nthat estimates the statistical significance of classifications using deep\nlearning architectures. In particular, a combination of autoencoders (AE) and\nsupport vector machines (SVM) is applied to: (i) a one-condition, within-group\ndesigns often of normal controls (NC) and; (ii) a two-condition, between-group\ndesigns which contrast, for example, Alzheimer's disease (AD) patients with NC\n(the extension to multi-class analyses is also included). A random-effects\ninference based on a label permutation test is proposed in both studies using\ncross-validation (CV) and resubstitution with upper bound correction (RUB) as\nvalidation methods. This allows both false positives and classifier overfitting\nto be detected as well as estimating the statistical power of the test. Several\nexperiments were carried out using the Alzheimer's Disease Neuroimaging\nInitiative (ADNI) dataset, the Dominantly Inherited Alzheimer Network (DIAN)\ndataset, and a MCI prediction dataset. We found in the permutation test that CV\nand RUB methods offer a false positive rate close to the significance level and\nan acceptable statistical power (although lower using cross-validation). A\nlarge separation between training and test accuracies using CV was observed,\nespecially in one-condition designs. This implies a low generalization ability\nas the model fitted in training is not informative with respect to the test\nset. We propose as solution by applying RUB, whereby similar results are\nobtained to those of the CV test set, but considering the whole set and with a\nlower computational cost per iteration.\n"}
+{"abstract": "  As an extension of an author's previous paper, we prove the Gersten's\nconjecture for the $p$-torsion subgroup of the generalized Brauer group over a\nhenselian regular local ring of mixed characteristic $(0, p)$. We also prove\nthe $\\mathbb{P}^{1}$-homotopy invariance for the generalized Brauer group which\nis an analogue of a result due to Auel-Bigazzi-B\\\"{o}hning-Graf von Bothmer in\nmixed characteristic cases.\n"}
+{"abstract": "  Structural analysis methods (e.g., probing and feature attribution) are\nincreasingly important tools for neural network analysis. We propose a new\nstructural analysis method grounded in a formal theory of causal abstraction\nthat provides rich characterizations of model-internal representations and\ntheir roles in input/output behavior. In this method, neural representations\nare aligned with variables in interpretable causal models, and then interchange\ninterventions are used to experimentally verify that the neural representations\nhave the causal properties of their aligned variables. We apply this method in\na case study to analyze neural models trained on Multiply Quantified Natural\nLanguage Inference (MQNLI) corpus, a highly complex NLI dataset that was\nconstructed with a tree-structured natural logic causal model. We discover that\na BERT-based model with state-of-the-art performance successfully realizes\nparts of the natural logic model's causal structure, whereas a simpler baseline\nmodel fails to show any such structure, demonstrating that BERT representations\nencode the compositional structure of MQNLI.\n"}
+{"abstract": "  We present a general scheme to approach the space - time evolution of\ndeformations, currents, and the electric field in charge density waves related\nto appearance of intrinsic topological defects: dislocations, their loops or\npairs, and solitons. We derive general equations for the multi-fluid\nhydrodynamics taking into account the collective mode, electric field, normal\nelectrons, and the intrinsic defects. These equations may allow to study the\ntransformation of injected carriers from normal electrons to new periods of the\ncharge density wave, the collective motion in constrained geometry, and the\nplastic states and flows. As an application, we present analytical and\nnumerical solutions for distributions of fields around an isolated dislocation\nline in the regime of nonlinear screening by the gas of phase solitons.\n"}
+{"abstract": "  An approach for the description of stochastic systems is derived. Some of the\nvariables in the system are studied forward in time, others backward in time.\nThe approach is based on a perturbation expansion in the strength of the\ncoupling between forward and backward variables, and is well suited for\nsituations in which initial and final conditions are imposed on different\ncomponents of the system and the coupling between those components is weak. The\nform of the stochastic equations in our approach is determined by requiring\nthat they generate the same statistics obtained in a forward description of the\ndynamics. Numerical tests are carried out on a few simple two-degree-of-freedom\nsystems. The merit and the difficulties of the approach are discussed and\ncompared to more traditional strategies based on transition path sampling and\nsimple shooting algorithms.\n"}
+{"abstract": "  Policies are authoritative assets that are present in multiple domains to\nsupport decision-making. They describe what actions are allowed or recommended\nwhen domain entities and their attributes satisfy certain criteria. It is\ncommon to find policies that contain geographical rules, including distance and\ncontainment relationships among named locations. These locations' polygons can\noften be found encoded in geospatial datasets. We present an approach to\ntransform data from geospatial datasets into Linked Data using the OWL, PROV-O,\nand GeoSPARQL standards, and to leverage this representation to support\nautomated ontology-based policy decisions. We applied our approach to\nlocation-sensitive radio spectrum policies to identify relationships between\nradio transmitters coordinates and policy-regulated regions in Census.gov\ndatasets. Using a policy evaluation pipeline that mixes OWL reasoning and\nGeoSPARQL, our approach implements the relevant geospatial relationships,\naccording to a set of requirements elicited by radio spectrum domain experts.\n"}
+{"abstract": "  Natural Language Processing (NLP) models' current trend consists of using\nincreasingly more extra-data to build the best models as possible. It implies\nmore expensive computational costs and training time, difficulties for\ndeployment, and worries about these models' carbon footprint reveal a critical\nproblem in the future. Against this trend, our goal is to develop NLP models\nrequiring no extra-data and minimizing training time. To do so, in this paper,\nwe explore Markov chain models, Hidden Markov Chain (HMC) and Pairwise Markov\nChain (PMC), for NLP segmentation tasks. We apply these models for three\nclassic applications: POS Tagging, Named-Entity-Recognition, and Chunking. We\ndevelop an original method to adapt these models for text segmentation's\nspecific challenges to obtain relevant performances with very short training\nand execution times. PMC achieves equivalent results to those obtained by\nConditional Random Fields (CRF), one of the most applied models for these tasks\nwhen no extra-data are used. Moreover, PMC has training times 30 times shorter\nthan the CRF ones, which validates this model given our objectives.\n"}
+{"abstract": "  Engineering a low singlet-triplet energy gap ({\\Delta}EST) is necessary for\nefficient reverse intersystem crossing (rISC) in delayed fluorescence (DF)\norganic semiconductors, but results in a small radiative rate that limits\nperformance in LEDs. Here, we study a model DF material, BF2, that exhibits a\nstrong optical absorption (absorption coefficient =3.8x10^5 cm^-1) and a\nrelatively large {\\Delta}EST of 0.2 eV. In isolated BF2 molecules,\nintramolecular rISC is slow (260 {\\mu}s), but in aggregated films, BF2\ngenerates intermolecular CT (inter-CT) states on picosecond timescales. In\ncontrast to the microsecond intramolecular rISC that is promoted by spin-orbit\ninteractions in most isolated DF molecules, photoluminescence-detected magnetic\nresonance shows that these inter-CT states undergo rISC mediated by hyperfine\ninteractions on a ~24 ns timescale and have an average electron-hole separation\nof >1.5 nm. Transfer back to the emissive singlet exciton then enables\nefficient DF and LED operation. Thus, access to these inter-CT states resolves\nthe conflicting requirements of fast radiative emission and low {\\Delta}EST.\n"}
+{"abstract": "  The recent physical model-free dehazing methods have achieved\nstate-of-the-art performances. However, without the guidance of physical\nmodels, the performances degrade rapidly when applied to real scenarios due to\nthe unavailable or insufficient data problems. On the other hand, the physical\nmodel-based methods have better interpretability but suffer from\nmulti-objective optimizations of parameters, which may lead to sub-optimal\ndehazing results. In this paper, a progressive residual learning strategy has\nbeen proposed to combine the physical model-free dehazing process with\nreformulated scattering model-based dehazing operations, which enjoys the\nmerits of dehazing methods in both categories. Specifically, the global\natmosphere light and transmission maps are interactively optimized with the aid\nof accurate residual information and preliminary dehazed restorations from the\ninitial physical model-free dehazing process. The proposed method performs\nfavorably against the state-of-the-art methods on public dehazing benchmarks\nwith better model interpretability and adaptivity for complex hazy data.\n"}
+{"abstract": "  Node.js is one of the most popular frameworks for building web applications.\nAs software systems mature, the cost of running their entire regression test\nsuite can become significant. Selective Regression Testing (SRT) is a technique\nthat executes only a subset of tests the regression test suite can detect\nsoftware failures more efficiently. Previous SRT studies mainly focused on\nstandard desktop applications. Node.js applications are considered hard to\nperform test reduction because of Node's asynchronous, event-driven programming\nmodel and because JavaScript is a dynamic programming language. In this paper,\nwe present NodeSRT, a Selective Regression Testing framework for Node.js\napplications. By performing static and dynamic analysis, NodeSRT identifies the\nrelationship between changed methods and tests, then reduces the regression\ntest suite to only tests that are affected by the change to improve the\nexecution time of the regression test suite. To evaluate our selection\ntechnique, we applied NodeSRT to two open-source projects: Uppy and Simorgh,\nthen compared our approach with the retest-all strategy and current\nindustry-standard SRT technique: Jest OnlyChange. The results demonstrate that\nNodeSRT correctly selects affected tests based on changes and is 250% faster,\n450% more precise than the Jest OnlyChange.\n"}
+{"abstract": "  In the present work we prove that given any two unicycle graphs\n(pseudoforests)\n  that share the same degree sequence there is a finite sequence of 2-switches\n  transforming one into the other such that all the graphs in the sequence\n  are also unicyclic graphs (pseudoforests).\n"}
+{"abstract": "  In this paper, we propose a minimax model predictive control (MPC)-based\nenergy management system that is robust with respect to uncertainties in\nrenewable infeed and load. The MPC formulation includes a model of low-level\ndroop control with saturation at the power and energy limits of the units.\nRobust MPC-based energy management systems tend to under-utilize the renewable\nenergy sources to guarantee safe operation. In order to mitigate this effect,\nwe further consider droop control of renewable energy sources. For a microgrid\nwith droop-controlled units, we show that enhancing droop feedback with\nsaturation enlarges the space of feasible control actions. However, the\nresulting controller requires to solve a mixed-integer problem with additional\nvariables and equations representing saturation. We derive a computationally\ntractable formulation for this problem. Furthermore, we investigate the\nperformance gained by using droop with saturation, renewable droop and\ncombination of both in a case study.\n"}
+{"abstract": "  Sensor configuration, including the sensor selections and their installation\nlocations, serves a crucial role in autonomous driving. A well-designed sensor\nconfiguration significantly improves the performance upper bound of the\nperception system. However, as leveraging multiple sensors is becoming the\nmainstream setting, existing methods mainly focusing on single-sensor\nconfiguration problems are hardly utilized in practice. To tackle these issues,\nwe propose a novel method based on conditional entropy in Bayesian theory to\nevaluate the sensor configurations containing both cameras and LiDARs.\nCorrespondingly, an evaluation metric, perception entropy, is introduced to\nmeasure the difference between two configurations, which considers both the\nperception algorithm performance and the selections of the sensors. To the best\nof our knowledge, this is the first method to tackle the multi-sensor\nconfiguration problem for autonomous vehicles. The simulation results,\nextensive comparisons, and analysis all demonstrate the superior performance of\nour proposed approach.\n"}
+{"abstract": "  This note formally defines the concept of coinductive validity of judgements,\nand contrasts it with inductive validity. For both notions it shows how a\njudgement is valid iff it has a formal proof. Finally, it defines and\nillustrates the notion of a proof by coinduction.\n"}
+{"abstract": "  Self-supervised contrastive learning has demonstrated great potential in\nlearning visual representations. Despite their success on various downstream\ntasks such as image classification and object detection, self-supervised\npre-training for fine-grained scenarios is not fully explored. In this paper,\nwe first point out that current contrastive methods are prone to memorizing\nbackground/foreground texture and therefore have a limitation in localizing the\nforeground object. Analysis suggests that learning to extract discriminative\ntexture information and localization are equally crucial for self-supervised\npre-training in fine-grained scenarios. Based on our findings, we introduce\ncross-view saliency alignment (CVSA), a contrastive learning framework that\nfirst crops and swaps saliency regions of images as a novel view generation and\nthen guides the model to localize on the foreground object via a cross-view\nalignment loss. Extensive experiments on four popular fine-grained\nclassification benchmarks show that CVSA significantly improves the learned\nrepresentation.\n"}
+{"abstract": "  Cooling and heating faster a system is a crucial problem in science,\ntechnology and industry. Indeed, choosing the best thermal protocol to reach a\ndesired temperature or energy is not a trivial task. Noticeably, we find that\nthe phase transitions may speed up thermalization in systems where there are no\nconserved quantities. In particular, we show that the slow growth of magnetic\ndomains shortens the overall time that the system takes to reach a final\ndesired state. To prove that statement, we use intensive numerical simulations\nof a prototypical many-body system, namely the 2D Ising model.\n"}
+{"abstract": "  We present the first detailed dissection of the circumgalactic medium (CGM)\nof massive starburst galaxies at z > 2. Our target is a submillimeter galaxy\n(SMG) at z = 2.674 that has a star formation rate of 1200 $M_\\odot$/yr and a\nmolecular gas reservoir of $1.3\\times10^{11} M_\\odot$. We characterize its CGM\nwith two background QSOs at impact parameters of 93 kpc and 176 kpc. We detect\nstrong HI and metal-line absorption near the redshift of the SMG towards both\nQSOs, each consisting of three main subsystems spanning over 1500 km/s. The\nabsorbers show remarkable kinematic and metallicity coherence across a\nseparation of 86 kpc. In particular, the cool gas in the CGM of the SMG\nexhibits high HI column densities ($\\log N_{\\rm HI}/{\\rm cm}^{-2} = 20.2,\n18.6$), low metallicities ([M/H] $\\approx$ -2.0), and similar radial velocities\n($\\approx$ -300 km/s). While the HI column densities match previous results on\nthe CGM around QSOs at z > 2, the metallicities are lower by more than an order\nof magnitude, making it an outlier in the line width$-$metallicity relation of\ndamped Ly$\\alpha$ absorbers. The large physical extent, the velocity coherence,\nthe high surface density, and the low metallicity are all consistent with the\ncool, inflowing, and near-pristine gas streams predicted to penetrate hot\nmassive halos at z > 1.5. We estimate a total gas accretion rate of ~100\n$M_\\odot$/yr from three such streams, which falls short of the star formation\nrate but is consistent with simulations. At this rate, it takes about a\ngigayear to acquire the molecular gas reservoir of the central starburst.\n"}
+{"abstract": "  We investigate the connection between galactic outflows and star formation\nusing two independent data sets covering a sample of 22 galaxies between $1\n\\lesssim z \\lesssim 1.5$. The HST WFC3/G141 grism provides low spectral\nresolution, high spatial resolution spectroscopy yielding H$\\alpha$ emission\nline maps from which we measure the spatial extent and strength of star\nformation. In the rest-frame near-UV, Keck/DEIMOS observes Fe II and Mg II\ninterstellar absorption lines, which provide constraints on the intensity and\nvelocity of the outflows. We compare outflow properties from individual and\ncomposite spectra with the star formation rate (SFR) and SFR surface density\n(SigmaSFR), as well as the stellar mass and specific star formation rate\n(sSFR). The Fe II and Mg II equivalent widths (EWs) increase with both SFR and\nSigmaSFR at $\\gtrsim 3\\sigma$ significance, while the composite spectra show\nlarger Fe II EWs and outflow velocities in galaxies with higher SFR, SigmaSFR,\nand sSFR. Absorption line profiles of the composite spectra further indicate\nthat the differences between subsamples are driven by outflows rather than the\nISM. While these results are consistent with those of previous studies, the use\nof H$\\alpha$ images makes them the most direct test of the relationship between\nstar formation and outflows at $z>1$ to date. Future facilities such as the\nJames Webb Space Telescope and the upcoming Extremely Large Telescopes will\nextend these direct, H$\\alpha$-based studies to lower masses and star formation\nrates, probing galactic feedback across orders of magnitude in galaxy\nproperties and augmenting the correlations we find here.\n"}
+{"abstract": "  We show that the rate for dark matter-electron scattering in an arbitrary\nmaterial is determined by an experimentally measurable quantity, the complex\ndielectric function, for any dark matter interaction that couples to electron\ndensity. This formulation automatically includes many-body effects, eliminates\nall systematic theoretical uncertainties on the electronic wavefunctions, and\nallows a direct calibration of the spectrum by electromagnetic probes such as\ninfrared spectroscopy, X-ray scattering, and electron energy-loss spectroscopy\n(EELS). Our formalism applies for several common benchmark models, including\nspin-independent interactions through scalar and vector mediators of arbitrary\nmass. We discuss the consequences for standard semiconductor and superconductor\ntargets, and find that the true reach of superconductor detectors for light\nmediators exceeds previous estimates by several orders of magnitude, with\nfurther enhancements possible due to the low-energy tail of the plasmon. Using\na heavy-fermion superconductor as an example, we show how our formulation\nallows a rapid and systematic investigation of novel electron scattering\ntargets.\n"}
+{"abstract": "  Level density $\\rho(E,A)$ is derived for a one-component nucleon system with\na given energy $E$ and particle number $A$ within the mean-field semiclassical\nperiodic-orbit theory beyond the saddle-point method of the Fermi gas model. We\nobtain $~~\\rho \\propto I_\\nu(S)/S^\\nu$, with $I_\\nu(S)$ being the modified\nBessel function of the entropy $S$. Within the micro-macro-canonical\napproximation (MMA), for a small thermal excitation energy, $U$, with respect\nto rotational excitations, $E_{\\rm rot}$, one obtains $\\nu=3/2$ for\n$\\rho(E,A)$. In the case of excitation energy $U$ larger than $E_{\\rm rot}$ but\nsmaller than the neutron separation energy, one finds a larger value of\n$\\nu=5/2$. A role of the fixed spin variables for rotating nuclei is discussed.\nThe MMA level density $\\rho$ reaches the well-known grand-canonical ensemble\nlimit (Fermi gas asymptotic) for large $S$ related to large excitation\nenergies, and also reaches the finite micro-canonical limit for small\ncombinatorial entropy $S$ at low excitation energies (the constant\n\"temperature\" model). Fitting the $\\rho(E,A)$ of the MMA to the experimental\ndata for low excitation energies, taking into account shell and, qualitatively,\npairing effects, one obtains for the inverse level density parameter $K$ a\nvalue which differs essentially from that parameter derived from data on\nneutron resonances.\n"}
+{"abstract": "  Several studies with brain signals suggested that bottom-up and top-down\ninfluences are exerted through distinct frequency bands among visual cortical\nareas. It has been recently shown that theta and gamma rhythms subserve\nfeedforward, whereas the feedback influence is dominated by the alpha-beta\nrhythm in primates. A few theoretical models for reproducing these effects have\nbeen proposed so far. Here we show that a simple but biophysically plausible\ntwo-network motif composed of spiking-neuron models and chemical synapses can\nexhibit feedforward and feedback influences through distinct frequency bands.\nDifferently from previous studies, this kind of model allows us to study\ndirected influences not only at the population level, by using a proxy for the\nlocal field potential, but also at the cellular level, by using the neuronal\nspiking series.\n"}
+{"abstract": "  Context: Given the acknowledged need to understand the people processes\nenacted during software development, software repositories and mailing lists\nhave become a focus for many studies. However, researchers have tended to use\nmostly mathematical and frequency-based techniques to examine the software\nartifacts contained within them. Objective: There is growing recognition that\nthese approaches uncover only a partial picture of what happens during software\nprojects, and deeper contextual approaches may provide further understanding of\nthe intricate nature of software teams' dynamics. We demonstrate the relevance\nand utility of such approaches in this study. Method: We use psycholinguistics\nand directed content analysis (CA) to study the way project tasks drive teams'\nattitudes and knowledge sharing. We compare the outcomes of these two\napproaches and offer methodological advice for researchers using similar forms\nof repository data. Results: Our analysis reveals significant differences in\nthe way teams work given their portfolio of tasks and the distribution of\nroles. Conclusion: We overcome the limitations associated with employing purely\nquantitative approaches, while avoiding the time-intensive and potentially\ninvasive nature of field work required in full case studies.\n"}
+{"abstract": "  A discrete multidimensional system is the set of solutions to a system of\nlinear partial difference equations defined on the lattice $\\Z^n$. This paper\nshows that it is determined by a unique coarsest sublattice, in the sense that\nthe solutions of the system on this sublattice determine the solutions on\n$\\Z^n$; it is therefore the correct domain of definition of the discrete\nsystem. In turn, the defining sublattice is determined by a Galois group of\nsymmetries that leave invariant the equations defining the system. These\nresults find application in understanding properties of the system such as\ncontrollability and autonomy, and in its order reduction.\n"}
+{"abstract": "  Deep models trained in supervised mode have achieved remarkable success on a\nvariety of tasks. When labeled samples are limited, self-supervised learning\n(SSL) is emerging as a new paradigm for making use of large amounts of\nunlabeled samples. SSL has achieved promising performance on natural language\nand image learning tasks. Recently, there is a trend to extend such success to\ngraph data using graph neural networks (GNNs). In this survey, we provide a\nunified review of different ways of training GNNs using SSL. Specifically, we\ncategorize SSL methods into contrastive and predictive models. In either\ncategory, we provide a unified framework for methods as well as how these\nmethods differ in each component under the framework. Our unified treatment of\nSSL methods for GNNs sheds light on the similarities and differences of various\nmethods, setting the stage for developing new methods and algorithms. We also\nsummarize different SSL settings and the corresponding datasets used in each\nsetting. To facilitate methodological development and empirical comparison, we\ndevelop a standardized testbed for SSL in GNNs, including implementations of\ncommon baseline methods, datasets, and evaluation metrics.\n"}
+{"abstract": "  We investigate a recombination-drift-diffusion model coupled to Poisson's\nequation modelling the transport of charge within certain types of\nsemiconductors. In more detail, we study a two-level system for electrons and\nholes endowed with an intermediate energy level for electrons occupying trapped\nstates. As our main result, we establish an explicit functional inequality\nbetween relative entropy and entropy production, which leads to exponential\nconvergence to equilibrium. We stress that our approach is applied uniformly in\nthe lifetime of electrons on the trap level assuming that this lifetime is\nsufficiently small.\n"}
+{"abstract": "  A recurrent structure is a popular framework choice for the task of video\nsuper-resolution. The state-of-the-art method BasicVSR adopts bidirectional\npropagation with feature alignment to effectively exploit information from the\nentire input video. In this study, we redesign BasicVSR by proposing\nsecond-order grid propagation and flow-guided deformable alignment. We show\nthat by empowering the recurrent framework with the enhanced propagation and\nalignment, one can exploit spatiotemporal information across misaligned video\nframes more effectively. The new components lead to an improved performance\nunder a similar computational constraint. In particular, our model BasicVSR++\nsurpasses BasicVSR by 0.82 dB in PSNR with similar number of parameters. In\naddition to video super-resolution, BasicVSR++ generalizes well to other video\nrestoration tasks such as compressed video enhancement. In NTIRE 2021,\nBasicVSR++ obtains three champions and one runner-up in the Video\nSuper-Resolution and Compressed Video Enhancement Challenges. Codes and models\nwill be released to MMEditing.\n"}
+{"abstract": "  We consider (Enc, Dec) schemes which are used to encode a classical/quantum\nmessage $m$ and derive an $n$-qubit quantum codeword $\\psi_m$. The quantum\ncodeword $\\psi_m$ can adversarially tamper via a unitary $U \\in \\mathcal{U}$\nfrom some known tampering unitary family $\\mathcal{U}$, resulting in $U \\psi_m\nU^\\dagger$.\n  Firstly, we initiate the general study of quantum tamper detection codes,\nwhich must detect that tampering occurred with high probability. In case there\nwas no tampering, we would like to output the message $m$ with a probability of\n$1$. We show that quantum tamper detection codes exist for both classical\nmessages and quantum messages for any family of unitaries $\\mathcal{U}$, such\nthat $|\\mathcal{U}| < 2^{2^{\\alpha n}}$ for some known constant $\\alpha \\in\n(0,1)$ and all the unitaries satisfy one additional condition :\n  \\begin{itemize}\n  \\item Far from Identity : For each $U \\in \\mathcal{U}$, we require that its\nmodulus of trace value isn't too much i.e. $ |Trace(U)| \\leq \\phi N$, where\n$N=2^n.$\n  \\end{itemize}\n  Quantum tamper-detection codes are quantum generalizations of classical\ntamper detection codes studied by Jafargholi et al. \\cite{JW15}.\n  Additionally for classical message $m$, if we must either output message $m$\nor detect that tampering occurred and output $\\perp$ with high probability, we\nshow that it is possible without the restriction of Far from Identity condition\nfor any family of unitaries $\\mathcal{U}$, such that $|\\mathcal{U} | <\n2^{2^{\\alpha n}}$. We also provide efficient (Enc, Dec) schemes when the family\nof tampering unitaries are from Pauli group $\\mathcal{P}_n$, which can be\nthought of as a quantum version of the algebraic manipulation detection (AMD)\ncodes of Cramer et al. \\cite{CDFPW08}.\n"}
+{"abstract": "  We give new and efficient black-box reconstruction algorithms for some\nclasses of depth-$3$ arithmetic circuits. As a consequence, we obtain the first\nefficient algorithm for computing the tensor rank and for finding the optimal\ntensor decomposition as a sum of rank-one tensors when then input is a\nconstant-rank tensor. More specifically, we provide efficient learning\nalgorithms that run in randomized polynomial time over general fields and in\ndeterministic polynomial time over the reals and the complex numbers for the\nfollowing classes:\n  (1) Set-multilinear depth-$3$ circuits of constant top fan-in\n$\\Sigma\\Pi\\Sigma\\{\\sqcup_j X_j\\}(k)$ circuits). As a consequence of our\nalgorithm, we obtain the first polynomial time algorithm for tensor rank\ncomputation and optimal tensor decomposition of constant-rank tensors. This\nresult holds for $d$ dimensional tensors for any $d$, but is interesting even\nfor $d=3$.\n  (2) Sums of powers of constantly many linear forms ($\\Sigma\\wedge\\Sigma$\ncircuits). As a consequence we obtain the first polynomial-time algorithm for\ntensor rank computation and optimal tensor decomposition of constant-rank\nsymmetric tensors.\n  (3) Multilinear depth-3 circuits of constant top fan-in (multilinear\n$\\Sigma\\Pi\\Sigma(k)$ circuits). Our algorithm works over all fields of\ncharacteristic 0 or large enough characteristic. Prior to our work the only\nefficient algorithms known were over polynomially-sized finite fields (see.\nKarnin-Shpilka 09').\n  Prior to our work, the only polynomial-time or even subexponential-time\nalgorithms known (deterministic or randomized) for subclasses of\n$\\Sigma\\Pi\\Sigma(k)$ circuits that also work over large/infinite fields were\nfor the setting when the top fan-in $k$ is at most $2$ (see Sinha 16' and Sinha\n20').\n"}
+{"abstract": "  In many real-world applications, the mismatch between distributions of\ntraining data (source) and test data (target) significantly degrades the\nperformance of machine learning algorithms. In speech data, causes of this\nmismatch include different acoustic environments or speaker characteristics. In\nthis paper, we address this issue in the challenging context of dysarthric\nspeech, by multi-source domain/speaker adaptation (MSDA/MSSA). Specifically, we\npropose the use of an optimal-transport based approach, called MSDA via\nWeighted Joint Optimal Transport (MSDA-WDJOT). We confront the mismatch problem\nin dysarthria detection for which the proposed approach outperforms both the\nBaseline and the state-of-the-art MSDA models, improving the detection accuracy\nof 0.9% over the best competitor method. We then employ MSDA-WJDOT for\ndysarthric speaker adaptation in command speech recognition. This provides a\nCommand Error Rate relative reduction of 16% and 7% over the baseline and the\nbest competitor model, respectively. Interestingly, MSDA-WJDOT provides a\nsimilarity score between the source and the target, i.e. between speakers in\nthis case. We leverage this similarity measure to define a Dysarthric and\nHealthy score of the target speaker and diagnose the dysarthria with an\naccuracy of 95%.\n"}
+{"abstract": "  Rapidly changing business environments expose companies to high levels of\nuncertainty. This uncertainty manifests itself in significant changes that tend\nto occur over the lifetime of a process and possibly affect its performance. It\nis important to understand the root causes of such changes since this allows us\nto react to change or anticipate future changes. Research in process mining has\nso far only focused on detecting, locating and characterizing significant\nchanges in a process and not on finding root causes of such changes. In this\npaper, we aim to close this gap. We propose a framework that adds an\nexplainability level onto concept drift detection in process mining and\nprovides insights into the cause-effect relationships behind significant\nchanges. We define different perspectives of a process, detect concept drifts\nin these perspectives and plug the perspectives into a causality check that\ndetermines whether these concept drifts can be causal to each other. We\nshowcase the effectiveness of our framework by evaluating it on both synthetic\nand real event data. Our experiments show that our approach unravels\ncause-effect relationships and provides novel insights into executed processes.\n"}
+{"abstract": "  We demonstrate a computational scheme which drastically decreases the\nrequired time to get theoretical predictions based on chiral two- and\nthree-nucleon forces for observables in three-nucleon continuum. For a\nthree-nucleon force containing N short-range terms all workload is reduced to\nsolving N+1 Faddeev-type integral equations. That done, computation of\nobservables for any combination of strengths of the contact terms is done in a\nflash. We demonstrate on example of the elastic nucleon-deuteron scattering\nobservables the high precision of the proposed emulator and its capability to\nreproduce exact results.\n"}
+{"abstract": "  This study investigates how the location-routing decisions of the electric\nvehicle (EV) DCFC charging stations are impacted by the ambient temperature. We\nformulated this problem as a mixed-integer linear programming model that\ncaptures the realistic charging behavior of the DCFC in association with the\nambient temperature and their subsequent impact on the EV charging station\nlocation and routing decisions. Two innovative heuristics are proposed to solve\nthis challenging model in a realistic test setting, namely, the two-phase Tabu\nSearch-modified Clarke and Wright algorithm and the Sweep-based Iterative\nGreedy Adaptive Large Neighborhood algorithm. We use Fargo city in North Dakota\nas a testbed to visualize and validate the algorithm performances. The results\nclearly indicate that the EV DCFC charging station location decisions are\nhighly sensitive to the ambient temperature, the charging time, and the initial\nstate of charge.\n"}
+{"abstract": "  The detrimental impacts of climate change include stronger and more\ndestructive hurricanes happening all over the world. Identifying different\ndamaged structures of an area including buildings and roads are vital since it\nhelps the rescue team to plan their efforts to minimize the damage caused by a\nnatural disaster. Semantic segmentation helps to identify different parts of an\nimage. We implement a novel self-attention based semantic segmentation model on\na high resolution UAV dataset and attain Mean IoU score of around 88% on the\ntest set. The result inspires to use self-attention schemes in natural disaster\ndamage assessment which will save human lives and reduce economic losses.\n"}
+{"abstract": "  It is demonstrated in this paper that the propagation of the electric wave\nfield in a heterogeneous medium in 3D can sometimes be governed well by a\nsingle PDE, which is derived from the Maxwell's equations. The corresponding\ncomponent of the electric field dominates two other components. This justifies\nsome past results of the second author with coauthors about numerical solutions\nof coefficient inverse problems with experimental electromagnetic data. In\naddition, since it is simpler to work in applications with a single PDE rather\nthan with the complete Maxwell's system, then the result of this paper might be\nuseful to researchers working on applied issues of the propagation of\nelectromagnetic waves in inhomogeneous media.\n"}
+{"abstract": "  In this paper, we define `simplicial GKM orbifold complexes' and `simplicial\nGKM graph complexes'. We study some of their topological and combinatorial\nproperties. We discuss some necessary conditions to confirm an invariant\n$q$-cell structure on a simplicial GKM orbifold complex. We prove Thom\nisomorphism theorem for orbifold $G$-vector bundles for equivariant cohomology\nand equivariant K-theory with rational coefficients. We use this to extend the\nmain result of Harada-Henriques-Holm (2005) to the category of $G$-spaces\nequipped with `singular invariant stratification'. Then we introduce an\nequivalent definition of weighted Grassmann orbifolds which were introduced by\nCorti and Reid (2002) and explicitly studied by Abe and Matsumura (2015) to\ncompute their equivariant cohomology with rational coefficients. These\norbifolds are a generalization of Grassmann manifolds. We study their different\n$q$-cell structures and discuss some necessary conditions to determine if they\nhave invariant CW-structures. We employ the techniques from the first part to\nstudy some generalized equivariant cohomology theories of weighted Grassmann\norbifolds. We introduce `divisive' weighted Grassmann orbifolds and compute\ntheir equivariant cohomology, equivariant K-theory and equivariant cobordism\nring with integer coefficients. We discuss Schubert calculus for the\nequivariant cohomology ring and show how to compute the corresponding structure\nconstants with integer coefficients.\n"}
+{"abstract": "  We prove that, for an undirected graph with $n$ vertices and $m$ edges, each\nlabeled with a linear function of a parameter $\\lambda$, the number of\ndifferent minimum spanning trees obtained as the parameter varies can be\n$\\Omega(m\\log n)$.\n"}
+{"abstract": "  We investigate the homogenization through Gamma-convergence for the\nL^2(\\Omega)-weak topology of the conductivity functional with a zero-order term\nwhere the matrix-valued conductivity is assumed to be non strongly elliptic.\nUnder proper assumptions, we show that the homogenized matrix A^\\ast is\nprovided by the classical homogenization formula. We also give algebraic\nconditions for two and three dimensional 1-periodic rank-one laminates such\nthat the homogenization result holds. For this class of laminates, an explicit\nexpression of A^\\ast is provided which is a generalization of the classical\nlaminate formula. We construct a two-dimensional counter-example which shows an\nanomalous asymptotic behaviour of the conductivity functional.\n"}
+{"abstract": "  Neural methods have been shown to achieve high performance in Named Entity\nRecognition (NER), but rely on costly high-quality labeled data for training,\nwhich is not always available across languages. While previous works have shown\nthat unlabeled data in a target language can be used to improve cross-lingual\nmodel performance, we propose a novel adversarial approach (AdvPicker) to\nbetter leverage such data and further improve results. We design an adversarial\nlearning framework in which an encoder learns entity domain knowledge from\nlabeled source-language data and better shared features are captured via\nadversarial training - where a discriminator selects less language-dependent\ntarget-language data via similarity to the source language. Experimental\nresults on standard benchmark datasets well demonstrate that the proposed\nmethod benefits strongly from this data selection process and outperforms\nexisting state-of-the-art methods; without requiring any additional external\nresources (e.g., gazetteers or via machine translation). The code is available\nat https://aka.ms/AdvPicker\n"}
+{"abstract": "  Dynamical friction is typically regarded a secular process, in which the\nsubject ('perturber') evolves very slowly (secular approximation), and has been\nintroduced to the host over a long time (adiabatic approximation). These\nassumptions imply that dynamical friction arises from the LBK torque with\nnon-zero contribution only from pure resonance orbits. However, dynamical\nfriction is only of astrophysical interest if its timescale is shorter than the\nage of the Universe. In this paper we therefore relax the adiabatic and secular\napproximations. We first derive a generalized LBK torque, which reduces to the\nLBK torque in the adiabatic limit, and show that it gives rise to transient\noscillations due to non-resonant orbits that slowly damp out, giving way to the\nLBK torque. This is analogous to how a forced, damped oscillator undergoes\ntransients before settling to a steady state, except that here the damping is\ndue to phase mixing rather than dissipation. Next, we present a self-consistent\ntreatment, that properly accounts for time-dependence of the perturber\npotential and circular frequency (memory effect), which we use to examine\norbital decay in a cored galaxy. We find that the memory effect results in a\nphase of accelerated, super-Chandrasekhar friction before the perturber stalls\nat a critical radius, $R_{\\mathrm{crit}}$, in the core (core-stalling). Inside\nof $R_{\\mathrm{crit}}$ the torque flips sign, giving rise to dynamical\nbuoyancy, which counteracts friction and causes the perturber to stall. This\nphenomenology is consistent with $N$-body simulations, but has thus far eluded\nproper explanation.\n"}
+{"abstract": "  We study a family of algorithms, which we refer to as local update methods,\ngeneralizing many federated and meta-learning algorithms. We prove that for\nquadratic models, local update methods are equivalent to first-order\noptimization on a surrogate loss we exactly characterize. Moreover, fundamental\nalgorithmic choices (such as learning rates) explicitly govern a trade-off\nbetween the condition number of the surrogate loss and its alignment with the\ntrue loss. We derive novel convergence rates showcasing these trade-offs and\nhighlight their importance in communication-limited settings. Using these\ninsights, we are able to compare local update methods based on their\nconvergence/accuracy trade-off, not just their convergence to critical points\nof the empirical loss. Our results shed new light on a broad range of\nphenomena, including the efficacy of server momentum in federated learning and\nthe impact of proximal client updates.\n"}
+{"abstract": "  Symmetry plays a fundamental role in the security analysis of quantum key\ndistribution (QKD). Here we review how symmetry is exploited in\ncontinuous-variable (CV) QKD to prove the optimality of Gaussian attacks in the\nfinite-size regime. We then apply these results to improve the efficiency, and\nthus the key rate, of these protocols. First we show how to improve the\nefficiency of the energy test, which is one important routine in a CV QKD\nprotocol aimed at establishing an upper bound on the effective dimensions of\nthe otherwise infinite-dimensional Hilbert space of CV systems. Second, we show\nhow the routine of parameter estimation can be made resource efficient in\nmeasurement-device independent (MDI) QKD. These results show that all the raw\ndata can be used both for key extraction and for the routines of energy test\nand parameter estimation.\n"}
+{"abstract": "  It is commonly believed that Bayesian optimization (BO) algorithms are highly\nefficient for optimizing numerically costly functions. However, BO is not often\ncompared to widely different alternatives, and is mostly tested on narrow sets\nof problems (multimodal, low-dimensional functions), which makes it difficult\nto assess where (or if) they actually achieve state-of-the-art performance.\nMoreover, several aspects in the design of these algorithms vary across\nimplementations without a clear recommendation emerging from current practices,\nand many of these design choices are not substantiated by authoritative test\ncampaigns. This article reports a large investigation about the effects on the\nperformance of (Gaussian process based) BO of common and less common design\nchoices. The experiments are carried out with the established COCO (COmparing\nContinuous Optimizers) software. It is found that a small initial budget, a\nquadratic trend, high-quality optimization of the acquisition criterion bring\nconsistent progress. Using the GP mean as an occasional acquisition contributes\nto a negligible additional improvement. Warping degrades performance. The\nMat\\'ern 5/2 kernel is a good default but it may be surpassed by the\nexponential kernel on irregular functions. Overall, the best EGO variants are\ncompetitive or improve over state-of-the-art algorithms in dimensions less or\nequal to 5 for multimodal functions. The code developed for this study makes\nthe new version (v2.1.1) of the R package DiceOptim available on CRAN. The\nstructure of the experiments by function groups allows to define priorities for\nfuture research on Bayesian optimization.\n"}
+{"abstract": "  We consider the Dirichlet problem for second-order linear elliptic equations\nin divergence form \\begin{equation*} -\\mathrm{div }(A\\nabla u)+\\mathbf{b} \\cdot\n\\nabla u+\\lambda u=f+\\mathrm{div } \\mathbf{F}\\quad \\text{in }\n\\Omega\\quad\\text{and}\\quad u=0\\quad \\text{on } \\partial\\Omega, \\end{equation*}\nin bounded Lipschitz domain $\\Omega$ in $\\mathbb{R}^2$, where\n$A:\\mathbb{R}^2\\rightarrow \\mathbb{R}^{2^2}$, $\\mathbf{b} : \\Omega\\rightarrow\n\\mathbb{R}^2$, and $\\lambda \\geq 0$ are given. If $2<p<\\infty$ and $A$ has a\nsmall mean oscillation in small balls, $\\Omega$ has small Lipschitz constant,\nand $\\mathrm{div } A,\\,\\mathbf{b} \\in L^{2}(\\Omega;\\mathbb{R}^2)$, then we\nprove existence and uniqueness of weak solutions in $W^{1,p}_0(\\Omega)$ of the\nproblem. Similar result also holds for the dual problem.\n"}
+{"abstract": "  We present Doppler-corrected position and velocity measurement with a\nfiber-coupled COFDR system based on the FMCW radar principle for high precision\nlocalization applications. A high measurement accuracy and the ability to track\ntargets are demonstrated.\n"}
+{"abstract": "  In this paper, we present a survey on the utility of machine learning (ML)\nalgorithms for applications in cognitive radio networks (CRN). We start with a\nhigh-level overview of some of the major challenges in CRNs, and mention the ML\narchitectures and algorithms that can be used to alleviate them. In particular,\nour focus is on two fundamental applications in CRNs, namely spectrum sensing\n-- with non-cooperative and cooperative scenarios, and dynamic spectrum access\n-- with spectrum auction and prediction. We present a detailed study of recent\nadvancements in the field of ML in CRNs for these applications, and briefly\ndiscuss the set of challenges in real-time implementation of ML techniques for\nCRNs.\n"}
+{"abstract": "  Positron Emission Particle Tracking (PEPT) is an imaging method that tracks\nindividual radioactive particles. PEPT relies on the detection of back-to-back\nphoton pairs emitted by positron annihilation. It requires an algorithm to\nlocate the radioactive particles based on the set of lines defined by\nsuccessive photon-pair detections. We propose and test a new algorithm for this\ntask. The algorithm relies on the maximization of a likelihood arising from a\nsimple Gaussian-mixture model defined in the space of lines. The model includes\na component that accounts for spurious lines caused by scattering and random\ncoincidence, and treats the relative activity of particles as well as their\npositions as parameters to be inferred. Values of these parameters that\napproximately maximize the likelihood are computed by application of an\nexpectation-maximization algorithm. A generalization of the model that includes\nthe particle velocities and accelerations as additional parameters takes\nadvantage of the information contained in the exact timing of positron\nannihilations to reconstruct pieces of trajectories rather than fixed\npositions, with clear benefits.\n  We test the algorithm on both simulated and experimental data. The results\nshow the algorithm to be highly effective for the simultaneous tracking of many\nparticles (up to 80 in one test). It provides estimates of particle positions\nthat are easily mapped to entire trajectories and handles a variable number of\nparticles in the field of view. The ability to track a large number of\nparticles robustly offers the possibility of a dramatic expansion of the scope\nof PEPT\n"}
+{"abstract": "  In this paper we propose and investigate a general approach to constructing\nlocal energy-preserving algorithms which can be of arbitrarily high order in\ntime for solving Hamiltonian PDEs. This approach is based on the temporal\ndiscretization using continuous Runge-Kutta-type methods, and the spatial\ndiscretization using pseudospectral methods or Gauss--Legendre collocation\nmethods. The local energy conservation law of our new schemes is analyzed in\ndetail. The effectiveness of the novel local energy-preserving integrators is\ndemonstrated by coupled nonlinear Schr\\\"odinger equations and 2D nonlinear\nSchr\\\"odinger equations with external fields. Our new schemes are compared with\nsome classical multi-symplectic and symplectic schemes in numerical\nexperiments. The numerical results show the remarkable \\emph{long-term}\nbehaviour of our new schemes.\n"}
+{"abstract": "  We study integrability properties of the non-chiral intermediate long wave\n(ncILW) equation with periodic boundary conditions. The ncILW equation was\nrecently introduced by the authors as a parity-invariant relative of the\nintermediate long wave equation. For this new equation we: (a) derive a Lax\npair, (b) derive a Hirota bilinear form, (c) use the Hirota method to construct\nthe periodic multi-soliton solutions, (d) derive a B\\\"{a}cklund transformation,\n(e) use the B\\\"{a}cklund transformation to obtain an infinite number of\nconservation laws.\n"}
+{"abstract": "  The advent of digital pathology presents opportunities for computer vision\nfor fast, accurate, and objective solutions for histopathological images and\naid in knowledge discovery. This work uses deep learning to predict genomic\nbiomarkers - TP53 mutation, PIK3CA mutation, ER status, PR status, HER2 status,\nand intrinsic subtypes, from breast cancer histopathology images. Furthermore,\nwe attempt to understand the underlying morphology as to how these genomic\nbiomarkers manifest in images. Since gene sequencing is expensive, not always\navailable, or even feasible, predicting these biomarkers from images would help\nin diagnosis, prognosis, and effective treatment planning. We outperform the\nexisting works with a minimum improvement of 0.02 and a maximum of 0.13 AUROC\nscores across all tasks. We also gain insights that can serve as hypotheses for\nfurther experimentations, including the presence of lymphocytes and\nkaryorrhexis. Moreover, our fully automated workflow can be extended to other\ntasks across other cancer subtypes.\n"}
+{"abstract": "  Images acquired with a telescope are blurred and corrupted by noise. The\nblurring is usually modeled by a convolution with the Point Spread Function and\nthe noise by Additive Gaussian Noise. Recovering the observed image is an\nill-posed inverse problem. Sparse deconvolution is well known to be an\nefficient deconvolution technique, leading to optimized pixel Mean Square\nErrors, but without any guarantee that the shapes of objects (e.g. galaxy\nimages) contained in the data will be preserved. In this paper, we introduce a\nnew shape constraint and exhibit its properties. By combining it with a\nstandard sparse regularization in the wavelet domain, we introduce the Shape\nCOnstraint REstoration algorithm (SCORE), which performs a standard sparse\ndeconvolution, while preserving galaxy shapes. We show through numerical\nexperiments that this new approach leads to a reduction of galaxy ellipticity\nmeasurement errors by at least 44%.\n"}
+{"abstract": "  This paper aims to gain a better understanding of the perspectives of\ncontributors to Spanish academic journals regarding open access, open peer\nreview, and altmetrics. It also explores how age, gender, professional\nexperience, career history, and perception and use of social media influence\nauthors opinions toward these developments in scholarly publishing. A sample of\ncontributors (n-1254) to Spanish academic journals was invited to participate\nin a survey about the aforementioned topics. The response rate was 24 per cent\n(n-295). Contributors to Spanish scholarly journals hold a favourable opinion\nof open access but were more cautious about open peer review and altmetrics.\nYounger and female scholars were more reluctant to accept open peer review\npractices. A positive attitude toward social networks did not necessarily\ntranslate into enthusiasm for emerging trends in scholarly publishing. Despite\nthis, ResearchGate users were more aware of altmetrics.\n"}
+{"abstract": "  We investigate the application of a holographic entanglement negativity\nconstruction to bipartite states of single subsystems in $CFT_d$s with a\nconserved charge dual to bulk $AdS_{d+1}$ geometries. In this context, we\nobtain the holographic entanglement negativity for single subsystems with long\nrectangular strip geometry in $CFT_d$s dual to bulk extremal and nonextremal\nReissner-Nordstr{\\\"o}m (RN)-$AdS_{d+1}$ black holes. Our results demonstrate\nthat for this configuration the holographic entanglement negativity involves\nsubtraction of the thermal entropy from the entanglement entropy confirming\nearlier results. This conforms to the characterization of entanglement\nnegativity as the upper bound on the distillable entanglement in quantum\ninformation theory and constitutes an important consistency check for our\nhigher dimensional construction.\n"}
+{"abstract": "  Compact groups (CGs) of galaxies appear to be the densest galaxy systems\ncontaining a few luminous galaxies in close proximity to each other, which have\na typical size of a few tens kilopacsec in observation. On the other hand, in\nthe modern hierarchical structure formation paradigm, galaxies are assembled\nand grouped in dark matter haloes, which have a typical size of a few hundreds\nof kiloparsec. Few studies have explored the physical connection between the\nobservation based CGs and halo model based galaxy groups to date. In this\nstudy, by matching the largest local CG catalog of Zheng & Shen (2020) to the\nhalo based group catalog of Yang et al. (2007), we find that the CGs are\nphysically heterogenous systems and can be mainly separated into two\ncategories, the isolated systems and those embedded in rich groups or clusters.\nBy examining the dynamical features of CGs, we find that the isolated CGs have\nsystematically lower dynamical masses than that of non-compact ones at the same\ngroup luminosity, indicating a more evolved stage of isolated CGs. On the other\nhand, the embedded CGs are mixtures of chance alignments in poor clusters and\nrecent infalling groups (sub-structures) of rich clusters.\n"}
+{"abstract": "  Hybrid Morphology Radio Sources (HyMoRS) are a very rare and newly discovered\nsubclass of radio galaxies that have mixed FR morphology i.e., these galaxies\nhave FR-I structure on one side of the core and FR-II structure on the other\nside of the core. We systematically searched for HyMoRS using VLA Faint Images\nof the Radio Sky at Twenty-cm (FIRST) survey at 1400 MHz and identified\nforty-five confirmed HyMoRS and five candidates HyMoRS. Our finding\nsignificantly increased the known sample size of HyMoRS. HyMoRS may play an\nessential role in understanding the interaction of jets with the interstellar\nmedium and a very debated topic of the FR dichotomy. We identified optical/IR\ncounterparts for thirty-nine sources in our catalogue. In our sample of\nsources, five sources had Quasar-like behavior. We had estimated the spectral\nindex and radio luminosity of HyMoR sources in our catalogue, when possible. We\nfound that the source J1336+2329 ($\\log L=26.93$ W Hz$^{-1}$sr$^{-1}$) was the\nmost luminous and the source J1204+3801, a Quasar, was the farthest HyMoRS\n(with redshift $z$=1.28) in our sample. With the help of a large sample size of\nthe newly discovered sources, various statistical properties were studied.\n"}
+{"abstract": "  The local Langlands correspondence matches irreducible representations of a\nreductive p-adic group G(F) with enhanced L-parameters. It is conjectured by\nHellmann and Zhu that it can be categorified. Then it should become a fully\nfaithful functor from a derived category of representations to a derived\ncategory of equivariant sheaves on some variety of L-parameters. We approach\nthis conjecture in the case of finite length G(F)-representations. Then it runs\nvia graded Hecke algebras associated to Bernstein components or to enhanced\nL-parameters. Here we work with graded Hecke algebras H on the Galois side,\nthose can be constructed entirely in terms of a complex reductive group,\nendowed with data from L-parameters. We fix an arbitrary central character\n(\\sigma,r) of H (which encodes the image of Frobenius by an L-parameter). That\nleads to a variety g_N^{\\sigma,r} of nilpotent elements in the Lie algebra of G\n(possibilities for the monodromy operator from an L-parameter) and to a complex\nof equivariant constructible sheaves K_{N,\\sigma,r} on g_N^{\\sigma,r}. We\nrelate the (derived) endomorphism algebra of (g_N^{\\sigma,r}, K_{N,\\sigma,r})\nto a localization of H, which yields an equivalence between the appropriate\ncategories of finite length modules of these algebras. From there we construct\na fully faithful functor between:\n  - the bounded derived category of finite length H-modules specified by the\ncentral character (\\sigma,r),\n  - the equivariant bounded derived category of constructible sheaves on\ng_N^{\\sigma,r}.\n  Also, we explicitly determine the images of standard modules under this\nfunctor. We expect that these results pave the way for more general instances\nof the aforementioned conjectural extension of the local Langlands\ncorrespondence.\n"}
+{"abstract": "  Recent work in equivariant deep learning bears strong similarities to\nphysics. Fields over a base space are fundamental entities in both subjects, as\nare equivariant maps between these fields. In deep learning, however, these\nmaps are usually defined by convolutions with a kernel, whereas they are\npartial differential operators (PDOs) in physics. Developing the theory of\nequivariant PDOs in the context of deep learning could bring these subjects\neven closer together and lead to a stronger flow of ideas. In this work, we\nderive a $G$-steerability constraint that completely characterizes when a PDO\nbetween feature vector fields is equivariant, for arbitrary symmetry groups\n$G$. We then fully solve this constraint for several important groups. We use\nour solutions as equivariant drop-in replacements for convolutional layers and\nbenchmark them in that role. Finally, we develop a framework for equivariant\nmaps based on Schwartz distributions that unifies classical convolutions and\ndifferential operators and gives insight about the relation between the two.\n"}
+{"abstract": "  The pyramids of ancient Egypt are said to have shone brilliantly in the sun.\nSurfaces of polished limestone would not only have reflected diffusely in all\ndirections, but would also likely have produced specular reflections in\nparticular directions. Reflections toward points on the horizon would have been\nvisible from large distances. On a particular day and time when the sun was\nproperly situated, an observer stationed at a distant site would have seen a\nmomentary flash as the sun's reflection moved across the face of the pyramid.\nThe positions of the sun that are reflected to the horizon are confined to\nnarrow arcs in the sky, one arc for each side of the pyramid. We model specular\nreflections from the pyramid of Khufu and derive the annual dates and times\nwhen they would have been visible at important ancient sites. Certain of these\nevents might have coincided with significant dates on the Egyptian calendar, as\nwell as with solar equinoxes, solstices and cross-quarter days. The celebration\nof Wepet-Renpet, which at the time of the pyramid's construction occurred near\nthe spring cross-quarter day, would have been marked by a specular sweep of\nsites on the southern horizon. On the autumn and winter cross-quarter days\nreflections would have been directed to Heliopolis. We suggest that on those\ndays the pyramidion of Khafre might have been visible in specular reflection\nover the truncated top of Khufu's pyramid.\n"}
+{"abstract": "  The tabla is a unique percussion instrument due to the combined harmonic and\npercussive nature of its timbre, and the contrasting harmonic frequency ranges\nof its two drums. This allows a tabla player to uniquely emphasize parts of the\nrhythmic cycle (theka) in order to mark the salient positions. An analysis of\nthe loudness dynamics and timing deviations at various cycle positions is an\nimportant part of musicological studies on the expressivity in tabla\naccompaniment. To achieve this at a corpus-level, and not restrict it to the\nfew recordings that manual annotation can afford, it is helpful to have access\nto an automatic tabla transcription system. Although a few systems have been\nbuilt by training models on labeled tabla strokes, the achieved accuracy does\nnot necessarily carry over to unseen instruments. In this article, we report\nour work towards building an instrument-independent stroke classification\nsystem for accompaniment tabla based on the more easily available tabla solo\naudio tracks. We present acoustic features that capture the distinctive\ncharacteristics of tabla strokes and build an automatic system to predict the\nlabel as one of a reduced, but musicologically motivated, target set of four\nstroke categories. To address the lack of sufficient labeled training data, we\nturn to common data augmentation methods and find the use of pitch-shifting\nbased augmentation to be most promising. We then analyse the important features\nand highlight the problem of their instrument-dependence while motivating the\nuse of more task-specific data augmentation strategies to improve the diversity\nof training data.\n"}
+{"abstract": "  We introduce an approach based on the Chapman-Kolmogorov equation to model\nheterogeneous stochastic circuits, namely, the circuits combining binary or\nmulti-state stochastic memristive devices and continuum reactive components\n(capacitors and/or inductors). Such circuits are described in terms of\noccupation probabilities of memristive states that are functions of reactive\nvariables. As an illustrative example, the series circuit of a binary memristor\nand capacitor is considered in detail. Some analytical solutions are found. Our\nwork offers a novel analytical/numerical tool for modeling complex stochastic\nnetworks, which may find a broad range of applications.\n"}
+{"abstract": "  Ground-based Whole Sky Imagers (WSIs) are increasingly being used for various\nremote sensing applications. While the fundamental requirements of a WSI are to\nmake it climate-proof with an ability to capture high resolution images, cost\nalso plays a significant role for wider scale adoption. This paper proposes an\nextremely low-cost alternative to the existing WSIs. In the designed model,\nhigh resolution images are captured with auto adjusting shutter speeds based on\nthe surrounding light intensity. Furthermore, a manual data backup option using\na portable memory drive is implemented for remote locations with no internet\naccess.\n"}
+{"abstract": "  Graph classification is a critical research problem in many applications from\ndifferent domains. In order to learn a graph classification model, the most\nwidely used supervision component is an output layer together with\nclassification loss (e.g.,cross-entropy loss together with softmax or margin\nloss). In fact, the discriminative information among instances are more\nfine-grained, which can benefit graph classification tasks. In this paper, we\npropose the novel Label Contrastive Coding based Graph Neural Network (LCGNN)\nto utilize label information more effectively and comprehensively. LCGNN still\nuses the classification loss to ensure the discriminability of classes.\nMeanwhile, LCGNN leverages the proposed Label Contrastive Loss derived from\nself-supervised learning to encourage instance-level intra-class compactness\nand inter-class separability. To power the contrastive learning, LCGNN\nintroduces a dynamic label memory bank and a momentum updated encoder. Our\nextensive evaluations with eight benchmark graph datasets demonstrate that\nLCGNN can outperform state-of-the-art graph classification models. Experimental\nresults also verify that LCGNN can achieve competitive performance with less\ntraining data because LCGNN exploits label information comprehensively.\n"}
+{"abstract": "  Precise segmentation of a lesion area is important for optimizing its\ntreatment. Deep learning makes it possible to detect and segment a lesion field\nusing annotated data. However, obtaining precisely annotated data is very\nchallenging in the medical domain. Moreover, labeling uncertainty and\nimprecision make segmentation results unreliable. In this paper, we address the\nuncertain boundary problem by a new evidential neural network with an\ninformation fusion strategy, and the scarcity of annotated data by\nsemi-supervised learning. Experimental results show that our proposal has\nbetter performance than state-of-the-art methods.\n"}
+{"abstract": "  In this paper, it is proved that the weak convergence of the $L_p$ Guassian\nsurface area measures implies the convergence of the corresponding convex\nbodies in the Hausdorff metric for $p\\geq 1$. Moreover, this paper obtains the\nsolution to the Guassian Minkowski problem is continuous with respect to $p$.\n"}
+{"abstract": "  In this paper we provide a way of taking $L^p$, $p > \\frac{m}{2}$ bounds on a\n$m-$ dimensional Riemannian metric and transforming that into H\\\"{o}lder bounds\nfor the corresponding distance function. One can think of this new estimate as\na type of Morrey inequality for Riemannian manifolds where one thinks of a\nRiemannian metric as the gradient of the corresponding distance function so\nthat the $L^p$, $p > \\frac{m}{2}$ bound analogously implies H\\\"{o}lder control\non the distance function. This new estimate is then used to state a compactness\ntheorem, another theorem which guarantees convergence to a particular\nRiemmanian manifold, and a new scalar torus stability result. We expect these\nresults to be useful for proving geometric stability results in the presence of\nscalar curvature bounds when Gromov-Hausdorff convergence is expected.\n"}
+{"abstract": "  We investigate here the nonlinear elliptic H\\'enon type equation:\n  $$\\D^{2} u= |x|^a|u|^{p-1}u \\; \\,\\,\\mbox{in}\\,\\,\\,\\, \\R^{n}_{+}, \\quad \\quad\nu =\\frac{\\partial u}{\\partial x_n} = 0 \\quad \\mbox{in}\\,\\,\\,\\, \\partial\n\\R^{n}_{+},$$ with $p>1$ and $n\\geq 2$. In particular, we prove some Liouville\ntype theorems for stable at infinity solutions. The main methods used are the\nintegral estimates, the Pohozaev-type identity and the monotonicity formula.\n"}
+{"abstract": "  Anisotropic, submicrometer sized particles are versatile systems providing\ninteresting features in creating ordering in 2-dimensional systems. Combining\nhard ellipsoids with a soft shell further enhances the opportunities to trigger\nand control order and alignment. In this work, we report a rich 2D phase\nbehavior and show how softness affects the ordering of anisotropic particles at\nfluid oil-water interfaces. Three different core-shell systems were synthesized\nsuch that they have the same elliptical hematite-silica core but differ with\nrespect to thickness and stiffness of the soft microgel shell. Compression\nisotherms, the shape of individual core-shell microgel as well as their 2D\norder at a decane-water interface are investigated by means of the\nLangmuir-Blodgett technique combined with ex-situ Atomic Force Microscopy (AFM)\nimaging, as well as by Disspiative Particle Dynamics (DPD) simulations. We show\nhow softness, size and anisotropy of the microgel shell affect the side-to-side\nvs. tip-to-tip ordering of anisotropic hybrid microgels as well as the\nalignment with respect to the direction of compression in the Langmuir trough.\nA large and soft microgel shell leads to an ordered structure with a tip-to-tip\nalignment directed perpendicular to the direction of compression. In contrast,\na thin and harder microgel shell leads to side-to-side ordering orientated\nparallel to the compression direction. In addition, the thin and harder\nmicrogel shell induces clustering of the microgels in the dilute state\nindicating the presence of strong capillary interactions. Our findings\nhighlight the relevance of softness for the complex ordering of anisotropic\nhybrid microgels at interfaces.\n"}
+{"abstract": "  Crystallization in a dense suspension of anisotropic spherical colloidal\nparticles with a Yukawa potential is numerically investigated in a\ntwo-dimensional plane. It is found that a strong anisotropy can hinder the\nparticles from crystallizing, while a weak anisotropy but super-strong coupling\nfacilitates colloids to freeze into a hexagonal crystal. Different criterions\nare employed to describe the phase transition, one can find that a competition\nbetween anisotropic degree and coupling strength shall widened the transition\nregion in the phase diagram, where the heterogeneous structures coexist, which\nrender as a quasi-platform stretched across the probability distribution curve\nof the local order parameter. Our study maybe helpful for the experiments\nrelating to the crystallizing behavior in statistical physics, materials\nscience and biophysical systems.\n"}
+{"abstract": "  As the scene information, including objectness and scene type, are important\nfor people with visual impairment, in this work we present a multi-task\nefficient perception system for the scene parsing and recognition tasks.\nBuilding on the compact ResNet backbone, our designed network architecture has\ntwo paths with shared parameters. In the structure, the semantic segmentation\npath integrates fast attention, with the aim of harvesting long-range\ncontextual information in an efficient manner. Simultaneously, the scene\nrecognition path attains the scene type inference by passing the semantic\nfeatures into semantic-driven attention networks and combining the semantic\nextracted representations with the RGB extracted representations through a\ngated attention module. In the experiments, we have verified the systems'\naccuracy and efficiency on both public datasets and real-world scenes. This\nsystem runs on a wearable belt with an Intel RealSense LiDAR camera and an\nNvidia Jetson AGX Xavier processor, which can accompany visually impaired\npeople and provide assistive scene information in their navigation tasks.\n"}
+{"abstract": "  Neutrinos from a Galactic core-collapse supernova will be measured by\nneutrino detectors minutes to days before an optical signal reaches Earth. We\npresent a novel calculation showing the ability of current and near-future\nneutrino detectors to make fast predictions of the progenitor distance and\nplace constraints on the zero-age main sequence mass in order to inform the\nobserving strategy for electromagnetic follow-up. We show that for typical\nGalactic supernovae, the distance can be constrained with an uncertainty of\n$\\sim$5\\% using IceCube or Hyper-K and, furthermore, the zero-age main sequence\nmass can be constrained for extremal values of compactness.\n"}
+{"abstract": "  In \\cite{JianQuan Liao 1} and \\cite{ZhongKai Li 3}, $H_{\\lambda}^p(\\RR)$ is\nintroduced to extend the results about the Hankel transform of Muckenhoupt and\nStein in \\cite{MS}. In this paper, we use the same way as\nBurkholder-Gundy-Silverstein theorem in\\,\\cite{BGS}, and obtain the Real\nCharacterization of the class $H_{\\lambda}^p(\\RR)$ for\n$\\frac{2\\lambda}{2\\lambda+1}<p\\leq1$.\n"}
+{"abstract": "  The increasing availability of textual corpora and data fetched from social\nnetworks is fuelling a huge production of works based on the model proposed by\npsychologist Robert Plutchik, often referred simply as the ``Plutchik Wheel''.\nRelated researches range from annotation tasks description to emotions\ndetection tools. Visualisation of such emotions is traditionally carried out\nusing the most popular layouts, as bar plots or tables, which are however\nsub-optimal. The classic representation of the Plutchik's wheel follows the\nprinciples of proximity and opposition between pairs of emotions: spatial\nproximity in this model is also a semantic proximity, as adjacent emotions\nelicit a complex emotion (a primary dyad) when triggered together; spatial\nopposition is a semantic opposition as well, as positive emotions are opposite\nto negative emotions. The most common layouts fail to preserve both features,\nnot to mention the need of visually allowing comparisons between different\ncorpora in a blink of an eye, that is hard with basic design solutions. We\nintroduce PyPlutchik, a Python library specifically designed for the\nvisualisation of Plutchik's emotions in texts or in corpora. PyPlutchik draws\nthe Plutchik's flower with each emotion petal sized after how much that emotion\nis detected or annotated in the corpus, also representing three degrees of\nintensity for each of them. Notably, PyPlutchik allows users to display also\nprimary, secondary, tertiary and opposite dyads in a compact, intuitive way. We\nsubstantiate our claim that PyPlutchik outperforms other classic visualisations\nwhen displaying Plutchik emotions and we showcase a few examples that display\nour library's most compelling features.\n"}
+{"abstract": "  We study LiFeP, LiFeAs and NaFeAs in their paramagnetic metallic phase\nincluding dynamical electronic correlations within a density functional theory\n+ slave-spin mean-field framework. The three compounds are found to lie next to\nthe crossover between a normal and a Hund's metal, where a region of enhanced\nelectronic compressibility that may boost superconductivity is systematically\npresent in this type of systems. We find that LiFeP lies in the normal metallic\nregime, LiFeAs at the crossover, and NaFeAs is in the Hund's metal regime,\nwhich possibly explains the different experimental trends for the pressure- and\ndoping-dependence of superconductivity in these compounds. Our picture captures\nthe orbitally-resolved mass renormalizations measured in these materials, while\nan analysis of the Sommerfeld specific-heat coefficient highlights some\nlimitations of currently used implementations of density-functional theory for\nthe correct prediction of the details of band structures in the iron-based\nsuperconductors.\n"}
+{"abstract": "  We study sets of local dimensions for self-similar measures in $\\mathbb{R}$\nsatisfying the finite neighbour condition, which is formally stronger than the\nweak separation condition but satisfied in all known examples. Under a mild\ntechnical assumption, we establish that the set of attainable local dimensions\nis a finite union of (possibly singleton) compact intervals. The number of\nintervals is bounded above by the number of non-trivial maximal strongly\nconnected components of a finite directed graph construction depending only on\nthe governing iterated function system. We also explain how our results allow\ncomputations of the sets of local dimensions in many explicit cases. This\ncontextualizes and generalizes a vast amount of prior work on sets of local\ndimensions for self-similar measures satisfying the weak separation condition.\n"}
+{"abstract": "  Hundreds of millions of people learn something new online every day.\nSimultaneously, the study of online education has blossomed within the human\ncomputer interaction community, with new systems, experiments, and observations\ncreating and exploring previously undiscovered online learning environments. In\nthis study we endeavor to characterize this entire landscape of online learning\nexperiences using a national survey of 2260 US adults who are balanced to match\nthe demographics of the U.S. We examine the online learning resources that they\nconsult, and we analyze the subjects that they pursue using those resources.\nFurthermore, we compare both formal and informal online learning experiences on\na larger scale than has ever been done before, to our knowledge, to better\nunderstand which subjects people are seeking for intensive study. We find that\nthere is a core set of online learning experiences that are central to other\nexperiences and these are shared among the majority of people who learn online.\nWe conclude by showing how looking outside of these core online learning\nexperiences can reveal opportunities for innovation in online education.\n"}
+{"abstract": "  With a growing demand for adopting ML models for a varietyof application\nservices, it is vital that the frameworks servingthese models are capable of\ndelivering highly accurate predic-tions with minimal latency along with reduced\ndeploymentcosts in a public cloud environment. Despite high latency,prior works\nin this domain are crucially limited by the accu-racy offered by individual\nmodels. Intuitively, model ensem-bling can address the accuracy gap by\nintelligently combiningdifferent models in parallel. However, selecting the\nappro-priate models dynamically at runtime to meet the desiredaccuracy with low\nlatency at minimal deployment cost is anontrivial problem. Towards this, we\nproposeCocktail, a costeffective ensembling-based model serving\nframework.Cock-tailcomprises of two key components: (i) a dynamic\nmodelselection framework, which reduces the number of modelsin the ensemble,\nwhile satisfying the accuracy and latencyrequirements; (ii) an adaptive\nresource management (RM)framework that employs a distributed proactive\nautoscalingpolicy combined with importance sampling, to efficiently allo-cate\nresources for the models. The RM framework leveragestransient virtual machine\n(VM) instances to reduce the de-ployment cost in a public cloud. A prototype\nimplementationofCocktailon the AWS EC2 platform and exhaustive evalua-tions\nusing a variety of workloads demonstrate thatCocktailcan reduce deployment cost\nby 1.45x, while providing 2xreduction in latency and satisfying the target\naccuracy for upto 96% of the requests, when compared to\nstate-of-the-artmodel-serving frameworks.\n"}
+{"abstract": "  The combinatorial invariance conjecture (due independently to G. Lusztig and\nM. Dyer) predicts that if $[x,y]$ and $[x',y']$ are isomorphic Bruhat posets\n(of possibly different Coxeter systems), then the corresponding Kazhdan-Lusztig\npolynomials are equal, i.e., $P_{x,y}(q)=P_{x',y'}(q)$. We prove this\nconjecture for the affine Weyl group of type $\\widetilde{A}_2$. This is the\nfirst non-trivial case where this conjecture is verified for an infinite group.\n"}
+{"abstract": "  Two-dimensional fully kinetic particle-in-cell simulations of an\nelectrodeless plasma thruster, which uses a magnetic nozzle, were conducted to\ninvestigate the thrust generation induced by the internal plasma current. The\nresults clearly show that the $\\bf{E} \\times \\bf{B}$ and diamagnetic current\ndensities are the major components of the internal plasma current. The\nsimulated pressure structures reproduced the experimentally observed structures\nwell. The results for various magnetic field strengths reveal that the $\\bf{E}\n\\times \\bf{B}$ effect decreases and the diamagnetic effect becomes dominant\nwith an increase in the magnetic field strength; this demonstrates the\nsignificant contribution of the diamagnetic effect in thrust generation.\n"}
+{"abstract": "  For an infinity of number rings we express stable motivic invariants in terms\nof topological data determined by the complex numbers, the real numbers, and\nfinite fields. We use this to extend Morel's identification of the endomorphism\nring of the motivic sphere with the Grothendieck-Witt ring of quadratic forms\nto deeper base schemes.\n"}
+{"abstract": "  Quantum-well (QW) devices have been extensively investigated in semiconductor\nstructures. More recently, spin-polarized QWs were integrated into magnetic\ntunnel junctions (MTJs). In this work, we demonstrate the spin-based control of\nthe quantized states in iron $3d$-band QWs, as observed in experiments and\ntheoretical calculations. We find that the magnetization rotation in the Fe QWs\nsignificantly shifts the QW quantization levels, which modulate the\nresonant-tunneling current in MTJs, resulting in a tunneling anisotropic\nmagnetoresistance (TAMR) effect of QWs. This QW-TAMR effect is sizable compared\nto other types of TAMR effect, and it is present above the room-temperature. In\na QW MTJ of Cr/Fe/MgAl$_2$O$_4$/top electrode, where the QW is formed by a\nmismatch between Cr and Fe in the $d$ band with $\\Delta_1$ symmetry, a QW-TAMR\nratio of up to 5.4 % was observed at 5 K, which persisted to 1.2 % even at\n380K. The magnetic control of QW transport can open new applications for\nspin-coupled optoelectronic devices, ultra-thin sensors, and memories.\n"}
+{"abstract": "  Multi-agent reinforcement learning methods have shown remarkable potential in\nsolving complex multi-agent problems but mostly lack theoretical guarantees.\nRecently, mean field control and mean field games have been established as a\ntractable solution for large-scale multi-agent problems with many agents. In\nthis work, driven by a motivating scheduling problem, we consider a\ndiscrete-time mean field control model with common environment states. We\nrigorously establish approximate optimality as the number of agents grows in\nthe finite agent case and find that a dynamic programming principle holds,\nresulting in the existence of an optimal stationary policy. As exact solutions\nare difficult in general due to the resulting continuous action space of the\nlimiting mean field Markov decision process, we apply established deep\nreinforcement learning methods to solve the associated mean field control\nproblem. The performance of the learned mean field control policy is compared\nto typical multi-agent reinforcement learning approaches and is found to\nconverge to the mean field performance for sufficiently many agents, verifying\nthe obtained theoretical results and reaching competitive solutions.\n"}
+{"abstract": "  E-commerce with major online retailers is changing the way people consume.\nThe goal of increasing delivery speed while remaining cost-effective poses\nsignificant new challenges for supply chains as they race to satisfy the\ngrowing and fast-changing demand. In this paper, we consider a warehouse with a\nRobotic Mobile Fulfillment System (RMFS), in which a fleet of robots stores and\nretrieves shelves of items and brings them to human pickers. To adapt to\nchanging demand, uncertainty, and differentiated service (e.g., prime vs.\nregular), one can dynamically modify the storage allocation of a shelf. The\nobjective is to define a dynamic storage policy to minimise the average cycle\ntime used by the robots to fulfil requests. We propose formulating this system\nas a Partially Observable Markov Decision Process, and using a Deep Q-learning\nagent from Reinforcement Learning, to learn an efficient real-time storage\npolicy that leverages repeated experiences and insightful forecasts using\nsimulations. Additionally, we develop a rollout strategy to enhance our method\nby leveraging more information available at a given time step. Using\nsimulations to compare our method to traditional storage rules used in the\nindustry showed preliminary results up to 14\\% better in terms of travelling\ntimes.\n"}
+{"abstract": "  The Colonel Blotto Problem proposed by Borel in 1921 has served as a widely\napplicable model of budget-constrained simultaneous winner-take-all\ncompetitions in the social sciences. Applications include elections,\nadvertising, R&D and more. However, the classic Blotto problem and variants\nlimit the study to competitions over a finite set of discrete battlefields. In\nthis paper, we extend the classical theory to study multiplayer Blotto games\nover arbitrary measurable battlegrounds, provide an algorithm to efficiently\nsample equilibria of symmetric \"equipartionable\" Generalized Blotto games, and\ncharacterize the symmetric fair equilibria of the Blotto game over the unit\ninterval.\n"}
+{"abstract": "  A novel quadcopter capable of changing shape mid-flight is presented,\nallowing for operation in four configurations with the capability of sustained\nhover in three. This is accomplished without requiring actuators beyond the\nfour motors typical of a quadcopter. Morphing is achieved through\nfreely-rotating hinges that allow the vehicle arms to fold downwards by either\nreducing or reversing thrust forces. Constraints placed on the control inputs\nof the vehicle prevent the arms from folding or unfolding unexpectedly. This\nallows for the use of existing quadcopter controllers and trajectory generation\nalgorithms with only minimal added complexity. For our experimental vehicle at\nhover, we find that these constraints result in a 36% reduction of the maximum\nyaw torque the vehicle can produce, but do not result in a reduction of the\nmaximum thrust or roll and pitch torques. Experimental results show that, for a\ntypical maneuver, the added limits have a negligible effect on trajectory\ntracking performance. Finally, the ability to change configurations is shown to\nenable the vehicle to traverse small passages, perch on hanging wires, and\nperform limited grasping tasks.\n"}
+{"abstract": "  We reconstruct spectra of secondary X-rays from a tunable 250-350 MeV laser\nwakefield electron accelerator from single-shot X-ray depth-energy measurements\nin a compact (7.5 $\\times$ 7.5 $\\times$ 15 cm), modular X-ray calorimeter made\nof alternating layers of absorbing materials and imaging plates. X-rays range\nfrom few-keV betatron to few-MeV inverse Compton to >100 MeV bremsstrahlung\nemission, and are characterized both individually and in mixtures. Geant4\nsimulations of energy deposition of single-energy X-rays in the stack generate\nan energy-vs-depth response matrix for a given stack configuration. An\niterative reconstruction algorithm based on analytic models of betatron,\ninverse Compton and bremsstrahlung photon energy distributions then unfolds\nX-ray spectra, typically within a minute. We discuss uncertainties, limitations\nand extensions of both measurement and reconstruction methods.\n"}
+{"abstract": "  In this paper we consider the influence of intercellular communication on the\ndevelopment and progression of Glioblastoma Multiforme (GBM), a grade IV\nmalignant glioma which is defined by an interplay Grow i.e. self renewal and Go\ni.e. invasiveness potential of multiple malignant glioma stem cells. Firstly,\nwe performed wet lab experiments with U87 malignant glioma cells to study the\nnode-stem growth pattern of GBM. Next we develop a model accounting for the\nstructural influence of multiple transmitter and receiver glioma stem cells\nresulting in the node-stem growth structure of GBM tumour. By using information\ntheory we study different properties associated with this communication model\nto show that the growth of GBM in a particular direction (node to stem) is\nrelated to an increase in mutual information. We further show that information\nflow between glioblastoma cells for different levels of invasiveness vary at\ndifferent points between node and stem. These findings are expected to\ncontribute significantly in the design of future therapeutic mechanisms for\nGBM.\n"}
+{"abstract": "  We consider the boomerang uniformity of an infinite class of (locally-APN)\npower maps and show that its boomerang uniformity over the finite field\n$\\F_{2^n}$ is $2$ and $4$, when $n \\equiv 0 \\pmod 4$ and $n \\equiv 2 \\pmod 4$,\nrespectively. As a consequence, we show that for this class of power maps, the\ndifferential uniformity is strictly greater than its boomerang uniformity.\n"}
+{"abstract": "  Traditional reactive approach of blacklisting botnets fails to adapt to the\nrapidly evolving landscape of cyberattacks. An automated and proactive approach\nto detect and block botnet hosts will immensely benefit the industry.\nBehavioral analysis of botnet is shown to be effective against a wide variety\nof attack types. Current works, however, focus solely on analyzing network\ntraffic from and to the bots. In this work we take a different approach of\nanalyzing the chain of commands input by attackers in a compromised host. We\nhave deployed several honeypots to simulate Linux shells and allowed attackers\naccess to the shells to collect a large dataset of commands. We have further\ndeveloped an automated mechanism to analyze these data. For the automation we\nhave developed a system called CYbersecurity information Exchange with Privacy\n(CYBEX-P). Finally, we have done a sequential analysis on the dataset to show\nthat we can successfully predict attacker behavior from the shell commands\nwithout analyzing network traffic like previous works.\n"}
+{"abstract": "  We re-consider Leadbetter's extremal index for stationary sequences. It has\ninterpretation as reciprocal of the expected size of an extremal cluster above\nhigh thresholds. We focus on heavy-tailed time series, in particular on\nregularly varying stationary sequences, and discuss recent research in extreme\nvalue theory for these models. A regularly varying time series has multivariate\nregularly varying finite-dimensional distributions. Thanks to results by Basrak\nand Segers we have explicit representations of the limiting cluster structure\nof extremes, leading to explicit expressions of the limiting point process of\nexceedances and the extremal index as a summary measure of extremal clustering.\nThe extremal index appears in various situations which do not seem to be\ndirectly related, like the convergence of maxima and point processes. We\nconsider different representations of the extremal index which arise from the\nconsidered context. We discuss the theory and apply it to a regularly varying\nAR(1) process and the solution to an affine stochastic recurrence equation\n"}
+{"abstract": "  Galactic outflows driven by starbursts can modify the galactic magnetic\nfields and drive them away from the galactic planes. Here, we quantify how\nthese fields may magnetize the intergalactic medium. We estimate the strength\nand structure of the fields in the starburst galaxy M82 using thermal polarized\nemission observations from SOFIA/HAWC+ and a potential field extrapolation\ncommonly used in solar physics. We modified the Davis-Chandrasekhar-Fermi\nmethod to account for the large-scale flow and the turbulent field. Results\nshow that the observed magnetic fields arise from the combination of a\nlarge-scale ordered potential field associated with the outflow and a\nsmall-scale turbulent field associated with bow-shock-like features. Within the\ncentral $900$ pc radius, the large-scale field accounts for $53\\pm4$% of the\nobserved turbulent magnetic energy with a median field strength of $305\\pm15$\n$\\mu$G, while small-scale turbulent magnetic fields account for the remaining\n$40\\pm5$% with a median field strength of $222\\pm19$ $\\mu$G. We estimate that\nthe turbulent kinetic and turbulent magnetic energies are in close\nequipartition up to $\\sim2$ kpc (measured), while the turbulent kinetic energy\ndominates at $\\sim7$ kpc (extrapolated). We conclude that the fields are frozen\ninto the ionized outflowing medium and driven away kinetically. The magnetic\nfield lines in the galactic wind of M82 are `open,' providing a direct channel\nbetween the starburst core and the intergalactic medium. Our novel approach\noffers the tools needed to quantify the effects of outflows on galactic\nmagnetic fields as well as their influence on the intergalactic medium and\nevolution of energetic particles.\n"}
+{"abstract": "  Systems composed of interacting self-propelled particles (SPPs) display\ndifferent forms of order-disorder phase transitions relevant to collective\nmotion. In this paper we propose a generalization of the Vicsek model\ncharacterized by an angular noise term following an arbitrary probability\ndensity function, which might depend on the state of the system and thus have a\nmultiplicative character. We show that the well established vectorial Vicsek\nmodel can be expressed in this general formalism by deriving the corresponding\nangular probability density function, as well as we propose two new\nmultiplicative models consisting on a bivariate Gaussian and a wrapped Gaussian\ndistributions. With the proposed formalism, the mean-field system can be solved\nusing the mean resultant length of the angular stochastic term. Accordingly,\nwhen the SPPs interact globally, the character of the phase transition depends\non the choice of the noise distribution, being first-order with an hybrid\nscaling for the vectorial and wrapped Gaussian distributions, and second order\nfor the bivariate Gaussian distribution. Numerical simulations reveal that this\nscenario also holds when the interactions among SPPs are given by a static\ncomplex network. On the other hand, using spatial short-range interactions\ndisplays, in all the considered instances, a discontinuous transition with a\ncoexistence region, consistent with the original formulation of the Vicsek\nmodel.\n"}
+{"abstract": "  Animals ranging from rats to humans can demonstrate cognitive map\ncapabilities. We evolved weights in a biologically plausible recurrent neural\nnetwork (RNN) using an evolutionary algorithm to replicate the behavior and\nneural activity observed in rats during a spatial and working memory task in a\ntriple T-maze. The rat was simulated in the Webots robot simulator and used\nvision, distance and accelerometer sensors to navigate a virtual maze. After\nevolving weights from sensory inputs to the RNN, within the RNN, and from the\nRNN to the robot's motors, the Webots agent successfully navigated the space to\nreach all four reward arms with minimal repeats before time-out. Our current\nfindings suggest that it is the RNN dynamics that are key to performance, and\nthat performance is not dependent on any one sensory type, which suggests that\nneurons in the RNN are performing mixed selectivity and conjunctive coding.\nMoreover, the RNN activity resembles spatial information and\ntrajectory-dependent coding observed in the hippocampus. Collectively, the\nevolved RNN exhibits navigation skills, spatial memory, and working memory. Our\nmethod demonstrates how the dynamic activity in evolved RNNs can capture\ninteresting and complex cognitive behavior and may be used to create RNN\ncontrollers for robotic applications.\n"}
+{"abstract": "  Urban 3D modeling from satellite images requires accurate semantic\nsegmentation to delineate urban features, multiple view stereo for 3D\nreconstruction of surface heights, and 3D model fitting to produce compact\nmodels with accurate surface slopes. In this work, we present a cumulative\nassessment metric that succinctly captures error contributions from each of\nthese components. We demonstrate our approach by providing challenging public\ndatasets and extending two open source projects to provide an end-to-end 3D\nmodeling baseline solution to stimulate further research and evaluation with a\npublic leaderboard.\n"}
+{"abstract": "  This paper is concerned with the problem of state estimation for\ndiscrete-time linear systems in the presence of additional (equality or\ninequality) constraints on the state (or estimate). By use of the minimum\nvariance duality, the estimation problem is converted into an optimal control\nproblem. Two algorithmic solutions are described: the full information\nestimator (FIE) and the moving horizon estimator (MHE). The main result is to\nshow that the proposed estimator is stable in the sense of an observer. The\nproposed algorithm is distinct from the standard algorithm for constrained\nstate estimation based upon the use of the minimum energy duality. The two are\ncompared numerically on the benchmark batch reactor process model.\n"}
+{"abstract": "  We take the tensor network describing explicit p-adic CFT partition functions\nproposed in [1], and considered boundary conditions of the network describing a\ndeformed Bruhat-Tits (BT) tree geometry. We demonstrate that this geometry\nsatisfies an emergent graph Einstein equation in a unique way that is\nconsistent with the bulk effective matter action encoding the same correlation\nfunction as the tensor network, at least in the perturbative limit order by\norder away from the pure BT tree. Moreover, the (perturbative) definition of\nthe graph curvature in the Mathematics literature naturally emerges from the\nconsistency requirements of the emergent Einstein equation. This could provide\nnew insights into the understanding of gravitational dynamics potentially\nencoded in more general tensor networks.\n"}
+{"abstract": "  The problem of fermion dynamics is studied using the Q-function for fermions.\nThis is a probabilistic phase-space representation, which we express using\nMajorana operators, so that the phase-space variable is a real antisymmetric\nmatrix. We consider a general interaction Hamiltonian with four Majorana\noperators and arbitrary properties. Our model includes the Majorana Hubbard and\nFermi Hubbard Hamiltonians, as well as general quantum field theories of\ninteracting fermions. Using the Majorana Q-function we derive a generalized\nFokker-Planck equation, with results for the drift and diffusion terms. The\ndiffusion term is proved to be traceless, which gives a dynamical\ninterpretation as a forwards-backwards stochastic process. This approach leads\nto a model of quantum measurement in terms of an ontology with real vacuum\nfluctuations.\n"}
+{"abstract": "  We show that the Plebanski-Demianski spacetime persists as a solution of\nGeneral Relativity when the theory is supplemented with both, a conformally\ncoupled scalar theory and with quadratic curvature corrections. The quadratic\nterms are of two types and are given by quadratic combinations of the Riemann\ntensor as well as a higher curvature interaction constructed with a scalar\nfield which is conformally coupled to quadratic terms in the curvature. The\nlater is built in terms of a four-rank tensor $S_{\\mu\\nu}^{\\ \\ \\lambda\\rho}$\nthat depends on the Riemann tensor and the scalar field, and that transforms\ncovariantly under local Weyl rescallings. Due to the generality of the\nPlebanski-Demianski family, several new hairy black hole solutions are obtained\nin this higher curvature model. We pay particular attention to the C-metric\nspacetime and the stationary Taub-NUT metric, which in the hyperbolic case can\nbe analytically extended leading to healthy, asymptotically AdS, wormhole\nconfigurations. Finally, we present a new general model for higher derivative,\nconformally coupled scalars, depending on an arbitrary function and that we\nhave dubbed Conformal K-essence. We also construct spherically symmetric hairy\nblack holes for these general models.\n"}
+{"abstract": "  We present a novel method for local image feature matching. Instead of\nperforming image feature detection, description, and matching sequentially, we\npropose to first establish pixel-wise dense matches at a coarse level and later\nrefine the good matches at a fine level. In contrast to dense methods that use\na cost volume to search correspondences, we use self and cross attention layers\nin Transformer to obtain feature descriptors that are conditioned on both\nimages. The global receptive field provided by Transformer enables our method\nto produce dense matches in low-texture areas, where feature detectors usually\nstruggle to produce repeatable interest points. The experiments on indoor and\noutdoor datasets show that LoFTR outperforms state-of-the-art methods by a\nlarge margin. LoFTR also ranks first on two public benchmarks of visual\nlocalization among the published methods.\n"}
+{"abstract": "  We analyze some relevant features of the primordial Universe as viewed in the\nJordan frame formulation of the f(R)-gravity, especially when the potential\nterm of the non-minimally coupled scalar field is negligible. We start\nformulating the Hamiltonian picture in the Jordan frame, using the 3-metric\ndeterminant as a basic variable and we outline that its conjugated momentum\nappears linearly only in the scalar constraint. Then, we construct the basic\nformalism to characterize the dynamics of a generic inhomogeneous cosmological\nmodel and specialize it in order to describe behaviors of the Bianchi\nUniverses, both on a classical and a quantum regime. As a fundamental issue, we\ndemonstrate that, when the potential term of the additional scalar mode is\nnegligible near enough to the initial singularity, the Bianchi IX cosmology is\nno longer affected by the chaotic behavior, typical in vacuum of the standard\nEinsteinian dynamics. In fact, the presence of stable Kasner stability region\nand its actractive character are properly characterized. Finally, we\ninvestigate the canonical quantization of the Bianchi I model, using as time\nvariable the non-minimally coupled scalar field and showing that the existence\nof a conserved current is outlined for the corresponding Wheeler-DeWitt\nequation. The behavior of a localized wave-packet for the isotropic Universe is\nalso evolved, demonstrating that the singularity is still present in this\nrevised quantum dynamics.\n"}
+{"abstract": "  Time inconsistency is prevalent in dynamic choice problems: a plan of actions\nto be taken in the future that is optimal for an agent today may not be optimal\nfor the same agent in the future. If the agent is aware of this intra-personal\nconflict but unable to commit herself in the future to following the optimal\nplan today, the rational strategy for her today is to reconcile with her future\nselves, namely to correctly anticipate her actions in the future and then act\ntoday accordingly. Such a strategy is named intra-personal equilibrium and has\nbeen studied since as early as in the 1950s. A rigorous treatment in\ncontinuous-time settings, however, had not been available until a decade ago.\nSince then, the study on intra-personal equilibrium for time-inconsistent\nproblems in continuous time has grown rapidly. In this chapter, we review the\nclassical results and some recent development in this literature.\n"}
+{"abstract": "  This paper explores the use of hyperbolic geometry and deep learning\ntechniques for recommendation. We present Hyperbolic Neural Collaborative\nRecommender (HNCR), a deep hyperbolic representation learning method that\nexploits mutual semantic relations among users/items for collaborative\nfiltering (CF) tasks. HNCR contains two major phases: neighbor construction and\nrecommendation framework. The first phase introduces a neighbor construction\nstrategy to construct a semantic neighbor set for each user and item according\nto the user-item historical interaction. In the second phase, we develop a deep\nframework based on hyperbolic geometry to integrate constructed neighbor sets\ninto recommendation. Via a series of extensive experiments, we show that HNCR\noutperforms its Euclidean counterpart and state-of-the-art baselines.\n"}
+{"abstract": "  Granular flow out of a silo is studied experimentally and numerically. The\ntime evolution of the discharge rate as well as the normal force (apparent\nweight) at the bottom of the container is monitored. We show, that particle\nstiffness has a strong effect on the qualitative features of silo discharge.\nFor deformable grains with a Young's modulus of about $Y_m\\approx 40$ kPa in a\nsilo with basal pressure of the order of 4 kPa lowering the friction\ncoefficient leads to a gradual change in the discharge curve: the flow rate\nbecomes filling height dependent, it decreases during the discharge process.\nFor hard grains with a Young's modulus of about $Y_m\\approx 500$ MPa the flow\nrate is much less sensitive to the value of the friction coefficient. Using DEM\ndata combined with a coarse-graining methodology allows us to compute all the\nrelevant macroscopic fields, namely, linear momentum, density and stress\ntensors. The observed difference in the discharge in the low friction limit is\nconnected to a strong difference in the pressure field: while for hard grains\nJanssen-screening is effective, leading to high vertical stress near the silo\nwall and small pressure above the orifice region, for deformable grains the\npressure above the orifice is larger and gradually decreases during the\ndischarge process. We have analyzed the momentum balance in the region of the\norifice (near the location of the outlet) for the case of soft particles with\nlow friction coefficient, and proposed a phenomenological formulation that\npredicts the linear decrease of the flow rate with decreasing filling height.\n"}
+{"abstract": "  Recent works have revealed that quantum extremal islands can contribute to\nthe fine-grained entropy of black hole radiation reproducing the unitary Page\ncurve. In this paper, we use these results to assess if an observer in de\nSitter space can decode information hidden behind their cosmological horizon.\nBy computing the fine-grained entropy of the Gibbons-Hawking radiation in a\nregion where gravity is weak we find that this is possible, but the observer's\ncuriosity comes at a price. At the same time the island appears, which happens\nmuch earlier than the Page time, a singularity forms which the observer will\neventually hit. We arrive at this conclusion by studying Jackiw-Teitelboim\ngravity in de Sitter space. We emphasize the role of the observer collecting\nradiation, breaking the thermal equilibrium studied so far in the literature.\nBy analytically solving for the backreacted geometry we show how an island\nappears in this out-of-equilibrium state.\n"}
+{"abstract": "  Whereas existing literature on unsupervised machine translation (MT) focuses\non exploiting unsupervised techniques for low-resource language pairs where\nbilingual training data is scare or unavailable, we investigate whether\nunsupervised MT can also improve translation quality of high-resource language\npairs where sufficient bitext does exist. We compare the style of correct\ntranslations generated by either supervised or unsupervised MT and find that\nthe unsupervised output is less monotonic and more natural than supervised\noutput. We demonstrate a way to combine the benefits of unsupervised and\nsupervised MT into a single system, resulting in better human evaluation of\nquality and fluency. Our results open the door to discussions about the\npotential contributions of unsupervised MT in high-resource settings, and how\nsupervised and unsupervised systems might be mutually-beneficial.\n"}
+{"abstract": "  Filters are fast, small and approximate set membership data structures. They\nare often used to filter out expensive accesses to a remote set S for negative\nqueries (that is, a query x not in S). Filters have one-sided errors: on a\nnegative query, a filter may say \"present\" with a tunable false-positve\nprobability of epsilon. Correctness is traded for space: filters only use log\n(1/\\epsilon) + O(1) bits per element.\n  The false-positive guarantees of most filters, however, hold only for a\nsingle query. In particular, if x is a false positive of a filter, a subsequent\nquery to x is a false positive with probability 1, not epsilon. With this in\nmind, recent work has introduced the notion of an adaptive filter. A filter is\nadaptive if each query has false positive epsilon, regardless of what queries\nwere made in the past. This requires \"fixing\" false positives as they occur.\n  Adaptive filters not only provide strong false positive guarantees in\nadversarial environments but also improve performance on query practical\nworkloads by eliminating repeated false positives.\n  Existing work on adaptive filters falls into two categories. First, there are\npractical filters based on cuckoo filters that attempt to fix false positives\nheuristically, without meeting the adaptivity guarantee. Meanwhile, the broom\nfilter is a very complex adaptive filter that meets the optimal theoretical\nbounds.\n  In this paper, we bridge this gap by designing a practical, provably adaptive\nfilter: the telescoping adaptive filter. We provide theoretical false-positive\nand space guarantees of our filter, along with empirical results where we\ncompare its false positive performance against state-of-the-art filters. We\nalso test the throughput of our filters, showing that they achieve comparable\nperformance to similar non-adaptive filters.\n"}
+{"abstract": "  Optimally solving a multi-armed bandit problem suffers the curse of\ndimensionality. Indeed, resorting to dynamic programming leads to an\nexponential growth of computing time, as the number of arms and the horizon\nincrease. We introduce a decompositioncoordination heuristic, DeCo, that turns\nthe initial problem into parallelly coordinated one-armed bandit problems. As a\nconsequence, we obtain a computing time which is essentially linear in the\nnumber of arms. In addition, the decomposition provides a theoretical lower\nbound on the regret. For the two-armed bandit case, dynamic programming\nprovides the exact solution, which is almost matched by the DeCo heuristic.\nMoreover, in numerical simulations with up to 100 rounds and 20 arms, DeCo\noutperforms classic algorithms (Thompson sampling and Kullback-Leibler\nupper-confidence bound) and almost matches the theoretical lower bound on the\nregret for 20 arms.\n"}
+{"abstract": "  Topological notions in physics have become a powerful perspective that leads\nto the discoveries of topological interface states (TISs). In this work, we\npresent a scheme to achieve negative refraction by leveraging the properties of\nTISs in a valley photonic crystal (VPC). Due to the chiral characteristics, one\ntype of the TISs deterministically possesses negative dispersion relation,\nwhich can cause an obliquely incident wave to undergo a negative lateral shift.\nBy stacking multiple VPC interfaces, the TIS-induced lateral shifts can relay\nin the transmitted wave towards the far side of incidence. The resultant\noutgoing wave appears to have undergone negative refraction. This finding is\nverified in microwave experiments. Our scheme opens new application scenarios\nfor topological systems in bulk wave manipulations.\n"}
+{"abstract": "  Single photon emitters in atomically-thin semiconductors can be\ndeterministically positioned using strain induced by underlying\nnano-structures. Here, we couple monolayer WSe$_2$ to high-refractive-index\ngallium phosphide dielectric nano-antennas providing both optical enhancement\nand monolayer deformation. For single photon emitters formed on such\nnano-antennas, we find very low (femto-Joule) saturation pulse energies and up\nto 10$^4$ times brighter photoluminescence than in WSe$_2$ placed on\nlow-refractive-index SiO$_2$ pillars. We show that the key to these\nobservations is the increase on average by a factor of 5 in the quantum\nefficiency of the emitters coupled to the nano-antennas. This further allowed\nus to gain new insights into their photoluminescence dynamics, revealing the\nroles of the dark exciton reservoir and Auger processes. We also find that the\ncoherence time of such emitters is limited by intrinsic dephasing processes.\nOur work establishes dielectric nano-antennas as a platform for high-efficiency\nquantum light generation in monolayer semiconductors.\n"}
+{"abstract": "  In certain systems which are subject to significant constant external forcing\nsuch as an airplane in wind or an underwater glider in ocean currents, the\nability to detect the forcing depends on both the measurements available and\nwhether appropriate control is being applied. Using analytical nonlinear\nobservability techniques, we define the necessary characteristics required of a\nmeasurement function for systems with unicycle dynamics subject to constant\nforcing to be observable. We further consider the necessary associated motion\ncharacteristics required of this class of systems to enable the desired sensing\ncapabilities. We then apply these results in combination with the empirical\nGramian to quantify relative observability, optimal sensor selection, and\nsensing quality for different motion primitive modes for a Dubins path.\n"}
+{"abstract": "  We argue that the global causal structure of the singularity is not a purely\ngeometric property but also depends on the collapsing matter-field leading to\nits formation. To show this, we investigate the global visibility of the end\nstate of a spherically symmetric marginally bound Lemaitre-Tolman-Bondi\ncollapsing cloud (which is well studied in general relativity) in the framework\nof modified gravity having the generalized Lagrangian $R+\\alpha R^2$ in the\nEinstein-Hilbert action. Here $R$ is the Ricci scalar, and $\\alpha>0$ is a\nconstant. As an example, we depict that for the same LTB metric governing the\nspacetime formed due to two different matter-fields, i.e., dust in general\nrelativity and imperfect viscous fluid in $f(R)$ gravity, the singularity is\nlocally visible in the former case, and globally visible in the latter case.\n"}
+{"abstract": "  A resilient multi-vehicle system cooperatively performs tasks by exchanging\ninformation, detecting, and removing cyber attacks that have the intent of\nhijacking or diminishing performance of the entire system. In this paper, we\npropose a framework to: i) detect and isolate misbehaving vehicles in the\nnetwork, and ii) securely encrypt information among the network to alert and\nattract nearby vehicles toward points of interest in the environment without\nexplicitly broadcasting safety-critical information. To accomplish these goals,\nwe leverage a decentralized virtual spring-damper mesh physics model for\nformation control on each vehicle. To discover inconsistent behavior of any\nvehicle in the network, we consider an approach that monitors for changes in\nsign behavior of an inter-vehicle residual that does not match with an\nexpectation. Similarly, to disguise important information and trigger vehicles\nto switch to different behaviors, we leverage side-channel information on the\nstate of the vehicles and characterize a hidden spring-damper signature model\ndetectable by neighbor vehicles. Our framework is demonstrated in simulation\nand experiments on formations of unmanned ground vehicles (UGVs) in the\npresence of malicious man-in-the-middle communication attacks.\n"}
+{"abstract": "  The coronal mass ejections (CMEs) from the Sun are known for their space\nweather and geomagnetic consequences. Among all CMEs, so-called radio-loud (RL)\nand halo CMEs are considered the most energetic in the sense that they are\nusually faster and wider than the general population of CMEs. Hence the study\nof RL and halo CMEs has become important and the prediction of their occurrence\nrate in a future cycle will give a warning in advance. In the present paper,\nthe occurrence rates of RL and halo CMEs in solar cycle (SC) 25 are predicted.\nFor this, we obtained good correlations between the numbers of RL and halo CMEs\nin each year and the yearly mean sunspot numbers in the previous two cycles.\nThe predicted values of sunspot numbers in SC 25 by NOAA/NASA were considered\nas representative indices and the corresponding numbers of RL and halo CMEs\nhave been determined using linear relations. Our results show that the maximum\nnumber of RL and halo CMEs will be around 39 $/pm$ 3 and 45 $/pm$ 4,\nrespectively. Removing backside events, a set of front-side events was also\nconsidered separately and the front-side events alone in SC 25 are predicted\nagain. The peak values of front-side RL and halo events have been estimated to\nbe around 31 $/pm$ 3 and 29 $/pm$ 3 respectively. These results are discussed\nin comparison with the predicted values of sunspots by different authors.\n"}
+{"abstract": "  We prove Burkholder inequality using Bregman divergence.\n"}
+{"abstract": "  In this paper, we propose a recent and under-researched paradigm for the task\nof event detection (ED) by casting it as a question-answering (QA) problem with\nthe possibility of multiple answers and the support of entities. The extraction\nof event triggers is, thus, transformed into the task of identifying answer\nspans from a context, while also focusing on the surrounding entities. The\narchitecture is based on a pre-trained and fine-tuned language model, where the\ninput context is augmented with entities marked at different levels, their\npositions, their types, and, finally, the argument roles. Experiments on the\nACE~2005 corpus demonstrate that the proposed paradigm is a viable solution for\nthe ED task and it significantly outperforms the state-of-the-art models.\nMoreover, we prove that our methods are also able to extract unseen event\ntypes.\n"}
+{"abstract": "  In a recent work [\\textit{J. Mol. Liq}, 2020, \\textbf{320}, 114420], using\nmolecular dynamics simulations and a core-softened potential approach, we have\nshown that adding a simple solute as methanol can \"kill\" the density anomalous\nbehavior as the LLCP is suppressed by the spontaneous crystallization in a\nhexagonal closed packing (HCP) crystal near the LLPT. Now, we extend this work\nin order to realize how longer-chain alcohols will affect the complex behavior\nof water-alcohol mixtures in the supercooled regime. Besides core-softened (CS)\nmethanol, ethanol and 1-propanol were added to a system of identical particles\nthat interact through the continuous shouldered well (CSW) potential. We\nobserved that the density anomaly gradually decreases its extension in phase\ndiagrams until disappearing with the growth of the non-polar chain and the\nalcohol concentration, differently from the liquid-liquid phase transition (and\nthe LLCP), which remained present in all analyzed mixtures, in according to\n\\textit{Nature}, 2001, \\textbf{409}, 692. For our model, the longer non-polar\nchains and higher concentrations gradually impact the competition between the\nscales in the CS potential, leading to a gradual disappearing of the anomalies\nuntil the TMD total disappearance is observed when the first coordination shell\nstructure is also affected: the short-range ordering is favored, leading to\nless competition between short- and long-range ordering and, consequently, to\nthe extinction of anomalies. Also, the non-polar chain size and concentration\nhave an effect on the solid phases, favoring the hexagonal closed packed (HCP)\nsolid and the amorphous solid phase over the body-centered cubic (BCC) crystal.\n"}
+{"abstract": "  Several recent studies have shown that Deep Neural Network (DNN)-based\nclassifiers are vulnerable against model extraction attacks. In model\nextraction attacks, an adversary exploits the target classifier to create a\nsurrogate classifier imitating the target classifier with respect to some\ncriteria. In this paper, we investigate the hardness degree of samples and\ndemonstrate that the hardness degree histogram of model extraction attacks\nsamples is distinguishable from the hardness degree histogram of normal\nsamples. Normal samples come from the target classifier's training data\ndistribution. As the training process of DNN-based classifiers is done in\nseveral epochs, we can consider this process as a sequence of subclassifiers so\nthat each subclassifier is created at the end of an epoch. We use the sequence\nof subclassifiers to calculate the hardness degree of samples. We investigate\nthe relation between hardness degree of samples and the trust in the classifier\noutputs. We propose Hardness-Oriented Detection Approach (HODA) to detect the\nsample sequences of model extraction attacks. The results demonstrate that HODA\ncan detect the sample sequences of model extraction attacks with a high success\nrate by only watching 100 attack samples. We also investigate the hardness\ndegree of adversarial examples and indicate that the hardness degree histogram\nof adversarial examples is distinct from the hardness degree histogram of\nnormal samples.\n"}
+{"abstract": "  In this work, we study the effects that interacting quark matter has on the\nstellar structure of strange and charm quark stars. Additionally, their\nstability against radial pulsations is analyzed using a first-order formalism\nfor adiabatic general relativistic oscillations. Besides, the early stage of\nstellar evolution of neutron stars after the supernovae explosion, i.e.\nprotoneutron stars, is investigated by considering the possibility of a\nfirst-order phase transition to quark matter rich in leptons, where the\ndynamics of conversion between phases is studied within a thermal nucleation\nmodel. For each kind of compact star mentioned we use for the quark phase the\nequation of state calculated within cold perturbative QCD (pQCD), parametrized\nonly by its renormalization scale. We note that the original pQCD framework is\nmanipulated appropriately to include neutrinos and extended to add heavy quarks\nto the original system composed only by up, down and strange quark flavors.\n"}
+{"abstract": "  In this article, we have investigated the role of bulk viscosity to study the\naccelerated expansion of the universe in the framework of modified $f(Q)$\ngravity. The gravitational action in this modified gravity theory has the form\n$f(Q)$, where $Q$ denote the non-metricity scalar. In the present manuscript,\nwe have considered a bulk viscous matter-dominated cosmological model with the\nbulk viscosity coefficient of the form $\\xi =\\xi _{0}+\\xi _{1}H+\\xi _{2}\\left(\n\\frac{\\dot{H}}{H}+H\\right) $ which is proportional to the velocity and\nacceleration of the expanding universe. Two sets of limiting conditions on the\nbulk viscous parameters $\\xi _{0},$ $\\xi _{1},$ $% \\xi _{2}$ and model\nparameter $\\alpha $ arose here out of which one condition favours the present\nscenario of cosmic acceleration with a phase transition and corresponds to the\nuniverse with a Big Bang origin. Moreover, we have discussed the cosmological\nbehaviour of some geometrical parameters. Then, we have obtained the best\nfitting values of the model parameters $\\xi _{0},$ $\\xi _{1},$ $\\xi _{2}$ and\n$\\alpha $ by constraining our model with updated Hubble datasets consisting of\n$57$ data points and recently released Pantheon datasets consisting of $1048$\ndata points which show that our obtained model has good compatibility with\nobservations. Further, we have also included the Baryon Acoustic Oscillation\n(BAO) datasets of six data points with the Hubble \\& Pantheon datasets and\nobtained slightly different values of the model parameters. Finally, we have\nanalyzed our model with the statefinder diagnostic analysis and found some\ninteresting results and are discussed in details.\n"}
+{"abstract": "  Smooth planar vector fields containing two hyperbolic saddles may possess\ncontours formed by heteroclinic connections between these saddles. We present\nan overview of the bifurcations of these contours based on papers by J.W. Reyn\nand A.V. Dukov. Additionally, two new explicit polynomial systems containing\nsuch contours are derived, which are studied using the bifurcation software\nMatCont and are shown to exhibit the theoretically predicted phenomena,\nincluding series of heteroclinic connections.\n"}
+{"abstract": "  We present a polynomial-time $\\frac{3}{2}$-approximation algorithm for the\nproblem of finding a maximum-cardinality stable matching in a many-to-many\nmatching model with ties and laminar constraints on both sides. We formulate\nour problem using a bipartite multigraph whose vertices are called workers and\nfirms, and edges are called contracts. Our algorithm is described as the\ncomputation of a stable matching in an auxiliary instance, in which each\ncontract is replaced with three of its copies and all agents have strict\npreferences on the copied contracts. The construction of this auxiliary\ninstance is symmetric for the two sides, which facilitates a simple symmetric\nanalysis. We use the notion of matroid-kernel for computation in the auxiliary\ninstance and exploit the base-orderability of laminar matroids to show the\napproximation ratio.\n  In a special case in which each worker is assigned at most one contract and\neach firm has a strict preference, our algorithm defines a\n$\\frac{3}{2}$-approximation mechanism that is strategy-proof for workers.\n"}
+{"abstract": "  Thermoelectric (TE) materials are among very few sustainable yet feasible\nenergy solutions of present time. This huge promise of energy harvesting is\ncontingent on identifying/designing materials having higher efficiency than\npresently available ones. However, due to the vastness of the chemical space of\nmaterials, only its small fraction was scanned experimentally and/or\ncomputationally so far. Employing a compressed-sensing based symbolic\nregression in an active-learning framework, we have not only identified a trend\nin materials' compositions for superior TE performance, but have also predicted\nand experimentally synthesized several extremely high performing novel TE\nmaterials. Among these, we found Ag$_{0.55}$Cu$_{0.45}$GaTe$_2$ to possess an\nexperimental figure of merit as high as ~2.8 at 827 K, which is a breakthrough\nin the field. The presented methodology demonstrates the importance and\ntremendous potential of physically informed descriptors in material science, in\nparticular for relatively small data sets typically available from experiments\nat well-controlled conditions.\n"}
+{"abstract": "  Stellar evolution and numerical hydrodynamics simulations depend critically\non access to fast, accurate, thermodynamically consistent equations of state.\nWe present Skye, a new equation of state for fully-ionized matter. Skye\nincludes the effects of positrons, relativity, electron degeneracy, Coulomb\ninteractions, non-linear mixing effects, and quantum corrections. Skye\ndetermines the point of Coulomb crystallization in a self-consistent manner,\naccounting for mixing and composition effects automatically. A defining feature\nof this equation of state is that it uses analytic free energy terms and\nprovides thermodynamic quantities using automatic differentiation machinery.\nBecause of this, Skye is easily extended to include new effects by simply\nwriting new terms in the free energy. We also introduce a novel thermodynamic\nextrapolation scheme for extending analytic fits to the free energy beyond the\nrange of the fitting data while preserving desirable properties like positive\nentropy and sound speed. We demonstrate Skye in action in the MESA stellar\nevolution software instrument by computing white dwarf cooling curves.\n"}
+{"abstract": "  Applying the triplicate form of the extended Gould--Hsu inverse series\nrelations to Dougall's summation theorem for the well--poised $_7F_6$-series,\nwe establish, from the dual series, several interesting Ramanujan--like\ninfinite series expressions for $\\pi^2$ and $\\pi^{\\pm1}$ with convergence rate\n\"$-\\frac{1}{27}$\".\n"}
+{"abstract": "  Wikipedia, the largest open-collaborative online encyclopedia, is a corpus of\ndocuments bound together by internal hyperlinks. These links form the building\nblocks of a large network whose structure contains important information on the\nconcepts covered in this encyclopedia. The presence of a link between two\narticles, materialised by an anchor text in the source page pointing to the\ntarget page, can increase readers' understanding of a topic. However, the\nprocess of linking follows specific editorial rules to avoid both under-linking\nand over-linking. In this paper, we study the transductive and the inductive\ntasks of link prediction on several subsets of the English Wikipedia and\nidentify some key challenges behind automatic linking based on anchor text\ninformation. We propose an appropriate evaluation sampling methodology and\ncompare several algorithms. Moreover, we propose baseline models that provide a\ngood estimation of the overall difficulty of the tasks.\n"}
+{"abstract": "  In this work, we propose a multigrid preconditioner for Jacobian-free\nNewton-Krylov (JFNK) methods. Our multigrid method does not require to store\nany representation of the Jacobian at any level of the multigrid hierarchy. As\nit is common in standard multigrid methods, the proposed method also relies on\nthree building blocks: transfer operators, smoothers, and a coarse level\nsolver. In addition to the restriction and prolongation operator, we also use a\nprojection operator to transfer the current Newton iterate to a coarser level.\nThe three-level Chebyshev semi-iterative method is employed as a smoother, as\nit has good smoothing properties and does not require the representation of the\nJacobian matrix. We replace the direct solver on the coarsest-level with a\nmatrix-free Krylov subspace method, thus giving rise to a truly Jacobian-free\nmultigrid preconditioner. We will discuss all building blocks of our multigrid\npreconditioner in detail and demonstrate the robustness and the efficiency of\nthe proposed method using several numerical examples.\n"}
+{"abstract": "  Digital contact tracing has been proposed to support the health authorities\nin fighting the current Covid-19 pandemic. In this paper we propose two\ncentralised protocols for digital contact tracing that, contrary to the common\nhypothesis that this is an inherent risk, do not allow (retroactive) tracking\nof the location of a device over time. The first protocol does not rely on\nsynchronised clocks. The second protocol does not require a handshake between\ntwo devices, at the expense of relying on real-time communication with a\ncentral server. We stress that digital contact tracing is a form of\ntechnological solutionism that should be used with care, especially given the\ninherent mass surveillance nature of such systems.\n"}
+{"abstract": "  The Vehicle Routing Problem (VRP) is the combinatorial optimization problem\nof designing routes for vehicles to visit customers in such a fashion that a\ncost function, typically the number of vehicles, or the total travelled\ndistance is minimized. The problem finds applications in industrial scenarios,\nfor example where Automated Guided Vehicles run through the plant to deliver\ncomponents from the warehouse. This specific problem, henceforth called the\nElectric Conflict-Free Vehicle Routing Problem (CF-EVRP), involves constraints\nsuch as limited operating range of the vehicles, time windows on the delivery\nto the customers, and limited capacity on the number of vehicles the road\nsegments can accommodate at the same time. Such a complex system results in a\nlarge model that cannot easily be solved to optimality in reasonable time. We\ntherefore developed a compositional model that breaks down the problem into\nsmaller and simpler sub-problems and provides sub-optimal, feasible solutions\nto the original problem. The algorithm exploits the strengths of SMT solvers,\nwhich proved in our previous work to be an efficient approach to deal with\nscheduling problems. Compared to a monolithic model for the CF-EVRP, written in\nthe SMT standard language and solved using a state-of-the-art SMT solver the\ncompositional model was found to be significantly faster.\n"}
+{"abstract": "  In the present study, the structural and hitherto uninvestigated mechanical\n(elastic stiffness constants, machinability index, Cauchy pressure, anisotropy\nindices, brittleness/ductility, Poissons ratio), electronic, optical, and\nthermodynamic properties of novel boron-rich compounds B6X (X = S, Se) have\nbeen explored using density functional theory. The estimated structural lattice\nparameters were consistent with the prior report. The mechanical and dynamical\nstability of these compounds have been established theoretically. The materials\nare brittle in nature and elastically anisotropic. The value of fracture\ntoughness, KIC for the B6S and B6Se are ~ 2.07 MPam0.5, evaluating the\nresistance to limit the crack propagation inside the materials. Both B6S and\nB6Se compounds possess high hardness values in the range 31-35 GPa, and have\nthe potential to be prominent members of the class of hard compounds. Strong\ncovalent bonding and sharp peak at low energy below the Fermi level confirmed\nby partial density of states (PDOS) resulted in the high hardness. The profile\nof band structure, as well as DOS, assesses the indirect semiconducting nature\nof the titled compounds. The comparatively high value of Debye temperature\n({\\Theta}D), minimum thermal conductivity (Kmin), lattice thermal conductivity\n(kph), low thermal expansion coefficient, and low density suggest that both\nboron-rich chalcogenides might be used as thermal management materials. Large\nabsorption capacities in the mid ultraviolet region (3.2-15 eV) of the studied\nmaterials and low reflectivity (~16 %) are significantly noted. Such favorable\nfeatures give promise to the compounds under investigation to be used in UV\nsurface-disinfection devices as well as medical sterilizer equipment\napplications. Excellent correlations are found among all the studied physical\nproperties of these compounds.\n"}
+{"abstract": "  We demonstrate that it is feasible to diacritize Hebrew script without any\nhuman-curated resources other than plain diacritized text. We present NAKDIMON,\na two-layer character level LSTM, that performs on par with much more\ncomplicated curation-dependent systems, across a diverse array of modern Hebrew\nsources.\n"}
+{"abstract": "  We consider 1D integrable systems supporting ballistic propagation of\nexcitations, perturbed by a localised defect that breaks most conservation laws\nand induces chaotic dynamics. Focusing on classical systems, we study an\nout-of-equilibrium protocol engineered activating the defect in an initially\nhomogeneous and far from the equilibrium state. We find that large enough\ndefects induce full thermalisation at their center, but nonetheless the\noutgoing flow of carriers emerging from the defect is non-thermal due to a\ngeneralization of the celebrated Boundary Thermal Resistance effect, occurring\nat the edges of the chaotic region. Our results are obtained combining\nab-initio numerical simulations for relatively small-sized defects, with the\nsolution of the Boltzmann equation, which becomes exact in the scaling limit of\nlarge, but weak defects.\n"}
+{"abstract": "  Streaming codes represent a packet-level FEC scheme for achieving reliable,\nlow-latency communication. In the literature on streaming codes, the\ncommonly-assumed Gilbert-Elliott channel model, is replaced by a more\ntractable, delay-constrained, sliding-window (DCSW) channel model that can\nintroduce either random or burst erasures. The known streaming codes that are\nrate optimal over the DCSW channel model are constructed by diagonally\nembedding a scalar block code across successive packets. These code\nconstructions have field size that is quadratic in the delay parameter $\\tau$\nand have a somewhat complex structure with an involved decoding procedure. This\nled to the introduction of simple streaming (SS) codes in which diagonal\nembedding is replaced by staggered-diagonal embedding (SDE). The SDE approach\nreduces the impact of a burst of erasures and makes it possible to construct\nnear-rate-optimal streaming codes using Maximum Distance Separable (MDS) code\nhaving linear field size. The present paper takes this development one step\nfurther, by retaining the staggered-diagonal feature, but permitting the\nplacement of more than one code symbol from a given scalar codeword within each\npacket. These generalized, simple streaming codes allow us to improve upon the\nrate of SS codes, while retaining the simplicity of working with MDS codes. We\ncharacterize the maximum code rate of streaming codes under a constraint on the\nnumber of contiguous packets over which symbols of the underlying scalar code\nare dispersed. Such a constraint leads to simplified code construction and\nreduced-complexity decoding.\n"}
+{"abstract": "  The relativistic charged spinor matter field is quantized in the background\nof a straight cosmic string with nonvanishing transverse size. The most general\nboundary conditions ensuring the impossibility for matter to penetrate through\nthe edge of the string core are considered. The role of discrete symmetries is\nelucidated, and analytic expressions for the temporal and spatial components of\nthe induced vacuum current are derived in the case of either $P$ or $CT$\ninvariant boundary condition with two parameters varying arbitrarily from point\nto point of the edge. The requirement of physical plausibility for the global\ninduced vacuum characteristics is shown to remove completely an arbitrariness\nin boundary conditions. We find out that a magnetic field is induced in the\nvacuum and that a sheath in the form of a tube of the magnetic flux lines\nencloses a cosmic string. The dependence of the induced vacuum magnetic field\nstrength on the string flux and tension, as well as on the transverse size of\nthe string and on the distance from the string, is unambiguously determined.\n"}
+{"abstract": "  Multifidelity Monte Carlo methods often rely on a preprocessing phase\nconsisting of standard Monte Carlo sampling to estimate correlation\ncoefficients between models of different fidelity to determine the weights and\nnumber of samples for each level. For computationally intensive models, as are\noften encountered in simulations of chaotic systems, this up-front cost can be\nprohibitive. In this work, a correlation estimation procedure is developed for\nthe case in which the highest and next highest fidelity models are generated\nvia discretizing the same mathematical model using different resolution. The\nprocedure uses discretization error estimates to estimate the required\ncorrelation coefficient without the need to sample the highest fidelity model,\nwhich can dramatically decrease the cost of the preprocessing phase. The method\nis extended to chaotic problems by using discretization error estimates that\naccount for the statistical nature of common quantities of interest and the\naccompanying finite sampling errors that pollute estimates of such quantities\nof interest. The methodology is then demonstrated on a model problem based on\nthe Kuramoto-Sivashinsky equation.\n"}
+{"abstract": "  The manuscript New Theory of Sunspots was identified and attributed to the\nJesuit astronomer and hydrographer from Marseille Esprit Pezenas (1692-1776)\nduring the year 2002, during the systematic search for papers concerning him in\nthe various French funds. The manuscript, in large part unpublished, New Theory\nof Sunspots was written by astronomer and professor of Marseilles Jesuit\nhydrography, Father Esprit Pezenas (1692-1776). The text was composed and\nrevised between the years 1766 and 1772. In this manuscript, of which we are\ngoing to study the conditions of its composition, P. Pezenas gives one of the\nlast geometric methods, related graphical methods, allowing the inclination of\nthe Sun's equator to be deduced on ecliptic, using three observations of a\nsunspot, at given times, and in taking into account the movement of the Earth\nwith respect to the Sun during the duration of observations. This text contains\nall the digital elements allowing to follow and understand the application of\ngeometric processing methods of observations and observation techniques\nmentioned above in the introduction: passages of edges and Sun spots at the\nmeridian, use of objective micrometers and wire micrometers. At detour of his\ncalculations, Father Pezenas announces a duration of about 26 days and 9 hours\nfor the rotation of the Sun around its axis, comments on and corrects some\nobservation data published in Le Monnier's Heavenly History.\n"}
+{"abstract": "  We propose a low-rank tensor approach to approximate linear transport and\nnonlinear Vlasov solutions and their associated flow maps. The approach takes\nadvantage of the fact that the differential operators in the Vlasov equation is\ntensor friendly, based on which we propose a novel way to dynamically and\nadaptively build up low-rank solution basis by adding new basis functions from\ndiscretization of the PDE, and removing basis from an SVD-type truncation\nprocedure. For the discretization, we adopt a high order finite difference\nspatial discretization and a second order strong stability preserving\nmulti-step time discretization. We apply the same procedure to evolve the\ndynamics of the flow map in a low-rank fashion, which proves to be advantageous\nwhen the flow map enjoys the low rank structure, while the solution suffers\nfrom high rank or displays filamentation structures. Hierarchical Tucker\ndecomposition is adopted for high dimensional problems. An extensive set of\nlinear and nonlinear Vlasov test examples are performed to show the high order\nspatial and temporal convergence of the algorithm with mesh refinement up to\nSVD-type truncation, the significant computational savings of the proposed\nlow-rank approach especially for high dimensional problems, the improved\nperformance of the flow map approach for solutions with filamentations.\n"}
+{"abstract": "  Grasping in dynamic environments presents a unique set of challenges. A\nstable and reachable grasp can become unreachable and unstable as the target\nobject moves, motion planning needs to be adaptive and in real time, the delay\nin computation makes prediction necessary. In this paper, we present a dynamic\ngrasping framework that is reachability-aware and motion-aware. Specifically,\nwe model the reachability space of the robot using a signed distance field\nwhich enables us to quickly screen unreachable grasps. Also, we train a neural\nnetwork to predict the grasp quality conditioned on the current motion of the\ntarget. Using these as ranking functions, we quickly filter a large grasp\ndatabase to a few grasps in real time. In addition, we present a seeding\napproach for arm motion generation that utilizes solution from previous time\nstep. This quickly generates a new arm trajectory that is close to the previous\nplan and prevents fluctuation. We implement a recurrent neural network (RNN)\nfor modelling and predicting the object motion. Our extensive experiments\ndemonstrate the importance of each of these components and we validate our\npipeline on a real robot.\n"}
+{"abstract": "  Semiconductor quantum dots embedded in optical cavities are promising\non-demand sources of single photons. Here, we theoretically study single photon\nemission from an optically driven two-photon Raman transition between the\nbiexciton and the ground state of a quantum dot. The advantage of this process\nis that it allows all-optical control of the properties of the emitted single\nphoton with a laser pulse. However, with the presence of other decay channels\nand excitation-induced quantum interference, on-demand emission of the single\nRaman photon is generally difficult to achieve. Here we show that laser pulses\nwith non-trivial shapes can be used to maintain excitation conditions for which\nwith increasing pulse intensities the on-demand regime is reached. To provide a\nrealistic picture of the achievable system performance, we include\nphonon-mediated processes in the theoretical caluclations. While preserving\nboth high photon purity and indistinguishability, we find that although based\non a higher-order emission process, for realistic system parameters on-demand\nRaman photon emission is indeed achievable with suitably tailored laser pulses.\n"}
+{"abstract": "  Stringent constraints on both reliability and latency must be guaranteed in\nultra-reliable low-latency communication (URLLC). To fulfill these constraints\nwith computationally constrained receivers, such as low-budget IoT receivers,\noptimal transmission parameters need to be studied in detail. In this paper, we\nintroduce a multi-objective optimization framework for the optimal design of\nURLLC in the presence of decoding complexity constraints. We consider\ntransmission of short-blocklength codewords that are encoded with linear block\nencoders, transmitted over a binary-input AWGN channel, and finally decoded\nwith order-statistics (OS) decoder. We investigate the optimal selection of a\ntransmission rate and power pair, while satisfying the constraints. For this\npurpose, a multi-objective optimization problem (MOOP) is formulated. Based on\nthe empirical model that accurately quantifies the trade-off between the\nperformance of an OS decoder and its computational complexity, the MOOP is\nsolved and the Pareto boundary is derived. In order to assess the overall\nperformance among several Pareto-optimal transmission pairs, two scalarization\nmethods are investigated. To exemplify the importance of the MOOP, a case study\non a battery-powered communication system is provided. It is shown that,\ncompared to the classical fixed rate-power transmissions, the MOOP provides the\noptimum usage of the battery and increases the energy efficiency of the\ncommunication system while maintaining the constraints.\n"}
+{"abstract": "  Few-shot object detection has made substantial progressby representing novel\nclass objects using the feature representation learned upon a set of base class\nobjects. However,an implicit contradiction between novel class classification\nand representation is unfortunately ignored. On the one hand, to achieve\naccurate novel class classification, the distributions of either two base\nclasses must be far away fromeach other (max-margin). On the other hand, to\nprecisely represent novel classes, the distributions of base classes should be\nclose to each other to reduce the intra-class distance of novel classes\n(min-margin). In this paper, we propose a class margin equilibrium (CME)\napproach, with the aim to optimize both feature space partition and novel class\nreconstruction in a systematic way. CME first converts the few-shot detection\nproblem to the few-shot classification problem by using a fully connected layer\nto decouple localization features. CME then reserves adequate margin space for\nnovel classes by introducing simple-yet-effective class margin loss during\nfeature learning. Finally, CME pursues margin equilibrium by disturbing the\nfeatures of novel class instances in an adversarial min-max fashion.\nExperiments on Pascal VOC and MS-COCO datasets show that CME significantly\nimproves upon two baseline detectors (up to $3\\sim 5\\%$ in average), achieving\nstate-of-the-art performance. Code is available at\nhttps://github.com/Bohao-Lee/CME .\n"}
+{"abstract": "  The fundamental nature of dark matter is entirely unknown. A compelling\ncandidate is Twin Higgs mirror matter, invisible hidden-sector cousins of the\nStandard Model particles and forces. This generically predicts mirror neutron\nstars, degenerate objects made entirely of mirror nuclear matter. We find their\nstructure using realistic equations of state, robustly modified based on\nfirst-principle quantum chromodynamic calculations. We predict their\ndetectability with gravitational waves and binary pulsars, suggesting an\nimpressive discovery potential and ability to probe the dark sector.\n"}
+{"abstract": "  We propose a novel Multi-Scale Spectrogram (MSS) modelling approach to\nsynthesise speech with an improved coarse and fine-grained prosody. We present\na generic multi-scale spectrogram prediction mechanism where the system first\npredicts coarser scale mel-spectrograms that capture the suprasegmental\ninformation in speech, and later uses these coarser scale mel-spectrograms to\npredict finer scale mel-spectrograms capturing fine-grained prosody.\n  We present details for two specific versions of MSS called Word-level MSS and\nSentence-level MSS where the scales in our system are motivated by the\nlinguistic units. The Word-level MSS models word, phoneme, and frame-level\nspectrograms while Sentence-level MSS models sentence-level spectrogram in\naddition.\n  Subjective evaluations show that Word-level MSS performs statistically\nsignificantly better compared to the baseline on two voices.\n"}
+{"abstract": "  We review the correspondence between integrable sigma models with complex\nhomogeneous target spaces and chiral bosonic (and possibly mixed\nbosonic/fermionic) Gross-Neveu models. Mathematically, the latter are models\nwith quiver variety phase spaces, which reduce to the more conventional sigma\nmodels in special cases. We discuss the geometry of the models, as well as\ntheir trigonometric and elliptic deformations, Ricci flow and the inclusion of\nfermions.\n"}
+{"abstract": "  Many approaches to 3D image segmentation are based on hierarchical clustering\nof supervoxels into image regions. Here we describe a distributed algorithm\ncapable of handling a tremendous number of supervoxels. The algorithm works\nrecursively, the regions are divided into chunks that are processed\nindependently in parallel by multiple workers. At each round of the recursive\nprocedure, the chunk size in all dimensions are doubled until a single chunk\nencompasses the entire image. The final result is provably independent of the\nchunking scheme, and the same as if the entire image were processed without\ndivision into chunks. This is nontrivial because a pair of adjacent regions is\nscored by some statistical property (e.g. mean or median) of the affinities at\nthe interface, and the interface may extend over arbitrarily many chunks. The\ntrick is to delay merge decisions for regions that touch chunk boundaries, and\nonly complete them in a later round after the regions are fully contained\nwithin a chunk. We demonstrate the algorithm by clustering an affinity graph\nwith over 1.5 trillion edges between 135 billion supervoxels derived from a 3D\nelectron microscopic brain image.\n"}
+{"abstract": "  When the available information is noisy zeroth-order (ZO) oracle, stochastic\napproximation methods are popular for estimating the root of the multivariate\ngradient equation. Inspired by the Stein's identity, this work establishes a\nnovel Hessian approximation scheme. We compare it alongside with second-order\nsimultaneous perturbation stochastic approximation (2SPSA). On the basis of the\nalmost sure convergence and the same convergence rate, 2SPSA requires four ZO\nqueries, while ours requires three ZO queries. Moreover, 2SPSA requires two\nstatistically independent perturbations and two differencing stepsizes, while\nours requires generating one perturbation vector only and tuning one\ndifferencing stepsize only. Besides, the weighting mechanism for the Hessian\nestimate is generalized and the smoothness restriction on the loss function is\nrelaxed compared to 2SPSA. Finally, we present numerical support for the\nreduced per-iteration ZO query complexity.\n"}
+{"abstract": "  This article presents a state feedback control design strategy for the\nstabilization of a vehicle along a reference collision avoidance maneuver. The\nstabilization of the vehicle is achieved through a combination of steering,\nacceleration and braking. A Linear Parameter-Varying (LPV) model is obtained\nfrom the linearization of a non-linear model along the reference trajectory. A\nrobust state feedback control law is computed for the LPV model. Finally,\nsimulation results illustrate the stabilization of the vehicle along the\nreference trajectory.\n"}
+{"abstract": "  It was shown that when one disposes of a parametric information of the\ntruncation distribution, the semiparametric estimator of the distribution\nfunction for truncated data (Wang, 1989) is more efficient than the\nnonparametric one. On the basis of this estimation method, we derive an\nestimator for the tail index of Pareto-type distributions that are randomly\nright-truncated and establish its consistency and asymptotic normality. The\nfinite sample behavior of the proposed estimator is carried out by simulation\nstudy. We point out that, in terms of both bias and root of the mean squared\nerror, our estimator performs better than those based on nonparametric\nestimation methods. An application to a real dataset of induction times of AIDS\ndiseases is given as well.\n"}
+{"abstract": "  Detached circumplanetary disks are unstable to tilting as a result of the\nstellar tidal potential. We examine how a tilted circumplanetary disk affects\nthe evolution of the spin axis of an oblate planet. The disk is evolved using\ntime-dependent equations for linear wave-like warp evolution, including terms\nrepresenting the effect of the tidal potential and planetary oblateness. For a\ndisk with a sufficiently large mass, we find that the planet spin quickly\naligns to the misaligned disk. The tilt of the planetary spin axis then\nincreases on the same timescale as the disk. This can be an efficient mechanism\nfor generating primordial obliquity in giant planets. We suggest that directly\nimaged exoplanets at large orbital radii, where the disk mass criterion is more\nlikely to be satisfied, could have significant obliquities due to the tilt\ninstability of their circumplanetary disks.\n"}
+{"abstract": "  A sequential recommender system aims to recommend attractive items to users\nbased on behaviour patterns. The predominant sequential recommendation models\nare based on natural language processing models, such as the gated recurrent\nunit, that embed items in some defined space and grasp the user's long-term and\nshort-term preferences based on the item embeddings. However, these approaches\nlack fundamental insight into how such models are related to the user's\ninherent decision-making process. To provide this insight, we propose a novel\nrecurrent cell, namely FaNC, from Freudian and Newtonian perspectives. FaNC\ndivides the user's state into conscious and unconscious states, and the user's\ndecision process is modelled by Freud's two principles: the pleasure principle\nand reality principle. To model the pleasure principle, i.e., free-floating\nuser's instinct, we place the user's unconscious state and item embeddings in\nthe same latent space and subject them to Newton's law of gravitation.\nMoreover, to recommend items to users, we model the reality principle, i.e.,\nbalancing the conscious and unconscious states, via a gating function. Based on\nextensive experiments on various benchmark datasets, this paper provides\ninsight into the characteristics of the proposed model. FaNC initiates a new\ndirection of sequential recommendations at the convergence of psychoanalysis\nand recommender systems.\n"}
+{"abstract": "  We present a novel generating function (GF) method for the self-condensing\nvinyl polymerization (SCVP) system with any initial distribution of preexisted\npolymers. Such a method was proven to be especially useful to investigate the\nsemi-batch SCVP system allowing a sequence of feeding operations during the\npolymerization. Consequently, the number-, weight-, and z-average molecular\nweights as well as polydispersity index of hyperbranched polymers can be\nexplicitly given, which are determined by predetermined feeding details and\nconversions in each polymerization step. These analytical results are further\nconfirmed by the corresponding Monte Carlo simulation. Therefore the present GF\nmethod has provided a unified treatment on the semi-batch SCVP system.\nAccordingly, hyperbranched polymers with desired properties can be prepared by\ndesigning feeding details and presetting conversions at each step based on the\npresent GF method.\n"}
+{"abstract": "  An inverse problem for a stationary heat transfer process is studied for a\ntotally isolated bar on its lateral surface, of negligible diameter, made up of\ntwo consecutive sections of different, isotropic and homogeneous materials. At\nthe left boundary, a Dirichlet type condition is imposed that represents a\nconstant temperature source while a Robin type condition that models the heat\ndissipation by convection is considered at the right one. Many articles in the\nliterature focus on thermal and stress analysis at the interface but no one is\ndedicated to the estimation of the contact point location between the two\nmaterials. In this work, it is assumed that the interface position is unknown.\nA technique to determine it from a unique noisy flow measurement at the right\nboundary is introduced. Necessary and sufficient conditions are derived in\norder to obtain the estimation of the interface point from a heat flux measured\nat the right boundary. Numerical solutions are obtained together with an\nexpression for the estimation error. Moreover, an elasticity analysis is\nincluded to study the influence of data errors. The results show that our\napproach is useful for determining the location of the materials interface.\n"}
+{"abstract": "  Fixed-order perturbative calculations of fiducial cross sections for two-body\ndecay processes at colliders show disturbing sensitivity to unphysically low\nmomentum scales and, in the case of $H\\to \\gamma \\gamma$ in gluon fusion, poor\nconvergence. Such problems have their origins in an interplay between the\nbehaviour of standard experimental cuts at small transverse momenta ($p_t$) and\nlogarithmic perturbative contributions. We illustrate how this interplay leads\nto a factorially divergent structure in the perturbative series that sets in\nalready from the first orders. We propose simple modifications of fiducial cuts\nto eliminate their key incriminating characteristic, a linear dependence of the\nacceptance on the Higgs or $Z$-boson $p_t$, replacing it with quadratic\ndependence. This brings major improvements in the behaviour of the perturbative\nexpansion. More elaborate cuts can achieve an acceptance that is independent of\nthe Higgs $p_t$ at low $p_t$, with a variety of consequent advantages.\n"}
+{"abstract": "  Power laws have been found to describe a wide variety of natural (physical,\nbiological, astronomic, meteorological, geological) and man-made (social,\nfinancial, computational) phenomena over a wide range of magnitudes, although\ntheir underlying mechanisms are not always clear. In statistics, power law\ndistribution is often found to fit data exceptionally well when the normal\n(Gaussian) distribution fails. Nevertheless, predicting power law phenomena is\nnotoriously difficult because some of its idiosyncratic properties such as lack\nof well-defined average value, and potentially unbounded variance. TPL\n(Taylor's power law), a power law first discovered to characterize the spatial\nand/or temporal distribution of biological populations and recently extended to\ndescribe the spatiotemporal heterogeneities (distributions) of human\nmicrobiomes and other natural and artificial systems such as fitness\ndistribution in computational (artificial) intelligence. The power law with\nexponential cutoff (PLEC) is a variant of power-law function that tapers off\nthe exponential growth of power-law function ultimately and can be particularly\nuseful for certain predictive problems such as biodiversity estimation and\nturning-point prediction for COVID-19 infection/fatality. Here, we propose\ncoupling (integration) of TPL and PLEC to offer improved prediction quality of\ncertain power-law phenomena. The coupling takes advantages of variance\nprediction using TPL and the asymptote estimation using PLEC and delivers\nconfidence interval for the asymptote. We demonstrate the integrated approach\nto the estimation of potential (dark) biodiversity and turning point of\nCOVID-19 fatality. We expect this integrative approach should have wide\napplications given the duel relationship between power law and normal\nstatistical distributions.\n"}
+{"abstract": "  The Central Molecular Zone (CMZ), a $\\sim$200 pc sized region around the\nGalactic Centre, is peculiar in that it shows a star formation rate (SFR) that\nis suppressed with respect to the available dense gas. To study the SFR in the\nCMZ, young stellar objects (YSOs) can be investigated. Here we present radio\nobservations of 334 2.2 $\\mu$m infrared sources that have been identified as\nYSO candidates. Our goal is to investigate the presence of centimetre\nwavelength radio continuum counterparts to this sample of YSO candidates which\nwe use to constrain the current SFR in the CMZ. As part of the GLOSTAR survey,\nD-configuration VLA data was obtained for the Galactic Centre, covering\n-2$^{\\circ}<l<$2$^{\\circ}$ and -1$^{\\circ}<b<$1$^{\\circ}$, with a frequency\ncoverage of 4-8 GHz. We matched YSOs with radio continuum sources based on\nselection criteria and classified these radio sources as potential HII regions\nand determined their physical properties. Of the 334 YSO candidates, we found\n35 with radio continuum counterparts. We find that 94 YSOs are associated with\ndense dust condensations identified in the 870 $\\mu$m ATLASGAL survey, of which\n14 have a GLOSTAR counterpart. Of the 35 YSOs with radio counterparts, 11 are\nconfirmed as HII regions, based on their spectral indices and the literature.\nWe estimated their Lyman continuum photon flux in order to estimate the mass of\nthe ionising star. Combining these with known sources, the present-day SFR in\nthe CMZ is calculated to be $\\sim$0.068 M$_{\\odot}$ yr$^{-1}$, which is\n$\\sim$6.8$\\%$ of the Galactic SFR. Candidate YSOs that lack radio counterparts\nmay not have yet evolved to the stage of exhibiting an HII region or,\nconversely, are older and have dispersed their natal clouds. Since many lack\ndust emission, the latter is more likely. Our SFR estimate in the CMZ is in\nagreement with previous estimates in the literature.\n"}
+{"abstract": "  In this paper, we show that Orlicz--Sobolev spaces $W^{1,\\phi}(\\Omega)$ can\nbe characterized with the ACL- and ACC-characterizations. ACL stands for\nabsolutely continuous on lines and ACC for absolutely continuous on curves. Our\nresults hold under the assumptions that $C^1(\\Omega)$ functions are dense in\n$W^{1,\\phi}(\\Omega)$, and $\\phi(x,\\beta) \\geq 1$ for some $\\beta > 0$ and\nalmost every $x \\in \\Omega$. The results are new even in the special cases of\nOrlicz and double phase growth.\n"}
+{"abstract": "  Dicke superradiance, i.e., the enhanced spontaneous emission of coherent\nradiation, is often attributed to radiation emitted by synchronized dipoles\ncoherently oscillating in phase. At the same time, Dicke derived superradiance\nassuming atoms in entangled Dicke states which do not display any dipole\nmoment. To shed light on this apparent paradox, we study the intensity\ndistribution arising from two identical two-level atoms prepared either in an\nentangled Dicke state or in a separable atomic state with non-vanishing dipole\nmoment. We find that the two configurations produce similar far field intensity\npatterns, however, stemming from fundamentally distinct types of coherence:\nwhile in the second case the atoms display coherence among the individual\nparticles leading to Young-type interference as known from classical dipoles,\natoms in Dicke states possess collective coherence leading to enhanced\nspontaneous emission. This demonstrates that the radiation generated by\nsynchronized dipoles and Dicke superradiance are fundamentally distinct\nphenomena and have to be interpreted in different ways.\n"}
+{"abstract": "  Amino acids are building-blocks of proteins, basic constituents of all\norganisms and essential to life on Earth. They are present in carbonaceous\nchondrite meteorites and comets, but their origin is still unknown. Formation\nof amino acids in the interstellar medium is posible via specific gas-phase\nreactions in dark clouds, however sensitive radiosearches at millimeter\nwavelengths have not revealed their existence yet. The mid-IR vibrational\nspectra of amino acids provides an alternative path for their identification.\nWe present Spitzer spectroscopic observations in the star-forming region IC 348\nof the Perseus Molecular Cloud showing the detection of mid-IR emission lines\nconsistent with the most intense laboratory bands of the three aromatic amino\nacids, tyrosine, phenylalanine and tryptophan and the aliphatic amino acids\nisoleucine and glycine. Estimates of column densities give values 10-100 times\nhigher for isoleucine and glycine than for the aromatic amino acids as in some\nmeteorites. The strongest bands of each amino acid are also found in the\ncombined spectrum of >30 interstellar locations in diverse star-forming regions\nsupporting the suggestion that amino acids are widely spread in interstellar\nspace. Future mid-IR searches for proteinogenic amino acids in protostars,\nprotoplanetary disks and in the interstellar medium will be key to establish an\nexogenous origin of meteoritic amino acids and to understand how the prebiotic\nconditions for life were set in the early Earth.\n"}
+{"abstract": "  In an attempt to better understand structural benefits and generalization\npower of deep neural networks, we firstly present a novel graph theoretical\nformulation of neural network models, including fully connected, residual\nnetwork (ResNet) and densely connected networks (DenseNet). Secondly, we extend\nthe error analysis of the population risk for two layer network\n\\cite{ew2019prioriTwo} and ResNet \\cite{e2019prioriRes} to DenseNet, and show\nfurther that for neural networks satisfying certain mild conditions, similar\nestimates can be obtained. These estimates are a priori in nature since they\ndepend sorely on the information prior to the training process, in particular,\nthe bounds for the estimation errors are independent of the input dimension.\n"}
+{"abstract": "  Defensive deception techniques have emerged as a promising proactive defense\nmechanism to mislead an attacker and thereby achieve attack failure. However,\nmost game-theoretic defensive deception approaches have assumed that players\nmaintain consistent views under uncertainty. They do not consider players'\npossible, subjective beliefs formed due to asymmetric information given to\nthem. In this work, we formulate a hypergame between an attacker and a defender\nwhere they can interpret the same game differently and accordingly choose their\nbest strategy based on their respective beliefs. This gives a chance for\ndefensive deception strategies to manipulate an attacker's belief, which is the\nkey to the attacker's decision making. We consider advanced persistent threat\n(APT) attacks, which perform multiple attacks in the stages of the cyber kill\nchain where both the attacker and the defender aim to select optimal strategies\nbased on their beliefs. Through extensive simulation experiments, we\ndemonstrated how effectively the defender can leverage defensive deception\ntechniques while dealing with multi-staged APT attacks in a hypergame in which\nthe imperfect information is reflected based on perceived uncertainty, cost,\nand expected utilities of both attacker and defender, the system lifetime\n(i.e., mean time to security failure), and improved false positive rates in\ndetecting attackers.\n"}
+{"abstract": "  We use a simulation-based modelling approach to analyse the anisotropic\nclustering of the BOSS LOWZ sample over the radial range $0.4 \\, h^{-1} \\,\n\\mathrm{Mpc}$ to $63 \\, h^{-1} \\, \\mathrm{Mpc}$, significantly extending what\nis possible with a purely analytic modelling framework. Our full-scale analysis\nyields constraints on the growth of structure that are a factor of two more\nstringent than any other study on large scales at similar redshifts. We infer\n$f \\sigma_8 = 0.471 \\pm 0.024$ at $z \\approx 0.25$, and $f \\sigma_8 = 0.431 \\pm\n0.025$ at $z \\approx 0.40$; the corresponding $\\Lambda$CDM predictions of the\nPlanck CMB analysis are $0.470 \\pm 0.006$ and $0.476 \\pm 0.005$, respectively.\nOur results are thus consistent with Planck, but also follow the trend seen in\nprevious low-redshift measurements of $f \\sigma_8$ falling slightly below the\n$\\Lambda$CDM+CMB prediction. We find that small and large radial scales yield\nmutually consistent values of $f \\sigma_8$, but there are $1-2.5 \\sigma$ hints\nof small scales ($< 10 \\, h^{-1} \\, \\mathrm{Mpc}$) preferring lower values for\n$f \\sigma_8$ relative to larger scales. We analyse the constraining power of\nthe full range of radial scales, finding that most of the multipole information\nabout $f\\sigma_8$ is contained in the scales $2 \\, h^{-1} \\, \\mathrm{Mpc}\n\\lesssim s \\lesssim 20 \\, h^{-1} \\, \\mathrm{Mpc}$. Evidently, once the\ncosmological information of the quasi-to-nonlinear regime has been harvested,\nlarge-scale modes contain only modest additional information about structure\ngrowth. Finally, we compare predictions for the galaxy-galaxy lensing amplitude\nof the two samples against measurements from SDSS and assess the lensing-is-low\neffect in light of our findings.\n"}
+{"abstract": "  This paper presents a systematic study the effects of compression on\nhyperspectral pixel classification task. We use five dimensionality reduction\nmethods -- PCA, KPCA, ICA, AE, and DAE -- to compress 301-dimensional\nhyperspectral pixels. Compressed pixels are subsequently used to perform\npixel-based classifications. Pixel classification accuracies together with\ncompression method, compression rates, and reconstruction errors provide a new\nlens to study the suitability of a compression method for the task of\npixel-based classification. We use three high-resolution hyperspectral image\ndatasets, representing three common landscape units (i.e. urban, transitional\nsuburban, and forests) collected by the Remote Sensing and Spatial Ecosystem\nModeling laboratory of the University of Toronto. We found that PCA, KPCA, and\nICA post greater signal reconstruction capability; however, when compression\nrate is more than 90\\% those methods showed lower classification scores. AE and\nDAE methods post better classification accuracy at 95\\% compression rate,\nhowever decreasing again at 97\\%, suggesting a sweet-spot at the 95\\% mark. Our\nresults demonstrate that the choice of a compression method with the\ncompression rate are important considerations when designing a hyperspectral\nimage classification pipeline.\n"}
+{"abstract": "  Conversational recommender systems (CRSs) have revolutionized the\nconventional recommendation paradigm by embracing dialogue agents to\ndynamically capture the fine-grained user preference. In a typical\nconversational recommendation scenario, a CRS firstly generates questions to\nlet the user clarify her/his demands and then makes suitable recommendations.\nHence, the ability to generate suitable clarifying questions is the key to\ntimely tracing users' dynamic preferences and achieving successful\nrecommendations. However, existing CRSs fall short in asking high-quality\nquestions because: (1) system-generated responses heavily depends on the\nperformance of the dialogue policy agent, which has to be trained with huge\nconversation corpus to cover all circumstances; and (2) current CRSs cannot\nfully utilize the learned latent user profiles for generating appropriate and\npersonalized responses.\n  To mitigate these issues, we propose the Knowledge-Based Question Generation\nSystem (KBQG), a novel framework for conversational recommendation. Distinct\nfrom previous conversational recommender systems, KBQG models a user's\npreference in a finer granularity by identifying the most relevant relations\nfrom a structured knowledge graph (KG). Conditioned on the varied importance of\ndifferent relations, the generated clarifying questions could perform better in\nimpelling users to provide more details on their preferences. Finially,\naccurate recommendations can be generated in fewer conversational turns.\nFurthermore, the proposed KBQG outperforms all baselines in our experiments on\ntwo real-world datasets.\n"}
+{"abstract": "  The faithful irreducible $*$-representations of the $C^*$-algebra of the\nquantum symplectic sphere $S_q^{4n-1}, n\\geq 2$, have been investigated by\nD'Andrea and Landi. They proved that the first $n-1$ generators are all zero\ninside $C^*(S_q^{4n-1})$, for $n\\geq 2$. The result is a generalisation of the\ncase where $n=2$, which was shown by Mikkelsen and Szyma\\'nski.\n  We will show that $C^*(S_q^{4n-1}), n\\geq 2$ is isomorphic to a graph\n$C^*$-algebra. From here it follows that $C^*(S_q^{4n-1})$ is isomorphic to the\nquantum $(2(n+1)-1)$-sphere by Vaksman and Soibelman.\n"}
+{"abstract": "  Analyses based on quantum metrology have shown that the ability to localize\nthe positions of two incoherent point sources can be significantly enhanced\nthrough the use of mode sorting. Here we theoretically and experimentally\ninvestigate the effect of partial coherence on the sub-diffraction limit\nlocalization of two sources based on parity sorting. With the prior information\nof a negative and real-valued degree of coherence, higher Fisher information is\nobtained than that for the incoherent case. Our results pave the way to\nclarifying the role of coherence in quantum limited metrology.\n"}
+{"abstract": "  We analytically study the Fermi-gas formulation of sphere correlation\nfunctions of the Coulomb branch operators for 3d $\\mathcal{N}=4$ ADHM theory\nwith a gauge group $U(N)$, an adjoint hypermultiplet and $l$ hypermultiplets\nwhich can describe a stack of $N$ M2-branes at $A_{l-1}$ singularities. We find\nthat the leading coefficients of the perturbative grand canonical correlation\nfunctions are invariant under a hidden triality symmetry conjectured from the\ntwisted M-theory. The triality symmetry also helps us to fix the\nnext-to-leading corrections analytically.\n"}
+{"abstract": "  Three-dimensional face dense alignment and reconstruction in the wild is a\nchallenging problem as partial facial information is commonly missing in\noccluded and large pose face images. Large head pose variations also increase\nthe solution space and make the modeling more difficult. Our key idea is to\nmodel occlusion and pose to decompose this challenging task into several\nrelatively more manageable subtasks. To this end, we propose an end-to-end\nframework, termed as Self-aligned Dual face Regression Network (SADRNet), which\npredicts a pose-dependent face, a pose-independent face. They are combined by\nan occlusion-aware self-alignment to generate the final 3D face. Extensive\nexperiments on two popular benchmarks, AFLW2000-3D and Florence, demonstrate\nthat the proposed method achieves significant superior performance over\nexisting state-of-the-art methods.\n"}
+{"abstract": "  Principal component analysis has been used to reduce dimensionality of\ndatasets for a long time. In this paper, we will demonstrate that in mode\ndetection the components of smallest variance, the pettiest components, are\nmore important. We prove that when the data follows a multivariate normal\ndistribution, by implementing \"pettiest component analysis\" when the data is\nnormally distributed, we obtain boxes of optimal size in the sense that their\nsize is minimal over all possible boxes with the same number of dimensions and\ngiven probability. We illustrate our result with a simulation revealing that\npettiest component analysis works better than its competitors.\n"}
+{"abstract": "  We prove that any minimal Lagrangian diffeomorphism between two closed\nspherical surfaces with cone singularities is an isometry, without any\nassumption on the multiangles of the two surfaces. As an application, we show\nthat every branched immersion of a closed surface of constant positive Gaussian\ncurvature in Euclidean three-space is a branched covering onto a round sphere,\nthus generalizing the classical rigidity theorem of Liebmann to branched\nimmersions.\n"}
+{"abstract": "  The quasi-steady-state approximation is widely used to develop simplified\ndeterministic or stochastic models of enzyme catalyzed reactions. In\ndeterministic models, the quasi-steady-state approximation can be\nmathematically justified from singular perturbation theory. For several closed\nenzymatic reactions, the homologous extension of the quasi-steady-state\napproximation to the stochastic regime, known as the stochastic\nquasi-steady-state approximation, has been shown to be accurate under the\nanalogous conditions that permit the quasi-steady-state reduction of the\ndeterministic counterpart. However, it was recently demonstrated that the\nextension of the stochastic quasi-steady-state approximation to an open\nMichaelis--Menten reaction mechanism is only valid under a condition that is\nfar more restrictive than the qualifier that ensures the validity of its\ncorresponding deterministic quasi-steady-state approximation. In this paper, we\nsuggest a possible explanation for this discrepancy from the lens of geometric\nsingular perturbation theory. In so doing, we illustrate a misconception in the\napplication of the quasi-steady-state approximation: timescale separation does\nnot imply singular perturbation.\n"}
+{"abstract": "  In wireless network communication environments, Spectral Efficiency (SE) and\nEnergy Efficiency (EE) are among the major indicators used for evaluating\nnetwork performance. However, given the high demand for data rate services and\nthe exponential growth of energy consumption, SE and EE continue to elicit\nincreasing attention in academia and industries. Consequently, a study of the\ntrade-off between these metrics is imperative. In contrast with existing works,\nthis study proposes an efficient SE and EE trade-off algorithm for saving\nenergy in downlink Long Term Evolution (LTE) networks to concurrently optimize\nSE and EE while considering battery life at the Base Station (BS). The scheme\nis formulated as a Multi-objective Optimization Problem (MOP) and its Pareto\noptimal solution is examined. In contrast with other algorithms that prolong\nbattery life by considering the idle state of a BS, thereby increasing average\ndelay and energy consumption, the proposed algorithm prolongs battery life by\nadjusting the initial and final states of a BS to minimize the average delay\nand the energy consumption. Similarly, the use of an omni-directional antenna\nto spread radio signals to the user equipment in all directions causes high\ninterference and low spatial reuse. We propose using a directional antenna\ninstead of an omni-directional antenna by transmitting signals in one direction\nwhich results in no or low interference and high spatial reuse. The proposed\nscheme has been extensively evaluated through simulation, where simulation\nresults prove that the proposed scheme is efficiently able to decrease the\naverage response delay, improve SE, and minimize energy consumption.\n"}
+{"abstract": "  In this paper we present an open database for the development of detection\nand enhancement algorithms of speech transmitted over HF radio channels. It\nconsists of audio samples recorded by various receivers at different locations\nacross Europe, all monitoring the same single-sideband modulated transmission\nfrom a base station in Paderborn, Germany. Transmitted and received speech\nsignals are precisely time aligned to offer parallel data for supervised\ntraining of deep learning based detection and enhancement algorithms. For the\ntask of speech activity detection two exemplary baseline systems are presented,\none based on statistical methods employing a multi-stage Wiener filter with\nminimum statistics noise floor estimation, and the other relying on a deep\nlearning approach.\n"}
+{"abstract": "  We exhibit a Quillen equivalence between two model categories encoding the\nhomotopy theory of stratified spaces : the model category of filtered\nsimplicial sets, and that of filtered spaces. Additionally, we introduce a new\nclass of filtered spaces, that of vertical filtered CW-complexes, providing a\nnice model for the homotopy category of stratified spaces.\n"}
+{"abstract": "  Graphically-rich applications such as games are ubiquitous with attractive\nvisual effects of Graphical User Interface (GUI) that offers a bridge between\nsoftware applications and end-users. However, various types of graphical\nglitches may arise from such GUI complexity and have become one of the main\ncomponent of software compatibility issues. Our study on bug reports from game\ndevelopment teams in NetEase Inc. indicates that graphical glitches frequently\noccur during the GUI rendering and severely degrade the quality of\ngraphically-rich applications such as video games. Existing automated testing\ntechniques for such applications focus mainly on generating various GUI test\nsequences and check whether the test sequences can cause crashes. These\ntechniques require constant human attention to captures non-crashing bugs such\nas bugs causing graphical glitches. In this paper, we present the first step in\nautomating the test oracle for detecting non-crashing bugs in graphically-rich\napplications. Specifically, we propose \\texttt{GLIB} based on a code-based data\naugmentation technique to detect game GUI glitches. We perform an evaluation of\n\\texttt{GLIB} on 20 real-world game apps (with bug reports available) and the\nresult shows that \\texttt{GLIB} can achieve 100\\% precision and 99.5\\% recall\nin detecting non-crashing bugs such as game GUI glitches. Practical application\nof \\texttt{GLIB} on another 14 real-world games (without bug reports) further\ndemonstrates that \\texttt{GLIB} can effectively uncover GUI glitches, with 48\nof 53 bugs reported by \\texttt{GLIB} having been confirmed and fixed so far.\n"}
+{"abstract": "  Many automatic sequences, such as the Thue-Morse sequence or the\nRudin-Shapiro sequence, have some desirable features of pseudorandomness such\nas a large linear complexity and a small well-distribution measure. However,\nthey also have some disastrous properties in view of certain applications. For\nexample, the majority of possible binary patterns never appears in automatic\nsequences and their correlation measure of order 2 is extremely large.\n  Certain subsequences, such as automatic sequences along squares, may keep the\ngood properties of the original sequence but avoid the bad ones.\n  In this survey we investigate properties of pseudorandomness and\nnon-randomness of automatic sequences and their subsequences and present\nresults on their behaviour under several measures of pseudorandomness including\nlinear complexity, correlation measure of order $k$, expansion complexity and\nnormality. We also mention some analogs for finite fields.\n"}
+{"abstract": "  Micro aerial vehicles are widely being researched and employed due to their\nrelative low operation costs and high flexibility in various applications. We\nstudy the under-actuated quadrotor perching problem, designing a trajectory\nplanner and controller which generates feasible trajectories and drives\nquadrotors to desired state in state space. This paper proposes a trajectory\ngenerating and tracking method for quadrotor perching that takes the advantages\nof reinforcement learning controller and traditional controller. The trained\nlow-level reinforcement learning controller would manipulate quadrotor toward\nthe perching point in simulation environment. Once the simulated quadrotor has\nsuccessfully perched, the relative trajectory information in simulation will be\nsent to tracking controller on real quadrotor and start the actual perching\ntask. Generating feasible trajectories via the trained reinforcement learning\ncontroller requires less time, and the traditional trajectory tracking\ncontroller could easily be modified to control the quadrotor and mathematically\nanalysis its stability and robustness. We show that this approach permits the\ncontrol structure of trajectories and controllers enabling such aggressive\nmaneuvers perching on vertical surfaces with high precision.\n"}
+{"abstract": "  Internet services leverage transport protocol port numbers to specify the\nsource and destination application layer protocols. While using port 0 is not\nallowed in most transport protocols, we see a non-negligible share of traffic\nusing port 0 in the Internet. In this study, we dissect port 0 traffic to infer\nits possible origins and causes using five complementing flow-level and\npacket-level datasets. We observe 73 GB of port 0 traffic in one week of IXP\ntraffic, most of which we identify as an artifact of packet fragmentation. In\nour packet-level datasets, most traffic is originated from a small number of\nhosts and while most of the packets have no payload, a major fraction of\npackets containing payload belong to the BitTorrent protocol. Moreover, we find\nunique traffic patterns commonly seen in scanning. In addition to analyzing\npassive traces, we also conduct an active measurement campaign to study how\ndifferent networks react to port 0 traffic. We find an unexpectedly high\nresponse rate for TCP port 0 probes in IPv4, with very low response rates with\nother protocol types. Finally, we will be running continuous port 0\nmeasurements and providing the results to the measurement community.\n"}
+{"abstract": "  The performance of voice-controlled systems is usually influenced by accented\nspeech. To make these systems more robust, the frontend accent recognition (AR)\ntechnologies have received increased attention in recent years. As accent is a\nhigh-level abstract feature that has a profound relationship with the language\nknowledge, AR is more challenging than other language-agnostic audio\nclassification tasks. In this paper, we use an auxiliary automatic speech\nrecognition (ASR) task to extract language-related phonetic features.\nFurthermore, we propose a hybrid structure that incorporates the embeddings of\nboth a fixed acoustic model and a trainable acoustic model, making the\nlanguage-related acoustic feature more robust. We conduct several experiments\non the Accented English Speech Recognition Challenge (AESRC) 2020 dataset. The\nresults demonstrate that our approach can obtain a 6.57% relative improvement\non the validation set. We also get a 7.28% relative improvement on the final\ntest set for this competition, showing the merits of the proposed method.\n"}
+{"abstract": "  This presentation reports the first known data from a field mill mounted on a\nground-based robotic platform. The robot's motor and electrostatic charging of\nits wheels do not perturb the field mill data, and electric field varies\nsmoothly whilst the robot is moving. Test measurements under a charged\npolystyrene plate are reduced in variability by a factor of 2 compared to a\nhand-held field mill. This technology has potential for autonomous measurements\nin inaccessible or hazardous environments.\n"}
+{"abstract": "  NLP has a rich history of representing our prior understanding of language in\nthe form of graphs. Recent work on analyzing contextualized text\nrepresentations has focused on hand-designed probe models to understand how and\nto what extent do these representations encode a particular linguistic\nphenomenon. However, due to the inter-dependence of various phenomena and\nrandomness of training probe models, detecting how these representations encode\nthe rich information in these linguistic graphs remains a challenging problem.\nIn this paper, we propose a new information-theoretic probe, Bird's Eye, which\nis a fairly simple probe method for detecting if and how these representations\nencode the information in these linguistic graphs. Instead of using classifier\nperformance, our probe takes an information-theoretic view of probing and\nestimates the mutual information between the linguistic graph embedded in a\ncontinuous space and the contextualized word representations. Furthermore, we\nalso propose an approach to use our probe to investigate localized linguistic\ninformation in the linguistic graphs using perturbation analysis. We call this\nprobing setup Worm's Eye. Using these probes, we analyze BERT models on their\nability to encode a syntactic and a semantic graph structure, and find that\nthese models encode to some degree both syntactic as well as semantic\ninformation; albeit syntactic information to a greater extent.\n"}
+{"abstract": "  We employ non-perturbative renormalisation group methods to compute the full\nmomentum dependence of propagators in quantum gravity in general dimensions. We\ndisentangle all different graviton and Faddeev-Popov ghost modes and find\nqualitative differences in the momentum dependence of their propagators. This\nallows us to reconstruct the form factors quadratic in curvature from first\nprinciples, which enter physical observables like scattering cross sections.\nThe results are qualitatively stable under variations of the gauge fixing\nchoice.\n"}
+{"abstract": "  Accretion onto black holes is an efficient mechanism in converting the gas\nmass-energy into energetic outputs as radiation, wind and jet. Tidal disruption\nevents, in which stars are tidally torn apart and then accreted onto\nsupermassive black holes, offer unique opportunities of studying the accretion\nphysics as well as the wind and jet launching physics across different\naccretion regimes. In this review, we systematically describe and discuss the\nmodels that have been developed to study the accretion flows and jets in tidal\ndisruption events. A good knowledge of these physics is not only needed for\nunderstanding the emissions of the observed events, but also crucial for\nprobing the general relativistic space-time around black holes and the\ndemographics of supermassive black holes via tidal disruption events.\n"}
+{"abstract": "  In computer-aided design (CAD), the ability to \"reverse engineer\" the\nmodeling steps used to create 3D shapes is a long-sought-after goal. This\nprocess can be decomposed into two sub-problems: converting an input mesh or\npoint cloud into a boundary representation (or B-rep), and then inferring\nmodeling operations which construct this B-rep. In this paper, we present a new\nsystem for solving the second sub-problem. Central to our approach is a new\ngeometric representation: the zone graph. Zones are the set of solid regions\nformed by extending all B-Rep faces and partitioning space with them; a zone\ngraph has these zones as its nodes, with edges denoting geometric adjacencies\nbetween them. Zone graphs allow us to tractably work with industry-standard CAD\noperations, unlike prior work using CSG with parametric primitives. We focus on\nCAD programs consisting of sketch + extrude + Boolean operations, which are\ncommon in CAD practice. We phrase our problem as search in the space of such\nextrusions permitted by the zone graph, and we train a graph neural network to\nscore potential extrusions in order to accelerate the search. We show that our\napproach outperforms an existing CSG inference baseline in terms of geometric\nreconstruction accuracy and reconstruction time, while also creating more\nplausible modeling sequences.\n"}
+{"abstract": "  The new Coronavirus is spreading rapidly, and it has taken the lives of many\npeople so far. The virus has destructive effects on the human lung, and early\ndetection is very important. Deep Convolution neural networks are such powerful\ntools in classifying images. Therefore, in this paper, a hybrid approach based\non a deep network is presented. Feature vectors were extracted by applying a\ndeep convolution neural network on the images, and useful features were\nselected by the binary differential meta-heuristic algorithm. These optimized\nfeatures were given to the SVM classifier. A database consisting of three\ncategories of images such as COVID-19, pneumonia, and healthy included in 1092\nX-ray samples was considered. The proposed method achieved an accuracy of\n99.43%, a sensitivity of 99.16%, and a specificity of 99.57%. Our results\ndemonstrate that the suggested approach is better than recent studies on\nCOVID-19 detection with X-ray images.\n"}
+{"abstract": "  In a (parameterized) graph edge modification problem, we are given a graph\n$G$, an integer $k$ and a (usually well-structured) class of graphs\n$\\mathcal{G}$, and ask whether it is possible to transform $G$ into a graph $G'\n\\in \\mathcal{G}$ by adding and/or removing at most $k$ edges. Parameterized\ngraph edge modification problems received considerable attention in the last\ndecades.\n  In this paper, we focus on finding small kernels for edge modification\nproblems. One of the most studied problems is the Cluster Editing problem, in\nwhich the goal is to partition the vertex set into a disjoint union of cliques.\nEven if this problem admits a $2k$ kernel [Cao, 2012], this kernel does not\nreduce the size of most instances. Therefore, we explore the question of\nwhether linear kernels are a theoretical limit in edge modification problems,\nin particular when the target graphs are very structured (such as a partition\ninto cliques for instance). We prove, as far as we know, the first sublinear\nkernel for an edge modification problem. Namely, we show that Clique +\nIndependent Set Deletion, which is a restriction of Cluster Deletion, admits a\nkernel of size $O(k/\\log k)$.\n  We also obtain small kernels for several other edge modification problems. We\nprove that Split Addition (and the equivalent Split Deletion) admits a linear\nkernel, improving the existing quadratic kernel of Ghosh et al. [Ghosh et al.,\n2015]. We complement this result by proving that Trivially Perfect Addition\nadmits a quadratic kernel (improving the cubic kernel of Guo [Guo, 2007]), and\nfinally prove that its triangle-free version (Starforest Deletion) admits a\nlinear kernel, which is optimal under ETH.\n"}
+{"abstract": "  Motor disturbances can affect the interaction with dynamic objects, such as\ncatching a ball. A classification of clinical catching trials might give\ninsight into the existence of pathological alterations in the relation of arm\nand ball movements. Accurate, but also early decisions are required to classify\na catching attempt before the catcher's first ball contact. To obtain\nclinically valuable results, a significant decision confidence of at least 75%\nis required. Hence, three competing objectives have to be optimized at the same\ntime: accuracy, earliness and decision-making confidence. Here we propose a\ncoupled classification and prediction approach for early time series\nclassification: a predictive, generative recurrent neural network (RNN)\nforecasts the next data points of ball trajectories based on already available\nobservations; a discriminative RNN continuously generates classification\nguesses based on the available data points and the unrolled sequence\npredictions. We compare our approach, which we refer to as predictive\nsequential classification (PSC), to state-of-the-art sequence learners,\nincluding various RNN and temporal convolutional network (TCN) architectures.\nOn this hard real-world task we can consistently demonstrate the superiority of\nPSC over all other models in terms of accuracy and confidence with respect to\nearliness of recognition. Specifically, PSC is able to confidently classify the\nsuccess of catching trials as early as 123 milliseconds before the first ball\ncontact. We conclude that PSC is a promising approach for early time series\nclassification, when accurate and confident decisions are required.\n"}
+{"abstract": "  A generalised numerical semigroup (GNS) is a submonoid $S$ of $\\mathbb{N}^d$\nfor which the complement $\\mathbb{N}^d\\setminus S$ is finite. The points in the\ncomplement $\\mathbb{N}^d\\setminus S$ are called gaps. A gap $F$ is considered\nFrobenius allowable if there is some relaxed monomial ordering on\n$\\mathbb{N}^d$ with respect to which $F$ is the largest gap. We characterise\nthe Frobenius allowable gaps of a GNS. A GNS that has only one Frobenius\nallowable gap is called a Frobenius GNS. We estimate the number of Frobenius\nGNS with a given Frobenius gap $F=(F^{(1)},\\dots,F^{(d)})\\in\\mathbb{N}^d$ and\nshow that it is close to $\\sqrt{3}^{(F^{(1)}+1)\\cdots (F^{(d)}+1)}$ for large\n$d$. We define notions of quasi-irreducibility and quasi-symmetry for GNS.\nWhile in the case of $d=1$ these notions coincide with irreducibility and\nsymmetry of GNS, they are distinct in higher dimensions.\n"}
+{"abstract": "  Optical quantum interferometry represents the oldest example of quantum\nmetrology and it is at the source of quantum technologies. The original\nsqueezed state scheme is now a significant element of the last version of\ngravitational wave detectors and various additional uses have been proposed.\n  Further quantum enhanced schemes, from SU(1,1) interferometer to twin beam\ncorrelation interferometry, have also reached the stage of proof of principle\nexperiments enlarging the field of experimental quantum interferometry and\npaving the way to several further applications ranging from Planck scale\nsignals search to small effects detection. In this review paper I introduce\nthese experimental achievements, describing their schemes, advantages,\napplications and possible further developments.\n"}
+{"abstract": "  I provide an axiomatization of stochastic utility over two periods, stating\ntestable necessary and sufficient conditions under which an agent's choice\nbehavior under exogenous menu selection can be modeled by a pair of random\nutility functions. Although static random utility is characterized by a single\naxiom, Block-Marschak nonnegativity, I demonstrate that an additional notion of\nmarginal consistency is needed for the two-period axiomatization. In\nparticular, when each period's choice set has size three, I restate the\ncharacterization using the simpler axiom of stochastic regularity. I conclude\nby stating several corollaries, including an axiomatization of stochastic\nutility with full support and an axiomatization of n-period stochastic utility.\n"}
+{"abstract": "  This paper deals with the convergence of the Doi-Navier-Stokes model of\nliquid crystals to the Ericksen-Leslie model in the limit of the Deborah number\ntending to zero. While the literature has investigated this problem by means of\nthe Hilbert expansion method, we develop the moment method, i.e. a method that\nexploits conservation relations obeyed by the collision operator. These are\nnon-classical conservation relations which are associated with a new concept,\nthat of Generalized Collision Invariant (GCI). In this paper, we develop the\nGCI concept and relate it to geometrical and analytical structures of the\ncollision operator. Then, the derivation of the limit model using the GCI is\nperformed in an arbitrary number of spatial dimensions and with non-constant\nand non-uniform polymer density. This non-uniformity generates new terms in the\nEricksen-Leslie model.\n"}
+{"abstract": "  We obtain a remarkable semi-analytic expression concerning the role of purely\ntidal curvature on accelerated probes, revealing some novel insights into the\nrole of absolute vs. tidal acceleration in the response of such probes. The key\nquantity we evaluate is the relation between geodesic ($\\tau_{\\rm geod}$) and\nproper time ($\\tau_{\\rm acc}$) intervals between events on the probe\ntrajectory.\n  This is obtained as a covariant power series in curvature using a combination\nof analytical and numerical tools. A serendipitous observation then reveals\nthat one can $exactly$ sum all terms involving the $purely\\;tidal$ component\n${\\mathscr E}_n= R_{abcd} \\varepsilon^{ab} \\varepsilon^{cd}$ of curvature, with\n$\\varepsilon^{ab}$ the bi-normal to the plane of motion: $$ \\tau_{\\rm geod} =\n\\frac{2}{\\sqrt{{-\\mathscr E}_n}} \\sinh ^{-1}\\Biggl[\\sqrt{\\frac{-{\\mathscr\nE}_n}{a^2-{\\mathscr E}_n}} \\sinh \\left(\\frac{1}{2} \\sqrt{a^2-{\\mathscr E}_n} \\;\n\\tau_{\\rm acc} \\right) \\Biggl] $$ For classical clocks, the above result\nrepresents an interesting closed form contribution of tidal curvature to the\ndifferential ageing of twins in the classic $Twin\\;paradox$. For quantum\nprobes, it gives a thermal contribution to the $detector\\;response$ with a\nmodified $Unruh\\;temperature$ $$ [k_{\\rm B} T]_{{\\mathscr E}_n} = \\frac{\\hbar\n\\sqrt{a^2- {\\mathscr E}_n }}{2 \\pi} $$ As an operational tool, the\ncomputational framework we present and the corresponding results should find\napplications to a wide range of physical problems that involve measurements and\nobservations by use of accelerated probes in curved spacetimes.\n"}
+{"abstract": "  Advancing the state-of-the-art in large-scale biomedical semantic indexing\nand question answering is the main focus of the BioASQ challenge. BioASQ\norganizes respective tasks where different teams develop systems that are\nevaluated on the same benchmark datasets that represent the real information\nneeds of experts in the biomedical domain. This paper presents an overview of\nthe ninth edition of the BioASQ challenge in the context of the Conference and\nLabs of the Evaluation Forum (CLEF) 2021. In this year, a new question\nanswering task, named Synergy, is introduced to support researchers studying\nthe COVID-19 disease and measure the ability of the participating teams to\ndiscern information while the problem is still developing. In total, 42 teams\nwith more than 170 systems were registered to participate in the four tasks of\nthe challenge. The evaluation results, similarly to previous years, show a\nperformance gain against the baselines which indicates the continuous\nimprovement of the state-of-the-art in this field.\n"}
+{"abstract": "  A lot of work has been done towards reconstructing the 3D facial structure\nfrom single images by capitalizing on the power of Deep Convolutional Neural\nNetworks (DCNNs). In the recent works, the texture features either correspond\nto components of a linear texture space or are learned by auto-encoders\ndirectly from in-the-wild images. In all cases, the quality of the facial\ntexture reconstruction is still not capable of modeling facial texture with\nhigh-frequency details. In this paper, we take a radically different approach\nand harness the power of Generative Adversarial Networks (GANs) and DCNNs in\norder to reconstruct the facial texture and shape from single images. That is,\nwe utilize GANs to train a very powerful facial texture prior \\edit{from a\nlarge-scale 3D texture dataset}. Then, we revisit the original 3D Morphable\nModels (3DMMs) fitting making use of non-linear optimization to find the\noptimal latent parameters that best reconstruct the test image but under a new\nperspective. In order to be robust towards initialisation and expedite the\nfitting process, we propose a novel self-supervised regression based approach.\nWe demonstrate excellent results in photorealistic and identity preserving 3D\nface reconstructions and achieve for the first time, to the best of our\nknowledge, facial texture reconstruction with high-frequency details.\n"}
+{"abstract": "  Narrow beams are key to wireless communications in millimeter wave frequency\nbands. Beam alignment (BA) allows the base station (BS) to adjust the direction\nand width of the beam used for communication. During BA, the BS transmits a\nnumber of scanning beams covering different angular regions. The goal is to\nminimize the expected width of the uncertainty region (UR) that includes the\nangle of departure of the user. Conventionally, in interactive BA, it is\nassumed that the feedback corresponding to each scanning packet is received\nprior to transmission of the next one. However, in practice, the feedback delay\ncould be larger because of propagation or system constraints. This paper\ninvestigates BA strategies that operate under arbitrary fixed feedback delays.\nThis problem is analyzed through a source coding prospective where the feedback\nsequences are viewed as source codewords. It is shown that these codewords form\na codebook with a particular characteristic which is used to define a new class\nof codes called d-unimodal codes. By analyzing the properties of these codes, a\nlower bound on the minimum achievable expected beamwidth is provided. The\nresults reveal potential performance improvements in terms of the BA duration\nit takes to achieve a fixed expected width of the UR over the state-of-the-art\nBA methods which do not consider the effect of delay.\n"}
+{"abstract": "  We study two disjoint universes in an entangled pure state. When only one\nuniverse contains gravity, the path integral for the $n^{\\text{th}}$ R\\'enyi\nentropy includes a wormhole between the $n$ copies of the gravitating universe,\nleading to a standard \"island formula\" for entanglement entropy consistent with\nunitarity of quantum information. When both universes contain gravity,\ngravitational corrections to this configuration lead to a violation of\nunitarity. However, the path integral is now dominated by a novel wormhole with\n$2n$ boundaries connecting replica copies of both universes. The analytic\ncontinuation of this contribution involves a quotient by $\\mathbb{Z}_n$ replica\nsymmetry, giving a cylinder connecting the two universes. When entanglement is\nlarge, this configuration has an effective description as a \"swap wormhole\", a\ngeometry in which the boundaries of the two universes are glued together by a\n\"swaperator\". This description allows precise computation of a generalized\nentropy-like formula for entanglement entropy. The quantum extremal surface\ncomputing the entropy lives on the Lorentzian continuation of the cylinder/swap\nwormhole, which has a connected Cauchy slice stretching between the universes\n-- a realization of the ER=EPR idea. The new wormhole restores unitarity of\nquantum information.\n"}
+{"abstract": "  The mechanisms for photodissociation of methyl halides (CH$_3$X, X= Cl, Br,\nI) have been studied for these molecules when adsorbed on thin films of\nC$_6$H$_6$ or C$_6$F$_6$ on copper single crystals, using time-of-flight\nspectroscopy with 248nm and 193nm light. For CH$_3$Cl and CH$_3$Br monolayers\nadsorbed on C$_6$H$_6$, two photodissociation pathways can be identified:\nneutral photodissociation similar to the gas-phase, and a dissociative electron\nattachment (DEA) pathway due to photoelectrons from the metal. The same methyl\nhalides adsorbed on a C$_6$F$_6$ thin film display only neutral\nphotodissociation, with the DEA pathway entirely absent due to intermolecular\nquenching via a LUMO-derived electronic band in the C$_6$F$_6$ thin film. For\nCH$_3$I adsorbed on a C$_6$F$_6$ thin film, illumination with 248nm light\nresults in CH$_3$ photofragments departing due to neutral photodissociation via\nthe A-band absorption. When CH$_3$I monolayers on C$_6$H$_6$ thin films are\nilluminated at the same wavelength, additional new photodissociation pathways\nare observed that are due to absorption in the molecular film with energy\ntransfer leading to dissociation of the CH$_3$I molecules adsorbed on top. The\nproposed mechanism for this photodissociation is via a charge-transfer complex\nfor the C$_6$H$_6$ layer and adsorbed CH$_3$I.\n"}
+{"abstract": "  Let $R$ be an associative ring with unit $1$, and $a, b, c\\in R$ satisfy\n$a(ba)^{2}=abaca=acaba=(ac)^{2}a$, this paper proves that $1-ac$ has\ngeneralized Drazin inverse (Drazin inverse, pseudo Drazin inverse,\nrespectively) if and only if $1-ba$ has generalized Drazin inverse (Drazin\ninverse, pseudo Drazin inverse, respectively). In particular, we obtain new\ncommon spectral properties for $ac$ and $ba$ in Banach algebras. As\napplications, new extension of Jacobson's lemma for B-Fredholm elements and\ngeneralized Fredholm elements in rings is established.\n"}
+{"abstract": "  Understanding dynamics of an outbreak like that of COVID-19 is important in\ndesigning effective control measures. This study aims to develop an agent based\nmodel that compares changes in infection progression by manipulating different\nparameters in a synthetic population. Model input includes population\ncharacteristics like age, sex, working status etc. of each individual and other\nfactors influencing disease dynamics. Depending on number of epicentres of\ninfection, location of primary cases, sensitivity, proportion of asymptomatic\nand frequency or duration of lockdown, our simulator tracks every individual\nand hence infection progression through community over time.\n  In a closed community of 10000 people, it is seen that without any lockdown,\nnumber of cases peak around 6th week and wanes off around 15th week. If primary\ncase is located inside dense population cluster like slums, cases peak early\nand wane off slowly. With introduction of lockdown, cases peak at slower rate.\nIf sensitivity of identifying infection decreases, cases and deaths increase.\nNumber of cases declines with increase in proportion of asymptomatic cases. The\nmodel is robust and provides reproducible estimates with realistic parameter\nvalues. It also guides in identifying measures to control outbreak in a\ncommunity. It is flexible in accommodating different parameters like\ninfectivity period, yield of testing, socio-economic strata, daily travel,\nawareness level, population density, social distancing, lockdown etc. and can\nbe tailored to study other infections with similar transmission pattern.\n"}
+{"abstract": "  In this paper we discuss the Morse potential on a quantum computer. The Morse\npotential is useful to describe diatomic molecules and has a finite number of\nbound states which can be measured through spectroscopy. It is also a example\nof an exactly soluble potential using supersymmetric quantum mechanics. Using\nthe the supersymmetric quantum mechanics formalism one can derive a heirachy of\nHamiltonians such that the ground state of the next rung on the heirarchy yeids\nthe first excited state of the hamiltonian below it. Using this method one can\ndetermine all the states of the Morse potential by calculating all the ground\nstates of the sequence of Hamiltonians in the heirarchy. We use the IBM QISKit\nsoftware together with the Variational Quantum Eiegensolver (VQE) algorithm to\ncalculate the ground state and first excited state energy of the Morse\npotential and find agreement with the exact expression for the bound state\nenergies of the Morse Potential. We analyze different optimizers to study the\nnumerical effect on the calculations. Finally we perform quantum computations\nfor diatomic and triatomic molecules to illustrate the application of these\ntechniques on near term quantum computers and find excellent agreement with\nexperimental data.\n"}
+{"abstract": "  Principal component analysis (PCA) is a classical and ubiquitous method for\nreducing data dimensionality, but it is suboptimal for heterogeneous data that\nare increasingly common in modern applications. PCA treats all samples\nuniformly so degrades when the noise is heteroscedastic across samples, as\noccurs, e.g., when samples come from sources of heterogeneous quality. This\npaper develops a probabilistic PCA variant that estimates and accounts for this\nheterogeneity by incorporating it in the statistical model. Unlike in the\nhomoscedastic setting, the resulting nonconvex optimization problem is not\nseemingly solved by singular value decomposition. This paper develops a\nheteroscedastic probabilistic PCA technique (HePPCAT) that uses efficient\nalternating maximization algorithms to jointly estimate both the underlying\nfactors and the unknown noise variances. Simulation experiments illustrate the\ncomparative speed of the algorithms, the benefit of accounting for\nheteroscedasticity, and the seemingly favorable optimization landscape of this\nproblem. Real data experiments on environmental air quality data show that\nHePPCAT can give a better PCA estimate than techniques that do not account for\nheteroscedasticity.\n"}
+{"abstract": "  This research was motivated by studying anti-drug antibody (ADA) formation\nand its potential impact on long-term benefit of a biologic treatment in a\nrandomized controlled trial, in which ADA status was not only unobserved in the\ncontrol arm but also in a subset of patients from the experimental treatment\narm. Recent literature considers the principal stratum estimand strategy to\nestimate treatment effect in groups of patients defined by an intercurrent\nstatus, i.e. in groups defined by a post-randomization variable only observed\nin one arm and potentially associated with the outcome. However, status\ninformation might be missing even for a non-negligible number of patients in\nthe experimental arm. For this setting, a novel weighted principal stratum\napproach is presented: Data from patients with missing intercurrent event\nstatus were re-weighted based on baseline covariates and additional\nlongitudinal information. A theoretical justification of the proposed approach\nis provided for different types of outcomes, and assumptions allowing for\ncausal conclusions on treatment effect are specified and investigated.\nSimulations demonstrated that the proposed method yielded valid inference and\nwas robust against certain violations of assumptions. The method was shown to\nperform well in a clinical study with ADA status as an intercurrent event.\n"}
+{"abstract": "  A thermophysical model was developed to test the viability of 57 candidate\nhollow-forming volatiles within the hollow-formation model framework of Blewett\net al. (2013). We find that the thermophysical properties of elemental sulfur\n(S) combined with the abundance of S on Mercury, make it the most likely\nhollow-forming volatile explored in this study. We propose a novel model for\nhollow formation in which a subsurface heat source drives sulfur-rich systems\nthat deposit volatiles (importantly, S) in the near-surface at night within a\n\"sulfur permafrost zone\", and daytime solar heating drives sublimation to form\nhollows.\n"}
+{"abstract": "  Recent advances in appearance-based models have shown improved eye tracking\nperformance in difficult scenarios like occlusion due to eyelashes, eyelids or\ncamera placement, and environmental reflections on the cornea and glasses. The\nkey reason for the improvement is the accurate and robust identification of eye\nparts (pupil, iris, and sclera regions). The improved accuracy often comes at\nthe cost of labeling an enormous dataset, which is complex and time-consuming.\nThis work presents two semi-supervised learning frameworks to identify\neye-parts by taking advantage of unlabeled images where labeled datasets are\nscarce. With these frameworks, leveraging the domain-specific augmentation and\nnovel spatially varying transformations for image segmentation, we show\nimproved performance on various test cases. For instance, for a model trained\non just 48 labeled images, these frameworks achieved an improvement of 0.38%\nand 0.65% in segmentation performance over the baseline model, which is trained\nonly with the labeled dataset.\n"}
+{"abstract": "  We present Ly$\\alpha$ and ultraviolet-continuum (UV) luminosity functions\n(LFs) of galaxies and active galactic nuclei (AGN) at $z=2.0-3.5$ determined by\nthe un-targetted optical spectroscopic survey of the Hobby-Eberly Telescope\nDark Energy Experiment (HETDEX). We combine deep Subaru imaging with HETDEX\nspectra resulting in $11.4$ deg$^2$ of fiber-spectra sky coverage, obtaining\n$18320$ galaxies spectroscopically identified with Ly$\\alpha$ emission, $2126$\nof which host type 1 AGN showing broad (FWHM$~>1000$ km s$^{-1}$) Ly$\\alpha$\nemission lines. We derive the Ly$\\alpha$ (UV) LF over 2 orders of magnitude\ncovering bright galaxies and AGN in $\\log\nL_\\mathrm{Ly\\alpha}/\\mathrm{[erg~s^{-1}]}=43.3-45.5$ ($-27<M_\\mathrm{UV}<-20$)\nby the $1/V_\\mathrm{max}$ estimator. Our results reveal the bright-end hump of\nthe Ly$\\alpha$ LF is composed of type 1 AGN. In conjunction with previous\nspectroscopic results at the faint end, we measure a slope of the best-fit\nSchechter function to be $\\alpha_\\mathrm{Sch}=-1.70^{+0.13}_{-0.14}$, which\nindicates $\\alpha_\\mathrm{Sch}$ steepens from $z=2-3$ towards high redshift.\nOur UV LF agrees well with previous AGN UV LFs, and extends to faint-AGN and\nbright-galaxy regimes. The number fraction of Ly$\\alpha$-emitting objects\n($X_\\mathrm{LAE}$) increases from $M_\\mathrm{UV}^*\\sim-21$ to bright magnitude\ndue to the contribution of type 1 AGN, while previous studies claim that\n$X_\\mathrm{Ly\\alpha}$ decreases from faint magnitude to $M_\\mathrm{UV}^*$,\nsuggesting a valley in the $X_\\mathrm{Ly\\alpha}-$magnitude relation at\n$M_\\mathrm{UV}^*$. Comparing our UV LF of type 1 AGN at $z=2-3$ with those at\n$z=0$, we find that the number density of faint ($M_\\mathrm{UV}>-21$) type 1\nAGN increases from $z\\sim2$ to $z\\sim0$ as opposed to the evolution of bright\n($M_\\mathrm{UV}<-21$) type 1 AGN, suggesting the AGN downsizing in the\nrest-frame UV luminosity.\n"}
+{"abstract": "  The old, solar metallicity open cluster Messier 67 has long been considered a\nlynchpin in the study and understanding of the structure and evolution of\nsolar-type stars. The same is arguably true for stellar remnants - the white\ndwarf population of M67 provides crucial observational data for understanding\nand interpreting white dwarf populations and evolution. In this work, we\ndetermine the white dwarf masses and derive their progenitor star masses using\nhigh signal-to-noise spectroscopy of warm ($\\gtrsim10,000$ K) DA white dwarfs\nin the cluster. From this we are able to derive each white dwarf's position on\nthe initial-final mass relation, with an average $M_{\\mathrm WD} = 0.60\\pm 0.01\nM_{\\odot}$ and progenitor mass $M_i = 1.52\\pm 0.04 M_{\\odot}$. These values are\nfully consistent with recently published linear and piecewise linear fits to\nthe semi-empirical initial-final mass relation and provide a crucial, precise\nanchor point for the initial-final mass relation for solar-metallicity,\nlow-mass stars. The mean mass of M67 white dwarfs is also consistent with the\nsharp narrow peak in the local field white dwarf mass distribution, indicating\nthat a majority of recently-formed field white dwarfs come from stars with\nprogenitor masses of $\\approx 1.5 M_{\\odot}$. Our results enable more precise\nmodeling of the Galactic star formation rate encoded in the field WD mass\ndistribution.\n"}
+{"abstract": "  Optimization in the latent space of variational autoencoders is a promising\napproach to generate high-dimensional discrete objects that maximize an\nexpensive black-box property (e.g., drug-likeness in molecular generation,\nfunction approximation with arithmetic expressions). However, existing methods\nlack robustness as they may decide to explore areas of the latent space for\nwhich no data was available during training and where the decoder can be\nunreliable, leading to the generation of unrealistic or invalid objects. We\npropose to leverage the epistemic uncertainty of the decoder to guide the\noptimization process. This is not trivial though, as a naive estimation of\nuncertainty in the high-dimensional and structured settings we consider would\nresult in high estimator variance. To solve this problem, we introduce an\nimportance sampling-based estimator that provides more robust estimates of\nepistemic uncertainty. Our uncertainty-guided optimization approach does not\nrequire modifications of the model architecture nor the training process. It\nproduces samples with a better trade-off between black-box objective and\nvalidity of the generated samples, sometimes improving both simultaneously. We\nillustrate these advantages across several experimental settings in digit\ngeneration, arithmetic expression approximation and molecule generation for\ndrug design.\n"}
+{"abstract": "  We derive an invariance principle for the lift to the rough path topology of\nstochastic processes with delayed regenerative increments under an optimal\nmoment condition. An interesting feature of the result is the emergence of area\nanomaly, a correction term in the second level of the limiting rough path which\nis identified as the average stochastic area on a regeneration interval. A few\napplications include random walks in random environment and additive\nfunctionals of recurrent Markov chains. The result is formulated in the\np-variation settings, where a rough Donsker Theorem is available under the\nsecond moment condition. The key renewal theorem is applied to obtain an\noptimal moment condition.\n"}
+{"abstract": "  A group-theoretical structure in a perturbative expansion of the Wilson loops\nin the 3d Chern-Simons theory with $SU(N)$ gauge group is studied in symmetric\napproach. A special basis in the center of the universal enveloping algebra\n$ZU(\\mathfrak{sl}_N)$ is introduced. This basis allows one to present group\nfactors in an arbitrary irreducible finite-dimensional representation.\nDeveloped methods have wide applications, the most straightforward and evident\nones are mentioned. Namely, Vassiliev invariants of higher orders are computed,\na conjecture about existence of new symmetries of the colored HOMFLY\npolynomials is stated, and the recently discovered tug-the-hook symmetry of the\ncolored HOMFLY polynomial is proved.\n"}
+{"abstract": "  We investigate large-scale latent variable models (LVMs) for neural story\ngeneration -- an under-explored application for open-domain long text -- with\nobjectives in two threads: generation effectiveness and controllability. LVMs,\nespecially the variational autoencoder (VAE), have achieved both effective and\ncontrollable generation through exploiting flexible distributional latent\nrepresentations. Recently, Transformers and its variants have achieved\nremarkable effectiveness without explicit latent representation learning, thus\nlack satisfying controllability in generation. In this paper, we advocate to\nrevive latent variable modeling, essentially the power of representation\nlearning, in the era of Transformers to enhance controllability without hurting\nstate-of-the-art generation effectiveness. Specifically, we integrate latent\nrepresentation vectors with a Transformer-based pre-trained architecture to\nbuild conditional variational autoencoder (CVAE). Model components such as\nencoder, decoder and the variational posterior are all built on top of\npre-trained language models -- GPT2 specifically in this paper. Experiments\ndemonstrate state-of-the-art conditional generation ability of our model, as\nwell as its excellent representation learning capability and controllability.\n"}
+{"abstract": "  Two general constructions of linear codes with functions over finite fields\nhave been extensively studied in the literature. The first one is given by\n$\\mathcal{C}(f)=\\left\\{ {\\rm Tr}(af(x)+bx)_{x \\in \\mathbb{F}_{q^m}^*}: a,b \\in\n\\mathbb{F}_{q^m} \\right\\}$, where $q$ is a prime power, $\\bF_{q^m}^*=\\bF_{q^m}\n\\setminus \\{0\\}$, $\\tr$ is the trace function from $\\bF_{q^m}$ to $\\bF_q$, and\n$f(x)$ is a function from $\\mathbb{F}_{q^m}$ to $\\mathbb{F}_{q^m}$ with\n$f(0)=0$. Almost bent functions, quadratic functions and some monomials on\n$\\bF_{2^m}$ were used in the first construction, and many families of binary\nlinear codes with few weights were obtained in the literature. This paper\nstudies some punctured codes of these binary codes. Several families of binary\nlinear codes with few weights and new parameters are obtained in this paper.\nSeveral families of distance-optimal binary linear codes with new parameters\nare also produced in this paper.\n"}
+{"abstract": "  Spherical nanoparticles (NPs) of size 14 nm, made of intermetallic Fe2CoAl\n(FCA) Heusler alloy, are synthesized via the co-precipitation and thermal\ndeoxidization method. X-ray diffraction (XRD) and selected area electron\ndiffraction (SAED) patterns confirm that the present nanoalloy is crystallized\nin A2-disordered cubic Heusler structure. Magnetic field (H) and temperature\n(T) dependent magnetization (M) results reveal that the NPs are soft\nferromagnetic (FM) with high saturation magnetization (Ms) and Curie\ntemperature (Tc). Fe2CoAl nanoalloy does not follow the Slater Pauling (SP)\nrule, possibly because of the disorder present in the system. We also\ninvestigate its magnetic phase transition (MPT) and magnetocaloric (MC)\nproperties. The peak value of the magnetic entropy change vs T curve at a\nmagnetic field change of 20 kOe corresponds to about 2.65 J/kg-K, and the\nobserved value of refrigeration capacity (RCP) is as large as 44 J/kg,\nsuggesting a large heat conversion in magnetic refrigeration cycle. The Arrott\nplot and the nature of the universal curve accomplish that the FM to\nparamagnetic (PM) phase transition in Fe2CoAl nanoalloy is of second-order. The\npresent study suggests that the Fe2CoAl nanoscale system is proficient, useful\nand a good candidate for the spintronics application and opens up a window for\nfurther research on full-Heusler based magnetic refrigerants.\n"}
+{"abstract": "  The purpose of the article is to generalize the concept of approximate\nBirkhoff-James orthogonality, in the semi-Hilbertian structure. Given a\npositive operator $ A $ on a Hilbert space $ \\mathbb{H}, $ we define $\n(\\epsilon,A)- $approximate orthogonality and $ (\\epsilon,A)- $approximate\northogonality in the sense of Chmieli$\\acute{n}$ski and establish a relation\nbetween them. We also characterize $ (\\epsilon,A)- $approximate orthogonality\nin the sense of Chmieli$\\acute{n}$ski for $A$-bounded and $A$-bounded compact\noperators. We further generalize the concept of right symmetric and left\nsymmetric operators on a Hilbert space. The utility of these notions are\nillustrated by extending some of the previous results obtained by various\nauthors in the setting of Hilbert spaces.\n"}
+{"abstract": "  Cyberbullying or Online harassment detection on social media for various\nmajor languages is currently being given a good amount of focus by researchers\nworldwide. Being the seventh most speaking language in the world and increasing\nusage of online platform among the Bengali speaking people urge to find\neffective detection technique to handle the online harassment. In this paper,\nwe have proposed binary and multiclass classification model using hybrid neural\nnetwork for bully expression detection in Bengali language. We have used 44,001\nusers comments from popular public Facebook pages, which fall into five classes\n- Non-bully, Sexual, Threat, Troll and Religious. We have examined the\nperformance of our proposed models from different perspective. Our binary\nclassification model gives 87.91% accuracy, whereas introducing ensemble\ntechnique after neural network for multiclass classification, we got 85%\naccuracy.\n"}
+{"abstract": "  Quasi-conformal models are an appealing scenario that can offer naturally a\nstrongly supercooled phase transition and a period of thermal inflation in the\nearly Universe. A crucial aspect for the viability of these models is how the\nUniverse escapes from the supercooled state. One possibility is that thermal\ninflation phase ends by nucleation and percolation of true vacuum bubbles. This\nroute is not, however, always efficient. In such case another escape mechanism,\nbased on the growth of quantum fluctuations of the scalar field that eventually\ndestabilize the false vacuum, becomes relevant. We study both of these cases in\ndetail in a simple yet representative model. We determine the duration of the\nthermal inflation, the curvature power spectrum generated for the scales that\nexit horizon during the thermal inflation, and the stochastic gravitational\nwave background from the phase transition. We show that these gravitational\nwaves provide an observable signal from the thermal inflation in almost the\nentire parameter space of interest. Furthermore, the shape of the gravitational\nwave spectrum can be used to ascertain how the Universe escaped from\nsupercooling.\n"}
+{"abstract": "  Element-selective techniques are central for the understanding of ultrafast\nspin dynamics in multi-element materials like magnetic alloys. Recently,\nthough, it turned out that the commonly used technique of transverse\nmagneto-optical Kerr effect (T-MOKE) in the EUV range may have linearity issues\nincluding unwanted cross talk between different elemental signals. This problem\ncan be sizeable, which puts recent observations of ultrafast spin transfer from\nFe to Ni sites in FeNi alloys into question. In this study, we investigate the\nFe-to-Ni spin transfer in a cross-talk-free time-resolved X-ray magnetic\ncircular dichroism (XMCD) experiment with a reliable time reference. We find a\nvery similar Fe and Ni dynamics with XMCD as with T-MOKE from identical\nsamples. Considering the non-linearities of the T-MOKE response, the agreement\nwith our findings appears fortuitous. We discuss possible reasons why T-MOKE\nseems to give accurate results in this case. Our data provide the ongoing\ndiscussion about ultrafast spin-transfer mechanisms in FeNi systems with a\nsound experimental basis.\n"}
+{"abstract": "  A three-way (resp., two-way) two-dimensional automaton has a read-only input\nhead that moves in three (resp., two) directions on a finite array of cells\nlabelled by symbols of the input alphabet. Restricting the input head movement\nof a two-dimensional automaton results in a model that is weaker in terms of\nrecognition power.\n  In this paper, we introduce the notion of \"degrees of restriction\" for\ntwo-dimensional automata, and we develop sets of extended two-dimensional\nautomaton models that allow for some bounded number of restricted moves. We\nestablish recognition hierarchies for both deterministic and nondeterministic\nextended three-way two-dimensional automata, and we find similar hierarchies\nfor both deterministic and nondeterministic extended two-way two-dimensional\nautomata. We also prove incomparability results between nondeterministic and\ndeterministic extended three-way two-dimensional automata. Lastly, we consider\nclosure properties for some operations on languages recognized by extended\nthree-way two-dimensional automata.\n"}
+{"abstract": "  We present examples of nearly integrable analytic Hamiltonian systems with\nseveral strong diffusion properties: topological weak mixing and diffusion at\nall times. These examples are obtained by AbC constructions with several\nfrequencies.\n"}
+{"abstract": "  This paper describes an application of the Cartographer graph SLAM stack as a\npose sensor in a UAV feedback control loop, with certain application-specific\nchanges in the SLAM stack such as smoothing of the optimized pose. Pose\nestimation is performed by fusing 3D LiDAR/IMU-based proprioception with GPS\nposition measurements by means of pose graph optimisation. Moreover, partial\nenvironment maps built from the LiDAR data (submaps) within the Cartographer\nSLAM stack are marshalled into OctoMap, an Octree-based voxel map\nimplementation. The OctoMap is further used for navigation tasks such as path\nplanning and obstacle avoidance.\n"}
+{"abstract": "  Many schools in large urban districts have more applicants than seats.\nCentralized school assignment algorithms ration seats at over-subscribed\nschools using randomly assigned lottery numbers, non-lottery tie-breakers like\ntest scores, or both. The New York City public high school match illustrates\nthe latter, using test scores and other criteria to rank applicants at\n``screened'' schools, combined with lottery tie-breaking at unscreened\n``lottery'' schools. We show how to identify causal effects of school\nattendance in such settings. Our approach generalizes regression discontinuity\nmethods to allow for multiple treatments and multiple running variables, some\nof which are randomly assigned. The key to this generalization is a local\npropensity score that quantifies the school assignment probabilities induced by\nlottery and non-lottery tie-breakers. The local propensity score is applied in\nan empirical assessment of the predictive value of New York City's school\nreport cards. Schools that receive a high grade indeed improve SAT math scores\nand increase graduation rates, though by much less than OLS estimates suggest.\nSelection bias in OLS estimates is egregious for screened schools.\n"}
+{"abstract": "  For Cyper-Physical Production Systems (CPPS), localization is becoming\nincreasingly important as wireless and mobile devices are considered an\nintegral part. While localizing targets in a wireless communication system\nbased on the Received Signal Strength Indicators (RSSIs) is a usual solution,\nit is limited by sensor quality. We consider the scenario of a car moving in\nand out of a chamber and propose to use a particle filter for sensor fusion,\nallowing us to incorporate non-idealities in our model and achieve a\nhigh-quality position estimate. Then, we use Machine Learning (ML) to classify\nthe vehicle position. Our results show that the location output of the particle\nfilter is a better input to the classifiers than the raw RSSI data, and we\nachieve improved accuracy while simultaneously reducing the number of features\nthat the ML has to consider. We also compare the performance of multiple ML\nalgorithms and show that SVMs provide the overall best performance for the\ngiven task.\n"}
+{"abstract": "  Despite the surging development and utilization of IoT devices, the security\nof IoT devices is still in infancy. The security pitfalls of IoT devices have\nmade it easy for hackers to take over IoT devices and use them for malicious\nactivities like botnet attacks. With the rampant emergence of IoT devices,\nbotnet attacks are surging. The botnet attacks are not only catastrophic for\nIoT device users but also for the rest of the world. Therefore, there is a\ncrucial need to identify and mitigate the possible threats in IoT devices\nduring the design phase. Threat modelling is a technique that is used to\nidentify the threats in the earlier stages of the system design activity. In\nthis paper, we propose a threat modelling approach to analyze and mitigate the\nbotnet attacks in an IoT smart home use case. The proposed methodology\nidentifies the development-level and application-level threats in smart home\nuse case using STRIDE and VAST threat modelling methods. Moreover, we\nreticulate the identified threats with botnet attacks. Finally, we propose the\nmitigation techniques for all identified threats including the botnet threats.\n"}
+{"abstract": "  In this paper, we present an algorithmic approach to design and construct\nplanar truss structures based on symmetric lattices using modular elements. The\nmethod of assembly is similar to Leonardo grids as they both rely on the\nproperty of interlocking. In theory, our modular elements can be assembled by\nthe same type of binary operations. Our modular elements embody the principle\nof geometric interlocking, a principle recently introduced in literature that\nallows for pieces of an assembly to be interlocked in a way that they can\nneither be assembled nor disassembled unless the pieces are subjected to\ndeformation or breakage. We demonstrate that breaking the pieces can indeed\nfacilitate the effective assembly of these pieces through the use of a simple\nkey-in-hole concept. As a result, these modular elements can be assembled\ntogether to form an interlocking structure, in which the locking pieces apply\nthe force necessary to hold the entire assembly together.\n"}
+{"abstract": "  Epidemic models often reflect characteristic features of infectious spreading\nprocesses by coupled non-linear differential equations considering different\nstates of health (such as Susceptible, Infected, or Recovered). This\ncompartmental modeling approach, however, delivers an incomplete picture of the\ndynamics of epidemics, as it neglects stochastic and network effects, and also\nthe role of the measurement process, on which the estimation of epidemiological\nparameters and incidence values relies. In order to study the related issues,\nwe extend established epidemiological spreading models with a model of the\nmeasurement (i.e. testing) process, considering the problems of false positives\nand false negatives as well as biased sampling. Studying a model-generated\nground truth in conjunction with simulated observation processes (virtual\nmeasurements) allows one to gain insights into the limitations of purely\ndata-driven methods to assess the epidemic situation. We conclude that epidemic\nmonitoring, simulation, and forecasting are wicked problems, as applying a\nconventional data-driven approach to a complex system with non-linear dynamics,\nnetwork effects, and uncertainty can be misleading. Nevertheless, some of the\nerrors can be corrected for, using scientific knowledge of the spreading\ndynamics and the measurement process. We conclude that such corrections should\ngenerally be part of epidemic monitoring, modeling, and forecasting efforts.\n"}
+{"abstract": "  We focus on the development of diagnostic tools and an R package called MNB\nfor a multivariate negative binomial (MNB) regression model for detecting\natypical and influential subjects. The MNB model is deduced from a Poisson\nmixed model in which the random intercept follows the generalized log-gamma\n(GLG) distribution. The MNB model for correlated count data leads to an MNB\nregression model that inherits the features of a hierarchical model to\naccommodate the intraclass correlation and the occurrence of overdispersion\nsimultaneously. The asymptotic consistency of the dispersion parameter\nestimator depends on the asymmetry of the GLG distribution. Inferential\nprocedures for the MNB regression model are simple, although it can provide\ninconsistent estimates of the asymptotic variance when the correlation\nstructure is misspecified. We propose the randomized quantile residual for\nchecking the adequacy of the multivariate model, and derive global and local\ninfluence measures from the multivariate model to assess influential subjects.\nFinally, two applications are presented in the data analysis section. The code\nfor installing the MNB package and the code used in the two examples is\nexhibited in the Appendix.\n"}
+{"abstract": "  Annihilation dynamics plays a fundamental role in the baryon$-$antibaryon\ninteraction (B$-\\rm{\\overline{B}}$) at low-energy and its strength and range\nare crucial in the assessment of possible baryon bound states. Experimental\ndata on annihilation cross sections are available for the p$-\\rm{\\overline{p}}$\nsystem but not in the low relative momentum region. Data regarding the\nB$\\rm{\\overline{B}}$ interaction with strange degrees of freedom are extremely\nscarce or absent, hence the modeling of the annihilation contributions is\nmainly based on nucleon$-$antinucleon (N$-\\rm{\\overline{N}}$) results, when\navailable. In this letter we present a measurement of the\np$-\\rm{\\overline{p}}$, p$-\\overline{\\Lambda}\\oplus\\rm{\\overline{p}}-\\Lambda$\nand $\\Lambda-\\overline{\\Lambda}$ interaction using correlation functions in the\nrelative momentum space in high-multiplicity triggered pp collisions at\n$\\sqrt{s} = 13$ TeV recorded by ALICE at the LHC. In the p$-\\rm{\\overline{p}}$\nsystem the couplings to the mesonic channels in different partial waves are\nextracted by adopting a coupled-channel approach with recent $\\chi\\rm{EFT}$\npotentials. The inclusion of these inelastic channels provides good agreement\nwith the data, showing a significant presence of the annihilation term down to\nzero momentum. Predictions obtained using the Lednick\\'y$-$Lyuboshits formula\nand scattering parameters obtained from heavy-ion collisions, hence mainly\nsensitive to elastic processes, are compared with the experimental\np$-\\overline{\\Lambda}\\oplus\\rm{\\overline{p}}-\\Lambda$ and\n$\\Lambda-\\overline{\\Lambda}$ correlations. The model describes the\n$\\Lambda-\\overline{\\Lambda}$ data and underestimates the\np$-\\overline{\\Lambda}\\oplus\\rm{\\overline{p}}-\\Lambda$ data in the region of\nmomenta below 200 MeV/$c$. The observed deviation indicates a different\ncontribution of annihilation channels to the two systems containing strange\nhadrons.\n"}
+{"abstract": "  For a monoidal $\\infty$-category $\\mathcal{M}$ with colimits, we study\ncolimits of $\\mathcal{M}$-functors $\\mathcal{A}\\to\\mathcal{B}$ where\n$\\mathcal{B}$ is left-tensored over $\\mathcal{M}$ and $\\mathcal{A}$ is an\n$\\mathcal{M}$-enriched category. We prove that the enriched Yoneda embedding\n$Y:\\mathcal{A}\\to P_{\\mathcal{M}}(\\mathcal{A})$ yields a universal\n$\\mathcal{M}$-functor and, in the case when $\\mathcal{A}$ has a certain\nmonoidal structure, the category of enriched presheaves\n$P_{\\mathcal{M}}(\\mathcal{A})$ inherits the same monoidal structure.\n"}
+{"abstract": "  A model to determine the dose rate of a planar alpha-emitting surface, has\nbeen developed. The approach presented is a computationally efficient\nmathematical model using stopping range data from the Stopping Ranges of Ions\nin Matter (SRIM) software. The alpha dose rates as a function of distance from\nirradiated UO2 spent fuel surfaces were produced for bench-marking with\nprevious modelling attempts. This method is able to replicate a Monte Carlo\n(MCNPX) study of an irradiated UO2 fuel surface within 0.6 % of the resulting\ntotal dose rate and displays a similar dose profile.\n"}
+{"abstract": "  The D68 ringlet is the innermost feature in Saturn's rings. Four clumps that\nappeared in D68 around 2014 remained evenly spaced about 30 degrees apart and\nmoved very slowly relative to each other from 2014 up until the last\nmeasurements were taken in 2017. D68's narrowness and the distribution of\nclumps could either indicate that we have a collection of source bodies in a\nco-orbital configuration or imply that an outside force confines the observed\ndust and any source bodies. In this paper we explore the possibility that these\nfour clumps arose from four source bodies in a co-orbital configuration. We\nfind that there are no solutions with four masses that produce the observed\nspacings. We therefore consider whether an unseen fifth co-orbital object could\naccount for the discrepancies in the angular separations and approach a stable\nstationary configuration. We find a range of solutions for five co-orbital\nobjects where their mass ratios depend on the assumed location of the fifth\nmass. Numerical simulations of five co-orbitals are highly sensitive to initial\nconditions, especially for the range of masses we would expect the D68 clumps\nto have. The fragility of our D68 co-orbital system model implies that there is\nprobably some outside force confining the material in this ringlet.\n"}
+{"abstract": "  Discovering speaker independent acoustic units purely from spoken input is\nknown to be a hard problem. In this work we propose an unsupervised speaker\nnormalization technique prior to unit discovery. It is based on separating\nspeaker related from content induced variations in a speech signal with an\nadversarial contrastive predictive coding approach. This technique does neither\nrequire transcribed speech nor speaker labels, and, furthermore, can be trained\nin a multilingual fashion, thus achieving speaker normalization even if only\nfew unlabeled data is available from the target language. The speaker\nnormalization is done by mapping all utterances to a medoid style which is\nrepresentative for the whole database. We demonstrate the effectiveness of the\napproach by conducting acoustic unit discovery with a hidden Markov model\nvariational autoencoder noting, however, that the proposed speaker\nnormalization can serve as a front end to any unit discovery system.\nExperiments on English, Yoruba and Mboshi show improvements compared to using\nnon-normalized input.\n"}
+{"abstract": "  A beautiful idea about the incompatibility of Physical Context(IPC) was\nintroduced in Phys. Rev. A 102, 050201(R) (2020). Here, a context is defined as\na set of a quantum state and two sharp rank-one measurements, and the\nincompatibility of physical context is defined as the leakage of information\nwhile implementing those two measurements successively in that quantum state.\nIn this work, we show the limitations in their approach. The three primary\nlimitations are that, (i) their approach is not positive operator-valued\nmeasurements and (ii), they restrict information-theoretic agents Alice, Eve\nand Bob to specific quantum operations and do not consider most general quantum\noperations i.e., quantum instruments and (iii), their measure of IPC can take\nnegative values in specific cases in a more general scenario which implies the\nlimitation of their information measure. Thereby, we have introduced a\ngeneralization and modification to their approach in more general and\nconvenient way, such that this idea is well-defined for generic measurements,\nwithout these limitations. We also present a comparison of the measure of the\nIPC through their and our method. Lastly, we show, how the IPC reduces in the\npresence of memory using our modification, which further validates our\napproach.\n"}
+{"abstract": "  Generative models are typically trained on grid-like data such as images. As\na result, the size of these models usually scales directly with the underlying\ngrid resolution. In this paper, we abandon discretized grids and instead\nparameterize individual data points by continuous functions. We then build\ngenerative models by learning distributions over such functions. By treating\ndata points as functions, we can abstract away from the specific type of data\nwe train on and construct models that are agnostic to discretization. To train\nour model, we use an adversarial approach with a discriminator that acts on\ncontinuous signals. Through experiments on a wide variety of data modalities\nincluding images, 3D shapes and climate data, we demonstrate that our model can\nlearn rich distributions of functions independently of data type and\nresolution.\n"}
+{"abstract": "  Industry 4.0 introduces new use cases, with more and more mobile devices\nappearing in the industrial landscape. These applications require both new\ntechnologies and smooth integration into existing brownfield deployments.\nEmerging mobile use cases can be divided into optional mobile and mandatory\nmobile, where the first point considers the use of wireless communications due\nto soft criteria such as cost savings and the second means use cases that\ncannot be covered by wireline technologies due to their movement, such as AGVs.\nFor most industrial applications, high determinism, E2E latency and\nsynchronicity are most important. Therefore, we provide a common table, based\non these requirements, listing both existing and emerging mobile use cases.\nSince time synchronization is particularly demanding for wireless use cases, we\npropose a concept for a simple but precise synchronization in IEEE 802.11 WLAN\nand a suitable integration using TSN in combination with OPC UA technology as\nexamples. Furthermore, the concept is evaluated with the help of a testbed\nutilizing state-of-the-art hardware. This means that this concept can be\ndirectly applied in existing industry solutions. It can be shown that the\nconcept is already suitable for a wide range of the mandatory mobile\napplications.\n"}
+{"abstract": "  Cardiovascular diseases and heart failures in particular are the main cause\nof non-communicable disease mortality in the world. Constant patient monitoring\nenables better medical treatment as it allows practitioners to react on time\nand provide the appropriate treatment. Telemedicine can provide constant remote\nmonitoring so patients can stay in their homes, only requiring medical sensing\nequipment and network connections. A limiting factor for telemedical centers is\nthe amount of patients that can be monitored simultaneously. We aim to increase\nthis amount by implementing a decision support system. This paper investigates\na machine learning model to estimate a risk score based on patient vital\nparameters that allows sorting all cases every day to help practitioners focus\ntheir limited capacities on the most severe cases. The model we propose reaches\nan AUCROC of 0.84, whereas the baseline rule-based model reaches an AUCROC of\n0.73. Our results indicate that the usage of deep learning to improve the\nefficiency of telemedical centers is feasible. This way more patients could\nbenefit from better health-care through remote monitoring.\n"}
+{"abstract": "  Molecular dynamics calculations of inelastic collisions of atomic oxygen with\nmolecular nitrogen are known to show orders of magnitude discrepancies with\nexperimental results in the range from room temperature to many thousands of\ndegrees Kelvin. In this work, we have achieved an unprecedented quantitative\nagreement with experiments even at low temperature, by including a\nnon-adiabatic treatment involving vibronic states on newly developed potential\nenergy surfaces. This result paves the way to the calculation of accurate and\ndetailed databases of vibrational energy exchange rates for this collisional\nsystem. This is bound to have an impact on air plasma simulations in a wide\nrange of conditions and on the development of Very Low Earth Orbit (VLEO)\nsatellites, operating in the low thermosphere, objects of great technological\ninterest due to their potential at a competitive cost.\n"}
+{"abstract": "  We present a unifying framework in which both the $\\nu$-Tamari lattice,\nintroduced by Pr\\'eville-Ratelle and Viennot, and principal order ideals in\nYoung's lattice indexed by lattice paths $\\nu$, are realized as the dual graphs\nof two combinatorially striking triangulations of a family of flow polytopes\nwhich we call the $\\nu$-caracol flow polytopes. The first triangulation gives a\nnew geometric realization of the $\\nu$-Tamari complex introduced by Ceballos,\nPadrol and Sarmiento. We use the second triangulation to show that the\n$h^*$-vector of the $\\nu$-caracol flow polytope is given by the $\\nu$-Narayana\nnumbers, extending a result of M\\'esz\\'aros when $\\nu$ is a staircase lattice\npath. Our work generalizes and unifies results on the dual structure of two\nsubdivisions of a polytope studied by Pitman and Stanley.\n"}
+{"abstract": "  Video object segmentation, aiming to segment the foreground objects given the\nannotation of the first frame, has been attracting increasing attentions. Many\nstate-of-the-art approaches have achieved great performance by relying on\nonline model updating or mask-propagation techniques. However, most online\nmodels require high computational cost due to model fine-tuning during\ninference. Most mask-propagation based models are faster but with relatively\nlow performance due to failure to adapt to object appearance variation. In this\npaper, we are aiming to design a new model to make a good balance between speed\nand performance. We propose a model, called NPMCA-net, which directly localizes\nforeground objects based on mask-propagation and non-local technique by\nmatching pixels in reference and target frames. Since we bring in information\nof both first and previous frames, our network is robust to large object\nappearance variation, and can better adapt to occlusions. Extensive experiments\nshow that our approach can achieve a new state-of-the-art performance with a\nfast speed at the same time (86.5% IoU on DAVIS-2016 and 72.2% IoU on\nDAVIS-2017, with speed of 0.11s per frame) under the same level comparison.\nSource code is available at https://github.com/siyueyu/NPMCA-net.\n"}
+{"abstract": "  Turbulent mixing layers in nature are often characterized by the presence of\na mean shear and an unstable buoyancy gradient between two streams of different\nvelocity. Depending on the relative strength of shear versus buoyancy, either\nthe former or the latter may dominate the turbulence and mixing between the two\nstreams. In this paper, we present a phenomenological theory that leads to the\nidentification of two distinct turbulent regimes: an early regime, dominated by\nthe mean shear, and a later regime dominated by the buoyancy. The main\ntheoretical result consists of the identification of a cross-over time-scale\nthat discerns between the shear- and the buoyancy-dominated turbulence. This\ncross-over time depends on three large-scale constants of the flow, namely the\nbuoyancy difference, the velocity difference between the two streams, and the\ngravitational acceleration. We validate our theory against direct numerical\nsimulations (DNSs) of a temporal turbulent mixing layer compounded with an\nunstable stratification. We observe that the cross-over time correctly predicts\nthe transition from shear to buoyancy driven turbulence, in terms of turbulent\nkinetic energy production, energy spectra scaling and mixing layer thickness.\n"}
+{"abstract": "  We use a superconducting microresonator as a cavity to sense absorption of\nmicrowaves by a superconducting quantum point contact defined by surface gates\nover a proximitized two-dimensional electron gas. Renormalization of the cavity\nfrequency with phase difference across the point contact is consistent with\nadiabatic coupling to Andreev bound states. Near $\\pi$ phase difference, we\nobserve random fluctuations in absorption with gate voltage, related to quantum\ninterference-induced modulations in the electron transmission. We identify\nfeatures consistent with the presence of single Andreev bound states and\ndescribe the Andreev-cavity interaction using a dispersive Jaynes-Cummings\nmodel. By fitting the weak Andreev-cavity coupling, we extract ~GHz decoherence\nconsistent with charge noise and the transmission dispersion associated with a\nlocalized state.\n"}
+{"abstract": "  Product descriptions on e-commerce websites often suffer from missing\nimportant aspects. Clarification question generation (CQGen) can be a promising\napproach to help alleviate the problem. Unlike traditional QGen assuming the\nexistence of answers in the context and generating questions accordingly, CQGen\nmimics user behaviors of asking for unstated information. The generated CQs can\nserve as a sanity check or proofreading to help e-commerce merchant to identify\npotential missing information before advertising their product, and improve\nconsumer experience consequently. Due to the variety of possible user\nbackgrounds and use cases, the information need can be quite diverse but also\nspecific to a detailed topic, while previous works assume generating one CQ per\ncontext and the results tend to be generic. We thus propose the task of Diverse\nCQGen and also tackle the challenge of specificity. We propose a new model\nnamed KPCNet, which generates CQs with Keyword Prediction and Conditioning, to\ndeal with the tasks. Automatic and human evaluation on 2 datasets (Home &\nKitchen, Office) showed that KPCNet can generate more specific questions and\npromote better group-level diversity than several competing baselines.\n"}
+{"abstract": "  We consider the $(1+1)$-dimensional quasilinear wave equation\n$g(x)w_{tt}-w_{xx}+h(x) (w_t^3)_t=0$ on $\\mathbb{R}\\times\\mathbb{R}$ which\narises in the study of localized electromagnetic waves modeled by\nKerr-nonlinear Maxwell equations. We are interested in time-periodic, spatially\nlocalized solutions. Here $g\\in L^{\\infty}(\\mathbb{R})$ is even with\n$g\\not\\equiv 0$ and $h(x)=\\gamma\\,\\delta_0(x)$ with\n$\\gamma\\in{\\mathbb{R}}\\backslash\\{0\\}$ and $\\delta_0$ the delta-distribution\nsupported in $0$. We assume that $0$ lies in a spectral gap of the operators\n$L_k=-\\frac{d^2}{dx^2}-k^2\\omega^2g$ on $L^2(\\mathbb{R})$ for all $k\\in\n2\\mathbb{Z}+1$ together with additional properties of the fundamental set of\nsolutions of $L_k$. By expanding $w$ into a Fourier series in time we transfer\nthe problem of finding a suitably defined weak solution to finding a minimizer\nof a functional on a sequence space. The solutions that we have found are\nexponentially localized in space. Moreover, we show that they can be well\napproximated by truncating the Fourier series in time. The guiding examples,\nwhere all assumptions are fulfilled, are explicitly given step potentials and\nperiodic step potentials $g$. In these examples we even find infinitely many\ndistinct breathers.\n"}
+{"abstract": "  Detection of higher order modes of gravitational waves in third-generation\n(3G) ground-based detectors such as Cosmic Explorer and Einstein Telescope is\nexplored. Using the astrophysical population of binary black holes based on\nevents reported in the second gravitational wave catalog by Laser\nInterferometer Gravitational Wave Observatory (LIGO) and Virgo (GWTC-2), in\nconjunction with the Madau-Dickinson model for redshift evolution of the binary\nblack hole mergers, we assess the detectability of these higher order modes\nusing a network consisting of three third-generation detectors. We find that\nthe two subleading modes [(3,3) and (4,4)] can be detected in approximately 30%\nof the population with a network signal-to-noise ratio of 3 or more, and for\nnearly 10% of the sources, the five leading modes will be detectable. Besides,\na study concerning the effect of binary's mass ratio and its orbital\ninclination with the observer's line-of-sight in detecting various modes is\npresented. For a few selected events of the LIGO-Virgo catalog, we identify the\nmodes that would have been detected if a third-generation detector was\noperational when these events were recorded. We also compute the detectability\nof higher modes by Voyager and find that only $\\sim$ 6 and 2% of the detectable\npopulation will have an associated detection of (3,3) and (4,4) modes,\nrespectively. Observing these higher order modes in the 3G era would have a\nhuge impact on the science possible with these detectors ranging from\nastrophysics and cosmology to testing strong-field gravity.\n"}
+{"abstract": "  Malicious applications (especially in the Android platform) are a serious\nthreat to developers and end-users. Many research efforts have hence been\ndevoted to developing effective approaches to defend Android malware. However,\nwith the explosive growth of Android malware and the continuous advancement of\nmalicious evasion technologies like obfuscation and reflection, android malware\ndefenses based on manual rules or traditional machine learning may not be\neffective due to limited apriori knowledge. In recent years, a dominant\nresearch field of deep learning (DL) with the powerful feature abstraction\nability has demonstrated a compelling and promising performance in various\nfields, like Nature Language processing and image processing. To this end,\nemploying deep learning techniques to thwart the attack of Android malware has\nrecently gained considerable research attention. Yet, there exists no\nsystematic literature review that focuses on deep learning approaches for\nAndroid Malware defenses. In this paper, we conducted a systematic literature\nreview to search and analyze how deep learning approaches have been applied in\nthe context of malware defenses in the Android environment. As a result, a\ntotal of 104 studies were identified over the period 2014-2020. The results of\nour investigation show that even though most of these studies still mainly\nconsider DL-based on Android malware detection, 35 primary studies (33.7\\%)\ndesign the defenses approaches based on other scenarios. This review also\ndescribes research trends, research focuses, challenges, and future research\ndirections in DL-based Android malware defenses.\n"}
+{"abstract": "  We obtain a complete characterization for doubly commuting mixed invariant\nsubspaces of the Hardy space over the unit polydisc. We say a closed subspace\n$\\mathcal{Q}$ of $H^2(\\mathbb{D}^n)$ is mixed invariant if\n$M_{z_{j}}(\\mathcal{Q}) \\subseteq \\mathcal{Q}$ for $1 \\leq j \\leq k$ and\n$M_{z_{j}}^*(\\mathcal{Q}) \\subseteq \\mathcal{Q}$, $k+1 \\leq j \\leq n$ for some\ninteger $k \\in \\{1, 2, \\ldots, n-1 \\}$. We prove that a mixed invariant\nsubspace $\\mathcal{Q}$ of $H^2(\\mathbb{D}^n)$ is doubly commuting if and only\nif \\[ \\mathcal{Q} = \\Theta H^2(\\mathbb{D}^k) \\otimes \\mathcal{Q}_{\\theta_1}\n\\otimes \\cdots \\otimes \\mathcal{Q}_{\\theta_{n-k}}, \\] where $\\Theta \\in\nH^{\\infty}(\\mathbb{D}^k)$ is some inner function and $\\mathcal{Q}_{\\theta_j}$\nis either a Jordan block $H^2(\\mathbb{D})\\ominus \\theta_j H^2(\\mathbb{D})$ for\nsome inner function $\\theta_j$ or the Hardy space $H^2(\\mathbb{D})$.\nFurthermore, an explicit representation for the commutant of an $n$-tuple of\ndoubly commuting shifts as well as a representation for the commutant of a\ndoubly commuting tuple of shifts and co-shifts are obtained. Finally, we\ndiscuss some concrete examples of mixed invariant subspaces.\n"}
+{"abstract": "  This paper concerns Saez and Stantcheva's (2016) generalized social marginal\nwelfare weights (GSMWW), which aggregate losses and gains due to tax policies,\nwhile incorporating non-utilitarian ethical considerations. The approach\nevaluates local tax changes without a global social objective. I argue that\nlocal tax policy comparisons implicitly entail global comparisons. Moreover,\nwhenever welfare weights do not have a utilitarian structure, these implied\nglobal comparisons are inconsistent. One motivation for GSMWW is that it\npreserves the Pareto principle. I argue that the approach's problems should\nspark a reconsideration of Pareto if one wants to represent broader values in\nformal policy analysis.\n"}
+{"abstract": "  We study the effect of two metallic slabs on the collective dynamics of\nelectrons in graphene positioned between the two slabs. We show that if the\nslabs are perfect conductors the plasmons of graphene display a linear\ndispersion relation. The velocity of these acoustic plasmons crucially depends\non the distance between the two metal gates and the graphene sheet. In the case\nof generic slabs, the dispersion relation of graphene plasmons is much more\ncomplicated but we find that acoustic plasmons can still be obtained under\nspecific conditions.\n"}
+{"abstract": "  We compute thermal 2-point correlation functions in the black brane $AdS_5$\nbackground dual to 4d CFT's at finite temperature for operators of large\nscaling dimension. We find a formula that matches the expected structure of the\nOPE. It exhibits an exponentiation property, whose origin we explain. We also\ncompute the first correction to the two-point function due to graviton\nemission, which encodes the proper time from the event horizon to the black\nhole singularity.\n"}
+{"abstract": "  This paper introduces a logic with a class of social network models that is\nbased on standard Linear Temporal Logic (LTL), leveraging the power of existing\nmodel checkers for the analysis of social networks. We provide a short\nliterature overview, and then define our logic and its axiomatization, present\nsome simple motivational examples of both models and formulas, and show its\nsoundness and completeness via a translation into propositional formulas.\nLastly, we briefly discuss model checking and time complexity analysis.\n"}
+{"abstract": "  We investigate the correlations that can arise between Alice and Bob in\nprepare-and-measure communication scenarios where the source (Alice) and the\nmeasurement device (Bob) can share prior entanglement. The paradigmatic example\nof such a scenario is the quantum dense coding protocol, where the\ncommunication capacity of a qudit can be doubled if a two-qudit entangled state\nis shared between Alice and Bob. We provide examples of correlations that\nactually require more general protocols based on higher-dimensional entangled\nstates. This motivates us to investigate the set of correlations that can be\nobtained from communicating either a classical or a quantum $d$-dimensional\nsystem in the presence of an unlimited amount of entanglement. We show how such\ncorrelations can be characterized by a hierarchy of semidefinite programming\nrelaxations by reducing the problem to a non-commutative polynomial\noptimization problem. We also introduce an alternative relaxation hierarchy\nbased on the notion of informationally-restricted quantum correlations, which,\nthough it represents a strict (non-converging) relaxation scheme, is less\ncomputationally demanding. As an application, we introduce device-independent\ntests of the dimension of classical and quantum systems that, in contrast to\nprevious results, do not make the implicit assumption that Alice and Bob share\nno entanglement. We also establish several relations between communication with\nand without entanglement as resources for creating correlations.\n"}
+{"abstract": "  RFID tags see a widespread use in modern security systems, including home\nintercoms, access control cards, etc. Here we focus on access control systems.\nCurrently they have either high cost or low security guarantees. Hence, the\ndevelopments focusing on improving access control security while lowering the\ncost is a rapidly developing field. The purpose of this work is to create an\nalternative access control scheme, where card scanners are replaced with\npassive RFID tags, and all the communications are done via user's smartphone\nWi-Fi. Based on the analysis of existing approaches, it was concluded that use\nof mobile systems is the most promising due to their expandability and presence\nof a large number of sensors, such as NFC, camera etc. In the proposed model\nRFID tags are mounted near a turnstile or a smart door. Tag reading and\nprogramming is done via NFC chip directly on an Android or iOS mobile device,\nwhich allows for a significant price cut for such a system implementation. A\ndetailed description of a tag writing procedure with the data required to\nperform it is provided. To enhance security, together with smartphone-based\nauthorization we require the user to provide his photograph while entering a\nsecure gate. The photograph is then displayed on a monitoring dashboard\nside-by-side with his registration picture, so that the two can then be matched\nagainst each other. The developed client-server application offers\nadministrative system used to configure gate access policies and monitor\nentrances with filters by access time, user and gate. Also, we propose a mobile\napplication that allows gate registration and serves as a door unlock key. The\nsuggested access control model reduces installation costs required, as it is\nfully wireless and uses cheap autonomous RFID-tags as its main component.\n"}
+{"abstract": "  The goal of this paper is to investigate an approach for derivative-free\noptimization that has not received sufficient attention in the literature and\nis yet one of the simplest to implement and parallelize. It consists of\ncomputing gradients of a smoothed approximation of the objective function (and\nconstraints), and employing them within established codes. These gradient\napproximations are calculated by finite differences, with a differencing\ninterval determined by the noise level in the functions and a bound on the\nsecond or third derivatives. It is assumed that noise level is known or can be\nestimated by means of difference tables or sampling. The use of finite\ndifferences has been largely dismissed in the derivative-free optimization\nliterature as too expensive in terms of function evaluations and/or as\nimpractical when the objective function contains noise. The test results\npresented in this paper suggest that such views should be re-examined and that\nthe finite-difference approach has much to be recommended. The tests compared\nNEWUOA, DFO-LS and COBYLA against the finite-difference approach on three\nclasses of problems: general unconstrained problems, nonlinear least squares,\nand general nonlinear programs with equality constraints.\n"}
+{"abstract": "  A four-wave-mixing, frequency-comb-based, hyperspectral imaging technique\nthat is spectrally precise, potentially rapid, and can in principle be applied\nto any material, is demonstrated in a near-diffraction-limited microscopy\napplication.\n"}
+{"abstract": "  Molecular simulations are playing an ever increasing role, finding\napplications in fields as varied as physics, chemistry, biology and material\nscience. However, many phenomena of interest take place on time scales that are\nout of reach of standard molecular simulations. This is known as the sampling\nproblem and over the years several enhanced sampling methods have been\ndeveloped to mitigate this issue. We propose a unified approach that puts on\nthe same footing the two most popular families of enhanced sampling methods,\nand paves the way for novel combined approaches. The on-the-fly probability\nenhanced sampling method provides an efficient implementation of such\ngeneralized approach, while also focusing on simplicity and robustness.\n"}
+{"abstract": "  Elastic $K^+ n\\to K^+ n$ and charge exchange $K^+n\\to K^0p$ reactions at low\nmomenta are investigated using the partial wave analysis. Isospin relation\ngives constrains for the partial $s$- and $p$-waves among elastic $K^+p$,\n$K^+n$ and charge exchange $K^+n\\to K^0p$ amplitudes. Two sets of the phase\nshift for these partial waves in the isoscalar channel are obtained from the\nfit of experimental data on total and differential cross sections. Polarization\nobservable leads to a good criterion to decide which set is valid. Differential\ncross section data near $P_{\\rm Lab}=434$ MeV/$c$ suggests a possibility of a\nresonance, e.g., the exotic $\\Theta^+(1540)$ baryon around $\\sqrt{s}\\simeq$\n1.54GeV.\n"}
+{"abstract": "  The ability to distinguish between the self and the background is of\nparamount importance for robotic tasks. The particular case of hands, as the\nend effectors of a robotic system that more often enter into contact with other\nelements of the environment, must be perceived and tracked with precision to\nexecute the intended tasks with dexterity and without colliding with obstacles.\nThey are fundamental for several applications, from Human-Robot Interaction\ntasks to object manipulation. Modern humanoid robots are characterized by high\nnumber of degrees of freedom which makes their forward kinematics models very\nsensitive to uncertainty. Thus, resorting to vision sensing can be the only\nsolution to endow these robots with a good perception of the self, being able\nto localize their body parts with precision. In this paper, we propose the use\nof a Convolution Neural Network (CNN) to segment the robot hand from an image\nin an egocentric view. It is known that CNNs require a huge amount of data to\nbe trained. To overcome the challenge of labeling real-world images, we propose\nthe use of simulated datasets exploiting domain randomization techniques. We\nfine-tuned the Mask-RCNN network for the specific task of segmenting the hand\nof the humanoid robot Vizzy. We focus our attention on developing a methodology\nthat requires low amounts of data to achieve reasonable performance while\ngiving detailed insight on how to properly generate variability in the training\ndataset. Moreover, we analyze the fine-tuning process within the complex model\nof Mask-RCNN, understanding which weights should be transferred to the new task\nof segmenting robot hands. Our final model was trained solely on synthetic\nimages and achieves an average IoU of 82% on synthetic validation data and\n56.3% on real test data. These results were achieved with only 1000 training\nimages and 3 hours of training time using a single GPU.\n"}
+{"abstract": "  The aim of the present work is to investigate the effects of strong magnetic\nfields on the hadron-quark phase transition point at zero temperature. To\ndescribe the hadronic phase, a relativistic mean field (RMF) model is used and\nto describe the quark phase a density dependent quark mass model (DDQM) is\nemployed. As compared with the results obtained with non-magnetised matter, we\nobserve a shift of the transition point towards higher pressures and, generally\nalso towards higher chemical potentials. An investigation of the phase\ntransitions that could sustain hybrid stars is also performed.\n"}
+{"abstract": "  In this paper we introduce a space-dependent multiscale model to describe the\nspatial spread of an infectious disease under uncertain data with particular\ninterest in simulating the onset of the COVID-19 epidemic in Italy. While virus\ntransmission is ruled by a SEIAR type compartmental model, within our approach\nthe population is given by a sum of commuters moving on a extra-urban scale and\nnon commuters interacting only on the smaller urban scale. A transport dynamic\nof the commuter population at large spatial scales, based on kinetic equations,\nis coupled with a diffusion model for non commuters at the urban scale. Thanks\nto a suitable scaling limit, the kinetic transport model used to describe the\ndynamics of commuters, within a given urban area coincides with the diffusion\nequations that characterize the movement of non-commuting individuals. Because\nof the high uncertainty in the data reported in the early phase of the\nepidemic, the presence of random inputs in both the initial data and the\nepidemic parameters is included in the model. A robust numerical method is\ndesigned to deal with the presence of multiple scales and the uncertainty\nquantification process. In our simulations, we considered a realistic\ngeographical domain, describing the Lombardy region, in which the size of the\ncities, the number of infected individuals, the average number of daily\ncommuters moving from one city to another, and the epidemic aspects are taken\ninto account through a calibration of the model parameters based on the actual\navailable data. The results show that the model is able to describe correctly\nthe main features of the spatial expansion of the first wave of COVID-19 in\nnorthern Italy.\n"}
+{"abstract": "  Adopting shared data resources requires scientists to place trust in the\noriginators of the data. When shared data is later used in the development of\nartificial intelligence (AI) systems or machine learning (ML) models, the trust\nlineage extends to the users of the system, typically practitioners in fields\nsuch as healthcare and finance. Practitioners rely on AI developers to have\nused relevant, trustworthy data, but may have limited insight and recourse.\nThis paper introduces a software architecture and implementation of a system\nbased on design patterns from the field of self-sovereign identity. Scientists\ncan issue signed credentials attesting to qualities of their data resources.\nData contributions to ML models are recorded in a bill of materials (BOM),\nwhich is stored with the model as a verifiable credential. The BOM provides a\ntraceable record of the supply chain for an AI system, which facilitates\non-going scrutiny of the qualities of the contributing components. The verified\nBOM, and its linkage to certified data qualities, is used in the AI Scrutineer,\na web-based tool designed to offer practitioners insight into ML model\nconstituents and highlight any problems with adopted datasets, should they be\nfound to have biased data or be otherwise discredited.\n"}
+{"abstract": "  We present a comparative study of the accuracy and precision of correlation\nfunction methods and full-field inference in cosmological data analysis. To do\nso, we examine a Bayesian hierarchical model that predicts log-normal fields\nand their two-point correlation function. Although a simplified analytic model,\nthe log-normal model produces fields that share many of the essential\ncharacteristics of the present-day non-Gaussian cosmological density fields. We\nuse three different statistical techniques: (i) a standard likelihood-based\nanalysis of the two-point correlation function; (ii) a likelihood-free\n(simulation-based) analysis of the two-point correlation function; (iii) a\nfield-level analysis, made possible by the more sophisticated data assimilation\ntechnique. We find that (a) standard assumptions made to write down a\nlikelihood for correlation functions can cause significant biases, a problem\nthat is alleviated with simulation-based inference; and (b) analysing the\nentire field offers considerable advantages over correlation functions, through\nhigher accuracy, higher precision, or both. The gains depend on the degree of\nnon-Gaussianity, but in all cases, including for weak non-Gaussianity, the\nadvantage of analysing the full field is substantial.\n"}
+{"abstract": "  In this work, we propose a novel strategy of adaptive sparse array beamformer\ndesign, referred to as regularized complementary antenna switching (RCAS), to\nswiftly adapt both array configuration and excitation weights in accordance to\nthe dynamic environment for enhancing interference suppression. In order to\nachieve an implementable design of array reconfiguration, the RCAS is conducted\nin the framework of regularized antenna switching, whereby the full array\naperture is collectively divided into separate groups and only one antenna in\neach group is switched on to connect with the processing channel. A set of\ndeterministic complementary sparse arrays with good quiescent beampatterns is\nfirst designed by RCAS and full array data is collected by switching among them\nwhile maintaining resilient interference suppression. Subsequently, adaptive\nsparse array tailored for the specific environment is calculated and\nreconfigured based on the information extracted from the full array data. The\nRCAS is devised as an exclusive cardinality-constrained optimization, which is\nreformulated by introducing an auxiliary variable combined with a piece-wise\nlinear function to approximate the $l_0$-norm function. A regularization\nformulation is proposed to solve the problem iteratively and eliminate the\nrequirement of feasible initial search point. A rigorous theoretical analysis\nis conducted, which proves that the proposed algorithm is essentially an\nequivalent transformation of the original cardinality-constrained optimization.\nSimulation results validate the effectiveness of the proposed RCAS strategy.\n"}
+{"abstract": "  We conduct an empirical study of neural machine translation (NMT) for truly\nlow-resource languages, and propose a training curriculum fit for cases when\nboth parallel training data and compute resource are lacking, reflecting the\nreality of most of the world's languages and the researchers working on these\nlanguages. Previously, unsupervised NMT, which employs back-translation (BT)\nand auto-encoding (AE) tasks has been shown barren for low-resource languages.\nWe demonstrate that leveraging comparable data and code-switching as weak\nsupervision, combined with BT and AE objectives, result in remarkable\nimprovements for low-resource languages even when using only modest compute\nresources. The training curriculum proposed in this work achieves BLEU scores\nthat improve over supervised NMT trained on the same backbone architecture by\n+12.2 BLEU for English to Gujarati and +3.7 BLEU for English to Kazakh,\nshowcasing the potential of weakly-supervised NMT for the low-resource\nlanguages. When trained on supervised data, our training curriculum achieves a\nnew state-of-the-art result on the Somali dataset (BLEU of 29.3 for Somali to\nEnglish). We also observe that adding more time and GPUs to training can\nfurther improve performance, which underscores the importance of reporting\ncompute resource usage in MT research.\n"}
+{"abstract": "  In a combinatorial exchange setting, players place sell (resp. buy) bids on\ncombinations of traded goods. Besides the question of finding an optimal\nselection of winning bids, the question of how to share the obtained profit is\nof high importance. The egalitarian allocation is a well-known solution concept\nof profit sharing games which tries to distribute profit among players in a\nmost equal way while respecting individual contributions to the obtained\nprofit. Given a set of winning bids, we construct a special network graph and\nshow that every flow in said graph corresponds to a core payment. Furthermore,\nwe show that the egalitarian allocation can be characterized as an almost equal\nmaximum flow which is a maximum flow with the additional property that the\ndifference of flow value on given edge sets is bounded by a constant. With\nthis, we are able to compute the egalitarian allocation in polynomial time.\n"}
+{"abstract": "  Two exact lens equations have been recently shown to be equivalent to each\nother, being consistent with the gravitational deflection angle of light from a\nsource to an observer, both of which can be within a finite distance from a\nlens object [Phys. Rev. D 102, 064060 (2020)]. We examine methods for iterative\nsolutions of the gravitational lens equations in the strong deflection limit.\nIt has been so far unclear whether a convergent series expansion can be\nprovided by the gravitational lens approach based on the geometrical optics for\nobtaining approximate solutions in the strong deflection limit in terms of a\nsmall offset angle. By using the ratio of the lens mass to the lens distance,\nwe discuss a slightly different method for iterative solutions and the behavior\nof the convergence. Finite distance effects begin at the third order in the\niterative method. The iterative solutions in the strong deflection limit are\nestimated for Sgr $A^{*}$ and M87. These results suggest that only the linear\norder solution can be relevant with current observations, while the finite\ndistance effects at the third order may be negligible in the Schwarzschild lens\nmodel for these astronomical objects.\n"}
+{"abstract": "  Language Identification in textual documents is the process of automatically\ndetecting the language contained in a document based on its content. The\npresent Language Identification techniques presume that a document contains\ntext in one of the fixed set of languages, however, this presumption is\nincorrect when dealing with multilingual document which includes content in\nmore than one possible language. Due to the unavailability of large standard\ncorpora for Hindi-English mixed lingual language processing tasks we propose\nthe language lexicons, a novel kind of lexical database that supports several\nmultilingual language processing tasks. These lexicons are built by learning\nclassifiers over transliterated Hindi and English vocabulary. The designed\nlexicons possess richer quantitative characteristic than its primary source of\ncollection which is revealed using the visualization techniques.\n"}
+{"abstract": "  In this paper, we propose a new greedy algorithm for sparse approximation,\ncalled SLS for Single L_1 Selection. SLS essentially consists of a greedy\nforward strategy, where the selection rule of a new component at each iteration\nis based on solving a least-squares optimization problem, penalized by the L_1\nnorm of the remaining variables. Then, the component with maximum amplitude is\nselected. Simulation results on difficult sparse deconvolution problems\ninvolving a highly correlated dictionary reveal the efficiency of the method,\nwhich outperforms popular greedy algorithms and Basis Pursuit Denoising when\nthe solution is sparse.\n"}
+{"abstract": "  Power allocation in spectrum sharing systems is challenging due to excessive\ninterference that the secondary system could impose on the primary system.\nTherefore, an interference threshold constraint is considered to regulate the\nsecondary system's activity. However, the primary receivers should measure the\ninterference and inform the secondary users accordingly. These cause design\ncomplexities, e.g., due to transceiver's hardware impairments, and impose a\nsubstantial signaling overhead. We set our main goal to mitigate these\nrequirements in order to make the spectrum sharing systems practically\nfeasible. To cope with the lack of a model we develop a coexisting deep\nreinforcement learning approach for continuous power allocation in both\nsystems. Importantly, via our solution, the two systems allocate power merely\nbased on geographical location of their users. Moreover, the inter-system\nsignaling requirement is reduced to exchanging only the number of primary users\nthat their QoS requirements are violated. We observe that compared to a\ncentralized agent that allocates power based on full (accurate) channel\ninformation, our solution is more robust and strictly guarantees QoS\nrequirements of the primary users. This implies that both systems can operate\nsimultaneously with almost-zero inter-system signaling overhead.\n"}
+{"abstract": "  Nowadays, neural network models achieve state-of-the-art results in many\nareas as computer vision or speech processing. For sequential data, especially\nfor Natural Language Processing (NLP) tasks, Recurrent Neural Networks (RNNs)\nand their extensions, the Long Short Term Memory (LSTM) network and the Gated\nRecurrent Unit (GRU), are among the most used models, having a \"term-to-term\"\nsequence processing. However, if many works create extensions and improvements\nof the RNN, few have focused on developing other ways for sequential data\nprocessing with neural networks in a \"term-to-term\" way. This paper proposes\nthe original Hidden Neural Markov Chain (HNMC) framework, a new family of\nsequential neural models. They are not based on the RNN but on the Hidden\nMarkov Model (HMM), a probabilistic graphical model. This neural extension is\npossible thanks to the recent Entropic Forward-Backward algorithm for HMM\nrestoration. We propose three different models: the classic HNMC, the HNMC2,\nand the HNMC-CN. After describing our models' whole construction, we compare\nthem with classic RNN and Bidirectional RNN (BiRNN) models for some sequence\nlabeling tasks: Chunking, Part-Of-Speech Tagging, and Named Entity Recognition.\nFor every experiment, whatever the architecture or the embedding method used,\none of our proposed models has the best results. It shows this new neural\nsequential framework's potential, which can open the way to new models, and\nmight eventually compete with the prevalent BiLSTM and BiGRU.\n"}
+{"abstract": "  The Navier-Stokes equations generate an infinite set of generalized Lyapunov\nexponents defined by different ways of measuring the distance between\nexponentially diverging perturbed and unperturbed solutions. This set is\ndemonstrated to be similar, yet different, from the generalized Lyapunov\nexponent that provides moments of distance between two fluid particles below\nthe Kolmogorov scale. We derive rigorous upper bounds on dimensionless Lyapunov\nexponent of the fluid particles that demonstrate the exponent's decay with\nReynolds number $Re$ in accord with previous studies. In contrast, terms of\ncumulant series for exponents of the moments have power-law growth with $Re$.\nWe demonstrate as an application that the growth of small fluctuations of\nmagnetic field in ideal conducting turbulence is hyper-intermittent, being\nexponential in both time and Reynolds number. We resolve the existing\ncontradiction between the theory, that predicts slow decrease of dimensionless\nLyapunov exponent of turbulence with $Re$, and observations exhibiting quite\nfast growth. We demonstrate that it is highly plausible that a pointwise limit\nfor the growth of small perturbations of the Navier-Stokes equations exists.\n"}
+{"abstract": "  Forecasting could be negatively impacted due to anonymization requirements in\ndata protection legislation. To measure the potential severity of this problem,\nwe derive theoretical bounds for the loss to forecasts from additive\nexponential smoothing models using protected data. Following the guidelines of\nanonymization from the General Data Protection Regulation (GDPR) and California\nConsumer Privacy Act (CCPA), we develop the $k$-nearest Time Series ($k$-nTS)\nSwapping and $k$-means Time Series ($k$-mTS) Shuffling methods to create\nprotected time series data that minimizes the loss to forecasts while\npreventing a data intruder from detecting privacy issues. For efficient and\neffective decision making, we formally model an integer programming problem for\na perfect matching for simultaneous data swapping in each cluster. We call it a\ntwo-party data privacy framework since our optimization model includes the\nutilities of a data provider and data intruder. We apply our data protection\nmethods to thousands of time series and find that it maintains the forecasts\nand patterns (level, trend, and seasonality) of time series well compared to\nstandard data protection methods suggested in legislation. Substantively, our\npaper addresses the challenge of protecting time series data when used for\nforecasting. Our findings suggest the managerial importance of incorporating\nthe concerns of forecasters into the data protection itself.\n"}
+{"abstract": "  We consider a linear mixed-effects model with a clustered structure, where\nthe parameters are estimated using maximum likelihood (ML) based on possibly\nunbalanced data. Inference with this model is typically done based on\nasymptotic theory, assuming that the number of clusters tends to infinity with\nthe sample size. However, when the number of clusters is fixed, classical\nasymptotic theory developed under a divergent number of clusters is no longer\nvalid and can lead to erroneous conclusions. In this paper, we establish the\nasymptotic properties of the ML estimators of random-effects parameters under a\ngeneral setting, which can be applied to conduct valid statistical inference\nwith fixed numbers of clusters. Our asymptotic theorems allow both fixed\neffects and random effects to be misspecified, and the dimensions of both\neffects to go to infinity with the sample size.\n"}
+{"abstract": "  We report on multi-band observations of the transient source Swift\nJ0840.7-3516, which was detected in outburst in 2020 February by the Neil\nGehrels Swift Observatory. The outburst episode lasted just ~5 days, during\nwhich the X-ray luminosity quickly decreased from ~3E37 erg/s at peak down to\n~5E33 erg/s in quiescence (0.3-10 keV; at 10 kpc). Such a marked and rapid\ndecrease in the flux was also registered at UV and optical wavelengths. In\noutburst, the source showed considerable aperiodic variability in the X-rays on\ntimescales as short as a few seconds. The spectrum of the source in the energy\nrange 0.3-20 keV was well described by a thermal, blackbody-like, component\nplus a non-thermal, power law-like, component and it softened considerably as\nthe source returned to quiescence. The spectrum of the optical counterpart in\nquiescence showed broad emission features associated mainly with ionised carbon\nand oxygen, superposed on a blue continuum. No evidence for bright continuum\nradio emission was found in quiescence. We discuss possible scenarios for the\nnature of this source, and show that the observed phenomenology points to a\ntransient ultra-compact X-ray binary system.\n"}
+{"abstract": "  We introduce the effectual topological complexity (ETC) of a $G$-space $X$.\nThis is a $G$-equivariant homotopy invariant sitting in between the effective\ntopological complexity of the pair $(X,G)$ and the (regular) topological\ncomplexity of the orbit space $X/G$. We study ETC for spheres and surfaces with\nantipodal involution, obtaining a full computation in the case of the torus.\nThis allows us to prove the vanishing of twice the non-trivial obstruction\nresponsible for the fact that the topological complexity of the Klein bottle is\n4. In addition, this gives a counterexample to the possibility -- suggested in\nPave\\v{s}i\\'c's work on the topological complexity of a map -- that ETC of\n$(X,G)$ would agree with Farber's $TC(X)$ whenever the projection map $X\\to\nX/G$ is finitely sheeted. We conjecture that ETC of spheres with antipodal\naction recasts the Hopf invariant one problem, and describe (conjecturally\noptimal) effectual motion planners.\n"}
+{"abstract": "  A pre-requisite for the design of wireless systems is the understanding of\nthe propagation channel. While a wealth of propagation knowledge exists for\nbands below 6 GHz, the same can not be said for bands approaching\nmillimeter-wave frequencies. In this paper, we present the design,\nimplementation and measurement-based verification of a re-configurable\n27.5-29.5 GHz channel sounder for measuring dynamic directional channels. Based\non the switched array principle, our design is capable of characterizing\n128$\\times$256 dual-polarized channels with snapshot times of around 600 ms.\nThis is in sharp contrast to measurement times on the order of tens-of-minutes\nwith rotating horn antenna sounders. Our design lends itself to high angular\nresolution at both link ends with calibrated antenna arrays sampled at\n2$^\\circ$ and 5$^\\circ$ intervals in the azimuth and elevation domains. This is\ncomplemented with a bandwidth of up to 2 GHz, enabling nanosecond-level delay\nresolution. The short measurement times and stable radio frequency design\nfacilitates real-time processing and averaging of the received wavefronts to\ngain measurement signal-to-noise ratio and dynamic range. After disclosing the\nsounder design and implementation, we demonstrate its capabilities by\npresenting dynamic and static measurements at 28 GHz over a 1 GHz bandwidth in\nan office corridor environment.\n"}
+{"abstract": "  We propose a numerical approximation method for the Cahn-Hilliard equations\nthat incorporates continuous data assimilation in order to achieve long time\naccuracy. The method uses a C$^0$ interior penalty spatial discretization of\nthe fourth order Cahn-Hilliard equations, together with a backward Euler\ntemporal discretization. We prove the method is long time stable and long time\naccurate, for arbitrarily inaccurate initial conditions, provided enough data\nmeasurements are incorporated into the simulation. Numerical experiments\nillustrate the effectiveness of the method on a benchmark test problem.\n"}
+{"abstract": "  Teleparallel gravity has significantly increased in popularity in recent\ndecades, bringing attention to Einstein's other theory of gravity. In this\nReview, we relate this form of geometry to the broader metric-affine approach\nto forming gravitational theories where we describe a systematic way of\nconstructing consistent teleparallel theories that respect certain physical\nconditions such as local Lorentz invariance. We first use teleparallel gravity\nto formulate a teleparallel equivalent of general relativity which is\ndynamically equivalent to general relativity but which may have different\nbehaviors for other scenarios, such as quantum gravity. After setting this\nfoundation, we describe the plethora of modified teleparallel theories of\ngravity that have been proposed in the literature. In the second part of the\nReview, we first survey works in teleparallel astrophysics literature where we\nfocus on the open questions in this regime of physics. We then discuss the\ncosmological consequences for the various formulations of teleparallel gravity.\nWe do this at background level by exploring works using various approaches\nranging from dynamical systems to Noether symmetries, and more. Naturally, we\nthen discuss perturbation theory, firstly by giving a concise approach in which\nthis can be applied in teleparallel gravity theories and then apply it to a\nnumber of important theories in the literature. Finally, we examine works in\nobservational and precision cosmology across the plethora of proposal theories.\nThis is done using some of the latest observations and is used to tackle\ncosmological tensions which may be alleviated in teleparallel cosmology. We\nalso introduce a number of recent works in the application of machine learning\nto gravity, we do this through deep learning and Gaussian processes, together\nwith discussions about other approaches in the literature.\n"}
+{"abstract": "  We revisit the task of quantum state redistribution in the one-shot setting,\nand design a protocol for this task with communication cost in terms of a\nmeasure of distance from quantum Markov chains. More precisely, the distance is\ndefined in terms of quantum max-relative entropy and quantum hypothesis testing\nentropy.\n  Our result is the first to operationally connect quantum state redistribution\nand quantum Markov chains, and can be interpreted as an operational\ninterpretation for a possible one-shot analogue of quantum conditional mutual\ninformation. The communication cost of our protocol is lower than all\npreviously known ones and asymptotically achieves the well-known rate of\nquantum conditional mutual information. Thus, our work takes a step towards the\nimportant open question of near-optimal characterization of the one-shot\nquantum state redistribution.\n"}
+{"abstract": "  Motivated by the possible non-spin-singlet superconductivity in the\nmagic-angle twisted trilayer graphene experiment, we investigate the\ntriplet-pairing superconductivity arising from a correlation-induced\nspin-fermion model on a honeycomb lattice. We find that the $f$-wave pairing is\nfavored due to the valley-sublattice structure, and the superconducting state\nis time-reversal symmetric, fully gapped, and non-topological. With a small\nin-plane magnetic field, the superconducting state becomes partially polarized,\nand the transition temperature can be slightly enhanced. Our results apply\nqualitatively for the triplet-pairing superconductivity in graphene-based\nmoir\\'e systems, which is fundamentally distinct from triplet superconductivity\nin $^3$He and ferromagnetic superconductors.\n"}
+{"abstract": "  Redshift-space distortions (RSD) generically affect any spatially-dependent\nobservable that is mapped using redshift information. The effect on the\nobserved clustering of galaxies is the primary example of this. This paper is\ndevoted to another example: the effect of RSD on the apparent peculiar motions\nof tracers as inferred from their positions in redshift space (i.e. the\nobserved distance). Our theoretical study is motivated by practical\nconsiderations, mainly, the direct estimation of the velocity power spectrum,\nwhich is preferably carried out using the tracer's redshift-space position (so\nas to avoid uncertainties in distance measurements). We formulate the\nredshift-space velocity field and show that RSD enters as a higher-order\neffect. Physically, this effect may be interpreted as a dissipative correction\nto the usual perfect-fluid description of dark matter. We show that the effect\non the power spectrum is a damping on relatively large, quasilinear scales\n($k>0.01\\,h\\,{\\rm Mpc}^{-1}$), as was observed, though unexplained, in $N$-body\nsimulations elsewhere. This paper presents two power spectrum models for the\npeculiar velocity field in redshift space, both of which can be considered\nvelocity analogues of existing clustering models. In particular, we show that\nthe \"Finger-of-God\" effect, while also present in the velocity field, cannot be\nentirely blamed for the observed damping in simulations. Our work provides some\nof the missing modelling ingredients required for a density--velocity\nmulti-tracer analysis, which has been proposed for upcoming redshift surveys.\n"}
+{"abstract": "  We consider a multi-source status update system in which status updates are\ntransmitted as packets containing the measured value of the monitored process\nand a time stamp representing the time when the sample was generated. The\npackets of each source are generated according to the Poisson process and the\npackets are served according to an exponentially distributed service time. We\nassume that the received status update packets needs further processing before\nbeing used (hence, computation-intensive). This is mathematically modeled by\nintroducing an additional server at the sink node. The sink server serves the\npackets according to an exponentially distributed service time. We introduce\ntwo packet management policies, namely, i) a preemptive policy and ii) a\nblocking policy and derive the moment generating function (MGF) of the AoI of\neach source under both policies. In the preemptive policy, a new arriving\npacket preempts any possible packet that is currently under service regardless\nof the packet's source index. In the blocking policy, when a server is busy at\nthe arrival instant of a packet the arriving packet is blocked and cleared. We\nassume that the same preemptive/blocking policy is employed in both transmitter\nand sink servers. Numerical results are provided to assess the results.\n"}
+{"abstract": "  Phase separation and transitions among different molecular states are\nubiquitous in living cells. Such transitions can be governed by local\nequilibrium thermodynamics or by active processes controlled by biological\nfuel. It remains largely unexplored how the behavior of phase-separating\nsystems with molecular transitions differs between thermodynamic equilibrium\nand cases where detailed balance of the molecular transition rates is broken\ndue to the presence of fuel. Here, we present a model of a phase-separating\nternary mixture where two components can convert into each other. At\nthermodynamic equilibrium, we find that molecular transitions can give rise to\na lower dissolution temperature and thus reentrant phase behavior. Moreover, we\nfind a discontinuous thermodynamic phase transition in the composition of the\ndroplet phase if both converting molecules attract themselves with similar\ninteraction strength. Breaking detailed-balance of the molecular transition\nleads to quasi-discontinuous changes in droplet composition by varying the fuel\namount for a larger range of inter-molecular interactions. Our findings\nshowcase that phase separation with molecular transitions provides a versatile\nmechanism to control properties of intra-cellular and synthetic condensates via\ndiscontinuous switches in droplet composition.\n"}
+{"abstract": "  We implement a Laplace method for the renormalised solution to the\ngeneralised 2D Parabolic Anderson Model (gPAM) driven by a small spatial white\nnoise. Our work rests upon Hairer's theory of regularity structures which\nallows to generalise classical ideas of Azencott and Ben Arous on path space as\nwell as Aida and Inahama and Kawabi on rough path space to the space of models.\nThe technical cornerstone of our argument is a Taylor expansion of the solution\nin the noise intensity parameter: We prove precise bounds for its terms and the\nremainder and use them to estimate asymptotically irrevelant terms to arbitrary\norder. While most of our arguments are not specific to gPAM, we also outline\nhow to adapt those that are.\n"}
+{"abstract": "  This paper describes the NTNU ASR system participating in the Formosa Speech\nRecognition Challenge 2020 (FSR-2020) supported by the Formosa Speech in the\nWild project (FSW). FSR-2020 aims at fostering the development of Taiwanese\nspeech recognition. Apart from the issues on tonal and dialectical variations\nof the Taiwanese language, speech artificially contaminated with different\ntypes of real-world noise also has to be dealt with in the final test stage;\nall of these make FSR-2020 much more challenging than before. To work around\nthe under-resourced issue, the main technical aspects of our ASR system include\nvarious deep learning techniques, such as transfer learning, semi-supervised\nlearning, front-end speech enhancement and model ensemble, as well as data\ncleansing and data augmentation conducted on the training data. With the best\nconfiguration, our system obtains 13.1 % syllable error rate (SER) on the\nfinal-test set, achieving the first place among all participating systems on\nTrack 3.\n"}
+{"abstract": "  Characterizing the structural properties of neural networks is crucial yet\npoorly understood, and there are no well-established similarity measures\nbetween networks. In this work, we observe that neural networks can be\nrepresented as abstract simplicial complex and analyzed using their topological\n'fingerprints' via Persistent Homology (PH). We then describe a PH-based\nrepresentation proposed for characterizing and measuring similarity of neural\nnetworks. We empirically show the effectiveness of this representation as a\ndescriptor of different architectures in several datasets. This approach based\non Topological Data Analysis is a step towards better understanding neural\nnetworks and serves as a useful similarity measure.\n"}
+{"abstract": "  Intelligent reflecting surface (IRS) enhanced multi-unmanned aerial vehicle\n(UAV) non-orthogonal multiple access (NOMA) networks are investigated. A new\ntransmission framework is proposed, where multiple UAV-mounted base stations\nemploy NOMA to serve multiple groups of ground users with the aid of an IRS.\nThe three-dimensional (3D) placement and transmit power of UAVs, the reflection\nmatrix of the IRS, and the NOMA decoding orders among users are jointly\noptimized for maximization of the sum rate of considered networks. To tackle\nthe formulated mixed-integer non-convex optimization problem with coupled\nvariables, a block coordinate descent (BCD)-based iterative algorithm is\ndeveloped. Specifically, the original problem is decomposed into three\nsubproblems, which are alternatingly solved by exploiting the penalty method\nand the successive convex approximation technique. The proposed BCD-based\nalgorithm is demonstrated to be able to obtain a stationary point of the\noriginal problem with polynomial time complexity. Numerical results show that:\n1) the proposed NOMA-IRS scheme for multi-UAV networks achieves a higher sum\nrate compared to the benchmark schemes, i.e., orthogonal multiple access\n(OMA)-IRS and NOMA without IRS; 2) the use of IRS is capable of providing\nperformance gain for multi-UAV networks by both enhancing channel qualities of\nUAVs to their served users and mitigating the inter-UAV interference; and 3)\noptimizing the UAV placement can make the sum rate gain brought by NOMA more\ndistinct due to the flexible decoding order design.\n"}
+{"abstract": "  Event suffix and remaining time prediction are sequence to sequence learning\ntasks. They have wide applications in different areas such as economics,\ndigital health, business process management and IT infrastructure monitoring.\nTimestamped event sequences contain ordered events which carry at least two\nattributes: the event's label and its timestamp. Suffix and remaining time\nprediction are about obtaining the most likely continuation of event labels and\nthe remaining time until the sequence finishes, respectively. Recent deep\nlearning-based works for such predictions are prone to potentially large\nprediction errors because of closed-loop training (i.e., the next event is\nconditioned on the ground truth of previous events) and open-loop inference\n(i.e., the next event is conditioned on previously predicted events). In this\nwork, we propose an encoder-decoder architecture for open-loop training to\nadvance the suffix and remaining time prediction of event sequences. To capture\nthe joint temporal dynamics of events, we harness the power of adversarial\nlearning techniques to boost prediction performance. We consider four real-life\ndatasets and three baselines in our experiments. The results show improvements\nup to four times compared to the state of the art in suffix and remaining time\nprediction of event sequences, specifically in the realm of business process\nexecutions. We also show that the obtained improvements of adversarial training\nare superior compared to standard training under the same experimental setup.\n"}
+{"abstract": "  Blotto Games are a popular model of multi-dimensional strategic resource\nallocation. Two players allocate resources in different battlefields in an\nauction setting. While competition with equal budgets is well understood,\nlittle is known about strategic behavior under asymmetry of resources. We\nintroduce a genetic algorithm, a search heuristic inspired from biological\nevolution, interpreted as social learning, to solve this problem. Most\nperformant strategies are combined to create more performant strategies.\nMutations allow the algorithm to efficiently scan the space of possible\nstrategies, and consider a wide diversity of deviations. We show that our\ngenetic algorithm converges to the analytical Nash equilibrium of the symmetric\nBlotto game. We present the solution concept it provides for asymmetrical\nBlotto games. It notably sees the emergence of \"guerilla warfare\" strategies,\nconsistent with empirical and experimental findings. The player with less\nresources learns to concentrate its resources to compensate for the asymmetry\nof competition. When players value battlefields heterogeneously, counter\nstrategies and bidding focus is obtained in equilibrium. These features are\nconsistent with empirical and experimental findings, and provide a learning\nfoundation for their existence.\n"}
+{"abstract": "  Recently proposed orthogonal time frequency space (OTFS) modulation has been\nconsidered as a promising candidate for accommodating various emerging\ncommunication and sensing applications in high-mobility environments. In this\npaper, we propose a novel cross domain iterative detection algorithm to enhance\nthe error performance of OTFS modulation. Different from conventional OTFS\ndetection methods, the proposed algorithm applies basic estimation/detection\napproaches to both the time domain and delay-Doppler (DD) domain and\niteratively updates the extrinsic information from two domains with the unitary\ntransformation. In doing so, the proposed algorithm exploits the time domain\nchannel sparsity and the DD domain symbol constellation constraints. We\nevaluate the estimation/detection error variance in each domain for each\niteration and derive the state evolution to investigate the detection error\nperformance. We show that the performance gain due to iterations comes from the\nnon-Gaussian constellation constraint in the DD domain. More importantly, we\nprove the proposed algorithm can indeed converge and, in the convergence, the\nproposed algorithm can achieve almost the same error performance as the\nmaximum-likelihood sequence detection even in the presence of fractional\nDoppler shifts. Furthermore, the computational complexity associated with the\ndomain transformation is low, thanks to the structure of the discrete Fourier\ntransform (DFT) kernel. Simulation results are consistent with our analysis and\ndemonstrate a significant performance improvement compared to conventional OTFS\ndetection methods.\n"}
+{"abstract": "  We propose MultiOpEd, an open-domain news editorial corpus that supports\nvarious tasks pertaining to the argumentation structure in news editorials,\nfocusing on automatic perspective discovery. News editorial is a genre of\npersuasive text, where the argumentation structure is usually implicit.\nHowever, the arguments presented in an editorial typically center around a\nconcise, focused thesis, which we refer to as their perspective. MultiOpEd aims\nat supporting the study of multiple tasks relevant to automatic perspective\ndiscovery, where a system is expected to produce a single-sentence thesis\nstatement summarizing the arguments presented. We argue that identifying and\nabstracting such natural language perspectives from editorials is a crucial\nstep toward studying the implicit argumentation structure in news editorials.\nWe first discuss the challenges and define a few conceptual tasks towards our\ngoal. To demonstrate the utility of MultiOpEd and the induced tasks, we study\nthe problem of perspective summarization in a multi-task learning setting, as a\ncase study. We show that, with the induced tasks as auxiliary tasks, we can\nimprove the quality of the perspective summary generated. We hope that\nMultiOpEd will be a useful resource for future studies on argumentation in the\nnews editorial domain.\n"}
+{"abstract": "  We generalize Conway-Sloane's constructions of the Leech lattice from\nNiemeier lattices using Lorentzian lattice to holomorphic vertex operator\nalgebras (VOA) of central charge 24. It provides a tool for analyzing the\nstructures and relations among holomorphic VOAs related by orbifold\nconstruction. In particular, we are able to get some useful information about\ncertain lattice subVOAs associated with the Cartan subalgebra of the weight one\nLie algebra. We also obtain a relatively elementary proof that any strongly\nregular holomorphic VOA of central charge $24$ with a non-trivial weight one\nsubspace can be constructed directly by a single orbifold construction from the\nLeech lattice VOA without using a dimension formula.\n"}
+{"abstract": "  Observations reveal a relatively small but statistically significant\nNorth-South (NS) asymmetry in sunspot activity varying on a time scale of\nseveral solar cycles. This paper proposes a dynamo model for the phenomenon of\nlong-term NS asymmetry. The model separates dynamo equations for magnetic\nfields of dipolar and quadrupolar equatorial parity. The NS asymmetry results\nfrom the superposition of dipolar and quadrupolar fields. Model computations\nconfirm the formerly proposed excitation of the quadrupolar dynamo mode by a\ndominant dipolar mode mediated by the equator-symmetric fluctuations in the\n$\\alpha$-effect as a mechanism for the long-term NS asymmetry. An analytically\nsolvable example of oscillations excited by short-term random forcing is given\nto justify the numerical result of NS asymmetry coherent on a time scale of\nseveral (about 6 in the present model) solar cycles resulting from random\nvariations in the $\\alpha$-effect on a time scale of one solar rotation. The\nmodel computations show the phase locking phenomenon of dipolar and quadrupolar\nfields oscillating predominantly in phase (northern type asymmetry) or in\nantiphase (southern type asymmetry) with relatively short irregular transitions\nbetween these two states. Large asymmetry in the simulated Grand minima is\nfound and explained by weak magnetic quenching of the $\\alpha$-effect during\nthe minima. The possibility of polar field asymmetry in activity minima as a\nprecursor of sunspot asymmetry in the following activity cycles is discussed\nbased on the dynamo model and observations.\n"}
+{"abstract": "  This paper describes the simulation framework of the Extreme Energy Events\n(EEE) experiment. EEE is a network of cosmic muon trackers, each made of three\nMulti-gap Resistive Plate Chambers (MRPC), able to precisely measure the\nabsolute muon crossing time and the muon integrated angular flux at the ground\nlevel. The response of a single MRPC and the combination of three chambers have\nbeen implemented in a GEANT4-based framework (GEMC) to study the telescope\nresponse. The detector geometry, as well as details about the surrounding\nmaterials and the location of the telescopes have been included in the\nsimulations in order to realistically reproduce the experimental set-up of each\ntelescope. A model based on the latest parametrization of the cosmic muon flux\nhas been used to generate single muon events. After validating the framework by\ncomparing simulations to selected EEE telescope data, it has been used to\ndetermine detector parameters not accessible by analysing experimental data\nonly, such as detection efficiency, angular and spatial resolution.\n"}
+{"abstract": "  Micro-expressions (MEs) are involuntary facial movements revealing people's\nhidden feelings in high-stake situations and have practical importance in\nmedical treatment, national security, interrogations and many human-computer\ninteraction systems. Early methods for MER mainly based on traditional\nappearance and geometry features. Recently, with the success of deep learning\n(DL) in various fields, neural networks have received increasing interests in\nMER. Different from macro-expressions, MEs are spontaneous, subtle, and rapid\nfacial movements, leading to difficult data collection, thus have small-scale\ndatasets. DL based MER becomes challenging due to above ME characters. To date,\nvarious DL approaches have been proposed to solve the ME issues and improve MER\nperformance. In this survey, we provide a comprehensive review of deep\nmicro-expression recognition (MER), including datasets, deep MER pipeline, and\nthe bench-marking of most influential methods. This survey defines a new\ntaxonomy for the field, encompassing all aspects of MER based on DL. For each\naspect, the basic approaches and advanced developments are summarized and\ndiscussed. In addition, we conclude the remaining challenges and and potential\ndirections for the design of robust deep MER systems. To the best of our\nknowledge, this is the first survey of deep MER methods, and this survey can\nserve as a reference point for future MER research.\n"}
+{"abstract": "  Multi-Target Multi-Camera Tracking has a wide range of applications and is\nthe basis for many advanced inferences and predictions. This paper describes\nour solution to the Track 3 multi-camera vehicle tracking task in 2021 AI City\nChallenge (AICITY21). This paper proposes a multi-target multi-camera vehicle\ntracking framework guided by the crossroad zones. The framework includes: (1)\nUse mature detection and vehicle re-identification models to extract targets\nand appearance features. (2) Use modified JDETracker (without detection module)\nto track single-camera vehicles and generate single-camera tracklets. (3)\nAccording to the characteristics of the crossroad, the Tracklet Filter Strategy\nand the Direction Based Temporal Mask are proposed. (4) Propose Sub-clustering\nin Adjacent Cameras for multi-camera tracklets matching. Through the above\ntechniques, our method obtained an IDF1 score of 0.8095, ranking first on the\nleaderboard. The code have released: https://github.com/LCFractal/AIC21-MTMC.\n"}
+{"abstract": "  We present the first application of a hierarchical Markov Chain Monte Carlo\n(MCMC) follow-up on continuous gravitational-wave candidates from real-data\nsearches. The follow-up uses an MCMC sampler to draw parameter-space points\nfrom the posterior distribution, constructed using the matched-filter as a\nlog-likelihood. As outliers are narrowed down, coherence time increases,\nimposing more restrictive phase-evolution templates. We introduce a novel Bayes\nfactor to compare results from different stages: The signal hypothesis is\nderived from first principles, while the noise hypothesis uses extreme value\ntheory to derive a background model. The effectiveness of our proposal is\nevaluated on fake Gaussian data and applied to a set of 30 outliers produced by\ndifferent continuous wave searches on O2 Advanced LIGO data. The results of our\nanalysis suggest all but five outliers are inconsistent with an astrophysical\norigin under the standard continuous wave signal model. We successfully ascribe\nfour of the surviving outliers to instrumental artifacts and a strong hardware\ninjection present in the data. The behavior of the fifth outlier suggests an\ninstrumental origin as well, but we could not relate it to any known\ninstrumental cause.\n"}
+{"abstract": "  We propose a scheme of lattice twisted-mass fermion regularization which is\nparticularly convenient for application to isospin breaking (IB) QCD and QED\ncalculations, based in particular on the so called RM123 approach, in which the\nIB terms of the action are treated as a perturbation. The main, practical\nadvantage of this scheme is that it allows the calculation of IB effects on\nsome mesonic observables, like e.g. the pi+ - pi0 mass splitting, using lattice\ncorrelation functions in which the quark and antiquark fields in the meson are\nregularized with opposite values of the Wilson parameter r. These correlation\nfunctions are found to be affected by much smaller statistical fluctuations,\nwith respect to the analogous functions in which quark and antiquark fields are\nregularized with the same value of r. Two numerical application of this scheme,\nthat we call \"rotated twisted-mass\", within pure QCD and QCD+QED respectively,\nare also provided for illustration.\n"}
+{"abstract": "  We construct a dynamical quantization for contact manifolds in terms of a\nflat connection acting on a Hilbert tractor bundle. We show that this contact\nquantization, which is independent of the choice of contact form, can be\nobtained by quantizing the Reeb dynamics of an ambient strict contact manifold\nequivariantly with respect to an R+-action. The contact quantization further\ndetermines a certain contact tractor connection whose parallel sections\ndetermine a distinguished choice of Reeb dynamics and their quantization. This\nrelationship relies on tractor constructions from parabolic geometries and\nmirrors the tight relationship between Einstein metrics and conformal\ngeometries. Finally, we construct in detail the dynamical quantization of the\nunique tight contact structure on the 3-sphere, where the Holstein-Primakoff\ntransformation makes a surprising appearance.\n"}
+{"abstract": "  In this paper we prove that every $2$-generator finite $p$-group $G$ has a\nnon-inner automorphism of order $p$ leaving $G^p\\gamma_4(G)$ elementwise fixed\n($p\\ge 5$). Moreover, we prove a $2$-generator finite $3$-group satisfying\n$|\\Omega_1(Z_2(G))|=p^2$ has a non-inner automorphism of order $p$ leaving\n$G^p\\gamma_3(G)$ elementwise fixed. As a consequence we prove the non-inner\nautomorphism conjecture for every finite $p$-group of coclass $4$ ($p\\ge 3$),\nand coclass $5$ ($p\\ge 5$).\n"}
+{"abstract": "  A pair of Banach spaces $(E, F)$ is said to have the weak maximizing property\n(WMP, for short) if for every bounded linear operator $T$ from $E$ into $F$,\nthe existence of a non-weakly null maximizing sequence for $T$ implies that $T$\nattains its norm. This property was recently introduced in an article by R.\nAron, D. Garc\\'ia, D. Pelegrino and E. Teixeira, raising several open\nquestions. The aim of the present paper is to contribute to the better\nknowledge of the WMP and its limitations. Namely, we provide sufficient\nconditions for a pair of Banach spaces to fail the WMP and study the behaviour\nof this property with respect to quotients, subspaces, and direct sums, which\nopen the gate to present several consequences. For instance, we deal with pairs\nof the form $(L_p[0,1], L_q[0,1])$, proving that these pairs fail the WMP\nwhenever $p>2$ or $q<2$. We also show that, under certain conditions on $E$,\nthe assumption that $(E, F)$ has the WMP for every Banach space $F$ implies\nthat $E$ must be finite dimensional. On the other hand, we show that $(E, F)$\nhas the WMP for every reflexive space $E$ if and only if $F$ has the Schur\nproperty. We also give a complete characterization for the pairs $(\\ell_s\n\\oplus_p \\ell_p, \\ell_s \\oplus_q \\ell_q)$ to have the WMP by calculating the\nmoduli of asymptotic uniform convexity of $\\ell_s \\oplus_p \\ell_p$ and of\nasymptotic uniform smoothness of $\\ell_s \\oplus_q \\ell_q$ when $1 < p \\leq s\n\\leq q < \\infty$. We conclude the paper by discussing some variants of the WMP\nand presenting a list of open problems on the topic of the paper.\n"}
+{"abstract": "  Code-Switching (CSW) is a common phenomenon that occurs in multilingual\ngeographic or social contexts, which raises challenging problems for natural\nlanguage processing tools. We focus here on Machine Translation (MT) of CSW\ntexts, where we aim to simultaneously disentangle and translate the two mixed\nlanguages. Due to the lack of actual translated CSW data, we generate\nartificial training data from regular parallel texts. Experiments show this\ntraining strategy yields MT systems that surpass multilingual systems for\ncode-switched texts. These results are confirmed in an alternative task aimed\nat providing contextual translations for a L2 writing assistant.\n"}
+{"abstract": "  We present heyoka, a new, modern and general-purpose implementation of\nTaylor's integration method for the numerical solution of ordinary differential\nequations. Detailed numerical tests focused on difficult high-precision\ngravitational problems in astrodynamics and celestial mechanics show how our\ngeneral-purpose integrator is competitive with and often superior to\nstate-of-the-art specialised symplectic and non-symplectic integrators in both\nspeed and accuracy. In particular, we show how Taylor methods are capable of\nsatisfying Brouwer's law for the conservation of energy in long-term\nintegrations of planetary systems over billions of dynamical timescales. We\nalso show how close encounters are modelled accurately during simulations of\nthe formation of the Kirkwood gaps and of Apophis' 2029 close encounter with\nthe Earth (where heyoka surpasses the speed and accuracy of domain-specific\nmethods). heyoka can be used from both C++ and Python, and it is publicly\navailable as an open-source project.\n"}
+{"abstract": "  The problem of recognizing various types of tissues present in\nmulti-gigapixel histology images is an important fundamental pre-requisite for\ndownstream analysis of the tumor microenvironment in a bottom-up analysis\nparadigm for computational pathology. In this paper, we propose a deep\ndictionary learning approach to solve the problem of tissue phenotyping in\nhistology images. We propose deep Multi-Resolution Dictionary Learning\n(deepMRDL) in order to benefit from deep texture descriptors at multiple\ndifferent spatial resolutions. We show the efficacy of the proposed approach\nthrough extensive experiments on four benchmark histology image datasets from\ndifferent organs (colorectal cancer, breast cancer and breast lymphnodes) and\ntasks (namely, cancer grading, tissue phenotyping, tumor detection and tissue\ntype classification). We also show that the proposed framework can employ most\noff-the-shelf CNNs models to generate effective deep texture descriptors.\n"}
+{"abstract": "  Unsupervised pretraining is an integral part of many natural language\nprocessing systems, and transfer learning with language models has achieved\nremarkable results in many downstream tasks. In the clinical application of\nmedical code assignment, diagnosis and procedure codes are inferred from\nlengthy clinical notes such as hospital discharge summaries. However, it is not\nclear if pretrained models are useful for medical code prediction without\nfurther architecture engineering. This paper conducts a comprehensive\nquantitative analysis of various contextualized language models' performance,\npretrained in different domains, for medical code assignment from clinical\nnotes. We propose a hierarchical fine-tuning architecture to capture\ninteractions between distant words and adopt label-wise attention to exploit\nlabel information. Contrary to current trends, we demonstrate that a carefully\ntrained classical CNN outperforms attention-based models on a MIMIC-III subset\nwith frequent codes. Our empirical findings suggest directions for improving\nthe medical code assignment application.\n"}
+{"abstract": "  The search of new physics~(NP) beyond the Standard Model is one of the most\nimportant tasks of high energy physics. A common characteristic of the NP\nsignals is that they are usually few and kinematically different. We use a\nmodel independent strategy to study the phenomenology of NP by directly picking\nout and studying the kinematically unusual events. For this purpose, the\nisolation forest~(IF) algorithm is applied, which is found to be efficient in\nidentifying the signal events of the anomalous quartic gauge couplings~(aQGCs).\nThe IF algorithm can also be used to constraint the coefficients of aQGCs. As a\nmachine learning algorithm, the IF algorithm shows a good prospect in the\nfuture studies of NP.\n"}
+{"abstract": "  We prove a generalized version of Renault's theorem for Cartan subalgebras.\nWe show that the original assumptions of second countability and separability\nare not needed. This weakens the assumption of topological principality of the\nunderlying groupoid to effectiveness.\n"}
+{"abstract": "  We consider chance-constrained binary knapsack problems, where the weights of\nitems are independent random variables with the means and standard deviations\nknown. The chance constraint can be reformulated as a second-order cone\nconstraint under some assumptions for the probability distribution of the\nweights. The problem becomes a second-order cone-constrained binary knapsack\nproblem, which is equivalent to a robust binary knapsack problem with an\nellipsoidal uncertainty set.\n  We demonstrate that optimal solutions to robust binary knapsack problems with\ninner and outer polyhedral approximations of the ellipsoidal uncertainty set\ncan provide both upper and lower bounds on the optimal value of the\nsecond-order cone-constrained binary knapsack problem, and they can be obtained\nby solving ordinary binary knapsack problems repeatedly. Moreover, we prove\nthat the solution providing the upper bound converges to the optimal solution\nto the second-order cone-constrained binary knapsack problem as the\napproximation of the uncertainty set becomes more accurate. Based on this, a\npseudo-polynomial time algorithm is obtained under the assumption that the\ncoefficients of the problem are integer-valued. We also propose an exact\nalgorithm, which iteratively improves the accuracy of the approximation until\nan exact optimal solution is obtained. The exact algorithm also runs in\npseudo-polynomial time. Computational results are presented to show the quality\nof the upper and lower bounds and efficiency of the exact algorithm compared to\nCPLEX and a previous study.\n"}
+{"abstract": "  In recent years, the use of multilevel inverter has become popular due to its\nmany advantages. Due to the popularity of multilevel inverters in industries\nand in applications that require a wide range of voltages, there have been many\nchallenges to achieve high-quality voltage. Many pieces of research have been\ndone to solve the problem of annoying harmonics in multilevel inverters. In\nthis study, pulse width modulation (PWM) has been proposed as a switching\nmethod. Using the Selective Harmonic Elimination (SHE) technique, the inverter\nswitching can be carried out in low frequency, and also, this method can reduce\nthe annoying harmonics significantly. However, in the lower modulation index,\neliminating such harmonics is challenging, resulting in a considerable increase\nin output voltage distortion. This study suggests a way to solve the problem.\nIn this research, eliminating selected harmonics in a multilevel inverter with\nvariable DC links is proposed. The DC-link variable method in this study is to\nuse a high-frequency isolated DC-DC converter. The proposed method is verified\non a 5-level Cascaded H-Bridge (CHB) inverter using the SHE-PWM method solved\nby particle swarm optimization (PSO) algorithm.\n"}
+{"abstract": "  A model with complete supersymmetric unification of the Standard Model matter\ncontent, interactions and families replication into a single $E_8$ gauge\nsuperfield in ten dimensions is presented. The gauge and extended Poincar\\'e\nsymmetries are broken through compactification of the\n$\\mathbb{T}^6/(\\mathbb{Z}_3\\times \\mathbb{Z}_3)$ orbifold with Wilson lines,\nwhich reduces the original symmetry and matter content into those of the\nStandard Model. Proton decay is strongly suppressed automatically, while the\nunification scale is required to be as low as $10^6-10^9\\ {\\rm GeV}$ so that\nthe corresponding physics may be potentially accessible and testable by future\ncollider measurements. Such a low-scale Grand Unification concept yields a\nhighly-constrained and predictive New Physics framework in a minimal and\nphenomenologically testable and consistent way.\n"}
+{"abstract": "  Almost paracontact Riemannian manifolds of the lowest dimension are studied,\nwhose paracontact distributions are equipped with an almost paracomplex\nstructure. These manifolds are constructed as a product of a real line and a\n2-dimensional Riemannian space-form. Their metric is obtained in two ways: as a\ncone metric and as a hyperbolic extension of the metric of the underlying\nparacomplex 2-manifold. The resulting manifolds are studied and characterized\nin terms of the classification used and their curvature properties.\n"}
+{"abstract": "  Entanglement is an indispensable quantum resource for quantum information\ntechnology. In continuous-variable quantum optics, photon subtraction can\nincrease the entanglement between Gaussian states of light, but for mixed\nstates the extent of this entanglement increase is poorly understood. In this\nwork, we use an entanglement measure based the R\\'enyi-2 entropy to prove that\nsingle-photon subtraction increases bipartite entanglement by no more than\nlog2. This value coincides with the maximal amount of bipartite entanglement\nthat can be achieved with one photon.\n"}
+{"abstract": "  We approach the important challenge of code autocompletion as an open-domain\ntask, in which a sequence-to-sequence code generator model is enhanced with the\nability to attend to reference code snippets supplied by a semantic code search\nengine. In this work, we present a novel framework to precisely retrieve\ntemplate functions as well as intent-snippet pairs and effectively train such a\nretrieval-guided code generator. To demonstrate the effectiveness of our model\ndesigns, we perform extensive experiments with CodeSearchNet which contains\ntemplate functions and CoNaLa which contains Stack Overflow intent-snippet\npairs. We also investigate different retrieval models, including Elasticsearch,\nDPR, and our fusion representation search model, which currently holds the\nnumber one spot on the CodeSearchNet leaderboard. We observe improvements by\nleveraging multiple database elements and further gain from retrieving diverse\ndata points by using Maximal Marginal Relevance. Overall, we see a 4%\nimprovement to cross-entropy loss, a 15% improvement to edit distance, and a\n44% improvement to BLEU score when retrieving template functions. We see\nsubtler improvements of 2%, 11%, and 6% respectively when retrieving Stack\nOverflow intent-snippet pairs. We also create a novel Stack Overflow-Function\nAlignment dataset, which consists of 150K tuples of functions and Stack\nOverflow intent-snippet pairs that are of help in writing the associated\nfunction, of which 1.7K are manually curated.\n"}
+{"abstract": "  We extend Sharkovskii's theorem to the cases of $N$-dimensional maps which\nare close to 1D maps, with an attracting $n$-periodic orbit. We prove that,\nwith relatively weak topological assumptions, there exist also $m$-periodic\norbits for all $m\\triangleright n$ in Sharkovskii's order, in the nearby.\n  We also show, as an example of application, how to obtain such a result for\nthe R\\\"ossler system with an attracting periodic orbit, for four sets of\nparameter values. The proofs are computer-assisted.\n"}
+{"abstract": "  We show that the attraction-repulsion chemotaxis system \\begin{equation*}\n\\begin{cases} u_t = \\Delta u - \\chi\\nabla\\cdot(u\\nabla v_1) +\n\\xi\\nabla\\cdot(u\\nabla v_2)\\\\\n  \\partial_t v_1 = \\Delta v_1 - \\beta v_1 + \\alpha u \\\\\n  \\partial_t v_2 = \\Delta v_2 - \\delta v_2 + \\gamma u, \\end{cases}\n\\end{equation*} posed with homogeneous Neumann boundary conditions in bounded\ndomains $\\Omega=B_R \\subset \\mathbb{R}^3$, $R>0$, admits radially symmetric\nsolutions which blow-up in finite time if it is attraction-dominated in the\nsense that $\\chi\\alpha-\\xi\\gamma>0$.\n"}
+{"abstract": "  Friedel oscillation is a well-known wave phenomenon, which represents the\noscillatory response of electron waves to imperfection. By utilizing the\npseudospin-momentum locking in gapless graphene, two recent experiments\ndemonstrate the measurement of the topological Berry phase by corresponding to\nthe unique number of wavefront dislocations in Friedel oscillations. Here, we\nstudy the Friedel oscillations in gapped graphene, in which the\npseudospin-momentum locking is broken. Unusually, the wavefront dislocations do\noccur as that in gapless graphene, which expects the immediate verification in\nthe current experimental condition. The number of wavefront dislocations is\nascribed to the invariant pseudospin winding number in gaped and gapless\ngraphene. This study deepens the understanding of correspondence between\ntopological quantity and wavefront dislocations in Friedel oscillations, and\nimplies the possibility to observe the wavefront dislocations of Friedel\noscillations in intrinsic gapped two-dimensional materials, e.g., transition\nmetal dichalcogenides.\n"}
+{"abstract": "  The scattering-adapted flexible inner region ensemble separator (SAFIRES) is\na partitioning scheme designed to divide a simulation cell into two regions to\nbe treated with different computational methodologies. SAFIRES prevents\nparticles from crossing between regions and resolves boundary events through\nelastic collisions of the particles mediated by the boundary, conserving energy\nand momenta. A multiple-time-step propagation algorithm is introduced where the\ntime step is scaled automatically to identify the moment a collision occurs. If\nthe length of the time step is kept constant, the new propagator reduces to a\nregular algorithm for Langevin dynamics, and to the velocity Verlet algorithm\nfor classical dynamics if the friction coefficient is set to zero. SAFIRES\nconstitutes the exact limit of the premise behind boundary-based methods such\nas FIRES, BEST, and BCC which take advantage of the indistinguishability of\nmolecules on opposite sides of the separator. It gives correct average ensemble\nstatistics despite the introduction of an ensemble separator. SAFIRES is tested\nin simulations where the molecules on the two sides are treated in the same\nway, for a Lennard-Jones (LJ) liquid and a LJ liquid in contact with a surface,\nas well as for liquid modelling simulations using the TIP4P force field.\nSimulations using SAFIRES are shown to reproduce the unconstrained reference\nsimulations without significant deviations.\n"}
+{"abstract": "  Energy-based models (EBMs) are generative models that are usually trained via\nmaximum likelihood estimation. This approach becomes challenging in generic\nsituations where the trained energy is nonconvex, due to the need to sample the\nGibbs distribution associated with this energy. Using general Fenchel duality\nresults, we derive variational principles dual to maximum likelihood EBMs with\nshallow overparametrized neural network energies, both in the active (aka\nfeature-learning) and lazy regimes. In the active regime, this dual formulation\nleads to a training algorithm in which one updates concurrently the particles\nin the sample space and the neurons in the parameter space of the energy. We\nalso consider a variant of this algorithm in which the particles are sometimes\nrestarted at random samples drawn from the data set, and show that performing\nthese restarts at every iteration step corresponds to score matching training.\nUsing intermediate parameter setups in our dual algorithm thereby gives a way\nto interpolate between maximum likelihood and score matching training. These\nresults are illustrated in simple numerical experiments.\n"}
+{"abstract": "  Models of asymmetric dark matter (ADM) seek to explain the apparent\ncoincidence between the present-day mass densities of visible and dark matter,\n$\\Omega_{\\mathrm{DM}} \\simeq 5\\Omega_{\\mathrm{VM}}$. However, most ADM models\nonly relate the number densities of visible and dark matter without motivating\nthe similar particle masses. We expand upon a recent work that obtained a\nnatural mass relationship in a mirror matter ADM model with two Higgs doublets\nin each sector, by looking to implement dark electroweak baryogenesis as the\nmeans of asymmetry generation. We explore two aspects of the mechanism: the\nnature of the dark electroweak phase transition, and the transfer of particle\nasymmetries between the sectors by the use of portal interactions. We find that\nboth aspects can be implemented successfully for various regions of the\nparameter space. We also analyse one portal interaction -- the neutron portal\n-- in greater detail, in order to satisfy the observational constraints on dark\nradiation.\n"}
+{"abstract": "  We report on the electronic properties of an artificial system obtained by\nthe intercalation of equiatomic FeCo layers under graphene grown on Ir(111).\nUpon intercalation, the FeCo film grows epitaxially on Ir(111), resulting in a\nlattice-mismatched system. By performing Density Functional Theory\ncalculations, we show that the intercalated FeCo layer leads to a pronounced\ncorrugation of the graphene film. At the same time, the FeCo intercalated\nlayers induce a clear transition from a nearly undisturbed to a strongly\nhybridized graphene {\\pi}-band, as measured by angle-resolved photoemission\nspectroscopy. A comparison of experimental results with the computed band\nstructure and the projected density of states unveils a spin-selective\nhybridization between the {\\pi} band of graphene and FeCo-3d states. Our\nresults demonstrate that the reduced dimensionality, as well as the\nhybridization within the FeCo layers, induce a narrowing and a clear splitting\nof Fe 3d-up and Fe 3d-down spin bands of the confined FeCo layers with respect\nto bulk Fe and Co.\n"}
+{"abstract": "  Estimating three-dimensional human poses from the positions of\ntwo-dimensional joints has shown promising results.However, using\ntwo-dimensional joint coordinates as input loses more information than\nimage-based approaches and results in ambiguity.In order to overcome this\nproblem, we combine bone length and camera parameters with two-dimensional\njoint coordinates for input.This combination is more discriminative than the\ntwo-dimensional joint coordinates in that it can improve the accuracy of the\nmodel's prediction depth and alleviate the ambiguity that comes from projecting\nthree-dimensional coordinates into two-dimensional space. Furthermore, we\nintroduce direction constraints which can better measure the difference between\nthe ground truth and the output of the proposed model. The experimental results\non the H36M show that the method performed better than other state-of-the-art\nthree-dimensional human pose estimation approaches.\n"}
+{"abstract": "  In this paper, we proposed a multi-document summarization system using\nsemantic role labeling (SRL) and semantic graph for Indonesian news articles.\nIn order to improve existing summarizer, our system modified summarizer that\nemployed subject, predicate, object, and adverbial (SVOA) extraction for\npredicate argument structure (PAS) extraction. SVOA extraction is replaced with\nSRL model for Indonesian. We also replace the genetic algorithm to identify\nimportant PAS with the decision tree classifier since the summarizer without\ngenetic algorithm gave better performance. The decision tree model is employed\nto identify important PAS. The decision tree model with 10 features achieved\nbetter performance than decision tree with 4 sentence features. Experiments and\nevaluations are conducted to generate 100 words summary and 200 words summary.\nThe evaluation shows the proposed model get 0.313 average ROUGE-2 recall in 100\nwords summary and 0.394 average ROUGE-2 recall in 200 words summary.\n"}
+{"abstract": "  The task of binary quantum hypothesis testing is to determine the state of a\nquantum system via measurements on it, given the side information that it is in\none of two possible states, say $\\rho$ and $\\sigma$. This task is generally\nstudied in either the symmetric setting, in which the two possible errors\nincurred in the task (the so-called type I and type II errors) are treated on\nan equal footing, or the asymmetric setting in which one minimizes the type II\nerror probability under the constraint that the corresponding type I error\nprobability is below a given threshold. Here we define a one-parameter family\nof binary quantum hypothesis testing tasks, which we call $s$-hypothesis\ntesting, and in which the relative significance of the two errors are weighted\nby a parameter $s$. In particular, $s$-hypothesis testing interpolates\ncontinuously between the regimes of symmetric and asymmetric hypothesis\ntesting. Moreover, if arbitrarily many identical copies of the system are\nassumed to be available, then the minimal error probability of $s$-hypothesis\ntesting is shown to decay exponentially in the number of copies, with a decay\nrate given by a quantum divergence which we denote as $\\xi_s(\\rho\\|\\sigma)$,\nand which satisfies a host of interesting properties. Moreover, this\none-parameter family of divergences interpolates continuously between the\ncorresponding decay rates for symmetric hypothesis testing (the quantum\nChernoff divergence) for $s = 1$, and asymmetric hypothesis testing (the\nUmegaki relative entropy) for $s = 0$.\n"}
+{"abstract": "  Recommendation systems often use online collaborative filtering (CF)\nalgorithms to identify items a given user likes over time, based on ratings\nthat this user and a large number of other users have provided in the past.\nThis problem has been studied extensively when users' preferences do not change\nover time (static case); an assumption that is often violated in practical\nsettings. In this paper, we introduce a novel model for online non-stationary\nrecommendation systems which allows for temporal uncertainties in the users'\npreferences. For this model, we propose a user-based CF algorithm, and provide\na theoretical analysis of its achievable reward. Compared to related\nnon-stationary multi-armed bandit literature, the main fundamental difficulty\nin our model lies in the fact that variations in the preferences of a certain\nuser may affect the recommendations for other users severely. We also test our\nalgorithm over real-world datasets, showing its effectiveness in real-world\napplications. One of the main surprising observations in our experiments is the\nfact our algorithm outperforms other static algorithms even when preferences do\nnot change over time. This hints toward the general conclusion that in\npractice, dynamic algorithms, such as the one we propose, might be beneficial\neven in stationary environments.\n"}
+{"abstract": "  Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA)\nhave been regarded as promising technologies to improve computation capability\nand offloading efficiency of the mobile devices in the sixth generation (6G)\nmobile system. This paper mainly focuses on the hybrid NOMA-MEC system, where\nmultiple users are first grouped into pairs, and users in each pair offload\ntheir tasks simultaneously by NOMA, and then a dedicated time duration is\nscheduled to the more delay-tolerable user for uploading the remaining data by\northogonal multiple access (OMA). For the conventional NOMA uplink\ntransmission, successive interference cancellation (SIC) is applied to decode\nthe superposed signals successively according to the channel state information\n(CSI) or the quality of service (QoS) requirement. In this work, we integrate\nthe hybrid SIC scheme which dynamically adapts the SIC decoding order among all\nNOMA groups. To solve the user grouping problem, a deep reinforcement learning\n(DRL) based algorithm is proposed to obtain a close-to-optimal user grouping\npolicy. Moreover, we optimally minimize the offloading energy consumption by\nobtaining the closed-form solution to the resource allocation problem.\nSimulation results show that the proposed algorithm converges fast, and the\nNOMA-MEC scheme outperforms the existing orthogonal multiple access (OMA)\nscheme.\n"}
+{"abstract": "  We consider the problem of computing shortest paths in weighted unit-disk\ngraphs in constant dimension $d$. Although the single-source and all-pairs\nvariants of this problem are well-studied in the plane case, no non-trivial\nexact distance oracles for unit-disk graphs have been known to date, even for\n$d=2$.\n  The classical result of Sedgewick and Vitter [Algorithmica '86] shows that\nfor weighted unit-disk graphs in the plane the $A^*$ search has average-case\nperformance superior to that of a standard shortest path algorithm, e.g.,\nDijkstra's algorithm. Specifically, if the $n$ corresponding points of a\nweighted unit-disk graph $G$ are picked from a unit square uniformly at random,\nand the connectivity radius is $r\\in (0,1)$, $A^*$ finds a shortest path in $G$\nin $O(n)$ expected time when $r=\\Omega(\\sqrt{\\log n/n})$, even though $G$ has\n$\\Theta((nr)^2)$ edges in expectation. In other words, the work done by the\nalgorithm is in expectation proportional to the number of vertices and not the\nnumber of edges.\n  In this paper, we break this natural barrier and show even stronger sublinear\ntime results. We propose a new heuristic approach to computing point-to-point\nexact shortest paths in unit-disk graphs. We analyze the average-case behavior\nof our heuristic using the same random graph model as used by Sedgewick and\nVitter and prove it superior to $A^*$. Specifically, we show that, if we are\nable to report the set of all $k$ points of $G$ from an arbitrary rectangular\nregion of the plane in $O(k + t(n))$ time, then a shortest path between\narbitrary two points of such a random graph on the plane can be found in\n$O(1/r^2 + t(n))$ expected time. In particular, the state-of-the-art range\nreporting data structures imply a sublinear expected bound for all\n$r=\\Omega(\\sqrt{\\log n/n})$ and $O(\\sqrt{n})$ expected bound for\n$r=\\Omega(n^{-1/4})$ after only near-linear preprocessing of the point set.\n"}
+{"abstract": "  After the 2017 TuSimple Lane Detection Challenge, its evaluation based on\naccuracy and F1 score has become the de facto standard to measure the\nperformance of lane detection methods. In this work, we conduct the first\nlarge-scale empirical study to evaluate the robustness of state-of-the-art lane\ndetection methods under physical-world adversarial attacks in autonomous\ndriving. We evaluate 4 major types of lane detection approaches with the\nconventional evaluation and end-to-end evaluation in autonomous driving\nscenarios and then discuss the security proprieties of each lane detection\nmodel. We demonstrate that the conventional evaluation fails to reflect the\nrobustness in end-to-end autonomous driving scenarios. Our results show that\nthe most robust model on the conventional metrics is the least robust in the\nend-to-end evaluation. Although the competition dataset and its metrics have\nplayed a substantial role in developing performant lane detection methods along\nwith the rapid development of deep neural networks, the conventional evaluation\nis becoming obsolete and the gap between the metrics and practicality is\ncritical. We hope that our study will help the community make further progress\nin building a more comprehensive framework to evaluate lane detection models.\n"}
+{"abstract": "  Our study of FeSe$ _ {1-x}$Te$ _ {x}$ crystals with x $<$ 0.15 shows that the\nphase separation in these compositions occurs into phases with a different\nstoichiometry of iron. This phase separation may indicate structural\ninstability of the iron plane in the studied range of compositions. To explain\nit, we discuss the bond polarity and the peculiarity of the direct $d$ exchange\nin the iron plane in the framework of the basic phenomenological description\nsuch as the Bethe-Slater curve. With this approach, when the distance between\niron atoms is close to the value at which the sign of the magnetic exchange for\nsome $d$ orbitals changes, structural and electronic instability can occur.\nAnomalies in the crystal field near the point of charge neutrality can also be\na significant component of this instability. A similar instability of the iron\nplane may also be an important factor for other series of iron-based\nsuperconductors.\n"}
+{"abstract": "  We establish an explicit formula for the character of an irreducible\nfinite-dimensional representation of $\\mathfrak{gl}(m|n)$. The formula is a\nfinite sum with integer coefficients in terms of a basis $\\mathcal{E}_{\\mu}$\n(Euler characters) of the character ring. We prove a simple formula for the\nbehaviour of the ``superversion'' of $\\mathcal{E}_{\\mu}$ in the\n$\\mathfrak{gl}(m|n)$ and $\\mathfrak{osp}(m|2n)$-case under the map $ds$ on the\nsupercharacter ring induced by the Duflo-Serganova cohomology functor $DS$. As\nan application we get combinatorial formulas for superdimensions, dimensions\nand $\\mathfrak{g}_0$-decompositions for $\\mathfrak{gl}(m|n)$ and\n$\\mathfrak{osp}(m|2n)$.\n"}
+{"abstract": "  A bounded container maintains a collection of elements that can be inserted\nand extracted as long as the number of stored elements does not exceed the\npredefined capacity. We consider the concurrent implementations of a bounded\ncontainer more or less memory-friendly depending on how much memory they use in\naddition to storing the elements. This way, memory-optimal implementation\nemploys the minimal possible memory overhead and ensures that data and metadata\nare stored most compactly, reducing cache misses and unnecessary memory\nreclamation.\n  In this paper, we show that non-blocking implementations of a large class of\nelement-independent bounded containers, including queues, stacks, and pools,\nmust incur linear in the number of concurrent processes memory overhead. In the\nspecial cases when the ABA problem is excluded, e.g., by assuming that the\nhardware supports LL/SC instructions or by storing distinct elements, we show\nthat the lower bound can be circumvented, by presenting lock-free bounded\nqueues with constant memory overhead. For the general case, we present a\nCAS-based bounded queue implementation that asymptotically matches our lower\nbound. We believe that these results open a new research avenue devoted to the\nmemory-optimality phenomenon.\n"}
+{"abstract": "  In this paper, we study the initial boundary value problem of the important\nhyperbolic Kirchhoff equation\n  $$u_{tt}-\\left(a \\int_\\Omega |\\nabla u|^2 \\dif x +b\\right)\\Delta u = \\lambda\nu+ |u|^{p-1}u\n  ,$$\n  where $a$, $b>0$, $p>1$, $\\lambda \\in \\mathbb{R}$ and the initial energy is\narbitrarily large. We prove several new theorems on the dynamics such as the\nboundedness or finite time blow-up of solution under the different range of\n$a$, $b$, $\\lambda$ and the initial data for the following cases: (i) $1<p<3$,\n(ii) $p=3$ and $a>1/\\Lambda$, (iii) $p=3$, $a \\leq 1/\\Lambda$ and $\\lam\n<b\\lam_1$, (iv) $p=3$, $a < 1/\\Lambda$ and $\\lam >b\\lam_1$, (v) $p>3$ and\n$\\lam\\leq b\\lam_1$, (vi) $p>3$ and $\\lam> b\\lam_1$, where $\\lam_1 =\n\\inf\\left\\{\\|\\nabla u\\|^2_2 :~ u\\in H^1_0(\\Omega)\\ {\\rm and}\\ \\|u\\|_2\n=1\\right\\}$, and $\\Lambda = \\inf\\left\\{\\|\\nabla u\\|^4_2 :~ u\\in H^1_0(\\Omega)\\\n{\\rm and}\\ \\|u\\|_4 =1\\right\\}$. Moreover, we prove the invariance of some\nstable and unstable sets of the solution for suitable $a$, $b$ and $\\lam$, and\ngive the sufficient conditions of initial data to generate a vacuum region of\nthe solution. Due to the nonlocal effect caused by the nonlocal\nintegro-differential term, we show many interesting differences between the\nblow-up phenomenon of the problem for $a>0$ and $a=0$.\n"}
+{"abstract": "  Lensed quasars and supernovae can be used to study galaxies' gravitational\npotential and measure cosmological parameters. The typical image separation of\nobjects lensed by galaxies is of the order of 0.5\". Therefore, finding the ones\nwith small separations, and measuring their time-delays using ground-based\nobservations is challenging. We suggest a new method to identify lensed quasars\nand simultaneously measure their time-delays, using seeing-limited synoptic\nobservations in which the lensed quasar images and the lensing galaxy are\nunresolved. We show that using the light curve of the combined flux, and the\nastrometric measurements of the center-of-light position of the lensed images,\nthe lensed nature of a quasar can be identified, and its time-delay can be\nmeasured. We provide the analytic formalism to do so, taking into account the\nmeasurement errors and the fact that the power spectra of quasar light curves\nis red (i.e., the light curve is highly correlated). We demonstrate our method\non simulated data, while its implementation to real data will be presented in\nfuture papers. Our simulations suggest that, under reasonable assumptions, the\nnew method can detect unresolved lensed quasars and measure their time delays,\neven when the image separation is below 0.1\", or the flux ratio between the\nfaintest and brightest images is as low as 0.03. Python and MATLAB\nimplementations are provided. In a companion paper, we present a method for\nmeasuring the time delay using the combined flux observations. Although the\nflux-only method is less powerful, it may be useful in cases in which the\nastrometric information is not relevant (e.g., reverberation mapping).\n"}
+{"abstract": "  We propose a homotopy sampling procedure, loosely based on importance\nsampling. Starting from a known probability distribution, the homotopy\nprocedure generates the unknown normalization of a target distribution. In the\ncontext of stationary distributions that are associated with physical systems\nthe method is an alternative way to estimate an unknown microcanonical\nensemble. The process is iterative and also generates samples from the target\ndistribution. In practice, the homotopy procedure does not circumvent using\nsample averages in the estimation of the normalization constant. The error in\nthe procedure depends on the errors incurred in sample averaging and the number\nof stages used in the computational implementation of the process. However, we\nshow that it is possible to exchange the number of homotopy stages and the\ntotal number of samples needed at each stage in order to enhance the\ncomputational efficiency of the implemented algorithm. Estimates of the error\nas a function of stages and sample averages are derived. These could guide\ncomputational efficiency decisions on how the calculation would be mapped to a\ngiven computer architecture. Consideration is given to how the procedure can be\nadapted to Bayesian estimation problems, both stationary and non-stationary.\nEmphasis is placed on the non-stationary problems, and in particular, on a\nsequential estimation technique known as particle filtering. It is shown that a\nmodification of the particle filter framework to include the homotopy process\ncan improve the computational robustness of particle filters. The homotopy\nprocess can ameliorate particle filter collapse, a common challenge to using\nparticle filters when the sample dimension is small compared with the state\nspace dimensions.\n"}
+{"abstract": "  In this paper, we consider an inverse conductivity problem on a bounded\ndomain $\\Omega\\subset\\mathbb{R}^n$, $n\\geq2$, also known as Electrical\nImpedance Tomography (EIT), for the case where unknown impenetrable obstacles\nare embedded into $\\Omega$. We show that a piecewise-constant conductivity\nfunction and embedded obstacles can be simultaneously recovered in terms of the\nlocal Dirichlet-to-Neumann map defined on an arbitrary small open subset of the\nboundary of the domain $\\Omega$. The method depends on the well-posedness of a\ncoupled PDE-system constructed for the conductivity equations in the\n$H^1$-space and some elementary a priori estimates for Harmonic functions.\n"}
+{"abstract": "  Deception detection is an important task that has been a hot research topic\ndue to its potential applications. It can be applied to many areas from\nnational security (e.g, airport security, jurisprudence, and law enforcement)\nto real-life applications (e.g., business and computer vision). However, some\ncritical problems still exist and worth more investigation. One of the major\nchallenges is the data scarcity problem. Until now, only one multimodal\nbenchmark dataset on deception detection has been published, which contains 121\nvideo clips for deception detection (61 for deceptive class and 60 for truthful\nclass). This amount of data is hard to drive deep neural network-based methods.\nHence, they often suffered from the overfitting problem and the bad\ngeneralization ability. Also, the ground truth data contains some unusable\nframes for many factors including the face is too small to be recognized the\nfacial expression, face is covered by text, file corruption, etc.\n  However, most of the literature did not consider these problems. In this\npaper, we design a series of data preprocessing methods to deal with the\nproblem first. Then, we propose a multimodal deception detection framework to\nconstruct our novel emotional state-based feature and used open toolkit\nopenSMILE to extract the features from audio modality. A voting scheme is also\ndesigned to combine the emotional state information obtained from both visual\nmodality and audio modality. Finally, the novel emotion state transformation\n(EST) feature is determined by our algorithm. The critical analysis and\ncomparison of the proposed methods with the state-of-the-art multimodal method\nare showed that the overall performance has a great improvement of accuracy\nfrom 84.16% to 91.67% and ROC-AUC from 0.9211 to 0.9244.\n"}
+{"abstract": "  In solving simulation-based stochastic root-finding or optimization problems\nthat involve rare events, such as in extreme quantile estimation, running crude\nMonte Carlo can be prohibitively inefficient. To address this issue, importance\nsampling can be employed to drive down the sampling error to a desirable level.\nHowever, selecting a good importance sampler requires knowledge of the solution\nto the problem at hand, which is the goal to begin with and thus forms a\ncircular challenge. We investigate the use of adaptive importance sampling to\nuntie this circularity. Our procedure sequentially updates the importance\nsampler to reach the optimal sampler and the optimal solution simultaneously,\nand can be embedded in both sample average approximation and stochastic\napproximation-type algorithms. Our theoretical analysis establishes strong\nconsistency and asymptotic normality of the resulting estimators. We also\ndemonstrate, via a minimax perspective, the key role of using adaptivity in\ncontrolling asymptotic errors. Finally, we illustrate the effectiveness of our\napproach via numerical experiments.\n"}
+{"abstract": "  The estimate of the remaining time of an ongoing wave of epidemic spreading\nis a critical issue. Due to the variations of a wide range of parameters in an\nepidemic, for simple models such as Susceptible-Infected-Removed (SIR) model,\nit is difficult to estimate such a time scale. On the other hand,\nmultidimensional data with a large set attributes are precisely what one can\nuse in statistical learning algorithms to make predictions. Here we show, how\nthe predictability of the SIR model changes with various parameters using a\nsupervised learning algorithm. We then estimate the condition in which the\nmodel gives the least error in predicting the duration of the first wave of the\nCOVID-19 pandemic in different states in India. Finally, we use the SIR model\nwith the above mentioned optimal conditions to generate a training data set and\nuse it in the supervised learning algorithm to estimate the end-time of the\nongoing second wave of the pandemic in different states in India.\n"}
+{"abstract": "  To better understand the potential of drug repurposing in COVID-19, we\nanalyzed control strategies over essential host factors for SARS-CoV-2\ninfection. We constructed comprehensive directed protein-protein interaction\nnetworks integrating the top ranked host factors, drug target proteins, and\ndirected protein-protein interaction data. We analyzed the networks to identify\ndrug targets and combinations thereof that offer efficient control over the\nhost factors. We validated our findings against clinical studies data and\nbioinformatics studies. Our method offers a new insight into the molecular\ndetails of the disease and into potentially new therapy targets for it. Our\napproach for drug repurposing is significant beyond COVID-19 and may be applied\nalso to other diseases.\n"}
+{"abstract": "  Laser-induced terahertz spin transport (TST) and ultrafast demagnetization\n(UDM) are central but so far disconnected phenomena in femtomagnetism and\nterahertz spintronics. Here, we use broadband terahertz emission spectroscopy\nto reliably measure both processes in one setup. We find that the rate of UDM\nof a single ferromagnetic metal film F has the same time evolution as the flux\nof TST from F into an adjacent normal-metal layer N. This remarkable agreement\nshows that UDM and TST are driven by the same force, which is fully determined\nby the state of the ferromagnet. An analytical model consistently and\nquantitatively explains our observations. It reveals that both UDM in F and TST\nin the F|N stack arise from a generalized spin voltage, which is defined for\narbitrary, nonthermal electron distributions. We also conclude that\ncontributions due to a possible temperature difference between F and N are\nminor and that the spin-current amplitude can, in principle, be increased by\none order of magnitude. In general, our findings allow one to apply the vast\nknowledge of UDM to TST, thereby opening up new pathways toward large-amplitude\nterahertz spin currents and, thus, energy-efficient ultrafast spintronic\ndevices.\n"}
+{"abstract": "  We further the theme of studying syntactic structures data from Longobardi\n(2017b), Collins (2010), Ceolin et al. (2020) and Koopman (2011) using general\nMarkov models initiated in Shu et al. (2017), exploring the question of how\nconsistent the data is with the idea that general Markov models. The ideas\nexplored in the present paper are more generally applicable than to the setting\nof syntactic structures, and can be used when analyzing consistency of data\nwith general Markov models. Additionally, we give an interpretation of the\nmethods of Ceolin et al. (2020) as an infinite sites evolutionary model and\ncompare it to the Markov model and explore each in the context of evolutionary\nprocesses acting on human language syntax.\n"}
+{"abstract": "  We consider the inverse problem of recovering the probability distribution\nfunction of $T_2$ relaxation times from NMR transverse relaxometry experiments.\nThis problem is a variant of the inverse Laplace transform and hence ill-posed.\nWe cast this within the framework of a Gaussian mixture model to obtain a\nleast-square problem with an $L_2$ regularization term. We propose a new method\nfor incorporating regularization into the solution; rather than seeking to\nreplace the native problem with a suitable mathematically close, regularized,\nversion, we instead augment the native formulation with regularization. We term\nthis new approach 'multi-regularization'; it avoids the treacherous process of\nselecting a single best regularization parameter $\\lambda$ and instead permits\nincorporation of several degrees of regularization into the solution. We\nillustrate the method with extensive simulation results as well as application\nto real experimental data.\n"}
+{"abstract": "  This paper presents a classification of 7-dimensional real and complex\nindecomposable solvable Lie algebras having some 5-dimensional nilradicals.\nAfterwards, we combine our results with those of Rubin and Winternitz (1993),\nNdogmo and Winternitz (1994), Snobl and Winternitz (2005, 2009), Snobl and\nKar\\'asek (2010) to obtain a complete classification of 7-dimensional real and\ncomplex indecomposable solvable Lie algebras with 5-dimensional nilradicals. In\nassociation with Gong (1998), Parry (2007), Hindeleh and Thompson (2008), we\nachieve a classification of 7-dimensional real and complex indecomposable\nsolvable Lie algebras.\n"}
+{"abstract": "  Let $\\Omega$ be a subdomain of $\\mathbb{C}$ and let $\\mu$ be a positive Borel\nmeasure on $\\Omega$. In this paper, we study the asymptotic behavior of the\neigenvalues of compact Toeplitz operator $T_\\mu$ acting on Bergman spaces on\n$\\Omega$. Let $(\\lambda_n(T_\\mu))$ be the decreasing sequence of the\neigenvalues of $T_\\mu$ and let $\\rho$ be an increasing function such that $\\rho\n(n)/n^A$ is decreasing for some $A>0$. We give an explicit necessary and\nsufficient geometric condition on $\\mu$ in order to have\n$\\lambda_n(T_\\mu)\\asymp 1/\\rho (n)$. As applications, we consider composition\noperators $C_\\varphi$, acting on some standard analytic spaces on the unit disc\n$\\mathbb{D}$. First, we give a general criterion ensuring that the singular\nvalues of $C_\\varphi$ satisfy $s_n(C_\\varphi ) \\asymp 1/\\rho(n)$. Next, we\nfocus our attention on composition operators with univalent symbols, where we\nexpress our general criterion in terms of the harmonic measure of $\\varphi\n\\mathbb{D})$. We finally study the case where $\\partial \\varphi (\\mathbb{D})$\nmeets the unit circle in one point and give several concrete examples. Our\nmethod is based on upper and lower estimates of the trace of $h(T_\\mu)$, where\n$h$ is suitable concave or convex functions.\n"}
+{"abstract": "  Recently, considerable literature has grown up around the theme of few-shot\nnamed entity recognition (NER), but little published benchmark data\nspecifically focused on the practical and challenging task. Current approaches\ncollect existing supervised NER datasets and re-organize them to the few-shot\nsetting for empirical study. These strategies conventionally aim to recognize\ncoarse-grained entity types with few examples, while in practice, most unseen\nentity types are fine-grained. In this paper, we present Few-NERD, a\nlarge-scale human-annotated few-shot NER dataset with a hierarchy of 8\ncoarse-grained and 66 fine-grained entity types. Few-NERD consists of 188,238\nsentences from Wikipedia, 4,601,160 words are included and each is annotated as\ncontext or a part of a two-level entity type. To the best of our knowledge,\nthis is the first few-shot NER dataset and the largest human-crafted NER\ndataset. We construct benchmark tasks with different emphases to\ncomprehensively assess the generalization capability of models. Extensive\nempirical results and analysis show that Few-NERD is challenging and the\nproblem requires further research. We make Few-NERD public at\nhttps://ningding97.github.io/fewnerd/.\n"}
+{"abstract": "  The process of heating and reionization of the Universe at high redshift\nlinks small scale structures/galaxy formation and large scale inter-galactic\nmedium properties. Even if the first one is difficult to observe, an\nobservation window is opening on the second one, with the promising development\nof current and future radio telescopes. They will permit to observe the 21cm\nbrightness temperature global signal and fluctuations. The need of large scale\nsimulations is therefore strong to understand the properties of the IGM that\nwill be observed. But at the same time the urge to resolve the structures\nresponsible of those process is important. We introduce in this study, a\ncoupled hydro-radiative transfer simulations of the Cosmic Dawn and\nReionization with a simple sub-grid star formation process developed and\ncalibrated on the state of the art simulation CoDaII. This scheme permits to\nfollow consistently dark matter, hydrodynamics and radiative transfer\nevolution's on large scales, while the sub-grid models bridges to the galaxy\nformation scale. We process the simulation to produce 21cm signal as close as\npossible to the observations.\n"}
+{"abstract": "  We examine the spectral stability and instability of periodic traveling waves\nfor regularized long-wave models. Examples include the regularized Boussinesq,\nBenney--Luke, and Benjamin--Bona--Mahony equations. Of particular interest is a\nstriking new instability phenomenon -- spectrum off the imaginary axis\nextending into infinity. The spectrum of the linearized operator of the\ngeneralized Korteweg--de Vries equation, for instance, lies along the imaginary\naxis outside a bounded set. The spectrum for a regularized long-wave model, by\ncontrast, can vary markedly with the parameters of the periodic traveling\nwaves. We carry out asymptotic spectral analysis to short wavelength\nperturbations, distinguishing whether the spectrum tends to infinity along the\nimaginary axis or some curve whose real part is nonzero. We conduct numerical\nexperiments to corroborate our analytical findings.\n"}
+{"abstract": "  Unmanned aerial vehicles (UAVs) can be integrated into wireless sensor\nnetworks (WSNs) for smart city applications in several ways. Among them, a UAV\ncan be employed as a relay in a \"store-carry and forward\" fashion by uploading\ndata from ground sensors and metering devices and, then, downloading it to a\ncentral unit. However, both the uploading and downloading phases can be prone\nto potential threats and attacks. As a legacy from traditional wireless\nnetworks, the jamming attack is still one of the major and serious threats to\nUAV-aided communications, especially when also the jammer is mobile, e.g., it\nis mounted on a UAV or inside a terrestrial vehicle. In this paper, we\ninvestigate anti-jamming communications for UAV-aided WSNs operating over\ndoubly-selective channels in the downloading phase. In such a scenario, the\nsignals transmitted by the UAV and the malicious mobile jammer undergo both\ntime dispersion due to multipath propagation effects and frequency dispersion\ncaused by their mobility. To suppress high-power jamming signals, we propose a\nblind physical-layer technique that jointly detects the UAV and jammer symbols\nthrough serial disturbance cancellation based on symbol-level post-sorting of\nthe detector output. Amplitudes, phases, time delays, and Doppler shifts -\nrequired to implement the proposed detection strategy - are blindly estimated\nfrom data through the use of algorithms that exploit the\nalmost-cyclostationarity properties of the received signal and the detailed\nstructure of multicarrier modulation format. Simulation results corroborate the\nanti-jamming capabilities of the proposed method, for different mobility\nscenarios of the jammer.\n"}
+{"abstract": "  It is known that unsupervised nonlinear dimensionality reduction and\nclustering is sensitive to the selection of hyperparameters, particularly for\ndeep learning based methods, which hinder its practical use. How to select a\nproper network structure that may be dramatically different in different\napplications is a hard issue for deep models, given little prior knowledge of\ndata. In this paper, we explore ensemble learning and selection techniques for\nautomatically determining the optimal network structure of a deep model, named\nmultilayer bootstrap networks (MBN). Specifically, we first propose an MBN\nensemble (MBN-E) algorithm which concatenates the sparse outputs of a set of\nMBN base models with different network structures into a new representation.\nBecause training an ensemble of MBN is expensive, we propose a fast version of\nMBN-E (fMBN-E), which replaces the step of random data resampling in MBN-E by\nthe random resampling of similarity scores. Theoretically, fMBN-E is even\nfaster than a single standard MBN. Then, we take the new representation\nproduced by MBN-E as a reference for selecting the optimal MBN base models. Two\nkinds of ensemble selection criteria, named optimization-like selection\ncriteria and distribution divergence criteria, are applied. Importantly, MBN-E\nand its ensemble selection techniques maintain the simple formulation of MBN\nthat is based on one-nearest-neighbor learning, and reach the state-of-the-art\nperformance without manual hyperparameter tuning. fMBN-E is empirically even\nhundreds of times faster than MBN-E without suffering performance degradation.\nThe source code is available at http://www.xiaolei-zhang.net/mbn-e.htm.\n"}
+{"abstract": "  Inspired by the notion of K\\\"onig graphs we introduce graded ideals of\nK\\\"onig type with respect to a monomial order $<$. It is shown that if $I$ is\nof K\\\"onig type, then the Cohen--Macaulay property of $\\ini_<(I)$ does not\ndepend on the characteristic of the base field. This happens to be the case\nalso for $I$ itself when $I$ is a binomial edge ideal. Attached to an ideal of\nK\\\"onig type is a sequence of linear forms, whose elements are variables or\ndifferences of variables. This sequence is a system of parameters for\n$\\ini_<(I)$, and is a potential system of parameters for $I$ itself. We study\nin detail the ideals of K\\\"onig type among the edge ideals, binomial edge\nideals and the toric ideal of a Hibi ring and use the K\\\"onig property to\ndetermine explicitly their canonical module.\n"}
+{"abstract": "  We present a comprehensive study of the linear response of interacting\nunderdamped Brownian particles to simple shear flow. We collect six different\nroutes for computing the response, two of which are based on the symmetry of\nthe considered system and observable with respect to the shear axes. We include\nthe extension of the Green-Kubo relation to underdamped cases, which shows two\nunexpected additional terms. These six computational methods are applied to\ninvestigate the relaxation of the response towards the steady state for\ndifferent observables, where interesting effects due to interactions and a\nfinite particle mass are observed. Moreover, we compare the different response\nrelations in terms of their statistical efficiency, identifying their relative\ndemand on experimental measurement time or computational resources in computer\nsimulations. Finally, several measures of breakdown of linear response theory\nfor larger shear rates are discussed.\n"}
+{"abstract": "  A plane non-parallel vortex flow in a square fluid domain is examined. The\nenergy dissipation of the flow is dominated by viscosity and linear friction\neffect of a Hartmann layer. This is a traditional Navier-Stokes flow when the\nlinear friction effect is not involved, whereas it is a magnetohydrodynamic\nflow when the energy dissipation is fundamentally dominated by the friction. It\nis proved that linear critical values of a spectral problem are nonlinear\nthresholds leading to the onset of secondary steady-state flows, the nonlinear\nphenomenon observed in laboratory experiments.\n"}
+{"abstract": "  We prove a number of new results on the large-scale geometry of the\n$L^p$-metrics on the group of area-preserving diffeomorphisms of each\norientable surface. Our proofs use in a key way the Fulton-MacPherson type\ncompactification of the configuration space of $n$ points on the surface due to\nAxelrod-Singer and Kontsevich. This allows us to apply the Schwarz-Milnor lemma\nto configuration spaces, a natural approach which we carry out successfully for\nthe first time. As sample results, we prove that all right-angled Artin groups\nadmit quasi-isometric embeddings into the group of area-preserving\ndiffeomorphisms endowed with the $L^p$-metric, and that all Gambaudo-Ghys\nquasi-morphisms on this metric group coming from the braid group on $n$ strands\nare Lipschitz. This was conjectured to hold, yet proven only for low values of\n$n$ and the genus $g$ of the surface.\n"}
+{"abstract": "  Reinforcement learning algorithms, just like any other Machine learning\nalgorithm pose a serious threat from adversaries. The adversaries can\nmanipulate the learning algorithm resulting in non-optimal policies. In this\npaper, we analyze the Multi-task Federated Reinforcement Learning algorithms,\nwhere multiple collaborative agents in various environments are trying to\nmaximize the sum of discounted return, in the presence of adversarial agents.\nWe argue that the common attack methods are not guaranteed to carry out a\nsuccessful attack on Multi-task Federated Reinforcement Learning and propose an\nadaptive attack method with better attack performance. Furthermore, we modify\nthe conventional federated reinforcement learning algorithm to address the\nissue of adversaries that works equally well with and without the adversaries.\nExperimentation on different small to mid-size reinforcement learning problems\nshow that the proposed attack method outperforms other general attack methods\nand the proposed modification to federated reinforcement learning algorithm was\nable to achieve near-optimal policies in the presence of adversarial agents.\n"}
+{"abstract": "  Heteroscedastic regression is the task of supervised learning where each\nlabel is subject to noise from a different distribution. This noise can be\ncaused by the labelling process, and impacts negatively the performance of the\nlearning algorithm as it violates the i.i.d. assumptions. In many situations\nhowever, the labelling process is able to estimate the variance of such\ndistribution for each label, which can be used as an additional information to\nmitigate this impact. We adapt an inverse-variance weighted mean square error,\nbased on the Gauss-Markov theorem, for parameter optimization on neural\nnetworks. We introduce Batch Inverse-Variance, a loss function which is robust\nto near-ground truth samples, and allows to control the effective learning\nrate. Our experimental results show that BIV improves significantly the\nperformance of the networks on two noisy datasets, compared to L2 loss,\ninverse-variance weighting, as well as a filtering-based baseline.\n"}
+{"abstract": "  In-situ measurements by several spacecraft have revealed that the solar wind\nis frequently perturbed by transient structures (magnetic folds, jets, waves,\nflux-ropes) that propagate rapidly away from the Sun over large distances.\nParker Solar Probe has detected frequent rotations of the magnetic field vector\nat small heliocentric distances, accompanied by surprisingly large solar wind\nrotation rates. The physical origin of such magnetic field bends, the\nconditions for their survival across the interplanetary space, and their\nrelation to solar wind rotation are yet to be clearly understood. We traced\nmeasured solar wind flows from the spacecraft position down to the surface of\nthe Sun to identify their potential source regions and used a global MHD model\nof the corona and solar wind to relate them to the rotational state of the low\nsolar corona. We identified regions of the solar corona for which solar wind\nspeed and rotational shear are important and long-lived, that can be favourable\nto the development of magnetic deflections and to their propagation across\nextended heights in the solar wind. We show that coronal rotation is highly\nstructured and that enhanced flow shear develops near the boundaries between\ncoronal holes and streamers, around and above pseudo-streamers, even when such\nboundaries are aligned with the direction of solar rotation. A large fraction\nof the switchbacks identified by PSP map back to these regions, both in terms\nof instantaneous magnetic field connectivity and of the trajectories of wind\nstreams that reach the spacecraft. These regions of strong shears are likely to\nleave an imprint on the solar wind over large distances and to increase the\ntransverse speed variability in the slow solar wind. The simulations and\nconnectivity analysis suggest they can be a source of the switchbacks and\nspikes observed by Parker Solar Probe.\n"}
+{"abstract": "  For a class $\\mathcal{G}$ of graphs, the problem SUBGRAPH COMPLEMENT TO\n$\\mathcal{G}$ asks whether one can find a subset $S$ of vertices of the input\ngraph $G$ such that complementing the subgraph induced by $S$ in $G$ results in\na graph in $\\mathcal{G}$. We investigate the complexity of the problem when\n$\\mathcal{G}$ is $H$-free for $H$ being a complete graph, a star, a path, or a\ncycle. We obtain the following results:\n  - When $H$ is a $K_t$ (a complete graph on $t$ vertices) for any fixed $t\\geq\n1$, the problem is solvable in polynomial-time. This applies even when\n$\\mathcal{G}$ is a subclass of $K_t$-free graphs recognizable in\npolynomial-time, for example, the class of $(t-2)$-degenerate graphs.\n  - When $H$ is a $K_{1,t}$ (a star graph on $t+1$ vertices), we obtain that\nthe problem is NP-complete for every $t\\geq 5$. This, along with known results,\nleaves only two unresolved cases - $K_{1,3}$ and $K_{1,4}$.\n  - When $H$ is a $P_t$ (a path on $t$ vertices), we obtain that the problem is\nNP-complete for every $t\\geq 7$, leaving behind only two unresolved cases -\n$P_5$ and $P_6$.\n  - When $H$ is a $C_t$ (a cycle on $t$ vertices), we obtain that the problem\nis NP-complete for every $t\\geq 8$, leaving behind four unresolved cases -\n$C_4, C_5, C_6,$ and $C_7$.\n  Further, we prove that these hard problems do not admit subexponential-time\nalgorithms (algorithms running in time $2^{o(|V(G)|)}$), assuming the\nExponential Time Hypothesis. A simple complementation argument implies that\nresults for $\\mathcal{G}$ are applicable for $\\overline{\\mathcal{G}}$, thereby\nobtaining similar results for $H$ being the complement of a complete graph, a\nstar, a path, or a cycle. Our results generalize two main results and resolve\none open question by Fomin et al. (Algorithmica, 2020).\n"}
+{"abstract": "  The concept of biological adaptation was closely connected to some\nmathematical, engineering and physical ideas from the very beginning. Cannon in\nhis \"The wisdom of the body\" (1932) used the engineering vision of regulation.\nIn 1938, Selye enriched this approach by the notion of adaptation energy. This\nterm causes much debate when one takes it literally, i.e. as a sort of energy.\nSelye did not use the language of mathematics, but the formalization of his\nphenomenological theory in the spirit of thermodynamics was simple and led to\nverifiable predictions. In 1980s, the dynamics of correlation and variance in\nsystems under adaptation to a load of environmental factors were studied and\nthe universal effect in ensembles of systems under a load of similar factors\nwas discovered: in a crisis, as a rule, even before the onset of obvious\nsymptoms of stress, the correlation increases together with variance (and\nvolatility). During 30 years, this effect has been supported by many\nobservations of groups of humans, mice, trees, grassy plants, and on financial\ntime series. In the last ten years, these results were supplemented by many new\nexperiments, from gene networks in cardiology and oncology to dynamics of\ndepression and clinical psychotherapy. Several systems of models were\ndeveloped: the thermodynamic-like theory of adaptation of ensembles and several\nfamilies of models of individual adaptation. Historically, the first group of\nmodels was based on Selye's concept of adaptation energy and used fitness\nestimates. Two other groups of models are based on the idea of hidden attractor\nbifurcation and on the advection--diffusion model for distribution of\npopulation in the space of physiological attributes. We explore this world of\nmodels and experiments, starting with classic works, with particular attention\nto the results of the last ten years and open questions.\n"}
+{"abstract": "  Several new improvements of the $A$-numerical radius inequalities for\noperators acting on a semi-Hilbert space, i.e., a space generated by a positive\noperator $A$, are proved. In particular, among other inequalities, we show that\n\\begin{align*} \\frac{1}{4}\\|T^{\\sharp_A} T+TT^{\\sharp_A}\\|_A\n\\leq\\frac{1}{4}\\Big(2\\omega_A^2(T)+\\gamma(T)\\Big) \\leq \\omega_A^2(T),\n\\end{align*} where\n$$\\gamma(T)=\\sqrt{\\left(\\|\\Re_A(T)\\|_A^2-\\|\\Im_A(T)\\|_A^2\\right)^2+4\\|\\Re_A(T)\\Im_A(T)\\|_A^2}.$$\nHere $\\omega_A(X)$ and $\\|X\\|_A$ denote respectively the $A$-numerical radius\nand the $A$-seminorm of an operator $X$. Also,\n$\\Re_A(T):=\\frac{T+T^{\\sharp_A}}{2}$ and $\\Im_A(T):=\\frac{T-T^{\\sharp_A}}{2i}$,\nwhere $T^{\\sharp_A}$ is a distinguished $A$-adjoint operator of $T$. Further,\nsome new refinements of the triangle inequality related to $\\|\\cdot\\|_A$ are\nestablished.\n"}
+{"abstract": "  We consider the problem of learning a nonlinear function over a network of\nlearners in a fully decentralized fashion. Online learning is additionally\nassumed, where every learner receives continuous streaming data locally. This\nlearning model is called a fully distributed online learning (or a fully\ndecentralized online federated learning). For this model, we propose a novel\nlearning framework with multiple kernels, which is named DOMKL. The proposed\nDOMKL is devised by harnessing the principles of an online alternating\ndirection method of multipliers and a distributed Hedge algorithm. We\ntheoretically prove that DOMKL over T time slots can achieve an optimal\nsublinear regret, implying that every learner in the network can learn a common\nfunction which has a diminishing gap from the best function in hindsight. Our\nanalysis also reveals that DOMKL yields the same asymptotic performance of the\nstate-of-the-art centralized approach while keeping local data at edge\nlearners. Via numerical tests with real datasets, we demonstrate the\neffectiveness of the proposed DOMKL on various online regression and\ntime-series prediction tasks.\n"}
+{"abstract": "  Scattered factor (circular) universality was firstly introduced by Barker et\nal. in 2020. A word $w$ is called $k$-universal for some natural number $k$, if\nevery word of length $k$ of $w$'s alphabet occurs as a scattered factor in $w$;\nit is called circular $k$-universal if a conjugate of $w$ is $k$-universal.\nHere, a word $u=u_1\\cdots u_n$ is called a scattered factor of $w$ if $u$ is\nobtained from $w$ by deleting parts of $w$, i.e. there exists (possibly empty)\nwords $v_1,\\dots,v_{n+1}$ with $w=v_1u_1v_2\\cdots v_nu_nv_{n+1}$. In this work,\nwe prove two problems, left open in the aforementioned paper, namely a\ngeneralisation of one of their main theorems to arbitrary alphabets and a\nslight modification of another theorem such that we characterise the circular\nuniversality by the universality. On the way, we present deep insights into the\nbehaviour of the remainder of the so called arch factorisation by Hebrard when\nrepetitions of words are considered.\n"}
+{"abstract": "  A theoretical investigation has been carried out to examine the ion-acoustic\nshock waves (IASHWs) in a magnetized degenerate quantum plasma system\ncontaining inertialess ultra-relativistically degenerate electrons, and\ninertial non-relativistic positively charged heavy and light ions. The Burgers'\nequation is derived by employing reductive perturbation method. It can be seen\nthat under consideration of non-relativistic positively charged heavy and light\nions, the plasma model supports only positive electrostatic shock structure. It\nis also observed that the charge state and number density of the\nnon-relativistic heavy and light ions enhance the amplitude of IASHWs, and the\nsteepness of the shock profile is decreased with ion kinematic viscosity\n($\\eta$). The findings of our present investigation will be helpful in\nunderstanding the nonlinear propagation of IASHWs in white dwarfs and neutron\nstars.\n"}
+{"abstract": "  We experimentally demonstrate that the next-nearest-neighbor(NNN)coupling in\nan array of waveguides can naturally be negative. To do so, dielectric zig-zag\nshaped waveguide arrays are fabricated with direct laser writing (DLW). By\nchanging the angle of the zig-zag shape it is possible to tune between positive\nand negative ratios of nearest and next-nearest-neighbor coupling, which also\nallows to reduce the impact of the NNN-coupling to zero at the correct\nrespective angle. We describe how the correct higher order coupling constants\nin tight-binding models can be derived, based on non-orthogonal coupled mode\ntheory. We confirm the existence of negative NNN-couplings experimentally and\nshow the improved accuracy of this refined tight-binding model. The negative\nNNN-coupling has a noticeable impact especially when higher order coupling\nterms can no longer be neglected. Our results are also of importance for other\ndiscrete systems in which the tight-binding model is often used.\n"}
+{"abstract": "  We study by numerical simulation in two and three dimensions the coupling\nlayer between the transducer and the microfluidic chip in ultrasound\nacoustofluidic devices. The model includes the transducer with electrodes, the\nmicrofluidic chip with a liquid-filled microchannel, and the coupling layer\nbetween the transducer and the chip. We consider two commonly used coupling\nmaterials, solid epoxy glue and viscous glycerol, as well as two commonly used\ndevice types, glass capillary tubes and silicon-glass chips. We study how\nacoustic resonances in ideal devices without a coupling layer is either\nsustained or attenuated as a coupling layer of increasing thickness is\ninserted. We establish a simple criterion based on the phase of the acoustic\nwave for whether a given zero-layer resonance is sustained or attenuated by the\naddition of a coupling layer. Finally, we show that by controlling the\nthickness and the material, the coupling layer can be used as a design\ncomponent for optimal and robust acoustofluidic resonances.\n"}
+{"abstract": "  We study text representation methods using deep models. Current methods, such\nas word-level embedding and character-level embedding schemes, treat texts as\neither a sequence of atomic words or a sequence of characters. These methods\neither ignore word morphologies or word boundaries. To overcome these\nlimitations, we propose to convert texts into 2-D representations and develop\nthe Sent2Matrix method. Our method allows for the explicit incorporation of\nboth word morphologies and boundaries. When coupled with a novel serpentine\npadding method, our Sent2Matrix method leads to an interesting visualization in\nwhich 1-D character sequences are folded into 2-D serpentine manifolds.\nNotably, our method is the first attempt to represent texts in 2-D formats.\nExperimental results on text classification tasks shown that our method\nconsistently outperforms prior embedding methods.\n"}
+{"abstract": "  In a recent measurement LHCb reported pronounced structures in the $J/\\psi\nJ/\\psi$ spectrum. One of the various possible explanations of those is that\nthey emerge from non-perturbative interactions of vector charmonia. It is thus\nimportant to understand whether it is possible to form a bound state of two\ncharmonia interacting through the exchange of gluons, which hadronise into two\npions at the longest distance. In this paper, we demonstrate that, given our\ncurrent understanding of hadron-hadron interactions, the exchange of correlated\nlight mesons (pions and kaons) is able to provide sizeable attraction to the\ndi-$J/\\psi$ system, and it is possible for two $J/\\psi$ mesons to form a bound\nstate. As a side result we find from an analysis of the data for the\n$\\psi(2S)\\to J/\\psi \\pi\\pi$ transition including both $\\pi\\pi$ and $K\\bar K$\nfinal state interactions an improved value for the $\\psi(2S)\\to J/\\psi$\ntransition chromo-electric polarisability: $|\\alpha_{\\psi(2S)J/\\psi}|= (1.8\\pm\n0.1)~\\mbox{GeV}^{-3}$, where the uncertainty also includes the one induced by\nthe final state interactions.\n"}
+{"abstract": "  We study the decoherence effect of charge noise on a single flip-flop qubit\nand two dipole-coupled qubits. We find that a single flip-flop qubit is highly\nresistant to charge noise at its sweet spots. However, due to the proximity of\nthe charge excited states to the flip-flop logical states, the presence of\ncharge noise greatly reduces the fidelity of two-qubit operations. We identify\nleakage from the qubit Hilbert space as the main culprit for the reduced gate\nfidelity. We also explore different bias conditions to mitigate this\ndecoherence channel.\n"}
+{"abstract": "  We define a new family of spectral invariants associated to certain\nLagrangian links in compact and connected surfaces of any genus. We show that\nour invariants recover the Calabi invariant of Hamiltonians in their limit. As\napplications, we resolve several open questions from topological surface\ndynamics and continuous symplectic topology: we show that the group of\nHamiltonian homeomorphisms of any compact surface with (possibly empty)\nboundary is not simple; we extend the Calabi homomorphism to the group of\nHameomorphisms constructed by Oh-M\\\"uller; and, we construct an infinite\ndimensional family of quasimorphisms on the group of area and orientation\npreserving homeomorphisms of the two-sphere. Our invariants are inspired by\nrecent work of Polterovich and Shelukhin defining and applying spectral\ninvariants for certain classes of links in the two-sphere.\n"}
+{"abstract": "  Model selection, via penalized likelihood type criteria, is a standard task\nin many statistical inference and machine learning problems. Progress has led\nto deriving criteria with asymptotic consistency results and an increasing\nemphasis on introducing non-asymptotic criteria. We focus on the problem of\nmodeling non-linear relationships in regression data with potential hidden\ngraph-structured interactions between the high-dimensional predictors, within\nthe mixture of experts modeling framework. In order to deal with such a complex\nsituation, we investigate a block-diagonal localized mixture of polynomial\nexperts (BLoMPE) regression model, which is constructed upon an inverse\nregression and block-diagonal structures of the Gaussian expert covariance\nmatrices. We introduce a penalized maximum likelihood selection criterion to\nestimate the unknown conditional density of the regression model. This model\nselection criterion allows us to handle the challenging problem of inferring\nthe number of mixture components, the degree of polynomial mean functions, and\nthe hidden block-diagonal structures of the covariance matrices, which reduces\nthe number of parameters to be estimated and leads to a trade-off between\ncomplexity and sparsity in the model. In particular, we provide a strong\ntheoretical guarantee: a finite-sample oracle inequality satisfied by the\npenalized maximum likelihood estimator with a Jensen-Kullback-Leibler type\nloss, to support the introduced non-asymptotic model selection criterion. The\npenalty shape of this criterion depends on the complexity of the considered\nrandom subcollection of BLoMPE models, including the relevant graph structures,\nthe degree of polynomial mean functions, and the number of mixture components.\n"}
+{"abstract": "  A continuous monitoring of the physical strength and mobility of elderly\npeople is important for maintaining their health and treating diseases at an\nearly stage. However, frequent screenings by physicians are exceeding the\nlogistic capacities. An alternate approach is the automatic and unobtrusive\ncollection of functional measures by ambient sensors. In the current\npublication, we show the correlation among data of ambient motion sensors and\nthe well-established mobility assessments Short-Physical-Performance-Battery,\nTinetti and Timed Up & Go. We use the average number of motion sensor events as\nactivity measure for correlation with the assessment scores. The evaluation on\na real-world dataset shows a moderate to strong correlation with the scores of\nstandardised geriatrics physical assessments.\n"}
+{"abstract": "  We study epidemic models where the infectivity of each individual is a random\nfunction of the infection age (the elapsed time of infection). To describe the\nepidemic evolution dynamics, we use a stochastic process that tracks the number\nof individuals at each time that have been infected for less than or equal to a\ncertain amount of time, together with the aggregate infectivity process. We\nestablish the functional law of large numbers (FLLN) for the stochastic\nprocesses that describe the epidemic dynamics. The limits are described by a\nset of deterministic integral equations, which has a further characterization\nusing PDEs under some regularity conditions. The solutions are characterized\nwith boundary conditions that are given by a system of Volterra equations. We\nalso characterize the equilibrium points for the PDEs in the SIS model with\ninfection-age dependent infectivity. To establish the FLLNs, we employ a useful\ncriterion for weak convergence for the two-parameter processes together with\nuseful representations for the relevant processes via Poisson random measures.\n"}
+{"abstract": "  We explicit some general properties regarding surfaces with Prym-canonical\nhyperplane sections and the geometric genus of their possible singularities.\nMoreover, we construct new examples of this type of surfaces.\n"}
+{"abstract": "  To reduce uploading bandwidth and address privacy concerns, deep learning at\nthe network edge has been an emerging topic. Typically, edge devices\ncollaboratively train a shared model using real-time generated data through the\nParameter Server framework. Although all the edge devices can share the\ncomputing workloads, the distributed training processes over edge networks are\nstill time-consuming due to the parameters and gradients transmission\nprocedures between parameter servers and edge devices. Focusing on accelerating\ndistributed Convolutional Neural Networks (CNNs) training at the network edge,\nwe present DynaComm, a novel scheduler that dynamically decomposes each\ntransmission procedure into several segments to achieve optimal layer-wise\ncommunications and computations overlapping during run-time. Through\nexperiments, we verify that DynaComm manages to achieve optimal layer-wise\nscheduling for all cases compared to competing strategies while the model\naccuracy remains untouched.\n"}
+{"abstract": "  We present a detailed 3D kinematic analysis of the central regions ($R<30''$)\nof the low-mass and dynamically evolved galactic globular cluster NGC 6362. The\nstudy is based on data obtained with ESO-VLT/MUSE used in combination with the\nadaptive optics module and providing $\\sim3000$ line-of-sight radial\nvelocities, which have been complemented with Hubble Space Telescope proper\nmotions. The quality of the data and the number of available radial velocities\nallowed us to detect for the first time a significant rotation signal along the\nline of sight in the cluster core with amplitude of $\\sim 1$ km/s and with a\npeak located at only $\\sim20''$ from the cluster center, corresponding to only\n$\\sim10\\%$ of the cluster half-light radius. This result is further supported\nby the detection of a central and significant tangential anisotropy in the\ncluster innermost regions. This is one of the most central rotation signals\never observed in a globular cluster to date. We also explore the rotational\nproperties of the multiple populations hosted by this cluster and find that\nNa-rich stars rotate about two times more rapidly than the Na-poor\nsub-population thus suggesting that the interpretation of the present-day\nglobular cluster properties require a multi-component chemo-dynamical approach.\nBoth the rotation amplitude and peak position would fit qualitatively the\ntheoretical expectations for a system that lost a significant fraction of its\noriginal mass because of the long-term dynamical evolution and interaction with\nthe Galaxy. However, to match the observations more quantitatively further\ntheoretical studies to explore the initial dynamical properties of the cluster\nare needed.\n"}
+{"abstract": "  Covalent organic frameworks (COFs) have attracted significant attention due\nto their chemical versatility combined with a significant number of potential\napplications. Of particular interest are two-dimensional COFs, where the\norganic building units are linked by covalent bonds within a plane. Most\nproperties of these COFs are determined by the relative arrangement of\nneighboring layers. These are typically found to be laterally displaced, which,\nfor example, reduces the electronic coupling between the layers. In the present\ncontribution, we use dispersion-corrected density-functional theory to\nelucidate the origin of that displacement, showing that the common notion that\nthe displacement is a consequence of electrostatic repulsions of polar building\nblocks can be misleading. For the representative case of COF-1, we find that\nelectrostatic and van der Waals interactions would, actually, favor a cofacial\narrangement of the layers and that Pauli repulsion is the crucial factor\ncausing the serrated AA stacking. A more in-depth analysis of the electrostatic\ncontribution reveals that the \"classical\" Coulomb repulsion between the\nboroxine building blocks of COF-1 suggested by chemical intuition does exist,\nbut is overcompensated by attractive effects due to charge-penetration in the\nphenylene units. The situation becomes more involved when additionally allowing\nthe interlayer distance to relax for each displacement, as then the different\ndistance-dependences of the various types of interactions come into play. The\noverall behavior calculated for COF-1 is recovered for several additional COFs\nwith differently sized pi-systems and topologies, implying that the presented\nresults are of more general relevance.\n"}
+{"abstract": "  Two key challenges within Reinforcement Learning involve improving (a) agent\nlearning within environments with sparse extrinsic rewards and (b) the\nexplainability of agent actions. We describe a curious subgoal focused agent to\naddress both these challenges. We use a novel method for curiosity produced\nfrom a Generative Adversarial Network (GAN) based model of environment\ntransitions that is robust to stochastic environment transitions. Additionally,\nwe use a subgoal generating network to guide navigation. The explainability of\nthe agent's behavior is increased by decomposing complex tasks into a sequence\nof interpretable subgoals that do not require any manual design. We show that\nthis method also enables the agent to solve challenging procedurally-generated\ntasks that contain stochastic transitions above other state-of-the-art methods.\n"}
+{"abstract": "  Telerehabilitation systems for stroke survivors have been predominantly\ndesigned to measure and quantify movement in order to guide and encourage\nrehabilitation regular exercises at home. We set out to study what aspect of\nthe movement data was essential, to better inform sensor design. We\ninvestigated face-to-face stroke rehabilitation sessions through a series of\ninterviews and observations involving 16 stroke rehabilitation specialists\nincluding physiatrists, physical therapists, and occupational therapists. We\nfound that specialists are not solely interested in movement data, and that\nexperiential information about stroke survivors' lived experience plays an\nessential role in specialists interpreting movement data and creating a\nrehabilitation plan. We argue for a reconceptualization in stroke\ntelerehabilitation that is more inclusive of non-movement data, and present\ndesign implications to better account for experiential information in\ntelerehabilitation systems.\n"}
+{"abstract": "  Motivated by entropic optimal transport, time reversal of Markov jump\nprocesses in $\\mathbb{R}^n$ is investigated. Relying on an abstract integration\nby parts formula for the carr\\'e du champ of a Markov process recently obtained\nby Cattiaux, Gentil and the authors, and using an entropic improvement strategy\ndiscovered by F\\\"ollmer in the eighties, we compute the semimartingale\ncharacteristics of the time reversed process for a wide class of jump processes\nwith possibly unbounded variation sample paths and singular intensities of\njump.\n"}
+{"abstract": "  Simultaneous X-ray and optical observations of black hole X-ray binaries have\nshown that the light curves contain multiple correlated and anti-correlated\nvariation components when the objects are in the hard state. In the case of the\nblack hole X-ray binary, GX 339-4, the cross correlation function (CCF) of the\nlight curves suggests a positive correlation with an optical lag of 0.15 s and\nanti-correlations with an optical lag of 1 s and X-ray lag of 4 s. This\nindicates the two light curves have some common signal components with\ndifferent delays. In this study, we extracted and reconstructed those signal\ncomponents from the data for GX 339-4. The results confirmed that correlation\nand anti-correlation with the optical lag are two common components. However,\nwe found that the reconstructed light curve for the anti-correlated component\nindicates a positively correlated variation with an X-ray lag of ~ +1 s. In\naddition, the CCF for this signal component shows anti-correlations not only\nwith the optical lag, but also with the X-ray lag, which is consistent with the\nCCF for the data. Therefore, our results suggest that the combination of the\ntwo positively correlated components, that is, the X-ray preceding signal with\nthe 0.15-s optical lag and the optical preceding signal with the 1-s X-ray lag,\ncan make the observed CCF without anti-correlated signals. The optical\npreceding signal may be caused by synchrotron emission in a magnetically\ndominated accretion flow or in a jet, while further study is required to\nunderstand the mechanism of the X-ray time lag.\n"}
+{"abstract": "  The Cohen-Kaplan-Nelson (CKN) bound formulates the condition that black hole\nis not produced by the low energy effective field theory dynamics. In de Sitter\nspace it also constrains the maximal size of the matter distribution to be\nsmaller than the cosmological horizon determined by black hole. On the other\nhand, the swampland distance conjecture (SDC) predicts that de Sitter space\nbecomes unstable by the descent of the low energy degrees of freedom from UV.\nThis results in the rapid increase in the energy inside the cosmological\nhorizon, the distribution of which can be constrained by the CKN bound. We\nstudy the CKN bound in de Sitter space in detail and point out that when the\nSDC is combined, the bound on the slow-roll parameter which forbids the eternal\ninflation is obtained.\n"}
+{"abstract": "  Efficient, reliable and reproducible target volume delineation is a key step\nin the effective planning of breast radiotherapy. However, post-operative\nbreast target delineation is challenging as the contrast between the tumor bed\nvolume (TBV) and normal breast tissue is relatively low in CT images. In this\nstudy, we propose to mimic the marker-guidance procedure in manual target\ndelineation. We developed a saliency-based deep learning segmentation (SDL-Seg)\nalgorithm for accurate TBV segmentation in post-operative breast irradiation.\nThe SDL-Seg algorithm incorporates saliency information in the form of markers'\nlocation cues into a U-Net model. The design forces the model to encode the\nlocation-related features, which underscores regions with high saliency levels\nand suppresses low saliency regions. The saliency maps were generated by\nidentifying markers on CT images. Markers' locations were then converted to\nprobability maps using a distance-transformation coupled with a Gaussian\nfilter. Subsequently, the CT images and the corresponding saliency maps formed\na multi-channel input for the SDL-Seg network. Our in-house dataset was\ncomprised of 145 prone CT images from 29 post-operative breast cancer patients,\nwho received 5-fraction partial breast irradiation (PBI) regimen on GammaPod.\nThe performance of the proposed method was compared against basic U-Net. Our\nmodel achieved mean (standard deviation) of 76.4 %, 6.76 mm, and 1.9 mm for\nDSC, HD95, and ASD respectively on the test set with computation time of below\n11 seconds per one CT volume. SDL-Seg showed superior performance relative to\nbasic U-Net for all the evaluation metrics while preserving low computation\ncost. The findings demonstrate that SDL-Seg is a promising approach for\nimproving the efficiency and accuracy of the on-line treatment planning\nprocedure of PBI, such as GammaPod based PBI.\n"}
+{"abstract": "  In recent years, many incidents have been reported where machine learning\nmodels exhibited discrimination among people based on race, sex, age, etc.\nResearch has been conducted to measure and mitigate unfairness in machine\nlearning models. For a machine learning task, it is a common practice to build\na pipeline that includes an ordered set of data preprocessing stages followed\nby a classifier. However, most of the research on fairness has considered a\nsingle classifier based prediction task. What are the fairness impacts of the\npreprocessing stages in machine learning pipeline? Furthermore, studies showed\nthat often the root cause of unfairness is ingrained in the data itself, rather\nthan the model. But no research has been conducted to measure the unfairness\ncaused by a specific transformation made in the data preprocessing stage. In\nthis paper, we introduced the causal method of fairness to reason about the\nfairness impact of data preprocessing stages in ML pipeline. We leveraged\nexisting metrics to define the fairness measures of the stages. Then we\nconducted a detailed fairness evaluation of the preprocessing stages in 37\npipelines collected from three different sources. Our results show that certain\ndata transformers are causing the model to exhibit unfairness. We identified a\nnumber of fairness patterns in several categories of data transformers.\nFinally, we showed how the local fairness of a preprocessing stage composes in\nthe global fairness of the pipeline. We used the fairness composition to choose\nappropriate downstream transformer that mitigates unfairness in the machine\nlearning pipeline.\n"}
+{"abstract": "  We introduce $k$-maximal sub-polygon graphs ($k$-MSP graphs), the\nintersection graphs of maximal polygons contained in a polygon with sides\nparallel to a regular $2k$-gon. We prove that all complete graphs are $k$-MSP\ngraphs for all $k>1$; trees are $2$-MSP graphs; trees are $k$-MSP graphs for\n$k>2$ if and only if they're caterpillars; and $n$-cycles are not $k$-MSP\ngraphs for $n>3$ and $k>1$. We derive bounds for which $j$-cycles appear as\ninduced subgraphs of $k$-MSP graphs. As our main result, we construct examples\nof graphs which are $k$-MSP graphs and not $j$-MSP graphs for all $k>1$, $j>1$,\n$k \\neq j$.\n"}
+{"abstract": "  A typical galaxy survey geometry results in galaxy pairs of different\nseparation and angle to the line-of-sight having different distributions in\nredshift and consequently a different effective redshift. However, clustering\nmeasurements are analysed assuming that the clustering is representative of\nthat at a single effective redshift. We investigate the impact of variations in\nthe galaxy-pair effective redshift on the large-scale clustering measured in\ngalaxy surveys. We find that galaxy surveys spanning a large redshift range\nhave different effective redshifts as a function of both pair separation and\nangle. Furthermore, when considering tracers whose clustering amplitude evolves\nstrongly with redshift, this combination can result in an additional\nscale-dependent clustering anisotropy. We demonstrate the size of this effect\non the eBOSS DR16 Quasar sample and show that, while the impact on monopole is\nnegligible, neglecting this effect can result in a large-scale tilt of $\\sim\n4\\%$ and $\\sim40\\%$ in quadrupole and hexadecapole, respectively. We discuss\nstrategies to mitigate this effect when making measurements.\n"}
+{"abstract": "  In Meta-Interpretive Learning (MIL) the metarules, second-order datalog\nclauses acting as inductive bias, are manually defined by the user. In this\nwork we show that second-order metarules for MIL can be learned by MIL. We\ndefine a generality ordering of metarules by $\\theta$-subsumption and show that\nuser-defined \\emph{sort metarules} are derivable by specialisation of the\nmost-general \\emph{matrix metarules} in a language class; and that these matrix\nmetarules are in turn derivable by specialisation of third-order \\emph{punch\nmetarules} with variables quantified over the set of atoms and for which only\nan upper bound on their number of literals need be user-defined. We show that\nthe cardinality of a metarule language is polynomial in the number of literals\nin punch metarules. We re-frame MIL as metarule specialisation by resolution.\nWe modify the MIL metarule specialisation operator to return new metarules\nrather than first-order clauses and prove the correctness of the new operator.\nWe implement the new operator as TOIL, a sub-system of the MIL system Louise.\nOur experiments show that as user-defined sort metarules are progressively\nreplaced by sort metarules learned by TOIL, Louise's predictive accuracy and\ntraining times are maintained. We conclude that automatically derived metarules\ncan replace user-defined metarules.\n"}
+{"abstract": "  Decentralized online learning for seeking generalized Nash equilibrium (GNE)\nof noncooperative games in dynamic environments is studied in this paper. Each\nplayer aims at selfishly minimizing its own time-varying cost function subject\nto time-varying coupled constraints and local feasible set constraints. Only\nlocal cost functions and local constraints are available to individual players,\nwho can receive their neighbors' information through a fixed and connected\ngraph. In addition, players have no prior knowledge of cost functions and local\nconstraint functions in the future time. In this setting, a novel distributed\nonline learning algorithm for seeking GNE of the studied game is devised based\non mirror descent and a primal-dual strategy. It is shown that the presented\nalgorithm can achieve sublinearly bounded dynamic regrets and constraint\nviolation by appropriately choosing decreasing stepsizes. Finally, the obtained\ntheoretical result is corroborated by a numerical simulation.\n"}
+{"abstract": "  We have implemented a control system for experiments in atomic, molecular and\noptical physics based on a commercial low-cost board, featuring a\nfield-programmable gate array as part of a system-on-a-chip on which a Linux\noperating system is running. The board features Gigabit Ethernet, allowing for\nfast data transmission and operation of remote experimental systems. A single\nboard can control a set of devices generating digital, analog and radio\nfrequency signals with a precise timing given either by an external or internal\nclock. Contiguous output and input sampling rates of up to 40 MHz are\nachievable. Several boards can run synchronously with a timing error\napproaching 1 ns. For this purpose, a novel auto-synchronization scheme is\ndemonstrated, with possible application in complex distributed experimental\nsetups with demanding timing requests.\n"}
+{"abstract": "  Traditional scene graph generation methods are trained using cross-entropy\nlosses that treat objects and relationships as independent entities. Such a\nformulation, however, ignores the structure in the output space, in an\ninherently structured prediction problem. In this work, we introduce a novel\nenergy-based learning framework for generating scene graphs. The proposed\nformulation allows for efficiently incorporating the structure of scene graphs\nin the output space. This additional constraint in the learning framework acts\nas an inductive bias and allows models to learn efficiently from a small number\nof labels. We use the proposed energy-based framework to train existing\nstate-of-the-art models and obtain a significant performance improvement, of up\nto 21% and 27%, on the Visual Genome and GQA benchmark datasets, respectively.\nFurthermore, we showcase the learning efficiency of the proposed framework by\ndemonstrating superior performance in the zero- and few-shot settings where\ndata is scarce.\n"}
+{"abstract": "  The integration of Information and Communication Technology (ICT) tools into\nmechanical devices found in aviation industry has raised security concerns. The\nmore integrated the system, the more vulnerable due to the inherent\nvulnerabilities found in ICT tools and software that drives the system. The\nsecurity concerns have become more heightened as the concept of\nelectronic-enabled aircraft and smart airports get refined and implemented\nunderway. In line with the above, this paper undertakes a review of\ncyber-security incidence in the aviation sector over the last 20 years. The\nessence is to understand the common threat actors, their motivations, the type\nof attacks, aviation infrastructure that is commonly attacked and then match\nthese so as to provide insight on the current state of the cyber-security in\nthe aviation sector. The review showed that the industry's threats come mainly\nfrom Advance Persistent Threat (APT) groups that work in collaboration with\nsome state actors to steal intellectual property and intelligence, in order to\nadvance their domestic aerospace capabilities as well as possibly monitor,\ninfiltrate and subvert other nations' capabilities. The segment of the aviation\nindustry commonly attacked is the Information Technology infrastructure, and\nthe prominent type of attacks is malicious hacking activities that aim at\ngaining unauthorised access using known malicious password cracking techniques\nsuch as Brute force attacks, Dictionary attacks and so on. The review further\nanalysed the different attack surfaces that exist in aviation industry, threat\ndynamics, and use these dynamics to predict future trends of cyberattacks in\nthe industry. The aim is to provide information for the cybersecurity\nprofessionals and aviation stakeholders for proactive actions in protecting\nthese critical infrastructures against cyberincidence for an optimal customer\nservice oriented industry.\n"}
+{"abstract": "  We study the validity of Bekenstein's entropy bound for a charged black hole\nin the context of nonlinear electrodynamics. Bekenstein's inequalities are\ncommonly understood as universal relations between the entropy, the charge, the\nmomentum, and the energy of a physical system but independent of its dynamics.\nIn particular, we consider the Born-Infeld electrodynamics coupled to gravity\nas described by General Relativity. Following the steps that lead to these\ninequalities, we study the absorption of a charged test particle by the black\nhole and verify that the entropy bound is violated. We find a modified upper\nbound for the entropy that depends on the maximum field parameter of the\nBorn-Infeld theory.\n"}
+{"abstract": "  We explore links between the thin concurrent games of Castellan, Clairambault\nand Winskel, and the weighted relational models of linear logic studied by\nLaird, Manzonetto, McCusker and Pagani. More precisely, we show that there is\nan interpretationpreserving \"collapse\" functor from the former to the latter.\nOn objects, the functor defines for each game a set of possible execution\nstates. Defining the action on morphisms is more subtle, and this is the main\ncontribution of the paper. Given a strategy and an execution state, our functor\nneeds to count the witnesses for this state within the strategy. Strategies in\nthin concurrent games describe non-linear behaviour explicitly, so in general\neach witness exists in countably many symmetric copies. The challenge is to\ndefine the right notion of witnesses, factoring out this infinity while\nmatching the weighted relational model. Understanding how witnesses compose is\nparticularly subtle and requires a delve into the combinatorics of witnesses\nand their symmetries. In its basic form, this functor connects thin concurrent\ngames and a relational model weighted by N $\\cup$ {+$\\infty$}. We will\nadditionally consider a generalised setting where both models are weighted by\nelements of an arbitrary continuous semiring; this covers the probabilistic\ncase, among others. Witnesses now additionally carry a value from the semiring,\nand our interpretation-preserving collapse functor extends to this setting.\n"}
+{"abstract": "  In this article we report a case study of a Language Learning Bauhaus VR\nhackathon with Goethe Institute. It was organized as an educational and\nresearch project to tap into the dynamics of transdisciplinary teams challenged\nwith a specific requirement. In our case, it was to build a Bauhaus-themed\nGerman Language Learning VR App. We constructed this experiment to simulate how\nrepresentatives of different disciplines may work together towards a very\nspecific purpose under time pressure. So, each participating team consisted of\nmembers of various expert-fields: software development (Unity or Unreal),\ndesign, psychology and linguistics. The results of this study cast light on the\nrecommended cycle of design thinking and customer-centered design in VR.\nEspecially in interdisciplinary rapid prototyping conditions, where\nstakeholders initially do not share competences. They also showcase educational\nbenefits of working in transdisciplinary environments. This study, combined\nwith our previous work on human factors in rapid software development and\nco-design, including hackathon dynamics, allowed us to formulate\nrecommendations for organizing content creation VR hackathons for specific\npurposes. We also provide guidelines on how to prepare the participants to work\nin rapid prototyping VR environments and benefit from such experiences in the\nlong term.\n"}
+{"abstract": "  We present new results on the equation of state and transition line of hot\nand dense strongly interacting QCD matter, obtained from a bottom-up\nEinstein-Maxwell-Dilaton holographic model. We considerably expand the previous\ncoverage in baryon densities in this model by implementing new numerical\nmethods to map the holographic black hole solutions onto the QCD phase diagram.\nWe are also able to obtain, for the first time, the first-order phase\ntransition line in a wide region of the phase diagram. Comparisons with the\nmost recent lattice results for the QCD thermodynamics are also presented.\n"}
+{"abstract": "  In the present paper we initiate the variational analysis of a super\nsinh-Gordon system on compact surfaces, yielding the first example of\nnon-trivial solution of min-max type. The proof is based on a linking argument\njointly with a suitably defined Nehari manifold and a careful analysis of\nPalais-Smale sequences. We complement this study with a multiplicity result\nexploiting the symmetry of the problem.\n"}
+{"abstract": "  R package krippendorffsalpha provides tools for measuring agreement using\nKrippendorff's Alpha coefficient, a well-known nonparametric measure of\nagreement (also called inter-rater reliability and various other names). This\narticle first develops Krippendorff's Alpha in a natural way, and situates\nAlpha among statistical procedures. Then the usage of package\nkrippendorffsalpha is illustrated via analyses of two datasets, the latter of\nwhich was collected during an imaging study of hip cartilage. The package\npermits users to apply the Alpha methodology using built-in distance functions\nfor the nominal, ordinal, interval, or ratio levels of measurement.\nUser-defined distance functions are also supported. The fitting function can\naccommodate any number of units, any number of coders, and missingness.\nBootstrap inference is supported, and the bootstrap computation can be carried\nout in parallel.\n"}
+{"abstract": "  Automatic modulation classification enables intelligent communications and it\nis of crucial importance in today's and future wireless communication networks.\nAlthough many automatic modulation classification schemes have been proposed,\nthey cannot tackle the intra-class diversity problem caused by the dynamic\nchanges of the wireless communication environment. In order to overcome this\nproblem, inspired by face recognition, a novel automatic modulation\nclassification scheme is proposed by using the multi-scale network in this\npaper. Moreover, a novel loss function that combines the center loss and the\ncross entropy loss is exploited to learn both discriminative and separable\nfeatures in order to further improve the classification performance. Extensive\nsimulation results demonstrate that our proposed automatic modulation\nclassification scheme can achieve better performance than the benchmark schemes\nin terms of the classification accuracy. The influence of the network\nparameters and the loss function with the two-stage training strategy on the\nclassification accuracy of our proposed scheme are investigated.\n"}
+{"abstract": "  We demonstrate collectively enhanced vacuum-induced quantum beat dynamics\nfrom a three-level V-type atomic system. Exciting a dilute atomic gas of\nmagneto-optically trapped $^{85}$Rb atoms with a weak drive resonant on one of\nthe transitions, we observe the forward-scattered field after a sudden shut-off\nof the laser. The subsequent radiative dynamics, measured for various optical\ndepths of the atomic cloud, exhibits superradiant decay rates, as well as\ncollectively enhanced quantum beats. Our work is also the first experimental\nillustration of quantum beats arising from atoms initially prepared in a single\nexcited level as a result of the vacuum-induced coupling between excited\nlevels.\n"}
+{"abstract": "  We consider the problem of Byzantine fault-tolerance in the peer-to-peer\n(P2P) distributed gradient-descent method -- a prominent algorithm for\ndistributed optimization in a P2P system. In this problem, the system comprises\nof multiple agents, and each agent has a local cost function. In the fault-free\ncase, when all the agents are honest, the P2P distributed gradient-descent\nmethod allows all the agents to reach a consensus on a solution that minimizes\ntheir aggregate cost. However, we consider a scenario where a certain number of\nagents may be Byzantine faulty. Such faulty agents may not follow an algorithm\ncorrectly, and may share arbitrary incorrect information to prevent other\nnon-faulty agents from solving the optimization problem. In the presence of\nByzantine faulty agents, a more reasonable goal is to allow all the non-faulty\nagents to reach a consensus on a solution that minimizes the aggregate cost of\nall the non-faulty agents. We refer to this fault-tolerance goal as\n$f$-resilience where $f$ is the maximum number of Byzantine faulty agents in a\nsystem of $n$ agents, with $f < n$. Most prior work on fault-tolerance in P2P\ndistributed optimization only consider approximate fault-tolerance wherein,\nunlike $f$-resilience, all the non-faulty agents' compute a minimum point of a\nnon-uniformly weighted aggregate of their cost functions. We propose a\nfault-tolerance mechanism that confers provable $f$-resilience to the P2P\ndistributed gradient-descent method, provided the non-faulty agents satisfy the\nnecessary condition of $2f$-redundancy, defined later in the paper. Moreover,\ncompared to prior work, our algorithm is applicable to a larger class of\nhigh-dimensional convex distributed optimization problems.\n"}
+{"abstract": "  Highly Oriented Pyrolytic Graphite was exfoliated via pulsed discharge plasma\nin liquid nitrogen. The potential mechanisms involved were investigated by\nobserving the treated surface of the graphitic material and the obtained\nparticles. Non-exfoliating defects from the plasma treatment were observed and\nexperimental parameter were modified to counteract those. One experiment was\nperformed without exposing the HOPG directly to the discharges so as to better\nunderstand the plasma role. The exfoliated particles were observed via TEM and\nSEM to evaluate the defects, the size, the purity and the crystallinity but no\nquantitative characterization of their thickness was possible so the actual\nnumber of layer of each particle is unknown. Nonetheless, few layers graphene\n(FLG) was successfully exfoliated through this process. The proposed mechanisms\nwere extrapolated from the observation of the damaged HOPG surface and the\nobtained particles but the correlation found does not prove causation.\n"}
+{"abstract": "  Unsupervised continual learning remains a relatively uncharted territory in\nthe existing literature because the vast majority of existing works call for\nunlimited access of ground truth incurring expensive labelling cost. Another\nissue lies in the problem of task boundaries and task IDs which must be known\nfor model's updates or model's predictions hindering feasibility for real-time\ndeployment. Knowledge Retention in Self-Adaptive Deep Continual Learner,\n(KIERA), is proposed in this paper. KIERA is developed from the notion of\nflexible deep clustering approach possessing an elastic network structure to\ncope with changing environments in the timely manner. The centroid-based\nexperience replay is put forward to overcome the catastrophic forgetting\nproblem. KIERA does not exploit any labelled samples for model updates while\nfeaturing a task-agnostic merit. The advantage of KIERA has been numerically\nvalidated in popular continual learning problems where it shows highly\ncompetitive performance compared to state-of-the art approaches. Our\nimplementation is available in\n\\textit{\\url{https://github.com/ContinualAL/KIERA}}.\n"}
+{"abstract": "  The prevailing framework for matching multimodal inputs is based on a\ntwo-stage process: 1) detecting proposals with an object detector and 2)\nmatching text queries with proposals. Existing two-stage solutions mostly focus\non the matching step. In this paper, we argue that these methods overlook an\nobvious \\emph{mismatch} between the roles of proposals in the two stages: they\ngenerate proposals solely based on the detection confidence (i.e.,\nquery-agnostic), hoping that the proposals contain all instances mentioned in\nthe text query (i.e., query-aware). Due to this mismatch, chances are that\nproposals relevant to the text query are suppressed during the filtering\nprocess, which in turn bounds the matching performance. To this end, we propose\nVL-NMS, which is the first method to yield query-aware proposals at the first\nstage. VL-NMS regards all mentioned instances as critical objects, and\nintroduces a lightweight module to predict a score for aligning each proposal\nwith a critical object. These scores can guide the NMS operation to filter out\nproposals irrelevant to the text query, increasing the recall of critical\nobjects, resulting in a significantly improved matching performance. Since\nVL-NMS is agnostic to the matching step, it can be easily integrated into any\nstate-of-the-art two-stage matching methods. We validate the effectiveness of\nVL-NMS on two multimodal matching tasks, namely referring expression grounding\nand image-text matching. Extensive ablation studies on several baselines and\nbenchmarks consistently demonstrate the superiority of VL-NMS.\n"}
+{"abstract": "  The passivation of thin Bi(1 1 1) films with hydrogen and oxide capping\nlayers is investigated from first principles. Considering termination related\nchanges of the crystal structure, we show how the bands and density of states\nare affected. In the context of the much discussed semimetal-to-semiconductor\ntransition and the band topology of the bulk material, we consider the effects\nof confinement in the whole Brillouin zone and go beyond standard density\nfunctional theory by including many-body interactions via the G$_0$W$_0$\napproximation. The conductivity of unterminated films is calculated via the\nBoltzmann transport equation using the simple constant relaxation time\napproximation and compared to experimental observations which have suggested a\ntwo-channel model.\n"}
+{"abstract": "  We determine the anti-Ramsey numbers for paths. This confirms a conjecture\nposed by Erd\\H{o}s, Simonovits and S\\'{o}s in 1970s.\n"}
+{"abstract": "  We study planar graphs with large negative curvature outside of a finite set\nand the spectral theory of Schr{\\\"o}dinger operators on these graphs. We obtain\nestimates on the first and second order term of the eigenvalue asymptotics.\nMoreover, we prove a unique continuation result for eigenfunctions and decay\nproperties of general eigenfunctions. The proofs rely on a detailed analysis of\nthe geometry which employs a Copy-and-Paste procedure based on the\nGau{\\ss}-Bonnet theorem.\n"}
+{"abstract": "  In the present paper, we study a two-player zero-sum deterministic\ndifferential game with both players adopting impulse controls, in infinite time\nhorizon, under rather weak assumptions on the cost functions. We prove by means\nof the dynamic programming principle (DPP) that the lower and upper value\nfunctions are continuous and viscosity solutions to the corresponding\nHamilton-Jacobi-Bellman-Isaacs (HJBI) quasi-variational inequality (QVI). We\ndefine a new HJBI QVI for which, under a proportional property assumption on\nthe maximizer cost, the value functions are the unique viscosity solution. We\nthen prove that the lower and upper value functions coincide.\n"}
+{"abstract": "  Researchers have compared machine learning (ML) classifiers and discrete\nchoice models (DCMs) in predicting travel behavior, but the generalizability of\nthe findings is limited by the specifics of data, contexts, and authors'\nexpertise. This study seeks to provide a generalizable empirical benchmark by\ncomparing hundreds of ML and DCM classifiers in a highly structured manner. The\nexperiments evaluate both prediction accuracy and computational cost by\nspanning four hyper-dimensions, including 105 ML and DCM classifiers from 12\nmodel families, 3 datasets, 3 sample sizes, and 3 outputs. This experimental\ndesign leads to an immense number of 6,970 experiments, which are corroborated\nwith a meta dataset of 136 experiment points from 35 previous studies. This\nstudy is hitherto the most comprehensive and almost exhaustive comparison of\nthe classifiers for travel behavioral prediction. We found that the ensemble\nmethods and deep neural networks achieve the highest predictive performance,\nbut at a relatively high computational cost. Random forests are the most\ncomputationally efficient, balancing between prediction and computation. While\ndiscrete choice models offer accuracy with only 3-4 percentage points lower\nthan the top ML classifiers, they have much longer computational time and\nbecome computationally impossible with large sample size, high input\ndimensions, or simulation-based estimation. The relative ranking of the ML and\nDCM classifiers is highly stable, while the absolute values of the prediction\naccuracy and computational time have large variations. Overall, this paper\nsuggests using deep neural networks, model ensembles, and random forests as\nbaseline models for future travel behavior prediction. For choice modeling, the\nDCM community should switch more attention from fitting models to improving\ncomputational efficiency, so that the DCMs can be widely adopted in the big\ndata context.\n"}
+{"abstract": "  In this paper, we derive an \\emph{a priori} second order estimate for\nsolutions which are in $\\Gamma_{k+1}$ cone to a class of complex Hessian\nequations with both sides of the equation depending on the gradient on compact\nHermitian manifolds.\n"}
+{"abstract": "  In this paper we consider $4$-dimensional steady soliton singularity models,\ni.e., complete steady gradient Ricci solitons that arise as the rescaled limit\nof a finite time singular solution of the Ricci flow on a closed $4$-manifold.\nIn particular, we study the geometry at infinity of such Ricci solitons under\nthe assumption that their tangent flow at infinity is the product of\n$\\mathbb{R}$ with a $3$-dimensional spherical space form. We also classify the\ntangent flows at infinity of $4$-dimensional steady soliton singularity models\nin general.\n"}
+{"abstract": "  In this paper we strengthen a result due to Li by showing that the third\nbi-power of a locally finite connected bipartite graph that admits a perfect\nmatching is Hamilton-laceable, i.e. any two vertices from different bipartition\nclasses are endpoints of some common Hamilton arc.\n"}
+{"abstract": "  We apply the theory of infinitesimal transformations for the study of a\nfamily of 1+1 fifth-order partial differential equations which have been\nproposed before for the description of multiple kink solutions. In this\nanalysis we perform a complete classification of the Lie symmetries and of the\none-dimensional optimal system. The results are applied for the derivation of\nsimilarity solutions and in particular we find travel-wave and scaling\nsolutions. We show that the kink-solution of these equations can be recovered\nby the use of the Lie symmetries, while new solutions are also derived.\n"}
+{"abstract": "  In a CMOS image sensor pixel, fast and complete charge transfer from pinned\nphotodiode (PPD) is desired and necessary in some applications. In special\ncases such as time-of-flight imaging or large pinned photodiodes, the PPD\npotential well shape highly affects the charge transfer performance and should\nbe engineered carefully. In the present work, a PPD structure named multi-level\nPPD is introduced and examined through simulation study. Moreover, a fast and\neffective way to analyze the pinning process for a lag-free design is\nintroduced. It is concluded that the proposed PPD achieves fast and complete\ncharge transfer without additional implementation masks or process steps. The\nproposed PPD is compared with a similar conventional rectangular pixel and 31%\nreduction in the charge transfer time is observed.\n"}
+{"abstract": "  In subscription-based businesses, the churn rate refers to the percentage of\ncustomers who discontinue their subscriptions within a given time period.\nParticularly, in the mobile games industry, the churn rate is often pronounced\ndue to the high competition and cost in customer acquisition; therefore, the\nprocess of minimizing the churn rate is crucial. This needs churn prediction,\npredicting users who will be churning within a given time period. Accurate\nchurn prediction can enable the businesses to devise and engage strategic\nremediations to maintain a low churn rate. The paper presents our highly\naccurate churn prediction method. We designed this method to take into account\neach individual user's distinct usage period in churn prediction. As presented\nin the paper, this approach was able to achieve 96.6% churn prediction accuracy\non a real game business. In addition, the paper shows that other existing churn\nprediction algorithms are improved in prediction accuracy when this method is\napplied.\n"}
+{"abstract": "  Few-shot segmentation~(FSS) performance has been extensively promoted by\nintroducing episodic training and class-wise prototypes. However, the FSS\nproblem remains challenging due to three limitations: (1) Models are distracted\nby task-unrelated information; (2) The representation ability of a single\nprototype is limited; (3) Class-related prototypes ignore the prior knowledge\nof base classes. We propose the Prior-Enhanced network with Meta-Prototypes to\ntackle these limitations. The prior-enhanced network leverages the support and\nquery (pseudo-) labels in feature extraction, which guides the model to focus\non the task-related features of the foreground objects, and suppress much noise\ndue to the lack of supervised knowledge. Moreover, we introduce multiple\nmeta-prototypes to encode hierarchical features and learn class-agnostic\nstructural information. The hierarchical features help the model highlight the\ndecision boundary and focus on hard pixels, and the structural information\nlearned from base classes is treated as the prior knowledge for novel classes.\nExperiments show that our method achieves the mean-IoU scores of 60.79% and\n41.16% on PASCAL-$5^i$ and COCO-$20^i$, outperforming the state-of-the-art\nmethod by 3.49% and 5.64% in the 5-shot setting. Moreover, comparing with\n1-shot results, our method promotes 5-shot accuracy by 3.73% and 10.32% on the\nabove two benchmarks. The source code of our method is available at\nhttps://github.com/Jarvis73/PEMP.\n"}
+{"abstract": "  Let $p$ be a prime number such that $p=2$ or $p\\equiv 1\\pmod 4$. Let\n$\\varepsilon_p$ denote the fundamental unit of $\\mathbb{Q}(\\sqrt{p})$ and let\n$a$ be a positive square-free integer. In the present paper, we construct the\nHilbert genus field of the real cyclic quartic fields\n$\\mathbb{Q}(\\sqrt{a\\varepsilon_p\\sqrt{p}})$.\n"}
+{"abstract": "  Computers used for data analytics are often NUMA systems with multiple\nsockets per machine, multiple cores per socket, and multiple thread contexts\nper core. To get the peak performance out of these machines requires the\ncorrect number of threads to be placed in the correct positions on the machine.\nOne particularly interesting element of the placement of memory and threads is\nthe way it effects the movement of data around the machine, and the increased\nlatency this can introduce to reads and writes. In this paper we describe work\non modeling the bandwidth requirements of an application on a NUMA compute node\nbased on the placement of threads. The model is parameterized by sampling\nperformance counters during 2 application runs with carefully chosen thread\nplacements. Evaluating the model with thousands of measurements shows a median\ndifference from predictions of 2.34% of the bandwidth. The results of this\nmodeling can be used in a number of ways varying from: Performance debugging\nduring development where the programmer can be alerted to potentially\nproblematic memory access patterns; To systems such as Pandia which take an\napplication and predict the performance and system load of a proposed thread\ncount and placement; To libraries of data structures such as Parallel\nCollections and Smart Arrays that can abstract from the user memory placement\nand thread placement issues when parallelizing code.\n"}
+{"abstract": "  We consider the problem of subset selection for $\\ell_{p}$ subspace\napproximation, i.e., given $n$ points in $d$ dimensions, we need to pick a\nsmall, representative subset of the given points such that its span gives\n$(1+\\epsilon)$ approximation to the best $k$-dimensional subspace that\nminimizes the sum of $p$-th powers of distances of all the points to this\nsubspace. Sampling-based subset selection techniques require adaptive sampling\niterations with multiple passes over the data. Matrix sketching techniques give\na single-pass $(1+\\epsilon)$ approximation for $\\ell_{p}$ subspace\napproximation but require additional passes for subset selection.\n  In this work, we propose an MCMC algorithm to reduce the number of passes\nrequired by previous subset selection algorithms based on adaptive sampling.\nFor $p=2$, our algorithm gives subset selection of nearly optimal size in only\n$2$ passes, whereas the number of passes required in previous work depend on\n$k$. Our algorithm picks a subset of size $\\mathrm{poly}(k/\\epsilon)$ that\ngives $(1+\\epsilon)$ approximation to the optimal subspace. The running time of\nthe algorithm is $nd + d~\\mathrm{poly}(k/\\epsilon)$. We extend our results to\nthe case when outliers are present in the datasets, and suggest a two pass\nalgorithm for the same. Our ideas also extend to give a reduction in the number\nof passes required by adaptive sampling algorithms for $\\ell_{p}$ subspace\napproximation and subset selection, for $p \\geq 2$.\n"}
+{"abstract": "  In this work, we propose a secret key generation procedure specifically\ndesigned for the inter-spacecraft communication links. As a novel secrecy\nsource, the spacecrafts utilize Doppler frequency shift based measurements. In\nthis way, the mobilities of the communication devices are exploited to generate\nsecret keys, where this resource can be utilized in the environments that the\nchannel fading based key generation methods are not available. The mobility of\na spacecraft is modeled as the superposition of a pre-determined component and\na dynamic component. We derive the maximum achievable secret key generation\nrate from the Doppler frequency shift. The proposed secret key generation\nprocedure extracts the Doppler frequency shift in the form of nominal power\nspectral density samples (NPSDS). We propose a maximum-likelihood (ML)\nestimation for the NPSDS at the spacecrafts, then a uniform quantizer is\nutilized to obtain secret key bits. The key disagreement rate (KDR) is\nanalytically obtained for the proposed key generation procedure. Through\nnumerical studies, the tightness of the provided approximations is shown. Both\nthe theoretical and numerical results demonstrate the validity and the\npracticality of the presented physical layer key generation procedure\nconsidering the security of the communication links of spacecrafts.\n"}
+{"abstract": "  The $^3{\\rm H}(d,n)^4{\\rm He}$ reaction is of significant interest in nuclear\nastrophysics and nuclear applications. It is an important, early step in\nbig-bang nucleosynthesis and a key process in nuclear fusion reactors. We use\none- and two-level $R$-matrix approximations to analyze data on the cross\nsection for this reaction at center-of-mass energies below 215 keV. We\ncritically examine the data sets using a Bayesian statistical model that allows\nfor both common-mode and additional point-to-point uncertainties. We use Markov\nChain Monte Carlo sampling to evaluate this $R$-matrix-plus-statistical model\nand find two-level $R$-matrix results that are stable with respect to\nvariations in the channel radii. The $S$ factor at 40 keV evaluates to\n$25.36(19)$ MeV b (68% credibility interval). We discuss our Bayesian analysis\nin detail and provide guidance for future applications of Bayesian methods to\n$R$-matrix analyses. We also discuss possible paths to further reduction of the\n$S$-factor uncertainty.\n"}
+{"abstract": "  In this paper, the multivariate tail covariance (MTCov) for generalized\nskew-elliptical distributions is considered. Some special cases for this\ndistribution, such as generalized skew-normal, generalized skew student-t,\ngeneralized skew-logistic and generalized skew-Laplace distributions, are also\nconsidered. In order to test the theoretical feasibility of our results, the\nMTCov for skewed and non skewed normal distributions are computed and compared.\nFinally, we give a special formula of the MTCov for generalized skew-elliptical\ndistributions.\n"}
+{"abstract": "  Graph pattern cardinality estimation is the problem of estimating the number\nof embeddings of a query graph in a data graph. This fundamental problem\narises, for example, during query planning in subgraph matching algorithms.\nThere are two major approaches to solving the problem: sampling and synopsis.\nSynopsis (or summary)-based methods are fast and accurate if synopses capture\ninformation of graphs well. However, these methods suffer from large errors due\nto loss of information during summarization and inherent assumptions.\nSampling-based methods are unbiased but suffer from large estimation variance\ndue to large sample space.\n  To address these limitations, we propose Alley, a hybrid method that combines\nboth sampling and synopses. Alley employs 1) a novel sampling strategy, random\nwalk with intersection, which effectively reduces the sample space, 2)\nbranching to further reduce variance, and 3) a novel mining approach that\nextracts and indexes tangled patterns as synopses which are inherently\ndifficult to estimate by sampling. By using them in the online estimation\nphase, we can effectively reduce the sample space while still ensuring\nunbiasedness. We establish that Alley has worst-case optimal runtime and\napproximation quality guarantees for any given error bound $\\epsilon$ and\nrequired confidence $\\mu$. In addition to the theoretical aspect of Alley, our\nextensive experiments show that Alley outperforms the state-of-the-art methods\nby up to orders of magnitude higher accuracy with similar efficiency.\n"}
+{"abstract": "  The Gell-Mann$-$Okubo (GMO) mass relations following from\n$\\text{SU}(3)$-flavor symmetry breaking in the strong sector have been proven\nto be of great success for describing hadron masses and classifying them into\nmultiplets. Nowadays, it is widely believed that baryons being fermions have to\nobey GMO relations which are linear in the baryon masses, whilst mesons as\nbosons follow quadratic GMO relations. In this work, I challenge the\ndistinction of GMO relations for baryons and mesons and conclude that both\nlinear and quadratic GMO relations apply to both baryons and mesons in most\ninstances (exceptions are the pseudoscalar meson octet and the mass relations\nfollowing from heavy quark symmetry). I support this claim by presenting an\nin-depth analysis of the GMO relations on a theoretical and experimental level.\nOn the theoretical side, two approaches to the derivation of the GMO relations\nare given and isospin symmetry breaking, electromagnetic contributions, and\nheavy quark symmetry are incorporated into the mass relations. Both linear and\nquadratic mass relations are checked against experimentally determined hadron\nmasses. Furthermore, the mass relations are used to classify unassigned\nresonances like $\\Lambda^+_c(2940)$ and $\\Xi_c(3123)$ into multiplets and to\npredict the masses of missing particles in yet incomplete multiplets.\n"}
+{"abstract": "  A $d$-dimensional random array on a nonempty set $I$ is a stochastic process\n$\\boldsymbol{X}=\\langle X_s:s\\in {I\\choose d}\\rangle$ indexed by the set\n${I\\choose d}$ of all $d$-element subsets of $I$. We obtain structural\ndecompositions of finite, high-dimensional random arrays whose distribution is\ninvariant under certain symmetries.\n  Our first main result is a distributional decomposition of finite,\n(approximately) spreadable, high-dimensional random arrays whose entries take\nvalues in a finite set; the two-dimensional case of this result is the finite\nversion of an infinitary decomposition due to Fremlin and Talagrand. Our second\nmain result is a physical decomposition of finite, spreadable, high-dimensional\nrandom arrays with square-integrable entries which is the analogue of the\nHoeffding/Efron--Stein decomposition. All proofs are effective.\n  We also present applications of these decompositions in the study of\nconcentration of functions of finite, high-dimensional random arrays.\n"}
+{"abstract": "  A pure excedance in a permutation $\\pi=\\pi_1\\pi_2\\ldots \\pi_n$ is a position\n$i<\\pi_i$ such that there is no $j<i$ with $i\\leq \\pi_j<\\pi_i$. We present a\none-to-one correspondence on the symmetric group that transports pure\nexcedances to descents of special kind. As a byproduct, we prove that the\npopularity of pure excedances equals those of pure descents on permutations,\nwhile their distributions are different.\n"}
+{"abstract": "  In this work Data Envelopment Analysis (DEA) is employed in thirty-day\nwindows to quantify temporal evolution of relative pandemic mitigation\ninefficiency of Brazilian municipalities. For each thirty-day window the\nresults of inefficiency scores of over five thousand Brazilian municipalities\nare displayed on maps, to address the spatial distribution of the corresponding\nvalues. This phenomenological spatiotemporal approach reveals location of the\nhotspots, in terms of relative pandemic inefficiency of the municipalities, at\ndifferent points in time along the pandemic. It is expected that the current\napproach may subsidize decision making through comparative analysis of previous\npractice, and thus contribute to the future pandemic mitigation efforts.\n"}
+{"abstract": "  We analyse new ALMA observations of the $^{29}$SiO ($\\nu$=0, $J$=8$-$7) and\nSO$_2$($\\nu$=0, $34_{3,31}$$-$$34_{2,32}$) line emissions of the circumstellar\nenvelope (CSE) of the oxygen-rich AGB star R Dor. With a spatial resolution of\n$\\sim$2.3 au, they cover distances below $\\sim$30 au from the star providing a\nlink between earlier observations and clarifying some open issues. The main\nconclusions are: 1) Rotation is confined below $\\sim$15 au from the star, with\nvelocity reaching a maximum below 10 au and morphology showing no significant\ndisc-like flattening. 2) In the south-eastern quadrant, a large Doppler\nvelocity gas stream is studied in more detail than previously possible and its\npossible association with an evaporating planetary companion is questioned. 3)\nA crude evaluation of the respective contributions of rotation, expansion and\nturbulence to the morpho-kinematics is presented. Significant line broadening\noccurs below $\\sim$12 au from the star and causes the presence of high Doppler\nvelocity components near the line of sight pointing to the centre of the star.\n4) Strong absorption of the continuum emission of the stellar disc and its\nimmediate dusty environment is observed to extend beyond the disc in the form\nof self-absorption. The presence of a cold SiO layer extending up to some 60 au\nfrom the star is shown to be the cause. 5) Line emissions from SO, $^{28}$SiO,\nCO and HCN molecules are used to probe the CSE up to some 100 au from the star\nand reveal the presence of two broad back-to-back outflows, the morphology of\nwhich is studied in finer detail than in earlier work.\n"}
+{"abstract": "  We observed a strong non-linearity in the system of quasiparticles of a\nsuperconducting aluminum resonator, due to the Cooper-pair breaking from the\nabsorbed readout power. We observed both negative and positive feedback\neffects, controlled by the detuning of the readout frequency, which are able to\nalter the relaxation time of quasiparticles by a factor greater than 10. We\nestimate that the (70+/-5) % of the total non-linearity of the device is due to\nquasiparticles.\n"}
+{"abstract": "  A method for evaluating the relative performance of local, cloud and combined\nprocessing of divisible (i.e. partitionable) data loads is presented. It is\nshown how to do this in the context of Amdahl's law. A single level (star)\nnetwork operating under each of three fundamental scheduling policies is used\nas an example. Applications include mobile computing, cloud computing and\nsignature searching.\n"}
+{"abstract": "  We prove the Bonnet theorem for statistical manifolds, which states that if a\nstatistical manifold admits tensors satisfying the Gauss--Codazzi--Ricci\nequations, then it is locally embeddable to a flat statistical manifold (or a\nHessian manifold). The proof is based on the notion of statistical embedding to\nthe product of a vector space and its dual space introduced by Lauritzen. As\nanother application of Lauritzen's embedding, we show that a statistical\nmanifold admitting an affine embedding of codimension 1 or 2 is locally\nembeddable to a flat statistical manifold of the same codimension.\n"}
+{"abstract": "  In this paper, the problem of load uncertainty in compliance problems is\naddressed where the uncertainty is described in the form of a set of finitely\nmany loading scenarios. Computationally more efficient methods are proposed to\nexactly evaluate and differentiate: 1) the mean compliance, or 2) any\nscalar-valued function of the individual load compliances such as the weighted\nsum of the mean and standard deviation. The computational time complexities of\nall the proposed algorithms are analyzed, compared with the naive approaches\nand then experimentally verified. Finally, a mean compliance minimization\nproblem, a risk-averse compliance minimization problem and a maximum compliance\nconstrained problem are solved to showcase the efficacy of the proposed\nalgorithms. The maximum compliance constrained problem is solved using the\naugmented Lagrangian method and the method proposed for handling scalar-valued\nfunctions of the load compliances, where the scalar-valued function is the\naugmented Lagrangian function.\n"}
+{"abstract": "  Face recognition has been extensively studied in computer vision and\nartificial intelligence communities in recent years. An important issue of face\nrecognition is data privacy, which receives more and more public concerns. As a\ncommon privacy-preserving technique, Federated Learning is proposed to train a\nmodel cooperatively without sharing data between parties. However, as far as we\nknow, it has not been successfully applied in face recognition. This paper\nproposes a framework named FedFace to innovate federated learning for face\nrecognition. Specifically, FedFace relies on two major innovative algorithms,\nPartially Federated Momentum (PFM) and Federated Validation (FV). PFM locally\napplies an estimated equivalent global momentum to approximating the\ncentralized momentum-SGD efficiently. FV repeatedly searches for better\nfederated aggregating weightings via testing the aggregated models on some\nprivate validation datasets, which can improve the model's generalization\nability. The ablation study and extensive experiments validate the\neffectiveness of the FedFace method and show that it is comparable to or even\nbetter than the centralized baseline in performance.\n"}
+{"abstract": "  Video game level generation based on machine learning (ML), in particular,\ndeep generative models, has attracted attention as a technique to automate\nlevel generation. However, applications of existing ML-based level generations\nare mostly limited to tile-based level representation. When ML techniques are\napplied to game domains with non-tile-based level representation, such as Angry\nBirds, where objects in a level are specified by real-valued parameters, ML\noften fails to generate playable levels. In this study, we develop a\ndeep-generative-model-based level generation for the game domain of Angry\nBirds. To overcome these drawbacks, we propose a sequential encoding of a level\nand process it as text data, whereas existing approaches employ a tile-based\nencoding and process it as an image. Experiments show that the proposed level\ngenerator drastically improves the stability and diversity of generated levels\ncompared with existing approaches. We apply latent variable evolution with the\nproposed generator to control the feature of a generated level computed through\nan AI agent's play, while keeping the level stable and natural.\n"}
+{"abstract": "  Hydrodynamic instabilities driven by a direct current are analyzed in 2D and\n3D relativisticlike systems with the Dyakonov-Shur boundary conditions\nsupplemented by a boundary condition for temperature. Besides the conventional\nDyakonov-Shur instability for plasmons, we find an entropy wave instability in\nboth 2D and 3D systems. The entropy wave instability is a manifestation of the\nrelativisticlike nature of electron quasiparticles and a nontrivial role of the\nenergy current in such systems. These two instabilities occur for the opposite\ndirections of fluid flow. While the Dyakonov-Shur instability is characterized\nby the plasma frequency in 3D and the system size in 2D, the frequency of the\nentropy wave instability is tunable by the system size and the flow velocity.\n"}
+{"abstract": "  Delivering reliable and high-capacity Internet connectivity to high-speed\ntrain users is a challenge. Modern railway cars act as Faraday cages and a\ntypical train consist comprises several hundreds of users moving at high\nvelocity. Furthermore, with the global availability of fourth generation (4G)\nLong Term Evolution (LTE), user expectations have dramatically increased: it is\nexpected to be online anytime and anywhere. Demand for mobile high-capacity is\nbeing driven by video and music streaming services, for lower latency and\nhigher availability by gaming, and for more reliability and even uplink\ncapacity by mission critical applications. Finally, the life-cycle of the\nrailway industry is much longer than for telecommunications, which makes\nsupporting 5G challenging. In this paper, we survey the challenges associated\nwith delivering high-capacity connectivity to train users, describe potential\noptions, and highlight how a leading western European operator is tackling\nthese challenges and preparing for 5G and beyond.\n"}
+{"abstract": "  A common library, TMDlib2, for Transverse-Momentum-Dependent distributions\n(TMDs) and unintegrated parton distributions (uPDFs) is described, which allows\nfor easy access of commonly used TMDs and uPDFs, providing a three-dimensional\n(3D) picture of the partonic structure of hadrons. The tool TMDplotter allows\nfor web-based plotting of distributions implemented in TMDlib2, together with\ncollinear pdfs as available in LHAPDF.\n"}
+{"abstract": "  This paper develops a framework that models and optimizes the operations of\ncomplex on-orbit servicing infrastructures involving one or more servicers and\norbital depots to provide multiple types of services to a fleet of\ngeostationary satellites. The proposed method extends the state-of-the-art\nspace logistics technique by addressing the unique challenges in on-orbit\nservicing applications, and integrate it with the Rolling Horizon decision\nmaking approach. The space logistics technique enables modeling of the on-orbit\nservicing logistical operations as a Mixed-Integer Linear Program whose optimal\nsolutions can efficiently be found. The Rolling Horizon approach enables the\nassessment of the long-term value of an on-orbit servicing infrastructure by\naccounting for the uncertain service needs that arise over time among the\ngeostationary satellites. Two case studies successfully demonstrate the\neffectiveness of the framework for (1) short-term operational scheduling and\n(2) long-term strategic decision making for on-orbit servicing architectures\nunder diverse market conditions.\n"}
+{"abstract": "  We present a tree tensor-network impurity solver suited for general\nmultiorbital systems. The network is constructed to efficiently capture the\nentanglement structure and symmetry of an impurity problem. The solver works\ndirectly on the real-time/frequency axis and generates spectral functions with\nenergy-independent resolution of the order of one percent of the correlated\nbandwidth. Combined with an optimized representation of the impurity bath, it\nefficiently solves self-consistent dynamical mean-field equations and\ncalculates various dynamical correlation functions for systems with\noff-diagonal Green's functions. For the archetypal correlated Hund's metal\nSr$_2$RuO$_4$, we show that both the low-energy quasiparticle spectra related\nto the van Hove singularity and the high-energy atomic multiplet excitations\ncan be faithfully resolved. In particular, we show that while the spin-orbit\ncoupling has only minor effects on the orbital-diagonal one-particle spectral\nfunctions, it has a more profound impact on the low-energy spin and orbital\nresponse functions.\n"}
+{"abstract": "  In this work, we develop an automated method to generate 3D human walking\nmotion in simulation which is comparable to real-world human motion. At the\ncore, our work leverages the ability of deep reinforcement learning methods to\nlearn high-dimensional motor skills while being robust to variations in the\nenvironment dynamics. Our approach iterates between policy learning and\nparameter identification to match the real-world bio-mechanical human data. We\npresent a thorough evaluation of the kinematics, kinetics and ground reaction\nforces generated by our learned virtual human agent. We also show that the\nmethod generalizes well across human-subjects with different kinematic\nstructure and gait-characteristics.\n"}
+{"abstract": "  We derive a covariant expression for the renormalized holographic\nentanglement entropy for Conformal Field Theories (CFTs) dual to Quadratic\nCurvature Gravity in arbitrary dimensions. This expression is written as the\nsum of the bare entanglement entropy functional obtained using standard conical\ndefect techniques, and a counterterm defined at the boundary of the extremal\nsurface of the functional. The latter corresponds to the cod-2 self-replicating\npart of the extrinsic counterterms when evaluated on the replica orbifold. This\nrenormalization method isolates the universal terms of the holographic\nentanglement entropy functional. We use it to compute the standard C-function\ncandidate for CFTs of arbitrary dimension, and the type-B anomaly coefficient c\nfor 4-dimensional CFTs.\n"}
+{"abstract": "  We use a trivial delta method with multiplicative characters for congruence\ndetection to prove the Weyl bound for GL(2) in $t$-aspect for a holomorphic or\nHecke-Maass cusp form of arbitrary level and nebentypus. This parallels the\nwork of Aggarwal in 2018, with the difference being multiplicative character\nhas a more natural connection to the twisted $L$-function. This provides\nanother view point to understand and explore the trivial and other delta\nmethods.\n"}
+{"abstract": "  This paper proposes a novel primal heuristic for Mixed Integer Programs, by\nemploying machine learning techniques. Mixed Integer Programming is a general\ntechnique for formulating combinatorial optimization problems. Inside a solver,\nprimal heuristics play a critical role in finding good feasible solutions that\nenable one to tighten the duality gap from the outset of the Branch-and-Bound\nalgorithm (B&B), greatly improving its performance by pruning the B&B tree\naggressively. In this paper, we investigate whether effective primal heuristics\ncan be automatically learned via machine learning. We propose a new method to\nrepresent an optimization problem as a graph, and train a Graph Convolutional\nNetwork on solved problem instances with known optimal solutions. This in turn\ncan predict the values of decision variables in the optimal solution for an\nunseen problem instance of a similar type. The prediction of variable solutions\nis then leveraged by a novel configuration of the B&B method, Probabilistic\nBranching with guided Depth-first Search (PB-DFS) approach, aiming to find\n(near-)optimal solutions quickly. The experimental results show that this new\nheuristic can find better primal solutions at a much earlier stage of the\nsolving process, compared to other state-of-the-art primal heuristics.\n"}
+{"abstract": "  We present the discovery and characterization of two sub-Neptunes in close\norbits, as well as a tentative outer planet of a similar size, orbiting\nTOI-1260 - a low metallicity K6V dwarf star. Photometry from TESS yields radii\nof $R_{\\rm b} = 2.33 \\pm 0.10$ $R_{\\oplus}$ and $R_{\\rm c} = 2.82 \\pm 0.15$\n$R_{\\oplus}$, and periods of 3.13 and 7.49 days for TOI-1260b and TOI-1260c,\nrespectively. We combined the TESS data with a series of ground-based follow-up\nobservations to characterize the planetary system. From HARPS-N high-precision\nradial velocities we obtain $M_{\\rm b} = 8.61_{ - 1.46 } ^ { + 1.36 }$\n$M_{\\oplus}$ and $M_{\\rm c} = 11.84_{ - 3.23 } ^ { + 3.38 }$ $M_{\\oplus}$. The\nstar is moderately active with a complex activity pattern, which necessitated\nthe use of Gaussian process regression for both the light curve detrending and\nthe radial velocity modelling, in the latter case guided by suitable activity\nindicators. We successfully disentangle the stellar-induced signal from the\nplanetary signals, underlining the importance and usefulness of the Gaussian\nProcess approach. We test the system's stability against atmospheric\nphotoevaporation and find that the TOI-1260 planets are classic examples of the\nstructure and composition ambiguity typical for the $2-3$ $R_{\\oplus}$ range.\n"}
+{"abstract": "  Articulatory information has been shown to be effective in improving the\nperformance of HMM-based and DNN-based text-to-speech synthesis. Speech\nsynthesis research focuses traditionally on text-to-speech conversion, when the\ninput is text or an estimated linguistic representation, and the target is\nsynthesized speech. However, a research field that has risen in the last decade\nis articulation-to-speech synthesis (with a target application of a Silent\nSpeech Interface, SSI), when the goal is to synthesize speech from some\nrepresentation of the movement of the articulatory organs. In this paper, we\nextend traditional (vocoder-based) DNN-TTS with articulatory input, estimated\nfrom ultrasound tongue images. We compare text-only, ultrasound-only, and\ncombined inputs. Using data from eight speakers, we show that that the combined\ntext and articulatory input can have advantages in limited-data scenarios,\nnamely, it may increase the naturalness of synthesized speech compared to\nsingle text input. Besides, we analyze the ultrasound tongue recordings of\nseveral speakers, and show that misalignments in the ultrasound transducer\npositioning can have a negative effect on the final synthesis performance.\n"}
+{"abstract": "  For many decades now, Bayesian Model Averaging (BMA) has been a popular\nframework to systematically account for model uncertainty that arises in\nsituations when multiple competing models are available to describe the same or\nsimilar physical process. The implementation of this framework, however, comes\nwith multitude of practical challenges including posterior approximation via\nMarkov Chain Monte Carlo and numerical integration. We present a Variational\nBayes Inference approach to BMA as a viable alternative to the standard\nsolutions which avoids many of the aforementioned pitfalls. The proposed method\nis 'black box' in the sense that it can be readily applied to many models with\nlittle to no model-specific derivation. We illustrate the utility of our\nvariational approach on a suite of standard examples and discuss all the\nnecessary implementation details. Fully documented Python code with all the\nexamples is provided as well.\n"}
+{"abstract": "  In this paper we study the effect of electroweak sphaleron transition or\nelectroweak phase transition (EWPT) in balancing baryon excess to the excess\nstable quarks of $4^{th}$ generation. Considering the conservation of all\nquantum number and charges, sphaleron transition between between baryons,\nleptons and $4^{th}$ family of leptons and quarks is possible. We have tried to\nestablished a possible definite relationship between the value and sign of the\n$4^{th}$ family excess relative to baryon asymmetry under the framework of\nsecond order EWPT. In passing by we show the small, yet negligible dilution in\nthe pre-existing dark matter density due the sphaleron transition.\n"}
+{"abstract": "  We prove the existence and $C^{1,\\alpha}$ regularity of solutions to nonlocal\nfully nonlinear elliptic double obstacle problems. We also obtain boundary\nregularity for these problems. The obstacles are assumed to be Lipschitz\nsemi-concave/semi-convex functions, and we do not require them to be $C^1$. Our\napproach is to adapt a penalization method to be applicable to the setting of\nnonlocal equations and their viscosity solutions.\n"}
+{"abstract": "  We study the Maximum Independent Set of Rectangles (MISR) problem, where we\nare given a set of axis-parallel rectangles in the plane and the goal is to\nselect a subset of non-overlapping rectangles of maximum cardinality. In a\nrecent breakthrough, Mitchell [2021] obtained the first constant-factor\napproximation algorithm for MISR. His algorithm achieves an approximation ratio\nof 10 and it is based on a dynamic program that intuitively recursively\npartitions the input plane into special polygons called corner-clipped\nrectangles (CCRs), without intersecting certain special horizontal line\nsegments called fences.\n  In this paper, we present a $(2+\\epsilon)$-approximation algorithm for MISR\nwhich is also based on a recursive partitioning scheme. First, we use a\npartition into a class of axis-parallel polygons with constant complexity each\nthat are more general than CCRs. This allows us to provide an arguably simpler\nanalysis and at the same time already improves the approximation ratio to 6.\nThen, using a more elaborate charging scheme and a recursive partitioning into\ngeneral axis-parallel polygons with constant complexity, we improve our\napproximation ratio to $2+\\epsilon$. In particular, we construct a recursive\npartitioning based on more general fences which can be sequences of up to\n$O(1/\\epsilon)$ line segments each. This partitioning routine and our other new\nideas may be useful for future work towards a PTAS for MISR.\n"}
+{"abstract": "  GG Carinae is a binary whose primary component is a B[e] supergiant. Using\nphotometric data from TESS, ASAS, OMC, and ASAS-SN, and spectroscopic data from\nthe Global Jet Watch to study visible He\\,I, Fe\\,II and Si\\,II emission lines,\nwe investigate the short-period variations which are exhibited in GG Car. We\nfind a hitherto neglected periodicity of $1.583156\\pm0.0002$\\,days that is\npresent in both its photometry and the radial velocities of its emission lines,\nalongside variability at the well-established $\\sim$31-day orbital period. We\nfind that the amplitudes of the shorter-period variations in both photometry\nand some of the emission lines are modulated by the orbital phase of the\nbinary, such that the short-period variations have largest amplitudes when the\nbinary is at periastron. There are no significant changes in the phases of the\nshort-period variations over the orbital period. We investigate potential\ncauses of the 1.583-day variability, and find that the observed period agrees\nwell with the expected period of the $l=2$ f-mode of the primary given its mass\nand radius. We propose that the primary is periodically pulled out of\nhydrostatic equilibrium by the quadrupolar tidal forces when the components are\nnear periastron in the binary's eccentric orbit ($e=0.5$) and the primary\nalmost fills its Roche lobe. This causes an oscillation at the $l=2$ f-mode\nfrequency which is damped as the distance between the components increases.\n"}
+{"abstract": "  Highly integrated active nanophotonics addressing both device footprint and\noperation speed demands is a key enabling technology for the next generation\noptical networks. Plasmonic systems have proven to be a serious contender to\nalleviate current performance limitations in electro-optic devices. Here, we\ndemonstrate a plasmonic optical phased array (OPA) consisting of two\n10-$\\mu$m-long plasmonic phase shifters, utilized to control the far-field\nradiation pattern of two subwavelength-separated emitters for aliasing-free\nbeam steering with an angular range of $\\pm5^\\circ$ and flat frequency response\nup to 18 GHz (with the potential bandwidth of 1.2 THz). Extreme optical and\nelectrostatic field confinement with great spatial overlap results in high\nphase modulation efficiency ($V_{\\pi}L=0.24\\: \\text{Vcm}$). The demonstrated\napproach of using plasmonic lithium niobate technology for optical beam\nmanipulation offers inertia-free, robust, ultra-compact and high-speed beam\nsteering.\n"}
+{"abstract": "  We discuss whether a multi-step electroweak phase transition (EWPT) occurs in\ntwo Higgs doublet models (2HDMs). The EWPT is related to interesting phenomena\nsuch as baryogenesis and a gravitational wave from it. We examine parameter\nregions in CP-conserving 2HDMs and find certain areas where the multi-step\nEWPTs occur. The parameter search shows the multi-step EWPT prefers the scalar\npotential with the approximate $Z_2$ symmetry and a mass hierarchy between the\nneutral CP-odd and CP-even extra scalar bosons $m_A<m_H$. By contrast, the\nmulti-step EWPT whose first step is strongly first order favors a mass\nhierarchy $m_A>m_H$. In addition, we compute the Higgs trilinear coupling in\nthe parameter region where the multi-step EWPTs occur, which can be observed at\nfuture colliders. We also discuss a multi-peaked gravitational wave from a\nmulti-step EWPT.\n"}
+{"abstract": "  We image equilibrium and non-equilibrium transport through a two-dimensional\nelectronic cavity using scanning gate microscopy (SGM). Injecting electrons\ninto the cavity through a quantum point contact close to equilibrium, we\nraster-scan a weakly invasive tip above the cavity regions and measure the\nmodulated conductance through the cavity. Varying the electron injection energy\nbetween $\\pm$ 2 meV, we observe that conductance minima turn into maxima beyond\nthe energy threshold of $\\pm$ 0.6 meV. This observation bears similarity to\nprevious measurements by Jura et al. [Jura et al., Phys. Rev. B 82, 155328\n(2010)] who used a strongly invasive tip potential to study electron injection\ninto an open two-dimensional electron gas. This resemblance suggests a similar\nmicroscopic origin based on electron-electron interactions.\n"}
+{"abstract": "  In the Open Data Portal Germany (OPAL) project, a pipeline of the following\ndata refinement steps has been developed: requirements analysis, data\nacquisition, analysis, conversion, integration and selection. 800,000 datasets\nin DCAT format have been produced.\n"}
+{"abstract": "  Consider a uniform rooted Cayley tree $T_{n}$ with $n$ vertices and let $m$\ncars arrive sequentially, independently, and uniformly on its vertices. Each\ncar tries to park on its arrival node, and if the spot is already occupied, it\ndrives towards the root of the tree and parks as soon as possible. Lackner &\nPanholzer (arXiv:1504.04972) established a phase transition for this process\nwhen $ m \\approx \\frac{n}{2}$. In this work, we couple this model with a\nvariant of the classical Erd\\\"os-R\\'enyi random graph process. This enables us\nto describe the phase transition for the size of the components of parked cars\nusing a modification of the multiplicative coalescent which we name the frozen\nmultiplicative coalescent. The geometry of critical parked clusters is also\nstudied. Those trees are very different from Bienaym\\'e-Galton-Watson trees and\nshould converge towards the growth-fragmentation trees canonically associated\nto the $3/2$-stable process that already appeared in the study of random planar\nmaps.\n"}
+{"abstract": "  Let G be a p-solvable group, P a p-subgroup and chi in Irr(G) such that\nchi(1)_p \\ge |G:P|_p. We prove that the restriction chi_P is a sum of\ncharacters induced from subgroups Q\\le P such that chi(1)_p=|G:Q|_p. This\ngeneralizes previous results by Giannelli--Navarro and Giannelli--Sambale on\nthe number of linear constituents of chi_P. Although this statement does not\nhold for arbitrary groups, we conjecture a weaker version which can be seen as\nan extension of Brauer--Nesbitt's theorem on characters of p-defect zero. It\nalso extends a conjecture of Wilde.\n"}
+{"abstract": "  Resonant hadronic systems often exhibit a complicated decay pattern in which\nthree-body dynamics play a relevant or even dominant role. In this work we\nfocus on the $a_1(1260)$ resonance. For the first time, the pole position and\nbranching ratios of a three-body resonance are calculated from lattice QCD\nusing one-, two-, and three-meson interpolators and a three-body finite-volume\nformalism extended to spin and coupled channels. This marks a new milestone for\nab-initio studies of ordinary resonances along with hybrid and exotic hadrons\ninvolving three-body dynamics.\n"}
+{"abstract": "  Few-shot and one-shot learning have been the subject of active and intensive\nresearch in recent years, with mounting evidence pointing to successful\nimplementation and exploitation of few-shot learning algorithms in practice.\nClassical statistical learning theories do not fully explain why few- or\none-shot learning is at all possible since traditional generalisation bounds\nnormally require large training and testing samples to be meaningful. This\nsharply contrasts with numerous examples of successful one- and few-shot\nlearning systems and applications.\n  In this work we present mathematical foundations for a theory of one-shot and\nfew-shot learning and reveal conditions specifying when such learning schemes\nare likely to succeed. Our theory is based on intrinsic properties of\nhigh-dimensional spaces. We show that if the ambient or latent decision space\nof a learning machine is sufficiently high-dimensional than a large class of\nobjects in this space can indeed be easily learned from few examples provided\nthat certain data non-concentration conditions are met.\n"}
+{"abstract": "  Machine learning (ML) methods are being used in almost every conceivable area\nof electronic structure theory and molecular simulation. In particular, ML has\nbecome firmly established in the construction of high-dimensional interatomic\npotentials. Not a day goes by without another proof of principle being\npublished on how ML methods can represent and predict quantum mechanical\nproperties - be they observable, such as molecular polarizabilities, or not,\nsuch as atomic charges. As ML is becoming pervasive in electronic structure\ntheory and molecular simulation, we provide an overview of how atomistic\ncomputational modeling is being transformed by the incorporation of ML\napproaches. From the perspective of the practitioner in the field, we assess\nhow common workflows to predict structure, dynamics, and spectroscopy are\naffected by ML. Finally, we discuss how a tighter and lasting integration of ML\nmethods with computational chemistry and materials science can be achieved and\nwhat it will mean for research practice, software development, and postgraduate\ntraining.\n"}
+{"abstract": "  We study the trace class perturbations of the whole-line, discrete Laplacian\nand obtain a new bound for the perturbation determinant of the corresponding\nnon-self-adjoint Jacobi operator. Based on this bound, we refine the\nLieb--Thirring inequality due to Hansmann--Katriel. The spectral enclosure for\nsuch operators is also discussed.\n"}
+{"abstract": "  We develop a new probabilistic method for deriving deviation estimates in\ndirected planar polymer and percolation models. The key estimates are for exit\npoints of geodesics as they cross transversal down-right boundaries. These\nbounds are of optimal cubic-exponential order. We derive them in the context of\nlast-passage percolation with exponential weights with near-stationary boundary\nconditions. As a result, the probabilistic coupling method is empowered to\ntreat a variety of problems optimally, which could previously be achieved only\nvia inputs from integrable probability. As applications in the bulk setting, we\nobtain upper bounds of cubic-exponential order for transversal fluctuations of\ngeodesics, and cube-root upper bounds with a logarithmic correction for\ndistributional Busemann limits and competition interface limits. Several other\napplications are already in the literature.\n"}
+{"abstract": "  In this paper, we prove a result on non-Baire sets in category bases which\nwhen applied together with a result of Grzegorek yeilds a comparatively\nstronger version of a decomposition theorem due to Ulam.\n"}
+{"abstract": "  This paper provides three nearly-optimal algorithms for scheduling $t$ jobs\nin the $\\mathsf{CLIQUE}$ model. First, we present a deterministic scheduling\nalgorithm that runs in $O(\\mathsf{GlobalCongestion} + \\mathsf{dilation})$\nrounds for jobs that are sufficiently efficient in terms of their memory. The\n$\\mathsf{dilation}$ is the maximum round complexity of any of the given jobs,\nand the $\\mathsf{GlobalCongestion}$ is the total number of messages in all jobs\ndivided by the per-round bandwidth of $n^2$ of the $\\mathsf{CLIQUE}$ model.\nBoth are inherent lower bounds for any scheduling algorithm.\n  Then, we present a randomized scheduling algorithm which runs $t$ jobs in\n$O(\\mathsf{GlobalCongestion} + \\mathsf{dilation}\\cdot\\log{n}+t)$ rounds and\nonly requires that inputs and outputs do not exceed $O(n\\log n)$ bits per node,\nwhich is met by, e.g., almost all graph problems. Lastly, we adjust the\n\\emph{random-delay-based} scheduling algorithm [Ghaffari, PODC'15] from the\n$\\mathsf{CLIQUE}$ model and obtain an algorithm that schedules any $t$ jobs in\n$O(t / n + \\mathsf{LocalCongestion} + \\mathsf{dilation}\\cdot\\log{n})$ rounds,\nwhere the $\\mathsf{LocalCongestion}$ relates to the congestion at a single node\nof the $\\mathsf{CLIQUE}$. We compare this algorithm to the previous approaches\nand show their benefit.\n  We schedule the set of jobs on-the-fly, without a priori knowledge of its\nparameters or the communication patterns of the jobs. In light of the inherent\nlower bounds, all of our algorithms are nearly-optimal.\n  We exemplify the power of our algorithms by analyzing the message complexity\nof the state-of-the-art MIS protocol [Ghaffari, Gouleakis, Konrad, Mitrovic and\nRubinfeld, PODC'18], and we show that we can solve $t$ instances of MIS in $O(t\n+ \\log\\log\\Delta\\log{n})$ rounds, that is, in $O(1)$ amortized time, for $t\\geq\n\\log\\log\\Delta\\log{n}$.\n"}
+{"abstract": "  The symmetry SU(2) and its geometric Bloch Sphere rendering are familiar for\na qubit (spin-1/2) but extension of symmetries and geometries have been\ninvestigated far less for multiple qubits, even just a pair of them, that are\ncentral to quantum information. In the last two decades, two different\napproaches with independent starting points and motivations have come together\nfor this purpose. One was to develop the unitary time evolution of two or more\nqubits for studying quantum correlations, exploiting the relevant Lie algebras\nand especially sub-algebras of the Hamiltonians involved, and arriving at\nconnections to finite projective geometries and combinatorial designs.\nIndependently, geometers studying projective ring lines and associated finite\ngeometries have come to parallel conclusions. This review brings together both\nthe Lie algebraic and group representation perspective of quantum physics and\nthe geometric algebraic one, along with connections to complex quaternions.\nTogether, all this may be seen as further development of Felix Klein's Erlangen\nProgram for symmetries and geometries. In particular, the fifteen generators of\nthe continuous SU(4) Lie group for two-qubits can be placed in one-to-one\ncorrespondence with finite projective geometries, combinatorial Steiner\ndesigns, and finite quaternionic groups. The very different perspectives may\nprovide further insight into problems in quantum information. Extensions are\nconsidered for multiple qubits and higher spin or higher dimensional qudits.\n"}
+{"abstract": "  In recent years, many studies have applied wearable devices to capture\npsychophysiological data from software developers. However, the current\nliterature lacks investigations that classify the studies and point out gaps to\nbe explored. This article, therefore, seeks to present a comprehensive overview\nof the literature by classifying and creating a systematic map of the works.\nBesides, it seeks to pinpoint research gaps, challenges, and trends. Based on\nwidely known guidelines, a systematic mapping of the literature was designed\nand run to answer eight research questions. After applying a careful filtering\nprocess, we selected 27 representative studies from a sample of 2,084\npotentially relevant works retrieved from seven digital libraries. The main\nresults are: a classification scheme of the published studies was produced;\nthere is no predominance of the devices used to capture psychophysiological\ndata; over 50% of the studies have explored indicators related to mental states\nand neural activity; and 80% have analyzed composite data to understand the\ncognitive load and in the context of understanding debugging programs and\nstrategies. Our findings can benefit researchers and students by creating a\nsystematic map of the literature, being a starting point for future research.\n"}
+{"abstract": "  Recent observations in extreme-ultraviolet (EUV) wavelengths reveal a new\nlate phase in some solar flares, which is seen as a second peak in warm coronal\nemissions ($\\sim3$ MK) several tens of minutes to a few hours after the soft\nX-ray (SXR) peak. The origin of the EUV late phase (ELP) is explained by either\na long-lasting cooling process in the long ELP loops, or a delayed energy\nejection into the ELP loops well after the main flare heating. Using the\nobservations with the \\emph{Solar Dynamics Observatory} (\\emph{SDO}), we\ninvestigate the production of the ELP in six homologous flares (F1--F6)\noriginating from a complex active region (AR) NOAA 11283, with an emphasis on\nthe emission characteristics of the flares. It is found that the main\nproduction mechanism of the ELP changes from additional heating in flare F1 to\nlong-lasting cooling in flares F3--F6, with both mechanisms playing a role in\nflare F2. The transition is evidenced by an abrupt decrease of the time lag of\nthe ELP peak, and the long-lasting cooling process in the majority of the\nflares is validated by a positive correlation between the flare ribbon fluence\nand the ELP peak intensity. We attribute the change in ELP production mechanism\nto an enhancement of the envelope magnetic field above the AR, which\nfacilitates a more prompt and energetic heating of the ELP loops. In addition,\nthe last and the only confined flare F6 exhibits an extremely large ELP. The\ndifferent emission pattern revealed in this flare may reflect a different\nenergy partitioning inside the ELP loops, which is due to a different magnetic\nreconnection process.\n"}
+{"abstract": "  We present a new mass function of galaxy clusters and groups using\noptical/near-infrared wavelength spectroscopic and photometric data from the\nObservations of Redshift Evolution in Large-Scale Environments (ORELSE) survey.\nAt $z\\sim1$, cluster mass function studies are rare regardless of wavelength\nand have never been attempted from an optical/near-infrared perspective. This\nwork serves as a proof of concept that $z\\sim1$ cluster mass functions are\nachievable without supplemental X-ray or Sunyaev-Zel'dovich (SZ) data.\nMeasurements of the cluster mass function provide important contraints on\ncosmological parameters and are complementary to other probes. With ORELSE, a\nnew cluster finding technique based on Voronoi tessellation Monte-Carlo (VMC)\nmapping, and rigorous purity and completeness testing, we have obtained\n$\\sim$240 galaxy overdensity candidates in the redshift range $0.55<z<1.37$ at\na mass range of $13.6<\\log(M/M_{\\odot})<14.8$. This mass range is comparable to\nexisting optical cluster mass function studies for the local universe. Our\ncandidate numbers vary based on the choice of multiple input parameters related\nto detection and characterization in our cluster finding algorithm, which we\nincorporated into the mass function analysis through a Monte-Carlo scheme. We\nfind cosmological constraints on the matter density of $\\Omega_{m} =\n0.250^{+0.104}_{-0.099}$ and on the amplitude of fluctuations of $\\sigma_{8} =\n1.150^{+0.260}_{-0.163}$. While our $\\Omega_{m}$ value is close to concordance,\nour $\\sigma_{8}$ value is $\\sim2\\sigma$ higher because of the inflated observed\nnumber densities compared to theoretical mass function models owing to how our\nsurvey targeted overdense regions. With Euclid and several other large,\nunbiased optical surveys on the horizon, VMC mapping will enable optical/NIR\ncluster cosmology at redshifts much higher than what has been possible before.\n"}
+{"abstract": "  In this work we analyse the parameterised complexity of propositional\ninclusion (PINC) and independence logic (PIND). The problems of interest are\nmodel checking (MC) and satisfiability (SAT). The complexity of these problems\nis well understood in the classical (non-parameterised) setting. Mahmood and\nMeier (FoIKS 2020) recently studied the parameterised complexity of\npropositional dependence logic (PDL). As a continuation of their work, we\nclassify inclusion and independence logic and thereby come closer to completing\nthe picture with respect to the parametrised complexity for the three most\nstudied logics in the propositional team semantics setting. We present results\nfor each problem with respect to 8 different parameterisations. It turns out\nthat for a team-based logic L such that L-atoms can be evaluated in polynomial\ntime, then MC parameterised by teamsize is FPT. As a corollary, we get an FPT\nmembership under the following parameterisations: formula-size, formula-depth,\ntreewidth, and number of variables. The parameter teamsize shows interesting\nbehavior for SAT. For PINC, the parameter teamsize is not meaningful, whereas\nfor PDL and PIND the satisfiability is paraNP-complete. Finally, we prove that\nwhen parameterised by arity, both MC and SAT are paraNP-complete for each of\nthe considered logics.\n"}
+{"abstract": "  Due to the lower ionospheric thermal pressure and existence of the crustal\nmagnetism at Mars, the Martian ionopause is expected to behave differently from\nthe ionopause at Venus. We study the solar wind interaction and pressure\nbalance at the ionopause of Mars using both in situ and remote sounding\nmeasurements from the MARSIS (Mars Advanced Radar for Subsurface and Ionosphere\nSounding) instrument on the Mars Express orbiter. We show that the magnetic\npressure usually dominates the thermal pressure to hold off the solar wind at\nthe ionopause at Mars, with only 13% of the cases where the ionospheric thermal\npressure plays a more important role in pressure balance. This percentage at\nVenus, however, is up to 65%. We also find that the ionopause altitude at Mars\ndecreases as the normal component of the solar wind dynamic pressure increases,\nsimilar to the altitude variation of the ionopauses at Venus. Moreover, our\nresults show that the ionopause thickness at Mars and Venus is mainly\ndetermined by the ion gyromotion and is equivalent to about 5 ion gyroradii.\n"}
+{"abstract": "  The Kaup-Newell (KN) system is a significant integrable system, which reveals\nmany physical phenomena and natural laws. Especially, since the solutions of\nhigher-order KN systems are more complex and various than that of second-order\nflow KN systems which contain the derivative nonlinear Schrodinger equation\n(DNLS), Chen-Lee-Liu equation, Gerdjikov-Ivanov equation and so on, the\nresearch on it will become more and more important. In this paper, we\ninvestigate the third-order flow equation of Kaup-Newell system (TOFKN) with\nzero boundary conditions (ZBCs) and non-zero boundary conditions (NZBCs) by\nusing the Riemann-Hilbert (RH) approach based on the inverse scattering\ntransformation. Starting from Lax pairs of the TOFKN equation, the direct\nscattering problem constructs the analyticity, symmetries, asymptotic behaviors\nand discrete spectral properties for the Jost solutions, modified Jost\nsolutions and scattering matrix. The inverse scattering problems are solved by\nestablishing the matrix RH problem, and the reconstruction formula of\npotential, trace formula and theta condition are also derived correspondingly.\nThe reflectionless potentials with double poles are exhibited explicitly by\nmeans of determinants. Specifically, vivid plots and dynamics analysis which\ncontain double-pole soliton solutions for the ZBCs and double-pole dark-bright\nsoliton solution, breather-breather solutions and breather-breather-dark-bright\ninteraction solutions for the NZBCs, are demonstrated in details. Furthermore,\ncompared with the most classical second-order flow KN system which also be\ncalled the DNLS equation, the third-order dispersion and quintic nonlinear term\nof the KN system change the trajectory and velocity of solutions.\n"}
+{"abstract": "  In this article, we will review Noether's Theorems and their application in\nGeneral Relativity. We will present Noether's Theorems in their original form\nand restate them as they are usually applied to physics. Some basic equations\nof Special Relativity will be reviewed and contrasted with the equations in\nGeneral Relativity. We will be most interested in the role of Noether's\nTheorems in conservation laws. Several applications in flat spaces are\nexamined. A formulation of Noether's Theorems in curved space is presented. Of\nparticular interest is the conservation of energy in General Relativity. Here,\nthere is no general form for a conserved tensor, as there is in a flat space\ncase. Pseudotensor formulations can be found and two examples are given. In\nspecial cases, one can formulate an equation for the conservation of energy.\nThe standard cosmology is an example.\n"}
+{"abstract": "  Stochastic resonance (SR) is a coherence enhancement effect due to noise that\noccurs in periodically-driven nonlinear dynamical systems. A very broad range\nof physical and biological systems present this effect such as climate change,\nneurons, neural networks, lasers, SQUIDS, and tunnel diodes, among many others.\nEarly theoretical models of SR dealt only with overdamped bistable oscillators.\nHere, we propose a simple model that accounts for SR in an underdamped driven\nDuffing oscillator with added white noise. Furthermore, we develop a\ntheoretical method to predict the effect of white noise on the pump, signal,\nand idler responses of a Duffing amplifier. We also calculate the power\nspectral density of the response of the Duffing amplifier. This approach may\nprove to be useful for assessing the robustness of acoustic, phononic, or\nmechanical frequency-comb generation to the presence of noise.\n"}
+{"abstract": "  All-dielectric optical metasurfaces are a workhorse in nano-optics due to\nboth their ability to manipulate light in different degrees of freedom and\ntheir excellent performance at light frequency conversion. Here, we demonstrate\nfirst-time generation of photon pairs via spontaneous parametric-down\nconversion in lithium niobate quantum optical metasurfaces with electric and\nmagnetic Mie-like resonances at various wavelengths. By engineering the quantum\noptical metasurface, we tailor the photon-pair spectrum in a controlled way.\nWithin a narrow bandwidth around the resonance, the rate of pair production is\nenhanced up to two orders of magnitude compared to an unpatterned film of the\nsame thickness and material. These results enable flat-optics sources of\nentangled photons -- a new promising platform for quantum optics experiments.\n"}
+{"abstract": "  We propose and develop an algebraic approach to revealed preference. Our\napproach dispenses with non algebraic structure, such as topological\nassumptions. We provide algebraic axioms of revealed preference that subsume\nprevious, classical revealed preference axioms, as well as generate new axioms\nfor behavioral theories, and show that a data set is rationalizable if and only\nif it is consistent with an algebraic axiom.\n"}
+{"abstract": "  The Dirichlet distribution, also known as multivariate beta, is the most used\nto analyse frequencies or proportions data. Maximum likelihood is widespread\nfor estimation of Dirichlet's parameters. However, for small sample sizes, the\nmaximum likelihood estimator may shows a significant bias. In this paper,\nDirchlet's parameters estimation is obtained through modified score functions\naiming at mean and median bias reduction of the maximum likelihood estimator,\nrespectively. A simulation study and an application compare the adjusted score\napproaches with maximum likelihood.\n"}
+{"abstract": "  Implicit feedback is widely explored by modern recommender systems. Since the\nfeedback is often sparse and imbalanced, it poses great challenges to the\nlearning of complex interactions among users and items. Metric learning has\nbeen proposed to capture user-item interactions from implicit feedback, but\nexisting methods only represent users and items in a single metric space,\nignoring the fact that users can have multiple preferences and items can have\nmultiple properties, which leads to potential conflicts limiting their\nperformance in recommendation. To capture the multiple facets of user\npreferences and item properties while resolving their potential conflicts, we\npropose the novel framework of Multi-fAcet Recommender networks with Spherical\noptimization (MARS). By designing a cross-facet similarity measurement, we\nproject users and items into multiple metric spaces for fine-grained\nrepresentation learning, and compare them only in the proper spaces.\nFurthermore, we devise a spherical optimization strategy to enhance the\neffectiveness and robustness of the multi-facet recommendation framework.\nExtensive experiments on six real-world benchmark datasets show drastic\nperformance gains brought by MARS, which constantly achieves up to 40\\%\nimprovements over the state-of-the-art baselines regarding both HR and nDCG\nmetrics.\n"}
+{"abstract": "  We have studied the gravitational baryogenesis in f(R) theory of gravity with\nan anisotropic Bianchi I space-time. The matter field is considered to be that\nof perfect fluid. Two models pertaining to specific form of Ricci scalar have\nbeen presented. The baryon-to-entropy ratio has been derived with the some\nspecific form of Ricci scalar in the anisotropic background. The gravitational\nbaryogenesis is examined and its behaviors are studied.\n"}
+{"abstract": "  Due to the practical importance of vehicle routing problems (VRP), there\nexists an ever-growing body of research in algorithms and (meta)heuristics for\nsolving such problems. However, the diversity of VRP domains creates the\nseparate problem of modeling such problems -- describing the domain entities\n(and, in particular, the planning decisions), the set of valid planning\ndecisions, and the preferences between different plans. In this paper, we\nreview the approaches for modeling vehicle routing problems. To make the\ncomparison more straightforward, we formulate several criteria for evaluating\nmodeling methods reflecting the practical requirements of the development of\noptimization algorithms for such problems. Finally, as a result of this\ncomparison, we discuss several future research avenues in the field of modeling\nVRP domains.\n"}
+{"abstract": "  The FeII/MgII line flux ratio has been used as an indicator of the Fe/Mg\nabundance ratio in the broad line region (BLR) of active galactic nuclei\n(AGNs). On the basis of archival rest-frame UV spectra obtained via the Hubble\nSpace Telescope and the Sloan Digital Sky Survey, we investigate the FeII/MgII\nratios of type 1 AGNs at z < 2. Over wide dynamic ranges of AGN properties\n(i.e., black hole mass, AGN luminosity, and Eddington ratio), we confirm that\nthe FeII/MgII ratio strongly correlates with Eddington ratio but not with black\nhole mass, AGN luminosity, or redshift. Our results suggest that the\nmetallicity in the BLR are physically related to the accretion activity of\nAGNs, but not to the global properties of galaxies (i.e., galaxy mass and\nluminosity). With regard to the relation between the BLR metallicity and the\naccretion rate of AGNs, we discuss that metal cooling may play an important\nrole in enhancing the gas inflow into the central region of host galaxies,\nresulting in the high accretion rate of AGNs.\n"}
+{"abstract": "  Balancing exploration and exploitation (EE) is a fundamental problem in\ncontex-tual bandit. One powerful principle for EE trade-off isOptimism in Face\nof Uncer-tainty(OFU), in which the agent takes the action according to an upper\nconfidencebound (UCB) of reward. OFU has achieved (near-)optimal regret bound\nfor lin-ear/kernel contextual bandits. However, it is in general unknown how to\nderiveefficient and effective EE trade-off methods for non-linearcomplex tasks,\nsuchas contextual bandit with deep neural network as the reward function. In\nthispaper, we propose a novel OFU algorithm namedregularized OFU(ROFU). InROFU,\nwe measure the uncertainty of the reward by a differentiable function\nandcompute the upper confidence bound by solving a regularized optimization\nprob-lem. We prove that, for multi-armed bandit, kernel contextual bandit and\nneuraltangent kernel bandit, ROFU achieves (near-)optimal regret bounds with\ncertainuncertainty measure, which theoretically justifies its effectiveness on\nEE trade-off.Importantly, ROFU admits a very efficient implementation with\ngradient-basedoptimizer, which easily extends to general deep neural network\nmodels beyondneural tangent kernel, in sharp contrast with previous OFU\nmethods. The em-pirical evaluation demonstrates that ROFU works extremelywell\nfor contextualbandits under various settings.\n"}
+{"abstract": "  In this paper we survey and analyze modern neural-network-based facial\nlandmark detection algorithms. We focus on approaches that have led to a\nsignificant increase in quality over the past few years on datasets with large\npose and emotion variability, high levels of face occlusions - all of which are\ntypical in real-world scenarios. We summarize the improvements into categories,\nprovide quality comparison on difficult and modern in-the-wild datasets: 300-W,\nAFLW, WFLW, COFW. Additionally, we compare algorithm speed on CPU, GPU and\nMobile devices. For completeness, we also briefly touch on established methods\nwith open implementations available. Besides, we cover applications and\nvulnerabilities of the landmark detection algorithms. Based on which, we raise\nproblems that as we hope will lead to further algorithm improvements.\n"}
+{"abstract": "  In this article we consider two particular examples of general construction\nproposed in arXiv:2012.15529. We consider the integrable extensions of the\nclassical elliptic Calogero-Moser model of N particles with spin and the\nintegrable Euler-Arnold top related to the group SL(N,C). The extended systems\nhas additional N-1 degrees of freedom and can be described in terms of the\nDarboux variables.\n"}
+{"abstract": "  This paper considers an enhancement of the classical iterated penalty Picard\n(IPP) method for the incompressible Navier-Stokes equations, where we restrict\nour attention to $O(1)$ penalty parameter, and Anderson acceleration (AA) is\nused to significantly improve its convergence properties. After showing the\nfixed point operator associated with the IPP iteration is Lipschitz continuous\nand Lipschitz continuously (Frechet) differentiable, we apply a recently\ndeveloped general theory for AA to conclude that IPP enhanced with AA improves\nits linear convergence rate by the gain factor associated with the underlying\nAA optimization problem. Results for several challenging numerical tests are\ngiven and show that IPP with penalty parameter 1 and enhanced with AA is a very\neffective solver.\n"}
+{"abstract": "  Recently, many multi-stream gaze estimation methods have been proposed. They\nestimate gaze from eye and face appearances and achieve reasonable accuracy.\nHowever, most of the methods simply concatenate the features extracted from eye\nand face appearance. The feature fusion process has been ignored. In this\npaper, we propose a novel Adaptive Feature Fusion Network (AFF-Net), which\nperforms gaze tracking task in mobile tablets. We stack two-eye feature maps\nand utilize Squeeze-and-Excitation layers to adaptively fuse two-eye features\naccording to their similarity on appearance. Meanwhile, we also propose\nAdaptive Group Normalization to recalibrate eye features with the guidance of\nfacial feature. Extensive experiments on both GazeCapture and MPIIFaceGaze\ndatasets demonstrate consistently superior performance of the proposed method.\n"}
+{"abstract": "  Experiencing images with suitable music can greatly enrich the overall user\nexperience. The proposed image analysis method treats an artwork image\ndifferently from a photograph image. Automatic image classification is\nperformed using deep-learning based models. An illustrative analysis showcasing\nthe ability of our deep-models to inherently learn and utilize perceptually\nrelevant features when classifying artworks is also presented. The Mean Opinion\nScore (MOS) obtained from subjective assessments of the respective image and\nrecommended music pairs supports the effectiveness of our approach.\n"}
+{"abstract": "  Hyperparameter optimization is a challenging problem in developing deep\nneural networks. Decision of transfer layers and trainable layers is a major\ntask for design of the transfer convolutional neural networks (CNN).\nConventional transfer CNN models are usually manually designed based on\nintuition. In this paper, a genetic algorithm is applied to select trainable\nlayers of the transfer model. The filter criterion is constructed by accuracy\nand the counts of the trainable layers. The results show that the method is\ncompetent in this task. The system will converge with a precision of 97% in the\nclassification of Cats and Dogs datasets, in no more than 15 generations.\nMoreover, backward inference according the results of the genetic algorithm\nshows that our method can capture the gradient features in network layers,\nwhich plays a part on understanding of the transfer AI models.\n"}
+{"abstract": "  We consider a nonlocal parabolic PDE, which may be regarded as the standard\nsemilinear heat equation with power nonlinearity, where the nonlinear term is\ndivided by some Sobolev norm of the solution. In this paper, we are interested\nin constructing blowup solutions under some critical regimes. We had a complete\nnew phenomenon of the blowup speed with a log correction term.\n"}
+{"abstract": "  In this work, a compact model is presented for a 14 nm CMOS-based FinFET\nResonant Body Transistor (fRBT) operating at a frequency of 11.8 GHz and\ntargeting RF frequency generation/filtering for next generation radio\ncommunication, clocking, and sensing applications. Analysis of the phononic\ndispersion characteristics of the device, which informs the model development,\nshows the presence of polarization exchange due to the periodic nature of the\nback-end-of-line (BEOL) metal PnC. An eigenfrequency-based extraction process,\napplicable to resonators based on electrostatic force transduction, has been\nused to model the resonance cavity. Augmented forms of the BSIM-CMG (Common\nMulti-Gate) model for FinFETs are used to model the drive and sense transistors\nin the fRBT. This model framework allows easy integration with the\nfoundry-supplied process design kits (PDKs) and circuit simulators while being\nflexible towards change in transduction mechanisms and device architecture.\nUltimately, the behaviour is validated against RF measured data for the\nfabricated fRBT device under different operating conditions, leading to the\ndemonstration of the first complete model for this class of resonant device\nintegrated seamlessly in the CMOS stack.\n"}
+{"abstract": "  Paraphrasing is often performed with less concern for controlled style\nconversion. Especially for questions and commands, style-variant paraphrasing\ncan be crucial in tone and manner, which also matters with industrial\napplications such as dialog system. In this paper, we attack this issue with a\ncorpus construction scheme that simultaneously considers the core content and\nstyle of directives, namely intent and formality, for the Korean language.\nUtilizing manually generated natural language queries on six daily topics, we\nexpand the corpus to formal and informal sentences by human rewriting and\ntransferring. We verify the validity and industrial applicability of our\napproach by checking the adequate classification and inference performance that\nfit with the fine-tuning approaches, at the same time proposing a supervised\nformality transfer task.\n"}
+{"abstract": "  Mobile system-on-chips (SoCs) are growing in their complexity and\nheterogeneity (e.g., Arm's Big-Little architecture) to meet the needs of\nemerging applications, including games and artificial intelligence. This makes\nit very challenging to optimally manage the resources (e.g., controlling the\nnumber and frequency of different types of cores) at runtime to meet the\ndesired trade-offs among multiple objectives such as performance and energy.\nThis paper proposes a novel information-theoretic framework referred to as\nPaRMIS to create Pareto-optimal resource management policies for given target\napplications and design objectives. PaRMIS specifies parametric policies to\nmanage resources and learns statistical models from candidate policy evaluation\ndata in the form of target design objective values. The key idea is to select a\ncandidate policy for evaluation in each iteration guided by statistical models\nthat maximize the information gain about the true Pareto front. Experiments on\na commercial heterogeneous SoC show that PaRMIS achieves better Pareto fronts\nand is easily usable to optimize complex objectives (e.g., performance per\nWatt) when compared to prior methods.\n"}
+{"abstract": "  End-to-end semantic role labeling (SRL) has been received increasing\ninterest. It performs the two subtasks of SRL: predicate identification and\nargument role labeling, jointly. Recent work is mostly focused on graph-based\nneural models, while the transition-based framework with neural networks which\nhas been widely used in a number of closely-related tasks, has not been studied\nfor the joint task yet. In this paper, we present the first work of\ntransition-based neural models for end-to-end SRL. Our transition model\nincrementally discovers all sentential predicates as well as their arguments by\na set of transition actions. The actions of the two subtasks are executed\nmutually for full interactions. Besides, we suggest high-order compositions to\nextract non-local features, which can enhance the proposed transition model\nfurther. Experimental results on CoNLL09 and Universal Proposition Bank show\nthat our final model can produce state-of-the-art performance, and meanwhile\nkeeps highly efficient in decoding. We also conduct detailed experimental\nanalysis for a deep understanding of our proposed model.\n"}
+{"abstract": "  Tidal effects in capped geometries computed in previous literature display no\ndynamics along internal (toroidal) directions. This goes against the intuition\ncoming from the dual CFT picture. To resolve this tension, we consider a set of\ninfalling null geodesics in a family of black hole microstate geometries with a\nsmooth cap at the bottom of a long BTZ-like throat. Using the Penrose limit, we\nshow that a string following one of these geodesics feels tidal stresses along\nall spatial directions, including internal toroidal directions. We find that\nthe tidal effects along the internal directions are of the same order of\nmagnitude as those along other, non-internal, directions. Furthermore, these\ntidal disruptions oscillate as a function of the distance from the cap -- as a\nstring falls down the throat, it alternately experiences compression and\nstretching. We explain some physical properties of this oscillation and comment\non the dual CFT interpretation.\n"}
+{"abstract": "  The reconstruction of sparse signal is an active area of research. Different\nfrom a typical i.i.d. assumption, this paper considers a non-independent prior\nof group structure. For this more practical setup, we propose EM-aided HyGEC, a\nnew algorithm to address the stability issue and the hyper-parameter issue\nfacing the other algorithms. The instability problem results from the ill\ncondition of the transform matrix, while the unavailability of the\nhyper-parameters is a ground truth that their values are not known beforehand.\nThe proposed algorithm is built on the paradigm of HyGAMP (proposed by Rangan\net al.) but we replace its inner engine, the GAMP, by a matrix-insensitive\nalternative, the GEC, so that the first issue is solved. For the second issue,\nwe take expectation-maximization as an outer loop, and together with the inner\nengine HyGEC, we learn the value of the hyper-parameters. Effectiveness of the\nproposed algorithm is also verified by means of numerical simulations.\n"}
+{"abstract": "  We prove a distribution-theoretic conjecture of Robert Coleman, thereby also\nobtaining an explicit description of the complete set of Euler systems for the\nmultiplicative group over Q.\n"}
+{"abstract": "  This work presents a general paradigm to construct finite combinatorial\nobjects, with extermal substructures. This is done by observing that there\nexist infinite combinatorial structures with desired extermal substructures,\ncoming from an action of a subgroup of the automorphism group. The crux of our\nidea, that we call the closed orbits method is a systematic way to construct a\nfinite quotient of the infinite structure, containing a simple shadow of the\ninfinite substructure, which maintains its extermal combinatorial property. In\nparticular, the shadow of a thin infinite substructure in the cover remains\nsmall in the quotient.\n  We give a couple of applications of our paradigm. The first application is to\nthe well-known question of vertex expansion of number theoretic Ramanujan\ngraphs. It has been a common belief that such graphs are lossless expanders. We\nshow that some number theoretic Ramanujan graphs are not even unique neighbor\nexpanders, which is a weaker notion. This holds, in particular, for the\nMorgenstern Ramanujan graphs.\n  The second application is in the field of graph quantum ergodicity, where we\nproduce number theoretic Ramanujan graphs with an eigenfunction of small\nsupport that corresponds to the zero eigenvalue. This again contradicts common\nexpectations.\n  The closed orbits method is based on an established idea from dynamics and\nnumber theory, of studying closed orbits of subgroups. The novelty of this work\nis in exploiting this idea to combinatorial questions.\n"}
+{"abstract": "  Mass vaccination offers a promising exit strategy for the COVID-19 pandemic.\nHowever, as vaccination progresses, demands to lift restrictions increase,\ndespite most of the population remaining susceptible. Using our age-stratified\nSEIRD-ICU compartmental model and curated epidemiological and vaccination data,\nwe quantified the rate (relative to vaccination progress) at which countries\ncan lift non-pharmaceutical interventions without overwhelming their healthcare\nsystems. We analyzed scenarios ranging from immediately lifting restrictions\n(accepting high mortality and morbidity) to reducing case numbers to a level\nwhere test-trace-and-isolate (TTI) programs efficiently compensate for local\nspreading events. In general, the age-dependent vaccination roll-out implies a\ntransient decrease of more than ten years in the average age of ICU patients\nand deceased. The pace of vaccination determines the speed of lifting\nrestrictions; Taking the European Union (EU) as an example case, all considered\nscenarios allow for steadily increasing contacts starting in May 2021 and\nrelaxing most restrictions by autumn 2021. Throughout summer 2021, only mild\ncontact restrictions will remain necessary. However, only high vaccine uptake\ncan prevent further severe waves. Across EU countries, seroprevalence impacts\nthe long-term success of vaccination campaigns more strongly than age\ndemographics. In addition, we highlight the need for preventive measures to\nreduce contagion in school settings throughout the year 2021, where children\nmight be drivers of contagion because of them remaining susceptible...\n"}
+{"abstract": "  Presence-only data are a typical occurrence in species distribution modeling.\nThey include the presence locations and no information on the absence. Their\nmodeling usually does not account for detection biases. In this work, we aim to\nmerge three different sources of information to model the presence of marine\nmammals. The approach is fully general and it is applied to two species of\ndolphins in the Central Tyrrhenian Sea (Italy) as a case study. Data come from\nthe Italian Environmental Protection Agency (ISPRA) and Sapienza University of\nRome research campaigns, and from a careful selection of social media (SM)\nimages and videos. We build a Log Gaussian Cox process where different\ndetection functions describe each data source. For the SM data, we analyze\nseveral choices that allow accounting for detection biases. Our findings allow\nfor a correct understanding of Stenella coeruleoalba and Tursiops truncatus\ndistribution in the study area. The results prove that the proposed approach is\nbroadly applicable, it can be widely used, and it is easily implemented in the\nR software using INLA and inlabru. We provide examples' code with simulated\ndata in the supplementary materials.\n"}
+{"abstract": "  Robust recommendation aims at capturing true preference of users from noisy\ndata, for which there are two lines of methods have been proposed. One is based\non noise injection, and the other is to adopt the generative model Variational\nAuto-encoder (VAE). However, the existing works still face two challenges.\nFirst, the noise injection based methods often draw the noise from a fixed\nnoise distribution given in advance, while in real world, the noise\ndistributions of different users and items may differ from each other due to\npersonal behaviors and item usage patterns. Second, the VAE based models are\nnot expressive enough to capture the true preference since VAE often yields an\nembedding space of a single modal, while in real world, user-item interactions\nusually exhibit multi-modality on user preference distribution. In this paper,\nwe propose a novel model called Dual Adversarial Variational Embedding (DAVE)\nfor robust recommendation, which can provide personalized noise reduction for\ndifferent users and items, and capture the multi-modality of the embedding\nspace, by combining the advantages of VAE and adversarial training between the\nintroduced auxiliary discriminators and the variational inference networks. The\nextensive experiments conducted on real datasets verify the effectiveness of\nDAVE on robust recommendation.\n"}
+{"abstract": "  We bring together topological data analysis, applied category theory, and\nmachine learning to study multiparameter hierarchical clustering. We begin by\nintroducing a procedure for flattening multiparameter hierarchical clusterings.\nWe demonstrate that this procedure is a functor from a category of\nmultiparameter hierarchical partitions to a category of binary integer\nprograms. We also include empirical results demonstrating its effectiveness.\nNext, we introduce a Bayesian update algorithm for learning clustering\nparameters from data. We demonstrate that the composition of this algorithm\nwith our flattening procedure satisfies a consistency property.\n"}
+{"abstract": "  $K2$ greatly extended $Kepler$'s ability to find new planets, but it was\ntypically limited to identifying transiting planets with orbital periods below\n40 days. While analyzing $K2$ data through the Exoplanet Explorers project,\ncitizen scientists helped discover one super-Earth and four sub-Neptune sized\nplanets in the relatively bright ($V=12.21$, $K=10.3$) K2-138 system, all which\norbit near 3:2 mean motion resonances. The $K2$ light curve showed two\nadditional transit events consistent with a sixth planet. Using $Spitzer$\nphotometry, we validate the sixth planet's orbital period of $41.966\\pm0.006$\ndays and measure a radius of $3.44^{+0.32}_{-0.31}\\,R_{\\oplus}$, solidifying\nK2-138 as the $K2$ system with the most currently known planets. There is a\nsizeable gap between the outer two planets, since the fifth planet in the\nsystem, K2-138 f, orbits at 12.76 days. We explore the possibility of\nadditional non-transiting planets in the gap between f and g. Due to the\nrelative brightness of the K2-138 host star, and the near resonance of the\ninner planets, K2-138 could be a key benchmark system for both radial velocity\nand transit timing variation mass measurements, and indeed radial velocity\nmasses for the inner four planets have already been obtained. With its five\nsub-Neptunes and one super-Earth, the K2-138 system provides a unique test bed\nfor comparative atmospheric studies of warm to temperate planets of similar\nsize, dynamical studies of near resonant planets, and models of planet\nformation and migration.\n"}
+{"abstract": "  Modern external memory is represented by several device classes. At present,\nHDD, SATA SSD and NVMe SSD are widely used. Recently ultra-low latency SSD such\nas Intel Optane became available on the market. Each of these types exhibits\nit's own pattern for throughput, latency and parallelism. To achieve the\nhighest performance one has to pick an appropriate I/O interface provided by\nthe operating system. In this work we present a detailed overview and\nevaluation of modern storage reading performance with regard to available Linux\nsynchronous and asynchronous interfaces. While throughout this work we aim for\nthe highest throughput we also measure latency and CPU usage. We provide this\nreport in hope the detailed results could be interesting to both researchers\nand practitioners.\n"}
+{"abstract": "  We present the first holographic simulations of non-equilibrium steady state\nformation in strongly coupled $\\mathcal{N}=4$ SYM theory in 3+1 dimensions. We\ninitially join together two thermal baths at different temperatures and\nchemical potentials and compare the subsequent evolution of the combined system\nto analytic solutions of the corresponding Riemann problem and to numeric\nsolutions of ideal and viscous hydrodynamics. The time evolution of the energy\ndensity that we obtain holographically is consistent with the combination of a\nshock and a rarefaction wave: A shock wave moves towards the cold bath, and a\nsmooth broadening wave towards the hot bath. Between the two waves emerges a\nsteady state with constant temperature and flow velocity, both of which are\naccurately described by a shock+rarefaction wave solution of the Riemann\nproblem. In the steady state region, a smooth crossover develops between two\nregions of different charge density. This is reminiscent of a contact\ndiscontinuity in the Riemann problem. We also obtain results for the\nentanglement entropy of regions crossed by shock and rarefaction waves and find\nboth of them to closely follow the evolution of the energy density.\n"}
+{"abstract": "  It is well known that photons can carry a spatial structure akin to a\n\"twisted\" or \"rippled\" wavefront. Such structured light fields have sparked\nsignificant interest in both classical and quantum physics, with applications\nranging from dense communications to light-matter interaction. Harnessing the\nfull advantage of transverse spatial photonic encoding using the\nLaguerre-Gaussian (LG) basis in the quantum domain requires control over both\nthe azimuthal (twisted) and radial (rippled) components of photons. However,\nprecise measurement of the radial photonic degree-of-freedom has proven to be\nexperimentally challenging primarily due to its transverse amplitude structure.\nHere we demonstrate the generation and certification of full-field\nLaguerre-Gaussian entanglement between photons pairs generated by spontaneous\nparametric down-conversion in the telecom regime. By precisely tuning the\noptical system parameters for state generation and collection, and adopting\nrecently developed techniques for precise spatial mode measurement, we are able\nto certify fidelities up to 85% and entanglement dimensionalities up to 26 in a\n43-dimensional radial and azimuthal LG mode space. Furthermore, we study\ntwo-photon quantum correlations between 9 LG mode groups, demonstrating a\ncorrelation structure related to mode group order and inter-modal cross-talk.\nIn addition, we show how the noise-robustness of high-dimensional entanglement\ncertification can be significantly increased by using measurements in multiple\nLG mutually unbiased bases. Our work demonstrates the potential offered by the\nfull spatial structure of the two-photon field for enhancing technologies for\nquantum information processing and communication.\n"}
+{"abstract": "  Transition and separation are difficult but important problems in the field\nof fluid mechanics. Hitherto, separation and transition problems have not been\ndescribed accurately in mathematical terms, leading to design errors and\nprediction problems in fluid machine engineering. The nonlinear uncertainty\ninvolved in separation and transition makes it difficult to accurately analyze\nthese phenomena using experimental methods. Thus, new ideas and methods are\nrequired for the mathematical prediction of fluid separation and transition. In\nthis article, after an axiomatic treatment of fluid mechanics, the concept of\nan excited state is derived by generating a fluctuation velocity, and it is\nrevealed that fluid separation and transition are special forms of this excited\nstate. This allows us to clarify the state conditions of fluid separation and\ntransition. Mathematical analysis of the Navier--Stokes equations leads to a\ngeneral excited state theorem suitable for flowfields. Finally, the conditions\nof separation and transition are derived, and the corresponding general laws\nare established. The results presented in this article provide a foundation for\nfuture research on the mechanism of turbulence and the solution of engineering\nproblems.\n"}
+{"abstract": "  Employment outcomes of resettled refugees depend strongly on where they are\nplaced inside the host country. While the United States sets refugee capacities\nfor communities on an annual basis, refugees arrive and must be placed over the\ncourse of the year. We introduce a dynamic allocation system based on two-stage\nstochastic programming to improve employment outcomes. Our algorithm is able to\nachieve over 98 percent of the hindsight-optimal employment compared to under\n90 percent of current greedy-like approaches. This dramatic improvement\npersists even when we incorporate a vast array of practical features of the\nrefugee resettlement process including indivisible families, batching, and\nuncertainty with respect to the number of future arrivals. Our algorithm is now\npart of the Annie MOORE optimization software used by a leading American\nrefugee resettlement agency.\n"}
+{"abstract": "  Using the Atacama Large Millimeter/submillimeter Array (ALMA), we\ninvestigated a peculiar millimeter source MMS 3 located in the Orion Molecular\nCloud 3 (OMC-3) region in the 1.3 mm continuum, CO ($J$=2-1), SiO ($J$=5-4),\nC$^{18}$O ($J$=2-1), N$_2$D$^+$ ($J$=3-2), and DCN ($J$=3-2) emissions. With\nthe ALMA high angular resolution ($\\sim$0''.2), we detected a very compact and\nhighly centrally condensed continuum emission with a size of 0''.45 $\\times$\n0''.32 (P.A.=0.22$^\\circ$). The peak position coincides with the locations of\npreviously reported $Spitzer$/IRAC and X-ray sources within their positional\nuncertainties. We also detected an envelope with a diameter of $\\sim$6800 au\n(P.A.=75$^\\circ$) in the C$^{18}$O ($J$=2-1) emission. Moreover, a bipolar\noutflow was detected in the CO ($J$=2-1) emission for the first time. The\noutflow elongates roughly perpendicular to the long axis of the envelope\ndetected in the C$^{18}$O ($J$=2-1) emission. Compact high-velocity CO gas in\nthe (red-shifted) velocity range of 22-30 km s$^{-1}$, presumably tracing a\njet, was detected near the 1.3 mm continuum peak. A compact and faint\nred-shifted SiO emission was marginally detected on the CO outflow lobe. The\nphysical quantities of the outflow in MMS 3 are relatively smaller than those\nin other sources in the OMC-3 region. The centrally condensed object associated\nwith the near-infrared and X-ray sources, the flattened envelope, and the faint\noutflow indicate that MMS 3 harbors a low mass protostar with an age of\n$\\sim$10$^3$ yr.\n"}
+{"abstract": "  Parametric stochastic simulators are ubiquitous in science, often featuring\nhigh-dimensional input parameters and/or an intractable likelihood. Performing\nBayesian parameter inference in this context can be challenging. We present a\nneural simulation-based inference algorithm which simultaneously offers\nsimulation efficiency and fast empirical posterior testability, which is unique\namong modern algorithms. Our approach is simulation efficient by simultaneously\nestimating low-dimensional marginal posteriors instead of the joint posterior\nand by proposing simulations targeted to an observation of interest via a prior\nsuitably truncated by an indicator function. Furthermore, by estimating a\nlocally amortized posterior our algorithm enables efficient empirical tests of\nthe robustness of the inference results. Since scientists cannot access the\nground truth, these tests are necessary for trusting inference in real-world\napplications. We perform experiments on a marginalized version of the\nsimulation-based inference benchmark and two complex and narrow posteriors,\nhighlighting the simulator efficiency of our algorithm as well as the quality\nof the estimated marginal posteriors.\n"}
+{"abstract": "  Flow and storage volume regulation is essential for the adequate transport\nand management of energy resources in district heating systems. In this letter,\nwe propose a novel and suitably tailored -- decentralized -- adaptive control\nscheme addressing this problem whilst offering closed-loop stability\nguarantees. We focus on a system configuration comprising multiple heat\nproducers, consumers and storage tanks exchanging energy through a common\ndistribution network, which are features of modern and prospective district\nheating installations. {\\color{black}The proposed controller is based on\npassivity, backstepping and (indirect) adaptive control theory.\n"}
+{"abstract": "  Metallic ion adsorption on surfactant aggregates were studied with Molecular\ndynamics simulations. Using ionic salts, such as lead sulfate (PbSO$_4$) and\naluminum sulfate [Al$_2$(SO$_4$)$_3$], adsorption of lead and aluminum were\ninvestigated at different salt concentrations and different surfactant\naggregates (micelles) sizes. The micelles were constructed with spherical\nshapes composed of sodium dodecyl sulfate (SDS) anionic surfactants. The\nelectrostatic interactions between the positive ions and the negative SDS\nheadgroups promote capture of the metal particles on the aggregate surface.\nMetal adsorption was analyzed in terms of radial density profiles, partial pair\ndistribution functions and adsorption isotherms. It is showed that SDS micelles\nadsorb better lead than aluminum ions regardless of the size of the aggregates\nand salt concentrations.\n"}
+{"abstract": "  In large infrastructures such as dams, which have a relatively high economic\nvalue, ensuring the proper operation of the associated hydraulic facilities in\ndifferent operating conditions is of utmost importance. To ensure the correct\nand successful operation of the dam's hydraulic equipment and prevent possible\ndamages, including gates and downstream tunnel, to build laboratory models and\nperform some tests are essential (the advancement of the smart sensors based on\nartificial intelligence is essential). One of the causes of damage to dam\nbottom outlets is cavitation in downstream and between the gates, which can\nimpact on dam facilities, and air aeration can be a solution to improve it. In\nthe present study, six dams in different provinces in Iran has been chosen to\nevaluate the air entrainment in the downstream tunnel experimentally. Three\nartificial neural networks (ANN) based machine learning (ML) algorithms are\nused to model and predict the air aeration in the bottom outlet. The proposed\nmodels are trained with genetic algorithms (GA), particle swarm optimization\n(PSO), i.e., ANN-GA, ANN-PSO, and ANFIS-PSO. Two hydrodynamic variables, namely\nvolume rate and opening percentage of the gate, are used as inputs into all\nbottom outlet models. The results showed that the most optimal model is\nANFIS-PSO to predict the dependent value compared with ANN-GA and ANN-PSO. The\nimportance of the volume rate and opening percentage of the dams' gate\nparameters is more effective for suitable air aeration.\n"}
+{"abstract": "  Concept normalization in free-form texts is a crucial step in every\ntext-mining pipeline. Neural architectures based on Bidirectional Encoder\nRepresentations from Transformers (BERT) have achieved state-of-the-art results\nin the biomedical domain. In the context of drug discovery and development,\nclinical trials are necessary to establish the efficacy and safety of drugs. We\ninvestigate the effectiveness of transferring concept normalization from the\ngeneral biomedical domain to the clinical trials domain in a zero-shot setting\nwith an absence of labeled data. We propose a simple and effective two-stage\nneural approach based on fine-tuned BERT architectures. In the first stage, we\ntrain a metric learning model that optimizes relative similarity of mentions\nand concepts via triplet loss. The model is trained on available labeled\ncorpora of scientific abstracts to obtain vector embeddings of concept names\nand entity mentions from texts. In the second stage, we find the closest\nconcept name representation in an embedding space to a given clinical mention.\nWe evaluated several models, including state-of-the-art architectures, on a\ndataset of abstracts and a real-world dataset of trial records with\ninterventions and conditions mapped to drug and disease terminologies.\nExtensive experiments validate the effectiveness of our approach in knowledge\ntransfer from the scientific literature to clinical trials.\n"}
+{"abstract": "  Invertible networks have various benefits for image denoising since they are\nlightweight, information-lossless, and memory-saving during back-propagation.\nHowever, applying invertible models to remove noise is challenging because the\ninput is noisy, and the reversed output is clean, following two different\ndistributions. We propose an invertible denoising network, InvDN, to address\nthis challenge. InvDN transforms the noisy input into a low-resolution clean\nimage and a latent representation containing noise. To discard noise and\nrestore the clean image, InvDN replaces the noisy latent representation with\nanother one sampled from a prior distribution during reversion. The denoising\nperformance of InvDN is better than all the existing competitive models,\nachieving a new state-of-the-art result for the SIDD dataset while enjoying\nless run time. Moreover, the size of InvDN is far smaller, only having 4.2% of\nthe number of parameters compared to the most recently proposed DANet. Further,\nvia manipulating the noisy latent representation, InvDN is also able to\ngenerate noise more similar to the original one. Our code is available at:\nhttps://github.com/Yang-Liu1082/InvDN.git.\n"}
+{"abstract": "  Traditional goal-oriented dialogue systems rely on various components such as\nnatural language understanding, dialogue state tracking, policy learning and\nresponse generation. Training each component requires annotations which are\nhard to obtain for every new domain, limiting scalability of such systems.\nSimilarly, rule-based dialogue systems require extensive writing and\nmaintenance of rules and do not scale either. End-to-End dialogue systems, on\nthe other hand, do not require module-specific annotations but need a large\namount of data for training. To overcome these problems, in this demo, we\npresent Alexa Conversations, a new approach for building goal-oriented dialogue\nsystems that is scalable, extensible as well as data efficient. The components\nof this system are trained in a data-driven manner, but instead of collecting\nannotated conversations for training, we generate them using a novel dialogue\nsimulator based on a few seed dialogues and specifications of APIs and entities\nprovided by the developer. Our approach provides out-of-the-box support for\nnatural conversational phenomena like entity sharing across turns or users\nchanging their mind during conversation without requiring developers to provide\nany such dialogue flows. We exemplify our approach using a simple pizza\nordering task and showcase its value in reducing the developer burden for\ncreating a robust experience. Finally, we evaluate our system using a typical\nmovie ticket booking task and show that the dialogue simulator is an essential\ncomponent of the system that leads to over $50\\%$ improvement in turn-level\naction signature prediction accuracy.\n"}
+{"abstract": "  The coupling behavior of time scales of structural relaxation is investigated\non the basis of five different response functions for 1,2,6-hexanetriol, a\nhydrogen-bonded liquid with a minor secondary contribution, and\n2,6,10,15,19,23-hexamethyl-tetracosane (squalane), a van der Waals bonded\nliquid with a prominent secondary relaxation process. Time scales of structural\nrelaxation are derived as inverse peak frequencies for each investigated\nresponse function. For 1,2,6-hexanetriol, the time-scale indices are\ntemperature-independent, while a decoupling of time scales is observed for\nsqualane in accordance with literature. An alternative evaluation approach is\nmade on the squalane data, extracting time scales from the terminal relaxation\nmode instead of the peak position, and in this case temperature-independent\ncoupling is also found for squalane, despite its strong secondary relaxation\ncontribution. Interestingly, the very same ordering of\nresponse-function-specific time scales is observed for these two liquids, which\nis also consistent with the observation made for simple van der Waals bonded\nliquids reported previously [Jakobsen \\textit{et al.}, J. Chem. Phys.\n\\textbf{136}, 081102 (2012)]. This time-scale ordering is based on the\nfollowing response functions, from fast to slow dynamics: shear modulus, bulk\nmodulus, dielectric permittivity, longitudinal thermal expansivity coefficient,\nand longitudinal specific heat. These findings indicate a general relation\nbetween the time scales of different response functions and, as inter-molecuar\ninteractions apparently play a subordinate role, suggest a rather generic\nnature of the process of structural relaxation.\n"}
+{"abstract": "  We present the results from a spectroscopic monitoring campaign to obtain\nreverberation-mapping measurements and investigate the broad-line region\nkinematics for active galactic nuclei (AGN) of Mrk~817 and NGC~7469. This\ncampaign was undertaken with the Lijiang 2.4-meter telescope, the median\nspectroscopic sampling is 2.0 days for Mrk~817 and 1.0 days for NGC~7469. We\ndetect time lags of the broad emission lines including H$\\beta$, H$\\gamma$,\nHe~{\\sc ii} and He~{\\sc i} for both AGNs, and including Fe~{\\sc ii} for Mrk~817\nwith respect to the varying AGN continuum at 5100~\\AA. Investigating the\nrelationship between line widths and time lags of the broad emission lines, we\nfind that the BLR dynamics of Mrk~817 and NGC~7469 are consistent with the\nvirial prediction. We estimate the masses of central supermassive black hole\n(SMBH) and the accretion rates of both AGNs. Using the data of this campaign,\nwe construct the velocity-resolved lag profiles of the broad H$\\gamma$,\nH$\\beta$, and He~{\\sc i} lines for Mrk~817, which show almost the same\nkinematic signatures that the time lags in the red wing are slightly larger\nthan the time lags in the blue wing. For NGC~7469, we only clearly construct\nthe velocity-resolved lag profiles of the broad H$\\gamma$ and H$\\beta$, which\nshow very similar kinematic signatures to the BLR of Mrk~817. These signatures\nindicate that the BLR of Keplerian motion in both AGNs seemingly has outflowing\ncomponents during the monitoring period. We discuss the kinematics of the BLR\nand the measurements including SMBH mass and accretion rates.\n"}
+{"abstract": "  In the context of generalized descriptive set theory, we systematically\ncompare and analyze various notions of Polish-like spaces and standard\n$\\kappa$-Borel spaces for $\\kappa$ an uncountable (regular) cardinal satisfying\n$\\kappa^{<\\kappa} = \\kappa$. As a result, we obtain a solid framework where one\ncan develop the theory in full generality. We also provide natural\ncharacterizations of the generalized Cantor and Baire spaces. Some of the\nresults obtained considerably extend previous work from [Coskey-Schlicht 2016,\nGaleotti 2019, Luecke-Schlicht 2015], and answer some questions contained\ntherein.\n"}
+{"abstract": "  Borexino is a 280-ton liquid scintillator detector located at the Laboratori\nNazionali del Gran Sasso in Italy. Since the start of its data taking in May\n2007, it has provided several measurements of low-energy neutrinos from various\nsources. At the base of its success, lie unprecedented levels of radio-purity\nand extensive thermal stabilisation, both resulting from a years-long effort of\nthe collaboration. Solar neutrinos, emitted in the hydrogen-to-helium fusion in\nthe solar core, are important for the understanding of our star, as well as\nneutrino properties. Borexino is the only experiment that has performed a\ncomplete spectroscopy of the \\emph{pp} chain solar neutrinos (with the\nexception of the \\emph{hep} neutrinos contributing to the total flux at\n$10^{-5}$ level), through the detection of \\emph{pp}, $^7$Be, \\emph{pep}, and\n$^8$B solar neutrinos and has experimentally confirmed the existence of the CNO\nfusion cycle in the Sun. Borexino has also detected geoneutrinos, antineutrinos\nfrom the decays of long-lived radioactive elements inside the Earth, that can\nbe exploited as a new and unique tool to study our planet. This paper reviews\nthe most recent Borexino results on solar and geoneutrinos, from highlighting\nthe key elements of the analyses up to the discussion and interpretation of the\nresults for neutrino, solar, and geophysics.\n"}
+{"abstract": "  In this paper, we will see that the Cartesian product of two 2-Banach spaces\nis also 2-Banach space and discuss some properties of closed linear operator in\nlinear 2-normed space. We also describe the concept of different types of\ncontinuity of b-linear functional and derive the Uniform Boundedness Principle\nand Hahn-Banach extension theorem for b-linear functionals in the case of\nlinear 2-normed spaces. We also introduce the notion of weak*convergence for\nthe sequence of bounded b-linear functionals relative to linear 2-normed space.\n"}
+{"abstract": "  We examine the collective states of run-and-tumble active matter disks driven\nover a periodic obstacle array. When the drive is applied along a symmetry\ndirection of the array, we find a clog-free uniform liquid state for low\nactivity, while at higher activity, the density becomes increasingly\nheterogeneous and an active clogged state emerges in which the mobility is\nstrongly reduced. For driving along non-symmetry or incommensurate directions,\nthere are two different clogging behaviors consisting of a drive dependent\nclogged state in the low activity thermal limit and a drive independent clogged\nstate at high activity. These regimes are separated by a uniform flowing liquid\nat intermediate activity. There is a critical activity level above which the\nthermal clogged state does not occur, as well as an optimal activity level that\nmaximizes the disk mobility. Thermal clogged states are dependent on the\ndriving direction while active clogged states are not. In the low activity\nregime, diluting the obstacles produces a monotonic increase in the mobility;\nhowever, for large activities, the mobility is more robust against obstacle\ndilution. We also examine the velocity-force curves for driving along\nnon-symmetry directions, and find that they are linear when the activity is low\nor intermediate, but become nonlinear at high activity and show behavior\nsimilar to that found for the plastic depinning of solids. At higher drives the\nactive clustering is lost. For low activity we also find a reentrant fluid\nphase, where the system transitions from a high mobility fluid at low drives to\na clogged state at higher drives and then back into another fluid phase at very\nhigh drives. We map the regions in which the thermally clogged, partially\nclogged, active uniform fluid, clustered fluid, active clogged, and\ndirectionally locked states occur as a function of disk density, drift force,\nand activity.\n"}
+{"abstract": "  We investigate in atomic scale the electronic phase separation occurring in\nthe well known quasi 1D charge-density wave (CDW) phase of the In atomic wire\narray on a Si(111) surface. The characteristic atomic scale defects, originated\nfrom excess In atoms, are found to be actively involved in the formation of the\nphase boundary between the metallic and the CDW phases by extensive analysis of\nscanning tunneling microscopy images at various temperatures. These particular\ndefects flip the phase of the quasi 1D CDW to impose strong local constraints\nin the CDW correlation. We show that such local constraints and the substantial\ninterwire CDW interaction induce local condensates of CDW and the phase\nseparation between the metallic and the CDW phases. This work unveils the\natomistic origin of the electronic phase separation, highlighting the\nimportance of atomic scale structures of defects and their collective\ninteraction in electronically inhomogeneous materials.\n"}
+{"abstract": "  We consider a one-phase free boundary problem governed by doubly degenerate\nfully non-linear elliptic PDEs with non-zero right hand side, which should be\nunderstood as an analog (non-variational) of certain double phase functionals\nin the theory of non-autonomous integrals. By way of brief elucidating example,\nsuch non-linear problems in force appear in the mathematical theory of\ncombustion, as well as in the study of some flame propagation problems. In such\nan environment we prove that solutions are Lipschitz continuous and they fulfil\na non-degeneracy property. Furthermore, we address the Caffarelli's\nclassification scheme: Flat and Lipschitz free boundaries are locally $C^{1,\n\\beta}$ for some $0 < \\beta (universal) < 1$.\n"}
+{"abstract": "  In order to improve performance of photovoltaic/thermal (or PV/T for\nsimplicity) collectors, this paper firstly validated a previous computational\nthermal model and then introduced an improved computational thermal model to\ninvestigate the effects of the major control parameters on the thermal\nperformance of PV/T collectors, including solar cell temperature, back surface\ntemperature, and outlet water temperature. Besides, a computational electrical\nmodel of PV/T system was also introduced to elaborate the relationship of\nvoltage, current and power of a PV module (MSX60 polycrystalline solar cell)\nused in an experiment in the literature. Simulation results agree with the\nexperimental data very well. The effects of the time-steps from 1 hour to\nminute, which is closed to the real time, were also reported. At last, several\nsuggestions to improve the efficiency of PV/T system were illustrated.\n"}
+{"abstract": "  Machine learning based interatomic potential energy surface (PES) models are\nrevolutionizing the field of molecular modeling. However, although much faster\nthan electronic structure schemes, these models suffer from a lower efficiency\nas compared to typical empirical force fields due to more sophisticated\ncomputations involved. Herein, we report a model compression scheme for\nboosting the performance of the Deep Potential (DP) model, a deep learning\nbased PES model. This scheme, we call DP Compress, is an efficient\npost-processing step after the training of DP models (DP Train). DP Compress\ncombines several DP-specific compression techniques, which typically speed up\nDP- based molecular dynamics simulations by an order of magnitude faster, and\nconsume an order of magnitude less memory. We demonstrate that DP Compress is\nsufficiently accurate by testing a variety of physical properties of Cu, H2O,\nand Al-Cu-Mg systems. DP Compress applies to both CPU and GPU machines and is\npublicly available at https://github.com/deepmodeling/deepmd-kit.\n"}
+{"abstract": "  Creating virtual avatars with realistic rendering is one of the most\nessential and challenging tasks to provide highly immersive virtual reality\n(VR) experiences. It requires not only sophisticated deep neural network (DNN)\nbased codec avatar decoders to ensure high visual quality and precise motion\nexpression, but also efficient hardware accelerators to guarantee smooth\nreal-time rendering using lightweight edge devices, like untethered VR\nheadsets. Existing hardware accelerators, however, fail to deliver sufficient\nperformance and efficiency targeting such decoders which consist of\nmulti-branch DNNs and require demanding compute and memory resources. To\naddress these problems, we propose an automation framework, called F-CAD\n(Facebook Codec avatar Accelerator Design), to explore and deliver optimized\nhardware accelerators for codec avatar decoding. Novel technologies include 1)\na new accelerator architecture to efficiently handle multi-branch DNNs; 2) a\nmulti-branch dynamic design space to enable fine-grained architecture\nconfigurations; and 3) an efficient architecture search for picking the\noptimized hardware design based on both application-specific demands and\nhardware resource constraints. To the best of our knowledge, F-CAD is the first\nautomation tool that supports the whole design flow of hardware acceleration of\ncodec avatar decoders, allowing joint optimization on decoder designs in\npopular machine learning frameworks and corresponding customized accelerator\ndesign with cycle-accurate evaluation. Results show that the accelerators\ngenerated by F-CAD can deliver up to 122.1 frames per second (FPS) and 91.6%\nhardware efficiency when running the latest codec avatar decoder. Compared to\nthe state-of-the-art designs, F-CAD achieves 4.0X and 2.8X higher throughput,\n62.5% and 21.2% higher efficiency than DNNBuilder and HybridDNN by targeting\nthe same hardware device.\n"}
+{"abstract": "  Affective computing with Electroencephalogram (EEG) is a challenging task\nthat requires cumbersome models to effectively learn the information contained\nin large-scale EEG signals, causing difficulties for real-time smart-device\ndeployment. In this paper, we propose a novel knowledge distillation pipeline\nto distill EEG representations via capsule-based architectures for both\nclassification and regression tasks. Our goal is to distill information from a\nheavy model to a lightweight model for subject-specific tasks. To this end, we\nfirst pre-train a large model (teacher network) on large number of training\nsamples. Then, we employ the teacher network to learn the discriminative\nfeatures embedded in capsules by adopting a lightweight model (student network)\nto mimic the teacher using the privileged knowledge. Such privileged\ninformation learned by the teacher contain similarities among capsules and are\nonly available during the training stage of the student network. We evaluate\nthe proposed architecture on two large-scale public EEG datasets, showing that\nour framework consistently enables student networks with different compression\nratios to effectively learn from the teacher, even when provided with limited\ntraining samples. Lastly, our method achieves state-of-the-art results on one\nof the two datasets.\n"}
+{"abstract": "  Dimensionality reduction (DR) and manifold learning (ManL) have been applied\nextensively in many machine learning tasks, including signal processing, speech\nrecognition, and neuroinformatics. However, the understanding of whether DR and\nManL models can generate valid learning results remains unclear. In this work,\nwe investigate the validity of learning results of some widely used DR and ManL\nmethods through the chart mapping function of a manifold. We identify a\nfundamental problem of these methods: the mapping functions induced by these\nmethods violate the basic settings of manifolds, and hence they are not\nlearning manifold in the mathematical sense. To address this problem, we\nprovide a provably correct algorithm called fixed points Laplacian mapping\n(FPLM), that has the geometric guarantee to find a valid manifold\nrepresentation (up to a homeomorphism). Combining one additional\ncondition(orientation preserving), we discuss a sufficient condition for an\nalgorithm to be bijective for any d-simplex decomposition result on a\nd-manifold. However, constructing such a mapping function and its computational\nmethod satisfying these conditions is still an open problem in mathematics.\n"}
+{"abstract": "  Single-beam scanning electron microscopes (SEM) are widely used to acquire\nmassive data sets for biomedical study, material analysis, and fabrication\ninspection. Datasets are typically acquired with uniform acquisition: applying\nthe electron beam with the same power and duration to all image pixels, even if\nthere is great variety in the pixels' importance for eventual use. Many SEMs\nare now able to move the beam to any pixel in the field of view without delay,\nenabling them, in principle, to invest their time budget more effectively with\nnon-uniform imaging.\n  In this paper, we show how to use deep learning to accelerate and optimize\nsingle-beam SEM acquisition of images. Our algorithm rapidly collects an\ninformation-lossy image (e.g. low resolution) and then applies a novel learning\nmethod to identify a small subset of pixels to be collected at higher\nresolution based on a trade-off between the saliency and spatial diversity. We\ndemonstrate the efficacy of this novel technique for active acquisition by\nspeeding up the task of collecting connectomic datasets for neurobiology by up\nto an order of magnitude.\n"}
+{"abstract": "  We improve the integrability of the state space of the $\\Phi ^4_3$-process\nand the support of the $\\Phi ^4_3$-measure on the torus obtained in [Albeverio,\nKusuoka, Ann. Sc. Norm. Super. Pisa Cl. Sci. (5), 2020]. For the improvement,\nwe improve the estimates of the H\\\"older continuity in time of the solutions to\napproximation equations. In the present paper, we only discuss the estimates\ndifferent from those in [Albeverio, Kusuoka, Ann. Sc. Norm. Super. Pisa Cl.\nSci. (5), 2020].\n"}
+{"abstract": "  Machine Learning (ML) provides important techniques for classification and\npredictions. Most of these are black-box models for users and do not provide\ndecision-makers with an explanation. For the sake of transparency or more\nvalidity of decisions, the need to develop explainable/interpretable ML-methods\nis gaining more and more importance. Certain questions need to be addressed:\n  How does an ML procedure derive the class for a particular entity? Why does a\nparticular clustering emerge from a particular unsupervised ML procedure? What\ncan we do if the number of attributes is very large? What are the possible\nreasons for the mistakes for concrete cases and models?\n  For binary attributes, Formal Concept Analysis (FCA) offers techniques in\nterms of intents of formal concepts, and thus provides plausible reasons for\nmodel prediction. However, from the interpretable machine learning viewpoint,\nwe still need to provide decision-makers with the importance of individual\nattributes to the classification of a particular object, which may facilitate\nexplanations by experts in various domains with high-cost errors like medicine\nor finance.\n  We discuss how notions from cooperative game theory can be used to assess the\ncontribution of individual attributes in classification and clustering\nprocesses in concept-based machine learning. To address the 3rd question, we\npresent some ideas on how to reduce the number of attributes using similarities\nin large contexts.\n"}
+{"abstract": "  Reinforcement learning, which acquires a policy maximizing long-term rewards,\nhas been actively studied. Unfortunately, this learning type is too slow and\ndifficult to use in practical situations because the state-action space becomes\nhuge in real environments. The essential factor for learning efficiency is\nrewards. Potential-based reward shaping is a basic method for enriching\nrewards. This method is required to define a specific real-value function\ncalled a potential function for every domain. It is often difficult to\nrepresent the potential function directly. SARSA-RS learns the potential\nfunction and acquires it. However, SARSA-RS can only be applied to the simple\nenvironment. The bottleneck of this method is the aggregation of states to make\nabstract states since it is almost impossible for designers to build an\naggregation function for all states. We propose a trajectory aggregation that\nuses subgoal series. This method dynamically aggregates states in an episode\nduring trial and error with only the subgoal series and subgoal identification\nfunction. It makes designer effort minimal and the application to environments\nwith high-dimensional observations possible. We obtained subgoal series from\nparticipants for experiments. We conducted the experiments in three domains,\nfour-rooms(discrete states and discrete actions), pinball(continuous and\ndiscrete), and picking(both continuous). We compared our method with a baseline\nreinforcement learning algorithm and other subgoal-based methods, including\nrandom subgoal and naive subgoal-based reward shaping. As a result, our reward\nshaping outperformed all other methods in learning efficiency.\n"}
+{"abstract": "  Liquid crystal elastomers (LCEs) can undergo large reversible contractions\nalong their nematic director upon heating or illumination. A spatially\npatterned director within a flat LCE sheet thus encodes a pattern of\ncontraction on heating, which can morph the sheet into a curved shell, akin to\nhow a pattern of growth sculpts a developing organism. Here we consider,\ntheoretically, numerically and experimentally, patterns constructed from\nregions of radial and circular director, which, in isolation, would form cones\nand anticones. The resultant surfaces contain curved ridges with sharp V-shaped\ncross-sections, associated with the boundaries between regions in the patterns.\nSuch ridges may be created in positively and negatively curved variants and,\nsince they bear Gauss curvature (quantified here via the Gauss-Bonnet theorem),\nthey cannot be flattened without energetically prohibitive stretch. Our\nexperiments and numerics highlight that, although such ridges cannot be\nflattened isometrically, they can deform isometrically by trading the\n(singular) curvature of the V angle against the (finite) curvature of the ridge\nline. Furthermore, in finite thickness sheets, the sharp ridges are inevitably\nnon-isometrically blunted to relieve bend, resulting in a modest smearing out\nof the encoded singular Gauss curvature. We close by discussing the use of such\nfeatures as actuating linear features, such as probes, tongues and limbs, and\nhighlighting the similarities between these patterns of shape change and those\nfound during the morphogenesis of several biological systems.\n"}
+{"abstract": "  Given a metric space $X$ and a subspace $A\\subset X$, we prove $A$ can\ngenerate various algebraic elements in persistent homology of $X$. We call such\nelements (algebraic) footprints of $A$. Our results imply that footprints\ntypically appear in dimensions above the dimension of $A$. Higher-dimensional\npersistent homology thus encodes lower-dimensional geometric features of $X$.\n  We pay special attention to a specific type of geodesics in a geodesic\nsurface $X$ called geodesic circles. We explain how they may generate\nnon-trivial odd-dimensional and two-dimensional footprints. In particular, we\ncan detect even some contractible geodesics using two- and three-dimensional\npersistent homology. This provides a link between persistent homology and\nlength spectrum in Riemannian geometry.\n"}
+{"abstract": "  Designing robotic tasks for co-manipulation necessitates to exploit not only\nproprioceptive but also exteroceptive information for improved safety and\nautonomy. Following such instinct, this research proposes to formulate\nintuitive robotic tasks following human viewpoint by incorporating\nvisuo-tactile perception. The visual data using depth cameras surveils and\ndetermines the object dimensions and human intentions while the tactile sensing\nensures to maintain the desired contact to avoid slippage. Experiment performed\non robot platform with human assistance under industrial settings validates the\nperformance and applicability of proposed intuitive task formulation.\n"}
+{"abstract": "  This paper presents an overview on how the PAL Robotics ARI robot is\nparticipating in the European SHAPES project to promote healthy and active\nliving among older people, by integrating digital solutions from project\npartners and adapting the system in order to improve human-robot interaction\nand user acceptability in a wide range of tasks.\n"}
+{"abstract": "  In this paper, we study the impact of non-trivial sound on the evolution of\ncosmological complexity in inflationary period. The vacuum state of curvature\nperturbation could be treated as squeezed states with two modes, characterized\nby the two most essential parameters: angle parameter $\\phi_k$ and squeezing\nparameter $r_k$. Through $Schr\\ddot{o}dinger$ equation, one can obtain the\ncorresponding evolution equation of $\\phi_k$ and $r_k$. Subsequently, the\nquantum circuit complexity between a squeezed vacuum state and squeezed states\nare evaluated in scalar curvature perturbation with a type of non-trivial sound\nspeed. Our results reveal that the evolution of complexity at early times shows\nthe rapid solution comparing with $c_S=1$, in which we implement the resonant\nsound speed with various values of $\\xi$. In these cases, it shows that the\nscrambling time will be lagged with non-vanishing $\\xi$. Further, our\nmethodology sheds a new way of distinguishing various inflationary models.\n"}
+{"abstract": "  The thermal properties of ice, liquid water and steam are at odds with\nstatistical theories applied to many-body systems. Here, these properties are\nquantitatively explained with a bulk-scale matter field emerging from the\nindefinite status of the microscopic constituents. Such a field is\ncharacterized by its symmetry in spacetime, its degree of degeneracy and its\neigenstates. There are several one-to-one correspondences bridging outcomes of\nclassical and quantum measurements. (i) The heat capacities are linked to the\nsymmetry of the field for each phase of water. (ii) The latent heats are linked\nto the change of the degree of degeneracy for each transition. (iii) The\ncritical temperatures are linked to the eigenstates of the potential operator.\nThe matter field leads to a complete representation of the phases of water,\nfree of hidden parameters and statistical ignorance\n"}
+{"abstract": "  Cryptocurrencies have emerged as a new form of digital money that has not\nescaped the eyes of cyber-attackers. Traditionally, they have been maliciously\nused as a medium of exchange for proceeds of crime in the cyber dark-market by\ncyber-criminals. However, cyber-criminals have devised an exploitative\ntechnique of directly acquiring cryptocurrencies from benign users' CPUs\nwithout their knowledge through a process called crypto mining. The presence of\ncrypto mining activities in a network is often an indicator of compromise of\nillegal usage of network resources for crypto mining purposes. Crypto mining\nhas had a financial toll on victims such as corporate networks and individual\nhome users. This paper addresses the detection of crypto mining attacks in a\ngeneric network environment using dynamic network characteristics. It tackles\nan in-depth overview of crypto mining operational details and proposes a\nsemi-supervised machine learning approach to detection using various crypto\nmining features derived from complex network characteristics. The results\ndemonstrate that the integration of semi-supervised learning with complex\nnetwork theory modeling is effective at detecting crypto mining activities in a\nnetwork environment. Such an approach is helpful during security mitigation by\nnetwork security administrators and law enforcement agencies.\n"}
+{"abstract": "  We have studied polarized image of a Schwarzschild black hole with an\nequatorial thin accretion disk as photon couples to Weyl tensor. The\nbirefringence of photon originating from the coupling affect the black hole\nshadow, the thin disk pattern and its luminosity distribution. We also analyze\nthe observed polarized intensity in the sky plane. The observed polarized\nintensity in the bright region is stronger than that in the darker region. The\nstronger effect of the coupling on the observed polarized vector appears only\nin the bright region close to black hole. These features in the polarized image\ncould help us to understand black hole shadow, the thin accretion disk and the\ncoupling between photon and Weyl tensor.\n"}
+{"abstract": "  A multiple operator integral (MOI) is an indispensable tool in several\nbranches of noncommutative analysis. However, there are substantial technical\nissues with the existing literature on the \"separation of variables\" approach\nto defining MOIs, especially when the Hilbert spaces on which they act are not\nseparable. In this paper, we provide a detailed development of this approach in\na very general setting that resolves existing technical issues. Along the way,\nwe characterize several kinds of \"weak\" operator-valued integrals in terms of\neasily checkable conditions and prove a highly useful Minkowski-type integral\ninequality for maps with values in a semifinite von Neumann algebra.\n"}
+{"abstract": "  The K x-ray spectra of different targets (Cu, Zn, and Ge) induced by 3 to 5\nMeV/u Si projectile ions have been measured to determine the K-shell ionization\ncross-section. A significant difference is observed between the measurements\nand theoretical estimates, with the latter being about 50% below the\nexperimental results. This underestimation} is attributed to the\ncharge-exchange from target K-shell to projectile K- and L-shells. Such\nobservation can only be possible if the projectile ions attain up to H- and\nHe-like charge states. Corresponding projectile charge state fractions have\nbeen evaluated from the Lorentzian charge state distribution, where mean charge\nstate is taken from the Fermi gas model [Phys. Rev. Lett. 30, 358 (1973)] and\nwidth from the Novikov and Teplova approach [Phys. Lett. A378, 1286-1289\n(2014)]. The sum of the direct ionization cross-section and K-K + K-L capture\ncross-sections gives a good agreement with the measured cross-sections.\nFurthermore, we have validated this methodology with available data for Si-ion\non Ti target. Such results may be useful in many solid target based\napplications.\n"}
+{"abstract": "  In this paper, we present a novel tone mapping algorithm that can be used for\ndisplaying wide dynamic range (WDR) images on low dynamic range (LDR) devices.\nThe proposed algorithm is mainly motivated by the logarithmic response and\nlocal adaptation features of the human visual system (HVS). HVS perceives\nluminance differently when under different adaptation levels, and therefore our\nalgorithm uses functions built upon different scales to tone map pixels to\ndifferent values. Functions of large scales are used to maintain image\nbrightness consistency and functions of small scales are used to preserve local\ndetail and contrast. An efficient method using local variance has been proposed\nto fuse the values of different scales and to remove artifacts. The algorithm\nutilizes integral images and integral histograms to reduce computation\ncomplexity and processing time. Experimental results show that the proposed\nalgorithm can generate high brightness, good contrast, and appealing images\nthat surpass the performance of many state-of-the-art tone mapping algorithms.\nThis project is available at\nhttps://github.com/jieyang1987/ToneMapping-Based-on-Multi-scale-Histogram-Synthesis.\n"}
+{"abstract": "  UVscope is an instrument, based on a multi-pixel photon detector, developed\nto support experimental activities for high-energy astrophysics and cosmic ray\nresearch. The instrument, working in single photon counting mode, is designed\nto directly measure light flux in the wavelengths range 300-650~nm. The\ninstrument can be used in a wide field of applications where the knowledge of\nthe nocturnal environmental luminosity is required. Currently, one UVscope\ninstrument is allocated onto the external structure of the ASTRI-Horn Cherenkov\ntelescope devoted to the gamma-ray astronomy at very high energies. Being\nco-aligned with the ASTRI-Horn camera axis, UVscope can measure the diffuse\nemission of the night sky background simultaneously with the ASTRI-Horn camera,\nwithout any interference with the main telescope data taking procedures.\nUVscope is properly calibrated and it is used as an independent reference\ninstrument for test and diagnostic of the novel ASTRI-Horn telescope.\n"}
+{"abstract": "  We construct a canonical chain of formality quasiisomorphisms for the operad\nof chains on framed little disks and the operad of chains on little disks. The\nconstruction is done in terms of logarithmic algebraic geometry and is\nremarkable for being rational (and indeed definable integrally) in de Rham\ncohomology.\n"}
+{"abstract": "  We describe the order type of range sets of compact ultrametrics and show\nthat an ultrametrizable infinite topological space $(X, \\tau)$ is compact iff\nthe range sets are order isomorphic for any two ultrametrics compatible with\nthe topology $\\tau$. It is also shown that an ultrametrizable topology is\nseparable iff every compatible with this topology ultrametric has at most\ncountable range set.\n"}
+{"abstract": "  We provide a direct proof of Weyl's law for the buckling eigenvalues of the\nbiharmonic operator on a wide class of domains of $\\mathbb R^d$ including\nbounded Lipschitz domains. The proof relies on asymptotically sharp lower and\nupper bounds that we develop for the Riesz mean $R_2(z)$. Lower bounds are\nobtained by making use of the so-called \"averaged variational principle\". Upper\nbounds are obtained in the spirit of Berezin-Li-Yau. Moreover, we state a\nconjecture for the second term in Weyl's law and prove its correctness in two\nspecial cases: balls in $\\mathbb R^d$ and bounded intervals in $\\mathbb R$.\n"}
+{"abstract": "  In this study, we report the first Cu-filled through silicon via (TSV)\nintegrated ion trap. TSVs are placed directly underneath electrodes as vertical\ninterconnections between ion trap and a glass interposer, facilitating the\narbitrary geometry design with increasing electrodes numbers and evolving\ncomplexity. The integration of TSVs reduces the form factor of ion trap by more\nthan 80%, minimizing parasitic capacitance from 32 to 3 pF. A low RF\ndissipation is achieved in spite of the absence of ground screening layer. The\nentire fabrication process is on 12-inch wafer and compatible with established\nCMOS back end process. We demonstrate the basic functionality of the trap by\nloading and laser-cooling single 88Sr+ ions. It is found that both heating rate\n(17 quanta/ms for an axial frequency of 300 kHz) and lifetime (~30 minutes) are\ncomparable with traps of similar dimensions. This work pioneers the development\nof TSV-integrated ion traps, enriching the toolbox for scalable quantum\ncomputing.\n"}
+{"abstract": "  We develop further the recently proposed event-by-event cumulants of\nazimuthal angles. The role of reflection symmetry, permutation symmetry, frame\nindependence, and relabeling of particle indices in the cumulant expansion is\ndiscussed in detail. We argue that mathematical and statistical properties of\ncumulants are preserved if cumulants of azimuthal angles are defined\nevent-by-event in terms of single-event averages of azimuthal angles, while\nthey are violated in the traditional approach in which cumulants are defined in\nterms of all-event averages. We derive for the first time the analytic\nsolutions for the contribution of combinatorial background in the measured 2-\nand 3-particle correlations. We demonstrate that these solutions for the\ncombinatorial background are universal as they can be written generically in\nterms of multiplicity-dependent combinatorial weights and marginal probability\ndensity functions of starting multivariate distribution. The new general\nresults between multiparticle azimuthal correlators and flow amplitudes and\nsymmetry planes are presented.\n"}
+{"abstract": "  With the goal of predicting the future rainfall intensity in a local region\nover a relatively short period time, precipitation nowcasting has been a\nlong-time scientific challenge with great social and economic impact. The radar\necho extrapolation approaches for precipitation nowcasting take radar echo\nimages as input, aiming to generate future radar echo images by learning from\nthe historical images. To effectively handle complex and high non-stationary\nevolution of radar echoes, we propose to decompose the movement into optical\nflow field motion and morphologic deformation. Following this idea, we\nintroduce Flow-Deformation Network (FDNet), a neural network that models flow\nand deformation in two parallel cross pathways. The flow encoder captures the\noptical flow field motion between consecutive images and the deformation\nencoder distinguishes the change of shape from the translational motion of\nradar echoes. We evaluate the proposed network architecture on two real-world\nradar echo datasets. Our model achieves state-of-the-art prediction results\ncompared with recent approaches. To the best of our knowledge, this is the\nfirst network architecture with flow and deformation separation to model the\nevolution of radar echoes for precipitation nowcasting. We believe that the\ngeneral idea of this work could not only inspire much more effective approaches\nbut also be applied to other similar spatiotemporal prediction tasks\n"}
+{"abstract": "  Transformer is important for text modeling. However, it has difficulty in\nhandling long documents due to the quadratic complexity with input text length.\nIn order to handle this problem, we propose a hierarchical interactive\nTransformer (Hi-Transformer) for efficient and effective long document\nmodeling. Hi-Transformer models documents in a hierarchical way, i.e., first\nlearns sentence representations and then learns document representations. It\ncan effectively reduce the complexity and meanwhile capture global document\ncontext in the modeling of each sentence. More specifically, we first use a\nsentence Transformer to learn the representations of each sentence. Then we use\na document Transformer to model the global document context from these sentence\nrepresentations. Next, we use another sentence Transformer to enhance sentence\nmodeling using the global document context. Finally, we use hierarchical\npooling method to obtain document embedding. Extensive experiments on three\nbenchmark datasets validate the efficiency and effectiveness of Hi-Transformer\nin long document modeling.\n"}
+{"abstract": "  Compressive sensing (CS) is a signal processing technique that enables\nsub-Nyquist sampling and near lossless reconstruction of a sparse signal. The\ntechnique is particularly appealing for neural signal processing since it\navoids the issues relevant to high sampling rate and large data storage. In\nthis project, different CS reconstruction algorithms were tested on raw action\npotential signals recorded in our lab. Two numerical criteria were set to\nevaluate the performance of different CS algorithms: Compression Ratio (CR) and\nSignal-to-Noise Ratio (SNR). In order to do this, individual CS algorithm\ntesting platforms for the EEG data were constructed within MATLAB scheme. The\nmain considerations for the project were the following. 1) Feasibility of the\ndictionary 2) Tolerance to non-sparsity 3) Applicability of thresholding or\ninterpolation.\n"}
+{"abstract": "  In this study, we investigated the influence of the topological defects\nspace-time with a spiral dislocation on a spin-zero boson field by using the\nDuffin-Kemmer-Petiau (DKP) equation. To be more specific, we solved the\ngeneralized spin-zero DKP equation in the presence of a spiral dislocation\nexactly. We derived the wave function and corresponding energy eigenvalues for\ntwo cases, in the absence and presence of a static potential by using\nanalytical methods. We numerically demonstrated the effect of the spiral\ndislocation on the solutions.\n"}
+{"abstract": "  A bad point of a positive semidefinite real polynomial f is a point at which\na pole appears in all expressions of f as a sum of squares of rational\nfunctions. We show that quartic polynomials in three variables never have bad\npoints. We give examples of positive semidefinite polynomials with a bad point\nat the origin, that are nevertheless sums of squares of formal power series,\nanswering a question of Brumfiel. We also give an example of a positive\nsemidefinite polynomial in three variables with a complex bad point that is not\nreal, answering a question of Scheiderer.\n"}
+{"abstract": "  The beneficial use of sewage sludge for valorization of carbon and nutrients\nis of increasing interest while micropollutants in sludge are of concern to the\nenvironment and human health. This study investigates the hydrothermal\nliquefaction (HTL) of sewage sludge in a continuous flow pilot scale reactor at\nconditions expected to reflect future industrial installations. The processing\nis evaluated in terms of energy efficiency, bio-crude yields and quality. The\nraw sludge and post-HTL process water and solid residues were analyzed\nextensively for micropollutants via HPLC-MS/MS for target pharmaceuticals\nincluding antibiotics, blood pressure medicine, antidepressants, analgesics,\nx-ray contrast media, angiotensin II receptor blockers, immunosuppressant drugs\nand biocides including triazines, triazoles, carbamates, a carboxamide, an\norganophosphate and a cationic surfactant. The results show that a positive\nenergy return on investment was achieved for all three HTL processing\ntemperatures of 300, 325 and 350 {\\deg}C with the most beneficial temperature\nidentified as 325 {\\deg}C. The analysis of the HTL by-products, process water\nand solids, indicates that HTL is indeed a suitable technology for the\ndestruction of micropollutants. However, due to the large matrix effect of the\nHTL process water it can only be stated with certainty that 9 out of 30\npharmaceuticals and 5 out of 7 biocides products were destroyed successfully\n(over 98% removal). One compound, the antidepressant citalopram, was shown to\nbe moderately recalcitrant at 300 {\\deg}C with 87% removal and was only\ndestroyed at temperatures $\\geq$325 {\\deg}C ($>$99% removal). Overall, the\nresults suggest that HTL is a suitable technology for energy efficient and\nvalue added sewage sludge treatment enabling destruction of micropollutants.\n"}
+{"abstract": "  A formula for the class number $h$ of the imaginary quadratic field\n$Q(\\sqrt{-p}$ is obtained by counting on a specific way the quadratic residues\nof a prime number of the form $p=4n-1.$ Formulas for the sum of the quadratic\nresidues are also found.\n"}
+{"abstract": "  Ensemble classifiers have been investigated by many in the artificial\nintelligence and machine learning community. Majority voting and weighted\nmajority voting are two commonly used combination schemes in ensemble learning.\nHowever, understanding of them is incomplete at best, with some properties even\nmisunderstood. In this paper, we present a group of properties of these two\nschemes formally under a dataset-level geometric framework. Two key factors,\nevery component base classifier's performance and dissimilarity between each\npair of component classifiers are evaluated by the same metric - the Euclidean\ndistance. Consequently, ensembling becomes a deterministic problem and the\nperformance of an ensemble can be calculated directly by a formula. We prove\nseveral theorems of interest and explain their implications for ensembles. In\nparticular, we compare and contrast the effect of the number of component\nclassifiers on these two types of ensemble schemes. Empirical investigation is\nalso conducted to verify the theoretical results when other metrics such as\naccuracy are used. We believe that the results from this paper are very useful\nfor us to understand the fundamental properties of these two combination\nschemes and the principles of ensemble classifiers in general. The results are\nalso helpful for us to investigate some issues in ensemble classifiers, such as\nensemble performance prediction, selecting a small number of base classifiers\nto obtain efficient and effective ensembles.\n"}
+{"abstract": "  A method involving intensity correlation measurements is described, which\nallows for the complete removal of Doppler broadening in the emission of\nelectromagnetic radiation from far-away sources that are inaccessible to\nconventional Doppler-free measurements. The technique, relying on a correction\nto g(2) of order N-1, probes the separation between neighboring spectral lines\nand is also applicable to the elimination of broadening due to collisions (N is\nthe number of emitting particles and g(2) is the second-order field correlation\nfunction). Possible applications include a determination of cosmological\nparameters from red shifts of gravitationally-lensed quasars.\n"}
+{"abstract": "  Can we define what it means to be \"creative,\" and if so, can our definition\ndrive artificial intelligence (AI) systems to feats of creativity\nindistinguishable from human efforts? This mixed question is considered from\ntechnological and social perspectives. Beginning with an exploration of the\nvalue we attach to authenticity in works of art, the article considers the\nability of AI to detect forgeries of renowned paintings and, in so doing,\nsomehow reveal the quiddity of a work of art. We conclude by considering\nwhether evolving technical capability can revise traditional relationships\namong art, artist, and the market.\n"}
+{"abstract": "  We investigate a well-known phenomenon of the appearance of the crossover\npoints, corresponding to the intersections of the solubility isotherms of the\nsolid compound in supercritical fluid. Opposed to the accepted understanding of\nthe existence of two fixed crossover points, which confine the region of the\ninverse isobaric temperature dependence of the solubility, we have found that\nthese points tend to shift with the change of the temperature and in the limit\nof the certain threshold value they converge to a single point. We demonstrate\nthis analyzing the solubility data of a set of poorly soluble drug compounds,\nwhich have been computed in a wide area of the phase diagram via the approach,\nbased on the classical density functional theory. Thorough analysis of the\navailable in the literature experimental solubility data is found to be in an\nagreement with our conclusions, as one can find that the wider temperature\nregion of the experimental study is, the more pronounced effect of the\ncrossover points drift can be observed.\n"}
+{"abstract": "  We study projective fourfolds of $K3^{[2]}$-type with a symplectic involution\nand the deformations of their quotients, called orbifolds of Nikulin types;\nthey are IHS orbifolds. We compute the Riemann--Roch formula for Weil divisors\non such orbifolds and describe the first complete family of orbifolds of\nNikulin type with a polarization of degree $2$ as double covers of special\ncomplete intersections $(3,4)$ in $\\mathbb{P}^6$.\n"}
+{"abstract": "  Word embeddings have been shown adept at capturing the semantic and syntactic\nregularities of the natural language text, as a result of which these\nrepresentations have found their utility in a wide variety of downstream\ncontent analysis tasks. Commonly, these word embedding techniques derive the\ndistributed representation of words based on the local context information.\nHowever, such approaches ignore the rich amount of explicit information present\nin knowledge-bases. This is problematic, as it might lead to poor\nrepresentation for words with insufficient local context such as domain\nspecific words. Furthermore, the problem becomes pronounced in domain such as\nbio-medicine where the presence of these domain specific words are relatively\nhigh. Towards this end, in this project, we propose a new word embedding based\nmodel for biomedical domain that jointly leverages the information from\navailable corpora and domain knowledge in order to generate knowledge-base\npowered embeddings. Unlike existing approaches, the proposed methodology is\nsimple but adept at capturing the precise knowledge available in domain\nresources in an accurate way. Experimental results on biomedical concept\nsimilarity and relatedness task validates the effectiveness of the proposed\napproach.\n"}
+{"abstract": "  Scientific literature contain important information related to cutting-edge\ninnovations in diverse domains. Advances in natural language processing have\nbeen driving the fast development in automated information extraction from\nscientific literature. However, scientific literature is often available in\nunstructured PDF format. While PDF is great for preserving basic visual\nelements, such as characters, lines, shapes, etc., on a canvas for presentation\nto humans, automatic processing of the PDF format by machines presents many\nchallenges. With over 2.5 trillion PDF documents in existence, these issues are\nprevalent in many other important application domains as well.\n  Our ICDAR 2021 Scientific Literature Parsing Competition (ICDAR2021-SLP) aims\nto drive the advances specifically in document understanding. ICDAR2021-SLP\nleverages the PubLayNet and PubTabNet datasets, which provide hundreds of\nthousands of training and evaluation examples. In Task A, Document Layout\nRecognition, submissions with the highest performance combine object detection\nand specialised solutions for the different categories. In Task B, Table\nRecognition, top submissions rely on methods to identify table components and\npost-processing methods to generate the table structure and content. Results\nfrom both tasks show an impressive performance and opens the possibility for\nhigh performance practical applications.\n"}
+{"abstract": "  The development of neuromorphic hardware and modeling of biological neural\nnetworks requires algorithms with local learning rules. Artificial neural\nnetworks using local learning rules to perform principal subspace analysis\n(PSA) and clustering have recently been derived from principled objective\nfunctions. However, no biologically plausible networks exist for minor subspace\nanalysis (MSA), a fundamental signal processing task. MSA extracts the\nlowest-variance subspace of the input signal covariance matrix. Here, we\nintroduce a novel similarity matching objective for extracting the minor\nsubspace, Minor Subspace Similarity Matching (MSSM). Moreover, we derive an\nadaptive MSSM algorithm that naturally maps onto a novel neural network with\nlocal learning rules and gives numerical results showing that our method\nconverges at a competitive rate.\n"}
+{"abstract": "  Human societies are characterized, besides others, by three constituent\nfeatures. (A) Options, as for jobs and societal positions, differ with respect\nto their associated monetary and non-monetary payoffs. (B) Competition leads to\nreduced payoffs when individuals compete for the same option with others. (C)\nPeople care how they are doing relatively to others. The latter trait, the\npropensity to compare one's own success with that of others, expresses itself\nas envy.\n  It is shown that the combination of (A)-(C) leads to spontaneous class\nstratification. Societies of agents split endogenously into two social classes,\nan upper and a lower class, when envy becomes relevant. A comprehensive\nanalysis of the Nash equilibria characterizing a basic reference game is\npresented. Class separation is due to the condensation of the strategies of\nlower-class agents, which play an identical mixed strategy. Upper class agents\ndo not condense, following individualist pure strategies.\n  Model and results are size-consistent, holding for arbitrary large numbers of\nagents and options. Analytic results are confirmed by extensive numerical\nsimulations. An analogy to interacting confined classical particles is\ndiscussed.\n"}
+{"abstract": "  Analog quantum simulation - the technique of using one experimentally\nwell-controlled physical system to mimic the behavior of another - has quickly\nemerged as one of the most promising near term strategies for studying strongly\ncorrelated quantum many-body systems. In particular, systems of interacting\nphotons, realizable in solid-state cavity and circuit QED frameworks, for\nexample, hold tremendous promise for the study of nonequilibrium many-body\nphenomena due to the capability to locally create and destroy photons. These\nsystems are typically modeled using a Jaynes-Cummings-Hubbard (JCH)\nHamiltonian, named due to similarities with the Bose-Hubbard (BH) model. Here,\nwe present a non-perturbative procedure for transforming the JC Hamiltonian\ninto a dressed operator representation that, in its most general form, admits\nan infinite sum of bosonic $k$-body terms where $k$ is bound only by the number\nof excitations in the system. We closely examine this result in both the\ndispersive and resonant coupling regimes, finding rapid convergence in the\nformer and contributions from $k\\gg1$ in the latter. Through extension to a\ntwo-site JCH system, we demonstrate that this approach facilitates close\ninspection of the analogy between the JCH and BH models and its breakdown for\nresonant light-matter coupling. Finally, we use this framework to survey the\nmany-body character of a two-site JCH for general system parameters,\nidentifying four unique quantum phases and the parameter regimes in which they\nare realized, thus highlighting phenomena realizable with finite JCH-based\nquantum simulators beyond the BH model. More broadly, this work is intended to\nserve as a clear mathematical exposition of bosonic many-body interactions\nunderlying JC-type systems, often postulated through analogy to Kerr-like\nnonlinear susceptibilities or by matching coefficients to obtain the\nappropriate eigenvalue spectrum.\n"}
+{"abstract": "  Tables in Web documents are pervasive and can be directly used to answer many\nof the queries searched on the Web, motivating their integration in question\nanswering. Very often information presented in tables is succinct and hard to\ninterpret with standard language representations. On the other hand, tables\noften appear within textual context, such as an article describing the table.\nUsing the information from an article as additional context can potentially\nenrich table representations. In this work we aim to improve question answering\nfrom tables by refining table representations based on information from\nsurrounding text. We also present an effective method to combine text and\ntable-based predictions for question answering from full documents, obtaining\nsignificant improvements on the Natural Questions dataset.\n"}
+{"abstract": "  In this paper, for a degenerate higher order equation with a fractional\nderivative in the sense of Caputo, a nonlocal problem with conjugation\nconditions in a rectangular domain is studied. The solution is constructed in\nthe form of a Fourier series in the eigenfunctions of a one-dimensional\nproblem. A criterion for the uniqueness of a solution is given.\n"}
+{"abstract": "  The estimation of periodicity is a fundamental task in many scientific areas\nof study. Existing methods rely on theoretical assumptions that the observation\ntimes have equal or i.i.d. spacings, and that common estimators, such as the\nperiodogram peak, are consistent and asymptotically normal. In practice,\nhowever, these assumptions are unrealistic as observation times usually exhibit\ndeterministic patterns -- e.g., the nightly observation cycle in astronomy --\nthat imprint nuisance periodicities in the data. These nuisance signals also\naffect the finite-sample distribution of estimators, which can substantially\ndeviate from normality. Here, we propose a set identification method, fusing\nideas from randomization inference and partial identification. In particular,\nwe develop a sharp test for any periodicity value, and then invert the test to\nbuild a confidence set. This approach is appropriate here because the\nconstruction of confidence sets does not rely on assumptions of regular or\nwell-behaved asymptotics. Notably, our inference is valid in finite samples\nwhen our method is fully implemented, while it can be asymptotically valid\nunder an approximate implementation designed to ease computation. Empirically,\nwe validate our method in exoplanet detection using radial velocity data. In\nthis context, our method correctly identifies the periodicity of the confirmed\nexoplanets in our sample. For some other, yet unconfirmed detections, we show\nthat the statistical evidence is weak, which illustrates the failure of\ntraditional statistical techniques. Last but not least, our method offers a\nconstructive way to resolve these identification issues via improved\nobservation designs. In exoplanet detection, these designs suggest meaningful\nimprovements in identifying periodicity even when a moderate amount of\nrandomization is introduced in scheduling radial velocity measurements.\n"}
+{"abstract": "  In [Hut16], Hutchings uses embedded contact homology to show the following\nfor area-preserving disc diffeomorphisms that are a rotation near the boundary\nof the disc: If the asymptotic mean action on the boundary is greater than the\nCalabi invariant, then the infimum of the mean action of the periodic points is\nless than or equal to the Calabi invariant. In this article, we extend this to\nall area-preserving disc diffeomorphisms. Our strategy is to extend the\ndiffeomorphism to a larger disc with nice properties and apply Hutchings'\ntheorem. As an application, we show that the Calabi invariant of any smooth\npseudo-rotation is equal to its rotation number.\n"}
+{"abstract": "  We present a gravitational theory that interpolates between JT gravity, and a\ngravity theory with a fixed boundary Hamiltonian. For this, we consider a\nmatrix integral with the insertion of a Gaussian with variance $\\sigma^2$,\ncentered around a matrix $\\textsf{H}_0$. Tightening the Gaussian renders the\nmatrix integral less random, and ultimately it collapses the ensemble to one\nHamiltonian $\\textsf{H}_0$. This model provides a concrete setup to study\nfactorization, and what the gravity dual of a single member of the ensemble is.\nWe find that as $\\sigma^2$ is decreased, the JT gravity dilaton potential gets\nmodified, and ultimately the gravity theory goes through a series of phase\ntransitions, corresponding to a proliferation of extra macroscopic holes in the\nspacetime. Furthermore, we observe that in the Efetov model approach to random\nmatrices, the non-averaged factorizing theory is described by one simple saddle\npoint.\n"}
+{"abstract": "  We present a heterogeneous localization framework for solving radar global\nlocalization and pose tracking on pre-built lidar maps. To bridge the gap of\nsensing modalities, deep neural networks are constructed to create shared\nembedding space for radar scans and lidar maps. Herein learned feature\nembeddings are supportive for similarity measurement, thus improving map\nretrieval and data matching respectively. In RobotCar and MulRan datasets, we\ndemonstrate the effectiveness of the proposed framework with the comparison to\nScan Context and RaLL. In addition, the proposed pose tracking pipeline is with\nless neural networks compared to the original RaLL.\n"}
+{"abstract": "  The frequencies of the analytic normal modes for N identical particles are\nstudied as a function of system parameters from the weakly interacting BCS\nregime to the strongly interacting unitary regime. The normal modes were\nobtained previously from a first-order L=0 group theoretic solution of a\nthree-dimensional Hamiltonian with a general two-body interaction for confined,\nidentical particles. In a precursor to this study, the collective behavior of\nthese normal modes was investigated as a function of N from few-body systems to\nmany-body systems analyzing the contribution of individual particles to the\ncollective macroscopic motions. A specific case, the Hamiltonian for Fermi\ngases in the unitary regime was studied in more detail. This regime is known to\nsupport collective behavior in the form of superfluidity and has previously\nbeen successfully described using normal modes. Two phenomena that could\nsustain the emergence and stability of superfluid behavior were revealed,\nincluding the behavior of the normal mode frequencies as N increases. In this\npaper, I focus on a more detailed analysis of these analytic frequencies,\nextending my investigation to include Hamiltonians with a range of\ninterparticle interaction strengths from the BCS regime to the unitary regime\nand analyzing the microscopic dynamics that lead to large gaps at unitarity.\nThe results of the current study suggest that in regimes where higher-order\neffects are small, normal modes an be used to describe the physics of\nsuperfluidity from the weakly interacting BCS regime with the emergence of\nsmall excitation gaps to unitarity with its large gaps, and can offer insight\ninto a possible microscopic understanding of the behavior at unitarity. This\napproach could thus offer an alternative to the two-body pairing models\ncommonly used to describe superfluidity along this transition.\n"}
+{"abstract": "  In this work, the aim is to study the diffusion of innovation of two\ncompeting products. The main focus has been to understand the effects of the\ncompetitive dynamic market on the diffusion of innovation. The global\nsmartphone operating system sales are chosen as an example. The availability of\nthe sales and the number of users data, as well as the predictions for the\nfuture number of users, make the smartphone diffusion a new laboratory to test\nthe innovation of diffusion models for the competitive markets. In this work,\nthe Bass model and its extensions which incorporate the competition between the\nbrands are used. The diffusion of smartphones can be considered on two levels:\nthe product level and the brand level. The diffusion of the smartphone as a\ncategory is studied by using the Bass equation (category-level diffusion). The\ndiffusion of each competing operating system (iOS and Android) are considered\nas the competition of the brands, and it is studied in the context of\ncompetitive market models (product-level diffusion). It is shown that the\neffects of personal interactions play the dominant role in the diffusion\nprocess. Moreover, the volume of near future sales can be predicted by\nintroducing appropriate dynamic market potential which helps to extrapolate the\nmodel results for the future.\n"}
+{"abstract": "  Integrating efficient connectivity, positioning and sensing functionalities\ninto 5G New Radio (NR) and beyond mobile cellular systems is one timely\nresearch paradigm, especially at mm-wave and sub-THz bands. In this article, we\naddress the radio-based sensing and environment mapping prospect with specific\nemphasis on the user equipment (UE) side. We first describe an efficient\nl1-regularized least-squares (LS) approach to obtain sparse range--angle charts\nat individual measurement or sensing locations. For the subsequent environment\nmapping, we then introduce a novel state model for mapping diffuse and specular\nscattering, which allows efficient tracking of individual scatterers over time\nusing interacting multiple model (IMM) extended Kalman filter and smoother. We\nprovide extensive numerical indoor mapping results at the 28~GHz band deploying\nOFDM-based 5G NR uplink waveform with 400~MHz channel bandwidth, covering both\naccurate ray-tracing based as well as actual RF measurement results. The\nresults illustrate the superiority of the dynamic tracking-based solutions,\ncompared to static reference methods, while overall demonstrate the excellent\nprospects of radio-based mobile environment sensing and mapping in future\nmm-wave networks.\n"}
+{"abstract": "  The Zernike wavefront sensor (ZWFS) is a concept belonging to the wide class\nFourier-filtering wavefront sensor (FFWFS). The ZWFS is known for its extremely\nhigh sensitivity while having a low dynamic range, which makes it a unique\nsensor for second stage adaptive optics (AO) systems or quasi-static\naberrations calibration sensor. This sensor is composed of a focal plane mask\nmade of a phase shifting dot fully described by two parameters: its diameter\nand depth. In this letter, we aim to improve the performance of this sensor by\nchanging the diameter of its phase shifting dot. We begin with a general\ntheoretical framework providing an analytical description of the FFWFS\nproperties, then we predict the expected ZWFS sensitivity for different\nconfigurations of dot diameters and depths. The analytical predictions are then\nvalidated with end-to-end simulations. From this, we propose a variation of the\nclassical ZWFS shape which exhibits extremely appealing properties. We show\nthat the ZWFS sensitivity can be optimized by modifying the dot diameter and\neven reach the optimal theoretical limit, with a trade-off for low spatial\nfrequencies sensitivity. As an example, we show that a ZWFS with a 2{\\lambda}/D\ndot diameter (where {\\lambda} is the sensing wavelength and D the telescope\ndiameter), hereafter called Z2WFS, exhibits a sensitivity twice higher than the\nclassical 1.06{\\lambda}/D ZWFS for all the phase spatial components except for\ntip-tilt modes. Furthermore, this gain in sensitivity does not impact the\ndynamic range of the sensor, and the Z2WFS exhibits a similar dynamical range\nas the classical 1.06{\\lambda}/D ZWFS. This study opens the path to the\nconception of diameter-optimized ZWFS.\n"}
+{"abstract": "  For an integer $r\\ge0$, we prove the $r$th order Reshetnyak formula for the\nray transform of rank $m$ symmetric tensor fields on $\\mathbb{R}^n$. Certain\ndifferential operators $A^{(m,r,l)}\\ (0\\le l\\le r)$ on the sphere\n$\\mathbb{S}^{n-1}$ are main ingredients of the formula. The operators are\ndefined by an algorithm that can be applied for any $r$ although the volume of\ncalculations grows fast with $r$. The algorithm is realized for small values of\n$r$ and Reshetnyak formulas of orders $0,1,2$ are presented in an explicit\nform.\n"}
+{"abstract": "  We prove an asymptotic result on the maximum number of k-vertex subtrees in\nbinary trees of given order. This problem turns out to be equivalent to\ndetermine the maximum number of k+2-cycles in n-vertex outerplanar graphs, thus\nwe settle the generalized outerplanar Tur\\'an number for all cycles. We also\ndetermine the exponential growth of the generalized outerplanar Tur\\'an number\nof paths Pk as a function of k which implies the order of magnitude of the\ngeneralized outerplanar Tur\\'an number of arbitrary trees. The bounds are\nstrongly related to the sequence of Catalan numbers.\n"}
+{"abstract": "  The weighted maximal bisection problem is, given an edge weighted graph, to\nfind a bipartition of the vertex set into two sets such that their cardinality\ndiffers by at most one and the sum of the weight of the edges between vertices\nthat are not in the same set is maximized. This problem is known to be NP-hard,\neven when a tree decomposition of width $t$ and $\\mathcal O(n)$ nodes is given\nas an advice as part of the input, where $n$ is the number of vertices of the\ninput graph. But, given such an advice, the problem is decidable in FPT time in\n$n$ parametrized by $t$. In particular Jansen et al. presented an algorithm\nwith running time $\\mathcal O(2^tn^3)$. Hanaka, Kobayashi, and Sone enhanced\nthe analysis of the complexity to $\\mathcal O(2^t(nt)^2)$. By slightly\nmodifying the approach, we improve the running time to $\\mathcal O(2^tn^2)$ in\nthe RAM model, which is asymptotically optimal in $n$ under the hardness of\nMinConv.\n  We proof that this is also asymptotically optimal in its dependence on $t$\nassuming SETH by showing for a slightly easier problem (maximal cut) that there\nis no $\\mathcal O(2^{\\epsilon t} \\operatorname{poly} n)$ algorithm for any\n$\\varepsilon < 1$ under SETH. This was already claimed by Hanaka, Kobayashi,\nand Sone but without a correct proof.\n  We also present a hardness result (no $\\mathcal O(2^t n^{2-\\varepsilon})$\nalgorithm for any $\\varepsilon > 0$) for a broad family of subclasses of the\nweighted maximal bisection problem that are characterized only by the\ndependence of $t$ from $n$, more precisely, all instances with $t = f(n)$ for\nan arbitrary but fixed $f(n) \\in \\operatorname o(\\log n)$. This holds even when\nonly considering planar graphs.\n  Moreover we present a detailed description of the implementation details and\nassumptions that are necessary to achieve the optimal running time.\n"}
+{"abstract": "  Deep neural network (DNN) accelerators received considerable attention in\nrecent years due to the potential to save energy compared to mainstream\nhardware. Low-voltage operation of DNN accelerators allows to further reduce\nenergy consumption significantly, however, causes bit-level failures in the\nmemory storing the quantized DNN weights. Furthermore, DNN accelerators have\nbeen shown to be vulnerable to adversarial attacks on voltage controllers or\nindividual bits. In this paper, we show that a combination of robust\nfixed-point quantization, weight clipping, as well as random bit error training\n(RandBET) or adversarial bit error training (AdvBET) improves robustness\nagainst random or adversarial bit errors in quantized DNN weights\nsignificantly. This leads not only to high energy savings for low-voltage\noperation as well as low-precision quantization, but also improves security of\nDNN accelerators. Our approach generalizes across operating voltages and\naccelerators, as demonstrated on bit errors from profiled SRAM arrays, and\nachieves robustness against both targeted and untargeted bit-level attacks.\nWithout losing more than 0.8%/2% in test accuracy, we can reduce energy\nconsumption on CIFAR10 by 20%/30% for 8/4-bit quantization using RandBET.\nAllowing up to 320 adversarial bit errors, AdvBET reduces test error from above\n90% (chance level) to 26.22% on CIFAR10.\n"}
+{"abstract": "  Among the properties shaping the light of a galaxy, the star formation\nhistory (SFH) is one of the most challenging to model due to the variety of\ncorrelated physical processes regulating star formation. In this work, we\nleverage the stellar population synthesis model FSPS, together with SFHs\npredicted by the hydrodynamical simulation IllustrisTNG and the empirical model\nUNIVERSEMACHINE, to study the impact of star formation variability on galaxy\ncolours. We start by introducing a model-independent metric to quantify the\nburstiness of a galaxy formation model, and we use this metric to demonstrate\nthat UNIVERSEMACHINE predicts SFHs with more burstiness relative to\nIllustrisTNG. Using this metric and principal component analysis, we construct\nfamilies of SFH models with adjustable variability, and we show that the\nprecision of broad-band optical and near-infrared colours degrades as the level\nof unresolved short-term variability increases. We use the same technique to\ndemonstrate that variability in metallicity and dust attenuation presents a\npractically negligible impact on colours relative to star formation\nvariability. We additionally provide a model-independent fitting function\ncapturing how the level of unresolved star formation variability translates\ninto imprecision in predictions for galaxy colours; our fitting function can be\nused to determine the minimal SFH model that reproduces colours with some\ntarget precision. Finally, we show that modelling the colours of individual\ngalaxies with percent-level precision demands resorting to complex SFH models,\nwhile producing precise colours for galaxy populations can be achieved using\nmodels with just a few degrees of freedom.\n"}
+{"abstract": "  Magnetic structures and the relationship between spin and charge-orbital\norderings of an A-site ordered double-perovskite manganite SmBaMn2O6, an\nanticipated multiferroic material, were investigated by means of neutron\ndiffraction. The spin arrangement in MnO2 planes perpendicular to the c axis is\nrevealed to be the same as that in the A-site disordered half-doped manganites\nCE-type but the stacking pattern is found to be different displaying a unique\ntwofold period. The temperature dependence of the superlattice magnetic and\nnuclear reflections clarifies that the antiferromagnetic spin ordering occurs\nat a temperature slightly lower than the temperature at which a rearrangement\nof the charge-orbital orderings occurs. The result evidences that the\nrearrangement leads the spin ordering. The intensities of the magnetic\nreflections are found to change across Tf = 10 K, suggesting a spin-flop by 90\n[deg.] while keeping the Mn spin ordering pattern unchanged.\n"}
+{"abstract": "  We study Fourier and Laplace transforms for Fourier hyperfunctions with\nvalues in a complex locally convex Hausdorff space. Since any hyperfunction\nwith values in a wide class of locally convex Hausdorff spaces can be extended\nto a Fourier hyperfunction, this gives simple notions of asymptotic Fourier and\nLaplace transforms for vector-valued hyperfunctions, which improves the\nexisting models of Komatsu, B\\\"aumer, Lumer and Neubrander and Langenbruch.\n"}
+{"abstract": "  The decentralized state estimation is one of the most fundamental components\nfor autonomous aerial swarm systems in GPS-denied areas, which still remains a\nhighly challenging research topic. To address this research niche, the\nOmni-swarm, a decentralized omnidirectional visual-inertial-UWB state\nestimation system for the aerial swarm is proposed in this paper. In order to\nsolve the issues of observability, complicated initialization, insufficient\naccuracy and lack of global consistency, we introduce an omnidirectional\nperception system as the front-end of the Omni-swarm, consisting of\nomnidirectional sensors, which includes stereo fisheye cameras and\nultra-wideband (UWB) sensors, and algorithms, which includes fisheye visual\ninertial odometry (VIO), multi-drone map-based localization and visual object\ndetector. A graph-based optimization and forward propagation working as the\nback-end of the Omni-swarm to fuse the measurements from the front-end.\nAccording to the experiment result, the proposed decentralized state estimation\nmethod on the swarm system achieves centimeter-level relative state estimation\naccuracy while ensuring global consistency. Moreover, supported by the\nOmni-swarm, inter-drone collision avoidance can be accomplished in a whole\ndecentralized scheme without any external device, demonstrating the potential\nof Omni-swarm to be the foundation of autonomous aerial swarm flights in\ndifferent scenarios.\n"}
+{"abstract": "  A family of reversible deterministic cellular automata, including the rules\n54 and 201 of [Bobenko et al., Commun. Math. Phys. 158, 127 (1993)] as well as\ntheir kinetically constrained quantum (unitary) or stochastic deformations, is\nshown to correspond to integrable Floquet circuit models with local\ninteractions round-a-face. Using inhomogeneous solutions of the star-triangle\nrelation with a one or two dimensional spectral parameter, changing their\nfunctional form depending on the orientation, we provide an explicit\nconstruction of the transfer matrix and establish its conservation law and\ninvolutivity properties. Integrability is independently demonstrated by\nnumerically exploring the spectral statistics via the Berry-Tabor conjecture.\nCuriously, we find that the deformed rule 54 model generically possesses no\nother local conserved quantities besides the net soliton current.\n"}
+{"abstract": "  We propose an approach for unsupervised domain adaptation for the task of\nestimating someone's age from a given face image. In order to avoid the\npropagation of racial bias in most publicly available face image datasets into\nthe inefficacy of models trained on them, we perform domain adaptation to\nmotivate the predictor to learn features that are invariant to ethnicity,\nenhancing the generalization performance across faces of people from different\nethnic backgrounds. Exploiting the ordinality of age, we also impose ranking\nconstraints on the prediction of the model and design our model such that it\ntakes as input a pair of images, and outputs both the relative age difference\nand the rank of the first identity with respect to the other in terms of their\nages. Furthermore, we implement Multi-Dimensional Scaling to retrieve absolute\nages from the predicted age differences from as few as two labeled images from\nthe domain to be adapted to. We experiment with a publicly available dataset\nwith age labels, dividing it into subsets based on the ethnicity labels, and\nevaluating the performance of our approach on the data from an ethnicity\ndifferent from the one that the model is trained on. Additionally, we impose a\nconstraint to preserve the sanity of the predictions with respect to relative\nand absolute ages, and another to ensure the smoothness of the predictions with\nrespect to the input. We experiment extensively and compare various domain\nadaptation approaches for the task of regression.\n"}
+{"abstract": "  We investigate potential correlations between radio source counts (after\nbackground corrections) of 22 Galactic globular clusters (GCs) from the MAVERIC\nsurvey, and stellar encounter rates ($\\Gamma$) and masses ($M$) of the GCs.\nApplying a radio luminosity limit of $L_\\mathrm{lim}=5.0\\times\n10^{27}~\\mathrm{erg~s^{-1}}$, we take a census of radio sources in the core and\nthose within the half-light radius. By following a maximum likelihood method\nand adopting a simplified linear model, we find an unambiguous dependence of\ncore radio source counts on $\\Gamma$ and/or $M$ at 90% confidence, but no clear\ndependence of source counts within the half-light radius on either $\\Gamma$ or\n$M$. Five of the identified radio sources in cores above our adopted limit are\nmillisecond pulsars or neutron star X-ray binaries (XRBs), the dependence of\nwhich on $\\Gamma$ is well-known, but another is a published black hole (BH) XRB\ncandidate, and ten others are not identified. Accounting for the verified\ncluster members increases the significance of correlation with $M$ and/or\n$\\Gamma$ (to 99% confidence), for fits to core and half-light region source\ncounts, while excluding a dependence on $\\Gamma$ alone at 90% (core) and 68%\n(half-light) confidence. This is consistent with published dynamical\nsimulations of GC BH interactions that argue $\\Gamma$ will be a poor predictor\nof the distribution of accreting BHs in GCs. Future multiwavelength follow-up\nto verify cluster membership will enable stronger constraints on the dependence\nof radio source classes on cluster properties, promising a new view on the\ndynamics of BHs in GCs.\n"}
+{"abstract": "  Intelligent reflecting surfaces (IRSs) are promising enablers for\nnext-generation wireless communications due to their reconfigurability and high\nenergy efficiency in improving poor propagation condition of channels, e.g.,\nlimited scattering environment. However, most existing works assumed full-rank\nchannels requiring rich scatters, which may not be available in practice. To\nanalyze the impact of rank-deficient channels and mitigate the ensued\nperformance loss, we consider a large-scale IRS-aided MIMO system with\nstatistical channel state information (CSI), where the double-scattering\nchannel is adopted to model rank deficiency. By leveraging random matrix theory\n(RMT), we first derive a deterministic approximation (DA) of the ergodic rate\nwith low computational complexity and prove the existence and uniqueness of the\nDA parameters. Then, we propose an alternating optimization algorithm for\nmaximizing the DA with respect to phase shifts and signal covariance matrices.\nNumerical results will show that the DA is tight and our proposed method can\neffectively mitigate the performance loss induced by channel rank deficiency.\n"}
+{"abstract": "  We survey recent studies and results on the following problem: which\nnumerical functions can be the depth function of powers and symbolic powers of\nhomogeneous ideals.\n"}
+{"abstract": "  In December 2019, the first case of SARS-CoV-2 infection was identified in\nWuhan, China. Since that day, COVID-19 has spread worldwide, affecting 153\nmillion people. Education, as many other sectors, has managed to adapt to the\nrequirements and barriers implied by the impossibility to teach students\nface-to-face as it was done before. Yet, little is known about the implications\nof emergency remote teaching (ERT) during the pandemic. This study describes\nand analyzes the impact of the pandemic on the study patterns of higher\neducation students. The analysis was performed by the integration of three main\ncomponents: (1) interaction with the learning management system (LMS), (2)\nAssignment submission rate, and (3) Teachers' perspective. Several variables\nwere created to analyze the study patterns, clicks on different LMS components,\nusage during the day, week and part of the term, the time span of interaction\nwith the LMS, and grade categories. The results showed significant differences\nin study patterns depending on the year of study, and the variables reflecting\nthe effect of teachers' changes in the course structure are identified. This\nstudy outlines the first insights of higher education's new normality,\nproviding important implications for supporting teachers in creating academic\nmaterial that adequately addresses students' particular needs depending on\ntheir year of study, changes in study pattern, and distribution of time and\nactivity through the term.\n"}
+{"abstract": "  The reinforcement learning problem of finding a control policy that minimizes\nthe minimum time objective for the Mountain Car environment is considered.\nParticularly, a class of parameterized nonlinear feedback policies is optimized\nover to reach the top of the highest mountain peak in minimum time. The\noptimization is carried out using quasi-Stochastic Gradient Descent (qSGD)\nmethods. In attempting to find the optimal minimum time policy, a new\nparameterized policy approach is considered that seeks to learn an optimal\npolicy parameter for different regions of the state space, rather than rely on\na single macroscopic policy parameter for the entire state space. This\npartitioned parameterized policy approach is shown to outperform the uniform\nparameterized policy approach and lead to greater generalization than prior\nmethods, where the Mountain Car became trapped in circular trajectories in the\nstate space.\n"}
+{"abstract": "  For a graph $G$, and two distinct vertices $u$ and $v$ of $G$, let $n_G(u,v)$\nbe the number of vertices of $G$ that are closer in $G$ to $u$ than to $v$.\nMiklavi\\v{c} and \\v{S}parl (arXiv:2011.01635v1) define the\ndistance-unbalancedness ${\\rm uB}(G)$ of $G$ as the sum of\n$|n_G(u,v)-n_G(v,u)|$ over all unordered pairs of distinct vertices $u$ and $v$\nof $G$. For positive integers $n$ up to $15$, they determine the trees $T$ of\nfixed order $n$ with the smallest and the largest values of ${\\rm uB}(T)$,\nrespectively. While the smallest value is achieved by the star $K_{1,n-1}$ for\nthese $n$, which we then proved for general $n$ (Minimum\ndistance-unbalancedness of trees, Journal of Mathematical Chemistry, DOI\n10.1007/s10910-021-01228-4), the structure of the trees maximizing the\ndistance-unbalancedness remained unclear. For $n$ up to $15$ at least, all\nthese trees were subdivided stars. Contributing to problems posed by\nMiklavi\\v{c} and \\v{S}parl, we show $$\\max\\Big\\{{\\rm uB}(T):T\\mbox{ is a tree\nof order }n\\Big\\} =\\frac{n^3}{2}+o(n^3)$$ and $$\\max\\Big\\{{\\rm\nuB}(S(n_1,\\ldots,n_k)):1+n_1+\\cdots+n_k=n\\Big\\}\n=\\left(\\frac{1}{2}-\\frac{5}{6k}+\\frac{1}{3k^2}\\right)n^3+O(kn^2),$$ where\n$S(n_1,\\ldots,n_k)$ is the subdivided star such that removing its center vertex\nleaves paths of orders $n_1,\\ldots,n_k$.\n"}
+{"abstract": "  We consider families of parameterizations of reduced curve singularities over\na Noetherian base scheme and prove that the delta invariant is semicontinuous.\nIn our setting, each curve singularity in the family is the image of a\nparameterization and not the fiber of a morphism. The problem came up in\nconnection with the right-left classification of parameterizations of curve\nsingularities defined over a field of positive characteristic. We prove a bound\nfor right-left determinacy of a parameterization in terms of delta and the\nsemicontinuity theorem provides a simultaneous bound for the determinacy in a\nfamily. The fact that the base space can be an arbitrary Noetherian scheme\ncauses some difficulties but is (not only) of interest for computational\npurposes.\n"}
+{"abstract": "  We suggest a mathematical model for computing and regularly updating the next\npreventive maintenance plan for a wind farm. Our optimization criterium takes\ninto account the current ages of the key components, the major maintenance\ncosts including eventual energy production losses as well as the available data\nmonitoring the condition of the wind turbines. We illustrate our approach with\na case study based on data collected from several wind farms located in Sweden.\nOur results show that preventive maintenance planning gives some effect, if the\nwind turbine components in question live significantly shorter than the turbine\nitself.\n"}
+{"abstract": "  We consider a general tractable model for default contagion and systemic risk\nin a heterogeneous financial network, subject to an exogenous macroeconomic\nshock. We show that, under some regularity assumptions, the default cascade\nmodel could be transferred to a death process problem represented by\nballs-and-bins model. We also reduce the dimension of the problem by\nclassifying banks according to different types, in an appropriate type space.\nThese types may be calibrated to real-world data by using machine learning\ntechniques. We then state various limit theorems regarding the final size of\ndefault cascade over different types. In particular, under suitable assumptions\non the degree and threshold distributions, we show that the final size of\ndefault cascade has asymptotically Gaussian fluctuations. We next state limit\ntheorems for different system-wide wealth aggregation functions and show how\nthe systemic risk measure, in a given stress test scenario, could be related to\nthe structure and heterogeneity of financial networks. We finally show how\nthese results could be used by a social planner to optimally target\ninterventions during a financial crisis, with a budget constraint and under\npartial information of the financial network.\n"}
+{"abstract": "  The signatures of topological superconductivity (TSC) in the superconducting\nmaterials with topological nontrivial states prompt intensive researches\nrecently. Utilizing high-resolution angle-resolved photoemission spectroscopy\nand first-principles calculations, we demonstrate multiple Dirac fermions and\nsurface states in superconductor BaSn$_3$ with a critical transition\ntemperature of about 4.4 K. We predict and then unveil the existence of two\npairs of type-\\uppercase\\expandafter{\\romannumeral1} topological Dirac fermions\nresiding on the rotational axis. Type-\\uppercase\\expandafter{\\romannumeral2}\nDirac fermions protected by screw axis are confirmed in the same compound.\nFurther calculation for the spin helical texture of the observed surface states\noriginating from the Dirac fermions give an opportunity for realization of TSC\nin one single material. Hosting multiple Dirac fermions and topological surface\nstates, the intrinsic superconductor BaSn$_3$ is expected to be a new platform\nfor further investigation of the topological quantum materials as well as TSC.\n"}
+{"abstract": "  Sleeping problems have become one of the major diseases all over the world.\nTo tackle this issue, the basic tool used by specialists is the Polysomnogram,\nwhich is a collection of different signals recorded during sleep. After its\nrecording, the specialists have to score the different signals according to one\nof the standard guidelines. This process is carried out manually, which can be\nhighly time-consuming and very prone to annotation errors. Therefore, over the\nyears, many approaches have been explored in an attempt to support the\nspecialists in this task. In this paper, an approach based on convolutional\nneural networks is presented, where an in-depth comparison is performed in\norder to determine the convenience of using more than one signal simultaneously\nas input. Additionally, the models were also used as parts of an ensemble model\nto check whether any useful information can be extracted from signal processing\na single signal at a time which the dual-signal model cannot identify. Tests\nhave been performed by using a well-known dataset called expanded sleep-EDF,\nwhich is the most commonly used dataset as the benchmark for this problem. The\ntests were carried out with a leave-one-out cross-validation over the patients,\nwhich ensures that there is no possible contamination between training and\ntesting. The resulting proposal is a network smaller than previously published\nones, but which overcomes the results of any previous models on the same\ndataset. The best result shows an accuracy of 92.67\\% and a Cohen's Kappa value\nover 0.84 compared to human experts.\n"}
+{"abstract": "  We study generalized symmetries of quantum field theories in 1+1D generated\nby topological defect lines with no inverse. This paper follows our companion\npaper on gapped phases and anomalies associated with these symmetries. In the\npresent work we focus on identifying fusion category symmetries, using both\nspecialized 1+1D methods such as the modular bootstrap and (rational) conformal\nfield theory (CFT), as well as general methods based on gauging finite\nsymmetries, that extend to all dimensions. We apply these methods to $c = 1$\nCFTs and uncover a rich structure. We find that even those $c = 1$ CFTs with\nonly finite group-like symmetries can have continuous fusion category\nsymmetries, and prove a Noether theorem that relates such symmetries in general\nto non-local conserved currents. We also use these symmetries to derive new\nconstraints on RG flows between 1+1D CFTs.\n"}
+{"abstract": "  Diagnosing pre-existing heart diseases early in life is important as it helps\nprevent complications such as pulmonary hypertension, heart rhythm problems,\nblood clots, heart failure and sudden cardiac arrest. To identify such\ndiseases, phonocardiogram (PCG) and electrocardiogram (ECG) waveforms convey\nimportant information. Therefore, effectively using these two modalities of\ndata has the potential to improve the disease screening process. We evaluate\nthis hypothesis on a subset of the PhysioNet Challenge 2016 Dataset which\ncontains simultaneously acquired PCG and ECG recordings. Our novel\nDual-Convolutional Neural Network based approach uses transfer learning to\ntackle the problem of having limited amounts of simultaneous PCG and ECG data\nthat is publicly available, while having the potential to adapt to larger\ndatasets. In addition, we introduce two main evaluation frameworks named\nrecord-wise and sample-wise evaluation which leads to a rich performance\nevaluation for the transfer learning approach. Comparisons with methods which\nused single or dual modality data show that our method can lead to better\nperformance. Furthermore, our results show that individually collected ECG or\nPCG waveforms are able to provide transferable features which could effectively\nhelp to make use of a limited number of synchronized PCG and ECG waveforms and\nstill achieve significant classification performance.\n"}
+{"abstract": "  In part I (arXiv:2006.12393) (accepted in JPP), we simulated individual\ndynamics of a rising bubble and a falling droplet in an incompressible limit of\na strongly coupled dusty plasma (SCDP) using a generalized hydrodynamic\nviscoelastic fluid model. In interest to understand hetero- (bubble-drop)\ninteractions between them, we extend this study to their combined evolution\nthrough two arrangements: First, both are placed side-by-side in a row at the\nsame height. We observe that the overall dynamic governs by the competition\nbetween the rotational motion induces due to the pairing between two\nco-rotating inner vorticity lobes and net vertical motion of two\ndipolar-vorticities (upward for bubble and downward for droplet) induces due to\nthe gravity. In second arrangement where the vertically aligned bubble (below)\nand droplet (above) after collision exchange their blobs/lobes and subsequently\nstart to move horizontally in opposite directions away from each other. This\nhorizontal movement gets slower with increasing coupling strength. For the\nthese arrangement, we consider the varying distance between the fixed-size\nbubble and droplet, and varying coupling strength which represents the nature\nof our medium. A series of 2D numerical simulations have been conducted to\nvisualize these interactions\n"}
+{"abstract": "  We show that the weakest versions of Foreman's minimal generic hugeness\naxioms cannot hold simultaneously on adjacent cardinals. Moreover, conventional\nforcing techniques cannot produce a model of one of these axioms.\n"}
+{"abstract": "  Chan, Har-Peled, and Jones [2020] recently developed locality-sensitive\nordering (LSO), a new tool that allows one to reduce problems in the Euclidean\nspace $\\mathbb{R}^d$ to the $1$-dimensional line. They used LSO's to solve a\nhost of problems. Later, Buchin, Har-Peled, and Ol{\\'{a}}h [2019,2020] used the\nLSO of Chan {\\em et al. } to construct very sparse \\emph{reliable spanners} for\nthe Euclidean space. A highly desirable feature of a reliable spanner is its\nability to withstand a massive failure: the network remains functioning even if\n90\\% of the nodes fail. In a follow-up work, Har-Peled, Mendel, and Ol{\\'{a}}h\n[2021] constructed reliable spanners for general and topologically structured\nmetrics. Their construction used a different approach, and is based on sparse\ncovers.\n  In this paper, we develop the theory of LSO's to non-Euclidean metrics by\nintroducing new types of LSO's suitable for general and topologically\nstructured metrics. We then construct such LSO's, as well as constructing\nconsiderably improved LSO's for doubling metrics. Afterwards, we use our new\nLSO's to construct reliable spanners with improved stretch and sparsity\nparameters. Most prominently, we construct $\\tilde{O}(n)$-size reliable\nspanners for trees and planar graphs with the optimal stretch of $2$. Along the\nway to the construction of LSO's and reliable spanners, we introduce and\nconstruct ultrametric covers, and construct $2$-hop reliable spanners for the\nline.\n"}
+{"abstract": "  As is known, for each fixed $\\nu\\in\\{0,1\\},$ the spectra of two operators\ngenerated by $-y''(x)+q(x)y(x-a)$ and the boundary conditions\n$y^{(\\nu)}(0)=y^{(j)}(\\pi)=0,$ $j=0,1,$ uniquely determine the complex-valued\nsquare-integrable potential $q(x)$ vanishing on $(0,a)$ as soon as\n$a\\in[2\\pi/5,\\pi).$ Meanwhile, it actually became the main question of the\ninverse spectral theory for Sturm-Liouville operators with constant delay\nwhether the uniqueness holds also for smaller values of $a.$ Recently, a\nnegative answer was given by the authors [Appl. Math. Lett. 113 (2021) 106862]\nfor $a\\in[\\pi/3,2\\pi/5)$ in the case $\\nu=0$ by constructing an infinite family\nof iso-bispectral potentials. Moreover, an essential and dramatic reason was\nestablished why this strategy, generally speaking, fails in the remarkable case\nwhen $\\nu=1.$ Here we construct a counterexample giving a negative answer for\n$\\nu=1,$ which is an important subcase of the Robin boundary condition at zero.\nWe also refine the former counterexample for $\\nu=0$ to $W_2^1$-potentials.\n"}
+{"abstract": "  Acoustic wave resonators are promising for gravimetric biosensing. However,\nthey generally suffer from strong acoustic radiation in liquid, which limits\ntheir quality factor and increases their frequency noise. This article presents\nan acoustic-radiation-free gravimetric biosensor based on a locally-resonant\nsurface phononic crystal (SPC) consisting of periodic high aspect ratio\nelectrodes to ad-dress the above issue. The acoustic wave generated in the SPC\nis slower than the sound wave in water, hence preventing acoustic propagation\nin the fluid and resulting in energy confinement near the electrode surface.\nThis energy confinement results in a significant quality factor improvement and\nthus reduces the frequency noise. The proposed SPC resonator is numerically\nstudied by finite element analysis and experimentally implemented by an\nelectroplating based fabrication process. Experimental results show that the\nSPC resonator exhibits an in-liquid quality factor 15 times higher than a\nconventional Rayleigh wave resonator with a similar operating frequency. The\nproposed radiation suppression method using SPC can also be applied in other\ntypes of acoustic wave resonators. It can thus serve as a general technique for\nboosting the in-liquid quality factor and the sensing performance of many\nacoustic biosensors.\n"}
+{"abstract": "  A fundamental premise of SMS One-Time Password (OTP) is that the used\npseudo-random numbers (PRNs) are uniquely unpredictable for each login session.\nHence, the process of generating PRNs is the most critical step in the OTP\nauthentication. An improper implementation of the pseudo-random number\ngenerator (PRNG) will result in predictable or even static OTP values, making\nthem vulnerable to potential attacks. In this paper, we present a vulnerability\nstudy against PRNGs implemented for Android apps. A key challenge is that PRNGs\nare typically implemented on the server-side, and thus the source code is not\naccessible. To resolve this issue, we build an analysis tool, \\sysname, to\nassess implementations of the PRNGs in an automated manner without the source\ncode requirement. Through reverse engineering, \\sysname identifies the apps\nusing SMS OTP and triggers each app's login functionality to retrieve OTP\nvalues. It further assesses the randomness of the OTP values to identify\nvulnerable PRNGs. By analyzing 6,431 commercially used Android apps downloaded\nfrom \\tool{Google Play} and \\tool{Tencent Myapp}, \\sysname identified 399\nvulnerable apps that generate predictable OTP values. Even worse, 194\nvulnerable apps use the OTP authentication alone without any additional\nsecurity mechanisms, leading to insecure authentication against guessing\nattacks and replay attacks.\n"}
+{"abstract": "  We study maximal operators associated to singular averages along finite\nsubsets $\\Sigma$ of the Grassmannian $\\mathrm{Gr}(d,n)$ of $d$-dimensional\nsubspaces of $\\mathbb R^n$. The well studied $d=1$ case corresponds to the the\ndirectional maximal function with respect to arbitrary finite subsets of\n$\\mathrm{Gr}(1,n)=\\mathbb S^{n-1}$. We provide a systematic study of all cases\n$1\\leq d<n$ and prove essentially sharp $L^2(\\mathbb R^n)$ bounds for the\nmaximal subspace averaging operator in terms of the cardinality of $\\Sigma$,\nwith no assumption on the structure of $\\Sigma$. In the codimension $1$ case,\nthat is $n=d+1$, we prove the precise critical weak $(2,2)$-bound.\n  Drawing on the analogy between maximal subspace averages and $(d,n)$-Nikodym\nmaximal averages, we also formulate the appropriate maximal Nikodym conjecture\nfor general $1<d<n$ by providing examples that determine the critical\n$L^p$-space for the $(d,n)$-Nikodym problem. Unlike the $d=1$ case, the maximal\nKakeya and Nikodym problems are shown not to be equivalent when $d>1$. In this\ncontext, we prove the best possible $L^2(\\mathbb R^n)$-bound for the\n$(d,n)$-Nikodym maximal function for all combinations of dimension and\ncodimension.\n  Our estimates rely on Fourier analytic almost orthogonality principles,\ncombined with polynomial partitioning, but we also use spatial analysis based\non the precise calculation of intersections of $d$-dimensional plates in\n$\\mathbb R^n$.\n"}
+{"abstract": "  In the process of studying a conjecture of Holly M. Green and Martin W.\nLiebeck, we obtain two interesting identities by elementary methods, one is a\ncombinatorial identity, and the other is a number theoretic identity.\n"}
+{"abstract": "  We propose injective generative models called Trumpets that generalize\ninvertible normalizing flows. The proposed generators progressively increase\ndimension from a low-dimensional latent space. We demonstrate that Trumpets can\nbe trained orders of magnitudes faster than standard flows while yielding\nsamples of comparable or better quality. They retain many of the advantages of\nthe standard flows such as training based on maximum likelihood and a fast,\nexact inverse of the generator. Since Trumpets are injective and have fast\ninverses, they can be effectively used for downstream Bayesian inference. To\nwit, we use Trumpet priors for maximum a posteriori estimation in the context\nof image reconstruction from compressive measurements, outperforming\ncompetitive baselines in terms of reconstruction quality and speed. We then\npropose an efficient method for posterior characterization and uncertainty\nquantification with Trumpets by taking advantage of the low-dimensional latent\nspace.\n"}
+{"abstract": "  We consider quantum many-body dynamics under quantum measurements, where the\nmeasurement-induced phase transitions (MIPs) occur when changing the frequency\nof the measurement. In this work, we consider the robustness of the MIP for\nlong-range interaction that decays as $r^{-\\alpha}$ with distance $r$. The\neffects of long-range interactions are classified into two regimes: (i) the MIP\nis observed $(\\alpha > \\alpha_c)$, and (ii) the MIP is absent even for\narbitrarily strong measurements $(\\alpha<\\alpha_c)$. Using fermion models, we\ndemonstrate both regimes in integrable and non-integrable cases. We identify\nthe underlying mechanism and propose sufficient conditions to observe the MIP,\nthat is, $\\alpha > d/2+1$ for general bilinear systems and $\\alpha > d+1$ for\ngeneral non-integrable systems ($d$: spatial dimension). Numerical calculation\nindicates that these conditions are optimal.\n"}
+{"abstract": "  We study confinement in 4d $\\mathcal{N}=1$ $SU(N)$ Super-Yang Mills (SYM)\nfrom a holographic point of view, focusing on the 1-form symmetry and its\nrelation to chiral symmetry breaking. In the 5d supergravity dual, obtained by\ntruncation of the Klebanov-Strassler solution, we identify the topological\ncouplings that determine the 1-form symmetry and its 't Hooft anomalies. One\nsuch coupling is a mixed 0-form/1-form symmetry anomaly closely related to\nchiral symmetry breaking in gapped confining vacua. From the dual gravity\ndescription we also identify the infra-red (IR) 4d topological field theory\n(TQFT), which realises chiral symmetry breaking and matches the mixed anomaly.\nFinally, complementing this, we derive the chiral and mixed anomalies from the\nLittle String Theory realization of pure SYM.\n"}
+{"abstract": "  In classical approaches of dynamic network identification, in order to\nidentify a system (module) embedded in a dynamic network, one has to formulate\na Multi-input-Single-output (MISO) identification problem that requires\nidentification of a parametric model for all the modules constituting the MISO\nsetup including (possibly) the noise model, and determine their model order.\nThis requirement leads to model order selection steps for modules that are of\nno interest to the experimenter which increases the computational complexity\nfor large-sized networks. Also, identification using a parametric noise model\n(like BJ method) can suffer from local minima, however neglecting the noise\nmodel has its impact on the variance of the estimates. In this paper, we\nprovide a two-step identification approach to avoid these problems. The first\nstep involves performing a non-parametric indirect approach for a MISO\nidentification problem to get the non-parametric frequency response function\n(FRF) estimates and its variance as a function of frequency. In the second\nstep, the estimated non-parametric FRF of the target module is smoothed using a\nparametric frequency domain estimator with the estimated variance from the\nprevious step as the non-parametric noise model. The developed approach is\npractical with weak assumptions on noise, uses the available toolbox, requires\na parametric model only for the target module of interest, and uses a\nnon-parametric noise model to reduce the variance of the estimates. Numerical\nsimulations illustrate the potentials of the introduced method in comparison\nwith the classical identification methods.\n"}
+{"abstract": "  This paper pushes the envelope on decomposing camouflaged regions in an image\ninto meaningful components, namely, camouflaged instances. To promote the new\ntask of camouflaged instance segmentation of in-the-wild images, we introduce a\ndataset, dubbed CAMO++, that extends our preliminary CAMO dataset (camouflaged\nobject segmentation) in terms of quantity and diversity. The new dataset\nsubstantially increases the number of images with hierarchical pixel-wise\nground truths. We also provide a benchmark suite for the task of camouflaged\ninstance segmentation. In particular, we present an extensive evaluation of\nstate-of-the-art instance segmentation methods on our newly constructed CAMO++\ndataset in various scenarios. We also present a camouflage fusion learning\n(CFL) framework for camouflaged instance segmentation to further improve the\nperformance of state-of-the-art methods. The dataset, model, evaluation suite,\nand benchmark will be made publicly available on our project page:\nhttps://sites.google.com/view/ltnghia/research/camo_plus_plus\n"}
+{"abstract": "  Nowadays, Light Detection And Ranging (LiDAR) has been widely used in\nautonomous vehicles for perception and localization. However, the cost of a\nhigh-resolution LiDAR is still prohibitively expensive, while its\nlow-resolution counterpart is much more affordable. Therefore, using\nlow-resolution LiDAR for autonomous driving perception tasks instead of\nhigh-resolution LiDAR is an economically feasible solution. In this paper, we\npropose a novel framework for 3D object detection in Bird-Eye View (BEV) using\na low-resolution LiDAR and a monocular camera. Taking the low-resolution LiDAR\npoint cloud and the monocular image as input, our depth completion network is\nable to produce dense point cloud that is subsequently processed by a\nvoxel-based network for 3D object detection. Evaluated with KITTI dataset, the\nexperimental results shows that the proposed approach performs significantly\nbetter than directly applying the 16-line LiDAR point cloud for object\ndetection. For both easy and moderate cases, our detection results are\ncomparable to those from 64-line high-resolution LiDAR. The network\narchitecture and performance evaluations are analyzed in detail.\n"}
+{"abstract": "  The Latent River Problem has emerged as a flagship problem for causal\ndiscovery in extreme value statistics. This paper gives QTree, a simple and\nefficient algorithm to solve the Latent River Problem that outperforms existing\nmethods. QTree returns a directed graph and achieves almost perfect recovery on\nthe Upper Danube, the existing benchmark dataset, as well as on new data from\nthe Lower Colorado River in Texas. It can handle missing data, has an automated\nparameter tuning procedure, and runs in time $O(n |V|^2)$, where $n$ is the\nnumber of observations and $|V|$ the number of nodes in the graph. In addition,\nunder a Bayesian network model for extreme values with propagating noise, we\nshow that the QTree estimator returns for $n\\to\\infty$ a.s. the correct tree.\n"}
+{"abstract": "  Interconnectivity, fault tolerance, and dynamic evolution of the circuitry\nare long sought-after objectives of bio-inspired engineering. Here, we propose\ndendritic transistors composed of organic semiconductors as building blocks for\nneuromorphic computing. These devices, owning to their voltage-triggered growth\nand resemblance to neural structures, respond to action potentials to achieve\ncomplex brain-like features, such as Pavlovian learning, pattern recognition,\nand spike-timing-dependent plasticity. The dynamic formation of the connections\nis reminiscent of a biological learning mechanism known as synaptogenesis, and\nit is carried out by an electrochemical reaction that we name field-directed\npolymerization. We employ it to dendritic connections and, by modulating the\ngrowth parameters, control material properties such as the resistance and the\ntime constants relevant for plasticity. We believe these results will inspire\nfurther research towards the complex integration of polymerized synapses for\nbrain-inspired computing.\n"}
+{"abstract": "  High-mass gamma-ray binaries consist of a presumptive pulsar in orbit with a\nmassive star. The intense outflows from the star can absorb radio emission from\nthe pulsar, making the detection of pulsation difficult. In this work, we\npresent the basic geometry and formulae that describe the absorption process of\na pulsar in binary with an O/B star and apply our model to two typical and\nwell-studied binaries: PSR~B1259-63/LS~2883 and LS 5039. We investigate the\ninfluences of the equatorial disc of LS 2883 with different orientations on the\ndispersion measure and free-free absorption of the radio pulsation from PSR\nB1259-63. The observed data are consistent with the disc inserted on the\norbital plane with a relatively large inclination angle. For LS 5039, due to\nits tight orbit, it was believed that the strong wind absorption makes\ndetecting radio emissions from the putative pulsar unlikely. However,\nconsidering the wind interaction and orbital motion, a bow shock cavity and a\nCoriolis shock would be formed, thereby allowing the pulsations to partially\navoid stellar outflow absorption. We investigate the dependence of the radio\noptical depth on the observing frequencies, the orbital inclination angle, and\nthe wind parameters. We suppose that the presumptive pulsar in LS 5039 is\nsimilar to PSR B1259-63 with pulsed emission extending to several tens of\ngigahertz. In that case, there could be a transparent window for radio\npulsations when the pulsar is moving around the inferior conjunction. The\nfollowing deep monitoring of LS 5039 and other systems by radio telescopes at\nhigh radio frequencies might reveal the nature of compact objects in the\nfuture. Alternatively, even a null detection could still provide further\nconstraints on the properties of the putative pulsar and stellar outflows.\n"}
+{"abstract": "  With the rise in Internet of Things (IoT) devices, home network management\nand security are becoming complex. There is an urgent requirement to make smart\nhome network management efficient. This work proposes an SDN-based architecture\nto secure smart home networks through K-Nearest Neighbor (KNN) based device\nclassifications and malicious traffic detection. The efficiency is further\nenhanced by offloading the computation-intensive KNN model to Field\nProgrammable Gate Arrays (FPGA), which offers parallel processing power of GPU\nplatforms at lower costs and higher efficiencies, and can be used to accelerate\ntime-sensitive tasks. The proposed parallelization and implementation of KNN on\nFPGA are achieved by using the Vivado Design Suite from Xilinx and High-Level\nSynthesis (HLS). When optimized with 10-fold cross-validation, the proposed\nsolution for KNN consistently exhibits the best performances on FPGA when\ncompared with four alternative KNN instances (i.e., 78% faster than the\nparallel bubble sort-based implementation and 99\\% faster than the other three\nsorting algorithms). Moreover, with 36,225 training samples, the proposed KNN\nsolution classifies a test query with 95% accuracy in approximately 4\nmilliseconds on FPGA compared to 57 seconds on a CPU platform.\n"}
+{"abstract": "  Several Type Ia supernova analyses make use of non-simultaneous regressions\nbetween observed supernova and host galaxy properties and supernova luminosity:\nfirst the supernova magnitudes are corrected for their light curve shape and\ncolor, and then they are separately corrected for their host galaxy masses.\nThis two-step regression methodology does not introduce any biases when there\nare no correlations between the variables regressed in each correction step.\nHowever, correlations between these covariates will bias estimates of the size\nof the corrections, as well as estimates of the variance of the final\nresiduals. In this work, we analyze the general case of non-simultaneous\nregression with correlated covariates to derive the functional forms of these\nbiases. We also simulate this effect on data from the literature to provide\ncorrections to remove these biases from the data sets studied. The biases\nexamined here can be entirely avoided by using simultaneous regression\ntechniques.\n"}
+{"abstract": "  Hashtag annotation for microblog posts has been recently formulated as a\nsequence generation problem to handle emerging hashtags that are unseen in the\ntraining set. The state-of-the-art method leverages conversations initiated by\nposts to enrich contextual information for the short posts. However, it is\nunrealistic to assume the existence of conversations before the hashtag\nannotation itself. Therefore, we propose to leverage news articles published\nbefore the microblog post to generate hashtags following a Retriever-Generator\nframework. Extensive experiments on English Twitter datasets demonstrate\nsuperior performance and significant advantages of leveraging news articles to\ngenerate hashtags.\n"}
+{"abstract": "  End-to-end automatic speech recognition (ASR) can achieve promising\nperformance with large-scale training data. However, it is known that domain\nmismatch between training and testing data often leads to a degradation of\nrecognition accuracy. In this work, we focus on the unsupervised domain\nadaptation for ASR and propose CMatch, a Character-level distribution matching\nmethod to perform fine-grained adaptation between each character in two\ndomains. First, to obtain labels for the features belonging to each character,\nwe achieve frame-level label assignment using the Connectionist Temporal\nClassification (CTC) pseudo labels. Then, we match the character-level\ndistributions using Maximum Mean Discrepancy. We train our algorithm using the\nself-training technique. Experiments on the Libri-Adapt dataset show that our\nproposed approach achieves 14.39% and 16.50% relative Word Error Rate (WER)\nreduction on both cross-device and cross-environment ASR. We also\ncomprehensively analyze the different strategies for frame-level label\nassignment and Transformer adaptations.\n"}
+{"abstract": "  We present a complete set of transverse momentum dependent and collinear\nparton densities including the photon over a wide phase space. The photon\ndensity appears when evolving parton distributions with QED corrections. The\nphotons are produced by perturbative radiation using the Parton Branching\nmethod. The QCD partons are constrained by a fit to HERA data. The lepton pair\nproduction at high masses is described within the extracted TMD densities.\n"}
+{"abstract": "  Next-generation, high-brilliance x-ray photon sources call for new x-ray\noptics. Here we demonstrate the feasibility of using monolithic diamond\nchannel-cut crystals as high-heat-load, beam-multiplexing, narrow-band,\nmechanically-stable x-ray monochromators with high-power x-ray beams at\ncutting-edge, high-repetition-rate x-ray free-electron laser (XFEL) facilities.\nThe diamond channel-cut crystals fabricated and characterized in these studies\nare designed as two-bounce Bragg reflection monochromators directing 14.4-keV\nor 12.4-keV x-rays within a 15-meV-bandwidth to $^{57}$Fe or $^{45}$Sc nuclear\nresonant scattering experiments, respectively. The crystal design allows\nout-of-band x-rays within a $\\simeq 1$-eV XFEL bandwidth to be transmitted with\nminimal losses to alternative simultaneous experiments. Only $\\lesssim 2$\\% of\nthe incident $\\simeq 100$-W x-ray beam is absorbed in a 50-$\\mu$m-thick first\ndiamond crystal reflector, ensuring that the monochromator crystal is highly\nstable. Other x-ray optics applications of diamond channel-cut crystals are\nanticipated.\n"}
+{"abstract": "  Maintaining computational load balance is important to the performant\nbehavior of codes which operate under a distributed computing model. This is\nespecially true for GPU architectures, which can suffer from memory\noversubscription if improperly load balanced. We present enhancements to\ntraditional load balancing approaches and explicitly target GPU architectures,\nexploring the resulting performance. A key component of our enhancements is the\nintroduction of several GPU-amenable strategies for assessing compute work.\nThese strategies are implemented and benchmarked to find the most optimal data\ncollection methodology for in-situ assessment of GPU compute work. For the\nfully kinetic particle-in-cell code WarpX, which supports MPI+CUDA parallelism,\nwe investigate the performance of the improved dynamic load balancing via a\nstrong scaling-based performance model and show that, for a laser-ion\nacceleration test problem run with up to 6144 GPUs on Summit, the enhanced\ndynamic load balancing achieves from 62%--74% (88% when running on 6 GPUs) of\nthe theoretically predicted maximum speedup; for the 96-GPU case, we find that\ndynamic load balancing improves performance relative to baselines without load\nbalancing (3.8x speedup) and with static load balancing (1.2x speedup). Our\nresults provide important insights into dynamic load balancing and performance\nassessment, and are particularly relevant in the context of distributed memory\napplications ran on GPUs.\n"}
+{"abstract": "  In this paper, we propose a framework for environmental sound synthesis from\nonomatopoeic words. As one way of expressing an environmental sound, we can use\nan onomatopoeic word, which is a character sequence for phonetically imitating\na sound. An onomatopoeic word is effective for describing diverse sound\nfeatures. Therefore, using onomatopoeic words for environmental sound synthesis\nwill enable us to generate diverse environmental sounds. To generate diverse\nsounds, we propose a method based on a sequence-to-sequence framework for\nsynthesizing environmental sounds from onomatopoeic words. We also propose a\nmethod of environmental sound synthesis using onomatopoeic words and sound\nevent labels. The use of sound event labels in addition to onomatopoeic words\nenables us to capture each sound event's feature depending on the input sound\nevent label. Our subjective experiments show that our proposed methods achieve\nhigher diversity and naturalness than conventional methods using sound event\nlabels.\n"}
+{"abstract": "  As ancient, gravitationally bound stellar populations, globular clusters are\nabundant, vibrant laboratories characterized by high frequencies of dynamical\ninteractions coupled to complex stellar evolution. Using surface brightness and\nvelocity dispersion profiles from the literature, we fit $59$ Milky Way\nglobular clusters to dynamical models from the \\texttt{CMC Cluster Catalog}.\nWithout doing any interpolation, and without any directed effort to fit any\nparticular cluster, $26$ globular clusters are well-matched by at least one of\nour models. We discuss in particular the core-collapsed clusters NGC 6293, NGC\n6397, NGC 6681, and NGC 6624, and the non-core-collapsed clusters NGC 288, NGC\n4372, and NGC 5897. As NGC 6624 lacks well-fitting snapshots on the main\n\\texttt{CMC Cluster Catalog}, we run six additional models in order to refine\nthe fit. We calculate metrics for mass segregation, explore the production of\ncompact object sources such as millisecond pulsars, cataclysmic variables,\nlow-mass X-ray binaries, and stellar-mass black holes, finding reasonable\nagreement with observations. Additionally, closely mimicking observational\ncuts, we extract the binary fraction from our models, finding good agreement\nexcept in the dense core regions of core-collapsed clusters. Accompanying this\npaper are a number of \\textsf{python} methods for examining the publicly\naccessible \\texttt{CMC Cluster Catalog}, as well as any other models generated\nusing \\texttt{CMC}.\n"}
+{"abstract": "  The structure of $^{33}$Mg was investigated by means of two knockout\nreactions, one-neutron removal from $^{34}$Mg and one-proton removal from\n$^{34}$Al. Using comparative analysis of the population of observed excited\nstates in the residual $^{33}$Mg, the nature of these states can be deciphered.\nIn addition, the long-standing controversy about the parity of the $^{33}$Mg\nground state is resolved using momentum distribution analysis, showing a clear\nsignature for negative parity. Partial cross section measurements are compared\nwith the results of eikonal reaction theory combined with large-scale shell\nmodel calculations of this complex nucleus located in the island of inversion,\nwhere configuration mixing plays a major role.\n"}
+{"abstract": "  In the present paper we prove that every local and $2$-local derivation on\nconservative algebras of $2$-dimensional algebras are derivations. Also, we\nprove that every local and $2$-local automorphism on conservative algebras of\n$2$-dimensional algebras are automorphisms.\n"}
+{"abstract": "  Consider a field $k$ of characteristic $0$, not necessarily algebraically\nclosed, and a fixed algebraic curve $f=0$ defined by a tame polynomial $f\\in\nk[x,y]$ with only quasi-homogeneous singularities. We prove that the space of\nholomorphic foliations in the plane ${mathbb A}^2_\\k$ having $f=0$ as a fixed\ninvariant curve is generated as $k[x,y]$-module by at most four elements, three\nof them are the trivial foliations $fdx,fdy$ and $df$. Our proof is algorithmic\nand constructs the fourth foliation explicitly. Using Serre's GAGA and\nQuillen-Suslin theorem, we show that for a suitable field extension $K$ of $k$\nsuch a module over $K[x,y]$ is actually generated by two elements, and\ntherefore, such curves are free divisors in the sense of K. Saito. After\nperforming Groebner basis for this module, we observe that in many well-known\nexamples, $K=k$.\n"}
+{"abstract": "  We present an experimental and theoretical study of the absorption and\nemission spectra of Yb atoms in a solid Ne matrix at a resolution of 0.025 nm.\nFive absorption bands were identified as due to transitions from the\n$4f^{14}5d^06s^2\\ ^1\\!S_0$ ground state configuration to $4f^{14}5d^06s6p$ and\n$4f^{13}5d^16s^2$ configurations. The two lowest energy bands were assigned to\nouter-shell transitions to $6s6p\\ ^3P_1$ and $^1P_1$ atomic states and\ndisplayed the structure of a broad doublet and an asymmetric triplet,\nrespectively. The remaining three higher-frequency bands were assigned to\ninner-shell transitions to distinct $J=1$ states arising from the\n$4f^{13}5d^16s^2$ configuration and were highly structured with narrow\nlinewidths. A classical simulation was performed to identify the stability and\nsymmetry of possible trapping sites in the Ne crystal. It showed that the\noverarching 1+2 structure of the high frequency bands could be predominantly\nascribed to crystal field splitting in the axial field of a 10-atom vacancy of\n$C_{4v}$ symmetry. Their prominent substructures were shown to be\nmanifestations of phonon sidebands associated with the zero-phonon lines on\neach crystal field state. Unprecedented for a metal-rare gas system, resolution\nof individual phonon states on an allowed electronic transition was possible\nunder excitation spectroscopy which reflects the semi-quantum nature of solid\nNe. In contrast to the absorption spectra, emission spectra produced by\nsteady-state excitation into the $^1P_1$ absorption band consisted of simple,\nunstructured fluorescence bands.\n"}
+{"abstract": "  A study of the gravitationally lensed blazar PKS 1830-211 was carried out\nusing multi waveband data collected by Fermi-LAT, Swift-XRT and Swift-UVOT\ntelescopes between MJD 58400 to MJD 58800 (9 Oct 2018 to 13 Nov 2019). Flaring\nstates were identified by analysing the gamma-ray light curve. Simultaneous\nmulti-waveband SED were obtained for those flaring periods. A cross-correlation\nanalysis of the multi-waveband data was carried out, which suggested a common\norigin of the gamma-ray and X-ray emission. The broadband emission mechanism\nwas studied by modelling the SED using a leptonic model. Physical parameters of\nthe blazar were estimated from the broadband SED modelling. The blazar PKS\n1830-211 is gravitationally lensed by at least two galaxies and has been\nextensively studied in the literature because of this property. The\nself-correlation of the gamma-ray light curve was studied to identify the\nsignature of lensing, but no conclusive evidence of correlation was found at\nthe expected time delay of 26 days.\n"}
+{"abstract": "  Reinforcement learning in large-scale environments is challenging due to the\nmany possible actions that can be taken in specific situations. We have\npreviously developed a means of constraining, and hence speeding up, the search\nprocess through the use of motion primitives; motion primitives are sequences\nof pre-specified actions taken across a state series. As a byproduct of this\nwork, we have found that if the motion primitives' motions and actions are\nlabeled, then the search can be sped up further. Since motion primitives may\ninitially lack such details, we propose a theoretically viewpoint-insensitive\nand speed-insensitive means of automatically annotating the underlying motions\nand actions. We do this through a differential-geometric, spatio-temporal\nkinematics descriptor, which analyzes how the poses of entities in two motion\nsequences change over time. We use this descriptor in conjunction with a\nweighted-nearest-neighbor classifier to label the primitives using a limited\nset of training examples.\n  In our experiments, we achieve high motion and action annotation rates for\nhuman-action-derived primitives with as few as one training sample. We also\ndemonstrate that reinforcement learning using accurately labeled trajectories\nleads to high-performing policies more quickly than standard reinforcement\nlearning techniques. This is partly because motion primitives encode prior\ndomain knowledge and preempt the need to re-discover that knowledge during\ntraining. It is also because agents can leverage the labels to systematically\nignore action classes that do not facilitate task objectives, thereby reducing\nthe action space.\n"}
+{"abstract": "  Analyzing temporal data (e.g., wearable device data) requires a decision\nabout how to combine information from the recent and distant past. In the\ncontext of classifying sleep status from actigraphy, Webster's rescoring rules\noffer one popular solution based on the long-term patterns in the output of a\nmoving-window model. Unfortunately, the question of how to optimize rescoring\nrules for any given setting has remained unsolved. To address this problem and\nexpand the possible use cases of rescoring rules, we propose rephrasing these\nrules in terms of epoch-specific features. Our features take two general forms:\n(1) the time lag between now and the most recent [or closest upcoming] bout of\ntime spent in a given state, and (2) the length of the most recent [or closest\nupcoming] bout of time spent in a given state. Given any initial moving window\nmodel, these features can be defined recursively, allowing for straightforward\noptimization of rescoring rules. Joint optimization of the moving window model\nand the subsequent rescoring rules can also be implemented using gradient-based\noptimization software, such as Tensorflow. Beyond binary classification\nproblems (e.g., sleep-wake), the same approach can be applied to summarize\nlong-term patterns for multi-state classification problems (e.g., sitting,\nwalking, or stair climbing). We find that optimized rescoring rules improve the\nperformance of sleep-wake classifiers, achieving accuracy comparable to that of\ncertain neural network architectures.\n"}
+{"abstract": "  This paper proposes to study the impact of image selective encryption on both\nforensics and privacy preserving mechanisms. The proposed selective encryption\nscheme works independently on each bitplane by encrypting the s most\nsignificant bits of each pixel. We show that this mechanism can be used to\nincrease privacy by mitigating image recognition tasks. In order to guarantee a\ntrade-off between forensics analysis and privacy, the signal of interest used\nfor forensics purposes is extracted from the 8--s least significant bits of the\nprotected image. We show on the CASIA2 database that good tampering detection\ncapabilities can be achieved for s $\\in$ {3,. .. , 5} with an accuracy above\n80% using SRMQ1 features, while preventing class recognition tasks using CNN\nwith an accuracy smaller than 50%.\n"}
+{"abstract": "  Through in-depth analysis of ultra high frequency (UHF) stock price change\ndata, more reasonable discrete dynamic distribution models are constructed in\nthis paper. Firstly, we classify the price changes into several categories.\nThen, temporal convolutional network (TCN) is utilized to predict the\nconditional probability for each category. Furthermore, attention mechanism is\nadded into the TCN architecture to model the time-varying distribution for\nstock price change data. Empirical research on constituent stocks of Chinese\nShenzhen Stock Exchange 100 Index (SZSE 100) found that the TCN framework model\nand the TCN (attention) framework have a better overall performance than GARCH\nfamily models and the long short-term memory (LSTM) framework model for the\ndescription of the dynamic process of the UHF stock price change sequence. In\naddition, the scale of the dataset reached nearly 10 million, to the best of\nour knowledge, there has been no previous attempt to apply TCN to such a\nlarge-scale UHF transaction price dataset in Chinese stock market.\n"}
+{"abstract": "  The $^9$C nucleus and related capture reaction,\n${^8\\mathrm{B}}(p,\\gamma){^9\\mathrm{C}}$, have been intensively studied with an\nastrophysical interest. Due to the weakly-bound nature of $^9$C, its structure\nis likely to be described as the three-body (${^7\\mathrm{Be}}+p+p$). Its\ncontinuum structure is also important to describe reaction processes of $^9$C,\nwith which the reaction rate of the ${^8\\mathrm{B}}(p,\\gamma){^9\\mathrm{C}}$\nprocess have been extracted indirectly. We perform three-body calculations on\n$^9$C and discuss properties of its ground and low-lying states via breakup\nreactions. We employ the three-body model of $^9$C using the Gaussian-expansion\nmethod combined with the complex-scaling method. This model is implemented in\nthe four-body version of the continuum-discretized coupled-channels method, by\nwhich breakup reactions of $^9$C are studied. The intrinsic spin of $^7$Be is\ndisregarded. By tuning a three-body interaction in the Hamiltonian of $^9$C, we\nobtain the low-lying $2^+$ state with the resonant energy 0.781 MeV and the\ndecay width 0.137 MeV, which is consistent with the available experimental\ninformation and a relatively high-lying second $2^+$ wider resonant state. Our\ncalculation predicts also sole $0^+$ and three $1^-$ resonant states. We\ndiscuss the role of these resonances in the elastic breakup cross section of\n$^9$C on $^{208}$Pb at 65 and 160 MeV/A. The low-lying 2$^+$ state is probed as\na sharp peak of the breakup cross section, while the 1$^-$ states enhance the\ncross section around 3 MeV. Our calculations will further support the future\nand ongoing experimental campaigns for extracting astrophysical information and\nevaluating the two-proton removal cross-sections.\n"}
+{"abstract": "  We studied the weak-lined T Tauri star Hubble 4, a known long-period binary,\nand its starspot phenomena. We used optical radial velocity (RV) data taken\nover a span of 14 years (2004-2010, 2017-2019) at the McDonald Observatory 2.7m\nHarlan J. Smith telescope and single epoch imaging from the HST/WFC3\ninstrument. The observed and apparent RV variations show contributions,\nrespectively, from the binary motion as well as from a large spot group on one\nof the stars, presumed to be the primary. Fitting and removing the orbital\nsignal from the RVs, we found the lower bound on the lifetime of a previously\nidentified large spot group on the surface of the star to be at least 5.1\nyears. A $\\sim5$ year lower limit is a long, but not unprecedented, duration\nfor a single spot group. The later epoch data indicate significant spot\nevolution has occurred, placing an upper bound on the spot group lifetime at 12\nyears. We find that pre-main sequence evolutionary models for the age of Taurus\n($\\sim2$ Myr), combined with component mass estimates from the literature,\npermit us to reproduce the HST relative photometry and the binary-induced\ncontribution to the apparent RV variations. The long-lived star spot we find on\nHubble 4 has significant implications for dynamo models in young stars, as it\nadds evidence for long lifetimes of magnetic field topologies. There are also\nsignificant implications for young star exoplanet searches as long-lived\ncoherent RV signals may be spot-induced and not the result of planetary motion.\n(This paper includes data taken at The McDonald Observatory of The University\nof Texas at Austin.)\n"}
+{"abstract": "  It is well-known that stochastic gradient noise (SGN) acts as implicit\nregularization for deep learning and is essentially important for both\noptimization and generalization of deep networks. Some works attempted to\nartificially simulate SGN by injecting random noise to improve deep learning.\nHowever, it turned out that the injected simple random noise cannot work as\nwell as SGN, which is anisotropic and parameter-dependent. For simulating SGN\nat low computational costs and without changing the learning rate or batch\nsize, we propose the Positive-Negative Momentum (PNM) approach that is a\npowerful alternative to conventional Momentum in classic optimizers. The\nintroduced PNM method maintains two approximate independent momentum terms.\nThen, we can control the magnitude of SGN explicitly by adjusting the momentum\ndifference. We theoretically prove the convergence guarantee and the\ngeneralization advantage of PNM over Stochastic Gradient Descent (SGD). By\nincorporating PNM into the two conventional optimizers, SGD with Momentum and\nAdam, our extensive experiments empirically verified the significant advantage\nof the PNM-based variants over the corresponding conventional Momentum-based\noptimizers.\n"}
+{"abstract": "  In this paper we show that ensemble Kalman inversion for linear inverse\nproblems can equivalently be formulated as a stochastic low-rank approximation\nof Tikhonov regularization. This point of view allows us to introduce an\nalternative sampling scheme based on the Nystr\\\"om method that improves\npractical performance. Furthermore, we formulate an adaptive version of\nensemble Kalman inversion where the sample size is coupled with the\nregularization parameter. We prove under standard assumptions that the proposed\nscheme yields an order optimal regularization method if the discrepancy\nprinciple is used as a stopping criterion.\n"}
+{"abstract": "  With the formation of next generation wireless communication, a growing\nnumber of new applications like internet of things, autonomous car, and drone\nis crowding the unlicensed spectrum. Licensed network such as the long-term\nevolution (LTE) also comes to the unlicensed spectrum for better providing\nhigh-capacity contents with low cost. However, LTE was not designed for sharing\nspectrum with others. A cooperation center for these networks is costly because\nthey possess heterogeneous properties and everyone can enter and leave the\nspectrum unrestrictedly, so the design will be challenging. Since it is\ninfeasible to incorporate potentially infinite scenarios with one unified\ndesign, an alternative solution is to let each network learn its own\ncoexistence policy. Previous solutions only work on fixed scenarios. In this\nwork a reinforcement learning algorithm is presented to cope with the\ncoexistence between Wi-Fi and LTE agents in 5 GHz unlicensed spectrum. The\ncoexistence problem was modeled as a decentralized partially observable Markov\ndecision process (Dec-POMDP) and Bayesian approach was adopted for policy\nlearning with nonparametric prior to accommodate the uncertainty of policy for\ndifferent agents. A fairness measure was introduced in the reward function to\nencourage fair sharing between agents. The reinforcement learning was turned\ninto an optimization problem by transforming the value function as likelihood\nand variational inference for posterior approximation. Simulation results\ndemonstrate that this algorithm can reach high value with compact policy\nrepresentations, and stay computationally efficient when applying to agent set.\n"}
+{"abstract": "  Policy Space Response Oracles (PSRO) is a deep reinforcement learning\nalgorithm for two-player zero-sum games that has empirically found approximate\nNash equilibria in large games. Although PSRO is guaranteed to converge to a\nNash equilibrium, it may take an exponential number of iterations as the number\nof infostates grows. We propose Extensive-Form Double Oracle (XDO), an\nextensive-form double oracle algorithm that is guaranteed to converge to an\napproximate Nash equilibrium linearly in the number of infostates. Unlike PSRO,\nwhich mixes best responses at the root of the game, XDO mixes best responses at\nevery infostate. We also introduce Neural XDO (NXDO), where the best response\nis learned through deep RL. In tabular experiments on Leduc poker, we find that\nXDO achieves an approximate Nash equilibrium in a number of iterations 1-2\norders of magnitude smaller than PSRO. In experiments on a modified Leduc poker\ngame, we show that tabular XDO achieves over 11x lower exploitability than CFR\nand over 82x lower exploitability than PSRO and XFP in the same amount of time.\nWe also show that NXDO beats PSRO and is competitive with NFSP on a large\nno-limit poker game.\n"}
+{"abstract": "  The pressure strain correlation plays a critical role in the Reynolds stress\ntransport modelling. Accurate modelling of the pressure strain correlation\nleads to proper prediction of turbulence stresses and subsequently the other\nterms of engineering interest. However, classical pressure strain correlation\nmodels are often unreliable for complex turbulent flows. Machine learning based\nmodels have shown promise in turbulence modeling but their application has been\nlargely restricted to eddy viscosity based models. In this article, we outline\na rationale for the preferential application of machine learning and turbulence\ndata to develop models at the level of Reynolds stress modeling. As the\nillustration We develop data driven models for the pressure strain correlation\nfor turbulent channel flow using neural networks. The input features of the\nneural networks are chosen using physics based rationale. The networks are\ntrained with the high resolution DNS data of turbulent channel flow at\ndifferent friction Reynolds numbers. The testing of the models are performed\nfor unknown flow statistics at other friction Reynolds numbers and also for\nturbulent plane Couette flows. Based on the results presented in this article,\nthe proposed machine learning framework exhibits considerable promise and may\nbe utilized for the development of accurate Reynolds stress models for flow\nprediction.\n"}
+{"abstract": "  We introduce an Eulerian Perturbation Theory to study the clustering of\ntracers for cosmologies in the presence of massive neutrinos. Our approach is\nbased on mapping recently-obtained Lagrangian Perturbation Theory results to\nthe Eulerian framework. We add Effective Field Theory counterterms,\nIR-resummations and a biasing scheme to compute the one-loop redshift-space\npower spectrum. To assess our predictions, we compare the power spectrum\nmultipoles against synthetic halo catalogues from the Quijote simulations,\nfinding excellent agreement on scales $k\\lesssim 0.25 \\,h \\text{Mpc}^{-1}$. One\ncan obtain the same fitting accuracy using higher wave-numbers, but then the\ntheory fails to give a correct estimation of the linear bias parameter. We\nfurther discuss the implications for the tree-level bispectrum. Finally,\ncalculating loop corrections is computationally costly, hence we derive an\naccurate approximation wherein we retain only the main features of the kernels,\nas produced by changes to the growth rate. As a result, we show how FFTLog\nmethods can be used to further accelerate the loop computations with these\nreduced kernels.\n"}
+{"abstract": "  Combining the closed-world reasoning of answer set programming (ASP) with the\nopen-world reasoning of ontologies broadens the space of applications of\nreasoners. Disjunctive hybrid MKNF knowledge bases succinctly extend ASP and in\nsome cases without increasing the complexity of reasoning tasks. However, in\nmany cases, solver development is lagging behind. As the result, the only known\nmethod of solving disjunctive hybrid MKNF knowledge bases is based on\nguess-and-verify, as formulated by Motik and Rosati in their original work. A\nmain obstacle is understanding how constraint propagation may be performed by a\nsolver, which, in the context of ASP, centers around the computation of\n\\textit{unfounded atoms}, the atoms that are false given a partial\ninterpretation. In this work, we build towards improving solvers for hybrid\nMKNF knowledge bases with disjunctive rules: We formalize a notion of unfounded\nsets for these knowledge bases, identify lower complexity bounds, and\ndemonstrate how we might integrate these developments into a solver. We discuss\nchallenges introduced by ontologies that are not present in the development of\nsolvers for disjunctive logic programs, which warrant some deviations from\ntraditional definitions of unfounded sets. We compare our work with prior\ndefinitions of unfounded sets.\n"}
+{"abstract": "  It is a classical result that the category of finitely-generated free monoids\nserves as a PROP for commutative bialgebras. Attaching permutations to fix the\norder of multiplication, we construct an extension of this category that is\nequivalent to the PROP for bialgebras.\n"}
+{"abstract": "  We study the $2k$-th moment of central values of the family of Dirichlet\n$L$-functions to a fixed prime modulus. We establish sharp lower bounds for all\nreal $k \\geq 0$ and sharp upper bounds for $k$ in the range $0 \\leq k \\leq 1$.\n"}
+{"abstract": "  We make use of the Parker Solar Probe (PSP) data to explore the nature of\nsolar wind turbulence focusing on the Alfv\\'enic character and power spectra of\nthe fluctuations and their dependence on distance and context (i.e. large scale\nsolar wind properties), aiming to understand the role that different effects\nsuch as source properties, solar wind expansion, stream interaction might play\nin determining the turbulent state. We carry out a statistical survey of the\ndata from the first five orbits of PSP with a focus on how the fluctuation\nproperties at the large, MHD scales, vary with different solar wind streams and\ndistance from the Sun. A more in-depth analysis from several selected periods\nis also presented. Our results show that as fluctuations are transported\noutward by the solar wind, the magnetic field spectrum steepens while the shape\nof the velocity spectrum remains unchanged. The steepening process is\ncontrolled by the \"age\" of the turbulence, determined by the wind speed\ntogether with the radial distance. Statistically, faster solar wind has higher\n\"Alfv\\'enicity\", with more dominant outward propagating wave component and more\nbalanced magnetic/kinetic energies. The outward wave dominance gradually\nweakens with radial distance, while the excess of magnetic energy is found to\nbe stronger as we move closer toward the Sun. We show that the turbulence\nproperties can vary significantly stream to stream even if these streams are of\nsimilar speed, indicating very different origins of these streams. Especially,\nthe slow wind that originates near the polar coronal holes has much lower\nAlfv\\'enicity compared with the slow wind that originates from the active\nregions/pseudostreamers. We show that structures such as heliospheric current\nsheets and velocity shears can play an important role in modifying the\nproperties of the turbulence.\n"}
+{"abstract": "  We describe an algorithm to compute the extremal eigenvalues and\ncorresponding eigenvectors of a symmetric matrix by solving a sequence of\nQuadratic Binary Optimization problems. This algorithm is robust across many\ndifferent classes of symmetric matrices, can compute the eigenvector/eigenvalue\npair to essentially arbitrary precision, and with minor modifications can also\nsolve the generalized eigenvalue problem. Performance is analyzed on small\nrandom matrices and selected larger matrices from practical applications.\n"}
+{"abstract": "  In this work, we report on the integrated sensorimotor control of the\nPisa/IIT SoftHand, an anthropomorphic soft robot hand designed around the\nprinciple of adaptive synergies, with the BRL tactile fingertip (TacTip), a\nsoft biomimetic optical tactile sensor based on the human sense of touch. Our\nfocus is how a sense of touch can be used to control an anthropomorphic hand\nwith one degree of actuation, based on an integration that respects the hand's\nmechanical functionality. We consider: (i) closed-loop tactile control to\nestablish a light contact on an unknown held object, based on the structural\nsimilarity with an undeformed tactile image; and (ii) controlling the estimated\npose of an edge feature of a held object, using a convolutional neural network\napproach developed for controlling other sensors in the TacTip family. Overall,\nthis gives a foundation to endow soft robotic hands with human-like touch, with\nimplications for autonomous grasping, manipulation, human-robot interaction and\nprosthetics. Supplemental video: https://youtu.be/ndsxj659bkQ\n"}
+{"abstract": "  Recent researches show that deep learning model is susceptible to backdoor\nattacks. Many defenses against backdoor attacks have been proposed. However,\nexisting defense works require high computational overhead or backdoor attack\ninformation such as the trigger size, which is difficult to satisfy in\nrealistic scenarios. In this paper, a novel backdoor detection method based on\nadversarial examples is proposed. The proposed method leverages intentional\nadversarial perturbations to detect whether an image contains a trigger, which\ncan be applied in both the training stage and the inference stage (sanitize the\ntraining set in training stage and detect the backdoor instances in inference\nstage). Specifically, given an untrusted image, the adversarial perturbation is\nadded to the image intentionally. If the prediction of the model on the\nperturbed image is consistent with that on the unperturbed image, the input\nimage will be considered as a backdoor instance. Compared with most existing\ndefense works, the proposed adversarial perturbation based method requires low\ncomputational resources and maintains the visual quality of the images.\nExperimental results show that, the backdoor detection rate of the proposed\ndefense method is 99.63%, 99.76% and 99.91% on Fashion-MNIST, CIFAR-10 and\nGTSRB datasets, respectively. Besides, the proposed method maintains the visual\nquality of the image as the l2 norm of the added perturbation are as low as\n2.8715, 3.0513 and 2.4362 on Fashion-MNIST, CIFAR-10 and GTSRB datasets,\nrespectively. In addition, it is also demonstrated that the proposed method can\nachieve high defense performance against backdoor attacks under different\nattack settings (trigger transparency, trigger size and trigger pattern).\nCompared with the existing defense work (STRIP), the proposed method has better\ndetection performance on all the three datasets, and is more efficient than\nSTRIP.\n"}
+{"abstract": "  Increasing volatilities within power transmission and distribution force\npower grid operators to amplify their use of communication infrastructure to\nmonitor and control their grid. The resulting increase in communication creates\na larger attack surface for malicious actors. Indeed, cyber attacks on power\ngrids have already succeeded in causing temporary, large-scale blackouts in the\nrecent past. In this paper, we analyze the communication infrastructure of\npower grids to derive resulting fundamental challenges of power grids with\nrespect to cybersecurity. Based on these challenges, we identify a broad set of\nresulting attack vectors and attack scenarios that threaten the security of\npower grids. To address these challenges, we propose to rely on a\ndefense-in-depth strategy, which encompasses measures for (i) device and\napplication security, (ii) network security, (iii) physical security, as well\nas (iv) policies, procedures, and awareness. For each of these categories, we\ndistill and discuss a comprehensive set of state-of-the art approaches, and\nidentify further opportunities to strengthen cybersecurity in interconnected\npower grids.\n"}
+{"abstract": "  The Track-1 of DSTC9 aims to effectively answer user requests or questions\nduring task-oriented dialogues, which are out of the scope of APIs/DB. By\nleveraging external knowledge resources, relevant information can be retrieved\nand encoded into the response generation for these out-of-API-coverage queries.\nIn this work, we have explored several advanced techniques to enhance the\nutilization of external knowledge and boost the quality of response generation,\nincluding schema guided knowledge decision, negatives enhanced knowledge\nselection, and knowledge grounded response generation. To evaluate the\nperformance of our proposed method, comprehensive experiments have been carried\nout on the publicly available dataset. Our approach was ranked as the best in\nhuman evaluation of DSTC9 Track-1.\n"}
+{"abstract": "  This paper presents a numerical method for div-curl systems with normal\nboundary conditions by using a finite element technique known as primal-dual\nweak Galerkin (PDWG). The PDWG finite element scheme for the div-curl system\nhas two prominent features in that it offers not only an accurate and reliable\nnumerical solution to the div-curl system under the low $H^\\alpha$-regularity\n($\\alpha>0$) assumption for the true solution, but also an effective\napproximation of normal harmonic vector fields regardless the topology of the\ndomain. Results of seven numerical experiments are presented to demonstrate the\nperformance of the PDWG algorithm, including one example on the computation of\ndiscrete normal harmonic vector fields.\n"}
+{"abstract": "  We introduce various colouring principles which generalise the so-called\n\"onto mapping principle\" of Sierpinski to larger cardinals and general ideals.\nWe prove that these principles capture the notion of an Ulam matrix and allow\nto characterise large cardinals, most notably weakly compact and ineffable\ncardinals. We also develop the basic theory of these colouring principles,\nconnecting them to the classical negative square bracket partition relations,\nproving pumping-up theorems, and deciding various instances of theirs.\n  We also demonstrate that our principles provide a uniform way of obtaining\nnon-saturation results for ideals satisfying a property we call subnormality in\ncontexts where Ulam matrices might not be available.\n"}
+{"abstract": "  The accurate computational determination of chemical, materials, biological,\nand atmospheric properties has critical impact on a wide range of health and\nenvironmental problems, but is deeply limited by the computational scaling of\nquantum-mechanical methods. The complexity of quantum-chemical studies arises\nfrom the steep algebraic scaling of electron correlation methods, and the\nexponential scaling in studying nuclear dynamics and molecular flexibility. To\ndate, efforts to apply quantum hardware to such quantum chemistry problems have\nfocused primarily on electron correlation. Here, we provide a framework which\nallows for the solution of quantum chemical nuclear dynamics by mapping these\nto quantum spin-lattice simulators. Using the example case of a short-strong\nhydrogen bonded system, we construct the Hamiltonian for the nuclear degrees of\nfreedom on a single Born-Oppenheimer surface and show how it can be transformed\nto a generalized Ising model Hamiltonian. We then demonstrate a method to\ndetermine the local fields and spin-spin couplings needed to identically match\nthe molecular and spin-lattice Hamiltonians. We describe a protocol to\ndetermine the on-site and inter-site coupling parameters of this Ising\nHamiltonian from the Born-Oppenheimer potential and nuclear kinetic energy\noperator. Our approach represents a paradigm shift in the methods used to study\nquantum nuclear dynamics, opening the possibility to solve both electronic\nstructure and nuclear dynamics problems using quantum computing systems.\n"}
+{"abstract": "  Eclipsing binaries (EBs) are unique benchmarks for stellar evolution. On the\none hand, detached EBs hosting at least one star with detectable solar-like\noscillations constitute ideal test objects to calibrate asteroseismic\nmeasurements. On the other hand, the oscillations and surface activity of stars\nthat belong to EBs offer unique information about the evolution of binary\nsystems. This paper builds upon previous works dedicated to red giant stars\n(RG) in EBs -- 20 known systems so far -- discovered by the NASA Kepler\nmission. Here we report the discovery of 16 RGs in EBs also from the Kepler\ndata. This new sample includes three SB2-EBs with oscillations and six close\nsystems where the RG display a clear surface activity and complete oscillation\nsuppression. Based on dedicated high-resolution spectroscopic observations\n(Apache Point Observatory, Observatoire de Haute Provence), we focus on three\nmain aspects. From the extended sample of 14 SB2-EBs, we first confirm that the\nsimple application of the asteroseismic scaling relations to RGs overestimates\nmasses and radii of RGs, by about 15% and 5%. This bias can be reduced by\nemploying either new asteroseismic reference values for RGs, or model-based\ncorrections of the asteroseismic parameters. Secondly, we confirm that close\nbinarity leads to a high level of photometric modulation (up to 10%), and a\nsuppression of solar-like oscillations. In particular, we show that it reduces\nthe lifetime of radial modes by a factor of up to 10. Thirdly, we use our 16\nnew systems to complement previous observational studies that aimed at\nconstraining tidal dissipation in interacting binaries. In particular, we\nidentify systems with circular orbits despite relatively young ages, which\nsuggests exploring complementary tidal dissipation mechanisms in the future.\nFinally, we report the measurements of mass, radius, and age of three M-dwarf\ncompanion stars.\n"}
+{"abstract": "  This proceeding broadly overviews the current landscape of heavy exotic\nspectroscopy. Such work includes the composition of certain X, Y , and Z\nstates, and proceeds to discuss tetraquarks made exclusively of four quarks.\n"}
+{"abstract": "  Multimodal affect recognition constitutes an important aspect for enhancing\ninterpersonal relationships in human-computer interaction. However, relevant\ndata is hard to come by and notably costly to annotate, which poses a\nchallenging barrier to build robust multimodal affect recognition systems.\nModels trained on these relatively small datasets tend to overfit and the\nimprovement gained by using complex state-of-the-art models is marginal\ncompared to simple baselines. Meanwhile, there are many different multimodal\naffect recognition datasets, though each may be small. In this paper, we\npropose to leverage these datasets using weakly-supervised multi-task learning\nto improve the generalization performance on each of them. Specifically, we\nexplore three multimodal affect recognition tasks: 1) emotion recognition; 2)\nsentiment analysis; and 3) sarcasm recognition. Our experimental results show\nthat multi-tasking can benefit all these tasks, achieving an improvement up to\n2.9% accuracy and 3.3% F1-score. Furthermore, our method also helps to improve\nthe stability of model performance. In addition, our analysis suggests that\nweak supervision can provide a comparable contribution to strong supervision if\nthe tasks are highly correlated.\n"}
+{"abstract": "  We study the decay of global energy for the wave equation with H\\\"older\ncontinuous damping placed on the $C^{1,1}$-boundary of compact and non-compact\nwaveguides with star-shaped cross-sections. We show there is sharp\n$t^{-1/2}$-decay when the damping is uniformly bounded from below on the\ncylindrical wall of product cylinders where the Geometric Control Condition is\nviolated. On non-product cylinders, we also show that there is $t^{-1/3}$-decay\nwhen the damping is uniformly bounded from below on the cylindrical wall.\n"}
+{"abstract": "  It has been argued that one role of social constructs, such as institutions,\ntrust and norms, is to coordinate the expectations of autonomous entities in\norder to resolve collective action situations (such as collective risk\ndilemmas) through the coordination of behaviour. While much work has addressed\nthe formal representation of these social constructs, in this paper we focus\nspecifically on the formal representation of, and associated reasoning with,\nthe expectations themselves. In particular, we investigate how explicit\nreasoning about expectations can be used to encode both traditional game theory\nsolution concepts and social mechanisms for the social dilemma situation. We\nuse the Collective Action Simulation Platform (CASP) to model a collective risk\ndilemma based on a flood plain scenario and show how using expectations in the\nreasoning mechanisms of the agents making decisions supports the choice of\ncooperative behaviour.\n"}
+{"abstract": "  Fast Radio Bursts (FRBs) are extremely energetic pulses of millisecond\nduration and unknown origin. In order to understand the phenomenon that emits\nthese pulses, targeted and untargeted searches have been performed for\nmulti-wavelength counterparts, including the optical. The objective of this\nwork is to search for optical transients at the position of 8 well-localized\nFRBs, after the arrival of the burst on different time-scales (typically at one\nday, several months, and one year after FRB detection) in order to compare with\nknown transient optical light curves. We used the Las Cumbres Observatory\nGlobal Telescope Network (LCOGT), which allows us to promptly take images owing\nto its network of twenty-three telescopes working around the world. We used a\ntemplate subtraction technique on all the images we collected at different\nepochs. We have divided the subtractions into two groups, in one group we use\nthe image of the last epoch as a template and in the other group we use the\nimage of the first epoch as a template. We have searched for bright optical\ntransients at the localizations of the FRBs (<1 arcsec) in the template\nsubtracted images. We have found no optical transients, so we have set limiting\nmagnitudes of optical counterparts. Typical limiting magnitudes in apparent\n(absolute) magnitudes for our LCOGT data are ~22 (-19) mag in the r-band. We\nhave compared our limiting magnitudes with light curves of superluminous\nsupernovae (SLSNe), type Ia supernovae (SNe), supernovae associated with\ngamma-ray bursts (GRB SNe), a kilonova, and tidal disruption events (TDEs). We\nrule out that FRBs are associated with SLSN at a confidence of ~99.9%. We can\nalso rule out the brightest sub-types of type Ia SNe, GRB SNe and TDEs (under\nsome conditions) at similar confidence, though we cannot exclude scenarios\nwhere FRBs are associated with the faintest sub-type of each of these transient\nclasses.\n"}
+{"abstract": "  To investigate the interaction between the nucleon $N$ and nucleon resonance\n$N(1535)1/2^-$, the $\\eta d$ threshold structure connected to the isoscalar\n$S$-wave $N$-$N(1535)1/2^-$ system has been experimentally studied in the\n$\\gamma{d}${$\\to$}$\\pi^0\\eta{d}$ reaction at incident photon energies ranging\nfrom the reaction threshold to 1.15 GeV. A strong enhancement is observed near\nthe $\\eta d$ threshold over the three-body phase-space contribution in the\n$\\eta d$ invariant-mass distribution. An analysis incorporating the known\nisovector resonance $\\mathcal{D}_{12}$ with a spin-parity of $2^+$ in the\n$\\pi^0d$ channel reveals the existence of a narrow isoscalar resonance-like\nstructure with $1^-$ in the $\\eta d$ system. Using a Flatt\\'e parametrization,\nthe mass is found to be $2.427_{-0.006}^{+0.013}$ GeV, close to the $\\eta d$\nthreshold, and the width is $\\left(0.029_{-0.029}^{+0.006}{\\rm\\\nGeV}\\right)+\\left(0.00_{-0.00}^{+0.41}\\right) p_\\eta c$, where $p_\\eta$ denotes\nthe $\\eta$ momentum in the rest frame of the $\\eta d$ system. The observed\nstructure would be attributed to a predicted isoscalar $1^-$ $\\eta NN$ bound\nstate from $\\eta NN$ and $\\pi NN$ coupled-channel calculation, or an $\\eta d$\nvirtual state owing to strong $\\eta d$ attraction.\n"}
+{"abstract": "  Quantum Monte Carlo and quantum simulation are both important tools for\nunderstanding quantum many-body systems. As a classical algorithm, quantum\nMonte Carlo suffers from the sign problem, preventing its application to most\nfermion systems and real time dynamics. In this paper, we introduce a novel\nnon-variational algorithm using quantum simulation as a subroutine to\naccelerate quantum Monte Carlo by easing the sign problem. The quantum\nsubroutine can be implemented with shallow circuits and, by incorporating error\nmitigation, can reduce the Monte Carlo variance by several orders of magnitude\neven when the circuit noise is significant. As such, the proposed quantum\nalgorithm is applicable to near-term noisy quantum hardware.\n"}
+{"abstract": "  Some interactions between classical or quantum fields and matter are known to\nbe irreversible processes. Here we associate an entropy to the electromagnetic\nfield from well-known notions of statistical quantum mechanics, in particular\nthe notion of diagonal entropy. We base our work on the study of spontaneous\nemission and light diffusion. We obtain a quantity which allows to quantify\nirreversibility for a quantum and classical description of the electromagnetic\nfield, that we can study and interpret from a thermodynamical point of view.\n"}
+{"abstract": "  In this paper, we introduce the notion of a topological groupoid extension\nand relate it to the already existing notion of a gerbe over a topological\nstack. We further study the properties of a gerbe over a Serre, Hurewicz stack.\n"}
+{"abstract": "  This paper provides a new approach for offensive language and hate speech\ndetection on social media. Our approach incorporates an offensive lexicon\ncomposed of implicit and explicit offensive and swearing expressions annotated\nwith binary classes: context-dependent and context-independent offensive. Due\nto the severity of the hate speech and offensive comments in Brazil, and the\nlack of research in Portuguese, Brazilian Portuguese is the language used to\nvalidate the proposed method. Nevertheless, our proposal may be applied to any\nother language or domain. Based on the obtained results, the proposed approach\nshowed high-performance overcoming the current baselines for European and\nBrazilian Portuguese.\n"}
+{"abstract": "  In this project, we have used the computer vision algorithm SSD (Single Shot\ndetector) computer vision algorithm and trained this algorithm from the dataset\nwhich consists of 139 Pictures. Images were labeled using Intel CVAT (Computer\nVision Annotation Tool)\n  We trained this model for facial detection. We have deployed our trained\nmodel and software in the Nvidia Jetson Nano Developer kit. Model code is\nwritten in Pytorch's deep learning framework. The programming language used is\nPython.\n"}
+{"abstract": "  A stochastic gravitational wave background is expected to emerge from the\nsuperposition of numerous gravitational wave sources of both astrophysical and\ncosmological origin. A number of cosmological models can have a parity\nviolation, resulting in the generation of circularly polarised gravitational\nwaves. We present a method to search for parity violation in the gravitational\nwave data. We first apply this method to the most recent, third, LIGO-Virgo\nobserving run. We then investigate the constraining power of future A+\nLIGO-Virgo detectors, including KAGRA to the network, for a gravitational wave\nbackground generated by early universe cosmological turbulence.\n"}
+{"abstract": "  In IEEE 802.11bc, the broadcast mode on wireless local area networks (WLANs),\ndata rate control that is based on acknowledgement (ACK) mechanism similar to\nthe one in the current IEEE 802.11 WLANs is not applicable because ACK\nmechanism is not implemented. This paper addresses this challenge by proposing\nACK-less data rate adaptation methods by capturing non-broadcast uplink frames\nof STAs. In IEEE 802.11bc, an use case is assumed, where a part of STAs in the\nbroadcast recipients is also associated with non-broadcast APs, and such STAs\nperiodically transmit uplink frames including ACK frames. The proposed method\nis based on the idea that by overhearing such uplink frames, the broadcast AP\nsurveys channel conditions at partial STAs, thereby setting appropriate data\nrates for the STAs. Furthermore, in order to avoid reception failures in a\nlarge portion of STAs, this paper proposes deep reinforcement learning\n(DRL)-based data rate adaptation framework that uses a sim-to-real approach.\nTherein, information of reception success/failure at broadcast recipient STAs,\nthat could not be notified to the broadcast AP in real deployments, are made\navailable by simulations beforehand, thereby forming data rate adaptation\nstrategies. Numerical results show that utilizing overheard uplink frames of\nrecipients makes it feasible to manage data rates in ACK-less broadcast WLANs,\nand using the sim-to-real DRL framework can decrease reception failures.\n"}
+{"abstract": "  The objective of this paper is to present a meta-corpus of diplomatic\ndocuments entitled Cartae Europae Medii Aevi or CEMA. It shows the logic and\nlimits of this meta-corpus, which contains 250,000 documents, by specifying\nboth its structure and its possible future extensions. The second part of the\npaper is devoted to specific examples that will attempt to show the interest of\nsuch a database. The third part examine the possibilities opened up by the\ncorpus in terms of historical semantics.\n"}
+{"abstract": "  The fundamental relationship of traffic flow is empirically estimated by\nfitting a regression curve to a cloud of observations of traffic variables.\nSuch estimates, however, may suffer from the confounding/endogeneity bias due\nto omitted variables such as driving behaviour and weather. To this end, this\npaper adopts a causal approach to obtain an unbiased estimate of the\nfundamental flow-density relationship using traffic detector data. In\nparticular, we apply a Bayesian non-parametric spline-based regression approach\nwith instrumental variables to adjust for the aforementioned confounding bias.\nThe proposed approach is benchmarked against standard curve-fitting methods in\nestimating the flow-density relationship for three highway bottlenecks in the\nUnited States. Our empirical results suggest that the saturated (or\nhypercongested) regime of the estimated flow-density relationship using\ncorrelational curve fitting methods may be severely biased, which in turn leads\nto biased estimates of important traffic control inputs such as capacity and\ncapacity-drop. We emphasise that our causal approach is based on the physical\nlaws of vehicle movement in a traffic stream as opposed to a demand-supply\nframework adopted in the economics literature. By doing so, we also aim to\nconciliate the engineering and economics approaches to this empirical problem.\nOur results, thus, have important implications both for traffic engineers and\ntransport economists.\n"}
+{"abstract": "  We theoretically investigate the threshold for the director reorientation\nfrom the homeotropic state to the hybrid homeotropic-planar state and vice\nversa in a cell filled with a flexoelectric nematic liquid crystal (NLC)\nsubjected to an electric field. The liquid crystal is doped by a CTAB-like\nsubstance, a part of molecules of which dissociates into positive and negative\nions. The anchoring on one of the cell surfaces is assumed to be strong and\nhomeotropic, while the other surface can adsorb positive ions which play the\nrole of an orienting surfactant for NLC molecules on this surface. At certain\nvoltages, the orientational transitions in the bulk of the NLC are possible due\nto the changing conditions for the director on the adsorbing surface. We\ncalculate respective threshold voltages as functions of anchoring parameters.\nThe existence of the critical values of these parameters, beyond which the\norientational transitions do not take place, is established.\n"}
+{"abstract": "  Exploring whether different patterns emerge across networks generated by\norganizational structures, co-authorships and citations for characterizing and\nevaluating cooperative relationships is particularly important for transferring\nthe research results into practice. This research-in-progress paper focuses on\nusing the structure of scientific collaborations and mapping knowledge transfer\nto gain insight into the influence of collaborative research centres linked to\nthe German Research Foundation (DFG) funding. Within the German Centre for\nIntegrative Biodiversity Research (iDiv), the DFG sponsors research conducted\nacross all participating universities and institutes by more than hundred\nresearch groups who bring their expertise to the manifold research fields of\nbiodiversity. Using iDiv research from 2013-2020, we build co-authorship\nnetworks and identify the most cohesive communities in terms of collaboration\nand compare them with groups presented on its website. Corresponding cited and\nciting works are analysed by distributions to investigate disciplinary\ncollaboration. Our findings show that the number of publications and the\nintensity of research collaboration have maintained a steady increase. Despite\nthe highly cohesive cooperation structure addressed by iDiv, the internal\nscientific collaboration has not gained strong momentum compared with its\ngrowing trends in international collaborations. The tendency towards covering\ncross-disciplinary research foci is not evident.\n"}
+{"abstract": "  Conservative mechanism is a desirable property in decision-making problems\nwhich balance the tradeoff between the exploration and exploitation. We propose\nthe novel \\emph{conservative contextual combinatorial cascading bandit\n($C^4$-bandit)}, a cascading online learning game which incorporates the\nconservative mechanism. At each time step, the learning agent is given some\ncontexts and has to recommend a list of items but not worse than the base\nstrategy and then observes the reward by some stopping rules. We design the\n$C^4$-UCB algorithm to solve the problem and prove its n-step upper regret\nbound for two situations: known baseline reward and unknown baseline reward.\nThe regret in both situations can be decomposed into two terms: (a) the upper\nbound for the general contextual combinatorial cascading bandit; and (b) a\nconstant term for the regret from the conservative mechanism. We also improve\nthe bound of the conservative contextual combinatorial bandit as a by-product.\nExperiments on synthetic data demonstrate its advantages and validate our\ntheoretical analysis.\n"}
+{"abstract": "  Plasma emission (PE), i.e., electromagnetic radiation at the plasma frequency\nand its second harmonic, is a general process occurring in both astrophysical\nand laboratory plasmas. The prevailing theory presents a multi-stage process\nattributed to the resonant coupling of beam-excited Langmuir waves with\nion-acoustic waves. Here we examine another possibility of the fundamental PE\ninduced by the resonant coupling of Z-mode and whistler (W) waves. Earlier\nstudies have been controversial in the plausibility and significance of such\nprocess in plasmas. In this study we show that the matching condition of three\nwave resonant interaction (Z+W ! O) can be satisfied over a wide regime of\nparameters based on the magnetoionic theory, demonstrate the occurrence of such\nprocess and further evaluate the rate of energy conversion from the pumped Z or\nW mode to the fundamental O mode with particle-in-cell (PIC) simulations of\nwave pumping. The study presents an alternative form of the fundamental PE,\nwhich could possibly play a role in various astrophysical and laboratory\nscenarios with both Z and W modes readily excited through the electron\ncyclotron maser instability.\n"}
+{"abstract": "  One of the first and easy to use techniques for proving run time bounds for\nevolutionary algorithms is the so-called method of fitness levels by Wegener.\nIt uses a partition of the search space into a sequence of levels which are\ntraversed by the algorithm in increasing order, possibly skipping levels. An\neasy, but often strong upper bound for the run time can then be derived by\nadding the reciprocals of the probabilities to leave the levels (or upper\nbounds for these). Unfortunately, a similarly effective method for proving\nlower bounds has not yet been established. The strongest such method, proposed\nby Sudholt (2013), requires a careful choice of the viscosity parameters\n$\\gamma_{i,j}$, $0 \\le i < j \\le n$.\n  In this paper we present two new variants of the method, one for upper and\none for lower bounds. Besides the level leaving probabilities, they only rely\non the probabilities that levels are visited at all. We show that these can be\ncomputed or estimated without greater difficulties and apply our method to\nreprove the following known results in an easy and natural way. (i) The precise\nrun time of the (1+1) EA on \\textsc{LeadingOnes}. (ii) A lower bound for the\nrun time of the (1+1) EA on \\textsc{OneMax}, tight apart from an $O(n)$ term.\n(iii) A lower bound for the run time of the (1+1) EA on long $k$-paths. We also\nprove a tighter lower bound for the run time of the (1+1) EA on jump functions\nby showing that, regardless of the jump size, only with probability $O(2^{-n})$\nthe algorithm can avoid to jump over the valley of low fitness.\n"}
+{"abstract": "  Two signed graphs are called switching isomorphic to each other if one is\nisomorphic to a switching of the other. The wheel $W_n$ is the join of the\ncycle $C_n$ and a vertex. For $0 \\leq p \\leq n$, $\\psi_{p}(n)$ is defined to be\nthe number of switching non-isomorphic signed $W_n$ with exactly $p$ negative\nedges on $C_n$. The number of switching non-isomorphic signed $W_n$ is denoted\nby $\\psi(n)$. In this paper, we compute the values of $\\psi_{p}(n)$ for\n$p=0,1,2,3,4,n-4,n-3,n-2,n-1,n$ and of $\\psi(n)$ for $n=4,5,...,10$. Our method\nof obtaining $\\psi_{p}(n)$ not only count the switching non-isomorphic signed\nwheels but also generates them.\n"}
+{"abstract": "  Supervised learning techniques are at the center of many tasks in remote\nsensing. Unfortunately, these methods, especially recent deep learning methods,\noften require large amounts of labeled data for training. Even though\nsatellites acquire large amounts of data, labeling the data is often tedious,\nexpensive and requires expert knowledge. Hence, improved methods that require\nfewer labeled samples are needed. We present MSMatch, the first semi-supervised\nlearning approach competitive with supervised methods on scene classification\non the EuroSAT and UC Merced Land Use benchmark datasets. We test both RGB and\nmultispectral images of EuroSAT and perform various ablation studies to\nidentify the critical parts of the model. The trained neural network achieves\nstate-of-the-art results on EuroSAT with an accuracy that is up to 19.76%\nbetter than previous methods depending on the number of labeled training\nexamples. With just five labeled examples per class, we reach 94.53% and 95.86%\naccuracy on the EuroSAT RGB and multispectral datasets, respectively. On the UC\nMerced Land Use dataset, we outperform previous works by up to 5.59% and reach\n90.71% with five labeled examples. Our results show that MSMatch is capable of\ngreatly reducing the requirements for labeled data. It translates well to\nmultispectral data and should enable various applications that are currently\ninfeasible due to a lack of labeled data. We provide the source code of MSMatch\nonline to enable easy reproduction and quick adoption.\n"}
+{"abstract": "  Anomaly detection is a task that recognizes whether an input sample is\nincluded in the distribution of a target normal class or an anomaly class.\nConventional generative adversarial network (GAN)-based methods utilize an\nentire image including foreground and background as an input. However, in these\nmethods, a useless region unrelated to the normal class (e.g., unrelated\nbackground) is learned as normal class distribution, thereby leading to false\ndetection. To alleviate this problem, this paper proposes a novel two-stage\nnetwork consisting of an attention network and an anomaly detection GAN\n(ADGAN). The attention network generates an attention map that can indicate the\nregion representing the normal class distribution. To generate an accurate\nattention map, we propose the attention loss and the adversarial anomaly loss\nbased on synthetic anomaly samples generated from hard augmentation. By\napplying the attention map to an image feature map, ADGAN learns the normal\nclass distribution from which the useless region is removed, and it is possible\nto greatly reduce the problem difficulty of the anomaly detection task.\nAdditionally, the estimated attention map can be used for anomaly segmentation\nbecause it can distinguish between normal and anomaly regions. As a result, the\nproposed method outperforms the state-of-the-art anomaly detection and anomaly\nsegmentation methods for widely used datasets.\n"}
+{"abstract": "  We present a new, stable, mixed finite element (FE) method for linear\nelastostatics of nearly incompressible solids. The method is the automatic\nvariationally stable FE (AVS-FE) method of Calo, Romkes and Valseth, in which\nwe consider a Petrov-Galerkin weak formulation where the stress and\ndisplacement variables are in the space H(div)xH1, respectively. This allows us\nto employ a fully conforming FE discretization for any elastic solid using\nclassical FE subspaces of H(div) and H1. Hence, the resulting FE approximation\nyields both continuous stresses and displacements.\n  To ensure stability of the method, we employ the philosophy of the\ndiscontinuous Petrov-Galerkin (DPG) method of Demkowicz and Gopalakrishnan and\nuse optimal test spaces. Thus, the resulting FE discretization is stable even\nas the Poisson ratio approaches 0.5, and the system of linear algebraic\nequations is symmetric and positive definite. Our method also comes with a\nbuilt-in a posteriori error estimator as well as well as indicators which are\nused to drive mesh adaptive refinements. We present several numerical\nverifications of our method including comparisons to existing FE technologies.\n"}
+{"abstract": "  Methods for linking individuals across historical data sets, typically in\ncombination with AI based transcription models, are developing rapidly.\nProbably the single most important identifier for linking is personal names.\nHowever, personal names are prone to enumeration and transcription errors and\nalthough modern linking methods are designed to handle such challenges these\nsources of errors are critical and should be minimized. For this purpose,\nimproved transcription methods and large-scale databases are crucial\ncomponents. This paper describes and provides documentation for HANA, a newly\nconstructed large-scale database which consists of more than 1.1 million images\nof handwritten word-groups. The database is a collection of personal names,\ncontaining more than 105 thousand unique names with a total of more than 3.3\nmillion examples. In addition, we present benchmark results for deep learning\nmodels that automatically can transcribe the personal names from the scanned\ndocuments. Focusing mainly on personal names, due to its vital role in linking,\nwe hope to foster more sophisticated, accurate, and robust models for\nhandwritten text recognition through making more challenging large-scale\ndatabases publicly available. This paper describes the data source, the\ncollection process, and the image-processing procedures and methods that are\ninvolved in extracting the handwritten personal names and handwritten text in\ngeneral from the forms.\n"}
+{"abstract": "  Feedback-controlled electric breakdown of graphene in air or vacuum is a\nwell-established way of fabricating tunnel junctions, nanogaps, and quantum\ndots. We show that the method is equally applicable to encapsulated graphene\nconstrictions fabricated using hydrogen silsesquioxane. The silica-like layer\nleft by hydrogen silsesquioxane resist after electron-beam exposure remains\nintact after electric breakdown of the graphene. We explore the conductance\nswitching behavior that is common in graphene nanostructures fabricated via\nfeedback-controlled breakdown, and show that it can be attributed to\natomic-scale fluctuations of graphene below the encapsulating layer. Our\nfindings open up new ways of fabricating encapsulated room-temperature\nsingle-electron nanodevices and shed light on the underlying physical mechanism\nof conductance switching in these graphene nanodevices.\n"}
+{"abstract": "  First-principles calculations of thermal transport in homogeneous materials\nhave reached remarkable predicting power. Modeling deterministically phonon\ntransport in nanostructures, however, poses novel challenges; notably, it\nentails solving as many algebraic equations as the number of combinations of\nwave vectors in the discretized Brillouen and polarizations. We show that,\nwithin the relaxation time approximation of the Boltzmann transport equation\n(BTE), this issue is resolved by interpolating the phonon distributions in the\nvectorial phonon mean free paths (MFP) space. The coupling between structure\nand mode-resolved heat transport is investigated in terms of angular-resolved\nbulk thermal conductivity and phonon suppression function, the latter being\nassociated primarily to the material's geometry. Our method, termed the\nanisotropic MFP-BTE (aMFP-BTE), allows for fast and accurate thermal\nconductivity calculations in nanomaterials regardless of the number of phonon\nbranches and wave vectors. Furthermore, it naturally blends with\nfirst-principles thermal transport calculations, therefore allowing for\nmultiscale, parameter-free simulations. We apply the aMFP-BTE to compute the\nmode-resolved effective thermal conductivity of porous Si membranes, achieving\nup to 50x speed with respect to the case with no interpolation. The proposed\napproach unlocks the engineering of novel nanostructures, with applications to\nthermoelectrics and heat management.\n"}
+{"abstract": "  With the emergence of Information and Communication Technologies (ICT) and\nwireless embedded sensing devices into modern vehicles, Intelligent Transport\nSystem (ITS) becomes a reality and an indispensable component of smart cities.\nThe purpose of ITS is to improve road safety and traffic efficiency as well as\noffering infotainments services. In fact, warning drivers in the right time\nabout dangerous situations on the road and providing them with prior\ninformation about traffic will undoubtedly leads to enhance driver's safety and\nreduce traffic congestion. Technically speaking, ITS is based on\nself-organizing wireless networks, known as vehicular ad-hoc networks (VANETs).\nMobile vehicles in VANET might play the role of stationary sensors in\ninfrastructure-based networks. They can detect, gather and disseminate\nreal-time data about traffic, driving conditions and potential hazards on\nroads. In this respect, we review in this study, recent developments on the\ndesign of VANET protocols and applications. We first introduce the architecture\nof VANETs then we review their unique characteristics and applications.\nThereafter, we discuss the main research challenges and open issues to be\nconsidered for designing efficient and a cost-effective VANET protocols and\napplications.\n"}
+{"abstract": "  Despite the recent success of deep reinforcement learning (RL), domain\nadaptation remains an open problem. Although the generalization ability of RL\nagents is critical for the real-world applicability of Deep RL, zero-shot\npolicy transfer is still a challenging problem since even minor visual changes\ncould make the trained agent completely fail in the new task. To address this\nissue, we propose a two-stage RL agent that first learns a latent unified state\nrepresentation (LUSR) which is consistent across multiple domains in the first\nstage, and then do RL training in one source domain based on LUSR in the second\nstage. The cross-domain consistency of LUSR allows the policy acquired from the\nsource domain to generalize to other target domains without extra training. We\nfirst demonstrate our approach in variants of CarRacing games with customized\nmanipulations, and then verify it in CARLA, an autonomous driving simulator\nwith more complex and realistic visual observations. Our results show that this\napproach can achieve state-of-the-art domain adaptation performance in related\nRL tasks and outperforms prior approaches based on latent-representation based\nRL and image-to-image translation.\n"}
+{"abstract": "  Most works in IRS-assisted systems have ignored the impact of the inevitable\nresidual hardware impairments (HWIs) at both the transceiver hardware and the\nIRS while any relevant works have addressed only simple scenarios, e.g., with\nsingle-antenna network nodes and/or without taking the randomness of phase\nnoise at the IRS into account. In this work, we aim at filling up this gap by\nconsidering a general IRS-assisted multi-user (MU) multiple-input single-output\n(MISO) system with imperfect CSI and correlated Rayleigh fading. In parallel,\nwe present a general computationally efficient methodology for IRS reflect\nbeamforming (RB) optimization. Specifically, we introduce an advantageous\nchannel estimation (CE) method for such systems accounting for the HWIs.\nMoreover, we derive the uplink achievable spectral efficiency (SE) with\nmaximal-ratio combining (MRC) receiver, displaying three significant advantages\nbeing: 1) its closed-form expression, 2) its dependence only on large-scale\nstatistics, and 3) its low training overhead. Notably, by exploiting the first\ntwo benefits, we achieve to perform optimization with respect to the reflect\nbeamforming matrix (RBM) that can take place only at every several coherence\nintervals, and thus, reduces significantly the computational cost compared to\nother methods which require frequent phase optimization. Among the insightful\nobservations, we highlight that uncorrelated Rayleigh fading does not allow\noptimization of the SE, which makes the application of an IRS ineffective.\nAlso, in the case that the phase drifts, describing the distortion of the\nphases in the RBM, are uniformly distributed, the presence of an IRS provides\nno advantage. The analytical results outperform previous works and are verified\nby Monte-Carlo (MC) simulations.\n"}
+{"abstract": "  We present a comprehensive study of the quark sector of $2+1$ flavour QCD,\nbased on a self-consistent treatment of the coupled system of Schwinger-Dyson\nequations for the quark propagator and the full quark-gluon vertex. The\nindividual form factors of the quark-gluon vertex are expressed in a special\ntensor basis obtained from a set of gauge-invariant operators. The sole\nexternal ingredient used as input to our equations is the Landau gauge gluon\npropagator with $2+1$ dynamical quark flavours, obtained from studies with\nSchwinger-Dyson equations, the functional renormalisation group approach, and\nlarge volume lattice simulations. The appropriate renormalisation procedure\nrequired in order to self-consistently accommodate external inputs stemming\nfrom other functional approaches or the lattice is discussed in detail, and the\nvalue of the gauge coupling is accurately determined at two vastly separated\nrenormalisation group scales.\n  Our analysis establishes a clear hierarchy among the vertex form factors. We\nidentify only three dominant ones, in agreement with previous results. The\ncomponents of the quark propagator obtained from our approach are in excellent\nagreement with the results from Schwinger-Dyson equations, the functional\nrenormalisation group, and lattice QCD simulation, a simple benchmark\nobservable being the chiral condensate in the chiral limit, which is computed\nas $(245\\,\\textrm{MeV})^3$. The present approach has a wide range of\napplications, including the self-consistent computation of bound-state\nproperties and finite temperature and density physics, which are briefly\ndiscussed.\n"}
+{"abstract": "  A theoretical framework which unifies the conventional Mori-Zwanzig formalism\nand the approximate Koopman learning is presented. In this framework, the\nMori-Zwanzig formalism, developed in statistical mechanics to tackle the hard\nproblem of construction of reduced-order dynamics for high-dimensional\ndynamical systems, can be considered as a natural generalization of the Koopman\ndescription of the dynamical system. We next show that similar to the\napproximate Koopman learning methods, data-driven methods can be developed for\nthe Mori-Zwanzig formalism with Mori's linear projection operator. We developed\ntwo algorithms to extract the key operators, the Markov and the memory kernel,\nusing time series of a reduced set of observables in a dynamical system. We\nadopted the Lorenz `96 system as a test problem and solved for the operators,\nwhich exhibit complex behaviors which are unlikely to be captured by\ntraditional modeling approaches, in Mori-Zwanzig analysis. The nontrivial\nGeneralized Fluctuation Dissipation relationship, which relates the memory\nkernel with the two-time correlation statistics of the orthogonal dynamics, was\nnumerically verified as a validation of the solved operators. We present\nnumerical evidence that the Generalized Langevin Equation, a key construct in\nthe Mori-Zwanzig formalism, is more advantageous in predicting the evolution of\nthe reduced set of observables than the conventional approximate Koopman\noperators.\n"}
+{"abstract": "  Gate-controlled supercurrent (GCS) in superconductor nanobridges has recently\nattracted attention as a means to create superconducting field effect\ntransistors. Despite the clear advantage for applications with low power\nconsumption and high switching speeds, the microscopic mechanism of the field\neffect is still under debate. In this work, we realize GCS for the first time\nin an epitaxial superconductor, which is created as a shell on an InAs\nnanowire. We show that the supercurrent in the epitaxial Al layer can be\nswitched to the normal state by applying $\\simeq\\pm$ 23$\\,$V on a bottom gate\ninsulated from the nanowire by a crystalline hBN layer. Our extensive study on\nthe temperature and magnetic field dependencies of GCS suggests that hot\nelectron injection alone cannot explain our experimental findings.\n"}
+{"abstract": "  The growing energy and performance costs of deep learning have driven the\ncommunity to reduce the size of neural networks by selectively pruning\ncomponents. Similarly to their biological counterparts, sparse networks\ngeneralize just as well, if not better than, the original dense networks.\nSparsity can reduce the memory footprint of regular networks to fit mobile\ndevices, as well as shorten training time for ever growing networks. In this\npaper, we survey prior work on sparsity in deep learning and provide an\nextensive tutorial of sparsification for both inference and training. We\ndescribe approaches to remove and add elements of neural networks, different\ntraining strategies to achieve model sparsity, and mechanisms to exploit\nsparsity in practice. Our work distills ideas from more than 300 research\npapers and provides guidance to practitioners who wish to utilize sparsity\ntoday, as well as to researchers whose goal is to push the frontier forward. We\ninclude the necessary background on mathematical methods in sparsification,\ndescribe phenomena such as early structure adaptation, the intricate relations\nbetween sparsity and the training process, and show techniques for achieving\nacceleration on real hardware. We also define a metric of pruned parameter\nefficiency that could serve as a baseline for comparison of different sparse\nnetworks. We close by speculating on how sparsity can improve future workloads\nand outline major open problems in the field.\n"}
+{"abstract": "  Semantic relationships, such as hyponym-hypernym, cause-effect,\nmeronym-holonym etc. between a pair of entities in a sentence are usually\nreflected through syntactic patterns. Automatic extraction of such patterns\nbenefits several downstream tasks, including, entity extraction, ontology\nbuilding, and question answering. Unfortunately, automatic extraction of such\npatterns has not yet received much attention from NLP and information retrieval\nresearchers. In this work, we propose an attention-based supervised deep\nlearning model, ASPER, which extracts syntactic patterns between entities\nexhibiting a given semantic relation in the sentential context. We validate the\nperformance of ASPER on three distinct semantic relations -- hyponym-hypernym,\ncause-effect, and meronym-holonym on six datasets. Experimental results show\nthat for all these semantic relations, ASPER can automatically identify a\ncollection of syntactic patterns reflecting the existence of such a relation\nbetween a pair of entities in a sentence. In comparison to the existing\nmethodologies of syntactic pattern extraction, ASPER's performance is\nsubstantially superior.\n"}
+{"abstract": "  Given a graph, the shortest-path problem requires finding a sequence of edges\nwith minimum cumulative length that connects a source vertex to a target\nvertex. We consider a generalization of this classical problem in which the\nposition of each vertex in the graph is a continuous decision variable,\nconstrained to lie in a corresponding convex set. The length of an edge is then\ndefined as a convex function of the positions of the vertices it connects.\nProblems of this form arise naturally in motion planning of autonomous\nvehicles, robot navigation, and even optimal control of hybrid dynamical\nsystems. The price for such a wide applicability is the complexity of this\nproblem, which is easily seen to be NP-hard. Our main contribution is a strong\nmixed-integer convex formulation based on perspective functions. This\nformulation has a very tight convex relaxation and makes it possible to\nefficiently find globally-optimal paths in large graphs and in high-dimensional\nspaces.\n"}
+{"abstract": "  Deep Neural Networks (DNNs) are generated by sequentially performing linear\nand non-linear processes. Using a combination of linear and non-linear\nprocedures is critical for generating a sufficiently deep feature space. The\nmajority of non-linear operators are derivations of activation functions or\npooling functions. Mathematical morphology is a branch of mathematics that\nprovides non-linear operators for a variety of image processing problems. We\ninvestigate the utility of integrating these operations in an end-to-end deep\nlearning framework in this paper. DNNs are designed to acquire a realistic\nrepresentation for a particular job. Morphological operators give topological\ndescriptors that convey salient information about the shapes of objects\ndepicted in images. We propose a method based on meta-learning to incorporate\nmorphological operators into DNNs. The learned architecture demonstrates how\nour novel morphological operations significantly increase DNN performance on\nvarious tasks, including picture classification and edge detection.\n"}
+{"abstract": "  In this paper we study the behaviour of scalar flat K\\\"ahler ALE spaces and\ntheir ADM mass under blow ups. In particular we prove that by blowing up\nsufficiently many points at sufficiently big mutual distance one can produce\nscalar flat metrics with arbitrarily large ADM mass. A general machinery for\nproducing scalar flat non Ricci flat ALE spaces of zero ADM mass is also\npresented, using and integrating previous work by Rollin-Singer and\nHein-LeBrun.\n"}
+{"abstract": "  Masked language models have revolutionized natural language processing\nsystems in the past few years. A recently introduced generalization of masked\nlanguage models called warped language models are trained to be more robust to\nthe types of errors that appear in automatic or manual transcriptions of spoken\nlanguage by exposing the language model to the same types of errors during\ntraining. In this work we propose a novel approach that takes advantage of the\nrobustness of warped language models to transcription noise for correcting\ntranscriptions of spoken language. We show that our proposed approach is able\nto achieve up to 10% reduction in word error rates of both automatic and manual\ntranscriptions of spoken language.\n"}
+{"abstract": "  Inspired by the UNet architecture of semantic image segmentation, we propose\na lightweight UNet using depthwise separable convolutions (DSUNet) for\nend-to-end learning of lane detection and path prediction (PP) in autonomous\ndriving. We also design and integrate a PP algorithm with convolutional neural\nnetwork (CNN) to form a simulation model (CNN-PP) that can be used to assess\nCNN's performance qualitatively, quantitatively, and dynamically in a host\nagent car driving along with other agents all in a real-time autonomous manner.\nDSUNet is 5.16x lighter in model size and 1.61x faster in inference than UNet.\nDSUNet-PP outperforms UNet-PP in mean average errors of predicted curvature and\nlateral offset for path planning in dynamic simulation. DSUNet-PP outperforms a\nmodified UNet in lateral error, which is tested in a real car on real road.\nThese results show that DSUNet is efficient and effective for lane detection\nand path prediction in autonomous driving.\n"}
+{"abstract": "  With the down-scaling of CMOS technology, the design complexity of very\nlarge-scale integrated (VLSI) is increasing. Although the application of\nmachine learning (ML) techniques in electronic design automation (EDA) can\ntrace its history back to the 90s, the recent breakthrough of ML and the\nincreasing complexity of EDA tasks have aroused more interests in incorporating\nML to solve EDA tasks. In this paper, we present a comprehensive review of\nexisting ML for EDA studies, organized following the EDA hierarchy.\n"}
+{"abstract": "  We study quasi two-dimensional, monodisperse systems of active Brownian\nparticles (ABPs) for a range of activities, stiffnesses, and densities. We\ndevelop a microscopic, analytical method for predicting the dense phase\nstructure formed after motility-induced phase separation (MIPS) has occurred,\nincluding the dense cluster's area fraction, interparticle pressure, and\nradius. Our predictions are in good agreement with our Brownian dynamics\nsimulations. We, then, derive a continuum model to investigate the relationship\nbetween the predicted interparticle pressure, the swim pressure, and the\nmacroscopic pressure in the momentum equation. We find that formulating the\npoint-wise macroscopic pressure as the interparticle pressure and modeling the\nparticle activity through a spatially variant body force -- as opposed to a\nvolume-averaged swim pressure -- results in consistent predictions of pressure\nin both the continuum model and the microscopic theory. This formulation of\npressure also results in nearly zero surface tension for the phase separated\ndomains, irrespective of activity, stiffness, and area fraction. Furthermore,\nusing Brownian dynamics simulations and our continuum model, we showed that\nboth the interface width and surface tension, are intrinsic characteristics of\nthe system. On the other hand, if we were to exclude the body force induced by\nactivity, we find that the resulting surface tension values are linearly\ndependent on the size of the simulation, in contrast to the statistical\nmechanical definition of surface tension.\n"}
+{"abstract": "  Delusive attacks aim to substantially deteriorate the test accuracy of the\nlearning model by slightly perturbing the features of correctly labeled\ntraining examples. By formalizing this malicious attack as finding the\nworst-case training data within a specific $\\infty$-Wasserstein ball, we show\nthat minimizing adversarial risk on the perturbed data is equivalent to\noptimizing an upper bound of natural risk on the original data. This implies\nthat adversarial training can serve as a principled defense against delusive\nattacks. Thus, the test accuracy decreased by delusive attacks can be largely\nrecovered by adversarial training. To further understand the internal mechanism\nof the defense, we disclose that adversarial training can resist the delusive\nperturbations by preventing the learner from overly relying on non-robust\nfeatures in a natural setting. Finally, we complement our theoretical findings\nwith a set of experiments on popular benchmark datasets, which show that the\ndefense withstands six different practical attacks. Both theoretical and\nempirical results vote for adversarial training when confronted with delusive\nadversaries.\n"}
+{"abstract": "  Neurons exhibit complex geometry in their branched networks of neurites which\nis essential to the function of individual neuron but also brings challenges to\ntransport a wide variety of essential materials throughout their neurite\nnetworks for their survival and function. While numerical methods like\nisogeometric analysis (IGA) have been used for modeling the material transport\nprocess via solving partial differential equations (PDEs), they require long\ncomputation time and huge computation resources to ensure accurate geometry\nrepresentation and solution, thus limit their biomedical application. Here we\npresent a graph neural network (GNN)-based deep learning model to learn the\nIGA-based material transport simulation and provide fast material concentration\nprediction within neurite networks of any topology. Given input boundary\nconditions and geometry configurations, the well-trained model can predict the\ndynamical concentration change during the transport process with an average\nerror less than 10% and 120~330 times faster compared to IGA simulations. The\neffectiveness of the proposed model is demonstrated within several complex\nneurite networks.\n"}
+{"abstract": "  We have performed an unbiased dense core survey toward the Orion A Giant\nMolecular Cloud in the C$^{18}$O ($J$=1--0) emission line taken with the\nNobeyama Radio Observatory (NRO) 45-m telescope. The effective angular\nresolution of the map is 26\", which corresponds to $\\sim$ 0.05 pc at a distance\nof 414 pc. By using the Herschel-Planck H$_2$ column density map, we calculate\nthe C$^{18}$O fractional abundance and find that it is roughly constant over\nthe column density range of $\\lesssim$ 5 $\\times$ 10$^{22}$ cm$^{-3}$, although\na trend of C$^{18}$O depletion is determined toward higher column density.\nTherefore, C$^{18}$O intensity can follow the cloud structure reasonably well.\nThe mean C$^{18}$O abundance in Orion A is estimated to be\n5.7$\\times$10$^{-7}$, which is about 3 times larger than the fiducial value. We\nidentified 746 C$^{18}$O cores with astrodendro and classified 709 cores as\nstarless cores. We compute the core masses by decomposing the Herschel-Planck\ndust column density using the relative proportions of the C$^{18}$O integrated\nintensities of line-of-sight components. Applying this procedure, we attempt to\nremove the contribution of the background emission, i.e., the ambient gas\noutside the cores. Then, we derived mass function for starless cores and found\nthat it resembles the stellar initial mass function (IMF). The CMF for starless\ncores, $dN/dM$, is fitted with a power-law relation of $M^\\alpha$ with a power\nindex of $\\alpha = -$2.25$\\pm$ 0.16 at the high-mass slope ($\\gtrsim$ 0.44\n$M_\\odot$). We also found that the ratio of each core mass to the total mass\nintegrated along the line of sight is significantly large. Therefore, in the\nprevious studies, the core masses derived from the dust image are likely to be\noverestimated at least by a factor of a few. Accordingly, such previous studies\nmay underestimate the star formation efficiency of individual cores.\n"}
+{"abstract": "  SDSS J134244.4+053056 is a tidal disruption event candidate with strong\ntemporal coronal line emitters and a long fading, mid-infrared dust echo. We\npresent detailed analyses of X-ray emission from a Swift/XRT observation in\n2009 and the most recent XMM-Newton/pn observation in 2020. The two spectra can\nbe modeled with hard and soft components. While no significant variability is\ndetected in the hard component above 2 keV between these two observations, the\nsoft X-ray emission in 0.3-2 keV varies by a factor of $\\sim5$. The luminosity\nof this soft component fades from $\\sim1.8\\times10^{41}$ to\n$\\sim3.7\\times10^{40}$ erg s$^{-1}$ from the observation in Swift to that of\nXMM-Newton, which are 8 and 19 years after the outburst occurred, respectively.\nThe evolution of luminosity matches with the $t^{-5/3}$ decline law well; there\nis a soft X-ray peak luminosity of 10$^{44}$ erg s$^{-1}$ at the time of the\noptical flare. Furthermore, the spectra of the soft component harden slightly\nin the decay phase, in which the photon index $\\Gamma$ varies from\n$4.8^{+1.2}_{-0.9}$ to $3.7\\pm0.5$, although they are consistent with each\nother if we consider the uncertainties. Additionally, by comparing the BH mass\nestimate between the $M-\\sigma$ correlation, the broad H$\\alpha$ emission, and\nthe fundamental plane relation of BH accretion, we find that a value of\n$\\sim10^{5}$Msun is favored. If so, taking its X-ray spectral variation,\nluminosity evolution, and further support from theory into account, we suggest\nthat SDSS J134244.4+053056 is a long-lived tidal disruption event candidate\nlasting more than 18 years with an intermediate-mass black hole.\n"}
+{"abstract": "  We formulate a problem on diamagnetic levitation, which may be suitable for\nspecialized high-school or first-year students in physical sciences. We guide\nthe students, step-by-step, through the physics of diamagnetic levitation. The\ncalculations are simplified by assuming a ring-shaped geometry of the\ndiamagnetic object residing above a magnetic dipole. This problem was\noriginally intended for the International Physics Olympiad 2016 (IPHO 2016),\nbut was finally deemed surplus and therefore not set.\n"}
+{"abstract": "  Despite impressive success of machine learning algorithms in clinical natural\nlanguage processing (cNLP), rule-based approaches still have a prominent role.\nIn this paper, we introduce medspaCy, an extensible, open-source cNLP library\nbased on spaCy framework that allows flexible integration of rule-based and\nmachine learning-based algorithms adapted to clinical text. MedspaCy includes a\nvariety of components that meet common cNLP needs such as context analysis and\nmapping to standard terminologies. By utilizing spaCy's clear and easy-to-use\nconventions, medspaCy enables development of custom pipelines that integrate\neasily with other spaCy-based modules. Our toolkit includes several core\ncomponents and facilitates rapid development of pipelines for clinical text.\n"}
+{"abstract": "  Computing the expectation of kernel functions is a ubiquitous task in machine\nlearning, with applications from classical support vector machines to\nexploiting kernel embeddings of distributions in probabilistic modeling,\nstatistical inference, causal discovery, and deep learning. In all these\nscenarios, we tend to resort to Monte Carlo estimates as expectations of\nkernels are intractable in general. In this work, we characterize the\nconditions under which we can compute expected kernels exactly and efficiently,\nby leveraging recent advances in probabilistic circuit representations. We\nfirst construct a circuit representation for kernels and propose an approach to\nsuch tractable computation. We then demonstrate possible advancements for\nkernel embedding frameworks by exploiting tractable expected kernels to derive\nnew algorithms for two challenging scenarios: 1) reasoning under missing data\nwith kernel support vector regressors; 2) devising a collapsed black-box\nimportance sampling scheme. Finally, we empirically evaluate both algorithms\nand show that they outperform standard baselines on a variety of datasets.\n"}
+{"abstract": "  Iterating the skew RSK correspondence discovered by Sagan and Stanley in the\nlate '80s, we define a deterministic dynamics on the space of pairs of skew\nYoung tableaux $(P,Q)$. We find that this skew RSK dynamics displays\nconservation laws which, in the picture of Viennot's shadow line construction,\nidentify generalizations of Greene invariants.\n  The introduction of a novel realization of $0$-th Kashiwara operators reveals\nthat the skew RSK dynamics possesses symmetries induced by an affine bicrystal\nstructure,\n  which, combined with connectedness properties of Demazure crystals, leads to\nits linearization.\n  Studying asymptotic evolution of the dynamics started from a pair of skew\ntableaux $(P,Q)$, we discover a new bijection $\\Upsilon : (P,Q) \\mapsto (V,W;\n\\kappa, \\nu)$. Here $(V,W)$ is a pair of\n  vertically strict tableaux, i.e., column strict filling of Young diagrams\nwith no condition on rows, with shape prescribed by the Greene invariant,\n$\\kappa$ is an array of non-negative weights and $\\nu$ is a partition.\n  An application of this construction is the first bijective proof of Cauchy\nand Littlewood identities involving $q$-Whittaker polynomials. New identities\nrelating sums of $q$-Whittaker and Schur polynomials are also presented.\n"}
+{"abstract": "  Recently, the EuS/InAs interface has attracted attention for the possibility\nof inducing magnetic exchange correlations in a strong spin-orbit\nsemiconductor, which could be useful for topological quantum devices. We use\ndensity functional theory (DFT) with a machine-learned Hubbard $U$ correction\n[npj Comput. Mater. 6, 180 (2020)] to elucidate the effect of the bonding\nconfiguration at the interface on the electronic structure. For all interface\nconfigurations considered here, we find that the EuS valence band maximum (VBM)\nlies below the InAs VBM. In addition, dispersed states emerge at the top of the\nInAs VBM at the interface, which do not exist in either material separately.\nThese states are contributed mainly by the InAs layer adjacent to the\ninterface. They are localized at the interface and may be attributed to charge\ntransfer from the EuS to the InAs. The interface configuration affects the\nposition of the EuS VBM with respect to the InAs VBM, as well as the dispersion\nof the interface state. For all interface configurations studied here, the\ninduced magnetic moment in the InAs is small. This suggests that this\ninterface, in its coherent form studied here, is not promising for inducing\nequilibrium magnetic properties in InAs.\n"}
+{"abstract": "  The goal of mediation analysis is to study the effect of exposure on an\noutcome interceded by a mediator. Two simple hypotheses are tested: the effect\nof the exposure on the mediator, and the effect of the mediator on the outcome.\nWhen either of these hypotheses is true, a predetermined significance level can\nbe assured. When both nulls are true, the same test becomes conservative.\nAdaptively finding the correct scenario enables customizing the tests and\nconsequently enlarges their efficiency, which is most important in a multiple\ntesting framework. In this work, we link between adaptive two-stage procedures\nand shrinkage estimators. We first study the properties of shrinkage\nestimators, and characterize their behavior at different parameter points using\nlocal asymptotics. We formulate theoretical results regarding shrinkage\nestimators, compared to regular estimators. We then discuss the\nmultiple-testing framework and state results about using shrinkage estimator in\ntwo-stage procedures for controlling the FWER. Taking advantage of these\ntheoretical results, we suggest a number of estimators and test statistics for\nthe two-stage mediation procedures. We then investigate their empirical FWER\nand power, compared to regular estimators and tests, through simulations.\n"}
+{"abstract": "  3C 84 (NGC 1275) is the bright radio core of the Perseus Cluster. Even in the\nabsence of strong relativistic effects, the source has been detected at\nGamma-rays up to TeV energies. Despite its intensive study, the physical\nprocesses responsible for the high-energy emission in the source remain\nunanswered. We present a detailed kinematics study of the source and its\nconnection to Gamma-ray emission. The sub-parsec scale radio structure is\ndominated by slow-moving features in both the eastern and western lanes of the\njet. The jet appears to have accelerated to its maximum speed within less than\n125 000 gravitational radii. The fastest reliably detected speed in the jet was\n~0.9 c. This leads to a minimum Lorentz factor of ~1.35. Our analysis suggests\nthe presence of multiple high-energy sites in the source. If Gamma-rays are\nassociated with kinematic changes in the jet, they are being produced in both\neastern and western lanes in the jet. Three Gamma-ray flares are\ncontemporaneous with epochs where the slowly moving emission region splits into\ntwo sub-regions. We estimate the significance of these events being associated\nas ~2-3 sigma. We tested our results against theoretical predictions for\nmagnetic reconnection-induced mini-jets and turbulence and find them\ncompatible.\n"}
+{"abstract": "  This paper presents a novel unsupervised abstractive summarization method for\nopinionated texts. While the basic variational autoencoder-based models assume\na unimodal Gaussian prior for the latent code of sentences, we alternate it\nwith a recursive Gaussian mixture, where each mixture component corresponds to\nthe latent code of a topic sentence and is mixed by a tree-structured topic\ndistribution. By decoding each Gaussian component, we generate sentences with\ntree-structured topic guidance, where the root sentence conveys generic\ncontent, and the leaf sentences describe specific topics. Experimental results\ndemonstrate that the generated topic sentences are appropriate as a summary of\nopinionated texts, which are more informative and cover more input contents\nthan those generated by the recent unsupervised summarization model\n(Bra\\v{z}inskas et al., 2020). Furthermore, we demonstrate that the variance of\nlatent Gaussians represents the granularity of sentences, analogous to Gaussian\nword embedding (Vilnis and McCallum, 2015).\n"}
+{"abstract": "  In this letter, a novel framework is proposed for analyzing data offloading\nin a multi-access edge computing system. Specifically, a two-phase algorithm,\nis proposed, including two key phases: user association phase and task\noffloading phase. In the first phase, a ruin theory-based approach is developed\nto obtain the users association considering the users' transmission\nreliability. Meanwhile, in the second phase, an optimization-based algorithm is\nused to optimize the data offloading process. In particular, ruin theory is\nused to manage the user association phase, and a ruin probability-based\npreference profile is considered to control the priority of proposing users.\nHere, ruin probability is derived by the surplus buffer space of each edge node\nat each time slot. Giving the association results, an optimization problem is\nformulated to optimize the amount of offloaded data aiming at minimizing the\nenergy consumption of users. Simulation results show that the developed\nsolutions guarantee system reliability under a tolerable value of surplus\nbuffer size and minimize the total energy consumption of all users.\n"}
+{"abstract": "  The relaxation dynamics of superexcited superfluid He nanodroplets is\nthoroughly investigated by means of extreme-ultraviolet (XUV) femtosecond\nelectron and ion spectroscopy complemented by time-dependent density functional\ntheory (TDDFT). Three main paths leading to the emission of electrons and ions\nare identified: Droplet autoionization, pump-probe photoionization, and\nautoionization induced by re-excitation of droplets relaxing into levels below\nthe droplet ionization threshold. The most abundant product of both droplet\nautoionization and photoionization is He$_2^+$, whereas the delayed appearance\nof He$^+$ is indicative of the ejection of excited He atoms from the droplets.\nThe state-resolved time-dependent photoelectron spectra reveal that\nintermediate excited states of the droplets are populated in the course of the\nrelaxation, terminating in the lowest-lying metastable singlet and triplet He\natomic states. The slightly faster relaxation of the triplet state compared to\nthe singlet state is in agreement with the simulation showing faster formation\nof a bubble around a He atom in the triplet state.\n"}
+{"abstract": "  This paper presents a new hybrid architecture for voice activity detection\n(VAD) incorporating both convolutional neural network (CNN) and bidirectional\nlong short-term memory (BiLSTM) layers trained in an end-to-end manner. In\naddition, we focus specifically on optimising the computational efficiency of\nour architecture in order to deliver robust performance in difficult\nin-the-wild noise conditions in a severely under-resourced setting. Nested\nk-fold cross-validation was used to explore the hyperparameter space, and the\ntrade-off between optimal parameters and model size is discussed. The\nperformance effect of a BiLSTM layer compared to a unidirectional LSTM layer\nwas also considered. We compare our systems with three established baselines on\nthe AVA-Speech dataset. We find that significantly smaller models with near\noptimal parameters perform on par with larger models trained with optimal\nparameters. BiLSTM layers were shown to improve accuracy over unidirectional\nlayers by $\\approx$2% absolute on average. With an area under the curve (AUC)\nof 0.951, our system outperforms all baselines, including a much larger ResNet\nsystem, particularly in difficult noise conditions.\n"}
+{"abstract": "  This work explores how to design a single neural network that is capable of\nadapting to multiple heterogeneous tasks of computer vision, such as image\nsegmentation, 3D detection, and video recognition. This goal is challenging\nbecause network architecture designs in different tasks are inconsistent. We\nsolve this challenge by proposing Network Coding Propagation (NCP), a novel\n\"neural predictor\", which is able to predict an architecture's performance in\nmultiple datasets and tasks. Unlike prior arts of neural architecture search\n(NAS) that typically focus on a single task, NCP has several unique benefits.\n(1) NCP can be trained on different NAS benchmarks, such as NAS-Bench-201 and\nNAS-Bench-MR, which contains a novel network space designed by us for jointly\nsearching an architecture among multiple tasks, including ImageNet, Cityscapes,\nKITTI, and HMDB51. (2) NCP learns from network codes but not original data,\nenabling it to update the architecture efficiently across datasets. (3)\nExtensive experiments evaluate NCP on object classification, detection,\nsegmentation, and video recognition. For example, with 17\\% fewer FLOPs, a\nsingle architecture returned by NCP achieves 86\\% and 77.16\\% on\nImageNet-50-1000 and Cityscapes respectively, outperforming its counterparts.\nMore interestingly, NCP enables a single architecture applicable to both image\nsegmentation and video recognition, which achieves competitive performance on\nboth HMDB51 and ADE20K compared to the singular counterparts. Code is available\nat https://github.com/dingmyu/NCP}{https://github.com/dingmyu/NCP.\n"}
+{"abstract": "  Collective excitations of bound electron-hole pairs -- known as excitons --\nare ubiquitous in condensed matter, emerging in systems as diverse as band\nsemiconductors, molecular crystals, and proteins. Recently, their existence in\nstrongly correlated electron materials has attracted increasing interest due to\nthe excitons' unique coupling to spin and orbital degrees of freedom. The\nnon-equilibrium driving of such dressed quasiparticles offers a promising\nplatform for realizing unconventional many-body phenomena and phases beyond\nthermodynamic equilibrium. Here, we achieve this in the van der Waals\ncorrelated insulator NiPS$_3$ by photoexciting its newly discovered\nspin-orbit-entangled excitons that arise from Zhang-Rice states. By monitoring\nthe time evolution of the terahertz conductivity, we observe the coexistence of\nitinerant carriers produced by exciton dissociation and the long-wavelength\nantiferromagnetic magnon that coherently precesses in time. These results\ndemonstrate the emergence of a transient metallic state that preserves\nlong-range antiferromagnetism, a phase that cannot be reached by simply tuning\nthe temperature. More broadly, our findings open an avenue toward the\nexciton-mediated optical manipulation of magnetism.\n"}
+{"abstract": "  A new unsupervised learning method of depth and ego-motion using multiple\nmasks from monocular video is proposed in this paper. The depth estimation\nnetwork and the ego-motion estimation network are trained according to the\nconstraints of depth and ego-motion without truth values. The main contribution\nof our method is to carefully consider the occlusion of the pixels generated\nwhen the adjacent frames are projected to each other, and the blank problem\ngenerated in the projection target imaging plane. Two fine masks are designed\nto solve most of the image pixel mismatch caused by the movement of the camera.\nIn addition, some relatively rare circumstances are considered, and repeated\nmasking is proposed. To some extent, the method is to use a geometric\nrelationship to filter the mismatched pixels for training, making unsupervised\nlearning more efficient and accurate. The experiments on KITTI dataset show our\nmethod achieves good performance in terms of depth and ego-motion. The\ngeneralization capability of our method is demonstrated by training on the\nlow-quality uncalibrated bike video dataset and evaluating on KITTI dataset,\nand the results are still good.\n"}
+{"abstract": "  Unmanned aerial vehicles (UAVs) have been increasingly used in a wide area of\nmilitary and civilian applications such as data collection and monitoring. A\nreliable network for command and control, communication, and data transfer is\ncrucial, not only for mission purposes but also for safety concerns. The\nalready deployed cellular networks are appropriate candidates for UAV\ncommunication given the solid security and wide coverage of these networks.\nHowever, the reliability of such networks needs a comprehensive investigation.\nIn this paper, we use the long-term evolution (LTE) network as the\ninfrastructure for drone communication and data transfer, in a rural area. We\nstudy the communication characteristics of an LTE-connected drone during\nlow-altitude flights, for different altitudes and UAV speeds. We show that, in\nsuch areas, the higher elevation benefits from a better signal quality and\nexperiences a fewer number of handover processes. Higher speed flights also\nslightly degrade the communication performance.\n"}
+{"abstract": "  Relational and noSQL storages are developed for the fast processing of the\nlarge data sets having a stable structure, while the ontologies are used to\nrep-resent complex and dynamic sets of information of a limited size. In the\nin-dustrial applications it is often needed to maintain the large warehouses of\ndata consolidated from various sources. The ontologies are useful to repre-sent\nthe structure of that data, but RDF triple stores are not well suitable for\nstoring it. We offer an approach and a system allowing to use the\nopportuni-ties of fast storage engines along with the flexibility of\nontology-based data management tools, including SPARQL queries. The system\nimplements a multi-model data abstraction layer which allows working with the\ndata as if it is situated in RDF triple store, executes SPARQL queries over it\nand ap-plies SHACL constraints and rules.\n"}
+{"abstract": "  In this paper we reformulate the problem of pricing options in a quantum\nsetting. Our proposed algorithm involves preparing an initial state,\nrepresenting the option price, and then evolving it using existing imaginary\ntime simulation algorithms. This way of pricing options boils down to mapping\nan initial option price to a quantum state and then simulating the time\ndependence in Wick's imaginary time space. We numerically verify our algorithm\nfor European options using a particular imaginary time evolution algorithm as\nproof of concept and show how it can be extended to path dependent options like\nAsian options. As the proposed method uses a hybrid variational algorithm, it\nis bound to be relevant for near-term quantum computers.\n"}
+{"abstract": "  In this paper, we propose a dual-mixed formulation for stationary\nviscoplastic flows with yield, such as the Bingham or the Herschel-Bulkley\nflow. The approach is based on a Huber regularization of the viscosity term and\na two-fold saddle point nonlinear operator equation for the resulting weak\nformulation. We provide the uniqueness of solutions for the continuous\nformulation and propose a discrete scheme based on Arnold-Falk-Winther finite\nelements. The discretization scheme yields a system of slantly differentiable\nnonlinear equations, for which a semismooth Newton algorithm is proposed and\nimplemented. Local superlinear convergence of the method is also proved.\nFinally, we perform several numerical experiments in two and three dimensions\nto investigate the behavior and efficiency of the method.\n"}
+{"abstract": "  In this paper DeWitt's formalism for field theories is presented; it provides\na framework in which the quantization of fields possessing infinite dimensional\ninvariance groups may be carried out in a manifestly covariant\n(non-Hamiltonian) fashion, even in curved space-time. Another important virtue\nof DeWitt's approach is that it emphasizes the common features of apparently\nvery different theories such as Yang-Mills theories and General Relativity;\nmoreover, it makes it possible to classify all gauge theories in three\ncategories characterized in a purely geometrical way, i.e., by the algebra\nwhich the generators of the gauge group obey; the geometry of such theories is\nthe fundamental reason underlying the emergence of ghost fields in the\ncorresponding quantum theories, too. These \"tricky extra particles\", as Feynman\ncalled them in 1964, contribute to a physical observable such as the\nstress-energy tensor, which can be expressed in terms of Feynman's Green\nfunction itself. Therefore, an entire section is devoted to the study of the\nGreen functions of the neutron scalar meson: in flat space-time, the choice of\na particular Green's function is the choice of an integration contour in the\n\"momentum\" space; in curved space-time the momentum space is no longer\navailable, and the definition of the different Green functions requires a\ncareful discussion itself. After the necessary introduction of bitensors, world\nfunction and parallel displacement tensor, an expansion for the Feynman\npropagator in curved space-time is obtained. Most calculations are explicitly\nshown.\n"}
+{"abstract": "  The matching complex of a graph $G$ is a simplicial complex whose simplices\nare matchings in $G$. In the last few years the matching complexes of grid\ngraphs have gained much attention among the topological combinatorists. In\n2017, Braun and Hough obtained homological results related to the matching\ncomplexes of $2 \\times n$ grid graphs. Further in 2019, Matsushita showed that\nthe matching complexes of $2 \\times n$ grid graphs are homotopy equivalent to a\nwedge of spheres. In this article we prove that the matching complexes of\n$3\\times n$ grid graphs are homotopy equivalent to a wedge of spheres. We also\ngive the comprehensive list of the dimensions of spheres appearing in the\nwedge.\n"}
+{"abstract": "  We present results of two-year photometric monitoring of Comet C/2011 J2\n(LINEAR) that spans the time period from February 2013 through December 2014,\nbefore and after perihelion passage. The observations were conducted with\nbroadband R filter. Analysis of observations of Comet C/2011 J2 (LINEAR) allows\nestimating the nucleus radius as function of geometric albedo and\nphase-darkening coefficient. Furthermore, our observations showed split of the\ncomet. Fragment (B) additional to the primary C/2011 J2 nucleus was\nunambiguously detected; relative velocity is estimated to be near 0.17 ''/day.\nWe derive the Afrho parameter and estimate the dust production rate in Comet\nC/2011 J2 (LINEAR) over the entire run of observations. We found a noticeable\nincrease in Afrho parameter between September 18, 2014 and November 5, 2014,\nepoch when the nucleus presumably got split. On September 28, 2014, we observed\na transit of the 16-magnitude star (USNO-A2 1275-18299027) near nucleus of the\nComet C/2011 J2 (LINEAR). We retrieve the optical depth of the coma 0.034. The\nfilling factor f that corresponds to such optical depth is in good quantitative\nagreement with the value that can be derived from the Afrho parameter under\nreasonable assumption on geometric albedo of cometary dust.\n"}
+{"abstract": "  Evidence of triggered star formation at large spatial scales involving\nstellar clusters is scarce. We investigate a Galactic region (l=130.0, b=0.35)\npopulated by several open stellar clusters that according to the last GAIA data\nrelease, are located at a distance of about 2.9 kpc. By analyzing the\ninterstellar medium (ISM) at infrared, centimeter, and millimeter wavelengths\ntowards this group of clusters we discovered a shell of material of about 2\ndegree in size at the same distance. We suggest that the shell, mainly observed\nat 12 um and in the Hi emission at 21 cm, was generated by the action of\nmassive stars belonging to clusters Berkeley 7 and UBC 414, that lie at its\ncenter. Five clusters (MWSC0152, Czernik 6, Czernik 7, Berkeley 6, NGC 663, and\nNGC 654) lie at the border of this shell. From the comparison between the\ndynamical time of the discovered Hi shell and the analysis of the ages of\nstellar populations in these clusters, we conclude that the expansion of the\nshell could have triggered in the past the formation of stars in some of them.\nWe point out that in order to find physical evidence supporting a genetic\nconnection between stellar clusters, it is necessary not only to study the\nindividual clusters and their stellar populations, but also to investigate\ntheir surrounding ISM at a large spatial scale.\n"}
+{"abstract": "  We present a detailed spectral-timing analysis of the Kilohertz\nquasi-periodic oscillations (kHz QPOs) in Sco X-1 using the data of Rossi X-ray\nTiming Explorer ($RXTE$) and the Hard X-ray Modulation Telescope\n($Insight$-HXMT). The energy band with detectable kHz QPOs is studied for the\nfirst time: on the horizontal branch, it is $\\sim$ 6.89--24.01 keV and $\\sim$\n8.68--21.78 keV for the upper and lower kHz QPOs detected by $RXTE$, and $\\sim$\n9--27.5 keV for the upper kHz QPOs by $Insight$-HXMT; on the lower normal\nbranch, the energy band is narrower. The fractional root mean square (rms) of\nthe kHz QPOs increases with energy at lower energy, reaches a plateau at about\n16 keV and 20 keV for the lower and upper peaks, and then levels off though\nwith a large uncertainty. The simulation of the deadtime effect of $RXTE$/PCA\nshows that the deadtime does not affect much the search of the kHz QPOs but\nmakes the rms amplitude underestimated. No significant QPO is detected below\n$\\sim$ 6 keV as shown by the $RXTE$ data, implying that the kHz QPOs do not\noriginate from the black body emission of the accretion disk and neutron star\nsurface. In addition, with the combined analysis of the energy spectra and the\nabsolute rms spectra of kHz QPOs, we suggest that the kHz QPOs in Sco X-1\noriginate from the Comptonization of the inner part of the transition layer,\nwhere the rotation sets the frequency and the inward bulk motion makes the\nspectrum harder.\n"}
+{"abstract": "  We propose an architecture and training scheme to predict video frames by\nexplicitly modeling dis-occlusions and capturing the evolution of semantically\nconsistent regions in the video. The scene layout (semantic map) and motion\n(optical flow) are decomposed into layers, which are predicted and fused with\ntheir context to generate future layouts and motions. The appearance of the\nscene is warped from past frames using the predicted motion in co-visible\nregions; dis-occluded regions are synthesized with content-aware inpainting\nutilizing the predicted scene layout. The result is a predictive model that\nexplicitly represents objects and learns their class-specific motion, which we\nevaluate on video prediction benchmarks.\n"}
+{"abstract": "  We prove mixing convergence of the least squares estimator of autoregressive\nparameters for supercritical Gaussian autoregressive processes of order 2\nhaving real characteristic roots with different absolute values. We use an\nappropriate random scaling such that the limit distribution is a\ntwo-dimensional normal distribution concentrated on a one-dimensional ray\ndetermined by the characteristic root having the larger absolute value.\n"}
+{"abstract": "  The SNO+ experiment is located 2 km underground at SNOLAB in Sudbury, Canada.\nA low background search for neutrinoless double beta ($0\\nu\\beta\\beta$) decay\nwill be conducted using 780 tonnes of liquid scintillator loaded with 3.9\ntonnes of natural tellurium, corresponding to 1.3 tonnes of $^{130}$Te. This\npaper provides a general overview of the SNO+ experiment, including detector\ndesign, construction of process plants, commissioning efforts, electronics\nupgrades, data acquisition systems, and calibration techniques. The SNO+\ncollaboration is reusing the acrylic vessel, PMT array, and electronics of the\nSNO detector, having made a number of experimental upgrades and essential\nadaptations for use with the liquid scintillator. With low backgrounds and a\nlow energy threshold, the SNO+ collaboration will also pursue a rich physics\nprogram beyond the search for $0\\nu\\beta\\beta$ decay, including studies of geo-\nand reactor antineutrinos, supernova and solar neutrinos, and exotic physics\nsuch as the search for invisible nucleon decay. The SNO+ approach to the search\nfor $0\\nu\\beta\\beta$ decay is scalable: a future phase with high\n$^{130}$Te-loading is envisioned to probe an effective Majorana mass in the\ninverted mass ordering region.\n"}
+{"abstract": "  Non-negative Matrix Factorization (NMF) is one of the most popular techniques\nfor data representation and clustering, and has been widely used in machine\nlearning and data analysis. NMF concentrates the features of each sample into a\nvector, and approximates it by the linear combination of basis vectors, such\nthat the low-dimensional representations are achieved. However, in real-world\napplications, the features are usually with different importances. To exploit\nthe discriminative features, some methods project the samples into the subspace\nwith a transformation matrix, which disturbs the original feature attributes\nand neglects the diversity of samples. To alleviate the above problems, we\npropose the Feature weighted Non-negative Matrix Factorization (FNMF) in this\npaper. The salient properties of FNMF can be summarized as threefold: 1) it\nlearns the weights of features adaptively according to their importances; 2) it\nutilizes multiple feature weighting components to preserve the diversity; 3) it\ncan be solved efficiently with the suggested optimization algorithm.\nPerformance on synthetic and real-world datasets demonstrate that the proposed\nmethod obtains the state-of-the-art performance.\n"}
+{"abstract": "  The electrostatic theory of magnetoplasmons on a semi-infinite magnetized\nelectron gas is generalized to mixed Faraday-Voigt configurations. We analyze a\nnew type of electrostatic surface waves that is strongly direction-dependent,\nand may be realized on narrow-gap semiconductors in the THz regime. A general\nexpression for the dispersion relation is presented, with its dependence on the\nmagnitude and orientation of the applied magnetic field. Remarkably, the group\nvelocity is always perpendicular to the phase velocity. Both velocity and\nenergy relations of the found magnetoplasmons are discussed in detail. In the\nappropriate limits the known magnetoplasmons in the higher-symmetry Faraday and\nVoigt configurations are recovered.\n"}
+{"abstract": "  Binary decision-making process is ubiquitous in social life and is of vital\nsignificance in many real-world issues, ranging from public health to political\ncampaigns. While continuous opinion evolution independent of discrete choice\nbehavior has been extensively studied, few works unveil how the group binary\ndecision-making result is determined by the coupled dynamics of these two\nprocesses. To this end, we propose an agent-based model to study the collective\nbehaviors of individual binary decision-making process through competitive\nopinion dynamics on social networks. Three key factors are considered: bounded\nconfidence that describes the cognitive scope of the population, stubbornness\nlevel that characterizes the opinion updating speed, and the opinion strength\nthat represents the asymmetry power or attractiveness of the two choices. We\nfind that bounded confidence plays an important role in determining competing\nevolution results. As bounded confidence grows, population opinions experience\npolarization to consensus, leading to the emergence of phase transition from\nco-existence to winner-takes-all state under binary decisions. Of particular\ninterest, we show how the combined effects of bounded confidence and asymmetry\nopinion strength may reverse the initial supportive advantage in competitive\ndynamics. Notably, our model qualitatively reproduces the important dynamical\npattern during a brutal competition, namely, cascading collapse, as observed by\nreal data. Finally and intriguingly, we find that individual cognitive\nheterogeneity can bring about randomness and unpredictability in binary\ndecision-making process, leading to the emergence of indeterministic\noscillation. Our results reveal how the diverse behavioral patterns of binary\ndecision-making can be interpreted by the complicated interactions of the\nproposed elements, which provides important insights toward competitive\ndynamics\n"}
+{"abstract": "  Special atom spaces have been around for quite awhile since the introduction\nof atoms by R. Coifman in his seminal paper who led to another proof that the\ndual of the Hardy space $H^1$ is in fact the space of functions of bounded\nmeans oscillations (BMO). Special atom spaces enjoy quite a few attributes of\ntheir own, among which the fact that they have an analytic extension to the\nunit disc. Recently, an extension of special atom spaces to higher dimensions\nwas proposed, making ripe the possible exploration of the above extension in\nhigher dimensions. In this paper we propose an analytic characterization of\nspecial atom spaces in higher dimensions.\n"}
+{"abstract": "  Nanofluids are suspensions of nanoparticles in a base heat-transfer liquid.\nThey have been widely investigated to boost heat transfer since they were\nproposed in the 1990's. We present a statistical correlation analysis of\nexperimentally measured thermal conductivity of water-based nanofluids\navailable in the literature. The influences of particle concentration, particle\nsize, temperature and surfactants are investigated. For specific materials\n(alumina, titania, copper oxide, copper, silica and silicon carbide), separate\nanalyses are performed. The conductivity increases with the concentration in\nqualitative agreement with Maxwell's theory of homogeneous media. The\nconductivity also increases with the temperature (in addition to the\nimprovement due to the increased conductivity with water). Surprisingly, only\nsilica nanofluids exhibit a statistically significant effect of particle size,\nwhereby smaller particles lead to faster heat transfer. Overall, the large\nscatter in the experimental data prevents a compelling, unambiguous assessment\nof these effects. Taken together, the results of our analysis suggest that more\ncomprehensive experimental characterizations of nanofluids are necessary to\nestimate their practical potential.\n"}
+{"abstract": "  Human pose estimation is a fundamental yet challenging task in computer\nvision, which aims at localizing human anatomical keypoints. However, unlike\nhuman vision that is robust to various data corruptions such as blur and\npixelation, current pose estimators are easily confused by these corruptions.\nThis work comprehensively studies and addresses this problem by building\nrigorous robust benchmarks, termed COCO-C, MPII-C, and OCHuman-C, to evaluate\nthe weaknesses of current advanced pose estimators, and a new algorithm termed\nAdvMix is proposed to improve their robustness in different corruptions. Our\nwork has several unique benefits. (1) AdvMix is model-agnostic and capable in a\nwide-spectrum of pose estimation models. (2) AdvMix consists of adversarial\naugmentation and knowledge distillation. Adversarial augmentation contains two\nneural network modules that are trained jointly and competitively in an\nadversarial manner, where a generator network mixes different corrupted images\nto confuse a pose estimator, improving the robustness of the pose estimator by\nlearning from harder samples. To compensate for the noise patterns by\nadversarial augmentation, knowledge distillation is applied to transfer clean\npose structure knowledge to the target pose estimator. (3) Extensive\nexperiments show that AdvMix significantly increases the robustness of pose\nestimations across a wide range of corruptions, while maintaining accuracy on\nclean data in various challenging benchmark datasets.\n"}
+{"abstract": "  The processing of binary alloys consisting of ferromagnetic Fe and\nantiferromagnetic Cr by severe plastic deformation (SPD) with different\nchemical compositions has been investigated. Although the phase diagram\nexhibits a large gap in the thermodynamical equilibrium at lower temperatures,\nit is shown that techniques based on SPD help to overcome common processing\nlimits. Different processing routes including initial ball milling (BM) and arc\nmelting (AM) and a concatenation with annealing treatments prior to\nhigh-pressure torsion (HPT) deformation are compared in this work.\nInvestigation of the deformed microstructures by electron microscopy and\nsynchrotron X-ray diffraction reveal homogeneous, nanocrystalline\nmicrostructures for HPT deformed AM alloys. HPT deformation of powder blends\nand BM powders leads to an exorbitant increase in hardness or an unusual fast\nformation of a $\\sigma$-phase and therefore impede successful processing.\n"}
+{"abstract": "  Policy optimization, which learns the policy of interest by maximizing the\nvalue function via large-scale optimization techniques, lies at the heart of\nmodern reinforcement learning (RL). In addition to value maximization, other\npractical considerations arise commonly as well, including the need of\nencouraging exploration, and that of ensuring certain structural properties of\nthe learned policy due to safety, resource and operational constraints. These\nconsiderations can often be accounted for by resorting to regularized RL, which\naugments the target value function with a structure-promoting regularization\nterm. Focusing on an infinite-horizon discounted Markov decision process, this\npaper proposes a generalized policy mirror descent (GPMD) algorithm for solving\nregularized RL. As a generalization of policy mirror descent Lan (2021), the\nproposed algorithm accommodates a general class of convex regularizers as well\nas a broad family of Bregman divergence in cognizant of the regularizer in use.\nWe demonstrate that our algorithm converges linearly over an entire range of\nlearning rates, in a dimension-free fashion, to the global solution, even when\nthe regularizer lacks strong convexity and smoothness. In addition, this linear\nconvergence feature is provably stable in the face of inexact policy evaluation\nand imperfect policy updates. Numerical experiments are provided to corroborate\nthe applicability and appealing performance of GPMD.\n"}
+{"abstract": "  There are now more Particle-in-Cell (PIC) codes than ever before that\nresearchers use to simulate intense laser-plasma interactions. To date, there\nhave been relatively few direct comparisons of these codes in the literature,\nespecially for relativistic intensity lasers interacting with thin overdense\ntargets. To address this we perform a code comparison of three PIC codes:\nEPOCH, LSP, and WarpX for the problem of laser-driven ion acceleration in a\n2D(3v) geometry for a $10^{20}$ W cm$^{-2}$ intensity laser. We examine the\nplasma density, ion energy spectra, and laser-plasma coupling of the three\ncodes and find strong agreement. We also run the same simulation 20 times with\ndifferent random seeds to explore statistical fluctuations of the outputs. We\nthen compare the execution times and memory usage of the codes (without\n\"tuning\" to improve performance) using between 1 and 48 processors on one node.\nWe provide input files to encourage larger and more frequent code comparisons\nin this field.\n"}
+{"abstract": "  The abolitionist movement of the nineteenth-century United States remains\namong the most significant social and political movements in US history.\nAbolitionist newspapers played a crucial role in spreading information and\nshaping public opinion around a range of issues relating to the abolition of\nslavery. These newspapers also serve as a primary source of information about\nthe movement for scholars today, resulting in powerful new accounts of the\nmovement and its leaders. This paper supplements recent qualitative work on the\nrole of women in abolition's vanguard, as well as the role of the Black press,\nwith a quantitative text modeling approach. Using diachronic word embeddings,\nwe identify which newspapers tended to lead lexical semantic innovations -- the\nintroduction of new usages of specific words -- and which newspapers tended to\nfollow. We then aggregate the evidence across hundreds of changes into a\nweighted network with the newspapers as nodes; directed edge weights represent\nthe frequency with which each newspaper led the other in the adoption of a\nlexical semantic change. Analysis of this network reveals pathways of lexical\nsemantic influence, distinguishing leaders from followers, as well as others\nwho stood apart from the semantic changes that swept through this period. More\nspecifically, we find that two newspapers edited by women -- THE PROVINCIAL\nFREEMAN and THE LILY -- led a large number of semantic changes in our corpus,\nlending additional credence to the argument that a multiracial coalition of\nwomen led the abolitionist movement in terms of both thought and action. It\nalso contributes additional complexity to the scholarship that has sought to\ntease apart the relation of the abolitionist movement to the women's suffrage\nmovement, and the vexed racial politics that characterized their relation.\n"}
+{"abstract": "  The associated production of a single-top with opposite-sign same-flavor\n(OSSF) di-leptons, $pp \\to t \\ell^+ \\ell^-$ and $ pp \\to t \\ell^+ \\ell^- + j$\n($j=$light jet), can lead to striking tri-lepton $pp \\to \\ell^\\prime \\ell^+\n\\ell^- + X$ and di-lepton $pp \\to \\ell^+ \\ell^- + j_b + X$ ($j_b=b$-jet) events\nat the LHC, after the top decays. Although these rather generic multi-lepton\nsignals are flavor-blind, they can be generated by new 4-Fermi flavor changing\n(FC) $u_i t \\ell \\ell$ scalar, vector and tensor interactions ($u_i \\in u,c$),\nwhich we study in this paper; we match the FC $u_i t \\ell \\ell$ 4-Fermi terms\nto the SMEFT operators and also to different types of FC underlying heavy\nphysics. The main backgrounds to these di- and tri-lepton signals arise from $t\n\\bar t$, $Z$+jets and $VV$ ($V=W,Z$) production, but they can be essentially\neliminated with a sufficiently high invariant mass selection on the OSSF\ndi-leptons, $m_{\\ell^+ \\ell^-}^{\\tt min}(OSSF) > 1$ TeV; the use of $b$-tagging\nas an additional selection in the di-lepton final state case also proves very\nuseful. We find, for example, that the expected 95\\% CL bounds on the scale of\na tensor(vector) $u t \\mu \\mu$ interaction, with the current $\\sim 140$\nfb$^{-1}$ of LHC data, are $\\Lambda < 5(3.2) $ TeV or $\\Lambda < 4.1(2.7)$ TeV,\nif analyzed via the di-muon $\\mu^+ \\mu^- + j_b$ signal or the $e \\mu^+ \\mu^-$\ntri-lepton one, respectively. The expected reach at the HL-LHC with 3000\nfb$^{-1}$ of data is $\\Lambda < 7.1(4.7)$ TeV and $\\Lambda < 2.4(1.5)$ TeV for\nthe corresponding $u t \\mu \\mu$ and $c t \\mu \\mu$ operators. We also study the\npotential sensitivity at future 27 TeV and 100 TeV high-energy LHC successors\nand also discuss the possible implications of this class of FC 4-Fermi\neffective interactions on lepton non-universality tests at the LHC.\n"}
+{"abstract": "  Round-the-clock monitoring of human behavior and emotions is required in many\nhealthcare applications which is very expensive but can be automated using\nmachine learning (ML) and sensor technologies. Unfortunately, the lack of\ninfrastructure for collection and sharing of such data is a bottleneck for ML\nresearch applied to healthcare. Our goal is to circumvent this bottleneck by\nsimulating a human body in virtual environment. This will allow generation of\npotentially infinite amounts of shareable data from an individual as a function\nof his actions, interactions and emotions in a care facility or at home, with\nno risk of confidentiality breach or privacy invasion. In this paper, we\ndevelop for the first time a system consisting of computational models for\nsynchronously synthesizing skeletal motion, electrocardiogram, blood pressure,\nrespiration, and skin conductance signals as a function of an open-ended set of\nactions and emotions. Our experimental evaluations, involving user studies,\nbenchmark datasets and comparison to findings in the literature, show that our\nmodels can generate skeletal motion and physiological signals with high\nfidelity. The proposed framework is modular and allows the flexibility to\nexperiment with different models. In addition to facilitating ML research for\nround-the-clock monitoring at a reduced cost, the proposed framework will allow\nreusability of code and data, and may be used as a training tool for ML\npractitioners and healthcare professionals.\n"}
+{"abstract": "  Given a natural number $n \\geq 4$ we show that there exists infinitely many\npolynomials $f_{n}(x):= \\prod_{i=1}^{n} (x^{2} - a_{i})$ such that (i)\n$f_{n}(x)$ has a root modulo every positive integer, (ii) $f_{n}(x)$ has no\nrational roots, and (iii) every proper divisor of $f_{n}(x)$ fails to have root\nmodulo some positive integer. We exhibit a process to explicitly construct such\n$f_{n}$ and this process demonstrates that the set of natural numbers $a_{n}$,\nsuch that the polynomial $f_{n}(x):= \\prod_{i=1}^{n} (x^{2} - a_{i})$ satisfies\nthe properties (i), (ii) and (iii), is of positive asymptotic density in\n$\\mathbb{N}$.\n"}
+{"abstract": "  We prove a number of results regarding odd values of the Ramanujan\n$\\tau$-function. For example, we prove the existence of an effectively\ncomputable positive constant $\\kappa$ such that if $\\tau(n)$ is odd and $n \\ge\n25$ then either \\[\n  P(\\tau(n)) \\; > \\; \\kappa \\cdot \\frac{\\log\\log\\log{n}}{\\log\\log\\log\\log{n}}\n\\] or there exists a prime $p \\mid n$ with $\\tau(p)=0$. Here $P(m)$ denotes the\nlargest prime factor of $m$. We also solve the equation $\\tau(n)=\\pm 3^{b_1}\n5^{b_2} 7^{b_3} 11^{b_4}$ and the equations $\\tau(n)=\\pm q^b$ where $3\\le q <\n100$ is prime and the exponents are arbitrary nonnegative integers. We make use\nof a variety of methods, including the Primitive Divisor Theorem of Bilu,\nHanrot and Voutier, bounds for solutions to Thue--Mahler equations due to\nBugeaud and Gy\\H{o}ry, and the modular approach via Galois representations of\nFrey-Hellegouarch elliptic curves.\n"}
+{"abstract": "  We propose a novel method to reconstruct high-resolution three-dimensional\nmass maps using data from photometric weak-lensing surveys. We apply an\nadaptive LASSO algorithm to perform a sparsity-based reconstruction on the\nassumption that the underlying cosmic density field is represented by a sum of\nNavarro-Frenk-White halos. We generate realistic mock galaxy shape catalogues\nby considering the shear distortions from isolated halos for the configurations\nmatched to Subaru Hyper Suprime-Cam Survey with its photometric redshift\nestimates. We show that the adaptive method significantly reduces line-of-sight\nsmearing that is caused by the correlation between the lensing kernels at\ndifferent redshifts. Lensing clusters with lower mass limits of $10^{14.0}\nh^{-1}M_{\\odot}$, $10^{14.7} h^{-1}M_{\\odot}$, $10^{15.0} h^{-1}M_{\\odot}$ can\nbe detected with 1.5-$\\sigma$ confidence at the low ($z<0.3$), median ($0.3\\leq\nz< 0.6$) and high ($0.6\\leq z< 0.85$) redshifts, respectively, with an average\nfalse detection rate of 0.022 deg$^{-2}$. The estimated redshifts of the\ndetected clusters are systematically lower than the true values by $\\Delta z\n\\sim 0.03$ for halos at $z\\leq 0.4$, but the relative redshift bias is below\n$0.5\\%$ for clusters at $0.4<z\\leq 0.85$. The standard deviation of the\nredshift estimation is $0.092$. Our method enables direct three-dimensional\ncluster detection with accurate redshift estimates.\n"}
+{"abstract": "  Following our work [Phys. Rev. Lett. 125, 020401 (2020)], we discuss a\nsemiclassical description of one-dimensional quantum tunneling through\nmultibarrier potentials in terms of complex time. We start by defining a\ncomplex-extended continuum level density of unbound systems and show its\nrelation to a complex time shift of the transmitted wave. While the real part\nof the level density and time shift describes the passage of the particle\nthrough classically allowed coordinate regions, the imaginary part is connected\nwith an instanton-like picture of the tunneling through forbidden regions. We\ndescribe singularities in the real and imaginary parts of the level density and\ntime shift caused by stationary points of the tunneling potential, and show\nthat they represent a dual extension of excited-state quantum phase transitions\nfrom bound to continuum systems. Using the complex scaling method, we\nnumerically verify the predicted effects in several tunneling potentials.\n"}
+{"abstract": "  This concept paper aims to provide a brief outline of quantum computers,\nexplore existing methods of quantum image classification techniques, so\nfocusing on remote sensing applications, and discuss the bottlenecks of\nperforming these algorithms on currently available open source platforms.\nInitial results demonstrate feasibility. Next steps include expanding the size\nof the quantum hidden layer and increasing the variety of output image options.\n"}
+{"abstract": "  A composite drag control (CDC) combining the opposition (OC) and spanwise\nopposed wall-jet forcing (SOJF) methods is studied in a turbulent channel flow\nvia direct numerical simulation of the incompressible Navier-Stokes equations.\nA maximum drag reduction of about 33% is obtained for CDC -- much higher than\nthat produced by either individual method (namely, 19% for SOJF and 23% for\nOC). Due to the small power input required for both OC and SOJF methods, a\nsignificant net power saving (about 32%) is achieved via CDC. Flow analysis\nshows that CDC can take advantage of both OC and SOJF methods to better\nsuppress drag producing, near-wall turbulent structures -- vortices and\nstreaks. In particular, due to the presence of the large-scale coherent swirls\ngenerated by SOJF, it is more effective than OC in suppressing the random\nturbulence. Moreover, due to the OC's role in suppressing random small-scale\nturbulence, CDC requires weaker large-scale coherent swirls than those using\nSOJF only -- hence decreasing the drag contribution associated with large-scale\nswirls. In summary, our results suggest prospects of employing composite\ncontrol strategy for effective skin friction drag reduction, particularly at\nvery high Reynolds numbers.\n"}
+{"abstract": "  We study the fundamental problem of allocating indivisible goods to agents\nwith additive preferences. We consider eliciting from each agent only a ranking\nof her $k$ most preferred goods instead of her full cardinal valuations. We\ncharacterize the value of $k$ needed to achieve envy-freeness up to one good\nand approximate maximin share guarantee, two widely studied fairness notions.\nWe also analyze the multiplicative loss in social welfare incurred due to the\nlack of full information with and without the fairness requirements.\n"}
+{"abstract": "  In this letter we report the existence of nondegenerate fundamental bright\nsoliton solution for coupled multi-component nonlinear Schr\\\"{o}dinger\nequations of Manakov type. To derive this class of nondegenerate vector soliton\nsolutions, we adopt the Hirota bilinear method with appopriate general class of\nseed solutions. Very interestingly the obtained nondegenerate fundamental\nsoliton solution of the $N$-coupled nonlinear Schr\\\"{o}dinger (CNLS) system\nadmits multi-hump natured intensity profiles. We explicitly demonstrate this\nspecific property by considering the nondegenerate soliton solutions for $3$\nand $4$-CNLS systems. We also point out the existence of a special class of\npartially nondegenerate soliton solutions by imposing appropriate restrictions\non the wavenumbers in the already obtained completely nondegenerate soliton\nsolution. Such class of soliton solutions can also exhibit multi-hump profile\nstructures. Finally, we present the stability analysis of nondegenerate\nfundamental soliton of the $3$-CNLS system as an example. The numerical results\nconfirm the stability of triple-humped profile nature against perturbations of\n5\\% and 10\\% white noise. The multi-hump nature of nondegenerate fundamental\nsoliton solution will be usefull in multi-level optical communication\napplications with enhanced flow of data in multi-mode fibers.\n"}
+{"abstract": "  This paper introduces Broad Infinity, a new and arguably intuitive axiom\nscheme. It states that \"broad numbers\", which are three-dimensional trees whose\ngrowth is controlled, form a set. If the Axiom of Choice is assumed, then Broad\nInfinity is equivalent to the Ord-is-Mahlo scheme: every closed unbounded class\nof ordinals contains a regular ordinal.\n  Whereas the axiom of Infinity leads to generation principles for sets and\nfamilies and ordinals, Broad Infinity leads to more advanced versions of these\nprinciples. The paper relates these principles under various prior assumptions:\nthe Axiom of Choice, the Law of Excluded Middle, and weaker assumptions.\n"}
+{"abstract": "  It is commonly assumed that topological phase transitions in topological\nsuperconductors are accompanied by a closing of the topological gap or a change\nof the symmetry of the system. We demonstrate that an unconventional\ntopological phase transition with neither gap closing nor a change of symmetry\nis possible. We consider a nanoscopic length ladder of atoms on a\nsuperconducting substrate, comprising self-organized magnetic moments coupled\nto itinerant electrons. For a range of conditions, the ground state of such a\nsystem prefers helical magnetic textures, self-sustaining topologically\nnontrivial phase. Abrupt changes in the magnetic order as a function of induced\nsuperconducting pairing or chemical potential can cause topological phase\ntransitions without closing the topological gap. Furthermore, the ground state\nprefers either parallel or anti-parallel configurations along the rungs, and\nthe anti-parallel configuration causes an emergent time reversal asymmetry\nprotecting Kramer's pair's of Majorana zero modes, but in a BDI topological\nsuperconductor. We determine the topological invariant and inspect the boundary\nMajorana zero modes.\n"}
+{"abstract": "  Chorus emission in planetary magnetospheres is taken as working paradigm to\nmotivate a short tutorial trip through theoretical plasma physics methods and\ntheir applications. Starting from basic linear theory, readers are first made\ncomfortable with whistler wave packets and their propagation in slowly varying\nweakly nonuniform media, such as the Earth's magnetosphere, where they can be\namplified by a population of supra-thermal electrons. The nonlinear dynamic\ndescription of energetic electrons in the phase space in the presence of\nself-consistently evolving whistler fluctuation spectrum is progressively\nintroduced by addressing renormalization of the electron response and spectrum\nevolution equations. Analytical and numerical results on chorus frequency\nchirping are obtained and compared with existing observations and particle in\ncell simulations. Finally, the general theoretical framework constructed during\nthis short trip through chorus physics is used to draw analogies with condensed\nmatter and laser physics as well as magnetic confinement fusion research.\nDiscussing these analogies ultimately presents plasma physics as an exciting\ncross-disciplinary field to study.\n"}
+{"abstract": "  A set of unified relativistic mean-field equations of state for hyperonic\ncompact stars recently built in [M. Fortin, Ad. R. Raduta, S. Avancini, and C.\nProvidencia, Phys. Rev. D {\\bf 101}, 034017 (2020)] is used to study the\nthermal evolution of non-magnetized and non-rotating spherically-symmetric\nisolated and accreting neutron stars under different hypothesis concerning\nproton $S$-wave superfluidity.\n  These equations of state have been obtained in the following way: the slope\nof the symmetry energy is in agreement with experimental data; the coupling\nconstants of $\\Lambda$ and $\\Xi$-hyperons are determined from experimental\nhypernuclear data; uncertainties in the nucleon-$\\Sigma$ interaction potential\nare accounted for; current constraints on the lower bound of the maximum\nneutron star mass are satisfied.\n  Within the considered set of equations of state, the presence of hyperons is\nessential for the description of the cooling/heating curves.\n  One of the conclusions we reach is that the criterion of best agreement with\nobservational data leads to different equations of states and proton $S$-wave\nsuperfluidity gaps when applied separately for isolated neutron stars and\naccreting neutron stars in quiescence.\n  This means that at least in one situation the traditional simulation\nframework that we employ is not complete and/or the equations of state are\ninappropriate.\n  Another result is that, considering equations of state which do not allow for\nnucleonic dUrca or allow for it only in very massive NS, the low luminosity of\nSAX J1808 requires a repulsive $\\Sigma$-hyperon potential in symmetric nuclear\nmatter in the range $U_\\Sigma^{(N)}\\approx 10-30$ MeV. This range of values for\n$U_\\Sigma^{(N)} $ is also supported by the criterion of best agreement with all\navailable data from INS and XRT.\n"}
+{"abstract": "  We show that, if $w_1, \\ldots , w_6$ are words which are not an identity of\nany (non-abelian) finite simple group, then $w_1(G)w_2(G) \\cdots w_6(G) = G$\nfor all (non-abelian) finite simple groups $G$. In particular, for every word\n$w$, either $w(G)^6 = G$ for all finite simple groups, or $w(G)=1$ for some\nfinite simple groups. These theorems follow from more general results we obtain\non characteristic collections of finite groups and their covering numbers,\nwhich are of independent interest and have additional applications.\n"}
+{"abstract": "  Online stores often utilize product relationships such as bundles and\nsubstitutes to improve their catalog quality and guide customers through myriad\nchoices. Entity resolution using pairwise product matching models offers a\nmeans of inferring relationships between products. In mature data repositories,\nthe relationships may be mostly correct but require incremental improvements\nowing to errors in the original data or in the entity resolution system. It is\ncritical to devise incremental entity resolution (IER) approaches for improving\nthe health of relationships. However, most existing research on IER focuses on\nthe addition of new products or information into existing relationships.\nRelatively little research has been done for detecting low quality within\ncurrent relationships.\n  This paper proposes a novel method for identifying inconsistent clusters\n(IC), existing groups of related products that do not belong together. We\npropose to treat the identification of inconsistent clusters as a supervised\nlearning task which predicts whether a graph of products with similarities as\nweighted edges should be partitioned into multiple clusters. In this case, the\nproblem becomes a classification task on weighted graphs and represents an\ninteresting application area for modern tools such as Graph Neural Networks\n(GNNs). We demonstrate that existing Message Passing neural networks perform\nwell at this task, exceeding traditional graph processing techniques. We also\ndevelop a novel message aggregation scheme for Message Passing Neural Networks\nthat further improves the performance of GNNs on this task. We apply the model\nto synthetic datasets, a public benchmark dataset, and an internal application.\nOur results demonstrate the value of graph classification in IER and the\nability of graph neural networks to develop useful representations for graph\npartitioning.\n"}
+{"abstract": "  We classify transcendental entire functions that are compositions of a\npolynomial and the exponential for which all singular values escape on disjoint\nrays. We focus on the case where the escape is degenerate in the sense that\npoints from different singular orbits are arbitrarily close to each other. As\nin the general case we employ an iteration procedure on an infinite-dimensional\nTeichm\\\"uller space, analogously to the Thurston's classical Topological\nCharacterization of Rational Functions, but for an infinite set of marked\npoints.\n"}
+{"abstract": "  Motivated by recent anomalies in FCNC $b\\to s\\ell^+\\ell^-$, we study $B_1\\to\nB_2\\ell^+\\ell^-(\\ell=e,\\mu,\\tau)$ semi-leptonic weak decays with the SU(3)\nflavor symmetry, where $B_{1,2}$ are the spin-1/2 baryons of single bottomed\nanti-triplet $T_{b3}$, single charmed anti-triplet $T_{c3}$ or light baryon\noctet $T_{8}$. Using the SU(3) irreducible representation approach, we first\nobtain the amplitude relations among different decays, and then predict the\nrelevant not-yet measured observables of these decays. (a) We calculate the\nbranching ratios of the $T_{b3}\\to T_8 \\mu^+\\mu^-$ and $T_{b3}\\to T_8\n\\tau^+\\tau^-$ in the whole $q^2$ region and in the different $q^2$ bins by the\nmeasurement of $\\Lambda^0_b\\to \\Lambda^0 \\mu^+\\mu^-$. Many of them are obtained\nfor the first time. In addition, the longitudinal polarization fractions and\nthe leptonic forward-backward asymmetries of all $T_{b3}\\to T_{8}\\ell^+\\ell^-$\ndecays are very similar to each other in certain $q^2$ bin due to the SU(3)\nflavor symmetry. (b) We analyze the upper limits of $B(T_{c3}\\to\nT_{8}\\ell^+\\ell^-)$ by using the experimental upper limits of $B(\\Lambda^+_c\\to\np\\mu^+\\mu^-)$ and $B(\\Lambda^+_c\\to pe^+e^-)$, and find the experimental upper\nlimit of $B(\\Lambda^+_c\\to p\\mu^+\\mu^-)$ giving effective bounds on the\nrelevant SU(3) flavor symmetry parameters. The predictions of $B(\\Xi^0_c \\to\n\\Xi^0e^+e^-)$ and $B(\\Xi^0_c \\to \\Xi^0\\mu^+\\mu^-)$ will be different between\nthe single-quark transition dominant contributions and the W-exchange dominant\nones. (c) As for $T_{8}\\to T'_8 \\ell^+\\ell^-$ decays, we analyze the\nsingle-quark transition contributions and the W-exchange contributions by using\ntwo measurements of $ B(\\Xi^0\\to \\Lambda^0 e^+e^-)$ and $ B(\\Sigma^+\\to\np\\mu^+\\mu^-)$, and give the branching ratio predictions by assuming either\nsingle-quark transition dominant contributions or the W-exchange dominant ones.\n"}
+{"abstract": "  Facial expressions are an ideal means of communicating one's emotions or\nintentions to others. This overview will focus on human facial expression\nrecognition as well as robotic facial expression generation. In case of human\nfacial expression recognition, both facial expression recognition on predefined\ndatasets as well as in real time will be covered. For robotic facial expression\ngeneration, hand coded and automated methods i.e., facial expressions of a\nrobot are generated by moving the features (eyes, mouth) of the robot by hand\ncoding or automatically using machine learning techniques, will also be\ncovered. There are already plenty of studies that achieve high accuracy for\nemotion expression recognition on predefined datasets, but the accuracy for\nfacial expression recognition in real time is comparatively lower. In case of\nexpression generation in robots, while most of the robots are capable of making\nbasic facial expressions, there are not many studies that enable robots to do\nso automatically.\n"}
+{"abstract": "  A measurement of the magnitude of the electric dipole moment of the electron\n(eEDM) larger than that predicted by the Standard Model (SM) of particle\nphysics is expected to have a huge impact on the search for physics beyond the\nSM. Polar diatomic molecules containing heavy elements experience enhanced\nsensitivity to parity ($P$) and time-reversal ($T$)-violating phenomena, such\nas the eEDM and the scalar-pseudoscalar (S-PS) interaction between the nucleons\nand the electrons, and are thus promising candidates for measurements. The\nNL-\\textit{e}EDM collaboration is preparing an experiment to measure the eEDM\nand S-PS interaction in a slow beam of cold BaF molecules [Eur. Phys. J. D, 72,\n197 (2018)]. Accurate knowledge of the electronic structure parameters, $W_d$\nand $W_s$, connecting the eEDM and the S-PS interaction to the measurable\nenergy shifts is crucial for the interpretation of these measurements.\n  In this work we use the finite field relativistic coupled cluster approach to\ncalculate the $W_d$ and $W_s$ parameters in the ground state of the BaF\nmolecule. Special attention was paid to providing a reliable theoretical\nuncertainty estimate based on investigations of the basis set, electron\ncorrelation, relativistic effects and geometry. Our recommended values of the\ntwo parameters, including conservative uncertainty estimates, are 3.13 $\\pm$\n$0.12 \\times 10^{24}\\frac{\\text{Hz}}{e\\cdot \\text{cm}}$ for $W_d$ and 8.29\n$\\pm$ 0.12 kHz for $W_s$.\n"}
+{"abstract": "  In globally distributed projects, virtual teams are often partially\ndispersed. One common setup occurs when several members from one company work\nwith a large outsourcing vendor based in another country. Further, the\nintroduction of the popular BizDevOps concept has increased the necessity to\ncooperate across departments and reduce the age-old disconnection between the\nbusiness strategy and technical development. Establishing a good collaboration\nin partially distributed BizDevOps teams requires extensive collaboration and\ncommunication techniques. Nowadays, a common approach is to rely on\ncollaboration through pull requests and frequent communication on Slack. To\ninvestigate barriers for pull requests in distributed teams, we examined an\norganization located in Scandinavia where cross-functional BizDevOps teams\ncollaborated with off-site team members in India. Data were collected by\nconducting 14 interviews, observing 23 entire days with the team, and observing\n37 meetings. We found that the pull-request approach worked very well locally\nbut not across sites. We found barriers such as domain complexity, different\nagile processes (timeboxed vs. flow-based development), and employee turnover.\nUsing an intellectual capital lens on our findings, we discuss barriers and\npositive and negative effects on the success of the pull-request approach.\n"}
+{"abstract": "  Heterogeneous information networks(HINs) become popular in recent years for\nits strong capability of modelling objects with abundant information using\nexplicit network structure. Network embedding has been proved as an effective\nmethod to convert information networks into lower-dimensional space, whereas\nthe core information can be well preserved. However, traditional network\nembedding algorithms are sub-optimal in capturing rich while potentially\nincompatible semantics provided by HINs. To address this issue, a novel\nmeta-path-based HIN representation learning framework named mSHINE is designed\nto simultaneously learn multiple node representations for different meta-paths.\nMore specifically, one representation learning module inspired by the RNN\nstructure is developed and multiple node representations can be learned\nsimultaneously, where each representation is associated with one respective\nmeta-path. By measuring the relevance between nodes with the designed objective\nfunction, the learned module can be applied in downstream link prediction\ntasks. A set of criteria for selecting initial meta-paths is proposed as the\nother module in mSHINE which is important to reduce the optimal meta-path\nselection cost when no prior knowledge of suitable meta-paths is available. To\ncorroborate the effectiveness of mSHINE, extensive experimental studies\nincluding node classification and link prediction are conducted on five\nreal-world datasets. The results demonstrate that mSHINE outperforms other\nstate-of-the-art HIN embedding methods.\n"}
+{"abstract": "  The Linearization Theorem for proper Lie groupoids organizes and generalizes\nseveral results for classic geometries. Despite the various approaches and\nrecent works on the subject, the problem of understanding invariant\nlinearization remains somehow open. We address it here, by first giving a\ncounter-example to a previous conjecture, and then proving a sufficient\ncriterion that uses compatible complete metrics and covers the case of proper\ngroup actions. We also show a partial converse that fixes and extends previous\nresults in the literature.\n"}
+{"abstract": "  Chromatin is known to be organised into multiple domains of varying sizes and\ncompaction. While these domains are often imagined as static structures, they\nare highly dynamic and show cell-to-cell variability. Since processes such as\ngene regulation and DNA replication occur in the context of these domains, it\nis important to understand their organization, fluctuation and dynamics. To\nsimulate chromatin domains, one requires knowledge of interaction strengths\namong chromatin segments. Here, we derive interaction strength parameters from\nexperimentally known contact maps, and use it to predict chromatin organization\nand dynamics. Taking $\\alpha$-globin domain as an example, we investigate its\n3D organization, size/shape fluctuations, and dynamics of different segments\nwithin a domain, accounting for hydrodynamic effects. Perturbing the\ninteraction strengths systematically, we quantify how epigenetic changes can\nalter the spatio-temporal nature of the domains. Computing\ndistance-distributions and relaxation times for different chromatin states, we\nshow that weak and strong interactions cooperatively determine the organization\nof the domains and how the solid-like and liquid-like nature of chromatin gets\naltered as we vary epigenetic states. Quantifying dynamics of chromatin\nsegments within a domain, we show how the competition between polymer entropy\nand interaction energy influence the timescales of loop formation and\nmaintenance of stable loops.\n"}
+{"abstract": "  We study a system of two non-interacting quantum wires with fermions of\nopposite chirality with a point contact junction at the origin across which\ntunneling can take place when an arbitrary time-dependent bias between the\nwires is applied. We obtain the exact dynamical non-equilibrium Green function\nby solving Dyson's equation analytically. Both the space-time dependent two and\nfour-point functions are written down in a closed form in terms of simple\nfunctions of position and time. This allows us to obtain, among other things,\nthe I-V characteristics for an arbitrary time-dependent bias. Our method is a\nsuperior alternative to competing approaches to non-equilibrium as we are able\nto account for transient phenomena as well as the steady state. We study the\napproach to steady state by computing the time evolution of the equal-time\none-particle Green function. We also show how the presence of the point-contact\naffects the density profile of the electrons by inducing Friedel oscillations\nin the system and how a bias influences these oscillations. We then consider\nthe case of a finite bandwidth in the point contact and calculate the\nnon-equilibrium transport properties which exhibit non-Markovian behaviour.\nWhen a subsequently constant bias is suddenly switched on, the current shows a\ntransient build up before approaching its steady state value in contrast to the\ninfinite bandwidth case. This transient property is consistent with numerical\nsimulations of lattice systems using time-dependent DMRG (tDMRG) suggesting\nthereby that this transient build up is merely due to the presence of a short\ndistance cutoff in the problem description and not on the other details.\n"}
+{"abstract": "  Machine learning algorithms deployed on edge devices must meet certain\nresource constraints and efficiency requirements. Random Vector Functional Link\n(RVFL) networks are favored for such applications due to their simple design\nand training efficiency. We propose a modified RVFL network that avoids\ncomputationally expensive matrix operations during training, thus expanding the\nnetwork's range of potential applications. Our modification replaces the\nleast-squares classifier with the Generalized Learning Vector Quantization\n(GLVQ) classifier, which only employs simple vector and distance calculations.\nThe GLVQ classifier can also be considered an improvement upon certain\nclassification algorithms popularly used in the area of Hyperdimensional\nComputing. The proposed approach achieved state-of-the-art accuracy on a\ncollection of datasets from the UCI Machine Learning Repository - higher than\npreviously proposed RVFL networks. We further demonstrate that our approach\nstill achieves high accuracy while severely limited in training iterations\n(using on average only 21% of the least-squares classifier computational\ncosts).\n"}
+{"abstract": "  Machine learning and wireless communication technologies are jointly\nfacilitating an intelligent edge, where federated edge learning (FEEL) is a\npromising training framework. As wireless devices involved in FEEL are resource\nlimited in terms of communication bandwidth, computing power and battery\ncapacity, it is important to carefully schedule them to optimize the training\nperformance. In this work, we consider an over-the-air FEEL system with analog\ngradient aggregation, and propose an energy-aware dynamic device scheduling\nalgorithm to optimize the training performance under energy constraints of\ndevices, where both communication energy for gradient aggregation and\ncomputation energy for local training are included. The consideration of\ncomputation energy makes dynamic scheduling challenging, as devices are\nscheduled before local training, but the communication energy for over-the-air\naggregation depends on the l2-norm of local gradient, which is known after\nlocal training. We thus incorporate estimation methods into scheduling to\npredict the gradient norm. Taking the estimation error into account, we\ncharacterize the performance gap between the proposed algorithm and its offline\ncounterpart. Experimental results show that, under a highly unbalanced local\ndata distribution, the proposed algorithm can increase the accuracy by 4.9% on\nCIFAR-10 dataset compared with the myopic benchmark, while satisfying the\nenergy constraints.\n"}
+{"abstract": "  In video-based dynamic point cloud compression (V-PCC), 3D point clouds are\nprojected onto 2D images for compressing with the existing video codecs.\nHowever, the existing video codecs are originally designed for natural visual\nsignals, and it fails to account for the characteristics of point clouds. Thus,\nthere are still problems in the compression of geometry information generated\nfrom the point clouds. Firstly, the distortion model in the existing\nrate-distortion optimization (RDO) is not consistent with the geometry quality\nassessment metrics. Secondly, the prediction methods in video codecs fail to\naccount for the fact that the highest depth values of a far layer is greater\nthan or equal to the corresponding lowest depth values of a near layer. This\npaper proposes an advanced geometry surface coding (AGSC) method for dynamic\npoint clouds (DPC) compression. The proposed method consists of two modules,\nincluding an error projection model-based (EPM-based) RDO and an occupancy\nmap-based (OM-based) merge prediction. Firstly, the EPM model is proposed to\ndescribe the relationship between the distortion model in the existing video\ncodec and the geometry quality metric. Secondly, the EPM-based RDO method is\npresented to project the existing distortion model on the plane normal and is\nsimplified to estimate the average normal vectors of coding units (CUs).\nFinally, we propose the OM-based merge prediction approach, in which the\nprediction pixels of merge modes are refined based on the occupancy map.\nExperiments tested on the standard point clouds show that the proposed method\nachieves an average 9.84\\% bitrate saving for geometry compression.\n"}
+{"abstract": "  Interacting fermion systems in one dimension, which in the low energy\napproximation are described by Luttinger liquid theory, can be reformulated as\nsystems of weakly interacting particles with fractional exchange statistics.\nThis is shown by use of Landau's Fermi liquid theory, with quasiparticles\ninterpreted as adiabatically dressed fermions. An application of this method is\nincluded, where boundary excitations of a two-dimensional quantum Hall electron\nsystem are studied.\n"}
+{"abstract": "  In this paper we introduce a novel objective prior distribution levering on\nthe connections between information, divergence and scoring rules. In\nparticular, we do so from the starting point of convex functions representing\ninformation in density functions. This provides a natural route to proper local\nscoring rules using Bregman divergence. Specifically, we determine the prior\nwhich solves setting the score function to be a constant. While in itself this\nprovides motivation for an objective prior, the prior also minimizes a\ncorresponding information criterion.\n"}
+{"abstract": "  Autism is one of the most important neurological disorders which leads to\nproblems in a person's social interactions. Improvement of brain imaging\ntechnologies and techniques help us to build brain structural and functional\nnetworks. Finding networks topology pattern in each of the groups (autism and\nhealthy control) can aid us to achieve an autism disorder screening model. In\nthe present study, we have utilized the genetic algorithm to extract a\ndiscriminative sub-network that represents differences between two groups\nbetter. In the fitness evaluation phase, for each sub-network, a machine\nlearning model was trained using various entropy features of the sub-network\nand its performance was measured. Proper model performance implies extracting a\ngood discriminative sub-network. Network entropies can be used as network\ntopological descriptors. The evaluation results indicate the acceptable\nperformance of the proposed screening method based on extracted discriminative\nsub-networks and the machine learning models succeeded in obtaining a maximum\naccuracy of 73.1% in structural networks of the UCLA dataset, 82.2% in\nfunctional networks of the UCLA dataset, and 66.1% in functional networks of\nABIDE datasets.\n"}
+{"abstract": "  Physical properties of two-dimensional van der Waals (vdWs) structures depend\nsensitively on both stacking orders and interlayer interactions. Yet, in most\ncases studied to date, the interlayer interaction is considered to be a static\nproperty of the vdWs structures. Here we demonstrate that applying a scanning\ntunneling microscopy (STM) tip pulse on twisted bilayer graphene (TBG) can\ninduce sub-Angstrom fluctuations of the interlayer separation in the TBG, which\nare equivalent to dynamic vertical external pressure of about 10 GPa on the\nTBG. The sub-Angstrom fluctuations of the interlayer separation result in large\noscillations of the energy separations between two van Hove singularities\n(VHSs) in the TBG. The period of the oscillations of the VHSs spacing is\nextremely long, about 500-1000 seconds, attributing to tip-induced local stress\nin the atomic-thick TBG. Our result provides an efficient method to tune and\nmeasure the physical properties of the vdWs structures dynamically.\n"}
+{"abstract": "  Effective Field Theories are an established framework to bridge the gap\nbetween UV and low energy theories. In the context of the Standard Model, the\nbottom-up approach extends its operator set and thus equips us to astutely\nprobe its observables while encapsulating indirect evidence of unknown high\nscale theories. While the top-down approach, on the other hand, employs\nfunctional techniques to integrate out the heavy fields from a BSM Lagrangian\nleading to a set of SMEFT operators. An intricate interplay of the two\napproaches enhances the efficacy of the SMEFT in making meaningful predictions\nwhile providing a platform for conducting a coherent comparison of new physics\nscenarios. However, while the bottom-up approach fails to indicate the origin\nof the effective operators, the top-down approach is highly dependent on the\nspecific model assumptions of the UV theory. We, for the first time, are\nproposing a diagrammatic approach to establish selection criteria for the\nallowed heavy field representations corresponding to each SMEFT operator. This,\nin turn, paves the way to construct observable driven new physics models. While\nwe take a predominantly minimalistic approach, we also highlight the necessity\nfor non-minimal interactions for certain operators.\n"}
+{"abstract": "  Let $\\Omega\\subset\\mathbb{C}$ be a bounded domain such that there exists an\nalgebraic harmonic function of degree two vanishing on the boundary of\n$\\Omega.$ Then we show that the Khavinson-Shapiro conjecture holds for\n$\\Omega:$ if the Dirichlet problem on $\\Omega$ with all polynomial boundary\ndata have polynomial solutions, then $\\Omega$ must be an ellipse. We also prove\nthat if there exists a rational function with a singularity in $\\Omega$, such\nthat the Dirichlet problem for its restriction on $\\partial\\Omega$ along with\nall polynomial functions have rational solutions, then $\\Omega$ must be a disc.\nThis generalizes a well-known result by Bell, Ebenfelt, Khavinson, and Shapiro.\nOur proofs are purely algebraic.\n"}
+{"abstract": "  We explore the clustering of galaxy groups in the Galaxy and Mass Assembly\n(GAMA) survey to investigate the dependence of group bias and profile on\nseparation scale and group mass. Due to the inherent uncertainty in estimating\nthe group selection function, and hence the group auto-correlation function, we\ninstead measure the projected galaxy--group cross-correlation function. We find\nthat the group profile has a strong dependence on scale and group mass on\nscales $r_\\bot \\lesssim 1 h^{-1} \\mathrm{Mpc}$. We also find evidence that the\nmost massive groups live in extended, overdense, structures. In the first\napplication of marked clustering statistics to groups, we find that group-mass\nmarked clustering peaks on scales comparable to the typical group radius of\n$r_\\bot \\approx 0.5 h^{-1} \\mathrm{Mpc}$. While massive galaxies are associated\nwith massive groups, the marked statistics show no indication of galaxy mass\nsegregation within groups. We show similar results from the IllustrisTNG\nsimulations and the L-Galaxies model, although L-Galaxies shows an enhanced\nbias and galaxy mass dependence on small scales.\n"}
+{"abstract": "  We present a row reduction algorithm to compute the barcode decomposition of\npersistence modules. This algorithm dualises the standard persistence one and\nclarifies the symmetry between clear and compress optimisations.\n"}
+{"abstract": "  Let us consider a semilinear boundary value problem $ - \\Delta u= f(x,u),$ in\n$\\Omega,$ with Dirichlet boundary conditions, where $ \\Omega \\subset\n\\mathbb{R}^N $, $N> 2,$ is a bounded smooth domain. We provide sufficient\nconditions guarantying that semi-stable weak positive solutions to subcritical\nsemilinear elliptic equations are smooth in any dimension, and as a\nconsequence, classical solutions. By a subcritical nonlinearity we mean\n$f(x,s)/s^\\frac{N+2}{N-2} \\to 0$ as $s\\to\\infty$, including non-power\nnonlinearities, and enlarging the class of subcritical nonlinearities, which is\nusually reserved for power like nonlinearities.\n"}
+{"abstract": "  In this paper, we obtain two Lichnerowicz type formulas for the Dirac-Witten\noperators. And we give the proof of Kastler-Kalau-Walze type theorems for the\nDirac-Witten operators on 4-dimensional and 6- dimensional compact manifolds\nwith (resp.without) boundary\n"}
+{"abstract": "  In this paper we investigate iteration of maps on lattices and the\ncorresponding polynomial-like iterative equation. Since a lattice need not have\na metric space structure, neither the Schauder fixed point theorem nor the\nBanach fixed point theorem is available. Using Tarski's fixed point theorem, we\nprove the existence of order-preserving solutions on convex complete\nsublattices of Riesz spaces. Further, in $\\mathbb{R}^n$ and $\\mathbb{R}$,\nspecial cases of Riesz space, we discuss upper semi-continuous solutions and\nintegrable solutions respectively. Finally, we indicate more special cases of\nRiesz space for discussion on the iterative equation.\n"}
+{"abstract": "  In the present paper we deal with parabolic fractional initial-boundary value\nproblems of Sturm Liouville type in an interval and in a general star graph. We\nfirst give several existence, uniqueness and regularity results of weak and\nvery-weak solutions. We prove the existence and uniqueness of solutions to a\nquadratic boundary optimal control problem and provide a characterization of\nthe optimal contol via the Euler Lagrange first order optimality conditions. We\nthen investigate the analogous problems for a fractional Sturm Liouville\nproblem in a general star graph with mixed Dirichlet and Neumann boundary\ncontrols. The existence and uniqueness of minimizers, and the characterization\nof the first order optimality conditions are obtained in a general star graph\nby using the method of Lagrange multipliers.\n"}
+{"abstract": "  We propose a one-dimensional (1D) model for the three-dimensional(3D)\nincompressible ideal magnetohydrodynamics. We establish a regularity criterion\nof the Beale-Kato-Majda type for this 1D model. Without the stretching effect,\nthe model with only transport effect equipped with a proper sign is shown to\nhave global in time strong solution. Some numerical simulations suggest that\nsolutions of this model with smooth periodic initial data do not tend to\ndevelop singularities at finite time.\n"}
+{"abstract": "  Networked systems are widely applicable in real-world scenarios such as\nsocial networks, infrastructure networks, and biological networks. Among those\napplications, we are interested in social networks due to their complexity and\npopularity. One crucial task on the social network is to recommend new content\nbased on special characteristics of the graph structure. In this project, we\naim to enhance the recommender systems by preventing the recommendations from\nleaning towards contents from closed communities. To counteract the bias, we\nwill consider information dissemination across network as a metric to assess\nthe recommendation for contents e.g. new connections and news feed. We use\nacademic collaboration network and user-item interaction datasets from Yelp to\nsimulate an environment for connection recommendations and to validate the\nproposed algorithm.\n"}
+{"abstract": "  Axisymmetric disks of eccentric orbits in near-Keplerian potentials are\nunstable to an out-of-plane buckling. Recently, Zderic et al. (2020) showed\nthat an idealized disk saturates to a lopsided mode. Here we show that this\napsidal clustering also occurs in a primordial scattered disk in the outer\nsolar system which includes the orbit-averaged gravitational influence of the\ngiant planets. We explain the dynamics using Lynden-Bell (1979)'s mechanism for\nbar formation in galaxies. We also show surface density and line of sight\nvelocity plots at different times during the instability, highlighting the\nformation of concentric circles and spiral arms in velocity space.\n"}
+{"abstract": "  Many machine learning frameworks have been proposed and used in wireless\ncommunications for realizing diverse goals. However, their incapability of\nadapting to the dynamic wireless environment and tasks and of self-learning\nlimit their extensive applications and achievable performance. Inspired by the\ngreat flexibility and adaptation of primate behaviors due to the brain\ncognitive mechanism, a unified cognitive learning (CL) framework is proposed\nfor the dynamic wireless environment and tasks. The mathematical framework for\nour proposed CL is established. Using the public and authoritative dataset, we\ndemonstrate that our proposed CL framework has three advantages, namely, the\ncapability of adapting to the dynamic environment and tasks, the self-learning\ncapability and the capability of 'good money driving out bad money' by taking\nmodulation recognition as an example. The proposed CL framework can enrich the\ncurrent learning frameworks and widen the applications.\n"}
+{"abstract": "  We discuss renormalization group flows in two-dimensional quantum field\ntheories with (0,2) supersymmetry. We focus on theories with UV described by a\nLandau-Ginzburg Lagrangian and use the chiral algebra to constrain the IR\ndynamics. We present examples where the structure of the chiral algebra is\nincompatible with unitarity of the IR superconformal theory and discuss the\nimplications of this result for programs of classifying (0,2) SCFTs as\nendpoints of flows from simple Lagrangian theories.\n"}
+{"abstract": "  In this paper, we study the maximum edge augmentation problem in directed\nLaplacian networks to improve their robustness while preserving lower bounds on\ntheir strong structural controllability (SSC). Since adding edges could\nadversely impact network controllability, the main objective is to maximally\ndensify a given network by selectively adding missing edges while ensuring that\nSSC of the network does not deteriorate beyond certain levels specified by the\nSSC bounds. We consider two widely used bounds: first is based on the notion of\nzero forcing (ZF), and the second relies on the distances between nodes in a\ngraph. We provide an edge augmentation algorithm that adds the maximum number\nof edges in a graph while preserving the ZF-based SSC bound, and also derive a\nclosed-form expression for the exact number of edges added to the graph. Then,\nwe examine the edge augmentation problem while preserving the distance-based\nbound and present a randomized algorithm that guarantees an approximate\nsolution with high probability. Finally, we numerically evaluate and compare\nthese edge augmentation solutions.\n"}
+{"abstract": "  Phase can be reliably estimated from a single diffracted intensity image, if\na faithful prior information about the object is available. Examples include\namplitude bounds, object support, sparsity in the spatial or a transform\ndomain, deep image prior and the prior learnt from the labelled datasets by a\ndeep neural network. Deep learning facilitates state of art reconstruction\nquality but requires a large labelled dataset (ground truth-measurement pair\nacquired in the same experimental conditions) for training. To alleviate this\ndata requirement problem, this letter proposes a zero-shot learning method. The\nletter demonstrates that the object-prior learnt by a deep neural network while\nbeing trained for a denoising task can also be utilized for the phase\nretrieval, if the diffraction physics is effectively enforced on the network\noutput. The letter additionally demonstrates that the incorporation of total\nvariation in the proposed zero-shot framework facilitates the reconstruction of\nsimilar quality in lesser time (e.g. ~8.5 fold, for a test reported in this\nletter).\n"}
+{"abstract": "  We consider coincidence Reidemeister zeta functions for tame endomorphism\npairs of nilpotent groups of finite rank, shedding new light on the subject by\nmeans of profinite completion techniques.\n  In particular, we provide a closed formula for coincidence Reidemeister\nnumbers for iterations of endomorphism pairs of torsion-free nilpotent groups\nof finite rank, based on a weak commutativity condition, which derives from\nsimultaneous triangularisability on abelian sections. Furthermore, we present\nresults in support of a P\\'olya-Carlson dichotomy between rationality and a\nnatural boundary for the analytic behaviour of the zeta functions in question.\n"}
+{"abstract": "  This study identifies and proposes techniques to alleviate two key\nbottlenecks to executing deep neural networks in trusted execution environments\n(TEEs): page thrashing during the execution of convolutional layers and the\ndecryption of large weight matrices in fully-connected layers. For the former,\nwe propose a novel partitioning scheme, y-plane partitioning, designed to (i)\nprovide consistent execution time when the layer output is large compared to\nthe TEE secure memory; and (ii) significantly reduce the memory footprint of\nconvolutional layers. For the latter, we leverage quantization and compression.\nIn our evaluation, the proposed optimizations incurred latency overheads\nranging from 1.09X to 2X baseline for a wide range of TEE sizes; in contrast,\nan unmodified implementation incurred latencies of up to 26X when running\ninside of the TEE.\n"}
+{"abstract": "  In this article, we propose a new hypothesis testing method for directed\nacyclic graph (DAG). While there is a rich class of DAG estimation methods,\nthere is a relative paucity of DAG inference solutions. Moreover, the existing\nmethods often impose some specific model structures such as linear models or\nadditive models, and assume independent data observations. Our proposed test\ninstead allows the associations among the random variables to be nonlinear and\nthe data to be time-dependent. We build the test based on some highly flexible\nneural networks learners. We establish the asymptotic guarantees of the test,\nwhile allowing either the number of subjects or the number of time points for\neach subject to diverge to infinity. We demonstrate the efficacy of the test\nthrough simulations and a brain connectivity network analysis.\n"}
+{"abstract": "  TOI-216 hosts a pair of warm, large exoplanets discovered by the TESS\nMission. These planets were found to be in or near the 2:1 resonance, and both\nof them exhibit transit timing variations (TTVs). Precise characterization of\nthe planets' masses and radii, orbital properties, and resonant behavior can\ntest theories for the origins of planets orbiting close to their stars.\nPrevious characterization of the system using the first six sectors of TESS\ndata suffered from a degeneracy between planet mass and orbital eccentricity.\nRadial velocity measurements using HARPS, FEROS, and PFS break that degeneracy,\nand an expanded TTV baseline from TESS and an ongoing ground-based transit\nobserving campaign increase the precision of the mass and eccentricity\nmeasurements. We determine that TOI-216c is a warm Jupiter, TOI-216b is an\neccentric warm Neptune, and that they librate in the 2:1 resonance with a\nmoderate libration amplitude of 60 +/- 2 degrees; small but significant free\neccentricity of 0.0222 +0.0005/-0.0003 for TOI-216b; and small but significant\nmutual inclination of 1.2-3.9 degrees (95% confidence interval). The libration\namplitude, free eccentricity, and mutual inclination imply a disturbance of\nTOI-216b before or after resonance capture, perhaps by an undetected third\nplanet.\n"}
+{"abstract": "  The selection of time step plays a crucial role in improving stability and\nefficiency in the Discontinuous Galerkin (DG) solution of hyperbolic\nconservation laws on adaptive moving meshes that typically employs explicit\nstepping. A commonly used selection of time step has been based on CFL\nconditions established for fixed and uniform meshes. This work provides a\nmathematical justification for those time step selection strategies used in\npractical adaptive DG computations. A stability analysis is presented for a\nmoving mesh DG method for linear scalar conservation laws. Based on the\nanalysis, a new selection strategy of the time step is proposed, which takes\ninto consideration the coupling of the $\\alpha$-function (that is related to\nthe eigenvalues of the Jacobian matrix of the flux and the mesh movement\nvelocity) and the heights of the mesh elements. The analysis also suggests\nseveral stable combinations of the choices of the $\\alpha$-function in the\nnumerical scheme and in the time step selection. Numerical results obtained\nwith a moving mesh DG method for Burgers' and Euler equations are presented.\n"}
+{"abstract": "  Based on first-principles calculations including a Coulomb repulsion term, we\nidentify trends in the electronic reconstruction of $A$NiO$_2$/SrTiO$_3$(001)\n($A=$ Pr, La) and $A$CuO$_2$/SrTiO$_3$(001) ($A=$ Ca, Sr). Common to all cases\nis the emergence of a quasi-two-dimensional electron gas (q2DEG) in\nSrTiO$_3$(001), albeit the higher polarity mismatch at the interface of\nnickelates vs. cuprates to the nonpolar SrTiO$_3(001)$ substrate (${3+}/0$ vs.\n${2+}/0$) results in an enhanced q2DEG carrier density. The simulations reveal\na significant dependence of the interfacial Ti $3d_{xy}$ band bending on the\nrare-earth ion in the nickelate films, being $20$-$30\\%$ larger for PrNiO$_2$\nand NdNiO$_2$ than for LaNiO$_2$. Contrary to expectations from the formal\npolarity mismatch, the electrostatic doping in the films is twice as strong in\ncuprates as in nickelates. We demonstrate that the depletion of the self-doping\nrare-earth $5d$ states enhances the similarity of nickelate and cuprate Fermi\nsurfaces in film geometry, reflecting a single hole in the Ni and Cu\n$3d_{x^2-y^2}$ orbitals. Finally, we show that NdNiO$_2$ films grown on a polar\nNdGaO$_3(001)$ substrate feature a simultaneous suppression of q2DEG formation\nas well as Nd~$5d$ self-doping.\n"}
+{"abstract": "  The great success of modern machine learning models on large datasets is\ncontingent on extensive computational resources with high financial and\nenvironmental costs. One way to address this is by extracting subsets that\ngeneralize on par with the full data. In this work, we propose a general\nframework, GRAD-MATCH, which finds subsets that closely match the gradient of\nthe training or validation set. We find such subsets effectively using an\northogonal matching pursuit algorithm. We show rigorous theoretical and\nconvergence guarantees of the proposed algorithm and, through our extensive\nexperiments on real-world datasets, show the effectiveness of our proposed\nframework. We show that GRAD-MATCH significantly and consistently outperforms\nseveral recent data-selection algorithms and achieves the best\naccuracy-efficiency trade-off. GRAD-MATCH is available as a part of the CORDS\ntoolkit: \\url{https://github.com/decile-team/cords}.\n"}
+{"abstract": "  Molecular dynamics calculations were preformed on organic crystals\n4-N,N-dimethylamino-4'-N'-methyl-stilbazolium tosylate (DAST) and\n4-N,N-dimethylamino-4'-N'-methylstilbazolium 2,4,6-trimethylbenzenesulfonate\n(DSTMS). Vibrational modes of the structures were investigated to examine the\nsingle unit cell phononic contribution of the organic crystals to their\nterahertz generating capabilities. Linear optical properties were also\ncalculated from snapshots of the molecular dynamics structures through\nGreen-Kubo relations, and compared with experimental transmission.\n"}
+{"abstract": "  Crater chronologies are a fundamental tool to assess relative and absolute\nages of planetary surfaces when direct radiometric dating is not available.\nMartian crater chronologies are derived from lunar crater spatial densities on\nterrains with known radiometric ages, and thus they critically depend on the\nextrapolation Moon to Mars. This extrapolation requires knowledge of the time\nevolution of the impact flux, including contributions from various impactor\npopulations, factors that are not trivially connected to the dynamical\nevolution of the early Solar System. In this paper, we will present a new\nmartian crater chronology based on current dynamical models, and consider the\nmain sources of uncertainties. The new martian crater chronology is discussed\nusing two interesting applications: Jezero crater's dark terrain (relevant to\nthe NASA Mars 2020 mission) and the southern heavily cratered highlands.\n[abridged]\n"}
+{"abstract": "  Thurston obtained a combinatorial characterization for generic branched\nself-coverings that preserve the orientation of the oriented 2-sphere by\nassociating a planar graph to them [arXiv:1502.04760]. In this work, the\nThurston result is generalized to any branched covering of the oriented\n2-sphere. To achieve that the notion of local balance introduced by Thurston is\ngeneralized. As an application, a new proof for a Theorem of\nEremenko-Gabrielov-Mukhin-Tarasov-Varchenko [MR1888795], [MR2552110] is\nobtained. This theorem corresponded to a special case of the B. \\& M. Shapiro\nconjecture. In this case, it refers to generic rational functions stating that\na generic rational function $ R : \\mathbb{C}\\mathbb{P}^1 \\rightarrow\n\\mathbb{C}\\mathbb{P}^1$ with only real critical points can be transformed by\npost-composition with an automorphism of $\\mathbb{C}\\mathbb{P}^1$ into a\nquotient of polynomials with real coefficients. Operations against balanced\ngraphs are introduced.\n"}
+{"abstract": "  Computer vision is increasingly effective at segmenting objects in images and\nvideos; however, scene effects related to the objects -- shadows, reflections,\ngenerated smoke, etc -- are typically overlooked. Identifying such scene\neffects and associating them with the objects producing them is important for\nimproving our fundamental understanding of visual scenes, and can also assist a\nvariety of applications such as removing, duplicating, or enhancing objects in\nvideo. In this work, we take a step towards solving this novel problem of\nautomatically associating objects with their effects in video. Given an\nordinary video and a rough segmentation mask over time of one or more subjects\nof interest, we estimate an omnimatte for each subject -- an alpha matte and\ncolor image that includes the subject along with all its related time-varying\nscene elements. Our model is trained only on the input video in a\nself-supervised manner, without any manual labels, and is generic -- it\nproduces omnimattes automatically for arbitrary objects and a variety of\neffects. We show results on real-world videos containing interactions between\ndifferent types of subjects (cars, animals, people) and complex effects,\nranging from semi-transparent elements such as smoke and reflections, to fully\nopaque effects such as objects attached to the subject.\n"}
+{"abstract": "  It is shown how the vector space $V_{8,8}$ arises from the Clifford algebra\n$Cl(1,3)$ of spacetime. The latter algebra describes fundamental objects such\nas strings and branes in terms of their $r$-volume degrees of freedom,\n$x^{\\mu_1 \\mu_2 ...\\mu_r}$ $\\equiv x^M$, $r=0,1,2,3$, that generalizethe\nconcept of center of mass. Taking into account that there are sixteen $x^M$,\n$M=1,2,3,...,16$, and in general $16 \\times 15/2 = 120$ rotations of the form\n$x'^M = {R^M}_N x^N$, we can consider $x^M$ as components of a vector $X=x^M\nq_M$, where $q_M$ are generators of the Clifford algebra $Cl(8,8)$. The vector\nspace $V_{8,8}$ has enough room for the unification of the fundamental\nparticles and forces of the standard model. The rotations in $V_{8,8}\\otimes\n\\mathbb{C}$ contain the grand unification group $SO(10)$ as a subgroup, and\nalso the Lorentz group $SO(1,3)$. It is shown how the Coleman-Mandula no go\ntheorem can be avoided. Spinors in $V_{8,8}\\otimes \\mathbb{C}$ are constructed\nin terms of the wedge products of the basis vectors rewritten in the Witt\nbasis. They satisfy the massless Dirac equation in $M_{8,8}$ with the internal\npart of the Dirac operator giving the non vanishing masses in four dimensions.\n"}
+{"abstract": "  One of the most important achievements of inflationary cosmology is to\npredict a departure from scale invariance of the power spectrum for scalar\ncurvature cosmological fluctuations. This tilt is understood as a consequence\nof a quasi de Sitter classical equation of state describing the inflationary\ndark energy dominated era. Here, following previous work, we find a departure\nof scale invariance for the quantum Fisher information associated to de Sitter\nvacuum for scalar quantum spectator modes. This gives rise to a purely quantum\ncosmological tilt with a well defined dependence on energy scale. This quantum\ntilt is imprinted, in a scale dependent energy uncertainty for the spectator\nmodes. The effective quasi de Sitter description of this model independent\nenergy uncertainty uniquely sets the effective quasi de Sitter parameters at\nall energy scales. In particular, in the slow-roll regime characterized by an\nalmost constant $\\epsilon$, the quantum Fisher -- model independent --\nprediction for the spectral index is $(1-n_s) = 0.0328$ ($n_s=0.9672$).\nMoreover, the energy scale dependence of the quantum cosmological tilt implies\nthe existence of a cosmological phase transition at energies higher than the\nCMB scale where the tilt goes from red into blue. This strongly suggest the\nexistence of a pre-inflationary phase where the effective scalaron contributes\nto the spectral index as normal relativistic matter and where the corresponding\ngrowth of the power spectrum can result in dark matter in the form of small\nmass primordial black holes. The source and features of the quantum\ncosmological tilt leading to these predictions are determined by the\nentanglement features of the de Sitter vacuum states.\n"}
+{"abstract": "  Tensor recovery has recently arisen in a lot of application fields, such as\ntransportation, medical imaging and remote sensing. Under the assumption that\nsignals possess sparse and/or low-rank structures, many tensor recovery methods\nhave been developed to apply various regularization techniques together with\nthe operator-splitting type of algorithms. Due to the unprecedented growth of\ndata, it becomes increasingly desirable to use streamlined algorithms to\nachieve real-time computation, such as stochastic optimization algorithms that\nhave recently emerged as an efficient family of methods in machine learning. In\nthis work, we propose a novel algorithmic framework based on the Kaczmarz\nalgorithm for tensor recovery. We provide thorough convergence analysis and its\napplications from the vector case to the tensor one. Numerical results on a\nvariety of tensor recovery applications, including sparse signal recovery,\nlow-rank tensor recovery, image inpainting and deconvolution, illustrate the\nenormous potential of the proposed methods.\n"}
+{"abstract": "  The trajectory deflection and gravitational-electromagnetic dual lensing\n(GEL) of charged signal in general charged static and spherically symmetric\nspacetimes are considered in this work. We showed that the perturbative\napproach previously developed for neutral particles can be extended to the\nelectromagnetic interaction case. The deflection angle still takes a\n(quasi-)series form and the finite distance effect of both the source and\nobserver can be taken into account. Comparing to pure gravitational case, the\napparent angles of the images in the GEL, their magnifications and time delay\nall receive the electromagnetic corrections starting from the first non-trivial\norder. The sign and relative size of the leading corrections are determined by\n$\\sim \\frac{Q}{M}\\frac{q}{E}$ where $M,~Q,~q,~E$ are the spacetime mass and\ncharge, and signal particle charge and energy respectively. It is found that\nfor $qQ>0$ (or $<0$), the electromagnetic interaction will decrease (or\nincrease) the deflection angle, and in GEL the impact parameters, apparent\nangles, magnifications and total travel time for each image. The time delay is\nincreased for small $\\beta$ and $qQ>0$, and otherwise always increased\nregardless the sign of $qQ$. The results are then applied to the deflection and\nGEL of charged protons in cosmic rays in Reissner-Nordstrom, charged dilaton\nand charged Horndeski spacetimes.\n"}
+{"abstract": "  Music Information Retrieval (MIR) is a collaborative scientific study that\nhelp to build innovative information research themes, novel frameworks, and\ndeveloping connected delivery mechanisms in addition to making the world's\nmassive collection of music open for everyone. Modern rock music proved to be\ndifficult to estimate tempo and chord recognition did not work. All of the\nfindings indicate that modern rock and metal music can be analysed, despite its\ncomplexity, but that further research is needed in this area to make it useful.\nUsing a neural network has been one of the simplest ways of dealing with it.\nThe pitch class profile vector is used in the neural network method. Because\nthe vector only contains 12 elements of semi-tone values, it is enough for\nchord recognition. Of course, there are other ways of achieving this work, most\nof them depend on pitch class profiling to transform the chord into a type that\ncan be recognised, but the recognition process is time-consuming centred on\nextremely complicated and memory-intensive methods.\n"}
+{"abstract": "  In this paper, we prove an optimal inequality between the potential function\nof a complete Schouten soliton and the norm of its gradient. We also prove that\nthese metrics have bounded scalar curvature of defined sign. As an application,\nwe prove that the potential function of a Shrinking Schouten soliton grows\nlinearly and provide optimal estimates for the growth of the volume of geodesic\nballs, without any further assumption.\n"}
+{"abstract": "  The problem of categorizing short speech sentences according to their\nsemantic features with high accuracy is a subject studied in natural language\nprocessing. In this study, a data set created with samples classified in 46\ndifferent categories was used. Examples consist of sentences taken from chat\nconversations between a company's customer representatives and the company's\nwebsite visitors. The primary purpose is to automatically tag questions and\nrequests from visitors in the most accurate way for 46 predetermined categories\nfor use in a chat application to generate meaningful answers to the questions\nasked by the website visitors. For this, different BERT models and one GPT-2\nmodel, pre-trained in Turkish, were preferred. The classification performances\nof the relevant models were analyzed in detail and reported accordingly.\n"}
+{"abstract": "  During their evolution, proteins explore sequence space via an interplay\nbetween random mutations and phenotypic selection. Here we build upon recent\nprogress in reconstructing data-driven fitness landscapes for families of\nhomologous proteins, to propose stochastic models of experimental protein\nevolution. These models predict quantitatively important features of\nexperimentally evolved sequence libraries, like fitness distributions and\nposition-specific mutational spectra. They also allow us to efficiently\nsimulate sequence libraries for a vast array of combinations of experimental\nparameters like sequence divergence, selection strength and library size. We\nshowcase the potential of the approach in re-analyzing two recent experiments\nto determine protein structure from signals of epistasis emerging in\nexperimental sequence libraries. To be detectable, these signals require\nsufficiently large and sufficiently diverged libraries. Our modeling framework\noffers a quantitative explanation for the variable success of recently\npublished experiments. Furthermore, we can forecast the outcome of time- and\nresource-intensive evolution experiments, opening thereby a way to\ncomputationally optimize experimental protocols.\n"}
+{"abstract": "  When personal, assistive, and interactive robots make mistakes, humans\nnaturally and intuitively correct those mistakes through physical interaction.\nIn simple situations, one correction is sufficient to convey what the human\nwants. But when humans are working with multiple robots or the robot is\nperforming an intricate task often the human must make several corrections to\nfix the robot's behavior. Prior research assumes each of these physical\ncorrections are independent events, and learns from them one-at-a-time.\nHowever, this misses out on crucial information: each of these interactions are\ninterconnected, and may only make sense if viewed together. Alternatively,\nother work reasons over the final trajectory produced by all of the human's\ncorrections. But this method must wait until the end of the task to learn from\ncorrections, as opposed to inferring from the corrections in an online fashion.\nIn this paper we formalize an approach for learning from sequences of physical\ncorrections during the current task. To do this we introduce an auxiliary\nreward that captures the human's trade-off between making corrections which\nimprove the robot's immediate reward and long-term performance. We evaluate the\nresulting algorithm in remote and in-person human-robot experiments, and\ncompare to both independent and final baselines. Our results indicate that\nusers are best able to convey their objective when the robot reasons over their\nsequence of corrections.\n"}
+{"abstract": "  We study the use of spike and slab priors for consistent estimation of the\nnumber of change points and their locations. Leveraging recent results in the\nvariable selection literature, we show that an estimator based on spike and\nslab priors achieves optimal localization rate in the multiple offline change\npoint detection problem. Based on this estimator, we propose a Bayesian change\npoint detection method, which is one of the fastest Bayesian methodologies, and\nit is more robust to misspecification of the error terms than the competing\nmethods. We demonstrate through empirical work the good performance of our\napproach vis-a-vis some state-of-the-art benchmarks.\n"}
+{"abstract": "  We consider frequency-weighted damping optimization for vibrating systems\ndescribed by a second-order differential equation. The goal is to determine\nviscosity values such that eigenvalues are kept away from certain undesirable\nareas on the imaginary axis. To this end, we present two complementary\ntechniques. First, we propose new frameworks using nonsmooth constrained\noptimization problems, whose solutions both damp undesirable frequency bands\nand maintain stability of the system. These frameworks also allow us to weight\nwhich frequency bands are the most important to damp. Second, we also propose a\nfast new eigensolver for the structured quadratic eigenvalue problems that\nappear in such vibrating systems. In order to be efficient, our new eigensolver\nexploits special properties of diagonal-plus-rank-one complex symmetric\nmatrices, which we leverage by showing how each quadratic eigenvalue problem\ncan be transformed into a short sequence of such linear eigenvalue problems.\nThe result is an eigensolver that is substantially faster than standard\ntechniques. By combining this new solver with our new optimization frameworks,\nwe obtain our overall algorithm for fast computation of optimal viscosities.\nThe efficiency and performance of our new methods are verified and illustrated\non several numerical examples.\n"}
+{"abstract": "  A search for production of the supersymmetric partners of the top quark, top\nsquarks, is presented. The search is based on proton-proton collision events\ncontaining multiple jets, no leptons, and large transverse momentum imbalance.\nThe data were collected with the CMS detector at the CERN LHC at a\ncenter-of-mass energy of 13 TeV, and correspond to an integrated luminosity of\n137 fb$^{-1}$. The targeted signal production scenarios are direct and\ngluino-mediated top squark production, including scenarios in which the top\nsquark and neutralino masses are nearly degenerate. The search utilizes novel\nalgorithms based on deep neural networks that identify hadronically decaying\ntop quarks and W bosons, which are expected in many of the targeted signal\nmodels. No statistically significant excess of events is observed relative to\nthe expectation from the standard model, and limits on the top squark\nproduction cross section are obtained in the context of simplified\nsupersymmetric models for various production and decay modes. Exclusion limits\nas high as 1310 GeV are established at the 95% confidence level on the mass of\nthe top squark for direct top squark production models, and as high as 2260 GeV\non the mass of the gluino for gluino-mediated top squark production models.\nThese results represent a significant improvement over the results of previous\nsearches for supersymmetry by CMS in the same final state.\n"}
+{"abstract": "  We investigate the high-temperature dynamical conductivity $\\sigma(\\omega)$\nin two one-dimensional integrable quantum lattice models: the anisotropic XXZ\nspin chain and the Hubbard chain. The emphasis is on the metallic regime of\nboth models, where besides the ballistic component, the regular part of\nconductivity might reveal a diffusive-like transport. To resolve the\nlow-frequency dynamics, we upgrade the microcanonical Lanczos method enabling\nstudies of finite-size systems with up to $L\\leq 32$ sites for XXZ spin model\nwith the frequency resolution $\\delta \\omega \\sim 10^{-3} J$. Results for the\nXXZ chain reveal a fine structure of $\\sigma(\\omega)$ spectra, which originates\nfrom the discontinuous variation of the stiffness, previously found at\ncommensurate values of the anisotropy parameter $\\Delta$. Still, we do not find\na clear evidence for a diffusive component, at least not for commensurate\nvalues of $\\Delta$, particularly for $\\Delta =0.5$, as well as for $\\Delta \\to\n0$. Similar is the conclusion for the Hubbard model away from half-filling,\nwhere the spectra reveal more universal behavior.\n"}
+{"abstract": "  We present the photometric and spectroscopic evolution of supernova (SN)\n2019cad during the first $\\sim100$ days from explosion. Based on the light\ncurve morphology, we find that SN 2019cad resembles the double-peaked type Ib/c\nSN 2005bf and the type Ic PTF11mnb. Unlike those two objects, SN 2019cad also\nshows the initial peak in the redder bands. Inspection of the g-band light\ncurve indicates the initial peak is reached in $\\sim8$ days, while the r band\npeak occurred $\\sim15$ days post-explosion. A second and more prominent peak is\nreached in all bands at $\\sim45$ days past explosion, followed by and fast\ndecline from $\\sim60$ days. During the first 30 days, the spectra of SN 2019cad\nshow the typical features of a type Ic SN, however, after 40 days, a blue\ncontinuum with prominent lines of Si II ${\\lambda}6355$ and C II\n${\\lambda}6580$ is observed again. Comparing the bolometric light curve to\nhydrodynamical models, we find that SN 2019cad is consistent with a pre-SN mass\nof 11 M$_{\\odot}$, and an explosion energy of $3.5\\times 10^{51}$ erg. The\nlight curve morphology can be reproduced either by a double-peaked $^{56}$Ni\ndistribution with an external component of 0.041 M$_{\\odot}$ and an internal\ncomponent of 0.3 M$_{\\odot}$ or a double-peaked $^{56}$Ni distribution plus\nmagnetar model (P $\\sim11$ ms and B $\\sim26\\times 10^{14}$ G). If SN 2019cad\nwere to suffer from significant host reddening (which cannot be ruled out), the\n$^{56}$Ni model would require extreme values, while the magnetar model would\nstill be feasible.\n"}
+{"abstract": "  The notions of transposed Hom-Poisson and Hom-pre-Lie Poisson algebras are\nintroduced. Their bimodules and matched pairs are defined and the relevant\nproperties and theorems are given. The notion of Manin triple of transposed\nHom-Poisson algebras is introduced, and its equivalence to the transposed\nHom-Poisson bialgebras is investigated. The notion of $\\mathcal{O}$-operator is\nexploited to illustrate the relations existing between transposed Hom-Poisson\nand Hom-pre-Lie Poisson algebras.\n"}
+{"abstract": "  Panoptic segmentation of point clouds is a crucial task that enables\nautonomous vehicles to comprehend their vicinity using their highly accurate\nand reliable LiDAR sensors. Existing top-down approaches tackle this problem by\neither combining independent task-specific networks or translating methods from\nthe image domain ignoring the intricacies of LiDAR data and thus often\nresulting in sub-optimal performance. In this paper, we present the novel\ntop-down Efficient LiDAR Panoptic Segmentation (EfficientLPS) architecture that\naddresses multiple challenges in segmenting LiDAR point clouds including\ndistance-dependent sparsity, severe occlusions, large scale-variations, and\nre-projection errors. EfficientLPS comprises of a novel shared backbone that\nencodes with strengthened geometric transformation modeling capacity and\naggregates semantically rich range-aware multi-scale features. It incorporates\nnew scale-invariant semantic and instance segmentation heads along with the\npanoptic fusion module which is supervised by our proposed panoptic periphery\nloss function. Additionally, we formulate a regularized pseudo labeling\nframework to further improve the performance of EfficientLPS by training on\nunlabelled data. We benchmark our proposed model on two large-scale LiDAR\ndatasets: nuScenes, for which we also provide ground truth annotations, and\nSemanticKITTI. Notably, EfficientLPS sets the new state-of-the-art on both\nthese datasets.\n"}
+{"abstract": "  Most of the adversarial attack methods suffer from large perceptual\ndistortions such as visible artifacts, when the attack strength is relatively\nhigh. These perceptual distortions contain a certain portion which contributes\nless to the attack success rate. This portion of distortions, which is induced\nby unnecessary modifications and lack of proper perceptual distortion\nconstraint, is the target of the proposed framework. In this paper, we propose\na perceptual distortion reduction framework to tackle this problem from two\nperspectives. Firstly, we propose a perceptual distortion constraint and add it\ninto the objective function to jointly optimize the perceptual distortions and\nattack success rate. Secondly, we propose an adaptive penalty factor $\\lambda$\nto balance the discrepancies between different samples. Since SGD and\nMomentum-SGD cannot optimize our complex non-convex problem, we exploit Adam in\noptimization. Extensive experiments have verified the superiority of our\nproposed framework.\n"}
+{"abstract": "  Humans have a remarkable ability to make decisions by accurately reasoning\nabout future events, including the future behaviors and states of mind of other\nagents. Consider driving a car through a busy intersection: it is necessary to\nreason about the physics of the vehicle, the intentions of other drivers, and\ntheir beliefs about your own intentions. If you signal a turn, another driver\nmight yield to you, or if you enter the passing lane, another driver might\ndecelerate to give you room to merge in front. Competent drivers must plan how\nthey can safely react to a variety of potential future behaviors of other\nagents before they make their next move. This requires contingency planning:\nexplicitly planning a set of conditional actions that depend on the stochastic\noutcome of future events. In this work, we develop a general-purpose\ncontingency planner that is learned end-to-end using high-dimensional scene\nobservations and low-dimensional behavioral observations. We use a conditional\nautoregressive flow model to create a compact contingency planning space, and\nshow how this model can tractably learn contingencies from behavioral\nobservations. We developed a closed-loop control benchmark of realistic\nmulti-agent scenarios in a driving simulator (CARLA), on which we compare our\nmethod to various noncontingent methods that reason about multi-agent future\nbehavior, including several state-of-the-art deep learning-based planning\napproaches. We illustrate that these noncontingent planning methods\nfundamentally fail on this benchmark, and find that our deep contingency\nplanning method achieves significantly superior performance. Code to run our\nbenchmark and reproduce our results is available at\nhttps://sites.google.com/view/contingency-planning\n"}
+{"abstract": "  We present a photometric redshift (photo-$z$) estimation technique for\ngalaxies in the P\\lowercase{an}-STARRS1 (PS1) $3\\pi $ survey. Specifically, we\ntrain and test a regression and a classification Random-Forest (RF) models\nusing photometric features (magnitudes, colors and moments of the radiation\nintensity) from the optical PS1 data release 2 (PS1-DR2) and from the\nAllWISE/unWISE infrared source catalogs. The classification RF model\n($RF_{clas}$) has better performance in the local universe ($z\\lesssim 0.1$),\nwhile the second one ($RF_{reg}$) is on average better for $0.1 \\lesssim\nz\\lesssim1$. We adopt as labels the spectroscopic redshift of the galaxies from\nthe Sloan Digital Sky Survey (SDSS) data release 16 (SDSS-DR16). We find that\nthe combination of AllWISE/unWISE and PS1-DR2 features leads to an average bias\nof $\\overline{\\Delta z_{norm}}=1\\times 10^{-3}$, a standard deviation\n$\\sigma(\\Delta z_{norm})=0.0225$, (where $\\Delta z_{norm} \\equiv\n(z_{phot}-z_{spec})/(1+z_{spec})$), and an outlier rate of $P_0=1.48 \\%$ in the\ntest set for the $RF_{clas}$ model. In the low-redshift Universe ($z<0.1$) that\nis of primary interest to many astronomical transient studies, our model\nproduces an error estimate on the inferred magnitude of an object of $\\le$1 mag\nin 87\\% of the test sample.\n"}
+{"abstract": "  The aim of this paper is to obtain new inequalities for a large family of\ntopological indices restricted to unicyclic graphs and to characterize the set\nof extremal unicyclic graphs with respect to them. This family includes\nvariable first Zagreb, variable sum exdeg, multiplicative second Zagreb and\nNarumi-Katayama indices. Our main results provide upper and lower bounds for\nthese topological indices on unicyclic graphs, fixing or not the maximum degree\nor the number of pendant vertices.\n"}
+{"abstract": "  A wide choice of cinematic lenses enables motion-picture creators to adapt\nimage visual-appearance to their creative vision. Such choice does not exist in\nthe realm of real-time computer graphics, where only one type of perspective\nprojection is widely used. This work provides a perspective imaging model that\nin an artistically convincing manner resembles anamorphic photography lens\nvariety and more. It presents an asymmetrical-anamorphic azimuthal projection\nmap with natural vignetting and realistic chromatic aberration. The\nmathematical model for this projection has been chosen such that its parameters\nreflect the psycho-physiological aspects of visual perception. That enables its\nuse in artistic and professional environments, where specific aspects of the\nphotographed space are to be presented.\n"}
+{"abstract": "  Deep learning has successfully been leveraged for medical image segmentation.\nIt employs convolutional neural networks (CNN) to learn distinctive image\nfeatures from a defined pixel-wise objective function. However, this approach\ncan lead to less output pixel interdependence producing incomplete and\nunrealistic segmentation results. In this paper, we present a fully automatic\ndeep learning method for robust medical image segmentation by formulating the\nsegmentation problem as a recurrent framework using two systems. The first one\nis a forward system of an encoder-decoder CNN that predicts the segmentation\nresult from the input image. The predicted probabilistic output of the forward\nsystem is then encoded by a fully convolutional network (FCN)-based context\nfeedback system. The encoded feature space of the FCN is then integrated back\ninto the forward system's feed-forward learning process. Using the FCN-based\ncontext feedback loop allows the forward system to learn and extract more\nhigh-level image features and fix previous mistakes, thereby improving\nprediction accuracy over time. Experimental results, performed on four\ndifferent clinical datasets, demonstrate our method's potential application for\nsingle and multi-structure medical image segmentation by outperforming the\nstate of the art methods. With the feedback loop, deep learning methods can now\nproduce results that are both anatomically plausible and robust to low contrast\nimages. Therefore, formulating image segmentation as a recurrent framework of\ntwo interconnected networks via context feedback loop can be a potential method\nfor robust and efficient medical image analysis.\n"}
+{"abstract": "  We report here the first detection in the interstellar medium of the\ncyanomidyl radical (HNCN). Using the Yebes 40m and the IRAM 30m telescopes, we\nhave targeted the doublets of the $N$=2$-$1, 4$-$3, 5$-$4, 6$-$5, and 7$-$6\ntransitions of HNCN toward the molecular cloud G+0.693-0.027. We have detected\nthree unblended lines of HNCN, these are the $N$=6$-$5 doublet and one line of\nthe $N$=4$-$3 transition. Additionally we present one line of the $N$=5$-$4\ntransition partially blended with emission from other species. The Local\nThermodynamic Equilibrium best fit to the data gives a molecular abundance of\n(0.91$\\pm$0.05)$\\times$10$^{-10}$ with respect to H$_2$. The relatively low\nabundance of this species in G+0.693-0.027, and its high reactivity, suggest\nthat HNCN is possibly produced by gas-phase chemistry. Our work shows that this\nhighly reactive molecule is present in interstellar space, and thus it\nrepresents a plausible precursor of larger prebiotic molecules with the NCN\nbackbone such as cyanamide (NH$_2$CN), carbodiimide (HNCNH) and formamidine\n(NH$_2$CHNH).\n"}
+{"abstract": "  The results from the ESA Gaia astrometric mission and deep photometric\nsurveys have revolutionized our knowledge of the Milky Way. There are many\nongoing efforts to search these data for stellar substructure to find evidence\nof individual accretion events that built up the Milky Way and its halo. One of\nthese newly identified features, called Nyx, was announced as an accreted\nstellar stream traveling in the plane of the disk. Using a combination of\nelemental abundances and stellar parameters from the GALAH and APOGEE surveys,\nwe find that the abundances of the highest likelihood Nyx members are entirely\nconsistent with membership of the thick disk, and inconsistent with a dwarf\ngalaxy origin. We conclude that the postulated Nyx stream is most probably a\nhigh-velocity component of the Milky Way's thick disk. With the growing\navailability of large data sets including kinematics, stellar parameters, and\ndetailed abundances, the probability of detecting chance associations\nincreases, and hence new searches for substructure require confirmation across\nas many data dimensions as possible.\n"}
+{"abstract": "  Bayesian neural networks are theoretically well-understood only in the\ninfinite-width limit, where Gaussian priors over network weights yield Gaussian\npriors over network outputs. Recent work has suggested that finite Bayesian\nnetworks may outperform their infinite counterparts, but their non-Gaussian\nfunction space priors have been characterized only though perturbative\napproaches. Here, we derive exact solutions for the function space priors for\nindividual input examples of a class of finite fully-connected feedforward\nBayesian neural networks. For deep linear networks, the prior has a simple\nexpression in terms of the Meijer $G$-function. The prior of a finite ReLU\nnetwork is a mixture of the priors of linear networks of smaller widths,\ncorresponding to different numbers of active units in each layer. Our results\nunify previous descriptions of finite network priors in terms of their tail\ndecay and large-width behavior.\n"}
+{"abstract": "  Inpainting arbitrary missing regions is challenging because learning valid\nfeatures for various masked regions is nontrivial. Though U-shaped\nencoder-decoder frameworks have been witnessed to be successful, most of them\nshare a common drawback of mask unawareness in feature extraction because all\nconvolution windows (or regions), including those with various shapes of\nmissing pixels, are treated equally and filtered with fixed learned kernels. To\nthis end, we propose our novel mask-aware inpainting solution. Firstly, a\nMask-Aware Dynamic Filtering (MADF) module is designed to effectively learn\nmulti-scale features for missing regions in the encoding phase. Specifically,\nfilters for each convolution window are generated from features of the\ncorresponding region of the mask. The second fold of mask awareness is achieved\nby adopting Point-wise Normalization (PN) in our decoding phase, considering\nthat statistical natures of features at masked points differentiate from those\nof unmasked points. The proposed PN can tackle this issue by dynamically\nassigning point-wise scaling factor and bias. Lastly, our model is designed to\nbe an end-to-end cascaded refinement one. Supervision information such as\nreconstruction loss, perceptual loss and total variation loss is incrementally\nleveraged to boost the inpainting results from coarse to fine. Effectiveness of\nthe proposed framework is validated both quantitatively and qualitatively via\nextensive experiments on three public datasets including Places2, CelebA and\nParis StreetView.\n"}
+{"abstract": "  Antibiotic resistance is a growing public health problem. To gain a\nfundamental understanding of resistance evolution, a combination of systematic\nexperimental and theoretical approaches is required. Evolution experiments\ncombined with next-generation sequencing techniques, laboratory automation, and\nmathematical modeling are enabling the investigation of resistance development\nat an unprecedented level of detail. Recent work has directly tracked the\nintricate stochastic dynamics of bacterial populations in which resistant\nmutants emerge and compete. In addition, new approaches have enabled measuring\nhow prone a large number of genetically perturbed strains are to evolve\nresistance. Based on advances in quantitative cell physiology, predictive\ntheoretical models of resistance are increasingly being developed. Taken\ntogether, a new strategy for observing, predicting, and ultimately controlling\nresistance evolution is emerging.\n"}
+{"abstract": "  The Reinforcement Learning (RL) building blocks, i.e. Q-functions and policy\nnetworks, usually take elements from the cartesian product of two domains as\ninput. In particular, the input of the Q-function is both the state and the\naction, and in multi-task problems (Meta-RL) the policy can take a state and a\ncontext. Standard architectures tend to ignore these variables' underlying\ninterpretations and simply concatenate their features into a single vector. In\nthis work, we argue that this choice may lead to poor gradient estimation in\nactor-critic algorithms and high variance learning steps in Meta-RL algorithms.\nTo consider the interaction between the input variables, we suggest using a\nHypernetwork architecture where a primary network determines the weights of a\nconditional dynamic network. We show that this approach improves the gradient\napproximation and reduces the learning step variance, which both accelerates\nlearning and improves the final performance. We demonstrate a consistent\nimprovement across different locomotion tasks and different algorithms both in\nRL (TD3 and SAC) and in Meta-RL (MAML and PEARL).\n"}
+{"abstract": "  The computation of determinants plays a central role in diagrammatic Monte\nCarlo algorithms for strongly correlated systems. The evaluation of large\nnumbers of determinants can often be the limiting computational factor\ndetermining the number of attainable diagrammatic expansion orders. In this\nwork we build upon the algorithm presented in [\\emph{Linear Algebra and its\napplications} 419.1 (2006), 107-124] which computes all principal minors of a\nmatrix in $O(2^n)$ operations. We present multiple generalizations of the\nalgorithm to the efficient evaluation of certain subsets of all principal\nminors with immediate applications to Connected Determinant Diagrammatic Monte\nCarlo within the normal and symmetry-broken phases as well as Continuous-time\nQuantum Monte Carlo. Additionally, we improve the asymptotic scaling of\ndiagrammatic Monte Carlo formulated in real-time to $O(2^n)$ and report\nspeedups of up to a factor $25$ at computationally realistic expansion orders.\nWe further show that all permanent-principal-minors, corresponding to sums of\nbosonic Feynman diagrams, can be computed in $O(3^n)$, making diagrammatic\nMonte Carlo for bosonic and mixed systems a viable path worth exploring.\n"}
+{"abstract": "  We show that the word problem for braided monoidal categories is at least as\nhard as the unknotting problem. As a corollary, so is the word problem for\ntricategories. We conjecture that the word problem for tricategories is\ndecidable.\n"}
+{"abstract": "  We review the basic definitions and properties of trapped surfaces and\ndiscuss them in the context of Kerr-Vaidya line-element. Our study shows that\nthe apparent horizon does not exist in general for axisymmetric space-times.\nThe reason being the surface at which the null tangent vectors are geodesic and\nthe surface at which the expansion of such vectors vanishes do not coincide.\nCalculation of an approximate apparent horizon for space-times that ensure its\nexistence seems to be the only way to get away with this problem. The\napproximate apparent horizon, however, turned out to be non-unique. The choice\nof the shear free null geodesics, at least in the leading order, seem to remove\nthis non-uniqueness. We also propose a new definition of the black hole\nboundary.\n"}
+{"abstract": "  In this talk, we will describe a framework for assertion-based verification\n(ABV) of quantum circuits by applying model checking techniques for quantum\nsystems developed in our previous work, in which:\n  (i) Noiseless and noisy quantum circuits are modelled as operator- and\nsuper-operator-valued transition systems, respectively, both of which can be\nfurther represented by tensor networks.\n  (ii) Quantum assertions are specified by a temporal extension of Birkhoff-von\nNeumann quantum logic. Their semantics is defined based on the design decision:\nthey will be used in verification of quantum circuits by simulation on\nclassical computers or human reasoning rather than by quantum physics\nexperiments (e.g. testing through measurements);\n  (iii) Algorithms for reachability analysis and model checking of quantum\ncircuits are developed based on contraction of tensor networks. We observe that\nmany optimisation techniques for computing relational products used in\nBDD-based model checking algorithms can be generalised for contracting tensor\nnetworks of quantum circuits.\n"}
+{"abstract": "  Scintillators play an important role in radiation detection and imaging.\nThallium-doped cesium iodide (CsI:Tl) and sodium iodide (NaI:Tl) are two of the\nmajor scintillators that have been used for many applications for many decades.\nIn this paper we will present an improved scintillation performance of Bridgman\nmethod grown CsI(Tl) and NaI(Tl) crystals developed by Xtallized Intelligence,\nInc. (XI, Inc.) and we will compare the performance with commercially available\nCsI:Tl and NaI:Tl. In a preliminary testing using MicroFJ-60035-TSV silicon\nphotomultipliers (ON Semiconductor), the newly developed and improved\n12.5x12.5x6 mm3 CsI:Tl crystal has shown an energy resolution of 4.8% (FWHM) at\n662 keV, compared to 7.2% obtained by a commercially available CsI:Tl with a\nsize of 12.5x12.5x25 mm3. Energy resolution of 5.4% (FWHM) at 662 keV is\nobtained for the newly improved NaI:Tl crystal, compared to 7% obtained by a\ncommercially available 1 in. dia. x 1 in. NaI(Tl). Comparison of the photo peak\nchannel locations shows that the improved CsI:Tl and NaI:Tl developed by XI,\nInc. have produced much larger signal amplitudes when compared to the\ncommercially available CsI(Tl) and NaI:Tl. Improved decay time constants are\npresented as well.\n"}
+{"abstract": "  Submodular optimization has numerous applications such as crowdsourcing and\nviral marketing. In this paper, we study the fundamental problem of\nnon-negative submodular function maximization subject to a $k$-system\nconstraint, which generalizes many other important constraints in submodular\noptimization such as cardinality constraint, matroid constraint, and\n$k$-extendible system constraint. The existing approaches for this problem\nachieve the best-known approximation ratio of $k+2\\sqrt{k+2}+3$ (for a general\nsubmodular function) based on deterministic algorithmic frameworks. We propose\nseveral randomized algorithms that improve upon the state-of-the-art algorithms\nin terms of approximation ratio and time complexity, both under the\nnon-adaptive setting and the adaptive setting. The empirical performance of our\nalgorithms is extensively evaluated in several applications related to data\nmining and social computing, and the experimental results demonstrate the\nsuperiorities of our algorithms in terms of both utility and efficiency.\n"}
+{"abstract": "  The representative cuprate, La$_{2-x}$M$_x$CuO$_4$, with M = Sr and $x = 1/8$\nis studied via first-principles calculations in the high-temperature tetragonal\n(HTT), low-temperature orthorhombic (LTO), and low-temperature\nless-orthorhombic (LTLO) structures. By suppressing the magnetism and\nsuperconductivity, the LTLO phase, which has rarely been observed in\nLa$_{2-x}$Sr$_x$CuO$_4$, is found to be the ground state, where the structural\nphase transitions, HTT$\\rightarrow$LTO$\\rightarrow$LTLO, can be understood via\nphonon instability. While the La-O composition is identified to be responsible\nfor the phonon softening, the superconducting CuO$_2$ layer is dynamically\nstable. The LTLO phase, which can exhibit a $\\sim$20 meV splitting in the\ndensity of states, is proposed to have an intimate relationship with the\nobserved pseudogap and the charge density wave giving the stripe. We argue that\nat low temperatures, the superconducting LTO La$_{1.875}$Sr$_{0.125}$CuO$_4$\ncompetes with the phonon-preferred LTLO phase by spontaneously forming the\nCooper pairs, resulting in suppressing the stripe. Therefore, the revealed LTLO\nphase is indispensable for understanding La$_{2-x}$Sr$_x$CuO$_4$.\n"}
+{"abstract": "  Consider a set P of points in the unit square U, one of them being the\norigin. For each point p in P you may draw a rectangle in U with its lower-left\ncorner in p. What is the maximum area such rectangles can cover without\noverlapping each other? Freedman [1969] posed this problem in 1969, asking\nwhether one can always cover at least 50% of U. Over 40 years later, Dumitrescu\nand T\\'oth [2011] achieved the first constant coverage of 9.1%; since then, no\nsignificant progress was made. While 9.1% might seem low, the authors could not\nfind any instance where their algorithm covers less than 50%, nourishing the\nhope to eventually prove a 50% bound. While we indeed significantly raise the\nalgorithm's coverage to 39%, we extinguish the hope of reaching 50% by giving\npoints for which the coverage is below 43.3%. Our analysis studies the\nalgorithm's average and worst-case density of so-called tiles, which represent\nthe area where a given point can freely choose its maximum-area rectangle. Our\napproachis comparatively general and may potentially help in analyzing related\nalgorithms.\n"}
+{"abstract": "  We propose an artificial life framework aimed at facilitating the emergence\nof intelligent organisms. In this framework there is no explicit notion of an\nagent: instead there is an environment made of atomic elements. These elements\ncontain neural operations and interact through exchanges of information and\nthrough physics-like rules contained in the environment. We discuss how an\nevolutionary process can lead to the emergence of different organisms made of\nmany such atomic elements which can coexist and thrive in the environment. We\ndiscuss how this forms the basis of a general AI generating algorithm. We\nprovide a simplified implementation of such system and discuss what advances\nneed to be made to scale it up further.\n"}
+{"abstract": "  We present a second-order ensemble method based on a blended three-step\nbackward differentiation formula (BDF) timestepping scheme to compute an\nensemble of Navier-Stokes equations. Compared with the only existing\nsecond-order ensemble method that combines the two-step BDF timestepping scheme\nand a special explicit second-order Adams-Bashforth treatment of the advection\nterm, this method is more accurate with nominal increase in computational cost.\nWe give comprehensive stability and error analysis for the method. Numerical\nexamples are also provided to verify theoretical results and demonstrate the\nimproved accuracy of the method.\n"}
+{"abstract": "  The optical and UV variability of the majority of AGN may be related to the\nreprocessing of rapidly-changing X-ray emission from a more compact region near\nthe central black hole. Such a reprocessing model would be characterised by\nlags between X-ray and optical/UV emission due to differences in light travel\ntime. Observationally however, such lag features have been difficult to detect\ndue to gaps in the lightcurves introduced through factors such as source\nvisibility or limited telescope time. In this work, Gaussian process regression\nis employed to interpolate the gaps in the Swift X-ray and UV lightcurves of\nthe narrow-line Seyfert 1 galaxy Mrk 335. In a simulation study of five\ncommonly-employed analytic Gaussian process kernels, we conclude that the\nMatern 1/2 and rational quadratic kernels yield the most well-specified models\nfor the X-ray and UVW2 bands of Mrk 335. In analysing the structure functions\nof the Gaussian process lightcurves, we obtain a broken power law with a break\npoint at 125 days in the UVW2 band. In the X-ray band, the structure function\nof the Gaussian process lightcurve is consistent with a power law in the case\nof the rational quadratic kernel whilst a broken power law with a breakpoint at\n66 days is obtained from the Matern 1/2 kernel. The subsequent\ncross-correlation analysis is consistent with previous studies and furthermore,\nshows tentative evidence for a broad X-ray-UV lag feature of up to 30 days in\nthe lag-frequency spectrum where the significance of the lag depends on the\nchoice of Gaussian process kernel.\n"}
+{"abstract": "  Remote collaboration systems have become increasingly important in today's\nsociety, especially during times where physical distancing is advised.\nIndustry, research and individuals face the challenging task of collaborating\nand networking over long distances. While video and teleconferencing are\nalready widespread, collaboration systems in augmented, virtual, and mixed\nreality are still a niche technology. We provide an overview of recent\ndevelopments of synchronous remote collaboration systems and create a taxonomy\nby dividing them into three main components that form such systems:\nEnvironment, Avatars, and Interaction. A thorough overview of existing systems\nis given, categorising their main contributions in order to help researchers\nworking in different fields by providing concise information about specific\ntopics such as avatars, virtual environment, visualisation styles and\ninteraction. The focus of this work is clearly on synchronised collaboration\nfrom a distance. A total of 82 unique systems for remote collaboration are\ndiscussed, including more than 100 publications and 25 commercial systems.\n"}
+{"abstract": "  Starting from the construction of the free quantum scalar field of mass\n$m\\geq 0$ we give mathematically precise and rigorous versions of three\ndifferent approaches to computing the Casimir forces between compact obstacles.\nWe then prove that they are equivalent.\n"}
+{"abstract": "  Self-supervised representation learning is able to learn semantically\nmeaningful features; however, much of its recent success relies on multiple\ncrops of an image with very few objects. Instead of learning view-invariant\nrepresentation from simple images, humans learn representations in a complex\nworld with changing scenes by observing object movement, deformation, pose\nvariation, and ego motion. Motivated by this ability, we present a new\nself-supervised learning representation framework that can be directly deployed\non a video stream of complex scenes with many moving objects. Our framework\nfeatures a simple flow equivariance objective that encourages the network to\npredict the features of another frame by applying a flow transformation to the\nfeatures of the current frame. Our representations, learned from\nhigh-resolution raw video, can be readily used for downstream tasks on static\nimages. Readout experiments on challenging semantic segmentation, instance\nsegmentation, and object detection benchmarks show that we are able to\noutperform representations obtained from previous state-of-the-art methods\nincluding SimCLR and BYOL.\n"}
+{"abstract": "  We consider the discrete-time quantum walk whose local dynamics is denoted by\n$C$ at the perturbed region $\\{0,1,\\dots,M-1\\}$ and free at the other\npositions. We obtain the stationary state with a bounded initial state. The\ninitial state is set so that the perturbed region receives the inflow\n$\\omega^n$ at time $n$ $(|\\omega|=1)$. From this expression, we compute the\nscattering on the surface of $-1$ and $M$ and also compute the quantity how\nquantum walker accumulates in the perturbed region; namely the energy of the\nquantum walk, in the long time limit. We find a discontinuity of the energy\nwith respect to the frequency of the inflow.\n"}
+{"abstract": "  The primary mechanism governing the emergence of near-room-temperature\nsuperconductivity in superhydrides is widely accepted to be the electron-phonon\ninteraction. If so, the temperature dependent resistance, R(T), in these\nmaterials should obey the Bloch-Gr\\\"uneisen equation, where the power-law\nexponent, p, should be equal to the exact integer value of p=5. On the other\nhand, there is a well-established theoretical result that pure electron-magnon\ninteraction should be manifested by p=3, and p=2 is the value for pure\nelectron-electron interaction. Here we aimed to reveal the type of charge\ncarrier interaction in the layered transition metal dichalcogenides PdTe2,\nhigh-entropy alloy (ScZrNb)0.65[RhPd]0.35, and highly-compressed elemental\nboron and superhydrides H3S, LaHx, PrH9 and BaH12 by fitting the temperature\ndependent resistance of these materials to the Bloch-Gr\\\"uneisen equation where\nthe power-law exponent, p, is a free-fitting parameter. In the result, we\nshowed that the high-entropy alloy (ScZrNb)0.65[RhPd]0.35 exhibited pure\nelectron-phonon mediated superconductivity with p = 4.9. Unexpectedly we\nrevealed that all studied superhydrides exhibit 1.8 < p < 3.2. This implies\nthat it is unlikely that the electron-phonon interaction is the primary\nmechanism for the Cooper pairs formation in highly-compressed superhydrides and\nalternative pairing mechanisms, for instance, the electron-magnon, the\nelectron-polaron, the electron-electron or other, should be considered as the\norigin for the emergence of near-room-temperature superconductivity in these\ncompounds.\n"}
+{"abstract": "  A non-local hidden variables theory for non-relativisitic quantum theory is\npresented, which gives a realist completion of quantum mechanics, in the sense\nof a complete description of individual events. The proposed fundamental theory\nis an extension of an energetic causal set theory, which assumes that time,\nevents, causal structure, momentum and energy are fundamental. But space and\nthe wave function are emergent.\n  The beables of the theory are the views of the events, which are a subset of\ntheir causal pasts. Thus, this theory asserts that the universe is a causal\nnetwork of events, which consists of partial views of itself as seen by looking\nbackwards from each event.\n  The fundamental dynamics is based on an action whose potential energy is\nproportional to the variety, which is a measure of the diversity of the views\nof the events, while the kinetic energy is proportional to its rate of change.\nThe Schroedinger equation is derived to leading order in an expansion in\ndensity of the events of the fundamental histories. To higher order, there are\ncomputable corrections, non-linear in the wave function, from which new\nphysical effects may be predicted.\n"}
+{"abstract": "  Knowledge distillation (KD) is one of the most useful techniques for\nlight-weight neural networks. Although neural networks have a clear purpose of\nembedding datasets into the low-dimensional space, the existing knowledge was\nquite far from this purpose and provided only limited information. We argue\nthat good knowledge should be able to interpret the embedding procedure. This\npaper proposes a method of generating interpretable embedding procedure (IEP)\nknowledge based on principal component analysis, and distilling it based on a\nmessage passing neural network. Experimental results show that the student\nnetwork trained by the proposed KD method improves 2.28% in the CIFAR100\ndataset, which is higher performance than the state-of-the-art (SOTA) method.\nWe also demonstrate that the embedding procedure knowledge is interpretable via\nvisualization of the proposed KD process. The implemented code is available at\nhttps://github.com/sseung0703/IEPKT.\n"}
+{"abstract": "  Financial disclosure analysis and Knowledge extraction is an important\nfinancial analysis problem. Prevailing methods depend predominantly on\nquantitative ratios and techniques, which suffer from limitations like window\ndressing and past focus. Most of the information in a firm's financial\ndisclosures is in unstructured text and contains valuable information about its\nhealth. Humans and machines fail to analyze it satisfactorily due to the\nenormous volume and unstructured nature, respectively. Researchers have started\nanalyzing text content in disclosures recently. This paper covers the previous\nwork in unstructured data analysis in Finance and Accounting. It also explores\nthe state of art methods in computational linguistics and reviews the current\nmethodologies in Natural Language Processing (NLP). Specifically, it focuses on\nresearch related to text source, linguistic attributes, firm attributes, and\nmathematical models employed in the text analysis approach. This work\ncontributes to disclosure analysis methods by highlighting the limitations of\nthe current focus on sentiment metrics and highlighting broader future research\nareas\n"}
+{"abstract": "  Electromagnetic energy backflow is a phenomenon occurring in regions where\nthe direction of the Poynting vector is opposite to that of the propagation of\nthe wave field. It is particularly remarkable in the nonparaxial regime and has\nbeen exhibited in the focal region of sharply focused beams, for vector Bessel\nbeams, and vector-valued spatiotemporally localized waves. A detailed study is\nundertaken of this phenomenon and the conditions for its appearance are\nexamined in detail in the case of a superposition of four plane waves in free\nspace, the simplest electromagnetic arrangement for the observation of negative\nenergy flow, as well as its comprehensive and transparent physical\ninterpretation. It is shown that the state of polarization of the constituent\ncomponents of the electromagnetic plane wave quartet determines whether energy\nbackflow takes place or not and what values the energy flow velocity assumes.\nDepending on the polarization angles, the latter can assume any value from c\n(the speed of light in vacuum) to -c in certain spatiotemporal regions of the\nfield.\n"}
+{"abstract": "  In this thesis, we present a flexible framework for specifying and\nconstructing operads which are suited to reasoning about network construction.\nThe data used to present these operads is called a \\emph{network model}, a\nmonoidal variant of Joyal's combinatorial species. The construction of the\noperad required that we develop a monoidal lift of the Grothendieck\nconstruction. We then demonstrate how concepts like priority and dependency can\nbe represented in this framework. For the former, we generalize Green's graph\nproducts of groups to the context of universal algebra. For the latter, we\nexamine the emergence of monoidal fibrations from the presence of catalysts in\nPetri nets.\n"}
+{"abstract": "  A novel $\\gamma$-ray supernova remnant (SNR) G206.9+2.3 is first reported in\nthis study. We arrived at this conclusion after analyzing 12.4 years of\nobservation data of the Fermi Large Area Telescope (Fermi-LAT). The photon flux\nof the remnant was (1.19$\\pm$0.59) 10$^{-9}$ cm$^{-2}$ s$^{-1}$, and its\npower-law spectral index was 2.22$\\pm$0.19 in the 0.2-500 GeV energy band.\nMoreover, we found that the test statistic values of the global fit from the\nfour different energy bands were greater than 9. We identified that this was a\nreal $\\gamma$-ray signal. Furthermore, we found that its GeV spatial location\nwas in good agreement with that of its radio band. Its spectral energy\ndistribution and light curve properties were similar to those of SNRs. We\nsuggest that the novel $\\gamma$-ray source is a likely counterpart to SNR\nG206.9+2.3. Consequently, we discuss its likely leptonic or hadronic origin.\n"}
+{"abstract": "  It is shown that the famous Allen -- Dynes asymtotic limit for\nsuperconducting transition temperature in very strong coupling region\n$T_{c}>\\frac{1}{2\\pi}\\sqrt{\\lambda}\\Omega_0$ (where $\\lambda\\gg 1$ - is\nEliashberg - McMillan electron - phonon coupling constant and $\\Omega_0$ - the\ncharacteristic frequency of phonons) in antiadiabatic limit of Eliashberg\nequations $\\Omega_0/D\\gg 1$ ($D\\sim E_F$ is conduction band half-width and\n$E_F$ is Fermi energy) is replaced by $T_c>(2\\pi^4)^{-1/3}(\\lambda\nD\\Omega_0^2)^{1/3}$, with the upper limit for $T_c$ given by\n$T_c<\\frac{2}{\\pi^2}\\lambda D$.\n"}
+{"abstract": "  While many works on Continual Learning have shown promising results for\nmitigating catastrophic forgetting, they have relied on supervised training. To\nsuccessfully learn in a label-agnostic incremental setting, a model must\ndistinguish between learned and novel classes to properly include samples for\ntraining. We introduce a novelty detection method that leverages network\nconfusion caused by training incoming data as a new class. We found that\nincorporating a class-imbalance during this detection method substantially\nenhances performance. The effectiveness of our approach is demonstrated across\na set of image classification benchmarks: MNIST, SVHN, CIFAR-10, CIFAR-100, and\nCRIB.\n"}
+{"abstract": "  In a paper from 1973 R.D. Hill studied linear matrix maps\n$\\mathcal{L}:\\mathbb{C}^{q \\times q}\\to\\mathbb{C}^{n \\times n}$ which map\nHermitian matrices to Hermitian matrices, or equivalently, preserve adjoints,\ni.e., $\\mathcal{L}(V^*)=\\mathcal{L}(V)^*$, via representations of the form\n\\begin{equation*} \\mathcal{L}(V)=\\sum_{k,l=1}^m \\mathbb{H}_{kl}\\, A_l V\nA_k^*,\\quad V\\in\\mathbb{C}^{q \\times q}, \\end{equation*} for matrices\n$A_1,\\ldots,A_m \\in\\mathbb{C}^{n \\times q}$ and continued his study of such\nrepresentations in later work, sometimes with co-authors, to completely\npositive matrix maps and associated matrix reorderings. In this paper we expand\nthe study of such representations, referred to as Hill representations here, in\nvarious directions. In particular, we describe which matrices $A_1,\\ldots, A_m$\ncan appear in Hill representations (provided the number $m$ is minimal) and\ndetermine the associated Hill matrix $\\mathbb{H}=\\left[\\mathbb{H}_{kl}\\right]$\nexplicitly. Also, we describe how different Hill representations of\n$\\mathcal{L}$ (again with $m$ minimal) are related and investigate further the\nimplication of $*$-linearity on the linear map $\\mathcal{L}$.\n"}
+{"abstract": "  Seizures are one of the defining symptoms in patients with epilepsy, and due\nto their unannounced occurrence, they can pose a severe risk for the individual\nthat suffers it. New research efforts are showing a promising future for the\nprediction and preemption of imminent seizures, and with those efforts, a vast\nand diverse set of features have been proposed for seizure prediction\nalgorithms. However, the data-driven discovery of nonsinusoidal waveforms for\nseizure prediction is lacking in the literature, which is in stark contrast\nwith recent works that show the close connection between the waveform\nmorphology of neural oscillations and the physiology and pathophysiology of the\nbrain, and especially its use in effectively discriminating between normal and\nabnormal oscillations in electrocorticographic (ECoG) recordings of epileptic\npatients. Here, we explore a scalable, energy-guided waveform search strategy\non spatially-projected continuous multi-day ECoG data sets. Our work shows that\ndata-driven waveform learning methods have the potential to not only contribute\nfeatures with predictive power for seizure prediction, but also to facilitate\nthe discovery of oscillatory patterns that could contribute to our\nunderstanding of the pathophysiology and etiology of seizures.\n"}
+{"abstract": "  We investigate the Lyapunov Exponents of a variation of Hodge structure which\nhas $G_2$ as geometric monodromy group, and discuss formulas for the sum of\npositive Lyapunov Exponents of variations of Hodge structures of any weight.\n"}
+{"abstract": "  Affect preferences vary with user demographics, and tapping into demographic\ninformation provides important cues about the users' language preferences. In\nthis paper, we utilize the user demographics, and propose EmpathBERT, a\ndemographic-aware framework for empathy prediction based on BERT. Through\nseveral comparative experiments, we show that EmpathBERT surpasses traditional\nmachine learning and deep learning models, and illustrate the importance of\nuser demographics to predict empathy and distress in user responses to\nstimulative news articles. We also highlight the importance of affect\ninformation in the responses by developing affect-aware models to predict user\ndemographic attributes.\n"}
+{"abstract": "  Distantly supervised (DS) relation extraction (RE) has attracted much\nattention in the past few years as it can utilize large-scale auto-labeled\ndata. However, its evaluation has long been a problem: previous works either\ntook costly and inconsistent methods to manually examine a small sample of\nmodel predictions, or directly test models on auto-labeled data -- which, by\nour check, produce as much as 53% wrong labels at the entity pair level in the\npopular NYT10 dataset. This problem has not only led to inaccurate evaluation,\nbut also made it hard to understand where we are and what's left to improve in\nthe research of DS-RE. To evaluate DS-RE models in a more credible way, we\nbuild manually-annotated test sets for two DS-RE datasets, NYT10 and Wiki20,\nand thoroughly evaluate several competitive models, especially the latest\npre-trained ones. The experimental results show that the manual evaluation can\nindicate very different conclusions from automatic ones, especially some\nunexpected observations, e.g., pre-trained models can achieve dominating\nperformance while being more susceptible to false-positives compared to\nprevious methods. We hope that both our manual test sets and novel observations\ncan help advance future DS-RE research.\n"}
+{"abstract": "  We use a three-flavor Nambu--Jona-Lasinio model to study the thermodynamics\nof strange quark matter under a strong magnetic field. The model Lagrangian\nfeatures flavor SU(3) four-quark interactions and six-quark interactions that\nbreak the UA(1) symmetry. We incorporate thermomagnetic effects in the\nfour-quark coupling. The model predicts magnetic catalysis at low temperatures\nand inverse magnetic catalysis at temperatures close to the pseudocritical\ntemperature of the QCD transition, in agreement with lattice QCD results. We\ncompute the pressure at the mean field level and obtain the magnetization of\nquark matter. We adopt the recently proposed vacuum magnetic regularization\n(VMR) scheme, in that divergent quark mass independent contributions are not\nsubtracted, thereby avoiding unphysical results for the magnetization. We\ndevote special attention to the renormalized magnetization, a projected\nquantity that allows for direct comparisons with lattice QCD simulations. Our\nresults are in very good agreement with lattice data indicating a paramagnetic\nbehavior for quark matter.\n"}
+{"abstract": "  We present ECLIPSE (Efficient Cmb poLarization and Intensity Power Spectra\nEstimator), an optimized implementation of the Quadratic Maximum Likelihood\n(QML) method for the estimation of the power spectra of the Cosmic Microwave\nBackground (CMB). This approach allows one to reduce significantly the\ncomputational costs associated to this technique, allowing to estimate the\npower spectra up to higher multipoles than previous implementations. In\nparticular, for a resolution of $N_\\mathrm{side}=64$, $\\ell_{\\mathrm{max}}=192$\nand a typical Galactic mask, the number of operations can be reduced by\napproximately a factor of 1000 in a full analysis including intensity and\npolarization with respect to an efficient direct implementation of the method.\nIn addition, if one is interested in studying only polarization, it is possible\nto obtain the power spectra of the E and B modes with a further reduction of\ncomputational resources without degrading the results. We also show that for\nexperiments observing a small fraction of the sky, the Fisher matrix becomes\nsingular and, in this case, the standard QML can not be applied. To solve this\nproblem, we have developed a binned version of the method that is unbiased and\nof minimum variance. We also test the robustness of the QML estimator when the\nassumed fiducial model differs from that of the sky and show the performance of\nan iterative approach. Finally, we present a comparison of the results obtained\nby QML and a pseudo-$C_{\\ell}$ estimator (NaMaster) for a next-generation\nsatellite, showing that, as expected, QML produces significantly smaller errors\nat low multipoles. The ECLIPSE fast QML code developed in this work will be\nmade publicly available.\n"}
+{"abstract": "  Leveraging the framework of Optimal Transport, we introduce a new family of\ngenerative autoencoders with a learnable prior, called Symmetric Wasserstein\nAutoencoders (SWAEs). We propose to symmetrically match the joint distributions\nof the observed data and the latent representation induced by the encoder and\nthe decoder. The resulting algorithm jointly optimizes the modelling losses in\nboth the data and the latent spaces with the loss in the data space leading to\nthe denoising effect. With the symmetric treatment of the data and the latent\nrepresentation, the algorithm implicitly preserves the local structure of the\ndata in the latent space. To further improve the quality of the latent\nrepresentation, we incorporate a reconstruction loss into the objective, which\nsignificantly benefits both the generation and reconstruction. We empirically\nshow the superior performance of SWAEs over the state-of-the-art generative\nautoencoders in terms of classification, reconstruction, and generation.\n"}
+{"abstract": "  Modelers use automatic differentiation (AD) of computation graphs to\nimplement complex Deep Learning models without defining gradient computations.\nStochastic AD extends AD to stochastic computation graphs with sampling steps,\nwhich arise when modelers handle the intractable expectations common in\nReinforcement Learning and Variational Inference. However, current methods for\nstochastic AD are limited: They are either only applicable to continuous random\nvariables and differentiable functions, or can only use simple but high\nvariance score-function estimators. To overcome these limitations, we introduce\nStorchastic, a new framework for AD of stochastic computation graphs.\nStorchastic allows the modeler to choose from a wide variety of gradient\nestimation methods at each sampling step, to optimally reduce the variance of\nthe gradient estimates. Furthermore, Storchastic is provably unbiased for\nestimation of any-order gradients, and generalizes variance reduction\ntechniques to higher-order gradient estimates. Finally, we implement\nStorchastic as a PyTorch library at https://github.com/HEmile/storchastic.\n"}
+{"abstract": "  The Gaia satellite will observe the positions and velocities of over a\nbillion Milky Way stars. In the early data releases, the majority of observed\nstars do not have complete 6D phase-space information. In this Letter, we\ndemonstrate the ability to infer the missing line-of-sight velocities until\nmore spectroscopic observations become available. We utilize a novel neural\nnetwork architecture that, after being trained on a subset of data with\ncomplete phase-space information, takes in a star's 5D astrometry (angular\ncoordinates, proper motions, and parallax) and outputs a predicted\nline-of-sight velocity with an associated uncertainty. Working with a mock Gaia\ncatalog, we show that the network can successfully recover the distributions\nand correlations of each velocity component for stars that fall within ~5 kpc\nof the Sun. We also demonstrate that the network can accurately reconstruct the\nvelocity distribution of a kinematic substructure in the stellar halo that is\nspatially uniform, even when it comprises a small fraction of the total star\ncount.\n"}
+{"abstract": "  Soiling, which consists of dust, dirt and particles accumulated on the\nsurface of conventional or concentrator photovoltaic modules, absorbs,\nscatters, and reflects part of the incoming sunlight. Therefore, it reduces the\namount of energy converted by the semiconductor solar cells. This work focuses\non the effect of soiling on the spectral performance of multi-junction (MJ)\ncells, widely used in concentrator photovoltaic (CPV) applications. Novel\nindexes, useful to quantify the spectral impact of soiling are introduced, and\ntheir meanings are discussed. The results of a one-year experimental\ninvestigation conducted in Spain are presented and are used to discuss how\nsoiling impacts each of the subcells of a MJ cell, as well as the cell\ncurrent-matching. Results show that soiling affects the current balance among\nthe junctions, i.e. the transmittance losses have found to be around 4% higher\nin the top than in the middle subcell. The spectral nature of soiling has\ndemonstrated to increase the annual spectral losses of around 2%. Ideal\nconditions for the mitigation of soiling are also discussed and found to be in\nblue-rich environments, where the higher light intensity at the shorter\nwavelengths can limit the impact of soiling on the overall production of the\nCPV system.\n"}
+{"abstract": "  We formulate sufficient conditions for the strong integrability of dressing\ncosets. We provide several sigma-model backgrounds solving those conditions,\nsome of them are new and some of them were not so far formulated as the\ndressing cosets. The new models are based on the Drinfeld doubles having the\nstructure of higher order jet bundles of quadratic Lie groups.\n"}
+{"abstract": "  With electric power systems becoming more compact and increasingly powerful,\nthe relevance of thermal stress especially during overload operation is\nexpected to increase ceaselessly. Whenever critical temperatures cannot be\nmeasured economically on a sensor base, a thermal model lends itself to\nestimate those unknown quantities. Thermal models for electric power systems\nare usually required to be both, real-time capable and of high estimation\naccuracy. Moreover, ease of implementation and time to production play an\nincreasingly important role. In this work, the thermal neural network (TNN) is\nintroduced, which unifies both, consolidated knowledge in the form of\nheat-transfer-based lumped-parameter models, and data-driven nonlinear function\napproximation with supervised machine learning. A quasi-linear\nparameter-varying system is identified solely from empirical data, where\nrelationships between scheduling variables and system matrices are inferred\nstatistically and automatically. At the same time, a TNN has physically\ninterpretable states through its state-space representation, is end-to-end\ntrainable -- similar to deep learning models -- with automatic differentiation,\nand requires no material, geometry, nor expert knowledge for its design.\nExperiments on an electric motor data set show that a TNN achieves higher\ntemperature estimation accuracies than previous white-/grey- or black-box\nmodels with a mean squared error of $3.18~\\text{K}^2$ and a worst-case error of\n$5.84~\\text{K}$ at 64 model parameters.\n"}
+{"abstract": "  Recently, general salient object detection (SOD) has made great progress with\nthe rapid development of deep neural networks. However, task-aware SOD has\nhardly been studied due to the lack of task-specific datasets. In this paper,\nwe construct a driving task-oriented dataset where pixel-level masks of salient\nobjects have been annotated. Comparing with general SOD datasets, we find that\nthe cross-domain knowledge difference and task-specific scene gap are two main\nchallenges to focus the salient objects when driving. Inspired by these\nfindings, we proposed a baseline model for the driving task-aware SOD via a\nknowledge transfer convolutional neural network. In this network, we construct\nan attentionbased knowledge transfer module to make up the knowledge\ndifference. In addition, an efficient boundary-aware feature decoding module is\nintroduced to perform fine feature decoding for objects in the complex\ntask-specific scenes. The whole network integrates the knowledge transfer and\nfeature decoding modules in a progressive manner. Experiments show that the\nproposed dataset is very challenging, and the proposed method outperforms 12\nstate-of-the-art methods on the dataset, which facilitates the development of\ntask-aware SOD.\n"}
+{"abstract": "  Complex network analyses have provided clues to improve power-grid stability\nwith the help of numerical models. The high computational cost of numerical\nsimulations, however, has inhibited the approach, especially when it deals with\nthe dynamic properties of power grids such as frequency synchronization. In\nthis study, we investigate machine learning techniques to estimate the\nstability of power-grid synchronization. We test three different machine\nlearning algorithms -- random forest, support vector machine, and artificial\nneural network -- training them with two different types of synthetic power\ngrids consisting of homogeneous and heterogeneous input-power distribution,\nrespectively. We find that the three machine learning models better predict the\nsynchronization stability of power-grid nodes when they are trained with the\nheterogeneous input-power distribution than the homogeneous one. With the\nreal-world power grids of Great Britain, Spain, France, and Germany, we also\ndemonstrate that the machine learning algorithms trained on synthetic power\ngrids are transferable to the stability prediction of the real-world power\ngrids, which implies the prospective applicability of machine learning\ntechniques on power-grid studies.\n"}
+{"abstract": "  We study a novel dark matter production mechanism based on the freeze-in\nthrough semi-production, i.e. the inverse semi-annihilation processes. A\npeculiar feature of this scenario is that the production rate is suppressed by\na small initial abundance of dark matter and consequently creating the observed\nabundance requires much larger coupling values than for the usual freeze-in. We\nprovide a concrete example model exhibiting such production mechanism and study\nit in detail, extending the standard formalism to include the evolution of dark\nmatter temperature alongside its number density and discuss the importance of\nthis improved treatment. Finally, we confront the relic density constraint with\nthe limits and prospects for the dark matter indirect detection searches. We\nshow that, even if it was never in full thermal equilibrium in the early\nUniverse, dark matter could, nevertheless, have strong enough present-day\nannihilation cross section to lead to observable signals.\n"}
+{"abstract": "  The Spectrometer/Telescope for Imaging X-rays (STIX) is the HXR instrument\nonboard Solar Orbiter designed to observe solar flares over a broad range of\nflare sizes, between 4-150 keV. We report the first STIX observations of\nmicroflares recorded during the instrument commissioning phase in order to\ninvestigate the STIX performance at its detection limit. This first result\npaper focuses on the temporal and spectral evolution of STIX microflares\noccuring in the AR12765 in June 2020, and compares the STIX measurements with\nGOES/XRS, SDO/AIA, and Hinode/XRT. For the observed microflares of the GOES A\nand B class, the STIX peak time at lowest energies is located in the impulsive\nphase of the flares, well before the GOES peak time. Such a behavior can either\nbe explained by the higher sensitivity of STIX to higher temperatures compared\nto GOES, or due to the existence of a nonthermal component reaching down to low\nenergies. The interpretation is inconclusive due to limited counting statistics\nfor all but the largest flare in our sample. For this largest flare, the\nlow-energy peak time is clearly due to thermal emission, and the nonthermal\ncomponent seen at higher energies occurs even earlier. This suggests that the\nclassic thermal explanation might also be favored for the majority of the\nsmaller flares. In combination with EUV and SXR observations, STIX corroborates\nearlier findings that an isothermal assumption is of limited validity. Future\ndiagnostic efforts should focus on multi-wavelength studies to derive\ndifferential emission measure distributions over a wide range of temperatures\nto accurately describe the energetics of solar flares. Commissioning\nobservations confirm that STIX is working as designed. As a rule of thumb, STIX\ndetects flares as small as the GOES A class. For flares above the GOES B class,\ndetailed spectral and imaging analyses can be performed.\n"}
+{"abstract": "  On a class of four-dimensional Lifshitz spacetimes with critical exponent\n$z=2$, including a hyperbolic and a spherical Lifshitz topological black hole,\nwe consider a real Klein-Gordon field. Using a mode-decomposition, we split the\nequation of motion into a radial and into an angular component. As first step,\nwe discuss under which conditions on the underlying parameters, we can impose\nto the radial equation boundary conditions of Robin type and whether bound\nstate solutions do occur. Subsequently, we show that, whenever bound states are\nabsent, one can associate to each admissible boundary condition a ground and a\nKMS state whose associated two-point correlation function is of local Hadamard\nform.\n"}
+{"abstract": "  Occlusions pose a significant challenge to optical flow algorithms that rely\non local evidences. We consider an occluded point to be one that is imaged in\nthe first frame but not in the next, a slight overloading of the standard\ndefinition since it also includes points that move out-of-frame. Estimating the\nmotion of these points is extremely difficult, particularly in the two-frame\nsetting. Previous work relies on CNNs to learn occlusions, without much\nsuccess, or requires multiple frames to reason about occlusions using temporal\nsmoothness. In this paper, we argue that the occlusion problem can be better\nsolved in the two-frame case by modelling image self-similarities. We introduce\na global motion aggregation module, a transformer-based approach to find\nlong-range dependencies between pixels in the first image, and perform global\naggregation on the corresponding motion features. We demonstrate that the\noptical flow estimates in the occluded regions can be significantly improved\nwithout damaging the performance in non-occluded regions. This approach obtains\nnew state-of-the-art results on the challenging Sintel dataset, improving the\naverage end-point error by 13.6% on Sintel Final and 13.7% on Sintel Clean. At\nthe time of submission, our method ranks first on these benchmarks among all\npublished and unpublished approaches. Code is available at\nhttps://github.com/zacjiang/GMA\n"}
+{"abstract": "  The Alcock-Paczynski (AP) effect is a geometrical distortion in\nthree-dimensional observed galaxy statistics. In anticipation of precision\ncosmology based on ongoing and upcoming all-sky galaxy surveys, we build an\nefficient method to compute the AP-distorted correlations of galaxy number\ndensity and peculiar velocity fields for any larger angular scale not relying\non the conventionally used plane-parallel (PP) approximation. Here, instead of\nthe usual Legendre polynomial basis, the correlation functions are decomposed\nusing tripolar spherical harmonic basis; hence, characteristic angular\ndependence due to the wide-angle AP effect can be rigorously captured. By means\nof this, we demonstrate the computation of the AP-distorted correlations over\nthe various scales. Comparing our results with the PP-limit ones, we confirm\nthat the errors due to the PP approximation become more remarkable as the\nvisual angle of separation between target galaxies, $\\Theta$, enlarges, and\nespecially for the density auto correlation, the error exceeds $10\\%$ when\n$\\Theta \\gtrsim 30^\\circ$. This highlights the importance of the analysis\nbeyond the PP approximation.\n"}
+{"abstract": "  We study features of the 3D anisotropy of galactic cosmic rays (GCR) for\n1965-2014. We analyze the 27-day variations of the 2D GCR anisotropy in the\necliptic plane, and the north-south anisotropy normal to the ecliptic plane. We\nstudy the dependence of the 27-day variation of the 3D GCR anisotropy on the\nsolar cycle and solar magnetic cycle. We demonstrate that the 27-day variations\nof the GCR intensity and anisotropy can be used as an important tool to study\nsolar wind, solar activity and heliosphere. We use the components of the 3D GCR\nanisotropy found based on hourly data of neutron monitors (NMs) and muon\ntelescopes (MTs) using the harmonic analyses and spectrographic methods. We\ncorrect 2D diurnal variation of the GCR intensity for the influence of the\nEarth magnetic field. We derive the north-south component of the GCR anisotropy\nbased on the GG index calculated as the difference in GCR intensities of Nagoya\nmultidirectional MTs. We show that behavior of the 27-variation of the 3D\nanisotropy verifies an existence of a stable long-lived active heliolongitudes\non the sun. This finding illustrates usefulness of the 27-day variation of the\nGCR anisotropy as a unique proxy to study solar wind, solar activity and\nheliosphere. We distinguish a tendency of the 22-year changes of the amplitudes\nof the 27-day variation of the 2D anisotropy connected with the solar magnetic\ncycle. We demonstrate that the amplitudes of the 27-day variation of the\nnorth-south component of the anisotropy vary upon the 11 year solar cycle,\nhowever, a dependence of the solar magnetic polarity hardly can be recognized.\nWe show that the 27-day recurrences of the $GG$ index and At component are in a\nhigh positive correlation, and both are highly correlated with By component of\nthe heliospheric magnetic field.\n"}
+{"abstract": "  A text to image generation (T2I) model aims to generate photo-realistic\nimages which are semantically consistent with the text descriptions. Built upon\nthe recent advances in generative adversarial networks (GANs), existing T2I\nmodels have made great progress. However, a close inspection of their generated\nimages reveals two major limitations: (1) The condition batch normalization\nmethods are applied on the whole image feature maps equally, ignoring the local\nsemantics; (2) The text encoder is fixed during training, which should be\ntrained with the image generator jointly to learn better text representations\nfor image generation. To address these limitations, we propose a novel\nframework Semantic-Spatial Aware GAN, which is trained in an end-to-end fashion\nso that the text encoder can exploit better text information. Concretely, we\nintroduce a novel Semantic-Spatial Aware Convolution Network, which (1) learns\nsemantic-adaptive transformation conditioned on text to effectively fuse text\nfeatures and image features, and (2) learns a mask map in a weakly-supervised\nway that depends on the current text-image fusion process in order to guide the\ntransformation spatially. Experiments on the challenging COCO and CUB bird\ndatasets demonstrate the advantage of our method over the recent\nstate-of-the-art approaches, regarding both visual fidelity and alignment with\ninput text description. Code is available at\nhttps://github.com/wtliao/text2image.\n"}
+{"abstract": "  We introduce StockBabble, a conversational agent designed to support\nunderstanding and engagement with the stock market. StockBabble's value and\nnovelty is in its ability to empower retail investors -- many of which may be\nnew to investing -- and supplement their informational needs using a\nuser-friendly agent. Users have the ability to query information on companies\nto retrieve a general and financial overview of a stock, including accessing\nthe latest news and trading recommendations. They can also request charts which\ncontain live prices and technical investment indicators, and add shares to a\npersonal portfolio to allow performance monitoring over time. To evaluate our\nagent's potential, we conducted a user study with 15 participants. In total,\n73% (11/15) of respondents said that they felt more confident in investing\nafter using StockBabble, and all 15 would consider recommending it to others.\nThese results are encouraging and suggest a wider appeal for such agents.\nMoreover, we believe this research can help to inform the design and\ndevelopment of future intelligent, financial personal assistants.\n"}
+{"abstract": "  In many real-world scenarios, a team of agents coordinate with each other to\ncompete against an opponent. The challenge of solving this type of game is that\nthe team's joint action space grows exponentially with the number of agents,\nwhich results in the inefficiency of the existing algorithms, e.g.,\nCounterfactual Regret Minimization (CFR). To address this problem, we propose a\nnew framework of CFR: CFR-MIX. Firstly, we propose a new strategy\nrepresentation that represents a joint action strategy using individual\nstrategies of all agents and a consistency relationship to maintain the\ncooperation between agents. To compute the equilibrium with individual\nstrategies under the CFR framework, we transform the consistency relationship\nbetween strategies to the consistency relationship between the cumulative\nregret values. Furthermore, we propose a novel decomposition method over\ncumulative regret values to guarantee the consistency relationship between the\ncumulative regret values. Finally, we introduce our new algorithm CFR-MIX which\nemploys a mixing layer to estimate cumulative regret values of joint actions as\na non-linear combination of cumulative regret values of individual actions.\nExperimental results show that CFR-MIX outperforms existing algorithms on\nvarious games significantly.\n"}
+{"abstract": "  We use IGETS absolute gravitational acceleration measurement data to study\nthe gravitational acceleration variance. The relative variance of $\\delta g /g$\nin 22 years is less than $4\\times 10^{-8}$. Since $\\delta G /G\\lessapprox\\delta\ng /g $, this implies the relative variance of Newtonian constant is less than\n$3\\times 10^{-9}$ based on an sine-like oscillation hypothesis. This limit is\nat least 4 orders of magnitude better than the existing $G$ measurements. The\nscattered values of reported $G$ measurements coming from different experiments\nare most probably coming from systematic errors associated with these\nexperiments and not due to intrinsic time variation of $G$. We also find that\n$\\dot{ G} /G<5.61\\times 10^{-10} \\text{yr}^{-1}$ based on a linear hypothesis.\nThis is the best terrestrial result so far.\n"}
+{"abstract": "  Autonomous mobile robots are usually faced with challenging situations when\ndriving in complex environments. Namely, they have to recognize the static and\ndynamic obstacles, plan the driving path and execute their motion. For\naddressing the issue of perception and path planning, in this paper, we\nintroduce OctoPath , which is an encoder-decoder deep neural network, trained\nin a self-supervised manner to predict the local optimal trajectory for the\nego-vehicle. Using the discretization provided by a 3D octree environment\nmodel, our approach reformulates trajectory prediction as a classification\nproblem with a configurable resolution. During training, OctoPath minimizes the\nerror between the predicted and the manually driven trajectories in a given\ntraining dataset. This allows us to avoid the pitfall of regression-based\ntrajectory estimation, in which there is an infinite state space for the output\ntrajectory points. Environment sensing is performed using a 40-channel\nmechanical LiDAR sensor, fused with an inertial measurement unit and wheels\nodometry for state estimation. The experiments are performed both in simulation\nand real-life, using our own developed GridSim simulator and RovisLab's\nAutonomous Mobile Test Unit platform. We evaluate the predictions of OctoPath\nin different driving scenarios, both indoor and outdoor, while benchmarking our\nsystem against a baseline hybrid A-Star algorithm and a regression-based\nsupervised learning method, as well as against a CNN learning-based optimal\npath planning method.\n"}
+{"abstract": "  The amount, size, and complexity of astronomical data-sets and databases are\ngrowing rapidly in the last decades, due to new technologies and dedicated\nsurvey telescopes. Besides dealing with poly-structured and complex data,\nsparse data has become a field of growing scientific interest. A specific field\nof Astroinformatics research is the estimation of redshifts of extra-galactic\nsources by using sparse photometric observations. Many techniques have been\ndeveloped to produce those estimates with increasing precision. In recent\nyears, models have been favored which instead of providing a point estimate\nonly, are able to generate probabilistic density functions (PDFs) in order to\ncharacterize and quantify the uncertainties of their estimates.\n  Crucial to the development of those models is a proper, mathematically\nprincipled way to evaluate and characterize their performances, based on\nscoring functions as well as on tools for assessing calibration. Still, in\nliterature inappropriate methods are being used to express the quality of the\nestimates that are often not sufficient and can potentially generate misleading\ninterpretations. In this work we summarize how to correctly evaluate errors and\nforecast quality when dealing with PDFs. We describe the use of the\nlog-likelihood, the continuous ranked probability score (CRPS) and the\nprobability integral transform (PIT) to characterize the calibration as well as\nthe sharpness of predicted PDFs. We present what we achieved when using proper\nscoring rules to train deep neural networks as well as to evaluate the model\nestimates and how this work led from well calibrated redshift estimates to\nimprovements in probabilistic weather forecasting. The presented work is an\nexample of interdisciplinarity in data-science and illustrates how methods can\nhelp to bridge gaps between different fields of application.\n"}
+{"abstract": "  We extend the single-stage stellarator coil design approach for\nquasi-symmetry on axis from [Giuliani et al, 2020] to additionally take into\naccount coil manufacturing errors. By modeling coil errors independently from\nthe coil discretization, we have the flexibility to consider realistic forms of\ncoil errors. The corresponding stochastic optimization problems are formulated\nusing a risk-neutral approach and risk-averse approaches. We present an\nefficient, gradient-based descent algorithm which relies on analytical\nderivatives to solve these problems. In a comprehensive numerical study, we\ncompare the coil designs resulting from deterministic and risk-neutral\nstochastic optimization and find that the risk-neutral formulation results in\nmore robust configurations and reduces the number of local minima of the\noptimization problem. We also compare deterministic and risk-neutral approaches\nin terms of quasi-symmetry on and away from the magnetic axis, and in terms of\nthe confinement of particles released close to the axis. Finally, we show that\nfor the optimization problems we consider, a risk-averse objective using the\nConditional Value-at-Risk leads to results which are similar to the\nrisk-neutral objective.\n"}
+{"abstract": "  Alinhac solved a long-standing open problem in 2001 and established that\nquasilinear wave equations in two space dimensions with quadratic null\nnonlinearities admit global-in-time solutions, provided that the initial data\nare compactly supported and sufficiently small in Sobolev norm. In this work,\nAlinhac obtained an upper bound with polynomial growth in time for the\ntop-order energy of the solutions. A natural question then arises whether the\ntime-growth is a true phenomena, despite the possible conservation of basic\nenergy. Analogous problems are also of central importance for Schr\\\"odinger\nequations and the incompressible Euler equations in two space dimensions, as\nstudied by Bourgain, Colliander-Keel-Staffilani-Takaoka-Tao, Kiselev-Sverak,\nand others. In the present paper, we establish that the top-order energy of the\nsolutions in Alinhac theorem remains globally bounded in time, which is\nopposite to Alinhac's blowup-at-infinity conjecture.\n"}
+{"abstract": "  We develop a model for lead-time quotation in a Markovian make-to-order\nproduction or service system with strategic customers who exhibit risk\naversion. Based on a CARA utility function of their net benefit, customers make\nindividual decisions to join the system or balk by observing the state of the\nqueue. The decisions of arriving customers result in a symmetric join/balk\ngame. Regarding the firm's strategy, the provider announces a lead-time\nquotation for each state and a respective balking threshold. There is also a\nfixed entrance fee and compensation rate for the part of a customer' delay\nexceeding the quoted lead-time. Moreover, we consider the problem from the\npoint of view of a social optimizer who maximizes the total net benefit of the\nsystem.\n  We analyze the provider's and social optimizer's maximization problems and we\nconsider two cases regarding the class of lead-time quotation policies, i.e.,\ndynamic and single. We identify the optimal entrance thresholds in each case.\nFinally, through computational experiments we quantify the effect of risk\naversion on the profits and the degree of flexibility that the compensation\npolicy offers. It is shown that the detrimental effects of risk aversion can be\naddressed more efficiently for the provider's problem compared to the social\noptimizer's one. Furthermore, the profit loss when setting a single lead-time\nquote is generally small compared to the optimal dynamic quotation policy.\n"}
+{"abstract": "  Mastitis is a billion dollar health problem for the modern dairy industry,\nwith implications for antibiotic resistance. The use of AI techniques to\nidentify the early onset of this disease, thus has significant implications for\nthe sustainability of this agricultural sector. Current approaches to treating\nmastitis involve antibiotics and this practice is coming under ever increasing\nscrutiny. Using machine learning models to identify cows at risk of developing\nmastitis and applying targeted treatment regimes to only those animals promotes\na more sustainable approach. Incorrect predictions from such models, however,\ncan lead to monetary losses, unnecessary use of antibiotics, and even the\npremature death of animals, so it is important to generate compelling\nexplanations for predictions to build trust with users and to better support\ntheir decision making. In this paper we demonstrate a system developed to\npredict mastitis infections in cows and provide explanations of these\npredictions using counterfactuals. We demonstrate the system and describe the\nengagement with farmers undertaken to build it.\n"}
+{"abstract": "  A dessin d'enfant, or dessin, is a bicolored graph embedded into a Riemann\nsurface, and the monodromy group is an algebraic invariant of the dessin\ngenerated by rotations of edges about black and white vertices. A rational\nbilliards surface is a two dimensional surface that allows one to view the path\nof a billiards ball as a continuous path. In this paper, we classify the\nmonodromy groups of dessins associated to rational triangular billiards\nsurfaces.\n"}
+{"abstract": "  We study analytically the two dimensional deformed bosonic oscillator\nequations for charged particles (both spin 0 and spin 1 particles) subject to\nthe effect of a uniform magnetic field. We consider the presence of a minimal\nuncertainty in momentum caused by the Anti-deSitter model and we use the\nNikiforov-Uvarov method to solve the system. The exact energy eigenvalues and\nthe corresponding wave functions are analytically obtained for both\nKlein-Gordon and scalar Duffin-Kemmer-Petiau cases. For spin 1 DKP case, we\ndeduce the behaviour of the DKP equation and write the non-relativistic\nenergies where we show the fundamental role of the spin in this case. Finally,\nwe study the thermodynamic properties of the system.\n"}
+{"abstract": "  In this paper, we present a systematic investigation on simple inverse seesaw\nmodels for neutrino masses and flavor mixing based on the modular $S^{}_4$\nsymmetry. Two right-handed neutrinos and three extra fermion singlets are\nintroduced to account for light neutrino masses through the inverse seesaw\nmechanism, and to provide a keV-mass sterile neutrino as the candidate for warm\ndark matter in our Universe. Considering all possible modular forms with\nweights no larger than four, we obtain twelve models, among which we find one\nis in excellent agreement with the observed lepton mass spectra and flavor\nmixing. Moreover, we explore the allowed range of the sterile neutrino mass and\nmixing angles, by taking into account the direct search of $X$-ray line and the\nLyman-$\\alpha$ observations. The model predictions for neutrino mixing\nparameters and the dark matter abundance will be readily testable in future\nneutrino oscillation experiments and cosmological observations.\n"}
+{"abstract": "  We consider a stochastic individual based model where each predator searches\nduring a random time and then manipulates its prey or rests. The time\ndistributions may be non-exponential. An age structure allows to describe these\ninteractions and get a Markovian setting. The process is characterized by a\nmeasure-valued stochastic differential equation. We prove averaging results in\nthis infinite dimensional setting and get the convergence of the slow-fast\nmacroscopic prey predator process to a two dimensional dynamical system. We\nrecover classical functional responses. We also get new forms arising in\nparticular when births and deaths of predators are affected by the lack of\nfood.\n"}
+{"abstract": "  In recent years, with the explosive growth of visual sensors and a large\nnumber of related video applications in Internet of Things (IoT), massive video\ndata is generated by IoT devices. Since the volume of video data is far greater\nthan traditional data in IoT, it is challenging to ensure high Quality of\nService (QoS) for video uplinking in IoT. To address this challenge, we\nintegrate non-orthogonal multiple access (NOMA) and scalable video coding (SVC)\nin IoT. To improve the video quality, we formulate a power allocation problem\nto maximize the average QoS in the proposed integrated system. Due to that the\nproblem is non-convex, we transform it into a monotonic problem based on its\nhidden monotonicity. Then a power allocation algorithm based on polyblock outer\napproximation is proposed to solve the problem effectively. Finally, simulation\nresults demonstrate that the proposed algorithm outperforms existing OMA and\nNOMA based schemes for video uplinking in IoT in terms of QoS and energy\nefficiency.\n"}
+{"abstract": "  Recent experimental measurements of light absorption in few-layer black\nphosphorus (BP) reveal a series of high and sharp peaks, interspersed by pairs\nof lower and broader features. Here, we propose a theoretical model for these\nexcitonic states in few-layer black phosphorus (BP) within a continuum approach\nfor the in-plane degrees of freedom and a tight-binding approximation that\naccounts for inter-layer couplings. This yields excitonic transitions between\ndifferent combinations of the sub-bands created by the coupled BP layers, which\nleads to a series of high and low oscillator strength excitonic states,\nconsistent with the experimentally observed bright and dark exciton peaks,\nrespectively. The main characteristics of such sub-band exciton states, as well\nas the possibility to control their energies and oscillator strengths via\napplied electric and magnetic fields, are discussed, towards a full\nunderstanding of the excitonic spectrum of few-layer BP and its tunability.\n"}
+{"abstract": "  In this paper, we have investigated a bulk viscous anisotropic Universe and\nconstrained its model parameters with recent $H(z)$ and Pantheon compilation\ndata. Using cosmic chronometric technique, we estimate the present value of\nHubble's constant as $H_{0} = 69.39 \\pm 1.54~km~s^{-1}Mpc^{-1}$, $70.016 \\pm\n1.65~km~s^{-1}Mpc^{-1}$ and $69.36 \\pm 1.42~km~s^{-1}Mpc^{-1}$ by bounding our\nderived model with recent $H(z)$ data, Pantheon and joint $H(z)$ and Pantheon\ndata respectively. The present age of the Universe is specified as $t_0=\n0.9796H_0^{-1}\\sim 13.79$ Gyrs. The model favours a transitioning Universe with\nthe transition red-shift as $z_{t} = 0.73$. We have reconstructed the jerk\nparameter using the observational data sets. From the analysis of the jerk\nparameter, it is observed that, our derived model shows a marginal departure\nfrom the concordance $\\Lambda$CDM model.\n"}
+{"abstract": "  An one-parameter regularization freedom of the Hamiltonian constraint for\nloop quantum gravity is analyzed. The corresponding spatially flat, homogenous\nand isotropic model includes the two well-known models of loop quantum\ncosmology as special cases. The quantum bounce nature is tenable in the\ngeneralized cases. For positive value of the regularization parameter, the\neffective Hamiltonian leads to an asymptotic de-Sitter branch of the Universe\nconnecting to the standard Friedmann branch by the quantum bounce. Remarkably,\nby suitably choosing the value of the regularization parameter, the\nobservational cosmological constant can emerge at large volume limit from the\neffect of quantum gravity, and the effective Newtonian constant satisfies the\nexperimental restrictions in the meantime.\n"}
+{"abstract": "  The large volumes of structured data currently available, from Web tables to\nopen-data portals and enterprise data, open up new opportunities for progress\nin answering many important scientific, societal, and business questions.\nHowever, finding relevant data is difficult. While search engines have\naddressed this problem for Web documents, there are many new challenges\ninvolved in supporting the discovery of structured data. We demonstrate how the\nAuctus dataset search engine addresses some of these challenges. We describe\nthe system architecture and how users can explore datasets through a rich set\nof queries. We also present case studies which show how Auctus supports data\naugmentation to improve machine learning models as well as to enrich analytics.\n"}
+{"abstract": "  In the era of big data, the utilization of credit-scoring models to determine\nthe credit risk of applicants accurately becomes a trend in the future. The\nconventional machine learning on credit scoring data sets tends to have poor\nclassification for the minority class, which may bring huge commercial harm to\nbanks. In order to classify imbalanced data sets, we propose a new ensemble\nalgorithm, namely, Weighted-Hybrid-Sampling-Boost (WHSBoost). In data sampling,\nwe process the imbalanced data sets with weights by the Weighted-SMOTE method\nand the Weighted-Under-Sampling method, and thus obtain a balanced training\nsample data set with equal weight. In ensemble algorithm, each time we train\nthe base classifier, the balanced data set is given by the method above. In\norder to verify the applicability and robustness of the WHSBoost algorithm, we\nperformed experiments on the simulation data sets, real benchmark data sets and\nreal credit scoring data sets, comparing WHSBoost with SMOTE, SMOTEBoost and\nHSBoost based on SVM, BPNN, DT and KNN.\n"}
+{"abstract": "  In this article, we present a method to construct a positivity preserving\nnumerical scheme for both the jump-extended CEV process and jump-extended CIR\nprocess, whose jumps are governed by a (compensated) spectrally positive\n$\\alpha$-stable process with $\\alpha \\in (1,2)$. The proposed scheme is\nobtained by making the diffusion coefficient partially implicit and then\nfinding the appropriate adjustment factor. We show that the proposed scheme\nconverges and theoretically achieves a strong convergence rate of at least\n$\\frac{1}{2}\\left(\\frac{\\alpha_-}{2} \\wedge \\frac{1}{\\alpha}\\right)$, where the\nconstant $\\alpha_- < \\alpha$ can be chosen arbitrarily close to $\\alpha \\in\n(1,2)$. Finally, to support our result, we present some numerical simulations\nwhich suggest that the optimal rate of convergence is $\\frac{\\alpha_-}{4}$.\n"}
+{"abstract": "  In this work we investigate a coupled system of degenerate and nonlinear\npartial differential equations governing the transport of reactive solutes in\ngroundwater. We show that the system admits a unique weak solution provided the\nnonlinear adsorption isotherm associated with the reaction process satisfies\ncertain physically reasonable structural conditions. We conclude, moreover,\nthat the solute concentrations stay non-negative if the source term is\ncomponentwise non-negative and investigate numerically the finite speed of\npropagation of compactly supported initial concentrations, in a two-component\ntest case.\n"}
+{"abstract": "  We theoretically clarify the functional form to be used in $t \\to 0$\nextrapolation in the small flow time expansion (SF$t$X) method for the\nenergy-momentum tensor (EMT), which facilitates lattice simulation of the EMT\nbased on the gradient flow. We argue that in the $t \\to 0$ extrapolation\nanalysis, lattice data should be fitted by a power function in $g(\\mu(t))$, the\nflow time dependent running coupling, where the power is determined by the\nperturbation order we consider. From actual lattice data, we confirm the\nvalidity of the extrapolation function. Using the new extrapolation function,\nwe present updated lattice results for thermodynamics quantities in quenched\nQCD; our results are consistent with the previous study [arXiv:1812.06444] but\nwe obtain smaller errors due to reduction of systematic errors.\n"}
+{"abstract": "  We address the Threshold Information Disclosure (TID) problem on Ethereum: An\narbitrary number of users commit to the scheduled disclosure of their\nindividual messages recorded on the Ethereum blockchain if and only if all such\nmessages are disclosed. Before a disclosure, only the original sender of each\nmessage should know its contents. To accomplish this, we task a small council\nwith executing a distributed generation and threshold sharing of an asymmetric\nkey pair. The public key can be used to encrypt messages which only become\nreadable once the threshold-shared decryption key is reconstructed at a\npredefined point in time and recorded on-chain. With blockchains like Ethereum,\nit is possible to coordinate such procedures and attach economic stakes to the\nactions of participating individuals. In this paper, we present ETHTID, an\nEthereum smart contract application to coordinate Threshold Information\nDisclosure. We base our implementation on ETHDKG [1], a smart contract\napplication for distributed key generation and threshold sharing, and adapt it\nto fit our differing use case as well as add functionality to oversee a\nscheduled reconstruction of the decryption key. For our main cost saving\noptimisation, we show that the security of the underlying cryptographic scheme\nis maintained. We evaluate how the execution costs depend on the size of the\ncouncil and the threshold and show that the presented protocol is deployable on\nEthereum with a council of more than 200 members with gas savings of 20-40%\ncompared to ETHDKG.\n"}
+{"abstract": "  For fixed training data and network parameters in the other layers the L1\nloss of a ReLU neural network as a function of the first layer's parameters is\na piece-wise affine function. We use the Deep ReLU Simplex algorithm to\niteratively minimize the loss monotonically on adjacent vertices and analyze\nthe trajectory of these vertex positions. We empirically observe that in a\nneighbourhood around a local minimum, the iterations behave differently such\nthat conclusions on loss level and proximity of the local minimum can be made\nbefore it has been found: Firstly the loss seems to decay exponentially slow at\niterated adjacent vertices such that the loss level at the local minimum can be\nestimated from the loss levels of subsequently iterated vertices, and secondly\nwe observe a strong increase of the vertex density around local minima. This\ncould have far-reaching consequences for the design of new gradient-descent\nalgorithms that might improve convergence rate by exploiting these facts.\n"}
+{"abstract": "  Two recently published papers propose some very simple key distribution\nschemes designed to enable two or more parties to establish a shared secret key\nwith the aid of a third party. Unfortunately, as we show, most of the schemes\nare inherently insecure and all are incompletely specified - moreover, claims\nthat the schemes are inherently lightweight are shown to be highly misleading.\nWe also briefly critique a somewhat related very recent paper by the same\nauthors that uses similar techniques to achieve what are claimed to be secure\nmultiparty computations.\n"}
+{"abstract": "  We have examined the star formation history (SFH) of Andromeda VII (And VII),\nthe brightest and most massive dwarf spheroidal (dSph) satellite of the\nAndromeda galaxy (M 31). Although M 31 is surrounded by several dSph companions\nwith old stellar populations and low metallicity, it has a metal-rich stellar\nhalo with an age of 6$-$8 Gyr. This indicates that any evolutionary association\nbetween the stellar halo of M 31 and its dSph system is frail. Therefore, the\nquestion is whether And VII (a high-metallicity dSph located $\\sim$220 kpc from\nM 31), can be associated with M 31's young, metal-rich halo. Here, we perform\nthe first reconstruction of the SFH of And VII employing long-period variable\n(LPV) stars. As the most-evolved asymptotic giant branch (AGB) and red\nsupergiant (RSG) stars, the birth mass of LPVs can be determined by connecting\ntheir near-infrared photometry to theoretical evolutionary tracks. We found 55\nLPV candidates within two half-light radii, using multi-epoch imaging with the\nIsaac Newton Telescope in the $i$ and $V$ bands. Based on their birth mass\nfunction, the star-formation rate (SFR) of And VII was obtained as a function\nof cosmic time. The main epoch of star formation occurred $\\simeq 6.2$ Gyr ago\nwith a SFR of $0.006\\pm0.002$ M$_\\odot$ yr$^{-1}$. Over the past 6 Gyr, we find\nslow star formation, which continued until 500 Myr ago with a SFR\n$\\sim0.0005\\pm0.0002$ M$_\\odot$ yr$^{-1}$. We determined And VII's stellar mass\n$M=(13.3\\pm5.3)\\times10^6$ M$_\\odot$ within a half-light radius\n$r_{\\frac{1}{2}}=3.8\\pm0.3$ arcmin and metallicity $Z=0.0007$, and also derived\nits distance modulus of $\\mu=24.38$ mag.\n"}
+{"abstract": "  Knowledge graphs (KG) have become increasingly important to endow modern\nrecommender systems with the ability to generate traceable reasoning paths to\nexplain the recommendation process. However, prior research rarely considers\nthe faithfulness of the derived explanations to justify the decision making\nprocess. To the best of our knowledge, this is the first work that models and\nevaluates faithfully explainable recommendation under the framework of KG\nreasoning. Specifically, we propose neural logic reasoning for explainable\nrecommendation (LOGER) by drawing on interpretable logical rules to guide the\npath reasoning process for explanation generation. We experiment on three\nlarge-scale datasets in the e-commerce domain, demonstrating the effectiveness\nof our method in delivering high-quality recommendations as well as\nascertaining the faithfulness of the derived explanation.\n"}
+{"abstract": "  A high sensitivity, 7mm Very Long Baseline Array image of M\\,87 is analyzed\nin order to estimate the jet velocity within 0.65 mas of the point of origin.\nThe image captured a high signal to noise, double-ridged, counter-jet extending\n$\\sim 1$ mas from the nucleus. After defining conditions and requirements that\njustify approximate time averaged bilateral symmetry, a continuous set of\nLorentz transformations are found that map the double-ridged counter-jet\nintensity profile into the double-ridged jet intensity profile. The mapping is\nrealized by a uniformly accelerating flow with intrinsic velocity of $\\sim\n0.27$c at 0.4 mas (a de-projected distance of 0.38 lt-yrs) to $0.38$c at 0.65\nmas (a de-projected distance of 0.61 lt-yrs) from the nucleus. Since the\nvelocity field is derived from the global surface brightness profile and does\nnot depend on the motion of enhanced features, it is most likely a bulk flow\nvelocity as opposed to a pattern velocity. This interpretation is corroborated\nby the fact that the distribution of the apparent velocities of previously\nidentified individual features (from the literature) within 0.65 mas of the\nnucleus are consistent with local hydrodynamic shocks being advected with the\nlocal bulk flow velocity. The bulk flow velocity of the visible inner jet is a\nconstraint that can potentially break degeneracies between numerical\nsimulations that are designed to replicate both the annulus that was imaged by\nthe Event Horizon Telescope as well as the base of the inner jet.\n"}
+{"abstract": "  We consider k-essence, a scalar-tensor theory with first-order derivative\nself-interactions that can screen local scales from scalar fifth forces, while\nallowing for sizeable deviations from General Relativity on cosmological\nscales. We construct fully nonlinear static stellar solutions that show the\npresence of this screening mechanism, and we use them as initial data for\nsimulations of stellar oscillations and gravitational collapse in spherical\nsymmetry. We find that for k-essence theories of relevance for cosmology, the\nscreening mechanism works in the case of stellar oscillation and suppresses the\nmonopole scalar emission to undetectable levels. In collapsing stars, we find\nthat the Cauchy problem, although locally well posed, can lead to diverging\ncharacteristic speeds for the scalar field. By introducing a ''fixing\nequation'' in the spirit of J. Cayuso, N. Ortiz, and L. Lehner [Phys. Rev. D\n96, 084043 (2017)], inspired in turn by dissipative relativistic hydrodynamics,\nwe manage to evolve collapsing neutron stars past the divergence of the\ncharacteristic speeds. We show that, in these systems, the screening mechanism\nis less efficient than for oscillating and static stars, because the collapsing\nstar must shed away all of its scalar hair before forming a black hole. For\nk-essence theories of relevance for cosmology, the characteristic frequency of\nthe resulting scalar monopole signal is too low for terrestrial detectors, but\nwe conjecture that space-borne interferometers such as LISA might detect it if\na supernova explodes in the Galaxy.\n"}
+{"abstract": "  Dynamical mass estimates of simple systems such globular clusters (GCs) still\nsuffer from up to a factor of 2 uncertainty. This is primarily due to the\noversimplifications of standard dynamical models that often neglect the effects\nof the long-term evolution of GCs. Here, we introduce a new approach to measure\nthe dynamical properties of GCs, based on the combination of a deep-learning\nframework and the large amount of data from direct $N$-body simulations. Our\nalgorithm, $\\texttt{$\\pi$-DOC}$ ($\\textit{Predicting Images for the Dynamics Of\nstellar Clusters}$) is composed of two convolutional networks, trained to learn\nthe non-trivial transformation between an observed GC luminosity map and its\nassociated mass distribution, age, and distance. The training set is made of\nV-band luminosity and mass maps constructed as mock observations from $N$-body\nsimulations. The tests on $\\texttt{$\\pi$-DOC}$ demonstrate that we can predict\nthe mass distribution with a mean error per pixel of 27%, and the age and\ndistance with an accuracy of 1.5 Gyr and 6 kpc, respectively. In turn, we\nrecover the shape of the mass-to-light profile and its global value with a mean\nerror of 12%, which implies that we efficiently trace mass segregation. A\npreliminary comparison with observations indicates that our algorithm is able\nto predict the dynamical properties of GCs within the limits of the training\nset. These encouraging results demonstrate that our deep-learning framework and\nits forward modelling approach can offer a rapid and adaptable tool competitive\nwith standard dynamical models.\n"}
+{"abstract": "  An idealized 1:2 scale demonstrator and a numerical parameter optimization\nalgorithm are proposed to closely reproduce the deformation shape and, thus,\nspatial strain directions of a real aerodynamically loaded civil aircraft\nspoiler using only four concentrated loads. Cost-efficient experimental studies\non demonstrators of increasing complexity are required to transfer knowledge\nfrom coupons to full-scale structures and to build up confidence in novel\nstructural health monitoring (SHM) technologies. Especially for testing novel\nsensor systems that depend on or are affected by mechanical strains, e.g.,\nstrain-based SHM methods, it is essential that the considered lab-scale\nstructures reflect the strain states of the real structure at operational\nloading conditions. Finite element simulations with detailed models were\nperformed for static strength analysis and for comparison to experimental\nmeasurements. The simulated and measured deformations and spatial strain\ndirections of the idealized demonstrator correlated well with the numerical\nresults of the real aircraft spoiler. Thus, using the developed idealized\ndemonstrator, strain-based SHM systems can be tested under conditions that\nreflect operational aerodynamic pressure loads, while the test effort and costs\nare significantly reduced. Furthermore, the presented loading optimization\nalgorithm can be easily adapted to mimic other pressure loads in plate-like\nstructures to reproduce specific structural conditions.\n"}
+{"abstract": "  An interesting proposal for detecting gravitational waves involves quantum\nmetrology of Bose-Einstein condensates (BECs). We consider a forced modulation\nof the BEC trap, whose frequency matches that of an incoming continuous\ngravitational wave. The trap modulation induces parametric resonance in the\nBEC, which in turn enhances sensitivity of the BEC to gravitational waves. We\nfind that such a BEC detector could potentially be used to detect gravitational\nwaves across several orders of magnitude in frequency, with the sensitivity\ndepending on the speed of sound, size of the condensate, and frequency of the\nphonons. We outline a possible BEC experiment and discuss the current\ntechnological limitations. We also comment on the potential noise sources as\nwell as what is necessary for such a detector to become feasible.\n"}
+{"abstract": "  This paper details the modeling pipeline and validates the baseline analysis\nchoices of the DES Year 3 joint analysis of galaxy clustering and weak lensing\n(a so-called \"3$\\times$2pt\" analysis). These analysis choices include the\nspecific combination of cosmological probes, priors on cosmological and\nsystematics parameters, model parameterizations for systematic effects and\nrelated approximations, and angular scales where the model assumptions are\nvalidated. We run a large number of simulated likelihood analyses using\nsynthetic data vectors to test the robustness of our baseline analysis. We\ndemonstrate that the DES Year 3 modeling pipeline, including the calibrated\nscale cuts, is sufficiently accurate relative to the constraining power of the\nDES Year 3 analyses. Our systematics mitigation strategy accounts for\nastrophysical systematics, such as galaxy bias, intrinsic alignments, source\nand lens magnification, baryonic effects, and source clustering, as well as for\nuncertainties in modeling the matter power spectrum, reduced shear, and\nestimator effects. We further demonstrate excellent agreement between two\nindependently-developed modeling pipelines, and thus rule out any residual\nuncertainties due to the numerical implementation.\n"}
+{"abstract": "  The Milky Way contains thousands of H II region candidates identified by\ntheir characteristic mid-infrared morphology, but lacking detections of ionized\ngas tracers such as radio continuum or radio recombination line emission. These\ntargets thus remain unconfirmed as H II regions. With only $\\sim$2500 confirmed\nH II regions in the Milky Way, Galactic surveys are deficient by several\nthousand nebulae when compared to external galaxies with similar star formation\nrates. Using sensitive 9 GHz radio continuum observations with the Karl G.\nJansky Very Large Array (VLA), we explore a sample of H II region candidates in\norder to set observational limits on the actual total population of Galactic H\nII regions. We target all infrared-identified \"radio quiet\" sources from the\nWISE Catalog of Galactic H II regions between\n$245^{\\circ}\\geq\\ell\\geq90^{\\circ}$ with infrared diameters less than\n80$^{\\prime\\prime}$. We detect radio continuum emission from 50% of the\ntargeted H II region candidates, providing strong evidence that most of the\nradio quiet candidates are bona fide HII regions. We measure the peak and\nintegrated radio flux densities and compare the inferred Lyman continuum fluxes\nusing models of OB-stars. We conclude that stars of approximately spectral type\nB2 and earlier are able to create H II regions with similar infrared and radio\ncontinuum morphologies as the more luminous H II regions created by O-stars.\nFrom our 50% detection rate of \"radio quiet\" sources, we set a lower limit of\n$\\sim$7000 for the H II region population of the Galaxy. Thus the vast majority\nof the Milky Way's H II regions remain to be discovered.\n"}
+{"abstract": "  Grapevine winter pruning is a complex task, that requires skilled workers to\nexecute it correctly. The complexity of this task is also the reason why it is\ntime consuming. Considering that this operation takes about 80-120 hours/ha to\nbe completed, and therefore is even more crucial in large-size vineyards, an\nautomated system can help to speed up the process. To this end, this paper\npresents a novel multidisciplinary approach that tackles this challenging task\nby performing object segmentation on grapevine images, used to create a\nrepresentative model of the grapevine plants. Second, a set of potential\npruning points is generated from this plant representation. We will describe\n(a) a methodology for data acquisition and annotation, (b) a neural network\nfine-tuning for grapevine segmentation, (c) an image processing based method\nfor creating the representative model of grapevines, starting from the inferred\nsegmentation and (d) potential pruning points detection and localization, based\non the plant model which is a simplification of the grapevine structure. With\nthis approach, we are able to identify a significant set of potential pruning\npoints on the canes, that can be used, with further selection, to derive the\nfinal set of the real pruning points.\n"}
+{"abstract": "  This paper is concerned with the large time behavior of the Cauchy problem\nfor Navier-Stokes/Allen-Cahn system describing the interface motion of\nimmiscible two-phase flow in 3-D. The existence and uniqueness of global\nsolutions and the stability of the phase separation state is proved under the\nsmall initial perturbations. Moreover, the optimal time decay rates are\nobtained for higher-order spatial derivatives of density, velocity and phase.\nOur results implies that if the immiscible two-phase flow is initially located\nnear the phase separation state, then under small perturbation conditions, the\nsolution exists globally and decays algebraically to the complete separation\nstate of the two-phase flow, that is, there will be no interface fracture,\nvacuum, shock wave, mass concentration at any time, and the interface thickness\ntends to zero as the time $t\\rightarrow+\\infty$.\n"}
+{"abstract": "  We study orbital diamagnetism in $(2+1)$-dimensional Dirac electrons with a\nshort-range interaction at zero temperature. We introduce orbital magnetic\nfields into spinless Dirac electrons on the $\\pi$-flux square lattice, and\nanalyze them by using infinite density matrix renormalization group. It is\nfound that the diamagnetism remains intact when the short-range interaction $V$\nis weak, while it is monotonically suppressed for larger interactions. Around\nthe quantum critical point (QCP) of a charge density wave phase transition, we\nfind a scaling behavior of the ground state energy density characteristic of\nthe chiral Ising universality class. This leads to universal orbital\ndiamagnetism in correlated Dirac electrons around the QCP, which may be\nregarded as a quantum, magnetic analogue of critical Casimir effect which has\nbeen widely studied for classical phase transitions.\n"}
+{"abstract": "  We review the current status and recent progress of microscopic many-body\napproaches and phenomenological models, which are employed to construct the\nequation of state of neutron stars. The equation of state is relevant for the\ndescription of their structure and dynamical properties, and it rules also the\ndynamics of core-collapse supernovae and binary neutron star mergers. We\ndescribe neutron star matter assuming that the main degrees of freedom are\nnucleons and hyperons, disregarding the appearance of quark matter. We compare\nthe theoretical predictions of the different equation-of-state models with the\ncurrently available data coming from both terrestrial laboratory experiments\nand recent astrophysical observations. We also analyse the importance of the\nnuclear strong interaction and equation of state for the cooling properties of\nneutron stars. We discuss the main open challenges in the description of the\nequation of state, mainly focusing on the limits of the different many-body\ntechniques, the so-called \"hyperon puzzle,\" and the dependence of the direct\nURCA processes on the equation of state.\n"}
+{"abstract": "  In this work, we present a new, algorithm for multi-domain learning. Given a\npretrained architecture and a set of visual domains received sequentially, the\ngoal of multi-domain learning is to produce a single model performing a task in\nall the domains together. Recent works showed how we can address this problem\nby masking the internal weights of a given original conv-net through learned\nbinary variables. In this work, we provide a general formulation of binary mask\nbased models for multi-domain learning by affine transformations of the\noriginal network parameters. Our formulation obtains significantly higher\nlevels of adaptation to new domains, achieving performances comparable to\ndomain-specific models while requiring slightly more than 1 bit per network\nparameter per additional domain. Experiments on two popular benchmarks showcase\nthe power of our approach, achieving performances close to state-of-the-art\nmethods on the Visual Decathlon Challenge.\n"}
+{"abstract": "  There is a growing interest in the learning-to-learn paradigm, also known as\nmeta-learning, where models infer on new tasks using a few training examples.\nRecently, meta-learning based methods have been widely used in few-shot\nclassification, regression, reinforcement learning, and domain adaptation. The\nmodel-agnostic meta-learning (MAML) algorithm is a well-known algorithm that\nobtains model parameter initialization at meta-training phase. In the meta-test\nphase, this initialization is rapidly adapted to new tasks by using gradient\ndescent. However, meta-learning models are prone to overfitting since there are\ninsufficient training tasks resulting in over-parameterized models with poor\ngeneralization performance for unseen tasks. In this paper, we propose a\nBayesian neural network based MAML algorithm, which we refer to as the B-SMALL\nalgorithm. The proposed framework incorporates a sparse variational loss term\nalongside the loss function of MAML, which uses a sparsifying approximated KL\ndivergence as a regularizer. We demonstrate the performance of B-MAML using\nclassification and regression tasks, and highlight that training a sparsifying\nBNN using MAML indeed improves the parameter footprint of the model while\nperforming at par or even outperforming the MAML approach. We also illustrate\napplicability of our approach in distributed sensor networks, where sparsity\nand meta-learning can be beneficial.\n"}
+{"abstract": "  Graph convolutional networks are becoming indispensable for deep learning\nfrom graph-structured data. Most of the existing graph convolutional networks\nshare two big shortcomings. First, they are essentially low-pass filters, thus\nthe potentially useful middle and high frequency band of graph signals are\nignored. Second, the bandwidth of existing graph convolutional filters is\nfixed. Parameters of a graph convolutional filter only transform the graph\ninputs without changing the curvature of a graph convolutional filter function.\nIn reality, we are uncertain about whether we should retain or cut off the\nfrequency at a certain point unless we have expert domain knowledge. In this\npaper, we propose Automatic Graph Convolutional Networks (AutoGCN) to capture\nthe full spectrum of graph signals and automatically update the bandwidth of\ngraph convolutional filters. While it is based on graph spectral theory, our\nAutoGCN is also localized in space and has a spatial form. Experimental results\nshow that AutoGCN achieves significant improvement over baseline methods which\nonly work as low-pass filters.\n"}
+{"abstract": "  Star formation in galaxies is a hierarchical process with a wide range of\nscales from smaller clusters to larger stellar complexes. Here, we present an\nultra-violet imaging study of the nearby flocculent spiral galaxy NGC 7793,\nobserved with the Ultra-Violet Imaging Telescope (UVIT). We find that the disk\nscale-length estimated in Far-UV (2.64$\\pm$0.16 kpc) is larger than that in\nNear-UV (2.21$\\pm$0.21 kpc) and optical (1.08 kpc), which supports the\ninside-out growth scenario of the galaxy disk. The star-forming UV disk is also\nfound to be contained within the extent of H~I gas of column density greater\nthan $10^{21}$cm$^{-2}$. With the spatial resolution of UVIT (1 pixel $\\sim$\n6.8 pc), we identified 2046 young star-forming clumps in the galaxy with radii\nbetween $\\sim$ 12 - 70 pc, which matches well with the size of GMCs detected in\nthe galaxy. Around 61\\% of the regions identified in our study have age younger\nthan 20 Myr, which points to a recent enhancement of star formation across the\ngalaxy. We also noticed that the youngest star-forming regions, with age $<$ 10\nMyr, distinctly trace the flocculent arms of the galaxy. The estimated mass of\nthe clumps cover a range between $10^3 - 10 ^6 M_{\\odot}$. We noticed a\ngradient in the mass distribution of identified clumps along the spiral arms.\nWe have also studied the nuclear star cluster of the galaxy and found that the\nstellar populations in the cluster outskirts are younger than the inner part.\n"}
+{"abstract": "  Identifying the intrinsic and extrinsic origins of magneto-transport in\nspin-orbit coupled systems has long been a central theme in condensed matter\nphysics. However, it has been elusive owing to the lack of an appropriate\nexperimental tool. In this work, using terahertz time-domain spectroscopy, we\nunambiguously disentangle the intrinsic and extrinsic contributions to the\nanisotropic magnetoresistance (AMR) of a permalloy film. We find that the\nscattering-independent intrinsic contribution to AMR is sizable and is as large\nas the scattering-dependent extrinsic contribution to AMR. Moreover, the\nportion of intrinsic contribution to total AMR increases with increasing\ntemperature due to the reduction of extrinsic contribution. Further\ninvestigation reveals that the reduction of extrinsic contribution is caused by\nthe phonon/magnon-induced negative AMR. Our result will stimulate further\nresearches on other spin-orbit-interaction-induced phenomena for which\nidentifying the intrinsic and extrinsic contributions is important.\n"}
+{"abstract": "  The starting point to understanding cluster properties is the putative global\nminimum and all the nearby local energy minima; however, locating them is\ncomputationally expensive and challenging due to a combinatorial explosion\nproblem. The relative populations and spectroscopic properties of a molecule\nthat are a function of temperature can be approximately computed by employing\nstatistical thermodynamics. Here, we investigate temperature-entropy-driven\nisomers distribution on Be$_6$B$_{11}^{-}$ fluxional cluster and the effect of\ntemperature on their infrared spectroscopy and relative populations. We\nidentify the vibration modes possessed by the cluster that significantly\ncontribute to the zero-point energy. A couple of steps are considered for\ncomputing the temperature-dependent relative population: First, using a genetic\nalgorithm coupled to density functional theory, we performed an extensive and\nsystematic exploration of the potential/free energy surface of\nBe$_6$B$_{11}^{-}$ cluster to locate the putative global minimum and elucidate\nthe low-energy structures. Second, the relative populations' temperature\neffects are determined by considering the thermodynamic properties and\nBoltzmann factors. The temperature-dependent relative populations show that the\nentropies and temperature are essential for determining the global minimum. We\ncompute the temperature-dependent total infrared spectra employing the\nBoltzmann factor weighted sums of each isomer's infrared spectrum and find that\nat finite temperature, the total infrared spectrum is composed of an admixture\nof infrared spectra that corresponds to the spectrum of the lowest energy\nstructure and its isomers located at higher energies. The methodology and\nresults describe the thermal effects in the relative population and the\ninfrared spectra.\n"}
+{"abstract": "  We provide a new five dimensional black string--like solution by means of the\nembedding of a four dimensional regular black hole into a compact extra\ndimension. We enunciate a list of constraints in order to that the five\ndimensional black string--like solution to be regular, and, following these\nconstraints we construct our solution. Instead of the usual singularity, it is\nformed a core whose topology corresponds to the product between the de--Sitter\ncore of the four dimensional Hayward solution and $S^1$. The horizon has\ntopology $S^2 \\times S^1$. At infinity of the radial coordinate the regular\nfour dimensional geometry is asymptotically flat, {\\it i.e}, at this place the\ntopology of the complete solution corresponds to the product between Minkowski\nand $S^1$. At the induced four dimensional geometry we compute the correct\nvalues of temperature and entropy.\n"}
+{"abstract": "  To reversify an arbitrary sequential algorithm $A$, we gently instrument $A$\nwith bookkeeping machinery. The result is a step-for-step reversible algorithm\nthat mimics $A$ step-for-step and stops exactly when $A$ does.\n  Without loss of generality, we presume that algorithm $A$ is presented as an\nabstract state machine that is behaviorally identical to $A$. The existence of\nsuch representation has been proven theoretically, and the practicality of such\nrepresentation has been amply demonstrated.\n"}
+{"abstract": "  Quantum machine learning (QML) has been identified as one of the key fields\nthat could reap advantages from near-term quantum devices, next to optimization\nand quantum chemistry. Research in this area has focused primarily on\nvariational quantum algorithms (VQAs), and several proposals to enhance\nsupervised, unsupervised and reinforcement learning (RL) algorithms with VQAs\nhave been put forward. Out of the three, RL is the least studied and it is\nstill an open question whether VQAs can be competitive with state-of-the-art\nclassical algorithms based on neural networks (NNs) even on simple benchmark\ntasks. In this work, we introduce a training method for parametrized quantum\ncircuits (PQCs) to solve RL tasks for discrete and continuous state spaces\nbased on the deep Q-learning algorithm. To evaluate our model, we use it to\nsolve two benchmark environments from the OpenAI Gym, Frozen Lake and Cart\nPole. We provide insight into why the performance of a VQA-based Q-learning\nalgorithm crucially depends on the observables of the quantum model and show\nhow to choose suitable observables based on the RL task at hand. We compare the\nperformance of our model to that of a NN for agents that need similar time to\nconvergence, and find that our quantum model needs approximately one-third of\nthe parameters of the classical model to solve the Cart Pole environment in a\nsimilar number of episodes on average. We also show how recent separation\nresults between classical and quantum agents for policy gradient RL can be\nadapted to quantum Q-learning agents, which yields a quantum speed-up for\nQ-learning. This work paves the way towards new ideas on how a quantum\nadvantage may be obtained for real-world problems in the future.\n"}
+{"abstract": "  In this work a mismatched section of a transmission line is examined for its\nutility in a bandpass filtering application. A model to analyze the situation\nis given. It was found that the transfer characteristics of such mismatched\ntransmission line resemble with that of a narrow bandpass filter with steeper\nroll-off. It has been shown that the center frequency of the response depends\non the length of the mismatched section as well as the dielectric material of\nthe line. Further it has been shown that the center frequency can be tuned to a\ndesired value by adjusting the dielectric profile along the length of the line.\nThis was tested on a set of parallel wire transmission lines of three lengths\nand liquid dielectrics. During the study it was noticed that a sensing\nmechanism to detect variation in small volumes of the liquid dielectrics with\nhigh sensitivity can be developed using mismatched transmission lines.\n"}
+{"abstract": "  Organ transplantation is often the last resort for treating end-stage\nillness, but the probability of a successful transplantation depends greatly on\ncompatibility between donors and recipients. Current medical practice relies on\ncoarse rules for donor-recipient matching, but is short of domain knowledge\nregarding the complex factors underlying organ compatibility. In this paper, we\nformulate the problem of learning data-driven rules for organ matching using\nobservational data for organ allocations and transplant outcomes. This problem\ndeparts from the standard supervised learning setup in that it involves\nmatching the two feature spaces (i.e., donors and recipients), and requires\nestimating transplant outcomes under counterfactual matches not observed in the\ndata. To address these problems, we propose a model based on representation\nlearning to predict donor-recipient compatibility; our model learns\nrepresentations that cluster donor features, and applies donor-invariant\ntransformations to recipient features to predict outcomes for a given\ndonor-recipient feature instance. Experiments on semi-synthetic and real-world\ndatasets show that our model outperforms state-of-art allocation methods and\npolicies executed by human experts.\n"}
+{"abstract": "  One of the limiting factors in training data-driven, rare-event prediction\nalgorithms is the scarcity of the events of interest resulting in an extreme\nimbalance in the data. There have been many methods introduced in the\nliterature for overcoming this issue; simple data manipulation through\nundersampling and oversampling, utilizing cost-sensitive learning algorithms,\nor by generating synthetic data points following the distribution of the\nexisting data. While synthetic data generation has recently received a great\ndeal of attention, there are real challenges involved in doing so for\nhigh-dimensional data such as multivariate time series. In this study, we\nexplore the usefulness of the conditional generative adversarial network (CGAN)\nas a means to perform data-informed oversampling in order to balance a large\ndataset of multivariate time series. We utilize a flare forecasting benchmark\ndataset, named SWAN-SF, and design two verification methods to both\nquantitatively and qualitatively evaluate the similarity between the generated\nminority and the ground-truth samples. We further assess the quality of the\ngenerated samples by training a classical, supervised machine learning\nalgorithm on synthetic data, and testing the trained model on the unseen, real\ndata. The results show that the classifier trained on the data augmented with\nthe synthetic multivariate time series achieves a significant improvement\ncompared with the case where no augmentation is used. The popular flare\nforecasting evaluation metrics, TSS and HSS, report 20-fold and 5-fold\nimprovements, respectively, indicating the remarkable statistical similarities,\nand the usefulness of CGAN-based data generation for complicated tasks such as\nflare forecasting.\n"}
+{"abstract": "  The shape of the anisotropic velocity distribution function, beyond the realm\nof strict Maxwellians can play a significant role in determining the evolution\nof the Weibel instability dictating the dynamics of self-generated magnetic\nfields. For non-Maxwellian distribution functions, we show that the direction\nof the maximum growth rate wavevector changes with shape. We investigate\ndifferent laser-plasma interaction model distributions which show that their\nWeibel generated magnetic fields may require closer scrutiny beyond the second\nmoment (temperature) anisotropy ratio characterization.\n"}
+{"abstract": "  Correlated topological magnets are emerging as a new class of quantum\nmaterials, exhibiting exotic interacting fermions and unconventional phase\ntransitions. Despite considerable interest, direct observation of the magnetic\nmanipulation of topological quasiparticles remains limited. Here we report a\ncorrelated topological phase transition in a kagome spin-orbit semimetal,\nexamined by high-resolution photoemission spectroscopy. By modulating the\nmagnetic order, we observe a clear exchange gap collapse in our spectra,\nassociated with a large renormalization of a spin-orbit-gapped Weyl loop at the\nFermi level. This unexpected response suggests the collapse of opposite-spin\npartner ferromagnetic Weyl loops into a paramagnetic Dirac loop. Taken together\nwith $ab$ $initio$ calculation, our results further indicate that\noppositely-charged Weyl points pair up and annihilate under collapse, and the\nFermi arc surface states are removed. Our findings suggest a novel topological\nphase transition driven by magnetic interactions, guiding future exploration of\nrenormalized topology under correlated order parameters.\n"}
+{"abstract": "  CrSb is an attractive material for room-temperature antiferromagnetic\nspintronic applications because of its high N\\'{e}el temperature $\\sim$700 K\nand semi-metallic character. We study the magnetic properties of CrSb bilayers\non few-layer topological insulator thin films using \\emph{ab initio} density\nfunctional theory. We find that the intrinsic parts of the total anomalous Hall\nconductivities of the thin films are non-zero, and approximately quantized. The\nN\\'{e}el temperature of CrSb bilayers on few-layer topological insulator thin\nfilms is found to be approximately two times larger than that of an isolated\nCrSb thin film. Due to the low Fermi level density of states of CrSb, Hall\nquantization might be achievable by introducing disorder. CrSb bilayers on\ntopological insulator surfaces are therefore attractive candidates for\nhigh-temperature quantum anomalous Hall effects.\n"}
+{"abstract": "  Clustering is grouping of data by the proximity of some properties. We report\non the possibility of increasing the efficiency of clustering of points in a\nplane using artificial quantum neural networks after the replacement of the\ntwo-level neurons called qubits represented by the spins S = 1/2 by the\nthree-level neurons called qutrits represented by the spins S = 1. The problem\nhas been solved by the slow adiabatic change of the Hamiltonian in time. The\nmethods for controlling a qutrit system using projection operators have been\ndeveloped and the numerical simulation has been performed. The Hamiltonians for\ntwo well-known cluster-ing methods, one-hot encoding and k-means ++, have been\nbuilt. The first method has been used to partition a set of six points into\nthree or two clusters and the second method, to partition a set of nine points\ninto three clusters and seven points into four clusters. The simulation has\nshown that the clustering problem can be ef-fectively solved on qutrits\nrepresented by the spins S = 1. The advantages of clustering on qutrits over\nthat on qubits have been demonstrated. In particular, the number of qutrits\nrequired to represent data points is smaller than the number of qubits by a\nfactor of log2 N / log3 N . Since, for qutrits, it is easier to partition the\ndata points into three clusters rather than two ones, the approximate\nhierarchical procedure of data partition-ing into a larger number of clusters\nis accelerated. At the exact data partition into more than three clusters, it\nhas been proposed to number the clusters by the numbers of states of the\ncorresponding multi-spin subsys-tems, instead of using the numbers of\nindividual spins. This reduces even more the number of qutrits (N log3 K\ninstead of NK ) required to implement the algorithm.\n"}
+{"abstract": "  We propose a globally convergent numerical method, called the\nconvexification, to numerically compute the viscosity solution to first-order\nHamilton-Jacobi equations through the vanishing viscosity process where the\nviscosity parameter is a fixed small number. By convexification, we mean that\nwe employ a suitable Carleman weight function to convexify the cost functional\ndefined directly from the form of the Hamilton-Jacobi equation under\nconsideration. The strict convexity of this functional is rigorously proved\nusing a new Carleman estimate. We also prove that the unique minimizer of the\nthis strictly convex functional can be reached by the gradient descent method.\nMoreover, we show that the minimizer well approximates the viscosity solution\nof the Hamilton-Jacobi equation as the noise contained in the boundary data\ntends to zero. Some interesting numerical illustrations are presented.\n"}
+{"abstract": "  Nanocellulose was extracted from short bast fibers, from hemp (Cannabis\nSativa L.) plants harvested at seed maturity, non-retted, mechanically\ndecorticated in a defibering apparatus giving non-aligned fibers. A chemical\npretreatment with NaOH and HCl allowed the removal of most of the\nnon-cellulosic components of the fibers. No bleaching was performed. The\nchemically pretreated fibers were then refined in a beater and treated with a\ncellulase enzyme, before the mechanical defibrillation in an ultrafine friction\ngrinder. The fibers were characterized by microscopy, infrared spectroscopy,\nthermogravimetric analysis, and X-ray diffraction after each step of the\nprocess, to understand the evolution of their morphology and composition. The\nobtained nanocellulose suspension was composed of rod-like fibrils with widths\nof 5-12 nm, stacks of nanofibrils with widths of 20-200 nm, and some larger\nfibers. The crystallinity index was found to increase from 74% for the raw\nfibers to 80% for the nanocellulose. The nanocellulose retained a yellowish\ncolor indicating the presence of some residual lignin. Properties of nanopaper\nprepared with the hemp nanocellulose were similar to those of nanopapers\nprepared with wood pulp derived rod-like nanofibrils.\n"}
+{"abstract": "  We consider electrodiffusion of ions in fluids, described by the\nNernst-Planck-Navier-Stokes system, in three dimensional bounded domains, with\nblocking (no-flux) boundary conditions for the ionic concentrations and Robin\nboundary conditions for the electric potential, representing the presence of an\nelectrical double layer. We prove global existence of strong solutions for\narbitrary initial data in the case of two oppositely charged ionic species and\nalso in the case of more than two species when the diffusivities are equal and\nthe magnitudes of the valences are also equal. This result holds\nunconditionally in the case where fluid flow is described by the Stokes\nequations. In the case of Navier-Stokes coupling, the result holds\nconditionally on Navier-Stokes regularity. We also consider a case of mixed\nblocking-Dirichlet boundary conditions for the ionic concentrations and prove\nglobal regularity for two oppositely charged species when the total\nconcentration of one species is sufficiently low.\n"}
+{"abstract": "  Previous cycle-consistency correspondence learning methods usually leverage\nimage patches for training. In this paper, we present a fully convolutional\nmethod, which is simpler and more coherent to the inference process. While\ndirectly applying fully convolutional training results in model collapse, we\nstudy the underline reason behind this collapse phenomenon, indicating that the\nabsolute positions of pixels provide a shortcut to easily accomplish\ncycle-consistence, which hinders the learning of meaningful visual\nrepresentations. To break this absolute position shortcut, we propose to apply\ndifferent crops for forward and backward frames, and adopt feature warping to\nestablish correspondence between two crops of a same frame. The former\ntechnique enforces the corresponding pixels at forward and back tracks to have\ndifferent absolute positions, and the latter effectively blocks the shortcuts\ngoing between forward and back tracks. In three label propagation benchmarks\nfor pose tracking, face landmark tracking and video object segmentation, our\nmethod largely improves the results of vanilla fully convolutional\ncycle-consistency method, achieving very competitive performance compared with\nthe self-supervised state-of-the-art approaches. Our trained model and code are\navailable at \\url{https://github.com/Steve-Tod/STFC3}.\n"}
+{"abstract": "  This paper develops an algorithmic-based approach for proving inductive\nproperties of propositional sequent systems such as admissibility,\ninvertibility, cut-elimination, and identity expansion. Although undecidable in\ngeneral, these structural properties are crucial in proof theory because they\ncan reduce the proof-search effort and further be used as scaffolding for\nobtaining other meta-results such as consistency. The algorithms -- which take\nadvantage of the rewriting logic meta-logical framework, and use rewrite- and\nnarrowing-based reasoning -- are explained in detail and illustrated with\nexamples throughout the paper. They have been fully mechanized in the\nL-Framework, thus offering both a formal specification language and\noff-the-shelf mechanization of the proof-search algorithms coming together with\nsemi-decision procedures for proving theorems and meta-theorems of the object\nsystem. As illustrated with case studies in the paper, the L-Framework,\nachieves a great degree of automation when used on several propositional\nsequent systems, including single conclusion and multi-conclusion\nintuitionistic logic, classical logic, classical linear logic and its dyadic\nsystem, intuitionistic linear logic, and normal modal logics.\n"}
+{"abstract": "  For mobile robots to be effectively applied to real world unstructured\nenvironments -- such as large scale farming -- they require the ability to\ngenerate adaptive plans that account both for limited onboard resources, and\nthe presence of dynamic changes, including nearby moving individuals. This work\nprovides a real world empirical evaluation of our proposed hierarchical\nframework for long-term autonomy of field robots, conducted on University of\nSydney's Swagbot agricultural robot platform. We demonstrate the ability of the\nframework to navigate an unstructured and dynamic environment in an effective\nmanner, validating its use for long-term deployment in large scale farming, for\ntasks such as autonomous weeding in the presence of moving individuals.\n"}
+{"abstract": "  We derive a new twist-3 partonic sum rule for the transverse spin of the\nproton, which involves the well-know quark spin structure function\n$g_T(x)=g_1(x)+g_2(x)$, the less-studied but known transverse gluon\npolarization density $\\Delta G_T(x)$, and quark and gluon canonical orbital\nangular momentum densities associated with transverse polarization. This is the\ncounter part of the sum rule for the longitudinal spin of the proton derived by\nJaffe and Manohar previously. We relate the partonic canonical orbital angular\nmomentum densities to a new class of twist-3 generalized parton distribution\nfunctions which are potentially measurable in deep-virtual exclusive processes.\nWe also discuss in detail an important technicality related to the transverse\npolarization in the infinite momentum frame, i.e., separation of intrinsic\ncontributions from the extrinsic ones. We apply our finding to the\ntransverse-space distributions of partons, angular momentum, and magnetic\nmoment, respectively, in a transversely polarized proton.\n"}
+{"abstract": "  Molecular Communications (MC) is a bio-inspired communication technique that\nuses molecules to transfer information among bio-nano devices. In this paper,\nwe focus on the detection problem for biological MC receivers employing ligand\nreceptors to infer the transmitted messages encoded into the concentration of\nmolecules, i.e., ligands. In practice, receptors are not ideally selective\nagainst target ligands, and in physiological environments, they can interact\nwith multiple types of ligands at different reaction rates depending on their\nbinding affinity. This molecular cross-talk can cause a substantial\ninterference on MC. Here we consider a particular scenario, where there is\nnon-negligible concentration of interferer molecules in the channel, which have\nsimilar receptor-binding characteristics with the information molecules, and\nthe receiver employs single type of receptors. We investigate the performance\nof four different detection methods, which make use of different statistics of\nthe ligand-receptor binding reactions: instantaneous number of bound receptors,\nunbound time durations of receptors, bound time durations of receptors, and\ncombination of unbound and bound time durations of receptors within a sampling\ntime interval. The performance of the introduced detection methods are\nevaluated in terms of bit error probability for varying strength of molecular\ninterference, similarity between information and interferer molecules, number\nof receptors, and received concentration difference between bit-0 and bit-1\ntransmissions. We propose synthetic receptor designs that can convert the\nrequired receptor statistics to the concentration of intracellular molecules,\nand chemical reaction networks that can chemically perform the computations\nrequired for detection.\n"}
+{"abstract": "  We have analytically explored the Rayleigh-Sommerfeld scalar diffraction for\noblique incidence. We have explicitly derived the Fraunhofer diffraction\nformulae for oblique incidence of plane scalar wave on various apertures, such\nas single-slit, circular aperture, and diffraction grating. Such derivations in\nthe background of Rayleigh-Sommerfeld scalar diffraction theory would be\nimportant for an undergraduate course on optics.\n"}
+{"abstract": "  High performance rack-scale offerings package disaggregated pools of compute,\nmemory and storage hardware in a single rack to run diverse workloads with\nvarying requirements, including applications that need low and predictable\nlatency. The intra-rack network is typically high speed Ethernet, which can\nsuffer from congestion leading to packet drops and may not satisfy the\nstringent tail latency requirements for some workloads (including remote\nmemory/storage accesses). In this paper, we design a Predictable Low\nLatency(PL2) network architecture for rack-scale systems with Ethernet as\ninterconnecting fabric. PL2 leverages programmable Ethernet switches to\ncarefully schedule packets such that they incur no loss with NIC and switch\nqueues maintained at small, near-zero levels. In our 100 Gbps rack-prototype,\nPL2 keeps 99th-percentile memcached RPC latencies under 60us even when the RPCs\ncompete with extreme offered-loads of 400%, without losing traffic. Network\ntransfers for a machine learning training task complete 30% faster than a\nreceiver-driven scheme implementation modeled after Homa (222ms vs 321ms 99%ile\nlatency per iteration).\n"}
+{"abstract": "  Customer support via chat requires agents to resolve customer queries with\nminimum wait time and maximum customer satisfaction. Given that the agents as\nwell as the customers can have varying levels of literacy, the overall quality\nof responses provided by the agents tend to be poor if they are not predefined.\nBut using only static responses can lead to customer detraction as the\ncustomers tend to feel that they are no longer interacting with a human. Hence,\nit is vital to have variations of the static responses to reduce monotonicity\nof the responses. However, maintaining a list of such variations can be\nexpensive. Given the conversation context and the agent response, we propose an\nunsupervised frame-work to generate contextual paraphrases using autoregressive\nmodels. We also propose an automated metric based on Semantic Similarity,\nTextual Entailment, Expression Diversity and Fluency to evaluate the quality of\ncontextual paraphrases and demonstrate performance improvement with\nReinforcement Learning (RL) fine-tuning using the automated metric as the\nreward function.\n"}
+{"abstract": "  We study the problem $\\#\\mathrm{EdgeSub}(\\Phi)$ of counting $k$-edge\nsubgraphs satisfying a given graph property $\\Phi$ in a large host graph $G$.\nBuilding upon the breakthrough result of Curticapean, Dell and Marx (STOC 17),\nwe express the number of such subgraphs as a finite linear combination of graph\nhomomorphism counts and derive the complexity of computing this number by\nstudying its coefficients.\n  Our approach relies on novel constructions of low-degree Cayley graph\nexpanders of $p$-groups, which might be of independent interest. The properties\nof those expanders allow us to analyse the coefficients in the aforementioned\nlinear combinations over the field $\\mathbb{F}_p$ which gives us significantly\nmore control over the cancellation behaviour of the coefficients. Our main\nresult is an exhaustive and fine-grained complexity classification of\n$\\#\\mathrm{EdgeSub}(\\Phi)$ for minor-closed properties $\\Phi$, closing the\nmissing gap in previous work by Roth, Schmitt and Wellnitz (ICALP 21).\n  Additionally, we observe that our methods also apply to modular counting.\nAmong others, we investigate the problems of modular counting of paths, cycles,\nforests and matroid bases. In the course of our investigations we also provide\nan exhaustive parameterized complexity classification for the problem of\ncounting graph homomorphisms modulo a prime $p$.\n"}
+{"abstract": "  Matter-wave interferometry with atoms propagating in a guiding potential is\nexpected to provide compact, scalable and precise inertial sensing. However, a\nrotation sensing device based on the Sagnac effect with atoms guided in a ring\nhas not yet been implemented despite continuous efforts during the last two\ndecades. Here we discuss some intrinsic effects that limit the coherence in\nsuch a device and propose a scheme that overcomes these limitations and enables\na multi-pass guiding Sagnac interferometer with a Bose-Einstein condensate\n(BEC) on a chip in a ring potential. We analyze crucial dephasing effects:\npotential roughness, phase diffusion due to atom-atom interactions and number\nuncertainty, and phase fluctuations. Owing to the recent progress in achieving\nhigh momentum beam splitting, creating smooth guides, and manipulating the\nmatter-wavepacket propagation, guided interferometry can be implemented within\nthe coherence time allowed by phase diffusion. Despite the lower particle flux\nin a guided Sagnac ring and the miniaturization of the interferometer, the\nestimated sensitivity, for reasonable and practical realizations of an atom\nchip-based gyroscope, is comparable to that of free-space interferometers,\nreaching 45 nrads^{-1}Hz^{-1/2}. A significant improvement over\nstate-of-the-art free-space gyrocope sensitivities can be envisioned by using\nthermal atoms instead of a BEC, whereby the interferometer can be operated in a\ncontinuous fashion with the coherence limited by the scattering rate of the\natoms with the background gas. Taking into account the sensitivity times length\nof the interferometer as the figure of merit which takes into account\ncompactness, our configuration is expected to deliver a potential improvement\nof 2-4 orders of magnitude over state-of-the-art free-space gyroscopes for a\nBEC, and 4-6 orders of magnitude for thermal atoms.\n"}
+{"abstract": "  We introduce wave encoded acquisition and reconstruction techniques for\nhighly accelerated echo planar imaging (EPI) with reduced g-factor penalty and\nimage artifacts. Wave-EPI involves playing sinusoidal gradients during the EPI\nreadout while employing interslice shifts as in blipped-CAIPI acquisitions.\nThis spreads the aliasing in all spatial directions, thereby taking better\nadvantage of 3D coil sensitivity profiles. The amount of voxel spreading that\ncan be achieved by the wave gradients during the short EPI readout period is\nconstrained by the slew rate of the gradient coils and peripheral nerve\nstimulation (PNS) monitor. We propose to use a half-cycle sinusoidal gradient\nto increase the amount of voxel spreading that can be achieved while respecting\nthe slew and stimulation constraints. Extending wave-EPI to multi-shot\nacquisition minimizes geometric distortion and voxel blurring at high in-plane\nresolution, while structured low-rank regularization mitigates shot-to-shot\nphase variations without additional navigators. We propose to use different\npoint spread functions (PSFs) for the k-space lines with positive and negative\npolarities, which are calibrated with a FLEET-based reference scan and allow\nfor addressing gradient imperfections. Wave-EPI provided whole-brain\nsingle-shot gradient echo (GE) and multi-shot spin echo (SE) EPI acquisitions\nat high acceleration factors and was combined with g-Slider slab encoding to\nboost the SNR level in 1mm isotropic diffusion imaging. Relative to\nblipped-CAIPI, wave-EPI reduced average and maximum g-factors by up to 1.21-\nand 1.37-fold, respectively. In conclusion, wave-EPI allows highly accelerated\nsingle- and multi-shot EPI with reduced g-factor and artifacts and may\nfacilitate clinical and neuroscientific applications of EPI by improving the\nspatial and temporal resolution in functional and diffusion imaging.\n"}
+{"abstract": "  I give an elementary proof that a quasi-symmetric design without repeated\nblocks on $v$ points has at most $v\\choose2$ blocks, with equality if and only\nif it is a tight $4$-design.\n"}
+{"abstract": "  Resonant beam communications (RBCom) is capable of providing wide bandwidth\nwhen using light as the carrier. Besides, the RBCom system possesses the\ncharacteristics of mobility, high signal-to-noise ratio (SNR), and\nmultiplexing. Nevertheless, the channel of the RBCom system is distinct from\nother light communication technologies due to the echo interference issue. In\nthis paper, we reveal the mechanism of the echo interference and propose the\nmethod to eliminate the interference. Moreover, we present an exemplary design\nbased on frequency shifting and optical filtering, along with its mathematic\nmodel and performance analysis. The numerical evaluation shows that the channel\ncapacity is greater than 15 bit/s/Hz.\n"}
+{"abstract": "  The simplest scheme for predicting real galaxy properties after performing a\ndark matter simulation is to rank order the real systems by stellar mass and\nthe simulated systems by halo mass and then simply assume monotonicity - that\nthe more massive halos host the more massive galaxies. This has had some\nsuccess, but we study here if a better motivated and more accurate matching\nscheme is easily constructed by looking carefully at how well one could predict\nthe simulated IllustrisTNG galaxy sample from its dark matter computations. We\nfind that using the dark matter rotation curve peak velocity, $v_{max}$, for\nnormal galaxies reduces the error of the prediction by 30% (18% for central\ngalaxies and 60% for satellite systems) - following expectations from the\nphysics of monolithic collapse. For massive systems with halo mass $>$\n10$^{12.5}$ M$_{\\odot}$ hierarchical merger driven formation is the better\nmodel and dark matter halo mass remains the best single metric. Using a new\nsingle variable that combines these effects, $\\phi$ $=$\n$v_{max}$/$v_{max,12.7}$ + M$_{peak}$/(10$^{12.7}$ M$_{\\odot}$) allows further\nimprovement and reduces the error, as compared to ranking by dark matter mass\nat $z=0$ by another 6% from $v_{max}$ ranking. Two parameter fits -- including\nenvironmental effects produce only minimal further impact.\n"}
+{"abstract": "  Given a family of locally Lipschitz vector fields\n$X(x)=(X_1(x),\\dots,X_m(x))$ on $\\mathbb{R}^n$, $m\\leq n$, we study integral\nfunctionals depending on $X$. Using the results in \\cite{MPSC1}, we study the\nconvergence of minima, minimizers and momenta of those functionals. Moreover,\nwe apply these results to the periodic homogenization in Carnot groups and to\nprove a $H$-compactness theorem for linear differential operators of the second\norder depending on $X$.\n"}
+{"abstract": "  The inner representation of deep neural networks (DNNs) is indecipherable,\nwhich makes it difficult to tune DNN models, control their training process,\nand interpret their outputs. In this paper, we propose a novel approach to\ninvestigate the inner representation of DNNs through topological data analysis\n(TDA). Persistent homology (PH), one of the outstanding methods in TDA, was\nemployed for investigating the complexities of trained DNNs. We constructed\nclique complexes on trained DNNs and calculated the one-dimensional PH of DNNs.\nThe PH reveals the combinational effects of multiple neurons in DNNs at\ndifferent resolutions, which is difficult to be captured without using PH.\nEvaluations were conducted using fully connected networks (FCNs) and networks\ncombining FCNs and convolutional neural networks (CNNs) trained on the MNIST\nand CIFAR-10 data sets. Evaluation results demonstrate that the PH of DNNs\nreflects both the excess of neurons and problem difficulty, making PH one of\nthe prominent methods for investigating the inner representation of DNNs.\n"}
+{"abstract": "  Covariance matrix estimation techniques require high acquisition costs that\nchallenge the sampling systems' storing and transmission capabilities. For this\nreason, various acquisition approaches have been developed to simultaneously\nsense and compress the relevant information of the signal using random\nprojections. However, estimating the covariance matrix from the random\nprojections is an ill-posed problem that requires further information about the\ndata, such as sparsity, low rank, or stationary behavior. Furthermore, this\napproach fails using high compression ratios. Therefore, this paper proposes an\nalgorithm based on the projected gradient method to recover a low-rank or\nToeplitz approximation of the covariance matrix. The proposed algorithm divides\nthe data into subsets projected onto different subspaces, assuming that each\nsubset contains an approximation of the signal statistics, improving the\ninverse problem's condition. The error induced by this assumption is\nanalytically derived along with the convergence guarantees of the proposed\nmethod. Extensive simulations show that the proposed algorithm can effectively\nrecover the covariance matrix of hyperspectral images with high compression\nratios (8-15% approx) in noisy scenarios. Additionally, simulations and\ntheoretical results show that filtering the gradient reduces the estimator's\nerror recovering up to twice the number of eigenvectors.\n"}
+{"abstract": "  Ly$\\alpha$-emitting galaxies (LAEs) are easily detectable in the\nhigh-redshift Universe and are potentially efficient tracers of large scale\nstructure at early epochs, as long as their observed properties do not strongly\ndepend on environment. We investigate the luminosity and equivalent width\nfunctions of LAEs in the overdense field of a protocluster at redshift $z\n\\simeq 3.78$. Using a large sample of LAEs (many spectroscopically confirmed),\nwe find that the Ly$\\alpha$ luminosity distribution is well-represented by a\nSchechter (1976) function with $\\log(L^{\\ast}/{\\rm erg s^{-1}}) =\n43.26^{+0.20}_{-0.22}$ and $\\log(\\phi^{\\ast}/{\\rm\nMpc^{-3}})=-3.40^{+0.03}_{-0.04}$ with $\\alpha=-1.5$. Fitting the equivalent\nwidth distribution as an exponential, we find a scale factor of\n$\\omega=79^{+15}_{-15}$ Angstroms. We also measured the Ly$\\alpha$ luminosity\nand equivalent width functions using the subset of LAEs lying within the\ndensest cores of the protocluster, finding similar values for $L^*$ and\n$\\omega$. Hence, despite having a mean overdensity more than 2$\\times$ that of\nthe general field, the shape of the Ly$\\alpha$ luminosity function and\nequivalent width distributions in the protocluster region are comparable to\nthose measured in the field LAE population by other studies at similar\nredshift. While the observed Ly$\\alpha$ luminosities and equivalent widths show\ncorrelations with the UV continuum luminosity in this LAE sample, we find that\nthese are likely due to selection biases and are consistent with no intrinsic\ncorrelations within the sample. This protocluster sample supports the strong\nevolutionary trend observed in the Ly$\\alpha$ escape fraction and suggest that\nlower redshift LAEs can be on average significantly more dusty that their\ncounterparts at higher redshift.\n"}
+{"abstract": "  This paper proposes a generalization of Gaussian mixture models, where the\nmixture weight is allowed to behave as an unknown function of time. This model\nis capable of successfully capturing the features of the data, as demonstrated\nby simulated and real datasets. It can be useful in studies such as clustering,\nchange-point and process control. In order to estimate the mixture weight\nfunction, we propose two new Bayesian nonlinear dynamic approaches for\npolynomial models, that can be extended to other problems involving polynomial\nnonlinear dynamic models. One of the methods, called here component-wise\nMetropolis-Hastings, apply the Metropolis-Hastings algorithm to each local\nlevel component of the state equation. It is more general and can be used in\nany situation where the observation and state equations are nonlinearly\nconnected. The other method tends to be faster, but is applied specifically to\nbinary data (using the probit link function). The performance of these methods\nof estimation, in the context of the proposed dynamic Gaussian mixture model,\nis evaluated through simulated datasets. Also, an application to an array\nComparative Genomic Hybridization (aCGH) dataset from glioblastoma cancer\nillustrates our proposal, highlighting the ability of the method to detect\nchromosome aberrations.\n"}
+{"abstract": "  Hyperdimensional computing (HDC) has emerged as a new light-weight learning\nalgorithm with smaller computation and energy requirements compared to\nconventional techniques. In HDC, data points are represented by\nhigh-dimensional vectors (hypervectors), which are mapped to high-dimensional\nspace (hyperspace). Typically, a large hypervector dimension ($\\geq1000$) is\nrequired to achieve accuracies comparable to conventional alternatives.\nHowever, unnecessarily large hypervectors increase hardware and energy costs,\nwhich can undermine their benefits. This paper presents a technique to minimize\nthe hypervector dimension while maintaining the accuracy and improving the\nrobustness of the classifier. To this end, we formulate the hypervector design\nas a multi-objective optimization problem for the first time in the literature.\nThe proposed approach decreases the hypervector dimension by more than\n$32\\times$ while maintaining or increasing the accuracy achieved by\nconventional HDC. Experiments on a commercial hardware platform show that the\nproposed approach achieves more than one order of magnitude reduction in model\nsize, inference time, and energy consumption. We also demonstrate the trade-off\nbetween accuracy and robustness to noise and provide Pareto front solutions as\na design parameter in our hypervector design.\n"}
+{"abstract": "  The conventional photon blockade for high-frequency mode is investigated in a\ntwo-mode second-order nonlinear system embedded with a two-level atom. By\nsolving the master equation and calculating the zero-delay-time second-order\ncorrelation function $g^{(2)}(0)$, we obtain that strong photon antibunching\ncan be achieved in this scheme. In particular, we find that by increasing the\nlinear coupling coefficient of the system, a perfect blockade region will be\nformed near the zero second-order nonlinear coupling coefficient. Similarly, by\nincreasing the nonlinear coupling coefficient of the system, the perfect\nblockade zone will appear. And this scheme is not sensitive to the reservoir\ntemperature, both of which make the current system easier to implement\nexperimentally.\n"}
+{"abstract": "  We develop an exact analytical approach to the optical response of a quantum\ndot-microcavity system for arbitrary excitation strengths. The response is\ndetermined in terms of the complex amplitudes of transitions between the rungs\nof the Jaynes-Cummings ladder, explicitly isolating nonlinearities of different\norders. Increasing the pulse area of the excitation field, we demonstrate the\nformation of a quantum Mollow quadruplet (QMQ), quantizing the semi-classical\nMollow triplet into a coherent superposition of a large number of transitions\nbetween rungs of the ladder, with inner and outer doublets of the QMQ formed by\ndensely lying inner and outer quantum transitions between the split rungs.\nRemarkably, a closed-form analytic approximation for the QMQ of any order of\nnonlinearity is found in the high-field low-damping limit.\n"}
+{"abstract": "  There is a parameter ubiquitous throughout the deep learning world: learning\nrate. There is likewise a ubiquitous question: what should that learning rate\nbe? The true answer to this question is often tedious and time consuming to\nobtain, and a great deal of arcane knowledge has accumulated in recent years\nover how to pick and modify learning rates to achieve optimal training\nperformance. Moreover, the long hours spent carefully crafting the perfect\nlearning rate can come to nothing the moment your network architecture,\noptimizer, dataset, or initial conditions change ever so slightly. But it need\nnot be this way. We propose a new answer to the great learning rate question:\nthe Autonomous Learning Rate Controller. Find it at\nhttps://github.com/fastestimator/ARC\n"}
+{"abstract": "  The Petty projection inequality for sets of finite perimeter is proved. Our\napproach is based on Steiner symmetrization. Neither the affine Sobolev\ninequality nor the functional Minkowski problem is used in our proof. Moreover,\nfor sets of finite perimeter, we prove the Petty projection inequality with\nrespect to Steiner symmetrization.\n"}
+{"abstract": "  This work introduces a second-order differential inclusion for unconstrained\nconvex optimization. In continuous level, solution existence in proper sense is\nobtained and exponential decay of a novel Lyapunov function along with the\nsolution trajectory is derived as well. Then in discrete level, based on\nnumerical discretizations of the continuous differential inclusion, both an\ninexact accelerated proximal point algorithm and an inexact accelerated\nproximal gradient method are proposed, and some new convergence rates are\nestablished via a discrete Lyapunov function.\n"}
+{"abstract": "  An edge stream is a common form of presentation of dynamic networks. It can\nevolve with time, with new types of nodes or edges being continuously added.\nExisting methods for anomaly detection rely on edge occurrence counts or\ncompare pattern snippets found in historical records. In this work, we propose\nIsconna, which focuses on both the frequency and the pattern of edge records.\nThe burst detection component targets anomalies between individual timestamps,\nwhile the pattern detection component highlights anomalies across segments of\ntimestamps. These two components together produce three intermediate scores,\nwhich are aggregated into the final anomaly score. Isconna does not actively\nexplore or maintain pattern snippets; it instead measures the consecutive\npresence and absence of edge records. Isconna is an online algorithm, it does\nnot keep the original information of edge records; only statistical values are\nmaintained in a few count-min sketches (CMS). Isconna's space complexity\n$O(rc)$ is determined by two user-specific parameters, the size of CMSs. In\nworst case, Isconna's time complexity can be up to $O(rc)$, but it can be\namortized in practice. Experiments show that Isconna outperforms five\nstate-of-the-art frequency- and/or pattern-based baselines on six real-world\ndatasets with up to 20 million edge records.\n"}
+{"abstract": "  Contrastive learning is a key technique of modern self-supervised learning.\nThe broader accessibility of earlier approaches is hindered by the need of\nheavy computational resources (e.g., at least 8 GPUs or 32 TPU cores), which\naccommodate for large-scale negative samples or momentum. The more recent\nSimSiam approach addresses such key limitations via stop-gradient without\nmomentum encoders. In medical image analysis, multiple instances can be\nachieved from the same patient or tissue. Inspired by these advances, we\npropose a simple triplet representation learning (SimTriplet) approach on\npathological images. The contribution of the paper is three-fold: (1) The\nproposed SimTriplet method takes advantage of the multi-view nature of medical\nimages beyond self-augmentation; (2) The method maximizes both intra-sample and\ninter-sample similarities via triplets from positive pairs, without using\nnegative samples; and (3) The recent mix precision training is employed to\nadvance the training by only using a single GPU with 16GB memory. By learning\nfrom 79,000 unlabeled pathological patch images, SimTriplet achieved 10.58%\nbetter performance compared with supervised learning. It also achieved 2.13%\nbetter performance compared with SimSiam. Our proposed SimTriplet can achieve\ndecent performance using only 1% labeled data. The code and data are available\nat https://github.com/hrlblab/SimTriple.\n"}
+{"abstract": "  The Rabi Hamiltonian, describing the interaction between a two-level atomic\nsystem and a single cavity mode of the electromagnetic field, is one of the\nfundamental models in quantum optics. The model becomes exactly solvable by\nconsidering an atom without permanent dipole moments, whose excitation energy\nis quasi-resonant with the cavity photon energy, and by neglecting the non\nresonant (counter-rotating) terms. In this case, after including the decay of\neither the atom or the cavity mode to a continuum, one is able to derive the\nwell-known phenomenology of quasi-resonant transitions, including the\nfluorescence triplets. In this work we consider the most general Rabi model,\nincorporating the effects of permanent atomic electric dipole moments, and,\nbased on a perturbative analysis, we compare the intensities of emission lines\ninduced by rotating terms, counter-rotating terms and parity-symmetry-breaking\nterms. The analysis reveals that the emission strength related to the existence\nof permanent dipoles may surpass the one due to the counter-rotating\ninteraction terms, but is usually much weaker than the emission due to the\nmain, resonant coupling. This ratio can be modified in systems with a reduced\ndimensionality or by engineering the energy spectral density of the continuum.\n"}
+{"abstract": "  As Field-programmable gate arrays (FPGAs) are widely adopted in clouds to\naccelerate Deep Neural Networks (DNN), such virtualization environments have\nposed many new security issues. This work investigates the integrity of DNN\nFPGA accelerators in clouds. It proposes DeepStrike, a remotely-guided attack\nbased on power glitching fault injections targeting DNN execution. We\ncharacterize the vulnerabilities of different DNN layers against fault\ninjections on FPGAs and leverage time-to-digital converter (TDC) sensors to\nprecisely control the timing of fault injections. Experimental results show\nthat our proposed attack can successfully disrupt the FPGA DSP kernel and\nmisclassify the target victim DNN application.\n"}
+{"abstract": "  We report on a compact and spectrally intense extreme-ultraviolet (XUV)\nsource, which is based on high-harmonic generation (HHG) driven by 395 nm\npulses. In order to minimize the XUV virtual source size and to maximize the\nXUV flux, HHG is performed several Rayleigh lengths away from the driving laser\nfocal plane in a high-density gas jet. As a result, a high focused XUV\nintensity of $5 \\times 10^{13}$ W/cm$^2$ is achieved, using a beamline with a\nlength of only two meters and a modest driving laser pulse energy of 3 mJ. The\nhigh XUV intensity is demonstrated by performing a nonlinear ionization\nexperiment in argon, using an XUV spectrum that is dominated by a single\nharmonic at 22 eV. Ion charge states up to Ar$^{3+}$ are observed, which\nrequires the absorption of at least four XUV photons. The high XUV intensity\nand the narrow bandwidth are ideally suited for a variety of applications\nincluding photoelectron spectroscopy, the coherent control of resonant\ntransitions and the imaging of nanoscale structures.\n"}
+{"abstract": "  Let $(M,J,g,\\omega)$ be a K\\\"ahler manifold. We prove a $W^{1,2}$ weak\nBott-Chern decomposition and a $W^{1,2}$ weak Dolbeault decomposition,\nfollowing the $L^2$ weak Kodaira decomposition on Riemannian manifolds.\nMoreover, if the K\\\"ahler metric is complete and the sectional curvature is\nbounded, the $W^{1,2}$ Bott-Chern decomposition is strictly related to the\nspace of $W^{1,2}$ Bott-Chern harmonic forms, i.e., $W^{1,2}$ smooth\ndifferential forms which are in the kernel of an elliptic differential operator\nof order $4$, called Bott-Chern Laplacian. We also generalize to the non\ncompact case the well known property that on compact K\\\"ahler manifolds the\nkernel of the Dolbeault Laplacian and the kernel of the Bott-Chern Laplacian\ncoincide.\n"}
+{"abstract": "  The ensemble methods are meta-algorithms that combine several base machine\nlearning techniques to increase the effectiveness of the classification. Many\nexisting committees of classifiers use the classifier selection process to\ndetermine the optimal set of base classifiers. In this article, we propose the\nclassifiers selection framework with relearning base classifiers. Additionally,\nwe use in the proposed framework the new generated feature, which can be\nobtained after the relearning process. The proposed technique was compared with\nstate-of-the-art ensemble methods using three benchmark datasets and one\nsynthetic dataset. Four classification performance measures are used to\nevaluate the proposed method.\n"}
+{"abstract": "  K(BEDT-TTF)2Cu(NCS)2 has been investigated by Raman scattering in both bulk\nand nanoparticle compounds. Phonon modes from 20 to 1600 cm-1 have been\nassigned. Focusing on the unexplored low frequency phonons, a plateau in\nfrequencies is observed in the bulk phonons between 50 and 100 K and assigned\nto the signature of the bad metal phase. Nanoparticles of K(BEDT-TTF)2Cu(NCS)2\nexhibit anomalies at 50 K associated to the crossover from a bad metal to a\nFermi liquid whose origins are discussed.\n"}
+{"abstract": "  We construct intrinsic Lipschitz graphs in Carnot groups with the property\nthat, at every point, there exist infinitely many different blow-up limits,\nnone of which is a homogeneous subgroup. This provides counterexamples to a\nRademacher theorem for intrinsic Lipschitz graphs.\n"}
+{"abstract": "  We consider the use of rational basis functions to compute the scattering and\ninverse scattering transforms associated with the AKNS system. The proposed\nnumerical forward scattering transform computes the solution of the AKNS system\nthat is valid on the entire real axis and thereby computes a reflection\ncoefficient at a point by solving a single linear system. The proposed\nnumerical inverse scattering transform makes use of a novel improvement in the\nrational function approach to the oscillatory Cauchy operator, enabling the\nefficient solution of certain Riemann--Hilbert problems without contour\ndeformations. The latter development enables access to high-precision\ncomputations and this is demonstrated on the inverse scattering transform for\nthe one-dimensional Schr\\\"odinger operator with a $\\mathrm{sech}^2$ potential.\n"}
+{"abstract": "  Robotic grippers are widely used in industries for handling objects. This\npaper presents the procedure of design and fabrication of a stepper motor\ncontrolled robotic gripper to be used in industries for handling small objects.\nThe gripper has one degree of freedom for linear motion and one degree of\nfreedom for rotational motion. A linear actuator has been employed to give\ntranslational motion to the gripper. Based on the design requirement data, a\nkinematic analysis has been done to accurately predict the design parameters\nuseful in proper selection of motor and linear actuator.\n"}
+{"abstract": "  We investigate flux-grown Sm-deficient Sm$_x$B$_6$ ($x < 1$) by global and\nlocal tools, including X-ray diffraction (XRD), electronic transport, and\nscanning tunneling microscopy (STM) and spectroscopy (STS). All these tools\nindicate a remarkable persistence of the SmB$_6$ local structure in the\nflux-grown samples even for nominal Sm concentrations as low as $x=0.75$. As a\nconsequence, the overall electronic properties of Sm$_x$B$_6$, and particularly\nthe surface conductance at low temperature, is only affected locally by the\nSm-deficiency.\n"}
+{"abstract": "  The information of a quantum system acquired by a Maxwell demon can be used\nfor either work extraction or entanglement preparation. We study these two\ntasks by using a thermal qubit, in which a demon obtains her information from\nmeasurements on the environment of the qubit. The allowed entanglement, between\nthe qubit and an auxiliary system, is enhanced by the information. And, the\nincrement is find to be equivalent to the extractable work. The Maxwell demon\nis called to be quantum by Beyer \\textit{et al.} [Phys. Rev. Lett 123, 250606\n(2019) ] if there is quantum steering from the environment to the qubit. In\nthis case, the postmeasured states of the qubit, after the measurements on its\nenvironment, cannot be simulated by an objective local statistical ensemble. We\npresent a upper bound of extractable work, and equivalently of the allowed\nentanglement, for unsteerable demons, considering two measurements inducing two\northogonal changes of the Bloch vector of the qubit.\n"}
+{"abstract": "  Knowledge distillation is classically a procedure where a neural network is\ntrained on the output of another network along with the original targets in\norder to transfer knowledge between the architectures. The special case of\nself-distillation, where the network architectures are identical, has been\nobserved to improve generalization accuracy. In this paper, we consider an\niterative variant of self-distillation in a kernel regression setting, in which\nsuccessive steps incorporate both model outputs and the ground-truth targets.\nThis allows us to provide the first theoretical results on the importance of\nusing the weighted ground-truth targets in self-distillation. Our focus is on\nfitting nonlinear functions to training data with a weighted mean square error\nobjective function suitable for distillation, subject to $\\ell_2$\nregularization of the model parameters. We show that any such function obtained\nwith self-distillation can be calculated directly as a function of the initial\nfit, and that infinite distillation steps yields the same optimization problem\nas the original with amplified regularization. Furthermore, we provide a closed\nform solution for the optimal choice of weighting parameter at each step, and\nshow how to efficiently estimate this weighting parameter for deep learning and\nsignificantly reduce the computational requirements compared to a grid search.\n"}
+{"abstract": "  We consider the problem of ON-OFF privacy in which a user is interested in\nthe latest message generated by one of n sources available at a server. The\nuser has the choice to turn privacy ON or OFF depending on whether he wants to\nhide his interest at the time or not. The challenge of allowing the privacy to\nbe toggled between ON and OFF is that the user's online behavior is correlated\nover time. Therefore, the user cannot simply ignore the privacy requirement\nwhen privacy is OFF. We represent the user's correlated requests by an n-state\nMarkov chain. Our goal is to design ON-OFF privacy schemes with optimal\ndownload rate that ensure privacy for past and future requests. We devise a\npolynomial-time algorithm to construct an ON-OFF privacy scheme. Moreover, we\npresent an upper bound on the achievable rate. We show that the proposed scheme\nis optimal and the upper bound is tight for some special families of Markov\nchains. We also give an implicit characterization of the optimal achievable\nrate as a linear programming (LP).\n"}
+{"abstract": "  We use a classical characterisation to prove that functions which are bounded\naway from zero cannot be elements of reproducing kernel Hilbert spaces whose\nreproducing kernels decays to zero in a suitable way. The result is used to\nstudy Hilbert spaces on subsets of the real line induced by analytic\ntranslation-invariant kernels which decay to zero at infinity.\n"}
+{"abstract": "  We introduce a new correlated percolation model on the $d$-dimensional\nlattice $\\mathbb{Z}^d$ called the random length worms model. Assume given a\nprobability distribution on the set of positive integers (the length\ndistribution) and $v \\in (0,\\infty)$ (the intensity parameter). From each site\nof $\\mathbb{Z}^d$ we start $\\mathrm{POI}(v)$ independent simple random walks\nwith this length distribution. We investigate the connectivity properties of\nthe set $\\mathcal{S}^v$ of sites visited by this cloud of random walks. It is\neasy to show that if the second moment of the length distribution is finite\nthen $\\mathcal{S}^v$ undergoes a percolation phase transition as $v$ varies.\nOur main contribution is a sufficient condition on the length distribution\nwhich guarantees that $\\mathcal{S}^v$ percolates for all $v>0$ if $d \\geq 5$.\nE.g., if the probability mass function of the length distribution is $ m(\\ell)=\nc \\cdot \\ln(\\ln(\\ell))^{\\varepsilon}/ (\\ell^3 \\ln(\\ell)) 1[\\ell \\geq \\ell_0] $\nfor some $\\ell_0>e^e$ and $\\varepsilon>0$ then $\\mathcal{S}^v$ percolates for\nall $v>0$. Note that the second moment of this length distribution is only\n\"barely\" infinite. In order to put our result in the context of earlier results\nabout similar models (e.g., finitary random interlacements, loop percolation,\nPoisson Boolean model, ellipses percolation, etc.), we define a natural family\nof percolation models called the Poisson zoo and argue that the percolative\nbehaviour of the random length worms model is quite close to being \"extremal\"\nin this family of models.\n"}
+{"abstract": "  We apply the renormalization group optimized perturbation theory (RGOPT) to\nevaluate the quark contribution to the QCD pressure at finite temperatures and\nbaryonic densities, at next-to-leading order (NLO). Our results are compared to\nNLO and state-of-the-art higher orders of standard perturbative QCD (pQCD) and\nhard thermal loop perturbation theory (HTLpt). The RGOPT resummation provides a\nnonperturbative approximation, exhibiting a drastically better remnant\nrenormalization scale dependence than pQCD, thanks to built-in renormalization\ngroup invariance consistency. At NLO, upon simply adding to the RGOPT-resummed\nquark contributions the purely perturbative NLO glue contribution, our results\nshow a remarkable agreement with ab initio lattice simulation data for\ntemperatures $0.25 \\lesssim T \\lesssim 1 \\, {\\rm GeV}$, with a remnant scale\ndependence drastically reduced as compared to HTLpt.\n"}
+{"abstract": "  In this paper, a novel machine learning regression based single event\ntransient (SET) modeling method is proposed. The proposed method can obtain a\nreasonable and accurate model without considering the complex physical\nmechanism. We got plenty of SET current data of SMIC 130nm bulk CMOS by TCAD\nsimulation under different conditions (e.g. different LET and different drain\nbias voltage). A multilayer feedfordward neural network is used to build the\nSET pulse current model by learning the data from TCAD simulation. The proposed\nmodel is validated with the simulation results from TCAD simulation. The\ntrained SET pulse current model is implemented as a Verilog-A current source in\nthe Cadence Spectre circuit simulator and an inverter with five fan-outs is\nused to show the practicability and reasonableness of the proposed SET pulse\ncurrent model for circuit-level single-event effect (SEE) simulation.\n"}
+{"abstract": "  We construct examples of solutions to the incompressible porous media (IPM)\nequation that must exhibit infinite in time growth of derivatives provided they\nremain smooth. As an application, this allows us to obtain nonlinear\ninstability for a class of stratified steady states of IPM.\n"}
+{"abstract": "  In recent years, reference-based and supervised summarization evaluation\nmetrics have been widely explored. However, collecting human-annotated\nreferences and ratings are costly and time-consuming. To avoid these\nlimitations, we propose a training-free and reference-free summarization\nevaluation metric. Our metric consists of a centrality-weighted relevance score\nand a self-referenced redundancy score. The relevance score is computed between\nthe pseudo reference built from the source document and the given summary,\nwhere the pseudo reference content is weighted by the sentence centrality to\nprovide importance guidance. Besides an $F_1$-based relevance score, we also\ndesign an $F_\\beta$-based variant that pays more attention to the recall score.\nAs for the redundancy score of the summary, we compute a self-masked similarity\nscore with the summary itself to evaluate the redundant information in the\nsummary. Finally, we combine the relevance and redundancy scores to produce the\nfinal evaluation score of the given summary. Extensive experiments show that\nour methods can significantly outperform existing methods on both\nmulti-document and single-document summarization evaluation.\n"}
+{"abstract": "  Achieving fully tunable quantum confinement of excitons has been a\nlong-standing goal in optoelectronics and quantum photonics. We demonstrate\nelectrically controlled 1D quantum confinement of neutral excitons by means of\na lateral p-i-n junction in a monolayer transition metal dichalcogenide\nsemiconductor. Exciton trapping in the i-region occurs due to the dc Stark\neffect induced by in-plane electric fields. Remarkably, we observe a new\nconfinement mechanism arising from the repulsive polaronic dressing of excitons\nby electrons and holes in the surrounding regions. The overall confinement\npotential leads to quantization of excitonic motion, which manifests in the\nemergence of multiple spectrally narrow, voltage-dependent resonances in\nreflectance and photoluminescence measurements. Additionally, the\nphotoluminescence from confined excitonic states exhibits high degree of linear\npolarization, highlighting the 1D nature of quantum confinement. Electrically\ntunable quantum confined excitons may provide a scalable platform for arrays of\nidentical single photon sources and constitute building blocks of strongly\ncorrelated photonic many-body systems.\n"}
+{"abstract": "  Many objects commonly found in household and industrial environments are\nrepresented by cylindrical and cubic shapes. Thus, it is available for robots\nto manipulate them through the real-time detection of elliptic and rectangle\nshape primitives formed by the circular and rectangle tops of these objects. We\ndevise a robust grasping system that enables a robot to manipulate cylindrical\nand cubic objects in collaboration scenarios by the proposed perception\nstrategy including the detection of elliptic and rectangle shape primitives and\ndepth information. The proposed method of detecting ellipses and rectangles\nincorporates a one-stage detection backbone and then, accommodates the proposed\nadaptive multi-branch multi-scale net with a designed iterative feature pyramid\nnetwork, local inception net, and multi-receptive-filed feature fusion net to\ngenerate object detection recommendations. In terms of manipulating objects\nwith different shapes, we propose the grasp synthetic to align the grasp pose\nof the gripper with an object's pose based on the proposed detector and\nregistered depth information. The proposed robotic perception algorithm has\nbeen integrated on a robot to demonstrate the ability to carry out human-robot\ncollaborative manipulations of cylindrical and cubic objects in real-time. We\nshow that the robotic manipulator, empowered by the proposed detector, performs\nwell in practical manipulation scenarios.(An experiment video is available in\nYouTube, https://www.youtube.com/watch?v=Amcs8lwvNK8.)\n"}
+{"abstract": "  The software development industry has been evolving with new development\nstandards and service delivery models. Agile methodologies have reached their\ncompletion with DevOps, thereby increasing the quality of the software and\ncreating greater speed in delivery. However, a gap regarding the formalization\nof its adoption and implementation doubts became relevant. My hypothesis is\nthat, by systematizing the introduction of DevOps into the software development\nprocess and defining the function of the members of the DevOps team members,\nmay well make it quicker to implement this process, thus reducing conflicts\nbetween the teams. As part of the investigation of this hypothesis, the result\nof the research will be applied in practical development environments i.e. in a\nTechnology Agency of the State of the Brazilian Government and also at the\nBrazilian Company Neurotech in order to evaluate its effectiveness from metrics\nappropriate for DevOps environments.\n"}
+{"abstract": "  ExploitDB is one of the important public websites, which contributes a large\nnumber of vulnerabilities to official CVE database. Over 60\\% of these\nvulnerabilities have high- or critical-security risks. Unfortunately, over 73\\%\nof exploits appear publicly earlier than the corresponding CVEs, and about 40\\%\nof exploits do not even have CVEs. To assist in documenting CVEs for the\nExploitDB posts, we propose an open information method to extract 9 key\nvulnerability aspects (vulnerable product/version/component, vulnerability\ntype, vendor, attacker type, root cause, attack vector and impact) from the\nverbose and noisy ExploitDB posts. The extracted aspects from an ExploitDB post\nare then composed into a CVE description according to the suggested CVE\ndescription templates, which is must-provided information for requesting new\nCVEs. Through the evaluation on 13,017 manually labeled sentences and the\nstatistically sampling of 3,456 extracted aspects, we confirm the high accuracy\nof our extraction method. Compared with 27,230 reference CVE descriptions. Our\ncomposed CVE descriptions achieve high ROUGH-L (0.38), a longest common\nsubsequence based metric for evaluating text summarization methods.\n"}
+{"abstract": "  The interface plays a critical role in the mechanical properties of\ncomposites. In the present work, a novel phase-field based cohesive zone model\n(CZM) is proposed for the cracking simulation. The competition and interaction\nbetween the bulk and interfacial cracking are taken into consideration directly\nin both displacement- and phase-field. A modified family of degradation\nfunctions is utilized to describe traction-separation law in the CZM. Finite\nelement implementation of the present CZM was carried out with a completely\nstaggered algorithm. Several numerical examples, including a single bar tension\ntest, a double cantilever beam test, a three-point bending test, and a single\nfiber-reinforced composite test, are carried out to validate the present model\nby comparison with existing numerical and experimental results.The present\nmodel shows its advantage on modeling interaction between bulk and interfacial\ncracking. 1. Introduction\n"}
+{"abstract": "  Reinforcement learning techniques successfully generate convincing agent\nbehaviors, but it is still difficult to tailor the behavior to align with a\nuser's specific preferences. What is missing is a communication method for the\nsystem to explain the behavior and for the user to repair it. In this paper, we\npresent a novel interaction method that uses interactive explanations using\ntemplates of natural language as a communication method. The main advantage of\nthis interaction method is that it enables a two-way communication channel\nbetween users and the agent; the bot can explain its thinking procedure to the\nusers, and the users can communicate their behavior preferences to the bot\nusing the same interactive explanations. In this manner, the thinking procedure\nof the bot is transparent, and users can provide corrections to the bot that\ninclude a suggested action to take, a goal to achieve, and the reasons behind\nthese decisions. We tested our proposed method in a clone of the video game\nnamed \\textit{Super Mario Bros.}, and the results demonstrate that our\ninteractive explanation approach is effective at diagnosing and repairing bot\nbehaviors.\n"}
+{"abstract": "  Many real-world data comes in the form of graphs, such as social networks and\nprotein structure. To fully utilize the information contained in graph data, a\nnew family of machine learning (ML) models, namely graph neural networks\n(GNNs), has been introduced. Previous studies have shown that machine learning\nmodels are vulnerable to privacy attacks. However, most of the current efforts\nconcentrate on ML models trained on data from the Euclidean space, like images\nand texts. On the other hand, privacy risks stemming from GNNs remain largely\nunstudied.\n  In this paper, we fill the gap by performing the first comprehensive analysis\nof node-level membership inference attacks against GNNs. We systematically\ndefine the threat models and propose three node-level membership inference\nattacks based on an adversary's background knowledge. Our evaluation on three\nGNN structures and four benchmark datasets shows that GNNs are vulnerable to\nnode-level membership inference even when the adversary has minimal background\nknowledge. Besides, we show that graph density and feature similarity have a\nmajor impact on the attack's success. We further investigate two defense\nmechanisms and the empirical results indicate that these defenses can reduce\nthe attack performance but with moderate utility loss.\n"}
+{"abstract": "  We discuss and illustrate the properties of several parton-shower algorithms\navailable in Pythia and Vincia, in the context of Higgs production via vector\nboson fusion (VBF). In particular, the distinctive colour topology of VBF\nprocesses allows to define observables sensitive to the coherent radiation\npattern of additional jets. We study a set of such observables, using the\nVincia sector-antenna shower as our main reference, and contrast it to Pythia's\ntransverse-momentum-ordered DGLAP shower as well as Pythia's dipole-improved\nshower. We then investigate the robustness of these predictions as successive\nlevels of higher-order perturbative matrix elements are incorporated, including\nnext-to-leading-order matched and tree-level merged calculations, using Powheg\nBox and Sherpa respectively to generate the hard events.\n"}
+{"abstract": "  This paper develops change-point methods for the spectrum of a locally\nstationary time series. We focus on series with a bounded spectral density that\nchange smoothly under the null hypothesis but exhibits change-points or becomes\nless smooth under the alternative. We address two local problems. The first is\nthe detection of discontinuities (or breaks) in the spectrum at unknown dates\nand frequencies. The second involves abrupt yet continuous changes in the\nspectrum over a short time period at an unknown frequency without signifying a\nbreak. Both problems can be cast into changes in the degree of smoothness of\nthe spectral density over time. We consider estimation and minimax-optimal\ntesting. We determine the optimal rate for the minimax distinguishable\nboundary, i.e., the minimum break magnitude such that we are able to uniformly\ncontrol type I and type II errors. We propose a novel procedure for the\nestimation of the change-points based on a wild sequential top-down algorithm\nand show its consistency under shrinking shifts and possibly growing number of\nchange-points. Our method can be used across many fields and a companion\nprogram is made available in popular software packages.\n"}
+{"abstract": "  Notoriously, the two main problems of the standard $\\Lambda$CDM model of\ncosmology are the cosmological constant $\\Lambda$ and the cold dark matter,\nCDM. This essay shows that both the $\\Lambda$ and the CDM arise as integration\nconstants in a careful derivation of Einstein's equations from first principles\nin a Lorentz gauge theory. The dark sector of the universe might only reflect\nthe geometry of a spontaneous symmetry breaking that is necessary for the\nexistence of a spacetime and an observer therein.\n"}
+{"abstract": "  Consider simple random walk $(S_n)_{n\\geq0}$ on a transitive graph with\nspectral radius $\\rho$. Let $u_n=\\mathbb{P}[S_n=S_0]$ be the $n$-step return\nprobability. It is a folklore conjecture that on transient, transitive graphs\n$u_n/\\rho^n$ is at most of the order $n^{-3/2}$. We prove this conjecture for\ngraphs with a closed, transitive, amenable and nonunimodular subgroup of\nautomorphisms.\n  We also study the first return probability $f_n$. For a graph $G$ with a\nclosed, transitive, nonunimodular subgroup of automorphisms, we show that there\nis a positive constant $c$ such that $f_n\\geq \\frac{u_n}{cn^c}$. We also make\nsome conjectures related to $f_n$ and $u_n$ for transient, transitive graphs.\n"}
+{"abstract": "  In a material prone to a nematic instability, anisotropic strain in principle\nprovides a preferred symmetry-breaking direction for the electronic nematic\nstate to follow. This is consistent with experimental observations, where\nelectronic nematicity and structural anisotropy typically appear hand-in-hand.\nIn this work, we discover that electronic nematicity can be locally decoupled\nfrom the underlying structural anisotropy in strain-engineered iron-selenide\n(FeSe) thin films. We use heteroepitaxial molecular beam epitaxy to grow FeSe\nwith a nanoscale network of modulations that give rise to spatially varying\nstrain. We map local anisotropic strain by analyzing scanning tunneling\nmicroscopy topographs, and visualize electronic nematic domains from\nconcomitant spectroscopic maps. While the domains form so that the energy of\nnemato-elastic coupling is minimized, we observe distinct regions where\nelectronic nematic ordering fails to flip direction, even though the underlying\nstructural anisotropy is locally reversed. The findings point towards a\nnanometer-scale stiffness of the nematic order parameter.\n"}
+{"abstract": "  Frei et al. [6] showed that the problem to decide whether a graph is stable\nwith respect to some graph parameter under adding or removing either edges or\nvertices is $\\Theta_2^{\\text{P}}$-complete. They studied the common graph\nparameters $\\alpha$ (independence number), $\\beta$ (vertex cover number),\n$\\omega$ (clique number), and $\\chi$ (chromatic number) for certain variants of\nthe stability problem. We follow their approach and provide a large number of\npolynomial-time algorithms solving these problems for special graph classes,\nnamely for graphs without edges, complete graphs, paths, trees, forests,\nbipartite graphs, and co-graphs.\n"}
+{"abstract": "  We present a graph neural network model for solving graph-to-graph learning\nproblems. Most deep learning on graphs considers ``simple'' problems such as\ngraph classification or regressing real-valued graph properties. For such\ntasks, the main requirement for intermediate representations of the data is to\nmaintain the structure needed for output, i.e., keeping classes separated or\nmaintaining the order indicated by the regressor. However, a number of learning\ntasks, such as regressing graph-valued output, generative models, or graph\nautoencoders, aim to predict a graph-structured output. In order to\nsuccessfully do this, the learned representations need to preserve far more\nstructure. We present a conditional auto-regressive model for graph-to-graph\nlearning and illustrate its representational capabilities via experiments on\nchallenging subgraph predictions from graph algorithmics; as a graph\nautoencoder for reconstruction and visualization; and on pretraining\nrepresentations that allow graph classification with limited labeled data.\n"}
+{"abstract": "  In this paper, we propose a multi-target image tracking algorithm based on\ncontinuously apative mean-shift (Cam-shift) and unscented Kalman filter. We\nimproved the single-lamp tracking algorithm proposed in our previous work to\nmulti-target tracking, and achieved better robustness in the case of occlusion,\nthe real-time performance to complete one positioning and relatively high\naccuracy by dynamically adjusting the weights of the multi-target motion\nstates. Our previous algorithm is limited to the analysis of tracking error. In\nthis paper, the results of the tracking algorithm are evaluated with the\ntracking error we defined. Then combined with the double-lamp positioning\nalgorithm, the real position of the terminal is calculated and evaluated with\nthe positioning error we defined. Experiments show that the defined tracking\nerror is 0.61cm and the defined positioning error for 3-D positioning is 3.29cm\nwith the average processing time of 91.63ms per frame. Even if nearly half of\nthe LED area is occluded, the tracking error remains at 5.25cm. All of this\nshows that the proposed visible light positioning (VLP) method can track\nmultiple targets for positioning at the same time with good robustness,\nreal-time performance and accuracy. In addition, the definition and analysis of\ntracking errors and positioning errors indicates the direction for future\nefforts to reduce errors.\n"}
+{"abstract": "  We present a novel, fast differentiable simulator for soft-body learning and\ncontrol applications. Existing differentiable soft-body simulators can be\nclassified into two categories based on their time integration methods:\nSimulators using explicit time-stepping schemes require tiny time steps to\navoid numerical instabilities in gradient computation, and simulators using\nimplicit time integration typically compute gradients by employing the adjoint\nmethod and solving the expensive linearized dynamics. Inspired by Projective\nDynamics (PD), we present Differentiable Projective Dynamics (DiffPD), an\nefficient differentiable soft-body simulator based on PD with implicit time\nintegration. The key idea in DiffPD is to speed up backpropagation by\nexploiting the prefactorized Cholesky decomposition in forward PD simulation.\nIn terms of contact handling, DiffPD supports two types of contacts: a\npenalty-based model describing contact and friction forces and a\ncomplementarity-based model enforcing non-penetration conditions and static\nfriction. We evaluate the performance of DiffPD and observe it is 4-19 times\nfaster compared with the standard Newton's method in various applications\nincluding system identification, inverse design problems, trajectory\noptimization, and closed-loop control. We also apply DiffPD in a\nreality-to-simulation (real-to-sim) example with contact and collisions and\nshow its capability of reconstructing a digital twin of real-world scenes.\n"}
+{"abstract": "  Let $G$ be a finite group and $\\sigma_1(G)=\\frac{1}{|G|}\\sum_{H\\leq G}\\,|H|$.\nIn this paper, we prove that if $\\sigma_1(G)<2+\\frac{11}{|G|}$\\,, then $G$ is\nsupersolvable. In particular, some new characterizations of the well-known\ngroups $\\mathbb{Z}_2\\times\\mathbb{Z}_4$ and $A_4$ are obtained. We also show\nthat $\\sigma_1(G)<c$ does not imply the supersolvability of $G$ for no constant\n$c\\in(2,\\infty)$.\n"}
+{"abstract": "  Port-based teleportation (PBT) is a protocol of quantum teleportation in\nwhich a receiver does not have to apply correction to the transmitted state. In\nthis protocol two spatially separated parties can teleport an unknown quantum\nstate only by exploiting joint measurements on shared $d-$dimensional maximally\nentangled states (resource state) together with a state to be teleported and\none way classical communication. In this paper we analyse degradation of the\nresource state after one round of PBT and implications for the recycling\nprotocol for deterministic PBT introduced earlier. In the recycling protocol\nthe main idea is to re-use the remaining resource state after one or many\nrounds of PBT for further processes of teleportation. It was stated by other\nauthors that the recycling protocol is by arguing that the resource state does\nnot degrade too much after each round of teleportation process. In particular,\nthere is a claim that the fidelity between ideal resource state and its real\nversion, each of them after one round of PBT, reaches asymptotically 1 when the\nnumber of shared entangled pairs tends to infinity. Here, considering original\nsetup for the recycling protocol, we disprove these claims. We show the\nresource state is heavily distorted after even one round of PBT with fidelity\nnot exceeding the value $1/d$. This bound was obtained by referring only to\nSchwarz inequality and general properties of measurements exploited in the\nprotocol. As additional results we present explicit formula for the mentioned\nfidelity involving group-theoretic parameters describing irreducible\nrepresentations in the Schur-Weyl duality. For the first time, we also analyse\nthe degradation of the resource state for the optimal PBT scheme and show its\nsubstantial distortion for all $d\\geq 2$. (...)\n"}
+{"abstract": "  Interstellar clouds can act as target material for hadronic cosmic rays;\ngamma rays subsequently produced through inelastic proton-proton collisions and\nspatially associated with such clouds can provide a key indicator of efficient\nparticle acceleration. However, even in the case that particle acceleration\nproceeds up to PeV energies, the system of accelerator and nearby target\nmaterial must fulfil a specific set of conditions in order to produce a\ndetectable gamma-ray flux. In this study, we rigorously characterise the\nnecessary properties of both cloud and accelerator. By using available\nSupernova Remnant (SNR) and interstellar cloud catalogues, we produce a ranked\nshortlist of the most promising target systems, those for which a detectable\ngamma-ray flux is predicted, in the case that particles are accelerated to PeV\nenergies in a nearby SNR. We discuss detection prospects for future facilities\nincluding CTA, LHAASO and SWGO; and compare our predictions with known\ngamma-ray sources. The four interstellar clouds with the brightest predicted\nfluxes >100 TeV identified by this model are located at (l,b) = (330.05, 0.13),\n(15.82, -0.46), (271.09, -1.26), and (21.97, -0.29). These clouds are\nconsistently bright under a range of model scenarios, including variation in\nthe diffusion coefficient and particle spectrum. On average, a detectable\ngamma-ray flux is more likely for more massive clouds; systems with lower\nseparation distance between the SNR and cloud; and for slightly older SNRs.\n"}
+{"abstract": "  Recently, the application of machine learning models has gained momentum in\nnatural sciences and engineering, which is a natural fit due to the abundance\nof data in these fields. However, the modeling of physical processes from\nsimulation data without first principle solutions remains difficult. Here, we\npresent a Graph Neural Networks approach towards accurate modeling of complex\n3D granular flow simulation processes created by the discrete element method\nLIGGGHTS and concentrate on simulations of physical systems found in real world\napplications like rotating drums and hoppers. We discuss how to implement Graph\nNeural Networks that deal with 3D objects, boundary conditions, particle -\nparticle, and particle - boundary interactions such that an accurate modeling\nof relevant physical quantities is made possible. Finally, we compare the\nmachine learning based trajectories to LIGGGHTS trajectories in terms of\nparticle flows and mixing entropies.\n"}
+{"abstract": "  We propose NeRF-VAE, a 3D scene generative model that incorporates geometric\nstructure via NeRF and differentiable volume rendering. In contrast to NeRF,\nour model takes into account shared structure across scenes, and is able to\ninfer the structure of a novel scene -- without the need to re-train -- using\namortized inference. NeRF-VAE's explicit 3D rendering process further contrasts\nprevious generative models with convolution-based rendering which lacks\ngeometric structure. Our model is a VAE that learns a distribution over\nradiance fields by conditioning them on a latent scene representation. We show\nthat, once trained, NeRF-VAE is able to infer and render\ngeometrically-consistent scenes from previously unseen 3D environments using\nvery few input images. We further demonstrate that NeRF-VAE generalizes well to\nout-of-distribution cameras, while convolutional models do not. Finally, we\nintroduce and study an attention-based conditioning mechanism of NeRF-VAE's\ndecoder, which improves model performance.\n"}
+{"abstract": "  Our interest in this work is in group extensions of minimal flows with\ncompact abelian groups in the fibres. We study their structure from categorical\nand algebraic points of view, and describe relations of their dynamics to the\none-dimensional algebraic-topological invariants. We determine the first\ncohomology groups of flows with simply connected acting groups and those of\ntopologically free flows possessing a free cycle. As an application we show\nthat minimal extensions of these flows not only do exist, but they have a rich\nalgebraic structure.\n"}
+{"abstract": "  Transformer encoding networks have been proved to be a powerful tool of\nunderstanding natural languages. They are playing a critical role in native ads\nservice, which facilitates the recommendation of appropriate ads based on\nuser's web browsing history. For the sake of efficient recommendation,\nconventional methods would generate user and advertisement embeddings\nindependently with a siamese transformer encoder, such that approximate nearest\nneighbour search (ANN) can be leveraged. Given that the underlying semantic\nabout user and ad can be complicated, such independently generated embeddings\nare prone to information loss, which leads to inferior recommendation quality.\nAlthough another encoding strategy, the cross encoder, can be much more\naccurate, it will lead to huge running cost and become infeasible for realtime\nservices, like native ads recommendation. In this work, we propose hybrid\nencoder, which makes efficient and precise native ads recommendation through\ntwo consecutive steps: retrieval and ranking. In the retrieval step, user and\nad are encoded with a siamese component, which enables relevant candidates to\nbe retrieved via ANN search. In the ranking step, it further represents each ad\nwith disentangled embeddings and each user with ad-related embeddings, which\ncontributes to the fine-grained selection of high-quality ads from the\ncandidate set. Both steps are light-weighted, thanks to the pre-computed and\ncached intermedia results. To optimize the hybrid encoder's performance in this\ntwo-stage workflow, a progressive training pipeline is developed, which builds\nup the model's capability in the retrieval and ranking task step-by-step. The\nhybrid encoder's effectiveness is experimentally verified: with very little\nadditional cost, it outperforms the siamese encoder significantly and achieves\ncomparable recommendation quality as the cross encoder.\n"}
+{"abstract": "  Affective Computing has recently attracted the attention of the research\ncommunity, due to its numerous applications in diverse areas. In this context,\nthe emergence of video-based data allows to enrich the widely used spatial\nfeatures with the inclusion of temporal information. However, such\nspatio-temporal modelling often results in very high-dimensional feature spaces\nand large volumes of data, making training difficult and time consuming. This\npaper addresses these shortcomings by proposing a novel model that efficiently\nextracts both spatial and temporal features of the data by means of its\nenhanced temporal modelling based on latent features. Our proposed model\nconsists of three major networks, coined Generator, Discriminator, and\nCombiner, which are trained in an adversarial setting combined with curriculum\nlearning to enable our adaptive attention modules. In our experiments, we show\nthe effectiveness of our approach by reporting our competitive results on both\nthe AFEW-VA and SEWA datasets, suggesting that temporal modelling improves the\naffect estimates both in qualitative and quantitative terms. Furthermore, we\nfind that the inclusion of attention mechanisms leads to the highest accuracy\nimprovements, as its weights seem to correlate well with the appearance of\nfacial movements, both in terms of temporal localisation and intensity.\nFinally, we observe the sequence length of around 160\\,ms to be the optimum one\nfor temporal modelling, which is consistent with other relevant findings\nutilising similar lengths.\n"}
+{"abstract": "  We show that a general but shear-free perturbation of homogeneous and\nisotropic universes are necessarily silent, without any gravitational waves. We\nprove this in two steps. First we establish that a shear free perturbation of\nthese universes are acceleration-free and the fluid flow geodesics of the\nbackground universe maps onto themselves in the perturbed universe. This effect\nthen decouples the evolution equations of the electric and magnetic parts of\nthe Weyl tensor in the perturbed spacetimes and the magnetic part no longer\ncontains any tensor modes. Although the electric part, that drives the tidal\nforces, do have tensor modes sourced by the anisotropic stress, these modes\nhave homogeneous oscillations at every point on a time slice without any wave\npropagation. We also show the presence of vorticity vector waves that are\nsourced by the curl of heat flux. This analysis shows the critical role of the\nshear tensor in generating cosmological gravitational waves in an expanding\nuniverse.\n"}
+{"abstract": "  Recently developed concept of dissipative measure-valued solution for\ncompressible flows is a suitable tool to describe oscillations and\nsingularities possibly developed in solutions of multidimensional Euler\nequations. In this paper we study the convergence of the first-order finite\nvolume method based on the exact Riemann solver for the complete compressible\nEuler equations. Specifically, we derive entropy inequality and prove the\nconsistency of numerical method. Passing to the limit, we show the weak and\nstrong convergence of numerical solutions and identify their limit. The\nnumerical results presented for the spiral, Kelvin-Helmholtz and the\nRichtmyer-Meshkov problem are consistent with our theoretical analysis.\n"}
+{"abstract": "  We propose an inflationary primordial feature model that can explain both the\nlarge and small-scale anomalies in the currently measured cosmic microwave\nbackground (CMB) anisotropy spectra, revealing a clip of adventurous history of\nthe Universe during its primordial epoch. Although the model is currently\nstatistically indistinguishable from the Standard Model, we show that future\nobservations such as the Simons Observatory and LiteBIRD will complement each\nother in distinguishing the model differences due to their accurate E-mode\npolarization measurements, and the PICO mission, if funded, can put stringent\nconstraints on all characteristic properties. The model predicts a signal of\nclassical primordial standard clock, which can also be used to distinguish the\ninflation and alternative scenarios in a model-independent fashion.\n"}
+{"abstract": "  While quantum accelerometers sense with extremely low drift and low bias,\ntheir practical sensing capabilities face two limitations compared with\nclassical accelerometers: a lower sample rate due to cold atom interrogation\ntime, and a reduced dynamic range due to signal phase wrapping. In this paper,\nwe propose a maximum likelihood probabilistic data fusion method, under which\nthe actual phase of the quantum accelerometer can be unwrapped by fusing it\nwith the output of a classical accelerometer on the platform. Consequently, the\nproposed method enables quantum accelerometers to be applied in practical\ninertial navigation scenarios with enhanced performance. The recovered\nmeasurement from the quantum accelerometer is also used to re-calibrate the\nclassical accelerometer. We demonstrate the enhanced error performance achieved\nby the proposed fusion method using a simulated 1D inertial navigation\nscenario. We conclude with a discussion on fusion error and potential\nsolutions.\n"}
+{"abstract": "  The attractor equations for an arbitrary one-parameter family of Calabi-Yau\nmanifolds are studied in the large complex structure region. These equations\nare solved iteratively, generating what we term an N-expansion, which is a\npower series in the Gromov-Witten invariants of the manifold. The coefficients\nof this series are associated with integer partitions. In important cases we\nare able to find closed-form expressions for the general term of this\nexpansion. To our knowledge, these are the first generic solutions to attractor\nequations that incorporate instanton contributions. In particular, we find a\nsimple closed-form formula for the entropy associated to rank two attractor\npoints, including those recently discovered. The applications of our solutions\nare briefly discussed. Most importantly, we are able to give an expression for\nthe Wald entropy of black holes that includes all genus 0 instanton\ncorrections.\n"}
+{"abstract": "  We have investigated the stellar and interstellar content of the distant star\nformation region IRAS 17591-2228 (WISE HII region GAL 007.47+0.06). It is\nassociated to a water maser, whose parallax distance is d=20.4^{+2.8} {-2.2}\nkpc, supported by independent measurements of proper motion and radial\nvelocity. It is projected in the same direction as an extremely red (J-Ks ~ 6\nmag) group of stars, and a shell of mid-infrared emission. We qualify the group\nof stars as a cluster candidate, VVV CL177. Its radius spans between 0.45' and\n1' and contains at least two young stellar objects with an extreme extinction\nnear Av ~ 40 mag. Yet more analysis will be required to determine is it is a\nreal single cluster associated with the water maser. The 13CO emissions at the\nradial velocity of the maser corresponds to the mid-infrared emission.\n"}
+{"abstract": "  We study modulational instability (MI) in optical fibers with random group\nvelocity dispersion (GVD) generated by sharply localized perturbations of a\nnormal GVD fiber that are either randomly or periodically placed along the\nfiber and that have random strength. This perturbation leads to the appearance\nof low frequency MI side lobes that grow with the strength of the\nperturbations, whereas they are faded by randomness in their position. If the\nrandom perturbations exhibit a finite average value, they can be compared with\nperiodically perturbed fibers, where Arnold tongues appear. In that case,\nincreased randomness in the strengths of the variations tends to affect the\nArnold tongues less than increased randomness in their positions.\n"}
+{"abstract": "  With $T_c \\sim 9.6~\\mathrm{K}$, Be$_{22}$Re exhibits one of the highest\ncritical temperatures among Be-rich compounds. We have carried out a series of\nhigh-pressure electrical resistivity measurements on this compound to 30 GPa.\nThe data show that the critical temperature $T_c$ is suppressed gradually at a\nrate of $dT_c/dP = -0.05~\\mathrm{K/GPa}$. Using density functional theory (DFT)\ncalculations of the electronic and phonon density of states (DOS) and the\nmeasured critical temperature, we estimate that the rapid increase in lattice\nstiffening in Be$_{22}$Re overwhelms a moderate increase in the electron-ion\ninteraction with pressure, resulting in the decrease in $T_c$. High pressure\nx-ray diffraction measurements show that the ambient pressure crystal structure\nof Be$_{22}$Re persists to at least 154 GPa. We discuss the relationship\nbetween low-Z Be-rich superconductors and the high-$T_c$ superhydrides.\n"}
+{"abstract": "  Most Video Super-Resolution (VSR) methods enhance a video reference frame by\naligning its neighboring frames and mining information on these frames.\nRecently, deformable alignment has drawn extensive attention in VSR community\nfor its remarkable performance, which can adaptively align neighboring frames\nwith the reference one. However, we experimentally find that deformable\nalignment methods still suffer from fast motion due to locally loss-driven\noffset prediction and lack explicit motion constraints. Hence, we propose a\nMatching-based Flow Estimation (MFE) module to conduct global semantic feature\nmatching and estimate optical flow as coarse offset for each location. And a\nFlow-guided Deformable Module (FDM) is proposed to integrate optical flow into\ndeformable convolution. The FDM uses the optical flow to warp the neighboring\nframes at first. And then, the warped neighboring frames and the reference one\nare used to predict a set of fine offsets for each coarse offset. In general,\nwe propose an end-to-end deep network called Flow-guided Deformable Alignment\nNetwork (FDAN), which reaches the state-of-the-art performance on two benchmark\ndatasets while is still competitive in computation and memory consumption.\n"}
+{"abstract": "  In \\cite{J}, Jab\\l o\\'{n}ski proved that a piecewise expanding $C^{2}$\nmultidimensional Jab\\l o\\'{n}ski map admits an absolutely continuous invariant\nprobability measure (ACIP). In \\cite{BL}, Boyarsky and Lou extended this result\nto the case of i.i.d. compositions of the above maps, with an on average\nexpanding condition. We generalize these results to the (quenched) setting of\nrandom Jab\\l o\\'{n}ski maps, where the randomness is governed by an ergodic,\ninvertible and measure preserving transformation. We prove that the skew\nproduct associated to this random dynamical system admits a finite number of\nergodic ACIPs. Furthermore, we provide two different upper bounds on the number\nof mutually singular ergodic ACIP's, motivated by the works of Buzzi \\cite{B}\nin one dimension and Gora, Boyarsky and Proppe \\cite{GBP} in higher dimensions.\n"}
+{"abstract": "  Assistive robot arms enable people with disabilities to conduct everyday\ntasks on their own. These arms are dexterous and high-dimensional; however, the\ninterfaces people must use to control their robots are low-dimensional.\nConsider teleoperating a 7-DoF robot arm with a 2-DoF joystick. The robot is\nhelping you eat dinner, and currently you want to cut a piece of tofu. Today's\nrobots assume a pre-defined mapping between joystick inputs and robot actions:\nin one mode the joystick controls the robot's motion in the x-y plane, in\nanother mode the joystick controls the robot's z-yaw motion, and so on. But\nthis mapping misses out on the task you are trying to perform! Ideally, one\njoystick axis should control how the robot stabs the tofu and the other axis\nshould control different cutting motions. Our insight is that we can achieve\nintuitive, user-friendly control of assistive robots by embedding the robot's\nhigh-dimensional actions into low-dimensional and human-controllable latent\nactions. We divide this process into three parts. First, we explore models for\nlearning latent actions from offline task demonstrations, and formalize the\nproperties that latent actions should satisfy. Next, we combine learned latent\nactions with autonomous robot assistance to help the user reach and maintain\ntheir high-level goals. Finally, we learn a personalized alignment model\nbetween joystick inputs and latent actions. We evaluate our resulting approach\nin four user studies where non-disabled participants reach marshmallows, cook\napple pie, cut tofu, and assemble dessert. We then test our approach with two\ndisabled adults who leverage assistive devices on a daily basis.\n"}
+{"abstract": "  We showcase applications of nonlinear algebra in the sciences and\nengineering. Our review is organized into eight themes: polynomial\noptimization, partial differential equations, algebraic statistics, integrable\nsystems, configuration spaces of frameworks, biochemical reaction networks,\nalgebraic vision, and tensor decompositions. Conversely, developments on these\ntopics inspire new questions and algorithms for algebraic geometry.\n"}
+{"abstract": "  We present the results of a study of the two-time correlation functions of a\ndichotomously driven two-level system in contact with a thermal bath by using\ncorrections beyond the quantum regression theorem. In the strong\nsystem-environment coupling regime, it is found that the noise parameters could\nbe tuned to control the magnitude of corrections at low environmental\ntemperatures. The motional averaging and narrowing effect of the external noise\nwas observed on the absorption and emission spectra of the two-state system.\nFurthermore, effects similar to the destruction of tunneling and noise\nenhancement of transport are observed in the dynamics of the two-time\ncorrelations.\n"}
+{"abstract": "  Intelligent agents powered by AI planning assist people in complex scenarios,\nsuch as managing teams of semi-autonomous vehicles. However, AI planning models\nmay be incomplete, leading to plans that do not adequately meet the stated\nobjectives, especially in unpredicted situations. Humans, who are apt at\nidentifying and adapting to unusual situations, may be able to assist planning\nagents in these situations by encoding their knowledge into a planner at\nrun-time. We investigate whether people can collaborate with agents by\nproviding their knowledge to an agent using linear temporal logic (LTL) at\nrun-time without changing the agent's domain model. We presented 24\nparticipants with baseline plans for situations in which a planner had\nlimitations, and asked the participants for workarounds for these limitations.\nWe encoded these workarounds as LTL constraints. Results show that\nparticipants' constraints improved the expected return of the plans by 10% ($p\n< 0.05$) relative to baseline plans, demonstrating that human insight can be\nused in collaborative planning for resilience. However, participants used more\ndeclarative than control constraints over time, but declarative constraints\nproduced plans less similar to the expectation of the participants, which could\nlead to potential trust issues.\n"}
+{"abstract": "  Violence against women occurs predominantly in the family and domestic\ncontext. The COVID-19 pandemic led Brazil to recommend and, at times, impose\nsocial distancing, with the partial closure of economic activities, schools,\nand restrictions on events and public services. Preliminary evidence shows that\nintense coexistence increases domestic violence, while social distancing\nmeasures may have prevented access to public services and networks,\ninformation, and help. We propose an agent-based model (ABM), called VIDA, to\nillustrate and examine multi-causal factors that influence events that generate\nviolence. A central part of the model is the multi-causal stress indicator,\ncreated as a probability trigger of domestic violence occurring within the\nfamily environment. Two experimental design tests were performed: (a) absence\nor presence of the deterrence system of domestic violence against women and (b)\nmeasures to increase social distancing. VIDA presents comparative results for\nmetropolitan regions and neighbourhoods considered in the experiments. Results\nsuggest that social distancing measures, particularly those encouraging staying\nat home, may have increased domestic violence against women by about 10%. VIDA\nsuggests further that more populated areas have comparatively fewer cases per\nhundred thousand women than less populous capitals or rural areas of urban\nconcentrations. This paper contributes to the literature by formalising, to the\nbest of our knowledge, the first model of domestic violence through agent-based\nmodelling, using empirical detailed socioeconomic, demographic, educational,\ngender, and race data at the intraurban level (census sectors).\n"}
+{"abstract": "  We report here our finding of two new blueberry galaxies using optical IFU\nspectroscopic data from the MaNGA survey. Both the blueberries are found to be\ncompact ($\\leq$ $1 - 2$ kpc) starburst systems located at the outskirts of Low\nSurface Brightness (LSB) galaxies. Our blueberries have the lowest stellar\nmasses $\\sim$ 10$^{5}$ M$_{\\odot}$ amongst the locally known blueberry\ngalaxies. We find a significantly large mean metallicity difference ($\\sim$ 0.5\ndex) between the blueberry sources and their associated LSBs. Moreover, the\nradial metallicity gradients in our blueberries are also different than their\nrespective LSBs - suggesting that these had significantly different metallicity\nhistories. The likelihood of survival of these blueberries as TDGs is analyzed\nbased on their structural and kinematic properties. Our analysis shows that\nalthough the two blueberries are stable against internal motions, they would\nnot have survived against the tidal force of the host galaxy. Based on the\nvelocity difference between the host LSBs and the blueberries, it appears that\nthey are compact, starburst systems in their advanced stage of merger with\nthese LSBs situated in a dense environment. Implications of our findings are\ndiscussed.\n"}
+{"abstract": "  The Higgs bosons and the top quark decay into rich and diverse final states,\ncontaining both light and heavy quarks, gluons, photons as well as W and Z\nbosons. This article reviews the challenges involved in reconstructing Higgs\nand top events at the FCC-ee and identifies the areas where novel developments\nare needed. The precise identification and reconstruction of these final states\nat the FCC-ee rely on the capability of the detector to provide excellent\nflavour tagging, jet energy and angular resolution, and global kinematic event\nreconstruction. Excellent flavour tagging performance requires low-material\nvertex and tracking detectors, and advanced machine learning techniques as\nsuccessfully employed in LHC experiments. In addition, the Z pole run will\nprovide abundant samples of heavy flavour partons that can be used for\ncalibration of the tagging algorithms. For the reconstruction of jets, leptons,\nand missing energy, particle-flow algorithms are crucial to explore the full\npotential of the highly granular tracking and calorimeter systems, and give\naccess to excellent energy-momentum resolution and precise identification of\nheavy bosons in their hadronic decays. This enables, among many other key\nelements, the reconstruction of Higgsstrahlung processes with leptonically and\nhadronically decaying Z bosons, and an almost background-free identification of\ntop quark pair events. Exploiting the full available kinematic constraints\ntogether with exclusive jet clustering algorithms will allow for the\noptimisation of global event reconstruction with kinematic fitting techniques.\n"}
+{"abstract": "  Accurate characterization of the inner surface of X-ray monocapillary optics\n(XMCO) is of great significance in X-ray optics research. Compared with other\ncharacterization methods, the micro computed tomography (micro-CT) method has\nits unique advantages but also has some disadvantages, such as a long scanning\ntime, long image reconstruction time, and inconvenient scanning process. In\nthis paper, sparse sampling was proposed to shorten the scanning time, GPU\nacceleration technology was used to improve the speed of image reconstruction,\nand a simple geometric calibration algorithm was proposed to avoid the\ncalibration phantom and simplify the scanning process. These methodologies will\npopularize the use of the micro-CT method in XMCO characterization.\n"}
+{"abstract": "  We evaluate the entanglement wedge cross section (EWCS) in asymptotically AdS\ngeometries which are dual to boundary excited states. We carry out a\nperturbative analysis for calculating EWCS between the vacuum and other states\nfor a symmetric configuration consisting of two disjoint strips and obtain\nanalytical results in the specific regimes of the parameter space. In\nparticular, when the states described by purely gravitational excitations in\nthe bulk we find that the leading correction to EWCS is negative and hence the\ncorrelation between the boundary subregions decreases. We also study other\ntypes of excitations upon adding the extra matter fields including current and\nscalar condensate. Our study reveals some generic properties of boundary\ninformation measures dual to EWCS, e.g., entanglement of purification,\nlogarithmic negativity and reflected entropy. Finally, we discuss how these\nresults are consistent with the behavior of other correlation measures\nincluding the holographic mutual information.\n"}
+{"abstract": "  Conversational passage retrieval relies on question rewriting to modify the\noriginal question so that it no longer depends on the conversation history.\nSeveral methods for question rewriting have recently been proposed, but they\nwere compared under different retrieval pipelines. We bridge this gap by\nthoroughly evaluating those question rewriting methods on the TREC CAsT 2019\nand 2020 datasets under the same retrieval pipeline. We analyze the effect of\ndifferent types of question rewriting methods on retrieval performance and show\nthat by combining question rewriting methods of different types we can achieve\nstate-of-the-art performance on both datasets.\n"}
+{"abstract": "  We formulate and prove a local arithmetic Siegel--Weil formula for GSpin\nRapoport--Zink spaces, which is a precise identity between the arithmetic\nintersection numbers of special cycles on GSpin Rapoport--Zink spaces and the\nderivatives of local representation densities of quadratic forms. As a first\napplication, we prove a semi-global arithmetic Siegel--Weil formula as\nconjectured by Kudla, which relates the arithmetic intersection numbers of\nspecial cycles on GSpin Shimura varieties at a place of good reduction and the\ncentral derivatives of nonsingular Fourier coefficients of incoherent Siegel\nEisenstein series.\n"}
+{"abstract": "  Hyers-Ulam stability of the sequence $ \\{z_n\\}_{n \\in \\mathbb{N}} $ generated\nby linear maps is studied. The difference equation $ z_{n+1} = g_n(z_n) $ where\n$ g_n(z) = a_nz + b_n $ and $ a_n \\neq 0 $. Hyers-Ulam stability is determined\nby the the limit of the geometric average of $ a_n $ rather than the type of\neach linear maps. In particular, if $ \\prod_{j=1}^{n-1}|a_j| $ is\nsubexponentially increasing sequence, then $ \\{z_n\\}_{n \\in \\mathbb{N}} $ has\nno Hyers-Ulam stability. Other complementary results are also established with\nproperties of $ \\prod_{j=1}^{n-1}|a_j| $. This result contains Hyers-Ulam\nstability of the first order difference equation with periodic coefficients\nalso.\n"}
+{"abstract": "  This volume of EPTCS contains the proceedings of the Seventh Workshop on\nProof Exchange for Theorem Proving (PxTP 2021), held on 11 July 2021 as part of\nthe CADE-28 online conference in Pittsburgh, USA. The PxTP workshop series\nbrings together researchers working on various aspects of communication,\nintegration, and cooperation between reasoning systems and formalisms, with a\nspecial focus on proofs. The progress in computer-aided reasoning, both\nautomated and interactive, during the past decades, made it possible to build\ndeduction tools that are increasingly more applicable to a wider range of\nproblems and are able to tackle larger problems progressively faster. In recent\nyears, cooperation between such tools in larger systems has demonstrated the\npotential to reduce the amount of manual intervention. Cooperation between\nreasoning systems relies on availability of theoretical formalisms and\npractical tools to exchange problems, proofs, and models. The PxTP workshop\nseries strives to encourage such cooperation by inviting contributions on all\naspects of cooperation between reasoning tools, whether automatic or\ninteractive.\n"}
+{"abstract": "  Recently, representation learning for text and speech has successfully\nimproved many language related tasks. However, all existing methods suffer from\ntwo limitations: (a) they only learn from one input modality, while a unified\nrepresentation for both speech and text is needed by tasks such as end-to-end\nspeech translation, and as a result,(b) they can not exploit various\nlarge-scale text and speech data and their performance is limited by the\nscarcity of parallel speech translation data.To address these problems, we\npropose a Fused Acoustic and Text Masked Language Model (FAT-MLM) which jointly\nlearns a unified representation for both acoustic and text input from various\ntypes of corpora including parallel data for speech recognition and machine\ntranslation, and even pure speech and text data. Within this cross-modal\nrepresentation learning framework, we further present an end-to-end model for\nFused Acoustic and Text Speech Translation (FAT-ST). Experiments on three\ntranslation directions show that by fine-tuning from FAT-MLM, our proposed\nspeech translation models substantially improve translation quality by up to\n+5.9 BLEU.\n"}
+{"abstract": "  In this paper, we propose a novel learnable image encryption method for\nprivacy-preserving deep neural networks (DNNs). The proposed method is carried\nout on the basis of block scrambling used in combination with data augmentation\ntechniques such as random cropping, horizontal flip and grid mask. The use of\nblock scrambling enhances robustness against various attacks, and in contrast,\nthe combination with data augmentation enables us to maintain a high\nclassification accuracy even when using encrypted images. In an image\nclassification experiment, the proposed method is demonstrated to be effective\nin privacy-preserving DNNs.\n"}
+{"abstract": "  The Reynolds-averaged Navier-Stokes (RANS) equations are widely used in\nturbulence applications. They require accurately modeling the anisotropic\nReynolds stress tensor, for which traditional Reynolds stress closure models\nonly yield reliable results in some flow configurations. In the last few years,\nthere has been a surge of work aiming at using data-driven approaches to tackle\nthis problem. The majority of previous work has focused on the development of\nfully-connected networks for modeling the anisotropic Reynolds stress tensor.\nIn this paper, we expand upon recent work for turbulent channel flow and\ndevelop new convolutional neural network (CNN) models that are able to\naccurately predict the normalized anisotropic Reynolds stress tensor. We apply\nthe new CNN model to a number of one-dimensional turbulent flows. Additionally,\nwe present interpretability techniques that help drive the model design and\nprovide guidance on the model behavior in relation to the underlying physics.\n"}
+{"abstract": "  A standard pipeline of current face recognition frameworks consists of four\nindividual steps: locating a face with a rough bounding box and several\nfiducial landmarks, aligning the face image using a pre-defined template,\nextracting representations and comparing. Among them, face detection, landmark\ndetection and representation learning have long been studied and a lot of works\nhave been proposed. As an essential step with a significant impact on\nrecognition performance, the alignment step has attracted little attention. In\nthis paper, we first explore and highlight the effects of different alignment\ntemplates on face recognition. Then, for the first time, we try to search for\nthe optimal template automatically. We construct a well-defined searching space\nby decomposing the template searching into the crop size and vertical shift,\nand propose an efficient method Face Alignment Policy Search (FAPS). Besides, a\nwell-designed benchmark is proposed to evaluate the searched policy.\nExperiments on our proposed benchmark validate the effectiveness of our method\nto improve face recognition performance.\n"}
+{"abstract": "  Deep learning approaches are nowadays ubiquitously used to tackle computer\nvision tasks such as semantic segmentation, requiring large datasets and\nsubstantial computational power. Continual learning for semantic segmentation\n(CSS) is an emerging trend that consists in updating an old model by\nsequentially adding new classes. However, continual learning methods are\nusually prone to catastrophic forgetting. This issue is further aggravated in\nCSS where, at each step, old classes from previous iterations are collapsed\ninto the background. In this paper, we propose Local POD, a multi-scale pooling\ndistillation scheme that preserves long- and short-range spatial relationships\nat feature level. Furthermore, we design an entropy-based pseudo-labelling of\nthe background w.r.t. classes predicted by the old model to deal with\nbackground shift and avoid catastrophic forgetting of the old classes. Finally,\nwe introduce a novel rehearsal method that is particularly suited for\nsegmentation. Our approach, called PLOP, significantly outperforms\nstate-of-the-art methods in existing CSS scenarios, as well as in newly\nproposed challenging benchmarks.\n"}
+{"abstract": "  We consider the Klein-Gordon operator on an $n$-dimensional asymptotically\nanti-de Sitter spacetime $(M,g)$ together with arbitrary boundary conditions\nencoded by a self-adjoint pseudodifferential operator on $\\partial M$ of order\nup to $2$. Using techniques from $b$-calculus and a propagation of\nsingularities theorem, we prove that there exist advanced and retarded\nfundamental solutions, characterizing in addition their structural and\nmicrolocal properties. We apply this result to the problem of constructing\nHadamard two-point distributions. These are bi-distributions which are weak\nbi-solutions of the underlying equations of motion with a prescribed form of\ntheir wavefront set and whose anti-symmetric part is proportional to the\ndifference between the advanced and the retarded fundamental solutions. In\nparticular, under a suitable restriction of the class of admissible boundary\nconditions and setting to zero the mass, we prove their existence extending to\nthe case under scrutiny a deformation argument which is typically used on\nglobally hyperbolic spacetimes with empty boundary.\n"}
+{"abstract": "  We investigate strong field gravitational lensing by rotating Simpson-Visser\nblack hole, which has an additional parameter ($0\\leq l/2M \\leq1$), apart from\nmass ($M$) and rotation parameter ($a$). A rotating Simpson-Visser metric\ncorrespond to (i) a Schwarzschild metric for $l/2M=a/2M=0$ and $ M \\neq 0 $,\n(ii) a Kerr metric for $l/2M=0$, $|a/2M|< 0.5$ and $ M \\neq 0 $ (iii) a\nrotating regular black hole metric for $|a/2M|< 0.5$, $ M \\neq 0 $ and $l/2M$\nin the range $0<l/2M<0.5 + \\sqrt{\\left(0.5\\right)^2-(a/2M)^2}$, and (iv) a\ntraversable wormhole for a $|a/2M| >0.5$ and $l/2M\\neq 0$. We find a decrease\nin the deflection angle $\\alpha_D$ and also in the ratio of the flux of the\nfirst image and all other images $ r_{mag}$. On the other hand, angular\nposition $\\theta_{1}$ increases more slowly and photon sphere radius $x_{m}$\ndecreases more quickly, but angular separation $s$ increases more rapidly, and\ntheir behaviour is similar to that of the Kerr black hole. The formalism is\napplied to discuss the astrophysical consequences in the supermassive black\nholes and find that the rotating Simpson-Visser black holes can be\ndistinguished from the Kerr black hole via gravitational lensing. The deviation\nof the lensing observables $\\Delta\\theta_1$ and $\\Delta s$ of rotating Simpson\nVisser black holes from Kerr black hole for $0<l/2M <0.6$ ($a/2M=0.45$), for\nsupermassive black holes Sgr A* and M87, respectively, are in the range\n$0.0422-0.11658~\\mu$as and $0.031709-0.08758~\\mu$as while $|\\Delta r_{mag}|$ is\nin the range $0.2037 - 0.95668$. It is difficult to distinguish the two black\nholes because the departure are in $\\mathcal{O}(\\mu$as), which are unlikely to\nget resolved by the current EHT observations. We also derive a two-dimensional\nlens equation and formula for deflection angle in the strong field limit by\nfocusing on trajectories close to the equatorial plane.\n"}
+{"abstract": "  We provide a Liouville principle for integration in terms of elliptic\nintegrals. Our methods are essentially those of Abel and Liouville changed to\nmodern notation. We expose Lie theoretic aspect of Liouville's work.\n"}
+{"abstract": "  The Bangla language is the seventh most spoken language, with 265 million\nnative and non-native speakers worldwide. However, English is the predominant\nlanguage for online resources and technical knowledge, journals, and\ndocumentation. Consequently, many Bangla-speaking people, who have limited\ncommand of English, face hurdles to utilize English resources. To bridge the\ngap between limited support and increasing demand, researchers conducted many\nexperiments and developed valuable tools and techniques to create and process\nBangla language materials. Many efforts are also ongoing to make it easy to use\nthe Bangla language in the online and technical domains. There are some review\npapers to understand the past, previous, and future Bangla Natural Language\nProcessing (BNLP) trends. The studies are mainly concentrated on the specific\ndomains of BNLP, such as sentiment analysis, speech recognition, optical\ncharacter recognition, and text summarization. There is an apparent scarcity of\nresources that contain a comprehensive study of the recent BNLP tools and\nmethods. Therefore, in this paper, we present a thorough review of 71 BNLP\nresearch papers and categorize them into 11 categories, namely Information\nExtraction, Machine Translation, Named Entity Recognition, Parsing, Parts of\nSpeech Tagging, Question Answering System, Sentiment Analysis, Spam and Fake\nDetection, Text Summarization, Word Sense Disambiguation, and Speech Processing\nand Recognition. We study articles published between 1999 to 2021, and 50% of\nthe papers were published after 2015. We discuss Classical, Machine Learning\nand Deep Learning approaches with different datasets while addressing the\nlimitations and current and future trends of the BNLP.\n"}
+{"abstract": "  Recently there has been an increased interest to apply the sensitive\n$\\beta$-decay asymmetry detected nuclear magnetic resonance ($\\beta$-NMR)\ntechnique to biological studies. A liquid-sample $\\beta$-NMR setup was build at\nISOLDE to allow such investigations and to use the resolution gain of\nliquid-state NMR in nuclear physics. As part of this setup a magnetic field\nlocking system, a set of printed circuit board shimming coils, a sample\nexchange system, a set of compact $\\beta$-detectors and a custom experimental\nvacuum chamber were developed. The main magnetic field was stabilized down to\nthe ppm level by the locking system while allowing the direct determination of\nthe absolute magnetic field. The homogeneity of the magnetic field was improved\nto $\\leq$~5~ppm over the sample volume by the shimming coils. Time spent on\nchanging samples was reduced by a factor of five by the liquid sample exchange\nsystem. During experiments it was possible to continuously observe the liquid\nsample thanks to the custom chamber and compact $\\beta$-detectors. The absolute\nfield determination allows for a novel way to reference $\\beta$-NMR\nmeasurements, removing the need for time consuming reference measurements. The\nimproved accuracy and resolution resulting from these innovations allows the\nstudy of the distribution of nuclear magnetization and (bio)chemicals using\nhigh-accuracy liquid-sample $\\beta$-NMR.\n"}
+{"abstract": "  The distribution of hot interstellar medium in early-type galaxies bears the\nimprint of the various astrophysical processes it underwent during its\nevolution. The X-ray observations of these galaxies have identified various\nstructural features related to AGN and stellar feedback and environmental\neffects such as merging and sloshing. In our XMM-Newton Galaxy Atlas (NGA)\nproject, we analyze archival observations of 38 ETGs, utilizing the high\nsensitivity and large field of view of XMM-Newton to construct spatially\nresolved 2D spectral maps of the hot gas halos. To illustrate our NGA data\nproducts in conjunction with the Chandra Galaxy Atlas (Kim et al. 2019), we\ndescribe two distinct galaxies - NGC 4636 and NGC 1550, in detail. We discuss\ntheir evolutionary history with a particular focus on the asymmetric\ndistribution of metal-enriched, low-entropy gas caused by sloshing and AGN-\ndriven uplift. We will release the NGA data products to a dedicated website,\nwhich users can download to perform further analyses.\n"}
+{"abstract": "  This work presents twelve fine-tuned deep learning architectures to solve the\nbacterial classification problem over the Digital Image of Bacterial Species\nDataset. The base architectures were mainly published as mobile or efficient\nsolutions to the ImageNet challenge, and all experiments presented in this work\nconsisted of making several modifications to the original designs, in order to\nmake them able to solve the bacterial classification problem by using\nfine-tuning and transfer learning techniques. This work also proposes a novel\ndata augmentation technique for this dataset, which is based on the idea of\nartificial zooming, strongly increasing the performance of every tested\narchitecture, even doubling it in some cases. In order to get robust and\ncomplete evaluations, all experiments were performed with 10-fold\ncross-validation and evaluated with five different metrics: top-1 and top-5\naccuracy, precision, recall, and F1 score. This paper presents a complete\ncomparison of the twelve different architectures, cross-validated with the\noriginal and the augmented version of the dataset, the results are also\ncompared with several literature methods. Overall, eight of the eleven\narchitectures surpassed the 0.95 scores in top-1 accuracy with our data\naugmentation method, being 0.9738 the highest top-1 accuracy. The impact of the\ndata augmentation technique is reported with relative improvement scores.\n"}
+{"abstract": "  The notion of Cyber-Physical-Social System (CPSS) is an emerging concept\ndeveloped as a result of the need to understand the impact of Cyber-Physical\nSystems (CPS) on humans and vice versa. This paradigm shift from CPS to CPSS\nwas mainly attributed to the increasing use of sensor-enabled smart devices and\nthe tight link with the users. The concept of CPSS has been around for over a\ndecade and it has gained increasing attention over the past few years. The\nevolution to incorporate human aspects in the CPS research has unlocked a\nnumber of research challenges. Particularly human dynamics brings additional\ncomplexity that is yet to be explored. The exploration to conceptualise the\nnotion of CPSS has been partially addressed in few scientific literatures.\nAlthough its conceptualisation has always been use-case dependent. Thus, there\nis a lack of generic view as most works focus on specific domains. Furthermore,\nthe systemic core and design principles linking it with the theory of systems\nare loose. This work aims at addressing these issues by first exploring and\nanalysing scientific literature to understand the complete spectrum of CPSS\nthrough a Systematic Literature Review (SLR). Thereby identifying the\nstate-of-the-art perspectives on CPSS regarding definitions, underlining\nprinciples and application areas. Subsequently, based on the findings of the\nSLR, we propose a domain-independent definition and a meta-model for CPSS,\ngrounded in the Theory of Systems. Finally, a discussion on feasible future\nresearch directions is presented based on the systemic notion and the proposed\nmeta-models.\n"}
+{"abstract": "  Robust estimation of a mean vector, a topic regarded as obsolete in the\ntraditional robust statistics community, has recently surged in machine\nlearning literature in the last decade. The latest focus is on the sub-Gaussian\nperformance and computability of the estimators in a non-asymptotic setting.\nNumerous traditional robust estimators are computationally intractable, which\npartly contributes to the renewal of the interest in the robust mean\nestimation.\n  Robust centrality estimators, however, include the trimmed mean and the\nsample median. The latter has the best robustness but suffers a low-efficiency\ndrawback. Trimmed mean and median of means, %as robust alternatives to the\nsample mean, and achieving sub-Gaussian performance have been proposed and\nstudied in the literature.\n  This article investigates the robustness of leading sub-Gaussian estimators\nof mean and reveals that none of them can resist greater than $25\\%$\ncontamination in data and consequently introduces an outlyingness induced\nwinsorized mean which has the best possible robustness (can resist up to $50\\%$\ncontamination without breakdown) meanwhile achieving high efficiency.\nFurthermore, it has a sub-Gaussian performance for uncontaminated samples and a\nbounded estimation error for contaminated samples at a given confidence level\nin a finite sample setting. It can be computed in linear time.\n"}
+{"abstract": "  In this note we prove a spectral gap for various Markov chains on various\nfunctional spaces. While proving that a spectral gap exists is relatively\ncommon, explicit estimates seems somewhat rare.These estimates are then used to\napply the concentration inequalities of \"Effective limit theorems for Markov\nchains with a spectral gap\" (most of the present material was part of Section 3\nof that article, which has been reduced to its core in the published version).\n"}
+{"abstract": "  This paper generalizes the method of deducing Dirac's equation to\nconstructing a family of equations that represent the $N$-th root of the basic\nEnergy-Momentum relation for a free particle \\cite{Dattoli1, Dattoli2,\nDattoli3, Dattoli4}. Then these equations are recast in a form that allows one\nto interpret them as the fundamental dynamical equations for particles with\nfractional spin, which we study in detail for the case of $N=4$ and $N=3$. We\nexplicitly prove that the equation is invariant to rotations and boosts and\nindeed represents spin-$\\frac{3}{8}$ and spin-$\\frac{1}{8}$ particles for $N=4$\nand spin-$\\frac{1}{6}$ and $0$ for $N=3$.\n"}
+{"abstract": "  In this paper, we solve the multiple product price optimization problem under\ninterval uncertainties of the price sensitivity parameters in the demand\nfunction. The objective of the price optimization problem is to maximize the\noverall revenue of the firm where the decision variables are the prices of the\nproducts supplied by the firm. We propose an approach that yields optimal\nsolutions under different variations of the estimated price sensitivity\nparameters. We adopt a robust optimization approach by building a data-driven\nuncertainty set for the parameters, and then construct a deterministic\ncounterpart for the robust optimization model. The numerical results show that\ntwo objectives are fulfilled: the method reflects the uncertainty embedded in\nparameter estimations, and also an interval is obtained for optimal prices. We\nalso conducted a simulation study to which we compared the results of our\napproach. The comparisons show that although robust optimization is deemed to\nbe conservative, the results of the proposed approach show little loss compared\nto those from the simulation.\n"}
+{"abstract": "  This manual describes bch, an efficient program written in the C programming\nlanguage for the fast computation of the Baker-Campbell-Hausdorff (BCH) and\nsimilar Lie series. The Lie series can be represented in the Lyndon basis, in\nthe classical Hall basis, or in the right-normed basis of E.S. Chibrikov. In\nthe Lyndon basis, which proves to be particularly efficient for this purpose,\nthe computation of 111013 coefficients for the BCH series up to terms of degree\n20 takes less than half a second on an ordinary personal computer and requires\nnegligible 11MB of memory. Up to terms of degree 30, which is the maximum\ndegree the program can handle, the computation of 74248451 coefficients takes\n55 hours but still requires only a modest 5.5GB of memory.\n"}
+{"abstract": "  Deep fakes became extremely popular in the last years, also thanks to their\nincreasing realism. Therefore, there is the need to measures human's ability to\ndistinguish between real and synthetic face images when confronted with\ncutting-edge creation technologies. We describe the design and results of a\nperceptual experiment we have conducted, where a wide and diverse group of\nvolunteers has been exposed to synthetic face images produced by\nstate-of-the-art Generative Adversarial Networks (namely, PG-GAN, StyleGAN,\nStyleGAN2). The experiment outcomes reveal how strongly we should call into\nquestion our human ability to discriminate real faces from synthetic ones\ngenerated through modern AI.\n"}
+{"abstract": "  Recently Marcus, Spielman and Srivastava proved Weaver's ${\\rm{KS}}_r$\nconjecture, which gives a positive solution to the Kadison-Singer problem.\nCohen and Br\\\"and\\'en independently extended this result to obtain the\narbitrary-rank version of Weaver's ${\\rm{KS}}_r$ conjecture. In this paper, we\npresent a new bound in Weaver's ${\\rm{KS}}_r$ conjecture for the arbitrary-rank\ncase. To do that, we introduce the definition of $(k,m)$-characteristic\npolynomials and employ it to improve the previous estimate on the largest root\nof the mixed characteristic polynomials. For the rank-one case, our bound\nagrees with the Bownik-Casazza-Marcus-Speegle's bound when $r=2$ and with the\nRavichandran-Leake's bound when $r>2$. For the higher-rank case, we sharpen the\nprevious bounds from Cohen and from Br\\\"and\\'en .\n"}
+{"abstract": "  A CCD photometry of the dwarf nova MASTER OT J172758.09 +380021.5 was carried\nout in 2019 during 134 nights. Observations covered three superoutbursts, five\nnormal outbursts and quiescence between them. The available ASASSN and ZTF data\nfor 2014-2020 were also examined. Spectral observations were done in 2020 when\nthe object was in quiescence. Spectra and photometry revealed that the star is\nan H-rich active ER UMa-type dwarf nova with a highly variable supercycle of\n~50-100 d that implies a high and variable mass-transfer rate. This object\ndemonstrated peculiar behaviour: short-lasted superoutbursts (a week); a slow\nsuperoutburst decline and cases of rebrightenings; low frequency (from none to\na few) of the normal outbursts during the supercycle. In 2019 a mean period of\npositive superhumps was found to be 0.05829 d during the superoutbursts. Late\nsuperhumps with a mean period of 0.057915 d which lasted about ~20 d after the\nend of superoutburst and were replaced by an orbital period of 0.057026 d or\nits orbital-negative superhump beat period were detected. An absence of eclipse\nin the orbital light curve and its moderate amplitude are consistent with the\norbital inclination of about 40 degr found from spectroscopy. The blue peaks of\nthe V-Ic and B-Rc of superhumps during the superoutburst coincided with minima\nof the light curves, while B-Rc of the late superhumps coincided with a rising\nbranch of the light curves. We found that a low mass ratio q=0.08 could explain\nmost of the peculiarities of this dwarf nova. The mass-transfer rate should be\naccordingly higher than what is expected from gravitational radiation only,\nthis assumes the object is in a post-nova state and underwent a nova eruption\nrelatively recently -- hundreds of years ago. This object would provide\nprobably the first observational evidence that a nova eruption can occur even\nin CVs near the period minimum.\n"}
+{"abstract": "  We establish the theory for pretty good state transfer in discrete-time\nquantum walks. For a class of walks, we show that pretty good state transfer is\ncharacterized by the spectrum of certain Hermitian adjacency matrix of the\ngraph; more specifically, the vertices involved in pretty good state transfer\nmust be $m$-strongly cospectral relative to this matrix, and the arccosines of\nits eigenvalues must satisfy some number theoretic conditions. Using normalized\nadjacency matrices, cyclic covers, and the theory on linear relations between\ngeodetic angles, we construct several infinite families of walks that exhibits\nthis phenomenon.\n"}
+{"abstract": "  We study ternary sequences associated with a multidimensional continued\nfraction algorithm introduced by the first author. The algorithm is defined by\ntwo matrices and we show that it is measurably isomorphic to the shift on the\nset $\\{1,2\\}^\\mathbb{N}$ of directive sequences. For a given set $\\mathcal{C}$\nof two substitutions, we show that there exists a $\\mathcal{C}$-adic sequence\nfor every vector of letter frequencies or, equivalently, for every directive\nsequence. We show that their factor complexity is at most $2n+1$ and is $2n+1$\nif and only if the letter frequencies are rationally independent if and only if\nthe $\\mathcal{C}$-adic representation is primitive. It turns out that in this\ncase, the sequences are dendric. We also prove that $\\mu$-almost every\n$\\mathcal{C}$-adic sequence is balanced, where $\\mu$ is any shift-invariant\nergodic Borel probability measure on $\\{1,2\\}^\\mathbb{N}$ giving a positive\nmeasure to the cylinder $[12121212]$. We also prove that the second Lyapunov\nexponent of the matrix cocycle associated with the measure $\\mu$ is negative.\n"}
+{"abstract": "  We classify which local problems with inputs on oriented paths have so-called\nBorel solution and show that this class of problems remains the same if we\ninstead require a measurable solution, a factor of iid solution, or a solution\nwith the property of Baire.\n  Together with the work from the field of distributed computing [Balliu et al.\nPODC 2019], the work from the field of descriptive combinatorics [Gao et al.\narXiv:1803.03872, Bernshteyn arXiv:2004.04905] and the work from the field of\nrandom processes [Holroyd et al. Annals of Prob. 2017, Greb\\'ik, Rozho\\v{n}\narXiv:2103.08394], this finishes the classification of local problems with\ninputs on oriented paths using complexity classes from these three fields.\n  A simple picture emerges: there are four classes of local problems and most\nclasses have natural definitions in all three fields.\n  Moreover, we now know that randomness does \\emph{not} help with solving local\nproblems on oriented paths.\n"}
+{"abstract": "  Dense depth estimation is essential to scene-understanding for autonomous\ndriving. However, recent self-supervised approaches on monocular videos suffer\nfrom scale-inconsistency across long sequences. Utilizing data from the\nubiquitously copresent global positioning systems (GPS), we tackle this\nchallenge by proposing a dynamically-weighted GPS-to-Scale (g2s) loss to\ncomplement the appearance-based losses. We emphasize that the GPS is needed\nonly during the multimodal training, and not at inference. The relative\ndistance between frames captured through the GPS provides a scale signal that\nis independent of the camera setup and scene distribution, resulting in richer\nlearned feature representations. Through extensive evaluation on multiple\ndatasets, we demonstrate scale-consistent and -aware depth estimation during\ninference, improving the performance even when training with low-frequency GPS\ndata.\n"}
+{"abstract": "  Animal behavior and neural recordings show that the brain is able to measure\nboth the intensity of an odor and the timing of odor encounters. However,\nwhether intensity or timing of odor detections is more informative for\nolfactory-driven behavior is not understood. To tackle this question, we\nconsider the problem of locating a target using the odor it releases. We ask\nwhether the position of a target is best predicted by measures of timing\nintensity of its odor, sampled for a short period of time. To answer this\nquestion, we feed data from accurate numerical simulations of odor transport to\nmachine learning algorithms that learn how to connect odor to target location.\nWe find that both intensity and timing can separately predict target location\neven from a distance of several meters; however their efficacy varies with the\ndilution of the odor in space. Thus organisms that use olfaction from different\nranges may have to switch among different modalities. This has implications on\nhow the brain should represent odors as the target is approached. We\ndemonstrate simple strategies to improve accuracy and robustness of the\nprediction by modifying odor sampling and appropriately combining distinct\nmeasures together. To test the predictions, animal behavior and odor\nrepresentation should be monitored as the animal moves relative to the target,\nor in virtual conditions that mimic concentrated dilute environments.\n"}
+{"abstract": "  We report on a flexible 300 mm process that optimally combines optical and\nelectron beam lithography to fabricate silicon spin qubits. It enables\non-the-fly layout design modifications while allowing devices with either n- or\np-type ohmic implants, a pitch smaller than 100 nm, and uniform critical\ndimensions down to 30 nm with a standard deviation ~ 1.6 nm. Various n- and\np-type qubits are characterized in a dilution refrigerator at temperatures ~ 10\nmK. Electrical measurements demonstrate well-defined quantum dots, tunable\ntunnel couplings, and coherent spin control, which are essential requirements\nfor the implementation of a large-scale quantum processor.\n"}
+{"abstract": "  Distant supervision tackles the data bottleneck in NER by automatically\ngenerating training instances via dictionary matching. Unfortunately, the\nlearning of DS-NER is severely dictionary-biased, which suffers from spurious\ncorrelations and therefore undermines the effectiveness and the robustness of\nthe learned models. In this paper, we fundamentally explain the dictionary bias\nvia a Structural Causal Model (SCM), categorize the bias into intra-dictionary\nand inter-dictionary biases, and identify their causes. Based on the SCM, we\nlearn de-biased DS-NER via causal interventions. For intra-dictionary bias, we\nconduct backdoor adjustment to remove the spurious correlations introduced by\nthe dictionary confounder. For inter-dictionary bias, we propose a causal\ninvariance regularizer which will make DS-NER models more robust to the\nperturbation of dictionaries. Experiments on four datasets and three DS-NER\nmodels show that our method can significantly improve the performance of\nDS-NER.\n"}
+{"abstract": "  This chapter presents some novel information theoretic results for the\nanalysis of stationary time series in the frequency domain. In particular, the\nspectral distribution that corresponds to the most uncertain or unpredictable\ntime series with some values of the autocovariance function fixed, is the\ngeneralized von Mises spectral distribution. It is thus a maximum entropy\nspectral distribution and the corresponding stationary time series is called\nthe generalized von Mises time series. The generalized von Mises distribution\nis used in directional statistics for modelling planar directions that follow a\nmultimodal distribution. Furthermore, the Gaussian-generalized von Mises times\nseries is presented as the stationary time series that maximizes entropies in\nfrequency and time domains, respectively referred to as spectral and temporal\nentropies. Parameter estimation and some computational aspects with this time\nseries are briefly analyzed.\n"}
+{"abstract": "  We study the convergence towards a unique equilibrium distribution of the\nsolutions to a time-discrete model with non-overlapping generations arising in\nquantitative genetics. The model describes the dynamics of a phenotypic\ndistribution with respect to a multi-dimensional trait, which is shaped by\nselection and Fisher's infinitesimal model of sexual reproduction. We extend\nsome previous works devoted to the time-continuous analogues, that followed a\nperturbative approach in the regime of weak selection, by exploiting the\ncontractivity of the infinitesimal model operator in the Wasserstein metric.\nHere, we tackle the case of quadratic selection by a global approach. We\nestablish uniqueness of the equilibrium distribution and exponential\nconvergence of the renormalized profile. Our technique relies on an accurate\ncontrol of the propagation of information across the large binary trees of\nancestors (the pedigree chart), and reveals an ergodicity property, meaning\nthat the shape of the initial datum is quickly forgotten across generations. We\ncombine this information with appropriate estimates for the emergence of\nGaussian tails and propagation of quadratic and exponential moments to derive\nquantitative convergence rates. Our result can be interpreted as a\ngeneralization of the Krein-Rutman theorem in a genuinely non-linear, and\nnon-monotone setting.\n"}
+{"abstract": "  In this paper, we generalize text infilling (e.g., masked language models) by\nproposing Sequence Span Rewriting (SSR) as a self-supervised\nsequence-to-sequence (seq2seq) pre-training objective. SSR provides more\nfine-grained learning signals for text representations by supervising the model\nto rewrite imperfect spans to ground truth, and it is more consistent than text\ninfilling with many downstream seq2seq tasks that rewrite a source sentences\ninto a target sentence. Our experiments with T5 models on various seq2seq tasks\nshow that SSR can substantially improve seq2seq pre-training. Moreover, we\nobserve SSR is especially helpful to improve pre-training a small-size seq2seq\nmodel with a powerful imperfect span generator, which indicates a new\nperspective of transferring knowledge from a large model to a smaller model for\nseq2seq pre-training.\n"}
+{"abstract": "  We explore the effects on nuclear bulk properties of using regularization\ncutoffs larger than the nucleon mass within the chiral effective field theory\nusing a power counting that ensures order-by-order renormalization in the\ntwo-nucleon system. To do so we calculate ground-state properties of the\n$^{16}$O nucleus in the Hartree--Fock approach in a basis made up of plane\nwaves confined in a cube. We find a strong sensitivity to the regularization\ncutoff through the counter-terms in attractive singular partial waves and to\nthe correction for spurious deeply bound states. This questions the possibility\nof testing in nuclei the renormalization-group invariance of renormalizable\npotentials from chiral effective field theory at leading order. A possible way\nout of this problem is proposed.\n"}
+{"abstract": "  The bond fluctuation method is used to simulate both non-concatenated\nentangled and interpenetrating melts of ring polymers. We find that the\nswelling of interpenetrating rings upon dilution follows the same laws as for\nlinear chains. Knotting and linking probabilities of ring polymers in\nsemi-dilute solution are analyzed using the HOMFLY polynomial. We find an\nexponential decay of the knotting probability of rings. The correlation length\nof the semi-dilute solution can be used to superimpose knotting data at\ndifferent concentrations. A power law dependence $f_{n}\\sim\\phi\nR^{2}\\sim\\phi^{0.77}N$ for the average number $f_{n}$ of linked rings per ring\nat concentrations larger than the overlap volume fraction of rings $\\phi^{*}$\nis determined from the simulation data. The fraction of non-concatenated rings\ndisplays an exponential decay $P_{OO}\\sim\\exp(-f_{n})$, which indicates $f_{n}$\nto provide the entropic effort for not forming concatenated conformations.\nBased upon this results we find four different regimes for the conformations of\nrings in melts that are separated by a critical lengths $N_{OO}$, $N_{C}$ and\n$N^{*}$. $N_{OO}$ describes the onset of the effect of non-concatenation below\nwhich topological effects are not important, $N_{C}$ is the cross-over between\nweak and strong compression of rings, and $N^{*}$ is defined by the cross-over\nfrom a non-concatenation contribution $f_{n}\\sim\\phi R^{2}$ to an overlap\ndominated concatenation contribution $f_{n}\\sim\\phi N^{1/2}$ at $N>N^{*}$. For\n$N_{OO}<N<N_{C}$, the scaling of ring sizes $R\\sim N^{2/5}$ results from\nbalancing non-concatenation with weak compression of rings. For\n$N_{C}<N<N^{*}$, non-concatenation and strong compression imply $R\\sim\nN^{3/8}$. Our simulation data for non-interpenetrating rings up to $N=1024$ are\nin good agreement with the prediction for weakly compressed rings.\n"}
+{"abstract": "  Some large freight railroads ship a number of shipments over the rail network\nannually. To reduce unnecessary reclassifications of shipments on their routes,\neach railroad is willing to operate the entire train for an individual\nshipment. In other words, the motivation for providing the entire train service\nlies in a simple realization that door to door transportation (directly from\norigin to destination) can reduce operating costs by decreasing classification.\nHowever, this mode will increase inventory costs for customers when commodities\nare transported by low frequency entire train services. Thus, this study\nproposes the trade-off strategy to keep a balance between saving operating\ncosts of railroads and increasing inventory costs of customers. We analyze the\nrevenue and losses after a shipment shifting from the transfer transportation\nwhich contains a series of train services to the direct transportation by\nentire train service.\n"}
+{"abstract": "  We propose that the optomechanical systems can be potential platforms to\nimplement the Fr\\\"{o}hlich condensate of phonons. We consider a one-dimensional\narray of membranes coupled to the cavity field via a quadratic interaction, and\nthe cavity is pumped by an external laser. Analytical and numerical results\npredict that the high phonon occupancy of the lowest or highest mechanical mode\nis achievable depending on the detuning of the driving laser, the optomechnical\nstrength, and the temperature. The decoherence of the Fr\\\"{o}hlich condensate\ncan be largely suppressed by the large number of membranes. Our results shed\nlight on narrow-linewidth phonon laser, energy conversion/transfer, and\nefficient multimode cooling.\n"}
+{"abstract": "  Cryptic crosswords, the dominant crossword variety in the UK, are a promising\ntarget for advancing NLP systems that seek to process semantically complex,\nhighly compositional language. Cryptic clues read like fluent natural language\nbut are adversarially composed of two parts: a definition and a wordplay cipher\nrequiring character-level manipulations. Expert humans use creative\nintelligence to solve cryptics, flexibly combining linguistic, world, and\ndomain knowledge. In this paper, we make two main contributions. First, we\npresent a dataset of cryptic clues as a challenging new benchmark for NLP\nsystems that seek to process compositional language in more creative,\nhuman-like ways. After showing that three non-neural approaches and T5, a\nstate-of-the-art neural language model, do not achieve good performance, we\nmake our second main contribution: a novel curriculum approach, in which the\nmodel is first fine-tuned on related tasks such as unscrambling words.We also\nintroduce a challenging data split, examine the meta-linguistic capabilities of\nsubword-tokenized models, and investigate model systematicity by perturbing the\nwordplay part of clues, showing that T5 exhibits behavior partially consistent\nwith human solving strategies. Although our curricular approach considerably\nimproves on the T5 baseline, our best-performing model still fails to\ngeneralize to the extent that humans can. Thus, cryptic crosswords remain an\nunsolved challenge for NLP systems and a potential source of future innovation.\n"}
+{"abstract": "  China has made great achievements in electric power industry during the\nlong-term deepening of reform and opening up. However, the complex regional\neconomic, social and natural conditions, electricity resources are not evenly\ndistributed, which accounts for the electricity deficiency in some regions of\nChina. It is desirable to develop a robust electricity forecasting model.\nMotivated by which, we propose a Panel Semiparametric Quantile Regression\nNeural Network (PSQRNN) by utilizing the artificial neural network and\nsemiparametric quantile regression. The PSQRNN can explore a potential linear\nand nonlinear relationships among the variables, interpret the unobserved\nprovincial heterogeneity, and maintain the interpretability of parametric\nmodels simultaneously. And the PSQRNN is trained by combining the penalized\nquantile regression with LASSO, ridge regression and backpropagation algorithm.\nTo evaluate the prediction accuracy, an empirical analysis is conducted to\nanalyze the provincial electricity consumption from 1999 to 2018 in China based\non three scenarios. From which, one finds that the PSQRNN model performs better\nfor electricity consumption forecasting by considering the economic and\nclimatic factors. Finally, the provincial electricity consumptions of the next\n$5$ years (2019-2023) in China are reported by forecasting.\n"}
+{"abstract": "  We propose an extension of the Schwinger parametric representation for\nFeynman amplitudes in $D$ euclidean dimensions to a scenario where $d$\ndimensions are compactified ($d<D$) through the introduction of periodic\nboundary conditions in space. We obtain two valid representations, one useful\nnear the bulk (large compactification length) and another useful near the\ndimensional reduction (small compactification length). Also, to illustrate, we\nexhibit some Feynman amplitudes up to three loops in a compactified scalar\nfield theory.\n"}
+{"abstract": "  Double Q-learning is a popular reinforcement learning algorithm in Markov\ndecision process (MDP) problems. Clipped Double Q-learning, as an effective\nvariant of Double Q-learning, employs the clipped double estimator to\napproximate the maximum expected action value. Due to the underestimation bias\nof the clipped double estimator, performance of clipped Double Q-learning may\nbe degraded in some stochastic environments. In this paper, in order to reduce\nthe underestimation bias, we propose an action candidate based clipped double\nestimator for Double Q-learning. Specifically, we first select a set of elite\naction candidates with the high action values from one set of estimators. Then,\namong these candidates, we choose the highest valued action from the other set\nof estimators. Finally, we use the maximum value in the second set of\nestimators to clip the action value of the chosen action in the first set of\nestimators and the clipped value is used for approximating the maximum expected\naction value. Theoretically, the underestimation bias in our clipped Double\nQ-learning decays monotonically as the number of the action candidates\ndecreases. Moreover, the number of action candidates controls the trade-off\nbetween the overestimation and underestimation biases. In addition, we also\nextend our clipped Double Q-learning to continuous action tasks via\napproximating the elite continuous action candidates. We empirically verify\nthat our algorithm can more accurately estimate the maximum expected action\nvalue on some toy environments and yield good performance on several benchmark\nproblems.\n"}
+{"abstract": "  Matching and recommending products is beneficial for both customers and\ncompanies. With the rapid increase in home goods e-commerce, there is an\nincreasing demand for quantitative methods for providing such recommendations\nfor millions of products. This approach is facilitated largely by online stores\nsuch as Amazon and Wayfair, in which the goal is to maximize overall sales.\nInstead of focusing on overall sales, we take a product design perspective, by\nemploying big-data analysis for determining the design qualities of a highly\nrecommended product. Specifically, we focus on the visual style compatibility\nof such products. We build off previous work which implemented a style-based\nsimilarity metric for thousands of furniture products. Using analysis and\nvisualization, we extract attributes of furniture products that are highly\ncompatible style-wise. We propose a designer in-the-loop workflow that mirrors\nmethods of displaying similar products to consumers browsing e-commerce\nwebsites. Our findings are useful when designing new products, since they\nprovide insight regarding what furniture will be strongly compatible across\nmultiple styles, and hence, more likely to be recommended.\n"}
+{"abstract": "  In background dynamics of a spatially flat FLRW model of the universe, we\ninvestigate an interacting scenario of quintessence scalar field as dark energy\nwhich interacts with pressure-less dust as dark matter, mass of which varies\nwith time through scalar field in context of Lyra's geometry. Mass of dark\nmatter particles and the potential of the quintessence field are considered to\nbe functions depending exponentially on scalar field. Cosmological evolution\nequations in this context are converted into an autonomous system of ordinary\ndifferential equations by suitable transformation of the cosmological variables\ninto dimensionless dynamical variables and the nature of critical points are\nfound by applying linear stability theory. We have obtained cosmologically\ninterested critical points which describe the attractor solutions at late\ntimes. Some of which representing the accelerated scalar field-dark matter\nscaling solutions in attractor regime with similar order of energy densities\ncan provide the possible solution of the coincidence problem.\n"}
+{"abstract": "  In order to explore the practical potential and needs of interdisciplinary\nknowledge and competence requirements of Blockchain technology, the project\nactivity \"Development of Interdisciplinary Blockchain Skills Concept\" starts\nwith the literature review identifying the state of the art of Blockchain in\nSupply Chain Management and Logistics, Business and Finance, as well as\nComputer Science and IT-Security. The project activity further explores the\nacademic and industry landscape of existing initiatives in education which\noffer Blockchain courses. Moreover, job descriptions and adverts are analyzed\nin order to specify today's competence requirements from enterprises. To\ndiscuss and define the future required competence, expert workshops are\norganized to validate the findings by academic experts. Based on the research\noutcome and validation, an interdisciplinary approach for Blockchain competence\nis developed.\n  A second part focuses on the development of the Blockchain Best Practices\nactivity while conducting qualitative empirical research based on case studies\nwith industry representatives. Therefore, company interviews, based on the\ntheoretical basis of Output 1, explore existing Blockchain use cases in\ndifferent sectors. Due to the interdisciplinary importance of Blockchain\ntechnology, these skills will be defined by different perspectives of\nBlockchain from across multiple mentioned disciplines. The use cases and\ncompanies for the interviews will be selected based on various sampling\ncriteria to gain results valid for a broad scale. The analysis of the various\nuse cases will be conducted and defined in a standardized format to identify\nthe key drivers and competence requirements for Blockchain technology\napplications and their adoption. On the one hand, this approach ensures\ncomparability, on the other hand, it facilitates the development of a\nstructured and systematic framework.\n"}
+{"abstract": "  Mean-Field Ring Polymer Molecular Dynamics (MF-RPMD) is a powerful,\nefficient, and accurate method for approximate quantum dynamic simulations of\nmulti-level system dynamics. Initial efforts to compute nonadiabatic reaction\nrates using MF-RPMD were not successful; recent work showed that this can be\nremedied by including a simple, if adhoc, correction term that accounts for the\nformation of `kinked' or mixed electronic state ring polymer configurations.\nHere, we build on this idea, introducing a electronic state population based\nreaction coordinate and novel skew dividing surface that constrains nuclear\npositions to configurations where the reactant and product state potentials are\nnear-degenerate and that samples kinked electronic state configurations. We\nthen demonstrate the numerical accuracy of this method in computing rates for a\nseries of nonadiabatic model systems.\n"}
+{"abstract": "  Intravascular ultrasound and optical coherence tomography are widely\navailable for characterizing coronary stenoses and provide critical vessel\nparameters to optimize percutaneous intervention. Intravascular\npolarization-sensitive optical coherence tomography (PS-OCT) simultaneously\nprovides high-resolution cross-sectional images of vascular structures while\nalso revealing preponderant tissue components such as collagen and smooth\nmuscle and thereby enhances plaque characterization. Automated interpretation\nof these features promises to facilitate the objective clinical investigation\nof the natural history and significance of coronary atheromas. Here, we propose\na convolutional neural network model, optimized using a new multi-term loss\nfunction, to classify the lumen, intima, and media layers in addition to the\nguidewire and plaque shadows. We demonstrate that our multi-class\nclassification model outperforms state-of-the-art methods in detecting the\ncoronary anatomical layers. Furthermore, the proposed model segments two\nclasses of common imaging artifacts and detects the anatomical layers within\nthe thickened vessel wall regions that were excluded from analysis by other\nstudies. The source code and the trained model are publicly available at\nhttps://github.com/mhaft/OCTseg\n"}
+{"abstract": "  This work presents a small-signal high-frequency (HF) equivalent circuit (EC)\nto model AC performances of black-phosphorous field-effect transistors\n(BPFETs). The proposed EC is able to describe correctly both the experimental\nHF intrinsic and extrinsic figures of merit, as well as S-parameters, from\ndifferent BPFET technologies. Single- and double-stage radio frequency gain\namplifiers, are designed at 2.4 GHz using the experimentally-calibrated\nsmall-signal BPFET EC. Results show high-gain high-selective BPFET-based\namplifiers.\n"}
+{"abstract": "  We consider a combination of local and nonlocal $p$-Laplace equations and\ndiscuss several regularity properties of weak solutions. More precisely, we\nestablish local boundedness of weak subsolutions, local H\\\"older continuity of\nweak solutions, Harnack inequality for weak solutions and weak Harnack\ninequality for weak supersolutions. We also discuss lower semicontinuity of\nweak supersolutions as well as upper semicontinuity of weak subsolutions. Our\napproach is purely analytic and it is based on the De Giorgi-Nash-Moser theory,\nthe expansion of positivity and estimates involving a tail term. The main\nresults apply to sign changing solutions and capture both local and nonlocal\nfeatures of the equation.\n"}
+{"abstract": "  Modeling realistic fluid and plasma flows is computationally intensive,\nmotivating the use of reduced-order models for a variety of scientific and\nengineering tasks. However, it is challenging to characterize, much less\nguarantee, the global stability (i.e., long-time boundedness) of these models.\nThe seminal work of Schlegel and Noack (JFM, 2015) provided a theorem outlining\nnecessary and sufficient conditions to ensure global stability in systems with\nenergy-preserving, quadratic nonlinearities, with the goal of evaluating the\nstability of projection-based models. In this work, we incorporate this theorem\ninto modern data-driven models obtained via machine learning. First, we propose\nthat this theorem should be a standard diagnostic for the stability of\nprojection-based and data-driven models, examining the conditions under which\nit holds. Second, we illustrate how to modify the objective function in machine\nlearning algorithms to promote globally stable models, with implications for\nthe modeling of fluid and plasma flows. Specifically, we introduce a modified\n\"trapping SINDy\" algorithm based on the sparse identification of nonlinear\ndynamics (SINDy) method. This method enables the identification of models that,\nby construction, only produce bounded trajectories. The effectiveness and\naccuracy of this approach are demonstrated on a broad set of examples of\nvarying model complexity and physical origin, including the vortex shedding in\nthe wake of a circular cylinder.\n"}
+{"abstract": "  We present the confirmation of the eccentric warm giant planet TOI-201 b,\nfirst identified as a candidate in \\textit{TESS} photometry (Sectors 1-8,\n10-13, and 27-28) and confirmed using ground-based photometry from NGTS and\nradial velocities from FEROS, HARPS, CORALIE, and \\textsc{Minerva}-Australis.\nTOI-201 b orbits a young ($\\mathrm{0.87^{+0.46}_{-0.49} \\, Gyr}$) and\nbright(V=9.07 mag) F-type star with a $\\mathrm{52.9781 \\, d}$ period. The\nplanet has a mass of $\\mathrm{0.42^{+0.05}_{-0.03}\\, M_J}$, a radius of\n$\\mathrm{1.008^{+0.012}_{-0.015}\\, R_J}$, and an orbital eccentricity of\n$0.28^{+0.06}_{-0.09}$; it appears to still be undergoing fairly rapid cooling,\nas expected given the youth of the host star. The star also shows long-term\nvariability in both the radial velocities and several activity indicators,\nwhich we attribute to stellar activity. The discovery and characterization of\nwarm giant planets such as TOI-201 b is important for constraining formation\nand evolution theories for giant planets.\n"}
+{"abstract": "  Data augmentation has been an indispensable tool to improve the performance\nof deep neural networks, however the augmentation can hardly transfer among\ndifferent tasks and datasets. Consequently, a recent trend is to adopt AutoML\ntechnique to learn proper augmentation policy without extensive hand-crafted\ntuning. In this paper, we propose an efficient differentiable search algorithm\ncalled Direct Differentiable Augmentation Search (DDAS). It exploits\nmeta-learning with one-step gradient update and continuous relaxation to the\nexpected training loss for efficient search. Our DDAS can achieve efficient\naugmentation search without relying on approximations such as Gumbel Softmax or\nsecond order gradient approximation. To further reduce the adverse effect of\nimproper augmentations, we organize the search space into a two level\nhierarchy, in which we first decide whether to apply augmentation, and then\ndetermine the specific augmentation policy. On standard image classification\nbenchmarks, our DDAS achieves state-of-the-art performance and efficiency\ntradeoff while reducing the search cost dramatically, e.g. 0.15 GPU hours for\nCIFAR-10. In addition, we also use DDAS to search augmentation for object\ndetection task and achieve comparable performance with AutoAugment, while being\n1000x faster.\n"}
+{"abstract": "  Multi-agent path finding (MAPF) determines an ensemble of collision-free\npaths for multiple agents between their respective start and goal locations.\nAmong the available MAPF planners for workspace modeled as a graph, A*-based\napproaches have been widely investigated due to their guarantees on\ncompleteness and solution optimality, and have demonstrated their efficiency in\nmany scenarios. However, almost all of these A*-based methods assume that each\nagent executes an action concurrently in that all agents start and stop\ntogether. This article presents a natural generalization of MAPF with\nasynchronous actions (MAPF-AA) where agents do not necessarily start and stop\nconcurrently. The main contribution of the work is a proposed approach called\nLoosely Synchronized Search (LSS) that extends A*-based MAPF planners to handle\nasynchronous actions. We show LSS is complete and finds an optimal solution if\none exists. We also combine LSS with other existing MAPF methods that aims to\ntrade-off optimality for computational efficiency. Numerical results are\npresented to corroborate the performance of LSS and the applicability of the\nproposed method is verified in the Robotarium, a remotely accessible swarm\nrobotics research platform.\n"}
+{"abstract": "  By definition, reciprocal matrices are tridiagonal $n$-by-$n$ matrices $A$\nwith constant main diagonal and such that $a_{i,i+1}a_{i+1,i}=1$ for\n$i=1,\\ldots,n-1$. For $n\\leq 6$, we establish criteria under which the\nnumerical range generating curves (also called Kippenhahn curves) of such\nmatrices consist of elliptical components only. As a corollary, we also provide\na complete description of higher-rank numerical ranges when the criteria are\nmet.\n"}
+{"abstract": "  CoMoGAN is a continuous GAN relying on the unsupervised reorganization of the\ntarget data on a functional manifold. To that matter, we introduce a new\nFunctional Instance Normalization layer and residual mechanism, which together\ndisentangle image content from position on target manifold. We rely on naive\nphysics-inspired models to guide the training while allowing private\nmodel/translations features. CoMoGAN can be used with any GAN backbone and\nallows new types of image translation, such as cyclic image translation like\ntimelapse generation, or detached linear translation. On all datasets, it\noutperforms the literature. Our code is available at\nhttp://github.com/cv-rits/CoMoGAN .\n"}
+{"abstract": "  In this paper, we consider the predual spaces of weak Orlicz spaces. As an\napplication, we provide the Fefferman-Stein vector-valued maximal inequality\nfor the weak Orlicz spaces. In order to prove this statement, we introduced the\nOrlicz-Lorentz spaces, and showed the boundedness of the Hardy-Littlewood\nmaximal operator on these spaces.\n"}
+{"abstract": "  Domain-specific quantitative modeling and analysis approaches are fundamental\nin scenarios in which qualitative approaches are inappropriate or unfeasible.\nIn this paper, we present a tool-supported approach to quantitative graph-based\nsecurity risk modeling and analysis based on attack-defense trees. Our approach\nis based on QFLan, a successful domain-specific approach to support\nquantitative modeling and analysis of highly configurable systems, whose\ndomain-specific components have been decoupled to facilitate the instantiation\nof the QFLan approach in the domain of graph-based security risk modeling and\nanalysis. Our approach incorporates distinctive features from three popular\nkinds of attack trees, namely enhanced attack trees, capabilities-based attack\ntrees and attack countermeasure trees, into the domain-specific modeling\nlanguage. The result is a new framework, called RisQFLan, to support\nquantitative security risk modeling and analysis based on attack-defense\ndiagrams. By offering either exact or statistical verification of probabilistic\nattack scenarios, RisQFLan constitutes a significant novel contribution to the\nexisting toolsets in that domain. We validate our approach by highlighting the\nadditional features offered by RisQFLan in three illustrative case studies from\nseminal approaches to graph-based security risk modeling analysis based on\nattack trees.\n"}
+{"abstract": "  Application domains of Bayesian optimization include optimizing black-box\n  functions or very complex functions. The functions we are interested in\ndescribe\n  complex real-world systems applied in industrial settings. Even though\n  they do have explicit representations, standard optimization\n  techniques fail to provide validated solutions and correctness\n  guarantees for them.\n  In this paper we present a combination of Bayesian optimisation and SMT-based\nconstraint solving to achieve safe and stable solutions with optimality\nguarantees.\n"}
+{"abstract": "  Nitrogen is one of the most abundant metals in the interstellar medium (ISM),\nand thus it constitutes an excellent test to study a variety of astrophysical\nenvironments, ranging from nova to active galactic nuclei. We present a\ndetailed analysis of the gaseous component of the N K~edge using\nhigh-resolution {\\it XMM-Newton} spectra of 12 Galactic and 40 extragalactic\nsources. For each source, we have estimated column densities for {\\rm N}~{\\sc\ni}, {\\rm N}~{\\sc ii}, {\\rm N}~{\\sc iii}, {\\rm N}~{\\sc v}, {\\rm N}~{\\sc vi} and\n{\\rm N}~{\\sc vii} ionic species, which trace the cold, warm and hot phases of\nthe local Galactic interstellar medium. We have found that the cold-warm\ncomponent column densities decrease with the Galactic latitude while the hot\ncomponent does not. Moreover, the cold column density distribution is in good\nagreement with UV measurements. This is the first detailed analysis of the\nnitrogen K-edge absorption due to ISM using high-resolution X-ray spectra.\n"}
+{"abstract": "  Traditional academic competitions that foster collaboration from student\ncommunities all around the globe have either been postponed indefinitely or\ncancelled due to restrictions posed upon in-person gathering. Owing to this,\nvirtual competitions are gaining importance as they provide the student\ncommunity with an outlet for exposure and academic growth whilst having the\nconvenience of being online and safe. Mars Society South Asia (MSSA) has\ndeveloped a software stack capable of replicating on-site competition tasks via\nROS-based simulations. The software enables users to perform necessary\nsimulations without them having to interact with ROS or Gazebo. Currently, the\nsoftware has been set out to be used for the Virtual Mars Rover Challenge\n(VMRC), a competition that focuses on simulating the next generation of Mars\nrovers and thereby providing the student community with a substitute to their\non-site competitions. The objective of this white paper is to explain the\nsoftware architecture, installation, usage, maintenance and ease of use which\nis the singular most important factor in software adoption.\n"}
+{"abstract": "  Diabetes is one of the deadliest diseases in the world and affects nearly 10\npercent of the global adult population. Fortunately, powerful new technologies\nallow for a consistent and reliable treatment plan for people with diabetes.\nOne major development is a system called continuous blood glucose monitoring\n(CGM). In this review, we look at three different continuous meal detection\nalgorithms that were developed using given CGM data from patients with\ndiabetes. From this analysis, an initial meal prediction algorithm was also\ndeveloped utilizing these methods.\n"}
+{"abstract": "  The transmission system operators (TSOs) are responsible to provide secure\nand efficient access to the transmission system for all stakeholders. This task\nis gradually getting challenging due to the demand growth, demand uncertainty,\nrapid changes in generation mix, and market policies. Traditionally, the TSOs\ntry to maximize the technical performance of the transmission network via\nbuilding new overhead lines or physical hardening. However, obtaining public\nacceptance for building new lines is not an easy step in this procedure. For\nthis reason, the TSOs try to capture the capabilities of existing assets. This\npaper investigates the use of modular FACTS devices (to alter the line\ncharacteristics) for improving the capability of transmission network in\nserving the uncertain demand without the need for building new overhead lines.\nThe proposed method considers the uncertainty of demands and controls the\nutilization of existing transmission assets. The mathematical results obtained\nare validated with a complete non-linear simulation model of three transmission\nnetworks.\n"}
+{"abstract": "  We consider the time-convex hull problem in the presence of two orthogonal\nhighways H. In this problem, the travelling speed on the highway is faster than\noff the highway, and the time-convex hull of a point set P is the closure of P\nwith respect to the inclusion of shortest time-paths. In this paper, we provide\nthe algorithm for constructing the time-convex hull with two orthogonal\nhighways. We reach the optimal result of O(n log n) time for arbitrary highway\nspeed in the L1-metric. For the L2-metric with infinite highway speed, we hit\nthe goal of O(n log n) time as well.\n"}
+{"abstract": "  Structural credit assignment for recurrent learning is challenging. An\nalgorithm called RTRL can compute gradients for recurrent networks online but\nis computationally intractable for large networks. Alternatives, such as BPTT,\nare not online. In this work, we propose a credit-assignment algorithm --\n\\algoname{} -- that approximates the gradients for recurrent learning in\nreal-time using $O(n)$ operations and memory per-step. Our method builds on the\nidea that for modular recurrent networks, composed of columns with scalar\nstates, it is sufficient for a parameter to only track its influence on the\nstate of its column. We empirically show that as long as connections between\ncolumns are sparse, our method approximates the true gradient well. In the\nspecial case when there are no connections between columns, the $O(n)$ gradient\nestimate is exact. We demonstrate the utility of the approach for both\nrecurrent state learning and meta-learning by comparing the estimated gradient\nto the true gradient on a synthetic test-bed.\n"}
+{"abstract": "  We show that for every countable recursively saturated model $M$ of Peano\nArithmetic and every subset $A \\subseteq M$, there exists a full satisfaction\nclass $S_A \\subset M^2$ such that $A$ is definable in $(M,S_A)$ without\nparametres. It follows that in every such model, there exists a full\nsatisfaction class which makes every element definable and thus the expanded\nmodel is minimal and rigid. On the other hand, we show that for every full\nsatisfaction class $S$ there are two elements which have the same arithmetical\ntype, but exactly one of them is in $S$. In particular, the automorphism group\nof a model expanded with a satisfaction class is never equal to the\nautomorphism group of the original model. The analogue of many of the results\nproved here for full satisfaction classes were obtained by Roman Kossak for\npartial inductive satisfaction classes. However, most of the proofs relied\nheavily on the induction scheme in a crucial way, so recapturing the results in\nthe setting of full satisfaction classes requires quite different arguments.\n"}
+{"abstract": "  The purpose of this paper is to illustrate the problem of energy and momentum\ndistributions of Van Stockum space-time within the framework of two different\ntheories of gravity, general relativity and teleparallel gravity.\n  We have shown that for all homogeneous space-times with metric components\n$g_{\\mu\\nu}$ being functions of time variable, $t$, alone and independent of\nspace variables the total gravitational energy for any finite volume is\nidentically zero. By working with general relativity, we have calculated the\nenergy-momentum density for Van Stockum space-time using double index complexes\nand in the framework teleparallel gravity, we used the energy-momentum\ncomplexes of Einstein, Bergmann-Thomson and Landau-Lifshitz. In our analysis,\nwe sustained that general relativity and teleparallel gravity are equivalent\ntheories of space-time under consideration. For space-time under consideration,\nwe have shown that different complexes of energy-momentum density do not\nprovide the same results neither in general relativity nor in teleparallel\ngravity.\n"}
+{"abstract": "  In this work we marry multi-index Monte Carlo with ensemble Kalman filtering\n(EnKF) to produce the multi-index EnKF method (MIEnKF). The MIEnKF method is\nbased on independent samples of four-coupled EnKF estimators on a multi-index\nhierarchy of resolution levels, and it may be viewed as an extension of the\nmultilevel EnKF (MLEnKF) method developed by the same authors in 2020.\nMulti-index here refers to a two-index method, consisting of a hierarchy of\nEnKF estimators that are coupled in two degrees of freedom: time discretization\nand ensemble size. Under certain assumptions, the MIEnKF method is proven to be\nmore tractable than EnKF and MLEnKF, and this is also verified in numerical\nexamples.\n"}
+{"abstract": "  The modeling of fast flavor evolution of neutrinos in dense environments has\nbeen traditionally carried out by relying on a two flavor approximation for\nsimplicity. In addition, vacuum mixing has been deemed negligible. For the\nfirst time, we highlight that the fast flavor evolution in three flavors is\nintrinsically different from the one obtained in the two flavor approximation.\nThis is due to the exponential growth of flavor mixing in the $e$--$\\mu$ and\n$e$--$\\tau$ sectors generated by the vacuum term in the Hamiltonian. As a\nresult, substantially larger flavor mixing is found in three flavors. Our\nfindings highlight that the two flavor approximation is not justified for fast\npairwise conversion, even if the angular distributions of non-electron type\nneutrinos are initially identical.\n"}
+{"abstract": "  Our goal in this work is to generate realistic videos given just one initial\nframe as input. Existing unsupervised approaches to this task do not consider\nthe fact that a video typically shows a 3D environment, and that this should\nremain coherent from frame to frame even as the camera and objects move. We\naddress this by developing a model that first estimates the latent 3D structure\nof the scene, including the segmentation of any moving objects. It then\npredicts future frames by simulating the object and camera dynamics, and\nrendering the resulting views. Importantly, it is trained end-to-end using only\nthe unsupervised objective of predicting future frames, without any 3D\ninformation nor segmentation annotations. Experiments on two challenging\ndatasets of natural videos show that our model can estimate 3D structure and\nmotion segmentation from a single frame, and hence generate plausible and\nvaried predictions.\n"}
+{"abstract": "  Charge density waves (CDW) are modulations of the electron density and the\natomic lattice that develop in some crystalline materials at low temperature.\nWe report an unusual example of a CDW in BaFe$_2$Al$_9$ below 100 K. In\ncontrast to the canonical CDW phase transition, temperature dependent physical\nproperties of single crystals reveal a first-order phase transition. This is\naccompanied by a discontinuous change in the size of the crystal lattice. In\nfact, this large strain has catastrophic consequences for the crystals causing\nthem to physically shatter. Single crystal x-ray diffraction reveals\nsuper-lattice peaks in the low-temperature phase signaling the development of a\nCDW lattice modulation. No similar low-temperature transitions are observed in\nBaCo$_2$Al$_9$. Electronic structure calculations provide one hint to the\ndifferent behavior of these two compounds; the d-orbital states in the Fe\ncompound are not completely filled. Iron compounds are renowned for their\nmagnetism and partly filled d-states play a key role. It is therefore\nsurprising that BaFe$_2$Al$_9$ develops a structural modulation instead at low\ntemperature instead of magnetic order.\n"}
+{"abstract": "  We study the scattering of open-beauty mesons and Goldstone bosons as\npredicted by the chiral SU(3) Lagrangian. The impact of subleading order chiral\ninteractions to systems with $J^P= 0^+$ and $J^P=1^+$ quantum numbers is worked\nout. We estimate the relevant low-energy coefficients from the open-charm\nsector, for which their values have been determined previously from sets of QCD\nlattice data. The leading order heavy-quark symmetry breaking effects are\nestimated by matching the $B$-meson ground-state chiral mass formula to the\nmass formula from the heavy-quark effective theory. We make refined predictions\nfor the flavor anti-triplet and sextet resonances that are generated\ndynamically by coupled-channel interactions.\n"}
+{"abstract": "  The cold start problem in recommender systems is a long-standing challenge,\nwhich requires recommending to new users (items) based on attributes without\nany historical interaction records. In these recommendation systems, warm users\n(items) have privileged collaborative signals of interaction records compared\nto cold start users (items), and these Collaborative Filtering (CF) signals are\nshown to have competing performance for recommendation. Many researchers\nproposed to learn the correlation between collaborative signal embedding space\nand the attribute embedding space to improve the cold start recommendation, in\nwhich user and item categorical attributes are available in many online\nplatforms. However, the cold start recommendation is still limited by two\nembedding spaces modeling and simple assumptions of space transformation. As\nuser-item interaction behaviors and user (item) attributes naturally form a\nheterogeneous graph structure, in this paper, we propose a privileged graph\ndistillation model~(PGD). The teacher model is composed of a heterogeneous\ngraph structure for warm users and items with privileged CF links. The student\nmodel is composed of an entity-attribute graph without CF links. Specifically,\nthe teacher model can learn better embeddings of each entity by injecting\ncomplex higher-order relationships from the constructed heterogeneous graph.\nThe student model can learn the distilled output with privileged CF embeddings\nfrom the teacher embeddings. Our proposed model is generally applicable to\ndifferent cold start scenarios with new user, new item, or new user-new item.\nFinally, extensive experimental results on the real-world datasets clearly show\nthe effectiveness of our proposed model on different types of cold start\nproblems, with average $6.6\\%, 5.6\\%, $ and $17.1\\%$ improvement over\nstate-of-the-art baselines on three datasets, respectively.\n"}
+{"abstract": "  We study discrete-time mirror descent applied to the unregularized empirical\nrisk in matrix sensing. In both the general case of rectangular matrices and\nthe particular case of positive semidefinite matrices, a simple potential-based\nanalysis in terms of the Bregman divergence allows us to establish convergence\nof mirror descent -- with different choices of the mirror maps -- to a matrix\nthat, among all global minimizers of the empirical risk, minimizes a quantity\nexplicitly related to the nuclear norm, the Frobenius norm, and the von Neumann\nentropy. In both cases, this characterization implies that mirror descent, a\nfirst-order algorithm minimizing the unregularized empirical risk, recovers\nlow-rank matrices under the same set of assumptions that are sufficient to\nguarantee recovery for nuclear-norm minimization. When the sensing matrices are\nsymmetric and commute, we show that gradient descent with full-rank factorized\nparametrization is a first-order approximation to mirror descent, in which case\nwe obtain an explicit characterization of the implicit bias of gradient flow as\na by-product.\n"}
+{"abstract": "  An increase in radiotherapy-induced secondary malignancies has led to recent\ndevelopments in analytical modelling of out-of-field dose. These models must be\nvalidated against measurements, but currently available datasets are outdated\nor limited in scope. This study aimed to address these shortcomings by\nproducing a large dataset of out-of-field dose profiles measured with modern\nequipment. A novel method was developed with the intention of allowing\nphysicists in all clinics to perform these measurements themselves using\ncommonly available dosimetry equipment. A standard 3D scanning water tank was\nused to collect 36 extended profiles. Each profile was measured in two\nsections, with the inner section measured with the beam directly incident on\nthe tank, and the outer section with the beam incident on a water-equivalent\nphantom abutted next to the tank. The two sections were then stitched using a\nnovel feature-matching approach. The profiles were compared against linac\ncommissioning data and manually inspected for discontinuities in the overlap\nregion. The dataset is presented as a publicly accessible comma separated\nvariable file containing off-axis ratios at a range of off-axis distances. This\ndataset may be applied to the development and validation of analytical models\nof out-of-field dose. Additionally, it may be used to inform dose estimates to\nradiosensitive implants and anatomy. Physicists are encouraged to perform these\nout-of-field measurements in their own clinics and share their results with the\ncommunity.\n"}
+{"abstract": "  We come back to the construction of p-adic L-functions attached to cusp forms\nof even weight k in the spirit of G. Stevens, R. Pollack [7] and M. Greenberg\n[3] with a new unified presentation including the non-ordinary case. This\nconstruction is based on Stevens's modular symbols rather than q-developments.\nWe review the proofs in order to obtain an effective algorithm guaranteeing a\ngiven p-adic accuracy.\n"}
+{"abstract": "  In this paper, we propose self-reorganizing and rejuvenating convolutional\nneural networks; a biologically inspired method for improving the computational\nresource utilization of neural networks. The proposed method utilizes the\nchannel activations of a convolution layer in order to reorganize that layers\nparameters. The reorganized parameters are clustered to avoid parameter\nredundancies. As such, redundant neurons with similar activations are merged\nleaving room for the remaining parameters to rejuvenate. The rejuvenated\nparameters learn different features to supplement those learned by the\nreorganized surviving parameters. As a result, the network capacity utilization\nincreases improving the baseline network performance without any changes to the\nnetwork structure. The proposed method can be applied to various network\narchitectures during the training stage, or applied to a pre-trained model\nimproving its performance. Experimental results showed that the proposed method\nis model-agnostic and can be applied to any backbone architecture increasing\nits performance due to the elevated utilization of the network capacity.\n"}
+{"abstract": "  Let $k$ be a field of characteristic two. We prove that a non constant monic\npolynomial $f\\in k[X]$ of degree $n$ is the minimal/characteristic polynomial\nof a symmetric matrix with entries in $k$ if and only if it is not the product\nof pairwise distinct inseparable irreducible polynomials. In this case, we\nprove that $f$ is the minimal polynomial of a symmetric matrix of size $n$. We\nalso prove that any element $\\alpha\\in k_{alg}$ of degree $n\\geq 1$ is the\neigenvalue of a symmetrix matrix of size $n$ or $n+1$, the first case happening\nif and only if the minimal polynomial of $\\alpha$ is not the product of\npairwise distinct inseparable irreducible polynomials.\n"}
+{"abstract": "  A concise method of deriving expressions for Gaussian-like solutions of the\nparaxial and d'Alembert equations is presented. This method is based on the\nHankel transform. Choosing some Gaussian base functions with slight\nmodifications of the prefactor all basic beams of cylindrical character can be\neasily obtained. This refers to Gaussian, Bessel-Gaussian, modified\nBessel-Gaussian, Laguerre-Gaussian and Kummer-Gaussian (i.e.,\nHypergeometric-Gaussian) beams although potentially other beams can come into\nplay as well. For instance a new type of a beam that can be derived in this way\nis described through the incomplete gamma function so it may be called a\n$\\gamma$ beam.\n"}
+{"abstract": "  The latest measurement of the muon anomalous magnetic moment $a^{}_{\\mu}\n\\equiv (g^{}_\\mu - 2)/2$ at the Fermi Laboratory has found a $4.2\\,\\sigma$\ndiscrepancy with the theoretical prediction of the Standard Model (SM).\nMotivated by this exciting progress, we investigate in the present paper the\ngeneral one-loop contributions to $a^{}_\\mu$ within the SM and beyond. First,\ndifferent from previous works, the analytical formulae of relevant loop\nfunctions after integration are now derived and put into compact forms with the\nhelp of the Passarino-Veltman functions. Second, given the interactions of muon\nwith new particles running in the loop, we clarify when the one-loop\ncontribution to $a^{}_\\mu$ could take the correct positive sign as desired.\nThird, possible divergences in the zero- and infinite-mass limits are examined,\nand the absence of any divergences in the calculations leads to some\nconsistency conditions for the construction of ultraviolet complete models.\nApplications of our general formulae to specific models, such as the SM, seesaw\nmodels, $Z^\\prime$ and leptoquark models, are also discussed.\n"}
+{"abstract": "  To the best of our knowledge, there are no specific calculations of\ngravity-darkening exponents for white dwarfs in the literature. On the other\nhand, the number of known eclipsing binaries whose components are tidally\nand/or rotationally distorted white dwarfs is increasing year on year.\n  Our main objective is to present the first theoretical approaches to the\nproblem of the distribution of temperatures on the surfaces of compact stars\ndistorted by rotation and/or tides in order to compare with relevant\nobservational data.\n  We find discrepancies between the gravity-darkening exponents calculated with\nour methods and the predictions of the von Zeipel theorem, particularly in the\ncases of cold white dwarfs; although the discrepancy also applies to higher\neffective temperatures under determined physical conditions. We find physical\nconnections between the gravity-darkening exponents calculated using our\nmodified method of triangles strategy with the convective efficiency (defined\nhere as the ratio of the convective to the total flux). A connection between\nthe entropy and the gravity-darkening coefficients is also found: variations of\nthe former cause changes in the way the temperature is distributed on distorted\nstellar surfaces.\n  On the other hand, we have generalised the von Zeipel theorem for the case of\nhot white dwarfs. Such a generalisation allows us to predict that, under\ncertain circumstances, the value of the gravity-darkening exponent may be\nsmaller than 1.0, even in the case of high effective temperatures.\n"}
+{"abstract": "  We outline the development of a general-purpose Python-based data analysis\ntool for OpenFOAM. Our implementation relies on the construction of OpenFOAM\napplications that have bindings to data analysis libraries in Python. Double\nprecision data in OpenFOAM is cast to a NumPy array using the NumPy C-API and\nPython modules may then be used for arbitrary data analysis and manipulation on\nflow-field information. We highlight how the proposed wrapper may be used for\nan in-situ online singular value decomposition (SVD) implemented in Python and\naccessed from the OpenFOAM solver PimpleFOAM. Here, `in-situ' refers to a\nprogramming paradigm that allows for a concurrent computation of the data\nanalysis on the same computational resources utilized for the partial\ndifferential equation solver. In addition, to demonstrate data-parallel\nanalyses, we deploy a distributed SVD, which collects snapshot data across the\nranks of a distributed simulation to compute the global left singular vectors.\nCrucially, both OpenFOAM and Python share the same message passing interface\n(MPI) communicator for this deployment which allows Python objects and\nfunctions to exchange NumPy arrays across ranks. Subsequently, we provide\nscaling assessments of this distributed SVD on multiple nodes of Intel\nBroadwell and KNL architectures for canonical test cases such as the large eddy\nsimulations of a backward facing step and a channel flow at friction Reynolds\nnumber of 395. Finally, we demonstrate the deployment of a deep neural network\nfor compressing the flow-field information using an autoencoder to demonstrate\nan ability to use state-of-the-art machine learning tools in the Python\necosystem.\n"}
+{"abstract": "  Let $(f_1,f_2)$ a $2$-tuple of arithmetic functions and\n$$\\Delta_3(n,f_1,f_2):=\\sup\\limits_{\\substack{(u_1,u_2) \\in \\mathbb{R}^{2}\n\\\\(v_1,v_2) \\in [0,1]^{2}}}\\Big\\lvert \\sum\\limits_{\\substack{d_1 d_{2} \\mid n\n\\\\ e^{u_i}<d_i\\leqslant e^{u_i+v_i}}}{f_1(d_1) f_{2}(d_{2})} \\Big\\rvert{\\rm\n.}$$ In this paper, we give an upper bound of the second moment of\n$\\Delta_3(n,\\chi_1,\\chi_2)$ when $\\chi_1$ and $\\chi_2$ are two non principal\nDirichlet characters, following methods developed by La Bret\\`eche and\nTenenbaum. This upper bound is a main step for the asymptotic counting of the\nnumber of ideals of norm fixed, which will be developped in another article.\n"}
+{"abstract": "  We review 15 years of theoretical and experimental work on the non-linear\nresponse of glassy systems. We argue that an anomalous growth of the peak value\nof non-linear susceptibilities is a signature of growing \"amorphous order\" in\nthe system, with spin-glasses as a case in point. Experimental results on\nsupercooled liquids are fully compatible with the RFOT prediction of compact\n\"glassites\" of increasing volume as temperature is decreased, or as the system\nages. We clarify why such a behaviour is hard to explain within purely kinetic\ntheories of glass formation, despite recent claims to the contrary.\n"}
+{"abstract": "  The outer crust of a cold nonaccreting neutron star has been generally\nassumed to be stratified into different layers, each of which consists of a\npure body-centered cubic ionic crystal in a charge compensating background of\nhighly degenerate electrons. The validity of this assumption is examined by\nanalyzing the stability of multinary ionic compounds in dense stellar matter.\nIt is thus shown that their stability against phase separation is uniquely\ndetermined by their structure and their composition irrespective of the stellar\nconditions. However, equilibrium with respect to weak and strong nuclear\nprocesses imposes very stringent constraints on the composition of multinary\ncompounds, and thereby on their formation. By examining different cubic and\nnoncubic lattices, it is found that substitutional compounds having the same\nstructure as cesium chloride are the most likely to exist in the outer crust of\na nonaccreting neutron star. The presence of ternary compounds is also\ninvestigated. Very accurate analytical expressions are obtained for the\nthreshold pressure, as well as for the densities of the different phases\nirrespective of the degree of relativity of the electron gas. Finally,\nnumerical calculations of the ground-state structure and of the equation of\nstate of the outer crust of a cold nonaccreting neutron star are carried out\nusing recent experimental and microscopic nuclear mass tables.\n"}
+{"abstract": "  We study the Hardy identities and inequalities on Cartan-Hadamard manifolds\nusing the notion of a Bessel pair. These Hardy identities offer significantly\nmore information on the existence/nonexistence of the extremal functions of the\nHardy inequalities. These Hardy inequalities are in the spirit of\nBrezis-V\\'{a}zquez in the Euclidean spaces. As direct consequences, we\nestablish several Hardy type inequalities that provide substantial improvements\nas well as simple understandings to many known Hardy inequalities and\nHardy-Poincar\\'{e}-Sobolev type inequalities on hyperbolic spaces in the\nliterature.\n"}
+{"abstract": "  Crowdfunding offers inventors and entrepreneurs alternative access to\nresources with which they can develop and realize their ideas. Besides helping\nto secure capital, crowdfunding also connects creators with engaged early\nsupporters who provide public feedback. But does this process foster truly\ninnovative outcomes? Does the proliferation of crowdfunding in an industry make\nit more innovative overall? Prior studies investigating the link between\ncrowdfunding and innovation do not compare traditional and crowdfunded products\nand so while claims that crowdfunding supports innovation are theoretically\nsound, they lack empirical backing. We address this gap using a unique dataset\nof board games, an industry with significant crowdfunding activity in recent\nyears. Each game is described by how it combines fundamental mechanisms such as\ndice-rolling, negotiation, and resource-management, from which we develop\nquantitative measures of innovation in game design. Using these measures to\ncompare games, we find that crowdfunded games tend to be more distinctive from\nprevious games than their traditionally published counterparts. They are also\nsignificantly more likely to implement novel combinations of mechanisms.\nCrowdfunded games are not just transient experiments: subsequent games imitate\ntheir novel ideas. These results hold in regression models controlling for game\nand designer-level confounders. Our findings demonstrate that the innovative\npotential of crowdfunding goes beyond individual products to entire industries,\nas new ideas spill over to traditionally funded products.\n"}
+{"abstract": "  The color-magnitude diagrams (CMDs) of young star clusters show that,\nparticularly at ultraviolet wavelengths, their upper main sequences (MSs)\nbifurcate into a sequence comprising the bulk population and a blue periphery.\nThe spatial distribution of stars is crucial to understand the reasons for\nthese distinct stellar populations. This study uses high-resolution photometric\ndata obtained with the Hubble Space Telescope to study the spatial\ndistributions of the stellar populations in seven Magellanic Cloud star\nclusters. The cumulative radial number fractions of blue stars within four\nclusters are strongly anti-correlated with those of the high-mass-ratio\nbinaries in the bifurcated region, with negative Pearson coefficients < -0.7.\nThose clusters generally are young or in an early dynamical evolutionary stage.\nIn addition, a supporting N-body simulation suggests the increasing percentage\nof blue-MS stars from the cluster centers to their outskirts may be associated\nwith the dissolution of soft binaries. This study provides a different\nperspective to explore the MS bimodalities in young clusters and adds extra\npuzzles. A more comprehensive study combined with detailed simulations is\nneeded in the future.\n"}
+{"abstract": "  This paper presents a comparison of two multi-fidelity methods for the\nforward uncertainty quantification of a naval engineering problem.\nSpecifically, we consider the problem of quantifying the uncertainty of the\nhydrodynamic resistance of a roll-on/roll-off passengers ferry advancing in\ncalm water and subject to two operational uncertainties (ship speed and\npayload). The first four statistical moments (mean, variance, skewness,\nkurtosis), and the probability density function for such quantity of interest\n(QoI) are computed with two multi-fidelity methods, i.e., the Multi-Index\nStochastic Collocation (MISC) method and an adaptive multi-fidelity Stochastic\nRadial Basis Functions (SRBF) algorithm. The QoI is evaluated via computational\nfluid dynamics simulations, which are performed with the in-house unsteady\nReynolds-Averaged Navier-Stokes (RANS) multi-grid solver $\\chi$navis. The\ndifferent fidelities employed by both methods are obtained by stopping the RANS\nsolver at different grid levels of the multi-grid cycle. The performance of\nboth methods are presented and discussed: in a nutshell, the findings suggest\nthat, at least for the current implementations of both algorithms, MISC could\nbe preferred whenever a limited computational budget is available, whereas for\na larger computational budget SRBFs seem to be preferable, thanks to its\nrobustness to the numerical noise in the evaluations of the QoI.\n"}
+{"abstract": "  Recently, pure transformer-based models have shown great potentials for\nvision tasks such as image classification and detection. However, the design of\ntransformer networks is challenging. It has been observed that the depth,\nembedding dimension, and number of heads can largely affect the performance of\nvision transformers. Previous models configure these dimensions based upon\nmanual crafting. In this work, we propose a new one-shot architecture search\nframework, namely AutoFormer, dedicated to vision transformer search.\nAutoFormer entangles the weights of different blocks in the same layers during\nsupernet training. Benefiting from the strategy, the trained supernet allows\nthousands of subnets to be very well-trained. Specifically, the performance of\nthese subnets with weights inherited from the supernet is comparable to those\nretrained from scratch. Besides, the searched models, which we refer to\nAutoFormers, surpass the recent state-of-the-arts such as ViT and DeiT. In\nparticular, AutoFormer-tiny/small/base achieve 74.7%/81.7%/82.4% top-1 accuracy\non ImageNet with 5.7M/22.9M/53.7M parameters, respectively. Lastly, we verify\nthe transferability of AutoFormer by providing the performance on downstream\nbenchmarks and distillation experiments. Code and models are available at\nhttps://github.com/microsoft/AutoML.\n"}
+{"abstract": "  We give new parametrisations of elliptic curves in Weierstrass normal form\n$y^2=x^3+ax^2+bx$ with torsion groups $\\mathbb{Z}/10\\mathbb{Z}$ and\n$\\mathbb{Z}/12\\mathbb{Z}$ over $\\mathbb{Q}$, and with $\\mathbb{Z}/14\\mathbb{Z}$\nand $\\mathbb{Z}/16\\mathbb{Z}$ over quadratic fields. Even though the\nparametrisations are equivalent to those given by Kubert and Rabarison,\nrespectively, with the new parametrisations we found three infinite families of\nelliptic curves with torsion group $\\mathbb{Z}/12\\mathbb{Z}$ and positive rank.\nFurthermore, we found elliptic curves with torsion group\n$\\mathbb{Z}/14\\mathbb{Z}$ and rank $3$, which is a new record for such curves,\nas well as some new elliptic curves with torsion group\n$\\mathbb{Z}/16\\mathbb{Z}$ and rank $3$.\n"}
+{"abstract": "  We develop a new stock market index that captures the chaos existing in the\nmarket by measuring the mutual changes of asset prices. This new index relies\non a tensor-based embedding of the stock market information, which in turn\nfrees it from the restrictive value- or capitalization-weighting assumptions\nthat commonly underlie other various popular indexes. We show that our index is\na robust estimator of the market volatility which enables us to characterize\nthe market by performing the task of segmentation with a high degree of\nreliability. In addition, we analyze the dynamics and kinematics of the\nrealized market volatility as compared to the implied volatility by introducing\na time-dependent dynamical system model. Our computational results which\npertain to the time period from January 1990 to December 2019 imply that there\nexist a bidirectional causal relation between the processes underlying the\nrealized and implied volatility of the stock market within the given time\nperiod, where it is shown that the later has a stronger causal effect on the\nformer as compared to the opposite. This result connotes that the implied\nvolatility of the market plays a key role in characterization of the market's\nrealized volatility.\n"}
+{"abstract": "  The first measurement of the temperature dependence of the muon transfer rate\nfrom muonic hydrogen to oxygen was performed by the FAMU collaboration in 2016.\nThe results provide evidence that the transfer rate rises with the temperature\nin the range 104-300 K. This paper presents the results of the experiment done\nin 2018 to extend the measurements towards lower (70 K) and higher (336 K)\ntemperatures. The 2018 results confirm the temperature dependence of the muon\ntransfer rate observed in 2016 and sets firm ground for comparison with the\ntheoretical predictions.\n"}
+{"abstract": "  We address the problem of strategyproof (SP) facility location mechanisms on\ndiscrete trees. Our main result is a full characterization of onto and SP\nmechanisms. In particular, we prove that when a single agent significantly\naffects the outcome, the trajectory of the facility is almost contained in the\ntrajectory of the agent, and both move in the same direction along the common\nedges. We show tight relations of our characterization to previous results on\ndiscrete lines and on continuous trees. We then derive further implications of\nthe main result for infinite discrete lines.\n"}
+{"abstract": "  Most segmentation losses are arguably variants of the Cross-Entropy (CE) or\nDice losses. In the abundant segmentation literature, there is no clear\nconsensus as to which of these losses is a better choice, with varying\nperformances for each across different benchmarks and applications. In this\nwork, we develop a theoretical analysis that links these two types of losses,\nexposing their advantages and weaknesses. First, we provide a\nconstrained-optimization perspective showing that CE and Dice share a much\ndeeper connection than previously thought: They both decompose into\nlabel-marginal penalties and closely related ground-truth matching penalties.\nThen, we provide bound relationships and an information-theoretic analysis,\nwhich uncover hidden label-marginal biases: Dice has an intrinsic bias towards\nspecific extremely imbalanced solutions, whereas CE implicitly encourages the\nground-truth region proportions. Our theoretical results explain the wide\nexperimental evidence in the medical-imaging literature, whereby Dice losses\nbring improvements for imbalanced segmentation. It also explains why CE\ndominates natural-image problems with diverse class proportions, in which case\nDice might have difficulty adapting to different label-marginal distributions.\nBased on our theoretical analysis, we propose a principled and simple solution,\nwhich enables to control explicitly the label-marginal bias. Our loss\nintegrates CE with explicit ${\\cal L}_1$ regularization, which encourages label\nmarginals to match target class proportions, thereby mitigating class imbalance\nbut without losing generality. Comprehensive experiments and ablation studies\nover different losses and applications validate our theoretical analysis, as\nwell as the effectiveness of our explicit label-marginal regularizers.\n"}
+{"abstract": "  In this work, we prove an optimal global-in-time L p --L q estimate for\nsolutions to the Kramers-Fokker-Planck equation with short range potential in\ndimension three. Our result shows that the decay rate as t $\\rightarrow$\n+$\\infty$ is the same as the heat equation in x-variables and the divergence\nrate as t $\\rightarrow$ 0 + is related to the sub-ellipticity with loss of 1/3\nderivatives of the Kramers-Fokker-Planck operator.\n"}
+{"abstract": "  We consider the strong solution of the 2D Navier-Stokes equations in a torus\nsubject to an additive noise. We implement a fully implicit time numerical\nscheme and a finite element method in space. We prove that the rate of\nconvergence of the schemes is $\\eta\\in[0,1/2)$ in time and 1 in space. Let us\nmention that the coefficient $\\eta$ is equal to the time regularity of the\nsolution with values in $\\LL^2$. Our method relies on the existence of finite\nexponential moments for both the solution and its time approximation. Our main\nidea is to use a discrete Gronwall lemma for the error estimate without any\nlocalization.\n"}
+{"abstract": "  Loss functions are widely used to compare several competing forecasts.\nHowever, forecast comparisons are often based on mismeasured proxy variables\nfor the true target. We introduce the concept of exact robustness to\nmeasurement error for loss functions and fully characterize this class of loss\nfunctions as the Bregman class. For such exactly robust loss functions,\nforecast loss differences are on average unaffected by the use of proxy\nvariables and, thus, inference on conditional predictive ability can be carried\nout as usual. Moreover, we show that more precise proxies give predictive\nability tests higher power in discriminating between competing forecasts.\nSimulations illustrate the different behavior of exactly robust and non-robust\nloss functions. An empirical application to US GDP growth rates demonstrates\nthat it is easier to discriminate between forecasts issued at different\nhorizons if a better proxy for GDP growth is used.\n"}
+{"abstract": "  The identification and counting of small graph patterns, called network\nmotifs, is a fundamental primitive in the analysis of networks, with\napplication in various domains, from social networks to neuroscience. Several\ntechniques have been designed to count the occurrences of motifs in static\nnetworks, with recent work focusing on the computational challenges provided by\nlarge networks. Modern networked datasets contain rich information, such as the\ntime at which the events modeled by the networks edges happened, which can\nprovide useful insights into the process modeled by the network. The analysis\nof motifs in temporal networks, called temporal motifs, is becoming an\nimportant component in the analysis of modern networked datasets. Several\nmethods have been recently designed to count the number of instances of\ntemporal motifs in temporal networks, which is even more challenging than its\ncounterpart for static networks. Such methods are either exact, and not\napplicable to large networks, or approximate, but provide only weak guarantees\non the estimates they produce and do not scale to very large networks. In this\nwork we present an efficient and scalable algorithm to obtain rigorous\napproximations of the count of temporal motifs. Our algorithm is based on a\nsimple but effective sampling approach, which renders our algorithm practical\nfor very large datasets. Our extensive experimental evaluation shows that our\nalgorithm provides estimates of temporal motif counts which are more accurate\nthan the state-of-the-art sampling algorithms, with significantly lower running\ntime than exact approaches, enabling the study of temporal motifs, of size\nlarger than the ones considered in previous works, on billion edges networks.\n"}
+{"abstract": "  Many important applications are modelled by differential equations with\npositive solutions. However, it remains an outstanding open problem to develop\nnumerical methods that are both (i) of a high order of accuracy and (ii)\ncapable of preserving positivity. It is known that the two main families of\nnumerical methods, Runge-Kutta methods and multistep methods, face an order\nbarrier: if they preserve positivity, then they are constrained to low\naccuracy: they cannot be better than first order. We propose novel methods that\novercome this barrier: our methods are of second order, and they are guaranteed\nto preserve positivity. Our methods apply to a large class of differential\nequations that have a special graph Laplacian structure, which we elucidate.\nThe equations need be neither linear nor autonomous and the graph Laplacian\nneed not be symmetric. This algebraic structure arises naturally in many\nimportant applications where positivity is required. We showcase our new\nmethods on applications where standard high order methods fail to preserve\npositivity, including infectious diseases, Markov processes, master equations\nand chemical reactions.\n"}
+{"abstract": "  We study polarons in the one-dimensional Bond-Peierls electron-phonon model,\nin which phonons on bonds of a lattice modulate the hopping of electrons\nbetween lattice sites, and contrast the results to those known for the\nBreathing-Mode Peierls problem. By inspecting the atomic limit, we show that\npolaronic dressing and mass enhancement of Bond-Peierls polarons depend on the\nmomentum dependence of the phonons. For dispersionless phonons, Bond-Peierls\npolarons are perfectly localized in the atomic limit, unlike their\nBreathing-mode counterparts, because the carrier creates a string of phonon\nexcitations that can only be annihilated via processes that retrace the carrier\nto its original site. However, inclusion of phonon dispersion leads to a\nnon-divergent polaron mass even in the atomic limit and depending on the form\nof the phonon dispersion may lead to a transition to a non-zero-momentum ground\nstate akin to that found in the Breathing-mode Peierls model.\n"}
+{"abstract": "  By generalizing our automated algebra approach from homogeneous space to\nharmonically trapped systems, we have calculated the fourth- and fifth-order\nvirial coefficients of universal spin-1/2 fermions in the unitary limit,\nconfined in an isotropic harmonic potential. We present results for said\ncoefficients as a function of trapping frequency (or, equivalently,\ntemperature), which compare favorably with previous Monte Carlo calculations\n(available only at fourth order) as well as with our previous estimates in the\nuntrapped limit (high temperature, low frequency). We use our estimates of the\nvirial expansion, together with resummation techniques, to calculate the\ncompressibility and spin susceptibility.\n"}
+{"abstract": "  The ability to understand the ways to interact with objects from visual cues,\na.k.a. visual affordance, is essential to vision-guided robotic research. This\ninvolves categorizing, segmenting and reasoning of visual affordance. Relevant\nstudies in 2D and 2.5D image domains have been made previously, however, a\ntruly functional understanding of object affordance requires learning and\nprediction in the 3D physical domain, which is still absent in the community.\nIn this work, we present a 3D AffordanceNet dataset, a benchmark of 23k shapes\nfrom 23 semantic object categories, annotated with 18 visual affordance\ncategories. Based on this dataset, we provide three benchmarking tasks for\nevaluating visual affordance understanding, including full-shape, partial-view\nand rotation-invariant affordance estimations. Three state-of-the-art point\ncloud deep learning networks are evaluated on all tasks. In addition we also\ninvestigate a semi-supervised learning setup to explore the possibility to\nbenefit from unlabeled data. Comprehensive results on our contributed dataset\nshow the promise of visual affordance understanding as a valuable yet\nchallenging benchmark.\n"}
+{"abstract": "  This paper presents a regularization theory for numerical computation of\npolynomial greatest common divisors and a convergence analysis, along with a\ndetailed description of a blackbox-type algorithm. The root of the\nill-posedness in conventional GCD computation is identified by its geometry\nwhere polynomials form differentiable manifolds entangled in a stratification\nstructure. With a proper regularization, the numerical GCD is proved to be\nstrongly well-posed. Most importantly, the numerical GCD solves the problem of\nfinding the GCD accurately using floating point arithmetic even if the data are\nperturbed. A sensitivity measurement, error bounds at each computing stage, and\nthe overall convergence are established rigorously. The computing results of\nselected test examples show that the algorithm and software appear to be robust\nand accurate.\n"}
+{"abstract": "  Geometrically frustrated spin-chain compounds such as Ca3Co2O6 exhibit\nextremely slow relaxation under a changing magnetic field. Consequently, both\nlow-temperature laboratory experiments and Monte Carlo simulations have shown\npeculiar out-of-equilibrium magnetization curves, which arise from trapping in\nmetastable configurations. In this work we simulate this phenomenon in a\nsuperconducting quantum annealing processor, allowing us to probe the impact of\nquantum fluctuations on both equilibrium and dynamics of the system. Increasing\nthe quantum fluctuations with a transverse field reduces the impact of\nmetastable traps in out-of-equilibrium samples, and aids the development of\nthree-sublattice ferrimagnetic (up-up-down) long-range order. At equilibrium we\nidentify a finite-temperature shoulder in the 1/3-to-saturated phase\ntransition, promoted by quantum fluctuations but with entropic origin. This\nwork demonstrates the viability of dynamical as well as equilibrium studies of\nfrustrated magnetism using large-scale programmable quantum systems, and is\ntherefore an important step toward programmable simulation of dynamics in\nmaterials using quantum hardware.\n"}
+{"abstract": "  Motivated by work of Haythorpe, Thomassen and the author showed that there\nexists a positive constant $c$ such that there is an infinite family of\n4-regular 4-connected graphs, each containing exactly $c$ hamiltonian cycles.\nWe complement this by proving that the same conclusion holds for planar\n4-regular 3-connected graphs, although it does not hold for planar 4-regular\n4-connected graphs by a result of Brinkmann and Van Cleemput, and that it holds\nfor 4-regular graphs of connectivity~2 with the constant $144 < c$, which we\nbelieve to be minimal among all hamiltonian 4-regular graphs of sufficiently\nlarge order. We then disprove a conjecture of Haythorpe by showing that for\nevery non-negative integer $k$ there is a 5-regular graph on $26 + 6k$ vertices\nwith $2^{k+10} \\cdot 3^{k+3}$ hamiltonian cycles. We prove that for every $d\n\\ge 3$ there is an infinite family of hamiltonian 3-connected graphs with\nminimum degree $d$, with a bounded number of hamiltonian cycles. It is shown\nthat if a 3-regular graph $G$ has a unique longest cycle $C$, at least two\ncomponents of $G - E(C)$ have an odd number of vertices on $C$, and that there\nexist 3-regular graphs with exactly two such components.\n"}
+{"abstract": "  The objects under inspection, on a given probability space, are noise(-type)\nBoolean algebras -- distributive non-empty sublattices of the lattice of all\ncomplete sub-$\\sigma$-fields, whose every element admits an independent\ncomplement. Special attention is given to the spectral decompositions of the\nalgebras of operators generated by the conditional expectations of their\nmembers (acting on $\\mathrm{L}^2$).\n  Atoms of the spectra are identified in explicit terms. For a reverse\nfiltration admitting an innovating sequence of equiprobable random signs, a\ndiscreteness property of the spectral measure of the associated noise Boolean\nalgebra is shown to imply product-typeness.\n  Noise projections on the spectral space are introduced, which correspond to\nrestricting a noise to a part of its domain space. They appear to play a\nnatural (albeit technical) r\\^ole in the general analysis. In particular\nthrough them manifests itself the tensor structure of the spectral\ndecomposition.\n  The spectrum is precisely delineated in the classical case (i.e. when the\nnoise Boolean algebra is complete), a kind-of standard \"symmetric Fock space\"\nform thereof is procured. The latter result leads to a new characterization of\nclassicality and blackness involving \"spectral independence\".\n"}
+{"abstract": "  This paper presents a study of the interaction between Alfv\\'en modes and\nzonal structures, considering a realistic ASDEX Upgrade equilibrium. The\nresults of gyrokinetic simulations with the global, electromagnetic,\nparticle-in-cell code ORB5 are presented, where the modes are driven unstable\nby energetic particles with a bump-on-tail equilibrium distribution function,\nwith radial density gradient. Two regimes have been observed: at low energetic\nparticles concentration, the Alfv\\'en mode saturates at much higher level in\npresence of zonal structures; on the other hand at high energetic particles\nconcentration the difference is less pronounced. The former regime is\ncharacterized by the zonal structure (identified as an energetic particle\ndriven geodesic acoustic mode), being more unstable than the Alfv\\'en mode. In\nthe latter regime the Alfv\\'en mode is more unstable than the zonal structure.\nThe theoretical explanation is given in terms of a 3-wave coupling of the\nenergetic particle driven geodesic acoustic mode and Alfv\\'en mode, mediated by\nthe curvature-pressure coupling term of the energetic particles.\n"}
+{"abstract": "  We present a new scientific machine learning method that learns from data a\ncomputationally inexpensive surrogate model for predicting the evolution of a\nsystem governed by a time-dependent nonlinear partial differential equation\n(PDE), an enabling technology for many computational algorithms used in\nengineering settings. Our formulation generalizes to the PDE setting the\nOperator Inference method previously developed in [B. Peherstorfer and K.\nWillcox, Data-driven operator inference for non-intrusive projection-based\nmodel reduction, Computer Methods in Applied Mechanics and Engineering, 306\n(2016)] for systems governed by ordinary differential equations. The method\nbrings together two main elements. First, ideas from projection-based model\nreduction are used to explicitly parametrize the learned model by\nlow-dimensional polynomial operators which reflect the known form of the\ngoverning PDE. Second, supervised machine learning tools are used to infer from\ndata the reduced operators of this physics-informed parametrization. For\nsystems whose governing PDEs contain more general (non-polynomial)\nnonlinearities, the learned model performance can be improved through the use\nof lifting variable transformations, which expose polynomial structure in the\nPDE. The proposed method is demonstrated on a three-dimensional combustion\nsimulation with over 18 million degrees of freedom, for which the learned\nreduced models achieve accurate predictions with a dimension reduction of six\norders of magnitude and model runtime reduction of 5-6 orders of magnitude.\n"}
+{"abstract": "  Next-generation large-scale structure surveys will deliver a significant\nincrease in the precision of growth data, allowing us to use `agnostic' methods\nto study the evolution of perturbations without the assumption of a\ncosmological model. We focus on a particular machine learning tool, Gaussian\nprocesses, to reconstruct the growth rate $f$, the root mean square of matter\nfluctuations $\\sigma_8$, and their product $f\\sigma_8$. We apply this method to\nsimulated data, representing the precision of upcoming Stage IV galaxy surveys.\nWe extend the standard single-task approach to a multi-task approach that\nreconstructs the three functions simultaneously, thereby taking into account\ntheir inter-dependence. We find that this multi-task approach outperforms the\nsingle-task approach for future surveys and will allow us to detect departures\nfrom the standard model with higher significance. By contrast, the limited\nsensitivity of current data severely hinders the use of agnostic methods, since\nthe Gaussian processes parameters need to be fine tuned in order to obtain\nrobust reconstructions.\n"}
+{"abstract": "  The non-exponential Schilder-type theorem in Backhoff-Veraguas, Lacker and\nTangpi [Ann. Appl. Probab., 30 (2020), pp. 1321-1367] is expressed as a\nconvergence result for path-dependent partial differential equations with\nappropriate notions of generalized solutions. This entails a non-Markovian\ncounterpart to the vanishing viscosity method.\n  We show uniqueness of maximal subsolutions for path-dependent viscous\nHamilton-Jacobi equations related to convex super-quadratic backward stochastic\ndifferential equations.\n  We establish well-posedness for the Hamilton-Jacobi-Bellman equation\nassociated to a Bolza problem of the calculus of variations with path-dependent\nterminal cost. In particular, uniqueness among lower semi-continuous solutions\nholds and state constraints are admitted.\n"}
+{"abstract": "  In three previous papers I showed that the series of the midpoints of the\ntimes of all the X-ray flares of Sgr A* that have been detected so far harbor a\nstatistical trend termed pacemaker regularity. This means that X-ray flares are\ndetected more frequently around time points that are a subset of a periodic\ngrid on the time axis of period P_X=0.1032 day=149 minutes. The series of the\ntimes of detection of the peaks of near IR (NIR) flares of the object are also\nregulated by a pacemaker with a period of P_IR=0.028 day=41 minutes. Here, I\nshow that the series of the midpoints of the times of recorded NIR flares are\nalso regulated by a pacemaker of the period P_IRM=0.039 day=56 minutes. The two\npacemakers found in the previous papers were interpreted as signals of a star\nthat revolves around the black hole of Sgr A* in orbit with a mean radius of\n~3.2 Schwarzschild radii of the black hole, here corrected to ~3.13. The\nfinding of the period of the third pacemaker is consistent with the suggested\nrevolving star model. Here, I present the specific orbit of the star as well as\na plausible description of its sidereal rotation. The model also implies that\nthe star has an unusual internal structure. I show that the discovery of the\nGRAVITY collaboration of the motion of hotspots at distances from the black\nhole that are of the order of very few Schwarzschild radii of it may well be\nunderstood within the context of the revolving star model.\n"}
+{"abstract": "  Transformer-based pre-trained language models have significantly improved the\nperformance of various natural language processing (NLP) tasks in the recent\nyears. While effective and prevalent, these models are usually prohibitively\nlarge for resource-limited deployment scenarios. A thread of research has thus\nbeen working on applying network pruning techniques under the\npretrain-then-finetune paradigm widely adopted in NLP. However, the existing\npruning results on benchmark transformers, such as BERT, are not as remarkable\nas the pruning results in the literature of convolutional neural networks\n(CNNs). In particular, common wisdom in pruning CNN states that sparse pruning\ntechnique compresses a model more than that obtained by reducing number of\nchannels and layers (Elsen et al., 2020; Zhu and Gupta, 2017), while existing\nworks on sparse pruning of BERT yields inferior results than its small-dense\ncounterparts such as TinyBERT (Jiao et al., 2020). In this work, we aim to fill\nthis gap by studying how knowledge are transferred and lost during the\npre-train, fine-tune, and pruning process, and proposing a knowledge-aware\nsparse pruning process that achieves significantly superior results than\nexisting literature. We show for the first time that sparse pruning compresses\na BERT model significantly more than reducing its number of channels and\nlayers. Experiments on multiple data sets of GLUE benchmark show that our\nmethod outperforms the leading competitors with a 20-times weight/FLOPs\ncompression and neglectable loss in prediction accuracy.\n"}
+{"abstract": "  A TL-LUE model modified with a radiation scalar (RTL-LUE) is developed in\nthis paper. The same maximum LUE is used for both sunlit and shaded leaves, and\nthe difference in LUE between sunlit and shaded leaf groups is determined by\nthe same radiation scalar. The RTL-LUE model was calibrated and validated at\nglobal 169 FLUXNET eddy covariance (EC) sites. Results indicate that although\nGPP simulations from the TL-LUE model match well with the EC GPP, the RTL-LUE\nmodel can further improve the simulation, for half-hour, 8-day, and yearly time\nscales. The TL-LUE model tends to overestimate GPP under conditions of high\nincoming photosynthetically active radiation (PAR), because the\nradiation-independent LUE values for both sunlit and shaded leaves are only\nsuitable for low-medium (e.g. average) incoming PAR conditions. The errors in\nthe RTL-LUE model show lower sensitivity to PAR, and its GPP simulations can\nbetter track the diurnal and seasonal variations of EC GPP by alleviating the\noverestimation at noon and growing seasons associated with the TL-LUE model.\nThis study demonstrates the necessity of considering a radiation scalar in GPP\nsimulation in LUE models even if the first-order effect of radiation is already\nconsidered through differentiating sunlit and shaded leaves. The simple RTL-LUE\ndeveloped in this study would be a useful alternative to complex process-based\nmodels for global carbon cycle research.\n"}
+{"abstract": "  Using task-specific pre-training and leveraging cross-lingual transfer are\ntwo of the most popular ways to handle code-switched data. In this paper, we\naim to compare the effects of both for the task of sentiment analysis. We work\nwith two Dravidian Code-Switched languages - Tamil-Engish and Malayalam-English\nand four different BERT based models. We compare the effects of task-specific\npre-training and cross-lingual transfer and find that task-specific\npre-training results in superior zero-shot and supervised performance when\ncompared to performance achieved by leveraging cross-lingual transfer from\nmultilingual BERT models.\n"}
+{"abstract": "  Online updating of the object model via samples from historical frames is of\ngreat importance for accurate visual object tracking. Recent works mainly focus\non constructing effective and efficient updating methods while neglecting the\ntraining samples for learning discriminative object models, which is also a key\npart of a learning problem. In this paper, we propose the DeepMix that takes\nhistorical samples' embeddings as input and generates augmented embeddings\nonline, enhancing the state-of-the-art online learning methods for visual\nobject tracking. More specifically, we first propose the online data\naugmentation for tracking that online augments the historical samples through\nobject-aware filtering. Then, we propose MixNet which is an offline trained\nnetwork for performing online data augmentation within one-step, enhancing the\ntracking accuracy while preserving high speeds of the state-of-the-art online\nlearning methods. The extensive experiments on three different tracking\nframeworks, i.e., DiMP, DSiam, and SiamRPN++, and three large-scale and\nchallenging datasets, \\ie, OTB-2015, LaSOT, and VOT, demonstrate the\neffectiveness and advantages of the proposed method.\n"}
+{"abstract": "  Does a new product spread faster among heterogeneous or homogeneous\nconsumers? We analyze this question using the stochastic discrete Bass model,\nin which consumers may differ in their individual external influence rates\n$\\{p_j \\}$ and in their individual internal influence rates $\\{ q_j \\}$. When\nthe network is complete and the heterogeneity is only manifested in $\\{p_j \\}$\nor only in $\\{ q_j \\}$, it always slows down the diffusion, compared to the\ncorresponding homogeneous network. When, however, consumers are heterogeneous\nin both $\\{p_j\\}$ and $\\{q_{j}\\}$, heterogeneity slows down the diffusion in\nsome cases, but accelerates it in others. Moreover, the dominance between the\nheterogeneous and homogeneous adoption levels is global in time in some cases,\nbut changes with time in others. Perhaps surprisingly, global dominance between\ntwo networks is not always preserved under \"additive transformations\", such as\nadding an identical node to both networks. When the network is not complete,\nthe effect of heterogeneity depends also on its spatial distribution within the\nnetwork.\n"}
+{"abstract": "  We derive an upper bound on the reliability function of mismatched decoding\nfor zero-rate codes. The bound is based on a result by Koml\\'os that shows the\nexistence of a subcode with certain symmetry properties. The bound is shown to\ncoincide with the expurgated exponent at rate zero for a broad family of\nchannel-decoding metric pairs.\n"}
+{"abstract": "  For the first time, the hybrid Frequency Division Multiple Access (FDMA) Code\nDivision Multiple Access (CDMA) mode of the CAOS (i.e., Coded Access Optical\nSensor) camera is demonstrated. The FDMA CDMA mode is a time frequency double\nsignal encoding design for robust and faster linear High Dynamic Range (HDR)\nimage irradiance extraction. Specifically, it simultaneously combines the\nstrength of the FDMA-mode linear HDR Fast Fourier Transform (FFT) Digital\nSignal Processing (DSP) based spectrum analysis with the CDMA mode provided\nmany simultaneous CAOS pixels high Signal to Noise Ratio (SNR) photo-detection.\nThe FDMA CDMA mode with P FDMA channels provides a faster camera operation\nversus the linear HDR Frequency Modulation (FM) CDMA mode. Visible band imaging\nexperiments using a Digital Micromirror Device (DMD) based CAOS camera\ndemonstrate a P equal to 4 channels FDMA CDMA mode high quality image recovery\nof a calibrated 64 dB 6 patches HDR target versus the CDMA and FM CDMA CAOS\nmodes that limit dynamic range and speed, respectively. Simultaneous dual image\ncapture capability of the FDMA-CDMA mode is also demonstrated for the first\ntime in Ultraviolet (UV) to Near Infrared (NIR) 350 to 1800 nm full spectrum\nusing Silicon (Si) and Germanium (Ge) point photo-detectors.\n"}
+{"abstract": "  We consider a linear Fermi-Pasta-Ulam-Tsingou lattice with random spatially\nvarying material coefficients. Using the methods of stochastic homogenization\nwe show that solutions with long wave initial data converge in an appropriate\nsense to solutions of a wave equation. The convergence is strong and both\nalmost sure and in expectation, but the rate is quite slow. The technique\ncombines energy estimates with powerful classical results about random walks,\nspecifically the law of the iterated logarithm.\n"}
+{"abstract": "  Steganography is a solution for covert communication and blockchain is a p2p\nnetwork for data transmission, so the benefits of blockchain can be used in\nsteganography. In this paper, we discuss the advantages of blockchain in\nsteganography, which include the ability to embed hidden data without manual\nchange in the original data, as well as the readiness of the blockchain\nplatform for data transmission and storage, which eliminates the need for the\nSteganographer to design and implement a new platform for data transmission and\nstorage. We have proposed two algorithms for steganography in blockchain, the\nfirst one is a high-capacity algorithm for the key and the steganography\nalgorithm exchange and switching, and the second one is a medium-capacity\nalgorithm for embedding hidden data. Also, by reviewing the previous three\nsteganography schemes in blockchain, we have examined their drawback and have\nshowed that none of them are practical schemes for steganography in blockchain.\nThen, we have explained the challenges of steganography in blockchain from the\nsteganographers and steganalyzers point of view.\n"}
+{"abstract": "  The exact reconstruction of many-body quantum systems is one of the major\nchallenges in modern physics, because it is impractical to overcome the\nexponential complexity problem brought by high-dimensional quantum many-body\nsystems. Recently, machine learning techniques are well used to promote quantum\ninformation research and quantum state tomography has been also developed by\nneural network generative models. We propose a quantum state tomography method,\nwhich is based on Bidirectional Gated Recurrent Unit neural network (BiGRU), to\nlearn and reconstruct both easy quantum states and hard quantum states in this\npaper. We are able to use fewer measurement samples in our method to\nreconstruct these quantum states and obtain high fidelity.\n"}
+{"abstract": "  Computer vision approaches are widely used by autonomous robotic systems to\nsense the world around them and to guide their decision making as they perform\ndiverse tasks such as collision avoidance, search and rescue, and object\nmanipulation. High accuracy is critical, particularly for Human-on-the-loop\n(HoTL) systems where decisions are made autonomously by the system, and humans\nplay only a supervisory role. Failures of the vision model can lead to\nerroneous decisions with potentially life or death consequences. In this paper,\nwe propose a solution based upon adaptive autonomy levels, whereby the system\ndetects loss of reliability of these models and responds by temporarily\nlowering its own autonomy levels and increasing engagement of the human in the\ndecision-making process. Our solution is applicable for vision-based tasks in\nwhich humans have time to react and provide guidance. When implemented, our\napproach would estimate the reliability of the vision task by considering\nuncertainty in its model, and by performing covariate analysis to determine\nwhen the current operating environment is ill-matched to the model's training\ndata. We provide examples from DroneResponse, in which small Unmanned Aerial\nSystems are deployed for Emergency Response missions, and show how the vision\nmodel's reliability would be used in addition to confidence scores to drive and\nspecify the behavior and adaptation of the system's autonomy. This workshop\npaper outlines our proposed approach and describes open challenges at the\nintersection of Computer Vision and Software Engineering for the safe and\nreliable deployment of vision models in the decision making of autonomous\nsystems.\n"}
+{"abstract": "  We answer Mundici's problem number 3 (D. Mundici. Advanced {\\L}ukasiewicz\ncalculus. Trends in Logic Vol. 35. Springer 2011, p. 235): Is the category of\nlocally finite MV-algebras equivalent to an equational class? We prove: (i) The\ncategory of locally finite MV-algebras is not equivalent to any finitary\nvariety. (ii) More is true: the category of locally finite MV-algebras is not\nequivalent to any finitely-sorted finitary quasi-variety. (iii) The category of\nlocally finite MV-algebras is equivalent to an infinitary variety; with\noperations of at most countable arity. (iv) The category of locally finite\nMV-algebras is equivalent to a countably-sorted finitary variety.\n  Our proofs rest upon the duality between locally finite MV-algebras and the\ncategory of multisets by R. Cignoli, E. J. Dubuc and D. Mundici, and\ncategorical characterisations of varieties and quasi-varieties proved by J.\nDuskin, J. R. Isbell, F. W. Lawvere and others. In fact no knowledge on\nMV-algebras is needed, apart from the aforementioned duality.\n"}
+{"abstract": "  In this Letter we construct the Hubble diagram for a Universe where dark\nmatter is universally charged under a dark non-linear electromagnetic force\nwhich features a screening mechanism of the K-mouflage type for repulsive\nforces. By resorting to the Newtonian approximation, we explicitly show that\nthe cosmological evolution generates an inhomogeneous Hubble diagram that\ncorresponds to a curvature dominated expansion at short distances and converges\nto the cosmological one of $\\Lambda$CDM. We discuss the potential impact of\nthis inhomogeneous profile on the Hubble tension. For completeness, we\nexplicitly show how the Newtonian approximation can be derived from an\ninhomogeneous relativistic Lema\\^itre model.\n"}
+{"abstract": "  We study the two-phase Stokes flow driven by surface tension for two fluids\nof different viscosities, separated by an asymptotically flat interface\nrepresentable as graph of a differentiable function. The flow is assumed to be\ntwo-dimensional with the fluids filling the entire space. We prove\nwell-posedness and parabolic smoothing in Sobolev spaces up to critical\nregularity. The main technical tools are an analysis of nonlinear singular\nintegral operators arising from the hydrodynamic single and double layer\npotential, spectral results on the corresponding integral operators, and\nabstract results on nonlinear parabolic evolution equations.\n"}
+{"abstract": "  We give conditions for minimality of $\\mathbb Z/N\\mathbb Z$ extensions of a\nrotation of angle $\\alpha$ with one marked point, solving the problem for any\nprime $N$: for $N=2$, these correspond to the Veech 1969 examples, for which a\nnecessary and sufficient condition was not known yet. We provide also a word\ncombinatorial criterion of minimality valid for general interval exchange\ntransformations, which applies to $\\mathbb Z/N\\mathbb Z$ extensions of any\ninterval exchange transformation with any number of marked points. Then we give\na condition for unique ergodicity of these extensions when the initial interval\nexchange transformation is linearly recurrent and there are one or two marked\npoints.\n"}
+{"abstract": "  We have employed Gd55Co30NixAl15-x (x = 10, 5 and 0) amorphous microwires as\na model system to unravel the impact of multiple magnetic interactions on the\nmagnetism and the magnetocaloric behavior in Gd-alloy microwire systems. Our\nstudy shows that in addition to the RKKY ferromagnetic (FM) interaction\n(Gd-Gd), antiferromagnetic (AFM) interactions (Gd-Co, Gd-Ni) coexist and\ncontribute to the magnetic and magnetocaloric response of the system. The\ndilution effect of Al element on the FM Gd-Gd interaction is responsible for\nthe decrease of the Curie temperature (TC), whereas the increase of the\nsaturation magnetization (MS) is originated from the reduced AFM Gd-Ni\ninteraction. A thorough analysis of critical exponents suggests that the\npresence of the AFM interactions hinders the system to exhibit a long-range FM\norder below the TC. Adjusting these interactions is shown to preserve the large\nrefrigerant capacity (RC) while tuning the TC over a wide temperature range,\nwhich is desirable for active magnetic refrigeration.\n"}
+{"abstract": "  Low-light image enhancement aims to improve an image's visibility while\nkeeping its visual naturalness. Different from existing methods tending to\naccomplish the relighting task directly by ignoring the fidelity and\nnaturalness recovery, we investigate the intrinsic degradation and relight the\nlow-light image while refining the details and color in two steps. Inspired by\nthe color image formulation (diffuse illumination color plus environment\nillumination color), we first estimate the degradation from low-light inputs to\nsimulate the distortion of environment illumination color, and then refine the\ncontent to recover the loss of diffuse illumination color. To this end, we\npropose a novel Degradation-to-Refinement Generation Network (DRGN). Its\ndistinctive features can be summarized as 1) A novel two-step generation\nnetwork for degradation learning and content refinement. It is not only\nsuperior to one-step methods, but also capable of synthesizing sufficient\npaired samples to benefit the model training; 2) A multi-resolution fusion\nnetwork to represent the target information (degradation or contents) in a\nmulti-scale cooperative manner, which is more effective to address the complex\nunmixing problems. Extensive experiments on both the enhancement task and joint\ndetection task have verified the effectiveness and efficiency of our proposed\nmethod, surpassing the SOTA by \\textit{0.70dB on average and 3.18\\% in mAP},\nrespectively. The code is available at\n\\url{https://github.com/kuijiang0802/DRGN}.\n"}
+{"abstract": "  The entropy production scenarios due to electroweak phase transition (EWPT)\nin the framework of the minimal extension of standard model namely two Higgs\ndoublet model(2HDM) is revisited. The possibility of first order phase\ntransition is discussed. Intense parameter scanning is done with the help of\nBSMPT, a C++ package. Numerical calculations are performed in order to\ncalculate the entropy production with numerous benchmark points.\n"}
+{"abstract": "  Electrically actuated optomechanical resonators provide a route to\nquantum-coherent, bidirectional conversion of microwave and optical photons.\nSuch devices could enable optical interconnection of quantum computers based on\nqubits operating at microwave frequencies. Here we present a novel platform for\nmicrowave-to-optical conversion comprising a photonic crystal cavity made of\nsingle-crystal, piezoelectric gallium phosphide integrated on pre-fabricated\nniobium circuits on an intrinsic silicon substrate. The devices exploit\nspatially extended, sideband-resolved mechanical breathing modes at $\\sim$ 3.2\nGHz, with vacuum optomechanical coupling rates of up to $g_0/2\\pi \\approx$ 300\nkHz. The mechanical modes are driven by integrated microwave electrodes via the\ninverse piezoelectric effect. We estimate that the system could achieve an\nelectromechanical coupling rate to a superconducting transmon qubit of $\\sim$\n200 kHz. Our work represents a decisive step towards integration of\npiezoelectro-optomechanical interfaces with superconducting quantum processors.\n"}
+{"abstract": "  Conventional approaches to image-text retrieval mainly focus on indexing\nvisual objects appearing in pictures but ignore the interactions between these\nobjects. Such objects occurrences and interactions are equivalently useful and\nimportant in this field as they are usually mentioned in the text. Scene graph\npresentation is a suitable method for the image-text matching challenge and\nobtained good results due to its ability to capture the inter-relationship\ninformation. Both images and text are represented in scene graph levels and\nformulate the retrieval challenge as a scene graph matching challenge. In this\npaper, we introduce the Local and Global Scene Graph Matching (LGSGM) model\nthat enhances the state-of-the-art method by integrating an extra graph\nconvolution network to capture the general information of a graph.\nSpecifically, for a pair of scene graphs of an image and its caption, two\nseparate models are used to learn the features of each graph's nodes and edges.\nThen a Siamese-structure graph convolution model is employed to embed graphs\ninto vector forms. We finally combine the graph-level and the vector-level to\ncalculate the similarity of this image-text pair. The empirical experiments\nshow that our enhancement with the combination of levels can improve the\nperformance of the baseline method by increasing the recall by more than 10% on\nthe Flickr30k dataset.\n"}
+{"abstract": "  Proper scoring rules are commonly applied to quantify the accuracy of\ndistribution forecasts. Given an observation they assign a scalar score to each\ndistribution forecast, with the the lowest expected score attributed to the\ntrue distribution. The energy and variogram scores are two rules that have\nrecently gained some popularity in multivariate settings because their\ncomputation does not require a forecast to have parametric density function and\nso they are broadly applicable. Here we conduct a simulation study to compare\nthe discrimination ability between the energy score and three variogram scores.\nCompared with other studies, our simulation design is more realistic because it\nis supported by a historical data set containing commodity prices, currencies\nand interest rates, and our data generating processes include a diverse\nselection of models with different marginal distributions, dependence\nstructure, and calibration windows. This facilitates a comprehensive comparison\nof the performance of proper scoring rules in different settings. To compare\nthe scores we use three metrics: the mean relative score, error rate and a\ngeneralised discrimination heuristic. Overall, we find that the variogram score\nwith parameter p=0.5 outperforms the energy score and the other two variogram\nscores.\n"}
+{"abstract": "  It is conjectured that the canonical models of varieties (not of general\ntype) are bounded when the Iitaka volume is fixed. We confirm this conjecture\nwhen the general fibers of the corresponding Iitaka fibration are in a fixed\nbounded family of polarized Calabi-Yau pairs. As a consequence, we prove that\nin this case, the fibration is birationally bounded, and when it has terminal\nsingularities, the corresponding minimal model is bounded in codimension 1.\n"}
+{"abstract": "  A Just-In-Time (JIT) defect prediction model is a classifier to predict if a\ncommit is defect-introducing. Recently, CC2Vec -- a deep learning approach for\nJust-In-Time defect prediction -- has been proposed. However, CC2Vec requires\nthe whole dataset (i.e., training + testing) for model training, assuming that\nall unlabelled testing datasets would be available beforehand, which does not\nfollow the key principles of just-in-time defect predictions. Our replication\nstudy shows that, after excluding the testing dataset for model training, the\nF-measure of CC2Vec is decreased by 38.5% for OpenStack and 45.7% for Qt,\nhighlighting the negative impact of excluding the testing dataset for\nJust-In-Time defect prediction. In addition, CC2Vec cannot perform fine-grained\npredictions at the line level (i.e., which lines are most risky for a given\ncommit).\n  In this paper, we propose JITLine -- a Just-In-Time defect prediction\napproach for predicting defect-introducing commits and identifying lines that\nare associated with that defect-introducing commit (i.e., defective lines).\nThrough a case study of 37,524 commits from OpenStack and Qt, we find that our\nJITLine approach is at least 26%-38% more accurate (F-measure), 17%-51% more\ncost-effective (PCI@20%LOC), 70-100 times faster than the state-of-the-art\napproaches (i.e., CC2Vec and DeepJIT) and the fine-grained predictions at the\nline level by our approach are 133%-150% more accurate (Top-10 Accuracy) than\nthe baseline NLP approach. Therefore, our JITLine approach may help\npractitioners to better prioritize defect-introducing commits and better\nidentify defective lines.\n"}
+{"abstract": "  Gaisi Takeuti introduced Boolean valued analysis around 1974 to provide\nsystematic applications of Boolean valued models of set theory to analysis.\nLater, his methods were further developed by his followers, leading to solving\nseveral open problems in analysis and algebra. Using the methods of Boolean\nvalued analysis, he further stepped forward to construct set theory based on\nquantum logic, as the first step to construct \"quantum mathematics\", a\nmathematics based on quantum logic. While it is known that the distributive law\ndoes not apply to quantum logic, and the equality axiom turns out not to hold\nin quantum set theory, he showed that the real numbers in quantum set theory\nare in one-to-one correspondence with the self-adjoint operators on a Hilbert\nspace, or equivalently the physical quantities of the corresponding quantum\nsystem. As quantum logic is intrinsic and empirical, the results of the quantum\nset theory can be experimentally verified by quantum mechanics. In this paper,\nwe analyze Takeuti's mathematical world view underlying his program from two\nperspectives: set theoretical foundations of modern mathematics and extending\nthe notion of sets to multi-valued logic. We outlook the present status of his\nprogram, and envisage the further development of the program, by which we would\nbe able to take a huge step forward toward unraveling the mysteries of quantum\nmechanics that have persisted for many years.\n"}
+{"abstract": "  We prove that the number of irreducible ordinary characters in the principal\n$p$-block of a finite group $G$ of order divisible by $p$ is always at least\n$2\\sqrt{p-1}$. This confirms a conjecture of H\\'{e}thelyi and K\\\"{u}lshammer\nfor principal blocks and provides an affirmative answer to Brauer's Problem 21\nfor principal blocks of bounded defect. Our proof relies on recent works of\nMar\\'{o}ti and Malle-Mar\\'{o}ti on bounding the conjugacy class number and the\nnumber of $p'$-degree irreducible characters of finite groups, earlier works of\nBrou\\'{e}-Malle-Michel and Cabanes-Enguehard on the distribution of characters\ninto unipotent blocks and $e$-Harish-Chandra series of finite reductive groups,\nand known cases of the Alperin-McKay conjecture.\n"}
+{"abstract": "  Diffusion channels are critical to determining the adoption scale which leads\nto the ultimate impact of an innovation. The aim of this study is to develop an\nintegrative understanding of the impact of two diffusion channels (i.e.,\nbroadcasting vs virality) on innovation adoption. Using citations of a series\nof classic algorithms and the time series of co-authorship as the footprints of\ntheir diffusion trajectories, we propose a novel method to analyze the\nintertwining relationships between broadcasting and virality in the innovation\ndiffusion process. Our findings show that broadcasting and virality have\nsimilar diffusion power, but play different roles across diffusion stages.\nBroadcasting is more powerful in the early stages but may be gradually caught\nup or even surpassed by virality in the later period. Meanwhile, diffusion\nspeed in virality is significantly faster than broadcasting and members from\nvirality channels tend to adopt the same innovation repetitively.\n"}
+{"abstract": "  Achieving large-size two-dimensional (2D) crystals is key to fully exploiting\ntheir remarkable functionalities and application potentials. Chemical vapor\ndeposition (CVD) growth of 2D semiconductors such as monolayer MoS2 has been\nreported to be activated by halide salts, yet clear identification of the\nunderlying mechanism remains elusive. Here we provide unambiguous experimental\nevidence showing that the MoS2 growth dynamics are halogen-dependent through\nthe Br{\\o}nsted-Evans-Polanyi relation, based on which we build a growth model\nby considering MoS2 edge passivation by halogens, and theoretically reproduces\nthe trend of our experimental observations. These mechanistic understandings\nenable us to further optimize the fast growth of MoS2 and reach record-large\ndomain sizes that should facilitate practical applications.\n"}
+{"abstract": "  The spectrum of cosmic ray protons and electrons released by supernova\nremnants throughout their evolution is poorly known, because of the difficulty\nin accounting for particle escape and confinement in the downstream of a shock\nfront, where both adiabatic and radiative losses are present. Here we calculate\nthe spectrum of cosmic ray protons released during the evolution of supernovae\nof different types, accounting for the escape from upstream and for adiabatic\nlosses of particles advected downstream of the shock and liberated at later\ntimes. The same calculation is carried out for electrons. The magnetic field in\nthe post-shock region is calculated by using an analytic treatment of the\nmagnetic field amplification due to non--resonant and resonant streaming\ninstability and their saturation. We find that when the field is the result of\nthe growth of the cosmic-ray--driven non--resonant instability alone, the\nspectrum of electrons and protons released by a supernova remnant are indeed\ndifferent, but such a difference becomes appreciable only at energies $\\gtrsim\n100-1000$ GeV, while observations of the electron spectrum require such a\ndifference to be present at energies as low as $\\sim 10$ GeV. An effect at such\nlow energies requires substantial magnetic field amplification in the late\nstages of the supernova remnant evolution (shock velocity $\\ll 1000$ km/s),\nperhaps not due to streaming instability but hydrodynamical processes. We\ncomment on the feasibility of such conditions and speculate on the possibility\nthat the difference in spectral shape between electrons and protons may reflect\neither some unknown acceleration effect, or additional energy losses in cocoons\naround the sources.\n"}
+{"abstract": "  The computational underpinnings of positive psychotic symptoms have recently\nreceived significant attention. Candidate mechanisms include some combination\nof maladaptive priors and reduced updating of these priors during perception. A\npotential benefit of models with such mechanisms is their ability to link\nmultiple levels of explanation. This is key to improving how we understand the\nexperience of psychosis. Moreover, it points us towards more comprehensive\navenues for therapeutic research by providing a putative mechanism that could\nallow for the generation of new treatments from first principles. In order to\ndemonstrate this, our conceptual paper will discuss the application of the\ninsights from previous computational models to an important and complex set of\nevidence-based clinical interventions with strong social elements, such as\ncoordinated specialty care clinics in early psychosis and assertive community\ntreatment. These interventions may include but also go beyond\npsychopharmacology, providing, we argue, structure and predictability for\npatients experiencing psychosis. We develop the argument that this structure\nand predictability directly counteract the relatively low precision afforded to\nsensory information in psychosis, while also providing the patient more access\nto external cognitive resources in the form of providers and the structure of\nthe programs themselves. We discuss how computational models explain the\nresulting reduction in symptoms, as well as the predictions these models make\nabout potential responses of patients to modifications or to different\nvariations of these interventions. We also link, via the framework of\ncomputational models, the experiences of patients and response to interventions\nto putative neurobiology.\n"}
+{"abstract": "  As predictive models are increasingly being deployed in high-stakes decision\nmaking (e.g., loan approvals), there has been growing interest in post hoc\ntechniques which provide recourse to affected individuals. These techniques\ngenerate recourses under the assumption that the underlying predictive model\ndoes not change. However, in practice, models are often regularly updated for a\nvariety of reasons (e.g., dataset shifts), thereby rendering previously\nprescribed recourses ineffective. To address this problem, we propose a novel\nframework, RObust Algorithmic Recourse (ROAR), that leverages adversarial\ntraining for finding recourses that are robust to model shifts. To the best of\nour knowledge, this work proposes the first solution to this critical problem.\nWe also carry out detailed theoretical analysis which underscores the\nimportance of constructing recourses that are robust to model shifts: 1) we\nderive a lower bound on the probability of invalidation of recourses generated\nby existing approaches which are not robust to model shifts. 2) we prove that\nthe additional cost incurred due to the robust recourses output by our\nframework is bounded. Experimental evaluation on multiple synthetic and\nreal-world datasets demonstrates the efficacy of the proposed framework and\nsupports our theoretical findings.\n"}
+{"abstract": "  This paper presents a four-stage algorithm for the realization of\nmulti-input/multi-output (MIMO) switched linear systems (SLSs) from Markov\nparameters. In the first stage, a linear time-varying (LTV) realization that is\ntopologically equivalent to the true SLS is derived from the Markov parameters\nassuming that the submodels have a common MacMillan degree and a mild condition\non their dwell times holds. In the second stage, zero sets of LTV Hankel\nmatrices where the realized system has a linear time-invariant (LTI) pulse\nresponse matching that of the original SLS are exploited to extract the\nsubmodels, up to arbitrary similarity transformations, by a clustering\nalgorithm using a statistics that is invariant to similarity transformations.\nRecovery is shown to be complete if the dwell times are sufficiently long and\nsome mild identifiability conditions are met. In the third stage, the switching\nsequence is estimated by three schemes. The first scheme is based on\nforward/backward corrections and works on the short segments. The second scheme\nmatches Markov parameter estimates to the true parameters for LTV systems and\nworks on the medium-to-long segments. The third scheme also matches Markov\nparameters, but for LTI systems only and works on the very short segments. In\nthe fourth stage, the submodels estimated in Stage 2 are brought to a common\nbasis by applying a novel basis transformation method which is necessary before\nperforming output predictions to given inputs. A numerical example illustrates\nthe properties of the realization algorithm. A key role in this algorithm is\nplayed by time-dependent switching sequences that partition the state-space\naccording to time, unlike many other works in the literature in which\npartitioning is state and/or input dependent.\n"}
+{"abstract": "  Recent tracking-by-detection approaches use deep object detectors as target\ndetection baseline, because of their high performance on still images. For\neffective video object tracking, object detection is integrated with a data\nassociation step performed by either a custom design inference architecture or\nan end-to-end joint training for tracking purpose. In this work, we adopt the\nformer approach and use the pre-trained Mask R-CNN deep object detector as the\nbaseline. We introduce a novel inference architecture placed on top of\nFPN-ResNet101 backbone of Mask R-CNN to jointly perform detection and tracking,\nwithout requiring additional training for tracking purpose. The proposed single\nobject tracker, TDIOT, applies an appearance similarity-based temporal matching\nfor data association. In order to tackle tracking discontinuities, we\nincorporate a local search and matching module into the inference head layer\nthat exploits SiamFC for short term tracking. Moreover, in order to improve\nrobustness to scale changes, we introduce a scale adaptive region proposal\nnetwork that enables to search the target at an adaptively enlarged spatial\nneighborhood specified by the trace of the target. In order to meet long term\ntracking requirements, a low cost verification layer is incorporated into the\ninference architecture to monitor presence of the target based on its LBP\nhistogram model. Performance evaluation on videos from VOT2016, VOT2018 and\nVOT-LT2018 datasets demonstrate that TDIOT achieves higher accuracy compared to\nthe state-of-the-art short-term trackers while it provides comparable\nperformance in long term tracking.\n"}
+{"abstract": "  The \"track event theory\" (TET) has been developed in recent years as an\nalternative to the phenomenological linear-quadratic model for cell survival\nunder exposure to ionizing radiation, particularly for heavy charged particles.\nThe TET is based on a few simple model assumptions including the possibility to\nderive some of the model parameters from nanodosimetry. This work intends to\ncarve out more clearly the basic assumptions behind the TET and to critically\nreview the resulting mathematical model equations. It is demonstrated that the\nmodel assumptions of Poisson distribution and statistical independence of the\nfrequency distributions of so-called one-track and two-track events follow from\nthe Poisson distribution of the number of tracks affecting the considered\ntarget volume. It is also shown that the modified TET model equation used in\nthe literature for consideration of repair is inconsistent with the model\nassumptions and requires an additional model parameter. Furthermore, the\nderivation of the model parameters from nanodosimetric properties of particle\ntrack structure is revealed to lead to a pure exponential dose dependence when\nthe potentially large number of relevant nanometric target volumes inside a\ncell nucleus is accounted for.\n"}
+{"abstract": "  We study the hedging and valuation of European and American claims on a\nnon-traded asset $Y$, when a traded stock $S$ is available for hedging, with\n$S$ and $Y$ following correlated geometric Brownian motions. This is an\nincomplete market, often called a basis risk model. The market agent's risk\npreferences are modelled using a so-called forward performance process (forward\nutility), which is a time-decreasing utility of exponential type. Moreover, the\nmarket agent (investor) does not know with certainty the values of the asset\nprice drifts. This market setting with drift parameter uncertainty is the\npartial information scenario. We discuss the stochastic control problem\nobtained by setting up the hedging portfolio and derive the optimal hedging\nstrategy. Furthermore, a (dual) forward indifference price representation of\nthe claim and its PDE are obtained. With these results, the residual risk\nprocess representing the basis risk (hedging error), pay-off decompositions and\nasymptotic expansions of the indifference price in the European case are\nderived. We develop the analogous stochastic control and stopping problem with\nan American claim and obtain the corresponding forward indifference price\nvaluation formula.\n"}
+{"abstract": "  Independently exploring unknown spaces or finding objects in an indoor\nenvironment is a daily but challenging task for visually impaired people.\nHowever, common 2D assistive systems lack depth relationships between various\nobjects, resulting in difficulty to obtain accurate spatial layout and relative\npositions of objects. To tackle these issues, we propose HIDA, a lightweight\nassistive system based on 3D point cloud instance segmentation with a\nsolid-state LiDAR sensor, for holistic indoor detection and avoidance. Our\nentire system consists of three hardware components, two interactive\nfunctions~(obstacle avoidance and object finding) and a voice user interface.\nBased on voice guidance, the point cloud from the most recent state of the\nchanging indoor environment is captured through an on-site scanning performed\nby the user. In addition, we design a point cloud segmentation model with dual\nlightweight decoders for semantic and offset predictions, which satisfies the\nefficiency of the whole system. After the 3D instance segmentation, we\npost-process the segmented point cloud by removing outliers and projecting all\npoints onto a top-view 2D map representation. The system integrates the\ninformation above and interacts with users intuitively by acoustic feedback.\nThe proposed 3D instance segmentation model has achieved state-of-the-art\nperformance on ScanNet v2 dataset. Comprehensive field tests with various tasks\nin a user study verify the usability and effectiveness of our system for\nassisting visually impaired people in holistic indoor understanding, obstacle\navoidance and object search.\n"}
+{"abstract": "  We address the problem of computing the smallest symplectic eigenvalues and\nthe corresponding eigenvectors of symmetric positive-definite matrices in the\nsense of Williamson's theorem. It is formulated as minimizing a trace cost\nfunction over the symplectic Stiefel manifold. We first investigate various\ntheoretical aspects of this optimization problem such as characterizing the\nsets of critical points, saddle points, and global minimizers as well as\nproving that non-global local minimizers do not exist. Based on our recent\nresults on constructing Riemannian structures on the symplectic Stiefel\nmanifold and the associated optimization algorithms, we then propose solving\nthe symplectic eigenvalue problem in the framework of Riemannian optimization.\nMoreover, a connection of the sought solution with the eigenvalues of a special\nclass of Hamiltonian matrices is discussed. Numerical examples are presented.\n"}
+{"abstract": "  Coma Berenices (Coma Ber), an open cluster about the same age as Praesepe and\nthe Hyades (700-800 Myr) is, despite being only 85 pc away, less well studied\nthan its famous cousins. This is due principally to its sparseness and low\nproper motion, which together made Coma Ber's membership challenging to\nestablish pre-Gaia. We have curated a new list of its members based on Gaia DR2\nastrometry, derived its metallicity and interstellar reddening using LAMOST\ndata, and inferred the cluster's age by fitting PARSEC isochrones to its\ncolor$-$magnitude diagram. We then measured rotation periods for Coma Ber's\nlow-mass members using TESS and ZTF photometry. Our isochrone fitting and the\nTESS- and ZTF-derived rotation periods confirm that Coma Ber is coeval with the\nHyades and Praesepe. This work is the first step toward re-establishing Coma\nBer as another valuable benchmark cluster for age$-$rotation$-$activity\nstudies.\n"}
+{"abstract": "  The thermal modeling of biological systems has increasing importance in\ndeveloping more advanced, more precise techniques such as ultrasound surgery.\nOne of the primary barriers is the complexity of biological materials: the\ngeometrical, structural, and material properties vary in a wide range, and they\ndepend on many factors. Despite these difficulties, there is a tremendous\neffort to develop a reliable and implementable thermal model. In the present\npaper, we focus on the continuum modeling of heterogeneous materials with\nbiological origin. There are numerous examples in the literature for\nnon-Fourier thermal models. However, as we realized, they are associated with a\nfew common misconceptions. Therefore, we first aim to clarify the basic\nconcepts of non-Fourier thermal models. These concepts are demonstrated by\nrevisiting two experiments from the literature in which the Cattaneo-Vernotte\nand the dual phase lag models are utilized. Our investigation revealed that\nusing these non-Fourier models is based on misinterpretations of the measured\ndata, and the seeming deviation from Fourier's law originates in the source\nterms and boundary conditions.\n"}
+{"abstract": "  Blockchain technologies have been rapidly enhanced in recent years. However,\nits scalability still has limitations in terms of throughput and broadcast\ndelay as the network and the amount of transaction data increase. To improve\nscalability of blockchain networks, we propose a novel approach named PiChu\nthat accelerates block propagation in blockchain networks by pipelining and\nverifying chunks of a block in parallel. Accelerating block propagation reduces\nthe mining interval and chance of fork occurring, which in turn increases\nthroughput. Our approach can be applied to the blockchain networks either\ndirectly or with a minor modification to the consensus. Through an extensive\nand large scale simulations, we validate that the proposed PiChu scheme\nsignificantly enhances the scalability of blockchain networks. For instance, a\n64 MB block can be broadcasted in just 80 seconds in a blockchain network with\na million nodes. The efficiency of PiChu broadcasting increases with bigger\nblock sizes and a larger number of nodes in the network.\n"}
+{"abstract": "  Topic classification systems on spoken documents usually consist of two\nmodules: an automatic speech recognition (ASR) module to convert speech into\ntext and a text topic classification (TTC) module to predict the topic class\nfrom the decoded text. In this paper, instead of using the ASR transcripts, the\nfusion of deep acoustic and linguistic features is used for topic\nclassification on spoken documents. More specifically, a conventional CTC-based\nacoustic model (AM) using phonemes as output units is first trained, and the\noutputs of the layer before the linear phoneme classifier in the trained AM are\nused as the deep acoustic features of spoken documents. Furthermore, these deep\nacoustic features are fed to a phoneme-to-word (P2W) module to obtain deep\nlinguistic features. Finally, a local multi-head attention module is proposed\nto fuse these two types of deep features for topic classification. Experiments\nconducted on a subset selected from Switchboard corpus show that our proposed\nframework outperforms the conventional ASR+TTC systems and achieves a 3.13%\nimprovement in ACC.\n"}
+{"abstract": "  We present a method for localizing a single camera with respect to a point\ncloud map in indoor and outdoor scenes. The problem is challenging because\ncorrespondences of local invariant features are inconsistent across the domains\nbetween image and 3D. The problem is even more challenging as the method must\nhandle various environmental conditions such as illumination, weather, and\nseasonal changes. Our method can match equirectangular images to the 3D range\nprojections by extracting cross-domain symmetric place descriptors. Our key\ninsight is to retain condition-invariant 3D geometry features from limited data\nsamples while eliminating the condition-related features by a designed\nGenerative Adversarial Network. Based on such features, we further design a\nspherical convolution network to learn viewpoint-invariant symmetric place\ndescriptors. We evaluate our method on extensive self-collected datasets, which\ninvolve \\textit{Long-term} (variant appearance conditions),\n\\textit{Large-scale} (up to $2km$ structure/unstructured environment), and\n\\textit{Multistory} (four-floor confined space). Our method surpasses other\ncurrent state-of-the-arts by achieving around $3$ times higher place retrievals\nto inconsistent environments, and above $3$ times accuracy on online\nlocalization. To highlight our method's generalization capabilities, we also\nevaluate the recognition across different datasets. With a single trained\nmodel, i3dLoc can demonstrate reliable visual localization in random\nconditions.\n"}
+{"abstract": "  The efficient and accurate calculation of how ionic quantum and thermal\nfluctuations impact the free energy of a crystal, its atomic structure, and\nphonon spectrum is one of the main challenges of solid state physics,\nespecially when strong anharmonicy invalidates any perturbative approach. To\ntackle this problem, we present the implementation on a modular Python code of\nthe stochastic self-consistent harmonic approximation method. This technique\nrigorously describes the full thermodyamics of crystals accounting for nuclear\nquantum and thermal anharmonic fluctuations. The approach requires the\nevaluation of the Born-Oppenheimer energy, as well as its derivatives with\nrespect to ionic positions (forces) and cell parameters (stress tensor) in\nsupercells, which can be provided, for instance, by first principles\ndensity-functional-theory codes. The method performs crystal geometry\nrelaxation on the quantum free energy landscape, optimizing the free energy\nwith respect to all degrees of freedom of the crystal structure. It can be used\nto determine the phase diagram of any crystal at finite temperature. It enables\nthe calculation of phase boundaries for both first-order and second-order phase\ntransitions from the Hessian of the free energy. Finally, the code can also\ncompute the anharmonic phonon spectra, including the phonon linewidths, as well\nas phonon spectral functions. We review the theoretical framework of the\nstochastic self-consistent harmonic approximation and its dynamical extension,\nmaking particular emphasis on the physical interpretation of the variables\npresent in the theory that can enlighten the comparison with any other\nanharmonic theory. A modular and flexible Python environment is used for the\nimplementation, which allows for a clean interaction with other packages. We\nbriefly present a toy-model calculation to illustrate the potential of the\ncode.\n"}
+{"abstract": "  Recent works of multi-source domain adaptation focus on learning a\ndomain-agnostic model, of which the parameters are static. However, such a\nstatic model is difficult to handle conflicts across multiple domains, and\nsuffers from a performance degradation in both source domains and target\ndomain. In this paper, we present dynamic transfer to address domain conflicts,\nwhere the model parameters are adapted to samples. The key insight is that\nadapting model across domains is achieved via adapting model across samples.\nThus, it breaks down source domain barriers and turns multi-source domains into\na single-source domain. This also simplifies the alignment between source and\ntarget domains, as it only requires the target domain to be aligned with any\npart of the union of source domains. Furthermore, we find dynamic transfer can\nbe simply modeled by aggregating residual matrices and a static convolution\nmatrix. Experimental results show that, without using domain labels, our\ndynamic transfer outperforms the state-of-the-art method by more than 3% on the\nlarge multi-source domain adaptation datasets -- DomainNet. Source code is at\nhttps://github.com/liyunsheng13/DRT.\n"}
+{"abstract": "  Human mobility similarity comparison plays a critical role in mobility\nestimation/prediction model evaluation, mobility clustering and mobility\nmatching, which exerts an enormous impact on improving urban mobility,\naccessibility, and reliability. By expanding origin-destination matrix, we\npropose a concept named mobility tableau, which is an aggregated tableau\nrepresentation to the population flow distributed between different location\npairs of a study site and can be represented by a vector graph. Compared with\ntraditional OD matrix-based mobility comparison, mobility tableau comparison\nprovides high-dimensional similarity information, including volume similarity,\nspatial similarity, mass inclusiveness and structure similarity. A novel\nmobility tableaus similarity measurement method is proposed by optimizing the\nleast spatial cost of transforming the vector graph for one mobility tableau\ninto the other and is optimized to be efficient. The robustness of the measure\nis supported through several sensitive analysis on GPS based mobility tableau.\nThe better performance of the approach compared with traditional mobility\ncomparison methods in two case studies demonstrate the practicality and\nsuperiority, while one study is estimated mobility tableaus validation and the\nother is different cities' mobility tableaus comparison.\n"}
+{"abstract": "  Session-based recommenders, used for making predictions out of users'\nuninterrupted sequences of actions, are attractive for many applications. Here,\nfor this task we propose using metric learning, where a common embedding space\nfor sessions and items is created, and distance measures dissimilarity between\nthe provided sequence of users' events and the next action. We discuss and\ncompare metric learning approaches to commonly used learning-to-rank methods,\nwhere some synergies exist. We propose a simple architecture for problem\nanalysis and demonstrate that neither extensively big nor deep architectures\nare necessary in order to outperform existing methods. The experimental results\nagainst strong baselines on four datasets are provided with an ablation study.\n"}
+{"abstract": "  We present and analyze a parallel implementation of a parallel-in-time method\nbased on $\\alpha$-circulant preconditioned Richardson iterations. While there\nare a lot of papers exploring this new class of single-level, time-parallel\nintegrators from many perspectives, performance results of actual parallel runs\nare still missing. This leaves a critical gap, because the efficiency and\napplicability heavily rely on the actual parallel performance, with only\nlimited guidance from theoretical considerations. Also, many challenges like\nselecting good parameters, finding suitable communication strategies, and\nperforming a fair comparison to sequential time-stepping methods can be easily\nmissed. In this paper, we first extend the original idea by using a collocation\nmethod of arbitrary order, which adds another level of parallelization in time.\nWe derive an adaptive strategy to select a new $\\alpha$-circulant\npreconditioner for each iteration during runtime for balancing convergence\nrates, round-off errors and inexactness in the individual time-steps. After\naddressing these more theoretical challenges, we present an open-source space-\nand doubly-time-parallel implementation and evaluate its performance for two\ndifferent test problems.\n"}
+{"abstract": "  Betweenness centrality is a popular centrality measure with applications in\nseveral domains, and whose exact computation is impractical for modern-sized\nnetworks. We present SILVAN, a novel, efficient algorithm to compute, with high\nprobability, accurate estimates of the betweenness centrality of all nodes of a\ngraph and a high-quality approximation of the k most central nodes of a graph.\nSILVAN follows a progressive sampling approach, and builds on recently improved\nbounds on Monte-Carlo Empirical Rademacher Averages, a fundamental tool from\nstatistical learning theory. SILVAN relies on a novel estimation scheme that\nleads to non-uniform bounds on the deviation of the estimates from the true\nvalues of the between centrality of all the nodes, providing tight guarantees\non the quality of the approximation. Our extensive experimental evaluation\nshows that SILVAN extracts high-quality approximations while outperforming, in\nterms of number of samples and accuracy, the state-of-the-art approximation\nalgorithm with comparable quality guarantees.\n"}
+{"abstract": "  A charged particle in a magnetic field possesses discrete energy levels\nassociated with particle rotation around the field lines. A bound complex of\nparticles with a nonzero net charge possesses an analogous levels associated\nwith its center-of-mass motion and, in addition, the levels associated with\ninternal degrees of freedom, that is with relative motions of its constituent\nparticles. The center-of-mass and internal motions are mutually dependent,\nwhich complicates theoretical studies of the binding energies, radiative\ntransitions and other properties of the complex ions moving in quantizing\nmagnetic fields. In this work, we present a detailed derivation of practical\nexpressions for the numerical treatment of such properties of the hydrogenlike\nions moving in strong quantizing magnetic fields, which follows and supplements\nthe previous works of Bezchastnov et al. Second, we derive asymptotic analytic\nexpressions for the binding energies, oscillator strengths, and photoionization\ncross sections of the moving hydrogenlike ions in the limit of an ultra-strong\nmagnetic field.\n"}
+{"abstract": "  It is shown that a magnetic monopole appears as the tension state of the\nprimary electric charge at its motion through each section of the path equal to\nthe particle's de Broglie wavelength. This conclusion is followed from a\nsubmicroscopic consideration of particles and their motion in the framework of\nthe theory of physical space in the form of a tessellattice developed by Michel\nBounias and the author. The periodical change of the particle's charged state\nto its monopole state can easily be introduced in the conventional Maxwell\nequations and the magnetic monopole automatically shows up in the structure of\nMaxwell's equations. The monopole is also presented in any quark system as a\nquark obeys dynamics that are also characterized by the appropriate de Broglie\nwavelength and hence the electric charge changes periodically to the magnetic\nmonopole. A (anti)neutrino emerges as the typical electron's monopole. When the\ncharged particle becomes the monopole, it also loses its mass (the mass passes\nto the particle's inerton cloud) and thus the neutrino is a massless particle,\nor more correctly massless monopole.\n"}
+{"abstract": "  In a two-ion-species plasma with disparate ion masses, heavy ions tend to\nconcentrate in the low-temperature region of collisionally magnetized plasma\nand in the high-temperature region of collisionally unmagnetized plasma,\nrespectively. Moreover, collisional magnetization can be determined as the\nratio of the light ion gyrofrequency to the collision frequency of light and\nheavy ion species, and the behavior of this effect in the intermediate regime\nof partially magnetized plasma is predominantly dependent on this Hall\nparameter. Multi-ion cross-field transport has been described before in the\ncollisionally magnetized plasma regime, and generalized pinch relations, which\ndescribe densities of ion species in equilibrium in that plasma, are found in\nthe literature. In this paper, the role of collisional magnetization and Larmor\nmagnetization in multi-ion collisional transport is clarified and generalized\npinch relations are extended to the partially magnetized regime, in which the\nion Hall parameter may be small, as long as electrons remain collisionally\nmagnetized. Equilibrium ion density profiles have the same dependence on\nexternal forces and on each other regardless of collisional magnetization of\nions. The expansion of the range of validity of multi-ion collisional transport\nmodels makes them applicable to a wider range of laboratory plasma conditions.\nIn particular, ion density profiles evolve sufficiently fast for radial\nimpurity transport to be observable around stagnation on MagLIF, leading to\nexpulsion of heavy ion impurities from the hotspot as long as plasma becomes\nsufficiently collisionally magnetized during the implosion.\n"}
+{"abstract": "  This work proposes a Prolog-dialect for the found and prioritised problems on\nexpressibility and automation. Given some given C-like program, if dynamic\nmemory is allocated, altered and freed on runtime, then a description of\ndesired dynamic memory is a heap specification. The check of calculated memory\nstate against a given specification is dynamic memory verification. This\ncontribution only considers formal specification and verification in a Hoare\ncalculus. Issues found include: invalid assignment, (temporary) unavailable\ndata in memory cells, excessive memory allocation, (accidental) heap alteration\nin unexpected regions and others. Excessive memory allocation is nowadays\nsuccessfully resolved by memory analysers like Valgrind. Essentially, papers in\nthose areas did not bring any big breakthrough. Possible reasons may also\ninclude the decrease of tension due to more available memory and parallel\nthreads. However, starting with Apt, problems related to variable modes have\nnot yet been resolved -- neither entirely nor in an acceptable way. Research\ncontributions over the last decades show again and again that heap issues\nremain and remain complex and still important. A significant contribution was\nreached in 2016 by Peter O'Hearn, who accepted the G\\\"{o}del prize for his\nparallel approach on a spatial heap operation.\n"}
+{"abstract": "  If a single line of sight (LOS) intercepts multiple dust clouds of different\nspectral energy distributions and magnetic field orientations, the frequency\nscaling of each of the Stokes $Q$ and $U$ parameters of thermal dust emission\nmay be different (\"LOS frequency decorrelation\"). We present first evidence for\nLOS frequency decorrelation in $Planck$ data. We use independent,\nneutral-hydrogen--measurements of the number of clouds per LOS and the magnetic\nfield orientation in each cloud to select two sets of sightlines: (i) a target\nsample (pixels likely to exhibit LOS frequency decorrelation); (ii) a control\nsample (pixels lacking complex LOS structure). We test the null hypothesis that\nLOS frequency decorrelation is not detectable in $Planck$ 353 and 217~GHz\npolarization data at high Galactic latitudes. The data reject this hypothesis\nat high significance. The detection is robust against choice of CMB map and\nmap-making pipeline. The observed change in polarization angle due to LOS\nfrequency decorrelation is detectable above the $Planck$ noise level. The\nprobability that the detected effect is due to noise alone ranges from $5\\times\n10^{-2}$ to $4\\times 10^{-7}$, depending on the CMB subtraction algorithm and\ntreatment of residual systematics; correcting for residual systematics\nincreases the significance of the effect. The LOS decorrelation effect is\nstronger for sightlines with more misaligned magnetic fields, as expected. We\nestimate that an intrinsic variation of $\\sim15\\%$ in the ratio of 353 to\n217~GHz polarized emission between clouds is sufficient to reproduce the\nmeasured effect. Our finding underlines the importance of ongoing studies to\nmap the 3D structure of the magnetized dusty ISM that could help component\nseparation methods to account for frequency decorrelation effects in CMB\npolarization studies.\n"}
+{"abstract": "  To overcome the limitations of original local binary patterns (LBP), this\narticle proposes a new texture descriptor aided by complex networks (CN) and\nLBP, named CN-LBP. Specifically, we first abstract a texture image (TI) as\ndirected graphs over different bands with the help of pixel distance,\nintensity, and gradient (magnitude and angle). Second, several CN-based feature\nmeasurements (including clustering coefficient, in-degree centrality,\nout-degree centrality, and eigenvector centrality) are selected to further\ndecipher the texture features, which generates four feature images that can\nretain the image information as much as possible. Third, given the original\nTIs, gradient images (GI), and generated feature images, we can obtain the\ndiscriminative representation of texture images based on uniform LBP (ULBP).\nFinally, the feature vector is obtained by jointly calculating and\nconcatenating the spatial histograms. In contrast to original LBP, the proposed\ntexture descriptor contains more detailed image information, and shows\nresistance to imaging and noise. Experiment results on four datasets\ndemonstrate that the proposed texture descriptor can significantly improve the\nclassification accuracies compared with the state-of-the-art LBP-based variants\nand deep learning-based methods.\n"}
+{"abstract": "  In a target communication system, a delicately designed frequency offset\nestimation scheme is required to meet certain decoding performance. In this\npaper, we proposed at wo-step estimation scheme, coarse and residual, with\ndifferent value of an time interval parameter. A result of RF conduction test\nshows that the proposed method has an 1dB gain of SNR compared to coarse-only\nestimator. A result of the commercial test also indicates the proposed method\noutperforms coarse-only estimator especially in low SNR condition.\n"}
+{"abstract": "  Refractory producers face many challenges in terms of producing\nMgO-containing castables due to the high likelihood of magnesia to hydrate in\ncontact with water, resulting in Mg(OH)2 generation. The expansive feature of\nthis transformation affects the performance of such refractories, as (i) if\nthis hydrated phase is not accommodated in the formed microstructure, ceramic\nlinings with cracks and low green mechanical strength will be obtained; and\n(ii) if crack-free pieces are prepared, they should present low porosity and\nreduced permeability, which require special attention when heating these\nmaterials. This work investigated the ability of various additives in the\noptimization of the drying behavior of Al2O3-MgO castables. Vibratable\ncompositions were tested after incorporating polymeric fibers (PF), an organic\nsalt (OAS), SiO2-based additive (SM) or permeability enhancing active compound\n(MP) into the dry-mixtures. Various experimental measurements were performed to\ninfer the role of the drying agents to prevent the samples explosion and\nwhether they would also influence other properties of the castables. As\nobserved, OAS and MP helped to inhibit the MgO-bonded samples explosion even\nunder severe heating conditions (2-20C/min) and increased their green\nmechanical strength and slag infiltration resistance when compared to the\nadditive-free composition. On the other hand, the addition of polymeric fibers\n(PF) or silica-based compound (SM) to the formulations was not able to prevent\nthe castables explosion when using a high heating rate and other side effects\ncould also be observed when testing these materials. Thus, the selection of\nsuitable drying agents is a key issue, as they may allow the development of\nMgO-bonded castables with enhanced properties and lower spalling risk during\ntheir first thermal treatment.\n"}
+{"abstract": "  We present new short proofs of known spanning tree enumeration formulae for\nthreshold and Ferrers graphs by showing that the Laplacian matrices of such\ngraphs admit triangular rank-one perturbations. We then characterize the set of\ngraphs whose Laplacian matrices admit triangular rank-one perturbations as the\nclass of special 2-threshold graphs, introduced by Hung, Kloks, and Villaamil.\nOur work introduces (1) a new characterization of special 2-threshold graphs\nthat generalizes the characterization of threshold graphs in terms of isolated\nand dominating vertices, and (2) a spanning tree enumeration formula for\nspecial 2-threshold graphs that reduces to the aforementioned formulae for\nthreshold and Ferrers graphs. We consider both unweighted and weighted spanning\ntree enumeration.\n"}
+{"abstract": "  We study three structurally different states of nanocrystalline 316 steel and\nshow that the state, where boundaries containing excess concentration of\nalloying elements are combined with mobile dislocations in grain interiors,\nallows maintaining extraordinarily high strength and remarkably enhanced\nplasticity. Underlying mechanisms featuring interaction between the\nsegregations and mobile dislocations are discussed.\n"}
+{"abstract": "  Recently, audio-visual speech enhancement has been tackled in the\nunsupervised settings based on variational auto-encoders (VAEs), where during\ntraining only clean data is used to train a generative model for speech, which\nat test time is combined with a noise model, e.g. nonnegative matrix\nfactorization (NMF), whose parameters are learned without supervision.\nConsequently, the proposed model is agnostic to the noise type. When visual\ndata are clean, audio-visual VAE-based architectures usually outperform the\naudio-only counterpart. The opposite happens when the visual data are corrupted\nby clutter, e.g. the speaker not facing the camera. In this paper, we propose\nto find the optimal combination of these two architectures through time. More\nprecisely, we introduce the use of a latent sequential variable with Markovian\ndependencies to switch between different VAE architectures through time in an\nunsupervised manner: leading to switching variational auto-encoder (SwVAE). We\npropose a variational factorization to approximate the computationally\nintractable posterior distribution. We also derive the corresponding\nvariational expectation-maximization algorithm to estimate the parameters of\nthe model and enhance the speech signal. Our experiments demonstrate the\npromising performance of SwVAE.\n"}
+{"abstract": "  We propose an algorithm to compute the $C^\\infty$-ring structure of arbitrary\nWeil algebra. It allows us to do some analysis with higher infinitesimals\nnumerically and symbolically. To that end, we first give a brief description of\nthe (Forward-mode) automatic differentiation (AD) in terms of $C^\\infty$-rings.\nThe notion of a $C^\\infty$-ring was introduced by Lawvere and used as the\nfundamental building block of smooth infinitesimal analysis and synthetic\ndifferential geometry. We argue that interpreting AD in terms of\n$C^\\infty$-rings gives us a unifying theoretical framework and modular ways to\nexpress multivariate partial derivatives. In particular, we can \"package\"\nhigher-order Forward-mode AD as a Weil algebra, and take tensor products to\ncompose them to achieve multivariate higher-order AD. The algorithms in the\npresent paper can also be used for a pedagogical purpose in learning and\nstudying smooth infinitesimal analysis as well.\n"}
+{"abstract": "  We study boson-fermion dualities in one-dimensional many-body problems of\nidentical particles interacting only through two-body contacts. By using the\npath-integral formalism as well as the configuration-space approach to\nindistinguishable particles, we find a generalization of the boson-fermion\nduality between the Lieb-Liniger model and the Cheon-Shigehara model. We\npresent an explicit construction of $n$-boson and $n$-fermion models which are\ndual to each other and characterized by $n-1$ distinct (coordinate-dependent)\ncoupling constants. These models enjoy the spectral equivalence, the\nboson-fermion mapping, and the strong-weak duality. We also discuss a\nscale-invariant generalization of the boson-fermion duality.\n"}
+{"abstract": "  This letter introduces a real valued summation known as Complex Conjugate\nPair Sum (CCPS). The space spanned by CCPS and its one circular downshift is\ncalled {\\em Complex Conjugate Subspace (CCS)}. For a given positive integer\n$N\\geq3$, there exists $\\frac{\\varphi(N)}{2}$ CCPSs forming\n$\\frac{\\varphi(N)}{2}$ CCSs, where $\\varphi(N)$ is the Euler's totient\nfunction. We prove that these CCSs are mutually orthogonal and their direct sum\nform a $\\varphi(N)$ dimensional subspace $s_N$ of $\\mathbb{C}^N$. We propose\nthat any signal of finite length $N$ is represented as a linear combination of\nelements from a special basis of $s_d$, for each divisor $d$ of $N$. This\ndefines a new transform named as Complex Conjugate Periodic Transform (CCPT).\nLater, we compared CCPT with DFT (Discrete Fourier Transform) and RPT\n(Ramanujan Periodic Transform). It is shown that, using CCPT we can estimate\nthe period, hidden periods and frequency information of a signal. Whereas, RPT\ndoes not provide the frequency information. For a complex valued input signal,\nCCPT offers computational benefit over DFT. A CCPT dictionary based method is\nproposed to extract non-divisor period information.\n"}
+{"abstract": "  Behaviors of the synthetic characters in current military simulations are\nlimited since they are generally generated by rule-based and reactive\ncomputational models with minimal intelligence. Such computational models\ncannot adapt to reflect the experience of the characters, resulting in brittle\nintelligence for even the most effective behavior models devised via costly and\nlabor-intensive processes. Observation-based behavior model adaptation that\nleverages machine learning and the experience of synthetic entities in\ncombination with appropriate prior knowledge can address the issues in the\nexisting computational behavior models to create a better training experience\nin military training simulations. In this paper, we introduce a framework that\naims to create autonomous synthetic characters that can perform coherent\nsequences of believable behavior while being aware of human trainees and their\nneeds within a training simulation. This framework brings together three\nmutually complementary components. The first component is a Unity-based\nsimulation environment - Rapid Integration and Development Environment (RIDE) -\nsupporting One World Terrain (OWT) models and capable of running and supporting\nmachine learning experiments. The second is Shiva, a novel multi-agent\nreinforcement and imitation learning framework that can interface with a\nvariety of simulation environments, and that can additionally utilize a variety\nof learning algorithms. The final component is the Sigma Cognitive Architecture\nthat will augment the behavior models with symbolic and probabilistic reasoning\ncapabilities. We have successfully created proof-of-concept behavior models\nleveraging this framework on realistic terrain as an essential step towards\nbringing machine learning into military simulations.\n"}
+{"abstract": "  One of the key ways in which quantum mechanics differs from relativity is\nthat it requires a fixed background reference frame for spacetime. In fact,\nthis appears to be one of the main conceptual obstacles to uniting the two\ntheories. Additionally, a combination of the two theories is expected to yield\nnon-classical, or \"indefinite\", causal structures. In this paper, we present a\nbackground-independent formulation of the process matrix formalism - a form of\nquantum mechanics that allows for indefinite causal structure - while retaining\noperationally well-defined measurement statistics. We do this by postulating an\narbitrary probability distribution of measurement outcomes across discrete\n\"chunks\" of spacetime, which we think of as physical laboratories, and then\nrequiring that this distribution be invariant under any permutation of\nlaboratories. We find (a) that one still obtains nontrivial, indefinite causal\nstructures with background independence, (b) that we lose the idea of local\noperations in distinct laboratories, but can recover it by encoding a reference\nframe into the physical states of our system, and (c) that permutation\ninvariance imposes surprising symmetry constraints that, although formally\nsimilar to a superselection rule, cannot be interpreted as such.\n"}
+{"abstract": "  In the past twenty years several events disrupted global economies and social\nwell-being and generally shook the confidence in the stability of western\nsocieties such as during the 2008 financial crisis and its economic aftermath.\nHere, we aim to identify and illustrate underlying drivers of such societal\ninstability or even consequential collapse. For this purpose we propose a\nlow-dimensional and stylised model of two classes of networked agents (termed\n\"labourers\" and \"administrators\" hereafter) that is inspired by Joseph\nTainter's theory of collapse of complex societies. We numerically model the\ndynamics of societal complexity, measured as the share of \"administrators\",\nwhich is assumed to affect the productivity of energy-producing `labourers\". We\nshow that collapse becomes increasingly likely if the model society's\ncomplexity continuously increases in response to external stresses that emulate\nTainter's abstract notion of problems that societies need to solve. We\nadditionally provide an analytical approximation of the system's dominant\ndynamics which matches well with the numerical experiments, thus, allowing for\na precise estimate of tipping points beyond which societal collapse is\ntriggered. The administration's ability for increasing productivity of labour\nshows to be the most influential parameter for longer survival times which is\nadditionally fostered by a minimum level of social connectivity. Finally, we\nshow that agents' stochastic transitions between labour force and\nadministration, i.e. social mobility, can increase the survival time of the\nmodeled society even further. Our work fosters the understanding of\nsocio-ecological collapse and illustrates its potentially direct link to an\never increasing complexity in response to external shocks or stress via a\nself-reinforcing feedback.\n"}
+{"abstract": "  Non-parametric inference for functional data over two-dimensional domains\nentails additional computational and statistical challenges, compared to the\none-dimensional case. Separability of the covariance is commonly assumed to\naddress these issues in the densely observed regime. Instead, we consider the\nsparse regime, where the latent surfaces are observed only at few irregular\nlocations with additive measurement error, and propose an estimator of\ncovariance based on local linear smoothers. Consequently, the assumption of\nseparability reduces the intrinsically four-dimensional smoothing problem into\nseveral two-dimensional smoothers and allows the proposed estimator to retain\nthe classical minimax-optimal convergence rate for two-dimensional smoothers.\nEven when separability fails to hold, imposing it can be still advantageous as\na form of regularization. A simulation study reveals a favorable bias-variance\ntrade-off and massive speed-ups achieved by our approach. Finally, the proposed\nmethodology is used for qualitative analysis of implied volatility surfaces\ncorresponding to call options, and for prediction of the latent surfaces based\non information from the entire data set, allowing for uncertainty\nquantification. Our cross-validated out-of-sample quantitative results show\nthat the proposed methodology outperforms the common approach of pre-smoothing\nevery implied volatility surface separately.\n"}
+{"abstract": "  We present design and realization of an ultra-broadband optical spectrometer\ncapable of measuring the spectral intensity of multi-octave-spanning light\nsources on a single-pulse basis with a dynamic range of up to 8 orders of\nmagnitude. The instrument is optimized for the characterization of the temporal\nstructure of femtosecond long electron bunches by analyzing the emitted\ncoherent transition radiation (CTR) spectra. The spectrometer operates within\nthe spectral range of 250nm to 11.35$\\mu$m, corresponding to 5.5 optical\noctaves. This is achieved by dividing the signal beam into three spectral\ngroups, each analyzed by a dedicated spectrometer and detector unit. The\ncomplete instrument was characterized with regard to wavelength, relative\nspectral sensitivity, and absolute photo-metric sensitivity, always accounting\nfor the light polarization and comparing different calibration methods.\nFinally, the capability of the spectrometer is demonstrated with a CTR\nmeasurement of a laser wakefield accelerated electron bunch, enabling to\ndetermine temporal pulse structures at unprecedented resolution.\n"}
+{"abstract": "  In the paper, our main aim is to generalize the width integrals to the Orlicz\nspace. Under the framework of Orlicz Brunn-Minkowski theory, we introduce a new\naffine geometric quantity by calculating Orlicz first order variation of the\nwidth integrals, and call as Orlicz mixed width integrals. The fundamental\nnotions and conclusions of the width integrals and Minkoswki and\nBrunn-Minkowski inequalities for the width integrals are extended to an Orlicz\nsetting and the related concepts and inequalities of Lp mixed width integrals\nof convex body are also derived.\n"}
+{"abstract": "  Atomic properties of $n=3$ states of the W$^{56+}$ $-$ W$^{61+}$ ions are\nsystematically investigated through two state-of-the-art methods, namely, the\nsecond-order many-body perturbation theory, and the multi-configuration\nDirac-Hartree-Fock method combined with the relativistic configuration\ninteraction approach. The contributions of valence-valence and core-valence\nelectron correlations, the Breit interaction, the higher-order retardation\ncorrection beyond the Breit interaction through the transverse photon\ninteraction, and the quantum electrodynamical corrections to the excitation\nenergies are studied in detail. The excitation energies and wavelengths\nobtained with the two methods agree with each other within \\approx 0.01 %. The\npresent results achieve spectroscopic accuracy and provide a benchmark test for\nvarious applications and other theoretical calculations of W$^{56+}$ $-$\nW$^{61+}$ ions. They will assist spectroscopists in their assignment and direct\nidentification of observed lines in complex spectra.\n"}
+{"abstract": "  Three typical one-dimensional (1D)/quasi-1D nanocarbons, linear carbon\nchains, carbon nanotubes, and graphene nanoribbons have been proven to grow\ninside single-wall carbon nanotubes. This gives rise to three types of hybrid\nmaterials whose behaviour and properties compared among each other are far from\nunderstood. After proving successful the synthesis of these nanostructured\nmaterials in recently published work, we have now been able to study their\noxidation stability systematically by using resonance Raman spectroscopy.\nSurprisingly, the linear carbon chains, which have been theoretically predicted\nto be very unstable, are actually thermally stable up to 500 {\\deg}C assisted\nby the protection of the carbon nanotube hosts. Besides, longer linear carbon\nchains inside narrower CNTs are more stable than the shorter ones inside larger\ntubes, suggesting that the thermal stability not only depends on the length of\nlinear carbon chains alone, but it is correlated with the confinement of the\nhost tubes in a more complicated manner. In addition, graphene nanoribbons come\noverall in view as the most stable confined structures. On the other hand,\npeculiarities like the higher stability of the (6,5) CNT over its (6,4)\ncounterpart allow this study to provide a solid platform for further studies on\nthe application of these 1D nanocarbons (including true 1D linear carbon\nchains) at ambient conditions.\n"}
+{"abstract": "  The central regions of star-forming barred spiral galaxies can be devoid of\nstar formation because of the redistribution of gas along the length of the\nbar. However, there can be gas outside the length of the bar that can host star\nformation. We study a sample of barred disc galaxies in the local Universe with\nan aim to discriminate between centrally quenched and globally quenched\ngalaxies based on their positions on star-formation-rate(SFR)--stellar mass\nplots and to find a connection between the SFR of quenched galaxies and the\nlength of their bar. We classified barred galaxies as centrally quenched and\nglobally quenched based on their position on SFR--stellar mass plots, with SFRs\nderived from H$\\alpha$ flux and spectral energy distribution fits on combined\nultraviolet and optical flux. We selected galaxies as passive based on the\ndistance from the main sequence relation. From a total 2514 barred galaxies\nstudied here, we present 651 with suppressed star formation in their central\nregion but hosting star formation outside. We also find a possible correlation\nbetween bar length and SFR for the galaxies that are fully quenched because of\nthe stellar bar.\n"}
+{"abstract": "  Recent work on explainable clustering allows describing clusters when the\nfeatures are interpretable. However, much modern machine learning focuses on\ncomplex data such as images, text, and graphs where deep learning is used but\nthe raw features of data are not interpretable. This paper explores a novel\nsetting for performing clustering on complex data while simultaneously\ngenerating explanations using interpretable tags. We propose deep descriptive\nclustering that performs sub-symbolic representation learning on complex data\nwhile generating explanations based on symbolic data. We form good clusters by\nmaximizing the mutual information between empirical distribution on the inputs\nand the induced clustering labels for clustering objectives. We generate\nexplanations by solving an integer linear programming that generates concise\nand orthogonal descriptions for each cluster. Finally, we allow the explanation\nto inform better clustering by proposing a novel pairwise loss with\nself-generated constraints to maximize the clustering and explanation module's\nconsistency. Experimental results on public data demonstrate that our model\noutperforms competitive baselines in clustering performance while offering\nhigh-quality cluster-level explanations.\n"}
+{"abstract": "  We develop a ready-to-use comprehensive theory for (super) 2-vector bundles\nover smooth manifolds. It is based on the bicategory of (super) algebras,\nbimodules, and intertwiners as a model for 2-vector spaces. We show that our\n2-vector bundles form a symmetric monoidal 2-stack, we discuss the dualizable,\nfully dualizable, and invertible objects, and we derive a classification in\nterms of non-abelian Cech cohomology. One important feature of our 2-vector\nbundles is that they contain bundle gerbes as well as ordinary algebra bundles\nas full sub-bicategories, and hence provide a unifying framework for these so\nfar distinct objects. We provide several examples of isomorphisms between\nbundle gerbes and algebra bundles, coming from representation theory, twisted\nK-theory, and spin geometry.\n"}
+{"abstract": "  The COVID-19 epidemic is the last of a long list of pandemics that have\naffected humankind in the last century. In this paper, we propose a novel\nmathematical epidemiological model named SUIHTER from the names of the seven\ncompartments that it comprises: susceptible uninfected individuals (S),\nundetected (both asymptomatic and symptomatic) infected (U), isolated (I),\nhospitalized (H), threatened (T), extinct (E), and recovered (R). A suitable\nparameter calibration that is based on the combined use of least squares method\nand Markov Chain Monte Carlo (MCMC) method is proposed with the aim of\nreproducing the past history of the epidemic in Italy, surfaced in late\nFebruary and still ongoing to date, and of validating SUIHTER in terms of its\npredicting capabilities. A distinctive feature of the new model is that it\nallows a one-to-one calibration strategy between the model compartments and the\ndata that are daily made available from the Italian Civil Protection. The new\nmodel is then applied to the analysis of the Italian epidemic with emphasis on\nthe second outbreak emerged in Fall 2020. In particular, we show that the\nepidemiological model SUIHTER can be suitably used in a predictive manner to\nperform scenario analysis at national level.\n"}
+{"abstract": "  In this document the density of electrically-charged positive and negative\nparticles in a two component plasma (TCP) will be studied. Particularly, we\nfocus on a two dimensional system confined in a large rectangular box for\n$\\Gamma=2$ in the presence of two electric impurities. A method for solution,\nwhich will be called, {\\it dimensional reduction}, will be applied in order to\nstudy the redistribution of electrically charged particles along the line\njoining both impurities. Numerical results, by means of a finite elements\nmethod approach, show, due to the electric field generated by the impurities,\nan increase in the density of charges of opposite sign in the neighborhood of\neach impurity. On the other hand, the presence of charges of the same sign\ndiminishes in the same region due to the existing electric repulsion; some of\nthe repelled particles accumulate in the border of the box. Numerical\nexpansions around the borders of the impurities and the box show an almost\nlinear power law relation of the net density for the particular cases that have\nbeen analyzed. It is also studied how the maximum and minimum values of the net\ndensity depend on the electric charges of the impurities, under some particular\nconditions.\n"}
+{"abstract": "  Mesh distortion optimization is a popular research topic and has wide range\nof applications in computer graphics, including geometry modeling, variational\nshape interpolation, UV parameterization, elastoplastic simulation, etc. In\nrecent years, many solvers have been proposed to solve this nonlinear\noptimization efficiently, among which projected Newton has been shown to have\nbest convergence rate and work well in both 2D and 3D applications. Traditional\nNewton approach suffers from ill conditioning and indefiniteness of local\nenergy approximation. A crucial step in projected Newton is to fix this issue\nby projecting energy Hessian onto symmetric positive definite (SPD) cone so as\nto guarantee the search direction always pointing to decrease the energy\nlocally. Such step relies on time consuming eigen decomposition of element\nHessian, which has been addressed by several work before on how to obtain a\nconjugacy that is as diagonal as possible. In this report, we demonstrate an\nanalytic form of Hessian eigen system for distortion energy defined using\nprincipal stretches, which is the most general representation. Compared with\nexisting projected Newton diagonalization approaches, our formulation is more\ngeneral as it doesn't require the energy to be representable by tensor\ninvariants. In this report, we will only show the derivation for 3D and the\nextension to 2D case is straightforward.\n"}
+{"abstract": "  We consider a stochastic partial differential equation (SPDE) model for\nchemorepulsion, with non-linear sensitivity on the one-dimensional torus. We\nshow that for any suitable initial data there exists a pathwise unique, global\nsolution to the SPDE. Furthermore we show that the associated semi-group is\nMarkov and possesses a unique invariant measure, supported on a H\\\"older-Besov\nspace of positive regularity, which the solution law converges to exponentially\nfast. We also establish tail bounds on the invariant measure that are heavier\nthan Gaussian when measured using any $L^p$ norm.\n"}
+{"abstract": "  Hybrid quantum systems aim at combining the advantages of different physical\nsystems and to produce novel quantum devices. In particular, the hybrid\ncombination of superconducting circuits and spins in solid-state crystals is a\nversatile platform to explore many quantum electrodynamics problems. Recently,\nthe remote coupling of nitrogen-vacancy center spins in diamond via a\nsuperconducting bus was demonstrated. However, a rigorous experimental test of\nthe quantum nature of this hybrid system and in particular entanglement is\nstill missing. We review the theoretical ideas to generate and detect\nentanglement, and present our own scheme to achieve this.\n"}
+{"abstract": "  Specification synthesis is the process of deriving a model from the\ninput-output traces of a system. It is used extensively in test design, reverse\nengineering, and system identification. One type of the resulting artifact of\nthis process for cyber-physical systems is hybrid automata. They are intuitive,\nprecise, tool independent, and at a high level of abstraction, and can model\nsystems with both discrete and continuous variables. In this paper, we propose\na new technique for synthesizing hybrid automaton from the input-output traces\nof a non-linear cyber-physical system. Similarity detection in non-linear\nbehaviors is the main challenge for extracting such models. We address this\nproblem by utilizing the Dynamic Time Warping technique. Our approach is\npassive, meaning that it does not need interaction with the system during\nautomata synthesis from the logged traces; and online, which means that each\ninput/output trace is used only once in the procedure. In other words, each new\ntrace can be used to improve the already synthesized automaton. We evaluated\nour algorithm in two industrial and simulated case studies. The accuracy of the\nderived automata show promising results.\n"}
+{"abstract": "  Relation classification aims to predict a relation between two entities in a\nsentence. The existing methods regard all relations as the candidate relations\nfor the two entities in a sentence. These methods neglect the restrictions on\ncandidate relations by entity types, which leads to some inappropriate\nrelations being candidate relations. In this paper, we propose a novel\nparadigm, RElation Classification with ENtity Type restriction (RECENT), which\nexploits entity types to restrict candidate relations. Specially, the mutual\nrestrictions of relations and entity types are formalized and introduced into\nrelation classification. Besides, the proposed paradigm, RECENT, is\nmodel-agnostic. Based on two representative models GCN and SpanBERT\nrespectively, RECENT_GCN and RECENT_SpanBERT are trained in RECENT.\nExperimental results on a standard dataset indicate that RECENT improves the\nperformance of GCN and SpanBERT by 6.9 and 4.4 F1 points, respectively.\nEspecially, RECENT_SpanBERT achieves a new state-of-the-art on TACRED.\n"}
+{"abstract": "  The friction force on a test particle traveling through a plasma that is both\nstrongly coupled and strongly magnetized is studied using molecular dynamics\nsimulations. In addition to the usual stopping power component aligned\nantiparallel to the velocity, a transverse component that is perpendicular to\nboth the velocity and Lorentz force is observed. This component, which was\nrecently discovered in weakly coupled plasmas, is found to increase in both\nabsolute and relative magnitude in the strongly coupled regime. Strong coupling\nis also observed to induce a third component of the friction force in the\ndirection of the Lorentz force. These first-principles simulations reveal novel\nphysics associated with collisions in strongly coupled, strongly magnetized,\nplasmas that are not predicted by existing kinetic theories. The effect is\nexpected to influence macroscopic transport in a number of laboratory\nexperiments and astrophysical plasmas.\n"}
+{"abstract": "  In this paper we consider the question of sampling for spaces of entire\nfunctions of exponential type in several variables. The novelty resides in the\ngrowth condition we impose, that is, that their restriction to a hypersurface\nis square integrable with respect to a natural measure. The hypersurface we\nconsider is the boundary $b\\mathcal U$ of the Siegel upper half-space $\\mathcal\nU$ and it is fundamental that $b\\mathcal U$ can be identified with the\nHeisenberg group $\\mathbb H_n$. We consider entire functions in $\\mathbb\nC^{n+1}$ of exponential type with respect to the hypersurface $b\\mathcal U$\nwhose restriction to $b\\mathcal U$ are square integrable with respect to the\nHaar measure on $\\mathbb H_n$. For these functions we prove a version of the\nWhittaker--Kotelnikov--Shannon Theorem. Instrumental in our work are spaces of\nentire functions in $\\mathbb C^{n+1}$ of exponential type with respect to the\nhypersurface $b\\mathcal U$ whose restrictions to $b\\mathcal U$ belong to some\nhomogeneous Sobolev space on $\\mathbb H_n$. For these spaces, using the group\nFourier transform on $\\mathbb H_n$, we prove a Paley--Wiener type theorem and a\nPlancherel--P\\'olya type inequality.\n"}
+{"abstract": "  We prove a result on the asymptotic proportion of randomly chosen pairs of\npermutations in the symmetric group $S_n$ which \"invariably\" generate a\nnonsolvable subgroup, i.e., whose cycle structures cannot possibly both occur\nin the same solvable subgroup of $S_n$. As an application, we obtain that for a\nlarge degree \"random\" integer polynomial $f$, reduction modulo two different\nprimes can be expected to suffice to prove the nonsolvability of\n$Gal(f/\\mathbb{Q})$.\n"}
+{"abstract": "  We consider the influence of transverse confinement on the instability\nproperties of velocity and density distributions reminiscent of those\npertaining to exchange flows in stratified inclined ducts, such as the recent\nexperiment of Lefauve et al. (J. Fluid Mech. 848, 508-544, 2018). Using a\nnormal mode streamwise and temporal expansion for flows in ducts with various\naspect ratios $B$ and non-trivial transverse velocity profiles, we calculate\ntwo-dimensional (2D) dispersion relations with associated eigenfunctions\nvarying in the 'crosswise' direction, in which the density varies, and the\nspanwise direction, both normal to the duct walls and to the flow direction. We\nalso compare these 2D dispersion relations to the so-called one-dimensional\n(1D) dispersion relation obtained for spanwise invariant perturbations, for\ndifferent aspect ratios $B$ and bulk Richardson numbers $Ri_b$. In this limited\nparameter space, the presence of lateral walls has a stabilizing effect.\nFurthermore, accounting for spanwise-varying perturbations results in a\nplethora of unstable modes, the number of which increases as the aspect ratio\nis increased. These modes present an odd-even regularity in their spatial\nstructures, which is rationalized by comparison to the so-called\none-dimensional oblique (1D-O) dispersion relation obtained for oblique waves.\nFinally, we show that in most cases, the most unstable 2D mode is the one that\noscillates the least in the spanwise direction, as a consequence of viscous\ndamping. However, in a limited region of the parameter space and in the absence\nof stratification, we show that a secondary mode with a more complex `twisted'\nstructure dominated by crosswise vorticity becomes more unstable than the least\noscillating Kelvin-Helmholtz mode associated with spanwise vorticity.\n"}
+{"abstract": "  In this paper, the $q$-th dual curvature measure is extended to convex\nfunctions and the associated Minkowski problem is posed. A special case\nincludes the $q$-th dual curvature measure of convex bodies which defined by\nHuang, Lutwak, Yang and Zhang. Existence for the functional dual Minkowski\nproblem is showed when $q\\leq0$ and the uniqueness part is obtained with some\nassumptions.\n"}
+{"abstract": "  The use of optimal transport cost for learning generative models has become\npopular with Wasserstein Generative Adversarial Networks (WGAN). Training of\nWGAN relies on a theoretical background: the calculation of the gradient of the\noptimal transport cost with respect to the generative model parameters. We\nfirst demonstrate that such gradient may not be defined, which can result in\nnumerical instabilities during gradient-based optimization. We address this\nissue by stating a valid differentiation theorem in the case of entropic\nregularized transport and specify conditions under which existence is ensured.\nBy exploiting the discrete nature of empirical data, we formulate the gradient\nin a semi-discrete setting and propose an algorithm for the optimization of the\ngenerative model parameters. Finally, we illustrate numerically the advantage\nof the proposed framework.\n"}
+{"abstract": "  Algorithms engineered to leverage rich behavioral and biometric data to\npredict individual attributes and actions continue to permeate public and\nprivate life. A fundamental risk may emerge from misconceptions about the\nsensitivity of such data, as well as the agency of individuals to protect their\nprivacy when fine-grained (and possibly involuntary) behavior is tracked. In\nthis work, we examine how individuals adjust their behavior when incentivized\nto avoid the algorithmic prediction of their intent. We present results from a\nvirtual reality task in which gaze, movement, and other physiological signals\nare tracked. Participants are asked to decide which card to select without an\nalgorithmic adversary anticipating their choice. We find that while\nparticipants use a variety of strategies, data collected remains highly\npredictive of choice (80% accuracy). Additionally, a significant portion of\nparticipants became more predictable despite efforts to obfuscate, possibly\nindicating mistaken priors about the dynamics of algorithmic prediction.\n"}
+{"abstract": "  Nonlinear networks can host spatially compact time periodic solutions called\ncompact breathers. Such solutions can exist accidentally (i.e. for specific\nnonlinear strength values) or parametrically (i.e. for any nonlinear strength).\nIn this work we introduce an efficient generator scheme for one-dimensional\nnonlinear lattices which support either types of compact breathers spanned over\na given number U of lattice's unit cells and any number of sites v per cell -\nscheme which can be straightforwardly extended to higher dimensions. This\nscheme in particular allows to show the existence and explicitly construct\nexamples of parametric compact breathers with inhomogeneous spatial profiles --\nextending previous results which indicated that only homogeneous parametric\ncompact breathers exist. We provide explicit d=1 lattices with different v\nsupporting compact breather solutions for U=1,2.\n"}
+{"abstract": "  All the laws of physics are time-reversible. Time arrow emerges only when\nensembles of classical particles are treated probabilistically, outside of\nphysics laws, and the entropy and the second law of thermodynamics are\nintroduced. In quantum physics, no mechanism for a time arrow has been proposed\ndespite its intrinsic probabilistic nature. In consequence, one cannot explain\nwhy an electron in an excited state will \"spontaneously\" transition into a\nground state as a photon is created and emitted, instead of continuing in its\nreversible unitary evolution. To address such phenomena, we introduce an\nentropy for quantum physics, which will conduce to the emergence of a time\narrow.\n  The entropy is a measure of randomness over the degrees of freedom of a\nquantum state. It is dimensionless; it is a relativistic scalar, it is\ninvariant under coordinate transformation of position and momentum that\nmaintain conjugate properties and under CPT transformations; and its minimum is\npositive due to the uncertainty principle.\n  To excogitate why some quantum physical processes cannot take place even\nthough they obey conservation laws, we partition the set of all evolutions of\nan initial state into four blocks, based on whether the entropy is (i)\nincreasing but not a constant, (ii) decreasing but not a constant, (iii) a\nconstant, (iv) oscillating. We propose a law that in quantum physics entropy\n(weakly) increases over time. Thus, evolutions in the set (ii) are disallowed,\nand evolutions in set (iv) are barred from completing an oscillation period by\ninstantaneously transitioning to a new state. This law for quantum physics\nlimits physical scenarios beyond conservation laws, providing causality\nreasoning by defining a time arrow.\n"}
+{"abstract": "  Detecting arguments in online interactions is useful to understand how\nconflicts arise and get resolved. Users often use figurative language, such as\nsarcasm, either as persuasive devices or to attack the opponent by an ad\nhominem argument. To further our understanding of the role of sarcasm in\nshaping the disagreement space, we present a thorough experimental setup using\na corpus annotated with both argumentative moves (agree/disagree) and sarcasm.\nWe exploit joint modeling in terms of (a) applying discrete features that are\nuseful in detecting sarcasm to the task of argumentative relation\nclassification (agree/disagree/none), and (b) multitask learning for\nargumentative relation classification and sarcasm detection using deep learning\narchitectures (e.g., dual Long Short-Term Memory (LSTM) with hierarchical\nattention and Transformer-based architectures). We demonstrate that modeling\nsarcasm improves the argumentative relation classification task\n(agree/disagree/none) in all setups.\n"}
+{"abstract": "  Using Ginzburg-Landau formalism, we theoretically study the isothermal shear\nmodulus anomaly of an unconventional superconductor with a multicomponent order\nparameter, when the superconducting transition is split by a symmetry-breaking\nfield. Experimental signatures are proposed for both chiral and nematic\nsuperconductors. Particularly striking is the vanishing of $C_{66}$ across the\nlower transition to a nematic superconducting state. Our findings can guide\nfuture experiments and shed new lights on materials such as Sr$_2$RuO$_4$ and\n$M_x$Bi$_2$Se$_3$.\n"}
+{"abstract": "  We develop a systematic and unified approach to estimate all possible\nsecondary (i.e. non-primordial) nonlinear effects to the cosmic microwave\nbackground (CMB) polarization, named curve-of-sight integration approach. In\nthis approach, the Boltzmann equation for polarized photons is rewritten in a\nline-of-sight integral along an exact geodesic in the perturbed universe,\nrather than a geodesic in the background universe used in the linear-order CMB\ncalculation. This approach resolves the difficulty to solve the Boltzmann\nhierarchy with the nonlinear gravitational effects in the photon free-streaming\nregime and thus unifies the standard remapping approach for CMB lensing into\nthe direct approach solving the Boltzmann equation for the nonlinear\ncollisional effects. In this paper, we derive formulae that: (i) include all\nthe nonlinear effects; (ii) can treat extended sources such as the\ncontributions after the reionization. It offers a solid framework to discuss\npossible systematics in the standard estimation of CMB lensing by the remapping\napproach. As an explicit demonstration, we estimate the secondary B-mode power\nspectrum induced by all foreground gravitational effects: lensing, redshift,\ntime-delay, emission-angle, and polarization-rotation effects. We define these\neffects properly so that they do not have any overlap, also without overlooking\nany effect. Then, we show that these effects only give corrections of the order\nof 0.001-0.01% to the standard lensing-induced B-mode power spectrum in the\nconcordance $\\Lambda$ cold dark matter model. Our result confirms the\nreliability of using the remapping approach in upcoming CMB experiments aiming\nto detect the primordial gravitational waves with the tensor-to-scalar ratio of\n$r \\sim 10^{-3}$.\n"}
+{"abstract": "  We provide explicit sequence space representations for the test function and\ndistribution spaces occurring in the Valdivia-Vogt structure tables by making\nuse of Wilson bases generated by compactly supported smooth windows.\nFurthermore, we show that these kind of bases are common unconditional Schauder\nbases for all separable spaces occurring in these tables. Our work implies that\nthe Valdivia-Vogt structure tables for test functions and distributions may be\ninterpreted as one large commutative diagram.\n"}
+{"abstract": "  We study the quantum chaos in the Bose-Fermi Kondo model in which the\nimpurity spin interacts with conduction electrons and a bosonic bath at the\nintermediate temperature in the large $N$ limit. The out-of-time-ordered\ncorrelator is calculated based on the Bethe-Salpeter equation and the Lyapunov\nexponent $\\lambda_L$ is extracted. Our calculation shows that the Lyapunov\nexponent monotonically increases as the Kondo coupling $J_K$ increases, and it\ncan reach an order of $\\lambda_L\\sim T$ as $J_K$ approaches the $MCK$ point.\nFurthermore, we also demonstrate that $\\lambda_L$ decreases monotonously as the\nimpurity and bosonic bath coupling $g$ increases, which is contrary to the\ngeneral expectation that the most chaotic property occurs at the quantum\ncritical point with the non-Fermi liquid nature.\n"}
+{"abstract": "  An excitation of a large-amplitude out-of-plane magnetization oscillation in\na ferromagnet by the spin Hall effect is of great interest for practical\napplications such as microwave generator and neuromorphic computing. However,\nboth experimental and theoretical works have revealed that only small-amplitude\noscillation around an in-plane easy axis can be excited via the spin Hall\neffect. Here, we propose that an out-of-plane oscillation can be excited due to\nan assistance of field-like torque. We focus on an in-plane magnetized\nferromagnet with an easy axis parallel to current direction. We notice that the\nfield-like torque with an appropriate sign provides an additional field\nmodifying the dynamic trajectory of the magnetization and drives the\nauto-oscillation. The condition on the sign of the field-like torque is\nsatisfied for typical nonmagnet used in spin Hall devices such as tungsten.\n"}
+{"abstract": "  We study a stochastic particle system which is motivated from grain boundary\ncoarsening in two-dimensional networks. Each particles lives on the positive\nreal line and is labeled as belonging to either Species 1 or Species 2. Species\n1 particles drift at unit speed toward the origin, while Species 2 particles do\nnot move. When a particle in Species 1 hits the origin, it is removed, and a\nrandomly selected particle mutates from Species 2 to Species 1. The process\ndescribed is an example of a high-dimensional piecewise deterministic Markov\nprocess (PDMP), in which deterministic flow is punctuated with stochastic\njumps. Our main result is a proof of exponential concentration inequalities of\nthe Kolmogorov-Smirnoff distance between empirical measures of the particle\nsystem and solutions of limiting nonlinear kinetic equations. Our method of\nproof involves a time and space discretization of the kinetic equations, which\nwe compare with the particle system to derive recurrence inequalities for\ncomparing total numbers in small intervals. To show these recurrences occur\nwith high probability, we appeal to a state dependent Hoeffding type inequality\nat each time increment.\n"}
+{"abstract": "  Analyzing particle data plays an important role in many scientific\napplications such as fluid simulation, cosmology simulation and molecular\ndynamics. While there exist methods that can perform feature extraction and\ntracking for volumetric data, performing those tasks for particle data is more\nchallenging because of the lack of explicit connectivity information. Although\none may convert the particle data to volume first, this approach is at risk of\nincurring error and increasing the size of the data. In this paper, we take a\ndeep learning approach to create feature representations for scientific\nparticle data to assist feature extraction and tracking. We employ a deep\nlearning model, which produces latent vectors to represent the relation between\nspatial locations and physical attributes in a local neighborhood. With the\nlatent vectors, features can be extracted by clustering these vectors. To\nachieve fast feature tracking, the mean-shift tracking algorithm is applied in\nthe feature space, which only requires inference of the latent vector for\nselected regions of interest. We validate our approach using two datasets and\ncompare our method with other existing methods.\n"}
+{"abstract": "  In Disquisitiones Arithmeticae, Gauss studied binary quadratic forms and\nintroduced a very general version of a composition operator that allows\ncomposing even forms of different discriminants and imprimitive forms. Section\nV of Disquisitiones contains truly revolutionary ideas and involves very\ncomplicated computations, sometimes left to the reader. With this theory, Gauss\ndevelops a method of factorization using quadratic residues obtained by binary\nquadratic forms. In this work we explore Gauss method making many of the\ncomputations necessary to better understand his work.\n"}
+{"abstract": "  The {\\it exchange} interaction arising from the particle indistinguishability\nis of central importance to physics of many-particle quantum systems. Here we\nstudy analytically the dynamical generation of quantum entanglement induced by\nthis interaction in an isolated system, namely, an ideal Fermi gas confined in\na chaotic cavity, which evolves unitarily from a non-Gaussian pure state. We\nfind that the breakdown of the quantum-classical correspondence of particle\nmotion, via dramatically changing the spatial structure of many-body\nwavefunction, leads to profound changes of the entanglement structure.\nFurthermore, for a class of initial states, such change leads to the approach\nto thermal equilibrium everywhere in the cavity, with the well-known Ehrenfest\ntime in quantum chaos as the thermalization time. Specifically, the quantum\nexpectation values of various correlation functions at different spatial scales\nare all determined by the Fermi-Dirac distribution. In addition, by using the\nreduced density matrix (RDM) and the entanglement entropy (EE) as local probes,\nwe find that the gas inside a subsystem is at equilibrium with that outside,\nand its thermal entropy is the EE, even though the whole system is in a pure\nstate. As a by-product of this work, we provide an analytical solution\nsupporting an important conjecture on thermalization, made and numerically\nstudied by Garrison and Grover in: Phys. Rev. X \\textbf{8}, 021026 (2018), and\nstrengthen its statement.\n"}
+{"abstract": "  In this work, we develop a method to generate infinite high-resolution images\nwith diverse and complex content. It is based on a perfectly equivariant\ngenerator with synchronous interpolations in the image and latent spaces.\nLatent codes, when sampled, are positioned on the coordinate grid, and each\npixel is computed from an interpolation of the nearby style codes. We modify\nthe AdaIN mechanism to work in such a setup and train the generator in an\nadversarial setting to produce images positioned between any two latent\nvectors. At test time, this allows for generating complex and diverse infinite\nimages and connecting any two unrelated scenes into a single arbitrarily large\npanorama. Apart from that, we introduce LHQ: a new dataset of \\lhqsize\nhigh-resolution nature landscapes. We test the approach on LHQ, LSUN Tower and\nLSUN Bridge and outperform the baselines by at least 4 times in terms of\nquality and diversity of the produced infinite images. The project page is\nlocated at https://universome.github.io/alis.\n"}
+{"abstract": "  Few-shot learning deals with the fundamental and challenging problem of\nlearning from a few annotated samples, while being able to generalize well on\nnew tasks. The crux of few-shot learning is to extract prior knowledge from\nrelated tasks to enable fast adaptation to a new task with a limited amount of\ndata. In this paper, we propose meta-learning kernels with random Fourier\nfeatures for few-shot learning, we call MetaKernel. Specifically, we propose\nlearning variational random features in a data-driven manner to obtain\ntask-specific kernels by leveraging the shared knowledge provided by related\ntasks in a meta-learning setting. We treat the random feature basis as the\nlatent variable, which is estimated by variational inference. The shared\nknowledge from related tasks is incorporated into a context inference of the\nposterior, which we achieve via a long-short term memory module. To establish\nmore expressive kernels, we deploy conditional normalizing flows based on\ncoupling layers to achieve a richer posterior distribution over random Fourier\nbases. The resultant kernels are more informative and discriminative, which\nfurther improves the few-shot learning. To evaluate our method, we conduct\nextensive experiments on both few-shot image classification and regression\ntasks. A thorough ablation study demonstrates that the effectiveness of each\nintroduced component in our method. The benchmark results on fourteen datasets\ndemonstrate MetaKernel consistently delivers at least comparable and often\nbetter performance than state-of-the-art alternatives.\n"}
+{"abstract": "  Observations and modelling of stars with near-solar masses in their early\nphases of evolution is critical for a better understanding of how dynamos of\nsolar-type stars evolve. We examine the chemical composition and the spot\ndistribution of the pre-main-sequence solar analogue EK Dra. Using spectra from\nthe HERMES Spectrograph (La Palma), we obtain the abundances of 23 elements\nwith respect to the solar ones, which lead to a $[{\\rm Fe/H}]=0.03$, with\nsignificant overabundance of Li and Ba. The s-process elements Sr, Y, and Ce\nare marginally overabundant, while Co, Ni, Cu, Zn are marginally deficient\ncompared to solar abundances. The overabundance of Ba is most likely due to the\nassumption of depth-independent microturbulent velocity. Li abundance is\nconsistent with the age and the other abundances may indicate distinct initial\nconditions of the pre-stellar nebula. We estimate a mass of 1.04 $M_\\odot$ and\nan age of $27^{+11}_{-8}$\\,Myr using various spectroscopic and photometric\nindicators. We study the surface distribution of dark spots, using 17 spectra\ncollected during 15 nights using the CAFE Spectrograph (Calar Alto). We also\nconduct flux emergence and transport (FEAT) simulations for EK Dra's parameters\nand produce 15-day-averaged synoptic maps of the likely starspot distributions.\nUsing Doppler imaging, we reconstruct the surface brightness distributions for\nthe observed spectra and FEAT simulations, which show overall agreement for\npolar and mid-latitude spots, while in the simulations there is a lack of\nlow-latitude spots compared to the observed image. We find indications that\ncross-equatorial extensions of mid-latitude spots can be artefacts of the less\nvisible southern-hemisphere activity.\n"}
+{"abstract": "  Income verification is the problem of validating a person's stated income\ngiven basic identity information such as name, location, job title and\nemployer. It is widely used in the context of mortgage lending, rental\napplications and other financial risk models. However, the current processes\nsurrounding verification involve significant human effort and document\ngathering which can be both time-consuming and expensive. In this paper, we\npropose a novel model for verifying an individual's income given very limited\nidentity information typically available in loan applications. Our model is a\ncombination of a deep neural network and hand-engineered features. The hand\nengineered features are based upon matching the input information against\nincome records extracted automatically from various publicly available online\nsources (e.g. payscale.com, H-1B filings, government employee salaries). We\nconduct experiments on two data sets, one simulated from H-1B records and the\nother from a real-world data set of peer-to-peer (P2P) loan applications\nobtained from one of the world's largest P2P lending platform. Our results show\na significant reduction in error of 3-6% relative to several strong baselines.\nWe also perform ablation studies to demonstrate that a combined model is indeed\nnecessary to achieve state-of-the-art performance on this task.\n"}
+{"abstract": "  We derive constraints on scalar field theories coupled to gravity by using\nrecently developed positivity bounds in the presence of gravity. It is found\nthat a canonically-normalized real scalar cannot have an arbitrarily flat\npotential unless some new physics enters well below the Planck scale. An upper\nbound on the scale of new physics is determined by loop corrections to the\nself-energy. Our result provides a swampland condition for scalar potentials.\n"}
+{"abstract": "  Let $F(x)$ be an integer coefficient polynomial with degree at least $2.$\nDefine the sequence $a_n$ by $a_n=F(a_{n-1})$ for all $n\\ge 1$ and $a_0=0.$ Let\n$\\mathscr{B}_{F,G,k}$ be the set of all positive integers $n$ such that $k\\mid\n\\gcd(G(n),a_n)$ and if $p\\mid \\gcd(G(n),a_n)$ for some $p$, then $p\\mid k.$ And\n$\\mathscr{A}_{F,G,k}$ be the subset of $\\mathscr{B}_{F,G,k}$ such that\n$\\mathscr{A}_{F,G,k}=\\{n\\ge 1 : \\gcd(G(n),a_n)=k\\}.$ In this article, we\nexplain the asymptotic density of $\\mathscr{A}_{F,G,k}$ and\n$\\mathscr{B}_{F,G,k}$ for a class of $(F,G)$ and also compute the explicit\ndensity of $\\mathscr{A}_{F,G,k}$ and $\\mathscr{B}_{F,G,k}$ for $G(x)=x.$\n"}
+{"abstract": "  Immersive 3D media is an emerging type of media that captures, encodes and\nreconstructs the 3D appearance of people and objects, with applications in\ntele-presence, teleconference, entertainment, gaming and other fields. In this\npaper, we discuss a novel concept of live 3D immersive media streaming in a\nserverless setting. In particular, we present a novel network-centric adaptive\nstreaming framework which deviates from a traditional client-based adaptive\nstreaming used in 2D video. In our framework, the decisions for the production\nof the transcoding profiles are taken in a centralized manner, by considering\nconsumer metrics vs provisioning costs and inferring an expected consumer\nquality of experience and behaviour based on them. In addition, we demonstrate\nthat a naive application of the serverless paradigm might be sub optimal under\nsome common immersive 3D media scenarios.\n"}
+{"abstract": "  Acoustic holograms have promising applications in sound-field reconstruction,\nparticle manipulation, ultrasonic haptics and therapy. This paper reports on\nthe theoretical, numerical, and experimental investigation of multiplexed\nacoustic holograms at both audio and ultrasonic frequencies via a rationally\ndesigned transmission-type acoustic metamaterial. The proposed meta-hologram is\ncomposed of two Fabry-Perot resonant channels per unit cell, which enables the\nsimultaneous modulation of the transmitted amplitude and phase at two desired\nfrequencies. In contrast to conventional acoustic metamaterial-based holograms,\nthe design strategy proposed here, provides a new degree of freedom (frequency)\nthat can actively tailor holograms that are otherwise completely passive and\nhence significantly enhances the information encoded in acoustic metamaterials.\nTo demonstrate the multiplexed acoustic metamaterial, we first show the\nprojection of two different high-quality meta-holograms at 14 kHz and 17 kHz,\nwith the patterns of the letters, N and S. We then demonstrate two-channel\nultrasound focusing and annular beams generation for the incident ultrasonic\nfrequencies of 35 kHz and 42.5 kHz. These multiplexed acoustic meta-holograms\noffer a technical advance to tackle the rising challenges in the fields of\nacoustic metamaterials, architectural acoustics, and medical ultrasound.\n"}
+{"abstract": "  In this work we will focus on the existence of weak solutions for a system\ndescribing a general compressible viscous fluid in the case of the pressure\nbeing a linear function of the density and the viscous stress tensor being a\nnon-linear function of the symmetric velocity gradient. More precisely, we will\nfirst prove the existence of dissipative solutions and study under which\nconditions it is possible to guarantee the existence of weak solutions.\n"}
+{"abstract": "  In 2017 a definition of spiral tilings was given, thereby answering a\nquestion posed by Gr\\\"unbaum and Shephard in the late 1970s. The author had the\npleasure to discuss the topic via e-mail with Branko Gr\\\"unbaum in his 87th\nyear. During this correspondence the question arose whether a spiral structure\n(given a certain definition of it) could be recognized automatically or whether\n\"to some extent, at least, the spiral effect is psychological\", as Gr\\\"unbaum\nand Shephard had conjectured in 1987 (see exercise section of chapter 9.5 in\n\"Tilings and Patterns\"). In this paper, an algorithm for automatic detection of\nsuch a tiling's spiral structure and its first implementation results will be\ndiscussed. Finally, the definitions for several types of spiral tilings are\nrefined based on this investigation.\n"}
+{"abstract": "  We propose a combination of a variational autoencoder and a transformer based\nmodel which fully utilises graph convolutional and graph pooling layers to\noperate directly on graphs. The transformer model implements a novel node\nencoding layer, replacing the position encoding typically used in transformers,\nto create a transformer with no position information that operates on graphs,\nencoding adjacent node properties into the edge generation process. The\nproposed model builds on graph generative work operating on graphs with edge\nfeatures, creating a model that offers improved scalability with the number of\nnodes in a graph. In addition, our model is capable of learning a disentangled,\ninterpretable latent space that represents graph properties through a mapping\nbetween latent variables and graph properties. In experiments we chose a\nbenchmark task of molecular generation, given the importance of both generated\nnode and edge features. Using the QM9 dataset we demonstrate that our model\nperforms strongly across the task of generating valid, unique and novel\nmolecules. Finally, we demonstrate that the model is interpretable by\ngenerating molecules controlled by molecular properties, and we then analyse\nand visualise the learned latent representation.\n"}
+{"abstract": "  We measure the transverse baryon acoustic oscillations (BAO) signal in the\nlocal Universe using a sample of blue galaxies from the Sloan Digital Sky\nSurvey (SDSS) survey as a cosmological tracer. The method is weakly dependent\non a cosmological model and is suitable for 2D analyses in thin redshift bins\nto investigate the SDSS data in the interval $z {\\in} [0.105, 0.115]$. We\ndetect the transverse BAO signal ${\\theta}_{BAO} = 19.8^{\\deg} {\\pm}\n1.05^{\\deg}$ at $z_{eff} = 0.11$, with a statistical significance of $2.2\n{\\sigma}$. Additionally, we perform tests that confirm the robustness of this\nangular BAO signature. Supported by a large set of log-normal simulations, our\nerror analyses include statistical and systematic contributions. In addition,\nconsidering the sound horizon scale calculated by the Planck Collaboration,\n$r_{s}^{Planck}$, and the ${\\theta}_{BAO}$ value obtained here, we obtain a\nmeasurement of the angular diameter distance $D_{A}(0.11) = 258.31 {\\pm} 13.71\n\\,Mpc/h$. Moreover, combining this ${\\theta}_{BAO}$ measurement at low redshift\nwith other BAO angular scale data reported in the literature, we perform\nstatistical analyses for the cosmological parameters of some Lambda cold dark\nmatter (${\\Lambda}$CDM) type models.\n"}
+{"abstract": "  We consider the problem of guaranteeing that the transient voltages and\ncurrents stay within prescribed bounds in Direct Current (DC) microgrids, when\nthe controller does not have access to accurate system dynamics due to the load\nbeing unknown and/or time-varying. To achieve this, we propose an optimization\nbased controller design using control barrier functions. We show that the\nproposed controller has a decentralized structure and is robust with respect to\nthe uncertainty in the precise values of the system parameters, such as the\nload.\n"}
+{"abstract": "  The computational cost of ab initio nuclear structure calculations is\nrendered particularly acute by the presence of (at least) three-nucleon\ninteractions. This feature becomes especially critical now that many-body\nmethods aim at extending their reach beyond mid-mass nuclei. Consequently,\nstate-of-the-art ab initio calculations are typically performed while\napproximating three-nucleon interactions in terms of effective, i.e.\nsystem-dependent, zero-, one- and two-nucleon operators. While straightforward\nin doubly closed-shell nuclei, existing approximation methods based on\nnormal-ordering techniques involve either two- and three-body density matrices\nor a symmetry-breaking one-body density matrix in open-shell systems. In order\nto avoid such complications, a simple, flexible, universal and accurate\napproximation technique involving the convolution of the initial operator with\na sole symmetry-invariant one-body matrix is presently formulated and tested\nnumerically. Employed with a low-resolution Hamiltonian, the novel\napproximation method is shown to induce errors below $2-3\\%$ across a large\nrange of nuclei, observables and many-body methods.\n"}
+{"abstract": "  We utilize simulations of electron scattering by a chain of dynamical quantum\nspins, to analyze the interplay between the spin transfer effect and the\nmagnetization dynamics. We show that the complex interactions between the\nspin-polarized electrons and the dynamical states of the local spins can be\ndecomposed into separate processes involving electron reflection and\ntransmission, as well as absorption and emission of magnons - the quanta of\nmagnetization dynamics. Analysis shows that these processes are substantially\nconstrained by the energy and momentum conversation laws, resulting in a\nsignificant dependence of spin transfer on the electron's energy and the\ndynamical state of the local spins. Our results suggest that exquisite control\nof spin transfer efficiency and of the resulting dynamical magnetization states\nmay be achievable by tailoring the spectral characteristics of the conduction\nelectrons and of the magnetic systems.\n"}
+{"abstract": "  In this work we present a novel approach to hierarchical reinforcement\nlearning for linearly-solvable Markov decision processes. Our approach assumes\nthat the state space is partitioned, and the subtasks consist in moving between\nthe partitions. We represent value functions on several levels of abstraction,\nand use the compositionality of subtasks to estimate the optimal values of the\nstates in each partition. The policy is implicitly defined on these optimal\nvalue estimates, rather than being decomposed among the subtasks. As a\nconsequence, our approach can learn the globally optimal policy, and does not\nsuffer from the non-stationarity of high-level decisions. If several partitions\nhave equivalent dynamics, the subtasks of those partitions can be shared. If\nthe set of boundary states is smaller than the entire state space, our approach\ncan have significantly smaller sample complexity than that of a flat learner,\nand we validate this empirically in several experiments.\n"}
+{"abstract": "  We consider the derivative nonlinear Schr\\\"odinger equation in one space\ndimension, posed both on the line and on the circle. This model is known to be\ncompletely integrable and $L^2$-critical with respect to scaling.\n  The first question we discuss is whether ensembles of orbits with\n$L^2$-equicontinuous initial data remain equicontinuous under evolution. We\nprove that this is true under the restriction $M(q)=\\int |q|^2 < 4\\pi$. We\nconjecture that this restriction is unnecessary.\n  Further, we prove that the problem is globally well-posed for initial data in\n$H^{1/6}$ under the same restriction on $M$. Moreover, we show that this\nrestriction would be removed by a successful resolution of our equicontinuity\nconjecture.\n"}
+{"abstract": "  We introduce a new method permitting the analytical determination of\nentanglement entropy (and related quantities) between configurations of a\nquantum field, which is either free or in interaction with a classical source,\nat two distinct spatial locations. We show how such a setup can be described by\na bipartite, continuous Gaussian system. This allows us to derive explicit and\nexact formulas for the entanglement entropy, the mutual information and the\nquantum discord, solely in terms of the Fourier-space power spectra of the\nfield. This contrasts with previous studies, which mostly rely on numerical\nconsiderations. As an illustration, we apply our formalism to massless fields\nin flat space, where exact expressions are derived that only involve the ratio\nbetween the size of the regions over which the field is coarse-grained, and the\ndistance between these regions. In particular, we recover the well-known fact\nthat mutual information decays as the fourth power of this ratio at large\ndistances, as previously observed in numerical works. Our method leads to the\nfirst analytical derivation of this result, and to an exact formula that also\napplies to arbitrary distances. Finally, we determine the quantum discord and\nfind that it identically vanishes (unless coarse-graining is performed over\nsmeared spheres, in which case it obeys the same suppression at large distance\nas mutual information).\n"}
+{"abstract": "  We present a planning and control framework for physics-based manipulation\nunder uncertainty. The key idea is to interleave robust open-loop execution\nwith closed-loop control. We derive robustness metrics through contraction\ntheory. We use these metrics to plan trajectories that are robust to both state\nuncertainty and model inaccuracies. However, fully robust trajectories are\nextremely difficult to find or may not exist for many multi-contact\nmanipulation problems. We separate a trajectory into robust and non-robust\nsegments through a minimum cost path search on a robustness graph. Robust\nsegments are executed open-loop and non-robust segments are executed with\nmodel-predictive control. We conduct experiments on a real robotic system for\nreaching in clutter. Our results suggest that the open and closed-loop approach\nresults in up to 35% more real-world success compared to open-loop baselines\nand a 40% reduction in execution time compared to model-predictive control. We\nshow for the first time that partially open-loop manipulation plans generated\nwith our approach reach similar success rates to model-predictive control,\nwhile achieving a more fluent/real-time execution. A video showing real-robot\nexecutions can be found at https://youtu.be/rPOPCwHfV4g.\n"}
+{"abstract": "  This paper presents a personalized adaptive cruise control (PACC) design that\ncan learn driver behavior and adaptively control the semi-autonomous vehicle\n(SAV) in the car-following scenario, and investigates its impacts on mixed\ntraffic. In mixed traffic where the SAV and human-driven vehicles share the\nroad, the SAV's driver can choose a PACC tuning that better fits the driver's\npreferred driving behaviors. The individual driver's preferences are learned\nthrough the inverse reinforcement learning (IRL) approach by recovering a\nunique cost function from the driver's demonstrated driving data that best\nexplains the observed driving style. The proposed PACC design plans the motion\nof the SAV by minimizing the learned unique cost function considering the short\npreview information of the preceding human-driven vehicle. The results reveal\nthat the learned driver model can identify and replicate the personalized\ndriving behaviors accurately and consistently when following the preceding\nvehicle in a variety of traffic conditions. Furthermore, we investigated the\nimpacts of the PACC with different drivers on mixed traffic by considering time\nheadway, gap distance, and fuel economy assessments. A statistical\ninvestigation shows that the impacts of the PACC on mixed traffic vary among\ntested drivers due to their intrinsic driving preferences.\n"}
+{"abstract": "  The quark contribution to the QCD pressure, $P_q$, is evaluated up to\nnext-to-leading order (NLO) within the renormalization group optimized\nperturbation theory (RGOPT) resummation approach. To evaluate the complete QCD\npressure we simply add the perturbative NLO contribution from massless gluons\nto the resummed $P_q$. Despite of this unsophisticated approximation our\nresults for $P = P_q +P_g$ at the central scale $M\\sim 2\\pi T$ show a\nremarkable agreement with ab initio lattice predictions for $0.25 \\lesssim T\n\\lesssim 1 \\, {\\rm GeV}$. We also show that by being imbued with RG properties,\nthe RGOPT produces a drastic reduction of the embarrassing remnant scale\ndependence that plagues both standard thermal perturbative QCD and hard thermal\nloop perturbation theory (HTLpt) applications.\n"}
+{"abstract": "  This paper studies time changes of Brownian motions by positive continuous\nadditive functionals. Under a certain regularity condition on the associated\nRevuz measures, we prove that the resolvents of the time-changed Brownian\nmotions are locally H\\\"{o}lder continuous in the spatial components. We also\nobtain lower bounds for the indice of the H\\\"{o}lder continuity.\n"}
+{"abstract": "  In this brief review, we present some cosmological models with a Hybrid Scale\nFactor (HSF) in the framework of general relativity (GR). The hybrid scale\nfactor fosters an early deceleration as well as a late time acceleration and\nmimics the present Universe. The dynamical aspects of different cosmological\nmodels with HSF in the presence of different matter fields have been discussed.\n"}
+{"abstract": "  The simultaneous presence of liquid and gas in porous media increases flow\nheterogeneity compared to saturated flows. However, so far the impact of\nsaturation on flow statistics and transport dynamics remained unclear. Here, we\ndevelop a theoretical framework that captures the impact of flow reorganization\non the statistics of pore-scale fluid velocities, due to the presence of gas in\nthe pore space, which leads to the development of a highly-structured flow\nfield. Preferential flow is distributed spatially through the denoted backbone\nand flow recirculation occurs in dead-end regions branching from it. This\ninduces a marked change in the scaling of the velocity PDF compared to the\nsaturated case, and a sharp transition to strongly anomalous transport. We\ndevelop a transport model based on the continuous time random walk theory that\nsuccessfully predicts advective transport dynamics for all saturation degrees.\nOur results provide a new modelling framework linking phase heterogeneity to\nflow heterogeneity and to transport in unsaturated media.\n"}
+{"abstract": "  Text lines are important parts of handwritten document images and easier to\nanalyze by further applications. Despite recent progress in text line\ndetection, text line extraction from a handwritten document remains an unsolved\ntask. This paper proposes to use a fully convolutional network for text line\ndetection and energy minimization for text line extraction. Detected text lines\nare represented by blob lines that strike through the text lines. These blob\nlines assist an energy function for text line extraction. The detection stage\ncan locate arbitrarily oriented text lines. Furthermore, the extraction stage\nis capable of finding out the pixels of text lines with various heights and\ninterline proximity independent of their orientations. Besides, it can finely\nsplit the touching and overlapping text lines without an orientation\nassumption. We evaluate the proposed method on VML-AHTE, VML-MOC, and\nDiva-HisDB datasets. The VML-AHTE dataset contains overlapping, touching and\nclose text lines with rich diacritics. The VML-MOC dataset is very challenging\nby its multiply oriented and skewed text lines. The Diva-HisDB dataset exhibits\ndistinct text line heights and touching text lines. The results demonstrate the\neffectiveness of the method despite various types of challenges, yet using the\nsame parameters in all the experiments.\n"}
+{"abstract": "  Citation recommendation is an important task to assist scholars in finding\ncandidate literature to cite. Traditional studies focus on static models of\nrecommending citations, which do not explicitly distinguish differences between\npapers that are caused by temporal variations. Although, some researchers have\ninvestigated chronological citation recommendation by adding time related\nfunction or modeling textual topics dynamically. These solutions can hardly\ncope with function generalization or cold-start problems when there is no\ninformation for user profiling or there are isolated papers never being cited.\nWith the rise and fall of science paradigms, scientific topics tend to change\nand evolve over time. People would have the time preference when citing papers,\nsince most of the theoretical basis exist in classical readings that published\nin old time, while new techniques are proposed in more recent papers. To\nexplore chronological citation recommendation, this paper wants to predict the\ntime preference based on user queries, which is a probability distribution of\nciting papers published in different time slices. Then, we use this time\npreference to re-rank the initial citation list obtained by content-based\nfiltering. Experimental results demonstrate that task performance can be\nfurther enhanced by time preference and it's flexible to be added in other\ncitation recommendation frameworks.\n"}
+{"abstract": "  Forward-looking correlations are of interest in different financial\napplications, including factor-based asset pricing, forecasting stock-price\nmovements or pricing index options. With a focus on non-FX markets, this paper\ndefines necessary conditions for option implied correlation matrices to be\nmathematically and economically feasible and argues, that existing models are\ntypically not capable of guaranteeing so. To overcome this difficulty, the\nproblem is addressed from the underlying factor structure and introduces two\napproaches to solve it. Under the quantitative approach, the puzzle is\nreformulated into a nearest correlation matrix problem which can be used either\nas a stand-alone estimate or to re-establish positive-semi-definiteness of any\nother model's estimate. From an economic approach, it is discussed how expected\ncorrelations between stocks and risk factors (like CAPM, Fama-French) can be\ntranslated into a feasible implied correlation matrix. Empirical experiments\nare carried out on monthly option data of the S\\&P 100 and S\\&P 500 index\n(1996-2020).\n"}
+{"abstract": "  We test the consistency of the data on the nucleon structure functions with\nanalyticity and the Regge asymptotics of the virtual Compton amplitude. By\nsolving a functional extremal problem, we derive an optimal lower bound on the\nmaximum difference between the exact amplitude and the dominant Reggeon\ncontribution for energies $\\nu$ above a certain high value $\\nu_h(Q^2)$.\nConsidering in particular the difference of the amplitudes $T_1^\\inel(\\nu,\nQ^2)$ for the proton and neutron, we find that the lower bound decreases in an\nimpressive way when $\\nu_h(Q^2)$ is increased, and represents a very small\nfraction of the magnitude of the dominant Reggeon. While the method cannot rule\nout the hypothesis of a fixed Regge pole, the results indicate that the data on\nthe structure function are consistent with an asymptotic behaviour given by\nleading Reggeon contributions. We also show that the minimum of the lower bound\nas a function of the subtraction constant $S_1^\\inel(Q^2)$ provides a\nreasonable estimate of this quantity, in a frame similar, but not identical to\nthe Reggeon dominance hypothesis.\n"}
+{"abstract": "  The network structure offers in principle the possibility for novel forms of\nquantum nonlocal correlations, that are proper to networks and cannot be traced\nback to standard quantum Bell nonlocality. Here we define a notion of genuine\nnetwork quantum nonlocality. Our approach is operational and views standard\nquantum nonlocality as a resource for producing correlations in networks. We\nshow several examples of correlations that are genuine network nonlocal,\nconsidering the so-called bilocality network of entanglement swapping. In\nparticular, we present an example of quantum self-testing which relies on the\nnetwork structure; the considered correlations are non-bilocal, but are local\naccording to the usual definition of Bell locality.\n"}
+{"abstract": "  We analyse the canonical quantum dynamics of the isotropic universe, as\nemerging from the Hamiltonian formulation of a metric f(R) gravity, viewed in\nthe Jordan frame. The canonical method of quantization is performed by solving\nthe Hamiltonian constraint before quantizing and adopting like a relational\ntime the non-minimally coupled scalar field emerging in the Jordan frame. The\nresulting Schr\\\"oedinger evolution is then investigated both in the vacuum and\nin the presence of a massless scalar field, though as the kinetic component of\nan inflaton. We show that, in vacuum, the morphology of localized wave packets\nis that of a non-spreading profile up to the cosmological singularity. When the\nexternal scalar field is included into the dynamics, we see that the wave\npackets acquire the surprising feature of increasing localization of the\nuniverse volume, as it expands. This result suggests that in the metric f(R)\nformulation of gravity, a spontaneous mechanism arises for the universe\nclassicization. Actually, when the phase space of the scalar field is fully\nexplored, such an increasing localization in the Universe volume is valid up to\na given value of the time, i.e. of the non-minimally coupled mode after which\nthe wave packets spread again. We conclude our analysis by inferring that\nbefore this critical transition age is reached, the inflationary phase could\ntake place, here modelled via a cosmological constant. This point of view\nprovides an interesting scenario for the transition from a Planckian Universe\nto a classical de-Sitter phase, which in the f(R) gravity appears more natural\nthan in the Einsteinian picture.\n"}
+{"abstract": "  In this paper we derive non-classical Tauberian asymptotic at infinity for\nthe tail, the density and the derivatives thereof of a large class of\nexponential functionals of subordinators. More precisely, we consider the case\nwhen the L\\'evy measure of the subordinator satisfies the well-known and mild\ncondition of positive increase. This is achieved via a convoluted application\nof the saddle point method to the Mellin transform of these exponential\nfunctionals which is given in terms of Bernstein-gamma functions. To apply the\nsaddle point method we improved the Stirling type of asymptotic for\nBernstein-gamma functions and the latter is of interest beyond this paper as\nthe Bernstein-gamma functions are applicable in different settings especially\nthrough their asymptotic behaviour in the complex plane. As an application we\nhave derived the asymptotic of the density and its derivatives for all\nexponential functionals of non-decreasing, potentially compound Poisson\nprocesses which turns out to be precisely as that of an exponentially\ndistributed random variable. We show further that a large class of densities\nare even analytic in a cone of the complex plane.\n"}
+{"abstract": "  Let $\\mathcal{D}$ be the class of radial weights on the unit disk which\nsatisfy both forward and reverse doubling conditions. Let $g$ be an analytic\nfunction on the unit disk $\\mathbb{D}$. We characterize bounded and compact\nVolterra type integration operators \\[\nJ_{g}(f)(z)=\\int_{0}^{z}f(\\lambda)g'(\\lambda)d\\lambda \\] between weighted\nBergman spaces $L_{a}^{p}(\\omega )$ induced by $\\mathcal{D}$ weights and Hardy\nspaces $H^{q}$ for $0<p,q<\\infty$.\n"}
+{"abstract": "  The conditions in the protoplanetary disc are determinant for the various\nplanet formation mechanisms. We present a framework which combines\nself-consistent disc structures with the calculations of the growth rates of\nplanetary embryos via pebble accretion, in order to study the formation of\nSuper-Earths. We first perform 2D hydrodynamical simulations of the inner\ndiscs, considering a grain size distribution with multiple chemical species and\ntheir corresponding size and composition dependent opacities. The resulting\naspect ratios are almost constant with orbital distance, resulting in radially\nconstant pebble isolation masses, the mass where pebble accretion stops. This\nsupports the \"peas-in-a-pod\" constraint from the Kepler observations. The\nderived pebble sizes are used to calculate the growth rates of planetary\nembryos via pebble accretion. Discs with low levels of turbulence (expressed\nthrough the $\\alpha$-viscosity) and/or high dust fragmentation velocities allow\nlarger particles, hence lead to smaller pebble isolation masses, and the\ncontrary. At the same time, small pebble sizes lead to low accretion rates. We\nfind that there is a trade-off between the pebble isolation mass and the growth\ntimescale with the best set of parameters being an $\\alpha$-viscosity of\n$10^{-3}$ and a dust fragmentation velocity of 10 m/s, mainly for an initial\ngas surface density (at 1 AU) greater than 1000 $\\rm{g/cm^2}$. A\nself-consistent treatment between the disc structures and the pebble sizes is\nthus of crucial importance for planet formation simulations.\n"}
+{"abstract": "  Stars are ubiquitous X-ray emitters and will be a substantial fraction of the\nX-ray sources detected in the on-going all-sky survey performed by the eROSITA\ninstrument aboard the Spectrum Roentgen Gamma (SRG) observatory. We use the\nX-ray sources in the eROSITA Final Equatorial-Depth Survey (eFEDS) field\nobserved during the SRG performance verification phase to investigate different\nstrategies to identify the stars among other source categories. We focus here\non Support Vector Machine (SVM) and Bayesian approaches, and our approaches are\nbased on a cross-match with the Gaia catalog, which will eventually contain\ncounterparts to virtually all stellar eROSITA sources. We estimate that 2060\nstars are among the eFEDS sources based on the geometric match distance\ndistribution, and we identify the 2060 most likely stellar sources with the SVM\nand Bayesian methods, the latter being named HamStars in the eROSITA context.\nBoth methods reach completeness and reliability percentages of almost 90%, and\nthe agreement between both methods is, incidentally, also about 90%. Knowing\nthe true number of stellar sources allowed us to derive association\nprobabilities $p_{ij}$ for the SVM method similar to the Bayesian method so\nthat one can construct samples with defined completeness and reliability\nproperties using appropriate cuts in $p_{ij}$. The thus identified stellar\nsources show the typical characteristics known for magnetically active stars,\nspecifically, they are generally compatible with the saturation level, show a\nlarge spread in activity for stars of spectral F to G, and have comparatively\nhigh fractional X-ray luminosities for later spectral types.\n"}
+{"abstract": "  We find excellent agreement between tunneling and optical conductivity gap\nvalues in superconducting granular aluminum films, up to the metal-to-insulator\ntransition. This behavior, in strong contrast with that recently reported in\natomically disordered samples for which the optical gap becomes smaller than\nthe tunneling gap, suggests that disorder is not at the origin of the\ntransition. The large increase seen in the strong coupling ratio and a finite\nvalue of the gap at T_c near the metal-to-insulator transition are consistent\nwith a BCS to BEC crossover.\n"}
+{"abstract": "  In this paper we study couples of finite separable extensions of the function\nfield $\\mathbb{F}_q(T)$ which are arithmetically equivalent, i.e. such that\nprime ideals of $\\mathbb{F}_q[T]$ decompose with the same inertia degrees in\nthe two fields, up to finitely many exceptions. In the first part of this work,\nwe extend previous results by Cornelissen, Kontogeorgis and Van der Zalm to the\ncase of non-geometric extensions of $\\mathbb{F}_q(T)$, which are fields such\nthat their field of constants may be bigger than $\\mathbb{F}_q$. In the second\npart, we explicitly produce examples of non-geometric extensions of\n$\\mathbb{F}_2(T)$ which are equivalent and non-isomorphic over\n$\\mathbb{F}_2(T)$ and non-equivalent over $\\mathbb{F}_4(T)$, solving a\nparticular Inverse Galois Problem.\n"}
+{"abstract": "  We use a combined generalized spectroscopic ellipsometry and density\nfunctional theory approach to determine and analyze the anisotropic dielectric\nfunctions of an $\\alpha$-Ga$_2$O$_3$ thin film. The sample is grown epitaxially\nby plasma-assisted molecular beam epitaxy on $m$-plane sapphire. Generalized\nspectroscopic ellipsometry data from multiple sample azimuths in the spectral\nrange from 0.73 eV to 8.75 eV are simultaneously analyzed. Density functional\ntheory is used to calculate the valence and conduction band structure. We\nidentify, for the indirect-bandgap material, two direct band-to-band\ntransitions with $M_0$-type van Hove singularities for polarization\nperpendicular to the $c$ axis, $E_{0,\\perp}=5.46(6)$ eV and\n$E_{0,\\perp}=6.04(1)$ eV, and one direct band-to-band transition with\n$M_1$-type van Hove singularity for polarization parallel with\n$E_{0,||}=5.44(2)$ eV. We further identify excitonic contributions with small\nbinding energy of 7 meV associated with the lowest ordinary transition, and a\nhyperbolic exciton at the $M_1$-type critical point with large binding energy\nof 178 meV.\n"}
+{"abstract": "  The stability conditions of a relativistic hydrodynamic theory can be derived\ndirectly from the requirement that the entropy should be maximised in\nequilibrium. Here we use a simple geometrical argument to prove that, if the\nhydrodynamic theory is stable according to this entropic criterion, then\nlocalised perturbations to the equilibrium state cannot propagate outside their\nfuture light-cone. In other words, within relativistic hydrodynamics, acausal\ntheories must be thermodynamically unstable, at least close to equilibrium. We\nshow that the physical origin of this deep connection between stability and\ncausality lies in the relationship between entropy and information. Our result\nmay be interpreted as an ``equilibrium conservation theorem'', which\ngeneralizes the Hawking-Ellis vacuum conservation theorem to finite temperature\nand chemical potential.\n"}
+{"abstract": "  Most of the existing isolated sound event datasets comprise a small number of\nsound event classes, usually 10 to 15, restricted to a small domain, such as\ndomestic and urban sound events. In this work, we introduce GISE-51, a dataset\nspanning 51 isolated sound events belonging to a broad domain of event types.\nWe also release GISE-51-Mixtures, a dataset of 5-second soundscapes with\nhard-labelled event boundaries synthesized from GISE-51 isolated sound events.\nWe conduct baseline sound event recognition (SER) experiments on the\nGISE-51-Mixtures dataset, benchmarking prominent convolutional neural networks,\nand models trained with the dataset demonstrate strong transfer learning\nperformance on existing audio recognition benchmarks. Together, GISE-51 and\nGISE-51-Mixtures attempt to address some of the shortcomings of recent sound\nevent datasets, providing an open, reproducible benchmark for future research\nalong with the freedom to adapt the included isolated sound events for\ndomain-specific applications.\n"}
+{"abstract": "  In the present letter we show the existence of a fully nonlinear dynamical\nmechanism for the formation of scalarized black holes which is different from\nthe spontaneous scalarization. We consider a class of scalar-Gauss-Bonnet\ngravity theories within which no tachyonic instability can occur. Although the\nSchwarzschild black holes are linearly stable against scalar perturbations, we\nshow dynamically that for certain choices of the coupling function they are\nunstable against nonlinear scalar perturbations. This nonlinear instability\nleads to the formation of new black holes with scalar hair. The fully nonlinear\nand self-consistent study of the equilibrium black holes reveals that the\nspectrum of solutions is more complicated and more than one scalarized branch\ncan exist. We have also considered classes of scalar-Gauss-Bonnet theories\nwhere both the standard and the nonlinear scalarization can be present, and\nthey are smoothly connected that completes in an interesting way the picture of\nblack hole scalarization. The fully nonlinear (de)scalarization of a\nSchwarzschild black hole will always happen with a jump because the stable\n\"scalarized branch\" is not continuously connected to the Schwarzschild one that\ncan leave clear observational signatures.\n"}
+{"abstract": "  We have investigated the directional dichroism of magnetic resonance spectra\nin the polar ferromagnet GaV$_4$S$_8$. While four types of structural domains\nare energetically degenerated under zero field, the magnetic resonance for each\ndomain is well separated by applying magnetic fields due to uniaxial magnetic\nanisotropy. Consequently, the directional dichroism as large as 20 % is clearly\nobserved without domain cancellation. The present observation therefore\ndemonstrates that not only magnetoelectric mono-domain crystals but also\nmagnetoelectric multi-domain specimens can be used to realize microwave\n(optical) diodes owing to the lack of inversion domains.\n"}
+{"abstract": "  An important transition from a homogeneous steady state to an inhomogeneous\nsteady state via the Turing bifurcation in coupled oscillators was reported in\n[Phys. Rev. Lett. {\\bf 111}, 024103 (2013)]. However, the same in the quantum\ndomain is yet to be observed. In this paper, we discover the quantum analogue\nof the Turing bifurcation in coupled quantum oscillators. We show that a\nhomogeneous steady state is transformed into an inhomogeneous steady state\nthrough this bifurcation in coupled quantum van der Pol oscillators. We\ndemonstrate our results by a direct simulation of the quantum master equation\nin the Lindblad form. We further support our observations through an analytical\ntreatment of the noisy classical model. Our study explores the paradigmatic\nTuring bifurcation at the quantum-classical interface and opens up the door\ntowards its broader understanding.\n"}
+{"abstract": "  Motivational digital systems offer capabilities to engage and motivate\nend-users to foster behavioral changes towards a common goal. In general these\nsystems use gamification principles in non-games contexts. Over the years,\ngamification has gained consensus among researchers and practitioners as a tool\nto motivate people to perform activities with the ultimate goal of promoting\nbehavioural change, or engaging the users to perform activities that can offer\nrelevant benefits but which can be seen as unrewarding and even tedious.\n  There exists a plethora of heterogeneous application scenarios towards\nreaching the common good that can benefit from gamification. However, an open\nproblem is how to effectively combine multiple motivational campaigns to\nmaximise the degree of participation without exposing the system to\ncounterproductive behaviours.\n  We conceive motivational digital systems as multi-agent systems:\nself-adaptation is a feature of the overall system, while individual agents may\nself-adapt in order to leverage other agents' resources, functionalities and\ncapabilities to perform tasks more efficiently and effectively. Consequently,\nmultiple campaigns can be run and adapted to reach common good. At the same\ntime, agents are grouped into micro-communities in which agents contribute with\ntheir own social capital and leverage others' capabilities to balance their\nweaknesses.\n  In this paper we propose our vision on how the principles at the base of the\nautonomous and multi-agent systems can be exploited to design multi-challenge\nmotivational systems to engage smart communities towards common goals. We\npresent an initial version of a general framework based on the MAPE-K loop and\na set of research challenges that characterise our research roadmap for the\nimplementation of our vision.\n"}
+{"abstract": "  We are interested in the general Choquard equation \\begin{multline*}\n  \\sqrt{\\strut -\\Delta + m^2} \\ u - mu + V(x)u - \\frac{\\mu}{|x|} u\n  =\n  \\left( \\int_{\\mathbb{R}^N} \\frac{F(y,u(y))}{|x-y|^{N-\\alpha}} \\, dy \\right)\n  f(x,u) - K (x) |u|^{q-2}u \\end{multline*} under suitable assumptions on the\nbounded potential \\(V\\) and on the nonlinearity \\(f\\). Our analysis extends\nrecent results by the second and third author on the problem with $\\mu = 0$ and\npure-power nonlinearity $f(x,u)=|u|^{p-2}u$. We show that, under appropriate\nassumptions on the potential, whether the ground state does exist or not.\nFinally, we study the asymptotic behaviour of ground states as $\\mu \\to 0^+$.\n"}
+{"abstract": "  Enclaves, such as those enabled by Intel SGX, offer a hardware primitive for\nshielding user-level applications from the OS. While enclaves are a useful\nstarting point, code running in the enclave requires additional checks whenever\ncontrol or data is transferred to/from the untrusted OS. The enclave-OS\ninterface on SGX, however, can be extremely large if we wish to run existing\nunmodified binaries inside enclaves. This paper presents Ratel, a dynamic\nbinary translation engine running inside SGX enclaves on Linux. Ratel offers\ncomplete interposition, the ability to interpose on all executed instructions\nin the enclave and monitor all interactions with the OS. Instruction-level\ninterposition offers a general foundation for implementing a large variety of\ninline security monitors in the future.\n  We take a principled approach in explaining why complete interposition on SGX\nis challenging. We draw attention to 5 design decisions in SGX that create\nfundamental trade-offs between performance and ensuring complete interposition,\nand we explain how to resolve them in the favor of complete interposition. To\nillustrate the utility of the Ratel framework, we present the first attempt to\noffer binary compatibility with existing software on SGX. We report that Ratel\noffers binary compatibility with over 200 programs we tested, including\nmicro-benchmarks and real applications such as Linux shell utilities. Runtimes\nfor two programming languages, namely Python and R, tested with standard\nbenchmarks work out-of-the-box on Ratel without any specialized handling.\n"}
+{"abstract": "  In this paper we study the large N limit of the $O(N)$-invariant linear sigma\nmodel, which is a vector-valued generalization of the $\\Phi^4$ quantum field\ntheory, on the three dimensional torus. We study the problem via its stochastic\nquantization, which yields a coupled system of N interacting SPDEs. We prove\ntightness of the invariant measures in the large N limit. For large enough mass\nor small enough coupling constant, they converge to the (massive) Gaussian free\nfield at a rate of order $1/\\sqrt N$ with respect to the Wasserstein distance.\nWe also obtain tightness results for certain $O(N)$ invariant observables.\nThese generalize some of the results in \\cite{SSZZ20} from two dimensions to\nthree dimensions. The proof leverages the method recently developed by\n\\cite{GH18} and combines many new techniques such as uniform in $N$ estimates\non perturbative objects as well as the solutions.\n"}
+{"abstract": "  Magnesium (Mg) has the lowest density of all structural metals and has\nexcellent potential for wide use in structural applications. While pure Mg has\ninferior mechanical properties; the addition of further elements at various\nconcentrations has produced alloys with enhanced mechanical performance and\ncorrosion resistance. An important consequence of adding such elements is that\nthe saturated Mg matrix can locally decompose to form solute clusters and\nintermetallic particles, often referred to as precipitates. Controlling the\nshape, number density, volume fraction, and spatial distribution of solute\nclusters and precipitates significantly impacts the alloy's plastic response.\nConversely, plastic deformation during thermomechanical processing can\ndramatically impact solute clustering and precipitation. In this paper, we\nfirst discuss how solute atoms, solute clusters, and precipitates can improve\nthe mechanical properties of Mg alloys. We do so by primarily comparing three\nalloy systems: Mg-Al, Mg-Zn, and Mg-Y-based alloys. In the second part, we\nprovide strategies for optimizing such microstructures by controlling\nnucleation and growth of solute clusters and precipitates during\nthermomechanical processing. In the third part, we briefly highlight how one\ncan enable inverse design of Mg alloys by a more robust Integrated\nComputational Materials Design (ICMD) approach.\n"}
+{"abstract": "  Time series forecasting is an extensively studied subject in statistics,\neconomics, and computer science. Exploration of the correlation and causation\namong the variables in a multivariate time series shows promise in enhancing\nthe performance of a time series model. When using deep neural networks as\nforecasting models, we hypothesize that exploiting the pairwise information\namong multiple (multivariate) time series also improves their forecast. If an\nexplicit graph structure is known, graph neural networks (GNNs) have been\ndemonstrated as powerful tools to exploit the structure. In this work, we\npropose learning the structure simultaneously with the GNN if the graph is\nunknown. We cast the problem as learning a probabilistic graph model through\noptimizing the mean performance over the graph distribution. The distribution\nis parameterized by a neural network so that discrete graphs can be sampled\ndifferentiably through reparameterization. Empirical evaluations show that our\nmethod is simpler, more efficient, and better performing than a recently\nproposed bilevel learning approach for graph structure learning, as well as a\nbroad array of forecasting models, either deep or non-deep learning based, and\ngraph or non-graph based.\n"}
+{"abstract": "  Recent work has shown that entanglement and the structure of spacetime are\nintimately related. One way to investigate this is to begin with an\nentanglement entropy in a conformal field theory (CFT) and use the AdS/CFT\ncorrespondence to calculate the bulk metric. We perform this calculation for\nABJM, a particular 3-dimensional supersymmetric CFT (SCFT), in its ground\nstate. In particular we are able to reconstruct the pure AdS4 metric from the\nholographic entanglement entropy of the boundary ABJM theory in its ground\nstate. Moreover, we are able to predict the correct AdS radius purely from\nentanglement. We also address the general philosophy of relating entanglement\nand spacetime through the Holographic Principle, as well as some of the\nphilosophy behind our calculations.\n"}
+{"abstract": "  Fine-tuning from a collection of models pre-trained on different domains (a\n\"model zoo\") is emerging as a technique to improve test accuracy in the\nlow-data regime. However, model selection, i.e. how to pre-select the right\nmodel to fine-tune from a model zoo without performing any training, remains an\nopen topic. We use a linearized framework to approximate fine-tuning, and\nintroduce two new baselines for model selection -- Label-Gradient and\nLabel-Feature Correlation. Since all model selection algorithms in the\nliterature have been tested on different use-cases and never compared directly,\nwe introduce a new comprehensive benchmark for model selection comprising of:\ni) A model zoo of single and multi-domain models, and ii) Many target tasks.\nOur benchmark highlights accuracy gain with model zoo compared to fine-tuning\nImagenet models. We show our model selection baseline can select optimal models\nto fine-tune in few selections and has the highest ranking correlation to\nfine-tuning accuracy compared to existing algorithms.\n"}
+{"abstract": "  While current state-of-the-art Automatic Speech Recognition (ASR) systems\nachieve high accuracy on typical speech, they suffer from significant\nperformance degradation on disordered speech and other atypical speech\npatterns. Personalization of ASR models, a commonly applied solution to this\nproblem, is usually performed in a server-based training environment posing\nproblems around data privacy, delayed model-update times, and communication\ncost for copying data and models between mobile device and server\ninfrastructure. In this paper, we present an approach to on-device based ASR\npersonalization with very small amounts of speaker-specific data. We test our\napproach on a diverse set of 100 speakers with disordered speech and find\nmedian relative word error rate improvement of 71% with only 50 short\nutterances required per speaker. When tested on a voice-controlled home\nautomation platform, on-device personalized models show a median task success\nrate of 81%, compared to only 40% of the unadapted models.\n"}
+{"abstract": "  The World Trade Web (WTW) is the network of international trade relationships\namong world countries. Characterizing both the local link weights (observed\ntrade volumes) and the global network structure (large-scale topology) of the\nWTW via a single model is still an open issue. While the traditional Gravity\nModel (GM) successfully replicates the observed trade volumes by employing\nmacroeconomic properties such as GDP and geographic distance, it,\nunfortunately, predicts a fully connected network, thus returning a completely\nunrealistic topology of the WTW. To overcome this problem, two different\nclasses of models have been introduced in econometrics and statistical physics.\nEconometric approaches interpret the traditional GM as the expected value of a\nprobability distribution that can be chosen arbitrarily and tested against\nalternative distributions. Statistical physics approaches construct\nmaximum-entropy probability distributions of (weighted) graphs from a chosen\nset of measurable structural constraints and test distributions resulting from\ndifferent constraints. Here we compare and integrate the two approaches by\nconsidering a class of maximum-entropy models that can incorporate\nmacroeconomic properties used in standard econometric models. We find that the\nintegrated approach achieves a better performance than the purely econometric\none. These results suggest that the maximum-entropy construction can serve as a\nviable econometric framework wherein extensive and intensive margins can be\nseparately controlled for, by combining topological constraints and dyadic\nmacroeconomic variables.\n"}
+{"abstract": "  Morse foliated open books were introduced by the autors (along with abstract\nand embedded versions) as a tool for studying contact manifolds with boundary,\nand this article illustrates the advantages of the Morse perspective. We use\nthis to extend the definition of \\textit{right-veering} to foliated open books\nand we show that it plays a similar role in detecting overtwistedness as in\nother versions of open books.\n"}
+{"abstract": "  The Cooper pairs in superconducting condensates are shown to acquire a\ntemperature-dependent dc magnetic moment under the effect of the circularly\npolarized electromagnetic radiation. The mechanisms of this inverse Faraday\neffect are investigated within the simplest version of the phenomenological\ndynamic theory for superfluids, namely, the time-dependent Ginzburg-Landau (GL)\nmodel. The light-induced magnetic moment is shown to be strongly affected by\nthe nondissipative oscillatory contribution to the superconducting order\nparameter dynamics which appears due to the nonzero imaginary part of the GL\nrelaxation time. The relevance of the latter quantity to the Hall effect in\nsuperconducting state allows to establish the connection between the direct and\ninverse Faraday phenomena.\n"}
+{"abstract": "  Using high-resolution Hubble Space Telescope imaging data, we perform a\nvisual morphological classification of $\\sim 36,000$ galaxies at $z < 1$ in the\nDEVILS/COSMOS region. As the main goal of this study, we derive the stellar\nmass function (SMF) and stellar mass density (SMD) sub-divided by morphological\ntypes. We find that visual morphological classification using optical imaging\nis increasingly difficult at $z > 1$ as the fraction of irregular galaxies and\nmerger systems (when observed at rest-frame UV/blue wavelengths) dramatically\nincreases. We determine that roughly two-thirds of the total stellar mass of\nthe Universe today was in place by $z \\sim 1$. Double-component galaxies\ndominate the SMD at all epochs and increase in their contribution to the\nstellar mass budget to the present day. Elliptical galaxies are the second most\ndominant morphological type and increase their SMD by $\\sim 2.5$ times, while\nby contrast, the pure-disk population significantly decreases by $\\sim 85\\%$.\nAccording to the evolution of both high- and low-mass ends of the SMF, we find\nthat mergers and in-situ evolution in disks are both present at $z < 1$, and\nconclude that double-component galaxies are predominantly being built by the\nin-situ evolution in disks (apparent as the growth of the low-mass end with\ntime), while mergers are likely responsible for the growth of ellipticals\n(apparent as the increase of intermediate/high-mass end).\n"}
+{"abstract": "  In recent years, a vast increase in spectroscopic observations of transiting\nexoplanets has for the first time allowed us to search for broad trends in\ntheir atmospheric properties. Analysis of these observations has revealed that,\neven for the highly irradiated hot Jupiters, aerosol is a common presence and\nmust be accounted for in modelling efforts. An additional challenge for hot\nJupiters is the large variation in temperature across the planet, which is\nlikely to result in partial or patchy cloud cover. As our observational\ncapability is due to increase further with the launch of the James Webb Space\nTelescope, anticipated in autumn 2021, community efforts are underway to\nprepare modelling and analysis tools capable of recovering information about\nvariable and patchy cloud coverage on hot exoplanets\n"}
+{"abstract": "  We consider intersections of n diagonal forms of degrees k 1 < $\\bullet$\n$\\bullet$ $\\bullet$ < kn, and we prove an asymptotic formula for the number of\nrational points of bounded height on these varieties. The proof uses the\nHardy-Littlewood method and recent breakthroughs on the Vinogradov system. We\nalso give a sharper result for one specific value of (k 1 ,. .. , kn), using a\ntechnique due to Wooley and an estimate on exponential sums derived from a\nrecent approach in the van der Corput's method.\n"}
+{"abstract": "  Machine Learning (ML) techniques have been rapidly adopted by smart\nCyber-Physical Systems (CPS) and Internet-of-Things (IoT) due to their powerful\ndecision-making capabilities. However, they are vulnerable to various security\nand reliability threats, at both hardware and software levels, that compromise\ntheir accuracy. These threats get aggravated in emerging edge ML devices that\nhave stringent constraints in terms of resources (e.g., compute, memory,\npower/energy), and that therefore cannot employ costly security and reliability\nmeasures. Security, reliability, and vulnerability mitigation techniques span\nfrom network security measures to hardware protection, with an increased\ninterest towards formal verification of trained ML models.\n  This paper summarizes the prominent vulnerabilities of modern ML systems,\nhighlights successful defenses and mitigation techniques against these\nvulnerabilities, both at the cloud (i.e., during the ML training phase) and\nedge (i.e., during the ML inference stage), discusses the implications of a\nresource-constrained design on the reliability and security of the system,\nidentifies verification methodologies to ensure correct system behavior, and\ndescribes open research challenges for building secure and reliable ML systems\nat both the edge and the cloud.\n"}
+{"abstract": "  We show that the non-perturbative dynamics of $\\mathcal{N}=2$ super\nYang-Mills theories in a self-dual $\\Omega$-background and with an arbitrary\nsimple gauge group is fully determined by studying renormalization group\nequations of vevs of surface operators generating one-form symmetries. The\ncorresponding system of equations is a {\\it non-autonomous} Toda chain, the\ntime being the RG scale. We obtain new recurrence relations which provide a\nsystematic algorithm computing multi-instanton corrections from the tree-level\none-loop prepotential as the asymptotic boundary condition of the RGE. We\nexemplify by computing the $E_6$ and $G_2$ cases up to two-instantons.\n"}
+{"abstract": "  This paper proposes a composable \"Just in Time Architecture\" for Data Science\n(DS) Pipelines named JITA-4DS and associated resource management techniques for\nconfiguring disaggregated data centers (DCs). DCs under our approach are\ncomposable based on vertical integration of the application,\nmiddleware/operating system, and hardware layers customized dynamically to meet\napplication Service Level Objectives (SLO - application-aware management).\nThereby, pipelines utilize a set of flexible building blocks that can be\ndynamically and automatically assembled and re-assembled to meet the dynamic\nchanges in the workload's SLOs. To assess disaggregated DC's, we study how to\nmodel and validate their performance in large-scale settings.\n"}
+{"abstract": "  This paper proposes a 1D residual convolutional neural network (CNN)\narchitecture for music genre classification and compares it with other recent\n1D CNN architectures. The 1D CNNs learn a representation and a discriminant\ndirectly from the raw audio signal. Several convolutional layers capture the\ntime-frequency characteristics of the audio signal and learn various filters\nrelevant to the music genre recognition task. The proposed approach splits the\naudio signal into overlapped segments using a sliding window to comply with the\nfixed-length input constraint of the 1D CNNs. As a result, music genre\nclassification can be carried out on a single audio segment or on the\naggregation of the predictions on several audio segments, which improves the\nfinal accuracy. The performance of the proposed 1D residual CNN is assessed on\na public dataset of 1,000 audio clips. The experimental results have shown that\nit achieves 80.93% of mean accuracy in classifying music genres and outperforms\nother 1D CNN architectures.\n"}
+{"abstract": "  Finding an expert plays a crucial role in driving successful collaborations\nand speeding up high-quality research development and innovations. However, the\nrapid growth of scientific publications and digital expertise data makes\nidentifying the right experts a challenging problem. Existing approaches for\nfinding experts given a topic can be categorised into information retrieval\ntechniques based on vector space models, document language models, and\ngraph-based models. In this paper, we propose $\\textit{ExpFinder}$, a new\nensemble model for expert finding, that integrates a novel $N$-gram vector\nspace model, denoted as $n$VSM, and a graph-based model, denoted as\n$\\textit{$\\mu$CO-HITS}$, that is a proposed variation of the CO-HITS algorithm.\nThe key of $n$VSM is to exploit recent inverse document frequency weighting\nmethod for $N$-gram words and $\\textit{ExpFinder}$ incorporates $n$VSM into\n$\\textit{$\\mu$CO-HITS}$ to achieve expert finding. We comprehensively evaluate\n$\\textit{ExpFinder}$ on four different datasets from the academic domains in\ncomparison with six different expert finding models. The evaluation results\nshow that $\\textit{ExpFinder}$ is a highly effective model for expert finding,\nsubstantially outperforming all the compared models in 19% to 160.2%.\n"}
+{"abstract": "  In this paper, we introduce a self-supervised learning method to enhance the\ngraph-level representations with the help of a set of subgraphs. For this\npurpose, we propose a universal framework to generate subgraphs in an\nauto-regressive way and then using these subgraphs to guide the learning of\ngraph representation by Graph Neural Networks. Under this framework, we can get\na comprehensive understanding of the graph structure in a learnable way. And to\nfully capture enough information of original graphs, we design three\ninformation aggregators: \\textbf{attribute-conv}, \\textbf{layer-conv} and\n\\textbf{subgraph-conv} to gather information from different aspects. And to\nachieve efficient and effective contrastive learning, a Head-Tail contrastive\nconstruction is proposed to provide abundant negative samples. Under all\nproposed components which can be generalized to any Graph Neural Networks, in\nthe unsupervised case, we achieve new state-of-the-art results in several\nbenchmarks. We also evaluate our model on semi-supervised learning tasks and\nmake a fair comparison to state-of-the-art semi-supervised methods.\n"}
+{"abstract": "  Effective computation of resultants is a central problem in elimination\ntheory and polynomial system solving. Commonly, we compute the resultant as a\nquotient of determinants of matrices and we say that there exists a\ndeterminantal formula when we can express it as a determinant of a matrix whose\nelements are the coefficients of the input polynomials. We study the resultant\nin the context of mixed multilinear polynomial systems, that is multilinear\nsystems with polynomials having different supports, on which determinantal\nformulas were not known. We construct determinantal formulas for two kind of\nmultilinear systems related to the Multiparameter Eigenvalue Problem (MEP):\nfirst, when the polynomials agree in all but one block of variables; second,\nwhen the polynomials are bilinear with different supports, related to a\nbipartite graph. We use the Weyman complex to construct Koszul-type\ndeterminantal formulas that generalize Sylvester-type formulas. We can use the\nmatrices associated to these formulas to solve square systems without computing\nthe resultant. The combination of the resultant matrices with the eigenvalue\nand eigenvector criterion for polynomial systems leads to a new approach for\nsolving MEP.\n"}
+{"abstract": "  In this work we propose a deep neural network based surrogate model for a\nplasma shadowgraph - a technique for visualization of perturbations in a\ntransparent medium. We are substituting the numerical code by a computationally\ncheaper projection based surrogate model that is able to approximate the\nelectric fields at a given time without computing all preceding electric fields\nas required by numerical methods. This means that the projection based\nsurrogate model allows to recover the solution of the governing 3D partial\ndifferential equation, 3D wave equation, at any point of a given compute domain\nand configuration without the need to run a full simulation. This model has\nshown a good quality of reconstruction in a problem of interpolation of data\nwithin a narrow range of simulation parameters and can be used for input data\nof large size.\n"}
+{"abstract": "  We present two classical algorithms for the simulation of universal quantum\ncircuits on $n$ qubits constructed from $c$ instances of Clifford gates and $t$\narbitrary-angle $Z$-rotation gates such as $T$ gates. Our algorithms complement\neach other by performing best in different parameter regimes. The\n$\\tt{Estimate}$ algorithm produces an additive precision estimate of the Born\nrule probability of a chosen measurement outcome with the only source of\nrun-time inefficiency being a linear dependence on the stabilizer extent (which\nscales like $\\approx 1.17^t$ for $T$ gates). Our algorithm is state-of-the-art\nfor this task: as an example, in approximately $13$ hours (on a standard\ndesktop computer), we estimated the Born rule probability to within an additive\nerror of $0.03$, for a $50$ qubit, $60$ non-Clifford gate quantum circuit with\nmore than $2000$ Clifford gates. Our second algorithm, $\\tt{Compute}$,\ncalculates the probability of a chosen measurement outcome to machine precision\nwith run-time $O(2^{t-r} t)$ where $r$ is an efficiently computable,\ncircuit-specific quantity. With high probability, $r$ is very close to $\\min\n\\{t,n-w\\}$ for random circuits with many Clifford gates, where $w$ is the\nnumber of measured qubits. $\\tt{Compute}$ can be effective in surprisingly\nchallenging parameter regimes, e.g., we can randomly sample Clifford+$T$\ncircuits with $n=55$, $w=5$, $c=10^5$ and $t=80$ $T$ gates, and then compute\nthe Born rule probability with a run-time consistently less than $10$ minutes\nusing a single core of a standard desktop computer. We provide a C+Python\nimplementation of our algorithms.\n"}
+{"abstract": "  In this work we solve the problem of robustly learning a high-dimensional\nGaussian mixture model with $k$ components from $\\epsilon$-corrupted samples up\nto accuracy $\\widetilde{O}(\\epsilon)$ in total variation distance for any\nconstant $k$ and with mild assumptions on the mixture. This robustness\nguarantee is optimal up to polylogarithmic factors. The main challenge is that\nmost earlier works rely on learning individual components in the mixture, but\nthis is impossible in our setting, at least for the types of strong robustness\nguarantees we are aiming for. Instead we introduce a new framework which we\ncall {\\em strong observability} that gives us a route to circumvent this\nobstacle.\n"}
+{"abstract": "  Several researches and evidence show the increasing likelihood of pandemics\n(large-scale outbreaks of infectious disease) which has far reaching sequels in\nall aspects of human lives ranging from rapid mortality rates to economic and\nsocial disruption across the world. In the recent time, COVID-19 (Coronavirus\nDisease 2019) pandemic disrupted normal human lives, and motivated by the\nurgent need of combating COVID-19, researchers have put significant efforts in\nmodelling and analysing the disease spread patterns for effective preventive\nmeasures (in addition to developing pharmaceutical solutions, like vaccine). In\nthis regards, it is absolutely necessary to develop an analytics framework by\nextracting and incorporating the knowledge of heterogeneous datasources to\ndeliver insights in improving administrative policy and enhance the\npreparedness to combat the pandemic. Specifically, human mobility, travel\nhistory and other transport statistics have significant impacts on the spread\nof any infectious disease. In this direction, this paper proposes a\nspatio-temporal knowledge mining framework, named STOPPAGE to model the impact\nof human mobility and other contextual information over large geographic area\nin different temporal scales. The framework has two major modules: (i)\nSpatio-temporal data and computing infrastructure using fog/edge based\narchitecture; and (ii) Spatio-temporal data analytics module to efficiently\nextract knowledge from heterogeneous data sources. Typically, we develop a\nPandemic-knowledge graph to discover correlations among mobility information\nand disease spread, a deep learning architecture to predict the next hot-spot\nzones; and provide necessary support in home-health monitoring utilizing\nFemtolet and fog/edge based solutions. The experimental evaluations on\nreal-life datasets related to COVID-19 in India illustrate the efficacy of the\nproposed methods.\n"}
+{"abstract": "  Many healthcare decisions involve navigating through a multitude of treatment\noptions in a sequential and iterative manner to find an optimal treatment\npathway with the goal of an optimal patient outcome. Such optimization problems\nmay be amenable to reinforcement learning. A reinforcement learning agent could\nbe trained to provide treatment recommendations for physicians, acting as a\ndecision support tool. However, a number of difficulties arise when using RL\nbeyond benchmark environments, such as specifying the reward function, choosing\nan appropriate state representation and evaluating the learned policy.\n"}
+{"abstract": "  Regret minimization has proved to be a versatile tool for tree-form\nsequential decision making and extensive-form games. In large two-player\nzero-sum imperfect-information games, modern extensions of counterfactual\nregret minimization (CFR) are currently the practical state of the art for\ncomputing a Nash equilibrium. Most regret-minimization algorithms for tree-form\nsequential decision making, including CFR, require (i) an exact model of the\nplayer's decision nodes, observation nodes, and how they are linked, and (ii)\nfull knowledge, at all times t, about the payoffs -- even in parts of the\ndecision space that are not encountered at time t. Recently, there has been\ngrowing interest towards relaxing some of those restrictions and making regret\nminimization applicable to settings for which reinforcement learning methods\nhave traditionally been used -- for example, those in which only black-box\naccess to the environment is available. We give the first, to our knowledge,\nregret-minimization algorithm that guarantees sublinear regret with high\nprobability even when requirement (i) -- and thus also (ii) -- is dropped. We\nformalize an online learning setting in which the strategy space is not known\nto the agent and gets revealed incrementally whenever the agent encounters new\ndecision points. We give an efficient algorithm that achieves $O(T^{3/4})$\nregret with high probability for that setting, even when the agent faces an\nadversarial environment. Our experiments show it significantly outperforms the\nprior algorithms for the problem, which do not have such guarantees. It can be\nused in any application for which regret minimization is useful: approximating\nNash equilibrium or quantal response equilibrium, approximating coarse\ncorrelated equilibrium in multi-player games, learning a best response,\nlearning safe opponent exploitation, and online play against an unknown\nopponent/environment.\n"}
+{"abstract": "  For a sequence of extrinsic or intrinsic biharmonic maps $u_j: M_j\\rightarrow\nN$ from a sequence of non-collapsed degenerating closed Einstein 4-manifolds\n$(M_j,g_j)$ with bounded Einstein constants, bounded diameters and bounded\n$L^2$ curvature energy into a compact Riemannian manifold $(N,h)$ with\nuniformly bounded biharmonic energy, we establish a compactness theory modular\nfinitely many bubbles, which are finite energy biharmonic maps from\n$\\mathbb{R}^4$, or from $\\mathbb{R}^4 / \\Gamma$ for some nontrivial finite\ngroup $\\Gamma \\subset SO(4)$, or from some complete, noncompact, Ricci flat,\nnon-flat ALE 4-manifold (orbifold). To achieve this, we develop a sophisticated\nasymptotic analysis for solutions over degenerating neck regions.\n"}
+{"abstract": "  Reinforcement Learning (RL) methods have been widely applied for robotic\nmanipulations via sim-to-real transfer, typically with proprioceptive and\nvisual information. However, the incorporation of tactile sensing into RL for\ncontact-rich tasks lacks investigation. In this paper, we model a tactile\nsensor in simulation and study the effects of its feedback in RL-based robotic\ncontrol via a zero-shot sim-to-real approach with domain randomization. We\ndemonstrate that learning and controlling with feedback from tactile sensor\narrays at the gripper, both in simulation and reality, can enhance grasping\nstability, which leads to a significant improvement in robotic manipulation\nperformance for a door opening task. In real-world experiments, the door open\nangle was increased by 45% on average for transferred policies with tactile\nsensing over those without it.\n"}
+{"abstract": "  Data-driven dynamic models of cell biology can be used to predict cell\nresponse to unseen perturbations. Recent work (CellBox) had demonstrated the\nderivation of interpretable models with explicit interaction terms, in which\nthe parameters were optimized using machine learning techniques. While the\nprevious work was tested only in a single biological setting, this work aims to\nextend the range of applicability of this model inference approach to a\ndiversity of biological systems. Here we adapted CellBox in Julia differential\nprogramming and augmented the method with adjoint algorithms, which has\nrecently been used in the context of neural ODEs. We trained the models using\nsimulated data from both abstract and biology-inspired networks, which afford\nthe ability to evaluate the recovery of the ground truth network structure. The\nresulting accuracy of prediction by these models is high both in terms of low\nerror against data and excellent agreement with the network structure used for\nthe simulated training data. While there is no analogous ground truth for real\nlife biological systems, this work demonstrates the ability to construct and\nparameterize a considerable diversity of network models with high predictive\nability. The expectation is that this kind of procedure can be used on real\nperturbation-response data to derive models applicable to diverse biological\nsystems.\n"}
+{"abstract": "  A thermodynamic framework has been developed for a class of amorphous\npolymers used in fused deposition modeling (FDM), in order to predict the\nresidual stresses and the accompanying distortion of the geometry of the\nprinted part (warping). When a polymeric melt is cooled, the inhomogeneous\ndistribution of temperature causes spatially varying volumetric shrinkage\nresulting in the generation of residual stresses. Shrinkage is incorporated\ninto the framework by introducing an isotropic volumetric expansion/contraction\nin the kinematics of the body. We show that the parameter for shrinkage also\nappears in the systematically derived rate-type constitutive relation for the\nstress. The solidification of the melt around the glass transition temperature\nis emulated by drastically increasing the viscosity of the melt.\n  In order to illustrate the usefulness and efficacy of the derived\nconstitutive relation, we consider four ribbons of polymeric melt stacked on\neach other such as those extruded using a flat nozzle: each layer laid\ninstantaneously and allowed to cool for one second before another layer is laid\non it. Each layer cools, shrinks and warps until a new layer is laid, at which\ntime the heat from the newly laid layer flows and heats up the bottom layers.\nThe residual stresses of the existing and newly laid layers readjust to satisfy\nequilibrium. Such mechanical and thermal interactions amongst layers result in\na complex distribution of residual stresses. The plane strain approximation\npredicts nearly equibiaxial tensile stress conditions in the core region of the\nsolidified part, implying that a pre-existing crack in that region is likely to\npropagate and cause failure of the part during service. The free-end of the\ninterface between the first and the second layer is subjected to the largest\nmagnitude of combined shear and tension in the plane with a propensity for\ndelamination.\n"}
+{"abstract": "  Twin-field quantum key distribution (TF-QKD) and its variants can overcome\nthe fundamental rate-distance limit of QKD which has been demonstrated in the\nlaboratory and field while their physical implementations with side channels\nremains to be further researched. We find the external modulation of different\nintensity states through the test, required in those TF-QKD with post-phase\ncompensation, shows a side channel in frequency domain. Based on this, we\npropose a complete and undetected eavesdropping attack, named passive frequency\nshift attack, on sending or not-sending (SNS) TF-QKD protocol given any\ndifference between signal and decoy states in frequency domain which can be\nextended to other imperfections with distinguishable decoy states. We analyze\nthis attack by giving the formula of upper bound of real secure key rate and\ncomparing it with lower bound of secret key rate under Alice and Bob's\nestimation with the consideration of actively odd-parity pairing (AOPP) method\nand finite key effects. The simulation results show that Eve can get full\ninformation about the secret key bits without being detected at long distance.\nOur results emphasize the importance of practical security at source and might\nprovide a valuable reference for the practical implementation of TF-QKD.\n"}
+{"abstract": "  An algorithm is proposed to verify whether a finite game is a weighted\npotential game (WPG) without pre-knowledge on its weights. Then the algorithm\nis also applied to find the closest WPG for a given finite game. The concept\nand criterion of near weighted potential games (NWPGs) are given, indicating\nthe evolutionary equivalence between a finite game and its closest WPG. Based\non this, a method is proposed for replacing PGs by NWPGs in game theoretic\ncontrol, which improved the applicability of PG-based optimization.\n"}
+{"abstract": "  Triangular numbers that are multiple of other triangular numbers are\ninvestigated. It is known that for any positive non-square integer multiplier,\nthere is an infinity of multiples of triangular numbers which are triangular\nnumbers. If the multiplier is a squared integer, there is either one or no\nsolution, depending on the multiplier value. Instead of recurrent relations, we\ndevelop in this paper closed form equations to calculate directly the values of\ntriangular numbers and their indices without the need of knowing the previous\nsolutions. We develop the theoretical equations for four cases of ranks from 1\nto 4 and we give several examples for non-square multipliers 2, 3, 5 and 8.\n"}
+{"abstract": "  This paper developed a fatigue-driven residual strength model considering the\neffects of low-velocity impact (LVI) damage and stress ratio. New fatigue\nfailure criteria based on fatigue-driven residual strength concept and fatigue\nprogressive damage model were developed to simulate fatigue damage growth and\npredict fatigue life for plain-weave composite laminates with LVI damage. To\nvalidate the proposed model, LVI tests of plain-weave glass fibre reinforced\npolymer 3238A/EW250F laminates were conducted, followed by post-impact constant\namplitude tension-tension, compression-compression fatigue tests and multi-step\nfatigue tests. Experimental results indicate that the LVI damage degrades\nfatigue strength of plain-weave glass fibre composite laminate drastically. The\nload history also plays an important role on the fatigue accumulation damage of\npost-impact laminates. The new fatigue progressive damage model achieves a good\nagreement with fatigue life of post-impact laminates and is able to capture the\nload sequence effect, opening a new avenue to predict fatigue failure of\ncomposite laminates.\n"}
+{"abstract": "  We theoretically study an optomechanical system, which consists of a\ntwo-sided cavity and a mechanical membrane that is placed outside of it. The\nmembrane is positioned close to one of its mirrors, and the cavity is coupled\nto the external light field through the other mirror. Our study is focused on\nthe regime where the dispersive optomechanical coupling in the system vanishes.\nSuch a regime is found to be possible if the membrane is less reflecting than\nthe adjacent mirror, yielding a potentially very strong dissipative\noptomechanical coupling. Specifically, if the absolute values of amplitude\ntransmission coefficients of the membrane and the mirror, $t$ and $t_m$\nrespectively, obey the condition $ t_m^2< t\\ll t_m\\ll 1$, the dissipative\ncoupling constant of the setup exceeds the dispersive coupling constant for an\noptomechanical cavity of the same length. The dissipative coupling constant and\nthe corresponding optomechanical cooperativity of the proposed system are also\ncompared with those of the Michelson-Sagnac interferometer and the so-called\n\"membrane-at-the-edge\" system, which are known for a strong optomechanical\ndissipative interaction. It is shown that under the above condition, the system\nproposed here is advantageous in both aspects. It also enables an efficient\nrealization of the two-port configuration, which was recently proposed as a\npromising optomechanical system, providing, among other benefits, a possibility\nof quantum limited optomechanical measurements in a system, which does not\nsuffer from any optomechanical instability.\n"}
+{"abstract": "  To identify the 3alpha BEC state with the excess neutron, we have\ninvestigated the monopole strength of the excited states of 13C by using the\ntheoretical framework of the real-time evolution method. The calculations have\nrevealed several candidates of the Hoyle-analog states in a highly excited\nregion.\n"}
+{"abstract": "  A.Regev proved that the codimension growth of an associative PI-algebra is at\nmost exponential. The author established a scale for the codimension growth of\nLie PI-algebras, which includes a series of functions between exponential and\nfactorial functions. We prove that the same scale stratifies the ordinary\ncodimension growth of Poisson PI-algebras satisfying Lie identical relations.\nAs a byproduct, we obtain a new bound on the codimension growth of Lie\nPI-algebras in terms of complexity functions. We also study very fast\ncodimension growth of some Poisson algebras without Lie identities. We\nessentially use techniques of exponential generating functions and growth of\nrespective fast growing entire functions.\n"}
+{"abstract": "  Current anchor-free object detectors are quite simple and effective yet lack\naccurate label assignment methods, which limits their potential in competing\nwith classic anchor-based models that are supported by well-designed assignment\nmethods based on the Intersection-over-Union~(IoU) metric. In this paper, we\npresent \\textbf{Pseudo-Intersection-over-Union~(Pseudo-IoU)}: a simple metric\nthat brings more standardized and accurate assignment rule into anchor-free\nobject detection frameworks without any additional computational cost or extra\nparameters for training and testing, making it possible to further improve\nanchor-free object detection by utilizing training samples of good quality\nunder effective assignment rules that have been previously applied in\nanchor-based methods. By incorporating Pseudo-IoU metric into an end-to-end\nsingle-stage anchor-free object detection framework, we observe consistent\nimprovements in their performance on general object detection benchmarks such\nas PASCAL VOC and MSCOCO. Our method (single-model and single-scale) also\nachieves comparable performance to other recent state-of-the-art anchor-free\nmethods without bells and whistles. Our code is based on mmdetection toolbox\nand will be made publicly available at\nhttps://github.com/SHI-Labs/Pseudo-IoU-for-Anchor-Free-Object-Detection.\n"}
+{"abstract": "  Modern kernel-based two-sample tests have shown great success in\ndistinguishing complex, high-dimensional distributions with appropriate learned\nkernels. Previous work has demonstrated that this kernel learning procedure\nsucceeds, assuming a considerable number of observed samples from each\ndistribution. In realistic scenarios with very limited numbers of data samples,\nhowever, it can be challenging to identify a kernel powerful enough to\ndistinguish complex distributions. We address this issue by introducing the\nproblem of meta two-sample testing (M2ST), which aims to exploit (abundant)\nauxiliary data on related tasks to find an algorithm that can quickly identify\na powerful test on new target tasks. We propose two specific algorithms for\nthis task: a generic scheme which improves over baselines and a more tailored\napproach which performs even better. We provide both theoretical justification\nand empirical evidence that our proposed meta-testing schemes out-perform\nlearning kernel-based tests directly from scarce observations, and identify\nwhen such schemes will be successful.\n"}
+{"abstract": "  Virtual screening is a computational technique widely used for identifying\nsmall molecules which are most likely to bind to a protein target. Here, we\nperformed a molecular docking study to propose potential candidates to prevent\nthe RBD/ACE2 attachment. These candidates are sixteen different flavonoids\npresent in the peppermint leaf. Results showed that Luteolin\n7-O-neohesperidoside is the peppermint flavonoid with a higher binding affinity\nregarding the RBD/ACE2 complex (about -9.18 Kcal/mol). On the other hand,\nSakuranetin presented the lowest affinity (about -6.38 Kcal/mol). Binding\naffinities of the other peppermint flavonoids ranged from -6.44 Kcal/mol up to\n-9.05 Kcal/mol. The binding site surface analysis showed pocket-like regions on\nthe RBD/ACE2 complex that yield several interactions (mostly hydrogen bonds)\nbetween the flavonoid and the amino acid residues of the proteins. This study\ncan open channels for the understanding of the roles of flavonoids against\nCOVID-19 infection.\n"}
+{"abstract": "  In this paper, we compute and demonstrate the equivalence of the joint\ndistribution of the first letter and descent statistics on six avoidance\nclasses of permutations corresponding to two patterns of length four. This\ndistribution is in turn shown to be equivalent to the distribution on a\nrestricted class of inversion sequences for the statistics that record the last\nletter and number of distinct positive letters, affirming a recent conjecture\nof Lin and Kim. Members of each avoidance class of permutations and also of the\nclass of inversion sequences are enumerated by the $n$-th large Schr\\\"oder\nnumber and thus one obtains a new bivariate refinement of these numbers as a\nconsequence. We make use of auxiliary combinatorial statistics, special\ngenerating functions (specific to each class) and the kernel method to\nestablish our results. In some cases, we utilize the conjecture itself in a\ncreative way to aid in solving the system of functional equations satisfied by\nthe associated generating functions.\n"}
+{"abstract": "  Recently the drift-reduced Landau fluid six-field turbulence model within the\nBOUT++ framework has been upgraded. In particular, this new model employs a new\nnormalization, adds a volumetric flux-driven source option, the Landau fluid\nclosure for parallel heat flux and a Laplacian inversion solver which is able\nto capture n=0 axisymmetric mode evolution in realistic tokamak configurations.\nThese improvements substantially extended model's capability to study a wider\nrange of tokamak edge phenomena, and are essential to build a fully\nself-consistent edge turbulence model capable of both transient (e.g., ELM,\ndisruption) and transport time-scale simulations.\n"}
+{"abstract": "  We enumerate Hurwitz orbits of shortest reflection factorizations of an\narbitrary element in the infinite family $G(m, p, n)$ of complex reflection\ngroups. As a consequence, we characterize the elements for which the action is\ntransitive and give a simple criterion to tell when two shortest reflection\nfactorizations belong to the same Hurwitz orbit. We also characterize the\nquasi-Coxeter elements (those with a shortest reflection factorization that\ngenerates the whole group) in $G(m, p, n)$.\n"}
+{"abstract": "  We consider the problem of constructing nonparametric undirected graphical\nmodels for high-dimensional functional data. Most existing statistical methods\nin this context assume either a Gaussian distribution on the vertices or linear\nconditional means. In this article we provide a more flexible model which\nrelaxes the linearity assumption by replacing it by an arbitrary additive form.\nThe use of functional principal components offers an estimation strategy that\nuses a group lasso penalty to estimate the relevant edges of the graph. We\nestablish statistical guarantees for the resulting estimators, which can be\nused to prove consistency if the dimension and the number of functional\nprincipal components diverge to infinity with the sample size. We also\ninvestigate the empirical performance of our method through simulation studies\nand a real data application.\n"}
+{"abstract": "  We show fully polynomial time randomized approximation schemes (FPRAS) for\ncounting matchings of a given size, or more generally sampling/counting\nmonomer-dimer systems in planar, not-necessarily-bipartite, graphs. While\nperfect matchings on planar graphs can be counted exactly in polynomial time,\ncounting non-perfect matchings was shown by [Jer87] to be #P-hard, who also\nraised the question of whether efficient approximate counting is possible. We\nanswer this affirmatively by showing that the multi-site Glauber dynamics on\nthe set of monomers in a monomer-dimer system always mixes rapidly, and that\nthis dynamics can be implemented efficiently on downward-closed families of\ngraphs where counting perfect matchings is tractable. As further applications\nof our results, we show how to sample efficiently using multi-site Glauber\ndynamics from partition-constrained strongly Rayleigh distributions, and\nnonsymmetric determinantal point processes.\n  In order to analyze mixing properties of the multi-site Glauber dynamics, we\nestablish two notions for generating polynomials of discrete set-valued\ndistributions: sector-stability and fractional log-concavity. These notions\ngeneralize well-studied properties like real-stability and log-concavity, but\nunlike them robustly degrade under useful transformations applied to the\ndistribution. We relate these notions to pairwise correlations in the\nunderlying distribution and the notion of spectral independence introduced by\n[ALO20], providing a new tool for establishing spectral independence based on\ngeometry of polynomials. As a byproduct of our techniques, we show that\npolynomials avoiding roots in a sector of the complex plane must satisfy what\nwe call fractional log-concavity; this extends a classic result established by\n[Gar59] who showed homogeneous polynomials that have no roots in a half-plane\nmust be log-concave over the positive orthant.\n"}
+{"abstract": "  Nuclear magnetic resonance imaging (MRI) at the atomic scale offers exciting\nprospects for determining the structure and function of individual molecules\nand proteins. Quantum defects in diamond have recently emerged as a promising\nplatform towards reaching this goal, and allowed for the detection and\nlocalization of single nuclear spins under ambient conditions. We present an\nefficient strategy for extending imaging to large nuclear spin clusters,\nfulfilling an important requirement towards a single-molecule MRI technique.\nOur method combines the concepts of weak quantum measurements, phase encoding\nand simulated annealing to detect three-dimensional positions from many nuclei\nin parallel. Detection is spatially selective, allowing us to probe nuclei at a\nchosen target radius while avoiding interference from strongly-coupled proximal\nnuclei. We demonstrate our strategy by imaging clusters containing more than 20\ncarbon-13 nuclear spins within a radius of 2.4 nm from single, near-surface\nnitrogen--vacancy centers at room temperature. The radius extrapolates to 7-8\nnm for protons. Beside taking an important step in nanoscale MRI, our\nexperiment also provides an efficient tool for the characterization of large\nnuclear spin registers in the context of quantum simulators and quantum network\nnodes.\n"}
+{"abstract": "  Urban areas are negatively impacted by Carbon Dioxide (CO2 ) and Nitrogen\nOxide (NOx) emissions. In order to achieve a cost-effective reduction of\ngreenhouse gas emissions and to combat climate change, the European Union (EU)\nintroduced an Emissions Trading System (ETS) where organizations can buy or\nreceive emission allowances as needed. The current ETS is a centralized one,\nconsisting of a set of complex rules. It is currently administered at the\norganizational level and is used for fixed-point sources of pollution such as\nfactories, power plants, and refineries. However, the current ETS cannot\nefficiently cope with vehicle mobility, even though vehicles are one of the\nprimary sources of CO2 and NOx emissions. In this study, we propose a new\ndistributed Blockchain-based emissions allowance trading system called B-ETS.\nThis system enables transparent and trustworthy data exchange as well as\ntrading of allowances among vehicles, relying on vehicle-to-vehicle\ncommunication. In addition, we introduce an economic incentive-based mechanism\nthat appeals to individual drivers and leads them to modify their driving\nbehavior in order to reduce emissions. The efficiency of the proposed system is\nstudied through extensive simulations, showing how increased vehicle\nconnectivity can lead to a reduction of the emissions generated from those\nvehicles. We demonstrate that our method can be used for full life-cycle\nmonitoring and fuel economy reporting. This leads us to conjecture that the\nproposed system could lead to important behavioral changes among the drivers\n"}
+{"abstract": "  Recently, the chiral superconductivity of the cosmic string in the axion\nmodel has gathered attention. The superconductive nature can alter the standard\nunderstanding of the cosmology of the axion model. For example, a string loop\nwith a sizable superconducting current can become a stable configuration, which\nis called a Vorton. The superconductive nature can also affect the cosmological\nevolution of the string network. In this paper, we study the stability of the\nsuperconducting current in the string. We find the superconductivity is indeed\nstable for a straight string or infinitely small string core size, even if the\ncarrier particles are unstable in the vacuum. However we also find that the\ncarrier particle decays in a curved string in typical axion models, if the\ncarrier particles are unstable in the vacuum. Accordingly, the lifetime of the\nVorton is not far from that of the carrier particle in the vacuum.\n"}
+{"abstract": "  Diabetic retinopathy (DR) remains the most prevalent cause of vision\nimpairment and irreversible blindness in the working-age adults. Due to the\nrenaissance of deep learning (DL), DL-based DR diagnosis has become a promising\ntool for the early screening and severity grading of DR. However, training deep\nneural networks (DNNs) requires an enormous amount of carefully labeled data.\nNoisy label data may be introduced when labeling plenty of data, degrading the\nperformance of models. In this work, we propose a novel label management\nmechanism (LMM) for the DNN to overcome overfitting on the noisy data. LMM\nutilizes maximum posteriori probability (MAP) in the Bayesian statistic and\ntime-weighted technique to selectively correct the labels of unclean data,\nwhich gradually purify the training data and improve classification\nperformance. Comprehensive experiments on both synthetic noise data (Messidor\n\\& our collected DR dataset) and real-world noise data (ANIMAL-10N)\ndemonstrated that LMM could boost performance of models and is superior to\nthree state-of-the-art methods.\n"}
+{"abstract": "  Person detection is a crucial task for mobile robots navigating in\nhuman-populated environments and LiDAR sensors are promising for this task,\ngiven their accurate depth measurements and large field of view. This paper\nstudies existing LiDAR-based person detectors with a particular focus on mobile\nrobot scenarios (e.g. service robot or social robot), where persons are\nobserved more frequently and in much closer ranges, compared to the driving\nscenarios. We conduct a series of experiments, using the recently released\nJackRabbot dataset and the state-of-the-art detectors based on 3D or 2D LiDAR\nsensors (CenterPoint and DR-SPAAM respectively). These experiments revolve\naround the domain gap between driving and mobile robot scenarios, as well as\nthe modality gap between 3D and 2D LiDAR sensors. For the domain gap, we aim to\nunderstand if detectors pretrained on driving datasets can achieve good\nperformance on the mobile robot scenarios, for which there are currently no\ntrained models readily available. For the modality gap, we compare detectors\nthat use 3D or 2D LiDAR, from various aspects, including performance, runtime,\nlocalization accuracy, robustness to range and crowdedness. The results from\nour experiments provide practical insights into LiDAR-based person detection\nand facilitate informed decisions for relevant mobile robot designs and\napplications.\n"}
+{"abstract": "  Data augmentation methods have been shown to be a fundamental technique to\nimprove generalization in tasks such as image, text and audio classification.\nRecently, automated augmentation methods have led to further improvements on\nimage classification and object detection leading to state-of-the-art\nperformances. Nevertheless, little work has been done on time-series data, an\narea that could greatly benefit from automated data augmentation given the\nusually limited size of the datasets. We present two sample-adaptive automatic\nweighting schemes for data augmentation: the first learns to weight the\ncontribution of the augmented samples to the loss, and the second method\nselects a subset of transformations based on the ranking of the predicted\ntraining loss. We validate our proposed methods on a large, noisy financial\ndataset and on time-series datasets from the UCR archive. On the financial\ndataset, we show that the methods in combination with a trading strategy lead\nto improvements in annualized returns of over 50$\\%$, and on the time-series\ndata we outperform state-of-the-art models on over half of the datasets, and\nachieve similar performance in accuracy on the others.\n"}
+{"abstract": "  Wireless power transfer using inductive coupling is commonly used for medical\nimplantable devices. The design of the secondary coil on the implantable device\nis important as it will affect the power transfer efficiency, the size of the\nimplant, and also the data transmission between the implant and the in-vitro\ncontroller. In this paper, we present a design of the secondary coil on a\npolyimide-based flexible substrate to achieve high power transfer efficiency.\nLoad shift keying modulation is used for the data communication between the\nprimary and secondary coils. A thorough analysis is done for the ideal and\npractical scenario and it shows that a mismatched secondary LC tank will affect\nthe communication range and communication correctness. A solution to achieve\nrobust data transmission is proposed and then verified by SPICE simulations.\n"}
+{"abstract": "  We show that, for points along the moment curve, the bar-and-joint rigidity\nmatroid and the hyperconnectivity matroid coincide, and that both coincide with\nthe $C^{d-2}_{d-1}$-cofactor rigidity of points along any (non-degenerate)\nconic in the plane. We also show that, for hyperconnectivity in dimension two,\nhaving the points in the moment curve is no loss of generality.\n"}
+{"abstract": "  We present numerical methods based on the fast Fourier transform (FFT) to\nsolve convolution integral equations on a semi-infinite interval (Wiener-Hopf\nequation) or on a finite interval (Fredholm equation). We extend and improve a\nFFT-based method for the Wiener-Hopf equation due to Henery, expressing it in\nterms of the Hilbert transform, and computing the latter in a more\nsophisticated way with sinc functions. We then generalise our method to the\nFredholm equation reformulating it as two coupled Wiener-Hopf equations and\nsolving them iteratively. We provide numerical tests and open-source code.\n"}
+{"abstract": "  Ultracold YbAg molecules have been recently proposed as promising candidates\nfor electron electric dipole moment searches [Verma, Jayich, and Vutha, Phys.\nRev. Lett. 125, 153201 (2020)]. Here, we calculate potential energy curves,\npermanent electric dipole and quadrupole moments, and static electric dipole\npolarizabilities for the YbCu, YbAg, and YbAu molecules in their ground\nelectronic states. We use the coupled cluster method restricted to single,\ndouble, and noniterative triple excitations with large Gaussian basis sets,\nwhile the scalar relativistic effects are included within the small-core\nenergy-consistent pseudopotentials. We find that the studied molecules are\nrelatively strongly bound with the well depths of 5708$\\,$cm$^{-1}$,\n5253$\\,$cm$^{-1}$, 13349$\\,$cm$^{-1}$ and equilibrium distances of\n5.50$\\,$bohr, 5.79$\\,$bohr, 5.55$\\,$bohr for the YbCu, YbAg, and YbAu,\nrespectively. They have large permanent electric dipole moments of 3.2$\\,$D,\n3.3$\\,$D, and 5.3$\\,$D at equilibrium distances, respectively. We also\ncalculate equilibrium geometries and energies of corresponding trimers. The\nstudied molecules are chemically reactive unless they are segregated in an\noptical lattice or shielded with external fields. The investigated molecules\nmay find application in ultracold controlled chemistry, dipolar many-body\nphysics, or precision measurement experiments.\n"}
+{"abstract": "  Hyperspectral Image (HSI) classification using Convolutional Neural Networks\n(CNN) is widely found in the current literature. Approaches vary from using\nSVMs to 2D CNNs, 3D CNNs, 3D-2D CNNs. Besides 3D-2D CNNs and FuSENet, the other\napproaches do not consider both the spectral and spatial features together for\nHSI classification task, thereby resulting in poor performances. 3D CNNs are\ncomputationally heavy and are not widely used, while 2D CNNs do not consider\nmulti-resolution processing of images, and only limits itself to the spatial\nfeatures. Even though 3D-2D CNNs try to model the spectral and spatial features\ntheir performance seems limited when applied over multiple dataset. In this\narticle, we propose SpectralNET, a wavelet CNN, which is a variation of 2D CNN\nfor multi-resolution HSI classification. A wavelet CNN uses layers of wavelet\ntransform to bring out spectral features. Computing a wavelet transform is\nlighter than computing 3D CNN. The spectral features extracted are then\nconnected to the 2D CNN which bring out the spatial features, thereby creating\na spatial-spectral feature vector for classification. Overall a better model is\nachieved that can classify multi-resolution HSI data with high accuracy.\nExperiments performed with SpectralNET on benchmark dataset, i.e. Indian Pines,\nUniversity of Pavia, and Salinas Scenes confirm the superiority of proposed\nSpectralNET with respect to the state-of-the-art methods. The code is publicly\navailable in https://github.com/tanmay-ty/SpectralNET.\n"}
+{"abstract": "  We consider the offline reinforcement learning (RL) setting where the agent\naims to optimize the policy solely from the data without further environment\ninteractions. In offline RL, the distributional shift becomes the primary\nsource of difficulty, which arises from the deviation of the target policy\nbeing optimized from the behavior policy used for data collection. This\ntypically causes overestimation of action values, which poses severe problems\nfor model-free algorithms that use bootstrapping. To mitigate the problem,\nprior offline RL algorithms often used sophisticated techniques that encourage\nunderestimation of action values, which introduces an additional set of\nhyperparameters that need to be tuned properly. In this paper, we present an\noffline RL algorithm that prevents overestimation in a more principled way. Our\nalgorithm, OptiDICE, directly estimates the stationary distribution corrections\nof the optimal policy and does not rely on policy-gradients, unlike previous\noffline RL algorithms. Using an extensive set of benchmark datasets for offline\nRL, we show that OptiDICE performs competitively with the state-of-the-art\nmethods.\n"}
+{"abstract": "  We consider a market setting of agents with additive valuations over\nheterogeneous divisible resources. Agents are assigned a budget of tokens\n(possibly unequal budgets) they can use to obtain resources; leftover tokens\nare worthless. We show how to support any Pareto efficient allocation in\nequilibrium, using anonymous resource prices and agent specific budgets. We\nalso give computationally efficient algorithms for those tasks. In particular,\nthis allows us to support the Rawlsian max-min allocation.\n"}
+{"abstract": "  We study two dyadic models for incompressible ideal magnetohydrodynamics, one\nwith a uni-directional energy cascade and the other one with both forward and\nbackward energy cascades. Global existence of weak solutions and local\nwell-posedness are established for both models. In addition, solutions to the\nmodel with uni-directional energy cascade associated with positive initial data\nare shown to develop blow-up at a finite time. Moreover, a set of fixed points\nis found for each model. Linear instability about some particular fixed points\nis proved.\n"}
+{"abstract": "  When fitting statistical models to variables in geoscientific disciplines\nsuch as hydrology, it is a customary practice to regionalize - to divide a\nlarge spatial domain into multiple regions and study each region separately -\ninstead of fitting a single model on the entire data (also known as\nunification). Traditional wisdom in these fields suggests that models built for\neach region separately will have higher performance because of homogeneity\nwithin each region. However, by partitioning the training data, each model has\naccess to fewer data points and cannot learn from commonalities between\nregions. Here, through two hydrologic examples (soil moisture and streamflow),\nwe argue that unification can often significantly outperform regionalization in\nthe era of big data and deep learning (DL). Common DL architectures, even\nwithout bespoke customization, can automatically build models that benefit from\nregional commonality while accurately learning region-specific differences. We\nhighlight an effect we call data synergy, where the results of the DL models\nimproved when data were pooled together from characteristically different\nregions. In fact, the performance of the DL models benefited from more diverse\nrather than more homogeneous training data. We hypothesize that DL models\nautomatically adjust their internal representations to identify commonalities\nwhile also providing sufficient discriminatory information to the model. The\nresults here advocate for pooling together larger datasets, and suggest the\nacademic community should place greater emphasis on data sharing and\ncompilation.\n"}
+{"abstract": "  Trading frictions are stochastic. They are, moreover, in many instances\nfast-mean reverting. Here, we study how to optimally trade in a market with\nstochastic price impact and study approximations to the resulting optimal\ncontrol problem using singular perturbation methods. We prove, by constructing\nsub- and super-solutions, that the approximations are accurate to the specified\norder. Finally, we perform some numerical experiments to illustrate the effect\nthat stochastic trading frictions have on optimal trading.\n"}
+{"abstract": "  Biologically inspired algorithms for simultaneous localization and mapping\n(SLAM) such as RatSLAM have been shown to yield effective and robust robot\nnavigation in both indoor and outdoor environments. One drawback however is the\nsensitivity to perceptual aliasing due to the template matching of\nlow-dimensional sensory templates. In this paper, we propose an unsupervised\nrepresentation learning method that yields low-dimensional latent state\ndescriptors that can be used for RatSLAM. Our method is sensor agnostic and can\nbe applied to any sensor modality, as we illustrate for camera images, radar\nrange-doppler maps and lidar scans. We also show how combining multiple sensors\ncan increase the robustness, by reducing the number of false matches. We\nevaluate on a dataset captured with a mobile robot navigating in a\nwarehouse-like environment, moving through different aisles with similar\nappearance, making it hard for the SLAM algorithms to disambiguate locations.\n"}
+{"abstract": "  Nanostructured materials enable high thermal transport tunability, holding\npromises for thermal management and heat harvesting applications. Predicting\nthe effect that nanostructuring has on thermal conductivity requires models,\nsuch as the Boltzmann transport equation (BTE), that capture the non-diffusive\ntransport of phonons. Although the BTE has been well validated against several\nkey experiments, notably those on nanoporous materials, its applicability is\ncomputationally expensive. Several effective model theories have been put\nforward to estimate the effective thermal conductivity; however, most of them\nare either based on simple geometries, e.g., thin films, or simplified material\ndescriptions such as the gray-model approximation. To fill this gap, we propose\na model that takes into account the whole mean-free-path (MFP) distribution as\nwell as the complexity of the material's boundaries in infinitely thick films\nwith extruded porosity using uniparameter logistic regression. We validate our\napproach, which is called the \"Ballistic Correction Model\" (BCM), against full\nBTE simulations of a selection of three base materials (GaAs, InAs, and Si)\nwith nanoscale porosity, obtaining excellent agreement. While the key\nparameters of our method, associated with the geometry of the bulk material,\nare obtained from the BTE, they can be decoupled and used in arbitrary\ncombinations and scales. We tabulated these parameters for a few cases,\nenabling the exploration of systems that are beyond those considered in this\nwork. Providing a simple yet accurate estimation of thermal transport in\nnanostructures, our work sets out to accelerate the discovery of materials for\nthermal-related applications.\n"}
+{"abstract": "  Recent applications employ publish/subscribe (Pub/Sub) systems so that\npublishers can easily receive attentions of customers and subscribers can\nmonitor useful information generated by publishers. Due to the prevalence of\nsmart devices and social networking services, a large number of objects that\ncontain both spatial and keyword information have been generated continuously,\nand the number of subscribers also continues to increase. This poses a\nchallenge to Pub/Sub systems: they need to continuously extract useful\ninformation from massive objects for each subscriber in real time. In this\npaper, we address the problem of k nearest neighbor monitoring on a\nspatial-keyword data stream for a large number of subscriptions. To scale well\nto massive objects and subscriptions, we propose a distributed solution, namely\nDkM-SKS. Given m workers, DkM-SKS divides a set of subscriptions into m\ndisjoint subsets based on a cost model so that each worker has almost the same\nkNN-update cost, to maintain load balancing. DkM-SKS allows an arbitrary\napproach to updating kNN of each subscription, so with a suitable in-memory\nindex, DkM-SKS can accelerate update efficiency by pruning irrelevant\nsubscriptions for a given new object. We conduct experiments on real datasets,\nand the results demonstrate the efficiency and scalability of DkM-SKS.\n"}
+{"abstract": "  Robotic applications nowadays are widely adopted to enhance operational\nautomation and performance of real-world Cyber-Physical Systems (CPSs)\nincluding Industry 4.0, agriculture, healthcare, and disaster management. These\napplications are composed of latency-sensitive, data-heavy, and\ncompute-intensive tasks. The robots, however, are constrained in the\ncomputational power and storage capacity. The concept of multi-agent cloud\nrobotics enables robot-to-robot cooperation and creates a complementary\nenvironment for the robots in executing large-scale applications with the\ncapability to utilize the edge and cloud resources. However, in such a\ncollaborative environment, the optimal resource allocation for robotic tasks is\nchallenging to achieve. Heterogeneous energy consumption rates and application\nof execution costs associated with the robots and computing instances make it\neven more complex. In addition, the data transmission delay between local\nrobots, edge nodes, and cloud data centres adversely affects the real-time\ninteractions and impedes service performance guarantee. Taking all these issues\ninto account, this paper comprehensively surveys the state-of-the-art on\nresource allocation and service provisioning in multi-agent cloud robotics. The\npaper presents the application domains of multi-agent cloud robotics through\nexplicit comparison with the contemporary computing paradigms and identifies\nthe specific research challenges. A complete taxonomy on resource allocation is\npresented for the first time, together with the discussion of resource pooling,\ncomputation offloading, and task scheduling for efficient service provisioning.\nFurthermore, we highlight the research gaps from the learned lessons, and\npresent future directions deemed beneficial to further advance this emerging\nfield.\n"}
+{"abstract": "  We build a rigorous statistical framework to provide constraints on the\nchemical and dust evolution parameters for nearby late-type galaxies with a\nwide range of gas fractions ($3\\%<f_g<94\\%$). A Bayesian Monte Carlo Markov\nChain framework provides statistical constraints on the parameters used in\nchemical evolution models. Nearly a million one-zone chemical and dust\nevolution models were compared to 340 galaxies. Relative probabilities were\ncalculated from the $\\chi^2$ between data and models, marginalised over the\ndifferent time steps, galaxy masses and star formation histories. We applied\nthis method to find `best fitting' model parameters related to metallicity, and\nsubsequently fix these metal parameters to study the dust parameters. For the\nmetal parameters, a degeneracy was found between the choice of initial mass\nfunction, supernova metal yield tables and outflow prescription. For the dust\nparameters, the uncertainties on the best fit values are often large except for\nthe fraction of metals available for grain growth, which is well constrained.\nWe find a number of degeneracies between the dust parameters, limiting our\nability to discriminate between chemical models using observations only. For\nexample, we show that the low dust content of low-metallicity galaxies can be\nresolved by either reducing the supernova dust yields and/or including\nphoto-fragmentation. We also show that supernova dust dominates the dust mass\nfor low metallicity galaxies and grain growth dominates for high metallicity\ngalaxies. The transition occurs around $12+\\log({\\rm O/H})=7.75$, which is\nlower than found in most studies in the literature.\n"}
+{"abstract": "  This paper presents a new Vision Transformer (ViT) architecture Multi-Scale\nVision Longformer, which significantly enhances the ViT of\n\\cite{dosovitskiy2020image} for encoding high-resolution images using two\ntechniques. The first is the multi-scale model structure, which provides image\nencodings at multiple scales with manageable computational cost. The second is\nthe attention mechanism of vision Longformer, which is a variant of Longformer\n\\cite{beltagy2020longformer}, originally developed for natural language\nprocessing, and achieves a linear complexity w.r.t. the number of input tokens.\nA comprehensive empirical study shows that the new ViT significantly\noutperforms several strong baselines, including the existing ViT models and\ntheir ResNet counterparts, and the Pyramid Vision Transformer from a concurrent\nwork \\cite{wang2021pyramid}, on a range of vision tasks, including image\nclassification, object detection, and segmentation. The models and source code\nare released at \\url{https://github.com/microsoft/vision-longformer}.\n"}
+{"abstract": "  A balanced sampling design should always be the adopted strategies if\nauxiliary information is available. Besides, integrating a stratified structure\nof the population in the sampling process can considerably reduce the variance\nof the estimators. We propose here a new method to handle the selection of a\nbalanced sample in a highly stratified population. The method improves\nsubstantially the commonly used sampling design and reduces the time-consuming\nproblem that could arise if inclusion probabilities within strata do not sum to\nan integer.\n"}
+{"abstract": "  I briefly discuss recent results of numerical simulations addressing the\ngeneration of a cosmological gravitational wave background produced by\nturbulence sources in the early universe. Contribution to the 2021 Gravitation\nsession of the 55th Rencontres de Moriond.\n"}
+{"abstract": "  Petroni et al. (2019) demonstrated that it is possible to retrieve world\nfacts from a pre-trained language model by expressing them as cloze-style\nprompts and interpret the model's prediction accuracy as a lower bound on the\namount of factual information it encodes. Subsequent work has attempted to\ntighten the estimate by searching for better prompts, using a disjoint set of\nfacts as training data. In this work, we make two complementary contributions\nto better understand these factual probing techniques. First, we propose\nOptiPrompt, a novel and efficient method which directly optimizes in continuous\nembedding space. We find this simple method is able to predict an additional\n6.4% of facts in the LAMA benchmark. Second, we raise a more important\nquestion: Can we really interpret these probing results as a lower bound? Is it\npossible that these prompt-search methods learn from the training data too? We\nfind, somewhat surprisingly, that the training data used by these methods\ncontains certain regularities of the underlying fact distribution, and all the\nexisting prompt methods, including ours, are able to exploit them for better\nfact prediction. We conduct a set of control experiments to disentangle\n\"learning\" from \"learning to recall\", providing a more detailed picture of what\ndifferent prompts can reveal about pre-trained language models.\n"}
+{"abstract": "  Much of what is known about high-temperature cuprate superconductors stems\nfrom studies based on two surface analytical tools, angle-resolved\nphotoemission spectroscopy (ARPES) and spectroscopic imaging scanning tunneling\nmicroscopy (SI-STM). A question of general interest is whether and when the\nsurface properties probed by ARPES and SI-STM are representative of the\nintrinsic properties of bulk materials. We find this question is prominent in\nthin films of a rarely studied cuprate DBCO. We synthesize DBCO films by oxide\nmolecular beam epitaxy and study them by in situ ARPES and SI-STM. Both ARPES\nand SI-STM show that the surface DBCO layer is different from the bulk of the\nfilm. It is heavily underdoped, while the doping level in the bulk is close to\noptimal doping evidenced by bulk-sensitive mutual inductance measurements.\nARPES shows the typical electronic structure of a heavily underdoped CuO2 plane\nand two sets of one-dimensional bands originating from the CuO chains with one\nof them gapped. SI-STM reveals two different energy scales in the local density\nof states, with one corresponding to the superconductivity and the other one to\nthe pseudogap. While the pseudogap shows large variations over the length scale\nof a few nanometers, the superconducting gap is very homogeneous. This\nindicates that the pseudogap and superconductivity are of different origins.\n"}
+{"abstract": "  Deep Learning (DL) is a machine learning procedure for artificial\nintelligence that analyzes the input data in detail by increasing neuron sizes\nand number of the hidden layers. DL has a popularity with the common\nimprovements on the graphical processing unit capabilities. Increasing number\nof the neuron sizes at each layer and hidden layers is directly related to the\ncomputation time and training speed of the classifier models. The\nclassification parameters including neuron weights, output weights, and biases\nneed to be optimized for obtaining an optimum model. Most of the popular DL\nalgorithms require long training times for optimization of the parameters with\nfeature learning progresses and back-propagated training procedures. Reducing\nthe training time and providing a real-time decision system are the basic focus\npoints of the novel approaches. Deep Extreme Learning machines (Deep ELM)\nclassifier model is one of the fastest and effective way to meet fast\nclassification problems. In this study, Deep ELM model, its superiorities and\nweaknesses are discussed, the problems that are more suitable for the\nclassifiers against Convolutional neural network based DL algorithms.\n"}
+{"abstract": "  Accurate estimates of network parameters are essential for modeling,\nmonitoring, and control in power distribution systems. In this paper, we\ndevelop a physics-informed graphical learning algorithm to estimate network\nparameters of three-phase power distribution systems. Our proposed algorithm\nuses only readily available smart meter data to estimate the three-phase series\nresistance and reactance of the primary distribution line segments. We first\ndevelop a parametric physics-based model to replace the black-box deep neural\nnetworks in the conventional graphical neural network (GNN). Then we derive the\ngradient of the loss function with respect to the network parameters and use\nstochastic gradient descent (SGD) to estimate the physical parameters. Prior\nknowledge of network parameters is also considered to further improve the\naccuracy of estimation. Comprehensive numerical study results show that our\nproposed algorithm yields high accuracy and outperforms existing methods.\n"}
+{"abstract": "  The typical contrastive self-supervised algorithm uses a similarity measure\nin latent space as the supervision signal by contrasting positive and negative\nimages directly or indirectly. Although the utility of self-supervised\nalgorithms has improved recently, there are still bottlenecks hindering their\nwidespread use, such as the compute needed. In this paper, we propose a module\nthat serves as an additional objective in the self-supervised contrastive\nlearning paradigm. We show how the inclusion of this module to regress the\nparameters of an affine transformation or homography, in addition to the\noriginal contrastive objective, improves both performance and learning speed.\nImportantly, we ensure that this module does not enforce invariance to the\nvarious components of the affine transform, as this is not always ideal. We\ndemonstrate the effectiveness of the additional objective on two recent,\npopular self-supervised algorithms. We perform an extensive experimental\nanalysis of the proposed method and show an improvement in performance for all\nconsidered datasets. Further, we find that although both the general homography\nand affine transformation are sufficient to improve performance and\nconvergence, the affine transformation performs better in all cases.\n"}
+{"abstract": "  We treat the change point problem in ergodic diffusion processes from\ndiscrete observations. Tonaki et al. (2020) proposed adaptive tests for\ndetecting changes in the diffusion and drift parameters in ergodic diffusion\nmodels. When any changes are detected by this method, the next question to be\nconsidered is where the change point is. Therefore, we propose the method to\nestimate the change point of the parameter for two cases: the case where there\nis a change in the diffusion parameter, and the case where there is no change\nin the diffusion parameter but a change in the drift parameter. Furthermore, we\npresent rates of convergence and distributional results of the change point\nestimators. Some examples and simulation results are also given.\n"}
+{"abstract": "  We consider Dirac-like operators with piecewise constant mass terms on spin\nmanifolds, and we study the behaviour of their spectra when the mass parameters\nbecome large. In several asymptotic regimes, effective operators appear: the\nextrinsic Dirac operator and a generalized MIT Bag Dirac operator. This extends\nsome results previously known for the Euclidean spaces to the case of general\nspin geometry.\n"}
+{"abstract": "  We derive a model based on the structure of dependence between a Brownian\nmotion and its reflection according to a barrier. The structure of dependence\npresents two states of correlation: one of comonotonicity with a positive\ncorrelation and one of countermonotonicity with a negative correlation. This\nmodel of dependence between two Brownian motions $B^1$ and $B^2$ allows for the\nvalue of $\\mathbb{P}\\left(B^1_t - B^2_t \\geq x\\right)$ to be higher than\n$\\frac{1}{2}$ when $x$ is close to 0, which is not the case when the dependence\nis modeled by a constant correlation. It can be used for risk management and\noption pricing in commodity energy markets. In particular, it allows to capture\nthe asymmetry in the distribution of the difference between electricity prices\nand its combustible prices.\n"}
+{"abstract": "  Galaxy clusters are an important tool for cosmology, and their detection and\ncharacterization are key goals for current and future surveys. Using data from\nthe Wide-field Infrared Survey Explorer (WISE), the Massive and Distant\nClusters of WISE Survey (MaDCoWS) located 2,839 significant galaxy\noverdensities at redshifts $0.7\\lesssim z\\lesssim 1.5$, which included\nextensive follow-up imaging from the Spitzer Space Telescope to determine\ncluster richnesses. Concurrently, the Atacama Cosmology Telescope (ACT) has\nproduced large area mm-wave maps in three frequency bands along with a large\ncatalog of Sunyaev-Zeldovich (SZ) selected clusters, as part of its Data\nRelease 5 (DR5). Using the maps and cluster catalog from DR5, we explore the\nscaling between SZ mass and cluster richness. We use complementary radio survey\ndata from the Very Large Array, submillimeter data from Herschel, and ACT\n224~GHz data to assess the impact of contaminating sources on the SZ signals.\nWe then use a hierarchical Bayesian model to fit the mass-richness scaling\nrelation. We find that MaDCoWS clusters have submillimeter contamination which\nis consistent with a gray-body spectrum, while the ACT clusters are consistent\nwith no submillimeter emission on average. We find the best fit ACT SZ mass vs.\nMaDCoWS richness scaling relation has a slope of $\\kappa =\n1.84^{+0.15}_{-0.14}$, where the slope is defined as $M\\propto\n\\lambda_{15}^{\\kappa}$ where $\\lambda_{15}$ is the richness. Additionally, we\nfind that the approximate level of in-fill of the ACT and MaDCoWS cluster SZ\nsignals to be at the percent level\n"}
+{"abstract": "  Classical soft graviton theorem gives the gravitational wave-form at future\nnull infinity at late retarded time $u$ for a general classical scattering. The\nlarge $u$ expansion has three known universal terms: the constant term, the\nterm proportional to $1/u$ and the term proportional to $\\ln u/u^2$, whose\ncoefficients are determined solely in terms of the momenta of incoming and the\noutgoing hard particles, including the momenta carried by outgoing\ngravitational and electromagnetic radiation produced during scattering. For the\nconstant term, also known as the memory effect, the dependence on the momenta\ncarried away by the final state radiation / massless particles is known as\nnon-linear memory or null memory. It was shown earlier that for the coefficient\nof the $1/u$ term the dependence on the momenta of the final state massless\nparticles / radiation cancels and the result can be written solely in terms of\nthe momenta of the incoming particles / radiation and the final state massive\nparticles. In this note we show that the same result holds for the coefficient\nof the $\\ln u/u^2$ term. Our result implies that for scattering of massless\nparticles the coefficients of the $1/u$ and $\\ln u/u^2$ terms are determined\nsolely by the incoming momenta, even if the particles coalesce to form a black\nhole and massless radiation. We use our result to compute the low frequency\nflux of gravitational radiation from the collision of massless particles at\nlarge impact parameter.\n"}
+{"abstract": "  This paper presents AppealNet, a novel edge/cloud collaborative architecture\nthat runs deep learning (DL) tasks more efficiently than state-of-the-art\nsolutions. For a given input, AppealNet accurately predicts on-the-fly whether\nit can be successfully processed by the DL model deployed on the\nresource-constrained edge device, and if not, appeals to the more powerful DL\nmodel deployed at the cloud. This is achieved by employing a two-head neural\nnetwork architecture that explicitly takes inference difficulty into\nconsideration and optimizes the tradeoff between accuracy and\ncomputation/communication cost of the edge/cloud collaborative architecture.\nExperimental results on several image classification datasets show up to more\nthan 40% energy savings compared to existing techniques without sacrificing\naccuracy.\n"}
+{"abstract": "  We theoretically investigate Majorana zero modes emerging in an anisotropic\nXY spin chain with second neighbor interactions. The spin chain is\nmathematically equivalent to the Kitaev chain composing of spinless fermions if\nonly nearest-neighbor interactions are considered. Recent studies on the Kitaev\nchain with long-ranged interactions, have presented coexistence of several\nMajorana zero modes. Investigating the topological phase diagram of the\nanisotropic XY spin chain with second neighbor interactions, we find\ncoexistence of several Majorana zero modes similar to the Kitaev chain.\nHowever, we confirm that one of zero modes is restricted into a Hilbert\nsubspace. The mode is regarded as a so-called weak zero mode that should\nexhibit thermodynamical properties different from a (strong) zero mode\nappearing in the whole Hilbert space. Since the quantum statistics of spin is\nthe same as a hard-core boson, we expect an experimental realization of the\nweak mode in not only quantum spin materials but also optical lattices for cold\natom systems.\n"}
+{"abstract": "  For an algebraic K3 surface with complex multiplication (CM), algebraic\nfibres of the associated twistor space away from the equator are again of CM\ntype. In this paper, we show that algebraic fibres corresponding to points at\nthe same altitude of the twistor base $S^2\\simeq \\mathbb{P}^1_\\mathbb{C}$ share\nthe same CM endomorphism field. Moreover, we determine all the admissible\nPicard numbers of the twistor fibres.\n"}
+{"abstract": "  This paper proposes a service restoration model for unbalanced distribution\nsystems and inverter-dominated microgrids (MGs), in which frequency dynamics\nconstraints are developed to optimize the amount of load restoration and\nguarantee the dynamic performance of system frequency response during the\nrestoration process. After extreme events, the damaged distribution systems can\nbe sectionalized into several isolated MGs to restore critical loads and\ntripped non-black start distributed generations (DGs) by black start DGs.\nHowever, the high penetration of inverter-based DGs reduces the system inertia,\nwhich results in low-inertia issues and large frequency fluctuation during the\nrestoration process. To address this challenge, we propose a two-level\nsimulation-assisted sequential service restoration model, which includes a\nmixed integer linear programming (MILP)-based optimization model and a\ntransient simulation model. The proposed MILP model explicitly incorporates the\nfrequency response into constraints, by interfacing with transient simulation\nof inverter-dominated MGs. Numerical results on a modified IEEE 123-bus system\nhave validated that the frequency dynamic performance of the proposed service\nrestoration model are indeed improved.\n"}
+{"abstract": "  Researchers and practitioners across many disciplines have recently adopted\ncomputational notebooks to develop, document, and share their scientific\nworkflows - and the GIS community is no exception. This chapter introduces\ncomputational notebooks in the geographical context. It begins by explaining\nthe computational paradigm and philosophy that underlie notebooks. Next it\nunpacks their architecture to illustrate a notebook user's typical workflow.\nThen it discusses the main benefits notebooks offer GIS researchers and\npractitioners, including better integration with modern software, more natural\naccess to new forms of data, and better alignment with the principles and\nbenefits of open science. In this context, it identifies notebooks as the glue\nthat binds together a broader ecosystem of open source packages and\ntransferable platforms for computational geography. The chapter concludes with\na brief illustration of using notebooks for a set of basic GIS operations.\nCompared to traditional desktop GIS, notebooks can make spatial analysis more\nnimble, extensible, and reproducible and have thus evolved into an important\ncomponent of the geospatial science toolkit.\n"}
+{"abstract": "  Context: In order to preserve the value of Systematic Reviews (SRs), they\nshould be frequently updated considering new evidence that has been produced\nsince the completion of the previous version of the reviews. However, the\nupdate of an SR is a time consuming, manual task. Thus, many SRs have not been\nupdated as they should be and, therefore, they are currently outdated.\nObjective: The main contribution of this paper is to support the update of SRs.\nMethod: We propose USR-VTM, an approach based on Visual Text Mining (VTM)\ntechniques, to support selection of new evidence in the form of primary\nstudies. We then present a tool, named Revis, which supports our approach.\nFinally, we evaluate our approach through a comparison of outcomes achieved\nusing USR-VTM versus the traditional (manual) approach. Results: Our results\nshow that USR-VTM increases the number of studies correctly included compared\nto the traditional approach. Conclusions: USR-VTM effectively supports the\nupdate of SRs.\n"}
+{"abstract": "  This paper is a theoretical and numerical study of the uniform growth of a\nrepeating sinusoidal imperfection in the line of a strut on a nonlinear elastic\nWinkler type foundation. The imperfection is introduced by considering an\ninitially deformed shape which is a sine function with an half wavelength. The\nrestoring force is either a bi-linear or an exponential profile. Periodic\nsolutions of the equilibrium problem are found using three different\napproaches: a semi-analytical method, an explicit solution of a Galerkin method\nand a direct numerical resolution. These methods are found in very good\nagreement and show the existence of a maximum imperfection size which leads to\na limit point in the equilibrium curve of the system. The existence of this\nlimit point is very important since it governs the appearance of localization\nphenomena.\n  Using the Galerkin method, we then establish an exact formula for this\nmaximum imperfection size and we show that it does not depend on the choice of\nthe restoring force. We also show that this method provides a better estimate\nwith respect to previous publications. The decrease of the maximum compressive\nforce supported by the beam as a function of the imperfection magnitude is also\ndetermined. We show that the leading term of the development has a different\nexponent than in subcritical buckling of elastic systems, and that the exponent\nvalues depend on the choice of the restoring force.\n"}
+{"abstract": "  The determination of the ground state of quantum many-body systems via\ndigital quantum computers rests upon the initialization of a sufficiently\neducated guess. This requirement becomes more stringent the greater the system.\nPreparing physically-motivated ans\\\"{a}tze on quantum hardware is therefore\nimportant to achieve quantum advantage in the simulation of correlated\nelectrons. In this spirit, we introduce the Gutzwiller Wave Function (GWF)\nwithin the context of the digital quantum simulation of the Fermi-Hubbard\nmodel. We present a quantum routine to initialize the GWF that comprises two\nparts. In the first, the noninteracting state associated with the $U = 0$ limit\nof the model is prepared. In the second, the non-unitary Gutzwiller projection\nthat selectively removes states with doubly-occupied sites from the wave\nfunction is performed by adding to every lattice site an ancilla qubit, the\nmeasurement of which in the $|0\\rangle$ state confirms the projection was made.\nDue to its non-deterministic nature, we estimate the success rate of the\nalgorithm in generating the GWF as a function of the lattice size and the\ninteraction strength $U/t$. The scaling of the quantum circuit metrics and its\nintegration in general quantum simulation algorithms are also discussed.\n"}
+{"abstract": "  In Twin Higgs models which contain the minimal particle content required to\naddress the little hierarchy problem (i.e. fraternal models), the twin tau has\nbeen identified as a promising candidate for dark matter. In this class of\nscenarios, however, the elastic scattering cross section of the twin tau with\nnuclei exceeds the bounds from XENON1T and other recent direct detection\nexperiments. In this paper, we propose a modification to the Fraternal Twin\nHiggs scenario that we call $\\mathbb{Z}_2$FTH, incorporating visible and twin\nhypercharged scalars (with $Y = 2$) which break twin electromagnetism. This\nleads to new mass terms for the twin tau that are unrelated to its Yukawa\ncoupling, as well as additional annihilation channels via the massive twin\nphoton. We show that these features make it possible for the right-handed twin\ntau to freeze out with an acceptable thermal relic abundance while scattering\nwith nuclei at a rate that is well below existing constraints. Nonetheless,\nlarge portions of the currently viable parameter space in this model are within\nthe reach of planned direct detection experiments. The prospects for indirect\ndetection using gamma rays and cosmic-ray antiprotons are also promising in\nthis model. Furthermore, if the twin neutrino is light, the predicted deviation\nof $\\Delta N_\\mathrm{eff} \\approx 0.1$ would be within reach of Stage 4 CMB\nexperiments. Finally, the high luminosity LHC should be able to probe the\nentire parameter space of the $\\mathbb{Z}_2$FTH model through charged scalar\nsearches. We also discuss how searches for long-lived particles are starting to\nconstrain Fraternal Twin Higgs models.\n"}
+{"abstract": "  We define \"robotic contact juggling\" to be the purposeful control of the\nmotion of a three-dimensional smooth object as it rolls freely on a\nmotion-controlled robot manipulator, or \"hand.\" While specific examples of\nrobotic contact juggling have been studied before, in this paper we provide the\nfirst general formulation and solution method for the case of an arbitrary\nsmooth object in single-point rolling contact on an arbitrary smooth hand. Our\nformulation splits the problem into four subproblems: (1) deriving the\nsecond-order rolling kinematics; (2) deriving the three-dimensional rolling\ndynamics; (3) planning rolling motions that satisfy the rolling dynamics; and\n(4) feedback stabilization of planned rolling trajectories. The theoretical\nresults are demonstrated in simulation and experiment using feedback from a\nhigh-speed vision system.\n"}
+{"abstract": "  Non-equilibrium defects often dictate macroscopic functional properties of\nmaterials. In intercalation hosts, widely used in rechargeable batteries,\nhigh-dimensional defects largely define reversibility and kinetics1,2,3,4.\nHowever, transient defects briefly appearing during ionic transport have been\nchallenging to capture, limiting the understanding of their life cycle and\nimpact. Here, we overcome this challenge and track operando the interaction and\nimpact of metastable defects within NaxNi1-xMnyO2 intercalation hosts in a\ncharging sodium-ion battery. Three-dimensional coherent X-ray imaging3,4,5\nreveals transformation and self-healing of a metastable domain boundary,\nglissile dislocation loop, and stacking fault. A local strain gradient suggests\na quantifiable difference in ion diffusion, coincident with the macroscopic\nchange in diffusion coefficient. Analysis of the unexpected4,6 defect\nanisotropy highlights the importance of mesostructure, suggesting a possible\ncontrol approach and disputing the rigidity of the framework layers. The shared\nnature of oxygen framework layers makes our results applicable to a wide range\nof intercalation materials.\n"}
+{"abstract": "  It is known that the Grothendieck group of the category of Schur functors is\nthe ring of symmetric functions. This ring has a rich structure, much of which\nis encapsulated in the fact that it is a \"plethory\": a monoid in the category\nof birings with its substitution monoidal structure. We show that similarly the\ncategory of Schur functors is a \"2-plethory\", which descends to give the\nplethory structure on symmetric functions. Thus, much of the structure of\nsymmetric functions exists at a higher level in the category of Schur functors.\n"}
+{"abstract": "  Voice activity detection is an essential pre-processing component for\nspeech-related tasks such as automatic speech recognition (ASR). Traditional\nsupervised VAD systems obtain frame-level labels from an ASR pipeline by using,\ne.g., a Hidden Markov model. These ASR models are commonly trained on clean and\nfully transcribed data, limiting VAD systems to be trained on clean or\nsynthetically noised datasets. Therefore, a major challenge for supervised VAD\nsystems is their generalization towards noisy, real-world data. This work\nproposes a data-driven teacher-student approach for VAD, which utilizes vast\nand unconstrained audio data for training. Unlike previous approaches, only\nweak labels during teacher training are required, enabling the utilization of\nany real-world, potentially noisy dataset. Our approach firstly trains a\nteacher model on a source dataset (Audioset) using clip-level supervision.\nAfter training, the teacher provides frame-level guidance to a student model on\nan unlabeled, target dataset. A multitude of student models trained on mid- to\nlarge-sized datasets are investigated (Audioset, Voxceleb, NIST SRE). Our\napproach is then respectively evaluated on clean, artificially noised, and\nreal-world data. We observe significant performance gains in artificially\nnoised and real-world scenarios. Lastly, we compare our approach against other\nunsupervised and supervised VAD methods, demonstrating our method's\nsuperiority.\n"}
+{"abstract": "  Abell 3266 is one of the X-ray brightest galaxy clusters in the sky and is a\nwell-known merging system. Using the ability of the eROSITA telescope onboard\nSRG (Spectrum R\\\"ontgen Gamma) to observe a wide field with a single pointing,\nwe analysed a new observation of the cluster out to a radius of R_200. The\nX-ray images highlight sub-structures present in the cluster, including the\nnorth-east--south-west merger seen in previous ASCA, Chandra, and XMM-Newton\ndata, a merging group towards the north-west, and filamentary structures\nbetween the core and one or more groups towards the west. We compute spatially\nresolved spectroscopic maps of the thermodynamic properties of the cluster,\nincluding the metallicity. The merging subclusters are seen as low entropy\nmaterial within the cluster. The filamentary structures could be the rims of a\npowerful outburst of an active galactic nucleus, or most likely material\nstripped from the western group(s) as they passed through the cluster core.\nSeen in two directions is a pressure jump at a radius of 1.1 Mpc, which is\nconsistent with a shock with a Mach number of ~1.5-1.7. The eROSITA data\nconfirm that the cluster is not a simple merging system, but it is made up of\nseveral subclusters which are merging or will shortly merge. We computed a\nhydrostatic mass from the eROSITA data, finding good agreement with a previous\nXMM-Newton result. With this pointing we detect several extended sources, where\nwe find secure associations between z=0.36-1.0 for seven of them, that is\nbackground galaxy groups and clusters, highlighting the power of eROSITA to\nfind such systems.\n"}
+{"abstract": "  In this paper we describe an algebraic/geometrical approach to quantum\nscrambling. Generalized quantum subsystems are described by a $*$-closed unital\nsubalgebra $\\cal A$ of operators evolving through a unitary channel. Quantum\nscrambling is defined by how the associated physical degrees of freedom get\nmixed up with others by the dynamics. This is accomplished by introducing a\nquantity, the geometric algebra anti-correlator (GAAC), which measures the\ndegree of self-orthogonalization of the commutant of $\\cal A$ induced by the\ndynamics. This approach extends and unifies averaged bipartite OTOC, operator\nentanglement, coherence generating power and Loschmidt echo. Each of these\nconcepts is indeed recovered by a special choice of $\\cal A$. We compute\ntypical values of GAAC for random unitaries, we prove upper bounds and\ncharacterize their saturation. For generic energy spectrum we find explicit\nexpressions for the infinite-time average of the GAAC which encode the relation\nbetween $\\cal A$ and the full system of Hamiltonian eigenstates. Finally, a\nnotion of ${\\cal A}$-chaoticity is suggested.\n"}
+{"abstract": "  In this work we introduce a new concept, namely, $\\tau_{s}$-extending modules\n(rings) which is torsion-theoretic analogues of extending modules and then we\nextend many results from extending modules to this new concept. For instance we\nshow that for any ring $R$ with unit, if $R$ is purely $\\tau_{s}$-extending\nthen every cyclic $\\tau$-nonsingular $R$-module is flat and we show that this\nfact is true over a principal ideal domain as well. Also, we make a\nclassification for the direct sums of the rings to be purely\n$\\tau_{s}$-extending.\n"}
+{"abstract": "  Time series subject to change in regime have attracted much interest in\ndomains such as econometry, finance or meteorology. For discrete-valued\nregimes, some models such as the popular Hidden Markov Chain (HMC) describe\ntime series whose state process is unknown at all time-steps. Sometimes, time\nseries are firstly labelled thanks to some annotation function. Thus, another\ncategory of models handles the case with regimes observed at all time-steps. We\npresent a novel model which addresses the intermediate case: (i) state\nprocesses associated to such time series are modelled by Partially Hidden\nMarkov Chains (PHMCs); (ii) a linear autoregressive (LAR) model drives the\ndynamics of the time series, within each regime. We describe a variant of the\nexpection maximization (EM) algorithm devoted to PHMC-LAR model learning. We\npropose a hidden state inference procedure and a forecasting function that take\ninto account the observed states when existing. We assess inference and\nprediction performances, and analyze EM convergence times for the new model,\nusing simulated data. We show the benefits of using partially observed states\nto decrease EM convergence times. A fully labelled scheme with unreliable\nlabels also speeds up EM. This offers promising prospects to enhance PHMC-LAR\nmodel selection. We also point out the robustness of PHMC-LAR to labelling\nerrors in inference task, when large training datasets and moderate labelling\nerror rates are considered. Finally, we highlight the remarkable robustness to\nerror labelling in the prediction task, over the whole range of error rates.\n"}
+{"abstract": "  We study two classes of summary-based cardinality estimators that use\nstatistics about input relations and small-size joins in the context of graph\ndatabase management systems: (i) optimistic estimators that make uniformity and\nconditional independence assumptions; and (ii) the recent pessimistic\nestimators that use information theoretic linear programs. We begin by\naddressing the problem of how to make accurate estimates for optimistic\nestimators. We model these estimators as picking bottom-to-top paths in a\ncardinality estimation graph (CEG), which contains sub-queries as nodes and\nweighted edges between sub-queries that represent average degrees. We outline a\nspace of heuristics to make an optimistic estimate in this framework and show\nthat effective heuristics depend on the structure of the input queries. We\nobserve that on acyclic queries and queries with small-size cycles, using the\nmaximum-weight path is an effective technique to address the well known\nunderestimation problem for optimistic estimators. We show that on a large\nsuite of datasets and workloads, the accuracy of such estimates is up to three\norders of magnitude more accurate in mean q-error than some prior heuristics\nthat have been proposed in prior work. In contrast, we show that on queries\nwith larger cycles these estimators tend to overestimate, which can partially\nbe addressed by using minimum weight paths and more effectively by using an\nalternative CEG. We then show that CEGs can also model the recent pessimistic\nestimators. This surprising result allows us to connect two disparate lines of\nwork on optimistic and pessimistic estimators, adopt an optimization from\npessimistic estimators to optimistic ones, and provide insights into the\npessimistic estimators, such as showing that there are alternative\ncombinatorial solutions to the linear programs that define them.\n"}
+{"abstract": "  Epileptic seizures are a type of neurological disorder that affect many\npeople worldwide. Specialist physicians and neurologists take advantage of\nstructural and functional neuroimaging modalities to diagnose various types of\nepileptic seizures. Neuroimaging modalities assist specialist physicians\nconsiderably in analyzing brain tissue and the changes made in it. One method\nto accelerate the accurate and fast diagnosis of epileptic seizures is to\nemploy computer aided diagnosis systems (CADS) based on artificial intelligence\n(AI) and functional and structural neuroimaging modalities. AI encompasses a\nvariety of areas, and one of its branches is deep learning (DL). Not long ago,\nand before the rise of DL algorithms, feature extraction was an essential part\nof every conventional machine learning method, yet handcrafting features limit\nthese models' performances to the knowledge of system designers. DL methods\nresolved this issue entirely by automating the feature extraction and\nclassification process; applications of these methods in many fields of\nmedicine, such as the diagnosis of epileptic seizures, have made notable\nimprovements. In this paper, a comprehensive overview of the types of DL\nmethods exploited to diagnose epileptic seizures from various neuroimaging\nmodalities has been studied. Additionally, rehabilitation systems and cloud\ncomputing in epileptic seizures diagnosis applications have been exactly\ninvestigated using various modalities.\n"}
+{"abstract": "  Brain Computer Interface (BCI) helps in processing and extraction of useful\ninformation from the acquired brain signals having applications in diverse\nfields such as military, medicine, neuroscience, and rehabilitation. BCI has\nbeen used to support paralytic patients having speech impediments with severe\ndisabilities. To help paralytic patients communicate with ease, BCI based\nsystems convert silent speech (thoughts) to text. However, these systems have\nan inconvenient graphical user interface, high latency, limited typing speed,\nand low accuracy rate. Apart from these limitations, the existing systems do\nnot incorporate the inevitable factor of a patient's emotional states and\nsentiment analysis. The proposed system EmoWrite implements a dynamic keyboard\nwith contextualized appearance of characters reducing the traversal time and\nimproving the utilization of the screen space. The proposed system has been\nevaluated and compared with the existing systems for accuracy, convenience,\nsentimental analysis, and typing speed. This system results in 6.58 Words Per\nMinute (WPM) and 31.92 Characters Per Minute (CPM) with an accuracy of 90.36\npercent. EmoWrite also gives remarkable results when it comes to the\nintegration of emotional states. Its Information Transfer Rate (ITR) is also\nhigh as compared to other systems i.e., 87.55 bits per min with commands and\n72.52 bits per min for letters. Furthermore, it provides easy to use interface\nwith a latency of 2.685 sec.\n"}
+{"abstract": "  In this paper, we design and simulate a terahertz (THz) controllable active\nplasmonic waveguide structure based on a single graphene layer that is placed\non a random silicon grating substrate. Optical gain in the proposed THz active\nplasmonic waveguide structure is provided by the stimulated emission process in\nthe photoexcited graphene monolayer that leads to the amplification of surface\nplasmon polariton (SPP) waves. We use a random grating substrate to introduce\nAnderson localization of the SPP waves propagating through the graphene\nmonolayer to enhance their optical amplification at resonant frequencies. It is\nshown that the enhancement factor of the resonant peaks corresponding to the\ngraphene SPPs can be as high as 175. We also analyze their corresponding field\nintensity distributions along the graphene monolayer and find out that their\nintensities and localization positions are different from each other. By\ninvestigating the pump dependent and temperature dependent variations of the\ntransmittance of the structure, it is shown that the resonant peak frequencies\nare blue-shifted by increasing the temperature and the external pump intensity.\nAlso, we show that increasing the ambient temperature by 60 K can dramatically\nreduce the output amplified intensity by a factor of 70. This property of the\nproposed graphene-based THz plasmonic waveguide structure makes it useful in\ntemperature sensing applications and on/off switchable laser devices.\n"}
+{"abstract": "  We classify Einstein metrics on $\\mathbb{R}^4$ invariant under a\nfour-dimensional group of isometries including a principal action of the\nHeisenberg group. The metrics are either Ricci-flat or of negative Ricci\ncurvature. We show that all of the Ricci-flat metrics, including the simplest\nones which are hyper-K\\\"ahler, are incomplete. By contrast, those of negative\nRicci curvature contain precisely two complete examples: the complex hyperbolic\nmetric and a metric of cohomogeneity one known as the one-loop deformed\nuniversal hypermultiplet.\n"}
+{"abstract": "  We provide a complete elaboration of the $L^2$-Hilbert space hypocoercivity\ntheorem for the degenerate Langevin dynamics with multiplicative noise,\nstudying the longtime behaviour of the strongly continuous contraction\nsemigroup solving the abstract Cauchy problem for the associated backward\nKolmogorov operator. Hypocoercivity for the Langevin dynamics with constant\ndiffusion matrix was proven previously by Dolbeault, Mouhot and Schmeiser in\nthe corresponding Fokker-Planck framework, and made rigorous in the Kolmogorov\nbackwards setting by Grothaus and Stilgenbauer. We extend these results to\nweakly differentiable diffusion coefficient matrices, introducing\nmultiplicative noise for the corresponding stochastic differential equation.\nThe rate of convergence is explicitly computed depending on the choice of these\ncoefficients and the potential giving the outer force. In order to obtain a\nsolution to the abstract Cauchy problem, we first prove essential\nself-adjointness of non-degenerate elliptic Dirichlet operators on Hilbert\nspaces, using prior elliptic regularity results and techniques from Bogachev,\nKrylov and R\\\"ockner. We apply operator perturbation theory to obtain essential\nm-dissipativity of the Kolmogorov operator, extending the m-dissipativity\nresults from Conrad and Grothaus. We emphasize that the chosen Kolmogorov\napproach is natural, as the theory of generalized Dirichlet forms implies a\nstochastic representation of the Langevin semigroup as the transition kernel of\na diffusion process which provides a martingale solution to the Langevin\nequation with multiplicative noise. Moreover, we show that even a weak solution\nis obtained this way.\n"}
+{"abstract": "  High-quality dielectric-semiconductor interfaces are critical for reliable\nhigh-performance transistors. We report the in-situ metalorganic chemical vapor\ndeposition (MOCVD) of Al$_2$O$_3$ on $\\beta$-Ga$_2$O$_3$ as a potentially\nbetter alternative to the most commonly used atomic layer deposition (ALD). The\ngrowth of Al$_2$O$_3$ is performed in the same reactor as Ga$_2$O$_3$ using\ntrimethylaluminum and O$_2$ as precursors without breaking the vacuum at a\ngrowth temperature of 600 $^0$C. The fast and slow near interface traps at the\nAl$_2$O$_3$/ $\\beta$-Ga$_2$O$_3$ interface are identified and quantified using\nstressed capacitance-voltage (CV) measurements on metal oxide semiconductor\ncapacitor (MOSCAP) structures. The density of shallow and deep level initially\nfilled traps (D$_{it}$) are measured using ultra-violet (UV) assisted CV\ntechnique. The average D$_{it}$ for the MOSCAP is determined to be 7.8 $\\times$\n10$^{11}$ cm$^{-2}$eV$^{-1}$. The conduction band offset of the Al$_2$O$_3$/\nGa$_2$O$_3$ interface is also determined from CV measurements and found out to\nbe 1.7 eV which is in close agreement with the existing literature reports of\nALD Al$_2$O$_3$/ Ga$_2$O$_3$ interface. The current-voltage characteristics are\nalso analyzed and the average breakdown field is extracted to be approximately\n5.8 MV/cm. This in-situ Al$_2$O$_3$ dielectric on $\\beta$-Ga$_2$O$_3$ with\nimproved dielectric properties can enable Ga$_2$O$_3$-based high performance\ndevices.\n"}
+{"abstract": "  Due to the distributed characteristics of Federated Learning (FL), the\nvulnerability of global model and coordination of devices are the main\nobstacle. As a promising solution of decentralization, scalability and\nsecurity, leveraging blockchain in FL has attracted much attention in recent\nyears. However, the traditional consensus mechanisms designed for blockchain\nlike Proof of Work (PoW) would cause extreme resource consumption, which\nreduces the efficiency of FL greatly, especially when the participating devices\nare wireless and resource-limited. In order to address device asynchrony and\nanomaly detection in FL while avoiding the extra resource consumption caused by\nblockchain, this paper introduces a framework for empowering FL using Direct\nAcyclic Graph (DAG)-based blockchain systematically (DAG-FL). Accordingly,\nDAG-FL is first introduced from a three-layer architecture in details, and then\ntwo algorithms DAG-FL Controlling and DAG-FL Updating are designed running on\ndifferent nodes to elaborate the operation of DAG-FL consensus mechanism. After\nthat, a Poisson process model is formulated to discuss that how to set\ndeployment parameters to maintain DAG-FL stably in different federated learning\ntasks. The extensive simulations and experiments show that DAG-FL can achieve\nbetter performance in terms of training efficiency and model accuracy compared\nwith the typical existing on-device federated learning systems as the\nbenchmarks.\n"}
+{"abstract": "  Ki67 is a significant biomarker in the diagnosis and prognosis of cancer,\nwhose index can be evaluated by quantifying its expression in Ki67\nimmunohistochemistry (IHC) stained images. However, quantitative analysis on\nmulti-source Ki67 images is yet a challenging task in practice due to\ncross-domain distribution differences, which result from imaging variation,\nstaining styles, and lesion types. Many recent studies have made some efforts\non domain generalization (DG), whereas there are still some noteworthy\nlimitations. Specifically in the case of Ki67 images, learning invariant\nrepresentation is at the mercy of the insufficient number of domains and the\ncell categories mismatching in different domains. In this paper, we propose a\nnovel method to improve DG by searching the domain-agnostic subnetwork in a\ndomain merging scenario. Partial model parameters are iteratively pruned\naccording to the domain gap, which is caused by the data converting from a\nsingle domain into merged domains during training. In addition, the model is\noptimized by fine-tuning on merged domains to eliminate the interference of\nclass mismatching among various domains. Furthermore, an appropriate\nimplementation is attained by applying the pruning method to different parts of\nthe framework. Compared with known DG methods, our method yields excellent\nperformance in multiclass nucleus recognition of Ki67 IHC images, especially in\nthe lost category cases. Moreover, our competitive results are also evaluated\non the public dataset over the state-of-the-art DG methods.\n"}
+{"abstract": "  Convolutional Neural Networks(CNNs) has achieved remarkable performance\nbreakthrough in a variety of tasks. Recently, CNNs based methods that are fed\nwith hand-extracted EEG features gradually produce a powerful performance on\nthe EEG data based emotion recognition task. In this paper, we propose a novel\nconvolutional layer allowing to adaptively extract effective data-driven\nspectrogram-like features from raw EEG signals, which we reference as scaling\nlayer. Further, it leverages convolutional kernels scaled from one data-driven\npattern to exposed a frequency-like dimension to address the shortcomings of\nprior methods requiring hand-extracted features or their approximations. The\nproposed neural network architecture based on the scaling layer, references as\nScalingNet, has achieved the state-of-the-art result across the established\nDEAP benchmark dataset.\n"}
+{"abstract": "  This paper considers an information bottleneck problem with the objective of\nobtaining a most informative representation of a hidden feature subject to a\nR\\'enyi entropy complexity constraint. The optimal bottleneck trade-off between\nrelevance (measured via Shannon's mutual information) and R\\'enyi entropy cost\nis defined and an iterative algorithm for finding approximate solutions is\nprovided. We also derive an operational characterization for the optimal\ntrade-off by demonstrating that the optimal R\\'enyi entropy-relevance trade-off\nis achievable by a simple time-sharing scalar coding scheme and that no coding\nscheme can provide better performance. Two examples where the optimal Shannon\nentropy-relevance trade-off can be exactly determined are further given.\n"}
+{"abstract": "  With the advance in user-friendly and powerful video editing tools, anyone\ncan easily manipulate videos without leaving prominent visual traces.\nFrame-rate up-conversion (FRUC), a representative temporal-domain operation,\nincreases the motion continuity of videos with a lower frame-rate and is used\nby malicious counterfeiters in video tampering such as generating fake\nframe-rate video without improving the quality or mixing temporally spliced\nvideos. FRUC is based on frame interpolation schemes and subtle artifacts that\nremain in interpolated frames are often difficult to distinguish. Hence,\ndetecting such forgery traces is a critical issue in video forensics. This\npaper proposes a frame-rate conversion detection network (FCDNet) that learns\nforensic features caused by FRUC in an end-to-end fashion. The proposed network\nuses a stack of consecutive frames as the input and effectively learns\ninterpolation artifacts using network blocks to learn spatiotemporal features.\nThis study is the first attempt to apply a neural network to the detection of\nFRUC. Moreover, it can cover the following three types of frame interpolation\nschemes: nearest neighbor interpolation, bilinear interpolation, and\nmotion-compensated interpolation. In contrast to existing methods that exploit\nall frames to verify integrity, the proposed approach achieves a high detection\nspeed because it observes only six frames to test its authenticity. Extensive\nexperiments were conducted with conventional forensic methods and neural\nnetworks for video forensic tasks to validate our research. The proposed\nnetwork achieved state-of-the-art performance in terms of detecting the\ninterpolated artifacts of FRUC. The experimental results also demonstrate that\nour trained model is robust for an unseen dataset, unlearned frame-rate, and\nunlearned quality factor.\n"}
+{"abstract": "  The influence of magnetism on the properties of screw dislocations in\nbody-centered cubic chromium is investigated by means of ab initio\ncalculations. Screw dislocations having Burgers vectors 1/2 111 and 100 are\nconsidered, following experimental observations showing activity for both slip\nsystems. At low temperature, chromium has a magnetic order close to\nantiferromagnetism along 100 directions, for which 1/2 111 is not a periodicity\nvector. Hence, dislocations with Burgers vectors 1/2 111 generate magnetic\nfaults when shearing the crystal, which constrain them to coexist and move\npairwise, leading to dissociated 111 super-dislocations. On the other side, 100\nis a periodicity vector of the magnetic order of chromium, and no such magnetic\nfault are generated when 100 dislocations glide. Dislocation properties are\ncomputed in the magnetically ordered and non magnetic phases of chromium for\ncomparison purposes. We report a marginal impact of magnetism on the structural\nproperties and energies of dislocations for both slip systems. The Peierls\nenergy barrier opposing dislocation glide in {110} planes is comparable for\nboth 1/2 111 {110} and 100 {110} slip systems, with lower Peierls stresses in\nthe magnetically ordered phase of chromium.\n"}
+{"abstract": "  An approach is presented treating decision theory as a probabilistic theory\nbased on quantum techniques. Accurate definitions are given and thorough\nanalysis is accomplished for the quantum probabilities describing the choice\nbetween separate alternatives, sequential alternatives characterizing\nconditional quantum probabilities, and behavioral quantum probabilities taking\ninto account rational-irrational duality of decision making. The comparison\nbetween quantum and classical probabilities is explained. The analysis\ndemonstrates that quantum probabilities serve as an essentially more powerful\ntool of characterizing various decision-making situations including the\ninfluence of psychological behavioral effects.\n"}
+{"abstract": "  Relic neutrinos from the Big Bang decoupled from the hot plasma predominantly\nin helicity eigenstates. Their subsequent propagation through gravitational\ninhomogeneities of the Universe alters the helicities of both Dirac and\nMajorana neutrinos, thus providing an independent probe of the evolving\nuniverse. We determine here the probability that relic neutrinos flip their\nhelicity, in terms of the spectrum of density inhomogeneities measured in the\nCosmic Microwave Background. As we find, for Dirac neutrinos the gravitational\nhelicity modifications are intermediate between the effects of magnetic fields\nif the neutrino magnetic moment is of the magnitude predicted in the Standard\nModel and the much larger effects if the magnetic moment is of the scale\nconsistent with the excess of low energy electron events seen by the XENON1T\nexperiment. We give succinct derivations, within general relativity, of the\nsemi-classical response of a spinning particle to a weak gravitational field in\nan expanding universe, and estimate the helicity modifications of neutrinos\nemitted by the Sun caused by the Sun's gravity.\n"}
+{"abstract": "  We apply the framework of optimal nonlinear control to steer the dynamics of\na whole-brain network of FitzHugh-Nagumo oscillators. Its nodes correspond to\nthe cortical areas of an atlas-based segmentation of the human cerebral cortex,\nand the inter-node coupling strengths are derived from Diffusion Tensor Imaging\ndata of the connectome of the human brain. Nodes are coupled using an additive\nscheme without delays and are driven by background inputs with fixed mean and\nadditive Gaussian noise. Optimal control inputs to nodes are determined by\nminimizing a cost functional that penalizes the deviations from a desired\nnetwork dynamic, the control energy, and spatially non-sparse control inputs.\nUsing the strength of the background input and the overall coupling strength as\norder parameters, the network's state-space decomposes into regions of low and\nhigh activity fixed points separated by a high amplitude limit cycle all of\nwhich qualitatively correspond to the states of an isolated network node. Along\nthe borders, however, additional limit cycles, asynchronous states and\nmultistability can be observed. Optimal control is applied to several\nstate-switching and network synchronization tasks, and the results are compared\nto controllability measures from linear control theory for the same connectome.\nWe find that intuitions from the latter about the roles of nodes in steering\nthe network dynamics, which are solely based on connectome features, do not\ngenerally carry over to nonlinear systems, as had been previously implied.\nInstead, the role of nodes under optimal nonlinear control critically depends\non the specified task and the system's location in state space. Our results\nshed new light on the controllability of brain network states and may serve as\nan inspiration for the design of new paradigms for non-invasive brain\nstimulation.\n"}
+{"abstract": "  The time resolution (TR) is one of the most important characteristic of a\ndetector. For the case of a scintillator, the collection of light also is\nimportant, it depends of the effective area of the photo-sensor\n(\\textit{Scorer}). The Photomultipliers Tubes (PMTs), normally, have an\neffective area greater than the Silicon Photomultipliers (SiPMs), however, due\nto its voltage of operation, their size or cost, in some cases it can be\ndifficult to work with them. In which case, it is preferable to use SiPMs. The\nvalue of TR also depends of the size and geometry of the scintillator, number\nof photo-sensors and the electronic part.\\\\ In this work, we study the mean\noptical photon arrival time distribution (AT) to a Scorer from a SiPM of\n6$\\times$6~mm$^2$. We define the variation of AT as \\textit{the intrinsic time\nresolution} (ITR). In Geant4, we simulated two different geometries: square and\nhexagonal, for a BC-404 plastic scintillator coupled to one Scorer. The sources\nsimulated were Sr$^{90}$, Co$^{60}$, Cs$^{137}$, Na$^{22}$ and $\\mu^-$ of\n1~GeV. It is shown that AT and ITR depends of the geometry and size of the\nplastic scintillator, the location of the Scorer, the incident particle and its\nenergy. Then, the ITR and therefore the TR is not a constant for a detector.\nFinally, we show the relation between AT and the deposited energy by the\nparticle incident, which are related in the experiment to the response time\nevent of the detector and the deposited charge by the incident particle,\nrespectively.\n"}
+{"abstract": "  Real-world scenarios often require the anticipation of object interactions in\nunknown future, which would assist the decision-making process of both humans\nand agents. To meet this challenge, we present a new task named Visual\nRelationship Forecasting (VRF) in videos to explore the prediction of visual\nrelationships in a reasoning manner. Specifically, given a subject-object pair\nwith H existing frames, VRF aims to predict their future interactions for the\nnext T frames without visual evidence. To evaluate the VRF task, we introduce\ntwo video datasets named VRF-AG and VRF-VidOR, with a series of\nspatio-temporally localized visual relation annotations in a video. These two\ndatasets densely annotate 13 and 35 visual relationships in 1923 and 13447\nvideo clips, respectively. In addition, we present a novel Graph Convolutional\nTransformer (GCT) framework, which captures both object-level and frame-level\ndependencies by spatio-temporal Graph Convolution Network and Transformer.\nExperimental results on both VRF-AG and VRF-VidOR datasets demonstrate that GCT\noutperforms the state-of-the-art sequence modelling methods on visual\nrelationship forecasting.\n"}
+{"abstract": "  Multimedia related research and development has evolved rapidly in the last\nfew years with advancements in hardware, software and network infrastructures.\nAs a result, multimedia has been integrated into domains like Healthcare and\nMedicine, Human facial feature extraction and tracking, pose recognition,\ndisparity estimation, etc. This survey gives an overview of the various\nmultimedia technologies and algorithms developed in the domains mentioned.\n"}
+{"abstract": "  In this paper, we deal with reversing and extended symmetries of shifts\ngenerated by bijective substitutions. We provide equivalent conditions for a\npermutation on the alphabet to generate a reversing/extended symmetry, and\nalgorithms how to check them. Moreover, we show that, for any finite group $G$\nand any subgroup $P$ of the $d$-dimensional hyperoctahedral group, there is a\nbijective substitution which generates an aperiodic hull with symmetry group\n$\\mathbb{Z}^{d}\\times G$ and extended symmetry group $(\\mathbb{Z}^{d} \\rtimes\nP)\\times G$.\n"}
+{"abstract": "  While larger neural models are pushing the boundaries of what deep learning\ncan do, often more weights are needed to train models rather than to run\ninference for tasks. This paper seeks to understand this behavior using search\nspaces -- adding weights creates extra degrees of freedom that form new paths\nfor optimization (or wider search spaces) rendering neural model training more\neffective. We then show how we can augment search spaces to train sparse models\nattaining competitive scores across dozens of deep learning workloads. They are\nalso are tolerant of structures targeting current hardware, opening avenues for\ntraining and inference acceleration. Our work encourages research to explore\nbeyond massive neural models being used today.\n"}
+{"abstract": "  Non-convex optimization problems can be approximately solved via relaxation\nor local algorithms. For many practical problems such as optimal power flow\n(OPF) problems, both approaches tend to succeed in the sense that relaxation is\nusually exact and local algorithms usually converge to a global optimum. In\nthis paper, we study conditions which are sufficient or necessary for such\nnon-convex problems to simultaneously have exact relaxation and no spurious\nlocal optima. Those conditions help us explain the widespread empirical\nexperience that local algorithms for OPF problems often work extremely well.\n"}
+{"abstract": "  In this paper, we consider the following non-local semi-linear parabolic\nequation with advection: for $1 \\le p<1+\\frac{2}{N}$, \\begin{equation*}\n\\begin{cases} u_t+v \\cdot \\nabla u-\\Delta u=|u|^p-\\int_{\\mathbb T^N} |u|^p\n\\quad & \\textrm{on} \\quad \\mathbb T^N, \\\\ \\\\ u \\ \\textrm{periodic} \\quad &\n\\textrm{on} \\quad \\partial \\mathbb T^N \\end{cases} \\end{equation*} with initial\ndata $u_0$ defined on $\\mathbb T^N$. Here $v$ is an incompressible flow, and\n$\\mathbb T^N=[0, 1]^N$ is the $N$-torus with $N$ being the dimension.\n  We first prove the local existence of mild solutions to the above equation\nfor arbitrary data in $L^2$. We then study the global existence of the\nsolutions under the following two scenarios: (1). when $v$ is a mixing flow;\n(2). when $v$ is a shear flow. More precisely, we show that under these\nassumptions, there exists a global solution to the above equation in the sense\nof $L^2$.\n"}
+{"abstract": "  The free boundary problem for a two-dimensional fluid filtered in porous\nmedia is studied. This is known as the one-phase Muskat problem and is\nmathematically equivalent to the vertical Hele-Shaw problem driven by gravity\nforce. We prove that if the initial free boundary is the graph of a periodic\nLipschitz function, then there exists a global-in-time Lipschitz solution in\nthe strong $L^\\infty_t L^2_x$ sense and it is the unique viscosity solution.\nThe proof requires quantitative estimates for layer potentials and pointwise\nelliptic regularity in Lipschitz domains. This is the first construction of\nunique global strong solutions for the Muskat problem with initial data of\narbitrary size.\n"}
+{"abstract": "  Following the recent progress in Terahertz (THz) signal generation and\nradiation methods, joint THz communications and sensing applications are\nshaping the future of wireless systems. Towards this end, THz spectroscopy is\nexpected to be carried over user equipment devices to identify material and\ngaseous components of interest. THz-specific signal processing techniques\nshould complement this re-surged interest in THz sensing for efficient\nutilization of the THz band. In this paper, we present an overview of these\ntechniques, with an emphasis on signal pre-processing (standard normal variate\nnormalization, min-max normalization, and Savitzky-Golay filtering), feature\nextraction (principal component analysis, partial least squares, t-distributed\nstochastic neighbor embedding, and nonnegative matrix factorization), and\nclassification techniques (support vector machines, k-nearest neighbor,\ndiscriminant analysis, and naive Bayes). We also address the effectiveness of\ndeep learning techniques by exploring their promising sensing capabilities at\nthe THz band. Lastly, we investigate the performance and complexity trade-offs\nof the studied methods in the context of joint communications and sensing; we\nmotivate the corresponding use-cases, and we present few future research\ndirections in the field.\n"}
+{"abstract": "  Since 2015 the gravitational-wave observations of LIGO and Virgo have\ntransformed our understanding of compact-object binaries. In the years to come,\nground-based gravitational-wave observatories such as LIGO, Virgo, and their\nsuccessors will increase in sensitivity, discovering thousands of stellar-mass\nbinaries. In the 2030s, the space-based LISA will provide gravitational-wave\nobservations of massive black holes binaries. Between the $\\sim\n10$-$10^3~\\mathrm{Hz}$ band of ground-based observatories and the\n$\\sim10^{-4}$-$10^{-1}~\\mathrm{Hz}$ band of LISA lies the uncharted decihertz\ngravitational-wave band. We propose a Decihertz Observatory to study this\nfrequency range, and to complement observations made by other detectors.\nDecihertz observatories are well suited to observation of intermediate-mass\n($\\sim10^2$-$10^4 M_\\odot$) black holes; they will be able to detect\nstellar-mass binaries days to years before they merge, providing early warning\nof nearby binary neutron star mergers and measurements of the eccentricity of\nbinary black holes, and they will enable new tests of general relativity and\nthe Standard Model of particle physics. Here we summarise how a Decihertz\nObservatory could provide unique insights into how black holes form and evolve\nacross cosmic time, improve prospects for both multimessenger astronomy and\nmultiband gravitational-wave astronomy, and enable new probes of gravity,\nparticle physics and cosmology.\n"}
+{"abstract": "  We study homogeneous Besov and Triebel--Lizorkin spaces defined on doubling\nmetric measure spaces in terms of a self-adjoint operator whose heat kernel\nsatisfies Gaussian estimates together with its derivatives. When the measure\nspace is a smooth manifold and such operator is a sum of squares of smooth\nvector fields, we prove that their intersection with $L^\\infty$ is an algebra\nfor pointwise multiplication. Our results apply to nilpotent Lie groups and\nGrushin settings.\n"}
+{"abstract": "  Quantum fluctuations endow spacetime with a foamy texture. The degree of\nfoaminess is dictated by blackhole physics to be of the holographic type.\nApplied to cosmology, the holographic foam model predicts the existence of dark\nenergy with critical energy density in the current (late) universe, the quanta\nof which obey infinite statistics. Furthermore we use the deep similarities\nbetween turbulence and the spacetime foam phase of strong quantum gravity to\nargue that the early universe was in a turbulent regime when it underwent a\nbrief cosmic inflation with a \"graceful\" transition to a laminar regime. In\nthis scenario, both the late and the early cosmic accelerations have their\norigins in spacetime foam.\n"}
+{"abstract": "  A graph is called Ricci-flat if its Ricci curvatures vanish on all edges,\nhere the definition of Ricci curvature on graphs was given by Lin-Lu-Yau. The\nauthors in arXiv:1301.0102 and arXiv:1802.02982 obtained a complete\ncharacterization for all Ricci-flat graphs with girth at least five. In this\npaper, we completely determined all Ricci-flat graphs with maximum degree at\nmost 4.\n"}
+{"abstract": "  In this paper, we present a new analysis on the $P$-wave charmonium\nannihilation into two photons up to next-to-next-to-leading order (NNLO) QCD\ncorrections by using the principle of maximum conformality (PMC). The\nconventional perturbative QCD prediction shows strong scale dependence and\ndeviates largely from the BESIII measurements. After applying the PMC, we\nobtain a more precise scale-invariant pQCD prediction, which also agrees with\nthe BESIII measurements within errors, i.e.\n$R={\\Gamma_{\\gamma\\gamma}(\\chi_{c2})}\n/{\\Gamma_{\\gamma\\gamma}(\\chi_{c0})}=0.246\\pm0.013$, where the error is for\n$\\Delta\\alpha_s(M_\\tau)=\\pm0.016$. By further considering the color-octet\ncontributions, even the central value can be in agreement with the data. This\nshows the importance of a correct scale-setting approach. We also give a\nprediction for the ratio involving $\\chi_{b0, b2} \\to\\gamma\\gamma$, which could\nbe tested in future Belle II experiment.\n"}
+{"abstract": "  Time-reversal symmetry breaking and entropy production are universal features\nof nonequilibrium phenomena. Despite its importance in the physics of active\nand living systems, the entropy production of systems with many degrees of\nfreedom has remained of little practical significance because the\nhigh-dimensionality of their state space makes it difficult to measure. We\nintroduce a local measure of entropy production and a numerical protocol to\nestimate it. We establish a connection between the entropy production and\nextractability of work in a given region of the system and show how this\nquantity depends crucially on the degrees of freedom being tracked. We validate\nour approach in theory, simulation, and experiments by considering systems of\nactive Brownian particles undergoing motility induced phase separation, as well\nas active Brownian particles and E. Coli in a rectifying device in which the\ntime-reversal asymmetry of the particle dynamics couples to spatial asymmetry\nto reveal its effects on a macroscopic scale.\n"}
+{"abstract": "  We introduce a partitioned coupling approach for iterative coupling of flow\nprocesses in deformable fractures embedded in a poro-elastic medium that is\nenhanced by interface quasi-Newton (IQN) methods. In this scope, a unique\ncomputational decomposition into a fracture flow and a poro-elastic domain is\ndeveloped, where communication and numerical coupling of the individual solvers\nare realized by consulting the open-source library preCICE. The underlying\nphysical problem is introduced by a brief derivation of the governing equations\nand interface conditions of fracture flow and poro-elastic domain followed by a\ndetailed discussion of the partitioned coupling scheme. We evaluate the\nproposed implementation and undertake a convergence study to compare a\nclassical interface quasi-Newton inverse least-squares (IQN-ILS) with the more\nadvanced interface quasi-Newton inverse multi-vector Jacobian (IQN-IMVJ)\nmethod. These coupling approaches are verified for an academic test case before\nthe generality of the proposed strategy is demonstrated by simulations of two\ncomplex fracture networks. In contrast to the development of specific solvers,\nwe promote the simplicity and computational efficiency of the proposed\npartitioned coupling approach using preCICE and FEniCS for parallel\ncomputations of hydro-mechanical processes in complex, three-dimensional\nfracture networks.\n"}
+{"abstract": "  The need to recover high-dimensional signals from their noisy low-resolution\nquantized measurements is widely encountered in communications and sensing. In\nthis paper, we focus on the extreme case of one-bit quantizers, and propose a\ndeep detector entitled LoRD-Net for recovering information symbols from one-bit\nmeasurements. Our method is a model-aware data-driven architecture based on\ndeep unfolding of first-order optimization iterations. LoRD-Net has a\ntask-based architecture dedicated to recovering the underlying signal of\ninterest from the one-bit noisy measurements without requiring prior knowledge\nof the channel matrix through which the one-bit measurements are obtained. The\nproposed deep detector has much fewer parameters compared to black-box deep\nnetworks due to the incorporation of domain-knowledge in the design of its\narchitecture, allowing it to operate in a data-driven fashion while benefiting\nfrom the flexibility, versatility, and reliability of model-based optimization\nmethods. LoRD-Net operates in a blind fashion, which requires addressing both\nthe non-linear nature of the data-acquisition system as well as identifying a\nproper optimization objective for signal recovery. Accordingly, we propose a\ntwo-stage training method for LoRD-Net, in which the first stage is dedicated\nto identifying the proper form of the optimization process to unfold, while the\nlatter trains the resulting model in an end-to-end manner. We numerically\nevaluate the proposed receiver architecture for one-bit signal recovery in\nwireless communications and demonstrate that the proposed hybrid methodology\noutperforms both data-driven and model-based state-of-the-art methods, while\nutilizing small datasets, on the order of merely $\\sim 500$ samples, for\ntraining.\n"}
+{"abstract": "  We show that the de Rham Hilbert complex with mixed boundary conditions on\nbounded strong Lipschitz domains is closed and compact. The crucial results are\ncompact embeddings which follow by abstract arguments using functional analysis\ntogether with particular regular decompositions. Higher Sobolev order results\nare proved as well.\n"}
+{"abstract": "  Most neural vocoders employ band-limited mel-spectrograms to generate\nwaveforms. If full-band spectral features are used as the input, the vocoder\ncan be provided with as much acoustic information as possible. However, in some\nmodels employing full-band mel-spectrograms, an over-smoothing problem occurs\nas part of which non-sharp spectrograms are generated. To address this problem,\nwe propose UnivNet, a neural vocoder that synthesizes high-fidelity waveforms\nin real time. Inspired by works in the field of voice activity detection, we\nadded a multi-resolution spectrogram discriminator that employs multiple linear\nspectrogram magnitudes computed using various parameter sets. Using full-band\nmel-spectrograms as input, we expect to generate high-resolution signals by\nadding a discriminator that employs spectrograms of multiple resolutions as the\ninput. In an evaluation on a dataset containing information on hundreds of\nspeakers, UnivNet obtained the best objective and subjective results among\ncompeting models for both seen and unseen speakers. These results, including\nthe best subjective score for text-to-speech, demonstrate the potential for\nfast adaptation to new speakers without a need for training from scratch.\n"}
+{"abstract": "  Single-beam spectrally controlled (SBSC) two-dimensional (2D) Raman\nspectroscopy is a unique 2D vibrational measurement technique utilizing trains\nof short pulses that are generated from a single broadband pulse by pulse\nshaping. This approach overcomes the difficulty of 2D Raman spectroscopy in\ndealing with small-signal extraction and avoids complicated low-order cascading\neffects, thus providing a new possibility for measuring the intramolecular and\nintermolecular modes of molecular liquids using fifth-order 2D Raman\nspectroscopy. Recently, for quantitatively investigating the mode-mode coupling\nmechanism, Hurwitz et al. [Optics Express 28, 3803 (2020)] have developed a new\npulse design for this measurement to separate the contributions of the fifth-\nand third-order polarizations, which are often overlapped in the original\nsingle-beam measurements. Here, we describe a method for simulating these\noriginal measurements and the new 2D Raman measurements on the basis of a\nsecond-order response function approach. We carry out full molecular dynamics\nsimulations for carbon tetrachloride and liquid water using an\nequilibrium-nonequilibrium hybrid algorithm, with the aim of explaining the key\nfeatures of the SBSC 2D Raman spectroscopic method from a theoretical point of\nview. The predicted signal profiles and intensities provide valuable\ninformation that can be applied to 2D spectroscopy experiments, allowing them\nto be carried out more efficiently.\n"}
+{"abstract": "  In this paper, we study the problem of inferring time-varying Markov random\nfields (MRF), where the underlying graphical model is both sparse and changes\nsparsely over time. Most of the existing methods for the inference of\ntime-varying MRFs rely on the regularized maximum likelihood estimation (MLE),\nthat typically suffer from weak statistical guarantees and high computational\ntime. Instead, we introduce a new class of constrained optimization problems\nfor the inference of sparsely-changing MRFs. The proposed optimization problem\nis formulated based on the exact $\\ell_0$ regularization, and can be solved in\nnear-linear time and memory. Moreover, we show that the proposed estimator\nenjoys a provably small estimation error. As a special case, we derive sharp\nstatistical guarantees for the inference of sparsely-changing Gaussian MRFs\n(GMRF) in the high-dimensional regime, showing that such problems can be\nlearned with as few as one sample per time. Our proposed method is extremely\nefficient in practice: it can accurately estimate sparsely-changing graphical\nmodels with more than 500 million variables in less than one hour.\n"}
+{"abstract": "  Although deep neural networks have been widely employed and proven effective\nin sentiment analysis tasks, it remains challenging for model developers to\nassess their models for erroneous predictions that might exist prior to\ndeployment. Once deployed, emergent errors can be hard to identify in\nprediction run-time and impossible to trace back to their sources. To address\nsuch gaps, in this paper we propose an error detection framework for sentiment\nanalysis based on explainable features. We perform global-level feature\nvalidation with human-in-the-loop assessment, followed by an integration of\nglobal and local-level feature contribution analysis. Experimental results show\nthat, given limited human-in-the-loop intervention, our method is able to\nidentify erroneous model predictions on unseen data with high precision.\n"}
+{"abstract": "  This is the second year of the TREC Deep Learning Track, with the goal of\nstudying ad hoc ranking in the large training data regime. We again have a\ndocument retrieval task and a passage retrieval task, each with hundreds of\nthousands of human-labeled training queries. We evaluate using single-shot\nTREC-style evaluation, to give us a picture of which ranking methods work best\nwhen large data is available, with much more comprehensive relevance labeling\non the small number of test queries. This year we have further evidence that\nrankers with BERT-style pretraining outperform other rankers in the large data\nregime.\n"}
+{"abstract": "  Emotion detection is an important task that can be applied to social media\ndata to discover new knowledge. While the use of deep learning methods for this\ntask has been prevalent, they are black-box models, making their decisions hard\nto interpret for a human operator. Therefore, in this paper, we propose an\napproach using weighted $k$ Nearest Neighbours (kNN), a simple, easy to\nimplement, and explainable machine learning model. These qualities can help to\nenhance results' reliability and guide error analysis. In particular, we apply\nthe weighted kNN model to the shared emotion detection task in tweets from\nSemEval-2018. Tweets are represented using different text embedding methods and\nemotion lexicon vocabulary scores, and classification is done by an ensemble of\nweighted kNN models. Our best approaches obtain results competitive with\nstate-of-the-art solutions and open up a promising alternative path to neural\nnetwork methods.\n"}
+{"abstract": "  The burgeoning field of \"algorithmic fairness\" provides a novel set of\nmethods for reasoning about the fairness of algorithmic predictions and\ndecisions. Yet even as algorithmic fairness has become a prominent component of\nefforts to enhance equality in domains such public policy, it also faces\nsignificant limitations and critiques. The most fundamental issue is the\nmathematical result known as the \"impossibility of fairness\" (an\nincompatibility between mathematical definitions of fairness). Furthermore,\nmany algorithms that satisfy standards of fairness actually exacerbate\noppression. These two issues call into question whether algorithmic fairness\ncan play a productive role in the pursuit of equality. In this paper, I\ndiagnose these issues as the product of algorithmic fairness methodology and\npropose an alternative path forward for the field. The dominant approach of\n\"formal algorithmic fairness\" suffers from a fundamental limitation: it relies\non a narrow frame of analysis that is limited to specific decision-making\nprocesses, in isolation from the context of those decisions. In light of this\nshortcoming, I draw on theories of substantive equality from law and philosophy\nto propose an alternative method: \"substantive algorithmic fairness.\"\nSubstantive algorithmic fairness takes a more expansive scope to analyzing\nfairness, looking beyond specific decision points to account for social\nhierarchies and the impacts of decisions facilitated by algorithms. As a\nresult, substantive algorithmic fairness suggests reforms that combat\noppression and that provide an escape from the impossibility of fairness.\nMoreover, substantive algorithmic fairness presents a new direction for the\nfield of algorithmic fairness: away from formal mathematical models of\n\"fairness\" and towards substantive evaluations of how algorithms can (and\ncannot) promote equality.\n"}
+{"abstract": "  Using the relationship between Siegel cusp forms of degree $2$ and cuspidal\nautomorphic representations of $\\mathrm{GSp}(4,\\mathbb{A}_{\\mathbb{Q}})$, we\nderive some congruences involving dimensions of spaces of Siegel cusp forms of\ndegree $2$ and the class number of $\\mathbb{Q}(\\sqrt{-p})$. We also obtain some\ncongruences between the $4$-core partition function $c_4(n)$ and dimensions of\nspaces of Siegel cusp forms of degree $2$.\n"}
+{"abstract": "  This paper proposes a novel nature-inspired meta-heuristic algorithm called\nthe Golden Tortoise Beetle Optimizer (GTBO) to solve optimization problems. It\nmimics golden tortoise beetle's behavior of changing colors to attract opposite\nsex for mating and its protective strategy that uses a kind of anal fork to\ndeter predators. The algorithm is modeled based on the beetle's dual\nattractiveness and survival strategy to generate new solutions for optimization\nproblems. To measure its performance, the proposed GTBO is compared with five\nother nature-inspired evolutionary algorithms on 24 well-known benchmark\nfunctions investigating the trade-off between exploration and exploitation,\nlocal optima avoidance, and convergence towards the global optima is\nstatistically significant. We particularly applied GTBO to two well-known\nengineering problems including the welded beam design problem and the gear\ntrain design problem. The results demonstrate that the new algorithm is more\nefficient than the five baseline algorithms for both problems. A sensitivity\nanalysis is also performed to reveal different impacts of the algorithm's key\ncontrol parameters and operators on GTBO's performance.\n"}
+{"abstract": "  Let $n > k > t \\geq j \\geq 1$ be integers. Let $X$ be an $n$-element set,\n${X\\choose k}$ the collection of its $k$-subsets. A family $\\mathcal F \\subset\n{X\\choose k}$ is called $t$-intersecting if $|F \\cap F'| \\geq t$ for all $F, F'\n\\in \\mathcal F$. The $j$'th shadow $\\partial^j \\mathcal F$ is the collection of\nall $(k - j)$-subsets that are contained in some member of~$\\mathcal F$.\nEstimating $|\\partial^j \\mathcal F|$ as a function of $|\\mathcal F|$ is a\nwidely used tool in extremal set theory. A classical result of the second\nauthor (Theorem \\ref{th:1.3}) provides such a bound for $t$-intersecting\nfamilies. It is best possible for $|\\mathcal F| = {2k - t\\choose k}$.\n  Our main result is Theorem \\ref{th:1.4} which gives an asymptotically optimal\nbound on $|\\partial^j \\mathcal F| / |\\mathcal F|$ for $|\\mathcal F|$ slightly\nlarger, e.g., $|\\mathcal F| > \\frac32 {2k - t\\choose k}$. We provide further\nimprovements for $|\\mathcal F|$ very large as well.\n"}
+{"abstract": "  We use coherence theory to explain why it is necessary to modify the\nconventional setup of a Michelson interferometer to obtain Haidinger rings with\nan extended source of white light. The modification consists of introducing a\nglass slide into one of the two arms of the interferometer. This insertion\ncircumvents the drastic restriction imposed by the low temporal coherence of\nwhite light, which prevents the observation of interference rings with the\nconventional setup. In order to understand this restriction, we developed and\nimplemented a criterion for observing interference fringes. The modified setup\nalso makes it possible to perform a spectral interferometry experiment to\nanalyze the output of the interferometer and determine the refractive index of\nthe glass slide over the whole visible spectrum. The fit of measured data using\nSellmeier's law gives the extrapolated value of the refractive index to the IR\n$n_\\mathrm{IR}=1.5307\\pm0.0003$ and the value of the characteristic wavelength\n$\\lambda_0=(164.5\\pm0.4)\\;\\mathrm{nm}$ of the oscillator responsible for the\ndispersion.\n"}
+{"abstract": "  Developing mechanisms that flexibly adapt dialog systems to unseen tasks and\ndomains is a major challenge in dialog research. Neural models implicitly\nmemorize task-specific dialog policies from the training data. We posit that\nthis implicit memorization has precluded zero-shot transfer learning. To this\nend, we leverage the schema-guided paradigm, wherein the task-specific dialog\npolicy is explicitly provided to the model. We introduce the Schema Attention\nModel (SAM) and improved schema representations for the STAR corpus. SAM\nobtains significant improvement in zero-shot settings, with a +22 F1 score\nimprovement over prior work. These results validate the feasibility of\nzero-shot generalizability in dialog. Ablation experiments are also presented\nto demonstrate the efficacy of SAM.\n"}
+{"abstract": "  We introduce six quantities that generalize the equinoctial orbital elements\nwhen some or all the perturbing forces that act on the propagated body are\nderived from a disturbing potential. Three of the elements define a\nnon-osculating ellipse on the orbital plane, other two fix the orientation of\nthe equinoctial reference frame, and the last allows one to determine the true\nlongitude of the body. The Jacobian matrices of the transformations between the\nnew elements and the position and velocity are explicitly given. As a possible\napplication we investigate their use in the propagation of Earth's artificial\nsatellites showing a remarkable improvement compared to the equinoctial orbital\nelements.\n"}
+{"abstract": "  We consider the Yang-Mills equations in $(1+d)$-dimensional Minkowski\nspacetime. It is known that in the supercritical case, i.e., for $d \\geq 5$,\nthese equations admit closed form equivariant self-similar blowup solutions\n\\cite{BieBiz15}. These solutions are furthermore conjectured to be the\nuniversal attractors for generic large data equivariant evolutions. In this\npaper we partially prove this conjecture. Namely, we show that for all odd $d\n\\geq 5$ the blowup mechanism exhibited by these solutions is stable.\n"}
+{"abstract": "  In this paper the equations of motion associated with a Lagrangian inspired\nby relativistic optics in nonuniform moving media are considered. The model\ndescribes optical effects in the nonuniform dispersionless moving medium. When\nusing the optical metric restricted to the Minkowski manifold, we have\nestablished the Euler-Lagrange equations for geodesics. We have specified the\ngeneral model to the special case when the refractive index increases along the\ndirection $Z$. The exact analytical solutions of the corresponding\nEuler-Lagrange equations have been constructed. Analysis of the solutions shows\nthat the light beams are bending to the axes $Z$ along which the refractive\nindex increases.\n"}
+{"abstract": "  Mainstream deep models for three-dimensional MRI synthesis are either\ncross-sectional or volumetric depending on the input. Cross-sectional models\ncan decrease the model complexity, but they may lead to discontinuity\nartifacts. On the other hand, volumetric models can alleviate the discontinuity\nartifacts, but they might suffer from loss of spatial resolution due to\nincreased model complexity coupled with scarce training data. To mitigate the\nlimitations of both approaches, we propose a novel model that progressively\nrecovers the target volume via simpler synthesis tasks across individual\norientations.\n"}
+{"abstract": "  A dimension reduction method based on the \"Nonlinear Level set Learning\"\n(NLL) approach is presented for the pointwise prediction of functions which\nhave been sparsely sampled. Leveraging geometric information provided by the\nImplicit Function Theorem, the proposed algorithm effectively reduces the input\ndimension to the theoretical lower bound with minor accuracy loss, providing a\none-dimensional representation of the function which can be used for regression\nand sensitivity analysis. Experiments and applications are presented which\ncompare this modified NLL with the original NLL and the Active Subspaces (AS)\nmethod. While accommodating sparse input data, the proposed algorithm is shown\nto train quickly and provide a much more accurate and informative reduction\nthan either AS or the original NLL on two example functions with\nhigh-dimensional domains, as well as two state-dependent quantities depending\non the solutions to parametric differential equations.\n"}
+{"abstract": "  The Insulator-Metal-Transition adjoined by a structural transition of VO2 is\ninduced above room temperature (340 K) by heating or self-heating. A steep\nresistance-jump of up to five orders of magnitude occurs at this transition in\nhigh quality, unstrained single crystals. Insulating domains sliding in the\nsense of the electric current within the metallic background were found so far\nexclusively in the current induced mixed metal-insulator phase of VO2 single\ncrystals; it is known for a long time that their uniformity and speed as\nfunction of current density are very sensitive to crystal quality. The high\nenergetical cost of domain emission is the focus of our present investigations.\nIn this Communication we report on the surprising behavior of a needle-like VO2\nsingle crystal. Several I-V closed loops traced at room temperature\nconcurrently with video recording of the crystal under the microscope, were\nfollowed by R(T) measurements during three slow heating-cooling cycles between\nroom temperature and above 340 K, followed in their turn by an additional set\nof I-V measurements and video recordings. The results show that the slow\ncycling through the transition using external heat had a healing effect on the\nreproducibility of R(T) while increasing the activation energy of conduction in\nthe insulating state and in reducing the damping term in the domains' sliding\nvelocity. The intriguing result of this set of measurements was that the energy\ncost of the domain emission, was higher after healing than prior to it.\n"}
+{"abstract": "  In this work, we present to the NLP community, and to the wider research\ncommunity as a whole, an application for the diachronic analysis of research\ncorpora. We open source an easy-to-use tool coined: DRIFT, which allows\nresearchers to track research trends and development over the years. The\nanalysis methods are collated from well-cited research works, with a few of our\nown methods added for good measure. Succinctly put, some of the analysis\nmethods are: keyword extraction, word clouds, predicting\ndeclining/stagnant/growing trends using Productivity, tracking bi-grams using\nAcceleration plots, finding the Semantic Drift of words, tracking trends using\nsimilarity, etc. To demonstrate the utility and efficacy of our tool, we\nperform a case study on the cs.CL corpus of the arXiv repository and draw\ninferences from the analysis methods. The toolkit and the associated code are\navailable here: https://github.com/rajaswa/DRIFT.\n"}
+{"abstract": "  Recent studies stressed the fact that covariance matrices computed from\nempirical financial time series appear to contain a high amount of noise. This\nmakes the classical Markowitz Mean-Variance Optimization model unable to\ncorrectly evaluate the performance associated to selected portfolios. Since the\nMarkowitz model is still one of the most used practitioner-oriented tool,\nseveral filtering methods have been proposed in the literature to fix the\nproblem. Among them, the two most promising ones refer to the Random Matrix\nTheory or to the Power Mapping strategy. The basic idea of these methods is to\ntransform the correlation matrix maintaining the Mean-Variance Optimization\nmodel. However, experimental analysis shows that these two strategies are not\nadequately effective when applied to real financial datasets.\n  In this paper we propose an alternative filtering method based on\nCombinatorial Optimization. We advance a new Mixed Integer Quadratic\nProgramming model to filter those observations that may influence the\nperformance of a portfolio in the future. We discuss the properties of this new\nmodel and we test it on some real financial datasets. We compare the\nout-of-sample performance of our portfolios with the one of the portfolios\nprovided by the two above mentioned alternative strategies. We show that our\nmethod outperforms them. Although our model can be solved efficiently with\nstandard optimization solvers the computational burden increases for large\ndatasets. To overcome this issue we also propose a heuristic procedure that\nempirically showed to be both efficient and effective.\n"}
+{"abstract": "  Quantifying anisotropy in the chemical reactions of mesoscopic crystals has\nmostly resorted on the combination of electron microscopy and diffraction. In\nthis work, we established crystal-facet-resolved kinetic measurements of\noxidation reactions in 2D transition metal dichalcogenides (TMDs) using optical\nsecond-harmonic generation spectroscopy and scanning probe microscopy. We show\nthe in-plane anisotropy of their bond-breaking reactions is governed by their\nstructure and strongly material-dependent among four TMDs. The facet-resolved\nanalysis directly revealed that the reactions proceed fastest (slowest) for\nchalcogen (metal)-terminated zigzag edges with armchair edges in the middle.\nThe degree of the anisotropy inducing trigonal oxidation patterns was much\nhigher in MoS2 and MoSe2 than WS2 and WSe2. Kinetic Wulff construction based on\nedge-specific reaction rates verified the material-dependent mesoscopic\nreaction patterns. We also show that the reactions are initiated at\nsubstrate-mediated defects located on the bottom and top surfaces of 2D TMDs.\n"}
+{"abstract": "  The paper concerns the image, level and sojourn time sets associated with\nsample paths of the Rosenblatt process. We obtain results regarding the\nHausdorff (both classical and macroscopic), packing and intermediate\ndimensions, and the logarithmic and pixel densities. As a preliminary step we\nalso establish the time inversion property of the Rosenblatt process, as well\nas some technical points regarding the distribution of $Z$.\n"}
+{"abstract": "  Nuclei instance segmentation plays an important role in the analysis of\nHematoxylin and Eosin (H&E)-stained images. While supervised deep learning\n(DL)-based approaches represent the state-of-the-art in automatic nuclei\ninstance segmentation, annotated datasets are required to train these models.\nThere are two main types of tissue processing protocols, namely formalin-fixed\nparaffin-embedded samples (FFPE) and frozen tissue samples (FS). Although\nFFPE-derived H&E stained tissue sections are the most widely used samples, H&E\nstaining on frozen sections derived from FS samples is a relevant method in\nintra-operative surgical sessions as it can be performed fast. Due to\ndifferences in the protocols of these two types of samples, the derived images\nand in particular the nuclei appearance may be different in the acquired whole\nslide images. Analysis of FS-derived H&E stained images can be more challenging\nas rapid preparation, staining, and scanning of FS sections may lead to\ndeterioration in image quality.\n  In this paper, we introduce CryoNuSeg, the first fully annotated FS-derived\ncryosectioned and H&E-stained nuclei instance segmentation dataset. The dataset\ncontains images from 10 human organs that were not exploited in other publicly\navailable datasets, and is provided with three manual mark-ups to allow\nmeasuring intra-observer and inter-observer variability. Moreover, we\ninvestigate the effects of tissue fixation/embedding protocol (i.e., FS or\nFFPE) on the automatic nuclei instance segmentation performance of one of the\nstate-of-the-art DL approaches. We also create a baseline segmentation\nbenchmark for the dataset that can be used in future research.\n  A step-by-step guide to generate the dataset as well as the full dataset and\nother detailed information are made available to fellow researchers at\nhttps://github.com/masih4/CryoNuSeg.\n"}
+{"abstract": "  Motivated by the mushy zones of sea ice, volcanoes, and icy moons of the\nouter solar system, we perform a theoretical and numerical study of\nboundary-layer convection along a vertical heated wall in a bounded ideal mushy\nregion. The mush is comprised of a porous and reactive binary alloy with a\nmixture of saline liquid in a solid matrix, and is studied in the near-eutectic\napproximation. Here we demonstrate the existence of four regions and study\ntheir behavior asymptotically. Starting from the bottom of the wall, the four\nregions are (i) an isotropic corner region; (ii) a buoyancy dominated vertical\nboundary layer; (iii) an isotropic connection region; and (iv) a horizontal\nboundary layer at the top boundary with strong gradients of pressure and\nbuoyancy. Scalings from numerical simulations are consistent with the\ntheoretical predictions. Close to the heated wall, the convection in the mushy\nlayer is similar to a rising buoyant plume abruptly stopped at the top, leading\nto increased pressure and temperature in the upper region, whose impact is\ndiscussed as an efficient melting mechanism.\n"}
+{"abstract": "  The multi analytical study of terrestrial analogues is a useful strategy to\ndeepen the knowledge about the geological and environmental evolution of Mars\nand other extraterrestrial bodies. In spite of the increasing importance that\nLIBS, NIR and Raman techniques are acquiring in the field of space exploration,\nthere is a lack web-based platform providing free access to a wide\nmulti-spectral database of terrestrial analogue materials. The Planetary\nTerrestrial Analogue Library (PTAL) project aims at responding to this critical\nneed by developing and providing free web accessibility to LIBS, NIR and Raman\ndata from more than 94 terrestrial analogues selected according to their\ncongruence with Martian geological contexts. In this framework, the present\nmanuscript provides the scientific community with a complete overview of the\nover 4500 Raman spectra collected to feed the PTAL database. Raman data,\nobtained through the complementary use of laboratory and spacecraft-simulator\nsystems, confirmed the effectiveness of this spectroscopic technique for the\ndetection of major and minor mineralogical phases of the samples, the latter\nbeing of critical importance for the recognition of geological processes that\ncould have occurred on Mars and other planets. In light of the forthcoming\nmissions to Mars, the results obtained through the RLS ExoMars Simulator offer\na valuable insight on the scientific outcome that could derive from the RLS\nspectrometer that will soon land on Mars as part of the ExoMars rover payload.\n"}
+{"abstract": "  We study the differentially private multi group aggregation (PMGA) problem.\nThis setting involves a single server and $n$ users. Each user belongs to one\nof $k$ distinct groups and holds a discrete value. The goal is to design\nschemes that allow the server to find the aggregate (sum) of the values in each\ngroup (with high accuracy) under communication and local differential privacy\nconstraints. The privacy constraint guarantees that the user's group remains\nprivate. This is motivated by applications where a user's group can reveal\nsensitive information, such as his religious and political beliefs, health\ncondition, or race. We propose a novel scheme, dubbed Query and Aggregate\n(Q\\&A) for PMGA. The novelty of Q\\&A is that it is an interactive aggregation\nscheme. In Q\\&A, each user is assigned a random query matrix, to which he sends\nthe server an answer based on his group and value. We characterize the Q\\&A\nscheme's performance in terms of accuracy (MSE), privacy, and communication. We\ncompare Q\\&A to the Randomized Group (RG) scheme, which is non-interactive and\nadapts existing randomized response schemes to the PMGA setting. We observe\nthat typically Q\\&A outperforms RG, in terms of privacy vs. utility, in the\nhigh privacy regime.\n"}
+{"abstract": "  The event-by-event correlations between three flow amplitudes are measured\nfor the first time in Pb--Pb collisions, using higher-order Symmetric\nCumulants. We find that different three-harmonic correlations develop during\nthe collective evolution of the medium, when compared with correlations that\nexist in the initial state. These new results cannot be interpreted in terms of\nprevious lower-order flow measurements, since contributions from two-harmonic\ncorrelations are explicitly removed in the new observables. Comparison with\nMonte Carlo simulations provides new and independent constraints for the\ninitial conditions and system properties of nuclear matter created in heavy-ion\ncollisions.\n"}
+{"abstract": "  We propose a robust framework for interpretable, few-shot analysis of\nnon-stationary sequential data based on flexible graphical models to express\nthe structured distribution of sequential events, using prototype radial basis\nfunction (RBF) neural network emissions. A motivational link is demonstrated\nbetween prototypical neural network architectures for few-shot learning and the\nproposed RBF network infinite hidden Markov model (RBF-iHMM). We show that RBF\nnetworks can be efficiently specified via prototypes allowing us to express\ncomplex nonstationary patterns, while hidden Markov models are used to infer\nprincipled high-level Markov dynamics. The utility of the framework is\ndemonstrated on biomedical signal processing applications such as automated\nseizure detection from EEG data where RBF networks achieve state-of-the-art\nperformance using a fraction of the data needed to train long-short-term memory\nvariational autoencoders.\n"}
+{"abstract": "  EU needs an effective exit strategy from the crisis, with a special attention\nto SMEs that represent the 99% of the enterprises active in the European\nproduction system. To this end, innovation appears to be a key factor to\nrelaunch the EU industrial system. The BIVEE project proceeded for almost 4\nyears to develop a rich framework, i.e., a methodology and a cloud-based\nsoftware environment, that includes business principles, models, and best\npractices, plus a number of advanced software services, to support and promote\nproduction improvement and business innovation in virtual enterprise\nenvironments (essentially, enterprise networks.)\n"}
+{"abstract": "  Knowledge Graph has been proven effective in modeling structured information\nand conceptual knowledge, especially in the medical domain. However, the lack\nof high-quality annotated corpora remains a crucial problem for advancing the\nresearch and applications on this task. In order to accelerate the research for\ndomain-specific knowledge graphs in the medical domain, we introduce DiaKG, a\nhigh-quality Chinese dataset for Diabetes knowledge graph, which contains\n22,050 entities and 6,890 relations in total. We implement recent typical\nmethods for Named Entity Recognition and Relation Extraction as a benchmark to\nevaluate the proposed dataset thoroughly. Empirical results show that the DiaKG\nis challenging for most existing methods and further analysis is conducted to\ndiscuss future research direction for improvements. We hope the release of this\ndataset can assist the construction of diabetes knowledge graphs and facilitate\nAI-based applications.\n"}
+{"abstract": "  The vorticity of world lines of observers associated to the rotation of a\nmassive body was reported by Lense and Thirring more than a century ago. In\ntheir example the frame dragging effect induced by the vorticity, is directly\n(explicitly) related to the rotation of the source. However in many other cases\nit is not so, and the origin of vorticity remains obscure and difficult to\nidentify. Accordingly, in order to unravel this issue, and looking for the\nultimate origin of vorticity associated to frame dragging, we analyze in this\nmanuscript very different scenarios where frame dragging effect is present.\nSpecifically we consider general vacuum stationary spacetimes, general\nelectro-vacuum spacetimes, radiating electro-vacuum spacetimes and Bondi--Sachs\nradiating spacetimes. We identify the physical quantities present in all these\ncases, which determine the vorticity and may legitimately be considered as\nresponsible for the frame dragging. Doing so we provide a comprehensive\nphysical picture of frame dragging. Some observational consequences of our\nresults are discussed.\n"}
+{"abstract": "  eXtended Reality (XR) autism research, ranging from Augmented Reality to\nVirtual Reality, focuses on socio-emotional abilities and high-functioning\nautism. However common autism interventions address the entire spectrum over\nsocial, sensory and mediation issues. To bridge the gap between autism research\nand real interventions, we compared existing literature on XR and autism with\nstakeholders' needs obtained by interviewing 34 skateholders, mainly\npractitioners. It allow us first to suggest XR use cases that could better\nsupport practitioners' interventions, and second to derive design guidelines\naccordingly. Findings demonstrate that collaborative XR sensory-based and\nmediation approaches would benefit the entire spectrum, and encourage to\nconsider the overall intervention context when designing XR protocols.\n"}
+{"abstract": "  Software Engineering researchers are increasingly using Natural Language\nProcessing (NLP) techniques to automate Software Vulnerabilities (SVs)\nassessment using the descriptions in public repositories. However, the existing\nNLP-based approaches suffer from concept drift. This problem is caused by a\nlack of proper treatment of new (out-of-vocabulary) terms for the evaluation of\nunseen SVs over time. To perform automated SVs assessment with concept drift\nusing SVs' descriptions, we propose a systematic approach that combines both\ncharacter and word features. The proposed approach is used to predict seven\nVulnerability Characteristics (VCs). The optimal model of each VC is selected\nusing our customized time-based cross-validation method from a list of eight\nNLP representations and six well-known Machine Learning models. We have used\nthe proposed approach to conduct large-scale experiments on more than 100,000\nSVs in the National Vulnerability Database (NVD). The results show that our\napproach can effectively tackle the concept drift issue of the SVs'\ndescriptions reported from 2000 to 2018 in NVD even without retraining the\nmodel. In addition, our approach performs competitively compared to the\nexisting word-only method. We also investigate how to build compact\nconcept-drift-aware models with much fewer features and give some\nrecommendations on the choice of classifiers and NLP representations for SVs\nassessment.\n"}
+{"abstract": "  We present a novel remote gas detection and identification technique based on\ncorrelation spectroscopy with a piezoelectric tunable fibre-optic Fabry-P\\'erot\nfilter. We show that the spectral correlation amplitude between the filter\ntransmission window and gas absorption features is related to the gas\nabsorption optical depth, and that different gases can be distinguished from\none another using their correlation signal phase. Using an observed\ntelluric-corrected, high-resolution near-infrared spectrum of Venus, we show\nvia simulation that the Doppler shift of gases lines can be extracted from the\nphase of the lock-in signal using low-cost, compact, and lightweight\nfibre-optic components with lock-in amplification to improve the\nsignal-to-noise ratio. This correlation spectroscopy technique has applications\nin the detection and radial velocity determination of faint spectral features\nin astronomy and remote sensing. We experimentally demonstrate remote CO2\ndetection system using a lock-in amplifier, fibre-optic Fabry-P\\'erot filter,\nand single channel photodiode.\n"}
+{"abstract": "  We prove a metastability result for finitary microstates which are good\nmodels for a Gibbs measure for a nearest-neighbor interaction on a\nfinitely-generated group. This is used to show that any maximal-entropy joining\nof two such Gibbs states is a relative product over the tail $\\sigma$-algebra,\nexcept in degenerate cases.\n  We also use results on extremal cuts of random graphs to further investigate\noptimal self-joinings of the Ising model on a free group.\n"}
+{"abstract": "  We present new $AdS_5$ solutions in 11d supergravity and identify them as the\ngravity duals of a class of 4d $\\mathcal N = 2$ SCFTs of Argyres-Douglas type,\nengineered by a stack of M5-branes wrapped on a sphere with one irregular\npuncture and one regular puncture. The gravity solutions feature an internal\nM5-brane source, which is dual to the irregular puncture. Thanks to a novel\nSt\\\"uckelberg mechanism involving an axion originating from the expansion of\nthe M-theory 3-form, one of the isometry generators on the gravity side is not\nmapped to a continuous symmetry on the SCFT side.\n"}
+{"abstract": "  This paper presents the development of a control system for vision-guided\npick-and-place tasks using a robot arm equipped with a 3D camera. The main\nsteps include camera intrinsic and extrinsic calibration, hand-eye calibration,\ninitial object pose registration, objects pose alignment algorithm, and\npick-and-place execution. The proposed system allows the robot be able to to\npick and place object with limited times of registering a new object and the\ndeveloped software can be applied for new object scenario quickly. The\nintegrated system was tested using the hardware combination of kuka iiwa,\nRobotiq grippers (two finger gripper and three finger gripper) and 3D cameras\n(Intel realsense D415 camera, Intel realsense D435 camera, Microsoft Kinect\nV2). The whole system can also be modified for the combination of other robotic\narm, gripper and 3D camera.\n"}
+{"abstract": "  In this paper, we make a substantial step towards an encoding of Cubical Type\nTheory (CTT) in the Dedukti logical framework. Type-checking CTT expressions\nfeatures a decision procedure in a de Morgan algebra that so far could not be\nexpressed by the rewrite rules of Dedukti. As an alternative, 2 Layer Type\nTheories are variants of Martin-L\\\"of Type Theory where all or part of the\ndefinitional equality can be represented in terms of a so-called external\nequality. We propose to split the encoding by giving an encoding of 2 Layer\nType Theories (2LTT) in Dedukti, and a partial encoding of CTT in 2LTT.\n"}
+{"abstract": "  By means of the extended Gould-Hsu inverse series relations, we find that the\ndual relation of Dougall's summation theorem for the well--poised\n$_7F_6$-series can be utilized to construct numerous interesting\nRamanujan--like infinite series expressions for $\\pi^{\\pm1}$ and $\\pi^{\\pm2}$,\nincluding an elegant formula of $\\pi^{-2}$ due to Guillera.\n"}
+{"abstract": "  This work proposes a novel framework for visual tracking based on the\nintegration of an iterative particle filter, a deep convolutional neural\nnetwork, and a correlation filter. The iterative particle filter enables the\nparticles to correct themselves and converge to the correct target position. We\nemploy a novel strategy to assess the likelihood of the particles after the\niterations by applying K-means clustering. Our approach ensures a consistent\nsupport for the posterior distribution. Thus, we do not need to perform\nresampling at every video frame, improving the utilization of prior\ndistribution information. Experimental results on two different benchmark\ndatasets show that our tracker performs favorably against state-of-the-art\nmethods.\n"}
+{"abstract": "  In this paper, we study a new micro-macro model for a reactive polymeric\nfluid, which is derived recently in [Y. Wang, T.-F. Zhang, and C. Liu, \\emph{J.\nNon-Newton. Fluid Mech.} 293 (2021), 104559, 13 pp], by using the energetic\nvariational approach. The model couples the breaking/reforming reaction scheme\nof the microscopic polymers with other mechanical effects in usual viscoelastic\ncomplex fluids. We establish the global existence of classical solutions near\nthe global equilibrium, in which the treatment on the chemo-mechanical coupling\neffect is the most crucial part. In particular, a weighted Poincar\\'e\ninequality with a mean value is employed to overcome the difficulty that arises\nfrom the non-conservative number density distribution of each species.\n"}
+{"abstract": "  The recent development of electron sensitive and pixelated detectors has\nattracted the use of four-dimensional scanning transmission electron microscopy\n(4D-STEM). Here, we present a precession electron diffraction assisted 4D-STEM\ntechnique for automated orientation mapping using diffraction spot patterns\ndirectly captured by an in-column scintillator based complementary\nmetal-oxide-semiconductor (CMOS) detector. We compare the results to a\nconventional approach, which utilizes a fluorescent screen filmed by an\nexternal CCD camera. The high dynamic range and signal-to-noise characteristics\nof the detector largely improve the image quality of the diffraction patterns,\nespecially the visibility of diffraction spots at high scattering angles. In\nthe orientation maps reconstructed via the template matching process, the CMOS\ndata yields a significant reduction of false indexing and higher reliability\ncompared to the conventional approach. The angular resolution of misorientation\nmeasurement could also be improved by masking reflections close to the direct\nbeam. This is because the orientation sensitive, weak and small diffraction\nspots at high scattering angle are more significant. The results show that fine\ndetails such as nanograins, nanotwins and sub-grain boundaries can be resolved\nwith a sub-degree angular resolution which is comparable to orientation mapping\nusing Kikuchi diffraction patterns.\n"}
+{"abstract": "  We explore properties of the doubly charged vector tetraquark $Z_{\\mathrm{V}\n}^{++}=[cu][\\overline{s}\\overline{d}]$ built of four quarks of different\nflavors using the QCD sum rule methods. The mass and current coupling of $Z_{\n\\mathrm{V}}^{++}$ are computed in the framework of the QCD two-point sum rule\napproach by taking into account quark, gluon and mixed vacuum condensates up to\ndimension $10$. The full width of this tetraquark is saturated by $S$-wave\n$Z_{\\mathrm{V}}^{++} \\to \\pi ^{+}D_{s1}(2460)^{+},\\ \\rho^{+}D_{s0}^{\\ast\n}(2317)^{+}$, and $P$-wave $Z_{\\mathrm{V}}^{++} \\to \\pi ^{+}D_{s}^{+},\\\nK^{+}D^{+}$ decays. Strong couplings required to find partial widths of\naforementioned decays are calculated in the context of the QCD light-cone sum\nrule method and a soft-meson approximation. Our predictions for the mass\n$m=(3515 \\pm 125)~\\mathrm{MeV}$ and full width $ \\Gamma\n_{\\mathrm{full}}=156_{-30}^{+56}~\\mathrm{MeV}$ of $Z_{\\mathrm{V} }^{++} $ are\nuseful to search for this exotic meson in various processes. Recently, the LHCb\ncollaboration discovered neutral states $X_{0(1)}(2900)$ as resonance-like\npeaks in $D^{-}K^{+}$ invariant mass distribution in the decay $B^{+} \\to\nD^{+}D^{-}K^{+}$. We argue that mass distribution of $ D^{+}K^{+}$ mesons in\nthe same $B$ decay can be used to observe the doubly charged scalar\n$Z_{\\mathrm{S}}^{++}$ and vector $Z_{\\mathrm{V}}^{++}$ tetraquarks.\n"}
+{"abstract": "  Novel effects induced by nonmagnetic impurities in frustrated magnets and\nquantum spin liquid represent a highly nontrivial and interesting problem. A\ntheoretical proposal of extended modulated spin structures induced by doping of\nsuch magnets, distinct from the well-known skyrmions has attracted significant\ninterest. Here, we demonstrate that nonmagnetic impurities can produce such\nextended spin structures in h-YMnO3, a triangular antiferromagnet with\nnoncollinear magnetic order. Using inelastic neutron scattering (INS), we\nmeasured the full dynamical structure factor in Al-doped h-YMnO3 and confirmed\nthe presence of magnon damping with a clear momentum dependence. Our\ntheoretical calculations can reproduce the key features of the INS data,\nsupporting the formation of the proposed spin textures. As such, our study\nprovides the first experimental confirmation of the impurity-induced spin\ntextures. It offers new insights and understanding of the impurity effects in a\nbroad class of noncollinear magnetic systems.\n"}
+{"abstract": "  The abundant production of beauty and charm hadrons in the $5 \\times 10^{12}$\n$Z^0$ decays expected at FCC-ee offers outstanding opportunities in flavour\nphysics that in general exceed those available at Belle II, and are\ncomplementary to the heavy-flavour programme of the LHC. A wide range of\nmeasurements will be possible in heavy-flavour spectroscopy, rare decays of\nheavy-flavoured particles and $C\\!P$-violation studies, which will benefit from\nthe low-background experimental environment, the high Lorentz boost, and the\navailability of the full spectrum of hadron species. This essay first surveys\nthe important questions in heavy-flavour physics, and assesses the likely\ntheoretical and experimental landscape at the turn-on of FCC-ee. From this\ncertain measurements are identified where the impact of FCC-ee will be\nparticularly important. A full exploitation of the heavy-flavour potential of\nFCC-ee places specific constraints and challenges on detector design, which in\nsome cases are in tension with those imposed by the other physics goals of the\nfacility. These requirements and conflicts are discussed.\n"}
+{"abstract": "  In the past decade, model-free reinforcement learning (RL) has provided\nsolutions to challenging domains such as robotics. Model-based RL shows the\nprospect of being more sample-efficient than model-free methods in terms of\nagent-environment interactions, because the model enables to extrapolate to\nunseen situations. In the more recent past, model-based methods have shown\nsuperior results compared to model-free methods in some challenging domains\nwith non-linear state transitions. At the same time, it has become apparent\nthat RL is not market-ready yet and that many real-world applications are going\nto require model-based approaches, because model-free methods are too\nsample-inefficient and show poor performance in early stages of training. The\nlatter is particularly important in industry, e.g. in production systems that\ndirectly impact a company's revenue. This demonstrates the necessity for a\ntoolbox to push the boundaries for model-based RL. While there is a plethora of\ntoolboxes for model-free RL, model-based RL has received little attention in\nterms of toolbox development. Bellman aims to fill this gap and introduces the\nfirst thoroughly designed and tested model-based RL toolbox using\nstate-of-the-art software engineering practices. Our modular approach enables\nto combine a wide range of environment models with generic model-based agent\nclasses that recover state-of-the-art algorithms. We also provide an experiment\nharness to compare both model-free and model-based agents in a systematic\nfashion w.r.t. user-defined evaluation metrics (e.g. cumulative reward). This\npaves the way for new research directions, e.g. investigating uncertainty-aware\nenvironment models that are not necessarily neural-network-based, or developing\nalgorithms to solve industrially-motivated benchmarks that share\ncharacteristics with real-world problems.\n"}
+{"abstract": "  The present study investigates the passive flow control phenomena over a\ntwo-dimensional circular cylinder using numerical simulations in the laminar\nregime. The aim is to explore one of the passive control techniques, which\ninvolves the introduction of a slit to the geometry of the cylinder. The two\nparameters, slit width ratio S/D (slit width/diameter) and slit angle (measured\nwith respect to the incoming flow direction), play an essential role in\ndetermining the trend of critical Reynolds number (Rec). Most of the analysis\ninvokes flow visualization and saturation amplitude methods to obtain the\ncritical Reynolds number (indicative of the onset of vortex shedding) for\ndifferent cases. Further, Hopf bifurcation analysis using Stuart-Landau\nequation and global stability analysis confirm the accuracy and consistency in\nthe predicted solutions. The additional amount of flow through slit increases\nthe pressure downstream of the cylinder, which consequently leads to an\nincrease in Rec of the modified cylinder. The critical Reynolds number\nincreases with S/D of the modified cylinder at 0 deg slit angle (as an\nadditional amount of flow grows with S/D). The critical Reynolds number shows\nan increasing trend with the slit angle in the range of S/D (0.05-0.15) as the\nfluctuation intensity reduces with slit angle in this range. For S/D=\n0.15-0.25, the extra amount of flow through slit induces the instability in\nwake which causes a decrease in Rec with slit angle. A correlation is obtained\nthat estimates the critical Reynolds number for a given S/D and slit angle.\n"}
+{"abstract": "  We propose an experimental architecture where an array of optical tweezers\naffords site-dependent control over the confining potential of a conventional\nradio-frequency ion trap. The site-dependent control enables programmable\nmanipulation of phonon modes of ions, with many potential applications in\nquantum information processing (QIP) and thermodynamics. We describe protocols\nfor programming the array of optical tweezers to attain a set of target phonon\nmodes with high accuracy. We propose applications of such controls in\nsimulating quantum thermodynamics of a particle of programmable effective mass\nvia Jarzynski's equality and improving the efficiency of sympathetic cooling\nand quantum logic gates in a multi-species ion system of disparate masses. We\ndiscuss the required optical parameters in a realistic ion trap system and\npotential adverse effects of optical tweezers in QIP. Our scheme extends the\nutility of trapped-ions as a platform for quantum computation and simulation.\n"}
+{"abstract": "  In this PhD thesis a wide variety of cosmological models beyond the\n$\\Lambda$CDM are studied in detail. Great emphasis is put on the running vacuum\nmodels (RVM's), which can be motivated in the context of Quantum Field Theory\nin curved spacetime. They consider the possibility of a smoothly evolving\nvacuum energy density that inherits its time-dependence from cosmological\nvariables, such as the Hubble rate and its time derivative. The analysis\npresented, however, is not limited only to the dynamical vacuum models but\nconsiders also models of dark energy such as the Peebles \\& Ratra scalar field\nmodel or simple parameterizations like the XCDM or the CPL. Their theoretical\npredictions are tested against the wealth of cosmological data and a fitting\nprocedure is carried out in order to obtain constraints over the free\nparameters that characterize the models under study. Finally, an extended\ndiscussion about the $\\sigma_8$ and $H_0$ tensions is provided and a possible\nsolution for both of them is described within the framework of Brans and Dicke\ngravity with a cosmological constant, which mimics the RVM with a mild\ntime-evolving gravitational coupling $G$. The main conclusion of this study is\nthat appreciable signals in favour of a time-evolving dark energy density are\nfound in the current data.\n"}
+{"abstract": "  Consideration of slamming loads within the structural design of planning\nhulls is of critical importance in ensuring adequate structural performance in\norder to avoid potential catastrophic consequences. However, because of the\nintricacy in the interplay between complex fluid flows and nonlinear structural\ndeformations that accompany the phenomenology of slamming, a general\nengineering theory in slamming has yet to be uncovered, and so design relies on\nspecialized theories. In this paper, we propose one such theory for a design\ncase that has, until now, eluded a proper description. In pursuit of this\ntheory, we employ a specialized implicit, partitioned fluid-structural\ninteraction (FSI) simulation approach, in order to study the underlying\nphysical mechanisms accompanying the oblique impact of a flexible plate during\nwater entry. In the present work, we first present validation results from\nflexible plate water entry experiments, to confirm the veracity of the\ndeveloped FSI solver. Subsequent to validation, we carry out a series of\nnumerical analyses, in an effort to characterize the regimes in impact force\nand plate out-of-plane deformations, as a function of impact velocities and\nplate flexural rigidity. Finally, we use our FSI solver, as a kind of\n\"microscope\", to study the mechanistic evolution of fluid flows and elastic\nplate deformations that occur during slamming. Based on these observations, we\npropose a novel, but simple engineering theory for flexible plates obliquely\nimpacting the water free surface (e.g. high speed porpoising water craft\nreentry).\n"}
+{"abstract": "  We present results from a suite of binary merging cluster simulations. The\nhydrodynamical cluster simulations are performed employing a smoothed particle\nhydrodynamics (SPH) formulation in which gradient errors are strongly reduced\nby means of an integral approach. We consider adiabatic as well as radiative\nsimulations, in which we include gas cooling, star formation and energy\nfeedback from supernovae. We explore the effects of merging on the\nthermodynamic structure of the intracluster gas of the final merger remnant. In\nparticular, we study how core entropy is generated during the merging and the\nstability properties of the initial cool-core profile against disruption. To\nthis end, we consider a range of initial mass ratio and impact parameters.\nFinal entropy profiles of our adiabatic merging simulations are in good accord\nwith previous findings (ZuHone 2011), with cool-cores being disrupted for all\nof the initial merging setups. For equal-mass off-axis mergers, we find that a\nsignificant contribution to the final primary core entropy is due to\nhydrodynamic instabilities generated by rotational motions, which are induced\nby tidal torques during the first pericenter passage. In radiative simulations,\ncool-cores are more resilient against heating processes; nonetheless, they are\nable to maintain their integrity only in the case of off-axis mergers with very\nunequal masses. We suggest that these results are robust against changes in the\ngas physical modeling, in particular to the inclusion of AGN thermal feedback.\n  Our findings support the view that the observed core cluster morphology\nemerges naturally in a merging cluster context, and conclude that the merging\nangular momentum is a key parameter in shaping the thermodynamical properties\nof the final merger remnant.\n"}
+{"abstract": "  To extend several known centered Gaussian processes, we introduce a new\ncentered mixed self-similar Gaussian process called the mixed generalized\nfractional Brownian motion, which could serve as a good model for a larger\nclass of natural phenomena. This process generalizes both the well known mixed\nfractional Brownian motion introduced by Cheridito [10] and the generalized\nfractional Brownian motion introduced by Zili [31]. We study its main\nstochastic properties, its non-Markovian and non-stationarity characteristics\nand the conditions under which it is not a semimartingale. We prove the long\nrange dependence properties of this process.\n"}
+{"abstract": "  In this paper, we provide a non-homogeneous $T(1)$ theorem on product spaces\n$(X_1 \\times X_2, \\rho_1 \\times \\rho_2, \\mu_1 \\times \\mu_2)$ equipped with a\nquasimetric $\\rho_1 \\times \\rho_2$ and a Borel measure $\\mu_1 \\times \\mu_2$,\nwhich, need not be doubling but satisfies an upper control on the size of\nquasiballs.\n"}
+{"abstract": "  We elucidate the fate of neighboring two and three-$\\alpha$ particles in cold\nneutron matter by focusing on an analogy between such $\\alpha$ systems and\nFermi polarons realized in ultracold atoms. We describe in-medium excitation\nproperties of an $\\alpha$ particle and neutron-mediated two- and three-$\\alpha$\ninteractions using theoretical approaches developed for studies of cold atomic\nsystems. We numerically solve the few-body Schr\\\"odinger equation of $\\alpha$\nparticles within standard $\\alpha$ cluster models combined with in-medium\nproperties of $\\alpha$ particles. We point out that the resultant two-$\\alpha$\nground state and three-$\\alpha$ first excited state, which correspond to $^8$Be\nand the Hoyle state, respectively, known as main components in the\ntriple-$\\alpha$ reaction, can become bound states in such a many-neutron\nbackground although these states are unstable in vacuum. Our results suggest a\nsignificance of these in-medium cluster states not only in astrophysical\nenvironments such as core-collapsed supernova explosions and neutron star\nmergers but also in neutron-rich nuclei.\n"}
+{"abstract": "  MIMO transmit arrays allow for flexible design of the transmit beampattern.\nHowever, the large number of elements required to achieve certain performance\nusing uniform linear arrays (ULA) maybe be too costly. This motivated the need\nfor thinned arrays by appropriately selecting a small number of elements so\nthat the full array beampattern is preserved. In this paper, we propose\nLearn-to-Select (L2S), a novel machine learning model for selecting antennas\nfrom a dense ULA employing a combination of multiple Softmax layers constrained\nby an orthogonalization criterion. The proposed approach can be efficiently\nscaled for larger problems as it avoids the combinatorial explosion of the\nselection problem. It also offers a flexible array design framework as the\nselection problem can be easily formulated for any metric.\n"}
+{"abstract": "  Exploiting multi-scale features has shown great potential in tackling\nsemantic segmentation problems. The aggregation is commonly done with sum or\nconcatenation (concat) followed by convolutional (conv) layers. However, it\nfully passes down the high-level context to the following hierarchy without\nconsidering their interrelation. In this work, we aim to enable the low-level\nfeature to aggregate the complementary context from adjacent high-level feature\nmaps by a cross-scale pixel-to-region relation operation. We leverage\ncross-scale context propagation to make the long-range dependency capturable\neven by the high-resolution low-level features. To this end, we employ an\nefficient feature pyramid network to obtain multi-scale features. We propose a\nRelational Semantics Extractor (RSE) and Relational Semantics Propagator (RSP)\nfor context extraction and propagation respectively. Then we stack several RSP\ninto an RSP head to achieve the progressive top-down distribution of the\ncontext. Experiment results on two challenging datasets Cityscapes and COCO\ndemonstrate that the RSP head performs competitively on both semantic\nsegmentation and panoptic segmentation with high efficiency. It outperforms\nDeeplabV3 [1] by 0.7% with 75% fewer FLOPs (multiply-adds) in the semantic\nsegmentation task.\n"}
+{"abstract": "  Metal Artifacts creates often difficulties for a high quality visual\nassessment of post-operative imaging in {c}omputed {t}omography (CT). A vast\nbody of methods have been proposed to tackle this issue, but {these} methods\nwere designed for regular CT scans and their performance is usually\ninsufficient when imaging tiny implants. In the context of post-operative\nhigh-resolution {CT} imaging, we propose a 3D metal {artifact} reduction\nalgorithm based on a generative adversarial neural network. It is based on the\nsimulation of physically realistic CT metal artifacts created by cochlea\nimplant electrodes on preoperative images. The generated images serve to train\na 3D generative adversarial networks for artifacts reduction. The proposed\napproach was assessed qualitatively and quantitatively on clinical conventional\nand cone-beam CT of cochlear implant postoperative images. These experiments\nshow that the proposed method {outperforms other} general metal artifact\nreduction approaches.\n"}
+{"abstract": "  Federated learning (FL) utilizes edge computing devices to collaboratively\ntrain a shared model while each device can fully control its local data access.\nGenerally, FL techniques focus on learning model on independent and identically\ndistributed (iid) dataset and cannot achieve satisfiable performance on non-iid\ndatasets (e.g. learning a multi-class classifier but each client only has a\nsingle class dataset). Some personalized approaches have been proposed to\nmitigate non-iid issues. However, such approaches cannot handle underlying data\ndistribution shift, namely data distribution skew, which is quite common in\nreal scenarios (e.g. recommendation systems learn user behaviors which change\nover time). In this work, we provide a solution to the challenge by leveraging\nsmart-contract with federated learning to build optimized, personalized deep\nlearning models. Specifically, our approach utilizes smart contract to reach\nconsensus among distributed trainers on the optimal weights of personalized\nmodels. We conduct experiments across multiple models (CNN and MLP) and\nmultiple datasets (MNIST and CIFAR-10). The experimental results demonstrate\nthat our personalized learning models can achieve better accuracy and faster\nconvergence compared to classic federated and personalized learning. Compared\nwith the model given by baseline FedAvg algorithm, the average accuracy of our\npersonalized learning models is improved by 2% to 20%, and the convergence rate\nis about 2$\\times$ faster. Moreover, we also illustrate that our approach is\nsecure against recent attack on distributed learning.\n"}
+{"abstract": "  We report on the strong coupling between a metallic ferromagnetic Fe75Co25\nthin film patterned element and a range of superconducting Nb half-wavelength\nco-planar waveguide (CPW) resonators. By varying the volume of the ferromagnet\nwe demonstrate that the coupling rate scales linearly with the square root of\nthe number of spins and achieve a coupling rate over 700 MHz, approaching the\nultrastrong coupling regime. Experiments varying the center conductor width\nwhile maintaining constant magnetic volume verify that decreasing the center\nconductor width increases coupling and cooperativity. Our results show that the\nfrequency dependence of the coupling rate is linear with the fundamental and\nhigher order odd harmonics of the CPW, but with differing efficiencies. The\nresults show promise for scaling planar superconducting resonator/magnetic\nhybrid systems to smaller dimensions.\n"}
+{"abstract": "  In this work we study the impact of noise on the training of object detection\nnetworks for the medical domain, and how it can be mitigated by improving the\ntraining procedure. Annotating large medical datasets for training data-hungry\ndeep learning models is expensive and time consuming. Leveraging information\nthat is already collected in clinical practice, in the form of text reports,\nbookmarks or lesion measurements would substantially reduce this cost.\nObtaining precise lesion bounding boxes through automatic mining procedures,\nhowever, is difficult. We provide here a quantitative evaluation of the effect\nof bounding box coordinate noise on the performance of Faster R-CNN object\ndetection networks for breast mass detection. Varying degrees of noise are\nsimulated by randomly modifying the bounding boxes: in our experiments,\nbounding boxes could be enlarged up to six times the original size. The noise\nis injected in the CBIS-DDSM collection, a well curated public mammography\ndataset for which accurate lesion location is available. We show how, due to an\nimperfect matching between the ground truth and the network bounding box\nproposals, the noise is propagated during training and reduces the ability of\nthe network to correctly classify lesions from background. When using the\nstandard Intersection over Union criterion, the area under the FROC curve\ndecreases by up to 9%. A novel matching criterion is proposed to improve\ntolerance to noise.\n"}
+{"abstract": "  In this paper, we introduce a novel semi-supervised learning framework for\nend-to-end speech separation. The proposed method first uses mixtures of\nunseparated sources and the mixture invariant training (MixIT) criterion to\ntrain a teacher model. The teacher model then estimates separated sources that\nare used to train a student model with standard permutation invariant training\n(PIT). The student model can be fine-tuned with supervised data, i.e., paired\nartificial mixtures and clean speech sources, and further improved via model\ndistillation. Experiments with single and multi channel mixtures show that the\nteacher-student training resolves the over-separation problem observed in the\noriginal MixIT method. Further, the semisupervised performance is comparable to\na fully-supervised separation system trained using ten times the amount of\nsupervised data.\n"}
+{"abstract": "  Reconfigurable Intelligent Surface (RIS) has becoming a useful tool in future\nwireless communication systems for close-distance communication network. This\npaper we use Reconfigurable Intelligent Surface (RIS) for downlink multi-user\ncommunication designed to improve energy collection performance while\nsatisfying wireless information and Power Transfer (WIPT). The designed system\nconsists of an IRS-assisted system consists of a multi-antenna assisted base\nstation (BS) and two opposite multi-antenna assisted information receiver\ncooperated (RIS) as energy receiver (ERs) that meets energy collection\nrequirements. Based on the electromagnetic property of Reconfigurable\nIntelligent Surface (RIS), like two mirrors that are opposite each other,\nsetting two Reconfigurable Intelligent Surface (RIS) attached to the city\nbuildings to reflect the sending signals. The transmitting precoding of the\nMulti-antenna Auxiliary Base Station (BS) and the angular phase transfer matrix\nof the multi-antenna Auxiliary Information Receiver (IRs) need to be optimized\ntogether to maximize the energy harvesting of IoT devices for energy efficiency\n(EE) of the IRs system and to provide users with the efficiency of the received\nsignal. In order to solve the joint optimization problem effectively, we turn\nthe non-convex maximize problem into the equivalent formal error method based\non the mean square, and finally use the iterative algorithm for optimization.\nAs for algorithm, we respectively use MSE method, semidefinite relaxation\ntechniques to simplify transmitting beamforming matrix and the matrix phase\nshift. Through the observation of simulation data, it can be concluded that the\nperformance optimization method of SDR based on RIS is effective.\n"}
+{"abstract": "  Variational quantum algorithms (VQAs) have been considered to be useful\napplications of noisy intermediate-scale quantum (NISQ) devices. Typically, in\nthe VQAs, a parametrized ansatz circuit is used to generate a trial wave\nfunction, and the parameters are optimized to minimize a cost function. On the\nother hand, blind quantum computing (BQC) has been studied in order to provide\nthe quantum algorithm with security by using cloud networks. A client with a\nlimited ability to perform quantum operations hopes to have access to a quantum\ncomputer of a server, and BQC allows the client to use the server's computer\nwithout leakage of the client's information (such as input, running quantum\nalgorithms, and output) to the server. However, BQC is designed for\nfault-tolerant quantum computing, and this requires many ancillary qubits,\nwhich may not be suitable for NISQ devices. Here, we propose an efficient way\nto implement the NISQ computing with guaranteed security for the client. In our\narchitecture, only N+ 1 qubits are required, under an assumption that the form\nof ansatzes is known to the server, where N denotes the necessary number of the\nqubits in the original NISQ algorithms. The client only performs single-qubit\nmeasurements on an ancillary qubit sent from the server, and the measurement\nangles can specify the parameters for the ansatzes of the NISQ algorithms.\nNo-signaling principle guarantees that neither parameters chosen by the client\nnor the outputs of the algorithm are leaked to the server. This work paves the\nway for new applications of NISQ devices.\n"}
+{"abstract": "  In this paper, we discuss complete minimal immersions in\n$\\mathbb{R}^N$($N\\geq4$) with finite total curvature and embedded planar ends.\nFirst, we prove nonexistence for the following cases: (1) genus 1 with 2\nembedded planar ends, (2) genus $\\neq4$, hyperelliptic with 2 embedded planar\nends like the Lagrangian catenoid. Then we show the existence of embedded\nminimal spheres in $\\mathbb{R}^4$ with 3 embedded planar ends. Moreover, we\nconstruct genus $g$ examples in $\\mathbb{R}^4$ with $d$ embedded planar ends\nsuch that $g\\geq 1$ and $g+2\\leq d\\leq 2g+1$. These examples include a family\nof embedded minimal tori with 3 embedded planar ends.\n"}
+{"abstract": "  In this report we present a systematic study of the magnonic modes in the\ndisordered Fe$_{0.5}$Co$_{0.5}$ alloy based on the Heisenberg Hamiltonian using\ntwo complementary approaches. In order to account for substitutional disorder,\non the one hand we directly average the transverse magnetic susceptibility in\nreal space over different disorder configurations and on the other hand we use\nthe coherent potential approximation (CPA). While the method of direct\naveraging is numerically exact, it is computationally expensive and limited by\nthe maximal size of the supercell which can be simulated on a computer. On the\ncontrary the CPA does not suffer from this drawback and yields a cheap\nnumerical scheme. Therefore, we additionally compare the results of these two\napproaches and show that the CPA gives very good results for most of the\nmagnetic properties, including the magnon energies and the spatial shape of the\neigenmodes. However, it turns out that while reproducing the general trend, the\nCPA systematically underestimates the disorder induced damping of the magnons.\nThis provides evidence that the physics of impurity scattering in this system\nis governed by non-local effects missing in the CPA. Finally, we study the real\nspace eigenmodes of the system, including their spatial shapes, and analyze\ntheir temperature dependence within the random phase approximation.\n"}
+{"abstract": "  This paper deals with design of an integrated secure Blockchain network\nframework to prevent damages from attackers. The multi-layer concept which\ncould handle multiple number of networks is adapted on the top of Blockchain\nGovernance Game frameworks. This new integrated theoretical model is designed\nto find the best strategies toward preparation for preventing whole network\nsystems malfunction from attackers and it is developed based on the combination\nof the Blockchain Governance Game and the Strategic Alliance for Blockchain\nGovernance Game. Analytically tractable results for executing a safety mode are\nfully obtained and simulated results are demonstrated to obtain the optimal\nvalues of hyper parameters of a Blockchain based security network. This\nresearch helps those whom are constructing a multiple layer network by\nenhancing security features through multi-layer framework in a decentralized\nnetwork.\n"}
+{"abstract": "  Motivated by the recent experiment demonstration of stacking dependent\ninterlayer magnetic interaction [T. Song et al., Nat. Mater. 18, 1298 (2019);\nT. Li et al., Nat. Mater. 18, 1303 (2019); W. Chen et al., Science 366, 983\n(2019)], we investigate the magnetization textures and the control\npossibilities in the moir\\'{e} pattern formed of twisted bilayer\ntwo-dimensional (2D) magnets CrX$_3$ (X=Br, I). We find that the stacking\ndependent interlayer magnetic interaction results in the formation of periodic\nmagnetization domains in a long-period moir\\'{e} pattern. Magnetization\ntextures with various topological numbers can be constructed, depending on the\nwinding of the textures around the domain walls. A uniform external magnetic\nfield competes with the lateral modulated interlayer magnetic interaction and\ncan be utilized to tune the magnetization textures.\n"}
+{"abstract": "  We consider a class of multiplicative processes which, added with stochastic\nreset events, give origin to stationary distributions with power-law tails --\nubiquitous in the statistics of social, economic, and ecological systems. Our\nmain goal is to provide a series of exact results on the dynamics and\nasymptotic behaviour of increasingly complex versions of a basic multiplicative\nprocess with resets, including discrete and continuous-time variants and\nseveral degrees of randomness in the parameters that control the process. In\nparticular, we show how the power-law distributions are built up as time\nelapses, how their moments behave with time, and how their stationary profiles\nbecome quantitatively determined by those parameters. Our discussion emphasizes\nthe connection with financial systems, but these stochastic processes are also\nexpected to be fruitful in modeling a wide variety of social and biological\nphenomena.\n"}
+{"abstract": "  We prove the consistency of the theory ZFC + there is a strongly compact\ncardinal from the existence of a cardinal preserving embedding from the\nuniverse into an inner model. The proof almost shows that under SCH, every\ncardinal preserving embedding preserves the cofinality and continuum functions.\n"}
+{"abstract": "  Recently, deep learning (DL) methods such as convolutional neural networks\n(CNNs) have gained prominence in the area of image denoising. This is owing to\ntheir proven ability to surpass state-of-the-art classical image denoising\nalgorithms such as BM3D. Deep denoising CNNs (DnCNNs) use many feedforward\nconvolution layers with added regularization methods of batch normalization and\nresidual learning to improve denoising performance significantly. However, this\ncomes at the expense of a huge number of trainable parameters. In this paper,\nwe address this issue by reducing the number of parameters while achieving a\ncomparable level of performance. We derive motivation from the improved\nperformance obtained by training networks using the dense-sparse-dense (DSD)\ntraining approach. We extend this training approach to a reduced DnCNN (RDnCNN)\nnetwork resulting in a faster denoising network with significantly reduced\nparameters and comparable performance to the DnCNN.\n"}
+{"abstract": "  Second harmonic generation (SHG) is a non-linear optical process, where two\nphotons coherently combine into one photon of twice their energy. Efficient SHG\noccurs for crystals with broken inversion symmetry, such as transition metal\ndichalcogenide monolayers. Here we show tuning of non-linear optical processes\nin an inversion symmetric crystal. This tunability is based on the unique\nproperties of bilayer MoS2, that shows strong optical oscillator strength for\nthe intra- but also inter-layer exciton resonances. As we tune the SHG signal\nonto these resonances by varying the laser energy, the SHG amplitude is\nenhanced by several orders of magnitude. In the resonant case the bilayer SHG\nsignal reaches amplitudes comparable to the off-resonant signal from a\nmonolayer. In applied electric fields the interlayer exciton energies can be\ntuned due to their in-built electric dipole via the Stark effect. As a result\nthe interlayer exciton degeneracy is lifted and the bilayer SHG response is\nfurther enhanced by an additional two orders of magnitude, well reproduced by\nour model calculations.\n"}
+{"abstract": "  We present the results of ALMA band-5 (~170 GHz) observations of the merging\nultraluminous infrared galaxy, the \"Superantennae\" (IRAS 19254-7245) at\nz=0.0617, which has been diagnosed as containing a luminous obscured active\ngalactic nucleus (AGN). In addition to dense molecular line emission (HCN,\nHCO+, and HNC J = 2-1), we detect a highly luminous (~6e4Lsun) 183 GHz H2O\n3(1,3)-2(2,0) emission line. We interpret the strong H2O emission as largely\noriginating in maser amplification in AGN-illuminated dense and warm molecular\ngas, based on (1) the spatially compact (<220 pc) nature of the H2O emission,\nunlike spatially resolved (>500 pc) dense molecular emission, and (2) a\nstrikingly different velocity profile from, and (3) significantly elevated flux\nratio relative to, dense molecular emission lines. H2O maser emission, other\nthan the widely studied 22 GHz 6(1,6)-5(2,3) line, has been expected to provide\nimportant information on the physical properties of gas in the vicinity of a\ncentral mass-accreting supermassive black hole (SMBH), because of different\nexcitation energy. We here demonstrate that with highly sensitive ALMA,\nmillimetre 183 GHz H2O maser detection is feasible out to >270 Mpc, opening a\nnew window to scrutinize molecular gas properties around a mass-accreting SMBH\nfar beyond the immediately local universe.\n"}
+{"abstract": "  For a Hilbert space valued martingale $(f_n)$ and an adapted sequence of\npositive random variables $(w_n)$, we show the weighted Davis type inequality\n\\[ \\mathbb{E} \\Bigl( |f_0| w_0 + \\frac{1}{4} \\sum_{n=1}^{N}\n\\frac{|df_n|^2}{f^*_n} w_n \\Bigr) \\leq \\mathbb{E} ( f^*_N w^*_N). \\] This\ninequality is sharp and implies several results about the martingale square\nfunction. We also obtain a variant of this inequality for martingales with\nvalues in uniformly convex Banach spaces.\n"}
+{"abstract": "  Asteroseismic observations are crucial to constrain stellar models with\nprecision. Bayesian Estimation of STellar Parameters (BESTP) is a tool that\nutilizes Bayesian statistics and nested sampling Monte Carlo algorithm to\nsearch for the stellar models that best match a given set of classical and\nasteroseismic constraints from observations. The computation and evaluation of\nmodels are efficiently performed in an automated and a multi-threaded way. To\nillustrate the capabilities of BESTP, we estimate fundamental stellar\nproperties for the Sun and the red-giant star HD 222076. In both cases, we find\nmodels that are consistent with the observations. We also evaluate the\nimprovement in the precision of stellar parameters when the oscillation\nfrequencies of individual modes are included as constraints, compared to the\ncase when only the the large frequency separation is included. For the solar\ncase, the uncertainties of estimated masses, radii and ages are reduced by\n0.7%, 0.3% and 8% respectively. For HD 222076, they are reduced even more\nnoticeably by 2%, 0.5% and 4.7%. We also note an improvement of 10% for the age\nof HD 222076 when the Gaia parallax is included as a constraint compared to the\ncase when only the large separation is included as constraint.\n"}
+{"abstract": "  The direct detection of gravitational waves by ground-based interferometers\nopened an unprecedented channel to probe alternative theories of gravitation.\nSeveral theories predict a dispersion of the gravitational waves during their\npropagation, distorting the signals observed by LIGO and Virgo compared to\ntheir predictions from general relativity. Such dispersion could induce a\nmodification of the luminosity distance inferred with gravitational radiation\nwith regards to electromagnetic radiation. By analysing two multimessenger\nevents, we set constraints on a large class of proposed theories, including\nextra-dimensional and scalar-tensor theories. The multimessenger events are the\nbinary neutron star merger GW170817 associated to GRB170817A, and the binary\nblack hole merger GW190521 with postulated candidate electromagnetic\ncounterpart ZTF19abanrhr. Without relying on multimessenger emission, a class\nof proposed theories predict a frequency-dependent dispersion of the\ngravitational waves breaking Lorentz invariance. By analysing 31 GW events from\nbinary-black holes coalescence, we constrain several coefficients\nparameterising Lorentz violation, including the best constraint on the graviton\nmass.\n"}
+{"abstract": "  We give debiased machine learners of parameters of interest that depend on\ngeneralized linear regressions, which regressions make a residual orthogonal to\nregressors. The parameters of interest include many causal and policy effects.\nWe give neural net learners of the bias correction that are automatic in only\ndepending on the object of interest and the regression residual. Convergence\nrates are given for these neural nets and for more general learners of the bias\ncorrection. We also give conditions for asymptotic normality and consistent\nasymptotic variance estimation of the learner of the object of interest.\n"}
+{"abstract": "  The present theory considers the transient evolution of the temperature and\nfuel mass fraction distributions, and it can determine the critical heating\npower and minimum ignition energy for successful ignition. The flame initiation\nprocess subject to central heating can be approximately decomposed into four\nstages, i.e., the fast establishment of the ignition kernel, the\nignition-energy-supported flame kernel propagation, unsteady transition of the\nflame kernel, and quasi-steady spherical flame propagation. Comparison between\npresent transient theory and previous quasi-steady theory indicates that the\nunsteady effects directly lead to the appearance of flame kernel establishing\nstage and considerably affect the subsequent flame kernel development by\nlowering the flame propagation speed. Time scale analysis is conducted, and it\nis found that the transient formulation completely degenerates to the\nquasi-steady theory either in the neighborhood of stationary flame ball or as\napproaching the planar flame. Previous quasi-steady theory shows that the\ncritical heating power for successful ignition is proportional to the cube of\nthe critical flame radius. However, the critical heating power predicted by the\npresent transient formulation deviates this scaling law, which can be\nattributed to the unsteady thermal conduction from heating center to the flame\nfront. Furthermore, in the transient formulation the central heating with\nfinite duration is considered and the minimum ignition energy is obtained. For\nthe first time, the memory effect that persistently drives flame propagation\nsubsequent to central heating switching-off is examined. It is found that as\nthe heating power grows, the memory effect becomes increasingly important and\nit can greatly reduce the minimum ignition energy.\n"}
+{"abstract": "  This paper proposes a mesh-free computational framework and machine learning\ntheory for solving elliptic PDEs on unknown manifolds, identified with point\nclouds, based on diffusion maps (DM) and deep learning. The PDE solver is\nformulated as a supervised learning task to solve a least-squares regression\nproblem that imposes an algebraic equation approximating a PDE (and boundary\nconditions if applicable). This algebraic equation involves a graph-Laplacian\ntype matrix obtained via DM asymptotic expansion, which is a consistent\nestimator of second-order elliptic differential operators. The resulting\nnumerical method is to solve a highly non-convex empirical risk minimization\nproblem subjected to a solution from a hypothesis space of neural-network type\nfunctions. In a well-posed elliptic PDE setting, when the hypothesis space\nconsists of feedforward neural networks with either infinite width or depth, we\nshow that the global minimizer of the empirical loss function is a consistent\nsolution in the limit of large training data. When the hypothesis space is a\ntwo-layer neural network, we show that for a sufficiently large width, the\ngradient descent method can identify a global minimizer of the empirical loss\nfunction. Supporting numerical examples demonstrate the convergence of the\nsolutions and the effectiveness of the proposed solver in avoiding numerical\nissues that hampers the traditional approach when a large data set becomes\navailable, e.g., large matrix inversion.\n"}
+{"abstract": "  For a local complete intersection morphism, we establish fiberwise denseness\nin the $n$-dimensional irreducible components of the compactification Nisnevich\nlocally.\n"}
+{"abstract": "  The Soft X-ray Telescope (SXT) aboard the $AstroSat$ satellite is the first\nIndian X-ray telescope in space. It is a modest size X-ray telescope with a\ncharge coupled device (CCD) camera in the focal plane, which provides X-ray\nimages in the $\\sim 0.3-8.0$ keV band. A forte of SXT is in providing\nundistorted spectra of relatively bright X-ray sources, in which it excels some\ncurrent large CCD-based X-ray telescopes. Here, we highlight some of the\npublished spectral and timing results obtained using the SXT data to\ndemonstrate the capabilities and overall performance of this telescope.\n"}
+{"abstract": "  Large Eddy Simulation (LES) with dynamic Smagorinsky model has been applied\nto numerically investigate the complicated flow structures that evolve in the\nnear wake of a cylindrical after body aligned with a uniform Mach 2.46 flow.\nMean flow field properties obtained from numerical simulations, such as axial\nvelocity, pressure on base surface, have been compared with the experimental\nmeasurements as well as with other published results. It has been found that\nstandard k-epsilon model fails to predict the flow properties in the\nrecirculation region where better agreement has been observed between the data\nobtained from LES and experimental measurements. Flow Statistics like turbulent\nkinetic energy and primary Reynolds stress have also been calculated and\ncompared with the results obtained from experiments in order to quantitatively\nassess the ability of LES technique to predict the turbulence properties of\nflow field in the highly compressible shear layer region. The data obtained\nfrom LES has been further analyzed to understand the evolution of coherent\nstructures in the flow field. Proper Orthogonal Decomposition (POD) of the data\nobtained from central plane in the wake region has been performed in order to\nreveal the most energetic structures present in the flow field.\n"}
+{"abstract": "  We propose a general construction of commuting projector lattice models for\n2D and 3D topological phases enriched by U(1) symmetry, with finite-dimensional\nHilbert space per site. The construction starts from a commuting projector\nmodel of the topological phase and decorates U(1) charges to the state space in\na consistent manner. We show that all 2D U(1) symmetry-enriched topological\nphases which allow gapped boundary without breaking symmetry, can be realized\nthrough our construction. We also construct a large class of 3D topological\nphases with U(1) symmetry fractionalized on particles or loop excitations.\n"}
+{"abstract": "  Existing pre-trained models for knowledge-graph-to-text (KG-to-text)\ngeneration simply fine-tune text-to-text pre-trained models such as BART or T5\non KG-to-text datasets, which largely ignore the graph structure during\nencoding and lack elaborate pre-training tasks to explicitly model graph-text\nalignments. To tackle these problems, we propose a graph-text joint\nrepresentation learning model called JointGT. During encoding, we devise a\nstructure-aware semantic aggregation module which is plugged into each\nTransformer layer to preserve the graph structure. Furthermore, we propose\nthree new pre-training tasks to explicitly enhance the graph-text alignment\nincluding respective text / graph reconstruction, and graph-text alignment in\nthe embedding space via Optimal Transport. Experiments show that JointGT\nobtains new state-of-the-art performance on various KG-to-text datasets.\n"}
+{"abstract": "  The search for extraterrestrial intelligence (SETI) has been conducted for\nnearly 60 years. A Dyson Sphere, a spherical structure that surrounds a star\nand transports its radiative energy outward as an energy source for an advanced\ncivilisation, is one of the main targets of SETI. In this study, we discuss\nwhether building a Dyson Sphere around a black hole is effective. We consider\nsix energy sources: (i) the cosmic microwave background, (ii) the Hawking\nradiation, (iii) an accretion disk, (iv) Bondi accretion, (v) a corona, and\n(vi) relativistic jets. To develop future civilisations (for example, a Type II\ncivilisation), $4\\times10^{26}\\,{\\rm W}$($1\\,{\\rm L_{\\odot}}$) is expected to\nbe needed. Among (iii) to (vi), the largest luminosity can be collected from an\naccretion disk, reaching $10^{5}\\,{\\rm L_{\\odot}}$, enough to maintain a Type\nII civilisation. Moreover, if a Dyson Sphere collects not only the\nelectromagnetic radiation but also other types of energy (e.g., kinetic energy)\nfrom the jets, the total collected energy would be approximately 5 times\nlarger. Considering the emission from a Dyson Sphere, our results show that the\nDyson Sphere around a stellar-mass black hole in the Milky Way ($10\\,\\rm kpc$\naway from us) is detectable in the ultraviolet$(\\rm 10-400\\,{\\rm nm)}$,\noptical$(\\rm 400-760\\,{\\rm nm)}$, near-infrared($\\rm 760\\,{\\rm nm}-5\\,{\\rm \\mu\nm}$), and mid-infrared($\\rm 5-40\\,{\\rm \\mu m}$) wavelengths via the waste heat\nradiation using current telescopes such as Galaxy Evolution Explorer\nUltraviolet Sky Surveys. Performing model fitting to observed spectral energy\ndistributions and measuring the variability of radial velocity may help us to\nidentify these possible artificial structures.\n"}
+{"abstract": "  We propose a general purpose approach to detect landmarks with improved\ntemporal consistency, and personalization. Most sparse landmark detection\nmethods rely on laborious, manually labelled landmarks, where inconsistency in\nannotations over a temporal volume leads to sub-optimal landmark learning.\nFurther, high-quality landmarks with personalization is often hard to achieve.\nWe pose landmark detection as an image translation problem. We capture two sets\nof unpaired marked (with paint) and unmarked videos. We then use a generative\nadversarial network and cyclic consistency to predict deformations of landmark\ntemplates that simulate markers on unmarked images until these images are\nindistinguishable from ground-truth marked images. Our novel method does not\nrely on manually labelled priors, is temporally consistent, and image class\nagnostic -- face, and hand landmarks detection examples are shown.\n"}
+{"abstract": "  We show that two-mode squeezed vacuum-like states may be engineered in the\nBohm-Madelung formalism by adequately choosing the phase of the wavefunction.\nThe difference between our wavefunction and the one of the squeezed vacuum\nstates is given precisely by the phase we choose.\n"}
+{"abstract": "  We generalize the concept of local states (LS) for the prediction of\nhigh-dimensional, potentially mixed chaotic systems. The construction of\ngeneralized local states (GLS) relies on defining distances between time series\non the basis of their (non-)linear correlations. We demonstrate the prediction\ncapabilities of our approach based on the reservoir computing (RC) paradigm\nusing the Kuramoto-Sivashinsky (KS), the Lorenz-96 (L96) and a combination of\nboth systems. In the mixed system a separation of the time series belonging to\nthe two different systems is made possible with GLS. More importantly,\nprediction remains possible with GLS, where the LS approach must naturally\nfail. Applications for the prediction of very heterogeneous time series with\nGLSs are briefly outlined.\n"}
+{"abstract": "  We study neutrino and antineutrino emission from the direct Urca process in\nneutron-star matter in the presence of strong magnetic fields. We calculate the\nneutrino emissivity of the direct Urca process, whereby a neutron converts to a\nproton, an electron and an antineutrino, or a proton-electron pair converts to\na neutron-neutrino pair. We solve exact wave functions for protons and\nelectrons in the states described with Landau levels. We find that the direct\nUrca process can satisfy the kinematic constraints even in density regions\nwhere this process could not normally occur in the absence of a magnetic field.\n"}
+{"abstract": "  It is well known that adversarial attacks can fool deep neural networks with\nimperceptible perturbations. Although adversarial training significantly\nimproves model robustness, failure cases of defense still broadly exist. In\nthis work, we find that the adversarial attacks can also be vulnerable to small\nperturbations. Namely, on adversarially-trained models, perturbing adversarial\nexamples with a small random noise may invalidate their misled predictions.\nAfter carefully examining state-of-the-art attacks of various kinds, we find\nthat all these attacks have this deficiency to different extents. Enlightened\nby this finding, we propose to counter attacks by crafting more effective\ndefensive perturbations. Our defensive perturbations leverage the advantage\nthat adversarial training endows the ground-truth class with smaller local\nLipschitzness. By simultaneously attacking all the classes, the misled\npredictions with larger Lipschitzness can be flipped into correct ones. We\nverify our defensive perturbation with both empirical experiments and\ntheoretical analyses on a linear model. On CIFAR10, it boosts the\nstate-of-the-art model from 66.16% to 72.66% against the four attacks of\nAutoAttack, including 71.76% to 83.30% against the Square attack. On ImageNet,\nthe top-1 robust accuracy of FastAT is improved from 33.18% to 38.54% under the\n100-step PGD attack.\n"}
+{"abstract": "  The Gromov-Wasserstein (GW) framework adapts ideas from optimal transport to\nallow for the comparison of probability distributions defined on different\nmetric spaces. Scalable computation of GW distances and associated matchings on\ngraphs and point clouds have recently been made possible by state-of-the-art\nalgorithms such as S-GWL and MREC. Each of these algorithmic breakthroughs\nrelies on decomposing the underlying spaces into parts and performing matchings\non these parts, adding recursion as needed. While very successful in practice,\ntheoretical guarantees on such methods are limited. Inspired by recent advances\nin the theory of quantization for metric measure spaces, we define Quantized\nGromov Wasserstein (qGW): a metric that treats parts as fundamental objects and\nfits into a hierarchy of theoretical upper bounds for the GW problem. This\nformulation motivates a new algorithm for approximating optimal GW matchings\nwhich yields algorithmic speedups and reductions in memory complexity.\nConsequently, we are able to go beyond outperforming state-of-the-art and apply\nGW matching at scales that are an order of magnitude larger than in the\nexisting literature, including datasets containing over 1M points.\n"}
+{"abstract": "  Newspapers are trustworthy media where people get the most reliable and\ncredible information compared with other sources. On the other hand, social\nmedia often spread rumors and misleading news to get more traffic and\nattention. Careful characterization, evaluation, and interpretation of\nnewspaper data can provide insight into intrigue and passionate social issues\nto monitor any big social incidence. This study analyzed a large set of\nspatio-temporal Bangladeshi newspaper data related to the COVID-19 pandemic.\nThe methodology included volume analysis, topic analysis, automated\nclassification, and sentiment analysis of news articles to get insight into the\nCOVID-19 pandemic in different sectors and regions in Bangladesh over a period\nof time. This analysis will help the government and other organizations to\nfigure out the challenges that have arisen in society due to this pandemic,\nwhat steps should be taken immediately and in the post-pandemic period, how the\ngovernment and its allies can come together to address the crisis in the\nfuture, keeping these problems in mind.\n"}
+{"abstract": "  In 2017, Lienert and Tumulka proved Born's rule on arbitrary Cauchy surfaces\nin Minkowski space-time assuming Born's rule and a corresponding collapse rule\non horizontal surfaces relative to a fixed Lorentz frame, as well as a given\nunitary time evolution between any two Cauchy surfaces, satisfying that there\nis no interaction faster than light and no propagation faster than light. Here,\nwe prove Born's rule on arbitrary Cauchy surfaces from a different, but equally\nreasonable, set of assumptions. The conclusion is that if detectors are placed\nalong any Cauchy surface $\\Sigma$, then the observed particle configuration on\n$\\Sigma$ is a random variable with distribution density $|\\Psi_\\Sigma|^2$,\nsuitably understood. The main different assumption is that the Born and\ncollapse rules hold on any spacelike hyperplane, i.e., at any time coordinate\nin any Lorentz frame. Heuristically, this follows if the dynamics of the\ndetectors is Lorentz invariant.\n"}
+{"abstract": "  Motivated by the development of high-dispersion spectrographs in the\nmid-infrared (MIR) range, we study their application to the atmospheric\ncharacterization of nearby non-transiting temperate terrestrial planets around\nM-type stars. We examine the detectability of CO$_2$, H$_2$O, N$_2$O, and O$_3$\nin high-resolution planetary thermal emission spectra at 12-18 $\\mu $m assuming\nan Earth-like profile and a simplified thermal structure. The molecular line\nwidth of such planets can be comparable to or broader than the Doppler shift\ndue to the planetary orbital motion. Given the likely difficulty in knowing the\nhigh-resolution MIR spectrum of the host star with sufficient accuracy, we\npropose to observe the target system at two quadrature phases and extract the\ndifferential spectra as the planetary signal. In this case, the signals can be\nsubstantially suppressed compared with the case where the host star spectrum is\nperfectly known, as some parts of the spectral features do not remain in the\ndifferential spectra. Despite this self-subtraction, the CO$_2$ and H$_2$O\nfeatures of nearby ($\\lesssim $ 5~pc) systems with mid-/late-M host stars would\nbe practical with a 6.5-meter-class cryogenic telescope, and orbital\ninclination could also be constrained for some of them. For CO$_2$ and N$_2$O\nin a 1~bar Earth-like atmosphere, this method would be sensitive when the\nmixing ratio is 1-10$^3$ ppm. The detectability of molecules except O$_3$ is\nnot significantly improved when the spectral resolution is higher than\n$\\mathcal{R}\\gtrsim 10,000$, although the constraint on the orbital inclination\nis improved. This study provides some benchmark cases useful for assessing the\nvalue of MIR high-resolution spectroscopy in terms of characterization of\npotentially habitable planets.\n"}
+{"abstract": "  Let $A$ be a bounded linear operator on a complex Hilbert space and $\\Re(A)$\n( $\\Im(A)$ ) denote the real part (imaginary part) of A. Among other\nrefinements of the lower bounds for the numerical radius of $A$, we prove that\n\\begin{eqnarray*} w(A)&\\geq &\\frac{1}{2} \\left \\|A \\right\\| + \\frac{ 1}{2} \\mid\n\\|\\Re(A)\\|-\\|\\Im(A)\\|\\mid,\\,\\,\\mbox{and}\\\\ w^2(A)&\\geq& \\frac{1}{4} \\left\n\\|A^*A+AA^* \\right\\| + \\frac{1}{2}\\mid \\|\\Re(A)\\|^2-\\|\\Im(A)\\|^2 \\mid,\n\\end{eqnarray*} where $w(A)$ is the numerical radius of the operator $A$. We\nstudy the equality conditions for $w(A)=\\frac{1}{2}\\sqrt{\\|A^*A+AA^*\\|}$ and\nprove that $w(A)=\\frac{1}{2}\\sqrt{\\|A^*A+AA^*\\|} $ if and only if the numerical\nrange of $A$ is a circular disk with center at the origin and radius\n$\\frac{1}{2}\\sqrt{\\|A^*A+AA^*\\|} $.\n  We also obtain upper bounds for the numerical radius of commutators of\noperators which improve on the existing ones.\n"}
+{"abstract": "  As an attempt to bridge between numerical analysis and algebraic geometry,\nthis paper formulates the multiplicity for the general nonlinear system at an\nisolated zero, presents an algorithm for computing the multiplicity structure,\nproposes a depth-deflation method for accurate computation of multiple zeros,\nand introduces the basic algebraic theory of the multiplicity. Furthermore,\nthis paper elaborates and proves some fundamental properties of the\nmultiplicity, including local finiteness, consistency, perturbation invariance,\nand depth-deflatability. As a justification of this formulation, the\nmultiplicity is proved to be consistent with the multiplicity defined in\nalgebraic geometry for the special case of polynomial systems. The proposed\nalgorithms can accurately compute the multiplicity and the multiple zeros using\nfloating point arithmetic even if the nonlinear system is perturbed.\n"}
+{"abstract": "  The ribbon cocycle invariant is defined by means of a partition function\nusing ternary cohomology of self-distributive structures (TSD) and colorings of\nribbon diagrams of a framed link, following the same paradigm introduced by\nCarter, Jelsovsky, Kamada, Langfor and Saito in Transactions of the American\nMathematical Society 2003;355(10):3947-89, for the quandle cocycle invariant.\nIn this article we show that the ribbon cocycle invariant is a quantum\ninvariant. We do so by constructing a ribbon category from a TSD set whose\ntwisting and braiding morphisms entail a given TSD $2$-cocycle. Then we show\nthat the quantum invariant naturally associated to this braided category\ncoincides with the cocycle invariant. We generalize this construction to\nsymmetric monoidal categories and provide classes of examples obtained from\nHopf monoids and Lie algebras. We further introduce examples from\nHopf-Frobenius algebras, objects studied in quantum computing.\n"}
+{"abstract": "  We present two methods to algorithmically compute both least and greatest\nsolutions of polynomial equation systems over absorptive semirings (with\ncertain completeness and continuity assumptions), such as the tropical\nsemiring. Both methods require a polynomial number of semiring operations,\nincluding semiring addition, multiplication and an infinitary power operation.\nOur main result is a closed-form solution for least and greatest fixed points\nbased on the fixed-point iteration. The proof builds on the notion of (possibly\ninfinite) derivation trees; a careful analysis of the shape of these trees\nallows us to collapse the fixed-point iteration to a linear number of steps.\nThe second method is an iterative symbolic computation in the semiring of\ngeneralized absorptive polynomials, largely based on results on Kleene\nalgebras.\n"}
+{"abstract": "  The future interior of black holes in AdS/CFT can be described in terms of a\nquantum circuit. We investigate boundary quantities detecting properties of\nthis quantum circuit. We discuss relations between operator size, quantum\ncomplexity, and the momentum of an infalling particle in the black hole\ninterior. We argue that the trajectory of the infalling particle in the\ninterior close to the horizon is related to the growth of operator size. The\nnotion of size here differs slightly from the size which has previously been\nrelated to momentum of exterior particles and provides an interesting\ngeneralization. The fact that both exterior and interior momentum are related\nto operator size growth is a manifestation of complementarity.\n"}
+{"abstract": "  The increased mobile connectivity, the range and number of services available\nin various computing environments in the network, demand mobile applications to\nbe highly dynamic to be able to efficiently incorporate those services into\napplications, along with other local capabilities on mobile devices. However,\nthe monolithic structure and mostly static configuration of mobile application\ncomponents today limit application's ability to dynamically manage internal\ncomponents, to be able to adapt to the user and the environment, and utilize\nvarious services in the network for improving the application experience.\n  In this paper, we present REACT, a new Android-based framework that enables\napps to be developed as a collection of loosely coupled microservices (MS). It\nallows individual distribution, dynamic management and offloading of MS to be\nexecuted by services in the network, based on contextual changes. REACT aims to\nprovide i) a framework as an Android Library for creating MS-based apps that\nadapt to contextual changes ii) a unified HTTP-based communication mechanism,\nusing Android Inter-Process Communication (IPC) for transporting requests\nbetween locally running MS, while allowing flexible and transparent switching\nbetween network and IPC requests, when offloading. We evaluate REACT by\nimplementing a video streaming app that dynamically offloads MS to web services\nin the network, adapting to contextual changes. The evaluation shows the\nadaptability to contextual changes and reductions in power consumption when\noffloading, while our communication mechanism overcomes performance limitations\nof Android IPC by enabling efficient transferring of large payloads between\nmobile MS.\n"}
+{"abstract": "  The natural association between visual observations and their corresponding\nsound provides powerful self-supervisory signals for learning video\nrepresentations, which makes the ever-growing amount of online videos an\nattractive source of training data. However, large portions of online videos\ncontain irrelevant audio-visual signals because of edited/overdubbed audio, and\nmodels trained on such uncurated videos have shown to learn suboptimal\nrepresentations. Therefore, existing approaches rely almost exclusively on\ndatasets with predetermined taxonomies of semantic concepts, where there is a\nhigh chance of audio-visual correspondence. Unfortunately, constructing such\ndatasets require labor intensive manual annotation and/or verification, which\nseverely limits the utility of online videos for large-scale learning. In this\nwork, we present an automatic dataset curation approach based on subset\noptimization where the objective is to maximize the mutual information between\naudio and visual channels in videos. We demonstrate that our approach finds\nvideos with high audio-visual correspondence and show that self-supervised\nmodels trained on our data achieve competitive performances compared to models\ntrained on existing manually curated datasets. The most significant benefit of\nour approach is scalability: We release ACAV100M that contains 100 million\nvideos with high audio-visual correspondence, ideal for self-supervised video\nrepresentation learning.\n"}
+{"abstract": "  In this work the experimental realization of a tunable high photon flux\nextreme ultraviolet light source is presented. This is enabled by high harmonic\ngeneration of two temporally delayed driving pulses, resulting in a tuning\nrange of 0.8 eV at the 19th harmonic at 22.8 eV. The implemented approach\nallows for fast tuning of the spectrum, is highly flexible and is scalable\ntowards full spectral coverage at higher photon energies.\n"}
+{"abstract": "  One of the most significant current discussions in the field of data mining\nis classifying imbalanced data. In recent years, several ways are proposed such\nas algorithm level (internal) approaches, data level (external) techniques, and\ncost-sensitive methods. Although extensive research has been carried out on\nimbalanced data classification, however, several unsolved challenges remain\nsuch as no attention to the importance of samples to balance, determine the\nappropriate number of classifiers, and no optimization of classifiers in the\ncombination of classifiers. The purpose of this paper is to improve the\nefficiency of the ensemble method in the sampling of training data sets,\nespecially in the minority class, and to determine better basic classifiers for\ncombining classifiers than existing methods. We proposed a hybrid ensemble\nalgorithm based on Genetic Programming (GP) for two classes of imbalanced data\nclassification. In this study uses historical data from UCI Machine Learning\nRepository to assess minority classes in imbalanced datasets. The performance\nof our proposed algorithm is evaluated by Rapid-miner studio v.7.5.\nExperimental results show the performance of the proposed method on the\nspecified data sets in the size of the training set shows 40% and 50% better\naccuracy than other dimensions of the minority class prediction.\n"}
+{"abstract": "  For positive integers $s,t,u,v$, we define a bipartite graph\n$\\Gamma_{\\mathbb{R}}(X^s Y^t,X^u Y^v)$ where each partite set is a copy of\n$\\mathbb{R}^3$, and a vertex $(a_1,a_2,a_3)$ in the first partite set is\nadjacent to a vertex $[x_1,x_2,x_3]$ in the second partite set if and only if\n  \\[\n  a_2 + x_2 = a_1^s x_1^t\n  \\quad\n  \\text{and}\n  \\quad\n  a_3+x_3=a_1^ux_1^v.\n  \\] In this paper, we classify all such graphs according to girth.\n"}
+{"abstract": "  One of the main requirements of tumor extraction is the annotation and\nsegmentation of tumor boundaries correctly. For this purpose, we present a\nthreefold deep learning architecture. First classifiers are implemented with a\ndeep convolutional neural network(CNN) andsecond a region-based convolutional\nneural network (R-CNN) is performed on the classified images to localize the\ntumor regions of interest. As the third and final stage, the concentratedtumor\nboundary is contoured for the segmentation process by using the\nChan-Vesesegmentation algorithm. As the typical edge detection algorithms based\non gradients of pixel intensity tend to fail in the medical image segmentation\nprocess, an active contour algorithm defined with the level set function is\nproposed. Specifically, Chan- Vese algorithm was applied to detect the tumor\nboundaries for the segmentation process. To evaluate the performance of the\noverall system, Dice Score,Rand Index (RI), Variation of Information (VOI),\nGlobal Consistency Error (GCE), Boundary Displacement Error (BDE), Mean\nabsolute error (MAE), and Peak Signal to Noise Ratio (PSNR) werecalculated by\ncomparing the segmented boundary area which is the final output of the\nproposed, against the demarcations of the subject specialists which is the gold\nstandard. Overall performance of the proposed architecture for both glioma and\nmeningioma segmentation is with average dice score of 0.92, (also, with RI of\n0.9936, VOI of 0.0301, GCE of 0.004, BDE of 2.099, PSNR of 77.076 and MAE of\n52.946), pointing to high reliability of the proposed architecture.\n"}
+{"abstract": "  We show that closed subsets with vanishing first homology in two-dimensional\nspaces inherit the upper curvature bound from their ambient spaces and discuss\ntopological applications.\n"}
+{"abstract": "  This paper exhibits two families of unravelled abstract regular polytopes in\nCoxeter groups of type Bn. For one family they have rank 4 while the other\nfamily has arbitrarily large rank.\n"}
+{"abstract": "  Manipulating the spin degrees of freedom of electrons affords an excellent\nplatform for exploring novel quantum states in condensed-matter physics and\nmaterial science. Based on first-principles calculations and analysis of\ncrystal symmetries, we propose a fully spin-polarized composite semimetal\nstate, which is combined with the one-dimensional nodal lines and\ntwo-dimensional nodal surfaces, in the half-metal material CaFeO$_3$. In the\nnodal line-surface states, the Baguenaudier-like nodal lines feature six rings\nlinked together, which are protected by the three independent symmetry\noperations:$\\mathcal{PT}$, $\\mathcal{M}_{y}$, and\n$\\mathcal{\\widetilde{M}}_{z}$. Near the Fermi level, the spin-polarized nodal\nsurface states are guaranteed by the joint operation\n$\\mathcal{T}\\mathcal{S}_{2i}$ in the $k_{i(i=x,y,z)}=\\pi$ plane. Furthermore,\nhigh-quality CaFeO$_3$ harbors ultra-clean energy dispersion, which is rather\nrobust against strong triaxial compressional strain and correlation effect. The\nrealization of the Weyl nodal line-surface half-metal presents great potential\nfor spintronics applications with high speed and low power consumption.\n"}
+{"abstract": "  Experimental demonstrations of quantum annealing with native implementation\nof Boolean logic Hamiltonians are reported. As a superconducting integrated\ncircuit, a problem Hamiltonian whose set of ground states is consistent with a\ngiven truth table is implemented for quantum annealing with no redundant\nqubits. As examples of the truth table, NAND and NOR are successfully\nfabricated as an identical circuit. Similarly, a native implementation of a\nmultiplier comprising six superconducting flux qubits is also demonstrated.\nThese native implementations of Hamiltonians consistent with Boolean logic\nprovide an efficient and scalable way of applying annealing computation to\nso-called circuit satisfiability problems that aim to find a set of inputs\nconsistent with a given output over any Boolean logic functions, especially\nthose like factorization through a multiplier Hamiltonian. A proof-of-concept\ndemonstration of a hybrid computing architecture for domain-specific quantum\ncomputing is described.\n"}
+{"abstract": "  Spin-polarized samples and spin mixtures of quantum degenerate fermionic\natoms are prepared in selected excited Bloch bands of an optical chequerboard\nsquare lattice. For the spin-polarized case, extreme band lifetimes above\n$10\\,$s are observed, reflecting the suppression of collisions by Pauli's\nexclusion principle. For spin mixtures, lifetimes are reduced by an order of\nmagnitude by two-body collisions between different spin components, but still\nremarkably large values of about one second are found. By analyzing momentum\nspectra, we can directly observe the orbital character of the optical lattice.\nThe observations demonstrated here form the basis for exploring the physics of\nFermi gases with two paired spin components in orbital optical lattices,\nincluding the regime of unitarity.\n"}
+{"abstract": "  Direction-of-arrival (DOA) estimation is one of the most demanding tasks for\nthe millimeter wave (mmWave) communication of massive multiple-input\nmultiple-output (MIMO) systems with the hybrid beamforming (HBF) architecture.\nIn this paper, we focus on the optimization of the HBF matrix for receiving\npilots to enhance the DOA estimation performance. Motivated by the fact that\nmany existing DOA estimation algorithms can achieve the Cram\\'{e}r-Rao bound\n(CRB), we formulate the HBF optimization problem aiming at minimizing the CRB\nwith the prior knowledge of the rough DOA range. Then, to tackle the problem\nwith intractable non-convex constraint introduced by the analog beamformers, we\npropose an efficient manifold optimization (MO) based algorithm. Simulation\nresults demonstrate the significant improvement of the proposed CRB-MO\nalgorithm over the conventional random HBF algorithm, and provide insights for\nthe HBF design in the beam training stage for practical applications.\n"}
+{"abstract": "  The control of structured waves has recently opened innovative scenarios in\nthe perspective of radiation propagation and light-matter interaction. In\nparticular, the transmission of customized electromagnetic fields is\ninvestigated for telecommunications, with the aim of exploring new modulation\nformats besides the traditional, almost saturated, division multiplexing\ntechniques. Beams carrying twisted wavefronts have long been recognized as the\npromising candidates, however their phase singularities and efficient\nmultiplexing still raise open issues. In a more general insight into\nstructured-phase beams, we introduce and develop here a new and unique paradigm\nbased on the transmission of beams with harmonic phases having a multipole\nstructure. The outlined framework encompasses multiplexing, transmission, and\ndemultiplexing as a whole for the first time, describing wavefields evolution\nin terms of conformal mappings, and solving straightforwardly the critical\nissues of previous solutions. Because of its potentialities, versatility, and\nease of implementation, we expect this completely new paradigm to find\nwidespread applications for space division multiplexing especially in free\nspace, from the optical to the microwave and radio regimes.\n"}
+{"abstract": "  We study the Yamabe flow starting from an asymptotically flat manifold\n$(M^n,g_0)$. We show that the flow converges to an asymptotically flat, scalar\nflat metric in a weighted global sense if $Y(M,[g_0])>0$, and show that the\nflow does not converge otherwise. If the scalar curvature is nonnegative and\nintegrable, then the ADM mass at time infinity drops by the limit of the total\nscalar curvature along the flow.\n"}
+{"abstract": "  In this paper, an approach for hate speech detection against women in Arabic\ncommunity on social media (e.g. Youtube) is proposed. In the literature,\nsimilar works have been presented for other languages such as English. However,\nto the best of our knowledge, not much work has been conducted in the Arabic\nlanguage. A new hate speech corpus (Arabic\\_fr\\_en) is developed using three\ndifferent annotators. For corpus validation, three different machine learning\nalgorithms are used, including deep Convolutional Neural Network (CNN), long\nshort-term memory (LSTM) network and Bi-directional LSTM (Bi-LSTM) network.\nSimulation results demonstrate the best performance of the CNN model, which\nachieved F1-score up to 86\\% for the unbalanced corpus as compared to LSTM and\nBi-LSTM.\n"}
+{"abstract": "  This paper reviews recent literature results on the mechanics of structures\nformed by layers of pentamode lattices alternating with stiffening plates,\nwhich can be effectively employed for the development of seismic isolation\ndevices and vibration attenuation tools, with nearly complete band gaps for\nshear waves. It is shown that such structures, named pentamode bearings, can\nrespond either in the stretching-dominated regime, or in the bending-dominated\nregime, depending on the nature of the joints connecting the different members.\nTheir response is characterized by high vertical stiffness and theoretically\nzero shear stiffness in the stretching dominated regime, or considerably low\nvalues of such a quantity in the bending dominated regime. Available results on\nthe experimental response of 3d printed models to combined compression and\nshear loading highlight that the examined structures are able to exhibit energy\ndissipation capacity and effective damping that are suitable for seismic\nisolation devices. Their fabrication does not necessarily require heavy\nindustry, and expensive materials, being possible with ordinary 3-D printers.\n"}
+{"abstract": "  The same-sign tetralepton signature via mixing of neutral Higgs bosons and\ntheir cascade decays to charged Higgs bosons is a unique signal in the type-II\nseesaw model. In this paper, we study this signature at future lepton\ncolliders, such as ILC, CLIC, and MuC. Constrained by direct search,\n$H^{\\pm\\pm}\\to W^\\pm W^\\pm$ is the only viable decay mode for $M_{A^0}=400$ GeV\nat $\\sqrt{s}=1$ TeV ILC. With an integrated luminosity of $\\mathcal{L}=8~\n\\mathrm{ab}^{-1}$, the promising region with about 150 signal events\ncorresponds to a narrow band in the range of $10^{-4}~\\text{GeV}\\lesssim\nv_\\Delta \\lesssim10^{-2}$ GeV. For heavier triplet scalars $M_{A^0}\\gtrsim 900$\nGeV, although the $H^{\\pm\\pm}\\to \\ell^\\pm \\ell^\\pm$ decay mode is allowed, the\ncascade decays are suppressed. A maximum event number $\\sim 16$ can be obtained\naround $v_\\Delta\\sim4\\times10^{-4}$ GeV and $\\lambda_4\\sim0.26$ for\n$M_{A^0}=1000$ GeV with $\\mathcal{L}=5~ \\mathrm{ab}^{-1}$ at $\\sqrt{s}=3$ TeV\nCLIC. Meanwhile, we find that this signature is not promising for\n$M_{A^0}=1500$ GeV at $\\sqrt{s}=6$ TeV MuC.\n"}
+{"abstract": "  Microstructure reconstruction is a key enabler of process-structure-property\nlinkages, a central topic in materials engineering. Revisiting classical\noptimization-based reconstruction techniques,they are recognized as a powerful\nframework to reconstruct random heterogeneous media, especially due to their\ngenerality and controllability. The stochasticity of the available approaches\nis, however, identified as a performance bottleneck. In this work,\nreconstruction is approached as a differentiable optimization problem, where\nthe error of a generic prescribed descriptor is minimized under consideration\nof its derivative. As an exemplary descriptor, a suitable differentiable\nversion of spatial correlations is formulated, along with a multigrid scheme to\nensure scalability. The applicability of differentiable optimization realized\nthrough this descriptor is demonstrated using a wide variety of heterogeneous\nmedia, achieving exact statistical equivalence with errors as low as 0 in a\nshort time. We conclude that, while still in an early stage of development,\nthis approach has the potential to significantly alleviate the computational\neffort currently associated with reconstructing general random heterogeneous\nmedia.\n"}
+{"abstract": "  Translating text into a language unknown to the text's author, dubbed\noutbound translation, is a modern need for which the user experience has\nsignificant room for improvement, beyond the basic machine translation\nfacility. We demonstrate this by showing three ways in which user confidence in\nthe outbound translation, as well as its overall final quality, can be\naffected: backward translation, quality estimation (with alignment) and source\nparaphrasing. In this paper, we describe an experiment on outbound translation\nfrom English to Czech and Estonian. We examine the effects of each proposed\nfeedback module and further focus on how the quality of machine translation\nsystems influence these findings and the user perception of success. We show\nthat backward translation feedback has a mixed effect on the whole process: it\nincreases user confidence in the produced translation, but not the objective\nquality.\n"}
+{"abstract": "  The analysis of contingency tables is a powerful statistical tool used in\nexperiments with categorical variables. This study improves parts of the theory\nunderlying the use of contingency tables. Specifically, the linkage\ndisequilibrium parameter as a measure of two-way interactions applied to\nthree-way tables makes it possible to quantify Simpsons paradox by a simple\nformula. With tests on three-way interactions, there is only one that\ndetermines whether the partial interactions of all variables agree or whether\nthere is at least one variable whose partial interactions disagree. To date,\nthere has been no test available that determines whether the partial\ninteractions of a certain variable agree or disagree, and the presented work\ncloses this gap. This work reveals the relation of the multiplicative and the\nadditive measure of a three-way interaction. Another contribution addresses the\nquestion of which cells in a contingency table are fixed when the first- and\nsecond-order marginal totals are given. The proposed procedure not only detects\nfixed zero counts but also fixed positive counts. This impacts the\ndetermination of the degrees of freedom. Furthermore, limitations of methods\nthat simulate contingency tables with given pairwise associations are\naddressed.\n"}
+{"abstract": "  The alternating directions method of multipliers (ADMM) is a powerful\nsplitting algorithm for linearly constrained convex optimization problems. In\nview of its popularity and applicability, a growing attention is drawn towards\nthe ADMM in nonconvex settings. Recent studies of minimization problems for\nnoncovex functions include various combinations of assumptions on the objective\nfunction including, in particular, a Lipschitz gradient assumption. We consider\nthe case where the objective is the sum of a strongly convex function and a\nweakly convex function. To this end we present and study an adaptive version of\nthe ADMM which incorporates generalized notions of convexity and varying\npenalty parameters adapted to the convexity constants of the functions. We\nprove convergence of the scheme under natural assumptions. To this end we\nemploy the recent adaptive Douglas--Rachford algorithm by revisiting the well\nknown duality relation between the classical ADMM and the Douglas--Rachford\nsplitting algorithm, generalizing this connection to our setting. We illustrate\nour approach by relating and comparing to alternatives, and by numerical\nexperiments on a signal denoising problem.\n"}
+{"abstract": "  A novel matrix pencil-based interference mitigation approach for FMCW radars\nis proposed in this paper. The interference-contaminated segment of the beat\nsignal is firstly cut out and then the signal samples in the cut-out region are\nreconstructed by modeling the beat signal as a sum of complex exponentials and\nusing the matrix pencil method to estimate their parameters. The efficiency of\nthe proposed approach for the interference with different parameters (i.e.\ninterference duration, signal-to-noise ratio (SNR), and different target\nscenarios) is investigated by means of numerical simulations. The proposed\ninterference mitigation approach is intensively verified on experimental data.\nComparisons of the proposed approach with the zeroing and other beat-frequency\ninterpolation techniques are presented. The results indicate the broad\napplicability and superiority of the proposed approach, especially in low SNR\nand long interference duration situations.\n"}
+{"abstract": "  Human motion prediction is an essential component for enabling closer\nhuman-robot collaboration. The task of accurately predicting human motion is\nnon-trivial. It is compounded by the variability of human motion, both at a\nskeletal level due to the varying size of humans and at a motion level due to\nindividual movement's idiosyncrasies. These variables make it challenging for\nlearning algorithms to obtain a general representation that is robust to the\ndiverse spatio-temporal patterns of human motion. In this work, we propose a\nmodular sequence learning approach that allows end-to-end training while also\nhaving the flexibility of being fine-tuned. Our approach relies on the\ndiversity of training samples to first learn a robust representation, which can\nthen be fine-tuned in a continual learning setup to predict the motion of new\nsubjects. We evaluated the proposed approach by comparing its performance\nagainst state-of-the-art baselines. The results suggest that our approach\noutperforms other methods over all the evaluated temporal horizons, using a\nsmall amount of data for fine-tuning. The improved performance of our approach\nopens up the possibility of using continual learning for personalized and\nreliable motion prediction.\n"}
+{"abstract": "  Day after day, the number of mobile applications deployed on cloud computing\ncontinues in increasing because o f smartphone capabilities improvement. Cloud\ncomputing has already succeeded in the web based application, for that reason,\nthe demand for context aware services provided by cloud computing increases. To\ncustomize a cloud service that takes into account th e consumer requirements,\nwhich depend on information change, it brings to light many recent challenges\nto cloud computing about environment aware, location aware, time aware. The\ncloud provider, moreover, has to manage personalized applications and the con\nstraints of mobile devices in matters of interaction abilities and\ncommunication restrictions. This paper proposes a strategy for selecting\nautomatically an appropriate cloud environment that runs out whole\nrequirements, defines a configuration for the ass ociated cloud environment and\nable to easily adapt to the change of the environment on either the user or the\ncloud side or both. This process builds on the principles of dynamic software\nproduct lines, Agent oriented software engineering, and the MAPE k m odel to\nselect and configure cloud environments according to the consumer needs and the\ncontext change.\n"}
+{"abstract": "  Two papers recently reported the detection of gaseous nickel and iron in the\ncomae of over 20 comets from observations collected over two decades, including\ninterstellar comet 2I/Borisov. To evaluate the state of the laboratory data in\nsupport of these identifications, we re-analyzed archived spectra of comet\nC/1996 B2 (Hyakutake), one of the nearest and brightest comets of the last\ncentury, using a combined experimental and computational approach. We developed\na new, many-level fluorescence model that indicates that the fluorescence\nemission of Fe I and Ni I vary greatly with heliocentric velocity. Combining\nthis model with laboratory spectra of an Fe-Ni plasma, we identified 22 lines\nof Fe I and 14 lines of Ni I in the spectrum of Hyakutake. Using Haser models,\nwe estimate the nickel and iron production rates as Q(Ni) = 2.6 - 4.1 x 10^22\ns^-1 and Q(Fe) = 0.4 - 2.8 x 10^23 s^-1. From derived column densities, the\nNi/Fe abundance ratio log10[Ni/Fe] = -0.15 +/- 0.07 deviates significantly from\nsolar abundance ratios, and it is consistent with the ratios observed in solar\nsystem comets. Possible production and emission mechanisms are analyzed in\ncontext of existing laboratory measurements. Based on the observed spatial\ndistributions, excellent fluorescence model agreement, and Ni/Fe ratio, our\nfindings support an origin consisting of a short-lived unknown parent followed\nby fluorescence emission. Our models suggest that the strong heliocentric\nvelocity dependence of the fluorescence efficiencies can provide a meaningful\ntest of the physical process responsible for the Fe I and Ni I emission.\n"}
+{"abstract": "  Applications towards 6G have brought a huge interest towards arrays with a\nhigh number of antennas and operating within the millimeter and sub-THz\nbandwidths for joint communication and localization. With such large arrays,\nthe plane wave approximation is often not accurate because the system may\noperate in the near-field propagation region (Fresnel region) where the\nelectromagnetic field wavefront is spherical. In this case, the curvature of\narrival (CoA) is a measure of the spherical wavefront that can be used to infer\nthe source position using only a single large array. In this paper, we study a\nnear-field tracking problem for inferring the state (i.e., the position and\nvelocity) of a moving source with an ad-hoc observation model that accounts for\nthe phase profile of a large receiving array. For this tracking problem, we\nderive the posterior Cram\\'er-Rao Lower Bound (P-CRLB) and show the effects\nwhen the source moves inside and outside the Fresnel region. We provide\ninsights on how the loss of positioning information outside Fresnel comes from\nan increase of the ranging error rather than from inaccuracies of angular\nestimation. Then, we investigate the performance of different Bayesian tracking\nalgorithms in the presence of model mismatches and abrupt trajectory changes.\nOur results demonstrate the feasibility and high accuracy for most of the\ntracking approaches without the need of wideband signals and of any\nsynchronization scheme. signals and of any synchronization scheme.\n"}
+{"abstract": "  In magnetic confinement fusion devices, the equilibrium configuration of a\nplasma is determined by the balance between the hydrostatic pressure in the\nfluid and the magnetic forces generated by an array of external coils and the\nplasma itself. The location of the plasma is not known a priori and must be\nobtained as the solution to a free boundary problem. The partial differential\nequation that determines the behavior of the combined magnetic field depends on\na set of physical parameters (location of the coils, intensity of the electric\ncurrents going through them, magnetic permeability, etc.) that are subject to\nuncertainty and variability. The confinement region is in turn a function of\nthese stochastic parameters as well. In this work, we consider variations on\nthe current intensities running through the external coils as the dominant\nsource of uncertainty. This leads to a parameter space of dimension equal to\nthe number of coils in the reactor. With the aid of a surrogate function built\non a sparse grid in parameter space, a Monte Carlo strategy is used to explore\nthe effect that stochasticity in the parameters has on important features of\nthe plasma boundary such as the location of the x-point, the strike points, and\nshaping attributes such as triangularity and elongation. The use of the\nsurrogate function reduces the time required for the Monte Carlo simulations by\nfactors that range between 7 and over 30.\n"}
+{"abstract": "  Understanding and predicting lattice dynamics in strongly anharmonic crystals\nis one of the long-standing challenges in condensed matter physics. Here we\npropose a first-principles method that gives accurate quasiparticle (QP) peaks\nof the phonon spectrum with strong anharmonic broadening. On top of the\nconventional first-order self-consistent phonon (SC1) dynamical matrix, the\nproposed method incorporates frequency renormalization effects by the bubble\nself-energy within the QP approximation. We apply the developed methodology to\nthe strongly anharmonic $\\alpha$-CsPbBr$_3$ that displays phonon instability\nwithin the harmonic approximation in the whole Brillouin zone. While the SC1\ntheory significantly underestimates the cubic-to-tetragonal phase transition\ntemperature (\\tc) by more than 50\\%, we show that our approach yields \\tc =\n404--423~K, in excellent agreement with the experimental value of 403~K. We\nalso demonstrate that an accurate determination of QP peaks is paramount for\nquantitative prediction and elucidation of lattice thermal conductivity.\n"}
+{"abstract": "  The Hard Lefschetz Property (HLP) is an important property which has been\nstudied in several categories of the symplectic world. For Sasakian manifolds,\nthis duality is satisfied by the basic cohomology (so, it is a transverse\nproperty), but a new version of the HLP has been recently given in terms of\nduality of the cohomology of the manifold itself in arXiv:1306.2896. Both\nproperties were proved to be equivalent (see arXiv:1311.1431) in the case of\nK-contact flows. In this paper we extend both versions of the HLP (transverse\nand not) to the more general category of isometric flows, and show that they\nare equivalent. We also give some explicit examples which illustrate the\ncategories where the HLP could be considered.\n"}
+{"abstract": "  On-demand shared mobility is a promising and sustainable transportation\napproach that can mitigate vehicle externalities, such as traffic congestion\nand emission. On-demand shared mobility systems require matching of one\n(one-to-one) or multiple riders (many-to-one) to a vehicle based on real-time\ninformation. We propose a novel Graph-based Many-to-One ride-Matching\n(GMOMatch) algorithm for the dynamic many-to-one matching problem in the\npresence of traffic congestion. GMOMatch, which is an iterative two-step\nmethod, provides high service quality and is efficient in terms of\ncomputational complexity. It starts with a one-to-one matching in Step 1 and is\nfollowed by solving a maximum weight matching problem in Step 2 to combine the\ntravel requests. To evaluate the performance, it is compared with a\nride-matching algorithm developed by Simonetto et al. (2019). Both algorithms\nare implemented in a micro-traffic simulator to assess their performance and\ntheir impact on traffic congestion in Downtown, Toronto road network. In\ncomparison to the Simonetto, GMOMatch improved the service rate, vehicle\nkilometer traveled and traffic travel time by 32%, 16.07%, and 4%,\nrespectively. The sensitivity analysis indicated that utilizing vehicles with a\ncapacity of 10 can achieve 25% service rate improvement compared to a capacity\nof 4.\n"}
+{"abstract": "  How to combine uncertain information from different sources has been a hot\ntopic for years. However, with respect to ordinal quantum evidences contained\nin information, there is no any referable work which is able to provide a\nsolution to this kind of problem. Besides, the method to dispel uncertainty of\nquantum information is still an open issue. Therefore, in this paper, a\nspecially designed method is designed to provide an excellent method which\nimproves the combination of ordinal quantum evidences reasonably and reduce the\neffects brought by uncertainty contained in quantum information simultaneously.\nBesides, some actual applications are provided to verify the correctness and\nvalidity of the proposed method.\n"}
+{"abstract": "  This paper considers the integration of gap-based local navigation methods\nwith artificial potential field (APF) methods to derive a local planning module\nfor hierarchical navigation systems that has provable collision-free\nproperties. Given that APF theory applies to idealized robot models, the\nprovable properties are lost when applied to more realistic models. We describe\na set of algorithm modifications that correct for these errors and enhance\nrobustness to non-ideal models. Central to the construction of the local\nplanner is the use of sensory-derived local free-space models that detect gaps\nand use them for the synthesis of the APF. Modifications are given for a\nnonholonomic robot model. Integration of the local planner, called potential\ngap, into a hierarchical navigation system provides the local goals and\ntrajectories needed for collision-free navigation through unknown environments.\nMonte Carlo experiments in benchmark worlds confirm the asserted safety and\nrobustness properties by testing under various robot models.\n"}
+{"abstract": "  Quantum key distribution (QKD) networks provide an infrastructure for\nestablishing information-theoretic secure keys between legitimate parties via\nquantum and authentic classical channels. The deployment of QKD networks in\nreal-world conditions faces several challenges, which are related in particular\nto the high costs of QKD devices and the condition to provide reasonable secret\nkey rates. In this work, we present a QKD network architecture that provides a\nsignificant reduction in the cost of deploying QKD networks by using optical\nswitches and reducing the number of QKD receiver devices, which use\nsingle-photon detectors. We describe the corresponding modification of the QKD\nnetwork protocol. We also provide estimations for a network link of a total of\n670 km length consisting of 8 nodes, and demonstrate that the switch-based\narchitecture allows achieving significant resource savings of up to 28%, while\nthe throughput is reduced by 8% only.\n"}
+{"abstract": "  Self-organized coherence-incoherence patterns, called chimera states, have\nfirst been reported in systems of Kuramoto oscillators. For coupled excitable\nunits similar patterns, where coherent units are at rest, are called bump\nstates. Here, we study bumps in an array of active rotators coupled by\nnon-local attraction and global repulsion. We demonstrate how they can emerge\nin a supercritical scenario from completely coherent Turing patterns: single\nincoherent units appear in a homoclinic bifurcation with a subsequent\ntransition via quasiperiodic and chaotic behavior, eventually transforming into\nextensive chaos with many incoherent units. We present different types of\ntransitions and explain the formation of coherence-incoherence patterns\naccording to the classical paradigm of short-range activation and long-range\ninhibition.\n"}
+{"abstract": "  Although using convolutional neural networks (CNNs) as backbones achieves\ngreat successes in computer vision, this work investigates a simple backbone\nnetwork useful for many dense prediction tasks without convolutions. Unlike the\nrecently-proposed Transformer model (e.g., ViT) that is specially designed for\nimage classification, we propose Pyramid Vision Transformer~(PVT), which\novercomes the difficulties of porting Transformer to various dense prediction\ntasks. PVT has several merits compared to prior arts. (1) Different from ViT\nthat typically has low-resolution outputs and high computational and memory\ncost, PVT can be not only trained on dense partitions of the image to achieve\nhigh output resolution, which is important for dense predictions but also using\na progressive shrinking pyramid to reduce computations of large feature maps.\n(2) PVT inherits the advantages from both CNN and Transformer, making it a\nunified backbone in various vision tasks without convolutions by simply\nreplacing CNN backbones. (3) We validate PVT by conducting extensive\nexperiments, showing that it boosts the performance of many downstream tasks,\ne.g., object detection, semantic, and instance segmentation. For example, with\na comparable number of parameters, RetinaNet+PVT achieves 40.4 AP on the COCO\ndataset, surpassing RetinNet+ResNet50 (36.3 AP) by 4.1 absolute AP. We hope PVT\ncould serve as an alternative and useful backbone for pixel-level predictions\nand facilitate future researches. Code is available at\nhttps://github.com/whai362/PVT.\n"}
+{"abstract": "  Anomaly detection on attributed networks attracts considerable research\ninterests due to wide applications of attributed networks in modeling a wide\nrange of complex systems. Recently, the deep learning-based anomaly detection\nmethods have shown promising results over shallow approaches, especially on\nnetworks with high-dimensional attributes and complex structures. However,\nexisting approaches, which employ graph autoencoder as their backbone, do not\nfully exploit the rich information of the network, resulting in suboptimal\nperformance. Furthermore, these methods do not directly target anomaly\ndetection in their learning objective and fail to scale to large networks due\nto the full graph training mechanism. To overcome these limitations, in this\npaper, we present a novel contrastive self-supervised learning framework for\nanomaly detection on attributed networks. Our framework fully exploits the\nlocal information from network data by sampling a novel type of contrastive\ninstance pair, which can capture the relationship between each node and its\nneighboring substructure in an unsupervised way. Meanwhile, a well-designed\ngraph neural network-based contrastive learning model is proposed to learn\ninformative embedding from high-dimensional attributes and local structure and\nmeasure the agreement of each instance pairs with its outputted scores. The\nmulti-round predicted scores by the contrastive learning model are further used\nto evaluate the abnormality of each node with statistical estimation. In this\nway, the learning model is trained by a specific anomaly detection-aware\ntarget. Furthermore, since the input of the graph neural network module is\nbatches of instance pairs instead of the full network, our framework can adapt\nto large networks flexibly. Experimental results show that our proposed\nframework outperforms the state-of-the-art baseline methods on all seven\nbenchmark datasets.\n"}
+{"abstract": "  Consider a shrinking neighborhood of a cusp of the unit tangent bundle of a\nnoncompact hyperbolic surface of finite area, and let the neighborhood shrink\ninto the cusp at a rate of $T^{-1}$ as $T \\rightarrow \\infty$. We show that a\nclosed horocycle whose length $\\ell$ goes to infinity or even a segment of that\nhorocycle becomes equidistributed on the shrinking neighborhood when normalized\nby the rate $T^{-1}$ provided that $T/\\ell \\rightarrow 0$ and, for any\n$\\delta>0$, the segment remains larger than\n$\\max\\left\\{T^{-1/6},\\left(T/\\ell\\right)^{1/2}\\right\\}\\left(T/\\ell\\right)^{-\\delta}$.\nWe also have an effective result for a smaller range of rates of growth of $T$\nand $\\ell$.\n"}
+{"abstract": "  The COVID-19 pandemic presented itself as a challenge for separate societal\nsectors. On the information technology (IT) standpoint, it does include the\nmaintenance of the infrastructure required to hold collaborative activities\nthat went to happen online; the implementation of projects in a scenario of\nuncertainty; and keep the software engineering and information security best\npractices in place. This article presents the context of a data science team\norganized as a skunk works group composed of professionals with experience in\nboth the industry and academia, located in an IT department working with a team\nof seasoned data engineers. At the time the pandemic started, the relatively\nnew data science team was positioning itself as a Center of Excellence in\nAdvanced Analytics. With the pandemic, it had to keep up with the expectations\nfrom the stakeholders; manage current and upcoming data science projects within\nthe methodology practiced in IT; and maintain a high level in the quality of\nservice delivered. This article discusses how did the COVID-19 pandemic\naffected the team productivity and its practices as well as the lessons learned\nwith it.\n"}
+{"abstract": "  One of the main challenges of the machine reading comprehension (MRC) models\nis their fragile out-of-domain generalization, which makes these models not\nproperly applicable to real-world general-purpose question answering problems.\nIn this paper, we leverage a zero-shot weighted ensemble method for improving\nthe robustness of out-of-domain generalization in MRC models. In the proposed\nmethod, a weight estimation module is used to estimate out-of-domain weights,\nand an ensemble module aggregate several base models' predictions based on\ntheir weights. The experiments indicate that the proposed method not only\nimproves the final accuracy, but also is robust against domain changes.\n"}
+{"abstract": "  We performed a Lorentz covariant quantization of the spin-1/2 fermion field\nassuming the space-like energy-momentum dispersion relation. We a\\-chieved the\ntask in the following steps: ($i$) determining the unitary realizations of the\ninhomogenous Lorentz group in the preferred frame scenario by means of the\nWigner-Mackey induction procedure and constructing the Fock space; ($ii$)\nformulating the theory in a manifestly covariant way by constructing the field\namplitudes according to the Weinberg method; ($iii$) obtaining the final\nconstraints on the amplitudes by postulating a Dirac-like free field equation.\nOur theory allows to predict all chiral properties of the neutrinos, preserving\nthe Standard Model dynamics. We discussed the form of the fundamental\nobservables, energy and helicity, and show that non-observation of the\n$+\\tfrac{1}{2}$ helicity state of the neutrino and the $-\\tfrac{1}{2}$ helicity\nstate of the antineutrino could be a direct consequence of the \"tachyoneity\" of\nneutrinos at the free level. We found that the free field theory of the\nspace-like neutrino is not invariant under the C and P transformations\nseparately but is CP-invariant. We calculated and analyzed the electron energy\nspectrum in tritium decay within the framework of our theory and found an\nexcellent agreement with the recent measurement of KATRIN. In our formalism the\nquestions of negative/imaginary energies and the causality problem does not\nappear.\n"}
+{"abstract": "  Second-order methods have the capability of accelerating optimization by\nusing much richer curvature information than first-order methods. However, most\nare impractical in a deep learning setting where the number of training\nparameters is huge. In this paper, we propose KF-QN-CNN, a new\nKronecker-factored quasi-Newton method for training convolutional neural\nnetworks (CNNs), where the Hessian is approximated by a layer-wise block\ndiagonal matrix and each layer's diagonal block is further approximated by a\nKronecker product corresponding to the structure of the Hessian restricted to\nthat layer. New damping and Hessian-action techniques for BFGS are designed to\ndeal with the non-convexity and the particularly large size of Kronecker\nmatrices in CNN models and convergence results are proved for a variant of\nKF-QN-CNN under relatively mild conditions. KF-QN-CNN has memory requirements\ncomparable to first-order methods and much less per-iteration time complexity\nthan traditional second-order methods. Compared with state-of-the-art first-\nand second-order methods on several CNN models, KF-QN-CNN consistently\nexhibited superior performance in all of our tests.\n"}
+{"abstract": "  Episodic memory-based methods can rapidly latch onto past successful\nstrategies by a non-parametric memory and improve sample efficiency of\ntraditional reinforcement learning. However, little effort is put into the\ncontinuous domain, where a state is never visited twice, and previous episodic\nmethods fail to efficiently aggregate experience across trajectories. To\naddress this problem, we propose Generalizable Episodic Memory (GEM), which\neffectively organizes the state-action values of episodic memory in a\ngeneralizable manner and supports implicit planning on memorized trajectories.\nGEM utilizes a double estimator to reduce the overestimation bias induced by\nvalue propagation in the planning process. Empirical evaluation shows that our\nmethod significantly outperforms existing trajectory-based methods on various\nMuJoCo continuous control tasks. To further show the general applicability, we\nevaluate our method on Atari games with discrete action space, which also shows\na significant improvement over baseline algorithms.\n"}
+{"abstract": "  Potassium channels play critical roles in many physiological processes,\nproviding a selective permeation route for K+ ions in and out of a cell, by\nemploying a carefully designed selectivity filter, evolutionarily conserved\nfrom viruses to mammals. The structure of the selectivity filter was determined\nat atomic resolution by x-ray crystallography, showing a tight coordination of\ndesolvated K+ ions by the channel. However, the molecular mechanism of K+ ions\npermeation through potassium channels remains unclear, with structural,\nfunctional and computational studies often providing conflicting data and\ninterpretations. In this review, we will present the proposed mechanisms,\ndiscuss their origins, and will critically assess them against all available\ndata. General properties shared by all potassium channels are introduced first,\nfollowed by the introduction of two main mechanisms of ion permeation: soft and\ndirect knock-on. Then, we will discuss critical computational and experimental\nstudies that shaped the field. We will especially focus on molecular dynamics\n(MD) simulations, that provided mechanistic and energetic aspects of K+\npermeation, but at the same time created long-standing controversies. Further\nchallenges and possible solutions are presented as well.\n"}
+{"abstract": "  Braces were introduced by W. Rump in 2006 as an algebraic system related to\nthe quantum Yang-Baxter equation. In 2017, L. Guarnieri and L. Vendramin\ndefined for the same purposes a more general notion of a skew left brace.\nRecently, L. Guo, H. Lang, Y. Sheng [arXiv:2009.03492] gave a definition of\nwhat is a Rota-Baxter operator on a group. We connect these two notions as\nfollows. It is shown that every Rota-Baxter group gives rise to a skew left\nbrace. Moreover, every skew left brace can be injectively embedded into a\nRota-Baxter group. When the additive group of a skew left brace is complete,\nthen this brace is induced by a Rota-Baxter group. We interpret theory of skew\nleft braces in terms of Rota -- Baxter operators.\n"}
+{"abstract": "  Canonical expressions are representative of implicative propositions upto\nrenaming of variables. In this paper we explore, using a Monte-Carlo approach,\nthe model of canonical expressions in order to confirm the paradox that says\nthat asymptotically almost all classical theorems are intuitionistic.\n"}
+{"abstract": "  The increasing availability of video recordings made by multiple cameras has\noffered new means for mitigatingocclusion and depth ambiguities in pose and\nmotion reconstruction methods. Yet, multi-view algorithms strongly depend on\ncamera parameters; particularly, the relativepositions between the cameras.\nSuch a dependency becomes a hurdle once shifting to dynamic capture in\nuncontrolled settings. We introduce FLEX (Free muLti-view rEconstruXion), an\nend-to-end parameter-free multi-viewmodel. FLEX is parameter-free in the sense\nthat it does not require any camera parameters, neither intrinsic nor\nextrinsic. Our key idea is that the 3D angles between skeletal parts, as well\nas bone lengths, are invariant to the camera position. Hence, learning 3D\nrotations and bone lengths rather than locations allows predicting common\nvalues for all camera views. Our network takes multiple video streams, learns\nfused deep features through a novel multi-view fusion layer, and reconstructs a\nsingle consistent skeleton with temporally coherent joint rotations. We\ndemonstrate quantitative and qualitative results on the Human3.6M and KTH\nMulti-view Football II datasets, and on synthetic multi-person video streams\ncaptured by dynamic cameras. We compare our model to state-of-the-art methods\nthat are not parameter-free and show that in the absence of camera parameters,\nwe outperform them by a large margin while obtaining comparable results when\ncamera parameters are available. Code, trained models, video examples, and more\nmaterial will be available on our project page.\n"}
+{"abstract": "  Context: Grey Literature (GL) recently has grown in Software Engineering (SE)\nresearch since the increased use of online communication channels by software\nengineers. However, there is still a limited understanding of how SE research\nis taking advantage of GL. Objective: This research aimed to understand how SE\nresearchers use GL in their secondary studies. Method: We conducted a tertiary\nstudy of studies published between 2011 and 2018 in high-quality software\nengineering conferences and journals. We then applied qualitative and\nquantitative analysis to investigate 446 potential studies. Results: From the\n446 selected studies, 126 studies cited GL but only 95 of those used GL to\nanswer a specific research question representing almost 21% of all the 446\nsecondary studies. Interestingly, we identified that few studies employed\nspecific search mechanisms and used additional criteria for assessing GL.\nMoreover, by the time we conducted this research, 49% of the GL URLs are not\nworking anymore. Based on our findings, we discuss some challenges in using GL\nand potential mitigation plans. Conclusion: In this paper, we summarized the\nlast 10 years of software engineering research that uses GL, showing that GL\nhas been essential for bringing practical new perspectives that are scarce in\ntraditional literature. By drawing the current landscape of use, we also raise\nsome awareness of related challenges (and strategies to deal with them).\n"}
+{"abstract": "  Let $n\\in\\mathbb{N}$ and $\\X(n) =(X_1(n), \\ldots, X_{d(n)}(n))$ be a sequence\nof random vectors. We prove that, under certain dependence conditions, the cdf\nof the maximum of $\\X_i(n)$ asymptotically equals to the cdf of the maximum of\na random vector with the same but independent marginal distributions. To prove\nour result on extremal independence, we obtain new lower and upper bounds on\nthe probability that none of a given finite set of events occurs. Using our\nresult, we show that, under certain conditions, including Berman-type\ncondition, a sequence of Gaussian random vectors possesses the extremal\nindependence property. We also prove that certain extremal characterstics of\nbinomial random graphs and hypergraphs, after an appropriate rescaling, have\nasymptotical Gumbel distribution (in particular, maximum codegrees in random\nhypergraphs and the maximum number of cliques sharing a given vertex in random\ngraphs).\n"}
+{"abstract": "  We examine a pair of graph generative models for the therapeutic design of\nnovel drug candidates targeting SARS-CoV-2 viral proteins. Due to a sense of\nurgency, we chose well-validated models with unique strengths: an autoencoder\nthat generates molecules with similar structures to a dataset of drugs with\nanti-SARS activity and a reinforcement learning algorithm that generates highly\nnovel molecules. During generation, we explore optimization toward several\ndesign targets to balance druglikeness, synthetic accessability, and anti-SARS\nactivity based on \\icfifty. This generative\nframework\\footnote{https://github.com/exalearn/covid-drug-design} will\naccelerate drug discovery in future pandemics through the high-throughput\ngeneration of targeted therapeutic candidates.\n"}
+{"abstract": "  We show that every Kac-Moody algebra is birationally equivalent to a smash\nbiproduct of two copies of a Weyl algebra together with a polynomial algebra.\nWe also show that the same is true for quantized Kac-Moody algebras where one\nreplaces Weyl algebras with their quantum analogues.\n"}
+{"abstract": "  The large variation of datasets is a huge barrier for image classification\ntasks. In this paper, we embraced this observation and introduce the finite\ntemperature tensor network (FTTN), which imports the thermal perturbation into\nthe matrix product states framework by placing all images in an environment\nwith constant temperature, in analog to energy-based learning. Tensor network\nis chosen since it is the best platform to introduce thermal fluctuation.\nDifferent from traditional network structure which directly takes the summation\nof individual losses as its loss function, FTTN regards it as thermal average\nloss computed from the entanglement with the environment. The temperature-like\nparameter can be automatically optimized, which gives each database an\nindividual temperature. FTTN obtains improvement in both test accuracy and\nconvergence speed in several datasets. The non-zero temperature automatically\nseparates similar features, avoiding the wrong classification in previous\narchitecture. The thermal fluctuation may give a better improvement in other\nframeworks, and we may also implement the temperature of database to improve\nthe training effect.\n"}
+{"abstract": "  We show how to learn a map that takes a content code, derived from a face\nimage, and a randomly chosen style code to an anime image. We derive an\nadversarial loss from our simple and effective definitions of style and\ncontent. This adversarial loss guarantees the map is diverse -- a very wide\nrange of anime can be produced from a single content code. Under plausible\nassumptions, the map is not just diverse, but also correctly represents the\nprobability of an anime, conditioned on an input face. In contrast, current\nmultimodal generation procedures cannot capture the complex styles that appear\nin anime. Extensive quantitative experiments support the idea the map is\ncorrect. Extensive qualitative results show that the method can generate a much\nmore diverse range of styles than SOTA comparisons. Finally, we show that our\nformalization of content and style allows us to perform video to video\ntranslation without ever training on videos.\n"}
+{"abstract": "  It is known for some time that there exists an energy-minimal diffeomorphism\nbetween two doubly-connected domains $\\Omega$ and $D$ provided that\n$\\mathrm{Mod}(\\Omega)\\le \\mathrm{Mod}{D}$ and that diffeomorphism is harmonic\n\\cite{tedi}. In this note we give a short proof of the fact that for given\nannuli $\\Omega$ and $D$ satisfying the condition $\\mathrm{Mod}(\\Omega)\\le\n\\mathrm{Mod}(D)$ there exist a $K-$quasiconformal harmonic diffeomorphism\n$f:\\Omega\\onto D$, where $K=K(\\tau)$, $\\tau=\\mathrm{Mod}(\\Omega)/\n\\mathrm{Mod}(D)$ and $\\lim_{\\tau\\to 1}K(\\tau)=1$.\n"}
+{"abstract": "  This paper is concerned with a compositional scheme for the construction of\ncontrol barrier certificates for interconnected discrete-time stochastic\nsystems. The main objective is to synthesize switching control policies against\n$\\omega$-regular properties that can be described by accepting languages of\ndeterministic Streett automata (DSA) along with providing probabilistic\nguarantees for the satisfaction of such specifications. The proposed framework\nleverages the interconnection topology and a notion of so-called control\nsub-barrier certificates of subsystems, which are used to compositionally\nconstruct control barrier certificates of interconnected systems by imposing\nsome dissipativity-type compositionality conditions. We propose a systematic\napproach to decompose high-level $\\omega$-regular specifications into simpler\ntasks by utilizing the automata corresponding to the complement of\nspecifications. In addition, we formulate an alternating direction method of\nmultipliers (ADMM) optimization problem in order to obtain suitable control\nsub-barrier certificates of subsystems while satisfying compositionality\nconditions. We also provide a sum-of-squares (SOS) optimization problem for the\ncomputation of control sub-barrier certificates and local control policies of\nsubsystems. Finally, we demonstrate the effectiveness of our proposed\napproaches by applying them to a physical case study.\n"}
+{"abstract": "  We examine the question how string theory achieves a sum over bulk geometries\nwith fixed asymptotic boundary conditions. We discuss this problem with the\nhelp of the tensionless string on $\\mathcal{M}_3 \\times \\mathrm{S}^3 \\times\n\\mathbb{T}^4$ (with one unit of NS-NS flux) that was recently understood to be\ndual to the symmetric orbifold $\\text{Sym}^N(\\mathbb{T}^4)$. We strengthen the\nanalysis of arXiv:2008.07533 and show that the perturbative string partition\nfunction around a fixed bulk background already includes a sum over\nsemi-classical geometries and large stringy corrections can be interpreted as\nvarious semi-classical geometries. We argue in particular that the string\npartition function on a Euclidean wormhole geometry factorizes completely into\nfactors associated to the two boundaries of spacetime. Central to this is the\nremarkable property of the moduli space integral of string theory to localize\non covering spaces of the conformal boundary of $\\mathcal{M}_3$. We also\nemphasize the fact that string perturbation theory computes the grand canonical\npartition function of the family of theories\n$\\bigoplus_N\\text{Sym}^N(\\mathbb{T}^4)$. The boundary partition function is\nnaturally expressed as a sum over winding worldsheets, each of which we\ninterpret as a `stringy geometry'. We argue that the semi-classical bulk\ngeometry can be understood as a condensate of such stringy geometries. We also\nbriefly discuss the effect of ensemble averaging over the Narain moduli space\nof $\\mathbb{T}^4$ and of deforming away from the orbifold by the marginal\ndeformation.\n"}
+{"abstract": "  Nonlinear light sources are central to a myriad of applications, driving a\nquest for their miniaturisation down to the nanoscale. In this quest, nonlinear\nmetasurfaces hold a great promise, as they enhance nonlinear effects through\ntheir resonant photonic environment and high refractive index, such as in\nhigh-index dielectric metasurfaces. However, despite the sub-diffractive\noperation of dielectric metasurfaces at the fundamental wave, this condition is\nnot fulfilled for the nonlinearly generated harmonic waves, thereby all\nnonlinear metasurfaces to date emit multiple diffractive beams. Here, we\ndemonstrate the enhanced single-beam second- and third-harmonic generation in a\nmetasurface of crystalline transition-metal-dichalcogenide material, offering\nthe highest refractive index. We show that the interplay between the resonances\nof the metasurface allows for tuning of the unidirectional second-harmonic\nradiation in forward or backward direction, not possible in any bulk nonlinear\ncrystal. Our results open new opportunities for metasurface-based nonlinear\nlight-sources, including nonlinear mirrors and entangled-photon generation.\n"}
+{"abstract": "  Lattice QCD has reached a mature status. State of the art lattice\ncomputations include $u,d,s$ (and even the $c$) sea quark effects, together\nwith an estimate of electromagnetic and isospin breaking corrections for\nhadronic observables. This precise and first principles description of the\nstandard model at low energies allows the determination of multiple quantities\nthat are essential inputs for phenomenology and not accessible to perturbation\ntheory.\n  One of the fundamental parameters that are determined from simulations of\nlattice QCD is the strong coupling constant, which plays a central role in the\nquest for precision at the LHC. Lattice calculations currently provide its best\ndeterminations, and will play a central role in future phenomenological\nstudies. For this reason we believe that it is timely to provide a pedagogical\nintroduction to the lattice determinations of the strong coupling. Rather than\nanalysing individual studies, the emphasis will be on the methodologies and the\nsystematic errors that arise in these determinations. We hope that these notes\nwill help lattice practitioners, and QCD phenomenologists at large, by\nproviding a self-contained introduction to the methodology and the possible\nsources of systematic error.\n  The limiting factors in the determination of the strong coupling turn out to\nbe different from the ones that limit other lattice precision observables. We\nhope to collect enough information here to allow the reader to appreciate the\nchallenges that arise in order to improve further our knowledge of a quantity\nthat is crucial for LHC phenomenology.\n"}
+{"abstract": "  Applications of machine learning (ML) to high-stakes policy settings -- such\nas education, criminal justice, healthcare, and social service delivery -- have\ngrown rapidly in recent years, sparking important conversations about how to\nensure fair outcomes from these systems. The machine learning research\ncommunity has responded to this challenge with a wide array of proposed\nfairness-enhancing strategies for ML models, but despite the large number of\nmethods that have been developed, little empirical work exists evaluating these\nmethods in real-world settings. Here, we seek to fill this research gap by\ninvestigating the performance of several methods that operate at different\npoints in the ML pipeline across four real-world public policy and social good\nproblems. Across these problems, we find a wide degree of variability and\ninconsistency in the ability of many of these methods to improve model\nfairness, but post-processing by choosing group-specific score thresholds\nconsistently removes disparities, with important implications for both the ML\nresearch community and practitioners deploying machine learning to inform\nconsequential policy decisions.\n"}
+{"abstract": "  Transformer is an attention-based neural network, which consists of two\nsublayers, namely, Self-Attention Network (SAN) and Feed-Forward Network (FFN).\nExisting research explores to enhance the two sublayers separately to improve\nthe capability of Transformer for text representation. In this paper, we\npresent a novel understanding of SAN and FFN as Mask Attention Networks (MANs)\nand show that they are two special cases of MANs with static mask matrices.\nHowever, their static mask matrices limit the capability for localness modeling\nin text representation learning. We therefore introduce a new layer named\ndynamic mask attention network (DMAN) with a learnable mask matrix which is\nable to model localness adaptively. To incorporate advantages of DMAN, SAN, and\nFFN, we propose a sequential layered structure to combine the three types of\nlayers. Extensive experiments on various tasks, including neural machine\ntranslation and text summarization demonstrate that our model outperforms the\noriginal Transformer.\n"}
+{"abstract": "  A recent construction by Amarra, Devillers and Praeger of block designs with\nspecific parameters depends on certain quadratic polynomials, with integer\ncoefficients, taking prime power values. The Bunyakovsky Conjecture, if true,\nwould imply that each of them takes infinitely many prime values, giving an\ninfinite family of block designs with the required parameters. We have found\nlarge numbers of prime values of these polynomials, and the numbers found agree\nvery closely with the estimates for them provided by Li's recent modification\nof the Bateman-Horn Conjecture. While this does not prove that these\npolynomials take infinitely many prime values, it provides strong evidence for\nthis, and it also adds extra support for the validity of the Bunyakovsky and\nBateman-Horn Conjectures.\n"}
+{"abstract": "  Consider the normalized adjacency matrices of random $d$-regular graphs on\n$N$ vertices with fixed degree $d\\geq3$. We prove that, with probability\n$1-N^{-1+{\\varepsilon}}$ for any ${\\varepsilon} >0$, the following two\nproperties hold as $N \\to \\infty$ provided that $d\\geq3$: (i) The eigenvalues\nare close to the classical eigenvalue locations given by the Kesten-McKay\ndistribution. In particular, the extremal eigenvalues are concentrated with\npolynomial error bound in $N$, i.e. $\\lambda_2, |\\lambda_N|\\leq 2+N^{-c}$. (ii)\nAll eigenvectors of random $d$-regular graphs are completely delocalized.\n"}
+{"abstract": "  The state of art of electromagnetic integral equations has seen significant\ngrowth over the past few decades, overcoming some of the fundamental\nbottlenecks: computational complexity, low frequency and dense discretization\nbreakdown, preconditioning, and so on. Likewise, the community has seen\nextensive investment in development of methods for higher order analysis, in\nboth geometry and physics. Unfortunately, these standard geometric descriptors\nare $C^0$ at the boundary between patches with a few exceptions; as a result,\none needs to define additional mathematical infrastructure to define physical\nbasis sets for vector problems. In stark contrast, the geometric representation\nused for design is higher-order differentiable over the entire surface.\nGeometric descriptions that have $C^{2}$-continuity almost everywhere on the\nsurfaces are common in computer graphics. Using these description for analysis\nopens the door to several possibilities, and is the area we explore in this\npaper. Our focus is on Loop subdivision based isogeometric methods. In this\npaper, our goals are two fold: (i) development of computational infrastructure\nnecessary to effect efficient methods for isogeometric analysis of electrically\nlarge simply connected objects, and (ii) to introduce the notion of manifold\nharmonics transforms and its utility in computational electromagnetics. Several\nresults highlighting the efficacy of these two methods are presented.\n"}
+{"abstract": "  We study a model of random colliding particles interacting with an infinite\nreservoir at fixed temperature and chemical potential. Interaction between the\nparticles is modeled via a Kac master equation \\cite{kac}. Moreover, particles\ncan leave the system toward the reservoir or enter the system from the\nreservoir. The system admits a unique steady state given by the Grand Canonical\nEnsemble at temperature $T=\\beta^{-1}$ and chemical potential $\\chi$. We show\nthat any initial state converges exponentially to equilibrium by computing the\nspectral gap of the generator in a suitable $L^2$ space and by showing\nexponential decrease of the relative entropy with respect to the steady state.\nWe also show propagation of chaos and thus the validity of a Boltzmann-Kac type\nequation for the particle density in the infinite system limit.\n"}
+{"abstract": "  We provide minimality criteria by construction of calibrations for\nfunctionals arising in the theory of Thermal Insulation.\n"}
+{"abstract": "  In this paper, we investigate the impact of channel aging on the uplink\nperformance of a cell-free~(CF) massive multiple-input multiple-output (mMIMO)\nsystem with a minimum mean squared error (MMSE) receiver. To this end, we\npresent a new model for the temporal evolution of the channel, which allows the\nchannel to age at different rates at different access points (APs). Under this\nsetting, we derive the deterministic equivalent of the per-user achievable\nsignal-to-interference-plus-noise ratio (SINR). In addition to validating the\ntheoretical expressions, our simulation results reveal that, {at low user\nmobilities,} the SINR of CF-mMIMO is nearly 5 dB higher than its cellular\ncounterpart with the same number of antennas, and about 8 dB higher than that\nof an equivalent small-cell network with the same number of APs. {On the other\nhand, at very high user velocities, and when the channel between the UEs the\ndifferent APs age at same rate, the relative impact of aging is higher for\nCF-mMIMO compared to cellular mMIMO. However, when the channel ages at the\ndifferent APs with different rates, the effect of aging on CF-mMIMO is\nmarginally mitigated, especially for larger frame durations.\n"}
+{"abstract": "  The prohibitive expense of automatic performance tuning at scale has largely\nlimited the use of autotuning to libraries for shared-memory and GPU\narchitectures. We introduce a framework for approximate autotuning that\nachieves a desired confidence in each algorithm configuration's performance by\nconstructing confidence intervals to describe the performance of individual\nkernels (subroutines of benchmarked programs). Once a kernel's performance is\ndeemed sufficiently predictable for a set of inputs, subsequent invocations are\navoided and replaced with a predictive model of the execution time. We then\nleverage online execution path analysis to coordinate selective kernel\nexecution and propagate each kernel's statistical profile. This strategy is\neffective in the presence of frequently-recurring computation and communication\nkernels, which is characteristic to algorithms in numerical linear algebra. We\nencapsulate this framework as part of a new profiling tool, Critter, that\nautomates kernel execution decisions and propagates statistical profiles along\ncritical paths of execution. We evaluate performance prediction accuracy\nobtained by our selective execution methods using state-of-the-art\ndistributed-memory implementations of Cholesky and QR factorization on\nStampede2, and demonstrate speed-ups of up to 7.1x with 98% prediction\naccuracy.\n"}
+{"abstract": "  Oversampled adaptive sensing (OAS) is a Bayesian framework recently proposed\nfor effective sensing of structured signals in a time-limited setting. In\ncontrast to the conventional blind oversampling, OAS uses the prior information\non the signal to construct posterior beliefs sequentially. These beliefs help\nin constructive oversampling which iteratively evolves through a sequence of\ntime sub-frames.\n  The initial studies of OAS consider the idealistic assumption of full control\non sensing coefficients which is not feasible in many applications. In this\nwork, we extend the initial investigations on OAS to more realistic settings in\nwhich the sensing coefficients are selected from a predefined set of possible\nchoices, referred to as the codebook. We extend the OAS framework to these\nsettings and compare its performance with classical non-adaptive approaches.\n"}
+{"abstract": "  Let $N$ be an $n$-dimensional $C^\\infty$ manifold and let\n$\\widetilde{\\varphi}: N\\to \\mathbb{R}^{n+1}$, $\\widetilde{\\nu}: N\\to S^n$ be\n$C^\\infty$ mappings. We first give a necessary and sufficient condition for the\nhyperplane family $\\mathcal{H}_{(\\widetilde{\\varphi}, \\widetilde{\\nu})}$\ndefined by $\\mathcal{H}_{(\\widetilde{\\varphi}, \\widetilde{\\nu})} =\\cup_{x\\in\nN}\\left\\{X\\in \\mathbb{R}^{n+1}\\; |\\; \\left(X-\\widetilde{\\varphi}(x)\\right)\\cdot\n\\widetilde{\\nu}(x)=0\\right\\}$ to create an envelope (Theorem 1). As a\nby-product of the proof of Theorem 1, when the given hyperplane family\n$\\mathcal{H}_{(\\widetilde{\\varphi}, \\widetilde{\\nu})}$ creates an envelope\n$\\widetilde{f}: N\\to \\mathbb{R}^{n+1}$, an explicit expression of the envelope\n$\\widetilde{f}$ is obtained in terms of $\\widetilde{\\varphi}$ and\n$\\widetilde{\\nu}$ (Corollary 2). The vector formula given in Corollary 2 holds\neven at a singular point of $\\widetilde{\\nu}$ so long as the hyperplane family\n$\\mathcal{H}_{(\\widetilde{\\varphi}, \\widetilde{\\nu})}$ creates an envelope. In\nthis sense, Corollary 2 may be regarded as a complete generalization of the\ncelebrated Cahn-Hoffman vector formula. Moreover, we give a criterion when and\nonly when $\\mathcal{H}_{\\left(\\widetilde{\\varphi}, \\widetilde{\\nu}\\right)}$\ncreates a unique envelope (Theorem 2).\n"}
+{"abstract": "  A summation is a shift-invariant ${\\rm R}$-module homomorphism from a\nsubmodule of ${\\rm R}[[\\sigma]]$ to ${\\rm R}$ or another ring. [11] formalized\na method for extending a summation to a larger domain by telescoping. In this\npaper, we revisit telescoping, we study multiplicative closures of summations\n(such as the usual summation on convergent series) that are not themselves\nmultiplicatively closed, and we study rational extensions as a generalization\nof telescoping.\n"}
+{"abstract": "  Intelligent transport systems have efficiently and effectively proved\nthemselves in settling up the problem of traffic congestion around the world.\nThe multi-agent based transportation system is one of the most important\nintelligent transport systems, which represents an interaction among the\nneighbouring vehicles, drivers, roads, infrastructure and vehicles. In this\npaper, two traffic management models have been created to mitigate congestion\nand to ensure that emergency vehicles arrive as quickly as possible. A\ntool-chain SUMO-JADE is employed to create a microscopic simulation symbolizing\nthe interactions of traffic. The simulation model has showed a significant\nreduction of at least 50% in the average time delay and thus a real improvement\nin the entire journey time.\n"}
+{"abstract": "  We offer a theoretical characterization of off-policy evaluation (OPE) in\nreinforcement learning using function approximation for marginal importance\nweights and $q$-functions when these are estimated using recent minimax\nmethods. Under various combinations of realizability and completeness\nassumptions, we show that the minimax approach enables us to achieve a fast\nrate of convergence for weights and quality functions, characterized by the\ncritical inequality \\citep{bartlett2005}. Based on this result, we analyze\nconvergence rates for OPE. In particular, we introduce novel alternative\ncompleteness conditions under which OPE is feasible and we present the first\nfinite-sample result with first-order efficiency in non-tabular environments,\ni.e., having the minimal coefficient in the leading term.\n"}
+{"abstract": "  Representational learning forms the backbone of most deep learning\napplications, and the value of a learned representation is intimately tied to\nits information content regarding different factors of variation. Finding good\nrepresentations depends on the nature of supervision and the learning\nalgorithm. We propose a novel algorithm that utilizes a weak form of\nsupervision where the data is partitioned into sets according to certain\ninactive (common) factors of variation which are invariant across elements of\neach set. Our key insight is that by seeking correspondence between elements of\ndifferent sets, we learn strong representations that exclude the inactive\nfactors of variation and isolate the active (varying) factors which vary within\nall sets. As a consequence of focusing on the active factors, our method can\nleverage a mix of set-supervised and wholly unsupervised data, which can even\nbelong to a different domain. We tackle the challenging problem of\nsynthetic-to-real object pose transfer, by isolating from images pose\ninformation which generalizes to the category level and across the\nsynthetic/real domain gap, even without pose annotations on anything. The\nmethod can also boost performance in supervised settings, by strengthening\nintermediate representations.\n"}
+{"abstract": "  This paper studies a model of two-phase flow with an immersed material\nviscous interface and a finite element method for numerical solution of the\nresulting system of PDEs. The interaction between the bulk and surface media is\ncharacterized by no-penetration and slip with friction interface conditions.\nThe system is shown to be dissipative and a model stationary problem is proved\nto be well-posed. The finite element method applied in this paper belongs to a\nfamily of unfitted discretizations. The performance of the method when model\nand discretization parameters vary is assessed. Moreover, an iterative\nprocedure based on the splitting of the system into bulk and surface problems\nis introduced and studied numerically.\n"}
+{"abstract": "  In previous work, we showed that an anomaly in the one point function of\nmarginal operators is related by the Wess-Zumino condition to the Euler density\nanomaly on a two dimensional defect or boundary. Here we analyze in detail the\ntwo point functions of marginal operators with the stress tensor and with the\ndisplacement operator in three dimensions. We show how to get the boundary\nanomaly from these bulk two point functions and find perfect agreement with our\nanomaly effective action. For a higher dimensional conformal field theory with\na four dimensional defect, we describe for the first time the anomaly effective\naction that relates the Euler density term to the one point function anomaly,\ngeneralizing our result for two dimensional defects.\n"}
+{"abstract": "  Image denoising and artefact removal are complex inverse problems admitting\nmany potential solutions. Variational Autoencoders (VAEs) can be used to learn\na whole distribution of sensible solutions, from which one can sample\nefficiently. However, such a generative approach to image restoration is only\nstudied in the context of pixel-wise noise removal (e.g. Poisson or Gaussian\nnoise). While important, a plethora of application domains suffer from imaging\nartefacts (structured noises) that alter groups of pixels in correlated ways.\nIn this work we show, for the first time, that generative diversity denoising\n(GDD) approaches can learn to remove structured noises without supervision. To\nthis end, we investigate two existing GDD architectures, introduce a new one\nbased on hierarchical VAEs, and compare their performances against a total of\nseven state-of-the-art baseline methods on five sources of structured noise\n(including tomography reconstruction artefacts and microscopy artefacts). We\nfind that GDD methods outperform all unsupervised baselines and in many cases\nnot lagging far behind supervised results (in some occasions even superseding\nthem). In addition to structured noise removal, we also show that our new GDD\nmethod produces new state-of-the-art (SOTA) results on seven out of eight\nbenchmark datasets for pixel-noise removal. Finally, we offer insights into the\ndaunting question of how GDD methods distinguish structured noise, which we\nlike to see removed, from image signals, which we want to see retained.\n"}
+{"abstract": "  Source traffic prediction is one of the main challenges of enabling\npredictive resource allocation in machine type communications (MTC). In this\npaper, a Long Short-Term Memory (LSTM) based deep learning approach is proposed\nfor event-driven source traffic prediction. The source traffic prediction\nproblem can be formulated as a sequence generation task where the main focus is\npredicting the transmission states of machine-type devices (MTDs) based on\ntheir past transmission data. This is done by restructuring the transmission\ndata in a way that the LSTM network can identify the causal relationship\nbetween the devices. Knowledge of such a causal relationship can enable\nevent-driven traffic prediction. The performance of the proposed approach is\nstudied using data regarding events from MTDs with different ranges of entropy.\nOur model outperforms existing baseline solutions in saving resources and\naccuracy with a margin of around 9%. Reduction in Random Access (RA) requests\nby our model is also analyzed to demonstrate the low amount of signaling\nrequired as a result of our proposed LSTM based source traffic prediction\napproach.\n"}
+{"abstract": "  The torsion models have stood out among the proposals for an alternative\ndescription of gravity. The simplest of them, the Teleparallel theory, is\nequivalent to General Relativity and there are many studies that seek to study\nits extension to more general functions of the torsion $T$. The purpose of {our\nstudy } is to consider a family of $f(T)$ models and apply their corresponding\nTolman-Oppenheimer-Volkof equations to compact objects such as neutron stars.\nConsequently, through a numerical analysis, calculate, among other things, the\nmaximum mass allowed by the model for a neutron star, which would also allow us\nto evaluate which models are in accordance with observations. In the present\npaper, the first in the series, we show explicitly the set of equations that\nmust be solved, and how to solve it, in order to model compact stars in $f(T)$\ngravity without the need to adopt any particular form for the metric functions\nor consider any perturbative approach, as has been done in some works in the\nliterature.\n"}
+{"abstract": "  Twisted bilayers of van der Waals materials have recently attracted great\nattention due to their tunable strongly correlated phenomena. Here, we\ninvestigate the chirality-specific physics in 3D moir\\'e superlattices induced\nby Eshelby twist. Our direct DFT calculations reveal helical rotation leads to\noptical circular dichroism, and chirality-specific nonlinear Hall effect, even\nthough there is no magnetization or magnetic field. Both these phenomena can be\nreversed by changing the structural chirality. This provides a way to\nconstructing chirality-specific materials.\n"}
+{"abstract": "  We introduce an impartial combinatorial game on Steiner triple systems called\nNofil. Players move alternately, choosing points of the triple system. If a\nplayer is forced to fill a block on their turn, they lose. We explore the play\nof Nofil on all Steiner triple systems up to order 15 and a sampling for orders\n19, 21, and 25. We determine the optimal strategies by computing the nim-values\nfor each game and its subgames. The game Nofil can be thought of in terms of\nplay on a corresponding hypergraph. As game play progresses, the hypergraph\nshrinks and will eventually be equivalent to playing the game Node Kayles on an\nisomorphic graph. Node Kayles is well studied and understood. Motivated by\nthis, we study which Node Kayles positions can be reached, i.e. embedded into a\nSteiner triple system. We prove necessary conditions and sufficient conditions\nfor the existence of such graph embeddings and conclude that the complexity of\ndetermining the outcome of the game Nofil on Steiner triple systems is\nPSPACE-complete.\n"}
+{"abstract": "  Federated edge learning (FEEL) has emerged as an effective approach to reduce\nthe large communication latency in Cloud-based machine learning solutions,\nwhile preserving data privacy. Unfortunately, the learning performance of FEEL\nmay be compromised due to limited training data in a single edge cluster. In\nthis paper, we investigate a novel framework of FEEL, namely semi-decentralized\nfederated edge learning (SD-FEEL). By allowing model aggregation across\ndifferent edge clusters, SD-FEEL enjoys the benefit of FEEL in reducing the\ntraining latency, while improving the learning performance by accessing richer\ntraining data from multiple edge clusters. A training algorithm for SD-FEEL\nwith three main procedures in each round is presented, including local model\nupdates, intra-cluster and inter-cluster model aggregations, which is proved to\nconverge on non-independent and identically distributed (non-IID) data. We also\ncharacterize the interplay between the network topology of the edge servers and\nthe communication overhead of inter-cluster model aggregation on the training\nperformance. Experiment results corroborate our analysis and demonstrate the\neffectiveness of SD-FFEL in achieving faster convergence than traditional\nfederated learning architectures. Besides, guidelines on choosing critical\nhyper-parameters of the training algorithm are also provided.\n"}
+{"abstract": "  Mobile manipulation tasks remain one of the critical challenges for the\nwidespread adoption of autonomous robots in both service and industrial\nscenarios. While planning approaches are good at generating feasible whole-body\nrobot trajectories, they struggle with dynamic environments as well as the\nincorporation of constraints given by the task and the environment. On the\nother hand, dynamic motion models in the action space struggle with generating\nkinematically feasible trajectories for mobile manipulation actions. We propose\na deep reinforcement learning approach to learn feasible dynamic motions for a\nmobile base while the end-effector follows a trajectory in task space generated\nby an arbitrary system to fulfill the task at hand. This modular formulation\nhas several benefits: it enables us to readily transform a broad range of\nend-effector motions into mobile applications, it allows us to use the\nkinematic feasibility of the end-effector trajectory as a dense reward signal\nand its modular formulation allows it to generalise to unseen end-effector\nmotions at test time. We demonstrate the capabilities of our approach on\nmultiple mobile robot platforms with different kinematic abilities and\ndifferent types of wheeled platforms in extensive simulated as well as\nreal-world experiments.\n"}
+{"abstract": "  Recognizing human activity plays a significant role in the advancements of\nhuman-interaction applications in healthcare, personal fitness, and smart\ndevices. Many papers presented various techniques for human activity\nrepresentation that resulted in distinguishable progress. In this study, we\nconduct an extensive literature review on recent, top-performing techniques in\nhuman activity recognition based on wearable sensors. Due to the lack of\nstandardized evaluation and to assess and ensure a fair comparison between the\nstate-of-the-art techniques, we applied a standardized evaluation benchmark on\nthe state-of-the-art techniques using six publicly available data-sets:\nMHealth, USCHAD, UTD-MHAD, WISDM, WHARF, and OPPORTUNITY. Also, we propose an\nexperimental, improved approach that is a hybrid of enhanced handcrafted\nfeatures and a neural network architecture which outperformed top-performing\ntechniques with the same standardized evaluation benchmark applied concerning\nMHealth, USCHAD, UTD-MHAD data-sets.\n"}
+{"abstract": "  By means the envelope function approximation, 8-band Kane model and a\nfinite-difference scheme with the coordinate space discretization, we\nnumerically performed calculations of the spin-orbit coupling parameters for 2D\nelectron gas confined in both symmetric and asymmetric [0 0 1] quantum wells\nbased on zinc-blende III-V semiconductors. Influence of the quantum well band\nparameters and width as well as the magnitude of the external electric field\napplied along the growth direction on the Dresselhaus and Rashba spin-orbit\ncoupling parameters is investigated. It has been found that in the symmetric\nInGaAs/GaAs quantum wells linear-in-momentum spin-orbit coupling disappears for\nthe third electron subband at certain values of well width and the indium\ncontent. It is also shown that in asymmetric InGaAs/GaAs structures the\nspin-orbit coupling parameters can be equal at a certain electric field that is\nthe condition for the realization of the SU(2) spin symmetry and formation of\npersistent spin helices. Besides, we calculated the spin-orbit coupling in the\npersistent spin helix regime as a function of the well width, indium content\nand external field. The proposed approach for the calculation of the spin-orbit\ncoupling parameters can be applied to other 2D structures with the spin-orbit\ncoupling.\n"}
+{"abstract": "  Some luminous and ultraluminous infrared galaxies (LIRGs and ULIRGs) host\nextremely compact and dusty nuclei. The intense infrared radiation arising from\nwarm dust in these sources is prone to excite vibrational levels of molecules\nsuch as HCN. This results in emission from the rotational transitions of\nvibrationally excited HCN (HCN-vib), with the brightest emission found in\ncompact obscured nuclei (CONs). We aim to establish how common CONs are in the\nlocal Universe, and whether their prevalence depends on the luminosity or other\nproperties of the host galaxy. We have conducted an Atacama Large\nMillimeter/submillimeter Array (ALMA) survey of the rotational J=3-2 transition\nof HCN-vib in a sample of 46 far-infrared luminous galaxies. Compact obscured\nnuclei are identified in 38 percent of ULIRGs, 21 percent of LIRGs, and 0\npercent of lower luminosity galaxies. We find no dependence on the inclination\nof the host galaxy, but strong evidence of lower IRAS 25 to 60 {\\mu}m flux\ndensity ratios (f25/f60) in CONs compared to the rest of the sample.\nFurthermore, we find that CONs have stronger silicate features (s9.7{\\mu}m) but\nsimilar PAH equivalent widths (EQW6.2{\\mu}m) compared to other galaxies. In the\nlocal Universe, CONs are primarily found in (U)LIRGs. High resolution continuum\nobservations of the individual nuclei are required to determine if the CON\nphenomenon is related to the inclinations of the nuclear disks. The lower\nf25/f60 ratios in CONs as well as the results for the mid-infrared diagnostics\ninvestigated are consistent with large dust columns shifting the nuclear\nradiation to longer wavelengths, making the mid- and far-infrared\n\"photospheres\" significantly cooler than the interior regions. To assess the\nimportance of CONs in the context of galaxy evolution, it is necessary to\nextend this study to higher redshifts where (U)LIRGs are more common.\n"}
+{"abstract": "  Numerous examples of ram pressure stripping in galaxy clusters are present in\nliterature; however, substantially less work has been focused on ram pressure\nstripping in lower mass groups. In this work we use the LOFAR Two-metre Sky\nSurvey (LoTSS) to search for jellyfish galaxies in ~500 SDSS groups (z<0.05),\nmaking this the most comprehensive search for ram pressure stripping in groups\nto date. We identify 60 jellyfish galaxies in groups with extended, asymmetric\nradio continuum tails, which are found across the entire range of group mass\nfrom $10^{12.5} < M_\\mathrm{group} < 10^{14}\\,h^{-1}\\,\\mathrm{M_\\odot}$. We\ncompare the group jellyfish galaxies identified in this work with the LoTSS\njellyfish galaxies in clusters presented in Roberts et al. (2021), allowing us\nto compare the effects of ram pressure stripping across three decades in\ngroup/cluster mass. We find that jellyfish galaxies are most commonly found in\nclusters, with the frequency decreasing towards the lowest mass groups. Both\nthe orientation of observed radio continuum tails, and the positions of group\njellyfish galaxies in phase space, suggest that galaxies are stripped more\nslowly in groups relative to clusters. Finally, we find that the star formation\nrates of jellyfish galaxies in groups are consistent with `normal' star-forming\ngroup galaxies, which is in contrast to cluster jellyfish galaxies that have\nclearly enhanced star formation rates. On the whole, there is clear evidence\nfor ongoing ram pressure stripping in galaxy groups (down to very low group\nmasses), though the frequency of jellyfish galaxies and the strength of ram\npressure stripping appears smaller in groups than clusters. Differences in the\nefficiency of ram pressure stripping in groups versus clusters likely\ncontributes to the positive trend between quenched fraction and host halo mass\nobserved in the local Universe.\n"}
+{"abstract": "  There are three approaches in the current social network analysis study:\nGraph Representation, Content Mining, and Semantic Analysis. Graph\nRepresentation has been used for analyzing social network topology, structural\nmodeling, tie-strength, community detection, group cohesion visualization, and\nmetrics computations. This paper provides a taxonomy of social network analysis\nbased on its graph representation.\n"}
+{"abstract": "  We propose a large, scalable engineering knowledge graph, comprising sets of\n(entity, relationship, entity) triples that are real-world engineering facts\nfound in the patent database. We apply a set of rules based on the syntactic\nand lexical properties of claims in a patent document to extract facts. We\naggregate these facts within each patent document and integrate the aggregated\nsets of facts across the patent database to obtain the engineering knowledge\ngraph. Such a knowledge graph is expected to support inference, reasoning, and\nrecalling in various engineering tasks. The knowledge graph has a greater size\nand coverage in comparison with the previously used knowledge graphs and\nsemantic networks in the engineering literature.\n"}
+{"abstract": "  Understanding the dynamics of complex systems is a central task in many\ndifferent areas ranging from biology via epidemics to economics and\nengineering. Unexpected behaviour of dynamic systems or even system failure is\nsometimes difficult to comprehend. Such a data-mismatch can be caused by\nendogenous model errors including misspecified interactions and inaccurate\nparameter values. These are often difficult to distinguish from unmodelled\nprocess influencing the real system like unknown inputs or faults. Localizing\nthe root cause of these errors or faults and reconstructing their dynamics is\nonly possible if the measured outputs of the system are sufficiently\ninformative.\n  Here, we present criteria for the measurements required to localize the\nposition of error sources in large dynamic networks. We assume that faults or\nerrors occur at a limited number of positions in the network. This invariable\nsparsity differs from previous sparsity definitions for inputs to dynamic\nsystems. We provide an exact criterion for the recovery of invariable sparse\ninputs to nonlinear systems and formulate an optimization criterion for\ninvariable sparse input reconstruction. For linear systems we can provide exact\nerror bounds for this reconstruction method.\n"}
+{"abstract": "  Anomia (word-finding difficulties) is the hallmark of aphasia, an acquired\nlanguage disorder most commonly caused by stroke. Assessment of speech\nperformance using picture naming tasks is a key method for both diagnosis and\nmonitoring of responses to treatment interventions by people with aphasia\n(PWA). Currently, this assessment is conducted manually by speech and language\ntherapists (SLT). Surprisingly, despite advancements in automatic speech\nrecognition (ASR) and artificial intelligence with technologies like deep\nlearning, research on developing automated systems for this task has been\nscarce. Here we present NUVA, an utterance verification system incorporating a\ndeep learning element that classifies 'correct' versus' incorrect' naming\nattempts from aphasic stroke patients. When tested on eight native\nBritish-English speaking PWA the system's performance accuracy ranged between\n83.6% to 93.6%, with a 10-fold cross-validation mean of 89.5%. This performance\nwas not only significantly better than a baseline created for this study using\none of the leading commercially available ASRs (Google speech-to-text service)\nbut also comparable in some instances with two independent SLT ratings for the\nsame dataset.\n"}
+{"abstract": "  The advent of powerful prediction algorithms led to increased automation of\nhigh-stake decisions regarding the allocation of scarce resources such as\ngovernment spending and welfare support. This automation bears the risk of\nperpetuating unwanted discrimination against vulnerable and historically\ndisadvantaged groups. Research on algorithmic discrimination in computer\nscience and other disciplines developed a plethora of fairness metrics to\ndetect and correct discriminatory algorithms. Drawing on robust sociological\nand philosophical discourse on distributive justice, we identify the\nlimitations and problematic implications of prominent fairness metrics. We show\nthat metrics implementing equality of opportunity only apply when resource\nallocations are based on deservingness, but fail when allocations should\nreflect concerns about egalitarianism, sufficiency, and priority. We argue that\nby cleanly distinguishing between prediction tasks and decision tasks, research\non fair machine learning could take better advantage of the rich literature on\ndistributive justice.\n"}
+{"abstract": "  In recent years, adversarial attacks have drawn more attention for their\nvalue on evaluating and improving the robustness of machine learning models,\nespecially, neural network models. However, previous attack methods have mainly\nfocused on applying some $l^p$ norm-bounded noise perturbations. In this paper,\nwe instead introduce a novel adversarial attack method based on haze, which is\na common phenomenon in real-world scenery. Our method can synthesize\npotentially adversarial haze into an image based on the atmospheric scattering\nmodel with high realisticity and mislead classifiers to predict an incorrect\nclass. We launch experiments on two popular datasets, i.e., ImageNet and\nNIPS~2017. We demonstrate that the proposed method achieves a high success\nrate, and holds better transferability across different classification models\nthan the baselines. We also visualize the correlation matrices, which inspire\nus to jointly apply different perturbations to improve the success rate of the\nattack. We hope this work can boost the development of non-noise-based\nadversarial attacks and help evaluate and improve the robustness of DNNs.\n"}
+{"abstract": "  Sublinear circuits are generalizations of the affine circuits in matroid\ntheory, and they arise as the convex-combinatorial core underlying constrained\nnon-negativity certificates of exponential sums and of polynomials based on the\narithmetic-geometric inequality. Here, we study the polyhedral combinatorics of\nsublinear circuits for polyhedral constraint sets.\n  We give results on the relation between the sublinear circuits and their\nsupports and provide necessary as well as sufficient criteria for sublinear\ncircuits. Based on these characterizations, we provide some explicit results\nand enumerations for two prominent polyhedral cases, namely the non-negative\northant and the cube $[-1,1]^n$.\n"}
+{"abstract": "  Being able to efficiently and accurately select the top-$k$ elements with\ndifferential privacy is an integral component of various private data analysis\ntasks. In this paper, we present the oneshot Laplace mechanism, which\ngeneralizes the well-known Report Noisy Max mechanism to reporting noisy\ntop-$k$ elements. We show that the oneshot Laplace mechanism with a noise level\nof $\\widetilde{O}(\\sqrt{k}/\\eps)$ is approximately differentially private.\nCompared to the previous peeling approach of running Report Noisy Max $k$\ntimes, the oneshot Laplace mechanism only adds noises and computes the top $k$\nelements once, hence much more efficient for large $k$. In addition, our proof\nof privacy relies on a novel coupling technique that bypasses the use of\ncomposition theorems. Finally, we present a novel application of efficient\ntop-$k$ selection in the classical problem of ranking from pairwise\ncomparisons.\n"}
+{"abstract": "  Structural properties of large random maps and lambda-terms may be gleaned by\nstudying the limit distributions of various parameters of interest. In our work\nwe focus on restricted classes of maps and their counterparts in the\nlambda-calculus, building on recent bijective connections between these two\ndomains. In such cases, parameters in maps naturally correspond to parameters\nin lambda-terms and vice versa. By an interplay between lambda-terms and maps,\nwe obtain various combinatorial specifications which allow us to access the\ndistributions of pairs of related parameters such as: the number of bridges in\nrooted trivalent maps and of subterms in closed linear lambda-terms, the number\nof vertices of degree 1 in (1,3)-valent maps and of free variables in open\nlinear lambda-terms etc. To analyse asymptotically these distributions, we\nintroduce appropriate tools: a moment-pumping schema for differential equations\nand a composition schema inspired by Bender's theorem.\n"}
+{"abstract": "  We present a new operator method in the Heisenberg representation to obtain\nthe signal of parity measurement within a lossless SU(1,1) interferometer.\nBased on this method, it is convenient to derive the parity signal directly in\nterms of input states, including general Gaussian or non-Gaussian state. As\napplications, we revisit the signal of parity measurement within an SU(1,1)\ninterferometer when a coherent or thermal state and a squeezed vacuum state are\nconsidered as input states. In addition, we also obtain the parity signal of a\nFock state when it passes through an SU(1,1) interferometer, which is also a\nnew result. Therefore, the operator method proposed in this work may bring\nconvenience to the study of quantum metrology, particularly the phase\nestimation based on an SU(1,1) interferometer.\n"}
+{"abstract": "  We study the localization properties of the eigenmodes of the staggered Dirac\noperator in finite-temperature $\\mathbb{Z}_2$ pure gauge theory on the lattice\nin 2+1 dimensions. We find that the low modes turn from delocalized to\nlocalized as the system crosses over from the confined to the deconfined phase\nin the \"physical\" sector (positive average Polyakov loop) selected by external\nfermionic probes, while they remain delocalized in the \"unphysical\" sector\n(negative average Polyakov loop). This confirms that the close connection\nbetween deconfinement and localization of the low Dirac modes in the physical\nsector, already observed in other models, holds also in the simplest gauge\ntheory displaying a deconfinement transition. We also observe a clear\ncorrelation of localized modes with fluctuations of the Polyakov loop away from\nthe ordered value, as expected according to the \"sea/islands\" picture of\nlocalization, and with clusters of negative plaquettes. A novel finding is the\npresence of localized modes at the high end of the Dirac spectrum in all\nphases/sectors of the theory.\n"}
+{"abstract": "  Identifying how a file has been created is often interesting in security. It\ncan be used by both attackers and defenders. Attackers can exploit this\ninformation to tune their attacks and defenders can understand how a malicious\nfile has been created after an incident. In this work, we want to identify how\na PDF file has been created. This problem is important because PDF files are\nextremely popular: many organizations publish PDF files online and malicious\nPDF files are commonly used by attackers. Our approach to detect which software\nhas been used to produce a PDF file is based on coding style: given patterns\nthat are only created by certain PDF producers. We have analyzed the coding\nstyle of 900 PDF files produced using 11 PDF producers on 3 different Operating\nSystems. We have obtained a set of 192 rules which can be used to identify 11\nPDF producers. We have tested our detection tool on 508836 PDF files published\non scientific preprints servers. Our tool is able to detect certain producers\nwith an accuracy of 100%. Its overall detection is still high (74%). We were\nable to apply our tool to identify how online PDF services work and to spot\ninconsistency.\n"}
+{"abstract": "  In this paper, the use of the Smith-McMillan form in decoupling\nmultiple-input multiple-output system dynamics is analyzed. In short, from a\ntransfer matrix plant model one can obtain a decoupling compensator which leads\nto a decoupled plant that contains the same transmission poles and zeros of the\ntransfer matrix of the original system. As a result, full decoupling of the\nplant transfer matrix is obtained for all frequencies, which can be\nindividually addressed by single-input single-output control. The aim of this\npaper is to present conditions for the decoupled system that guarantee internal\nstability and performance requirements for the overall control system.\nPerformance specifications are defined in terms of magnitude limits for the\nmaximum singular value of the closed-loop transfer matrices. The potential of\nthe decoupling procedure is shown in a simulation study of a mechanical system.\n"}
+{"abstract": "  We show the ferro-rotational nature of the commensurate charge density wave\n(CCDW) in $1T$-$\\mathrm{TaS}_2$ and track its dynamic modulations by\ntemperature-dependent and time-resolved electric quadrupole rotation\nanisotropy-second harmonic generation (EQ RA-SHG), respectively. The ultrafast\nmodulations manifest as the breathing and the rotation of the EQ RA-SHG\npatterns at three frequencies around the reported single CCDW amplitude mode\nfrequency. A sudden shift of the triplet frequencies and a dramatic increase in\nthe breathing and rotation magnitude further reveal a photo-induced transient\nCDW phase across a critical pump fluence of ~ 0.5 mJ/cm$^2$.\n"}
+{"abstract": "  We consider high dimensional variants of the maximum flow and minimum cut\nproblems in the setting of simplicial complexes and provide both algorithmic\nand hardness results. By viewing flows and cuts topologically in terms of the\nsimplicial (co)boundary operator we can state these problems as linear programs\nand show that they are dual to one another. Unlike graphs, complexes with\nintegral capacity constraints may have fractional max-flows. We show that\ncomputing a maximum integral flow is NP-hard. Moreover, we give a combinatorial\ndefinition of a simplicial cut that seems more natural in the context of\noptimization problems and show that computing such a cut is NP-hard. However,\nwe provide conditions on the simplicial complex for when the cut found by the\nlinear program is a combinatorial cut. For $d$-dimensional simplicial complexes\nembedded into $\\mathbb{R}^{d+1}$ we provide algorithms operating on the dual\ngraph: computing a maximum flow is dual to computing a shortest path and\ncomputing a minimum cut is dual to computing a minimum cost circulation.\nFinally, we investigate the Ford-Fulkerson algorithm on simplicial complexes,\nprove its correctness, and provide a heuristic which guarantees it to halt.\n"}
+{"abstract": "  We provide several algorithms for constrained optimization of a large class\nof convex problems, including softmax, $\\ell_p$ regression, and logistic\nregression. Central to our approach is the notion of width reduction, a\ntechnique which has proven immensely useful in the context of maximum flow\n[Christiano et al., STOC'11] and, more recently, $\\ell_p$ regression [Adil et\nal., SODA'19], in terms of improving the iteration complexity from $O(m^{1/2})$\nto $\\tilde{O}(m^{1/3})$, where $m$ is the number of rows of the design matrix,\nand where each iteration amounts to a linear system solve. However, a\nconsiderable drawback is that these methods require both problem-specific\npotentials and individually tailored analyses.\n  As our main contribution, we initiate a new direction of study by presenting\nthe first unified approach to achieving $m^{1/3}$-type rates. Notably, our\nmethod goes beyond these previously considered problems to more broadly capture\nquasi-self-concordant losses, a class which has recently generated much\ninterest and includes the well-studied problem of logistic regression, among\nothers. In order to do so, we develop a unified width reduction method for\ncarefully handling these losses based on a more general set of potentials.\nAdditionally, we directly achieve $m^{1/3}$-type rates in the constrained\nsetting without the need for any explicit acceleration schemes, thus naturally\ncomplementing recent work based on a ball-oracle approach [Carmon et al.,\nNeurIPS'20].\n"}
+{"abstract": "  We present MT-lib, an efficient message transfer library for messages gather\nand scatter in benchmarks like Graph500 for Supercomputers. Our library\nincludes MST version as well as new-MST version. The MT-lib is deliberately\nkept light-weight, efficient and friendly interfaces for massive graph\ntraverse. MST provides (1) a novel non-blocking communication scheme with\nsending and receiving messages asynchronously to overlap calculation and\ncommunication;(2) merging messages according to the target process for reducing\ncommunication overhead;(3) a new communication mode of gathering intra-group\nmessages before forwarding between groups for reducing communication traffic.\nIn MT-lib, there are (1) one-sided message; (2) two-sided messages; and (3)\ntwo-sided messages with buffer, in which dynamic buffer expansion is built for\nmessages delivery. We experimented with MST and then testing Graph500 with MST\non Tianhe supercomputers. Experimental results show high communication\nefficiency and high throughputs for both BFS and SSSP communication operations.\n"}
+{"abstract": "  The space-borne missions CoRoT and Kepler opened up a new opportunity for\nbetter understanding stellar evolution by probing stellar interiors with\nunrivalled high-precision photometric data. Kepler has observed stellar\noscillation for four years, which gave access to excellent frequency resolution\nthat enables deciphering the oscillation spectrum of evolved red giant branch\nand asymptotic giant branch stars. The internal structure of stars in the upper\nparts of the red and asymptotic giant branches is poorly constrained, which\nmakes the distinction between red and asymptotic giants difficult. We perform a\nthorough seismic analysis to address the physical conditions inside these stars\nand to distinguish them. We studied the oscillation mode properties of about\n2.000 evolved giants in a model described by the asymptotic pressure-mode\npattern of red giants, which includes the signature of the helium\nsecond-ionisation zone. We extracted the mode properties up to the degree l = 3\nand investigated their dependence on stellar mass, metallicity, and\nevolutionary status. We identify a clear difference in the signature of the\nhelium second-ionisation zone between red and asymptotic giants. We also detect\na clear shortage of the energy of l = 1 modes after the core-He-burning phase.\nFurthermore, we note that the mode damping observed on the asymptotic giant\nbranch is similar to that observed on the red giant branch. We highlight that\nthe signature of the helium second-ionisation zone varies with stellar\nevolution. This provides us with a physical basis for distinguishing red giant\nbranch stars from asymptotic giants. Here, our investigation of stellar\noscillations allows us to constrain the physical processes and the key events\nthat occur during the advanced stages of stellar evolution, with emphasis on\nthe ascent along the asymptotic giant branch, including the asymptotic giant\nbranch bump.\n"}
+{"abstract": "  We point out that common evaluation practices for cross-document coreference\nresolution have been unrealistically permissive in their assumed settings,\nyielding inflated results. We propose addressing this issue via two evaluation\nmethodology principles. First, as in other tasks, models should be evaluated on\npredicted mentions rather than on gold mentions. Doing this raises a subtle\nissue regarding singleton coreference clusters, which we address by decoupling\nthe evaluation of mention detection from that of coreference linking. Second,\nwe argue that models should not exploit the synthetic topic structure of the\nstandard ECB+ dataset, forcing models to confront the lexical ambiguity\nchallenge, as intended by the dataset creators. We demonstrate empirically the\ndrastic impact of our more realistic evaluation principles on a competitive\nmodel, yielding a score which is 33 F1 lower compared to evaluating by prior\nlenient practices.\n"}
+{"abstract": "  In frequency-division duplexing systems, the downlink channel state\ninformation (CSI) acquisition scheme leads to high training and feedback\noverheads. In this paper, we propose an uplink-aided downlink channel\nacquisition framework using deep learning to reduce these overheads. Unlike\nmost existing works that focus only on channel estimation or feedback modules,\nto the best of our knowledge, this is the first study that considers the entire\ndownlink CSI acquisition process, including downlink pilot design, channel\nestimation, and feedback. First, we propose an adaptive pilot design module by\nexploiting the correlation in magnitude among bidirectional channels in the\nangular domain to improve channel estimation. Next, to avoid the bit allocation\nproblem during the feedback module, we concatenate the complex channel and\nembed the uplink channel magnitude to the channel reconstruction at the base\nstation. Lastly, we combine the above two modules and compare two popular\ndownlink channel acquisition frameworks. The former framework estimates and\nfeeds back the channel at the user equipment subsequently. The user equipment\nin the latter one directly feeds back the received pilot signals to the base\nstation. Our results reveal that, with the help of uplink, directly feeding\nback the pilot signals can save approximately 20% of feedback bits, which\nprovides a guideline for future research.\n"}
+{"abstract": "  We present a study of the Corona Borealis (CB) supercluster. We determined\nthe high-density cores of the CB and the richest galaxy clusters in them, and\nstudied their dynamical state and galaxy content. We determined filaments in\nthe supercluster to analyse the connectivity of clusters. We compared the mass\ndistribution in the CB with predictions from the spherical collapse model and\nanalysed the acceleration field in the CB. We found that at a radius\n$R_{\\mathrm{30}}$ around clusters in the CB (A2065, A2061, A2089, and Gr2064)\n(corresponding to the density contrast $\\Delta\\rho \\approx 30$), the galaxy\ndistribution shows a minimum. The $R_{30}$ values for individual clusters lie\nin the range of $3 - 6$ $h^{-1}$ Mpc. The radii of the clusters (splashback\nradii) lie in the range of $R_{\\mathrm{cl}} \\approx 2 - 3$ $R_{\\mathrm{vir}}$.\nThe projected phase space diagrams and the comparison with the spherical\ncollapse model suggest that $R_{\\mathrm{30}}$ regions have passed turnaround\nand are collapsing. Galaxy content in clusters varies strongly. The cluster\nA2061 has the highest fraction of galaxies with old stellar populations, and\nA2065 has the highest fraction of galaxies with young stellar populations. The\nnumber of long filaments near clusters vary from one at A2089 to five at A2061.\nDuring the future evolution, the clusters in the main part of the CB may merge\nand form one of the largest bound systems in the nearby Universe. Another part\nof the CB, with the cluster Gr2064, will form a separate system. The structures\nwith a current density contrast $\\Delta\\rho \\approx 30$ have passed turnaround\nand started to collapse at redshifts $z \\approx 0.3 - 0.4$. The comparison of\nthe number and properties of the most massive collapsing supercluster cores\nfrom observations and simulations may serve as a test for cosmological models.\n"}
+{"abstract": "  Automatic speech recognition (ASR) systems promise to deliver objective\ninterpretation of human speech. Practice and recent evidence suggests that the\nstate-of-the-art (SotA) ASRs struggle with the large variation in speech due to\ne.g., gender, age, speech impairment, race, and accents. Many factors can cause\nthe bias of an ASR system. Our overarching goal is to uncover bias in ASR\nsystems to work towards proactive bias mitigation in ASR. This paper is a first\nstep towards this goal and systematically quantifies the bias of a Dutch SotA\nASR system against gender, age, regional accents and non-native accents. Word\nerror rates are compared, and an in-depth phoneme-level error analysis is\nconducted to understand where bias is occurring. We primarily focus on bias due\nto articulation differences in the dataset. Based on our findings, we suggest\nbias mitigation strategies for ASR development.\n"}
+{"abstract": "  Scenario-based stochastic optimal control problems suffer from the curse of\ndimensionality as they can easily grow to six and seven figure sizes.\nFirst-order methods are suitable as they can deal with such large-scale\nproblems, but may fail to achieve accurate solutions within a reasonable number\nof iterations. To achieve solutions of higher accuracy and high speed, in this\npaper we propose two proximal quasi-Newtonian limited-memory algorithms -\nMinFBE applied to the dual problem and the Newton-type alternating minimization\nalgorithm (NAMA) - which can be massively parallelized on lockstep hardware\nsuch as graphics processing units (GPUs). We demonstrate the performance of\nthese methods, in terms of convergence speed and parallelizability, on\nlarge-scale problems involving millions of variables.\n"}
+{"abstract": "  When the input to a deep neural network (DNN) is a video signal, a sequence\nof feature tensors is produced at the intermediate layers of the model. If\nneighboring frames of the input video are related through motion, a natural\nquestion is, \"what is the relationship between the corresponding feature\ntensors?\" By analyzing the effect of common DNN operations on optical flow, we\nshow that the motion present in each channel of a feature tensor is\napproximately equal to the scaled version of the input motion. The analysis is\nvalidated through experiments utilizing common motion models. %These results\nwill be useful in collaborative intelligence applications where sequences of\nfeature tensors need to be compressed or further analyzed.\n"}
+{"abstract": "  Imitation learning aims to extract knowledge from human experts'\ndemonstrations or artificially created agents in order to replicate their\nbehaviors. Its success has been demonstrated in areas such as video games,\nautonomous driving, robotic simulations and object manipulation. However, this\nreplicating process could be problematic, such as the performance is highly\ndependent on the demonstration quality, and most trained agents are limited to\nperform well in task-specific environments. In this survey, we provide a\nsystematic review on imitation learning. We first introduce the background\nknowledge from development history and preliminaries, followed by presenting\ndifferent taxonomies within Imitation Learning and key milestones of the field.\nWe then detail challenges in learning strategies and present research\nopportunities with learning policy from suboptimal demonstration, voice\ninstructions and other associated optimization schemes.\n"}
+{"abstract": "  In this paper, we address the problem of global-scale image geolocation,\nproposing a mixed classification-retrieval scheme. Unlike other methods that\nstrictly tackle the problem as a classification or retrieval task, we combine\nthe two practices in a unified solution leveraging the advantages of each\napproach with two different modules. The first leverages the EfficientNet\narchitecture to assign images to a specific geographic cell in a robust way.\nThe second introduces a new residual architecture that is trained with\ncontrastive learning to map input images to an embedding space that minimizes\nthe pairwise geodesic distance of same-location images. For the final location\nestimation, the two modules are combined with a search-within-cell scheme,\nwhere the locations of most similar images from the predicted geographic cell\nare aggregated based on a spatial clustering scheme. Our approach demonstrates\nvery competitive performance on four public datasets, achieving new\nstate-of-the-art performance in fine granularity scales, i.e., 15.0% at 1km\nrange on Im2GPS3k.\n"}
+{"abstract": "  Colorectal cancer (CRC) is one of the most common types of cancer with a high\nmortality rate. Colonoscopy is the preferred procedure for CRC screening and\nhas proven to be effective in reducing CRC mortality. Thus, a reliable\ncomputer-aided polyp detection and classification system can significantly\nincrease the effectiveness of colonoscopy. In this paper, we create an\nendoscopic dataset collected from various sources and annotate the ground truth\nof polyp location and classification results with the help of experienced\ngastroenterologists. The dataset can serve as a benchmark platform to train and\nevaluate the machine learning models for polyp classification. We have also\ncompared the performance of eight state-of-the-art deep learning-based object\ndetection models. The results demonstrate that deep CNN models are promising in\nCRC screening. This work can serve as a baseline for future research in polyp\ndetection and classification.\n"}
+{"abstract": "  A combination of high strength and reasonable ductility has been achieved in\na copper-1.7 at.%titanium alloy deformed by high-pressure torsion. Grain\nrefinement and a spinodal microstructure provided a hardness of 254 +/- 2 HV ,\nyield strength of 800 MPa and elongation of 10%. The spinodal structure\npersisted during isothermal ageing, further increasing the yield strength to\n890MPa while retaining an elongation of 7%. This work demonstrates the\npotential for spinodal microstructures to overcome the difficulties in\nretaining ductility in ultra-fine grained or nanocrystalline alloys, especially\nupon post-deformation heating where strain softening normally results in\nbrittle behavior.\n"}
+{"abstract": "  We present a method to over-approximate reachable tubes over compact\ntime-intervals, for linear continuous-time, time-varying control systems whose\ninitial states and inputs are subject to compact convex uncertainty. The method\nuses numerical approximations of transition matrices, is convergent of first\norder, and assumes the ability to compute with compact convex sets in finite\ndimension. We also present a variant that applies to the case of zonotopic\nuncertainties, uses only linear algebraic operations, and yields zonotopic\nover-approximations. The performance of the latter variant is demonstrated on\nan example.\n"}
+{"abstract": "  Assume ZFC. Let $\\kappa$ be a cardinal. Recall that a ${<\\kappa}$-ground is a\ntransitive proper class $W$ modelling ZFC such that $V$ is a generic extension\nof $W$ via a forcing $\\mathbb{P}\\in W$ of cardinality ${<\\kappa}$, and the\n$\\kappa$-mantle is the intersection of all ${<\\kappa}$-grounds.\n  Assume there is a Woodin cardinal and a proper class of measurables, and let\n$x$ be a real of sufficiently high Turing degree. Let $\\kappa$ be a limit\ncardinal of $L[x]$ of uncountable cofinality in $L[x]$. Using methods from\nWoodin's analysis of $\\mathrm{HOD}^{L[x,G]}$, we analyze the $\\kappa$-mantle of\n$L[x]$, and show that it models ZFC + GCH + \"There is a Woodin cardinal\".\nMoreover, we show that it is a fully iterable strategy mouse (analogous to\n$\\mathrm{HOD}^{L[x,G]}$). We also analyze another form of \"local mantle\",\npartly assuming also a weak form of Turing determinacy. We also compute bounds\non how much iteration strategy can be added to $M_1$ before $M_1^\\#$ is added.\n"}
+{"abstract": "  Contrastive self-supervised learning has largely narrowed the gap to\nsupervised pre-training on ImageNet. However, its success highly relies on the\nobject-centric priors of ImageNet, i.e., different augmented views of the same\nimage correspond to the same object. Such a heavily curated constraint becomes\nimmediately infeasible when pre-trained on more complex scene images with many\nobjects. To overcome this limitation, we introduce Object-level Representation\nLearning (ORL), a new self-supervised learning framework towards scene images.\nOur key insight is to leverage image-level self-supervised pre-training as the\nprior to discover object-level semantic correspondence, thus realizing\nobject-level representation learning from scene images. Extensive experiments\non COCO show that ORL significantly improves the performance of self-supervised\nlearning on scene images, even surpassing supervised ImageNet pre-training on\nseveral downstream tasks. Furthermore, ORL improves the downstream performance\nwhen more unlabeled scene images are available, demonstrating its great\npotential of harnessing unlabeled data in the wild. We hope our approach can\nmotivate future research on more general-purpose unsupervised representation\nlearning from scene data.\n"}
+{"abstract": "  Eta Carinae is a massive interacting binary system shrouded in a complex\ncircumstellar environment whose evolution is the source of the long-term\nbrightening observed during the last 80 years. An occulter, acting as a natural\ncoronagraph, impacts observations from our perspective, but not from most other\ndirections. Other sight-lines are visible to us through studies of the\nHomunculus reflection nebula. The coronagraph appears to be vanishing,\ndecreasing the extinction towards the central star, and causing the star's\nsecular brightening. In contrast, the Homunculus remains at an almost constant\nbrightness. The coronagraph primarily suppresses the stellar continuum, to a\nlesser extent the wind lines, and not the circumstellar emission lines. This\nexplains why the absolute values of equivalent widths (EWs) of the emission\nlines in our direct view are larger than those seen in reflected by the\nHomunculus, why the direct view absolute EWs are decreasing with time, and why\nlower-excitation spectral wind lines formed at larger radii (e.g. FeII 4585A)\ndecrease in intensity at a faster pace than higher excitation lines that form\ncloser to the star (e.g. H\\delta). Our main result is that the star, despite\nits 10-fold brightening over two decades, is relatively stable. A vanishing\ncoronagraph that can explain both the large flux evolution and the much weaker\nspectral evolution. This is contrary to suggestions that the long-term\nvariability is intrinsic to the primary star that is still recovering from the\nGreat Eruption with a decreasing mass-loss rate and a polar wind that is\nevolving at a slower pace than at the equator.\n"}
+{"abstract": "  We investigate some properties and convergence theorem of\nKluv\\'{a}nek-Lewis-Henstock $\\m-$integrability for $\\m-$measurable functions\nthat we introduced in \\cite{ABH}. We give a $\\m-$a.e. convergence version of\nDominated (resp. Bounded) Convergence Theorem for $\\m.$ We introduce\nKluv\\'{a}nek-Lewis-Henstock integrable of scalar-valued functions with respect\nto a set valued measure in a Banach space. Finally we introduce $(KL)-$type\nDominated Convergence Theorem for the set-valued Kluv\\'{a}nek-Lewis-Henstock\nintegral.\n"}
+{"abstract": "  As is known, the irreducible projective representations (Reps) of\nanti-unitary groups contain three different situations, namely, the real, the\ncomplex and quaternion types with torsion number 1,2,4 respectively. This\nsubtlety increases the complexity in obtaining irreducible projective Reps of\nanti-unitary groups. In the present work, a physical approach is introduced to\nderive the condition of irreducibility for projective Reps of anti-unitary\ngroups. Then a practical procedure is provided to reduce an arbitrary\nprojective Rep into direct sum of irreducible ones. The central idea is to\nconstruct a hermitian Hamiltonian matrix which commutes with the representation\nof every group element $g\\in G$, such that each of its eigenspaces forms an\nirreducible representation space of the group $G$. Thus the Rep is completely\nreduced in the eigenspaces of the Hamiltonian. This approach is applied in the\n$k\\cdot p$ effective theory at the high symmetry points (HSPs) of the Brillouin\nzone for quasi-particle excitations in magnetic materials. After giving the\ncriterion to judge the power of single-particle dispersion around a HSP, we\nthen provide a systematic procedure to construct the $k\\cdot p$ effective\nmodel.\n"}
+{"abstract": "  In this work, we utilize progressive growth-based Generative Adversarial\nNetworks (GANs) to develop the Clarkson Fingerprint Generator (CFG). We\ndemonstrate that the CFG is capable of generating realistic, high fidelity,\n$512\\times512$ pixels, full, plain impression fingerprints. Our results suggest\nthat the fingerprints generated by the CFG are unique, diverse, and resemble\nthe training dataset in terms of minutiae configuration and quality, while not\nrevealing the underlying identities of the training data. We make the\npre-trained CFG model and the synthetically generated dataset publicly\navailable at https://github.com/keivanB/Clarkson_Finger_Gen\n"}
+{"abstract": "  Gaussian Process Regression is a popular nonparametric regression method\nbased on Bayesian principles that provides uncertainty estimates for its\npredictions. However, these estimates are of a Bayesian nature, whereas for\nsome important applications, like learning-based control with safety\nguarantees, frequentist uncertainty bounds are required. Although such rigorous\nbounds are available for Gaussian Processes, they are too conservative to be\nuseful in applications. This often leads practitioners to replacing these\nbounds by heuristics, thus breaking all theoretical guarantees. To address this\nproblem, we introduce new uncertainty bounds that are rigorous, yet practically\nuseful at the same time. In particular, the bounds can be explicitly evaluated\nand are much less conservative than state of the art results. Furthermore, we\nshow that certain model misspecifications lead to only graceful degradation. We\ndemonstrate these advantages and the usefulness of our results for\nlearning-based control with numerical examples.\n"}
+{"abstract": "  We established a method for obtaining set-theoretical solutions to the 3D\nreflection equation by using known ones to the Zamolodchikov tetrahedron\nequation, where the former equation was proposed by Isaev and Kulish as a\nboundary analog of the latter. By applying our method to Sergeev's electrical\nsolution and a two-component solution associated with the discrete modified KP\nequation, we obtain new solutions to the 3D reflection equation. Our approach\nis closely related to a relation between the transition maps of Lusztig's\nparametrizations of the totally positive part of $SL_3$ and $SO_5$, which is\nobtained via folding the Dynkin diagram of $A_3$ into one of $B_2$.\n"}
+{"abstract": "  The Gaussian Process with a deep kernel is an extension of the classic GP\nregression model and this extended model usually constructs a new kernel\nfunction by deploying deep learning techniques like long short-term memory\nnetworks. A Gaussian Process with the kernel learned by LSTM, abbreviated as\nGP-LSTM, has the advantage of capturing the complex dependency of financial\nsequential data, while retaining the ability of probabilistic inference.\nHowever, the deep kernel Gaussian Process has not been applied to forecast the\nconditional returns and volatility in financial market to the best of our\nknowledge. In this paper, grid search algorithm, used for performing\nhyper-parameter optimization, is integrated with GP-LSTM to predict both the\nconditional mean and volatility of stock returns, which are then combined\ntogether to calculate the conditional Sharpe Ratio for constructing a\nlong-short portfolio. The experiments are performed on a dataset covering all\nconstituents of Shenzhen Stock Exchange Component Index. Based on empirical\nresults, we find that the GP-LSTM model can provide more accurate forecasts in\nstock returns and volatility, which are jointly evaluated by the performance of\nconstructed portfolios. Further sub-period analysis of the experiment results\nindicates that the superiority of GP-LSTM model over the benchmark models stems\nfrom better performance in highly volatile periods.\n"}
+{"abstract": "  We develop fractional buffer layers (FBLs) to absorb propagating waves\nwithout reflection in bounded domains. Our formulation is based on\nvariable-order spatial fractional derivatives. We select a proper\nvariable-order function so that dissipation is induced to absorb the coming\nwaves in the buffer layers attached to the domain. In particular, we first\ndesign proper FBsL for the one-dimensional one-way and two-way wave\npropagation. Then, we extend our formulation to two-dimensional problems, where\nwe introduce a consistent variable-order fractional wave equation. In each\ncase, we obtain the fully discretized equations by employing a spectral\ncollocation method in space and Crank-Nicolson or Adams-Bashforth method in\ntime. We compare our results with the perfectly matched layer (PML) method and\nshow the effectiveness of FBL in accurately suppressing any erroneously\nreflected waves, including corner reflections in two-dimensional rectangular\ndomains. FBLs can be used in conjunction with any discretization method\nappropriate for fractional operators describing wave propagation in bounded or\ntruncated domains.\n"}
+{"abstract": "  This paper presents iBatch, a middleware system running on top of an\noperational Ethereum network to enable secure batching of smart-contract\ninvocations against an untrusted relay server off-chain. iBatch does so at a\nlow overhead by validating the server's batched invocations in smart contracts\nwithout additional states. The iBatch mechanism supports a variety of policies,\nranging from conservative to aggressive batching, and can be configured\nadaptively to the current workloads. iBatch automatically rewrites smart\ncontracts to integrate with legacy applications and support large-scale\ndeployment.\n  For cost evaluation, we develop a platform with fast and cost-accurate\ntransaction replaying, build real transaction benchmarks on popular Ethereum\napplications, and build a functional prototype of iBatch on Ethereum. The\nevaluation results show that iBatch saves 14.6%-59.1% Gas cost per invocation\nwith a moderate 2-minute delay and 19.06%-31.52% Ether cost per invocation with\na delay of 0.26-1.66 blocks.\n"}
+{"abstract": "  Dynamical systems that are subject to continuous uncertain fluctuations can\nbe modelled using Stochastic Differential Equations (SDEs). Controlling such\nsystems results in solving path constrained SDEs. Broadly, these problems fall\nunder the category of Stochastic Differential-Algebraic Equations (SDAEs). In\nthis article, the focus is on combining ideas from the local theory of\nDifferential-Algebraic Equations with that of Stochastic Differential\nEquations. The question of existence and uniqueness of the solution for SDAEs\nis addressed by using contraction mapping theorem in an appropriate Banach\nspace to arrive at a sufficient condition. From the geometric point of view, a\nnecessary condition is derived for the existence of the solution. It is\nobserved that there exists a class of high index SDAEs for which there is no\nsolution. Hence, computational methods to find approximate solution of high\nindex equations are presented. The techniques are illustrated in form of\nalgorithms with examples and numerical computations.\n"}
+{"abstract": "  It was recently demonstrated that the Matrix Action Key Exchange (MAKE)\nalgorithm, a new type of key exchange protocol using the semidirect product of\nmatrix groups, is vulnerable to a linear algebraic attack if the matrices are\nover a commutative ring. In this note, we establish conditions under which\nprotocols using matrices over a non-commutative ring are also vulnerable to\nthis attack. We then demonstrate that group rings $R[G]$ are examples of\nnon-commutative rings that satisfy these conditions.\n"}
+{"abstract": "  Convolutional Neural Networks (CNNs) have proven to be a powerful\nstate-of-the-art method for image classification tasks. One drawback however is\nthe high computational complexity and high memory consumption of CNNs which\nmakes them unfeasible for execution on embedded platforms which are constrained\non physical resources needed to support CNNs. Quantization has often been used\nto efficiently optimize CNNs for memory and computational complexity at the\ncost of a loss of prediction accuracy. We therefore propose a method to\noptimally quantize the weights, biases and activations of each layer of a\npre-trained CNN while controlling the loss in inference accuracy to enable\nquantized inference. We quantize the 32-bit floating-point precision parameters\nto low bitwidth fixed-point representations thereby finding optimal bitwidths\nand fractional offsets for parameters of each layer of a given CNN. We quantize\nparameters of a CNN post-training without re-training it. Our method is\ndesigned to quantize parameters of a CNN taking into account how other\nparameters are quantized because ignoring quantization errors due to other\nquantized parameters leads to a low precision CNN with accuracy losses of up to\n50% which is far beyond what is acceptable. Our final method therefore gives a\nlow precision CNN with accuracy losses of less than 1%. As compared to a method\nused by commercial tools that quantize all parameters to 8-bits, our approach\nprovides quantized CNN with averages of 53% lower memory consumption and 77.5%\nlower cost of executing multiplications for the two CNNs trained on the four\ndatasets that we tested our work on. We find that layer-wise quantization of\nparameters significantly helps in this process.\n"}
+{"abstract": "  We introduce a functorial construction $\\mathsf{C}$ which takes unitary\nmagmas $\\mathcal{M}$ as input and produces operads. The obtained operads\ninvolve configurations of chords labeled by elements of $\\mathcal{M}$, called\n$\\mathcal{M}$-decorated cliques and generalizing usual configurations of\nchords. By considering combinatorial subfamilies of $\\mathcal{M}$-decorated\ncliques defined, for instance, by limiting the maximal number of crossing\ndiagonals or the maximal degree of the vertices, we obtain suboperads and\nquotients of $\\mathsf{C} \\mathcal{M}$. This leads to a new hierarchy of operads\ncontaining, among others, operads on noncrossing configurations, Motzkin\nconfigurations, forests, dissections of polygons, and involutions. Besides, the\nconstruction $\\mathsf{C}$ leads to alternative definitions of the operads of\nsimple and double multi-tildes, and of the gravity operad.\n"}
+{"abstract": "  We formalize and study the problem of optimal allocation strategies for a\n(perfect) vaccine in the infinite-dimensional SIS model. The question may be\nviewed as a bi-objective minimization problem, where one tries to minimize\nsimultaneously the cost of the vaccination, and a loss that may be either the\neffective reproduction number, or the proportion of the infected population in\nthe endemic state. We prove the existence of Pareto optimal strategies,\ndescribe the corresponding Pareto frontier in both cases, and study its\nconvexity and stability properties. We also show that vaccinating according to\nthe profile of the endemic state is a critical allocation, in the sense that,\nif the initial reproduction number is larger than 1, then this vaccination\nstrategy yields an effective reproduction number equal to 1.\n"}
+{"abstract": "  Among other remote sensing technologies, synthetic aperture radar (SAR) has\nbecome firmly established in the practice of oceanographic research. Despite\nsolid experience in this field, comprehensive knowledge and interpretation of\nocean/sea and vessel wave signatures on radar images are still very\nchallenging. Many technical parameters and scanning conditions vary for\ndifferent SAR platforms, which also imposes some restrictions on the\ncross-analysis of their respective images. Numerical simulation of SAR images\nallows the analysis of many radar imaging parameters including environmental,\nship, or platform related. In this paper, we present a universal simulation\nframework for SAR imagery of the sea surface, which includes the superposition\nof sea-ship waves. This paper is the first attempt to cover exhaustively all\nSAR imaging effects for the sea waves and ship wakes scene. The study is based\non well proven concepts: the linear theory of sea surface modeling, Michell\nthin-ship theory for Kelvin wake modeling, and ocean SAR imaging theory. We\ndemonstrate the role of two main factors that affect imaging of both types of\nwaves: (i) SAR parameters and (ii) Hydrodynamic related parameters such as wind\nstate and Froude number. The SAR parameters include frequency, signal\npolarization, mean incidence angle, image resolution, variation by scanning\nplatform (airborne or spaceborne) of the range-to-velocity (R/V) ratio, and\nvelocity bunching with associated shifting, smearing and azimuthal cutoff\neffects. We perform modeling for five wave frequency spectra and four ship\nmodels. We also compare spectra in two aspects: with Cox and Munk's probability\ndensity function (PDF), and with a novel proposed evaluation of ship wake\ndetectability. The simulation results agree well with SAR imaging theory. The\nstudy gives a fuller understanding of radar imaging mechanisms for sea waves\nand ship wakes.\n"}
+{"abstract": "  We present BPFroid -- a novel dynamic analysis framework for Android that\nuses the eBPF technology of the Linux kernel to continuously monitor events of\nuser applications running on a real device. The monitored events are collected\nfrom different components of the Android software stack: internal kernel\nfunctions, system calls, native library functions, and the Java API framework.\nAs BPFroid hooks these events in the kernel, a malware is unable to trivially\nbypass monitoring. Moreover, using eBPF doesn't require any change to the\nAndroid system or the monitored applications. We also present an analytical\ncomparison of BPFroid to other malware detection methods and demonstrate its\nusage by developing novel signatures to detect suspicious behavior that are\nbased on it. These signatures are then evaluated using real apps. We also\ndemonstrate how BPFroid can be used to capture forensic artifacts for further\ninvestigation. Our results show that BPFroid successfully alerts in real time\nwhen a suspicious behavioral signature is detected, without incurring a\nsignificant runtime performance overhead.\n"}
+{"abstract": "  Multi-label text classification (MLTC) aims to annotate documents with the\nmost relevant labels from a number of candidate labels. In real applications,\nthe distribution of label frequency often exhibits a long tail, i.e., a few\nlabels are associated with a large number of documents (a.k.a. head labels),\nwhile a large fraction of labels are associated with a small number of\ndocuments (a.k.a. tail labels). To address the challenge of insufficient\ntraining data on tail label classification, we propose a Head-to-Tail Network\n(HTTN) to transfer the meta-knowledge from the data-rich head labels to\ndata-poor tail labels. The meta-knowledge is the mapping from few-shot network\nparameters to many-shot network parameters, which aims to promote the\ngeneralizability of tail classifiers. Extensive experimental results on three\nbenchmark datasets demonstrate that HTTN consistently outperforms the\nstate-of-the-art methods. The code and hyper-parameter settings are released\nfor reproducibility\n"}
+{"abstract": "  We construct a CCCZ gate using six T gates, assisted by stabilizer operations\nand classical feedback. More generally, we reduce the T cost of a $C^{n}Z$ gate\nfrom $4n-4$ to $4n-6$, for $n > 2$.\n"}
+{"abstract": "  It is well understood that boundary conditions (BCs) may cause global radial\nbasis function (RBF) methods to become unstable for hyperbolic conservation\nlaws (CLs). Here we investigate this phenomenon and identify the strong\nenforcement of BCs as the mechanism triggering such stability issues. Based on\nthis observation we propose a technique to weakly enforce BCs in RBF methods.\nIn the case of hyperbolic CLs, this is achieved by carefully building RBF\nmethods from the weak form of the CL, rather than the typically enforced strong\nform. Furthermore, we demonstrate that global RBF methods may violate\nconservation, yielding physically unreasonable solutions when the approximation\ndoes not take into account these considerations. Numerical experiments validate\nour theoretical results.\n"}
+{"abstract": "  The quantum illumination is examined by making use of the three-mode\nmaximally entangled Gaussian state, which involves one signal and two idler\nbeams. It is shown that the quantum Bhattacharyya bound between $\\rho$ (state\nfor target absence) and $\\sigma$ (state for target presence) is less than the\nprevious result derived by two-mode Gaussian state when $N_S$, average photon\nnumber per signal, is less than $0.295$. This indicates that the quantum\nillumination with three-mode Gaussian state gives less error probability\ncompared to that with two-mode Gaussian state when $N_S < 0.295$.\n"}
+{"abstract": "  Optimization in machine learning typically deals with the minimization of\nempirical objectives defined by training data. However, the ultimate goal of\nlearning is to minimize the error on future data (test error), for which the\ntraining data provides only partial information. In this view, the optimization\nproblems that are practically feasible are based on inexact quantities that are\nstochastic in nature. In this paper, we show how probabilistic results,\nspecifically gradient concentration, can be combined with results from inexact\noptimization to derive sharp test error guarantees. By considering\nunconstrained objectives we highlight the implicit regularization properties of\noptimization for learning.\n"}
+{"abstract": "  Predicting user intent and detecting the corresponding slots from text are\ntwo key problems in Natural Language Understanding (NLU). In the context of\nzero-shot learning, this task is typically approached by either using\nrepresentations from pre-trained multilingual transformers such as mBERT, or by\nmachine translating the source data into the known target language and then\nfine-tuning. Our work focuses on a particular scenario where the target\nlanguage is unknown during training. To this goal, we propose a novel method to\naugment the monolingual source data using multilingual code-switching via\nrandom translations to enhance a transformer's language neutrality when\nfine-tuning it for a downstream task. This method also helps discover novel\ninsights on how code-switching with different language families around the\nworld impact the performance on the target language. Experiments on the\nbenchmark dataset of MultiATIS++ yielded an average improvement of +4.2% in\naccuracy for intent task and +1.8% in F1 for slot task using our method over\nthe state-of-the-art across 8 different languages. Furthermore, we present an\napplication of our method for crisis informatics using a new human-annotated\ntweet dataset of slot filling in English and Haitian Creole, collected during\nHaiti earthquake disaster.\n"}
+{"abstract": "  Measurements of the ac response represent a widely-used method for probing\nthe properties of superconductors. In the surface superconducting state (SSS),\nincrease of the current beyond the surface critical current $I_c$ leads to\nbreakdown of SSS and penetration of external magnetic field into the sample\nbulk. An interesting free-of-bulk system in SSS is offered by thin-walled\nsuperconducting cylinders. The critical state model (CSM) asserts the ac\nsusceptibility $\\chi$ to exhibit jumps as a function of the external ac field\namplitude $H_{ac}$, because of the periodic destruction and restoration of SSS\nin the cylinder wall. Here, we investigate experimentally the low-frequency\n(128-8192\\,Hz) ac response of thin-walled superconducting cylinders in\nsuperimposed dc and ac magnetic fields applied parallel to the cylinder axis.\nDistinct from the CSM predictions, experiments reveal that $\\chi$ is a smooth\nfunction of $H_{ac}$. For the explanation of our observations we propose a\nphenomenological model of partial penetration of magnetic flux (PPMF). The PPMF\nmodel implies that after a restoration of the superconducting state, the\nmagnetic fields inside and outside the cylinder are not equal, and the value of\nthe penetrating flux is random for each penetration. This model fits very well\nto the experimental data on the temperature dependence of the first-harmonic\n$\\chi_1$ at any $H_{ac}$ and dc field magnitude. However, in a certain\ntemperature range the values of physical parameters deduced within the\nframework of the PPMF model are questionable.\n"}
+{"abstract": "  For a relative equilibrium of a symmetric simple mechanical system, if the\nMorse index of the corresponding amended potential is odd, whether the nullity\nis zero or not, it is linearly unstable. We also provide a sufficient condition\nfor spectral instability.\n"}
+{"abstract": "  Graph colorings is a fundamental topic in graph theory that require an\nassignment of labels (or colors) to vertices or edges subject to various\nconstraints. We focus on the harmonious coloring of a graph, which is a proper\nvertex coloring such that for every two distinct colors i, j at most one pair\nof adjacent vertices are colored with i and j. This type of coloring is\nedge-distinguishing and has potential applications in transportation network,\ncomputer network, airway network system.\n  The results presented in this paper fall into two categories: in the first\npart of the paper we are concerned with the computational aspects of finding a\nminimum harmonious coloring and in the second part we determine the exact value\nof the harmonious chromatic number for some particular graphs and classes of\ngraphs. More precisely, in the first part we show that finding a minimum\nharmonious coloring for arbitrary graphs is APX-hard, the natural greedy\nalgorithm is a $\\Omega(\\sqrt{n})$-approximation, and, moreover, we show a\nrelationship between the vertex cover and the harmonious chromatic number. In\nthe second part we determine the exact value of the harmonious chromatic number\nfor all 3-regular planar graphs of diameter 3, some non-planar regular graphs\nand cycle-related graphs.\n"}
+{"abstract": "  We derive thermodynamically consistent models for diblock copolymer solutions\ncoupled with the electric and magnetic field, respectively. These models\nsatisfy the second law of thermodynamics and therefore are therefore\nthermodynamically consistent. We then design a set of 2nd order, linear,\nsemi-discrete schemes for the models using the energy quadratization method and\nthe supplementary variable method, respectively, which preserve energy\ndissipation rates of the models. The spatial discretization is carried out\nsubsequently using 2nd order finite difference methods, leading to fully\ndiscrete algorithms that preserve discrete energy-dissipation-rates of the\nmodels so that the resulting fully discrete models are thermodynamically\nconsistent. Convergence rates are numerically confirmed through mesh refinement\ntests and several numerical examples are given to demonstrate the role of the\nmobility in pattern formation, defect removing effect of both electric and\nmagnetic fields as well as the hysteresis effect for applied external fields in\ncopolymer solutions.\n"}
+{"abstract": "  The year 2019 marked the quincentenary of the arrival in the southern\nhemisphere of Ferdinand Magellan, the namesake of the Magellanic Clouds, our\nnearest example of dwarf galaxies in the early stages of a minor merging event.\nThese galaxies have been firmly established as laboratories for the study of\nvariable stars, stellar evolution, and galaxy interaction, as well as being\nanchors for the extragalactic distance scale. The goal of this conference was\nto provide fertile ground for shaping future research related to the Magellanic\nClouds by combining state-of-the-art results based on advanced observational\nprogrammes with discussions of the highly multiplexed wide-field spectroscopic\nsurveys that will come online in the 2020s.\n"}
+{"abstract": "  We present Low Distortion Local Eigenmaps (LDLE), a manifold learning\ntechnique which constructs a set of low distortion local views of a dataset in\nlower dimension and registers them to obtain a global embedding. The local\nviews are constructed using the global eigenvectors of the graph Laplacian and\nare registered using Procrustes analysis. The choice of these eigenvectors may\nvary across the regions. In contrast to existing techniques, LDLE can embed\nclosed and non-orientable manifolds into their intrinsic dimension by tearing\nthem apart. It also provides gluing instruction on the boundary of the torn\nembedding to help identify the topology of the original manifold. Our\nexperimental results will show that LDLE largely preserved distances up to a\nconstant scale while other techniques produced higher distortion. We also\ndemonstrate that LDLE produces high quality embeddings even when the data is\nnoisy or sparse.\n"}
+{"abstract": "  New relations involving the Riemann, Ricci and Einstein tensors that have to\nhold for a given geometry to admit Killing-Yano tensors are described. These\nrelations are then used to introduce novel conserved \"currents\" involving such\nKilling-Yano tensors. For a particular current based on the Einstein tensor, we\ndiscuss the issue of conserved charges and consider implications for matter\ncoupled to gravity. The condition on the background geometry to allow\nasymptotic conserved charges for a current introduced by Kastor and Traschen is\nfound and a number of other new aspects of this current are commented on. In\nparticular we show that it vanishes for rank $(D-1)$ Killing-Yano tensors in\n$D$ dimensions.\n"}
+{"abstract": "  This paper introduces SelfMatch, a semi-supervised learning method that\ncombines the power of contrastive self-supervised learning and consistency\nregularization. SelfMatch consists of two stages: (1) self-supervised\npre-training based on contrastive learning and (2) semi-supervised fine-tuning\nbased on augmentation consistency regularization. We empirically demonstrate\nthat SelfMatch achieves the state-of-the-art results on standard benchmark\ndatasets such as CIFAR-10 and SVHN. For example, for CIFAR-10 with 40 labeled\nexamples, SelfMatch achieves 93.19% accuracy that outperforms the strong\nprevious methods such as MixMatch (52.46%), UDA (70.95%), ReMixMatch (80.9%),\nand FixMatch (86.19%). We note that SelfMatch can close the gap between\nsupervised learning (95.87%) and semi-supervised learning (93.19%) by using\nonly a few labels for each class.\n"}
+{"abstract": "  This paper studies the essential normality of Bergman modules over the\nintersection of complex ellipsoids, as well as their quotients by monomial\nideals.\n"}
+{"abstract": "  This paper studies an unmanned aerial vehicle (UAV)-assisted wireless\nnetwork, where a UAV is dispatched to gather information from ground sensor\nnodes (SN) and transfer the collected data to the depot. The information\nfreshness is captured by the age of information (AoI) metric, whilst the energy\nconsumption of the UAV is seen as another performance criterion. Most\nimportantly, the AoI and energy efficiency are inherently competing metrics,\nsince decreasing the AoI requires the UAV returning to the depot more\nfrequently, leading to a higher energy consumption. To this end, we design UAV\npaths that optimize these two competing metrics and reveal the Pareto frontier.\nTo formulate this problem, a multi-objective mixed integer linear programming\n(MILP) is proposed with a flow-based constraint set and we apply Bender's\ndecomposition on the proposed formulation. The overall outcome shows that the\nproposed method allows deriving non-dominated solutions for decision making for\nUAV based wireless data collection. Numerical results are provided to\ncorroborate our study by presenting the Pareto front of the two objectives and\nthe effect on the UAV trajectory.\n"}
+{"abstract": "  We discuss the relation between the linear Tschebyshev-Pad\\'e approximations\nto analytic function $f$ and the diagonal type I Hermite-Pad\\'e polynomials for\nthe tuple of functions $[1,f_1,f_2]$ where the pair of functions $f_1,f_2$\nforms certain Nikishin system. An approach is proposed of how to extend the\nseminal Stahl's Theory for Pad\\'e approximations for multivalued analytic\nfunctions to the Tschebyshev-Pad\\'e approximations. The approach is based on\nthe relation between Tschebyshev-Pad\\'e approximations and Hermite-Pad\\'e\npolynomials and also on a connection of Hermite-Pad\\'e polynomials and\nmultipoint Pad\\'e approximants.\n"}
+{"abstract": "  Previous research on nonlinear oscillator networks has shown that chaos\nsynchronization is attainable for identical oscillators but deteriorates in the\npresence of parameter mismatches. Here, we identify regimes for which the\nopposite occurs and show that oscillator heterogeneity can synchronize chaos\nfor conditions under which identical oscillators cannot. This effect is not\nlimited to small mismatches and is observed for random oscillator heterogeneity\non both homogeneous and heterogeneous network structures. The results are\ndemonstrated experimentally using networks of Chua's oscillators and are\nfurther supported by numerical simulations and theoretical analysis. In\nparticular, we propose a general mechanism based on heterogeneity-induced mode\nmixing that provides insights into the observed phenomenon. Since individual\ndifferences are ubiquitous and often unavoidable in real systems, it follows\nthat such imperfections can be an unexpected source of synchronization\nstability.\n"}
+{"abstract": "  We report the results of tests of data transmission and signal stability in\ntime of two different wide-band multiplexing (MUX) schemes, each in a\npoint-to-point configuration, based on electromagnetic waves carrying Orbital\nAngular Momentum (OAM) in noisy real-world settings. Each radio link\ntransmitted two high definition wide--band analog TV channels in the same\nfrequency band with FM-carrier centered at $2.414$ GHz and $27$ MHz bandwidth,\nencoded with different OAM modes in the same polarization state,\nuninterruptedly for $5$ months during the world exhibition\n``Globale--Digitale'' at ZKM in Karlsruhe and in other $2$ months time slots\ntaken in the following $4$ years, $24$ hours per day. We show the practical\nfeasibility of the use of stable OAM radio/TV links in the real world for a\nlong time, paving the way for for secure and efficient communication schemes\nalso under electromagnetic jamming conditions.\n"}
+{"abstract": "  Modeling real-world phenomena is a focus of many science and engineering\nefforts, such as ecological modeling and financial forecasting, to name a few.\nBuilding an accurate model for complex and dynamic systems improves\nunderstanding of underlying processes and leads to resource efficiency. Towards\nthis goal, knowledge-driven modeling builds a model based on human expertise,\nyet is often suboptimal. At the opposite extreme, data-driven modeling learns a\nmodel directly from data, requiring extensive data and potentially generating\noverfitting. We focus on an intermediate approach, model revision, in which\nprior knowledge and data are combined to achieve the best of both worlds. In\nthis paper, we propose a genetic model revision framework based on\ntree-adjoining grammar (TAG) guided genetic programming (GP), using the TAG\nformalism and GP operators in an effective mechanism to incorporate prior\nknowledge and make data-driven revisions in a way that complies with prior\nknowledge. Our framework is designed to address the high computational cost of\nevolutionary modeling of complex systems. Via a case study on the challenging\nproblem of river water quality modeling, we show that the framework efficiently\nlearns an interpretable model, with higher modeling accuracy than existing\nmethods.\n"}
+{"abstract": "  In this letter we use the Carath\\'{e}odory's approach to thermodynamics, to\nconstruct the thermodynamic manifold of the Hayward black hole. The Pfaffian\nform representing the infinitesimal heat exchange reversibly is considered to\nbe $\\delta Q_{\\mathrm{rev}}\\equiv {\\rm d}r_s-\\mathcal{F}_H {\\rm d}l$,\npreviously obtained by Molina \\& Villanueva \\cite{fmv20}, where $r_s$ is the\nSchwarzschild radius, $l$ is the Hayward's parameter responsible for the\npossible regularization of the Schwarzschild black hole, and $\\mathcal{F}_H$ is\nthe intensive variable called the Hayward's force. By solving the associated\nCauchy problem, the adiabatic paths are confined to the non-extremal manifold,\nand therefore, the status of the second and third laws are preserved.\nConsequently, the extremal sub-manifold corresponds to the {adiabatically\ndisconnected} boundary of the manifold. In addition, the merger of two extremal\nHayward black holes is analyzed.\n"}
+{"abstract": "  Conventional transfer learning leverages weights of pre-trained networks, but\nmandates the need for similar neural architectures. Alternatively, knowledge\ndistillation can transfer knowledge between heterogeneous networks but often\nrequires access to the original training data or additional generative\nnetworks. Knowledge transfer between networks can be improved by being agnostic\nto the choice of network architecture and reducing the dependence on original\ntraining data. We propose a knowledge transfer approach from a teacher to a\nstudent network wherein we train the student on an independent transferal\ndataset, whose annotations are generated by the teacher. Experiments were\nconducted on five state-of-the-art networks for semantic segmentation and seven\ndatasets across three imaging modalities. We studied knowledge transfer from a\nsingle teacher, combination of knowledge transfer and fine-tuning, and\nknowledge transfer from multiple teachers. The student model with a single\nteacher achieved similar performance as the teacher; and the student model with\nmultiple teachers achieved better performance than the teachers. The salient\nfeatures of our algorithm include: 1)no need for original training data or\ngenerative networks, 2) knowledge transfer between different architectures, 3)\nease of implementation for downstream tasks by using the downstream task\ndataset as the transferal dataset, 4) knowledge transfer of an ensemble of\nmodels, trained independently, into one student model. Extensive experiments\ndemonstrate that the proposed algorithm is effective for knowledge transfer and\neasily tunable.\n"}
+{"abstract": "  The thermodynamic uncertainty relations (TURs) provide lower bounds on the\nentropy production (EP) of a system in terms of the statistical precision of an\narbitrary current in that system. All conventional TURs derived so far have\nconcerned a single physical system, differing from one another in what\nproperties they require the system to have. However, many physical scenarios of\ninterest involve multiple interacting systems, e.g. organelles within a\nbiological cell. Here we show how to extend the conventional TURs to those\nscenarios. A common feature of these extended versions of the TURs is that they\nbound the global EP, jointly generated by the set of interacting systems, in\nterms of a weighted sum of the precisions of the local currents generated\nwithin those systems -- plus an information-theoretic correction term.\nImportantly, these extended TURs can bound the global EP even when the global\nsystem does not meet any of the requirements of the conventional TURs. After\nderiving these extended TURs we use them to obtain bounds that do not involve\nthe global EP, but instead relate the local EPs of the individual systems and\nthe statistical coupling among the currents generated within those systems. We\nderive such bounds for both scalar-valued and vector-valued currents within\neach system. We illustrate our results with numerical experiments.\n"}
+{"abstract": "  Current neural architecture search (NAS) algorithms still require expert\nknowledge and effort to design a search space for network construction. In this\npaper, we consider automating the search space design to minimize human\ninterference, which however faces two challenges: the explosive complexity of\nthe exploration space and the expensive computation cost to evaluate the\nquality of different search spaces. To solve them, we propose a novel\ndifferentiable evolutionary framework named AutoSpace, which evolves the search\nspace to an optimal one with following novel techniques: a differentiable\nfitness scoring function to efficiently evaluate the performance of cells and a\nreference architecture to speedup the evolution procedure and avoid falling\ninto sub-optimal solutions. The framework is generic and compatible with\nadditional computational constraints, making it feasible to learn specialized\nsearch spaces that fit different computational budgets. With the learned search\nspace, the performance of recent NAS algorithms can be improved significantly\ncompared with using previously manually designed spaces. Remarkably, the models\ngenerated from the new search space achieve 77.8% top-1 accuracy on ImageNet\nunder the mobile setting (MAdds < 500M), out-performing previous SOTA\nEfficientNet-B0 by 0.7%. All codes will be made public.\n"}
+{"abstract": "  We introduce a discriminative multimodal descriptor based on a pair of sensor\nreadings: a point cloud from a LiDAR and an image from an RGB camera. Our\ndescriptor, named MinkLoc++, can be used for place recognition, re-localization\nand loop closure purposes in robotics or autonomous vehicles applications. We\nuse late fusion approach, where each modality is processed separately and fused\nin the final part of the processing pipeline. The proposed method achieves\nstate-of-the-art performance on standard place recognition benchmarks. We also\nidentify dominating modality problem when training a multimodal descriptor. The\nproblem manifests itself when the network focuses on a modality with a larger\noverfit to the training data. This drives the loss down during the training but\nleads to suboptimal performance on the evaluation set. In this work we describe\nhow to detect and mitigate such risk when using a deep metric learning approach\nto train a multimodal neural network. Our code is publicly available on the\nproject website: https://github.com/jac99/MinkLocMultimodal.\n"}
+{"abstract": "  Liquid xenon offers several features, which make it suitable for applications\nin nuclear medicine, such as high scintillation yield and fast scintillation\ndecay time. Moreover, being a continuous medium with a uniform response, liquid\nxenon allows one to avoid most of the geometrical distortions of conventional\ndetectors based on scintillating crystals. In this paper, we describe how these\nproperties have motivated the development of a novel concept for positron\nemission tomography scanners with Time-Of-Flight measurement, which combines a\nliquid xenon scintillating volume and silicon photomultipliers as sensors. A\nMonte Carlo investigation has pointed out that this technology would provide an\nexcellent intrinsic time resolution, down to 70 ps. Also, the transparency of\nliquid xenon to UV and blue wavelengths opens the possibility of exploiting\nboth scintillation and Cherenkov light for a high-sensitivity positron emission\ntomography scanner with Time-Of-Flight capabilities. Monte Carlo simulations\npoint to a time resolution of 30-50 ps obtained using Cherenkov light. A\nprototype is being built to demonstrate the high resolution in energy, time and\nreconstruction of spatial coordinates of this concept, using a ring of 30 cm\ninternal diameter and a depth of 3 cm instrumented with VUV-sensitive silicon\nphotomultipliers.\n"}
+{"abstract": "  Over-parametrized deep neural networks trained by stochastic gradient descent\nare successful in performing many tasks of practical relevance. One aspect of\nover-parametrization is the possibility that the student network has a larger\nexpressivity than the data generating process. In the context of a\nstudent-teacher scenario, this corresponds to the so-called over-realizable\ncase, where the student network has a larger number of hidden units than the\nteacher. For on-line learning of a two-layer soft committee machine in the\nover-realizable case, we find that the approach to perfect learning occurs in a\npower-law fashion rather than exponentially as in the realizable case. All\nstudent nodes learn and replicate one of the teacher nodes if teacher and\nstudent outputs are suitably rescaled.\n"}
+{"abstract": "  Fractures occur in the shoulder area, which has a wider range of motion than\nother joints in the body, for various reasons. To diagnose these fractures,\ndata gathered from Xradiation (X-ray), magnetic resonance imaging (MRI), or\ncomputed tomography (CT) are used. This study aims to help physicians by\nclassifying shoulder images taken from X-ray devices as fracture / non-fracture\nwith artificial intelligence. For this purpose, the performances of 26 deep\nlearning-based pretrained models in the detection of shoulder fractures were\nevaluated on the musculoskeletal radiographs (MURA) dataset, and two ensemble\nlearning models (EL1 and EL2) were developed. The pretrained models used are\nResNet, ResNeXt, DenseNet, VGG, Inception, MobileNet, and their spinal fully\nconnected (Spinal FC) versions. In the EL1 and EL2 models developed using\npretrained models with the best performance, test accuracy was 0.8455,0.8472,\nCohens kappa was 0.6907, 0.6942 and the area that was related with fracture\nclass under the receiver operating characteristic (ROC) curve (AUC) was\n0.8862,0.8695. As a result of 28 different classifications in total, the\nhighest test accuracy and Cohens kappa values were obtained in the EL2 model,\nand the highest AUC value was obtained in the EL1 model.\n"}
+{"abstract": "  Given the voluminous nature of the multimedia sensed data, the Multimedia\nInternet of Things (MIoT) devices and networks will present several limitations\nin terms of power and communication overhead. One traditional solution to cope\nwith the large-size data challenge is to use lossy compression. However,\ncurrent lossy compression schemes require low compression rate to guarantee\nacceptable perceived image quality, which results in a low reduction of the\ncommunicated data size and consequently a low reduction in the energy and\nbandwidth consumption. Thus, an efficient compression solution is required for\nstriking a good balance between data size (and consequently communication\noverhead) and visual degradation. In this paper, a Deep-Learning (DL)\nsuper-resolution model is applied to recuperate high quality images (at the\napplication server side) given as input degraded images with a high compression\nratio (at the sender side). The experimental analysis shows the effectiveness\nof the proposed solution in enhancing the visual quality of the compressed and\ndown-scaled images. Consequently, the proposed solution reduces the overall\ncommunication overhead and power consumption of limited MIoT devices.\n"}
+{"abstract": "  Volume of fluid(VOF) method is a sharp interface method employed for\nsimulations of two phase flows. Interface in VOF is usually represented using\npiecewise linear line segments in each computational grid based on the volume\nfraction field. While VOF for cartesian coordinates conserve mass exactly,\nexisting algorithms do not show machine-precision mass conservation for\naxisymmetric coordinate systems. In this work, we propose analytic formulae for\ninterface reconstruction in axisymmetric coordinates, similar to those proposed\nby Scardovelli and Zaleski (J. Comput. Phys. 2000) for cartesian coordinates.\nWe also propose modifications to the existing advection schemes in VOF for\naxisymmetric coordinates to obtain higher accuracy in mass conservation\n"}
+{"abstract": "  We investigate the uniform boundedness of the fronts of the solutions to the\nrandomized Fisher-KPP equation and to its linearization, the parabolic Anderson\nmodel. It has been known that for the standard (i.e. deterministic) Fisher-KPP\nequation, as well as for the special case of a randomized Fisher-KPP equation\nwith so-called ignition type nonlinearity, one has a uniformly bounded (in\ntime) transition front. Here, we show that this property of having a uniformly\nbounded transition front fails to hold for the general randomized Fisher-KPP\nequation. Nevertheless, we establish that this property does hold true for the\nparabolic Anderson model.\n"}
+{"abstract": "  Memory effects can have a profound impact on the resistivity of semiconductor\nsystems, resulting in giant negative magnetoresistance and MIRO phenomena. This\nwork opens the discussion of the memory effects in 3D conducting systems\nfeatured by the presence of the extended one-dimensional defects, such as screw\ndislocations or static charge stripes. We demonstrate that accounting for the\nmemory effect, that is the capture of electrons on collisionless spiral\ntrajectories winding around extended defects, leads to the strong negative\nmagnetoresistance in case when the external magnetic field direction becomes\nparallel to the defects axis. This effect gives rise to a significant\nmagnetoresistance anisotropy already for an isotropic Fermi surface and no\nspin-orbit effects. The proposed resistivity feature can be used to detect\none-dimensional scattering defects in these systems.\n"}
+{"abstract": "  The Partial Least Square Regression (PLSR) algorithm exhibits exceptional\ncompetence for predicting continuous variables from inter-correlated brain\nrecordings in brain-computer interfaces, which achieved successful prediction\nfrom epidural electrocorticography of macaques to three-dimensional continuous\nhand trajectories recently. Nevertheless, PLSR is in essence formulated based\non the least square criterion, thus, being non-robust with respect to\ncomplicated noises consequently. The aim of the present study is to propose a\nrobust version of PLSR. To this end, the maximum correntropy criterion is\nadopted to structure a new robust variant of PLSR, namely Partial Maximum\nCorrentropy Regression (PMCR). Half-quadratic optimization technique is\nutilized to calculate the robust latent variables. We assess the proposed PMCR\non a synthetic example and the public Neurotycho dataset. Compared with the\nconventional PLSR and the state-of-the-art variant, PMCR realized superior\nprediction competence on three different performance indicators with\ncontaminated training set. The proposed PMCR was demonstrated as an effective\napproach for robust decoding from noisy brain measurements, which could reduce\nthe performance degradation resulting from adverse noises, thus, improving the\ndecoding robustness of brain-computer interfaces.\n"}
+{"abstract": "  The body of experimental measurements of intermediate-energy reactions that\nremove a single nucleon from a secondary beam of neutron- or proton-rich nuclei\ncontinues to grow. These data have been analysed consistently using an\napproximate, eikonal-model treatment of the reaction dynamics combined with\nappropriate shell-model descriptions of the projectile initial state, the bound\nfinal states spectrum of the reaction residue and single-particle removal\nstrengths computed from their wave-function overlaps. The systematics of the\nratio $R_s$ of the measured inclusive cross-section to all bound final states\nand the calculated cross-section to bound shell-model states -- in different\nregions of the nuclear chart and involving both very weakly-bound and\nstrongly-bound valence nucleons -- is important in relating the empirically\ndeduced orbital occupancies to those from the best available shell-model\npredictions. Importantly, several new higher-energy measurements, for which the\nsudden-approximation aspect of the dynamical description is placed on an even\nstronger footing, now supplement the previously-analysed measurements. These\nadditional data sets are discussed. Their $R_s$ values are shown to conform to\nand reinforce the earlier-observed systematics, with no indication that the\napproximately linear reduction in $R_s$ with increasing nucleon separation\nenergy is a consequence of a breakdown of the sudden approximation.\n"}
+{"abstract": "  We search for ultra-luminous Quasi-Stellar Objects (QSOs) at high redshift\nusing photometry from the SkyMapper Southern Survey Data Release 3 (DR3), in\ncombination with 2MASS, VHS DR6, VIKING DR5, AllWISE, and CatWISE2020, as well\nas parallaxes and proper motions from Gaia DR2 and eDR3. We report 142 newly\ndiscovered Southern QSOs at $3.8<z<5.5$, of which 126 have $M_{145} <-27$ ABmag\nand are found in a search area of 14,486 deg$^2$. This Southern sample,\nutilising the Gaia astrometry to offset wider photometric colour criteria,\nachieves unprecedented completeness for an ultra-luminous QSO search at high\nredshift. In combination with already known QSOs, we construct a sample that is\n$>80$ per cent complete for $M_{145}<-27.33$ ABmag at $z=4.7$ and for\n$M_{145}<-27.73$ ABmag at $z=5.4$. We derive the bright end of the QSO\nluminosity function at restframe 145 nm for $z=4.7-5.4$ and measure its slope\nto be $\\beta = -3.60\\pm0.37$ and $\\beta = -3.38\\pm0.32$ for two different\nestimates of the faint-end QSO density adopted from the literature. We also\npresent the first $z\\sim 5$ QSO luminosity function at restframe 300 nm.\n"}
+{"abstract": "  A single-hole ground state ansatz for the two-dimensional t-J model has been\nrecently studied by variational Monte Carlo (VMC) method. Such a doped hole\nbehaves like a ``twisted'' non-Landau quasiparticle characterized by an\nemergent quantum number in agreement with exact numerics. In this work, we\nfurther investigate the ground state of two holes by VMC. It is found that the\ntwo holes strongly attract each other to form a pairing state with a new\nquantum number the same as obtained by the numerical exact diagonalization (ED)\nand density matrix renormalization group (DMRG) calculations. A unique feature\nof this pairing state is a dichotomy in the pairing symmetry, i.e., a d-wave in\nterms of the electron c-operators and an s-wave in terms of the new\nquasiparticles, as explicitly illustrated in the ground state wave function. A\nsimilar VMC study of a two-hole wave function for the t-J two-leg ladder also\nyields a good agreement with the DMRG result. We demonstrate that the pairing\nmechanism responsible for the strong binding here is not due to the long-range\nantiferromagnetic nor the resonating-valence-bound pairing in the spin\nbackground, but is the consequence of the quantum phase strings created by the\nhopping of holes. The resulting spin current pattern mediating the pairing\nforce is explicitly illustrated in the VMC calculation. Physical implications\nto superconductivity at finite doping will be also discussed.\n"}
+{"abstract": "  Author name disambiguation results are often evaluated by measures such as\nCluster-F, K-metric, Pairwise-F, Splitting & Lumping Error, and B-cubed.\nAlthough these measures have distinctive evaluation schemes, this paper shows\nthat they can be calculated in a single framework by a set of common steps that\ncompare truth and predicted clusters through two hash tables recording\ninformation about name instances with their predicted cluster indices and\nfrequencies of those indices per truth cluster. This integrative calculation\nreduces greatly calculation runtime, which is scalable to a clustering task\ninvolving millions of name instances within a few seconds. During the\nintegration process, B-cubed and K-metric are shown to produce the same\nprecision and recall scores. In this framework, especially, name instance pairs\nfor Pairwise-F are counted using a heuristic, surpassing a state-of-the-art\nalgorithm in speedy calculation. Details of the integrative calculation are\ndescribed with examples and pseudo-code to assist scholars to implement each\nmeasure easily and validate the correctness of implementation. The integrative\ncalculation will help scholars compare similarities and differences of multiple\nmeasures before they select ones that characterize best the clustering\nperformances of their disambiguation methods.\n"}
+{"abstract": "  The possibility of two interaction regions (IRs) is a design requirement for\nthe Electron Ion Collider (the EIC). There is also a significant interest from\nthe nuclear physics community in a 2nd IR with measurements capabilities\ncomplementary to those of the first IR. While the 2nd IR will be in operation\nover the entire energy range of ~20GeV to ~140GeV center of mass (CM). The 2nd\nIR can also provide an acceptance coverage complementary to that of the first.\nWe present a brief overview and the current progress of the 2nd IR design in\nterms of the parameters, magnet layout, and beam dynamics.\n"}
+{"abstract": "  A quiet point, an operating point of pump-resonance detuning that minimizes\nfrequency fluctuation due to nonlinear effects inside a resonator, has been\nemployed for phase noise reduction of a soliton Kerr microresonator frequency\ncomb (microcomb). Naturally, it is expected that the use of the point will also\nimprove performances of a microcomb in terms of frequency stability and\nfaithfulness in a phase locked loop. In this study, we experimentally\ninvestigate the effect in a microcomb with a repetition frequency of 300~GHz.\nWe obtain a lowest fractional frequency instability at a quiet point of\n$1.5\\times 10^{-9}$ at 1 second, which is 44 times lower than free-running\ninstability. Phase-locking of a microcomb to a stabilized fiber comb is\ndemonstrated to evaluate performance in a feedback loop, where in-loop-limited\nrelative fractional frequency instability between the microcomb and the fiber\ncomb of $6.8 \\times 10^{-13}$ is obtained as an indicator of the stability\nlimitation.\n"}
+{"abstract": "  Recent work has highlighted roles for thermodynamic phase behavior in diverse\ncellular processes. Proteins and nucleic acids can phase separate into\nthree-dimensional liquid droplets in the cytoplasm and nucleus and the plasma\nmembrane of animal cells appears tuned close to a two-dimensional liquid-liquid\ncritical point. In some examples, cytoplasmic proteins aggregate at plasma\nmembrane domains, forming structures such as the post-synaptic density and\ndiverse signaling clusters. Here we examine the physics of these surface\ndensities, employing minimal simulations of co-acervating polymers coupled to\nan Ising membrane surface in conjunction with a complementary Landau theory. We\nargue that these surface densities are a novel phase reminiscent of\npre-wetting, in which a molecularly thin three-dimensional liquid forms on a\nusually solid surface. However, in surface densities the solid surface is\nreplaced by a membrane with an independent propensity to phase separate. We\nshow that proximity to criticality in the membrane dramatically increases the\nparameter regime in which a pre-wetting-like transition occurs, leading to a\nbroad region where coexisting surface phases can form even when a bulk phase is\nunstable. Our simulations naturally exhibit three surface phase coexistence\neven though both the membrane and the polymer bulk can only display two phase\ncoexistence on their own. We argue that the physics of these surface densities\nenables diverse functions seen in Eukaryotic cells.\n"}
+{"abstract": "  Colexification refers to the phenomenon of multiple meanings sharing one word\nin a language. Cross-linguistic lexification patterns have been shown to be\nlargely predictable, as similar concepts are often colexified. We test a recent\nclaim that, beyond this general tendency, communicative needs play an important\nrole in shaping colexification patterns. We approach this question by means of\na series of human experiments, using an artificial language communication game\nparadigm. Our results across four experiments match the previous\ncross-linguistic findings: all other things being equal, speakers do prefer to\ncolexify similar concepts. However, we also find evidence supporting the\ncommunicative need hypothesis: when faced with a frequent need to distinguish\nsimilar pairs of meanings, speakers adjust their colexification preferences to\nmaintain communicative efficiency, and avoid colexifying those similar meanings\nwhich need to be distinguished in communication. This research provides further\nevidence to support the argument that languages are shaped by the needs and\npreferences of their speakers.\n"}
+{"abstract": "  To improve the efficiency of flood early warning systems (FEWS), it is\nimportant to understand the interactions between natural and social systems.\nThe high level of trust in authorities and experts is necessary to improve the\nlikeliness of individuals to take preparedness actions responding to warnings.\nDespite a lot of efforts to develop the dynamic model of human and water in\nsocio-hydrology, no socio-hydrological models explicitly simulate social\ncollective trust in FEWS. Here we develop the stylized model to simulate the\ninteractions of flood, social collective memory, social collective trust in\nFEWS, and preparedness actions responding to warnings by extending the existing\nsocio-hydrological model. We realistically simulate the cry wolf effect, in\nwhich many false alarms undermine the credibility of the early warning systems\nand make it difficult to induce preparedness actions. We found (1) considering\nthe dynamics of social collective trust in FEWS is more important in the\ntechnological society with infrequent flood events than in the green society\nwith frequent flood events; (2) as the natural scientific skill to predict\nflood events is improved, the efficiency of FEWS gets more sensitive to the\nbehavior of social collective trust, so that forecasters need to determine\ntheir warning threshold by considering the social aspects.\n"}
+{"abstract": "  Designing novel protein sequences for a desired 3D topological fold is a\nfundamental yet non-trivial task in protein engineering. Challenges exist due\nto the complex sequence--fold relationship, as well as the difficulties to\ncapture the diversity of the sequences (therefore structures and functions)\nwithin a fold. To overcome these challenges, we propose Fold2Seq, a novel\ntransformer-based generative framework for designing protein sequences\nconditioned on a specific target fold. To model the complex sequence--structure\nrelationship, Fold2Seq jointly learns a sequence embedding using a transformer\nand a fold embedding from the density of secondary structural elements in 3D\nvoxels. On test sets with single, high-resolution and complete structure inputs\nfor individual folds, our experiments demonstrate improved or comparable\nperformance of Fold2Seq in terms of speed, coverage, and reliability for\nsequence design, when compared to existing state-of-the-art methods that\ninclude data-driven deep generative models and physics-based RosettaDesign. The\nunique advantages of fold-based Fold2Seq, in comparison to a structure-based\ndeep model and RosettaDesign, become more evident on three additional\nreal-world challenges originating from low-quality, incomplete, or ambiguous\ninput structures. Source code and data are available at\nhttps://github.com/IBM/fold2seq.\n"}
+{"abstract": "  We exploit the relationship between the stochastic Koopman operator and the\nKolmogorov backward equation to construct importance sampling schemes for\nstochastic differential equations. Specifically, we propose using\neigenfunctions of the stochastic Koopman operator to approximate the Doob\ntransform for an observable of interest (e.g., associated with a rare event)\nwhich in turn yields an approximation of the corresponding zero-variance\nimportance sampling estimator. Our approach is broadly applicable and\nsystematic, treating non-normal systems, non-gradient systems, and systems with\noscillatory dynamics or rank-deficient noise in a common framework. In\nnonlinear settings where the stochastic Koopman eigenfunctions cannot be\nderived analytically, we use dynamic mode decomposition (DMD) methods to\ncompute them numerically, but the framework is agnostic to the particular\nnumerical method employed. Numerical experiments demonstrate that even coarse\napproximations of a few eigenfunctions, where the latter are built from\nnon-rare trajectories, can produce effective importance sampling schemes for\nrare events.\n"}
+{"abstract": "  This is a sequel to [Xu18] on the $L$-packets of quasisplit general\nsymplectic and even orthogonal groups. We show the existence of global\n$L$-packets and establish the functoriality of endoscopic transfer for them in\nmany cases.\n"}
+{"abstract": "  The Ramsey sequence is a canonical example of a quantum phase measurement for\na spin qubit. In Ramsey measurements, the measurement efficiency can be\noptimized through careful selection of settings for the phase accumulation time\nsetting, $\\tau$. This paper implements a sequential Bayesian experiment design\nprotocol for the phase accumulation time in low-fidelity Ramsey measurements,\nand performance is compared to both a previously reported adaptive heuristic\nprotocol and random setting choices. A workflow allowing measurements and\ndesign calculations to run concurrently largely eliminates computation time\nfrom measurement overhead. When precession frequency is the lone parameter to\nestimate, the Bayesian design is faster by factors of 2 and 4 relative to the\nadaptive heuristic and random protocols respectively.\n"}
+{"abstract": "  Hybrid spin-optomechanical quantum systems offer high flexibility,\nintegrability and applicability for quantum science and technology.\nParticularly, on-chip surface acoustic waves (SAWs) can efficiently drive spin\ntransitions in the ground states (GSs) of atomic-scale, color centre qubits,\nwhich are forbidden in case of the more frequently used electromagnetic fields.\nHere, we demonstrate that strain-induced spin interactions within their\noptically excited state (ES) can exceed by two orders of magnitude the ones\nwithin the GS. This gives rise to novel physical phenomena, such as the\nacoustically induced coherent spin trapping (CST) unvealed here. The CST\nmanifests itself as the spin preservation along one particular direction under\nthe coherent drive of the GS and ES by the same acoustic field. Our findings\nprovide new opportunities for the coherent control of spin qubits with\ndynamically generated strain fields that can lead towards the realization of\nfuture spin-acoustic quantum devices.\n"}
+{"abstract": "  We propose new mathematical models of inventory management in a reverse\nlogistics system. The proposed models extend the model introduced by Nahmias\nand Rivera with the assumption that the demand for newly produced and repaired\n(remanufacturing) items are not the same. We derive two mathematical models and\nformulate unconstrained and constrained optimization problems to optimize the\nholding cost. We also introduce the solution procedures of the proposed\nproblems. The exactness of the proposed solutions has been tested by numerical\nexperiments. Nowadays, it is an essential commitment for industries to reduce\ngreenhouse gas (GHG) emissions as well as energy consumption during the\nproduction and remanufacturing processes. This paper also extends along this\nline of research, and therewith develops a three-objective mathematical model\nand provides an algorithm to obtain the Pareto solution.\n"}
+{"abstract": "  The boundaries identification of Kelvin-Helmholtz vortices in observational\ndata has been addressed by searching for single-spacecraft small-scale\nsignatures. A recent hybrid Vlasov-Maxwell simulation of Kelvin-Helmholtz\ninstability has pointed out clear kinetic features which uniquely characterize\nthe vortex during both the nonlinear and turbulent stage of the instability. We\ncompare the simulation results with \\textit{in situ} observations of\nKelvin-Helmholtz vortices by the Magnetospheric MultiScale satellites. We find\ngood agreement between simulation and observations. In particular, the edges of\nthe vortex are associated with strong current sheets, while the center is\ncharacterized by a low value for the magnitude of the total current density and\nstrong deviation of the ion distribution function from a Maxwellian\ndistribution. We also find a significant temperature anisotropy parallel to the\nmagnetic field inside the vortex region and strong agyrotropies near the edges.\nWe suggest that these kinetic features can be useful for the identification of\nKelvin-Helmholtz vortices in \\textit{in situ} data.\n"}
+{"abstract": "  This paper aims at studying the sample complexity of graph convolutional\nnetworks (GCNs), by providing tight upper bounds of Rademacher complexity for\nGCN models with a single hidden layer. Under regularity conditions, theses\nderived complexity bounds explicitly depend on the largest eigenvalue of graph\nconvolution filter and the degree distribution of the graph. Again, we provide\na lower bound of Rademacher complexity for GCNs to show optimality of our\nderived upper bounds. Taking two commonly used examples as representatives, we\ndiscuss the implications of our results in designing graph convolution filters\nan graph distribution.\n"}
+{"abstract": "  Named entity recognition (NER) is a fundamental component in many\napplications, such as Web Search and Voice Assistants. Although deep neural\nnetworks greatly improve the performance of NER, due to the requirement of\nlarge amounts of training data, deep neural networks can hardly scale out to\nmany languages in an industry setting. To tackle this challenge, cross-lingual\nNER transfers knowledge from a rich-resource language to languages with low\nresources through pre-trained multilingual language models. Instead of using\ntraining data in target languages, cross-lingual NER has to rely on only\ntraining data in source languages, and optionally adds the translated training\ndata derived from source languages. However, the existing cross-lingual NER\nmethods do not make good use of rich unlabeled data in target languages, which\nis relatively easy to collect in industry applications. To address the\nopportunities and challenges, in this paper we describe our novel practice in\nMicrosoft to leverage such large amounts of unlabeled data in target languages\nin real production settings. To effectively extract weak supervision signals\nfrom the unlabeled data, we develop a novel approach based on the ideas of\nsemi-supervised learning and reinforcement learning. The empirical study on\nthree benchmark data sets verifies that our approach establishes the new\nstate-of-the-art performance with clear edges. Now, the NER techniques reported\nin this paper are on their way to become a fundamental component for Web\nranking, Entity Pane, Answers Triggering, and Question Answering in the\nMicrosoft Bing search engine. Moreover, our techniques will also serve as part\nof the Spoken Language Understanding module for a commercial voice assistant.\nWe plan to open source the code of the prototype framework after deployment.\n"}
+{"abstract": "  The randomized Gauss--Seidel method and its extension have attracted much\nattention recently and their convergence rates have been considered\nextensively. However, the convergence rates are usually determined by upper\nbounds, which cannot fully reflect the actual convergence. In this paper, we\nmake a detailed analysis of their convergence behaviors. The analysis shows\nthat the larger the singular value of $A$ is, the faster the error decays in\nthe corresponding singular vector space, and the convergence directions are\nmainly driven by the large singular values at the beginning, then gradually\ndriven by the small singular values, and finally by the smallest nonzero\nsingular value. These results explain the phenomenon found in the extensive\nnumerical experiments appearing in the literature that these two methods seem\nto converge faster at the beginning. Numerical examples are provided to confirm\nthe above findings.\n"}
+{"abstract": "  We explore strain-modulated helimagnetism in highly crystalline MnP nanorod\nfilms grown on Si(100) substrates using molecular beam epitaxy. The strained\nMnP film exhibits a paramagnetic to ferromagnetic (PM-FM) phase transition at\nTC ~ 279 K, and the FM to helical phase transition at TN ~ 110 K. The value of\nTN is greater than TN ~ 47 K for the MnP single crystal, indicating strong\nstrain-modulated helimagnetic states in the MnP nanorod film. The presence of\nsignificant thermal hysteresis in the helical phase indicates coexistence of\ncompeting magnetic interactions, leading to the first-order metamagnetic\ntransition. Similar to its single crystal counterpart, an anisotropic magnetic\neffect is observed in the MnP film, which is independently confirmed by\nmagnetic hysteresis loop and radio-frequency transverse susceptibility (TS)\nmeasurements. The evolution of screw (SCR) to CONE and FAN phase is precisely\ntracked from magnetization versus magnetic field/temperature measurements. The\ntemperature dependence of the anisotropy fields, extracted from the TS spectra,\nyields further insight into the competing nature of the magnetic phases.\nUnfolding of the different helical phases at T < 120 K (~TN) is analyzed by the\ntemperature- and field-dependent magnetic entropy change. Based on these\nfindings, the comprehensive magnetic phase diagrams of the MnP nanorod film are\nconstructed for the first time for both the in-plane and out-of-plane magnetic\nfield directions, revealing emergent strain/dimensionality-driven helical\nmagnetic features that are absent in the magnetic phase diagram of the MnP\nsingle crystal.\n"}
+{"abstract": "  Photons radiated from an evaporating black hole in principle provide complete\ninformation on the particle spectrum of nature up to the Planck scale. If an\nevaporating black hole were to be observed, it would open a unique window onto\nmodels beyond the Standard Model of particle physics. To demonstrate this, we\ncompute the limits that could be placed on the size of a dark sector. We find\nthat observation of an evaporating black hole at a distance of 0.01 parsecs\ncould probe dark sector models containing one or more copies of the Standard\nModel particles, with any mass scale up to 300 TeV.\n"}
+{"abstract": "  Gaussian processes (GPs) are nonparametric Bayesian models that have been\napplied to regression and classification problems. One of the approaches to\nalleviate their cubic training cost is the use of local GP experts trained on\nsubsets of the data. In particular, product-of-expert models combine the\npredictive distributions of local experts through a tractable product\noperation. While these expert models allow for massively distributed\ncomputation, their predictions typically suffer from erratic behaviour of the\nmean or uncalibrated uncertainty quantification. By calibrating predictions via\na tempered softmax weighting, we provide a solution to these problems for\nmultiple product-of-expert models, including the generalised product of experts\nand the robust Bayesian committee machine. Furthermore, we leverage the optimal\ntransport literature and propose a new product-of-expert model that combines\npredictions of local experts by computing their Wasserstein barycenter, which\ncan be applied to both regression and classification.\n"}
+{"abstract": "  We present the cumulative star-formation histories (SFHs) of >15000 dwarf\ngalaxies ($M_{*}=10^{7-10}M_{\\odot}$) from the TNG50 run of the IllustrisTNG\nsuite across a vast range of environments. The key factors determining the\ndwarfs' SFHs are their status as central or satellite and their stellar mass,\nwith centrals and more massive dwarfs assembling their stellar mass at later\ntimes on average compared to satellites and lower mass dwarfs. The satellites\n(in hosts of total mass $M_{200c,\\,host}=10^{12-14.3}M_{\\odot}$) assembled 90%\nof their z=0 stellar mass ~$7.0_{-5.5}^{+3.3}$ Gyr ago, while the centrals did\nso only ~$1.0_{-0.5}^{+4.0}$ Gyr ago. TNG50 predicts a large diversity in SFHs\nfor both centrals and satellites, so that the stacked cumulative SFHs are\nrepresentative of the TNG50 dwarf populations only in an average sense and\nindividual dwarfs can have significantly different cumulative SFHs. Satellite\ndwarfs with the highest stellar mass to host mass ratios have the latest\nstellar mass assembly. Satellites at fixed stellar and host halo mass, found\ncloser to the cluster centre, or accreted at earlier times, show significantly\nearlier stellar mass assembly. These trends, as well as the shapes of the SFHs\nthemselves, are a manifestation of the varying proportions within a given\nsubsample of quenched vs. star-forming galaxies, which exhibit markedly\ndistinct SFH shapes. We also find a subtle effect whereby satellite dwarfs in\nthe most massive hosts at z=0 have higher SFRs at early times, well before\nfinal infall into their z=0 host, compared to a control sample of centrals\nmass-matched at the time of accretion. This suggests that the large-scale\nenvironment can have a mild effect even on future satellites by providing the\nconditions for enhanced SF at early epochs. Our results are useful theoretical\npredictions for comparison to future resolved-stellar-population observations.\n"}
+{"abstract": "  The class of port-Hamiltonian systems incorporates many physical models, such\nas mechanical systems in the finite-dimensional case and wave and beam\nequations in the infinite-dimensional case. In this paper we study a subclass\nof linear first order port-Hamiltonian systems. These systems are exactly\nobservable when the energy is not dissipated internally and when sufficient\nobservations are made at the boundary. In this article we study the\nobservability properties for these systems when internal dissipation of energy\nis possible. We cannot show the exact observability, but we do show that the\nHautus test is satisfied. In general, the Hautus test is weaker than exact\nobservability, but stronger than approximate observability. Hence we conclude\nthat these systems are approximately observable.\n"}
+{"abstract": "  End-to-end encryption (E2EE) by messaging platforms enable people to securely\nand privately communicate with one another. Its widespread adoption however\nraised concerns that illegal content might now be shared undetected. Following\nthe global pushback against key escrow systems, client-side scanning based on\nperceptual hashing has been recently proposed by tech companies, governments\nand researchers to detect illegal content in E2EE communications. We here\npropose the first framework to evaluate the robustness of perceptual\nhashing-based client-side scanning to detection avoidance attacks and show\ncurrent systems to not be robust. More specifically, we propose three\nadversarial attacks--a general black-box attack and two white-box attacks for\ndiscrete cosine transform-based algorithms--against perceptual hashing\nalgorithms. In a large-scale evaluation, we show perceptual hashing-based\nclient-side scanning mechanisms to be highly vulnerable to detection avoidance\nattacks in a black-box setting, with more than 99.9\\% of images successfully\nattacked while preserving the content of the image. We furthermore show our\nattack to generate diverse perturbations, strongly suggesting that\nstraightforward mitigation strategies would be ineffective. Finally, we show\nthat the larger thresholds necessary to make the attack harder would probably\nrequire more than one billion images to be flagged and decrypted daily, raising\nstrong privacy concerns. Taken together, our results shed serious doubts on the\nrobustness of perceptual hashing-based client-side scanning mechanisms\ncurrently proposed by governments, organizations, and researchers around the\nworld.\n"}
+{"abstract": "  Despite pre-trained language models have proven useful for learning\nhigh-quality semantic representations, these models are still vulnerable to\nsimple perturbations. Recent works aimed to improve the robustness of\npre-trained models mainly focus on adversarial training from perturbed examples\nwith similar semantics, neglecting the utilization of different or even\nopposite semantics. Different from the image processing field, the text is\ndiscrete and few word substitutions can cause significant semantic changes. To\nstudy the impact of semantics caused by small perturbations, we conduct a\nseries of pilot experiments and surprisingly find that adversarial training is\nuseless or even harmful for the model to detect these semantic changes. To\naddress this problem, we propose Contrastive Learning with semantIc Negative\nExamples (CLINE), which constructs semantic negative examples unsupervised to\nimprove the robustness under semantically adversarial attacking. By comparing\nwith similar and opposite semantic examples, the model can effectively perceive\nthe semantic changes caused by small perturbations. Empirical results show that\nour approach yields substantial improvements on a range of sentiment analysis,\nreasoning, and reading comprehension tasks. And CLINE also ensures the\ncompactness within the same semantics and separability across different\nsemantics in sentence-level.\n"}
+{"abstract": "  We study deterministic power-law quantum hopping model with an amplitude\n$J(r) \\propto - r^{-\\beta}$ and local Gaussian disorder in low dimensions\n$d=1,2$ under the condition $d < \\beta < 3d/2$. We demonstrate unusual\ncombination of exponentially decreasing density of the \"tail states\" and\nlocalization-delocalization transition (as function of disorder strength $w$)\npertinent to a small (vanishing in thermodynamic limit) fraction of\neigenstates. At sub-critical disorder $w < w_c$ delocalized eigenstates with\nenergies near the bare band edge co-exist with a strongly localized eigenstates\nin the same energy window. At higher disorder $w > w_c$ all eigenstates are\nlocalized. In a broad range of parameters density of states $\\nu(E)$ decays\ninto the tail region $E <0$ as simple exponential, $ \\nu(E) = \\nu_0 e^{E/E_0}\n$, while characteristic energy $E_0$ varies smoothly across edge localization\ntransition. We develop simple analytic theory which describes $E_0$ dependence\non power-law exponent $\\beta$, dimensionality $d$ and disorder strength $W$,\nand compare its predictions with exact diagonalization results. At low energies\nwithin the bare \"conduction band\", all eigenstates are localized due to strong\nquantum interference at $d=1,2$; however localization length grows fast with\nenergy decrease, contrary to the case of usual Schrodinger equation with local\ndisorder.\n"}
+{"abstract": "  We investigate the existence and non-existence of maximal green sequences for\nquivers arising from weighted projective lines. Let $Q$ be the Gabreil quiver\nof the endomorphism algebra of a basic cluster-tilting object in the cluster\ncategory $\\mathcal{C}_\\mathbb{X}$ of a weighted projective line $\\mathbb{X}$.\nIt is proved that there exists a quiver $Q'$ in the mutation equivalence class\n$\\operatorname{Mut}(Q)$ such that $Q'$ admits a maximal green sequence. On the\nother hand, there is a quiver in $\\operatorname{Mut}(Q)$ which does not admit a\nmaximal green sequence if and only if $\\mathbb{X}$ is of wild type.\n"}
+{"abstract": "  Much attention has been devoted to water's metastable phase behavior,\nincluding polyamorphism (multiple amorphous solid phases), and the hypothesized\nliquid-liquid transition and associated critical point. However, the possible\nrelationship between these phenomena remains incompletely understood. Using\nmolecular dynamics simulations of the realistic TIP4P/2005 model, we found a\nstriking signature of the liquid-liquid critical point in the structure of\nwater glasses, manifested as a pronounced increase in long-range density\nfluctuations at pressures proximate to the critical pressure. By contrast,\nthese signatures were absent in glasses of two model systems that lack a\ncritical point. We also characterized the departure from equilibrium upon\nvitrification via the non-equilibrium index; water-like systems exhibited a\nstrong pressure dependence in this metric, whereas simple liquids did not.\nThese results reflect a surprising relationship between the metastable\nequilibrium phenomenon of liquid-liquid criticality and the non-equilibrium\nstructure of glassy water, with implications for our understanding of water\nphase behavior and glass physics. Our calculations suggest a possible\nexperimental route to probing the existence of the liquid-liquid transition in\nwater and other fluids.\n"}
+{"abstract": "  Spontaneous emergence of correlated states such as flocks and vortices are\nprime examples of remarkable collective dynamics and self-organization observed\nin active matter. The formation of globally correlated polar states in\ngeometrically confined systems proceeds through the emergence of a macroscopic\nsteadily rotating vortex that spontaneously selects a clockwise or\ncounterclockwise global chiral state. Here, we reveal that a global vortex\nformed by colloidal rollers exhibits state memory. The information remains\nstored even when the energy injection is ceased and the activity is terminated.\nWe show that a subsequent formation of the collective states upon re-energizing\nthe system is not random. We combine experiments and simulations to elucidate\nhow a combination of hydrodynamic and electrostatic interactions leads to\nhidden asymmetries in the local particle positional order encoding the chiral\nstate of the system. The stored information can be accessed and exploited to\nsystematically command subsequent polar states of active liquid through\ntemporal control of the activity. With the chirality of the emergent collective\nstates controlled on-demand, active liquids offer new possibilities for flow\nmanipulation, transport, and mixing at the microscale.\n"}
+{"abstract": "  We report the fabrication of small (Ba,A)Fe2As2 (A: Na, K) coils using 10\nm-class long round wires, fabricated by powder-in-tube (PIT) method. Coils are\nsintered using hot-isostatic-press (HIP) technique after glass-fiber\ninsulations are installed. Critical current (Ic) of the whole coil using\n(Ba,Na)Fe2As2 and (Ba,K)Fe2As2 are 60 A and 66 A under the self-field, and the\ngenerated magnetic fields at the center of the coil reach 2.6 kOe and 2.5 kOe,\nrespectively. Furthermore, the largest transport critical current density (Jc)\nand Ic in (Ba,Na)Fe2As2 wires picked up from the coil reach 54 kAcm-2 and 51.8\nA at T = 4.2 K under a magnetic field of 100 kOe, respectively. This value\nexceeds transport Jc of all previous iron-based superconducting round wires.\nTexturing of grains in the core of the wire due to the improvement of the wire\ndrawing process plays a key role for the enhancement of Jc.\n"}
+{"abstract": "  The emergence of technologies such as 5G and mobile edge computing has\nenabled provisioning of different types of services with different resource and\nservice requirements to the vehicles in a vehicular network.The growing\ncomplexity of traffic mobility patterns and dynamics in the requests for\ndifferent types of services has made service placement a challenging task. A\ntypical static placement solution is not effective as it does not consider the\ntraffic mobility and service dynamics. In this paper, we propose a\nreinforcement learning-based dynamic (RL-Dynamic) service placement framework\nto find the optimal placement of services at the edge servers while considering\nthe vehicle's mobility and dynamics in the requests for different types of\nservices. We use SUMO and MATLAB to carry out simulation experiments. In our\nlearning framework, for the decision module, we consider two alternative\nobjective functions-minimizing delay and minimizing edge server utilization. We\ndeveloped an ILP based problem formulation for the two objective functions. The\nexperimental results show that 1) compared to static service placement,\nRL-based dynamic service placement achieves fair utilization of edge server\nresources and low service delay, and 2) compared to delay-optimized placement,\nserver utilization optimized placement utilizes resources more effectively,\nachieving higher fairness with lower edge-server utilization.\n"}
+{"abstract": "  Latent Gaussian models and boosting are widely used techniques in statistics\nand machine learning. Tree-boosting shows excellent prediction accuracy on many\ndata sets, but potential drawbacks are that it assumes conditional independence\nof samples, produces discontinuous predictions for, e.g., spatial data, and it\ncan have difficulty with high-cardinality categorical variables. Latent\nGaussian models, such as Gaussian process and grouped random effects models,\nare flexible prior models which explicitly model dependence among samples and\nwhich allow for efficient learning of predictor functions and for making\nprobabilistic predictions. However, existing latent Gaussian models usually\nassume either a zero or a linear prior mean function which can be an\nunrealistic assumption. This article introduces a novel approach that combines\nboosting and latent Gaussian models to remedy the above-mentioned drawbacks and\nto leverage the advantages of both techniques. We obtain increased prediction\naccuracy compared to existing approaches in both simulated and real-world data\nexperiments.\n"}
+{"abstract": "  We consider a mean-field control problem with linear dynamics and quadratic\ncontrol. We apply the vanishing viscosity method: we add a (regularizing) heat\ndiffusion with a small viscosity coefficient and let such coefficient go to\nzero. The main result is that, in this case, the limit optimal control is\nexactly the optimal control of the original problem.\n"}
+{"abstract": "  The human somatosensory cortex is intimately linked to other central brain\nfunctions such as vision, audition, mechanoreception, and motor planning and\ncontrol. These links are established through brain learning, and display a\nconsiderable functional plasticity. This latter fulfills an important adaptive\nrole and ensures, for example, that humans are able to reliably manipulate and\ncontrol objects in the physical world under constantly changing conditions in\ntheir immediate sensory environment. Variations in human grip force are a\ndirect reflection of this specific kind of functional plasticity. Data from\npreliminary experiments where wearable wireless sensor technology (sensor\ngloves) was exploited to measure human grip force variations under varying\nsensory input conditions (eyes open or shut, soft music or hard music during\ngripping) are discussed here to show the extent to which grip force sensing\npermits quantifying somatosensory brain interactions and their functional\nplasticity. Experiments to take this preliminary work further are suggested.\nImplications for robotics, in particular the development of end-effector robots\nfor upper limb movement planning and control, are brought forward.\n"}
+{"abstract": "  We investigate the rate-dependent fracture of vitrimers by conducting a tear\ntest. Based on the relationship between the frac-ture energy and the thickness\nof vitrimer films, we, for the first time, obtain the intrinsic fracture energy\nand bulk dissipation of vitrimers during crack extension. The intrinsic\nfracture energy strongly depends on tear speed, and such dependence can be well\nexplained by Eyring theory. In contrast, the bulk dissipation only weakly\ndepends on tear speed, which is drastically different from observations on\ntraditional viscoelastic polymers. We ascribe such a weak rate-dependence to\nthe strong force-sensitivity of the exchange reaction of the dynamic covalent\nbond in the vitrimer.\n"}
+{"abstract": "  Mathematical modeling of lithium-ion batteries (LiBs) is a central challenge\nin advanced battery management. This paper presents a new approach to integrate\na physics-based model with machine learning to achieve high-precision modeling\nfor LiBs. This approach uniquely proposes to inform the machine learning model\nof the dynamic state of the physical model, enabling a deep integration between\nphysics and machine learning. We propose two hybrid physics-machine learning\nmodels based on the approach, which blend a single particle model with thermal\ndynamics (SPMT) with a feedforward neural network (FNN) to perform\nphysics-informed learning of a LiB's dynamic behavior. The proposed models are\nrelatively parsimonious in structure and can provide considerable predictive\naccuracy even at high C-rates, as shown by extensive simulations.\n"}
+{"abstract": "  In this paper, we introduce UnifiedM2, a general-purpose misinformation model\nthat jointly models multiple domains of misinformation with a single, unified\nsetup. The model is trained to handle four tasks: detecting news bias,\nclickbait, fake news, and verifying rumors. By grouping these tasks together,\nUnifiedM2learns a richer representation of misinformation, which leads to\nstate-of-the-art or comparable performance across all tasks. Furthermore, we\ndemonstrate that UnifiedM2's learned representation is helpful for few-shot\nlearning of unseen misinformation tasks/datasets and model's generalizability\nto unseen events.\n"}
+{"abstract": "  A new domain decomposition preconditioner is introduced for efficiently\nsolving linear systems Ax = b with a symmetric positive definite matrix A. The\nparticularity of the new preconditioner is that it is not necessary to have\naccess to the so-called Neumann matrices (i.e.: the matrices that result from\nassembling the variational problem underlying A restricted to each subdomain).\nAll the components in the preconditioner can be computed with the knowledge\nonly of A (and this is the meaning given here to the word algebraic). The new\npreconditioner relies on the GenEO coarse space for a matrix that is a low-rank\nmodification of A and on the Woodbury matrix identity. The idea underlying the\nnew preconditioner is introduced here for the first time with a first version\nof the preconditioner. Some numerical illustrations are presented. A more\nextensive presentation including some improved variants of the new\npreconditioner can be found in [7]\n(https://hal.archives-ouvertes.fr/hal-03258644).\n"}
+{"abstract": "  Construction equipment is designed to maintain good traction, even when\noperating in difficult off-road conditions. To curb wheel slip, the vehicles\nare equipped with differential locks. A driver may engage/disengage the locks\nto switch between two distinct operating modes: the closed mode is\ncharacterized by greater off-road passability while the open mode allows better\nmanoeuvrability. However, many drivers lack the education and experience\nrequired to correctly judge the terrain ahead of the vehicle and therefore\nengage/disengage the locks in a suboptimal fashion. An automatic traction\ncontrol solution for locking and opening the differentials is hence desirable.\nThis paper compares three on/off differential lock control algorithms, all\nderived from the same kinematic vehicle model but each relying on the\navailability of output signals from different sensors. The validity of the\nkinematic model and the algorithms' sensitivity to the values assumed by a\ncouple of unobservable states, the wheel slip angles, is investigated by\ncomparison to a realistic articulated hauler model in the multibody physics\nsimulator MSC ADAMS.\n"}
+{"abstract": "  We investigate the one-dimensional telegraph random process in the presence\nof an elastic boundary at the origin. This process describes a finite-velocity\nrandom motion that alternates between two possible directions of motion\n(positive or negative). When the particle hits the origin, it is either\nabsorbed, with probability $\\alpha$, or reflected upwards, with probability\n$1-\\alpha$. In the case of exponentially distributed random times between\nconsecutive changes of direction, we obtain the distribution of the renewal\ncycles and of the absorption time at the origin. This investigation is\nperformed both in the case of motion starting from the origin and non-zero\ninitial state. We also study the probability law of the process within a\nrenewal cycle.\n"}
+{"abstract": "  The growth of sky surveys and the large amount of stellar spectra in the\ncurrent databases, has generated the necessity of developing new methods to\nestimate atmospheric parameters, a fundamental task on stellar research. In\nthis work we present a comparison of different machine learning algorithms,\nusing for the classification of stellar synthetic spectra and the estimation of\nfundamental stellar parameters included T_eff(K), log(L/Lo), log g, M/Mo, and\nVrot. For both tasks, we established a group of supervised learning models, and\npropose a database of measures with the same structure to train the algorithms.\nThis data includes equivalent-width types measurements over noisy synthetic\nspectra in order to replicate the natural noise on a real observed spectrum.\nDifferent levels of signal to noise ratio are considered for this analysis.\n"}
+{"abstract": "  Using $p$-adic numbers, we partially categorize the cycles of a sizable class\nof polynomial dynamical systems. In turn, we prove a few results related to the\nnon-trivial cycles of the $\\textit{Collatz map}$ $\\text{Col} : \\mathbb{Z}_+ \\to\n\\mathbb{Z}_+$ defined by $$\\text{Col}(n) = \\begin{cases} 3n + 1, & n \\text{ is\nodd;} \\\\ n/2, & \\text{otherwise.}\\end{cases}$$ Proving the non-existence of\nnon-trivial Collatz cycles would reduce the Collatz conjecture to whether the\nCollatz map can diverge to infinity.\n"}
+{"abstract": "  Here we consider a non-perturbative description of the non-interacting\nresonant model using a scattering approach employed in JCP 152(24) 244126\n(2020). We showed that such description coincides with the standard NEGF and\nLandauer-Buttiker approaches.\n"}
+{"abstract": "  Deep learning models, such as convolutional neural networks, have long been\napplied to image and multi-media tasks, particularly those with structured\ndata. More recently, there has been more attention to unstructured data that\ncan be represented via graphs. These types of data are often found in health\nand medicine, social networks, and research data repositories. Graph\nconvolutional neural networks have recently gained attention in the field of\ndeep learning that takes advantage of graph-based data representation with\nautomatic feature extraction via convolutions. Given the popularity of these\nmethods in a wide range of applications, robust uncertainty quantification is\nvital. This remains a challenge for large models and unstructured datasets.\nBayesian inference provides a principled approach to uncertainty quantification\nof model parameters for deep learning models. Although Bayesian inference has\nbeen used extensively elsewhere, its application to deep learning remains\nlimited due to the computational requirements of the Markov Chain Monte Carlo\n(MCMC) methods. Recent advances in parallel computing and advanced proposal\nschemes in MCMC sampling methods has opened the path for Bayesian deep\nlearning. In this paper, we present Bayesian graph convolutional neural\nnetworks that employ tempered MCMC sampling with Langevin-gradient proposal\ndistribution implemented via parallel computing. Our results show that the\nproposed method can provide accuracy similar to advanced optimisers while\nproviding uncertainty quantification for key benchmark problems.\n"}
+{"abstract": "  We study Lorentzian supersymmetric configurations in $D=4$ and $D=5$ gauged\n$\\mathcal{N}=2$ supergravity. We show that there are smooth $1/2$ BPS solutions\nwhich are asymptotically AdS$_{4}$ and AdS$_{5}$ with a planar boundary, a\ncompact spacelike direction and with a Wilson line on that circle. There are\nsolitons where the $S^{1}$ shrinks smoothly to zero in the interior, with a\nmagnetic flux through the circle determined by the Wilson line, which are AdS\nanalogues of the Melvin fluxtube. There is also a solution with a constant\ngauge field, which is pure AdS. Both solutions preserve half of the\nsupersymmetries at a special value of the Wilson line. There is a phase\ntransition between these two saddle-points as a function of the Wilson line\nprecisely at the supersymmetric point. Thus, the supersymmetric solutions are\ndegenerate, at least at the supergravity level. We extend this discussion to\none of the Romans solutions in four dimensions when the Euclidean boundary is\n$S^{1}\\times\\Sigma_{g}$ where $\\Sigma_{g}$ is a Riemann surface with genus $g >\n0$. We speculate that the supersymmetric state of the CFT on the boundary is\ndual to a superposition of the two degenerate geometries.\n"}
+{"abstract": "  In this paper, we focus on the use of Low-Eart Orbit (LEO) satellites\nproviding the Narrowband Internet of Things (NB-IoT) connectivity to the\non-ground user equipment (UEs). Conventional resource allocation algorithms for\nthe NBIoT systems are particularly designed for terrestrial infrastructures,\nwhere devices are under the coverage of a specific base station and the whole\nsystem varies very slowly in time. The existing methods in the literature\ncannot be applied over LEO satellite-based NB-IoT systems for several reasons.\nFirst, with the movement of the LEO satellite, the corresponding channel\nparameters for each user will quickly change over time. Delaying the scheduling\nof a certain user would result in a resource allocation based on outdated\nparameters. Second, the differential Doppler shift, which is a typical\nimpairment in communications over LEO, directly depends on the relative\ndistance among users. Scheduling at the same radio frame users that overcome a\ncertain distance would violate the differential Doppler limit supported by the\nNB-IoT standard. Third, the propagation delay over a LEO satellite channel is\naround 4-16 times higher compared to a terrestrial system, imposing the need\nfor message exchange minimization between the users and the base station. In\nthis work, we propose a novel uplink resource allocation strategy that jointly\nincorporates the new design considerations previously mentioned together with\nthe distinct channel conditions, satellite coverage times and data demands of\nvarious users on Earth. The novel methodology proposed in this paper can act as\na framework for future works in the field.\n"}
+{"abstract": "  UK Biobank (UKB) conducts large-scale examinations of more than half a\nmillion volunteers, collecting health-related information on genetics,\nlifestyle, blood biochemistry, and more. Medical imaging of 100,000 subjects,\nwith 70,000 follow-up sessions, enables measurements of organs, muscle, and\nbody composition. With up to 170,000 mounting MR images, various methodologies\nare accordingly engaged in large-scale image analysis. This work presents an\nexperimental inference engine that can automatically predict a comprehensive\nprofile of subject metadata from UKB neck-to-knee body MRI. It was evaluated in\ncross-validation for baseline characteristics such as age, height, weight, and\nsex, but also measurements of body composition, organ volumes, and abstract\nproperties like grip strength, pulse rate, and type 2 diabetic status. It\npredicted subsequently released test data covering twelve body composition\nmetrics with a 3% median error. The proposed system can automatically analyze\none thousand subjects within ten minutes, providing individual confidence\nintervals. The underlying methodology utilizes convolutional neural networks\nfor image-based mean-variance regression on two-dimensional representations of\nthe MRI data. This work aims to make the proposed system available for free to\nresearchers, who can use it to obtain fast and fully-automated estimates of 72\ndifferent measurements immediately upon release of new UKB image data.\n"}
+{"abstract": "  Two-dimensional (2D) surface of the topological materials is an attractive\nchannel for the electrical conduction reflecting the linearly-dispersive\nelectronic bands. By applying a reliable systematic thickness t dependent\nmeasurement of sheet conductance, here we elucidate the dimensionality of the\nelectrical conduction paths of a Weyl semimetal Co3Sn2S2. Under the\nferromagnetic phase, the 2D conduction path clearly emerges in Co3Sn2S2 thin\nfilms, indicating a formation of the Fermi arcs projected from Weyl nodes.\nComparison between 3D conductivity and 2D conductance provides the effective\nthickness of the surface conducting region being estimated to be approximately\n20 nm, which is rather thicker than 5 nm in topological insulator Bi2Se3. This\nlarge value may come from the narrow gap at Weyl point and relatively weak\nspin-orbit interaction of the Co3Sn2S2. The emergent surface conduction will\nprovide a pathway to activate quantum and spintronic transport features\nstemming from a Weyl node in thin-film-based devices.\n"}
+{"abstract": "  The triple-based knowledge in large-scale knowledge bases is most likely\nlacking in structural logic and problematic of conducting knowledge hierarchy.\nIn this paper, we introduce the concept of metaknowledge to knowledge\nengineering research for the purpose of structural knowledge construction.\nTherefore, the Metaknowledge Extraction Framework and Document Structure Tree\nmodel are presented to extract and organize metaknowledge elements (titles,\nauthors, abstracts, sections, paragraphs, etc.), so that it is feasible to\nextract the structural knowledge from multi-modal documents. Experiment results\nhave proved the effectiveness of metaknowledge elements extraction by our\nframework. Meanwhile, detailed examples are given to demonstrate what exactly\nmetaknowledge is and how to generate it. At the end of this paper, we propose\nand analyze the task flow of metaknowledge applications and the associations\nbetween knowledge and metaknowledge.\n"}
+{"abstract": "  The competing effect of heterogeneity and symmetry breaking coupling on the\nemerging dynamics in a system of N globally coupled Stuart-Landau oscillators\nis investigated. Increasing the heterogeneity, using the standard deviation of\nthe Hopf bifurcation parameter, favors the macroscopic oscillatory state for\nlow values of the symmetry breaking coupling and inhomogeneous steady state for\nlarger values of the coupling. There is also a transition, tipping, to\nhomogeneous steady state (aging state) from the macroscopic oscillatory state.\nThe limiting factor in the diffusive coupling favors the macroscopic\noscillatory state even in the presence of a large fraction of inactive\noscillators in the network thereby increasing the robustness of the network.\nThe globally coupled oscillators are reduced to a system of two evolution\nequations for the macroscopic order parameters, corresponding to the mean-field\nand the shape parameter, using the self-consistent field approach. The\nbifurcation diagrams obtained from the mean-field variables elucidate various\nbifurcation scenarios responsible for the dynamical transitions observed in N\nglobally coupled Stuart-Landau oscillators. In particular, tipping to the aging\nstate is found to occur via the Hopf and pitchfork bifurcations illustrating\nthe phenomenon of bifurcation induced tipping. Analytical stability (critical)\ncurves of these bifurcations, deduced from the mean-field variables, are found\nto fairly well agree with the simulation results.\n"}
+{"abstract": "  Stock price prediction can be made more efficient by considering the price\nfluctuations and understanding the sentiments of people. A limited number of\nmodels understand financial jargon or have labelled datasets concerning stock\nprice change. To overcome this challenge, we introduced FinALBERT, an ALBERT\nbased model trained to handle financial domain text classification tasks by\nlabelling Stocktwits text data based on stock price change. We collected\nStocktwits data for over ten years for 25 different companies, including the\nmajor five FAANG (Facebook, Amazon, Apple, Netflix, Google). These datasets\nwere labelled with three labelling techniques based on stock price changes. Our\nproposed model FinALBERT is fine-tuned with these labels to achieve optimal\nresults. We experimented with the labelled dataset by training it on\ntraditional machine learning, BERT, and FinBERT models, which helped us\nunderstand how these labels behaved with different model architectures. Our\nlabelling method competitive advantage is that it can help analyse the\nhistorical data effectively, and the mathematical function can be easily\ncustomised to predict stock movement.\n"}
+{"abstract": "  In this second part of a two-part paper, we discuss numerical simulations of\na head-on merger of two non-spinning black holes. We resolve the fate of the\noriginal two apparent horizons by showing that after intersecting, their world\ntubes \"turn around\" and continue backwards in time. Using the method presented\nin the first paper to locate these surfaces, we resolve several such world\ntubes evolving and connecting through various bifurcations and annihilations.\nThis also draws a consistent picture of the full merger in terms of apparent\nhorizons, or more generally, marginally outer trapped surfaces (MOTSs). The\nMOTS stability operator provides a natural mechanism to identify MOTSs which\nshould be thought of as black hole boundaries. These are the two initial ones\nand the final remnant. All other MOTSs lie in the interior and are neither\nstable nor inner trapped.\n"}
+{"abstract": "  We complete the task of optimal probabilistic coherence distillation\nprotocol, whose aim is to transform a general state into a set of n-level\nmaximally coherent states via strictly incoherent operations (SIO).\nSpecifically, we present the necessary and sufficient condition for the\ntransformation from a mixed state into a pure-state ensemble via SIO. With the\naid of this condition and the simplex algorithm, we can accomplish the\nprobabilistic distillation protocol by presenting an analytical expression of\nthe maximal average distillable coherence of a general state and constructing\nthe corresponding operation achieving this bound. Our protocol is a universal\nprotocol since it can be applied to any coherence measure.\n"}
+{"abstract": "  We study the transverse instability and dynamics of bright soliton stripes in\ntwo-dimensional nonlocal nonlinear media. Using a multiscale perturbation\nmethod, we derive analytically the first-order correction to the soliton shape,\nwhich features an exponential growth in time -- a signature of the transverse\ninstability. The soliton's characteristic timescale associated with its\nexponential growth,is found to depend on the square root of the nonlocality\nparameter. This, in turn, highlights the nonlocality-induced suppression of the\ntransverse instability. Our analytical predictions are corroborated by direct\nnumerical simulations, with the analytical results being in good agreement with\nthe numerical ones.\n"}
+{"abstract": "  It is widely believed that range resolution, the ability to distinguish\nbetween two closely situated targets, depends inversely on the bandwidth of the\ntransmitted radar signal. Here we demonstrate a different type of ranging\nsystem, which possesses superior range resolution that is almost completely\nfree of bandwidth limitations. By sweeping over the coherence length of the\ntransmitted signal, the partially coherent radar experimentally demonstrates an\nimprovement of over an order of magnitude in resolving targets, compared to\nstandard coherent radars with the same bandwidth.. A theoretical framework is\ndeveloped to show that the resolution could be further improved without a\nbound, revealing a tradeoff between bandwidth and sweep time. This concept\noffers solutions to problems which require high range resolution and accuracy\nbut available bandwidth is limited, as is the case for the autonomous car\nindustry, optical imaging, and astronomy to name just few.\n"}
+{"abstract": "  We study families of faces for convex semi-algebraic sets via the normal\ncycle which is a semi-algebraic set similar to the conormal variety in\nprojective duality theory. We propose a convex algebraic notion of a \"patch\" --\na term recently coined by Ciripoi, Kaihnsa, L\\\"ohne, and Sturmfels as a tool\nfor approximating the convex hull of a semi-algebraic set. We discuss geometric\nconsequences, both for the semi-algebraic and convex geometry of the families\nof faces, as well as variations of our definition and their consequences.\n"}
+{"abstract": "  To capture spatial relationships and temporal dynamics in traffic data,\nspatio-temporal models for traffic forecasting have drawn significant attention\nin recent years. Most of the recent works employed graph neural networks(GNN)\nwith multiple layers to capture the spatial dependency. However, road junctions\nwith different hop-distance can carry distinct traffic information which should\nbe exploited separately but existing multi-layer GNNs are incompetent to\ndiscriminate between their impact. Again, to capture the temporal\ninterrelationship, recurrent neural networks are common in state-of-the-art\napproaches that often fail to capture long-range dependencies. Furthermore,\ntraffic data shows repeated patterns in a daily or weekly period which should\nbe addressed explicitly. To address these limitations, we have designed a\nSimplified Spatio-temporal Traffic forecasting GNN(SST-GNN) that effectively\nencodes the spatial dependency by separately aggregating different neighborhood\nrepresentations rather than with multiple layers and capture the temporal\ndependency with a simple yet effective weighted spatio-temporal aggregation\nmechanism. We capture the periodic traffic patterns by using a novel position\nencoding scheme with historical and current data in two different models. With\nextensive experimental analysis, we have shown that our model has significantly\noutperformed the state-of-the-art models on three real-world traffic datasets\nfrom the Performance Measurement System (PeMS).\n"}
+{"abstract": "  Neutron irradiation tends to promote disorder in ordered alloys through the\naction of the thermal spikes that it generates, while simultaneously\nintroducing point defects and defect clusters. As they migrate, these point\ndefects will promote reordering of the alloys, acting against\nirradiation-induced disordering. In this study, classical molecular dynamics\nand a highly parallel accelerated sampling method are used to study the\nreordering kinetics of Ni$_3$Al under the diffusion of self-interstitial atoms\n(SIA). By monitoring the order parameter and potential energy from atomistic\nsimulations, we show that the SIA acts as a reordering agent in Ni$_3$Al. A\nmean-field rate theory model of the interstitialcy-based reordering kinetics is\nintroduced, which reproduces simulation data and predicts reordering at\ntemperatures as low as 500 K.\n"}
+{"abstract": "  We present the concept of a Generalized Feedback Nash Equilibrium (GFNE) in\ndynamic games, extending the Feedback Nash Equilibrium concept to games in\nwhich players are subject to state and input constraints. We formalize\nnecessary and sufficient conditions for (local) GFNE solutions at the\ntrajectory level, which enable the development of efficient numerical methods\nfor their computation. Specifically, we propose a Newton-style method for\nfinding game trajectories which satisfy the necessary conditions, which can\nthen be checked against the sufficiency conditions. We show that the evaluation\nof the necessary conditions in general requires computing a series of nested,\nimplicitly-defined derivatives, which quickly becomes intractable. To this end,\nwe introduce an approximation to the necessary conditions which is amenable to\nefficient evaluation, and in turn, computation of solutions. We term the\nsolutions to the approximate necessary conditions Generalized Feedback Quasi\nNash Equilibria (GFQNE), and we introduce numerical methods for their\ncomputation. In particular, we develop a Sequential Linear-Quadratic Game\napproach, in which a locally approximate LQ game is solved at each iteration.\nThe development of this method relies on the ability to compute a GFNE to\ninequality- and equality-constrained LQ games, and therefore specific methods\nfor the solution of these special cases are developed in detail. We demonstrate\nthe effectiveness of the proposed solution approach on a dynamic game arising\nin an autonomous driving application.\n"}
+{"abstract": "  Price discrimination for maximizing expected profit is a well-studied concept\nin economics and there are various methods that achieve the maximum given the\nuser type distribution and the budget constraints. In many applications,\nparticularly with regards to engineering and computing, it is often the case\nthan the user type distribution is unknown or not accurately known. In this\npaper, we therefore propose and study a mathematical framework for price\ndiscrimination with \\emph{target} profits under the contract-theoretic model.\nWe first consider service providers with a given user type profile and\ndetermine sufficient conditions for achieving a target profit. Our proof is\nconstructive in that it also provides a method to compute the quality-price tag\nmenu. Next we consider a dual scenario where the offered service qualities are\npredetermined and describe an iterative method to obtain nominal demand values\nthat best match the qualities offered by the service provider while achieving a\ntarget profit-user satisfaction margin. We also illustrate our methods with\ndesign examples in both cases.\n"}
+{"abstract": "  The (1,2)-TSP is a special case of the TSP where each edge has cost either 1\nor 2. In this paper we give a lower bound of $\\frac{3}{2}$ for the\napproximation ratio of the 2-Opt algorithm for the (1,2)-TSP. Moreover, we show\nthat the 3-Opt algorithm has an exact approximation ratio of $\\frac{11}{8}$ for\nthe (1,2)-TSP. Furthermore, we introduce the 3-Opt++-algorithm, an improved\nversion of the 3-Opt algorithm for the (1-2)-TSP with an exact approximation\nratio of $\\frac{4}{3}$.\n"}
+{"abstract": "  In this work, we study the extension of two variants of the facility location\nproblem (FL) to make them robust towards a few distantly located clients.\nFirst, $k$-facility location problem ($k$FL), a common generalization of FL and\n$k$ median problems, is a well studied problem in literature. In the second\nvariant, lower bounded facility location (LBFL), we are given a bound on the\nminimum number of clients that an opened facility must serve. Lower bounds are\nrequired in many applications like profitability in commerce and load balancing\nin transportation problem. In both the cases, the cost of the solution may be\nincreased grossly by a few distantly located clients, called the outliers.\nThus, in this work, we extend $k$FL and LBFL to make them robust towards the\noutliers. For $k$FL with outliers ($k$FLO) we present the first (constant)\nfactor approximation violating the cardinality requirement by +1. As a\nby-product, we also obtain the first approximation for FLO based on\nLP-rounding. For LBFLO, we present a tri-criteria solution with a trade-off\nbetween the violations in lower bounds and the number of outliers. With a\nviolation of $1/2$ in lower bounds, we get a violation of $2$ in outliers.\n"}
+{"abstract": "  This paper describes the system developed by the NPU team for the 2020\npersonalized voice trigger challenge. Our submitted system consists of two\nindependently trained subsystems: a small footprint keyword spotting (KWS)\nsystem and a speaker verification (SV) system. For the KWS system, a\nmulti-scale dilated temporal convolutional (MDTC) network is proposed to detect\nwake-up word (WuW). For SV system, Write something here. The KWS predicts\nposterior probabilities of whether an audio utterance contains WuW and\nestimates the location of WuW at the same time. When the posterior probability\nofWuW reaches a predefined threshold, the identity information of triggered\nsegment is determined by the SV system. On evaluation dataset, our submitted\nsystem obtains detection costs of 0.081and 0.091 in close talking and far-field\ntasks, respectively.\n"}
+{"abstract": "  The concept of the hafnian first appeared in the works on quantum field\ntheory by E. R. Caianiello. However, it also has an important combinatorial\nproperty: the hafnian of the adjacency matrix of an undirected weighted graph\nis equal to the total sum of the weights of perfect matchings in this graph. In\ngeneral, the use of the hafnian is limited by the complexity of its\ncomputation. In this paper, we present an efficient method for the exact\ncalculation of the hafnian of two-parameter matrices. In terms of graphs, we\ncount the total sum of the weights of perfect matchings in graphs whose edge\nweights take only two values. This method is based on the formula expressing\nthe hafnian of a sum of two matrices through the product of the hafnians of\ntheir submatrices. The necessary condition for the application of this method\nis the possibility to count the number of k-edge matchings in some graphs. We\nconsider two special cases in detail using a Toeplitz matrix as the\ntwo-parameter matrix. As an example, we propose a new interpretation of some of\nthe sequences from the On-Line Encyclopedia of Integer Sequences and then\nprovide new analytical formulas to count the number of certain linear chord\ndiagrams.\n"}
+{"abstract": "  Offensive language is pervasive in social media. Individuals frequently take\nadvantage of the perceived anonymity of computer-mediated communication, using\nthis to engage in behavior that many of them would not consider in real life.\nThe automatic identification of offensive content online is an important task\nthat has gained more attention in recent years. This task can be modeled as a\nsupervised classification problem in which systems are trained using a dataset\ncontaining posts that are annotated with respect to the presence of some\nform(s) of abusive or offensive content. The objective of this study is to\nprovide a description of a classification system built for SemEval-2019 Task 6:\nOffensEval. This system classifies a tweet as either offensive or not offensive\n(Sub-task A) and further classifies offensive tweets into categories (Sub-tasks\nB \\& C). We trained machine learning and deep learning models along with data\npreprocessing and sampling techniques to come up with the best results. Models\ndiscussed include Naive Bayes, SVM, Logistic Regression, Random Forest and\nLSTM.\n"}
+{"abstract": "  Caustics are a striking phenomena in natural optics and hydrodynamics:\nhigh-amplitude characteristic patterns that are singular in the short\nwavelength limit. We use exact numerical and approximate semiclassical analytic\nmethods to study quantum versions of caustics that form in Fock space during\nfar-from-equilibrium dynamics following a quench in the Bose Hubbard dimer and\ntrimer models. Due to interparticle interactions, both models are nonlinear and\nthe trimer is also nonintegrable. The caustics exhibit a hierarchy of\nmorphologies described by catastrophe theory that are stable against\nperturbations and hence occur generically. The dimer case gives rise to\ndiscretized versions of folds and cusps which are the simplest two of Thom's\ncatastrophes. The extra dimension available in the trimer case allows for\nhigher catastrophes including the codimension-3 hyperbolic umbilic and elliptic\numbilic catastrophes which we argue are organized by, and projections of, an\n8-dimensional corank-2 catastrophe known as $X_9$. These results indicate a\nhitherto unrecognized form of universality in quantum many-body dynamics\norganized by singularities.\n"}
+{"abstract": "  Quantifying the risk of global warming exceeding critical targets such as 2.0\nK requires reliable projections of uncertainty as well as best estimates of\nGlobal Mean Surface Temperature (GMST). However, uncertainty bands on GMST\nprojections are often calculated heuristically and have several potential\nshortcomings. In particular, the uncertainty bands shown in IPCC plume\nprojections of GMST are based on the distribution of GMST anomalies from\nclimate model runs and so are strongly determined by model characteristics with\nlittle influence from observations of the real-world. Physically motivated\ntime-series approaches are proposed based on fitting energy balance models\n(EBMs) to climate model outputs and observations in order to constrain future\nprojections. It is shown that EBMs fitted to one forcing scenario will not\nproduce reliable projections when different forcing scenarios are applied. The\nerrors in the EBM projections can be interpreted as arising due to a\ndiscrepancy in the effective forcing felt by the model. A simple time-series\napproach to correcting the projections is proposed based on learning the\nevolution of the forcing discrepancy so that it can be projected into the\nfuture. These approaches give reliable projections of GMST when tested in a\nperfect model setting, and when applied to observations lead to well\nconstrained projections with lower mean warming and narrower projection bands\nthan previous estimates. Despite the reduced uncertainty, the lower warming\nleads to a greatly reduced probability of exceeding the 2.0 K warming target.\n"}
+{"abstract": "  Magnetic field may distort neutron stars (NSs), but the effect has not been\nrobustly tested through gravitational-wave observation yet due to the absence\nof a fast rotating Galactic magnetar. The central objects of Gamma-ray bursts\n(GRBs) could be millisecond magnetars. Under the magnetar scenario on the X-ray\nplateaus of GRB afterglows,the spindown evolution modulated by the\ngravitational-wave radiation may be inferred from some special samples, so that\nthe magnetically-induced distorting can be further estimated. According to two\nsamples, GRB 060807 and GRB 070521, we found that the correlation between the\neffective ellipticity, $\\varepsilon_{\\rm B,eff}$, and effective dipole magnetic\nfield strength on a neutron star (NS) surface, $B_{\\rm eff}$, is\n$\\varepsilon_{\\rm B,eff}\\sim 10^{-4} (\\frac{B_{\\rm eff}}{10^{14}\\;\\rm G})^{2}$.\nThis result demands that $B_{\\rm eff}\\sim 0.01 B_{\\rm t}$ with $B_{\\rm t}$\nbeing the internal toroidal magnetic field strength of NSs. We suggested that\nthe torque generated during few unsymmetrical massive-star collapses may induce\ndifferential rotations in proto-NSs to amplify the internal toroidal fields.\n"}
+{"abstract": "  Neutron star (NS) is a unique astronomical compact object where the four\nfundamental interactions have been revealed from the observation and studied in\ndifferent ways. While the macroscopic properties of NS like mass and radius can\nbe determined within the General Relativity using a realistic equation of state\n(EOS) of NS matter, such an EOS is usually generated by a nuclear structure\nmodel like, e.g., the nuclear mean-field approach to asymmetric nuclear matter.\nGiven the radius of NS extended to above 10 km and its mass up to twice the\nsolar mass, NS is expected to be tidally deformed when it is embedded in a\nstrong tidal field. Such a tidal effect was confirmed unambiguously in the\ngravitation wave signals detected recently by the LIGO and Virgo laser\ninterferometers from GW170817, the first ever direct observation of a binary NS\nmerger. A nonrelativistic mean-field study is carried out in the present work\nwithin the Hartree-Fock formalism to construct the EOS of NS matter, which is\nthen used to determine the tidal deformability, gravitational mass, and radius\nof NS. The mean-field results are compared with the constraints imposed for\nthese quantities by the global analysis of the observed GW170817 data, and a\nstrong impact by the incompressibility of nuclear matter on the hydrostatic\nconfiguration of NS is shown.\n"}
+{"abstract": "  We implement stacked denoising autoencoders, a class of neural networks that\nare capable of learning powerful representations of high dimensional data. We\ndescribe stochastic gradient descent for unsupervised training of autoencoders,\nas well as a novel genetic algorithm based approach that makes use of gradient\ninformation. We analyze the performance of both optimization algorithms and\nalso the representation learning ability of the autoencoder when it is trained\non standard image classification datasets.\n"}
+{"abstract": "  The application of the energy-efficient thermal and energy management\nmechanism in Geothermal Heat Pumps (GHPs) is indispensable to reduce the\noverall energy consumption and carbon emission across the building sector.\nBesides, in the Virtual Power Plant (VPP) system, the demand response of\nclustered GHP systems can improve the operating flexibility of the power grid.\nThis paper presents an integration and operation framework of GHPs in the\ndistribution network, applying a layered communication and optimization method\nto coordinate multiple clustered GHPs in a community. In the proposed\nhierarchical operation scheme, the operator of regional GHPs collects the\nthermal zone information and the disturbance prediction of buildings in a short\ntime granularity, predicts the energy demand, and transmits the information to\nan aggregator. Using a novel linearized optimal power flow model, the\naggregator coordinates and aggregates load resources of GHP systems in the\ndistribution network. In this way, GHP systems with thermal and energy\nmanagement mechanisms can be applied to achieve the demand response in the VPP\nand offer more energy flexibility to the community.\n"}
+{"abstract": "  We prove that a closed $n$-manifold $M$ with positive scalar curvature and\nabelian fundamental group admits a finite covering $M'$ which is strongly\ninessential. The latter means that a classifying map $u:M'\\to K(\\pi_1(M'),1)$\ncan be deformed to the $(n-2)$-skeleton. This is proven for all $n$-manifolds\nwith the exception of 4-manifolds with spin universal coverings.\n"}
+{"abstract": "  The article provides a brief overview of existing models describing guided\nflux motion. The first model was proposed by Niessen et al. This model works in\nthe single-vortex approximation and provides qualitative explanation of the\neffect, but in most cases it gives an overestimated guiding angle. The\nstochastic model explains the experimentally observed effect of decreasing the\nguiding angle, the so-called slipping effect, by the influence of thermal\nfluctuations. However, the performed estimates show that thermal fluctuations\nshould not be so significant. An alternative guiding model is the anisotropic\npinning model. This model works in the critical state approximation, and the\nslipping effect is the result of the combined action of anisotropic pinning and\nvortex interaction. The predictions of all three models are verified by the\nexample of a superconducting Nb-Ti tape containing about 6 vol. % {/alpha}-Ti,\nwhich acts as strong pinning centers. Cutting samples at different angles to\nrolling allows one to control the direction of the driving force. It was found\nthat when the standard criterion of the electric field is reached, the guiding\nangle varies significantly at different places along the sample, which is\nexplained by the plastic mode of the vortex matter motion. In this case, the\nanisotropic pinning model semi-quantitatively predicts the direction angle\naveraged over the sample length. With an increase in the driving force, the\nvortex matter motion passes into an elastic mode, and the guiding angle becomes\nuniform along the sample.\n"}
+{"abstract": "  Classical model-based imaging methods for ultrasound elasticity inverse\nproblem require prior constraints about the underlying elasticity patterns,\nwhile finding the appropriate hand-crafted prior for each tissue type is a\nchallenge. In contrast, standard data-driven methods count solely on supervised\nlearning on the training data pairs leading to massive network parameters for\nunnecessary physical model relearning which might not be consistent with the\ngoverning physical models of the imaging system. Fusing the physical forward\nmodel and noise statistics with data-adaptive priors leads to a united\nreconstruction framework that guarantees the learned reconstruction agrees with\nthe physical models while coping with the limited training data. In this paper,\nwe propose a new methodology for estimating the elasticity image by solving a\nregularized optimization problem which benefits from the physics-based modeling\nvia a data-fidelity term and adversarially learned priors via a regularization\nterm. In this method, the regularizer is trained based on the Wasserstein\nGenerative Adversarial Network (WGAN) objective function which tries to\ndistinguish the distribution of clean and noisy images. Leveraging such an\nadversarial regularizer for parameterizing the distribution of latent images\nand using gradient descent (GD) for solving the corresponding regularized\noptimization task leads to stability and convergence of the reconstruction\ncompared to pixel-wise supervised learning schemes. Our simulation results\nverify the effectiveness and robustness of the proposed methodology with\nlimited training datasets.\n"}
+{"abstract": "  Transformer is a powerful tool for many natural language tasks which is based\non self-attention, a mechanism that encodes the dependence of other tokens on\neach specific token, but the computation of self-attention is a bottleneck due\nto its quadratic time complexity. There are various approaches to reduce the\ntime complexity and approximation of matrix is one such. In Nystr\\\"omformer,\nthe authors used Nystr\\\"om based method for approximation of softmax. The\nNystr\\\"om method generates a fast approximation to any large-scale symmetric\npositive semidefinite (SPSD) matrix using only a few columns of the SPSD\nmatrix. However, since the Nystr\\\"om approximation is low-rank when the\nspectrum of the SPSD matrix decays slowly, the Nystr\\\"om approximation is of\nlow accuracy. Here an alternative method is proposed for approximation which\nhas a much stronger error bound than the Nystr\\\"om method. The time complexity\nof this same as Nystr\\\"omformer which is $O\\left({n}\\right)$.\n"}
+{"abstract": "  A thermodynamically consistent model for soft deformable viscoelastic magnets\nis formulated in actual space (Eulerian) coordinates. The possibility of a\nferro-paramagnetic-type (or ferri-antiferromagnetic) transition exploiting\nLandau-Lifshitz phase transition theory as well as mechanical melting or\nsolidification is considered, being motivated and applicable to paleomagnetism\n(involving both thermo- and isothermal and viscous remanent magnetization) in\nrocks in Earth's crust and to rock-magma transition. The temperature-dependent\nJeffreys rheology in the deviatoric part combined with the Kelvin-Voigt\nrheology in the spherical (volumetric) part is used. The energy balance and the\nentropy imbalance behind the model are demonstrated, and its analysis is\nperformed by time discretization, proving existence of weak solutions.\n"}
+{"abstract": "  This paper presents an automatic merging algorithm for autonomous driving\nvehicles, which decouples the specific motion planning problem into a\nDual-Layer Automatic Merge Planning (DL_AMP) and a Descent-Based Trajectory\nOptimization (DBTO). This work leads to great improvements in finding the best\nmerge opportunity, lateral and longitudinal merge planning and control,\ntrajectory postprocessing and driving comfort.\n"}
+{"abstract": "  In imperfect-information games, subgame solving is significantly more\nchallenging than in perfect-information games, but in the last few years, such\ntechniques have been developed. They were the key ingredient to the milestone\nof superhuman play in no-limit Texas hold'em poker. Current subgame-solving\ntechniques analyze the entire common-knowledge closure of the player's current\ninformation set, that is, the smallest set of nodes within which it is common\nknowledge that the current node lies. While this is acceptable in games like\npoker where the common-knowledge closure is relatively small, many practical\ngames have more complex information structure, which renders the\ncommon-knowledge closure impractically large to enumerate or even reasonably\napproximate. We introduce an approach that overcomes this obstacle, by instead\nworking with only low-order knowledge. Our approach allows an agent, upon\narriving at an infoset, to basically prune any node that is no longer\nreachable, thereby massively reducing the game tree size relative to the\ncommon-knowledge subgame. We prove that, as is, our approach can increase\nexploitability compared to the blueprint strategy. However, we develop three\navenues by which safety can be guaranteed. Even without the safety-guaranteeing\nadditions, experiments on medium-sized games show that our approach always\nreduced exploitability in practical games even when applied at every infoset,\nand a depth-limited version of it led to -- to our knowledge -- the first\nstrong AI for the challenge problem dark chess.\n"}
+{"abstract": "  The spin-orbit coupling field, an atomic magnetic field inside a Kramer's\nsystem, or discrete symmetries can create a topological torus in the Brillouin\nZone and provide protected edge or surface states, which can contain\nrelativistic fermions, namely, Dirac and Weyl Fermions. The topology-protected\nhelical edge or surface states and the bulk electronic energy band define\ndifferent quantum or topological phases of matters, offering an excellent\nprospect for some unique device applications. Device applications of the\nquantum materials rely primarily on understanding the topological properties,\ntheir mutual conversion processes under different external stimuli, and the\nphysical system for achieving the phase conversion. There have been tremendous\nefforts in finding new topological materials with exotic topological phases.\nHowever, the application of the topological properties in devices is still\nlimited due to the slow progress in developing the physical structures for\ncontrolling the topological phase conversions. Such control systems often\nrequire extreme tuning conditions or the fabrication of complex multi-layered\ntopological structures. This review article highlights the details of the\ntopological phases, their conversion processes, along with their potential\nphysical systems, and the prospective application fields. A general overview of\nthe critical factors for topological phases and the materials properties are\nfurther discussed to provide the necessary background for the following\nsections.\n"}
+{"abstract": "  We consider thermodynamically consistent autonomous Markov jump processes\ndisplaying a macroscopic limit in which the logarithm of the probability\ndistribution is proportional to a scale-independent rate function (i.e., a\nlarge deviations principle is satisfied). In order to provide an explicit\nexpression for the probability distribution valid away from equilibrium, we\npropose a linear response theory performed at the level of the rate function.\nWe show that the first order non-equilibrium contribution to the steady state\nrate function, $g(x)$, satisfies $u(x)\\cdot \\nabla g(x) = -\\beta \\dot W(x)$\nwhere the vector field $u(x)$ defines the macroscopic deterministic dynamics,\nand the scalar field $\\dot W(x)$ equals the rate at which work is performed on\nthe system in a given state $x$. This equation provides a practical way to\ndetermine $g(x)$, significantly outperforms standard linear response theory\napplied at the level of the probability distribution, and approximates the rate\nfunction surprisingly well in some far-from-equilibrium conditions. The method\napplies to a wealth of physical and chemical systems, that we exemplify by two\nanalytically tractable models - an electrical circuit and an autocatalytic\nchemical reaction network - both undergoing a non-equilibrium transition from a\nmonostable phase to a bistable phase. Our approach can be easily generalized to\ntransient probabilities and non-autonomous dynamics. Moreover, its recursive\napplication generates a virtual flow in the probability space which allows to\ndetermine the steady state rate function arbitrarily far from equilibrium.\n"}
+{"abstract": "  Globular clusters (GCs) are bright objects that span a wide range of\ngalactocentric distances, and are thus probes of the structure of dark matter\n(DM) haloes. In this work, we explore whether the projected radial profiles of\nGCs can be used to infer the structural properties of their host DM haloes. We\nuse the simulated GC populations in a sample of 166 central galaxies from the\n$(34.4~\\rm cMpc)^3$ periodic volume of the E-MOSAICS project. We find that more\nmassive galaxies host stellar and GC populations with shallower density\nprofiles that are more radially extended. In addition, the metal-poor GC\nsubpopulations tend to have shallower and more extended profiles than the\nmetal-rich subsamples, which we relate to the preferentially accreted origin of\nthe metal-poor GCs. We find strong correlations between the slopes and\neffective radii of the radial profiles of the GC populations and the structural\nproperties of the DM haloes, such as their power-law slopes, scale radii, and\nconcentration parameters. Accounting for a dependence on the galaxy stellar\nmass decreases the scatter of the two-dimensional relations. This suggests that\nthe projected number counts of GCs, combined with their galaxy mass, trace the\ndensity profile of the DM halo of their host galaxy. When applied to\nextragalactic GC systems, we recover the scale radii and the extent of the DM\nhaloes of a sample of ETGs with uncertainties smaller than $0.2~\\rm dex$. Thus,\nextragalactic GC systems provide a novel avenue to explore the structure of DM\nhaloes beyond the Local Group.\n"}
+{"abstract": "  Architecture information is vital for Open Source Software (OSS) development,\nand mailing list is one of the widely used channels for developers to share and\ncommunicate architecture information. This work investigates the nature of\narchitecture information communication (i.e., why, who, when, and what) by OSS\ndevelopers via developer mailing lists. We employed a multiple case study\napproach to extract and analyze the architecture information communication from\nthe developer mailing lists of two OSS projects, ArgoUML and Hibernate, during\ntheir development life-cycle of over 18 years. Our main findings are: (a)\narchitecture negotiation and interpretation are the two main reasons (i.e.,\nwhy) of architecture communication; (b) the amount of architecture information\ncommunicated in developer mailing lists decreases after the first stable\nrelease (i.e., when); (c) architecture communications centered around a few\ncore developers (i.e., who); (d) and the most frequently communicated\narchitecture elements (i.e., what) are Architecture Rationale and Architecture\nModel. There are a few similarities of architecture communication between the\ntwo OSS projects. Such similarities point to how OSS developers naturally\ngravitate towards the four aspects of architecture communication in OSS\ndevelopment.\n"}
+{"abstract": "  Magnetic Resonance Imaging (MRI) is a principal diagnostic approach used in\nthe field of radiology to create images of the anatomical and physiological\nstructure of patients. MRI is the prevalent medical imaging practice to find\nabnormalities in soft tissues. Traditionally they are analyzed by a radiologist\nto detect abnormalities in soft tissues, especially the brain. The process of\ninterpreting a massive volume of patient's MRI is laborious. Hence, the use of\nMachine Learning methodologies can aid in detecting abnormalities in soft\ntissues with considerable accuracy. In this research, we have curated a novel\ndataset and developed a framework that uses Deep Transfer Learning to perform a\nmulti-classification of tumors in the brain MRI images. In this paper, we\nadopted the Deep Residual Convolutional Neural Network (ResNet50) architecture\nfor the experiments along with discriminative learning techniques to train the\nmodel. Using the novel dataset and two publicly available MRI brain datasets,\nthis proposed approach attained a classification accuracy of 86.40% on the\ncurated dataset, 93.80% on the Harvard Whole Brain Atlas dataset, and 97.05%\naccuracy on the School of Biomedical Engineering dataset. Results of our\nexperiments significantly demonstrate our proposed framework for transfer\nlearning is a potential and effective method for brain tumor\nmulti-classification tasks.\n"}
+{"abstract": "  We present a three-periodic, chiral, tensegrity structure and demonstrate\nthat it is auxetic. Our tensegrity structure is constructed using the chiral\nsymmetry {\\Pi}+ cylinder packing, transforming the cylinders themselves to the\nelastic elements and cylinder contacts to incompressible rods. The resulting\nstructure displays local re-entrant geometry at its vertices, and is shown to\nbe auxetic when modelled as an equilibrium configuration of spatial constraints\nsubject to a quasi-static deformation. When the tensegrity structure is\nsubsequently modelled as a lattice material with elastic elements, the auxetic\nbehaviour is again confirmed through finite element modelling as well as\ndemonstrated with a 3d printed example. The cubic symmetry of the structure\nmeans that the behaviour is independent of the chosen stretching direction and\nthe auxetic behaviour is observed in both perpendicular directions. This\nstructure could be the simplest three-dimensional analogue to the\ntwo-dimensional re-entrant honeycomb. This, alongside the chirality of the\nstructure, make it an interesting design target for multifunctional materials.\n"}
+{"abstract": "  We demonstrate the DC-Kerr effect in PECVD Silicon-rich Nitride (SRN) and use\nit to demonstrate a third order nonlinear susceptibility, \\c{hi}^((3)), as high\nas (6 +/- 0.58)x10-19 m2/v2. We employ spectral shift versus applied voltage\nmeasurements in a racetrack ring resonator as a tool by which to characterize\nthe nonlinear susceptibilities of these films. In doing so we demonstrate a\n\\c{hi}^((3)) larger than that of silicon and argue that PECVD SRN can provide a\nversatile platform for employing optical phase-shifters while maintain a low\nthermal budget using a deposition technique readily available in CMOS process\nflows.\n"}
+{"abstract": "  Entity Linking is one of the essential tasks of information extraction and\nnatural language understanding. Entity linking mainly consists of two tasks:\nrecognition and disambiguation of named entities. Most studies address these\ntwo tasks separately or focus only on one of them. Moreover, most of the\nstate-of-the -art entity linking algorithms are either supervised, which have\npoor performance in the absence of annotated corpora or language-dependent,\nwhich are not appropriate for multi-lingual applications. In this paper, we\nintroduce an Unsupervised Language-Independent Entity Disambiguation (ULIED),\nwhich utilizes a novel approach to disambiguate and link named entities.\nEvaluation of ULIED on different English entity linking datasets as well as the\nonly available Persian dataset illustrates that ULIED in most of the cases\noutperforms the state-of-the-art unsupervised multi-lingual approaches.\n"}
+{"abstract": "  Let $K$ be a local field with residue characteristic $p$ and let $L/K$ be a\ntotally ramified extension of degree $p^k$. In this paper we show that if $L/K$\nhas only two distinct indices of inseparability then there exists a uniformizer\n$\\pi_L$ for $L$ whose minimum polynomial over $K$ has at most three terms. This\nleads to an explicit classification of extensions with two indices of\ninseparability. Our classification extends work of Amano, who considered the\ncase $k=1$.\n"}
+{"abstract": "  One of the most popular approaches being pursued to achieve a quantum\nadvantage with practical hardware are superconducting circuit devices. Although\nsignificant progress has been made over the previous two decades, substantial\nengineering efforts are required to scale these devices so they can be used to\nsolve many problems of interest. Unfortunately, much of this exciting field is\ndescribed using technical jargon and concepts from physics that are unfamiliar\nto a classically trained electromagnetic engineer. As a result, this work is\noften difficult for engineers to become engaged in. We hope to lower the\nbarrier to this field by providing an accessible review of one of the most\nprevalently used quantum bits (qubits) in superconducting circuit systems, the\ntransmon qubit. Most of the physics of these systems can be understood\nintuitively with only some background in quantum mechanics. As a result, we\navoid invoking quantum mechanical concepts except where it is necessary to ease\nthe transition between details in this work and those that would be encountered\nin the literature. We believe this leads to a gentler introduction to this\nfascinating field, and hope that more researchers from the classical\nelectromagnetic community become engaged in this area in the future.\n"}
+{"abstract": "  Determining the border between ergodic and localized behavior is of central\ninterest for interacting many-body systems. We consider here the recently very\npopular spin-chain model that is periodically excited. A convenient description\nof such a many-body system is achieved by the dual operator that evolves the\nsystem in contrast to the time-evolution operator not in time but in particle\ndirection. We identify in this paper the largest eigenvalue of a function based\non the dual operator as a convenient tool to determine if the system shows\nergodic or many-body localized features. By perturbation theory in the vicinity\nof the noninteracting system we explain analytically the eigenvalue structure\nand compare it with numerics in [P. Braun, D. Waltner, M. Akila, B. Gutkin, T.\nGuhr, Phys. Rev. E $\\bf{101}$, 052201 (2020)] for small times. Furthermore we\nidentify a quantity that allows based on extensive large-time numerical\ncomputations of the spectral form factor to distinguish between localized and\nergodic system features and to determine the Thouless time, i.e. the transition\ntime between these regimes in the thermodynamic limit.\n"}
+{"abstract": "  Rerandomization discards assignments with covariates unbalanced in the\ntreatment and control groups to improve the estimation and inference\nefficiency. However, the acceptance-rejection sampling method used by\nrerandomization is computationally inefficient. As a result, it is\ntime-consuming for classical rerandomization to draw numerous independent\nassignments, which are necessary for constructing Fisher randomization tests.\nTo address this problem, we propose a pair-switching rerandomization method to\ndraw balanced assignments much efficiently. We show that the\ndifference-in-means estimator is unbiased for the average treatment effect and\nthe Fisher randomization tests are valid under pair-switching rerandomization.\nIn addition, our method is applicable in both non-sequentially and sequentially\nrandomized experiments. We conduct comprehensive simulation studies to compare\nthe finite-sample performances of the proposed method and classical\nrerandomization. Simulation results indicate that pair-switching\nrerandomization leads to comparable power of Fisher randomization tests and is\n4-18 times faster than classical rerandomization. Finally, we apply the\npair-switching rerandomization method to analyze two clinical trial data sets,\nboth demonstrating the advantages of our method.\n"}
+{"abstract": "  The Leggett-Garg inequality (LGI) distinguishes nonmacrorealistic channels\nfrom macrorealistic ones by constraining the experimental outcomes of the\nunderlying system. In this work, we propose a class of channels which,\ninitially, cannot violate the LGI (in the form of the temporal Bell inequality)\nbut can violate it after the application of stochastic pre- and post-\noperations (SPPOs). As a proof-of-principle experiment, we demonstrate the\nstochastic pre- and post- operations in an amplitude-damping channel with\nphotonic qubits. We denote the above phenomenon as hidden nonmacrorealistic\nchannels. We also discuss the relationship between this hidden\nnonmacrorealistic channels (in terms of the temporal Clauser-Horne-Shimony-Holt\n(CHSH) inequality) and the strongly nonlocality-breaking channel, which breaks\nthe hidden spatial CHSH nonlocality for arbitrary states. In general, if the\nchannel satisfies hidden nonmacrorealism, it is not a strongly CHSH\nnonlocality-breaking channel.\n"}
+{"abstract": "  A practical application of universal wall scalings is near-wall turbulence\nmodeling. In this paper, we exploit temperature's semi-local scaling [Patel,\nBoersma, and Pecnik, {Scalar statistics in variable property turbulent channel\nflows}, Phys. Rev. Fluids, 2017, 2(8), 084604] and derive an eddy conductivity\nclosure for wall-modeled large-eddy simulation of high-speed flows. We show\nthat while the semi-local scaling does not collapse high-speed direct numerical\nsimulation (DNS) data, the resulting eddy conductivity and the wall model work\nfairly well. The paper attempts to answer the following outstanding question:\nwhy the semi-local scaling fails but the resulting eddy conductivity works\nwell. We conduct DNSs of Couette flows at Mach numbers from $M=1.4$ to 6. We\nadd a source term in the energy equation to get a cold, a close-to-adiabatic\nwall, and a hot wall. Detailed analysis of the flows' energy budgets shows that\naerodynamic heating is the answer to our question: aerodynamic heating is not\naccounted for in Patel et al.'s semi-local scaling but is modeled in the\nequilibrium wall model. We incorporate aerodynamic heating in semi-local\nscaling and show that the new scaling successfully collapses the high-speed DNS\ndata. We also show that incorporating aerodynamic heating or not, the\nsemi-local scaling gives rise to the exact same eddy conductivity, thereby\nanswering the outstanding question.\n"}
+{"abstract": "  State-of-the-art deep neural networks require large-scale labeled training\ndata that is often expensive to obtain or not available for many tasks. Weak\nsupervision in the form of domain-specific rules has been shown to be useful in\nsuch settings to automatically generate weakly labeled training data. However,\nlearning with weak rules is challenging due to their inherent heuristic and\nnoisy nature. An additional challenge is rule coverage and overlap, where prior\nwork on weak supervision only considers instances that are covered by weak\nrules, thus leaving valuable unlabeled data behind.\n  In this work, we develop a weak supervision framework (ASTRA) that leverages\nall the available data for a given task. To this end, we leverage task-specific\nunlabeled data through self-training with a model (student) that considers\ncontextualized representations and predicts pseudo-labels for instances that\nmay not be covered by weak rules. We further develop a rule attention network\n(teacher) that learns how to aggregate student pseudo-labels with weak rule\nlabels, conditioned on their fidelity and the underlying context of an\ninstance. Finally, we construct a semi-supervised learning objective for\nend-to-end training with unlabeled data, domain-specific rules, and a small\namount of labeled data. Extensive experiments on six benchmark datasets for\ntext classification demonstrate the effectiveness of our approach with\nsignificant improvements over state-of-the-art baselines.\n"}
+{"abstract": "  Real-time data analytics systems such as SAP HANA, MemSQL, and IBM Wildfire\nemploy hybrid data layouts, in which data are stored in different formats\nthroughout their lifecycle. Recent data are stored in a row-oriented format to\nserve OLTP workloads and support high data rates, while older data are\ntransformed to a column-oriented format for OLAP access patterns. We observe\nthat a Log-Structured Merge (LSM) Tree is a natural fit for a lifecycle-aware\nstorage engine due to its high write throughput and level-oriented structure,\nin which records propagate from one level to the next over time. To build a\nlifecycle-aware storage engine using an LSM-Tree, we make a crucial\nmodification to allow different data layouts in different levels, ranging from\npurely row-oriented to purely column-oriented, leading to a Real-Time LSM-Tree.\nWe give a cost model and an algorithm to design a Real-Time LSM-Tree that is\nsuitable for a given workload, followed by an experimental evaluation of LASER\n- a prototype implementation of our idea built on top of the RocksDB key-value\nstore. In our evaluation, LASER is almost 5x faster than Postgres (a pure\nrow-store) and two orders of magnitude faster than MonetDB (a pure\ncolumn-store) for real-time data analytics workloads.\n"}
+{"abstract": "  Over the past several years, in order to solve the problem of malicious abuse\nof facial manipulation technology, face manipulation detection technology has\nobtained considerable attention and achieved remarkable progress. However, most\nexisting methods have very impoverished generalization ability and robustness.\nIn this paper, we propose a novel method for face manipulation detection, which\ncan improve the generalization ability and robustness by bag-of-local-feature.\nSpecifically, we extend Transformers using bag-of-feature approach to encode\ninter-patch relationships, allowing it to learn local forgery features without\nany explicit supervision. Extensive experiments demonstrate that our method can\noutperform competing state-of-the-art methods on FaceForensics++, Celeb-DF and\nDeeperForensics-1.0 datasets.\n"}
+{"abstract": "  The infinite-dimensional Iwahori--Hecke algebras $\\mathcal{H}_\\infty(q)$ are\ndirect limits of the usual finite-dimensional Iwahori--Hecke algebras. They\narise in a natural way as convolution algebras of bi-invariant functions on\ngroups $\\mathrm{GLB}(\\mathbb{F}_q)$ of infinite-dimensional matrices over\nfinite-fields having only finite number of non-zero matrix elements under the\ndiagonal. In 1988 Vershik and Kerov classified all indecomposable positive\ntraces on $\\mathcal{H}_\\infty(q)$. Any such trace generates a representation of\nthe double $\\mathcal{H}_\\infty(q)\\otimes \\mathcal{H}_\\infty(q)$ and of the\ndouble $\\mathrm{GLB}(\\mathbb{F}_q)\\times \\mathrm{GLB}(\\mathbb{F}_q)$. We\npresent constructions of such representations; the traces are some\ndistinguished matrix elements. We also obtain some (simple) general statements\non relations between unitary representations of groups and representations of\nconvolution algebras of measures bi-invariant with respect to compact\nsubgroups.\n"}
+{"abstract": "  In one-dimensional, heterogeneous systems, the whole traffic dynamics depend\nstrongly on the behavior of the leading vehicle. This result holds for a class\nof vehicular traffic models satisfying the following properties. The\ninteractions are unidirectional. The dynamics of the particles maximize the\nvelocity or reduces the gap between particles. The particles are hard. We use\nthis result to show a link between traffic models and graphs theory with the\nconsequence that as driving styles spread through social contagion and appear\nrandomly, the inhomogeneities of the system becomes dynamical, or\n\\textit{annealed}, toward specific regions in the space of parameters.\nInterpreting parameters as entries of vectors defined in the parameters space\nappear analogies between the evolutionary dynamics of these systems and\nasymptotic behaviors of the \\textit {n-vector model}. When the time-scale ratio\nof imitation to the mutation processes, $ \\tau_i / \\tau_m $, is small an\norganized state where \"orientation\" corresponds to the set of parameters of the\nslowest strategies is favored, and if this ratio is big an unorganized state\nwithout a preferential orientation is favored.\n"}
+{"abstract": "  Industrial processes produce a considerable volume of data and thus\ninformation. Whether it is structured sensory data or semi- to unstructured\ntextual data, the knowledge that can be derived from it is critical to the\nsustainable development of the industrial process. A key challenge of this\nsustainability is the intelligent management of the generated data, as well as\nthe knowledge extracted from it, in order to utilize this knowledge for\nimproving future procedures. This challenge is a result of the tailored\ndocumentation methods and domain-specific requirements, which include the need\nfor quick visibility of the documented knowledge. In this paper, we utilize the\nexpert knowledge documented in chip-design failure reports in supporting user\naccess to information that is relevant to a current chip design. Unstructured,\nfree, textual data in previous failure documentations provides a valuable\nsource of lessons-learned, which expert design-engineers have experienced,\nsolved and documented. To achieve a sustainable utilization of knowledge within\nthe company, not only the inherent knowledge has to be mined from unstructured\ntextual data, but also the relations between the lessons-learned, uncovering\npotentially unknown links. In this research, a knowledge graph is constructed,\nin order to represent and use the interconnections between reported design\nfailures. A search engine is developed and applied onto the graph to answer\nqueries. In contrast to mere keyword-based searching, the searchability of the\nknowledge graph offers enhanced search results beyond direct matches and acts\nas a mean for generating explainable results and result recommendations.\nResults are provided to the design engineer through an interactive search\ninterface, in which, the feedback from the user is used to further optimize\nrelations for future iterations of the knowledge graph.\n"}
+{"abstract": "  The creation of unstable heavy particles at the Large Hadron Collider is the\nmost direct way to address some of the deepest open questions in physics.\nCollisions typically produce variable-size sets of observed particles which\nhave inherent ambiguities complicating the assignment of observed particles to\nthe decay products of the heavy particles. Current strategies for tackling\nthese challenges in the physics community ignore the physical symmetries of the\ndecay products and consider all possible assignment permutations and do not\nscale to complex configurations. Attention based deep learning methods for\nsequence modelling have achieved state-of-the-art performance in natural\nlanguage processing, but they lack built-in mechanisms to deal with the unique\nsymmetries found in physical set-assignment problems. We introduce a novel\nmethod for constructing symmetry-preserving attention networks which reflect\nthe problem's natural invariances to efficiently find assignments without\nevaluating all permutations. This general approach is applicable to arbitrarily\ncomplex configurations and significantly outperforms current methods, improving\nreconstruction efficiency between 19\\% - 35\\% on typical benchmark problems\nwhile decreasing inference time by two to five orders of magnitude on the most\ncomplex events, making many important and previously intractable cases\ntractable.\n  A full code repository containing a general library, the specific\nconfiguration used, and a complete dataset release, are avaiable at\nhttps://github.com/Alexanders101/SPANet\n"}
+{"abstract": "  Spatial and channel attentions, modelling the semantic interdependencies in\nspatial and channel dimensions respectively, have recently been widely used for\nsemantic segmentation. However, computing spatial and channel attentions\nseparately sometimes causes errors, especially for those difficult cases. In\nthis paper, we propose Channelized Axial Attention (CAA) to seamlessly\nintegrate channel attention and spatial attention into a single operation with\nnegligible computation overhead. Specifically, we break down the dot-product\noperation of the spatial attention into two parts and insert channel relation\nin between, allowing for independently optimized channel attention on each\nspatial location. We further develop grouped vectorization, which allows our\nmodel to run with very little memory consumption without slowing down the\nrunning speed. Comparative experiments conducted on multiple benchmark\ndatasets, including Cityscapes, PASCAL Context, and COCO-Stuff, demonstrate\nthat our CAA outperforms many state-of-the-art segmentation models (including\ndual attention) on all tested datasets.\n"}
+{"abstract": "  The existence of Ulrich modules for local domains has been a difficult and\nelusive open question. For over thirty years, it was unknown whether local\ndomains always have Ulrich modules. In this paper, we answer the question of\nexistence for both Ulrich modules and weakly lim Ulrich sequences -- a weaker\nnotion recently introduced by Ma -- in the negative. We construct many local\ndomains in all dimensions $d \\geq 2$ that do not have any Ulrich modules.\nMoreover, we show that when $d = 2$, these local domains do not have weakly lim\nUlrich sequences. A key insight in our proofs is the classification of MCM\n$R$-modules via the $S_2$-ification of $R$. For local domains of dimension $2$,\nwe show that the existence of weakly lim Ulrich sequences implies the existence\nof lim Ulrich sequences.\n"}
+{"abstract": "  Line-intensity mapping observations will find fluctuations of integrated line\nemission are attenuated by varying degrees at small scales due to the width of\nthe line emission profiles. This attenuation may significantly impact estimates\nof astrophysical or cosmological quantities derived from measurements. We\nconsider a theoretical treatment of the effect of line broadening on both the\nclustering and shot-noise components of the power spectrum of a generic\nline-intensity power spectrum using a halo model. We then consider possible\nsimplifications to allow easier application in analysis, particularly in the\ncontext of inferences that require numerous, repeated, fast computations of\nmodel line-intensity signals across a large parameter space. For the CO Mapping\nArray Project (COMAP) and the CO(1-0) line-intensity field at $z\\sim3$ serving\nas our primary case study, we expect a $\\sim10\\%$ attenuation of the\nspherically averaged power spectrum on average at relevant scales of\n$k\\approx0.2$-$0.3$ Mpc$^{-1}$, compared to $\\sim25\\%$ for the interferometric\nMillimetre-wave Intensity Mapping Experiment (mmIME) targeting shot noise from\nCO lines at $z\\sim1$-$5$ at scales of $k\\gtrsim1$ Mpc$^{-1}$. We also consider\nthe nature and amplitude of errors introduced by simplified treatments of line\nbroadening, and find that while an approximation using a single effective\nvelocity scale is sufficient for spherically-averaged power spectra, a more\ncareful treatment is necessary when considering other statistics such as higher\nmultipoles of the anisotropic power spectrum or the voxel intensity\ndistribution.\n"}
+{"abstract": "  In this article, we show and discuss our experience in applying the flipped\nclassroom method for teaching Conditional Random Fields in a Natural Language\nProcessing course. We present the activities that we developed together with\ntheir relationship to a cognitive complexity model (Bloom's taxonomy). After\nthis, we provide our own reflections and expectations of the model itself.\nBased on the evaluation got from students, it seems that students learn about\nthe topic and also that the method is rewarding for some students.\nAdditionally, we discuss some shortcomings and we propose possible solutions to\nthem. We conclude the paper with some possible future work.\n"}
+{"abstract": "  Software bots have been facilitating several development activities in Open\nSource Software (OSS) projects, including code review. However, these bots may\nbring unexpected impacts to group dynamics, as frequently occurs with new\ntechnology adoption. Understanding and anticipating such effects is important\nfor planning and management. To analyze these effects, we investigate how\nseveral activity indicators change after the adoption of a code review bot. We\nemployed a regression discontinuity design on 1,194 software projects from\nGitHub. We also interviewed 12 practitioners, including open-source maintainers\nand contributors. Our results indicate that the adoption of code review bots\nincreases the number of monthly merged pull requests, decreases monthly\nnon-merged pull requests, and decreases communication among developers. From\nthe developers' perspective, these effects are explained by the transparency\nand confidence the bot comments introduce, in addition to the changes in the\ndiscussion focused on pull requests. Practitioners and maintainers may leverage\nour results to understand, or even predict, bot effects on their projects.\n"}
+{"abstract": "  We consider a simple extension of the standard model, which could give a\nsolution for its $CP$ issues such as the origin of both CKM and PMNS phases and\nthe strong $CP$ problem. The model is extended with singlet scalars which could\ncause spontaneous $CP$ violation to result in these phases at low energy\nregions. The singlet scalars could give a good inflaton candidate if they have\na suitable nonminimal coupling with the Ricci scalar. $CP$ issues and inflation\ncould be closely related through these singlet scalars in a natural way. In a\ncase where inflaton is a mixture of the singlet scalars, we study reheating and\nleptogenesis as notable phenomena affected by the fields introduced in this\nextension.\n"}
+{"abstract": "  Some cosmological solutions of massive strings are obtained in Bianchi I\nspace-time following the techniques used by Letelier and Stachel. A class of\nsolutions corresponds to string cosmology associated with/without a magnetic\nfield and the other class consists of pure massive strings, obeying the\nTakabayashi equation of state.\n"}
+{"abstract": "  Recent advances have lead towards first prototypes of a quantum internet in\nwhich entanglement is distributed by sources producing bipartite entangled\nstates with high fidelities. This raises the question which states can be\ngenerated in quantum networks based on bipartite sources using local operations\nand classical communication. In this work we study state transformations under\nfinite rounds of local operations and classical communication in networks based\non maximally entangled two-qubit states. We first derive the symmetries for\narbitrary network structures as these determine which transformations are\npossible. Then we show that contrary to tree graphs for which it has already\nbeen shown that any state within the same entanglement class can be reached\nthere exist states which can be reached probabilistically but not\ndeterministically if the network contains a cycle. Furthermore, we provide a\nsystematic way to determine states which are not reachable in networks\nconsisting of a cycle. Moreover, we provide a complete characterization of the\nstates which can be reached in a cycle network with a protocol where each party\nmeasures only once and each step of the protocol results in a deterministic\ntransformation. Finally, we present an example which cannot be reached with\nsuch a simple protocol.\n"}
+{"abstract": "  By telescoping method, Sun gave some hypergeometric series whose sums are\nrelated to $\\pi$ recently. We investigate these series from the point of view\nof Gosper's algorithm. Given a hypergeometric term $t_k$, we consider the\nGosper summability of $r(k)t_k$ for $r(k)$ being a rational function of $k$. We\ngive an upper bound and a lower bound on the degree of the numerator of $r(k)$\nsuch that $r(k)t_k$ is Gosper summable. We also show that the denominator of\nthe $r(k)$ can read from the Gosper representation of $t_{k+1}/t_k$. Based on\nthese results, we give a systematic method to construct series whose sums can\nbe derived from the known ones. We also illustrated the corresponding\nsuper-congruences and the $q$-analogue of the approach.\n"}
+{"abstract": "  Conditional Variational Auto Encoders (VAE) are gathering significant\nattention as an Explainable Artificial Intelligence (XAI) tool. The codes in\nthe latent space provide a theoretically sound way to produce counterfactuals,\ni.e. alterations resulting from an intervention on a targeted semantic feature.\nTo be applied on real images more complex models are needed, such as\nHierarchical CVAE. This comes with a challenge as the naive conditioning is no\nlonger effective. In this paper we show how relaxing the effect of the\nposterior leads to successful counterfactuals and we introduce VAEX an\nHierarchical VAE designed for this approach that can visually audit a\nclassifier in applications.\n"}
+{"abstract": "  We present a self-improving, Neural Tree Expansion (NTE) method for\nmulti-robot online planning in non-cooperative environments, where each robot\nattempts to maximize its cumulative reward while interacting with other\nself-interested robots. Our algorithm adapts the centralized, perfect\ninformation, discrete-action space method from AlphaZero to a decentralized,\npartial information, continuous action space setting for multi-robot\napplications. Our method has three interacting components: (i) a centralized,\nperfect-information \"expert\" Monte Carlo Tree Search (MCTS) with large\ncomputation resources that provides expert demonstrations, (ii) a\ndecentralized, partial-information \"learner\" MCTS with small computation\nresources that runs in real-time and provides self-play examples, and (iii)\npolicy & value neural networks that are trained with the expert demonstrations\nand bias both the expert and the learner tree growth. Our numerical experiments\ndemonstrate Neural Tree Expansion's computational advantage by finding better\nsolutions than a MCTS with 20 times more resources. The resulting policies are\ndynamically sophisticated, demonstrate coordination between robots, and play\nthe Reach-Target-Avoid differential game significantly better than the\nstate-of-the-art control-theoretic baseline for multi-robot, double-integrator\nsystems. Our hardware experiments on an aerial swarm demonstrate the\ncomputational advantage of Neural Tree Expansion, enabling online planning at\n20Hz with effective policies in complex scenarios.\n"}
+{"abstract": "  We provide statistical analysis methods for samples of curves when the image\nbut not the parametrisation of the curves is of interest. A parametrisation\ninvariant analysis can be based on the elastic distance of the curves modulo\nwarping, but existing methods have limitations in common realistic settings\nwhere curves are irregularly and potentially sparsely observed. We provide\nmethods and algorithms to approximate the elastic distance for such curves via\ninterpreting them as polygons. Moreover, we propose to use spline curves for\nmodelling smooth or polygonal Fr\\'echet means of open or closed curves with\nrespect to the elastic distance and show identifiability of the spline model\nmodulo warping. We illustrate the use of our methods for elastic mean and\ndistance computation by application to two datasets. The first application\nclusters sparsely sampled GPS tracks based on the elastic distance and computes\nsmooth means for each cluster to find new paths on Tempelhof field in Berlin.\nThe second classifies irregularly sampled handwritten spirals of Parkinson's\npatients and controls based on the elastic distance to a mean spiral curve\ncomputed using our approach. All developed methods are implemented in the\n\\texttt{R}-package \\texttt{elasdics} and evaluated in simulations.\n"}
+{"abstract": "  In this work we prove a strong maximum principle for fractional elliptic\nproblems with mixed Dirichlet-Neumann boundary data which extends the one\nproved by J. D\\'avila to the fractional setting. In particular, we present a\ncomparison result for two solutions of the fractional Laplace equation\ninvolving the spectral fractional Laplacian endowed with homogeneous mixed\nboundary condition. This result represents a non-local counterpart to a Hopf's\nLemma for fractional elliptic problems with mixed boundary data.\n"}
+{"abstract": "  Examining cables using many conductor transmission line theory has shed light\non the modes supported by various cable types. However, so far the theory\ndisregards the fundamental surface wave mode whose lateral confinement\nincreases with frequency and hence is expected to play an important role in\nhigh frequency applications. To address this issue, we propose an extension to\nthe theory which incorporates surface waves on uncoated, cylindrical wires.\nCrucially, this requires new definitions of the per unit length transmission\nline parameters which are derived using the single wire surface wave solution.\nBy closely examining a two wire and three wire system, we show that these new\nparameters can predict surface waves as well as modes found using conventional\nmany conductor transmission line theory. Furthermore, all calculated modes are\nvalidated experimentally by diagonalization of a measured channel transfer\nmatrix. Additionally, the theoretically predicted propagation constants for the\nmodes are validated against full numerical simulation for the two wire case and\ngood agreement is observed when proximity effects can be neglected.\n"}
+{"abstract": "  Cervical cancer is a very common and fatal type of cancer in women.\nCytopathology images are often used to screen for this cancer. Given that there\nis a possibility that many errors can occur during manual screening, a\ncomputer-aided diagnosis system based on deep learning has been developed. Deep\nlearning methods require a fixed dimension of input images, but the dimensions\nof clinical medical images are inconsistent. The aspect ratios of the images\nsuffer while resizing them directly. Clinically, the aspect ratios of cells\ninside cytopathological images provide important information for doctors to\ndiagnose cancer. Therefore, it is difficult to resize directly. However, many\nexisting studies have resized the images directly and have obtained highly\nrobust classification results. To determine a reasonable interpretation, we\nhave conducted a series of comparative experiments. First, the raw data of the\nSIPaKMeD dataset are pre-processed to obtain standard and scaled datasets.\nThen, the datasets are resized to 224 x 224 pixels. Finally, 22 deep learning\nmodels are used to classify the standard and scaled datasets. The results of\nthe study indicate that deep learning models are robust to changes in the\naspect ratio of cells in cervical cytopathological images. This conclusion is\nalso validated via the Herlev dataset.\n"}
+{"abstract": "  Single Image Super-resolution (SISR) produces high-resolution images with\nfine spatial resolutions from aremotely sensed image with low spatial\nresolution. Recently, deep learning and generative adversarial networks(GANs)\nhave made breakthroughs for the challenging task of single image\nsuper-resolution (SISR). However, thegenerated image still suffers from\nundesirable artifacts such as, the absence of texture-feature representationand\nhigh-frequency information. We propose a frequency domain-based spatio-temporal\nremote sensingsingle image super-resolution technique to reconstruct the HR\nimage combined with generative adversarialnetworks (GANs) on various frequency\nbands (TWIST-GAN). We have introduced a new method incorporatingWavelet\nTransform (WT) characteristics and transferred generative adversarial network.\nThe LR image hasbeen split into various frequency bands by using the WT,\nwhereas, the transfer generative adversarial networkpredicts high-frequency\ncomponents via a proposed architecture. Finally, the inverse transfer of\nwaveletsproduces a reconstructed image with super-resolution. The model is\nfirst trained on an external DIV2 Kdataset and validated with the UC Merceed\nLandsat remote sensing dataset and Set14 with each image sizeof 256x256.\nFollowing that, transferred GANs are used to process spatio-temporal remote\nsensing images inorder to minimize computation cost differences and improve\ntexture information. The findings are comparedqualitatively and qualitatively\nwith the current state-of-art approaches. In addition, we saved about 43% of\ntheGPU memory during training and accelerated the execution of our simplified\nversion by eliminating batchnormalization layers.\n"}
+{"abstract": "  Medical imaging has been employed to support medical diagnosis and treatment.\nIt may also provide crucial information to surgeons to facilitate optimal\nsurgical preplanning and perioperative management. Essentially, semi-automatic\norgan and tumor segmentation has been studied by many researchers. Recently,\nwith the development of Deep Learning (DL) algorithms, automatic organ\nsegmentation has been gathered lots of attention from the researchers. This\npaper addresses to propose the most efficient DL architectures for Liver\nsegmentation by adapting and comparing state-of-the-art DL frameworks, studied\nin different disciplines. These frameworks are implemented and adapted into a\nCommercial software, 'LiverVision'. It is aimed to reveal the most effective\nand accurate DL architecture for fully automatic liver segmentation. Equal\nconditions were provided to all architectures in the experiments so as to\nmeasure the effectiveness of algorithms accuracy, and Dice coefficient metrics\nwere also employed to support comparative analysis. Experimental results prove\nthat 'U-Net' and 'SegNet' have been superior in line with the experiments\nconducted considering the concepts of time, cost, and effectiveness.\nConsidering both architectures, 'SegNet' was observed to be more successful in\neliminating false-positive values. Besides, it was seen that the accuracy\nmetric used to measure effectiveness in image segmentation alone was not\nenough. Results reveal that DL algorithms are able to automate organ\nsegmentation from DICOM images with high accuracy. This contribution is\ncritical for surgical preplanning and motivates author to apply this approach\nto the different organs and field of medicine.\n"}
+{"abstract": "  Mechanical metamaterials owe their extraordinary properties and\nfunctionalities to their micro-/nanoscale design of which shape, including both\ngeometry and topology, is perhaps the most important aspect. 4D printing\nenables programmed, predictable, and precise change in the shape of mechanical\nmetamaterials to achieve multi-functionality, adaptive properties, and the\nother types of desired behaviors that cannot be achieved using simple 3D\nprinting. This paper presents an overview of 4D printing as applied to\nmechanical metamaterials. It starts by presenting a systematic definition of\nwhat 4D printing is and what shape aspects (e.g., geometry, topology) are\nrelevant for the 4D printing of mechanical metamaterials. Instead of focusing\non different printing processes and materials, the paper addresses the most\nfundamental aspects of the shapeshifting behaviors required for transforming a\nflat construct to a target 3D shape (i.e., 2D to 3D shapeshifting) or\ntransforming a 3D shape to another 3D shape (i.e., 3D to 3D shapeshifting). In\neither case, we will discuss the rigid-body shape morphing (e.g., rigid\norigami) as well as deformable-body shapeshifting. The paper concludes with a\ndiscussion of the major challenges ahead of us for applying 4D printing to\nmechanical metamaterials and suggests several areas for future research.\n"}
+{"abstract": "  A quantum version of the Monge--Kantorovich optimal transport problem is\nanalyzed. The transport cost is minimized over the set of all bipartite\ncoupling states $\\rho^{AB}$, such that both of its reduced density matrices\n$\\rho^A$ and $\\rho^B$ of dimension $N$ are fixed. We show that, selecting the\nquantum cost matrix to be proportional to the projector on the antisymmetric\nsubspace, the minimal transport cost leads to a semidistance between $\\rho^A$\nand $\\rho^B$, which is bounded from below by the rescaled Bures distance and\nfrom above by the root infidelity. In the single qubit case we provide a\nsemi-analytic expression for the optimal transport cost between any two states\nand prove that its square root satisfies the triangle inequality and yields an\nanalogue of the Wasserstein distance of order two on the set of density\nmatrices. We introduce an associated measure of proximity of quantum states,\ncalled SWAP-fidelity, and discuss its properties and applications in quantum\nmachine learning.\n"}
+{"abstract": "  Cells and tissues exhibit oscillatory deformations during remodelling,\nmigration or embryogenesis. Although it has been shown that these oscillations\ncorrelate with cell biochemical signalling, it is yet unclear the role of these\noscillations in triggering drastic cell reorganisation events or instabilities,\nand the coupling of this oscillatory response with tested visco-elastic\nproperties.\n  We here present a rheological model that incorporates elastic, viscous and\nfrictional components, and that is able to generate oscillatory response\nthrough a delay adaptive process of the rest-length. We analyse its stability\nproperties as a function of the model parameters and deduce analytical bounds\nof the stable domain. While increasing values of the delay and remodelling rate\nrender the model unstable, we also show that increasing friction with the\nsubstrate destabilise the oscillatory response. Furthermore, we numerically\nverify that the extension of the model to non-linear strain measures is able to\ngenerate sustained oscillations that alternate between stable and unstable\nregions.\n"}
+{"abstract": "  We show that the number of partial triangulations of a set of $n$ points on\nthe plane is at least the $(n-2)$-nd Catalan number. This is tight for convex\n$n$-gons. We also describe all the equality cases.\n"}
+{"abstract": "  Ultra-cold atoms provide ideal platforms for interferometry. The macroscopic\nmatter-wave property of ultra-cold atoms leads to large coherent length and\nlong coherent time, which enable high accuracy and sensitivity to measurement.\nHere, we review our efforts to improve the performance of the interferometer.\nWe demonstrate a shortcut method for manipulating ultra-cold atoms in an\noptical lattice. Compared with traditional ones, this shortcut method can\nreduce manipulation time by up to three orders of magnitude. We construct a\nmatter-wave Ramsey interferometer for trapped motional quantum states and\nsignificantly increase its coherence time by one order of magnitude with an\necho technique based on this method. Efforts have also been made to enhance the\nresolution by multimode scheme. Application of a noise-resilient\nmulti-component interferometer shows that increasing the number of paths could\nsharpen the peaks in the time-domain interference fringes, which leads to a\nresolution nearly twice compared with that of a conventional double-path\ntwo-mode interferometer. With the shortcut method mentioned above, improvement\nof the momentum resolution could also be fulfilled, which leads to atomic\nmomentum patterns less than 0.6 $\\hbar k_L$. To identify and remove systematic\nnoises, we introduce the methods based on the principal component analysis\n(PCA) that reduce the noise in detection close to the $1/\\sqrt{2}$ of the\nphoton-shot noise and separate and identify or even eliminate noises.\nFurthermore, we give a proposal to measure precisely the local gravity\nacceleration within a few centimeters based on our study of ultracold atoms in\nprecision measurements.\n"}
+{"abstract": "  Within the last decade much progress has been made in the experimental\nrealisation of quantum computing hardware based on a variety of physical\nsystems. Rapid progress has been fuelled by the conviction that sufficiently\npowerful quantum machines will herald enormous computational advantages in many\nfields, including chemical research. A quantum computer capable of simulating\nthe electronic structures of complex molecules would be a game changer for the\ndesign of new drugs and materials. Given the potential implications of this\ntechnology, there is a need within the chemistry community to keep abreast with\nthe latest developments as well as becoming involved in experimentation with\nquantum prototypes. To facilitate this, here we review the types of quantum\ncomputing hardware that have been made available to the public through cloud\nservices. We focus on three architectures, namely superconductors, trapped ions\nand semiconductors. For each one we summarise the basic physical operations,\nrequirements and performance. We discuss to what extent each system has been\nused for molecular chemistry problems and highlight the most pressing hardware\nissues to be solved for a chemistry-relevant quantum advantage to eventually\nemerge.\n"}
+{"abstract": "  In the present work, a general formulation is proposed to implement the\ncontact angle boundary conditions for the second-order Phase-Field models,\nwhich is applicable to $N$-phase $(N \\geqslant 2)$ moving contact line\nproblems. To remedy the issue of mass change due to the contact angle boundary\ncondition, a Lagrange multiplier is added to the original second-order\nPhase-Field models, which is determined by the consistent and conservative\nvolume distribution algorithm so that the summation of the order parameters and\nthe \\textit{consistency of reduction} are not influenced. To physically couple\nthe proposed formulation to the hydrodynamics, especially for\nlarge-density-ratio problems, the consistent formulation is employed. The\nreduction-consistent conservative Allen-Cahn models are chosen as examples to\nillustrate the application of the proposed formulation. The numerical scheme\nthat preserves the consistency and conservation of the proposed formulation is\nemployed to demonstrate its effectiveness. Results produced by the proposed\nformulation are in good agreement with the exact and/or asymptotic solutions.\nThe proposed method captures complex dynamics of moving contact line problems\nhaving large density ratios.\n"}
+{"abstract": "  The numerical simulation of the physical systems has become in recent years a\nfundamental tool to perform analyses and predictions in several application\nfields, spanning from industry to the academy. As far as large scale\nsimulations are concerned, one of the most computationally expensive task is\nthe solution of linear systems arising from the discretization of the partial\ndifferential equations governing the physical processes.This work presents\nChronos, a collection of linear algebra functions specifically designed for the\nsolution of large, sparse linear systems on massively parallel computers\n(https://www.m3eweb.it/chronos/). Its emphasis is on modern, effective and\nscalable AMG preconditioners for High Performance Computing (HPC). This work\ndescribes the numerical algorithms and the main structures of this software\nsuite, especially from the implementation standpoint. Several numerical results\narising from practical mechanics and fluid dynamics applications with hundreds\nof millions of unknowns are addressed and compared with other state-of-the-art\nlinear solvers, proving Chronos efficiency and robustness.\n"}
+{"abstract": "  We shortly describe the algorithms behind some of the functions provided by\nthe Macaulay2 package MultiprojectiveVarieties, a package for multi-projective\nvarieties and rational maps between them.\n"}
+{"abstract": "  This study presents torsional and transverse vibration analysis of a solar\npanel including a rectangular thin plate locally supported by an elastic beam.\nThe plate is totally free in all boundaries, except for the local part attached\nto the beam. The response of the system, which is subjected to a combination of\ntorsional and transverse vibration, identifies with a couple of PDEs developed\nby the Euler-Bernoulli assumption and classical plate theory. To calculate the\nsystem's natural frequencies, the domain of the solution is discretized by\nzeroes of the Chebyshev polynomials to apply the Modified Generalized\nDifferential Quadrature method (MGDQ). Furthermore, governing equations along\nwith continuity and boundary conditions are discretized. After obtaining\nsolutions to the eigenvalue problem, several studies are investigated to\nvalidate the accuracy of the proposed method. As can be concluded from the\ntables, MGDQ improves the accuracy of results obtained by GDQ. Results for\nvarious case studies reveal that MGDQ is properly devised for the vibration\nanalysis of systems with local boundary and continuity conditions.\n"}
+{"abstract": "  We introduce a modification of standard Martin-Lof type theory in which we\neliminate definitional equality and replace all computation rules by\npropositional equalities. We show that type checking for such a system can be\ndone in quadratic time and that it has a natural homotopy-theoretic semantics.\n"}
+{"abstract": "  Fifth generation cellular systems support a broad range of services,\nincluding mobile broadband, critical and massive Internet of Things and are\nused in a variety of scenarios. In many of these scenarios, the main challenge\nis maintaining high throughput and ensuring proper quality of service (QoS) in\nirregular topologies. In multiple input multiple output systems, this challenge\ntranslates to designing linear transmit and receive beamformers that maximize\nthe system throughput and manage QoS constraints. In this paper, we argue that\nthis basic design task in 5G and beyond systems must be extended such that\nbeamforming design and user scheduling are managed jointly. Specifically, we\npropose a fully decentralized joint beamforming design and user scheduling\nalgorithm that manages QoS. A novel feature of this scheme is its ability to\nreduce the initial rate requirements in case of infeasibility. By means of\nsimulations that model contemporary 5G scenarios, we show that the proposed\ndecentralized scheme outperforms benchmarking algorithms that do not support\nminimum rate requirements and previously proposed algorithms that support QoS\nrequirements.\n"}
+{"abstract": "  Pommerman is a hybrid cooperative/adversarial multi-agent environment, with\nchallenging characteristics in terms of partial observability, limited or no\ncommunication, sparse and delayed rewards, and restrictive computational time\nlimits. This makes it a challenging environment for reinforcement learning (RL)\napproaches. In this paper, we focus on developing a curriculum for learning a\nrobust and promising policy in a constrained computational budget of 100,000\ngames, starting from a fixed base policy (which is itself trained to imitate a\nnoisy expert policy). All RL algorithms starting from the base policy use\nvanilla proximal-policy optimization (PPO) with the same reward function, and\nthe only difference between their training is the mix and sequence of opponent\npolicies. One expects that beginning training with simpler opponents and then\ngradually increasing the opponent difficulty will facilitate faster learning,\nleading to more robust policies compared against a baseline where all available\nopponent policies are introduced from the start. We test this hypothesis and\nshow that within constrained computational budgets, it is in fact better to\n\"learn in the school of hard knocks\", i.e., against all available opponent\npolicies nearly from the start. We also include ablation studies where we study\nthe effect of modifying the base environment properties of ammo and bomb blast\nstrength on the agent performance.\n"}
+{"abstract": "  COVID-19 has been devastating the world since the end of 2019 and has\ncontinued to play a significant role in major national and worldwide events,\nand consequently, the news. In its wake, it has left no life unaffected. Having\nearned the world's attention, social media platforms have served as a vehicle\nfor the global conversation about COVID-19. In particular, many people have\nused these sites in order to express their feelings, experiences, and\nobservations about the pandemic. We provide a multi-faceted analysis of\ncritical properties exhibited by these conversations on social media regarding\nthe novel coronavirus pandemic. We present a framework for analysis, mining,\nand tracking the critical content and characteristics of social media\nconversations around the pandemic. Focusing on Twitter and Reddit, we have\ngathered a large-scale dataset on COVID-19 social media conversations. Our\nanalyses cover tracking potential reports on virus acquisition, symptoms,\nconversation topics, and language complexity measures through time and by\nregion across the United States. We also present a BERT-based model for\nrecognizing instances of hateful tweets in COVID-19 conversations, which\nachieves a lower error-rate than the state-of-the-art performance. Our results\nprovide empirical validation for the effectiveness of our proposed framework\nand further demonstrate that social media data can be efficiently leveraged to\nprovide public health experts with inexpensive but thorough insight over the\ncourse of an outbreak.\n"}
+{"abstract": "  As an in situ combustion diagnostic tool, Tunable Diode Laser Absorption\nSpectroscopy (TDLAS) tomography has been widely used for imaging of\ntwo-dimensional temperature distributions in reactive flows. Compared with the\ncomputational tomographic algorithms, Convolutional Neural Networks (CNNs) have\nbeen proofed to be more robust and accurate for image reconstruction,\nparticularly in case of limited access of laser beams in the Region of Interest\n(RoI). In practice, flame in the RoI that requires to be reconstructed with\ngood spatial resolution is commonly surrounded by low-temperature background.\nAlthough the background is not of high interest, spectroscopic absorption still\nexists due to heat dissipation and gas convection. Therefore, we propose a\nPseudo-Inversed CNN (PI-CNN) for hierarchical temperature imaging that (a) uses\nefficiently the training and learning resources for temperature imaging in the\nRoI with good spatial resolution, and (b) reconstructs the less spatially\nresolved background temperature by adequately addressing the integrity of the\nspectroscopic absorption model. In comparison with the traditional CNN, the\nnewly introduced pseudo inversion of the RoI sensitivity matrix is more\npenetrating for revealing the inherent correlation between the projection data\nand the RoI to be reconstructed, thus prioritising the temperature imaging in\nthe RoI with high accuracy and high computational efficiency. In this paper,\nthe proposed algorithm was validated by both numerical simulation and lab-scale\nexperiment, indicating good agreement between the phantoms and the\nhigh-fidelity reconstructions.\n"}
+{"abstract": "  This paper presents a game, controlled by computer vision, in identification\nof hand gestures (hand-tracking). The proposed work is based on image\nsegmentation and construction of a convex hull with Jarvis Algorithm , and\ndetermination of the pattern based on the extraction of area characteristics in\nthe convex hull.\n"}
+{"abstract": "  Hypotheses: Additives are commonly used to tune macromolecular conformational\ntransitions. Among additives, trehalose is an excellent bioprotectant and among\nresponsive polymers, PNIPAM is the most studied material. Nevertheless, their\ninteraction mechanism so far has only been hinted without direct investigation,\nand, crucially, never elucidated in comparison to proteins. Detailed insights\nwould help understand to what extent PNIPAM microgels can effectively be used\nas synthetic biomimetic materials, to reproduce and study, at the colloidal\nscale, isolated protein behavior and its sensitivity to interactions with\nspecific cosolvents or cosolutes. Experiments: The effect of trehalose on the\nswelling behavior of PNIPAM microgels was monitored by dynamic light\nscattering; Raman spectroscopy and molecular dynamics simulations were used to\nexplore changes of solvation and dynamics across the swelling-deswelling\ntransition at the molecular scale. Findings: Strongly hydrated trehalose\nmolecules develop water-mediated interactions with PNIPAM microgels, thereby\npreserving polymer hydration below and above the transition while drastically\ninhibiting local motions of the polymer and of its hydration shell. Our study,\nfor the first time, demonstrates that slowdown of dynamics and preferential\nexclusion are the principal mechanisms governing trehalose effect on PNIPAM\nmicrogels, at odds with preferential adsorption of alcohols, but in full\nanalogy with the behavior observed in trehalose-protein systems.\n"}
+{"abstract": "  In this paper we extensively study the notion of Hamiltonian structure for\nnonabelian differential-difference systems, exploring the link between the\ndifferent algebraic (in terms of double Poisson algebras and vertex algebras)\nand geometric (in terms of nonabelian Poisson bivectors) definitions. We\nintroduce multiplicative double Poisson vertex algebras (PVAs) as the suitable\nnoncommutative counterpart to multiplicative PVAs, used to describe Hamiltonian\ndifferential-difference equations in the commutative setting, and prove that\nthese algebras are in one-to-one correspondence with the Poisson structures\ndefined by difference operators, providing a sufficient condition for the\nfulfilment of the Jacobi identity. Moreover, we define nonabelian polyvector\nfields and their Schouten brackets, for both finitely generated noncommutative\nalgebras and infinitely generated difference ones: this allows us to provide a\nunified characterisation of Poisson bivectors and double quasi-Poisson algebra\nstructures. Finally, as an application we obtain some results towards the\nclassification of local scalar Hamiltonian difference structures and construct\nthe Hamiltonian structures for the nonabelian Kaup, Ablowitz-Ladik and\nChen-Lee-Liu integrable lattices.\n"}
+{"abstract": "  We discuss the scalability of superconducting quantum computers, especially\nin a wiring problem. The number of wiring inside a cryostat is almost\nproportional to the number of qubits in current wiring architectures. We\nintroduce regularity, modularity, and hierarchy to an architecture design of\nsuperconducting quantum computers. The key to the wiring elimination is found\nin the quantum error correction codes having thresholds and spatial\ntranslational symmetry, i.e., the surface code. We show a\nsuperconducting-digital-logic-based architecture and introduce a stacked\nheterogeneous structure of the quantum module.\n"}
+{"abstract": "  We prove the (optimal) $W^{1,\\infty}$-regularity of weak solutions to the\nequation $-\\Delta u = Q \\; \\mathcal{H}^{n-1} \\llcorner \\Gamma$ in a domain\n$\\Omega \\subset \\mathbb{R}^n$ with Dirichlet boundary conditions, where $\\Gamma\n\\subset \\subset \\Omega$ is a compact (Lipschitz) manifold and $Q \\in\nL^\\infty(\\Gamma)$.\n  We also discuss optimality and necessity of the assumptions on $Q$ and\n$\\Gamma$.\n  Our findings can be applied to study the regularity of solutions for several\nfree boundary problems, in particular the biharmonic Alt-Caffarelli Problem.\n"}
+{"abstract": "  Inter-observer variation is a significant problem in clinical target\nvolume(CTV) segmentation in postoperative settings, where there is no gross\ntumor present. In this scenario, the CTV is not an anatomically established\nstructure, but one determined by the physician based on the clinical guideline\nused, the preferred tradeoff between tumor control and toxicity, their\nexperience and training background, and other factors. This results in high\ninter-observer variability between physicians. This variability has been\nconsidered an issue, but the absence of multiple physician CTV contours for\neach patient and the significant amount of time required for dose planning have\nmade it impractical to study its dosimetric consequences. In this study, we\nanalyze the impact that variations in physician style have on dose to\norgans-at-risk(OAR) by simulating the clinical workflow via deep learning. For\na given patient previously treated by one physician, we use deep learning-based\ntools to simulate how other physicians would contour the CTV and how the\ncorresponding dose distributions would look for this patient. To simulate\nmultiple physician styles, we use a previously developed in-house CTV\nsegmentation model that can produce physician style-aware segmentations. The\ncorresponding dose distribution is predicted using another in-house deep\nlearning tool, which, can predict dose within 3% of the prescription dose, on\naverage, on the test data. For every test patient, four different physician\nstyle CTVs are considered, and four different dose distributions are analyzed.\nOAR dose metrics are compared, showing that even though physician style\nvariations result in organs getting different doses, all the important dose\nmetrics except Maximum Dose point are within the clinically acceptable limit.\n"}
+{"abstract": "  Darija Open Dataset (DODa) is an open-source project for the Moroccan\ndialect. With more than 10,000 entries DODa is arguably the largest open-source\ncollaborative project for Darija-English translation built for Natural Language\nProcessing purposes. In fact, besides semantic categorization, DODa also adopts\na syntactic one, presents words under different spellings, offers verb-to-noun\nand masculine-to-feminine correspondences, contains the conjugation of hundreds\nof verbs in different tenses, and many other subsets to help researchers better\nunderstand and study Moroccan dialect. This data paper presents a description\nof DODa, its features, how it was collected, as well as a first application in\nImage Classification using ImageNet labels translated to Darija. This\ncollaborative project is hosted on GitHub platform under MIT's Open-Source\nlicense and aims to be a standard resource for researchers, students, and\nanyone who is interested in Moroccan Dialect\n"}
+{"abstract": "  In this paper, we study an asymptotic expansion of the heat kernel for a\nLaplace operator on a smooth Riemannian manifold without a boundary at enough\nsmall values of the proper time. The Seeley-DeWitt coefficients of this\ndecomposition satisfy a set of recurrence relations, which we use to construct\ntwo function families of a special kind. Using these functions, we find the\nexpansion of a heat kernel for the inverse Laplace operator for an arbitrary\ndimension of space. We show that the new functions have some important\nproperties. For example, we can consider the Laplace operator on the function\nset as a shift one. Also we describe various applications useful in theoretical\nphysics and, in particular, we find the decomposition of Green functions in\nterms of new functions.\n"}
+{"abstract": "  Both performance and efficiency are crucial factors for sequence labeling\ntasks in many real-world scenarios. Although the pre-trained models (PTMs) have\nsignificantly improved the performance of various sequence labeling tasks,\ntheir computational cost is expensive. To alleviate this problem, we extend the\nrecent successful early-exit mechanism to accelerate the inference of PTMs for\nsequence labeling tasks. However, existing early-exit mechanisms are\nspecifically designed for sequence-level tasks, rather than sequence labeling.\nIn this paper, we first propose a simple extension of sentence-level early-exit\nfor sequence labeling tasks. To further reduce the computational cost, we also\npropose a token-level early-exit mechanism that allows partial tokens to exit\nearly at different layers. Considering the local dependency inherent in\nsequence labeling, we employed a window-based criterion to decide for a token\nwhether or not to exit. The token-level early-exit brings the gap between\ntraining and inference, so we introduce an extra self-sampling fine-tuning\nstage to alleviate it. The extensive experiments on three popular sequence\nlabeling tasks show that our approach can save up to 66%-75% inference cost\nwith minimal performance degradation. Compared with competitive compressed\nmodels such as DistilBERT, our approach can achieve better performance under\nthe same speed-up ratios of 2X, 3X, and 4X.\n"}
+{"abstract": "  Almost three decades ago, the problem of long term polar wander on a dynamic\nplanet was formulated and simplified within the framework of normal mode\ntheory. The underlying simplifications have been debated ever since, recently\nin a series of papers by Hu et al. 2017a, 2017b, and 2019, who clarify the role\nof neglecting short-term relaxation modes of the body. However, the authors\nstill do not solve the governing equations in full, because they make\napproximations to the Liouville equation (LE). In this paper I use a time\ndomain approach and for previously studied test loads I solve both the\nrelaxation of the body and the LE in full. I also compute the energy balance of\ntrue polar wander (TPW) in order to analyze the existing LE approximations. For\nfast rotating bodies such as the Earth, I show that the rotation axis is always\naligned with the maximum principal axis of inertia (w||MMOI) once free\noscillations are damped. The w||MMOI assumption is also re-derived\ntheoretically. Contrary to previous beliefs, I demonstrate that it is not\nnecessarily linked to the quasi-fluid simplification of the body's viscoelastic\nresponse to loading and rotation, but that it is an expression of neglecting\nthe Coriolis and Euler forces in the equation of motion. For slowly rotating\nbodies such as Venus, the full LE together with energy analysis indicate that\nprevious estimates of TPW in the normal direction need to be revisited. The\nnumerical code LIOUSHELL is made freely available.\n"}
+{"abstract": "  End-to-end models for robust automatic speech recognition (ASR) have not been\nsufficiently well-explored in prior work. With end-to-end models, one could\nchoose to preprocess the input speech using speech enhancement techniques and\ntrain the model using enhanced speech. Another alternative is to pass the noisy\nspeech as input and modify the model architecture to adapt to noisy speech. A\nsystematic comparison of these two approaches for end-to-end robust ASR has not\nbeen attempted before. We address this gap and present a detailed comparison of\nspeech enhancement-based techniques and three different model-based adaptation\ntechniques covering data augmentation, multi-task learning, and adversarial\nlearning for robust ASR. While adversarial learning is the best-performing\ntechnique on certain noise types, it comes at the cost of degrading clean\nspeech WER. On other relatively stationary noise types, a new speech\nenhancement technique outperformed all the model-based adaptation techniques.\nThis suggests that knowledge of the underlying noise type can meaningfully\ninform the choice of adaptation technique.\n"}
+{"abstract": "  We investigated the development and breaking of the dineutron correlation in\n$^{10}$Be by analyzing the elastic and inelastic scatterings with a framework\ncombing the microscopic structure and reaction models. For studying the\nstructure, the $^{10}$Be nucleus was constructed under the assumption of a\nfour-body ($\\alpha + \\alpha + n + n$) cluster model. In this work, we focused\non the change in the inner structure for the 0$_1^+$, 2$_1^+$, and 2$_2^+$\nstates when the strength of the spin-orbit interaction is varied. The inner\nstructure, including various physical quantities such as energy, radius, and\ntransition strength, is drastically influenced by the strength of the\nspin-orbit interaction. In particular, the development and breaking of the\ndineutron correlation is governed by the spin-orbit strength. The differences\nin the inner structure can be manifested by applying the obtained wave\nfunctions to elastic and inelastic scatterings with a proton target at $E/A =$\n59.4 and 200 MeV. Although the 0$_1^+$ and 2$_1^+$ states are significantly\ninfluenced by the spin-orbit strength of the nuclear structure calculation, the\nelastic and inelastic cross sections are not much affected. On the other hand,\nthe inelastic cross section of the 2$_2^+$ state depends greatly on the\nspin-orbit strength of the structure calculation. Thus, we discovered a way to\nmeasure the degree of the development of dineutron cluster structure based on\nits sensitivity to the inelastic cross section of the 2$_2^+$ state of\n$^{10}$Be.\n"}
+{"abstract": "  In this work, we consider the following inhomogeneous nonlinear Schr\\\"odinger\nequation \\begin{align*}\n  i\\partial_t u+\\Delta u +|x|^{-b}|u|^{p-1}u=0,\\quad (x, t)\\in\n\\mathbb{R}^N\\times\\mathbb{R} \\end{align*} with $0<b<\\mbox{min}\\{2, N\\}$ and\n$1+\\frac{4-2b}{N}<p<2^{\\ast}$. We assume that $u_0 \\in H^{1}(\\mathbb{R}^N)$ and\nsatisfies \\begin{align*}\n  E(u_0)^{s_c}M(u_0)^{1-s_c}<E(Q)^{s_c}M(Q)^{1-s_c}, \\end{align*} and\n\\begin{align*}\n  \\|\\nabla u_0\\|_{L^2}^{s_c}\\|u_0\\|_{L^2}^{1-s_c}>\\|\\nabla\nQ\\|_{L^2}^{s_c}\\|Q\\|_{L^2}^{1-s_c}, \\end{align*} where $Q$ is the ground state\nsolution of the corresponding elliptic equation. We prove that when\n$1+\\frac{4-2b}{N}<p\\leq1+\\frac{4}{N}$, then the corresponding solution blows up\nin finite time. Besides, when $1+\\frac{4}{N}<p<2^{\\ast}$, we prove the finite\nor infinite time blow-up and obtain a precise lower bound of blow-up rate.\n"}
+{"abstract": "  The Super-Kamiokande detector can be used to search for neutrinos in time\ncoincidence with gravitational waves detected by the LIGO-Virgo Collaboration\n(LVC). Both low-energy ($7-100$ MeV) and high-energy ($0.1-10^5$ GeV) samples\nwere analyzed in order to cover a very wide neutrino spectrum. Follow-ups of 36\n(out of 39) gravitational waves reported in the GWTC-2 catalog were examined;\nno significant excess above the background was observed, with 10 (24) observed\nneutrinos compared with 4.8 (25.0) expected events in the high-energy\n(low-energy) samples. A statistical approach was used to compute the\nsignificance of potential coincidences. For each observation, p-values were\nestimated using neutrino direction and LVC sky map ; the most significant event\n(GW190602_175927) is associated with a post-trial p-value of $7.8\\%$\n($1.4\\sigma$). Additionally, flux limits were computed independently for each\nsample and by combining the samples. The energy emitted as neutrinos by the\nidentified gravitational wave sources was constrained, both for given flavors\nand for all-flavors assuming equipartition between the different flavors,\nindependently for each trigger and by combining sources of the same nature.\n"}
+{"abstract": "  By means of in situ synchrotron X-ray diffraction and Raman spectroscopy\nunder hydrostatic pressure, we investigate the structural stability of the\nquadruple perovskite LaMn7O12. At 34 GPa, the data unveil a first-order\nstructural phase transition from monoclinic I2/m to cubic Im-3 symmetry\ncharacterized by a pronounced contraction of the unit cell and by a significant\nmodifications in the Raman phonon modes. The phase transition is also marked by\nthe suppression of Jahn-Teller distortion which is present in the ambient\nmonoclinic phase. In addition, above 20 GPa pressure, a sudden and simultaneous\nbroadening is observed in several Raman modes which suggests the onset of a\nsizable electron-phonon interaction and incipient charge mobility. Considering\nthat LaMn7O12 is paramagnetic insulator at ambient, and Jahn-Teller distortion\nis frozen in the high-pressure Im-3 phase, we argue that this phase could be a\npotential candidate to host a purely electronic insulator-metal transition with\nno participation of the lattice\n"}
+{"abstract": "  We present a simple proof of the minimum time for the quantum evolution\nbetween two arbitrary states. This proof is performed in the absence of any\ngeometrical arguments. Then, being in the geometric framework of quantum\nevolutions based upon the geometry of the projective Hilbert space, we discuss\nthe roles played by either minimum-time or maximum-energy uncertainty concepts\nin defining a geometric efficiency measure of quantum evolutions between two\narbitrary quantum states. Finally, we provide a quantitative justification of\nthe validity of the efficiency inequality even when the system passes only\nthrough nonorthogonal quantum states.\n"}
+{"abstract": "  This paper develops a grey-box approach to small-signal stability analysis of\ncomplex power systems that facilitates root-cause tracing without requiring\ndisclosure of the full details of the internal control structure of apparatus\nconnected to the system. The grey-box enables participation analysis in\nimpedance models, which is popular in power electronics and increasingly\naccepted in power systems for stability analysis. The Impedance participation\nfactor is proposed and defined in terms of the residue of the whole-system\nadmittance matrix. It is proved that, the so defined impedance participation\nfactor equals the sensitivity of the whole-system eigenvalue with respect to\napparatus impedance. The classic state participation factor is related to the\nimpedance participation factor via a chain-rule. Based on the chain-rule, a\nthree-layer grey-box approach, with three degrees of transparency, is proposed\nfor root-cause tracing to different depths, i.e. apparatus, states, and\nparameters, according to the available information. The association of\nimpedance participation factor with eigenvalue sensitivity points to the\nre-tuning that would stabilize the system. The impedance participation factor\ncan be measured in the field or calculated from the black-box impedance spectra\nwith little prior knowledge required.\n"}
+{"abstract": "  We solve a weakly supervised regression problem. Under \"weakly\" we understand\nthat for some training points the labels are known, for some unknown, and for\nothers uncertain due to the presence of random noise or other reasons such as\nlack of resources. The solution process requires to optimize a certain\nobjective function (the loss function), which combines manifold regularization\nand low-rank matrix decomposition techniques. These low-rank approximations\nallow us to speed up all matrix calculations and reduce storage requirements.\nThis is especially crucial for large datasets. Ensemble clustering is used for\nobtaining the co-association matrix, which we consider as the similarity\nmatrix. The utilization of these techniques allows us to increase the quality\nand stability of the solution. In the numerical section, we applied the\nsuggested method to artificial and real datasets using Monte-Carlo modeling.\n"}
+{"abstract": "  We propose a low-cost reconfigurable metasurface transmitter to implement\ngeneralized polarization-space modulation (GPSM) that has been proposed for\nnext-generation wireless communications. Unlike conventional multiple-input\nmultiple-output GPSM, our electromagnetically-compliant, reduced form-factor\ndesign using Barium-Strontium-Titanate film yields more than two polarization\nstates. Full-wave simulations show our optimized design achieves 0.08-0.95\npolarization conversion ratio with 5% bandwidth at 28 GHz and bit error rates\ncomparable or better than conventional systems.\n"}
+{"abstract": "  The Analog Ensemble (AnEn) technique has been shown effective on several\nweather problems. Unlike previous weather analogs that are sought within a\nlarge spatial domain and an extended temporal window, AnEn strictly confines\nspace and time, and independently generates results at each grid point within a\nshort time window. AnEn can find similar forecasts that lead to accurate and\ncalibrated ensemble forecasts. The central core of the AnEn technique is a\nsimilarity metric that sorts historical forecasts with respect to a new target\nprediction. A commonly used metric is Euclidean distance. However, a\nsignificant difficulty using this metric is the definition of the weights for\nall the parameters. Generally, feature selection and extensive weight search\nare needed.\n  This paper proposes a novel definition of weather analogs through a Machine\nLearning (ML) based similarity metric. The similarity metric uses neural\nnetworks that are trained and instantiated to search for weather analogs. This\nnew metric allows incorporating all variables without requiring a prior feature\nselection and weight optimization. Experiments are presented on the application\nof this new metric to forecast wind speed and solar irradiance. Results show\nthat the ML metric generally outperforms the original metric. The ML metric has\na better capability to correct for larger errors and to take advantage of a\nlarger search repository. Spatial predictions using a learned metric also show\nthe ability to define effective latent features that are transferable to other\nlocations.\n"}
+{"abstract": "  This article presents a new set of proton parton distribution functions,\nATLASepWZVjet20, produced in an analysis at next-to-next-to-leading order in\nQCD. The new data sets considered are the measurements of $W^+$ and $W^-$ boson\nand $Z$ boson production in association with jets in $pp$ collisions at\n$\\sqrt{s} = 8~\\mathrm{TeV}$ performed by the ATLAS experiment at the LHC with\nintegrated luminosities of $20.2~\\mathrm{fb}^{-1}$ and $19.9~\\mathrm{fb}^{-1}$,\nrespectively. The analysis also considers the ATLAS measurements of\ndifferential $W^{\\pm}$ and $Z$ boson production at $\\sqrt{s} = 7~\\mathrm{TeV}$\nwith an integrated luminosity of $4.6~\\mathrm{fb}^{-1}$ and\ndeep-inelastic-scattering data from $e^{\\pm}p$ collisions at the HERA\naccelerator. An improved determination of the sea-quark densities at high\nBjorken $x$ is shown, while confirming a strange-quark density similar in size\nto the up- and down-sea-quark densities in the range $x \\lesssim 0.02$ found by\nprevious ATLAS analyses.\n"}
+{"abstract": "  The radiometric force on a sphere due to its thermal polarization in a\nrarefied gas flow being in equilibrium is investigated on the basis of a\nkinetic model to the linearized Boltzmann equation. The scattering kernel\nproposed by Cercignani and Lampis to model the gas-surface interaction using\ntwo accommodation coefficients, namely the tangential momentum accommodation\ncoefficient and the normal energy accommodation coefficient, is employed as the\nboundary condition. The radiometric force on the sphere, as well as the flow\nfield of the gas around it, are calculated in a wide range of the gas\nrarefaction, defined as the ratio of the sphere radius to an equivalent free\npath of gaseous particles, covering the free molecular, transition and\ncontinuum regimes. The discrete velocity method is employed to solve the\nkinetic equation numerically. The calculations are carried out for values of\naccommodation coefficients considering most situations encountered in practice.\nTo confirm the reliability of the calculations, the reciprocity relation\nbetween the cross phenomena is verified numerically within a numerical error of\n0.1%. The temperature drop between two diametrically opposite points of the\nspherical surface in the direction of the gas flow stream, which characterizes\nthe thermal polarization effect, is compared to experimental data for a\nspherical particle of Pyrex glass immersed in helium and argon gases.\n"}
+{"abstract": "  Anomalies in images occur in various scales from a small hole on a carpet to\na large stain. However, anomaly detection based on sparse coding, one of the\nwidely used anomaly detection methods, has an issue in dealing with anomalies\nthat are out of the patch size employed to sparsely represent images. A large\nanomaly can be considered normal if seen in a small scale, but it is not easy\nto determine a single scale (patch size) that works well for all images. Then,\nwe propose to incorporate multi-scale features to sparse coding and improve the\nperformance of anomaly detection. The proposed method, multi-layer feature\nsparse coding (MLF-SC), employs a neural network for feature extraction, and\nfeature maps from intermediate layers of the network are given to sparse\ncoding, whereas the standard sparse-coding-based anomaly detection method\ndirectly works on given images. We show that MLF-SC outperforms\nstate-of-the-art anomaly detection methods including those employing deep\nlearning. Our target data are the texture categories of the MVTec Anomaly\nDetection (MVTec AD) dataset, which is a modern benchmark dataset consisting of\nimages from the real world. Our idea can be a simple and practical option to\ndeal with practical data.\n"}
+{"abstract": "  Social networks have triumphed in communicating people online, but they have\nalso been exploited to launch influence operations for manipulating society.\nThe deployment of software-controlled accounts (e.g., social bots) has proven\nto be one of the most effective enablers for that purpose, and tools for their\ndetection have been developed and widely adopted. However, the way to analyze\nthese accounts and measure their impact is heterogeneous in the literature,\nwhere each case study performs unique measurements. To unify these efforts, we\npropose a common framework to analyze the interference of social bots in\nTwitter. The methodology compares the non-authentic actors with the rest of\nusers from different perspectives, thus building objective metrics to measure\ntheir actual impact. We validate the framework by applying it to a dataset of\nTwitter iterations dated in the weeks preceding the 2019 Spanish general\nelection. In this sense, we check that our framework facilitates the\nquantitative evaluation of unauthentic groups, particularly discovering that\nsocial bots changed the natural dynamics of the network in these days, but did\nnot have a significant impact. We also consider this methodology as a practical\ntool for the qualitative interpretation of experimental results, particularly\nsuggesting within the aforementioned electoral context that semi-automated\naccounts are potentially more threatening than fully automated ones.\n"}
+{"abstract": "  We study local spin polarization in the relativistic hydrodynamic model.\nGeneralizing the Wigner functions previously obtained from chiral kinetic\ntheory by Y. Hidaka et al. [Phys. Rev. D 97, 016004 (2018)] to the massive\ncase, we present the possible contributions up to the order of $\\hbar$ from\nthermal vorticity, shear viscous tensor, other terms associated with the\ntemperature and chemical-potential gradients, and electromagnetic fields to the\nlocal spin polarization. We then implement the (3+1)-dimensional viscous\nhydrodynamic model to study the spin polarizations from these sources with a\nsmall chemical potential and ignorance of electromagnetic fields by adopting an\nequation of state different from those in other recent studies. Although the\nshear correction alone upon the local polarization results in a sign and\nazimuthal-angle dependence more consistent with experimental observations, as\nalso discovered in other recent studies, it is mostly suppressed by the\ncontributions from thermal vorticity and other terms that yield an opposite\ntrend. It is found that the total local spin polarization can be very sensitive\nto the equation of states, the ratio of shear viscosity to entropy density, and\nthe freeze-out temperature.\n"}
+{"abstract": "  We study the deformation theory of rational curves on primitive symplectic\nvarieties and show that if the rational curves cover a divisor, then, as in the\nsmooth case, they deform along their Hodge locus in the universal locally\ntrivial deformation. As applications, we extend Markman's deformation\ninvariance of prime exceptional divisors along their Hodge locus to this\nsingular framework and provide existence results for uniruled ample divisors on\nprimitive symplectic varieties which are locally trivial deformations of any\nmoduli space of semistable objects on a projective $K3$ or fibers of the\nAlbanese map of those on an abelian surface. We also present an application to\nthe existence of prime exceptional divisors.\n"}
+{"abstract": "  In this paper we present a class of potentials derived by no-scale\nsupergravity in order to explain the production of primordial black holes\n(PBHs) in the Universe. By breaking the SU(2,1)/SU(2)$\\times$U(1) symmetry we\nfix one of the two chiral fields and we derive effective scalar potentials\nwhich are capable of generating PBHs. Specifically, we modify well-known\nsuperpotentials, which reduce to Starobinsky-like effective scalar potentials.\nThus, we derive scalar potentials which, on the one hand, explain the\nproduction of PBHs and, on the other hand, they conserve the transformation\nlaws, which yield from the parametrization of the coset\nSU(2,1)/SU(2)$\\times$U(1). Moreover, we generate PBHs by modifying the kinetic\nterm of the Langrangian (or the K\\\"ahler potential) and we keep the\nsuperpotentials unmodified. In all cases we evaluate the fractional abundances\nof PBHs by comparing Press-Schechter approach and peak theory, while focusing\non explaining the dark matter in the Universe. All models are in complete\nconsistence with Planck constraints.\n"}
+{"abstract": "  Polyps in the colon are widely known as cancer precursors identified by\ncolonoscopy either related to diagnostic work-up for symptoms, colorectal\ncancer screening or systematic surveillance of certain diseases. Whilst most\npolyps are benign, the number, size and the surface structure of the polyp are\ntightly linked to the risk of colon cancer. There exists a high missed\ndetection rate and incomplete removal of colon polyps due to the variable\nnature, difficulties to delineate the abnormality, high recurrence rates and\nthe anatomical topography of the colon. In the past, several methods have been\nbuilt to automate polyp detection and segmentation. However, the key issue of\nmost methods is that they have not been tested rigorously on a large\nmulti-center purpose-built dataset. Thus, these methods may not generalise to\ndifferent population datasets as they overfit to a specific population and\nendoscopic surveillance. To this extent, we have curated a dataset from 6\ndifferent centers incorporating more than 300 patients. The dataset includes\nboth single frame and sequence data with 3446 annotated polyp labels with\nprecise delineation of polyp boundaries verified by six senior\ngastroenterologists. To our knowledge, this is the most comprehensive detection\nand pixel-level segmentation dataset curated by a team of computational\nscientists and expert gastroenterologists. This dataset has been originated as\nthe part of the Endocv2021 challenge aimed at addressing generalisability in\npolyp detection and segmentation. In this paper, we provide comprehensive\ninsight into data construction and annotation strategies, annotation quality\nassurance and technical validation for our extended EndoCV2021 dataset which we\nrefer to as PolypGen.\n"}
+{"abstract": "  In this work we demonstrate spectral-temporal correlation measurements of the\nHong-Ou-Mandel (HOM) interference effect with the use of a spectrometer based\non a photon-counting camera. This setup allows us to take, within seconds,\nspectral temporal correlation measurements on entangled photon sources with\nsub-nanometer spectral resolution and nanosecond timing resolution. Through\npost processing, we can observe the HOM behaviour for any number of spectral\nfilters of any shape and width at any wavelength over the observable spectral\nrange. Our setup also offers great versatility in that it is capable of\noperating at a wide spectral range from the visible to the near infrared and\ndoes not require a pulsed pump laser for timing purposes. This work offers the\nability to gain large amounts of spectral and temporal information from a HOM\ninterferometer quickly and efficiently and will be a very useful tool for many\nquantum technology applications and fundamental quantum optics research.\n"}
+{"abstract": "  Liu, Hong, Gu, and Lai proved if the second largest eigenvalue of the\nadjacency matrix of graph $G$ with minimum degree $\\delta \\ge 2m+2 \\ge 4$\nsatisfies $\\lambda_2(G) < \\delta - \\frac{2m+1}{\\delta+1}$, then $G$ contains at\nleast $m+1$ edge-disjoint spanning trees, which verified a generalization of a\nconjecture by Cioab\\u{a} and Wong. We show this bound is essentially the best\npossible by constructing $d$-regular graphs $\\mathcal{G}_{m,d}$ for all $d \\ge\n2m+2 \\ge 4$ with at most $m$ edge-disjoint spanning trees and\n$\\lambda_2(\\mathcal{G}_{m,d}) < d-\\frac{2m+1}{d+3}$. As a corollary, we show\nthat a spectral inequality on graph rigidity by Cioab\\u{a}, Dewar, and Gu is\nessentially tight.\n"}
+{"abstract": "  The mainstay of canine rabies control is fixed point mass dog vaccination\ncampaigns (MDVC). However, in some regions, ideal vaccination coverage in dogs\nis not obtained due to low participation in the MDVC. Travel distance to the\nvaccination sites has been identified as an important barrier to participation.\nWe aim to increase MDVC participation by optimally placing fixed point\nvaccination locations to minimize walking distance to the nearest vaccination\nlocation. We quantified participation probability based on walking distance to\nthe nearest vaccination point using a Poisson regression model. The regression\nwas fit with survey data collected from 2016-2019. We then used a computational\nrecursive interchange technique to solve the facility location problem to find\na set of optimal placements of fixed point vaccination locations. Finally, we\ncompared predicted participation of optimally placed vaccination sites to\nhistorical participation data from surveys collected from 2016-2019. We\nidentified the p-median algorithm to solve the facility location problem as\nideal for fixed point vaccination placement. We found a predicted increase in\nMDVC participation if vaccination locations are placed optimally. We also found\na more even vaccination coverage with optimized vaccination sites; however, the\nworkload in some optimized locations increased significantly. We developed a\ndata-driven computational algorithm to combat an ongoing rabies epidemic by\noptimally using limited resources to maximize vaccination coverage. The main\npositive effects we expect if this algorithm is to be implemented would be\nincreased overall vaccination coverage and increased spatial evenness of\ncoverage. A potential negative effect could be the presence of long waiting\nlines as participation increases.\n"}
+{"abstract": "  In this work we leverage recent advances in context-sensitive language models\nto improve the task of query expansion. Contextualized word representation\nmodels, such as ELMo and BERT, are rapidly replacing static embedding models.\nWe propose a new model, Contextualized Embeddings for Query Expansion (CEQE),\nthat utilizes query-focused contextualized embedding vectors. We study the\nbehavior of contextual representations generated for query expansion in ad-hoc\ndocument retrieval. We conduct our experiments on probabilistic retrieval\nmodels as well as in combination with neural ranking models. We evaluate CEQE\non two standard TREC collections: Robust and Deep Learning. We find that CEQE\noutperforms static embedding-based expansion methods on multiple collections\n(by up to 18% on Robust and 31% on Deep Learning on average precision) and also\nimproves over proven probabilistic pseudo-relevance feedback (PRF) models. We\nfurther find that multiple passes of expansion and reranking result in\ncontinued gains in effectiveness with CEQE-based approaches outperforming other\napproaches. The final model incorporating neural and CEQE-based expansion score\nachieves gains of up to 5% in P@20 and 2% in AP on Robust over the\nstate-of-the-art transformer-based re-ranking model, Birch.\n"}
+{"abstract": "  In news articles the lead bias is a common phenomenon that usually dominates\nthe learning signals for neural extractive summarizers, severely limiting their\nperformance on data with different or even no bias. In this paper, we introduce\na novel technique to demote lead bias and make the summarizer focus more on the\ncontent semantics. Experiments on two news corpora with different degrees of\nlead bias show that our method can effectively demote the model's learned lead\nbias and improve its generality on out-of-distribution data, with little to no\nperformance loss on in-distribution data.\n"}
+{"abstract": "  This paper proposes an active attack detection scheme for constrained\ncyber-physical systems. Despite passive approaches where the detection is based\non the analysis of the input-output data, active approaches interact with the\nsystem by designing the control input so to improve detection. This paper\nfocuses on the prevented actuation attack, where the attacker prevents the\nexchange of information between the controller and actuators. The proposed\nscheme consists of two units: 1) detection, and 2) control. The detection unit\nincludes a set of parallel detectors, which are designed based on the\nmultiple-model adaptive estimation approach to detect the attack and to\nidentify the attacked actuator(s). For what regards the control unit, a\nconstrained optimization approach is developed to determine the control input\nsuch that the control and detection aims are achieved. In the formulation of\nthe detection and control objective functions, a probabilistic approach is used\nto reap the benefits of the \\textit{a priori} information availability. The\neffectiveness of the proposed scheme is demonstrated through a simulation study\non an irrigation channel.\n"}
+{"abstract": "  Weakly supervised learning has drawn considerable attention recently to\nreduce the expensive time and labor consumption of labeling massive data. In\nthis paper, we investigate a novel weakly supervised learning problem of\nlearning from similarity-confidence (Sconf) data, where we aim to learn an\neffective binary classifier from only unlabeled data pairs equipped with\nconfidence that illustrates their degree of similarity (two examples are\nsimilar if they belong to the same class). To solve this problem, we propose an\nunbiased estimator of the classification risk that can be calculated from only\nSconf data and show that the estimation error bound achieves the optimal\nconvergence rate. To alleviate potential overfitting when flexible models are\nused, we further employ a risk correction scheme on the proposed risk\nestimator. Experimental results demonstrate the effectiveness of the proposed\nmethods.\n"}
+{"abstract": "  This paper aims to mitigate straggler effects in synchronous distributed\nlearning for multi-agent reinforcement learning (MARL) problems. Stragglers\narise frequently in a distributed learning system, due to the existence of\nvarious system disturbances such as slow-downs or failures of compute nodes and\ncommunication bottlenecks. To resolve this issue, we propose a coded\ndistributed learning framework, which speeds up the training of MARL algorithms\nin the presence of stragglers, while maintaining the same accuracy as the\ncentralized approach. As an illustration, a coded distributed version of the\nmulti-agent deep deterministic policy gradient(MADDPG) algorithm is developed\nand evaluated. Different coding schemes, including maximum distance separable\n(MDS)code, random sparse code, replication-based code, and regular low density\nparity check (LDPC) code are also investigated. Simulations in several\nmulti-robot problems demonstrate the promising performance of the proposed\nframework.\n"}
+{"abstract": "  In 1971, J. Lipman introduced the notion of strict closure of a ring in\nanother, and established the underlying theory in connection with a conjecture\nof O. Zariski. In this paper, for further developments of the theory, we\ninvestigate three different topics related to strict closure of rings. The\nfirst one concerns construction of the closure, and the second one is the study\nregarding the question of whether the strict closedness is inherited under flat\nhomomorphisms. We finally handle the question of when the Arf closure coincides\nwith the strict closure. Examples are explored to illustrate our theorems.\n"}
+{"abstract": "  We report on an asymmetric high energy dual optical parametric amplifier\n(OPA) which is capable of having either the idlers, signals, or depleted pumps,\nrelatively phase locked at commensurate or incommensurate wavelengths. Idlers\nand signals can be locked on the order of 200 mrad rms or better, corresponding\nto a 212 as jitter at $\\lambda$=2$\\mu$m. The high energy arm of the OPA outputs\na combined 3.5 mJ of signal and idler, while the low energy arm outputs 1.5 mJ,\nwith the entire system being pumped with a 1 kHz, 18 mJ Ti:Sapphire laser. Both\narms are independently tunable from 1080 nm-2600 nm. The combination of\nrelative phase locking, high output power and peak intensity, and large\ntunability makes our OPA an ideal tool for use in difference frequency\ngeneration (DFG) in the strong pump regime, and for high peak field waveform\nsynthesis in the near-infrared. To demonstrate this ability we generate\nterahertz radiation through two color waveform synthesis in air plasma and show\nthe influence of the relative phase on the generated terahertz intensity. The\nability to phase lock multiple incommensurate wavelengths at high energies\nopens the door to a multitude of possibilities of strong pump DFG and waveform\nsynthesis.\n"}
+{"abstract": "  In bilayers consisting of a normal metal (N) with spin-orbit coupling and a\nferromagnet (F), the combination of the spin-Hall effect, the spin-transfer\ntorque, and the inverse spin-Hall effect gives a small correction to the\nin-plane conductivity of N, which is referred to as spin-Hall magnetoresistance\n(SMR). We here present a theory of the SMR and the associated off-diagonal\nconductivity corrections for frequencies up to the terahertz regime. We show\nthat the SMR signal has pronounced singularities at the spin-wave frequencies\nof F, which identifies it as a potential tool for all-electric spectroscopy of\nmagnon modes. A systematic change of the magnitude of the SMR at lower\nfrequencies is associated with the onset of a longitudinal magnonic\ncontribution to spin transport across the F-N interface.\n"}
+{"abstract": "  Inverse problems defined naturally on the sphere are becoming increasingly of\ninterest. In this article we provide a general framework for evaluation of\ninverse problems on the sphere, with a strong emphasis on flexibility and\nscalability. We consider flexibility with respect to the prior selection\n(regularization), the problem definition - specifically the problem formulation\n(constrained/unconstrained) and problem setting (analysis/synthesis) - and\noptimization adopted to solve the problem. We discuss and quantify the\ntrade-offs between problem formulation and setting. Crucially, we consider the\nBayesian interpretation of the unconstrained problem which, combined with\nrecent developments in probability density theory, permits rapid, statistically\nprincipled uncertainty quantification (UQ) in the spherical setting. Linearity\nis exploited to significantly increase the computational efficiency of such UQ\ntechniques, which in some cases are shown to permit analytic solutions. We\nshowcase this reconstruction framework and UQ techniques on a variety of\nspherical inverse problems. The code discussed throughout is provided under a\nGNU general public license, in both C++ and Python.\n"}
+{"abstract": "  By using distribution dependent Zvonkin's transforms and Malliavin calculus,\nthe Bismut type formula is derived for the intrinisc/Lions derivatives of\ndistribution dependent SDEs with singular drifts, which generalizes the\ncorresponding results derived for classical SDEs and regular distribution\ndependent SDEs.\n"}
+{"abstract": "  In this paper, we prove that the group $\\mathrm{Aut}_\\mathbb{Q}(X)$ of\nnumerically trivial automorphisms are uniformly bounded for smooth projective\nthreefolds $X$ of general type which either satisfy $q(X)\\geq 3$ or have a\nGorenstein minimal model. If $X$ is furthermore of maximal Albanese dimension,\nthen $|\\mathrm{Aut}_\\mathbb{Q}(X)|\\leq 4$, and equality can be achieved by an\nunbounded family of threefolds previously constructed by the third author.\nAlong the way we prove a Noether type inequality for log canonical pairs of\ngeneral type with the coefficients of the boundary divisor from a given subset\n$\\mathcal{C}\\subset (0,1]$ such that $\\mathcal{C}\\cup\\{1\\}$ attains the\nminimum.\n"}
+{"abstract": "  Here we present stringent low-frequency 185MHz limits on coherent radio\nemission associated with a short gamma-ray burst (SGRB). Our observations of\nthe short GRB 180805A were taken with the upgraded Murchison Widefield Array\n(MWA) rapid-response system, which triggered within 20s of receiving the\ntransient alert from Swift, corresponding to 83.7s post-burst. The SGRB was\nobserved for 30m, resulting in a 3sigma persistent flux density upper-limit of\n40.2mJy/beam. Transient searches were conducted at the Swift position of this\nGRB on 0.5s, 5s, 30s, and 2m timescales, resulting in 3sigma limits of\n570-1830, 270-630, 200-420, and 100-200mJy/beam, respectively. We also\nperformed a dedispersion search for prompt signals at the position of the SGRB\nwith a temporal and spectral resolution of 0.5s and 1.28MHz, resulting in a\n6sigma fluence upper-limit range from 570Jyms at DM=3000pc/cm^3 (z~2.5) to\n1750Jyms at DM=200pc/cm^3 (z~0.1). We compare the fluence prompt emission limit\nand the persistent upper-limit to SGRB coherent emission models assuming the\nmerger resulted in a stable magnetar. Our observations were not sensitive\nenough to detect prompt emission associated with the alignment of magnetic\nfields of a binary neutron star just prior to the merger, from the interaction\nbetween the relativistic jet and the interstellar medium or persistent\npulsar-like emission from the spin-down of the magnetar. However, in the case\nof a more powerful SGRB (a gamma-ray fluence an order of magnitude higher than\nGRB 180805A and/or a brighter X-ray counterpart), our MWA observations may be\nsensitive enough to detect coherent radio emission from the jet-ISM interaction\nand/or the magnetar remnant. Finally, we demonstrate that of all current\nlow-frequency radio telescopes, only the MWA has the sensitivity and response\ntimes capable of probing prompt emission models associated with the initial\nSGRB merger event.\n"}
+{"abstract": "  Using insight from numerical approximation of ODEs and the problem\nformulation and solution methodology of TD learning through a Galerkin\nrelaxation, I propose a new class of TD learning algorithms. After applying the\nimproved numerical methods, the parameter being approximated has a guaranteed\norder of magnitude reduction in the Taylor Series error of the solution to the\nODE for the parameter $\\theta(t)$ that is used in constructing the linearly\nparameterized value function. Predictor-Corrector Temporal Difference (PCTD) is\nwhat I call the translated discrete time Reinforcement Learning(RL) algorithm\nfrom the continuous time ODE using the theory of Stochastic Approximation(SA).\nBoth causal and non-causal implementations of the algorithm are provided, and\nsimulation results are listed for an infinite horizon task to compare the\noriginal TD(0) algorithm against both versions of PCTD(0).\n"}
+{"abstract": "  This paper proposes an advanced method for contrast enhancement of capsule\nendoscopic images, with the main objective to obtain sufficient information\nabout the vessels and structures in more distant (or darker) parts of capsule\nendoscopic images. The proposed method (PM) combines two algorithms for the\nenhancement of darker and brighter areas of capsule endoscopic images,\nrespectively. The half-unit weighted bilinear algorithm (HWB) proposed in our\nprevious work is used to enhance darker areas according to the darker map\ncontent of its HSV's component V. Enhancement of brighter areas is achieved\nthanks to the novel thresholded weighted-bilinear algorithm (TWB) developed to\navoid overexposure and enlargement of specular highlight spots while preserving\nthe hue, in such areas. The TWB performs enhancement operations following a\ngradual increment of the brightness of the brighter map content of its HSV's\ncomponent V. In other words, the TWB decreases its averaged-weights as the\nintensity content of the component V increases. Extensive experimental\ndemonstrations were conducted, and based on evaluation of the reference and PM\nenhanced images, a gastroenterologist ({\\O}H) concluded that the PM enhanced\nimages were the best ones based on the information about the vessels, contrast\nin the images, and the view or visibility of the structures in more distant\nparts of the capsule endoscopy images.\n"}
+{"abstract": "  This paper presents a new primal-dual method for computing an equilibrium of\ngeneralized (continuous) Nash game (referred to as generalized Nash equilibrium\nproblem (GNEP)) where each player's feasible strategy set depends on the other\nplayers' strategies. The method is based on a new form of Lagrangian function\nwith a quadratic approximation. First, we reformulate a GNEP as a saddle point\ncomputation using the new Lagrangian and establish equivalence between a saddle\npoint of the Lagrangian and an equilibrium of the GNEP. We then propose a\nsimple algorithm that is convergent to the saddle point. Furthermore, we\nestablish global convergence by assuming that the Lagrangian function satisfies\nthe Kurdyka-{\\L}ojasiewicz property. A distinctive feature of our analysis is\nto make use of the new Lagrangian as a potential function to guide the iterate\nconvergence, which is based on the idea of turning a descent method into a\nmultiplier method. Our method has two novel features over existing approaches:\n(i) it requires neither boundedness assumptions on the strategy set and the set\nof multipliers of each player, nor any boundedness assumptions on the iterates\ngenerated by the algorithm; (ii) it leads to a Jacobi-type decomposition\nscheme, which, to the best of our knowledge, is the first development of a\ndistributed algorithm to solve a general class of GNEPs. Numerical experiments\nare performed on benchmark test problems and the results demonstrate the\neffectiveness of the proposed method.\n"}
+{"abstract": "  Context: Classical nova progenitors are cataclysmic variables and very old\nnovae are observed to match high mass transfer rate and (relatively) long\norbital period systems. However, the aftermath of a classical nova has never\nbeen studied in detail. Aims: To probe the aftermath of a classical nova\nexplosion in cataclysmic variables and observe as the binary system relaxes to\nquiescence. Methods: We used multi-wavelength time resolved optical and\nnear-infrared spectroscopy for a bright, well studied classical nova five years\nafter outburst. We were able to disentangle the contribution of the ejecta at\nthis late epoch using its previous characterization, separating the ejecta\nemission from that of the binary system. Results: We determined the binary\norbital period (P=3.76 hr), the system separation and mass ratio (q>=0.17 for\nan assumed white dwarf mass of 1.2 solar masses). We find evidence of an\nirradiated secondary star and no unambiguous signature of an accretion disk,\nalthough we identify a second emission line source tied to the white dwarf with\nan impact point. The data are consistent with a bloated white dwarf envelope\nand the presence of unsettled gas within the white dwarf Roche lobe.\nConclusions: At more than 5 years after eruption, it appears that this\nclassical nova has not yet relaxed.\n"}
+{"abstract": "  In this work, we propose a new signal routing method for solving routing\nproblems that occur in the design process of semiconductor package substrates.\nOur work uses a topological transformation of the layers of the package\nsubstrate in order to simplify the routing problem into a problem of connecting\npoints on a circle with non-intersecting straight line segments. The circle,\nwhich we call the Circular Frame, is a polygonal schema, which is originally\nused in topology to study the topological structure of 2-manifolds. We show\nthrough experiments that our new routing method based on the Circular Frame\ncompetes with certain grid-based routing algorithms.\n"}
+{"abstract": "  We study a frustrated antiferromagnetic Heisenberg model on a triangular\nlattice with the Dzyaloshinskii-Moriya interaction (DMI) in the presence of an\nexternal magnetic field and a single-ion anisotropy. Phase diagrams in the\ntemperature-field plane for both easy-axis and easy-plane anisotropy of a\nvarying strength are constructed in the regimes of a moderate and strong DMI by\nparallel tempering Monte Carlo simulations. For the considered range of\nparameters the phase diagrams featuring up to four ordered phases are\nidentified. A skyrmion lattice (SkX) phase is found to be stabilized within\nsome temperature and field window for sufficiently large DMI and not too strong\nanisotropy. For the systems with moderate (larger) DMI, a small easy-plane\n(easy-axis) anisotropy is concluded to be beneficial by extending (shifting)\nthe field window of the SkX appearance to lower values. The Metropolis dynamics\nis employed to probe the persistence of SkX upon quenching the field to zero or\nsmall finite values. The most favorable conditions for the skyrmions\npersistence are confirmed at low temperatures, small DMI and small easy-plane\nanisotropy values.\n"}
+{"abstract": "  Best match graphs (BMGs) are vertex-colored digraphs that naturally arise in\nmathematical phylogenetics to formalize the notion of evolutionary closest\ngenes w.r.t. an a priori unknown phylogenetic tree. BMGs are explained by\nunique least resolved trees. We prove that the property of a rooted,\nleaf-colored tree to be least resolved for some BMG is preserved by the\ncontraction of inner edges. For the special case of two-colored BMGs, this\nleads to a characterization of the least resolved trees (LRTs) of\nbinary-explainable trees and a simple, polynomial-time algorithm for the\nminimum cardinality completion of the arc set of a BMG to reach a BMG that can\nbe explained by a binary tree.\n"}
+{"abstract": "  In this paper, we propose a novel computational implicit method, which we\ncall Isogeometric Residual Minimization (iGRM) with direction splitting. The\nmethod mixes the benefits resulting from isogeometric analysis, implicit\ndynamics, residual minimization, and alternating direction solver. We utilize\ntensor product B-spline basis functions in space, implicit second order time\nintegration schemes, residual minimization in every time step, and we exploit\nKronecker product structure of the matrix to employ linear computational cost\nalternating direction solver. We implement an implicit time integration scheme\nand apply, for each space-direction, a stabilized mixed method based on\nresidual minimization. We show that the resulting system of linear equations\nhas a Kronecker product structure, which results in a linear computational cost\nof the direct solver, even using implicit time integration schemes together\nwith the stabilized mixed formulation. We test our method on three\nadvection-diffusion computational examples, including model ``membrane''\nproblem, the circular wind problem, and the simulations modeling pollution\npropagating from a chimney.\n"}
+{"abstract": "  In this paper Gabor system of certain type based on the unitary dual of the\nHeisenberg group $\\mathbb{H}^n$ is introduced and a sufficient condition is\nobtained for the Gabor system to be a Bessel sequence for\n$L^2(\\mathbb{R}^*,\\mathcal{B}_2;d\\kappa)$ using the $Schr\\\"{o}dinger$\nrepresentation of $\\mathbb{H}^n$, where $\\mathcal{B}_2$ denotes the class of\nHilbert-Schmidt operators on $L^2(\\mathbb{R}^n)$ and $d\\kappa$ denotes the Haar\nmeasure on $\\mathbb{R}^*$. Further a necessary and sufficient condition is\nprovided for the Gabor system to be an orthonormal system, a Parseval frame\nsequence, a frame sequence and a Riesz sequence.\n"}
+{"abstract": "  We present a high-dimensional Winfree model in this paper. The Winfree model\nis the first mathematical model for synchronization on the circle. We\ngeneralize this model to the high-dimensional sphere and we call it \"the\nWinfree sphere model.\" We restricted the support of the influence function in\nthe neighborhood of the attraction point with a small diameter to mimic the\ninfluence function as the Dirac-delta distribution. Restricting the support of\nthe influence function allows several new conditions of the complete\nphase-locking states for the identical Winfree sphere model compare to previous\nresults. We also provide the exponential $\\ell^1$-stability and the existence\nof the equilibrium solution to obtain the complete oscillator death state of\nthe Winfree sphere model.\n"}
+{"abstract": "  A numerical method to design nonlinear double- and multi-bend achromat (DBA\nand MBA) lattices with approximate invariants of motion is investigated. The\nsearch for such nonlinear lattices is motivated by Fermilab's Integrable Optics\nTest Accelerator (IOTA), whose design is based on an integrable Hamiltonian\nsystem with two invariants of motion. While it may not be possible to design an\nachromatic lattice for a dedicated synchrotron light source storage ring with\none or more exact invariants of motion, it is possible to tune the sextupoles\nand octupoles in existing DBA and MBA lattices to produce approximate\ninvariants. In our procedure, the lattice is tuned while minimizing the\nturn-by-turn fluctuations of the Courant-Snyder actions $J_x$ and $J_y$ at\nseveral distinct amplitudes, while simultaneously minimizing diffusion of the\non-energy betatron tunes. The resulting lattices share some important features\nwith integrable ones, such as a large dynamic aperture, trajectories confined\nto invariant tori, robustness to resonances and errors, and a large\namplitude-dependent tune-spread. Compared to the nominal NSLS-II lattice, the\nsingle- and multi-bunch instability thresholds are increased and the\nbunch-by-bunch feedback gain can be reduced.\n"}
+{"abstract": "  In this work we analyze the quantum correlations in a spin-1/2 Ising-XXZ\ndiamond chain with one plaquette distorted impurity. We have shown that the\nintroduction of impurity into the chain can significantly increase entanglement\nas well as quantum correlations compared to the original model, without\nimpurity. Due to the great flexibility in the choice of impurity parameters,\nthe model presented is very general and this fact can be very useful for future\nexperimental measurements. In addition to entanglement and quantum coherence,\nwe studied quantum teleportation through a quantum channel composed by a\ncoupled of Heisenberg dimers with distorted impurity in an Ising-XXZ diamond\nchain, as well as fidelity in teleportation. Our analysis shows that the\nappropriate choice of parameters can greatly increase all the measures\nanalyzed. For comparison purposes, we present all our results together with the\nresults of the measurements made for the original model, without impurity,\nstudied in previous works.\n"}
+{"abstract": "  We present the design of ATJ graphite rods developed for ablation experiments\nunder high heat flux (up to 50 MW/m2) in the lower divertor of the DIII-D\ntokamak [1], a magnetic plasma confinement device. This work is motivated by\nthe need to test ablation models relevant to carbon-based thermal shields used\nin high-speed spacecraft atmospheric entries, where the heat fluxes encountered\ncan be comparable to those achieved in the DIII-D divertor plasma. Several\ndifferent designs for the flow-facing side of the rod are analyzed, including\n\"sharp nose,\" \"blunt,\" and \"concave\". The last shape is studied for its\npotential to lower heat fluxes at the rod surface by increased radiation from\ntrapped neutrals and reduced parallel plasma pressure. We also analyze the\npossibility of applying a thin (approximately 30 microns) layer of silicon\ncarbide (SiC) to the exposed part of several carbon ablation rods to benchmark\nits erosion calculations and lifetime predictions. Such calculations are of\ninterest as SiC represents a promising material for both thermal protection\nsystems (TPS) and a fusion plasma-facing material (PFM). Preliminary results\nfrom the DIII-D rod ablation experiments are also discussed.\n"}
+{"abstract": "  The discovery of topological order has revolutionized the understanding of\nquantum matter in modern physics and provided the theoretical foundation for\nmany quantum error correcting codes. Realizing topologically ordered states has\nproven to be extremely challenging in both condensed matter and synthetic\nquantum systems. Here, we prepare the ground state of the toric code\nHamiltonian using an efficient quantum circuit on a superconducting quantum\nprocessor. We measure a topological entanglement entropy near the expected\nvalue of $\\ln2$, and simulate anyon interferometry to extract the braiding\nstatistics of the emergent excitations. Furthermore, we investigate key aspects\nof the surface code, including logical state injection and the decay of the\nnon-local order parameter. Our results demonstrate the potential for quantum\nprocessors to provide key insights into topological quantum matter and quantum\nerror correction.\n"}
+{"abstract": "  We consider the 2D delta-Bose gas by a smooth mollification of the delta\npotential, where the coupling constant is in the critical window. The main\nresult proves that for two particles, the approximate semigroups on $\\mathscr\nB_b(\\Bbb R^2)$ for the Schr\\\"odinger operator with singular interaction at the\norigin converge pointwise in the initial condition. This convergence extends\nearlier functional analytic results for the convergence in the $L_2$-norm\nresolvent sense. The central methods introduced here apply the excursion theory\nof the 2D Bessel process and the ergodicity of the winding number of planar\nBrownian motion. The limiting semigroup thus shows both the Kallianpur--Robbins\nlaw for additive functionals of planar Brownian motion and Kasahara's\nsecond-order law for the fluctuations. As an application, the mode of\nconvergence is extended to the $N$-particle delta-Bose gas for all $N\\geq 3$.\n"}
+{"abstract": "  This paper presents a series of experiments in collective social robotics,\nspanning more than 10 years, with the long-term aim of building embodied models\nof (aspects) of cultural evolution. Initial experiments demonstrated the\nemergence of behavioural traditions in a group of social robots programmed to\nimitate each other's behaviours (we call these Copybots). These experiments\nshow that the noisy (i.e. less than perfect fidelity) imitation that comes for\nfree with real physical robots gives rise naturally to variation in social\nlearning. More recent experimental work extends the robots' cognitive\ncapabilities with simulation-based internal models, equipping them with a\nsimple artificial theory of mind. With this extended capability we explore, in\nour current work, social learning not via imitation but robot-robot\nstorytelling, in an effort to model this very human mode of cultural\ntransmission. In this paper we give an account of the methods and inspiration\nfor these experiments, the experiments and their results, and an outline of\npossible directions for this programme of research. It is our hope that this\npaper stimulates not only discussion but suggestions for hypotheses to test\nwith the Storybots.\n"}
+{"abstract": "  Multiple reflections between transducer and imaged object can naturally occur\nin ultrasound imaging and other acoustic sensing applications such as sonar.\nThe repeated interaction of the emitted wavefront with the imaged object is\ntraditionally regarded an undesired reverberation artifact, often\nmisinterpreted as fictitious acoustic boundaries. Here we introduce high order\nreflection pulse-echo (HOPE)-ultrasound, a novel method that leverages high\norder reflections to improve on several aspects of conventional ultrasound\nimaging. HOPE is experimentally demonstrated to resolve sub-micrometer features\nby breaking through the Nyquist resolution limit. The major contrast\nenhancement of the high reflection orders allowed to reveal defects within\nmaterials invisible to conventional scanning acoustic microscopy. The technique\nis further shown to improve accuracy of frequency-dependent ultrasound\nattenuation measurements from biological tissues. HOPE ultrasound requires no\nadditional hardware and is easy to implement, underscoring its potential to\nboost imaging performance in a broad range of biomedical imaging,\nnondestructive testing, and other acoustic sensing applications.\n"}
+{"abstract": "  Discrete event systems (DES) have been established and deeply developed in\nthe framework of probabilistic and fuzzy computing models due to the necessity\nof practical applications in fuzzy and probabilistic systems. With the\ndevelopment of quantum computing and quantum control, a natural problem is to\nsimulate DES by means of quantum computing models and to establish {\\it quantum\nDES} (QDES). The motivation is twofold: on the one hand, QDES have potential\napplications when DES are simulated and processed by quantum computers, where\nquantum systems are employed to simulate the evolution of states driven by\ndiscrete events, and on the other hand, QDES may have essential advantages over\nDES concerning state complexity for imitating some practical problems. The goal\nof this paper is to establish a basic framework of QDES by using {\\it quantum\nfinite automata} (QFA) as the modelling formalisms, and the supervisory control\ntheorems of QDES are established and proved. Then we present a polynomial-time\nalgorithm to decide whether or not the controllability condition holds. In\nparticular, we construct a number of new examples of QFA to illustrate the\nsupervisory control of QDES and to verify the essential advantages of QDES over\nDES in state complexity.\n"}
+{"abstract": "  The search for neural architecture is producing many of the most exciting\nresults in artificial intelligence. It has increasingly become apparent that\ntask-specific neural architecture plays a crucial role for effectively solving\nproblems. This paper presents a simple method for learning neural architecture\nthrough random mutation. This method demonstrates 1) neural architecture may be\nlearned during the agent's lifetime, 2) neural architecture may be constructed\nover a single lifetime without any initial connections or neurons, and 3)\narchitectural modifications enable rapid adaptation to dynamic and novel task\nscenarios. Starting without any neurons or connections, this method constructs\na neural architecture capable of high-performance on several tasks. The\nlifelong learning capabilities of this method are demonstrated in an\nenvironment without episodic resets, even learning with constantly changing\nmorphology, limb disablement, and changing task goals all without losing\nlocomotion capabilities.\n"}
+{"abstract": "  Spatially-coupled (SC) codes, known for their threshold saturation phenomenon\nand low-latency windowed decoding algorithms, are ideal for streaming\napplications. They also find application in various data storage systems\nbecause of their excellent performance. SC codes are constructed by\npartitioning an underlying block code, followed by rearranging and\nconcatenating the partitioned components in a \"convolutional\" manner. The\nnumber of partitioned components determines the \"memory\" of SC codes. While\nadopting higher memories results in improved SC code performance, obtaining\noptimal SC codes with high memory is known to be hard. In this paper, we\ninvestigate the relation between the performance of SC codes and the density\ndistribution of partitioning matrices. We propose a probabilistic framework\nthat obtains (locally) optimal density distributions via gradient descent.\nStarting from random partitioning matrices abiding by the obtained\ndistribution, we perform low complexity optimization algorithms over the cycle\nproperties to construct high memory, high performance quasi-cyclic SC codes.\nSimulation results show that codes obtained through our proposed method notably\noutperform state-of-the-art SC codes with the same constraint length and codes\nwith uniform partitioning.\n"}
+{"abstract": "  We report three new FRBs discovered by the Five-hundred-meter Aperture\nSpherical radio Telescope (FAST), namely FRB 181017.J0036+11, FRB 181118 and\nFRB 181130, through the Commensal Radio Astronomy FAST Survey (CRAFTS).\nTogether with FRB 181123 that was reported earlier, all four FAST-discovered\nFRBs share the same characteristics of low fluence ($\\leq$0.2 Jy ms) and high\ndispersion measure (DM, $>1000$ \\dmu), consistent with the anti-correlation\nbetween DM and fluence of the entire FRB population. FRB 181118 and FRB 181130\nexhibit band-limited features. FRB 181130 is prominently scattered\n($\\tau_s\\simeq8$ ms) at 1.25 GHz. FRB 181017.J0036+11 has full-bandwidth\nemission with a fluence of 0.042 Jy ms, which is one of the faintest FRB\nsources detected so far. CRAFTS starts to built a new sample of FRBs that fills\nthe region for more distant and fainter FRBs in the fluence-$\\rm DM_E$ diagram,\npreviously out of reach of other surveys. The implied all sky event rate of\nFRBs is $1.24^{+1.94}_{-0.90} \\times 10^5$ sky$^{-1}$ day$^{-1}$ at the $95\\%$\nconfidence interval above 0.0146 Jy ms. We also demonstrate here that the\nprobability density function of CRAFTS FRB detections is sensitive to the\nassumed intrinsic FRB luminosity function and cosmological evolution, which may\nbe further constrained with more discoveries.\n"}
+{"abstract": "  Inspired by biological evolution, we explain the rationality of Vision\nTransformer by analogy with the proven practical Evolutionary Algorithm (EA)\nand derive that both of them have consistent mathematical representation.\nAnalogous to the dynamic local population in EA, we improve the existing\ntransformer structure and propose a more efficient EAT model, and design\ntask-related heads to deal with different tasks more flexibly. Moreover, we\nintroduce the spatial-filling curve into the current vision transformer to\nsequence image data into a uniform sequential format. Thus we can design a\nunified EAT framework to address multi-modal tasks, separating the network\narchitecture from the data format adaptation. Our approach achieves\nstate-of-the-art results on the ImageNet classification task compared with\nrecent vision transformer works while having smaller parameters and greater\nthroughput. We further conduct multi-modal tasks to demonstrate the superiority\nof the unified EAT, e.g., Text-Based Image Retrieval, and our approach improves\nthe rank-1 by +3.7 points over the baseline on the CSS dataset.\n"}
+{"abstract": "  SARS-CoV-2, also known as COVID-19 or Coronavirus, is a viral contagious\ndisease that is infected by a novel coronavirus, and has been rapidly spreading\nacross the globe. It is very important to test and isolate people to reduce\nspread, and from here comes the need to do this quickly and efficiently.\nAccording to some studies, Chest-CT outperforms RT-PCR lab testing, which is\nthe current standard, when diagnosing COVID-19 patients. Due to this, computer\nvision researchers have developed various deep learning systems that can\npredict COVID-19 using a Chest-CT scan correctly to a certain degree. The\naccuracy of these systems is limited since deep learning neural networks such\nas CNNs (Convolutional Neural Networks) need a significantly large quantity of\ndata for training in order to produce good quality results. Since the disease\nis relatively recent and more focus has been on CXR (Chest XRay) images, the\navailable chest CT Scan image dataset is much less. We propose a method, by\nutilizing GANs, to generate synthetic chest CT images of both positive and\nnegative COVID-19 patients. Using a pre-built predictive model, we concluded\nthat around 40% of the generated images are correctly predicted as COVID-19\npositive. The dataset thus generated can be used to train a CNN-based\nclassifier which can help determine COVID-19 in a patient with greater\naccuracy.\n"}
+{"abstract": "  In the paper [On superspecial abelian surfaces over finite fields II. J.\nMath. Soc. Japan, 72(1):303--331, 2020], Tse-Chung Yang and the first two\ncurrent authors computed explicitly the number $\\lvert\n\\mathrm{SSp}_2(\\mathbb{F}_q)\\rvert$ of isomorphism classes of superspecial\nabelian surfaces over an arbitrary finite field $\\mathbb{F}_q$ of even degree\nover the prime field $\\mathbb{F}_p$. There it was assumed that certain\ncommutative $\\mathbb{Z}_p$-orders satisfy an \\'etale condition that excludes\nthe primes $p=2, 3, 5$. We treat these remaining primes in the present paper,\nwhere the computations are more involved because of the ramifications. This\ncompletes the calculation of $\\lvert \\mathrm{SSp}_2(\\mathbb{F}_q)\\rvert$ in the\neven degree case. The odd degree case was previous treated by Tse-Chung Yang\nand the first two current authors in [On superspecial abelian surfaces over\nfinite fields. Doc. Math., 21:1607--1643, 2016]. Along the proof of our main\ntheorem, we give the classification of lattices over local quaternion Bass\norders, which is a new input to our previous works.\n"}
+{"abstract": "  Simulated DAG models may exhibit properties that, perhaps inadvertently,\nrender their structure identifiable and unexpectedly affect structure learning\nalgorithms. Here, we show that marginal variance tends to increase along the\ncausal order for generically sampled additive noise models. We introduce\nvarsortability as a measure of the agreement between the order of increasing\nmarginal variance and the causal order. For commonly sampled graphs and model\nparameters, we show that the remarkable performance of some continuous\nstructure learning algorithms can be explained by high varsortability and\nmatched by a simple baseline method. Yet, this performance may not transfer to\nreal-world data where varsortability may be moderate or dependent on the choice\nof measurement scales. On standardized data, the same algorithms fail to\nidentify the ground-truth DAG or its Markov equivalence class. While\nstandardization removes the pattern in marginal variance, we show that data\ngenerating processes that incur high varsortability also leave a distinct\ncovariance pattern that may be exploited even after standardization. Our\nfindings challenge the significance of generic benchmarks with independently\ndrawn parameters. The code is available at\nhttps://github.com/Scriddie/Varsortability.\n"}
+{"abstract": "  The nonlinear dispersive wave equation inside the cylindrical elastic rod is\nderived by applying the Navier-Bernoulli hypothesis and Love's relation in\n\\cite{5}. The elastic rod is assumed to be composed of the Murnaghan's\nmaterials such as Lam$\\acute{e}$'s coefficient, Poisson ratio and constitutive\nconstant which are compressible in nature. In this research paper we apply the\ntwo integral architectures namely extended sine-Gordon method and modified\nexponential function method to study the dispersive wave and solved for the\nsolitons and their classifications. The existence of the number of solutions\nare proved with respect to the linear equation obtained by balancing principle.\nThe related two and three dimensional graphs are simulated and drawn to show\nthe complex structures.\n"}
+{"abstract": "  We study the minor relation for algebra homomorphims in finitely generated\nquasivarieties that admit a logarithmic natural duality. We characterize the\nminor homomorphism posets of finite algebras in terms of disjoint unions of\ndual partition lattices and investigate reconstruction problems for\nhomomorphisms.\n"}
+{"abstract": "  We establish decidability for the infinitely many axiomatic extensions of the\ncommutative Full Lambek logic with weakening FLew (i.e. IMALLW) that have a\ncut-free hypersequent proof calculus (specifically: every analytic structural\nrule extension). Decidability for the corresponding extensions of its\ncontraction counterpart FLec was established recently but their computational\ncomplexity was left unanswered. In the second part of this paper, we introduce\njust enough on length functions for well-quasi-orderings and the fast-growing\ncomplexity classes to obtain complexity upper bounds for both the weakening and\ncontraction extensions. A specific instance of this result yields the first\ncomplexity bound for the prominent fuzzy logic MTL (monoidal t-norm based\nlogic) providing an answer to a long-standing open problem.\n"}
+{"abstract": "  Algorithmic risk assessments hold the promise of greatly advancing accurate\ndecision-making, but in practice, multiple real-world examples have been shown\nto distribute errors disproportionately across demographic groups. In this\npaper, we characterize why error disparities arise in the first place. We show\nthat predictive uncertainty often leads classifiers to systematically\ndisadvantage groups with lower-mean outcomes, assigning them smaller true and\nfalse positive rates than their higher-mean counterparts. This can occur even\nwhen prediction is group-blind. We prove that to avoid these error imbalances,\nindividuals in lower-mean groups must either be over-represented among positive\nclassifications or be assigned more accurate predictions than those in\nhigher-mean groups. We focus on the latter condition as a solution to bridge\nerror rate divides and show that data acquisition for low-mean groups can\nincrease access to opportunity. We call the strategy \"affirmative information\"\nand compare it to traditional affirmative action in the classification task of\nidentifying creditworthy borrowers.\n"}
+{"abstract": "  6G will likely be the first generation of mobile communication that will\nfeature tight integration of localization and sensing with communication\nfunctionalities. Among several worldwide initiatives, the Hexa-X flagship\nproject stands out as it brings together 25 key players from adjacent\nindustries and academia, and has among its explicit goals to research\nfundamentally new radio access technologies and high-resolution localization\nand sensing. Such features will not only enable novel use cases requiring\nextreme localization performance, but also provide a means to support and\nimprove communication functionalities. This paper provides an overview of the\nHexa-X vision alongside the envisioned use cases. To close the required\nperformance gap of these use cases with respect to 5G, several technical\nenablers will be discussed, together with the associated research challenges\nfor the coming years.\n"}
+{"abstract": "  We explore a field theoretical approach to quantum computing and control.\nThis book consists of three parts. The basics of systems theory and field\ntheory are reviewed in Part I. In Part II, a gauge theory is reinterpreted from\na systems theoretical perspective and applied to the formulation of quantum\ngates. Then quantum systems are defined by introducing feedback to the gates.\nIn Part III, quantum gates and systems are reformulated from a quantum field\ntheoretical perspective using S-matrices. We also discuss how gauge fields are\nrelated to feedback.\n"}
+{"abstract": "  The COVID19 pandemic has forced Indian engineering institutions (EIs) to\nbring their previous half shut shades completely down. Fetching new admissions\nto EI campuses during the pandemic has become a now or never situation for EIs.\nDuring crisis situations, institutions have struggled to return to the normal\ntrack. The pandemic has drastically changed students behavior and family\npreferences due to mental stress and the emotional life attached to it.\nConsequently, it becomes a prerequisite, and emergencies need to examine the\nchoice characteristics influencing the selection of EI during the COVID19\npandemic situation.\n  The purpose of this study is to critically examine institutional influence\nand pandemic influence due to COVID19 that affects students choice about an\nengineering institution (EI) and consequently to explore relationships between\ninstitutional and pandemic influence. The findings of this quantitative\nresearch, conducted through a self-reported survey, have revealed that\ninstitutional and pandemic influence have governed EI choice under the COVID19\npandemic. Second, pandemic influence is positively affected by institutional\ninfluence. The study demonstrated that EIs will have to reposition themselves\nto normalize pandemic influence by tuning institutional characteristics that\nregulate situational influence and new enrollments. It can be yardstick for\npolicy makers to attract new enrollments under pandemic situations.\n"}
+{"abstract": "  A search for time-dependent violation of the charge-parity symmetry in $D^0\n\\to K^+ K^-$ and $D^0 \\to \\pi^+ \\pi^-$ decays is performed at the LHCb\nexperiment using proton-proton collision data recorded from 2015 to 2018 at a\ncentre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 6\nfb$^{-1}$. The $D^0$ meson is required to originate from a $D^*(2010)^+ \\to D^0\n\\pi^+$ decay, such that its flavour at production is identified by the charge\nof the accompanying pion. The slope of the time-dependent asymmetry of the\ndecay rates of $D^0$ and $\\bar{D}^0$ mesons into the final states under\nconsideration is measured to be $\\Delta Y_{K^+ K^-} = (-2.3 \\pm 1.5 \\pm 0.3)\n\\times 10^{-4}$, $\\Delta Y_{\\pi^+ \\pi^-} = (-4.0 \\pm 2.8 \\pm 0.4)\\times\n10^{-4}$, where the first uncertainties are statistical and the second are\nsystematic. These results are compatible with the conservation of the\ncharge-parity symmetry at the level of 2 standard deviations and improve the\nprecision by nearly a factor of two.\n"}
+{"abstract": "  Understanding non-Landau Fermi liquids in dimensions higher than one, has\nbeen a subject of great interest. Such phases may serve as parent states for\nother unconventional phases of quantum matter, in a similar manner that\nconventional broken symmetry states can be understood as instabilities of the\nLandau Fermi liquid. In this work, we investigate the emergence of a novel\nnon-Landau Fermi liquid in two dimensions, where the fermions with quadratic\nband-touching dispersion interact with a Bose metal. The bosonic excitations in\nthe Bose metal possess an extended nodal-line spectrum in momentum space, which\narises due to the subsystem symmetry or the restricted motion of bosons. Using\nrenormalization group analysis and direct computations, we show that the\nextended infrared (IR) singularity of the Bose metal leads to a line of\ninteracting fixed points of novel non-Landau Fermi liquids, where the anomalous\ndimension of the fermions varies continuously, akin to the Luttinger liquid in\none dimension. Further, the multi-patch generalization of the model is used to\nexplore other unusual features of the resulting ground state.\n"}
+{"abstract": "  The false data injection (FDI) attack is a crucial form of cyber-physical\nsecurity problems facing cyber-physical power systems. However, there is no\nresearch revealing the problem of FDI attacks facing voltage source converter\nbased high voltage direct current transmission (VSC-HVDC) systems. Firstly, the\ngeneral form of the model of FDI attack strategies is proposed and the essence\nof the problem of FDI attack strategies is further analyzed. Moreover, the\nmodel of FDI attack strategies aiming at disrupting the operation security of\nconverter stations in VSC-HVDC systems is proposed and its solving algorithm is\nthen presented. And finally, the modified IEEE-14 bus system is utilized to\nreveal the problem of FDI attacks facing VSC-HVDC systems, demonstrating that\nattackers are capable of disrupting the operation security of converter\nstations in VSC-HVDC systems by FDI attacks.\n"}
+{"abstract": "  Stalagmites are an extraordinarily powerful resource for the reconstruction\nof climatological palaeoseasonality. Here, we provide a comprehensive review of\ndifferent types of seasonality preserved by stalagmites and methods for\nextracting this information. A new drip classification scheme is introduced,\nwhich facilitates the identification of stalagmites fed by seasonally\nresponsive drips and which highlights the wide variability in drip types\nfeeding stalagmites. This hydrological variability, combined with seasonality\nin Earth atmospheric processes, meteoric precipitation, biological processes\nwithin the soil, and cave atmosphere composition means that every stalagmite\nretains a different and distinct (but correct) record of environmental\nconditions. Replication of a record is extremely useful but should not be\nexpected unless comparing stalagmites affected by the same processes in the\nsame proportion. A short overview of common microanalytical techniques is\npresented, and suggested best practice discussed. In addition to geochemical\nmethods, a new modelling technique for extracting meteoric precipitation and\ntemperature palaeoseasonality from stalagmite d18O data is discussed and tested\nwith both synthetic and real-world datasets. Finally, world maps of\ntemperature, meteoric precipitation amount, and meteoric precipitation oxygen\nisotope ratio seasonality are presented and discussed, with an aim of helping\nto identify regions most sensitive to shifts in seasonality.\n"}
+{"abstract": "  L\\'{e}vy walks are a particular type of continuous-time random walks which\nresults in a super-diffusive spreading of an initially localized packet. The\noriginal one-dimensional model has a simple schematization that is based on\nstarting a new unidirectional motion event either in the positive or in the\nnegative direction. We consider two-dimensional generalization of L\\'{e}vy\nwalks in the form of the so-called XY-model. It describes a particle moving\nwith a constant velocity along one of the four basic directions and randomly\nswitching between them when starting a new motion event. We address the\nballistic regime and derive solutions for the asymptotic density profiles. The\nsolutions have a form of first-order integrals which can be evaluated\nnumerically. For specific values of parameters we derive an exact expression.\nThe analytic results are in perfect agreement with the results of finite-time\nnumerical samplings.\n"}
+{"abstract": "  The role of differential equations in the process of calculating Feynman\nintegrals is reviewed. An example of a diagram is given for which the method of\ndifferential equations was introduced, the properties of the\ninverse-mass-expansion coefficients are shown, and modern methods based on\ndifferential equations are considered.\n"}
+{"abstract": "  This work presents a multilevel variant of Stein variational gradient descent\nto more efficiently sample from target distributions. The key ingredient is a\nsequence of distributions with growing fidelity and costs that converges to the\ntarget distribution of interest. For example, such a sequence of distributions\nis given by a hierarchy of ever finer discretization levels of the forward\nmodel in Bayesian inverse problems. The proposed multilevel Stein variational\ngradient descent moves most of the iterations to lower, cheaper levels with the\naim of requiring only a few iterations on the higher, more expensive levels\nwhen compared to the traditional, single-level Stein variational gradient\ndescent variant that uses the highest-level distribution only. Under certain\nassumptions, in the mean-field limit, the error of the proposed multilevel\nStein method decays by a log factor faster than the error of the single-level\ncounterpart with respect to computational costs. Numerical experiments with\nBayesian inverse problems show speedups of more than one order of magnitude of\nthe proposed multilevel Stein method compared to the single-level variant that\nuses the highest level only.\n"}
+{"abstract": "  This paper provides a theoretical and numerical approach to show existence,\nuniqueness, and the numerical determination of metalenses refracting radiation\nwith energy patterns. The theoretical part uses ideas from optimal transport\nand for the numerical solution we study and implement a damped Newton algorithm\nto solve the semi discrete problem. A detailed analysis is carried out to solve\nthe near field one source refraction problem and extensions to the far field\nare also mentioned.\n"}
+{"abstract": "  The universal scaling law of cortical morphology describes cortical folding\nas the covariance of average grey matter thickness, pial surface area, and\nexposed surface area. It applies for mammalian species, humans, and across\nlobes, however it remains to be shown that local cortical folding obeys the\nsame rules. Here, we develop a method to obtain morphological measures for\nsmall regions across the cortex and correct surface areas by curvature to\naccount for differences in patch size, resulting in a map of local morphology.\nIt enables a near-pointwise analysis of morphological variables and their\nregional changes due to processes such as healthy ageing. We confirm\nempirically that the theorised covariance of morphological measures still holds\nat this level of local partition sizes as predicted, justifying the use of\nindependent variables derived from the scaling law to identify regional\ndifferences in folding, subject-specific abnormalities, and local effects of\nageing.\n"}
+{"abstract": "  For every $\\mathcal{U} \\subset \\mathrm{Diff}^\\infty_{vol}(\\mathbb{T}^2)$\nthere is a measure of finite support contained in $\\mathcal{U}$ which is\nuniformly expanding.\n"}
+{"abstract": "  Motivated by the initial stages of high-energy heavy-ion collisions, we study\nexcitations of far-from-equilibrium 2+1 dimensional gauge theories using\nclassical-statistical lattice simulations. We evolve field perturbations over a\nstrongly overoccupied background undergoing self-similar evolution. While in\n3+1D the excitations are described by hard-thermal loop theory, their structure\nin 2+1D is nontrivial and nonperturbative. These nonperturbative interactions\nlead to broad excitation peaks in spectral and statistical correlation\nfunctions. Their width is comparable to the frequency of soft excitations,\ndemonstrating the absence of soft quasiparticles in these theories. Our results\nalso suggest that excitations at higher momenta are sufficiently long-lived,\nsuch that an effective kinetic theory description for 2+1 dimensional\nGlasma-like systems may exist, but its collision kernel must be\nnonperturbatively determined.\n"}
+{"abstract": "  We propose a framework where generation and transmission capacities are\nplanned concurrently in market environments with a focus on the prosumers. This\npaper is a continuation of Part I and presents numerical results from three\narchetypal case studies. Following the proposed framework, optimal planning\ndecisions are shown in all the cases. Furthermore, in case study I, no-regret\nplanning decisions considering the uncertainties in future electricity market\ndesigns are discussed. In case study II, we look at a situation where the\nsocial resistance of wind energy prevails and the prosumers choose to not\ninvest in wind energy. This preference results in an increased system cost, and\nthus may harm other prosumers. The framework is used as a negotiation simulator\nto deal with this problem. Case study III presents numerical results for a\nmixed bilateral/pool market. The case studies utilize realistic data from the\nDutch power system and the European power system to provide policy-relevant\nresults that aid their decarbonization in various market environments.\n"}
+{"abstract": "  We study the problem of matching a string in a labeled graph. Previous\nresearch has shown that unless the Orthogonal Vectors Hypothesis (OVH) is\nfalse, one cannot solve this problem in strongly sub-quadratic time, nor index\nthe graph in polynomial time to answer queries efficiently (Equi et al. ICALP\n2019, SOFSEM 2021). These conditional lower-bounds cover even deterministic\ngraphs with binary alphabet, but there naturally exist also graph classes that\nare easy to index: E.g. Wheeler graphs (Gagie et al. Theor. Comp. Sci. 2017)\ncover graphs admitting a Burrows-Wheeler transform -based indexing scheme.\nHowever, it is NP-complete to recognize if a graph is a Wheeler graph (Gibney,\nThankachan, ESA 2019).\n  We propose an approach to alleviate the construction bottleneck of Wheeler\ngraphs. Rather than starting from an arbitrary graph, we study graphs induced\nfrom multiple sequence alignments (MSAs). Elastic degenerate strings (Bernadini\net al. SPIRE 2017, ICALP 2019) can be seen as such graphs, and we introduce\nhere their generalization: elastic founder graphs. We first prove that even\nsuch induced graphs are hard to index under OVH. Then we introduce two\nsubclasses, repeat-free and semi-repeat-free graphs, that are easy to index. We\ngive a linear time algorithm to construct a repeat-free non-elastic founder\ngraph from a gapless MSA, and (parameterized) near-linear time algorithms to\nconstruct semi-repeat-free (repeat-free, respectively) elastic founder graphs\nfrom general MSAs. Finally, we show that repeat-free elastic founder graphs\nadmit a reduction to Wheeler graphs in polynomial time.\n"}
+{"abstract": "  State of the art language models return a natural language text continuation\nfrom any piece of input text. This ability to generate coherent text extensions\nimplies significant sophistication, including a knowledge of grammar and\nsemantics. In this paper, we propose a mathematical framework for passing from\nprobability distributions on extensions of given texts, such as the ones\nlearned by today's large language models, to an enriched category containing\nsemantic information. Roughly speaking, we model probability distributions on\ntexts as a category enriched over the unit interval. Objects of this category\nare expressions in language, and hom objects are conditional probabilities that\none expression is an extension of another. This category is syntactical -- it\ndescribes what goes with what. Then, via the Yoneda embedding, we pass to the\nenriched category of unit interval-valued copresheaves on this syntactical\ncategory. This category of enriched copresheaves is semantic -- it is where we\nfind meaning, logical operations such as entailment, and the building blocks\nfor more elaborate semantic concepts.\n"}
+{"abstract": "  Among several subpopulations of a given species with a normally-distributed\ntrait, such as height, blood pressure, or various test scores in humans, one\nand only one of the subpopulations will always strongly dominate all the others\nin the right tail, that is, in the high-end values of that trait. Examples are\ngiven to show that this is not true in general for other common classes of\ncontinuous centrally-symmetric unimodal distributions such as Laplace, nor even\nfor other bell-shaped distributions such as Cauchy.\n"}
+{"abstract": "  Can artificial agents benefit from human conventions? Human societies manage\nto successfully self-organize and resolve the tragedy of the commons in\ncommon-pool resources, in spite of the bleak prediction of non-cooperative game\ntheory. On top of that, real-world problems are inherently large-scale and of\nlow observability. One key concept that facilitates human coordination in such\nsettings is the use of conventions. Inspired by human behavior, we investigate\nthe learning dynamics and emergence of temporal conventions, focusing on\ncommon-pool resources. Extra emphasis was given in designing a realistic\nevaluation setting: (a) environment dynamics are modeled on real-world\nfisheries, (b) we assume decentralized learning, where agents can observe only\ntheir own history, and (c) we run large-scale simulations (up to 64 agents).\n  Uncoupled policies and low observability make cooperation hard to achieve; as\nthe number of agents grow, the probability of taking a correct gradient\ndirection decreases exponentially. By introducing an arbitrary common signal\n(e.g., date, time, or any periodic set of numbers) as a means to couple the\nlearning process, we show that temporal conventions can emerge and agents reach\nsustainable harvesting strategies. The introduction of the signal consistently\nimproves the social welfare (by 258% on average, up to 3306%), the range of\nenvironmental parameters where sustainability can be achieved (by 46% on\naverage, up to 300%), and the convergence speed in low abundance settings (by\n13% on average, up to 53%).\n"}
+{"abstract": "  Scene flow in 3D point clouds plays an important role in understanding\ndynamic environments. Although significant advances have been made by deep\nneural networks, the performance is far from satisfactory as only per-point\ntranslational motion is considered, neglecting the constraints of the rigid\nmotion in local regions. To address the issue, we propose to introduce the\nmotion consistency to force the smoothness among neighboring points. In\naddition, constraints on the rigidity of the local transformation are also\nadded by sharing unique rigid motion parameters for all points within each\nlocal region. To this end, a high-order CRFs based relation module (Con-HCRFs)\nis deployed to explore both point-wise smoothness and region-wise rigidity. To\nempower the CRFs to have a discriminative unary term, we also introduce a\nposition-aware flow estimation module to be incorporated into the Con-HCRFs.\nComprehensive experiments on FlyingThings3D and KITTI show that our proposed\nframework (HCRF-Flow) achieves state-of-the-art performance and significantly\noutperforms previous approaches substantially.\n"}
+{"abstract": "  Intelligence necessitates memory. Without memory, humans fail to perform\nvarious nontrivial tasks such as reading novels, playing games or solving\nmaths. As the ultimate goal of machine learning is to derive intelligent\nsystems that learn and act automatically just like human, memory construction\nfor machine is inevitable. Artificial neural networks model neurons and\nsynapses in the brain by interconnecting computational units via weights, which\nis a typical class of machine learning algorithms that resembles memory\nstructure. Their descendants with more complicated modeling techniques (a.k.a\ndeep learning) have been successfully applied to many practical problems and\ndemonstrated the importance of memory in the learning process of machinery\nsystems. Recent progresses on modeling memory in deep learning have revolved\naround external memory constructions, which are highly inspired by\ncomputational Turing models and biological neuronal systems. Attention\nmechanisms are derived to support acquisition and retention operations on the\nexternal memory. Despite the lack of theoretical foundations, these approaches\nhave shown promises to help machinery systems reach a higher level of\nintelligence. The aim of this thesis is to advance the understanding on memory\nand attention in deep learning. Its contributions include: (i) presenting a\ncollection of taxonomies for memory, (ii) constructing new memory-augmented\nneural networks (MANNs) that support multiple control and memory units, (iii)\nintroducing variability via memory in sequential generative models, (iv)\nsearching for optimal writing operations to maximise the memorisation capacity\nin slot-based memory networks, and (v) simulating the Universal Turing Machine\nvia Neural Stored-program Memory-a new kind of external memory for neural\nnetworks.\n"}
+{"abstract": "  Current blockchain approaches for business contracts are based on smart\ncontracts, namely, software programs placed on a blockchain that are\nautomatically executed to realize a contract. However, smart contracts lack\nflexibility and interfere with the autonomy of the parties concerned.\n  We propose Hercule, an approach for declaratively specifying blockchain\napplications in a manner that reflects business contracts. Hercule represents a\ncontract via regulatory norms that capture the involved parties' expectations\nof one another. It computes the states of norms (hence, of contracts) from\nevents in the blockchain. Hercule's novelty and significance lie in that it\noperationalizes declarative contracts over semistructured databases, the\nunderlying representation for practical blockchain such as Hyperledger Fabric\nand Ethereum. Specifically, it exploits the map-reduce capabilities of such\nstores to compute norm states.\n  We demonstrate that our implementation over Hyperledger Fabric can process\nthousands of events per second, sufficient for many applications.\n"}
+{"abstract": "  We study the problem of whether the curvature-dimension condition with\nnegative values of the generalized dimension parameter is stable under a\nsuitable notion of convergence. To this purpose, first of all we introduce an\nappropriate setting to introduce the CD(K, N)-condition for $N < 0$, allowing\nmetric measure structures in which the reference measure is quasi-Radon. Then\nin this class of spaces we introduce the distance $d_{\\mathsf{iKRW}}$, which\nextends the already existing notions of distance between metric measure spaces.\nFinally, we prove that if a sequence of metric measure spaces satisfying the\nCD(K, N)-condition with $N < 0$ is converging with respect to the distance\n$d_{\\mathsf{iKRW}}$ to some metric measure space, then this limit structure is\nstill a CD(K, N) space.\n"}
+{"abstract": "  In this paper, we establish the well-posedness for the third grade fluid\nequation perturbed by a multiplicative white noise. This equation describes the\nmotion of a non-Newtonian fluid of differential type with relevant viscoelastic\nproperties. We are faced with a strongly nonlinear stochastic partial\ndifferential equation supplemented with a Navier slip boundary condition.\nTaking the initial condition in the Sobolev space H^2, we show the existence\nand the uniqueness of the strong (in the probability sense) solution in a two\ndimensional and non axisymmetric bounded domain.\n"}
+{"abstract": "  In this paper, we apply the long short-term memory (LSTM), an advanced\nrecurrent neural network based machine learning (ML) technique, to the problem\nof transmitter selection (TS) for secrecy in an underlay small-cell cognitive\nradio network with unreliable backhaul connections. The cognitive communication\nscenario under consideration has a secondary small-cell network that shares the\nsame spectrum of the primary network with an agreement to always maintain a\ndesired outage probability constraint in the primary network. Due to the\ninterference from the secondary transmitter common to all primary\ntransmissions, the secrecy rates for the different transmitters are correlated.\nLSTM exploits this correlation and matches the performance of the conventional\ntechnique when the number of transmitters is small. As the number grows, the\nperformance degrades in the same manner as other ML techniques such as support\nvector machine, $k$-nearest neighbors, naive Bayes, and deep neural network.\nHowever, LSTM still significantly outperforms these techniques in\nmisclassification ratio and secrecy outage probability. It also reduces the\nfeedback overhead against conventional TS.\n"}
+{"abstract": "  In this note, we present constructive bijections from Dyck and Motzkin\nmeanders with catastrophes to Dyck paths avoiding some patterns. As a\nbyproduct, we deduce correspondences from Dyck and Motzkin excursions to\nrestricted Dyck paths.\n"}
+{"abstract": "  A number of stability criteria exist for dark energy theories, associated\nwith requiring the absence of ghost, gradient and tachyonic instabilities.\nTachyonic instabilities are the least well explored of these in the dark energy\ncontext and we here discuss and derive criteria for their presence and size in\ndetail. Our findings suggest that, while the absence of ghost and gradient\ninstabilities is indeed essential for physically viable models and so priors\nassociated with the absence of such instabilities significantly increase the\nefficiency of parameter estimations without introducing unphysical biases, this\nis not the case for tachyonic instabilities. Even strong such instabilities can\nbe present without spoiling the cosmological validity of the underlying models.\nTherefore, we caution against using exclusion priors based on requiring the\nabsence of (strong) tachyonic instabilities in deriving cosmological parameter\nconstraints. We illustrate this by explicitly computing such constraints within\nthe context of Horndeski theories, while quantifying the size and effect of\nrelated tachyonic instabilities.\n"}
+{"abstract": "  Monte Carlo (MC) generators are crucial for analyzing data at hadron\ncolliders, however, even a small mismatch between the MC simulations and the\nexperimental data can undermine the interpretation of LHC searches in the SM\nand beyond. The jet multiplicity distributions used in four-top searches, one\nof the ultimate rare processes in the SM currently being explored at the LHC,\nmakes $pp\\to t\\bar tt \\bar t$ an ideal testing ground to explore for new ways\nto reduce the impact of MC mismodelling on such observables. In this Letter, we\npropose a novel weakly-supervised method capable of disentangling the $t\\bar t\nt\\bar t$ signal from the dominant background, while partially correcting for\npossible MC imperfections. A mixture of multinomial distributions is used to\nmodel the light-jet and $b$-jet multiplicities under the assumption that these\nare conditionally independent given a categorical latent variable. The signal\nand background distributions generated from a deliberately untuned MC simulator\nare used as model priors. The posterior distributions, as well as the signal\nfraction, are then learned from the data using Bayesian inference. We\ndemonstrate that our method can mitigate the effects of large MC mismodellings\nusing a realistic $t\\bar tt\\bar t$ search in the same-sign dilepton channel,\nleading to corrected posterior distributions that better approximate the\nunderlying truth-level spectra.\n"}
+{"abstract": "  In this work, retrograde mean motion resonances (MMRs) are investigated by\nmeans of analytical and numerical approaches. Initially, we define a new\nresonant angle to describe the retrograde MMRs and then perform a series of\ncanonical transformations to formulate the resonant model, in which the phase\nportrait, resonant centre and resonant width can be analytically determined. To\nvalidate the analytical developments, the non-perturbative analysis is made by\ntaking advantage of Poincar\\'e surfaces of section. Some modifications are\nintroduced in the production of Poincar\\'e sections and, in particular, it\nbecomes possible to make direct comparisons between the analytical and\nnumerical results. It is found that there exists an excellent correspondence\nbetween the phase portraits and the associated Poincar\\'e sections, and the\nanalytical results agree well with the numerical results in terms of the\nresonant width and the location of resonant centre. Finally, the numerical\napproach is utilized to determine the resonant widths and resonant centres over\nthe full range of eccentricity. In particular, seven known examples of\nretrograde asteroids including 2015 BZ509, 2008 SO218, 1999 LE31, 2000 DG8,\n2014 AT28, 2016 LS and 2016 JK24 are found inside the libration zones of\nretrograde MMRs with Jupiter. The results obtained in this work may be helpful\nfor understanding the dynamical evolution for asteroids inside retrograde MMRs.\n"}
+{"abstract": "  We formulate the path integral for Jackiw-Teitelboim gravity in the second\norder formalism working directly with the metric and the dilaton. We consider\nthe theory both in Anti-de Sitter(AdS) and de Sitter space(dS) and analyze the\npath integral for the disk topology and the \"double trumpet\" topology with two\nboundaries. We also consider its behavior in the presence of conformal matter.\nIn the dS case the path integral evaluates the wavefunction of the universe\nwhich arises in the no-boundary proposal. In the asymptotic AdS or dS limit\nwithout matter we get agreement with the first order formalism. More generally,\naway from this limit, the path integral is more complicated due to the presence\nof modes from the gravity-dilaton sector and also matter sector with short\nwavelengths along the boundary that are smaller than the AdS or dS scales. In\nthe double trumpet case, for both AdS and dS, we find that bosonic matter gives\nrise to a diverging contribution in the moduli space integral rendering the\npath integral ill-defined. The divergence occurs when the size of the wormhole\nneck vanishes and is related to the Casimir effect. For fermions this\ndivergence can be avoided by imposing suitable boundary conditions. In this\ncase, in dS space the resulting path integral gives a finite contribution for\ntwo disconnected universes to be produced by quantum tunneling.\n"}
+{"abstract": "  While most moving groups are young and nearby, a small number have been\nidentified in the Galactic halo. Understanding the origin and evolution of\nthese groups is an important piece of reconstructing the formation history of\nthe halo. Here we report on our analysis of three putative halo moving groups:\nG03-37, G18-39, and G21-22. Based on Gaia EDR3 data, the stars associated with\neach group show some scatter in velocity (e.g., Toomre diagram) and integrals\nof motion (energy, angular momentum) spaces, counter to expectations of\nmoving-group stars. We choose the best candidate of the three groups, G21-22,\nfor follow-up chemical analysis based on high-resolution spectroscopy of six\npresumptive members. Using a new Python code that uses a Bayesian method to\nself-consistently propagate uncertainties from stellar atmosphere solutions in\ncalculating individual abundances and spectral synthesis, we derive the\nabundances of $\\alpha$- (Mg, Si, Ca, Ti), Fe-peak (Cr, Sc, Mn, Fe, Ni), odd-$Z$\n(Na, Al, V), and neutron-capture (Ba, Eu) elements for each star. We find that\nthe G21-22 stars are not chemically homogeneous. Based on the kinematic\nanalysis for all three groups and the chemical analysis for G21-22, we conclude\nthe three are not genuine moving groups. The case for G21-22 demonstrates the\nbenefit of combining kinematic and chemical information in identifying conatal\npopulations when either alone may be insufficient. Comparing the integrals of\nmotion and velocities of the six G21-22 stars with those of known structures in\nthe halo, we tentatively associate them with the Gaia-Enceladus accretion\nevent.\n"}
+{"abstract": "  The goal of this short paper is to find Lagrangian for bosonic string with\ndeformed dispersion relation proposed by J.Magueijo and L. Smolin in 2004.\n"}
+{"abstract": "  We perform a detailed calculation of the various contributions to the\nfluctuation conductivity of a granular metal close to its superconducting\ntransition. We find three distinct regions of power law behavior in reduced\ntemperature, $\\eta=(T-T_c)/T_c$, with crossovers at $\\Gamma/T_c$ and\n$E_{Th}/T_c$, where $\\Gamma$ is the electron tunneling rate, and $E_{Th}$ is\nthe Thouless energy of a grain. The calculation includes both intergrain and\nintragrain degrees of freedom. This complete theory of the fluctuation region\nin granular superconductors is then compared to experimental results from\nboron-doped nanocrystalline diamond, using the assumption of a constant phase\nbreaking rate, $\\tau_{\\phi}^{-1}$. We find a semi-quantitative agreement\nbetween the theoretical and experimental results only in the case of large\nphase breaking. We argue that there may be a novel phase breaking mechanism in\ngranular metals worthy of further experimental and theoretical investigation.\n"}
+{"abstract": "  In 2017, Google announced Kotlin as an official Android programming language,\nand more recently, as the preferred programming language to build applications.\nThese facts motivated developers to migrate their applications, which is\nchallenging because each migrated piece of code must be tested after the\nmigration to ensure it preserves the expected behavior. Due to the\ninteroperability between Java and Kotlin, most developers decided to migrate\ntheir applications gradually. Thus, developers have to decide which file(s) to\nmigrate first on each migration step. However, there are no tools available to\nhelp developers make these choices. This paper presents an approach to support\na gradual migration of Android applications that given a version of an\napplication written in Java and eventually, in Kotlin, it suggests the most\nconvenient files to migrate. To this end, we built a large-scale corpus of\nopen-source projects that migrated Java files to Kotlin. Then, we trained a\nlearning to rank model using the information extracted from these projects. To\nvalidate our model, we verify whether these recommendations made by them\ncorrespond to real migrations. The results showed our approach modestly\noutperforms random approaches. Since most Android applications are written in\nJava, we conclude that our approach may significantly impact Android\napplications' development. Therefore, we consider this result is the first step\ninto long-term research towards a model capable of predicting precisely\nfile-level migration, establishing the initial baseline on file migrations.\n"}
+{"abstract": "  By successfully solving the problem of forecasting, the processes in the work\nof various companies are optimized and savings are achieved. In this process,\nthe analysis of time series data is of particular importance. Since the\ncreation of Facebook's Prophet, and Amazon's DeepAR+ and CNN-QR forecasting\nmodels, algorithms have attracted a great deal of attention. The paper presents\nthe application and comparison of the above algorithms for sales forecasting in\ndistribution companies. A detailed comparison of the performance of algorithms\nover real data with different lengths of sales history was made. The results\nshow that Prophet gives better results for items with a longer history and\nfrequent sales, while Amazon's algorithms show superiority for items without a\nlong history and items that are rarely sold.\n"}
+{"abstract": "  Corrupted sensing concerns the problem of recovering a high-dimensional\nstructured signal from a collection of measurements that are contaminated by\nunknown structured corruption and unstructured noise. In the case of linear\nmeasurements, the recovery performance of different convex programming\nprocedures (e.g., generalized Lasso and its variants) is well established in\nthe literature. However, in practical applications of digital signal\nprocessing, the quantization process is inevitable, which often leads to\nnon-linear measurements. This paper is devoted to studying corrupted sensing\nunder quantized measurements. Specifically, we demonstrate that, with the aid\nof uniform dithering, both constrained and unconstrained Lassos are able to\nrecover signal and corruption from the quantized samples when the measurement\nmatrix is sub-Gaussian. Our theoretical results reveal the role of quantization\nresolution in the recovery performance of Lassos. Numerical experiments are\nprovided to confirm our theoretical results.\n"}
+{"abstract": "  One of the well-known measurements of vulnerability in graph theory is\ndomination. There are many kinds of dominating and relative types of sets in\ngraphs. However, we are going to focus on Roman domination, which is a type of\ndomination that came up with an article by Ian Stewart. The Roman domination\nnumbers of the comet, double comet, and comb graphs are given in this paper.\n"}
+{"abstract": "  We revisit the computation of string worldsheet correlators on Euclidean\n$\\text{AdS}_3$ with pure NS-NS background. We compute correlation functions\nwith insertions of spectrally flowed operators. We explicitly solve all the\nknown constraints of the model and for the first time conjecture a closed\nformula for three-point functions with arbitrary amount of spectral flow. We\nexplain the relation of our results with previous computations in the\nliterature and derive the fusion rules of the model. This paper is the first in\na series with several installments.\n"}
+{"abstract": "  Fano Bott manifolds bijectively correspond to signed rooted forests with some\nequivalence relation. Using this bijective correspondence, we enumerate the\nisomorphism classes of Fano Bott manifolds and the diffeomorphism classes of\nindecomposable Fano Bott manifolds. We also observe that the signed rooted\nforests with the equivalence relation bijectively correspond to rooted\ntriangular cacti.\n"}
+{"abstract": "  Recent advancements in deep learning-based modeling of molecules promise to\naccelerate in silico drug discovery. A plethora of generative models is\navailable, building molecules either atom-by-atom and bond-by-bond or\nfragment-by-fragment. However, many drug discovery projects require a fixed\nscaffold to be present in the generated molecule, and incorporating that\nconstraint has only recently been explored. Here, we propose MoLeR, a\ngraph-based model that naturally supports scaffolds as initial seed of the\ngenerative procedure, which is possible because it is not conditioned on the\ngeneration history. Our experiments show that MoLeR performs comparably to\nstate-of-the-art methods on unconstrained molecular optimization tasks, and\noutperforms them on scaffold-based tasks, while being an order of magnitude\nfaster to train and sample from than existing approaches. Furthermore, we show\nthe influence of a number of seemingly minor design choices on the overall\nperformance.\n"}
+{"abstract": "  Avalanche photodiodes fabricated from AlInAsSb grown as a digital alloy\nexhibit low excess noise. In this paper, we investigate the band\nstructure-related mechanisms that influence impact ionization. Band-structures\ncalculated using an empirical tight-binding method and Monte Carlo simulations\nreveal that the mini-gaps in the conduction band do not inhibit electron impact\nionization. Good agreement between the full band Monte Carlo simulations and\nmeasured noise characteristics is demonstrated.\n"}
+{"abstract": "  In this work, we consider symmetric positive definite pencils depending on\ntwo parameters. That is, we are concerned with the generalized eigenvalue\nproblem $A(x)-\\lambda B(x)$, where $A$ and $B$ are symmetric matrix valued\nfunctions in ${\\mathbb R}^{n\\times n}$, smoothly depending on parameters $x\\in\n\\Omega\\subset {\\mathbb R}^2$; further, $B$ is also positive definite. In\ngeneral, the eigenvalues of this multiparameter problem will not be smooth, the\nlack of smoothness resulting from eigenvalues being equal at some parameter\nvalues (conical intersections). We first give general theoretical results on\nthe smoothness of eigenvalues and eigenvectors for the present generalized\neigenvalue problem, and hence for the corresponding projections, and then\nperform a numerical study of the statistical properties of coalescing\neigenvalues for pencils where $A$ and $B$ are either full or banded, for\nseveral bandwidths. Our numerical study will be performed with respect to a\nrandom matrix ensemble which respects the underlying engineering problems\nmotivating our study.\n"}
+{"abstract": "  A quantity of interest to characterise continuous-valued stochastic processes\nis the differential entropy rate. The rate of convergence of many properties of\nLRD processes is slower than might be expected, based on the intuition for\nconventional processes, e.g. Markov processes. Is this also true of the entropy\nrate?\n  In this paper we consider the properties of the differential entropy rate of\nstochastic processes that have an autocorrelation function that decays as a\npower law. We show that power law decaying processes with similar\nautocorrelation and spectral density functions, Fractional Gaussian Noise and\nARFIMA(0,d,0), have different entropic properties, particularly for negatively\ncorrelated parameterisations. Then we provide an equivalence between the mutual\ninformation between past and future and the differential excess entropy for\nstationary Gaussian processes, showing the finiteness of this quantity is the\nboundary between long and short range dependence. Finally, we analyse the\nconvergence of the conditional entropy to the differential entropy rate and\nshow that for short range dependence that the rate of convergence is of the\norder $O(n^{-1})$, but it is slower for long range dependent processes and\ndepends on the Hurst parameter.\n"}
+{"abstract": "  The computer science research community and the broader public have become\nincreasingly aware of negative consequences of algorithmic systems. In\nresponse, the top-tier Neural Information Processing Systems (NeurIPS)\nconference for machine learning and artificial intelligence research required\nthat authors include a statement of broader impact to reflect on potential\npositive and negative consequences of their work. We present the results of a\nqualitative thematic analysis of a sample of statements written for the 2020\nconference. The themes we identify broadly fall into categories related to how\nconsequences are expressed (e.g., valence, specificity, uncertainty), areas of\nimpacts expressed (e.g., bias, the environment, labor, privacy), and\nresearchers' recommendations for mitigating negative consequences in the\nfuture. In light of our results, we offer perspectives on how the broader\nimpact statement can be implemented in future iterations to better align with\npotential goals.\n"}
+{"abstract": "  Many phenomena in the climate system lie in the gray zone between weather and\nclimate: they are not amenable to deterministic forecast, but they still depend\non the initial condition. A natural example is medium-range forecasting, which\nis inherently probabilistic because it lies beyond the deterministic\npredictability time of the atmosphere, but for which statistically significant\nprediction can be made which depend on the current state of the system.\nSimilarly, one may ask the probability of occurrence of an El Ni\\~no event\nseveral months ahead of time. In this paper, we introduce a quantity which\ncorresponds precisely to this type of prediction problem: the committor\nfunction is the probability that an event takes place within a given time\nwindow, as a function of the initial condition. We explain the main\nmathematical properties of this probabilistic concept, and compute it in the\ncase of a low-dimensional stochastic model for El-Ni\\~no, the Jin and Timmerman\nmodel. In this context, we show that the ability to predict the probability of\noccurrence of the event of interest may differ strongly depending on the\ninitial state. The main result is the new distinction between intrinsic\nprobabilistic predictability (when the committor function is smooth and\nprobability can be computed which does not depend sensitively on the initial\ncondition) and intrinsic probabilistic unpredictability (when the committor\nfunction depends sensitively on the initial condition). We also demonstrate\nthat the Jin and Timmerman model might be the first example of a stochastic\ndifferential equation with weak noise for which transition between attractors\ndo not follow the Arrhenius law, which is expected based on large deviation\ntheory and generic hypothesis.\n"}
+{"abstract": "  We generalize our previous new definition of Euler Gamma function to higher\nGamma functions. With this unified approach, we characterize Barnes higher\nGamma functions, Mellin Gamma functions, Barnes multiple Gamma functions,\nJackson $q$-Gamma function, and Nishizawa higher $q$-Gamma functions. This\napproach extends to more general functional equations. This generalization\nreveals the multiplicative group structure of solutions of the functional\nequation that appears as a cocycle equation. We also generalize Barnes\nhierarchy of higher Gamma function and multiple Gamma functions. In this new\napproach, Barnes-Hurwitz zeta functions are no longer required for the\ndefinition of Barnes multiple Gamma functions. This simplifies the classical\ndefinition, without the necessary analytic preliminaries about the meromorphic\nextension of Barnes-Hurwitz zeta functions, and defines a larger class of Gamma\nfunctions. For some algebraic independence conditions on the parameters, we\nhave uniqueness of the solutions, which implies the coincidence of our multiple\nGamma functions with Barnes multiple Gamma functions.\n"}
+{"abstract": "  Extending the Standard Model with three right-handed neutrinos and a simple\nQCD axion sector can account for neutrino oscillations, dark matter and baryon\nasymmetry; at the same time, it solves the strong CP problem, stabilizes the\nelectroweak vacuum and can implement critical Higgs inflation (satisfying all\ncurrent observational bounds). We perform here a general analysis of dark\nmatter (DM) in such a model, which we call the $a\\nu$MSM. Although critical\nHiggs inflation features a (quasi) inflection point of the inflaton potential\nwe show that DM cannot receive a contribution from primordial black holes in\nthe $a\\nu$MSM. This leads to a multicomponent axion-sterile-neutrino DM and\nallows us to relate the axion parameters, such as the axion decay constant, to\nthe neutrino parameters. We include several DM production mechanisms: the axion\nproduction via misalignment and decay of topological defects as well as the\nsterile-neutrino production through the resonant and non-resonant mechanisms\nand in the recently proposed CPT-symmetric universe.\n"}
+{"abstract": "  Continuous-variable quantum information processing through quantum optics\noffers a promising platform for building the next generation of scalable\nfault-tolerant information processors. To achieve quantum computational\nadvantages and fault tolerance, non-Gaussian resources are essential. In this\nwork, we propose and analyze a method to generate a variety of non-Gaussian\nstates using coherent photon subtraction from a two-mode squeezed state\nfollowed by photon-number-resolving measurements. The proposed method offers a\npromising way to generate rotation-symmetric states conventionally used for\nquantum error correction with binomial codes and truncated Schr\\\"{o}dinger cat\ncodes. We consider the deleterious effects of experimental imperfections such\nas detection inefficiencies and losses in the state engineering protocol. Our\nmethod can be readily implemented with current quantum photonic technologies.\n"}
+{"abstract": "  It is often desired to train 6D pose estimation systems on synthetic data\nbecause manual annotation is expensive. However, due to the large domain gap\nbetween the synthetic and real images, synthesizing color images is expensive.\nIn contrast, this domain gap is considerably smaller and easier to fill for\ndepth information. In this work, we present a system that regresses 6D object\npose from depth information represented by point clouds, and a lightweight data\nsynthesis pipeline that creates synthetic point cloud segments for training. We\nuse an augmented autoencoder (AAE) for learning a latent code that encodes 6D\nobject pose information for pose regression. The data synthesis pipeline only\nrequires texture-less 3D object models and desired viewpoints, and it is cheap\nin terms of both time and hardware storage. Our data synthesis process is up to\nthree orders of magnitude faster than commonly applied approaches that render\nRGB image data. We show the effectiveness of our system on the LineMOD, LineMOD\nOcclusion, and YCB Video datasets. The implementation of our system is\navailable at: https://github.com/GeeeG/CloudAAE.\n"}
+{"abstract": "  We apply the Dirac procedure for constrained systems to the\nArnowitt-Deser-Misner formalism linearized around the Bianchi I universe. We\ndiscuss and employ basic concepts such as Dirac observables, Dirac brackets,\ngauge-fixing conditions, reduced phase space, physical Hamiltonian, canonical\nisomorphism between different gauge-fixing surfaces and spacetime\nreconstruction. We relate this approach to the gauge-fixing procedure for\nnon-perturbative canonical relativity. We discuss the issue of propagating a\nbasis for the scalar-vector-tensor decomposition as, in an anisotropic\nuniverse, the wavefronts of plane waves undergo a nontrivial evolution. We show\nthat the definition of a gravitational wave as a traceless-transverse mode of\nthe metric perturbation needs to be revised. Moreover there exist coordinate\nsystems in which a polarization mode of the gravitational wave is given\nentirely in terms of a scalar metric perturbation. We first develop the\nformalism for the universe with a single scalar field and then extend it to the\nmulti-field case. The obtained fully canonical formalism will serve as a\nstarting point for a complete quantization of the cosmological perturbations\nand the cosmological background.\n"}
+{"abstract": "  Motivation: Bacteriophages (aka phages), which mainly infect bacteria, play\nkey roles in the biology of microbes. As the most abundant biological entities\non the planet, the number of discovered phages is only the tip of the iceberg.\nRecently, many new phages have been revealed using high throughput sequencing,\nparticularly metagenomic sequencing. Compared to the fast accumulation of\nphage-like sequences, there is a serious lag in taxonomic classification of\nphages. High diversity, abundance, and limited known phages pose great\nchallenges for taxonomic analysis. In particular, alignment-based tools have\ndifficulty in classifying fast accumulating contigs assembled from metagenomic\ndata. Results: In this work, we present a novel semi-supervised learning model,\nnamed PhaGCN, to conduct taxonomic classification for phage contigs. In this\nlearning model, we construct a knowledge graph by combining the DNA sequence\nfeatures learned by convolutional neural network (CNN) and protein sequence\nsimilarity gained from gene-sharing network. Then we apply graph convolutional\nnetwork (GCN) to utilize both the labeled and unlabeled samples in training to\nenhance the learning ability. We tested PhaGCN on both simulated and real\nsequencing data. The results clearly show that our method competes favorably\nagainst available phage classification tools.\n"}
+{"abstract": "  In this article, we introduce the first degrees of a cochain complex\nassociated to a strict Lie 2-group whose cohomology is shown to extend the\nclassical cohomology theory of Lie groups. In particular, we show that the\nsecond cohomology group classifies an appropriate type of extension. We\nconclude by putting forward evidence that this complex can be extended to\narbitrary degrees.\n"}
+{"abstract": "  We present tight bounds and heuristics for personalized, multi-product\npricing problems. Under mild conditions we show that the best price in the\ndirection of a positive vector results in profits that are guaranteed to be at\nleast as large as a fraction of the profits from optimal personalized pricing.\nFor unconstrained problems, the fraction depends on the factor and on optimal\nprice vectors for the different customer types. For constrained problems the\nfactor depends on the factor and a ratio of the constraints. Using a factor\nvector with equal components results in uniform pricing and has exceedingly\nmild sufficient conditions for the bound to hold. A robust factor is presented\nthat achieves the best possible performance guarantee. As an application, our\nmodel yields a tight lower-bound on the performance of linear pricing relative\nto optimal personalized non-linear pricing, and suggests effective non-linear\nprice heuristics relative to personalized solutions. Additionally, our model\nprovides guarantees for simple strategies such as bundle-size pricing and\ncomponent-pricing with respect to optimal personalized mixed bundle pricing.\nHeuristics to cluster customer types are also developed with the goal of\nimproving performance by allowing each cluster to price along its own factor.\nNumerical results are presented for a variety of demand models that illustrate\nthe tradeoffs between using the economic factor and the robust factor for each\ncluster, as well as the tradeoffs between using a clustering heuristic with a\nworst case performance of two and a machine learning clustering algorithm. In\nour experiments economically motivated factors coupled with machine learning\nclustering heuristics performed best.\n"}
+{"abstract": "  Resilience to class imbalance and confounding biases, together with the\nassurance of fairness guarantees are highly desirable properties of autonomous\ndecision-making systems with real-life impact. Many different targeted\nsolutions have been proposed to address separately these three problems,\nhowever a unifying perspective seems to be missing. With this work, we provide\na general formalization, showing that they are different expressions of\nunbalance. Following this intuition, we formulate a unified loss correction to\naddress issues related to Fairness, Biases and Imbalances (FBI-loss). The\ncorrection capabilities of the proposed approach are assessed on three\nreal-world benchmarks, each associated to one of the issues under\nconsideration, and on a family of synthetic data in order to better investigate\nthe effectiveness of our loss on tasks with different complexities. The\nempirical results highlight that the flexible formulation of the FBI-loss leads\nalso to competitive performances with respect to literature solutions\nspecialised for the single problems.\n"}
+{"abstract": "  Quantization is a technique used in deep neural networks (DNNs) to increase\nexecution performance and hardware efficiency. Uniform post-training\nquantization (PTQ) methods are common, since they can be implemented\nefficiently in hardware and do not require extensive hardware resources or a\ntraining set. Mapping FP32 models to INT8 using uniform PTQ yields models with\nnegligible accuracy degradation; however, reducing precision below 8 bits with\nPTQ is challenging, as accuracy degradation becomes noticeable, due to the\nincrease in quantization noise. In this paper, we propose a sparsity-aware\nquantization (SPARQ) method, in which the unstructured and dynamic activation\nsparsity is leveraged in different representation granularities. 4-bit\nquantization, for example, is employed by dynamically examining the bits of\n8-bit values and choosing a window of 4 bits, while first skipping zero-value\nbits. Moreover, instead of quantizing activation-by-activation to 4 bits, we\nfocus on pairs of 8-bit activations and examine whether one of the two is equal\nto zero. If one is equal to zero, the second can opportunistically use the\nother's 4-bit budget; if both do not equal zero, then each is dynamically\nquantized to 4 bits, as described. SPARQ achieves minor accuracy degradation\nand a practical hardware implementation. The code is available at\nhttps://github.com/gilshm/sparq.\n"}
+{"abstract": "  Several applications in medical imaging and non-destructive material testing\nlead to inverse elliptic coefficient problems, where an unknown coefficient\nfunction in an elliptic PDE is to be determined from partial knowledge of its\nsolutions. This is usually a highly non-linear ill-posed inverse problem, for\nwhich unique reconstructability results, stability estimates and global\nconvergence of numerical methods are very hard to achieve.\n  The aim of this note is to point out a new connection between inverse\ncoefficient problems and semidefinite programming that may help addressing\nthese challenges. We show that an inverse elliptic Robin transmission problem\nwith finitely many measurements can be equivalently rewritten as a uniquely\nsolvable convex non-linear semidefinite optimization problem. This allows to\nexplicitly estimate the number of measurements that is required to achieve a\ndesired resolution, to derive an error estimate for noisy data, and to overcome\nthe problem of local minima that usually appears in optimization-based\napproaches for inverse coefficient problems.\n"}
+{"abstract": "  We conducted a field study at a K-12 private school in the suburbs of\nMelbourne, Australia. The data capture contained two elements: First, a 5-month\nlongitudinal field study In-Gauge using two outdoor weather stations, as well\nas indoor weather stations in 17 classrooms and temperature sensors on the\nvents of occupant-controlled room air-conditioners; these were collated into\nindividual datasets for each classroom at a 5-minute logging frequency,\nincluding additional data on occupant presence. The dataset was used to derive\npredictive models of how occupants operate room air-conditioning units. Second,\nwe tracked 23 students and 6 teachers in a 4-week cross-sectional study\nEn-Gage, using wearable sensors to log physiological data, as well as daily\nsurveys to query the occupants' thermal comfort, learning engagement, emotions\nand seating behaviours. This is the first publicly available dataset studying\nthe daily behaviours and engagement of high school students using heterogeneous\nmethods. The combined data could be used to analyse the relationships between\nindoor climates and mental states of school students.\n"}
+{"abstract": "  Recent work has highlighted several advantages of enforcing orthogonality in\nthe weight layers of deep networks, such as maintaining the stability of\nactivations, preserving gradient norms, and enhancing adversarial robustness by\nenforcing low Lipschitz constants. Although numerous methods exist for\nenforcing the orthogonality of fully-connected layers, those for convolutional\nlayers are more heuristic in nature, often focusing on penalty methods or\nlimited classes of convolutions. In this work, we propose and evaluate an\nalternative approach to directly parameterize convolutional layers that are\nconstrained to be orthogonal. Specifically, we propose to apply the Cayley\ntransform to a skew-symmetric convolution in the Fourier domain, so that the\ninverse convolution needed by the Cayley transform can be computed efficiently.\nWe compare our method to previous Lipschitz-constrained and orthogonal\nconvolutional layers and show that it indeed preserves orthogonality to a high\ndegree even for large convolutions. Applied to the problem of certified\nadversarial robustness, we show that networks incorporating the layer\noutperform existing deterministic methods for certified defense against\n$\\ell_2$-norm-bounded adversaries, while scaling to larger architectures than\npreviously investigated. Code is available at\nhttps://github.com/locuslab/orthogonal-convolutions.\n"}
+{"abstract": "  The critical nature of vehicular communications requires their extensive\ntesting and evaluation. Analytical models can represent an attractive and\ncost-effective approach for such evaluation if they can adequately model all\nunderlying effects that impact the performance of vehicular communications.\nSeveral analytical models have been proposed to date to model vehicular\ncommunications based on the IEEE 802.11p (or DSRC) standard. However, existing\nmodels normally model in detail the MAC (Medium Access Control), and generally\nsimplify the propagation and interference effects. This reduces their value as\nan alternative to evaluate the performance of vehicular communications. This\npaper addresses this gap, and presents new analytical models that accurately\nmodel the performance of vehicle-to-vehicle communications based on the IEEE\n802.11p standard. The models jointly account for a detailed modeling of the\npropagation and interference effects, as well as the impact of the hidden\nterminal problem. The model quantifies the PDR (Packet Delivery Ratio) as a\nfunction of the distance between transmitter and receiver. The paper also\npresents new analytical models to quantify the probability of the four\ndifferent types of packet errors in IEEE 802.11p. In addition, the paper\npresents the first analytical model capable to accurately estimate the Channel\nBusy Ratio (CBR) metric even under high channel load levels. All the analytical\nmodels are validated by means of simulation for a wide range of parameters,\nincluding traffic densities, packet transmission frequencies, transmission\npower levels, data rates and packet sizes. An implementation of the models is\nprovided openly to facilitate their use by the community.\n"}
+{"abstract": "  We synthesized lead sulfide (PbS) nanowires with high crystallinity by\nsolvothermal method and successfully fabricated devices based on single PbS\nnanowires. The devices exhibited memristive property which depends on the\napplied voltage and the power density of light. In summary, these results\nindicated that PbS nanowires prepared by solvothermal method have potential\napplications in the fields of electronics and optoelectronics.\n"}
+{"abstract": "  Transformer-based language models have been changing the modern Natural\nLanguage Processing (NLP) landscape for high-resource languages such as\nEnglish, Chinese, Russian, etc. However, this technology does not yet exist for\nany Ghanaian language. In this paper, we introduce the first of such models for\nTwi or Akan, the most widely spoken Ghanaian language. The specific\ncontribution of this research work is the development of several pretrained\ntransformer language models for the Akuapem and Asante dialects of Twi, paving\nthe way for advances in application areas such as Named Entity Recognition\n(NER), Neural Machine Translation (NMT), Sentiment Analysis (SA) and\nPart-of-Speech (POS) tagging. Specifically, we introduce four different\nflavours of ABENA -- A BERT model Now in Akan that is fine-tuned on a set of\nAkan corpora, and BAKO - BERT with Akan Knowledge only, which is trained from\nscratch. We open-source the model through the Hugging Face model hub and\ndemonstrate its use via a simple sentiment classification example.\n"}
+{"abstract": "  The time- or temperature-resolved detector signal from a thermoluminescence\ndosimeter can reveal additional information about circumstances of an exposure\nto ionizing irradiation. We present studies using deep neural networks to\nestimate the date of a single irradiation with 12 mSv within a monitoring\ninterval of 42 days from glow curves of novel TL-DOS personal dosimeters\ndeveloped by the Materialpr\\\"ufungsamt NRW in cooperation with TU Dortmund\nUniversity. Using a deep convolutional network, the irradiation date can be\npredicted from raw time-resolved glow curve data with an uncertainty of roughly\n1-2 days on a 68% confidence level without the need for a prior transformation\ninto temperature space and a subsequent glow curve deconvolution. This\ncorresponds to a significant improvement in prediction accuracy compared to a\nprior publication, which yielded a prediction uncertainty of 2-4 days using\nfeatures obtained from a glow curve deconvolution as input to a neural network.\n"}
+{"abstract": "  The vast majority of image recovery tasks are ill-posed problems. As such,\nmethods that are based on optimization use cost functions that consist of both\nfidelity and prior (regularization) terms. A recent line of works imposes the\nprior by the Regularization by Denoising (RED) approach, which exploits the\ngood performance of existing image denoising engines. Yet, the relation of RED\nto explicit prior terms is still not well understood, as previous work requires\ntoo strong assumptions on the denoisers. In this paper, we make two\ncontributions. First, we show that the RED gradient can be seen as a\n(sub)gradient of a prior function--but taken at a denoised version of the\npoint. As RED is typically applied with a relatively small noise level, this\ninterpretation indicates a similarity between RED and traditional gradients.\nThis leads to our second contribution: We propose to combine RED with the\nBack-Projection (BP) fidelity term rather than the common Least Squares (LS)\nterm that is used in previous works. We show that the advantages of BP over LS\nfor image deblurring and super-resolution, which have been demonstrated for\ntraditional gradients, carry on to the RED approach.\n"}
+{"abstract": "  Single-electron avalanche distributions in gaseous multipliers affect their\nefficient detection and that of single UV-photons. In this work, we\ninvestigated the shape of single-photo-electron spectra in single- and\ndouble-stage Resistive Plate WELL (RPWELL) detector configurations, operated in\n$\\mathrm{Ne/CH_{4}}$ and $\\mathrm{Ar/CH_{4}}$. Discharge-free operation was\nreached over a broad dynamic range, with charge gains of \\numrange[range-phrase\n= -]{e4}{e6}. Compared to the usual exponential ones, the observed Polya-like\ncharge spectra pave the way towards higher single-electrons detection\nefficiencies. The latter were evaluated here, using experimental data combined\nwith numerical simulations. The effects of the gas mixtures, electric field\nconfiguration and detector geometry on the Polya spectra and their related\n\"$\\theta$\" parameter are presented.\n"}
+{"abstract": "  Optically trapped mixed-species single atom arrays with arbitrary geometries\nare an attractive and promising platform for various applications, because\ntunable quantum systems with multiple components provide extra degrees of\nfreedom for experimental control. Here, we report the first demonstration of\ntwo-dimensional $6\\times4$ dual-species atom assembly with a filling fraction\nof 0.88 (0.89) for $^{85}$Rb ($^{87}$Rb) atoms. This mixed-species atomic\nsynthetic is achieved via rearranging initially randomly distributed atoms\nusing a sorting algorithm (heuristic heteronuclear algorithm) which is proposed\nfor bottom-up atom assembly with both user-defined geometries and two-species\natom number ratios. Our fully tunable hybrid-atom system of scalable advantages\nis a good starting point for high-fidelity quantum logic, many-body quantum\nsimulation and forming defect-free single molecule arrays.\n"}
+{"abstract": "  We study the van der Waals (vdW) interaction between a polarizable particle\nand a grounded conducting corrugated surface. For sinusoidal corrugations, one\nknows that, under the action of the lateral vdW force, an isotropic particle is\nalways attracted to the nearest corrugation peak, with such behavior called in\nthe present paper as peak regime. Here, considering an anisotropic polarizable\nparticle, and making analytical calculations valid beyond the proximity force\napproximation (PFA), we show that the attraction is not only toward the peaks,\nbut, for certain particle orientations and distances from the surface, the\nlateral force attracts the particle to the nearest corrugation valley (valley\nregime), or even to an intermediate point between a peak and a valley\n(intermediate regime). We also show that in the configurations of transition\nbetween the peak and valley regimes the lateral vdW force vanishes, even in the\npresence of a corrugated surface. In addition, we find that these new regimes\noccur in general, for periodic and nonperiodic corrugated surfaces. Moreover,\nwe demonstrate that similar regimes arise in the classical interaction between\na neutral polarized particle and a rough surface. The description of these\nvalley and intermediate regimes, which are out of reach of the predictions\nbased on the PFA, may be relevant for a better understanding of the interaction\nbetween anisotropic particles and corrugated surfaces in classical and quantum\nphysics, with experimental verifications feasible in both domains.\n"}
+{"abstract": "  Interpretability is a critical factor in applying complex deep learning\nmodels to advance the understanding of brain disorders in neuroimaging studies.\nTo interpret the decision process of a trained classifier, existing techniques\ntypically rely on saliency maps to quantify the voxel-wise or feature-level\nimportance for classification through partial derivatives. Despite providing\nsome level of localization, these maps are not human-understandable from the\nneuroscience perspective as they do not inform the specific meaning of the\nalteration linked to the brain disorder. Inspired by the image-to-image\ntranslation scheme, we propose to train simulator networks that can warp a\ngiven image to inject or remove patterns of the disease. These networks are\ntrained such that the classifier produces consistently increased or decreased\nprediction logits for the simulated images. Moreover, we propose to couple all\nthe simulators into a unified model based on conditional convolution. We\napplied our approach to interpreting classifiers trained on a synthetic dataset\nand two neuroimaging datasets to visualize the effect of the Alzheimer's\ndisease and alcohol use disorder. Compared to the saliency maps generated by\nbaseline approaches, our simulations and visualizations based on the Jacobian\ndeterminants of the warping field reveal meaningful and understandable patterns\nrelated to the diseases.\n"}
+{"abstract": "  We prove that a complete properly embedded surface in $\\mathbb\nH^2\\times\\mathbb R$ with constant mean curvature $0<H\\leq \\frac{1}{2}$ and an\nannular end can not be contained in any horizontal slab. Moreover, we show that\na properly embedded surface with constant mean curvature $0<H\\leq \\frac{1}{2}$\ncontained in $\\mathbb H^2\\times[0,+\\infty)$ and with finite topology is\nnecessarily a graph. For the case $H=\\frac{1}{2}$, the graph is entire.\n"}
+{"abstract": "  We apply the Hybrid-Multi-Determinant method using the recent chiral two-body\ninteractions of Entem-Machleidt-Nosyk (EMN) without renormalization to few\nnuclei up to A=48. Mostly we use the bare fifth order NN interaction N4LO-450.\nFor ${}^{24}Mg$ and ${}^{48}Cr$ the excitation energies of the $2^+_1$ states\nare far larger than the corresponding experimental values.\n"}
+{"abstract": "  We prove an equation of Euler characteristics of tautological sheaves on the\nHilbert scheme of points on the fibers of a simple degeneration.\n"}
+{"abstract": "  Generative Adversarial Networks can learn the mapping of random noise to\nrealistic images in a semi-supervised framework. This mapping ability can be\nused for semi-supervised image classification to detect images of an unknown\nclass where there is no training data to be used for supervised classification.\nHowever, if the unknown class shares similar characteristics to the known\nclass(es), GANs can learn to generalize and generate images that look like both\nclasses. This generalization ability can hinder the classification performance.\nIn this work, we propose the Vanishing Twin GAN. By training a weak GAN and\nusing its generated output image parallel to the regular GAN, the Vanishing\nTwin training improves semi-supervised image classification where image\nsimilarity can hurt classification tasks.\n"}
+{"abstract": "  We report measurements of the branching fractions of singly\nCabibbo-suppressed decays $\\Lambda_c^+ \\to p \\eta$ and $\\Lambda_c^+ \\to p\n\\pi^0$ using the full Belle data sample corresponding to an integrated\nluminosity of 980.6 $\\rm fb^{-1}$. The data were collected by the Belle\ndetector at the KEKB $e^{+}$$e^{-}$ asymmetric-energy collider. A clear\n$\\Lambda_c^+$ signal is seen in the invariant mass distribution of $p \\eta$.\nThe signal yield of the $\\Lambda_c^+ \\to p \\eta$ process is $7734 \\pm 263$;\nfrom this, we measure the ratio of branching fractions ${\\cal B}(\\Lambda_c^+\n\\to p \\eta)/{\\cal B}(\\Lambda_c^+ \\to p K^- \\pi^+) = (2.258 \\pm 0. 077(\\rm stat.\n) \\pm 0.136(\\rm syst. ))\\times 10^{-2}$, from which we infer the branching\nfraction ${\\cal B}(\\Lambda_c^+ \\to p \\eta) = (1.42 \\pm 0.05(\\rm stat.) \\pm\n0.11(\\rm syst.)) \\times 10^{-3}$. In addition, no significant signal for\n$\\Lambda_c^+ \\to p \\pi^0$ is found so an upper limit on the branching fraction\nof ${\\cal B}(\\Lambda_c^+ \\to p \\pi^0)<8.0 \\times 10^{-5}$ at 90\\% credibility\nlevel is set, more than three times better than the best current upper limit.\n"}
+{"abstract": "  Higher Spin Gravities are scarce, but covariant actions for them are even\nscarcer. We construct covariant actions for contractions of Chiral Higher Spin\nGravity that represent higher spin extensions of self-dual Yang-Mills and\nself-dual Gravity theories. The actions give examples of complete higher spin\ntheories both in flat and (anti)-de Sitter spaces that feature gauge and\ngravitational interactions. The actions are based on a new description of\nhigher spin fields, whose origin can be traced to early works on twistor\ntheory. The new description simplifies the structure of interactions. In\nparticular, we find a covariant form of the minimal gravitational interaction\nfor higher spin fields both in flat and anti-de Sitter space, which resolves\nsome of the puzzles in the literature.\n"}
+{"abstract": "  Sounds recorded with smartphones or IoT devices often have partially\nunreliable observations caused by clipping, wind noise, and completely missing\nparts due to microphone failure and packet loss in data transmission over the\nnetwork. In this paper, we investigate the impact of the partially missing\nchannels on the performance of acoustic scene classification using multichannel\naudio recordings, especially for a distributed microphone array. Missing\nobservations cause not only losses of time-frequency and spatial information on\nsound sources but also a mismatch between a trained model and evaluation data.\nWe thus investigate how a missing channel affects the performance of acoustic\nscene classification in detail. We also propose simple data augmentation\nmethods for scene classification using multichannel observations with partially\nmissing channels and evaluate the scene classification performance using the\ndata augmentation methods.\n"}
+{"abstract": "  We argue that primordial black-hole formation must be described by means of\nextreme-value theory. This is a consequence of the large values of the energy\ndensity required to initiate the collapse of black holes in the early Universe\nand the finite duration of their collapse. Compared to the Gaussian description\nof the most extreme primordial density fluctuations, the holes' mass function\nis narrower and peaks towards larger masses. Secondly, thanks to the shallower\nfall-off of extreme-value distributions, the predicted abundance of primordial\nblack holes is boosted by $10^{7}$ orders of magnitude when extrapolating the\nobserved nearly scale-free power spectrum of the cosmic large-scale structure\nto primordial black-hole mass scales.\n"}
+{"abstract": "  We investigate the entanglement entropy (EE) and charge fluctuations in\nmodels where the low energy physics is governed by massless Dirac fermions. We\nfocus on the response to flux insertion which, for the EE, is widely assumed to\nbe universal, \\emph{i.e.}, independent of the microscopic details. We provide\nan analytical derivation of the EE and charge fluctuations for the seminal\nexample of graphene, using the dimensional reduction of its tight-binding model\nto the one-dimensional Su-Schrieffer-Heeger model. Our asymptotic expression\nfor the EE matches the conformal field theory prediction. We show that the\ncharge variance has the same asymptotic behavior, up to a constant prefactor.\nTo check the validity of universality arguments, we numerically consider\nseveral models, with different geometries and number of Dirac cones, and either\nfor strictly two-dimensional models or for gapless surface mode of\nthree-dimensional topological insulators. We also show that the flux response\ndoes not depend on the entangling surface geometry as long as it encloses the\nflux. Finally we consider the universal corner contributions to the EE. We show\nthat in the presence of corners, the Kitaev-Preskill subtraction scheme\nprovides non-universal, geometry dependent results.\n"}
+{"abstract": "  Let $\\Sigma_{g,p}$ be a oriented connected surface of genus $g$ with $p$\npunctures. We denote by $\\mathcal{M}_{g,p}$ and $\\mathcal{M}_{g,p}^\\pm$ the\nmapping class group and the extended mapping class group of $\\Sigma_{g,p}$,\nrespectively. In this paper, we show that $\\mathcal{M}_{g,p}$ and\n$\\mathcal{M}_{g,p}^\\pm$ are generated by two element for $g\\geq 3$ and $p\\geq\n0$.\n"}
+{"abstract": "  Recently, we have shown that thermal effects play a crucial role in speeding\nup the rate of bond-dissociation reactions. This was done by applying a simple\ntemperature-shifted Arrhenius Law to the experimental data, corroborated with\ndetailed account of the heat diffusion occurring within the relevant samples\nand identification of errors in the temperature measurements. Here, we provide\nthree important extensions of our previous studies. First, we analyze thermal\neffects in reduction-oxidation (redox) reactions, where charge transfer is an\nintegral part of the reaction. Second, we analyze not only the spatial\ndistribution of the temperature, but also its temporal dynamics. Third, we also\nmodel the fluid convection and stirring. An analysis of two exemplary\nexperimental studies allows us to show that thermal effects can explain the\nexperimental data in one of experiments (Baumberg and coworkers), but not in\nthe other (Jain and coworkers), showing that redox reactions are not\nnecessarily driven by non-thermal charge carriers.\n"}
+{"abstract": "  Millimeter-wave (mmWave) radars are being increasingly integrated in\ncommercial vehicles to support new Adaptive Driver Assisted Systems (ADAS)\nfeatures that require accurate location and Doppler velocity estimates of\nobjects, independent of environmental conditions. To explore radar-based ADAS\napplications, we have updated our test-bed with Texas Instrument's 4-chip\ncascaded FMCW radar (TIDEP-01012) that forms a non-uniform 2D MIMO virtual\narray. In this paper, we develop the necessary received signal models for\napplying different direction of arrival (DoA) estimation algorithms and\nexperimentally validating their performance on formed virtual array under\ncontrolled scenarios. To test the robustness of mmWave radars under adverse\nweather conditions, we collected raw radar dataset (I-Q samples post\ndemodulated) for various objects by a driven vehicle-mounted platform,\nspecifically for snowy and foggy situations where cameras are largely\nineffective. Initial results from radar imaging algorithms to this dataset are\npresented.\n"}
+{"abstract": "  In this work, with the help of fractional calculus, it is shown a time\ndependence of entropy more general than the well known Pesin relation is\nderived. Here the equiprobability postulate is not assumed, the system dynamic\nin the phase space is not necessarily Markovian and the system is not in a\nsteady state at all. Different possibilities for the time evolution of entropy\nby considering different features of the phase space and processes involved are\nobtained.\n"}
+{"abstract": "  The study of coordinated manipulation of conversations on social media has\nbecome more prevalent as social media's role in amplifying misinformation,\nhate, and polarization has come under scrutiny. We discuss the implications of\nsuccessful coordination detection algorithms based on shifts of power, and\nconsider how responsible coordination detection may be carried out through\nsynchronized action. We then propose a Synchronized Action Framework for\ndetection of automated coordination through construction and analysis of\nmulti-view networks. We validate our framework by examining the Reopen America\nconversation on Twitter, discovering three coordinated campaigns. We further\ninvestigate covert coordination surrounding the protests and find the task to\nbe far more complex than examples seen in prior work, demonstrating the need\nfor our multi-view approach. A cluster of suspicious users is identified and\nthe activity of three members is detailed. These users amplify protest messages\nusing the same hashtags at very similar times, though they all focus on\ndifferent states. Through this analysis, we emphasize both the potential\nusefulness of coordination detection algorithms in investigating amplification,\nand the need for careful and responsible deployment of such tools.\n"}
+{"abstract": "  Layered tin selenide (SnSe) has recently emerged as a high-performance\nthermoelectric material with the current record for the figure of merit (ZT)\nobserved in the high-temperature Cmcm phase. So far, access of the Cmcm phase\nhas been mainly obtained via thermal equilibrium methods based on sample\nheating or application of external pressure, thus restricting the current\nunderstanding only to ground-state conditions. Here, we investigate the\nultrafast carrier and phononic dynamics in SnSe. Our results demonstrate that\noptical excitations can transiently switch the point-group symmetry of the\ncrystal from Pnma to Cmcm at room temperature in a few hundreds of femtoseconds\nwith an ultralow threshold for the excitation carrier density. This\nnon-equilibrium Cmcm phase is found to be driven by the displacive excitation\nof coherent Ag phonons and, given the absence of low-energy thermal phonons,\nexists in SnSe with the status of 'cold lattice with hot carriers'. Our\nfindings provide important insight for understanding non-equilibrium\nthermoelectric properties of SnSe.\n"}
+{"abstract": "  Social networks are widely used for information consumption and\ndissemination, especially during time-critical events such as natural\ndisasters. Despite its significantly large volume, social media content is\noften too noisy for direct use in any application. Therefore, it is important\nto filter, categorize, and concisely summarize the available content to\nfacilitate effective consumption and decision-making. To address such issues\nautomatic classification systems have been developed using supervised modeling\napproaches, thanks to the earlier efforts on creating labeled datasets.\nHowever, existing datasets are limited in different aspects (e.g., size,\ncontains duplicates) and less suitable to support more advanced and data-hungry\ndeep learning models. In this paper, we present a new large-scale dataset with\n~77K human-labeled tweets, sampled from a pool of ~24 million tweets across 19\ndisaster events that happened between 2016 and 2019. Moreover, we propose a\ndata collection and sampling pipeline, which is important for social media data\nsampling for human annotation. We report multiclass classification results\nusing classic and deep learning (fastText and transformer) based models to set\nthe ground for future studies. The dataset and associated resources are\npublicly available. https://crisisnlp.qcri.org/humaid_dataset.html\n"}
+{"abstract": "  Although ferromagnets are found in all kinds of technological applications,\nonly few substances are known to be intrinsically ferromagnetic at room\ntemperature. In the past twenty years, a plethora of new artificial\nferromagnetic materials have been found by introducing defects into\nnon-magnetic host materials. In contrast to the intrinsic ferromagnetic\nmaterials, they offer an outstanding degree of material engineering freedom,\nprovided one finds a type of defect to functionalize every possible host\nmaterial to add magnetism to its intrinsic properties. Still, one controversial\nquestion remains: Are these materials really technologically relevant\nferromagnets? To answer this question, in this work the emergence of a\nferromagnetic phase upon ion irradiation is systematically investigated both\ntheoretically and experimentally. Quantitative predictions are validated\nagainst experimental data from the literature of SiC hosts irradiated with high\nenergy Ne ions and own experiments on low energy Ar ion irradiation of TiO$_2$\nhosts. In the high energy regime, a bulk magnetic phase emerges, which is\nlimited by host lattice amorphization, whereas at low ion energies an ultrathin\nmagnetic layer forms at the surface and evolves into full magnetic percolation.\nLowering the ion energy, the magnetic layer thickness reduces down to a\nbilayer, where a perpendicular magnetic anisotropy appears due to magnetic\nsurface states.\n"}
+{"abstract": "  Commercial application of facial recognition demands robustness to a variety\nof challenges such as illumination, occlusion, spoofing, disguise, etc.\nDisguised face recognition is one of the emerging issues for access control\nsystems, such as security checkpoints at the borders. However, the lack of\navailability of face databases with a variety of disguise addons limits the\ndevelopment of academic research in the area. In this paper, we present a\nmultimodal disguised face dataset to facilitate the disguised face recognition\nresearch. The presented database contains 8 facial add-ons and 7 additional\ncombinations of these add-ons to create a variety of disguised face images.\nEach facial image is captured in visible, visible plus infrared, infrared, and\nthermal spectra. Specifically, the database contains 100 subjects divided into\nsubset-A (30 subjects, 1 image per modality) and subset-B (70 subjects, 5 plus\nimages per modality). We also present baseline face detection results performed\non the proposed database to provide reference results and compare the\nperformance in different modalities. Qualitative and quantitative analysis is\nperformed to evaluate the challenging nature of disguise addons. The dataset\nwill be publicly available with the acceptance of the research article. The\ndatabase is available at: https://github.com/usmancheema89/SejongFaceDatabase.\n"}
+{"abstract": "  This paper revisits the classical question of the stability of the nonlinear\nWonham filter. The novel contributions of this paper are two-fold: (i)\ndefinition of the stabilizability for the (control-theoretic) dual to the\nnonlinear filter; and (ii) the use of this definition to obtain conclusions on\nthe stability of the Wonham filter. Specifically, it is shown that the\nstabilizability of the dual system is necessary for filter stability and\nconversely stabilizability implies that the filter asymptotically detects the\ncorrect ergodic class. The formulation and the proofs are based upon a recently\ndiscovered duality result whereby the nonlinear filtering problem is cast as a\nstochastic optimal control problem for a backward stochastic differential\nequation (BSDE). The control-theoretic proof techniques and results may be\nviewed as a generalization of the classical work on the stability of the Kalman\nfilter.\n"}
+{"abstract": "  In any other circumstance, it might make sense to define the extent of the\nterrain (Data Science) first, and then locate and describe the landmarks\n(Principles). But this data revolution we are experiencing defies a cadastral\nsurvey. Areas are continually being annexed into Data Science. For example,\nbiometrics was traditionally statistics for agriculture in all its forms but\nnow, in Data Science, it means the study of characteristics that can be used to\nidentify an individual. Examples of non-intrusive measurements include height,\nweight, fingerprints, retina scan, voice, photograph/video (facial landmarks\nand facial expressions), and gait. A multivariate analysis of such data would\nbe a complex project for a statistician, but a software engineer might appear\nto have no trouble with it at all. In any applied-statistics project, the\nstatistician worries about uncertainty and quantifies it by modelling data as\nrealisations generated from a probability space. Another approach to\nuncertainty quantification is to find similar data sets, and then use the\nvariability of results between these data sets to capture the uncertainty. Both\napproaches allow 'error bars' to be put on estimates obtained from the original\ndata set, although the interpretations are different. A third approach, that\nconcentrates on giving a single answer and gives up on uncertainty\nquantification, could be considered as Data Engineering, although it has staked\na claim in the Data Science terrain. This article presents a few (actually\nnine) statistical principles for data scientists that have helped me, and\ncontinue to help me, when I work on complex interdisciplinary projects.\n"}
+{"abstract": "  In 2008, Langevin and Leander determined the dual function of three classes\nof monomial bent functions with the help of Stickelberger's theorem: Dillon,\nGold and Kasami. In their paper, they proposed one very strong condition such\nthat their method works, and showed that both Gold exponent and Kasami exponent\nsatisfy this condition. In 2018, Pott {\\em et al.} investigated the issue of\nvectorial functions with maximal number of bent components. They found one\nclass of binomial functions which attains the upper bound. They also proposed\nan open problem regarding monomial function with maximal number of bent\ncomponents.\n  In this paper, we obtain an interesting result about the condition of\nLangevin and Leander, and solve the open problem of Pott {\\em et al.}.\nSpecifically, we show that: 1) for a monomial bent function over\n$\\mathbb{F}_{2^{2k}}$, if the exponent satisfies the first part of the\ncondition of Langevin and Leander, then it satisfies the entire condition; 2)\n$x^{2^k+1}$ is the only monomial function over $\\mathbb{F}_{2^{2k}}$ which has\nmaximal number of bent components. Fortunately, as a consequence, we also solve\nan open problem of Ness and Helleseth in 2006.\n"}
+{"abstract": "  In this paper, we propose a path re-planning algorithm that makes robots able\nto work in scenarios with moving obstacles. The algorithm switches between a\nset of pre-computed paths to avoid collisions with moving obstacles. It also\nimproves the current path in an anytime fashion. The use of informed sampling\nenhances the search speed. Numerical results show the effectiveness of the\nstrategy in different simulation scenarios.\n"}
+{"abstract": "  The perpendicular shape anisotropy-spin transfer torque-magnetic random\naccess memories (PSASTT-MRAMs) takes advantage of the nanopillar free-layer\ngeometry for securing a good thermal stability factor from the shape anisotropy\nof the nanomagnet. Such a concept is particularly well-suited for small\njunctions down to a few nanometers. At such a volume size, the nanopillar can\nbe effectively modeled as a Stoner-Wohlfarth (SW) particle, and the shape\nanisotropy scales with the spontaneous magnetization by ~ Ms^2. For almost all\nferromagnets, Ms is a strong function of temperature, therefore, the\ntemperature-dependent shape anisotropy is an important factor to be considered\nin any modeling of the temperature-dependent performance of PSA-STT-MRAMs. In\nthis work, we summarize and discuss various possible temperature-dependent\ncontributions to the thermal stability factor and coercivity of the\nPSA-STT-MRAMs by modeling and comparing different temperature scaling and\nparameters. We reveal nontrivial corrections to the thermal stability factor by\nconsidering both temperature-dependent shape and interfacial anisotropies. The\ncoercivity, blocking temperature, and electrical switching characteristics that\nresulted from incorporating such a temperature dependence are also discussed,\nin conjugation with the nanomagnet dimension and coherence volume.\n"}
+{"abstract": "  2D Convolutional neural network (CNN) has arguably become the de facto\nstandard for computer vision tasks. Recent findings, however, suggest that CNN\nmay not be the best option for 1D pattern recognition, especially for datasets\nwith over 1 M training samples, e.g., existing CNN-based methods for 1D signals\nare highly reliant on human pre-processing. Common practices include utilizing\ndiscrete Fourier transform (DFT) to reconstruct 1D signal into 2D array. To add\nto extant knowledge, in this paper, a novel 1D data processing algorithm is\nproposed for 1D big data analysis through learning a deep\ndeconvolutional-convolutional network. Rather than resorting to human-based\ntechniques, we employed deconvolution layers to convert 1 D signals into 2D\ndata. On top of the deconvolution model, the data was identified by a 2D CNN.\nCompared with the existing 1D signal processing algorithms, DCNet boasts the\nadvantages of less human-made inference and higher generalization performance.\nOur experimental results from a varying number of training patterns (50 K to 11\nM) from classification and regression demonstrate the desirability of our new\napproach.\n"}
+{"abstract": "  Dinuclear systems that occur in the post-saddle to scission stage in nuclear\nfission process are special transient formations. The diabatic evolution at\nthis stage has been studied using the methods of non-equilibrium\nthermodynamics. A novel explanation for the spontaneous emergence of collective\nrotational modes in a dinucleus has been developed by identifying these modes\ntogether as an open subsystem that exchange matter and energy with its\nenvironment. The environment consists of all remaining degrees of freedom of\nthe dinucleus, and a weak coupling of the subsystem to this environment\nfacilitate a diabatic energy exchange. Under appropriate non-equilibrium\nconditions, the available energy will be coupled to the work needed for the\nemergence of self organizing rotational modes. This comes at the expense of\nincreasing microscopic disorder in the form of intrinsic excitations in the\nfragments. A simple formalism is presented such that the magnitudes of relevant\nenergy flow through the subsystem are obtained in terms of entropy production\nrate, entropy expulsion rate and net rate of change of entropy in unit of $MeV\nzs^{-1} K^{-1}$. An experimental approach with which the entropy rates can be\nmeasured is discussed schematically.\n"}
+{"abstract": "  Despite frequent references in modern reviews to a seventeenth-century\nVenetian longitude prize, only a single, circumstantial reference to the\nalleged prize is known from contemporary sources. Edward Harrison's scathing\nassessment of the conditions governing the award of an alleged Venetian\nlongitude prize simultaneously disparages the rewards offered by the Dutch\nStates General. However, the latter had long run its course by 1696, the year\nof the citation, thus rendering Harrison's reference unreliable. Whereas other\nlongitude awards offered by the leading European maritime nations attracted\napplicants from far and wide, often accompanied by extensive, self-published\npamphlets, the alleged Venetian prize does not seem to have been subject to\nsimilar hype. The alleged existence of seventeenth-century Venetian award is\nparticularly curious, because the city's fortune was clearly in decline, and\nlongitude determination on the open seas does not appear to have been a\npriority; the city's mariners already had access to excellent \"portolan\"\ncharts. It is therefore recommended that authors refrain from referring to a\npotentially phantom Venetian longitude prize in the same context as the major\nsixteenth- to eighteenth-century European awards offered by the dominant\nsea-faring nations.\n"}
+{"abstract": "  As our lives migrate to the digital realm, our online identity has evolved to\nbecome an increasingly robust collection of data about every aspect of our\nonline and offline lives. This data is extremely appealing to companies who\nwish to use it for a variety of analytics. In this report, we create awareness\nfor the consumers around the world who have only a vague understanding of how\nmuch of their data is being tracked, where, when, and by which companies.\n"}
+{"abstract": "  This presentation for the AIES 21 doctoral consortium examines the Latin\nAmerican crowdsourcing market through a decolonial lens. This research is based\non the analysis of the web traffic of ninety-three platforms, interviews with\nVenezuelan data workers of four platforms, and the analysis of the\ndocumentation issued by these organizations. The findings show that (1)\ncenturies-old global divisions of labor persist, in this case, with requesters\nlocated in advanced economies and workers in the Global South. (2) That the\nplatforms' configuration of the labor process constrains the agency of these\nworkers when producing annotations. And, (3) that ideologies originating from\nthe Global North serve to legitimize and reinforce this global labor market\nconfiguration.\n"}
+{"abstract": "  Recognizing and categorizing human actions is an important task with\napplications in various fields such as human-robot interaction, video analysis,\nsurveillance, video retrieval, health care system and entertainment industry.\nThis thesis presents a novel computational approach for human action\nrecognition through different implementations of multi-layer architectures\nbased on artificial neural networks. Each system level development is designed\nto solve different aspects of the action recognition problem including online\nreal-time processing, action segmentation and the involvement of objects. The\nanalysis of the experimental results are illustrated and described in six\narticles. The proposed action recognition architecture of this thesis is\ncomposed of several processing layers including a preprocessing layer, an\nordered vector representation layer and three layers of neural networks. It\nutilizes self-organizing neural networks such as Kohonen feature maps and\ngrowing grids as the main neural network layers. Thus the architecture presents\na biological plausible approach with certain features such as topographic\norganization of the neurons, lateral interactions, semi-supervised learning and\nthe ability to represent high dimensional input space in lower dimensional\nmaps. For each level of development the system is trained with the input data\nconsisting of consecutive 3D body postures and tested with generalized input\ndata that the system has never met before. The experimental results of\ndifferent system level developments show that the system performs well with\nquite high accuracy for recognizing human actions.\n"}
+{"abstract": "  In this paper, an enhancement technique for the class activation mapping\nmethods such as gradient-weighted class activation maps or excitation\nbackpropagation is proposed to present the visual explanations of decisions\nfrom convolutional neural network-based models. The proposed idea, called\nGradual Extrapolation, can supplement any method that generates a heatmap\npicture by sharpening the output. Instead of producing a coarse localization\nmap that highlights the important predictive regions in the image, the proposed\nmethod outputs the specific shape that most contributes to the model output.\nThus, the proposed method improves the accuracy of saliency maps. The effect\nhas been achieved by the gradual propagation of the crude map obtained in the\ndeep layer through all preceding layers with respect to their activations. In\nvalidation tests conducted on a selected set of images, the faithfulness,\ninterpretability, and applicability of the method are evaluated. The proposed\ntechnique significantly improves the localization detection of the neural\nnetworks attention at low additional computational costs. Furthermore, the\nproposed method is applicable to a variety deep neural network models. The code\nfor the method can be found at\nhttps://github.com/szandala/gradual-extrapolation\n"}
+{"abstract": "  Conservation properties of iterative methods applied to implicit finite\nvolume discretizations of nonlinear conservation laws are analyzed. It is shown\nthat any consistent multistep or Runge-Kutta method is globally conservative.\nFurther, it is shown that Newton's method, Krylov subspace methods and\npseudo-time iterations are globally conservative while the Jacobi and\nGauss-Seidel methods are not in general. If pseudo-time iterations using an\nexplicit Runge-Kutta method are applied to a locally conservative\ndiscretization, then the resulting scheme is also locally conservative.\nHowever, the corresponding numerical flux can be inconsistent with the\nconservation law. We prove an extension of the Lax-Wendroff theorem, which\nreveals that numerical solutions based on these methods converge to weak\nsolutions of a modified conservation law where the flux function is multiplied\nby a particular constant. This constant depends on the choice of Runge-Kutta\nmethod but is independent of both the conservation law and the discretization.\nConsistency is maintained by ensuring that this constant equals unity and a\nstrategy for achieving this is presented. Experiments show that this strategy\nimproves the convergence rate of the pseudo-time iterations.\n"}
+{"abstract": "  In common law, the outcome of a new case is determined mostly by precedent\ncases, rather than by existing statutes. However, how exactly does the\nprecedent influence the outcome of a new case? Answering this question is\ncrucial for guaranteeing fair and consistent judicial decision-making. We are\nthe first to approach this question computationally by comparing two\nlongstanding jurisprudential views; Halsbury's, who believes that the arguments\nof the precedent are the main determinant of the outcome, and Goodhart's, who\nbelieves that what matters most is the precedent's facts. We base our study on\nthe corpus of legal cases from the European Court of Human Rights (ECtHR),\nwhich allows us to access not only the case itself, but also cases cited in the\njudges' arguments (i.e. the precedent cases). Taking an information-theoretic\nview, and modelling the question as a case outcome classification task, we find\nthat the precedent's arguments share 0.38 nats of information with the case's\noutcome, whereas precedent's facts only share 0.18 nats of information (i.e.,\n58% less); suggesting Halsbury's view may be more accurate in this specific\ncourt. We found however in a qualitative analysis that there are specific\nstatues where Goodhart's view dominates, and present some evidence these are\nthe ones where the legal concept at hand is less straightforward.\n"}
+{"abstract": "  Textures contain a wealth of image information and are widely used in various\nfields such as computer graphics and computer vision. With the development of\nmachine learning, the texture synthesis and generation have been greatly\nimproved. As a very common element in everyday life, wallpapers contain a\nwealth of texture information, making it difficult to annotate with a simple\nsingle label. Moreover, wallpaper designers spend significant time to create\ndifferent styles of wallpaper. For this purpose, this paper proposes to\ndescribe wallpaper texture images by using multi-label semantics. Based on\nthese labels and generative adversarial networks, we present a framework for\nperception driven wallpaper texture generation and style transfer. In this\nframework, a perceptual model is trained to recognize whether the wallpapers\nproduced by the generator network are sufficiently realistic and have the\nattribute designated by given perceptual description; these multi-label\nsemantic attributes are treated as condition variables to generate wallpaper\nimages. The generated wallpaper images can be converted to those with\nwell-known artist styles using CycleGAN. Finally, using the aesthetic\nevaluation method, the generated wallpaper images are quantitatively measured.\nThe experimental results demonstrate that the proposed method can generate\nwallpaper textures conforming to human aesthetics and have artistic\ncharacteristics.\n"}
+{"abstract": "  This paper addresses the challenge of classifying polarimetric SAR images by\nleveraging the peculiar characteristics of the polarimetric covariance matrix\n(PCM). To this end, a general framework to solve a multiple hypothesis test is\nintroduced with the aim to detect and classify contextual spatial variations in\npolarimetric SAR images. Specifically, under the null hypothesis, only an\nunknown structure is assumed for data belonging to a 2-dimensional spatial\nsliding window, whereas under each alternative hypothesis, data are partitioned\ninto subsets sharing different structures. The problem of partition estimation\nis solved by resorting to hidden random variables representative of covariance\nstructure classes and the expectation-maximization algorithm. The effectiveness\nof the proposed detection strategies is demonstrated on both simulated and real\npolarimetric SAR data also in comparison with existing classification\nalgorithms.\n"}
+{"abstract": "  We consider the lepton-number-violating processes in $\\Lambda_b$/$\\Lambda_c$\ndecays mediated by on-shell GeV-scale sterile neutrino. We calculate the\nbranching ratio for the following processes: $\\Lambda_b^{0} \\rightarrow\n\\mathcal{B}^+ \\mu^{-}\\mu^- \\pi^+$, where $\\mathcal{B}^+$ is $\\Lambda_c^+$ or\nproton, and $\\Lambda_c^{+} \\rightarrow \\Lambda \\mu^{+}\\mu^+ \\pi^-$, as function\nof the mass of the sterile neutrino $m_N$ and the heavy-light mixing\ncoefficient of the extended Pontecorvo-Maki-Nakagawa-Sakata (PMNS) matrix\n$|U_{\\mu N}|^2$. The effect of finite detector size is included in our\ncalculation. After comparing the theoretical effective branching ratio with the\nexpected experimental ability of LHCb, we give the sensitivity upper bounds on\n$|U_{\\mu N}|^2$ on the ($|U_{\\mu N}|^2$-$m_N$) plane. These channels give\ncomparable results with the bounds given by different search strategies (e.g.,\nat Belle, NuTeV, DELPHI and BEBC) in the mass region $m_N \\simeq$ 0.25 GeV -\n4.5 GeV, and the limits are stronger in the mass range 2 GeV $<m_N<$ 4.5 GeV.\n"}
+{"abstract": "  Dielectric and antiferroelectric materials are particularly promising for\nhigh-power energy-storage applications. However, relatively low energy density\ngreatly hinders their usage in storage technologies. Here, we report\nfirst-principles-based calculations predicting that epitaxial and initially\nnon-polar AlN/ScN superlattices can achieve an ultrahigh energy density of up\nto 200 J/cm$^{\\textrm{3}}$, accompanied by an ideal efficiency of 100%. We also\nshow that high energy density requires the system being neither too close nor\ntoo far from a ferroelectric phase transition under zero electric field. A\nphenomenological model is further proposed to rationalize such striking\nfeatures.\n"}
+{"abstract": "  We present HyperMorph, a learning-based strategy for deformable image\nregistration that removes the need to tune important registration\nhyperparameters during training. Classical registration methods solve an\noptimization problem to find a set of spatial correspondences between two\nimages, while learning-based methods leverage a training dataset to learn a\nfunction that generates these correspondences. The quality of the results for\nboth types of techniques depends greatly on the choice of hyperparameters.\nUnfortunately, hyperparameter tuning is time-consuming and typically involves\ntraining many separate models with various hyperparameter values, potentially\nleading to suboptimal results. To address this inefficiency, we introduce\namortized hyperparameter learning for image registration, a novel strategy to\nlearn the effects of hyperparameters on deformation fields. The proposed\nframework learns a hypernetwork that takes in an input hyperparameter and\nmodulates a registration network to produce the optimal deformation field for\nthat hyperparameter value. In effect, this strategy trains a single, rich model\nthat enables rapid, fine-grained discovery of hyperparameter values from a\ncontinuous interval at test-time. We demonstrate that this approach can be used\nto optimize multiple hyperparameters considerably faster than existing search\nstrategies, leading to a reduced computational and human burden as well as\nincreased flexibility. We also show several important benefits, including\nincreased robustness to initialization and the ability to rapidly identify\noptimal hyperparameter values specific to a registration task, dataset, or even\na single anatomical region, all without retraining the HyperMorph model. Our\ncode is publicly available at http://voxelmorph.mit.edu.\n"}
+{"abstract": "  In recent times, federated machine learning has been very useful in building\nintelligent intrusion detection systems for IoT devices. As IoT devices are\nequipped with a security architecture vulnerable to various attacks, these\nsecurity loopholes may bring a risk during federated training of decentralized\nIoT devices. Adversaries can take control over these IoT devices and inject\nfalse gradients to degrade the global model performance. In this paper, we have\nproposed an approach that detects the adversaries with the help of a clustering\nalgorithm. After clustering, it further rewards the clients for detecting\nhonest and malicious clients. Our proposed gradient filtration approach does\nnot require any processing power from the client-side and does not use\nexcessive bandwidth, making it very much feasible for IoT devices. Further, our\napproach has been very successful in boosting the global model accuracy, up to\n99% even in the presence of 40% adversaries.\n"}
+{"abstract": "  Edit-based approaches have recently shown promising results on multiple\nmonolingual sequence transduction tasks. In contrast to conventional\nsequence-to-sequence (Seq2Seq) models, which learn to generate text from\nscratch as they are trained on parallel corpora, these methods have proven to\nbe much more effective since they are able to learn to make fast and accurate\ntransformations while leveraging powerful pre-trained language models. Inspired\nby these ideas, we present TST, a simple and efficient Text Simplification\nsystem based on sequence Tagging, leveraging pre-trained Transformer-based\nencoders. Our system makes simplistic data augmentations and tweaks in training\nand inference on a pre-existing system, which makes it less reliant on large\namounts of parallel training data, provides more control over the outputs and\nenables faster inference speeds. Our best model achieves near state-of-the-art\nperformance on benchmark test datasets for the task. Since it is fully\nnon-autoregressive, it achieves faster inference speeds by over 11 times than\nthe current state-of-the-art text simplification system.\n"}
+{"abstract": "  In this paper, we study the low-rank matrix minimization problem, where the\nloss function is convex but nonsmooth and the penalty term is defined by the\ncardinality function. We first introduce an exact continuous relaxation, that\nis, both problems have the same minimzers and the same optimal value. In\nparticular, we introduce a class of lifted stationary point of the relaxed\nproblem and show that any local minimizer of the relaxed problem must be a\nlifted stationary point. In addition, we derive lower bound property for the\nnonzero singular values of the lifted stationary point and hence also of the\nlocal minimizers of the relaxed problem. Then the smoothing proximal gradient\n(SPG) algorithm is proposed to find a lifted stationary point of the continuous\nrelaxation model. Moreover, it is shown that the whole sequence generated by\nSPG algorithm converges to a lifted stationary point. At last, numerical\nexamples show the efficiency of the SPG algorithm.\n"}
+{"abstract": "  In the pioneering work of Stern, level sets of harmonic functions have been\nshown to be an effective tool in the study of scalar curvature in dimension 3.\nGeneralizations of this idea, utilizing level sets of so called spacetime\nharmonic functions as well as other elliptic equations, are similarly effective\nin treating geometric inequalities involving the ADM mass. In this paper, we\nsurvey recent results in this context, focusing on applications of spacetime\nharmonic functions to the asymptotically flat and asymptotically hyperbolic\nversions of the spacetime positive mass theorem, and additionally introduce a\nnew concept of total mass valid in both settings which is encoded in\ninterpolation regions between generic initial data and model geometries.\nFurthermore, a novel and elementary proof of the positive mass theorem with\ncharge is presented, and the level set approach to the Penrose inequality given\nby Huisken and Ilmanen is related to the current developments. Lastly, we\ndiscuss several open problems.\n"}
+{"abstract": "  Asymptotic efficiency of targeted maximum likelihood estimators (TMLE) of\ntarget features of the data distribution relies on a a second order remainder\nbeing asymptotically negligible. In previous work we proposed a nonparametric\nMLE termed Highly Adaptive Lasso (HAL) which parametrizes the relevant\nfunctional of the data distribution in terms of a multivariate real valued\ncadlag function that is assumed to have finite variation norm. We showed that\nthe HAL-MLE converges in Kullback-Leibler dissimilarity at a rate n-1/3 up till\nlogn factors. Therefore, by using HAL as initial density estimator in the TMLE,\nthe resulting HAL-TMLE is an asymptotically efficient estimator only assuming\nthat the relevant nuisance functions of the data density are cadlag and have\nfinite variation norm. However, in finite samples, the second order remainder\ncan dominate the sampling distribution so that inference based on asymptotic\nnormality would be anti-conservative.\n  In this article we propose a new higher order TMLE, generalizing the regular\nfirst order TMLE. We prove that it satisfies an exact linear expansion, in\nterms of efficient influence functions of sequentially defined higher order\nfluctuations of the target parameter, with a remainder that is a k+1th order\nremainder. As a consequence, this k-th order TMLE allows statistical inference\nonly relying on the k+1th order remainder being negligible. We also provide a\nrationale for the higher order TMLE that it will be superior to the first order\nTMLE by (iteratively) locally minimizing the exact finite sample remainder of\nthe first order TMLE. The second order TMLE is demonstrated for nonparametric\nestimation of the integrated squared density and for the treatment specific\nmean outcome. We also provide an initial simulation study for the second order\nTMLE of the treatment specific mean confirming the theoretical analysis.\n"}
+{"abstract": "  The transition kernel of a continuous-state-action Markov decision process\n(MDP) admits a natural tensor structure. This paper proposes a tensor-inspired\nunsupervised learning method to identify meaningful low-dimensional state and\naction representations from empirical trajectories. The method exploits the\nMDP's tensor structure by kernelization, importance sampling and\nlow-Tucker-rank approximation. This method can be further used to cluster\nstates and actions respectively and find the best discrete MDP abstraction. We\nprovide sharp statistical error bounds for tensor concentration and the\npreservation of diffusion distance after embedding.\n"}
+{"abstract": "  A systematic procedure is proposed for inclusion of Stueckelberg fields. The\nprocedure begins with the involutive closure when the original Lagrangian\nequations are complemented by all the lower order consequences. The involutive\nclosure can be viewed as Lagrangian analogue of complementing constrained\nHamiltonian system with secondary constraints. The involutively closed form of\nthe field equations allows for explicitly covariant degree of freedom number\ncount, which is stable with respect to deformations. If the original Lagrangian\nequations are not involutive, the involutive closure will be a non-Lagrangian\nsystem. The Stueckelberg fields are assigned to all the consequences included\ninto the involutive closure of the Lagrangian system. The iterative procedure\nis proposed for constructing the gauge invariant action functional involving\nStueckelberg fields such that Lagrangian equations are equivalent to the\ninvolutive closure of the original theory. The generators of the Stueckelberg\ngauge symmetry begin with the operators generating the closure of original\nLagrangian system. These operators are not assumed to be a generators of gauge\nsymmetry of any part of the original action, nor are they supposed to form an\non shell integrable distribution. With the most general closure generators, the\nconsistent Stueckelberg gauge invariant theory is iteratively constructed,\nwithout obstructions at any stage. The Batalin-Vilkovisky form of inclusion the\nStueckelberg fields is worked out and existence theorem for the Stueckelberg\naction is proven.\n"}
+{"abstract": "  SPICA is a mid to far infra-red space mission to explore the processes that\nform galaxies, stars and planets. SPICA/SAFARI is the far infrared spectrometer\nthat provides near-background limited observations between 34 and 230\nmicrometers. The core of SAFARI consists of 4 grating modules, dispersing light\nonto 5 arrays of TES detectors per module. The grating modules provide low\nresolution (250) instantaneous spectra over the entire wavelength range. The\nhigh resolution (1500 to 12000) mode is accomplished by placing a Fourier\nTransform Spectrometer (FTS) in front of the gratings. Each grating module\ndetector sees an interferogram from which the high resolution spectrum can be\nconstructed. SAFARI data will be a convolution of complex spectral, temporal\nand spatial information. Along with spectral calibration accuracy of <1%, a\nrelative flux calibration of 1% and an absolute flux calibration accuracy of\n10% are required. This paper will discuss the calibration strategy and its\nimpact on the instrument design of SAFARI\n"}
+{"abstract": "  In this paper, we define a new class of dynamic games played in large\npopulations of anonymous agents. The behavior of agents in these games depends\non a time-homogeneous type and a time-varying state, which are private to each\nagent and characterize their available actions and motifs. We consider finite\ntype, state, and action spaces. On the individual agent level, the state\nevolves in discrete-time as the agent participates in interactions, in which\nthe state transitions are affected by the agent's individual action and the\ndistribution of other agents' states and actions. On the societal level, we\nconsider that the agents form a continuum of mass and that interactions occur\neither synchronously or asynchronously, and derive models for the evolution of\nthe agents' state distribution. We characterize the stationary equilibrium as\nthe solution concept in our games, which is a condition where all agents are\nplaying their best response and the state distribution is stationary. At least\none stationary equilibrium is guaranteed to exist in every dynamic population\ngame. Our approach intersects with previous works on anonymous sequential\ngames, mean-field games, and Markov decision evolutionary games, but it is\nnovel in how we relate the dynamic setting to a classical, static population\ngame setting. In particular, stationary equilibria can be reduced to standard\nNash equilibria in classical population games. This simplifies the analysis of\nthese games and inspires the formulation of an evolutionary model for the\ncoupled dynamics of both the agents' actions and states.\n"}
+{"abstract": "  We consider the lower bounds of differentially private empirical risk\nminimization for general convex functions in this paper. For convex generalized\nlinear models (GLMs), the well-known tight bound of DP-ERM in the constrained\ncase is $\\tilde{\\Theta}(\\frac{\\sqrt{p}}{\\epsilon n})$, while recently,\n\\cite{sstt21} find the tight bound of DP-ERM in the unconstrained case is\n$\\tilde{\\Theta}(\\frac{\\sqrt{\\text{rank}}}{\\epsilon n})$ where $p$ is the\ndimension, $n$ is the sample size and $\\text{rank}$ is the rank of the feature\nmatrix of the GLM objective function. As $\\text{rank}\\leq \\min\\{n,p\\}$, a\nnatural and important question arises that whether we can evade the curse of\ndimensionality for over-parameterized models where $n\\ll p$, for more general\nconvex functions beyond GLM. We answer this question negatively by giving the\nfirst and tight lower bound of unconstrained private ERM for the general convex\nfunction, matching the current upper bound\n$\\tilde{O}(\\frac{\\sqrt{p}}{n\\epsilon})$ for unconstrained private ERM. We also\ngive an $\\Omega(\\frac{p}{n\\epsilon})$ lower bound for unconstrained pure-DP ERM\nwhich recovers the result in the constrained case.\n"}
+{"abstract": "  We survey partial geometric designs and investigate their concurrences of\npoints. The concurrence matrix of a design, which encodes the concurrences of\npairs of points, can be used in the classification of designs in some extent.\nAn ordinary 2-$(v,k,\\lambda)$ design has concurrence $\\lambda$ for any pair of\ndistinct points, and its concurrence matrix is circulant. A partial geometry\nhas two concurrences $1$ and $0$ and a transversal design TD$_{\\lambda}(k, u)$\nhas two concurrences $\\lambda$ and $0$. It is also known that the concurrence\nmatrix of a partial geometric design can have at most three distinct\neigenvalues, all of which are non-negative integers. In this paper, we show the\nexistence of other partial geometric designs having two or three distinct\nconcurrences, and investigate which symmetric circulant matrices are realized\nas the concurrence matrices of partial geometric designs. We collect known\nsources of partial geometric designs and study their structural characteristics\nand construction methods. We then give a list of feasible parameter sets for\npartial geometric designs of order up to 12 each of which has a circulant\nconcurrence matrix. We also consider the combinatorial properties and\nconstructions of partial geometric designs satisfying these parameter sets.\n"}
+{"abstract": "  The ability for an Open Source Software (OSS) project to attract, onboard,\nand retain any newcomer is vital to its livelihood. Evidence suggests more new\nusers are joining GitHub, however, the extent to which they contribute to OSS\nprojects is unknown. In this study, we coin the term newcomer candidate to\ndescribe a novice developer that is a new user to the GitHub platform, with the\nintention to later onboard an OSS project. Our objective is to track and\ncharacterize their initial contributions using a mixed-method approach. Our\nresults show that 68% of newcomer candidates are more likely to practice\nnon-social coding, 86% tend to work on forward-engineering activities in their\nfirst commits, and 53% show their interest of targeting non-software\nrepositories. Our quantitative analysis did match only 3% of newcomer\ncandidates contributions to established OSS repositories, yet 70% of newcomer\ncandidates claim to already onboard an OSS project. This study opens up new\navenues for future work, especially in terms of targeting potential\ncontributors to onboard an existing OSS project. More practical applications\nwould be tool support to (i) recommend practical examples that OSS project\nteams can use to lower their barriers for a newcomer candidate to successfully\nmake a contribution and (ii) recommend suitable repositories for newcomer\ncandidates based on their preference. Researchers can explore strategies to\nsustain newcomer candidate activities until they are ready to onboard an OSS\nproject.\n"}
+{"abstract": "  This paper proposes a novel nonlinear activation mechanism typically for\nconvolutional neural network (CNN), named as reborn mechanism. In sharp\ncontrast to ReLU which cuts off the negative phase value, the reborn mechanism\nenjoys the capacity to reborn and reconstruct dead neurons. Compared to other\nimproved ReLU functions, reborn mechanism introduces a more proper way to\nutilize the negative phase information. Extensive experiments validate that\nthis activation mechanism is able to enhance the model representation ability\nmore significantly and make the better use of the input data information while\nmaintaining the advantages of the original ReLU function. Moreover, reborn\nmechanism enables a non-symmetry that is hardly achieved by traditional CNNs\nand can act as a channel compensation method, offering competitive or even\nbetter performance but with fewer learned parameters than traditional methods.\nReborn mechanism was tested on various benchmark datasets, all obtaining better\nperformance than previous nonlinear activation functions.\n"}
+{"abstract": "  In this paper we study gapless fermionic and bosonic systems in\n$d$-dimensional continuum space with $U(1)$ particle-number conservation and\n$\\mathbb{R}^d$ translation symmetry. We write down low energy effective field\ntheories for several gapless phases where $U(1)\\times \\mathbb{R}^d$ is viewed\nas internal symmetry. The $U(1)\\times \\mathbb{R}^d$ symmetry, when viewed as an\ninternal symmetry, has a mixed anomaly, and the different effective field\ntheories for different phases must have the same mixed anomaly. Such a mixed\nanomaly is proportional to the particle number density, and can be measured\nfrom the distribution of the total momentum $\\boldsymbol{k}_\\text{tot}$ for low\nenergy many-body states (\\ie how such a distribution is shifted by $U(1)$\nsymmetry twist $\\boldsymbol{a}$), as well as some other low energy universal\nproperties of the systems. In particular, we write down low energy effective\nfield theory for Fermi liquid with infinite number of fields, in the presence\nof both real space magnetic field and $\\boldsymbol{k}$-space \"magnetic\" field.\nThe effective field theory also captures the mixed anomaly, which constraints\nthe low energy dynamics, such as determine the volume of Fermi surface (which\nis another formulation of Luttinger-Ward-Oshikawa theorem).\n"}
+{"abstract": "  Unabated coal power in India must be phased out by mid-century to achieve\nglobal climate targets under the Paris Agreement. Here we estimate the costs of\nhybrid power plants - lithium-ion battery storage with wind and solar PV - to\nreplace coal generation. We design least cost mixes of these technologies to\nsupply baseload and load-following generation profiles in three Indian states -\nKarnataka, Gujarat, and Tamil Nadu. Our analysis shows that availability of low\ncost capital, solar PV installation costs of $<$\\$300/kW, and battery storage\ncapacity costs of $<$\\$75/kWh will be required to phase out existing coal power\nplants. Phaseout by 2040 requires a 5% annual decline in the cost of hybrid\nsystems over the next two decades. Solar PV is more suited to pairing with\nshort duration storage than wind power. Our results describe the challenging\ntechnological and policy advances needed to achieve the goals of the Paris\nAgreement.\n"}
+{"abstract": "  We propose a new method in which a generative adversarial network (GAN) is\nused to quantify the uncertainty of forward simulations in the presence of\nobserved data. Previously, a method has been developed which enables GANs to\nmake time series predictions and data assimilation by training a GAN with\nunconditional simulations of a high-fidelity numerical model. After training,\nthe GAN can be used to predict the evolution of the spatial distribution of the\nsimulation states and observed data is assimilated. In this paper, we describe\nthe process required in order to quantify uncertainty, during which no\nadditional simulations of the high-fidelity numerical model are required. These\nmethods take advantage of the adjoint-like capabilities of generative models\nand the ability to simulate forwards and backwards in time. Set within a\nreduced-order model framework for efficiency, we apply these methods to a\ncompartmental model in epidemiology to predict the spread of COVID-19 in an\nidealised town. The results show that the proposed method can efficiently\nquantify uncertainty in the presence of measurements using only unconditional\nsimulations of the high-fidelity numerical model.\n"}
+{"abstract": "  Adoption processes in socio-technological systems have been widely studied\nboth empirically and theoretically. The way in which social norms, behaviors,\nand even items such as books, music, or other commercial or technological\nproduct spread in a population is usually modeled as a process of social\ncontagion, in which the agents of a social system can infect their neighbors on\nthe underlying network of social contacts. More recently, various models have\nalso been proposed to reproduce the typical dynamics of a process of discovery,\nin which an agent explores a space of relations between ideas or items in\nsearch for novelties. In both types of processes, the structure of the\nunderlying networks, respectively, the network of social contacts in the first\ncase, and the network of relations among items in the second one, plays a\nfundamental role. However, the two processes have been traditionally seen and\nstudied independently. Here, we provide a brief overview of the existing models\nof social spreading and exploration and of the latest advancements in both\ndirections. We propose to look at them as two complementary aspects of the same\nadoption process: on the one hand, there are items spreading over a social\nnetwork of individuals influencing each other, and on the other hand,\nindividuals explore a network of similarities among items to adopt. The\ntwo-fold nature of the approach proposed opens up new stimulating challenges\nfor the scientific community of network and data scientists. We conclude by\noutlining some possible directions that we believe may be relevant to be\nexplored in the coming years.\n"}
+{"abstract": "  We analyze recent results concerning the hypothesis of a privileged direction\nin the space-time that is made by considering a background of the Lorentz\nsymmetry violation determined by a fixed spacelike vector field and the\nanalysis of quantum effects of this background on the interaction of a\nnonrelativistic electron with a nonuniform electric field produced by a uniform\nelectric charge distribution. We show that the conclusions derived by the\nauthors are an artifact of the truncation of the Frobenius series by means of\nthe tree-term recurrence relation for the expansion coefficients. Thus, the\nexistence of allowed angular frequencies stemming from this procedure is\nmeaningless and unphysical.\n"}
+{"abstract": "  \\textit{Let $E$ be an infinite set on which a property $(\\bf P)$ is defined.\nSuppose that $E=\\cup_{i\\in I} E_i$ is a partition, where each $E_i$ is\ninfinite. Suppose also that, in each $E_i$, the number of elements satisfying\n$(\\bf P)$ is finite. Then, clearly the density of the elements satisfying $(\\bf\nP)$ is 0 in every $E_i$. Is it possible that the density of the subset of $E$\ncontaining all the elements satisfying $(\\bf P)$ will be at least equal to $\n1/2$?}\n  We were first confronted with this situation while reading the paper of Arno\net al. [1]. In fact, it is in the paper [1] where it is shown that the density\nof certain algebraic numbers in $\\overline{\\mathbb{Q}}$, which we will call\nArno et al. numbers in section 5, is equal to $1/\\zeta(3)$. We have partitioned\n$\\overline{\\mathbb{Q}}$ in a way that suggests these Arno et al. numbers are\nrare. This phenomenom struck us as contradictory, which lead us to consider the\nsituation in greater detail. We will show in the sequel, through two examples,\nthat the answer to the above question may be positive. At first glance, this\nproblem resembles to the so called Simpson paradox in probability and\nstatistics. In this paper, when we say the density, we mean the natural\ndensity.\n"}
+{"abstract": "  Bibliometrics is useful to analyze the research impact for measuring the\nresearch quality. Different bibliographic databases like Scopus, Web of\nScience, Google Scholar etc. are accessed for evaluating the trend of\npublications and citations from time to time. Some of these databases are free\nand some are subscription based. Its always debatable that which bibliographic\ndatabase is better and in what terms. To provide an optimal solution to\navailability of multiple bibliographic databases, we have implemented a single\nauthentic database named as ``conflate'' which can be used for fetching\npublication and citation trend of an author. To further strengthen the\ngenerated database and to provide the transparent system to the stakeholders, a\nconsensus mechanism ``proof of reference (PoR)'' is proposed. Due to three\nconsent based checks implemented in PoR, we feel that it could be considered as\na authentic and honest citation data source for the calculation of unified\ninformetrics for an author.\n"}
+{"abstract": "  Special generic maps are smooth maps at each singular point of which we can\nrepresent as $(x_1, \\cdots, x_m) \\mapsto\n(x_1,\\cdots,x_{n-1},\\sum_{k=n}^{m}{x_k}^2)$ for suitable coordinates. Morse\nfunctions with exactly two singular points on homotopy spheres and canonical\nprojections of unit spheres are special generic. They are known to restrict the\ntopologies and the differentiable structures of the manifolds in various\nsituations. On the other hands, various manifolds admit such maps.\n  This article first presents a special generic map on a $7$-dimensional\nmanifold and the image. This results also seems to present a new example of\n$7$-dimensional closed and simply-connected manifolds having non-vanishing\ntriple Massey products and seems to be a new work related to similar works by\nDranishnikov and Rudyak. We also review results on vanishing of products of\ncohomology classes, previously obtained by the author. The images of special\ngeneric maps are smoothly immersed manifolds whose dimensions are equal to the\ndimensions of the targets. The author studied the topologies of these images\npreviously and studies on homology groups, cohomology rings and structures of\nthem for special generic maps having simple structures are presented as new\nresults.\n"}
+{"abstract": "  Many methods have been developed to understand complex predictive models and\nhigh expectations are placed on post-hoc model explainability. It turns out\nthat such explanations are not robust nor trustworthy, and they can be fooled.\nThis paper presents techniques for attacking Partial Dependence (plots,\nprofiles, PDP), which are among the most popular methods of explaining any\npredictive model trained on tabular data. We showcase that PD can be\nmanipulated in an adversarial manner, which is alarming, especially in\nfinancial or medical applications where auditability became a must-have trait\nsupporting black-box models. The fooling is performed via poisoning the data to\nbend and shift explanations in the desired direction using genetic and gradient\nalgorithms. To the best of our knowledge, this is the first work performing\nattacks on variable dependence explanations. The novel approach of using a\ngenetic algorithm for doing so is highly transferable as it generalizes both\nways: in a model-agnostic and an explanation-agnostic manner.\n"}
+{"abstract": "  Motivated by the phenomenology in the condensed-matter flat-band Dirac\nsystems, we here construct a holographic model that imprints the symmetry\nbreaking pattern of a rather simple Dirac fermion model at zero chemical\npotential.In the bulk we explicitly include the backreaction to the\ncorresponding Lifshitz geometry and compute the dynamical critical exponent.\nMost importantly, we find that such a geometry is unstable towards a nematic\nphase, exhibiting an anomalous Hall effect and featuring a Drude-like shift of\nits spectral weight. Our findings should motivate further studies of the\nquantum phases emerging from such holographic models.\n"}
+{"abstract": "  An `open' or $(\\mu,P,T)$-ensemble describes equilibrium systems whose control\nparameters are chemical potential $\\mu$, pressure $P$ and temperature $T$. Such\nan unconstrained ensemble is seldom used for applications to standard\nthermodynamic systems due to the fact that the corresponding free energy\nidentically vanishes as a result of the Euler relation. However, an open\nensemble is perfectly regular for the case of black holes, as the entropy is a\nquasi-homogeneous function of extensive thermodynamic variables with scaling\ndictated by the Smarr formula. Following a brief discussion on thermodynamics\nin the open ensemble, we compute the general form of logarithmic corrections to\nthe entropy of a typical system, due to fluctuations in energy, thermodynamic\nvolume and a generic charge $N$. This is then used to obtain the exact analytic\nform of the logarithmically corrected black hole entropy for charged and\nrotating black holes in anti-de Sitter spacetimes.\n"}
+{"abstract": "  Referring image segmentation aims to segment the objects referred by a\nnatural language expression. Previous methods usually focus on designing an\nimplicit and recurrent feature interaction mechanism to fuse the\nvisual-linguistic features to directly generate the final segmentation mask\nwithout explicitly modeling the localization information of the referent\ninstances. To tackle these problems, we view this task from another perspective\nby decoupling it into a \"Locate-Then-Segment\" (LTS) scheme. Given a language\nexpression, people generally first perform attention to the corresponding\ntarget image regions, then generate a fine segmentation mask about the object\nbased on its context. The LTS first extracts and fuses both visual and textual\nfeatures to get a cross-modal representation, then applies a cross-model\ninteraction on the visual-textual features to locate the referred object with\nposition prior, and finally generates the segmentation result with a\nlight-weight segmentation network. Our LTS is simple but surprisingly\neffective. On three popular benchmark datasets, the LTS outperforms all the\nprevious state-of-the-art methods by a large margin (e.g., +3.2% on RefCOCO+\nand +3.4% on RefCOCOg). In addition, our model is more interpretable with\nexplicitly locating the object, which is also proved by visualization\nexperiments. We believe this framework is promising to serve as a strong\nbaseline for referring image segmentation.\n"}
+{"abstract": "  As the usage of deep learning becomes increasingly popular in mobile and\nembedded solutions, it is necessary to convert the framework-specific network\nrepresentations into executable code for these embedded platforms. This paper\nconsists of two parts: The first section is made up of a survey and benchmark\nof the available open source deep learning compiler toolchains, which focus on\nthe capabilities and performance of the individual solutions in regard to\ntargeting embedded devices and microcontrollers that are combined with a\ndedicated accelerator in a heterogeneous fashion. The second part explores the\nimplementation and evaluation of a compilation flow for such a heterogeneous\ndevice and reuses one of the existing toolchains to demonstrate the necessary\nsteps for hardware developers that plan to build a software flow for their own\nhardware.\n"}
+{"abstract": "  In a multi-dimensional diffusion framework, the price of a financial\nderivative can be expressed as an iterated conditional expectation, where the\ninner conditional expectation conditions on the future of an auxiliary process\nthat enters into the dynamics for the spot. Inspired by results from non-linear\nfiltering theory, we show that this inner conditional expectation solves a\nbackward SPDE (a so-called `conditional Feynman-Kac formula'), thereby\nestablishing a connection between SPDE and derivative pricing theory. The\nbenefits of this representation are potentially significant and of both\ntheoretical and practical interest. In particular, this representation leads to\nan alternative class of so-called mixed Monte-Carlo / PDE numerical methods.\n"}
+{"abstract": "  We investigate Hardy-Rellich inequalities for perturbed Laplacians. In\nparticular, we show that a non-trivial angular perturbation of the free\noperator typically improves the inequality, and may also provide an estimate\nwhich does not hold in the free case. The main examples are related to the\nintroduction of a magnetic field: this is a manifestation of the diamagnetic\nphenomenon, which has been observed by Laptev and Weidl in \\cite{LW1999} for\nthe Hardy inequality, later by Evans and Lewis in \\cite{EL2005} for the Rellich\ninequality; however, to the best of our knowledge, the so called Hardy-Rellich\ninequality has not yet been investigated in this regards. After showing the\noptimal inequality, we prove that the best constant is not attained by any\nfunction in the domain of the estimate.\n"}
+{"abstract": "  The quantum indeterminacy caused by non-commutativity of observables at\ndifferent times sets a lower bound on the voltage noise power spectrum in any\nconducting material. This bound is calculated explicitly in the case of\nsemiconductors with a conical energy-momentum dispersion of charge carriers. It\npossesses all characteristic properties of 1/f noise. Its momentum\ndecomposition is found to be singular at zero particle momentum, a measurable\nconsequence being a sharp peak in the noise magnitude at small charge carrier\ndensity. In application to monolayer graphene, this peak becomes M-shaped on\naccount of a continuous transition from the electron to hole conductivity. A\ncomparison with experimental data is made which demonstrates that the\ncalculated power spectrum is close in magnitude and congruent to the observed.\n"}
+{"abstract": "  We introduce a protocol for dynamical dispersion engineering in an atomic\nchain consisting of an ordered array of multi-level atoms with subwavelength\nlattice constant. This chain supports dark states that are protected from\ndissipation in the form of photon emission and can be understood as propagating\nspin waves traveling along the array. By using an external control field with a\nspatially-varying elliptical polarization we correlate internal and external\ndegrees of freedom of the array in a controllable way. The coherent control\nover the atomic states translates into control over the group velocity of the\nspin waves. A traveling excitation can be stored and released without\ndissipation by adiabatically changing the control field amplitude. This\nprotocol is an alternative to the more conventional electromagnetically-induced\ntransparency, and exemplifies the rich physics born of the interplay between\ncoherent control and correlated decay.\n"}
+{"abstract": "  Human trajectory prediction is critical for autonomous platforms like\nself-driving cars or social robots. We present a latent belief energy-based\nmodel (LB-EBM) for diverse human trajectory forecast. LB-EBM is a probabilistic\nmodel with cost function defined in the latent space to account for the\nmovement history and social context. The low-dimensionality of the latent space\nand the high expressivity of the EBM make it easy for the model to capture the\nmultimodality of pedestrian trajectory distributions. LB-EBM is learned from\nexpert demonstrations (i.e., human trajectories) projected into the latent\nspace. Sampling from or optimizing the learned LB-EBM yields a belief vector\nwhich is used to make a path plan, which then in turn helps to predict a\nlong-range trajectory. The effectiveness of LB-EBM and the two-step approach\nare supported by strong empirical results. Our model is able to make accurate,\nmulti-modal, and social compliant trajectory predictions and improves over\nprior state-of-the-arts performance on the Stanford Drone trajectory prediction\nbenchmark by 10.9% and on the ETH-UCY benchmark by 27.6%.\n"}
+{"abstract": "  The spatial decorrelation of dense molecular gas and young stars observed on\n$\\lesssim 1$ kiloparsec scales in nearby galaxies indicates rapid dispersal of\nstar-forming regions by stellar feedback. We explore the sensitivity of this\ndecorrelation to different processes controlling the structure of the\ninterstellar medium, the abundance of molecular gas, star formation, and\nfeedback in a suite of simulations of an isolated dwarf galaxy with structural\nproperties similar to NGC300 that self-consistently model radiative transfer\nand molecular chemistry. Our fiducial simulation reproduces the magnitude of\ndecorrelation and its scale dependence measured in NGC300, and we show that\nthis agreement is due to different aspects of feedback, including H$_2$\ndissociation, gas heating by the locally variable UV field, early mechanical\nfeedback, and supernovae. In particular, early radiative and mechanical\nfeedback affects the correlation on $\\lesssim 100$ pc scales, while supernovae\nplay a significant role on $\\gtrsim 100$ pc scales. The correlation is also\nsensitive to the choice of the local star formation efficiency per freefall\ntime, $\\epsilon_{\\rm ff}$, which provides a strong observational constraint on\n$\\epsilon_{\\rm ff}$ when the global star formation rate is independent of its\nvalue. Finally, we explicitly show that the degree of correlation between the\npeaks of molecular gas and star formation density is directly related to the\ndistribution of the lifetimes of star-forming regions.\n"}
+{"abstract": "  In 1975 Bollob\\'as, Erd\\H os, and Szemer\\'edi asked the following question:\ngiven positive integers $n, t, r$ with $2\\le t\\le r-1$, what is the largest\nminimum degree $\\delta(G)$ among all $r$-partite graphs $G$ with parts of size\n$n$ and which do not contain a copy of $K_{t+1}$? The $r=t+1$ case has\nattracted a lot of attention and was fully resolved by Haxell and Szab\\'{o},\nand Szab\\'{o} and Tardos in 2006. In this paper we investigate the $r>t+1$ case\nof the problem, which has remained dormant for over forty years. We resolve the\nproblem exactly in the case when $r \\equiv -1 \\pmod{t}$, and up to an additive\nconstant for many other cases, including when $r \\geq (3t-1)(t-1)$. Our\napproach utilizes a connection to the related problem of determining the\nmaximum of the minimum degrees among the family of balanced $r$-partite\n$rn$-vertex graphs of chromatic number at most $t$.\n"}
+{"abstract": "  We show that, for a generic measure preserving transformation $T$, the closed\ngroup generated by $T$ is not isomorphic to the topological group $L^0(\\lambda,\n{\\mathbb T})$ of all Lebesgue measurable functions from $[0,1]$ to $\\mathbb T$\n(taken with pointwise multiplication and the topology of convergence in\nmeasure). This result answers a question of Glasner and Weiss. The main step in\nthe proof consists of showing that Koopman representations of ergodic boolean\nactions of $L^0(\\lambda, {\\mathbb T})$ possess a non-trivial property not\nshared by all unitary representations of $L^0(\\lambda, {\\mathbb T})$. In\nproving that theorem, we use an ergodic theorem for ergodic boolean actions of\n$L^0(\\lambda, {\\mathbb T})$ also established in the present paper. The main\ntool underlying our arguments is a theorem on the form of unitary\nrepresentations of $L^0(\\lambda, {\\mathbb T})$ from our earlier work.\n"}
+{"abstract": "  Observationally, the interstellar gas-phase abundance of deuterium (D) is\nconsiderably depleted and the missing D atoms are often postulated to have been\nlocked up into carbonaceous solids and polycyclic aromatic hydrocarbon (PAH)\nmolecules. An accurate knowledge of the fractional amount of D (relative to H)\ntied up in carbon dust and PAHs has important cosmological implications since D\noriginated exclusively from the Big Bang and the present-day D abundance, after\naccounting for the astration it has experienced during the Galactic evolution,\nprovides essential clues to the primordial nucleosynthesis and the cosmological\nparameters. To quantitatively explore the extent to which PAHs could possibly\naccommodate the observed D depletion, we have previously quantum-chemically\ncomputed the infrared vibrational spectra of mono-deuterated PAHs and derived\nthe mean intrinsic band strengths of the 3.3 $\\mu$m C--H stretch (A$_{3.3}$)\nand the 4.4 $\\mu$m C--D stretch (A$_{4.4}$). Here we extend our previous work\nto multi-deuterated PAH species of different deuterations, sizes and\nstructures. We find that both the intrinsic band strengths A$_{3.3}$ and\nA$_{4.4}$ and their ratios A$_{4.4}$/A$_{3.3}$ not only show little variations\namong PAHs of different deuterations, sizes and structures, they are also\nclosely similar to that of mono-deuterated PAHs. Therefore, a PAH deuteration\nlevel (i.e., the fraction of peripheral atoms attached to C atoms in the form\nof D) of ~2.4% previously estimated from the observed 4.4 $\\mu$m to 3.3 $\\mu$m\nband ratio based on the A$_{4.4}$/A$_{3.3}$ ratio of mono-deuterated PAHs is\nrobust.\n"}
+{"abstract": "  Attractive interaction between spinless fermions in a two-dimensional lattice\ndrives the formation of a topological superfluid. But the topological phase is\ndynamically unstable towards phase separation when the system has a high\ndensity of states and large interaction strength. This limits the critical\ntemperature to an experimentally challenging regime where, for example, even\nultracold atoms and molecules in optical lattices would struggle to realize the\ntopological superfluid. We propose that the introduction of a weaker\nlonger-range repulsion, in addition to the short-range attraction between\nlattice fermions, will suppress the phase separation instability. Taking the\nhoneycomb lattice as an example, we show that our proposal significantly\nenlarges the stable portion of the topological superfluid phase and increases\nthe critical temperature by an order of magnitude. Our work opens a route to\nenhance the stability of topological superfluids by engineering inter-particle\ninteractions.\n"}
+{"abstract": "  I study costly information acquisition in a two-sided matching problem, such\nas matching applicants to schools. An applicant's utility is a sum of common\nand idiosyncratic components. The idiosyncratic component is unknown to the\napplicant but can be learned at a cost. When applicants are assigned using an\nordinal strategy-proof mechanism, too few acquire information, generating a\nsignificant welfare loss. Affirmative action and other realistic policies may\nlead to a Pareto improvement. As incentives to acquire information differ\nacross mechanisms, ignoring such incentives may lead to incorrect welfare\nassessments, for example, in comparing a popular Immediate Assignment and an\nordinal strategy-proof mechanism.\n"}
+{"abstract": "  The Assmus-Mattson theorem gives a way to identify block designs arising from\ncodes. This result was broadened to matroids and weighted designs. In this work\nwe present a further two-fold generalisation: first from matroids to\npolymatroids and also from sets to vector spaces. To achieve this, we introduce\nthe characteristic polynomial of a $q$-polymatroid and outline several of its\nproperties.\n"}
+{"abstract": "  Emotions are reactions that can be expressed through a variety of social\nsignals. For example, anger can be expressed through a scowl, narrowed eyes, a\nlong stare, or many other expressions. This complexity is problematic when\nattempting to recognize a human's expression in a human-robot interaction:\ncategorical emotion models used in HRI typically use only a few prototypical\nclasses, and do not cover the wide array of expressions in the wild. We propose\na data-driven method towards increasing the number of known emotion classes\npresent in human-robot interactions, to 28 classes or more. The method includes\nthe use of automatic segmentation of video streams into short (<10s) videos,\nand annotation using the large set of widely-understood emojis as categories.\nIn this work, we showcase our initial results using a large in-the-wild HRI\ndataset (UE-HRI), with 61 clips randomly sampled from the dataset, labeled with\n28 different emojis. In particular, our results showed that the \"skeptical\"\nemoji was a common expression in our dataset, which is not often considered in\ntypical emotion taxonomies. This is the first step in developing a rich\ntaxonomy of emotional expressions that can be used in the future as labels for\ntraining machine learning models, towards more accurate perception of humans by\nrobots.\n"}
+{"abstract": "  Deep neural networks are known to be data-driven and label noise can have a\nmarked impact on model performance. Recent studies have shown great robustness\nto classic image recognition even under a high noisy rate. In medical\napplications, learning from datasets with label noise is more challenging since\nmedical imaging datasets tend to have asymmetric (class-dependent) noise and\nsuffer from high observer variability.\n  In this paper, we systematically discuss and define the two common types of\nlabel noise in medical images - disagreement label noise from inconsistency\nexpert opinions and single-target label noise from wrong diagnosis record. We\nthen propose an uncertainty estimation-based framework to handle these two\nlabel noise amid the medical image classification task. We design a\ndual-uncertainty estimation approach to measure the disagreement label noise\nand single-target label noise via Direct Uncertainty Prediction and\nMonte-Carlo-Dropout.\n  A boosting-based curriculum training procedure is later introduced for robust\nlearning. We demonstrate the effectiveness of our method by conducting\nextensive experiments on three different diseases: skin lesions, prostate\ncancer, and retinal diseases. We also release a large re-engineered database\nthat consists of annotations from more than ten ophthalmologists with an\nunbiased golden standard dataset for evaluation and benchmarking.\n"}
+{"abstract": "  We investigate ultrafast phonon dynamics in the Bi$_{1-x}$Sb$_{x}$ alloy\nsystem for various compositions $x$ using a reflective femtosecond pump-probe\ntechnique. The coherent optical phonons corresponding to the A$_{1g}$ local\nvibrational modes of Bi-Bi, Bi-Sb, and Sb-Sb are generated and observed in the\ntime domain with a few picoseconds dephasing time. The frequencies of the\ncoherent optical phonons were found to change as the Sb composition $x$ was\nvaried, and more importantly, the relaxation time of those phonon modes was\ndramatically reduced for $x$ values in the range 0.5--0.8. We argue that the\nphonon relaxation dynamics are not simply governed by alloy scattering, but are\nsignificantly modified by anharmonic phonon-phonon scattering with implied\nminor contributions from electron-phonon scattering in a Weyl-semimetal phase.\n"}
+{"abstract": "  A high-accuracy time discretization is discussed to numerically solve the\nnonlinear fractional diffusion equation forced by a space-time white noise. The\nmain purpose of this paper is to improve the temporal convergence rate by\nmodifying the semi-implicit Euler scheme. The solution of the equation is only\nH\\\"older continuous in time, which is disadvantageous to improve the temporal\nconvergence rate. Firstly, the system is transformed into an equivalent form\nhaving better regularity than the original one in time. But the regularity of\nnonlinear term remains unchanged. Then, combining Lagrange mean value theorem\nand independent increments of Brownian motion leads to a higher accuracy\ndiscretization of nonlinear term which ensures the implementation of the\nproposed time discretization scheme without loss of convergence rate. Our\nscheme can improve the convergence rate from\n${\\min\\{\\frac{\\gamma}{2\\alpha},\\frac{1}{2}\\}}$ to\n${\\min\\{\\frac{\\gamma}{\\alpha},1\\}}$ in the sense of mean-squared $L^2$-norm.\nThe theoretical error estimates are confirmed by extensive numerical\nexperiments.\n"}
+{"abstract": "  Human recognition of the actions of other humans is very efficient and is\nbased on patterns of movements. Our theoretical starting point is that the\ndynamics of the joint movements is important to action categorization. On the\nbasis of this theory, we present a novel action recognition system that employs\na hierarchy of Self-Organizing Maps together with a custom supervised neural\nnetwork that learns to categorize actions. The system preprocesses the input\nfrom a Kinect like 3D camera to exploit the information not only about joint\npositions, but also their first and second order dynamics. We evaluate our\nsystem in two experiments with publicly available data sets, and compare its\nperformance to the performance with less sophisticated preprocessing of the\ninput. The results show that including the dynamics of the actions improves the\nperformance. We also apply an attention mechanism that focuses on the parts of\nthe body that are the most involved in performing the actions.\n"}
+{"abstract": "  The Simons Observatory (SO) Large Aperture Telescope Receiver (LATR) will be\ncoupled to the Large Aperture Telescope located at an elevation of 5,200 m on\nCerro Toco in Chile. The resulting instrument will produce arcminute-resolution\nmillimeter-wave maps of half the sky with unprecedented precision. The LATR is\nthe largest cryogenic millimeter-wave camera built to date with a diameter of\n2.4 m and a length of 2.6 m. It cools 1200 kg of material to 4 K and 200 kg to\n100 mk, the operating temperature of the bolometric detectors with bands\ncentered around 27, 39, 93, 145, 225, and 280 GHz. Ultimately, the LATR will\naccommodate 13 40 cm diameter optics tubes, each with three detector wafers and\na total of 62,000 detectors. The LATR design must simultaneously maintain the\noptical alignment of the system, control stray light, provide cryogenic\nisolation, limit thermal gradients, and minimize the time to cool the system\nfrom room temperature to 100 mK. The interplay between these competing factors\nposes unique challenges. We discuss the trade studies involved with the design,\nthe final optimization, the construction, and ultimate performance of the\nsystem.\n"}
+{"abstract": "  In two recent papers by Pore et al. and Khuyagbaatar et al. discovery of the\nnew isotope Md-244 was reported. The decay data, however, are conflicting.\nWhile Pore et al. report two isomeric states decaying by alpha emission with\nE(1)=8.66(2) MeV, T_1/2=0.4+0.4/-0.1s and E(2)=8.31(2) MeV, T_1/2 approx 6 s,\nKhuyagbaatar et al. report only a single transition with a broad energy\ndistribution of E=(8.73-8.86) MeV and T_1/2=0.30/-0.09 s. The data published by\nPore et al. very similar to those published for Md-245 (E=8.64(2), 8.68(2) MeV\nT_1/2=0.35+0.23/-0.16}$ s ). Therefore, we compare the data presented for\nMd-244 by Pore et al. with those reported for Md-245 by Ninov et al. and also\nby Khuyagbaatar et al.. We conclude that the data presented by Pore et al.\nshall be attributed to Md-245 with small contributions (one event each) from\nFm-245 and probably Md-246.\n"}
+{"abstract": "  We study the Lagrangian structure of relativistic Vlasov systems, such as the\nrelativistic Vlasov-Poisson and the relativistic quasi-eletrostatic limit of\nVlasov-Maxwell equations. We show that renormalized solutions of these systems\nare Lagrangian and that these notions of solution, in fact, coincide. As a\nconsequence, finite-energy solutions are shown to be transported by a global\nflow. Moreover, we extend the notion of generalized solution for \"effective\"\ndensities and we prove its existence. Finally, under a higher integrability\nassumption of the initial condition, we show that solutions have every energy\nbounded, even in the gravitational case. These results extend to our setting\nthose obtained by Ambrosio, Colombo, and Figalli \\cite{vlasovpoisson} for the\nVlasov-Poisson system; here, we analyse relativistic systems and we consider\nthe contribution of the magnetic force into the evolution equation.\n"}
+{"abstract": "  Proton imaging is a powerful technique for imaging electromagnetic fields\nwithin an experimental volume, in which spatial variations in proton fluence\nare a result of deflections to proton trajectories due to interaction with the\nfields. When deflections are large, proton trajectories can overlap, and this\nnonlinearity creates regions of greatly increased proton fluence on the image,\nknown as caustics. The formation of caustics has been a persistent barrier to\nreconstructing the underlying fields from proton images. We have developed a\nnew method for reconstructing the path-integrated magnetic fields which begins\nto address the problem posed by caustics. Our method uses multiple proton\nimages of the same object, each image at a different energy, to fill in the\ninformation gaps and provide some uniqueness when reconstructing caustic\nfeatures. We use a differential evolution algorithm to iteratively estimate the\nunderlying deflection function which accurately reproduces the observed proton\nfluence at multiple proton energies simultaneously. We test this reconstruction\nmethod using synthetic proton images generated for three different,\ncylindrically symmetric field geometries at various field amplitudes and levels\nof proton statistics, and present reconstruction results from a set of\nexperimental images. The method we propose requires no assumption of deflection\nlinearity and can reliably solve for fields underlying linear, nonlinear, and\ncaustic proton image features for the selected geometries, and is shown to be\nfairly robust to noise in the input proton intensity.\n"}
+{"abstract": "  We study a model for the collective behavior of self-propelled particles\nsubject to pairwise copying interactions and noise. Particles move at a\nconstant speed $v$ on a two--dimensional space and, in a single step of the\ndynamics, each particle adopts the direction of motion of a randomly chosen\nneighboring particle, with the addition of a perturbation of amplitude $\\eta$\n(noise). We investigate how the global level of particles' alignment (order) is\naffected by their motion and the noise amplitude $\\eta$. In the static case\nscenario $v=0$ where particles are fixed at the sites of a square lattice and\ninteract with their first neighbors, we find that for any noise $\\eta_c>0$ the\nsystem reaches a steady state of complete disorder in the thermodynamic limit,\nwhile for $\\eta=0$ full order is eventually achieved for a system with any\nnumber of particles $N$. Therefore, the model displays a transition at zero\nnoise when particles are static, and thus there are no ordered steady states\nfor a finite noise ($\\eta>0$). We show that the finite-size transition noise\nvanishes with $N$ as $\\eta_c^{1D} \\sim N^{-1}$ and $\\eta_c^{2D} \\sim \\left(N\n\\ln N \\right)^{-1/2}$ in one and two--dimensional lattices, respectively, which\nis linked to known results on the behavior of a type of noisy voter model for\ncatalytic reactions. When particles are allowed to move in the space at a\nfinite speed $v>0$, an ordered phase emerges, characterized by a fraction of\nparticles moving in a similar direction. The system exhibits an order-disorder\nphase transition at a noise amplitude $\\eta_c>0$ that is proportional to $v$,\nand that scales approximately as $\\eta_c \\sim v \\, (-\\ln v)^{-1/2}$ for $v \\ll\n1$. These results show that the motion of particles is able to sustain a state\nof global order in a system with voter-like interactions.\n"}
+{"abstract": "  Certain fractional quantum Hall wavefunctions -- particularly including the\nLaughlin, Moore-Read, and Read-Rezayi wavefunctions -- have special structure\nthat makes them amenable to analysis using an exeptionally wide range of\ntechniques including conformal field theory (CFT), thin cylinder or torus\nlimit, study of symmetric polynomials and Jack polynomials, and so-called\n``special\" parent Hamiltonians. This review discusses these techniques as well\nas explaining to what degree some other quantum Hall wavefunctions share this\nspecial structure. Along the way we will explore the physics of quantum Hall\nedges, entanglement spectra, quasiparticles, nonabelian braiding statistics,\nand Hall viscosity, among other topics. As compared to a number of other recent\nreviews, most of this review is written so as to {\\it not} rely on results from\nconformal field theory -- although a short discussion of a few key relations to\nCFT are included near the end.\n"}
+{"abstract": "  The success of deep denoisers on real-world color photographs usually relies\non the modeling of sensor noise and in-camera signal processing (ISP) pipeline.\nPerformance drop will inevitably happen when the sensor and ISP pipeline of\ntest images are different from those for training the deep denoisers (i.e.,\nnoise discrepancy). In this paper, we present an unpaired learning scheme to\nadapt a color image denoiser for handling test images with noise discrepancy.\nWe consider a practical training setting, i.e., a pre-trained denoiser, a set\nof test noisy images, and an unpaired set of clean images. To begin with, the\npre-trained denoiser is used to generate the pseudo clean images for the test\nimages. Pseudo-ISP is then suggested to jointly learn the pseudo ISP pipeline\nand signal-dependent rawRGB noise model using the pairs of test and pseudo\nclean images. We further apply the learned pseudo ISP and rawRGB noise model to\nclean color images to synthesize realistic noisy images for denoiser adaption.\nPseudo-ISP is effective in synthesizing realistic noisy sRGB images, and\nimproved denoising performance can be achieved by alternating between\nPseudo-ISP training and denoiser adaption. Experiments show that our Pseudo-ISP\nnot only can boost simple Gaussian blurring-based denoiser to achieve\ncompetitive performance against CBDNet, but also is effective in improving\nstate-of-the-art deep denoisers, e.g., CBDNet and RIDNet.\n"}
+{"abstract": "  We propose using Carrier Sensing (CS) for distributed interference management\nin millimeter-wave (mmWave) cellular networks where spectrum is shared by\nmultiple operators that do not coordinate among themselves. In addition, even\nthe base station sites can be shared by the operators. We describe important\nchallenges in using traditional CS in this setting and propose enhanced CS\nprotocols to address these challenges. Using stochastic geometry, we develop a\ngeneral framework for downlink coverage probability analysis of our shared\nmmWave network in the presence of CS and derive the downlink coverage\nprobability expressions for several CS protocols. To the best of our knowledge,\nour work is the first to investigate and analyze (using stochastic geometry) CS\nfor mmWave networks with spectrum and BS sites shared among non-coordinating\noperators. We evaluate the downlink coverage probability of our shared mmWave\nnetwork using simulations as well as numerical examples based on our analysis.\nOur evaluations show that our proposed enhancements lead to an improvement in\ndownlink coverage probability, compared to the downlink coverage probability\nwith no CS, for higher values of signal-to-interference and noise ratio (SINR).\nInterestingly, our evaluations also reveal that for lower values of SINR, not\nusing any CS is the best strategy in terms of the downlink coverage\nprobability.\n"}
+{"abstract": "  We investigate the ultrafast electron dynamics triggered by terahertz and\noptical pulses in thin platinum and gold films by probing their transient\noptical reflectivity. The response of the platinum film to an intense terahertz\npulse is similar to the optically-induced dynamics of both films and can be\ndescribed by a two-temperature model. Surprisingly, gold can exhibit a much\nsmaller terahertz pulse-induced reflectivity change and with opposite sign. For\nplatinum, we estimate a 20% larger electron-phonon coupling for the\nterahertz-driven dynamics compared to the optically-induced one, which we\nascribe to an additional nonthermal electron-phonon coupling contribution. We\nexplain the remarkable response of gold to terahertz radiation with the field\nemission of electrons due the Fowler-Nordheim tunneling process, in samples\nwith thickness below the structural percolation threshold where near-field\nenhancement is possible. Our results provide a fundamental insight into the\nultrafast processes relevant to modern electro- and magneto-optical\napplications.\n"}
+{"abstract": "  We introduce a long-period generic spatial modulation into a typical model of\nthe Thouless pump, namely, the Rice--Mele (RM) model, to examine the lattice\nanalog of the fermion charge in quantum field theory. We derive a Diophantine\nequation relating the fermion charge and the pumped charge, which leads to the\none-dimensional (1D) analog of the Streda formula in the quantum Hall effect\n(QHE). This formula implies that an adiabatic change of the periodicity of the\nspatial modulation yields a spatial charge pump such that the rightmost charge\nis pumped to the right by the Chern number compared with the leftmost charge.\nThis causes a change in the length of the fermion chain by an integer, thus\nproviding the opportunity for direct measurement of the Streda formula in 1D\nsystems.\n"}
+{"abstract": "  In this work, we estimate the background components in muography using the\nMuTe: a hybrid muon telescope composed of two subdetectors -a scintillator\nhodoscope and a Water Cherenkov Detector (WCD). The hodoscope records the\ntrajectories of particles crossing the telescope, while the WCD measures their\nenergy loss. The MuTe hodoscope reconstructs 3841 different directions with an\nangular resolution of 32 mrad for an inter-panel distance of 2.5 m. The spatial\nresolution can reach $\\sim$25.6 m assuming an 800 m distance to the target. The\nWCD measures the deposited energy from 50 MeV to 1.5 GeV with a resolution of\n0.72 MeV.\n  MuTe discriminates muography background sources such as: upward coming muons,\nscattered muons, the soft component of Extensive Air Showers (EAS), and\nparticles arriving simultaneously. They are filtered by using measurements of\ndeposited energy (WCD) and Time-of-Flight. The WCD differentiates single muons,\nelectrons/positrons, and multiparticle events. On the other hand, the ToF\nmeasurements allow us to estimate the muon momentum establishing an energy\nthreshold to decrease the background contribution of scattered muons. Upward\ncoming muons are rejected by means of the particle arrival direction determined\nby the ToF sign.\n  We concluded that near 36% of the recorded events belong to the\nelectromagnetic component (electrons and positrons), roughly 30.4% is caused by\nmultiple particle events that arrive with time differences < 100 ns and the\nlast 34% are caused by muons. The muonic soft component (< 1 GeV/c) represents\n46% of the single-muon events. The upward going particles add up the 22% of the\ntotal flux crossing the MuTe.\n"}
+{"abstract": "  The Jiangmen Underground Neutrino Observatory (JUNO) is designed to determine\nthe neutrino mass ordering and measure neutrino oscillation parameters. A\nprecise muon reconstruction is crucial to reduce one of the major backgrounds\ninduced by cosmic muons. This article proposes a novel muon reconstruction\nmethod based on convolutional neural network (CNN) models. In this method, the\ntrack information reconstructed by the top tracker is used for network\ntraining. The training dataset is augmented by applying a rotation to muon\ntracks to compensate for the limited angular coverage of the top tracker. The\nmuon reconstruction with the CNN model can produce unbiased tracks with\nperformance that spatial resolution is better than 10 cm and angular resolution\nis better than 0.6 degrees. By using a GPU accelerated implementation a speedup\nfactor of 100 compared to existing CPU techniques has been demonstrated.\n"}
+{"abstract": "  Digital Twin, as an emerging technology related to Cyber-Physical Systems\n(CPS) and Internet of Things (IoT), has attracted increasing attentions during\nthe past decade. Conceptually, a Digital Twin is a digital replica of a\nphysical entity in the real world, and this technology is leveraged in this\nstudy to design a cooperative driving system at non-signalized intersections,\nallowing connected vehicles to cooperate with each other to cross intersections\nwithout any full stops. Within the proposed Digital Twin framework, we\ndeveloped an enhanced first-in-first-out (FIFO) slot reservation algorithm to\nschedule the sequence of crossing vehicles, a consensus motion control\nalgorithm to calculate vehicles' referenced longitudinal motion, and a\nmodel-based motion estimation algorithm to tackle communication delay and\npacket loss. Additionally, an augmented reality (AR) human-machine-interface\n(HMI) is designed to provide the guidance to drivers to cooperate with other\nconnected vehicles. Agent-based modeling and simulation of the proposed system\nis conducted in Unity game engine based on a real-world map in San Francisco,\nand the human-in-the-loop (HITL) simulation results prove the benefits of the\nproposed algorithms with 20% reduction in travel time and 23.7% reduction in\nenergy consumption, respectively, when compared with traditional signalized\nintersections.\n"}
+{"abstract": "  For every natural number $n$ we introduce a new weak choice principle\n$\\mathrm{nRC_{fin}}$: Given any infinite set $x$, there is an infinite subset\n$y\\subseteq x$ and a selection function $f$ that chooses an $n$-element subset\nfrom every finite $z\\subseteq y$ containing at least $n$ elements. By\nconstructing new permutation models built on a set of atoms obtained as\nFra\\\"iss\\'e limits, we will study the relation of $\\mathrm{nRC_{fin}}$ to the\nweak choice principles $\\mathrm{RC_m}$ (that has already been studied by\nMontenegro, Halbeisen and Tachtsis): Given any infinite set $x$, there is an\ninfinite subset $y\\subseteq x$ with a choice function $f$ on the family of all\n$m$-element subsets of $y$. Moreover, we prove a stronger analogue of\nMontenegros results when we study the relation between $\\mathrm{nRC_{fin}}$ and\n$\\mathrm{kC_{fin}^-}$ which is defined by: Given any infinite family\n$\\mathcal{F}$ of finite sets of cardinality greater than $k$, there is an\ninfinite subfamily $\\mathcal{A}\\subseteq \\mathcal{F}$ with a selection function\n$f$ that chooses a $k$-element subset from each $A\\in\\mathcal{A}$.\n"}
+{"abstract": "  Nuclear physics experiments are always in need of more and more advanced\ndetection systems. During the last years relevant technological developments\nhave come out with many improvements in terms of performance and compactness of\ndetector materials, transducers, electronics, computing and data transmission.\nIn light of these achievements some applications previously prohibitive, mainly\nbecause of size and cost, have become feasible. A few applications of nuclear\ndetection techniques are discussed, starting from the neighboring field of\nparticle beam diagnostics, moving to the medical diagnostics and ending up into\nthe radioactive waste handling. New radiation sensors are shown and explained,\nas exploited in the DMNR project for the radioactive waste online monitoring\nwhich merged into the MICADO Euratom project.\n"}
+{"abstract": "  Humans' experience of the world is profoundly multimodal from the beginning,\nso why do existing state-of-the-art language models only use text as a modality\nto learn and represent semantic meaning? In this paper we review the literature\non the role of embodiment and emotion in the interactive setting of spoken\ndialogue as necessary prerequisites for language learning for human children,\nincluding how words in child vocabularies are largely concrete, then shift to\nbecome more abstract as the children get older. We sketch a model of semantics\nthat leverages current transformer-based models and a word-level grounded\nmodel, then explain the robot-dialogue system that will make use of our\nsemantic model, the setting for the system to learn language, and existing\nbenchmarks for evaluation.\n"}
+{"abstract": "  Sampling from probability distributions of quantum circuits is a\nfundamentally and practically important task which can be used to demonstrate\nquantum supremacy using noisy intermediate-scale quantum devices. In the\npresent work, we examine classical simulability of sampling from the output\nphoton-number distribution of linear-optical circuits composed of random beam\nsplitters with equally distributed squeezed vacuum states and single-photon\nstates input. We provide efficient classical algorithms to simulate\nlinear-optical random circuits and show that the algorithms' error is\nexponentially small up to a depth less than quadratic in the distance between\nsources using a classical random walk behavior of random linear-optical\ncircuits. Notably, the average-case depth allowing an efficient classical\nsimulation is larger than the worst-case depth limit, which is linear in the\ndistance. Besides, our results together with the hardness of boson sampling\ngive a lower-bound on the depth for constituting global Haar-random unitary\ncircuits.\n"}
+{"abstract": "  This paper presents BSTC (Baidu Speech Translation Corpus), a large-scale\nChinese-English speech translation dataset. This dataset is constructed based\non a collection of licensed videos of talks or lectures, including about 68\nhours of Mandarin data, their manual transcripts and translations into English,\nas well as automated transcripts by an automatic speech recognition (ASR)\nmodel. We have further asked three experienced interpreters to simultaneously\ninterpret the testing talks in a mock conference setting. This corpus is\nexpected to promote the research of automatic simultaneous translation as well\nas the development of practical systems. We have organized simultaneous\ntranslation tasks and used this corpus to evaluate automatic simultaneous\ntranslation systems.\n"}
+{"abstract": "  The intelligibility of speech severely degrades in the presence of\nenvironmental noise and reverberation. In this paper, we propose a novel deep\nlearning based system for modifying the speech signal to increase its\nintelligibility under the equal-power constraint, i.e., signal power before and\nafter modification must be the same. To achieve this, we use generative\nadversarial networks (GANs) to obtain time-frequency dependent amplification\nfactors, which are then applied to the input raw speech to reallocate the\nspeech energy. Instead of optimizing only a single, simple metric, we train a\ndeep neural network (DNN) model to simultaneously optimize multiple advanced\nspeech metrics, including both intelligibility- and quality-related ones, which\nresults in notable improvements in performance and robustness. Our system can\nnot only work in non-realtime mode for offline audio playback but also support\npractical real-time speech applications. Experimental results using both\nobjective measurements and subjective listening tests indicate that the\nproposed system significantly outperforms state-ofthe-art baseline systems\nunder various noisy and reverberant listening conditions.\n"}
+{"abstract": "  In the d-dimensional online bin packing problem, d-dimensional cubes of\npositive sizes no larger than 1 are presented one by one to be assigned to\npositions in d-dimensional unit cube bins. In this work, we provide improved\nupper bounds on the asymptotic competitive ratio for square and cube bin\npacking problems, where our bounds do not exceed 2.0885 and 2.5735 for square\nand cube packing, respectively. To achieve these results, we adapt and improve\na previously designed harmonic-type algorithm, and apply a different method for\ndefining weight functions. We detect deficiencies in the state-of-the-art\nresults by providing counter-examples to the current best algorithms and the\nanalysis, where the claimed bounds were 2.1187 for square packing and 2.6161\nfor cube packing.\n"}
+{"abstract": "  We study two methods for differentially private analysis of bounded data and\nextend these to nonnegative queries. We first recall that for the Laplace\nmechanism, boundary inflated truncation (BIT) applied to nonnegative queries\nand truncation both lead to strictly positive bias. We then consider a\ngeneralization of BIT using translated ramp functions. We explicitly\ncharacterise the optimal function in this class for worst case bias. We show\nthat applying any square-integrable post-processing function to a Laplace\nmechanism leads to a strictly positive maximal absolute bias. A corresponding\nresult is also shown for a generalisation of truncation, which we refer to as\nrestriction. We also briefly consider an alternative approach based on\nmultiplicative mechanisms for positive data and show that, without additional\nrestrictions, these mechanisms can lead to infinite bias.\n"}
+{"abstract": "  Complex networks contain complete subgraphs such as nodes, edges, triangles,\netc., referred to as simplices and cliques of different orders. Notably,\ncavities consisting of higher-order cliques play an important role in brain\nfunctions. Since searching for maximum cliques is an NP-complete problem, we\nuse k-core decomposition to determine the computability of a given network. For\na computable network, we design a search method with an implementable algorithm\nfor finding cliques of different orders, obtaining also the Euler\ncharacteristic number. Then, we compute the Betti numbers by using the ranks of\nboundary matrices of adjacent cliques. Furthermore, we design an optimized\nalgorithm for finding cavities of different orders. Finally, we apply the\nalgorithm to the neuronal network of C. elegans with data from one typical\ndataset, and find all of its cliques and some cavities of different orders,\nproviding a basis for further mathematical analysis and computation of its\nstructure and function.\n"}
+{"abstract": "  A Maximum Likelihood recursive state estimator is derived for non-linear and\nnon-Gaussian state-space models. The estimator combines a particle filter to\ngenerate the conditional density and the Expectation Maximization algorithm to\ncompute the maximum likelihood state estimate iteratively. Algorithms for\nmaximum likelihood state filtering, prediction and smoothing are presented. The\nconvergence properties of these algorithms, which are inherited from the\nExpectation Maximization algorithm, are proven and examined in two examples. It\nis shown that, with randomized reinitialization, which is feasible because of\nthe algorithm simplicity, these methods are able to converge to the Maximum\nLikelihood Estimate (MLE) of multimodal, truncated and skewed densities, as\nwell as those of disjoint support.\n"}
+{"abstract": "  The baryon form factor of charged pions arises since isospin symmetry is\nbroken with unequal up and down quark masses, $m_d>m_u$, as well as\nelectromagnetic effects. We obtain estimates for this basic property in two\nphenomenological ways: from simple constituent quark models, as well as from\nfitting the $e^+e^- \\to \\pi^+ \\pi^-$ data. All our methods yield the result\nthat the baryon mean square radius, extracted from the slope of the form\nfactor, is positive for $\\pi^+$, hence a picture where the outer region has a\nnet baryon, and the inner region a net antibaryon density, both compensating\neach other such that the total baryon number is zero. For $\\pi^-$ the effect is\nequal and opposite. We estimate the corresponding mean squared baryon radius as\n$\\langle r^2 \\rangle_B^{\\pi^{+}} = (0.03-0.04~{\\rm fm})^2$.\n"}
+{"abstract": "  High-dimensional data sets are often analyzed and explored via the\nconstruction of a latent low-dimensional space which enables convenient\nvisualization and efficient predictive modeling or clustering. For complex data\nstructures, linear dimensionality reduction techniques like PCA may not be\nsufficiently flexible to enable low-dimensional representation. Non-linear\ndimension reduction techniques, like kernel PCA and autoencoders, suffer from\nloss of interpretability since each latent variable is dependent of all input\ndimensions. To address this limitation, we here present path lasso penalized\nautoencoders. This structured regularization enhances interpretability by\npenalizing each path through the encoder from an input to a latent variable,\nthus restricting how many input variables are represented in each latent\ndimension. Our algorithm uses a group lasso penalty and non-negative matrix\nfactorization to construct a sparse, non-linear latent representation. We\ncompare the path lasso regularized autoencoder to PCA, sparse PCA, autoencoders\nand sparse autoencoders on real and simulated data sets. We show that the\nalgorithm exhibits much lower reconstruction errors than sparse PCA and\nparameter-wise lasso regularized autoencoders for low-dimensional\nrepresentations. Moreover, path lasso representations provide a more accurate\nreconstruction match, i.e. preserved relative distance between objects in the\noriginal and reconstructed spaces.\n"}
+{"abstract": "  In this work, we consider a multi-user mobile edge computing system with\nmultiple computing access points (CAPs). Each mobile user has multiple\ndependent tasks that must be processed in a round-by-round schedule. In every\nround, a user may process their individual task locally, or choose to offload\ntheir task to one of the $M$ CAPs or the remote cloud server, in order to\npossibly reduce their processing cost. We aim to jointly optimize the\noffloading decisions of the users and the resource allocation decisions for\neach CAP over a global objective function, defined as a weighted sum of total\nenergy consumption and the round time. We first present a centralized heuristic\nsolution, termed MCAP, where the original problem is relaxed to a semi-definite\nprogram (SDP) to probabilistically generate the offloading decision. Then,\nrecognizing that the users often exhibit selfish behavior to reduce their\nindividual cost, we propose a game-theoretical approach, termed MCAP-NE, which\nallows us to compute a Nash Equilibrium (NE) through a finite improvement\nmethod starting from the previous SDP solution. This approach leads to a\nsolution from which the users have no incentive to deviate, with substantially\nreduced NE computation time. In simulation, we compare the system cost of the\nNE solution with those of MCAP, MCAP-NE, a random mapping, and the optimal\nsolution, showing that our NE solution attains near optimal performance under a\nwide set of parameter settings, as well as demonstrating the advantages of\nusing MCAP to produce the initial point for MCAP-NE.\n"}
+{"abstract": "  Although Shapley Values (SV) are widely used in explainable AI, they can be\npoorly understood and estimated, which implies that their analysis may lead to\nspurious inferences and explanations. As a starting point, we remind an\ninvariance principle for SV and derive the correct approach for computing the\nSV of categorical variables that are particularly sensitive to the encoding\nused. In the case of tree-based models, we introduce two estimators of Shapley\nValues that exploit efficiently the tree structure and are more accurate than\nstate-of-the-art methods. For interpreting additive explanations, we recommend\nto filter the non-influential variables and to compute the Shapley Values only\nfor groups of influential variables. For this purpose, we use the concept of\n\"Same Decision Probability\" (SDP) that evaluates the robustness of a prediction\nwhen some variables are missing. This prior selection procedure produces sparse\nadditive explanations easier to visualize and analyse. Simulations and\ncomparisons are performed with state-of-the-art algorithm, and show the\npractical gain of our approach.\n"}
+{"abstract": "  Monte Carlo rendering algorithms are widely used to produce photorealistic\ncomputer graphics images. However, these algorithms need to sample a\nsubstantial amount of rays per pixel to enable proper global illumination and\nthus require an immense amount of computation. In this paper, we present a\nhybrid rendering method to speed up Monte Carlo rendering algorithms. Our\nmethod first generates two versions of a rendering: one at a low resolution\nwith a high sample rate (LRHS) and the other at a high resolution with a low\nsample rate (HRLS). We then develop a deep convolutional neural network to fuse\nthese two renderings into a high-quality image as if it were rendered at a high\nresolution with a high sample rate. Specifically, we formulate this fusion task\nas a super resolution problem that generates a high resolution rendering from a\nlow resolution input (LRHS), assisted with the HRLS rendering. The HRLS\nrendering provides critical high frequency details which are difficult to\nrecover from the LRHS for any super resolution methods. Our experiments show\nthat our hybrid rendering algorithm is significantly faster than the\nstate-of-the-art Monte Carlo denoising methods while rendering high-quality\nimages when tested on both our own BCR dataset and the Gharbi dataset.\n\\url{https://github.com/hqqxyy/msspl}\n"}
+{"abstract": "  Popular social media networks provide the perfect environment to study the\nopinions and attitudes expressed by users. While interactions in social media\nsuch as Twitter occur in many natural languages, research on stance detection\n(the position or attitude expressed with respect to a specific topic) within\nthe Natural Language Processing field has largely been done for English.\nAlthough some efforts have recently been made to develop annotated data in\nother languages, there is a telling lack of resources to facilitate\nmultilingual and crosslingual research on stance detection. This is partially\ndue to the fact that manually annotating a corpus of social media texts is a\ndifficult, slow and costly process. Furthermore, as stance is a highly domain-\nand topic-specific phenomenon, the need for annotated data is specially\ndemanding. As a result, most of the manually labeled resources are hindered by\ntheir relatively small size and skewed class distribution. This paper presents\na method to obtain multilingual datasets for stance detection in Twitter.\nInstead of manually annotating on a per tweet basis, we leverage user-based\ninformation to semi-automatically label large amounts of tweets. Empirical\nmonolingual and cross-lingual experimentation and qualitative analysis show\nthat our method helps to overcome the aforementioned difficulties to build\nlarge, balanced and multilingual labeled corpora. We believe that our method\ncan be easily adapted to easily generate labeled social media data for other\nNatural Language Processing tasks and domains.\n"}
+{"abstract": "  Using the Attouch-Th\\'era duality, we study the cycles, gap vectors and fixed\npoint sets of compositions of proximal mappings. Sufficient conditions are\ngiven for the existence of cycles and gap vectors. A primal-dual framework\nprovides an exact relationship between the cycles and gap vectors. We also\nintroduce the generalized cycle and gap vectors to tackle the case when the\nclassical ones do not exist. Examples are given to illustrate our results.\n"}
+{"abstract": "  Chaotic attractors, chaotic saddles and periodic orbits are examples of\nchain-recurrent sets. Using arbitrary small controls, a trajectory starting\nfrom any point in a chain-recurrent set can be steered to any other in that\nset. The qualitative behavior of a dynamical system can be encapsulated in a\ngraph. Its nodes are chain-recurrent sets. There is an edge from node A to node\nB if, using arbitrary small controls, a trajectory starting from any point of A\ncan be steered to any point of B. We discuss physical systems that have\ninfinitely many disjoint coexisting nodes. Such infinite collections can occur\nfor many carefully chosen parameter values. The logistic map is such a system,\nas we showed in arXiv:2008.08338. To illustrate these very common phenomena, we\ncompare the Lorenz system and the logistic map and we show how extremely\nsimilar their bifurcation diagrams are in some parameter ranges.\n"}
+{"abstract": "  We present a method to infer the 3D pose of mice, including the limbs and\nfeet, from monocular videos. Many human clinical conditions and their\ncorresponding animal models result in abnormal motion, and accurately measuring\n3D motion at scale offers insights into health. The 3D poses improve\nclassification of health-related attributes over 2D representations. The\ninferred poses are accurate enough to estimate stride length even when the feet\nare mostly occluded. This method could be applied as part of a continuous\nmonitoring system to non-invasively measure animal health.\n"}
+{"abstract": "  As a statistical tool to assist formative assessments in educational\nsettings, diagnostic classification models (DCMs) have been increasingly used\nto provide diagnostic information regarding examinees' attributes. DCMs often\nadopt dichotomous division such as mastery and non-mastery of attributes to\nexpress mastery states of attributes. However, many practical settings involve\ndifferent levels of mastery states rather than a simple dichotomy in a single\nattribute. Although this practical demand can be addressed by\npolytomous-attribute DCMs, their computational cost in a Markov chain Monte\nCarlo estimation impedes their large-scale applications due to the larger\nnumber of polytomous-attribute mastery patterns than that of binary-attribute\nones. This study considers a scalable Bayesian estimation method for\npolytomous-attribute DCMs and developed a variational Bayesian (VB) algorithm\nfor a polytomous-attribute saturated DCM -- a generalization of\npolytomous-attribute DCMs -- by building on the existing literature in VB for\nbinary-attribute DCMs and polytomous-attribute DCMs. Furthermore, we proposed\nthe configuration of parallel computing for the proposed VB algorithm to\nachieve better computational efficiency. Monte Carlo simulations revealed that\nour method exhibited the high performance in parameter recovery under a wide\nrange of conditions. An empirical example is used to demonstrate the utility of\nour method.\n"}
+{"abstract": "  Light projection displays play an increasingly important role in our modern\nlife. Core projection systems including liquid crystal displays and digital\nmicromirror devices can impose spatial light modulation and actively shape\nlight waves. Recently, the advent of metasurfaces has revolutionized design\nconcepts in nanophotonics, enabling a new family of optical elements with\nexceptional degrees of freedom. Here, we demonstrate a light projection display\ntechnology based on optical metasurfaces, called digital metasurface device\n(DMSD). Each metasurface pixel in a DMSD is electrically reconfigurable with\nwell-controlled programmability and addressability. The DMSD can not only\ncontinuously modulate the intensity of light with high contrast, but also shape\nthe wavefront of light generated by each metasurface pixel and dynamically\nswitch between arbitrary holographic patterns. Our approach will pave an avenue\ntowards the development of customized light projection devices. It will also\ndrive the field of dynamic optical metasurfaces with fresh momentum and\ninspiring concepts.\n"}
+{"abstract": "  The advancement of convolutional neural networks (CNNs) on various vision\napplications has attracted lots of attention. Yet the majority of CNNs are\nunable to satisfy the strict requirement for real-world deployment. To overcome\nthis, the recent popular network pruning is an effective method to reduce the\nredundancy of the models. However, the ranking of filters according to their\n\"importance\" on different pruning criteria may be inconsistent. One filter\ncould be important according to a certain criterion, while it is unnecessary\naccording to another one, which indicates that each criterion is only a partial\nview of the comprehensive \"importance\". From this motivation, we propose a\nnovel framework to integrate the existing filter pruning criteria by exploring\nthe criteria diversity. The proposed framework contains two stages: Criteria\nClustering and Filters Importance Calibration. First, we condense the pruning\ncriteria via layerwise clustering based on the rank of \"importance\" score.\nSecond, within each cluster, we propose a calibration factor to adjust their\nsignificance for each selected blending candidates and search for the optimal\nblending criterion via Evolutionary Algorithm. Quantitative results on the\nCIFAR-100 and ImageNet benchmarks show that our framework outperforms the\nstate-of-the-art baselines, regrading to the compact model performance after\npruning.\n"}
+{"abstract": "  The estimate of the cumulative incidence of SARS-CoV-2 of 76% in Manaus by\nOctober 2020 by Buss et al. relies on the assumption of an\nexponentially-declining probability of sero-reversion over time. We present an\nalternative, empirically-supported approach that is based on the observed\ndynamics of antibody titers in sero-positive cases. Through this approach we\nrevise the cumulative incidence estimate to 66% (63.3% - 68.5%) by October\n2020. This estimate has implications for the proximity to herd immunity and\nfuture estimates of fitness advantages of virus variants, such as the P.1\nvariant of concern. This methodology is also relevant for any other sero-survey\nanalysis that has to adjust for sero-reversion.\n"}
+{"abstract": "  We formally prove the existence of a quantization procedure that makes the\npath integral of a general diffeomorphism-invariant theory of gravity, with\nfixed total spacetime volume, equivalent to that of its unimodular version.\nThis is achieved by means of a partial gauge fixing of diffeomorphisms together\nwith a careful definition of the unimodular measure. The statement holds also\nin the presence of matter. As an explicit example, we consider scalar-tensor\ntheories and compute the corresponding logarithmic divergences in both\nsettings. In spite of significant differences in the coupling of the scalar\nfield to gravity, the results are equivalent for all couplings, including\nnon-minimal ones.\n"}
+{"abstract": "  Two of the most important technological advancements currently underway are\nthe advent of quantum technologies, and the transitioning of global financial\nsystems towards cryptographic assets, notably blockchain-based cryptocurrencies\nand smart contracts. There is, however, an important interplay between the two,\ngiven that, in due course, quantum technology will have the ability to directly\ncompromise the cryptographic foundations of blockchain. We explore this complex\ninterplay by building financial models for quantum failure in various\nscenarios, including pricing quantum risk premiums. We call this quantum\ncrypto-economics.\n"}
+{"abstract": "  We prove Archimedes' principle for a macroscopic ball in ideal gas consisting\nof point particles with non-zero mass. The main result is an asymptotic\ntheorem, as the number of point particles goes to infinity and their total mass\nremains constant. We also show that, asymptotically, the gas has an exponential\ndensity as a function of height. We find the asymptotic inverse temperature of\nthe gas. We derive an accurate estimate of the volume of the phase space using\nthe local central limit theorem.\n"}
+{"abstract": "  What is the creative process through which an artist goes from an original\nimage to a painting? Can we examine this process using techniques from computer\nvision and pattern recognition? Here we set the first preliminary steps to\nalgorithmically deconstruct some of the transformations that an artist applies\nto an original image in order to establish centres of interest, which are focal\nareas of a painting that carry meaning. We introduce a comparative methodology\nthat first cuts out the minimal segment from the original image on which the\npainting is based, then aligns the painting with this source, investigates\nmicro-differences to identify centres of interest and attempts to understand\ntheir role. In this paper we focus exclusively on micro-differences with\nrespect to edges. We believe that research into where and how artists create\ncentres of interest in paintings is valuable for curators, art historians,\nviewers, and art educators, and might even help artists to understand and\nrefine their own artistic method.\n"}
+{"abstract": "  Review articles summarize state-of-the-art work and provide a means to\norganize the growing number of scholarly publications. However, the current\nreview method and publication mechanisms hinder the impact review articles can\npotentially have. Among other limitations, reviews only provide a snapshot of\nthe current literature and are generally not readable by machines. In this\nwork, we identify the weaknesses of the current review method. Afterwards, we\npresent the SmartReview approach addressing those weaknesses. The approach\npushes towards semantic community-maintained review articles. At the core of\nour approach, knowledge graphs are employed to make articles more\nmachine-actionable and maintainable.\n"}
+{"abstract": "  We consider the dynamics of fluctuations in the quantum asymmetric simple\nexclusion process (Q-ASEP) with periodic boundary conditions. The Q-ASEP\ndescribes a chain of spinless fermions with random hoppings that are induced by\na Markovian environment. We show that fluctuations of the fermionic degrees of\nfreedom obey evolution equations of Lindblad type, and derive the corresponding\nLindbladians. We identify the underlying algebraic structure by mapping them to\nnon-Hermitian spin chains and demonstrate that the operator space fragments\ninto exponentially many (in system size) sectors that are invariant under time\nevolution. At the level of quadratic fluctuations we consider the Lindbladian\non the sectors that determine the late time dynamics for the particular case of\nthe quantum symmetric simple exclusion process (Q-SSEP). We show that the\ncorresponding blocks in some cases correspond to known Yang-Baxter integrable\nmodels and investigate the level-spacing statistics in others. We carry out a\ndetailed analysis of the steady states and slow modes that govern the late time\nbehaviour and show that the dynamics of fluctuations of observables is\ndescribed in terms of closed sets of coupled linear differential-difference\nequations. The behaviour of the solutions to these equations is essentially\ndiffusive but with relevant deviations, that at sufficiently late times and\nlarge distances can be described in terms of a continuum scaling limit which we\nconstruct. We numerically check the validity of this scaling limit over a\nsignificant range of time and space scales. These results are then applied to\nthe study of operator spreading at large scales, focusing on out-of-time\nordered correlators and operator entanglement.\n"}
+{"abstract": "  Health economic models simulate the costs and effects of health technologies\nfor use in health technology assessment (HTA) to inform efficient use of scarce\nresources. Models have historically been developed using spreadsheet software\n(e.g., Microsoft Excel) and while use of R is growing, general purpose modeling\nsoftware is still limited. hesim is an R package that helps fill this gap by\nfacilitating parameterization, simulation, and analysis of economic models in\nan integrated manner. Supported model types include cohort discrete time state\ntransition models (cDTSTMs), individual continuous time state transition models\n(iCTSTMs), and partitioned survival models (PSMs), encompassing Markov\n(time-homogeneous and time-inhomogeneous) and semi-Markov processes. A modular\ndesign based on R6 and S3 classes allows users to combine separate submodels\nfor disease progression, costs, and utility in a flexible way. Probabilistic\nsensitivity analysis (PSA) is used to propagate uncertainty in model parameters\nto model outputs. Simulation code is written in C++ so complex simulations such\nas those combining PSA and individual simulation can be run much more quickly\nthan previously possible. Decision analysis within a cost-effectiveness\nframework is performed using simulated costs and quality-adjusted life years\n(QALYs) from a PSA.\n"}
+{"abstract": "  We prove global existence of solutions of a loglog energy-supercritical\nKlein-Gordon equation for n=3,4,5. Assuming that blow-up occurs at a time of\nmaximal existence, we perform an analysis close to this time in order to find a\nfinite bound of a Strichartz-type norm, which eventually leads to a contraction\nwith the blow-up assumption.\n"}
+{"abstract": "  We propose a modern method to estimate population size based on\ncapture-recapture designs of K samples. The observed data is formulated as a\nsample of n i.i.d. K-dimensional vectors of binary indicators, where the k-th\ncomponent of each vector indicates the subject being caught by the k-th sample,\nsuch that only subjects with nonzero capture vectors are observed. The target\nquantity is the unconditional probability of the vector being nonzero across\nboth observed and unobserved subjects. We cover models assuming a single\nconstraint (identification assumption) on the K-dimensional distribution such\nthat the target quantity is identified and the statistical model is\nunrestricted. We present solutions for linear and non-linear constraints\ncommonly assumed to identify capture-recapture models, including no K-way\ninteraction in linear and log-linear models, independence or conditional\nindependence. We demonstrate that the choice of constraint has a dramatic\nimpact on the value of the estimand, showing that it is crucial that the\nconstraint is known to hold by design. For the commonly assumed constraint of\nno K-way interaction in a log-linear model, the statistical target parameter is\nonly defined when each of the $2^K - 1$ observable capture patterns is present,\nand therefore suffers from the curse of dimensionality. We propose a targeted\nMLE based on undersmoothed lasso model to smooth across the cells while\ntargeting the fit towards the single valued target parameter of interest. For\neach identification assumption, we provide simulated inference and confidence\nintervals to assess the performance on the estimator under correct and\nincorrect identifying assumptions. We apply the proposed method, alongside\nexisting estimators, to estimate prevalence of a parasitic infection using\nmulti-source surveillance data from a region in southwestern China, under the\nfour identification assumptions.\n"}
+{"abstract": "  As surgical robots become more common, automating away some of the burden of\ncomplex direct human operation becomes ever more feasible. Model-free\nreinforcement learning (RL) is a promising direction toward generalizable\nautomated surgical performance, but progress has been slowed by the lack of\nefficient and realistic learning environments. In this paper, we describe\nadding reinforcement learning support to the da Vinci Skill Simulator, a\ntraining simulation used around the world to allow surgeons to learn and\nrehearse technical skills. We successfully teach an RL-based agent to perform\nsub-tasks in the simulator environment, using either image or state data. As\nfar as we know, this is the first time an RL-based agent is taught from visual\ndata in a surgical robotics environment. Additionally, we tackle the sample\ninefficiency of RL using a simple-to-implement system which we term\nhybrid-batch learning (HBL), effectively adding a second, long-term replay\nbuffer to the Q-learning process. Additionally, this allows us to bootstrap\nlearning from images from the data collected using the easier task of learning\nfrom state. We show that HBL decreases our learning times significantly.\n"}
+{"abstract": "  We consider the Scale-Free Adversarial Multi Armed Bandits(MAB) problem. At\nthe beginning of the game, the player only knows the number of arms $n$. It\ndoes not know the scale and magnitude of the losses chosen by the adversary or\nthe number of rounds $T$. In each round, it sees bandit feedback about the loss\nvectors $l_1,\\dots, l_T \\in \\mathbb{R}^n$. The goal is to bound its regret as a\nfunction of $n$ and norms of $l_1,\\dots, l_T$. We design a bandit Follow The\nRegularized Leader (FTRL) algorithm, that uses an adaptive learning rate and\ngive two different regret bounds, based on the exploration parameter used. With\nnon-adaptive exploration, our algorithm has a regret of\n$\\tilde{\\mathcal{O}}(\\sqrt{nL_2} + L_\\infty\\sqrt{nT})$ and with adaptive\nexploration, it has a regret of $\\tilde{\\mathcal{O}}(\\sqrt{nL_2} +\nL_\\infty\\sqrt{nL_1})$. Here $L_\\infty = \\sup_t \\| l_t\\|_\\infty$, $L_2 =\n\\sum_{t=1}^T \\|l_t\\|_2^2$, $L_1 = \\sum_{t=1}^T \\|l_t\\|_1$ and the\n$\\tilde{\\mathcal{O}}$ notation suppress logarithmic factors. These are the\nfirst MAB bounds that adapt to the $\\|\\cdot\\|_2$, $\\|\\cdot\\|_1$ norms of the\nlosses. The second bound is the first data-dependent scale-free MAB bound as\n$T$ does not directly appear in the regret. We also develop a new technique for\nobtaining a rich class of local-norm lower-bounds for Bregman Divergences. This\ntechnique plays a crucial role in our analysis for controlling the regret when\nusing importance weighted estimators of unbounded losses. This technique could\nbe of independent interest.\n"}
+{"abstract": "  A class of noncanonical effective potentials is introduced allowing stable,\nradially symmetric, solutions to first order Bogomol'nyi equations for a real\nscalar field in a fixed spacetime background. This class of effective\npotentials generalizes those found previously by Bazeia, Menezes, and Menezes\n[Phys.Rev.Lett. 91 (2003) 241601] for radially symmetric defects in a flat\nspacetime. Use is made of the \"on-shell method\" introduced by Atmaja and\nRamadhan [Phys.Rev.D 90 (2014) 10, 105009] of reducing the second order\nequation of motion to a first order one, along with a constraint equation. This\nmethod and class of potentials admits radially symmetric, stable solutions for\nfour dimensional static, radially symmetric spacetimes. Stability against\nradial fluctuations is established with a modified version of Derrick's\ntheorem, along with demonstrating that the radial stress vanishes. Several\nexamples of scalar field configurations are given.\n"}
+{"abstract": "  Slot filling and intent detection have become a significant theme in the\nfield of natural language understanding. Even though slot filling is\nintensively associated with intent detection, the characteristics of the\ninformation required for both tasks are different while most of those\napproaches may not fully aware of this problem. In addition, balancing the\naccuracy of two tasks effectively is an inevitable problem for the joint\nlearning model. In this paper, a Continual Learning Interrelated Model (CLIM)\nis proposed to consider semantic information with different characteristics and\nbalance the accuracy between intent detection and slot filling effectively. The\nexperimental results show that CLIM achieves state-of-the-art performace on\nslot filling and intent detection on ATIS and Snips.\n"}
+{"abstract": "  After recent observational events like the LIGO-Virgo detections of\ngravitational waves and the shadow image of M87* supermassive black hole by\nEvent Horizon Telescope (EHT), the theoretical study of black holes was\nsignificantly improved. Quantities as quasinormal frequencies, shadows, and\nlight deflection become more important to analyze black hole models. In this\ncontext, an interesting scenario to study is a black hole in the bumblebee\ngravity. The bumblebee vector field imposes a spontaneous symmetry breaking\nthat allows the field to acquire a vacuum expectation value that generates\nLorentz-Violating (LV) into the black hole. In order to compute the quasinormal\nmodes (QNMs) via the WKB method, we obtain the Reege-Wheeler's equation with a\nbell-shaped potential for this black hole. Both QNMs, the scalar and tensorial\nmodes, are computed for the black hole in the bumblebee scenario. Moreover, the\nstability of the Schwarzschild-like solution and some bounds to LV parameters\nare analyzed.\n"}
+{"abstract": "  Demand response for electrical power networks is a mature field that has\nyielded numerous efficiency and resilience benefits like managing the peaks and\nvalleys of electricity usage, reduction in peak electricity usage, to name a\nfew. However, only recently has the study of the counterpart to demand response\nfor electric power in natural gas started to receive similar levels of\nattention. Natural gas systems are increasingly operating at or near capacity,\nwhich challenges these systems to meet all the needs for gas, especially during\nsevere winter weather. However, unlike demand response programs in electrical\nnetworks, demand response in gas networks cannot shift peak usage. Here, we\ndevelop analogues to demand response that can help improve the resilience of\nnatural gas systems by reducing peak consumption and thereby limiting potential\ndisruptions such events can cause. This paper develops a mathematical\nformulation to support a residential-level demand response analogue for natural\ngas based on current and anticipated smart thermostat technologies. The\nmathematical formulation takes the form of an optimal control problem (OCP)\nthat leverages physics constraints to model temperature changes, balance\nequitable service, and optimize the gas consumption for collections of houses.\nOn test problems, the formulation demonstrates significant benefits, including\nthe ability to cut peak demand by 15% while still ensuring equitable service to\ncustomers.\n"}
+{"abstract": "  An expansion of row Markov matrices in terms of matrices related to\npermutations with repetitions, is introduced.It generalises the Birkhoff-von\nNeumann expansion of doubly stochastic matrices in terms of permutation\nmatrices (without repetitions).An interpretation of the formalism in terms of\nsequences of integers that open random safes described by the Markov matrices,\nis presented. Various quantities that describe probabilities and correlations\nin this context, are discussed. The Gini index is used to quantify the sparsity\n(certainty) of various probability vectors. The formalism is used in the\ncontext of multipartite quantum systems with finite dimensional Hilbert space,\nwhich can be viewed as quantum permutations with repetitions or as quantum\nsafes. The scalar product of row Markov matrices, the various Gini indices,\netc, are novel probabilistic quantities that describe the statistics of\nmultipartite quantum systems. Local and global Fourier transforms are used to\ndefine locally dual and also globally dual statistical quantities. The latter\ndepend on off-diagonal elements that entangle (in general) the various\ncomponents of the system. Examples which demonstrate these ideas are also\npresented.\n"}
+{"abstract": "  Generalized Linear Bandits (GLBs) are powerful extensions to the Linear\nBandit (LB) setting, broadening the benefits of reward parametrization beyond\nlinearity. In this paper we study GLBs in non-stationary environments,\ncharacterized by a general metric of non-stationarity known as the\nvariation-budget or \\emph{parameter-drift}, denoted $B_T$. While previous\nattempts have been made to extend LB algorithms to this setting, they overlook\na salient feature of GLBs which flaws their results. In this work, we introduce\na new algorithm that addresses this difficulty. We prove that under a geometric\nassumption on the action set, our approach enjoys a\n$\\tilde{\\mathcal{O}}(B_T^{1/3}T^{2/3})$ regret bound. In the general case, we\nshow that it suffers at most a $\\tilde{\\mathcal{O}}(B_T^{1/5}T^{4/5})$ regret.\nAt the core of our contribution is a generalization of the projection step\nintroduced in Filippi et al. (2010), adapted to the non-stationary nature of\nthe problem. Our analysis sheds light on central mechanisms inherited from the\nsetting by explicitly splitting the treatment of the learning and tracking\naspects of the problem.\n"}
+{"abstract": "  An innovative technique, called conversion, is introduced to model\ncircumferential cracks in thin cylindrical shells. The semi-analytical finite\nelement method is applied to investigate the modal deformation of the cylinder.\nAn element including the crack is divided into three sub-elements with four\nnodes in which the stiffness matrix is enriched. The crack characteristics are\nincluded in the finite element method relations through conversion matrices and\na rotational spring corresponding to the crack. Conversion matrices obtained by\napplying continuity conditions at the crack tip are used to transform\ndisplacements of the middle nodes to those of the main nodes. Moreover, another\ntechnique, called spring set, is represented based on a set of springs to model\nthe crack as a separated element. Components of the stiffness matrix related to\nthe separated element are incorporated while the geometric boundary conditions\nat the crack tip are satisfied. The effects of the circumferential mode number,\nthe crack depth and the length of the cylinder on the critical buckling load\nare investigated. Experimental tests, ABAQUS modeling and results from\nliterature are used to verify and validate the results and derived relations.\nIn addition, the crack effect on the natural frequency is examined using the\nvibration analysis based on the conversion technique.\n"}
+{"abstract": "  Light scattering by tissue severely limits how deep beneath the surface one\ncan image, and the spatial resolution one can obtain from these images. Diffuse\noptical tomography (DOT) is one of the most powerful techniques for imaging\ndeep within tissue -- well beyond the conventional $\\sim$10-15 mean scattering\nlengths tolerated by ballistic imaging techniques such as confocal and\ntwo-photon microscopy. Unfortunately, existing DOT systems are limited,\nachieving only centimeter-scale resolution. Furthermore, they suffer from slow\nacquisition times and slow reconstruction speeds making real-time imaging\ninfeasible. We show that time-of-flight diffuse optical tomography (ToF-DOT)\nand its confocal variant (CToF-DOT), by exploiting the photon travel time\ninformation, allow us to achieve millimeter spatial resolution in the highly\nscattered diffusion regime ($> 50 $ mean free paths). In addition, we\ndemonstrate two additional innovations: focusing on confocal measurements, and\nmultiplexing the illumination sources allow us to significantly reduce the\nmeasurement acquisition time. Finally, we rely on a novel convolutional\napproximation that allows us to develop a fast reconstruction algorithm,\nachieving a 100$\\times$ speedup in reconstruction time compared to traditional\nDOT reconstruction techniques. Together, we believe that these technical\nadvances serve as the first step towards real-time, millimeter resolution, deep\ntissue imaging using DOT.\n"}
+{"abstract": "  Nanophotonic chiral sensing has recently attracted a lot of attention. The\nidea is to exploit the strong light-matter interaction in nanophotonic\nresonators to determine the concentration of chiral molecules at ultra-low\nthresholds, which is highly attractive for numerous applications in life\nscience and chemistry. However, a thorough understanding of the underlying\ninteractions is still missing. The theoretical description relies on either\nsimple approximations or on purely numerical approaches. We close this gap and\npresent a general theory of chiral light-matter interactions in arbitrary\nresonators. Our theory describes the chiral interaction as a perturbation of\nthe resonator modes, also known as resonant states or quasi-normal modes. We\nobserve two dominant contributions: A chirality-induced resonance shift and\nchanges in the modes excitation and emission efficiencies. Our theory brings\nnew and deep insights for tailoring and enhancing chiral light-matter\ninteractions. Furthermore, it allows to predict spectra much more efficiently\nin comparison to conventional approaches. This is particularly true as chiral\ninteractions are inherently weak and therefore perturbation theory fits\nextremely well for this problem.\n"}
+{"abstract": "  Let $V$ be a valuation ring and $K$ be its field of fraction. We show that\nthe canonical map $\\Br(V) \\to \\Br(K)$ is injective.\n"}
+{"abstract": "  Stars can either be formed in or captured by the accretion disks in Active\nGalactic Nuclei (AGN). These AGN stars are irradiated and subject to extreme\nlevels of accretion, which can turn even low-mass stars into very massive ones\n($M > 100 {\\rm M}_\\odot$) whose evolution may result in the formation of\nmassive compact objects ($M > 10 {\\rm M}_\\odot$). Here we explore the spins of\nthese AGN stars and the remnants they leave behind. We find that AGN stars\nrapidly spin up via accretion, eventually reaching near-critical rotation\nrates. They further maintain near-critical rotation even as they shed their\nenvelopes, become compact, and undergo late stages of burning. This makes them\ngood candidates to produce high-spin massive black holes, such as the ones seen\nby LIGO-Virgo in GW190521g, as well as long Gamma Ray Bursts (GRBs) and the\nassociated chemical pollution of the AGN disk.\n"}
+{"abstract": "  The Stockpile blending problem is an important component of mine production\nscheduling, where stockpiles are used to store and blend raw material. The goal\nof blending material from stockpiles is to create parcels of concentrate which\ncontain optimal metal grades based on the material available. The volume of\nmaterial that each stockpile provides to a given parcel is dependent on a set\nof mine schedule conditions and customer demands. Therefore, the problem can be\nformulated as a continuous optimization problem. In the real-world application,\nthere are several constraints required to guarantee parcels that meet the\ndemand of downstream customers. It is a challenge in solving the stockpile\nblending problems since its scale can be very large. We introduce two repaired\noperators for the problems to convert the infeasible solutions into the\nsolutions without violating the two tight constraints. Besides, we introduce a\nmulti-component fitness function for solving the large-scale stockpile blending\nproblem which can maximize the volume of metal over the plan and maintain the\nbalance between stockpiles according to the usage of metal. Furthermore, we\ninvestigate the well-known approach in this paper, which is used to solve\noptimization problems over continuous space, namely the differential evolution\n(DE) algorithm. The experimental results show that the DE algorithm combined\nwith two proposed duration repair methods is significantly better in terms of\nthe values of results than the results on real-world instances for both\none-month problems and large-scale problems.\n"}
+{"abstract": "  Current Artificial Intelligence (AI) methods, most based on deep learning,\nhave facilitated progress in several fields, including computer vision and\nnatural language understanding. The progress of these AI methods is measured\nusing benchmarks designed to solve challenging tasks, such as visual question\nanswering. A question remains of how much understanding is leveraged by these\nmethods and how appropriate are the current benchmarks to measure understanding\ncapabilities. To answer these questions, we have analysed existing benchmarks\nand their understanding capabilities, defined by a set of understanding\ncapabilities, and current research streams. We show how progress has been made\nin benchmark development to measure understanding capabilities of AI methods\nand we review as well how current methods develop understanding capabilities.\n"}
+{"abstract": "  In this paper we propose an extension of the notion of deviation-based\naggregation function tailored to aggregate multidimensional data. Our objective\nis both to improve the results obtained by other methods that try to select the\nbest aggregation function for a particular set of data, such as penalty\nfunctions, and to reduce the temporal complexity required by such approaches.\nWe discuss how this notion can be defined and present three illustrative\nexamples of the applicability of our new proposal in areas where temporal\nconstraints can be strict, such as image processing, deep learning and decision\nmaking, obtaining favourable results in the process.\n"}
+{"abstract": "  In current Medium Voltage DC (MVDC) Shipboard Power Systems (SPSs), multiple\nsources exist to supply power to a common dc bus. Conventionally, the power\nmanagement of such systems is performed by controlling Power Generation Modules\n(PGMs) which include fuel operated generators and underlying converters.\nMoreover, energy management is performed by the emerging single or hybrid\nEnergy Storage Systems (ESSs). This paper presents a model and load predictive\ncontrol framework for power and energy management of SPSs. Here, MPC with load\nprediction is used for three main objectives: (1) to request power and energy\nfrom generators and energy storage elements according to their individual State\nof Power (SOP) and ramp-rate limitations, (2) to consider and integrate the\ngenerator cost and degradation, and (3) to reach a specific parking (final)\nState of Charge (SOC) for the ESSs at the end of the prediction horizon. The\nsolution of the optimization problem is demonstrated using MATLAB and the\nfunctionality of the control framework is validated in real-time simulation\nenvironment.\n"}
+{"abstract": "  Nowadays, an important topic that is considered a lot is how to integrate\nMachine Learning(ML) to cloud resources management. In this study, our goal is\nto explore the most important cloud resources management issues that have been\ncombined with ML and which present many promising results. To accomplish this,\nwe used chronological charts based on some keywords that we considered\nimportant and tried to answer the question: is ML suitable for resources\nmanagement problems in the cloud? Furthermore, a short discussion takes place\non the data that are available and the open challenges on it. A big collection\nof researches is used to make sensible comparisons between the ML techniques\nthat are used in the different kinds of cloud resources management fields and\nwe propose the most suitable ML model for each field. 1\n"}
+{"abstract": "  Let $0 \\leq s \\leq 1$ and $0 \\leq t \\leq 2$. An $(s,t)$-Furstenberg set is a\nset $K \\subset \\mathbb{R}^{2}$ with the following property: there exists a line\nset $\\mathcal{L}$ of Hausdorff dimension $\\dim_{\\mathrm{H}} \\mathcal{L} \\geq t$\nsuch that $\\dim_{\\mathrm{H}} (K \\cap \\ell) \\geq s$ for all $\\ell \\in\n\\mathcal{L}$. We prove that for $s\\in (0,1)$, and $t \\in (s,2]$, the Hausdorff\ndimension of $(s,t)$-Furstenberg sets in $\\mathbb{R}^{2}$ is no smaller than\n$2s + \\epsilon$, where $\\epsilon > 0$ depends only on $s$ and $t$. For $s>1/2$\nand $t = 1$, this is an $\\epsilon$-improvement over a result of Wolff from\n1999.\n  The same method also yields an $\\epsilon$-improvement to Kaufman's projection\ntheorem from 1968. We show that if $s \\in (0,1)$, $t \\in (s,2]$ and $K \\subset\n\\mathbb{R}^{2}$ is an analytic set with $\\dim_{\\mathrm{H}} K = t$, then\n$$\\dim_{\\mathrm{H}} \\{e \\in S^{1} : \\dim_{\\mathrm{H}} \\pi_{e}(K) \\leq s\\} \\leq\ns - \\epsilon,$$ where $\\epsilon > 0$ only depends on $s$ and $t$. Here\n$\\pi_{e}$ is the orthogonal projection to $\\mathrm{span}(e)$.\n"}
+{"abstract": "  We study equilibrium properties of binary lattice-gases comprising $A$ and\n$B$ particles, which undergo continuous exchanges with their respective\nreservoirs, maintained at chemical potentials $\\mu_A = \\mu_B = \\mu$. The\nparticles interact via on-site hard-core exclusion and also between the\nnearest-neighbours: there are a soft repulsion for $AB$ pairs and interactions\nof arbitrary strength $J$, positive or negative, for $AA$ and $BB$ pairs. For\ntree-like Bethe and Husimi lattices, we determine the full phase diagram of\nsuch a ternary mixture of particles and voids. We show that for $J$ being above\na lattice-dependent threshold value, the critical behaviour is similar: the\nsystem undergoes a transition at $\\mu = \\mu_c$ from a phase with equal mean\ndensities of species into a phase with a spontaneously broken symmetry, in\nwhich the mean densities are no longer equal. Depending on the value of $J$,\nthis transition can be either continuous or of the first order. For\nsufficiently big negative $J$, the behaviour on the two lattices becomes\nmarkedly different: while for the Bethe lattice there exists a continuous\ntransition into a phase with an alternating order followed by a continuous\nre-entrant transition into a disordered phase, an alternating order phase is\nabsent on the Husimi lattice due to strong frustration effects.\n"}
+{"abstract": "  Deep learning empirically achieves high performance in many applications, but\nits training dynamics has not been fully understood theoretically. In this\npaper, we explore theoretical analysis on training two-layer ReLU neural\nnetworks in a teacher-student regression model, in which a student network\nlearns an unknown teacher network through its outputs. We show that with a\nspecific regularization and sufficient over-parameterization, the student\nnetwork can identify the parameters of the teacher network with high\nprobability via gradient descent with a norm dependent stepsize even though the\nobjective function is highly non-convex. The key theoretical tool is the\nmeasure representation of the neural networks and a novel application of a dual\ncertificate argument for sparse estimation on a measure space. We analyze the\nglobal minima and global convergence property in the measure space.\n"}
+{"abstract": "  We investigate the problem dependent regime in the stochastic Thresholding\nBandit problem (TBP) under several shape constraints. In the TBP, the objective\nof the learner is to output, at the end of a sequential game, the set of arms\nwhose means are above a given threshold. The vanilla, unstructured, case is\nalready well studied in the literature. Taking $K$ as the number of arms, we\nconsider the case where (i) the sequence of arm's means $(\\mu_k)_{k=1}^K$ is\nmonotonically increasing (MTBP) and (ii) the case where $(\\mu_k)_{k=1}^K$ is\nconcave (CTBP). We consider both cases in the problem dependent regime and\nstudy the probability of error - i.e. the probability to mis-classify at least\none arm. In the fixed budget setting, we provide upper and lower bounds for the\nprobability of error in both the concave and monotone settings, as well as\nassociated algorithms. In both settings the bounds match in the problem\ndependent regime up to universal constants in the exponential.\n"}
+{"abstract": "  This paper presents an attempt to employ the mask language modeling approach\nof BERT to pre-train a 12-layer Transformer model over 4,166 pieces of\npolyphonic piano MIDI files for tackling a number of symbolic-domain\ndiscriminative music understanding tasks. These include two note-level\nclassification tasks, i.e., melody extraction and velocity prediction, as well\nas two sequence-level classification tasks, i.e., composer classification and\nemotion classification. We find that, given a pre-trained Transformer, our\nmodels outperform recurrent neural network based baselines with less than 10\nepochs of fine-tuning. Ablation studies show that the pre-training remains\neffective even if none of the MIDI data of the downstream tasks are seen at the\npre-training stage, and that freezing the self-attention layers of the\nTransformer at the fine-tuning stage slightly degrades performance. All the\nfive datasets employed in this work are publicly available, as well as\ncheckpoints of our pre-trained and fine-tuned models. As such, our research can\nbe taken as a benchmark for symbolic-domain music understanding.\n"}
+{"abstract": "  In this paper, we characterize quasicrystalline interacting topological\nphases of matter i.e., phases protected by some quasicrystalline structure. We\nshow that the elasticity theory of quasicrystals, which accounts for both\n\"phonon\" and \"phason\" modes, admits non-trivial quantized topological terms\nwith far richer structure than their crystalline counterparts. We show that\nthese terms correspond to distinct phases of matter and also uncover\nintrinsically quasicrystalline phases, which have no crystalline analogues. For\nquasicrystals with internal $\\mathrm{U}(1)$ symmetry, we discuss a number of\ninterpretations and physical implications of the topological terms, including\nconstraints on the mobility of dislocations in $d=2$ quasicrystals and a\nquasicrystalline generalization of the Lieb-Schultz-Mattis-Oshikawa-Hastings\ntheorem. We then extend these ideas much further and address the complete\nclassification of quasicrystalline topological phases, including systems with\npoint-group symmetry as well as non-invertible phases. We hence obtain the\n\"Quasicrystalline Equivalence Principle,\" which generalizes the classification\nof crystalline topological phases to the quasicrystalline setting.\n"}
+{"abstract": "  This paper is the third and final component of a three-part effort on\nobservers contracting a Riemannian distance between the state of the system and\nits estimate. In Part I, we showed that such a contraction property holds if\nthe system dynamics and the Riemannian metric satisfy two key conditions: a\ndifferential detectability property and a geodesic monotonicity property. With\nthe former condition being the focus of Part II, in this Part III, we study the\nlatter condition in relationship to the nullity of the second fundamental form\nof the output function. We formulate sufficient and necessary conditions for it\nto hold. We establish a link between it and the infinite gain margin property,\nand we provide a systematic way for constructing a metric satisfying this\ncondition. Finally, we illustrate cases where both conditions hold.\n"}
+{"abstract": "  Quantum secret sharing (QSS) is an essential primitive for the future quantum\ninternet, which promises secure multiparty communication. However, developing a\nlarge-scale QSS network is a huge challenge due to the channel loss and the\nrequirement of multiphoton interference or high-fidelity multipartite\nentanglement distribution. Here, we propose a three-user QSS protocol without\nmonitoring signal disturbance, which is capable of ensuring the unconditional\nsecurity. The final key rate of our protocol can be demonstrated to break the\nPirandola-Laurenza-Ottaviani-Banchi bound of quantum channel and its simulated\ntransmission distance can approach over 600 km using current techniques. Our\nresults pave the way to realizing high-rate and large-scale QSS networks.\n"}
+{"abstract": "  We introduce a new binary detection technique, Binary INformation from Open\nClusters using SEDs (binocs), which we show is able to determine reliable\nstellar multiplicity and masses over a much larger mass range than current\napproaches. This new technique determines accurate component masses of binary\nand single systems of the open clusters main sequence by comparing observed\nmagnitudes from multiple photometric filters to synthetic star spectral energy\ndistributions (SEDs) allowing systematically probing the binary population for\nlow mass stars in clusters for 8 well-studied open clusters. We provide new\ndeep, infrared photometric catalogs (1.2 - 8.0 microns) for the key open\nclusters NGC 1960 (M36), NGC 2099 (M37), NGC 2420, and NGC2682 (M67), using\nobservation from NOAO/NEWFIRM and Spitzer}/IRAC. Using these deep\nmulti-wavelength catalogs, the binocs method is applied to these clusters to\ndetermine accurate component masses for unresolved cluster binaries. We explore\nbinary fractions as a function of cluster age, Galactic location and\nmetallicity.\n"}
+{"abstract": "  In a celebrated paper of 1893, Hadamard established the maximal determinant\ntheorem, which establishes an upper bound on the determinant of a matrix with\ncomplex entries of norm at most $1$. His paper concludes with the suggestion\nthat mathematicians study the maximum value of the determinant of an $n \\times\nn$ matrix with entries in $\\{ \\pm 1\\}$. This is the Hadamard maximal\ndeterminant problem.\n  This survey provides complete proofs of the major results obtained thus far.\nWe focus equally on upper bounds for the determinant (achieved largely via the\nstudy of the Gram matrices), and constructive lower bounds (achieved largely\nvia quadratic residues in finite fields and concepts from design theory). To\nprovide an impression of the historical development of the subject, we have\nattempted to modernise many of the original proofs, while maintaining the\nunderlying ideas. Thus some of the proofs have the flavour of determinant\ntheory, and some appear in print in English for the first time.\n  We survey constructions of matrices in order $n \\equiv 3 \\mod 4$, giving\nasymptotic analysis which has not previously appeared in the literature. We\nprove that there exists an infinite family of matrices achieving at least\n$0.48$ of the maximal determinant bound. Previously the best known constant for\na result of this type was $0.34$.\n"}
+{"abstract": "  In this paper we study properties of regular solutions of quaternionic\nRiccati equations. The obtained results we use for study of the asymptotic\nbehavior of solutions of two first-order linear quaternionic ordinary\ndifferential equations.\n"}
+{"abstract": "  Machine learning models present a risk of adversarial attack when deployed in\nproduction. Quantifying the contributing factors and uncertainties using\nempirical measures could assist the industry with assessing the risk of\ndownloading and deploying common model types. This work proposes modifying the\ntraditional Drake Equation's formalism to estimate the number of potentially\nsuccessful adversarial attacks on a deployed model. The Drake Equation is\nfamously used for parameterizing uncertainties and it has been used in many\nresearch fields outside of its original intentions to estimate the number of\nradio-capable extra-terrestrial civilizations. While previous work has outlined\nmethods for discovering vulnerabilities in public model architectures, the\nproposed equation seeks to provide a semi-quantitative benchmark for evaluating\nand estimating the potential risk factors for adversarial attacks.\n"}
+{"abstract": "  The velocities of the quasiparticles that form Cooper pairs in a\nsuperconductor are revealed by the upper critical magnetic field. Here we use\nthis property to assess superconductivity in magic-angle twisted bilayer\ngraphene (MATBG), which has been observed over a range of moir\\'e band filling,\ntwist angle, and screening environment conditions. We find that for pairing\nmechanisms that are unrelated to correlations within the MATBG flat bands,\nminima in an average Fermi velocity $v_F^* \\equiv k_B T_c \\ell_c /\\hbar $,\nwhere $\\ell_c$ is the magnetic length at the critical perpendicular magnetic\nfield, are always coincident with transition temperature maxima. Both extrema\noccur near flat-band van Hove singularities. Since no such association is\npresent in MATBG experimental data, we conclude that electronic correlations\nthat yield a band-filling-dependent pairing glue must play a crucial role in\nMATBG superconductivity.\n"}
+{"abstract": "  This paper deals with a version of the two-timing method which describes\nvarious `slow' effects caused by externally imposed `fast' oscillations. Such\nsmall oscillations are often called \\emph{vibrations} and the research area can\nbe referred as \\emph{vibrodynamics}. The governing equations represent a\ngeneric system of first-order ODEs containing a prescribed oscillating velocity\nu, given in a general form. Two basic small parameters stand in for the inverse\nfrequency and the ratio of two time-scales; they appear in equations as regular\nperturbations. The proper connections between these parameters yield the\n\\emph{distinguished limits}, leading to the existence of closed systems of\nasymptotic equations. The aim of this paper is twofold: (i) to clarify (or to\ndemystify) the choices of a slow variable, and (ii) to give a coherent\nexposition which is accessible for practical users in applied mathematics,\nsciences and engineering. We focus our study on the usually hidden aspects of\nthe two-timing method such as the \\emph{uniqueness or multiplicity of\ndistinguished limits} and \\emph{universal structures of averaged equations}.\nThe main result is the demonstration that there are two (and only two)\ndifferent distinguished limits. The explicit instruction for practically\nsolving ODEs for different classes of u is presented. The key roles of drift\nvelocity and the qualitatively new appearance of the linearized equations are\ndiscussed. To illustrate the broadness of our approach, two examples from\nmathematical biology are shown.\n"}
+{"abstract": "  In recent years, significant advances have been made in the design and\nanalysis of fully dynamic algorithms. However, these theoretical results have\nreceived very little attention from the practical perspective. Few of the\nalgorithms are implemented and tested on real datasets, and their practical\npotential is far from understood. Here, we present a quick reference guide to\nrecent engineering and theory results in the area of fully dynamic graph\nalgorithms.\n"}
+{"abstract": "  New spectrometric data on V Pup are combined with satellite photometry\n(HIPPARCOS and recent TESS) to allow a revision of the absolute parameters with\nincreased precision. We find: $M_1$ = 14.0$\\pm$0.5, $M_2$ = 7.3$\\pm$0.3\n(M$_\\odot$); $R_{1}$ = 5.48$\\pm$0.18, $R_2$ = 4.59$\\pm$0.15 (R$_\\odot$);\n$T_{1}$ 26000$\\pm 1000$, $T_2$ 24000 $\\pm$1000 (K), age 5 $\\pm$1 (Myr),\nphotometric distance 320 $\\pm$10 (pc). The TESS photometry reveals\nlow-amplitude ($\\sim$0.002 mag) variations of the $\\beta$ Cep kind, consistent\nwith the deduced evolutionary condition and age of the optical primary. This\nfact provides independent support to our understanding of the system as in a\nprocess of Case A type interactive evolution that can be compared with $\\mu^1$\nSco. The $\\sim$10 M$_{\\odot}$ amount of matter shed by the over-luminous\npresent secondary must have been mostly ejected from the system rather than\ntransferred, thus taking angular momentum out of the orbit and keeping the pair\nin relative close proximity. New times of minima for V Pup have been studied\nand the results compared with previous analyses. The implied variation of\nperiod is consistent with the Case A evolutionary model, though we offer only a\ntentative sketch of the original arrangement of this massive system. We are not\nable to confirm the previously reported cyclical variations having a 5.47 yr\nperiod with the new data, though a direct comparison between the HIPPARCOS and\nTESS photometry points to the presence of third light from a star that is\ncooler than those of the close binary, as mentioned in previous literature.\n"}
+{"abstract": "  We study the maximum matching problem in the random-order semi-streaming\nsetting. In this problem, the edges of an arbitrary $n$-vertex graph $G=(V, E)$\narrive in a stream one by one and in a random order. The goal is to have a\nsingle pass over the stream, use $n \\cdot poly(\\log n)$ space, and output a\nlarge matching of $G$.\n  We prove that for an absolute constant $\\epsilon_0 > 0$, one can find a $(2/3\n+ \\epsilon_0)$-approximate maximum matching of $G$ using $O(n \\log n)$ space\nwith high probability. This breaks the natural boundary of $2/3$ for this\nproblem prevalent in the prior work and resolves an open problem of Bernstein\n[ICALP'20] on whether a $(2/3 + \\Omega(1))$-approximation is achievable.\n"}
+{"abstract": "  Using the recently constructed covariant Ito-Langevin dynamics, we develop a\ncovariant theory of non-equilibrium thermodynamics that is applicable to small\nsystems with multiplicative noises and with slow variables forming curved\nmanifolds. Assuming instantaneous detailed balance, we derive expressions for\nwork, heat, entropy production, and free energy both at ensemble level, as well\nas at the level of individual dynamic trajectory. We also relate time-reversal\nasymmetry to entropy production, and derive its consequences such as\nfluctuation theorem and work relation. The theory is based on Ito-calculus, is\nfully covariant under time-independent nonlinear transformation of variables,\nand is applicable to systems strongly coupled to environments.\n"}
+{"abstract": "  Most of the existing deep learning based end-to-end image/video coding (DLEC)\narchitectures are designed for non-subsampled RGB color format. However, in\norder to achieve a superior coding performance, many state-of-the-art\nblock-based compression standards such as High Efficiency Video Coding\n(HEVC/H.265) and Versatile Video Coding (VVC/H.266) are designed primarily for\nYUV 4:2:0 format, where U and V components are subsampled by considering the\nhuman visual system. This paper investigates various DLEC designs to support\nYUV 4:2:0 format by comparing their performance against the main profiles of\nHEVC and VVC standards under a common evaluation framework. Moreover, a new\ntransform network architecture is proposed to improve the efficiency of coding\nYUV 4:2:0 data. The experimental results on YUV 4:2:0 datasets show that the\nproposed architecture significantly outperforms naive extensions of existing\narchitectures designed for RGB format and achieves about 10% average BD-rate\nimprovement over the intra-frame coding in HEVC.\n"}
+{"abstract": "  Dense circumstellar material (CSM) is thought to play an important role in\nobserved luminous optical transients: if such CSM is shocked, e.g. by ejecta\nexpelled from the progenitor during core-collapse, then radiation produced by\nthe shock-heated CSM can power bright UV/optical emission. If the initial CSM\nhas an `outer edge' where most of the mass is contained and at which the\noptical depth is large, then shock breakout -- when photons are first able to\nescape the shocked CSM -- occurs near this outer edge. The $\\sim$thin shell of\nshocked CSM subsequently expands, and in the ensuing cooling-envelope phase,\nradiative and adiabatic losses compete to expend the CSM thermal energy. Here\nwe derive an analytic solution to the bolometric light-curve produced by such\nshocked CSM. For the first time, we provide a solution to the cooling-envelope\nphase that is applicable already starting from shock breakout. In particular,\nwe account for the planar CSM geometry that is relevant at early times and\nimpose physically-motivated initial conditions. We show that these effects can\ndramatically impact the resulting light-curves, particularly if the CSM optical\ndepth is only marginally larger than $c/v_{\\rm sh}$ (where $v_{\\rm sh}$ is the\nshock velocity). This has important implications for interpreting observed fast\noptical transients, which have previously been modeled using either\ncomputationally-expensive numerical simulations or more simplified models that\ndo not properly capture the early light-curve evolution.\n"}
+{"abstract": "  A failed core-collapse supernova from a non-rotating progenitor can eject\nmass due to a weakening of gravity associated to neutrino emission by the\nprotoneutron star. This mechanism yields observable transients and sets an\nupper limit to the mass of the black hole (BH) remnant. Previous global\nsimulations of this mechanism have included neutrino losses parametrically,\nhowever, with direct implications for the ejecta mass and energy. Here we\nevolve the inner supernova core with a spherically-symmetric,\ngeneral-relativistic neutrino radiation-hydrodynamic code until BH formation.\nWe then use the result in a Newtonian code that follows the response of the\nouter layers of the star to the change in gravity and resolves the surface\npressure scale height. We find that the dense-matter equation of state (EOS)\ncan introduce a factor $\\sim 2$ variation in gravitational mass lost to\nneutrinos, with a stiff EOS matching previous parametric results, and a soft\nEOS yielding lower ejecta masses and energies by a factor of several. This\ndifference is caused primarily by the longer time to BH formation in stiffer\nEOSs. With a soft EOS, our red and yellow supergiant progenitors fail to unbind\nmass if hydrogen recombination energy is not included. Using a linear ramp in\ntime for mass-energy lost to neutrinos (with suitable parameters) yields a\nstellar response within $\\sim 10\\%$ of that obtained using the detailed history\nof neutrino losses. Our results imply quantitative but not qualitative\nmodifications to previous predictions for shock breakout, plateau emission, and\nfinal BH masses from these events.\n"}
+{"abstract": "  Natural-gradient descent (NGD) on structured parameter spaces (e.g., low-rank\ncovariances) is computationally challenging due to difficult Fisher-matrix\ncomputations. We address this issue by using \\emph{local-parameter coordinates}\nto obtain a flexible and efficient NGD method that works well for a\nwide-variety of structured parameterizations. We show four applications where\nour method (1) generalizes the exponential natural evolutionary strategy, (2)\nrecovers existing Newton-like algorithms, (3) yields new structured\nsecond-order algorithms via matrix groups, and (4) gives new algorithms to\nlearn covariances of Gaussian and Wishart-based distributions. We show results\non a range of problems from deep learning, variational inference, and evolution\nstrategies. Our work opens a new direction for scalable structured geometric\nmethods.\n"}
+{"abstract": "  Relation extraction is used to populate knowledge bases that are important to\nmany applications. Prior datasets used to train relation extraction models\neither suffer from noisy labels due to distant supervision, are limited to\ncertain domains or are too small to train high-capacity models. This constrains\ndownstream applications of relation extraction. We therefore introduce: WebRED\n(Web Relation Extraction Dataset), a strongly-supervised human annotated\ndataset for extracting relationships from a variety of text found on the World\nWide Web, consisting of ~110K examples. We also describe the methods we used to\ncollect ~200M examples as pre-training data for this task. We show that\ncombining pre-training on a large weakly supervised dataset with fine-tuning on\na small strongly-supervised dataset leads to better relation extraction\nperformance. We provide baselines for this new dataset and present a case for\nthe importance of human annotation in improving the performance of relation\nextraction from text found on the web.\n"}
+{"abstract": "  This paper reports methods and results in the DeeperForensics Challenge 2020\non real-world face forgery detection. The challenge employs the\nDeeperForensics-1.0 dataset, one of the most extensive publicly available\nreal-world face forgery detection datasets, with 60,000 videos constituted by a\ntotal of 17.6 million frames. The model evaluation is conducted online on a\nhigh-quality hidden test set with multiple sources and diverse distortions. A\ntotal of 115 participants registered for the competition, and 25 teams made\nvalid submissions. We will summarize the winning solutions and present some\ndiscussions on potential research directions.\n"}
+{"abstract": "  Creating high performance implementations of deep learning primitives on CPUs\nis a challenging task. Multiple considerations including multi-level cache\nhierarchy, and wide SIMD units of CPU platforms influence the choice of program\ntransformations to apply for performance optimization. In this paper, we\npresent machine learning powered compiler techniques to optimize loop nests. We\ntake a two-pronged approach to code optimization: We first apply high level\noptimizations to optimize the code to take optimal advantage of the cache\nmemories. Then, we perform low level, target-specific optimizations to\neffectively vectorize the code to run well on the SIMD units of the machine.\nFor high level optimizations, we use polyhedral compilation techniques and deep\nlearning approaches. For low level optimization, we use a target specific code\ngenerator that generates code using vector intrinsics and Reinforcement\nLearning (RL) techniques to find the optimal parameters for the code generator.\nWe perform experimental evaluation of the developed techniques on various\nmatrix multiplications that occur in popular deep learning workloads. The\nexperimental results show that the compiler techniques presented in the paper\nachieve 7.6X and 8.2X speed-ups over a baseline for sequential and parallel\nruns respectively.\n"}
+{"abstract": "  For data given on a nonlinear space, like angles, symmetric positive\nmatrices, the sphere, or the hyperbolic space, there is often enough structure\nto form a Riemannian manifold. We present the Julia package Manifolds.jl,\nproviding a fast and easy to use library of Riemannian manifolds and Lie\ngroups. We introduce a common interface, available in ManifoldsBase.jl, with\nwhich new manifolds, applications, and algorithms can be implemented. We\ndemonstrate the utility of Manifolds.jl using B\\'ezier splines, an optimization\ntask on manifolds, and a principal component analysis on nonlinear data. In a\nbenchmark, Manifolds.jl outperforms existing packages in Matlab or Python by\nseveral orders of magnitude and is about twice as fast as a comparable package\nimplemented in C++.\n"}
+{"abstract": "  This paper presents computer simulations of Cu$_x$Zr$_{100-x}$ $(x=36,50,64)$\nin the liquid and glass phases. The simulations are based on the\neffective-medium theory (EMT) potentials. We find good invariance of both\nstructure and dynamics in reduced units along the isomorphs of the systems. The\nstate points studied involve a density variation of almost a factor of two and\ntemperatures going from 1500 K to above 4000 K for the liquids and from 500 K\nto above 1500 K for the glasses. For comparison, results are presented also for\nsimilar temperature variations along isochores, showing little invariance. In\ngeneral for a binary system the phase diagram has three axes: composition,\ntemperature and pressure (or density). When isomorphs are present, there are\neffectively only two axes, and for a fixed composition just one. We conclude\nthat the liquid and glass parts of the thermodynamic phase diagram of this\nmetallic glass former at a fixed composition is effectively one-dimensional in\nthe sense that many physical properties are invariant along the same curves,\nimplying that in order to investigate the phase diagram, it is only necessary\nto go across these curves.\n"}
+{"abstract": "  The ability of bacteria to colonize and grow on different surfaces is an\nessential process for biofilm development and depends on complex biomechanical\ninteractions between the biofilm and the underlying substrate. Changes in the\nphysical properties of the underlying substrate are known to alter biofilm\nexpansion, but the mechanisms by which biofilms sense and respond to physical\nfeatures of their environment are still poorly understood. Here, we report the\nuse of synthetic polyacrylamide hydrogels with tunable stiffness and\ncontrollable pore size to assess physical effects of the substrate on biofilm\ndevelopment. Using time lapse microscopy to track the growth of expanding\nSerratia marcescens colonies, we find that biofilm colony growth can increase\nwith increasing substrate stiffness on purely elastic substrates, unlike what\nis found on traditional agar substrates. Using traction force microscopy, we\nfind that biofilms exert transient stresses correlated over length scales much\nlarger than a single bacterium. Our results are consistent with a model of\nbiofilm development in which the interplay between osmotic pressure arising\nfrom the biofilm and the poroelastic response of the underlying substrate\ncontrols biofilm growth and morphology.\n"}
+{"abstract": "  A theoretical investigation has been made on the propagation of ion-acoustic\n(IA) shock waves (IASHWs) in a magnetized pair-ion plasma having inertial warm\npositive and negative ions, and inertialess super-thermal electrons and\npositrons. The well known Burgers' equation has been derived by employing the\nreductive perturbation method. The plasma model supports both positive and\nnegative shock structures under consideration of super-thermal electrons and\npositrons. It is found that the oblique angle ($\\delta$) enhances the magnitude\nof the amplitude of both positive and negative shock profiles. It is also\nobserved that the steepness of the shock profiles decreases with the kinematic\nviscosity of the ion, and the height of the shock profile increases (decreases)\nwith the mass of the positive (negative) ion. The implications of the results\nhave been briefly discussed for space and laboratory plasmas.\n"}
+{"abstract": "  Highly heterogeneous, anisotropic coefficients, e.g. in the simulation of\ncarbon-fibre composite components, can lead to extremely challenging finite\nelement systems. Direct solvers for the resulting large and sparse linear\nsystems suffer from severe memory requirements and limited parallel\nscalability, while iterative solvers in general lack robustness. Two-level\nspectral domain decomposition methods can provide such robustness for symmetric\npositive definite linear systems, by using coarse spaces based on independent\ngeneralized eigenproblems in the subdomains. Rigorous condition number bounds\nare independent of mesh size, number of subdomains, as well as coefficient\ncontrast. However, their parallel scalability is still limited by the fact that\n(in order to guarantee robustness) the coarse problem is solved via a direct\nmethod. In this paper, we introduce a multilevel variant in the context of\nsubspace correction methods and provide a general convergence theory for its\nrobust convergence for abstract, elliptic variational problems. Assumptions of\nthe theory are verified for conforming, as well as for discontinuous Galerkin\nmethods applied to a scalar diffusion problem. Numerical results illustrate the\nperformance of the method for two- and three-dimensional problems and for\nvarious discretization schemes, in the context of scalar diffusion and linear\nelasticity.\n"}
+{"abstract": "  Positioning systems and frameworks use various techniques to determine the\nposition of an object. Some of the existing solutions combine different sensory\ndata at the time of positioning in order to compute more accurate positions by\nreducing the error introduced by the used individual positioning techniques. We\npresent OpenHPS, a generic hybrid positioning system implemented in TypeScript,\nthat can not only reduce the error during tracking by fusing different sensory\ndata based on different algorithms, but also also make use of combined tracking\ntechniques when calibrating or training the system. In addition to a detailed\ndiscussion of the architecture, features and implementation of the extensible\nopen source OpenHPS framework, we illustrate the use of our solution in a\ndemonstrator application fusing different positioning techniques. While OpenHPS\noffers a number of positioning techniques, future extensions might integrate\nnew positioning methods or algorithms and support additional levels of\nabstraction including symbolic locations.\n"}
+{"abstract": "  We propose and demonstrate a new phase retrieval method for imaging through\nrandom media. Although methods to recover the Fourier amplitude through random\ndistortions are well established, recovery of the Fourier phase has been a more\ndifficult problem and is still a very active research area. Here, we show that\nby simply ensemble averaging shift-corrected images, the Fourier phase of an\nobject obscured by random distortions can be accurately retrieved up to the\ndiffraction limit. The method is simple, fast, does not have any optimization\nparameters, and does not require prior knowledge or assumptions about the\nsample. We demonstrate the feasibility and robustness of our method by\nrealizing all computational diffraction-limited imaging through atmospheric\nturbulence as well as imaging through multiple scattering media.\n"}
+{"abstract": "  We study the high-energy phase diagram of a two-dimensional\nspin-$\\frac{1}{2}$ Heisenberg model on a square lattice in the presence of\ndisorder. The use of large-scale tensor network numerics allows us to compute\nthe bi-partite entanglement entropy for systems of up to $30\\times7$ lattice\nsites. We demonstrate the existence of a finite many-body localized phase for\nlarge disorder strength $W$ for which the eigenstate thermalization hypothesis\nis violated. Moreover, we show explicitly that the area law holds for excited\nstates in this phase and determine an estimate for the critical $W_{\\rm{c}}$\nwhere the transition to the ergodic phase occurs.\n"}
+{"abstract": "  Given a deterministic finite automaton and its implementation with at most\none single fault, that we can test on a set of inputs, we provide an algorithm\nto find a test set that guarantees finding whether the fault exists.\n"}
+{"abstract": "  Gas electron multipliers (GEMs) with wire (WGEMs) or metal electrodes\n(MGEMs), which don't use any plastic insulators between electrodes are created.\nThe chambers containing MGEMs (WGEMs) with pin-anodes are proposed as detectors\nfor searching of spin-dependent interactions between Dark Matter (DM) particles\nand gases with nonzero-spin nuclei (H2, D2, 3He, 20Ne, CF4, CH4, etc.). In this\npaper, we present a review of such chambers. For investigation of the gas\nmixtures Ne+10%H2, H2 (D2) +3ppmTMAE, the chamber containing WGEM with\npin-anode detection system was constructed. In this paper we present the\nresults of an experimental study of these gaseous mixtures exited by an\n{\\alpha}-source. Mixture of Ar + 40 ppm C2H4 and mixture 50% Xe + 50%CF4 have\nbeen investigated. The spatial distributions of photoelectron clouds produced\nby primary scintillations on {\\alpha}- and \\b{eta}-particle tracks, as well as\nthe distributions of photoelectron clouds due to photons from avalanches at the\npin-anode, have been measured for the first time. In our experiments as another\nfilling of the chambers for search of low-mas WIMP (<10 GeV/c2), solar neutrino\nand solar axions with spin-dependent interaction we propose to use the\nmixtures: D2 +3ppmTMAE, 3He + 3%CH4, 20Ne + 10%H2, at pressure 10-17 bar. And\nin our experiment with liquid gases is used the mixtures with 19F (LAr + CF4,\nLXe + CF4) and mixture LCH4 + 40ppm TMAE. The time projection chamber (TPC)\nwith the mixture D2 + 3ppmTMAE filling allow to search of spin-dependent\ninteractions of solar axions and deuterium. As well as we present the detecting\nsystems for search of narrow pp-resonances (quarks) in accelerators\nexperiments.\n"}
+{"abstract": "  Domain adaptation aims to mitigate the domain gap when transferring knowledge\nfrom an existing labeled domain to a new domain. However, existing\ndisentanglement-based methods do not fully consider separation between\ndomain-invariant and domain-specific features, which means the domain-invariant\nfeatures are not discriminative. The reconstructed features are also not\nsufficiently used during training. In this paper, we propose a novel enhanced\nseparable disentanglement (ESD) model. We first employ a disentangler to\ndistill domain-invariant and domain-specific features. Then, we apply feature\nseparation enhancement processes to minimize contamination between\ndomain-invariant and domain-specific features. Finally, our model reconstructs\ncomplete feature vectors, which are used for further disentanglement during the\ntraining phase. Extensive experiments from three benchmark datasets outperform\nstate-of-the-art methods, especially on challenging cross-domain tasks.\n"}
+{"abstract": "  We first investigate the one-shot static entanglement cost to simulate a\nbipartite quantum channel under the set of non-entangling channels. The lower\nbound on the static entanglement cost is given by the generalized robustness of\nthe target channel as well as the robust-generating power of the channel, which\nimplies that the cost necessary to generate a bipartite quantum channel might\nnot be retrievable in general. Next, we conceive a set of quantum channels that\nextends the set of non-entangling channels. We find out that the one-shot\nstatic entanglement cost under the extended set of channels is given by the\nchannel's standard log-robustness. This gives the one-shot dynamic entanglement\ncost under a set of free superchannels that do not generate dynamic\nentanglement resource; the extended set is shown to be equivalent to the set of\nfree superchannels.\n"}
+{"abstract": "  While the front of a fluid shock is a few mean-free-paths thick, the front of\na collisionless shock can be orders of magnitude thinner. By bridging between a\ncollisional and a collisionless formalism, we assess the transition between\nthese two regimes. We consider non-relativistic, un-magnetized, planar shocks\nin electron/ion plasmas. In addition, our treatment of the collisionless regime\nis restricted to high Mach number electrostatic shocks. We find that the\ntransition can be parameterized by the upstream plasma parameter $\\Lambda$\nwhich measures the coupling of the upstream medium. For $\\Lambda \\lesssim\n1.12$, the upstream is collisional, i.e. strongly coupled, and the strong shock\nfront is about $\\mathcal{M}_1 \\lambda_{\\mathrm{mfp},1}$ thick, where\n$\\lambda_{\\mathrm{mfp},1}$ and $\\mathcal{M}_1 $ are the upstream mean-free-path\nand Mach number respectively. A transition occurs for $\\Lambda \\sim 1.12$\nbeyond which the front is $\\sim \\mathcal{M}_1\\lambda_{\\mathrm{mfp},1}\\ln\n\\Lambda/\\Lambda$ thick for $\\Lambda\\gtrsim 1.12$. Considering $\\Lambda$ can\nreach billions in astrophysical settings, this allows to understand how the\nfront of a collisionless shock can be orders of magnitude smaller than the\nmean-free-path, and how physics transitions continuously between these 2\nextremes.\n"}
+{"abstract": "  We consider the complexity of deciding the winner of an election under the\nSlater rule. In this setting we are given a tournament $T = (V, A)$, where the\nvertices of V represent candidates and the direction of each arc indicates\nwhich of the two endpoints is preferable for the majority of voters. The Slater\nscore of a vertex $v\\in V$ is defined as the minimum number of arcs that need\nto be reversed so that $T$ becomes acyclic and $v$ becomes the winner. We say\nthat $v$ is a Slater winner in $T$ if $v$ has minimum Slater score in $T$.\n  Deciding if a vertex is a Slater winner in a tournament has long been known\nto be NP-hard. However, the best known complexity upper bound for this problem\nis the class $\\Theta_2^p$, which corresponds to polynomial-time Turing machines\nwith parallel access to an NP oracle. In this paper we close this gap by\nshowing that the problem is $\\Theta_2^p$-complete, and that this hardness\napplies to instances constructible by aggregating the preferences of 7 voters.\n"}
+{"abstract": "  Detecting hidden convexity is one of the tools to address nonconvex\nminimization problems. After giving a formal definition of hidden convexity, we\nintroduce the notion of conditional infimum, as it will prove instrumental in\ndetecting hidden convexity. We develop the theory of the conditional infimum,\nand we establish a tower property, relevant for minimization problems. Thus\nequipped, we provide a sufficient condition for hidden convexity in nonconvex\nminimization problems. We illustrate our result on nonconvex quadratic\nminimization problems. We conclude with perspectives for using the conditional\ninfimum in relation to the so-called S-procedure, to couplings and conjugacies,\nand to lower bound convex programs.\n"}
+{"abstract": "  We consider sets of factors that can be avoided in square-free words on\ntwo-generator free groups. The elements of the group are presented in terms of\n0,1,2,3 such that 0 and 2 (resp.,1 and 3) are inverses of each other so that\n02, 20, 13 and 31 do not occur in a reduced word. A Dean word is a reduced word\nthat does not contain occurrences of $uu$ for any nonempty $u$. Dean showed in\n1965 that there exist infinite square-free reduced words. We show that if $w$\nis a Dean word of length at least 59 then there are at most six reduced words\nof length 3 avoided by $w$. We construct an infinite Dean word avoiding six\nreduced words of length~3. We also construct infinite Dean words with low\ncritical exponent and avoiding fewer reduced words of length 3. Finally, we\nshow that the minimal frequency of a letter in a Dean word is $8/59$ and the\ngrowth rate is close to 1.45818.\n"}
+{"abstract": "  A string $w$ is called a minimal absent word (MAW) for another string $T$ if\n$w$ does not occur in $T$ but the proper substrings of $w$ occur in $T$. For\nexample, let $\\Sigma = \\{\\mathtt{a, b, c}\\}$ be the alphabet. Then, the set of\nMAWs for string $w = \\mathtt{abaab}$ is $\\{\\mathtt{aaa, aaba, bab, bb, c}\\}$.\nIn this paper, we study combinatorial properties of MAWs in the sliding window\nmodel, namely, how the set of MAWs changes when a sliding window of fixed\nlength $d$ is shifted over the input string $T$ of length $n$, where $1 \\leq d\n< n$. We present \\emph{tight} upper and lower bounds on the maximum number of\nchanges in the set of MAWs for a sliding window over $T$, both in the cases of\ngeneral alphabets and binary alphabets. Our bounds improve on the previously\nknown best bounds [Crochemore et al., 2020].\n"}
+{"abstract": "  Financial literacy and financial education are important components of modern\nlife. The importance of financial literacy is increasing for financial\nconsumers because of the weakening of both government and employer-based\nretirement systems. Unfortunately, empirical research shows that financial\nconsumers are not fully informed and are not able to make proper choices even\nwhen appropriate information is available. More research is needed as to how\nfinancial consumers obtain investment and financial planning information. A\nprimary data study was conducted to understand the differences between the\ndemographic categories of gender, age, education-level, and income-level with\nthe means of obtaining investment and financial planning information. In this\nresearch study, which selected a population from the LinkedIn platform,\nstatistical differences between gender, age, education-level, and income-level\nwere confirmed. These differences helped to confirm prior research in this\nfield of study. Practical opportunities for commercial outreach to specific\npopulations became evident through this type of research. Providers of\ninvestment and financial planning information can access their targeted\naudience more effectively by understanding the demographic profile of the\naudience, as well as the propensity of the demographic profile of the audience\nto respond. As this type of research is relatively easy to construct and\nadminister, commercial outreach for providers of investment and financial\nplanning information can be conducted in a cost-efficient and effective manner.\n"}
+{"abstract": "  In this paper, we propose several models, which can realize synchronization\nof complex networks in finite time effectively. The results apply to\nheterogeneous dynamic networks, too. The mechanism of finite time convergence\nis revealed. Different from many models, which assume the coupling matrix being\nsymmetric (or the connecting graph is undirected), here, the coupling matrix is\nasymmetric (or the connecting graph is directed).\n"}
+{"abstract": "  Dynamical localization is the analog of Anderson localization in momentum\nspace, where the system's energy saturates and the single-particle\nwave-functions are exponentially localized in momentum space. In the presence\nof interactions, in the context of a periodically kicked Bose gas, it has been\nargued that dynamical localization persists. Focusing on the Tonks (strongly\ninteracting) regime, we show that the many-body dynamically localized phase is\neffectively thermal, a clear deviation from the breaking of ergodicity observed\nin standard many-body localized systems. We relate the effective temperature to\nthe driving parameters, and thus quantitatively describe the loss of coherence\nat large distances in this phase. Contrary to the non-interacting case, the\nmomentum distribution decays as a power-law at large momenta, characterized by\nan effectively thermal Tan's contact. This is a rare example where driving and\nmany-body (dynamical) localization lead to an effectively ergodic state.\n"}
+{"abstract": "  Time discretizations of phase-field systems have been studied. For example, a\ntime discretization and an error estimate for a parabolic-parabolic phase-field\nsystem have been studied by Colli--K. [Commun. Pure Appl. Anal. 18 (2019)].\nAlso, a time discretization and an error estimate for a simultaneous abstract\nevolution equation applying parabolic-hyperbolic phase field systems and the\nlinearized equations of coupled sound and heat flow have been studied (see K.\n[ESAIM Math. Model. Numer. Anal.54 (2020), Electron. J. Differential Equations\n2020, Paper No. 96]). On the other hand, although existence, continuous\ndependence estimates and behavior of solutions to nonlocal phase-field systems\nwith inertial terms have been studied by Grasselli--Petzeltov\\'a--Schimperna\n[Quart. Appl. Math. 65 (2007)], time discretizations of these systems seem to\nbe not studied yet. In this paper we focus on employing a time discretization\nscheme for a nonlocal phase-field system with inertial term and establishing an\nerror estimate for the difference between continuous and discrete solutions.\n"}
+{"abstract": "  We describe a technique to measure the position of a dipolar scatterer based\non self-homodyne detection of the scattered light. The method can theoretically\nreach the Heisenberg limit, at which information gained about the position is\nconstrained only by the back-action of the scattered light. The technique has\napplications in the fields of levitated optomechanics and trapped ions and is\ngenerally applicable to the position determination of confined light\nscatterers.\n"}
+{"abstract": "  Braneworld scenarios consider our observable universe as a brane embedded in\na five-dimensional bulk. In this work, we consider thick braneworld systems in\nthe recently proposed dynamically equivalent scalar-tensor representation of\n$f(R,T)$ gravity, where $R$ is the Ricci scalar and $T$ the trace of the\nstress-energy tensor. In the general $f\\left(R,T\\right)$ case we consider two\ndifferent models: a brane model without matter fields where the geometry is\nsupported solely by the gravitational fields, and a second model where matter\nis described by a scalar field with a potential. The particular cases for which\nthe function $f\\left(R,T\\right)$ is separable in the forms $F\\left(R\\right)+T$\nand $R+G\\left(T\\right)$, which give rise to scalar-tensor representations with\na single auxiliary scalar field, are studied separately. The stability of the\ngravitational sector is investigated and the models are shown to be stable\nagainst small perturbations of the metric. Furthermore, we show that in the\n$f\\left(R,T\\right)$ model in the presence of an extra matter field, the shape\nof the graviton zero-mode develops internal structure under appropriate choices\nof the parameters of the model.\n"}
+{"abstract": "  We propose a two-dimensional spectroscopic protocol for measuring the\ntime-dependent coherences between the stationary states of a system induced by\na time-dependent system-bath interaction. We also investigate the role of\ntemporally-correlated noise on coherence dephasing. This protocol enables\ndynamical information about the system and its coupling to the environment to\nbe determined. Our results are based on the quantum-trajectory method, and are\nobtained from both approximate, analytical and exact, numerical solutions of\nthe time-dependent Schroedinger equation. As an example, we show how this\nprotocol can be used to investigate exciton dynameds in conjugated polymers\ninduced by the coupling of their torsional modes with the environment.\n"}
+{"abstract": "  In the semileptonic decays of heavy mesons and baryons the lepton-mass\ndependence factors out in the quadratic $\\cos^2\\theta$ coefficient of the\ndifferential $\\cos\\theta$ distribution. We call the corresponding normalized\ncoefficient the convexity parameter. This observation opens the path to a test\nof lepton universality in semileptonic heavy meson and baryon decays that is\nindependent of form-factor effects. By projecting out the quadratic rate\ncoefficient, dividing out the lepton-mass-dependent factor and restricting the\nphase space integration to the $\\tau$ lepton phase space, one can define\noptimized partial rates which, in the Standard Model, are the same for all\nthree $(e,\\mu,\\tau)$ modes in a given semileptonic decay process. We discuss\nhow the identity is spoiled by New Physics effects. We discuss semileptonic\nheavy meson decays such as $\\bar{B}^0 \\to D^{(\\ast)+} \\ell^- \\bar\\nu_\\ell$ and\n$B_c^- \\to J/\\psi (\\eta_c)\\ell^- \\bar\\nu_\\ell$, and semileptonic heavy baryon\ndecays such as $\\Lambda_b \\to \\Lambda_c \\ell^- \\bar\\nu_\\ell$ for each\n$\\ell=e,\\mu,\\tau$.\n"}
+{"abstract": "  Quantifying quantum states' complexity is a key problem in various subfields\nof science, from quantum computing to black-hole physics. We prove a prominent\nconjecture by Brown and Susskind about how random quantum circuits' complexity\nincreases. Consider constructing a unitary from Haar-random two-qubit quantum\ngates. Implementing the unitary exactly requires a circuit of some minimal\nnumber of gates - the unitary's exact circuit complexity. We prove that this\ncomplexity grows linearly with the number of random gates, with unit\nprobability, until saturating after exponentially many random gates. Our proof\nis surprisingly short, given the established difficulty of lower-bounding the\nexact circuit complexity. Our strategy combines differential topology and\nelementary algebraic geometry with an inductive construction of Clifford\ncircuits.\n"}
+{"abstract": "  Pebbles of millimeter sizes are abundant in protoplanetary discs around young\nstars. Chondrules inside primitive meteorites - formed by melting of dust\naggregate pebbles or in impacts between planetesimals - have similar sizes. The\nrole of pebble accretion for terrestrial planet formation is nevertheless\nunclear. Here we present a model where inwards-drifting pebbles feed the growth\nof terrestrial planets. The masses and orbits of Venus, Earth, Theia (which\nlater collided with the Earth to form the Moon) and Mars are all consistent\nwith pebble accretion onto protoplanets that formed around Mars' orbit and\nmigrated to their final positions while growing. The isotopic compositions of\nEarth and Mars are matched qualitatively by accretion of two generations of\npebbles, carrying distinct isotopic signatures. Finally, we show that the water\nand carbon budget of Earth can be delivered by pebbles from the early\ngeneration before the gas envelope became hot enough to vaporise volatiles.\n"}
+{"abstract": "  Existing neural information retrieval (IR) models have often been studied in\nhomogeneous and narrow settings, which has considerably limited insights into\ntheir out-of-distribution (OOD) generalization capabilities. To address this,\nand to facilitate researchers to broadly evaluate the effectiveness of their\nmodels, we introduce Benchmarking-IR (BEIR), a robust and heterogeneous\nevaluation benchmark for information retrieval. We leverage a careful selection\nof 18 publicly available datasets from diverse text retrieval tasks and domains\nand evaluate 10 state-of-the-art retrieval systems including lexical, sparse,\ndense, late-interaction and re-ranking architectures on the BEIR benchmark. Our\nresults show BM25 is a robust baseline and re-ranking and\nlate-interaction-based models on average achieve the best zero-shot\nperformances, however, at high computational costs. In contrast, dense and\nsparse-retrieval models are computationally more efficient but often\nunderperform other approaches, highlighting the considerable room for\nimprovement in their generalization capabilities. We hope this framework allows\nus to better evaluate and understand existing retrieval systems, and\ncontributes to accelerating progress towards better robust and generalizable\nsystems in the future. BEIR is publicly available at\nhttps://github.com/UKPLab/beir.\n"}
+{"abstract": "  This paper aims at solving mass precise peg-in-hole assembly. First, a\nfeature space and a response space are constructed according to the relative\npose and equivalent forces and moments. Then the contact states are segmented\nin the feature space and the segmentation boundaries are mapped into the\nresponse space. Further, a feature-based compliance control (FBCC) algorithm is\nproposed based on boundary mapping. In the FBCC algorithm, a direction matrix\nis designed to execute accurate adjustment and an integrator is applied to\neliminate the residual responses. Finally, the simulations and experiments\ndemonstrate the superiority, robustness, and generalization ability of the\nFBCC.\n"}
+{"abstract": "  In a recent comment [Feller et. al, arXiv:2101.09186] on our Letter [Phys.\nRev. Lett. 122, 010403 (2019)], Feller et. al identified a mistake in our\nmathematical expression of \"strong independence\" for states that satisfy\nSpectrum Broadcast Structure. We concede that we wrote a mathematical condition\nthat is necessary but not sufficient. However, we used the original and correct\nqualitative definition for \"strong independence\" throughout the paper and in\nour proofs, therefore the proofs and statements, aside from the aforementioned\nmathematical expression, remain correct.\n"}
+{"abstract": "  We measure the phase velocities of surface acoustic waves (SAWs) propagating\nat different crystal orientations on (001)-cut GaAs substrates and their\ntemperature dependance. We design and fabricate sets of interdigital\ntransducers (IDTs) to induce 4 {\\mu}m SAWs via the inverse piezoelectric (PZE)\neffect between the PZE [110] direction (set as {\\theta} = 0{\\deg}) and the\nnon-PZE [100] direction ({\\theta} = 45{\\deg}) on GaAs. We also prepare ZnO film\nsputtered GaAs substrates in order to launch SAWs efficiently by IDTs even in\nthe non-PZE direction. We quantify acoustic velocities between 1.4 and 300 K\nfrom the resonant frequencies in the S11 parameter using a network analyzer. We\nobserve parabolic velocity-temperature trends at all {\\theta}-values both on\nGaAs and ZnO/GaAs substrates. Below 200 K, in ZnO/GaAs substrates slower SAW\nmodes appear around the [110] direction, which are unseen at RT.\n"}
+{"abstract": "  Optical power measurements are needed in practically all technologies based\non light. Here we report a general-purpose optical power detector based on the\nphotoacoustic effect. Optical power incident on the detector's black absorber\nproduces an acoustic signal, which is further converted into an electrical\nsignal using a silicon-cantilever pressure transducer. We demonstrate an\nexceptionally large spectral coverage from ultraviolet to far infrared, with\nthe possibility for further extension to the terahertz region. The linear\ndynamic range of the detector reaches 80 dB, ranging from a noise-equivalent\npower of 6 nW to 600 mW.\n"}
+{"abstract": "  Open-source software (OSS) is a critical part of the software supply chain.\nRecent social engineering attacks against OSS development teams have enabled\nattackers to become code contributors and later inject malicious code or\nvulnerabilities into the project with the goal of compromising dependent\nsoftware. The attackers have exploited interactions among development team\nmembers and the social dynamics of team behavior to enable their attacks. We\nintroduce a security approach that leverages signatures and patterns of team\ndynamics to predict the susceptibility of a software development team to social\nengineering attacks that enable access to the OSS project code. The proposed\napproach is programming language-, platform-, and vulnerability-agnostic\nbecause it assesses the artifacts of OSS team interactions, rather than OSS\ncode.\n"}
+{"abstract": "  The balancing provided by hydropower reservoirs is essential in the\ntransition towards a decarbonised European energy system, but the resource\nmight be impacted by future climate change. In this work, we first analyse the\nhydropower operation needed to balance a wind and solar dominated European\nenergy system, to signify whether and to what extent hydropower is required to\noperate differently due to the decarbonisation of the energy system. Second, we\napply runoff data achieved with 10 dynamically downscaled climate models with\n0.11 x 0.11 deg horizontal and daily resolution to project the future reservoir\ninflow at three CO2 emissions scenarios: low (RCP2.6), mid (RCP4.5), and high\nemissions (RCP8.5). We show that the decarbonised energy system increases the\nramp rates and seasonality of the hydropower operation. Despite large\ninterannual and intermodel variability, we found a significant change in annual\ninflow due to climate change in 20 out of 22 European countries at the mid and\nhigh emissions scenarios. The seasonal profile, as well as the frequency and\nduration of droughts and floods, is also projected to be impacted.\n"}
+{"abstract": "  Spectral geometric methods have brought revolutionary changes to the field of\ngeometry processing -- however, when the data to be processed exhibits severe\npartiality, such methods fail to generalize. As a result, there exists a big\nperformance gap between methods dealing with complete shapes, and methods that\naddress missing geometry. In this paper, we propose a possible way to fill this\ngap. We introduce the first method to compute compositions of non-rigidly\ndeforming shapes, without requiring to solve first for a dense correspondence\nbetween the given partial shapes. We do so by operating in a purely spectral\ndomain, where we define a union operation between short sequences of\neigenvalues. Working with eigenvalues allows to deal with unknown\ncorrespondence, different sampling, and different discretization (point clouds\nand meshes alike), making this operation especially robust and general. Our\napproach is data-driven, and can generalize to isometric and non-isometric\ndeformations of the surface, as long as these stay within the same semantic\nclass (e.g., human bodies), as well as to partiality artifacts not seen at\ntraining time.\n"}
+{"abstract": "  Recent developments in data acquisition technology allow us to collect 3D\ntexture meshes quickly. Those can help us understand and analyse the urban\nenvironment, and as a consequence are useful for several applications like\nspatial analysis and urban planning. Semantic segmentation of texture meshes\nthrough deep learning methods can enhance this understanding, but it requires a\nlot of labelled data. The contributions of this work are threefold: (1) a new\nbenchmark dataset of semantic urban meshes, (2) a novel semi-automatic\nannotation framework, and (3) an annotation tool for 3D meshes. In particular,\nour dataset covers about 4 km2 in Helsinki (Finland), with six classes, and we\nestimate that we save about 600 hours of labelling work using our annotation\nframework, which includes initial segmentation and interactive refinement. We\nalso compare the performance of several state-of-theart 3D semantic\nsegmentation methods on the new benchmark dataset. Other researchers can use\nour results to train their networks: the dataset is publicly available, and the\nannotation tool is released as open-source.\n"}
+{"abstract": "  Freshwater from rivers influences Indian summer monsoon rainfall and regional\ntropical cyclones by shallowing the upper layer and warming the subsurface\nocean in the Bay of Bengal. Here, we use in situ and satellite data with\nreanalysis products to showcase how river water can experience a significant\nincrease in salinity on subseasonal timescales. This involves the trapping and\nhomogenization of freshwater by a cyclonic eddy in the Bay. Specifically, in\nOctober 2015, river water is shown to enter a particularly long-lived eddy\nalong with its attracting manifolds within a period of two weeks. The eddy\nitself is quite unique in that it lasted for 16 months in the Bay where average\nlifespans are of the order of 2-3 months. This low salinity water results in\nthe formation of a highly stratified surface layer. In fact, when freshest, the\neddy has the highest sea-level anomalies, spins fastest, and supports strong\nlateral gradients in salinity. Subsequently, observations reveal progressive\nhomogenization of salinity and relaxation of sea-level anomalies and salinity\ngradients within a month. In particular, salty water spirals in, and freshwater\nis pulled out across the eddy boundary. Lagrangian experiments elucidate this\nprocess, whereby horizontal chaotic mixing provides a mechanism for the rapid\nincrease in surface salinity on the order of timescale of a month. This pathway\nis distinct from vertical mixing and likely to be important in the eddy-rich\nBay of Bengal.\n"}
+{"abstract": "  The hadron-quark/gluon duality formulated in terms of a topology change at a\ndensity $n\\gsim 2n_0$ $n_0\\simeq 0.16$fm$^{-3}$ is found to describe the core\nof massive compact stars in terms of quasiparticles of fractional baryon\ncharges, behaving neither like pure baryons nor like deconfined quarks. This\ncan be considered as the Cheshire-Cat mechanism~\\cite{CC} for the hadron-quark\ncontinuity arrived at bottom-up from skyrmions that is equivalent to the\n\"MIT-bag\"-to-skyrmion continuity arrived at top-down from quarks/gluons. Hidden\nsymmetries, both local gauge and pseudo-conformal (or broken scale), emerge and\ngive rise both to the long-standing \"effective $g_A^\\ast\\approx 1$\" in nuclear\nGamow-Teller transitions at $\\lsim n_0$ and to the pseudo-conformal sound\nvelocity $v_{pcs}^2/c^2\\approx 1/3$ at $\\gsim 3n_0$. It is suggested that what\nhas been referred to, since a long time, as \"quenched $g_A$\" in light nuclei\nreflects what leads to the dilaton-limit $g_A^{\\rm DL}=1$ at near the\n(putative) infrared fixed point of scale invariance. These properties are\nconfronted with the recent observations in Gamow-Teller transitions and in\nastrophysical observations.\n"}
+{"abstract": "  We demonstrate that cosmic string loops may provide a joint resolution of two\nmysteries surrounding recently observed black holes. For a string tension in an\nappropriate range, large radius string loops have the potential to provide the\nnonlinearities in the early universe which seed supermassive black holes. The\nmore numerous smaller radius string loops can then seed intermediate mass black\nholes, including those with a mass in the region between 65 and 135 solar\nmasses in which standard black hole formation scenarios predict no black holes\nare able to form, but which have recently been detected by the LIGO/VIRGO\ncollaboration. We find that there could be as many as $10^6$ of intermediate\nmass black holes per galaxy, providing a tantalizing target for gravitational\nwave observatories to look for.\n"}
+{"abstract": "  Quantum and random walks were proven to be equivalent on finite graphs by\ndemonstrating how to construct a time-dependent random walk sharing the exact\nsame evolution of vertex probability of any given discrete-time coined quantum\nwalk. Such equivalence stipulated a deep connection between the processes that\nis far stronger than simply considering quantum walks as quantum analogues of\nrandom walks. This article expands on the connection between quantum and random\nwalks by demonstrating a procedure that constructs a time-dependent quantum\nwalk matching the evolution of vertex probability of any given random walk in a\nunitary way. It is a trivial fact that a quantum walk measured at all time\nsteps of its evolution degrades to a random walk. More interestingly, the\nmethod presented describes a quantum walk that matches a random walk without\nmeasurement operations, such that the unitary evolution of the quantum walk\ncaptures the probability evolution of the random walk. The construction\nprocedure is general, covering both homogeneous and non-homogeneous random\nwalks. For the homogeneous random walk case, the properties of unitary\nevolution imply that the quantum walk described is time-dependent since\nhomogeneous quantum walks do not converge for arbitrary initial conditions\n"}
+{"abstract": "  Mutation testing is a well-established technique for assessing a test suite's\nquality by injecting artificial faults into production code. In recent years,\nmutation testing has been extended to machine learning (ML) systems, and deep\nlearning (DL) in particular; researchers have proposed approaches, tools, and\nstatistically sound heuristics to determine whether mutants in DL systems are\nkilled or not. However, as we will argue in this work, questions can be raised\nto what extent currently used mutation testing techniques in DL are actually in\nline with the classical interpretation of mutation testing. We observe that ML\nmodel development resembles a test-driven development (TDD) process, in which a\ntraining algorithm (`programmer') generates a model (program) that fits the\ndata points (test data) to labels (implicit assertions), up to a certain\nthreshold. However, considering proposed mutation testing techniques for ML\nsystems under this TDD metaphor, in current approaches, the distinction between\nproduction and test code is blurry, and the realism of mutation operators can\nbe challenged. We also consider the fundamental hypotheses underlying classical\nmutation testing: the competent programmer hypothesis and coupling effect\nhypothesis. As we will illustrate, these hypotheses do not trivially translate\nto ML system development, and more conscious and explicit scoping and concept\nmapping will be needed to truly draw parallels. Based on our observations, we\npropose several action points for better alignment of mutation testing\ntechniques for ML with paradigms and vocabularies of classical mutation\ntesting.\n"}
+{"abstract": "  Context. Many low-redshift active galactic nuclei harbor a supermassive black\nhole accreting matter at low or medium rates. At such rates, the accretion flow\nusually consists of a cold optically thick disk, plus a hot, low density,\ncollisionless corona. In the latter component, charged particles can be\naccelerated to high energies by various mechanisms. Aims. We aim to\ninvestigate, in detail, nonthermal processes in hot accretion flows onto\nsupermassive black holes, covering a wide range of accretion rates and\nluminosities. Methods. We developed a model consisting of a thin\nShakura-Sunyaev disk plus an inner hot accretion flow or corona, modeled as a\nradiatively inefficient accretion flow, where nonthermal processes take place.\nWe solved the transport equations for relativistic particles and estimated the\nspectral energy distributions resulting from nonthermal interactions between\nthe various particle species and the fields in the source. Results. We covered\na variety of scenarios, from low accretion rates up to 10% of the Eddington\nlimit, and identified the relevant cooling mechanisms in each case. The\npresence of hadrons in the hot flow is decisive for the spectral shape, giving\nrise to secondary particles and gamma-ray cascades. We applied our model to the\nsource IC 4329A, confirming earlier results that showed evidence of nonthermal\nparticles in the corona.\n"}
+{"abstract": "  Graph-level representations are critical in various real-world applications,\nsuch as predicting the properties of molecules. But in practice, precise graph\nannotations are generally very expensive and time-consuming. To address this\nissue, graph contrastive learning constructs instance discrimination task which\npulls together positive pairs (augmentation pairs of the same graph) and pushes\naway negative pairs (augmentation pairs of different graphs) for unsupervised\nrepresentation learning. However, since for a query, its negatives are\nuniformly sampled from all graphs, existing methods suffer from the critical\nsampling bias issue, i.e., the negatives likely having the same semantic\nstructure with the query, leading to performance degradation. To mitigate this\nsampling bias issue, in this paper, we propose a Prototypical Graph Contrastive\nLearning (PGCL) approach. Specifically, PGCL models the underlying semantic\nstructure of the graph data via clustering semantically similar graphs into the\nsame group, and simultaneously encourages the clustering consistency for\ndifferent augmentations of the same graph. Then given a query, it performs\nnegative sampling via drawing the graphs from those clusters that differ from\nthe cluster of query, which ensures the semantic difference between query and\nits negative samples. Moreover, for a query, PGCL further reweights its\nnegative samples based on the distance between their prototypes (cluster\ncentroids) and the query prototype such that those negatives having moderate\nprototype distance enjoy relatively large weights. This reweighting strategy is\nproved to be more effective than uniform sampling. Experimental results on\nvarious graph benchmarks testify the advantages of our PGCL over\nstate-of-the-art methods.\n"}
+{"abstract": "  Let $F_1,\\ldots,F_R$ be homogeneous polynomials of degree $d\\ge 2$ with\ninteger coefficients in $n$ variables, and let $\\mathbf{F}=(F_1,\\ldots,F_R)$.\nSuppose that $F_1,\\ldots,F_R$ is a non-singular system and $n\\ge\n4^{d+2}d^2R^5$. We prove that there are infinitely many solutions to\n$\\mathbf{F}(\\mathbf{x})=\\mathbf{0}$ in prime coordinates if (i)\n$\\mathbf{F}(\\mathbf{x})=\\mathbf{0}$ has a non-singular solution over the\n$p$-adic units $\\U_p$ for all prime numbers $p$, and (ii)\n$\\mathbf{F}(\\mathbf{x})=\\mathbf{0}$ has a non-singular solution in the open\ncube $(0,1)^n$.\n"}
+{"abstract": "  Millimeter wave (mmWave) is a practical solution to provide high data rate\nfor the vehicle-to-everything (V2X) communications. This enables the future\nautonomous vehicles to exchange big data with the base stations (BSs) such as\nthe velocity and the location to improve the awareness of the advanced driving\nassistance system (ADAS). In this context, we consider a single-cell multiuser\ndoubly-selective system wherein the BS simultaneously serves multiple vehicles.\nTo accomplish this requirement, the BS is implemented in hybrid architecture to\nsupport multiple spatial streams while the vehicles have analog-only\nstructures. In this work, we develop a low-complexity hybrid precoding\nalgorithm wherein the design of the hybrid precoder at the BS and the analog\ncombiner at the vehicles require small training and feedback overhead. We\npropose a two-stage hybrid precoding algorithm wherein the first stage designs\nthe analog beamformers as in single user scenario while the second stage\ndesigns the multiuser digital precoder at the BS. In the second stage, we\nderive closed-form digital precoders such as Maximum Ratio Transmission (MRT),\nZero-Forcing (ZF) and Minimum Mean Square Error (MMSE) as a first variant while\nwe propose iterative digital precoder as a second variant. The design of the\ndigital precoders for the two variants requires the limited feedback sent from\nthe vehicles to BS. We refer to the random vector quantization (RVQ) and the\nbeamsteering codebooks to quantize the feedbacks for variants I and II,\nrespectively, since the perfect feedback requires long overhead and large\ntraining. We evaluate the rate loss incurred by the quantization of the digital\nand analog codebooks against the perfect channel state information at the\ntransmitter (CSIT).\n"}
+{"abstract": "  We propose a new algorithm for computing the tensor rank decomposition or\ncanonical polyadic decomposition of higher-order tensors subject to a rank and\ngenericity constraint. Reformulating this as a system of polynomial equations\nallows us to leverage recent numerical linear algebra tools from computational\nalgebraic geometry. We describe the complexity of our algorithm in terms of the\nmultigraded regularity of a multihomogeneous ideal. We prove effective bounds\nfor many formats and ranks and conjecture a general formula. These bounds are\nof independent interest for overconstrained polynomial system solving. Our\nexperiments show that our algorithm can outperform state-of-the-art algebraic\nalgorithms by an order of magnitude in terms of accuracy, computation time, and\nmemory consumption.\n"}
+{"abstract": "  Legged robots have excellent terrestrial mobility for traversing diverse\nenvironments and thus have the potential to be deployed in a wide variety of\nscenarios. However, they are susceptible to falling and leg malfunction during\nlocomotion. Although the use of a large number of legs can overcome these\nproblems, it makes the body long and leads to many legs being constrained to\ncontact with the ground to support the long body, which impedes\nmaneuverability. To improve the locomotion maneuverability of such robots, the\npresent study focuses on dynamic instability, which induces rapid and large\nmovement changes, and uses a 12-legged robot with a flexible body axis. Our\nprevious work found that the straight walk of the robot becomes unstable\nthrough Hopf bifurcation when the body axis flexibility is changed, which\ninduces body undulations. Furthermore, we developed a simple controller based\non the Hopf bifurcation and showed that the instability facilitates the turning\nof the robot. In this study, we newly found that the straight walk becomes\nunstable through pitchfork bifurcation when the body-axis flexibility is\nchanged in a way different from that in our previous work. In addition, the\npitchfork bifurcation induces a transition into a curved walk, whose curvature\ncan be controlled by the body-axis flexibility. We developed a simple\ncontroller based on the pitchfork-bifurcation characteristics and demonstrated\nthat the robot can perform a turning maneuver superior to that with the\nprevious controller. This study provides a novel design principle for\nmaneuverable locomotion of many-legged robots using intrinsic dynamic\nproperties.\n"}
+{"abstract": "  The reliability of software that has a Deep Neural Network (DNN) as a\ncomponent is urgently important today given the increasing number of critical\napplications being deployed with DNNs. The need for reliability raises a need\nfor rigorous testing of the safety and trustworthiness of these systems. In the\nlast few years, there have been a number of research efforts focused on testing\nDNNs. However the test generation techniques proposed so far lack a check to\ndetermine whether the test inputs they are generating are valid, and thus\ninvalid inputs are produced. To illustrate this situation, we explored three\nrecent DNN testing techniques. Using deep generative model based input\nvalidation, we show that all the three techniques generate significant number\nof invalid test inputs. We further analyzed the test coverage achieved by the\ntest inputs generated by the DNN testing techniques and showed how invalid test\ninputs can falsely inflate test coverage metrics.\n  To overcome the inclusion of invalid inputs in testing, we propose a\ntechnique to incorporate the valid input space of the DNN model under test in\nthe test generation process. Our technique uses a deep generative model-based\nalgorithm to generate only valid inputs. Results of our empirical studies show\nthat our technique is effective in eliminating invalid tests and boosting the\nnumber of valid test inputs generated.\n"}
+{"abstract": "  We prove that a matrix from the split orthogonal group over a polynomial ring\nwith coefficients in a small-dimensional ring can be reduced to a smaller\nmatrix by a bounded number of elementary orthogonal transformations. The bound\nis given explicitly. This result is an effective version of the early\nstabilisation of the orthogonal K1 functor proven by Suslin and Kopeiko. Since\nthe similar effective results for special linear and symplectic groups were\nobtained by Vaserstein, the present paper closes the problem for split\nclassical groups.\n"}
+{"abstract": "  Biological agents are known to learn many different tasks over the course of\ntheir lives, and to be able to revisit previous tasks and behaviors with little\nto no loss in performance. In contrast, artificial agents are prone to\n'catastrophic forgetting' whereby performance on previous tasks deteriorates\nrapidly as new ones are acquired. This shortcoming has recently been addressed\nusing methods that encourage parameters to stay close to those used for\nprevious tasks. This can be done by (i) using specific parameter regularizers\nthat map out suitable destinations in parameter space, or (ii) guiding the\noptimization journey by projecting gradients into subspaces that do not\ninterfere with previous tasks. However, these methods often exhibit subpar\nperformance in both feedforward and recurrent neural networks, with recurrent\nnetworks being of interest to the study of neural dynamics supporting\nbiological continual learning. In this work, we propose Natural Continual\nLearning (NCL), a new method that unifies weight regularization and projected\ngradient descent. NCL uses Bayesian weight regularization to encourage good\nperformance on all tasks at convergence and combines this with gradient\nprojection using the prior precision, which prevents catastrophic forgetting\nduring optimization. Our method outperforms both standard weight regularization\ntechniques and projection based approaches when applied to continual learning\nproblems in feedforward and recurrent networks. Finally, the trained networks\nevolve task-specific dynamics that are strongly preserved as new tasks are\nlearned, similar to experimental findings in biological circuits.\n"}
+{"abstract": "  The recently discovered electrical-induced switching of antiferromagnetic\n(AF) materials that have spatial inversion asymmetry has enriched the field of\nspintronics immensely and opened the door for the concept of antiferromagnetic\nMRAM. CuMnAs is one promising AF material that exhibits such electrical\nswitching ability, and has been studied to switch using electrical pulses of\nlength millisecond down to picosecond, but with little focus on nanosecond\nregime. We demonstrate here switching of CuMnAs/GaP using nanosecond pulses.\nOur results showed that in the nanosecond regime low-energy switching, high\nreadout signal with highly reproducible behaviour down to a single pulse can be\nachieved. Moreover, a comparison of the two switching methods of orthogonal\nswitching and polarity switching was done on same device showing two different\nbehaviours that can be exploited selectively for different future\nmemory/processing applications.\n"}
+{"abstract": "  Medical image diagnosis can be achieved by deep neural networks, provided\nthere is enough varied training data for each disease class. However, a\nhitherto unknown disease class not encountered during training will inevitably\nbe misclassified, even if predicted with low probability. This problem is\nespecially important for medical image diagnosis, when an image of a hitherto\nunknown disease is presented for diagnosis, especially when the images come\nfrom the same image domain, such as dermoscopic skin images.\n  Current out-of-distribution detection algorithms act unfairly when the\nin-distribution classes are imbalanced, by favouring the most numerous disease\nin the training sets. This could lead to false diagnoses for rare cases which\nare often medically important. We developed a novel yet simple method to train\nneural networks, which enables them to classify in-distribution dermoscopic\nskin disease images and also detect novel diseases from dermoscopic images at\ntest time. We show that our BinaryHeads model not only does not hurt\nclassification balanced accuracy when the data is imbalanced, but also\nconsistently improves the balanced accuracy. We also introduce an important\nmethod to investigate the effectiveness of out-of-distribution detection\nmethods based on presence of varying amounts of out-of-distribution data, which\nmay arise in real-world settings.\n"}
+{"abstract": "  Time series data have grown at an explosive rate in numerous domains and have\nstimulated a surge of time series modeling research. A comprehensive comparison\nof different time series models, for a considered data analytics task, provides\nuseful guidance on model selection for data analytics practitioners. Data\nscarcity is a universal issue that occurs in a vast range of data analytics\nproblems, due to the high costs associated with collecting, generating, and\nlabeling data as well as some data quality issues such as missing data. In this\npaper, we focus on the temporal classification/regression problem that attempts\nto build a mathematical mapping from multivariate time series inputs to a\ndiscrete class label or a real-valued response variable. For this specific\nproblem, we identify two types of scarce data: scarce data with small samples\nand scarce data with sparsely and irregularly observed time series covariates.\nObserving that all existing works are incapable of utilizing the sparse time\nseries inputs for proper modeling building, we propose a model called sparse\nfunctional multilayer perceptron (SFMLP) for handling the sparsity in the time\nseries covariates. The effectiveness of the proposed SFMLP under each of the\ntwo types of data scarcity, in comparison with the conventional deep sequential\nlearning models (e.g., Recurrent Neural Network, and Long Short-Term Memory),\nis investigated through mathematical arguments and numerical experiments.\n"}
+{"abstract": "  The concurrency features of the Go language have proven versatile in the\ndevelopment of a number of concurrency systems. However, correctness methods to\naddress challenges in Go concurrency debugging have not received much\nattention. In this work, we present an automatic dynamic tracing mechanism that\nefficiently captures and helps analyze the whole-program concurrency model.\nUsing an enhancement to the built-in tracer package of Go and a framework that\ncollects dynamic traces from application execution, we enable thorough\npost-mortem analysis for concurrency debugging. Preliminary results about the\neffectiveness and scalability (up to more than 2K goroutines) of our proposed\ndynamic tracing for concurrent debugging are presented. We discuss the future\ndirection for exploiting dynamic tracing towards accelerating concurrent bug\nexposure.\n"}
+{"abstract": "  Active, selective and stable catalysts are imperative for sustainable energy\nconversion, and engineering materials with such properties are highly desired.\nHigh-entropy alloys (HEAs) offer a vast compositional space for tuning such\nproperties. Too vast, however, to traverse without the proper tools. Here, we\nreport the use of Bayesian optimization on a model based on density functional\ntheory (DFT) to predict the most active compositions for the electrochemical\noxygen reduction reaction (ORR) with the least possible number of sampled\ncompositions for the two HEAs Ag-Ir-Pd-Pt-Ru and Ir-Pd-Pt-Rh-Ru. The discovered\noptima are then scrutinized with DFT and subjected to experimental validation\nwhere optimal catalytic activities are verified for Ag-Pd, Ir-Pt, and Pd-Ru\nbinary systems. This study offers insight into the number of experiments needed\nfor exploring the vast compositional space of multimetallic alloys which has\nbeen determined to be on the order of 50 for ORR on these HEAs.\n"}
+{"abstract": "  In this paper we examine the conditions that an object $E$ with\n$\\text{ch}_0(E)=0$ has to satisfy in order for it to be asymptotically\n(semi)stable with regard to Weak or Bridgeland stability conditions. This\nnotion turned out to be equivalent to sheaf Gieseker-Simpson (semi)stability or\na dual of it, depending on the curve considered.\n"}
+{"abstract": "  In this paper, we investigate the driving factors behind concatenation, a\nsimple but effective data augmentation method for low-resource neural machine\ntranslation. Our experiments suggest that discourse context is unlikely the\ncause for the improvement of about +1 BLEU across four language pairs. Instead,\nwe demonstrate that the improvement comes from three other factors unrelated to\ndiscourse: context diversity, length diversity, and (to a lesser extent)\nposition shifting.\n"}
+{"abstract": "  As mobile traffic is dominated by content services (e.g., video), which\ntypically use recommendation systems, the paradigm of network-friendly\nrecommendations (NFR) has been proposed recently to boost the network\nperformance by promoting content that can be efficiently delivered (e.g.,\ncached at the edge). NFR increase the network performance, however, at the cost\nof being unfair towards certain contents when compared to the standard\nrecommendations. This unfairness is a side effect of NFR that has not been\nstudied in literature. Nevertheless, retaining fairness among contents is a key\noperational requirement for content providers. This paper is the first to study\nthe fairness in NFR, and design fair-NFR. Specifically, we use a set of metrics\nthat capture different notions of fairness, and study the unfairness created by\nexisting NFR schemes. Our analysis reveals that NFR can be significantly\nunfair. We identify an inherent trade-off between the network gains achieved by\nNFR and the resulting unfairness, and derive bounds for this trade-off. We show\nthat existing NFR schemes frequently operate far from the bounds, i.e., there\nis room for improvement. To this end, we formulate the design of Fair-NFR\n(i.e., NFR with fairness guarantees compared to the baseline recommendations)\nas a linear optimization problem. Our results show that the Fair-NFR can\nachieve high network gains (similar to non-fair-NFR) with little unfairness.\n"}
+{"abstract": "  We explore induced mappings between character varieties by mappings between\nsurfaces. It is shown that these mappings are generally Poisson. We also\nexplicitly calculate the Poisson bi-vector in a new case.\n"}
+{"abstract": "  Over the years, families of forecasting models, such as the exponential\nsmoothing family and Autoregressive Integrated Moving Average, have expanded to\ncontain multiple possible forms and forecasting profiles. In this paper, we\nquestion the need to consider such large families of models. We argue that\nparsimoniously identifying suitable subsets of models will not decrease the\nforecasting accuracy nor will it reduce the ability to estimate the forecast\nuncertainty. We propose a framework that balances forecasting performance\nversus computational cost, resulting in a set of reduced families of models and\nempirically demonstrate this trade-offs. We translate computational benefits to\nmonetary cost savings and discuss the implications of our results in the\ncontext of large retailers.\n"}
+{"abstract": "  It has been known that the Schwarzschild-de Sitter (Sch-dS) black hole may\nnot be in thermal equilibrium and also be found to be thermodynamically\nunstable in the standard black hole thermodynamics. In the present work, we\ninvestigate the possibility to realize the thermodynamical stability of the\nSch-dS black hole as an effective system by using the R\\'{e}nyi statistics,\nwhich includes the non-extensive nature of black holes. Our results indicate\nthat the non-extensivity allows the black hole to be thermodynamically stable\nwhich gives rise to the lower bound on the non-extensive parameter. By\ncomparing the results to ones in the separated system approach, we find that\nthe effective temperature is always smaller than the black hole horizon\ntemperature and the thermodynamically stable black hole in effective approach\nis always larger than one in separated approach at a certain temperature. There\nexists only the zeroth-order phase transition from the the hot gas phase to the\nblack hole phase for the effective system while it is possible to have the\ntransition of both the zeroth order and the first order for the separated\nsystem.\n"}
+{"abstract": "  The weakest known criterion for local rotational symmetry (LRS) in spacetimes\nof Petrov type D is due to Goode and Wainwright (1986). Here it is shown, using\nmethods related to the Cartan-Karlhede procedure, to be equivalent to local\nspatial rotation invariance of the Riemann tensor and its first derivatives.\nConformally flat spacetimes are similarly studied and it is shown that for\nalmost all cases the same criterion ensures LRS. Only for conformally flat\naccelerated perfect fluids are three curvature derivatives required to ensure\nLRS, showing that Ellis's original condition for that case is necessary as well\nas sufficient.\n"}
+{"abstract": "  There is long research interest in electron-induced reactions of halogenated\nmolecules. It has been two decades since the cosmic-ray (CR) driven\nelectron-induced reaction (CRE) mechanism for the ozone hole formation was\nproposed. The derived CRE equation with stratospheric equivalent chlorine level\nand CR intensity as only two variables has well reproduced the observed data of\nstratospheric O3 and temperatures over the past 40 years. The CRE predictions\nof 11-year cyclic variations of the Antarctic O3 hole and associated\nstratospheric cooling have also been well confirmed. Measured altitude profiles\nof ozone and temperatures in Antarctic ozone holes provide convincing\nfingerprints of the CRE mechanism. A quantitative estimate indicates that the\nCRE-produced Cl atoms could completely deplete or even over-kill ozone in the\nCR-peak polar stratospheric region, consistent with observed altitude profiles\nof severest Antarctic ozone holes. After removing the natural CR effect, the\nhidden recovery in the Antarctic O3 hole since around 1995 is clearly\ndiscovered, while the recovery of O3 loss at mid-latitudes is being delayed by\n>=10 years. These results have provided strong evidence of the CRE mechanism.\nIf the CR intensity keeps the current rising trend, the Antarctic O3 hole will\nreturn to the 1980 level by around 2060, while the returning of the O3 layer at\nmid-latitudes to the 1980 level will largely be delayed or will not even occur\nby the end of this century. The results strongly indicate that the CRE\nmechanism must be considered as a key factor in evaluating the O3 hole.\n"}
+{"abstract": "  We consider the existence of convolution of Roumieu type ultradistribution\nwith the kernel $e^{s(1+|x|^2)^{q/2}}, q\\geq 1$, $s\\in\\RR\\backslash\\{0\\}$.\n"}
+{"abstract": "  Checkerboard framings are an extension of checkerboard colorings for virtual\nlinks. According to checkerboard framings, in 2017, Dye obtained an independent\ninvariant of virtual links: the cut point number. Checkerboard framings and cut\npoints can be used as a tool to extend other classical invariants to virtual\nlinks. We prove that one of the conjectures in Dye's paper is correct.\nMoreover, we analyze the connection and difference between checkerboard framing\nobtained from virtual link diagram by adapting cut points and twisted link\ndiagram obtained from virtual link diagram by introducing bars. By adjusting\nthe normalized arrow polynomial of virtual links, we generalize it to twisted\nlinks. And we show that it is an invariant for twisted link. Finally, we figure\nout three characteristics of the normalized arrow polynomial of a checkerboard\ncolorable twisted link, which is a tool of detecting checkerboard colorability\nof a twisted link. The latter two characteristics are the same as in the case\nof checkerboard colorable virtual link diagram.\n"}
+{"abstract": "  Machine learning problems have an intrinsic geometric structure as central\nobjects including a neural network's weight space and the loss function\nassociated with a particular task can be viewed as encoding the intrinsic\ngeometry of a given machine learning problem. Therefore, geometric concepts can\nbe applied to analyze and understand theoretical properties of machine learning\nstrategies as well as to develop new algorithms. In this paper, we address\nthree seemingly unrelated open questions in machine learning by viewing them\nthrough a unified framework grounded in differential geometry. Specifically, we\nview the weight space of a neural network as a manifold endowed with a\nRiemannian metric that encodes performance on specific tasks. By defining a\nmetric, we can construct geodesic, minimum length, paths in weight space that\nrepresent sets of networks of equivalent or near equivalent functional\nperformance on a specific task. We, then, traverse geodesic paths while\nidentifying networks that satisfy a second objective. Inspired by the geometric\ninsight, we apply our geodesic framework to 3 major applications: (i) Network\nsparsification (ii) Mitigating catastrophic forgetting by constructing networks\nwith high performance on a series of objectives and (iii) Finding high-accuracy\npaths connecting distinct local optima of deep networks in the non-convex loss\nlandscape. Our results are obtained on a wide range of network architectures\n(MLP, VGG11/16) trained on MNIST, CIFAR-10/100. Broadly, we introduce a\ngeometric framework that unifies a range of machine learning objectives and\nthat can be applied to multiple classes of neural network architectures.\n"}
+{"abstract": "  Smart Contracts (SCs) in Ethereum can automate tasks and provide different\nfunctionalities to a user. Such automation is enabled by the `Turing-complete'\nnature of the programming language (Solidity) in which SCs are written. This\nalso opens up different vulnerabilities and bugs in SCs that malicious actors\nexploit to carry out malicious or illegal activities on the cryptocurrency\nplatform. In this work, we study the correlation between malicious activities\nand the vulnerabilities present in SCs and find that some malicious activities\nare correlated with certain types of vulnerabilities. We then develop and study\nthe feasibility of a scoring mechanism that corresponds to the severity of the\nvulnerabilities present in SCs to determine if it is a relevant feature to\nidentify suspicious SCs. We analyze the utility of severity score towards\ndetection of suspicious SCs using unsupervised machine learning (ML) algorithms\nacross different temporal granularities and identify behavioral changes. In our\nexperiments with on-chain SCs, we were able to find a total of 1094 benign SCs\nacross different granularities which behave similar to malicious SCs, with the\ninclusion of the smart contract vulnerability scores in the feature set.\n"}
+{"abstract": "  Face anti-spoofing (FAS) has lately attracted increasing attention due to its\nvital role in securing face recognition systems from presentation attacks\n(PAs). As more and more realistic PAs with novel types spring up, traditional\nFAS methods based on handcrafted features become unreliable due to their\nlimited representation capacity. With the emergence of large-scale academic\ndatasets in the recent decade, deep learning based FAS achieves remarkable\nperformance and dominates this area. However, existing reviews in this field\nmainly focus on the handcrafted features, which are outdated and uninspiring\nfor the progress of FAS community. In this paper, to stimulate future research,\nwe present the first comprehensive review of recent advances in deep learning\nbased FAS. It covers several novel and insightful components: 1) besides\nsupervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also\ninvestigate recent methods with pixel-wise supervision (e.g., pseudo depth\nmap); 2) in addition to traditional intra-dataset evaluation, we collect and\nanalyze the latest methods specially designed for domain generalization and\nopen-set FAS; and 3) besides commercial RGB camera, we summarize the deep\nlearning applications under multi-modal (e.g., depth and infrared) or\nspecialized (e.g., light field and flash) sensors. We conclude this survey by\nemphasizing current open issues and highlighting potential prospects.\n"}
+{"abstract": "  Recent water line observations toward several low-mass protostars suggest low\nwater gas fractional abundances in the inner warm envelopes. Water destruction\nby X-rays has been proposed to influence the water abundances in these regions,\nbut the detailed chemistry, including the nature of alternative oxygen\ncarriers, is not yet understood. In this study, we aim to understand the impact\nof X-rays on the composition of low-mass protostellar envelopes, focusing\nspecifically on water and related oxygen bearing species. We compute the\nchemical composition of two low-mass protostellar envelopes using a 1D\ngas-grain chemical reaction network, under various X-ray field strengths.\nAccording to our calculations, outside the water snowline, the water gas\nabundance increases with $L_{\\mathrm{X}}$. Inside the water snowline, water\nmaintains a high abundance of $\\sim 10^{-4}$ for small $L_{\\mathrm{X}}$, with\nwater and CO being the dominant oxygen carriers. For large $L_{\\mathrm{X}}$,\nthe water gas abundances significantly decrease just inside the water snowline\n(down to $\\sim10^{-8}-10^{-7}$) and in the innermost regions ($\\sim10^{-6}$).\nFor these cases, the O$_{2}$ and O gas abundances reach $\\sim 10^{-4}$ within\nthe water snowline, and they become the dominant oxygen carriers. The HCO$^{+}$\nand CH$_{3}$OH abundances, which have been used as tracers of the water\nsnowline, significantly increase/decrease within the water snowline,\nrespectively, as the X-ray fluxes become larger. The abundances of some other\ndominant molecules, such as CO$_{2}$, OH, CH$_{4}$, HCN, and NH$_{3}$, are also\naffected by strong X-ray fields, especially within their own snowlines. These\nX-ray effects are larger in lower density envelope models. Future observations\nof water and related molecules (using e.g., ALMA and ngVLA) will access the\nregions around protostars where such X-ray induced chemistry is effective.\n"}
+{"abstract": "  This paper shows experimental results from the TCV tokamak that indicate\nplasma-molecule interactions involving $D_2^+$ and possibly $D^-$ play an\nimportant role as sinks of energy (through hydrogenic radiation as well as\ndissociation) and particles during divertor detachment if low target\ntemperatures ($< 3$ eV) are achieved. Both molecular activated recombination\n(MAR) and ion source reduction due to a power limitation effect are shown to be\nimportant in reducing the ion target flux during a density ramp. In contrast,\nthe electron-ion recombination (EIR) ion sink is too small to play an important\nrole in reducing the ion target flux. MAR or EIR do not occur during $N_2$\nseeding induced detachment as the target temperatures are not sufficiently low.\n  The impact of $D_2^+$ is shown to be underestimated in present (vibrationally\nunresolved) SOLPS-ITER simulations, which could result from an underestimated\n$D_2 + D^+ \\rightarrow D_2^+ + D$ rate. The converged SOLPS-ITER simulations\nare post-processed with alternative reaction rates, resulting in considerable\ncontributions of $D_2^+$ to particle and power losses as well as dissociation\nbelow the $D_2$ dissociation area. Those findings are in quantitative agreement\nwith the experimental results.\n"}
+{"abstract": "  The scattering equivalence of quantum field theories formulated with\nlight-front and instant-form kinematic subgroups is established using\nnon-perturbative methods. The difficulty with field theoretic formulations of\nDirac's forms of dynamics is that the free and interacting unitary\nrepresentations of the Poincar\\'e group are defined on inequivalent\nrepresentations of the Hilbert space, which means that the concept of kinematic\ntransformations must be modified on the Hilbert space of the field theory. This\nwork addresses this problem by assuming the existence of a field theory with\nthe expected properties and constructs equivalent representations with instant\nand front form kinematic subgroups. In this construction both the light-front\nand instant-form formulations share the same vacuum and one-particle states.\nThe free field Fock space plays no role. There is no \"quantization\" of a\nclassical theory. The property that survives from the perturbative approach is\nthe notion of a kinematic subgroup, which means kinematic Poincar\\'e\ntransformations can be trivially implemented by acting on suitable basis\nvectors. This non-perturbative approach avoids dealing with issues that arise\nin perturbative treatments where is it necessary to have a consistent treatment\nof renormalization, rotational covariance, and the structure of the light-front\nvacuum. While addressing these issues in a computational framework is important\nfor applications, this work may provide some insight into the nature of the\nexpected resolution and identifies the origin of some of differences between\nthe perturbative and non-perturbative approaches.\n"}
+{"abstract": "  Citizen Science aims to engage people in research activities on important\nissues related to their well-being. Smart Cities aim to provide them with\nservices that improve the quality of their life. Both concepts have seen\nsignificant growth in the last years, and can be further enhanced by combining\ntheir purposes with IoT technologies that allow for dynamic and large-scale\ncommunication and interaction. However, exciting and retaining the interest of\nparticipants is a key factor for such initiatives. In this paper we suggest\nthat engagement in Citizen Science projects applied on Smart Cities\ninfrastructure can be enhanced through contextual and structural game elements\nrealized through augmented audio interactive mechanisms. Our inter-disciplinary\nframework is described through the paradigm of a collaborative bird call\nrecognition game, in which users collect and submit audio data, which are then\nclassified and used for augmenting physical space with virtual soundscape maps.\nWe discuss the Playful Learning, Internet of Audio Things, and Bird Monitoring\nprinciples that shaped the design of our paradigm, and analyze its potential\ntechnical implementation.\n"}
+{"abstract": "  The Ewens sampling formula is a distribution related to the random partition\nof a positive integer. In this study, we investigate the issue of non-existence\nsolutions in parameter estimation under the distribution. As a result, the\nfirst and second moments matching estimators to the uniformly minimum variance\nunbiased estimator are derived using the Ewens sampling formula in asymptotic\nsense. A Monte Carlo simulation study is performed to evaluate the efficiency\nof the resulting estimators.\n"}
+{"abstract": "  Distributional Reinforcement Learning (RL) maintains the entire probability\ndistribution of the reward-to-go, i.e. the return, providing more learning\nsignals that account for the uncertainty associated with policy performance,\nwhich may be beneficial for trading off exploration and exploitation and policy\nlearning in general. Previous works in distributional RL focused mainly on\ncomputing the state-action-return distributions, here we model the state-return\ndistributions. This enables us to translate successful conventional RL\nalgorithms that are based on state values into distributional RL. We formulate\nthe distributional Bellman operation as an inference-based auto-encoding\nprocess that minimises Wasserstein metrics between target/model return\ndistributions. The proposed algorithm, BDPG (Bayesian Distributional Policy\nGradients), uses adversarial training in joint-contrastive learning to estimate\na variational posterior from the returns. Moreover, we can now interpret the\nreturn prediction uncertainty as an information gain, which allows to obtain a\nnew curiosity measure that helps BDPG steer exploration actively and\nefficiently. We demonstrate in a suite of Atari 2600 games and MuJoCo tasks,\nincluding well known hard-exploration challenges, how BDPG learns generally\nfaster and with higher asymptotic performance than reference distributional RL\nalgorithms.\n"}
+{"abstract": "  Covariate shift arises when the labelled training (source) data is not\nrepresentative of the unlabelled (target) data due to systematic differences in\nthe covariate distributions. A supervised model trained on the source data\nsubject to covariate shift may suffer from poor generalization on the target\ndata. We propose a novel, statistically principled and theoretically justified\nmethod to improve learning under covariate shift conditions, based on\npropensity score stratification, a well-established methodology in causal\ninference. We show that the effects of covariate shift can be reduced or\naltogether eliminated by conditioning on propensity scores. In practice, this\nis achieved by fitting learners on subgroups (\"strata\") constructed by\npartitioning the data based on the estimated propensity scores, leading to\nbalanced covariates and much-improved target prediction. We demonstrate the\neffectiveness of our general-purpose method on contemporary research questions\nin observational cosmology, and on additional benchmark examples, matching or\noutperforming state-of-the-art importance weighting methods, widely studied in\nthe covariate shift literature. We obtain the best reported AUC (0.958) on the\nupdated \"Supernovae photometric classification challenge\" and improve upon\nexisting conditional density estimation of galaxy redshift from Sloan Data Sky\nSurvey (SDSS) data.\n"}
+{"abstract": "  We prove that a valued field of positive characteristic $p$ that has only\nfinitely many distinct Artin-Schreier extensions (which is a property of\ninfinite NTP$_2$ fields) is dense in its perfect hull. As a consequence, it is\na deeply ramified field and has $p$-divisible value group and perfect residue\nfield. Further, we prove a partial analogue for valued fields of mixed\ncharacteristic and observe an open problem about 1-units in this setting.\nFinally, we fill a gap that occurred in a proof in an earlier paper in which we\nfirst introduced a classification of Artin-Schreier defect extensions.\n"}
+{"abstract": "  There is a growing demand for high-resolution (HR) medical images in both the\nclinical and research applications. Image quality is inevitably traded off with\nthe acquisition time for better patient comfort, lower examination costs, dose,\nand fewer motion-induced artifacts. For many image-based tasks, increasing the\napparent resolution in the perpendicular plane to produce multi-planar\nreformats or 3D images is commonly used. Single image super-resolution (SR) is\na promising technique to provide HR images based on unsupervised learning to\nincrease resolution of a 2D image, but there are few reports on 3D SR. Further,\nperceptual loss is proposed in the literature to better capture the textual\ndetails and edges than using pixel-wise loss functions, by comparing the\nsemantic distances in the high-dimensional feature space of a pre-trained 2D\nnetwork (e.g., VGG). However, it is not clear how one should generalize it to\n3D medical images, and the attendant implications are still unclear. In this\npaper, we propose a framework called SOUP-GAN: Super-resolution Optimized Using\nPerceptual-tuned Generative Adversarial Network (GAN), in order to produce\nthinner slice (e.g., high resolution in the 'Z' plane) medical images with\nanti-aliasing and deblurring. The proposed method outperforms other\nconventional resolution-enhancement methods and previous SR work on medical\nimages upon both qualitative and quantitative comparisons. Specifically, we\nexamine the model in terms of its generalization for various SR ratios and\nimaging modalities. By addressing those limitations, our model shows promise as\na novel 3D SR interpolation technique, providing potential applications in both\nclinical and research settings.\n"}
+{"abstract": "  We construct characteristic identities for the split (polarized) Casimir\noperators of the simple Lie algebras in defining (minimal fundamental) and\nadjoint representations. By means of these characteristic identities, for all\nsimple Lie algebras we derive explicit formulae for invariant projectors onto\nirreducible subrepresentations in T^{\\otimes 2} in two cases, when T is the\ndefining and the adjoint representation. In the case when T is the defining\nrepresentation, these projectors and the split Casimir operator are used to\nexplicitly write down invariant solutions of the Yang-Baxter equations. In the\ncase when T is the adjoint representation, these projectors and characteristic\nidentities are considered from the viewpoint of the universal description of\nthe simple Lie algebras in terms of the Vogel parameters.\n"}
+{"abstract": "  Recent years have seen the proliferation of disinformation and misinformation\nonline, thanks to the freedom of expression on the Internet and to the rise of\nsocial media. Two solutions were proposed to address the problem: (i) manual\nfact-checking, which is accurate and credible, but slow and non-scalable, and\n(ii) automatic fact-checking, which is fast and scalable, but lacks\nexplainability and credibility. With the accumulation of enough manually\nfact-checked claims, a middle-ground approach has emerged: checking whether a\ngiven claim has previously been fact-checked. This can be made automatically,\nand thus fast, while also offering credibility and explainability, thanks to\nthe human fact-checking and explanations in the associated fact-checking\narticle. This is a relatively new and understudied research direction, and here\nwe focus on claims made in a political debate, where context really matters.\nThus, we study the impact of modeling the context of the claim: both on the\nsource side, i.e., in the debate, as well as on the target side, i.e., in the\nfact-checking explanation document. We do this by modeling the local context,\nthe global context, as well as by means of co-reference resolution, and\nreasoning over the target text using Transformer-XH. The experimental results\nshow that each of these represents a valuable information source, but that\nmodeling the source-side context is more important, and can yield 10+ points of\nabsolute improvement.\n"}
+{"abstract": "  To maintain blockchain-based services with ensuring its security, it is an\nimportant issue how to decide a mining reward so that the number of miners\nparticipating in the mining increases. We propose a dynamical model of\ndecision-making for miners using an evolutionary game approach and analyze the\nstability of equilibrium points of the proposed model. The proposed model is\ndescribed by the 1st-order differential equation. So, it is simple but its\ntheoretical analysis gives an insight into the characteristics of the\ndecision-making. Through the analysis of the equilibrium points, we show the\ntranscritical bifurcations and hysteresis phenomena of the equilibrium points.\nWe also design a controller that determines the mining reward based on the\nnumber of participating miners to stabilize the state that all miners\nparticipate in the mining. Numerical simulation shows that there is a trade-off\nin the choice of the design parameters.\n"}
+{"abstract": "  A Cayley (di)graph $Cay(G,S)$ of a group $G$ with respect to $S$ is said to\nbe normal if the right regular representation of $G$ is normal in the\nautomorphism group of $Cay(G,S)$, and is called a CI-(di)graph if there is\n$\\alpha\\in Aut(G)$ such that $S^\\alpha=T$, whenever $Cay(G,S)\\cong Cay(G,T)$\nfor a Cayley (di)graph $Cay(G,T)$. A finite group $G$ is called a DCI-group or\na NDCI-group if all Cayley digraphs or normal Cayley digraphs of $G$ are\nCI-digraphs, and is called a CI-group or a NCI-group if all Cayley graphs or\nnormal Cayley graphs of $G$ are CI-graphs, respectively.\n  Motivated by a conjecture proposed by \\'Ad\\'am in 1967, CI-groups and\nDCI-groups have been actively studied during the last fifty years by many\nresearchers in algebraic graph theory. It takes about thirty years to obtain\nthe classification of cyclic CI-groups and DCI-groups, and recently, the first\ntwo authors, among others, classified cyclic NCI-groups and NDCI-groups. Even\nthough there are many partial results on dihedral CI-groups and DCI-groups,\ntheir classification is still elusive. In this paper, we prove that a dihedral\ngroup of order $2n$ is a NCI-group or a NDCI-group if and only if $n=2,4$ or\n$n$ is odd. As a direct consequence, we have that if a dihedral group $D_{2n}$\nof order $2n$ is a DCI-group then $n=2$ or $n$ is odd-square-free, and that if\n$D_{2n}$ is a CI-group then $n=2,9$ or $n$ is odd-square-free, throwing some\nnew light on classification of dihedral CI-groups and DCI-groups.\n"}
+{"abstract": "  Samples were examined using a superconducting (Nb) neutron imaging system\nemploying a delay-line technique which in previous studies was shown to have\nhigh spatial resolution. We found excellent correspondence between neutron\ntransmission and scanning electron microscope (SEM) images of Gd islands with\nsizes between 15 and 130 micrometer which were thermally-sprayed onto a Si\nsubstrate. Neutron transmission images could be used to identify tiny voids in\na thermally-sprayed continuous Gd2O3 film on a Si substrate which could not be\nseen in SEM images. We also found that neutron transmission images revealed\npattern formations, mosaic features and co-existing dendritic phases in Wood's\nmetal samples with constituent elements Bi, Pb, Sn and Cd. These results\ndemonstrate the merits of the current-biased kinetic inductance detector\n(CB-KID) system for practical studies in materials science. Moreover, we found\nthat operating the detector at a more optimal temperature (7.9 K) appreciably\nimproved the effective detection efficiency when compared to previous studies\nconducted at 4 K. This is because the effective size of hot-spots in the\nsuperconducting meanderline planes increases with temperature, which makes\nparticle detections more likely.\n"}
+{"abstract": "  In this paper, we study a mobile edge computing (MEC) system in which the\nmobile device is assisted by a base station (BS) and a cooperative node. The\nmobile device has sequential tasks to complete, whereas the cooperative node\nassists the mobile device on both task offloading and task computation. In\nspecific, two cases are investigated, which are 1) the cooperative node has no\ntasks to complete itself, and 2) the cooperative node has tasks to complete\nitself. Our target is to minimize the total energy consumption of the mobile\ndevice and the cooperative node through optimizing the transmit duration in\ntask offloading, CPU frequency in task computing along with the task index to\noffload in the sequential tasks. In the first case, we decompose the\nmixed-integer non-convex problem into two levels. In the lower level problem,\nthanks to the convexity, Karush-Kuhn-Tucker (KKT) conditions are utilized to\nsimplify the problem, which is then solved with bisection search. In the upper\nlevel problem, to find solution of the task index to offload, rather than\nutilizing traversal method, we develop a monotonic condition to simplify the\nsearching process. In the second case, in order to guarantee the successful\ncomputation of the mobile device and cooperative node, the uploading\ntransmission is classified into three schemes. Within each scheme, the\nnon-convex problem is decomposed. In the lower level problem, semi-closed\nsolution is found by Lagrangian dual method. In the upper level problem,\ntraversal method is applied to find the optimal offloading index.\n"}
+{"abstract": "  We provide a generalization of quantum polar codes to quantum channels with\nqudit-input, achieving the symmetric coherent information of the channel. Our\nscheme relies on a channel combining and splitting construction, where a\ntwo-qudit unitary randomly chosen from a unitary 2-design is used to combine\ntwo instances of a qudit-input channel. The inputs to the synthesized bad\nchannels are frozen by sharing EPR pairs between the sender and the receiver,\nso our scheme is entanglement assisted. Using the fact that the generalized\ntwo-qudit Clifford group forms a unitary 2-design, we conclude that the channel\ncombining operation can be chosen from this set. Moreover, we show that\npolarization also happens for a much smaller subset of two-qudit Cliffords,\nwhich is not a unitary 2-design. Finally, we show how to decode the proposed\nquantum polar codes on Pauli qudit channels.\n"}
+{"abstract": "  Using a sample of $1.31\\times10^9$ $J/\\psi$ events collected with the BESIII\ndetector at the electron-positron collider BEPCII, we analyse the full $J/\\psi\n\\to$ $\\Xi^-\\overline{\\Xi}^+$, $\\Xi^-\\to \\Lambda \\pi^-$, $\\Lambda\\to p\\pi^-$,\n$\\overline{\\Xi}^+\\to\\overline{\\Lambda}\\pi^+$,\n$\\overline{\\Lambda}\\to\\overline{p}\\pi^+$ decay chain. A new method, exploiting\nthe fact that the $\\Xi^-\\overline{\\Xi}^+$ pair is entangled and sequentially\ndecaying, and where the complete decay chains are reconstructed, is applied for\nthe first time. This enables precision measurements of the decay parameters for\nthe $\\Xi^-\\to\\Lambda\\pi^-$ decay ($\\alpha_{\\Xi}$, $\\phi_{\\Xi}$) as well as the\n$\\overline{\\Xi}^+\\to\\overline{\\Lambda}\\pi^+$ decay ($\\overline{\\alpha}_{\\Xi}$,\n$\\overline{\\phi}_{\\Xi}$). From the decay parameters, two independent CP tests\nwere performed, quantified by the observables $A_{\\rm CP}^{\\Xi}$ and $\\Delta\n\\phi_\\Xi$. Our results, $A_{\\rm CP}^{\\Xi}$ = $(6.0\\pm13.4\\pm5.6)\\times10^{-3}$\nand $\\Delta \\phi_\\Xi= (-4.8\\pm13.7\\pm2.9)\\times10^{-3}~{\\rm rad}$, are\nconsistent with CP symmetry. Furthermore, our method enables a separation of\nstrong and weak $\\Xi\\to\\Lambda\\pi$ decay amplitudes. This results in the first\ndirect measurement of the weak phase difference for any baryon decay. The\nresult is found to be $(\\xi_{P} - \\xi_{S}) = (1.2\\pm3.4\\pm0.8)\\times10^{-2}$\nrad and is one of the most precise tests of CP symmetry for strange baryons.\nThe strong phase difference is measured to be $(\\delta_P - \\delta_S) =\n(-4.0\\pm3.3\\pm1.7)\\times10^{-2}$ rad. In addition, we provide an independent\nmeasurement of the recently debated $\\Lambda$ decay parameter,\n$\\alpha_{\\Lambda} = 0.757 \\pm 0.011 \\pm 0.008 $. The\n$\\Lambda\\overline{\\Lambda}$ asymmetry is measured to be $A_{\\rm CP}^{\\Lambda} =\n(-3.7\\pm11.7\\pm9.0)\\times10^{-3}$.\n"}
+{"abstract": "  In data processing and machine learning, an important challenge is to recover\nand exploit models that can represent accurately the data. We consider the\nproblem of recovering Gaussian mixture models from datasets. We investigate\nsymmetric tensor decomposition methods for tackling this problem, where the\ntensor is built from empirical moments of the data distribution. We consider\nidentifiable tensors, which have a unique decomposition, showing that moment\ntensors built from spherical Gaussian mixtures have this property. We prove\nthat symmetric tensors with interpolation degree strictly less than half their\norder are identifiable and we present an algorithm, based on simple linear\nalgebra operations, to compute their decomposition. Illustrative\nexperimentations show the impact of the tensor decomposition method for\nrecovering Gaussian mixtures, in comparison with other state-of-the-art\napproaches.\n"}
+{"abstract": "  A machine learning algorithm for partitioning the nuclear vibrational space\ninto subspaces is introduced. The subdivision criterion is based on Liouville's\ntheorem, i.e. best preservation of the unitary of the reduced dimensionality\nJacobian determinant within each subspace along a probe full-dimensional\nclassical trajectory. The algorithm is based on the idea of evolutionary\nselection and it is implemented through a probability graph representation of\nthe vibrational space partitioning. We interface this customized version of\ngenetic algorithms with our divide-and-conquer semiclassical initial value\nrepresentation method for calculation of molecular power spectra. First, we\nbenchmark the algorithm by calculating the vibrational power spectra of two\nmodel systems, for which the exact subspace division is known. Then, we apply\nit to the calculation of the power spectrum of methane. Exact calculations and\nfull-dimensional semiclassical spectra of this small molecule are available and\nprovide an additional test of the accuracy of the new approach. Finally, the\nalgorithm is applied to the divide-and-conquer semiclassical calculation of the\npower spectrum of 12-atom trans-N-Methylacetamide.\n"}
+{"abstract": "  We demonstrate the design, fabrication and use of a dual electrode PMUT\n(Piezoelectric Micromachined Ultrasound Transducer) integrated with a\nmicrofluidic channel as a fluid density sensor in both static and dynamic\ndensity-change conditions. The dual electrode configuration makes the PMUT\nresonator a self-contained resonant peak-shift sensor and the microfluidic\nintegration makes this system a versatile fluid density sensing platform that\ncan be used with extremely low volumes of fluids in various industrial and\nhealthcare applications. The density measurements carried out here under\nflowing fluid conditions demonstrate the potential of this system as a\nreal-time fluid density monitoring system. We include results of density\nmeasurements in the range of 1020 - 1090 kg/m3 that corresponds to the human\nblood density variation generally due to the change in its hemoglobin content.\nThe sensitivity of the sensor-26.3 Hz/(kg/m3)-is good enough to reliably detect\neven 1% change in the hemoglobin content of the human blood. Thus, this system\ncould potentially be used also as a hemoglobin measurement sensor in healthcare\napplications.\n"}
+{"abstract": "  We show that the gravitational waves generated by the perturbations of\ngeneral relativistic black holes can be considered as a direct probe of the\nexistence of dark sector interactions. Working within the framework of\nHorndeski theory and linear perturbations, we show that dark sector\ninteractions effectively reduce to an interaction charge that influences both\nscalar and tensor waveforms. Furthermore, we show that the total dark matter\nfield, including the effects of dark sector interactions, satisfies a\nconservation equation embodying the equivalence principle. We exploit this\nrealization to setup the Regge-Wheeler equation and the coupled Zerilli and\nscalar wave equations for a Schwarzschild-(anti) de Sitter black hole. We then\npresent numerical integration of the coupled even-parity wave equations for the\ncase of a dark matter particle falling straight down into a Schwarzschild black\nhole.\n"}
+{"abstract": "  The Neural Tangent Kernel (NTK) characterizes the behavior of infinitely wide\nneural nets trained under least squares loss by gradient descent. However,\ndespite its importance, the super-quadratic runtime of kernel methods limits\nthe use of NTK in large-scale learning tasks. To accelerate kernel machines\nwith NTK, we propose a near input sparsity time algorithm that maps the input\ndata to a randomized low-dimensional feature space so that the inner product of\nthe transformed data approximates their NTK evaluation. Our transformation\nworks by sketching the polynomial expansions of arc-cosine kernels.\nFurthermore, we propose a feature map for approximating the convolutional\ncounterpart of the NTK, which can transform any image using a runtime that is\nonly linear in the number of pixels. We show that in standard large-scale\nregression and classification tasks a linear regressor trained on our features\noutperforms trained Neural Nets and Nystrom approximation of NTK kernel.\n"}
+{"abstract": "  This paper presents novel reconfigurable architectures for reducing the\nlatency of recurrent neural networks (RNNs) that are used for detecting\ngravitational waves. Gravitational interferometers such as the LIGO detectors\ncapture cosmic events such as black hole mergers which happen at unknown times\nand of varying durations, producing time-series data. We have developed a new\narchitecture capable of accelerating RNN inference for analyzing time-series\ndata from LIGO detectors. This architecture is based on optimizing the\ninitiation intervals (II) in a multi-layer LSTM (Long Short-Term Memory)\nnetwork, by identifying appropriate reuse factors for each layer. A\ncustomizable template for this architecture has been designed, which enables\nthe generation of low-latency FPGA designs with efficient resource utilization\nusing high-level synthesis tools. The proposed approach has been evaluated\nbased on two LSTM models, targeting a ZYNQ 7045 FPGA and a U250 FPGA.\nExperimental results show that with balanced II, the number of DSPs can be\nreduced up to 42% while achieving the same IIs. When compared to other\nFPGA-based LSTM designs, our design can achieve about 4.92 to 12.4 times lower\nlatency.\n"}
+{"abstract": "  We design and implement Zooid, a domain specific language for certified\nmultiparty communication, embedded in Coq and implemented atop our\nmechanisation framework of asynchronous multiparty session types (the first of\nits kind). Zooid provides a fully mechanised metatheory for the semantics of\nglobal and local types, and a fully verified end-point process language that\nfaithfully reflects the type-level behaviours and thus inherits the global\ntypes properties such as deadlock freedom, protocol compliance, and liveness\nguarantees.\n"}
+{"abstract": "  In 2015, Felsner, Trotter, and Wiechert showed that posets with outerplanar\ncover graphs have bounded dimension. We generalise this result to posets with\n$k$-outerplanar cover graphs. Namely, we show that posets with $k$-outerplanar\ncover graph have dimension $\\mathcal{O}(k^3)$. As a consequence, we show that\nevery poset with a planar cover graph and height $h$ has dimension\n$\\mathcal{O}(h^3)$. This improves the previously best known bound of\n$\\mathcal{O}(h^6)$ by Kozik, Micek and Trotter.\n"}
+{"abstract": "  A first-order Electroweak Phase Transition (EWPT) could explain the observed\nbaryon-antibaryon asymmetry and its dynamics could yield a detectable\ngravitational wave signature, while the underlying physics would be within the\nreach of colliders. The Standard Model, however, predicts a crossover\ntransition. We therefore study the EWPT in the Standard Model Effective Field\nTheory (SMEFT) including dimension-six operators. A first-order EWPT has\npreviously been shown to be possible in the SMEFT. Phenomenology studies have\nfocused on scenarios with a tree-level barrier between minima, which requires a\nnegative Higgs quartic coupling and a new physics scale low enough to raise\nquestions about the validity of the EFT approach. In this work we stress that a\nfirst-order EWPT is also possible when the barrier between minima is generated\nradiatively, the quartic coupling is positive, the scale of new physics is\nhigher, and there is good agreement with experimental bounds. Our calculation\nis done in a consistent, gauge-invariant way, and we carefully analyze the\nscaling of parameters necessary to generate a barrier in the potential. We\nperform a global fit in the relevant parameter space and explicitly find the\npoints with a first-order transition that agree with experimental data. We also\nbriefly discuss the prospects for probing the allowed parameter space using\ndi-Higgs production in colliders.\n"}
+{"abstract": "  Time-varying parameter (TVP) regressions commonly assume that time-variation\nin the coefficients is determined by a simple stochastic process such as a\nrandom walk. While such models are capable of capturing a wide range of dynamic\npatterns, the true nature of time variation might stem from other sources, or\narise from different laws of motion. In this paper, we propose a flexible TVP\nVAR that assumes the TVPs to depend on a panel of partially latent covariates.\nThe latent part of these covariates differ in their state dynamics and thus\ncapture smoothly evolving or abruptly changing coefficients. To determine which\nof these covariates are important, and thus to decide on the appropriate state\nevolution, we introduce Bayesian shrinkage priors to perform model selection.\nAs an empirical application, we forecast the US term structure of interest\nrates and show that our approach performs well relative to a set of competing\nmodels. We then show how the model can be used to explain structural breaks in\ncoefficients related to the US yield curve.\n"}
+{"abstract": "  Graphs are naturally used to describe the structures of various real-world\nsystems in biology, society, computer science etc., where subgraphs or motifs\nas basic blocks play an important role in function expression and information\nprocessing. However, existing research focuses on the basic statistics of\ncertain motifs, largely ignoring the connection patterns among them. Recently,\na subgraph network (SGN) model is proposed to study the potential structure\namong motifs, and it was found that the integration of SGN can enhance a series\nof graph classification methods. However, SGN model lacks diversity and is of\nquite high time complexity, making it difficult to widely apply in practice. In\nthis paper, we introduce sampling strategies into SGN, and design a novel\nsampling subgraph network model, which is scale-controllable and of higher\ndiversity. We also present a hierarchical feature fusion framework to integrate\nthe structural features of diverse sampling SGNs, so as to improve the\nperformance of graph classification. Extensive experiments demonstrate that, by\ncomparing with the SGN model, our new model indeed has much lower time\ncomplexity (reduced by two orders of magnitude) and can better enhance a series\nof graph classification methods (doubling the performance enhancement).\n"}
+{"abstract": "  Background: Stochastic resonance (SR) refers to a faint signal being enhanced\nwith the addition of white noise. Previous studies have found that vestibular\nperceptual thresholds are lowered with noisy galvanic vestibular stimulation\n(i.e., \"in-channel\" SR). Auditory white noise has been shown to improve tactile\nand visual thresholds, suggesting \"cross-modal\" SR. Objective: We aimed to\nstudy the cross-modal impact of noisy galvanic vestibular stimulation (nGVS)\n(n=9 subjects) on visual and auditory thresholds. Methods: We measured auditory\nand visual perceptual thresholds of human subjects across a swath of different\nnGVS levels in order to determine if a subject-specific best nGVS level\nelicited a reduction in thresholds as compared the no noise condition (sham).\nResults: We found an 18% improvement in visual thresholds (p = 0.026). Among\nthe 7 of 9 subjects with reduced thresholds, the average improvement was 26%.\nSubjects with higher (worse) visual thresholds with no stimulation (sham)\nimproved more than those with lower thresholds (p = 0.005). Auditory thresholds\nwere unchanged by vestibular stimulation. Conclusions: These results are the\nfirst demonstration of cross-modal improvement with nGVS, indicating galvanic\nvestibular white noise can produce cross-modal improvements in some sensory\nchannels, but not all.\n"}
+{"abstract": "  In this paper, we construct a lightweight, high-precision and high-speed\nobject tracking using a trained CNN. Conventional methods with trained CNNs use\nVGG16 network which requires powerful computational resources. Therefore, there\nis a problem that it is difficult to apply in low computation resources\nenvironments. To solve this problem, we use MobileNetV3, which is a CNN for\nmobile terminals.Based on Feature Map Selection Tracking, we propose a new\narchitecture that extracts effective features of MobileNet for object tracking.\nThe architecture requires no online learning but only offline learning. In\naddition, by using features of objects other than tracking target, the features\nof tracking target are extracted more efficiently. We measure the tracking\naccuracy with Visual Tracker Benchmark and confirm that the proposed method can\nperform high-precision and high-speed calculation even in low computation\nresource environments.\n"}
+{"abstract": "  Glaucoma is a severe eye disease that is known to deteriorate optic never\nfibers, causing cup size to increase, which could result in permanent loss of\nvision. Glaucoma is the second leading cause of blindness after cataract, but\nglaucoma being more dangerous as it is not curable. Early diagnoses and\ntreatment of glaucoma can help to slow the progression of glaucoma and its\ndamages. For the detection of glaucoma, the Cup to Disc ratio (CDR) provides\nsignificant information. The CDR depends heavily on the accurate segmentation\nof cup and disc regions. In this paper, we have proposed a modified M-Net with\nbidirectional convolution long short-term memory (LSTM), based on joint cup and\ndisc segmentation. The proposed network combines features of encoder and\ndecoder, with bidirectional LSTM. Our proposed model segments cup and disc\nregions based on which the abnormalities in cup to disc ratio can be observed.\nThe proposed model is tested on REFUGE2 data, where our model achieves a dice\nscore of 0.92 for optic disc and an accuracy of 98.99% in segmenting cup and\ndisc regions\n"}
+{"abstract": "  We study the constraint equations for a class of scalar-tensor effective\nfield theories of gravity, including the operators up to $4$ derivatives in the\naction ($4\\partial$ST). We extend the conformal transverse traceless and\nconformal thin sandwich methods of General Relativity to rewrite the constraint\nequations of the scalar-tensor theory as a set of elliptic partial differential\nequations. It is shown that, at weak coupling, a unique solution exists to the\ncorresponding elliptic boundary value problems on asymptotically Euclidean\ninitial slices under similar conditions as in the case of General Relativity.\nFurthermore, we find a generalization of the Bowen-York solution for\n$4\\partial$ST theories, too. These results demonstrate that standard methods\nfor constructing initial data in General Relativity are applicable (with\nminimal modification) to weakly coupled $4\\partial$ST theories.\n"}
+{"abstract": "  Six binary-merger progenitors of Supernova 1987A (SN 1987A) with properties\nclose to those of the blue supergiant Sanduleak -69 202 are exploded by\nneutrino heating and evolved until long after shock breakout in three\ndimensions (3D), and continued for light-curve calculations in spherical\nsymmetry. Our results confirm previous findings for single-star progenitors:\n(1) 3D neutrino-driven explosions with SN 1987A-like energies synthesize Ni-56\nmasses consistent with the radioactive light-curve tail; (2) hydrodynamic\nmodels mix hydrogen inward to minimum velocities below 40 km/s compatible with\nspectral observations of SN 1987A; and (3) for given explosion energy the\nefficiency of outward radioactive Ni-56 mixing depends mainly on high growth\nfactors of Rayleigh-Taylor instabilities at the (C+O)/He and He/H composition\ninterfaces and a weak interaction of fast plumes with the reverse shock\noccurring below the He/H interface. All binary-merger models possess\npresupernova radii matching the photometric radius of Sanduleak -69 202 and a\nstructure of the outer layers allowing them to reproduce the observed initial\nluminosity peak in the first about 7 days. Models that mix about 0.5 Msun of\nhydrogen into the He-shell and exhibit strong outward mixing of Ni-56 with\nmaximum velocities exceeding the 3000 km/s observed for the bulk of ejected\nNi-56 have light-curve shapes in good agreement with the dome of the SN 1987A\nlight curve. A comparative analysis of the best representatives of our 3D\nneutrino-driven explosion models of SN 1987A based on single-star and\nbinary-merger progenitors reveals that only one binary model fulfills all\nobservational constraints, except one.\n"}
+{"abstract": "  The stranglehold of low temperatures on fascinating quantum phenomena in\none-dimensional quantum magnets has been challenged recently by the discovery\nof anomalous spin transport at high temperatures. Whereas both regimes have\nbeen investigated separately, no study has attempted to reconcile them. For\ninstance, the paradigmatic quantum Heisenberg spin-$1/2$ chain falls at low\ntemperature within the Tomonaga-Luttinger liquid framework, while its\nhigh-temperature dynamics is superdiffusive and relates to the\nKardar-Parisi-Zhang universality class in $1+1$ dimensions. This Letter aims at\nreconciling the two regimes. Building on large-scale matrix product state\nsimulations, we find that they are connected by a temperature-dependent\nspatiotemporal crossover. As the temperature $T$ is reduced, we show that the\nonset of superdiffusion takes place at longer length and timescales $\\propto\n1/T$. This prediction has direct consequences for experiments including nuclear\nmagnetic resonance: it is consistent with earlier measurements on the nearly\nideal Heisenberg $S=1/2$ chain compound Sr$_2$CuO$_3$, yet calls for new and\ndedicated experiments.\n"}
+{"abstract": "  This project takes the factors of keyword selection behavior as the research\nobject. Qualitative analysis methods such as interview and grounded theory were\nused to construct causal influence path model. Combined with computer\nsimulation technology such as multi-agent simulation experiment method was used\nto study the factors of keyword selection from two dimensions of individual to\ngroup. The research was carried out according to the path of factor analysis at\nindividual level macro situation simulation optimization of scientific research\ndata management. Based on the aforementioned review of existing researches and\nexplanations of keywords selection, this study adopts a qualitative research\ndesign to expand the explanation, and macro simulation based on the results of\nqualitative research. There are two steps in this study, one is do interview\nwith authors and then design macro simulation according the deductive and\nqualitative content analysis results.\n"}
+{"abstract": "  Assessing the quality of arguments and of the claims the arguments are\ncomposed of has become a key task in computational argumentation. However, even\nif different claims share the same stance on the same topic, their assessment\ndepends on the prior perception and weighting of the different aspects of the\ntopic being discussed. This renders it difficult to learn topic-independent\nquality indicators. In this paper, we study claim quality assessment\nirrespective of discussed aspects by comparing different revisions of the same\nclaim. We compile a large-scale corpus with over 377k claim revision pairs of\nvarious types from kialo.com, covering diverse topics from politics, ethics,\nentertainment, and others. We then propose two tasks: (a) assessing which claim\nof a revision pair is better, and (b) ranking all versions of a claim by\nquality. Our first experiments with embedding-based logistic regression and\ntransformer-based neural networks show promising results, suggesting that\nlearned indicators generalize well across topics. In a detailed error analysis,\nwe give insights into what quality dimensions of claims can be assessed\nreliably. We provide the data and scripts needed to reproduce all results.\n"}
+{"abstract": "  The generation of high-fidelity polarization-entangled photon pairs, to date,\nhas been demonstrated on specific spatial modes or over relatively narrow\napertures. We put forward and demonstrate an experimental scheme to extend the\ntemporal and spatial indistinguishability of polarization-entangled photons\nover wide emission angles, which can be applied to cover the whole SPDC cone.\nOver such wide angular extent, while the time-delay map is almost flat which\nrenders the conventional compensation via a birefringent element an appropriate\napproach, the relative-phase map -- verified as a quadratic function --\nnecessitates a tunable compensation paradigm. Here, to do so, we employ a\nphase-only two-dimensional spatial light modulator (2D SLM) loaded by the\ncomplementary of the relative phase map to equalize the phase variations for\none third of the noncollinear spontaneous parametric down conversion (SPDC)\nemission. After eliminating the temporal and spatial distinguishability over\nthe 2D SLM area, a 97% polarization visibility is verified for the entangled\nphoton pairs scattered widely across the SPDC cone.\n"}
+{"abstract": "  Let $(p_k)_{k\\in\\mathbb{N}}$ be a discrete probability distribution for which\nthe counting function $x\\mapsto \\#\\{k\\in\\mathbb{N}: p_k\\geq 1/x\\}$ belongs to\nthe de Haan class $\\Pi$. Consider a deterministic weighted branching process\ngenerated by $(p_k)_{k\\in\\mathbb{N}}$. A nested Karlin's occupancy scheme is\nthe sequence of Karlin balls-in-boxes schemes in which boxes of the $j$th\nlevel, $j=1,2,\\ldots$ are identified with the $j$th generation individuals and\nthe hitting probabilities of boxes are identified with the corresponding\nweights. The collection of balls is the same for all generations, and each ball\nstarts at the root and moves along the tree of the deterministic weighted\nbranching process according to the following rule: transition from a mother box\nto a daughter box occurs with probability given by the ratio of the daughter\nand mother weights.\n  Assuming there are $n$ balls, denote by $\\mathcal{K}_n(j)$ the number of\noccupied (ever hit) boxes in the $j$th level. For each $j\\in\\mathbb{N}$, we\nprove a functional limit theorem for the vector-valued process\n$(\\mathcal{K}^{(1)}_{\\lfloor e^{T+u}\\rfloor},\\ldots, \\mathcal{K}^{(j)}_{\\lfloor\ne^{T+u}\\rfloor})_{u\\in\\mathbb{R}}$, properly normalized and centered, as\n$T\\to\\infty$. The limit is a vector-valued process whose components are\nindependent stationary Gaussian processes. An integral representation of the\nlimit process is obtained.\n"}
+{"abstract": "  Polarization is implicated in the erosion of democracy and the progression to\nviolence, which makes the polarization properties of large algorithmic content\nselection systems (recommender systems) a matter of concern for peace and\nsecurity. While algorithm-driven social media does not seem to be a primary\ndriver of polarization at the country level, it could be a useful intervention\npoint in polarized societies. This paper examines algorithmic depolarization\ninterventions with the goal of conflict transformation: not suppressing or\neliminating conflict but moving towards more constructive conflict. Algorithmic\nintervention is considered at three stages: which content is available\n(moderation), how content is selected and personalized (ranking), and content\npresentation and controls (user interface). Empirical studies of online\nconflict suggest that the exposure diversity intervention proposed as an\nantidote to \"filter bubbles\" can be improved and can even worsen polarization\nunder some conditions. Using civility metrics in conjunction with diversity in\ncontent selection may be more effective. However, diversity-based interventions\nhave not been tested at scale and may not work in the diverse and dynamic\ncontexts of real platforms. Instead, intervening in platform polarization\ndynamics will likely require continuous monitoring of polarization metrics,\nsuch as the widely used \"feeling thermometer.\" These metrics can be used to\nevaluate product features, and potentially engineered as algorithmic\nobjectives. It may further prove necessary to include polarization measures in\nthe objective functions of recommender algorithms to prevent optimization\nprocesses from creating conflict as a side effect.\n"}
+{"abstract": "  The conflict between individual and collective interests is in the heart of\nevery social dilemmas established by evolutionary game theory. We cannot avoid\nthese conflicts but sometimes we may choose which interaction framework to use\nas a battlefield. For instance some people like to be part of a larger group\nwhile other persons prefer to interact in a more personalized, individual way.\nBoth attitudes can be formulated via appropriately chosen traditional games. In\nparticular, the prisoner's dilemma game is based on pair interaction while the\npublic goods game represents multi-point interactions of group members. To\nreveal the possible advantage of a certain attitude we extend these models by\nallowing players not simply to change their strategies but also let them to\nvary their attitudes for a higher individual income. We show that both\nattitudes could be the winner at a specific parameter value. Interestingly,\nhowever, the subtle interplay between different states may result in a\ncounterintuitive evolutionary outcome where the increase of the multiplication\nfactor of public goods game drives the population to a fully defector state. We\npoint out that the accompanying pattern formation can only be understood via\nthe multipoint or multi-player interactions of different microscopic states\nwhere the vicinity of a particular state may influence the relation of two\nother competitors.\n"}
+{"abstract": "  Black-box optimization is a very active area of research, with many new\nalgorithms being developed every year. This variety is needed, on the one hand,\nsince different algorithms are most suitable for different types of\noptimization problems. But the variety also poses a meta-problem: which\nalgorithm to choose for a given problem at hand? Past research has shown that\nper-instance algorithm selection based on exploratory landscape analysis (ELA)\ncan be an efficient mean to tackle this meta-problem. Existing approaches,\nhowever, require the approximation of problem features based on a significant\nnumber of samples, which are typically selected through uniform sampling or\nLatin Hypercube Designs. The evaluation of these points is costly, and the\nbenefit of an ELA-based algorithm selection over a default algorithm must\ntherefore be significant in order to pay off. One could hope to by-pass the\nevaluations for the feature approximations by using the samples that a default\nalgorithm would anyway perform, i.e., by using the points of the default\nalgorithm's trajectory.\n  We analyze in this paper how well such an approach can work. Concretely, we\ntest how accurately trajectory-based ELA approaches can predict the final\nsolution quality of the CMA-ES after a fixed budget of function evaluations. We\nobserve that the loss of trajectory-based predictions can be surprisingly small\ncompared to the classical global sampling approach, if the remaining budget for\nwhich solution quality shall be predicted is not too large. Feature selection,\nin contrast, did not show any advantage in our experiments and rather led to\nworsened prediction accuracy. The inclusion of state variables of CMA-ES only\nhas a moderate effect on the prediction accuracy.\n"}
+{"abstract": "  Consider first passage percolation with identical and independent weight\ndistributions and first passage time ${\\rm T}$. In this paper, we study the\nupper tail large deviations $\\mathbb{P}({\\rm T}(0,nx)>n(\\mu+\\xi))$, for $\\xi>0$\nand $x\\neq 0$ with a time constant $\\mu$ and a dimension $d$, for weights that\nsatisfy a tail assumption $ \\beta_1\\exp{(-\\alpha t^r)}\\leq \\mathbb\nP(\\tau_e>t)\\leq \\beta_2\\exp{(-\\alpha t^r)}.$ When $r\\leq 1$ (this includes the\nwell-known Eden growth model), we show that the upper tail large deviation\ndecays as $\\exp{(-(2d\\xi +o(1))n)}$. When $1< r\\leq d$, we find that the rate\nfunction can be naturally described by a variational formula, called the\ndiscrete p-Capacity, and we study its asymptotics. For $r<d$, we show that the\nlarge deviation event ${\\rm T}(0,nx)>n(\\mu+\\xi)$ is described by a localization\nof high weights around the origin. The picture changes for $r\\geq d$ where the\nconfiguration is not anymore localized.\n"}
+{"abstract": "  We present the study of a proof-of-concept integrated device that can be used\nas a nonlinear broadband isolator. The device is based on the asymmetric\nloading of a highly-confining silicon-slot photonic coupler with graphene\nlayers, whose ultrafast and low-threshold saturable absorption can be exploited\nfor nonreciprocal transmission between the cross-ports of the coupler. The\nstructure is essentially a non-Hermitian system, whose exceptional points are\nbriefly discussed. The nonlinear device is modeled with a coupled Schrodinger\nequation system whose validity is checked by full-vector finite element-based\nbeam-propagation method simulations in CW. The numerically computed performance\nreveals a nonreciprocal intensity range (NRIR) in the vicinity of 100 mW peak\npower with a bandwidth spanning tens of nanometers, from CW down to ps-long\npulses. Finally, the combination of saturable absorption and self-phase\nmodulation (Kerr effect) in graphene is studied, indicating the existence of\ntwo NRIR with opposite directionality.\n"}
+{"abstract": "  We present the results on the physical properties of filaments and dense\ncores in IC 5146, as a part of the TRAO FUNS project. We carried out On-The-Fly\nmapping observations using the Taeduk Radio Astronomy Observatory (TRAO) 14m\ntelescope covering about 1 square degree of the area of IC 5146 using various\nmolecular lines. We identified 14 filaments (24 in total, including\nsub-filaments) from the C$^{18}$O (1-0) data cube and 22 dense cores from the\n$\\rm N_{2}H^{+}$ (1-0) data. We examined the filaments' gravitational\ncriticality, turbulence properties, accretion rate from filaments to dense\ncores, and relative evolutionary stages of cores. Most filaments in IC 5146 are\ngravitationally supercritical within the uncertainty, and most dense cores are\nformed in them. We found that dense cores in the hubs show a systemic velocity\nshift of ~0.3 km/s between the $\\rm N_{2}H^{+}$ and C$^{18}$O gas. Besides,\nthese cores are subsonic or transonic, while the surrounding filament gas is\ntransonic or supersonic, indicating that the cores in the hubs are likely\nformed by the turbulence dissipation in the colliding turbulent filaments and\nthe merging is still ongoing. We estimated the mass accretion rate of $15 -\n35~M_{\\odot}~\\rm Myr^{-1}$ from the filaments to the dense cores, and the\nrequired time scales to collect the current core mass are consistent with the\nlifetime of the dense cores. The structures of filaments and dense cores in the\nhub can form by a collision of turbulent converging flows, and mass flow along\nthe filaments to the dense cores may play an important role in forming dense\ncores.\n"}
+{"abstract": "  Software architecture refers to the high-level abstraction of a system\nincluding the configuration of the involved elements and the interactions and\nrelationships that exist between them. Source codes can be easily built by\nreferring to the software architectures. However, the reverse process i.e.\nderivation of the software architecture from the source code is a challenging\ntask. Further, such an architecture consists of multiple layers, and\ndistributing the existing elements into these layers should be done accurately\nand efficiently. In this paper, a novel approach is presented for the recovery\nof layered architectures from Java-based software systems using the concept of\nego networks. Ego networks have traditionally been used for social network\nanalysis, but in this paper, they are modified in a particular way and tuned to\nsuit the mentioned task. Specifically, a dependency network is extracted from\nthe source code to create an ego network. The ego network is processed to\ncreate and optimize ego layers in a particular structure. These ego layers when\nintegrated and optimized together give the final layered architecture. The\nproposed approach is evaluated in two ways: on static versions of three\nopen-source software, and a continuously evolving software system. The\ndistribution of nodes amongst the proposed layers and the committed violations\nare observed on both class level and package level. The proposed method is seen\nto outperform the existing standard approaches over multiple performance\nmetrics. We also carry out the analysis of variation in the results concerning\nthe change in the node selection strategy and the frequency. The empirical\nobservations show promising signs for recovering software architecture layers\nfrom source codes using this technique and also extending it further to other\nlanguages and software.\n"}
+{"abstract": "  We prove that there is a unique nonnegative and diagram-preserving\nquasiprobability representation of the stabilizer subtheory in all odd\ndimensions, namely Gross's discrete Wigner function. This representation is\nequivalent to Spekkens' epistemically restricted toy theory, which is\nconsequently singled out as the unique noncontextual ontological model for the\nstabilizer subtheory. Strikingly, the principle of noncontextuality is powerful\nenough (at least in this setting) to single out one particular classical\nrealist interpretation. Our result explains the practical utility of Gross's\nrepresentation, e.g. why (in the setting of the stabilizer subtheory)\nnegativity in this particular representation implies generalized contextuality,\nand hence sheds light on why negativity of this particular representation is a\nresource for quantum computational speedup. It also allows us to prove that\ngeneralized contextuality is a necessary resource for universal quantum\ncomputation in the state injection model. In all even dimensions, we prove that\nthere does not exist any nonnegative and diagram-preserving quasiprobability\nrepresentation of the stabilizer subtheory, and, hence, that the stabilizer\nsubtheory is contextual in all even dimensions. Together, these results\nconstitute a complete characterization of the (non)classicality of all\nstabilizer subtheories.\n"}
+{"abstract": "  Deploying transformer models in practice is challenging due to their\ninference cost, which scales quadratically with input sequence length. To\naddress this, we present a novel Learned Token Pruning (LTP) method which\nadaptively removes unimportant tokens as an input sequence passes through\ntransformer layers. In particular, LTP prunes tokens with an attention score\nbelow a threshold value which is learned for each layer during training. Our\nthreshold-based method allows the length of the pruned sequence to vary\nadaptively based on the input sequence, and avoids algorithmically expensive\noperations such as top-k token selection. We extensively test the performance\nof LTP on GLUE tasks and show that our method outperforms the prior\nstate-of-the-art token pruning methods by up to ~2.5% higher accuracy with the\nsame amount of FLOPs. In particular, LTP achieves up to 2.1x FLOPs reduction\nwith less than 1% accuracy drop, which results in up to 1.9x and 2.0x\nthroughput improvement on Intel Haswell CPUs and NVIDIA V100 GPUs,\nrespectively. Furthermore, we demonstrate that LTP is more robust than prior\nmethods to variations on input sentence lengths. Our code has been developed in\nPyTorch and has been open-sourced.\n"}
+{"abstract": "  Neural networks have achieved tremendous empirical success in many areas. It\nhas been observed that a randomly initialized neural network trained by\nfirst-order methods is able to achieve near-zero training loss, although its\nloss landscape is non-convex and non-smooth. There are few theoretical\nexplanations for this phenomenon. Recently, some attempts have been made to\nbridge this gap between practice and theory by analyzing the trajectories of\ngradient descent~(GD) and heavy-ball method~(HB) in an over-parameterized\nregime. In this work, we make further progress by considering Nesterov's\naccelerated gradient method~(NAG) with a constant momentum parameter. We\nanalyze its convergence for an over-parameterized two-layer fully connected\nneural network with ReLU activation. Specifically, we prove that the training\nerror of NAG converges to zero at a non-asymptotic linear convergence rate\n$(1-\\Theta(1/\\sqrt{\\kappa}))^t$ after $t$ iterations, where $\\kappa > 1$ is\ndetermined by the initialization and the architecture of the neural network.\nBesides, we present a comparison between NAG and the existing convergence\nresults of GD and HB. Our theoretical results show that NAG achieves an\nacceleration over GD and its convergence rate is comparable to HB. Furthermore,\nthe numerical experiments validate the correctness of our theoretical analysis.\n"}
+{"abstract": "  IFOSMONDI iterative algorithm for implicit co-simulation of coupled physical\nsystems (introduced by the authors in july 2019 during the Simultech\nconference, p.176-186) enables us to solve the nonlinear coupling function\nwhile keeping the smoothness of interfaces without introducing a delay.\nMoreover, it automatically adapts the size of the steps between data exchanges\namong the systems according to the difficulty of the solving of the coupling\nconstraint. The latter was solved by a fixed-point algorithm in the original\nimplementation whereas this paper introduces the JFM version (standing for\nJacobian-Free Methods). Most implementations of Newton-like methods require a\njacobian matrix which can be difficult to compute in the co-simulation context,\nexcept in the case where the interfaces are represented by a Zero-Order-Hold\n(ZOH). As far as IFOSMONDI coupling algorithm uses Hermite interpolation for\nsmoothness enhancement (up to Third-Order-Hold), we propose hereafter a new\nformulation of the non-linear coupling function including both the values and\nthe time-derivatives of the coupling variables. This formulation is well\ndesigned for solving the coupling through jacobian-free Newton type methods.\nConsequently, successive function evaluations consist in multiple simulations\nof the systems on a co-simulation time step using rollback. The\norchestrator-workers structure of the algorithm enables us to combine the PETSc\nframework on the orchestrator side for the non-linear Newton-type solvers with\nthe parallel integrations of the systems on the workers side thanks to MPI\nprocesses. Different nonlinear methods will be compared to one another and to\nthe original fixed-point implementation on a newly proposed 2-systems academic\ntest-case (mass-spring-damper type) with direct feedthrough on both sides.\n"}
+{"abstract": "  To quantify the dependence between two random vectors of possibly different\ndimensions, we propose to rely on the properties of the 2-Wasserstein distance.\nWe first propose two coefficients that are based on the Wasserstein distance\nbetween the actual distribution and a reference distribution with independent\ncomponents. The coefficients are normalized to take values between 0 and 1,\nwhere 1 represents the maximal amount of dependence possible given the two\nmultivariate margins. We then make a quasi-Gaussian assumption that yields two\nadditional coefficients rooted in the same ideas as the first two. These\ndifferent coefficients are more amenable for distributional results and admit\nattractive formulas in terms of the joint covariance or correlation matrix.\nFurthermore, maximal dependence is proved to occur at the covariance matrix\nwith minimal von Neumann entropy given the covariance matrices of the two\nmultivariate margins. This result also helps us revisit the RV coefficient by\nproposing a sharper normalisation. The two coefficients based on the\nquasi-Gaussian approach can be estimated easily via the empirical covariance\nmatrix. The estimators are asymptotically normal and their asymptotic variances\nare explicit functions of the covariance matrix, which can thus be estimated\nconsistently too. The results extend to the Gaussian copula case, in which case\nthe estimators are rank-based. The results are illustrated through theoretical\nexamples, Monte Carlo simulations, and a case study involving\nelectroencephalography data.\n"}
+{"abstract": "  We extend non-emtpyness and irreducibility of Hassett divisors to the moduli\nspaces of $M$-polarizable cubic fourfolds for higher rank lattices $M$, which\nin turn provides a systematic approach for describing the irreducible\ncomponents of intersection of Hassett divisors. We show that Fermat cubic\nfourfold is contained in every Hassett divisor, which yields a new proof of\nHassett's existence theorem of special cubic fourfolds. We obtain an algorithm\nto determine the irreducible components of the intersection of any two Hassett\ndivisors and we give new examples of rational cubic fourfolds. Moreover, we\nderive a numerical criterion for the algebraic cohomology of a cubic fourfold\nhaving an associated K3 surface and answer a question of Laza by realizing\ninfinitely many rank $11$ lattices as the algebraic cohomologies of cubic\nfourfolds having no associated K3 surfaces.\n"}
+{"abstract": "  Motivated by a possible convergence of terrestrial limbless locomotion\nstrategies ultimately determined by interfacial effects, we show how both 3D\ngait alterations and locomotory adaptations to heterogeneous terrains can be\nunderstood through the lens of local friction modulation. Via an\n`everything-is-friction' modeling approach, compounded by 3D simulations, the\nemergence and disappearance of a range of locomotory behaviors observed in\nnature is systematically explained in relation to inhabited environments. Our\napproach also simplifies the treatment of terrain heterogeneity, whereby even\nsolid obstacles may be seen as high friction regions, which we confirm against\nexperiments of snakes `diffracting' while traversing rows of posts [1], similar\nto optical waves. We further this optic analogy by illustrating snake\nrefraction, reflection and lens focusing. We use these insights to engineer\nsurface friction patterns and demonstrate passive snake navigation in complex\ntopographies. Overall, our study outlines a unified view that connects active\nand passive 3D mechanics with heterogeneous interfacial effects to explain a\nbroad set of biological observations, and potentially inspire engineering\ndesign.\n"}
+{"abstract": "  We consider the problem of allocating multiple indivisible items to a set of\nnetworked agents to maximize the social welfare subject to network\nexternalities. Here, the social welfare is given by the sum of agents'\nutilities and externalities capture the effect that one user of an item has on\nthe item's value to others. We first provide a general formulation that\ncaptures some of the existing models as a special case. We then show that the\nsocial welfare maximization problem benefits some nice diminishing or\nincreasing marginal return properties. That allows us to devise polynomial-time\napproximation algorithms using the Lovasz extension and multilinear extension\nof the objective functions. Our principled approach recovers or improves some\nof the existing algorithms and provides a simple and unifying framework for\nmaximizing social welfare subject to network externalities.\n"}
+{"abstract": "  Let $X$ be one of the $28$ Atkin-Lehner quotients of a curve $X_0(N)$ such\nthat $X$ has genus $2$ and its Jacobian variety $J$ is absolutely simple. We\nshow that the Shafarevich-Tate group of $J/\\mathbb{Q}$ is trivial. This\nverifies the strong BSD conjecture for $J$.\n"}
+{"abstract": "  Conversational machine reading (CMR) tools have seen a rapid progress in the\nrecent past. The current existing tools rely on the supervised learning\ntechnique which require labeled dataset for their training. The supervised\ntechnique necessitates that for every new rule text, a manually labeled dataset\nmust be created. This is tedious and error prone. This paper introduces and\ndemonstrates how unsupervised learning technique can be applied in the\ndevelopment of CMR. Specifically, we demonstrate how unsupervised learning can\nbe used in rule extraction and entailment modules of CMR. Compared to the\ncurrent best CMR tool, our developed framework reports 3.3% improvement in\nmicro averaged accuracy and 1.4 % improvement in macro averaged accuracy.\n"}
+{"abstract": "  We have optimized the growth of superconducting TaN thin films on \\ch{SiO2}\nsubstrates via dc magnetron sputtering and extract a maximum superconducting\ntransition temperature of $T_{\\mathrm{c}}=5$ K as well as a maximum critical\nfield $\\mu_0H_{\\mathrm{c2}}=(13.8\\pm0.1)$ T. To investigate the impact of\nspin-orbit interaction in superconductor/ferromagnet heterostructures, we then\nanalyze the magnetization dynamics of both normal state and superconducting\nTaN/\\ch{Ni80Fe20}(Permalloy, Py)-bilayers as a function of temperature using\nbroadband ferromagnetic resonance (bbFMR) spectroscopy. The phase sensitive\ndetection of the microwave transmission signal is used to quantitatively\nextract the inverse current-induced torques of the bilayers. The results are\ncompared to our previous study on NbN/Py-bilayers. In the normal state of TaN,\nwe detect a positive damping-like current-induced torque $\\sigma_{\\mathrm{d}}$\nfrom the inverse spin Hall effect (iSHE) and a small field-like torque\n$\\sigma_{\\mathrm{f}}$ attributed to the inverse Rashba-Edelstein effect (iREE)\nat the TaN/Py-interface. In the superconducting state of TaN, we detect a\nnegative $\\sigma_{\\mathrm{d}}$ attributed to the quasiparticle mediated inverse\nspin Hall effect (QMiSHE) and the unexpected manifestation of a large positive\nfield-like $\\sigma_{\\mathrm{f}}$ of unknown origin matching our previous\nresults for NbN/Py-bilayers.\n"}
+{"abstract": "  We work with lattice walks in $\\mathbb{Z}^{r+1}$ using step set $\\{\\pm\n1\\}^{r+1}$ that finish with $x_{r+1} = 0$. We further impose conditions of\navoiding backtracking (i.e. $[v,-v]$) and avoiding consecutive steps (i.e.\n$[v,v]$) each possibly combined with restricting to the half-space $x_{r+1}\n\\geq 0$. We find in all cases the generating functions for such walks are\nalgebraic and give explicit formulas for them. We also find polynomial\nrecurrences for their coefficients. From the generating functions we find the\nasymptotic enumeration of each family of walks considered. The enumeration in\nspecial cases includes central binomial coefficients and Catalan numbers as\nwell as relations to enumeration of another family of walks previously studied\nfor which we provide bijection.\n"}
+{"abstract": "  A common developmental process, called branching morphogenesis, generates the\nepithelial trees in a variety of organs, including the lungs, kidneys, and\nglands. How branching morphogenesis can create epithelial architectures of very\ndifferent shapes and functions remains elusive. In this review, we compare\nbranching morphogenesis and its regulation in lungs and kidneys and discuss the\nrole of signaling pathways, the mesenchyme, the extracellular matrix, and the\ncytoskeleton as potential organ-specific determinants of branch position,\norientation, and shape. Identifying the determinants of branch and organ shape\nand their adaptation in different organs may reveal how a highly conserved\ndevelopmental process can be adapted to different structural and functional\nframeworks and should provide important insights into epithelial morphogenesis\nand developmental disorders.\n"}
+{"abstract": "  Frames are redundant system which are useful in the reconstruction of certain\nclasses of spaces. Duffin and Schaeffer introduced frames for Hilbert spaces,\nwhile addressing some deep problems in non harmonic Fourier series. The dual of\na frame (Hilbert) always exists and can be obtained in a natural way. In this\npaper we introduce the notion of $\\Omega_0$-type duality of retro\n$(\\Omega,\\mu)$-frames are given. Necessary and sufficient conditions for the\nexistence of the dual of retro $(\\Omega,\\mu)$-frames are obtained. A special\nclass of retro $(\\Omega,\\mu)$-frames which always admit a dual frame is\ndiscussed.\n"}
+{"abstract": "  We develop a general formalism to study laser operation in active micro-ring\nresonators supporting two counterpropagating modes. Our formalism is based on\nthe coupled-mode equations of motion for the field amplitudes in the two\ncounterpropagating modes and a linearized analysis of the small perturbations\naround the steady state. We show that the devices including an additional\nS-shaped waveguide establishing an unidirectional coupling between both modes\n-- the so-called Taiji resonators (TJR) -- feature a preferred chirality on the\nlaser emission and can ultimately lead to unidirectional lasing even in the\npresence of sizable backscattering. The efficiency of this mode selection\nprocess is further reinforced by the Kerr nonlinearity of the material. This\nstable unidirectional laser operation can be seen as an effective breaking of\n$\\mathcal{T}$-reversal symmetry dynamically induced by the breaking of the\n$\\mathcal{P}$-symmetry of the underlying device geometry. This mechanism\nappears as a promising building block to ensure non-reciprocal behaviors in\nintegrated photonic networks and topological lasers without the need for\nmagnetic elements.\n"}
+{"abstract": "  Turbulent winds and gusts fluctuate on a wide range of timescales from\nmilliseconds to minutes and longer, a range that overlaps the timescales of\navian flight behavior, yet the importance of turbulence to avian behavior is\nunclear. By combining wind speed data with the measured accelerations of a\ngolden eagle (Aquila chrysaetos) flying in the wild, we show that the eagle's\naccelerations can be explained by a linear interaction with turbulence for\ntimescales between about 1/2 and 10 s. These timescales are comparable to those\nof typical eagle behaviors, corresponding to between about 1 and 25 wingbeats,\nand to those of turbulent gusts both larger than the eagle's wingspan and\nsmaller than large-scale atmospheric phenomena such as convection cells. The\neagle's accelerations exhibit power spectra and intermittent activity\ncharacteristic of turbulence, and increase in proportion to the turbulence\nintensity. Intermittency results in accelerations that are occasionally several\ntimes stronger than gravity, and much larger than the ones we experience while\ndriving, for instance. These imprints of turbulence on the bird's movements\nneed to be further explored to understand the energetics of birds and other\nvolant lifeforms, to improve our own methods of flying through ceaselessly\nturbulent environments, and to engage airborne wildlife as distributed probes\nof the changing conditions in the atmosphere.\n"}
+{"abstract": "  We present results from a spectroscopically blind search for associated and\nintervening HI 21-cm and OH 18-cm absorption lines towards 88 AGNs at $2\\le\nz\\le5$ using the upgraded Giant Metrewave Radio Telescope (uGMRT). The sample\nof AGNs with 1.4 GHz spectral luminosity in the range, $10^{27 - 29.3}$ W/Hz,\nis selected using mid-infrared colors and closely resembles the distribution of\nthe underlying quasar population. The search for associated or proximate\nabsorption, defined to be within 3000 km/s of the AGN redshift, led to one HI\n21-cm absorption detection (M1540-1453; $z_{abs}$= 2.1139). This is only the\nfourth known absorption at $z>2$. The detection rate ($1.6^{+3.8}_{-1.4}$%)\nsuggests low covering factor of cold neutral medium (CNM; T$\\sim$100 K)\nassociated with these powerful AGNs. The intervening absorption line search,\nwith a sensitivity to detect CNM in damped Ly$\\alpha$ systems (DLAs), has\ncomoving absorption path lengths of $\\Delta$X = 130.1 and 167.7 for HI and OH,\nrespectively. The corresponding number of absorber per unit comoving path\nlengths are $\\le$0.014 and $\\le$0.011, respectively. The former is at least 4.5\ntimes lower than that of DLAs and consistent with the CNM cross-section\nestimated using H$_2$ and CI absorbers at $z>2$. Our AGN sample is optically\nfainter compared to the quasars used to search for DLAs in the past. In our\noptical spectra obtained using the Southern African Large Telescope (SALT) and\nthe Nordic Optical Telescope (NOT), we detect 5 intervening (redshift\npath$\\sim9.3$) and 2 proximate DLAs. This is slightly excessive compared to the\nstatistics based on optically selected quasars. The non-detection of HI 21-cm\nabsorption from these DLAs suggests small CNM covering fraction around galaxies\nat $z>2$.\n"}
+{"abstract": "  Balanced steady-state free precession (bSSFP) imaging enables high scan\nefficiency in MRI, but differs from conventional sequences in terms of elevated\nsensitivity to main field inhomogeneity and nonstandard T2/T1-weighted tissue\ncontrast. To address these limitations, multiple bSSFP images of the same\nanatomy are commonly acquired with a set of different RF phase-cycling\nincrements. Joint processing of phase-cycled acquisitions serves to mitigate\nsensitivity to field inhomogeneity. Recently phase-cycled bSSFP acquisitions\nwere also leveraged to estimate relaxation parameters based on explicit signal\nmodels. While effective, these model-based methods often involve a large number\nof acquisitions (N~10-16), degrading scan efficiency. Here, we propose a new\nconstrained ellipse fitting method (CELF) for parameter estimation with\nimproved efficiency and accuracy in phase-cycled bSSFP MRI. CELF is based on\nthe elliptical signal model framework for complex bSSFP signals; and it\nintroduces geometrical constraints on ellipse properties to improve estimation\nefficiency, and dictionary-based identification to improve estimation accuracy.\nCELF generates maps of T1, T2, off-resonance and on-resonant bSSFP signal by\nemploying a separate B1 map to mitigate sensitivity to flip angle variations.\nOur results indicate that CELF can produce accurate off-resonance and\nbanding-free bSSFP maps with as few as N=4 acquisitions, while estimation\naccuracy for relaxation parameters is notably limited by biases from\nmicrostructural sensitivity of bSSFP imaging.\n"}
+{"abstract": "  This paper develops a version of dependent type theory in which isomorphism\nis handled through a direct generalization of the 1939 definitions of Bourbaki.\nMore specifically we generalize the Bourbaki definition of structure from\nsimple type signatures to dependent type signatures. Both the original Bourbaki\nnotion of isomorphism and its generalization given here define an isomorphism\nbetween two structures $N$ and $N'$ to consist of bijections between their\nsorts that transport the structure of $N$ to the structure of $N'$. Here\ntransport is defined by commutativity conditions stated with set-theoretic\nequality. This differs from the dependent type theoretic treatments of\nisomorphism given in the groupoid model and homotopy type theory where no\nanalogously straightforward set-theoretic definition of transport is specified.\nThe straightforward definition of transport also leads to a straightforward\nconstructive proof (constructive content) for the validity of the substitution\nof isomorphics -- something that is difficult in the groupoid model or homotopy\ntype theory.\n"}
+{"abstract": "  Chiral exceptional points (CEPs) have been shown to emerge in traveling wave\nresonators via asymmetric back scattering from two or more nano-scatterers.\nHere, we provide a new perspective on the formation of CEPs based on the\ncoupled oscillator model. Our approach provides an intuitive understanding for\nthe modal coalescence that signals the emergence of CEPs, and emphasizes the\nrole played by dissipation throughout this process. In doing so, our model also\nunveils an otherwise unexplored connection between CEPs and other types of\nexceptional points associated with parity-time symmetric photonic arrangements.\nIn addition, our model also explains qualitative results observed in recent\nexperimental work involving CEPs. Importantly, the tight-binding nature of our\napproach allows us to extend the notion of CEP to discrete photonics setups\nthat consist coupled resonator and waveguide arrays, thus opening new avenues\nfor exploring the exotic features of CEPs in conjunction with other interesting\nphysical effects such as nonlinearities and topological protections.\n"}
+{"abstract": "  Graphs are fundamental mathematical structures used in various fields to\nmodel statistical and physical relationships between data, signals, and\nprocesses. In some applications, such as data processing in graphs that\nrepresent physical networks, the initial network topology is known. However,\ndisconnections of edges in the network change the topology and may affect the\nsignals and processes over the network. In this paper, we consider the problem\nof edge disconnection identification in networks by using concepts from graph\nsignal processing (GSP). We assume that the graph signals measured over the\nvertices of the network can be represented as white noise that has been\nfiltered on the graph topology by a smooth graph filter. We develop the\nlikelihood ratio test (LRT) to detect a specific set of edge disconnections.\nThen, we provide the maximum likelihood (ML) decision rule for identifying\ngeneral scenarios of edge disconnections in the network. It is shown that the\nsufficient statistics of the LRT and ML decision rule are the graph frequency\nenergy levels in the graph spectral domain. However, the ML decision rule leads\nto a high-complexity exhaustive search over the edges in the network and is\npractically infeasible. Thus, we propose a low-complexity greedy method that\nidentifies a single disconnected edge at each iteration. Moreover, by using the\nsmoothness of the considered graph filter, we suggest a local implementation of\nthe decision rule, which is based solely on the measurements at neighboring\nvertices. Simulation results demonstrate that the proposed methods outperform\nexisting detection and identification methods on a synthetic dataset and for\nline outage identification in power systems from the IEEE 118-bus test case.\n"}
+{"abstract": "  The performance of a cross-sectional currency strategy depends crucially on\naccurately ranking instruments prior to portfolio construction. While this\nranking step is traditionally performed using heuristics, or by sorting outputs\nproduced by pointwise regression or classification models, Learning to Rank\nalgorithms have recently presented themselves as competitive and viable\nalternatives. Despite improving ranking accuracy on average however, these\ntechniques do not account for the possibility that assets positioned at the\nextreme ends of the ranked list -- which are ultimately used to construct the\nlong/short portfolios -- can assume different distributions in the input space,\nand thus lead to sub-optimal strategy performance. Drawing from research in\nInformation Retrieval that demonstrates the utility of contextual information\nembedded within top-ranked documents to learn the query's characteristics to\nimprove ranking, we propose an analogous approach: exploiting the features of\nboth out- and under-performing instruments to learn a model for refining the\noriginal ranked list. Under a re-ranking framework, we adapt the Transformer\narchitecture to encode the features of extreme assets for refining our\nselection of long/short instruments obtained with an initial retrieval.\nBacktesting on a set of 31 currencies, our proposed methodology significantly\nboosts Sharpe ratios -- by approximately 20% over the original LTR algorithms\nand double that of traditional baselines.\n"}
+{"abstract": "  With the surge of inexpensive computational and memory resources, neural\nnetworks (NNs) have experienced an unprecedented growth in architectural and\ncomputational complexity. Introducing NNs to resource-constrained devices\nenables cost-efficient deployments, widespread availability, and the\npreservation of sensitive data. This work addresses the challenges of bringing\nMachine Learning to MCUs, where we focus on the ubiquitous ARM Cortex-M\narchitecture. The detailed effects and trade-offs that optimization methods,\nsoftware frameworks, and MCU hardware architecture have on key performance\nmetrics such as inference latency and energy consumption have not been\npreviously studied in depth for state-of-the-art frameworks such as TensorFlow\nLite Micro. We find that empirical investigations which measure the perceptible\nmetrics - performance as experienced by the user - are indispensable, as the\nimpact of specialized instructions and layer types can be subtle. To this end,\nwe propose an implementation-aware design as a cost-effective method for\nverification and benchmarking. Employing our developed toolchain, we\ndemonstrate how existing NN deployments on resource-constrained devices can be\nimproved by systematically optimizing NNs to their targeted application\nscenario.\n"}
+{"abstract": "  We propose a new quantum state reconstruction method that combines ideas from\ncompressed sensing, non-convex optimization, and acceleration methods. The\nalgorithm, called Momentum-Inspired Factored Gradient Descent (\\texttt{MiFGD}),\nextends the applicability of quantum tomography for larger systems. Despite\nbeing a non-convex method, \\texttt{MiFGD} converges \\emph{provably} to the true\ndensity matrix at a linear rate, in the absence of experimental and statistical\nnoise, and under common assumptions. With this manuscript, we present the\nmethod, prove its convergence property and provide Frobenius norm bound\nguarantees with respect to the true density matrix. From a practical point of\nview, we benchmark the algorithm performance with respect to other existing\nmethods, in both synthetic and real experiments performed on an IBM's quantum\nprocessing unit. We find that the proposed algorithm performs orders of\nmagnitude faster than state of the art approaches, with the same or better\naccuracy. In both synthetic and real experiments, we observed accurate and\nrobust reconstruction, despite experimental and statistical noise in the\ntomographic data. Finally, we provide a ready-to-use code for state tomography\nof multi-qubit systems.\n"}
+{"abstract": "  The transition mechanism of metal-insulator in metal oxides is discussed in\ndetail, which is a part of the mechanism of superconductivity. Through the\nstudy of magic angle twisted bilayer graphene superconductor and other new\nfindings on superconductivity, we further demonstrate that the physical\nmechanism of superconductivity proposed in the Part I is the only correct way\nto handle the properties of superconductivity in various materials. We propose\nthat superfluid helium consists of normal liquid helium mixed with high-energy\nhelium atoms. Based on this new model, all peculiar features discovered in\nsuperfluid helium can be truly understood, such as its climb on the container's\nwall, its fountain effect, the discontinuity of specific heat capacity at phase\ntransition point, as well as the maintaining mass current in ring-shaped\ncontainer. We demonstrate that the high-energy particles play a driving force\nrole in both superconductors and superfluid helium, and therefore dominate\ntheir properties.\n"}
+{"abstract": "  Deeply virtual Compton scattering (DVCS) and timelike Compton scattering\n(TCS) leading twist amplitudes are intimately related thanks to their analytic\nproperties as a function of $Q^2$. We exploit this feature to use Compton form\nfactors previously extracted from available DVCS data and derive data-driven\npredictions for TCS observables to be measured in near future experiments. Our\nresults quantitatively illustrate the complementarity of DVCS and TCS\nexperiments.\n"}
+{"abstract": "  The vector Apodizing Phase Plate (vAPP) is a class of pupil plane coronagraph\nthat enables high-contrast imaging by modifying the Point Spread Function (PSF)\nto create a dark hole of deep flux suppression adjacent to the PSF core. Here,\nwe recover the known brown dwarf HR 2562 B using a vAPP coronagraph, in\nconjunction with the Magellan Adaptive Optics (MagAO) system, at a\nsignal-to-noise of S/N = 3.04 in the lesser studied L-band regime. The data\ncontained a mix of field and pupil-stabilised observations, hence we explored\nthree different processing techniques to extract the companion, including\nFlipped Differential Imaging (FDI), a newly devised Principal Component\nAnalysis (PCA)-based method for vAPP data. Despite the partial\nfield-stabilisation, the companion is recovered sufficiently to measure a 3.94\n$\\mu$m narrow-band contrast of (3.05$\\pm$1.00) $\\times$ 10$^{-4}$\n($\\Delta$m$_{3.94 {\\mu}m}$ = 8.79$\\pm$0.36 mag). Combined with archival GPI and\nSPHERE observations, our atmospheric modelling indicates a spectral type at the\nL/T transition with mass M = 29$\\pm$15 M$_{\\text{Jup}}$, consistent with\nliterature results. However, effective temperature and surface gravity vary\nsignificantly depending on the wavebands considered\n(1200$\\leq$T$_{\\text{eff}}$(K)$\\leq$1700 and 4.0$\\leq$log(g)(dex)$\\leq$5.0),\nreflecting the challenges of modelling objects at the L/T transition.\nObservations between 2.4-3.2 $\\mu$m will be more effective in distinguishing\ncooler brown dwarfs due to the onset of absorption bands in this region. We\nexplain that instrumental scattered light and wind-driven halo can be\ndetrimental to FDI+PCA and thus must be sufficiently mitigated to use this\nprocessing technique. We thus demonstrate the potential of vAPP coronagraphs in\nthe characterisation of high-contrast substellar companions, even in\nsub-optimal conditions, and provide new, complementary photometry of HR 2562 B.\n"}
+{"abstract": "  Exotic electromagnetic energy injection in the early Universe may alter\ncosmological recombination, and ultimately cosmic microwave background (CMB)\nanisotropies. Moreover, if energy injection is inhomogeneous, it may induce a\nspatially-varying ionization fraction, and non-Gaussianity in the CMB. The\nobservability of these signals, however, is contingent upon how far the\ninjected particles propagate and deposit their energy into the primordial\nplasma, relative to the characteristic scale of energy injection fluctuations.\nIn this study we inspect the spatial properties of energy deposition and\nperturbed recombination resulting from an inhomogeneous energy injection of\nsub-10 MeV photons, relevant to accreting primordial black holes (PBHs). We\ndevelop a novel Monte-Carlo radiation transport code accounting for all\nrelevant photon interactions in this energy range, and including secondary\nelectron energy deposition efficiency through a new analytic approximation. For\na specified injected photon spectrum, the code outputs an\ninjection-to-deposition Green's function depending on time and distance from\nthe injection point. Combining this output with a linearized solution of the\nperturbed recombination problem, we derive time- and scale-dependent\ndeposition-to-ionization Green's functions. We apply this general framework to\naccreting PBHs, whose luminosity is strongly spatially modulated by supersonic\nrelative velocities between cold dark matter and baryons. We find that the\nresulting spatial fluctuations of the free-electron fraction are of the same\nmagnitude as its mean deviation from standard recombination, from which current\nCMB power spectra constraints are derived. This work suggests that the\nsensitivity to accreting PBHs might be substantially improved by propagating\nthese inhomogeneities to CMB anisotropy power spectra and non-Gaussian\nstatistics, which we study in subsequent papers.\n"}
+{"abstract": "  Multilevel regression and poststratification (MRP) is a flexible modeling\ntechnique that has been used in a broad range of small-area estimation\nproblems. Traditionally, MRP studies have been focused on non-causal settings,\nwhere estimating a single population value using a nonrepresentative sample was\nof primary interest. In this manuscript, MRP-style estimators will be evaluated\nin an experimental causal inference setting. We simulate a large-scale\nrandomized control trial with a stratified cluster sampling design, and compare\ntraditional and nonparametric treatment effect estimation methods with MRP\nmethodology. Using MRP-style estimators, treatment effect estimates for areas\nas small as 1.3$\\%$ of the population have lower bias and variance than\nstandard causal inference methods, even in the presence of treatment effect\nheterogeneity. The design of our simulation studies also requires us to build\nupon a MRP variant that allows for non-census covariates to be incorporated\ninto poststratification.\n"}
+{"abstract": "  The emergence of multi-parametric magnetic resonance imaging (mpMRI) has had\na profound impact on the diagnosis of prostate cancers (PCa), which is the most\nprevalent malignancy in males in the western world, enabling a better selection\nof patients for confirmation biopsy. However, analyzing these images is complex\neven for experts, hence opening an opportunity for computer-aided diagnosis\nsystems to seize. This paper proposes a fully automatic system based on Deep\nLearning that takes a prostate mpMRI from a PCa-suspect patient and, by\nleveraging the Retina U-Net detection framework, locates PCa lesions, segments\nthem, and predicts their most likely Gleason grade group (GGG). It uses 490\nmpMRIs for training/validation, and 75 patients for testing from two different\ndatasets: ProstateX and IVO (Valencia Oncology Institute Foundation). In the\ntest set, it achieves an excellent lesion-level AUC/sensitivity/specificity for\nthe GGG$\\geq$2 significance criterion of 0.96/1.00/0.79 for the ProstateX\ndataset, and 0.95/1.00/0.80 for the IVO dataset. Evaluated at a patient level,\nthe results are 0.87/1.00/0.375 in ProstateX, and 0.91/1.00/0.762 in IVO.\nFurthermore, on the online ProstateX grand challenge, the model obtained an AUC\nof 0.85 (0.87 when trained only on the ProstateX data, tying up with the\noriginal winner of the challenge). For expert comparison, IVO radiologist's\nPI-RADS 4 sensitivity/specificity were 0.88/0.56 at a lesion level, and\n0.85/0.58 at a patient level. Additional subsystems for automatic prostate\nzonal segmentation and mpMRI non-rigid sequence registration were also employed\nto produce the final fully automated system. The code for the ProstateX-trained\nsystem has been made openly available at\nhttps://github.com/OscarPellicer/prostate_lesion_detection. We hope that this\nwill represent a landmark for future research to use, compare and improve upon.\n"}
+{"abstract": "  Gravitational waves (GWs) directly measure the luminosity distance to the\nmerger, which, when combined with an independent measurement of the source's\nredshift, provides a novel probe of cosmology. The proposed next generation of\nground-based GW detectors, Einstein Telescope and Cosmic Explorer, will detect\ntens of thousands of binary neutron stars (BNSs) out to cosmological distances\n($z>2$), beyond the peak of the star formation rate (SFR), or \"cosmic noon.\" At\nthese distances, it will be challenging to measure the sources' redshifts by\nobserving electromagnetic (EM) counterparts or statistically marginalizing over\na galaxy catalog. In the absence of an EM counterpart or galaxy catalog, Ding\net al. showed that theoretical priors on the merger redshift distribution can\nbe used to infer parameters in a $w$CDM cosmology. We argue that in the BNS\ncase, the redshift distribution will be measured by independent observations of\nshort gamma ray bursts (GRBs), kilonovae, and known BNS host galaxies. We show\nthat, in addition to measuring the background cosmology, this method can\nconstrain the effects of dark energy on modified GW propagation. We consider\nthe simple case in which the BNS rate is \\textit{a priori} known to follow the\nSFR. If the SFR is perfectly known, $\\mathcal{O}(10,000)$ events (to be\nexpected within a year of observation with Cosmic Explorer) would yield a\nsub-tenth percent measurement of the combination $H_0^{2.8}\\Omega_M$. Fixing\n$H_0$ and $\\Omega_M$, this method may enable a 5\\% measurement of the dark\nenergy equation of state parameter. Fixing the background cosmology and probing\nmodified GW propagation, the running of the Planck mass parameter $c_M$ may be\nmeasured to $\\pm0.02$. Although realistically, the redshift evolution of the\nmerger rate will be uncertain, prior knowledge of the peak redshift will\nprovide valuable information for standard siren analyses.\n"}
+{"abstract": "  We consider a projection method for time-dependent incompressible\nNavier-Stokes equations with a total pressure boundary condition. The\nprojection method is one of the numerical calculation methods for\nincompressible viscous fluids often used in engineering. In general, the\nprojection method needs additional boundary conditions to solve a\npressure-Poisson equation, which does not appear in the original Navier-Stokes\nproblem. On the other hand, many mechanisms generate flow by creating a\npressure difference, such as water distribution systems and blood circulation.\nWe propose a new additional boundary condition for the projection method with a\nDirichlet-type pressure boundary condition and no tangent flow. We demonstrate\nstability for the scheme and establish error estimates for the velocity and\npressure under suitable norms. A numerical experiment verifies the theoretical\nconvergence results. Furthermore, the existence of a weak solution to the\noriginal Navier-Stokes problem is proved by using the stability.\n"}
+{"abstract": "  Waiting time distributions of solar flares and {\\sl coronal mass ejections\n(CMEs)} exhibit power law-like distribution functions with slopes in the range\nof $\\alpha_{\\tau} \\approx 1.4-3.2$, as observed in annual data sets during 4\nsolar cycles (1974-2012). We find a close correlation between the waiting time\npower law slope $\\alpha_\\tau$ and the {\\sl sunspot number (SN)}, i.e.,\n$\\alpha_\\tau$ = 1.38 + 0.01 $\\times$ SN. The waiting time distribution can be\nfitted with a Pareto-type function of the form $N(\\tau) = N_0$ $(\\tau_0 +\n\\tau)^{-\\alpha_{\\tau}}$, where the offset $\\tau_0$ depends on the instrumental\nsensitivity, the detection threshold of events, and pulse pile-up effects. The\ntime-dependent power law slope $\\alpha_{\\tau}(t)$ of waiting time distributions\ndepends only on the global solar magnetic flux (quantified by the sunspot\nnumber) or flaring rate, independent of other physical parameters of {\\sl\nself-organized criticality (SOC)} or {\\sl magneto-hydrodynamic (MHD)}\nturbulence models. Power law slopes of $\\alpha_{\\tau}\\approx 1.2-1.6$ were also\nfound in solar wind switchback events, as observed with the {\\sl Parker Solar\nProbe (PSP)}. We conclude that the annual variability of switchback events in\nthe heliospheric solar wind is modulated by flare and CME rates originating in\nthe photosphere and lower corona.\n"}
+{"abstract": "  Communication networks are important infrastructures in contemporary society.\nThere are still many challenges that are not fully solved and new solutions are\nproposed continuously in this active research area. In recent years, to model\nthe network topology, graph-based deep learning has achieved the\nstate-of-the-art performance in a series of problems in communication networks.\nIn this survey, we review the rapidly growing body of research using different\ngraph-based deep learning models, e.g. graph convolutional and graph attention\nnetworks, in various problems from different types of communication networks,\ne.g. wireless networks, wired networks, and software defined networks. We also\npresent a well-organized list of the problem and solution for each study and\nidentify future research directions. To the best of our knowledge, this paper\nis the first survey that focuses on the application of graph-based deep\nlearning methods in communication networks involving both wired and wireless\nscenarios. To track the follow-up research, a public GitHub repository is\ncreated, where the relevant papers will be updated continuously.\n"}
+{"abstract": "  One-loop correction to the power spectrum in generic single-field inflation\nis calculated by using standard perturbation theory. Due to the enhancement\ninversely proportional to the observed red-tilt of the spectral index of\ncurvature perturbation, the correction turns out to be much larger than\npreviously anticipated. As a result, the primordial non-Gaussianity must be\nmuch smaller than the current observational bound in order to warrant the\nvalidity of cosmological perturbation theory.\n"}
+{"abstract": "  We study the cohomology of families of $(\\varphi,\\Gamma)$-modules with\ncoefficients in pseudoaffinoid algebras. We prove that they have finite\ncohomology, and we deduce an Euler characteristic formula and Tate local\nduality. We classify rank-$1$ $(\\varphi, \\Gamma)$-modules and deduce that\ntriangulations of pseudorigid families of $(\\varphi,\\Gamma)$-modules can be\ninterpolated, extending a result of [KPX14]. We then apply this to study\nextended eigenvarieties at the boundary of weight space, proving in particular\nthat the eigencurve is proper at the boundary and that Galois representations\nattached to certain characteristic $p$ points are trianguline.\n"}
+{"abstract": "  We use two-dimensional transverse laser cooling to produce an ultracold beam\nof YbF molecules. Through experiments and numerical simulations, we study how\nthe cooling is influenced by the polarization configuration, laser intensity,\nlaser detuning and applied magnetic field. The ultracold part of the beam\ncontains more than $2 \\times 10^5$ molecules per shot and has a temperature\nbelow 200 $\\mu$K, and the cooling yields a 300-fold increase in the brightness\nof the beam. The method can improve the precision of experiments that use\nmolecules to test fundamental physics. In particular, the beam is suitable for\nmeasuring the electron electric dipole moment with a statistical precision\nbetter than $10^{-30}$ e cm.\n"}
+{"abstract": "  Graph Convolutional Networks (GCNs) have achieved impressive empirical\nadvancement across a wide variety of semi-supervised node classification tasks.\nDespite their great success, training GCNs on large graphs suffers from\ncomputational and memory issues. A potential path to circumvent these obstacles\nis sampling-based methods, where at each layer a subset of nodes is sampled.\nAlthough recent studies have empirically demonstrated the effectiveness of\nsampling-based methods, these works lack theoretical convergence guarantees\nunder realistic settings and cannot fully leverage the information of evolving\nparameters during optimization. In this paper, we describe and analyze a\ngeneral doubly variance reduction schema that can accelerate any sampling\nmethod under the memory budget. The motivating impetus for the proposed schema\nis a careful analysis of the variance of sampling methods where it is shown\nthat the induced variance can be decomposed into node embedding approximation\nvariance (zeroth-order variance) during forward propagation and\nlayerwise-gradient variance (first-order variance) during backward propagation.\nWe theoretically analyze the convergence of the proposed schema and show that\nit enjoys an $\\mathcal{O}(1/T)$ convergence rate. We complement our theoretical\nresults by integrating the proposed schema in different sampling methods and\napplying them to different large real-world graphs.\n"}
+{"abstract": "  The state space of Android apps is huge and its thorough exploration during\ntesting remains a major challenge. In fact, the best exploration strategy is\nhighly dependent on the features of the app under test. Reinforcement Learning\n(RL) is a machine learning technique that learns the optimal strategy to solve\na task by trial and error, guided by positive or negative reward, rather than\nby explicit supervision. Deep RL is a recent extension of RL that takes\nadvantage of the learning capabilities of neural networks. Such capabilities\nmake Deep RL suitable for complex exploration spaces such as the one of Android\napps. However, state of the art, publicly available tools only support basic,\ntabular RL. We have developed ARES, a Deep RL approach for black-box testing of\nAndroid apps. Experimental results show that it achieves higher coverage and\nfault revelation than the baselines, which include state of the art RL based\ntools, such as TimeMachine and Q-Testing. We also investigated qualitatively\nthe reasons behind such performance and we have identified the key features of\nAndroid apps that make Deep RL particularly effective on them to be the\npresence of chained and blocking activities.\n"}
+{"abstract": "  We propose a functional view of matrix decomposition problems on graphs such\nas geometric matrix completion and graph regularized dimensionality reduction.\nOur unifying framework is based on the key idea that using a reduced basis to\nrepresent functions on the product space is sufficient to recover a low rank\nmatrix approximation even from a sparse signal. We validate our framework on\nseveral real and synthetic benchmarks (for both problems) where it either\noutperforms state of the art or achieves competitive results at a fraction of\nthe computational effort of prior work.\n"}
+{"abstract": "  Photoexcitation and shaping of a propagating surface plasmon polariton (SPP)\non silver and gold microstructures are well established and lead to the\ndiscovery of the plasmonic spin-Hall effect recently. Whereas silver is often\nthe material of choice due to its exceptional low plasma frequency and weak\ndamping, similar observations have not been reported for ferromagnetic metals.\nIn this work, we report on propagating SPPs on Ni$_{80}$Fe$_{20}$\nmicrostructures imaged by photoemission electron microscopy (PEEM) in\ncombination with a tunable femtosecond laser system at MHz repetition rate.\nCircular dichroic (CD) images in threshold PEEM show clear edge-induced SPPs\nwith sub-micrometer wavelength and propagation length of about 3.5 $\\mu$m.\nAnalysis of the interference patterns as well as the coupling of the optical\nspin angular momentum to the observed fringe fields reveal propagation\ncharacteristics exclusive to evanescent waves and the presence of the plasmonic\nspin-Hall effect. Our work provides direct evidence that many materials with a\nhigh plasma frequency allow for excitation and observation of propagating SPPs\nat the dielectric/metal interface via CD PEEM imaging, enabling\nmagnetoplasmonic investigation of common ferromagnets on nanometer length and\nfemtosecond time scales.\n"}
+{"abstract": "  The adaptive changes in synaptic efficacy that occur between spiking neurons\nhave been demonstrated to play a critical role in learning for biological\nneural networks. Despite this source of inspiration, many learning focused\napplications using Spiking Neural Networks (SNNs) retain static synaptic\nconnections, preventing additional learning after the initial training period.\nHere, we introduce a framework for simultaneously learning the underlying\nfixed-weights and the rules governing the dynamics of synaptic plasticity and\nneuromodulated synaptic plasticity in SNNs through gradient descent. We further\ndemonstrate the capabilities of this framework on a series of challenging\nbenchmarks, learning the parameters of several plasticity rules including BCM,\nOja's, and their respective set of neuromodulatory variants. The experimental\nresults display that SNNs augmented with differentiable plasticity are\nsufficient for solving a set of challenging temporal learning tasks that a\ntraditional SNN fails to solve, even in the presence of significant noise.\nThese networks are also shown to be capable of producing locomotion on a\nhigh-dimensional robotic learning task, where near-minimal degradation in\nperformance is observed in the presence of novel conditions not seen during the\ninitial training period.\n"}
+{"abstract": "  The emergent induction of spiral magnets that was proposed [Jpn. J. Appl.\nPhys. 58, 120909 (2019)] and recently demonstrated [Nature 586, 232 (2020)] is\nshown to be further extended by a comprehensive treatment of the Rashba\nspin-orbit coupling and the electron spin relaxation that affect the underlying\nprocesses of spin-transfer torque and spinmotive force. Within adiabatic\napproximation, we show that the output voltages are widely altered\nintrinsically via the Rashba effect whereas extrinsically via the nonadiabatic\ncorrection due to the spin relaxation and sample disorder. The findings\nrespectively clarify the origins for the amplitude modulation and sign change\nof the emergent inductance with tunability by electrical gating and careful\nsample preparation.\n"}
+{"abstract": "  Nonlinear dynamics of an optical pulse or a beam continue to be one of the\nactive areas of research in the field of optical solitons. Especially, in\nmulti-mode fibers or fiber arrays and photorefractive materials, the vector\nsolitons display rich nonlinear phenomena. Due to their fascinating and\nintriguing novel properties, the theory of optical vector solitons has been\ndeveloped considerably both from theoretical and experimental points of view\nleading to soliton based promising potential applications. In the recent past,\nmany types of vector solitons have been identified both in the integrable and\nnon-integrable coupled nonlinear Schr\\\"{o}dinger (CNLS) equations framework. In\nthis article, we review some of the recent progress in understanding the\ndynamics of the so called nondegenerate vector bright solitons in nonlinear\noptics, where the fundamental soliton can have more than one propagation\nconstant. We address this theme by considering the integrable two CNLS family\nof equations, namely Manakov system, mixed 2-CNLS system, coherently CNLS\nsystem, generalized CNLS system and two-component long-wave short-wave\nresonance interaction (LSRI) system. In these models, we discuss the existence\nof nondegenerate vector solitons and their associated novel multi-hump\ngeometrical profile nature by deriving their analytical forms through the\nHirota bilinear method. Then we reveal the novel collision properties of the\nnondegenerate solitons in the Manakov system as an example. The asymptotic\nanalysis shows that the nondegenerate solitons, in general, undergo three types\nof elastic collisions without any energy redistribution among the modes.\nFurther, we show that the energy sharing collision exhibiting vector solitons\narises as a special case of the newly reported nondegenerate vector solitons.\nFinally, we point out the possible further developments in this subject and\npotential applications.\n"}
+{"abstract": "  Flow in fractured porous media is modeled frequently by discrete\nfracture-matrix approaches where fractures are treated as dimensionally reduced\nmanifolds. Generalizing earlier work we focus on two-phase flow in\ntime-dependent fracture geometries including the fracture's aperture. We\npresent the derivation of a reduced model for immiscible two-phase flow in\nporous media. For the reduced model we present a fully conforming finite-volume\ndiscretization coupled with a moving-mesh method. This method permits arbitrary\nmovement of facets of the triangulation while being fully conservative. In\nnumerical examples we show the performance of the scheme and investigate the\nmodeling error of the reduced model.\n"}
+{"abstract": "  Estimation of bone age from hand radiographs is essential to determine\nskeletal age in diagnosing endocrine disorders and depicting the growth status\nof children. However, existing automatic methods only apply their models to\ntest images without considering the discrepancy between training samples and\ntest samples, which will lead to a lower generalization ability. In this paper,\nwe propose an adversarial regression learning network (ARLNet) for bone age\nestimation. Specifically, we first extract bone features from a fine-tuned\nInception V3 neural network and propose regression percentage loss for\ntraining. To reduce the discrepancy between training and test data, we then\npropose adversarial regression loss and feature reconstruction loss to\nguarantee the transition from training data to test data and vice versa,\npreserving invariant features from both training and test data. Experimental\nresults show that the proposed model outperforms state-of-the-art methods.\n"}
+{"abstract": "  Large-scale unlabeled data has allowed recent progress in self-supervised\nlearning methods that learn rich visual representations. State-of-the-art\nself-supervised methods for learning representations from images (MoCo and\nBYOL) use an inductive bias that different augmentations (e.g. random crops) of\nan image should produce similar embeddings. We show that such methods are\nvulnerable to backdoor attacks where an attacker poisons a part of the\nunlabeled data by adding a small trigger (known to the attacker) to the images.\nThe model performance is good on clean test images but the attacker can\nmanipulate the decision of the model by showing the trigger at test time.\nBackdoor attacks have been studied extensively in supervised learning and to\nthe best of our knowledge, we are the first to study them for self-supervised\nlearning. Backdoor attacks are more practical in self-supervised learning since\nthe unlabeled data is large and as a result, an inspection of the data to avoid\nthe presence of poisoned data is prohibitive. We show that in our targeted\nattack, the attacker can produce many false positives for the target category\nby using the trigger at test time. We also propose a knowledge distillation\nbased defense algorithm that succeeds in neutralizing the attack. Our code is\navailable here: https://github.com/UMBCvision/SSL-Backdoor .\n"}
+{"abstract": "  Design Fiction is known for its provocative and often dystopian speculations\nabout the future. In this paper, we present an alternative approach that\nfocuses primarily on the positive. We propose to imagine, enact, and evaluate\nutopia with participants. By doing so, we react to four main critiques\nconcerning Design Fiction: (1) its negativity, (2) its contextlessness, (3) its\nelitist authorship, and (4) its missing evaluation methods.\n"}
+{"abstract": "  In e-commerce advertising, it is crucial to jointly consider various\nperformance metrics, e.g., user experience, advertiser utility, and platform\nrevenue. Traditional auction mechanisms, such as GSP and VCG auctions, can be\nsuboptimal due to their fixed allocation rules to optimize a single performance\nmetric (e.g., revenue or social welfare). Recently, data-driven auctions,\nlearned directly from auction outcomes to optimize multiple performance\nmetrics, have attracted increasing research interests. However, the procedure\nof auction mechanisms involves various discrete calculation operations, making\nit challenging to be compatible with continuous optimization pipelines in\nmachine learning. In this paper, we design \\underline{D}eep \\underline{N}eural\n\\underline{A}uctions (DNAs) to enable end-to-end auction learning by proposing\na differentiable model to relax the discrete sorting operation, a key component\nin auctions. We optimize the performance metrics by developing deep models to\nefficiently extract contexts from auctions, providing rich features for auction\ndesign. We further integrate the game theoretical conditions within the model\ndesign, to guarantee the stability of the auctions. DNAs have been successfully\ndeployed in the e-commerce advertising system at Taobao. Experimental\nevaluation results on both large-scale data set as well as online A/B test\ndemonstrated that DNAs significantly outperformed other mechanisms widely\nadopted in industry.\n"}
+{"abstract": "  We show how to compute electromagnetic polarizabilities of charged hadrons\nusing four-point functions in lattice QCD. The low-energy behavior of Compton\nscattering amplitude is matched to matrix elements of current-current\ncorrelation functions on the lattice. Working in momentum space, formulas for\nelectric polarizability ($\\alpha_E$) and magnetic polarizability ($\\beta_M$)\nare derived for both charged pion and proton. Lattice four-point correlation\nfunctions are constructed from quark and gluon fields to be used in Monte-Carlo\nsimulations. The content of the functions is assessed in detail and specific\nprescriptions are given to isolate the polarizabilities. The connected\nquark-line diagrams can be done today as a small lattice project. The\ndisconnected diagrams are more challenging but are within reach of dedicated\nresources for medium to large lattice projects. We also draw attention to the\npotential of four-point functions as a multi-purpose tool for hadron structure.\n"}
+{"abstract": "  A celebrated unresolved conjecture of Erd\\\"{o}s and Hajnal states that for\nevery undirected graph $H$ there exists $ \\epsilon(H) > 0 $ such that every\nundirected graph on $ n $ vertices that does not contain $H$ as an induced\nsubgraph contains a clique or a stable set of size at least $ n^{\\epsilon(H)}\n$. This conjecture has a directed equivalent version stating that for every\ntournament $H$ there exists $ \\epsilon(H) > 0 $ such that every $H-$free\n$n-$vertex tournament $T$ contains a transitive subtournament of size at least\n$ n^{\\epsilon(H)} $. Recently the conjecture was proved for all six-vertex\ntournaments, except $K_{6}$. In this paper we construct two infinite families\nof tournaments for which the conjecture is still open for infinitely many\ntournaments in these two families $-$ the family of so-called super nebulas and\nthe family of so-called super triangular galaxies. We prove that for every\nsuper nebula $H_{1}$ and every $\\Delta$galaxy $H_{2}$ there exist\n$\\epsilon(H_{1},H_{2})$ such that every $\\lbrace H_{1},H_{2}\\rbrace$$-$free\ntournament $T$ contains a transitive subtournament of size at least\n$\\mid$$T$$\\mid^{\\epsilon(H_{1},H_{2})}$. We also prove that for every central\ntriangular galaxy $H$ there exist $\\epsilon(K_{6},H)$ such that every $\\lbrace\nK_{6},H\\rbrace$$-$free tournament $T$ contains a transitive subtournament of\nsize at least $\\mid$$T$$\\mid^{\\epsilon(K_{6},H)}$. And we give an extension of\nour results.\n"}
+{"abstract": "  We study the sub-grid scale characteristics of a vorticity-transport-based\napproach for large-eddy simulations. In particular, we consider a\nmulti-dimensional upwind scheme for the vorticity transport equations and\nestablish its properties in the under-resolved regime. The asymptotic behavior\nof key turbulence statistics of velocity gradients, vorticity, and invariants\nis studied in detail. Modified equation analysis indicates that dissipation can\nbe controlled locally via non-linear limiting of the gradient employed for the\nvorticity reconstruction on the cell face such that low numerical diffusion is\nobtained in well-resolved regimes and high numerical diffusion is realized in\nunder-resolved regions. The enstrophy budget highlights the remarkable ability\nof the truncation terms to mimic the true sub-grid scale dissipation and\ndiffusion. The modified equation also reveals diffusive terms that are similar\nto several commonly employed sub-grid scale models including tensor-gradient\nand hyper-viscosity models. Investigations on several canonical turbulence flow\ncases show that large-scale features are adequately represented and remain\nconsistent in terms of spectral energy over a range of grid resolutions.\nNumerical dissipation in under-resolved simulations is consistent and can be\ncharacterized by diffusion terms discovered in the modified equation analysis.\nA minimum state of scale separation necessary to obtain asymptotic behavior is\ncharacterized using metrics such as effective Reynolds number and effective\ngrid spacing. Temporally-evolving jet simulations, characterized by large-scale\nvortical structures, demonstrate that high Reynolds number vortex-dominated\nflows are captured when criteria is met and necessitate diffusive non-linear\nlimiting of vorticity reconstruction be employed to realize accuracy in\nunder-resolved simulations.\n"}
+{"abstract": "  The concept of thermal machines has evolved from the canonical steam engine\nto the recently proposed nanoscopic quantum systems as working fluids. The\nlatter obey quantum open system dynamics and frequently operate in\nnon-equilibrium conditions. However, the role of this dynamics in the overall\nperformance of quantum heat engines remains an open problem. Here, we analyse\nand optimize the efficiency and power output of two-stage quantum heat engines\nfuelled by non-equilibrium steady states. In a charging first stage, the\nquantum working fluid consisting of a qutrit or two coupled qubits is connected\nto two reservoirs at different temperatures, which establish a heat current\nthat stores ergotropy in the system; the second stage comprises a coherent\ndriving force that extracts work from the machine in finite a amount of time;\nfinally, the external drive is switched off and the machine enters a new cycle.\n"}
+{"abstract": "  The interaction of spin-polarized one-dimensional (1D) topological edge modes\nlocalized along single-atomic steps of the topological crystalline insulator\n$Pb_{0.7}Sn_{0.3}Se(001)$ has been studied systematically by scanning tunneling\nspectroscopy. Our results reveal that the coupling of adjacent edge modes sets\nin at a step{to{step distance $d_{ss} < 25$ nm, resulting in a characteristic\nsplitting of a single peak at the Dirac point in tunneling spectra. Whereas the\nenergy splitting exponentially increases with decreasing $d_{ss}$ for\nsingle-atomic steps running almost parallel, we find no splitting for\nsingle-atomic step edges under an angle of $90^{o}$. The results are discussed\nin terms of overlapping wave functions with $p_x; p_y$ orbital character.\n"}
+{"abstract": "  We consider the regression problem of estimating functions on $\\mathbb{R}^D$\nbut supported on a $d$-dimensional manifold $ \\mathcal{M} \\subset \\mathbb{R}^D\n$ with $ d \\ll D $. Drawing ideas from multi-resolution analysis and nonlinear\napproximation, we construct low-dimensional coordinates on $\\mathcal{M}$ at\nmultiple scales, and perform multiscale regression by local polynomial fitting.\nWe propose a data-driven wavelet thresholding scheme that automatically adapts\nto the unknown regularity of the function, allowing for efficient estimation of\nfunctions exhibiting nonuniform regularity at different locations and scales.\nWe analyze the generalization error of our method by proving finite sample\nbounds in high probability on rich classes of priors. Our estimator attains\noptimal learning rates (up to logarithmic factors) as if the function was\ndefined on a known Euclidean domain of dimension $d$, instead of an unknown\nmanifold embedded in $\\mathbb{R}^D$. The implemented algorithm has quasilinear\ncomplexity in the sample size, with constants linear in $D$ and exponential in\n$d$. Our work therefore establishes a new framework for regression on\nlow-dimensional sets embedded in high dimensions, with fast implementation and\nstrong theoretical guarantees.\n"}
+{"abstract": "  We show how to characterize the indistinguishability of up to four identical,\nbosonic or fermionic particles, which are rendered partially distinguishable\nthrough their internal degrees of freedom prepared in mixed states. This is\naccomplished via their counting statistics when subjected to a highly symmetric\nunitary acting upon their external (i.e., dynamical) degrees of freedom. For\npure internal states, we further extract information on the particles'\ncollective phases, which ultimately allows for an experimental reconstruction\nof the full many-particle density operator up to complex conjugation.\n"}
+{"abstract": "  Twisted van der Waals materials have been shown to host a variety of tunable\nelectronic structures. Here we put forward twisted trilayer graphene (TTG) as a\nplatform to emulate heavy fermion physics. We demonstrate that TTG hosts\nextended and localized modes with an electronic structure that can be\ncontrolled by interlayer bias. In the presence of interactions, the existence\nof localized modes leads to the development of local moments, which are Kondo\ncoupled to coexisting extended states. By electrically controlling the\neffective exchange between local moments, the system can be driven from a\nmagnetic into a heavy fermion regime, passing through a quantum critical point.\nOur results put forward twisted graphene multilayers as a platform for the\nrealization of strongly correlated heavy fermion physics in a purely\ncarbon-based platform.\n"}
+{"abstract": "  The paper studies planar surface reconstruction of indoor scenes from two\nviews with unknown camera poses. While prior approaches have successfully\ncreated object-centric reconstructions of many scenes, they fail to exploit\nother structures, such as planes, which are typically the dominant components\nof indoor scenes. In this paper, we reconstruct planar surfaces from multiple\nviews, while jointly estimating camera pose. Our experiments demonstrate that\nour method is able to advance the state of the art of reconstruction from\nsparse views, on challenging scenes from Matterport3D. Project site:\nhttps://jinlinyi.github.io/SparsePlanes/\n"}
+{"abstract": "  Traditional approaches to ensure group fairness in algorithmic decision\nmaking aim to equalize ``total'' error rates for different subgroups in the\npopulation. In contrast, we argue that the fairness approaches should instead\nfocus only on equalizing errors arising due to model uncertainty (a.k.a\nepistemic uncertainty), caused due to lack of knowledge about the best model or\ndue to lack of data. In other words, our proposal calls for ignoring the errors\nthat occur due to uncertainty inherent in the data, i.e., aleatoric\nuncertainty. We draw a connection between predictive multiplicity and model\nuncertainty and argue that the techniques from predictive multiplicity could be\nused to identify errors made due to model uncertainty. We propose scalable\nconvex proxies to come up with classifiers that exhibit predictive multiplicity\nand empirically show that our methods are comparable in performance and up to\nfour orders of magnitude faster than the current state-of-the-art. We further\npropose methods to achieve our goal of equalizing group error rates arising due\nto model uncertainty in algorithmic decision making and demonstrate the\neffectiveness of these methods using synthetic and real-world datasets.\n"}
+{"abstract": "  We discuss unpolarized neutrino- and anti-neutrino-nucleon deep inelastic\nscattering (DIS) using a chiral doublet of baryonic sources with explicit\nsymmetry breaking, in a slice of AdS$_5$ with both a hard and soft wall. We\nexplicitly derive the direct and transition form factors for the vector and\naxial-vector currents for the holographic dual of a proton and neutron. We use\nthem to derive the s-channel structure functions for neutrino and anti-neutrino\nscattering on a proton and neutron in bulk. The t-channel contributions\nstemming from the Pomeron and Reggeon exchanges are also evaluated explicitly.\nThe pertinent even and odd structure functions in the limit of large and small\nparton momentum fraction $x$ are given. The results allow for the extraction of\nthe nonperterbative parton distribution functions carried by the sea and\nvalence quarks both at large-x and small-x regimes. Our holographic PDF sets\ncompare well with LHAPDF and CTEQ PDF sets in the large-x and small-x regimes\nin the intermediate range of $Q^2<10~\\rm{GeV^2}$.\n"}
+{"abstract": "  Emotional expressions are the behaviors that communicate our emotional state\nor attitude to others. They are expressed through verbal and non-verbal\ncommunication. Complex human behavior can be understood by studying physical\nfeatures from multiple modalities; mainly facial, vocal and physical gestures.\nRecently, spontaneous multi-modal emotion recognition has been extensively\nstudied for human behavior analysis. In this paper, we propose a new deep\nlearning-based approach for audio-visual emotion recognition. Our approach\nleverages recent advances in deep learning like knowledge distillation and\nhigh-performing deep architectures. The deep feature representations of the\naudio and visual modalities are fused based on a model-level fusion strategy. A\nrecurrent neural network is then used to capture the temporal dynamics. Our\nproposed approach substantially outperforms state-of-the-art approaches in\npredicting valence on the RECOLA dataset. Moreover, our proposed visual facial\nexpression feature extraction network outperforms state-of-the-art results on\nthe AffectNet and Google Facial Expression Comparison datasets.\n"}
+{"abstract": "  Economic Policy Uncertainty (EPU) is a critical indicator in economic\nstudies, while it can be used to forecast a recession. Under higher levels of\nuncertainty, firms' owners cut their investment, which leads to a longer\npost-recession recovery. EPU index is computed by counting news articles\ncontaining pre-defined keywords related to policy-making and economy and convey\nuncertainty. Unfortunately, this method is sensitive to the original keyword\nset, its richness, and the news coverage. Thus, reproducing its results for\ndifferent countries is challenging. In this paper, we propose an unsupervised\ntext mining method that uses word-embedding representation space to select\nrelevant keywords. This method is not strictly sensitive to the semantic\nsimilarity threshold applied to the word embedding vectors and does not require\na pre-defined dictionary. Our experiments using a massive repository of Persian\nnews show that the EPU series computed by the proposed method precisely follows\nmajor events affecting Iran's economy and is compatible with the World\nUncertainty Index (WUI) of Iran.\n"}
+{"abstract": "  During the past few years, mostly as a result of the GDPR and the CCPA,\nwebsites have started to present users with cookie consent banners. These\nbanners are web forms where the users can state their preference and declare\nwhich cookies they would like to accept, if such option exists. Although\nrequesting consent before storing any identifiable information is a good start\ntowards respecting the user privacy, yet previous research has shown that\nwebsites do not always respect user choices. Furthermore, considering the ever\ndecreasing reliance of trackers on cookies and actions browser vendors take by\nblocking or restricting third-party cookies, we anticipate a world where\nstateless tracking emerges, either because trackers or websites do not use\ncookies, or because users simply refuse to accept any.\n  In this paper, we explore whether websites use more persistent and\nsophisticated forms of tracking in order to track users who said they do not\nwant cookies. Such forms of tracking include first-party ID leaking, ID\nsynchronization, and browser fingerprinting. Our results suggest that websites\ndo use such modern forms of tracking even before users had the opportunity to\nregister their choice with respect to cookies. To add insult to injury, when\nusers choose to raise their voice and reject all cookies, user tracking only\nintensifies. As a result, users' choices play very little role with respect to\ntracking: we measured that more than 75% of tracking activities happened before\nusers had the opportunity to make a selection in the cookie consent banner, or\nwhen users chose to reject all cookies.\n"}
+{"abstract": "  Free-space optics (FSO) offer promising solutions in wireless systems where\nmany different devices need to communicate and interoperate, exchanging massive\namounts of data as in communication and sensor networks, and in technologies\nfor positioning and ranging. In these applications, control and manipulation of\nthe light field is required for properly generating and correctly receiving FSO\nbeams even in the presence of unpredictable objects and turbulence in the light\npath. In this work, we use a programmable mesh of Mach-Zehnder Interferometers\n(MZIs) to automatically control the complex field radiated and captured by an\narray of optical antennas. Several functionalities are demonstrated, including\nthe generation of perfectly shaped beams with non-perfect optical antennas, the\nimaging of a desired field pattern through an obstacle or a diffusive medium,\nand the identification of an unknown obstacle inserted in the FSO path.\nCompared to conventional devices used for the manipulation of FSO beams, such\nas spatial light modulators (SLMs), our programmable circuit can self-configure\nthrough automated control strategies and can be integrated with other\nfunctionalities implemented onto the same photonic chip.\n"}
+{"abstract": "  Recently, there has been discussions on the ill-posed nature of\nsuper-resolution that multiple possible reconstructions exist for a given\nlow-resolution image. Using normalizing flows, SRflow[23] achieves\nstate-of-the-art perceptual quality by learning the distribution of the output\ninstead of a deterministic output to one estimate. In this paper, we adapt the\nconcepts of SRFlow to improve GAN-based super-resolution by properly\nimplementing the one-to-many property. We modify the generator to estimate a\ndistribution as a mapping from random noise. We improve the content loss that\nhampers the perceptual training objectives. We also propose additional training\ntechniques to further enhance the perceptual quality of generated images. Using\nour proposed methods, we were able to improve the performance of ESRGAN[1] in\nx4 perceptual SR and achieve the state-of-the-art LPIPS score in x16 perceptual\nextreme SR by applying our methods to RFB-ESRGAN[21].\n"}
+{"abstract": "  Deep learning has shown its power in many applications, including object\ndetection in images, natural-language understanding, and speech recognition. To\nmake it more accessible to end users, many deep learning models are now\nembedded in mobile apps. Compared to offloading deep learning from smartphones\nto the cloud, performing machine learning on-device can help improve latency,\nconnectivity, and power consumption. However, most deep learning models within\nAndroid apps can easily be obtained via mature reverse engineering, while the\nmodels' exposure may invite adversarial attacks. In this study, we propose a\nsimple but effective approach to hacking deep learning models using adversarial\nattacks by identifying highly similar pre-trained models from TensorFlow Hub.\nAll 10 real-world Android apps in the experiment are successfully attacked by\nour approach. Apart from the feasibility of the model attack, we also carry out\nan empirical study that investigates the characteristics of deep learning\nmodels used by hundreds of Android apps on Google Play. The results show that\nmany of them are similar to each other and widely use fine-tuning techniques to\npre-trained models on the Internet.\n"}
+{"abstract": "  Sound event detection (SED) is the task of tagging the absence or presence of\naudio events and their corresponding interval within a given audio clip. While\nSED can be done using supervised machine learning, where training data is fully\nlabeled with access to per event timestamps and duration, our work focuses on\nweakly-supervised sound event detection (WSSED), where prior knowledge about an\nevent's duration is unavailable. Recent research within the field focuses on\nimproving segment- and event-level localization performance for specific\ndatasets regarding specific evaluation metrics. Specifically, well-performing\nevent-level localization requires fully labeled development subsets to obtain\nevent duration estimates, which significantly benefits localization\nperformance. Moreover, well-performing segment-level localization models output\npredictions at a coarse-scale (e.g., 1 second), hindering their deployment on\ndatasets containing very short events (< 1 second). This work proposes a\nduration robust CRNN (CDur) framework, which aims to achieve competitive\nperformance in terms of segment- and event-level localization. This paper\nproposes a new post-processing strategy named \"Triple Threshold\" and\ninvestigates two data augmentation methods along with a label smoothing method\nwithin the scope of WSSED. Evaluation of our model is done on the DCASE2017 and\n2018 Task 4 datasets, and URBAN-SED. Our model outperforms other approaches on\nthe DCASE2018 and URBAN-SED datasets without requiring prior duration\nknowledge. In particular, our model is capable of similar performance to\nstrongly-labeled supervised models on the URBAN-SED dataset. Lastly, ablation\nexperiments to reveal that without post-processing, our model's localization\nperformance drop is significantly lower compared with other approaches.\n"}
+{"abstract": "  The nth r-extended Lah-Bell number is defined as the number of ways a set\nwith $n+r$ elements can be partitioned into ordered blocks such that r\ndistinguished elements have to be in distinct ordered blocks. The aim of this\npaper is to introduce incomplete r-extended Lah-Bell polynomials and complete\n$r$-extended Lah-Bell polynomials respectively as multivariate versions of\n$r$-Lah numbers and the r-extended Lah-Bell numbers and to investigate some\nproperties and identities for these polynomials. From these investigations, we\nobtain some expressions for the r-Lah numbers and the r-extended Lah-Bell\nnumbers as finite sums.\n"}
+{"abstract": "  Conformational transitions of flexible molecules, especially those driven by\nhydrophobic effects, tend to be hindered by desolvation barriers. For such\ntransitions, it is thus important to characterize and understand the interplay\nbetween solvation and conformation. Using specialized molecular simulations,\nhere we perform such a characterization for a hydrophobic polymer solvated in\nwater. We find that an external potential, which unfavorably perturbs the\npolymer hydration waters, can trigger a coil-to-globule or collapse transition,\nand that the relative stabilities of the collapsed and extended states can be\nquantified by the strength of the requisite potential. Our results also provide\nmechanistic insights into the collapse transition, highlighting that polymer\ncollapse proceeds through the formation of a sufficiently large non-polar\ncluster, and that collective water density fluctuations play an important role\nin stabilizing such a cluster. We also study the collapse of the hydrophobic\npolymer in octane, a non-polar solvent, and interestingly, we find that the\nmechanistic details of the transition are qualitatively similar to that in\nwater.\n"}
+{"abstract": "  Motivated by the recent muon anomalous magnetic moment (g-2) measurement at\nFERMILAB and non-zero neutrino masses, we propose a model based on the $SU(3)_C\n\\times SU(3)_L \\times U(1)_X$ (3-3-1) gauge symmetry. The most popular 3-3-1\nmodels in the literature require the presence of a scalar sextet to address\nneutrino masses. In our work, we show that we can successfully implement an\none-loop linear seesaw mechanism with right-handed neutrinos, and vector-like\nfermions to nicely explain the active neutrino masses, and additionally\nreproduce the recent Muon g-2 result, in agreement with existing bounds.\n"}
+{"abstract": "  Context. In this paper we present ultra deep images of the compact group of\ngalaxies HCG 86 as part of the VEGAS survey. Aims. Our main goals are to\nestimate the amount of intragroup light (IGL), to study the light and color\ndistributions in order to address the main formation process of the IGL\ncomponent in groups of galaxies. Methods. We derived the azimuthally averaged\nsurface brightness profiles in the g,r and i bands with g - r and r - i average\ncolors and color profiles for all group members. By fitting the light\ndistribution, we have extrapolated the contribution of the stellar halos plus\nthe diffuse light from the brightest component of each galaxy. The results are\ncompared with theoretical predictions. Results. The long integration time and\nwide area covered make our data deeper than previous literature studies of the\nIGL in compact groups of galaxies and allow us to produce an extended (~160\nkpc) map of the IGL, down to a surface brightness level of about 30\nmag/arcsec^2 in the g band. The IGL in HCG 86 is mainly in diffuse form and has\naverage colors of g - r ~ 0.8 mag and r - i ~ 0.4 mag. The fraction of IGL in\nHCG 86 is ~ 16% of the total luminosity of the group, and this is consistent\nwith estimates available for other compact groups and loose groups of galaxies\nof similar virial masses. A weak trend is present between the amount of IGL and\nthe early-type to late-type galaxy ratio. Conclusions. By comparing the IGL\nfraction and colors with those predicted by simulations, the amount of IGL in\nHCG 86 would be the result of the disruption of satellites at an epoch of z ~\n0.4. At this redshift, observed colors are consistent with the scenario where\nthe main contribution to the mass of the IGL comes from the\nintermediate-massive galaxies.\n"}
+{"abstract": "  We introduce the novel phenomena of CP-violating inflation in the frameworks\nof a 3-Higgs doublet model where the inflaton doublets have a non-minimal\ncoupling to gravity. We allow for this coupling to be complex, thereby\nintroducing CP-violation - a necessary source of the baryon asymmetry - in the\ninflaton couplings. We investigate the inflationary dynamics of such a\nframework and the inflaton decay in the reheating phase. We discuss how the\nCP-violation of the model is imprinted on the particle asymmetries.\n"}
+{"abstract": "  The bulk photovoltaic effect (BPVE) has attracted an increasing interest due\nto its potential to overcome the efficiency limit of traditional photovoltaics,\nand much effort has been devoted to understanding its underlying physics.\nHowever, previous work has shown that theoretical models of the shift current\nand the phonon-assisted ballistic current in real materials do not fully\naccount for the experimental BPVE photocurrent, and so other mechanisms should\nbe investigated in order to obtain a complete picture of BPVE. In this Letter,\nwe demonstrate two approaches that enable the ab initio calculation of the\nballistic current originating from the electron-hole interaction in\nsemiconductors. Using BaTiO$_3$ and MoS$_2$ as two examples, we show clearly\nthat for them the asymmetric scattering from electron-hole interaction is less\nappreciable than that from electron-phonon interaction, indicating more\nscattering processes need to be included to further improve the BPVE theory.\nMoreover, our approaches build up a venue for predicting and designing\nmaterials with larger ballistic current due to electron-hole interactions.\n"}
+{"abstract": "  Background: Testing IoT software is challenging due to large scale, volume of\ndata and heterogeneity. Testing automation is a much-needed feature in the\ndomain. Aims: The first goal of this research is to explore the requirements\nand challenges of IoT testing automation. The second goal is to integrate\ntesting automation tools used in commercial software into the IoT context.\nMethod: A systematic literature review is carried out to elicit requirements\nfor testing automation in IoT. A design science approach is followed to build a\ntesting automation tool for IoT applications written in the Node-Red platform,\nusing the commercial testing automation tool Selenium. The resulting framework\nuses the Selenium Web Driver for browser-based testing automation for IoT\napplications. Results: The proposed framework has been functionally tested on\nmultiple browsers with preliminary evaluation on maintainability, browser\ncapability and comprehensiveness. Conclusions: The use of commercial tools for\ntesting automation in IoT is feasible. However, major challenges like high data\nvolumes and parallel transmission and processing of data need to be addressed\ncomprehensively for complete integration.\n"}
+{"abstract": "  Shared autonomy teleoperation can guarantee safety, but does so by reducing\nthe human operator's control authority, which can lead to reduced levels of\nhuman-robot agreement and user satisfaction. This paper presents a novel haptic\nshared autonomy teleoperation paradigm that uses haptic feedback to inform the\nuser about the inner state of a shared autonomy paradigm, while still\nguaranteeing safety. This differs from haptic shared control, which uses haptic\nfeedback to inform the user's actions, but gives the human operator full\ncontrol over the robot's actions. We conducted a user study in which twelve\nusers flew a simulated UAV in a search-and-rescue task with no assistance or\nassistance provided by haptic shared control, shared autonomy, or haptic shared\nautonomy. All assistive teleoperation methods use control barrier functions to\nfind a control command that is both safe and as close as possible to the\nhuman-generated control command. For assistive teleoperation conditions with\nhaptic feedback, we apply a force to the user that is proportional to the\ndifference between the human-generated control and the safe control. We find\nthat haptic shared autonomy improves the user's task performance and\nsatisfaction. We also find that haptic feedback in assistive teleoperation can\nimprove the user's situational awareness. Finally, results show that adding\nhaptic feedback to shared-autonomy teleoperation can improve human-robot\nagreement.\n"}
+{"abstract": "  We consider the problem of controlling the spread of harmful items in\nnetworks, such as the contagion proliferation of diseases or the diffusion of\nfake news. We assume the linear threshold model of diffusion where each node\nhas a threshold that measures the node resistance to the contagion. We study\nthe parameterized complexity of the problem: Given a network, a set of\ninitially contaminated nodes, and two integers $k$ and $\\ell$, is it possible\nto limit the diffusion to at most $k$ other nodes of the network by immunizing\nat most $\\ell$ nodes? We consider several parameters associated to the input,\nincluding: the bounds $k$ and $\\ell$, the maximum node degree $\\Delta$, the\ntreewidth, and the neighborhood diversity of the network. We first give $W[1]$\nor $W[2]$-hardness results for each of the considered parameters. Then we give\nfixed-parameter algorithms for some parameter combinations.\n"}
+{"abstract": "  We characterize the common range of the adjoints of cyclic multiplication\noperators on the Drury--Arveson space. We show that a function belongs to this\ncommon range if and only if its Taylor coefficients satisfy a simple decay\ncondition. To achieve this, we introduce the uniform Smirnov class on the ball\nand determine its dual space. We show that the dual space of the uniform\nSmirnov class equals the dual space of the strictly smaller Smirnov class of\nthe Drury-Arveson space, and that this in turn equals the common range of the\nadjoints of cyclic multiplication operators.\n"}
+{"abstract": "  The voyage of the \"First Fleet\" from Britain to the new colony of New South\nWales was not only a military enterprise, it also had a distinct scientific\npurpose. Britain's fifth Astronomer Royal, Nevil Maskelyne, had selected\nWilliam Dawes, a promising young Marine with a propensity for astronomical\nobservations, as his prot\\'eg\\'e. Maskelyne convinced the British Board of\nLongitude to supply Dawes with a suite of state-of-the-art instruments and\nallow the young Marine to establish an observatory in the new settlement. The\nAstronomer Royal may have had a dual motivation, one driven by strategic\nnational interests combined with a personal investment linked to the suggested\nre-appearance of a comet in the southern sky. With the unexpected assistance of\nthe French Lap\\'erouse expedition, between 1788 and 1791 Dawes established not\none but two observatories within a kilometre of Sydney's present-day city\ncentre. Motivated by persisting confusion in the literature, we explore the\nhistorical record to narrow down the precise location of Dawes' observatory. We\nconclude that the memorial plaque attached to Sydney Harbour Bridge indicates\nan incorrect location. Overwhelming contemporary evidence -- maps, charts and\npictorial representations -- implies that Dawes' observatory was located on the\nnortheastern tip of the promontory presently known as The Rocks (formerly\nDawes' Point), with any remains having vanished during the construction of the\nHarbour Bridge.\n"}
+{"abstract": "  Nuclear Magnetic Ressonance (NMR) is a widely used technique, with a long\nhistory of applications in chemestry, medicine, and material science. Twenty\nyears ago, it emerged as a reliable source for quantum computing too, since the\nwork of Cory. One of its major advantage is the ease with which arbitrary\nunitary transformations can be implemented, together with its experimental\nsimplicity, that can be traced back to very simple NMR routines, which were\nbeing extensively used long before. However, some disadvantages came along,\nmostly related to experimental effort in the initialisation and measure\nprocesses, and scalability. In this work, we have successfully probed some\nsimple quantum gates (Pauli-Z, Pauli-X and Hadamard) in one and two-qubit\nsystems, realised in a NMR experiment. The work comprised a pseudo-pure state\npreparation, followed by the application of the gates, and a quantum tomography\nmethod, necessary to reconstruct the density matrix. The experiments were\nconducted with a chloroform sample, placed in a 300 MHz Bruker Avance II\nspectrometer, equipped with a superconducting magnet with a 7 T magnetic field.\n"}
+{"abstract": "  Data privacy has become an increasingly important issue in Machine Learning\n(ML), where many approaches have been developed to tackle this challenge, e.g.\ncryptography (Homomorphic Encryption (HE), Differential Privacy (DP), etc.) and\ncollaborative training (Secure Multi-Party Computation (MPC), Distributed\nLearning and Federated Learning (FL)). These techniques have a particular focus\non data encryption or secure local computation. They transfer the intermediate\ninformation to the third party to compute the final result. Gradient exchanging\nis commonly considered to be a secure way of training a robust model\ncollaboratively in Deep Learning (DL). However, recent researches have\ndemonstrated that sensitive information can be recovered from the shared\ngradient. Generative Adversarial Network (GAN), in particular, has shown to be\neffective in recovering such information. However, GAN based techniques require\nadditional information, such as class labels which are generally unavailable\nfor privacy-preserved learning. In this paper, we show that, in the FL system,\nimage-based privacy data can be easily recovered in full from the shared\ngradient only via our proposed Generative Regression Neural Network (GRNN). We\nformulate the attack to be a regression problem and optimize two branches of\nthe generative model by minimizing the distance between gradients. We evaluate\nour method on several image classification tasks. The results illustrate that\nour proposed GRNN outperforms state-of-the-art methods with better stability,\nstronger robustness, and higher accuracy. It also has no convergence\nrequirement to the global FL model. Moreover, we demonstrate information\nleakage using face re-identification. Some defense strategies are also\ndiscussed in this work.\n"}
+{"abstract": "  We study the dimension theory of limit sets of iterated function systems\nconsisting of a countably infinite number of contractions. Our primary focus is\non the intermediate dimensions: a family of dimensions depending on a parameter\n$\\theta \\in [0,1]$ which interpolate between the Hausdorff and box dimensions.\nOur main results are in the case when all the contractions are conformal. Under\na natural separation condition we prove that the intermediate dimensions of the\nlimit set are the maximum of the Hausdorff dimension of the limit set and the\nintermediate dimensions of the set of fixed points of the contractions. This\nbuilds on work of Mauldin and Urba\\'nski concerning the Hausdorff and upper box\ndimension. We give several (often counter-intuitive) applications of our work\nto dimensions of projections, fractional Brownian images, and general H\\\"older\nimages. These applications apply to well-studied examples such as sets of\nnumbers which have real or complex continued fraction expansions with\nrestricted entries.\n  We also obtain several results without assuming conformality or any\nseparation conditions. We prove general upper bounds for the Hausdorff, box and\nintermediate dimensions of infinitely generated attractors in terms of a\ntopological pressure function. We also show that the limit set of a 'generic'\ninfinite iterated function system has box and intermediate dimensions equal to\nthe ambient spatial dimension, where 'generic' can refer to any one of (i) full\nmeasure; (ii) prevalent; or (iii) comeagre.\n"}
+{"abstract": "  Non-trivial spin structures in itinerant magnets can give rise to topological\nHall effect (THE) due to the interacting local magnetic moments and conductive\nelectrons. While, in series of materials, THE has mostly been observed at low\ntemperatures far below room temperature (RT) limiting its potential\napplications. Here, we report the anisotropic anomalous Hall effect (AHE) near\nRT in LaMn2Ge2, a noncollinear ferromagnetic (FM) with Curie temperature TC=325\nK. Large topological Hall resistivity of ~1.0 10-6 ohmcm in broad temperature\nrange (190 K <T< 300 K) is realized as field (H) parallel to the ab-plane (H //\nab) and current along c axis (I // c), in contrast to the conventional AHE for\nH // c and I // ab. The emergence of THE is attributed to the spin chirality of\nnoncoplanar spin configurations stabilized by thermal fluctuation during spin\nflop process. Moreover, the constructed temperature-field (H-T) phase diagrams\nbased on the isothermal topological Hall resistivity reveal a field-induced\ntransition from the noncoplanar spin configuration to polarized ferromagnetic\nstates. Our experimental realization of large THE near RT highlights LaMn2Ge2\nas a promising system for functional applications in novel spintronic devices.\n"}
+{"abstract": "  We revisit the calculation of the fermion self-energy in QED in the presence\nof a magnetic field. We show that, after carrying out the renormalization\nprocedure and identifying the most general perturbative tensor structure for\nthe modified fermion {mass operator} in the large field limit, the mass\ndevelops an imaginary part. This happens when account is made of the\nsub-leading contributions associated to Landau levels other than the lowest\none. The imaginary part is associated to a spectral density describing the\nspread of the mass function in momentum. The center of the distribution\ncorresponds to the magnetic-field modified mass. The width becomes small as the\nfield intensity increases in such a way that for asymptotically large values of\nthe field, when the separation between Landau levels becomes also large, the\nmass function describes a stable particle occupying only the lowest Landau\nlevel. For large but finite values of the magnetic field, the spectral density\nrepresents a finite probability for the fermion to occupy Landau levels other\nthan the LLL.\n"}
+{"abstract": "  This paper revisits the discussion on determinants of budget balances and\ninvestigates the change in their effect in light of the COVID-19 crisis by\nutilizing data on 43 countries and a system generalized method of moments\napproach. The results show that the overall impact of the global pandemic led\nto a disproportionate increase in the magnitude of the estimated effects of the\nmacroeconomic determinants on the budget balance. However, we also find that\nmore developed economies were able to undertake higher stimulus packages for\nthe relatively same level of primary balance. We believe that one of the\nfactors affecting this outcome is that that they exhibit a higher government\ndebt position in domestic currency denomination.\n"}
+{"abstract": "  By applying the formula for essential Whittaker functions established by\nMatringe and Miyauchi, we study five integral representations for irreducible\nadmissible generic representations of ${\\rm GL}_n$ over $p$-adic fields. In\neach case, we show that the integrals achieve local formal $L$-functions\ndefined by Langlands parameters, when the test vector is associated to the\nnewform. We give the relation between local periods involving essential\nWhittaker functions and special values of formal $L$-factors at $s=1$ for\ncertain distinguished or unitary representations. The period integrals are also\nserved as standard non-zero distinguished forms.\n"}
+{"abstract": "  Proportional ranking rules aggregate approval-style preferences of agents\ninto a collective ranking such that groups of agents with similar preferences\nare adequately represented. Motivated by the application of live Q&A platforms,\nwhere submitted questions need to be ranked based on the interests of the\naudience, we study a dynamic extension of the proportional rankings setting. In\nour setting, the goal is to maintain the proportionality of a ranking when\nalternatives (i.e., questions) -- not necessarily from the top of the ranking\n-- get selected sequentially. We propose generalizations of well-known\naggregation rules to this setting and study their monotonicity and\nproportionality properties. We also evaluate the performance of these rules\nexperimentally, using realistic probabilistic assumptions on the selection\nprocedure.\n"}
+{"abstract": "  We introduce and evaluate the degree of convexity of an unit ball, so-called,\ncharacteristic of convexity (COC) for the Grand Lebesgue Spaces, (GLS), which\nis a slight analog of the classical notion of the modulus of convexity (MOC).\n"}
+{"abstract": "  This paper proposes an algorithmic method to heuristically solve the famous\nTravelling Salesman Problem (TSP) when the salesman's path evolves in\ncontinuous state space and discrete time but with otherwise arbitrary\n(nonlinear) dynamics. The presented method is based on the framework of\nSymbolic Control. In this way, our method returns a provably correct\nstate-feedback controller for the underlying coverage specification, which is\nthe TSP leaving out the requirement for optimality on the route. In addition,\nwe utilize the Lin-Kernighan-Helsgaun TSP solver to heuristically optimize the\ncost for the overall taken route. Two examples, an urban parcel delivery task\nand a UAV reconnaissance mission, greatly illustrate the powerfulness of the\nproposed heuristic.\n"}
+{"abstract": "  In this paper we provide the basic setup for a project, initiated by Felix\nRehren, aiming at classifying all $2$-generated primitive axial agebras of\nMonster type $(\\alpha, \\beta)$. We first revise Rehren's construction of an\ninitial object in the cathegory of primitive $n$-generated axial algebras\ngiving a formal one, filling some gaps and, though correcting some\ninaccuracies, confirm Rehren's results. Then we focus on $2$-generated algebras\nwhich naturally part into three cases: the two critical cases $\\alpha=2\\beta$\nand $\\alpha=4\\beta$, and the generic case (i.e. all the rest). About these\ncases, which will be dealt in detail in subsequent papers, we give bounds on\nthe dimensions (the generic case already treated by Rehen) and classify all\n2-generated primitive axial algebras of Monster type $(\\alpha, \\beta)$ over\n$\\mathbb Q(\\alpha, \\beta)$ for $\\alpha$ and $\\beta$ algebraically independent\nindeterminates over $\\mathbb Q$. Finally we restrict to the $2$-generated\nMajorana algebras (i.e. when $\\alpha=\\frac{1}{4}$ and $\\beta=\\frac{1}{32}$),\nshowing that these fall precisely into the nine isomorphism types of the\nNorton-Sakuma algebras.\n"}
+{"abstract": "  We present an ensemble X-ray analysis of systematic perturbations in the\ncentral hot gas properties for a sample of 28 nearby strong cool-core systems\nselected from the HIghest X-ray FLUx Galaxy Cluster Sample (HIFLUGCS). We\nanalyze their cool-core features observed with the Chandra X-ray Observatory.\nAll individual systems in our sample exhibit at least a pair of positive and\nnegative excess perturbations in the X-ray residual image after subtracting the\nglobal brightness profile. We extract and analyze X-ray spectra of the\nintracluster medium (ICM) in the detected perturbed regions. To investigate\npossible origins of the gas perturbations, we characterize thermodynamic\nproperties of the ICM in the perturbed regions and characterize their\ncorrelations between positive and negative excess regions. The best-fit\nrelations for temperature and entropy show a clear offset from the one-to-one\nrelation, $T_\\mathrm{neg}/T_\\mathrm{pos}=1.20^{+0.04}_{-0.03}$ and\n$K_\\mathrm{neg}/K_\\mathrm{pos}=1.43\\pm 0.07$, whereas the best-fit relation for\npressure is found to be remarkably consistent with the one-to-one relation\n$P_\\mathrm{neg}=P_\\mathrm{pos}$, indicating that the ICM in the perturbed\nregions is in pressure equilibrium. These observed features in the HIFLUGCS\nsample are in agreement with the hypothesis that the gas perturbations in cool\ncores are generated by gas sloshing. We also analyze synthetic observations of\nperturbed cluster cores created from binary merger simulations, finding that\nthe observed temperature ratio agrees with the simulations,\n$T_\\mathrm{neg}/T_\\mathrm{pos}\\sim 1.3$. We conclude that gas sloshing induced\nby infalling substructures plays a major role in producing the characteristic\ngas perturbations in cool cores. The ubiquitous presence of gas perturbations\nin cool cores may suggest a significant contribution of gas sloshing to\nsuppressing runaway cooling of the ICM.\n"}
+{"abstract": "  The past two decades have witnessed the great success of the algorithmic\nmodeling framework advocated by Breiman et al. (2001). Nevertheless, the\nexcellent prediction performance of these black-box models rely heavily on the\navailability of strong supervision, i.e. a large set of accurate and exact\nground-truth labels. In practice, strong supervision can be unavailable or\nexpensive, which calls for modeling techniques under weak supervision. In this\ncomment, we summarize the key concepts in weakly supervised learning and\ndiscuss some recent developments in the field. Using algorithmic modeling alone\nunder a weak supervision might lead to unstable and misleading results. A\npromising direction would be integrating the data modeling culture into such a\nframework.\n"}
+{"abstract": "  We introduce a notion of scalar curvature of a twisted generalized Kahler\nmanifold in terms of pure spinors formalism. A moment map framework with a\nmodified action of generalized Hamiltonians on an arbitrary compact generalized\nKahler manifold is developed. Then it turns out that a moment map is given by\nthe scalar curvature, which is a generalization of the result of the scalar\ncurvature as a moment map in the ordinary Kahler geometry, due to Fujiki and\nDonaldson.\n  A noncommutative compact Lie group $G$ does not have any Kahler structure.\nHowever, we show that every compact Lie group admits generalized Kahler\nstructures with constant scalar curvature. In particular, generalized Kahler\nstructures with constant scalar curvature on the standard Hopf surface are\nexplicitly given.\n"}
+{"abstract": "  Lithium-salt-based deep eutectic solvents, where the only cation is Li+, are\npromising candidates as electrolytes in electrochemical energy-storage devices\nlike batteries. We have performed broadband dielectric spectroscopy on three\nsuch systems, covering a broad temperature and dynamic range that extends from\nthe low-viscosity liquid around room temperature down to the glassy state\napproaching the glass-transition temperature. We detect a relaxational process\nthat can be ascribed to dipolar reorientational dynamics and exhibits the clear\nsignatures of glassy freezing. We find that the temperature dependence of the\nionic dc conductivity and its room-temperature value also are governed by the\nglassy dynamics of these systems, depending, e.g., on the glass-transition\ntemperature and fragility. Compared to the previously investigated\ncorresponding systems, containing choline chloride instead of a lithium salt,\nboth the reorientational and ionic dynamics are significantly reduced due to\nvariations of the glass-transition temperature and the higher ionic potential\nof the lithium ions. These lithium-based deep eutectic solvents partly exhibit\nsignificant decoupling of the dipolar reorientational and the ionic\ntranslational dynamics and approximately follow a fractional\nDebye-Stokes-Einstein relation, leading to an enhancement of the dc\nconductivity, especially at low temperatures. The presented results clearly\nreveal the importance of decoupling effects and of the typical glass-forming\nproperties of these systems for the technically relevant room-temperature\nconductivity.\n"}
+{"abstract": "  We give a new proof of a partition theorem popularly known as Elder's\ntheorem, but which is also credited to Stanley and Fine. We extend the theorem\nto the context of colored partitions (or prefabs). More specifically, we give\nanalogous results for $b$-colored partitions, where each part occurs in $b$\ncolors; for $b$-colored partitions with odd parts (or distinct parts); for\npartitions where the part $k$ comes in $k$ colors; and, overpartitions.\n"}
+{"abstract": "  Optical microresonators are of paramount importance in photonic circuits\nrequiring fine spectral filtering or resonant light recirculation. Key\nperformance metrics improve with increasing resonance quality factor (Q) across\nall applications. The performance of silicon photonic circuits is often\nhampered by the low-quality factor of planar silicon microresonators, typically\nof Q~10^4-10^5. On the other hand, bulk whispering gallery mode resonators\nprovide a wide range of materials with intriguing optical properties and\nexceptionally high resonant quality factors Q>10^7. However, the efficient\ncoupling between bulk resonators and planar Si photonic waveguides is\nconsidered challenging, if not impossible, due to remarkably large mismatch in\nsize and refractive index. Here, we show an efficient method to couple bulk\nresonators and Si waveguides based on subwavelength metamaterial engineering of\nsilicon. Based on this approach, we experimentally demonstrate coupling between\n220-nm-thick Si waveguides and bulk microresonators made of silica, lithium\nniobate and calcium fluoride with diameters in the 0.3-3.5 mm range, achieving\nhigh coupling efficiency of 75-99% and exceptional Q of 10^6-10^7. These\nresults open a new route for the heterogeneous integration of bulk resonators\nand silicon photonic circuits, with great potential for applications in\nsensing, microwave-photonics, and quantum photonics, to name a few.\n"}
+{"abstract": "  The emergence of CNNs in mainstream deployment has necessitated methods to\ndesign and train efficient architectures tailored to maximize the accuracy\nunder diverse hardware & latency constraints. To scale these resource-intensive\ntasks with an increasing number of deployment targets, Once-For-All (OFA)\nproposed an approach to jointly train several models at once with a constant\ntraining cost. However, this cost remains as high as 40-50 GPU days and also\nsuffers from a combinatorial explosion of sub-optimal model configurations. We\nseek to reduce this search space -- and hence the training budget -- by\nconstraining search to models close to the accuracy-latency Pareto frontier. We\nincorporate insights of compound relationships between model dimensions to\nbuild CompOFA, a design space smaller by several orders of magnitude. Through\nexperiments on ImageNet, we demonstrate that even with simple heuristics we can\nachieve a 2x reduction in training time and 216x speedup in model\nsearch/extraction time compared to the state of the art, without loss of Pareto\noptimality! We also show that this smaller design space is dense enough to\nsupport equally accurate models for a similar diversity of hardware and latency\ntargets, while also reducing the complexity of the training and subsequent\nextraction algorithms.\n"}
+{"abstract": "  We demonstrate the use of a single trapped ion as a sensor to probe\nelectric-field noise from interchangeable test surfaces. As proof of principle,\nwe measure the magnitude and distance dependence of electric-field noise from\ntwo ion-trap-like samples with patterned Au electrodes. This trapped-ion sensor\ncould be combined with other surface characterization tools to help elucidate\nthe mechanisms that give rise to electric-field noise from ion-trap surfaces.\nSuch noise presents a significant hurdle for performing large-scale trapped-ion\nquantum computations.\n"}
+{"abstract": "  In several examples it has been observed that a module category of a vertex\noperator algebra (VOA) is equivalent to a category of representations of some\nquantum group. The present article is concerned with developing such a duality\nin the case of the Virasoro VOA at generic central charge; arguably the most\nrudimentary of all VOAs, yet structurally complicated. We do not address the\ncategory of all modules of the generic Virasoro VOA, but we consider the\ninfinitely many modules from the first row of the Kac table. Building on an\nexplicit quantum group method of Coulomb gas integrals, we give a new proof of\nthe fusion rules, we prove the analyticity of compositions of intertwining\noperators, and we show that the conformal blocks are fully determined by the\nquantum group method. Crucially, we prove the associativity of the intertwining\noperators among the first-row modules, and find that the associativity is\ngoverned by the $6j$-symbols of the quantum group. Our results constitute a\nconcrete duality between a VOA and a quantum group, and they will serve as the\nkey tools to establish the equivalence of the first-row subcategory of modules\nof the generic Virasoro VOA and the category of (type-1) finite-dimensional\nrepresentations of $\\mathcal{U}_{q}(\\mathfrak{sl}_{2})$.\n"}
+{"abstract": "  The first evidence for X(3872) production in relativistic heavy ion\ncollisions is reported. The X(3872) production is studied in lead-lead (PbPb)\ncollisions at a center-of-mass energy of $\\sqrt{s_\\mathrm{NN}}=$ 5.02 TeV per\nnucleon pair, using the decay chain X(3872) $\\to$ J$/\\psi\\, \\pi^+\\pi^- \\to\n\\mu^+\\mu^-\\pi^+\\pi^-$. The data were recorded with the CMS detector in 2018 and\ncorrespond to an integrated luminosity of 1.7 nb$^{-1}$. The measurement is\nperformed in the rapidity and transverse momentum ranges $|y|\\lt$ 1.6 and 15\n$\\lt p_\\mathrm{T} \\lt$ 50 GeV$/c$. The significance of the inclusive X(3872)\nsignal is 4.2 standard deviations. The prompt X(3872) to $\\psi$(2S) yield ratio\nis found to be $\\rho^\\mathrm{PbPb} = $ 1.08 $\\pm$ 0.49 (stat) $\\pm$ 0.52\n(syst), to be compared with typical values of 0.1 for pp collisions. This\nresult provides a unique experimental input to theoretical models of the\nX(3872) production mechanism, and of the nature of this exotic state.\n"}
+{"abstract": "  In most websites, the online rating system provides the ratings of products\nand services to users. Lack of trust in data integrity and its manipulation has\nhindered fulfilling user satisfaction. Since existing online rating systems\ndeal with a central server, all rating data is stored on the central server.\nTherefore, all rating data can be removed, modified, and manipulated by the\nsystem manager to change the ratings in favor of the service or product\nprovider. In this paper, an online rating system using distributed ledger\ntechnologies has been presented as the proposed system to solve all the\nweaknesses of current systems. Distributed ledger technologies are completely\ndecentralized and there is no centralization on them by any institution.\nDistributed ledger technologies have different variants. Among distributed\nledger technologies, blockchain technology has been used in the proposed rating\nsystem because of its support for smart contracts. In the proposed online\nrating system, the Ethereum platform has been chosen from different blockchain\nplatforms that have a public permission network. In this system, the raters\ncannot rate unless they submit a request to the system and be authorized to\ntake part in the online product rating process. The important feature of the\nEthereum platform is its support for smart contracts, which can be used to\nwrite the rating contract in the Solidity language. Also, using Proof of\nAuthority consensus mechanisms, all rating transactions are approved by the\nsurveyors. Since in the real Ethereum system, each rating transaction is sent\nto the network by the raters, some gas must be paid for each rating\ntransaction. However, since this method is expensive, TestNet blockchain can be\nused in the rating system. Finally, the proposed rating system was used for\nrating the restaurants of a website and its features were tested.\n"}
+{"abstract": "  We report on Bayesian estimation of the radius, mass, and hot surface regions\nof the massive millisecond pulsar PSR J0740$+$6620, conditional on\npulse-profile modeling of Neutron Star Interior Composition Explorer X-ray\nTiming Instrument (NICER XTI) event data. We condition on informative pulsar\nmass, distance, and orbital inclination priors derived from the joint NANOGrav\nand CHIME/Pulsar wideband radio timing measurements of arXiv:2104.00880. We use\nXMM European Photon Imaging Camera spectroscopic event data to inform our X-ray\nlikelihood function. The prior support of the pulsar radius is truncated at 16\nkm to ensure coverage of current dense matter models. We assume conservative\npriors on instrument calibration uncertainty. We constrain the equatorial\nradius and mass of PSR J0740$+$6620 to be $12.39_{-0.98}^{+1.30}$ km and\n$2.072_{-0.066}^{+0.067}$ M$_{\\odot}$ respectively, each reported as the\nposterior credible interval bounded by the 16% and 84% quantiles, conditional\non surface hot regions that are non-overlapping spherical caps of fully-ionized\nhydrogen atmosphere with uniform effective temperature; a posteriori, the\ntemperature is $\\log_{10}(T$ [K]$)=5.99_{-0.06}^{+0.05}$ for each hot region.\nAll software for the X-ray modeling framework is open-source and all data,\nmodel, and sample information is publicly available, including analysis\nnotebooks and model modules in the Python language. Our marginal likelihood\nfunction of mass and equatorial radius is proportional to the marginal joint\nposterior density of those parameters (within the prior support) and can thus\nbe computed from the posterior samples.\n"}
+{"abstract": "  Monolayer graphene aligned with hexagonal boron nitride (hBN) develops a gap\nat the charge neutrality point (CNP). This gap has previously been extensively\nstudied by electrical transport through thermal activation measurements. Here,\nwe report the determination of the gap size at the CNP of graphene/hBN\nsuperlattice through photocurrent spectroscopy study. We demonstrate two\ndistinct measurement approaches to extract the gap size. A maximum of ~ 14 meV\ngap is observed for devices with a twist angle of less than 1 degree. This\nvalue is significantly smaller than that obtained from thermal activation\nmeasurements, yet larger than the theoretically predicted single-particle gap.\nOur results suggest that lattice relaxation and moderate electron-electron\ninteraction effects may enhance the CNP gap in hBN/graphene superlattice.\n"}
+{"abstract": "  We consider a parallel server system with so-called cancel-on-completion\nredundancy. There are $n$ servers and multiple job classes $j$. An arriving\nclass $j$ job consists of $d_j$ components, placed on a randomly selected\nsubset of servers; the job service is complete as soon as $k_j$ components out\nof $d_j$ complete their service, at which point the service of all remaining\n$d_j-k_j$ components is canceled. The system is in general non-work-conserving\n-- the average amount of new workload added to the system by an arriving class\n$j$ job depends on the system state. This poses the main challenge for the\nsystem analysis.\n  The results of this paper concern both the system with fixed number of\nservers $n$ and the mean-field asymptotic regime when $n\\to\\infty$ while each\njob class arrival rate per server remains constant. The main question we\naddress for the asymptotic regime is whether the steady-state asymptotic\nindependence of server workloads holds. We prove that this property does hold\nunder certain conditions, including the important special case when job\ncomponents of each class $j$ are i.i.d. with an increasing-hazard-rate\ndistribution.\n"}
+{"abstract": "  In this work, a hybrid integration of few-layer transition metal\ndichalcogenides (TMDCs) and ferroelectric polymer is designed to achieve\npassive control of optical properties in-situ. The electrical polarization in\nferroelectric P(VDF-TrFE) polymer can regulate the photoluminescence (PL) in\nfew-layer TMDCs. The total PL intensity is substantially suppressed or enhanced\nunder opposite polarization in bilayer WSe2. This is because electrons transfer\nbetween valley K and {\\Lambda} in the conduction band induced by the built-in\nelectric field in P(VDF-TrFE) polymer. This charge transfer further changes the\ncompeting dynamics between direct and indirect exciton recombination path and\noverall optical radiation efficiency. We also illustrate that the engineered PL\noriginates from external electric field dependent transferred electron effect.\nThe theoretical result matches the experimental data well. This work\ndemonstrates a device platform in which passive regulation is achieved using 2D\nTMDCs modulated by polarized ferroelectric materials.\n"}
+{"abstract": "  Inertial Measurement Unit (IMU) sensors are present in everyday devices such\nas smartphones and fitness watches. As a result, the array of health-related\nresearch and applications that tap onto this data has been growing, but little\nattention has been devoted to the prediction of an individual's heart rate (HR)\nfrom IMU data, when undergoing a physical activity. Would that be even\npossible? If so, this could be used to design personalized sets of aerobic\nexercises, for instance. In this work, we show that it is viable to obtain\naccurate HR predictions from IMU data using Recurrent Neural Networks, provided\nonly access to HR and IMU data from a short-lived, previously executed\nactivity. We propose a novel method for initializing an RNN's hidden state\nvectors, using a specialized network that attempts to extract an embedding of\nthe physical conditioning (PCE) of a subject. We show that using a\ndiscriminator in the training phase to help the model learn whether two PCEs\nbelong to the same individual further reduces the prediction error. We evaluate\nthe proposed model when predicting the HR of 23 subjects performing a variety\nof physical activities from IMU data available in public datasets (PAMAP2,\nPPG-DaLiA). For comparison, we use as baselines the only model specifically\nproposed for this task and an adapted state-of-the-art model for Human Activity\nRecognition (HAR), a closely related task. Our method, PCE-LSTM, yields over\n10% lower mean absolute error. We demonstrate empirically that this error\nreduction is in part due to the use of the PCE. Last, we use the two datasets\n(PPG-DaLiA, WESAD) to show that PCE-LSTM can also be successfully applied when\nphotoplethysmography (PPG) sensors are available, outperforming the\nstate-of-the-art deep learning baselines by more than 30%.\n"}
+{"abstract": "  For $X$ a smooth projective variety and $D=D_1+\\ldots+D_n$ a simple normal\ncrossings divisor, we establish a precise cycle-level correspondence between\nthe genus zero local Gromov-Witten theory of the bundle $\\oplus_{i=1}^n\n\\mathcal{O}_X(-D_i)$ and the maximal contact Gromov-Witten theory of the\nmulti-root stack $X_{D,\\vec{r}}$. The proof is an implementation of the rank\nreduction strategy. We use this point of view to clarify the relationship\nbetween logarithmic and orbifold invariants.\n"}
+{"abstract": "  The Best Estimate plus Uncertainty (BEPU) approach for nuclear systems\nmodeling and simulation requires that the prediction uncertainty must be\nquantified in order to prove that the investigated design stays within\nacceptance criteria. A rigorous Uncertainty Quantification (UQ) process should\nsimultaneously consider multiple sources of quantifiable uncertainties: (1)\nparameter uncertainty due to randomness or lack of knowledge; (2) experimental\nuncertainty due to measurement noise; (3) model uncertainty caused by\nmissing/incomplete physics and numerical approximation errors, and (4) code\nuncertainty when surrogate models are used. In this paper, we propose a\ncomprehensive framework to integrate results from inverse UQ and quantitative\nvalidation to provide robust predictions so that all these sources of\nuncertainties can be taken into consideration.\n  Inverse UQ quantifies the parameter uncertainties based on experimental data\nwhile taking into account uncertainties from model, code and measurement. In\nthe validation step, we use a quantitative validation metric based on Bayesian\nhypothesis testing. The resulting metric, called the Bayes factor, is then used\nto form weighting factors to combine the prior and posterior knowledge of the\nparameter uncertainties in a Bayesian model averaging process. In this way,\nmodel predictions will be able to integrate the results from inverse UQ and\nvalidation to account for all available sources of uncertainties. This\nframework is a step towards addressing the ANS Nuclear Grand Challenge on\n\"Simulation/Experimentation\" by bridging the gap between models and data.\n"}
+{"abstract": "  This letter studies the problem of minimizing increasing set functions, or\nequivalently, maximizing decreasing set functions, over the base of a matroid.\nThis setting has received great interest, since it generalizes several applied\nproblems including actuator and sensor placement problems in control theory,\nmulti-robot task allocation problems, video summarization, and many others. We\nstudy two greedy heuristics, namely, the forward and the reverse greedy\nalgorithms. We provide two novel performance guarantees for the approximate\nsolutions obtained by these heuristics depending on both the submodularity\nratio and the curvature.\n"}
+{"abstract": "  This article investigates why the genus two, supermoduli space of curves will\nsplit in contrast to, potentially, almost all other supermoduli spaces. We use\nthat the dimension of the odd, versal deformation space of a genus two, super\nRiemann surface is two dimensional. As a consequence, the odd versal\ndeformations can be generated by Schiffer variations at the associated points\nof a Szego kernel. This idea is present in D'Hoker and Phong's two loop,\nsuperstring amplitude calculation. We show how this idea, combined with Donagi\nand Witten's characterization of supermoduli obstructions, will result in a\nsplitting of supermoduli space in genus two.\n"}
+{"abstract": "  In tasks such as tracking, time-series data inevitably carry missing\nobservations. While traditional tracking approaches can handle missing\nobservations, recurrent neural networks (RNNs) are designed to receive input\ndata in every step. Furthermore, current solutions for RNNs, like omitting the\nmissing data or data imputation, are not sufficient to account for the\nresulting increased uncertainty. Towards this end, this paper introduces an\nRNN-based approach that provides a full temporal filtering cycle for motion\nstate estimation. The Kalman filter inspired approach, enables to deal with\nmissing observations and outliers. For providing a full temporal filtering\ncycle, a basic RNN is extended to take observations and the associated belief\nabout its accuracy into account for updating the current state. An RNN\nprediction model, which generates a parametrized distribution to capture the\npredicted states, is combined with an RNN update model, which relies on the\nprediction model output and the current observation. By providing the model\nwith masking information, binary-encoded missing events, the model can overcome\nlimitations of standard techniques for dealing with missing input values. The\nmodel abilities are demonstrated on synthetic data reflecting prototypical\npedestrian tracking scenarios.\n"}
+{"abstract": "  Biological systems perceive the world by simultaneously processing\nhigh-dimensional inputs from modalities as diverse as vision, audition, touch,\nproprioception, etc. The perception models used in deep learning on the other\nhand are designed for individual modalities, often relying on domain-specific\nassumptions such as the local grid structures exploited by virtually all\nexisting vision models. These priors introduce helpful inductive biases, but\nalso lock models to individual modalities. In this paper we introduce the\nPerceiver - a model that builds upon Transformers and hence makes few\narchitectural assumptions about the relationship between its inputs, but that\nalso scales to hundreds of thousands of inputs, like ConvNets. The model\nleverages an asymmetric attention mechanism to iteratively distill inputs into\na tight latent bottleneck, allowing it to scale to handle very large inputs. We\nshow that this architecture is competitive with or outperforms strong,\nspecialized models on classification tasks across various modalities: images,\npoint clouds, audio, video, and video+audio. The Perceiver obtains performance\ncomparable to ResNet-50 and ViT on ImageNet without 2D convolutions by directly\nattending to 50,000 pixels. It is also competitive in all modalities in\nAudioSet.\n"}
+{"abstract": "  Masked Language Models (MLMs) have shown superior performances in numerous\ndownstream NLP tasks when used as text encoders. Unfortunately, MLMs also\ndemonstrate significantly worrying levels of social biases. We show that the\npreviously proposed evaluation metrics for quantifying the social biases in\nMLMs are problematic due to following reasons: (1) prediction accuracy of the\nmasked tokens itself tend to be low in some MLMs, which raises questions\nregarding the reliability of the evaluation metrics that use the (pseudo)\nlikelihood of the predicted tokens, and (2) the correlation between the\nprediction accuracy of the mask and the performance in downstream NLP tasks is\nnot taken into consideration, and (3) high frequency words in the training data\nare masked more often, introducing noise due to this selection bias in the test\ncases. To overcome the above-mentioned disfluencies, we propose All Unmasked\nLikelihood (AUL), a bias evaluation measure that predicts all tokens in a test\ncase given the MLM embedding of the unmasked input. We find that AUL accurately\ndetects different types of biases in MLMs. We also propose AUL with attention\nweights (AULA) to evaluate tokens based on their importance in a sentence.\nHowever, unlike AUL and AULA, previously proposed bias evaluation measures for\nMLMs systematically overestimate the measured biases, and are heavily\ninfluenced by the unmasked tokens in the context.\n"}
+{"abstract": "  Energy storage technologies that can meet the unprecedented demands of a\nsustainable energy system based on intermittent energy sources require new\nbattery materials. We investigate high ionic conductors, Li$_3$ClO glasses. In\nthe present work we use a first principles method to model the amorphous\nstructure of the glass. We characterize the structure by means of radial\ndistribution functions, radial disctributions functions and coordination\nnumbers. We compare to their crystalline counterparts. The electronic structure\nof the glass is compared to that of the crystalline material. The band gap of\nthe glass appears to be slighly reduced compared to that of the crystal. We\nalso investigate the chemical stability of the glass against Li metal\nelectrode. The electrochemical stability of the glassy electrolite is evaluated\nagainst Li metal.\n"}
+{"abstract": "  Steady plasma flows have been studied almost exclusively in systems with\ncontinuous symmetry or in open domains. In the absence of continuous symmetry,\nthe lack of a conserved quantity makes the study of flows intrinsically\nchallenging. In a toroidal domain, the requirement of double-periodicity for\nphysical quantities adds to the complications. In particular, the\nmagnetohydrodynamics (MHD) model of plasma steady-state with the flow in a\nnon-symmetric toroidal domain allows the development of singularities when the\nrotational transform of the magnetic field is rational, much like the\nequilibrium MHD model. In this work, we show that steady flows can still be\nmaintained provided the rotational transform is close to rational and the\nmagnetic shear is weak. We extend the techniques developed in carrying out\nperturbation methods to all orders for static MHD equilibrium by Weitzner\n(Physics of Plasmas 21, 022515 (2014)) to MHD equilibrium with flows. We\nconstruct perturbative MHD equilibrium in a doubly-periodic domain with nearly\nparallel flows by systematically eliminating magnetic resonances order by\norder. We then utilize an additional symmetry of the flow problem, first\ndiscussed by E. Hameiri in (J. Math. Phys. \\textbf{22}, 2080 (1981) Sec. III),\nto obtain a generalized Grad-Shafranov equation for a class of non-symmetric\nthree-dimensional MHD equilibrium with flows both parallel and perpendicular to\nthe magnetic field. For this class of flows, we are able to obtain\nnon-symmetric generalizations of integrals of motion, such as Bernoulli's\nfunction and angular momentum. Finally, we obtain the generalized Hamada\nconditions, which are consistency conditions necessary to suppress singular\ncurrents in such a system when the magnetic field lines are closed. We do not\nattempt to address the question of neoclassical damping of flows.\n"}
+{"abstract": "  Reliable automation of the labor-intensive manual task of scoring animal\nsleep can facilitate the analysis of long-term sleep studies. In recent years,\ndeep-learning-based systems, which learn optimal features from the data,\nincreased scoring accuracies for the classical sleep stages of Wake, REM, and\nNon-REM. Meanwhile, it has been recognized that the statistics of transitional\nstages such as pre-REM, found between Non-REM and REM, may hold additional\ninsight into the physiology of sleep and are now under vivid investigation. We\npropose a classification system based on a simple neural network architecture\nthat scores the classical stages as well as pre-REM sleep in mice. When\nrestricted to the classical stages, the optimized network showed\nstate-of-the-art classification performance with an out-of-sample F1 score of\n0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1\nscores on pre-REM (0.5) compared to the classical stages. The result is\ncomparable to previous attempts to score transitional stages in other species\nsuch as transition sleep in rats or N1 sleep in humans. Nevertheless, we\nobserved that the sequence of predictions including pre-REM typically\ntransitioned from Non-REM to REM reflecting sleep dynamics observed by human\nscorers. Our findings provide further evidence for the difficulty of scoring\ntransitional sleep stages, likely because such stages of sleep are\nunder-represented in typical data sets or show large inter-scorer variability.\nWe further provide our source code and an online platform to run predictions\nwith our trained network.\n"}
+{"abstract": "  While large self-supervised models have rivalled the performance of their\nsupervised counterparts, small models still struggle. In this report, we\nexplore simple baselines for improving small self-supervised models via\ndistillation, called SimDis. Specifically, we present an offline-distillation\nbaseline, which establishes a new state-of-the-art, and an online-distillation\nbaseline, which achieves similar performance with minimal computational\noverhead. We hope these baselines will provide useful experience for relevant\nfuture research. Code is available at: https://github.com/JindongGu/SimDis/\n"}
+{"abstract": "  In this paper, we construct defects (domain walls) that connect different\nphases of two-dimensional gauged linear sigma models (GLSMs), as well as\ndefects that embed those phases into the GLSMs. Via their action on boundary\nconditions these defects give rise to functors between the D-brane categories,\nwhich respectively describe the transport of D-branes between different phases,\nand embed the D-brane categories of the phases into the category of D-branes of\nthe GLSMs.\n"}
+{"abstract": "  Traditional Cyber-physical Systems(CPSs) were not built with cybersecurity in\nmind. They operated on separate Operational Technology (OT) networks. As these\nsystems now become more integrated with Information Technology (IT) networks\nbased on IP, they expose vulnerabilities that can be exploited by the attackers\nthrough these IT networks. The attackers can control such systems and cause\nbehavior that jeopardizes the performance and safety measures that were\noriginally designed into the system. In this paper, we explore the approaches\nto identify threats to CPSs and ensure the quality of the created threat\nmodels. The study involves interviews with eleven security experts working in\nsecurity consultation companies, software engineering companies, an Original\nEquipment Manufacturer (OEM),and ground and areal vehicles integrators. We\nfound through these interviews that the practitioners use a combination of\nvarious threat modeling methods, approaches, and standards together when they\nperform threat modeling of given CPSs. key challenges practitioners face are:\nthey cannot transfer the threat modeling knowledge that they acquire in a\ncyber-physical domain to other domains, threat models of modified systems are\noften not updated, and the reliance on mostly peer-evaluation and quality\nchecklists to ensure the quality of threat models. The study warns about the\ndifficulty to develop secure CPSs and calls for research on developing\npractical threat modeling methods for CPSs, techniques for continuous threat\nmodeling, and techniques to ensure the quality of threat models.\n"}
+{"abstract": "  Modality is the linguistic ability to describe events with added information\nsuch as how desirable, plausible, or feasible they are. Modality is important\nfor many NLP downstream tasks such as the detection of hedging, uncertainty,\nspeculation, and more. Previous studies that address modality detection in NLP\noften restrict modal expressions to a closed syntactic class, and the modal\nsense labels are vastly different across different studies, lacking an accepted\nstandard. Furthermore, these senses are often analyzed independently of the\nevents that they modify. This work builds on the theoretical foundations of the\nGeorgetown Gradable Modal Expressions (GME) work by Rubinstein et al. (2013) to\npropose an event-based modality detection task where modal expressions can be\nwords of any syntactic class and sense labels are drawn from a comprehensive\ntaxonomy which harmonizes the modal concepts contributed by the different\nstudies. We present experiments on the GME corpus aiming to detect and classify\nfine-grained modal concepts and associate them with their modified events. We\nshow that detecting and classifying modal expressions is not only feasible, but\nalso improves the detection of modal events in their own right.\n"}
+{"abstract": "  Universal difference quotient representations are introduced for the exact\nself-sameness principles (SSP) as rules for rates of change introduced in\n[Clemence-Mkhope, D.P (2021, Preprint). The Exact Spectral Derivative\nDiscretization Finite Difference (ESDDFD) Method for Wave Models. arXiv].\nProperties are presented for the fundamental rule, a generalized derivative\nrepresentation which is shown to yield some known non-integer derivatives as\nlimit cases of such natural derivative measures; this is shown for some local\nderivatives of conformable, fractional, or fractal type and non-local\nderivatives of Caputo and Riemann-Liouville type. The SSP-inspired exact\nspectral derivative discretization finite difference method is presented for\nthe Fokker-Planck non-fractional and time-fractional equations; the resulting\ndiscrete models recover exactly some known behaviors predicted for the\nprocesses modeled, such as the Gibbs-Boltzmann distribution and the\nEinstein-Stokes-Smoluchowski relation; new ones are predicted.\n"}
+{"abstract": "  Quantum transport simulations often use explicit, yet finite, electronic\nreservoirs. These should converge to the correct continuum limit, albeit with a\ntrade-off between discretization and computational cost. Here, we study this\ninterplay for extended reservoir simulations, where relaxation maintains a bias\nor temperature drop across the system. Our analysis begins in the\nnon-interacting limit, where we parameterize different discretizations to\ncompare them on an even footing. For many-body systems, we develop a method to\nestimate the relaxation that best approximates the continuum by controlling\nvirtual transitions in Kramers' turnover for the current. While some\ndiscretizations are more efficient for calculating currents, there is little\nbenefit with regard to the overall state of the system. Any gains become\nmarginal for many-body, tensor network simulations, where the relative\nperformance of discretizations varies when sweeping other numerical controls.\nThese results indicate that a given reservoir discretization may have little\nimpact on numerical efficiency for certain computational tools. The choice of a\nrelaxation parameter, however, is crucial, and the method we develop provides a\nreliable estimate of the optimal relaxation for finite reservoirs.\n"}
+{"abstract": "  We report on an all-sky search for continuous gravitational waves in the\nfrequency band 20-2000\\,Hz and with a frequency time derivative in the range of\n$[-1.0, +0.1]\\times10^{-8}$\\,Hz/s. Such a signal could be produced by a nearby,\nspinning and slightly non-axisymmetric isolated neutron star in our galaxy.\nThis search uses the LIGO data from the first six months of Advanced LIGO's and\nAdvanced Virgo's third observational run, O3. No periodic gravitational wave\nsignals are observed, and 95\\%\\ confidence-level (CL) frequentist upper limits\nare placed on their strengths. The lowest upper limits on worst-case (linearly\npolarized) strain amplitude $h_0$ are $~1.7\\times10^{-25}$ near 200\\,Hz. For a\ncircularly polarized source (most favorable orientation), the lowest upper\nlimits are $\\sim6.3\\times10^{-26}$. These strict frequentist upper limits refer\nto all sky locations and the entire range of frequency derivative values. For a\npopulation-averaged ensemble of sky locations and stellar orientations, the\nlowest 95\\%\\ CL upper limits on the strain amplitude are\n$\\sim1.\\times10^{-25}$. These upper limits improve upon our previously\npublished all-sky results, with the greatest improvement (factor of $\\sim$2)\nseen at higher frequencies, in part because quantum squeezing has dramatically\nimproved the detector noise level relative to the second observational run, O2.\nThese limits are the most constraining to date over most of the parameter space\nsearched.\n"}
+{"abstract": "  Cloud-native Applications are 'distributed, elastic and horizontal-scalable\nsystems composed of (micro)services which isolate states in a minimum of\nstateful components'. Hence, an important property is to ensure a low coupling\nand a high cohesion among the (micro)services composing the cloud-native\napplication. Loosely coupled and highly cohesive services allow development\nteams to work in parallel, reducing the communication overhead between teams.\nHowever, despite both practitioners and researchers agree on the importance of\nthis general property, there are no validated metrics to effectively measure or\ntest the actual coupling level between services. In this work, we propose ways\nto compute and visualize the coupling between microservices, by extending and\nadapting the concepts behind the computation of the traditional structural\ncoupling. We validate these measures with a case study involving 17 open-source\nprojects and we provide an automatic approach to measure them. The results of\nthis study highlight how these metrics provide to practitioners a quantitative\nand visual view of services compositions, which can be useful to conceive\nadvanced systems to monitor the evolution of the service.\n"}
+{"abstract": "  The low-energy $U(1)_{B-L}$ gauge symmetry is well-motivated as part of\nbeyond Standard Model physics related to neutrino mass generation. We show that\na light $B-L$ gauge boson $Z{'}$ and the associated $U(1)_{B-L}$-breaking\nscalar $\\varphi$ can both be effectively searched for at high-intensity\nfacilities such as the near detector complex of the Deep Underground Neutrino\nExperiment (DUNE). Without the scalar $\\varphi$, the $Z{'}$ can be probed at\nDUNE up to mass of 1 GeV, with the corresponding gauge coupling $g_{BL}$ as low\nas $10^{-9}$. In the presence of the scalar $\\varphi$ with gauge coupling to\n$Z{'}$, the DUNE capability of discovering the gauge boson $Z{'}$ can be\nsignificantly improved, even by one order of magnitude in $g_{BL}$, due to\nadditional production from the decay $\\varphi \\to Z{'}Z{'}$. The DUNE\nsensitivity is largely complementary to other long-lived $Z{'}$ searches at\nbeam-dump facilities such as FASER and SHiP, as well as astrophysical and\ncosmological probes. On the other hand, the prospects of detecting $\\varphi$\nitself at DUNE are to some extent weakened in presence of $Z{'}$, compared to\nthe case without the gauge interaction.\n"}
+{"abstract": "  To calculate the conductivity of a material having full knowledge of its\ncomposition is a reasonably simple task. To do the same in reverse, i.e., to\nfind information about the composition of a device from its conductivity\nresponse alone, is very challenging and even more so in the presence of\ndisorder. An inversion methodology capable of decoding the information\ncontained in the conductivity response of disordered structures has been\nrecently proposed but despite claims of generality and robustness, the method\nhas only been used with 2D systems possessing relatively simple electronic\nstructures. Here we put these claims to the test and generalise the inversion\nmethod to the case of monolayer MoS$_2$, a material whose electronic structure\nis far more complex and elaborate. Starting from the spectral function that\ndescribes the DC conductivity of a disordered sample of a single layered\nMoS$_2$ containing a small concentration of randomly dispersed vacancies, we\nare able to invert the signal and find the exact composition of defects with an\nimpressive degree of accuracy. Remarkably, equally accurate results are\nobtained with the optical conductivity. This is indicative of a methodology\nthat is indeed suitable to extract composition information from different 2D\nmaterials, regardless of their electronic structure complexity. Calculated\nconductivity results were used as a proxy for their experimental counterpart\nand were obtained with an efficient quantum transport code (KITE) based on a\nreal-space multi-orbital tight-binding model with parameters generated by\ndensity functional theory.\n"}
+{"abstract": "  We study the problem of aligning two sets of 3D geometric primitives given\nknown correspondences. Our first contribution is to show that this primitive\nalignment framework unifies five perception problems including point cloud\nregistration, primitive (mesh) registration, category-level 3D registration,\nabsolution pose estimation (APE), and category-level APE. Our second\ncontribution is to propose DynAMical Pose estimation (DAMP), the first general\nand practical algorithm to solve primitive alignment problem by simulating\nrigid body dynamics arising from virtual springs and damping, where the springs\nspan the shortest distances between corresponding primitives. We evaluate DAMP\nin simulated and real datasets across all five problems, and demonstrate (i)\nDAMP always converges to the globally optimal solution in the first three\nproblems with 3D-3D correspondences; (ii) although DAMP sometimes converges to\nsuboptimal solutions in the last two problems with 2D-3D correspondences, using\na scheme for escaping local minima, DAMP always succeeds. Our third\ncontribution is to demystify the surprising empirical performance of DAMP and\nformally prove a global convergence result in the case of point cloud\nregistration by charactering local stability of the equilibrium points of the\nunderlying dynamical system.\n"}
+{"abstract": "  Recently the Ihara zeta function for the finite graph was extended to\ninfinite one by Clair and Chinta et al. In this paper, we obtain the same\nexpressions by a different approach from their analytical method. Our new\napproach is to take a suitable limit of a sequence of finite graphs via the\nKonno-Sato theorem. This theorem is related to explicit formulas of\ncharacteristic polynomials for the evolution matrix of the Grover walk. The\nwalk is one of the most well-investigated quantum walks which are quantum\ncounterpart of classical random walks. We call the relation between the Grover\nwalk and the zeta function based on the Konno-Sato theorem \"Grover/Zeta\nCorrespondence\" here.\n"}
+{"abstract": "  A zero-dimensional (volume-averaged) and a pseudo-one-dimensional (plug-flow)\nmodel are developed to investigate atmospheric-pressure plasma jet devices\noperated with He, He/O$_2$, He/N$_2$ and He/N$_2$/O$_2$ mixtures. The models\nare coupled with the Boltzmann equation under the two-term approximation to\nself-consistently calculate the electron energy distribution function (EEDF).\nThe simulation results are verified against spatially resolved model\ncalculations and validated against a wide variety of measurement data. The\nnitric oxide (NO) concentration is thoroughly characterized for a variation of\nthe gas mixture ratio, helium flow rate and absorbed power. The concentration\nmeasurements at low power are better captured by the simulation with a larger\nhypothetical \"effective\" rate coefficient value for the reactive quenching\nN$_2$(A$^3\\Sigma$,B$^3\\Pi$) + O($^3$P) $\\to$ NO + N($^2$D). This suggests that\nthe NO production at low power is also covered by the species\nN$_2$(A$^3\\Sigma$,B$^3\\Pi$;v>0) and multiple higher N$_2$ electronically\nexcited states instead of only N$_2$(A$^3{\\Sigma}$,B$^3{\\Pi}$;v=0) in this\nquenching. Furthermore, the O($^3$P) density measurements under the same\noperation conditions are also better predicted by the simulations with a\nconsideration of the aforementioned hypothetical rate coefficient value. It is\nfound that the contribution of the vibrationally excited nitrogen molecules\nN$_2$(v$\\geqslant$13) to the net NO formation rate gains more significance at\nhigher power. The vibrational distribution functions (VDFs) of O$_2$(v<41) and\nN$_2$(v<58) are investigated. The sensitivity of the zero-dimensional model\nwith respect to a variation of the VDF resolutions, wall reaction probabilities\nand synthetic air impurity levels is presented. The simulated plasma properties\nare sensitive to the variation especially for a feeding gas mixture containing\nnitrogen.\n"}
+{"abstract": "  In this paper, we examine the Nash equilibrium convergence properties of\nno-regret learning in general N-player games. For concreteness, we focus on the\narchetypal follow the regularized leader (FTRL) family of algorithms, and we\nconsider the full spectrum of uncertainty that the players may encounter - from\nnoisy, oracle-based feedback, to bandit, payoff-based information. In this\ngeneral context, we establish a comprehensive equivalence between the stability\nof a Nash equilibrium and its support: a Nash equilibrium is stable and\nattracting with arbitrarily high probability if and only if it is strict (i.e.,\neach equilibrium strategy has a unique best response). This equivalence extends\nexisting continuous-time versions of the folk theorem of evolutionary game\ntheory to a bona fide algorithmic learning setting, and it provides a clear\nrefinement criterion for the prediction of the day-to-day behavior of no-regret\nlearning in games\n"}
+{"abstract": "  Within the model of self-consistent connection of quasi-Rayleigh wave with\nadsorbed atoms, a method of constructing a new class of radiometric sensors of\nthe temperature and concentration of adsorbed atoms on the surface acoustic\nwaves is proposed. Based on the developed theory of the dispersion and acoustic\nmode width of a quasi-Rayleigh wave on the adsorbed surface of monocrystals\nwith a Zinc blende structure, the temperature coefficient of the resonant\nfrequency of the surface acoustic wave is calculated depending on the\ntemperature and on the concentration of adsorbed atoms.\n"}
+{"abstract": "  Recent studies have shown that neural network (NN) based image classifiers\nare highly vulnerable to adversarial examples, which poses a threat to\nsecurity-sensitive image recognition task. Prior work has shown that JPEG\ncompression can combat the drop in classification accuracy on adversarial\nexamples to some extent. But, as the compression ratio increases, traditional\nJPEG compression is insufficient to defend those attacks but can cause an\nabrupt accuracy decline to the benign images. In this paper, with the aim of\nfully filtering the adversarial perturbations, we firstly make modifications to\ntraditional JPEG compression algorithm which becomes more favorable for NN.\nSpecifically, based on an analysis of the frequency coefficient, we design a\nNN-favored quantization table for compression. Considering compression as a\ndata augmentation strategy, we then combine our model-agnostic preprocess with\nnoisy training. We fine-tune the pre-trained model by training with images\nencoded at different compression levels, thus generating multiple classifiers.\nFinally, since lower (higher) compression ratio can remove both perturbations\nand original features slightly (aggressively), we use these trained multiple\nmodels for model ensemble. The majority vote of the ensemble of models is\nadopted as final predictions. Experiments results show our method can improve\ndefense efficiency while maintaining original accuracy.\n"}
+{"abstract": "  In this note we report some advances in the study of thermodynamic formalism\nfor a class of partially hyperbolic system -- center isometries, that includes\nregular elements in Anosov actions. The techniques are of geometric flavor (in\nparticular, not relying in symbolic dynamics) and even provide new information\nin the classical case.\n  For such systems, we give in particular a constructive proof of the existence\nof the SRB measure and of the entropy maximizing measure. It is also\nestablished very fine statistical properties (Bernoulliness), and it is given a\ncharacterization of equilibrium states in terms of their conditional measures\nin the stable/unstable lamination, similar to the SRB case. The construction is\napplied to obtain the uniqueness of quasi-invariant measures associated to\nH\\\"older Jacobian for the horocyclic flow.\n"}
+{"abstract": "  Internet survey experiment is conducted to examine how providing peer\ninformation of evaluation about progressive firms changed individual's\nevaluations. Using large sample including over 13,000 observations collected by\ntwo-step experimental surveys, I found; (1) provision of the information leads\nindividuals to expect higher probability of rising of stocks and be more\nwilling to buy it. (2) the effect on willingness to buy is larger than the\nexpected probability of stock price rising, (3) The effect for woman is larger\nthan for man. (4) individuals who prefer environment (woman's empowerment)\nbecome more willing to buy stock of pro-environment (gender-balanced) firms\nthan others if they have the information. (5) The effect of the peer\ninformation is larger for individuals with \"warm -glow\" motivation.\n"}
+{"abstract": "  We study zero-sum repeated games where the minimizing player has to pay a\ncertain cost each time he changes his action. Our contribution is twofold.\nFirst, we show that the value of the game exists in stationary strategies,\ndepending solely on the previous action of the minimizing player, not the\nentire history. We provide a full characterization of the value and the optimal\nstrategies. The strategies exhibit a robustness property and typically do not\nchange with a small perturbation of the switching costs. Second, we consider a\ncase where the minimizing player is limited to playing simpler strategies that\nare completely history-independent. Here too, we provide a full\ncharacterization of the (minimax) value and the strategies for obtaining it.\nMoreover, we present several bounds on the loss due to this limitation.\n"}
+{"abstract": "  This paper is devoted to the maximal $L^1$ regularity and asymptotic behavior\nfor solutions to the inhomogeneous incompressible Navier-Stokes equations under\na scaling-invariant smallness assumption on the initial velocity. We obtain a\nnew global $L^1$-in-time estimate for the Lipschitz seminorm of the velocity\nfield without any smallness assumption on the initial density fluctuation. In\nthe derivation of this estimate, we study the maximal $L^1$ regularity for a\nlinear Stokes system with variable coefficients. The analysis tools are a use\nof the semigroup generated by a generalized Stokes operator to characterize\nsome Besov norms and a new gradient estimate for a class of second-order\nelliptic equations of divergence form. Our method might be used to study some\nother issues arising from incompressible or compressible viscous fluids.\n"}
+{"abstract": "  The behavior of self driving cars may differ from people expectations, (e.g.\nan autopilot may unexpectedly relinquish control). This expectation mismatch\ncan cause potential and existing users to distrust self driving technology and\ncan increase the likelihood of accidents. We propose a simple but effective\nframework, AutoPreview, to enable consumers to preview a target autopilot\npotential actions in the real world driving context before deployment. For a\ngiven target autopilot, we design a delegate policy that replicates the target\nautopilot behavior with explainable action representations, which can then be\nqueried online for comparison and to build an accurate mental model. To\ndemonstrate its practicality, we present a prototype of AutoPreview integrated\nwith the CARLA simulator along with two potential use cases of the framework.\nWe conduct a pilot study to investigate whether or not AutoPreview provides\ndeeper understanding about autopilot behavior when experiencing a new autopilot\npolicy for the first time. Our results suggest that the AutoPreview method\nhelps users understand autopilot behavior in terms of driving style\ncomprehension, deployment preference, and exact action timing prediction.\n"}
+{"abstract": "  We consider amplituhedron-like geometries which are defined in a similar way\nto the intrinsic definition of the amplituhedron but with non-maximal winding\nnumber. We propose that for the cases with minimal number of points the\ncanonical form of these geometries corresponds to the product of parity\nconjugate amplitudes at tree as well as loop level. The product of amplitudes\nin superspace lifts to a star product in bosonised superspace which we give a\nprecise definition of. We give an alternative definition of amplituhedron-like\ngeometries, analogous to the original amplituhedron definition, and also a\ncharacterisation as a sum over pairs of on-shell diagrams that we use to prove\nthe conjecture at tree level. The union of all amplituhedron-like geometries\nhas a very simple definition given by only physical inequalities. Although such\na union does not give a positive geometry, a natural extension of the standard\ndefinition of canonical form, the globally oriented canonical form, acts on\nthis union and gives the square of the amplitude.\n"}
+{"abstract": "  Motivated by recent experiments with Rydberg atoms in an optical tweezer\narray, we accurately map out the ground-state phase diagram of the\nantiferromagnetic Ising model on a square lattice with longitudinal and\ntransverse magnetic fields using the quantum Monte Carlo method. For a small\nbut nonzero transverse field, the transition longitudinal field is found to\nremain nearly constant. By scrutinizing the phase diagram, we uncover a narrow\nregion where the system exhibits reentrant transitions between the disordered\nand antiferromagnetic phases with increasing transverse field. Our phase\ndiagram provides a useful benchmark for quantum simulation of a Rydberg atom\nsystem.\n"}
+{"abstract": "  This paper addresses the ability to enable machines to automatically detect\nfailures on machine tool components as well as estimating the severity of the\nfailures, which is a critical step towards autonomous production machines.\nExtracting information about the severity of failures has been a substantial\npart of classical, as well as Machine Learning based machine vision systems.\nEfforts have been undertaken to automatically predict the severity of failures\non machine tool components for predictive maintenance purposes. Though, most\napproaches only partly cover a completely automatic system from detecting\nfailures to the prognosis of their future severity. To the best of the authors\nknowledge, this is the first time a vision-based system for defect detection\nand prognosis of failures on metallic surfaces in general and on Ball Screw\nDrives in specific has been proposed. The authors show that they can do both,\ndetect and prognose the evolution of a failure on the surface of a Ball Screw\nDrive.\n"}
+{"abstract": "  A serious and ubiquitous issue in existing mapped WENO schemes is that most\nof them can hardly preserve high resolutions and in the meantime prevent\nspurious oscillations on solving hyperbolic conservation laws with long output\ntimes. Our goal in this article is to address this widely concerned problem [3,\n4, 15, 29, 16, 18]. In our previous work [18], the order-preserving (OP)\ncriterion was originally introduced and carefully used to devise a new mapped\nWENO scheme that performs satisfactorily in long-run simulations, and hence it\nwas indicated that the OP criterion plays a critical role in the maintenance of\nlow-dissipation and robustness for the mapped WENO schemes. Thus, in our\npresent work, we firstly define the family of the mapped WENO schemes, whose\nmappings meet the OP criterion, as OP-Mapped WENO. Next, we attentively take a\ncloser look at the mappings of various existing mapped WENO schemes and devise\na general formula for them. It helps us to extend the OP criterion into the\ndesign of the improved mappings. Then, we propose the generalized\nimplementation of obtaining a group of OP-Mapped WENO schemes, named MOP-WENO-X\nas they are developed from the existing mapped WENO-X schemes, where the\nnotation \"X\" is used to identify the version of the existing mapped WENO\nscheme. Finally, extensive numerical experiments and comparisons with competing\nschemes are conducted to demonstrate the enhanced performances of the\nMOP-WENO-X schemes.\n"}
+{"abstract": "  The significance of heating load (HL) accurate approximation is the primary\nmotivation of this research to distinguish the most efficient predictive model\namong several neural-metaheuristic models. The proposed models are through\nsynthesizing multi-layer perceptron network (MLP) with ant lion optimization\n(ALO), biogeography-based optimization (BBO), dragonfly algorithm (DA),\nevolutionary strategy (ES), invasive weed optimization (IWO), and league\nchampion optimization (LCA) hybrid algorithms. Each ensemble is optimized in\nterms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP,\nES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population\nsizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was\ncarried out by implementing a ranking system. Based on the obtained overall\nscores (OSs), the BBO (OS = 36) featured as the most capable optimization\ntechnique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient\nperformance of these algorithms, the corresponding MLPs can be promising\nsubstitutes for traditional methods used for HL analysis.\n"}
+{"abstract": "  In this paper, we study the existence of extremal functions of the discrete\nSobolev inequality and Hardy-Littlewood-Sobolev inequality on lattice graphs.\n  We introduce the discrete Concentration-Compactness principle, and prove the\nexistence of extremal functions for the best constants in the supercritical\ncases.\n"}
+{"abstract": "  In this paper we define and explore the analytic spread $\\ell(\\mathcal I)$ of\na filtration in a local ring. We show that, especially for divisorial and\nsymbolic filtrations, some basic properties of the analytic spread of an ideal\nextend to filtrations, even when the filtration is non Noetherian. We also\nillustrate some significant differences between the analytic spread of a\nfiltration and the analytic spread of an ideal with examples.\n  In the case of an ideal $I$, we have the classical bounds\n$\\mbox{ht}(I)\\le\\ell(I)\\le \\dim R$. The upper bound $\\ell(\\mathcal I)\\le \\dim\nR$ is true for filtrations $\\mathcal I$, but the lower bound is not true for\nall filtrations. We show that for the filtration $\\mathcal I$ of symbolic\npowers of a height two prime ideal $\\mathfrak p$ in a regular local ring of\ndimension three (a space curve singularity), so that $\\mbox{ht}(\\mathcal I) =2$\nand $\\dim R=3$, we have that $0\\le \\ell(\\mathcal I)\\le 2$ and all values of 0,1\nand 2 can occur. In the cases of analytic spread 0 and 1 the symbolic algebra\nis necessarily non-Noetherian. The symbolic algebra is non-Noetherian if and\nonly if $\\ell(\\mathfrak p^{(n)})=3$ for all symbolic powers of $\\mathfrak p$\nand if and only if $\\ell(\\mathcal I_a)=3$ for all truncations $\\mathcal I_a$ of\n$\\mathcal I$.\n"}
+{"abstract": "  We analyze the propagation of high-frequency gravitational waves (GW) in\nscalar-tensor theories of gravity, with the aim of examining properties of\ncosmological distances as inferred from GW measurements. By using symmetry\nprinciples, we first determine the most general structure of the GW linearized\nequations and of the GW energy momentum tensor, assuming that GW move with the\nspeed of light. Modified gravity effects are encoded in a small number of\nparameters, and we study the conditions for ensuring graviton number\nconservation in our covariant set-up. We then apply our general findings to the\ncase of GW propagating through a perturbed cosmological space-time, deriving\nthe expressions for the GW luminosity distance $d_L^{({\\rm GW})}$ and the GW\nangular distance $d_A^{({\\rm GW})}$. We prove for the first time the validity\nof Etherington reciprocity law $d_L^{({\\rm GW})}\\,=\\,(1+z)^2\\,d_A^{({\\rm GW})}$\nfor a perturbed universe within a scalar-tensor framework. We find that besides\nthe GW luminosity distance, also the GW angular distance can be modified with\nrespect to General Relativity. We discuss implications of this result for\ngravitational lensing, focussing on time-delays of lensed GW and lensed photons\nemitted simultaneously during a multimessenger event. We explicitly show how\nmodified gravity effects compensate between different coefficients in the GW\ntime-delay formula: lensed GW arrive at the same time as their lensed\nelectromagnetic counterparts, in agreement with causality constraints.\n"}
+{"abstract": "  This paper develops a novel method to detect cold pools from atmospheric\nsoundings over tropical oceans and applies it to sounding data from the EUREC4A\nfield campaign, which took place south and east of Barbados in January-February\n2020. The proposed method exploits the fact that the air in a cold pool is\ndenser than the air above it. It leads us to define cold pool soundings as\nthose for which the mixed-layer height is smaller than 400 m. We first test\nthis criterion by verifying its consistency with surface temperature and\nprecipitation in a realistic high-resolution simulation over the western\ntropical Atlantic. Applying it to EUREC4A data, we then identify 7% of EUREC4A\ndropsondes and radiosondes as cold pool soundings. In two selected case\nstudies, we find that cold pools soundings coincide with mesoscale cloud arcs\nand temperature drops in the surface time series. Statistics for the entire\ncampaign further characterize the signature of cold pools in temperature,\nhumidity and wind profiles. In the presence of wind shear, we show in\nparticular that the spreading of cold pools is favored downshear, suggesting\ndownward momentum transport by unsaturated downdrafts. These results support\nthe robustness of our simple method in different environmental conditions and\nillustrate the new insights it offers for the characterization of cold pools\nand their environment.\n"}
+{"abstract": "  In recent work, Gourv\\`es, Lesca, and Wilczynski propose a variant of the\nclassic housing markets model where the matching between agents and objects\nevolves through Pareto-improving swaps between pairs of adjacent agents in a\nsocial network. To explore the swap dynamics of their model, they pose several\nbasic questions concerning the set of reachable matchings. In their work and\nother follow-up works, these questions have been studied for various classes of\ngraphs: stars, paths, generalized stars (i.e., trees where at most one vertex\nhas degree greater than two), trees, and cliques. For generalized stars and\ntrees, it remains open whether a Pareto-efficient reachable matching can be\nfound in polynomial time.\n  In this paper, we pursue the same set of questions under a natural variant of\ntheir model. In our model, the social network is replaced by a network of\nobjects, and a swap is allowed to take place between two agents if it is\nPareto-improving and the associated objects are adjacent in the network. In\nthose cases where the question of polynomial-time solvability versus\nNP-hardness has been resolved for the social network model, we are able to show\nthat the same result holds for the network-of-objects model. In addition, for\nour model, we present a polynomial-time algorithm for computing a\nPareto-efficient reachable matching in generalized star networks. Moreover, the\nobject reachability algorithm that we present for path networks is\nsignificantly faster than the known polynomial-time algorithms for the same\nquestion in the social network model.\n"}
+{"abstract": "  Real-time identification of electrical equivalent circuit models is a\ncritical requirement in many practical systems, such as batteries and electric\nmotors. Significant work has been done in the past developing different types\nof algorithms for system identification using reduced equivalent circuit\nmodels. However, little work was done in analyzing the theoretical performance\nbounds of these approaches. Proper understanding of theoretical bounds will\nhelp in designing a system that is economical in cost and robust in\nperformance. In this paper, we analyze the performance of a linear recursive\nleast squares approach to equivalent circuit model identification and show that\nthe least squares approach is both unbiased and efficient when the\nsignal-to-noise ratio is high enough. However, we show that, when the\nsignal-to-noise ratio is low - resembling the case in many practical\napplications - the least squares estimator becomes significantly biased.\nConsequently, we develop a parameter estimation approach based on total least\nsquares method and show it to be asymptotically unbiased and efficient at\npractically low signal-to-noise ratio regions. Further, we develop a recursive\nimplementation of the total least square algorithm and find it to be slow to\nconverge; for this, we employ a Kalman filter to improve the convergence speed\nof the total least squares method. The resulting total Kalman filter is shown\nto be both unbiased and efficient in equivalent circuit model parameter\nidentification. The performance of this filter is analyzed using real-world\ncurrent profile under fluctuating signal-to-noise ratios. Finally, the\napplicability of the algorithms and analysis in this paper in identifying\nhigher order electrical equivalent circuit models is explained.\n"}
+{"abstract": "  The composition of dark matter is one of the puzzling topics in astrophysics.\nTo address this issue, several experiments searching for the existence of\naxions have been designed, built and realized in the last twenty years. Among\nall the others, light shining through walls experiments promise to push the\nexclusion limits to lower energies. For this reason, effort is put for the\ndevelopment of single-photon detectors operating at frequencies $<100$ GHz.\nHere, we review recent advancements in superconducting single-photon detection.\nIn particular, we present two sensors based on one-dimensional Josephson\njunctions with the capability to be in situ tuned by simple current bias: the\nnanoscale transition edge sensor (nano-TES) and the Josephson escape sensor\n(JES). These two sensors are the ideal candidates for the realization of\nmicrowave light shining through walls (LSW) experiments, since they show\nunprecedented frequency resolutions of about 100 GHz and 2 GHz for the nano-TES\nand JES, respectively.\n"}
+{"abstract": "  We study the propagation of 0.05-1 TW power, ultrafast laser pulses in a 10\nmeter long rubidium vapor cell. The central wavelength of the laser is resonant\nwith the $D_2$ line of rubidium and the peak intensity in the $10^{12}-10^{14}\n~W/cm^2$ range, enough to create a plasma channel with single electron\nionization. We observe the absorption of the laser pulse for low energy, a\nregime of transverse confinement of the laser beam by the strong resonant\nnonlinearity for higher energies and the transverse broadening of the output\nbeam when the nonlinearity is saturated due to full medium ionization. We\ncompare experimental observations of transmitted pulse energy and transverse\nfluence profile with the results of computer simulations modeling pulse\npropagation. We find a qualitative agreement between theory and experiment that\ncorroborates the validity of our propagation model. While the quantitative\ndifferences are substantial, the results show that the model can be used to\ninterpret the observed phenomena in terms of self-focusing and channeling of\nthe laser pulses by the saturable nonlinearity and the transparency of the\nfully ionized medium along the propagation axis.\n"}
+{"abstract": "  Deep ensembles perform better than a single network thanks to the diversity\namong their members. Recent approaches regularize predictions to increase\ndiversity; however, they also drastically decrease individual members'\nperformances. In this paper, we argue that learning strategies for deep\nensembles need to tackle the trade-off between ensemble diversity and\nindividual accuracies. Motivated by arguments from information theory and\nleveraging recent advances in neural estimation of conditional mutual\ninformation, we introduce a novel training criterion called DICE: it increases\ndiversity by reducing spurious correlations among features. The main idea is\nthat features extracted from pairs of members should only share information\nuseful for target class prediction without being conditionally redundant.\nTherefore, besides the classification loss with information bottleneck, we\nadversarially prevent features from being conditionally predictable from each\nother. We manage to reduce simultaneous errors while protecting class\ninformation. We obtain state-of-the-art accuracy results on CIFAR-10/100: for\nexample, an ensemble of 5 networks trained with DICE matches an ensemble of 7\nnetworks trained independently. We further analyze the consequences on\ncalibration, uncertainty estimation, out-of-distribution detection and online\nco-distillation.\n"}
+{"abstract": "  The gap between a recently developed dynamical version of relaxed\nmagnetohydrodynamics (RxMHD) and ideal MHD (IMHD) is bridged by approximating\nthe zero-resistivity \"Ideal\" Ohm's Law (IOL) constraint using an augmented\nLagrangian method borrowed from optimization theory. The augmentation combines\na pointwise vector Lagrange multiplier method and global penalty function\nmethod and can be used either for iterative enforcement of the IOL to arbitrary\naccuracy, or for constructing a continuous sequence of magnetofluid dynamics\nmodels running between RxMHD (no IOL) and weak IMHD (IOL almost everywhere).\nThis is illustrated by deriving dispersion relations for linear waves on an MHD\nequilibrium.\n"}
+{"abstract": "  We define graded, quasi-coherent $\\mathcal{O}_S$-algebras over a given base\nderived scheme $S$, and show that these are equivalent to derived\n$\\mathbb{G}_{m,S}$-schemes which are affine over $S$. We then use this\n$\\mathbb{G}_{m,S}$-action to define the projective spectrum $\\mathrm{Proj}\n(\\mathcal{B})$ of a graded algebra $\\mathcal{B}$ as a quotient stack, show that\n$\\mathrm{Proj} (\\mathcal{B})$ is representable by a derived scheme over $S$,\nand describe the functor of points of $\\mathrm{Proj} (\\mathcal{B})$ in terms of\nline bundles. The theory of graded algebras and projective spectra is then used\nto define the blow-up of a closed immersion of derived schemes. Our\nconstruction will coincide with the existing one for the quasi-smooth case. The\nconstruction is done by generalizing the extended Rees algebra to the derived\nsetting, using Weil restrictions. We close by also generalizing the deformation\nto the normal cone to the derived setting.\n"}
+{"abstract": "  In this note we consider asymptotics of the multipoint Pad\\'e approximants to\nCauchy integrals of analytic non-vanishing densities defined on a Jordan arc\nconnecting $ -1 $ and $ 1 $. We allow for the situation where the (symmetric)\ncontour attracting the poles of the approximants does separate the plane.\n"}
+{"abstract": "  Cyber-physical systems (CPS) and Internet-of-Things (IoT) devices are\nincreasingly being deployed across multiple functionalities, ranging from\nhealthcare devices and wearables to critical infrastructures, e.g., nuclear\npower plants, autonomous vehicles, smart cities, and smart homes. These devices\nare inherently not secure across their comprehensive software, hardware, and\nnetwork stacks, thus presenting a large attack surface that can be exploited by\nhackers. In this article, we present an innovative technique for detecting\nunknown system vulnerabilities, managing these vulnerabilities, and improving\nincident response when such vulnerabilities are exploited. The novelty of this\napproach lies in extracting intelligence from known real-world CPS/IoT attacks,\nrepresenting them in the form of regular expressions, and employing machine\nlearning (ML) techniques on this ensemble of regular expressions to generate\nnew attack vectors and security vulnerabilities. Our results show that 10 new\nattack vectors and 122 new vulnerability exploits can be successfully generated\nthat have the potential to exploit a CPS or an IoT ecosystem. The ML\nmethodology achieves an accuracy of 97.4% and enables us to predict these\nattacks efficiently with an 87.2% reduction in the search space. We demonstrate\nthe application of our method to the hacking of the in-vehicle network of a\nconnected car. To defend against the known attacks and possible novel exploits,\nwe discuss a defense-in-depth mechanism for various classes of attacks and the\nclassification of data targeted by such attacks. This defense mechanism\noptimizes the cost of security measures based on the sensitivity of the\nprotected resource, thus incentivizing its adoption in real-world CPS/IoT by\ncybersecurity practitioners.\n"}
+{"abstract": "  Prolonged focused work periods decrease efficiency with related decline of\nattention and performance. Therefore, emergency fleet break scheduling should\nconsider both area coverage by idle vehicles (related to the fleet's target\narrival time to incidents) and vehicle crews' service requirements for breaks\nto avoid fatigue. In this paper, we propose a break assignment problem\nconsidering area coverage (BAPCAC) addressing this issue. Moreover, we\nformulate a mathematical model for the BAPCAC problem. Based on available\nhistorical spatio-temporal incident data and service requirements, the BAPCAC\nmodel can be used not only to dimension the size of the fleet at the tactical\nlevel, but also to decide upon the strategies related with break scheduling.\nMoreover, the model could be used to compute (suboptimal) locations for idle\nvehicles in each time period and arrange vehicles' crews' work breaks\nconsidering given break and coverage constraints.\n"}
+{"abstract": "  In this work, we investigate crystallization from droplets formed on\nmicro-patterned surfaces. By solvent exchange in a micro-chamber, a ternary\nsolution consisting of a model compound beta-alanine, water, and isopropanol,\nwas displaced by a flow of isopropanol. In the process, oiling-out droplets\nformed and crystallized. Our results showed that the shape and size of the\ncrystals on micro-patterned surfaces could be simply mediated by the flow\nconditions of solvent exchange. Varying flow rate, concentration, or channel\nheight led to the formation of a thin film with micro-holes, connected network\nof crystals, or small diamond-shaped crystals. Rough micro-structures on the\nsurface allowed the easy detachment of crystals from the surface. Beyond\noiling-out crystallization, we demonstrated that the crystal formation from\nanother solute dissolved in the droplets could be triggered by solvent\nexchange. The length of crystal fibers after the solvent exchange process was\nshorter at a faster flow rate. This study may provide further understanding to\neffectively obtain crystallization from surface droplets through the solvent\nexchange approach.\n"}
+{"abstract": "  We study shadowing and chain recurrence in the context of linear operators\nacting on Banach spaces or even on normed vector spaces. We show that for\nlinear operators there is only one chain recurrent set, and this set is\nactually a closed invariant subspace. We prove that every chain transitive\nlinear dynamical system with the shadowing property is frequently hypercyclic,\nand as a corollary obtain that every shadowing hypercyclic linear dynamical\nsystem is frequently hypercyclic.\n"}
+{"abstract": "  The Hong-Ou-Mandel effect lies at the heart of quantum interferometry, having\nmultiple applications in the field of quantum information processing and no\nclassical counterpart. Despite its popularity, only a few works have considered\npolarization-frequency interaction within the interferometer. In this paper, we\nfill this gap. Our system of interest is a general biphoton polarization state\nthat experiences effective dephasing noise by becoming entangled with the same\nphotons' frequency state, as the photons propagate through birefringent media.\nThe photons then meet at a beam splitter, where either coincidence or bunching\noccurs, after which the polarization-frequency interaction continues on the\noutput paths. Alongside performing extensive theoretical analysis on the\ncoincidence probability and different polarization states, we outline multiple\ninteresting applications that range from constructing Bell states to an\nalternative delayed choice quantum eraser.\n"}
+{"abstract": "  Convolutional neural networks (CNNs) have been the de facto standard for\nnowadays 3D medical image segmentation. The convolutional operations used in\nthese networks, however, inevitably have limitations in modeling the long-range\ndependency due to their inductive bias of locality and weight sharing. Although\nTransformer was born to address this issue, it suffers from extreme\ncomputational and spatial complexities in processing high-resolution 3D feature\nmaps. In this paper, we propose a novel framework that efficiently bridges a\n{\\bf Co}nvolutional neural network and a {\\bf Tr}ansformer {\\bf (CoTr)} for\naccurate 3D medical image segmentation. Under this framework, the CNN is\nconstructed to extract feature representations and an efficient deformable\nTransformer (DeTrans) is built to model the long-range dependency on the\nextracted feature maps. Different from the vanilla Transformer which treats all\nimage positions equally, our DeTrans pays attention only to a small set of key\npositions by introducing the deformable self-attention mechanism. Thus, the\ncomputational and spatial complexities of DeTrans have been greatly reduced,\nmaking it possible to process the multi-scale and high-resolution feature maps,\nwhich are usually of paramount importance for image segmentation. We conduct an\nextensive evaluation on the Multi-Atlas Labeling Beyond the Cranial Vault (BCV)\ndataset that covers 11 major human organs. The results indicate that our CoTr\nleads to a substantial performance improvement over other CNN-based,\ntransformer-based, and hybrid methods on the 3D multi-organ segmentation task.\nCode is available at \\def\\UrlFont{\\rm\\small\\ttfamily}\n\\url{https://github.com/YtongXie/CoTr}\n"}
+{"abstract": "  Nieh-Yan anomaly describes the non-conservation of chiral charges induced by\nthe coupling between Dirac fermions and torsion fields. Since the torsion field\nis beyond general relativity, this effect remains hypothetical and its\nrelevance to our universe is unclear in the context of high-energy physics. In\nthis work, we propose that the phonons can induce a torsion field for the\nKramers-Weyl fermions through electron-phonon interaction in a non-magnetic\nchiral crystal, thus leading to the occurrence of the Nieh-Yan anomaly in this\ncondensed matter system. As a consequence, the Nieh-Yan term can strongly\ninfluence the phonon dynamics and lead to the helicity of acoustic phonons,\nnamely, two transverse phonon modes mix with each other to form a circular\npolarization with a non-zero angular momentum at a finite phonon momentum and\nthe phonon angular momentum reverses its sign for opposite momenta due to time\nreversal symmetry. The phonon helicity can be probed through measuring the\ntotal phonon angular momentum driven by a temperature gradient.\n"}
+{"abstract": "  Knowledge of how science is consumed in public domains is essential for a\ndeeper understanding of the role of science in human society. While science is\nheavily supported by public funding, common depictions suggest that scientific\nresearch remains an isolated or 'ivory tower' activity, with weak connectivity\nto public use, little relationship between the quality of research and its\npublic use, and little correspondence between the funding of science and its\npublic use. This paper introduces a measurement framework to examine public\ngood features of science, allowing us to study public uses of science, the\npublic funding of science, and how use and funding relate. Specifically, we\nintegrate five large-scale datasets that link scientific publications from all\nscientific fields to their upstream funding support and downstream public uses\nacross three public domains - government documents, the news media, and\nmarketplace invention. We find that the public uses of science are extremely\ndiverse, with different public domains drawing distinctively across scientific\nfields. Yet amidst these differences, we find key forms of alignment in the\ninterface between science and society. First, despite concerns that the public\ndoes not engage high-quality science, we find universal alignment, in each\nscientific field and public domain, between what the public consumes and what\nis highly impactful within science. Second, despite myriad factors underpinning\nthe public funding of science, the resulting allocation across fields presents\na striking alignment with the field's collective public use. Overall, public\nuses of science present a rich landscape of specialized consumption, yet\ncollectively science and society interface with remarkable, quantifiable\nalignment between scientific use, public use, and funding.\n"}
+{"abstract": "  Over the last decade, artificial intelligence has found many applications\nareas in the society. As AI solutions have become more sophistication and the\nuse cases grew, they highlighted the need to address performance and energy\nefficiency challenges faced during the implementation process. To address these\nchallenges, there has been growing interest in neuromorphic chips. Neuromorphic\ncomputing relies on non von Neumann architectures as well as novel devices,\ncircuits and manufacturing technologies to mimic the human brain. Among such\ntechnologies, 3D integration is an important enabler for AI hardware and the\ncontinuation of the scaling laws. In this paper, we overview the unique\nopportunities 3D integration provides in neuromorphic chip design, discuss the\nemerging opportunities in next generation neuromorphic architectures and review\nthe obstacles. Neuromorphic architectures, which relied on the brain for\ninspiration and emulation purposes, face grand challenges due to the limited\nunderstanding of the functionality and the architecture of the human brain.\nYet, high-levels of investments are dedicated to develop neuromorphic chips. We\nargue that 3D integration not only provides strategic advantages to the\ncost-effective and flexible design of neuromorphic chips, it may provide design\nflexibility in incorporating advanced capabilities to further benefits the\ndesigns in the future.\n"}
+{"abstract": "  Intersectional bias is a bias caused by an overlap of multiple social factors\nlike gender, sexuality, race, disability, religion, etc. A recent study has\nshown that word embedding models can be laden with biases against\nintersectional groups like African American females, etc. The first step\ntowards tackling such intersectional biases is to identify them. However,\ndiscovering biases against different intersectional groups remains a\nchallenging task. In this work, we present WordBias, an interactive visual tool\ndesigned to explore biases against intersectional groups encoded in static word\nembeddings. Given a pretrained static word embedding, WordBias computes the\nassociation of each word along different groups based on race, age, etc. and\nthen visualizes them using a novel interactive interface. Using a case study,\nwe demonstrate how WordBias can help uncover biases against intersectional\ngroups like Black Muslim Males, Poor Females, etc. encoded in word embedding.\nIn addition, we also evaluate our tool using qualitative feedback from expert\ninterviews. The source code for this tool can be publicly accessed for\nreproducibility at github.com/bhavyaghai/WordBias.\n"}
+{"abstract": "  Massive star evolution is still poorly understood, and observational tests\nare required to discriminate between different implementations of physical\nphenomenon in stellar evolution codes. By confronting stellar evolution models\nwith observed properties of blue supergiants, such as pulsations, chemical\ncomposition and position in the Hertzsprung-Russell diagram, we aim at\ndetermining which of the criterion used for convection (Schwarzschild or\nLedoux) is best able to explain the observations. We compute state-of-the-art\nstellar evolution models with either the Schwarzschild or the Ledoux criterion\nfor convection. Models are for $14$ to $35\\,M_\\odot$ at solar or Large\nMagellanic Cloud metallicity. For each model, we compute the pulsation\nproperties to know when radial modes are excited. We then compare our results\nwith the position of blue supergiants in the Hertzsprung-Russell diagram, with\ntheir surface chemical composition, and with their variability. Our results at\nLarge Magellanic Cloud metallicity shows only a slight preference for the\nLedoux criterion over the Schwarzschild one in reproducing at the same time the\nobserved properties of blue supergiants, even if the Schwarzschild criterion\ncannot be excluded at this metallicity. We check that changing the overshoot\nparameter at solar metallicity does not improve the situation. We also check\nthat our models are able to reproduce the position of Galactic blue supergiant\nin the flux-weighted-gravity -- luminosity relation. We confirm that overall,\nmodels computed with the Ledoux criterion are slightly better in matching\nobservations. Our results also support the idea that most Galactic $\\alpha$ Cyg\nvariables are blue supergiants of the group 2, i.e. stars that have been\nthrough a previous red supergiant phase where they have lost large amount of\nmass.\n"}
+{"abstract": "  Accurate segmentation of the pelvic CTs is crucial for the clinical diagnosis\nof pelvic bone diseases and for planning patient-specific hip surgeries. With\nthe emergence and advancements of deep learning for digital healthcare, several\nmethodologies have been proposed for such segmentation tasks. But in a low data\nscenario, the lack of abundant data needed to train a Deep Neural Network is a\nsignificant bottle-neck. In this work, we propose a methodology based on\nmodulation of image tonal distributions and deep learning to boost the\nperformance of networks trained on limited data. The strategy involves\npre-processing of test data through histogram specification. This simple yet\neffective approach can be viewed as a style transfer methodology. The\nsegmentation task uses a U-Net configuration with an EfficientNet-B0 backbone,\noptimized using an augmented BCE-IoU loss function. This configuration is\nvalidated on a total of 284 images taken from two publicly available CT\ndatasets, TCIA (a cancer imaging archive) and the Visible Human Project. The\naverage performance measures for the Dice coefficient and Intersection over\nUnion are 95.7% and 91.9%, respectively, give strong evidence for the\neffectiveness of the approach, which is highly competitive with\nstate-of-the-art methodologies.\n"}
+{"abstract": "  We find an analytic solution of the backreacted coupled fermion-baby-Skyrmion\nsystem valid at all values of the coupling parameter. The solution, built on a\nfinite cylinder, is generally given in terms of the Heun functions and\nsatisfies the physical requirements of finite energy. For a special value of\nthe coupling parameter, the solution becomes a periodic crystal of\nbaby-Skyrmions and fermions defined on the plane $\\mathbb{R}^2$. These\nsolutions are trivially extended to multi-solitonic branches of higher Baryon\nnumber.\n"}
+{"abstract": "  We investigate, using numerical simulations, the conformations of isolated\nactive ring polymers. We find that the their behaviour depends crucially on\ntheir size: short rings ($N \\lesssim$ 100) are swelled whereas longer rings ($N\n\\gtrsim$ 200) collapse, at sufficiently high activity. By investigating the\nnon-equilibrium process leading to the steady state, we find a universal route\ndriving both outcomes; we highlight the central role of steric interactions, at\nvariance with linear chains, and of topology conservation. We further show that\nthe collapsed rings are arrested by looking at different observables, all\nunderlining the presence of an extremely long time scales at the steady state,\nassociated with the internal dynamics of the collapsed section. Finally, we\nfound that is some circumstances the collapsed state spins about its axis.\n"}
+{"abstract": "  In the present paper, we consider multidimensional nonlinear backward\nstochastic differential equations (BSDEs) with a driver depending on the\nmartingale part $M$ of a solution. We assume that the nonlinear term is merely\nmonotone continuous with respect to the state variable. As to the regularity of\nthe driver with respect to the martingale variable, we consider a very general\ncondition which permits path-dependence on \"the future\" of the process $M$ as\nwell as a dependence of its law (McKean-Vlasov-type equations). For such\ndriver, we prove the existence and uniqueness of a global solution (i.e. for\nany maturity $T>0$) to BSDE with data satisfying natural integrability\nconditions.\n"}
+{"abstract": "  We find the relation between reliability and entropy production in a\nrealistic model of electronic memory (low-power MOS-based SRAM) where logical\nvalues are encoded as metastable non-equilibrium states. We employ large\ndeviations techniques to obtain an analytical expression for the bistable\nquasipotential describing the non-equilibrium steady state and use it to derive\nan explicit expression bounding the error rate of the memory. Our results go\nbeyond the dominant contribution given by classical instanton theory and\nprovide accurate estimates of the error rate as confirmed by comparison with\nstochastic simulations. The methods developed can be adapted to study the\nreliability of broad classes of nonlinear devices subjected to thermal noise.\n"}
+{"abstract": "  Creating a good drum track to imitate a skilled performer in digital audio\nworkstations (DAWs) can be a time-consuming process, especially for those\nunfamiliar with drums. In this work, we introduce PocketVAE, a groove\ngeneration system that applies grooves to users' rudimentary MIDI tracks, i.e,\ntemplates. Grooves can be either transferred from a reference track, generated\nrandomly or with conditions, such as genres. Our system, consisting of\ndifferent modules for each groove component, takes a two-step approach that is\nanalogous to a music creation process. First, the note module updates the user\ntemplate through addition and deletion of notes; Second, the velocity and\nmicrotiming modules add details to this generated note score. In order to model\nthe drum notes, we apply a discrete latent representation method via Vector\nQuantized Variational Autoencoder (VQ-VAE), as drum notes have a discrete\nproperty, unlike velocity and microtiming values. We show that our two-step\napproach and the usage of a discrete encoding space improves the learning of\nthe original data distribution. Additionally, we discuss the benefit of\nincorporating control elements - genre, velocity and microtiming patterns -\ninto the model.\n"}
+{"abstract": "  This short note regards an observation about the recent theory of prismatic\ncohomology developed by Bhatt and Scholze. In particular, by applying a functor\nof Mandell, we see that the \\'etale comparison theorem in the prismatic theory\nreproduces the $p$-adic homotopy type for a smooth proper complex variety with\ngood reduction mod $p$.\n"}
+{"abstract": "  How do healthcare organizations (from small Practices to large HDOs) evaluate\nadherence to the cybersecurity and privacy protection of Medical Internet of\nThings (MIoT) used in clinical settings? This paper suggests an approach for\nsuch evaluation using National Institute of Standards and Technology (NIST)\nguidance. Through application of NISTIR 8228 Expectations it is possible to\nquantitatively assess cybersecurity and privacy protection, and determine\nrelative compliance with recommended standards. This approach allows\norganizations to evaluate the level of risk a MiOT device poses to IT systems\nand to determine whether or not to permit its use in healthcare/IT\nenvironments.\n  This paper reviews the current state of IoT/MiOT cybersecurity and privacy\nprotection using historical and current industry guidance & best-practices;\nrecommendations by federal agencies; NIST publications; and federal law. It\nthen presents similarities and differences between IOT/MiOT devices and\n\"traditional\" (or classic) Information Technology (IT) hardware, and cites\nseveral challenges IoT/MiOT pose to cybersecurity and privacy protection.\n  Finally, a practical approach to evaluating cybersecurity and privacy\nprotection is offered along with enhancements for validating assessment\nresults. In so doing it will demonstrate general compliance with both NIST\nguidance and HIPAA/HITECH requirements.\n"}
+{"abstract": "  Naviers Stokes and energy equations of hydrocarbon liquid droplet in\nevaporation in natural convection are solved numerically using PLIC VOF method.\nOur new technique allows us to observe clearly the droplet radius regression in\neach cell of the mesh. This regression is not affected by the changing of the\ndroplet shape and temperature.\n"}
+{"abstract": "  In this article we study modules over wild canonical algebras which\ncorrespond to extension bundles [9] over weighted projective lines. We prove\nthat all modules attached to extension bundles can be established by matrices\nwith coefficients related to the relations of the considered algebra. Moreover,\nwe expand the concept of extension bundles over weighted projective lines with\nthree weights to general weight type and establish similar results in this\nsituation. Finally, we present a method to compute matrices for all modules\nattached to extension bundles using cokernels of maps between direct sums of\nline bundles.\n"}
+{"abstract": "  We address the questions of identifying pairs of interacting neurons from the\nobservation of their spiking activity. The neuronal network is modeled by a\nsystem of interacting point processes with memory of variable length. The\ninfluence of a neuron on another can be either excitatory or inhibitory. To\nidentify the existence and the nature of an interaction we propose an algorithm\nbased only on the observation of joint activity of the two neurons in\nsuccessive time slots. This reduces the amount of computation and storage\nrequired to run the algorithm, thereby making the algorithm suitable for the\nanalysis of real neuronal data sets. We obtain computable upper bounds for the\nprobabilities of false positive and false negative detection. As a corollary we\nprove the consistency of the identification algorithm.\n"}
+{"abstract": "  When transporting an object, we unconsciously adapt our movement to its\nproperties, for instance by slowing down when the item is fragile. The most\nrelevant features of an object are immediately revealed to a human observer by\nthe way the handling occurs, without any need for verbal description. It would\ngreatly facilitate collaboration to enable humanoid robots to perform movements\nthat convey similar intuitive cues to the observers. In this work, we focus on\nhow to generate robot motion adapted to the hidden properties of the\nmanipulated objects, such as their weight and fragility. We explore the\npossibility of leveraging Generative Adversarial Networks to synthesize new\nactions coherent with the properties of the object. The use of a generative\napproach allows us to create new and consistent motion patterns, without the\nneed of collecting a large number of recorded human-led demonstrations.\nBesides, the informative content of the actions is preserved. Our results show\nthat Generative Adversarial Nets can be a powerful tool for the generation of\nnovel and meaningful transportation actions, which result effectively modulated\nas a function of the object weight and the carefulness required in its\nhandling.\n"}
+{"abstract": "  Locally recoverable codes (LRCs) were proposed for the recovery of data in\ndistributed and cloud storage systems about nine years ago. A lot of progress\non the study of LRCs has been made by now. However, there is a lack of general\ntheory on the minimum linear locality of linear codes. In addition, the minimum\nlinear locality of many known families of linear codes is not studied in the\nliterature. Motivated by these two facts, this paper develops some general\ntheory about the minimum linear locality of linear codes, and investigates the\nminimum linear locality of a number of families of linear codes, such as\n$q$-ary Hamming codes, $q$-ary Simplex codes, generalized Reed-Muller codes,\novoid codes, maximum arc codes, the extended hyperoval codes, and near MDS\ncodes. Many classes of both distance-optimal and dimension-optimal LRCs are\npresented in this paper. The minimum linear locality of many families of linear\ncodes are settled with the general theory developed in this paper.\n"}
+{"abstract": "  First-order primal-dual methods are appealing for their low memory overhead,\nfast iterations, and effective parallelization. However, they are often slow at\nfinding high accuracy solutions, which creates a barrier to their use in\ntraditional linear programming (LP) applications. This paper exploits the\nsharpness of primal-dual formulations of LP instances to achieve linear\nconvergence using restarts in a general setting that applies to ADMM\n(alternating direction method of multipliers), PDHG (primal-dual hybrid\ngradient method) and EGM (extragradient method). In the special case of PDHG,\nwithout restarts we show a lower bound of $\\Omega(\\kappa^2 \\log(1/\\epsilon))$,\nwhile with restarts we show an upper bound of $O(\\kappa \\log(1/\\epsilon))$,\nwhere $\\kappa$ is a condition number and $\\epsilon$ is the desired accuracy.\nMoreover, the upper bound is optimal for a wide class of primal-dual methods,\nand applies to the strictly more general class of sharp primal-dual problems.\nWe develop an adaptive restart scheme and verify that restarts significantly\nimprove the ability of PDHG to find high accuracy solutions to LP problems.\n"}
+{"abstract": "  The Horseshoe is a widely used and popular continuous shrinkage prior for\nhigh-dimensional Bayesian linear regression. Recently, regularized versions of\nthe Horseshoe prior have also been introduced in the literature. Various Gibbs\nsampling Markov chains have been developed in the literature to generate\napproximate samples from the corresponding intractable posterior densities.\nEstablishing geometric ergodicity of these Markov chains provides crucial\ntechnical justification for the accuracy of asymptotic standard errors for\nMarkov chain based estimates of posterior quantities. In this paper, we\nestablish geometric ergodicity for various Gibbs samplers corresponding to the\nHorseshoe prior and its regularized variants in the context of linear\nregression. First, we establish geometric ergodicity of a Gibbs sampler for the\noriginal Horseshoe posterior under strictly weaker conditions than existing\nanalyses in the literature. Second, we consider the regularized Horseshoe prior\nintroduced in Piironen and Vehtari (2017), and prove geometric ergodicity for a\nGibbs sampling Markov chain to sample from the corresponding posterior without\nany truncation constraint on the global and local shrinkage parameters.\nFinally, we consider a variant of this regularized Horseshoe prior introduced\nin Nishimura and Suchard (2020), and again establish geometric ergodicity for a\nGibbs sampling Markov chain to sample from the corresponding posterior.\n"}
+{"abstract": "  We observe and comprehend the dynamical Coulomb blockade suppression of the\nelectrical conductance across an electronic quantum channel submitted to a\ntemperature difference. A broadly tunable, spin-polarized Ga(Al)As quantum\nchannel is connected on-chip, through a micron-scale metallic node, to a linear\n$RC$ circuit. The latter is made up of the node's geometrical capacitance $C$\nin parallel with an adjustable resistance $R\\in \\{1/2,1/3,1/4\\}\\times h/e^2$\nformed by 2--4 quantum Hall channels. The system is characterized by three\ntemperatures: a temperature of the electrons in the large electrodes ($T$) and\nin the node ($T_\\mathrm{node}$), and a temperature of the electromagnetic modes\nof the $RC$ circuit ($T_\\mathrm{env}$). The temperature in the node is\nselectively increased by local Joule dissipation, and characterized from\ncurrent fluctuations. For a quantum channel in the tunnel regime, a close match\nis found between conductance measurements and tunnel dynamical Coulomb blockade\ntheory. In the opposite near ballistic regime, we develop a theory that\naccounts for different electronic and electromagnetic bath temperatures, again\nin very good agreement with experimental data. Beyond these regimes, for an\narbitrary quantum channel set in the far out-of-equilibrium situation where the\ntemperature in the node significantly exceeds the one in the large electrodes,\nthe equilibrium (uniform temperature) prediction for the conductance is\nrecovered, albeit at a rescaled temperature $\\alpha T_\\mathrm{node}$.\n"}
+{"abstract": "  Constrained Horn Clauses (CHCs) are an intermediate program representation\nthat can be generated by several verification tools, and that can be processed\nand solved by a number of Horn solvers. One of the main challenges when using\nCHCs in verification is the encoding of heap-allocated data-structures: such\ndata-structures are today either represented explicitly using the theory of\narrays, or transformed away with the help of invariants or refinement types,\ndefeating the purpose of CHCs as a representation that is language-independent\nas well as agnostic of the algorithm implemented by the Horn solver. This paper\npresents an SMT-LIB theory of heap tailored to CHCs, with the goal of enabling\na standard interchange format for programs with heap data-structures. We\nintroduce the syntax of the theory of heap, define its semantics in terms of\naxioms and using a reduction to SMT-LIB arrays and data-types, and discuss its\nproperties and outline possible extensions and future work.\n"}
+{"abstract": "  Quantum secure direct communication (QSDC) based on entanglement can directly\ntransmit confidential information. However, the inability to simultaneously\ndistinguish the four sets of encoded entangled states limits its practical\napplication. Here, we explore a deterministic QSDC network based on time-energy\nentanglement and sum-frequency generation. 15 users are in a fully connected\nQSDC network, and the fidelity of the entangled state shared by any two users\nis greater than 97%. The results show that when any two users are performing\nQSDC over 40 kilometers of optical fiber, the fidelity of the entangled state\nshared by them is still greater than 95%, and the rate of information\ntransmission can be maintained above 1Kbp/s. Our Letter demonstrates the\nfeasibility of a proposed QSDC network, and hence lays the foundation for the\nrealization of satellite-based long-distance and global QSDC in the future.\n"}
+{"abstract": "  In this work we introduce, test and discuss three quantum methods for\ncomputing hub and authority centrality scores in directed networks. The methods\nare based on unitary, continuous-time quantum walks; the construction of a\nsuitable Hermitian Hamiltonian is achieved by performing a quantum walk on the\nassociated bipartite graph.\n  Two methods, called CQAu and CQAw, use the same evolution operator, inspired\nby the classical HITS algorithm, but with different initial states; the\ncomputation of hub and authority scores is performed simultaneously. The third\nmethod, called CQG and inspired by classical PageRank, requires instead two\nseparate runs with different evolution operators, one for hub and one for\nauthority scores.\n  The methods are tested on several directed graphs with different sizes and\nproperties; a comparison with other well-established ranking algorithms is\nprovided. CQAw emerges as the most reliable of the three methods and yields\nrankings that are largely compatible with results from HITS, although CQAu and\nCQG also present interesting features and potential for applications.\n"}
+{"abstract": "  We study the coherent transport of one or two photons in a 1D waveguide\nchirally coupled to a nonlinear resonator. Analytic solutions of the one-photon\nand two-photon scattering is derived. Although the resonator acts as a\nnon-reciprocal phase shifter, light transmission is reciprocal at one-photon\nlevel. However, the forward and reverse transmitted probabilities for two\nphotons incident from either the left side or the right side of the nonlinear\nresonator are nonreciprocal due to the energy redistribution of the two-photon\nbound state. Hence, the nonlinear resonator acts as an optical diode at\ntwo-photon level.\n"}
+{"abstract": "  We investigate the non-perturbative renormalization group flow of the scalar\nGalileon model in flat space. We discuss different expansion schemes of the\nGalileon truncation, including a heat-kernel based derivative expansion, a\nvertex expansion in momentum space and a curvature expansion in terms of a\ncovariant geometric formulation. We find that the Galileon symmetry prevents a\nquantum induced renormalization group running of the Galileon couplings.\nConsequently, the Galileon truncation only features a trivial Gaussian fixed\npoint.\n"}
+{"abstract": "  In this work, we are interested in the determination of the shape of the\nscatterer for the two dimensional time harmonic inverse medium scattering\nproblems in acoustics. The scatterer is assumed to be a piecewise constant\nfunction with a known value inside inhomogeneities, and its shape is\nrepresented by the level set functions for which we investigate the information\nusing the Bayesian method. In the Bayesian framework, the solution of the\ngeometric inverse problem is defined as a posterior probability distribution.\nThe well-posedness of the posterior distribution would be discussed, and the\nMarkov chain Monte Carlo (MCMC) methods will be applied to generate samples\nfrom the arising posterior distribution. Numerical experiments will be\npresented to demonstrate the effectiveness of the proposed method.\n"}
+{"abstract": "  In this paper, we present a deep reinforcement learning platform named FIXAR\nwhich employs fixed-point data types and arithmetic units for the first time\nusing a SW/HW co-design approach. Starting from 32-bit fixed-point data,\nQuantization-Aware Training (QAT) reduces its data precision based on the range\nof activations and performs retraining to minimize the reward degradation.\nFIXAR proposes the adaptive array processing core composed of configurable\nprocessing elements to support both intra-layer parallelism and intra-batch\nparallelism for high-throughput inference and training. Finally, FIXAR was\nimplemented on Xilinx U50 and achieves 25293.3 inferences per second (IPS)\ntraining throughput and 2638.0 IPS/W accelerator efficiency, which is 2.7 times\nfaster and 15.4 times more energy efficient than those of the CPU-GPU platform\nwithout any accuracy degradation.\n"}
+{"abstract": "  Quiescent black hole X-ray binaries (X-ray luminosities <1e34 erg/s) are\nbelieved to be fed by hot accretion flows that launch compact, relativistic\njets. However, due to their low luminosities, quiescent jets have been detected\nin the radio waveband from only five systems so far. Here, we present radio\nobservations of two quiescent black hole X-ray binaries with the Australia\nTelescope Compact Array. One system, GS 1124-684, was not detected. The other\nsystem, BW Cir, was detected over two different epochs in 2018 and 2020, for\nwhich we also obtained quasi-simultaneous X-ray detections with Chandra and\nSwift. BW Cir is now the sixth quiescent X-ray binary with a confirmed radio\njet. However, the distance to BW Cir is uncertain, and we find that BW Cir\nshows different behaviour in the radio/X-ray luminosity plane depending on the\ncorrect distance. Estimates based on its G-type subgiant donor star place BW\nCir at >25 kpc, while initial optical astrometric measurements from Gaia Data\nRelease 2 suggested likely distances of just a few kpc. Here, we use the most\nrecent measurements from Gaia Early Data Release 3 and find a distance\nd=7.1(+4.8/-3.9) kpc and a potential kick velocity PKV=165(+81/-17) km/s, with\ndistances up to ~20 kpc possible based on its parallax and proper motion. Even\nthough there is now less tension between the parallax and donor-star based\ndistance measurements, it remains an unresolved matter, and we conclude with\nsuggestions on how to reconcile the two measurements.\n"}
+{"abstract": "  To develop a machine sound monitoring system, a method for detecting\nanomalous sound is proposed. Exact likelihood estimation using Normalizing\nFlows is a promising technique for unsupervised anomaly detection, but it can\nfail at out-of-distribution detection since the likelihood is affected by the\nsmoothness of the data. To improve the detection performance, we train the\nmodel to assign higher likelihood to target machine sounds and lower likelihood\nto sounds from other machines of the same machine type. We demonstrate that\nthis enables the model to incorporate a self-supervised classification-based\napproach. Experiments conducted using the DCASE 2020 Challenge Task2 dataset\nshowed that the proposed method improves the AUC by 4.6% on average when using\nMasked Autoregressive Flow (MAF) and by 5.8% when using Glow, which is a\nsignificant improvement over the previous method.\n"}
+{"abstract": "  We show that the exceptional surfaces of linear three-dimensional\nnon-Hermitian parity-time-symmetric two-band models attain the form of\ntopologically stable tilted exceptional cones. By relating the exceptional\ncones to the eigenvalues of Dirac operators, we find a connection between the\nexceptional cone and the light cone of an observer in the vicinity of a\nSchwarzschild black hole. When the cone overtilts, light-like\nparticle-antiparticle pairs are created resembling Hawking radiation. We also\ninvestigate dissipative features of the non-Hermitian Hamiltonian related to\nthe latter and comment on potential realizations in laboratory setups.\n"}
+{"abstract": "  Measuring the star-forming properties of AGN hosts is key to our\nunderstanding of galaxy formation and evolution. However, this topic remains\ndebated, partly due to the difficulties in separating the infrared (i.e.\n1--1000 $\\mu$m) emission into AGN and star-forming components. Taking advantage\nof archival far-infrared data from Herschel, we present a new set of AGN and\ngalaxy infrared templates, and introduce the spectral energy distribution\nfitting code IRAGNSEP. Both can be used to measure infrared host galaxy\nproperties, free of AGN contamination. To build these, we used a sample of 100\nlocal ($z$ < 0.3), low-to-high luminosity AGNs (i.e. $L_{\\rm\nbol}~\\sim~10^{42--46}~\\rm erg~s^{-1}$), selected from the 105-month Swift - BAT\nX-ray survey, which have archival Spitzer - IRS spectra and Herschel\nphotometry. We first built a set of seven galaxy templates using a sample of 55\nstar-forming galaxies selected via infrared diagnostics. Using these templates,\ncombined with a flexible model for the AGN contribution, we extracted the\nintrinsic infrared emission of our AGN sample. We further demonstrate that we\ncan reduce the diversity in the intrinsic shapes of AGN spectral energy\ndistributions down to a set of three AGN templates, of which two represent AGN\ncontinuum, and one represents silicate emission. Our results indicate that, on\naverage, the contribution of AGNs to the far-infrared ($\\lambda~\\gtrsim$ 50\n$\\mu$m) is not as high as suggested by some recent work. We further show that\nthe need for two infrared AGN continuum templates could be related to nuclear\nobscuration, where one of our templates appears dominated by the emission of\nthe extended polar dust.\n"}
+{"abstract": "  We introduce two simple online activities to explore the physics of neutron\nstars. These provide an introduction to the basic properties of compact\nobjects, like their masses and radii, for secondary school students. The first\nactivity explores the idea of the minimum mass of a neutron star. It is\ndirectly linked to the concept of binding energy and follows on from our\nprevious activities. The second activity focuses on the maximum mass of neutron\nstars using a solvable model of the neutron star interior. The activities are\nbased on spreadsheets, provided as Supplementary Material, and can be easily\nadapted to different levels, age groups and discussion topics. In particular,\nthese activities can naturally lead towards discussions on extrapolations and\nlimits of theoretical models.\n"}
+{"abstract": "  Edge computing has been an efficient way to provide prompt and near-data\ncomputing services for resource-and-delay sensitive IoT applications via\ncomputation offloading. Effective computation offloading strategies need to\ncomprehensively cope with several major issues, including the allocation of\ndynamic communication and computational resources, the deadline constraints of\nheterogeneous tasks, and the requirements for computationally inexpensive and\ndistributed algorithms. However, most of the existing works mainly focus on\npart of these issues, which would not suffice to achieve expected performance\nin complex and practical scenarios. To tackle this challenge, in this paper, we\nsystematically study a distributed computation offloading problem with hard\ndelay constraints, where heterogeneous computational tasks require continually\noffloading to a set of edge servers via a limiting number of stochastic\ncommunication channels. The task offloading problem is then cast as a\ndelay-constrained long-term stochastic optimization problem under unknown\npriori statistical knowledge. To resolve this problem, we first provide a\ntechnical path to transform and decompose it into several slot-level\nsubproblems, then we develop a distributed online algorithm, namely TODG, to\nefficiently allocate the resources and schedule the offloading tasks with delay\nguarantees. Further, we present a comprehensive analysis for TODG, in terms of\nthe optimality gap, the delay guarantees, and the impact of system parameters.\nExtensive simulation results demonstrate the effectiveness and efficiency of\nTODG.\n"}
+{"abstract": "  We study the problem of bounding path-dependent expectations (within any\nfinite time horizon $d$) over the class of discrete-time martingales whose\nmarginal distributions lie within a prescribed tolerance of a given collection\nof benchmark marginal distributions. This problem is a relaxation of the\nmartingale optimal transport (MOT) problem and is motivated by applications to\nsuper-hedging in financial markets. We show that the empirical version of our\nrelaxed MOT problem can be approximated within $O\\left( n^{-1/2}\\right)$ error\nwhere $n$ is the number of samples of each of the individual marginal\ndistributions (generated independently) and using a suitably constructed\nfinite-dimensional linear programming problem.\n"}
+{"abstract": "  We consider positive critical points of Caffarelli-Kohn-Nirenberg\ninequalities and prove a Liouville type result which allows us to give a\ncomplete classification of the solutions in a certain range of parameters,\nproviding a symmetry result for positive solutions. The governing operator is a\nweighted $p$-Laplace operator, which we consider for a general $p \\in (1,d)$.\nFor $p=2$, the symmetry breaking region for extremals of\nCaffarelli-Kohn-Nirenberg inequalities was completely characterized in [J.\nDolbeault, M. Esteban, M. Loss, Invent. Math. 44 (2016)]. Our results extend\nthis result to a general $p$ and are optimal in some cases.\n"}
+{"abstract": "  The aim of this work is to give a pointfree description of the Cantor set. It\ncan be shown that the Cantor set is homeomorphic to the $p$-adic integers\n$\\mathbb{Z}_{p}:=\\{x\\in\\mathbb{Q}_{p}: |x|_p\\leq 1\\}$ for every prime number\n$p$. To give a pointfree description of the Cantor set, we specify the frame of\n$\\mathbb{Z}_{p}$ by generators and relations. We use the fact that the open\nballs centered at integers generate the open subsets of $\\mathbb{Z}_{p}$ and\nthus we think of them as the basic generators; on this poset we impose some\nrelations and then the resulting quotient is the frame of the Cantor set\n$\\mathcal{L}(\\mathbb{Z}_{p})$. We prove that $\\mathcal{L}(\\mathbb{Z}_{p})$ is a\nspatial frame whose space of points is homeomorphic to $\\mathbb{Z}_{p}$. In\nparticular, we show with pointfree arguments that $\\mathcal{L}(\\mathbb{Z}_{p})$\nis $0$-dimensional, (completely) regular, compact, and metrizable (it admits a\ncountably generated uniformity). Finally, we give a point-free counterpart of\nthe Hausdorff-Alexandroff Theorem which states that \\emph{every compact metric\nspace is a continuous image of the Cantor space} (see, e.g. \\cite{Alexandroff}\nand \\cite{Hausdorff}). We prove the point-free analog: if $L$ is a compact\nmetrizable frame, then there is an injective frame homomorphism from $L$ into\n$\\mathcal{L}(\\mathbb{Z}_{2})$.\n"}
+{"abstract": "  The theoretical approach to a sequential heavy ion double charge exchange\nreaction is presented. A brief introduction into the formal theory of\nsecond-order nuclear reactions and their application to Double Single Charge\nExchange (DSCE) reactions by distorted wave theory is given, thereby completing\nthe theoretical background to our recent work [1]. Formally, the DSCE reaction\namplitudes are shown to be separable into superpositions of distortion factors,\naccounting for initial and final state ion--ion interactions, and nuclear\nmatrix elements. A broad space is given to the construction of nuclear DSCE\nresponse functions on the basis of polarization propagator theory. The nuclear\nresponse tensors resemble the nuclear matrix elements of $2\\nu\\beta\\beta$ decay\nin structure but contain in general a considerable more complex multipole and\nspin structure. The QRPA theory is used to derive explicit expressions for\nnuclear matrix elements (NMEs). The differences between the NME of the first\nand the second interaction vertexes in a DSCE reaction is elucidated. Reduction\nschemes for the transition form factors are discussed by investigating the\nclosure approximation and the momentum structure of form factors. DSCE unit\nstrength cross sections are derived.\n"}
+{"abstract": "  Let $f$ be a martingale with values in a uniformly $p$-smooth Banach space\nand $w$ any positive weight. We show that $\\mathbb{E} (f^* \\cdot w) \\lesssim\n\\mathbb{E}(S_p f \\cdot w^*)$, where $\\cdot^*$ is the martingale maximal\noperator and $S_p$ is the $\\ell^p$ sum of martingale increments.\n"}
+{"abstract": "  Given the semantic descriptions of classes, Zero-Shot Learning (ZSL) aims to\nrecognize unseen classes without labeled training data by exploiting semantic\ninformation, which contains knowledge between seen and unseen classes. Existing\nZSL methods mainly use vectors to represent the embeddings to the semantic\nspace. Despite the popularity, such vector representation limits the\nexpressivity in terms of modeling the intra-class variability for each class.\nWe address this issue by leveraging the use of distribution embeddings. More\nspecifically, both image embeddings and class embeddings are modeled as\nGaussian distributions, where their similarity relationships are preserved\nthrough the use of triplet constraints. The key intuition which guides our\napproach is that for each image, the embedding of the correct class label\nshould be closer than that of any other class label. Extensive experiments on\nmultiple benchmark data sets show that the proposed method achieves highly\ncompetitive results for both traditional ZSL and more challenging Generalized\nZero-Shot Learning (GZSL) settings.\n"}
+{"abstract": "  Knowledge distillation constitutes a simple yet effective way to improve the\nperformance of a compact student network by exploiting the knowledge of a more\npowerful teacher. Nevertheless, the knowledge distillation literature remains\nlimited to the scenario where the student and the teacher tackle the same task.\nHere, we investigate the problem of transferring knowledge not only across\narchitectures but also across tasks. To this end, we study the case of object\ndetection and, instead of following the standard detector-to-detector\ndistillation approach, introduce a classifier-to-detector knowledge transfer\nframework. In particular, we propose strategies to exploit the classification\nteacher to improve both the detector's recognition accuracy and localization\nperformance. Our experiments on several detectors with different backbones\ndemonstrate the effectiveness of our approach, allowing us to outperform the\nstate-of-the-art detector-to-detector distillation methods.\n"}
+{"abstract": "  The lowest critical point of one unitary matrix model with cosine plus\nlogarithmic potential is known to correspond with the $(A_1, A_3)$\nArgyres-Douglas (AD) theory and its double scaling limit derives the\nPainlev\\'{e} II equation with parameter. Here, we consider the critical points\nassociated with all cosine potentials and determine the scaling operators,\ntheir vevs and their scaling dimensions from perturbed string equations at\nplanar level. These dimensions agree with those of $(A_1,A_{4k-1})$ AD theory.\n"}
+{"abstract": "  We consider the effects of electron-hole interaction, 2D confinement and\napplied electric field on direct allowed transitions in III-V semiconductors,\nwith InGaAs as a study case. Instead of Coulomb interaction, we use Gaussian\npotential. It is finite at the origin and has a finite effective range, which\nallows for a more efficient numerical solution of Schr\\\"{o}dinger equation.\nYet, we can expect electroabsorption phenomena to remain qualitatively similar\nto the ones observed for Coulomb excitons. Moreover, we use variation of\nparameters to fit both position and magnitude of the first absorption peak to\nthe Coulomb case. We combine and compare several numerical and approximate\nmethods, including spectral expansion, finite differences, separation of\nvariables and variational approximation. We find that separation of variables\napproach works only for quantum well widths smaller than the exciton radius.\nAfter separation of variables, finite difference solution of the resulting\ninteraction equation gives a much better agreement with the full spectral\nsolution than naive variational approximation. We observe that electric field\nhas a critical effect on the magnitudes of exciton absorption peaks,\nsuppressing previously allowed transitions and enhancing forbidden ones.\nMoreover, for excited states, initially suppressed transitions are enhanced\nagain at higher field strengths.\n"}
+{"abstract": "  We investigate the gravitational settling of a long, model elastic filament\nin homogeneous isotropic turbulence. We show that the flow produces a strongly\nfluctuating settling velocity, whose mean is moderately enhanced over the\nstill-fluid terminal velocity, and whose variance has a power-law dependence on\nthe filament's weight but is surprisingly unaffected by its elasticity. In\ncontrast, the tumbling of the filament is shown to be closely coupled to its\nstretching, and manifests as a Poisson process with a tumbling time that\nincreases with the elastic relaxation time of the filament.\n"}
+{"abstract": "  Meta-learning models, or models that learn to learn, have been a long-desired\ntarget for their ability to quickly solve new tasks. Traditional meta-learning\nmethods can require expensive inner and outer loops, thus there is demand for\nalgorithms that discover strong learners without explicitly searching for them.\nWe draw parallels to the study of evolvable genomes in evolutionary systems --\ngenomes with a strong capacity to adapt -- and propose that meta-learning and\nadaptive evolvability optimize for the same objective: high performance after a\nset of learning iterations. We argue that population-based evolutionary systems\nwith non-static fitness landscapes naturally bias towards high-evolvability\ngenomes, and therefore optimize for populations with strong learning ability.\nWe demonstrate this claim with a simple evolutionary algorithm,\nPopulation-Based Meta Learning (PBML), that consistently discovers genomes\nwhich display higher rates of improvement over generations, and can rapidly\nadapt to solve sparse fitness and robotic control tasks.\n"}
+{"abstract": "  In this paper, we introduce the class of $(A,(m,n))$-isosymmetric operators\nand we study some of their properties, for a positive semi-definite operator\n$A$ and $ m,n\\in\\mathbb{ N}$, which extend, by changing the initial inner\nproduct with the semi-inner product induced by $A$, the well-known class of\n$(m,n)$-isosymmetric operators introduced by Mark Stankus (\\cite{mark1},\n\\cite{mark}). In particular, we characterize a family of $A$-isosymmetric\n$(2\\times2)$ upper triangular operator matrices. Moreover, we show that that if\n$T$ is $(A,(m,n))$-isosymmetric and if $Q$ is a nilpotent operator of order $r$\ndoubly commuting with $T$, then $T^p$ is $(A,(m,n))$-isosymmetric symmetric for\nany $p\\in \\mathbb{N}$ and $(T +Q)$ is $\\big(A,(m+2r -2, n+2r\n-1)\\big)$-isosymmetric. Some properties of the spectrum are also investigated.\n"}
+{"abstract": "  The dynamical properties of free and bound domain-wall excitations in\nIsing-chain materials have recently become the focus of intense research\ninterest. New materials and spectrometers have made it possible to control the\nenvironment of coupled Ising chains by both effective internal and applied\nexternal fields, which can be both longitudinal and transverse, and thus to\ndemonstrate how the resulting magnetic phase transitions and the nature of the\nassociated excited states obey fundamental symmetry properties. In RbCoCl$_3$,\nthe weakly coupled Ising chains form a triangular lattice whose frustrated\ngeometry and magnetic ordering transitions at low temperature open new\npossibilities for the Ising-chain environment. We have investigated the\nstructure and magnetism in RbCoCl$_3$ by high-resolution x-ray diffraction and\nneutron scattering measurements on powder and single crystal samples between\n1.5 K and 300 K. Upon cooling, the Co$^{2+}$ spins develop one-dimensional\nantiferromagnetic correlations along the chain axis ($c$-axis) below 90 K.\nBelow the first N\\'eel temperature, $T_{N1}$ = 28 K, a partial 3D magnetic\norder sets in, with propagation vector ${\\vec k}_1$ = (1/3,1/3,1), the moments\naligned along the $c$-axis and every third chain uncorrelated from its\nneighbours. Only below a second magnetic phase transition at $T_{N2}$ = 13 K\ndoes the system achieve a fully ordered state, with two additional propagation\nvectors: ${\\vec k}_2$ = (0,0,1) establishes a \"honeycomb\" $c$-axis order, in\nwhich 1/3 of the chains are subject to a strong effective mean field due to\ntheir neighbours whereas 2/3 experience no net field, while ${\\vec k}_3$ =\n(1/2,0,1) governs a small, staggered in-plane ordered moment. We conclude that\nRbCoCl$_3$ is an excellent material to study the physics of Ising chains in a\nwide variety of temperature-controlled environments.\n"}
+{"abstract": "  Out-of-domain (OOD) input detection is vital in a task-oriented dialogue\nsystem since the acceptance of unsupported inputs could lead to an incorrect\nresponse of the system. This paper proposes OutFlip, a method to generate\nout-of-domain samples using only in-domain training dataset automatically. A\nwhite-box natural language attack method HotFlip is revised to generate\nout-of-domain samples instead of adversarial examples. Our evaluation results\nshowed that integrating OutFlip-generated out-of-domain samples into the\ntraining dataset could significantly improve an intent classification model's\nout-of-domain detection performance.\n"}
+{"abstract": "  Shubnikov-de Haas oscillation measurements were performed on CaFeAsF up to a\nhigh temperature of $T$ = 9 K. The oscillation frequency of the $\\alpha$ Dirac\nelectron cylinder exhibits a negative shift as the temperature is raised, while\nthat of the $\\beta$ Schr\\\"odinger hole cylinder shows a positive shift. At high\ntemperatures ($T \\geqslant 5$ K) where the $\\beta$ oscillation is negligible,\nthe shift of the $\\alpha$ frequency is proportional to $T^2$ and its magnitude\nagrees within experimental accuracy with the topological frequency shift\nproposed by Guo, Alexandradinata, \\textit{et al.} (arXiv:1910.07608), who\nargues that the energy dependence of the effective mass peculiar to a linear\nband dispersion of Dirac/Weyl fermions gives rise to a frequency shift\nproportional to $T^2$. At low temperatures where the $\\beta$ oscillation is not\nnegligible, the two frequencies ($\\alpha$ and $\\beta$) could not be determined\nso accurately as to allow quantitative comparison to the theory.\n"}
+{"abstract": "  Image segmentation is a common and challenging task in autonomous driving.\nAvailability of sufficient pixel-level annotations for the training data is a\nhurdle. Active learning helps learning from small amounts of data by suggesting\nthe most promising samples for labeling. In this work, we propose a new\npool-based method for active learning, which proposes promising patches\nextracted from full image, in each acquisition step. The problem is framed in\nan exploration-exploitation framework by combining an embedding based on\nUniform Manifold Approximation to model representativeness with entropy as\nuncertainty measure to model informativeness. We applied our proposed method to\nthe autonomous driving datasets CamVid and Cityscapes and performed a\nquantitative comparison with state-of-the-art baselines. We find that our\nactive learning method achieves better performance compared to previous\nmethods.\n"}
+{"abstract": "  Graphical User Interface (GUI) provides visual bridges between software apps\nand end users. However, due to the compatibility of software or hardware, UI\ndisplay issues such as text overlap, blurred screen, image missing always occur\nduring GUI rendering on different devices. Because these UI display issues can\nbe found directly by human eyes, in this paper, we implement an online UI\ndisplay issue detection tool OwlEyes-Online, which provides a simple and\neasy-to-use platform for users to realize the automatic detection and\nlocalization of UI display issues. The OwlEyes-Online can automatically run the\napp and get its screenshots and XML files, and then detect the existence of\nissues by analyzing the screenshots. In addition, OwlEyes-Online can also find\nthe detailed area of the issue in the given screenshots to further remind\ndevelopers. Finally, OwlEyes-Online will automatically generate test reports\nwith UI display issues detected in app screenshots and send them to users. The\nOwlEyes-Online was evaluated and proved to be able to accurately detect UI\ndisplay issues. Tool Link: http://www.owleyes.online:7476 Github Link:\nhttps://github.com/franklinbill/owleyes Demo Video Link:\nhttps://youtu.be/002nHZBxtCY\n"}
+{"abstract": "  In twisted homobilayer transition metal dichalcogenides, intra- and\ninter-layer valley excitons hybridize with the layer configurations spatially\nvarying in the moir\\'e. The ground state valley excitons are trapped at two\nhigh-symmetry points with opposite electric dipoles in a moir\\'e supercell,\nforming a honeycomb superlattice of nearest-neighbor dipolar attraction. We\nfind that the spatial texture of layer configuration results in a luminescence\nanomaly of the moir\\'e trapped excitons, where a tiny displacement by\ninteractions dramatically increases the brightness and changes polarization\nfrom circular to linear. At full filling, radiative recombination predominantly\noccurs at edges and vacancies of the exciton superlattice. The anomaly also\nmanifests in the cascaded emission of small clusters, producing chains of\npolarization entangled photons. An interlayer bias can switch the superlattice\ninto a single-orbital triangular lattice with repulsive interactions only,\nwhere the luminescence anomaly can be exploited to distinguish ordered states\nand domain boundaries at fractional filling.\n"}
+{"abstract": "  In this work we propose and analyze a new framework to learn feedback control\npolicies that exhibit provable guarantees on the closed-loop performance and\nrobustness to bounded (adversarial) perturbations. These policies are learned\nfrom expert demonstrations without any prior knowledge of the task, its cost\nfunction, and system dynamics. In contrast to the existing algorithms in\nimitation learning and inverse reinforcement learning, we use a\nLipschitz-constrained loss minimization scheme to learn control policies with\ncertified robustness. We establish robust stability of the closed-loop system\nunder the learned control policy and derive an upper bound on its regret, which\nbounds the sub-optimality of the closed-loop performance with respect to the\nexpert policy. We also derive a robustness bound for the deterioration of the\nclosed-loop performance under bounded (adversarial) perturbations on the state\nmeasurements. Ultimately, our results suggest the existence of an underlying\ntradeoff between nominal closed-loop performance and adversarial robustness,\nand that improvements in nominal closed-loop performance can only be made at\nthe expense of robustness to adversarial perturbations. Numerical results\nvalidate our analysis and demonstrate the effectiveness of our robust feedback\npolicy learning framework.\n"}
+{"abstract": "  Two-dimensional (2D) MoS$_2$ has been intensively investigated for its use in\nthe fields of microelectronics, nanoelectronics, and optoelectronics. However,\nintrinsic 2D MoS$_2$ is usually used as the n-type semiconductor due to the\nunintentional sulphur vacancies and surface gas adsorption.The synthesis and\ncharacterization of 2D MoS$_2$ semiconductor of p-type are crucial for the\ndevelopment of relevant p-n junction devices, as well as the practical\napplications of 2D MoS$_2$ in the next-generation CMOS integrated circuit.\nHere, we synthesize high-quality, wafer-scale, 2D p-type MoS$_2$\n(Mo$_{1-x}$Nb$_x$S$_2$) with various niobium (Nb) mole fractions from 0 to 7.6%\nby a creative two-step method. The dielectric functions of 2D Mo1-xNbxS2 are\naccurately determined by spectroscopic ellipsometry. We find that the\nincreasing fraction of Nb dopant in 2D MoS$_2$ can modulate and promote the\ncombination of A and B exciton peaks of 2D MoS$_2$. The direct causes of this\nimpurity-tunable combination are interpreted as the joint influence of\ndecreasing peak A and broadening peak B. We explain the broadening peak B as\nthe multiple transitions from the impurity-induced valance bands to the\nconductive band minimum at K point of Brillouin zone by comparing and analyzing\nthe simulated electronic structure of intrinsic and 2D Nb-doped MoS$_2$. A\np-type FET based on the 2D Nb-doped MoS$_2$ was fabricated for\ncharacterization, and its working performance is expected to be adjustable as a\nfunction of concentration of Nb dopant according to our theoretical research.\nOur study is informative for comprehending optical and electronic properties of\nextrinsic 2D transitional metal dichalcogenides, which is important and\nimperative for the development and optimization of corresponding photonics and\noptoelectronics devices.\n"}
+{"abstract": "  We discuss the realization of a universal set of ultrafast single- and\ntwo-qubit operations with superconducting quantum circuits and investigate the\nmost relevant physical and technical limitations that arise when pushing for\nfaster and faster gates. With the help of numerical optimization techniques, we\nestablish a fundamental bound on the minimal gate time, which is determined\nindependently of the qubit design solely by its nonlinearity. In addition,\nimportant practical restrictions arise from the finite qubit transition\nfrequency and the limited bandwidth of the control pulses. We show that for\nhighly anharmonic flux qubits and commercially available control electronics,\nelementary single- and two-qubit operations can be implemented in about 100\npicoseconds with residual gate errors below $10^{-4}$. Under the same\nconditions, we simulate the complete execution of a compressed version of\nShor's algorithm for factoring the number 15 in about one nanosecond. These\nresults demonstrate that compared to state-of-the-art implementations with\ntransmon qubits, a hundredfold increase in the speed of gate operations with\nsuperconducting circuits is still feasible.\n"}
+{"abstract": "  A measure-preserving formalism (MPF) is constructed and applied to spin/band\nmodels, which yield observations about pumping. It occurs at topological phase\ntransition (TPT), i.e., a $Z_2$-flip, suggesting that $Z_2$ can imply bulk\neffects. The model's asymptotic behavior is analytically solved via MPF. The\npumping probability is geometric, fractional, and has a ceiling of\n$\\frac{1}{2}$. Intriguingly, theorems are proved about occurrence conditions,\nwhich are linked to the system's dimension and the distinction between rational\nand irrational numbers. Experimental detection is discussed.\n"}
+{"abstract": "  Word embeddings are a basic building block of modern NLP pipelines. Efforts\nhave been made to learn rich, efficient, and interpretable embeddings for large\ngeneric datasets available in the public domain. However, these embeddings have\nlimited applicability for small corpora from specific domains such as\nautomotive, manufacturing, maintenance and support, etc. In this work, we\npresent a comprehensive notion of interpretability for word embeddings and\npropose a novel method to generate highly interpretable and efficient\nembeddings for a domain-specific small corpus. We report the evaluation results\nof our resulting word embeddings and demonstrate their novel features for\nenhanced interpretability.\n"}
+{"abstract": "  Optical bottle beams can be used to trap atoms and small low-index particles.\nWe introduce a figure of merit for optical bottle beams, specifically in the\ncontext of optical traps, and use it to compare optical bottle-beam traps\nobtained by three different methods. Using this figure of merit and an\noptimization algorithm, we identified optical bottle-beam traps based on a\nGaussian beam illuminating a metasurface that are superior in terms of power\nefficiency than existing approaches. We numerically demonstrate a silicon\nmetasurface for creating an optical bottle-beam trap.\n"}
+{"abstract": "  An effective and efficient method that solves the high-order and the\nnon-linear ordinary differential equations is provided. The method is based on\nthe ratio net. By comparing the method with existing methods such as the\npolynomial based method and the multilayer perceptron network based method, we\nshow that the ratio net gives good results and has higher efficiency.\n"}
+{"abstract": "  Automatic speech recognition (ASR) is a relevant area in multiple settings\nbecause it provides a natural communication mechanism between applications and\nusers. ASRs often fail in environments that use language specific to particular\napplication domains. Some strategies have been explored to reduce errors in\nclosed ASRs through post-processing, particularly automatic spell checking, and\ndeep learning approaches. In this article, we explore using a deep neural\nnetwork to refine the results of a phonetic correction algorithm applied to a\ntelesales audio database. The results exhibit a reduction in the word error\nrate (WER), both in the original transcription and in the phonetic correction,\nwhich shows the viability of deep learning models together with post-processing\ncorrection strategies to reduce errors made by closed ASRs in specific language\ndomains.\n"}
+{"abstract": "  Over the last few decades, many architectures have been developed that\nharness the power of neural networks to detect objects in near real-time.\nTraining such systems requires substantial time across multiple GPUs and\nmassive labeled training datasets. Although the goal of these systems is\ngeneralizability, they are often impractical in real-life applications due to\nflexibility, robustness, or speed issues. This paper proposes RMOPP: A robust\nmulti-objective post-processing algorithm to boost the performance of fast\npre-trained object detectors with a negligible impact on their speed.\nSpecifically, RMOPP is a statistically driven, post-processing algorithm that\nallows for simultaneous optimization of precision and recall. A unique feature\nof RMOPP is the Pareto frontier that identifies dominant possible\npost-processed detectors to optimize for both precision and recall. RMOPP\nexplores the full potential of a pre-trained object detector and is deployable\nfor near real-time predictions. We also provide a compelling test case on\nYOLOv2 using the MS-COCO dataset.\n"}
+{"abstract": "  Surface stresses, in nano-sized anode particles undergoing chemomechanical\ninteractions, have a relaxing effect on the diffusion-induced stresses thus\nimproving the mechanical endurance of the particles, whereas, the compressive\neffect of surface stresses degrades the electrochemical performance. However,\nthis straightforward prediction of an improved mechanical performance is\nchallenged in this work. Silicon nanowires undergoing huge volumetric changes\nduring lithiation, may undergo significant axial length-increase, which serves\nas an important criterion in determining the mechanical performance of SiNWs.\nInterestingly, surface stresses tend to reduce the length-increase under\npotentiostatic charging condition, but under galvanostatic charging, the\nlength-increase gets enhanced, thus degrading the mechanical performance of the\nSiNWs. To further make the study more inclusive, the nanowire is modelled with\na constraining material at its core, and a competitive analysis is presented\nfor the overall performance of the anode particles under the combined effects\nof surface stresses and constraining material. The mathematical model is based\non large deformation theory, considering two-way coupling of diffusion-induced\nstresses and stress-enhanced diffusion. It is hoped that this study will\nprovide a fresh perspective in designing next-generation lithium-ion battery\nparticles.\n"}
+{"abstract": "  In this paper, the ground state Wigner function of a many-body system is\nexplored theoretically and numerically. First, an eigenvalue problem for Wigner\nfunction is derived based on the energy operator of the system. The validity of\nfinding the ground state through solving this eigenvalue problem is obtained by\nbuilding a correspondence between its solution and the solution of stationary\nSchr\\\"odinger equation. Then, a numerical method is designed for solving\nproposed eigenvalue problem in one dimensional case, which can be briefly\ndescribed by i) a simplified model is derived based on a quantum hydrodynamic\nmodel [Z. Cai et al, J. Math. Chem., 2013] to reduce the dimension of the\nproblem, ii) an imaginary time propagation method is designed for solving the\nmodel, and numerical techniques such as solution reconstruction are proposed\nfor the feasibility of the method. Results of several numerical experiments\nverify our method, in which the potential application of the method for large\nscale system is demonstrated by examples with density functional theory.\n"}
+{"abstract": "  We study Density Functional Theory models for systems which are\ntranslationally invariant in some directions, such as a homogeneous 2-d slab in\nthe 3-d space. We show how the different terms of the energy are modified and\nwe derive reduced equations in the remaining directions. In the Thomas-Fermi\nmodel, we prove that there is perfect screening, and provide decay estimates\nfor the electronic density away from the slab. In Kohn-Sham models, we prove\nthat the Pauli principle is replaced by a penalization term in the energy. In\nthe reduced Hartree-Fock model in particular, we prove that the resulting model\nis well-posed, and provide some properties for the minimizer.\n"}
+{"abstract": "  Quantum information processing tasks require exotic quantum states as a\nprerequisite. They are usually prepared with many different methods tailored to\nthe specific resource state. Here we provide a versatile unified state\npreparation scheme based on a driven quantum network composed of\nrandomly-coupled fermionic nodes. The output of such a system is then\nsuperposed with the help of linear mixing where weights and phases are trained\nin order to obtain desired output quantum states. We explicitly show that our\nmethod is robust and can be utilized to create almost perfect maximally\nentangled, NOON, W, cluster, and discorded states. Furthermore, the treatment\nincludes energy decay in the system as well as dephasing and depolarization.\nUnder these noisy conditions we show that the target states are achieved with\nhigh fidelity by tuning controllable parameters and providing sufficient\nstrength to the driving of the quantum network. Finally, in very noisy systems,\nwhere noise is comparable to the driving strength, we show how to concentrate\nentanglement by mixing more states in a larger network.\n"}
+{"abstract": "  Deep neural networks (DNNs) have been shown to be vulnerable against\nadversarial examples (AEs), which are maliciously designed to cause dramatic\nmodel output errors. In this work, we reveal that normal examples (NEs) are\ninsensitive to the fluctuations occurring at the highly-curved region of the\ndecision boundary, while AEs typically designed over one single domain (mostly\nspatial domain) exhibit exorbitant sensitivity on such fluctuations. This\nphenomenon motivates us to design another classifier (called dual classifier)\nwith transformed decision boundary, which can be collaboratively used with the\noriginal classifier (called primal classifier) to detect AEs, by virtue of the\nsensitivity inconsistency. When comparing with the state-of-the-art algorithms\nbased on Local Intrinsic Dimensionality (LID), Mahalanobis Distance (MD), and\nFeature Squeezing (FS), our proposed Sensitivity Inconsistency Detector (SID)\nachieves improved AE detection performance and superior generalization\ncapabilities, especially in the challenging cases where the adversarial\nperturbation levels are small. Intensive experimental results on ResNet and VGG\nvalidate the superiority of the proposed SID.\n"}
+{"abstract": "  In this paper, some generalized metric properties in strongly topological\ngyrogroups are studied.\n"}
+{"abstract": "  A magnon Bose-Einstein condensate in superfluid $^3$He is a fine instrument\nfor studying the surrounding macroscopic quantum system. At zero temperature,\nthe BEC is subject to a few, distinct forms of decay into other collective\nexcitations, owing to momentum and energy conservation in a quantum vacuum. We\nstudy the vortex-Higgs mechanism: the vortices relax the requirement for\nmomentum conservation, allowing the optical magnons of the BEC to transform\ninto light Higgs quasiparticles. This observation expands the spectrum of\npossible interactions between magnetic quasiparticles in $^3$He-B, opens\npathways for hunting down elusive phenomena such as the Kelvin wave cascade or\nbound Majorana fermions, and lays groundwork for building magnon-based quantum\ndevices.\n"}
+{"abstract": "  Natural language-based vehicle retrieval is a task to find a target vehicle\nwithin a given image based on a natural language description as a query. This\ntechnology can be applied to various areas including police searching for a\nsuspect vehicle. However, it is challenging due to the ambiguity of language\ndescriptions and the difficulty of processing multi-modal data. To tackle this\nproblem, we propose a deep neural network called SBNet that performs natural\nlanguage-based segmentation for vehicle retrieval. We also propose two\ntask-specific modules to improve performance: a substitution module that helps\nfeatures from different domains to be embedded in the same space and a future\nprediction module that learns temporal information. SBnet has been trained\nusing the CityFlow-NL dataset that contains 2,498 tracks of vehicles with three\nunique natural language descriptions each and tested 530 unique vehicle tracks\nand their corresponding query sets. SBNet achieved a significant improvement\nover the baseline in the natural language-based vehicle tracking track in the\nAI City Challenge 2021.\n"}
+{"abstract": "  Let $n \\ge 2$ be an integer and consider the defining ideal of the Fermat\nconfiguration of points in $\\mathbb{P}^2$:\n$I_n=(x(y^n-z^n),y(z^n-x^n),z(x^n-y^n)) \\subset R=\\mathbb{C}[x,y,z]$. In this\npaper, we compute explicitly the least degree of generators of its symbolic\npowers in all unknown cases. As direct applications, we easily verify\nChudnovsky's Conjecture, Demailly's Conjecture and Harbourne-Huneke Containment\nproblem as well as calculating explicitly the Waldschmidt constant and\n(asymptotic) resurgence number.\n"}
+{"abstract": "  In a classical plasma the momentum distribution, $n(k)$, decays\nexponentially, for large $k$, and the same is observed for an ideal Fermi gas.\nHowever, when quantum and correlation effects are relevant simultaneously, an\nalgebraic decay, $n_\\infty(k)\\sim k^{-8}$ has been predicted. This is of\nrelevance for cross sections and threshold processes in dense plasmas that\ndepend on the number of energetic particles. Here we present extensive\n\\textit{ab initio} results for the momentum distribution of the nonideal\nuniform electron gas at warm dense matter conditions. Our results are based on\nfirst principle fermionic path integral Monte Carlo (CPIMC) simulations and\nclearly confirm the $k^{-8}$ asymptotic. This asymptotic behavior is directly\nlinked to short-range correlations which are analyzed via the on-top pair\ndistribution function (on-top PDF), i.e. the PDF of electrons with opposite\nspin. We present extensive results for the density and temperature dependence\nof the on-top PDF and for the momentum distribution in the entire momentum\nrange.\n"}
+{"abstract": "  Natural Language Processing offers new insights into language data across\nalmost all disciplines and domains, and allows us to corroborate and/or\nchallenge existing knowledge. The primary hurdles to widening participation in\nand use of these new research tools are, first, a lack of coding skills in\nstudents across K-16, and in the population at large, and second, a lack of\nknowledge of how NLP-methods can be used to answer questions of disciplinary\ninterest outside of linguistics and/or computer science. To broaden\nparticipation in NLP and improve NLP-literacy, we introduced a new tool\nweb-based tool called Natural Language Processing 4 All (NLP4All). The intended\npurpose of NLP4All is to help teachers facilitate learning with and about NLP,\nby providing easy-to-use interfaces to NLP-methods, data, and analyses, making\nit possible for non- and novice-programmers to learn NLP concepts\ninteractively.\n"}
+{"abstract": "  In this paper, we propose and analyze a diffuse interface model for\ninductionless magnetohydrodynamic fluids. The model couples a convective\nCahn-Hilliard equation for the evolution of the interface, the Navier-Stokes\nsystem for fluid flow and the possion quation for electrostatics. The model is\nderived from Onsager's variational principle and conservation laws\nsystematically. We perform formally matched asymptotic expansions and develop\nseveral sharp interface models in the limit when the interfacial thickness\ntends to zero. It is shown that the sharp interface limit of the models are the\nstandard incompressible inductionless magnetohydrodynamic equations coupled\nwith several different interface conditions for different choice of the\nmobilities. Numerical results verify the convergence of the diffuse interface\nmodel with different mobilitiess.\n"}
+{"abstract": "  We consider the concatenation of a convolutional code (CC) with an optimized\ncyclic redundancy check (CRC) code as a promising paradigm for good short\nblocklength codes. The resulting CRC-aided convolutional code naturally permits\nthe use of serial list Viterbi decoding (SLVD) to achieve maximum-likelihood\ndecoding. The convolutional encoder of interest is of rate-$1/\\omega$ and the\nconvolutional code is either zero-terminated (ZT) or tail-biting (TB). The\nresulting CRC-aided convolutional code is called a CRC-ZTCC or a CRC-TBCC. To\ndesign a good CRC-aided convolutional code, we propose the distance-spectrum\noptimal (DSO) CRC polynomial. A DSO CRC search algorithm for the TBCC is\nprovided. Our analysis reveals that the complexity of SLVD is governed by the\nexpected list rank which converges to $1$ at high SNR. This allows a good\nperformance to be achieved with a small increase in complexity. In this paper,\nwe focus on transmitting $64$ information bits with a rate-$1/2$ convolutional\nencoder. For a target error probability $10^{-4}$, simulations show that the\nbest CRC-ZTCC approaches the random-coding union (RCU) bound within $0.4$ dB.\nSeveral CRC-TBCCs outperform the RCU bound at moderate SNR values.\n"}
+{"abstract": "  In this paper, we obtain some results on precise large deviations for\nnon-random and random sums of widely dependent random variables with common\ndominatedly varying tail distribution or consistently varying tail distribution\non $(-\\infty,\\infty)$. Then we apply the results to reinsurance and insurance\nand give some asymptotic estimates on proportional reinsurance, random-time\nruin probability and the finite-time ruin probability.\n"}
+{"abstract": "  The energy range between about 100 keV and 1 GeV is of interest for a vast\nclass of astrophysical topics. In particular, (1) it is the missing ingredient\nfor understanding extreme processes in the multi-messenger era; (2) it allows\nlocalizing cosmic-ray interactions with background material and radiation in\nthe Universe, and spotting the reprocessing of these particles; (3) last but\nnot least, gamma-ray emission lines trace the formation of elements in the\nGalaxy and beyond. In addition, studying the still largely unexplored MeV\ndomain of astronomy would provide for a rich observatory science, including the\nstudy of compact objects, solar- and Earth-science, as well as fundamental\nphysics. The technological development of silicon microstrip detectors makes it\npossible now to detect MeV photons in space with high efficiency and low\nbackground. During the last decade, a concept of detector (\"ASTROGAM\") has been\nproposed to fulfil these goals, based on a silicon hodoscope, a 3D\nposition-sensitive calorimeter, and an anticoincidence detector. In this paper\nwe stress the importance of a medium size (M-class) space mission, dubbed\n\"ASTROMEV\", to fulfil these objectives.\n"}
+{"abstract": "  In network science complex systems are represented as a mathematical graphs\nconsisting of a set of nodes representing the components and a set of edges\nrepresenting their interactions. The framework of networks has led to\nsignificant advances in the understanding of the structure, formation and\nfunction of complex systems. Social and biological processes such as the\ndynamics of epidemics, the diffusion of information in social media, the\ninteractions between species in ecosystems or the communication between neurons\nin our brains are all actively studied using dynamical models on complex\nnetworks. In all of these systems, the patterns of connections at the\nindividual level play a fundamental role on the global dynamics and finding the\nmost important nodes allows one to better understand and predict their\nbehaviors. An important research effort in network science has therefore been\ndedicated to the development of methods allowing to find the most important\nnodes in networks. In this short entry, we describe network centrality measures\nbased on the notions of network traversal they rely on. This entry aims at\nbeing an introduction to this extremely vast topic, with many contributions\nfrom several fields, and is by no means an exhaustive review of all the\nliterature about network centralities.\n"}
+{"abstract": "  We propose a new method to define anomaly scores and apply this to particle\nphysics collider events. Anomalies can be either rare, meaning that these\nevents are a minority in the normal dataset, or different, meaning they have\nvalues that are not inside the dataset. We quantify these two properties using\nan ensemble of One-Class Deep Support Vector Data Description models, which\nquantifies differentness, and an autoregressive flow model, which quantifies\nrareness. These two parameters are then combined into a single anomaly score\nusing different combination algorithms. We train the models using a dataset\ncontaining only simulated collisions from the Standard Model of particle\nphysics and test it using various hypothetical signals in four different\nchannels and a secret dataset where the signals are unknown to us. The anomaly\ndetection method described here has been evaluated in a summary paper [1] where\nit performed very well compared to a large number of other methods. The method\nis simple to implement and is applicable to other datasets in other fields as\nwell.\n"}
+{"abstract": "  As a fundamental structure in real-world networks, communities can be\nreflected by abundant node attributes with the fusion of graph topology. In\nattribute-aware community detection, probabilistic generative models (PGMs)\nhave become the mainstream fusion method due to their principled\ncharacterization and interpretation. Here, we propose a novel PGM without\nimposing any distributional assumptions on attributes, which is superior to\nexisting PGMs that require attributes to be categorical or Gaussian\ndistributed. Based on the famous block model of graph structure, our model\nfuses the attribute by describing its effect on node popularity using an\nadditional term. To characterize the effect quantitatively, we analyze the\ndetectability of communities for the proposed model and then establish the\nrequirements of the attribute-popularity term, which leads to a new scheme for\nthe model selection problem in attribute-aware community detection. With the\nmodel determined, an efficient algorithm is developed to estimate the\nparameters and to infer the communities. The proposed method is validated from\ntwo aspects. First, the effectiveness of our algorithm is theoretically\nguaranteed by the detectability condition, whose correctness is verified by\nnumerical experiments on artificial graphs. Second, extensive experiments show\nthat our method outperforms the competing approaches on a variety of real-world\nnetworks.\n"}
+{"abstract": "  The link relation on simple subcoalgebras is used for decompositions of\ncoalgebras. In this paper, we provide more sufficient conditions for this link\nrelation, and prove a formula on the products between link-indecomposable\ncomponents of Hopf algebras with the dual Chevalley property. Furthermore, we\nshow that each of its component is generated by a simple subcoalgebra, as a\nfaithfully flat module (in fact, a projective generator) over a Hopf subalgebra\nwhich is the component containing the unit element. Our conclusions generalize\nsome relevant results on pointed Hopf algebras, which were established by\nMontgomery in 1995.\n"}
+{"abstract": "  Mycelium networks are promising substrates for designing unconventional\ncomputing devices providing rich topologies and geometries where signals\npropagate and interact. Fulfilling our long-term objectives of prototyping\nelectrical analog computers from living mycelium networks, including networks\nhybridised with nanoparticles, we explore the possibility of implementing\nBoolean logical gates based on electrical properties of fungal colonies. We\nconverted a 3D image-data stack of \\emph{Aspergillus niger} fungal colony to an\nEuclidean graph and modelled the colony as resistive and capacitive (RC)\nnetworks, where electrical parameters of edges were functions of the edges'\nlengths. We found that {\\sc and}, {\\sc or} and {\\sc and-not} gates are\nimplementable in RC networks derived from the geometrical structure of the real\nfungal colony.\n"}
+{"abstract": "  We report a comprehensive investigation of Ln2NiIrO6 (Ln = La, Pr, Nd) using\nthermodynamic and transport properties, neutron powder diffraction, resonant\ninelastic x-ray scattering, and density functional theory (DFT) calculations to\ninvestigate the role of A-site cations on the magnetic interactions in this\nfamily of hybrid 3d-5d-4f compositions. Magnetic structure determination using\nneutron diffraction reveals antiferromagnetism for La2NiIrO6, a collinear\nferrimagnetic Ni/Ir state that is driven to long range antiferromagnetism upon\nthe onset of Nd ordering in Nd2NiIrO6, and a non-collinear ferrimagnetic Ni/Ir\nsublattice interpenetrated by a ferromagnetic Pr lattice for Pr2NiIrO6. For\nPr2NiIrO6 heat capacity results reveal the presence of two independent magnetic\nsublattices and transport resistivity indicates insulating behavior and a\nconduction pathway that is thermally mediated. First principles DFT calculation\nelucidates the existence of the two independent magnetic sublattices within\nPr2NiIrO6 and offers insight into the behavior in La2NiIrO6 and Nd2NiIrO6.\nResonant inelastic x-ray scattering is consistent with spin-orbit coupling\nsplitting the t2g manifold of octahedral Ir4+ into a Jeff = 1/2 and Jeff = 3/2\nstate for all members of the series considered.\n"}
+{"abstract": "  Constraint satisfaction problems (CSP's) and data stream models are two\npowerful abstractions to capture a wide variety of problems arising in\ndifferent domains of computer science. Developments in the two communities have\nmostly occurred independently and with little interaction between them. In this\nwork, we seek to investigate whether bridging the seeming communication gap\nbetween the two communities may pave the way to richer fundamental insights. To\nthis end, we focus on two foundational problems: model counting for CSP's and\ncomputation of zeroth frequency moments ($F_0$) for data streams.\n  Our investigations lead us to observe striking similarity in the core\ntechniques employed in the algorithmic frameworks that have evolved separately\nfor model counting and $F_0$ computation. We design a recipe for translation of\nalgorithms developed for $F_0$ estimation to that of model counting, resulting\nin new algorithms for model counting. We then observe that algorithms in the\ncontext of distributed streaming can be transformed to distributed algorithms\nfor model counting. We next turn our attention to viewing streaming from the\nlens of counting and show that framing $F_0$ estimation as a special case of\n#DNF counting allows us to obtain a general recipe for a rich class of\nstreaming problems, which had been subjected to case-specific analysis in prior\nworks. In particular, our view yields a state-of-the art algorithm for\nmultidimensional range efficient $F_0$ estimation with a simpler analysis.\n"}
+{"abstract": "  Quantum chemistry calculations such as the prediction of molecular properties\nand modeling of chemical reactions are a few of the critical areas where\nnear-term quantum computers can showcase quantum advantage. We present a method\nto calculate energy derivatives for both ground state and excited state\nenergies with respect to the parameters of a chemical system based on the\nframework of the variational quantum eigensolver (VQE). A low-depth\nimplementation of quantum circuits within the hybrid variational paradigm is\ndesigned, and their computational costs are analyzed. We showcase the\neffectiveness of our method by incorporating it in some key quantum chemistry\napplications of energy derivatives, such as to perform minimum energy\nconfiguration search and estimate molecular response properties estimation of\nH$_2$ molecule, and also to find the transition state of H$_2$ + H\n$\\leftrightarrow$ H + H$_2$ reaction. The obtained results are shown to be in\ncomplete agreement with their respective full configuration interaction (FCI)\nvalues.\n"}
+{"abstract": "  We introduce shadow structures for singular knot theory. Precisely, we define\n\\emph{two} invariants of singular knots and links. First, we introduce a notion\nof action of a singquandle on a set to define a shadow counting invariant of\nsingular links which generalize the classical shadow colorings of knots by\nquandles. We then define a shadow polynomial invariant for shadow structures.\nLastly, we enhance the shadow counting invariant by combining both the shadow\ncounting invariant and the shadow polynomial invariant. Explicit examples of\ncomputations are given.\n"}
+{"abstract": "  Due to their simplicity and versatility of design, straight strip or\nrectangular pad anode structures are frequently employed with micro-pattern gas\ndetectors to reconstruct high precision space points for various tracking\napplications. The particle impact point is typically determined by\ninterpolating the charge collected by several neighboring pads. However, to\neffectively extract the inherent positional information, the lateral spacing of\nthe straight pads must be significantly smaller than the extent of the charge\ncloud. In contrast, highly interleaved anode patterns, such as zigzags, can\nadequately sample the charge with a pitch comparable to the size of the charge\ncloud or even larger. This has the considerable advantage of providing the same\nperformance while requiring far fewer instrumented channels. Additionally, the\ngeometric parameters defining such zigzag structures may be tuned to provide a\nuniform detector response without the need for so-called pad response\nfunctions, while simultaneously maintaining excellent position resolution. We\nhave measured the position resolution of a variety of zigzag shaped anode\npatterns optimized for various MPGDs, including GEM, Micromegas, and\nmicro-RWELL and compared this performance to the same detectors equipped with\nstraight pads of varying pitch. We report on the performance results of each\nreadout structure, evaluated under identical conditions in a test beam.\n"}
+{"abstract": "  Electroencephalography (EEG) is essential for the diagnosis of epilepsy, but\nit requires expertise and experience to identify abnormalities. It is thus\ncrucial to develop automated models for the detection of abnormalities in EEGs\nrelated to epilepsy. This paper describes the development of a novel class of\ncompact convolutional neural networks (CNNs) for detecting abnormal patterns\nand electrodes in EEGs for epilepsy. The designed model is inspired by a CNN\ndeveloped for brain-computer interfacing called multichannel EEGNet (mEEGNet).\nUnlike the EEGNet, the proposed model, mEEGNet, has the same number of\nelectrode inputs and outputs to detect abnormal patterns. The mEEGNet was\nevaluated with a clinical dataset consisting of 29 cases of juvenile and\nchildhood absence epilepsy labeled by a clinical expert. The labels were given\nto paroxysmal discharges visually observed in both ictal (seizure) and\ninterictal (nonseizure) durations. Results showed that the mEEGNet detected\nabnormalities with the area under the curve, F1-values, and sensitivity\nequivalent to or higher than those of existing CNNs. Moreover, the number of\nparameters is much smaller than other CNN models. To our knowledge, the dataset\nof absence epilepsy validated with machine learning through this research is\nthe largest in the literature.\n"}
+{"abstract": "  Single-cycle pulses with deterministic carrier-envelope phase enable the\nstudy and control of light-matter interactions at the sub-cycle timescale, as\nwell as the efficient generation of low-noise multi-octave frequency combs.\nHowever, current single-cycle light sources are difficult to implement and\noperate, hindering their application and accessibility in a wider range of\nresearch. In this paper, we present a single-cycle 100 MHz frequency comb in a\ncompact, turn-key, and reliable all-silica-fiber format. This is achieved by\namplifying 2 $\\mu$m seed pulses in heavily-doped Tm:fiber, followed by cascaded\nself-compression to yield 6.8 fs pulses with 215 kW peak power and 374 mW\naverage power. The corresponding spectrum covers more than two octaves, from\nbelow 700 nm up to 3500 nm. Driven by this single-cycle pump, supercontinuum\nwith 180 mW of integrated power and a smooth spectral amplitude between 2100\nand 2700 nm is generated directly in silica fibers. To broaden\napplications,few-cycle pulses extending from 6 $\\mu$m to beyond 22 $\\mu$m with\nlong-term stable carrier-envelope phase are created using intra-pulse\ndifference frequency, and electro-optic sampling yields comb-tooth-resolved\nspectra. Our work demonstrates the first all-fiber configuration that generates\nsingle-cycle pulses, and provides a practical source to study nonlinear optics\non the same timescale.\n"}
+{"abstract": "  We propose a novel generic reputation bootstrapping framework for composite\nservices. Multiple reputation-related indicators are considered in a\nlayer-based framework to implicitly reflect the reputation of the component\nservices. The importance of an indicator on the future performance of a\ncomponent service is learned using a modified Random Forest algorithm. We\npropose a topology-aware Forest Deep Neural Network (fDNN) to find the\ncorrelations between the reputation of a composite service and reputation\nindicators of component services. The trained fDNN model predicts the\nreputation of a new composite service with the confidence value. Experimental\nresults with real-world dataset prove the efficiency of the proposed approach.\n"}
+{"abstract": "  Face recognition technology related to recognizing identities is widely\nadopted in intelligence gathering, law enforcement, surveillance, and consumer\napplications. Recently, this technology has been ported to smartphones and\nbody-worn cameras (BWC). Face recognition technology in body-worn cameras is\nused for surveillance, situational awareness, and keeping the officer safe.\nOnly a handful of academic studies exist in face recognition using the\nbody-worn camera. A recent study has assembled BWCFace facial image dataset\nacquired using a body-worn camera and evaluated the ResNet-50 model for face\nidentification. However, for real-time inference in resource constraint\nbody-worn cameras and privacy concerns involving facial images, on-device face\nrecognition is required. To this end, this study evaluates lightweight\nMobileNet-V2, EfficientNet-B0, LightCNN-9 and LightCNN-29 models for face\nidentification using body-worn camera. Experiments are performed on a publicly\navailable BWCface dataset. The real-time inference is evaluated on three mobile\ndevices. The comparative analysis is done with heavy-weight VGG-16 and\nResNet-50 models along with six hand-crafted features to evaluate the trade-off\nbetween the performance and model size. Experimental results suggest the\ndifference in maximum rank-1 accuracy of lightweight LightCNN-29 over\nbest-performing ResNet-50 is \\textbf{1.85\\%} and the reduction in model\nparameters is \\textbf{23.49M}. Most of the deep models obtained similar\nperformances at rank-5 and rank-10. The inference time of LightCNNs is 2.1x\nfaster than other models on mobile devices. The least performance difference of\n\\textbf{14\\%} is noted between LightCNN-29 and Local Phase Quantization (LPQ)\ndescriptor at rank-1. In most of the experimental settings, lightweight\nLightCNN models offered the best trade-off between accuracy and the model size\nin comparison to most of the models.\n"}
+{"abstract": "  In this paper we first show that each Kummer quartic surface (a quartic\nsurface $X$ with 16 singular points) is, in canonical coordinates, equal to its\ndual surface, and that the Gauss map induces a fixpoint free involution\n$\\gamma$ on the minimal resolution $S$ of $X$. Then we study the corresponding\nEnriques surfaces $S/ \\gamma$. We also describe in detail the remarkable\nproperties of the most symmetric Kummer quartic, which we call the Cefal\\'u\nquartic. We also investigate the Kummer quartic surfaces whose associated\nAbelian surface is isogenous to a product of elliptic curves through an isogeny\nwith kernel $(\\mathbb{Z}/2)^2$, and show the existence of polarized nodal K3\nsurfaces $X$ of any degree $d=2k$ with the maximal number of nodes, such that\n$X$ and its nodes are defined over $\\mathbb{R}$. We take then as parameter\nspace for Kummer quartics an open set in $\\mathbb{P}^3$, parametrizing\nnondegenerate $(16_6, 16_6)$-configurations, and compare with other parameter\nspaces.\n  We also extend to positive characteristic some results which were previously\nknown over $\\mathbb{C}$.\n  We end with a section devoted to remarks on normal cubic surfaces, and\nproviding some other examples of strictly selfdual hypersurfaces.\n"}
+{"abstract": "  In this paper we focus on the problem of learning online an optimal policy\nfor Active Visual Search (AVS) of objects in unknown indoor environments. We\npropose POMP++, a planning strategy that introduces a novel formulation on top\nof the classic Partially Observable Monte Carlo Planning (POMCP) framework, to\nallow training-free online policy learning in unknown environments. We present\na new belief reinvigoration strategy which allows to use POMCP with a\ndynamically growing state space to address the online generation of the floor\nmap. We evaluate our method on two public benchmark datasets, AVD that is\nacquired by real robotic platforms and Habitat ObjectNav that is rendered from\nreal 3D scene scans, achieving the best success rate with an improvement of\n>10% over the state-of-the-art methods.\n"}
+{"abstract": "  An $r^{\\an}$-center of a compact body $\\Om$ in an $n$ dimensional Euclidean\nspace is a point that gives an extremal value of the regularized Riesz\npotential, which is the (Hadamard regularization of) integration on $\\Om$ of\nthe distance from the point to the power $\\an$. We show that for any real\nnumber $\\la$ if a compact body is sufficiently close to a ball in the sense of\nasphericity then the $r^{\\an}$-center is unique. We also study the regularized\npotentials of a unit ball.\n"}
+{"abstract": "  This paper is the second of a series that tackles the properties of molecular\ngas in galaxies residing in clusters and their related large-scale structures.\nOut of 21 targeted fields, 19 galaxies were detected in CO(3-2) with the\nAtacama Large Millimeter Array (ALMA), including two detections within a single\nfield. These galaxies are either bona fide members of the CL1301.7$-$1139\ncluster ($z=0.4828$, $\\sigma_{cl}=681$ km s$^{-1}$), or located within $\\sim 7\n\\times R_{200}$, its virial radius. They have been selected to sample the range\nof photometric local densities around CL1301.7$-$1139, with stellar masses\nabove log($M_{\\rm star}$) = 10, and to be located in the blue clump of\nstar-forming galaxies derived from the $u$, $g$, and $i$ photometric bands.\nUnlike previous works, our sample selection does not impose a minimum star\nformation rate or detection in the far-infrared. As such and as much as\npossible, it delivers an unbiased view of the gas content of normal\nstar-forming galaxies at $z \\sim 0.5$. Our study highlights the variety of\npaths to star formation quenching, and most likely the variety of physical\nproperties (i.e. temperature, density) of the corresponding galaxy's cold\nmolecular gas. Just as in the case of CL1411.1$-$1148, although to a smaller\nextent, we identify a number of galaxies with lower gas fraction than\nclassically found in other surveys. These galaxies can still be on the\nstar-forming main sequence. When these galaxies are not inside the cluster\nvirialised region, we provide hints that they are linked to their infall\nregions within $\\sim 4 \\times R_{200}$.\n"}
+{"abstract": "  In this paper, we establish quantitative estimates for nonlinear sampling\nKantorovich operators in terms of the modulus of continuity in the setting of\nOrlicz spaces. This general frame allows us to directly deduce some\nquantitative estimates of approximation in $L^{p}$-spaces, $1\\leq p<\\infty $,\nand in other well-known instances of Orlicz spaces, such as the Zygmung and the\nexponential spaces. Further, the qualitative order of approximation has been\nobtained assuming $f$ in suitable Lipschitz classes. The above estimates\nachieved in the general setting of Orlicz spaces, have been also improved in\nthe $L^p$-case, using a direct approach suitable to this context. At the end,\nwe consider the particular cases of the nonlinear sampling Kantorovich\noperators constructed by using some special kernels.\n"}
+{"abstract": "  Securing the communication between a web server and a browser is a\nfundamental task of securing the World Wide Web. Websites today rely heavily on\nHTTPS to set up secure connections. In recent years, several incidents\nundermined this trust and therefore the security of the HTTPS system. In this\npaper we introduce an approach allowing to secure JavaScript files in case a\nHTTPS connection between web server and browser is compromised. Our paper\npresents a solution to safeguard the user's browser so that it only processes\ncontent (e.g., JavaScript or HTML) that was genuinely provided by the web\napplication service providers themselves. Our solution makes use of service\nworkers, a recently proposed W3C Candidate Recommendation enabling applications\nto take advantage of persistent background processing, including hooks to\nenable bootstrapping of web applications while offline. It demonstrates how\nservice workers are able to validate the integrity of JavaScript files within\nthe client's browser and how service workers are used to detect and mitigate\nmalicious JavaScript files.\n"}
+{"abstract": "  Recently, Hyperbolic Spaces in the context of Non-Euclidean Deep Learning\nhave gained popularity because of their ability to represent hierarchical data.\nWe propose that it is possible to take advantage of the hierarchical\ncharacteristic present in the images by using hyperbolic neural networks in a\nGAN architecture. In this study, different configurations using fully connected\nhyperbolic layers in the GAN, CGAN, and WGAN are tested, in what we call the\nHGAN, HCGAN, and HWGAN, respectively. The results are measured using the\nInception Score (IS) and the Fr\\'echet Inception Distance (FID) on the MNIST\ndataset. Depending on the configuration and space curvature, better results are\nachieved for each proposed hyperbolic versions than their euclidean\ncounterpart.\n"}
+{"abstract": "  Ambipolar transport is a commonly occurring theme in semimetals and\nsemiconductors. Here we present an analytical formulation of the conductivity\nfor a two-band system. Electron and hole carrier densities and their respective\nconductivities are mapped into a two-dimensional unit-less phase space.\nProvided that one of the carrier densities is known, the dimensionless phase\nspace can be probed through magnetoresistance measurements. This formulation of\nthe two-band model for conductivity is applied to magnetoresistance experiments\non Ca$_3$Ru$_2$O$_7$. While previous such measurements focused on the\nlow-temperature limit, we cover a broad temperature range and find negative\nmagnetoresistance in an intermediate interval below the electronic transition\nat 48 K. The low-temperature magnetoresistance in Ca$_3$Ru$_2$O$_7$ is\nconsistent with a two-band structure. However, the model fails to describe the\nfull temperature and magnetic field dependence. Negative magnetoresistance\nfound in an intermediate temperature range is, for example, not captured by\nthis model. We thus conclude that the electronic and magnetic structure in this\nintermediate temperature range render the system beyond the most simple\ntwo-band model.\n"}
+{"abstract": "  In this work we show that the cosmological lithium problem and the $H_0$\ntension could be eased at the same time by allowing variations in the\nfundamental constants. We compute the primordial abundances of light elements\nresulting from Big Bang Nucleosynthesis considering the fine structure\nconstant, the Higgs' vacuum expectation value and Newton's constant as free\nparameters. Using the observational data for abundances, we set constraints on\nthe variations of the fundamental constants. An interpretation of the results\nin terms of the number of effective relativistic species gives $N_{\\rm\neff}=4.04\\pm0.12$. If one extrapolates the fit of {\\sffamily Planck}\nconsidering this $N_{\\rm eff}$, the value of the inferred Hubble's constant\nshifts to $H_0=(71.85\\pm0.77)\\,\\text{km}\\,\\text{s}^{-1} \\text{Mpc}^{-1}$,\ncompatible with current direct determinations.\n"}
+{"abstract": "  We report the study of sudden transitions or tipping in a collection of\nsystems induced due to multiplexing with another network of systems. The\nemergent dynamics of oscillators on one layer can undergo a sudden transition\nto steady state due to indirect coupling with a shared environment, mean field\ncouplings and conjugate couplings among them. In all these cases, when\nmultiplexed with another set of similar systems, the tipping phenomena are\ninduced on the second layer also with a similar pattern of behaviour. We\nconsider van der Pol oscillator as nodal dynamics with various network\ntopologies like scale free and regular networks with local and nonlocal\ncouplings. We also report how the coupling topology influences the nature of\ntransitions on both layers, under multiplexing.\n"}
+{"abstract": "  In this paper, we introduce central vertex corona, central edge corona, and\ncentral edge neighborhood corona of graphs using central graph. Also, we\ndetermine their adjacency spectrum, Laplacian spectrum and signless Laplacian\nspectrum. From our results, it is possible to obtain infinitely many pairs of\nadjacency (respectively, Laplacian and signless Laplacian) cospectral graphs.\nAs an application, we calculate the number of spanning trees and the Kirchhoff\nindex of the resulting graphs.\n"}
+{"abstract": "  We investigated the mechanism of magnetic noise due to both surface and bulk\nimpurities. For surface noise, we apply the Langevin method to spin fluctuation\ntheory to calculate the noise for paramagnetic surface impurities absorbed in a\nthin layer of water. We find that the mechanisms generating noise are spin flip\nand spin precession which depend on impurity spin relaxation and spin\nprecession time. For the bulk noise, we consider carbon-13 and nitrogen as\nimpurities and employ the correlated-cluster expansion to calculate noise.\nCarbon-13 noise is a few orders of magnitude larger than nitrogen due to the\nhigher impurity density in the typical NV center diamond system. We also find\nthat the noise in the secular approximation underestimates noise under low\napplied magnetic field. Overall, the major source of magnetic field noise is\nspin precession noise, which is more than five orders of magnitude larger than\nthe spin flip noise.\n"}
+{"abstract": "  Working in a variant of the intersection type assignment system of Coppo,\nDezani-Ciancaglini and Veneri [1981], we prove several facts about sets of\nterms having a given intersection type. Our main result is that every strongly\nnormalizing term $M$ admits a *uniqueness typing*, which is a pair $(\\Gamma,A)$\nsuch that\n  1) $\\Gamma \\vdash M : A$\n  2) $\\Gamma \\vdash N : A \\Longrightarrow M =_{\\beta\\eta} N$\n  We also discuss several presentations of intersection type algebras, and the\ncorresponding choices of type assignment rules.\n"}
+{"abstract": "  The Cadmium Zinc Telluride Imager (CZTI) on AstroSat is a hard X-ray\ncoded-aperture mask instrument with a primary field of view of 4.6 x 4.6\ndegrees (FWHM). The instrument collimators become increasingly transparent at\nenergies above $\\sim$100 keV, making CZTI sensitive to radiation from the\nentire sky. While this has enabled CZTI to detect a large number of off-axis\ntransient sources, calculating the source flux or spectrum requires knowledge\nof the direction and energy dependent attenuation of the radiation incident\nupon the detector. Here, we present a GEANT4-based mass model of CZTI and\nAstroSat that can be used to simulate the satellite response to the incident\nradiation, and to calculate an effective \"response file\" for converting the\nsource counts into fluxes and spectra. We provide details of the geometry and\ninteraction physics, and validate the model by comparing the simulations of\nimaging and flux studies with observations. Spectroscopic validation of the\nmass model is discussed in a companion paper, Chattopadhyay 2021.\n"}
+{"abstract": "  Purpose: The development of metaheuristic algorithms has increased by\nresearchers to use them extensively in the field of business, science, and\nengineering. One of the common metaheuristic optimization algorithms is called\nGrey Wolf Optimization (GWO). The algorithm works based on imitation of the\nwolves' searching and the process of attacking grey wolves. The main purpose of\nthis paper to overcome the GWO problem which is trapping into local optima.\n  Design or Methodology or Approach: In this paper, the K-means clustering\nalgorithm is used to enhance the performance of the original Grey Wolf\nOptimization by dividing the population into different parts. The proposed\nalgorithm is called K-means clustering Grey Wolf Optimization (KMGWO).\n  Findings: Results illustrate the efficiency of KMGWO is superior to GWO. To\nevaluate the performance of the KMGWO, KMGWO applied to solve 10 CEC2019\nbenchmark test functions. Results prove that KMGWO is better compared to GWO.\nKMGWO is also compared to Cat Swarm Optimization (CSO), Whale Optimization\nAlgorithm-Bat Algorithm (WOA-BAT), and WOA, so, KMGWO achieves the first rank\nin terms of performance. Statistical results proved that KMGWO achieved a\nhigher significant value compared to the compared algorithms. Also, the KMGWO\nis used to solve a pressure vessel design problem and it has outperformed\nresults.\n  Originality/value: Results prove that KMGWO is superior to GWO. KMGWO is also\ncompared to cat swarm optimization (CSO), whale optimization algorithm-bat\nalgorithm (WOA-BAT), WOA, and GWO so KMGWO achieved the first rank in terms of\nperformance. Also, the KMGWO is used to solve a classical engineering problem\nand it is superior\n"}
+{"abstract": "  Primal heuristics play a crucial role in exact solvers for Mixed Integer\nProgramming (MIP). While solvers are guaranteed to find optimal solutions given\nsufficient time, real-world applications typically require finding good\nsolutions early on in the search to enable fast decision-making. While much of\nMIP research focuses on designing effective heuristics, the question of how to\nmanage multiple MIP heuristics in a solver has not received equal attention.\nGenerally, solvers follow hard-coded rules derived from empirical testing on\nbroad sets of instances. Since the performance of heuristics is\ninstance-dependent, using these general rules for a particular problem might\nnot yield the best performance. In this work, we propose the first data-driven\nframework for scheduling heuristics in an exact MIP solver. By learning from\ndata describing the performance of primal heuristics, we obtain a\nproblem-specific schedule of heuristics that collectively find many solutions\nat minimal cost. We provide a formal description of the problem and propose an\nefficient algorithm for computing such a schedule. Compared to the default\nsettings of a state-of-the-art academic MIP solver, we are able to reduce the\naverage primal integral by up to 49% on a class of challenging instances.\n"}
+{"abstract": "  Functional Time Series are sequences of dependent random elements taking\nvalues on some functional space. Most of the research on this domain is focused\non producing a predictor able to forecast the value of the next function having\nobserved a part of the sequence. For this, the Autoregresive Hilbertian process\nis a suitable framework. We address here the problem of constructing\nsimultaneous predictive confidence bands for a stationary functional time\nseries. The method is based on an entropy measure for stochastic processes, in\nparticular functional time series. To construct predictive bands we use a\nfunctional bootstrap procedure that allow us to estimate the prediction law\nthrough the use of pseudo-predictions. Each pseudo-realisation is then\nprojected into a space of finite dimension, associated to a functional basis.\nWe use Reproducing Kernel Hilbert Spaces (RKHS) to represent the functions,\nconsidering then the basis associated to the reproducing kernel. Using a simple\ndecision rule, we classify the points on the projected space among those\nbelonging to the minimum entropy set and those that do not. We push back the\nminimum entropy set to the functional space and construct a band using the\nregularity property of the RKHS. The proposed methodology is illustrated\nthrough artificial and real-world data sets.\n"}
+{"abstract": "  In this article, we develop a graphical calculus for multi-qudit computations\nwith generalized Clifford algebras, using the algebraic framework we developed\nin our previous paper. We build our graphical calculus out of a fixed set of\ngraphical primitives defined by algebraic expressions constructed out of\nelements of a given generalized Clifford algebra, a graphical primitive\ncorresponding to the ground state, and also graphical primitives corresponding\nto projections onto the ground state of each qudit. We establish many\n\"superpower\"-like properties of the graphical calculus using purely algebraic\nmethods, including a novel algebraic proof of a Yang-Baxter-like equation. We\nalso derive several new identities for the braid elements, which are key to our\nproofs. In terms of physics, we connect these braid identities to physics by\nshowing the presence of a conserved charge. Furthermore, we demonstrate that in\nmany cases, the verification of involved vector identities can be reduced to\nthe combinatorial application of two basic vector identities. Finally, we show\nhow to explicitly compute various vector states in an efficient manner using\nalgebraic methods.\n"}
+{"abstract": "  How deep neural networks (DNNs) learn from noisy labels has been studied\nextensively in image classification but much less in image segmentation. So\nfar, our understanding of the learning behavior of DNNs trained by noisy\nsegmentation labels remains limited. In this study, we address this deficiency\nin both binary segmentation of biological microscopy images and multi-class\nsegmentation of natural images. We generate extremely noisy labels by randomly\nsampling a small fraction (e.g., 10%) or flipping a large fraction (e.g., 90%)\nof the ground truth labels. When trained with these noisy labels, DNNs provide\nlargely the same segmentation performance as trained by the original ground\ntruth. This indicates that DNNs learn structures hidden in labels rather than\npixel-level labels per se in their supervised training for semantic\nsegmentation. We refer to these hidden structures in labels as meta-structures.\nWhen DNNs are trained by labels with different perturbations to the\nmeta-structure, we find consistent degradation in their segmentation\nperformance. In contrast, incorporation of meta-structure information\nsubstantially improves performance of an unsupervised segmentation model\ndeveloped for binary semantic segmentation. We define meta-structures\nmathematically as spatial density distributions and show both theoretically and\nexperimentally how this formulation explains key observed learning behavior of\nDNNs.\n"}
+{"abstract": "  We survey the inclusion of interferometric elements in nonlinear spectroscopy\nperformed with quantum light. Controlled interference of electromagnetic fields\ncoupled to matter can induce constructive or destructive contributions of\nmicroscopic coupling sequences (histories) of matter. Since quantum fields do\nnot commute, quantum light signals are sensitive to the order of light-matter\ncoupling sequence. Matter correlation functions are thus imprinted by different\nfield factors, which depend on that order. We identify the associated quantum\ninformation obtained by controlling the weights of different contributing\npathways, and offer several experimental schemes for recovering it. Nonlinear\nquantum response functions include out-of-time-ordering matter correlators\n(OTOC) which reveal how perturbations spread throughout a quantum system\n(information scrambling). Their effect becomes most notable when using\nultrafast pulse sequences with respect to the path difference induced by the\ninterferometer. OTOC appear in quantum-informatics studies in other fields,\nincluding black holes, high energy, and condensed matter physics.\n"}
+{"abstract": "  As an extension of the weak perturbation theory obtained in recent analysis\non infinite-derivative non-local non-Abelian gauge theories motivated from\np-adic string field theory, and postulated as direction of UV-completion in 4-D\nQuantum Field Theory (QFT), here we investigate the confinement conditions and\n$\\beta-$function in the strong coupling regime. We extend the confinement\ncriterion, previously obtained by Kugo and Ojima for the local theory based on\nthe Becchi-Rouet-Stora-Tyutin (BRST) invariance, to the non-local theory, by\nusing a set of exact solutions of the corresponding local theory. We show that\nthe infinite-derivatives which are active in the UV provides finite\ncontributions also in the infrared (IR) limit and provide a proof of\nconfinement, granted by the absence of the Landau pole. The main difference\nwith the local case is that the IR fixed point is moved to infinity. We also\nshow that in the limit of the energy scale of non-locality $M \\rightarrow\n\\infty$ we reproduce the local theory results and see how asymptotic freedom is\nproperly recovered.\n"}
+{"abstract": "  The low temperature dependence of the nuclear magnetic resonance frequency\nand spin-lattice relaxation rate measured in the chiral magnet MnSi by Yasuoka\nand coworkers [J. Phys. Soc. Jpn. 85, 073701 (2016)] is interpreted in terms of\nhelimagnon excitations. The theoretically predicted gapless and anisotropic\ndispersion relation which is probed at extremely small energy is experimentally\nconfirmed. Whenever comparison is possible, the results are found\nquantitatively consistent with those of the inelastic neutron scattering and\nmuon spin rotation and relaxation techniques. Further studies are suggested.\n"}
+{"abstract": "  Convolutional Neural Networks have become the standard for image\nclassification tasks, however, these architectures are not invariant to\ntranslations of the input image. This lack of invariance is attributed to the\nuse of stride which ignores the sampling theorem, and fully connected layers\nwhich lack spatial reasoning. We show that stride can greatly benefit\ntranslation invariance given that it is combined with sufficient similarity\nbetween neighbouring pixels, a characteristic which we refer to as local\nhomogeneity. We also observe that this characteristic is dataset-specific and\ndictates the relationship between pooling kernel size and stride required for\ntranslation invariance. Furthermore we find that a trade-off exists between\ngeneralization and translation invariance in the case of pooling kernel size,\nas larger kernel sizes lead to better invariance but poorer generalization.\nFinally we explore the efficacy of other solutions proposed, namely global\naverage pooling, anti-aliasing, and data augmentation, both empirically and\nthrough the lens of local homogeneity.\n"}
+{"abstract": "  A thermoelectric effect of the noncentrosymmetric electron-phonon coupled\nsystem is proposed, in which a temperature difference between electrons and\nphonons, instead of a spatial temperature gradient, induces an electric\ncurrent. This is a realization of Seebeck ratchet in solids with the asymmetric\nenergy dispersion of electrons, and the dynamical phonons act as the\nfluctuation of the scalar potential and induce the dc current. Possible\nrealizations of this mechanism are also discussed.\n"}
+{"abstract": "  Smart electricity meters typically upload readings a few times a day. Utility\nproviders aim to increase the upload frequency in order to access consumption\ninformation in near real time, but the legacy compressors fail to provide\nsufficient savings on the low-bandwidth, high-cost data connection. We propose\na new compression method and data format for DLMS smart meter readings, which\nis significantly better with frequent uploads and enable reporting every\nreading in near real time with the same or lower data sizes than the currently\navailable compressors in the DLMS protocol.\n"}
+{"abstract": "  Population control is an essential component of any projector Monte Carlo\nalgorithm. This control mechanism usually introduces a bias in the sampled\nquantities that is inversely proportional to the population size. In this\npaper, we investigate the population control bias in the full configuration\ninteraction quantum Monte Carlo method. We identify the precise origin of this\nbias and quantify it in general. We show that it has different effects on\ndifferent estimators and that the shift estimator is particularly susceptible.\nWe derive a re-weighting technique, similar to the one used in diffusion Monte\nCarlo, for correcting this bias and apply it to the shift estimator. We also\nshow that by using importance sampling, the bias can be reduced substantially.\nWe demonstrate the necessity and the effectiveness of applying these techniques\nfor sign-problem-free systems where this bias is especially notable.\nSpecifically, we show results for large one-dimensional Hubbard models and the\ntwo-dimensional Heisenberg model, where corrected FCIQMC results are comparable\nto the other high-accuracy results.\n"}
+{"abstract": "  A multiscale asymptotic homogenization method for periodic microstructured\nmaterials in presence of thermoelasticity with periodic spatially dependent one\nrelaxation time is introduced. The asymptotic expansions of the\nmicro-displacement and the micro-temperature fields are rewritten on the\ntransformed Laplace space and expressed as power series of the microstructural\nlength scale, leading to a set of recursive differential problems over the\nperiodic unit cell. The solution of such cell problems leads to the\nperturbation functions. Up-scaling and down-scaling relations are then defined,\nand the latter allow expressing the microscopic fields in terms of the\nmacroscopic ones and their gradients. Average field equations of infinite order\nare also derived. The efficiency of the proposed technique was tested in\nrelation to a bi dimensional orthotropic layered body with orthotropy axis\nparallel to the direction of the layers, where the mechanical and temperature\nconstitutive properties were well stabilised. The dispersion curves of the\nhomogenized medium, truncated at the first order are compared with the\ndispersion curves of the heterogeneous continuum obtained by the Floquet-Bloch\ntheory. The results obtained with the two different approaches show a very good\nagreement.\n"}
+{"abstract": "  As a prevailing task in video surveillance and forensics field, person\nre-identification (re-ID) aims to match person images captured from\nnon-overlapped cameras. In unconstrained scenarios, person images often suffer\nfrom the resolution mismatch problem, i.e., \\emph{Cross-Resolution Person\nRe-ID}. To overcome this problem, most existing methods restore low resolution\n(LR) images to high resolution (HR) by super-resolution (SR). However, they\nonly focus on the HR feature extraction and ignore the valid information from\noriginal LR images. In this work, we explore the influence of resolutions on\nfeature extraction and develop a novel method for cross-resolution person re-ID\ncalled \\emph{\\textbf{M}ulti-Resolution \\textbf{R}epresentations \\textbf{J}oint\n\\textbf{L}earning} (\\textbf{MRJL}). Our method consists of a Resolution\nReconstruction Network (RRN) and a Dual Feature Fusion Network (DFFN). The RRN\nuses an input image to construct a HR version and a LR version with an encoder\nand two decoders, while the DFFN adopts a dual-branch structure to generate\nperson representations from multi-resolution images. Comprehensive experiments\non five benchmarks verify the superiority of the proposed MRJL over the\nrelevent state-of-the-art methods.\n"}
+{"abstract": "  DRAM is the dominant main memory technology used in modern computing systems.\nComputing systems implement a memory controller that interfaces with DRAM via\nDRAM commands. DRAM executes the given commands using internal components\n(e.g., access transistors, sense amplifiers) that are orchestrated by DRAM\ninternal timings, which are fixed foreach DRAM command. Unfortunately, the use\nof fixed internal timings limits the types of operations that DRAM can perform\nand hinders the implementation of new functionalities and custom mechanisms\nthat improve DRAM reliability, performance and energy. To overcome these\nlimitations, we propose enabling programmable DRAM internal timings for\ncontrolling in-DRAM components. To this end, we design CODIC, a new low-cost\nDRAM substrate that enables fine-grained control over four previously fixed\ninternal DRAM timings that are key to many DRAM operations. We implement CODIC\nwith only minimal changes to the DRAM chip and the DDRx interface. To\ndemonstrate the potential of CODIC, we propose two new CODIC-based security\nmechanisms that outperform state-of-the-art mechanisms in several ways: (1) a\nnew DRAM Physical Unclonable Function (PUF) that is more robust and has\nsignificantly higher throughput than state-of-the-art DRAM PUFs, and (2) the\nfirst cold boot attack prevention mechanism that does not introduce any\nperformance or energy overheads at runtime.\n"}
+{"abstract": "  Seismic full-waveform inversion (FWI) techniques aim to find a\nhigh-resolution subsurface geophysical model provided with waveform data. Some\nrecent effort in data-driven FWI has shown some encouraging results in\nobtaining 2D velocity maps. However, due to high computational complexity and\nlarge memory consumption, the reconstruction of 3D high-resolution velocity\nmaps via deep networks is still a great challenge. In this paper, we present\nInversionNet3D, an efficient and scalable encoder-decoder network for 3D FWI.\nThe proposed method employs group convolution in the encoder to establish an\neffective hierarchy for learning information from multiple sources while\ncutting down unnecessary parameters and operations at the same time. The\nintroduction of invertible layers further reduces the memory consumption of\nintermediate features during training and thus enables the development of\ndeeper networks with more layers and higher capacity as required by different\napplication scenarios. Experiments on the 3D Kimberlina dataset demonstrate\nthat InversionNet3D achieves state-of-the-art reconstruction performance with\nlower computational cost and lower memory footprint compared to the baseline.\n"}
+{"abstract": "  Many natural systems exhibit chaotic behaviour such as the weather,\nhydrology, neuroscience and population dynamics. Although many chaotic systems\ncan be described by relatively simple dynamical equations, characterizing these\nsystems can be challenging, due to sensitivity to initial conditions and\ndifficulties in differentiating chaotic behavior from noise. Ideally, one\nwishes to find a parsimonious set of equations that describe a dynamical\nsystem. However, model selection is more challenging when only a subset of the\nvariables are experimentally accessible. Manifold learning methods using\ntime-delay embeddings can successfully reconstruct the underlying structure of\nthe system from data with hidden variables, but not the equations. Recent work\nin sparse-optimization based model selection has enabled model discovery given\na library of possible terms, but regression-based methods require measurements\nof all state variables. We present a method combining variational annealing --\na technique previously used for parameter estimation in chaotic systems with\nhidden variables -- with sparse optimization methods to perform model\nidentification for chaotic systems with unmeasured variables. We applied the\nmethod to experimental data from an electrical circuit with Lorenz-system like\nbehavior to successfully recover the circuit equations with two measured and\none hidden variable. We discuss the robustness of our method to varying noise\nand manifold sampling using ground-truth time-series simulated from the classic\nLorenz system.\n"}
+{"abstract": "  A result of Farahat and Higman shows that there is a ``universal'' algebra,\n$\\mathrm{FH}$, interpolating the centres of symmetric group algebras,\n$Z(\\mathbb{Z}S_n)$. We explain that this algebra is isomorphic to $\\mathcal{R}\n\\otimes \\Lambda$, where $\\mathcal{R}$ is the ring of integer-valued polynomials\nand $\\Lambda$ is the ring of symmetric functions. Moreover, the isomorphism is\nvia ``evaluation at Jucys-Murphy elements'', which leads to character formulae\nfor symmetric groups. Then, we generalise this result to wreath products\n$\\Gamma \\wr S_n$ of a fixed finite group $\\Gamma$. This involves constructing\nwreath-product versions $\\mathcal{R}_\\Gamma$ and $\\Lambda(\\Gamma_*)$ of\n$\\mathcal{R}$ and $\\Lambda$, respectively, which are interesting in their own\nright (for example, both are Hopf algebras). We show that the universal algebra\nfor wreath products, $\\mathrm{FH}_\\Gamma$, is isomorphic to $\\mathcal{R}_\\Gamma\n\\otimes \\Lambda(\\Gamma_*)$ and use this to compute the $p$-blocks of wreath\nproducts.\n"}
+{"abstract": "  Bias evaluation in machine-learning based services (MLS) based on traditional\nalgorithmic fairness notions that rely on comparative principles is practically\ndifficult, making it necessary to rely on human auditor feedback. However, in\nspite of taking rigorous training on various comparative fairness notions,\nhuman auditors are known to disagree on various aspects of fairness notions in\npractice, making it difficult to collect reliable feedback. This paper offers a\nparadigm shift to the domain of algorithmic fairness via proposing a new\nfairness notion based on the principle of non-comparative justice. In contrary\nto traditional fairness notions where the outcomes of two individuals/groups\nare compared, our proposed notion compares the MLS' outcome with a desired\noutcome for each input. This desired outcome naturally describes a human\nauditor's expectation, and can be easily used to evaluate MLS on crowd-auditing\nplatforms. We show that any MLS can be deemed fair from the perspective of\ncomparative fairness (be it in terms of individual fairness, statistical\nparity, equal opportunity or calibration) if it is non-comparatively fair with\nrespect to a fair auditor. We also show that the converse holds true in the\ncontext of individual fairness. Given that such an evaluation relies on the\ntrustworthiness of the auditor, we also present an approach to identify fair\nand reliable auditors by estimating their biases with respect to a given set of\nsensitive attributes, as well as quantify the uncertainty in the estimation of\nbiases within a given MLS. Furthermore, all of the above results are also\nvalidated on COMPAS, German credit and Adult Census Income datasets.\n"}
+{"abstract": "  Copper oxide (CuO) thin films have been deposited on glass substrates by a\nfacile sol-gel dip-coating technique with varying withdrawal speeds from 0.73\nto 4.17 mm/s. The variation of film thickness manifested by dip-coating\nwithdrawal speeds was investigated in detail to investigate its effect on the\nstructural, morphological, optoelectrical, and wettability properties of CuO\nthin films for carbon dioxide (CO2) gas-sensing applications. The\ncrystallinity, as well as phase purity of dip-coated CuO, were confirmed by\nboth X-ray diffraction (XRD) and Raman spectral analyses. The surface\nmorphology of the films characterized by the scanning electron microscopy (SEM)\nrevealed that pore density decreases with the increase of withdrawal speeds and\ngrain size is found to increase with the increase of film thickness\ncorroborating the XRD results. The optical bandgap of dip-coated CuO films was\nestimated in the range of 1.47 - 1.52 eV from the UV-VIS-NIR transmission data\nand it is found to decrease with the increment of Urbach tail states\naccompanied by the increase of film thickness. The ratio of the electrical and\noptical conductivity of CuO films is found to decrease with increasing\nwithdrawal speeds due to the variation of carrier concentration. Among all the\nstudied films, the sample deposited to a 0.73 mm/s withdrawal speed exhibited\nthe highest crystallinity, porous morphology, highest pore density,\noptoelectrical conductivity as well as water contact angle, and therefore\nmaximum gas sensing response of CO2 vapor in the air recorded at room\ntemperature.\n"}
+{"abstract": "  We demonstrate that it is possible to efficiently control ultracold chemical\nreactions of alkali-metal atoms colliding with open-shell alkali-metal dimers\nin their metastable triplet states by choosing the internal hyperfine and\nrovibrational states of the reactants as well as by inducing magnetic Feshbach\nresonances with an external magnetic field. We base these conclusions on\ncoupled-channel statistical calculations that include the effects of hyperfine\ncontact and magnetic-field-induced Zeeman interactions on ultracold chemical\nreactions of hyperfine-resolved ground-state Na and the triplet\nNaLi(a$^3\\Sigma^+$) producing singlet Na$_2$($^1\\Sigma^+_g$) and a Li atom. We\nfind that the reaction rates are sensitive to the initial hyperfine states of\nthe reactants. The chemical reaction of fully spin-polarized, high-spin states\nof rotationless NaLi(a$^3\\Sigma^+, v = 0, N = 0$) molecules with fully\nspin-polarized Na is suppressed by a factor of 10-100 compared to that of\nunpolarized reactants. We interpret these findings within the adiabatic state\nmodel, which treats the reaction as a sequence of nonadiabatic transitions\nbetween the initial non-reactive high-spin state and the final low-spin states\nof the reaction complex. In addition, we show that magnetic Feshbach resonances\ncan similarly change reaction rate coefficients by several orders of magnitude.\nSome of these resonances are due to resonant trimer bound states dissociating\nto the $N=2$ rotational state of NaLi(a$^3\\Sigma^+, v = 0$) and would thus\nexist in systems without hyperfine interactions.\n"}
+{"abstract": "  A key desiderata for inclusive and accessible speech recognition technology\nis ensuring its robust performance to children's speech. Notably, this includes\nthe rapidly advancing neural network based end-to-end speech recognition\nsystems. Children speech recognition is more challenging due to the larger\nintra-inter speaker variability in terms of acoustic and linguistic\ncharacteristics compared to adult speech. Furthermore, the lack of adequate and\nappropriate children speech resources adds to the challenge of designing robust\nend-to-end neural architectures. This study provides a critical assessment of\nautomatic children speech recognition through an empirical study of\ncontemporary state-of-the-art end-to-end speech recognition systems. Insights\nare provided on the aspects of training data requirements, adaptation on\nchildren data, and the effect of children age, utterance lengths, different\narchitectures and loss functions for end-to-end systems and role of language\nmodels on the speech recognition performance.\n"}
+{"abstract": "  In 1878, Jordan proved that if a finite group $G$ has a faithful\nrepresentation of dimension $n$ over $\\mathbb{C}$, then $G$ has a normal\nabelian subgroup with index bounded above by a function of $n$. The same result\nfails if one replaces $\\mathbb{C}$ by a field of positive characteristic, due\nto the presence of large unipotent and/or Lie type subgroups. For this reason,\na long-standing problem in group and representation theory has been to find the\n\"correct analogue\" of Jordan's theorem in characteristic $p>0$. Progress has\nbeen made in a number of different directions, most notably by Brauer and Feit\nin 1966; by Collins in 2008; and by Larsen and Pink in 2011. With a 1968\ntheorem of Steinberg in mind (which shows that a significant proportion of\nelements in a simple group of Lie type are unipotent), we prove in this paper\nthat if a finite group $G$ has a faithful representation over a field of\ncharacteristic $p$, then a significant proportion of the elements of $G$ must\nhave $p$-power order. We prove similar results for permutation groups, and\npresent a general method for counting $p$-elements in finite groups. All of our\nresults are best possible.\n"}
+{"abstract": "  We study robust testing and estimation of discrete distributions in the\nstrong contamination model. We consider both the \"centralized setting\" and the\n\"distributed setting with information constraints\" including communication and\nlocal privacy (LDP) constraints. Our technique relates the strength of\nmanipulation attacks to the earth-mover distance using Hamming distance as the\nmetric between messages(samples) from the users. In the centralized setting, we\nprovide optimal error bounds for both learning and testing. Our lower bounds\nunder local information constraints build on the recent lower bound methods in\ndistributed inference. In the communication constrained setting, we develop\nnovel algorithms based on random hashing and an $\\ell_1/\\ell_1$ isometry.\n"}
+{"abstract": "  Limerick generation exemplifies some of the most difficult challenges faced\nin poetry generation, as the poems must tell a story in only five lines, with\nconstraints on rhyme, stress, and meter. To address these challenges, we\nintroduce LimGen, a novel and fully automated system for limerick generation\nthat outperforms state-of-the-art neural network-based poetry models, as well\nas prior rule-based poetry models. LimGen consists of three important pieces:\nthe Adaptive Multi-Templated Constraint algorithm that constrains our search to\nthe space of realistic poems, the Multi-Templated Beam Search algorithm which\nsearches efficiently through the space, and the probabilistic Storyline\nalgorithm that provides coherent storylines related to a user-provided prompt\nword. The resulting limericks satisfy poetic constraints and have thematically\ncoherent storylines, which are sometimes even funny (when we are lucky).\n"}
+{"abstract": "  This paper proposes a general multi-modal data learning method, which\nincludes Global Homogeneous Transformation, Local Homogeneous Transformation\nand their combination. During ReID model training, on the one hand, it randomly\nselected a rectangular area in the RGB image and replace its color with the\nsame rectangular area in corresponding homogeneous image, thus it generate a\ntraining image with different homogeneous areas; On the other hand, it convert\nan image into a homogeneous image. These two methods help the model to directly\nlearn the relationship between different modalities in the Special ReID task.\nIn single-modal ReID tasks, it can be used as an effective data augmentation.\nThe experimental results show that our method achieves a performance\nimprovement of up to 3.3% in single modal ReID task, and performance\nimprovement in the Sketch Re-identification more than 8%. In addition, our\nexperiments also show that this method is also very useful in adversarial\ntraining for adversarial defense. It can help the model learn faster and better\nfrom adversarial examples.\n"}
+{"abstract": "  In this paper, we consider the long time asymptotic behavior for the initial\nvalue problem of the Novikov equation \\begin{align} &u_{t}-u_{txx}+4\nu_{x}=3uu_xu_{xx}+u^2u_{xxx}, \\nonumber &u(x, 0)=u_{0}(x),\\nonumber \\end{align}\nwhere $u_0(x)$ is assumed to decay to a nonzero constant: $u(x)\\to\\kappa>0$,\n$x\\to\\pm\\infty$. The study makes crucial use of the inverse scattering\ntransform as well as of the $\\overline\\partial$ generalization of Deift-Zhou\nsteepest descent method for oscillatory Riemann-Hilbert (RH) problems. Based on\nthe spectral analysis of the Lax pair associated with the Novikov equation and\nscattering matrix, the solution of the Cauchy problem is characterized via the\nsolution of a RH problem. In different space-time solitonic region of $x/t$, we\nfurther compute the different long time asymptotic expansion of the solution\n$u(x,t)$, which implies soliton resolution conjecture and can be characterized\nwith an $N(\\Lambda)$-soliton whose parameters are modulated by a sum of\nlocalized soliton-soliton interactions as one moves through the region. And it\nhas diverse residual error order from $\\overline\\partial$ equation:\n$\\mathcal{O}(t^{-1+2\\rho})$ for\n$\\xi=\\frac{y}{t}\\in(-\\infty,-0.125)\\cup(1,+\\infty)$ and $\\mathcal{O}(t^{-3/4})$\nfor $\\xi=\\frac{y}{t}\\in(-0.125,1)$.\n"}
+{"abstract": "  Politically sensitive topics are still a challenge for open-domain chatbots.\nHowever, dealing with politically sensitive content in a responsible,\nnon-partisan, and safe behavior way is integral for these chatbots. Currently,\nthe main approach to handling political sensitivity is by simply changing such\na topic when it is detected. This is safe but evasive and results in a chatbot\nthat is less engaging. In this work, as a first step towards a politically safe\nchatbot, we propose a group of metrics for assessing their political prudence.\nWe then conduct political prudence analysis of various chatbots and discuss\ntheir behavior from multiple angles through our automatic metric and human\nevaluation metrics. The testsets and codebase are released to promote research\nin this area.\n"}
+{"abstract": "  We investigate the radio properties of a sample of 53 sources selected at 850\n$\\mu$m from the SCUBA-2 Cosmology Legacy Survey using new deep, low-frequency\nradio imaging of the Lockman Hole field from the Low Frequency Array. Combining\nthese data with additional radio observations from the GMRT and the JVLA, we\nfind a variety of radio spectral shapes and luminosities within our sample\ndespite their similarly bright submillimetre flux densities. We characterise\ntheir spectral shapes in terms of multi-band radio spectral indices. Finding\nstrong spectral flattening at low frequencies in ~20% of sources, we\ninvestigate the differences between sources with extremely flat low-frequency\nspectra and those with `normal' radio spectral indices. As there are no other\nstatistically significant differences between the two subgroups of our sample\nas split by the radio spectral index, we suggest that any differences are\nundetectable in galaxy-averaged properties that we can observe with our\nunresolved images, and likely relate to galaxy properties that we cannot\nresolve, on scales $\\lesssim$ 1 kpc. We attribute the observed spectral\nflattening in the radio to free-free absorption, proposing that those sources\nwith significant low-frequency spectral flattening have a clumpy distribution\nof star-forming gas. We estimate an average spatial extent of absorbing\nmaterial of at most several hundred parsecs to produce the levels of absorption\nobserved in the radio spectra. This estimate is consistent with the\nhighest-resolution observations of submillimetre galaxies in the literature,\nwhich find examples of non-uniform dust distributions on scales of ~100 pc,\nwith evidence for clumps and knots in the interstellar medium. Additionally, we\nfind two bright (> 6 mJy) submm sources undetected at all other wavelengths. We\nspeculate that these objects may be very high redshift sources, likely residing\nat z > 4.\n"}
+{"abstract": "  The nascent computational paradigm of quantum reservoir computing presents an\nattractive use of near-term, noisy-intermediate-scale quantum processors. To\nunderstand the potential power and use cases of quantum reservoir computing, it\nis necessary to define a conceptual framework to separate its constituent\ncomponents and determine their impacts on performance. In this manuscript, we\nutilize such a framework to isolate the input encoding component of\ncontemporary quantum reservoir computing schemes. We find that across the\nmajority of schemes the input encoding implements a nonlinear transformation on\nthe input data. As nonlinearity is known to be a key computational resource in\nreservoir computing, this calls into question the necessity and function of\nfurther, post-input, processing. Our findings will impact the design of future\nquantum reservoirs, as well as the interpretation of results and fair\ncomparison between proposed designs.\n"}
+{"abstract": "  The design of attacks for cyber physical systems is critical to assess CPS\nresilience at design time and run-time, and to generate rich datasets from\ntestbeds for research. Attacks against cyber physical systems distinguish\nthemselves from IT attacks in that the main objective is to harm the physical\nsystem. Therefore, both cyber and physical system knowledge are needed to\ndesign such attacks. The current practice to generate attacks either focuses on\nthe cyber part of the system using IT cyber security existing body of\nknowledge, or uses heuristics to inject attacks that could potentially harm the\nphysical process. In this paper, we present a systematic approach to\nautomatically generate integrity attacks from the CPS safety and control\nspecifications, without knowledge of the physical system or its dynamics. The\ngenerated attacks violate the system operational and safety requirements, hence\npresent a genuine test for system resilience. We present an algorithm to\nautomate the malware payload development. Several examples are given throughout\nthe paper to illustrate the proposed approach.\n"}
+{"abstract": "  We provide new ionization correction factors (ICFs) for carbon, nitrogen,\nneon, sulfur, chlorine, and argon in giant H II regions. The ICFs were computed\nusing the most representative photoionization models from a large initial grid.\nThe models were selected using an observational sample of 985 giant H II\nregions (GHR) in spiral galaxies and blue compact galaxies (BCG). The\nobservational sample was also used to assign a weight to each model describing\nhow well it agrees with observations in the [O III]/Hbeta versus [N II]/Halpha\ndiagram. In addition to the ICFs we provide, for the first time, analytical\nexpressions for their formal uncertainties. We use our ICFs to compute the\nabundances of nitrogen, neon, sulfur, and argon in our samples. Our abundances\nare robust within the adopted framework, but may require revision in the case\nof important changes in atomic data or in the spectral energy distribution of\nthe ionizing radiation in H II regions. Considering the abundance patterns we\nobtained for the BCG sample (abundances for the GHR sample are less reliable)\nwe find that oxygen is depleted into dust grains at a rate increasing with\nmetallicity and reaching 0.12 dex at solar abundances. The discussion of\npossible depletion of sulfur and argon requires considering recent Type Ia\nSupernova yields, which are still uncertain.\n"}
+{"abstract": "  By exploiting an innovative total-variation compressive sensing (TV-CS)\nformulation, a new method for the synthesis of physically contiguous clustered\nlinear arrays is presented. The computation of the feed network excitations is\nrecast as the maximization of the gradient sparsity of the excitation vector\nsubject to matching a user-defined pattern. The arising TV-CS functional is\nthen optimized by means of a deterministic alternating direction algorithm. A\nselected set of representative numerical results, drawn from a wide validation,\nis reported to illustrate the potentialities and the limitations of the\nproposed approach when clustering arrays of both ideal and realistic antenna\nelements. Comparisons with some competitive state-of-the-art subarraying\ntechniques are performed as well.\n"}
+{"abstract": "  Crystallization has long been the subject of research as one of the basic\nways in which solid materials are constructed. In particular, the nucleation\nstage has not been isolated, thus has been predicted through many calculations\nand achieved theoretical completion through the nucleation rate(J). Si nce most\nof these results were obtained through isotropic building blocks in\nthree-dimensional space, it was difficult to interpret nuclei formed by\nanisotropic building block in 2D or 1D structure. Recently, a lot of studies\nrelated to amyloid fibril have shown nucleation of anisotropic building block.\nHowever, due to the complexity of the amyloid fibrils, there is no unified\nexplanation of the thermodynamic method of classical nucleation theory which is\nthe energy loss from surface and energy gain from volume. We have\nexperimentally demonstrated the isolation of nuclei of the orthorhombic phase\nof HYLION-12 which is a Dirac metamaterial and provide the effect of anisotropy\nof the molecules on nucleation The thermal behavior of nuclei of Dirac\nmetamaterial through DSC has demonstrated that it can be crystallized to a\nDirac metamaterial through the first order phase transition. The growth process\nis verified at low temperature where no phase transition occurs. The\ncalculation of surface and bulk energy of the Dirac metamaterial was conducted.\nIt could explain the isolation of nuclei of the Dirac metamaterial by enlarging\nthe thermodynamic classical nucleation theory.\n"}
+{"abstract": "  Motivated by the probabilistic methods for nonlinear differential equations\nintroduced by McKean (1975) for the Kolmogorov-Petrovski-Piskunov (KPP)\nequation, and by Le Jan and Sznitman (1997) for the incompressible\nNavier-Stokes equations, we identify a new class of stochastic cascade models,\nreferred to as {\\it doubly stochastic Yule cascades}. We establish\nnon-explosion criteria under the assumption that the randomization of Yule\nintensities from generation to generation is by an ergodic time-reversible\nMarkov chain. In addition to the cascade models that arise in the analysis of\ncertain deterministic nonlinear differential equations, this model includes the\nmultiplicative branching random walks, the branching Markov processes, and the\nstochastic generalizations of the percolation and/or cell aging models\nintroduced by Aldous and Shields (1988) and independently by Athreya (1985).\n"}
+{"abstract": "  Multiple views of data, both naturally acquired (e.g., image and audio) and\nartificially produced (e.g., via adding different noise to data samples), have\nproven useful in enhancing representation learning. Natural views are often\nhandled by multiview analysis tools, e.g., (deep) canonical correlation\nanalysis [(D)CCA], while the artificial ones are frequently used in\nself-supervised learning (SSL) paradigms, e.g., SimCLR and Barlow Twins. Both\ntypes of approaches often involve learning neural feature extractors such that\nthe embeddings of data exhibit high cross-view correlations. Although\nintuitive, the effectiveness of correlation-based neural embedding is only\nempirically validated. This work puts forth a theory-backed framework for\nunsupervised multiview learning. Our development starts with proposing a\nmultiview model, where each view is a nonlinear mixture of shared and private\ncomponents. Consequently, the learning problem boils down to shared/private\ncomponent identification and disentanglement. Under this model, latent\ncorrelation maximization is shown to guarantee the extraction of the shared\ncomponents across views (up to certain ambiguities). In addition, the private\ninformation in each view can be provably disentangled from the shared using\nproper regularization design. The method is tested on a series of tasks, e.g.,\ndownstream clustering, which all show promising performance. Our development\nalso provides a unifying perspective for understanding various DCCA and SSL\nschemes.\n"}
+{"abstract": "  Interleaved texts, where posts belonging to different threads occur in a\nsequence, commonly occur in online chat posts, so that it can be time-consuming\nto quickly obtain an overview of the discussions. Existing systems first\ndisentangle the posts by threads and then extract summaries from those threads.\nA major issue with such systems is error propagation from the disentanglement\ncomponent. While end-to-end trainable summarization system could obviate\nexplicit disentanglement, such systems require a large amount of labeled data.\nTo address this, we propose to pretrain an end-to-end trainable hierarchical\nencoder-decoder system using synthetic interleaved texts. We show that by\nfine-tuning on a real-world meeting dataset (AMI), such a system out-performs a\ntraditional two-step system by 22%. We also compare against transformer models\nand observed that pretraining with synthetic data both the encoder and decoder\noutperforms the BertSumExtAbs transformer model which pretrains only the\nencoder on a large dataset.\n"}
+{"abstract": "  As the use of Internet of Things (IoT) devices for monitoring purposes\nbecomes ubiquitous, the efficiency of sensor communication is a major issue for\nthe modern Internet. Channel coding is less efficient for extremely short\npackets, and traditional techniques that rely on source compression require\nextensive signaling or pre-existing knowledge of the source dynamics. In this\nwork, we propose an encoding and decoding scheme that learns source dynamics\nonline using a Hidden Markov Model (HMM), puncturing a short packet code to\noutperform existing compression-based approaches. Our approach shows\nsignificant performance improvements for sources that are highly correlated in\ntime, with no additional complexity on the sender side.\n"}
+{"abstract": "  Many machine learning and data science tasks require solving non-convex\noptimization problems. When the loss function is a sum of multiple terms, a\npopular method is the stochastic gradient descent. Viewed as a process for\nsampling the loss function landscape, the stochastic gradient descent is known\nto prefer flat minima. Though this is desired for certain optimization problems\nsuch as in deep learning, it causes issues when the goal is to find the global\nminimum, especially if the global minimum resides in a sharp valley.\n  Illustrated with a simple motivating example, we show that the fundamental\nreason is that the difference in the Lipschitz constants of multiple terms in\nthe loss function causes stochastic gradient descent to experience different\nvariances at different minima. In order to mitigate this effect and perform\nfaithful optimization, we propose a combined resampling-reweighting scheme to\nbalance the variance at local minima and extend to general loss functions. We\nexplain from the numerical stability perspective how the proposed scheme is\nmore likely to select the true global minimum, and the local convergence\nanalysis perspective how it converges to a minimum faster when compared with\nthe vanilla stochastic gradient descent. Experiments from robust statistics and\ncomputational chemistry are provided to demonstrate the theoretical findings.\n"}
+{"abstract": "  In this work, we introduce the concept of the fractional quantum heat engine.\nWe examine the space-fractional quantum Szilard heat engine as an example to\nshow that the space-fractional quantum heat engines can produce higher\nefficiency than the conventional quantum heat engines.\n"}
+{"abstract": "  Computed tomography (CT) imaging is a promising approach to diagnosing the\nCOVID-19. Machine learning methods can be employed to train models from labeled\nCT images and predict whether a case is positive or negative. However, there\nexists no publicly-available and large-scale CT data to train accurate models.\nIn this work, we propose a multi-stage attentive transfer learning framework\nfor improving COVID-19 diagnosis. Our proposed framework consists of three\nstages to train accurate diagnosis models through learning knowledge from\nmultiple source tasks and data of different domains. Importantly, we propose a\nnovel self-supervised learning method to learn multi-scale representations for\nlung CT images. Our method captures semantic information from the whole lung\nand highlights the functionality of each lung region for better representation\nlearning. The method is then integrated to the last stage of the proposed\ntransfer learning framework to reuse the complex patterns learned from the same\nCT images. We use a base model integrating self-attention (ATTNs) and\nconvolutional operations. Experimental results show that networks with ATTNs\ninduce greater performance improvement through transfer learning than networks\nwithout ATTNs. This indicates attention exhibits higher transferability than\nconvolution. Our results also show that the proposed self-supervised learning\nmethod outperforms several baseline methods.\n"}
+{"abstract": "  Large-scale pre-trained models like BERT, have obtained a great success in\nvarious Natural Language Processing (NLP) tasks, while it is still a challenge\nto adapt them to the math-related tasks. Current pre-trained models neglect the\nstructural features and the semantic correspondence between formula and its\ncontext. To address these issues, we propose a novel pre-trained model, namely\n\\textbf{MathBERT}, which is jointly trained with mathematical formulas and\ntheir corresponding contexts. In addition, in order to further capture the\nsemantic-level structural features of formulas, a new pre-training task is\ndesigned to predict the masked formula substructures extracted from the\nOperator Tree (OPT), which is the semantic structural representation of\nformulas. We conduct various experiments on three downstream tasks to evaluate\nthe performance of MathBERT, including mathematical information retrieval,\nformula topic classification and formula headline generation. Experimental\nresults demonstrate that MathBERT significantly outperforms existing methods on\nall those three tasks. Moreover, we qualitatively show that this pre-trained\nmodel effectively captures the semantic-level structural information of\nformulas. To the best of our knowledge, MathBERT is the first pre-trained model\nfor mathematical formula understanding.\n"}
+{"abstract": "  Both brain science and the deep learning communities have the problem of\ninterpreting neural activity. For deep learning, even though we can access all\nneurons' activity data, interpretation of how the deep network solves the task\nis still challenging. Although a large amount of effort has been devoted to\ninterpreting a deep network, there is still no consensus of what interpretation\nis. This paper tries to push the discussion in this direction and proposes an\ninformation-theoretic progressive framework to synthesize interpretation.\nFirstly, we discuss intuitions of interpretation: interpretation is\nmeta-information; interpretation should be at the right level; inducing\nindependence is helpful to interpretation; interpretation is naturally\nprogressive; interpretation doesn't have to involve a human. Then, we build the\nframework with an information map splitting idea and implement it with the\nvariational information bottleneck technique. After that, we test the framework\nwith the CLEVR dataset. The framework is shown to be able to split information\nmaps and synthesize interpretation in the form of meta-information.\n"}
+{"abstract": "  In $2020$, Korea Disease Control and Prevention Agency reported three rounds\nof surveys on seroprevalence of severe acute respiratory syndrome coronavirus 2\n(SARS-CoV-2) antibodies in South Korea. We analyze the seroprevalence surveys\nusing a Bayesian method with an informative prior distribution on the\nseroprevalence parameter, and the sensitivity and specificity of the diagnostic\ntest. We construct the informative prior using the posterior distribution\nobtained from the clinical evaluation data based on the plaque reduction\nneutralization test. The constraint of the seroprevalence parameter induced\nfrom the known confirmed coronavirus 2019 cases can be imposed naturally in the\nproposed Bayesian model. We also prove that the confidence interval of the\nseroprevalence parameter based on the Rao's test can be the empty set, while\nthe Bayesian method renders a reasonable interval estimator. As of the $30$th\nof October $2020$, the $95\\%$ credible interval of the estimated SARS-CoV-2\npositive population does not exceed $307,448$, approximately $0.6\\%$ of the\nKorean population.\n"}
+{"abstract": "  The use of the Internet has become an urgent necessity in various fields and\nactivities. One of such important fields could be electronic business\n(E-Business). E-business includes all operations and activities related to\nInternet commerce, including e-commerce, e-marketing, eservices, e-payment, and\nelectronic transfer. This proposal focuses on two perspectives that are under\ne-commerce umbrella where many e-payment process models suffer from the lack of\nclarity in the special procedures of the verification of the quality of\nservices. This proposal, therefore, proposes a new E-payment process model to\nenhance the quality of service through maintaining availability. The proposed\nmodel focuses on the concepts of electronic payment model dimensions, which\nincludes integrity, non-repudiation, authenticity, confidentiality, privacy,\nand availability. Accordingly, the proposed model implements the availability\ndimension in order to improve the quality of services that are provided to the\nconsumer.\n"}
+{"abstract": "  Agile attitude maneuvering maximizes the utility of remote sensing satellite\nconstellations. By taking into account a satellite's physical properties and\nits actuator specifications, we may leverage the full performance potential of\nthe attitude control system to conduct agile remote sensing beyond conventional\nslew-and-stabilize maneuvers. Employing a constellation of agile satellites,\ncoordinated by an autonomous and responsive scheduler, can significantly\nincrease overall response rate, revisit time and global coverage for the\nmission. In this paper, we use recent advances in sequential convex programming\nbased trajectory optimization to enable rapid-target acquisition, pointing and\ntracking capabilities for a scheduler-based constellation. We present two\nproblem formulations. The Minimum-Time Slew Optimal Control Problem determines\nthe minimum time, required energy, and optimal trajectory to slew between any\ntwo orientations given nonlinear quaternion kinematics, gyrostat and actuator\ndynamics, and state/input constraints. By gridding the space of 3D rotations\nand efficiently solving this problem on the grid, we produce lookup tables or\nparametric fits off-line that can then be used on-line by a scheduler to\ncompute accurate estimates of minimum-time and maneuver energy for real-time\nconstellation scheduling. The Minimum-Effort Multi-Target Pointing Optimal\nControl Problem is used on-line by each satellite to produce continuous\nattitude-state and control-input trajectories that realize a given schedule\nwhile minimizing attitude error and control effort. The optimal trajectory may\nthen be achieved by a low-level tracking controller. We demonstrate our\napproach with an example of a reference satellite in Sun-synchronous orbit\npassing over globally-distributed, Earth-observation targets.\n"}
+{"abstract": "  We present the Python package ana_cont for the analytic continuation of\nfermionic and bosonic many-body Green's functions by means of either the Pade\napproximants or the maximum entropy method. The determination of\nhyperparameters and the implementation are described in detail. The code is\npublicly available on GitHub, where also documentation and learning resources\nare provided.\n"}
+{"abstract": "  Non-Hermitian non-reciprocal systems are known to be extremely sensitive to\nboundary conditions, exhibiting diverse localizing behaviors and spectrum\nstructures when translational invariance is locally broken, either by tuning\nthe boundary coupling strength, or by introducing an effective boundary using\nimpurities or defects. In this work, we consider the single impurity problem in\nthe Hatano-Nelson model and the Su-Schreieffer-Heeger model, which can be\nexactly solved with the single impurity being treated as an effective boundary\nof the system. From our exact solutions for finite-size systems, we unveil that\nincreasing the impurity strength can lead to a transition of the bulk states\nfrom non-skin states to skin states, accompanied by the change of the spectrum\nstructure from an ellipse in the complex plane to a segment along the real\naxis. These exact results indicate that the critical value of impurity strength\nis size-dependent, and increases exponentially with the lattice size when the\nimpurity is strong or the system is large enough. OUr exact solutions are also\nuseful for determining the point-gap topological transition in the concerned\nmodels.\n"}
+{"abstract": "  We report on temporal and spectral study of a flat spectrum radio quasar, PKS\nB1222+216, in flare state to get insight into acceleration and emission\nmechanisms inside the jet. This is one of the brightest and highly active\nblazar in the MeV-GeV regime. Long term multi-waveband light curves of this\nobject showed a flaring activity in 2014 with two distinct flares. Work\npresented here includes the study of flux-index variation, flare fitting,\nhardness ratio and spectral modelling of both X-ray and $\\gamma-$ray data. The\nflux-index correlation we have found in MeV-GeV regime indicates a 'softer when\nbrighter' feature. Modelling of $\\gamma-$ray light curves suggests that low\nenergy particles initiate both the flares followed by the injection of high\nenergy particles. The short rise time indicates the presence of Fermi first\norder acceleration. Multi-waveband spectral energy distributions (SEDs)\ngenerated for flares are fitted with a single-zone leptonic model. This SED\nmodelling shows the inverse Compton scattering of photon field reprocessed from\nBroad Line Region (BLR) primarily accounts for GeV emission. We have also\nreport a shift in break-energy in the soft X-ray regime during the flaring\nactivity which is the consequence of a rapid change in injection spectra.\n"}
+{"abstract": "  Let $\\mathfrak g$ be a finite-dimensional Lie algebra. The symmetric algebra\n$\\mathcal S(\\mathfrak g)$ is equipped with the standard Lie-Poisson bracket. In\nthis paper, we elaborate on a surprising observation that one naturally\nassociates the second compatible Poisson bracket on $\\mathcal S(\\mathfrak g)$\nto any finite order automorphism $\\theta$ of $\\mathfrak g$. We study related\nPoisson-commutative subalgebras $\\mathcal C$ of $\\mathcal S(\\mathfrak g)$ and\nassociated Lie algebra contractions of $\\mathfrak g$. To obtain substantial\nresults, we have to assume that $\\mathfrak g$ is semisimple. Then we can use\nVinberg's theory of $\\theta$-groups and the machinery of Invariant Theory.\n  If $\\mathfrak g=\\mathfrak h\\oplus\\dots \\oplus \\mathfrak h$ (sum of $k$\ncopies), where $\\mathfrak h$ is simple, and $\\theta$ is the cyclic permutation,\nthen we prove that the corresponding Poisson-commutative subalgebra $\\mathcal\nC$ is polynomial and maximal. Furthermore, we quantise this $\\mathcal C$ using\na Gaudin subalgebra in the enveloping algebra $\\mathcal U(\\mathfrak g)$.\n"}
+{"abstract": "  Understanding and controlling the rheology of polymeric fluids that are\nout-of-equilibrium is a fundamental problem in biology and industry. For\nexample, to package, repair, and replicate DNA, cells use enzymes to constantly\nmanipulate DNA topology, length, and structure. Inspired by this impressive\nfeat, we combine experiments with theory and simulations to show that complex\nfluids of entangled DNA display a rich range of non-equilibrium material\nproperties when undergoing enzymatic reactions that alter their topology and\nsize. We reveal that while enzymatically-active fluids of linear DNA display\nuniversal viscous thinning, circular DNA fluids - undergoing the same\nnon-equilibrium process - display thickening with a rate and degree that can be\ntuned by the DNA and enzyme concentrations. Our results open the way for the\ntopological functionalization of DNA-based materials via naturally occurring\nenzymes to create a new class of \"topologically-active\" materials that can\nautonomously alter their rheological properties in a programmable manner.\n"}
+{"abstract": "  Using EUV images and line-of-sight magnetograms from Solar Dynamics\nObservatory, we examine eight emerging bipolar magnetic regions (BMRs) in\ncentral-disk coronal holes for whether the emerging magnetic arch made any\nnoticeable coronal jets directly, via reconnection with ambient open field as\nmodeled by Yokoyama and Shibata (1995). During emergence, each BMR produced no\nobvious EUV coronal jet of normal brightness, but each produced one or more\nfaint EUV coronal jets that are discernible in AIA 193 {\\AA} images. The spires\nof these jets are much fainter and usually narrower than for typical EUV jets\nthat have been observed to be produced by minifilament eruptions in quiet\nregions and coronal holes. For each of 26 faint jets from the eight emerging\nBMRs, we examine whether the faint spire was evidently made a la Yokoyama and\nShibata (1995). We find: (1) 16 of these faint spires evidently originate from\nsites of converging opposite-polarity magnetic flux and show base brightenings\nlike those in minifilament-eruption-driven coronal jets, (2) the 10 other faint\nspires maybe were made by a burst of the external-magnetic-arcade-building\nreconnection of the emerging magnetic arch with the ambient open field,\nreconnection directly driven by the arch's emergence, but (3) none were\nunambiguously made by such emergence-driven reconnection. Thus, for these eight\nemerging BMRs the observations indicate that emergence-driven external\nreconnection of the emerging magnetic arch with ambient open field at most\nproduces a jet spire that is much fainter than in previously-reported, much\nmore obvious coronal jets driven by minifilament eruptions.\n"}
+{"abstract": "  The task of detecting morphed face images has become highly relevant in\nrecent years to ensure the security of automatic verification systems based on\nfacial images, e.g. automated border control gates. Detection methods based on\nDeep Neural Networks (DNN) have been shown to be very suitable to this end.\nHowever, they do not provide transparency in the decision making and it is not\nclear how they distinguish between genuine and morphed face images. This is\nparticularly relevant for systems intended to assist a human operator, who\nshould be able to understand the reasoning. In this paper, we tackle this\nproblem and present Focused Layer-wise Relevance Propagation (FLRP). This\nframework explains to a human inspector on a precise pixel level, which image\nregions are used by a Deep Neural Network to distinguish between a genuine and\na morphed face image. Additionally, we propose another framework to objectively\nanalyze the quality of our method and compare FLRP to other DNN\ninterpretability methods. This evaluation framework is based on removing\ndetected artifacts and analyzing the influence of these changes on the decision\nof the DNN. Especially, if the DNN is uncertain in its decision or even\nincorrect, FLRP performs much better in highlighting visible artifacts compared\nto other methods.\n"}
+{"abstract": "  We aim to investigate the potential impacts of smart homes on human behavior.\nTo this end, we simulate a series of human models capable of performing various\nactivities inside a reinforcement learning-based smart home. We then\ninvestigate the possibility of human behavior being altered as a result of the\nsmart home and the human model adapting to one-another. We design a semi-Markov\ndecision process human task interleaving model based on hierarchical\nreinforcement learning that learns to make decisions to either pursue or leave\nan activity. We then integrate our human model in the smart home which is based\non Q-learning. We show that a smart home trained on a generic human model is\nable to anticipate and learn the thermal preferences of human models with\nintrinsic rewards similar to the generic model. The hierarchical human model\nlearns to complete each activity and set optimal thermal settings for maximum\ncomfort. With the smart home, the number of time steps required to change the\nthermal settings are reduced for the human models. Interestingly, we observe\nthat small variations in the human model reward structures can lead to the\nopposite behavior in the form of unexpected switching between activities which\nsignals changes in human behavior due to the presence of the smart home.\n"}
+{"abstract": "  By harnessing quantum superposition and entanglement, remarkable progress has\nsprouted over the past three decades from different areas of research in\ncommunication computation and simulation. To further improve the processing\nability of microwave pho-tonics, here, we have demonstrated a quantum microwave\nphotonic processing system using a low jitter superconducting nanowire single\nphoton detector (SNSPD) and a time-correlated single-photon counting (TCSPC)\nmodule. This method uniquely combines extreme optical sensitivity, down to a\nsingle-photon level (below -100 dBm), and wide processing bandwidth, twice\nhigher than the transmission bandwidth of the cable. Moreover, benefitted from\nthe trigger, the system can selectively process the desired RF signal and\nattenuates the other in-tense noise and undesired RF components even the power\nis 15dB greater than the desired signal power. Using this method we show\nmicrowave phase shifting and frequency filtering for the desired RF signal on\nthe single-photon level. Besides its applications in space and under-water\ncommunications and testing and qualification of pre-packaged photonic\nmodulators and detectors. This RF signal processing capability at the\nsingle-photon level can lead to significant development in the high-speed\nquantum processing method.\n"}
+{"abstract": "  Context: In the context of the solar coronal heating problem, one possible\nexplanation for the high coronal temperature is the release of energy by\nmagnetohydrodynamic (MHD) waves. The energy transfer is believed to be\npossible, among others, by the development of the Kelvin-Helmholtz instability\n(KHI) in coronal loops. Aims: Our aim is to determine if standing slow waves in\nsolar atmospheric structures such as coronal loops, and also prominence\nthreads, sunspots, and pores, can trigger the KHI due to the oscillating shear\nflow at the structure's boundary. Methods: We used linearized nonstationary MHD\nto work out an analytical model in a cartesian reference frame. The model\ndescribes a compressible plasma near a discontinuous interface separating two\nregions of homogeneous plasma, each harboring an oscillating velocity field\nwith a constant amplitude which is parallel to the background magnetic field\nand aligned with the interface. The obtained analytical results were then used\nto determine the stability of said interface, both in coronal and photospheric\nconditions. Results: We find that the stability of the interface is determined\nby a Mathieu equation. In function of the parameters of this equation, the\ninterface can either be stable or unstable. For coronal as well as photospheric\nconditions, we find that the interface is stable with respect to the KHI.\nTheoretically, it can, however, be unstable with respect to a parametric\nresonance instability, although it seems physically unlikely. We conclude that,\nin this simplified setup, a standing slow wave does not trigger the KHI without\nthe involvement of additional physical processes.\n"}
+{"abstract": "  If $\\psi:M^n\\to \\mathbb{R}^{n+1}$ is a smooth immersed closed hypersurface,\nwe consider the functional $\\mathcal{F}_m(\\psi) = \\int_M 1 + |\\nabla^m \\nu |^2\n\\, d\\mu$, where $\\nu$ is a local unit normal vector along $\\psi$, $\\nabla$ is\nthe Levi-Civita connection of the Riemannian manifold $(M,g)$, with $g$ the\npull-back metric induced by the immersion and $\\mu$ the associated volume\nmeasure. We prove that if $m>\\lfloor n/2 \\rfloor$ then the unique globally\ndefined smooth solution to the $L^2$-gradient flow of $\\mathcal{F}_m$, for\nevery initial hypersurface, smoothly converges asymptotically to a critical\npoint of $\\mathcal{F}_m$, up to diffeomorphisms. The proof is based on the\napplication of a Lojasiewicz-Simon gradient inequality for the functional\n$\\mathcal{F}_m$.\n"}
+{"abstract": "  By design, average precision (AP) for object detection aims to treat all\nclasses independently: AP is computed independently per category and averaged.\nOn the one hand, this is desirable as it treats all classes, rare to frequent,\nequally. On the other hand, it ignores cross-category confidence calibration, a\nkey property in real-world use cases. Unfortunately, we find that on\nimbalanced, large-vocabulary datasets, the default implementation of AP is\nneither category independent, nor does it directly reward properly calibrated\ndetectors. In fact, we show that the default implementation produces a gameable\nmetric, where a simple, nonsensical re-ranking policy can improve AP by a large\nmargin. To address these limitations, we introduce two complementary metrics.\nFirst, we present a simple fix to the default AP implementation, ensuring that\nit is truly independent across categories as originally intended. We benchmark\nrecent advances in large-vocabulary detection and find that many reported gains\ndo not translate to improvements under our new per-class independent\nevaluation, suggesting recent improvements may arise from difficult to\ninterpret changes to cross-category rankings. Given the importance of reliably\nbenchmarking cross-category rankings, we consider a pooled version of AP\n(AP-pool) that rewards properly calibrated detectors by directly comparing\ncross-category rankings. Finally, we revisit classical approaches for\ncalibration and find that explicitly calibrating detectors improves\nstate-of-the-art on AP-pool by 1.7 points.\n"}
+{"abstract": "  In this paper, we are concerned with the uniqueness result for non-negative\nsolutions of the higher-order Lane-Emden equations involving the GJMS operators\non $\\mathbb{S}^n$. Since the classical moving-plane method based on the Kelvin\ntransform and maximum principle fails in dealing with the high-order elliptic\nequations in $\\mathbb{S}^n$, we first employ the Mobius transform between\n$\\mathbb{S}^n$ and $\\mathbb{R}^n$, poly-harmonic average and iteration\narguments to show that the higher-order Lane-Emden equation on $\\mathbb{S}^n$\nis equivalent to some integral equation in $\\mathbb{R}^n$. Then we apply the\nmethod of moving plane in integral forms and the symmetry of sphere to obtain\nthe uniqueness of nonnegative solutions to the higher-order Lane-Emden\nequations with subcritical polynomial growth on $\\mathbb{S}^n$. As an\napplication, we also identify the best constants and classify the extremals of\nthe sharp subcritical high-order Sobolev inequalities involving the GJMS\noperators on $\\mathbb{S}^n$. Our results do not seem to be in the literature\neven for the Lane-Emden equation and sharp subcritical Sobolev inequalities for\nfirst order derivatives on $\\mathbb{S}^n$.\n"}
+{"abstract": "  We consider reduction cohomology of modular functions defined on complex\ncurves via generalizations of holomorphic connections. The cohomology is\nexplicitly found in terms of higher genus counterparts of elliptic functions as\nanalytic continuations of solutions for functional equations. Examples of\nmodular functions on various genera are provided.\n"}
+{"abstract": "  Intelligent reflecting surface (IRS) is a promising technology to improve the\nperformance of backscatter communication systems by smartly reconfiguring the\nmulti-reflection channel. To fully exploit the passive beamforming gain of IRS\nin backscatter communication, channel state information (CSI) is indispensable\nbut more practically challenging to acquire than conventional IRS-assisted\nsystems, since IRS passively reflects signals over both the forward and\nbackward (backscattering) links between the reader and tag. To address this\nissue, we propose in this letter a new and efficient channel estimation scheme\nfor the IRS-assisted backscatter communication system. To minimize the\nmean-square error (MSE) of channel estimation, we formulate and solve an\noptimization problem by designing the IRS training reflection matrix for\nchannel estimation under the constraints of unit-modulus elements and full\nrank. Simulation results verify the effectiveness of the proposed channel\nestimation scheme as compared to other baseline schemes.\n"}
+{"abstract": "  Multilevel techniques are efficient approaches for solving the large linear\nsystems that arise from discretized partial differential equations and other\nproblems. While geometric multigrid requires detailed knowledge about the\nunderlying problem and its discretization, algebraic multigrid aims to be less\nintrusive, requiring less knowledge about the origin of the linear system. A\nkey step in algebraic multigrid is the choice of the coarse/fine partitioning,\naiming to balance the convergence of the iteration with its cost. In work by\nMacLachlan and Saad, a constrained combinatorial optimization problem is used\nto define the \"best\" coarse grid within the setting of a two-level\nreduction-based algebraic multigrid method and is shown to be NP-complete.\nHere, we develop a new coarsening algorithm based on simulated annealing to\napproximate solutions to this problem, which yields improved results over the\ngreedy algorithm developed previously. We present numerical results for test\nproblems on both structured and unstructured meshes, demonstrating the ability\nto exploit knowledge about the underlying grid structure if it is available.\n"}
+{"abstract": "  We propose and analyze the convergence of a novel stochastic algorithm for\nsolving monotone inclusions that are the sum of a maximal monotone operator and\na monotone, Lipschitzian operator. The propose algorithm requires only unbiased\nestimations of the Lipschitzian operator. We obtain the rate\n$\\mathcal{O}(log(n)/n)$ in expectation for the strongly monotone case, as well\nas almost sure convergence for the general case. Furthermore, in the context of\napplication to convex-concave saddle point problems, we derive the rate of the\nprimal-dual gap. In particular, we also obtain $\\mathcal{O}(1/n)$ rate\nconvergence of the primal-dual gap in the deterministic setting.\n"}
+{"abstract": "  We investigate pressure-induced structural changes to the Peierls-type\ndistorted low-temperature phase of the low-dimensional Sc$_3$CoC$_4$ as a\npossible origin of its pressure-enhanced superconductivity. By means of\ncryogenic high-pressure x-ray diffraction experiments we could reveal subtle,\nbut significant structural differences between the low-temperature phase at\nambient and elevated pressures. We could thus establish the structure of the\nsuperconducting phase of the title compound which interestingly still shows the\nmain features of the Peierls-type distorted low-temperature phase. This\nindicates that in contrast to other low-dimensional materials a suppression of\nperiodic structural distortions is no prerequisite for superconducitivity in\nthe transition metal carbide.\n"}
+{"abstract": "  Objective: In some situations that exist both scalar and functional data,\ncalled mixed and hybrid data, the hybrid PCA (HPCA) was introduced. Among the\nregression models for the hybrid data, we can count covariate-adjusted HPCA,\nthe Semi-functional partial linear regression, function-on-function (FOF)\nregression with signal compression, and functional additive regression, models.\nIn this article, we study the effects of HPCA decomposition of hybrid data on\nthe prediction accuracy of the FOF regression with signal compressions. Method:\nWe stated a two-step procedure for incorporating the HPCA in the functional\nregressions. The first step is reconstructing the data based on the HPCAs and\nthe second step is merging data on the other dimensions and calculate the\npoint-wise average of the desired functional dimension. We also choose the\nnumber of HPCA based on Mean Squared Perdition Error (MSPE). Result: In the two\nsimulations, we show that the regression models with the first HPCA have the\nbest accuracy prediction and model fit summaries among no HPCA and all HPCAs\nwith a training/testing approach. Finally, we applied our methodology to the\nEEG-fMRI dataset. Conclusions: We conclude that our methodology improves the\nprediction of the experiments with the EEG datasets. And we recommend that\ninstead of using the functional PCA on the desired dimension, reconstruct the\ndata with HPCA and average it on the other two dimensions for functional\nregression models.\n"}
+{"abstract": "  A corresponding comment, raised by Kao and Hwang, claims that the\nreconstructor Bob1 is unable to obtain the expected secret information in (t,\nn) Threshold d-level Quantum Secret Sharing (TDQSS)[Scientific Reports, Vol. 7,\nNo. 1 (2017), pp.6366] . In this reply, we show the TDQSS scheme can obtain the\ndealer's secret information in the condition of adding a step on\ndisentanglement.\n"}
+{"abstract": "  We investigate the existence of infinitely many radially symmetric solutions\nto the following problem $$(-\\Delta_p)^s u=g(u) \\ \\ \\textrm{ in } \\ \\\n\\mathbb{R}^N, \\ \\ u\\in W^{s,p}(\\mathbb{R}^N),$$ where $s\\in (0,1)$, $2 \\leq p <\n\\infty$, $sp \\leq N $, $2 \\leq N \\in \\mathbb{N}$ and $(-\\Delta_p)^s$ is the\nfractional $p$-Laplacian operator. We treat both of cases $sp=N$ and $sp<N.$\nThe nonlinearity $g$ is a function of Berestycki-Lions type with critical\nexponential growth if $sp=N$ and critical polynomial growth if $sp<N$. We also\nprove the existence of a ground state solution for the same problem.\n"}
+{"abstract": "  The recently discovered superconductor, UTe$_2$, has attracted immense\nscientific interest due to the experimental observations that suggest\nodd-parity superconductivity. It is believed that the material becomes a\nheavy-fermion metal at low temperatures although details of the normal state\nare unclear. Using Fourier transform infrared spectroscopy (FTIR), the normal\nstate electronic structure of UTe$_2$ was investigated at zero applied magnetic\nfield. Combining the measured reflectivity with the dc resistivity, the complex\noptical conductivity was obtained over a large frequency range. The frequency\ndependence of the real part of the optical conductivity exhibits a MIR peak\naround 4000 cm$^{-1}$ and a narrow Drude peak that develops below 40 K. A\ncombination of density functional and dynamic mean field theory (DFT + DMFT)\ngives spectra in close correspondence to the experiment. Via this comparison we\nattribute the prominent MIR peak to inter-band transitions involving a narrow U\n5$f$ feature that develops near the Fermi level. In this regard, our data gives\nspectroscopic evidence for the existence of a low energy Kondo resonance at\ntemperatures just above the onset of superconductivity and implicates heavy\nelectrons in the formation of the superconducting state. We find that the\ncoherent Kondo resonance is primarily associated with a collapse of scattering\nand less with a transfer of spectral weight.\n"}
+{"abstract": "  The partition function of the Symmetric Matrix Ensemble is identified with\nthe $\\tau-$function of a particular solution of the Pfaff Lattice. We show that\nin the case of even power interactions, in the thermodynamic limit, the\n$\\tau-$function corresponds to the solution of an integrable chain of\nhydrodynamic type. We prove that the hydrodynamic chain so obtained is\ndiagonalisable and admits hydrodynamic reductions in Riemann invariants in an\narbitrary number of components.\n"}
+{"abstract": "  Since the outbreak of the novel coronavirus, COVID-19, there has been\nincreased interest in the use of digital contact tracing as a means of stopping\nchains of viral transmission, provoking alarm from privacy advocates.\nConcerning the ethics of this technology, recent studies have predominantly\nfocused on (1) the formation of guidelines for ethical contact tracing, (2) the\nanalysis of specific implementations, or (3) the review of a select number of\ncontact tracing applications and their relevant privacy or ethical\nimplications. In this study, we provide a comprehensive survey of the evolving\necosystem of COVID-19 tracing applications, examining 152 contact tracing\napplications and assessing the extent to which they comply with existing\nguidelines for ethical contact tracing. The assessed criteria cover areas\nincluding data collection and storage, transparency and consent, and whether\nthe implementation is open source. We find that although many apps released\nearly in the pandemic fell short of best practices, apps released more\nrecently, following the publication of the Apple/Google exposure notification\nprotocol, have tended to be more closely aligned with ethical contact tracing\nprinciples. This dataset will be publicly available and may be updated as the\npandemic continues.\n"}
+{"abstract": "  Player modeling attempts to create a computational model which accurately\napproximates a player's behavior in a game. Most player modeling techniques\nrely on domain knowledge and are not transferable across games. Additionally,\nplayer models do not currently yield any explanatory insight about a player's\ncognitive processes, such as the creation and refinement of mental models. In\nthis paper, we present our findings with using action model learning (AML), in\nwhich an action model is learned given data in the form of a play trace, to\nlearn a player model in a domain-agnostic manner. We demonstrate the utility of\nthis model by introducing a technique to quantitatively estimate how well a\nplayer understands the mechanics of a game. We evaluate an existing AML\nalgorithm (FAMA) for player modeling and develop a novel algorithm called\nBlackout that is inspired by player cognition. We compare Blackout with FAMA\nusing the puzzle game Sokoban and show that Blackout generates better player\nmodels.\n"}
+{"abstract": "  Like many predictive models, random forests provide a point prediction for a\nnew observation. Besides the point prediction, it is important to quantify the\nuncertainty in the prediction. Prediction intervals provide information about\nthe reliability of the point predictions. We have developed a comprehensive R\npackage, RFpredInterval, that integrates 16 methods to build prediction\nintervals with random forests and boosted forests. The methods implemented in\nthe package are a new method to build prediction intervals with boosted forests\n(PIBF) and 15 different variants to produce prediction intervals with random\nforests proposed by Roy and Larocque (2020). We perform an extensive simulation\nstudy and apply real data analyses to compare the performance of the proposed\nmethod to ten existing methods to build prediction intervals with random\nforests. The results show that the proposed method is very competitive and,\nglobally, it outperforms the competing methods.\n"}
+{"abstract": "  The influence of oscillatory perturbations on autonomous strongly nonlinear\nsystems in the plane is investigated. It is assumed that the intensity of\nperturbations decays with time, and their frequency increases according to a\npower law. The long-term behaviour of perturbed trajectories is discussed. It\nis shown that, depending on the structure and the parameters of perturbations,\nthere are at least two different asymptotic regimes: a phase locking and a\nphase drifting. In the case of phase locking, resonant solutions with an\nunlimitedly growing energy occur. The stability and asymptotics at infinity of\nsuch solutions are investigated. The proposed analysis is based on a\ncombination of the averaging technique and the method of Lyapunov functions.\n"}
+{"abstract": "  Using $1310.6\\times10^{6}$ $J/\\psi$ and $448.1\\times10^{6}$ $\\psi(3686)$\nevents collected with the BESIII detector, the branching fractions of $J/\\psi$\nand $\\psi(3686)$ decays to $\\Sigma^{+}\\overline{\\Sigma}^{-}$ are measured to be\n$(10.61 \\pm 0.04 \\pm 0.36) \\times 10^{-4}$ and $(2.52 \\pm 0.04 \\pm 0.09) \\times\n10^{-4}$, respectively. In addition, the ratio of $\\mathcal{B}(\\psi(3686)\n\\rightarrow \\Sigma^{+}\\overline{\\Sigma}^{-})/\\mathcal{B}(J/\\psi \\rightarrow\n\\Sigma^{+}\\overline{\\Sigma}^{-})$ is determined to be $(23.8 \\pm 1.1)\\%$ which\nviolates the \"$12\\%$ rule\".\n"}
+{"abstract": "  Heavy quarkonium is one of the key candidates to study the fundamental\nproperties of Quark-Gluon Plasma (QGP) created in heavy-ion collisions.\nComparing the production of the $J/\\psi$ meson in proton-proton and heavy-ion\ncollisions, namely the nuclear modification factor ($R_{\\rm AA}$), can provide\nsome quantitative understanding of the QGP. Traditionally, the $R_{\\rm AA}$ is\nmeasured under the assumption that the quarkonium is unpolarized in both\nproton-proton and heavy-ion collisions. However, the recent measurements on the\n$J/\\psi$ polarization in heavy-ion collisions from the ALICE Collaboration\nsuggest that the $J/\\psi$ meson has small but non-negligible transverse\npolarization in both helicity and Collins-Soper frames. In this paper, we show\nthe effects from the $J/\\psi$ polarization to the measurement of $R_{\\rm AA}$\nin the forward rapidity region using the available data from the ALICE and LHCb\nexperiments and demonstrate the possible maximum effects from polarization in\nthe central barrel region at the RHIC and LHC energies. We would like to draw\npeople's attention that having precise measurements on the quarkonium\npolarization in heavy-ion collisions is important to obtain the full picture of\nhow heavy quarkonium interacting with the QGP.\n"}
+{"abstract": "  We describe limits of line bundles on nodal curves in terms of toric\narrangements associated to Voronoi tilings of Euclidean spaces. These tilings\nencode information on the relationship between the possibly infinitely many\nlimits, and ultimately give rise to a new definition of limit linear series.\nThis article and the first two that preceded it are the first in a series aimed\nto explore this new approach.\n  In Part I, we set up the combinatorial framework and showed how graphs\nweighted with integer lengths associated to the edges provide tilings of\nEuclidean spaces by certain polytopes associated to the graph itself and to its\nsubgraphs.\n  In Part II, we described the arrangements of toric varieties associated to\nthe tilings of Part I in several ways: using normal fans, as unions of orbits,\nby equations and as degenerations of tori.\n  In the present Part III, we show how these combinatorial and toric frameworks\nallow us to describe all stable limits of a family of line bundles along a\ndegenerating family of curves. Our main result asserts that the collection of\nall these limits is parametrized by a connected 0-dimensional closed substack\nof the Artin stack of all torsion-free rank-one sheaves on the limit curve.\nMoreover, we thoroughly describe this closed substack and all the closed\nsubstacks that arise in this way as certain torus quotients of the arrangements\nof toric varieties of Part II determined by the Voronoi tilings of Euclidean\nspaces studied in Part I.\n"}
+{"abstract": "  In this article, we study activity recognition in the context of sensor-rich\nenvironments. We address, in particular, the problem of inductive biases and\ntheir impact on the data collection process. To be effective and robust,\nactivity recognition systems must take these biases into account at all levels\nand model them as hyperparameters by which they can be controlled. Whether it\nis a bias related to sensor measurement, transmission protocol, sensor\ndeployment topology, heterogeneity, dynamicity, or stochastic effects, it is\nimportant to understand their substantial impact on the quality of activity\nrecognition models. This study highlights the need to separate the different\ntypes of biases arising in real situations so that machine learning models,\ne.g., adapt to the dynamicity of these environments, resist to sensor failures,\nand follow the evolution of the sensors topology. We propose a metamodeling\nprocess in which the sensor data is structured in layers. The lower layers\nencode the various biases linked to transformations, transmissions, and\ntopology of data. The upper layers encode biases related to the data itself.\nThis way, it becomes easier to model hyperparameters and follow changes in the\ndata acquisition infrastructure. We illustrate our approach on the SHL dataset\nwhich provides motion sensor data for a list of human activities collected\nunder real conditions. The trade-offs exposed and the broader implications of\nour approach are discussed with alternative techniques to encode and\nincorporate knowledge into activity recognition models.\n"}
+{"abstract": "  Attention has been proved to be an efficient mechanism to capture long-range\ndependencies. However, so far it has not been deployed in invertible networks.\nThis is due to the fact that in order to make a network invertible, every\ncomponent within the network needs to be a bijective transformation, but a\nnormal attention block is not. In this paper, we propose invertible attention\nthat can be plugged into existing invertible models. We mathematically and\nexperimentally prove that the invertibility of an attention model can be\nachieved by carefully constraining its Lipschitz constant. We validate the\ninvertibility of our invertible attention on image reconstruction task with 3\npopular datasets: CIFAR-10, SVHN, and CelebA. We also show that our invertible\nattention achieves similar performance in comparison with normal non-invertible\nattention on dense prediction tasks. The code is available at\nhttps://github.com/Schwartz-Zha/InvertibleAttention\n"}
+{"abstract": "  We perform energy dependent calculations of independent and cumulative\nfission product yields for $^{235}$U, $^{238}$U, and $^{239}$Pu in the first\nchance fission region. Starting with the primary fission fragment distributions\ntaken from available experimental data and analytical functions based on\nassumptions for the excitation energy and spin-parity distributions, the\nHauser-Feshbach statistical decay treatment for fission fragment de-excitation\nis applied to more than 1,000 fission fragments for the incident neutron\nenergies up to 5 MeV. The calculated independent fission product yields are\nthen used as an input of $\\beta$-decay to produce the cumulative yield, and\nsummation calculations are performed. Model parameters in these procedures are\nadjusted by applying the Bayesian technique at the thermal energy for $^{235}$U\nand $^{239}$Pu and in the fast energy range for $^{238}$U. The calculated\nfission observable quantities, such as the energy-dependent fission yields, and\nprompt and delayed neutron yields, are compared with available experimental\ndata. We also study a possible impact of the second chance fission opening on\nthe energy dependence of the delayed neutron yield by extrapolating the\ncalculation.\n"}
+{"abstract": "  Hierarchy and centrality are two popular notions used to characterize the\nimportance of entities in complex systems. Indeed, many complex systems exhibit\na natural hierarchical structure, and centrality is a fundamental\ncharacteristic allowing to identify key constituents. Several measures based on\nvarious aspects of network topology have been proposed in order to quantify\nthese concepts. While numerous studies have investigated whether centrality\nmeasures convey redundant information, how centrality and hierarchy measures\nare related is still an open issue. In this paper, we investigate the\ninteraction between centrality and hierarchy using several correlation and\nsimilarity evaluation measures. A series of experiments is performed in order\nto evaluate the combinations of 6 centrality measures with 4 hierarchy measures\nacross 28 diverse real-world networks with varying topological characteristics.\nResults show that network density and transitivity play a key role in shaping\nthe pattern of relations between centrality and hierarchy measures.\n"}
+{"abstract": "  The long wavelength modes lost to bright foregrounds in the interferometric\n21-cm surveys can partially be recovered using a forward modeling approach that\nexploits the non-linear coupling between small and large scales induced by\ngravitational evolution. In this work, we build upon this approach by\nconsidering how adding external galaxy distribution data can help to fill in\nthese modes. We consider supplementing the 21-cm data at two different\nredshifts with a spectroscopic sample (good radial resolution but low number\ndensity) loosely modeled on DESI-ELG at $z=1$ and a photometric sample (high\nnumber density but poor radial resolution) similar to LSST sample at $z=1$ and\n$z=4$ respectively. We find that both the galaxy samples are able to\nreconstruct the largest modes better than only using 21-cm data, with the\nspectroscopic sample performing significantly better than the photometric\nsample despite much lower number density. We demonstrate the synergies between\nsurveys by showing that the primordial initial density field is reconstructed\nbetter with the combination of surveys than using either of them individually.\nMethodologically, we also explore the importance of smoothing the density field\nwhen using bias models to forward model these tracers for reconstruction.\n"}
+{"abstract": "  Let $R$ be a Noetherian local ring. We prove that $R$ is regular of dimension\nat most four if, and only if, every prime ideal, defining a Gorenstein quotient\nring, is syzygetic. We deduce a characterization of these rings in terms of the\nAndr\\'e-Quillen homology.\n"}
+{"abstract": "  In the United States, from the start of the COVID-19 pandemic to December 31,\n2020, 341,199 deaths and more than 19,663,976 infections were recorded. Recent\nliterature establishes that communities with poverty-related health problems,\nsuch as obesity, cardiovascular disease, diabetes, and hypertension, are more\nsusceptible to mortality from SARS-CoV-2 infection. Additionally, controversial\npublic health policies implemented by the nation's political leaders have\nhighlighted the socioeconomic inequalities of minorities. Therefore, through\nmultivariate correlational analysis using machine learning techniques and\nstructural equations, we measure whether social determinants are associated\nwith increased infection and death from COVID-19 disease. The PLS least squares\nregression analysis allowed identifying a significant impact between social\ndeterminants and COVID-19 disease through a predictive value of R2 = .916,\n\\b{eta} = .836, p =. 000 (t-value = 66,137) shows that for each unit of\nincrease in social determinants, COVID-19 disease increases by 91.6%. The\nclustering index used for correlational analysis generated a new data set\ncomprising three groups: C1 Republicans, C2 and C3 Democrats from California,\nNew York, Texas, and Florida. This analysis made it possible to identify the\npoverty variable as the main risk factor related to the high rates of infection\nin Republican states and a high positive correlation between the population not\ninsured with a medical plan and high levels of virus contagion in the states of\ngroup C3. These findings explain the argument that poverty and lack of economic\nsecurity put the public or private health system at risk and calamity.\n"}
+{"abstract": "  The loop equations for the $\\beta$-ensembles are conventionally solved in\nterms of a $1/N$ expansion. We observe that it is also possible to fix $N$ and\nexpand in inverse powers of $\\beta$. At leading order, for the one-point\nfunction $W_1(x)$ corresponding to the average of the linear statistic $A =\n\\sum_{j=1}^N 1/(x - \\lambda_j)$, and specialising the classical weights, this\nreclaims well known results of Stieltjes relating the zeros of the classical\npolynomials to the minimum energy configuration of certain log-gas potential\nenergies. Moreover, it is observed that the differential equations satisfied by\n$W_1(x)$ in the case of classical weights -- which are particular Riccati\nequations -- are simply related to the differential equations satisfied by\n$W_1(x)$ in the high temperature scaled limit $\\beta = 2\\alpha/N$ ($\\alpha$\nfixed, $N \\to \\infty$), implying a certain high-low temperature duality. A\ngeneralisation of this duality, valid without any limiting procedure, is shown\nto hold for $W_1(x)$ and all its higher point analogues in the classical\n$\\beta$-ensembles.\n"}
+{"abstract": "  Disease classification relying solely on imaging data attracts great interest\nin medical image analysis. Current models could be further improved, however,\nby also employing Electronic Health Records (EHRs), which contain rich\ninformation on patients and findings from clinicians. It is challenging to\nincorporate this information into disease classification due to the high\nreliance on clinician input in EHRs, limiting the possibility for automated\ndiagnosis. In this paper, we propose \\textit{variational knowledge\ndistillation} (VKD), which is a new probabilistic inference framework for\ndisease classification based on X-rays that leverages knowledge from EHRs.\nSpecifically, we introduce a conditional latent variable model, where we infer\nthe latent representation of the X-ray image with the variational posterior\nconditioning on the associated EHR text. By doing so, the model acquires the\nability to extract the visual features relevant to the disease during learning\nand can therefore perform more accurate classification for unseen patients at\ninference based solely on their X-ray scans. We demonstrate the effectiveness\nof our method on three public benchmark datasets with paired X-ray images and\nEHRs. The results show that the proposed variational knowledge distillation can\nconsistently improve the performance of medical image classification and\nsignificantly surpasses current methods.\n"}
+{"abstract": "  Existing grasp synthesis methods are either analytical or data-driven. The\nformer one is oftentimes limited to specific application scope. The latter one\ndepends heavily on demonstrations, thus suffers from generalization issues;\n\\eg, models trained with human grasp data would be difficult to transfer to\n3-finger grippers. To tackle these deficiencies, we formulate a fast and\ndifferentiable force closure estimation method, capable of producing diverse\nand physically stable grasps with arbitrary hand structures, without any\ntraining data. Although force closure has commonly served as a measure of grasp\nquality, it has not been widely adopted as an optimization objective for grasp\nsynthesis primarily due to its high computational complexity; in comparison,\nthe proposed differentiable method can test a force closure within\nmilliseconds. In experiments, we validate the proposed method's efficacy in 6\ndifferent settings.\n"}
+{"abstract": "  In this paper, we present the spectroscopy of 6P$_{1/2}$ state in $^{87}$Rb\nusing double resonance technique at $780~\\text{nm}$ and $421~\\text{nm}$. The\ndouble resonance technique is implemented using electromagnetically induced\ntransparency (EIT) and optical pumping methods. Using these spectroscopy\nmethods, we have measured the hyperfine splitting of 6P$_{1/2}$ state with\nprecision of $<$400~kHz which agrees well with other spectroscopy methods such\nas electrical discharge and saturated absorption spectroscopy at\n$421~\\text{nm}$.\n"}
+{"abstract": "  In this paper, we study the dynamics of temporal difference learning with\nneural network-based value function approximation over a general state space,\nnamely, \\emph{Neural TD learning}. We consider two practically used algorithms,\nprojection-free and max-norm regularized Neural TD learning, and establish the\nfirst convergence bounds for these algorithms. An interesting observation from\nour results is that max-norm regularization can dramatically improve the\nperformance of TD learning algorithms, both in terms of sample complexity and\noverparameterization. In particular, we prove that max-norm regularization\nimproves state-of-the-art sample complexity and overparameterization bounds.\nThe results in this work rely on a novel Lyapunov drift analysis of the network\nparameters as a stopped and controlled random process.\n"}
+{"abstract": "  Vocoders received renewed attention as main components in statistical\nparametric text-to-speech (TTS) synthesis and speech transformation systems.\nEven though there are vocoding techniques give almost accepted synthesized\nspeech, their high computational complexity and irregular structures are still\nconsidered challenging concerns, which yield a variety of voice quality\ndegradation. Therefore, this paper presents new techniques in a continuous\nvocoder, that is all features are continuous and presents a flexible speech\nsynthesis system. First, a new continuous noise masking based on the phase\ndistortion is proposed to eliminate the perceptual impact of the residual noise\nand letting an accurate reconstruction of noise characteristics. Second, we\naddressed the need of neural sequence to sequence modeling approach for the\ntask of TTS based on recurrent networks. Bidirectional long short-term memory\n(LSTM) and gated recurrent unit (GRU) are studied and applied to model\ncontinuous parameters for more natural-sounding like a human. The evaluation\nresults proved that the proposed model achieves the state-of-the-art\nperformance of the speech synthesis compared with the other traditional\nmethods.\n"}
+{"abstract": "  ECG is an attractive option to assess stress in serious Virtual Reality (VR)\napplications due to its non-invasive nature. However, the existing Machine\nLearning (ML) models perform poorly. Moreover, existing studies only perform a\nbinary stress assessment, while to develop a more engaging biofeedback-based\napplication, multi-level assessment is necessary. Existing studies annotate and\nclassify a single experience (e.g. watching a VR video) to a single stress\nlevel, which again prevents design of dynamic experiences where real-time\nin-game stress assessment can be utilized. In this paper, we report our\nfindings on a new study on VR stress assessment, where three stress levels are\nassessed. ECG data was collected from 9 users experiencing a VR roller coaster.\nThe VR experience was then manually labeled in 10-seconds segments to three\nstress levels by three raters. We then propose a novel multimodal deep fusion\nmodel utilizing spectrogram and 1D ECG that can provide a stress prediction\nfrom just a 1-second window. Experimental results demonstrate that the proposed\nmodel outperforms the classical HRV-based ML models (9% increase in accuracy)\nand baseline deep learning models (2.5% increase in accuracy). We also report\nresults on the benchmark WESAD dataset to show the supremacy of the model.\n"}
+{"abstract": "  Deployment of machine learning models in real high-risk settings (e.g.\nhealthcare) often depends not only on model's accuracy but also on its\nfairness, robustness and interpretability. Generalized Additive Models (GAMs)\nare a class of interpretable models with a long history of use in these\nhigh-risk domains, but they lack desirable features of deep learning such as\ndifferentiability and scalability. In this work, we propose a neural GAM\n(NODE-GAM) and neural GA$^2$M (NODE-GA$^2$M) that scale well and perform better\nthan other GAMs on large datasets, while remaining interpretable compared to\nother ensemble and deep learning models. We demonstrate that our models find\ninteresting patterns in the data. Lastly, we show that we improve model\naccuracy via self-supervised pre-training, an improvement that is not possible\nfor non-differentiable GAMs.\n"}
+{"abstract": "  For the first time, we report a theoretical methodology to predict charge\nstate distribution of projectile ions inside a solid-target. The method\nutilizes either a simple Fermi gas model or an ab initio theoretical method and\na certain parameterization of width for the Lorentzian charge state\ndistributions. Results obtained from the two approaches are comparable, but the\nformer has a certain edge over the latter. The projectile charge state\ndistribution inside a solid-target plays a significant role in estimating\nelectron capture cross-sections and then to describe the observed K-shell\nionization dynamics. The electron capture process plays a certain role in\nL-shell ionization dynamics too, but in a test case of Si on Au target the\nsubshell charge sharing contributes a more vital role than the electron\ncapture. Thus, we have validated the present model as a reliable as well as\nuseful for many solid-target based applications viz. tumour therapy,\nbiophysics, accelerators, material science etc.\n"}
+{"abstract": "  We present experimental results using a single-electron resolution\nSkipper-CCD running above ground level to demonstrate the potential of this\ntechnology for its use in reactor neutrino observations and other low-energy\nparticle interaction experiments. Operating conditions and event-selection\ncriteria are provided to decouple most of the background rate at low energies.\nOur final results for events with energies as low as $5$ ionized electron-hole\npairs show that the exponentially increasing rate of events seen in other\ntechnologies is not present in our data. This demonstrates that the Skipper CCD\nproves to be among the best options to measure low energy and weakly\ninteracting particles at ground level.\n"}
+{"abstract": "  The minimization of electronics makes heat dissipation of related devices an\nincreasing challenge. When the size of materials is smaller than the phonon\nmean free paths, phonons transport without internal scatterings and laws of\ndiffusive thermal conduction fail, resulting in significant reduction in the\neffective thermal conductivity. This work reports, for the first time, the\ntemperature dependent thermal conductivity of doped epitaxial 6H-SiC and\nmonocrystalline porous 6H-SiC below room temperature probed by time-domain\nthermoreflectance. Strong quasi-ballistic thermal transport was observed in\nthese samples, especially at low temperatures. Doping and structural boundaries\nwere applied to tune the quasi-ballistic thermal transport since dopants\nselectively scatter high-frequency phonons while boundaries scatter phonons\nwith long mean free paths. Exceptionally strong phonon scattering by boron\ndopants are observed, compared to nitrogen dopants. Furthermore, orders of\nmagnitude reduction in the measured thermal conductivity was observed at low\ntemperatures for the porous 6H-SiC compared to the epitaxial 6H-SiC. Finally,\nfirst principles calculations and a simple Callaway model are built to\nunderstand the measured thermal conductivities. Our work sheds light on the\nfundamental understanding of thermal conduction in technologically-important\nwide bandgap semiconductors such as 6H-SiC and will impact applications such as\nthermal management of 6H-SiC-related electronics and devices.\n"}
+{"abstract": "  We consider probe-based quantum thermometry and show that machine\nclassification can provide model-independent estimation with quantifiable error\nassessment. Our approach is based on the k-nearest-neighbor algorithm. The\nmachine is trained using data from either computer simulations or a calibration\nexperiment. This yields a predictor which can be used to estimate the\ntemperature from new observations. The algorithm is highly flexible and works\nwith any kind of probe observable. It also allows to incorporate experimental\nerrors, as well as uncertainties about experimental parameters. We illustrate\nour method with an impurity thermometer in a Bose-gas, as well as in the\nestimation of the thermal phonon number in the Rabi model.\n"}
+{"abstract": "  We present a second-order recursive Fermi-operator expansion scheme using\nmixed precision floating point operations to perform electronic structure\ncalculations using tensor core units. A performance of over 100 teraFLOPs is\nachieved for half-precision floating point operations on Nvidia's A100 tensor\ncore units. The second-order recursive Fermi-operator scheme is formulated in\nterms of a generalized, differentiable deep neural network structure, which\nsolves the quantum mechanical electronic structure problem. We demonstrate how\nthis network can be accelerated by optimizing the weight and bias values to\nsubstantially reduce the number of layers required for convergence. We also\nshow how this machine learning approach can be used to optimize the\ncoefficients of the recursive Fermi-operator expansion to accurately represent\nfractional occupation numbers of the electronic states at finite temperatures.\n"}
+{"abstract": "  Existing end-to-end-encrypted (E2EE) email systems, mainly PGP, have long\nbeen evaluated in controlled lab settings. While these studies have exposed\nusability obstacles for the average user and offer design improvements, there\nexist users with an immediate need for private communication, who must cope\nwith existing software and its limitations. We seek to understand whether\nindividuals motivated by concrete privacy threats, such as those vulnerable to\nstate surveillance, can overcome usability issues to adopt complex E2EE tools\nfor long-term use. We surveyed regional activists, as surveillance of social\nmovements is well-documented. Our study group includes individuals from 9\nsocial movement groups in the US who had elected to participate in a workshop\non using Thunderbird+Enigmail for email encryption. These workshops tool place\nprior to mid-2017, via a partnership with a non-profit which supports social\nmovement groups. Six to 40 months after their PGP email encryption training,\nmore than half of the study participants were continuing to use PGP email\nencryption despite intervening widespread deployment of simple E2EE messaging\napps such as Signal. We study the interplay of usability with social factors\nsuch as motivation and the risks that individuals undertake through their\nactivism. We find that while usability is an important factor, it is not enough\nto explain long term use. For example, we find that riskiness of one's activism\nis negatively correlated with long-term PGP use. This study represents the\nfirst long-term study, and the first in-the-wild study, of PGP email encryption\nadoption.\n"}
+{"abstract": "  3D delineation of anatomical structures is a cardinal goal in medical imaging\nanalysis. Prior to deep learning, statistical shape models that imposed\nanatomical constraints and produced high quality surfaces were a core\ntechnology. Prior to deep learning, statistical shape models that imposed\nanatomical constraints and produced high quality surfaces were a core\ntechnology. Today fully-convolutional networks (FCNs), while dominant, do not\noffer these capabilities. We present deep implicit statistical shape models\n(DISSMs), a new approach to delineation that marries the representation power\nof convolutional neural networks (CNNs) with the robustness of SSMs. DISSMs use\na deep implicit surface representation to produce a compact and descriptive\nshape latent space that permits statistical models of anatomical variance. To\nreliably fit anatomically plausible shapes to an image, we introduce a novel\nrigid and non-rigid pose estimation pipeline that is modelled as a Markov\ndecision process(MDP). We outline a training regime that includes inverted\nepisodic training and a deep realization of marginal space learning (MSL).\nIntra-dataset experiments on the task of pathological liver segmentation\ndemonstrate that DISSMs can perform more robustly than three leading FCN\nmodels, including nnU-Net: reducing the mean Hausdorff distance (HD) by\n7.7-14.3mm and improving the worst case Dice-Sorensen coefficient (DSC) by\n1.2-2.3%. More critically, cross-dataset experiments on a dataset directly\nreflecting clinical deployment scenarios demonstrate that DISSMs improve the\nmean DSC and HD by 3.5-5.9% and 12.3-24.5mm, respectively, and the worst-case\nDSC by 5.4-7.3%. These improvements are over and above any benefits from\nrepresenting delineations with high-quality surface.\n"}
+{"abstract": "  Classification and identification of amino acids in aqueous solutions is\nimportant in the study of biomacromolecules. Laser Induced Breakdown\nSpectroscopy (LIBS) uses high energy laser-pulses for ablation of chemical\ncompounds whose radiated spectra are captured and recorded to reveal molecular\nstructure. Spectral peaks and noise from LIBS are impacted by experimental\nprotocols. Current methods for LIBS spectral analysis achieves promising\nresults using PCA, a linear method. It is well-known that the underlying\nphysical processes behind LIBS are highly nonlinear. Our work set out to\nunderstand the impact of LIBS spectra on suitable neighborhood size over which\nto consider pattern phenomena, if nonlinear methods capture pattern phenomena\nwith increased efficacy, and how they improve classification and identification\nof compounds. We analyzed four amino acids, polysaccharide, and a control\ngroup, water. We developed an information theoretic method for measurement of\nLIBS energy spectra, implemented manifold methods for nonlinear dimensionality\nreduction, and found while clustering results were not statistically\nsignificantly different, nonlinear methods lead to increased classification\naccuracy. Moreover, our approach uncovered the contribution of micro-wells\n(experimental protocol) in LIBS spectra. To the best of our knowledge, ours is\nthe first application of Manifold methods to LIBS amino-acid analysis in the\nresearch literature.\n"}
+{"abstract": "  Cross-modal retrieval is generally performed by projecting and aligning the\ndata from two different modalities onto a shared representation space. This\nshared space often also acts as a bridge for translating the modalities. We\naddress the problem of learning such representation space by proposing and\nexploiting the property of Discriminative Semantic Transitive Consistency --\nensuring that the data points are correctly classified even after being\ntransferred to the other modality. Along with semantic transitive consistency,\nwe also enforce the traditional distance minimizing constraint which makes the\nprojections of the corresponding data points from both the modalities to come\ncloser in the representation space. We analyze and compare the contribution of\nboth the loss terms and their interaction, for the task. In addition, we\nincorporate semantic cycle-consistency for each of the modality. We empirically\ndemonstrate better performance owing to the different components with clear\nablation studies. We also provide qualitative results to support the proposals.\n"}
+{"abstract": "  We report a single-shot-based projective readout of a semiconductor hybrid\nqubit formed by three electrons in a GaAs double quantum dot.\nVoltage-controlled adiabatic transitions between the qubit operations and\nreadout conditions allow high-fidelity mapping of quantum states. We show that\na large ratio both in relaxation time vs. tunneling time (~ 50) and\nsinglet-triplet splitting vs. thermal energy (~ 20) allow energy-selective\ntunneling-based spin-to-charge conversion with readout visibility ~ 92.6%.\nCombined with ac driving, we demonstrate high visibility coherent Rabi and\nRamsey oscillations of a hybrid qubit in GaAs. Further, we discuss the\ngenerality of the method for use in other materials, including silicon.\n"}
+{"abstract": "  Let $G$ be an acylindrically hyperbolic group and $E$ an exponential equation\nover $G$. We show that if $E$ is solvable in $G$, then there exists a solution\nwhose components, corresponding to loxodromic elements, can be linearly\nestimated in terms of lengths of the coefficients of $E$. We give a more\nprecise answer in the case where $G$ is a relatively hyperbolic group. Under\nsome assumption of general character, the solvability and the search problems\nfor exponential equations over $G$ can be reduced to the peripheral subgroups\nof $G$.\n"}
+{"abstract": "  For the Hodge--Laplace equation in finite element exterior calculus, we\nintroduce several families of discontinuous Galerkin methods in the extended\nGalerkin framework. For contractible domains, this framework utilizes seven\nfields and provides a unifying inf-sup analysis with respect to all\ndiscretization and penalty parameters. It is shown that the proposed methods\ncan be hybridized as a reduced two-field formulation.\n"}
+{"abstract": "  Based on decision trees, it is efficient to handle tabular data. Conventional\ndecision tree growth methods often result in suboptimal trees because of their\ngreedy nature. Their inherent structure limits the options of hardware to\nimplement decision trees in parallel. Here is a compact representation of\nbinary decision trees to overcome these deficiencies. We explicitly formulate\nthe dependence of prediction on binary tests for binary decision trees and\nconstruct a function to guide the input sample from the root to the appropriate\nleaf node. And based on this formulation we introduce a new interpretation of\nbinary decision trees. Then we approximate this formulation via continuous\nfunctions. Finally, we interpret the decision tree as a model combination\nmethod. And we propose the selection-prediction scheme to unify a few learning\nmethods.\n"}
+{"abstract": "  We prove bounds on the saturation degrees of homogeneous ideals (and their\npowers) defining smooth complex projective varieties. For example, we show that\na classical statement due to Macualay for zero-dimensional complete\nintersection ideals holds for any smooth variety. For curves, we bound the\nsaturation degree of powers in terms of the regularity.\n"}
+{"abstract": "  Vision transformers (ViTs) have been successfully applied in image\nclassification tasks recently. In this paper, we show that, unlike convolution\nneural networks (CNNs)that can be improved by stacking more convolutional\nlayers, the performance of ViTs saturate fast when scaled to be deeper. More\nspecifically, we empirically observe that such scaling difficulty is caused by\nthe attention collapse issue: as the transformer goes deeper, the attention\nmaps gradually become similar and even much the same after certain layers. In\nother words, the feature maps tend to be identical in the top layers of deep\nViT models. This fact demonstrates that in deeper layers of ViTs, the\nself-attention mechanism fails to learn effective concepts for representation\nlearning and hinders the model from getting expected performance gain. Based on\nabove observation, we propose a simple yet effective method, named\nRe-attention, to re-generate the attention maps to increase their diversity at\ndifferent layers with negligible computation and memory cost. The pro-posed\nmethod makes it feasible to train deeper ViT models with consistent performance\nimprovements via minor modification to existing ViT models. Notably, when\ntraining a deep ViT model with 32 transformer blocks, the Top-1 classification\naccuracy can be improved by 1.6% on ImageNet. Code is publicly available at\nhttps://github.com/zhoudaquan/dvit_repo.\n"}
+{"abstract": "  Despite their remarkable performance on a wide range of visual tasks, machine\nlearning technologies often succumb to data distribution shifts. Consequently,\na range of recent work explores techniques for detecting these shifts.\nUnfortunately, current techniques offer no explanations about what triggers the\ndetection of shifts, thus limiting their utility to provide actionable\ninsights. In this work, we present Concept Bottleneck Shift Detection (CBSD): a\nnovel explainable shift detection method. CBSD provides explanations by\nidentifying and ranking the degree to which high-level human-understandable\nconcepts are affected by shifts. Using two case studies (dSprites and\n3dshapes), we demonstrate how CBSD can accurately detect underlying concepts\nthat are affected by shifts and achieve higher detection accuracy compared to\nstate-of-the-art shift detection methods.\n"}
+{"abstract": "  A wall-resolved large eddy simulation is performed to study secondary tones\ngenerated by a NACA0012 airfoil at $\\alpha = 3^{\\circ}$ with freestream Mach\nnumber $M_{\\infty} = 0.3$ and Reynolds number $Re = 5 \\times 10^4$. Laminar\nseparation bubbles are observed over the suction side and near the trailing\nedge on the pressure side. Flow visualization and spectral analysis are\nemployed to investigate vortex shedding aft of the suction side separation\nbubble. Vortex interaction results in merging or bursting such that coherent\nstructures or turbulent packets are advected towards the trailing edge leading\nto different levels of noise emission. Despite the intermittent occurrence of\nlaminar-turbulent transition, the noise spectrum depicts a main tone with\nmultiple equidistant secondary tones. To understand the role of flow\ninstabilities on the tones, the linearized Navier-Stokes equations are examined\nin its operator form through bi-global stability and resolvent analyses, and by\ntime evolution of disturbances using a matrix-free method. These linear global\nanalyses reveal amplification of disturbances over the suction side separation\nbubble. Non-normality of the linear operator leads to further transient\namplification due to modal interaction among eigenvectors. Two-point\ncorrelations of pressure along the spanwise direction elucidate aspects of the\nacoustic feedback loop mechanism in both the linear and non-linear solutions.\nThis feedback process is self-sustained by acoustic waves radiated from the\ntrailing edge, which reach the most sensitive flow location between 10 and 18\\%\nof the airfoil chord as identified by the resolvent analysis.\n"}
+{"abstract": "  Earth's modern climate is characterized by wet, rainy deep tropics, however\npaleoclimate and planetary science have revealed a wide range of hydrological\ncycle regimes connected to different external parameters. Here we investigate\nhow surface wetness affects the tropical hydrological cycle. When surface\nwetness is decreased in an Earth-like general circulation model, the tropics\nremain wet but transition from a rainy to rain-free regime. The rain-free\nregime occurs when surface precipitation is suppressed as negative evaporation\n(surface condensation) balances moisture flux convergence. The regime\ntransition is dominated by near-surface relative humidity changes in contrast\nto the hypothesis that relative humidity changes are small. We show\nnear-surface relative humidity changes responsible for the regime transition\nare controlled by re-evaporation of stratiform precipitation near the lifting\ncondensation level. Re-evaporation impacts the near-surface through vertical\nmixing. Our results reveal a new rain-free tropical hydrological cycle regime\nthat goes beyond the wet/dry paradigm.\n"}
+{"abstract": "  With the widespread success of deep learning in biomedical image\nsegmentation, domain shift becomes a critical and challenging problem, as the\ngap between two domains can severely affect model performance when deployed to\nunseen data with heterogeneous features. To alleviate this problem, we present\na novel unsupervised domain adaptation network, for generalizing models learned\nfrom the labeled source domain to the unlabeled target domain for\ncross-modality biomedical image segmentation. Specifically, our approach\nconsists of two key modules, a conditional domain discriminator~(CDD) and a\ncategory-centric prototype aligner~(CCPA). The CDD, extended from conditional\ndomain adversarial networks in classifier tasks, is effective and robust in\nhandling complex cross-modality biomedical images. The CCPA, improved from the\ngraph-induced prototype alignment mechanism in cross-domain object detection,\ncan exploit precise instance-level features through an elaborate prototype\nrepresentation. In addition, it can address the negative effect of class\nimbalance via entropy-based loss. Extensive experiments on a public benchmark\nfor the cardiac substructure segmentation task demonstrate that our method\nsignificantly improves performance on the target domain.\n"}
+{"abstract": "  In quantum computation, the computation is achieved by linear operators in\nHilbert spaces. In this work, we explain an idea of a new computation scheme,\nin which the linear operators are replaced by (higher) functors between two\n(higher) categories. The fundamental problem in realizing this idea is the\nphysical realization of (higher) functors. We provide a theoretical idea of\nrealizing (higher) functors based on the physics of topological orders.\n"}
+{"abstract": "  In this paper, as the third part of the third step of our study on developing\ndata analysis procedures for using 3-dimensional information offered by\ndirectional direct Dark Matter detection experiments in the future, we\nintroduce a 3-dimensional effective velocity distribution of halo Weakly\nInteracting Massive Particles (WIMPs), which, instead of the theoretically\nprediction of the entire Galactic Dark Matter particles, describes the actual\nvelocity distribution of WIMPs scattering off (specified) target nuclei in an\nunderground detector. Its target and WIMP-mass dependences as well as (\"annual\"\nmodulations of) its \"anisotropy\" in the Equatorial/laboratory and even the\nGalactic coordinate systems will be demonstrated and discussed in detail. For\nreaders' reference, all simulation plots presented in this paper (and more) can\nbe found \"in animation\" on our online (interactive) demonstration webpage\n(http://www.tir.tw/phys/hep/dm/amidas-2d/).\n"}
+{"abstract": "  Two-way online correlated selection (two-way OCS) is an online algorithm\nthat, at each timestep, takes a pair of elements from the ground set and\nirrevocably chooses one of the two elements, while ensuring negative\ncorrelation in the algorithm's choices. Whilst OCS was initially invented by\nFahrbach, Huang, Tao, and Zadimoghaddam to solve the edge-weighted online\nbipartite matching problem, it is an interesting technique on its own due to\nits capability of introducing a powerful algorithmic tool, namely negative\ncorrelation, to online algorithms. As such, Fahrbach et al. posed two\ntantalizing open questions in their paper, one of which was the following: Can\nwe obtain n-way OCS for n>2, in which the algorithm can be given n>2 elements\nto choose from at each timestep?\n  In this paper, we affirmatively answer this open question by presenting a\nthree-way OCS. Our algorithm uses two-way OCS as its building block and is\nsimple to describe; however, as it internally runs two instances of two-way\nOCS, one of which is fed with the output of the other, the final output\nprobability distribution becomes highly elusive. We tackle this difficulty by\napproximating the output distribution of OCS by a flat, less correlated\nfunction and using it as a safe \"surrogate\" of the real distribution. Our\nthree-way OCS also yields a 0.5093-competitive algorithm for edge-weighted\nonline matching, demonstrating its usefulness.\n"}
+{"abstract": "  We determine the effect on the computational complexity of a conformal\nanomaly using the Complexity=Action prescription of the gauge/gravity\ncorrespondence. To allow the involvement of said anomaly, we extend previous\nstudies to include arbitrary values for the anisotropic parameter and the\nmagnetic field respectively on the Mateos-Trancanelli and the D'Hoker-Kraus\nholographic models. Our main result is that the rate of change of the\ncomputational complexity is independent of the conformal anomaly in both cases.\nIn addition, this allows us to also show that, if so desired, the saturation of\nLloyd's bound at infinite time can be used as a renormalization condition.\n"}
+{"abstract": "  We present the largest sample of brown dwarf (BD) protoplanetary disk\nspectral energy distributions modeled to date. We compile 49 objects with ALMA\nobservations from four star-forming regions: $\\rho$ Ophiuchus, Taurus, Lupus,\nand Upper Scorpius. Studying multiple regions with various ages enables us to\nprobe disk evolution over time. Specifically, from our models we obtain values\nfor dust grain sizes, dust settling, and disk mass; we compare how each of\nthese parameters vary between the regions. We find that disk mass decreases\nwith age. We also find evidence of disk evolution (i.e., grain growth and\nsignificant dust settling) in all four regions, indicating that planet\nformation and disk evolution may begin to occur at earlier stages. We generally\nfind these disks contain too little mass to form planetary companions, though\nwe cannot rule out that planet formation may have already occurred. Finally, we\nexamine the disk mass -- host mass relationship and find that BD disks are\nlargely consistent with previously-determined relationships for disks around T\nTauri stars.\n"}
+{"abstract": "  For graph $G$, $F$ and integer $n$, the generalized Tu\\'an number $ex(n,G,F)$\ndenotes the maximum number of copies of $G$ that an $F$-free $n$-vertex graph\ncan have. We study this parameter when both $G$ and $F$ are complete bipartite\ngraphs.\n"}
+{"abstract": "  Expanding on MacKay (1992), we argue that conventional model-based methods\nfor active learning - like BALD - have a fundamental shortfall: they fail to\ndirectly account for the test-time distribution of the input variables. This\ncan lead to pathologies in the acquisition strategy, as what is maximally\ninformative for model parameters may not be maximally informative for\nprediction: for example, when the data in the pool set is more dispersed than\nthat of the final prediction task, or when the distribution of pool and test\nsamples differs. To correct this, we revisit an acquisition strategy that is\nbased on maximizing the expected information gained about possible future\npredictions, referring to this as the Expected Predictive Information Gain\n(EPIG). As EPIG does not scale well for batch acquisition, we further examine\nan alternative strategy, a hybrid between BALD and EPIG, which we call the\nJoint Expected Predictive Information Gain (JEPIG). We consider using both for\nactive learning with Bayesian neural networks on a variety of datasets,\nexamining the behavior under distribution shift in the pool set.\n"}
+{"abstract": "  Prior works on formalizing explanations of a graph neural network (GNN) focus\non a single use case - to preserve the prediction results through identifying\nimportant edges and nodes. In this paper, we develop a multi-purpose\ninterpretation framework by acquiring a mask that indicates topology\nperturbations of the input graphs. We pack the framework into an interactive\nvisualization system (GNNViz) which can fulfill multiple purposes:\nPreserve,Promote, or Attack GNN's predictions. We illustrate our approach's\nnovelty and effectiveness with three case studies: First, GNNViz can assist non\nexpert users to easily explore the relationship between graph topology and\nGNN's decision (Preserve), or to manipulate the prediction (Promote or Attack)\nfor an image classification task on MS-COCO; Second, on the Pokec social\nnetwork dataset, our framework can uncover unfairness and demographic biases;\nLastly, it compares with state-of-the-art GNN explainer baseline on a synthetic\ndataset.\n"}
+{"abstract": "  In the present investigation compact stellar models are dealt with in the\nframework of the modified gravity theory, specifically of\n$f(\\mathbb{T},\\mathcal{T})$ type. We have considered that the compact objects\nare following a spherically symmetric static metric and obtained the Einstein\nfield equations in the spacetime of $f(\\mathbb{T},\\mathcal{T})$. To make the\nEinstein equations solvable we employ the methodology of conformal Killing\nvectors. Thereafter by using the MIT bag equation of state to the compact\nstars, considering that the stars are formed by strange quark, we find the\nsolutions set. The solutions are examined via several physical tastings which\nexhibit viability of the model.\n"}
+{"abstract": "  A recent study demonstrated that freedom of convection and strength of\nmagnetic field in the photospheric feet of active-region (AR) coronal loops,\ntogether, can engender or quench heating in them. Other studies stress that\nmagnetic flux cancellation at the loop-feet potentially drives heating in\nloops. We follow 24-hour movies of a bipolar AR, using EUV images from SDO/AIA\nand line-of-sight (LOS) magnetograms from SDO/HMI, to examine magnetic\npolarities at the feet of 23 of the brightest coronal loops. We derived FeXVIII\nemission (hot-94) images (using the Warren et al. method) to select the\nhottest/brightest loops, and confirm their footpoint locations via\nnon-force-free field extrapolations. From 6\"$\\times$6\" boxes centered at each\nloop foot in LOS magnetograms we find that $\\sim$40\\% of the loops have both\nfeet in unipolar flux, and $\\sim$60\\% of the loops have at least one foot in\nmixed-polarity flux. The loops with both feet unipolar are $\\sim$15\\% shorter\nlived on average than the loops having mixed-polarity foot-point flux, but\ntheir peak-intensity averages are equal. The presence of mixed-polarity\nmagnetic flux in at least one foot of majority of the loops suggests that flux\ncancellation at the footpoints may drive most of the heating. But, the absence\nof mixed-polarity magnetic flux (to the detection limit of HMI) in $\\sim$40\\%\nof the loops suggests that flux cancellation may not be necessary to drive\nheating in coronal loops -- magnetoconvection and field strength at both loop\nfeet possibly drive much of the heating, even in the cases where a loop foot\npresents mixed-polarity magnetic flux.\n"}
+{"abstract": "  What limits the value of the superconducting transition temperature ($T_c$)\nis a question of great fundamental and practical importance. Various heuristic\nupper bounds on $T_c$ have been proposed, expressed as fractions of the Fermi\ntemperature, $T_F$, the zero-temperature superfluid stiffness, $\\rho_s(0)$, or\na characteristic Debye frequency, $\\omega_0$. We show that while these bounds\nare physically motivated and are certainly useful in many relevant situations,\nnone of them serve as a fundamental bound on $T_c$. To demonstrate this, we\nprovide explicit models where $T_c/T_F$ (with an appropriately defined $T_F$),\n$T_c/\\rho_s(0)$, and $T_c/\\omega_0$ are unbounded.\n"}
+{"abstract": "  The specific heat capacity $c_v$ of glass formers undergoes a hysteresis when\nsubjected to a cooling-heating cycle, with a larger $c_v$ and a more pronounced\nhysteresis for fragile glasses than for strong ones. Here, we show that these\nexperimental features, including the unusually large magnitude of $c_v$ of\nfragile glasses, are well reproduced by kinetic Monte Carlo and equilibrium\nstudy of a distinguishable particle lattice model (DPLM) incorporating a\ntwo-state picture of particle interactions. The large $c_v$ in fragile glasses\nis caused by a dramatic transfer of probabilistic weight from high-energy\nparticle interactions to low-energy ones as temperature decreases.\n"}
+{"abstract": "  In this article, we give an in-depth analysis of the problem of optimising\nthe total population size for a standard logistic-diffusive model. This\noptimisation problem stems from the study of spatial ecology and amounts to the\nfollowing question: assuming a species evolves in a domain, what is the best\nway to spread resources in order to ensure a maximal population size at\nequilibrium? {In recent years, many authors contributed to this topic.} We\nsettle here the proof of two fundamental properties of optimisers: the\nbang-bang one which had so far only been proved under several strong\nassumptions, and the other one is the fragmentation of maximisers. Here, we\nprove the bang-bang property in all generality using a new spectral method. The\ntechnique introduced to demonstrate the bang-bang character of optimizers can\nbe adapted and generalized to many optimization problems with other classes of\nbilinear optimal control problems where the state equation is semilinear and\nelliptic. We comment on it in a conclusion section.Regarding the geometry of\nmaximisers, we exhibit a blow-up rate for the $BV$-norm of maximisers as the\ndiffusivity gets smaller: if $\\O$ is an orthotope and if $m_\\mu$ is an optimal\ncontrol, then $\\Vert m_\\mu\\Vert_{BV}\\gtrsim \\sqrt{\\mu}$. The proof of this\nresults relies on a very fine energy argument.\n"}
+{"abstract": "  The phenomenon of negative refraction generally requires negative refractive\nindices or phase discontinuities, which can be realized using metamaterials or\nmetasurfaces. Recent theories have proposed a novel mechanism for negative\nrefraction based on synthetic gauge fields, which affect classical waves as if\nthey were charged particles in electromagnetic fields, but this has not\nhitherto been demonstrated in experiment. Here, we report on the experimental\ndemonstration of gauge-field-induced negative refraction in a twisted bilayer\nacoustic metamaterial. The bilayer twisting produces a synthetic gauge field\nfor sound waves propagating within a projected two-dimensional geometry, with\nthe magnitude of the gauge field parameterized by the choice of wavenumber\nalong the third dimension. Waveguiding with backward propagating modes is also\ndemonstrated in a trilayer configuration that implements strong gauge fields.\nThese results provide an alternative route to achieving negative refraction in\nsynthetic materials.\n"}
+{"abstract": "  Accelerated gradient-based methods are being extensively used for solving\nnon-convex machine learning problems, especially when the data points are\nabundant or the available data is distributed across several agents. Two of the\nprominent accelerated gradient algorithms are AdaGrad and Adam. AdaGrad is the\nsimplest accelerated gradient method, which is particularly effective for\nsparse data. Adam has been shown to perform favorably in deep learning problems\ncompared to other methods. In this paper, we propose a new fast optimizer,\nGeneralized AdaGrad (G-AdaGrad), for accelerating the solution of potentially\nnon-convex machine learning problems. Specifically, we adopt a state-space\nperspective for analyzing the convergence of gradient acceleration algorithms,\nnamely G-AdaGrad and Adam, in machine learning. Our proposed state-space models\nare governed by ordinary differential equations. We present simple convergence\nproofs of these two algorithms in the deterministic settings with minimal\nassumptions. Our analysis also provides intuition behind improving upon\nAdaGrad's convergence rate. We provide empirical results on MNIST dataset to\nreinforce our claims on the convergence and performance of G-AdaGrad and Adam.\n"}
+{"abstract": "  In \"The Organization of Behavior\" (Hebb, 1949), Hebb suggested that the\npropagation of activity between transiently grouped neurons plays an important\nrole in behavior. Since then, multiple studies have provided evidence\nsupporting Hebb's claim; however, most findings have been found locally in\nconfined brain regions during unimodal tasks. Here we report on brain-wide\nbehavioral-specific sequences in humans performing a multimodal task. To\ninvestigate the structure of these sequences, we used MEG to record brain\nactivity in multiple brain regions simultaneously in participants performing a\nsensory-motor synchronization task. We detected local transient events\ncorresponding to synchronously activating populations of pyramidal neurons and\nsearched for their global organization as spatio-temporal patterns of\nactivation sequences between distant neural populations. We focused our\nanalysis on two types of spatio-temporal patterns: the most frequently\nrepeating patterns and the most discriminative patterns, to concentrate on\npatterns with high relevancy to behavior. The findings revealed that global\ntemporally precise sequences can be found and that these sequences have\npartially stereotypical characteristics, both temporally and spatially, with\nconsistent properties across subjects. By implementing a simplistic\nsingle-trial decoding approach, we found that brain-wide sequences have a\ntemporal precision of 17-31 milliseconds, which resembles the temporal\nprecision found locally in neural assemblies.\n"}
+{"abstract": "  Repeated Compton scattering of photons with thermal electrons is one of the\nfundamental processes at work in many astrophysical plasma. Solving the exact\nevolution equations is hard and one common simplification is based on\nFokker-Planck (FP) approximations of the Compton collision term. Here we carry\nout a detailed numerical comparison of several FP approaches with the exact\nscattering kernel solution for a range of test problems assuming isotropic\nmedia and thermal electrons at various temperatures. The Kompaneets equation,\nbeing one of the most widely used FP approximations, fails to account for\nKlein-Nishina corrections and enhanced Doppler boosts and recoil at high\nenergies. These can be accounted for with an alternative FP approach based on\nthe exact first and second moments of the scattering kernel. As demonstrated\nhere, the latter approach works very well in dilute media, but inherently fails\nto reproduce the correct equilibrium solution in the limit of many scattering.\nConditions for the applicability of the FP approximations are clarified,\noverall showing that the Kompaneets equation provides the most robust\napproximation to the full problem, even if inaccurate in many cases. We close\nour numerical analysis by briefly illustrating the solutions for the spectral\ndistortions of the cosmic microwave background (CMB) after photon injection at\nredshift $z\\lesssim 10^5$, when double Compton and Bremsstrahlung emission can\nbe omitted. We demonstrate that the exact treatment using the scattering kernel\ncomputed with {\\tt CSpack} is often needed. This work should provide an\nimportant step towards accurate computations of the CMB spectral distortions\nfrom high-energy particle cascades.\n"}
+{"abstract": "  Robot-assisted surgery is an established clinical practice. The automatic\nidentification of surgical actions is needed for a range of applications,\nincluding performance assessment of trainees and surgical process modeling for\nautonomous execution and monitoring. However, supervised action identification\nis not feasible, due to the burden of manually annotating recordings of\npotentially complex and long surgical executions. Moreover, often few example\nexecutions of a surgical procedure can be recorded. This paper proposes a novel\nfast algorithm for unsupervised identification of surgical actions in a\nstandard surgical training task, the ring transfer, executed with da Vinci\nResearch Kit. Exploiting kinematic and semantic visual features automatically\nextracted from a very limited dataset of executions, we are able to\nsignificantly outperform state-of-the-art results on a dataset of non-expert\nexecutions (58\\% vs. 24\\% F1-score), and improve performance in the presence of\nnoise, short actions and non-homogeneous workflows, i.e. non repetitive action\nsequences.\n"}
+{"abstract": "  Camera-based Deep Learning algorithms are increasingly needed for perception\nin Automated Driving systems. However, constraints from the automotive industry\nchallenge the deployment of CNNs by imposing embedded systems with limited\ncomputational resources. In this paper, we propose an approach to embed a\nmulti-task CNN network under such conditions on a commercial prototype\nplatform, i.e. a low power System on Chip (SoC) processing four surround-view\nfisheye cameras at 10 FPS.\n  The first focus is on designing an efficient and compact multi-task network\narchitecture. Secondly, a pruning method is applied to compress the CNN,\nhelping to reduce the runtime and memory usage by a factor of 2 without\nlowering the performances significantly. Finally, several embedded optimization\ntechniques such as mixed-quantization format usage and efficient data transfers\nbetween different memory areas are proposed to ensure real-time execution and\navoid bandwidth bottlenecks. The approach is evaluated on the hardware\nplatform, considering embedded detection performances, runtime and memory\nbandwidth. Unlike most works from the literature that focus on classification\ntask, we aim here to study the effect of pruning and quantization on a compact\nmulti-task network with object detection, semantic segmentation and soiling\ndetection tasks.\n"}
+{"abstract": "  A novel way to arrive at a single molecule junction in the partially wet\nphase is described. The fabrication of this device makes use of a natural\ndrying process. Details are provided concerning the fabrication of the device\nand the related material choices. In addition a process description is provided\nwith a step by step approach for creating the partially wet phase molecular MCB\njunction.\n"}
+{"abstract": "  The promise of Deep Neural Network (DNN) powered Internet of Thing (IoT)\ndevices has motivated a tremendous demand for automated solutions to enable\nfast development and deployment of efficient (1) DNNs equipped with\ninstantaneous accuracy-efficiency trade-off capability to accommodate the\ntime-varying resources at IoT devices and (2) dataflows to optimize DNNs'\nexecution efficiency on different devices. Therefore, we propose InstantNet to\nautomatically generate and deploy instantaneously switchable-precision networks\nwhich operate at variable bit-widths. Extensive experiments show that the\nproposed InstantNet consistently outperforms state-of-the-art designs.\n"}
+{"abstract": "  We prove that the Bowen-Franks group classifies the Leavitt path algebras of\npurely infinite simple finite graphs over a regular supercoherent commutative\nring with involution where $2$ is invertible, equipped with their standard\ninvolutions, up to matricial stabilization and involution preserving homotopy\nequivalence. We also consider a twisting of the standard involution on Leavitt\npath algebras and obtain partial results in the same direction for purely\ninfinite simple graphs. Our tools are $K$-theoretic, and we prove several\nresults about (Hermitian, bivariant) $K$-theory of Leavitt path algebras.\n"}
+{"abstract": "  Self-supervised pre-training using so-called \"pretext\" tasks has recently\nshown impressive performance across a wide range of modalities. In this work,\nwe advance self-supervised learning from permutations, by pre-training a model\nto reorder shuffled parts of the spectrogram of an audio signal, to improve\ndownstream classification performance. We make two main contributions. First,\nwe overcome the main challenges of integrating permutation inversions into an\nend-to-end training scheme, using recent advances in differentiable ranking.\nThis was heretofore sidestepped by casting the reordering task as\nclassification, fundamentally reducing the space of permutations that can be\nexploited. Our experiments validate that learning from all possible\npermutations improves the quality of the pre-trained representations over using\na limited, fixed set. Second, we show that inverting permutations is a\nmeaningful pretext task for learning audio representations in an unsupervised\nfashion. In particular, we improve instrument classification and pitch\nestimation of musical notes by reordering spectrogram patches in the\ntime-frequency space.\n"}
+{"abstract": "  Collaborative multi-agent robotic systems where agents coordinate by\nmodifying a shared environment often result in undesired dynamical couplings\nthat complicate the analysis and experiments when solving a specific problem or\ntask. Simultaneously, biologically-inspired robotics rely on simplifying agents\nand increasing their number to obtain more efficient solutions to such\nproblems, drawing similarities with natural processes. In this work we focus on\nthe problem of a biologically-inspired multi-agent system solving collaborative\nforaging. We show how mean field techniques can be used to re-formulate such a\nstochastic multi-agent problem into a deterministic autonomous system. This\nde-couples agent dynamics, enabling the computation of limit behaviours and the\nanalysis of optimality guarantees. Furthermore, we analyse how having finite\nnumber of agents affects the performance when compared to the mean field limit\nand we discuss the implications of such limit approximations in this\nmulti-agent system, which have impact on more general collaborative stochastic\nproblems.\n"}
+{"abstract": "  We compare three first-principles methods of calculating the Curie\ntemperature in two-dimensional (2D) ferromagnetic materials (FM), modeled using\nthe Heisenberg model, and propose a simple formula for estimating the Curie\ntemperature with high accuracy that works for all common 2D lattice types.\nFirst, we study the effect of exchange anisotropy on the Curie temperature\ncalculated using the Monte-Carlo (MC), the Green's function method, and the\nrenormalized spin-wave (RNSW). We find that the Green's function overestimates\nthe Curie temperature in high-anisotropy regimes compared to MC, whereas RNSW\nunderestimates the Curie temperature compared to the MC and the Green's\nfunction. Next, we propose a closed-form formula for calculating the Curie\ntemperature of 2D FMs, which provides an estimate of the Curie temperature\ngreatly improving over the mean-field expression for magnetic material\nscreening. We apply the closed-form formula to predict the Curie temperature 2D\nmagnets screened from the C2DB database and discover several high Curie\ntemperature FMs with Fe2F2 and MoI2 emerging as the most promising 2D\nferromagnets. Finally, comparing to experimental results for CrI3, CrCl3, and\nCrBr3, we conclude that for small effective anisotropies, the Green's\nfunction-based equations are preferable, while, for larger anisotropies\nMC-based results are more predictive.\n"}
+{"abstract": "  We investigate and quantify the impact of mixed (cold and warm) dark matter\nmodels on large-scale structure observables. In this scenario, dark matter\ncomes in two phases, a cold one (CDM) and a warm one (WDM): the presence of the\nlatter causes a suppression in the matter power spectrum which is allowed by\ncurrent constraints and may be detected in present-day and upcoming surveys. We\nrun a large set of $N$-body simulations in order to build an efficient and\naccurate emulator to predict the aforementioned suppression with percent\nprecision over a wide range of values for the WDM mass, $M_\\mathrm{wdm}$, and\nits fraction with respect to the totality of dark matter, $f_\\mathrm{wdm}$. The\nsuppression in the matter power spectrum is found to be independent of changes\nin the cosmological parameters at the 2% level for $k\\lesssim 10 \\ h/$Mpc and\n$z\\leq 3.5$. In the same ranges, by applying a baryonification procedure on\nboth $\\Lambda$CDM and CWDM simulations to account for the effect of feedback,\nwe find a similar level of agreement between the two scenarios. We examine the\nimpact that such suppression has on weak lensing and angular galaxy clustering\npower spectra. Finally, we discuss the impact of mixed dark matter on the shape\nof the halo mass function and which analytical prescription yields the best\nagreement with simulations. We provide the reader with an application to galaxy\ncluster number counts.\n"}
+{"abstract": "  The novel technique introduced here aims to accomplish the first stage of\ntransferring low-level cognitive skills between two individuals (e.g. from\nexpert to learner) to ease the consecutive higher level declarative learning\nprocess for the target \"learner\" individual in a game environment. Such\nlow-level cognitive skill is associated with the procedural knowledge and\nestablished at low-level of mind which can be unveiled and transferred by only\na novel technique (rather than by a traditional educational environment ) like\na highly interactive computer game domain in which a user exposes his/her\nunconscious mind behaviors via the game-hero non-deliberately during the game\nsessions. The cognitive data exposed by the game-hero would be recorded, and\nthen be modelled by the artificial intelligence technique like Bayesian\nnetworks for an early stage of cognitive skill transfer and the cognitive\nstimuli are also generated to be used as game agents to train the learner.\n"}
+{"abstract": "  Despite the continued successes of computationally efficient deep neural\nnetwork architectures for video object detection, performance continually\narrives at the great trilemma of speed versus accuracy versus computational\nresources (pick two). Current attempts to exploit temporal information in video\ndata to overcome this trilemma are bottlenecked by the state-of-the-art in\nobject detection models. We present, a technique which performs video object\ndetection through the use of off-the-shelf object detectors alongside existing\noptical flow based motion estimation techniques in parallel. Through a set of\nexperiments on the benchmark MOT20 dataset, we demonstrate that our approach\nsignificantly reduces the baseline latency of any given object detector without\nsacrificing any accuracy. Further latency reduction, up to 25x lower than the\noriginal latency, can be achieved with minimal accuracy loss. MOVEX enables low\nlatency video object detection on common CPU based systems, thus allowing for\nhigh performance video object detection beyond the domain of GPU computing. The\ncode is available at https://github.com/juliantrue/movex.\n"}
+{"abstract": "  We establish a DMV-strong uniqueness result for the compressible\nNavier-Stokes system with potential temperature transport. The concept of\ngeneralized, the so-called dissipative measure-valued (DMV), solutions was\nproposed in [7], where their global-in-time existence was proved. Here we show\nthat strong solutions are stable in the class of DMV solutions. More precisely,\na DMV solution coincides with a strong solution emanating from the same initial\ndata as long as the strong solution exists.\n"}
+{"abstract": "  We introduce \"Simulations Beyond The Local Universe\" (SIBELIUS) that connect\nthe Local Group to its cosmic environment. We show that introducing\nhierarchical small-scale perturbations to a density field constrained on large\nscales by observations provides an efficient way to explore the sample space of\nLocal Group analogues. From more than 60 000 simulations, we identify a\nhierarchy of Local Group characteristics emanating from different scales: the\ntotal mass, orientation, orbital energy and the angular momentum are largely\ndetermined by modes above $\\lambda$ = 1.6 comoving Mpc (cMpc) in the primordial\ndensity field.\n  Smaller scale variations are mostly manifest as perturbations to the MW-M31\norbit, and we find that the observables commonly used to describe the Local\nGroup -- the MW-M31 separation and radial velocity -- are transient and depend\non specifying scales down to 0.2 cMpc in the primordial density field. We\nfurther find that the presence of M33/LMC analogues significantly affects the\nMW-M31 orbit and its sensitivity to small-scale perturbations. We construct\ninitial conditions that lead to the formation of a Local Group whose primary\nobservables precisely match the current observations.\n"}
+{"abstract": "  Toxic Spans Detection(TSD) task is defined as highlighting spans that make a\ntext toxic. Many works have been done to classify a given comment or document\nas toxic or non-toxic. However, none of those proposed models work at the token\nlevel. In this paper, we propose a self-attention-based bidirectional gated\nrecurrent unit(BiGRU) with a multi-embedding representation of the tokens. Our\nproposed model enriches the representation by a combination of GPT-2, GloVe,\nand RoBERTa embeddings, which led to promising results. Experimental results\nshow that our proposed approach is very effective in detecting span tokens.\n"}
+{"abstract": "  In this work we present a spatial-temporal convolutional neural network for\npredicting future COVID-19 related symptoms severity among a population, per\nregion, given its past reported symptoms. This can help approximate the number\nof future Covid-19 patients in each region, thus enabling a faster response,\ne.g., preparing the local hospital or declaring a local lockdown where\nnecessary. Our model is based on a national symptom survey distributed in\nIsrael and can predict symptoms severity for different regions daily. The model\nincludes two main parts - (1) learned region-based survey responders profiles\nused for aggregating questionnaires data into features (2) Spatial-Temporal 3D\nconvolutional neural network which uses the above features to predict symptoms\nprogression.\n"}
+{"abstract": "  In Southern Italy, since 2013, there has been an ongoing Olive Quick Decline\nSyndrome (OQDS) outbreak, due to the bacterium Xylella fastidiosa. In a couple\nof previous papers, the authors have proposed a mathematical approach for\nidentifying possible control strategies for eliminating or at least reduce the\neconomic impact of such event. The main players involved in OQDS are\nrepresented by the insect vector, Philaenus spumarius, its host plants (olive\ntrees and weeds) and the bacterium, X. fastidiosa. A basic mathematical model\nhas been expressed in terms of a system of ordinary differential equations; a\npreliminary analysis already provided interesting results about possible\ncontrol strategies within an integrated pest management framework, not\nrequiring the removal of the productive resource represented by the olive\ntrees. The same conjectures have been later confirmed by analyzing the impact\nof possible spatial heterogeneities on controlling a X. fastidiosa epidemic.\nThese encouraging facts have stimulated a more detailed and rigorous\nmathematical analysis of the same system, as presented in this paper. A clear\npicture of the possible steady states (equilibria) and their stability\nproperties has been outlined, within a variety of different parameter\nscenarios, for the original spatially homogeneous ecosystem. The results\nobtained here confirm, in a mathematically rigorous way, what had been\nconjectured in the previous papers, i.e. that the removal of a suitable amount\nof weed biomass (reservoir of the juvenile stages of the insect vector of X.\nfastidiosa from olive orchards and surrounding areas is the most acceptable\nstrategy to control the spread of the OQDS. In addition, as expected, the\nadoption of more resistant olive tree cultivars has been shown to be a good\nstrategy, though less cost-effective, in controlling the pathogen.\n"}
+{"abstract": "  The increasing number of Photovoltaic (PV) systems connected to the power\ngrid are vulnerable to the projection of shadows from moving clouds. Global\nSolar Irradiance (GSI) forecasting allows smart grids to optimize the energy\ndispatch, preventing energy shortages caused by occlusion of the sun. This\ninvestigation compares the performances of machine learning algorithms (not\nrequiring labelled images for training) for real-time segmentation of clouds in\nimages acquired using a ground-based infrared sky imager. Real-time\nsegmentation is utilized to extract cloud features using only the pixels in\nwhich clouds are detected.\n"}
+{"abstract": "  In computer vision, recovering spatial information by filling in masked\nregions, e.g., inpainting, has been widely investigated for its usability and\nwide applicability to other various applications: image inpainting, image\nextrapolation, and environment map estimation. Most of them are studied\nseparately depending on the applications. Our focus, however, is on\naccommodating the opposite task, e.g., image outpainting, which would benefit\nthe target applications, e.g., image inpainting. Our self-supervision method,\nIn-N-Out, is summarized as a training approach that leverages the knowledge of\nthe opposite task into the target model. We empirically show that In-N-Out --\nwhich explores the complementary information -- effectively takes advantage\nover the traditional pipelines where only task-specific learning takes place in\ntraining. In experiments, we compare our method to the traditional procedure\nand analyze the effectiveness of our method on different applications: image\ninpainting, image extrapolation, and environment map estimation. For these\ntasks, we demonstrate that In-N-Out consistently improves the performance of\nthe recent works with In-N-Out self-supervision to their training procedure.\nAlso, we show that our approach achieves better results than an existing\ntraining approach for outpainting.\n"}
+{"abstract": "  The natural night sky brightness is a relevant input for monitoring the light\npollution evolution at observatory sites, by subtracting it from the overall\nsky brightness determined by direct measurements. It is also instrumental for\nassessing the expected darkness of the pristine night skies. The natural\nbrightness of the night sky is determined by the sum of the spectral radiances\ncoming from astrophysical sources, including zodiacal light, and the\natmospheric airglow. The resulting radiance is modified by absorption and\nscattering before it reaches the observer. Therefore, the natural night sky\nbrightness is a function of the location, time and atmospheric conditions. We\npresent in this work GAMBONS (GAia Map of the Brightness Of the Natural Sky), a\nmodel to map the natural night brightness of the sky in cloudless and moonless\nnights. Unlike previous maps, GAMBONS is based on the extra-atmospheric star\nradiance obtained from the Gaia catalogue. The Gaia-DR2 archive compiles\nastrometric and photometric information for more than 1.6 billion stars up to G\n= 21 magnitude. For the brightest stars, not included in Gaia-DR2, we have used\nthe Hipparcos catalogue instead. After adding up to the star radiance the\ncontributions of the diffuse galactic and extragalactic light, zodiacal light\nand airglow, and taking into account the effects of atmospheric attenuation and\nscattering, the radiance detected by ground-based observers can be estimated.\nThis methodology can be applied to any photometric band, if appropriate\ntransformations from the Gaia bands are available. In particular, we present\nthe expected sky brightness for V (Johnson), and visual photopic and scotopic\npassbands.\n"}
+{"abstract": "  We report the detection of 376.05 Hz (2.66 ms) coherent X-ray pulsations in\nNICER observations of a transient outburst of the low-mass X-ray binary IGR\nJ17494-3030 in 2020 October/November. The system is an accreting millisecond\nX-ray pulsar in a 75 minute ultracompact binary. The mass donor is most likely\na $\\simeq 0.02 M_\\odot$ finite-entropy white dwarf composed of He or C/O. The\nfractional rms pulsed amplitude is 7.4%, and the soft (1-3 keV) X-ray pulse\nprofile contains a significant second harmonic. The pulsed amplitude and pulse\nphase lag (relative to our mean timing model) are energy-dependent, each having\na local maximum at 4 keV and 1.5 keV, respectively. We also recovered the X-ray\npulsations in archival 2012 XMM-Newton observations, allowing us to measure a\nlong-term pulsar spin-down rate of $\\dot\\nu = -2.1(7)\\times10^{-14}$ Hz/s and\nto infer a pulsar surface dipole magnetic field strength of $\\simeq 10^9$ G. We\nshow that the mass transfer in the binary is likely non-conservative, and we\ndiscuss various scenarios for mass loss from the system.\n"}
+{"abstract": "  SVD serves as an exploratory tool in identifying the dominant features in the\nform of top rank-r singular factors corresponding to the largest singular\nvalues. For Big Data applications it is well known that Singular Value\nDecomposition (SVD) is restrictive due to main memory requirements. However, a\nnumber of applications such as community detection, clustering, or bottleneck\nidentification in large scale graph data-sets rely upon identifying the lowest\nsingular values and the singular corresponding vectors. For example, the lowest\nsingular values of a graph Laplacian reveal the number of isolated clusters\n(zero singular values) or bottlenecks (lowest non-zero singular values) for\nundirected, acyclic graphs. A naive approach here would be to perform a full\nSVD however, this quickly becomes infeasible for practical big data\napplications due to the enormous memory requirements. Furthermore, for such\napplications only a few lowest singular factors are desired making a full\ndecomposition computationally exorbitant. In this work, we trivially extend the\npreviously proposed Range-Net to \\textbf{Tail-Net} for a memory and compute\nefficient extraction of lowest singular factors of a given big dataset and a\nspecified rank-r. We present a number of numerical experiments on both\nsynthetic and practical data-sets for verification and bench-marking using\nconventional SVD as the baseline.\n"}
+{"abstract": "  Efficient video action recognition remains a challenging problem. One large\nmodel after another takes the place of the state-of-the-art on the Kinetics\ndataset, but real-world efficiency evaluations are often lacking. In this work,\nwe fill this gap and investigate the use of transformers for efficient action\nrecognition. We propose a novel, lightweight action recognition architecture,\nVideoLightFormer. In a factorized fashion, we carefully extend the 2D\nconvolutional Temporal Segment Network with transformers, while maintaining\nspatial and temporal video structure throughout the entire model. Existing\nmethods often resort to one of the two extremes, where they either apply huge\ntransformers to video features, or minimal transformers on highly pooled video\nfeatures. Our method differs from them by keeping the transformer models small,\nbut leveraging full spatiotemporal feature structure. We evaluate\nVideoLightFormer in a high-efficiency setting on the temporally-demanding\nEPIC-KITCHENS-100 and Something-Something-V2 (SSV2) datasets and find that it\nachieves a better mix of efficiency and accuracy than existing state-of-the-art\nmodels, apart from the Temporal Shift Module on SSV2.\n"}
+{"abstract": "  One of the ways blind people understand their surroundings is by clicking\nimages and relying on descriptions generated by image captioning systems.\nCurrent work on captioning images for the visually impaired do not use the\ntextual data present in the image when generating captions. This problem is\ncritical as many visual scenes contain text. Moreover, up to 21% of the\nquestions asked by blind people about the images they click pertain to the text\npresent in them. In this work, we propose altering AoANet, a state-of-the-art\nimage captioning model, to leverage the text detected in the image as an input\nfeature. In addition, we use a pointer-generator mechanism to copy the detected\ntext to the caption when tokens need to be reproduced accurately. Our model\noutperforms AoANet on the benchmark dataset VizWiz, giving a 35% and 16.2%\nperformance improvement on CIDEr and SPICE scores, respectively.\n"}
+{"abstract": "  Preprint for a book chapter introducing Audio Content Analysis. With a focus\non Music Information Retrieval systems, this chapter defines musical audio\ncontent, introduces the general process of audio content analysis, and surveys\nbasic approaches to audio content analysis. The various tasks in Audio Content\nAnalysis are categorized into three classes: music transcription, music\nperformance analysis, and music identification and categorization. The examples\nfor music transcription systems include music key detection, fundamental\nfrequency detection, and music structure detection. Music performance analysis\nsystems feature an overview of beat and tempo detection approaches as well as\nmusic performance assessment. The covered music classification systems are\naudio fingerprinting, music genre classification, and music emotion\nrecognition. The chapter concludes with a discussion and current challenges in\nthe field and a speculation on future perspectives.\n"}
+{"abstract": "  In this work, we address the question whether a sufficiently deep quantum\nneural network can approximate a target function as accurate as possible. We\nstart with simple but typical physical situations that the target functions are\nphysical observables, and then we extend our discussion to situations that the\nlearning targets are not directly physical observables, but can be expressed as\nphysical observables in an enlarged Hilbert space with multiple replicas, such\nas the Loshimidt echo and the Renyi entropy. The main finding is that an\naccurate approximation is possible only when the input wave functions in the\ndataset do not exhaust the entire Hilbert space that the quantum circuit acts\non, and more precisely, the Hilbert space dimension of the former has to be\nless than half of the Hilbert space dimension of the latter. In some cases,\nthis requirement can be satisfied automatically because of the intrinsic\nproperties of the dataset, for instance, when the input wave function has to be\nsymmetric between different replicas. And if this requirement cannot be\nsatisfied by the dataset, we show that the expressivity capabilities can be\nrestored by adding one ancillary qubit where the wave function is always fixed\nat input. Our studies point toward establishing a quantum neural network\nanalogy of the universal approximation theorem that lays the foundation for\nexpressivity of classical neural networks.\n"}
+{"abstract": "  The CM carbonaceous chondrite meteorites provide a record of low temperature\naqueous reactions in the early solar system. A number of CM chondrites also\nexperienced short-lived, post-hydration thermal metamorphism at temperatures of\n200C to over 750C. The exact conditions of thermal metamorphism and the\nrelationship between the unheated and heated CM chondrites are not well\nconstrained but are crucial to understanding the formation and evolution of\nhydrous asteroids. Here we have used position-sensitive-detector X-ray\ndiffraction (PSD-XRD), thermogravimetric analysis (TGA) and transmission\ninfrared (IR) spectroscopy to characterise the mineralogy and water contents of\n14 heated CM and ungrouped carbonaceous chondrites. We show that heated CM\nchondrites underwent the same degree of aqueous alteration as the unheated CMs,\nhowever upon thermal metamorphism their mineralogy initially (300 to 500C)\nchanged from hydrated phyllosilicates to a dehydrated amorphous phyllosilicate\nphase. At higher temperatures (over 500C) we observe recrystallisation of\nolivine and Fe-sulphides and the formation of metal. Thermal metamorphism also\ncaused the water contents of heated CM chondrites to decrease from 13 wt\npercent to 3 wt percent and a subsequent reduction in the intensity of the 3\nmicron feature in IR spectra. We estimate that the heated CM chondrites have\nlost 15 to 65 percent of the water they contained at the end of aqueous\nalteration. If impacts were the main cause of metamorphism, this is consistent\nwith shock pressures of 20 to 50 GPa. However, not all heated CM chondrites\nretain shock features suggesting that some were instead heated by solar\nradiation. Evidence from the Hayabusa2 and ORSIRS-REx missions suggest that\ndehydrated materials may be common on the surfaces of primitive asteroids and\nour results will support upcoming analysis of samples returned from asteroids\nRyugu and Bennu.\n"}
+{"abstract": "  Dense semantic forecasting anticipates future events in video by inferring\npixel-level semantics of an unobserved future image. We present a novel\napproach that is applicable to various single-frame architectures and tasks.\nOur approach consists of two modules. Feature-to-motion (F2M) module forecasts\na dense deformation field that warps past features into their future positions.\nFeature-to-feature (F2F) module regresses the future features directly and is\ntherefore able to account for emergent scenery. The compound F2MF model\ndecouples the effects of motion from the effects of novelty in a task-agnostic\nmanner. We aim to apply F2MF forecasting to the most subsampled and the most\nabstract representation of a desired single-frame model. Our design takes\nadvantage of deformable convolutions and spatial correlation coefficients\nacross neighbouring time instants. We perform experiments on three dense\nprediction tasks: semantic segmentation, instance-level segmentation, and\npanoptic segmentation. The results reveal state-of-the-art forecasting accuracy\nacross three dense prediction tasks.\n"}
+{"abstract": "  The ongoing COVID-19 pandemic is the first epidemic in human history in which\ndigital contact-tracing has been deployed at a global scale. Tracking and\nquarantining all the contacts of individuals who test positive to a virus can\nhelp slowing-down an epidemic, but the impact of contact-tracing is severely\nlimited by the generally low adoption of contact-tracing apps in the\npopulation. We derive here an analytical expression for the effectiveness of\ncontact-tracing app installation strategies in a SIR model on a given contact\ngraph. We propose a decentralised heuristic to improve the effectiveness of\ncontact tracing under fixed adoption rates, which targets a set of individuals\nto install contact-tracing apps, and can be easily implemented. Simulations on\na large number of real-world contact networks confirm that this heuristic\nrepresents a feasible alternative to the current state of the art.\n"}
+{"abstract": "  Extending the discovery by Giles Gardam of a concrete counterexample to\nKaplansky's unit conjecture in characteristic 2, a family of counterexamples\nfor every prime characteristic is presented.\n"}
+{"abstract": "  The role of the tensor part of the nuclear interaction is actively\ninvestigated in recent years due to experimental advancement yielding new data\nin nuclei far from the $\\beta$-stability line. In this article we study the\neffect of this part of the nuclear interaction on deformed neutron magic\nnumbers in the rare-earth region within the Skyrme energy-density functional\nfor various TIJ \\cite{lesinski:2007} parametrizations. Two quantities signaling\nmagic numbers are considered: two-neutron separation energies and\nsingle-particle energies. They are calculated in isotopic series involving\nwell-deformed rare-earth nuclei ranging from $Z=64$ to $Z=72$ in the $N=100$\nregion. Obtained results show that, whereas the neutron-proton tensor\ncontribution to binding energies is important to reproduce neutron sub-shell\nclosure at $N = 104$ in heavier rare earths Yb ($Z=70$) and Hf ($Z=72$)\nisotopes, like-particle tensor also plays a role in the single-particle\nspectrum around Fermi level and is even favored in lighter Gd ($Z=64$) and Dy\n($Z=66$) rare-earth isotopes.\n"}
+{"abstract": "  In this paper, we introduce a generalization of a class of tilings which\nappear in the literature: the tilings over which a height function can be\ndefined (for example, the famous tilings of polyominoes with dominoes). We show\nthat many properties of these tilings can be seen as the consequences of\nproperties of the generalized tilings we introduce. In particular, we show that\nany tiling problem which can be modelized in our generalized framework has the\nfollowing properties: the tilability of a region can be constructively decided\nin polynomial time, the number of connected components in the undirected\nflip-accessibility graph can be determined, and the directed flip-accessibility\ngraph induces a distributive lattice structure. Finally, we give a few examples\nof known tiling problems which can be viewed as particular cases of the new\nnotions we introduce.\n"}
+{"abstract": "  We present a survey of ways in which domain-knowledge has been included when\nconstructing models with neural networks. The inclusion of domain-knowledge is\nof special interest not just to constructing scientific assistants, but also,\nmany other areas that involve understanding data using human-machine\ncollaboration. In many such instances, machine-based model construction may\nbenefit significantly from being provided with human-knowledge of the domain\nencoded in a sufficiently precise form. This paper examines two broad\napproaches to encode such knowledge--as logical and numerical constraints--and\ndescribes techniques and results obtained in several sub-categories under each\nof these approaches.\n"}
+{"abstract": "  High-order topological phases, such as those with nontrivial quadrupole\nmoments, protect edge states that are themselves topological insulators in\nlower dimensions. So far, most quadrupole phases of light are explored in\nlinear optical systems, which are protected by spatial symmetries or synthetic\nsymmetries. Here we present Floquet quadrupole phases in driven nonlinear\nphotonic crystals (PhCs) that are protected by space-time screw symmetries. We\nstart by illustrating space-time symmetries by tracking the trajectory of\ninstantaneous optical axes of the driven media. Our Floquet quadrupole phase is\nthen confirmed in two independent ways: symmetry indices at high-symmetry\nmomentum points and calculations of the nested Wannier bands. Our work presents\na general framework to analyze symmetries in driven optical materials and paves\nthe way to further exploring symmetry-protected topological phases in Floquet\nsystems and their optoelectronic applications.\n"}
+{"abstract": "  In this paper we present the outcome of the first international comparison in\nthe terahertz frequency range among three different kinds of spectrometers. A\nFourier-Transform infrared spectrometer, a Vector Network Analyzer and a\nTime-Domain Spectrometer have been employed for measuring the complex\nrefractive index of three travelling standards made of selected dielectric\nmaterials in order to offer a wide enough range of parameters to be measured.\nThe three spectrometers have been compared in terms of measurement capability\nand uncertainty.\n"}
+{"abstract": "  The interplay between the spin-orbit and Zeeman interactions acting on a\nspinful Su-Schrieffer-Heeger model is studied based on an InAs nanowire\nsubjected to a periodic gate potential along the axial direction. It is shown\nthat a nontrivial topological phase can be achieved by regulating the\nconfining-potential configuration. In the absence of the Zeeman field, we prove\nthat the topology of the chain is not affected by the Rashba spin-orbit\ninteraction due to the persisting chiral symmetry. The energies of the edge\nmodes can be manipulated by varying the magnitude and direction of the external\nmagnetic field. Remarkably, the joint effect of the two spin-related\ninteractions leads to novel edge states that appear in the gap formed by the\nanti-crossing of the bands of a finite spinful dimerized chain, and can be\nmerged into the bulk states by tilting the magnetic-field direction.\n"}
+{"abstract": "  Speech enhancement has benefited from the success of deep learning in terms\nof intelligibility and perceptual quality. Conventional time-frequency (TF)\ndomain methods focus on predicting TF-masks or speech spectrum,via a naive\nconvolution neural network or recurrent neural network.Some recent studies were\nbased on Complex spectral Mapping convolution recurrent neural network (CRN) .\nThese models skiped directly from encoder layers' output and decoder layers'\ninput ,which maybe thoughtless. We proposed an attention mechanism based skip\nconnection between encoder and decoder layers,namely Complex Spectral Mapping\nWith Attention Based Convolution Recurrent Neural Network (CARN).Compared with\nCRN model,the proposed CARN model improved more than 10% relatively at several\nmetrics such as PESQ,CBAK,COVL,CSIG and son,and outperformed the place 1st\nmodel in both real time and non-real time track of the DNS Challenge 2020 at\nthese metrics.\n"}
+{"abstract": "  In this paper, a three-dimensional (3D) geometry based stochastic model\n(GBSM) for a massive multiple-input multiple-output (MIMO) communication system\nemploying practical discrete intelligent reflecting surface (IRS) is proposed.\nThe proposed channel model supports the scenario where both transceivers and\nenvironments move. The evolution of clusters in the space domain and the\npractical discrete phase shifts are considered in the channel model. The\nsteering vector is set at the base station for the cooperation with IRS.\nThrough studying statistical properties, the non-stationary properties are\nverified. We find that IRS plays a role in separating the whole channel and\nmake the absolute value of time autocorrelation function (ACF) larger than the\nsituation without employing IRS. Time ACF of the case using discrete phase\nshifts is also compared with the continuous case.\n"}
+{"abstract": "  Training high-accuracy object detection models requires large and diverse\nannotated datasets. However, creating these data-sets is time-consuming and\nexpensive since it relies on human annotators. We design, implement, and\nevaluate TagMe, a new approach for automatic object annotation in videos that\nuses GPS data. When the GPS trace of an object is available, TagMe matches the\nobject's motion from GPS trace and the pixels' motions in the video to find the\npixels belonging to the object in the video and creates the bounding box\nannotations of the object. TagMe works using passive data collection and can\ncontinuously generate new object annotations from outdoor video streams without\nany human annotators. We evaluate TagMe on a dataset of 100 video clips. We\nshow TagMe can produce high-quality object annotations in a fully-automatic and\nlow-cost way. Compared with the traditional human-in-the-loop solution, TagMe\ncan produce the same amount of annotations at a much lower cost, e.g., up to\n110x.\n"}
+{"abstract": "  Based on the Kazama-Suzuki type coset construction and its inverse coset\nbetween the subregular $\\mathcal{W}$-algebras for $\\mathfrak{sl}_n$ and the\nprincipal $\\mathcal{W}$-superalgebras for $\\mathfrak{sl}_{1|n}$, we prove\nweight-wise linear equivalences of their representation categories. Our main\nresults are then improvements of these correspondences incorporating the\nmonoidal structures. Firstly, in the rational case, we obtain the\nclassification of simple modules and their fusion rules via simple current\nextensions from their Heisenberg cosets. Secondly, beyond the rational case, we\nuse certain kernel VOAs together with relative semi-infinite cohomology\nfunctors to get functors from categories of modules for the subregular\n$\\mathcal{W}$-algebras for $\\mathfrak{sl}_n$ to categories of modules for the\nprincipal $\\mathcal{W}$-superalgebras for $\\mathfrak{sl}_{1|n}$ and vice versa.\nWe study these functors and in particular prove isomorphisms between the\nsuperspaces of logarithmic intertwining operators. As a corollary, we obtain\ncorrespondences of representation categories in the monoidal sense beyond the\nrational case as well.\n"}
+{"abstract": "  We introduce a data set called DCH-2, which contains 4,390 real\ncustomer-helpdesk dialogues in Chinese and their English translations. DCH-2\nalso contains dialogue-level annotations and turn-level annotations obtained\nindependently from either 19 or 20 annotators. The data set was built through\nour effort as organisers of the NTCIR-14 Short Text Conversation and NTCIR-15\nDialogue Evaluation tasks, to help researchers understand what constitutes an\neffective customer-helpdesk dialogue, and thereby build efficient and helpful\nhelpdesk systems that are available to customers at all times. In addition,\nDCH-2 may be utilised for other purposes, for example, as a repository for\nretrieval-based dialogue systems, or as a parallel corpus for machine\ntranslation in the helpdesk domain.\n"}
+{"abstract": "  As Machine Learning (ML) is now widely applied in many domains, in both\nresearch and industry, an understanding of what is happening inside the black\nbox is becoming a growing demand, especially by non-experts of these models.\nSeveral approaches had thus been developed to provide clear insights of a model\nprediction for a particular observation but at the cost of long computation\ntime or restrictive hypothesis that does not fully take into account\ninteraction between attributes. This paper provides methods based on the\ndetection of relevant groups of attributes -- named coalitions -- influencing a\nprediction and compares them with the literature. Our results show that these\ncoalitional methods are more efficient than existing ones such as SHapley\nAdditive exPlanation (SHAP). Computation time is shortened while preserving an\nacceptable accuracy of individual prediction explanations. Therefore, this\nenables wider practical use of explanation methods to increase trust between\ndeveloped ML models, end-users, and whoever impacted by any decision where\nthese models played a role.\n"}
+{"abstract": "  This paper presents a generalized and robust face manipulation detection\nmethod based on the edge region features appearing in images. Most contemporary\nface synthesis processes include color awkwardness reduction but damage the\nnatural fingerprint in the edge region. In addition, these color correction\nprocesses do not proceed in the non-face background region. We also observe\nthat the synthesis process does not consider the natural properties of the\nimage appearing in the time domain. Considering these observations, we propose\na facial forensic framework that utilizes pixel-level color features appearing\nin the edge region of the whole image. Furthermore, our framework includes a\n3D-CNN classification model that interprets the extracted color features\nspatially and temporally. Unlike other existing studies, we conduct\nauthenticity determination by considering all features extracted from multiple\nframes within one video. Through extensive experiments, including real-world\nscenarios to evaluate generalized detection ability, we show that our framework\noutperforms state-of-the-art facial manipulation detection technologies in\nterms of accuracy and robustness.\n"}
+{"abstract": "  The electromagnetic character of the $\\Delta I=1$ transitions connecting the\none- to zero-phonon and the two- to one-phonon wobbling bands should be\ndominated by an $E2$ component, due to the collective motion of the entire\nnuclear charge. In the present work it is shown, based on combined angular\ncorrelation and linear polarization measurements, that the mixing ratios of all\nanalyzed connecting transitions between low-lying bands in $^{135}$Pr\ninterpreted as zero-, one-, and two-phonon wobbling bands, have absolute values\nsmaller than one. This indicates predominant $M1$ magnetic character, which is\nincompatible with the proposed wobbling nature. All experimental observables\nare instead in good agreement with quasiparticle-plus-triaxial-rotor model\ncalculations, which describe the bands as resulting from a rapid re-alignment\nof the total angular momentum from the short to the intermediate nuclear axis.\n"}
+{"abstract": "  The development of an optical clock with ultimate accuracy and stability\nrequires lasers with very narrow linewidth. We present two ultrastable laser\nsystems based on 48 cm long Fabry-Perot cavities made of ULE glass in\nhorizontal and vertical configurations operating at 698 nm. Fractional\nfrequency instability of the beat signal between two lasers reaches 1.6e-15 at\nthe averaging time of 1 s. We experimentally characterized the contribution of\nthe different noise sources (power fluctuations, residual amplitude modulation,\nthe Doppler noise, sensitivity to the shock impact) and found that in our case\nthe laser frequency instability to a large extent is determined by an\noptoelectronic feedback loop. Although the vertical configuration proved to be\neasier to manufacture and to transport, it turned out that it is much more\nsensitive to acoustics and horizontal accelerations compared to the horizontal\none. Both laser systems were transported over a 60 km distance from the Lebedev\nPhysical Institute to the Russian National Metrology Institute (VNIIFTRI) where\nthey serve as local oscillators for spectroscopy of the clock transition in the\nrecently developed strontium optical clock.\n"}
+{"abstract": "  Based on $14.7~\\textrm{fb}^{-1}$ of $e^+e^-$ annihilation data collected with\nthe BESIII detector at the BEPCII collider at 17 different center-of-mass\nenergies between $3.7730~\\textrm{GeV}$ and $4.5995~\\textrm{GeV}$, Born cross\nsections of the two processes $e^+e^- \\to p\\bar{p}\\eta$ and $e^+e^- \\to\np\\bar{p}\\omega$ are measured for the first time. No indication of resonant\nproduction through a vector state $V$ is observed, and upper limits on the Born\ncross sections of $e^+e^- \\to V \\to p\\bar{p}\\eta$ and $e^+e^- \\to V \\to\np\\bar{p}\\omega$ at the $90\\%$ confidence level are calculated for a large\nparameter space in resonance masses and widths. For the current world average\nparameters of the $\\psi(4230)$ of $m=4.2187~\\textrm{GeV}/c^{2}$ and\n$\\Gamma=44~\\textrm{MeV}$, we find upper limits on resonant production of the\n$p\\bar{p}\\eta$ and $p\\bar{p}\\omega$ final states of $7.5~\\textrm{pb}$ and\n$10.4~\\textrm{pb}$ at the $90\\%$ CL, respectively.\n"}
+{"abstract": "  We study the release of energy during the gradual phase of a flare,\ncharacterized by faint bursts of non-thermal hard X-ray (HXR) emission\nassociated with decimetric radio spikes and type III radio bursts starting at\nhigh frequencies and extending to the heliosphere. We characterize the site of\nelectron acceleration in the corona and study the radial evolution of radio\nsource sizes in the high corona. Imaging and spectroscopy of the HXR emission\nwith Fermi and RHESSI provide a diagnostic of the accelerated electrons in the\ncorona as well as a lower limit on the height of the acceleration region. Radio\nobservations in the decimetric range with the ORFEES spectrograph provide radio\ndiagnostics close to the acceleration region. Radio spectro-imaging with LOFAR\nin the meter range provide the evolution of the radio source sizes with their\ndistance from the Sun, in the high corona. Non-thermal HXR bursts and radio\nspikes are well correlated on short timescales. The spectral index of\nnon-thermal HXR emitting electrons is -4 and their number is about $2\\times\n10^{33}$ electrons/s. The density of the acceleration region is constrained\nbetween $1-5 \\times 10^9$ cm$^{-3}$. Electrons accelerated upward rapidly\nbecome unstable to Langmuir wave production, leading to high starting\nfrequencies of the type III radio bursts, and the elongation of the radio beam\nat its source is between 0.5 and 11.4 Mm. The radio source sizes and their\ngradient observed with LOFAR are larger than the expected size and gradient of\nthe size of the electron beam, assuming it follows the expansion of the\nmagnetic flux tubes. These observations support the idea that the fragmentation\nof the radio emission into spikes is linked to the fragmentation of the\nacceleration process itself. The combination of HXR and radio diagnostics in\nthe corona provides strong constrains on the site of electron acceleration.\n"}
+{"abstract": "  Quality control (QC) in medical image analysis is time-consuming and\nlaborious, leading to increased interest in automated methods. However, what is\ndeemed suitable quality for algorithmic processing may be different from\nhuman-perceived measures of visual quality. In this work, we pose MR image\nquality assessment from an image reconstruction perspective. We train Bayesian\nCNNs using a heteroscedastic uncertainty model to recover clean images from\nnoisy data, providing measures of uncertainty over the predictions. This\nframework enables us to divide data corruption into learnable and non-learnable\ncomponents and leads us to interpret the predictive uncertainty as an\nestimation of the achievable recovery of an image. Thus, we argue that quality\ncontrol for visual assessment cannot be equated to quality control for\nalgorithmic processing. We validate this statement in a multi-task experiment\ncombining artefact recovery with uncertainty prediction and grey matter\nsegmentation. Recognising this distinction between visual and algorithmic\nquality has the impact that, depending on the downstream task, less data can be\nexcluded based on ``visual quality\" reasons alone.\n"}
+{"abstract": "  We construct the generating function for products of inverse central binomial\ncoefficients with harmonic numbers.\n"}
+{"abstract": "  We study the cosmology of a quadratic metric-compatible torsionful gravity\ntheory in the presence of a perfect hyperfluid. The gravitational action is an\nextension of the Einstein-Cartan theory given by the usual Einstein-Hilbert\ncontribution plus all the admitted quadratic parity even torsion scalars and\nthe matter action also exhibits a dependence on the connection. The equations\nof motion are obtained by regarding the metric and the metric-compatible\ntorsionful connection as independent variables. We then consider a\nFriedmann-Lema\\^itre-Robertson-Walker background, analyze the conservation\nlaws, and derive the torsion modified Friedmann equations for our theory.\nRemarkably, we are able to provide exact analytic solutions for the torsionful\ncosmology.\n"}
+{"abstract": "  The paper is devoted to a comprehensive study of smoothness of inertial\nmanifolds for abstract semilinear parabolic problems. It is well known that in\ngeneral we cannot expect more than $C^{1,\\varepsilon}$-regularity for such\nmanifolds (for some positive, but small $\\varepsilon$). Nevertheless, as shown\nin the paper, under the natural assumptions, the obstacles to the existence of\na $C^n$-smooth inertial manifold (where $n\\in\\mathbb N$ is any given number)\ncan be removed by increasing the dimension and by modifying properly the\nnonlinearity outside of the global attractor (or even outside the\n$C^{1,\\varepsilon}$-smooth IM of a minimal dimension). The proof is strongly\nbased on the Whitney extension theorem.\n"}
+{"abstract": "  We present the characteristic polynomial for the transition matrix of a\nvertex-face walk on a graph, and obtain its spectra. Furthermore, we express\nthe characteristic polynomial for the transition matrix of a vertex-face walk\non the 2-dimensional torus by using its adjacency matrix, and obtain its\nspectra. As an application, we define a new walk-type zeta function with\nrespect to the transition matrix of a vertex-face walk on the 2-dimensional\ntorus, and present its explicit formula.\n"}
+{"abstract": "  $\\Gamma$-maximin, $\\Gamma$-maximax and inteval dominance are familiar\ndecision criteria for making decisions under severe uncertainty, when\nprobability distributions can only be partially identified. One can apply these\nthree criteria by solving sequences of linear programs. In this study, we\npresent new algorithms for these criteria and compare their performance to\nexisting standard algorithms. Specifically, we use efficient ways, based on\nprevious work, to find common initial feasible points for these algorithms.\nExploiting these initial feasible points, we develop early stopping criteria to\ndetermine whether gambles are either $\\Gamma$-maximin, $\\Gamma$-maximax or\ninterval dominant. We observe that the primal-dual interior point method\nbenefits considerably from these improvements. In our simulation, we find that\nour proposed algorithms outperform the standard algorithms when the size of the\ndomain of lower previsions is less or equal to the sizes of decisions and\noutcomes. However, our proposed algorithms do not outperform the standard\nalgorithms in the case that the size of the domain of lower previsions is much\nlarger than the sizes of decisions and outcomes.\n"}
+{"abstract": "  During the last decades, scientific community has been investigating both\nbiological and hydrographic processes that affect fisheries. Such an\ninterdisciplinary and synergic approach is nowadays giving a fundamental\ncontribution, in particular, in connecting the dots between hydrographic\nphenomena and biomass variability and distribution of small pelagic fish. Here\nwe estimate impacts of hydrographic fluctuations on small pelagic fishery,\nfocusing on the inter-annual variability that characterizes connectivity\nbetween spawning and recruiting areas for the European anchovy (Engraulis\nencrasicolus, Linnaues 1758), in the Northern side of the Sicily Channel\n(Mediterranean Sea). Results show that coastal transport dynamics of a specific\nyear largely affect the biomass recorded the following year. Our work,\nmoreover, quantifies the specific monetary impacts on landings of European\nanchovy fishery due to hydrodynamics variability, connecting biomass\nfluctuations with fishery economics in a highly dynamic and exploited marine\nenvironment as the Sicily Channel. In particular, we build a model that\nattributes a monetary value to the hydrographic phenomena (i.e., cross-shore\nvs. alongshore eggs and larvae transport), registered in the FAO Geographical\nSub-Area (GSA) 16 (Southern Sicily). This allows us to provide a monetary\nestimation of catches, derived from different transport dynamics. Our results\nhighlight the paramount importance that hydrographic phenomena can have over\nthe socio-economic performance of a fishery.\n"}
+{"abstract": "  We introduce simplified models for enhancements in the matter power spectrum\nat small scales and study their implications for dark matter substructure and\ngravitational observables. These models capture the salient aspects of a\nvariety of early universe scenarios that predict enhanced small-scale\nstructure, such as axion-like particle dark matter, light vector dark matter,\nand epochs of early matter domination. We use a model-independent,\nsemi-analytic treatment to map bumps in the matter power spectrum to\nearly-forming sub-solar mass dark matter halos and estimate their evolution,\ndisruption, and contribution to substructure of clusters and galaxies at late\ntimes. We discuss the sensitivity of gravitational observables, including\npulsar timing arrays and caustic microlensing, to both the presence of bumps in\nthe power spectrum and variations in their basic properties.\n"}
+{"abstract": "  We explore methodologies to improve the robustness of generative adversarial\nimitation learning (GAIL) algorithms to observation noise. Towards this\nobjective, we study the effect of local Lipschitzness of the discriminator and\nthe generator on the robustness of policies learned by GAIL. In many robotics\napplications, the learned policies by GAIL typically suffer from a degraded\nperformance at test time since the observations from the environment might be\ncorrupted by noise. Hence, robustifying the learned policies against the\nobservation noise is of critical importance. To this end, we propose a\nregularization method to induce local Lipschitzness in the generator and the\ndiscriminator of adversarial imitation learning methods. We show that the\nmodified objective leads to learning significantly more robust policies.\nMoreover, we demonstrate --- both theoretically and experimentally --- that\ntraining a locally Lipschitz discriminator leads to a locally Lipschitz\ngenerator, thereby improving the robustness of the resultant policy. We perform\nextensive experiments on simulated robot locomotion environments from the\nMuJoCo suite that demonstrate the proposed method learns policies that\nsignificantly outperform the state-of-the-art generative adversarial imitation\nlearning algorithm when applied to test scenarios with noise-corrupted\nobservations.\n"}
+{"abstract": "  In most nuclear many-body methods, observables are calculated using many-body\nwave functions explicitly. The variational two-particle reduced density matrix\nmethod is one of the few exceptions to the rule. Ground-state energies of both\nclosed-shell and open-shell nuclear systems can indeed be evaluated by\nminimizing a constrained linear functional of the two-particle reduced density\nmatrix. However, it has virtually never been used in nuclear theory, because\nnuclear ground states were found to be well overbound, contrary to those of\natoms and molecules. Consequently, we introduced new constraints in the nuclear\nvariational two-particle reduced density matrix method, developed recently for\natomic and molecular systems. Our calculations then show that this approach can\nprovide a proper description of nuclear systems where only valence neutrons are\nincluded. For the nuclear systems where both neutrons and protons are active,\nhowever, the energies obtained with the variational two-particle reduced\ndensity matrix method are still overbound. The possible reasons for the noticed\ndiscrepancies and solutions to this problem will be discussed.\n"}
+{"abstract": "  Vesicle budding induced by protein binding that generates an isotropic\nspontaneous curvature is studied using a mean-field theory. Many spherical buds\nare formed via protein binding. As the binding chemical potential increases,\nthe proteins first bind to the buds and then to the remainder of the vesicle.\nFor a high spontaneous curvature and/or high bending rigidity of the bound\nmembrane, it is found that a first-order transition occurs between a small\nnumber of large buds and a large number of small buds. These two states coexist\naround the transition point. The proposed scheme is simple and easily\napplicable to many interaction types, so we investigate the effects of\ninter-protein interactions, the protein-insertion-induced changes in area, the\nvariation of the saddle-splay-modulus, and the area-difference-elasticity\nenergy. The differences in the preferred curvatures for curvature sensing and\ngeneration are also clarified.\n"}
+{"abstract": "  The variational determination of the two-fermion reduced density matrix is\ndescribed for trapped, ultracold few-fermion systems in one dimension with\nequal spin populations. This is accomplished by formulating the problem as a\nsemi-definite program, with the two-fermion reduced density matrix being\nsubject to the D, Q, G, T1, and T2 $N$-representability conditions. The\nground-state energies of $N=2,4$, and $8$ fermion systems are found by\nutilising an augmented Lagrangian method for semi-definite programming. The\nground-state energies are found to match extremely well to those determined by\nfull-configuration interaction and coupled-cluster calculations. This\ndemonstrates the utility of the reduced density matrix approach to strongly\ncorrelated, ultracold few-fermion systems.\n"}
+{"abstract": "  Similarities in the chemical composition of two of the closest Milky Way\nsatellites, namely the Large Magellanic Cloud (LMC) and the Sagittarius (Sgr)\ndwarf galaxy, have been proposed in the literature, suggesting similar chemical\nenrichment histories between the two galaxies. This proposition, however, rests\non different abundance analyses, which likely introduce various systematics\nthat hamper a fair comparison among the different data sets. In order to bypass\nthis issue (and highlight real similarities and differences between their\nabundance patterns), we present a homogeneous chemical analysis of 30 giant\nstars in LMC, 14 giant stars in Sgr and 14 giants in the Milky Way, based on\nhigh-resolution spectra taken with the spectrograph UVES-FLAMES. The LMC and\nSgr stars, in the considered metallicity range ([Fe/H]>-1.1 dex), show very\nsimilar abundance ratios for almost all the elements, with differences only in\nthe heavy s-process elements Ba, La and Nd, suggesting a different contribution\nby asymptotic giant branch stars. On the other hand, the two galaxies have\nchemical patterns clearly different from those measured in the Galactic stars,\nespecially for the elements produced by massive stars. This finding suggests\nthe massive stars contributed less to the chemical enrichment of these galaxies\nwith respect to the Milky Way. The derived abundances support similar chemical\nenrichment histories for the LMC and Sgr.\n"}
+{"abstract": "  In this paper, a comprehensive examination of the temperature- and\nbias-dependent diffusion regimes of underdamped Brownian particles is\npresented. A temperature threshold for a transition between anomalous and\nnormal diffusive behaviors is located, yielding a new phase diagram for the\nsystem. In the low-temperature regime, the system exhibits an apparent negative\ndifferential mobility due to persistent, long-time subdiffusion at low-bias; at\nhigh temperature (or critical bias,) the system rapidly approaches normal\ndiffusion below an intermediate barrier height, $U_o \\sim k_B T$. By\nconsideration of numerical results, comparison to the overdamped case, and the\nrelated Kramers multistable escape problems, it is demonstrated that the\nlow-bias non-monotonic temperature dependence of the diffusivity, persistent\nsubdiffusion, and negative differential mobility can be traced to inertial\neffects, which are evident in the oscillatory modes of the velocity power\nspectra at low bias. In the giant diffusion regime, the velocity power spectra\nexhibit coupling between the ``locked\" and ``running\" states, with a\ncharacteristic frequency corresponding to the principal frequency of the limit\ncycles of a damped, driven plane pendulum near critical bias. Non-linear second\nharmonic generation, corresponding to oscillatory transient anomalous\ndiffusivity, is observed with increasing bias and decreasing temperature,\nfurther emphasizing that the low-noise diffusion problem converges to\nnoise-free dynamics, complementing analytic results for the average velocity\n[L. Cheng and N.K. Yip, Physica D, 2015].\n"}
+{"abstract": "  Synthesizing tabular data is attracting much attention these days for various\npurposes. With sophisticate synthetic data, for instance, one can augment its\ntraining data. For the past couple of years, tabular data synthesis techniques\nhave been greatly improved. Recent work made progress to address many problems\nin synthesizing tabular data, such as the imbalanced distribution and\nmultimodality problems. However, the data utility of state-of-the-art methods\nis not satisfactory yet. In this work, we significantly improve the utility by\ndesigning our generator and discriminator based on neural ordinary differential\nequations (NODEs). After showing that NODEs have theoretically preferred\ncharacteristics for generating tabular data, we introduce our designs. The\nNODE-based discriminator performs a hidden vector evolution trajectory-based\nclassification rather than classifying with a hidden vector at the last layer\nonly. Our generator also adopts an ODE layer at the very beginning of its\narchitecture to transform its initial input vector (i.e., the concatenation of\na noisy vector and a condition vector in our case) onto another latent vector\nspace suitable for the generation process. We conduct experiments with 13\ndatasets, including but not limited to insurance fraud detection, online news\narticle prediction, and so on, and our presented method outperforms other\nstate-of-the-art tabular data synthesis methods in many cases of our\nclassification, regression, and clustering experiments.\n"}
+{"abstract": "  An \\emph{additive $+\\beta$ spanner} of a graph $G$ is a subgraph which\npreserves distances up to an additive $+\\beta$ error. Additive spanners are\nwell-studied in unweighted graphs but have only recently received attention in\nweighted graphs [Elkin et al.\\ 2019 and 2020, Ahmed et al.\\ 2020]. This paper\nmakes two new contributions to the theory of weighted additive spanners.\n  For weighted graphs, [Ahmed et al.\\ 2020] provided constructions of sparse\nspanners with \\emph{global} error $\\beta = cW$, where $W$ is the maximum edge\nweight in $G$ and $c$ is constant. We improve these to \\emph{local} error by\ngiving spanners with additive error $+cW(s,t)$ for each vertex pair $(s,t)$,\nwhere $W(s, t)$ is the maximum edge weight along the shortest $s$--$t$ path in\n$G$. These include pairwise $+(2+\\eps)W(\\cdot,\\cdot)$ and $+(6+\\eps) W(\\cdot,\n\\cdot)$ spanners over vertex pairs $\\Pc \\subseteq V \\times V$ on\n$O_{\\eps}(n|\\Pc|^{1/3})$ and $O_{\\eps}(n|\\Pc|^{1/4})$ edges for all $\\eps > 0$,\nwhich extend previously known unweighted results up to $\\eps$ dependence, as\nwell as an all-pairs $+4W(\\cdot,\\cdot)$ spanner on $\\widetilde{O}(n^{7/5})$\nedges.\n  Besides sparsity, another natural way to measure the quality of a spanner in\nweighted graphs is by its \\emph{lightness}, defined as the total edge weight of\nthe spanner divided by the weight of an MST of $G$. We provide a $+\\eps\nW(\\cdot,\\cdot)$ spanner with $O_{\\eps}(n)$ lightness, and a $+(4+\\eps)\nW(\\cdot,\\cdot)$ spanner with $O_{\\eps}(n^{2/3})$ lightness. These are the first\nknown additive spanners with nontrivial lightness guarantees. All of the above\nspanners can be constructed in polynomial time.\n"}
+{"abstract": "  Surface plasmon enhanced processes and hot-carrier dynamics in plasmonic\nnanostructures are of great fundamental interest to reveal light-matter\ninteractions at the nanoscale. Using plasmonic tunnel junctions as a platform\nsupporting both electrically- and optically excited localized surface plasmons,\nwe report a much greater (over 1000x) plasmonic light emission at upconverted\nphoton energies under combined electro-optical excitation, compared with\nelectrical or optical excitation separately. Two mechanisms compatible with the\nform of the observed spectra are interactions of plasmon-induced hot carriers\nand electronic anti-Stokes Raman scattering. Our measurement results are in\nexcellent agreement with a theoretical model combining electro-optical\ngeneration of hot carriers through non-radiative plasmon excitation and\nhot-carrier relaxation. We also discuss the challenge of distinguishing\nrelative contributions of hot carrier emission and the anti-Stokes electronic\nRaman process. This observed increase in above-threshold emission in plasmonic\nsystems may open avenues in on-chip nanophotonic switching and hot carrier\nphotocatalysis.\n"}
+{"abstract": "  Reasoning the human-object interactions (HOI) is essential for deeper scene\nunderstanding, while object affordances (or functionalities) are of great\nimportance for human to discover unseen HOIs with novel objects. Inspired by\nthis, we introduce an affordance transfer learning approach to jointly detect\nHOIs with novel objects and recognize affordances. Specifically, HOI\nrepresentations can be decoupled into a combination of affordance and object\nrepresentations, making it possible to compose novel interactions by combining\naffordance representations and novel object representations from additional\nimages, i.e. transferring the affordance to novel objects. With the proposed\naffordance transfer learning, the model is also capable of inferring the\naffordances of novel objects from known affordance representations. The\nproposed method can thus be used to 1) improve the performance of HOI\ndetection, especially for the HOIs with unseen objects; and 2) infer the\naffordances of novel objects. Experimental results on two datasets, HICO-DET\nand HOI-COCO (from V-COCO), demonstrate significant improvements over recent\nstate-of-the-art methods for HOI detection and object affordance detection.\nCode is available at https://github.com/zhihou7/HOI-CL\n"}
+{"abstract": "  Due to the COVID-19 pandemic, the focus on everydays mobility has been\nshifted from traditional means of transport to how to safely commute for work\nand/or move around the neighbourhood. Maintaining the safe distance among\npedestrian becomes crucial in big pedestrian networks. Looking at personal\ngoals, such as walking through the shortest path, could lead to congestion\nphenomena on both roadsand crossroads violating the imposed regulations. We\nsuggest a centralized multi-objective approach able to assign alternative fair\npaths for users while maintaining the congestion level as lower as possible.\nComputational results show that, even considering paths that are not longer\nthan the 1% with respect to the shortest path for each pedestrian, the\ncongestion phenomena are reduced of more than the 50%\n"}
+{"abstract": "  This paper introduces multiuser multiple-input multiple-output (MU-MIMO)\narchitectures based on non-linear precoding and stream combining techniques\nusing rate-splitting (RS), where the transmitter often has only partial\nknowledge of the channel state information (CSI). In contrast to existing\nworks, we consider deployments where the receivers may be equipped with\nmultiple antennas. This allows us to employ linear combining techniques based\non the Min-Max, the maximum ratio and the minimum mean-square error criteria\nalong with Tomlinson-Harashima precoders (THP) for RS-based MU-MIMO systems to\nenhance the sum-rate performance. Moreover, we incorporate the Multi-Branch\n(MB) concept into the RS architecture to further improve the sum-rate\nperformance. Closed-form expressions for the signal-to-interference-plus-noise\nratio and the sum-rate at the receiver end are devised through statistical\nanalysis. Simulation results show that the proposed RS-THP schemes achieve\nbetter performance than conventional linear and THP precoders.\n"}
+{"abstract": "  Coupled nonlinear systems have promise for parallel computing architectures.\nEn route to realizing complex networks for Ising machines, we report an\nexperimental and theoretical study of two coupled parametric resonators\n(parametrons). The coupling severely impacts the bifurcation topology and the\nnumber of available solutions of the system; in part of the stability diagram,\nwe can access fewer solutions than expected. When applying noise to probe the\nstability of the states, we find that the switching rates and the phase-space\ntrajectories of the system depend on the detuning in surprising ways. We\npresent a theoretical framework that heralds the existence of 'ghost\nbifurcations'. These bifurcations involve only unstable solutions and lead to\navoided zones in phase space. The emergence of such ghost bifurcations is an\nimportant feature of parametron networks that can influence their application\nfor parallel logic operations.\n"}
+{"abstract": "  We study the emergence of cooperation in large spatial public goods games.\nWithout employing severe social-pressure against \"defectors\", or alternatively,\nsignificantly rewarding \"cooperators\", theoretical models typically predict a\nsystem collapse in a way that is reminiscent of the \"tragedy-of-the-commons\"\nmetaphor. Drawing on a dynamic network model, this paper demonstrates how\ncooperation can emerge when the social-pressure is mild. This is achieved with\nthe aid of an additional behavior called \"hypocritical\", which appears to be\ncooperative from the external observer's perspective but in fact hardly\ncontributes to the social-welfare. Our model assumes that social-pressure is\ninduced over both defectors and hypocritical players, but the extent of which\nmay differ. Our main result indicates that the emergence of cooperation highly\ndepends on the extent of social-pressure applied against hypocritical players.\nSetting it to be at some intermediate range below the one employed against\ndefectors allows a system composed almost exclusively of defectors to transform\ninto a fully cooperative one quickly. Conversely, when the social-pressure\nagainst hypocritical players is either too low or too high, the system remains\nlocked in a degenerate configuration.\n"}
+{"abstract": "  Two-parameter perturbation theory (2PPT) is a framework designed to include\nthe relativistic gravitational effects of small-scale nonlinear structures on\nthe large-scale properties of the Universe. In this paper we use the 2PPT\nframework to calculate and study the bispectrum of matter in a spatially-flat\n$\\Lambda$CDM cosmology. This is achieved by deploying Newtonian perturbation\ntheory to model the gravitational fields of quasi-nonlinear structures, and\nthen subsequently using them as source terms for the large-scale cosmological\nperturbations. We find that our approach reproduces some of the expected\nrelativistic effects from second-order cosmological perturbation theory, but\nnot all. This work therefore provides a first step in deploying a formalism\nthat can simultaneously model the weak gravitational fields of both linear and\nnonlinear structures in a realistic model of the Universe.\n"}
+{"abstract": "  For a subspace $X$ of functions from $L_2$ we consider the minimal number $m$\nof nodes necessary for the exact discretization of the $L_2$-norm of the\nfunctions in $X$. We construct a subspace such that for any exact\ndiscretization with $m$ nodes there is at least one negative weight.\n"}
+{"abstract": "  We propose POLAR, a \\textbf{pol}ynomial \\textbf{ar}ithmetic framework that\nleverages polynomial overapproximations with interval remainders for\nbounded-time reachability analysis of neural network-controlled systems\n(NNCSs). Compared with existing arithmetic approaches that use standard Taylor\nmodels, our framework uses a novel approach to iteratively overapproximate the\nneuron output ranges layer-by-layer with a combination of Bernstein polynomial\ninterpolation for continuous activation functions and Taylor model arithmetic\nfor the other operations. This approach can overcome the main drawback in the\nstandard Taylor model arithmetic, i.e. its inability to handle functions that\ncannot be well approximated by Taylor polynomials, and significantly improve\nthe accuracy and efficiency of reachable states computation for NNCSs. To\nfurther tighten the overapproximation, our method keeps the Taylor model\nremainders symbolic under the linear mappings when estimating the output range\nof a neural network. We show that POLAR can be seamlessly integrated with\nexisting Taylor model flowpipe construction techniques, and demonstrate that\nPOLAR significantly outperforms the current state-of-the-art techniques on a\nsuite of benchmarks.\n"}
+{"abstract": "  For a Calabi-Yau manifold $X$, the Kawamata - Morrison movable cone\nconjecture connects the convex geometry of the movable cone\n$\\overline{\\mathrm{Mov}}(X)$ to the birational automorphism group. Using the\ntheory of Coxeter groups, Cantat and Oguiso proved that the conjecture is true\nfor general varieties of Wehler type, and they described explicitly\n$\\mathrm{Bir}(X)$. We generalize their argument to prove the conjecture and\ndescribe $\\mathrm{Bir}(X)$ for general complete intersections of ample divisors\nin arbitrary products of projective spaces. Then, under a certain condition, we\ngive a description of the boundary of $\\overline{\\mathrm{Mov}}(X)$ and an\napplication connected to the numerical dimension of divisors.\n"}
+{"abstract": "  Accelerated MRI shortens acquisition time by subsampling in the measurement\nk-space. Recovering a high-fidelity anatomical image from subsampled\nmeasurements requires close cooperation between two components: (1) a sampler\nthat chooses the subsampling pattern and (2) a reconstructor that recovers\nimages from incomplete measurements. In this paper, we leverage the sequential\nnature of MRI measurements, and propose a fully differentiable framework that\njointly learns a sequential sampling policy simultaneously with a\nreconstruction strategy. This co-designed framework is able to adapt during\nacquisition in order to capture the most informative measurements for a\nparticular target (Figure 1). Experimental results on the fastMRI knee dataset\ndemonstrate that the proposed approach successfully utilizes intermediate\ninformation during the sampling process to boost reconstruction performance. In\nparticular, our proposed method outperforms the current state-of-the-art\nlearned k-space sampling baseline on up to 96.96% of test samples. We also\ninvestigate the individual and collective benefits of the sequential sampling\nand co-design strategies. Code and more visualizations are available at\nhttp://imaging.cms.caltech.edu/seq-mri\n"}
+{"abstract": "  This paper is concerned with the recovery of (approximate) solutions to\nparabolic problems from incomplete and possibly inconsistent observational\ndata, given on a time-space cylinder that is a strict subset of the\ncomputational domain under consideration. Unlike previous approaches to this\nand related problems our starting point is a regularized least squares\nformulation in a continuous infinite-dimensional setting that is based on\nstable variational time-space formulations of the parabolic PDE. This allows us\nto derive a priori as well as a posteriori error bounds for the recovered\nstates with respect to a certain reference solution. In these bounds the\nregularization parameter is disentangled from the underlying discretization. An\nimportant ingredient for the derivation of a posteriori bounds is the\nconstruction of suitable Fortin operators which allow us to control oscillation\nerrors stemming from the discretization of dual norms. Moreover, the\nvariational framework allows us to contrive preconditioners for the discrete\nproblems whose application can be performed in linear time, and for which the\ncondition numbers of the preconditioned systems are uniformly proportional to\nthat of the regularized continuous problem.\n  In particular, we provide suitable stopping criteria for the iterative\nsolvers based on the a posteriori error bounds. The presented numerical\nexperiments quantify the theoretical findings and demonstrate the performance\nof the numerical scheme in relation with the underlying discretization and\nregularization.\n"}
+{"abstract": "  Across machine learning, the use of curricula has shown strong empirical\npotential to improve learning from data by avoiding local optima of training\nobjectives. For reinforcement learning (RL), curricula are especially\ninteresting, as the underlying optimization has a strong tendency to get stuck\nin local optima due to the exploration-exploitation trade-off. Recently, a\nnumber of approaches for an automatic generation of curricula for RL have been\nshown to increase performance while requiring less expert knowledge compared to\nmanually designed curricula. However, these approaches are seldomly\ninvestigated from a theoretical perspective, preventing a deeper understanding\nof their mechanics. In this paper, we present an approach for automated\ncurriculum generation in RL with a clear theoretical underpinning. More\nprecisely, we formalize the well-known self-paced learning paradigm as inducing\na distribution over training tasks, which trades off between task complexity\nand the objective to match a desired task distribution. Experiments show that\ntraining on this induced distribution helps to avoid poor local optima across\nRL algorithms in different tasks with uninformative rewards and challenging\nexploration requirements.\n"}
+{"abstract": "  In this paper, we derive a formula to express the maximum number of\nnon-intersecting diagonals of arbitrary length that can be drawn in n x n\nsquare arrays, where n is a multiple of l+1.\n"}
+{"abstract": "  In this work we explicitly show that the perturbative unitarity of analytic\ninfinite derivative (AID) scalar field theories can be achieved using a\nmodified prescription for computing scattering amplitudes. The crux of the new\nprescription is the analytic continuation of a result obtained in the Euclidean\nsignature to the Minkowski external momenta. We explicitly elaborate an example\nof a non-local $\\phi^4$ model for various infinite derivative operators.\nGeneral UV properties of amplitudes in non-local theories are discussed.\n"}
+{"abstract": "  We initiate the use of a multi-layer neural network to model two-sided\nmatching and to explore the design space between strategy-proofness and\nstability. It is well known that both properties cannot be achieved\nsimultaneously but the efficient frontier in this design space is not\nunderstood. We show empirically that it is possible to achieve a good\ncompromise between stability and strategy-proofness-substantially better than\nthat achievable through a convex combination of deferred acceptance (stable and\nstrategy-proof for only one side of the market) and randomized serial\ndictatorship (strategy-proof but not stable).\n"}
+{"abstract": "  Platooning on highways with connected and automated vehicles (CAVs) has\nattracted considerable attention, while how to mange and coordinate platoons in\nurban networks remains largely an open question. This scientific gap mainly\nresults from the maneuver complexity on urban roads, making it difficult to\nmodel the platoon formation process. Inspired by flocking behaviors in nature,\nthis paper proposed a two-dimensional model to describe CAV group dynamics. The\nmodel is formulated based on potential fields in planar coordinates, which is\ncomposed of the inter-vehicle potential field and the cross-section potential\nfield. The inter-vehicle potential field enables CAVs to attract each other\nwhen vehicle gaps are larger than the equilibrium distance, and repel each\nother otherwise. It also generates incentives for lane change maneuvers to join\na platoon or to comply with the traffic management layer. The cross-section\npotential field is able to mimic lane keeping behavior and it also creates\nresistance to avoid unnecessary lane changes at very low incentives. These\nmodeling principles can also be applied to human-driven vehicles in the mixed\ntraffic environment. Behavioral plausibility of the model in terms of\ncar-following rationality and car-following safety is demonstrated analytically\nand further verified with simulation in typical driving scenarios. The model is\ncomputationally efficient and can provide insights into platoon operations in\nurban networks.\n"}
+{"abstract": "  Graph drawing beyond planarity focuses on drawings of high visual quality for\nnon-planar graphs which are characterized by certain forbidden edge\nconfigurations. A natural criterion for the quality of a drawing is the number\nof edge crossings. The question then arises whether beyond-planar drawings have\na significantly larger crossing number than unrestricted drawings. Chimani et\nal. [GD'19] gave bounds for the ratio between the crossing number of three\nclasses of beyond-planar graphs and the unrestricted crossing number. In this\npaper we extend their results to the main currently known classes of\nbeyond-planar graphs characterized by forbidden edge configurations and answer\nseveral of their open questions.\n"}
+{"abstract": "  Radial superlattices are nanostructured materials obtained by rolling-up thin\nsolid films into spiral-like tubular structures. The formation of these\n\"high-order\" superlattices from two-dimensional crystals or ultrathin films is\nexpected to result in a transition of transport characteristics from\ntwo-dimensional to one-dimensional. Here, we show that a transport hallmark of\nradial superlattices is the appearance of magnetoconductance modulations in the\npresence of externally applied axial magnetic fields. This phenomenon\ncritically relies on electronic interlayer tunneling processes that activates\nan unconventional Aharonov-Bohm-like effect. Using a combination of density\nfunctional theory calculations and low-energy continuum models, we determine\nthe electronic states of a paradigmatic single-material radial superlattice --\na two-winding carbon nanoscroll -- and indeed show momentum-dependent\noscillations of the magnetic states in axial configuration, which we\ndemonstrate to be entirely due to hopping between the two windings of the\nspiral-shaped scroll.\n"}
+{"abstract": "  Purpose: To implement and evaluate a sequential approach to obtain\nsemi-quantitative T1-weighted MPRAGE images, unbiased by B1 inhomogeneities at\n7T.\n  Methods: In the reference gradient echo used for normalization of the MPRAGE\nimage, flip angle (aGE) and acquisition voxel size (Vref) was varied to\noptimize tissue contrast and acquisition time (Tacq). The finalized protocol\nwas implemented at three different resolutions and the reproducibility was\nevaluated. Maps of T1 were derived based on the normalized MPRAGE through\nforward signal modelling.\n  Results: A good compromise between tissue contrast and SNR was reached at\naGE=3{\\deg}. A reduction of the reference GE Tacq by a factor of 4, at the cost\nof negligible bias, was obtained by increasing Vref with a factor of 8 relative\nthe MPRAGE resolution. The coefficient-of-variation in segmented WM was 9+/-5%\nafter normalization, compared to 24+/-12% before. The T1 maps showed no obvious\nbias and had reasonable values with regard to literature, especially after\noptional B1 correction through separate flip angle mapping.\n  Conclusion: A non-interleaved acquisition for normalization of MPRAGE offers\na simple alternative to MP2RAGE to obtain semi-quantitative purely T1-weighted\nimages. These images can be converted to T1 maps analogously to the established\nMP2RAGE approach. Scan time can be reduced by increasing Vref which has a\nminiscule effect on image quality.\n"}
+{"abstract": "  Due to the fractal nature of retinal blood vessels, the retinal fractal\ndimension is a natural parameter for researchers to explore and has garnered\ninterest as a potential diagnostic tool. This review aims to summarize the\ncurrent scientific evidence regarding the relationship between fractal\ndimension and retinal pathology and thus assess the clinical value of retinal\nfractal dimension. Following the PRISMA guidelines, a literature search for\nresearch articles was conducted in several internet databases (Embase, PubMed,\nWeb of Science, Scopus). This led to a result of 28 studies included in the\nfinal review, which were analyzed via meta-analysis to determine whether the\nfractal dimension changes significantly in retinal disease versus normal\nindividuals\n"}
+{"abstract": "  We prove that in each degree divisible by 2 or 3, there are infinitely many\ntotally real number fields that require universal quadratic forms to have\narbitrarily large rank.\n"}
+{"abstract": "  A new discontinuous model of computation called one-way jumping finite\nautomata was defined by H. Chigahara et. al. This model was a restricted\nversion of the model jumping finite automata. These automata read an input\nsymbol-by-symbol and jump only in one direction. A generalized linear one-way\njumping finite automaton makes jumps after deleting a substring of an input\nstring and then changes its state. These automata can make sequence of jumps in\nonly one direction on an input string either from left to right or from right\nto left. We show that newly defined model is powerful than its original\ncounterpart. We define and compare the variants, generalized right linear\none-way jumping finite automata and generalized left linear one-way jumping\nfinite automata. We also compare the newly defined models with Chomsky\nhierarchy. Finally, we explore closure properties of the model.\n"}
+{"abstract": "  In this paper, we explore the stability of the energy landscape of an Ising\nHamiltonian when subjected to two kinds of perturbations: a perturbation on the\ncoupling coefficients and external fields, and a perturbation on the underlying\ngraph structure. We give sufficient conditions so that the ground states of a\ngiven Hamiltonian are stable under perturbations of the first kind in terms of\norder preservation. Here by order preservation we mean that the ordering of\nenergy corresponding to two spin configurations in a perturbed Hamiltonian will\nbe preserved in the original Hamiltonian up to a given error margin. We also\nestimate the probability that the energy gap between ground states for the\noriginal Hamiltonian and the perturbed Hamiltonian is bounded by a given error\nmargin when the coupling coefficients and local external magnetic fields of the\noriginal Hamiltonian are i.i.d. Gaussian random variables. In the end we show a\nconcrete example of a system which is stable under perturbations of the second\nkind.\n"}
+{"abstract": "  Similarity search is a fundamental problem for many data analysis techniques.\nMany efficient search techniques rely on the triangle inequality of metrics,\nwhich allows pruning parts of the search space based on transitive bounds on\ndistances. Recently, Cosine similarity has become a popular alternative choice\nto the standard Euclidean metric, in particular in the context of textual data\nand neural network embeddings. Unfortunately, Cosine similarity is not metric\nand does not satisfy the standard triangle inequality. Instead, many search\ntechniques for Cosine rely on approximation techniques such as locality\nsensitive hashing. In this paper, we derive a triangle inequality for Cosine\nsimilarity that is suitable for efficient similarity search with many standard\nsearch structures (such as the VP-tree, Cover-tree, and M-tree); show that this\nbound is tight and discuss fast approximations for it. We hope that this spurs\nnew research on accelerating exact similarity search for cosine similarity, and\npossible other similarity measures beyond the existing work for distance\nmetrics.\n"}
+{"abstract": "  Quantum phenomena appear in a macroscopic scale in Bose-Einstein condensates.\nThe Gross-Pitaevskii (GP) equation describes the dynamics of the weakly\ninteracting Bose-Einstein condensates. The GP equation has a form of the\nSchroedinger equation with self-interaction. The coupled Gross-Pitaevskii\nequations are used to describe some mixtures of Bose-Einstein condensates. In\nthis paper, we will show some numerical results of coupled Gross-Pitaevskii\nequations without self-interaction, which has a form of nonlinearly-coupled\nSchroedinger equations. We demonstrate that the macroscopic oscillation and the\ninterference of two quantum wave packets decay in time owing to mutual\ninteraction, which is analogous to the decoherence in quantum mechanics of many\nparticles.\n"}
+{"abstract": "  This paper proposes a very simple deterministic mathematical model, which, by\nusing a power-law, is a \\emph{non-integer power model} (or \\emph{fractional\npower model (FPM)}). Such a model, in non-integer power of time, namely $t^m$\nup to constants, enables representing at each day, with a good precision, the\ntotality of the contaminated individuals. Despite being enriched with knowledge\nthrough an internal structure based on a geometric sequence \"with variable\nratio\", the model (in its non-integer representation) has only three\nparameters, among which the non-integer power, $m$, that determines on its own,\naccording to its value, an aggravation or an improvement of the viral\nspreading. Its simplicity comes from the power-law, $t^m$, which simply\nexpresses the singular dynamics of the operator of non-integer differentiation\nor integration, of high parametric compactness, that governs diffusion\nphenomena and, as shown in this paper, the spreading phenomena by\ncontamination. The proposed model is indeed validated with the official data of\nMinistry of Health on the COVID-19 spreading. Used in prediction, it well\nenables justifying the choice of a lockdown, without which the spreading would\nhave highly worsened. The comparison of this model in $t^m$ with two known\nmodels having the same number of parameters, well shows that its\nrepresentativity of the real data is better or more general. Finally, in a more\nfundamental context and particularly in terms of complexity and simplicity, a\nself-filtering action enables showing the compatibility between the\n\\emph{internal complexity} that the internal structure and its stochastic\nbehavior present, and the \\emph{global simplicity} that the model in $t^m$\noffers in a deterministic manner: it is true that the non-integer power of a\npower-law is well a marker of complexity.\n"}
+{"abstract": "  This work focuses on low-energy collision avoidance and formation maintenance\nin autonomous swarms of drones. Here, the two main problems are: 1) how to\navoid collisions by temporarily breaking the formation, i.e., collision\navoidance reformation, and 2) how do such reformation while minimizing the\ndeviation resulting in minimization of the overall time and energy consumption\nof the drones. To address the first question, we use cellular automata based\ntechnique to find an efficient formation that avoids the obstacle while\nminimizing the time and energy. Concerning the second question, a near-optimal\nreformation of the swarm after successful collision avoidance is achieved by\napplying a temperature function reduction technique, originally used in the\npoint set registration process. The goal of the reformation process is to\nremove the disturbance while minimizing the overall time it takes for the swarm\nto reach the destination and consequently reducing the energy consumption\nrequired by this operation. To measure the degree of formation disturbance due\nto collision avoidance, deviation of the centroid of the swarm formation is\nused, inspired by the concept of the center of mass in classical mechanics.\nExperimental results show the efficiency of the proposed technique, in terms of\nperformance and energy.\n"}
+{"abstract": "  Despite extensive investigations of dissipation and deformation processes in\nmicro- and nano- sized metallic samples, the mechanisms at play during\ndeformation of systems with ultimate, molecular size remain elusive. While\nmetallic nanojunctions, obtained by stretching metallic wires down to the\natomic level are a system of choice to explore atomic scale contacts, it has\nnot been possible up to now to extract the full equilibrium and out of\nequilibrium rheological flow properties of matter at such scales. Here, by\nusing a quartz-tuning fork based Atomic Force Microscope (TF-AFM), we combine\nelectrical and rheological measurement on angstr\\\"om-size gold junctions to\nstudy the non linear rheology of this model atomic system. By submitting the\njunction to increasing sub-nanometric deformations we uncover a transition from\na purely elastic regime to a plastic, and eventually to a viscous-like\nfluidized regime, akin to the rheology of soft yielding materials, though\norders of magnitude difference in length scale. The fluidized state furthermore\nhighlights capillary attraction, as expected for liquid capillary bridges. This\nshear fluidization cannot be captured by classical models of friction between\natomic planes, pointing to unexpected dissipative behavior of defect-free\nmetallic junctions at the ultimate scales. Atomic rheology is therefore a\npowerful tool to probe the structural reorganization of atomic contacts.\n"}
+{"abstract": "  Sampling-based motion planners perform exceptionally well in robotic\napplications that operate in high-dimensional space. However, most works often\nconstrain the planning workspace rooted at some fixed locations, do not\nadaptively reason on strategy in narrow passages, and ignore valuable local\nstructure information. In this paper, we propose Rapidly-exploring Random\nForest (RRF*) -- a generalised multi-trees motion planner that combines the\nrapid exploring property of tree-based methods and adaptively learns to deploys\na Bayesian local sampling strategy in regions that are deemed to be\nbottlenecks. Local sampling exploits the local-connectivity of spaces via\nMarkov Chain random sampling, which is updated sequentially with a Bayesian\nproposal distribution to learns the local structure from past observations. The\ntrees selection problem is formulated as a multi-armed bandit problem, which\nefficiently allocates resources on the most promising tree to accelerate\nplanning runtime. RRF* learns the region that is difficult to perform tree\nextensions and adaptively deploys local sampling in those regions to maximise\nthe benefit of exploiting local structure. We provide rigorous proofs of\ncompleteness and optimal convergence guarantees, and we experimentally\ndemonstrate that the effectiveness of RRF*'s adaptive multi-trees approach\nallows it to performs well in a wide range of problems.\n"}
+{"abstract": "  We present spatially resolved ($0.1'' - 1.0''$) radio maps of Neptune taken\nfrom the Very Large Array and Atacama Large Submillimeter/Millimeter Array\nbetween $2015-2017$. Combined, these observations probe from just below the\nmain methane cloud deck at $\\sim 1$ bar down to the NH$_4$SH cloud at $\\sim50$\nbar. Prominent latitudinal variations in the brightness temperature are seen\nacross the disk. Depending on wavelength, the south polar region is $5-40$ K\nbrighter than the mid-latitudes and northern equatorial region. We use\nradiative transfer modeling coupled to Markov Chain Monte Carlo methods to\nretrieve H$_2$S, NH$_3$, and CH$_4$ abundance profiles across the disk, though\nonly strong constraints can be made for H$_2$S. Below all cloud formation, the\ndata are well fit by $53.8^{+18.9}_{-13.4}\\times$ and $3.9^{+2.1}_{-3.1}\\times$\nprotosolar enrichment in the H$_2$S and NH$_3$ abundances, respectively,\nassuming a dry adiabat. Models in which the radio-cold mid-latitudes and\nnorthern equatorial region are supersaturated in H$_2$S are statistically\nfavored over models following strict thermochemical equilibrium. H$_2$S is more\nabundant at the equatorial region than at the poles, indicative of strong,\npersistent global circulation. Our results imply that Neptune's\nsulfur-to-nitrogen ratio exceeds unity as H$_2$S is more abundant than NH$_3$\nin every retrieval. The absence of NH$_3$ above 50 bar can be explained either\nby partial dissolution of NH$_3$ in an ionic ocean at GPa pressures or by a\nplanet formation scenario in which hydrated clathrates preferentially delivered\nsulfur rather than nitrogen onto planetesimals, or a combination of these\nhypotheses.\n"}
+{"abstract": "  In this article, we use the theory of (non-abelian) exterior product of\nHom-Lie algebras to prove the Hopf formula for these algebras. As an\napplication, we construct an eight-term sequence in the homology of Hom-Lie\nalgebras. We also investigate the capability property of Hom-Lie algebras via\nthe exterior product.\n"}
+{"abstract": "  Earth Trojan Asteroids are an important but elusive population that co-orbit\nwith Earth at the L4 and L5 Lagrange points. There is only one known, but a\nlarge population is theoretically stable and could provide insight into our\nsolar system's past and present as well as planetary defense. In this paper, we\npresent the results of an Earth Trojan survey that uses a novel shift-and-stack\ndetection method on two nights of data from the Dark Energy Camera. We find no\nnew Earth Trojan Asteroids. We calculate an upper limit on the population that\nis consistent with previous searches despite much less sky coverage.\nAdditionally, we elaborate on previous upper limit calculations using current\nasteroid population statistics and an extensive asteroid simulation to provide\nthe most up to date population constraints. We find an L4 Earth Trojan\npopulation of NET < 1 for H = 13.93, NET < 7 for H = 16, and NET < 938 for H =\n22.\n"}
+{"abstract": "  The deformed Hermitian-Yang-Mills equation is a complex Hessian equation on\ncompact K\\\"ahler manifolds that corresponds to the special Lagrangian equation\nin the context of the Strominger-Yau-Zaslow mirror symmetry. Recently, Chen\nproved that the existence of the solution is equivalent to a uniform stability\ncondition in terms of holomorphic intersection numbers along test families. In\nthis paper, we establish an analogous stability result not involving a uniform\nconstant in accordance with a recent work on the $J$-equation by Song, which\nmakes further progress toward Collins-Jacob-Yau's original conjecture in the\nsupercritical phase case. In particular, we confirm this conjecture for\nprojective manifolds in the supercritical phase case.\n"}
+{"abstract": "  The mathematical analysis of random phylogenetic networks via analytic and\nalgorithmic methods has received increasing attention in the past years. In the\npresent work we introduce branching process methods to their study. This\napproach appears to be new in this context. Our main results focus on random\nlevel-$k$ networks with $n$ labelled leaves. Although the number of\nreticulation vertices in such networks is typically linear in $n$, we prove\nthat their asymptotic global and local shape is tree-like in a well-defined\nsense. We show that the depth process of vertices in a large network converges\ntowards a Brownian excursion after rescaling by $n^{-1/2}$. We also establish\nBenjamini--Schramm convergence of large random level-$k$ networks towards a\nnovel random infinite network.\n"}
+{"abstract": "  The capabilities of natural neural systems have inspired new generations of\nmachine learning algorithms as well as neuromorphic very large-scale integrated\n(VLSI) circuits capable of fast, low-power information processing. However, it\nhas been argued that most modern machine learning algorithms are not\nneurophysiologically plausible. In particular, the workhorse of modern deep\nlearning, the backpropagation algorithm, has proven difficult to translate to\nneuromorphic hardware. In this study, we present a neuromorphic, spiking\nbackpropagation algorithm based on synfire-gated dynamical information\ncoordination and processing, implemented on Intel's Loihi neuromorphic research\nprocessor. We demonstrate a proof-of-principle three-layer circuit that learns\nto classify digits from the MNIST dataset. To our knowledge, this is the first\nwork to show a Spiking Neural Network (SNN) implementation of the\nbackpropagation algorithm that is fully on-chip, without a computer in the\nloop. It is competitive in accuracy with off-chip trained SNNs and achieves an\nenergy-delay product suitable for edge computing. This implementation shows a\npath for using in-memory, massively parallel neuromorphic processors for\nlow-power, low-latency implementation of modern deep learning applications.\n"}
+{"abstract": "  This paper introduces decorated merge trees (DMTs) as a novel invariant for\npersistent spaces. DMTs combine both $\\pi_0$ and $H_n$ information into a\nsingle data structure that distinguishes filtrations that merge trees and\npersistent homology cannot distinguish alone. Three variants on DMTs, which\nemphasize category theory, representation theory and persistence barcodes,\nrespectively, offer different advantages in terms of theory and computation.\nTwo notions of distance -- an interleaving distance and bottleneck distance --\nfor DMTs are defined and a hierarchy of stability results that both refine and\ngeneralize existing stability results is proved here. To overcome some of the\ncomputational complexity inherent in these distances, we provide a novel use of\nGromov-Wasserstein couplings to compute optimal merge tree alignments for a\ncombinatorial version of our interleaving distance which can be tractably\nestimated. We introduce computational frameworks for generating, visualizing\nand comparing decorated merge trees derived from synthetic and real data.\nExample applications include comparison of point clouds, interpretation of\npersistent homology of sliding window embeddings of time series, visualization\nof topological features in segmented brain tumor images and topology-driven\ngraph alignment.\n"}
+{"abstract": "  CaCo$_{2-y}$As$_2$ is an unusual itinerant magnet with signatures of extreme\nmagnetic frustration. The conditions for establishing magnetic order in such\nitinerant frustrated magnets, either by reducing frustration or increasing\nelectronic correlations, is an open question. Here we use results from\ninelastic neutron scattering and magnetic susceptibility measurements and\ndensity functional theory calculations to show that hole doping in\nCa(Co$_{1-x}$Fe$_{x}$)$_{2-y}$As$_{2}$ suppresses magnetic order by quenching\nthe magnetic moment while maintaining the same level of magnetic frustration.\nThe suppression is due to tuning the Fermi energy away from a peak in the\nelectronic density of states originating from a flat conduction band. This\nresults in the complete elimination of the magnetic moment by $x\\approx0.25$,\nproviding a clear example of a Stoner-type transition.\n"}
+{"abstract": "  In a recent work it was shown that conformal Carroll geometries are\ncanonically equipped with a null-tractor bundle generalizing the tractor bundle\nof conformal geometry. We here show that in the case of the conformal boundary\nof an asymptotically flat space-time of any dimension d>=3, this null-tractor\nbundle over null-infinity can be canonically derived from the bulk geometry. As\nwas previously discussed, compatible normal connections on the null-tractor\nbundle are not unique: We prove that they are in fact in one-to-one\ncorrespondence with the germ of the asymptotically flat space-time to leading\norder.\n  In dimension d=3 the tractor connection invariantly encodes a choice of mass\nand angular momentum aspect, in dimension d>=4 a choice of asymptotic shear. In\ndimension $d=4$ the presence of tractor curvature correspond to gravitational\nradiations.\n  Even thought the construction is by construction geometrical and coordinate\ninvariant, we give explicit expressions in BMS coordinates for concreteness.\n"}
+{"abstract": "  Abstract. We present a tool to automatically perform the data-size analysis\nof imperative programs written in C. This tool, called pymwp, is inspired by a\nclassical work on complexity analysis [10], and allows to certify that the size\nof the values computed by a program will be bounded by a polynomial in the\nprogram's inputs. Strategies to provide meaningful feedback on non-polynomial\nprograms and to ``tame'' the non-determinism of the original analysis were\nimplemented following recent progresses [3], but required particular care to\naccommodate the growing complexity of the analysis. The Python source code is\nintensively documented, and our numerous example files encompass the original\nexamples as well as multiple test cases. A pip package should make it easy to\ninstall pymwp on any plat-form, but an on-line demo is also available for\nconvenience.\n"}
+{"abstract": "  At this paper, it is considered to find a way for defining non-commutative\nspaces by ordinary commutative ones and vice versa. A novel parameter which has\nnot been considered so far is represented. This parameter describes equivalent\nspaces. Also, we searched concepts of these new parameters with one problem.\nNoncommutativity in total space is important here because it could explain more\nconcepts. As we knew SW method (Seiberg-Witten) explained noncommutativity so\nhere, we showed that it was not suitable for some conditions.in the end we\nconsidered Hamiltonian of free particle in new noncommutativity and we found\nconcepts of new parameters.\n"}
+{"abstract": "  Convolutional Neural Networks (CNNs) are the go-to model for computer vision.\nRecently, attention-based networks, such as the Vision Transformer, have also\nbecome popular. In this paper we show that while convolutions and attention are\nboth sufficient for good performance, neither of them are necessary. We present\nMLP-Mixer, an architecture based exclusively on multi-layer perceptrons (MLPs).\nMLP-Mixer contains two types of layers: one with MLPs applied independently to\nimage patches (i.e. \"mixing\" the per-location features), and one with MLPs\napplied across patches (i.e. \"mixing\" spatial information). When trained on\nlarge datasets, or with modern regularization schemes, MLP-Mixer attains\ncompetitive scores on image classification benchmarks, with pre-training and\ninference cost comparable to state-of-the-art models. We hope that these\nresults spark further research beyond the realms of well established CNNs and\nTransformers.\n"}
+{"abstract": "  We use linear perturbation theory to study perturbations in dynamical dark\nenergy models. We compare quintessence and tachyonic dark energy models with\nidentical background evolution. We write the corresponding equations for\ndifferent models in a form that makes it easier to see that the two models are\nvery hard to distinguish in the linear regime, especially for models with $(1 +\nw) \\ll 1$. We use Cosmic Microwave Background data and parametric\nrepresentations for the two models to illustrate that they cannot be\ndistinguished for the same background evolution with existing observations.\nFurther, we constrain tachyonic models with the Planck data. We do this\nanalysis for exponential and inverse square potentials and find that the\nintrinsic parameters of the potentials remain very weakly constrained. In\nparticular, this is true in the regime allowed by low redshift observations.\n"}
+{"abstract": "  In this article, we study the impact of a change in the type of boundary\nconditions of an elliptic boundary value problem. In the context of the\nconductivity equation we consider a reference problem with mixed homogeneous\nDirichlet and Neumann boundary conditions. Two different perturbed versions of\nthis ``background'' situation are investigated, when (i) The homogeneous\nNeumann boundary condition is replaced by a homogeneous Dirichlet boundary\ncondition on a ``small'' subset $\\omega_\\varepsilon$ of the Neumann boundary;\nand when (ii) The homogeneous Dirichlet boundary condition is replaced by a\nhomogeneous Neumann boundary condition on a ``small'' subset\n$\\omega_\\varepsilon $ of the Dirichlet boundary. The relevant quantity that\nmeasures the ``smallness'' of the subset $\\omega_\\varepsilon $ differs in the\ntwo cases: while it is the harmonic capacity of $\\omega_\\varepsilon $ in the\nformer case, we introduce a notion of ``Neumann capacity'' to handle the\nlatter. In the first part of this work we derive representation formulas that\ncatch the structure of the first non trivial term in the asymptotic expansion\nof the voltage potential, for a general $\\omega_\\varepsilon $, under the sole\nassumption that it is ``small'' in the appropriate sense. In the second part,\nwe explicitly calculate the first non trivial term in the asymptotic expansion\nof the voltage potential, in the particular geometric situation where the\nsubset $\\omega_\\varepsilon $ is a vanishing surfacic ball.\n"}
+{"abstract": "  Recent advances in Computer Vision and Deep Learning have enabled astonishing\nresults in a variety of fields and applications. Motivated by this success, the\nSkyCam Dataset aims to enable image-based Deep Learning solutions for\nshort-term, precise prediction of solar radiation on a local level. For the\nspan of a year, three different cameras in three topographically different\nlocations in Switzerland are acquiring images of the sky every 10 seconds.\nThirteen high resolution images with different exposure times are captured and\nused to create an additional HDR image. The images are paired with highly\nprecise irradiance values gathered from a high-accuracy pyranometer.\n"}
+{"abstract": "  Zero-shot learning (ZSL) aims at understanding unseen categories with no\ntraining examples from class-level descriptions. To improve the discriminative\npower of ZSL, we model the visual learning process of unseen categories with\ninspiration from the psychology of human creativity for producing novel art.\nFirst, we propose CIZSL-v1 as a creativity inspired model for generative ZSL.\nWe relate ZSL to human creativity by observing that ZSL is about recognizing\nthe unseen, and creativity is about creating a likable unseen. We introduce a\nlearning signal inspired by creativity literature that explores the unseen\nspace with hallucinated class-descriptions and encourages careful deviation of\ntheir visual feature generations from seen classes while allowing knowledge\ntransfer from seen to unseen classes. Second, CIZSL-v2 is proposed as an\nimproved version of CIZSL-v1 for generative zero-shot learning. CIZSL-v2\nconsists of an investigation of additional inductive losses for unseen classes\nalong with a semantic guided discriminator. Empirically, we show consistently\nthat CIZSL losses can improve generative ZSL models on the challenging task of\ngeneralized ZSL from a noisy text on CUB and NABirds datasets. We also show the\nadvantage of our approach to Attribute-based ZSL on AwA2, aPY, and SUN\ndatasets. We also show that CIZSL-v2 has improved performance compared to\nCIZSL-v1.\n"}
+{"abstract": "  Let $n > 1$. Let $\\{U_{ij}\\}_{1 \\leq i < j \\leq n}$ be $\\binom{n}{2}$\ncommuting unitaries on some Hilbert space $\\mathcal{H}$, and suppose $U_{ji} :=\nU_{ij}^*$, $1 \\leq i < j \\leq n$. An $n$-tuple of isometries $V = (V_1, \\ldots\n,V_n)$ on $\\mathcal{H}$ is called $\\mathcal{U}_n$-twisted isometry with respect\nto $\\{U_{ij}\\}_{i<j}$ (or simply $\\mathcal{U}_n$-twisted isometry if\n$\\{U_{ij}\\}_{i<j}$ is clear from the context) if $V_i$'s are in the commutator\n$\\{U_{st}: s \\neq t\\}'$, and $V_i^*V_j=U_{ij}^*V_jV_i^*$, $i \\neq j$\n  We prove that each $\\mathcal{U}_n$-twisted isometry admits a von Neumann-Wold\ntype orthogonal decomposition, and prove that the universal $C^*$-algebra\ngenerated by $\\mathcal{U}_n$-twisted isometry is nuclear. We exhibit concrete\nanalytic models of $\\mathcal{U}_n$-twisted isometries, and establish\nconnections between unitary equivalence classes of the irreducible\nrepresentations of the $C^*$-algebras generated by $\\mathcal{U}_n$-twisted\nisometries and the unitary equivalence classes of the non-zero irreducible\nrepresentations of twisted noncommutative tori. Our motivation of\n$\\mathcal{U}_n$-twisted isometries stems from the classical rotation\n$C^*$-algebras, Heisenberg group $C^*$-algebras, and a recent work of de Jeu\nand Pinto.\n"}
+{"abstract": "  This note shows that the category of truncated spaces with finite homotopy\ninvariants (aka truncated coherent spaces, or truncated pi-finite spaces) has\nmany of the expected features of what should be an elementary infinity-topos.\nIt should be thought as the natural higher analogue of the elementary topos of\nfinite sets.\n"}
+{"abstract": "  As a step to establish the blockwise Alperin weight conjecture for all finite\ngroups, we verify the inductive blockwise Alperin weight condition introduced\nby Navarro--Tiep and Sp\\\"ath for simple groups of Lie type $\\mathsf A$, split\nor twisted. Key to the proofs is to reduce the verification of the inductive\ncondition to the isolated (that means unipotent) blocks, using the Jordan\ndecomposition for blocks of finite reductive groups given by Bonnaf\\'e, Dat and\nRouquier.\n"}
+{"abstract": "  In this article, we establish a near-optimal convergence rate for the CLT of\nlinear eigenvalue statistics of Wigner matrices, in Kolmogorov-Smirnov\ndistance. For all test functions $f\\in C^5(\\mathbb R)$, we show that the\nconvergence rate is either $N^{-1/2+\\varepsilon}$ or $N^{-1+\\varepsilon}$,\ndepending on the first Chebyshev coefficient of $f$ and the third moment of the\ndiagonal matrix entries. The condition that distinguishes these two rates is\nnecessary and sufficient. For a general class of test functions, we further\nidentify matching lower bounds for the convergence rates. In addition, we\nidentify an explicit, non-universal contribution in the linear eigenvalue\nstatistics, which is responsible for the slow rate $N^{-1/2+\\varepsilon}$ for\nnon-Gaussian ensembles. By removing this non-universal part, we show that the\nshifted linear eigenvalue statistics have the unified convergence rate\n$N^{-1+\\varepsilon}$ for all test functions.\n"}
+{"abstract": "  In this work, we present an alternative methodology to solve the\nparticle-fluid interaction in the resolved CFDEM coupling framework. This\nnumerical approach consists of coupling a Discrete Element Method (DEM) with a\nComputational Fluid Dynamics (CFD) scheme, solving the motion of immersed\nparticles in a fluid phase. As a novelty, our approach explicitly accounts for\nthe body force acting on the fluid phase when computing the local momentum\nbalance equations. Accordingly, we implement a fluid-particle interaction\ncomputing the buoyant and drag forces as a function of local shear strain and\npressure gradient. As a benchmark, we study the Stokesian limit of a single\nparticle. The validation is performed comparing our outcomes with the ones\nprovided by a previous resolved methodology and the analytical prediction. In\ngeneral, we find that the new implementation reproduces with very good accuracy\nthe Stokesian dynamics. Complementarily, we study the settling terminal\nvelocity of a sphere under confined conditions.\n"}
+{"abstract": "  Semantic segmentation is one of the basic, yet essential scene understanding\ntasks for an autonomous agent. The recent developments in supervised machine\nlearning and neural networks have enjoyed great success in enhancing the\nperformance of the state-of-the-art techniques for this task. However, their\nsuperior performance is highly reliant on the availability of a large-scale\nannotated dataset. In this paper, we propose a novel fully unsupervised\nsemantic segmentation method, the so-called Information Maximization and\nAdversarial Regularization Segmentation (InMARS). Inspired by human perception\nwhich parses a scene into perceptual groups, rather than analyzing each pixel\nindividually, our proposed approach first partitions an input image into\nmeaningful regions (also known as superpixels). Next, it utilizes\nMutual-Information-Maximization followed by an adversarial training strategy to\ncluster these regions into semantically meaningful classes. To customize an\nadversarial training scheme for the problem, we incorporate adversarial pixel\nnoise along with spatial perturbations to impose photometrical and geometrical\ninvariance on the deep neural network. Our experiments demonstrate that our\nmethod achieves the state-of-the-art performance on two commonly used\nunsupervised semantic segmentation datasets, COCO-Stuff, and Potsdam.\n"}
+{"abstract": "  Continuing [Fuchino, Ottenbreit and Sakai[9, 10]] and [Fuchino and\nOttenbreit[11]], we further study reflection principles in connection with the\nL\\\"owenheim-Skolem Theorems of stationary logics. In this paper, we mainly\nanalyze the situations in the models obtained by mixed support iteration of a\nsupercompact length and then collapsing another supercompact cardinal to make\nit $(2^{\\aleph_0})^+$. We show, among other things, that the reflection down to\n$< 2^{\\aleph_0}$ of the non-metrizability of topological spaces with small\ncharacter is independent from the reflection properties studied in [Fuchino,\nOttenbreit and Sakai[9, 10]] and [Fuchino and Ottenbreit[11]].\n"}
+{"abstract": "  We review the dispersion-theoretical analysis of the electromagnetic form\nfactors of the nucleon. We emphasize in particular the role of unitarity and\nanalyticity in the construction of the isoscalar and isovector spectral\nfunctions. We present new results on the extraction of the nucleon radii, the\nelectric and magnetic form factors and the extraction of $\\omega$-meson\ncouplings. All this is supplemented by a detailed calculation of the\ntheoretical uncertainties, using bootstrap and Bayesian methods to pin down the\nstatistical errors, while systematic errors are determined from variations of\nthe spectral functions. We also discuss the physics of the time-like form\nfactors and point out further issues to be addressed in this framework.\n"}
+{"abstract": "  Near-infrared to visible (NIR-VIS) face recognition is the most common case\nin heterogeneous face recognition, which aims to match a pair of face images\ncaptured from two different modalities. Existing deep learning based methods\nhave made remarkable progress in NIR-VIS face recognition, while it encounters\ncertain newly-emerged difficulties during the pandemic of COVID-19, since\npeople are supposed to wear facial masks to cut off the spread of the virus. We\ndefine this task as NIR-VIS masked face recognition, and find it problematic\nwith the masked face in the NIR probe image. First, the lack of masked face\ndata is a challenging issue for the network training. Second, most of the\nfacial parts (cheeks, mouth, nose etc.) are fully occluded by the mask, which\nleads to a large amount of loss of information. Third, the domain gap still\nexists in the remaining facial parts. In such scenario, the existing methods\nsuffer from significant performance degradation caused by the above issues. In\nthis paper, we aim to address the challenge of NIR-VIS masked face recognition\nfrom the perspectives of training data and training method. Specifically, we\npropose a novel heterogeneous training method to maximize the mutual\ninformation shared by the face representation of two domains with the help of\nsemi-siamese networks. In addition, a 3D face reconstruction based approach is\nemployed to synthesize masked face from the existing NIR image. Resorting to\nthese practices, our solution provides the domain-invariant face representation\nwhich is also robust to the mask occlusion. Extensive experiments on three\nNIR-VIS face datasets demonstrate the effectiveness and\ncross-dataset-generalization capacity of our method.\n"}
+{"abstract": "  In this paper, we mainly study the global well-posedness and $L^2$ decay rate\nfor the strong solutions of the compressible co-rotation finite extensible\nnonlinear elastic (FENE) dumbbell model with small initial data. This model is\na coupling of isentropic compressible Navier-Stokes equations with a nonlinear\nFokker-Planck equation. We first prove that the FENE dumbbell model admits a\nunique global strong solution provided the initial data are close to\nequilibrium state for $d\\geq 2$. Moreover, for $d\\geq3$, we show that optimal\n$L^2$ decay rates of global strong solutions by the linear spectral theory.\n"}
+{"abstract": "  The Terahertz band is envisioned to meet the demanding 100 Gbps data rates\nfor 6G wireless communications. Aiming at combating the distance limitation\nproblem with low hardware-cost, ultra-massive MIMO with hybrid beamforming is\npromising. However, relationships among wavelength, array size and antenna\nspacing give rise to the inaccuracy of planar-wave channel model (PWM), while\nan enlarged channel matrix dimension leads to excessive parameters of applying\nspherical-wave channel model (SWM). Moreover, due to the adoption of hybrid\nbeamforming, channel estimation (CE) needs to recover high-dimensional channels\nfrom severely compressed channel observation. In this paper, a hybrid\nspherical- and planar-wave channel model (HSPM) is investigated and proved to\nbe accurate and efficient by adopting PWM within subarray and SWM among\nsubarray. Furthermore, a two-phase HSPM CE mechanism is developed. A deep\nconvolutional-neural-network (DCNN) is designed in the first phase for\nparameter estimation of reference subarrays, while geometric relationships of\nthe remaining channel parameters between reference subarrays are leveraged to\ncomplete CE in the second phase. Extensive numerical results demonstrate the\nHSPM is accurate at various communication distances, array sizes and carrier\nfrequencies.The DCNN converges fast and achieves high accuracy with 5.2 dB\nimproved normalized-mean-square-error compared to literature methods, and owns\nsubstantially low complexity.\n"}
+{"abstract": "  Current two-stage TTS framework typically integrates an acoustic model with a\nvocoder -- the acoustic model predicts a low resolution intermediate\nrepresentation such as Mel-spectrum while the vocoder generates waveform from\nthe intermediate representation. Although the intermediate representation is\nserved as a bridge, there still exists critical mismatch between the acoustic\nmodel and the vocoder as they are commonly separately learned and work on\ndifferent distributions of representation, leading to inevitable artifacts in\nthe synthesized speech. In this work, different from using pre-designed\nintermediate representation in most previous studies, we propose to use VAE\ncombining with GAN to learn a latent representation directly from speech and\nthen utilize a flow-based acoustic model to model the distribution of the\nlatent representation from text. In this way, the mismatch problem is migrated\nas the two stages work on the same distribution. Results demonstrate that the\nflow-based acoustic model can exactly model the distribution of our learned\nspeech representation and the proposed TTS framework, namely Glow-WaveGAN, can\nproduce high fidelity speech outperforming the state-of-the-art GAN-based\nmodel.\n"}
+{"abstract": "  Lazy evaluation is a powerful tool for functional programmers. It enables the\nconcise expression of on-demand computation and a form of compositionality not\navailable under other evaluation strategies. However, the stateful nature of\nlazy evaluation makes it hard to analyze a program's computational cost, either\ninformally or formally. In this work, we present a novel and simple framework\nfor formally reasoning about lazy computation costs based on a recent model of\nlazy evaluation: clairvoyant call-by-value. The key feature of our framework is\nits simplicity, as expressed by our definition of the clairvoyance monad. This\nmonad is both simple to define (around 20 lines of Coq) and simple to reason\nabout. We show that this monad can be effectively used to mechanically reason\nabout the computational cost of lazy functional programs written in Coq.\n"}
+{"abstract": "  Complex lattices provide a versatile ground for fascinating quantum many-body\nphysics. Here, we propose an exotic mechanics for generating orbital\nfrustration in hexagonal lattices. We study two-component (pseudospin-$1/2$)\nBose gases in $p$-orbital bands of two-dimensional hexagonal lattices, and find\nthat the system exhibits previously untouched orbital frustration as a result\nof the interplay of spin and orbital degrees of freedom, in contrast to normal\nIsing-type orbital ordering of spinless $p$-orbital band bosons in\ntwo-dimensional hexagonal lattices. Based on the classification by symmetry\nanalysis, we find the interplay of orbital frustration and strong interaction\nleads to exotic Mott and superfluid phases with spin-orbital intertwined\norders, in spite of the complete absence of spin-orbital interaction in the\nHamiltonian. Our study implies many-body correlations in a multi-orbital\nsetting could induce rich spin-orbital intertwined physics in complex lattice\nstructures.\n"}
+{"abstract": "  Instance segmentation can detect where the objects are in an image, but hard\nto understand the relationship between them. We pay attention to a typical\nrelationship, relative saliency. A closely related task, salient object\ndetection, predicts a binary map highlighting a visually salient region while\nhard to distinguish multiple objects. Directly combining two tasks by\npost-processing also leads to poor performance. There is a lack of research on\nrelative saliency at present, limiting the practical applications such as\ncontent-aware image cropping, video summary, and image labeling.\n  In this paper, we study the Salient Object Ranking (SOR) task, which manages\nto assign a ranking order of each detected object according to its visual\nsaliency. We propose the first end-to-end framework of the SOR task and solve\nit in a multi-task learning fashion. The framework handles instance\nsegmentation and salient object ranking simultaneously. In this framework, the\nSOR branch is independent and flexible to cooperate with different detection\nmethods, so that easy to use as a plugin. We also introduce a\nPosition-Preserved Attention (PPA) module tailored for the SOR branch. It\nconsists of the position embedding stage and feature interaction stage.\nConsidering the importance of position in saliency comparison, we preserve\nabsolute coordinates of objects in ROI pooling operation and then fuse\npositional information with semantic features in the first stage. In the\nfeature interaction stage, we apply the attention mechanism to obtain\nproposals' contextualized representations to predict their relative ranking\norders. Extensive experiments have been conducted on the ASR dataset. Without\nbells and whistles, our proposed method outperforms the former state-of-the-art\nmethod significantly. The code will be released publicly available.\n"}
+{"abstract": "  We investigate the Hubbard Hamiltonian on ladders where the number of sites\nper rung alternates between two and three. These geometries are bipartite, with\na non-equal number of sites on the two sublattices. Thus they share a key\nfeature of the Hubbard model in a class of lattices which Lieb has shown\nanalytically to exhibit long-range ferrimagnetic order, while being amenable to\npowerful numeric approaches developed for quasi-one-dimensional geometries. The\nDensity Matrix Renormalization Group (DMRG) method is used to obtain the ground\nstate properties, e.g. excitation gaps, charge and spin densities as well as\ntheir correlation functions at half-filling. We show the existence of\nlong-range ferrimagnetic order in the one-dimensional ladder geometries. Our\nwork provides detailed quantitative results which complement the general\ntheorem of Lieb for generalized bipartite lattices. It also addresses the issue\nof how the alternation between quasi-long range order and spin liquid behavior\nfor uniform ladders with odd and even numbers of legs might be affected by a\nregular alternation pattern.\n"}
+{"abstract": "  The CMS collaboration reported a $\\sim 3 \\, \\sigma$ (local) excess at\n$96\\;$GeV in the search for light Higgs-boson decaying into two photons. This\nmass coincides with a $\\sim 2 \\, \\sigma$ (local) excess in the $b\\bar b$ final\nstate at LEP. We show an interpretation of these possible signals as the\nlightest Higgs boson in the 2 Higgs Doublet Model with an additional complex\nHiggs singlet (2HDMS). The interpretation is in agreement with all experimental\nand theoretical constraints. We concentrate on the 2HDMS type II, which\nresembles the Higgs and Yukawa structure of the Next-to Minimal Supersymmetric\nStandard Model. We discuss the experimental prospects for constraining our\nexplanation at future $e^+e^-$ colliders, with concrete analyses based on the\nILC prospects.\n"}
+{"abstract": "  We use the Hyper Suprime-Cam Subaru Strategic Program S19A shape catalog to\nconstruct weak lensing shear-selected cluster samples. From aperture mass maps\ncovering $\\sim 510$~deg$^2$ created using a truncated Gaussian filter, we\nconstruct a catalog of 187 shear-selected clusters that correspond to mass map\npeaks with the signal-to-noise ratio larger than 4.7. Most of the\nshear-selected clusters have counterparts in optically-selected clusters, from\nwhich we estimate the purity of the catalog to be higher than 95\\%. The sample\ncan be expanded to 418 shear-selected clusters with the same signal-to-noise\nratio cut by optimizing the shape of the filter function and by combining weak\nlensing mass maps created with several different background galaxy selections.\nWe argue that dilution and obscuration effects of cluster member galaxies can\nbe mitigated by using background source galaxy samples and adopting the filter\nfunction with its inner boundary larger than about $2'$. The large samples of\nshear-selected clusters that are selected without relying on any baryonic\ntracer are useful for detailed studies of cluster astrophysics and cosmology.\n"}
+{"abstract": "  Optical, reflected light eclipse observations provide a direct probe of the\nexoplanet scattering properties, such as from aerosols. We present here the\nphotometric, reflected light observations of WASP-43b using the HST WFC3/UVIS\ninstrument with the F350LP filter (346-822nm) encompassing the entire optical\nband. This is the first reflected light, photometric eclipse using UVIS in\nscanning mode; as such we further detail our scanning extraction and analysis\npipeline Arctor. Our HST WFC3/UVIS eclipse light curve for WASP-43 b derived a\n3-{\\sigma} upper limit of 67 ppm on the eclipse depth, which implies that\nWASP-43b has a very dark dayside atmosphere. With our atmospheric modeling\ncampaign, we compared our reflected light constraints with predictions from\nglobal circulation and cloud models, benchmarked with HST and Spitzer\nobservations of WASP-43b. We infer that we do not detect clouds on the dayside\nwithin the pressure levels probed by HST WFC3/UVIS with the F350LP filter (P >\n1 bar). This is consistent with the GCM predictions based on previous WASP-43b\nobservations. Dayside emission spectroscopy results from WASP-43b with HST and\nSpitzer observations are likely to not be significantly affected by\ncontributions from cloud particles.\n"}
+{"abstract": "  Scalar-tensor theories leaving significant modifications of gravity at\ncosmological scales rely on screening mechanisms to recover General Relativity\n(GR) in high-density regions and pass stringent tests with astrophysical\nobjects. Much focus has been placed on the signatures of such modifications of\ngravity on the propagation of gravitational waves (GWs) through cosmological\ndistances while typically assuming their emission from fully screened regions\nwith the wave generation strictly abiding by GR. Here, we closely analyse the\nimpact of screening mechanisms on the inspiral GW waveforms from compact\nsources by employing a scaling method that enables a post-Newtonian (PN)\nexpansion in screened regimes. Particularly, we derive the leading-order\ncorrections to a fully screened emission to first PN order in the near zone and\nwe also compute the modifications in the unscreened radiation zone to second PN\norder. For a concrete example, we apply our results to a cubic Galileon model.\nThe resulting GW amplitude from a binary black hole inspiral deviate from its\nGR counterpart at most by one part in $10^{2}$ for the modifications in the\nradiation zone and at most one part in $10^{11}$ due to next-order corrections\nto the fully screened near zone. We expect such modifications to be\nundetectable by the current generation of GW detectors, but the deviation is\nnot so small as to remain undetectable in future experiments.\n"}
+{"abstract": "  Accurate segmentation of brain tumors from multi-modal Magnetic Resonance\n(MR) images is essential in brain tumor diagnosis and treatment. However, due\nto the existence of domain shifts among different modalities, the performance\nof networks decreases dramatically when training on one modality and performing\non another, e.g., train on T1 image while performing on T2 image, which is\noften required in clinical applications. This also prohibits a network from\nbeing trained on labeled data and then transferred to unlabeled data from a\ndifferent domain. To overcome this, unsupervised domain adaptation (UDA)\nmethods provide effective solutions to alleviate the domain shift between\nlabeled source data and unlabeled target data. In this paper, we propose a\nnovel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA\nscheme. Specifically, a bidirectional image synthesis and segmentation module\nis proposed to segment the brain tumor using the intermediate data\ndistributions generated for the two domains, which includes an image-to-image\ntranslator and a shared-weighted segmentation network. Further, a\nglobal-to-local consistency learning module is proposed to build robust\nrepresentation alignments in an integrated way. Extensive experiments on a\nmulti-modal brain MR benchmark dataset demonstrate that the proposed method\noutperforms several state-of-the-art unsupervised domain adaptation methods by\na large margin, while a comprehensive ablation study validates the\neffectiveness of each key component. The implementation code of our method will\nbe released at \\url{https://github.com/KeleiHe/BiGL}.\n"}
+{"abstract": "  We report on controllable cavity modes through controlling the backscattering\nby two identical scatterers. Periodic changes of the backscattering coupling\nbetween two degenerate cavity modes are observed with the angle between two\nscatterers and elucidated by a theoretical model using two-mode approximation\nand numerical simulations. The periodically appearing single-peak cavity modes\nindicate mode degeneracy at diabolical points. Then interactions between single\nquantum dots and cavity modes are investigated. Enhanced emission of a quantum\ndot with a six-fold intensity increase is obtained in a microdisk at a\ndiabolical point. This method to control cavity modes allows large-scale\nintegration, high reproducibility and fexible design of the size, location,\nquantity and shape for scatterers, which can be applied for integrated photonic\nstructures with scatterer-modified light-matter interaction.\n"}
+{"abstract": "  Statistical fault localization (SFL) techniques use execution profiles and\nsuccess/failure information from software executions, in conjunction with\nstatistical inference, to automatically score program elements based on how\nlikely they are to be faulty. SFL techniques typically employ one type of\nprofile data: either coverage data, predicate outcomes, or variable values.\nMost SFL techniques actually measure correlation, not causation, between\nprofile values and success/failure, and so they are subject to confounding bias\nthat distorts the scores they produce. This paper presents a new SFL technique,\nnamed \\emph{UniVal}, that uses causal inference techniques and machine learning\nto integrate information about both predicate outcomes and variable values to\nmore accurately estimate the true failure-causing effect of program statements.\n\\emph{UniVal} was empirically compared to several coverage-based,\npredicate-based, and value-based SFL techniques on 800 program versions with\nreal faults.\n"}
+{"abstract": "  We prove upper bounds on the graph diameters of polytopes in two settings.\nThe first is a worst-case bound for integer polytopes in terms of the length of\nthe description of the polytope (in bits) and the minimum angle between facets\nof its polar. The second is a smoothed analysis bound: given an appropriately\nnormalized polytope, we add small Gaussian noise to each constraint. We\nconsider a natural geometric measure on the vertices of the perturbed polytope\n(corresponding to the mean curvature measure of its polar) and show that with\nhigh probability there exists a \"giant component\" of vertices, with measure\n$1-o(1)$ and polynomial diameter. Both bounds rely on spectral gaps -- of a\ncertain Schr\\\"odinger operator in the first case, and a certain continuous time\nMarkov chain in the second -- which arise from the log-concavity of the volume\nof a simple polytope in terms of its slack variables.\n"}
+{"abstract": "  A data-driven convergence criterion for the D'Agostini (Richardson-Lucy)\niterative unfolding is presented. It relies on the unregularized spectrum\n(infinite number of iterations), and allows a safe estimation of the bias and\nundercoverage induced by truncating the algorithm. In addition, situations\nwhere the response matrix is not perfectly known are also discussed, and show\nthat in most cases the unregularized spectrum is not an unbiased estimator of\nthe true distribution. Whenever a bias is introduced, either by truncation of\nby poor knowledge of the response, a way to retrieve appropriate coverage\nproperties is proposed.\n"}
+{"abstract": "  Warfarin, a commonly prescribed drug to prevent blood clots, has a highly\nvariable individual response. Determining a maintenance warfarin dose that\nachieves a therapeutic blood clotting time, as measured by the international\nnormalized ratio (INR), is crucial in preventing complications. Machine\nlearning algorithms are increasingly being used for warfarin dosing; usually,\nan initial dose is predicted with clinical and genotype factors, and this dose\nis revised after a few days based on previous doses and current INR. Since a\nsequence of prior doses and INR better capture the variability in individual\nwarfarin response, we hypothesized that longitudinal dose response data will\nimprove maintenance dose predictions. To test this hypothesis, we analyzed a\ndataset from the COAG warfarin dosing study, which includes clinical data,\nwarfarin doses and INR measurements over the study period, and maintenance dose\nwhen therapeutic INR was achieved. Various machine learning regression models\nto predict maintenance warfarin dose were trained with clinical factors and\ndosing history and INR data as features. Overall, dose revision algorithms with\na single dose and INR achieved comparable performance as the baseline dose\nrevision algorithm. In contrast, dose revision algorithms with longitudinal\ndose and INR data provided maintenance dose predictions that were statistically\nsignificantly much closer to the true maintenance dose. Focusing on the best\nperforming model, gradient boosting (GB), the proportion of ideal estimated\ndose, i.e., defined as within $\\pm$20% of the true dose, increased from the\nbaseline (54.92%) to the GB model with the single (63.11%) and longitudinal\n(75.41%) INR. More accurate maintenance dose predictions with longitudinal dose\nresponse data can potentially achieve therapeutic INR faster, reduce\ndrug-related complications and improve patient outcomes with warfarin therapy.\n"}
+{"abstract": "  The development of alternative platforms for computing has been a\nlongstanding goal for physics, and represents a particularly pressing concern\nas conventional transistors approach the limit of miniaturization. A potential\nalternatice paradigm is that of reservoir computing, which leverages unknown,\nbut highly non-linear transformations of input-data to perform computations.\nThis has the advantage that many physical systems exhibit precisely the type of\nnon-linear input-output relationships necessary for them to function as\nreservoirs. Consequently, the quantum effects which obstruct the further\ndevelopment of silicon electronics become an advantage for a reservoir\ncomputer. Here we demonstrate that even the most basic constituents of matter -\natoms - can act as a reservoir for optical computers, thanks to the phenomenon\nof High Harmonic Generation (HHG). A prototype single-atom computer for\nclassification problems is proposed, where parameters of the classification\nmodel are mapped to optical elements. We numerically demonstrate that this\n`all-optical' computer can successfully classify data with an accuracy that is\nstrongly dependent on dynamical non-linearities. This may pave the way for the\ndevelopment of petahertz information processing platforms.\n"}
+{"abstract": "  Rapid antigen tests detect proteins at the surface of virus particles,\nidentifying the disease during its infectious phase. In contrast, PCR tests\ndetect viral genomes; they can thus diagnose COVID-19 before the infectious\nphase but also react to remnants of the virus genome, even weeks after live\nvirus ceases to be detectable in the respiratory tract. Furthermore, the\nlogistics for administering the tests are different, with rapid antigen tests\nbeing much easier to administer at-scale. In this article, we discuss the\nrelative advantages of the different testing procedures and summarise evidence\nthat shows that using antigen tests 2-3 times per week could become a powerful\ntool to suppress the COVID-19 pandemic. We also discuss the results of recent\nlarge-scale rapid antigen testing in Austrian schools. While our report on\ntesting predates Delta, we have updated the review with recent data on viral\nloads in breakthrough infections and more information about testing efficacy,\nespecially in children.\n"}
+{"abstract": "  Collective oscillations and their suppression by external stimulation are\nanalyzed in a large-scale neural network consisting of two interacting\npopulations of excitatory and inhibitory quadratic integrate-and-fire neurons.\nIn the limit of an infinite number of neurons, the microscopic model of this\nnetwork can be reduced to an exact low-dimensional system of mean-field\nequations. Bifurcation analysis of these equations reveals three different\ndynamic modes in a free network: a stable resting state, a stable limit cycle,\nand bistability with a coexisting resting state and a limit cycle. We show that\nin the limit cycle mode, high-frequency stimulation of an inhibitory population\ncan stabilize an unstable resting state and effectively suppress collective\noscillations. We also show that in the bistable mode, the dynamics of the\nnetwork can be switched from a stable limit cycle to a stable resting state by\napplying an inhibitory pulse to the excitatory population. The results obtained\nfrom the mean-field equations are confirmed by numerical simulation of the\nmicroscopic model.\n"}
+{"abstract": "  Site Reliability Engineers (SREs) play a key role in issue identification and\nresolution. After an issue is reported, SREs come together in a virtual room\n(collaboration platform) to triage the issue. While doing so, they leave behind\na wealth of information which can be used later for triaging similar issues.\nHowever, usability of the conversations offer challenges due to them being i)\nnoisy and ii) unlabelled. This paper presents a novel approach for issue\nartefact extraction from the noisy conversations with minimal labelled data. We\npropose a combination of unsupervised and supervised model with minimum human\nintervention that leverages domain knowledge to predict artefacts for a small\namount of conversation data and use that for fine-tuning an already pretrained\nlanguage model for artefact prediction on a large amount of conversation data.\nExperimental results on our dataset show that the proposed ensemble of\nunsupervised and supervised model is better than using either one of them\nindividually.\n"}
+{"abstract": "  Backpropagation through time (BPTT) is a technique of updating tuned\nparameters within recurrent neural networks (RNNs). Several attempts at\ncreating such an algorithm have been made including: Nth Ordered Approximations\nand Truncated-BPTT. These methods approximate the backpropagation gradients\nunder the assumption that the RNN only utilises short-term dependencies. This\nis an acceptable assumption to make for the current state of artificial neural\nnetworks. As RNNs become more advanced, a shift towards influence by long-term\ndependencies is likely. Thus, a new method for backpropagation is required. We\npropose using the 'discrete forward sensitivity equation' and a variant of it\nfor single and multiple interacting recurrent loops respectively. This solution\nis exact and also allows the network's parameters to vary between each\nsubsequent step, however it does require the computation of a Jacobian.\n"}
+{"abstract": "  In this paper, we present the performance analysis of mixed RF/FSO system\nwith multiple relays. To select the best relay, we adopt partial relay\nselection with outdated CSI wherein we investigate the effect of the temporal\ncorrelation of the channels. Unlike the vast majority of work, we introduce the\nimpairments to the relays and the destination and we compare the performance\nagainst conventional RF relaying systems. We further derive the expressions of\nthe outage probability and the ergodic capacity as well as the bounds to unpack\nengineering insights into the system robustness.\n"}
+{"abstract": "  A group is called strongly bounded, if the speed with which it is generated\nby finitely many conjugacy classes has a positive, lower bound only dependent\non the number of the conjugacy classes in question rather than the actual\nconjugacy classes. Earlier papers by Kedra, Libman and Martin and myself have\nshown that this is a property common to split Chevalley groups defined using an\nirreducible root system of rank at least $2$ and the ring of all S-algebraic\nintegers and that the situation is dependent on the number theory of $R$ for\n${\\rm Sp}_4$ and $G_2.$ In this paper, we will show that ${\\rm SL}_2(R)$ is\nalso strongly bounded for $R$ the ring of all S-algebraic integers in a number\nfield $K$ with $R$ having infinitely many units and will give a complete\naccount of the existence of small conjugacy classes generating ${\\rm SL}_2(R)$\nin terms of the prime factorization of the rational primes $2$ and $3$ in $R.$\n"}
+{"abstract": "  The news ecosystem has become increasingly complex, encompassing a wide range\nof sources with varying levels of trustworthiness, and with public commentary\ngiving different spins to the same stories. In this paper, we present a\nmulti-platform measurement of this ecosystem. We compile a list of 1,073 news\nwebsites and extract posts from four Web communities (Twitter, Reddit, 4chan,\nand Gab) that contain URLs from these sources. This yields a dataset of 38M\nposts containing 15M news URLs, spanning almost three years.\n  We study the data along several axes, assessing the trustworthiness of shared\nnews, designing a method to group news articles into stories, analyzing these\nstories are discussed and measuring the influence various Web communities have\nin that. Our analysis shows that different communities discuss different types\nof news, with polarized communities like Gab and /r/The_Donald subreddit\ndisproportionately referencing untrustworthy sources. We also find that fringe\ncommunities often have a disproportionate influence on other platforms w.r.t.\npushing narratives around certain news, for example about political elections,\nimmigration, or foreign policy.\n"}
+{"abstract": "  Due to their small size and unique properties, single-molecule electronics\nhave long seen research interest from experimentalists and theoreticians alike.\nFrom a theoretical standpoint, modeling these systems using electronic\nstructure theory can be difficult due to the importance of electron correlation\nin the determination of molecular properties, and this electron correlation can\nbe computationally expensive to consider, particularly multiconfigurational\ncorrelation energy. In this work, we develop a new approach for the study of\nsingle-molecule electronic systems, denoted NEGF-MCPDFT, which combines\nmulticonfiguration pair-density functional theory (MC-PDFT) with the\nnon-equilibrium Green's function formalism (NEGF). The use of MC-PDFT with NEGF\nallows for the efficient inclusion of both static and dynamic electron\ncorrelation in the description of the junction's electronic structure. CASSCF\nwave functions are used as references in the MC-PDFT calculation, and like with\nany active space method, effort must be made to determine the proper orbital\ncharacter to include in the active space. We perform conductance and\ntransmission calculations on a series of alkanes (predominantly\nsingle-configurational character) and benzyne (multiconfigurational character),\nexploring the role that active space selection has on the computed results. For\nthe alkane junctions explored (where dynamic electron correlation dominates),\nthe MCPDFT-NEGF results agree well with DFT-NEGF results. For the benzyne\njunction (which has significant static correlation), we see clear differences\nin the MCPDFT-NEGF and DFT-NEGF results, and evidence that NEGF-MCPDFT is\ncapturing additional electron correlation effects beyond those provided by the\nPBE functional.\n"}
+{"abstract": "  The insights revealed from process mining heavily rely on the quality of\nevent logs. Activities extracted from healthcare information systems with the\nfree-text nature may lead to inconsistent labels. Such inconsistency would then\nlead to redundancy of activity labels, which refer to labels that have\ndifferent syntax but share the same behaviours. The identifications of these\nlabels from data-driven process discovery are difficult and rely heavily on\nresource-intensive human review. Existing work achieves low accuracy either\nredundant activity labels are in low occurrence frequency or the existence of\nnumerical data values as attributes in event logs. However, these phenomena are\ncommonly observed in healthcare information systems. In this paper, we propose\nan approach to detect redundant activity labels using control-flow relations\nand numerical data values from event logs. Natural Language Processing is also\nintegrated into our method to assess semantic similarity between labels, which\nprovides users with additional insights. We have evaluated our approach through\nsynthetic logs generated from the real-life Sepsis log and a case study using\nthe MIMIC-III data set. The results demonstrate that our approach can\nsuccessfully detect redundant activity labels. This approach can add value to\nthe preprocessing step to generate more representative event logs for process\nmining tasks in the healthcare domain.\n"}
+{"abstract": "  Quantum computing's greatest challenge is scaling up. Several decades ago,\nclassical computers faced the same problem and a single solution emerged:\nvery-large-scale integration using silicon. Today's silicon chips consist of\nbillions of field-effect transistors (FinFETs) in which current flow along the\nfin-shaped channel is controlled by wrap-around gates. The semiconductor\nindustry currently employs fins of sub-10$\\,$nm width, small enough for quantum\napplications: at low temperature, an electron or hole can be trapped under the\ngate and serve as a spin qubit. An attractive benefit of silicon's advantageous\nscaling properties is that quantum hardware and its classical control circuitry\ncan be integrated in the same package. This, however, requires qubit operation\nat temperatures greater than 1$\\,$K where the cooling is sufficient to overcome\nthe heat dissipation. Here, we demonstrate that a silicon FinFET is an\nexcellent host for spin qubits that operate even above 4$\\,$K. We achieve fast\nelectrical control of hole spins with driving frequencies up to 150$\\,$MHz and\nsingle-qubit gate fidelities at the fault-tolerance threshold. The number of\nspin rotations before coherence is lost at these \"hot\" temperatures already\nmatches or exceeds values on hole spin qubits at mK temperatures. While our\ndevices feature both industry compatibility and quality, they are fabricated in\na flexible and agile way to accelerate their development. This work paves the\nway towards large-scale integration of all-electrical and ultrafast spin\nqubits.\n"}
+{"abstract": "  We present the first interferometric blind HI survey of the Fornax galaxy\ncluster, which covers an area of 15 deg$^2$ out to the cluster $R_{vir}$. The\nsurvey has a resolution of 67''x95'' and 6.6 km$s^{-1}$ with a 3$\\sigma$\nsensitivity of N(HI)~2x10$^{19}$ cm$^{-2}$ and MHI 2x10$^7$ M$_\\odot$.\n  We detect 16 galaxies out of 200 spectroscopically confirmed Fornax cluster\nmembers. The detections cover ~3 orders of magnitude in HI mass, from 8x10$^6$\nto 1.5x10$^{10}$ M$_\\odot$. They avoid the central, virialised region of the\ncluster both on the sky and in projected phase-space, showing that they are\nrecent arrivals and that, in Fornax, HI is lost within a crossing time, ~2 Gyr.\nHalf of these galaxies exhibit a disturbed HI morphology, including several\ncases of asymmetries, tails, offsets between HI and optical centres, and a case\nof a truncated HI disc suggesting that they have been interacting within or on\ntheir way to Fornax. Our HI detections are HI-poorer and form stars at a lower\nrate than non-cluster galaxies in the same $M_\\star$ range. Low mass galaxies\nare more strongly affected throughout their infall towards the cluster. The\nMHI/$M_\\star$ ratio of Fornax galaxies is comparable to that in the Virgo\ncluster. At fixed $M_\\star$, our HI detections follow the non-cluster relation\nbetween MHI and the star formation rate, and we argue that this implies that so\nfar they have lost their HI on a timescale $\\gtrsim$1-2 Gyr. Deeper inside the\ncluster HI removal is likely to proceed faster, as confirmed by a population of\nHI-undetected but H$_2$-detected star-forming galaxies. Based on ALMA data, we\nfind a large scatter in H$_2$-to-HI mass ratio, with several galaxies showing\nan unusually high ratio that is probably caused by faster HI removal.\n  We identify an HI-rich subgroup of possible interacting galaxies dominated by\nNGC 1365, where pre-processing is likey to have taken place.\n"}
+{"abstract": "  Extreme-mass ratio inspirals (EMRIs), compact binaries with small mass ratios\n$\\epsilon\\ll 1$, will be important sources for low-frequency gravitational wave\ndetectors. Almost all EMRIs will evolve through important transient orbital\n$r\\theta$ resonances, which will enhance or diminish their gravitational wave\nflux, thereby affecting the phase evolution of the waveforms at\n$O(\\epsilon^{1/2})$ relative to leading order. While modeling the local\ngravitational self-force (GSF) during resonances is essential for generating\naccurate EMRI waveforms, so far the full GSF has not been calculated for an\n$r\\theta$-resonant orbit owing to computational demands of the problem. As a\nfirst step we employ a simpler model, calculating the scalar self-force (SSF)\nalong $r\\theta$-resonant geodesics in Kerr spacetime. We demonstrate two ways\nof calculating the $r\\theta$-resonant SSF (and likely GSF), with one method\nleaving the radial and polar motions initially independent as if the geodesic\nis nonresonant. We illustrate results by calculating the SSF along geodesics\ndefined by three $r\\theta$-resonant ratios (1:3, 1:2, 2:3). We show how the SSF\nand averaged evolution of the orbital constants vary with the initial phase at\nwhich an EMRI enters resonance. We then use our SSF data to test a previously\nproposed integrability conjecture, which argues that conservative effects\nvanish at adiabatic order during resonances. We find prominent contributions\nfrom the conservative SSF to the secular evolution of the Carter constant,\n$\\langle \\dot{\\mathcal{Q}}\\rangle$, but these nonvanishing contributions are on\nthe order of, or less than, the estimated uncertainties of our self-force\nresults. The uncertainties come from residual, incomplete removal of the\nsingular field in the regularization process. Higher order regularization\nparameters, once available, will allow definitive tests of the integrability\nconjecture.\n"}
+{"abstract": "  We propose new Riemannian preconditioned algorithms for low-rank tensor\ncompletion via the polyadic decomposition of a tensor. These algorithms exploit\na non-Euclidean metric on the product space of the factor matrices of the\nlow-rank tensor in the polyadic decomposition form. This new metric is designed\nusing an approximation of the diagonal blocks of the Hessian of the tensor\ncompletion cost function, thus has a preconditioning effect on these\nalgorithms. We prove that the proposed Riemannian gradient descent algorithm\nglobally converges to a stationary point of the tensor completion problem, with\nconvergence rate estimates using the $\\L{}$ojasiewicz property. Numerical\nresults on synthetic and real-world data suggest that the proposed algorithms\nare more efficient in memory and time compared to state-of-the-art algorithms.\nMoreover, the proposed algorithms display a greater tolerance for overestimated\nrank parameters in terms of the tensor recovery performance, thus enable a\nflexible choice of the rank parameter.\n"}
+{"abstract": "  In three generations or more, we derive the oscillation probabilities of both\nDirac and Majorana neutrinos relativistically by using the Dirac equation. We\npresent various oscillation probabilities for including wrong-helicity\nneutrinos, right-handed neutrinos, and anti-neutrinos. We summarize the\nrelations between these probabilities. As neutrinos have finite mass, there are\ntwo components for each chirality corresponding to positive and negative\nhelicities. We show that the probability is different for each component even\nif neutrinos have the same chirality. The probabilities derived by the\nrelativistic equation depend on not only the mass squared differences but also\nthe absolute masses of neutrinos. Besides, the new CP phases appear in the\nprobabilities of oscillations with chirality-flip. These new CP phases are\nequivalent to the Majorana CP phases in the case of Majorana neutrinos. We\ninvestigate the CP dependence of oscillation probabilities in vacuum. There are\nno direct CP violation in $\\nu_{\\alpha}\\leftrightarrow \\nu_{\\beta}^c$\noscillations even if the flavors, $\\alpha$ and $\\beta$, are different as in the\nsame as two generations. In other words, the difference between the\nCP-conjugate probabilities vanishes. However, in three generations or more, the\nsine terms of new CP phases appear in the probabilities in addition to the\ncosine terms. This is different from the result obtained in two generations.\nFurthermore, the zero-distance effect does not appear in our formulation.\n"}
+{"abstract": "  Living systems operate far from thermal equilibrium by converting the\nchemical potential of ATP into mechanical work to achieve growth, replication\nor locomotion. Given time series observations of intra-, inter- or\nmulticellular processes, a key challenge is to detect non-equilibrium behavior\nand quantify the rate of free energy consumption. Obtaining reliable bounds on\nenergy consumption and entropy production directly from experimental data\nremains difficult in practice as many degrees of freedom typically are hidden\nto the observer, so that the accessible coarse-grained dynamics may not\nobviously violate detailed balance. Here, we introduce a novel method for\nbounding the entropy production of physical and living systems which uses only\nthe waiting time statistics of hidden Markov processes and hence can be\ndirectly applied to experimental data. By determining a universal limiting\ncurve, we infer entropy production bounds from experimental data for gene\nregulatory networks, mammalian behavioral dynamics and numerous other\nbiological processes. Further considering the asymptotic limit of increasingly\nprecise biological timers, we estimate the necessary entropic cost of heartbeat\nregulation in humans, dogs and mice.\n"}
+{"abstract": "  Capturing interpretable variations has long been one of the goals in\ndisentanglement learning. However, unlike the independence assumption,\ninterpretability has rarely been exploited to encourage disentanglement in the\nunsupervised setting. In this paper, we examine the interpretability of\ndisentangled representations by investigating two questions: where to be\ninterpreted and what to be interpreted? A latent code is easily to be\ninterpreted if it would consistently impact a certain subarea of the resulting\ngenerated image. We thus propose to learn a spatial mask to localize the effect\nof each individual latent dimension. On the other hand, interpretability\nusually comes from latent dimensions that capture simple and basic variations\nin data. We thus impose a perturbation on a certain dimension of the latent\ncode, and expect to identify the perturbation along this dimension from the\ngenerated images so that the encoding of simple variations can be enforced.\nAdditionally, we develop an unsupervised model selection method, which\naccumulates perceptual distance scores along axes in the latent space. On\nvarious datasets, our models can learn high-quality disentangled\nrepresentations without supervision, showing the proposed modeling of\ninterpretability is an effective proxy for achieving unsupervised\ndisentanglement.\n"}
+{"abstract": "  The negatively charged nitrogen-vacancy (NV) center in nano- or micro-\ndiamonds has emerged as a promising magnetic field sensor, as a candidate for\nhyper-polarizing paramagnetic species, as well as a tool for spin-mechanics at\nthe nanoscale. However, NV-doped diamonds are presently not straightforwardly\nemployable for these applications in a liquid or when levitating under\natmospheric pressures due to the random angular Brownian motion which tends to\nrotate the NV quantization axis over the course of the measurments. Here, we\nreport on angle locking of the crystalline axis of a trapped micro-diamond\nalong an external magnetic field. Specifically, we use spin population\ninversion after a ground state level crossing of the NV center to turn the\ndiamond into a diamagnet. The diamond crystalline axis naturally aligns to the\nmagnetic field with high precision and in the absence of micro-wave, offering\nbright prospects for applications in biology and spin-mechanical platforms.\n"}
+{"abstract": "  In the past years, numerous methods have been introduced to reliably detect\ndigital face image manipulations. Lately, the generalizability of these schemes\nhas been questioned in particular with respect to image post-processing. Image\ncompression represents a post-processing which is frequently applied in diverse\nbiometric application scenarios. Severe compression might erase digital traces\nof face image manipulation and hence hamper a reliable detection thereof. In\nthis work, the effects of image compression on face image manipulation\ndetection are analyzed. In particular, a case study on facial retouching\ndetection under the influence of image compression is presented. To this end,\nICAO-compliant subsets of two public face databases are used to automatically\ncreate a database containing more than 9,000 retouched reference images\ntogether with unconstrained probe images. Subsequently, reference images are\ncompressed applying JPEG and JPEG 2000 at compression levels recommended for\nface image storage in electronic travel documents. Novel detection algorithms\nutilizing texture descriptors and deep face representations are proposed and\nevaluated in a single image and differential scenario. Results obtained from\nchallenging cross-database experiments in which the analyzed retouching\ntechnique is unknown during training yield interesting findings: (1) most\ncompetitive detection performance is achieved for differential scenarios\nemploying deep face representations; (2) image compression severely impacts the\nperformance of face image manipulation detection schemes based on texture\ndescriptors while methods utilizing deep face representations are found to be\nhighly robust; (3) in some cases, the application of image compression might as\nwell improve detection performance.\n"}
+{"abstract": "  We consider a system consisting of a server, which receives updates for $N$\nfiles according to independent Poisson processes. The goal of the server is to\ndeliver the latest version of the files to the user through a parallel network\nof $K$ caches. We consider an update received by the user successful, if the\nuser receives the same file version that is currently prevailing at the server.\nWe derive an analytical expression for information freshness at the user. We\nobserve that freshness for a file increases with increase in consolidation of\nrates across caches. To solve the multi-cache problem, we first solve the\nauxiliary problem of a single-cache system. We then rework this auxiliary\nsolution to our parallel-cache network by consolidating rates to single routes\nas much as possible. This yields an approximate (sub-optimal) solution for the\noriginal problem. We provide an upper bound on the gap between the sub-optimal\nsolution and the optimal solution. Numerical results show that the sub-optimal\npolicy closely approximates the optimal policy.\n"}
+{"abstract": "  Controlling the motion of macroscopic oscillators in the quantum regime has\nbeen the subject of intense research in recent decades. In this direction,\nopto-mechanical systems, where the motion of micro-objects is strongly coupled\nwith laser light radiation pressure, have had tremendous success. In\nparticular, the motion of levitating objects can be manipulated at the quantum\nlevel thanks to their very high isolation from the environment under ultra-low\nvacuum conditions. To enter the quantum regime, schemes using single long-lived\natomic spins, such as the electronic spin of nitrogen-vacancy (NV) centers in\ndiamond, coupled with levitating mechanical oscillators have been proposed. At\nthe single spin level, they offer the formidable prospect of transferring the\nspins' inherent quantum nature to the oscillators, with foreseeable\nfar-reaching implications in quantum sensing and tests of quantum mechanics.\nAdding the spin degrees of freedom to the experimentalists' toolbox would\nenable access to a very rich playground at the crossroads between condensed\nmatter and atomic physics. We review recent experimental work in the field of\nspin-mechanics that employ the interaction between trapped particles and\nelectronic spins in the solid state and discuss the challenges ahead. Our focus\nis on the theoretical background close to the current experiments, as well as\non the experimental limits, that, once overcome, will enable these systems to\nunleash their full potential.\n"}
+{"abstract": "  The young magnetically active solar-like stars are efficient generators of\nionizing radiation in the form of X-ray and Extreme UV (EUV) flux, stellar wind\nand eruptive events. These outputs are the critical factors affecting\natmospheric escape and chemistry of (exo)planets around active stars. While\nX-ray fluxes and surface magnetic fields can be derived from observations, the\nEUV emission and wind mass fluxes, Coronal Mass Ejections and associated\nStellar Energetic Particle events cannot be directly observed. Here, we present\nthe results of a three-dimensional magnetohydrodynamic (MHD) model with inputs\nconstrained by spectropolarimetric data, HST/STIS Far UV, X-ray data data and\nstellar magnetic maps reconstructed at two epochs separated by 11 months. The\nsimulations show that over the course of the year, the global stellar corona\nhad undergone a drastic transition from a simple dipole-like to a tilted dipole\nwith multipole field components, and thus, provided favorable conditions for\nCorotating Interaction Events (CIRs) that drive strong shocks. The dynamic\npressure exerted by CIRs are 1300 times larger than those observed from the Sun\nand can contribute to the atmospheric erosion of early Venus, Earth, Mars and\nyoung Earth-like exoplanets. Our data-constrained MHD model provides the\nframework to model coronal environments of G-M planet hosting dwarfs. The model\noutputs can serve as a realistic input for exoplanetary atmospheric models to\nevaluate the impact of stellar coronal emission, stellar winds and CIRs on\ntheir atmospheric escape and chemistry that can be tested in the upcoming JWST\nand ground-based observations.\n"}
+{"abstract": "  Now, the pre-training technique is ubiquitous in natural language processing\nfield. ProphetNet is a pre-training based natural language generation method\nwhich shows powerful performance on English text summarization and question\ngeneration tasks. In this paper, we extend ProphetNet into other domains and\nlanguages, and present the ProphetNet family pre-training models, named\nProphetNet-X, where X can be English, Chinese, Multi-lingual, and so on. We\npre-train a cross-lingual generation model ProphetNet-Multi, a Chinese\ngeneration model ProphetNet-Zh, two open-domain dialog generation models\nProphetNet-Dialog-En and ProphetNet-Dialog-Zh. And also, we provide a PLG\n(Programming Language Generation) model ProphetNet-Code to show the generation\nperformance besides NLG (Natural Language Generation) tasks. In our\nexperiments, ProphetNet-X models achieve new state-of-the-art performance on 10\nbenchmarks. All the models of ProphetNet-X share the same model structure,\nwhich allows users to easily switch between different models. We make the code\nand models publicly available, and we will keep updating more pre-training\nmodels and finetuning scripts.\n"}
+{"abstract": "  Stakeholders make various types of decisions with respect to requirements,\ndesign, management, and so on during the software development life cycle.\nNevertheless, these decisions are typically not well documented and classified\ndue to limited human resources, time, and budget. To this end, automatic\napproaches provide a promising way. In this paper, we aimed at automatically\nclassifying decisions into five types to help stakeholders better document and\nunderstand decisions. First, we collected a dataset from the Hibernate\ndeveloper mailing list. We then experimented and evaluated 270 configurations\nregarding feature selection, feature extraction techniques, and machine\nlearning classifiers to seek the best configuration for classifying decisions.\nEspecially, we applied an ensemble learning method and constructed ensemble\nclassifiers to compare the performance between ensemble classifiers and base\nclassifiers. Our experiment results show that (1) feature selection can\ndecently improve the classification results; (2) ensemble classifiers can\noutperform base classifiers provided that ensemble classifiers are well\nconstructed; (3) BoW + 50% features selected by feature selection with an\nensemble classifier that combines Na\\\"ive Bayes (NB), Logistic Regression (LR),\nand Support Vector Machine (SVM) achieves the best classification result (with\na weighted precision of 0.750, a weighted recall of 0.739, and a weighted\nF1-score of 0.727) among all the configurations. Our work can benefit various\ntypes of stakeholders in software development through providing an automatic\napproach for effectively classifying decisions into specific types that are\nrelevant to their interests.\n"}
+{"abstract": "  In this paper, we obtain dispersion relations corresponding to plane wave\nsolutions in various Lorentz-breaking extensions of gravity with dimensions 3,\n4, 5 and 6. We demonstrate that these dispersion relations display an usual\nLorentz-invariant mode when the corresponding additive term involves higher\nderivatives.\n"}
+{"abstract": "  Despite the substantial progress of active learning for image recognition,\nthere still lacks an instance-level active learning method specified for object\ndetection. In this paper, we propose Multiple Instance Active Object Detection\n(MI-AOD), to select the most informative images for detector training by\nobserving instance-level uncertainty. MI-AOD defines an instance uncertainty\nlearning module, which leverages the discrepancy of two adversarial instance\nclassifiers trained on the labeled set to predict instance uncertainty of the\nunlabeled set. MI-AOD treats unlabeled images as instance bags and feature\nanchors in images as instances, and estimates the image uncertainty by\nre-weighting instances in a multiple instance learning (MIL) fashion. Iterative\ninstance uncertainty learning and re-weighting facilitate suppressing noisy\ninstances, toward bridging the gap between instance uncertainty and image-level\nuncertainty. Experiments validate that MI-AOD sets a solid baseline for\ninstance-level active learning. On commonly used object detection datasets,\nMI-AOD outperforms state-of-the-art methods with significant margins,\nparticularly when the labeled sets are small. Code is available at\nhttps://github.com/yuantn/MI-AOD.\n"}
+{"abstract": "  Parallel one-dimensional semiconductor channels connected by a\nsuperconducting strip constitute the core platform in several recent quantum\ndevice proposals that rely e.g. on Andreev processes or topological effects. In\norder to realize these proposals, the actual material systems must have high\ncrystalline purity and the coupling between the different elements should be\ncontrollable in terms of their interfaces and geometry. We present a strategy\nfor synthesizing double InAs nanowires by the vapor-liquid-solid mechanism\nusing III-V molecular beam epitaxy. A superconducting layer is deposited onto\nnanowires without breaking vacuum, ensuring pristine interfaces between the\nsuperconductor and the two semiconductor nanowires. The method allows for a\nhigh yield of merged as well as separate parallel nanowires, with full or\nhalf-shell superconductor coatings. We demonstrate their utility in complex\nquantum devices by electron transport measurements.\n"}
+{"abstract": "  We compute the Nielsen-Borsuk-Ulam number for any selfmap of $n-$torus,\n$\\mathbb{T}^n$, as well as any free involution $\\tau$ in $\\mathbb{T}^n$, with\n$n \\leqslant 3$. Finally, we conclude that the tori, $\\mathbb{T}^1$,\n$\\mathbb{T}^2$ and $\\mathbb{T}^3$, are Wecken spaces in Nielsen-Borsuk-Ulam\ntheory. Such a number is a lower bound for the minimal number of pair of points\nsuch that $f(x)=f(\\tau(x))$ in a given homotopy class of maps.\n"}
+{"abstract": "  Predictive coding networks (PCNs) are an influential model for information\nprocessing in the brain. They have appealing theoretical interpretations and\noffer a single mechanism that accounts for diverse perceptual phenomena of the\nbrain. On the other hand, backpropagation (BP) is commonly regarded to be the\nmost successful learning method in modern machine learning. Thus, it is\nexciting that recent work formulates inference learning (IL) that trains PCNs\nto approximate BP. However, there are several remaining critical issues: (i) IL\nis an approximation to BP with unrealistic/non-trivial requirements, (ii) IL\napproximates BP in single-step weight updates; whether it leads to the same\npoint as BP after the weight updates are conducted for more steps is unknown,\nand (iii) IL is computationally significantly more costly than BP. To solve\nthese issues, a variant of IL that is strictly equivalent to BP in fully\nconnected networks has been proposed. In this work, we build on this result by\nshowing that it also holds for more complex architectures, namely,\nconvolutional neural networks and (many-to-one) recurrent neural networks. To\nour knowledge, we are the first to show that a biologically plausible algorithm\nis able to exactly replicate the accuracy of BP on such complex architectures,\nbridging the existing gap between IL and BP, and setting an unprecedented\nperformance for PCNs, which can now be considered as efficient alternatives to\nBP.\n"}
+{"abstract": "  Gamers Private Network (GPN) is a client/server technology that guarantees a\nconnection for online video games that is more reliable and lower latency than\na standard internet connection. Users of the GPN technology benefit from a\nstable and high-quality gaming experience for online games, which are hosted\nand played across the world. After transforming a massive volume of raw\nnetworking data collected by WTFast, we have structured the cleaned data into a\nspecial-purpose data warehouse and completed the extensive analysis using\nmachine learning and neural nets technologies, and business intelligence tools.\nThese analyses demonstrate the ability to predict and quantify changes in the\nnetwork and demonstrate the benefits gained from the use of a GPN for users\nwhen connected to an online game session.\n"}
+{"abstract": "  Multimodal self-supervised learning is getting more and more attention as it\nallows not only to train large networks without human supervision but also to\nsearch and retrieve data across various modalities. In this context, this paper\nproposes a self-supervised training framework that learns a common multimodal\nembedding space that, in addition to sharing representations across different\nmodalities, enforces a grouping of semantically similar instances. To this end,\nwe extend the concept of instance-level contrastive learning with a multimodal\nclustering step in the training pipeline to capture semantic similarities\nacross modalities. The resulting embedding space enables retrieval of samples\nacross all modalities, even from unseen datasets and different domains. To\nevaluate our approach, we train our model on the HowTo100M dataset and evaluate\nits zero-shot retrieval capabilities in two challenging domains, namely\ntext-to-video retrieval, and temporal action localization, showing\nstate-of-the-art results on four different datasets.\n"}
+{"abstract": "  With the overwhelming popularity of Knowledge Graphs (KGs), researchers have\npoured attention to link prediction to fill in missing facts for a long time.\nHowever, they mainly focus on link prediction on binary relational data, where\nfacts are usually represented as triples in the form of (head entity, relation,\ntail entity). In practice, n-ary relational facts are also ubiquitous. When\nencountering such facts, existing studies usually decompose them into triples\nby introducing a multitude of auxiliary virtual entities and additional\ntriples. These conversions result in the complexity of carrying out link\nprediction on n-ary relational data. It has even proven that they may cause\nloss of structure information. To overcome these problems, in this paper, we\nrepresent each n-ary relational fact as a set of its role and role-value pairs.\nWe then propose a method called NaLP to conduct link prediction on n-ary\nrelational data, which explicitly models the relatedness of all the role and\nrole-value pairs in an n-ary relational fact. We further extend NaLP by\nintroducing type constraints of roles and role-values without any external\ntype-specific supervision, and proposing a more reasonable negative sampling\nmechanism. Experimental results validate the effectiveness and merits of the\nproposed methods.\n"}
+{"abstract": "  A \\emph{fair competition}, based on the concept of envy-freeness, is a\nnon-eliminating competition where each contestant (team or individual player)\nmay not play against all other contestants, but the total difficulty for each\ncontestant is the same: the sum of the initial rankings of the opponents for\neach contestant is the same. Similar to other non-eliminating competitions like\nthe Round-robin competition or the Swiss-system competition, the winner of the\nfair competition is the contestant who wins the most games. The {\\sc Fair\nNon-Eliminating Tournament} ({\\sc Fair-NET}) problem can be used to schedule\nfair competitions whose infrastructure is known. In the {\\sc Fair-NET} problem,\nwe are given an infrastructure of a tournament represented by a graph $G$ and\nthe initial rankings of the contestants represented by a multiset of integers\n$S$. The objective is to decide whether $G$ is \\emph{$S$-fair}, i.e., there\nexists an assignment of the contestants to the vertices of $G$ such that the\nsum of the rankings of the neighbors of each contestant in $G$ is the same\nconstant $k\\in\\mathbb{N}$. We initiate a study of the classical and\nparameterized complexity of {\\sc Fair-NET} with respect to several central\nstructural parameters motivated by real world scenarios, thereby presenting a\ncomprehensive picture of it.\n"}
+{"abstract": "  Kinematic numerators of Yang-Mills scattering amplitudes possess a rich Lie\nalgebraic structure that suggest the existence of a hidden infinite-dimensional\nkinematic algebra. Explicitly realizing such a kinematic algebra is a\nlongstanding open problem that only has had partial success for simple helicity\nsectors. In past work, we introduced a framework using tensor currents and\nfusion rules to generate BCJ numerators of a special subsector of NMHV\namplitudes in Yang-Mills theory. Here we enlarge the scope and explicitly\nrealize a kinematic algebra for all NMHV amplitudes. Master numerators are\nobtained directly from the algebraic rules and through commutators and\nkinematic Jacobi identities other numerators can be generated. Inspecting the\noutput of the algebra, we conjecture a closed-form expression for the master\nBCJ numerator up to any multiplicity. We also introduce a new method, based on\ngroup algebra of the permutation group, to solve for the generalized gauge\nfreedom of BCJ numerators. It uses the recently introduced binary BCJ relations\nto provide a complete set of NMHV kinematic numerators that consist of pure\ngauge.\n"}
+{"abstract": "  In this article, we study the complexity involved in the computation of the\nreach in arbitrary dimension and in particular the computation of the critical\nspherical curvature points of an arbitrary algebraic variety. We present\nproperties of the critical spherical curvature points as well as an algorithm\nfor computing it.\n"}
+{"abstract": "  We have used B, V time series photometry collected with the Large Binocular\nTelescope to undertake the first study of variable stars in the Milky Way\nultra-faint dwarf (UFD) satellites, Pisces II and Pegasus III. In Pisces II we\nhave identified a RRab star, one confirmed and a candidate SX Phoenicis star\nand, a variable with uncertain classification. In Pegasus III we confirmed the\nvariability of two sources: an RRab star and a variable with uncertain\nclassification, similar to the case found in Pisces II. Using the\nintensity-averaged apparent magnitude of the bona-fide RRab star in each galaxy\nwe estimate distance moduli of (m - M)0= 21.22 \\pm 0.14 mag (d= 175 \\pm 11 kpc)\nand 21.21 \\pm 0.23 mag (d=174 \\pm 18 kpc) for Pisces II and Pegasus III,\nrespectively. Tests performed to disentangle the actual nature of variables\nwith an uncertain classification led us to conclude that they most likely are\nbright, long period and very metal poor RRab members of their respective hosts.\nThis may indicate that Pisces II and Pegasus III contain a dominant old stellar\npopulation (t>12 Gyr) with metallicity < [Fe=H] > -1.8 dex along with,\npossibly, a minor, more metal-poor component, as supported by the V , B-V\ncolor-magnitude diagrams of the two UFDs and their spectroscopically confirmed\nmembers. The metallicity spread that we derived from our data sample is 0.4 dex\nin both systems. Lastly, we built isodensity contour maps which do not reveal\nany irregular shape, thus making the existence of a physical connection between\nthese UFDs unlikely.\n"}
+{"abstract": "  The human proteome is enriched in proteins that do not fold into a stable 3D\nstructure. These intrinsically disordered proteins (IDPs) spontaneously\nfluctuate between a large number of configurations in their native form.\nRemarkably, the disorder does not lead to dysfunction as with denatured folded\nproteins. In fact, unlike denatured proteins, recent evidences strongly suggest\nthat multiple biological functions stem from such structural plasticity. Here,\nfocusing on the nanoscopic length-scale, we review the latest advances in IDP\nresearch and discuss some of the future directions in this highly promising\nfield.\n"}
+{"abstract": "  Massive star formation occurs in the interior of giant molecular clouds (GMC)\nand proceeds through many stages. In this work, we focus on massive young\nstellar objects (MYSOs) and Ultra-Compact HII regions (UCHII), where the former\nare enshrouded in dense envelopes of dust and gas, which the latter have begun\ndispersing. By selecting a complete sample of MYSOs and UCHII from the Red MSX\nSource (RMS) survey data base, we combine Planck and IRAS data and build their\nSpectral Energy Distributions (SEDs). With these, we estimate the physical\nproperties (dust temperatures, mass, luminosity) of the sample. Because the RMS\ndatabase provides unique solar distances, it also allows investigating the\ninstantaneous Star Formation Efficiency (SFE) as a function of Galactocentric\nradius. We find that the SFE increase between 2 and 4.5 kpc, where it reaches a\npeak, likely in correspondence of the accumulation of molecular material at the\nend of the Galactic bar. It then stays approximately constant up to 9 kpc,\nafter which it linearly declines, in agreement with predictions from\nextragalactic studies. This behavior suggests the presence of a significant\namount of undetected molecular gas at R$_G$ $>$ 8 kpc. Finally we present\ndiagnostic colors that can be used to identify sites of massive star formation.\n"}
+{"abstract": "  The properties of the first-discovered interstellar object (ISO), 1I/2017\n(`Oumuamua), differ from both Solar System asteroids and comets, casting doubt\non a protoplanetary disk origin. In this study, we investigate the possibility\nthat it formed with a substantial H2 ice component in the starless core of a\ngiant molecular cloud. While interstellar solid hydrogen has yet to be\ndetected, this constituent would explain a number of the ISO's properties. We\nconsider the relevant processes required to build decameter-sized, solid\nhydrogen bodies and assess the plausibility of growth in various size regimes.\nVia an energy balance argument, we find that the most severe barrier to\nformation is the extremely low temperature required for the favorability of\nmolecular hydrogen ice. However, if deposition occurs, we find that the\nturbulence within starless cores is conducive for growth into kilometer-sized\nbodies on sufficiently short timescales. Then, we analyze mass loss in the\ninterstellar medium and determine the necessary size for a hydrogen object to\nsurvive a journey to the Solar System as a function of ISO age. Finally, we\ndiscuss the implications if the H2 explanation is correct, and we assess the\nfuture prospects of ISO science. If hydrogen ice ISOs do exist, our\nhypothesized formation pathway would require a small population of porous, 100\nmicron dust in a starless core region that has cooled to 2.8K via adiabatic\nexpansion of the surrounding gas and excellent shielding from electromagnetic\nradiation and cosmic rays.\n"}
+{"abstract": "  Bojanczyk and Pilipczuk showed in their celebrated article \"Definability\nequals recognizability for graphs of bounded treewidth\" (LICS 2016) that\nmonadic second-order logic can define tree-decompositions in graphs of bounded\ntreewidth. This raises the question whether such decompositions can already be\ndefined in first-order logic (FO).\n  We start by introducing the notion of tree-decompositions of bounded span,\nwhich restricts the diameter of the subtree consisting of the bags containing a\nsame node of the structure. Having a bounded span is a natural property of\ntree-decompositions when dealing with FO, since equality of nodes cannot in\ngeneral be recovered in FO when it doesn't hold. In particular, it encompasses\nthe notion of domino tree-decompositions.\n  We show that path-decompositions of bounded span are not FO-continuous, in\nthe sense that there exist arbitrarily FO-similar graphs of bounded pathwidth\nwhich do not possess FO-similar path-decompositions of bounded span. Then, we\nshow that tree-decompositions of bounded span are not FO-continuous either.\n"}
+{"abstract": "  Electronic Health Records (EHR) have been heavily used in modern healthcare\nsystems for recording patients' admission information to hospitals. Many\ndata-driven approaches employ temporal features in EHR for predicting specific\ndiseases, readmission times, or diagnoses of patients. However, most existing\npredictive models cannot fully utilize EHR data, due to an inherent lack of\nlabels in supervised training for some temporal events. Moreover, it is hard\nfor existing works to simultaneously provide generic and personalized\ninterpretability. To address these challenges, we first propose a hyperbolic\nembedding method with information flow to pre-train medical code\nrepresentations in a hierarchical structure. We incorporate these pre-trained\nrepresentations into a graph neural network to detect disease complications,\nand design a multi-level attention method to compute the contributions of\nparticular diseases and admissions, thus enhancing personalized\ninterpretability. We present a new hierarchy-enhanced historical prediction\nproxy task in our self-supervised learning framework to fully utilize EHR data\nand exploit medical domain knowledge. We conduct a comprehensive set of\nexperiments and case studies on widely used publicly available EHR datasets to\nverify the effectiveness of our model. The results demonstrate our model's\nstrengths in both predictive tasks and interpretable abilities.\n"}
+{"abstract": "  Several lines of evidence suggest the Milky Way underwent a major merger at\nz~2 with a galaxy known as Gaia-Sausage-Enceladus (GSE). Here we use H3 Survey\ndata to argue that GSE entered the Galaxy on a retrograde orbit based on a\npopulation of highly retrograde stars with chemistry similar to the largely\nradial GSE debris. We present the first tailored, high-resolution N-body\nsimulations of the merger. From a grid of ~500 simulations we find a GSE with\n$M_{*}=5\\times10^{8}\\ M_{\\odot}, M_{\\rm{DM}}=2\\times10^{11} M_{\\odot}$ (a 2.5:1\ntotal mass merger) best matches the H3 data. This simulation shows the\nretrograde GSE stars are stripped from its outer disk early in the merger\nbefore the orbit loses significant angular momentum. Despite being selected\npurely on angular momenta and radial distributions, this simulation reproduces\nand explains the following empirical phenomena: (i) the elongated, triaxial\nshape of the inner halo (axis ratios $10:7.9:4.5$), whose major axis is at\n~35{\\deg} to the plane and connects GSE's apocenters, (ii) the Hercules-Aquila\nCloud & the Virgo Overdensity, which arise due to apocenter pile-up, (iii) the\n2 Gyr lag between the quenching of GSE and the truncation of the age\ndistribution of the in-situ halo, which tracks the 2 Gyr gap between the first\nand final GSE pericenters. We make the following predictions: (i) the inner\nhalo has a \"double-break\" density profile with breaks at both ~15-18 kpc and 30\nkpc, coincident with the GSE apocenters, (ii) the outer halo has retrograde\nstreams containing ~10% of GSE stars awaiting discovery at >30 kpc. The\nretrograde (radial) GSE debris originates from its outer (inner) disk --\nexploiting this trend we reconstruct the stellar metallicity gradient of GSE\n($-0.04\\pm0.01$ dex $r_{\\rm{50}}^{-1}$). These simulations imply GSE delivered\n~20% of the Milky Way's present-day dark matter and ~50% of its stellar halo.\n(ABRIDGED)\n"}
+{"abstract": "  The unitarity of quantum evolutions implies that the overlap between two\ninitial states does not change in time. This property is commonly believed to\nexplain the lack of state sensitivity in quantum theory, a feature that is\nprevailing in classical chaotic systems. However, a distance between two points\nin classical phase space is a completely different mathematical concept than an\noverlap distance between two points in Hilbert space. There is a possibility\nthat state sensitivity in quantum theory can be uncovered with a help of some\nother metric. Here we show that the recently introduced Weighted Bures Length\n(WBL) achieves this task. In particular, we numerically study a cellular\nautomaton-like unitary evolution of N qubits, known as Rule 54, and apply WBL\nto show that a single-qubit perturbation of a random initial state: (a) grows\nlinearly in time under the nearest neighbour interaction on a cycle, (b)\nappears to grow exponentially in time under interaction given by a random\nbipartite graph.\n"}
+{"abstract": "  The kagom\\'e lattice is a fruitful source of novel physical states of matter,\nincluding the quantum spin liquid and Dirac fermions. Here we report a\nstructural, thermodynamic, and transport study of the two-dimensional kagom\\'e\nmetal-organic frameworks Ni_3(HIB)v2 and Cu_3(HIB)_2 (HIB = hexaiminobenzene).\nMagnetization measurements yield Curie constants of 1.12 and 0.352 emu K mol\nf.u.-1 Oe-1 respectively, close to the values expected for ideal S=1 and S=1/2\nmoments. Weiss temperatures of -20.3 K and -6.52 K, respectively, indicate\nmoderate to weak magnetic interactions. Electrical transport measurements\nreveal that both materials are semiconducting, with gaps of Eg = 22.2 and 103\nmeV, respectively. Specific heat measurements reveal a large T-linear\ncontribution of {\\gamma} = 148(4) mJ mol-f.u.-1 K-2 in Ni_3(HIB)_2 with only a\ngradual upturn below T ~ 5 K and no evidence of a phase transition to an\nordered state down to T = 0.1 K. Cu_3(HIB)_2 also lacks evidence of a phase\ntransition above T = 0.1 K, with a substantial, field-dependent, magnetic\ncontribution below T ~ 5 K. Despite being superficially in agreement with\nexpectations of magnetic frustration and spin liquid physics, we are able to\nexplain these observations as arising due to known stacking disorder in these\nmaterials. Our results further state the art of kagom\\'e lattice physics,\nespecially in the rarely explored regime of semiconducting but not metallic\nbehavior.\n"}
+{"abstract": "  The increasing quantity of multi-omics data, such as methylomic and\ntranscriptomic profiles, collected on the same specimen, or even on the same\ncell, provide a unique opportunity to explore the complex interactions that\ndefine cell phenotype and govern cellular responses to perturbations. We\npropose a network approach based on Gaussian Graphical Models (GGMs) that\nfacilitates the joint analysis of paired omics data. This method, called DRAGON\n(Determining Regulatory Associations using Graphical models on multi-Omic\nNetworks), calibrates its parameters to achieve an optimal trade-off between\nthe network's complexity and estimation accuracy, while explicitly accounting\nfor the characteristics of each of the assessed omics layers. In simulation\nstudies, we show that DRAGON adapts to edge-density and feature-size\ndifferences between the omics layers, which improves model inference and the\nrecovery of associations compared to state-of-the-art network inference using\nGGMs. Next, we verified this performance gain on paired single-cell data, where\nthe transcriptome was assayed together with selected proteins. Finally, we used\nDRAGON to study joint transcriptome-methylome data of breast cancer specimens.\nIn this context, we provide examples of how DRAGON can identify key molecular\nmechanisms. For instance, we show how co-expression can be explained by\nco-methylation and how DRAGON gives hints about the spatial organization of the\ngenome. DRAGON is available as open-source code in python through the Network\nZoo package (netZooPy v0.8; netzoo.github.io).\n"}
+{"abstract": "  In modern drug development, the broader availability of high-dimensional\nobservational data provides opportunities for scientist to explore subgroup\nheterogeneity, especially when randomized clinical trials are unavailable due\nto cost and ethical constraints. However, a common practice that naively\nsearches the subgroup with a high treatment level is often misleading due to\nthe \"subgroup selection bias.\" More importantly, the nature of high-dimensional\nobservational data has further exacerbated the challenge of accurately\nestimating the subgroup treatment effects. To resolve these issues, we provide\nnew inferential tools based on resampling to assess the replicability of\npost-hoc identified subgroups from observational studies. Through careful\ntheoretical justification and extensive simulations, we show that our proposed\napproach delivers asymptotically sharp confidence intervals and debiased\nestimates for the selected subgroup treatment effects in the presence of\nhigh-dimensional covariates. We further demonstrate the merit of the proposed\nmethods by analyzing the UK Biobank data.\n"}
+{"abstract": "  We present results of two pilot studies that investigated human error\nbehaviours with an error prone in-air gesture recognizer. During the studies,\nusers performed a small set of simple in-air gestures. In the first study,\nthese gestures were abstract. The second study associated concrete tasks with\neach gesture. Interestingly, the error patterns observed in the two studies\nwere substantially different.\n"}
+{"abstract": "  The combination of deep neural networks and Differential Privacy has been of\nincreasing interest in recent years, as it offers important data protection\nguarantees to the individuals of the training datasets used. However, using\nDifferential Privacy in the training of neural networks comes with a set of\nshortcomings, like a decrease in validation accuracy and a significant increase\nin the use of resources and time in training. In this paper, we examine\nsuper-convergence as a way of greatly increasing training speed of\ndifferentially private neural networks, addressing the shortcoming of high\ntraining time and resource use. Super-convergence allows for acceleration in\nnetwork training using very high learning rates, and has been shown to achieve\nmodels with high utility in orders of magnitude less training iterations than\nconventional ways. Experiments in this paper show that this order-of-magnitude\nspeedup can also be seen when combining it with Differential Privacy, allowing\nfor higher validation accuracies in much fewer training iterations compared to\nnon-private, non-super convergent baseline models. Furthermore,\nsuper-convergence is shown to improve the privacy guarantees of private models.\n"}
+{"abstract": "  We formulate and study the microscopic statistical mechanics of systems of\nparticles with exclusion statistics in a discrete one-body spectrum. The\nstatistical mechanics of these systems can be expressed in terms of effective\nsingle-level grand partition functions obeying a generalization of the standard\nthermodynamic exclusion statistics equation of state. We derive explicit\nexpressions for the thermodynamic potential in terms of microscopic cluster\ncoefficients and show that the mean occupation numbers of levels satisfy a\nnesting relation involving a number of adjacent levels determined by the\nexclusion parameter. We apply the formalism to the harmonic Calogero model and\npoint out a relation with the Ramanujan continued fraction identity and\nappropriate generalizations.\n"}
+{"abstract": "  Let $\\Bbbk$ be a field of characteristic 0. Let $X$ be a smooth complete\nintersection over $k$ of dimension $n-k$ in the projective space\n$\\mathbf{P}^n_{k}$, for given positive integers $n$ and $k$. When\n$k=\\mathbb{C}$, Terasoma (\\cite{Ter90}) and Konno (\\cite{Ko91}) provided an\nexplicit representative (in terms of differential forms) of a basis for the\nprimitive middle-dimensional algebraic de Rham cohomology\n$H_{dR,\\operatorname{prim}}^{n-k}(X;\\mathbb{C})$. Later Dimca constructed\nanother explicit representative of a basis of\n$H_{dR,\\operatorname{prim}}^{n-k}(X;\\mathbb{C})$ in \\cite{Dim95}. Moreover, he\nproved that his representative gives the same cohomology class as the previous\nrepresentative of Terasoma and Konno. The goal of this article is to examine\nthe above two different approaches without assuming that $k=\\mathbb{C}$ and\nprovide a similar comparison result for any field $k$. Dimca's argument depends\nheavily on the condition $k=\\mathbb{C}$ and our idea is to find appropriate\nCech-de Rham complexes and spectral sequences corresponding to those two\napproaches, which work without restrictions on $k$.\n"}
+{"abstract": "  This report proposes a polyphonic sound event detection (SED) method for the\nDCASE 2021 Challenge Task 4. The proposed SED model consists of two stages: a\nmean-teacher model for providing target labels regarding weakly labeled or\nunlabeled data and a self-training-based noisy student model for predicting\nstrong labels for sound events. The mean-teacher model, which is based on the\nresidual convolutional recurrent neural network (RCRNN) for the teacher and\nstudent model, is first trained using all the training data from a weakly\nlabeled dataset, an unlabeled dataset, and a strongly labeled synthetic\ndataset. Then, the trained mean-teacher model predicts the strong label to each\nof the weakly labeled and unlabeled datasets, which is brought to the noisy\nstudent model in the second stage of the proposed SED model. Here, the\nstructure of the noisy student model is identical to the RCRNN-based student\nmodel of the mean-teacher model in the first stage. Then, it is self-trained by\nadding feature noises, such as time-frequency shift, mixup, SpecAugment, and\ndropout-based model noise. In addition, a semi-supervised loss function is\napplied to train the noisy student model, which acts as label noise injection.\nThe performance of the proposed SED model is evaluated on the validation set of\nthe DCASE 2021 Challenge Task 4, and then, several ensemble models that combine\nfive-fold validation models with different hyperparameters of the\nsemi-supervised loss function are finally selected as our final models.\n"}
+{"abstract": "  Let $G$ be a graph on $n$ vertices. For $i\\in \\{0,1\\}$ and a connected graph\n$G$, a spanning forest $F$ of $G$ is called an $i$-perfect forest if every tree\nin $F$ is an induced subgraph of $G$ and exactly $i$ vertices of $F$ have even\ndegree (including zero). A $i$-perfect forest of $G$ is proper if it has no\nvertices of degree zero. Scott (2001) showed that every connected graph with\neven number of vertices contains a (proper) 0-perfect forest. We prove that one\ncan find a 0-perfect forest with minimum number of edges in polynomial time,\nbut it is NP-hard to obtain a 0-perfect forest with maximum number of edges.\nMoreover, we show that to decide whether $G$ has a 0-perfect forest with at\nleast $|V(G)|/2+k$ edges, where $k$ is the parameter, is W[1]-hard. We also\nprove that for a prescribed edge $e$ of $G,$ it is NP-hard to obtain a\n0-perfect forest containing $e,$ but one can decide if there existsa 0-perfect\nforest not containing $e$ in polynomial time. It is easy to see that every\ngraph with odd number of vertices has a 1-perfect forest. It is not the case\nfor proper 1-perfect forests. We give a characterization of when a connected\ngraph has a proper 1-perfect forest.\n"}
+{"abstract": "  The mind-body problem is reviewed in the context of a non-technical account\nof quantum information theory. The importance of clearly defining: `what is\nphysical?' is highlighted, since only then can we give meaning to the concept\n`non-physical'. Physicality is defined in terms of interaction, which is in\nturn defined to be a correlated exchange of information. This is asserted to be\nthe basis of any meaningful concept of epistemology. Hence, it is argued that a\nnon-physical entity can not `know' anything about the world. Information\ntransfer is then discussed in terms of quantum entanglement and an argument for\nour perception of time is presented. It is then contended that the notion of\n`mind' may be meaningfully discussed in the context of a quantum theoretic\nframework.\n"}
+{"abstract": "  Several upper and lower bounds for the numerical radius of $2 \\times 2$\noperator matrices are developed which refine and generalize the earlier related\nbounds. In particular, we show that if $B,C$ are bounded linear operators on a\ncomplex Hilbert space, then \\begin{eqnarray*}\n  && \\frac{1}{2}\\max \\left \\{ \\|B\\|, \\|C\\| \\right \\}+\\frac{1}{4} \\left |\n\\|B+C^*\\|-\\|B-C^*\\| \\right |\n  &&\\leq w \\left(\\left[\\begin{array}{cc}\n  0 & B\n  C& 0\n  \\end{array}\\right]\\right)\\\\ &&\\leq \\frac{1}{2} \\max \\left\\{\\|B\\|,\\|C\\|\\right\n\\}+\\frac{1}{2}\\max \\left\n\\{r^{\\frac{1}{2}}(|B||C^*|),r^{\\frac{1}{2}}(|B^*||C|)\\right\\}, \\end{eqnarray*}\nwhere $w(.)$, $r(.)$ and $\\|.\\|$ are the numerical radius, spectral radius and\noperator norm of a bounded linear operator, respectively. We also obtain\nequality conditions for the numerical radius of the operator matrix\n$\\left[\\begin{array}{cc}\n  0 & B\n  C& 0 \\end{array}\\right]$. As application of results obtained, we show that if\n$B,C$ are self-adjoint operators then, $\\max \\Big \\{\\|B+C\\|^2 , \\|B-C\\|^2\n\\Big\\}\\leq \\left \\|B^2+C^2 \\right \\|+2w(|B||C|). $\n"}
+{"abstract": "  Developing ways to affordably deliver broadband connectivity is one of the\nmajor issues of our time. In challenging deployment locations with irregular\nterrain, traditional Clear-Line-Of-Sight (CLOS) wireless links can be\nuneconomical to deploy, as the number of required towers make infrastructure\ninvestment unviable. With new research focusing on developing wireless\ndiffractive backhaul technologies to provide Non-Line-Of-Sight (NLOS) links,\nthis paper evaluates the engineering-economic implications. A Three-Dimensional\n(3D) techno-economic assessment framework is developed, utilizing a combination\nof remote sensing and viewshed geospatial techniques, in order to quantify the\nimpact of different wireless backhaul strategies. This framework is applied to\nassess both Clear-Line-Of-Sight and diffractive Non-Line-Of-Sight strategies\nfor deployment in Peru, as well as the islands of Kalimantan and Papua, in\nIndonesia. The results find that a hybrid strategy combining the use of\nClear-Line-Of-Sight and diffractive Non-Line-Of-Sight links produces a 9-45\npercent cost-efficiency saving, relative to only using traditional\nClear-Line-Of-Sight wireless backhaul links.\n"}
+{"abstract": "  Measuring public opinion is a key focus during democratic elections, enabling\ncandidates to gauge their popularity and alter their campaign strategies\naccordingly. Traditional survey polling remains the most popular estimation\ntechnique, despite its cost and time intensity, measurement errors, lack of\nreal-time capabilities and lagged representation of public opinion. In recent\nyears, Twitter opinion mining has attempted to combat these issues. Despite\nachieving promising results, it experiences its own set of shortcomings such as\nan unrepresentative sample population and a lack of long term stability. This\npaper aims to merge data from both these techniques using Bayesian data\nassimilation to arrive at a more accurate estimate of true public opinion for\nthe Brexit referendum. This paper demonstrates the effectiveness of the\nproposed approach using Twitter opinion data and survey data from trusted\npollsters. Firstly, the possible existence of a time gap of 16 days between the\ntwo data sets is identified. This gap is subsequently incorporated into a\nproposed assimilation architecture. This method was found to adequately\nincorporate information from both sources and measure a strong upward trend in\nLeave support leading up to the Brexit referendum. The proposed technique\nprovides useful estimates of true opinion, which is essential to future opinion\nmeasurement and forecasting research.\n"}
+{"abstract": "  Identifying anomalies in multi-dimensional datasets is an important task in\nmany real-world applications. A special case arises when anomalies are occluded\nin a small set of attributes, typically referred to as a subspace, and not\nnecessarily over the entire data space. In this paper, we propose a new\nsubspace analysis approach named Agglomerative Attribute Grouping (AAG) that\naims to address this challenge by searching for subspaces that are comprised of\nhighly correlative attributes. Such correlations among attributes represent a\nsystematic interaction among the attributes that can better reflect the\nbehavior of normal observations and hence can be used to improve the\nidentification of two particularly interesting types of abnormal data samples:\nanomalies that are occluded in relatively small subsets of the attributes and\nanomalies that represent a new data class. AAG relies on a novel\nmulti-attribute measure, which is derived from information theory measures of\npartitions, for evaluating the \"information distance\" between groups of data\nattributes. To determine the set of subspaces to use, AAG applies a variation\nof the well-known agglomerative clustering algorithm with the proposed\nmulti-attribute measure as the underlying distance function. Finally, the set\nof subspaces is used in an ensemble for anomaly detection. Extensive evaluation\ndemonstrates that, in the vast majority of cases, the proposed AAG method (i)\noutperforms classical and state-of-the-art subspace analysis methods when used\nin anomaly detection ensembles, and (ii) generates fewer subspaces with a fewer\nnumber of attributes each (on average), thus resulting in a faster training\ntime for the anomaly detection ensemble. Furthermore, in contrast to existing\nmethods, the proposed AAG method does not require any tuning of parameters.\n"}
+{"abstract": "  We present a new and versatile implementation of rapid and localized\nimmunohistochemical staining of tissue sections. Immunohistochemistry (IHC)\nallows to detect specific proteins on tissue sections and comprises a sequence\nof specific biochemical reactions. For the rapid implementation of IHC, we\nfabricated horizontally oriented microfluidic probes (MFP) with functionally\ndesigned apertures to enable square and circular footprints, which we employ to\nlocally expose a tissue to time-optimized sequences of different biochemicals.\n"}
+{"abstract": "  A subalgebra S of a Leibniz algebra L is called self-idealizing in L if it\ncoincides with its idealizer IL(S). In this paper we study the structure of\nLeibniz algebras, whose subalgebras are either ideals or self-idealizing.\n"}
+{"abstract": "  High-precision stellar analyses require hydrodynamic 3D modeling. Such models\npredict changes across stellar disks of spectral line shapes, asymmetries, and\nwavelength shifts. For testing models in stars other than the Sun, spatially\nresolved observations are feasible from differential spectroscopy during\nexoplanet transits, retrieving spectra of stellar surface segments that\nsuccessively become hidden behind the transiting planet, as shown in Papers I,\nII, and III. Synthetic high-resolution spectra over extended spectral regions\nare now available from 3D models. Similar to other ab initio simulations, these\ndata contain patterns that have not been specifically modeled but may be\nrevealed after analyses analogous to those of a large volume of observations.\nFrom five 3D models spanning T=3964-6726K (approx. spectral types K8V-F3V),\nsynthetic spectra at hyper-high resolution (R>1,000,000) were analyzed.\nSelected FeI and FeII lines at various positions across stellar disks were\nsearched for patterns between different lines in the same star and for similar\nlines between different stars. Such patterns are identified for representative\nphotospheric lines of different strengths, excitation potential, and ionization\nlevel, encoding the hydrodynamic 3D structure. Line profiles and bisectors are\nshown for various stars at different positions across stellar disks. Absolute\nconvective wavelength shifts are obtained as differences to 1D models, where\nsuch shifts do not occur. Observable relationships for line properties are\nretrieved from realistically complex synthetic spectra. Such patterns may also\ntest very detailed 3D modeling, including non-LTE effects. While present\nresults are obtained at hyper-high spectral resolution, the subsequent Paper V\nexamines their practical observability at realistically lower resolutions, and\nin the presence of noise.\n"}
+{"abstract": "  We investigate whether certain non-classical communication channels can be\nsimulated by a classical channel with a given number of states and a given\n`amount' of noise. It is proved that any noisy quantum channel can be simulated\nby a corresponding classical channel with `the same amount' of noise. Classical\nsimulations of general probabilistic channels are also studied.\n"}
+{"abstract": "  In this paper, we focus on removing interference of motion blur by the\nderivation of motion blur invariants.Unlike earlier work, we don't restore any\nblurred image. Based on geometric moment and mathematical model of motion blur,\nwe prove that geometric moments of blurred image and original image are\nlinearly related. Depending on this property, we can analyse whether an\nexisting moment-based feature is invariant to motion blur. Surprisingly, we\nfind some geometric moment invariants are invariants to not only spatial\ntransform but also motion blur. Meanwhile, we test invariance and robustness of\nthese invariants using synthetic and real blur image datasets. And the results\nshow these invariants outperform some widely used blur moment invariants and\nnon-moment image features in image retrieval, classification and template\nmatching.\n"}
+{"abstract": "  Neural Networks require large amounts of memory and compute to process high\nresolution images, even when only a small part of the image is actually\ninformative for the task at hand. We propose a method based on a differentiable\nTop-K operator to select the most relevant parts of the input to efficiently\nprocess high resolution images. Our method may be interfaced with any\ndownstream neural network, is able to aggregate information from different\npatches in a flexible way, and allows the whole model to be trained end-to-end\nusing backpropagation. We show results for traffic sign recognition,\ninter-patch relationship reasoning, and fine-grained recognition without using\nobject/part bounding box annotations during training.\n"}
+{"abstract": "  Equilibrating proteins and other biomacromolecules is cardinal for molecular\ndynamics simulation of such biological systems in which they perform free\ndynamics without any externally-applied mechanical constraint, until\nthermodynamic equilibrium with the surrounding is attained. However, in some\nimportant cases, we have to equilibrate the system of interest in the constant\npresence of certain constraints, being referred to as constrained equilibration\nin the present work. A clear illustration of this type is a single amyloid\n\\b{eta}-strand or RNA, when the reaction coordinate is defined as the distance\nbetween the two ends of the strand and we are interested in carrying out\nreplica-exchange umbrella sampling to map the associated free energy profile as\nthe dependent quantity of interest. In such cases, each sample has to be\nequilibrated with the two ends fixed. Here, we introduced a simulation trick to\nperform this so-called constrained equilibration using steered molecular\ndynamics. We then applied this method to equilibrate a single, stretched\n\\b{eta}-strand of an amyloid beta dodecamer fibril with fixed ends. Examining\nthe associated curves of the total energy and the force exerted on the\npractically-fixed SMD atom over the total timespan broadly supported the\nvalidity of this kind of equilibration.\n"}
+{"abstract": "  Machine Learning has been applied in a wide range of tasks throughout the\nlast years, ranging from image classification to autonomous driving and natural\nlanguage processing. Restricted Boltzmann Machine (RBM) has received recent\nattention and relies on an energy-based structure to model data probability\ndistributions. Notwithstanding, such a technique is susceptible to adversarial\nmanipulation, i.e., slightly or profoundly modified data. An alternative to\novercome the adversarial problem lies in the Generative Adversarial Networks\n(GAN), capable of modeling data distributions and generating adversarial data\nthat resemble the original ones. Therefore, this work proposes to artificially\ngenerate RBMs using Adversarial Learning, where pre-trained weight matrices\nserve as the GAN inputs. Furthermore, it proposes to sample copious amounts of\nmatrices and combine them into ensembles, alleviating the burden of training\nnew models'. Experimental results demonstrate the suitability of the proposed\napproach under image reconstruction and image classification tasks, and\ndescribe how artificial-based ensembles are alternatives to pre-training vast\namounts of RBMs.\n"}
+{"abstract": "  In this paper, our main focus is to explore different classes of nearly\nnormally torsion-free ideals. We first characterize all finite simple connected\ngraphs with nearly normally torsion-free cover ideals. Next, we characterize\nall normally torsion-free $t$-spread principal Borel ideals that can also be\nviewed as edge ideals of uniform multipartite hypergraphs.\n"}
+{"abstract": "  The interaction of quantum light with matter like that inside a cavity is\nknown to give rise to mixed light-matter states called polaritons. We discuss\nthe impact of rotation of the cavity on the polaritons. It is shown that the\nnumber of polaritons increases due to this rotation. The structure of the\noriginal polaritons is modified and new ones are induced by the rotation that\nstrongly depend on the angular velocity and the choice of axis of rotation. In\nmolecules the rotation can change the number of light-induced conical\nintersections and their dimensionality and hence strongly impact their quantum\ndynamics. General consequences are discussed.\n"}
+{"abstract": "  By the use of homotopy perturbation method-Pad\\'e (HPM-Pad\\'e) technique, a\nnew analytical approximation of luminosity distance in the flat universe is\nproposed, which has the advantage of significant improvement for accuracy in\napproximating luminosity distance over cosmological redshift range within\n$0\\leq z\\leq 2.5$. Then we confront the analytical expression of luminosity\ndistance that is obtained by our new approach with the observational data, for\nthe purpose of checking whether it works well. In order to probe the robustness\nof the proposed method, we also confront it to supernova type Ia and recent\ndata on the Hubble expansion rate $H(z)$. Markov Chain Monte Carlo (MCMC) code\nemcee is used in the data fitting. The result indicates that it works fairly\nwell.\n"}
+{"abstract": "  We introduce Mini Slotted Threshold ALOHA (MiSTA), a slotted ALOHA\nmodification designed to minimize the network-wide time average Age of\nInformation (AoI). In MiSTA, sources whose ages are below a certain threshold\nstay silent. When a node with age above the threshold has data to send, it\nbecomes active in the next time frame with a certain probability. The active\nnode first transmits a short control sequence in a mini-slot ahead of actual\ndata transmission, and if collision is sensed, it backs off with a certain\nprobability. We derive the steady state distribution of the number of active\nsources and analyze its limiting behaviour. We show that MiSTA\nprobabilistically converges to a \"thinned\" slotted ALOHA, where the number of\nactive users at steady state adjusts to optimize age. With an optimal selection\nof parameters, MiSTA achieves an AoI scaling with the number of sources, n, as\n0.9641n, which is an improvement over the Threshold ALOHA policy proposed\nearlier (for which the lowest possible scaling is 1.4169n). While achieving\nthis reduction in age, MiSTA also increases achievable throughput to\napproximately 53%, from the 37% achievable by Threshold ALOHA and regular\nslotted ALOHA.\n"}
+{"abstract": "  The transformation of late-type galaxies has been suggested as the origin of\nearly-type dwarf galaxies in galaxy clusters. Venhola et al. analysed\ncorrelations between colour and surface brightness for galaxies in the Fornax\ncluster binned by luminosity or stellar mass. In the bins with $M_\\star<10^8\n{\\rm M}_\\odot$, the authors identified a correlation of redness with fainter\nsurface brightness and interpreted it as a consequence of the quenching of star\nformation by ram pressure stripping in the dwarf galaxies. We carry out a\ncorresponding analysis for the Virgo cluster and find great similarities in\nthese correlations between surface brightness and colour for the two clusters,\ndespite expected differences in the strength of the ram pressure. Furthermore,\nwe extend the analysis to a wider range of optical colours for both clusters\nand contrast the results with expectations for fading and reddening stellar\npopulations. Overall the slopes of the surface brightness-colour relations are\nconsistent with these models. In addition the sizes of the early- and late-type\ngalaxies at these low masses are comparable. These two results are compatible\nwith a transformation scenario. However, when analysing early- and late-type\ngalaxies separately, the consistency of the slope of the surface\nbrightness-colour relations with the model expectations for fading and\nreddening stellar population applies only to the late types. The lack of this\nimprint for the early-type dwarfs calls for some additional explanation, for\nwhich we discuss several possibilities. Finally, the Virgo cluster is an\natypical cluster with a low fraction of quiescent early-type galaxies at all\ngalaxy masses despite its large cluster mass. (abridged)\n"}
+{"abstract": "  Human body is a complex dynamic system composed of various sub-dynamic parts.\nEspecially, thoracic and abdominal organs have complex internal shape\nvariations with different frequencies by various reasons such as respiration\nwith fast motion and peristalsis with slower motion. CT protocols for abdominal\nlesions are multi-phase scans for various tumor detection to use different\nvascular contrast, however, they are not aligned well enough to visually check\nthe same area. In this paper, we propose a time-efficient and accurate\ndeformable registration algorithm for multi-phase CT scans considering\nabdominal organ motions, which can be applied for differentiable or\nnon-differentiable motions of abdominal organs. Experimental results shows the\nregistration accuracy as 0.85 +/- 0.45mm (mean +/- STD) for pancreas within 1\nminute for the whole abdominal region.\n"}
+{"abstract": "  An unsupervised clustering using a Gaussian mixture model is applied to Argo\nprofiles distributed over the entire ocean. We employ as the coordinate\ncomponents in feature space the path signature, which is a central notion in\nrough path theory. This allows us to better identify the oceanic condition at\neach horizontal point with distinct clusters, than by using conventional\ntemperature and salinity coordinate. To the best of my knowledge, it is the\nfirst attempt at clustering almost all of the existing Argo profiles with the\nfull use of measured sequences of temperature, salinity, and pressure. We will\nalso discuss why the path signature is relevant to representing the property of\na profile.\n"}
+{"abstract": "  We recently discovered an extragalactic transient, AT2020caa, using the\ncommunity alert broker ANTARES. This transient apparently exhibited two\noutbursts in a time span of a year (between 2020 and 2021). Based on a\ndecade-long historical light curve of the candidate host galaxy, we rule out an\nactivity from the galaxy nucleus to explain these outbursts. The measured peak\nmagnitudes (assuming the known spectroscopic redshift of the candidate host\ngalaxy) put AT2020caa in the realm of thermonuclear supernovae (SNe) or\nluminous core-collapse SNe. A handful of the latter are known to show prior\noutbursts (POs), thought to be linked to mass loss in massive stars. Using\nGemini/GMOS, we obtained a spectrum of the current outburst that shows it to be\na Type Ia supernova (SNIa). We examine the nature of AT2020caa's PO and\nconclude that it is likely a separate SN within the same galaxy.\n"}
+{"abstract": "  This paper addresses the derivation of sufficient linear matrix inequality\nconditions ensuring the stability of a coupled system composed of a\nreaction-diffusion partial differential equation (PDE), with possibly\nspatially-varying coefficients, and a finite-dimensional linear time invariant\nordinary differential equation (ODE). The coupling of the PDE with the ODE is\nlocated at both left and right boundary conditions of the reaction-diffusion\nequation and takes the form of the input and output of the ODE. We investigate\nthe four possible sets of left/right boundary couplings among Dirichlet and\nNeumann traces. The adopted approach relies on the spectral reduction of the\nproblem by projecting the trajectory of the PDE into a Hilbert basis composed\nof the eigenvectors of the underlying Sturm-Liouville operator. We propose\nnumerical examples, consisting of an unstable reaction-diffusion equation and\nan unstable ODE, such that the application of the derived stability conditions\nensure the stability of the resulting coupled PDE-ODE system.\n"}
+{"abstract": "  Let $\\mathbb{F}_{q}$ be the finite field with an odd prime power $q$. In this\npaper, we construct a new isometric invariant of combinatorial type on\n$(\\mathbb{F}^{n}_{q},\\text{dot}_{n})$, where\n$\\text{dot}_{n}(\\mathbf{x}):=x_{1}^{2}+\\cdots+x_{n}^{2}$. Additionally, using\ncounts from our new invariant, we give a new proof of Minkowski's formula on\nthe size of spheres over finite fields. We also show which types of quadratic\nsubspaces can be embedded in $(\\mathbb{F}_{q}^{n},\\text{dot}_{n})$.\n"}
+{"abstract": "  We present the first global model of prompt emission from a short gamma-ray\nburst that consistently describes the evolution of the central black-hole (BH)\ntorus system, the propagation of the jet through multi-component merger ejecta,\nthe transition into free expansion, and the photospheric emission from the\nrelativistic jet. To this end, we perform a special relativistic\nneutrino-hydrodynamics simulation of a viscous BH-torus system, which is formed\nabout 500ms after the merger and is surrounded by dynamical ejecta as well as\nneutron star winds, along with a jet that is injected in the vicinity of the\ncentral BH. In a post-processing step, we compute the photospheric emission\nusing a relativistic Monte-Carlo radiative transfer code. It is found that the\nwind from the torus leaves a strong imprint on the jet as well as on the\nemission causing narrow collimation and rapid time variability. The viewing\nangle dependence of the emission gives rise to correlations among the spectral\npeak energy, E_p, isotropic energy, E_iso, and peak luminosity, L_p, which may\nprovide natural explanations for the Amati- and Yonetoku-relations. We also\nfind that the degree of polarization is small for the emission from the jet\ncore (<2%), while it tends to increase with viewing angle outside of the core\nand can become as high as ~10-40% for energies larger than the peak energy.\nFinally, the comparison of our model with GRB170817A strongly disfavors the\nphotospheric emission scenario and therefore supports alternative scenarios,\nsuch as the cocoon shock breakout.\n"}
+{"abstract": "  The increasing need for economic, safe, and sustainable smart manufacturing\ncombined with novel technological enablers, has paved the way for Artificial\nIntelligence (AI) and Big Data in support of smart manufacturing. This implies\na substantial integration of AI, Industrial Internet of Things (IIoT),\nRobotics, Big data, Blockchain, 5G communications, in support of smart\nmanufacturing and the dynamical processes in modern industries. In this paper,\nwe provide a comprehensive overview of different aspects of AI and Big Data in\nIndustry 4.0 with a particular focus on key applications, techniques, the\nconcepts involved, key enabling technologies, challenges, and research\nperspective towards deployment of Industry 5.0. In detail, we highlight and\nanalyze how the duo of AI and Big Data is helping in different applications of\nIndustry 4.0. We also highlight key challenges in a successful deployment of AI\nand Big Data methods in smart industries with a particular emphasis on\ndata-related issues, such as availability, bias, auditing, management,\ninterpretability, communication, and different adversarial attacks and security\nissues. In a nutshell, we have explored the significance of AI and Big data\ntowards Industry 4.0 applications through panoramic reviews and discussions. We\nbelieve, this work will provide a baseline for future research in the domain.\n"}
+{"abstract": "  I explore the implications of informational robustness under the assumptions\nof common belief in rationality. That is, predictions for\nincomplete-information games which are valid across all possible information\nstructures. First, I address this question from a global perspective and then\ngeneralize the analysis to allow for localized informational robustness.\n"}
+{"abstract": "  Predicting multiple heterogeneous biological and medical targets is a\nchallenge for traditional deep learning models. In contrast to single-task\nlearning, in which a separate model is trained for each target, multi-task\nlearning (MTL) optimizes a single model to predict multiple related targets\nsimultaneously. To address this challenge, we propose the Multi-gate\nMixture-of-Experts with Exclusivity (MMoEEx). Our work aims to tackle the\nheterogeneous MTL setting, in which the same model optimizes multiple tasks\nwith different characteristics. Such a scenario can overwhelm current MTL\napproaches due to the challenges in balancing shared and task-specific\nrepresentations and the need to optimize tasks with competing optimization\npaths. Our method makes two key contributions: first, we introduce an approach\nto induce more diversity among experts, thus creating representations more\nsuitable for highly imbalanced and heterogenous MTL learning; second, we adopt\na two-step optimization [6, 11] approach to balancing the tasks at the gradient\nlevel. We validate our method on three MTL benchmark datasets, including\nMedical Information Mart for Intensive Care (MIMIC-III) and PubChem BioAssay\n(PCBA).\n"}
+{"abstract": "  Optical flow estimation is a fundamental problem of computer vision and has\nmany applications in the fields of robot learning and autonomous driving. This\npaper reveals novel geometric laws of optical flow based on the insight and\ndetailed definition of non-occlusion. Then, two novel loss functions are\nproposed for the unsupervised learning of optical flow based on the geometric\nlaws of non-occlusion. Specifically, after the occlusion part of the images are\nmasked, the flowing process of pixels is carefully considered and geometric\nconstraints are conducted based on the geometric laws of optical flow. First,\nneighboring pixels in the first frame will not intersect during the pixel\ndisplacement to the second frame. Secondly, when the cluster containing\nadjacent four pixels in the first frame moves to the second frame, no other\npixels will flow into the quadrilateral formed by them. According to the two\ngeometrical constraints, the optical flow non-intersection loss and the optical\nflow non-blocking loss in the non-occlusion regions are proposed. Two loss\nfunctions punish the irregular and inexact optical flows in the non-occlusion\nregions. The experiments on datasets demonstrated that the proposed\nunsupervised losses of optical flow based on the geometric laws in\nnon-occlusion regions make the estimated optical flow more refined in detail,\nand improve the performance of unsupervised learning of optical flow. In\naddition, the experiments training on synthetic data and evaluating on real\ndata show that the generalization ability of optical flow network is improved\nby our proposed unsupervised approach.\n"}
+{"abstract": "  Recent discovery of superconductive rare earth/actinide superhydrides has\nushered in a new era of superconductivity research at high pressures. This\ndistinct type of clathrate metal hydrides was first proposed for\nalkaline-earth-metal hydride CaH6 that, however, has long eluded experimental\nsynthesis, impeding an understanding of pertinent physics. Here, we report\nsuccessful synthesis of CaH6 and its measured superconducting critical\ntemperature Tc of 215 K at 172 GPa, which is evidenced by a sharp drop of\nresistivity to zero and a characteristic decrease of Tc under a magnetic field\nup to 9 T.An estimate based on the Werthamer-Helfand-Hohenberg model gives a\ngiant zero-temperature upper critical magnetic field of 203 T. These remarkable\nbenchmark superconducting properties place CaH6 among the most outstanding\nhigh-Tc superhydrides, marking it as the hitherto only clathrate metal hydride\noutside the family of rare earth/actinide hydrides. This exceptional case\nraises great prospects of expanding the extraordinary class of high-Tc\nsuperhydrides to a broader variety of compounds that possess more diverse\nmaterial features and physics characteristics.\n"}
+{"abstract": "  The analysis of second-order optimization methods based either on sampling,\nrandomization or sketching has two serious shortcomings compared to the\nconventional Newton method. The first shortcoming is that the analysis of the\niterates is not scale-invariant, and even if it is, restrictive assumptions are\nrequired on the problem structure. The second shortfall is that the fast\nconvergence rates of second-order methods have only been established by making\nassumptions regarding the input data. In this paper, we close the theoretical\ngap between the theoretical analysis of the conventional Newton method and\nrandomization-based second-order methods. We propose a Self-concordant\nIterative-minimization - Galerkin-based Multilevel Algorithm (SIGMA) and\nestablish its super-linear convergence rate using the well-established theory\nof self-concordant functions. Our analysis is global and entirely independent\nof unknown constants such as Lipschitz constants and strong convexity\nparameters. Our analysis is based on the connections between multigrid\noptimization methods, and the role of coarse-grained or reduced-order models in\nthe computation of search directions. We take advantage of the insights from\nthe analysis to significantly improve the performance of second-order methods\nin machine learning applications. We report encouraging initial experiments\nthat suggest SIGMA significantly outperforms the state-of-the-art\nsub-sampled/sketched Newton methods for both medium and large-scale problems.\n"}
+{"abstract": "  The study of superconductivity in compressed hydrides is of great interest\ndue to measurements of high critical temperatures (Tc) in the vicinity of room\ntemperature, beginning with the observations of LaH10 at 170-190 GPa. However,\nthe pressures required for synthesis of these high Tc superconducting hydrides\ncurrently remain extremely high. Here we show the investigation of crystal\nstructures and superconductivity in the La-B-H system under pressure with\nparticle-swarm intelligence structure searches methods in combination with\nfirst-principles calculations. Structures with six stoichiometries, LaBH,\nLaBH3, LaBH4, LaBH6, LaBH7 and LaBH8, were predicted to become stable under\npressure. Remarkably, the hydrogen atoms in LaBH8 were found to bond with B\natoms in a manner that is similar to that in H3S. Lattice dynamics calculations\nindicate that LaBH7 and LaBH8 become dynamically stable at pressures as low as\n109.2 and 48.3 GPa, respectively. Moreover, the two phases were predicted to be\nsuperconducting with a critical temperature (Tc) of 93 K and 156 K at 110 GPa\nand 55 GPa, respectively. Our results provide guidance for future experiments\ntargeting new hydride superconductors with both low synthesis pressures and\nhigh Tc.\n"}
+{"abstract": "  Micropolar continua are shown to be the generalisation of the nematic liquid\ncrystals through perspectives of order parameters, topological and geometrical\nconsiderations. Micropolar continua and nematic liquid crystals are recognised\nas the antipodals of $S^3$ and $S^2$ in projective geometry. We show that\nposition-dependent rotational axial fields in kinematic micropolar continua can\nbe considered as solutions of anisotropic Higgs fields, characterised by\nintegers N. We emphasise that the identical integers N are topological\ninvariants through homotopy classifications based on defects of order\nparameters and a finite energy requirement. Magnetic monopoles and Skyrmions\nare investigated based on the theories of defects of continua in Riemann-Cartan\nmanifolds.\n"}
+{"abstract": "  We compute the logarithmic contributions to the polarized massive Wilson\ncoefficients for deep-inelastic scattering in the asymptotic region $Q^2 \\gg\nm^2$ to 3-loop order in the fixed-flavor number scheme and present the\ncorresponding expressions for the polarized massive operator matrix elements\nneeded in the variable flavor number scheme. The calculation is performed in\nthe Larin scheme. For the massive operator matrix elements $A_{qq,Q}^{(3),\\rm\nPS}$ and $A_{qg,Q}^{(3),\\rm S}$ the complete results are presented. The\nexpressions are given in Mellin-$N$ space and in momentum fraction $z$-space.\n"}
+{"abstract": "  Continual Learning (CL) refers to a learning setup where data is non\nstationary and the model has to learn without forgetting existing knowledge.\nThe study of CL for sequential patterns revolves around trained recurrent\nnetworks. In this work, instead, we introduce CL in the context of Echo State\nNetworks (ESNs), where the recurrent component is kept fixed. We provide the\nfirst evaluation of catastrophic forgetting in ESNs and we highlight the\nbenefits in using CL strategies which are not applicable to trained recurrent\nmodels. Our results confirm the ESN as a promising model for CL and open to its\nuse in streaming scenarios.\n"}
+{"abstract": "  In our model, the acceleration and propagation of particles from the\nBohm-like diffusion region inside the SNR to the Galactic diffusion region\noutside the SNR are described through nonlinear diffusive shock acceleration\n(NLDSA). The main content of our NLDSA model is to solve the hydrodynamic\nequations numerically for gas density, gas velocity, gas pressure as well as\nthe equation for the quasi-isotropic particle momentum distribution. The\nconsequent multi-band non-thermal emission is from two different regions,\nnamely the acceleration region and the escaping region. Our model is capable of\nexplaining the multi-band photon emission via the dominant synchrotron\nradiation of the electrons accelerated inside the SNR; and the photons with\nenergy of $\\gtrsim$ GeV are naturally produced by the protons inside and\noutside the SNR. Moreover, the photons in the energy range of $\\sim 1 - \\sim\n100$ TeV are due to the interaction of escaped protons with dense molecular\nclouds. For the photons with energy of $E_\\gamma \\gtrsim $ 1 GeV from SNR\nG106.3+2.7, our results here favor a hadronic origin, where the photons in the\nenergy range of $\\sim 1$ GeV to $\\sim 1$ TeV are produced inside the SNR\nthrough proton-proton interaction, while the photons with $E_\\gamma \\gtrsim 1$\nTeV originate from the interaction of escaped protons with a dense molecular\ncloud.\n"}
+{"abstract": "  Parametric and nonparametric inference for stochastic processes driven by a\nfractional Brownian motion were investigated in Mishura (2008) and Prakasa\nRao(2010) among others. Similar problems for processes driven by an infinite\ndimensional fractional Brownian motion were studied in Prakasa Rao (2004,2013),\nCialenco (2009) and others. Parametric estimation for processes driven by\ninfinite dimensional mixed fractional Brownian motion is discussed in this\narticle.\n"}
+{"abstract": "  Reward function, as an incentive representation that recognizes humans'\nagency and rationalizes humans' actions, is particularly appealing for modeling\nhuman behavior in human-robot interaction. Inverse Reinforcement Learning is an\neffective way to retrieve reward functions from demonstrations. However, it has\nalways been challenging when applying it to multi-agent settings since the\nmutual influence between agents has to be appropriately modeled. To tackle this\nchallenge, previous work either exploits equilibrium solution concepts by\nassuming humans as perfectly rational optimizers with unbounded intelligence or\npre-assigns humans' interaction strategies a priori. In this work, we advocate\nthat humans are bounded rational and have different intelligence levels when\nreasoning about others' decision-making process, and such an inherent and\nlatent characteristic should be accounted for in reward learning algorithms.\nHence, we exploit such insights from Theory-of-Mind and propose a new\nmulti-agent Inverse Reinforcement Learning framework that reasons about humans'\nlatent intelligence levels during learning. We validate our approach in both\nzero-sum and general-sum games with synthetic agents and illustrate a practical\napplication to learning human drivers' reward functions from real driving data.\nWe compare our approach with two baseline algorithms. The results show that by\nreasoning about humans' latent intelligence levels, the proposed approach has\nmore flexibility and capability to retrieve reward functions that explain\nhumans' driving behaviors better.\n"}
+{"abstract": "  A search for the flavor-changing neutral-current decay $B^{+}\\to\nK^{+}\\nu\\bar{\\nu}$ is performed at the Belle II experiment at the SuperKEKB\nasymmetric energy electron-positron collider. The results are based on a data\nsample corresponding to an integrated luminosity of $63\\,\\mbox{fb}^{-1}$\ncollected at the $\\Upsilon{(4S)}$ resonance and a sample of $9\\,\\mbox{fb}^{-1}$\ncollected at an energy $60\\mathrm{\\,Me\\kern -0.1em V}$ below the resonance. A\nnovel measurement method is employed, which exploits topological properties of\nthe $B^{+}\\to K^{+}\\nu\\bar{\\nu}$ decay that differ from both generic\nbottom-meson decays and light-quark pair production. This inclusive tagging\napproach offers a higher signal efficiency compared to previous searches. No\nsignificant signal is observed. An upper limit on the branching fraction of\n$B^{+}\\to K^{+}\\nu\\bar{\\nu}$ of $4.1 \\times 10^{-5}$ is set at the 90%\nconfidence level.\n"}
+{"abstract": "  We present the discovery and follow-up observations of two CCSNe that\noccurred in the luminous infrared galaxy (LIRG), NGC3256. The first, SN2018ec,\nwas discovered using the ESO HAWK-I/GRAAL adaptive optics seeing enhancer, and\nwas classified as a Type Ic with a host galaxy extinction of\n$A_V=2.1^{+0.3}_{-0.1}$ mag. The second, AT2018cux, was discovered during the\ncourse of follow-up observations of SN2018ec, and is consistent with a\nsub-luminous Type IIP classification with an $A_V=2.1 \\pm 0.4$ mag of host\nextinction. A third CCSN, PSNJ10275082-4354034 in NGC3256, has previously been\nreported in 2014, and we recovered the source in late time archival HST\nimaging. Based on template light-curve fitting, we favour a Type IIn\nclassification for it with modest host galaxy extinction of\n$A_V=0.3^{+0.4}_{-0.3}$ mag. We also extend our study with follow-up data of\nthe recent Type IIb SN2019lqo and Type Ib SN2020fkb that occurred in the LIRG\nsystem Arp299 with host extinctions of $A_V=2.1^{+0.1}_{-0.3}$ and\n$A_V=0.4^{+0.1}_{-0.2}$ mag, respectively. Motivated by the above, we\ninspected, for the first time, a sample of 29 CCSNe located within a projected\ndistance of 2.5 kpc from the host galaxy nuclei in a sample of 16 LIRGs. We\nfind that, if star formation within these galaxies is modelled assuming a\nglobal starburst episode and normal IMF, there is evidence of a correlation\nbetween the starburst age and the CCSN subtype. We infer that the two subgroups\nof 14 H-poor (Type IIb/Ib/Ic/Ibn) and 15 H-rich (Type II/IIn) CCSNe have\ndifferent underlying progenitor age distributions, with the H-poor progenitors\nbeing younger at 3$\\sigma$ significance. However, we do note that the available\nsample sizes of CCSNe and host LIRGs are so far small, and the statistical\ncomparisons between subgroups do not take into account possible systematic or\nmodel errors related to the estimated starburst ages. (abridged)\n"}
+{"abstract": "  When modified theories of gravity are considered, at most six gravitational\nwave polarization modes are allowed and classified in tensor modes, the only\nones predicted by General Relativity (GR), along with additional vector and\nscalar modes. Therefore, gravitational waves represent a powerful tool to test\nalternative theories of gravitation. In this paper, we forecast the sensitivity\nof third-generation ground-based interferometers, Einstein Telescope and Cosmic\nExplorer, to non-GR polarization modes focusing on the stochastic gravitational\nwave background. We consider the latest technical specifications of the two\nindependent detectors and the full network in order to estimate both the\noptimal signal-to-noise ratio and the detectable energy density limits relative\nto all polarization modes in the stochastic background for several locations on\nEarth and orientations of the two observatories. By considering optimal\ndetector configurations, we find that in 5 years of observation the detection\nlimit for tensor and extra polarization modes could reach\n$h_0^2\\Omega^{T,V,S}_{GW} \\approx 10^{-12}-10^{-11}$, depending on the network\nconfiguration and the stochastic background (i.e., if only one among vector and\nscalar modes exists or both are present). This means that the network\nsensitivity to different polarization modes can be approximately improved by a\nfactor $10^3$ with respect to second-generation interferometers. We finally\ndiscuss the possibility of breaking the scalar modes degeneracy by considering\nboth detectors angular responses to sufficiently high gravitational wave\nfrequencies.\n"}
+{"abstract": "  We analyze the stochastic proximal subgradient descent in the case where the\nobjective functions are path differentiable and verify a Sard-type condition.\nWhile the accumulation set may not be reduced to unique point, we show that the\ntime spent by the iterates to move from one accumulation point to another goes\nto infinity. An oscillation-type behavior of the drift is established. These\nresults show a strong stability property of the proximal subgradient descent.\nUsing the theory of closed measures, Bolte, Pauwels and R\\'ios-Zertuche\nestablished this type of behavior for the deterministic subgradient descent.\nOur technique of proof relies on the classical work of Bena\\\"im, Hofbauer and\nSorin, which allows us to extend results in the deterministic case to a\nstochastic and proximal setting, as well as to treat these different cases in a\nunified manner.\n"}
+{"abstract": "  We prove a Yau's type gradient estimate for positive $f$-harmonic functions\nwith the Dirichlet boundary condition on smooth metric measure spaces with\ncompact boundary when the infinite dimensional Bakry-Emery Ricci tensor and the\nweighted mean curvature are bounded below. As an application, we give a\nLiouville type result for bounded $f$-harmonic functions with the Dirichlet\nboundary condition. Our results do not depend on any assumption on the\npotential function $f$.\n"}
+{"abstract": "  We systematically investigate examples of non-hyperbolic dynamical systems\nhaving irregular sets of full topological entropy and full Hausdorff dimension.\nThe examples include some partially hyperbolic systems and geometric Lorenz\nflows. We also pose interesting questions for future research.\n"}
+{"abstract": "  Many black hole solutions of General Relativity are known to be linearly\nexact. This opens a way to study them in gauge theories that apart from gravity\ncontain fields of higher spin $s>2$. Starting with a black brane in $AdS_4$ we\nfind its free field higher-spin generalization that respects static and planar\nsymmetry for all bosonic gauge fields $s\\geq 0$. The solution is found for both\nthe higher-spin curvatures and potentials in the form suitable for further\nnon-linear analysis and satisfies the multi copy relation.\n"}
+{"abstract": "  This paper addresses the problem of a boundary control design for traffic\nevolving in a large-scale urban network. The traffic state is described on a\nmacroscopic scale and corresponds to the vehicle density, whose dynamics are\ngoverned by a two-dimensional conservation law. We aim at designing a boundary\ncontrol law such that the throughput of vehicles in a congested area is\nmaximized. Thereby, the only knowledge we use is the network's topology,\ncapacities of its roads and speed limits. In order to achieve this goal, we\ntreat a 2D equation as a set of 1D equations by introducing curvilinear\ncoordinates satisfying special properties. The theoretical results are verified\non a numerical example, where an initially fully congested area is driven to\nthe state with maximum possible througput.\n"}
+{"abstract": "  Media-based modulation (MBM) is a novel modulation technique that can improve\nthe spectral efficiency of the existing wireless systems. In MBM, multiple\nradio frequency (RF) mirrors are placed near the transmit antenna(s) and are\nswitched ON/OFF to create different channel fade realizations. In such systems,\nadditional information is conveyed through the ON/OFF status of RF mirrors\nalong with conventional modulation symbols. A challenging task at the receiver\nis to detect the transmitted information symbols and extract the additional\ninformation from the channel fade realization used for transmission. In this\npaper, we consider a massive MIMO (mMIMO) system where each user relies on MBM\nfor transmitting information to the base station, and investigate the problem\nof symbol detection at the base station. First, we propose a mirror activation\npattern (MAP) selection based modified iterative sequential detection\nalgorithm. With the proposed algorithm, the most favorable MAP is selected,\nfollowed by the detection of symbol corresponding to the selected MAP. Each\nsolution is subjected to the reliability check before getting the update. Next,\nwe introduce a $K$ favorable MAP search based iterative interference\ncancellation (KMAP-IIC) algorithm. In particular, a selection rule is\nintroduced in KMAP-IIC for deciding the set of favorable MAPs over which\niterative interference cancellation is performed, followed by a greedy update\nscheme for detecting the MBM symbols corresponding to each user. Simulation\nresults show that the proposed detection algorithms exhibit superior\nperformance-complexity trade-off over the existing detection techniques in\nMBM-mMIMO systems.\n"}
+{"abstract": "  What is the \"right\" topological invariant of a large point cloud X? Prior\nresearch has focused on estimating the full persistence diagram of X, a\nquantity that is very expensive to compute, unstable to outliers, and far from\na sufficient statistic. We therefore propose that the correct invariant is not\nthe persistence diagram of X, but rather the collection of persistence diagrams\nof many small subsets. This invariant, which we call \"distributed persistence,\"\nis trivially parallelizable, more stable to outliers, and has a rich inverse\ntheory. The map from the space of point clouds (with the quasi-isometry metric)\nto the space of distributed persistence invariants (with the\nHausdorff-Bottleneck distance) is a global quasi-isometry. This is a much\nstronger property than simply being injective, as it implies that the inverse\nof a small neighborhood is a small neighborhood, and is to our knowledge the\nonly result of its kind in the TDA literature. Moreover, the quasi-isometry\nbounds depend on the size of the subsets taken, so that as the size of these\nsubsets goes from small to large, the invariant interpolates between a purely\ngeometric one and a topological one. Lastly, we note that our inverse results\ndo not actually require considering all subsets of a fixed size (an enormous\ncollection), but a relatively small collection satisfying certain covering\nproperties that arise with high probability when randomly sampling subsets.\nThese theoretical results are complemented by two synthetic experiments\ndemonstrating the use of distributed persistence in practice.\n"}
+{"abstract": "  We study the parity and time-reversal ($\\mathcal{PT}$) symmetric quantum\nphysics in a non-Hermitian non-relativistic hydrogen molecule with local\n(Hubbard type) Coulomb interaction. We consider non-Hermiticity generated from\nboth kinetic and orbital energies of the atoms and encounter the existence of\ntwo different types of exceptional points (EPs) in pairs. These two kinds of EP\nare characteristically different and depend differently on the interaction\nstrength. Our discovery may open the gates of a rich physics emerging out of a\nsimple Hamiltonian resembling a two-site Hubbard model.\n"}
+{"abstract": "  We study the stationary state of Hall devices composed of a load circuit\nconnected to the lateral edges of a Hall-bar. We follow the approach developed\nin a previous work (Creff et al. J. Appl. Phys 2020) in which the stationary\nstate of a ideal Hall bar is defined by the minimum power dissipation\nprinciple. The presence of both the lateral circuit and the magnetic field\ninduces the injection of a current: the so-called Hall current. Analytical\nexpressions for the longitudinal and the transverse currents are derived. It is\nshown that the efficiency of the power injection into the lateral circuit is\nquadratic in the Hall angle and obeys to the maximum transfer theorem. For\nusual values of the Hall angle, the main contribution of this power injection\nprovides from the longitudinal current flowing along the edges, instead of the\ntransverse current crossing the Hall bar.\n"}
+{"abstract": "  Measurements carried out or reinterpreted within the framework of effective\nfield theory (EFT) will constitute a key component of the LHC legacy in the\nquest towards physics beyond the standard model. Numerous EFT measurements were\nalready published by the CMS Collaboration. In the top quark sector, four\ndistinct approaches to EFT have been identified, each successfully applied in\nseveral analyses. We review the pros and cons of each approach, while\nillustrating them with recent analyses in which they were adopted.\n"}
+{"abstract": "  The second-largest order statistic is of special importance in reliability\ntheory since it represents the time to failure of a $2$-out-of-$n$ system.\nConsider two $2$-out-of-$n$ systems with heterogeneous random lifetimes. The\nlifetimes are assumed to follow heterogeneous general exponentiated\nlocation-scale models. In this communication, the usual stochastic and reversed\nhazard rate orders between the systems' lifetimes are established under two\ncases. For the case of independent random lifetimes, the usual stochastic order\nand the reversed hazard rate order between the second-largest order statistics\nare obtained by using the concept of vector majorization and related orders.\nFor the dependent case, the conditions under which the usual stochastic order\nbetween the second-largest order statistics holds are investigated. To\nillustrate the theoretical findings, some special cases of the exponentiated\nlocation-scale model are considered.\n"}
+{"abstract": "  The transmission of Lyman-{\\alpha} (Ly{\\alpha}) in the spectra of distant\nquasars depends on the density, temperature, and ionization state of the\nintergalactic medium (IGM). Therefore, high-redshift (z > 5) Ly{\\alpha} forests\ncould be invaluable in studying the late stages of the epoch of reionization\n(EoR), as well as properties of the sources that drive it. Indeed, high-quality\nquasar spectra have now firmly established the existence of large-scale opacity\nfluctuations at z > 5, whose physical origins are still debated. Here we\nintroduce a Bayesian framework capable of constraining the EoR and galaxy\nproperties by forward-modelling the high-z Ly{\\alpha} forest. Using priors from\ngalaxy and CMB observations, we demonstrate that the final overlap stages of\nthe EoR (when >95% of the volume was ionized) should occur at z < 5.6, in order\nto reproduce the large-scale opacity fluctuations seen in forest spectra.\nHowever, it is the combination of patchy reionization and the inhomogeneous UV\nbackground that produces the longest Gunn-Peterson troughs. Ly{\\alpha} forest\nobservations tighten existing constraints on the characteristic ionizing escape\nfraction of galaxies, with the combined observations suggesting f_{\\rm esc}\n\\approx 7^4_3%, and disfavoring a strong evolution with the galaxy's halo (or\nstellar) mass.\n"}
+{"abstract": "  In Budai et al. (2020) we argued that angular non-stationarities of gamma-ray\nburst (GRB) jets can result in a statistical connection between the angle\nvalues deduced from jet break times and the variabilities of prompt light\ncurves. The connection should be an anti-correlation if luminosity densities of\njets follow a power-law or a uniform profile, and a correlation if they have a\nGaussian profile. In this follow-up paper, we search for the connection by\nmeasuring Spearman's rank correlation coefficient in a sample of 19 long GRBs\nobserved by the Swift satellite. Using 16 of the GRBs with well-defined angle\nmeasurements, we find $\\rho = -0.20_{-0.09}^{+0.09}$ and $p =\n0.46_{-0.19}^{+0.23}$. Adding three more GRBs to the sample, each with a pair\nof equally possible angle values, can strengthen the anti-correlation to\n$\\rho=-0.31_{-0.08}^{+0.07}$ and $p=0.19_{-0.09}^{+0.14}$. We show that these\nresults are incompatible with non-stationary jets having Gaussian profiles, and\nthat $\\gtrsim\\!100$ GRBs with observed afterglows would be needed to confirm\nthe potential existence of the angle-variability anti-correlation with\n$3\\sigma$ significance. If the connection is real, GRB jet angles would be\nconstrainable from prompt gamma light curves, without the need of afterglow\nobservations.\n"}
+{"abstract": "  We explore the relation between dust and several fundamental properties of\nsimulated galaxies using the Dusty SAGE semi-analytic model. In addition to\ntracing the standard galaxy properties, Dusty SAGE also tracks cold dust mass\nin the interstellar medium (ISM), hot dust mass in the halo and dust mass\nejected by feedback activity. Based on their ISM dust content, we divide our\ngalaxies into two categories: ISM dust-poor and ISM dust-rich. We split the ISM\ndust-poor group into two subgroups: halo dust-rich and dust-poor (the latter\ncontains galaxies that lack dust in both the ISM and halo). Halo dust-rich\ngalaxies have high outflow rates of heated gas and dust and are more massive.\nWe divide ISM dust-rich galaxies based on their specific star formation rate\n(sSFR) into star-forming and quenched subgroups. At redshift z=0, we find that\nISM dust-rich galaxies have a relatively high sSFR, low bulge-to-total (BTT)\nmass ratio, and high gas metallicity. The high sSFR of ISM dust-rich galaxies\nallows them to produce dust in the stellar ejecta. Their metal-rich ISM enables\ndust growth via grain accretion. The opposite is seen in the ISM dust-poor\ngroup. Furthermore, ISM dust-rich galaxies are typically late-types, while ISM\ndust-poor galaxies resemble the early-type population, and we show how their\nISM content evolves from being dust-rich to dust-poor. Finally, we investigate\ndust production from z=3 to z=0 and find that all groups evolve similarly,\nexcept for the quenched ISM dust-rich group.\n"}
+{"abstract": "  The volume estimation of brain regions from MRI data is a key problem in many\nclinical applications, where the acquisition of data at high spatial resolution\nis desirable. While parallel MRI and constrained image reconstruction\nalgorithms can accelerate the scans, image reconstruction artifacts are\ninevitable, especially at high acceleration factors. We introduce a novel image\ndomain deep-learning framework for calibrationless parallel MRI reconstruction,\ncoupled with a segmentation network to improve image quality and to reduce the\nvulnerability of current segmentation algorithms to image artifacts resulting\nfrom acceleration. The combination of the proposed image domain deep\ncalibrationless approach with the segmentation algorithm offers improved image\nquality, while increasing the accuracy of the segmentations. The novel\narchitecture with an encoder shared between the reconstruction and segmentation\ntasks is seen to reduce the need for segmented training datasets. In\nparticular, the proposed few-shot training strategy requires only 10% of\nsegmented datasets to offer good performance.\n"}
+{"abstract": "  We introduce concurrent quantum non-local games, quantum output mirror games\nand concurrent classical-to-quantum non-local games, as quantum versions of\nsynchronous non-local games, and provide tracial characterisations of their\nperfect strategies belonging to various correlation classes. We define\n*-algebras and C*-algebras of concurrent classical-to-quantum and concurrent\nquantum non-local games, and algebraic versions of the orthogonal rank of a\ngraph. We show that quantum homomorphisms of quantum graphs can be viewed as\nentanglement assisted classical homomorphisms of the graphs, and give\ndescriptions of the perfect quantum commuting and the perfect approximately\nquantum strategies for the quantum graph homomorphism game. We specialise the\nlatter results to the case where the inputs of the game are based on a\nclassical graph.\n"}
+{"abstract": "  Background/Aims: Standard Automated Perimetry (SAP) is the gold standard to\nmonitor visual field (VF) loss in glaucoma management, but is prone to\nintra-subject variability. We developed and validated a deep learning (DL)\nregression model that estimates pointwise and overall VF loss from unsegmented\noptical coherence tomography (OCT) scans. Methods: Eight DL regression models\nwere trained with various retinal imaging modalities: circumpapillary OCT at\n3.5mm, 4.1mm, 4.7mm diameter, and scanning laser ophthalmoscopy (SLO) en face\nimages to estimate mean deviation (MD) and 52 threshold values. This\nretrospective study used data from patients who underwent a complete glaucoma\nexamination, including a reliable Humphrey Field Analyzer (HFA) 24-2 SITA\nStandard VF exam and a SPECTRALIS OCT scan using the Glaucoma Module Premium\nEdition. Results: A total of 1378 matched OCT-VF pairs of 496 patients (863\neyes) were included for training and evaluation of the DL models. Average\nsample MD was -7.53dB (from -33.8dB to +2.0dB). For 52 VF threshold values\nestimation, the circumpapillary OCT scan with the largest radius (4.7mm)\nachieved the best performance among all individual models (Pearson r=0.77, 95%\nCI=[0.72-0.82]). For MD, prediction averaging of OCT-trained models (3.5mm,\n4.1mm, 4.7mm) resulted in a Pearson r of 0.78 [0.73-0.83] on the validation set\nand comparable performance on the test set (Pearson r=0.79 [0.75-0.82]).\nConclusion: DL on unsegmented OCT scans accurately predicts pointwise and mean\ndeviation of 24-2 VF in glaucoma patients. Automated VF from unsegmented OCT\ncould be a solution for patients unable to produce reliable perimetry results.\n"}
+{"abstract": "  Many implicit inferences exist in text depending on how it is structured that\ncan critically impact the text's interpretation and meaning. One such\nstructural aspect present in text with chronology is the order of its\npresentation. For narratives or stories, this is known as the narrative order.\nReordering a narrative can impact the temporal, causal, event-based, and other\ninferences readers draw from it, which in turn can have strong effects both on\nits interpretation and interestingness. In this paper, we propose and\ninvestigate the task of Narrative Reordering (NAREOR) which involves rewriting\na given story in a different narrative order while preserving its plot. We\npresent a dataset, NAREORC, with human rewritings of stories within ROCStories\nin non-linear orders, and conduct a detailed analysis of it. Further, we\npropose novel task-specific training methods with suitable evaluation metrics.\nWe perform experiments on NAREORC using state-of-the-art models such as BART\nand T5 and conduct extensive automatic and human evaluations. We demonstrate\nthat although our models can perform decently, NAREOR is a challenging task\nwith potential for further exploration. We also investigate two applications of\nNAREOR: generation of more interesting variations of stories and serving as\nadversarial sets for temporal/event-related tasks, besides discussing other\nprospective ones, such as for pedagogical setups related to language skills\nlike essay writing and applications to medicine involving clinical narratives.\n"}
+{"abstract": "  In this brief note, it is shown (using the NP spin-coefficient formalism)\nthat, contrary to popular belief, the MOTS stability operator is self-adjoint\nfor a large class of geometric models in general relativity.\n"}
+{"abstract": "  We examine $3D$ flows $\\mathbf{\\dot{x}}=\\mathbf{v}({\\bf x})$ admitting vector\nidentity $M\\mathbf{v} = \\nabla \\times \\mathbf{A}$ for a multiplier $M$ and a\npotential field $\\mathbf{A}$. It is established that, for those systems, one\ncan complete the vector field $\\mathbf{v}$ into a basis fitting an\n$\\mathfrak{sl}(2)$-algebra. Accordingly, in terms of covariant quantities, the\nstructure equations determine a set of equations in Maurer-Cartan form. This\nrealization permits one to obtain the potential field as well as to investigate\nthe (bi-)Hamiltonian character of the system. The latter occurs if the system\nhas a time-independent first integral. In order to exhibit the theoretical\nresults on some concrete cases, three examples are provided, namely the Gulliot\nsystem, a system with a non-integrable potential, and the Darboux-Halphen\nsystem in symmetric polynomials.\n"}
+{"abstract": "  We prove that if $M$ is a strictly stable complete minimal hypersurface in\nEuclidean space with finite density at infinity and which lies on one side of a\nminimal cylinder with cross-section a strictly stable area minimizing\nhypercone, then $M$ must be cylindrical. Applications will be given in the\nreferences [Sim20a], [Sim20b].\n"}
+{"abstract": "  It is proven that the ground state is unique in the Edwards-Anderson model\nfor almost all continuous random exchange interactions, and any excited state\nwith the overlap less than its maximal value has large energy in dimensions\nhigher than two with probability one. Since the spin overlap is shown to be\nconcentrated at its maximal value in the ground state, replica symmetry\nbreaking does not occur in the Edwards-Anderson model near zero temperature.\n"}
+{"abstract": "  The precise positioning of dopant atoms within bulk crystal lattices could\nenable novel applications in areas including solid-state sensing and quantum\ncomputation. Established scanning probe techniques are capable tools for the\nmanipulation of surface atoms, but at a disadvantage due to their need to bring\na physical tip into contact with the sample. This has prompted interest in\nelectron-beam techniques, followed by the first proof-of-principle experiment\nof bismuth dopant manipulation in crystalline silicon. Here, we use first\nprinciples modeling to discover a novel indirect exchange mechanism that allows\nelectron impacts to non-destructively move dopants with atomic precision within\nthe silicon lattice. However, this mechanism only works for the two heaviest\ngroup V donors with split-vacancy configurations, Bi and Sb. We verify our\nmodel by directly imaging these configurations for Bi, and by demonstrating\nthat the promising nuclear spin qubit Sb can be manipulated using a focused\nelectron beam.\n"}
+{"abstract": "  We introduce a new pretraining approach geared for multi-document language\nmodeling, incorporating two key ideas into the masked language modeling\nself-supervised objective. First, instead of considering documents in\nisolation, we pretrain over sets of multiple related documents, encouraging the\nmodel to learn cross-document relationships. Second, we improve over recent\nlong-range transformers by introducing dynamic global attention that has access\nto the entire input to predict masked tokens. We release CDLM (Cross-Document\nLanguage Model), a new general language model for multi-document setting that\ncan be easily applied to downstream tasks. Our extensive analysis shows that\nboth ideas are essential for the success of CDLM, and work in synergy to set\nnew state-of-the-art results for several multi-text tasks. Code and models are\navailable at https://github.com/aviclu/CDLM.\n"}
+{"abstract": "  Making the gradients small is a fundamental optimization problem that has\neluded unifying and simple convergence arguments in first-order optimization,\nso far primarily reserved for other convergence criteria, such as reducing the\noptimality gap. We introduce a novel potential function-based framework to\nstudy the convergence of standard methods for making the gradients small in\nsmooth convex optimization and convex-concave min-max optimization. Our\nframework is intuitive and it provides a lens for viewing algorithms that make\nthe gradients small as being driven by a trade-off between reducing either the\ngradient norm or a certain notion of an optimality gap. On the lower bounds\nside, we discuss tightness of the obtained convergence results for the convex\nsetup and provide a new lower bound for minimizing norm of cocoercive operators\nthat allows us to argue about optimality of methods in the min-max setup.\n"}
+{"abstract": "  A class of high-order shock-capturing schemes, P$_n$T$_m$-BVD (Deng et al.,\nJ. Comp. Phys., 386:323-349, 2019; Comput. & Fluids, 200:104433, 2020.)\nschemes, have been devised to solve the Euler equations with substantially\nreduced numerical dissipation, which enable high-resolution simulations to\nresolve flow structures of wider range scales. In such simulations with low\ndissipation, errors of round-off level might grow and contaminate the numerical\nsolutions. A typical example of such problems is the loss of symmetry in the\nnumerical solutions for physical problems of symmetric configurations even if\nthe schemes are mathematically in line with the symmetry rules. In this study,\nthe mechanisms of symmetry-breaking in a finite volume framework with the\nP$_4$T$_2$-BVD reconstruction scheme are thoroughly examined. Particular\nattention has been paid to remove the possible causes due to the lack of\nassociativity in floating-point arithmetic which is associated with round-off\nerrors. Modifications and new techniques are proposed to completely remove the\npossible causes for symmetry breaking in different components of the\nP$_4$T$_2$-BVD finite volume solver. Benchmark tests that have symmetric\nsolution structures are used to verify the proposed methods. The numerical\nresults demonstrate the perfect symmetric solution structures.\n"}
+{"abstract": "  In this work we introduce and investigate the properties of the\n\"relativistic\" Hopfield model endowed with temporally correlated patterns.\nFirst, we review the \"relativistic\" Hopfield model and we briefly describe the\nexperimental evidence underlying correlation among patterns. Then, we face the\nstudy of the resulting model exploiting statistical-mechanics tools in a\nlow-load regime. More precisely, we prove the existence of the thermodynamic\nlimit of the related free-energy and we derive the self-consistence equations\nfor its order parameters. These equations are solved numerically to get a phase\ndiagram describing the performance of the system as an associative memory as a\nfunction of its intrinsic parameters (i.e., the degree of noise and of\ncorrelation among patterns). We find that, beyond the standard retrieval and\nergodic phases, the relativistic system exhibits correlated and symmetric\nregions -- that are genuine effects of temporal correlation -- whose width is,\nrespectively, reduced and increased with respect to the classical case.\n"}
+{"abstract": "  The excitations of nonlinear magnetosonic lump waves induced by orbiting\ncharged space debris objects in the Low Earth Orbital (LEO) plasma region are\ninvestigated in presence of the ambient magnetic field. These nonlinear waves\nare found to be governed by the forced Kadomtsev-Petviashvili (KP) type model\nequation, where the forcing term signifies the source current generated by\ndifferent possible motions of charged space debris particles in the LEO plasma\nregion. Different analytic lump wave solutions that are stable for both slow\nand fast magnetosonic waves in presence of charged space debris particles are\nfound for the first time. The dynamics of exact pinned accelerated lump waves\nis explored in detail. Approximate lump wave solutions with time-dependent\namplitudes and velocities are analyzed through perturbation methods for\ndifferent types of localized space debris functions; yielding approximate\npinned accelerated lump wave solutions. These new results may pave new\ndirection in this field of research.\n"}
+{"abstract": "  Conductance signatures that signal the presence of Majorana zero modes in a\nthree terminal nanowire-topological superconductor hybrid system are analyzed\nin detail, in both the clean nanowire limit and in the presence of non-coherent\ndephasing interactions. In the coherent transport regime for a clean wire, we\npoint out contributions of the local Andreev reflection and the non-local\ntransmissions toward the total conductance lineshapes while clarifying the role\nof contact broadening on the Majorana conductance lineshapes at the magnetic\nfield parity crossings. Interestingly, at larger $B$-field parity crossings,\nthe contribution of the Andreev reflection process decreases which is\ncompensated by the non-local processes in order to maintain the conductance\nquantum regardless of contact coupling strength. In the non-coherent transport\nregime, we include dephasing that is introduced by momentum randomization\nprocesses, that allows one to smoothly transition to the diffusive limit. Here,\nas expected, we note that while the Majorana character of the zero modes is\nunchanged, there is a reduction in the conductance peak magnitude that scales\nwith the strength of the impurity scattering potentials. Important distinctions\nbetween the effect of non-coherent dephasing processes and contact-induced\ntunnel broadenings in the coherent regime on the conductance lineshapes are\nelucidated. Most importantly our results reveal that the addition of dephasing\nin the set up does not lead to any notable length dependence to the conductance\nof the zero modes, contrary to what one would expect in a gradual transition to\nthe diffusive limit. We believe this work paves a way for a systematic\nintroduction of scattering processes into the realistic modeling of Majorana\nnanowire hybrid devices and assessing topological signatures in such systems in\nthe presence of non-coherent scattering processes.\n"}
+{"abstract": "  We present a novel framework for the reconstruction of 1D composite signals\nassumed to be a mixture of two additive components, one sparse and the other\nsmooth, given a finite number of linear measurements. We formulate the\nreconstruction problem as a continuous-domain regularized inverse problem with\nmultiple penalties. We prove that these penalties induce reconstructed signals\nthat indeed take the desired form of the sum of a sparse and a smooth\ncomponent. We then discretize this problem using Riesz bases, which yields a\ndiscrete problem that can be solved by standard algorithms. Our discretization\nis exact in the sense that we are solving the continuous-domain problem over\nthe search space specified by our bases without any discretization error. We\npropose a complete algorithmic pipeline and demonstrate its feasibility on\nsimulated data.\n"}
+{"abstract": "  Calculations of molecular spectral properties, like photodissociation rates\nand absorption bands, rely on knowledge of the excited state energies of the\nmolecule of interest. Protocols based on the variational quantum eigensolver\n(VQE) are promising candidates to calculate such energies on emerging noisy\nintermediate scale quantum (NISQ) computers. The successful implementation of\nthese protocols on NISQ computers, relies on ans\\\"atze that can accurately\napproximate the molecular states and that can be implemented by shallow quantum\ncircuits. In this paper, we introduce the excited qubit-excitation-based\nadaptive (e-QEB-ADAPT)-VQE protocol to calculate molecular excited state\nenergies. The e-QEB-ADAPT-VQE constructs efficient problem-tailored ans\\\"atze\nby iteratively appending evolutions of qubit excitation operators. The\ne-QEB-ADAPT-VQE is an adaptation of the QEB-ADAPT-VQE protocol, which is\ndesigned to be independent on the choice of an initial reference state. We\nperform classical numerical simulations for LiH and BeH$_2$ to benchmark the\nperformance of the e-QEB-ADAPT-VQE. We demonstrate that the e-QEB-ADAPT-VQE can\nconstruct highly accurate ans\\\"atze that require at least an order of magnitude\nfewer $CNOT$s than standard fixed UCC ans\\\"atze, such as the UCCSD and the\nGUCCSD.\n"}
+{"abstract": "  Modern compute nodes in high-performance computing provide a tremendous level\nof parallelism and processing power. However, as arithmetic performance has\nbeen observed to increase at a faster rate relative to memory and network\nbandwidths, optimizing data movement has become critical for achieving strong\nscaling in many communication-heavy applications. This performance gap has been\nfurther accentuated with the introduction of graphics processing units, which\ncan provide by multiple factors higher throughput in data-parallel tasks than\ncentral processing units. In this work, we explore the computational aspects of\niterative stencil loops and implement a generic communication scheme using\nCUDA-aware MPI, which we use to accelerate magnetohydrodynamics simulations\nbased on high-order finite differences and third-order Runge-Kutta integration.\nWe put particular focus on improving intra-node locality of workloads. In\ncomparison to a theoretical performance model, our implementation exhibits\nstrong scaling from one to $64$ devices at $50\\%$--$87\\%$ efficiency in\nsixth-order stencil computations when the problem domain consists of\n$256^3$--$1024^3$ cells.\n"}
+{"abstract": "  Alfv\\'{e}n waves as excited in black hole accretion disks and neutron star\nmagnetospheres are the building blocks of turbulence in relativistic,\nmagnetized plasmas. A large reservoir of magnetic energy is available in these\nsystems, such that the plasma can be heated significantly even in the weak\nturbulence regime. We perform high-resolution three-dimensional simulations of\ncounter-propagating Alfv\\'{e}n waves, showing that an $E_{B_{\\perp}}(k_{\\perp})\n\\propto k_{\\perp}^{-2}$ energy spectrum develops as a result of the weak\nturbulence cascade in relativistic magnetohydrodynamics and its infinitely\nmagnetized (force-free) limit. The plasma turbulence ubiquitously generates\ncurrent sheets, which act as locations where magnetic energy dissipates. We\nshow that current sheets form as a natural result of nonlinear interactions\nbetween counter-propagating Alfv\\'{e}n waves. These current sheets form due to\nthe compression of elongated eddies, driven by the shear induced by growing\nhigher order modes, and undergo a thinning process until they break-up into\nsmall-scale turbulent structures. We explore the formation of {current sheets}\nboth in overlapping waves and in localized wave packet collisions. The\nrelativistic interaction of localized Alfv\\'{e}n waves induces both Alfv\\'{e}n\nwaves and fast waves and efficiently mediates the conversion and dissipation of\nelectromagnetic energy in astrophysical systems. Plasma energization through\nreconnection in current sheets emerging during the interaction of Alfv\\'{e}n\nwaves can potentially explain X-ray emission in black hole accretion coronae\nand neutron star magnetospheres.\n"}
+{"abstract": "  All-optical switching of 77 fs pulses centered at 1560 nm, driven by 270 fs,\n1030 nm pulses in a dual-core optical fiber exhibiting high index contrast is\npresented. The fiber is made of a thermally matched pair of lead silicate and\nborosilicate glasses used as core and cladding material, respectively. The\nnovel switching approach is based on nonlinear balancing of dual-core\nasymmetry, by control pulse intensity induced group velocity reduction of the\nfast fiber channel. Due to the fiber core made of soft glass with high\nnonlinearity high switching contrast exceeding 20 dB is attained under\napplication of control pulses of only few nanojoule energy. The optimization of\nthe fiber length brought the best results at 14 mm, which is in good\ncorrespondence with the calculated coupling length at the signal wavelength.\nThe results express significant progress in comparison to similar studies based\non self-switching of solitonic pulses in dual-core fibers and represent high\napplication potential.\n"}
+{"abstract": "  An exact low-dimensional system of mean-field equations for an infinite-size\nnetwork of pulse coupled integrate-and-fire neurons with a bimodal distribution\nof an excitability parameter is derived. Bifurcation analysis of these\nequations shows a rich variety of dynamic modes that do not exist with a\nunimodal distribution of this parameter. New modes include multistable\nequilibrium states with different levels of the spiking rate, collective\noscillations and chaos. All oscillatory modes coexist with stable equilibrium\nstates. The mean field equations are a good approximation to the solutions of a\nmicroscopic model consisting of several thousand neurons.\n"}
+{"abstract": "  We continue the work done by the authors and before that by the second\nauthor, Kanovei and koepke. We prove that for every set $A$ in a Magidor-Radin\ngeneric extension using a coherent sequence such that\n$o^{\\vec{U}}(\\kappa)<\\kappa^+$, there is $C'\\subseteq C_G$, such that\n$V[A]=V[C']$. Also we prove that the supremum of a fresh set in a Prikry, tree\nPrikry, Magidor, Radin-Magidor and Radin forcing, changes cofinality to\n$\\omega$.\n"}
+{"abstract": "  We introduce a simple, accurate, and extremely efficient method for\nnumerically solving the multi-marginal optimal transport (MMOT) problems\narising in density functional theory. The method relies on (i) the sparsity of\noptimal plans [for $N$ marginals discretized by $\\ell$ gridpoints each, general\nKantorovich plans require $\\ell^N$ gridpoints but the support of optimizers is\nof size $O(\\ell\\cdot N)$ [FV18]], (ii) the method of column generation (CG)\nfrom discrete optimization which to our knowledge has not hitherto been used in\nMMOT, and (iii) ideas from machine learning. The well-known bottleneck in CG\nconsists in generating new candidate columns efficiently; we prove that in our\ncontext, finding the best new column is an NP-complete problem. To overcome\nthis bottleneck we use a genetic learning method tailormade for MMOT in which\nthe dual state within CG plays the role of an \"adversary\", in loose similarity\nto Wasserstein GANs. On a sequence of benchmark problems with up to 120\ngridpoints and up to 30 marginals, our method always found the exact\noptimizers. Moreover, empirically the number of computational steps needed to\nfind them appears to scale only polynomially when both $N$ and $\\ell$ are\nsimultaneously increased (while keeping their ratio fixed to mimic a\nthermodynamic limit of the particle system).\n"}
+{"abstract": "  The Dynamic Pickup and Delivery Problem (DPDP) is aimed at dynamically\nscheduling vehicles among multiple sites in order to minimize the cost when\ndelivery orders are not known a priori. Although DPDP plays an important role\nin modern logistics and supply chain management, state-of-the-art DPDP\nalgorithms are still limited on their solution quality and efficiency. In\npractice, they fail to provide a scalable solution as the numbers of vehicles\nand sites become large. In this paper, we propose a data-driven approach,\nSpatial-Temporal Aided Double Deep Graph Network (ST-DDGN), to solve\nindustry-scale DPDP. In our method, the delivery demands are first forecast\nusing spatial-temporal prediction method, which guides the neural network to\nperceive spatial-temporal distribution of delivery demand when dispatching\nvehicles. Besides, the relationships of individuals such as vehicles are\nmodelled by establishing a graph-based value function. ST-DDGN incorporates\nattention-based graph embedding with Double DQN (DDQN). As such, it can make\nthe inference across vehicles more efficiently compared with traditional\nmethods. Our method is entirely data driven and thus adaptive, i.e., the\nrelational representation of adjacent vehicles can be learned and corrected by\nST-DDGN from data periodically. We have conducted extensive experiments over\nreal-world data to evaluate our solution. The results show that ST-DDGN reduces\n11.27% number of the used vehicles and decreases 13.12% total transportation\ncost on average over the strong baselines, including the heuristic algorithm\ndeployed in our UAT (User Acceptance Test) environment and a variety of vanilla\nDRL methods. We are due to fully deploy our solution into our online logistics\nsystem and it is estimated that millions of USD logistics cost can be saved per\nyear.\n"}
+{"abstract": "  We argue for a foundational epistemic claim and a hypothesis about the\nproduction and uses of mathematical epidemiological models, exploring the\nconsequences for our political and socio-economic lives. First, in order to\nmake the best use of scientific models, we need to understand why models are\nnot truly representational of our world, but are already pitched towards\nvarious uses. Second, we need to understand the implicit power relations in\nnumbers and models in public policy, and, thus, the implications for good\ngovernance if numbers and models are used as the exclusive drivers of decision\nmaking.\n"}
+{"abstract": "  Deep learning is found to be vulnerable to adversarial examples. However, its\nadversarial susceptibility in image caption generation is under-explored. We\nstudy adversarial examples for vision and language models, which typically\nadopt an encoder-decoder framework consisting of two major components: a\nConvolutional Neural Network (i.e., CNN) for image feature extraction and a\nRecurrent Neural Network (RNN) for caption generation. In particular, we\ninvestigate attacks on the visual encoder's hidden layer that is fed to the\nsubsequent recurrent network. The existing methods either attack the\nclassification layer of the visual encoder or they back-propagate the gradients\nfrom the language model. In contrast, we propose a GAN-based algorithm for\ncrafting adversarial examples for neural image captioning that mimics the\ninternal representation of the CNN such that the resulting deep features of the\ninput image enable a controlled incorrect caption generation through the\nrecurrent network. Our contribution provides new insights for understanding\nadversarial attacks on vision systems with language component. The proposed\nmethod employs two strategies for a comprehensive evaluation. The first\nexamines if a neural image captioning system can be misled to output targeted\nimage captions. The second analyzes the possibility of keywords into the\npredicted captions. Experiments show that our algorithm can craft effective\nadversarial images based on the CNN hidden layers to fool captioning framework.\nMoreover, we discover the proposed attack to be highly transferable. Our work\nleads to new robustness implications for neural image captioning.\n"}
+{"abstract": "  This study investigates the convergence of two bibliometric approaches to the\nmeasurement of interdisciplinary research: one based on analyzing disciplinary\ndiversity in the reference list of publications, the other based on the\ndisciplinary diversity of authors of publications. In particular we measure the\nvariety, balance, disparity and integrated diversity index of, respectively,\nsingle-author, multi-author single-field, and multi-author multi-field\npublications. We find that, in general, the diversity of the reference list\ngrows with the number of fields reflected in a paper's authors' list and, to a\nlesser extent, with the number of authors being equal the number of fields.\nFurther, we find that when fields belonging to different disciplines are\nreflected in the authors' list, the disparity in the reference list is higher\nthan in the case of fields belonging to the same discipline. However, this\ngeneral tendency varies across disciplines, and noticeable exceptions are found\nat individual paper level.\n"}
+{"abstract": "  Model Agnostic Meta-Learning (MAML) has emerged as a standard framework for\nmeta-learning, where a meta-model is learned with the ability of fast adapting\nto new tasks. However, as a double-looped optimization problem, MAML needs to\ndifferentiate through the whole inner-loop optimization path for every\nouter-loop training step, which may lead to both computational inefficiency and\nsub-optimal solutions. In this paper, we generalize MAML to allow meta-learning\nto be defined in function spaces, and propose the first meta-learning paradigm\nin the Reproducing Kernel Hilbert Space (RKHS) induced by the meta-model's\nNeural Tangent Kernel (NTK). Within this paradigm, we introduce two\nmeta-learning algorithms in the RKHS, which no longer need a sub-optimal\niterative inner-loop adaptation as in the MAML framework. We achieve this goal\nby 1) replacing the adaptation with a fast-adaptive regularizer in the RKHS;\nand 2) solving the adaptation analytically based on the NTK theory. Extensive\nexperimental studies demonstrate advantages of our paradigm in both efficiency\nand quality of solutions compared to related meta-learning algorithms. Another\ninteresting feature of our proposed methods is that they are demonstrated to be\nmore robust to adversarial attacks and out-of-distribution adaptation than\npopular baselines, as demonstrated in our experiments.\n"}
+{"abstract": "  Visual appearance is an important aspect influencing the perception and\nconsequent acceptance of Embodied Conversational Agents (ECA). To this end, the\nUncanny Valley theory contradicts the common assumption that increased\nhumanization of characters leads to better acceptance. Rather, it shows that\nanthropomorphic behavior may trigger feelings of eeriness and rejection in\npeople. The work presented in this paper explores whether four different\nautonomous ECAs, specifically build for a European research project, are\naffected by this effect, and how they compare to two slightly more\nrealistically looking human-controlled, i.e. face-tracked, ECAs with respect to\nperceived humanness, eeriness, and attractiveness. Short videos of the ECAs in\ncombination with a validated questionnaire were used to investigate potential\ndifferences. Results support existing theories highlighting that increased\nperceived humanness correlates with increased perceived eeriness. Furthermore,\nit was found, that neither the gender of survey participants, their age, nor\nthe sex of the ECA influences this effect, and that female ECAs are perceived\nto be significantly more attractive than their male counterparts.\n"}
+{"abstract": "  This paper seeks a quantitative comparison between the curves in the KPZ line\nensemble [CH16] and a standard Brownian bridge under the $t^{1/3}$ vertical and\n$t^{2/3}$ horizontal scaling. The estimate we obtained is parallel to the one\nestablished in [Ham1], where the Airy line ensemble was studied. Our main tool\nis the soft Brownian Gibbs property enjoyed by the KPZ line ensemble. In view\nof the Gibbs property, the KPZ line ensemble differs from the Airy line\nensemble mainly due to the intersecting nature of its curves, which results in\nthe main technical difficulty in this paper. We develop a resampling framework,\nthe soft jump ensemble, to tackle this difficulty. Our method is highly\ninspired by the jump ensemble technique developed in [Ham1].\n"}
+{"abstract": "  We show that using renormalization-group summation to generate the QCD\nradiative corrections to the $\\pi-\\gamma$ transition form factor, calculated\nwith lightcone sum rules (LCSR), renders the strong coupling free of Landau\nsingularities while preserving the QCD form-factor asymptotics. This enables a\nreliable applicability of the LCSR method to momenta well below 1 GeV$^2$. This\nway, one can use the new preliminary BESIII data with unprecedented accuracy\nbelow 1.5 GeV$^2$ to fine tune the prefactor of the twist-six contribution.\nUsing a combined fit to all available data below 3.1 GeV$^2$, we are able to\ndetermine all nonperturbative scale parameters and a few Gegenbauer\ncoefficients entering the calculation of the form factor. Employing these\ningredients, we determine a pion distribution amplitude with conformal\ncoefficients $(b_2,b_4)$ that agree at the $1\\sigma$ level with the data for\n$Q^2 \\leqslant 3.1$ GeV$^2$ and fulfill at the same time the lattice\nconstraints on $b_2$ at N$^3$LO together with the constraints from QCD sum\nrules with nonlocal condensates.The form-factor prediction calculated herewith\nreproduces the data below 1 GeV$^2$ significantly better than analogous\npredictions based on a fixed-order power-series expansion in the strong\ncoupling constant.\n"}
+{"abstract": "  Promising results for subjective image quality prediction have been achieved\nduring the past few years by using convolutional neural networks (CNN).\nHowever, the use of CNNs for high resolution image quality assessment remains a\nchallenge, since typical CNN architectures have been designed for small\nresolution input images. In this study, we propose an image quality model that\nattempts to mimic the attention mechanism of human visual system (HVS) by using\na recurrent neural network (RNN) for spatial pooling of the features extracted\nfrom different spatial areas (patches) by a deep CNN-based feature extractor.\nThe experimental study, conducted by using images with different resolutions\nfrom two recently published image quality datasets, indicates that the quality\nprediction accuracy of the proposed method is competitive against benchmark\nmodels representing the state-of-the-art, and the proposed method also performs\nconsistently on different resolution versions of the same dataset.\n"}
+{"abstract": "  Commonsense generation is a challenging task of generating a plausible\nsentence describing an everyday scenario using provided concepts. Its\nrequirement of reasoning over commonsense knowledge and compositional\ngeneralization ability even puzzles strong pre-trained language generation\nmodels. We propose a novel framework using retrieval methods to enhance both\nthe pre-training and fine-tuning for commonsense generation. We retrieve\nprototype sentence candidates by concept matching and use them as auxiliary\ninput. For fine-tuning, we further boost its performance with a trainable\nsentence retriever. We demonstrate experimentally on the large-scale CommonGen\nbenchmark that our approach achieves new state-of-the-art results.\n"}
+{"abstract": "  In this paper, we investigate two methods that allow us to automatically\ncreate profitable DeFi trades, one well-suited to arbitrage and the other\napplicable to more complicated settings. We first adopt the Bellman-Ford-Moore\nalgorithm with DEFIPOSER-ARB and then create logical DeFi protocol models for a\ntheorem prover in DEFIPOSER-SMT. While DEFIPOSER-ARB focuses on DeFi\ntransactions that form a cycle and performs very well for arbitrage,\nDEFIPOSER-SMT can detect more complicated profitable transactions. We estimate\nthat DEFIPOSER-ARB and DEFIPOSER-SMT can generate an average weekly revenue of\n191.48ETH (76,592USD) and 72.44ETH (28,976USD) respectively, with the highest\ntransaction revenue being 81.31ETH(32,524USD) and22.40ETH (8,960USD)\nrespectively. We further show that DEFIPOSER-SMT finds the known economic bZx\nattack from February 2020, which yields 0.48M USD. Our forensic investigations\nshow that this opportunity existed for 69 days and could have yielded more\nrevenue if exploited one day earlier. Our evaluation spans 150 days, given 96\nDeFi protocol actions, and 25 assets.\n  Looking beyond the financial gains mentioned above, forks deteriorate the\nblockchain consensus security, as they increase the risks of double-spending\nand selfish mining. We explore the implications of DEFIPOSER-ARB and\nDEFIPOSER-SMT on blockchain consensus. Specifically, we show that the trades\nidentified by our tools exceed the Ethereum block reward by up to 874x. Given\noptimal adversarial strategies provided by a Markov Decision Process (MDP), we\nquantify the value threshold at which a profitable transaction qualifies as\nMiner ExtractableValue (MEV) and would incentivize MEV-aware miners to fork the\nblockchain. For instance, we find that on Ethereum, a miner with a hash rate of\n10% would fork the blockchain if an MEV opportunity exceeds 4x the block\nreward.\n"}
+{"abstract": "  The core for tackling the fine-grained visual categorization (FGVC) is to\nlearn subtle yet discriminative features. Most previous works achieve this by\nexplicitly selecting the discriminative parts or integrating the attention\nmechanism via CNN-based approaches.However, these methods enhance the\ncomputational complexity and make the modeldominated by the regions containing\nthe most of the objects. Recently, vision trans-former (ViT) has achieved SOTA\nperformance on general image recognition tasks. Theself-attention mechanism\naggregates and weights the information from all patches to the classification\ntoken, making it perfectly suitable for FGVC. Nonetheless, the classifi-cation\ntoken in the deep layer pays more attention to the global information, lacking\nthe local and low-level features that are essential for FGVC. In this work, we\nproposea novel pure transformer-based framework Feature Fusion Vision\nTransformer (FFVT)where we aggregate the important tokens from each transformer\nlayer to compensate thelocal, low-level and middle-level information. We design\na novel token selection mod-ule called mutual attention weight selection (MAWS)\nto guide the network effectively and efficiently towards selecting\ndiscriminative tokens without introducing extra param-eters. We verify the\neffectiveness of FFVT on three benchmarks where FFVT achieves the\nstate-of-the-art performance.\n"}
+{"abstract": "  We examine Serrin's classical overdetermined problem under a perturbation of\nthe Neumann boundary condition. The solution of the problem for a constant\nNeumann boundary condition exists provided that the underlying domain is a\nball. The question arises whether for a perturbation of the constant there\nstill are domains admitting solutions to the problem. Furthermore, one may ask\nwhether a domain that admits a solution for the perturbed problem is unique up\nto translation and whether it is close to the ball. We develop a new implicit\nfunction theorem for a pair of Banach triplets that is applicable to nonlinear\nproblems with loss of derivatives except at the point under consideration.\nCombined with a detailed analysis of the linearized operator, we prove the\nexistence and local uniqueness of a domain admitting a solution to the\nperturbed overdetermined problem. Moreover, an optimal linear stability\nestimate for the shape of a domain is established.\n"}
+{"abstract": "  Finding codes given natural language query isb eneficial to the productivity\nof software developers. Future progress towards better semantic matching\nbetween query and code requires richer supervised training resources. To remedy\nthis, we introduce the CoSQA dataset.It includes 20,604 labels for pairs of\nnatural language queries and codes, each annotated by at least 3 human\nannotators. We further introduce a contrastive learning method dubbed CoCLR to\nenhance query-code matching, which works as a data augmenter to bring more\nartificially generated training instances. We show that evaluated on CodeXGLUE\nwith the same CodeBERT model, training on CoSQA improves the accuracy of code\nquestion answering by 5.1%, and incorporating CoCLR brings a further\nimprovement of 10.5%.\n"}
+{"abstract": "  Structured pruning is an effective compression technique to reduce the\ncomputation of neural networks, which is usually achieved by adding\nperturbations to reduce network parameters at the cost of slightly increasing\ntraining loss. A more reasonable approach is to find a sparse minimizer along\nthe flat minimum valley found by optimizers, i.e. stochastic gradient descent,\nwhich keeps the training loss constant. To achieve this goal, we propose the\nstructured directional pruning based on orthogonal projecting the perturbations\nonto the flat minimum valley. We also propose a fast solver sDprun and further\nprove that it achieves directional pruning asymptotically after sufficient\ntraining. Experiments using VGG-Net and ResNet on CIFAR-10 and CIFAR-100\ndatasets show that our method obtains the state-of-the-art pruned accuracy\n(i.e. 93.97% on VGG16, CIFAR-10 task) without retraining. Experiments using\nDNN, VGG-Net and WRN28X10 on MNIST, CIFAR-10 and CIFAR-100 datasets demonstrate\nour method performs structured directional pruning, reaching the same minimum\nvalley as the optimizer.\n"}
+{"abstract": "  Comprehending the nature of action potentials is fundamental to our\nunderstanding of the functioning of nervous systems in general. Here we\nconsider their evolution and describe their functions of communication,\nmodulation and computation within nervous systems. The ionic mechanisms\nunderlying action potentials in the squid giant axon were first described by\nHodgkin and Huxley in 1952 and their findings have formed our orthodox view of\nhow the physiological action potential functions. However, substantial evidence\nhas now accumulated to show that the action potential is accompanied by a\nsynchronized coupled soliton pressure pulse in the cell membrane, the action\npotential pulse (APPulse). Here we explore the interactions between the soliton\nand the ionic mechanisms known to be associated with the action potential.\nComputational models of the action potential usually describe it as a binary\nevent, but we suggest that it is quantum ternary event known as the\ncomputational action potential (CAP), whose temporal fixed point is threshold,\nrather than the rather plastic action potential peak used in other models. The\nCAP accompanies the APPulse and the Physiological action potential. Therefore,\nwe conclude that nerve impulses appear to be an ensemble of three inseparable,\ninterdependent, concurrent states: the physiological action potential, the\nAPPulse and the CAP.\n"}
+{"abstract": "  HERMES-TP/SP (High Energy Rapid Modular Ensemble of Satellites Technologic\nand Scientific Pathfinder) is a constellation of six 3U nano-satellites hosting\nsimple but innovative X-ray detectors, characterized by a large energy band and\nexcellent temporal resolution, and thus optimized for the monitoring of Cosmic\nHigh Energy transients such as Gamma Ray Bursts and the electromagnetic\ncounterparts of Gravitational Wave Events, and for the determination of their\npositions. The projects are funded by the Italian Ministry of University and\nResearch and by the Italian Space Agency, and by the European Union Horizon\n2020 Research and Innovation Program under Grant Agreement No. 821896.\nHERMES-TP/SP is an in-orbit demonstration, that should be tested starting from\n2022. It is intrinsically a modular experiment that can be naturally expanded\nto provide a global, sensitive all sky monitor for high-energy transients.\n"}
+{"abstract": "  Blockchain technologies can enable secure computing environments among\nmistrusting parties. Permissioned blockchains are particularly enlightened by\ncompanies, enterprises, and government agencies due to their efficiency,\ncustomizability, and governance-friendly features. Obviously, seamlessly fusing\nblockchain and cloud computing can significantly benefit permissioned\nblockchains; nevertheless, most blockchains implemented on clouds are\noriginally designed for loosely-coupled networks where nodes communicate\nasynchronously, failing to take advantages of the closely-coupled nature of\ncloud servers. In this paper, we propose an innovative cloud-oriented\nblockchain -- CloudChain, which is a modularized three-layer system composed of\nthe network layer, consensus layer, and blockchain layer. CloudChain is based\non a shared-memory model where nodes communicate synchronously by direct memory\naccesses. We realize the shared-memory model with the Remote Direct Memory\nAccess technology, based on which we propose a shared-memory consensus\nalgorithm to ensure presistence and liveness, the two crucial blockchain\nsecurity properties countering Byzantine nodes. We also implement a CloudChain\nprototype based on a RoCEv2-based testbed to experimentally validate our\ndesign, and the results verify the feasibility and efficiency of CloudChain.\n"}
+{"abstract": "  In this work, we seek new insights into the underlying challenges of the\nScene Graph Generation (SGG) task. Quantitative and qualitative analysis of the\nVisual Genome dataset implies -- 1) Ambiguity: even if inter-object\nrelationship contains the same object (or predicate), they may not be visually\nor semantically similar, 2) Asymmetry: despite the nature of the relationship\nthat embodied the direction, it was not well addressed in previous studies, and\n3) Higher-order contexts: leveraging the identities of certain graph elements\ncan help to generate accurate scene graphs. Motivated by the analysis, we\ndesign a novel SGG framework, Local-to-Global Interaction Networks (LOGIN).\nLocally, interactions extract the essence between three instances - subject,\nobject, and background - while baking direction awareness into the network by\nconstraining the input order. Globally, interactions encode the contexts\nbetween every graph components -- nodes and edges. Also we introduce Attract &\nRepel loss which finely adjusts predicate embeddings. Our framework enables\npredicting the scene graph in a local-to-global manner by design, leveraging\nthe possible complementariness. To quantify how much LOGIN is aware of\nrelational direction, we propose a new diagnostic task called Bidirectional\nRelationship Classification (BRC). We see that LOGIN can successfully\ndistinguish relational direction than existing methods (in BRC task) while\nshowing state-of-the-art results on the Visual Genome benchmark (in SGG task).\n"}
+{"abstract": "  Learning temporal patterns from multivariate longitudinal data is challenging\nespecially in cases when data is sporadic, as often seen in, e.g., healthcare\napplications where the data can suffer from irregularity and asynchronicity as\nthe time between consecutive data points can vary across features and samples,\nhindering the application of existing deep learning models that are constructed\nfor complete, evenly spaced data with fixed sequence lengths. In this paper, a\nnovel deep learning-based model is developed for modeling multiple temporal\nfeatures in sporadic data using an integrated deep learning architecture based\non a recurrent neural network (RNN) unit and a continuous-time autoregressive\n(CAR) model. The proposed model, called CARRNN, uses a generalized\ndiscrete-time autoregressive model that is trainable end-to-end using neural\nnetworks modulated by time lags to describe the changes caused by the\nirregularity and asynchronicity. It is applied to multivariate time-series\nregression tasks using data provided for Alzheimer's disease progression\nmodeling and intensive care unit (ICU) mortality rate prediction, where the\nproposed model based on a gated recurrent unit (GRU) achieves the lowest\nprediction errors among the proposed RNN-based models and state-of-the-art\nmethods using GRUs and long short-term memory (LSTM) networks in their\narchitecture.\n"}
+{"abstract": "  The task of predicting smooth and edge-consistent depth maps is notoriously\ndifficult for single image depth estimation. This paper proposes a novel\nBilateral Grid based 3D convolutional neural network, dubbed as 3DBG-UNet, that\nparameterizes high dimensional feature space by encoding compact 3D bilateral\ngrids with UNets and infers sharp geometric layout of the scene. Further,\nanother novel 3DBGES-UNet model is introduced that integrate 3DBG-UNet for\ninferring an accurate depth map given a single color view. The 3DBGES-UNet\nconcatenates 3DBG-UNet geometry map with the inception network edge\naccentuation map and a spatial object's boundary map obtained by leveraging\nsemantic segmentation and train the UNet model with ResNet backbone. Both\nmodels are designed with a particular attention to explicitly account for edges\nor minute details. Preserving sharp discontinuities at depth edges is critical\nfor many applications such as realistic integration of virtual objects in AR\nvideo or occlusion-aware view synthesis for 3D display applications.The\nproposed depth prediction network achieves state-of-the-art performance in both\nqualitative and quantitative evaluations on the challenging NYUv2-Depth data.\nThe code and corresponding pre-trained weights will be made publicly available.\n"}
+{"abstract": "  The axion is a highly motivated elementary particle which could address two\nfundamental questions in physics - the strong CP problem and the dark matter\nmystery. Experimental searches for this hypothetical particle have started to\nreach theoretically interesting sensitivity levels, particularly in the $\\mu$eV\n(GHz) region. They rely on large volume solenoid magnetic fields and microwave\nresonators with signals read out by quantum noise limited amplifiers.\nConcurrently, there have been intensive experimental efforts to widen the\nsearch range by devising various techniques as well as to enhance sensitivities\nby implementing advanced technologies. The developments and improvements in\nthese orthogonal approaches will enable us to explore most of the parameter\nspace of the axion and axion-like particles within the next couple of decades,\nwith the 1-25 GHz frequency range to be conquered well within the first decade.\nWe review the experimental aspects of axion physics and discuss the past,\npresent and future of the direct search programs.\n"}
+{"abstract": "  Data driven models of dynamical systems help planners and controllers to\nprovide more precise and accurate motions. Most model learning algorithms will\ntry to minimize a loss function between the observed data and the model's\npredictions. This can be improved using prior knowledge about the task at hand,\nwhich can be encoded in the form of constraints. This turns the unconstrained\nmodel learning problem into a constrained one. These constraints allow models\nwith finite capacity to focus their expressive power on important aspects of\nthe system. This can lead to models that are better suited for certain tasks.\nThis paper introduces the constrained Sufficiently Accurate model learning\napproach, provides examples of such problems, and presents a theorem on how\nclose some approximate solutions can be. The approximate solution quality will\ndepend on the function parameterization, loss and constraint function\nsmoothness, and the number of samples in model learning.\n"}
+{"abstract": "  Most prior approaches to offline reinforcement learning (RL) have taken an\niterative actor-critic approach involving off-policy evaluation. In this paper\nwe show that simply doing one step of constrained/regularized policy\nimprovement using an on-policy Q estimate of the behavior policy performs\nsurprisingly well. This one-step algorithm beats the previously reported\nresults of iterative algorithms on a large portion of the D4RL benchmark. The\none-step baseline achieves this strong performance while being notably simpler\nand more robust to hyperparameters than previously proposed iterative\nalgorithms. We argue that the relatively poor performance of iterative\napproaches is a result of the high variance inherent in doing off-policy\nevaluation and magnified by the repeated optimization of policies against those\nestimates. In addition, we hypothesize that the strong performance of the\none-step algorithm is due to a combination of favorable structure in the\nenvironment and behavior policy.\n"}
+{"abstract": "  Thermally activated selective topography equilibration (TASTE) enables the\ncreation of 3D structures in resist using grayscale electron-beam lithography\nfollowed by a thermal treatment to induce a selective polymer reflow. A blazed\ngrating topography can be created by reflowing repeating staircase patterns in\nresist into wedge-like structures. Motivated by astronomical applications, such\npatterns with periodicities 840 nm and 400 nm have been fabricated in 130\nnm-thick PMMA using TASTE to provide a base for X-ray reflection gratings. A\npath forward to integrate this alternative blazing technique into grating\nfabrication recipes is discussed.\n"}
+{"abstract": "  Reflecting on the last few years, the biggest breakthroughs in deep\nreinforcement learning (RL) have been in the discrete action domain. Robotic\nmanipulation, however, is inherently a continuous control environment, but\nthese continuous control reinforcement learning algorithms often depend on\nactor-critic methods that are sample-inefficient and inherently difficult to\ntrain, due to the joint optimisation of the actor and critic. To that end, we\nexplore how we can bring the stability of discrete action RL algorithms to the\nrobot manipulation domain. We extend the recently released ARM algorithm, by\nreplacing the continuous next-best pose agent with a discrete next-best pose\nagent. Discretisation of rotation is trivial given its bounded nature, while\ntranslation is inherently unbounded, making discretisation difficult. We\nformulate the translation prediction as the voxel prediction problem by\ndiscretising the 3D space; however, voxelisation of a large workspace is memory\nintensive and would not work with a high density of voxels, crucial to\nobtaining the resolution needed for robotic manipulation. We therefore propose\nto apply this voxel prediction in a coarse-to-fine manner by gradually\nincreasing the resolution. In each step, we extract the highest valued voxel as\nthe predicted location, which is then used as the centre of the\nhigher-resolution voxelisation in the next step. This coarse-to-fine prediction\nis applied over several steps, giving a near-lossless prediction of the\ntranslation. We show that our new coarse-to-fine algorithm is able to\naccomplish RLBench tasks much more efficiently than the continuous control\nequivalent, and even train some real-world tasks, tabular rasa, in less than 7\nminutes, with only 3 demonstrations. Moreover, we show that by moving to a\nvoxel representation, we are able to easily incorporate observations from\nmultiple cameras.\n"}
+{"abstract": "  Stack Overflow (SO) platform has a huge dataset of questions and answers\ndriven by interactions between users. But the count of unanswered questions is\ncontinuously rising. This issue is common across various community Question &\nAnswering platforms (Q&A) such as Yahoo, Quora and so on. Clustering is one of\nthe approaches used by these communities to address this challenge.\nSpecifically, Intent-based clustering could be leveraged to answer unanswered\nquestions using other answered questions in the same cluster and can also\nimprove the response time for new questions. It is here, we propose SOCluster,\nan approach and a tool to cluster SO questions based on intent using a\ngraph-based clustering approach. We selected four datasets of 10k, 20k, 30k &\n40k SO questions without code-snippets or images involved, and performed\nintent-based clustering on them. We have done a preliminary evaluation of our\ntool by analyzing the resultant clusters using the commonly used metrics of\nSilhouette coefficient, Calinkski-Harabasz Index, & Davies-Bouldin Index. We\nperformed clustering for 8 different threshold similarity values and analyzed\nthe intriguing trends reflected by the output clusters through the three\nevaluation metrics. At 90% threshold similarity, it shows the best value for\nthe three evaluation metrics on all four datasets. The source code and tool are\navailable for download on Github at: https://github.com/Liveitabhi/SOCluster,\nand the demo can be found here: https://youtu.be/uyn8ie4h3NY.\n"}
+{"abstract": "  We consider the Euler equations for the incompressible flow of an ideal fluid\nwith an additional rough-in-time, divergence-free, Lie-advecting vector field.\nIn recent work, we have demonstrated that this system arises from Clebsch and\nHamilton-Pontryagin variational principles with a perturbative geometric rough\npath Lie-advection constraint. In this paper, we prove local well-posedness of\nthe system in $L^2$-Sobolev spaces $H^m$ with integer regularity $m\\ge \\lfloor\nd/2\\rfloor+2$ and establish a Beale-Kato-Majda (BKM) blow-up criterion in terms\nof the $L^1_tL^\\infty_x$-norm of the vorticity. In dimension two, we show that\nthe $L^p$-norms of the vorticity are conserved, which yields global\nwell-posedness and a Wong-Zakai approximation theorem for the stochastic\nversion of the equation.\n"}
+{"abstract": "  The topologically non-trivial nano-whirls, called magnetic skyrmions, are\noften considered attractive for spintronic applications like the racetrack data\nstorage device. However, skyrmions do not move parallel to applied currents and\ntypically do not coexist with other nano-objects, making the realization of\nsuch storage concepts difficult. Herein we consider materials with an\nanisotropic DMI, like tetragonal Heusler materials, and show that four distinct\ntypes of topologically trivial bubbles can coexist. We show that each of them\ncan be written by spin torques and that they can be distinguished using a\nsingle magnetic tunneling junction. Due to their trivial topology, the four\ntypes of bubbles move parallel to applied electrical currents and remain\nequidistant under motion. Still, the bubbles have a small size and high\nstability comparable to topologically non-trivial spin textures. This allows to\nconstruct a quaternary-digit-based racetrack storage device that overcomes the\ntwo mentioned drawbacks of skyrmion-based racetracks.\n"}
+{"abstract": "  One possible progenitor of short gamma-ray bursts (GRBs) is thought to be\nfrom a double neutron star (NS) merger, and the remnant of such a merger may be\na supramassive NS, which is supported by rigid rotation and through its\nsurvival of hundreds of seconds before collapsing into a black hole (BH). If\nthis is the case, an optical/infrared transient (namely merger-nova) is\ngenerated from the ejected materials and it is powered by radioactive decay\nfrom $r$-process, spin-down energy from a supramassive NS, as well as the\nmagnetic wind from a newborn BH. In this paper, we systematically search for\nthe signature of a supramassive NS central engine by analyzing the X-ray\nemission of short GRBs with internal plateau observed by {\\em Swift}, and we\nfind that five candidates of short GRBs have such feature with redshift\nmeasurement. Then, we calculate the possible merger-nova emission from those\ncandidates for given the typical model parameters by considering the above\nthree energy sources, and compare its brightness with the sensitivity of some\noptical telescopes. We find that the merger-nova emission of GRB 060801 in K-,\nr-, and U-bands with variations of $M_{\\rm ej}$ ($10^{-4}-10^{-2} M_{\\odot}$),\n$\\kappa$ ($0.1-10 ~\\rm cm^{2}~g^{-1}$), and $\\beta$ ($0.1-0.3$) is very\ndifficult to detect using the Vera C. Rubin, Panoramic Survey Telescope and\nRapid Response System (Pan-STARRS), the Zwicky Transient Facility, and the\nRoman Space Telescope (Roman), except for the case of large ejecta mass $M_{\\rm\nej}=10^{-2} M_{\\odot}$. However, we are very hopeful to detect the merger-nova\nemission of GRBs 090515, 100625A, and 101219A using more sensitive instruments,\nsuch as Vera C. Rubin, Pan-STARRS, and Roman. Moreover, the merger-nova\nemission of GRB 160821B is bright enough to detect in our calculations, and it\nis also consistent with current real observations of merger-nova emission.\n"}
+{"abstract": "  COVID-19 has hugely changed our lives, work, and interactions with people.\nWith more and more online activities, people are easily exposed to privacy\nthreats. In this paper, we explore how users self-disclose on social media and\nprivacy concerns raised from these behaviors. Based on recent news, techniques,\nand research, we indicate three increasing privacy threats caused by the\nCOVID-19 pandemic. After that, we provide a systematic analysis of potential\nprivacy issues related to the COVID pandemic. Furthermore, we propose a series\nof research directions about online user self-disclosure and privacy issues for\nfuture work as well as possible solutions.\n"}
+{"abstract": "  Metric of axially symmetric asymptotically flat black holes in an arbitrary\nmetric theory of gravity can be represented in the general form which depends\non infinite number of parameters. We constrain this general class of metrics by\nrequiring the existence of additional symmetries, which lead to the separation\nof variables in the Hamilton-Jacobi and Klein-Gordon equations, and show that\nonce the metric functions change sufficiently moderately in some region near\nthe black hole, the black-hole shadow depends on a few deformation parameters\nonly. We analyze the influence of these parameters on the black-hole shadow. We\nalso show that the shadow of the rotating black hole in the\nEinstein-dilaton-Gauss-Bonnet theory is well approximated if the terms\nviolating the separation of variables are neglected in the metric.\n"}
+{"abstract": "  Internetwork (IN) magnetic fields are highly dynamic, short-lived magnetic\nstructures that populate the interior of supergranular cells. Since they emerge\nall over the Sun, these small-scale fields bring a substantial amount of flux,\nand therefore energy, to the solar surface. Because of this, IN fields are\ncrucial for understanding the quiet Sun (QS) magnetism. However, they are weak\nand produce very small polarization signals, which is the reason why their\nproperties and impact on the energetics and dynamics of the solar atmosphere\nare poorly known. Here we use coordinated, high-resolution, multiwavelength\nobservations obtained with the Swedish 1-m Solar Telescope (SST) and the\n\\textit{Interface Region Imaging Spectrograph} (IRIS) to follow the evolution\nof IN magnetic loops as they emerge into the photosphere and reach the\nchromosphere and transition region. We studied in this paper three flux\nemergence events having total unsigned magnetic fluxes of $1.9\\times10^{18}$,\n$2.5\\times10^{18}$, and $5.3\\times10^{18}$~Mx. The footpoints of the emerging\nIN bipoles are clearly seen to appear in the photosphere and to rise up through\nthe solar atmosphere, as observed in \\ion{Fe}{1} 6173 \\AA\\/ and \\ion{Mg}{1}\nb$_2$ 5173 \\AA\\/ magnetograms, respectively. For the first time, our\npolarimetric measurements taken in the chromospheric \\ion{Ca}{2} 8542 \\AA\\/\nline provide direct observational evidence that IN fields are capable of\nreaching the chromosphere. Moreover, using IRIS data, we study the effects of\nthese weak fields on the heating of the chromosphere and transition region.\n"}
+{"abstract": "  We address the global-in-time existence, stability and long time behaviour of\nweak solutions of the three-dimensional compressible Navier-Stokes equations\nwith potential force. We show the details of the $\\alpha$-dependence of\ndifferent smoothing rates for weak solutions near $t=0$ under the assumption on\nthe initial velocity $u_0$ that $u_0\\in H^\\alpha$ for\n$\\alpha\\in(\\frac{1}{2},1]$ and obtain long time convergence of weak solutions\nin various norms. We then make use of the Lagrangean framework in comparing the\ninstantaneous states of corresponding fluid particles in two different\nsolutions. The present work provides qualitative results on the long time\nbehaviour of weak solutions and how the weak solutions depend continuously on\ninitial data and steady states.\n"}
+{"abstract": "  CNN (Convolutional Neural Network) models have been successfully used for\nsegmentation of the left ventricle (LV) in cardiac MRI (Magnetic Resonance\nImaging), providing clinical measurements. In practice, two questions arise\nwith deployment of CNNs: 1) when is it better to use a shallow model instead of\na deeper one? 2) how the size of a dataset might change the network\nperformance? We propose a framework to answer them, by experimenting with deep\nand shallow versions of three U-Net families, trained from scratch in six\nsubsets varying from 100 to 10,000 images, different network sizes, learning\nrates and regularization values. 1620 models were evaluated using 5-fold\ncross-validation by loss and DICE. The results indicate that: sample size\naffects performance more than architecture or hyper-parameters; in small\nsamples the performance is more sensitive to hyper-parameters than\narchitecture; the performance difference between shallow and deeper networks is\nnot the same across families.\n"}
+{"abstract": "  As a master's student, knowing how to manage your personal research is not\nonly useful for keeping track of your work, but it is also a process that\nshould be learned as a part of your training. As a doctoral student, however,\nresearch management is a fundamental part of your overall methodology and it\nshould be a well-planned process. Long-term research requires a good approach\nto documentation, otherwise you risk getting lost among your many surveyed\npapers, carried experiments, and results. This approach should be systematic,\naccessible (mainly to you), low-effort, and a natural part of your daily\nworkflow - it should be there to help you, and not the other way around. In\nthis article, we describe how we relied on a wiki to organize literature,\ndatasets, experiments and results, and we also show how such a systematic\napproach can lead to better insights through automated meta-analysis. In\naddition to this content, we provide a docker-based installation of a\npreconfigured wiki, with the required templates and extensions, along with some\nexamples pages, as well as a Jupyter notebook to analyze your documented work.\nSo read on.\n"}
+{"abstract": "  The direct and inverse scattering problems are analyzed for a first-order\ndiscrete system associated with the semi-discrete version of the derivative NLS\nsystem. The Jost solutions, the scattering coefficients, the bound-state\ndependency and norming constants are investigated and related to the\ncorresponding quantities for two particular discrete linear systems associated\nwith the semi-discrete version of the NLS system. The bound-state data set with\nany multiplicities is described in an elegant manner in terms of a pair of\nconstant matrix triplets. Several methods are presented to the solve the\ninverse problem. One of these methods involves a discrete Marchenko system\nusing as input the scattering data set consisting of the scattering\ncoefficients and the bound-state information, and this method is presented in a\nway generalizable to other first-order systems both in the discrete and\ncontinuous cases for which a Marchenko system is not yet available. Finally,\nusing the time-evolved scattering data set, the inverse scattering transform is\napplied on the corresponding semi-discrete derivative NLS system, and in the\nreflectionless case certain explicit solution formulas are presented in closed\nform expressed in terms of the two matrix triplets.\n"}
+{"abstract": "  We develop two new classes of tests for the Weibull distribution based on\nStein's method. The proposed tests are applied in the full sample case as well\nas in the framework of random right censoring. We investigate the finite sample\nperformance of the new tests using a comprehensive Monte Carlo study. In both\nthe absence and presence of censoring, it is found that the newly proposed\nclasses of tests outperform competing tests against the majority of the\ndistributions considered. In the cases where censoring is present we consider\nvarious censoring distributions. Some remarks on the asymptotic properties of\nthe proposed tests are included. The paper presents another result of\nindependent interest; the test initially proposed in Krit (2014) for use with\nfull samples is amended to allow for testing for the Weibull distribution in\nthe presence of censoring. The techniques developed in the paper are\nillustrated using two practical examples. In the first, we consider the\nsurvival times of patients with a certain type of leukemia. The second example\nis concerned with the initial remission times of leukemia patients, where the\nobserved remission times are subject to random right censoring. We further\ninclude some concluding remarks along with avenues for future research.\n"}
+{"abstract": "  In lattice calculations, the approach to the continuum limit is hindered by\nthe severe freezing of the topological charge, which prevents ergodic sampling\nin configuration space. In order to significantly reduce the autocorrelation\ntime of the topological charge, we develop a density of states approach with a\nsmooth constraint and use it to study SU(3) pure Yang Mills gauge theory near\nthe continuum limit. Our algorithm relies on simulated tempering across a range\nof couplings, which guarantees the decorrelation of the topological charge and\nergodic sampling of topological sectors. Particular emphasis is placed on\ntesting the accuracy, efficiency and scaling properties of the method. In their\nmost conservative interpretation, our results provide firm evidence of a\nsizeable reduction of the exponent z related to the growth of the\nautocorrelation time as a function of the inverse lattice spacing.\n"}
+{"abstract": "  In It\\^{o}-diffusion environments, we introduce and analyze $N$-player and\ncommon-noise mean-field games in the context of optimal portfolio choice in a\ncommon market. The players invest in a finite horizon and also interact, driven\neither by competition or homophily. We study an incomplete market model in\nwhich the players have constant individual risk tolerance coefficients (CARA\nutilities). We also consider the general case of random individual risk\ntolerances and analyze the related games in a complete market setting. This\nrandomness makes the problem substantially more complex as it leads to ($N$ or\na continuum of) auxiliary ''individual'' It\\^{o}-diffusion markets. For all\ncases, we derive explicit or closed-form solutions for the equilibrium\nstochastic processes, the optimal state processes, and the values of the games.\n"}
+{"abstract": "  In this paper, we propose methods for functional predictor selection and the\nestimation of smooth functional coefficients simultaneously in a\nscalar-on-function regression problem under high-dimensional multivariate\nfunctional data setting. In particular, we develop two methods for functional\ngroup-sparse regression under a generic Hilbert space of infinite dimension. We\nshow the convergence of algorithms and the consistency of the estimation and\nthe selection (oracle property) under infinite-dimensional Hilbert spaces.\nSimulation studies show the effectiveness of the methods in both the selection\nand the estimation of functional coefficients. The applications to the\nfunctional magnetic resonance imaging (fMRI) reveal the regions of the human\nbrain related to ADHD and IQ.\n"}
+{"abstract": "  There are considerable advancements in medical health care in recent years,\nresulting in rising older population. As the workforce for such a population is\nnot keeping pace, there is an urgent need to address this problem. Having\nrobots to stimulating recreational activities for older adults can reduce the\nworkload for caretakers and give them time to address the emotional needs of\nthe elderly. In this paper, we investigate the effects of the humanoid social\nrobot Nadine as an activity host for the elderly. This study aims to analyse if\nthe elderly feels comfortable and enjoy playing game/activity with the humanoid\nrobot Nadine. We propose to evaluate this by placing Nadine humanoid social\nrobot in a nursing home as a caretaker where she hosts bingo game. We record\nsessions with and without Nadine to understand the difference and acceptance of\nthese two scenarios. We use computer vision methods to analyse the activities\nof the elderly to detect emotions and their involvement in the game. We\nenvision that such humanoid robots will make recreational activities more\nreadily available for the elderly. Our results present positive enforcement\nduring recreational activity, Bingo, in the presence of Nadine.\n"}
+{"abstract": "  Recently, attributed community search, a related but different problem to\ncommunity detection and graph clustering, has been widely studied in the\nliterature. Compared with the community detection that finds all existing\nstatic communities from a graph, the attributed community search (ACS) is more\nchallenging since it aims to find dynamic communities with both cohesive\nstructures and homogeneous node attributes given arbitrary queries. To solve\nthe ACS problem, the most popular paradigm is to simplify the problem as two\nsub-problems, that is, structural matching and attribute filtering and deal\nwith them separately. However, in real-world graphs, the community structure\nand the node attributes are actually correlated to each other. In this vein,\ncurrent studies cannot capture these correlations which are vital for the ACS\nproblem.\n  In this paper, we propose Query-Driven Graph Convolutional Networks (QD-GCN),\nan end-to-end framework that unifies the community structure as well as node\nattribute to solve the ACS problem. In particular, QD-GCN leverages the Graph\nConvolutional Networks, which is a powerful tool to encode the graph topology\nand node attributes concurrently, as the backbones to extract the\nquery-dependent community information from the original graph. By utilizing\nthis query-dependent community information, QD-GCN is able to predict the\ntarget community given any queries. Experiments on real-world graphs with\nground-truth communities demonstrate that QD-GCN outperforms existing\nattributed community search algorithms in terms of both efficiency and\neffectiveness.\n"}
+{"abstract": "  We present benchmarks of the parity transformation for the Quantum\nApproximate Optimization Algorithm (QAOA). We analyse the gate resources\nrequired to implement a single QAOA cycle for real-world scenarios. In\nparticular, we consider random spin models with higher order terms, as well as\nthe problems of predicting financial crashes and finding the ground states of\nelectronic structure Hamiltonians. For the spin models studied our findings\nimply a significant advantage of the parity mapping compared to the standard\ngate model. In combination with full parallelizability of gates this has the\npotential to boost the race for demonstrating quantum advantage.\n"}
+{"abstract": "  This paper addresses a new variant of the multi-trip single vehicle routing\nproblem where the nodes are related to each other through AND-type precedence\nconstraints. The problem aims at determining a sequence of trips to visit all\nthe nodes respecting every precedence constraint within and among the routes so\nto minimize the total traveling cost. Our motivation comes from routing\nproblems where a node may have a set of predecessors (not just single one\nproposed in the dial-a-ride or pickup and delivery problems) resulting in a set\nof pairwise relations that specify which customers need to be visited before\nwhich other ones. We develop three Mixed Integer Programming (MIP) models to\nformulate the problem. The models are experimentally compared to determine the\nbest one. Moreover, a solution approach based on the Logic-Based Benders\nDecomposition (LBBD) algorithm is developed which allows to decompose the\noriginal problem into an assignment master problem and independent sequencing\nsubproblems. A new valid optimality cut is devised to achieve faster\nconvergence. The cut performance is experimentally investigated by comparing\nwith a recently proposed one in the literature. We further relax the algorithm\nto find the sub-optimal solution and demonstrate its efficiency. Extensive\ncomputational experiments are conducted to assess the proposed algorithms in\nterms of solution quality and CPU time.\n"}
+{"abstract": "  Weakly-Supervised Temporal Action Localization (WS-TAL) task aims to\nrecognize and localize temporal starts and ends of action instances in an\nuntrimmed video with only video-level label supervision. Due to lack of\nnegative samples of background category, it is difficult for the network to\nseparate foreground and background, resulting in poor detection performance. In\nthis report, we present our 2021 HACS Challenge - Weakly-supervised Learning\nTrack solution that based on BaSNet to address above problem. Specifically, we\nfirst adopt pre-trained CSN, Slowfast, TDN, and ViViT as feature extractors to\nget feature sequences. Then our proposed Local-Global Background Modeling\nNetwork (LGBM-Net) is trained to localize instances by using only video-level\nlabels based on Multi-Instance Learning (MIL). Finally, we ensemble multiple\nmodels to get the final detection results and reach 22.45% mAP on the test set\n"}
+{"abstract": "  Polycrystal microstructures, with their distinct physical, chemical,\nstructural and topological entities, play an important role in determining the\neffective properties of materials. Particularly for computational studies, the\nwell-known Voronoi tessellation technique is regularly used for obtaining\nmicrostructures. Standard Voronoi tessellations, however, exhibit statistics\nthat are generally far removed from those in real microstructures.\nNevertheless, such tessellations can be optimized to obtain certain key\nfeatures and statistics seen in real microstructures. In this work, we develop\nthe open-source software package OptiMic that enables the generation of\noptimized microstructures for both finite element as well as atomistic\nsimulations. OptiMic allows for both monodispersive grains as well as irregular\ngrains obtained currently via Voronoi tessellations. These initial\nmicrostructures can then be optimized to reflect desired statistical features.\nA key feature of the tool is that it gives the user extensive control on the\noptimization process via customizable cost functions. The software currently\nperforms tessellations with the Voronoi method and can be easily extended to\ninclude other methods like grain-growth, phase-field etc.\n"}
+{"abstract": "  Thermodynamic quantities, like heat, entropy, or work, are random variables,\nin stochastic systems. Here, we investigate the statistics of the heat\nexchanged by a Brownian particle subjected to a logarithm-harmonic potential.\nWe derive analytically the characteristic function and its moments for the\nheat. Through numerical integration, and numerical simulation, we calculate the\nprobability distribution as well, characterizing fully the statistical behavior\nof the heat. The results are also investigated in the asymptotic limit, where\nwe encounter the characteristic function in terms of hypergeometric functions.\n"}
+{"abstract": "  Efficient integration of solar energy into the electricity mix depends on a\nreliable anticipation of its intermittency. A promising approach to forecast\nthe temporal variability of solar irradiance resulting from the cloud cover\ndynamics is based on the analysis of sequences of ground-taken sky images or\nsatellite images. Despite encouraging results, a recurrent limitation of\nexisting deep learning approaches lies in the ubiquitous tendency of reacting\nto past observations rather than actively anticipating future events. This\nleads to a frequent temporal lag and limited ability to predict sudden events.\nTo address this challenge, we introduce ECLIPSE, a spatio-temporal neural\nnetwork architecture that models cloud motion from sky images to not only\npredict future irradiance levels but also segmented images, which provide\nricher information on the local irradiance map. We show that ECLIPSE\nanticipates critical events and reduces temporal delay while generating\nvisually realistic futures.\n"}
+{"abstract": "  The spin effect of electrons/positrons ($e^-$/$e^+$) and polarization effect\nof $\\gamma$ photons are investigated in the interaction of two\ncounter-propagating linearly polarized 10-PW-class laser pulses with a thin\nfoil target. The processes of nonlinear Compton scattering and nonlinear\nBreit-Wheeler pair production based on spin- and polarization-resolved\nprobabilities are implemented into the particle-in-cell (PIC) algorithm by\nMonte Carlo methods. It is found from PIC simulations that the average degree\nof linear polarization of emitted $\\gamma$ photons can exceed $50\\%$. This\npolarization effect leads to reduced positron yield by about $10\\%$. At some\nmedium positron energies, the reduction can reach $20\\%$. Furthermore, we also\nobserve that the local spin polarization of $e^-$/$e^+$ leads to a slight\ndecrease of the positron yield about $2\\%$ and some anomalous phenomena about\nthe positron spectrum and photon polarization at the high-energy range, due to\nspin-dependent photon emissions. Our results indicate that spin and\npolarization effects should be considered in calculating the pair production\nand laser-plasma interaction with the laser power of 10-PW class.\n"}
+{"abstract": "  We prove that if $P(D)$ is some constant coefficient partial differential\noperator and $f$ is a scalar field such that $P(D)f$ vanishes in a given open\nset, then the integrals of $f$ over all lines intersecting that open set\ndetermine the scalar field uniquely everywhere. This is done by proving a\nunique continuation property of fractional Laplacians which implies uniqueness\nfor the partial data problem. We also apply our results to partial data\nproblems of vector fields.\n"}
+{"abstract": "  Wavelet transformation stands as a cornerstone in modern data analysis and\nsignal processing. Its mathematical essence is an invertible transformation\nthat discerns slow patterns from fast patterns in the frequency domain, which\nrepeats at each level. Such an invertible transformation can be learned by a\ndesigned normalizing flow model. With a factor-out scheme resembling the\nwavelet downsampling mechanism, a mutually independent prior, and parameter\nsharing along the depth of the network, one can train normalizing flow models\nto factor-out variables corresponding to fast patterns at different levels,\nthus extending linear wavelet transformations to non-linear learnable models.\nIn this paper, a concrete way of building such flows is given. Then, a\ndemonstration of the model's ability in lossless compression task, progressive\nloading, and super-resolution (upsampling) task. Lastly, an analysis of the\nlearned model in terms of low-pass/high-pass filters is given.\n"}
+{"abstract": "  In recent years, several magnetic Mott insulators with strong spin-orbit\ncouplings were suggested to be proximate to the Kitaev quantum spin liquid\n(QSL) whose one of the most exciting features is the fractionalization of spin\nexcitations into itinerant Majorana fermions and static $Z_2$ fluxes. Motivated\nby the emergence of this plethora of 4d and 5d transition metal Kitaev\nmaterials and by the fact that some level of disorder is inevitable in real\nmaterials, here we study how the Kitaev QSL responds to various forms of\ndisorder, such as vacancies, impurities, and bond randomness. First, we argue\nthat the presence of the quenched disorder in the Kitaev QSL can lead to the\nAnderson localization of Majorana fermions and the appearance of Lifshitz\ntails. We point out that the Anderson localization of low-energy states is\nparticularly strong in the extended Kitaev model with the time reversal\nsymmetry breaking term. Second, we show that the disorder effects on the\nlow-energy Majorana fermion modes can be detected in thermal transport. Third,\nwe show that at finite temperatures the $Z_2$ fluxes become thermally excited\nand give rise to an additional disorder for the Majorana fermions. This source\nof the disorder dominates at high temperatures. Fourth, we demonstrate that\nboth the structure of the energy spectrum and the thermal transport properties\nof the disordered Kitaev QSL depend strongly on the character of disorder.\nWhile we find that both the site disorder and the bond randomness suppress the\nlongitudinal thermal conductivity, the low-energy localization is stronger in\nthe case of the site disorder.\n"}
+{"abstract": "  We introduce diversified risk parity embedded with various reward-risk\nmeasures and more generic allocation rules for portfolio construction. We\nempirically test advanced reward-risk parity strategies and compare their\nperformance with an equally-weighted risk portfolio in various asset universes.\nThe reward-risk parity strategies we tested exhibit consistent outperformance\nevidenced by higher average returns, Sharpe ratios, and Calmar ratios. The\nalternative allocations also reflect less downside risks in Value-at-Risk,\nconditional Value-at-Risk, and maximum drawdown. In addition to the enhanced\nperformance and reward-risk profile, transaction costs can be reduced by\nlowering turnover rates. The Carhart four-factor analysis also indicates that\nthe diversified reward-risk parity allocations gain superior performance.\n"}
+{"abstract": "  Transfer learning is a machine learning paradigm where the knowledge from one\ntask is utilized to resolve the problem in a related task. On the one hand, it\nis conceivable that knowledge from one task could be useful for solving a\nrelated problem. On the other hand, it is also recognized that if not executed\nproperly, transfer learning algorithms could in fact impair the learning\nperformance instead of improving it - commonly known as \"negative transfer\". In\nthis paper, we study the online transfer learning problems where the source\nsamples are given in an offline way while the target samples arrive\nsequentially. We define the expected regret of the online transfer learning\nproblem and provide upper bounds on the regret using information-theoretic\nquantities. We also obtain exact expressions for the bounds when the sample\nsize becomes large. Examples show that the derived bounds are accurate even for\nsmall sample sizes. Furthermore, the obtained bounds give valuable insight on\nthe effect of prior knowledge for transfer learning in our formulation. In\nparticular, we formally characterize the conditions under which negative\ntransfer occurs.\n"}
+{"abstract": "  We develop an analytical framework for self-stabilizing optical manipulation\nof freestanding metasurfaces in three dimensions. Our framework reveals that\nthe challenging problem of stabilization against translational and rotational\nperturbations in three dimensions is reduced to a simpler scattering analysis\nof the metasurface unit cell in two dimensions. We derive universal analytical\nstiffness coefficients applicable to arbitrary three-dimensional radial\nmetasurfaces and radial beam intensity profiles. The analytical nature of our\nframework facilitates highly efficient discovery of optimal optomechanical\nmetasurfaces. Such use of metasurfaces for mechanical stabilization enables\nmacroscale and long-range control in collimated, but otherwise unfocused light\nbeams, and could open up new avenues for manipulation beyond traditional\noptical tweezing and transport.\n"}
+{"abstract": "  This paper reports on the derivation and implementation of a shape\noptimization procedure for the minimization of hemolysis induction in\nbiomedical devices. Hemolysis is a blood damaging phenomenon that may occur in\nmechanical blood-processing applications where large velocity gradients are\nfound. An increased level of damaged blood can lead to deterioration of the\nimmune system and quality of life. It is, thus, important to minimize\nflow-induced hemolysis by improving the design of next-generation biomedical\nmachinery. Emphasis is given to the formulation of a continuous adjoint\ncomplement to a power-law hemolysis prediction model dedicated to efficiently\nidentifying the shape sensitivity to hemolysis. The computational approach is\nverified against the analytical solutions of a benchmark problem and computed\nsensitivity derivatives are validated by a finite differences study on a\ngeneric 2D stenosed geometry. The application included addresses a 3D ducted\ngeometry which features typical characteristics of biomedical devices. An\noptimized shape, leading to a potential improvement in hemolysis induction up\nto 22%, is identified. It is shown, that the improvement persists for\ndifferent, literature-reported hemolysis-evaluation parameters.\n"}
+{"abstract": "  In this paper, we prove the existence of infinitely many solutions for a\nclass of quasilinear elliptic $m(x)$-polyharmonic Kirchhoff equations where the\nnonlinear function has a quasicritical growth at infinity and without assuming\nthe Ambrosetti and Rabinowitz type condition. The new aspect consists in\nemploying the notion of a Schauder basis to verify the geometry of the\nsymmetric mountain pass theorem. Furthermore, for the case $m(x)\\equiv Const$,\nwe introduce a positive quantity $\\lambda_M$ similar to the first eigenvalue of\nthe $m$-polyharmonic operator to find a mountain pass solution, and also to\ndiscuss the sublinear case under large growth conditions at infinity and at\nzero. Our results are an improvement and generalization of the corresponding\nresults obtained by Colasuonno-Pucci (Nonlinear Analysis: Theory, Methods and\nApplications, $2011$) and Bae-Kim (Mathematical Methods in the Applied\nSciences, $2020$).\n"}
+{"abstract": "  We give a modified definition of a reproducing kernel Hilbert $C^*$-module\n(shortly, $RKHC^*M$) without using the condition of self-duality and discuss\nsome related aspects; in particular, an interpolation theorem is presented. We\ninvestigate the exterior tensor product of $RKHC^*M$s and find their\nreproducing kernel. In addition, we deal with left multipliers of $RKHC^*M$s.\nUnder some mild conditions, it is shown that one can make a new $RKHC^*M$ via a\nleft multiplier. Moreover, we introduce the Berezin transform of an operator in\nthe context of $RKHC^*M$s and construct a unital subalgebra of the unital\n$C^*$-algebra consisting of adjointable maps on an $RKHC^*M$ and show that it\nis closed with respect to a certain topology. Finally, the Papadakis theorem is\nextended to the setting of $RKHC^*M$, and in order for the multiplication of\ntwo specific functions to be in the Papadakis $RKHC^*M$, some conditions are\nexplored.\n"}
+{"abstract": "  Let $F_n(\\Sigma_{g,1})$ denote the configuration space of $n$ ordered points\non the surface $\\Sigma_{g,1}$ and let $\\Gamma_{g,1}$ denote the mapping class\ngroup of $\\Sigma_{g,1}$. We prove that the action of $\\Gamma_{g,1}$ on\n$H_i(F_n(\\Sigma_{g,1});\\mathbb{Z})$ is trivial when restricted to the $i^{th}$\nstage of the Johnson filtration $\\mathcal{J}(i)\\subset \\Gamma_{g,1}$. We give\nexamples showing that $\\mathcal{J}(2)$ acts nontrivially on\n$H_3(F_3(\\Sigma_{g,1}))$ for $g\\ge 2$, and provide two new conceptual\nreinterpretations of a certain group introduced by Moriyama.\n"}
+{"abstract": "  The Pauli Exclusion Operator (PEO) which ensures proper symmetry of the\neigenstates of multi-electron systems with respect to exchange of each pair of\nelectrons is introduced. Once PEO is added to the Hamiltonian, no additional\nconstraints on multi-electron wave function due to the Pauli exclusion\nprinciple are needed. For two-electron states in two dimensions ($2D$) the PEO\ncan be expressed in a closed form in terms of momentum operators, while in the\nposition representation PEO is a non-local operator. Generalizations of PEO for\nmulti-electron systems is introduced. Several approximations to PEO are\ndiscussed. Examples of analytical and numerical calculations of PEO are given\nfor isotropic and anisotropic Hooke's atom in~$2D$. Application of approximate\nand kernel forms of PEO for calculations of energies and states in~$2D$ Hooke's\natom are analyzed. Relation of PEO to standard variational calculations with\nthe use of Slater determinant is discussed.\n"}
+{"abstract": "  Generative adversarial networks (GANs) are able to generate high resolution\nphoto-realistic images of objects that \"do not exist.\" These synthetic images\nare rather difficult to detect as fake. However, the manner in which these\ngenerative models are trained hints at a potential for information leakage from\nthe supplied training data, especially in the context of synthetic faces. This\npaper presents experiments suggesting that identity information in face images\ncan flow from the training corpus into synthetic samples without any\nadversarial actions when building or using the existing model. This raises\nprivacy-related questions, but also stimulates discussions of (a) the face\nmanifold's characteristics in the feature space and (b) how to create\ngenerative models that do not inadvertently reveal identity information of real\nsubjects whose images were used for training. We used five different face\nmatchers (face_recognition, FaceNet, ArcFace, SphereFace and Neurotechnology\nMegaMatcher) and the StyleGAN2 synthesis model, and show that this identity\nleakage does exist for some, but not all methods. So, can we say that these\nsynthetically generated faces truly do not exist? Databases of real and\nsynthetically generated faces are made available with this paper to allow full\nreplicability of the results discussed in this work.\n"}
+{"abstract": "  We propose a novel reinforcement learning based framework PoBRL for solving\nmulti-document summarization. PoBRL jointly optimizes over the following three\nobjectives necessary for a high-quality summary: importance, relevance, and\nlength. Our strategy decouples this multi-objective optimization into different\nsubproblems that can be solved individually by reinforcement learning.\nUtilizing PoBRL, we then blend each learned policies together to produce a\nsummary that is a concise and complete representation of the original input.\nOur empirical analysis shows state-of-the-art performance on several\nmulti-document datasets. Human evaluation also shows that our method produces\nhigh-quality output.\n"}
+{"abstract": "  A rigorous and comprehensive testing plays a key role in training\nself-driving cars to handle variety of situations that they are expected to see\non public roads.\n  The physical testing on public roads is unsafe, costly, and not always\nreproducible. This is where testing in simulation helps fill the gap, however,\nthe problem with simulation testing is that it is only as good as the simulator\nused for testing and how representative the simulated scenarios are of the real\nenvironment. In this paper, we identify key requirements that a good simulator\nmust have. Further, we provide a comparison of commonly used simulators. Our\nanalysis shows that CARLA and LGSVL simulators are the current state-of-the-art\nsimulators for end to end testing of self-driving cars for the reasons\nmentioned in this paper. Finally, we also present current challenges that\nsimulation testing continues to face as we march towards building fully\nautonomous cars.\n"}
+{"abstract": "  We study the propagation in time of $1/2$-Gelfand-Shilov singularities, i.e.\nglobal analytic singularities, of tempered distributional solutions of the\ninitial value problem \\begin{align*} \\begin{cases} u_t + q^w(x,D) u = 0 \\\\\nu|_{t=0} = u_0, \\end{cases} \\end{align*} on $\\mathbb{R}^n$, where $u_0$ is a\ntempered distribution on $\\mathbb{R}^n$, $q=q(x,\\xi)$ is a complex-valued\nquadratic form on $\\mathbb{R}^{2n} = \\mathbb{R}^n_x \\times \\mathbb{R}^n_\\xi$\nwith nonnegative real part $\\textrm{Re} \\ q \\ge 0$, and $q^w(x,D)$ is the Weyl\nquantization of $q$. We prove that the $1/2$-Gelfand-Shilov singularities of\nthe initial data that are contained within a distinguished linear subspace of\nthe phase space $\\mathbb{R}^{2n}$, called the singular space of $q$, are\ntransported by the Hamilton flow of $\\textrm{Im} \\ q$, while all other\n$1/2$-Gelfand-Shilov singularities are instantaneously regularized. Our result\nextends the observation of Hitrik, Pravda-Starov, and Viola '18 that this\nevolution is instantaneously globally analytically regularizing when the\nsingular space of $q$ is trivial.\n"}
+{"abstract": "  A novel explainable AI method called CLEAR Image is introduced in this paper.\nCLEAR Image is based on the view that a satisfactory explanation should be\ncontrastive, counterfactual and measurable. CLEAR Image explains an image's\nclassification probability by contrasting the image with a corresponding image\ngenerated automatically via adversarial learning. This enables both salient\nsegmentation and perturbations that faithfully determine each segment's\nimportance. CLEAR Image was successfully applied to a medical imaging case\nstudy where it outperformed methods such as Grad-CAM and LIME by an average of\n27% using a novel pointing game metric. CLEAR Image excels in identifying cases\nof \"causal overdetermination\" where there are multiple patches in an image, any\none of which is sufficient by itself to cause the classification probability to\nbe close to one.\n"}
+{"abstract": "  A single spin in a Josephson junction can reverse the flow of the\nsupercurrent. At mesoscopic length scales, such $\\pi$-junctions are employed in\nvarious instances from finding the pairing symmetry to quantum computing. In\nYu-Shiba-Rusinov (YSR) states, the atomic scale counterpart of a single spin in\na superconducting tunnel junction, the supercurrent reversal so far has\nremained elusive. Using scanning tunneling microscopy (STM), we demonstrate\nsuch a 0 to $\\pi$ transition of a Josephson junction through a YSR state as we\ncontinuously change the impurity-superconductor coupling. We detect the sign\nchange in the critical current by exploiting a second transport channel as\nreference in analogy to a superconducting quantum interference device (SQUID),\nwhich provides the STM with the required phase sensitivity. The measured change\nin the Josephson current is a signature of the quantum phase transition and\nallows its characterization with unprecedented resolution.\n"}
+{"abstract": "  Temporal receptive fields of models play an important role in action\nsegmentation. Large receptive fields facilitate the long-term relations among\nvideo clips while small receptive fields help capture the local details.\nExisting methods construct models with hand-designed receptive fields in\nlayers. Can we effectively search for receptive field combinations to replace\nhand-designed patterns? To answer this question, we propose to find better\nreceptive field combinations through a global-to-local search scheme. Our\nsearch scheme exploits both global search to find the coarse combinations and\nlocal search to get the refined receptive field combination patterns further.\nThe global search finds possible coarse combinations other than human-designed\npatterns. On top of the global search, we propose an expectation guided\niterative local search scheme to refine combinations effectively. Our\nglobal-to-local search can be plugged into existing action segmentation methods\nto achieve state-of-the-art performance.\n"}
+{"abstract": "  A new exactly solvable case in strong-field quantum electrodynamics with a\ntime-dependent external electric field is presented. The corresponding field is\ngiven by an analytic function, which is asymmetric (in contrast to Sauter-like\nelectric field) with respect to the time instant, where it reaches its maximum\nvalue, that is why we call it the analytic asymmetric electric field. We\nmanaged to exactly solve the Dirac equation with such a field, which made it\npossible to calculate characteristics of the corresponding vacuum instability\nnonperturbatively. We construct the so-called in- and out-solutions and with\ntheir help calculate mean differential and total numbers of created charged\nparticles, probability of the vacuum to remain a vacuum, vacuum mean values of\ncurrent density and energy-momentum tensor of the particles. We study the\nvacuum instability in regimes of rapidly and slowly changing analytic\nasymmetric electric field, and compare the obtained results with corresponding\nones obtained earlier for the case of the symmetric Sauter-like electric field.\nWe also compare exact results in the regime of slowly changing field with\ncorresponding results obtained within the slowly varying field approximation\nrecently proposed by two of the authors, thus demonstrating the effectiveness\nof such an approximation.\n"}
+{"abstract": "  How to attribute responsibility for autonomous artificial intelligence (AI)\nsystems' actions has been widely debated across the humanities and social\nscience disciplines. This work presents two experiments ($N$=200 each) that\nmeasure people's perceptions of eight different notions of moral responsibility\nconcerning AI and human agents in the context of bail decision-making. Using\nreal-life adapted vignettes, our experiments show that AI agents are held\ncausally responsible and blamed similarly to human agents for an identical\ntask. However, there was a meaningful difference in how people perceived these\nagents' moral responsibility; human agents were ascribed to a higher degree of\npresent-looking and forward-looking notions of responsibility than AI agents.\nWe also found that people expect both AI and human decision-makers and advisors\nto justify their decisions regardless of their nature. We discuss policy and\nHCI implications of these findings, such as the need for explainable AI in\nhigh-stakes scenarios.\n"}
+{"abstract": "  In this note, we establish a Vorono\\\"i--Oppenheim summation formula for\ndivisor functions over an arbitrary number field.\n"}
+{"abstract": "  The discovery of structure from time series data is a key problem in fields\nof study working with complex systems. Most identifiability results and\nlearning algorithms assume the underlying dynamics to be discrete in time.\nComparatively few, in contrast, explicitly define dependencies in infinitesimal\nintervals of time, independently of the scale of observation and of the\nregularity of sampling. In this paper, we consider score-based graph learning\nfor the study of dynamical systems. We prove that for vector fields\nparameterized in a large class of neural networks, least squares optimization\nwith adaptive regularization schemes consistently recovers directed graphs of\nlocal independencies in systems of stochastic differential equations. Using\nthis insight, we propose a score-based learning algorithm based on penalized\nNeural Ordinary Differential Equations (modelling the mean process) that we\nshow to be applicable to the general setting of irregularly-sampled\nmultivariate time series and to outperform the state of the art across a range\nof dynamical systems.\n"}
+{"abstract": "  In this paper we propose the adaptive lasso for predictive quantile\nregression (ALQR). Reflecting empirical findings, we allow predictors to have\nvarious degrees of persistence and exhibit different signal strengths. The\nnumber of predictors is allowed to grow with the sample size. We study\nregularity conditions under which stationary, local unit root, and cointegrated\npredictors are present simultaneously. We next show the convergence rates and\nmodel selection consistency of ALQR. We apply the proposed method to the\nout-of-sample quantile prediction problem of stock returns and find that it\noutperforms the existing alternatives. We also provide numerical evidence from\nadditional Monte Carlo experiments, supporting the theoretical results.\n"}
+{"abstract": "  Deep neural networks' remarkable ability to correctly fit training data when\noptimized by gradient-based algorithms is yet to be fully understood. Recent\ntheoretical results explain the convergence for ReLU networks that are wider\nthan those used in practice by orders of magnitude. In this work, we take a\nstep towards closing the gap between theory and practice by significantly\nimproving the known theoretical bounds on both the network width and the\nconvergence time. We show that convergence to a global minimum is guaranteed\nfor networks with widths quadratic in the sample size and linear in their depth\nat a time logarithmic in both. Our analysis and convergence bounds are derived\nvia the construction of a surrogate network with fixed activation patterns that\ncan be transformed at any time to an equivalent ReLU network of a reasonable\nsize. This construction can be viewed as a novel technique to accelerate\ntraining, while its tight finite-width equivalence to Neural Tangent Kernel\n(NTK) suggests it can be utilized to study generalization as well.\n"}
+{"abstract": "  A Rb deficiency by a factor two with respect to the Sun has been found in M\ndwarfs of solar metallicity. This deficiency is difficult to understand from\nboth the observational and nucleosynthesis point of views. To test the\nreliability of this Rb deficiency, we study the Rb and Zr abundances in a\nsample of KM-type giant stars in a similar metallicity range extracted from the\nAMBRE Project. We derive Rb and Zr abundances in 54 giant stars with\nmetallicity close to solar by spectral synthesis in LTE and NLTE. The impact of\nthe Zeeman broadening in the RbI line is also studied. The LTE analysis results\nin a Rb deficiency in giant stars smaller than that obtained in M dwarfs, but\nthe NLTE [Rb/Fe] ratios are very close to solar in the full metallicity range.\nThis contrasts with the figure found in M dwarfs. We investigate the effect of\ngravitational settling and magnetic activity as possible causes of the Rb\ndeficiency found in M dwarfs. While, the former phenomenon has a negligible\nimpact on the surface Rb abundance, the existence of an average magnetic field\nwith intensity typical of that observed in M dwarfs may result in systematic Rb\nabundance underestimations if the Zeeman broadening is not considered in the\nspectral synthesis. The new [Rb,Zr/Fe] vs. [Fe/H] relationships can be\nexplained when the Rb production by rotating massive stars and\nlow-and-intermediate mass stars are considered, without the need of any\ndeviation from the standard s-process nucleosynthesis in AGB stars as\npreviously suggested.\n"}
+{"abstract": "  The complexity and size increase of software has extended the delay for\ndevelopers as they wait for code analysis and code merge. With the larger and\nmore complex software, more developers nowadays are developing software with\nlarge source code repositories. The tendency for software platforms to\nimmediately update software packages with feature updates and bug-fixes is a\nsignificant obstacle. Continuous integration systems may help prevent software\nflaws during the active development of software packages, even when they are\ndeployed and updated frequently. Herein, we present a portable and modular code\nreview automation system that inspects incoming code changes such as code\nformat and style, performance regression, static analysis, build and deployment\ntests, and dynamic analysis before merging and changing code. The proposed\nmechanisms are sufficiently lightweight to be hosted on a regular desktop\ncomputer even for numerous developers. The resulting reduced costs allow\ndevelopers to apply the proposed mechanism to many source code repositories.\nExperimental results demonstrate that the proposed mechanism drastically\nreduces overheads and improves usability compared with conventional mechanisms:\nexecution time (6x faster), CPU usage (40% lower), memory consumption (1/180),\nand no out-of-memory occurrence.\n"}
+{"abstract": "  We review theoretical findings, astrophysical modeling, and current\ngravitational-wave evidence of hierarchical stellar-mass black-hole mergers.\nWhile most of the compact binary mergers detected by LIGO and Virgo are\nexpected to consist of first-generation black holes formed from the collapse of\nstars, others might instead be of second (or higher) generation, containing the\nremnants of previous black-hole mergers. Such a subpopulation of hierarchically\nassembled black holes presents distinctive gravitational-wave signatures,\nnamely higher masses, possibly within the pair-instability mass gap, and\ndimensionless spins clustered at the characteristic value of $\\sim$0.7. In\norder to produce hierarchical mergers, astrophysical environments need to\novercome the relativistic recoils imparted to black-hole merger remnants, a\ncondition which prefers hosts with escape speeds $\\gtrsim$ 100 km/s. Promising\nlocations for efficient production of hierarchical mergers include nuclear star\nclusters and accretion disks surrounding active galactic nuclei, though\nenvironments that are less efficient at retaining merger products such as\nglobular clusters may still contribute significantly to the detectable\npopulation of repeated mergers. While GW190521 is the single most promising\nhierarchical-merger candidate to date, constraints coming from large population\nanalyses are becoming increasingly more powerful.\n"}
+{"abstract": "  Biological agents have adopted the principle of attention to limit the rate\nof incoming information from the environment. One question that arises is if an\nartificial agent has access to only a limited view of its surroundings, how can\nit control its attention to effectively solve tasks? We propose an approach for\nlearning how to control a hard attention window by maximizing the mutual\ninformation between the environment state and the attention location at each\nstep. The agent employs an internal world model to make predictions about its\nstate and focuses attention towards where the predictions may be wrong.\nAttention is trained jointly with a dynamic memory architecture that stores\npartial observations and keeps track of the unobserved state. We demonstrate\nthat our approach is effective in predicting the full state from a sequence of\npartial observations. We also show that the agent's internal representation of\nthe surroundings, a live mental map, can be used for control in two partially\nobservable reinforcement learning tasks. Videos of the trained agent can be\nfound at https://sites.google.com/view/hard-attention-control.\n"}
+{"abstract": "  Young stellar associations hold a star formation record that can persist for\nmillions of years, revealing the progression of star formation long after the\ndispersal of the natal cloud. To identify nearby young stellar populations that\ntrace this progression, we have designed a comprehensive framework for the\nidentification of young stars, and use it to identify $\\sim$3$\\times 10^4$\ncandidate young stars within a distance of 333 pc using Gaia DR2. Applying the\nHDBSCAN clustering algorithm to this sample, we identify 27 top-level groups,\nnearly half of which have little to no presence in previous literature. Ten of\nthese groups have visible substructure, including notable young associations\nsuch as Orion, Perseus, Taurus, and Sco-Cen. We provide a complete\nsubclustering analysis on all groups with substructure, using age estimates to\nreveal each region's star formation history. The patterns we reveal include an\napparent star formation origin for Sco-Cen along a semicircular arc, as well as\nclear evidence for sequential star formation moving away from that arc with a\npropagation speed of $\\sim$4 km s$^{-1}$ ($\\sim$4 pc Myr$^{-1}$). We also\nidentify earlier bursts of star formation in Perseus and Taurus that predate\ncurrent, kinematically identical active star-forming events, suggesting that\nthe mechanisms that collect gas can spark multiple generations of star\nformation, punctuated by gas dispersal and cloud regrowth. The large spatial\nscales and long temporal scales on which we observe star formation offer a\nbridge between the processes within individual molecular clouds and the broad\nforces guiding star formation at galactic scales.\n"}
+{"abstract": "  Flying Gyroscopes are fascinating flight objects, which, due to gyroscopic\nstabilization, can achieve surprisingly long flight distances when thrown with\nrapid spin. The most common example hereby is a traditional Frisbee disc. This\npaper focuses on a similar object called X-Zylo, that shows a remarkable\nstraight flight despite its simple geometry. The main aim of the present study\nis to investigate the flight behavior of the X-Zylo and to build a reliable\ngroundwork for further quantitative parameter studies on ring wing\nconfigurations. To achieve this goal, a six degree of freedom model to predict\nthe flight trajectory was developed. The trajectory computation uses\ninterpolated high-fidelity CFD simulation data to calculate the acting moments\nand forces on the object during flight. A launch contraption was built to be\nable to validate the theory systematically and reproducible in experiments\nwithout human factors involved in the launch. Despite the complexity of the\nflight, the theoretical simulations match the real world data qualitatively,\nhowever quantitative differences still prevail. The investigation shows that\nthe deviation between theory and experiment mostly stems from uncertainties in\nthe CFD data as well as the optical recording of the experimental data. Despite\nthe methods outperforming those of prior studies, advancements still have to be\nmade in those areas in order to obtain better quantitative accordance between\ntheory and experiment.\n"}
+{"abstract": "  With computers getting more and more powerful and integrated in our daily\nlives, the focus is increasingly shifting towards more human-friendly\ninterfaces, making Automatic Speech Recognition (ASR) a central player as the\nideal means of interaction with machines. Consequently, interest in speech\ntechnology has grown in the last few years, with more systems being proposed\nand higher accuracy levels being achieved, even surpassing \\textit{Human\nAccuracy}. While ASR systems become increasingly powerful, the computational\ncomplexity also increases, and the hardware support have to keep pace. In this\npaper, we propose a technique to improve the energy-efficiency and performance\nof ASR systems, focusing on low-power hardware for edge devices. We focus on\noptimizing the DNN-based Acoustic Model evaluation, as we have observed it to\nbe the main bottleneck in state-of-the-art ASR systems, by leveraging run-time\ninformation from the Beam Search. By doing so, we reduce energy and execution\ntime of the acoustic model evaluation by 25.6% and 25.9%, respectively, with\nnegligible accuracy loss.\n"}
+{"abstract": "  The symmetry energy and its density dependence are pivotal for many nuclear\nphysics and astrophysics applications, as they determine properties ranging\nfrom the neutron-skin thickness of nuclei to the crust thickness and the radius\nof neutron stars. Recently, PREX-II reported a value of $0.283\\pm0.071$ fm for\nthe neutron-skin thickness of $^{208}$Pb, $R_{\\rm skin}^{^{208}\\text{Pb}}$,\nimplying a symmetry-energy slope parameter $L$ of $106\\pm37$ MeV, larger than\nmost ranges obtained from microscopic calculations and other nuclear\nexperiments. We use a nonparametric equation of state representation based on\nGaussian processes to constrain the symmetry energy $S_0$, $L$, and $R_{\\rm\nskin}^{^{208}\\text{Pb}}$ directly from observations of neutron stars with\nminimal modeling assumptions. The resulting astrophysical constraints from\nheavy pulsar masses, LIGO/Virgo, and NICER favor smaller values of the neutron\nskin and $L$, as well as negative symmetry incompressibilities. Combining\nastrophysical data with chiral effective field theory ($\\chi$EFT) and PREX-II\nconstraints yields $S_0 = 33.0^{+2.0}_{-1.8}$ MeV, $L=53^{+14}_{-15}$ MeV, and\n$R_{\\rm skin}^{^{208}\\text{Pb}} = 0.17^{+0.04}_{-0.04}$ fm. We also examine the\nconsistency of several individual $\\chi$EFT calculations with astrophysical\nobservations and terrestrial experiments. We find that there is only mild\ntension between $\\chi$EFT, astrophysical data, and PREX-II's\n$R_\\mathrm{skin}^{^{208}\\mathrm{Pb}}$ measurement ($p$-value $= 12.3\\%$) and\nthat there is excellent agreement between $\\chi$EFT, astrophysical data, and\nother nuclear experiments.\n"}
+{"abstract": "  The Aubry-Andr\\'e-Harper model provides a paradigmatic example of aperiodic\norder in a one-dimensional lattice displaying a delocalization-localization\nphase transition at a finite critical value $V_c$ of the quasiperiodic\npotential amplitude $V$. In terms of dynamical behavior of the system, the\nphase transition is discontinuous when one measures the quantum diffusion\nexponent $\\delta$ of wave packet spreading, with $\\delta=1$ in the delocalized\nphase $V<V_c$ (ballistic transport), $\\delta \\simeq 1/2$ at the critical point\n$V=V_c$ (diffusive transport), and $\\delta=0$ in the localized phase $V>V_c$\n(dynamical localization). However, the phase transition turns out to be smooth\nwhen one measures, as a dynamical variable, the speed $v(V)$ of excitation\ntransport in the lattice, which is a continuous function of potential amplitude\n$V$ and vanishes as the localized phase is approached. Here we consider a\nnon-Hermitian extension of the Aubry-Andr\\'e-Harper model, in which hopping\nalong the lattice is asymmetric, and show that the dynamical\nlocalization-delocalization transition is discontinuous not only in the\ndiffusion exponent $\\delta$, but also in the speed $v$ of ballistic transport.\nThis means that, even very close to the spectral phase transition point, rather\ncounter-intuitively ballistic transport with a finite speed is allowed in the\nlattice. Also, we show that the ballistic velocity can increase as $V$ is\nincreased above zero, i.e. surprisingly disorder in the lattice can result in\nan enhancement of transport.\n"}
+{"abstract": "  In this paper we present a system that exploits different pre-trained\nLanguage Models for assigning domain labels to WordNet synsets without any kind\nof supervision. Furthermore, the system is not restricted to use a particular\nset of domain labels. We exploit the knowledge encoded within different\noff-the-shelf pre-trained Language Models and task formulations to infer the\ndomain label of a particular WordNet definition. The proposed zero-shot system\nachieves a new state-of-the-art on the English dataset used in the evaluation.\n"}
+{"abstract": "  We present nonlinear (NL) and higher twist (HT) corrections to the color\ndipole model (CDM) bounds at low values of $x$ and $Q^{2}$ using the\nparameterization method. Consistency between the bounds at this region describe\nthat a transition from the linear to the nonlinear behavior is dependence on\nthe behavior of the gluon distribution function. The parameters in the color\ndipole model are comparable with the color dipole bounds at low values of\n$Q^{2}$. Consequently, the obtained reduced cross sections at low and moderate\n$Q^{2}$ values due to the NL+HT effects show a good agreement with the H1 data.\n"}
+{"abstract": "  The material and exact shape of a nanostructure determine its optical\nresponse, which is especially strong for plasmonic metals. Unfortunately, only\na very few plasmonic metals are available, which limits the spectral range\nwhere these strong optical effects can be utilized. Alloying different\nplasmonic metals can overcome this limitation, at the expense of using a high\ntemperature alloying process, which adversely destroys the nanostructure shape.\nHere, we develop a low temperature alloying process at only 300{\\deg}C and\nfabricate Au-Ag nanostructures with a broad diversity of shapes, aspect ratios\nand stoichiometries. EDX and XPS analyses confirm the homogeneous alloying\nthrough the entire sample. Varying the alloy stoichiometry tunes the optical\nresponse of the nanostructure and controls spectral features such as Fano\nresonances. Binary metasurfaces that combine nanostructures with different\nstoichiometries are fabricated using multiple-step electron beam lithography,\nand their optical function as hologram or Fresnel zone plate is demonstrated at\nthe visible wavelength of 532 nm. This low temperature annealing technique\nprovides a versatile and cost-effective way of fabricating complex Au-Ag\nnanostructures with arbitrary stoichiometry.\n"}
+{"abstract": "  Traditional computer vision approaches, based on neural networks (NN), are\ntypically trained on a large amount of image data. By minimizing the\ncross-entropy loss between a prediction and a given class label, the NN and its\nvisual embedding space are learned to fulfill a given task. However, due to the\nsole dependence on the image data distribution of the training domain, these\nmodels tend to fail when applied to a target domain that differs from their\nsource domain. To learn a more robust NN to domain shifts, we propose the\nknowledge graph neural network (KG-NN), a neuro-symbolic approach that\nsupervises the training using image-data-invariant auxiliary knowledge. The\nauxiliary knowledge is first encoded in a knowledge graph with respective\nconcepts and their relationships, which is then transformed into a dense vector\nrepresentation via an embedding method. Using a contrastive loss function,\nKG-NN learns to adapt its visual embedding space and thus its weights according\nto the image-data invariant knowledge graph embedding space. We evaluate KG-NN\non visual transfer learning tasks for classification using the mini-ImageNet\ndataset and its derivatives, as well as road sign recognition datasets from\nGermany and China. The results show that a visual model trained with a\nknowledge graph as a trainer outperforms a model trained with cross-entropy in\nall experiments, in particular when the domain gap increases. Besides better\nperformance and stronger robustness to domain shifts, these KG-NN adapts to\nmultiple datasets and classes without suffering heavily from catastrophic\nforgetting.\n"}
+{"abstract": "  Relationship between agents can be conveniently represented by graphs. When\nthese relationships have different modalities, they are better modelled by\nmultilayer graphs where each layer is associated with one modality. Such graphs\narise naturally in many contexts including biological and social networks.\nClustering is a fundamental problem in network analysis where the goal is to\nregroup nodes with similar connectivity profiles. In the past decade, various\nclustering methods have been extended from the unilayer setting to multilayer\ngraphs in order to incorporate the information provided by each layer. While\nmost existing works assume - rather restrictively - that all layers share the\nsame set of nodes, we propose a new framework that allows for layers to be\ndefined on different sets of nodes. In particular, the nodes not recorded in a\nlayer are treated as missing. Within this paradigm, we investigate several\ngeneralizations of well-known clustering methods in the complete setting to the\nincomplete one and prove some consistency results under the Multi-Layer\nStochastic Block Model assumption. Our theoretical results are complemented by\nthorough numerical comparisons between our proposed algorithms on synthetic\ndata, and also on real datasets, thus highlighting the promising behaviour of\nour methods in various settings.\n"}
+{"abstract": "  Multifunctional semiconductors widen the application scope of existing\nsemiconductor devices. Here we report on the ferromagnetic/semiconductor\ncoupling obtained through nickel and porous-GaN composites. We realised\nnickel-infiltrated and nickel-coated porous-GaN structures, and examined the\nsubsequent magnetic and magnetotransport properties. We found that the\nmagnetization of the porous-GaN/Ni composites strongly depended on the amount\nof deposited nickel and evolves from relatively isotropic and remanent (up to\n90% remanent-to-saturation magnetization) response to an anisotropic response\ncharacteristic of thin-films. The magnetoresistance of nickel sputter-coated\nporous-GaN structures was measured at 300 and 200 K. The temperature dependant\nmeasurements suggested that transport in the GaN layers is dominating the\ncurrent. Nonetheless, depending on sample pore size, the magnetoresistance\ndisplayed high (12-fold) out-of-plane/in-plane anisotropy, and significant\nhysteresis. These results are uncharacteristic of GaN transport and point\ntowards magneto-piezo-resistive coupling between the nickel and the porous GaN.\nThese results encourage deeper investigation of magnetic nanostructure property\ntuning and of magnetic property coupling to GaN and similar materials.\n"}
+{"abstract": "  Purpose: Image classification is perhaps the most fundamental task in imaging\nAI. However, labeling images is time-consuming and tedious. We have recently\ndemonstrated that reinforcement learning (RL) can classify 2D slices of MRI\nbrain images with high accuracy. Here we make two important steps toward\nspeeding image classification: Firstly, we automatically extract class labels\nfrom the clinical reports. Secondly, we extend our prior 2D classification work\nto fully 3D image volumes from our institution. Hence, we proceed as follows:\nin Part 1, we extract labels from reports automatically using the SBERT natural\nlanguage processing approach. Then, in Part 2, we use these labels with RL to\ntrain a classification Deep-Q Network (DQN) for 3D image volumes.\n  Methods: For Part 1, we trained SBERT with 90 radiology report impressions.\nWe then used the trained SBERT to predict class labels for use in Part 2. In\nPart 2, we applied multi-step image classification to allow for combined Deep-Q\nlearning using 3D convolutions and TD(0) Q learning. We trained on a set of 90\nimages. We tested on a separate set of 61 images, again using the classes\npredicted from patient reports by the trained SBERT in Part 1. For comparison,\nwe also trained and tested a supervised deep learning classification network on\nthe same set of training and testing images using the same labels.\n  Results: Part 1: Upon training with the corpus of radiology reports, the\nSBERT model had 100% accuracy for both normal and metastasis-containing scans.\nPart 2: Then, using these labels, whereas the supervised approach quickly\noverfit the training data and as expected performed poorly on the testing set\n(66% accuracy, just over random guessing), the reinforcement learning approach\nachieved an accuracy of 92%. The results were found to be statistically\nsignificant, with a p-value of 3.1 x 10^-5.\n"}
+{"abstract": "  We report piezoelectric response in liquid phase exfoliated MoS2 nanosheets\nwith desired structure and morphology. The piezoelectric effect in liquid phase\nexfoliated few layers of MoS2 flakes is interesting as it may allow the\nscalable fabrication of electronic devices such as self-powered electronics,\npiezoelectric transformers, antennas and more. The piezo force microscopy (PFM)\nmeasurements were used to quantify the amplitude and phase loop, which shows\nstrong piezoelectric coefficient. Herein, the piezoelectric response in few\nlayers of MoS2 is attributed to the defects formed in it during the synthesis\nprocedure. The presence of defects is confirmed by XPS analysis\n"}
+{"abstract": "  In this paper we propose a method to compute a freeform reflector system for\ncollimating and shaping a beam from a point source. We construct these\nreflectors such that the radiant intensity of the source is converted into a\ndesired target. An important generalization in our approach compared to\nprevious research is that the output beam can be in an arbitrary direction. The\ndesign problem is approached by using a generalized Monge-Amp\\`ere equation.\nThis equation is solved using a least-squares algorithm for non-quadratic cost\nfunctions. This algorithm calculates the optical map, from which we can then\ncompute the surfaces. We test our algorithm on two cases. First we consider a\nuniform source and target distribution. Next, we use the model of a laser diode\nlight source and a ring-shaped target distribution.\n"}
+{"abstract": "  We present thermal observations of Ganymede from the Atacama Large Millimeter\nArray (ALMA) in 2016-2019 at a spatial resolution of 300-900 km (0.1-0.2''\nangular resolution) and frequencies of 97.5, 233, and 343.5 GHz (wavelengths of\n3, 1.3, and 0.87 mm); the observations collectively covered all Ganymede\nlongitudes. We determine the global thermophysical properties using a thermal\nmodel that considers subsurface emission and depth- and temperature-dependent\nthermophysical and dielectric properties, in combination with a retrieval\nalgorithm. The data are sensitive to emission from the upper $\\sim$0.5 meter of\nthe surface, and we find a millimeter emissivity of 0.75-0.78 and (sub)surface\nporosities of 10-40%, corresponding to effective thermal inertias of 400-800 J\nm^{-2} K^{-1} s^{-1/2}. Combined with past infrared results, as well as\nmodeling presented here of a previously-unpublished Galileo PPR nighttime\ninfrared observation, the multi-wavelength constraints are consistent with a\ncompaction profile whereby the porosity drops from ~85% at the surface to\n10{+30/-10}% at depth over a compaction length scale of tens of cm. We present\nmaps of temperature residuals from the best-fit global models which indicate\nlocalized variations in thermal surface properties at some (but not all) dark\nterrains and at impact craters, which appear 5-8 K colder than the model.\nEquatorial regions are warmer than predicted by the model, in particular near\nthe centers of the leading and trailing hemispheres, while the mid-latitudes\n(~30-60 degrees) are generally colder than predicted; these trends are\nsuggestive of an exogenic origin.\n"}
+{"abstract": "  Let $R$ be a ring and $S$ a multiplicative subset of $R$. An $R$-module $T$\nis called uniformly $S$-torsion provided that $sT=0$ for some $s\\in S$. The\nnotion of $S$-exact sequences is also introduced from the viewpoint of\nuniformity. An $R$-module $F$ is called $S$-flat provided that the induced\nsequence $0\\rightarrow A\\otimes_RF\\rightarrow B\\otimes_RF\\rightarrow\nC\\otimes_RF\\rightarrow 0$ is $S$-exact for any $S$-exact sequence $0\\rightarrow\nA\\rightarrow B\\rightarrow C\\rightarrow 0$. A ring $R$ is called $S$-von Neumann\nregular provided there exists an element $s\\in S$ satisfies that for any $a\\in\nR$ there exists $r\\in R$ such that $sa=ra^2$. We obtain that a ring $R$ an\n$S$-von Neumann regular ring if and only if any $R$-module is $S$-flat. Several\nproperties of $S$-flat modules and $S$-von Neumann regular rings are obtained.\n"}
+{"abstract": "  The majority of stochastic channel models rely on the electromagnetic\nfar-field assumption. This assumption breaks down in future applications that\npush towards the electromagnetic near-field region such as those where the use\nof very large antenna arrays is envisioned. Motivated by this consideration, we\nshow how physical principles can be used to derive a channel model that is also\nvalid in the electromagnetic near-field. We show that wave propagation through\na three-dimensional scattered medium can be generally modeled as a linear and\nspace-variant system. We first review the physics principles that lead to a\nclosed-form deterministic angular representation of the channel response. This\nserves as a basis for deriving a stochastic representation of the channel in\nterms of statistically independent Gaussian random coefficients for randomly\nspatially-stationary propagation environments. The very desirable property of\nspatial stationarity can always be retained by excluding reactive propagation\nmechanisms confined in the extreme near-field propagation region. Remarkably,\nthe provided stochastic representation is directly connected to the Fourier\nspectral representation of a general spatially-stationary random field.\n"}
+{"abstract": "  We present a novel LSTM cell architecture capable of learning both intra- and\ninter-perspective relationships available in visual sequences captured from\nmultiple perspectives. Our architecture adopts a novel recurrent joint learning\nstrategy that uses additional gates and memories at the cell level. We\ndemonstrate that by using the proposed cell to create a network, more effective\nand richer visual representations are learned for recognition tasks. We\nvalidate the performance of our proposed architecture in the context of two\nmulti-perspective visual recognition tasks namely lip reading and face\nrecognition. Three relevant datasets are considered and the results are\ncompared against fusion strategies, other existing multi-input LSTM\narchitectures, and alternative recognition solutions. The experiments show the\nsuperior performance of our solution over the considered benchmarks, both in\nterms of recognition accuracy and complexity. We make our code publicly\navailable at https://github.com/arsm/MPLSTM.\n"}
+{"abstract": "  This paper describes FBK's system submission to the IWSLT 2021 Offline Speech\nTranslation task. We participated with a direct model, which is a\nTransformer-based architecture trained to translate English speech audio data\ninto German texts. The training pipeline is characterized by knowledge\ndistillation and a two-step fine-tuning procedure. Both knowledge distillation\nand the first fine-tuning step are carried out on manually segmented real and\nsynthetic data, the latter being generated with an MT system trained on the\navailable corpora. Differently, the second fine-tuning step is carried out on a\nrandom segmentation of the MuST-C v2 En-De dataset. Its main goal is to reduce\nthe performance drops occurring when a speech translation model trained on\nmanually segmented data (i.e. an ideal, sentence-like segmentation) is\nevaluated on automatically segmented audio (i.e. actual, more realistic testing\nconditions). For the same purpose, a custom hybrid segmentation procedure that\naccounts for both audio content (pauses) and for the length of the produced\nsegments is applied to the test data before passing them to the system. At\ninference time, we compared this procedure with a baseline segmentation method\nbased on Voice Activity Detection (VAD). Our results indicate the effectiveness\nof the proposed hybrid approach, shown by a reduction of the gap with manual\nsegmentation from 8.3 to 1.4 BLEU points.\n"}
+{"abstract": "  Discrete time crystals (DTCs) refer to a novel many-body steady state that\nspontaneously breaks the discrete time-translational symmetry in a\nperiodically-driven quantum system. Here, we study DTCs in a Bose-Einstein\ncondensate (BEC) bouncing resonantly on an oscillating mirror, using a two-mode\nmodel derived from a standard quantum field theory. We investigate the validity\nof this model and apply it to study the long-time behavior of our system. A\nwide variety of initial states based on two Wannier modes are considered. We\nfind that in previous studies the investigated phenomena in the evolution\ntime-window ($\\lessapprox$2000 driving periods) are actually \"short-time\"\ntransient behavior though DTC formation signaled by the sub-harmonic responses\nis still shown if the inter-boson interaction is strong enough. After a much\nlonger (about 20 times) evolution time, initial states with no \"long-range\"\ncorrelations relax to a steady state, where time-symmetry breaking can be\nunambiguously defined. Quantum revivals also eventually occur. This long-time\nbehavior can be understood via the many-body Floquet quasi-eigenenergy spectrum\nof the two-mode model. A symmetry-breaking edge for DTC formation appears in\nthe spectrum for strong enough interaction, where all quasi-eigenstates below\nthe edge are symmetry-breaking while those above the edge are symmetric. The\nlate-time steady state's time-translational symmetry depends solely on whether\nthe initial energy is above or below the symmetry-breaking edge. A phase\ndiagram showing regions of symmetry-broken and symmetric phases for differing\ninitial energies and interaction strengths is presented. We find that according\nto this two-mode model, the discrete time crystal survives for times out to at\nleast 250,000 driving periods.\n"}
+{"abstract": "  The test bench time needed for model-based calibration can be reduced with\nactive learning methods for test design. This paper presents an improved\nstrategy for active output selection. This is the task of learning multiple\nmodels in the same input dimensions and suits the needs of calibration tasks.\nCompared to an existing strategy, we take into account the noise estimate,\nwhich is inherent to Gaussian processes. The method is validated on three\ndifferent toy examples. The performance compared to the existing best strategy\nis the same or better in each example. In a best case scenario, the new\nstrategy needs at least 10% less measurements compared to all other active or\npassive strategies. Further efforts will evaluate the strategy on a real-world\napplication. Moreover, the implementation of more sophisticated active-learning\nstrategies for the query placement will be realized.\n"}
+{"abstract": "  We study a certain wormholing phenomenon that takes place in the\nKoll\\'ar--Shepherd-Barron--Alexeev (KSBA) compactification of the moduli space\nof surfaces of general type. It occurs because of the appearance of particular\nextremal P-resolutions in surfaces on the KSBA boundary. We state a general\nwormhole conjecture, and we prove it for a wide range of cases. At the end, we\ndiscuss some topological properties and open questions.\n"}
+{"abstract": "  With the aim of evaluating image forensics tools, we propose a methodology to\ncreate forgeries traces, leaving intact the semantics of the image. Thus, the\nonly forgery cues left are the specific alterations of one or several aspects\nof the image formation pipeline. This methodology creates automatically forged\nimages that are challenging to detect for forensic tools and overcomes the\nproblem of creating convincing semantic forgeries. Based on this methodology,\nwe create the Trace database and conduct an evaluation of the main\nstate-of-the-art image forensics tools.\n"}
+{"abstract": "  The strong-field approximation for high-harmonic generation in near-infrared\nand infrared laser pulses is formulated in the accelerated Kramers-Henneberger\nframe. The accompanying physical picture is discussed and the nature of the\nleading-order term is contrasted with that of the three-step model following\nthe strong-field-approximation formulation in the length or velocity gauges.\nThe theory is illustrated by high-harmonic generation spectra for atomic\nhydrogen.\n"}
+{"abstract": "  The axion has emerged in recent years as a leading particle candidate to\nprovide the mysterious dark matter in the cosmos, as we review here for a\ngeneral scientific audience. We describe first the historical roots of the\naxion in the Standard Model of particle physics and the problem of\ncharge-parity invariance of the strong nuclear force. We then discuss how the\naxion emerges as a dark matter candidate, and how it is produced in the early\nUniverse. The symmetry properties of the axion dictate the form of its\ninteractions with ordinary matter. Astrophysical considerations restrict the\nparticle mass and interaction strengths to a limited range, which facilitates\nthe planning of experiments to detect the axion. A companion review discusses\nthe exciting prospect that the axion could indeed be detected in the near term\nin the laboratory.\n"}
+{"abstract": "  A continuous-time quantum walk on a dynamic graph evolves by Schr\\\"odinger's\nequation with a sequence of Hamiltonians encoding the edges of the graph. This\nprocess is universal for quantum computing, but in general, the dynamic graph\nthat implements a quantum circuit can be quite complicated. In this paper, we\ngive six scenarios under which a dynamic graph can be simplified, and they\nexploit commuting graphs, identical graphs, perfect state transfer,\ncomplementary graphs, isolated vertices, and uniform mixing on the hypercube.\nAs examples, we simplify dynamic graphs, in some instances allowing\nsingle-qubit gates to be implemented in parallel.\n"}
+{"abstract": "  SAR (VV and VH polarization) and optical data are widely used in image fusion\nto use the complimentary information of each other and to obtain the\nbetter-quality image (in terms of spatial and spectral features) for the\nimproved classification results. This paper uses anisotropic diffusion with PCA\nfor the fusion of SAR and optical data and patch-based SVM Classification with\nLBP (LBP-PSVM). Fusion results with VV polarization performed better than VH\npolarization using considered fusion method. For classification, the\nperformance of LBP-PSVM using S1 (VV) with S2, S1 (VH) with S2 is compared with\nSVM classifier (without patch) and PSVM classifier (with patch), respectively.\nClassification results suggests that the LBP-PSVM classifier is more effective\nin comparison to SVM and PSVM classifiers for considered data.\n"}
+{"abstract": "  The manuscript presents the $LU$ approach to matrix biorthogonal polynomials\nwhen all the even ordered entries in the Gram matrix are zero. This arises in\ncase of a quadratic transformation which is briefly discussed. Further, the\nmain diagonal of the Gram matrix is a zero diagonal and we present the theory\nthat follows from this fact. Precisely, we discuss the Christoffel\ntransformation and matrix representations of the kernel polynomials, usually\ncalled the ABC Theorem. Finally, we provide an illustration of our results\nassuming the Gram matrix has Hankel symmetry.\n"}
+{"abstract": "  The relevance of the Key Information Extraction (KIE) task is increasingly\nimportant in natural language processing problems. But there are still only a\nfew well-defined problems that serve as benchmarks for solutions in this area.\nTo bridge this gap, we introduce two new datasets (Kleister NDA and Kleister\nCharity). They involve a mix of scanned and born-digital long formal\nEnglish-language documents. In these datasets, an NLP system is expected to\nfind or infer various types of entities by employing both textual and\nstructural layout features. The Kleister Charity dataset consists of 2,788\nannual financial reports of charity organizations, with 61,643 unique pages and\n21,612 entities to extract. The Kleister NDA dataset has 540 Non-disclosure\nAgreements, with 3,229 unique pages and 2,160 entities to extract. We provide\nseveral state-of-the-art baseline systems from the KIE domain (Flair, BERT,\nRoBERTa, LayoutLM, LAMBERT), which show that our datasets pose a strong\nchallenge to existing models. The best model achieved an 81.77% and an 83.57%\nF1-score on respectively the Kleister NDA and the Kleister Charity datasets. We\nshare the datasets to encourage progress on more in-depth and complex\ninformation extraction tasks.\n"}
+{"abstract": "  The research of the derivative nonlinear Schrodinger equation (DNLS) has\nattracted more and more extensive attention in theoretical analysis and\nphysical application. The improved physicsinformed neural network (IPINN)\napproach with neuron-wise locally adaptive activation function is presented to\nderive the data-driven localized wave solutions, which contain rational\nsolution, soliton solution, rogue wave, periodic wave and rogue periodic wave\nfor the DNLS with initial and boundary conditions in complex space. Especially,\nthe data-driven periodic wave and rogue periodic wave of the DNLS are\ninvestigated by employing the IPINN method for the first time. Furthermore, the\nrelevant dynamical behaviors, error analysis and vivid plots have been\nexhibited in detail. The numerical results indicate the IPINN method can well\nsimulate the localized wave solutions of the DNLS under a small data set.\n"}
+{"abstract": "  The Internet of Vehicles (IoV) is an application of the Internet of things\n(IoT). It faces two main security problems: (1) the central server of the IoV\nmay not be powerful enough to support the centralized authentication of the\nrapidly increasing connected vehicles, (2) the IoV itself may not be robust\nenough to single-node attacks. To solve these problems, this paper proposes\nSG-PBFT: a secure and highly efficient PBFT consensus algorithm for Internet of\nVehicles, which is based on a distributed blockchain structure. The distributed\nstructure can reduce the pressure on the central server and decrease the risk\nof single-node attacks. The SG-PBFT consensus algorithm improves the\ntraditional PBFT consensus algorithm by using a score grouping mechanism to\nachieve a higher consensus efficiency. The experimental result shows that our\nmethod can greatly improve the consensus efficiency and prevent single-node\nattacks. Specifically, when the number of consensus nodes reaches 1000, the\nconsensus time of our algorithm is only about 27% of what is required for the\nstate-of-the-art consensus algorithm (PBFT). Our proposed SG-PBFT is versatile\nand can be used in other application scenarios which require high consensus\nefficiency.\n"}
+{"abstract": "  The cultivation of orchard meadows provides an ecological benefit for\nbiodiversity, which is significantly higher than in intensively cultivated\norchards. The goal of this research is to create a tree model to automatically\ndetermine possible pruning points for stand-alone trees within meadows. The\nalgorithm which is presented here is capable of building a skeleton model based\non a pre-segmented photogrammetric 3D point cloud. Good results were achieved\nin assigning the points to their leading branches and building a virtual tree\nmodel, reaching an overall accuracy of 95.19 %. This model provided the\nnecessary information about the geometry of the tree for automated pruning.\n"}
+{"abstract": "  Currently, memristor devices suffer from variability between devices and from\ncycle to cycle. In this work, we study the impact of device variations on\nmemristive Material Implication (IMPLY). New constraints for different\nparameters and variables are analytically derived and compared to extensive\nsimulation results, covering single gate and 1T1R crossbar structures. We show\nthat a static analysis based on switching conditions is not sufficient for an\noverall assessment of robustness against device variability. Furthermore, we\noutline parameter ranges within which the IMPLY gate is predicted to produce\ncorrect output values. Our study shows that threshold voltage is the most\ncritical parameter. This work helps scientists and engineers to understand the\npitfalls of designing reliable IMPLY-based calculation units better and design\nthem with more ease. Moreover, these analyses can be used to determine whether\na certain memristor technology is suitable for implementation of IMPLY-based\ncircuits and systems.\n"}
+{"abstract": "  I point out critical errors in the paper \"Bell's Theorem Versus Local Realism\nin a Quaternionic Model of Physical Space\" by J. Christian, published in IEEE\nAccess. Christian's paper in fact contains several conflicting models. None of\nthem form counterexamples to Bell's theorem. Most of Christian's paper is\ndevoted to a model based on the detection loophole due to Pearle (1970).\n"}
+{"abstract": "  Operations on the cohomology of spaces are important tools enhancing the\ndescriptive power of this computable invariant. For cohomology with mod 2\ncoefficients, Steenrod squares are the most significant of these operations.\nTheir effective computation relies on a cup-$i$ construction, a structure on\n(co)chains which is important in its own right, having connections to, among\nothers, lattice field theory, convex geometry and higher category theory. In\nthis article we present new formulas defining a cup-$i$ construction, and use\nthem to introduce a fast algorithm for the computation of Steenrod squares on\nthe cohomology of simplicial complexes.\n"}
+{"abstract": "  We demonstrate on-chip light amplification with integrated optical waveguide\nfabricated on erbium-doped thin film lithium niobate on insulator (TFLNOI)\nusing the photolithography assisted chemo-mechanical etching (PLACE) technique.\nA maximum internal net gain of 18 dB in the small-signal-gain regime is\nmeasured at the peak emission wavelength of 1530 nm for a waveguide length of\n3.6 cm, indicating a differential gain per unit length of 5 dB/cm. This work\npaves the way to the monolithic integration of diverse active and passive\nphotonic components on the TFLNOI platform.\n"}
+{"abstract": "  We obtain various new limit theorems for random walks on SL_2(C) under low\nmoment conditions. For non-elementary measures with a finite second moment, we\nprove a Local Limit Theorem for the norm cocycle, yielding the optimal version\nof a theorem of E. Le Page. For measures with a finite third moment, we obtain\nthe Local Limit Theorem for the matrix coefficients, improving a recent result\nof Grama-Quint-Xiao and the authors, and Berry-Esseen bounds with optimal rate\n$O(1 / \\sqrt n)$ for the norm cocycle and the matrix coefficients. The main\ntool is a detailed study of the spectral properties of the Markov operator and\nits purely imaginary perturbations acting on different function spaces. We\nintroduce, in particular, a new function space derived from the Sobolev space\n$W^{1,2}$ that provides uniform estimates.\n"}
+{"abstract": "  Proactive tile-based virtual reality video streaming computes and delivers\nthe predicted tiles to be requested before playback. All existing works\noverlook the important fact that computing and communication (CC) tasks for a\nsegment may squeeze the time for the tasks for the next segment, which will\ncause less and less available time for the latter segments. In this paper, we\njointly optimize the durations for CC tasks to maximize the completion rate of\nCC tasks under the task duration-squeezing-aware constraint. To ensure the\nlatter segments remain enough time for the tasks, the CC tasks for a segment\nare not allowed to squeeze the time for computing and delivering the subsequent\nsegment. We find the closed-form optimal solution, from which we find a\nminimum-resource-limited, an unconditional and a conditional resource-tradeoff\nregions, which are determined by the total time for proactive CC tasks and the\nplayback duration of a segment. Owing to the duration-squeezing-prohibited\nconstraints, the increase of the configured resources may not be always useful\nfor improving the completion rate of CC tasks. Numerical results validate the\nimpact of the duration-squeezing-prohibited constraints and illustrate the\nthree regions.\n"}
+{"abstract": "  Hyperspectral stimulated Raman scattering (SRS) microscopy is a label-free\ntechnique for biomedical and mineralogical imaging which can suffer from low\nsignal to noise ratios. Here we demonstrate the use of an unsupervised deep\nlearning neural network for rapid and automatic denoising of SRS images: UHRED\n(Unsupervised Hyperspectral Resolution Enhancement and Denoising). UHRED is\ncapable of one-shot learning; only one hyperspectral image is needed, with no\nrequirements for training on previously labelled datasets or images.\nFurthermore, by applying a k-means clustering algorithm to the processed data,\nwe demonstrate automatic, unsupervised image segmentation, yielding, without\nprior knowledge of the sample, intuitive chemical species maps, as shown here\nfor a lithium ore sample.\n"}
+{"abstract": "  String theory's holographic QCD duality makes predictions for hadron physics\nby building models that live in five-dimensional (5D) curved space. In this\npedagogical note, we explain how finding the hadron mass spectrum in these\nmodels amounts to finding the eigenvalues of a time-independent,\none-dimensional Schroedinger equation. Changing the structure of the 5D curved\nspace is equivalent to altering the potential in the Schroedinger equation,\nwhich in turn alters the hadron spectrum. We illustrate this concept with three\nholographic QCD models possessing exact analogs in basic quantum mechanics: the\nfree particle, the infinite square well, and the harmonic oscillator. In\naddition to making aspects of holographic QCD accessible to undergraduates,\nthis formulation can provide students with intuition for the meaning of curved\nspace. This paper is intended primarily as a tool for researchers interested in\ninvolving early-stage undergraduates in research, but is also a suitable\nintroduction to elements of holographic QCD for advanced undergraduate- and\nbeginning graduate students with some knowledge of general relativity and\nclassical field theory.\n"}
+{"abstract": "  Direct evidence of micro-turbulence effect on the onset of neoclassical\ntearing mode (NTM) is reported for the first time in this letter. A puzzling\npositive correlation between critical width of seed island of NTM and\nnormalized plasma pressure beta_p is first observed employing a novel method\nfor clearly separating the processes of seed island and the onset of NTM in the\nEAST tokamak. Different from the methods developed before, the width of the\nseed island is well controlled by slowly ramping up the current in resonant\nmagnetic perturbation coils. It is revealed that the positive correlation is\nmainly attributed to the enhancement of perpendicular transport by\nmicro-turbulence, which overcomes the destabilizing effect of beta_p on the\nonset of NTM. Reduced magnetohydrodynamics (MHD) modeling well reproduced the\ntwo states of nonlinear bifurcations observed in this experiment by including\nthe finite transport effect. This result provides a new route for understanding\nmulti-scale interaction in plasma physics.\n"}
+{"abstract": "  In this paper, we use the Bessel $\\delta$-method, along with new variants of\nthe van der Corput method in two dimensions, to prove non-trivial bounds for\n$\\mathrm{GL}(2)$ exponential sums beyond the Weyl barrier. More explicitly, for\nsums of $\\mathrm{GL}(2)$ Fourier coefficients twisted by $e(f(n))$, with length\n$N$ and phase $f(n)=N^{\\beta} \\log n / 2\\pi$ or $a n^{\\beta}$, non-trivial\nbounds are established for $ \\beta < 1.63651... $, which is beyond the Weyl\nbarrier at $\\beta = 3/2$.\n"}
+{"abstract": "  We study the recovery of multivariate functions from reproducing kernel\nHilbert spaces in the uniform norm. Our main interest is to obtain\npreasymptotic estimates for the corresponding sampling numbers. We obtain\nresults in terms of the decay of related singular numbers of the compact\nembedding into $L_2(D,\\varrho_D)$ multiplied with the supremum of the\nChristoffel function of the subspace spanned by the first $m$ singular\nfunctions. Here the measure $\\varrho_D$ is at our disposal. As an application\nwe obtain near optimal upper bounds for the sampling numbers for periodic\nSobolev type spaces with general smoothness weight. Those can be bounded in\nterms of the corresponding benchmark approximation number in the uniform norm,\nwhich allows for preasymptotic bounds. By applying a recently introduced\nsub-sampling technique related to Weaver's conjecture we mostly lose a\n$\\sqrt{\\log n}$ and sometimes even less. Finally we point out a relation to the\ncorresponding Kolmogorov numbers.\n"}
+{"abstract": "  This work presents a probabilistic channel pruning method to accelerate\nConvolutional Neural Networks (CNNs). Previous pruning methods often zero out\nunimportant channels in training in a deterministic manner, which reduces CNN's\nlearning capacity and results in suboptimal performance. To address this\nproblem, we develop a probability-based pruning algorithm, called batch\nwhitening channel pruning (BWCP), which can stochastically discard unimportant\nchannels by modeling the probability of a channel being activated. BWCP has\nseveral merits. (1) It simultaneously trains and prunes CNNs from scratch in a\nprobabilistic way, exploring larger network space than deterministic methods.\n(2) BWCP is empowered by the proposed batch whitening tool, which is able to\nempirically and theoretically increase the activation probability of useful\nchannels while keeping unimportant channels unchanged without adding any extra\nparameters and computational cost in inference. (3) Extensive experiments on\nCIFAR-10, CIFAR-100, and ImageNet with various network architectures show that\nBWCP outperforms its counterparts by achieving better accuracy given limited\ncomputational budgets. For example, ResNet50 pruned by BWCP has only 0.70\\%\nTop-1 accuracy drop on ImageNet, while reducing 43.1\\% FLOPs of the plain\nResNet50.\n"}
+{"abstract": "  A new basis has been found for the theory of self-organization of transport\navalanches and jet zonal flows in L-mode tokamak plasma, the so-called \"plasma\nstaircase.\" The jet zonal flows are considered as a wave packet of coupled\nnonlinear oscillators characterized by a complex time- and wave-number\ndependent wave function; in a mean-field approximation this function is argued\nto obey a discrete nonlinear Schr\\\"odinger equation with subquadratic power\nnonlinearity. It is shown that the subquadratic power leads directly to a white\nL\\'evy noise, and to a L\\'evy-fractional Fokker-Planck equation for radial\ntransport of test particles (via wave-particle interactions). In a\nself-consistent description the avalanches, which are driven by the white\nL\\'evy noise, interact with the jet zonal flows, which form a system of\nsemi-permeable barriers to radial transport. We argue that the plasma staircase\nsaturates at a state of marginal stability, in whose vicinity the avalanches\nundergo an ever-pursuing localization-delocalization transition. At the\ntransition point, the event-size distribution of the avalanches is found to be\na power-law $w_\\tau (\\Delta n) \\sim \\Delta n^{-\\tau}$, with the drop-off\nexponent $\\tau = ({\\sqrt{17}} + 1)/{2} \\simeq 2.56$. This value is an exact\nresult of the self-consistent model. The edge behavior bears signatures\nenabling to associate it with the dynamics of a self-organized critical (SOC)\nstate. At the same time the critical exponents, pertaining to this state, are\nfound to be inconsistent with classic models of avalanche transport based on\nsand-piles and their generalizations, suggesting that the coupled avalanche-jet\nzonal flow system operates on different organizing principles. The results\nobtained have been validated in a numerical simulation of the plasma staircase\nusing flux-driven gyrokinetic code for L-mode Tore-Supra plasma.\n"}
+{"abstract": "  This paper deals with a general system of equations and conditions arising\nfrom a mathematical model of prostate cancer growth with chemotherapy and\nantiangiogenic therapy that has been recently introduced and analyzed (see [P.\nColli et al., Mathematical analysis and simulation study of a phase-field model\nof prostate cancer growth with chemotherapy and antiangiogenic therapy effects,\nMath. Models Methods Appl. Sci. 30 (2020), 1253-1295], preprint in\narXiv:1907.11618 [math.AP]). The related system includes two evolutionary\noperator equations involving fractional powers of selfadjoint, nonnegative,\nunbounded linear operators having compact resolvents. Both equations contain\nnonlinearities and in particular the equation describing the dynamics of the\ntumor phase variable has the structure of a Allen-Cahn equation with\ndouble-well potential and additional nonlinearity depending also on the other\nvariable, which represents the nutrient concentration. The equation for the\nnutrient concentration is nonlinear as well, with a term coupling both\nvariables. For this system we design an existence, uniqueness and continuous\ndependence theory by setting up a careful analysis which allows the\nconsideration of nonsmooth potentials and the treatment of continuous\nnonlinearities with general growth properties.\n"}
+{"abstract": "  We study evolution equations of drift-diffusion type when various parameters\nare random. Motivated by applications in pedestrian dynamics, we focus on the\ncase when the total mass is, due to boundary or reaction terms, not conserved.\nAfter providing existence and stability for the deterministic problem, we\nconsider uncertainty in the data. Instead of a sensitivity analysis we propose\nto measure functionals of the solution, so-called quantities of interest (QoI),\nby involving scalarizing statistics. For these summarizing statistics we\nprovide probabilistic continuity results.\n"}
+{"abstract": "  We study the gravitational lensing properties of a massive object in a dark\nmatter halo, concentrating on the critical curves and caustics of the combined\nlens. We model the system in the simplest approximation by a point mass\nembedded in a spherical Navarro$-$Frenk$-$White density profile. The low number\nof parameters of such a model permits a systematic exploration of its parameter\nspace. We present galleries of critical curves and caustics for different\nmasses and positions of the point in the halo. We demonstrate the existence of\na critical mass, above which the gravitational influence of the centrally\npositioned point is strong enough to eliminate the radial critical curve and\ncaustic of the halo. In the point-mass parameter space we identify the\nboundaries at which critical-curve transitions and corresponding caustic\nmetamorphoses occur. The number of transitions as a function of position of the\npoint is surprisingly high, ranging from three for higher masses to as many as\neight for lower masses. On the caustics we identify the occurrence of six\ndifferent types of caustic metamorphoses. We illustrate the peculiar properties\nof the single radial critical curve and caustic appearing in an additional\nunusual non-local metamorphosis for a critical mass positioned at the halo\ncenter. Although we constructed the model primarily to study the lensing\ninfluence of individual galaxies in a galaxy cluster, it can also be used to\nstudy the lensing by dwarf satellite galaxies in the halo of a host galaxy, as\nwell as (super)massive black holes at a general position in a galactic halo.\n"}
+{"abstract": "  We analyse a central broadcast continuous variable quantum key distribution\nprotocol in which a beam produced by a thermal source is used to create a\nsecret key between two parties, Alice and Bob. A beam splitter divides the\ninitial beam into a pair of output beams, which are sent to Alice and Bob, with\nEve intercepting Bob's beam. We investigate the protocol in detail, calculating\nmutual informations through a pair of analytic methods and comparing the\nresults to the outputs of a Monte Carlo simulation of the protocol. In a\nlossless system, we find that a lower bound on the key rate remains positive in\nthe protocol under a beam splitter attack, provided Bob receives a nonzero\nproportion of the beam initially sent to him. This suggests that the thermal\nstate protocol could be used experimentally to produce secure keys.\n"}
+{"abstract": "  Atomic interference experiments can probe the gravitational redshift via the\ninternal energy splitting of atoms and thus give direct access to test the\nuniversality of the coupling between matter-energy and gravity at different\nspacetime points. By including possible violations of the equivalence principle\nin a fully quantized treatment of all degrees of freedom, we characterize how\nthe sensitivity to gravitational redshift violations arises in atomic clocks\nand atom interferometers, as well as their underlying limitations.\nSpecifically, we show that: (i.) Contributions beyond linear order to trapping\npotentials lead to such a sensitivity of trapped atomic clocks. (ii.) While\nBragg-type interferometers, even with a superposition of internal states, with\nstate-independent, linear interaction potentials are at first insensitive to\ngravitational redshift tests, modified configurations, for example by\nrelaunching the atoms, can mimic such tests tests under certain conditions.\n(iii.) Guided atom interferometers are comparable to atomic clocks. (iv.)\nInternal transitions lead to state-dependent interaction potentials through\nwhich light-pulse atom interferometers can become sensitive to gravitational\nredshift violations.\n"}
+{"abstract": "  Multi-agent reinforcement learning (MARL) has been increasingly used in a\nwide range of safety-critical applications, which require guaranteed safety\n(e.g., no unsafe states are ever visited) during the learning\nprocess.Unfortunately, current MARL methods do not have safety guarantees.\nTherefore, we present two shielding approaches for safe MARL. In centralized\nshielding, we synthesize a single shield to monitor all agents' joint actions\nand correct any unsafe action if necessary. In factored shielding, we\nsynthesize multiple shields based on a factorization of the joint state space\nobserved by all agents; the set of shields monitors agents concurrently and\neach shield is only responsible for a subset of agents at each\nstep.Experimental results show that both approaches can guarantee the safety of\nagents during learning without compromising the quality of learned policies;\nmoreover, factored shielding is more scalable in the number of agents than\ncentralized shielding.\n"}
+{"abstract": "  We introduce Position Adaptive Convolution (PAConv), a generic convolution\noperation for 3D point cloud processing. The key of PAConv is to construct the\nconvolution kernel by dynamically assembling basic weight matrices stored in\nWeight Bank, where the coefficients of these weight matrices are\nself-adaptively learned from point positions through ScoreNet. In this way, the\nkernel is built in a data-driven manner, endowing PAConv with more flexibility\nthan 2D convolutions to better handle the irregular and unordered point cloud\ndata. Besides, the complexity of the learning process is reduced by combining\nweight matrices instead of brutally predicting kernels from point positions.\n  Furthermore, different from the existing point convolution operators whose\nnetwork architectures are often heavily engineered, we integrate our PAConv\ninto classical MLP-based point cloud pipelines without changing network\nconfigurations. Even built on simple networks, our method still approaches or\neven surpasses the state-of-the-art models, and significantly improves baseline\nperformance on both classification and segmentation tasks, yet with decent\nefficiency. Thorough ablation studies and visualizations are provided to\nunderstand PAConv. Code is released on https://github.com/CVMI-Lab/PAConv.\n"}
+{"abstract": "  Robust laser sources are a fundamental building block for contemporary\ninformation technologies. Originating from condensed-matter physics, the\nconcept of topology has recently entered the realm of optics, offering\nfundamentally new design principles for lasers with enhanced robustness. In\nanalogy to the well-known Majorana fermions in topological superconductors,\nDirac-vortex states have recently been investigated in passive photonic systems\nand are now considered as a promising candidate for single-mode large-area\nlasers. Here, we experimentally realize the first Dirac-vortex topological\nlasers in InAs/InGaAs quantum-dot materials monolithically grown on a silicon\nsubstrate. We observe room-temperature continuous-wave single-mode linearly\npolarized vertical laser emission at a telecom wavelength. Most importantly, we\nconfirm that the wavelength of the Dirac-vortex laser is topologically robust\nagainst variations in the cavity size, and its free spectral range defies the\nuniversal inverse scaling law with the cavity size. These lasers will play an\nimportant role in CMOS-compatible photonic and optoelectronic systems on a\nchip.\n"}
+{"abstract": "  We find and classify self-similar solutions of the curve shortening flow in\nstandard hyperbolic 2-space. Together with earlier work of Halld\\'orsson on\ncurve shortening flow in the plane and Santos dos Reis and Tenenblat in the\n2-sphere, this completes the classification of self-similar curve shortening\nflows in the constant curvature model spaces in 2-dimensions.\n"}
+{"abstract": "  Quantum walks are the quantum mechanical analogue of classical random walks\nand an extremely powerful tool in quantum simulations, quantum search\nalgorithms, and even for universal quantum computing. In our work, we have\ndesigned and fabricated an 8x8 two-dimensional square superconducting qubit\narray composed of 62 functional qubits. We used this device to demonstrate high\nfidelity single and two particle quantum walks. Furthermore, with the high\nprogrammability of the quantum processor, we implemented a Mach-Zehnder\ninterferometer where the quantum walker coherently traverses in two paths\nbefore interfering and exiting. By tuning the disorders on the evolution paths,\nwe observed interference fringes with single and double walkers. Our work is an\nessential milestone in the field, brings future larger scale quantum\napplications closer to realization on these noisy intermediate-scale quantum\nprocessors.\n"}
+{"abstract": "  We prove the stability of the ball as global minimizer of an attractive shape\nfunctional under volume constraint, by means of mass transportation arguments.\nThe stability exponent is $1/2$ and it is sharp. Moreover, we use such\nstability result together with the quantitative (possibly fractional)\nisoperimetric inequality to prove that the ball is a global minimizer of a\nshape functional involving both an attractive and a repulsive term with a\nsufficiently large fixed volume and with a suitable (possibly fractional)\nperimeter penalization.\n"}
+{"abstract": "  We describe the development, characteristics and availability of a test\ncollection for the task of Web table retrieval, which uses a large-scale Web\nTable Corpora extracted from the Common Crawl. Since a Web table usually has\nrich context information such as the page title and surrounding paragraphs, we\nnot only provide relevance judgments of query-table pairs, but also the\nrelevance judgments of query-table context pairs with respect to a query, which\nare ignored by previous test collections. To facilitate future research with\nthis benchmark, we provide details about how the dataset is pre-processed and\nalso baseline results from both traditional and recently proposed table\nretrieval methods. Our experimental results show that proper usage of context\nlabels can benefit previous table retrieval methods.\n"}
+{"abstract": "  Recently, we employed electronic polarization-resolved Raman spectroscopy to\nreveal the strongly correlated excitonic insulator (EI) nature of Ta2NiSe5,\nVolkov et al. [arXiv:2007.07344], and also showed that for\nTa$_2$Ni(Se$_{1-x}$S$_x$)$_5$ alloys the critical excitonic fluctuations\ndiminish with sulfur concentration x exposing a cooperating lattice instability\nthat takes over for large x, Volkov et al. [arXiv:2104.07032]. Here we focus on\nthe lattice dynamics of this EI family. We identify all Raman-active optical\nphonons of fully symmetric and ac-quadrupole-like symmetries and study their\nevolution with temperature and sulfur concentration. We demonstrate the change\nof selection rules at temperatures below the orthorhombic-to-monoclinic\ntransition at Tc(x) that is related to the EI phase. We find that Tc(x)\ndecrease monotonically from 328 K for Ta2NiSe5 to 120 K for Ta2NiS5 and that\nthe magnitude of lattice distortion also decreases with the sulfur\nconcentration x. For x < 0.7, the two lowest-frequency B2g phonon modes show\nstrongly asymmetric lineshapes at high temperatures due to Fano interference\nwith the broad excitonic continuum present in a semimetallic state. Within the\nframework of extended Fano model, we develop a quantitative description of the\ninteracting exciton-phonon excitation lineshape, enabling us to derive the\nintrinsic phonon parameters and determine the exciton-phonon interaction\nstrength, that affects the transition temperature Tc(x). We also observe\nsignatures of the acoustic mode scattered assisted by the structural domain\nwalls formed below Tc. Based on our results, we additionally present a\nconsistent interpretation of the origin of oscillations observed in\ntime-resolved pump-probe experiments.\n"}
+{"abstract": "  Optical depth variations in the Galactic neutral interstellar medium (ISM)\nwith spatial scales from hundreds to thousands of astronomical units have been\nobserved through HI absorption against pulsars and continuum sources, while\nextremely small structures with spatial scales of tens of astronomical units\nremain largely unexplored. The nature and formation of such tiny-scale atomic\nstructures (TSAS) need to be better understood. We report a tentative detection\nof TSAS with a signal-to-noise ratio of 3.2 toward PSR B1557$-$50 in the second\nepoch of two Parkes sessions just 0.36 yr apart, which are the closest-spaced\nspectral observations toward this pulsar. One absorption component showing\nmarginal variations has been identified. Based on the pulsar's proper motion of\n14 mas $\\rm yr^{-1}$ and the component's kinematic distance of 3.3 kpc, the\ncorresponding characteristic spatial scale is 17 au, which is among the\nsmallest sizes of known TSAS. Assuming a similar line-of-sight (LOS) depth, the\ntentative TSAS cloud detected here is overdense and overpressured relative to\nthe cold neutral medium (CNM), and can radiatively cool fast enough to be in\nthermal equilibrium with the ambient environment. We find that turbulence is\nnot sufficient to confine the overpressured TSAS. We explore the LOS elongation\nthat would be required for the tentative TSAS to be at the canonical CNM\npressure, and find that it is $\\sim5000$ -- much larger than filaments observed\nin the ISM. We see some evidence of line width and temperature variations in\nthe CNM components observed at the two epochs, as predicted by models of\nTSAS-like cloud formation colliding warm neutral medium flows.\n"}
+{"abstract": "  For a target task where labeled data is unavailable, domain adaptation can\ntransfer a learner from a different source domain. Previous deep domain\nadaptation methods mainly learn a global domain shift, i.e., align the global\nsource and target distributions without considering the relationships between\ntwo subdomains within the same category of different domains, leading to\nunsatisfying transfer learning performance without capturing the fine-grained\ninformation. Recently, more and more researchers pay attention to Subdomain\nAdaptation which focuses on accurately aligning the distributions of the\nrelevant subdomains. However, most of them are adversarial methods which\ncontain several loss functions and converge slowly. Based on this, we present\nDeep Subdomain Adaptation Network (DSAN) which learns a transfer network by\naligning the relevant subdomain distributions of domain-specific layer\nactivations across different domains based on a local maximum mean discrepancy\n(LMMD). Our DSAN is very simple but effective which does not need adversarial\ntraining and converges fast. The adaptation can be achieved easily with most\nfeed-forward network models by extending them with LMMD loss, which can be\ntrained efficiently via back-propagation. Experiments demonstrate that DSAN can\nachieve remarkable results on both object recognition tasks and digit\nclassification tasks. Our code will be available at:\nhttps://github.com/easezyc/deep-transfer-learning\n"}
+{"abstract": "  We give pointwise upper estimate for the gradient of the heat kernel on some\nfractal-like cable systems including the Vicsek and the Sierpi\\'nski cable\nsystems. Applications to $L^p$-boundedness of quasi-Riesz transforms are\nderived.\n"}
+{"abstract": "  Concerted efforts are underway to establish an infrastructure for a global\nquantum internet to realise a spectrum of quantum technologies. This will\nenable more precise sensors, secure communications, and faster data processing.\nQuantum communications are a front-runner with quantum networks already\nimplemented in several metropolitan areas. A number of recent proposals have\nmodelled the use of space segments to overcome range limitations of purely\nterrestrial networks. Rapid progress in the design of quantum devices have\nenabled their deployment in space for in-orbit demonstrations. We review\ndevelopments in this emerging area of space-based quantum technologies and\nprovide a roadmap of key milestones towards a complete, global quantum\nnetworked landscape. Small satellites hold increasing promise to provide a cost\neffective coverage required to realised the quantum internet. We review the\nstate of art in small satellite missions and collate the most current in-field\ndemonstrations of quantum cryptography. We summarise important challenges in\nspace quantum technologies that must be overcome and recent efforts to mitigate\ntheir effects. A perspective on future developments that would improve the\nperformance of space quantum communications is included. We conclude with a\ndiscussion on fundamental physics experiments that could take advantage of a\nglobal, space-based quantum network.\n"}
+{"abstract": "  People learn motor activities best when they are conscious of their errors\nand make a concerted effort to correct them. While haptic interfaces can\nfacilitate motor training, existing interfaces are often bulky and do not\nalways ensure post-training skill retention. Here, we describe a programmable\nhaptic sleeve composed of textile-based electroadhesive clutches for skill\nacquisition and retention. We show its functionality in a motor learning study\nwhere users control a drone's movement using elbow joint rotation. Haptic\nfeedback is used to restrain elbow motion and make users aware of their errors.\nThis helps users consciously learn to avoid errors from occurring. While all\nsubjects exhibited similar performance during the baseline phase of motor\nlearning, those subjects who received haptic feedback from the haptic sleeve\ncommitted 23.5% fewer errors than subjects in the control group during the\nevaluation phase. The results show that the sleeve helps users retain and\ntransfer motor skills better than visual feedback alone. This work shows the\npotential for fabric-based haptic interfaces as a training aid for motor tasks\nin the fields of rehabilitation and teleoperation.\n"}
+{"abstract": "  Future precision measurements of Higgs boson decays will determine the\nbranching fraction for the decay into two photons with a precision at the one\npercent level. To fully exploit such measurements, equally precise theoretical\npredictions need to be available. To this end we compute four-loop QCD\ncorrections in the large top quark mass expansion to the Higgs boson--photon\nform factor, which enter the two-photon decay width at\nnext-to-next-to-next-to-leading order. Furthermore we obtain corrections to the\ntwo-photon decay width stemming from the emission of additional gluons, which\ncontribute for the first time at next-to-next-to-leading order. Finally, we\ncombine our results with other available perturbative corrections and estimate\nthe residual uncertainty due to missing higher-order contributions.\n"}
+{"abstract": "  We provide a short proof of the theorem that every real multivariate\npolynomial has a symmetric determinantal representation, which was first proved\nin J. W. Helton, S. A. McCullough, and V. Vinnikov, Noncommutative convexity\narises from linear matrix inequalities, J. Funct. Anal. 240 (2006), 105-191. We\nthen provide an example using our approach and extend our results from the real\nfield $\\mathbb{R}$ to an arbitrary field $\\mathbb{F}$ different from\ncharacteristic $2$. The new approach we take is only based on elementary\nresults from the theory of determinants, the theory of Schur complements, and\nbasic properties of polynomials.\n"}
+{"abstract": "  Automated source code summarization is a popular software engineering\nresearch topic wherein machine translation models are employed to \"translate\"\ncode snippets into relevant natural language descriptions. Most evaluations of\nsuch models are conducted using automatic reference-based metrics. However,\ngiven the relatively large semantic gap between programming languages and\nnatural language, we argue that this line of research would benefit from a\nqualitative investigation into the various error modes of current\nstate-of-the-art models. Therefore, in this work, we perform both a\nquantitative and qualitative comparison of three recently proposed source code\nsummarization models. In our quantitative evaluation, we compare the models\nbased on the smoothed BLEU-4, METEOR, and ROUGE-L machine translation metrics,\nand in our qualitative evaluation, we perform a manual open-coding of the most\ncommon errors committed by the models when compared to ground truth captions.\nOur investigation reveals new insights into the relationship between\nmetric-based performance and model prediction errors grounded in an empirically\nderived error taxonomy that can be used to drive future research efforts\n"}
+{"abstract": "  CDF-S XT1 is a fast-rising non-thermal X-ray transient detected by\n\\textit{Chandra} in the Deep-Field South Survey. Although various hypotheses\nhave been suggested, the origin of this transient remains unclear. Here, we\nshow that the observations of CDF-S XT1 are well explained as the X-ray\nafterglow produced by a relativistic structured jet viewed off-axis. We measure\nproperties of the jet, showing that they are similar to those of GRB170817A,\nalbeit at cosmological distances. We measure the observers viewing angle to be\n$\\theta_{\\textrm{obs}} = 10^{\\circ}\\pm3^{\\circ}$ and the core of the\nultra-relativistic jet to be $\\theta_{\\textrm{core}} =\n4.4^{\\circ}\\pm0.9^{\\circ}$, where the uncertainties are the $68\\%$ credible\ninterval. The inferred properties and host galaxy combined with Hubble, radio,\nand optical non detections favour the hypothesis that CDF-S XT1 is the off-axis\nafterglow of a binary neutron star merger. We find that other previously\nsuggested hypotheses are unable to explain all properties of CDF-S XT1. At a\nredshift of $z=2.23$, this is potentially the most distant observed neutron\nstar merger to date and the first orphan afterglow of a short gamma-ray burst.\nWe discuss the implications of a binary neutron star merger at such a high\nredshift for the star-formation rate in the early Universe, the nucleosynthesis\nof heavy elements, and the prospect of identifying other off-axis afterglows.\n"}
+{"abstract": "  This article compares the distributions of integer-valued random variables\nand Poisson random variables. It considers the total variation and the\nWasserstein distance and provides, in particular, explicit bounds on the\npointwise difference between the cumulative distribution functions. Special\nattention is dedicated to estimating the difference when the cumulative\ndistribution functions are evaluated at 0. This permits to approximate the\nminimum (or maximum) of a collection of random variables by a suitable random\nvariable in the Kolmogorov distance. The main theoretical results are obtained\nby combining the Chen-Stein method with size-bias coupling and a generalization\nof size-bias coupling for integer-valued random variables developed herein. A\nwide variety of applications are then discussed with a focus on stochastic\ngeometry.\n"}
+{"abstract": "  Logic-based argumentation is a well-established formalism modelling\nnonmonotonic reasoning. It has been playing a major role in AI for decades,\nnow. Informally, a set of formulas is the support for a given claim if it is\nconsistent, subset-minimal, and implies the claim. In such a case, the pair of\nthe support and the claim together is called an argument. In this paper, we\nstudy the propositional variants of the following three computational tasks\nstudied in argumentation: ARG (exists a support for a given claim with respect\nto a given set of formulas), ARG-Check (is a given set a support for a given\nclaim), and ARG-Rel (similarly as ARG plus requiring an additionally given\nformula to be contained in the support). ARG-Check is complete for the\ncomplexity class DP, and the other two problems are known to be complete for\nthe second level of the polynomial hierarchy (Parson et al., J. Log. Comput.,\n2003) and, accordingly, are highly intractable. Analyzing the reason for this\nintractability, we perform a two-dimensional classification: first, we consider\nall possible propositional fragments of the problem within Schaefer's framework\n(STOC 1978), and then study different parameterizations for each of the\nfragment. We identify a list of reasonable structural parameters (size of the\nclaim, support, knowledge-base) that are connected to the aforementioned\ndecision problems. Eventually, we thoroughly draw a fine border of\nparameterized intractability for each of the problems showing where the\nproblems are fixed-parameter tractable and when this exactly stops.\nSurprisingly, several cases are of very high intractability (paraNP and\nbeyond).\n"}
+{"abstract": "  We consider convergence of alternating projections between non-convex sets\nand obtain applications to convergence of the Gerchberg-Saxton error reduction\nmethod, of the Gaussian expectation-maximization algorithm, and of Cadzow's\nalgorithm.\n"}
+{"abstract": "  In this work, we propose the use of a fully managed machine learning service,\nwhich utilizes active learning to directly build models from unstructured data.\nWith this tool, business users can quickly and easily build machine learning\nmodels and then directly deploy them into a production ready hosted environment\nwithout much involvement from data scientists. Our approach leverages\nstate-of-the-art text representation like OpenAI's GPT2 and a fast\nimplementation of the active learning workflow that relies on a simple\nconstruction of incremental learning using linear models, thus providing a\nbrisk and efficient labeling experience for the users. Experiments on both\npublicly available and real-life insurance datasets empirically show why our\nchoices of simple and fast classification algorithms are ideal for the task at\nhand.\n"}
+{"abstract": "  Whereas proof assistants based on Higher-Order Logic benefit from external\nsolvers' automation, those based on Type Theory resist automation and thus\nrequire more expertise. Indeed, the latter use a more expressive logic which is\nfurther away from first-order logic, the logic of most automatic theorem\nprovers. In this article, we develop a methodology to transform a subset of Coq\ngoals into first-order statements that can be automatically discharged by\nautomatic provers. The general idea is to write modular, pairwise independent\ntransformations and combine them. Each of these eliminates a specific aspect of\nCoq logic towards first-order logic. As a proof of concept, we apply this\nmethodology to a set of simple but crucial transformations which extend the\nlocal context with proven first-order assertions that make Coq definitions and\nalgebraic types explicit. They allow users of Coq to solve non-trivial goals\nautomatically. This methodology paves the way towards the definition and\ncombination of more complex transformations, making Coq more accessible.\n"}
+{"abstract": "  We consider the derivation of data-dependent simultaneous bandwidths for\ndouble kernel heteroskedasticity and autocorrelation consistent (DK-HAC)\nestimators. In addition to the usual smoothing over lagged autocovariances for\nclassical HAC estimators, the DK-HAC estimator also applies smoothing over the\ntime direction. We obtain the optimal bandwidths that jointly minimize the\nglobal asymptotic MSE criterion and discuss the trade-off between bias and\nvariance with respect to smoothing over lagged autocovariances and over time.\nUnlike the MSE results of Andrews (1991), we establish how nonstationarity\naffects the bias-variance trade-o?. We use the plug-in approach to construct\ndata-dependent bandwidths for the DK-HAC estimators and compare them with the\nDK-HAC estimators from Casini (2021) that use data-dependent bandwidths\nobtained from a sequential MSE criterion. The former performs better in terms\nof size control, especially with stationary and close to stationary data.\nFinally, we consider long-run variance estimation under the assumption that the\nseries is a function of a nonparametric estimator rather than of a\nsemiparametric estimator that enjoys the usual T^(1/2) rate of convergence.\nThus, we also establish the validity of consistent long-run variance estimation\nin nonparametric parameter estimation settings.\n"}
+{"abstract": "  In living cells, protein-rich condensates can wet the cell membrane and\nsurfaces of membrane-bound organelles. Interestingly, many phase-separating\nproteins also bind to membranes leading to a molecular layer of bound\nmolecules. Here we investigate how binding to membranes affects surface phase\ntransitions such as wetting and prewetting. We derive a thermodynamic theory\nfor a three-dimensional bulk in the presence of a two-dimensional, flat\nmembrane. Above the saturation concentration, we find that membrane binding\nfacilitates complete wetting and lowers the wetting angle. Moreover, below the\nsaturation concentration, binding facilitates the formation of a thick layer at\nthe membrane and thereby shifts the prewetting phase transition far below the\nsaturation concentration. The distinction between bound and unbound molecules\nnear the surface leads to a large variety of prewetted states. Our work\nsuggests that surface phase transitions combined with molecular binding\nrepresent a versatile mechanism to control the formation of protein-rich\ndomains at intra-cellular surfaces.\n"}
+{"abstract": "  For the wavelet type orthonormal systems $\\phi_n$, we establish a new bound\n  \\begin{equation}\n  \\left\\|\\max_{1\\le m\\le n}\\left|\\sum_{j\\in G_m}\\langle f,\\phi_j\\rangle\n\\phi_j\\right|\\right\\|_p\\lesssim \\sqrt{\\log (n+1)}\\cdot \\|f\\|_p,\\quad\n1<p<\\infty,\n  \\end{equation} where $G_m\\subset N$ are arbitrary sets of indexes. Using this\nestimate, we prove that $\\log n$ is an almost everywhere convergence Weyl\nmultiplier for any orthonormal system of non-overlapping wavelet polynomials.\nIt will also be remarked that $\\log n$ is the optimal sequence in this context.\n"}
+{"abstract": "  Let $W$ be a compact smooth $4$-manifold that deformation retract to a PL\nembedded closed surface. One can arrange the embedding to have at most one\nnon-locally-flat point, and near the point the topology of the embedding is\nencoded in the singularity knot $K$. If $K$ is slice, then $W$ has a smooth\nspine, i.e., deformation retracts onto a smoothly embedded surface. Using the\nobstructions from the Heegaard Floer homology and the high-dimensional surgery\ntheory, we show that $W$ has no smooth spines if $K$ is a knot with nonzero Arf\ninvariant, a nontrivial L-space knot, the connected sum of nontrivial L-space\nknots, or an alternating knot of signature $<-4$. We also discuss examples\nwhere the interior of $W$ is negatively curved.\n"}
+{"abstract": "  This paper explores the potential for performing temporal semantic\nsegmentation in the context of agricultural robotics without temporally\nlabelled data. We achieve this by proposing to generate virtual temporal\nsamples from labelled still images. By exploiting the relatively static scene\nand assuming that the robot (camera) moves we are able to generate virtually\nlabelled temporal sequences with no extra annotation effort. Normally, to train\na recurrent neural network (RNN), labelled samples from a video (temporal)\nsequence are required which is laborious and has stymied work in this\ndirection. By generating virtual temporal samples, we demonstrate that it is\npossible to train a lightweight RNN to perform semantic segmentation on two\nchallenging agricultural datasets. Our results show that by training a temporal\nsemantic segmenter using virtual samples we can increase the performance by an\nabsolute amount of $4.6$ and $4.9$ on sweet pepper and sugar beet datasets,\nrespectively. This indicates that our virtual data augmentation technique is\nable to accurately classify agricultural images temporally without the use of\ncomplicated synthetic data generation techniques nor with the overhead of\nlabelling large amounts of temporal sequences.\n"}
+{"abstract": "  Let $A$ be an abelian variety and $G$ a finite group of automorphisms of $A$\nfixing the origin such that $A/G$ is smooth. The quotient $A/G$ can be seen as\na fibration over an abelian variety whose fibers are isomorphic to a product of\nprojective spaces. We classify how the fibers are glued in the case when the\nfibers are isomorphic to a projective space and we prove that, in general, the\nquotient $A/G$ is a fibered product of such fibrations.\n"}
+{"abstract": "  We report on the formation of a dispersive shock wave in a nonlinear optical\nmedium. We monitor the evolution of the shock by tuning the incoming beam\npower. The experimental observations for the position and intensity of the\nsolitonic edge of the shock, as well as the location of the nonlinear\noscillations are well described by recent developments of Whitham modulation\ntheory. Our work constitutes a detailed and accurate benchmark for this\napproach. It opens exciting possibilities to engineer specific configurations\nof optical shock wave for studying wave-mean flow interaction.\n"}
+{"abstract": "  Pansharpening is a widely used image enhancement technique for remote\nsensing. Its principle is to fuse the input high-resolution single-channel\npanchromatic (PAN) image and low-resolution multi-spectral image and to obtain\na high-resolution multi-spectral (HRMS) image. The existing deep learning\npansharpening method has two shortcomings. First, features of two input images\nneed to be concatenated along the channel dimension to reconstruct the HRMS\nimage, which makes the importance of PAN images not prominent, and also leads\nto high computational cost. Second, the implicit information of features is\ndifficult to extract through the manually designed loss function. To this end,\nwe propose a generative adversarial network via the fast guided filter (FGF)\nfor pansharpening. In generator, traditional channel concatenation is replaced\nby FGF to better retain the spatial information while reducing the number of\nparameters. Meanwhile, the fusion objects can be highlighted by the spatial\nattention module. In addition, the latent information of features can be\npreserved effectively through adversarial training. Numerous experiments\nillustrate that our network generates high-quality HRMS images that can surpass\nexisting methods, and with fewer parameters.\n"}
+{"abstract": "  Transition metal defects in SiC give rise to localized electronic states that\ncan be optically addressed in the telecom range in an industrially mature\nsemiconductor platform. This has led to intense scrutiny of the spin and\noptical properties of these defect centers. For spin-1/2 defects, a combination\nof the defect symmetry and the strong spin-orbit coupling may restrict the\nallowed spin transitions, giving rise to defect spins that are long lived, but\nhard to address via microwave spin manipulation. Here, we show via analytical\nand numerical results that the presence of a central nuclear spin can lead to a\nnon-trivial mixing of electronic spin states, while preserving the defect\nsymmetry. The interplay between a small applied magnetic field and hyperfine\ncoupling opens up magnetic microwave transitions that are forbidden in the\nabsence of hyperfine coupling, enabling efficient manipulation of the\nelectronic spin. We also find that an electric microwave field parallel to the\nc-axis can be used to manipulate the electronic spin via modulation of the\nrelative strength of the dipolar hyperfine term.\n"}
+{"abstract": "  Processes that lead to the critical-current suppression and change of\nimpedance of a superconductor under the application ofan external voltage is an\nactive area of research, especially due to various possible technological\napplications. In particular,field-effect transistors and radiation detectors\nhave been developed in the recent years, showing the potential for precision\nandsensitivity exceeding their normal-metal counterparts. In order to describe\nthe phenomenon that leads to the critical-currentsuppression in metallic\nsuperconducting structures, a field-effect hypothesis has been formulated,\nstating that an electricfield can penetrate the metallic superconductor and\naffect its characteristics. The existence of such an effect would imply\ntheincompleteness of the underlying theory, and hence indicate an important gap\nin the general comprehension of superconductors.In addition to its theoretical\nvalue, a complete understanding of the phenomenon underneath the electric-field\nresponse ofthe superconductor is important in the light of the related\ntechnological applications. In this paper, we study the changeof the\ncharacteristics of a superconductor implementing a coplanar-waveguide resonator\nas a tank circuit, by relating ourmeasurements to the reactance and resistance\nof the material. Namely, we track the state of the superconductor at\ndifferentvoltages and resulting leakage currents of a nearby gate electrode\nwhich is not galvanically connected to the resonator. Bycomparing the effects\nof the leakage current and of a change in the temperature of the system, we\nconclude that the observedbehaviour in the superconductor is [...]\n"}
+{"abstract": "  The origin of the interfacial perpendicular magnetic anisotropy (PMA) induced\nin the ultrathin Fe layer on the Au(111) surface was examined using\nsynchrotron-radiation-based M\\\"{o}ssbauer spectroscopy (MS), X-ray magnetic\ncircular dichroism (XMCD), and angle-resolved photoemission spectroscopy\n(ARPES). To probe the detailed interfacial electronic structure of orbital\nhybridization between the Fe 3$d$ and Au 6$p$ bands, we detected the\ninterfacial proximity effect, which modulates the valence-band electronic\nstructure of Fe, resulting in PMA. MS and XMCD measurements were used to detect\nthe interfacial magnetic structure and anisotropy in orbital magnetic moments,\nrespectively. $In$-$situ$ ARPES also confirms the initial growth of Fe on large\nspin-orbit coupled surface Shockley states under Au(111) modulated electronic\nstates in the vicinity of the Fermi level. This suggests that PMA in the\nFe/Au(111) interface originates from the cooperation effects among the spin,\norbital magnetic moments in Fe, and large spin-orbit coupling in Au. These\nfindings pave the way to develop interfacial PMA using $p$-$d$ hybridization\nwith a large spin-orbit interaction.\n"}
+{"abstract": "  For a unital $C^*$-algebra $\\mathcal A$ and a subspace $\\mathcal B$ of\n$\\mathcal A$, a characterization for a best approximation to an element of\n$\\mathcal A$ in $\\mathcal B$ is obtained. As an application, a formula for the\ndistance of an element of $\\mathcal A$ from $\\mathcal B$ has been obtained,\nwhen a best approximation of that element to $\\mathcal B$ exists. Further, a\ncharacterization for Birkhoff-James orthogonality of an element of a Hilbert\n$C^*$-module to a subspace is obtained.\n"}
+{"abstract": "  This paper explores the task of Difficulty-Controllable Question Generation\n(DCQG), which aims at generating questions with required difficulty levels.\nPrevious research on this task mainly defines the difficulty of a question as\nwhether it can be correctly answered by a Question Answering (QA) system,\nlacking interpretability and controllability. In our work, we redefine question\ndifficulty as the number of inference steps required to answer it and argue\nthat Question Generation (QG) systems should have stronger control over the\nlogic of generated questions. To this end, we propose a novel framework that\nprogressively increases question difficulty through step-by-step rewriting\nunder the guidance of an extracted reasoning chain. A dataset is automatically\nconstructed to facilitate the research, on which extensive experiments are\nconducted to test the performance of our method.\n"}
+{"abstract": "  We present a novel framework, InfinityGAN, for arbitrary-sized image\ngeneration. The task is associated with several key challenges. First, scaling\nexisting models to an arbitrarily large image size is resource-constrained, in\nterms of both computation and availability of large-field-of-view training\ndata. InfinityGAN trains and infers in a seamless patch-by-patch manner with\nlow computational resources. Second, large images should be locally and\nglobally consistent, avoid repetitive patterns, and look realistic. To address\nthese, InfinityGAN disentangles global appearances, local structures, and\ntextures. With this formulation, we can generate images with spatial size and\nlevel of details not attainable before. Experimental evaluation validates that\nInfinityGAN generates images with superior realism compared to baselines and\nfeatures parallelizable inference. Finally, we show several applications\nunlocked by our approach, such as spatial style fusion, multi-modal\noutpainting, and image inbetweening. All applications can be operated with\narbitrary input and output sizes.\n"}
+{"abstract": "  Survival in remote snow bounded areas is unsafe and risky for mankind. Many\nproblems like arthritis, frostbite, asthma, starvation can caused and lead to\ndeath. Indian Military provides transportation vehicles which are heavily built\nand needs manpower for monitoring. Hence it necessitates facilitating compact\ntransportation to fulfill all requirements. This research aimed at design and\nanalysis of mobile unmanned vehicle for transportation & providing medical\nhelp, food and other essential things necessary for surviving in such areas.\nThis can also be used for military services to save the life of solider with\nless risk. It is typical medium weight, high speed vehicle which carries up to\n35 kg load and can negotiate through loose snow, rough terrain with use of\ncaterpillar track. The noteworthy feature of the vehicle is that it constitutes\nof spiral blades and V shape snowplow to make its way through snow. Hence it\nwill repel the snow in outward direction for self-extraction. It also\nincorporates skis and hubs for changing the direction and smooth suspension. 3D\nmodel of the vehicle is drafted in CATIA and structural analysis is carried out\nin ANSYS. Control system design and mechatronics integration is proposed to\ndevelop the prototype by assembling various components.\n"}
+{"abstract": "  We investigate parity-time reversal (PT) phase transitions in open quantum\nsystems and discuss a criterion of Liouvillian PT symmetry proposed recently by\nJ. Huber, P. Kirton, S. Rotter and P. Rabl. Using the third quantization, which\nis a general method to solve the Lindblad equation for open quadratic systems,\nwe show, with a proposed criterion of PT symmetry, that the eigenvalue\nstructure of the Liouvillian clearly changes at the PT symmetry breaking point\nfor an open 2-spin model with exactly balanced gain and loss if the total spin\nis large. Specially, in a PT unbroken phase, some eigenvalues are pure\nimaginary numbers while in a PT broken phase, all the eigenvalues are real.\nFrom this result, it is analytically shown for open quantum system including\nquantum jumps that the dynamics in the long time limit changes from an\noscillatory to an overdamped behavior at the proposed PT symmetry breaking\npoint. Furthermore, we show a direct relation between the criterion of Huber et\nal. of Liouvillian PT symmetry and dynamics of the physical quantities for\nquadratic bosonic systems. Our results support validity of the proposed\ncriterion of Liouvillian PT symmetry.\n"}
+{"abstract": "  Photonics is a promising platform for demonstrating quantum computational\nsupremacy (QCS) by convincingly outperforming the most powerful classical\nsupercomputers on a well-defined computational task. Despite this promise,\nexisting photonics proposals and demonstrations face significant hurdles.\nExperimentally, current implementations of Gaussian boson sampling lack\nprogrammability or have prohibitive loss rates. Theoretically, there is a\ncomparative lack of rigorous evidence for the classical hardness of GBS. In\nthis work, we make significant progress in improving both the theoretical\nevidence and experimental prospects. On the theory side, we provide strong\nevidence for the hardness of Gaussian boson sampling, placing it on par with\nthe strongest theoretical proposals for QCS. On the experimental side, we\npropose a new QCS architecture, high-dimensional Gaussian boson sampling, which\nis programmable and can be implemented with low loss rates using few optical\ncomponents. We show that particular classical algorithms for simulating GBS are\nvastly outperformed by high-dimensional Gaussian boson sampling experiments at\nmodest system sizes. This work thus opens the path to demonstrating QCS with\nprogrammable photonic processors.\n"}
+{"abstract": "  It is possible to describe the scattering and the radiative capture with help\nof a Breit-Wigner resonance with negative energy.\n"}
+{"abstract": "  The LHCb Collaboration announced the observation of doubly charmed baryon\nthrough $\\Xi_{c c}^{++} \\rightarrow \\Lambda_{c}^{+} K^{-} \\pi^{+} \\pi^{+}$ in\n2017. Since then, a series of studies of doubly heavy baryons have been\npresented. $\\Xi_{cc}^{++}$ was discovered through nonleptonic four-body decay\nmode, and experimental data has indicated that the decay modes of $\\Xi_{c\nc}^{++}$ are not saturated by two and three-body intermediate states. In this\nwork, we analyze the four-body weak decays of doubly heavy baryons\n$\\Xi_{cc}^{++}, \\Xi_{cc}^+$, and $\\Omega_{cc}^+$. Decay amplitudes for various\nchannels are parametrized in terms of SU(3) irreducible amplitudes. We point\nout that branching fractions for Cabibbo-allowed processes\n$\\Xi_{cc}^{+}\\to\\Lambda_c^+\\pi^+ \\pi^0 K^-$,\n$\\Omega_{cc}^{+}\\to\\Lambda_c^+\\pi^+ \\overline K^0 K^-$ would be helpful to\nsearch for $\\Xi_{cc}^+$ and $\\Omega_{cc}^+$ in future measurements at\nexperimental facilities like LHC, Belle II, and CEPC.\n"}
+{"abstract": "  In this entry point into the subject, combining two elementary proofs, we\ndecrease the gap between the upper and lower bounds by $0.2\\%$ in a classical\ncombinatorial number theory problem. We show that the maximum size of a Sidon\nset of $\\{ 1, 2, \\ldots, n\\}$ is at most $\\sqrt{n}+ 0.998n^{1/4}$ for\nsufficiently large $n$.\n"}
+{"abstract": "  This paper elaborates the integral transformation technique of [K. Pachucki,\nW. Cencek, and J. Komasa, J. Chem. Phys. 122, 184101 (2005)] and uses it for\nthe case of the non-relativistic kinetic and Coulomb potential energy\noperators, as well as for the relativistic mass-velocity and Darwin terms. The\ntechniques are tested for the ground electronic state of the helium atom and\nnew results are reported for the ground electronic state of the H$_3^+$\nmolecular ion near its equilibrium structure.\n"}
+{"abstract": "  By modulating the intensity of laser light before the rotating groundglass,\nthe well-known pseudothermal light source can be modified into superbunching\npseudothermal light source, in which the degree of second-order coherence of\nthe scattered light is larger than 2. With the modulated intensities following\nbinary distribution, we experimentally observed the degree of second- and\nthird-order coherence equaling 20.45 and 227.07, which is much larger than the\nvalue of thermal or pseudothermal light, 2 and 6, respectively. Numerical\nsimulation predicts that the degree of second-order coherence can be further\nimproved by tuning the parameters of binary distribution. It is also predicted\nthat the quality of temporal ghost imaging can be improved with this\nsuperbunching pseudothermal light. This simple and efficient superbunching\npseudothermal light source provides an interesting alternative to study the\nsecond- and higher-order interference of light in these scenarios where thermal\nor pseudothermal light source were employed.\n"}
+{"abstract": "  The solar minimum 23/24 is considered to be unusual because it exhibits\nfeatures that differ notably from those commonly seen in pervious minima. In\nthis letter, we analyze the solar polar magnetic field, the potential-field\nsolution of the solar corona, and the in-situ solar wind measurements to see\nwhether the recent solar minimum 24/25 is another unusual one. While the\ndipolar configuration that are commonly seen during minimum 22/23 and earlier\nminima persist for about half a year after the absolute minimum of solar cycle\n24, the corona has a morphology more complex than a simple dipole before the\nabsolute minimum. The fast solar wind streams are less dominant than minimum\n23/24. The IMF strength, density and mass flux that are historically low in the\nminimum 23/24 are regained during minimum 24/25, but still do not reach the\nminimum 22/23 level. From the analysis of this Letter, it seems that the\nminimum 24/25 is only partially unusual, and the recovery of the commonly\nminimum features may result from the enhancement of the polar field.\n"}
+{"abstract": "  We use the general relativistic radiation magnetohydrodynamics code\n\\verb=KORAL= to simulate the early stages of accretion disk formation resulting\nfrom the tidal disruption of a solar mass star around a super massive black\nhole (BH) of mass $10^6\\,M_\\odot$. We simulate the disruption of artificially\nmore bound stars with orbital eccentricity $e\\leq0.99$ (compared to the more\nrealistic case of parabolic orbits with $e=1$) on close orbits with impact\nparameter $\\beta\\geq3$. We use a novel method of injecting the tidal stream\ninto the domain. For two simulations, we choose $e=0.99$ and inject mass at a\nrate that is similar to realistic TDEs. We find that the disk only becomes\nmildly circularized with eccentricity $e\\approx0.6$ within the $3.5$ days that\nwe simulate. The rate of circularization is faster for pericenter radii that\ncome closer to the BH. The emitted radiation is mildly super-Eddington with\n$L_{\\rm{bol}}\\approx3-5\\,L_{\\rm{Edd}}$ and the photosphere is highly asymmetric\nwith the photosphere being significantly closer to the inner accretion disk for\nviewing angles near pericenter. We find that soft X-ray radiation with\n$T_{\\rm{rad}} \\approx 3-5\\times 10^5$ K may be visible for chance viewing\nangles. Our simulations predict that TDEs should be radiatively inefficient\nwith $\\eta\\approx0.009-0.014$. These are the first simulations which\nsimultaneously capture the stream, disk formation, and emitted radiation.\n"}
+{"abstract": "  It's well-known that inverse problems are ill-posed and to solve them\nmeaningfully one has to employ regularization methods. Traditionally, the most\npopular regularization approaches are Variational-type approaches, i.e.,\npenalized/constrained functional minimization. In recent years, the classical\nregularization approaches have been replaced by the so-called plug-and-play\n(PnP) algorithms, which copies the proximal gradient minimization processes,\nsuch as ADMM or FISTA, but with any general denoiser. However, unlike the\ntraditional proximal gradient methods, the theoretical analysis and convergence\nresults have been insufficient for these PnP-algorithms. Hence, the results\nfrom these algorithms, though empirically outstanding, are not well-defined, in\nthe sense of, being a minimizer of a Variational problem. In this paper, we\naddress this question of \"well-definedness\", but from a different angle. We\nexplain these algorithms from the viewpoint of a semi-iterative regularization\nmethod. In addition, we expand the family of regularized solutions,\ncorresponding to the classical semi-iterative methods, to further generalize\nthe explainability of these algorithms, as well as, enhance the recovery\nprocess. We conclude with several numerical results which validate the\ndeveloped theories and reflect the improvements over the traditional\nPnP-algorithms, such as ADMM-PnP and FISTA-PnP.\n"}
+{"abstract": "  Let $(\\sigma_N^{(i)})_{i \\in I}$ be a family of symmetric permutations of the\nentries of a Wigner matrix $\\mathbf{W}_N$. We characterize the limiting traffic\ndistribution of the corresponding family of dependent Wigner matrices\n$(\\mathbf{W}_N^{\\sigma_N^{(i)}})_{i \\in I}$ in terms of the geometry of the\npermutations. We also consider the analogous problem for the limiting joint\ndistribution of $(\\mathbf{W}_N^{\\sigma_N^{(i)}})_{i \\in I}$. In particular, we\nobtain a description in terms of semicircular families with general covariance\nstructures. As a special case, we derive necessary and sufficient conditions\nfor traffic independence as well as sufficient conditions for free\nindependence.\n"}
+{"abstract": "  This paper presents an efficient equivalent circuit approach (ECA), based on\na Floquet modal expansion, for the study of the co- and cross-polarization in\nfrequency selective surfaces (FSS) formed by periodic arrays of\npatches/apertures in either single or stacked configurations. The ECA makes it\npossible the derivation of analytical expressions for the generalized\nscattering parameters associated with the proposed circuit networks.\nFurthermore, the proposed circuit approach is an efficient surrogate model that\ncan be combined with optimization techniques and artificial intelligence\nalgorithms for the efficient design of FSS structures, saving efforts in the\ncomputation compared to time-consuming full-wave simulators and tedious\nsynthesis (simulation-assisted) techniques. Due to the simplicity of the\ntopology of the involved networks, the ECA can also be advantageously used to\ngain physical insight. The proposed approach is applied and validated in\ndifferent FSS configurations where the cross-pol component plays a fundamental\nrole in the design, as in circular polarizers, polarization rotators, and\nreflectarray cells.\n"}
+{"abstract": "  Ongoing or upcoming surveys such as Gaia, ZTF, or LSST will observe\nlight-curves of billons or more astronomical sources. This presents new\nchallenges for identifying interesting and important types of variability.\nCollecting a sufficient number of labelled data for training is difficult,\nhowever, especially in the early stages of a new survey. Here we develop a\nsingle-band light-curve classifier based on deep neural networks, and use\ntransfer learning to address the training data paucity problem by conveying\nknowledge from one dataset to another. First we train a neural network on 16\nvariability features extracted from the light-curves of OGLE and EROS-2\nvariables. We then optimize this model using a small set (e.g. 5%) of periodic\nvariable light-curves from the ASAS dataset in order to transfer knowledge\ninferred from OGLE/EROS-2 to a new ASAS classifier. With this we achieve good\nclassification results on ASAS, thereby showing that knowledge can be\nsuccessfully transferred between datasets. We demonstrate similar transfer\nlearning using Hipparcos and ASAS-SN data. We therefore find that it is not\nnecessary to train a neural network from scratch for every new survey, but\nrather that transfer learning can be used even when only a small set of\nlabelled data is available in the new survey.\n"}
+{"abstract": "  Context. The birth environments of planetary systems are thought to influence\nplanet formation and orbital evolution, through external photoevaporation and\nstellar flybys. Recent work has claimed observational support for this, in the\nform of a correlation between the properties of planetary systems and the local\nGalactic phase space density of the host star. In particular, Hot Jupiters are\nfound overwhelmingly around stars in regions of high phase space density, which\nmay reflect a formation environment with high stellar density. Aims. We instead\ninvestigate whether the high phase space density may have a galactic kinematic\norigin: Hot Jupiter hosts may be biased towards being young and therefore\nkinematically cold, because tidal inspiral leads to the destruction of the\nplanets on Gyr timescales, and the velocity dispersion of stars in the Galaxy\nincreases on similar timescales. Methods. We use 6D positions and kinematics\nfrom Gaia for the Hot Jupiter hosts and their neighbours, and construct\ndistributions of the phase space density. We investigate correlations between\nthe stars' local phase space density and peculiar velocity. Results. We find a\nstrong anticorrelation between the phase space density and the host star's\npeculiar velocity with respect to the Local Standard of Rest. Therefore, most\nstars in \"high-density\" regions are kinematically cold, which may be caused by\nthe aforementioned bias towards detecting Hot Jupiters around young stars\nbefore the planets' tidal destruction. Conclusions. We do not find evidence in\nthe data for Hot Jupiter hosts preferentially being in phase space\noverdensities compared to other stars of similar kinematics, nor therefore for\ntheir originating in birth environments of high stellar density.\n"}
+{"abstract": "  In this paper, we study the importance of pruning in Deep Networks (DNs) and\nthe yin & yang relationship between (1) pruning highly overparametrized DNs\nthat have been trained from random initialization and (2) training small DNs\nthat have been \"cleverly\" initialized. As in most cases practitioners can only\nresort to random initialization, there is a strong need to develop a grounded\nunderstanding of DN pruning. Current literature remains largely empirical,\nlacking a theoretical understanding of how pruning affects DNs' decision\nboundary, how to interpret pruning, and how to design corresponding principled\npruning techniques. To tackle those questions, we propose to employ recent\nadvances in the theoretical analysis of Continuous Piecewise Affine (CPA) DNs.\nFrom this perspective, we will be able to detect the early-bird (EB) ticket\nphenomenon, provide interpretability into current pruning techniques, and\ndevelop a principled pruning strategy. In each step of our study, we conduct\nextensive experiments supporting our claims and results; while our main goal is\nto enhance the current understanding towards DN pruning instead of developing a\nnew pruning method, our spline pruning criteria in terms of layerwise and\nglobal pruning is on par with or even outperforms state-of-the-art pruning\nmethods.\n"}
+{"abstract": "  We develop a numerical method to nonperturbatively study scattering and gluon\nemission of a quark from a colored target using a light-front Hamiltonian\napproach. The target is described as a classical color field, as in the Color\nGlass Condensate effective theory. The Fock space of the scattering system is\nrestricted to the $\\ket{q}+\\ket{qg}$ sectors, but the time evolution of this\ntruncated system is solved exactly. This method allows us to study the\ninterplay between coherence and multiple scattering in gluon emission. It could\nbe applied both to studying subeikonal effects in high energy scattering and to\nunderstanding jet quenching in a hot plasma.\n"}
+{"abstract": "  Despite the great progress of person re-identification (ReID) with the\nadoption of Convolutional Neural Networks, current ReID models are opaque and\nonly outputs a scalar distance between two persons. There are few methods\nproviding users semantically understandable explanations for why two persons\nare the same one or not. In this paper, we propose a post-hoc method, named\nAttribute-guided Metric Distillation (AMD), to explain existing ReID models.\nThis is the first method to explore attributes to answer: 1) what and where the\nattributes make two persons different, and 2) how much each attribute\ncontributes to the difference. In AMD, we design a pluggable interpreter\nnetwork for target models to generate quantitative contributions of attributes\nand visualize accurate attention maps of the most discriminative attributes. To\nachieve this goal, we propose a metric distillation loss by which the\ninterpreter learns to decompose the distance of two persons into components of\nattributes with knowledge distilled from the target model. Moreover, we propose\nan attribute prior loss to make the interpreter generate attribute-guided\nattention maps and to eliminate biases caused by the imbalanced distribution of\nattributes. This loss can guide the interpreter to focus on the exclusive and\ndiscriminative attributes rather than the large-area but common attributes of\ntwo persons. Comprehensive experiments show that the interpreter can generate\neffective and intuitive explanations for varied models and generalize well\nunder cross-domain settings. As a by-product, the accuracy of target models can\nbe further improved with our interpreter.\n"}
+{"abstract": "  Software-defined networking (SDN) was devised to simplify network management\nand automate infrastructure sharing in wired networks. These benefits motivated\nthe application of SDN in wireless sensor networks to leverage solutions for\ncomplex applications. However, some of the core SDN traits turn the networks\nprone to denial of service attacks (DoS). There are proposals in the literature\nto detect DoS in wireless SDN networks, however, not without shortcomings:\nthere is little focus on resource constraints, high detection rates have been\nreported only for small networks, and the detection is disengaged from the\nidentification of the type of the attack or the attacker. Our work targets\nthese shortcomings by introducing a lightweight, online change point detector\nto monitor performance metrics that are impacted when the network is under\nattack. A key novelty is that the proposed detector is able to operate in\neither centralized or distributed mode. The centralized detector has very high\ndetection rates and can further distinguish the type of the attack (from a list\nof known attacks). On the other hand, the distributed detector provides\ninformation that allows to identify the nodes launching the attack. Our\nproposal is tested over IEEE 802.15.4 networks. The results show detection\nrates exceeding $96\\%$ in networks of 36 and 100 nodes and identification of\nthe type of the attack with a probability exceeding $0.89$ when using the\ncentralized approach. Additionally, for some types of attack it was possible to\npinpoint the attackers with an identification probability over $0.93$ when\nusing distributed detectors.\n"}
+{"abstract": "  Prediction tasks with high-dimensional nonorthogonal predictor sets pose a\nchallenge for least squares based fitting procedures. A large and productive\nliterature exists, discussing various regularized approaches to improving the\nout-of-sample robustness of parameter estimates. This paper proposes a novel\ncluster-based regularization - the hierarchical feature regression (HFR) -,\nwhich mobilizes insights from the domains of machine learning and graph theory\nto estimate parameters along a supervised hierarchical representation of the\npredictor set, shrinking parameters towards group targets. The method is\ninnovative in its ability to estimate optimal compositions of predictor groups,\nas well as the group targets endogenously. The HFR can be viewed as a\nsupervised factor regression, with the strength of shrinkage governed by a\npenalty on the extent of idiosyncratic variation captured in the fitting\nprocess. The method demonstrates good predictive accuracy and versatility,\noutperforming a panel of benchmark regularized estimators across a diverse set\nof simulated regression tasks, including dense, sparse and grouped data\ngenerating processes. An application to the prediction of economic growth is\nused to illustrate the HFR's effectiveness in an empirical setting, with\nfavorable comparisons to several frequentist and Bayesian alternatives.\n"}
+{"abstract": "  System-of-systems (SoS) approach is often used for simulating disruptions to\nbusiness and infrastructure system networks allowing for integration of several\nmodels into one simulation. However, the integration is frequently challenging\nas each system is designed individually with different characteristics, such as\ntime granularity. Understanding the impact of time granularity on propagation\nof disruptions between businesses and infrastructure systems and finding the\nappropriate granularity for the SoS simulation remain as major challenges. To\ntackle these, we explore how time granularity, recovery time, and disruption\nsize affect the propagation of disruptions between constituent systems of an\nSoS simulation. To address this issue, we developed a High Level Architecture\n(HLA) simulation of 3 networks and performed a series of simulation\nexperiments. Our results revealed that time granularity and especially recovery\ntime have huge impact on propagation of disruptions. Consequently, we developed\na model for selecting an appropriate time granularity for an SoS simulation\nbased on expected recovery time. Our simulation experiments show that time\ngranularity should be less than 1.13 of expected recovery time. We identified\nsome areas for future research centered around extending the experimental\nfactors space.\n"}
+{"abstract": "  We present Atacama Large Millimeter/submillimeter Array (ALMA) gas and dust\nobservations at band 7 (339~GHz: 0.89~mm) of the protoplanetary disk around a\nvery low mass star ZZ~Tau~IRS with a spatial resolution of 0\\farcs25. The\n$^{12}$CO~$J=3\\rightarrow2$ position--velocity diagram suggests a dynamical\nmass of ZZ~Tau~IRS of $\\sim$0.1--0.3~$M_{\\sun}$. The disk has a total flux\ndensity of 273.9 mJy, corresponding to an estimated mass of 24--50~$M_\\oplus$\nin dust. The dust emission map shows a ring at $r=$ 58~au and an azimuthal\nasymmetry at $r=$ \\jh{45}~au with a position angle of 135\\degr. The properties\nof the asymmetry, including radial width, aspect ratio, contrast, and\ncontribution to the total flux, were found to be similar to the asymmetries\naround intermediate mass stars ($\\sim$2~$M_{\\sun}$) such as MWC~758 and IRS~48.\nThis implies that the asymmetry in the ZZ~Tau~IRS disk shares a similar origin\nwith others, despite the star being $\\sim$10 times less massive. Our\nobservations also suggest that the inner and outer parts of the disk may be\nmisaligned. Overall, the ZZ~Tau~IRS disk shows evidence of giant planet\nformation at $\\sim$10 au scale at a few Myr. If confirmed, it will challenge\nexisting core accretion models, in which such planets have been predicted to be\nextremely hard to form around very low mass stars.\n"}
+{"abstract": "  In this paper, following Nourdin-Peccati's methodology, we combine the\nMalliavin calculus and Stein's method to provide general bounds on the\nWasserstein distance between functionals of a compound Hawkes process and a\ngiven Gaussian density. To achieve this, we rely on the Poisson embedding\nrepresentation of an Hawkes process to provide a Malliavin calculus for the\nHawkes processes, and more generally for compound Hawkes processes. As an\napplication, we close a gap in the literature by providing the first\nBerry-Ess\\'een bounds associated to Central Limit Theorems for the compound\nHawkes process.\n"}
+{"abstract": "  Phase transitions and critical phenomena, which are dominated by fluctuations\nand correlations, are one of the fields replete with physical paradigms and\nunexpected discoveries. Especially for two-dimensional magnetism, the\nlimitation of the Ginzburg criterion leads to enhanced fluctuations breaking\ndown the mean-field theory near a critical point. Here, by means of magnetic\nresonance, we investigate the behavior of critical fluctuations in the\ntwo-dimensional ferromagnetic insulators $\\rm CrXTe_3 (X=Si, Ge)$. After\nderiving the classical and quantum models of magnetic resonance, we deem the\ndramatic anisotropic shift of the measured $g$ factor to originate from\nfluctuations with anisotropic interactions. The deduction of the $g$ factor\nbehind the fluctuations is consistent with the spin-only state (${g\\approx}$\n2.050(10) for $\\rm CrSiTe_3$ and 2.039(10) for $\\rm CrGeTe_3$). Furthermore,\nthe abnormal enhancement of $g$ shift, supplemented by specific heat and\nmagnetometry measurements, suggests that $\\rm CrSiTe_3$ exhibits a more typical\ntwo-dimensional nature than $\\rm CrGeTe_3$ and may be closer to the quantum\ncritical point.\n"}
+{"abstract": "  We present an algorithm for determining whether a bipartite graph $G$ is\n2-chordal (formerly doubly chordal bipartite). At its core this algorithm is an\nextension of the existing efficient algorithm for determining whether a graph\nis chordal bipartite. We then introduce the notion of $k$-chordal bipartite\ngraphs and show by inductive means that a slight modification of our algorithm\nis sufficient to detect this property. We show that there are no nontrivial\n$k$-chordal bipartite graphs for $k \\geq 4$ and that both the 2-chordal\nbipartite and 3-chordal bipartite problem are contained within complexity class\nP.\n"}
+{"abstract": "  For a continuous state branching process with two types of individuals which\nare subject to selection and density dependent competition, we characterize the\njoint evolution of population size, type configurations and genealogies as the\nunique strong solution of a system of SDE's. Our construction is achieved in\nthe lookdown framework and provides a synthesis as well as a generalization of\ncases considered separately in two seminal papers by Donnelly and Kurtz (1999),\nnamely fluctuating population sizes under neutrality, and selection with\nconstant population size. As a conceptual core in our approach, we introduce\nthe selective lookdown space which is obtained from its neutral counterpart\nthrough a state-dependent thinning of \"potential\" selection/competition events\nwhose rates interact with the evolution of the type densities. The updates of\nthe genealogical distance matrix at the \"active\" selection/competition events\nare obtained through an appropriate sampling from the selective lookdown space.\nThe solution of the above mentioned system of SDE's is then mapped into the\njoint evolution of population size and symmetrized type configurations and\ngenealogies, i.e. marked distance matrix distributions. By means of Kurtz's\nMarkov mapping theorem, we characterize the latter process as the unique\nsolution of a martingale problem. For the sake of transparency we restrict the\nmain part of our presentation to a prototypical example with two types, which\ncontains the essential features. In the final section we outline an extension\nto processes with multiple types including mutation.\n"}
+{"abstract": "  We use mixed correlators in thermal CFT as clean probes of the strong gravity\neffects in their holographic duals. The dual interpretation of mixing is an\ninelastic conversion of one field to another field, induced by gravity: tidal\nexcitation. We find an enhanced mixing at high temperatures, corresponding to\nlarge AdS black holes, concentrated to small boundary momenta, dual to the deep\nbulk, where strong gravitational fields are expected. We also find large\n$\\mathcal{O}(1/G_{N})$ tidal conversion in the low temperature phase of the\n$U(N)$ vector model, strengthening suspicions that the bulk dual of this phase\nalso houses extremely compact objects.\n"}
+{"abstract": "  Fluctuation theorems are fundamental extensions of the second law of\nthermodynamics for small nonequilibrium systems. While work and heat are\nequally important forms of energy exchange, fluctuation relations have not been\nexperimentally assessed for the generic situation of simultaneous mechanical\nand thermal changes. Thermal driving is indeed generally slow and more\ndifficult to realize than mechanical driving. Here, we use feedback cooling\ntechniques to implement fast and controlled temperature variations of an\nunderdamped levitated microparticle that are one order of magnitude faster than\nthe equilibration time. Combining mechanical and thermal control, we verify the\nvalidity of a fluctuation theorem that accounts for both contributions, well\nbeyond the range of linear response theory. Our results allow the investigation\nof general far-from-equilibrium processes in microscopic systems that involve\nfast mechanical and thermal changes at the same time.\n"}
+{"abstract": "  We introduce a class of $\\gamma$-negatively dependent random samples. We\nprove that this class includes, apart from Monte Carlo samples, in particular\nLatin hypercube samples and Latin hypercube samples padded by Monte Carlo.\n  For a $\\gamma$-negatively dependent $N$-point sample in dimension $d$ we\nprovide probabilistic upper bounds for its star discrepancy with explicitly\nstated dependence on $N$, $d$, and $\\gamma$. These bounds generalize the\nprobabilistic bounds for Monte Carlo samples from [Heinrich et al., Acta Arith.\n96 (2001), 279--302] and [C.~Aistleitner, J.~Complexity 27 (2011), 531--540],\nand they are optimal for Monte Carlo and Latin hypercube samples. In the\nspecial case of Monte Carlo samples the constants that appear in our bounds\nimprove substantially on the constants presented in the latter paper and in\n[C.~Aistleitner, M.~T.~Hofer, Math. Comp.~83 (2014), 1373--1381].\n"}
+{"abstract": "  One key vertical application that will be enabled by 6G is the automation of\nthe processes with the increased use of robots. As a result, sensing and\nlocalization of the surrounding environment becomes a crucial factor for these\nrobots to operate. Light detection and ranging (LiDAR) has emerged as an\nappropriate method of sensing due to its capability of generating detail-rich\ninformation with high accuracy. However, LiDARs are power hungry devices that\ngenerate a lot of data, and these characteristics limit their use as on-board\nsensors in robots. In this paper, we present a novel approach on the\nmethodology of generating an enhanced 3D map with improved field-of-view using\nmultiple LiDAR sensors. We utilize an inherent property of LiDAR point clouds;\nrings and data from the inertial measurement unit (IMU) embedded in the sensor\nfor registration of the point clouds. The generated 3D map has an accuracy of\n10 cm when compared to the real-world measurements. We also carry out the\npractical implementation of the proposed method using two LiDAR sensors.\nFurthermore, we develop an application to utilize the generated map where a\nrobot navigates through the mapped environment with minimal support from the\nsensors on-board. The LiDARs are fixed in the infrastructure at elevated\npositions. Thus this is applicable to vehicular and factory scenarios. Our\nresults further validate the idea of using multiple elevated LiDARs as a part\nof the infrastructure for various applications.\n"}
+{"abstract": "  Upcoming measurements of the small-scale primary cosmic microwave background\n(CMB) temperature and polarization power spectra ($TT$/$TE$/$EE$) are\nanticipated to yield transformative constraints on new physics, including the\neffective number of relativistic species in the early universe ($N_{\\rm eff}$).\nHowever, at multipoles $\\ell \\gtrsim 3000$, the primary CMB power spectra\nreceive significant contributions from gravitational lensing. While these modes\nstill carry primordial information, their theoretical modeling requires\nknowledge of the CMB lensing convergence power spectrum, $C_L^{\\kappa\\kappa}$,\nincluding on small scales where it is affected by nonlinear gravitational\nevolution and baryonic feedback processes. Thus, the high-$\\ell$ primary CMB is\nsensitive to these late-time, nonlinear effects. Here, we show that\ninaccuracies in the modeling of $C_L^{\\kappa\\kappa}$ can yield surprisingly\nlarge biases on cosmological parameters inferred from the primary CMB power\nspectra measured by the upcoming Simons Observatory and CMB-S4 experiments. For\nCMB-S4, the biases can be as large as $1.6\\sigma$ on the Hubble constant $H_0$\nin a fit to $\\Lambda$CDM and $1.2\\sigma$ on $N_{\\rm eff}$ in a fit to\n$\\Lambda$CDM+$N_{\\rm eff}$.\n  We show that these biases can be mitigated by explicitly discarding all $TT$\ndata at $\\ell>3000$ or by marginalizing over parameters describing baryonic\nfeedback processes, both at the cost of slightly larger error bars. We also\ndiscuss an alternative, data-driven mitigation strategy based on delensing the\nCMB $T$ and $E$-mode maps. Finally, we show that analyses of upcoming data will\nrequire Einstein-Boltzmann codes to be run with much higher numerical precision\nsettings than is currently standard, so as to avoid similar -- or larger --\nparameter biases due to inaccurate theoretical predictions.\n"}
+{"abstract": "  The bus system is a critical component of sustainable urban transportation.\nHowever, due to the significant uncertainties in passenger demand and traffic\nconditions, bus operation is unstable in nature and bus bunching has become a\ncommon phenomenon that undermines the reliability and efficiency of bus\nservices. Despite recent advances in multi-agent reinforcement learning (MARL)\non traffic control, little research has focused on bus fleet control due to the\ntricky asynchronous characteristic -- control actions only happen when a bus\narrives at a bus stop and thus agents do not act simultaneously. In this study,\nwe formulate route-level bus fleet control as an asynchronous multi-agent\nreinforcement learning (ASMR) problem and extend the classical actor-critic\narchitecture to handle the asynchronous issue. Specifically, we design a novel\ncritic network to effectively approximate the marginal contribution for other\nagents, in which graph attention neural network is used to conduct inductive\nlearning for policy evaluation. The critic structure also helps the ego agent\noptimize its policy more efficiently. We evaluate the proposed framework on\nreal-world bus services and actual passenger demand derived from smart card\ndata. Our results show that the proposed model outperforms both traditional\nheadway-based control methods and existing MARL methods.\n"}
+{"abstract": "  Insight into the electroweak (EW) and Higgs sectors can be achieved through\nmeasurements of vector boson scattering (VBS) processes. The scattering of EW\nbosons are rare processes that are precisely predicted in the Standard Model\n(SM) and are closely related to the Higgs mechanism. Modifications to VBS\nprocesses are also predicted in models of physics beyond the SM (BSM), for\nexample through changes to the Higgs boson couplings to gauge bosons and the\nresonant production of new particles. In this review, experimental results and\ntheoretical developments of VBS at the Large Hadron Collider, its high\nluminosity upgrade, and future colliders are presented.\n"}
+{"abstract": "  Using an artificial neutral network we explore the parameter space of\nsupergravity grand unified models consistent with the combined Fermilab E989\nand Brookhaven E821 data on $(g-2)_\\mu$. The analysis indicates that the region\nfavored by the data is the one generated by gluino-driven radiative breaking of\nthe electroweak symmetry. This region naturally leads to a split sparticle\nspectrum with light sleptons and weakinos but heavy squarks, with the stau and\nthe chargino as the lightest charged particles. We show that if the entire\ndeviation from the standard model $(g-2)_{\\mu}$ arises from supersymmetry, then\nsupersymmetry is discoverable at HL-LHC and HE-LHC via production and decay of\nsleptons within the optimal integrated luminosity of HL-LHC and with a smaller\nintegrated luminosity at HE-LHC.\n"}
+{"abstract": "  Researchers have long observed that the \"small-world\" property, which\ncombines the concepts of high transitivity or clustering with a low average\npath length, is ubiquitous for networks obtained from a variety of disciplines\nincluding social sciences, biology, neuroscience, and ecology. However, we find\nthree shortcomings of the currently popular definition and detection methods\nrendering the concept less powerful. First, the classical definition combines\nhigh transitivity with a low average path length in a rather ad-hoc fashion\nwhich confounds the two separate aspects. We find that in several cases,\nnetworks get flagged as \"small world\" by the current methodology solely because\nof their high transitivity. Second, the detection methods lack a formal\nstatistical inference, and third, the comparison is typically performed against\nsimplistic random graph models as the baseline which ignores well-known network\ncharacteristics. We propose three innovations to address these issues. First,\nwe decouple the properties of high transitivity and low average path length as\nseparate events to test for. Second, we define the property as a statistical\ntest between a suitable null model and a superimposed alternative model. Third,\nthe test is performed using parametric bootstrap with several null models to\nallow a wide range of background structures in the network. In addition to the\nbootstrap tests, we also propose an asymptotic test under the\nErd\\\"{o}s-Ren\\'{y}i null model for which we provide theoretical guarantees on\nthe asymptotic level and power. Applying the proposed methods on a large number\nof network datasets, we uncover new insights about their small-world property.\n"}
+{"abstract": "  The zeroth law is one of the oldest conjecture in turbulence that is still\nunproven. Here, we consider weak solutions of one-dimensional compressible\nmagnetohydrodynamics and demonstrate that the lack of smoothness of the fields\nintroduces a new dissipative term, named inertial dissipation, into the\nexpression of energy conservation that is neither viscous nor resistive in\nnature. We propose exact solutions assuming that the kinematic viscosity and\nthe magnetic diffusivity are equal, and we demonstrate that the associated\ninertial dissipation is positive and equal on average to the mean viscous\ndissipation rate in the limit of small viscosity, proving the conjecture of the\nzeroth law of turbulence and the existence of an anomalous dissipation. As an\nillustration, we evaluate the shock heating produced by discontinuities\ndetected by Voyager in the solar wind around 5 AU. We deduce a heating rate of\n$\\sim 10^{-18}$J m$^{-3}$ s$^{-1}$, which is significantly higher than the\nvalue obtained from the turbulent fluctuations. This suggests that\ncollisionless shocks can be a dominant source of heating in the outer solar\nwind.\n"}
+{"abstract": "  The $\\Lambda$CDM model provides a good fit to a large span of cosmological\ndata but harbors areas of phenomenology. With the improvement of the number and\nthe accuracy of observations, discrepancies among key cosmological parameters\nof the model have emerged. The most statistically significant tension is the\n$4-6\\sigma$ disagreement between predictions of the Hubble constant $H_0$ by\nearly time probes with $\\Lambda$CDM model, and a number of late time,\nmodel-independent determinations of $H_0$ from local measurements of distances\nand redshifts. The high precision and consistency of the data at both ends\npresent strong challenges to the possible solution space and demand a\nhypothesis with enough rigor to explain multiple observations--whether these\ninvoke new physics, unexpected large-scale structures or multiple, unrelated\nerrors. We present a thorough review of the problem, including a discussion of\nrecent Hubble constant estimates and a summary of the proposed theoretical\nsolutions. Some of the models presented are formally successful, improving the\nfit to the data in light of their additional degrees of freedom, restoring\nagreement within $1-2\\sigma$ between {\\it Planck} 2018, using CMB power spectra\ndata, BAO, Pantheon SN data, and R20, the latest SH0ES Team measurement of the\nHubble constant ($H_0 = 73.2 \\pm 1.3{\\rm\\,km\\,s^{-1}\\,Mpc^{-1}}$ at 68\\%\nconfidence level). Reduced tension might not simply come from a change in $H_0$\nbut also from an increase in its uncertainty due to degeneracy with additional\nphysics, pointing to the need for additional probes. While no specific proposal\nmakes a strong case for being highly likely or far better than all others,\nsolutions involving early or dynamical dark energy, neutrino interactions,\ninteracting cosmologies, primordial magnetic fields, and modified gravity\nprovide the best options until a better alternative comes along.[Abridged]\n"}
+{"abstract": "  An iterative configuration interaction (iCI)-based multiconfigurational\nself-consistent field (SCF) theory, iCISCF, is proposed to handle systems that\nrequire large complete active spaces (CAS). The success of iCISCF stems from\nthree ingredients: (1) efficient selection of individual configuration state\nfunctions spanning the CAS, meanwhile maintaining full spin symmetry; (2) the\nuse of Jacobi rotation for the optimization of active orbitals, in conjunction\nwith a quasi-Newton algorithm for the core/active-virtual and core-active\norbital rotations; (3) a second-order perturbative treatment of the residual\nspace left over by the selection procedure (i.e., iCISCF(2)). Just like\nselected iCI being a very accurate approximation to CASCI, iCISCF(2) is a very\naccurate approximation to CASSCF. Several examples that go beyond the\ncapability of CASSCF are taken as showcases to reveal the performances of\niCISCF and iCISCF(2).\n"}
+{"abstract": "  We consider a homogeneous plasma composed of $N$ particles of the same\nelectric charge which interact through a Coulomb potential. In the large plasma\nparameter limit, classical kinetic theories justify that the empirical density\nis the solution of the Balescu-Guernsey-Lenard equation, at leading order. This\nis a law of large numbers. The Balescu-Guernsey-Lenard equation is approximated\nby the Landau equation for scales much smaller than the Debye length. In order\nto describe typical and rare fluctuations, we compute for the first time a\nlarge deviation principle for dynamical paths of the empirical density, within\nthe Landau approximation. We obtain a large deviation Hamiltonian that\ndescribes fluctuations and rare excursions of the empirical density, in the\nlarge plasma parameter limit. We obtain this large deviation Hamiltonian either\nfrom the Boltzmann large deviation Hamiltonian in the grazing collision limit,\nor directly from the dynamics, extending the classical kinetic theory for\nplasmas within the Landau approximation. We also derive the large deviation\nHamiltonian for the empirical density of $N$ particles, each of which is\ngoverned by a Markov process, and coupled in a mean field way. We explain that\nthe plasma large deviation Hamiltonian is not the one of $N$ particles coupled\nin a mean-field way.\n"}
+{"abstract": "  Disasters are constant threats to humankind, and beyond losses in lives, they\ncause many implicit yet profound societal issues such as wealth disparity and\ndigital divide. Among those recovery measures in the aftermath of disasters,\nrestoring and improving communication services is of vital importance. Although\nexisting works have proposed many architectural and protocol designs, none of\nthem have taken human factors and social equality into consideration. Recent\nsociological studies have shown that people from marginalized groups (e.g.,\nminority, low income, and poor education) are more vulnerable to communication\noutages. In this work, we take pioneering efforts in integrating human factors\ninto an empirical optimization model to determine strategies for post-disaster\ncommunication restoration. We cast the design into a mix-integer non-linear\nprogramming problem, which is proven too complex to be solved. Through a suite\nof convex relaxations, we then develop heuristic algorithms to efficiently\nsolve the transformed optimization problem. Based on a collected dataset, we\nfurther evaluate and demonstrate how our design will prioritize communication\nservices for vulnerable people and promote social equality compared with an\nexisting modeling benchmark.\n"}
+{"abstract": "  The Maryland model was introduced more than 30 years ago as an integrable\nmodel of localization by aperiodic order. Even though quite popular and rich of\nfascinating mathematical properties, this model has so far remained quite\nartificial, as compared to other models displaying dynamical localization like\nthe periodically-kicked quantum rotator or the Aubry-Andre$^{\\prime}$ model.\nHere we suggest that light propagation in a polygonal optical waveguide lattice\nprovides a photonic realization of the Maryland model and enables to observe a\nmain prediction of this model, namely fragility of wave localization in the\ncommensurate potential limit.\n"}
+{"abstract": "  An analysis involving the light front variables is performed over the\ninclusively produced $\\pi^{\\pm}$, $K^{\\pm}$ and $p(\\bar{p})$ in the Pb-Pb\ncollisions simulated using the UrQMD event generator at $\\sqrt{s} = 2.76$ TeV\nto study the possible formation of a thermalised medium in these collisions. It\nis demonstrated that there exist surfaces defined by constant values of the\nlight front variable in the phase space of these hadrons which can select a\ngroup of thermalised particles. The temperatures are extracted for each species\nand are compared with those obtained from a similar calculation involving the\nkinematical and acceptance restrictions which are typically bound to the\ncollider experiments. It is shown that the analysis is feasible to perform with\nthe data collected at the ALICE experiment at LHC. The analysis may be\nconsidered as a prototypical study to a similar experimental analysis of the\ndata from the heavy-ion collisions at RHIC and LHC after implementing the\nnecessary corrections and calibrations relevant to the experimental\napparatuses.\n"}
+{"abstract": "  Cryo-Electron Tomography (cryo-ET) is a new 3D imaging technique with\nunprecedented potential for resolving submicron structural detail. Existing\nvolume visualization methods, however, cannot cope with its very low\nsignal-to-noise ratio. In order to design more powerful transfer functions, we\npropose to leverage soft segmentation as an explicit component of visualization\nfor noisy volumes. Our technical realization is based on semi-supervised\nlearning where we combine the advantages of two segmentation algorithms. A\nfirst weak segmentation algorithm provides good results for propagating sparse\nuser provided labels to other voxels in the same volume. This weak segmentation\nalgorithm is used to generate dense pseudo labels. A second powerful\ndeep-learning based segmentation algorithm can learn from these pseudo labels\nto generalize the segmentation to other unseen volumes, a task that the weak\nsegmentation algorithm fails at completely. The proposed volume visualization\nuses the deep-learning based segmentation as a component for segmentation-aware\ntransfer function design. Appropriate ramp parameters can be suggested\nautomatically through histogram analysis. Finally, our visualization uses\ngradient-free ambient occlusion shading to further suppress visual presence of\nnoise, and to give structural detail desired prominence. The cryo-ET data\nstudied throughout our technical experiments is based on the highest-quality\ntilted series of intact SARS-CoV-2 virions. Our technique shows the high impact\nin target sciences for visual data analysis of very noisy volumes that cannot\nbe visualized with existing techniques.\n"}
+{"abstract": "  Magnetars have been proposed to be the origin of FRBs soon after its initial\ndiscovery. The detection of the first Galactic FRB 20200428 from SGR 1935+2154\nhas made this hypothesis more convincing. In October 2020, this source was\nsupposed to be in an extremely active state again. We then carried out a\n1.6-hours follow-up observation of SGR 1935+2154 using the new ultra-wideband\nlow (UWL) receiver of the Parkes 64\\,m radio telescope covering a frequency\nrange of 704$-$4032 MHz. However, no convincing signal was detected in either\nof our single pulse or periodicity searches. We obtained a limit on the flux\ndensity of periodic signal of $\\rm 3.6\\,\\mu Jy$ using the full 3.3GHz bandwidth\ndata sets, which is the strictest limit for that of SGR 1935+2154. Our full\nbandwidth limit on the single pulses fluence is 35mJy ms, which is well below\nthe brightest single pulses detected by the FAST radio telescope just two\nbefore our observation. Assuming that SGR 1935+2154 is active during our\nobservation, our results suggest that its radio bursts are either intrinsically\nnarrowband or show a steep spectrum.\n"}
+{"abstract": "  A unit fraction representation of a rational number $r$ is a finite sum of\nreciprocals of positive integers that equals $r$. Of particular interest is the\ncase when all denominators in the representation are distinct, resulting in an\nEgyptian fraction representation of $r$. Common algorithms for computing\nEgyptian fraction representations of a given rational number tend to result in\nextremely large denominators and cannot be adapted to restrictions on the\nallowed denominators. We describe an algorithm for finding all unit fraction\nrepresentations of a given rational number using denominators from a given\nfinite multiset of positive integers. The freely available algorithm,\nimplemented in Scheme, is particularly well suited to computing dense Egyptian\nfraction representations, where the allowed denominators have a prescribed\nmaximum.\n"}
+{"abstract": "  A variational modeling framework for hydraulically induced fracturing of\nelastic-plastic solids is developed in the present work. The developed\nvariational structure provides a global minimization problem. While fracture\npropagation is modeled by means of a phase-field approach to fracture, plastic\neffects are taken into account by using a Drucker-Prager-type yield-criterion\nfunction. This yield-criterion function governs the plastic evolution of the\nfluid-solid mixture. Fluid storage and transport are described by a\nDarcy-Biot-type formulation. Thereby the fluid storage is decomposed into a\ncontribution due to the elastic deformations and one due to the plastic\ndeformations. A local return mapping scheme is used for the update of the\nplastic quantities. The global minimization structure demands a\n$H($div$)$-conforming finite-element formulation. Furthermore this is combined\nwith an enhanced-assumed-strain formulation in order to overcome locking\nphenomena arising from the plastic deformations. The robustness and\ncapabilities of the presented framework will be shown in a sequence of\nnumerical examples.\n"}
+{"abstract": "  We initiate a systematic investigation of group actions on compact medain\nalgebras via the corresponding dynamics on their spaces of measures. We show\nthat a probability measure which is invariant under a natural push forward\noperation must be a uniform measure on a cube and use this to show that every\namenable group action on a locally convex compact median algebra fixed a\nsub-cube.\n"}
+{"abstract": "  The transport properties of the strongly-coupled quark-gluon plasma created\nin ultra-relativistic heavy-ion collisions are extracted by Bayesian parameter\nestimate methods with the latest collision beam energy data from LHC. This\nBayesian analysis includes sophisticated flow harmonic observables for the\nfirst time. We found that the temperature dependence of specific shear\nviscosity shows weaker than the previous studies. The results prefer a lower\nvalue of specific bulk viscosity and a higher switching temperature to\nreproduce additional observables. However, the improved statistical\nuncertainties both on the experimental data and hydrodynamic calculations with\nadditional observables do not help to reduce the final credibility ranges much,\nindicating a need for improving the dynamical collision model before the\nhydrodynamic takes place. In addition, the sensitivities of experimental\nobservables to the parameters in hydrodynamic model calculations are\nquantified. It is found that the analysis benefits most from the symmetric\ncumulants and non-linear flow modes, which mostly reflect non-linear\nhydrodynamic responses, in constraining the temperature dependence of the\nspecific shear and bulk viscosity in addition to the previously used flow\ncoefficients.\n"}
+{"abstract": "  We prove bounds for the volume of neighborhoods of algebraic sets, in the\neuclidean space or the sphere, in terms of the degree of the defining\npolynomials, the number of variables and the dimension of the algebraic set,\nwithout any smoothness assumption. This generalizes previous work of Lotz on\nsmooth complete intersections in the euclidean space and of B\\\"urgisser, Cucker\nand Lotz on hypersurfaces in the sphere, and gives a complete solution to\nProblem 17 in the book titled \"Condition\" by B\\\"urgisser and Cucker.\n"}
+{"abstract": "  The discovery of 3C 273 in 1963, and the emergence of the Kerr solution\nshortly thereafter, precipitated the current era in astrophysics focused on\nusing black holes to explain active galactic nuclei (AGN). But while partial\nsuccess was achieved in separately explaining the bright nuclei of some AGN via\nthin disks, as well as powerful jets with thick disks, the combination of both\npowerful jets in an AGN with a bright nucleus, such as in 3C 273, remained\nelusive. Although numerical simulations have taken center stage in the last 25\nyears, they have struggled to produce the conditions that explain them. This is\nbecause radiatively efficient disks have proved a challenge to simulate. Radio\nquasars have thus been the least understood objects in high energy\nastrophysics. But recent simulations have begun to change this. We explore this\nmilestone in light of scale-invariance and show that transitory jets, possibly\nrelated to the jets seen in these recent simulations, as some have proposed,\ncannot explain radio quasars. We then provide a road map for a resolution.\n"}
+{"abstract": "  We study quasi-convex optimization problems, where only a subset of the\nconstraints can be sampled, and yet one would like a probabilistic guarantee on\nthe obtained solution with respect to the initial (unknown) optimization\nproblem. Even though our results are partly applicable to general quasi-convex\nproblems, in this work we introduce and study a particular subclass, which we\ncall \"quasi-linear problems\". We provide optimality conditions for these\nproblems. Thriving on this, we extend the approach of chance-constrained convex\noptimization to quasi-linear optimization problems. Finally, we show that this\napproach is useful for the stability analysis of black-box switched linear\nsystems, from a finite set of sampled trajectories. It allows us to compute\nprobabilistic upper bounds on the JSR of a large class of switched linear\nsystems.\n"}
+{"abstract": "  Content-based music information retrieval has seen rapid progress with the\nadoption of deep learning. Current approaches to high-level music description\ntypically make use of classification models, such as in auto-tagging or genre\nand mood classification. In this work, we propose to address music description\nvia audio captioning, defined as the task of generating a natural language\ndescription of music audio content in a human-like manner. To this end, we\npresent the first music audio captioning model, MusCaps, consisting of an\nencoder-decoder with temporal attention. Our method combines convolutional and\nrecurrent neural network architectures to jointly process audio-text inputs\nthrough a multimodal encoder and leverages pre-training on audio data to obtain\nrepresentations that effectively capture and summarise musical features in the\ninput. Evaluation of the generated captions through automatic metrics shows\nthat our method outperforms a baseline designed for non-music audio captioning.\nThrough an ablation study, we unveil that this performance boost can be mainly\nattributed to pre-training of the audio encoder, while other design choices -\nmodality fusion, decoding strategy and the use of attention - contribute only\nmarginally. Our model represents a shift away from classification-based music\ndescription and combines tasks requiring both auditory and linguistic\nunderstanding to bridge the semantic gap in music information retrieval.\n"}
+{"abstract": "  The control of Covid 19 epidemics by public health policy in Italy during the\nfirst and the second epidemic waves has been driven by using reproductive\nnumber Rt(t) to identify the supercritical (percolative), the subcritical\n(arrested), separated by the critical regime. Here we show that to quantify the\nCovid-19 spreading rate with containment measures (CSRwCM) there is a need of a\n3D expanded parameter space phase diagram built by the combination of Rt(t) and\ndoubling time Td(t). In this space we identify the dynamics of the Covid-19\ndynamics Italy and its administrative Regions. The supercritical regime is\nmathematically characterized by i) the power law of Td vs. [Rt(t)-1] and ii)\nthe exponential behaviour of Td vs. time, either in the first and in the second\nwave. The novel 3D phase diagram shows clearly metastable states appearing\nbefore and after the second wave critical regime. for loosening quarantine and\ntracing of actives cases. The metastable states are precursors of the abrupt\nonset of a next nascent wave supercritical regime. This dynamic description\nallows epidemics predictions needed by policymakers to activate\nnon-pharmaceutical interventions (NPIs), a key issue for avoiding economical\nlosses, reduce fatalities and avoid new virus variant during vaccination\ncampaign\n"}
+{"abstract": "  Sensor placement and feature selection are critical steps in engineering,\nmodeling, and data science that share a common mathematical theme: the selected\nmeasurements should enable solution of an inverse problem. Most real-world\nsystems of interest are nonlinear, yet the majority of available techniques for\nfeature selection and sensor placement rely on assumptions of linearity or\nsimple statistical models. We show that when these assumptions are violated,\nstandard techniques can lead to costly over-sensing without guaranteeing that\nthe desired information can be recovered from the measurements. In order to\nremedy these problems, we introduce a novel data-driven approach for sensor\nplacement and feature selection for a general type of nonlinear inverse problem\nbased on the information contained in secant vectors between data points. Using\nthe secant-based approach, we develop three efficient greedy algorithms that\neach provide different types of robust, near-minimal reconstruction guarantees.\nWe demonstrate them on two problems where linear techniques consistently fail:\nsensor placement to reconstruct a fluid flow formed by a complicated\nshock-mixing layer interaction and selecting fundamental manifold learning\ncoordinates on a torus.\n"}
+{"abstract": "  Self-assembled topological structures of post-processed two-dimensional\nmaterials exhibit novel physical properties distinct from those of their parent\nmaterials. Herein, the critical role of desulphurization on self-assembled\ntopological morphologies of molybdenum disulfide ($MoS_2$) monolayer sheets is\nexplored using molecular dynamics (MD) simulations. MD results show that there\nare differences in atomic energetics of $MoS_2$ monolayer sheets with different\ndesulphurization contents. Both free-standing and substrate-hosted $MoS_2$\nmonolayer sheets show diversity in topological structures such as flat surface,\nwrinkles, folds and scrolls, depending on the desulphurization contents, planar\ndimensions and ratios of length-to-width of $MoS_2$ monolayer sheets.\nParticularly, at the critical desulphurization contents, they roll up into\nnanotube morphology, consistent with previous experimental observations.\nMoreover, the observed differences in the molecular morphological diagrams\nbetween free-standing and substrate-hosted $MoS_2$ monolayer sheets can be\nattributed to unique interatomic interactions and van der Waals interactions in\nthem. The study provides important insights into functionalizing structural\nmorphological properties of two-dimensional materials, e.g., $MoS_2$, via\ndefect engineering.\n"}
+{"abstract": "  In this paper, we consider the pointwise boundary Lipschitz regularity of\nsolutions for the semilinear elliptic equations in divergence form mainly under\nsome weaker assumptions on nonhomogeneous term and the boundary. If the domain\nsatisfies C^{1,\\text{Dini}} condition at a boundary point, and the\nnonhomogeneous term satisfies Dini continuous condition and Lipschitz Newtonian\npotential condition, then the solution is Lipschitz continuous at this point.\nFurthermore, we generalize this result to Reifenberg C^{1,\\text{Dini}} domains.\n"}
+{"abstract": "  In this paper we compute the cohomology of moduli space of cubic fourfolds\nwith ADE type singularities relying on Kirwan's blowup and Laza's GIT\nconstruction. More precisely, we obtain the Betti numbers of Kirwan's\nresolution of the moduli space. Furthermore, by applying decomposition theorem\nwe obtain the Betti numbers of the intersection cohomology of Baily-Borel\ncompactification of the moduli space.\n"}
+{"abstract": "  Theoretical and experimental studies of the interaction between spins and\ntemperature are vital for the development of spin caloritronics, as they\ndictate the design of future devices. In this work, we propose a two-terminal\ncold-atom simulator to study that interaction. The proposed quantum simulator\nconsists of strongly interacting atoms that occupy two temperature reservoirs\nconnected by a one-dimensional link. First, we argue that the dynamics in the\nlink can be described using an inhomogeneous Heisenberg spin chain whose\ncouplings are defined by the local temperature. Second, we show the existence\nof a spin current in a system with a temperature difference by studying the\ndynamics that follows the spin-flip of an atom in the link. A temperature\ngradient accelerates the impurity in one direction more than in the other,\nleading to an overall spin current similar to the spin Seebeck effect.\n"}
+{"abstract": "  Data representation techniques have made a substantial contribution to\nadvancing data processing and machine learning (ML). Improving predictive power\nwas the focus of previous representation techniques, which unfortunately\nperform rather poorly on the interpretability in terms of extracting underlying\ninsights of the data. Recently, Kolmogorov model (KM) was studied, which is an\ninterpretable and predictable representation approach to learning the\nunderlying probabilistic structure of a set of random variables. The existing\nKM learning algorithms using semi-definite relaxation with randomization\n(SDRwR) or discrete monotonic optimization (DMO) have, however, limited utility\nto big data applications because they do not scale well computationally. In\nthis paper, we propose a computationally scalable KM learning algorithm, based\non the regularized dual optimization combined with enhanced gradient descent\n(GD) method. To make our method more scalable to large-dimensional problems, we\npropose two acceleration schemes, namely, eigenvalue decomposition (EVD)\nelimination strategy and proximal EVD algorithm. Furthermore, a thresholding\ntechnique by exploiting the approximation error analysis and leveraging the\nnormalized Minkowski $\\ell_1$-norm and its bounds, is provided for the\nselection of the number of iterations of the proximal EVD algorithm. When\napplied to big data applications, it is demonstrated that the proposed method\ncan achieve compatible training/prediction performance with significantly\nreduced computational complexity; roughly two orders of magnitude improvement\nin terms of the time overhead, compared to the existing KM learning algorithms.\nFurthermore, it is shown that the accuracy of logical relation mining for\ninterpretability by using the proposed KM learning algorithm exceeds $80\\%$.\n"}
+{"abstract": "  A common classification task situation is where one has a large amount of\ndata available for training, but only a small portion is annotated with class\nlabels. The goal of semi-supervised training, in this context, is to improve\nclassification accuracy by leverage information not only from labeled data but\nalso from a large amount of unlabeled data. Recent works have developed\nsignificant improvements by exploring the consistency constrain between\ndifferently augmented labeled and unlabeled data. Following this path, we\npropose a novel unsupervised objective that focuses on the less studied\nrelationship between the high confidence unlabeled data that are similar to\neach other. The new proposed Pair Loss minimizes the statistical distance\nbetween high confidence pseudo labels with similarity above a certain\nthreshold. Combining the Pair Loss with the techniques developed by the\nMixMatch family, our proposed SimPLE algorithm shows significant performance\ngains over previous algorithms on CIFAR-100 and Mini-ImageNet, and is on par\nwith the state-of-the-art methods on CIFAR-10 and SVHN. Furthermore, SimPLE\nalso outperforms the state-of-the-art methods in the transfer learning setting,\nwhere models are initialized by the weights pre-trained on ImageNet or\nDomainNet-Real. The code is available at github.com/zijian-hu/SimPLE.\n"}
+{"abstract": "  We generically construct a model in which the ${\\Pi^1_3}$-uniformization\nproperty is true, thus lowering the best known consistency strength from the\nexistence of $M_1^{\\#}$ to just $\\mathsf{ZFC}$. The forcing construction can be\nadapted to work over canonical inner models with Woodin cardinals, which\nyields, for the first time, universes where the $\\Pi^1_{2n}$-uniformization\nproperty holds, thus producing models which contradict the natural\n$\\mathsf{PD}$-induced pattern. It can also be used to obtain models for the\n$\\Pi^1_1$-uniformization property in the generalized Baire space.\n"}
+{"abstract": "  Personalized recommender systems are playing an increasingly important role\nas more content and services become available and users struggle to identify\nwhat might interest them. Although matrix factorization and deep learning based\nmethods have proved effective in user preference modeling, they violate the\ntriangle inequality and fail to capture fine-grained preference information. To\ntackle this, we develop a distance-based recommendation model with several\nnovel aspects: (i) each user and item are parameterized by Gaussian\ndistributions to capture the learning uncertainties; (ii) an adaptive margin\ngeneration scheme is proposed to generate the margins regarding different\ntraining triplets; (iii) explicit user-user/item-item similarity modeling is\nincorporated in the objective function. The Wasserstein distance is employed to\ndetermine preferences because it obeys the triangle inequality and can measure\nthe distance between probabilistic distributions. Via a comparison using five\nreal-world datasets with state-of-the-art methods, the proposed model\noutperforms the best existing models by 4-22% in terms of recall@K on Top-K\nrecommendation.\n"}
+{"abstract": "  Density functionals with asymptotic corrections to the long-range potential\nare recognized as entry-level methods for electronic structure calculations on\nmolecules that can sustain charge transfer. Even though charge transfer is also\nextremely common in Materials-Science, the application of such methods has been\nrare. We implement CAM-B3LYP within the VASP framework. Results obtained for 8\nrepresentative materials: aluminum, diamond, graphene, silicon, NaCl, MgO, 2D\nh-BN and 3D h-BN, indicate that CAM-B3LYP predictions embody mean-absolute\ndeviations (MAD) compared to HSE06 that are reduced by a factor of 4 for\nlattice parameters, 4 for quasiparticle band gaps, 3 for the lowest optical\nexcitation energies, and 6 for exciton binding energies; the only property for\nwhich HSE06 performed better was the band-gap of silicon. CAM-B3LYP also\nappears to provide similar or improved accuracy compared to ab initio G0W0 and\nBethe-Salpeter equation (BSE) approaches. The CAM-B3LYP implementation in VASP\nwas verified by comparison of optimized geometries and reaction energies for\nisolated molecules taken from the ACCDB database, evaluated in large periodic\nunit cells, to analogous results obtained using Gaussian-basis sets by\nGaussian-16. Using standard GW pseudopotentials and energy cutoffs for the\nplane-wave calculations and the aug-cc-pV5Z basis set for the atomic-basis\nones, the MAD in energy for 1738 chemical reactions was 0.34 kcal mol-1 (0.015\neV), whilst for 1291 bond lengths this was 0.0036 {\\AA}. An approximate scheme\nfor the speedy implementation of CAM-B3LYP into other electronic-structure\ncodes for materials-science applications was considered and shown to be less\naccurate, but nevertheless useful. Analytical functional derivatives for\nCAM-B3LYP were also obtained and implemented, ca. halving computational cost\ncompared to use of standard numerical derivatives.\n"}
+{"abstract": "  Unforgeable quantum money tokens were the first invention of quantum\ninformation science, but remain technologically challenging as they require\nquantum memories and/or long distance quantum communication. More recently,\nvirtual \"S-money\" tokens were introduced. These are generated by quantum\ncryptography, do not require quantum memories or long distance quantum\ncommunication, and yet in principle guarantee many of the security advantages\nof quantum money. Here, we describe implementations of S-money schemes with\noff-the-shelf quantum key distribution technology, and analyse security in the\npresence of noise, losses, and experimental imperfection. Our schemes satisfy\nnear instant validation without cross-checking. We show that, given standard\nassumptions in mistrustful quantum cryptographic implementations,\nunforgeability and user privacy could be guaranteed with attainable refinements\nof our off-the-shelf setup. We discuss the possibilities for unconditionally\nsecure (assumption-free) implementations.\n"}
+{"abstract": "  3D hydrodynamics models of deep stellar convection exhibit turbulent\nentrainment at the convective-radiative boundary which follows the entrainment\nlaw, varying with boundary penetrability. We implement the entrainment law in\nthe 1D Geneva stellar evolution code. We then calculate models between 1.5 and\n60 M$_{\\odot}$ at solar metallicity ($Z=0.014$) and compare them to previous\ngenerations of models and observations on the main sequence. The boundary\npenetrability, quantified by the bulk Richardson number, $Ri_{\\mathrm{B}}$,\nvaries with mass and to a smaller extent with time. The variation of\n$Ri_{\\mathrm{B}}$ with mass is due to the mass dependence of typical convective\nvelocities in the core and hence the luminosity of the star. The chemical\ngradient above the convective core dominates the variation of $Ri_{\\mathrm{B}}$\nwith time. An entrainment law method can therefore explain the apparent mass\ndependence of convective boundary mixing through $Ri_{\\mathrm{B}}$. New models\nincluding entrainment can better reproduce the mass dependence of the main\nsequence width using entrainment law parameters $A \\sim 2 \\times 10^{-4}$ and\n$n=1$. We compare these empirically constrained values to the results of 3D\nhydrodynamics simulations and discuss implications.\n"}
+{"abstract": "  Logarithmic Sobolev inequalities play an important role in understanding the\nmixing times of Markov chains on finite state spaces. It is typically not easy\nto determine, or indeed approximate, the optimal constant for which such\ninequalities hold. In this paper, we describe a semidefinite programming\nrelaxation for the logarithmic Sobolev constant of a finite Markov chain. This\nrelaxation gives certified lower bounds on the logarithmic Sobolev constant.\nNumerical experiments show that the solution to this relaxation is often very\nclose to the true constant. Finally, we use this relaxation to obtain a\nsum-of-squares proof that the logarithmic Sobolev constant is equal to half the\nPoincar\\'e constant for the specific case of a simple random walk on the odd\n$n$-cycle, with $n\\in\\{5,7,\\dots,21\\}$. Previously this was known only for\n$n=5$ and even $n$.\n"}
+{"abstract": "  When unexpected incidents occur, new innovative and flexible solutions are\nrequired. If this event is something such radical and dramatic like the\nCOVID-19 pandemic, these solutions must aim to guarantee as much normality as\npossible while protecting lives. After a moment of shock our university decided\nthat the students have to be able to pursue their studies for guaranteeing a\ndegree in the expected time since most of them faced immediate financial\nproblems due to the loss of their student jobs. This implied, for us as\nteachers, that we had to reorganise not only the teaching methods from nearly\none day to the next, but we also had to come up with an adjusted way of\nexaminations which had to take place in person with pen and paper under strict\nhygiene rules. On the other hand the correction should avoid personal contacts.\nWe developed a framework which allowed us to correct the digitalised exams\nsafely at home while providing the high standards given by the general data\nprotection regulation of our country. Moreover, the time spent in the offices\ncould be reduced to a minimum thanks to automatically generated exam sheets,\nautomatically re-digitalised and sorted worked-on exams.\n"}
+{"abstract": "  Greedy-GQ is a value-based reinforcement learning (RL) algorithm for optimal\ncontrol. Recently, the finite-time analysis of Greedy-GQ has been developed\nunder linear function approximation and Markovian sampling, and the algorithm\nis shown to achieve an $\\epsilon$-stationary point with a sample complexity in\nthe order of $\\mathcal{O}(\\epsilon^{-3})$. Such a high sample complexity is due\nto the large variance induced by the Markovian samples. In this paper, we\npropose a variance-reduced Greedy-GQ (VR-Greedy-GQ) algorithm for off-policy\noptimal control. In particular, the algorithm applies the SVRG-based variance\nreduction scheme to reduce the stochastic variance of the two time-scale\nupdates. We study the finite-time convergence of VR-Greedy-GQ under linear\nfunction approximation and Markovian sampling and show that the algorithm\nachieves a much smaller bias and variance error than the original Greedy-GQ. In\nparticular, we prove that VR-Greedy-GQ achieves an improved sample complexity\nthat is in the order of $\\mathcal{O}(\\epsilon^{-2})$. We further compare the\nperformance of VR-Greedy-GQ with that of Greedy-GQ in various RL experiments to\ncorroborate our theoretical findings.\n"}
+{"abstract": "  One of the lessons from the COVID-19 pandemic is the importance of social\ndistancing, even in challenging circumstances such as pre-hurricane evacuation.\nTo explore the implications of integrating social distancing with evacuation\noperations, we describe this evacuation process as a Capacitated Vehicle\nRouting Problem (CVRP) and solve it using a DNN (Deep Neural Network)-based\nsolution (Deep Reinforcement Learning) and a non-DNN solution (Sweep\nAlgorithm). A central question is whether Deep Reinforcement Learning provides\nsufficient extra routing efficiency to accommodate increased social distancing\nin a time-constrained evacuation operation. We found that, in comparison to the\nSweep Algorithm, Deep Reinforcement Learning can provide decision-makers with\nmore efficient routing. However, the evacuation time saved by Deep\nReinforcement Learning does not come close to compensating for the extra time\nrequired for social distancing, and its advantage disappears as the emergency\nvehicle capacity approaches the number of people per household.\n"}
+{"abstract": "  We present an analysis of the spatial clustering of a large sample of\nhigh-resolution, interferometically identified, submillimetre galaxies (SMGs).\nWe measure the projected cross-correlation function of ~350 SMGs in the UKIDSS\nUltra Deep-Survey Field across a redshift range of $z=1.5-3$ utilising a method\nthat incorporates the uncertainties in the redshift measurements for both the\nSMGs and cross-correlated galaxies through sampling their full probability\ndistribution functions. By measuring the absolute linear bias of the SMGs we\nderive halo masses of $\\log_{10}(M_{\\rm halo}[{h^{-1}\\,\\rm\nM_{\\odot}}])\\sim12.8$ with no evidence of evolution in the halo masses with\nredshift, contrary to some previous work. From considering models of halo mass\ngrowth rates we predict that the SMGs will reside in haloes of mass\n$\\log_{10}(M_{\\rm halo}[{h^{-1}\\,\\rm M_{\\odot}}])\\sim13.2$ at $z=0$, consistent\nwith the expectation that the majority of $z=1.5-3$ SMGs will evolve into\npresent-day spheroidal galaxies. Finally, comparing to models of\nstellar-to-halo mass ratios, we show that SMGs may correspond to systems that\nare maximally efficient at converting their gas reservoirs into stars. We\ncompare them to a simple model for gas cooling in halos that suggests that the\nunique properties of the SMG population, including their high levels of\nstar-formation and their redshift distribution, are a result of the SMGs being\nthe most massive galaxies that are still able to accrete cool gas from their\nsurrounding intragalactic medium.\n"}
+{"abstract": "  For a compact $(2n+1)$-dimensional smooth manifold, let $\\mu_M : B\nDiff_\\partial (D^{2n+1}) \\to B Diff (M)$ be the map that is defined by\nextending diffeomorphisms on an embedded disc by the identity.\n  By a classical result of Farrell and Hsiang, the rational homotopy groups and\nthe rational homology of $ B Diff_\\partial (D^{2n+1})$ are known in the\nconcordance stable range.\n  We prove two results on the behaviour of the map $\\mu_M$ in the concordance\nstable range. Firstly, it is \\emph{injective} on rational homotopy groups, and\nsecondly, it is \\emph{trivial} on rational homology, if $M$ contains\nsufficiently many embedded copies of $S^n\\times S^{n+1} \\setminus\nint(D^{2n+1})$.\n  The homotopical statement is probably not new and follows from the theory of\nsmooth torsion invariants. The homological statement relies on work by\nBotvinnik and Perlmutter on diffeomorphism of odd-dimensional manifolds.\n"}
+{"abstract": "  LIGO and Virgo have initiated the era of gravitational-wave (GW) astronomy;\nbut in order to fully explore GW frequency spectrum, we must turn our attention\nto innovative techniques for GW detection. One such approach is to use binary\nsystems as dynamical GW detectors by studying the subtle perturbations to their\norbits caused by impinging GWs. We present a powerful new formalism for\ncalculating the orbital evolution of a generic binary coupled to a stochastic\nbackground of GWs, deriving from first principles a secularly-averaged\nFokker-Planck equation which fully characterises the statistical evolution of\nall six of the binary's orbital elements. We also develop practical tools for\nnumerically integrating this equation, and derive the necessary statistical\nformalism to search for GWs in observational data from binary pulsars and\nlaser-ranging experiments.\n"}
+{"abstract": "  The extreme timing stability of radio millisecond pulsars (MSPs) combined\nwith their exotic environment and evolutionary history makes them excellent\nlaboratories to probe matter in extreme condition. Population studies indicate\nthat we have discovered less than five per cent of the MSPs of our Galaxy,\nimplying that a huge majority of radio MSPs are waiting to be discovered with\nimproved search techniques and more sensitive surveys. In this chapter, we\nprovide an overview of the present status of ongoing and upcoming surveys for\nMSPs. Observed spectra, profile and polarisation properties of known radio MSPs\nare also summarised. Finally, we describe how the timing studies of radio MSPs\nenable a huge science return including attempts to detect gravitational waves\nusing an array of MSPs, gravity tests using individual interesting MSP systems,\nas well as probing the intra-binary material using eclipses observed in MSPs in\ncompact binary systems.\n"}
+{"abstract": "  Worldwide, several cases go undiagnosed due to poor healthcare support in\nremote areas. In this context, a centralized system is needed for effective\nmonitoring and analysis of the medical records. A web-based patient diagnostic\nsystem is a central platform to store the medical history and predict the\npossible disease based on the current symptoms experienced by a patient to\nensure faster and accurate diagnosis. Early disease prediction can help the\nusers determine the severity of the disease and take quick action. The proposed\nweb-based disease prediction system utilizes machine learning based\nclassification techniques on a data set acquired from the National Centre of\nDisease Control (NCDC). $K$-nearest neighbor (K-NN), random forest and naive\nbayes classification approaches are utilized and an ensemble voting algorithm\nis also proposed where each classifier is assigned weights dynamically based on\nthe prediction confidence. The proposed system is also equipped with a\nrecommendation scheme to recommend the type of tests based on the existing\nsymptoms of the patient, so that necessary precautions can be taken. A\ncentralized database ensures that the medical data is preserved and there is\ntransparency in the system. The tampering into the system is prevented by\ngiving the no \"updation\" rights once the diagnosis is created.\n"}
+{"abstract": "  Replication time is among the most important components of a bacterial cell's\nreproductive fitness. Paradoxically, larger cells replicate in less time than\nsmaller cells despite the fact that building a larger cell requires increased\nquantities of raw materials and energy. This feat is primarily accomplished by\nthe massive over expression of ribosomes, which permits translation of mRNA\ninto protein, the limiting step in reproduction, to occur at a scale that would\nbe impossible were it not for the use of parallel processing. In computer\nscience, spatial parallelism is the distribution of work across the nodes of a\ndistributed-memory multicomputer system. Despite the fact that a non-negligible\nfraction of artificial life research is grounded in formulations based on\nspatially parallel substrates, there have been no examples of artificial\norganisms that use spatial parallelism to replicate in less time than smaller\norganisms. This paper describes artificial cells defined using a\ncombinator-based artificial chemistry that replicate in less time than smaller\ncells. This is achieved by employing extra copies of programs implementing the\nlimiting steps in the process used by the cells to synthesize their component\nparts. Significant speedup is demonstrated, despite the increased complexity of\ncontrol and export processes necessitated by the use of a parallel replication\nstrategy.\n"}
+{"abstract": "  By considering the parity of the degrees and levels of nodes in increasing\ntrees, a new combinatorial interpretation for the coefficients of the Taylor\nexpansions of the Jacobi elliptic functions is found. As one application of\nthis new interpretation, a conjecture of Ma-Mansour-Wang-Yeh is solved.\nUnifying the concepts of increasing trees and plane trees, Lin-Ma-Ma-Zhou\nintroduced weakly increasing trees on a multiset. A symmetry joint distribution\nof \"even-degree nodes on odd levels\" and \"odd-degree nodes\" on weakly\nincreasing trees is found, extending the Schett polynomials, a generalization\nof the Jacobi elliptic functions introduced by Schett, to multisets. A\ncombinatorial proof and an algebraic proof of this symmetry are provided, as\nwell as several relevant interesting consequences. Moreover, via introducing a\ngroup action on trees, we prove the partial $\\gamma$-positivity of the multiset\nSchett polynomials, a result implies both the symmetry and the unimodality of\nthese polynomials.\n"}
+{"abstract": "  This article explores the required amount of time series points from a\nhigh-speed traffic network to accurately estimate the Hurst exponent. The\nmethodology consists in designing an experiment using estimators that are\napplied to time series, followed by addressing the minimum amount of points\nrequired to obtain accurate estimates of the Hurst exponent in real-time. The\nmethodology addresses the exhaustive analysis of the Hurst exponent considering\nbias behavior, standard deviation, mean square error, and convergence using\nfractional gaussian noise signals with stationary increases. Our results show\nthat the Whittle estimator successfully estimates the Hurst exponent in series\nwith few points. Based on the results obtained, a minimum length for the time\nseries is empirically proposed. Finally, to validate the results, the\nmethodology is applied to real traffic captures in a high-speed network based\non the IEEE 802.3ab standard.\n"}
+{"abstract": "  Gravitational wave-forms from coalescences of binary black hole systems and\nbinary neutron star systems with low tidal effects can hardly be distinguished\nif the two systems have similar masses. In the absence of discriminating power\nbased on the gravitational wave-forms, the classification of sources into\nbinary neutron stars, binary black holes and mixed systems containing a black\nhole and a neutron star can only be unambiguous when assuming the standard\nmodel of stellar evolution and using the fact that there exists a mass gap\nbetween neutron stars and black holes. This approach is however limited by its\nown assumptions: for instance the 2.6 solar mass object detected in the\nGW190814 event remains unclassified, and models of new physics can introduce\nnew compact objects, like primordial black holes, which may have masses in the\nsame range as neutron stars. Then, without an electromagnetic counterpart\n(kilonova), classifying mergers of compact objects without mass gap criteria\nremains a difficult task, unless the source is close enough. In what follows we\ninvestigate a procedure to discriminate a model between binary neutron star\nmerger and primordial binary black hole merger by using a Bayes factor in\nsimulated wave-forms that we superimpose to realistic detector noise.\n"}
+{"abstract": "  Sensor technologies are becoming increasingly prevalent in the biomedical\nfield, with applications ranging from telemonitoring of people at risk, to\nusing sensor derived information as objective endpoints in clinical trials. To\nfully utilize sensor information, signals from distinct sensors often have to\nbe temporally aligned. However, due to imperfect oscillators and significant\nnoise, commonly encountered with biomedical signals, temporal alignment of raw\nsignals is an all but trivial problem, with, to-date, no generally applicable\nsolution. In this work, we present Deep Canonical Correlation Alignment (DCCA),\na novel, generally applicable solution for the temporal alignment of raw\n(biomedical) sensor signals. DCCA allows practitioners to directly align raw\nsignals, from distinct sensors, without requiring deep domain knowledge. On a\nselection of artificial and real datasets, we demonstrate the performance and\nutility of DCCA under a variety of conditions. We compare the DCCA algorithm to\nother warping based methods, DCCA outperforms dynamic time warping and cross\ncorrelation based methods by an order of magnitude in terms of alignment error.\nDCCA performs especially well on almost periodic biomedical signals such as\nheart-beats and breathing patterns. In comparison to existing approaches, that\nare not tailored towards raw sensor data, DCCA is not only fast enough to work\non signals with billions of data points but also provides automatic filtering\nand transformation functionalities, allowing it to deal with very noisy and\neven morphologically distinct signals.\n"}
+{"abstract": "  We probe photo-induced loss for chemically stable bosonic $^{23}$Na$^{87}$Rb\nand $^{23}$Na$^{39}$K molecules in chopped optical dipole traps where the\nmolecules spend a significant time in the dark. We expect the effective\ntwo-body decay to be largely suppressed in chopped traps due to the small\nexpected complex lifetimes of about $13\\mu$s and $6\\mu$s for $^{23}$Na$^{87}$Rb\nand $^{23}$Na$^{39}$K respectively. However, instead we do observe\nnear-universal loss even at the lowest chopping frequencies we can probe. Our\ndata thus either suggest a so far unknown loss mechanism or a drastic\nunderestimation of the complex lifetime by at least one to two orders of\nmagnitude.\n"}
+{"abstract": "  A regularized version of Mixture Models is proposed to learn a principal\ngraph from a distribution of $D$-dimensional data points. In the particular\ncase of manifold learning for ridge detection, we assume that the underlying\nmanifold can be modeled as a graph structure acting like a topological prior\nfor the Gaussian clusters turning the problem into a maximum a posteriori\nestimation. Parameters of the model are iteratively estimated through an\nExpectation-Maximization procedure making the learning of the structure\ncomputationally efficient with guaranteed convergence for any graph prior in a\npolynomial time. We also embed in the formalism a natural way to make the\nalgorithm robust to outliers of the pattern and heteroscedasticity of the\nmanifold sampling coherently with the graph structure. The method uses a graph\nprior given by the minimum spanning tree that we extend using random\nsub-samplings of the dataset to take into account cycles that can be observed\nin the spatial distribution.\n"}
+{"abstract": "  We investigate the late-time evolution of the Universe within a cosmological\nmodel in which dark matter and dark energy are identified with two interacting\nscalar fields. Using methods of qualitative analysis of dynamical systems, we\nidentify all cosmological solutions of this model. We show that viable\nsolutions -- in the sense that they correspond to a cosmic evolution in which a\nlong enough matter-dominated era is followed by a current era of accelerated\nexpansion -- can be found in several regions of the parameter space. These\nsolutions can be divided into two categories, namely, solutions that evolve to\na state of everlasting accelerated expansion, in which the energy density of\nthe dark-matter field rapidly approaches zero and the evolution becomes\nentirely dominated by the dark-energy field, and solutions in which the stage\nof accelerated expansion is temporary and the ratio between the energy\ndensities of dark energy and dark matter tends, asymptotically, to a constant\nnonzero value.\n"}
+{"abstract": "  This paper focuses on monocular 3D object detection, one of the essential\nmodules in autonomous driving systems. A key challenge is that the depth\nrecovery problem is ill-posed in monocular data. In this work, we first conduct\na thorough analysis to reveal how existing methods fail to robustly estimate\ndepth when different geometry shifts occur. In particular, through a series of\nimage-based and instance-based manipulations for current detectors, we\nillustrate existing detectors are vulnerable in capturing the consistent\nrelationships between depth and both object apparent sizes and positions. To\nalleviate this issue and improve the robustness of detectors, we convert the\naforementioned manipulations into four corresponding 3D-aware data augmentation\ntechniques. At the image-level, we randomly manipulate the camera system,\nincluding its focal length, receptive field and location, to generate new\ntraining images with geometric shifts. At the instance level, we crop the\nforeground objects and randomly paste them to other scenes to generate new\ntraining instances. All the proposed augmentation techniques share the virtue\nthat geometry relationships in objects are preserved while their geometry is\nmanipulated. In light of the proposed data augmentation methods, not only the\ninstability of depth recovery is effectively alleviated, but also the final 3D\ndetection performance is significantly improved. This leads to superior\nimprovements on the KITTI and nuScenes monocular 3D detection benchmarks with\nstate-of-the-art results.\n"}
+{"abstract": "  Extended Hubbard models have proven to bear novel quantum states, but their\nexperimental realization remains challenging. In this work we propose to use\nbosonic quantum gases dressed with molecular bound states in Rydberg\ninteraction potentials for the observation of these quantum states. We study\nthe molecular Rabi coupling with respect to principal quantum number and\ntrapping frequency of the ground state atoms for various molecular potentials\nof Rubidium and Potassium, and the hereby resulting dressed interaction\nstrength. Additionally, we propose a two-color excitation scheme which\nsignificantly increases the dressed interaction and cancels AC Stark shifts\nlimiting the atomic motion in the itinerant regime. We study the various\nequilibrium phases of the corresponding extended Bose-Hubbard model by means of\nthe Cluster Gutzwiller approach and perform time evolution simulations via the\nLindblad master equation. We find a supersolid phase by slowly ramping the\nmolecular Rabi coupling of an initially prepared superfluid and discuss the\nrole of dissipation.\n"}
+{"abstract": "  As the signal-to-noise of Sunyaev-Zeldovich (SZ) cross-correlation\nmeasurements of galaxies improves our ability to infer properties about the\ncircumgalactic medium (CGM), we will transition from being limited by\nstatistical uncertainties to systematic uncertainties. Using thermodynamic\nprofiles of the CGM created from the IllustrisTNG (The Next Generation)\nsimulations we investigate the importance of specific choices in modeling the\ngalaxy sample. These choices include different sample selections in the\nsimulation (stellar versus halo mass, color selections) and different fitting\nmodels (matching by the shape of the mass distribution, inclusion of a two-halo\nterm). We forward model a mock galaxy sample into projected SZ observable\nprofiles and fit these profiles to a generalized Navarro-Frenk-White profile\nusing forecasted errors of the upcoming Simons Observatory experiment. We test\nthe number of free parameters in the fits and show that this is another\nmodeling choice that yields different results. Finally, we show how different\nfitting models can reproduce parameters of a fiducial profile, and show that\nthe addition of a two-halo term and matching by the mass distribution of the\nsample are extremely important modeling choices to consider.\n"}
+{"abstract": "  The Large High Altitude Air Shower Observatory~(LHAASO) is one of the most\nsensitive gamma-ray detector arrays currently operating at TeV and PeV\nenergies. Recently the LHAASO experiment detected ultra-high-energy~(UHE;\n$E_{\\gamma}\\gtrsim 100~\\mathrm{TeV}$) photon emissions up to $1.4~\\mathrm{PeV}$\nfrom twelve astrophysical gamma-ray sources. We point out that the detection of\ncosmic photons at such energies can constrain the photon self-decay motivated\nby superluminal Lorentz symmetry violation~(LV) to a higher level, thus can put\nstrong constraints to certain LV frameworks. Meanwhile, we suggest that the\ncurrent observation of the PeV-scale photon with LHAASO may provide hints to\npermit a subluminal type of Lorentz violation in the proximity of the Planckian\nregime, and may be compatible with the light speed variation at the scale of\n$3.6\\times 10^{17}~\\mathrm{GeV}$ recently suggested from gamma-ray burst~(GRB)\ntime delays. We further propose detecting PeV photons coming from extragalactic\nsources with future experiments, based on LV-induced threshold anomalies of\n$e^{+}e^{-}$ pair-production, as a crucial test of subluminal Lorentz\nviolation. We comment that these observations are consistent with a\nD-brane/string-inspired quantum-gravity framework, the space-time foam model.\n"}
+{"abstract": "  The $\\Lambda$CDM cosmology passes demanding tests that establish it as a good\napproximation to reality, but it could be improved. I present a list of\npossibly interesting and less well explored things that might yield hints to a\nbetter theory.\n"}
+{"abstract": "  Development of control algorithms for enhancing performance in\nsafety-critical systems such as the Autonomous Emergency Braking system (AEB)\nis an important issue in the emerging field of automated electric vehicles. In\nthis study, we design a safety distance-based hierarchical AEB control system\nconstituted of a high-level Rule-Based Supervisory control module, an\nintermediate-level switching algorithm and a low-level control module. The Rule\nBased supervisor determines the required deceleration command that is fed to\nthe low-level control module via the switching algorithm. In the low-level, two\nwheel slip control algorithms were developed, a Robust Sliding Mode controller\nand a Gain-Scheduled Linear Quadratic Regulator. For the needs of this control\ndesign, a non-linear dynamic vehicle model was implemented whereas a constant\ntire-road friction coefficient was considered. The proposed control system was\nvalidated in Simulink, assuming a straight-line braking maneuver on a flat dry\nroad. The simulation results demonstrated satisfactory emergency braking\nperformance with full collision avoidance in both proposed control system\ncombinations.\n"}
+{"abstract": "  The extensive timespan of modern radial velocity surveys have made the\ndiscovery of long-period substellar companions more common in recent years,\nhowever measuring the true masses of these objects remains challenging.\nAstrometry from the Gaia mission is expected to provide mass measurements for\nmany of these long-period companions, but this data is not yet available.\nHowever, combining proper motion data from Gaia DR2 and the earlier Hipparcos\nmission makes it possible to measure true masses of substellar companions in\nfavourable cases. In this work, we combine radial velocities with\nHipparcos-Gaia astrometry to measure the true masses of two recently discovered\nlong-period substellar companion candidates, HD 92987 B and HD 221420 b. In\nboth cases, we find that the true masses are significantly higher than implied\nby radial velocities alone. A $2087 \\pm 19$ m s$^{-1}$ astrometric signal\nreveals that HD 92987 B is not close to its $17$ $M_J$ minimum mass but is\ninstead a $0.2562 \\pm 0.0045$ $M_\\odot$ star viewed at a near-polar orbital\ninclination, whereas the $22.9 \\pm 2.2$ $M_J$ HD 221420 b can be plausibly\ninterpreted as a high-mass \"super-planet\" or a low-mass brown dwarf. With\nsemi-major axes of $\\sim$10 AU both companions are interesting targets for\ndirect imaging, and HD 221420 b in particular would be a benchmark metal-rich\nsubstellar object if it proves possible to directly detect. Our results\ndemonstrate the power of Hipparcos-Gaia astrometry for studying long-period\nplanet and brown dwarf candidates discovered from radial velocity surveys.\n"}
+{"abstract": "  The increasing use of cloud computing for latency-sensitive applications has\nsparked renewed interest in providing tight bounds on network tail latency.\nAchieving this in practice at reasonable network utilization has proved\nelusive, due to a combination of highly bursty application demand, faster link\nspeeds, and heavy-tailed message sizes. While priority scheduling can be used\nto reduce tail latency for some traffic, this comes at a cost of much worse\ndelay behavior for all other traffic on the network. Most operators choose to\nrun their networks at very low average utilization, despite the added cost, and\nyet still suffer poor tail behavior.\n  This paper takes a different approach. We build a system, swp, to help\noperators (and network designers) to understand and control tail latency\nwithout relying on priority scheduling. As network workload changes, swp is\ndesigned to give real-time advice on the network switch configurations needed\nto maintain tail latency objectives for each traffic class. The core of swp is\nan efficient model for simulating the combined effect of traffic\ncharacteristics, end-to-end congestion control, and switch scheduling on\nservice-level objectives (SLOs), along with an optimizer that adjusts\nswitch-level scheduling weights assigned to each class. Using simulation across\na diverse set of workloads with different SLOs, we show that to meet the same\nSLOs as swp provides, FIFO would require 65% greater link capacity, and 79%\nmore for scenarios with tight SLOs on bursty traffic classes.\n"}
+{"abstract": "  We discuss integrable discretizations of 3-dimensional cyclic systems, that\nis, orthogonal coordinate systems with one family of circular coordinate lines.\nIn particular, the underlying circle congruences are investigated in detail,\nand characterized by the existence of a certain flat connection. Within the\ndeveloped framework, discrete cyclic systems with a family of discrete flat\nfronts in hyperbolic space and discrete cyclic systems, where all coordinate\nsurfaces are discrete Dupin cyclides, are investigated.\n"}
+{"abstract": "  These are notes on the hyperbolic geometry of surfaces, Teichm{\\\"u}ller\nspaces and Thurston's metric on these spaces. They are associated with lectures\nI gave at the Morningside Center of Mathematics of the Chinese Academy of\nSciences in March 2019 and at the Chebyshev Laboratory of the Saint Petersburg\nState University in May 2019. In particular, I survey several results on the\nbehavior of stretch lines, a distinguished class of geodesics for Thurston's\nmetric and I point out several analogies between this metric and\nTeichm{\\\"u}ller's metric. Several open questions are addressed. The final\nversion of these notes will appear in the book \"Moduli Spaces and Locally\nSymmetric Spaces\", edited by L. Ji and S.-T. Yau, International Press, 2021.\n"}
+{"abstract": "  Polygenic risk score (PRS) analysis is a powerful method been used to\nestimate an individual's genetic risk towards targeted traits. PRS analysis\ncould be used to obtain evidence of a genetic effect beyond Genome-Wide\nAssociation Studies (GWAS) results i.e. when there are no significant markers.\nPRS analysis has been widely applied to investigate the genetic basis of\nseveral traits including rare diseases. However, the accuracy of PRS analysis\ndepends on the genomic data of the underlying population. For instance, several\nstudies showed that obtaining higher prediction power of PRS analysis is\nchallenging for non-Europeans. In this manuscript, we reviewed the conventional\nPRS methods and their application to sub-saharan Africa communities. We\nconcluded that the limiting factor of applying PRS analysis to sub-saharan\npopulations is the lack of sufficient GWAS data. Also, we recommended\ndeveloping African-specific PRS tools\n"}
+{"abstract": "  Well-defined jokes can be divided neatly into a setup and a punchline. While\nmost works on humor today talk about a joke as a whole, the idea of generating\npunchlines to a setup has applications in conversational humor, where funny\nremarks usually occur with a non-funny context. Thus, this paper is based\naround two core concepts: Classification and the Generation of a punchline from\na particular setup based on the Incongruity Theory. We first implement a\nfeature-based machine learning model to classify humor. For humor generation,\nwe use a neural model, and then merge the classical rule-based approaches with\nthe neural approach to create a hybrid model. The idea behind being: combining\ninsights gained from other tasks with the setup-punchline model and thus\napplying it to existing text generation approaches. We then use and compare our\nmodel with human written jokes with the help of human evaluators in a\ndouble-blind study.\n"}
+{"abstract": "  In the present paper we discuss about a set of geometric and physical\nproperties of hyper-generalised quasi-Einstein spacetime. At the beginning we\ndiscuss about pseudosymmetry over a hyper-generalised quasi-Einstein spacetime.\nHere we discuss about W2-Ricci pseudosymmetry, Z-Ricci pseudosymmetry, Ricci\npseudosymmetry and projective pseudosymmetry over a hyper-generalised\nquasi-Einstein spacetime. Later on we take over Ricci symmetric\nhyper-generalised quasi-Einstein spacetime and derive a set of important\ngeometric and physical theorems over it. Moving further we consider some\nphysical applications of the hyper-generalised quasi-Einstein spacetime. Lastly\nwe prove the existence of a hyper-generalised quasi-Einstein spacetime by\nconstructing a non-trivial example.\n"}
+{"abstract": "  Let $G$ be a finite group and let $\\{A_1,\\ldots,A_k\\}$ be a collection of\nsubsets of $G$ such that $G=A_1\\ldots A_k$ is the product of all the $A_i$ and\n${\\rm card}(G)={\\rm card}(A_1)\\ldots{\\rm card}(A_k)$. We shall write\n$G=A_1\\cdot\\ldots\\cdot A_k$, and call this a $k$-fold factorization of the form\n$({\\rm card}(A_1),\\ldots,{\\rm card}(A_k))$. We prove that for any integer\n$k\\geq3$ there exist a finite group $G$ of order $n$ and a factorization of\n$n=a_1\\ldots a_k$ into $k$ factors, with $a_i>1$, such that $G$ has no $k$-fold\nfactorization of the form $(a_1,\\ldots,a_k)$.\n"}
+{"abstract": "  We investigate the conduction-band structure and electron mobility in\nrocksalt ScN based on density functional theory. The first-principles band\nstructure allows us to obtain band velocities and effective masses as a\nfunction of energy. Electron-phonon scattering is assessed by explicitly\ncomputing the $q$-dependent electron-phonon matrix elements, with the inclusion\nof the long-range electrostatic interaction. The influence of free-carrier\nscreening on the electron transport is assessed using the random phase\napproximation. We find a notable enhancement of electron mobility when the\ncarrier concentration exceeds 10$^{20}$ cm$^{-3}$. We calculate the\nroom-temperature electron mobility in ScN to be 587 cm$^2$/Vs at low carrier\nconcentrations. When the carrier concentration is increased, the electron\nmobility starts to decrease significantly around $n=10^{19}$ cm$^{-3}$, and\ndrops to 240 cm$^2$/Vs at $n=10^{21}$ cm$^{-3}$. We also explore the influence\nof strain in (111)- and (100)-oriented ScN films. For (111) films, we find that\na 1.0\\% compressive epitaxial strain increases the in-plane mobility by 72\ncm$^2$/Vs and the out-of-plane mobility by 50 cm$^2$/Vs. For (100) films, a\n1.0\\% compressive epitaxial strain increases the out-of-plane mobility by as\nmuch as 172 cm$^2$/Vs, but has a weak impact on the in-plane mobility. Our\nstudy sheds light on electron transport in ScN at different electron\nconcentrations and shows how strain engineering could increase the electron\nmobility.\n"}
+{"abstract": "  Generalized Polynomial Chaos (gPC) theory has been widely used for\nrepresenting parametric uncertainty in a system, thanks to its ability to\npropagate uncertainty evolution. In an optimal control context, gPC can be\ncombined with several optimization techniques to achieve a control policy that\nhandles effectively this type of uncertainty. Such a suitable method is\nDifferential Dynamic Programming (DDP), leading to an algorithm that inherits\nthe scalability to high-dimensional systems and fast convergence nature of the\nlatter. In this paper, we expand this combination aiming to acquire\nprobabilistic guarantees on the satisfaction of nonlinear constraints. In\nparticular, we exploit the ability of gPC to express higher order moments of\nthe uncertainty distribution - without any Gaussianity assumption - and we\nincorporate chance constraints that lead to expressions involving the state\ncovariance. Furthermore, we demonstrate that by implementing our algorithm in a\nreceding horizon fashion, we are able to compute control policies that\neffectively reduce the accumulation of uncertainty on the trajectory. The\napplicability of our method is verified through simulation results on a\ndifferential wheeled robot and a quadrotor that perform obstacle avoidance\ntasks.\n"}
+{"abstract": "  The acoustic radiation force produced by ultrasonic waves is the \"workhorse\"\nof particle manipulation in acoustofluidics. Nonspherical particles are also\nsubjected to a mean torque known as the acoustic radiation torque. Together\nthey constitute the mean-acoustic fields exerted on the particle. Analytical\nmethods alone cannot calculate these fields on arbitrarily shaped particles in\nactual fluids and no longer fit for purpose. Here, a semi-analytical approach\nis introduced for handling subwavelength axisymmetric particles immersed in an\nisotropic Newtonian fluid. The obtained mean-acoustic fields depend on the\nscattering coefficients that reflect the monopole and dipole modes. These\ncoefficients are determined by numerically solving the scattering problem. Our\nmethod is benchmarked by comparison with the exact result for a subwavelength\nrigid sphere in water. Besides, a more realistic case of a red blood cell\nimmersed in blood plasma under a standing ultrasonic wave is investigated with\nour methodology.\n"}
+{"abstract": "  Facility Location (FL) problems as one of the most important problems in\noperations research aim to determine the location of a set of facilities in a\nway that the total costs, including costs of opening facilities and\ntransportation costs, are minimized. This study addresses an FL problem in\nwhich the capacity of each facility is limited. Because this problem is in the\ncategory of np-hard problems, we use the Benders Decomposition (BD) approach to\nefficiently solve the FL problem. In this paper, we implement the classic BD\nalgorithm and some accelerating BD methods such as Pareto-optimality cut and\nL-shaped decomposition methods. Furthermore, we propose and implement the\nhybrid Pareto-L-shaped (PL) method, and evaluate the performance of the\nimplemented algorithms. The results show that the L-shaped decomposition\noutperforms the other algorithms in terms of time and the number of iteration,\nwhile the classic BD converges slowly especially on large scales.\n"}
+{"abstract": "  We use the Gemini NIFS instrument to map the H$_2 2.1218\\mu$m and Br$\\gamma$\nflux distributions in the inner 0.04-2 kpc of a sample of 36 nearby active\ngalaxies ($0.001\\lesssim z\\lesssim0.056$) at spatial resolutions from 4 to 250\npc. We find extended emission in 34 galaxies. In $\\sim$55% of them, the\nemission in both lines is most extended along the galaxy major axis, while in\nthe other 45% the extent follows a distinct orientation. The emission of H$_2$\nis less concentrated than that of Br$\\gamma$, presenting a radius that contains\nhalf of the flux 60% greater, on average. The H$_2$ emission is driven by\nthermal processes - X-ray heating and shocks - at most locations for all\ngalaxies, where $0.4<H_2/Br\\gamma<6$. For regions where H$_2$/Br$\\gamma>6$\n(seen in 40% of the galaxies), shocks are the main H$_2$ excitation mechanism,\nwhile in regions with H$_2$/Br$\\gamma<0.4$ (25% of the sample) the H$_2$\nemission is produced by fluorescence. The only difference we found between type\n1 and type 2 AGN was in the nuclear emission-line equivalent widths, that are\nsmaller in type 1 than in type 2 due to a larger contribution to the continuum\nfrom the hot dusty torus in the former. The gas masses in the inner 125 pc\nradius are in the range $10^1-10^4$ M$_\\odot$ for the hot H$_2$ and $10^3-10^6$\nM$_\\odot$ for the ionised gas and would be enough to power the AGN in our\nsample for $10^5-10^8$ yr at their current accretion rates.\n"}
+{"abstract": "  The cues needed to detect spoofing attacks against automatic speaker\nverification are often located in specific spectral sub-bands or temporal\nsegments. Previous works show the potential to learn these using either\nspectral or temporal self-attention mechanisms but not the relationships\nbetween neighbouring sub-bands or segments. This paper reports our use of graph\nattention networks (GATs) to model these relationships and to improve spoofing\ndetection performance. GATs leverage a self-attention mechanism over graph\nstructured data to model the data manifold and the relationships between nodes.\nOur graph is constructed from representations produced by a ResNet. Nodes in\nthe graph represent information either in specific sub-bands or temporal\nsegments. Experiments performed on the ASVspoof 2019 logical access database\nshow that our GAT-based model with temporal attention outperforms all of our\nbaseline single systems. Furthermore, GAT-based systems are complementary to a\nset of existing systems. The fusion of GAT-based models with more conventional\ncountermeasures delivers a 47% relative improvement in performance compared to\nthe best performing single GAT system.\n"}
+{"abstract": "  Place Recognition is a crucial capability for mobile robot localization and\nnavigation. Image-based or Visual Place Recognition (VPR) is a challenging\nproblem as scene appearance and camera viewpoint can change significantly when\nplaces are revisited. Recent VPR methods based on ``sequential\nrepresentations'' have shown promising results as compared to traditional\nsequence score aggregation or single image based techniques. In parallel to\nthese endeavors, 3D point clouds based place recognition is also being explored\nfollowing the advances in deep learning based point cloud processing. However,\na key question remains: is an explicit 3D structure based place representation\nalways superior to an implicit ``spatial'' representation based on sequence of\nRGB images which can inherently learn scene structure. In this extended\nabstract, we attempt to compare these two types of methods by considering a\nsimilar ``metric span'' to represent places. We compare a 3D point cloud based\nmethod (PointNetVLAD) with image sequence based methods (SeqNet and others) and\nshowcase that image sequence based techniques approach, and can even surpass,\nthe performance achieved by point cloud based methods for a given metric span.\nThese performance variations can be attributed to differences in data richness\nof input sensors as well as data accumulation strategies for a mobile robot.\nWhile a perfect apple-to-apple comparison may not be feasible for these two\ndifferent modalities, the presented comparison takes a step in the direction of\nanswering deeper questions regarding spatial representations, relevant to\nseveral applications like Autonomous Driving and Augmented/Virtual Reality.\nSource code available publicly https://github.com/oravus/seqNet.\n"}
+{"abstract": "  Protection of gauge invariance in experimental realizations of lattice gauge\ntheories based on energy-penalty schemes has recently stimulated impressive\nefforts both theoretically and in setups of quantum synthetic matter. A major\nchallenge is the reliability of such schemes in non-Abelian gauge theories\nwhere local conservation laws do not commute. Here, we show through exact\ndiagonalization that non-Abelian gauge invariance can be reliably controlled\nusing gauge-protection terms that energetically stabilize the target gauge\nsector in Hilbert space, suppressing gauge violations due to unitary\ngauge-breaking errors. We present analytic arguments that predict a\nvolume-independent protection strength $V$, which when sufficiently large leads\nto the emergence of an \\textit{adjusted} gauge theory with the same local gauge\nsymmetry up to least a timescale $\\propto\\sqrt{V/V_0^3}$. Thereafter, a\n\\textit{renormalized} gauge theory dominates up to a timescale\n$\\propto\\exp(V/V_0)/V_0$ with $V_0$ a volume-independent energy factor, similar\nto the case of faulty Abelian gauge theories. Moreover, we show for certain\nexperimentally relevant errors that single-body protection terms robustly\nsuppress gauge violations up to all accessible evolution times in exact\ndiagonalization, and demonstrate that the adjusted gauge theory emerges in this\ncase as well. These single-body protection terms can be readily implemented\nwith fewer engineering requirements than the ideal gauge theory itself in\ncurrent ultracold-atom setups and NISQ devices.\n"}
+{"abstract": "  Susceptibility of neural networks to adversarial attack prompts serious\nsafety concerns for lane detection efforts, a domain where such models have\nbeen widely applied. Recent work on adversarial road patches have successfully\ninduced perception of lane lines with arbitrary form, presenting an avenue for\nrogue control of vehicle behavior. In this paper, we propose a modular lane\nverification system that can catch such threats before the autonomous driving\nsystem is misled while remaining agnostic to the particular lane detection\nmodel. Our experiments show that implementing the system with a simple\nconvolutional neural network (CNN) can defend against a wide gamut of attacks\non lane detection models. With a 10% impact to inference time, we can detect\n96% of bounded non-adaptive attacks, 90% of bounded adaptive attacks, and 98%\nof patch attacks while preserving accurate identification at least 95% of true\nlanes, indicating that our proposed verification system is effective at\nmitigating lane detection security risks with minimal overhead.\n"}
+{"abstract": "  We apply the finite-temperature variational cluster approach to a strongly\ncorrelated and spin-orbit coupled model for four electrons (i.e. two holes) in\nthe $t_{2g}$ subshell. We focus on parameters suitable for antiferromagnetic\nMott insulators, in particular Ca$_2$RuO$_4$, and identify a crossover from the\nlow-temperature regime, where spin-orbit coupling is essential, to the\nhigh-temperature regime where it leaves few signatures. The crossover is seen\nin one-particle spectra, where $xz$ and $yz$ spectra are almost one dimensional\n(as expected for weak spin-orbit coupling) at high temperature. At lower\ntemperature, where spin-orbit coupling mixes all three orbitals, they become\nmore two dimensional. However, stronger effects are seen in two-particle\nobservables like the weight in states with definite onsite angular momentum. We\nthus identify the enigmatic intermediate-temperature 'orbital-order phase\ntransition', which has been reported in various X-ray diffraction and\nabsorption experiments at $T\\approx 260\\;K$, as the signature of the onset of\nspin-orbital correlations.\n"}
+{"abstract": "  We present a study of exclusion process on a peculiar topology of network\nwith two intersected lanes, competing for the particles in a reservoir with\nfinite capacity. To provide a theoretical ground for our findings, we exploit\nmean-field approximation along with domain-wall theory. The stationary\nproperties of the system including phase transitions, density profiles and\nposition of the domain-wall are derived analytically. Under the similar\ndynamical rules, the particles of both the lanes interact only at the\nintersected site. The symmetry of system is maintained till number of particles\ndo not exceed total number of sites. However, beyond this the symmetry breaking\nphenomenon occurs resulting in the appearance of asymmetric phases and\ncontinues to persist even for infnite number of particles. The complexity of\nphase diagram shows a non-monotonic behaviour with increasing number of\nparticles in the system. A bulk induced shock appears in a symmetric phase,\nwhereas, a boundary induced shock is observed in symmetric as well as\nasymmetric phase. Monitoring the location of shock with increasing entry of\nparticles, we explain the possible phase transitions. The theoretical results\nare supported by extensive Monte Carlo simulations and explained using simple\nphysical arguments.\n"}
+{"abstract": "  On December 22, 2020 at about 23h 23m 33s UTC a very bright fireball was seen\nin the sky of south-eastern China. The fireball lasted for several seconds and\nended with an atmospheric explosion that was detected by US surveillance\nsatellites. According to CNEOS's data, the fireball moved with a mean speed of\n13.6 km/s and exploded at an altitude of about 35.5 km (Lat. $31.9^{\\circ}$ N;\nLong. $96.2^{\\circ} $ E). In this paper we estimate the atmospheric trajectory,\nthe heliocentric orbit and the strewn fields for different mass/section ratio\nof the fragments. The trajectory was about from north to south and with low\ninclination ($5^{\\circ} \\pm 2^{\\circ}$) with respect to the local surface. The\nexplosion height appear consistent with a fragmented rocky body and the\nheliocentric orbit supports an asteroidal origin. The probable strewn field\nextend between two points with coordinates ($+31.3^\\circ$ N; $96.3^\\circ$ E)\nand ($+30.3^\\circ$ N; $96.5^\\circ$ E), for a total area of about 4000\n$\\textrm{km}^2$. This large extension is a direct consequence of the low\ninclination of the trajectory. Given the unknown uncertainty of the CNEOS's\ndata, these results should be taken with caution.\n"}
+{"abstract": "  Crimean-Congo haemorrhagic fever (CCHF) is a tick-borne zoonotic disease\ncaused by the Crimean-Congo hemorrhagic fever virus (CCHFV). Ticks belonging to\nthe genus \\textit{Hyalomma} are the main vectors and reservoir for the virus.\nIt is maintained in nature in an endemic vertebrate-tick-vertebrate cycle.\nCCHFV is prevalent in wide geographical areas including Asia, Africa,\nSouth-Eastern Europe and the Middle East. Over the last decade, several\noutbreaks of CCHFV have been observed in Europe, mainly in Mediterranean\ncountries. Due to the high case/fatality ratio of CCHFV in human sometimes, it\nis of great importance for public health. Climate change and the invasion of\nCCHFV vectors in Central Europe suggest that the establishment of the\ntransmission in Central Europe may be possible in future. We developed a\ncompartment-based nonlinear Ordinary Differential Equation (ODE) system to\nmodel the disease transmission cycle including blood sucking ticks, livestock\nand human. Sensitivity analysis of the basic reproduction number $R_0$ shows\nthat decreasing in the tick survival time is an efficient method to eradicate\nthe disease. The model supports us in understanding the influence of different\nmodel parameters on the spread of CCHFV. Tick to tick transmission through\nco-feeding and the CCHFV circulation through trasstadial and transovarial\nstages are important factors to sustain the disease cycle. The proposed model\ndynamics are calibrated through an empirical multi-country analysis and\nmultidimensional scaling reveals the disease-parameter sets of different\ncountries burdened with CCHF are different. This necessary information may help\nus to select most efficient control strategies.\n"}
+{"abstract": "  For the field $\\mathbb{K} = \\mathbb{R}$ or $\\mathbb{C}$, and an integrable\ndistribution $F \\subseteq T_{\\mathbb{K}}M = T_M \\otimes_{\\mathbb{R}}\n\\mathbb{K}$ on a smooth manifold $M$, we study the Hochschild cohomology of the\ndg manifold $(F[1],d_F)$ and establish a canonical isomorphism with the\nHochschild cohomology of the transversal polydifferential operators of $F$. In\nparticular, for the dg manifold $(T_X^{0,1}[1],\\bar{\\partial})$ associated with\na complex manifold $X$, we prove that it is canonically isomorphic to the\nHochschild cohomology $HH^\\bullet (X)$ of the complex manifold. As an\napplication, we show that the Kontsevich-Duflo type theorem for the dg manifold\n$(T_X^{0,1}[1],\\bar{\\partial})$ implies the Kontsevich-Duflo theorem for\ncomplex manifolds.\n"}
+{"abstract": "  The 6-element Brandt monoid $B_2^1$ admits a unique addition under which it\nbecomes an additively idempotent semiring. We show that this addition is a term\noperation of $B_2^1$ as an inverse semigroup. As a consequence, we exhibit an\neasy proof that the semiring identities of $B_2^1$ are not finitely based.\n"}
+{"abstract": "  We study the behaviour of circular flexible loops sedimenting in a viscous\nfluid by numerical simulations and linear stability analysis. The numerical\nmodel involves a local slender-body theory approximation for the flow coupled\nto the Euler-Bernoulli elastic forces for an inextensible fibre. Starting from\nan inclined circle, we simulate the dynamics using truncated Fourier modes to\nobserve three distinct regimes of motion: absolute stability, two-, and\nthree-dimensional dynamics, depending on therelative importance of elastic and\ngravitational forces. We identify the governing parameter and develop a simple\nsemi-analytic stability criterion, which we verify numerically. In all cases,\nsedimenting loops converge to stable, planar shape equilibria with one free\nparameter related to the initial conditions and material properties of the\nfibre.\n"}
+{"abstract": "  In this article we study the numerical solution of the $L^1$-Optimal\nTransport Problem on 2D surfaces embedded in $R^3$, via the DMK formulation\nintroduced in [FaccaCardinPutti:2018]. We extend from the Euclidean into the\nRiemannian setting the DMK model and conjecture the equivalence with the\nsolution Monge-Kantorovich equations, a PDE-based formulation of the\n$L^1$-Optimal Transport Problem.\n  We generalize the numerical method proposed in\n[FaccaCardinPutti:2018,FaccaDaneriCardinPutti:2020] to 2D surfaces embedded in\n$\\REAL^3$ using the Surface Finite Element Model approach to approximate the\nLaplace-Beltrami equation arising from the model. We test the accuracy and\nefficiency of the proposed numerical scheme, comparing our approximate solution\nwith respect to an exact solution on a 2D sphere. The results show that the\nnumerical scheme is efficient, robust, and more accurate with respect to other\nnumerical schemes presented in the literature for the solution of\nls$L^1$-Optimal Transport Problem on 2D surfaces.\n"}
+{"abstract": "  A mathematical model of the evolution of plane perturbations in the\ncosmological statistical system of completely degenerate scalar-charged\nfermions with Higgs scalar interaction for short-wave perturbations is\nformulated. These disturbance modes have been found and investigated. It is\nshown that in such a model an additional oscillation mode arises, which is\nassociated directly with degenerate fermions, in which monotonically decreasing\nand increasing perturbations of the metric exist. In this case, there is such a\ntime instant in the system that the potential of the scalar field, together\nwith the energy density, tends to infinity. In this regard, an assumption is\nmade about a possible mechanism for the formation of dark matter condensates.\n"}
+{"abstract": "  We use tunable dipolar-interactions between the spins of nitrogen-vacancy\n(NV) centers in diamond to rotate a diamond crystal. Specifically, we employ\ncross-relaxation between the electronic spin of pairs of NV centers in a\ntrapped diamond to enhance the anisotropic NV paramagnetism and thus to\nincrease the associated spin torque. Our observations open a path towards the\nuse of mechanical oscillators to detect paramagnetic defects that lack optical\ntransitions, to investigation of angular momentum conservation in spin\nrelaxation processes and to novel means of cooling the motion of mechanical\noscillators.\n"}
+{"abstract": "  In this paper we provide an abstract characterization of the inverse hulls of\nsemigroups associated with Markov shifts. As an application of the\ncharacterization we give an example of Markov shifts that are not conjugate,\nbut have isomorphic inverse hulls.\n"}
+{"abstract": "  Many reinforcement learning algorithms can be seen as versions of approximate\npolicy iteration (API). While standard API often performs poorly, it has been\nshown that learning can be stabilized by regularizing each policy update by the\nKL-divergence to the previous policy. Popular practical algorithms such as\nTRPO, MPO, and VMPO replace regularization by a constraint on KL-divergence of\nconsecutive policies, arguing that this is easier to implement and tune. In\nthis work, we study this implementation choice in more detail. We compare the\nuse of KL divergence as a constraint vs. as a regularizer, and point out\nseveral optimization issues with the widely-used constrained approach. We show\nthat the constrained algorithm is not guaranteed to converge even on simple\nproblem instances where the constrained problem can be solved exactly, and in\nfact incurs linear expected regret. With approximate implementation using\nsoftmax policies, we show that regularization can improve the optimization\nlandscape of the original objective. We demonstrate these issues empirically on\nseveral bandit and RL environments.\n"}
+{"abstract": "  Transformer models are not only successful in natural language processing\n(NLP) but also demonstrate high potential in computer vision (CV). Despite\ngreat advance, most of works only focus on improvement of architectures but pay\nlittle attention to the classification head. For years transformer models base\nexclusively on classification token to construct the final classifier, without\nexplicitly harnessing high-level word tokens. In this paper, we propose a novel\ntransformer model called second-order transformer (SoT), exploiting\nsimultaneously the classification token and word tokens for the classifier.\nSpecifically, we empirically disclose that high-level word tokens contain rich\ninformation, which per se are very competent with the classifier and moreover,\nare complementary to the classification token. To effectively harness such rich\ninformation, we propose multi-headed global cross-covariance pooling with\nsingular value power normalization, which shares similar philosophy and thus is\ncompatible with the transformer block, better than commonly used pooling\nmethods. Then, we study comprehensively how to explicitly combine word tokens\nwith classification token for building the final classification head. For CV\ntasks, our SoT significantly improves state-of-the-art vision transformers on\nchallenging benchmarks including ImageNet and ImageNet-A. For NLP tasks,\nthrough fine-tuning based on pretrained language transformers including GPT and\nBERT, our SoT greatly boosts the performance on widely used tasks such as CoLA\nand RTE. Code will be available at https://peihuali.org/SoT\n"}
+{"abstract": "  The pentagram map, introduced by Schwartz in 1992, is a dynamical system on\nthe moduli space of polygons in the projective plane. Its real and complex\ndynamics have been explored in detail. We study the pentagram map over an\narbitrary algebraically closed field of characteristic not equal to 2. We prove\nthat the pentagram map on twisted polygons is a discrete integrable system, in\nthe sense of algebraic complete integrability: the pentagram map is birational\nto a self-map of a family of abelian varieties. This generalizes Soloviev's\nproof of complex integrability. In the course of the proof, we construct the\nmoduli space of twisted $n$-gons, derive formulas for the pentagram map, and\ncalculate the Lax representation by characteristic-independent methods.\n"}
+{"abstract": "  Guided by the intuition of coherent superposition of causal relations, recent\nworks presented quantum processes without classical common-cause and\ndirect-cause explanation, that is, processes which cannot be written as\nprobabilistic mixtures of quantum common-cause and quantum direct-cause\nrelations (CCDC). In this work, we analyze the minimum requirements for a\nquantum process to fail to admit a CCDC explanation and present \"simple\"\nprocesses, which we prove to be the most robust ones against general noise.\nThese simple processes can be realized by preparing a maximally entangled state\nand applying the identity quantum channel, thus not requiring an explicit\ncoherent mixture of common-cause and direct-cause, exploiting the possibility\nof a process to have both relations simultaneously. We then prove that,\nalthough all bipartite direct-cause processes are bipartite separable\noperators, there exist bipartite separable processes which are not\ndirect-cause. This shows that the problem of deciding weather a process is\ndirect-cause process is not equivalent to entanglement certification and points\nout the limitations of entanglement methods to detect non-classical CCDC\nprocesses. We also present a semi-definite programming hierarchy that can\ndetect and quantify the non-classical CCDC robustnesses of every non-classical\nCCDC process. Among other results, our numerical methods allow us to show that\nthe simple processes presented here are likely to be also the maximally robust\nagainst white noise. Finally, we explore the equivalence between bipartite\ndirect-cause processes and bipartite processes without quantum memory, to\npresent a separable process which cannot be realized as a process without\nquantum memory.\n"}
+{"abstract": "  Temporal graph signals are multivariate time series with individual\ncomponents associated with nodes of a fixed graph structure. Data of this kind\narises in many domains including activity of social network users, sensor\nnetwork readings over time, and time course gene expression within the\ninteraction network of a model organism. Traditional matrix decomposition\nmethods applied to such data fall short of exploiting structural regularities\nencoded in the underlying graph and also in the temporal patterns of the\nsignal. How can we take into account such structure to obtain a succinct and\ninterpretable representation of temporal graph signals?\n  We propose a general, dictionary-based framework for temporal graph signal\ndecomposition (TGSD). The key idea is to learn a low-rank, joint encoding of\nthe data via a combination of graph and time dictionaries. We propose a highly\nscalable decomposition algorithm for both complete and incomplete data, and\ndemonstrate its advantage for matrix decomposition, imputation of missing\nvalues, temporal interpolation, clustering, period estimation, and rank\nestimation in synthetic and real-world data ranging from traffic patterns to\nsocial media activity. Our framework achieves 28% reduction in RMSE compared to\nbaselines for temporal interpolation when as many as 75% of the observations\nare missing. It scales best among baselines taking under 20 seconds on 3.5\nmillion data points and produces the most parsimonious models. To the best of\nour knowledge, TGSD is the first framework to jointly model graph signals by\ntemporal and graph dictionaries.\n"}
+{"abstract": "  We proposed a new modeling method to promote the performance of prohibited\nitems recognition via X-ray image. We analyzed the characteristics of\nprohibited items and X-ray images. We found the fact that the scales of some\nitems are too small to be recognized which encumber the model performance. Then\nwe adopted a set of data augmentation and modified the model to adapt the field\nof prohibited items recognition. The Convolutional Block Attention Module(CBAM)\nand rescoring mechanism has been assembled into the model. By the modification,\nour model achieved a mAP of 89.9% on SIXray10, mAP of 74.8%.\n"}
+{"abstract": "  In transfer learning, we wish to make inference about a target population\nwhen we have access to data both from the distribution itself, and from a\ndifferent but related source distribution. We introduce a flexible framework\nfor transfer learning in the context of binary classification, allowing for\ncovariate-dependent relationships between the source and target distributions\nthat are not required to preserve the Bayes decision boundary. Our main\ncontributions are to derive the minimax optimal rates of convergence (up to\npoly-logarithmic factors) in this problem, and show that the optimal rate can\nbe achieved by an algorithm that adapts to key aspects of the unknown transfer\nrelationship, as well as the smoothness and tail parameters of our\ndistributional classes. This optimal rate turns out to have several regimes,\ndepending on the interplay between the relative sample sizes and the strength\nof the transfer relationship, and our algorithm achieves optimality by careful,\ndecision tree-based calibration of local nearest-neighbour procedures.\n"}
+{"abstract": "  In order to increase the bone forming ability of MBG-PCL composite scaffold,\nmicroporosity was created in the struts of 3D-printed MBG-PCL scaffolds for the\nmanufacturing of a construct with a multiscale porosity consisting of meso-,\nmicro- and macro-pores. 3D-printing imparted macroporosity while the\nmicroporosity was created by porogen removal from the struts, and the MBG\nparticles were responsible for the mesoporosity. The scaffolds were 3D-printed\nusing a mixture of PCL, MBG and phosphate buffered saline (PBS) particles,\nsubsequently leached out. Microporous-PCL (pPCL) as a negative control,\nmicroporous MBG-PCL (pMBG-PCL) and non-microporous-MBG-PCL (MBG-PCL) were\ninvestigated. Scanning electron microscopy, mercury intrusion porosimetry and\nmicro-computed tomography demonstrated that the PBS removal resulted in the\nformation of micropores inside the struts with porosity of around 30% for both\npPCL and pMBG-PCL, with both constructs displaying an overall porosity of\n80-90%. In contrast, the MBG-PCL group had a microporosity of 6% and an overall\nporosity of 70%. Early mineralisation was found in the pMBG-PCL post-leaching\nout and this resulted in the formation a more homogeneous calcium phosphate\nlayer when using a biomimetic mineralisation assay. Mechanical properties\nranged from 5 to 25 MPa for microporous and non-microporous specimens, hence\nmicroporosity was the determining factor affecting compressive properties.\nMC3T3-E1 metabolic activity was increased in the pMBG-PCL along with an\nincreased production of RUNX2. Therefore, the microporosity within a 3D-printed\nbioceramic composite construct may result in additional physical and biological\nbenefits.\n"}
+{"abstract": "  In this position paper we approach problems concerning critical digital and\ninformation literacy with ideas to provide more digestible explanations of\nabstract concepts through interface design. In particular, we focus on social\nmedia platforms where we see the possibility of counteracting the spread of\nmisinformation by providing users with more proficiency through our approaches.\nWe argue that the omnipresent trend to abstract away and hide information from\nusers via UI/UX design opposes their ability to self-learn. This leads us to\npropose a different framework in which we unify elegant and simple interfaces\nwith nudges that promote a look behind the curtain. Such designs serve to\nfoster a deeper understanding of employed technologies and aim to increase the\ncritical assessment of content encountered on social platforms. Furthermore, we\nconsider users with an intermediary skill level to be largely ignored in\ncurrent approaches, as they are given no tools to broaden their knowledge\nwithout consultation of expert material. The resulting stagnation is\nexemplified by the tactics of misinformation campaigns, which exploit the\nensuing lack of information literacy and critical thinking. We propose an\napproach to design that sufficiently emancipates users in both aspects by\npromoting a look behind the abstraction of UI/UX so that an autonomous learning\nprocess is given the chance to occur. Furthermore, we name ideas for future\nresearch within this area that take our considerations into account.\n"}
+{"abstract": "  Topological semimetals are a new class of metallic materials, which exist at\nband fillings that ordinarily correspond to insulators or compensated\naccidental semimetals with zero Luttinger volume. Their metallicity is a result\nof nontrivial topology in momentum space and crystal symmetry, wherein\ntopological charges may be assigned to point band-touching nodes, preventing\ngap opening, unless protecting crystal symmetries are violated. These\ntopological charges, however, are defined from noninteracting band eigenstates,\nwhich raises the possibility that the physics of topological semimetals may be\nmodified qualitatively by electron-electron interactions. Here we ask the\nfollowing question: what makes the topological semimetals nontrivial beyond\nband theory? Alternatively, can strong electron-electron interactions open a\ngap in topological semimetals without breaking the protecting symmetries or\nintroducing topological order? We demonstrate that the answer is generally no,\nand what prevents it is their topological response, or quantum anomalies. While\nthis is familiar in the case of magnetic Weyl semimetals, where the topological\nresponse takes the form of an anomalous Hall effect, analogous responses in\nother types of topological semimetals are more subtle and involve crystal\nsymmetry as well as electromagnetic gauge fields. Physically these responses\nare detectable as fractional symmetry charges induced on certain gauge defects.\nWe discuss the cases of type-I Dirac semimetals and time-reversal invariant\nWeyl semimetals in detail. For type-I Dirac semimetals, we also show that the\nanomaly vanishes, in a nontrivial manner, if the momenta of the Dirac nodes\nsatisfy certain exceptional conditions.\n"}
+{"abstract": "  Motivated by the recent experimental realization of twisted trilayer graphene\nand the observed superconductivity that is associated with its flat bands at\nspecific angles, we study trilayer graphene under the influence of different\nforms of light in the non-interacting limit. Specifically, we study four\ndifferent types of stacking configurations with a single twisted layer. In all\nfour cases, we study the impact of circularly polarized light and longitudinal\nlight coming from a waveguide. We derive effective time-independent Floquet\nHamiltonians and review light-induced changes to the band structure. For\ncircularly polarized light, we find band flattening effects as well as band gap\nopenings. We emphasize that there is a rich band topology, which we summarize\nin Chern number maps that are different for all four studied lattice\nconfigurations. The case of a so-called ABC stacking with top layer twist is\nespecially rich and shows a different phase diagram depending on the handedness\nof the circularly polarized light. Consequently, we propose an experiment where\nthis difference in typologies could be captured via optical conductivity\nmeasurements. In contrast for the case of longitudinal light that is coming\nfrom a waveguide, we find that the band structure is very closely related to\nthe equilibrium one but the magic angles can be tuned in-situ by varying the\nintensity of the incident beam of light.\n"}
+{"abstract": "  Hexahedral meshes are an ubiquitous domain for the numerical resolution of\npartial differential equations. Computing a pure hexahedral mesh from an\nadaptively refined grid is a prominent approach to automatic hexmeshing, and\nrequires the ability to restore the all hex property around the hanging nodes\nthat arise at the interface between cells having different size. The most\nadvanced tools to accomplish this task are based on mesh dualization. These\napproaches use topological schemes to regularize the valence of inner vertices\nand edges, such that dualizing the grid yields a pure hexahedral mesh. In this\npaper we study in detail the dual approach, and propose four main contributions\nto it: (i) we enumerate all the possible transitions that dual methods must be\nable to handle, showing that prior schemes do not natively cover all of them;\n(ii) we show that schemes are internally asymmetric, therefore not only their\nimplementation is ambiguous, but different implementation choices lead to\nhexahedral meshes with different singular structure; (iii) we explore the\ncombinatorial space of dual schemes, selecting the minimum set that covers all\nthe possible configurations and also yields the simplest singular structure in\nthe output hexmesh; (iv) we enlarge the class of adaptive grids that can be\ntransformed into pure hexahedral meshes, relaxing one of the tight requirements\nimposed by previous approaches, and ultimately permitting to obtain much\ncoarser meshes for same geometric accuracy. Last but not least, for the first\ntime we make grid-based hexmeshing truly reproducible, releasing our code and\nalso revealing a conspicuous amount of technical details that were always\noverlooked in previous literature, creating an entry barrier that was hard to\novercome for practitioners in the field.\n"}
+{"abstract": "  In this paper, we reveal the depreciation mechanism of representative money\n(banknotes) from the perspective of logistics warehousing costs. Although it\nhas long been the dream of economists to stabilize the buying power of the\nmonetary units, the goal we have honest money always broken since the central\nbank depreciate the currency without limit. From the point of view of modern\nlogistics, the key functions of money are the store of value and low logistics\n(circulation and warehouse) cost. Although commodity money (such as gold and\nsilver) has the advantages of a wealth store, its disadvantage is the high\nlogistics cost. In comparison to commodity money, credit currency and digital\ncurrency cannot protect wealth from loss over a long period while their\nlogistics costs are negligible. We proved that there is not such honest money\nfrom the perspective of logistics costs, which is both the store of value like\nprecious metal and without logistics costs in circulation like digital\ncurrency. The reason hidden in the back of the depreciation of banknotes is the\nblack hole of storage charge of the anchor overtime after digitizing commodity\nmoney. Accordingly, it is not difficult to infer the inevitable collapse of the\nBretton woods system. Therefore, we introduce a brand-new currency named honest\ndevalued stable-coin and built a attenuation model of intrinsic value of the\nhonest money based on the change mechanism of storage cost of anchor assets,\nlike gold, which will lay the theoretical foundation for a stable monetary\nsystem.\n"}
+{"abstract": "  Understanding common envelope (CE) evolution is an outstanding problem in\nbinary evolution. Although the CE phase is not driven by gravitational-wave\n(GW) emission, the in-spiraling binary emits GWs that passively trace the CE\ndynamics. Detecting this GW signal would provide direct insight into the\ngas-driven physics. Even a non-detection might offer invaluable constraints. We\ninvestigate the prospects of detection of a Galactic CE by LISA. While the\ndynamical phase of the CE is likely sufficiently loud for detection, it is\nshort and thus rare. We focus instead on the self-regulated phase that proceeds\non a thermal timescale. Based on population synthesis calculations and the\n(unknown) signal duration in the LISA band, we expect $\\sim 0.1-100$ sources in\nthe Galaxy during the mission duration. We map the GW observable parameter\nspace of frequency $f_\\mathrm{GW}$ and its derivative $\\dot f_\\mathrm{GW}$\nremaining agnostic on the specifics of the inspiral, and find that signals with\n$\\mathrm{SNR}>10$ are possible if the CE stalls at separations such that\n$f_\\mathrm{GW}\\gtrsim2\\times10^{-3}\\,\\mathrm{Hz}$. We investigate the\npossibility of misidentifying the signal with other known sources. If the\nsecond derivative $\\ddot f_\\mathrm{GW}$ can also be measured, the signal can be\ndistinguished from other sources using a GW braking-index. Alternatively,\ncoupling LISA with electromagnetic observations of peculiar red giant stars\nand/or infrared and optical transients might allow for the disentangling of a\nGalactic CE from other Galactic and extra-galactic GW sources.\n"}
+{"abstract": "  Modern approaches to sound synthesis using deep neural networks are hard to\ncontrol, especially when fine-grained conditioning information is not\navailable, hindering their adoption by musicians.\n  In this paper, we cast the generation of individual instrumental notes as an\ninpainting-based task, introducing novel and unique ways to iteratively shape\nsounds. To this end, we propose a two-step approach: first, we adapt the\nVQ-VAE-2 image generation architecture to spectrograms in order to convert\nreal-valued spectrograms into compact discrete codemaps, we then implement\ntoken-masked Transformers for the inpainting-based generation of these\ncodemaps.\n  We apply the proposed architecture on the NSynth dataset on masked resampling\ntasks. Most crucially, we open-source an interactive web interface to transform\nsounds by inpainting, for artists and practitioners alike, opening up to new,\ncreative uses.\n"}
+{"abstract": "  Kernel embeddings of distributions have recently gained significant attention\nin the machine learning community as a data-driven technique for representing\nprobability distributions. Broadly, these techniques enable efficient\ncomputation of expectations by representing integral operators as elements in a\nreproducing kernel Hilbert space. We apply these techniques to the area of\nstochastic optimal control theory and present a method to compute approximately\noptimal policies for stochastic systems with arbitrary disturbances. Our\napproach reduces the optimization problem to a linear program, which can easily\nbe solved via the Lagrangian dual, without resorting to gradient-based\noptimization algorithms. We focus on discrete-time dynamic programming, and\ndemonstrate our proposed approach on a linear regulation problem, and on a\nnonlinear target tracking problem. This approach is broadly applicable to a\nwide variety of optimal control problems, and provides a means of working with\nstochastic systems in a data-driven setting.\n"}
+{"abstract": "  We introduce a new approach for circuit anonymous communication based on\nLizama's non-invertible Key Exchange Protocol (ni-KEP) which has been conceived\nto work in the quantum era. Lizama's protocol has the smallest key size when\ncompared to main post-quantum schemes thus it becomes a promising alternative\nfor the quantum era. Circuit-based communication can be scaled to support the\nHidden Service Protocol (HSP) as well as cross-domain digital certificates that\npromise greater computing security, speed and efficiency.\n"}
+{"abstract": "  We prove a division algorithm for group rings of high genus surface groups\nand use it to show that some $2$-complexes with surface fundamental groups are\nstandard. We also give an application of division to cohomological dimension of\n$2$-relator groups acting on $\\mathbb H^n$.\n"}
+{"abstract": "  Process discovery methods have obtained remarkable achievements in Process\nMining, delivering comprehensible process models to enhance management\ncapabilities. However, selecting the suitable method for a specific event log\nhighly relies on human expertise, hindering its broad application. Solutions\nbased on Meta-learning (MtL) have been promising for creating systems with\nreduced human assistance. This paper presents a MtL solution for recommending\nprocess discovery methods that maximize model quality according to\ncomplementary dimensions. Thanks to our MtL pipeline, it was possible to\nrecommend a discovery method with 92% of accuracy using light-weight features\nthat describe the event log. Our experimental analysis also provided\nsignificant insights on the importance of log features in generating\nrecommendations, paving the way to a deeper understanding of the discovery\nalgorithms.\n"}
+{"abstract": "  Prediction based on Irregularly Sampled Time Series (ISTS) is of wide concern\nin the real-world applications. For more accurate prediction, the methods had\nbetter grasp more data characteristics. Different from ordinary time series,\nISTS is characterised with irregular time intervals of intra-series and\ndifferent sampling rates of inter-series. However, existing methods have\nsuboptimal predictions due to artificially introducing new dependencies in a\ntime series and biasedly learning relations among time series when modeling\nthese two characteristics. In this work, we propose a novel Time Encoding (TE)\nmechanism. TE can embed the time information as time vectors in the complex\ndomain. It has the the properties of absolute distance and relative distance\nunder different sampling rates, which helps to represent both two\nirregularities of ISTS. Meanwhile, we create a new model structure named Time\nEncoding Echo State Network (TE-ESN). It is the first ESNs-based model that can\nprocess ISTS data. Besides, TE-ESN can incorporate long short-term memories and\nseries fusion to grasp horizontal and vertical relations. Experiments on one\nchaos system and three real-world datasets show that TE-ESN performs better\nthan all baselines and has better reservoir property.\n"}
+{"abstract": "  Rendering tactile effects on a touch screen via electrovibration has many\npotential applications. However, our knowledge on tactile perception of change\nin friction and the underlying contact mechanics are both very limited. In this\nstudy, we investigate the tactile perception and the contact mechanics for a\nstep change in friction under electrovibration during a relative sliding\nbetween finger and the surface of a capacitive touchscreen. First, we conduct\nmagnitude estimation experiments to investigate the role of normal force and\nsliding velocity on the perceived tactile intensity for a step increase and\ndecrease in friction, called as rising friction (RF) and falling friction (FF).\nTo investigate the contact mechanics involved in RF and FF, we then measure the\nfrictional force, the apparent contact area, and the strains acting on the\nfingerpad during sliding at a constant velocity under three different normal\nloads using a custom-made experimental set-up. The results show that the\nparticipants perceived RF stronger than FF, and both the normal force and\nsliding velocity significantly influenced their perception. These results are\nsupported by our mechanical measurements; the relative change in friction, the\napparent contact area, and the strain in the sliding direction were all higher\nfor RF than those for FF, especially for low normal forces. Taken together, our\nresults suggest that different contact mechanics take place during RF and FF\ndue to the viscoelastic behavior of fingerpad skin, and those differences\ninfluence our tactile perception of a step change in friction.\n"}
+{"abstract": "  With the advances of data-driven machine learning research, a wide variety of\nprediction problems have been tackled. It has become critical to explore how\nmachine learning and specifically deep learning methods can be exploited to\nanalyse healthcare data. A major limitation of existing methods has been the\nfocus on grid-like data; however, the structure of physiological recordings are\noften irregular and unordered which makes it difficult to conceptualise them as\na matrix. As such, graph neural networks have attracted significant attention\nby exploiting implicit information that resides in a biological system, with\ninteractive nodes connected by edges whose weights can be either temporal\nassociations or anatomical junctions. In this survey, we thoroughly review the\ndifferent types of graph architectures and their applications in healthcare. We\nprovide an overview of these methods in a systematic manner, organized by their\ndomain of application including functional connectivity, anatomical structure\nand electrical-based analysis. We also outline the limitations of existing\ntechniques and discuss potential directions for future research.\n"}
+{"abstract": "  We study the renormalization group running of axion couplings while taking\ninto account that the Standard Model can be extended to its supersymmetric\nextension at a certain energy scale below the axion decay constant. We then\napply our results to three different classes of axion models, i.e. KSVZ-like,\nDFSZ-like, and string-theoretic axions, and examine if string-theoretic axions\ncan be distinguished from others by having a different pattern of low energy\ncouplings to the photon, nucleons and electron. We find that the low energy\ncouplings of string-theoretic axions have a similar pattern as those of\nKSVZ-like axions but yet reveal a sizable difference which might be testable in\nfuture axion search experiments. We also note that the coupling of KSVZ-like\nQCD axions to the electron is dominated by a three-loop contribution involving\nthe exotic heavy quark, gluons, top quark and Higgs field.\n"}
+{"abstract": "  Previous work by Mora and Sala provides the reduced Groebner basis of the\nideal formed by the elementary symmetric polynomials in $n$ variables of\ndegrees $k=1,\\dots,n$, $\\langle e_{1,n}(x), \\dots, e_{n,n}(x) \\rangle$.\nHaglund, Rhoades, and Shimonozo expand upon this, finding the reduced Groebner\nbasis of the ideal of elementary symmetric polynomials in $n$ variables of\ndegree $d$ for $d=n-k+1,\\dots,n$ for $k\\leq n$. In this paper, we further\ngeneralize their findings by using symbolic computation and experimentation to\nconstruct the reduced Groebner basis for the ideal generated by the elementary\nsymmetric polynomials in $n$ variables of arbitrary degrees.\n"}
+{"abstract": "  Optimizing inspection and maintenance (I&M) plans for a large deteriorating\nstructure is a computationally challenging task, in particular if one considers\ninterdependences among its components. This is due to the sheer number of\npossible decision alternatives over the lifetime of the structure and the\nuncertainty surrounding the deterioration processes, the structural performance\nand the outcomes of inspection and maintenance actions. To address this\nchallenge, Luque and Straub (2019) proposed a heuristic approach in which I&M\nplans for structural systems are defined through a set of simple decision\nrules. Here, we formalize the optimization of these decision rules and extend\nthe approach to enable adaptive planning. The initially optimal I&M plan is\nsuccessively adapted throughout the service life, based on past inspection and\nmonitoring results. The proposed methodology uses stochastic deterioration\nmodels and accounts for the interdependence among structural components. The\nheuristic-based adaptive planning is illustrated for a structural frame\nsubjected to fatigue.\n"}
+{"abstract": "  Using the density matrix equations (DME) for high scale leptogenesis based on\nthe type I seesaw mechanism, in which the CP violation (CPV) is provided by the\nlow-energy Dirac or/and Majorana phases of the neutrino mixing (PMNS) matrix,\nwe investigate the 1-to-2 and the 2-to-3 flavour regime transitions, where the\n1, 2 and 3 leptogenesis flavour regimes in the generation of the baryon\nasymmetry of the Universe $\\eta_B$ are described by the Boltzmann equations.\nConcentrating on the 1-to-2 flavour transition we determine the general\nconditions under which $\\eta_B$ goes through zero and changes sign in the\ntransition. Analysing in detail the behaviour of $\\eta_B$ in the transition in\nthe case of two heavy Majorana neutrinos $N_{1,2}$ with hierarchical masses,\n$M_1 \\ll M_2$, we find, in particular, that i) the Boltzmann equations in many\ncases fail to describe correctly the generation of $\\eta_B$ in the 1, 2 and 3\nflavour regimes, ii) the 2-flavour regime can persist above (below) $\\sim\n10^{12}$ GeV ($\\sim 10^9$ GeV), iii) the flavour effects in leptogenesis\npersist beyond the typically considered maximal for these effects leptogenesis\nscale of $10^{12}$ GeV. We further determine the minimal scale\n$M_{1\\text{min}}$ at which we can have successful leptogenesis when the CPV is\nprovided only by the Dirac or Majorana phases of the PMNS matrix as well as the\nranges of scales and values of the phases for having successful leptogenesis.\nWe show, in particular, that when the CPV is due to the Dirac phase $\\delta$,\nthere is a direct relation between the sign of $\\sin \\delta$ and the sign of\n$\\eta_B$ in the regions of viable leptogenesis in the case of normal\nhierarchical light neutrino mass spectrum; for the inverted hierarchical\nspectrum the same result holds for $M_1 \\lesssim 10^{13}$ GeV. The considered\nscenarios of leptogenesis are testable and falsifiable in low-energy neutrino\nexperiments.\n"}
+{"abstract": "  Concurrent analysis of composite materials can provide the interaction among\nscales for better composite design, analysis, and performance prediction. A\ndata-driven concurrent n-scale modeling theory ($\\textrm{FExSCA}^\\textrm{n-1}$)\nis proposed in this paper utilizing a mechanistic reduced order model (ROM)\ncalled self-consistent clustering analysis (SCA). We demonstrated this theory\nwith a $\\textrm{FExSCA}^2$ approach to study the 3-scale woven carbon fiber\nreinforced polymer (CFRP) laminate structure. $\\textrm{FExSCA}^2$ significantly\nreduced expensive 3D nested composite representative volume elements (RVEs)\ncomputation for woven and unidirectional (UD) composite structures by\ndeveloping a material database. The modeling procedure is established by\nintegrating the material database into a woven CFRP structural numerical model,\nformulating a concurrent 3-scale modeling framework. This framework provides an\naccurate prediction for the structural performance (e.g., nonlinear structural\nbehavior under tensile load), as well as the woven and UD physics field\nevolution. The concurrent modeling results are validated against physical tests\nthat link structural performance to the basic material microstructures. The\nproposed methodology provides a comprehensive predictive modeling procedure\napplicable to general composite materials aiming to reduce laborious\nexperiments needed.\n"}
+{"abstract": "  Bacterial cells navigate around their environment by directing their movement\nalong chemical gradients. This process, known as chemotaxis, can promote the\nrapid expansion of bacterial populations into previously unoccupied\nterritories. However, despite numerous experimental and theoretical studies on\nthis classical topic, chemotaxis-driven population expansion is not understood\nin quantitative terms. Building on recent experimental progress, we here\npresent a detailed analytical study that provides a quantitative understanding\nof how chemotaxis and cell growth lead to rapid and stable expansion of\nbacterial populations. We provide analytical relations that accurately describe\nthe dependence of the expansion speed and density profile of the expanding\npopulation on important molecular, cellular, and environmental parameters. In\nparticular, expansion speeds can be boosted by orders of magnitude when the\nenvironmental availability of chemicals relative to the cellular limits of\nchemical sensing is high. As analytical understanding of such complex\nspatiotemporal dynamic processes is rare, the results derived here provide a\nmathematical framework for further investigations of the different roles\nchemotaxis plays in diverse ecological contexts.\n"}
+{"abstract": "  For ensembles of Hamiltonians that fall under the Dyson classification of\nrandom matrices with $\\beta \\in \\{1,2,4\\}$, the low-temperature mean entropy\ncan be shown to vanish as $\\langle S(T)\\rangle\\sim \\kappa T^{\\beta+1}$. A\nsimilar relation holds for Altland-Zirnbauer ensembles. JT gravity has been\nshown to be dual to the double-scaling limit of a $\\beta =2$ ensemble, with a\nclassical eigenvalue density $\\propto e^{S_0}\\sqrt{E}$ when $0 < E \\ll 1$. We\nuse universal results about the distribution of the smallest eigenvalues in\nsuch ensembles to calculate $\\kappa$ up to corrections that we argue are doubly\nexponentially small in $S_0$.\n"}
+{"abstract": "  Aiming at developing high thermal conductivity copper/diamond composite, an\nunconventional approach applying self-assembled monolayer (SAM) prior to the\nhigh-temperature sintering of copper/diamond composite was utilized to enhance\nthe thermal boundary conductance (TBC) between copper and diamond. The\nenhancement was first systematically confirmed on a model interface system by\ndetailed SAM morphology characterization and TBC measurements. TBC\nsignificantly depends on the SAM coverage and ordering, and the formation of\nhigh-quality SAM promoted the TBC to 73 MW/m^2-K from 27 MW/m^2-K, the value\nwithout SAM. With the help of molecular dynamics simulations, the TBC\nenhancement was identified to be determined by the number of SAM bridges and\nthe overlap of vibrational density of states. The diamond particles of 210\n{\\micro\\metre} in size were simultaneously functionalized by SAM with the\ncondition giving the highest TBC in the model system and sintered together with\nthe copper to fabricate isotropic copper/diamond composite of 50% volume\nfraction. The measured thermal conductivity marked 711 W/m-K at room\ntemperature, the highest value among the ones with similar diamond-particles\nvolume fraction and size. This work demonstrates a novel strategy to enhance\nthe thermal conductivity of composite materials by SAM functionalization.\n"}
+{"abstract": "  This work is devoted to deriving the Onsager-Machlup action functional for a\nclass of stochastic differential equations with (non-Gaussian) L\\'{e}vy process\nas well as Brownian motion in high dimensions. This is achieved by applying the\nGirsanov transformation for probability measures and then by a path\nrepresentation. The Poincar\\'{e} lemma is essential to handle such path\nrepresentation problem in high dimensions. We provide a sufficient condition on\nthe vector field such that this path representation holds in high dimensions.\nMoreover, this Onsager-Machlup action functional may be considered as the\nintegral of a Lagrangian. Finally, by a variational principle, we investigate\nthe most probable transition pathways analytically and numerically.\n"}
+{"abstract": "  We show that the notions of weak solution to the total variation flow based\non the Anzellotti pairing and the variational inequality coincide under some\nrestrictions on the boundary data. The key ingredient in the argument is a\nduality result for the total variation functional, which is based on an\napproximation of the total variation by area-type functionals.\n"}
+{"abstract": "  Precision determinations of Standard Model (SM) Electro-Weak (EW) parameters\nat the Large Hadron Collider (LHC) are dominated by uncertainties due to Parton\nDistribution Functions (PDFs). Reweighting and profiling techniques are\nroutinely employed to treat this. We explore approaches based on combining\nmeasurements of charged current and neutral current Drell-Yan (DY) asymmetries\nto improve PDF uncertainties. We present the results of a numerical analysis\nperformed with the open-source platform xFitter. PDF uncertainties are examined\nfor lepton-charge and forward-backward asymmetries in regions of transverse and\ninvariant masses near the vector-boson peak, based on LHC Run III and HL-LHC\nluminosity scenarios. We discuss the complementarity of the asymmetries in\nreducing PDF uncertainties in observables relevant to both SM and Beyond the SM\n(BSM) physics.\n"}
+{"abstract": "  This paper considers the problem of approximating the \"maximal\" region of\nattraction (the set that contains all asymptotically stable sets) of any given\nset of locally exponentially stable nonlinear Ordinary Differential Equations\n(ODEs) with a sufficiently smooth vector field. Given a locally exponential\nstable ODE with a differentiable vector field, we show that there exists a\nglobally Lipschitz continuous converse Lyapunov function whose 1-sublevel set\nis equal to the maximal region of attraction of the ODE. We then propose a\nsequence of d-degree Sum-of-Squares (SOS) programming problems that yields a\nsequence of polynomials that converges to our proposed converse Lyapunov\nfunction uniformly from above in the L1 norm. We show that each member of the\nsequence of 1-sublevel sets of the polynomial solutions to our proposed\nsequence of SOS programming problems are certifiably contained inside the\nmaximal region of attraction of the ODE, and moreover, we show that this\nsequence of sublevel sets converges to the maximal region of attraction of the\nODE with respect to the volume metric. We provide numerical examples of\nestimations of the maximal region of attraction for the Van der Pol oscillator\nand a three dimensional servomechanism.\n"}
+{"abstract": "  Erythrosiderites with the formula A2FeX5H2O, where A = Rb, K, and (NH4) and X\n= Cl and Br are intriguing systems that possess various magnetic and electric\nphases, as well as multiferroic phases in which magnetism and ferroelectricity\nare coupled. In this report, we study the magnetic phase diagram of\nerythrosiderites as a function of superexchange interactions. To this end, we\nperform classical Monte Carlo simulations on magnetic Hamiltonians that contain\nfive different superexchange interactions with single-ion anisotropies. Our\nphase diagram contains all magnetic ground states that have been experimentally\nobserved in these materials. We argue that the ground states can be explained\nby varying the ratio of J4/J2. For J4/J2 > 0.95 a cycloidal spins structure is\nstabilized as observed in (NH4)2FeCl5H2O and otherwise, a collinear spin\nstructure is stabilized as observed in (K,Rb)2FeCl5H2O. We also show that the\ndifference in the single-ion anisotropy along a- and c- axes is essential to\nstabilize the intermediate state observed in (NH)2FeCl5H2O.\n"}
+{"abstract": "  Big data has been used widely in many areas including the transportation\nindustry. Using various data sources, traffic states can be well estimated and\nfurther predicted for improving the overall operation efficiency. Combined with\nthis trend, this study presents an up-to-date survey of open data and big data\ntools used for traffic estimation and prediction. Different data types are\ncategorized and the off-the-shelf tools are introduced. To further promote the\nuse of big data for traffic estimation and prediction tasks, challenges and\nfuture directions are given for future studies.\n"}
+{"abstract": "  We re-examine the jet probes of the nucleon spin and flavor structures. We\nfind for the first time the time-reversal odd (T-odd) component of a jet,\nconventionally thought to vanish, can survive due to the non-perturbative\nfragmentation and hadronization effects and could be testable. This additional\ncontribution of a jet will lead to novel jet phenomena relevant for unlocking\nthe access to several spin structures of the nucleon, which were thought to be\nimpossible by using jets. As examples, we show how the T-odd constituent can\ncouple to the proton transversity at the Electron Ion Collider (EIC) and can\ngive rise to the anisotropy in the jet production in $e^+e^-$ annihilations. We\nexpect the T-odd contribution of the jet to have broad applications in high\nenergy nuclear physics.\n"}
+{"abstract": "  In Navier-Stokes turbulence, energy and helicity injected at large scales are\nsubject to a joint direct cascade, with both quantities exhibiting a spectral\nscaling $\\propto k^{-5/3}$. We demonstrate via direct numerical simulations\nthat the two cascades are compatible due to the existence of a strong\nscale-dependent phase alignment between velocity and vorticity fluctuations,\nwith the phase alignment angle scaling as $\\cos\\alpha_k\\propto k^{-1}$.\n"}
+{"abstract": "  We present the results of a molecular survey of comet 46P/Wirtanen undertaken\nwith the IRAM 30-m and NOEMA radio telescopes in December 2018. Observations at\nIRAM 30-m during the 12-18 Dec. period comprise a 2 mm spectral survey covering\n25 GHz and a 1 mm survey covering 62 GHz. The gas outflow velocity and kinetic\ntemperature have been accurately constrained by the observations. We derive\nabundances of 11 molecules, some being identified remotely for the first time\nin a Jupiter-family comet, including complex organic molecules such as\nformamide, ethylene glycol, acetaldehyde, or ethanol. Sensitive upper limits on\nthe abundances of 24 other molecules are obtained. The comet is found to be\nrelatively rich in methanol (3.4 percent relative to water), but relatively\ndepleted in CO, CS, HNC, HNCO, and HCOOH.\n"}
+{"abstract": "  In this article, the temperature-density relation of the intergalactic medium\nwas studied in the region $1.6 \\leq z < 2.0$ divided into two bins. For this\npurpose, the Ly-$\\alpha$ forest decomposition into individual absorption\nprofiles was used for the study of 35 publicly available quasar spectra\nobtained by the Ultraviolet and Visual Echelle Spectrograph (UVES) on the Very\nLarge Telescope (ESO) and by the High Resolution Echelle Spectrometer (HIRES)\non the Keck Telescope. For the determination of the thermal state sensitive\ncut-off position in the $b - N_{HI}$ distribution, the iterative fitting\nprocedure was adopted. The measurements were calibrated using mock Ly-$\\alpha$\nforest data generated by 23 hydrodynamical simulations with different thermal\nhistories. The value of the temperature at mean density corresponds to the\ndecreasing trend predicted by various models at the lower redshifts. In the\ncase of power law index, determined values are close to 1.6, which is expected\nafter all reionization events in various models assuming the balance of\nphotoheating with adiabatic cooling.\n"}
+{"abstract": "  The temperature associated with gravitation as presented in the Unruh effect\nand Hawking temperature serves to link different areas of physics, such as\ngravity, statistical mechanics, and quantum physics. In this paper,\nteleparallel gravity is considered to study temperature effects on the de\nSitter space-time. The effects of temperature are introduced using the Thermo\nField Dynamics (TFD) formalism. The gravitational Stefan-Boltzmann law is\nobtained. Then the temperature of gravitation in the de Sitter space-time is\ncalculated. Here an Unruh-type effect is discussed. This effect relates the\ntemperature and the acceleration of a particle in the de Sitter space-time. The\ngravitational Casimir effect is calculated. The result shows that there is a\ntransition between an attractive and a repulsive Casimir effect.\n"}
+{"abstract": "  Given a $d$-dimensional tensor $T:A_1\\times\\dots\\times A_d\\rightarrow\n\\mathbb{F}$ (where $\\mathbb{F}$ is a field), the $i$-flattening rank of $T$ is\nthe rank of the matrix whose rows are indexed by $A_{i}$, columns are indexed\nby $B_{i}=A_1\\times\\dots\\times A_{i-1}\\times A_{i+1}\\times\\dots\\times A_{d}$\nand whose entries are given by the corresponding values of $T$. The\nmax-flattening rank of $T$ is defined as $\\text{mfrank}(T)=\\max_{i\\in\n[d]}\\text{frank}_{i}(T)$. A tensor $T:A^{d}\\rightarrow\\mathbb{F}$ is called\nsemi-diagonal, if $T(a,\\dots,a)\\neq 0$ for every $a\\in A$, and\n$T(a_{1},\\dots,a_{d})=0$ for every $a_{1},\\dots,a_{d}\\in A$ that are all\ndistinct. In this paper we prove that if $T:A^{d}\\rightarrow\\mathbb{F}$ is\nsemi-diagonal, then $\\text{mfrank}(T)\\geq \\frac{|A|}{d-1}$, and this bound is\nthe best possible.\n  We give several applications of this result, including a generalization of\nthe celebrated Frankl-Wilson theorem on forbidden intersections. Also,\naddressing a conjecture of Aharoni and Berger, we show that if the edges of an\n$r$-uniform multi-hypergraph $\\mathcal{H}$ are colored with $z$ colors such\nthat each colorclass is a matching of size $t$, then $\\mathcal{H}$ contains a\nrainbow matching of size $t$ provided $z>(t-1)\\binom{rt}{r}$. This improves\nprevious results of Alon and Glebov, Sudakov and Szab\\'o.\n"}
+{"abstract": "  The recent availability of high-resolution far-infrared (FIR) polarization\nobservations of galaxies using HAWC+/SOFIA has facilitated studies of\nextragalactic magnetic fields in the cold and dense molecular disks.We\ninvestigate if any significant structural differences are detectable in the\nkpc-scale magnetic field of the grand design face-on spiral galaxy M51 when\ntraced within the diffuse (radio) and the dense and cold (FIR) interstellar\nmedium (ISM). Our analysis reveals a complex scenario where radio and FIR\npolarization observations do not necessarily trace the same magnetic field\nstructure. We find that the magnetic field in the arms is wrapped tighter at\n154um than at 3 and 6 cm; statistically significant lower values for the\nmagnetic pitch angle are measured at FIR in the outskirts (R > 7 kpc) of the\ngalaxy. This difference is not detected in the interarm region. We find strong\ncorrelations of the polarization fraction and total intensity at FIR and radio\nwith the gas column density and 12CO(1-0) velocity dispersion. We conclude that\nthe arms show a relative increase of small-scale turbulent B-fields at regions\nwith increasing column density and dispersion velocities of the molecular gas.\nNo correlations are found with HI neutral gas. The star formation rate shows a\nclear correlation with the radio polarized intensity, which is not found in\nFIR, pointing to a small-scale dynamo-driven B-field amplification scenario.\nThis work shows that multi-wavelength polarization observations are key to\ndisentangling the interlocked relation between star formation, magnetic fields,\nand gas kinematics in the multi-phase ISM.\n"}
+{"abstract": "  This paper introduces a new concept called \"transferable visual words\"\n(TransVW), aiming to achieve annotation efficiency for deep learning in medical\nimage analysis. Medical imaging--focusing on particular parts of the body for\ndefined clinical purposes--generates images of great similarity in anatomy\nacross patients and yields sophisticated anatomical patterns across images,\nwhich are associated with rich semantics about human anatomy and which are\nnatural visual words. We show that these visual words can be automatically\nharvested according to anatomical consistency via self-discovery, and that the\nself-discovered visual words can serve as strong yet free supervision signals\nfor deep models to learn semantics-enriched generic image representation via\nself-supervision (self-classification and self-restoration). Our extensive\nexperiments demonstrate the annotation efficiency of TransVW by offering higher\nperformance and faster convergence with reduced annotation cost in several\napplications. Our TransVW has several important advantages, including (1)\nTransVW is a fully autodidactic scheme, which exploits the semantics of visual\nwords for self-supervised learning, requiring no expert annotation; (2) visual\nword learning is an add-on strategy, which complements existing self-supervised\nmethods, boosting their performance; and (3) the learned image representation\nis semantics-enriched models, which have proven to be more robust and\ngeneralizable, saving annotation efforts for a variety of applications through\ntransfer learning. Our code, pre-trained models, and curated visual words are\navailable at https://github.com/JLiangLab/TransVW.\n"}
+{"abstract": "  Local coexistence of species in large ecosystems is traditionally explained\nwithin the broad framework of niche theory. However, its rationale hardly\njustifies rich biodiversity observed in nearly homogeneous environments. Here\nwe consider a consumer-resource model in which a coarse-graining procedure\naccounts for a variety of ecological mechanisms and leads to effective spatial\neffects which favour species coexistence. Herein, we provide conditions for\nseveral species to live in an environment with very few resources. In fact, the\nmodel displays two different phases depending on whether the number of\nsurviving species is larger or smaller than the number of resources. We obtain\nconditions whereby a species can successfully colonize a pool of coexisting\nspecies. Finally, we analytically compute the distribution of the population\nsizes of coexisting species. Numerical simulations as well as empirical\ndistributions of population sizes support our analytical findings.\n"}
+{"abstract": "  By combining neutron scattering and magnetization measurements down to 80 mK,\nwe determine the $(H,T)$ phase diagram of the Nd$_2$(Zr$_{1-x}$Ti$_x$)$_2$O$_7$\npyrochlore magnet compounds. In those samples, Zr is partially substituted by\nTi, hence tuning the exchange parameters and testing the robustness of the\nvarious phases. In all samples, the ground state remains \"all in / all out\",\nwhile the field induces phase transitions towards new states characterized by\n\"2 in - 2 out\" or \"1 out - 3 in / 1 in - 3 out\" configurations. These\ntransitions manifest as metamagnetic singularities in the magnetization vs\nfield measurements. Strikingly, it is found that moderate substitution\nreinforces the stability of the \"all in / all out\" phase: the N\\'eel\ntemperature, the metamagnetic fields along with the ordered magnetic moment are\nhigher in substituted samples with $x <$ 10\\%.\n"}
+{"abstract": "  The results on the initial boundary value problem for Einstein's vacuum field\nequation obtained in \\cite{friedrich:nagy} rely on an unusual gauge. One of the\ndefining gauge source functions represents the mean extrinsic curvature of the\ntime-like leaves of a foliation that includes the boundary and covers a\nneighbourhoood of it. The others steer the development of a frame field and\ncoordinates on the leaves. In general their combined action is needed to\ncontrol in the context of the reduced field equations the evolution of the\nleaves. In this article are derived the hyperbolic equations implicit in that\ngauge. It is shown that the latter are independent of the Einstein equations\nand well defined on arbitrary space-times. The analysis simplifies if boundary\nconditions with constant mean extrinsic curvature are stipulated. It simplifies\nfurther if the boundary is required to be totally geodesic.\n"}
+{"abstract": "  Water (H$_{2}$O), in all forms, is an important constituent in planetary\nbodies, controlling habitability and influencing geological activity. Under\nconditions found in the interior of many planets, as the pressure increases,\nthe H-bonds in water gradually weaken and are replaced by ionic bonds. Recent\nexperimental measurements of the water equation of state (EOS) showed both a\nnew phase of H-bonded water ice, ice-VII$_t$, and a relatively low transition\npressure just above 30 GPa to ionic bonded ice-X, which has a bulk modulus 2.5\ntimes larger. The higher bulk modulus of ice-X produces larger planets for a\ngiven mass, thereby either reducing the atmospheric contribution to the volume\nof many exoplanets or limiting their water content. We investigate the impact\nof the new EOS measurements on the planetary mass-radius relation and interior\nstructure for water-rich planets. We find that the change in the planet\nmass-radius relation caused by the systematic differences between previous and\nnew experimental EOS measurements are comparable to the observational\nuncertainties in some planet sizes -- an issue that will become more important\nas observations continue to improve.\n"}
+{"abstract": "  The occurrence of stochastic resonance in bistable systems undergoing\nanomalous diffusions, which arise from density-dependent fluctuations, is\ninvestigated with emphasis on the analytical formulation of the problem as well\nas a possible analytical derivation of key quantifiers of stochastic resonance.\nThe nonlinear Fokker-Planck equation describing the system dynamics, together\nwith the corresponding Ito-Langevin equation, are formulated. In the\nlinear-response regime analytical expressions of the spectral amplification, of\nthe signal-to-noise ratio and of the hysteresis loop area are derived as\nquantifiers of stochastic resonance. These quantifiers are found to be strongly\ndependent on the parameters controlling the type of diffusion, in particular\nthe peak characterizing the signal-to-noise ratio occurs only in close ranges\nof parameters. Results introduce the relevant information that taking into\nconsideration the interactions of anomalous diffusive systems with a periodic\nsignal, can provide a better understanding of the physics of stochastic\nresonance in bistable systems driven by periodic forces.\n"}
+{"abstract": "  In a previous paper, the first two named authors established an isomorphism\nbetween the moduli space of framed flags of sheaves on the projective plane and\nthe moduli space of stable representations of a certain quiver. In the present\nnote, we substitute one of the claims made, namely [5, Theorem 17], for a\nweaker claim regarding the existence of unobstructed points in the quiver\nmoduli space. We also extend some of the results of the cited paper, concerning\nthe maximal stability chamber within which the isomorphism mentioned holds, and\nthe existence of a perfect obstruction theory for the quiver moduli space.\n"}
+{"abstract": "  Using the policy gradient algorithm, we train a single-hidden-layer neural\nnetwork to balance a physically accurate simulation of a single inverted\npendulum. The trained weights and biases can then be transferred to a physical\nagent, where they are robust enough to to balance a real inverted pendulum.\nThis hybrid approach of training a simulation allows thousands of trial runs to\nbe completed orders of magnitude faster than would be possible in the real\nworld, resulting in greatly reduced training time and more iterations,\nproducing a more robust model. When compared with existing reinforcement\nlearning methods, the resulting control is smoother, learned faster, and able\nto withstand forced disturbances.\n"}
+{"abstract": "  In this work, we study the binary neural networks (BNNs) of which both the\nweights and activations are binary (i.e., 1-bit representation). Feature\nrepresentation is critical for deep neural networks, while in BNNs, the\nfeatures only differ in signs. Prior work introduces scaling factors into\nbinary weights and activations to reduce the quantization error and effectively\nimproves the classification accuracy of BNNs. However, the scaling factors not\nonly increase the computational complexity of networks, but also make no sense\nto the signs of binary features. To this end, Self-Distribution Binary Neural\nNetwork (SD-BNN) is proposed. Firstly, we utilize Activation Self Distribution\n(ASD) to adaptively adjust the sign distribution of activations, thereby\nimprove the sign differences of the outputs of the convolution. Secondly, we\nadjust the sign distribution of weights through Weight Self Distribution (WSD)\nand then fine-tune the sign distribution of the outputs of the convolution.\nExtensive experiments on CIFAR-10 and ImageNet datasets with various network\nstructures show that the proposed SD-BNN consistently outperforms the\nstate-of-the-art (SOTA) BNNs (e.g., achieves 92.5% on CIFAR-10 and 66.5% on\nImageNet with ResNet-18) with less computation cost. Code is available at\nhttps://github.com/ pingxue-hfut/SD-BNN.\n"}
+{"abstract": "  Quantum coherent control of ultrafast bond making and the subsequent\nmolecular dynamics is crucial for the realization of a new photochemistry,\nwhere a shaped laser field is actively driving the chemical system in a\ncoherent way from the thermal initial state of the reactants to the final state\nof the desired products. We demonstrate here coherent control over the relative\nyields of Mg$_2$ molecules that are generated via photoassociation and\nsubsequently photodriven into different groups of final states. The\nstrong-field process involves non-resonant multiphoton femtosecond\nphotoassociation of a pair of thermally hot magnesium atoms into a bound\nMg$_{2}$ molecule and subsequent molecular dynamics on electronically excited\nstates. The branching-ratio control is achieved with linearly chirped laser\npulses, utilizing the different chirp dependence that various groups of final\nmolecular states display for their post-pulse population. Our study establishes\nthe feasibility of high degree coherent control over quantum molecular dynamics\nthat is initiated by femtosecond photoassociation of thermal atoms.\n"}
+{"abstract": "  In this paper we study the statistical properties of Laplacian smoothing, a\ngraph-based approach to nonparametric regression. Under standard regularity\nconditions, we establish upper bounds on the error of the Laplacian smoothing\nestimator $\\widehat{f}$, and a goodness-of-fit test also based on\n$\\widehat{f}$. These upper bounds match the minimax optimal estimation and\ntesting rates of convergence over the first-order Sobolev class\n$H^1(\\mathcal{X})$, for $\\mathcal{X}\\subseteq \\mathbb{R}^d$ and $1 \\leq d < 4$;\nin the estimation problem, for $d = 4$, they are optimal modulo a $\\log n$\nfactor. Additionally, we prove that Laplacian smoothing is manifold-adaptive:\nif $\\mathcal{X} \\subseteq \\mathbb{R}^d$ is an $m$-dimensional manifold with $m\n< d$, then the error rate of Laplacian smoothing (in either estimation or\ntesting) depends only on $m$, in the same way it would if $\\mathcal{X}$ were a\nfull-dimensional set in $\\mathbb{R}^d$.\n"}
+{"abstract": "  Ammonia is predicted to be one of the major components in the depths of the\nice giant planets Uranus and Neptune. Their dynamics, evolution, and interior\nstructure are insufficiently understood and models rely imperatively on data\nfor equation of state and transport properties. Despite its great significance,\nthe experimentally accessed region of the ammonia phase diagram today is still\nvery limited in pressure and temperature. Here we push the probed regime to\nunprecedented conditions, up to $\\sim$350 GPa and $\\sim$40000 K. Along the\nHugoniot, the temperature measured as a function of pressure shows a subtle\nchange in slope at $\\sim$7000 K and $\\sim$90 GPa, in agreement with ab initio\nsimulations we have performed. This feature coincides with the gradual\ntransition from a molecular liquid to a plasma state. Additionally, we\nperformed reflectivity measurements, providing the first experimental evidence\nof electronic conduction in high-pressure ammonia. Shock reflectance\ncontinuously rises with pressure above 50 GPa and reaches saturation values\nabove 120 GPa. Corresponding electrical conductivity values are up to 1 order\nof magnitude higher than in water in the 100 GPa regime, with possible\nsignificant contributions of the predicted ammonia-rich layers to the\ngeneration of magnetic dynamos in ice giant interiors.\n"}
+{"abstract": "  We construct an example of a Hilbert C*-module which shows that Troitsky's\ntheorem on the geometrical essence of A-compact operators between Hilbert\nC*-modules is not extendable to a not countably generated module case (even in\nthe case of a stronger uniform structure, which is also introduced). In\naddition, the constructed module admits no frames.\n"}
+{"abstract": "  We discuss high-order calculations in perturbative effective field theory for\nfermions at low energy scales. The Fermi-momentum or $k_{\\rm F} a_s$ expansion\nfor the ground-state energy of the dilute Fermi gas is calculated to fourth\norder, both in cutoff regularization and in dimensional regularization. For the\ncase of spin one-half fermions we find from a Bayesian analysis that the\nexpansion is well-converged at this order for ${| k_{\\rm F} a_s | \\lesssim\n0.5}$. Further, we show that Pad{\\'e}-Borel resummations can improve the\nconvergence for ${| k_{\\rm F} a_s | \\lesssim 1}$. Our results provide important\nconstraints for nonperturbative calculations of ultracold atoms and dilute\nneutron matter.\n"}
+{"abstract": "  The idea of constructing a time machine is not new and even received a boost\nthanks to the realization that a traversable wormhole could be converted to a\ntime machine. This also implied that it would be impossible to travel back in\ntime beyond the creation of the time machine. This paper addresses these\nissues, as well as the concomitant causality violations, by starting with a\nphysically acceptable model, a spacetime that is anti-de Sitter due to an extra\ntime-like dimension, thereby allowing the existence of closed time-like curves.\nBy assuming that the extra dimension is independent of the radial coordinate,\nthe wormhole retains its basic geometric properties regardless of its location\nand can therefore serve as a shortcut for any closed time-like curve, which, in\nturn, can extend indefinitely into the past. The same independence of the\nradial coordinate suggests that the wormhole could connect a region in the\npresent to a local region that (1) lies in the past and (2) does not contain\nany closed time-like curves. Previous studies have suggested that if such a\nregion is sufficiently localized, it may be possible to avoid a causality\nviolation.\n"}
+{"abstract": "  Land use mix is one of the central concepts in the urban planning field,\nthough its measure has been found to have many fallacies. In this study, we\npropose multiple alternative methods to the Conventional Shannon Entropy land\nuse mix index, which is generally employed to measure land use diversity. The\nstudy attempted to measure land use mix from two new perspectives and test the\nperformance of the new measures with economic, transportation, and social\noutcomes. We first offered an improved single measure, entropy-based land use\nmix index weighted by adding surrounding land use attributes and the regional\nland use context to mitigate the modifiable area unit problem (MAUP) and equal\ncomposition issue. Second, we designed a multi-measure method to describe land\nuse patterns via clustering. We found that the new single measure method is\nmore effective than the existing Conventional Shannon Entropy index in\naccurately delivering the vision of land use mix that Jane Jacobs originally\nproposed. We also found the multi-measure clustering method performed much\nbetter in the regression model on various effect variables, compared with both\nconventional and other new index. Upon further review, we found that land use\npattern complexity's relationship with various outcomes makes a single\ndefinition measure very difficult to represent true land use fully. Therefore,\nwe recommend that future research measure whether or not land use is mixed to\nthen assess land use pattern.\n"}
+{"abstract": "  We investigate the observational viability of a class of interacting dark\nenergy (iDE) models in the light of the latest Cosmic Microwave Background\n(CMB), type Ia supernovae (SNe) and SH0ES Hubble parameter measurements. Our\nanalysis explores the assumption of a non-zero spatial curvature, the\ncorrelation between the interaction parameter $\\alpha$ and the current\nexpansion rate $H_0$, and updates the results reported in \\cite{micol}.\nInitially, assuming a spatially flat universe, the results show that the\nbest-fit of our joint analysis clearly favours a positive interaction, i.e., an\nenergy flux from dark matter to dark energy, with $\\alpha \\approx 0.2$, while\nthe non-interacting case, $\\alpha = 0$, is ruled out by more than $3\\sigma$\nconfidence level. On the other hand, considering a non-zero spatial curvature,\nwe find a slight preference for a negative value of the curvature parameter,\nwhich seems to relax the correlation between the parameters $\\alpha$ and $H_0$,\nas well as between $H_0$ and the normalization of the matter power spectrum on\nscales of 8$h^{-1}$ Mpc ($\\sigma_8$). Finally, we discuss the influence of\nconsidering the SH$0$ES prior on $H_0$ in the joint analyses, and find that\nsuch a choice does not change considerably the standard cosmology predictions\nbut has a significant influence on the results of the iDE model.\n"}
+{"abstract": "  Crowdsourced design feedback systems are emerging resources for getting large\namounts of feedback in a short period of time. Traditionally, the feedback\ncomes in the form of a declarative statement, which often contains positive or\nnegative sentiment. Prior research has shown that overly negative or positive\nsentiment can strongly influence the perceived usefulness and acceptance of\nfeedback and, subsequently, lead to ineffective design revisions. To enhance\nthe effectiveness of crowdsourced design feedback, we investigate a new\napproach for mitigating the effects of negative or positive feedback by\ncombining open-ended and thought-provoking questions with declarative feedback\nstatements. We conducted two user studies to assess the effects of\nquestion-based feedback on the sentiment and quality of design revisions in the\ncontext of graphic design. We found that crowdsourced question-based feedback\ncontains more neutral sentiment than statement-based feedback. Moreover, we\nprovide evidence that presenting feedback as questions followed by statements\nleads to better design revisions than question- or statement-based feedback\nalone.\n"}
+{"abstract": "  In scientific computing and machine learning applications, matrices and more\ngeneral multidimensional arrays (tensors) can often be approximated with the\nhelp of low-rank decompositions. Since matrices and tensors of fixed rank form\nsmooth Riemannian manifolds, one of the popular tools for finding low-rank\napproximations is to use Riemannian optimization. Nevertheless, efficient\nimplementation of Riemannian gradients and Hessians, required in Riemannian\noptimization algorithms, can be a nontrivial task in practice. Moreover, in\nsome cases, analytic formulas are not even available. In this paper, we build\nupon automatic differentiation and propose a method that, given an\nimplementation of the function to be minimized, efficiently computes Riemannian\ngradients and matrix-by-vector products between an approximate Riemannian\nHessian and a given vector.\n"}
+{"abstract": "  Given two relations containing multiple measurements - possibly with\nuncertainties - our objective is to find which sets of attributes from the\nfirst have a corresponding set on the second, using exclusively a sample of the\ndata. This approach could be used even when the associated metadata is damaged,\nmissing or incomplete, or when the volume is too big for exact methods. This\nproblem is similar to the search of Inclusion Dependencies (IND), a type of\nrule over two relations asserting that for a set of attributes X from the\nfirst, every combination of values appears on a set Y from the second. Existing\nIND can be found exploiting the existence of a partial order relation called\nspecialization. However, this relation is based on set theory, requiring the\nvalues to be directly comparable. Statistical tests are an intuitive possible\nreplacement, but it has not been studied how would they affect the underlying\nassumptions. In this paper we formally review the effect that a statistical\napproach has over the inference rules applied to IND discovery. Our results\nconfirm the intuitive thought that statistical tests can be used, but not in a\ndirectly equivalent manner. We provide a workable alternative based on a\n\"hierarchy of null hypotheses\", allowing for the automatic discovery of\nmulti-dimensional equally distributed sets of attributes.\n"}
+{"abstract": "  In previous work by this author, the screening paradox - the loss of\npredictive power of screening tests over time $t$ - was mathematically\nformalized using Bayesian theory. Where $J$ is Youden's statistic, $b$ is the\nspecificity of the screening test and $\\phi$ is the prevalence of disease, the\nratio of positive predictive values at subsequent time $k$, $\\rho(\\phi_{k})$,\nover the original $\\rho(\\phi_{0})$ at $t_0$ is given by:\n  $\\zeta(\\phi_{0},k) = \\frac{\\rho(\\phi_{k})}{\\rho(\\phi_{0})}\n=\\frac{\\phi_k(1-b)+J\\phi_0\\phi_k}{\\phi_0(1-b)+J\\phi_0\\phi_k}$\n  Herein, we modify the traditional Kermack-McKendrick SIR Model to include the\nfluctuation of the positive predictive value $\\rho(\\phi)$ (PPV) of a screening\ntest over time as a function of the prevalence threshold $\\phi_e$. We term this\nmodified model the SIR-P model. Where a = sensitivity, b = specificity, $S$ =\nnumber susceptible, $I$ = number infected, $R$ = number recovered/dead, $\\beta$\n= infectious rate, $\\gamma$ = recovery rate, and $N$ is the total number in the\npopulation, the predictive value $\\rho(\\phi,t)$ over time $t$ is given by:\n  $\\rho(\\phi,t) = \\frac{a[\\frac{\\beta IS}{N}-\\gamma I]}{ a[\\frac{\\beta\nIS}{N}-\\gamma I]+(1-b)(1-[\\frac{\\beta IS}{N}-\\gamma I])}$\n  Otherwise stated:\n  $\\rho(\\phi,t) = \\frac{a\\frac{dI}{dt}}{\na\\frac{dI}{dt}+(1-b)(1-\\frac{dI}{dt})}$ where $\\frac{dI}{dt}$ is the\nfluctuation of infected individuals over time $t$.\n"}
+{"abstract": "  The transformation of graphs and graph-like structures is ubiquitous in\ncomputer science. When a system is described by graph-transformation rules, it\nis often desirable that the rules are both terminating and confluent so that\nrule applications in an arbitrary order produce unique resulting graphs.\nHowever, there are application scenarios where the rules are not globally\nconfluent but confluent on a subclass of graphs that are of interest. In other\nwords, non-resolvable conflicts can only occur on graphs that are considered as\n\"garbage\". In this paper, we introduce the notion of confluence up to garbage\nand generalise Plump's critical pair lemma for double-pushout graph\ntransformation, providing a sufficient condition for confluence up to garbage\nby non-garbage critical pair analysis. We apply our results in two case studies\nabout efficient language recognition: we present backtracking-free graph\nreduction systems which recognise a class of flow diagrams and a class of\nlabelled series-parallel graphs, respectively. Both systems are non-confluent\nbut confluent up to garbage. We also give a critical pair condition for\nsubcommutativity up to garbage which, together with closedness, implies\nconfluence up to garbage even in non-terminating systems.\n"}
+{"abstract": "  Extensive molecular simulations are applied to characterize the equilibrium\ndynamics, entanglement topology, and nonlinear extensional rheology of\nsymmetric ring-linear polymer blends with systematically varied ring fraction\n$\\phi_R$. Chains with degree of entanglement $Z\\approx14$ mixed to produce 10\nwell-entangled systems with $\\phi_R$ varying from neat linear to neat ring\nmelts. Primitive path analysis are used to visualize and quantify the structure\nof the composite ring-linear entanglement network. We directly measure the\nquantity of ring-linear threading and linear-linear entanglement as a function\nof $\\phi_R$, and identify with simple arguments a ring fraction\n$\\phi_R\\approx0.4$ where the topological constraints of the entanglement\nnetwork are maximized. These topological analyses are used to rationalize the\n$\\phi_R$-dependence of ring and linear chain dynamics, conformations, and blend\nviscosity. Complimentary simulations of startup uniaxial elongation flows\ndemonstrate the extensional stress overshoot observed in recent filament\nstretching experiments, and characterize how it depends on the blend\ncomposition and entanglement topology. The overshoot is driven by an\noverstretching and recoil of ring polymer conformations that is caused by the\nconvective unthreading of rings from linear chains. This produces significant\nchanges in the entanglement structure of blends that we directly visualize and\nquantify with primitive path analyses during flow.\n"}
+{"abstract": "  This paper provides the details of implementing two important policy gradient\nmethods to solve the inverted pendulum problem. These are namely the Deep\nDeterministic Policy Gradient (DDPG) and the Proximal Policy Optimization (PPO)\nalgorithm. The problem is solved by using an actor-critic model where an\nactor-network is used to learn the policy function and a critic network is to\nevaluate the actor-network by learning to estimate the Q function. Apart from\nbriefly explaining the mathematics behind these two algorithms, the details of\npython implementation are provided which helps in demystifying the underlying\ncomplexity of the algorithm. In the process, the readers will be introduced to\nOpenAI/Gym, Tensorflow 2.x and Keras utilities used for implementing the above\nconcepts.\n"}
+{"abstract": "  High-dimensional omics data contains intrinsic biomedical information that is\ncrucial for personalised medicine. Nevertheless, it is challenging to capture\nthem from the genome-wide data due to the large number of molecular features\nand small number of available samples, which is also called 'the curse of\ndimensionality' in machine learning. To tackle this problem and pave the way\nfor machine learning aided precision medicine, we proposed a unified multi-task\ndeep learning framework named OmiEmbed to capture biomedical information from\nhigh-dimensional omics data with the deep embedding and downstream task\nmodules. The deep embedding module learnt an omics embedding that mapped\nmultiple omics data types into a latent space with lower dimensionality. Based\non the new representation of multi-omics data, different downstream task\nmodules were trained simultaneously and efficiently with the multi-task\nstrategy to predict the comprehensive phenotype profile of each sample.\nOmiEmbed support multiple tasks for omics data including dimensionality\nreduction, tumour type classification, multi-omics integration, demographic and\nclinical feature reconstruction, and survival prediction. The framework\noutperformed other methods on all three types of downstream tasks and achieved\nbetter performance with the multi-task strategy comparing to training them\nindividually. OmiEmbed is a powerful and unified framework that can be widely\nadapted to various application of high-dimensional omics data and has a great\npotential to facilitate more accurate and personalised clinical decision\nmaking.\n"}
+{"abstract": "  In an empirical study of a two-sided large matching market (such as a college\nadmissions problem), the primary statistics of interest are often the empirical\nmatching probabilities which represent the fraction of students of a given type\nwho match with colleges of a given type. This paper studies the\nconcentration-of-measure phenomenon for the empirical matching probabilities\nand related statistics when the matching is generated from a student-optimal\nstable matching mechanism. We introduce a notion of partial homogeneity of\npreferences to express the degree of alignment in the preferences of colleges\nover students, and show that the more the preferences are aligned, the more the\nempirical matching frequencies tend to concentrate on their expected value\ngiven the colleges' types. This means that the empirical matching probabilities\nbecome more robust to realizations of students' preferences when the colleges'\npreferences are more aligned.\n"}
+{"abstract": "  In the field of machine learning, the well-trained model is assumed to be\nable to recover the training labels, i.e. the synthetic labels predicted by the\nmodel should be as close to the ground-truth labels as possible. Inspired by\nthis, we propose a self-guided curriculum strategy to encourage the learning of\nneural machine translation (NMT) models to follow the above recovery criterion,\nwhere we cast the recovery degree of each training example as its learning\ndifficulty. Specifically, we adopt the sentence level BLEU score as the proxy\nof recovery degree. Different from existing curricula relying on linguistic\nprior knowledge or third-party language models, our chosen learning difficulty\nis more suitable to measure the degree of knowledge mastery of the NMT models.\nExperiments on translation benchmarks, including WMT14\nEnglish$\\Rightarrow$German and WMT17 Chinese$\\Rightarrow$English, demonstrate\nthat our approach can consistently improve translation performance against\nstrong baseline Transformer.\n"}
+{"abstract": "  Equilibrium Propagation (EP) is an algorithm intrinsically adapted to the\ntraining of physical networks, thanks to the local updates of weights given by\nthe internal dynamics of the system. However, the construction of such a\nhardware requires to make the algorithm compatible with existing neuromorphic\nCMOS technologies, which generally exploit digital communication between\nneurons and offer a limited amount of local memory. In this work, we\ndemonstrate that EP can train dynamical networks with binary activations and\nweights. We first train systems with binary weights and full-precision\nactivations, achieving an accuracy equivalent to that of full-precision models\ntrained by standard EP on MNIST, and losing only 1.9% accuracy on CIFAR-10 with\nequal architecture. We then extend our method to the training of models with\nbinary activations and weights on MNIST, achieving an accuracy within 1% of the\nfull-precision reference for fully connected architectures and reaching the\nfull-precision accuracy for convolutional architectures. Our extension of EP to\nbinary networks opens new solutions for on-chip learning and provides a compact\nframework for training BNNs end-to-end with the same circuitry as for\ninference.\n"}
+{"abstract": "  The COVID-19 pandemic considerably affects public health systems around the\nworld. The lack of knowledge about the virus, the extension of this phenomenon,\nand the speed of the evolution of the infection are all factors that highlight\nthe necessity of employing new approaches to study these events. Artificial\nintelligence techniques may be useful in analyzing data related to areas\naffected by the virus. The aim of this work is to investigate any possible\nrelationships between air quality and confirmed cases of COVID-19 in Italian\ndistricts. Specifically, we report an analysis of the correlation between daily\nCOVID-19 cases and environmental factors, such as temperature, relative\nhumidity, and atmospheric pollutants. Our analysis confirms a significant\nassociation of some environmental parameters with the spread of the virus. This\nsuggests that machine learning models trained on the environmental parameters\nto predict the number of future infected cases may be accurate. Predictive\nmodels may be useful for helping institutions in making decisions for\nprotecting the population and contrasting the pandemic.\n"}
+{"abstract": "  Here we outline a description of paraxial light propagation from a modal\nperspective. By decomposing the initial transverse field into a spatial basis\nwhose elements have known and analytical propagation characteristics, we are\nable to analytically propagate any desired field, making the calculation fast\nand easy. By selecting a basis other than that of planes waves, we overcome the\nproblem of numerical artefacts in the angular spectrum approach and at the same\ntime are able to offer an intuitive understanding for why certain classes of\nfields propagate as they do. We outline the concept theoretically, compare it\nto the numerical angular spectrum approach, and confirm its veracity\nexperimentally using a range of instructive examples. We believe that this\nmodal approach to propagating light will be a useful addition to toolbox for\npropagating optical fields.\n"}
+{"abstract": "  Given positive integers $p$ and $q$, a $(p,q)$-coloring of the complete graph\n$K_n$ is an edge-coloring in which every $p$-clique receives at least $q$\ncolors. Erd\\H{o}s and Shelah posed the question of determining $f(n,p,q)$, the\nminimum number of colors needed for a $(p,q)$-coloring of $K_n$. In this paper,\nwe expand on the color energy technique introduced by Pohoata and Sheffer to\nprove new lower bounds on this function, making explicit the connection between\nbounds on extremal numbers and $f(n,p,q)$. Using results on the extremal\nnumbers of subdivided complete graphs, theta graphs, and subdivided complete\nbipartite graphs, we generalize results of Fish, Pohoata, and Sheffer, giving\nthe first nontrivial lower bounds on $f(n,p,q)$ for some pairs $(p,q)$ and\nimproving previous lower bounds for other pairs.\n"}
+{"abstract": "  This work considers a novel information design problem and studies how the\ncraft of payoff-relevant environmental signals solely can influence the\nbehaviors of intelligent agents. The agents' strategic interactions are\ncaptured by a Markov game, in which each agent first selects one external\nsignal from multiple signal sources as additional payoff-relevant information\nand then takes an action. There is a rational information designer (principal)\nwho possesses one signal source and aims to influence the equilibrium behaviors\nof the agents by designing the information structure of her signals sent to the\nagents. We propose a direct information design approach that incentivizes each\nagent to select the signal sent by the principal, such that the design process\navoids the predictions of the agents' strategic selection behaviors. We then\nintroduce the design protocol given a goal of the designer which we refer to as\nobedient implementability (OIL) and characterize the OIL in a class of obedient\nsequential Markov perfect equilibria (O-SMPE). A design regime is proposed\nbased on an approach which we refer to as the fixed-point alignment that\nincentivizes the agents to choose the signal sent by the principal, guarantees\nthat the agents' policy profile of taking actions is the policy component of an\nO-SMPE and the principal's goal is achieved. We then formulate the principal's\noptimal goal selection problem in terms of information design and characterize\nthe optimization problem by minimizing the fixed-point misalignments. The\nproposed approach can be applied to elicit desired behaviors of multi-agent\nsystems in competing as well as cooperating settings and be extended to\nheterogeneous stochastic games in the complete- and the incomplete-information\nenvironments.\n"}
+{"abstract": "  We investigate the interaction between a moving detector and a quantum field,\nespecially about how the trajectory of the detector would affect the vacuum\nfluctuations when the detector is moves in a quantum field (the Unruh effect).\nWe focus on two moving detectors system for future application in quantum\nteleportation. We find that the rajectory of a uniformly accelerated detector\nin Rindler space cannot be extended to a trajectory in which a detector moves\nat constant velocity. Based on our previous work, we redo the calculations and\nfind that a term is missing from the past calculations, and we also find that\nthere are some restrictions on the values for the parameters in the solutions.\nIn addition, without inclusion of the missing term, the variance from the\nquantum field for the inertial detector will be zero and is unlikely in such a\nsystem. When all these points are combined, there is a difference in the\ntwo-point correlation function between the inertial detector and the\naccelerated detector in the early-time region. The influence of proper\nacceleration can be seen in the two-point correlation functions. This might\nplay a role in the quantum teleportation process and be worth studying\nthoroughly.\n"}
+{"abstract": "  An object's interior material properties, while invisible to the human eye,\ndetermine motion observed on its surface. We propose an approach that estimates\nheterogeneous material properties of an object from a monocular video of its\nsurface vibrations. Specifically, we show how to estimate Young's modulus and\ndensity throughout a 3D object with known geometry. Knowledge of how these\nvalues change across the object is useful for simulating its motion and\ncharacterizing any defects. Traditional non-destructive testing approaches,\nwhich often require expensive instruments, generally estimate only homogenized\nmaterial properties or simply identify the presence of defects. In contrast,\nour approach leverages monocular video to (1) identify image-space modes from\nan object's sub-pixel motion, and (2) directly infer spatially-varying Young's\nmodulus and density values from the observed modes. We demonstrate our approach\non both simulated and real videos.\n"}
+{"abstract": "  In this paper, we propose an approach for solving an energy-optimal goal\nassignment problem to generate the desired formation in multi-agent systems.\nEach agent solves a decentralized optimization problem with only local\ninformation about its neighboring agents and the goals. The optimization\nproblem consists of two sub-problems. The first problem seeks to minimize the\nenergy for each agent to reach certain goals, while the second problem entreats\nan optimal combination of goal and agent pairs that minimizes the energy cost.\nBy assuming the goal trajectories are given in a polynomial form, we prove the\nsolution to the formulated problem exists globally. Finally, the effectiveness\nof the proposed approach is validated through the simulation.\n"}
+{"abstract": "  Let $f \\in M_+(\\mathbb{R}_+)$, the class of nonnegative, Lebesgure-measurable\nfunctions on $\\mathbb{R}_+=(0, \\infty)$. We deal with integral operators of the\nform \\[ (T_Kf)(x)=\\int_{\\mathbb{R}_+}K(x,y)f(y)\\, dy, \\quad x \\in \\mathbb{R}_+,\n\\] with $K \\in M_+(\\mathbb{R}_+^2)$.\n"}
+{"abstract": "  In this paper we present the details of Women in Computer Vision Workshop -\nWiCV 2020, organized in alongside virtual CVPR 2020. This event aims at\nencouraging the women researchers in the field of computer vision. It provides\na voice to a minority (female) group in computer vision community and focuses\non increasingly the visibility of these researchers, both in academia and\nindustry. WiCV believes that such an event can play an important role in\nlowering the gender imbalance in the field of computer vision. WiCV is\norganized each year where it provides a.) opportunity for collaboration with\nbetween researchers b.) mentorship to female junior researchers c.) financial\nsupport to presenters to overcome monetary burden and d.) large and diverse\nchoice of role models, who can serve as examples to younger researchers at the\nbeginning of their careers. In this paper, we present a report on the workshop\nprogram, trends over the past years, a summary of statistics regarding\npresenters, attendees, and sponsorship for the current workshop.\n"}
+{"abstract": "  We investigate the effect of strong emission line galaxies on the performance\nof empirical photometric redshift estimation methods. In order to artificially\ncontrol the contribution of photometric error and emission lines to total flux,\nwe develop a PCA-based stochastic mock catalogue generation technique that\nallows for generating infinite signal-to-noise ratio model spectra with\nrealistic emission lines on top of theoretical stellar continua. Instead of\nrunning the computationally expensive stellar population synthesis and nebular\nemission codes, our algorithm generates realistic spectra with a statistical\napproach, and - as an alternative to attempting to constrain the priors on\ninput model parameters - works by matching output observational parameters.\nHence, it can be used to match the luminosity, colour, emission line and\nphotometric error distribution of any photometric sample with sufficient\nflux-calibrated spectroscopic follow-up. We test three simple empirical\nphotometric estimation methods and compare the results with and without\nphotometric noise and strong emission lines. While photometric noise clearly\ndominates the uncertainty of photometric redshift estimates, the key findings\nare that emission lines play a significant role in resolving colour space\ndegeneracies and good spectroscopic coverage of the entire colour space is\nnecessary to achieve good results with empirical photo-z methods. Template\nfitting methods, on the other hand, must use a template set with sufficient\nvariation in emission line strengths and ratios, or even better, first estimate\nthe redshift empirically and fit the colours with templates at the best-fit\nredshift to calculate the K-correction and various physical parameters.\n"}
+{"abstract": "  We present the first results of an MMT/Hectospec campaign to measure the\nkinematics of globular clusters (GCs) around M49 -- the brightest galaxy in the\nVirgo galaxy cluster, which dominates the Virgo B subcluster. The data include\nkinematic tracers beyond 95 kpc (~5.2 effective radii) for M49 for the first\ntime, enabling us to achieve three key insights reported here. First, beyond\n~20'-30' (~100-150 kpc), the GC kinematics sampled along the minor photometric\naxis of M49 become increasingly hotter, indicating a transition from GCs\nrelated to M49 to those representing the Virgo B intra-cluster medium. Second,\nthere is an anomaly in the line-of-sight radial velocity dispersion\n($\\sigma_{r,los}$) profile in an annulus ~10-15' (~50-90 kpc) from M49 in which\nthe kinematics cool by $\\Delta \\sigma_{r,los}~150$ km s$^{-1}$ relative to\nthose in- or outward. The kinematic fingerprint of a previous accretion event\nis hinted at in projected phase-space, and we isolate GCs that both give rise\nto this feature, and are spatially co-located with two prominent stellar shells\nin the halo of M49. Third, we find a subsample of GCs with velocities\nrepresentative of the dwarf galaxy VCC1249 that is currently interacting with\nM49. The spatial distribution of these GCs closely resembles the morphology of\nVCC1249's isophotes, indicating that several of these GCs are likely in the act\nof being stripped from the dwarf during its passage through M49's halo. Taken\ntogether, these results point toward the opportunity of witnessing on-going\ngiant halo assembly in the depths of a cluster environment.\n"}
+{"abstract": "  Toda conformal field theories are natural generalizations of Liouville\nconformal field theory that enjoy an enhanced level of symmetry. In Toda\nconformal field theories this higher-spin symmetry can be made explicit, thanks\nto a path integral formulation of the model based on a Lie algebra structure.\nThe purpose of the present document is to explain how this higher level of\nsymmetry can manifest itself within the rigorous probabilistic framework\nintroduced by R. Rhodes, V. Vargas and the first author. One of its features is\nthe existence of holomorphic currents that are introduced via a rigorous\nderivation of the Miura transformation. More precisely, we prove that the\nspin-three Ward identities, that encode higher-spin symmetry, hold in the\n$\\mathfrak{sl}_3$ Toda conformal field theory; as an original input we provide\nexplicit expressions for the descendent fields which were left unidentified in\nthe physics literature. This representation of the descendent fields provides a\nnew systematic method to find the degenerate fields of the $\\mathfrak{sl}_3$\nToda (and Liouville) conformal field theory, which in turn implies that certain\nfour-point correlation functions are solutions of an hypergeometric\ndifferential equation of the third order.\n"}
+{"abstract": "  Aims. We study the 27-day variations of galactic cosmic rays (GCRs) based on\nneutron monitor (NM), ACE/CRIS, STEREO and SOHO/EPHIN measurements, in solar\nminima 23/24 and 24/25 characterized by the opposite polarities of solar\nmagnetic cycle. Now there is an opportunity to reanalyze the polarity\ndependence of the amplitudes of the recurrent GCR variations in 2007-2009 for\nnegative A < 0 solar magnetic polarity and to compare it with the clear\nperiodic variations related to solar rotation in 2017-2019 for positive A > 0.\nMethods. We use the Fourier analysis method to study the periodicity in the GCR\nfluxes. Since the GCR recurrence is a consequence of solar rotation, we analyze\nnot only GCR fluxes, but also solar and heliospheric parameters examining the\nrelationships between the 27-day GCR variations and heliospheric, as well as,\nsolar wind parameters. Results. We find that the polarity dependence of the\namplitudes of the 27-day variations of the GCR intensity and anisotropy for NMs\ndata is kept for the last two solar minima: 23/24 (2007-2009) and 24/25\n(2017-2019) with greater amplitudes in positive A > 0 solar magnetic polarity.\nACE/CRIS, SOHO/EPHIN and STEREO measurements are not governed by this principle\nof greater amplitudes in positive A > 0 polarity. GCR recurrence caused by the\nsolar rotation for low energy (< 1GeV) cosmic rays is more sensitive to the\nenhanced diffusion effects, resulting in the same level of the 27-day\namplitudes for positive and negative polarities. While high energy (> 1GeV)\ncosmic rays registered by NMs, are more sensitive to the large-scale drift\neffect leading to the 22-year Hale cycle in the 27-day GCR variation, with the\nlarger amplitudes in the A > 0 polarity than in the A < 0.\n"}
+{"abstract": "  Spatio-temporal models for count data are required in a wide range of\nscientific fields and they have become particularly crucial nowadays because of\ntheir ability to analyse COVID-19-related data. Models for count data are\nneeded when the variable of interest take only non-negative integer values and\nthese integers arise from counting occurrences. Several R-packages are\ncurrently available to deal with spatiotemporal areal count data. Each package\nfocuses on different models and/or statistical methodologies. Unfortunately,\nthe results generated by these models are rarely comparable due to differences\nin notation and methods. The main objective of this paper is to present a\nreview describing the most important approaches that can be used to model and\nanalyse count data when questions of scientific interest concern both their\nspatial and their temporal behaviour and we monitor their performance under the\nsame data set.\n  For this review, we focus on the three R-packages that can be used for this\npurpose and the different models assessed are representative of the two most\nwidespread methodologies used to analyse spatiotemporal count data: the\nclassical approach (based on Penalised Likelihood or Estimating Equations) and\nthe Bayesian point of view.\n  A case study is analysed as an illustration of these different methodologies.\nIn this case study, these packages are used to model and predict daily\nhospitalisations from COVID-19 in 24 health regions within the Valencian\nCommunity (Spain), with data corresponding to the period from 28 June to 13\nDecember 2020. Because of the current urgent need for monitoring and predicting\ndata in the COVID-19 pandemic, this case study is, in itself, of particular\nimportance and can be considered the secondary objective of this work.\nSatisfactory and promising results have been obtained in this second goal.\n"}
+{"abstract": "  The accurate representation of variable renewable generation (RES, e.g.,\nwind, solar PV) assets in capacity expansion planning (CEP) studies is\nparamount to capture spatial and temporal correlations that may exist between\nsites and impact both power system design and operation. However, it typically\nhas a high computational cost. This paper proposes a method to reduce the\nspatial dimension of CEP problems while preserving an accurate representation\nof renewable energy sources. A two-stage approach is proposed to this end. In\nthe first stage, relevant sites are identified via a screening routine that\ndiscards the locations with little impact on system design. In the second\nstage, the subset of relevant RES sites previously identified is used in a CEP\nproblem to determine the optimal configuration of the power system. The\nproposed method is tested on a realistic EU case study and its performance is\nbenchmarked against a CEP set-up in which the entire set of candidate RES sites\nis available. The method shows great promise, with the screening stage\nconsistently identifying 90% of the optimal RES sites while discarding up to\n54% of the total number of candidate locations. This leads to a peak memory\nreduction of up to 41% and solver runtime gains between 31% and 46%, depending\non the weather year considered.\n"}
+{"abstract": "  The rising adoption of machine learning in high energy physics and lattice\nfield theory necessitates the re-evaluation of common methods that are widely\nused in computer vision, which, when applied to problems in physics, can lead\nto significant drawbacks in terms of performance and generalizability. One\nparticular example for this is the use of neural network architectures that do\nnot reflect the underlying symmetries of the given physical problem. In this\nwork, we focus on complex scalar field theory on a two-dimensional lattice and\ninvestigate the benefits of using group equivariant convolutional neural\nnetwork architectures based on the translation group. For a meaningful\ncomparison, we conduct a systematic search for equivariant and non-equivariant\nneural network architectures and apply them to various regression and\nclassification tasks. We demonstrate that in most of these tasks our best\nequivariant architectures can perform and generalize significantly better than\ntheir non-equivariant counterparts, which applies not only to physical\nparameters beyond those represented in the training set, but also to different\nlattice sizes.\n"}
+{"abstract": "  Recently, reinforcement learning (RL) has been applied to task-oriented\ndialogue systems by using latent actions to solve shortcomings of supervised\nlearning (SL). In this paper, we propose a multi-domain task-oriented dialogue\nsystem, called Dialogue System with Optimizing a Recurrent Action Policy using\nEfficient Context (DORA), that uses SL, with subsequently applied RL to\noptimize dialogue systems using a recurrent dialogue policy. This dialogue\npolicy recurrently generates explicit system actions as a both word-level and\nhigh-level policy. As a result, DORA is clearly optimized during both SL and RL\nsteps by using an explicit system action policy that considers an efficient\ncontext instead of the entire dialogue history. The system actions are both\ninterpretable and controllable, whereas the latent actions are not. DORA\nimproved the success rate by 6.6 points on MultiWOZ 2.0 and by 10.9 points on\nMultiWOZ 2.1.\n"}
+{"abstract": "  By defining the Adjacency-Matrix for the Collatz Graph it is shown that it is\npossible, from the Adjacency-Matrix, to generate a 'picture' which defines the\nFlow-Diagram for the Collatz Graph. In a sense the Flow-Diagram is the Collatz\nGraph.\n  While defining a Double 2D Matrix containing all odd numbers it is shown,\nthat the 3N+1-Problem is in fact a problem concerning Groups of values not only\nindividual Values. This implicates, that it is possible to predict the number\nof Operations needed to reach the Origo/Root 1 for a Group of values, if the\nnumber of Operations is known for a single Value in the Group.\n  After the formal analysis of the 'Double 2D Matrix' a group of five\nAlgorithms is investigated.\n  Based on the fact that two different Algorithms (with different rules) can\ngenerate identical Odd-Series and the fact that two of the Algorithms are able\nto generate identical 'Branching-codes' in both directions UP and DOWN in a\n(binary) Tree, it is concluded that Collatz Conjecture is True.\n"}
+{"abstract": "  Based on a self-consistent $t$-matrix approximation, we explore the influence\nof magnetic and nonmagnetic doping on the surface electronic states and\nconductivity of topological insulators. We show that warping parameter has a\ncrucial impact on the density of states and dc conductivity of the doped\nsurfaces. As the warping strength is increased, the surface density of states\nat high energies is suppressed and the resonant states induced by impurities in\nthe vicinity of the Dirac point gradually disappear. It is found that\nnonmagnetic impurities break electron-hole symmetry at low warping strength,\nwhile the symmetry remains unchanged when the surface is magnetically doped.\nOur findings reveal that surface conductivity can be controlled by tuning the\ndoping, the direction of external magnetic field and that of impurity magnetic\nmoments. Also, the surface conductivity features in topological insulators with\nwarped energy dispersions are not significantly affected by the presence of\nimpurities compared to that of materials with circular energy contour.\n"}
+{"abstract": "  In the present paper we propose two new algorithms of tensor completion for\nthree-order tensors. The proposed methods consist in minimizing the average\nrank of the underlying tensor using its approximate function namely the tensor\nnuclear norm and then the recovered data will be obtained by using the total\nvariation regularisation technique. We will adopt the Alternating Direction\nMethod of Multipliers (ADM), using the tensor T-product, to solve the main\noptimization problems associated to the two algorithms. In the last section, we\npresent some numerical experiments and comparisons with the most known image\ncompletion methods.\n"}
+{"abstract": "  In this study, noise generation is investigated for a generic voluteless\ncentrifugal HVAC fan at an off-design operation point where tonal noise\nincreases. The simulations are performed by coupling IDDES with the FW-H\nacoustic analogy, and the experiments are conducted in a rig consisting of a\nplenum chamber and a reverberation room. In contrast to typical tonal noise\nsources induced by the fan blades, we find out that another predominant source\nis the turbulence stemming from the gap between the fan shroud and the inlet\nduct. The turbulence evolves along with the shroud and is swept downstream to\ninteract with the top side of the blade leading edge. The interaction accounts\nfor uneven surface pressure distribution on the blades. Moreover, the pressure\nis significantly unsteady near the shroud. The power spectral density (PSD) of\nthe noise shows obvious tones at 273Hz that is approximately equal to the\ndifference of the blade passing frequency (BPF0) and the fan rotation\nfrequency. By coarsening the mesh resolution near the inlet gap and shroud, we\nartificially deactivate the gap turbulence in the numerical simulations and,\nconsequently, detect that the tone at 273Hz disappears completely. At this\nfrequency, the PSD contours of surface pressure fluctuations are found potent\nat the inlet gap and the blade top side only if the gap turbulence is resolved.\nThese findings indicate that the tonal noise source at 273Hz is the interaction\nbetween the gap turbulence and blades. As the gap turbulence exists near the\nshroud wall upstream of the blades, the rotating wall introduces rotational\nmomentum into the turbulence due to the wall friction. Hence the tonal\nfrequency of the interaction is smaller than BPF0 with a decrement of the fan\nrotation frequency. To the authors' knowledge, it is the first time that\nvoluteless centrifugal fans are studied for the noise generation from the gap\nturbulence.\n"}
+{"abstract": "  Modeling thematic fit (a verb--argument compositional semantics task)\ncurrently requires a very large burden of data. We take a high-performing\nneural approach to modeling verb--argument fit, previously trained on a\nlinguistically machine-annotated large corpus, and replace corpus layers with\noutput from higher-quality taggers. Contrary to popular beliefs that, in the\ndeep learning era, more data is as effective as higher quality annotation, we\ndiscover that higher annotation quality dramatically reduces our data\nrequirement while demonstrating better supervised predicate-argument\nclassification. But in applying the model to a psycholinguistic task outside\nthe training objective, we saw only small gains in one of two thematic fit\nestimation tasks, and none in the other. We replicate previous studies while\nmodifying certain role representation details, and set a new state-of-the-art\nin event modeling, using a fraction of the data.\n"}
+{"abstract": "  In quantum mechanics time usually appears as classical parameter which means\nthat it is treated as being essentially different from spatial coordinates that\nare represented by operators. On the other hand, relativity theory demands to\ntreat space and time on an equal footing and there are many approaches to the\nproblem how to introduce time operator. In order to support these approaches we\npropose the experiment that will clearly expose the equivalence of space and\ntime regarding the relation towards quantum interference. The proposed\nexperiment can show that it is possible to have superposition of being created\nin two different moments in time, and by measuring standard interference\npattern, it will be shown that there is interference in time. It is similar to\nthe case of verification of the possibility of single quantum system to be in\nthe state that is superposition of two different positions in space. If so,\nthis would mean that there should be exactly the same formal approach to the\nspace coordinates and time in quantum mechanics since they are all quantum in\ntheir nature.\n"}
+{"abstract": "  The tunable bandgap from 0.3 eV to 2 eV of black phosphorus (BP) makes it to\nfill the gap in graphene. When studying the properties of BP more\ncomprehensive, scientists have discovered that many two-dimensional materials,\nsuch as tellurene, antimonene, bismuthene, indium selenide and tin sulfide,\nhave similar structures and properties to black phosphorus thus called black\nphosphorus analogs. In this review, we briefly introduce preparation methods of\nblack phosphorus and its analogs, with emphasis on the method of mechanical\nexfoliation (ME), liquid phase exfoliation (LPE) and chemical vapor deposition\n(CVD). And their characterization and properties according to their\nclassification of single-element materials and multi-element materials are\ndescribed. We focus on the performance of passively mode-locked fiber lasers\nusing BP and its analogs as saturable absorbers (SA) and demonstrated this part\nthrough classification of working wavelength. Finally, we introduce the\napplication of BP and its analogs, and discuss their future research prospects.\n"}
+{"abstract": "  Tolerance estimation problems are prevailing in engineering applications. For\nexample, in modern robotics, it remains challenging to efficiently estimate\njoint tolerance, \\ie the maximal allowable deviation from a reference robot\nstate such that safety constraints are still satisfied. This paper presented an\nefficient algorithm to estimate the joint tolerance using sum-of-squares\nprogramming. It is theoretically proved that the algorithm provides a tight\nlower bound of the joint tolerance. Extensive numerical studies demonstrate\nthat the proposed method is computationally efficient and near optimal. The\nalgorithm is implemented in the JTE toolbox and is available at\n\\url{https://github.com/intelligent-control-lab/Sum-of-Square-Safety-Optimization}.\n"}
+{"abstract": "  We revisit the pairing model of state spaces with new emergent states\nintroduced in J. Phys. A: Math. Theor. 51 375002, 2018.\n  We facilitate our analysis by introducing a simplified pairing model\nconsisting of balls able to form pairs but without any internal structure.\n  For both the simplified and the original model we compute exactly the\nprobability distribution for observing a state with $n_p$ pairs. We show this\ndistribution satisfies a large deviation principle with speed $n \\ln(n)$. We\npresent closed form expressions for a variety of statistical quantities\nincluding moments and marginal distributions.\n"}
+{"abstract": "  The observed emission lines of Be stars originate from a circumstellar\nKeplerian disk that are generally well explained by the Viscous Decretion Disk\nmodel. In an earlier work we performed the modeling of the full light curve of\nthe bright Be star $\\omega$ CMa (Ghoreyshi et al. 2018) with the 1-D\ntime-dependent hydrodynamics code SINGLEBE and the Monte Carlo\nradiative-transfer code HDUST.\n  We used the V -band light curve that probes the inner disk through four disk\nformation and dissipation cycles. This new study compares predictions of the\nsame set of model parameters with time-resolved photometry from the near UV\nthrough the mid-infrared, comprehensive series of optical spectra, and optical\nbroad-band polarimetry, that overall represent a larger volume of the disk.\nQualitatively, the models reproduce the trends in the observed data due to the\ngrowth and decay of the disk. However, quantitative differences exist, e.g., an\noverprediction of the flux increasing with wavelength, too slow decreases in\nBalmer emission-line strength that are too slow during disk dissipation, and\nthe discrepancy between the range of polarimetric data and the model. We find\nthat a larger value of the viscosity parameter alone, or a truncated disk by a\ncompanion star, reduces these discrepancies by increasing the dissipation rate\nin the outer regions of the disk.\n"}
+{"abstract": "  High-level synthesis (HLS) is a key component for the hardware acceleration\nof applications, especially thanks to the diffusion of reconfigurable devices\nin many domains, from data centers to edge devices. HLS reduces development\ntimes by allowing designers to raise the abstraction level and use automated\nmethods for hardware generation. Since security concerns are becoming more and\nmore relevant for data-intensive applications, we investigate how to abstract\nsecurity properties and use HLS for their integration with the accelerator\nfunctionality. We use the case of dynamic information flow tracking, showing\nhow classic software-level abstractions can be efficiently used to hide\nimplementation details to the designers.\n"}
+{"abstract": "  Many popular open source programming languages (Perl, Ruby or Python for\nexample) have systems for distributing packaged source code that software\ndevelopers can use when working in that particular programming language. This\npaper will consider the design principals that should be followed if designing\nsuch an open source code repository.\n"}
+{"abstract": "  Bottom-up design of high-entropy ceramics is a promising approach for\nrealizing materials with unique combination of high hardness and\nfracture-resistance at elevated temperature. This work offers a simple yet\nfundamental design criterion - valence electron concentration (VEC) > ~9.5\ne-/f.u. to populate bonding metallic states at the Fermi level - for selecting\nelemental compositions that may form rocksalt-structure (B1) high-entropy\nceramics with enhanced plasticity (reduced brittleness). Single-phase B1\n(HfTaTiWZr)C and (MoNbTaVW)C, chosen as representative systems due to their\nspecific VEC values, are here synthesized and tested. Nanoindentation arrays at\nvarious loads and depths statistically show that (HfTaTiWZr)C (VEC=8.6 e-/f.u.)\nis hard but brittle, whilst (MoNbTaVW)C (VEC=9.4 e-/f.u.) is hard and\nconsiderably more resistant to fracture than (HfTaTiWZr)C. Ab initio molecular\ndynamics simulations and electronic-structure analysis reveal that the improved\nfracture-resistance of (MoNbTaVW)C subject to tensile and shear deformation may\noriginate from the intrinsic material's ability to undergo local lattice\ntransformations beyond tensile yield points, as well as from relatively facile\nactivation of lattice slip. Additional simulations, carried out to follow the\nevolution in mechanical properties as a function of temperature, suggest that\n(MoNbTaVW)C may retain good resistance to fracture up to ~900-1200K, whereas\n(HfTaTiWZr)C is predicted to remain brittle at all investigated temperatures.\n"}
+{"abstract": "  It is widely known that Google Trends have become one of the most popular\nfree tools used by forecasters both in academics and in the private and public\nsectors. There are many papers, from several different fields, concluding that\nGoogle Trends improve forecasts' accuracy. However, what seems to be widely\nunknown, is that each sample of Google search data is different from the other,\neven if you set the same search term, data and location. This means that it is\npossible to find arbitrary conclusions merely by chance. This paper aims to\nshow why and when it can become a problem and how to overcome this obstacle.\n"}
+{"abstract": "  We present a general method to efficiently design quantum control procedures\nfor non-Markovian problems and illustrate it by optimizing the shape of a laser\npulse to prepare a quantum dot in a specific state. The optimization of open\nquantum system dynamics with strong coupling to structured environments --\nwhere time-local descriptions fail -- is a computationally challenging task. We\nmodify the numerically exact time evolving matrix product operator (TEMPO)\nmethod, such that it allows the repeated computation of the time evolution of\nthe reduced system density matrix for various sets of control parameters at\nvery low computational cost. This method is potentially useful for studying\nnumerous quantum optimal control problems, in particular in solid state quantum\ndevices where the coupling to vibrational modes is typically strong.\n"}
+{"abstract": "  Let $X$ be an arbitrary set and let $T(X)$ denote the full transformation\nmonoid on $X$. We prove that an element of $T(X)$ is unit-regular if and only\nif it is semi-balanced. For infinite $X$, we discuss regularity of the\nsubmonoid of $T(X)$ consisting of all injective (resp. surjective)\ntransformations. For a partition $\\mathcal{P}$ of $X$, we characterize\nunit-regular elements in the monoid $T(X, \\mathcal{P})$, under composition,\ndefined as \\[T(X, \\mathcal{P}) = \\{f\\in T(X)\\mid (\\forall X_i \\in \\mathcal{P})\n(\\exists X_j \\in \\mathcal{P})\\; X_i f \\subseteq X_j\\}.\\] We also characterize\n(unit-)regular elements in various known submonoids of $T(X, \\mathcal{P})$.\n"}
+{"abstract": "  Convolutional networks (ConvNets) have achieved promising accuracy for\nvarious anatomical segmentation tasks. Despite the success, these methods can\nbe sensitive to data appearance variations. Considering the large variability\nof scans caused by artifacts, pathologies, and scanning setups, robust ConvNets\nare vital for clinical applications, while have not been fully explored. In\nthis paper, we propose to mitigate the challenge by enabling ConvNets'\nawareness of the underlying anatomical invariances among imaging scans.\nSpecifically, we introduce a fully convolutional Constraint Adoption Module\n(CAM) that incorporates probabilistic atlas priors as explicit constraints for\npredictions over a locally connected Conditional Random Field (CFR), which\neffectively reinforces the anatomical consistency of the labeling outputs. We\ndesign the CAM to be flexible for boosting various ConvNet, and compact for\nco-optimizing with ConvNets for fusion parameters that leads to the optimal\nperformance. We show the advantage of such atlas priors fusion is two-fold with\ntwo brain parcellation tasks. First, our models achieve state-of-the-art\naccuracy among ConvNet-based methods on both datasets, by significantly\nreducing structural abnormalities of predictions. Second, we can largely boost\nthe robustness of existing ConvNets, proved by: (i) testing on scans with\nsynthetic pathologies, and (ii) training and evaluation on scans of different\nscanning setups across datasets. Our method is proposing to be easily adopted\nto existing ConvNets by fine-tuning with CAM plugged in for accuracy and\nrobustness boosts.\n"}
+{"abstract": "  We discuss recent experiments performed with an upgraded version of the main\nmagnetic focus ion source (MaMFIS) at the Joint Institute for Nuclear Research\n(JINR) in Dubna. The device operates in the range of electron beam energies\nextended up to $40$ keV. The achieved electron current densities are of the\norder of $10$ kA/cm$^{2}$. This assessment is consistent both with the very\nshort ionization time and with the utmost ionization degree of the produced\nhighly charged ions. Due to its high efficiency, the MaMFIS technology is\nespecially promising for ionization of short-lived radionuclides and heavy\nelements. A new scheme for charge breeding is proposed.\n"}
+{"abstract": "  Partially Observable Markov Decision Processes (POMDP) is a widely used model\nto represent the interaction of an environment and an agent, under state\nuncertainty. Since the agent does not observe the environment state, its\nuncertainty is typically represented through a probabilistic belief. While the\nset of possible beliefs is infinite, making exact planning intractable, the\nbelief space's complexity (and hence planning complexity) is characterized by\nits covering number. Many POMDP solvers uniformly discretize the belief space\nand give the planning error in terms of the (typically unknown) covering\nnumber. We instead propose an adaptive belief discretization scheme, and give\nits associated planning error. We furthermore characterize the covering number\nwith respect to the POMDP parameters. This allows us to specify the exact\nmemory requirements on the planner, needed to bound the value function error.\nWe then propose a novel, computationally efficient solver using this scheme. We\ndemonstrate that our algorithm is highly competitive with the state of the art\nin a variety of scenarios.\n"}
+{"abstract": "  We propose and demonstrate a fast, robust method for using satellite images\nto localize an Unmanned Aerial Vehicle (UAV). Previous work using satellite\nimages has large storage and computation costs and is unable to run in real\ntime. In this work, we collect Google Earth (GE) images for a desired flight\npath offline and an autoencoder is trained to compress these images to a\nlow-dimensional vector representation while retaining the key features. This\ntrained autoencoder is used to compress a real UAV image, which is then\ncompared to the precollected, nearby, autoencoded GE images using an\ninner-product kernel. This results in a distribution of weights over the\ncorresponding GE image poses and is used to generate a single localization and\nassociated covariance to represent uncertainty. Our localization is computed in\n1% of the time of the current standard and is able to achieve a comparable RMSE\nof less than 3m in our experiments, where we robustly matched UAV images from\nsix runs spanning the lighting conditions of a single day to the same map of\nsatellite images.\n"}
+{"abstract": "  Emulsion droplets trapped in an ultrasonic levitator behave in two ways that\nsolid spheres do not: (1) Individual droplets spin rapidly about an axis\nparallel to the trapping plane, and (2) coaxially spinning droplets form long\nchains aligned with their common axis of rotation. Acoustically-organized\nchains interact hydrodynamically, either to merge into longer chains or to form\nthree-dimensional bundles of chains. Solid spheres, by contrast, form\nclose-packed planar crystals drawn together by the sound-mediated secondary\nBjerknes interaction. We demonstrate the chain-to-crystal transition with a\nmodel system in which fluid emulsion droplets can be photopolymerized into\nsolid spheres without significantly changing other material properties. The\nbehavior of this experimental system is quantitatively consistent with an\nacoustohydrodynamic model for spinning spheres in an acoustic levitator. This\nstudy therefore introduces acoustically-driven spinning as a mechanism for\nguiding self-organization of acoustically levitated matter.\n"}
+{"abstract": "  We consider the Goursat problem for linear partial differential equations\nwith constant coefficients in two complex variables. We find the conditions for\nsummable solutions of the Goursat problem in the case when the Newton polygon\nhas exactly one side with a positive slope.\n"}
+{"abstract": "  Ensembles of machine learning models yield improved system performance as\nwell as robust and interpretable uncertainty estimates; however, their\ninference costs may often be prohibitively high. \\emph{Ensemble Distribution\nDistillation} is an approach that allows a single model to efficiently capture\nboth the predictive performance and uncertainty estimates of an ensemble. For\nclassification, this is achieved by training a Dirichlet distribution over the\nensemble members' output distributions via the maximum likelihood criterion.\nAlthough theoretically principled, this criterion exhibits poor convergence\nwhen applied to large-scale tasks where the number of classes is very high. In\nour work, we analyze this effect and show that the Dirichlet log-likelihood\ncriterion classes with low probability induce larger gradients than\nhigh-probability classes. This forces the model to focus on the distribution of\nthe ensemble tail-class probabilities. We propose a new training objective that\nminimizes the reverse KL-divergence to a \\emph{Proxy-Dirichlet} target derived\nfrom the ensemble. This loss resolves the gradient issues of Ensemble\nDistribution Distillation, as we demonstrate both theoretically and empirically\non the ImageNet and WMT17 En-De datasets containing 1000 and 40,000 classes,\nrespectively.\n"}
+{"abstract": "  In this paper, dyadic Green's function for a graphene-dielectric stack is\nformulated based on the scattering superposition method. To this end,\nscattering Green's function in each layer is expanded in terms of cylindrical\nvector wave functions with unknown coefficients. Using the Kronecker delta\nfunction in the field expansion, it is considered that the field and source\npoints lie in the arbitrary layers. Afterward, recurrence relations for\ncalculating the unknown expansion coefficients are derived by applying the\nimpedance boundary conditions at the interface of a graphene sheet surrounded\nby two adjacent dielectric layers. The verification of the calculated\ncoefficients is conducted by utilizing them in the analysis of graphene-based\nstructures with different numbers of layers, including 1) free-standing\nfrequency-selective surfaces (FSSs) and 2) parallel plates (PPs) with graphene\nwalls. A potential application of our proposed structure is investigating the\ninteraction of donor-acceptor pairs resided in the arbitrary layers of the\ngraphene-dielectric stack with a desired number of layers.\n"}
+{"abstract": "  Portfolio management aims at maximizing the return on investment while\nminimizing risk by continuously reallocating the assets forming the portfolio.\nThese assets are not independent but correlated during a short time period. A\ngraph convolutional reinforcement learning framework called DeepPocket is\nproposed whose objective is to exploit the time-varying interrelations between\nfinancial instruments. These interrelations are represented by a graph whose\nnodes correspond to the financial instruments while the edges correspond to a\npair-wise correlation function in between assets. DeepPocket consists of a\nrestricted, stacked autoencoder for feature extraction, a convolutional network\nto collect underlying local information shared among financial instruments, and\nan actor-critic reinforcement learning agent. The actor-critic structure\ncontains two convolutional networks in which the actor learns and enforces an\ninvestment policy which is, in turn, evaluated by the critic in order to\ndetermine the best course of action by constantly reallocating the various\nportfolio assets to optimize the expected return on investment. The agent is\ninitially trained offline with online stochastic batching on historical data.\nAs new data become available, it is trained online with a passive concept drift\napproach to handle unexpected changes in their distributions. DeepPocket is\nevaluated against five real-life datasets over three distinct investment\nperiods, including during the Covid-19 crisis, and clearly outperformed market\nindexes.\n"}
+{"abstract": "  Lagrangian cobordisms between Legendrian knots arise in Symplectic Field\nTheory and impose an interesting and not well-understood relation on Legendrian\nknots. There are some known \"elementary\" building blocks for Lagrangian\ncobordisms that are smoothly the attachment of $0$- and $1$-handles. An\nimportant question is whether every pair of non-empty Legendrians that are\nrelated by a connected Lagrangian cobordism can be related by a ribbon\nLagrangian cobordism, in particular one that is \"decomposable\" into a\ncomposition of these elementary building blocks. We will describe these and\nother combinatorial building blocks as well as some geometric methods,\ninvolving the theory of satellites, to construct Lagrangian cobordisms. We will\nthen survey some known results, derived through Heegaard Floer Homology and\ncontact surgery, that may provide a pathway to proving the existence of\nnondecomposable (nonribbon) Lagrangian cobordisms.\n"}
+{"abstract": "  We study the dynamics of a generic endomorphism $f$ of an Oka-Stein manifold\n$X$. Such manifolds include all connected linear algebraic groups and, more\ngenerally, all Stein homogeneous spaces of complex Lie groups. We give several\ndescriptions of the Fatou set and the Julia set of $f$. In particular, we show\nthat the Julia set is the derived set of the set of attracting periodic points\nof $f$ and that it is also the closure of the set of repelling periodic points\nof $f$. Among other results, we prove that $f$ is chaotic on the Julia set and\nthat every periodic point of $f$ is hyperbolic. We also give an explicit\ndescription of the \"Conley decomposition\" of $X$ induced by $f$ into\nchain-recurrence classes and basins of attractors. For $X=\\mathbb{C}$, we prove\nthat every Fatou component is a disc and that every point in the Fatou set is\nattracted to an attracting cycle or lies in a dynamically bounded wandering\ndomain (whether such domains exist is an open question).\n"}
+{"abstract": "  Integrated access and backhaul (IAB) networks have the potential to provide\nhigh data rate in both access and backhaul networks by sharing the same\nspectrum. Due to the dense deployment of small base stations (SBSs), IAB\nnetworks connect SBSs to the core network in a wireless manner without the\ndeployment of high-cost optical fiber. As large spectrum is available in mmWave\nbands and high data rate is achieved by using directional beamforming, the\naccess and backhaul links can be integrated in the same frequency band while\nsatisfying quality-of-service constraints. In this work, we optimize the\nscheduling of access and backhaul links such that the minimum throughput of the\naccess links is maximized based on the revised simplex method. By considering a\nprobability based line-ofsight (LOS) and non-line-of-sight (NLOS) path loss\nmodel and the antenna array gains, we compare the achievable minimum access\nthroughput of the IAB network with the network with only macro base stations,\nand study the effect of the network topology and antenna parameters on the\nachievable minimum throughput. Simulation results show that, for a broad range\nof parameter settings, the implementation of IABs improves the access minimum\nachievable throughput.\n"}
+{"abstract": "  What fits the human brain does not necessarily mean whatever is invented is\nmeant for human brain. This is exactly what is implied by the definition of\nalgorithm: \\textit{Reducing and Simplifying}. Our cognitive apparatus, although\nendowed with certain parameters, ceases to comprehend chains of mathematical\nand logical steps used for algorithmic or mathematical purposes. Such a\ncognitive limit allows us to pay attention to the symbolic regularities which\nare meaningful. The more meaningless these symbols become, the fuzzier is our\nmental representation for it. Needless to say, that we actually tend to\ninteract with activities that are meaningful, and discard those that are\nmeaningless. By activities we mean steps, steps that form an algorithm. Could\nit be just an assumption? Are we really aversing from algorithms in modern day\nlife?\n  --\n  Ziel dieser Arbeit war eine kritische Auseinandersetzung mit dem\nbiokybernetischen Denkansatz anhand des Sensitivit\\\"atsmodells von Frederic\nVester, das in die sozioinformatische Analyse eines sozio-technischen Systems\nintegriert und hinsichtlich der Fragestellung gepr\\\"uft wurde, inwieweit sich\ndieses Konzept zur Beurteilung von Planungsvorhaben bei sozio-technischen\nSystemen eignet. Dazu wurden F. Vesters Biokybernetischenn Grundregeln zur\nFeststellung der \\\"Uberlebensf\\\"ahigkeit eines Systems, auf ihre Anwendbarkeit\nin Bezug auf sozio-technische Systeme untersucht. Die Funktionalit\\\"at der\nAnalysemethode wurde auf das nach der Definition von Andrea Kienle und Gabriele\nKunau als sozio-technisches System charakterisierte Unternehmen Uber\nangewendet. Im Gegensatz zu unsystemischen Analysen besteht der Vorteil des\nSensitivit\\\"atsmodells in der Vermittlung von Erkenntnissen \\\"uber innere\nSystemzusammenh\\\"ange. Abschlie{\\ss}end wurde der vorgestellte Analyseansatz\nals transdisziplin\\\"are Weiterentwicklung des Sensitivit\\\"atsmodells\ncharakterisiert.\n"}
+{"abstract": "  A prototype of a single-gap glass Resistive Plate Chamber (RPC) is\nconstructed by the authors. To find the considerations required for better\noperation of the detector's gas system, we have simulated the flow of the Ar\ngas through the detector by using computational fluid dynamic methods.\nSimulations show that the pressure inside the chamber linearly depends on the\ngas flow rate and the chamber's output hose length. The simulation results were\ncompatible with experiments. We have found that the pressure-driven speed of\nthe gas molecules is two orders of magnitude larger in the inlet and outlet\nregions than the blocked corners of a 14x14 cm2 chamber, and most likely seems\nto be higher for larger detectors and different geometries.\n"}
+{"abstract": "  Daisy (Data Analysis Integrated Software System) has been designed for the\nanalysis and visualization of the X-ray experiments. To address an extensive\nrange of Chinese radiation facilities community's requirements from purely\nalgorithmic problems to scientific computing infrastructure, Daisy sets up a\ncloud-native platform to support on-site data analysis services with fast\nfeedback and interaction. The plugs-in based application is convenient to\nprocess the expected high throughput data flow in parallel at next-generation\nfacilities such as the High Energy Photon Source (HEPS). The objectives,\nfunctionality and architecture of Daisy are described in this article.\n"}
+{"abstract": "  A recent work [Phys. Rev. Lett. 125, 110602] showed that among a pair of\n\\textit{thermodynamically} equidistant quenches from a colder and a hotter\ninitial state at a fixed ambient temperature, the relaxation from the colder\ninitial state (\\textit{uphill} relaxation) is always faster, for dynamics close\nto stable minima. Here we show that this is not generically the case for open\nquantum systems with two or three energy levels. We find that both faster\nuphill and faster downhill relaxation and symmetric thermal relaxation can be\nobserved in equidistant quenches, depending on the transition rates and the\nchoice of the distance measure used. Furthermore, we obtain a phase diagram in\nthe parameter space for the three-level system corresponding to different\nthermalization behaviours.\n"}
+{"abstract": "  The Clustered Shortest-Path Tree Problem (CluSPT) plays an important role in\nvarious types of optimization problems in real-life. Recently, some\nMultifactorial Evolutionary Algorithm (MFEA) have been introduced to deal with\nthe CluSPT, however these researches still have some shortcomings such as\nevolution operators only perform on complete graphs, huge resource consumption\nfor finding the solution on large search spaces. To overcome these limitations,\nthis paper describes a MFEA-based approach to solve the CluSPT. The proposed\nalgorithm utilizes Dijkstra's algorithm to construct the spanning trees in\nclusters while using evolutionary operators for building the spanning tree\nconnecting clusters. This approach takes advantage of both exact and\napproximate algorithms so it enables the algorithm to function efficiently on\ncomplete and sparse graphs alike. Furthermore, evolutionary operators such as\nindividual encoding and decoding methods are also designed with great\nconsideration regarding performance and memory usage. We have included a proof\non the repairing method's efficacy in ensuring all solutions are valid. We have\nconducted tests on various types of Euclidean instances to assess the\neffectiveness of the proposed algorithm and methods. Experiment results point\nout the effectiveness of the proposed algorithm existing heuristic algorithms\nin most of the test cases. The impact of the proposed MFEA was analyzed and a\npossible influential factor that may be useful for further study was also\npointed out.\n"}
+{"abstract": "  Successful adoption of deep learning (DL) in the wild requires models to be:\n(1) compact, (2) accurate, and (3) robust to distributional shifts.\nUnfortunately, efforts towards simultaneously meeting these requirements have\nmostly been unsuccessful. This raises an important question: Is the inability\nto create Compact, Accurate, and Robust Deep neural networks (CARDs)\nfundamental? To answer this question, we perform a large-scale analysis of\npopular model compression techniques which uncovers several intriguing\npatterns. Notably, in contrast to traditional pruning approaches (e.g., fine\ntuning and gradual magnitude pruning), we find that \"lottery ticket-style\"\napproaches can surprisingly be used to produce CARDs, including binary-weight\nCARDs. Specifically, we are able to create extremely compact CARDs that,\ncompared to their larger counterparts, have similar test accuracy and matching\n(or better) robustness -- simply by pruning and (optionally) quantizing.\nLeveraging the compactness of CARDs, we develop a simple domain-adaptive\ntest-time ensembling approach (CARD-Decks) that uses a gating module to\ndynamically select appropriate CARDs from the CARD-Deck based on their\nspectral-similarity with test samples. The proposed approach builds a \"winning\nhand'' of CARDs that establishes a new state-of-the-art (on RobustBench) on\nCIFAR-10-C accuracies (i.e., 96.8% standard and 92.75% robust) and CIFAR-100-C\naccuracies (80.6% standard and 71.3% robust) with better memory usage than\nnon-compressed baselines (pretrained CARDs and CARD-Decks available at\nhttps://github.com/RobustBench/robustbench). Finally, we provide theoretical\nsupport for our empirical findings.\n"}
+{"abstract": "  We use sub-arcsecond resolution ($\\sim$0.4$''$) observations with NOEMA at\n1.37 mm to study the dust emission and molecular gas of 18 high-mass\nstar-forming regions. We combine the derived physical and chemical properties\nof individual cores in these regions to estimate their ages. The temperature\nstructure of these regions are determined by fitting H2CO and CH3CN line\nemission. The density profiles are inferred from the 1.37 mm continuum\nvisibilities. The column densities of 11 different species are determined by\nfitting the emission lines with XCLASS. Within the 18 observed regions, we\nidentify 22 individual cores with associated 1.37 mm continuum emission and\nwith a radially decreasing temperature profile. We find an average temperature\npower-law index of q = 0.4$\\pm$0.1 and an average density power-law index of p\n= 2.0$\\pm$0.2 on scales on the order of several 1 000 au. Comparing these\nresults with values of p derived in the literature suggest that the density\nprofiles remain unchanged from clump to core scales. The column densities\nrelative to N(C18O) between pairs of dense gas tracers show tight correlations.\nWe apply the physical-chemical model MUSCLE to the derived column densities of\neach core and find a mean chemical age of $\\sim$60 000 yrs and an age spread of\n20 000-100 000 yrs. With this paper we release all data products of the CORE\nproject available at https://www.mpia.de/core. The CORE sample reveals well\nconstrained density and temperature power-law distributions. Furthermore, we\ncharacterize a large variety in molecular richness that can be explained by an\nage spread confirmed by our physical-chemical modeling. The hot molecular cores\nshow the most emission lines, but we also find evolved cores at an evolutionary\nstage, in which most molecules are destroyed and thus the spectra appear\nline-poor again.\n"}
+{"abstract": "  In this paper, we propose the beginnings of a formal framework for modeling\nnarrative \\textit{qua} narrative. Our framework affords the ability to discuss\nkey qualities of stories and their communication, including the flow of\ninformation from a Narrator to a Reader, the evolution of a Reader's story\nmodel over time, and Reader uncertainty. We demonstrate its applicability to\ncomputational narratology by giving explicit algorithms for measuring the\naccuracy with which information was conveyed to the Reader and two novel\nmeasurements of story coherence.\n"}
+{"abstract": "  Design and construction of the instruments for ESO's Extremely Large\nTelescope (ELT) began in 2015. We present here a brief overview of the status\nof the ELT Instrumentation Plan. Dedicated articles on each instrument are\npresented elsewhere this volume.\n"}
+{"abstract": "  Time-dependent density functional theory has emerged as a method of choice\nfor calculations of spectra and response properties in physics, chemistry, and\nbiology, with its system-size scaling enabling computations on systems much\nlarger than possible otherwise. While increasingly complex and interesting\nsystems have been successfully tackled with relatively simple functional\napproximations, there has also been increasing awareness that these functionals\ntend to fail for certain classes of approximations. I review the fundamental\nchallenges the approximate functionals have in describing double-excitations\nand charge-transfer excitations, which are two of the most common impediments\nfor the theory to be applied in a black box way. At the same time, I describe\nthe progress made in recent decades in developing functional approximations\nthat give useful predictions for these excitations.\n"}
+{"abstract": "  A two-step concept for 3D segmentation on 5 abdominal organs inside\nvolumetric CT images is presented. First each relevant organ's volume of\ninterest is extracted as bounding box. The extracted volume acts as input for a\nsecond stage, wherein two compared U-Nets with different architectural\ndimensions re-construct an organ segmentation as label mask. In this work, we\nfocus on comparing 2D U-Nets vs. 3D U-Net counterparts. Our initial results\nindicate Dice improvements of about 6\\% at maximum. In this study to our\nsurprise, liver and kidneys for instance were tackled significantly better\nusing the faster and GPU-memory saving 2D U-Nets. For other abdominal key\norgans, there were no significant differences, but we observe highly\nsignificant advantages for the 2D U-Net in terms of GPU computational efforts\nfor all organs under study.\n"}
+{"abstract": "  Electronic transport in Weyl semimetals is quite extraordinary due to the\ntopological property of the chiral anomaly generating the charge pumping\nbetween two distant Weyl nodes with opposite chiralities under parallel\nelectric and magnetic fields. Here, we develop a full nonequilibrium quantum\ntransport theory of the chiral anomaly, based on the fact that the chiral\ncharge pumping is essentially nothing but the Bloch oscillation. Specifically,\nby using the Keldysh nonequilibrium Green function method, it is shown that\nthere is a rich structure in the chiral anomaly transport, including the\nnegative magnetoresistance, the non-Ohmic behavior, the Esaki-Tsu peak, and\nfinally the resonant oscillation of the DC electric current as a function of\nelectric field, called the electric quantum oscillation. We argue that, going\nbeyond the usual behavior of linear response, the non-Ohmic behavior observed\nin BiSb alloys can be regarded as a precursor to the occurrence of electric\nquantum oscillation, which is both topologically and energetically protected in\nWeyl semimetals.\n"}
+{"abstract": "  Let $F$ be a distribution function on the integer lattice $\\mathbb{Z}$ and\n$S=(S_n)$ the random walk with step distribution $F$. Suppose $S$ is\noscillatory and denote by $U_{\\rm a}(x)$ and $u_{\\rm a}(x)$ the renewal\nfunction and sequence, respectively, of the strictly ascending ladder height\nprocess associated with $S$. Putting $A(x) =\\int_0^x [1-F(t)-F(-t)] dt$,\n$H(x)=1-F(x)+F(-x)$ we suppose\n  $$A(x)/\\big(xH(x)\\big) \\to -\\infty \\quad (x\\to\\infty).$$ Under some\nadditional regularity condition on the positive tail of $F$, we show that\n$$u_{\\rm a}(x) \\sim U_{\\rm a}(x)[1-F(x)]/|A(x)|$$ as $x\\to\\infty$ and uniformly\nfor $0\\leq x\\leq R\\in \\mathbb{Z}$, as $R\\to\\infty$ $$P [ S\\; \\mbox{leaves\n$[0,R]$ on its upper side}\\, |\\, S_0=x] \\, \\sim\\, c^{-1}A(x)u_{\\rm a}(x), $$\nwhere $c= \\sum_{n=1}^\\infty P[S_{n}>S_0;\\, S_k < S_0$ for $0<k<n]$ and the\nregularity condition is satisfied at least if $S$ is recurrent, $\\limsup\n[1-F(x)]/F(-x)<1$, and $x[1-F(x)]/L(x) $ ($x\\geq 1$) is bounded away from zero\nand infinity for some slowly varying function $L$. We also give some asymptotic\nestimates of the probability that $S$ visits $R$ before entering the negative\nhalf-line for asymptotically stable walks and obtain asymptotic behaviour of\nthe probability that $R$ is ever hit by $S$ conditioned to avoid the negative\nhalf-line forever.\n"}
+{"abstract": "  Minimizing the number of necessary two-qubit gates is an important task in\nquantum information processing. By introducing non-computational quantum states\nin auxiliary spaces, we construct effective circuits for the controlled-NOT\n(CNOT) gate and the n-control-qubit Toffoli gate with (2n-1) qubit-qudit gates\nand (2n-2) single-qudit gates. We propose the polarization CNOT and Toffoli\ngates based on the designed quantum circuits in linear optics by operating on\nthe spatial-mode degree of freedom of photons. Our optical schemes can be\nachieved with a higher success probability and no extra auxiliary photons are\nneeded.\n"}
+{"abstract": "  This paper introduces a new class of right-angled Coxeter groups with totally\ndisconnected Morse boundaries. We construct this class recursively by examining\nhow the Morse boundary of a right-angled Coxeter group changes if we glue a\ngraph to its defining graph. More generally, we present a method to construct\namalgamated free products of CAT(0) groups with totally disconnected Morse\nboundaries that act geometrically on CAT(0) spaces that have a treelike block\ndecomposition.\n"}
+{"abstract": "  We apply the Noether Symmetry Approach to point-like teleparallel Lagrangians\nin view to derive minisuperspaces suitable for Quantum Cosmology. Adopting the\nArnowitt-Deser-Misner formalism, we find out related Wave Functions of the\nUniverse. Specifically, by means of appropriate changes of variables suggested\nby the existence of Noether symmetries, it is possible to obtain the\ncosmological Hamiltonians whose solutions are classical trajectories\ninterpretable as observable universes.\n"}
+{"abstract": "  Imitation learning (IL) enables robots to acquire skills quickly by\ntransferring expert knowledge, which is widely adopted in reinforcement\nlearning (RL) to initialize exploration. However, in long-horizon motion\nplanning tasks, a challenging problem in deploying IL and RL methods is how to\ngenerate and collect massive, broadly distributed data such that these methods\ncan generalize effectively. In this work, we solve this problem using our\nproposed approach called {self-imitation learning by planning (SILP)}, where\ndemonstration data are collected automatically by planning on the visited\nstates from the current policy. SILP is inspired by the observation that\nsuccessfully visited states in the early reinforcement learning stage are\ncollision-free nodes in the graph-search based motion planner, so we can plan\nand relabel robot's own trials as demonstrations for policy learning. Due to\nthese self-generated demonstrations, we relieve the human operator from the\nlaborious data preparation process required by IL and RL methods in solving\ncomplex motion planning tasks. The evaluation results show that our SILP method\nachieves higher success rates and enhances sample efficiency compared to\nselected baselines, and the policy learned in simulation performs well in a\nreal-world placement task with changing goals and obstacles.\n"}
+{"abstract": "  Jet reconstruction is critical for the precision measurement of Higgs boson\nproperties and the electroweak observables at the CEPC. We analyze the jet\nenergy and angular responses of benchmark 2- and 4-jet processes with fully\nsimulated samples with the CEPC baseline detector geometry. We observe a\nrelative resolution of 3.5$\\%$ and 1$\\%$ on the jet energy and angular\nmeasurement for jets in the detector barrel region ($|cos{\\theta}| < 0.6$) with\nenergy greater than 60 GeV. Meanwhile, the jet energy/angular scale can be\ncontrolled within 0.5/0.01$\\%$. The differential dependences of the jet\nresponse on the jet direction and energy are extracted. We also analyze the\nimpact on the jet responses induced by different jet clustering algorithms and\nmatching criteria, which yields a relative difference of up to 8$\\%$.\n"}
+{"abstract": "  This paper considers two types of boundary control problems for linear\ntransport equations. The first one shows that transport solutions on a\nsubdomain of a domain X can be controlled exactly from incoming boundary\nconditions for X under appropriate convexity assumptions. This is in contrast\nwith the only approximate control one typically obtains for elliptic equations\nby an application of a unique continuation property, a property which we prove\ndoes not hold for transport equations. We also consider the control of an\noutgoing solution from incoming conditions, a transport notion similar to the\nDirichlet-to-Neumann map for elliptic equations. We show that for well-chosen\ncoefficients in the transport equation, this control may not be possible. In\nsuch situations and by (Fredholm) duality, we obtain the existence of\nnon-trivial incoming conditions that are compatible with vanishing outgoing\nconditions.\n"}
+{"abstract": "  The field explainable artificial intelligence (XAI) has brought about an\narsenal of methods to render Machine Learning (ML) predictions more\ninterpretable. But how useful explanations provided by transparent ML methods\nare for humans remains difficult to assess. Here we investigate the quality of\ninterpretable computer vision algorithms using techniques from psychophysics.\nIn crowdsourced annotation tasks we study the impact of different\ninterpretability approaches on annotation accuracy and task time. We compare\nthese quality metrics with classical XAI, automated quality metrics. Our\nresults demonstrate that psychophysical experiments allow for robust quality\nassessment of transparency in machine learning. Interestingly the quality\nmetrics computed without humans in the loop did not provide a consistent\nranking of interpretability methods nor were they representative for how useful\nan explanation was for humans. These findings highlight the potential of\nmethods from classical psychophysics for modern machine learning applications.\nWe hope that our results provide convincing arguments for evaluating\ninterpretability in its natural habitat, human-ML interaction, if the goal is\nto obtain an authentic assessment of interpretability.\n"}
+{"abstract": "  Inspired by real-world applications such as the assignment of pupils to\nschools or the allocation of social housing, the one-sided matching problem\nstudies how a set of agents can be assigned to a set of objects when the agents\nhave preferences over the objects, but not vice versa. For fairness reasons,\nmost mechanisms use randomness, and therefore result in a probabilistic\nassignment. We study the problem of decomposing these probabilistic assignments\ninto a weighted sum of ex-post (Pareto-)efficient matchings, while maximizing\nthe worst-case number of assigned agents. This decomposition preserves all the\nassignments' desirable properties, most notably strategy-proofness. For a\nspecific class of probabilistic assignments, including the assignment by the\nProbabilistic Serial mechanism, we propose a polynomial-time algorithm for this\nproblem that obtains a decomposition in which all matchings assign at least the\nexpected number of assigned agents by the probabilistic assignment, rounded\ndown, thus achieving the theoretically best possible guarantee. For general\nprobabilistic assignments, the problem becomes NP-hard. For the Random Serial\nDictatorship mechanism, we show that the worst-case number of assigned agents\nis at least half of the optimal, and that this bound is asymptotically tight.\nLastly, we propose a column generation framework for the introduced problem,\nwhich we evaluate both on randomly generated data, and on real-world school\nchoice data from the Belgian cities Antwerp and Ghent.\n"}
+{"abstract": "  Sampling-based algorithms solve the path planning problem by generating\nrandom samples in the search-space and incrementally growing a connectivity\ngraph or a tree. Conventionally, the sampling strategy used in these algorithms\nis biased towards exploration to acquire information about the search-space. In\ncontrast, this work proposes an optimization-based procedure that generates new\nsamples to improve the cost-to-come value of vertices in a neighborhood. The\napplication of proposed algorithm adds an exploitative-bias to sampling and\nresults in a faster convergence to the optimal solution compared to other\nstate-of-the-art sampling techniques. This is demonstrated using benchmarking\nexperiments performed fora variety of higher dimensional robotic planning\ntasks.\n"}
+{"abstract": "  Face image manipulation via three-dimensional guidance has been widely\napplied in various interactive scenarios due to its semantically-meaningful\nunderstanding and user-friendly controllability. However, existing\n3D-morphable-model-based manipulation methods are not directly applicable to\nout-of-domain faces, such as non-photorealistic paintings, cartoon portraits,\nor even animals, mainly due to the formidable difficulties in building the\nmodel for each specific face domain. To overcome this challenge, we propose, as\nfar as we know, the first method to manipulate faces in arbitrary domains using\nhuman 3DMM. This is achieved through two major steps: 1) disentangled mapping\nfrom 3DMM parameters to the latent space embedding of a pre-trained StyleGAN2\nthat guarantees disentangled and precise controls for each semantic attribute;\nand 2) cross-domain adaptation that bridges domain discrepancies and makes\nhuman 3DMM applicable to out-of-domain faces by enforcing a consistent latent\nspace embedding. Experiments and comparisons demonstrate the superiority of our\nhigh-quality semantic manipulation method on a variety of face domains with all\nmajor 3D facial attributes controllable-pose, expression, shape, albedo, and\nillumination. Moreover, we develop an intuitive editing interface to support\nuser-friendly control and instant feedback. Our project page is\nhttps://cassiepython.github.io/cddfm3d/index.html\n"}
+{"abstract": "  It is well-known that a BKP tau-function is the square root of a certain KP\ntau-function, provided one puts the even KP times equal to zero. In this paper\nwe compute for all polynomial BKP tau-function its corresponding KP \"square\".\nWe also give, in the polynomial case, a representation theoretical proof of a\nrecent result by Alexandov, viz. that a KdV tau-function becomes a BKP\ntau-function when one divides all KdV times by 2.\n"}
+{"abstract": "  In shared spaces, motorized and non-motorized road users share the same space\nwith equal priority. Their movements are not regulated by traffic rules, hence\nthey interact more frequently to negotiate priority over the shared space. To\nestimate the safeness and efficiency of shared spaces, reproducing the traffic\nbehavior in such traffic places is important. In this paper, we consider and\ncombine different levels of interaction between pedestrians and cars in shared\nspace environments. Our proposed model consists of three layers: a layer to\nplan trajectories of road users; a force-based modeling layer to reproduce free\nflow movement and simple interactions; and a game-theoretic decision layer to\nhandle complex situations where road users need to make a decision over\ndifferent alternatives. We validate our model by simulating scenarios involving\nvarious interactions between pedestrians and cars and also car-to-car\ninteraction. The results indicate that simulated behaviors match observed\nbehaviors well.\n"}
+{"abstract": "  We construct infinite new classes of $AdS_4\\times S^1\\times S^5$ solutions of\ntype IIB string theory which have non-trivial $SL(2,\\mathbb{Z})$ monodromy\nalong the $S^1$ direction. The solutions are supersymmetric and holographically\ndual, generically, to $\\mathcal{N}=1$ SCFTs in $d=3$. The solutions are first\nconstructed as $AdS_4\\times \\mathbb{R}$ solutions in $D=5$ $SO(6)$ gauged\nsupergravity and then uplifted to $D=10$. Unlike the known $AdS_4\\times\n\\mathbb{R}$ S-fold solutions, there is no continuous symmetry associated with\nthe $\\mathbb{R}$ direction. The solutions all arise as limiting cases of Janus\nsolutions of $d=4$, $\\mathcal{N}=4$ SYM theory which are supported both by a\ndifferent value of the coupling constant on either side of the interface, as\nwell as by fermion and boson mass deformations. As special cases, the\nconstruction recovers three known S-fold constructions, preserving\n$\\mathcal{N}=1,2$ and 4 supersymmetry, as well as a recently constructed\n$\\mathcal{N}=1$ $AdS_4\\times S^1\\times S^5$ solution (not S-folded). We also\npresent some novel \"one-sided Janus\" solutions that are non-singular.\n"}
+{"abstract": "  In this paper, we develop a novel analytic method to prove the prime number\ntheorem in de la Vall\\'ee Poussin's form: $$\n\\pi(x)=\\operatorname{li}(x)+\\mathcal O(xe^{-c\\sqrt{\\log x}}) $$ Instead of\nperforming asymptotic expansion on Chebyshev functions as in conventional\nanalytic methods, this new approach uses contour-integration method to analyze\nRiemann's prime counting function $J(x)$, which only differs from $\\pi(x)$ by\n$\\mathcal O(\\sqrt x/\\log x)$.\n"}
+{"abstract": "  The viscous flow of two immiscible fluids in a porous medium on the Darcy\nscale is governed by a system of nonlinear parabolic equations. If infinite\nmobility of one phase can be assumed (e.g. in soil layers in contact with the\natmosphere) the system can be substituted by the scalar Richards model. Thus,\nthe domain of the porous medium may be partitioned into disjoint subdomains\nwith either the full two-phase or the simplified Richards model dynamics.\nExtending the one-model approach from [1, 2] we suggest coupling conditions for\nthis hybrid model approach. Based on an Euler implicit discretisation, a linear\niterative (-type) domain decomposition scheme is proposed, and proven to be\nconvergent. The theoretical findings are verified by a comparative numerical\nstudy that in particular confirms the efficiency of the hybrid ansatz as\ncompared to full two-phase model computations.\n"}
+{"abstract": "  IIoT services focused on industry-oriented services often require objects run\nmore than one task. IIoT objects poses the challenge of distributing and\nmanaging task allocation among them. The fairness of task allocation brings\nflexible network reconfiguration and maximizes the tasks to be performed.\nAlthough existing approaches optimize and manage the dynamics of objects, not\nall them consider both co-relationship between tasks and object capabilities\nand the distributed allocation over the cluster service. This paper introduces\nthe ACADIA mechanism for task allocation in IIoT networks in order to\ndistribute task among objects. It relies on relational consensus strategies to\nallocate tasks and similarity capabilities to determine which objects can play\nin accomplishing those tasks. Evaluation on NS-3 showed that ACADIA achieved\n98% of allocated tasks in an IIoT-Health considering all scenarios, average\nmore than 95% of clusters apt to performed tasks in a low response time, and\nachieved 50% more effectiveness in task allocation compared to the literature\nsolution CONTASKI.\n"}
+{"abstract": "  By the method of intense terahertz laser spectroscopy, we provide strong\nevidence that if an integer quantum Hall (IQH) system has asymmetric confining\npotential and the external quantizing magnetic field has a nonzero in-plane\ncomponent, then a quantum phase may arise with spatially-ordered\nquasi-one-dimensional orbit-like states similar to the current-carrying edging\nstates in conventional IQH system. The emergence of the phase may be\ninterpreted in terms of a quantum phase transition from the IQH system, which\nis accompanied by the breaking of translational symmetry in the direction of\nthe in-plane magnetic field. As a result, we get a fully-correlated macroscopic\nquantum object that gives one a unique opportunity to test the foundations of\nquantum mechanics (QM) at a macroscopic level. In particular, by means of this\nobject, we show experimentally that the concept of quantum spatial dynamics of\nelectrons without a definable trajectory remains relevant in the macro-world in\naccordance with the foundations of standard QM while an alternative description\nof such dynamics in terms of so-called Bohmian trajectories appears untenable.\n"}
+{"abstract": "  The role of gravity in human motor control is at the same time obvious and\ndifficult to isolate. It can be assessed by performing experiments in variable\ngravity. We propose that adiabatic invariant theory may be used to reveal\nnearly-conserved quantities in human voluntary rhythmic motion, an individual\nbeing seen as a complex time-dependent dynamical system with bounded motion in\nphase-space. We study an explicit realization of our proposal: An experiment in\nwhich we asked participants to perform $\\infty-$ shaped motion of their right\narm during a parabolic flight, either at self-selected pace or at a metronome's\ngiven pace. Gravity varied between $0$ and $1.8$ $g$ during a parabola. We\ncompute the adiabatic invariants in participant's frontal plane assuming a\nseparable dynamics. It appears that the adiabatic invariant in vertical\ndirection increases linearly with $g$, in agreement with our model. Differences\nbetween the free and metronome-driven conditions show that participants'\nadaptation to variable gravity is maximal without constraint. Furthermore,\nmotion in the participant's transverse plane induces trajectories that may be\nlinked to higher-derivative dynamics. Our results show that adiabatic\ninvariants are relevant quantities to show the changes in motor strategy in\ntime-dependent environments.\n"}
+{"abstract": "  The unprecedented sky coverage and observing cadence of the All-Sky Automated\nSurvey for SuperNovae (ASAS-SN) has resulted in the discovery and continued\nmonitoring of a large sample of Galactic transients. The vast majority of these\nare accretion-powered dwarf nova outbursts in cataclysmic variable systems, but\na small subset are thermonuclear-powered classical novae. Despite improved\nmonitoring of the Galaxy for novae from ASAS-SN and other surveys, the observed\nGalactic nova rate is still lower than predictions. One way classical novae\ncould be missed is if they are confused with the much larger population of\ndwarf novae. Here, we examine the properties of 1617 dwarf nova outbursts\ndetected by ASAS-SN and compare them to classical novae. We find that the mean\nclassical nova brightens by ~11 magnitudes during outburst, while the mean\ndwarf nova brightens by only ~5 magnitudes, with the outburst amplitude\ndistributions overlapping by roughly 15%. For the first time, we show that the\namplitude of an outburst and the time it takes to decline by two magnitudes\nfrom maximum are positively correlated for dwarf nova outbursts. For classical\nnovae, we find that these quantities are negatively correlated, but only\nweakly, compared to the strong anti-correlation of these quantities found in\nsome previous work. We show that, even if located at large distances, only a\nsmall number of putative dwarf novae could be mis-classified classical novae\nsuggesting that there is minimal confusion between these populations. Future\nspectroscopic follow-up of these candidates can show whether any are indeed\nclassical novae.\n"}
+{"abstract": "  We consider Hamiltonian simulation using the first order Lie-Trotter product\nformula under the assumption that the initial state has a high overlap with an\nenergy eigenstate, or a collection of eigenstates in a narrow energy band. This\nassumption is motivated by quantum phase estimation (QPE) and digital adiabatic\nsimulation (DAS). Treating the effective Hamiltonian that generates the\nTrotterized time evolution using rigorous perturbative methods, we show that\nthe Trotter step size needed to estimate an energy eigenvalue within precision\n$\\epsilon$ using QPE can be improved in scaling from $\\epsilon$ to\n$\\epsilon^{1/2}$ for a large class of systems (including any Hamiltonian which\ncan be decomposed as a sum of local terms or commuting layers that each have\nreal-valued matrix elements). For DAS we improve the asymptotic scaling of the\nTrotter error with the total number of gates $M$ from $\\mathcal{O}(M^{-1})$ to\n$\\mathcal{O}(M^{-2})$, and for any fixed circuit depth we calculate an\napproximately optimal step size that balances the error contributions from\nTrotterization and the adiabatic approximation. These results partially\ngeneralize to diabatic processes, which remain in a narrow energy band\nseparated from the rest of the spectrum by a gap, thereby contributing to the\nexplanation of the observed similarities between the quantum approximate\noptimization algorithm and diabatic quantum annealing at small system sizes.\nOur analysis depends on the perturbation of eigenvectors as well as\neigenvalues, and on quantifying the error using state fidelity (instead of the\nmatrix norm of the difference of unitaries which is sensitive to an overall\nglobal phase).\n"}
+{"abstract": "  Accurate force fields are necessary for predictive molecular simulations.\nHowever, developing force fields that accurately reproduce experimental\nproperties is challenging. Here, we present a machine learning directed,\nmultiobjective optimization workflow for force field parameterization that\nevaluates millions of prospective force field parameter sets while requiring\nonly a small fraction of them to be tested with molecular simulations. We\ndemonstrate the generality of the approach and identify multiple low-error\nparameter sets for two distinct test cases: simulations of hydrofluorocarbon\n(HFC) vapor-liquid equilibrium (VLE) and an ammonium perchlorate (AP) crystal\nphase. We discuss the challenges and implications of our force field\noptimization workflow.\n"}
+{"abstract": "  Minimally invasive image guided treatment procedures often employ advanced\nimage processing algorithms. The recent developments of artificial intelligence\nalgorithms harbor potential to further enhance this domain. In this article we\nexplore several application areas within the minimally invasive treatment space\nand discuss the deployment of artificial intelligence within these areas.\n"}
+{"abstract": "  Text analytics directly on compression (TADOC) has proven to be a promising\ntechnology for big data analytics. GPUs are extremely popular accelerators for\ndata analytics systems. Unfortunately, no work so far shows how to utilize GPUs\nto accelerate TADOC. We describe G-TADOC, the first framework that provides\nGPU-based text analytics directly on compression, effectively enabling\nefficient text analytics on GPUs without decompressing the input data. G-TADOC\nsolves three major challenges. First, TADOC involves a large amount of\ndependencies, which makes it difficult to exploit massive parallelism on a GPU.\nWe develop a novel fine-grained thread-level workload scheduling strategy for\nGPU threads, which partitions heavily-dependent loads adaptively in a\nfine-grained manner. Second, in developing G-TADOC, thousands of GPU threads\nwriting to the same result buffer leads to inconsistency while directly using\nlocks and atomic operations lead to large synchronization overheads. We develop\na memory pool with thread-safe data structures on GPUs to handle such\ndifficulties. Third, maintaining the sequence information among words is\nessential for lossless compression. We design a sequence-support strategy,\nwhich maintains high GPU parallelism while ensuring sequence information. Our\nexperimental evaluations show that G-TADOC provides 31.1x average speedup\ncompared to state-of-the-art TADOC.\n"}
+{"abstract": "  We design a virtual element method for the numerical treatment of the\ntwo-dimensional parabolic variational inequality problem on unstructured\npolygonal meshes. Due to the expected low regularity of the exact solution, the\nvirtual element method is based on the lowest-order virtual element space that\ncontains the subspace of the linear polynomials defined on each element. The\nconnection between the nonnegativity of the virtual element functions and the\nnonnegativity of the degrees of freedom, i.e., the values at the mesh vertices,\nis established by applying the Maximum and Minimum Principle Theorem. The mass\nmatrix is computed through an approximate L 2 polynomial projection, whose\nproperties are carefully investigated in the paper. We prove the well-posedness\nof the resulting scheme in two different ways that reveal the contractive\nnature of the VEM and its connection with the minimization of quadratic\nfunctionals. The convergence analysis requires the existence of a nonnegative\nquasi-interpolation operator, whose construction is also discussed in the\npaper. The variational crime introduced by the virtual element setting produces\nfive error terms that we control by estimating a suitable upper bound.\nNumerical experiments confirm the theoretical convergence rate for the\nrefinement in space and time on three different mesh families including\ndistorted squares, nonconvex elements, and Voronoi tesselations.\n"}
+{"abstract": "  A finite set of integers $A$ tiles the integers by translations if\n$\\mathbb{Z}$ can be covered by pairwise disjoint translated copies of $A$.\nRestricting attention to one tiling period, we have $A\\oplus B=\\mathbb{Z}_M$\nfor some $M\\in\\mathbb{N}$ and $B\\subset\\mathbb{Z}$. This can also be stated in\nterms of cyclotomic divisibility of the mask polynomials $A(X)$ and $B(X)$\nassociated with $A$ and $B$.\n  In this article, we introduce a new approach to a systematic study of such\ntilings. Our main new tools are the box product, multiscale cuboids, and\nsaturating spaces, developed through a combination of harmonic-analytic and\ncombinatorial methods. We provide new criteria for tiling and cyclotomic\ndivisibility in terms of these concepts. As an application, we can determine\nwhether a set $A$ containing certain configuration can tile a cyclic group\n$\\mathbb{Z}_M$, or recover a tiling set based on partial information about it.\nWe also develop tiling reductions where a given tiling can be replaced by one\nor more tilings with a simpler structure. The tools introduced here are crucial\nin our proof in a follow-up paper that all tilings of period $(pqr)^2$, where\n$p,q,r$ are distinct odd primes, satisfy a tiling condition proposed by Coven\nand Meyerowitz.\n"}
+{"abstract": "  Modifying Besicovitch's construction of a set $\\mathcal{B}$ of positive\nintegers whose set of multiples $\\mathcal{M}_{\\mathcal{B}}$ has no asymptotic\ndensity, we provide examples of such sets $\\mathcal{B}$ for which\n$\\eta:=1_{\\mathbb{Z}\\setminus\\mathcal{M}_{\\mathcal{B}}}\\in\\{0,1\\}^{\\mathbb{Z}}$\nis a Toeplitz sequence. Moreover our construction produces examples, for which\n$\\eta$ is not only quasi-generic for the Mirsky measure (which has discrete\ndynamical spectrum), but also for some measure of positive entropy. On the\nother hand, modifying slightly an example from Kasjan, Keller, and Lema\\'nczyk,\nwe construct a set $\\mathcal{B}$ for which $\\eta$ is an irregular Toeplitz\nsequence but for which the orbit closure of $\\eta$ in $\\{0,1\\}^{\\mathbb{Z}}$ is\nuniquely ergodic.\n"}
+{"abstract": "  We define a generalised model for three-stranded DNA consisting of two chains\nof one type and a third chain of a different type. The DNA strands are modelled\nby random walks on the three-dimensional cubic lattice with different\ninteractions between two chains of the same type and two chains of different\ntypes. This model may be thought of as a classical analogue of the quantum\nthree-body problem. In the quantum situation it is known that three identical\nquantum particles will form a triplet with an infinite tower of bound states at\nthe point where any pair of particles would have zero binding energy. The phase\ndiagram is mapped out, and the different phase transitions examined using\nfinite-size scaling. We look particularly at the scaling of the DNA model at\nthe equivalent Efimov point for chains up to 10000 steps in length. We find\nclear evidence of several bound states in the finite-size scaling. We compare\nthese states with the expected Efimov behaviour.\n"}
+{"abstract": "  The scalar field theory of cosmological inflation constitutes nowadays one of\nthe preferred scenarios for the physics of the early universe. In this paper we\naim at studying the inflationary universe making use of a numerical lattice\nsimulation. Various lattice codes have been written in the last decades and\nhave been extensively used for understating the reheating phase of the\nuniverse, but they have never been used to study the inflationary phase itself\nfar from the end of inflation (i.e. about 50 e-folds before the end of\ninflation). In this paper we use a lattice simulation to reproduce the\nwell-known results of some simple models of single-field inflation,\nparticularly for the scalar field perturbation. The main model that we consider\nis the standard slow-roll inflation with an harmonic potential for the inflaton\nfield. We explore the technical aspects that need to be accounted for in order\nto reproduce with precision the nearly scale invariant power spectrum of\ninflaton perturbations. We also consider the case of a step potential, and show\nthat the simulation is able to correctly reproduce the oscillatory features in\nthe power spectrum of this model. Even if a lattice simulation is not needed in\nthese cases, that are well within the regime of validity of linear perturbation\ntheory, this sets the basis to future work on using lattice simulations to\nstudy more complicated models of inflation.\n"}
+{"abstract": "  Multisite trials, in which treatment is randomized separately in multiple\nsites, offer a unique opportunity to disentangle treatment effect variation due\nto \"compositional\" differences in the distributions of unit-level features from\nvariation due to \"contextual\" differences in site-level features. In\nparticular, if we can re-weight (or \"transport\") each site to have a common\ndistribution of unit-level covariates, the remaining effect variation captures\ncontextual differences across sites. In this paper, we develop a framework for\ntransporting effects in multisite trials using approximate balancing weights,\nwhere the weights are chosen to directly optimize unit-level covariate balance\nbetween each site and the target distribution. We first develop our approach\nfor the general setting of transporting the effect of a single-site trial. We\nthen extend our method to multisite trials, assess its performance via\nsimulation, and use it to analyze a series of multisite trials of\nwelfare-to-work programs. Our method is available in the balancer R package.\n"}
+{"abstract": "  This study analyzes patterns of physical, mental, lifestyle, and personality\nfactors in college students in different periods over the course of a semester\nand models their relationships with students' academic performance. The data\nanalyzed was collected through smartphones and Fitbit. The use of machine\nlearning models derived from the gathered data was employed to observe the\nextent of students' behavior associated with their GPA, lifestyle, physical\nhealth, mental health, and personality attributes. A mutual agreement method\nwas used in which rather than looking at the accuracy of results, the model\nparameters and weights of features were used to find common behavioral trends.\nFrom the results of the model creation, it was determined that the most\nsignificant indicator of academic success defined as a higher GPA, was the\nplaces a student spent their time. Lifestyle and personality factors were\ndeemed more significant than mental and physical factors. This study will\nprovide insight into the impact of different factors and the timing of those\nfactors on students' academic performance.\n"}
+{"abstract": "  The current computational study investigates the changes in solvation\ndynamics of water when introducing hydrophobic side chains to the molecular\nprobe N-methyl-6-oxyquinolinium betaine. High precision transient fluorescence\nand absorption measurements published in the companion paper revealed an\ninfluence of hydrophobic sidechain alterations on the observed solvation\ndynamics of a chromophore in water. As the influence of shape, size and\nstructure of chromophores on the time-dependent Stokes shift was so far thought\nto play a role only in slowly rotating solvents compared to the solute or if\nthe hydrogen bonding ability of the solute changes, this finding is quite\nunexpected. Analysis of the time-dependent Stokes shift obtained from\nnonequilibrium simulations corroborates experimental retardation factors and\nactivation energies, and indicates that solute motion, namely vibration, is\nmainly responsible for the observed retardation of solvation dynamics. The\nfaster dynamics around the smaller chromophore is in fact achieved by some\nnormal modes located at the pyridinium part of the chromophore. Rotation also\ncontributes to a very small extent to hydration dynamics, but for small and\nlarge derivatives alike. Local residence times furthermore reveal slight\nretardations in the first solvent shell around the chromophores. The current\npicture of the solute acting as a passive molecular probe therefore needs to be\nrevised even for solvents like water.\n"}
+{"abstract": "  We study bifurcation phenomena in natural families of rational,\n(transcendental) entire or meromorphic functions of finite type $\\{f_\\lambda :=\n\\varphi_\\lambda \\circ f_{\\lambda_0} \\circ \\psi^{-1}_\\lambda\\}_{\\lambda\\in M}$,\nwhere $M$ is a complex connected manifold, $\\lambda_0\\in M$, $f_{\\lambda_0}$ is\na meromorphic map and $\\varphi_\\lambda$ and $\\psi_\\lambda$ are families of\nquasiconformal homeomorphisms depending holomorphically on $\\lambda$ and with\n$\\psi_\\lambda(\\infty)=\\infty$. There are fundamental differences compared to\nthe rational or entire setting due to the presence of poles and therefore of\nparameters for which singular values are eventually mapped to infinity\n(singular parameters). Under mild geometric conditions we show that singular\n(asymptotic) parameters are the endpoint of a curve of parameters for which an\nattracting cycle progressively exits de domain, while its multiplier tends to\nzero. This proves the main conjecture by Fagella and Keen (asymptotic\nparameters are virtual centers) in a very general setting. Other results in the\npaper show the connections between cycles exiting the domain, singular\nparameters, activity of singular orbits and $J$-unstability, converging to a\ntheorem in the spirit of the celebrated result by Ma\\~{n}\\'e-Sad-Sullivan and\nLyubich.\n"}
+{"abstract": "  The air source heat pump (ASHP) systems assisted by solar energy have drawn\ngreat attentions, owing to their great feasibility in buildings for space\nheating/cooling and hot water purposes. However, there are a variety of\nconfigurations, parameters and performance criteria of solar assisted ASHP\nsystems, leading to a major inconsistency that increase the degree of\ncomplexity to compare and implement different systems. A comparative literature\nreview is lacking, with the aim to evaluate the performance of various ASHP\nsystems from three main solar sources, such as solar thermal (ST), photovoltaic\n(PV) and hybrid photovoltaic/thermal (PV/T). This paper thus conducts a\nsystematic review of the prevailing solar assisted ASHP systems, including\ntheir boundary conditions, system configurations, performance indicators,\nresearch methodologies and system performance. The comparison result indicates\nthat PV-ASHP system has the best techno-economic performance, which performs\nbest in average with coefficient of performance (COP) of around 3.75, but with\nmoderate cost and payback time. While ST-ASHP and PV/T-ASHP systems have lower\nperformance with mean COP of 2.90 and 3.03, respectively. Moreover, PV/T-ASHP\nsystem has the highest cost and longest payback time, while ST-ASHP has the\nlowest ones. Future research are discussed from aspects of methodologies,\nsystem optimization and standard evaluation.\n"}
+{"abstract": "  We consider an entanglement harvesting protocol between two Unruh-deWitt\ndetectors in quantum superpositions of static trajectories in the static de\nSitter and thermal Minkowski spacetimes. We demonstrate for the first time that\nthe spatial superposition of each detector's path allows entanglement to be\nharvested from the quantum field in regimes where it would be otherwise\nimpossible for detectors on classical trajectories. Surprisingly, for detectors\non sufficiently delocalised trajectories in a thermal bath, the amount of\nharvested entanglement grows with the temperature of the field, violating a\nno-go theorem derived by Simidzija et. al. (Phys. Rev. D 98, 085007). We also\ndiscover interesting effects for mutual information harvesting, which depends\nsensitively on the nonlocal correlations between the superposed trajectories\nbetween the paths of the respective detectors.\n"}
+{"abstract": "  While multitask representation learning has become a popular approach in\nreinforcement learning (RL), theoretical understanding of why and when it works\nremains limited. This paper presents analyses for the statistical benefit of\nmultitask representation learning in linear Markov Decision Process (MDP) under\na generative model. In this paper, we consider an agent to learn a\nrepresentation function $\\phi$ out of a function class $\\Phi$ from $T$ source\ntasks with $N$ data per task, and then use the learned $\\hat{\\phi}$ to reduce\nthe required number of sample for a new task. We first discover a\n\\emph{Least-Activated-Feature-Abundance} (LAFA) criterion, denoted as $\\kappa$,\nwith which we prove that a straightforward least-square algorithm learns a\npolicy which is $\\tilde{O}(H^2\\sqrt{\\frac{\\mathcal{C}(\\Phi)^2 \\kappa\nd}{NT}+\\frac{\\kappa d}{n}})$ sub-optimal. Here $H$ is the planning horizon,\n$\\mathcal{C}(\\Phi)$ is $\\Phi$'s complexity measure, $d$ is the dimension of the\nrepresentation (usually $d\\ll \\mathcal{C}(\\Phi)$) and $n$ is the number of\nsamples for the new task. Thus the required $n$ is $O(\\kappa d H^4)$ for the\nsub-optimality to be close to zero, which is much smaller than\n$O(\\mathcal{C}(\\Phi)^2\\kappa d H^4)$ in the setting without multitask\nrepresentation learning, whose sub-optimality gap is\n$\\tilde{O}(H^2\\sqrt{\\frac{\\kappa \\mathcal{C}(\\Phi)^2d}{n}})$. This\ntheoretically explains the power of multitask representation learning in\nreducing sample complexity. Further, we note that to ensure high sample\nefficiency, the LAFA criterion $\\kappa$ should be small. In fact, $\\kappa$\nvaries widely in magnitude depending on the different sampling distribution for\nnew task. This indicates adaptive sampling technique is important to make\n$\\kappa$ solely depend on $d$. Finally, we provide empirical results of a noisy\ngrid-world environment to corroborate our theoretical findings.\n"}
+{"abstract": "  For gene expression data measured in a crossover trial, a multivariate\nmixed-effects model seems to be most appropriate. Standard statistical\ninference fails to provide reliable results when some responses are missing.\nParticularly for crossover studies, missingness is a serious concern as the\ntrial requires a small number of participants. A Monte Carlo EM (MCEM) based\ntechnique has been adopted to deal with this situation. Along with estimation,\na MCEM likelihood ratio test (LRTs) is developed for testing the fixed effects\nin such a multivariate crossover model with missing data. Intensive simulation\nstudies have been carried out prior to the analysis of the gene expression\ndata.\n"}
+{"abstract": "  Black-box optimization (BBO) has a broad range of applications, including\nautomatic machine learning, engineering, physics, and experimental design.\nHowever, it remains a challenge for users to apply BBO methods to their\nproblems at hand with existing software packages, in terms of applicability,\nperformance, and efficiency. In this paper, we build OpenBox, an open-source\nand general-purpose BBO service with improved usability. The modular design\nbehind OpenBox also facilitates flexible abstraction and optimization of basic\nBBO components that are common in other existing systems. OpenBox is\ndistributed, fault-tolerant, and scalable. To improve efficiency, OpenBox\nfurther utilizes \"algorithm agnostic\" parallelization and transfer learning.\nOur experimental results demonstrate the effectiveness and efficiency of\nOpenBox compared to existing systems.\n"}
+{"abstract": "  Descent methods for deep networks are notoriously capricious: they require\ncareful tuning of step size, momentum and weight decay, and which method will\nwork best on a new benchmark is a priori unclear. To address this problem, this\npaper conducts a combined study of neural architecture and optimisation,\nleading to a new optimiser called Nero: the neuronal rotator. Nero trains\nreliably without momentum or weight decay, works in situations where Adam and\nSGD fail, and requires little to no learning rate tuning. Also, Nero's memory\nfootprint is ~ square root that of Adam or LAMB. Nero combines two ideas: (1)\nprojected gradient descent over the space of balanced networks; (2)\nneuron-specific updates, where the step size sets the angle through which each\nneuron's hyperplane turns. The paper concludes by discussing how this geometric\nconnection between architecture and optimisation may impact theories of\ngeneralisation in deep learning.\n"}
+{"abstract": "  The end-to-end Human Mesh Recovery (HMR) approach has been successfully used\nfor 3D body reconstruction. However, most HMR-based frameworks reconstruct\nhuman body by directly learning mesh parameters from images or videos, while\nlacking explicit guidance of 3D human pose in visual data. As a result, the\ngenerated mesh often exhibits incorrect pose for complex activities. To tackle\nthis problem, we propose to exploit 3D pose to calibrate human mesh.\nSpecifically, we develop two novel Pose Calibration frameworks, i.e., Serial\nPC-HMR and Parallel PC-HMR. By coupling advanced 3D pose estimators and HMR in\na serial or parallel manner, these two frameworks can effectively correct human\nmesh with guidance of a concise pose calibration module. Furthermore, since the\ncalibration module is designed via non-rigid pose transformation, our PC-HMR\nframeworks can flexibly tackle bone length variations to alleviate misplacement\nin the calibrated mesh. Finally, our frameworks are based on generic and\ncomplementary integration of data-driven learning and geometrical modeling. Via\nplug-and-play modules, they can be efficiently adapted for both\nimage/video-based human mesh recovery. Additionally, they have no requirement\nof extra 3D pose annotations in the testing phase, which releases inference\ndifficulties in practice. We perform extensive experiments on the popular\nbench-marks, i.e., Human3.6M, 3DPW and SURREAL, where our PC-HMR frameworks\nachieve the SOTA results.\n"}
+{"abstract": "  Diffusion-weighted (DW) magnetic resonance imaging is essential for the\ndiagnosis and treatment of ischemic stroke. DW images (DWIs) are usually\nacquired in multi-slice settings where lesion areas in two consecutive 2D\nslices are highly discontinuous due to large slice thickness and sometimes even\nslice gaps. Therefore, although DWIs contain rich 3D information, they cannot\nbe treated as regular 3D or 2D images. Instead, DWIs are somewhere in-between\n(or 2.5D) due to the volumetric nature but inter-slice discontinuities. Thus,\nit is not ideal to apply most existing segmentation methods as they are\ndesigned for either 2D or 3D images. To tackle this problem, we propose a new\nneural network architecture tailored for segmenting highly-discontinuous 2.5D\ndata such as DWIs. Our network, termed LambdaUNet, extends UNet by replacing\nconvolutional layers with our proposed Lambda+ layers. In particular, Lambda+\nlayers transform both intra-slice and inter-slice context around a pixel into\nlinear functions, called lambdas, which are then applied to the pixel to\nproduce informative 2.5D features. LambdaUNet is simple yet effective in\ncombining sparse inter-slice information from adjacent slices while also\ncapturing dense contextual features within a single slice. Experiments on a\nunique clinical dataset demonstrate that LambdaUNet outperforms existing 3D/2D\nimage segmentation methods including recent variants of UNet. Code for\nLambdaUNet will be released with the publication to facilitate future research.\n"}
+{"abstract": "  In this paper, we prove various results for circle actions on compact unitary\nmanifolds with discrete fixed point sets, generalizing results for almost\ncomplex manifolds. For a circle action on a compact unitary manifold with a\ndiscrete fixed point set, we prove relationships between the weights at the\nfixed points. As a consequence, we show that there is a multigraph that encodes\nthe fixed point data (a collection of multisets of weights at the fixed points)\nof the manifold; this can be used to study unitary $S^1$-manifolds in terms of\nmultigraphs. We derive results regarding the first equivariant Chern class,\nobtaining a lower bound on the number of fixed points under an assumption on a\nmanifold. We determine the Hirzebruch $\\chi_y$-genus of a compact unitary\nmanifold admitting a semi-free $S^1$-action, and obtain a lower bound on the\nnumber of fixed points.\n"}
+{"abstract": "  We continue the study of fuzzy geometries inside Connes' spectral formalism\nand their relation to multimatrix models. In this companion paper to [arXiv\n2007:10914, Ann. Henri Poincar\\'e] we propose a gauge theory setting based on\nnoncommutative geometry, which -- just as the traditional formulation in terms\nof almost-commutative manifolds -- has the ability to also accommodate a Higgs\nfield. However, in contrast to \"almost-commutative manifolds\", the present\nframework employs only finite dimensional algebras which we call gauge matrix\nspectral triples. In a path-integral quantization approach to the Spectral\nAction, this allows to state Yang-Mills--Higgs theory (on four-dimensional\nEuclidean fuzzy space) as an explicit random multimatrix model obtained here,\nwhose matrix fields mirror those of the Yang-Mills--Higgs theory on a smooth\nmanifold.\n"}
+{"abstract": "  Twist defects in surface codes can be used to encode more logical qubits,\nimprove the code rate, and implement logical gates. In this work we provide a\nrigorous formalism for constructing surface codes with twists generalizing the\nwell-defined homological formalism introduced by Kitaev for describing CSS\nsurface codes. In particular, we associate a surface code to \\emph{any} graph\n$G$ embedded on \\emph{any} 2D-manifold, in such a way that (1) qubits are\nassociated to the vertices of the graph, (2) stabilizers are associated to\nfaces, (3) twist defects are associated to odd-degree vertices. In this way, we\nare able to reproduce the variety of surface codes, with and without twists, in\nthe literature and produce some new examples. We also calculate and bound\nvarious code properties such as the rate and distance in terms of topological\ngraph properties such as genus, systole, and face-width.\n"}
+{"abstract": "  The objective of this study is to predict the near-future flooding status of\nroad segments based on their own and adjacent road segments current status\nthrough the use of deep learning framework on fine-grained traffic data.\nPredictive flood monitoring for situational awareness of road network status\nplays a critical role to support crisis response activities such as evaluation\nof the loss of access to hospitals and shelters. Existing studies related to\nnear-future prediction of road network flooding status at road segment level\nare missing. Using fine-grained traffic speed data related to road sections,\nthis study designed and implemented three spatio-temporal graph convolutional\nnetwork (STGCN) models to predict road network status during flood events at\nthe road segment level in the context of the 2017 Hurricane Harvey in Harris\nCounty (Texas, USA). Model 1 consists of two spatio-temporal blocks considering\nthe adjacency and distance between road segments, while Model 2 contains an\nadditional elevation block to account for elevation difference between road\nsegments. Model 3 includes three blocks for considering the adjacency and the\nproduct of distance and elevation difference between road segments. The\nanalysis tested the STGCN models and evaluated their prediction performance.\nOur results indicated that Model 1 and Model 2 have reliable and accurate\nperformance for predicting road network flooding status in near future (e.g.,\n2-4 hours) with model precision and recall values larger than 98% and 96%,\nrespectively. With reliable road network status predictions in floods, the\nproposed model can benefit affected communities to avoid flooded roads and the\nemergency management agencies to implement evacuation and relief resource\ndelivery plans.\n"}
+{"abstract": "  We derive a Riemann--Hilbert representation for the solution of an integrable\nnonlinear evolution equation with a $3 \\times 3$ Lax pair. We use the derived\nrepresentation to obtain formulas for the long-time asymptotics.\n"}
+{"abstract": "  Medical image segmentation models are typically supervised by expert\nannotations at the pixel-level, which can be expensive to acquire. In this\nwork, we propose a method that combines the high quality of pixel-level expert\nannotations with the scale of coarse DNN-generated saliency maps for training\nmulti-label semantic segmentation models. We demonstrate the application of our\nsemi-supervised method, which we call CheXseg, on multi-label chest X-ray\ninterpretation. We find that CheXseg improves upon the performance (mIoU) of\nfully-supervised methods that use only pixel-level expert annotations by 9.7%\nand weakly-supervised methods that use only DNN-generated saliency maps by\n73.1%. Our best method is able to match radiologist agreement on three out of\nten pathologies and reduces the overall performance gap by 57.2% as compared to\nweakly-supervised methods.\n"}
+{"abstract": "  The Collatz problem with $3x+k$ is revisited. Positive and negative limit\ncycles are given up to k=9997 starting with $x_0=-2\\cdot10^7...+2\\cdot10^7$. A\nsimple relation between the probability distribution for the Syracuse iterates\nfor various k (not divisible by 2 and 3) is obtained. From this it follows that\nthe oscillation considered by Tao 2019 ( arXiv:1909.03562v2 ) does not depend\non k. Thus this piece of the proof of his theorem 1.3 \"Almost all Collatz\norbits attain almost bounded values\" holds for all k not divisible by 2 and 3.\n"}
+{"abstract": "  The topological order of a (2+1)D topological phase of matter is\ncharacterized by its chiral central charge and a unitary modular tensor\ncategory that describes the universal fusion and braiding properties of its\nanyonic quasiparticles. I discuss the topologically invariant quantities\nassociated with these and identify ones that are useful for determining the\ntopological order. I propose a variety of physical experiments that probe these\nquantities and detail the relation of the measured data to the topological\ninvariants.\n"}
+{"abstract": "  We calculate complete basis set (CBS) limit extrapolated ionization\npotentials (IP) and electron affinities (EA) with Slater Type Basis sets for\nthe molecules in the GW100 database. To this end, we present two new Slater\nType orbital (STO) basis sets of triple- (TZ) and quadruple-$\\zeta$ (QZ)\nquality whose polarization is adequate for correlated-electron methods and\nwhich contain extra diffuse functions to be able to correctly calculate\nelectron affinities of molecules with a positive Lowest Unoccupied Molecular\nOrbital (LUMO). We demonstrate, that going from TZ to QZ quality consistently\nreduces the basis set error of our computed IPs and EAs and we conclude that a\ngood estimate of these quantities at the CBS limit can be obtained by\nextrapolation. With MADs from 70 to 85 meV, our CBS limit extrapolated\nionization potentials are in good agreement with results from FHI-AIMS,\nTURBOMOLE, VASP and WEST while they differ by more than 130 meV on average from\nnanoGW. With a MAD of 160 meV, our electron affinities are also in good\nagreement with the WEST code. Especially for systems with positive LUMOs, the\nagreement is excellent. With respect to other codes, the STO type basis sets\ngenerally underestimate EAs of small molecules with strongly bound LUMOs. With\n62 meV for IPs and 93 meV for EAs, we find much better agreement to CBS limit\nextrapolated results from FHI-AIMS for a set of 250 medium to large organic\nmolecules.\n"}
+{"abstract": "  A few years ago, flow equations were introduced as a technique for\ncalculating the ground-state energies of cold Bose gases with and without\nimpurities. In this paper, we extend this approach to compute observables other\nthan the energy. As an example, we calculate the densities, and phase\nfluctuations of one-dimensional Bose gases with one and two impurities. For a\nsingle mobile impurity, we use flow equations to validate the mean-field\nresults obtained upon the Lee-Low-Pines transformation. We show that the\nmean-field approximation is accurate for all values of the boson-impurity\ninteraction strength as long as the phase coherence length is much larger than\nthe healing length of the condensate. For two static impurities, we calculate\nimpurity-impurity interactions induced by the Bose gas. We find that leading\norder perturbation theory fails when boson-impurity interactions are stronger\nthan boson-boson interactions. The mean-field approximation reproduces the flow\nequation results for all values of the boson-impurity interaction strength as\nlong as boson-boson interactions are weak.\n"}
+{"abstract": "  The black string solution is a model of black holes in (3+1) dimensions that\ncan be related to the BTZ (2+1) dimensions black hole solution. This object can\nbe seen as a cylindrical black hole. In this work it is considered the black\nstring Hawking temperature obtained by the Hamilton-Jacobi method and a\ndescription of a system composed by a black string and a thermal reservoir,\nconsidering the thermal equilibrium state.\n"}
+{"abstract": "  We present a reformulation of the functional renormalization group (fRG) for\nmany-electron systems, which relies on the recently introduced single boson\nexchange (SBE) representation of the parquet equations [Phys. Rev. B 100,\n155149 (2019)]. The latter exploits a diagrammatic decomposition, which\nclassifies the contributions to the full scattering amplitude in terms of their\nreducibility with respect to cutting one interaction line, naturally\ndistinguishing the processes mediated by the exchange of a single boson in the\ndifferent channels. We apply this idea to the fRG by splitting the one-loop fRG\nflow equations for the vertex function into SBE contributions and a residual\nfour-point fermionic vertex. Similarly as in the case of parquet solvers,\nrecasting the fRG algorithm in the SBE representation offers both computational\nand interpretative advantages: the SBE decomposition not only significantly\nreduces the numerical effort of treating the high-frequency asymptotics of the\nflowing vertices, but it also allows for a clear physical identification of the\ncollective degrees of freedom at play. We illustrate the advantages of an SBE\nformulation of fRG-based schemes, by computing through the merger of dynamical\nmean-field theory and fRG the susceptibilities and the Yukawa couplings of the\ntwo-dimensional Hubbard model from weak to strong coupling, for which we also\npresent an intuitive physical explanation of the results. The SBE formulation\nof the one-loop flow equations paves a promising route for future multiboson\nand multiloop extensions of fRG-based algorithms.\n"}
+{"abstract": "  An ultrafast fiber chirped-pulse amplifier comprising 8 coherently combined\namplifier channels is presented. The laser delivers 1 kW average power at 1 mJ\npulse energy and 260 fs pulse duration. Excellent beam quality and low noise\nperformance are confirmed. The laser has proven suitable for demanding\nscientific applications. Further power scaling is possible right away using\neven more amplifier channels\n"}
+{"abstract": "  Terao's factorization theorem shows that if an arrangement is free, then its\ncharacteristic polynomial factors into the product of linear polynomials over\nthe integer ring. This is not a necessary condition, but there are not so many\nnon-free arrangements whose characteristic polynomial factors over the integer\nring. On the other hand, the localization of a free arrangement is free, and\nits restriction is in many cases free, thus its characteristic polynomial\nfactors. In this paper, we consider how their integer, or real roots behave.\n"}
+{"abstract": "  In critical situations involving discrimination, gender inequality, economic\ndamage, and even the possibility of casualties, machine learning models must be\nable to provide clear interpretations for their decisions. Otherwise, their\nobscure decision-making processes can lead to socioethical issues as they\ninterfere with people's lives. In the aforementioned sectors, random forest\nalgorithms strive, thus their ability to explain themselves is an obvious\nrequirement. In this paper, we present LionForests, which relies on a\npreliminary work of ours. LionForests is a random forest-specific\ninterpretation technique, which provides rules as explanations. It is\napplicable from binary classification tasks to multi-class classification and\nregression tasks, and it is supported by a stable theoretical background.\nExperimentation, including sensitivity analysis and comparison with\nstate-of-the-art techniques, is also performed to demonstrate the efficacy of\nour contribution. Finally, we highlight a unique property of LionForests,\ncalled conclusiveness, that provides interpretation validity and distinguishes\nit from previous techniques.\n"}
+{"abstract": "  Specific heat measurements of hydrogenated amorphous silicon prepared by\nhot-wire chemical vapor deposition show a large density of two-level systems at\nlow temperature. Annealing at 200 {\\deg}C, well below the growth temperature,\ndoes not significantly affect the already-low internal friction or the sound\nvelocity, but irreversibly reduces the non-Debye specific heat by an order of\nmagnitude at 2 K, indicating a large reduction of the density of two-level\nsystems. Comparison of the specific heat to the internal friction suggests that\nthe two-level systems are uncharacteristically decoupled from acoustic waves,\nboth before and after annealing. Analysis yields an anomalously low value of\nthe coupling constant, which increases upon annealing but still remains\nanomalously low. The results suggest that the coupling constant value is\nlowered by the presence of hydrogen.\n"}
+{"abstract": "  We study the problem of learning predictors that are robust to adversarial\nexamples with respect to an unknown perturbation set, relying instead on\ninteraction with an adversarial attacker or access to attack oracles, examining\ndifferent models for such interactions. We obtain upper bounds on the sample\ncomplexity and upper and lower bounds on the number of required interactions,\nor number of successful attacks, in different interaction models, in terms of\nthe VC and Littlestone dimensions of the hypothesis class of predictors, and\nwithout any assumptions on the perturbation set.\n"}
+{"abstract": "  With the rise of deep learning models in the field of computer vision, new\npossibilities for their application in industrial processes proves to return\ngreat benefits. Nevertheless, the actual fit of machine learning for highly\nstandardised industrial processes is still under debate. This paper addresses\nthe challenges on the industrial realization of the AI tools, considering the\nuse case of Laser Beam Welding quality control as an example. We use object\ndetection algorithms from the TensorFlow object detection API and adapt them to\nour use case using transfer learning. The baseline models we develop are used\nas benchmarks and evaluated and compared to models that undergo dataset scaling\nand hyperparameter tuning. We find that moderate scaling of the dataset via\nimage augmentation leads to improvements in intersection over union (IoU) and\nrecall, whereas high levels of augmentation and scaling may lead to\ndeterioration of results. Finally, we put our results into perspective of the\nunderlying use case and evaluate their fit.\n"}
+{"abstract": "  Convolutional neural networks (CNNs) often have poor generalization\nperformance under domain shift. One way to improve domain generalization is to\ncollect diverse source data from multiple relevant domains so that a CNN model\nis allowed to learn more domain-invariant, and hence generalizable\nrepresentations. In this work, we address domain generalization with MixStyle,\na plug-and-play, parameter-free module that is simply inserted to shallow CNN\nlayers and requires no modification to training objectives. Specifically,\nMixStyle probabilistically mixes feature statistics between instances. This\nidea is inspired by the observation that visual domains can often be\ncharacterized by image styles which are in turn encapsulated within\ninstance-level feature statistics in shallow CNN layers. Therefore, inserting\nMixStyle modules in effect synthesizes novel domains albeit in an implicit way.\nMixStyle is not only simple and flexible, but also versatile -- it can be used\nfor problems whereby unlabeled images are available, such as semi-supervised\ndomain generalization and unsupervised domain adaptation, with a simple\nextension to mix feature statistics between labeled and pseudo-labeled\ninstances. We demonstrate through extensive experiments that MixStyle can\nsignificantly boost the out-of-distribution generalization performance across a\nwide range of tasks including object recognition, instance retrieval, and\nreinforcement learning.\n"}
+{"abstract": "  We proposed the method that translates the 2-D CSP for minimizing the number\nof cuts to the Ising model. After that, we conducted computer experiments of\nthe proposed model using the benchmark problem. From the above, the following\nresults are obtained. (1) The proposed Ising model adequately represents the\ntarget problem. (2) Acceptance rates were low as 0.2% to 9.8% and from 21.8% to\n49.4%. (3) Error rates from optimal solution were broad as 0% to 25.9%. As the\nfuture work, (1) Improve the Hamiltonian for Constraints. (2) Improve the\nproposed model to adjust more complex 2-D CSP and reduce the number of spins\nwhen it deals with large materials and components. (3) Conduct experiments\nusing a quantum annealer.\n"}
+{"abstract": "  The zeta-function of a manifold is closely related to, and sometimes can be\ncalculated completely, in terms of its periods. We report here on a practical\nand computationally rapid implementation of this procedure for families of\nCalabi-Yau manifolds with one complex structure parameter phi. Although partly\nconjectural, it turns out to be possible to compute the matrix of the Frobenius\nmap on the third cohomology group of X(phi) directly from the Picard-Fuchs\ndifferential operator of the family. To illustrate our method, we compute\ntables of the quartic numerators of the zeta-functions for six manifolds of\nincreasing complexity as the parameter phi varies in Fp. For four of these\nmanifolds, we do this for the 500 primes p=5,7,...,3583, while for two\nmanifolds we extend the calculation to 1000 primes. The tables for 5 <= p <= 97\nare part of this article while the remaining tables are attached in electronic\nform. Interest attaches to the cases for which the numerators factorise. Some\nof these factorisations can be associated with parameter values for which the\nunderlying manifold becomes singular. For the cases we consider here, the\nsingularities are all of conifold or hyperconifold type. In these cases the\nnumerator degenerates to a cubic and this factorises into the product of a\nlinear and a quadratic factor. The quadratic term contains a coefficient that\nis the p'th coefficient of a modular form. Some of our examples have\nsingularities when the parameter satisfies a polynomial equation that does not\nfactorise over Q. When this happens, the corresponding forms are modular forms\nwith neben type or Hilbert modular forms. The numerator can also factorise into\ntwo quadrics. This happens when the Hodge structure of the manifold splits,\nsometimes this happens for algebraic values of the parameter and we identify,\nin this way, attractor points of rank two of the parameter space.\n"}
+{"abstract": "  Building on the recent theoretical work of Wray, Duffy and Wilson [J. Fluid\nMech. 884, A45 (2020)] concerning the competitive diffusion-limited evaporation\nof multiple thin sessile droplets in proximity to each other, we obtain\ntheoretical predictions for the spatially non-uniform densities of the\ncontact-line deposits (often referred to as \"coffee stains\" or \"ring stains\")\nleft on the substrate after such droplets containing suspended solid particles\nhave completely evaporated. Neighbouring droplets interact via their vapour\nfields, which results in a spatially non-uniform \"shielding\" effect. We give\npredictions for the deposits from a pair of identical droplets, which show that\nthe deposit is reduced the most where the droplets are closest together, and\ndemonstrate excellent quantitative agreement with experimental results of\nPradhan and Panigrahi [Coll. Surf. A 482, 562-567 (2015)]. We also give\ncorresponding predictions for a triplet of identical droplets arranged in an\nequilateral triangle, which show that the effect of shielding on the deposit is\nmore subtle in this case.\n"}
+{"abstract": "  A myriad of explainability methods have been proposed in recent years, but\nthere is little consensus on how to evaluate them. While automatic metrics\nallow for quick benchmarking, it isn't clear how such metrics reflect human\ninteraction with explanations. Human evaluation is of paramount importance, but\nprevious protocols fail to account for belief biases affecting human\nperformance, which may lead to misleading conclusions. We provide an overview\nof belief bias, its role in human evaluation, and ideas for NLP practitioners\non how to account for it. For two experimental paradigms, we present a case\nstudy of gradient-based explainability introducing simple ways to account for\nhumans' prior beliefs: models of varying quality and adversarial examples. We\nshow that conclusions about the highest performing methods change when\nintroducing such controls, pointing to the importance of accounting for belief\nbias in evaluation.\n"}
+{"abstract": "  We report the results of optical spectroscopy of the Small Magellanic Cloud\nsupernova remnant (SNR) MCSNR J0127-7332 and the mass donor Be star, 2dFS 3831,\nin its associated high-mass X-ray binary SXP 1062 carried out with the Southern\nAfrican Large Telescope (SALT). Using high-resolution long-slit spectra, we\nmeasured the expansion velocity of the SNR shell of \\approx 140 km/s,\nindicating that MCSNR J0127-7332 is in the radiative phase. We found that the\nobserved line ratios in the SNR spectrum can be understood if the local\ninterstellar medium is ionized by 2dFS 3831 and/or OB stars around the SNR. We\npropose that MCSNR J0127-7332 is the result of supernova explosion within a\nbubble produced by the stellar wind of the supernova progenitor and that the\nbubble was surrounded by a massive shell at the moment of supernova explosion.\nWe estimated the age of MCSNR J0127-7332 to be \\la 10 000 yr. We found that the\nspectrum of 2dFS 3831 changes with orbital phase. Namely, the equivalent width\nof the Halpha emission line decreased by \\approx 40 per cent in \\approx 130 d\nafter periastron passage of the neutron star and then almost returned to its\noriginal value in the next \\approx 100 d. Also, the spectrum of 2dFS 3831\nobtained closest to the periastron epoch (about three weeks after the\nperiastron) shows a noticeable emission line of He II \\lambda 4686, which\ndisappeared in the next about two weeks. We interpret these changes as a result\nof the temporary perturbation and heating of the disk as the neutron star\npasses through it.\n"}
+{"abstract": "  Extensive infrared spectra of the weakly-bound CO$_2$-CO dimer are observed\nin the carbon dioxide $\\nu_3$ asymmetric stretch region (~2350 cm-1) using a\ntunable infrared OPO laser source to probe a pulsed slit jet supersonic\nexpansion. Both C-bonded and O-bonded isomers are analyzed for the normal\nisotopologue as well as for 13CO$_2$-CO and $^{16}$O$^{13}$C$^{18}$O-CO, the\nlatter being the first observation of an asymmetrically substituted form for\nwhich all values of Ka are allowed. Combination bands involving the lowest\nin-plane intermolecular bending modes are also studied for both isomers. Weak\nbands near 2337 cm-1 are assigned to CO$_2$ hot band transitions (v1, v2l2, v3)\n= (0111) <-- (0110), yielding the splitting of the degenerate CO$_2$ $\\nu_2$\nbend into in-plane and out-of-plane components due to the presence of the CO.\nThis splitting has rather different values for the C- and O- bonded isomers,\n4.56 and 1.59 cm-1, respectively, with the out-of-plane mode higher in energy\nthan the in-plane for both cases.\n"}
+{"abstract": "  For the interaction energy with repulsive-attractive potentials, we give\ngeneric conditions which guarantee the radial symmetry of the local minimizers\nin the infinite Wasserstein distance. As a consequence, we obtain the\nuniqueness of local minimizers in this topology for a class of interaction\npotentials. We introduce a novel notion of concavity of the interaction\npotential allowing us to show certain fractal-like behavior of the local\nminimizers. We provide a family of interaction potentials such that the support\nof the associated local minimizers has no isolated points and any superlevel\nset has no interior points.\n"}
+{"abstract": "  In Alexandrov's work \\cite{al2, al3} it has been shown that the extended\npartition function $\\exp(F^{o,ext}+F^c)$ introduced by Buryak in \\cite{bu, bu2}\nis a tau-function of the KP hierarchy. In this work, we compute the affine\ncoordinates of this tau-function on the Sato Grassmannian, and rewrite the\nVirasoro constraints as recursions for the affine coordinates in the fermionic\npicture. As applications we derive some formulas for the extended partition\nfunction and the connected $n$-point functions using methods developed by Zhou\nin \\cite{zhou1} based on the boson-fermion correspondence.\n"}
+{"abstract": "  This paper describes Resh, a new, statically typed, interpreted programming\nlanguage and associated runtime for orchestrating multirobot systems. The main\nfeatures of Resh are: (1) It offloads much of the tedious work of programming\nsuch systems away from the programmer and into the language runtime; (2) It is\nbased on a small set of temporal and locational operators; and (3) It is not\nrestricted to specific robot types or tasks. The Resh runtime consists of three\nengines that collaborate to run a Resh program using the available robots in\ntheir current environment. This paper describes both Resh and its runtime and\ngives examples of its use.\n"}
+{"abstract": "  We revisit the stability issue of determining the conductivity at the\nboundary from the corresponding Dirichlet-to-Neumann map. We discuss both the\nmethod based on singular solutions and the one built on the localized\noscillating solutions. Our primary objective is not establishing new results on\nthis subject even if the present work contains some new ones. We mainly clarify\nsome points in the existing proofs and make some comments. We also derive some\nconsequences of the stability inequality of the determination of the\nconductivity at the boundary from the Dirichlet-to-Neumann map.\n"}
+{"abstract": "  While AI has benefited humans, it may also harm humans if not appropriately\ndeveloped. The focus of HCI work is transiting from conventional human\ninteraction with non-AI computing systems to interaction with AI systems. We\nconducted a high-level literature review and a holistic analysis of current\nwork in developing AI systems from an HCI perspective. Our review and analysis\nhighlight the new changes introduced by AI technology and the new challenges\nthat HCI professionals face when applying the human-centered AI (HCAI) approach\nin the development of AI systems. We also identified seven main issues in human\ninteraction with AI systems, which HCI professionals did not encounter when\ndeveloping non-AI computing systems. To further enable the implementation of\nthe HCAI approach, we identified new HCI opportunities tied to specific\nHCAI-driven design goals to guide HCI professionals in addressing these new\nissues. Finally, our assessment of current HCI methods shows the limitations of\nthese methods in support of developing AI systems. We propose alternative\nmethods that can help overcome these limitations and effectively help HCI\nprofessionals apply the HCAI approach to the development of AI systems. We also\noffer strategic recommendations for HCI professionals to effectively influence\nthe development of AI systems with the HCAI approach, eventually developing\nHCAI systems.\n"}
+{"abstract": "  Recently, it was found that clipping can significantly improve the section\nerror rate (SER) performance of sparse regression (SR) codes if an optimal\nclipping threshold is chosen. In this paper, we propose irregularly clipped SR\ncodes, where multiple clipping thresholds are applied to symbols according to a\ndistribution, to further improve the SER performance of SR codes. Orthogonal\napproximate message passing (OAMP) algorithm is used for decoding. Using state\nevolution, the distribution of irregular clipping thresholds is optimized to\nminimize the SER of OAMP decoding. As a result, optimized irregularly clipped\nSR codes achieve a better tradeoff between clipping distortion and noise\ndistortion than regularly clipped SR codes. Numerical results demonstrate that\nirregularly clipped SR codes achieve 0.4 dB gain in signal-to-noise-ratio (SNR)\nover regularly clipped SR codes at code length$\\,\\approx2.5\\!\\times\\! 10^4$ and\nSER$\\,\\approx10^{-5}$. We further show that irregularly clipped SR codes are\nrobust over a wide range of code rates.\n"}
+{"abstract": "  We propose a \"small-uniform\" statistic for the inference of the functional\nPCA estimator in a functional linear regression model. The literature has shown\ntwo extreme behaviors: on the one hand, the FPCA estimator does not converge in\ndistribution in its norm topology; but on the other hand, the FPCA estimator\ndoes have a pointwise asymptotic normal distribution. Our statistic takes a\nmiddle ground between these two extremes: after a suitable rate normalization,\nour small-uniform statistic is constructed as the maximizer of a fractional\nprogramming problem of the FPCA estimator over a finite-dimensional subspace,\nand whose dimensions will grow with sample size. We show the rate for which our\nscalar statistic converges in probability to the supremum of a Gaussian\nprocess. The small-uniform statistic has applications in hypothesis testing.\nSimulations show our statistic has comparable to slightly better power\nproperties for hypothesis testing than the two statistics of Cardot, Ferraty,\nMas and Sarda (2003).\n"}
+{"abstract": "  We check the impact of the gluon distribution due to the number of active\nflavor employed in the calculation of heavy-quark deep-inelastic scattering\n(DIS) electro-production on the reduced cross section and structure functions\ndetermined in the leading order (LO) and next-to-leading order (NLO) quantum\nchromodynamics (QCD) analysis using the Hadron Electron Ring Accelerator (HERA)\ncombined data. The charm and beauty structure functions are determined and\ncompared with the HERA combined data. The results are in good agreement with\nrespect to the experimental data. We also compare the obtained charm and bottom\nstructure functions with the results from CTEQ6.6 [Phys.Rev.D{\\bf78},\n013004(2008)] and NNPDF3.1[Eur.Phys.J.C{\\bf77}, 663(2017)] parameterization\nmodels. The effect of gluon density on these calculations depends on the number\nof active flavor at asymptotical values of the momentum transfer $Q^{2}$. Also,\nin the HERA kinematic range, the ratio of $F_{2}^{c\\overline{c}}/F_{2}$ and\n$F_{2}^{b\\overline{b}}/F_{2}$ are obtained. These results and comparison with\nthe HERA combined data demonstrate that the suggested method can be applied in\nanalysis of new colliders.\n"}
+{"abstract": "  It has been shown that global scene understanding tasks like layout\nestimation can benefit from wider field of views, and specifically spherical\npanoramas. While much progress has been made recently, all previous approaches\nrely on intermediate representations and postprocessing to produce\nManhattan-aligned estimates. In this work we show how to estimate full room\nlayouts in a single-shot, eliminating the need for postprocessing. Our work is\nthe first to directly infer Manhattan-aligned outputs. To achieve this, our\ndata-driven model exploits direct coordinate regression and is supervised\nend-to-end. As a result, we can explicitly add quasi-Manhattan constraints,\nwhich set the necessary conditions for a homography-based Manhattan alignment\nmodule. Finally, we introduce the geodesic heatmaps and loss and a\nboundary-aware center of mass calculation that facilitate higher quality\nkeypoint estimation in the spherical domain. Our models and code are publicly\navailable at https://vcl3d.github.io/SingleShotCuboids/.\n"}
+{"abstract": "  The area of computing with uncertainty considers problems where some\ninformation about the input elements is uncertain, but can be obtained using\nqueries. For example, instead of the weight of an element, we may be given an\ninterval that is guaranteed to contain the weight, and a query can be performed\nto reveal the weight. While previous work has considered models where queries\nare asked either sequentially (adaptive model) or all at once (non-adaptive\nmodel), and the goal is to minimize the number of queries that are needed to\nsolve the given problem, we propose and study a new model where $k$ queries can\nbe made in parallel in each round, and the goal is to minimize the number of\nquery rounds. We use competitive analysis and present upper and lower bounds on\nthe number of query rounds required by any algorithm in comparison with the\noptimal number of query rounds. Given a set of uncertain elements and a family\nof $m$ subsets of that set, we present an algorithm for determining the value\nof the minimum of each of the subsets that requires at most $(2+\\varepsilon)\n\\cdot \\mathrm{opt}_k+\\mathrm{O}\\left(\\frac{1}{\\varepsilon} \\cdot \\lg m\\right)$\nrounds for every $0<\\varepsilon<1$, where $\\mathrm{opt}_k$ is the optimal\nnumber of rounds, as well as nearly matching lower bounds. For the problem of\ndetermining the $i$-th smallest value and identifying all elements with that\nvalue in a set of uncertain elements, we give a $2$-round-competitive\nalgorithm. We also show that the problem of sorting a family of sets of\nuncertain elements admits a $2$-round-competitive algorithm and this is the\nbest possible.\n"}
+{"abstract": "  Non-Abelian excitations are an interesting feature of many fractional quantum\nHall phases, including those phases described by the Moore-Read (or Pfaffian)\nwave function. However, the detection of the non-Abelian quasiparticles is\nchallenging. Here, we consider a system described by the Moore-Read wave\nfunction, and assume that impurity particles bind to its quasiholes. Then, the\nangular momentum of the impurities, reflected also by the impurity density,\nprovides a useful witness of the physics of the non-Abelian excitations. By\ndemanding that the impurities are constrained to the lowest Landau level, we\nare able to write down the corresponding many-body wave function describing\nboth the Moore-Read liquid and the impurities. Through Monte Carlo sampling we\ndetermine the impurity angular momentum, and we show that it suggests a\nquantum-statistical parameter $\\alpha = a\\nu -b +P/2$ for the quasiholes, where\n$\\alpha$ ranges from $0$ for bosons to $1$ for fermions. A reasonable agreement\nwith the Monte Carlo results is obtained for $a=1/4$, $b=1/8$ and $P=0,1$\ndepending on the parity of the particle number in the Moore-Read liquid. This\nparity-dependence of the angular momentum serves as an unambiguous\ndemonstration of the non-Abelian nature of the excitations. In addition to the\nstudies of excitations in the Moore-Read liquid, we also apply our scheme to\nLaughlin liquids, for which we focus on interacting bosonic impurities. With\nthis, the impurities themselves form Laughlin states, which allows for a study\nof hierarchical fractional quantum Hall states.\n"}
+{"abstract": "  From the Lytchak's result for polar foliations on an irreducible simply\nconnected symmetric space $G/K$ of compact type and rank greater than one, we\ncan derive that there exists no equifocal submanifold with non-flat section\nwhose codimension is greater than two in the symmetric space $G/K$. In the\nfirst-half part of this paper, we give a new proof of this non-existence\ntheorem. The recipi of our new proof is as follows. Suppose that there exists\nan equifocal submanifold $M$ with non-flat section whose codimension is greater\nthan two in an irreducible symmertric space $G/K$ of compact type and rank\ngreater than one. We introduce the notion of a slice topology of $G/K$\nassociated to $M$. We consider the universal covering $\\pi:\\widehat{G/K}\\to\nG/K$ of the slice topological space $G/K$ and give $\\widehat{G/K}$ the manifold\nstructure and the Riemannian metric such that $\\pi$ is a Riemannian submersion\nonto the symmetric space $G/K$. First we show that a simplicial decomposition\nof the Riemannian manifold $\\widehat{G/K}$ gives an irreducible topological\nTits building of spherical type and rank greater than two. By applying\nBurns-Spatzier's theorem to this topological Tits building, we show that the\nRiemannian manifold $\\widehat{G/K}$ is homothetic to the unit sphere.\nFurthermore, from this fact, we show that $G/K$ is isometric to a sphere, a\ncomplex projective space or a quaternionic projective space. This contradicts\nthat $G/K$ is of rank greater than one. This is the recipi of our proof. In the\nsecond-half part, we estimate the codimension of $M$ from above by using the\nmultiplicities of the roots of the root system of $G/K$. As its result, we can\nshow that there exists no equifocal submanifold with non-flat section in some\nirreducible simply connected symmetric spaces of compact type.\n"}
+{"abstract": "  The entropy produced when a quantum system is driven away from equilibrium\ncan be decomposed in two parts, one related with populations and the other with\nquantum coherences. The latter is usually based on the so-called relative\nentropy of coherence, a widely used quantifier in quantum resource theories. In\nthis paper we argue that, despite satisfying fluctuation theorems and having a\nclear resource-theoretic interpretation, this splitting has shortcomings.\nFirst, it predicts that at low temperatures the entropy production will always\nbe dominated by the classical term, irrespective of the quantum nature of the\nprocess. Second, for infinitesimal quenches, the radius of convergence diverges\nexponentially as the temperature decreases, rendering the functions\nnon-analytic. Motivated by this, we provide here a complementary approach,\nwhere the entropy production is split in a way such that the contributions from\npopulations and coherences are written in terms of a thermal state of a\nspecially dephased Hamiltonian. The physical interpretation of our proposal is\ndiscussed in detail. We also contrast the two approaches by studying work\nprotocols in a transverse field Ising chain, and a macrospin of varying\ndimension.\n"}
+{"abstract": "  The segmentation of data into stationary stretches also known as multiple\nchange point problem is important for many applications in time series analysis\nas well as signal processing. Based on strong invariance principles, we analyse\ndata segmentation methodology using moving sum (MOSUM) statistics for a class\nof regime-switching multivariate processes where each switch results in a\nchange in the drift. In particular, this framework includes the data\nsegmentation of multivariate partial sum, integrated diffusion and renewal\nprocesses even if the distance between change points is sublinear. We study the\nasymptotic behaviour of the corresponding change point estimators, show\nconsistency and derive the corresponding localisation rates which are minimax\noptimal in a variety of situations including an unbounded number of changes in\nWiener processes with drift. Furthermore, we derive the limit distribution of\nthe change point estimators for local changes - a result that can in principle\nbe used to derive confidence intervals for the change points.\n"}
+{"abstract": "  We introduce a notion of compatibility for families\n$(\\mathcal{F}_{\\ell})_{\\ell}$ of bounded constructible $\\ell$-adic complexes of\n\\'etale sheaves on schemes. For schemes of finite type over a field, this\nnotion is preserved by the usual six functors. We prove that the compatibility\nof a family is preserved by the nearby cycles functor and by the linearized\n$\\varepsilon$-factors introduced recently by the author. We establish\nindependence of $\\ell$ for the characteristic cycles and characteristic\n$\\varepsilon$-cycles of compatible families.\n"}
+{"abstract": "  Models of chemical enrichment and inhomogeneity in high-redshift galaxies are\nchallenging to constrain observationally. In this work, we discuss a novel\napproach to probe chemical inhomogeneities within long Gamma-Ray Burst (GRB)\nhost galaxies, by comparing the absorption metallicity, Z_abs, from the GRB\nafterglow (which probes the environment along the line of sight) with the\nemission-line metallicity, Z_emiss, measured via slit spectroscopy. Using the\nIllustrisTNG simulation, the theoretical relationship between these metallicity\nmetrics is explored for a range of GRB formation models, varying the GRB\nprogenitor metallicity threshold. For galaxies with fixed Z_emiss, the median\nvalue of Z_abs depends strongly on the GRB progenitor threshold metallicity,\nwith Z_abs significantly lower than Z_emiss for high metallicity hosts.\nConversely, at fixed Z_abs, the median value of Z_emiss depends primarily on\nthe metallicity distribution of galaxies in IllustrisTNG and their chemical\ninhomogeneities, offering a GRB-model-independent way to constrain these\nprocesses observationally. Currently, only one host galaxy has data for both\nabsorption and emission metallicities (GRB121014A). We re-analyse the emission\nspectrum and compare the inferred metallicity Z_emiss to a recent Bayesian\ndetermination of Z_abs, finding $\\log(Z_{\\rm emiss}/Z_{\\odot}) = \\log(Z_{\\rm\nabs}/Z_{\\odot}) +0.35^{+ 0.14}_{- 0.25}$, within ~2 standard deviations of\npredictions from the IllustrisTNG simulation. Future observations with the\nJames Webb Space Telescope will be able to measure Z_emiss for 4 other GRB\nhosts with known Z_abs values, using ~2 hour observations. While small, the\nsample will provide preliminary constraints on the Z_abs-Z_emiss relation to\ntest chemical enrichment schemes in cosmological simulations.\n"}
+{"abstract": "  The plethora of photometric data collected by the Kepler space telescope has\npromoted the detection of tens of thousands of stellar rotation periods.\nHowever, these periods are not found to an equal extent among different\nspectral types. Interestingly, early G-type stars with near-solar rotation\nperiods are strongly underrepresented among those stars with known rotation\nperiods. In this study we investigate whether the small number of such stars\ncan be explained by difficulties in the period determination from photometric\ntime series. For that purpose, we generate model light curves of early G-type\nstars with solar rotation periods for different inclination angles,\nmetallicities and (magnitude-dependent) noise levels. We find that the\ndetectability is determined by the predominant type of activity (i.e. spot or\nfaculae domination) on the surface, which defines the degree of irregularity of\nthe light curve, and further depends on the level of photometric noise. These\ntwo effects significantly complicate the period detection and explain the lack\nof solar-like stars with known near-solar rotation periods. We conclude that\nthe rotation periods of the majority of solar-like stars with near-solar\nrotation periods remain undetected to date. Finally, we promote the use of new\ntechniques to recover more periods of near-solar rotators.\n"}
+{"abstract": "  Smart Contracts are software programs that are deployed and executed within a\nblockchain infrastructure. Due to their immutable nature, directly resulting\nfrom the specific characteristics of the deploying infrastructure, smart\ncontracts must be thoroughly tested before their release. Testing is one of the\nmain activities that can help to improve the reliability of a smart contract,\nso as to possibly prevent considerable loss of valuable assets. It is therefore\nimportant to provide the testers with tools that permit them to assess the\nactivity they performed. Mutation testing is a powerful approach for assessing\nthe fault-detection capability of a test suite. In this paper, we propose SuMo,\na novel mutation testing tool for Ethereum Smart Contracts. SuMo implements a\nset of 44 mutation operators that were designed starting from the latest\nSolidity documentation, and from well-known mutation testing tools. These allow\nto simulate a wide variety of faults that can be made by smart contract\ndevelopers. The set of operators was designed to limit the generation of\nstillborn mutants, which slow down the mutation testing process and limit the\nusability of the tool. We report a first evaluation of SuMo on open-source\nprojects for which test suites were available. The results we got are\nencouraging, and they suggest that SuMo can effectively help developers to\ndeliver more reliable smart contracts.\n"}
+{"abstract": "  In this paper, we present SANTA, a scalable framework to automatically\nnormalize E-commerce attribute values (e.g. \"Win 10 Pro\") to a fixed set of\npre-defined canonical values (e.g. \"Windows 10\"). Earlier works on attribute\nnormalization focused on fuzzy string matching (also referred as syntactic\nmatching in this paper). In this work, we first perform an extensive study of\nnine syntactic matching algorithms and establish that 'cosine' similarity leads\nto best results, showing 2.7% improvement over commonly used Jaccard index.\nNext, we argue that string similarity alone is not sufficient for attribute\nnormalization as many surface forms require going beyond syntactic matching\n(e.g. \"720p\" and \"HD\" are synonyms). While semantic techniques like\nunsupervised embeddings (e.g. word2vec/fastText) have shown good results in\nword similarity tasks, we observed that they perform poorly to distinguish\nbetween close canonical forms, as these close forms often occur in similar\ncontexts. We propose to learn token embeddings using a twin network with\ntriplet loss. We propose an embedding learning task leveraging raw attribute\nvalues and product titles to learn these embeddings in a self-supervised\nfashion. We show that providing supervision using our proposed task improves\nover both syntactic and unsupervised embeddings based techniques for attribute\nnormalization. Experiments on a real-world attribute normalization dataset of\n50 attributes show that the embeddings trained using our proposed approach\nobtain 2.3% improvement over best string matching and 19.3% improvement over\nbest unsupervised embeddings.\n"}
+{"abstract": "  When a deep learning model is deployed in the wild, it can encounter test\ndata drawn from distributions different from the training data distribution and\nsuffer drop in performance. For safe deployment, it is essential to estimate\nthe accuracy of the pre-trained model on the test data. However, the labels for\nthe test inputs are usually not immediately available in practice, and\nobtaining them can be expensive. This observation leads to two challenging\ntasks: (1) unsupervised accuracy estimation, which aims to estimate the\naccuracy of a pre-trained classifier on a set of unlabeled test inputs; (2)\nerror detection, which aims to identify mis-classified test inputs. In this\npaper, we propose a principled and practically effective framework that\nsimultaneously addresses the two tasks. The proposed framework iteratively\nlearns an ensemble of models to identify mis-classified data points and\nperforms self-training to improve the ensemble with the identified points.\nTheoretical analysis demonstrates that our framework enjoys provable guarantees\nfor both accuracy estimation and error detection under mild conditions readily\nsatisfied by practical deep learning models. Along with the framework, we\nproposed and experimented with two instantiations and achieved state-of-the-art\nresults on 59 tasks. For example, on iWildCam, one instantiation reduces the\nestimation error for unsupervised accuracy estimation by at least 70% and\nimproves the F1 score for error detection by at least 4.7% compared to existing\nmethods.\n"}
+{"abstract": "  This short note revisit information metric, underlining that it is a pseudo\nmetric on manifolds of observables (random variables), rather than as usual on\nprobability laws. Geodesics are characterized in terms of their boundaries and\nconditional independence condition. Pythagorean theorem is given, providing in\nspecial case potentially interesting natural integer triplets. This metric is\ncomputed for illustration on Diabetes dataset using infotopo package.\n"}
+{"abstract": "  Biomedical entity linking is the task of identifying mentions of biomedical\nconcepts in text documents and mapping them to canonical entities in a target\nthesaurus. Recent advancements in entity linking using BERT-based models follow\na retrieve and rerank paradigm, where the candidate entities are first selected\nusing a retriever model, and then the retrieved candidates are ranked by a\nreranker model. While this paradigm produces state-of-the-art results, they are\nslow both at training and test time as they can process only one mention at a\ntime. To mitigate these issues, we propose a BERT-based dual encoder model that\nresolves multiple mentions in a document in one shot. We show that our proposed\nmodel is multiple times faster than existing BERT-based models while being\ncompetitive in accuracy for biomedical entity linking. Additionally, we modify\nour dual encoder model for end-to-end biomedical entity linking that performs\nboth mention span detection and entity disambiguation and out-performs two\nrecently proposed models.\n"}
+{"abstract": "  Spiking Neural Networks (SNNs) have been attached great importance due to\ntheir biological plausibility and high energy-efficiency on neuromorphic chips.\nAs these chips are usually resource-constrained, the compression of SNNs is\nthus crucial along the road of practical use of SNNs. Most existing methods\ndirectly apply pruning approaches in artificial neural networks (ANNs) to SNNs,\nwhich ignore the difference between ANNs and SNNs, thus limiting the\nperformance of the pruned SNNs. Besides, these methods are only suitable for\nshallow SNNs. In this paper, inspired by synaptogenesis and synapse elimination\nin the neural system, we propose gradient rewiring (Grad R), a joint learning\nalgorithm of connectivity and weight for SNNs, that enables us to seamlessly\noptimize network structure without retraining. Our key innovation is to\nredefine the gradient to a new synaptic parameter, allowing better exploration\nof network structures by taking full advantage of the competition between\npruning and regrowth of connections. The experimental results show that the\nproposed method achieves minimal loss of SNNs' performance on MNIST and\nCIFAR-10 dataset so far. Moreover, it reaches a $\\sim$3.5% accuracy loss under\nunprecedented 0.73% connectivity, which reveals remarkable structure refining\ncapability in SNNs. Our work suggests that there exists extremely high\nredundancy in deep SNNs. Our codes are available at\nhttps://github.com/Yanqi-Chen/Gradient-Rewiring.\n"}
+{"abstract": "  We propose and analyze computationally a new fictitious domain method, based\non higher order space-time finite element discretizations, for the simulation\nof the nonstationary, incompressible Navier-Stokes equations on evolving\ndomains. The physical domain is embedded into a fixed computational mesh such\nthat arbitrary intersections of the moving domain's boundaries with the\nbackground mesh occur. The potential of such cut finite element techniques for\nhigher order space-time finite element methods has rarely been studied in the\nliterature so far and deserves further elucidation. The key ingredients of the\napproach are the weak formulation of Dirichlet boundary conditions by Nitsche's\nmethod, the flexible and efficient integration over all types of intersections\nof cells by moving boundaries and the spatial extension of the discrete\nphysical quantities to the entire computational background mesh including\nfictitious (ghost) subdomains of fluid flow. Thereby, an expensive remeshing\nand adaptation of the sparse matrix data structure are avoided and the\ncomputations are accelerated. To prevent spurious oscillations caused by\nirregular intersections of mesh cells, a penalization, defining also implicitly\nthe extension to ghost domains, is added. These techniques are embedded in an\narbitrary order, discontinuous Galerkin discretization of the time variable and\nan inf-sup stable discretization of the spatial variables. The parallel\nimplementation of the matrix assembly is described. The optimal order\nconvergence properties of the algorithm are illustrated in a numerical\nexperiment for an evolving domain. The well-known 2d benchmark of flow around a\ncylinder as well as flow around moving obstacles with arising cut cells and\nfictitious domains are considered further.\n"}
+{"abstract": "  Laser-plasma accelerators (LPAs) outperform current radiofrequency technology\nin acceleration strength by orders of magnitude. Yet, enabling them to deliver\ncompetitive beam quality for demanding applications, particularly in terms of\nenergy spread and stability, remains a major challenge. In this Letter, we\npropose to combine bunch decompression and active plasma dechirping for\ndrastically improving the energy profile and stability of beams from LPAs.\nStart-to-end simulations demonstrate the efficacy of such post-acceleration\nphase-space manipulations and the potential to reduce current state-of-the-art\nenergy spread and jitter from $1\\%$ to $0.10\\%$ and $0.024\\%$, respectively,\nclosing the beam-quality gap to conventional acceleration schemes.\n"}
+{"abstract": "  Understanding how massive stars die as supernovae is a crucial question in\nmodern astrophysics. Supernovae are powerful stellar explosions and key drivers\nin the cosmic baryonic cycles by injecting their explosion energy and heavy\nelements to the interstellar medium that forms new stars. After decades of\neffort, astrophysicists have built up a stand model for the explosion mechanism\nof massive stars. However, this model is challenged by new kinds of stellar\nexplosions discovered in the recent transit surveys. In particular, the new\npopulation called superluminous supernovae, which are a hundred times brighter\nthan typical supernovae, is revolutionizing our understanding of supernovae.\nNew studies suggest the superluminous supernovae are associated with the\nunusual demise of very massive stars and their extreme supernovae powered by\nthe radioactive isotopes or compact objects formed after the explosion.\nStudying these supernovae fills a gap of knowledge between the death of massive\nstars and their explosions; furthermore, we may apply their intense luminosity\nto light up the distant universe. This paper aims to provide a timely review of\nsuperluminous supernovae physics, focusing on the latest development of their\ntheoretical models.\n"}
+{"abstract": "  Learning classifiers that are robust to adversarial examples has received a\ngreat deal of recent attention. A major drawback of the standard robust\nlearning framework is there is an artificial robustness radius $r$ that applies\nto all inputs. This ignores the fact that data may be highly heterogeneous, in\nwhich case it is plausible that robustness regions should be larger in some\nregions of data, and smaller in others. In this paper, we address this\nlimitation by proposing a new limit classifier, called the neighborhood optimal\nclassifier, that extends the Bayes optimal classifier outside its support by\nusing the label of the closest in-support point. We then argue that this\nclassifier maximizes the size of its robustness regions subject to the\nconstraint of having accuracy equal to the Bayes optimal. We then present\nsufficient conditions under which general non-parametric methods that can be\nrepresented as weight functions converge towards this limit, and show that both\nnearest neighbors and kernel classifiers satisfy them under certain conditions.\n"}
+{"abstract": "  In this paper we settle the two-dimensional case of a conjecture involving\nunknown semialgebraic functions with specified smoothness. More precisely, we\nprove the following result: Let $\\mathcal{H}$ be a semialgebraic bundle with\nrespect to $C^m_{loc}\\left( \\mathbb{R}^{2},\\mathbb{R}^{D}\\right) .$ If\n$\\mathcal{H}$ has a section, then it has a semialgebraic section.\n"}
+{"abstract": "  This note is devoted to the study of Hyt\\\"{o}nen's extrapolation theorem of\ncompactness on weighted Lebesgue spaces. Two criteria of compactness of linear\noperators in the two-weight setting are obtained. As applications, we obtain\ntwo-weight compactness of commutators of Calder\\'{o}n--Zygmund operators,\nfractional integrals and bilinear Calder\\'{o}n--Zygmund operators.\n"}
+{"abstract": "  Online class-incremental continual learning (CL) studies the problem of\nlearning new classes continually from an online non-stationary data stream,\nintending to adapt to new data while mitigating catastrophic forgetting. While\nmemory replay has shown promising results, the recency bias in online learning\ncaused by the commonly used Softmax classifier remains an unsolved challenge.\nAlthough the Nearest-Class-Mean (NCM) classifier is significantly undervalued\nin the CL community, we demonstrate that it is a simple yet effective\nsubstitute for the Softmax classifier. It addresses the recency bias and avoids\nstructural changes in the fully-connected layer for new classes. Moreover, we\nobserve considerable and consistent performance gains when replacing the\nSoftmax classifier with the NCM classifier for several state-of-the-art replay\nmethods. To leverage the NCM classifier more effectively, data embeddings\nbelonging to the same class should be clustered and well-separated from those\nwith a different class label. To this end, we contribute Supervised Contrastive\nReplay (SCR), which explicitly encourages samples from the same class to\ncluster tightly in embedding space while pushing those of different classes\nfurther apart during replay-based training. Overall, we observe that our\nproposed SCR substantially reduces catastrophic forgetting and outperforms\nstate-of-the-art CL methods by a significant margin on a variety of datasets.\n"}
+{"abstract": "  The conventional federated learning (FedL) architecture distributes machine\nlearning (ML) across worker devices by having them train local models that are\nperiodically aggregated by a server. FedL ignores two important characteristics\nof contemporary wireless networks, however: (i) the network may contain\nheterogeneous communication/computation resources, while (ii) there may be\nsignificant overlaps in devices' local data distributions. In this work, we\ndevelop a novel optimization methodology that jointly accounts for these\nfactors via intelligent device sampling complemented by device-to-device (D2D)\noffloading. Our optimization aims to select the best combination of sampled\nnodes and data offloading configuration to maximize FedL training accuracy\nsubject to realistic constraints on the network topology and device\ncapabilities. Theoretical analysis of the D2D offloading subproblem leads to\nnew FedL convergence bounds and an efficient sequential convex optimizer. Using\nthis result, we develop a sampling methodology based on graph convolutional\nnetworks (GCNs) which learns the relationship between network attributes,\nsampled nodes, and resulting offloading that maximizes FedL accuracy. Through\nevaluation on real-world datasets and network measurements from our IoT\ntestbed, we find that our methodology while sampling less than 5% of all\ndevices outperforms conventional FedL substantially both in terms of trained\nmodel accuracy and required resource utilization.\n"}
+{"abstract": "  Bayesian computation plays an important role in modern machine learning and\nstatistics to reason about uncertainty. A key computational challenge in\nBayesian inference is to develop efficient techniques to approximate, or draw\nsamples from posterior distributions. Stein variational gradient decent (SVGD)\nhas been shown to be a powerful approximate inference algorithm for this issue.\nHowever, the vanilla SVGD requires calculating the gradient of the target\ndensity and cannot be applied when the gradient is unavailable or too expensive\nto evaluate. In this paper we explore one way to address this challenge by the\nconstruction of a local surrogate for the target distribution in which the\ngradient can be obtained in a much more computationally feasible manner. More\nspecifically, we approximate the forward model using a deep neural network\n(DNN) which is trained on a carefully chosen training set, which also\ndetermines the quality of the surrogate. To this end, we propose a general\nadaptation procedure to refine the local approximation online without\ndestroying the convergence of the resulting SVGD. This significantly reduces\nthe computational cost of SVGD and leads to a suite of algorithms that are\nstraightforward to implement. The new algorithm is illustrated on a set of\nchallenging Bayesian inverse problems, and numerical experiments demonstrate a\nclear improvement in performance and applicability of standard SVGD.\n"}
+{"abstract": "  We determine a characterization of all 2-slices of equivariant spectra over\nthe Klein four-group $C_2\\times C_2$. We then describe all slices of integral\nsuspensions of the equivariant Eilenberg-MacLane spectrum\n$H\\underline{\\mathbb{Z}}$ for the constant Mackey functor over $C_2\\times C_2$.\n"}
+{"abstract": "  Beam stripping losses of H- ion beams by interactions with residual gas and\nelectromagnetic fields are evaluated. These processes play an important role in\ncompact cyclotrons where the beam is produced on an internal ion source, and\nthey operate under high magnetic field. The implementation of stripping\ninteractions into the beam dynamics code OPAL provides an adequate framework to\nestimate the stripping losses for compact cyclotrons such as AMIT. The analysis\nis focused on optimizing the high energy beam current delivered to the target.\nThe optimization is performed by adjusting parameters of the ion source to\nregulate the vacuum level inside the accelerator and minimize the beam\nstripping losses.\n"}
+{"abstract": "  In the manuscript, we report evidence on broad \\oiii components apparently\nobscured in Type-2 AGN under the framework of the Unified model, after checking\nproperties of broad \\oiii emissions in large samples of Type-1 and Type-2 AGN\nin SDSS DR12. We can well confirm the statistically lower flux ratios of the\nbroad to the core \\oiii components in Type-2 AGN than in Type-1 AGN, which can\nbe naturally explained by stronger obscured broad \\oiii components by central\ndust torus in Type-2 AGN, unless the Unified model for AGN was not appropriate\nto the narrow emission lines. The results provide further evidence to support\nbroad \\oiii components coming from emission regions nearer to central BHs, and\nalso indicate the core \\oiii component as the better indicator for central\nactivities in Type-2 AGN, due to few effects of obscuration on the core \\oiii\ncomponent. Considering the broad \\oiii components as signs of central outflows,\nthe results provide evidence for strong central outflows being preferentially\nobscured in Type-2 AGN. Furthermore, the obscured broad \\oiii component can be\napplied to explain the different flux ratios of \\oiiihb between Type-1 and\nType-2 AGN in the BPT diagram.\n"}
+{"abstract": "  We present the collective excitation spectrum analysis of binary\nBose-Einstein condensates (BECs) with spin-orbit (SO) and Rabi couplings in a\nquasi-two-dimensional system. In particular, we investigate the role of SO and\nRabi coupling strengths in determining the dynamical stability of the coupled\nBECs using Bogoliubov-de Gennes (BdG) theory. Using the eigenergy of BdG\nspectrum, we confirm the existence of phonon, roton, and maxon modes with weak\nrepulsive intra- and inter-species contact interactions. The depth of the\nminimum corresponding to the roton mode depends strongly on the coupling\nstrength. We find that the increase of the SO coupling leads to instability,\nwhile the increase in the Rabi coupling stabilizes the system. Also the\neigenvectors of BdG spectrum indicates the presence of density like mode in the\nstable regime and spin like modes in unstable regimes. A phase diagram\ndemonstrating the stability regime in the plane of SO and Rabi coupling\nstrengths is obtained. Finally, we complement the observation of the excitation\nspectrum with the direct numerical simulation results of coupled\nGross-Pitaevskii equations.\n"}
+{"abstract": "  The Ashtekar-Streubel fluxes give a proposed definition of the angular\nmomentum emitted by an isolated gravitationally radiating system. This was\nbased on identifying a \"phase space of radiative modes,\" independent of any\ninternal degrees of freedom, and using the Hamiltonian functions conjugate to\nthe action of the Bondi-Metzner-Sachs (BMS) group as the energy-momentum,\nsupermomentum and angular momentum. However, there are some difficulties in\nformulating this phase space so as to get the proper degrees of freedom. I\nconsider how to address this point, and also to identify circumstances in which\nthe radiative modes are sufficiently decoupled that they can be assigned their\nown angular momentum. Two different phase spaces are considered. One, which\nseems to reflect what previous workers have done (it leads to the usual\nformulas for the Ashtekar-Streubel fluxes), is mathematically simpler, but this\nhas unwanted degrees of freedom and is difficult to interpret physically. The\nsecond is a quotient of the first; it is better justified (at least in terms of\ndegrees of freedom), and plausibly decouples the radiative angular momentum\nfrom internal modes (at least for systems which ultimately become stationary).\nThe symplectic form for this quotient, and the corresponding angular momentum\nflux, involve highly non-local correlations. Both phase spaces are shown to\nhave Poisson brackets implementing the BMS algebra. For both phase spaces, for\naxisymmetric space-times vacuum near null infinity, there can be no\ngravitational radiation of angular momentum about the axis of symmetry,\nalthough matter can carry off angular momentum in such cases.\n"}
+{"abstract": "  The emergence of the modern gig economy introduces a new set of employment\nconsiderations for firms and laborers that include various trade-offs. With a\ngame-theoretical approach, we examine the influences of technology, policy and\nmarkets on firm and worker preferences for gig labor. Theoretically, we present\na new extension to the replicator equation and model oscillating dynamics in\ntwo-player asymmetric bi-matrix games with time-evolving environments,\nintroducing concepts of the attractor arc, trapping zone and escape. We\ndemonstrate how changing market conditions result in distinct evolutionary\npatterns for gig-labor preferences across high and low skill work-forces, which\nwe explain through their differing sensitivities to market-driven consumer\ndemand and financial incentives among other considerations. Informing tensions\nregarding the future of this new employment category, we present a novel payoff\nframework to analyze the role of technology on the growth of the gig economy.\nFinally, we explore regulatory implications within the gig economy,\ndemonstrating how intervals of lenient and strict policy alter firm and worker\nsensitivities between gig and employee labor strategies.\n"}
+{"abstract": "  We study the Closest Pair Problem in Hamming metric, which asks to find the\npair with the smallest Hamming distance in a collection of binary vectors. We\ngive a new randomized algorithm for the problem on uniformly random input\noutperforming previous approaches whenever the dimension of input points is\nsmall compared to the dataset size. For moderate to large dimensions, our\nalgorithm matches the time complexity of the previously best-known locality\nsensitive hashing based algorithms. Technically our algorithm follows similar\ndesign principles as Dubiner (IEEE Trans. Inf. Theory 2010) and May-Ozerov\n(Eurocrypt 2015). Besides improving the time complexity in the aforementioned\nareas, we significantly simplify the analysis of these previous works. We give\na modular analysis, which allows us to investigate the performance of the\nalgorithm also on non-uniform input distributions. Furthermore, we give a proof\nof concept implementation of our algorithm which performs well in comparison to\na quadratic search baseline. This is the first step towards answering an open\nquestion raised by May and Ozerov regarding the practicability of algorithms\nfollowing these design principles.\n"}
+{"abstract": "  Predicting the answer to a product-related question is an emerging field of\nresearch that recently attracted a lot of attention. Answering subjective and\nopinion-based questions is most challenging due to the dependency on\ncustomer-generated content. Previous works mostly focused on review-aware\nanswer prediction; however, these approaches fail for new or unpopular\nproducts, having no (or only a few) reviews at hand. In this work, we propose a\nnovel and complementary approach for predicting the answer for such questions,\nbased on the answers for similar questions asked on similar products. We\nmeasure the contextual similarity between products based on the answers they\nprovide for the same question. A mixture-of-expert framework is used to predict\nthe answer by aggregating the answers from contextually similar products.\nEmpirical results demonstrate that our model outperforms strong baselines on\nsome segments of questions, namely those that have roughly ten or more similar\nresolved questions in the corpus. We additionally publish two large-scale\ndatasets used in this work, one is of similar product question pairs, and the\nsecond is of product question-answer pairs.\n"}
+{"abstract": "  We use categorical methods to define a new flavor of Petri nets where\ntransitions can only fire a limited number of times, specified by a quantity\nthat we call mana. We do so with chemistry in mind, looking at ways of\nmodelling the behavior of chemical reactions that depend on enzymes to work. We\nprove that such nets can be either obtained as a result of a comonadic\nconstruction, or by enriching them with extra information encoded into a\nfunctor. We then use a well-established categorical result to prove that the\ntwo constructions are equivalent, and generalize them to the case where the\nfiring of some transitions can \"regenerate\" the mana of others. This allows us\nto represent the action of catalysts and also of biochemical processes where\nthe byproducts of some chemical reaction are exactly the enzymes that another\nreaction needs to work.\n"}
+{"abstract": "  Multidimensional packing problems generalize the classical packing problems\nsuch as Bin Packing, Multiprocessor Scheduling by allowing the jobs to be\n$d$-dimensional vectors. While the approximability of the scalar problems is\nwell understood, there has been a significant gap between the approximation\nalgorithms and the hardness results for the multidimensional variants. In this\npaper, we close this gap by giving almost tight hardness results for these\nproblems.\n  1. We show that Vector Bin Packing has no polynomial time $\\Omega( \\log d)$\nfactor asymptotic approximation algorithm when $d$ is a large constant,\nassuming $\\textsf{P}\\neq \\textsf{NP}$. This matches the $\\ln d + O(1)$ factor\napproximation algorithms (Chekuri, Khanna SICOMP 2004, Bansal, Caprara,\nSviridenko SICOMP 2009, Bansal, Eli\\'{a}s, Khan SODA 2016) upto constants.\n  2. We show that Vector Scheduling has no polynomial time algorithm with an\napproximation ratio of $\\Omega\\left( (\\log d)^{1-\\epsilon}\\right)$ when $d$ is\npart of the input, assuming $\\textsf{NP}\\nsubseteq \\textsf{ZPTIME}\\left(\nn^{(\\log n)^{O(1)}}\\right)$. This almost matches the $O\\left( \\frac{\\log\nd}{\\log \\log d}\\right)$ factor algorithms(Harris, Srinivasan JACM 2019, Im,\nKell, Kulkarni, Panigrahi SICOMP 2019). We also show that the problem is\nNP-hard to approximate within $(\\log \\log d)^{\\omega(1)}$.\n  3. We show that Vector Bin Covering is NP-hard to approximate within\n$\\Omega\\left( \\frac{\\log d}{\\log \\log d}\\right)$ when $d$ is part of the input,\nalmost matching the $O(\\log d)$ factor algorithm (Alon et al., Algorithmica\n1998).\n  Previously, no hardness results that grow with $d$ were known for Vector\nScheduling and Vector Bin Covering when $d$ is part of the input and for Vector\nBin Packing when $d$ is a fixed constant.\n"}
+{"abstract": "  Regularizers helped deep neural networks prevent feature co-adaptations.\nDropout,as a commonly used regularization technique, stochastically disables\nneuron ac-tivations during network optimization. However, such complete feature\ndisposal can affect the feature representation and network understanding.\nToward betterdescriptions of latent representations, we present DropGraph that\nlearns regularization function by constructing a stand-alone graph from the\nbackbone features. DropGraph first samples stochastic spatial feature vectors\nand then incorporates graph reasoning methods to generate feature map\ndistortions. This add-on graph regularizes the network during training and can\nbe completely skipped during inference. We provide intuitions on the linkage\nbetween graph reasoning andDropout with further discussions on how partial\ngraph reasoning method reduces feature correlations. To this end, we\nextensively study the modeling of graphvertex dependencies and the utilization\nof the graph for distorting backbone featuremaps. DropGraph was validated on\nfour tasks with a total of 7 different datasets.The experimental results show\nthat our method outperforms other state-of-the-art regularizers while leaving\nthe base model structure unmodified during inference.\n"}
+{"abstract": "  We extend the (continuous) multivariate Almkvist-Zeilberger algorithm in\norder to apply it for instance to special Feynman integrals emerging in\nrenormalizable Quantum field Theories. We will consider multidimensional\nintegrals over hyperexponential integrands and try to find closed form\nrepresentations in terms of nested sums and products or iterated integrals. In\naddition, if we fail to compute a closed form solution in full generality, we\nmay succeed in computing the first coefficients of the Laurent series\nexpansions of such integrals in terms of indefinite nested sums and products or\niterated integrals. In this article we present the corresponding methods and\nalgorithms. Our Mathematica package MultiIntegrate, can be considered as an\nenhanced implementation of the (continuous) multivariate Almkvist Zeilberger\nalgorithm to compute recurrences or differential equations for hyperexponential\nintegrands and integrals. Together with the summation package Sigma and the\npackage HarmonicSums our package provides methods to compute closed form\nrepresentations (or coefficients of the Laurent series expansions) of\nmultidimensional integrals over hyperexponential integrands in terms of nested\nsums or iterated integrals.\n"}
+{"abstract": "  A common optimization tool used in deep reinforcement learning is momentum,\nwhich consists in accumulating and discounting past gradients, reapplying them\nat each iteration. We argue that, unlike in supervised learning, momentum in\nTemporal Difference (TD) learning accumulates gradients that become doubly\nstale: not only does the gradient of the loss change due to parameter updates,\nthe loss itself changes due to bootstrapping. We first show that this\nphenomenon exists, and then propose a first-order correction term to momentum.\nWe show that this correction term improves sample efficiency in policy\nevaluation by correcting target value drift. An important insight of this work\nis that deep RL methods are not always best served by directly importing\ntechniques from the supervised setting.\n"}
+{"abstract": "  We propose a system to capture nearly-synchronous frame streams from multiple\nand moving handheld mobiles that is suitable for dynamic object 3D\nreconstruction. Each mobile executes Simultaneous Localisation and Mapping\non-board to estimate its pose, and uses a wireless communication channel to\nsend or receive synchronisation triggers. Our system can harvest frames and\nmobile poses in real time using a decentralised triggering strategy and a\ndata-relay architecture that can be deployed either at the Edge or in the\nCloud. We show the effectiveness of our system by employing it for 3D skeleton\nand volumetric reconstructions. Our triggering strategy achieves equal\nperformance to that of an NTP-based synchronisation approach, but offers higher\nflexibility, as it can be adjusted online based on application needs. We\ncreated a challenging new dataset, namely 4DM, that involves six handheld\naugmented reality mobiles recording an actor performing sports actions\noutdoors. We validate our system on 4DM, analyse its strengths and limitations,\nand compare its modules with alternative ones.\n"}
+{"abstract": "  Global 21cm experiments aim to measure the sky averaged HI absorption signal\nfrom cosmic dawn and the epoch of reionisation. However, antenna chromaticity\ncoupling to bright foregrounds can introduce distortions into the observational\ndata of such experiments. We demonstrate a method for guiding the antenna\ndesign of a global experiment through data analysis simulations. This is done\nby performing simulated observations for a range of inserted 21cm signals, then\nattempting to identify the signals with a data analysis pipeline. We\ndemonstrate this method on five antennae that were considered as potential\ndesigns for the Radio Experiment for the Analysis of Cosmic Hydrogen (REACH); a\nconical log spiral antenna, an inverted conical sinuous antenna and polygonal-,\nsquare- and elliptical-bladed dipoles. We find that the log spiral performs\nsignificantly better than the other antennae tested, able to correctly and\nconfidently identify every inserted 21cm signal. In second place is the\npolygonal dipole antenna, which was only unable to detect signals with both\nvery low amplitudes of 0.05K and low centre frequency of 80MHz. The conical\nsinuous antenna was found to perform least accurately, only able to detect the\nhighest amplitude 21cm signals, and even then with biases. We also demonstrate\nthat, due to the non-trivial nature of chromatic distortion and the processes\nof correcting for it, these are not the results that could have been expected\nsuperficially from the extent of chromatic variation in each antenna.\n"}
+{"abstract": "  We investigate the time evolution and asymptotic behavior of a system of two\ntwo-level atoms (qubits) interacting off-resonance with a single mode radiation\nfield. The two atoms are coupled to each other through dipole-dipole as well as\nIsing interactions. An exact analytic solution for the system dynamics that\nspans the entire phase space is provided. We focus on initial states that cause\nthe system to evolve to entanglement sudden death (ESD) between the two atoms.\nWe find that combining the Ising and dipole-dipole interactions is very\npowerful in controlling the entanglement dynamics and ESD compared with either\none of them separately. Their effects on eliminating ESD may add up\nconstructively or destructively depending on the type of Ising interaction\n(Ferromagnetic or anti-Ferromagnetic), the detuning parameter value, and the\ninitial state of the system. The asymptotic behavior of the ESD is found to\ndepend substantially on the initial state of the system, where ESD can be\nentirely eliminated by tuning the system parameters except in the case of an\ninitial correlated Bell state. Interestingly, the entanglement, atomic\npopulation and quantum correlation between the two atoms and the field\nsynchronize and reach asymptotically quasi-steady dynamic states. Each one of\nthem ends up as a continuous irregular oscillation, where the collapse periods\nvanish, with a limited amplitude and an approximately constant mean value that\ndepend on the initial state and the system parameters choice. This indicates an\nasymptotic continuous exchange of energy (and strong quantum correlation)\nbetween the atoms and the field takes place, accompanied by diminished ESD for\nthese chosen setups of the system. This system can be realized in spin states\nof quantum dots or Rydberg atoms in optical cavities, and superconducting or\nhybrid qubits in linear resonators.\n"}
+{"abstract": "  In a recent paper [1], it has been claimed that the outcomes of a quantum\ncoin toss which is idealized as an infinite binary sequence is 1-random. We\nalso defend the correctness of this claim and assert that the outcomes of\nquantum measurements can be considered as an infinite 1-random or n-random\nsequence. In this brief note we present our comments on this claim. We have\nmostly positive but also some negative comments on the arguments of the paper\n[1]. Furthermore, we speculate a logical-axiomatic study of nature which we\nbelieve can intrinsically provide quantum mechanical probabilities based on\n1(n)-randomness.\n"}
+{"abstract": "  This is the first part of a series of two papers aiming to construct a\ncategorification of the braiding on tensor products of Verma modules, and in\nparticular of the Lawrence--Krammer--Bigelow representations. \\\\ In this part,\nwe categorify all tensor products of Verma modules and integrable modules for\nquantum $\\mathfrak{sl_2}$. The categorification is given by derived categories\nof dg versions of KLRW algebras which generalize both the tensor product\nalgebras of Webster, and the dg-algebras used by Lacabanne, the second author\nand Vaz. We compute a basis for these dgKLRW algebras by using rewriting\nmethods modulo braid-like isotopy, which we develop in an Appendix.\n"}
+{"abstract": "  Let $R$ be a finite commutative ring with unity $1_R$ and $k \\in R$.\nProperties of one-sided $k$-orthogonal $n \\times n$ matrices over $R$ are\npresented. When $k$ is idempotent, these matrices form a semigroup structure.\nConsequently new families of matrix semigroups over certain finite semi-local\nrings are constructed. When $k=1_R$, the classical orthogonal group of degree\n$n$ is obtained. It is proved that, if $R$ is a semi-local ring, then these\nsemigroups are isomorphic to a finite product of $k$-orthogonal semigroups over\nfields. Finally, the antiorthogonal and self-orthogonal matrices that give rise\nto leading-systematic self-dual or weakly self-dual linear codes are discussed.\n"}
+{"abstract": "  It turned out that the partial sums $g_n(z) = \\sum_{k=0}^n \\frac{(a_1)_k ...\n(a_p)_k}{(b_1)_k ... (b_q)_k} \\frac{z^k}{k!}$, of the generalized\nhypergeometric series ${}_p F_q(a_1,...,a_p; b_1,...,b_q;z)$, with parameters\n$a_j,b_l\\in\\mathbb{C}\\backslash\\{ 0,-1,-2,... \\}$, are Sobolev orthogonal\npolynomials. The corresponding monic polynomials $G_n(z)$ are polynomials of\n$R_I$ type, and therefore they are related to biorthogonal rational functions.\nPolynomials $g_n$ possess a differential equation (in $z$), and a recurrence\nrelation (in $n$). We study integral representations for $g_n$, and some other\ntheir basic properties. Partial sums of arbitrary power series with non-zero\ncoefficients are shown to be also related to biorthogonal rational functions.\nWe obtain a relation of polynomials $g_n(z)$ to Jacobi-type pencils and their\nassociated polynomials.\n"}
+{"abstract": "  In this paper we demonstrate end-to-end precoded multi-user multiple-input\nsingle-output (MU-MISO) communications over a live GEO satellite link. Precoded\ncommunications enable full frequency reuse (FFR) schemes in satellite\ncommunications (SATCOM) to achieve broader service availability and higher\nspectrum efficiency than with the conventional four-color (4CR) and two-color\n(2CR) reuse approaches. In this scope, we develop an over-the-air test-bed for\nend-to-end precoding validations. We use an actual multi-beam satellite to\ntransmit and receive precoded signals using the DVB-S2X standard based gateway\nand user terminals. The developed system is capable of end-to-end real-time\ncommunications over the satellite link including channel measurements and\nprecompensation. It is shown, that by successfully canceling inter-user\ninterference in the actual satellite FFR link precoding brings gains in terms\nof enhanced SINR and increased system goodput.\n"}
+{"abstract": "  This paper presents the Runge-Kutta-Legendre finite difference scheme,\nallowing for an additional shift in its polynomial representation. A short\npresentation of the stability region, comparatively to the\nRunge-Kutta-Chebyshev scheme follows. We then explore the problem of pricing\nAmerican options with the Runge-Kutta-Legendre scheme under the one factor\nBlack-Scholes and the two factor Heston stochastic volatility models, as well\nas the pricing of butterfly spread and digital options under the uncertain\nvolatility model, where a Hamilton-Jacobi-Bellman partial differential equation\nneeds to be solved. We explore the order of convergence in these problems, as\nwell as the option greeks stability, compared to the literature and popular\nschemes such as Crank-Nicolson, with Rannacher time-stepping.\n"}
+{"abstract": "  The inductive biases of trained neural networks are difficult to understand\nand, consequently, to adapt to new settings. We study the inductive biases of\nlinearizations of neural networks, which we show to be surprisingly good\nsummaries of the full network functions. Inspired by this finding, we propose a\ntechnique for embedding these inductive biases into Gaussian processes through\na kernel designed from the Jacobian of the network. In this setting, domain\nadaptation takes the form of interpretable posterior inference, with\naccompanying uncertainty estimation. This inference is analytic and free of\nlocal optima issues found in standard techniques such as fine-tuning neural\nnetwork weights to a new task. We develop significant computational speed-ups\nbased on matrix multiplies, including a novel implementation for scalable\nFisher vector products. Our experiments on both image classification and\nregression demonstrate the promise and convenience of this framework for\ntransfer learning, compared to neural network fine-tuning. Code is available at\nhttps://github.com/amzn/xfer/tree/master/finite_ntk.\n"}
+{"abstract": "  Classical graph modeling approaches such as Erd\\H{o}s R\\'{e}nyi (ER) random\ngraphs or Barab\\'asi-Albert (BA) graphs, here referred to as stylized models,\naim to reproduce properties of real-world graphs in an interpretable way. While\nuseful, graph generation with stylized models requires domain knowledge and\niterative trial and error simulation. Previous work by Stoehr et al. (2019)\naddresses these issues by learning the generation process from graph data,\nusing a disentanglement-focused deep autoencoding framework, more specifically,\na $\\beta$-Variational Autoencoder ($\\beta$-VAE). While they successfully\nrecover the generative parameters of ER graphs through the model's latent\nvariables, their model performs badly on sequentially generated graphs such as\nBA graphs, due to their oversimplified decoder. We focus on recovering the\ngenerative parameters of BA graphs by replacing their $\\beta$-VAE decoder with\na sequential one. We first learn the generative BA parameters in a supervised\nfashion using a Graph Neural Network (GNN) and a Random Forest Regressor, by\nminimizing the squared loss between the true generative parameters and the\nlatent variables. Next, we train a $\\beta$-VAE model, combining the GNN encoder\nfrom the first stage with an LSTM-based decoder with a customized loss.\n"}
+{"abstract": "  We study theoretically the superconductor diodes, where the magnitude of the\ncritical current changes as the direction is reversed, in terms of a\ngeneralized Ginzburg-Landau model with the higher-order terms in the momentum\nof the order parameter. This theory is applied to Rashba spin-orbit coupled\nsystems, where analytical relations between the nonreciprocal critical currents\nand the system parameters are achieved. Numerical calculations with mean-field\ntheory are also obtained to study broader parameter regions. These results\noffer a rather general description and design principles of superconductor\ndiodes.\n"}
+{"abstract": "  Available algorithms for the initialization of volume fractions typically\nutilize exact functions to model fluid interfaces, or they rely on\ncomputationally costly intersections between volume meshes. Here, a new\nalgorithm is proposed that computes signed distances and volume fractions on\nunstructured meshes from arbitrarily shaped surfaces, e.g. originating from\nexperimental data. The proposed algorithm calculates signed distances\ngeometrically near the fluid interface, approximated as a triangle surface\nmesh, and propagates the inside/outside information by an approximate solution\nof a Laplace equation. Volume fractions are computed based on signed distances,\nusing either geometrical intersections between cells of the unstructured mesh\nand a sub-set of the surface mesh that represents the interface, or using a\npolynomial approximation and adaptive mesh refinement. Although primarily\ndeveloped for multiphase flow simulations, the proposed algorithm can\npotentially be used for other problems that require a phase-indicator:\ninside/outside information with respect to an arbitrarily shaped surface on\narbitrarily unstructured meshes.\n"}
+{"abstract": "  In the paper, we use the equivalent formulation of a finite state mean field\ngame as a control problem with mixed constraints to study the dependence of\nsolutions to finite state mean field game on an initial distribution of\nplayers. We introduce the concept of value multifunction of the mean field game\nthat is a mapping assigning to an initial time and an initial distribution a\nset of expected outcomes of the representative player corresponding to\nsolutions of the mean field game. Using the control reformulation of the finite\nstate mean field game, we give the sufficient condition on a given\nmultifunction to be a value multifunction in the terms of the viability theory.\nThe maximal multifunction (i.e. the mapping assigning to an initial time and\ndistribution the whole set of values corresponding to solutions of the mean\nfield game) is characterized via the backward attainability set for the certain\ncontrol systems.\n"}
+{"abstract": "  Locating each node in a wireless sensor network is essential for starting the\nmonitoring job and sending information about the area. One method that has been\nused in hard and inaccessible environments is randomly scattering each node in\nthe area. In order to reduce the cost of using GPS at each node, some nodes\nshould be equipped with GPS (anchors), Then using the belief propagation\nalgorithm, locate other nodes. The number of anchor nodes must be reduced since\nthey are expensive. Furthermore, the location of these nodes affects the\nalgorithm's performance. Using multi-objective optimization, an algorithm is\nintroduced in this paper that minimizes the estimated location error and the\nnumber of anchor nodes. According to simulation results, This algorithm\nproposes a set of solutions with less energy consumption and less error than\nsimilar algorithms.\n"}
+{"abstract": "  We investigate in the framework of quantum noise theory how the striking\nboundary-sensitivity recently discovered in the context of non-Hermitian (NH)\ntopological phases may be harnessed to devise novel quantum sensors.\nSpecifically, we study a quantum-optical setting of coupled modes arranged in\nan array with broken ring geometry that would realize a NH topological phase in\nthe classical limit. Using methods from quantum-information theory of Gaussian\nstates, we show that a small coupling induced between the ends of the broken\nring may be detected with a precision that increases exponentially in the\nnumber of coupled modes, e.g. by heterodyne detection of two output modes.\nWhile this robust effect only relies on reaching a NH topological regime, we\nidentify a resonance phenomenon without direct classical counterpart that\nprovides an experimental knob for drastically enhancing the aforementioned\nexponential growth. Our findings pave the way towards designing quantum NH\ntopological sensors (QUANTOS) that may observe with high precision any physical\nobservable that couples to the boundary conditions of the device.\n"}
+{"abstract": "  Optical vortex beams are profiled as helical wavefronts with a phase\nsingularity carrying an orbital angular momentum (OAM) associated with their\nspatial distribution. The transverse intensity distribution of a conventional\noptical vortex has a strong dependence on the carried topological charge.\nHowever, perfect optical vortex (POV) beams have their transverse intensity\ndistribution independent of their charge. Such `size-invariant' POV beams have\nfound exciting applications in optical manipulation, imaging and communication.\nIn this article, we investigate the use of POV modes in the efficient\ngeneration of high dimensional quantum states of light. We generate heralded\nsingle photons carrying OAM using spontaneous parametric down-conversion (SPDC)\nof POV beams. We show that the heralding efficiency of the SPDC single photons\ngenerated with POV pump is greater than that with normal optical vortex beams.\nThe dimensionality of the two-photon OAM states is increased with POV modes in\nthe pump and projective measurements using Bessel-Gaussian vortex modes that\ngive POV, instead of the Laguerre-Gaussian modes.\n"}
+{"abstract": "  In this paper we consider some results obtained for graphs using minimal\nvertex separators and generalized chordality and translate them to the context\nof Geometric Group Theory. Using these new tools, we are able to give two new\ncharacterizations for a group to be virtually free. Furthermore, we prove that\nthe Baumslag-Solitar group $BS(1,n)$ is $k$-chordal for some $k$ if and only if\n$|n|<3$ and we give an application of generalized chordality to the study of\nthe word problem.\n"}
+{"abstract": "  In many real-world scenarios where extrinsic rewards to the agent are\nextremely sparse, curiosity has emerged as a useful concept providing intrinsic\nrewards that enable the agent to explore its environment and acquire\ninformation to achieve its goals. Despite their strong performance on many\nsparse-reward tasks, existing curiosity approaches rely on an overly holistic\nview of state transitions, and do not allow for a structured understanding of\nspecific aspects of the environment. In this paper, we formulate curiosity\nbased on grounded question answering by encouraging the agent to ask questions\nabout the environment and be curious when the answers to these questions\nchange. We show that natural language questions encourage the agent to uncover\nspecific knowledge about their environment such as the physical properties of\nobjects as well as their spatial relationships with other objects, which serve\nas valuable curiosity rewards to solve sparse-reward tasks more efficiently.\n"}
+{"abstract": "  We provide sufficient conditions on the profile $\\varphi$, on the sequence of\nrandom variables $\\varepsilon_j>0$ and on the sequence of random vectors\n$y_j\\in\\mathbb{R}^n$ such that\n$\\mathscr{E}\\left(\\frac{1}{\\varepsilon_j^n(\\omega)}\\int_{z\\in\\mathbb{R}^n}\\varphi\\left(\\frac{|x-z-y_j(\\omega)|}{\\varepsilon_j(\\omega)}\\right)f(z)\ndz\\right)\\longrightarrow f(x)$ when $j\\to\\infty$ for almost every\n$x\\in\\mathbb{R}^n$, $f\\in L^p(\\mathbb{R}^n)$, $1\\leq p\\leq\\infty$, where\n$\\mathscr{E}$ denotes the expectation, $\\varepsilon_j$ tends to\n$0\\in\\mathbb{R}$ in law and $y_j$ tends to $\\mathbf{0}\\in\\mathbb{R}^n$ in law.\n"}
+{"abstract": "  Breast cancer is one of the most common cause of deaths among women.\nMammography is a widely used imaging modality that can be used for cancer\ndetection in its early stages. Deep learning is widely used for the detection\nof cancerous masses in the images obtained via mammography. The need to improve\naccuracy remains constant due to the sensitive nature of the datasets so we\nintroduce segmentation and wavelet transform to enhance the important features\nin the image scans. Our proposed system aids the radiologist in the screening\nphase of cancer detection by using a combination of segmentation and wavelet\ntransforms as pre-processing augmentation that leads to transfer learning in\nneural networks. The proposed system with these pre-processing techniques\nsignificantly increases the accuracy of detection on Mini-MIAS.\n"}
+{"abstract": "  The recent report of an association of the gravitational-wave (GW) binary\nblack hole (BBH) merger GW190521 with a flare in the Active Galactic Nuclei\n(AGN) J124942.3+344929 has generated tremendous excitement. However, GW190521\nhas one of the largest localization volumes amongst all of the GW events\ndetected so far. The 90\\% localization volume likely contains $7,400$\nunobscured AGN brighter than $g \\leq 20.5$ AB mag, and it results in a $\\gtrsim\n70\\%$ probability of chance coincidence for an AGN flare consistent with the GW\nevent. We present a Bayesian formalism to estimate the confidence of an AGN\nassociation by analyzing a population of BBH events with dedicated follow-up\nobservations. Depending on the fraction of BBH arising from AGNs, counterpart\nsearches of $\\mathcal{O}(1)-\\mathcal{O}(100)$ GW events are needed to establish\na confident association, and more than an order of magnitude more for searches\nwithout followup (i.e, using only the locations of AGNs and GW events).\nFollow-up campaigns of the top $\\sim 5\\%$ (based on volume localization and\nbinary mass) of BBH events with total rest frame mass $\\ge 50~M_\\odot$ are\nexpected to establish a confident association during the next LIGO/Virgo/KAGRA\nobserving run (O4), as long as the true value of the fraction of BBH giving\nrise to AGN flares is $>0.1$. Our formalism allows us to jointly infer\ncosmological parameters from a sample of BBH events that include chance\ncoincidence flares. Until the confidence of AGN associations is established,\nthe probability of chance coincidence must be taken into account to avoid\nbiasing astrophysical and cosmological constraints.\n"}
+{"abstract": "  The usage of environment sensor models for virtual testing is a promising\napproach to reduce the testing effort of autonomous driving. However, in order\nto deduce any statements regarding the performance of an autonomous driving\nfunction based on simulation, the sensor model has to be validated to determine\nthe discrepancy between the synthetic and real sensor data. Since a certain\ndegree of divergence can be assumed to exist, the sufficient level of fidelity\nmust be determined, which poses a major challenge. In particular, a method for\nquantifying the fidelity of a sensor model does not exist and the problem of\ndefining an appropriate metric remains. In this work, we train a neural network\nto distinguish real and simulated radar sensor data with the purpose of\nlearning the latent features of real radar point clouds. Furthermore, we\npropose the classifier's confidence score for the `real radar point cloud'\nclass as a metric to determine the degree of fidelity of synthetically\ngenerated radar data. The presented approach is evaluated and it can be\ndemonstrated that the proposed deep evaluation metric outperforms conventional\nmetrics in terms of its capability to identify characteristic differences\nbetween real and simulated radar data.\n"}
+{"abstract": "  We review and give detailed description for ${\\rm gl}_{NM}$ Gaudin models\nrelated to holomorphic vector bundles of rank $NM$ and degree $N$ over elliptic\ncurve with $n$ punctures. Then we introduce their generalizations constructed\nby means of $R$-matrices satisfying the associative Yang-Baxter equation. A\nnatural extension of the obtained models to the Schlesinger systems is given as\nwell.\n"}
+{"abstract": "  Deep CNN-based methods have so far achieved the state of the art results in\nmulti-view 3D object reconstruction. Despite the considerable progress, the two\ncore modules of these methods - multi-view feature extraction and fusion, are\nusually investigated separately, and the object relations in different views\nare rarely explored. In this paper, inspired by the recent great success in\nself-attention-based Transformer models, we reformulate the multi-view 3D\nreconstruction as a sequence-to-sequence prediction problem and propose a new\nframework named 3D Volume Transformer (VolT) for such a task. Unlike previous\nCNN-based methods using a separate design, we unify the feature extraction and\nview fusion in a single Transformer network. A natural advantage of our design\nlies in the exploration of view-to-view relationships using self-attention\namong multiple unordered inputs. On ShapeNet - a large-scale 3D reconstruction\nbenchmark dataset, our method achieves a new state-of-the-art accuracy in\nmulti-view reconstruction with fewer parameters ($70\\%$ less) than other\nCNN-based methods. Experimental results also suggest the strong scaling\ncapability of our method. Our code will be made publicly available.\n"}
+{"abstract": "  We construct, for any given positive integer $n$, Reeb flows on contact\nintegral homology 3-spheres which do not admit global surfaces of section with\nfewer than $n$ boundary components. We use a connected sum operation for open\nbooks to construct such a system. We prove that this property is stable with\nrespect to $C^{4+\\epsilon}$-small perturbations of the Hamiltonian given on the\nsymplectization.\n"}
+{"abstract": "  We introduce S++, a simple, robust, and deployable framework for training a\nneural network (NN) using private data from multiple sources, using\nsecret-shared secure function evaluation. In short, consider a virtual third\nparty to whom every data-holder sends their inputs, and which computes the\nneural network: in our case, this virtual third party is actually a set of\nservers which individually learn nothing, even with a malicious (but\nnon-colluding) adversary.\n  Previous work in this area has been limited to just one specific activation\nfunction: ReLU, rendering the approach impractical for many use-cases. For the\nfirst time, we provide fast and verifiable protocols for all common activation\nfunctions and optimize them for running in a secret-shared manner. The ability\nto quickly, verifiably, and robustly compute exponentiation, softmax, sigmoid,\netc., allows us to use previously written NNs without modification, vastly\nreducing developer effort and complexity of code. In recent times, ReLU has\nbeen found to converge much faster and be more computationally efficient as\ncompared to non-linear functions like sigmoid or tanh. However, we argue that\nit would be remiss not to extend the mechanism to non-linear functions such as\nthe logistic sigmoid, tanh, and softmax that are fundamental due to their\nability to express outputs as probabilities and their universal approximation\nproperty. Their contribution in RNNs and a few recent advancements also makes\nthem more relevant.\n"}
+{"abstract": "  Beginning with the 'conventional' two-dimensional (2D) quantum wells (QWs)\nbased on III-V and II-VI semiconductors in the 1970s, to the recent\natomically-thin sheets of van der Waals materials such as 2D semiconducting\ntransition metal dichalcogenides (TMDCs) and 2D magnets, the research in 2D\nmaterials is continuously evolving and providing new challenges. Magnetooptical\nspectroscopy has played a significant role in this area of research, both from\nfundamental physics and technological perspectives. A major challenge in 2D\nsemiconductors such as TMDCs is to understand their spin-valley-resolved\nphysics, and their implications in quantum computation and information\nresearch. Since the discovery of valley Zeeman effects, deep insights into the\nspin-valley physics of TMDCs and their heterostructures has emerged through\nmagnetooptical spectroscopy. In this perspective, we highlight the role of\nmagnetooptics in many milestones such as the discovery of interlayer excitons,\nphase control between coherently-excited valleys, determination of\nexciton-reduced masses, Bohr radii and binding energies, physics of the\noptically-bright and dark excitons, trions, other many-body species such as\nbiexcitons and their phonon replicas in TMDC monolayers. The discussion\naccompanies open questions, challenges and future prospects in the field\nincluding comments on the magnetooptics of van der Waals heterostructures\ninvolving TMDCs and 2D magnets.\n"}
+{"abstract": "  DA-type white dwarfs account for 80% of all white dwarfs and represent, for\nmost of them, the ultimate outcome of the typical evolution of\nlow-to-intermediate mass stars. Their internal chemical stratification is\nstrongly marked by passed, often uncertain, stellar evolution processes that\noccurred during the helium (core and shell) burning phases, i.e., from the\nhorizontal branch through AGB and post-AGB stages. Pulsating white dwarfs, in\nparticular the \"cool\" DA-type ZZ Ceti variables, offer an outstanding\nopportunity to dig into these stars by fully exploiting their asteroseismic\npotential. With our most recent tools dedicated to that purpose, we show that a\ncomplete cartography of the stratification of the main constituents of a white\ndwarf can be inferred, leading in particular to strong constraints on the C/O\ncore structure produced by the processes mentioned above. This opens up the way\ntoward a systematic exploration of white-dwarf internal properties.\n"}
+{"abstract": "  In this paper we prove a direct geometric relation between Deligne--Lusztig\nvarieties and Springer fibres in type $\\mathsf{A}$: For any rational unipotent\nelement, the Springer fibre cuts out a unique component of a specific\nDeligne--Lusztig variety; moreover, this component forms an open dense subset\nof a component of the Springer fibre. This boils down to a map from the\nunipotent variety to the Weyl group, and combines several constructions with a\ncombinatorial flavour (like Weyr normal forms, Robinson--Schensted\ncorrespondence, and Spaltenstein's and Steinberg's labellings); it also\nprovides a geometric interpretation of a classical dimension formula of\nunipotent centralisers.\n"}
+{"abstract": "  We introduce a weakening of the notion of fusion 2-category given in\narXiv:1812.11933. Then, we establish a number of properties of (multi)fusion\n2-categories. Finally, we describe the fusion rule of the fusion 2-categories\nassociated to certain pointed braided fusion categories.\n"}
+{"abstract": "  A heterogeneous graph consists of different vertices and edges types.\nLearning on heterogeneous graphs typically employs meta-paths to deal with the\nheterogeneity by reducing the graph to a homogeneous network, guide random\nwalks or capture semantics. These methods are however sensitive to the choice\nof meta-paths, with suboptimal paths leading to poor performance. In this\npaper, we propose an approach for learning on heterogeneous graphs without\nusing meta-paths. Specifically, we decompose a heterogeneous graph into\ndifferent homogeneous relation-type graphs, which are then combined to create\nhigher-order relation-type representations. These representations preserve the\nheterogeneity of edges and retain their edge directions while capturing the\ninteraction of different vertex types multiple hops apart. This is then\ncomplemented with attention mechanisms to distinguish the importance of the\nrelation-type based neighbors and the relation-types themselves. Experiments\ndemonstrate that our model generally outperforms other state-of-the-art\nbaselines in the vertex classification task on three commonly studied\nheterogeneous graph datasets.\n"}
+{"abstract": "  We examine the effect of the decoherence-induced reduction of correlation\nlength on a one-dimensional scattering problem by solving numerically the\nevolution equation for the Wigner function with decoherence proposed in [L.\nBarletti, G. Frosali and E. Giovannini, Journal of Computational and\nTheoretical Transport 47, 209 (2018)]. The numerical solution is achieved by\nthe Splitting-Scheme algorithm. Three cases are examined, corresponding to a\nreflection-dominated regime, a transmission-dominated regime and an\nintermediate one. The dynamic evolution of the Wigner function is followed\nuntil the separation process of the reflected and of the transmitted packets is\ncomplete and it is observed for three different values of the correlation\nlength. The outcomes show a broadening and flattening of the Wigner function\nwhich becomes progressively more pronounced as the correlation length is\ndecreased. This results in a reduced reflection at low energies and in a\nreduced transmission at high energies.\n"}
+{"abstract": "  Given a graph $G=(V,E)$ with a weight $w_v$ associated with each vertex $v\\in\nV$, the maximum weighted induced forest problem (MWIF) consists of encountering\na maximum weighted subset $V'\\subseteq V$ of the vertices such that $V'$\ninduces a forest. This NP-hard problem is known to be equivalent to the minimum\nweighted feedback vertex set problem, which has applicability in a variety of\ndomains. The closely related maximum weighted induced tree problem (MWIT), on\nthe other hand, requires that the subset $V'\\subseteq V$ induces a tree. We\npropose two new integer programming formulations with an exponential number of\nconstraints and branch-and-cut procedures for MWIF. Computational experiments\nusing benchmark instances are performed comparing several formulations,\nincluding the newly proposed approaches and those available in the literature,\nwhen solved by a standard commercial mixed integer programming solver. More\nspecifically, five formulations are compared, two compact (i.e., with a\npolynomial number of variables and constraints) ones and the three others with\nan exponential number of constraints. The experiments show that a new\nformulation for the problem based on directed cutset inequalities for\neliminating cycles (DCUT) offers stronger linear relaxation bounds earlier in\nthe search process. The results also indicate that the other new formulation,\ndenoted tree with cycle elimination (TCYC), outperforms those available in the\nliterature when it comes to the average times for proving optimality for the\nsmall instances, especially the more challenging ones. Additionally, this\nformulation can achieve much lower average times for solving the larger random\ninstances that can be optimally solved. Furthermore, we show how the\nformulations for MWIF can be easily extended for MWIT. Such extension allowed\nus to compare the optimal solution values of the two problems.\n"}
+{"abstract": "  This work is about the semantic segmentation of skin lesion boundary and\ntheir attributes using Image-to-Image Translation with Conditional Adversarial\nNets. Melanoma is a type of skin cancer that can be cured if detected in time.\nSegmentation into dermoscopic images is an essential procedure for\ncomputer-assisted diagnosis due to its existing artifacts typical of skin\nimages. To alleviate the image annotation process, we propose to use a modified\nPix2Pix network. The discriminator network learns the mapping from a dermal\nimage as an input and a mask image of six channels as an output. Likewise, the\ndiscriminative network output called PatchGAN is varied for one channel and six\noutput channels. The photos used come from the 2018 ISIC Challenge, where 500\nphotographs are used with their respective semantic map, divided into 75% for\ntraining and 35% for testing. Obtaining for 100 training epochs high Jaccard\nindices for all attributes of the segmentation map.\n"}
+{"abstract": "  Intelligent reflecting surfaces (IRSs) have been introduced into wireless\ncommunications systems due to their great potential to smartly customize and\nreconfigure radio propagation environments in a cost-effective manner. Despite\nthe promising advantages of IRSs, academic research on IRSs is still in its\ninfancy. In particular, the design and analysis of IRS-assisted wireless\ncommunication systems critically depend on an accurate and tractable modeling\nof the IRS. In this article, we first present and compare three IRS models,\nnamely the conventional independent diffusive scatterer-based model,\nphysics-based model, and impedance network-based model, in terms of their\naccuracy, tractability, and hardware complexity. Besides, a new framework based\non partitioning the IRS into tiles and employing codebooks of transmission\nmodes is introduced to enable scalable IRS optimization. Then, we investigate\nthe impact of the three considered IRS models on system design, where several\ncrucial technical challenges for the efficient design of IRS-assisted wireless\nsystems are identified and the corresponding solutions are unraveled.\nFurthermore, to illustrate the properties of the considered models and the\nefficiency of the proposed solution concepts, IRS-assisted secure wireless\nsystems and simultaneous wireless information and power transfer (SWIPT)\nsystems are studied in more detail. Finally, several promising future research\ndirections for IRS-assisted wireless systems are highlighted.\n"}
+{"abstract": "  In several domains it is crucial to store and manipulate data whose origin\nneeds to be completely traceable to guarantee the consistency, trustworthiness\nand reliability on the data itself typically for ethical and legal reasons. It\nis also important to guarantee that such properties are also carried further\nwhen such data is composed and processed into new data. In this article we\npresent the main requirements and theorethical problems that arise by the\ndesign of a system supporting data with such capabilities. We present an\narchitecture for implementing a system as well as a prototype developed in\nPharo.\n"}
+{"abstract": "  The Local Group is a unique environment in which to study the astrophysics of\ngalaxy formation. The proximity of the Milky Way and M31 causes a large\nfraction of the low-mass halo population to interact with more massive dark\nmatter haloes, which increases their concentrations and strips them of gas and\nother material. Some low-mass haloes pass through the haloes of the Milky Way\nor M31 and are either ejected into the field or exchanged between the two\nprimary hosts. We use high resolution gas-dynamical simulations to describe a\nnew class of field halo that passed through the haloes of both the Milky Way\nand M31 at early times and is almost twice as concentrated as isolated field\nhaloes. These 'Hermeian' haloes are distributed anisotropically at greater\ndistances from the Local Group barycentre than the primary haloes and appear to\ncluster close to the Milky Way and M31 in projection. We show that some\nHermeian haloes can host galaxies that are promising targets for indirect dark\nmatter searches and are competitive with signals from other dwarf galaxies.\nHermeian galaxies in the Local Group should be detectable by forthcoming\nwide-field imaging surveys.\n"}
+{"abstract": "  We investigate the $I=1$ $\\pi \\pi$ interaction using the HAL QCD method in\nlattice QCD. We employ the (2+1)-flavor gauge configurations on $32^3 \\times\n64$ lattice at the lattice spacing $a \\approx 0.0907$ fm and $m_{\\pi} \\approx\n411$ MeV, in which the $\\rho$ meson appears as a resonance state. We find that\nall-to-all propagators necessary in this calculation can be obtained with\nreasonable precision by a combination of three techniques, the one-end trick,\nthe sequential propagator, and the covariant approximation averaging (CAA). The\nnon-local $I=1$ $\\pi \\pi$ potential is determined at the\nnext-to-next-to-leading order (N$^2$LO) of the derivative expansion for the\nfirst time, and the resonance parameters of the $\\rho$ meson are extracted. The\nobtained $\\rho$ meson mass is found to be consistent with the value in the\nliterature, while the value of the coupling $g_{\\rho \\pi \\pi}$ turns out to be\nsomewhat larger. The latter observation is most likely attributed to the lack\nof low-energy information in our lattice setup with the center-of-mass frame.\nSuch a limitation may appear in other P-wave resonant systems and we discuss\npossible improvement in future. With this caution in mind, we positively\nconclude that we can reasonably extract the N$^2$LO potential and resonance\nparameters even in the system requiring the all-to-all propagators in the HAL\nQCD method, which opens up new possibilities for the study of resonances in\nlattice QCD.\n"}
+{"abstract": "  Biomolecular electrostatics is key in protein function and the chemical\nprocesses affecting it.Implicit-solvent models expressed by the\nPoisson-Boltzmann (PB) equation can provide insights with less computational\npower than full atomistic models, making large-system studies -- at the scale\nof viruses, for example -- accessible to more researchers. This paper presents\na high-productivity and high-performance computational workflow combining\nExafmm, a fast multipole method (FMM) library, and Bempp, a Galerkin boundary\nelement method (BEM) package. It integrates an easy-to-use Python interface\nwith well-optimized computational kernels that are written in compiled\nlanguages. Researchers can run PB simulations interactively via Jupyter\nnotebooks, enabling faster prototyping and analyzing. We provide results that\nshowcase the capability of the software, confirm correctness, and evaluate its\nperformance with problem sizes between 8,000 and 2 million boundary elements. A\nstudy comparing two variants of the boundary integral formulation in regards to\nalgebraic conditioning showcases the power of this interactive computing\nplatform to give useful answers with just a few lines of code. As a form of\nsolution verification, mesh refinement studies with a spherical geometry as\nwell as with a real biological structure (5PTI) confirm convergence at the\nexpected $1/N$ rate, for $N$ boundary elements. Performance results include\ntimings, breakdowns, and computational complexity. Exafmm offers evaluation\nspeeds of just a few seconds for tens of millions of points, and\n$\\mathcal{O}(N)$ scaling. This allowed computing the solvation free energy of a\nZika virus, represented by 1.6 million atoms and 10 million boundary elements,\nat 80-min runtime on a single compute node (dual 20-core Intel Xeon Gold 6148).\nAll results in the paper are presented with utmost care for reproducibility.\n"}
+{"abstract": "  The convergence theory for the gradient sampling algorithm is extended to\ndirectionally Lipschitz functions. Although directionally Lipschitz functions\nare not necessarily locally Lipschitz, they are almost everywhere\ndifferentiable and well approximated by gradients and so are a natural\ncandidate for the application of the gradient sampling algorithm. The main\nobstacle to this extension is the potential unboundedness or emptiness of the\nClarke subdifferential at points of interest. The convergence analysis we\npresent provides one path to addressing these issues. In particular, we recover\nthe usual convergence theory when the function is locally Lipschitz. Moreover,\nif the algorithm does not drive a certain measure of criticality to zero, then\nthe iterates must converge to a point at which either the Clarke\nsubdifferential is empty or the direction of steepest descent is degenerate in\nthe sense that it does lie in the interior of the domain of the regular\nsubderivative.\n"}
+{"abstract": "  Telescope Array (TA) is the largest ultrahigh energy cosmic-ray (UHECR)\nobservatory in the Northern Hemisphere. It explores the origin of UHECRs by\nmeasuring their energy spectrum, arrival-direction distribution, and mass\ncomposition using a surface detector (SD) array covering approximately 700\nkm$^2$ and fluorescence detector (FD) stations. TA has found evidence for a\ncluster of cosmic rays with energies greater than 57 EeV. In order to confirm\nthis evidence with more data, it is necessary to increase the data collection\nrate.We have begun building an expansion of TA that we call TAx4. In this\npaper, we explain the motivation, design, technical features, and expected\nperformance of the TAx4 SD. We also present TAx4's current status and examples\nof the data that have already been collected.\n"}
+{"abstract": "  Continual (or \"incremental\") learning approaches are employed when additional\nknowledge or tasks need to be learned from subsequent batches or from streaming\ndata. However these approaches are typically adversary agnostic, i.e., they do\nnot consider the possibility of a malicious attack. In our prior work, we\nexplored the vulnerabilities of Elastic Weight Consolidation (EWC) to the\nperceptible misinformation. We now explore the vulnerabilities of other\nregularization-based as well as generative replay-based continual learning\nalgorithms, and also extend the attack to imperceptible misinformation. We show\nthat an intelligent adversary can take advantage of a continual learning\nalgorithm's capabilities of retaining existing knowledge over time, and force\nit to learn and retain deliberately introduced misinformation. To demonstrate\nthis vulnerability, we inject backdoor attack samples into the training data.\nThese attack samples constitute the misinformation, allowing the attacker to\ncapture control of the model at test time. We evaluate the extent of this\nvulnerability on both rotated and split benchmark variants of the MNIST dataset\nunder two important domain and class incremental learning scenarios. We show\nthat the adversary can create a \"false memory\" about any task by inserting\ncarefully-designed backdoor samples to the test instances of that task thereby\ncontrolling the amount of forgetting of any task of its choosing. Perhaps most\nimportantly, we show this vulnerability to be very acute and damaging: the\nmodel memory can be easily compromised with the addition of backdoor samples\ninto as little as 1\\% of the training data, even when the misinformation is\nimperceptible to human eye.\n"}
+{"abstract": "  Tidal range structures have been considered for large scale electricity\ngeneration for their potential ability to produce reasonable predictable energy\nwithout the emission of greenhouse gases. Once the main forcing components for\ndriving the tides have deterministic dynamics, the available energy in a given\ntidal power plant has been estimated, through analytical and numerical\noptimisation routines, as a mostly predictable event. This constraint imposes\nstate-of-art flexible operation methods to rely on tidal predictions\n(concurrent with measured data and up to a multiple of half-tidal cycles into\nthe future) to infer best operational strategies for tidal lagoons, with the\nadditional cost of requiring to run optimisation routines for every new tide.\nIn this paper, we propose a novel optimised operation of tidal lagoons with\nproximal policy optimisation through Unity ML-Agents. We compare this technique\nwith 6 different operation optimisation approaches (baselines) devised from the\nliterature, utilising the Swansea Bay Tidal Lagoon as a case study. We show\nthat our approach is successful in maximising energy generation through an\noptimised operational policy of turbines and sluices, yielding competitive\nresults with state-of-the-art methods of optimisation, regardless of test data\nused, requiring training once and performing real-time flexible control with\nmeasured ocean data only.\n"}
+{"abstract": "  A two-dimensional inviscid incompressible fluid is governed by simple rules.\nYet, to characterise its long-time behaviour is a knotty problem. The fluid\nevolves according to Euler's equations: a non-linear Hamiltonian system with\ninfinitely many conservation laws. In both experiments and numerical\nsimulations, coherent vortex structures, or blobs, emerge after an initial\nstage. These formations dominate the large-scale dynamics, but small scales\nalso persist. Kraichnan describes in his classical work a forward cascade of\nenstrophy into smaller scales, and a backward cascade of energy into larger\nscales. Previous attempts to model Kraichnan's double cascade use filtering\ntechniques that enforce separation from the outset. Here we show that Euler's\nequations posses an intrinsic, canonical splitting of the vorticity function.\nThe splitting is remarkable in four ways: (i) it is defined solely via the\nPoisson bracket and the Hamiltonian, (ii) it characterises steady flows, (iii)\nwithout imposition it yields a separation of scales, enabling the dynamics\nbehind Kraichnan's qualitative description, and (iv) it accounts for the\n\"broken line\" in the power law for the energy spectrum, observed in both\nexperiments and numerical simulations. The splitting originates from Zeitlin's\ntruncated model of Euler's equations in combination with a standard\nquantum-tool: the spectral decomposition of Hermitian matrices. In addition to\ntheoretical insight, the scale separation dynamics could be used for stochastic\nmodel reduction, where small scales are modelled by multiplicative noise.\n"}
+{"abstract": "  We introduce a model of competing agents in a prophet setting, where rewards\narrive online, and decisions are made immediately and irrevocably. The rewards\nare unknown from the outset, but they are drawn from a known probability\ndistribution. In the standard prophet setting, a single agent makes selection\ndecisions in an attempt to maximize her expected reward. The novelty of our\nmodel is the introduction of a competition setting, where multiple agents\ncompete over the arriving rewards, and make online selection decisions\nsimultaneously, as rewards arrive. If a given reward is selected by more than a\nsingle agent, ties are broken either randomly or by a fixed ranking of the\nagents. The consideration of competition turns the prophet setting from an\nonline decision making scenario to a multi-agent game.\n  For both random and ranked tie-breaking rules, we present simple threshold\nstrategies for the agents that give them high guarantees, independent of the\nstrategies taken by others. In particular, for random tie-breaking, every agent\ncan guarantee herself at least $\\frac{1}{k+1}$ of the highest reward, and at\nleast $\\frac{1}{2k}$ of the optimal social welfare. For ranked tie-breaking,\nthe $i$th ranked agent can guarantee herself at least a half of the $i$th\nhighest reward. We complement these results by matching upper bounds, even with\nrespect to equilibrium profiles. For ranked tie-breaking rule, we also show a\ncorrespondence between the equilibrium of the $k$-agent game and the optimal\nstrategy of a single decision maker who can select up to $k$ rewards.\n"}
+{"abstract": "  Extremal clusters of stationary processes with long memory can be quite\nintricate. For certain stationary infinitely divisible processes with\nsubexponential tails, including both power-like tails and certain lighter\ntails, e.g. lognormal-like tails, such clusters may take the shape of stable\nregenerative sets. In this paper we show that for semi-exponential tails, which\nare even lighter, a new shape of extremal clusters arises. In this case each\nstable regenerative set supports a random panoply of varying extremes.\n"}
+{"abstract": "  Metal-insulator-metal (MIM) nanogaps in canonical nanoparticle-on-mirror\ngeometry (NPoM) provide deep-subwavelength confinement of light with mode\nvolumes smaller than $V/V_0$ $<$ $10^{-6}$. However, access to these hotspots\nis limited by the impendence mismatch between the high in-plane $k_{//}$ of\ntrapped light and free-space plane-waves, making the in- and out-coupling of\nlight difficult. Here, by constructing a nanoparticle-on-foil (NPoF) system\nwith thin metal films, we show the mixing of insulator-metal-insulator (IMI)\nmodes and MIM gap modes resulting in MIMI modes. This mixing provides\nmulti-channel access to the plasmonic nanocavity through light incident from\nboth sides of the metal film. The red-tuning and near-field strength of MIMI\nmodes for thinner foils is measured experimentally with white-light scattering\nand surface-enhanced Raman scattering from individual NPoFs. We discuss further\nthe utility of NPoF systems since the geometry allows tightly confined light to\nbe accessed simply and through different ports.\n"}
+{"abstract": "  Inspired by the progress of the End-to-End approach [1], this paper\nsystematically studies the effects of Number of Filters of convolutional layers\non the model prediction accuracy of CNN+RNN (Convolutional Neural Networks\nadding to Recurrent Neural Networks) for ASR Models (Automatic Speech\nRecognition). Experimental results show that only when the CNN Number of\nFilters exceeds a certain threshold value is adding CNN to RNN able to improve\nthe performance of the CNN+RNN speech recognition model, otherwise some\nparameter ranges of CNN can render it useless to add the CNN to the RNN model.\nOur results show a strong dependency of word accuracy on the Number of Filters\nof convolutional layers. Based on the experimental results, the paper suggests\na possible hypothesis of Sound-2-Vector Embedding (Convolutional Embedding) to\nexplain the above observations.\n  Based on this Embedding hypothesis and the optimization of parameters, the\npaper develops an End-to-End speech recognition system which has a high word\naccuracy but also has a light model-weight. The developed LVCSR (Large\nVocabulary Continuous Speech Recognition) model has achieved quite a high word\naccuracy of 90.2% only by its Acoustic Model alone, without any assistance from\nintermediate phonetic representation and any Language Model. Its acoustic model\ncontains only 4.4 million weight parameters, compared to the 35~68 million\nacoustic-model weight parameters in DeepSpeech2 [2] (one of the top\nstate-of-the-art LVCSR models) which can achieve a word accuracy of 91.5%. The\nlight-weighted model is good for improving the transcribing computing\nefficiency and also useful for mobile devices, Driverless Vehicles, etc. Our\nmodel weight is reduced to ~10% the size of DeepSpeech2, but our model accuracy\nremains close to that of DeepSpeech2. If combined with a Language Model, our\nLVCSR system is able to achieve 91.5% word accuracy.\n"}
+{"abstract": "  Topological superconductivity can be engineered in semiconductors with strong\nspin-orbit interaction coupled to a superconductor. Experimental advances in\nthis field have often been triggered by the development of new hybrid material\nsystems. Among these, two-dimensional electron gases (2DEGs) are of particular\ninterest due to their inherent design flexibility and scalability. Here we\ndiscuss results on a 2D platform based on a ternary 2DEG (InSbAs) coupled to\nin-situ grown Aluminum. The spin-orbit coupling in these 2DEGs can be tuned\nwith the As concentration, reaching values up to 400 meV$\\unicode{xC5}$, thus\nexceeding typical values measured in its binary constituents. In addition to a\nlarge Land\\'e g-factor $\\sim$ 55 (comparable to InSb), we show that the clean\nsuperconductor-semiconductor interface leads to a hard induced superconducting\ngap. Using this new platform we demonstrate the basic operation of\nphase-controllable Josephson junctions, superconducting islands and quasi-1D\nsystems, prototypical device geometries used to study Majorana zero modes.\n"}
+{"abstract": "  In the manuscript, we are interested in using kinetic theory to better\nunderstand the time evolution of wealth distribution and their large scale\nbehavior such as the evolution of inequality (e.g. Gini index). We investigate\nthree type of dynamics denoted unbiased, poor-biased and rich-biased dynamics.\nAt the particle level, one agent is picked randomly based on its wealth and one\nof its dollar is redistributed among the population. Proving the so-called\npropagation of chaos, we identify the limit of each dynamics as the number of\nindividual approaches infinity using both coupling techniques [48] and\nmartingale-based approach [36]. Equipped with the limit equation, we identify\nand prove the convergence to specific equilibrium for both the unbiased and\npoor-biased dynamics. In the rich-biased dynamics however, we observe a more\ncomplex behavior where a dispersive wave emerges. Although the dispersive wave\nis vanishing in time, its also accumulates all the wealth leading to a Gini\napproaching 1 (its maximum value). We characterize numerically the behavior of\ndispersive wave but further analytic investigation is needed to derive such\ndispersive wave directly from the dynamics.\n"}
+{"abstract": "  We consider an inverse source problem in the stationary radiating transport\nthrough a two dimensional absorbing and scattering medium. Of specific\ninterest, the exiting radiation is measured on an arc. The attenuation and\nscattering properties of the medium are assumed known. For scattering kernels\nof finite Fourier content in the angular variable, we show how to\nquantitatively recover the part of the isotropic sources restricted to the\nconvex hull of the measurement arc. The approach is based on the Cauchy problem\nwith partial data for a Beltrami-like equation associated with $A$-analytic\nmaps in the sense of Bukhgeim, and extends authors' previous work to this\nspecific partial data case. The robustness of the method is demonstrated by the\nresults of several numerical experiments.\n"}
+{"abstract": "  Atomtronics is a relatively new subfield of atomic physics that aims to\nrealize the device behavior of electronic components in ultracold atom-optical\nsystems. The fact that these systems are coherent makes them particularly\ninteresting since, in addition to current, one can impart quantum states onto\nthe current carriers themselves or perhaps perform quantum computational\noperations on them. After reviewing the fundamental ideas of this subfield, we\nreport on the theoretical and experimental progress made towards developing\nexternally-driven and closed loop devices. The functionality and potential\napplications for these atom analogs to electronic and spintronic systems is\nalso discussed.\n"}
+{"abstract": "  Positron Emission Tomography (PET) is an important tool for studying\nAlzheimer's disease (AD). PET scans can be used as diagnostics tools, and to\nprovide molecular characterization of patients with cognitive disorders.\nHowever, multiple tracers are needed to measure glucose metabolism (18F-FDG),\nsynaptic vesicle protein (11C-UCB-J), and $\\beta$-amyloid (11C-PiB).\nAdministering multiple tracers to patient will lead to high radiation dose and\ncost. In addition, access to PET scans using new or less-available tracers with\nsophisticated production methods and short half-life isotopes may be very\nlimited. Thus, it is desirable to develop an efficient multi-tracer PET\nsynthesis model that can generate multi-tracer PET from single-tracer PET.\nPrevious works on medical image synthesis focus on one-to-one fixed domain\ntranslations, and cannot simultaneously learn the feature from multi-tracer\ndomains. Given 3 or more tracers, relying on previous methods will also create\na heavy burden on the number of models to be trained. To tackle these issues,\nwe propose a 3D unified anatomy-aware cyclic adversarial network (UCAN) for\ntranslating multi-tracer PET volumes with one unified generative model, where\nMR with anatomical information is incorporated. Evaluations on a multi-tracer\nPET dataset demonstrate the feasibility that our UCAN can generate high-quality\nmulti-tracer PET volumes, with NMSE less than 15% for all PET tracers.\n"}
+{"abstract": "  A swarm of cooperating UAVs communicating with a distant multiantenna ground\nstation can leverage MIMO spatial multiplexing to scale the capacity. Due to\nthe line-of-sight propagation between the swarm and the ground station, the\nMIMO channel is highly correlated, leading to limited multiplexing gains. In\nthis paper, we optimize the UAV positions to attain the maximum MIMO capacity\ngiven by the single user bound. An infinite set of UAV placements that attains\nthe capacity bound is first derived. Given an initial swarm placement, we\nformulate the problem of minimizing the distance traveled by the UAVs to reach\na placement within the capacity maximizing set of positions. An offline\ncentralized solution to the problem using block coordinate descent is developed\nassuming known initial positions of UAVs. We also propose an online distributed\nalgorithm, where the UAVs iteratively adjust their positions to maximize the\ncapacity. Our proposed approaches are shown to significantly increase the\ncapacity at the expense of a bounded translation from the initial UAV\nplacements. This capacity increase persists when using a massive MIMO ground\nstation. Using numerical simulations, we show the robustness of our approaches\nin a Rician channel under UAV motion disturbances.\n"}
+{"abstract": "  Recent works have proposed new Byzantine consensus algorithms for blockchains\nbased on epidemics, a design which enables highly scalable performance at a low\ncost. These methods however critically depend on a secure random peer sampling\nservice: a service that provides a stream of random network nodes where no\nattacking entity can become over-represented. To ensure this security property,\ncurrent epidemic platforms use a Proof-of-Stake system to select peer samples.\nHowever such a system limits the openness of the system as only nodes with\nsignificant stake can participate in the consensus, leading to an oligopoly\nsituation. Moreover, this design introduces a complex interdependency between\nthe consensus algorithm and the cryptocurrency built upon it. In this paper, we\npropose a radically different security design for the peer sampling service,\nbased on the distribution of IP addresses to prevent Sybil attacks. We propose\na new algorithm, $\\scriptstyle{BASALT}$, that implements our design using a\nstubborn chaotic search to counter attackers' attempts at becoming\nover-represented. We show in theory and using Monte Carlo simulations that\n$\\scriptstyle{BASALT}$ provides samples which are extremely close to the\noptimal distribution even in adversarial scenarios such as tentative Eclipse\nattacks. Live experiments on a production cryptocurrency platform confirm that\nthe samples obtained using $\\scriptstyle{BASALT}$ are equitably distributed\namongst nodes, allowing for a system which is both open and where no single\nentity can gain excessive power.\n"}
+{"abstract": "  The phase transitions of random-field q-state Potts models in d=3 dimensions\nare studied by renormalization-group theory by exact solution of a hierarchical\nlattice and, equivalently, approximate Migdal-Kadanoff solutions of a cubic\nlattice. The recursion, under rescaling, of coupled random-field and\nrandom-bond (induced under rescaling by random fields) coupled probability\ndistributions is followed to obtain phase diagrams. Unlike the Ising model\n(q=2), several types of random fields can be defined for q >= 3 Potts models,\nincluding random-axis favored, random-axis disfavored, random-axis randomly\nfavored or disfavored cases, all of which are studied. Quantitatively very\nsimilar phase diagrams are obtained, for a given q for the three types of field\nrandomness, with the low-temperature ordered phase persisting, increasingly as\ntemperature is lowered, up to random-field threshold in d=3, which is\ncalculated for all temperatures below the zero-field critical temperature.\nPhase diagrams thus obtained are compared as a function of $q$. The ordered\nphase in the low-q models reaches higher temperatures, while in the high-q\nmodels it reaches higher random fields. This renormalization-group calculation\nresult is physically explained.\n"}
+{"abstract": "  We study monopole operators with the lowest possible topological charge\n$q=1/2$ at the infrared fixed point of scalar electrodynamics in $2+1$\ndimension (scalar QED$_3$) with $N$ complex scalars and Chern-Simons coupling\n$|k|=N$. In the large $N$ expansion, monopole operators in this theory with\nspins $\\ell<O(\\sqrt{N})$ and associated flavor representations are expected to\nhave the same scaling dimension to sub-leading order in $1/N$. We use the\nstate-operator correspondence to calculate the scaling dimension to sub-leading\norder with the result $N-0.2743+O(1/N)$, which improves on existing leading\norder results. We also compute the $\\ell^2/N$ term that breaks the degeneracy\nto sub-leading order for monopoles with spins $\\ell=O(\\sqrt{N})$.\n"}
+{"abstract": "  Removing the field redefinitions, the Bianchi identities and the total\nderivative freedoms from the general form of the gauge invariant couplings at\norder $\\ell_p^6$ for the bosonic fields of M-theory, we have found that the\nminimum number of independent couplings in the structures with even number of\nthe three-form, is 1062. We find that, except two couplings which have the\nRicci scalar, there are schemes in which there is no coupling involving\n$R,\\,R_{\\mu\\nu},\\,\\nabla_\\mu F^{\\mu\\alpha\\beta\\gamma}$. In these schemes, there\nare sub-schemes in which, except one coupling which has the second derivative\nof $F^{(4)}$, the couplings can have no term with more than two derivatives. We\nfind some of the parameters by dimensionally reducing the couplings on a circle\nand comparing them with the known couplings of the one-loop effective action of\ntype IIA superstring theory. In particular, we find the coupling which has term\nwith more than two derivatives is zero.\n"}
+{"abstract": "  We present a computation for inclusive charged-current deeply-inelastic\nscattering at NNLO (N$^2$LO) in QCD. Mass-dependent quark contributions are\nconsistently included across a wide range of momentum transfers in the\nSACOT-$\\chi$ general-mass scheme. When appropriate, we further include N$^3$LO\ncorrections in the zero-mass scheme. We show theoretical predictions for\nseveral experiments with neutrinos over a wide range of energies and at the\nupcoming Electron-Ion Collider. Our prediction reduces perturbative\nuncertainties to $\\sim$1%, sufficient for the high-precision objectives of\nfuture charged-current DIS measurements.\n"}
+{"abstract": "  End-to-end speech recognition generally uses hand-engineered acoustic\nfeatures as input and excludes the feature extraction module from its joint\noptimization. To extract learnable and adaptive features and mitigate\ninformation loss, we propose a new encoder that adopts globally attentive\nlocally recurrent (GALR) networks and directly takes raw waveform as input. We\nobserve improved ASR performance and robustness by applying GALR on different\nwindow lengths to aggregate fine-grain temporal information into multi-scale\nacoustic features. Experiments are conducted on a benchmark dataset AISHELL-2\nand two large-scale Mandarin speech corpus of 5,000 hours and 21,000 hours.\nWith faster speed and comparable model size, our proposed multi-scale GALR\nwaveform encoder achieved consistent character error rate reductions (CERRs)\nfrom 7.9% to 28.1% relative over strong baselines, including Conformer and\nTDNN-Conformer. In particular, our approach demonstrated notable robustness\nthan the traditional handcrafted features and outperformed the baseline\nMFCC-based TDNN-Conformer model by a 15.2% CERR on a music-mixed real-world\nspeech test set.\n"}
+{"abstract": "  Due to the wide application of average consensus algorithm, its security and\nprivacy problems have attracted great attention. In this paper, we consider the\nsystem threatened by a set of unknown agents that are both \"malicious\" and\n\"curious\", who add additional input signals to the system in order to perturb\nthe final consensus value or prevent consensus, and try to infer the initial\nstate of other agents. At the same time, we design a privacy-preserving average\nconsensus algorithm equipped with an attack detector with a time-varying\nexponentially decreasing threshold for every benign agent, which can guarantee\nthe initial state privacy of every benign agent, under mild conditions. The\nattack detector will trigger an alarm if it detects the presence of malicious\nattackers. An upper bound of false alarm rate in the absence of malicious\nattackers and the necessary and sufficient condition for there is no\nundetectable input by the attack detector in the system are given.\nSpecifically, we show that under this condition, the system can achieve\nasymptotic consensus almost surely when no alarm is triggered from beginning to\nend, and an upper bound of convergence rate and some quantitative estimates\nabout the error of final consensus value are given. Finally, numerical case is\nused to illustrate the effectiveness of some theoretical results.\n"}
+{"abstract": "  Battery energy storage boosts up the response speed of power system frequency\nregulation, but must be recharged carefully to minimize the distortion to the\nfrequency regulation response. This paper proposes a nonlinear feedback\ncontroller to optimize the recharge for storage resources in frequency\nregulation. This controller is designed using a data-driven best-hindsight\noptimization framework, the resulting nonlinear recharge controller's gain\ndepends on the storage state of charge as well as its power and energy rating.\nThe developed controller is compared with two benchmark automatic generation\ncontrol designs, one is a proportional-integral-based control from PJM\nInterconnection, the other one is based on linear-quadratic regulator.\nSimulation results using real area control error data from PJM Interconnection\nshow the proposed controller achieves smaller deviations in both the area\ncontrol error and the storage state of charge compared to the two benchmark\ncontrollers under various storage configurations.\n"}
+{"abstract": "  Monocular (relative or metric) depth estimation is a critical task for\nvarious applications, such as autonomous vehicles, augmented reality and image\nediting. In recent years, with the increasing availability of mobile devices,\naccurate and mobile-friendly depth models have gained importance. Increasingly\naccurate models typically require more computational resources, which inhibits\nthe use of such models on mobile devices. The mobile use case is arguably the\nmost unrestricted one, which requires highly accurate yet mobile-friendly\narchitectures. Therefore, we try to answer the following question: How can we\nimprove a model without adding further complexity (i.e. parameters)? Towards\nthis end, we systematically explore the design space of a relative depth\nestimation model from various dimensions and we show, with key design choices\nand ablation studies, even an existing architecture can reach highly\ncompetitive performance to the state of the art, with a fraction of the\ncomplexity. Our study spans an in-depth backbone model selection process,\nknowledge distillation, intermediate predictions, model pruning and loss\nrebalancing. We show that our model, using only DIW as the supervisory dataset,\nachieves 0.1156 WHDR on DIW with 2.6M parameters and reaches 37 FPS on a mobile\nGPU, without pruning or hardware-specific optimization. A pruned version of our\nmodel achieves 0.1208 WHDR on DIW with 1M parameters and reaches 44 FPS on a\nmobile GPU.\n"}
+{"abstract": "  In cavity-based axion dark matter detectors, quantum noise remains a primary\nbarrier to achieving the scan rate necessary for a comprehensive search of\naxion parameter space. Here we introduce a method of scan rate enhancement in\nwhich an axion-sensitive cavity is coupled to an auxiliary resonant circuit\nthrough simultaneous two-mode squeezing (entangling) and state swapping\ninteractions. We show analytically that when combined, these interactions can\namplify an axion signal before it becomes polluted by vacuum noise introduced\nby measurement. This internal amplification yields a wider bandwidth of axion\nsensitivity, increasing the rate at which the detector can search through\nfrequency space. With interaction rates predicted by circuit simulations of\nthis system, we show that this technique can increase the scan rate up to\n15-fold relative to the scan rate of a detector limited by vacuum noise.\n"}
+{"abstract": "  This paper presents a new large-scale Japanese speech corpus for training\nautomatic speech recognition (ASR) systems. This corpus contains over 2,000\nhours of speech with transcripts built on Japanese TV recordings and their\nsubtitles. We develop herein an iterative workflow to extract matching audio\nand subtitle segments from TV recordings based on a conventional method for\nlightly-supervised audio-to-text alignment. We evaluate a model trained with\nour corpus using an evaluation dataset built on Japanese TEDx presentation\nvideos and confirm that the performance is better than that trained with the\nCorpus of Spontaneous Japanese (CSJ). The experiment results show the\nusefulness of our corpus for training ASR systems. This corpus is made public\nfor the research community along with Kaldi scripts for training the models\nreported in this paper.\n"}
+{"abstract": "  Guaranteeing the security of transactional systems is a crucial priority of\nall institutions that process transactions, in order to protect their\nbusinesses against cyberattacks and fraudulent attempts. Adversarial attacks\nare novel techniques that, other than being proven to be effective to fool\nimage classification models, can also be applied to tabular data. Adversarial\nattacks aim at producing adversarial examples, in other words, slightly\nmodified inputs that induce the Artificial Intelligence (AI) system to return\nincorrect outputs that are advantageous for the attacker. In this paper we\nillustrate a novel approach to modify and adapt state-of-the-art algorithms to\nimbalanced tabular data, in the context of fraud detection. Experimental\nresults show that the proposed modifications lead to a perfect attack success\nrate, obtaining adversarial examples that are also less perceptible when\nanalyzed by humans. Moreover, when applied to a real-world production system,\nthe proposed techniques shows the possibility of posing a serious threat to the\nrobustness of advanced AI-based fraud detection procedures.\n"}
+{"abstract": "  In this paper, we introduce a Grasp Manifold Estimator (GraspME) to detect\ngrasp affordances for objects directly in 2D camera images. To perform\nmanipulation tasks autonomously it is crucial for robots to have such\ngraspability models of the surrounding objects. Grasp manifolds have the\nadvantage of providing continuously infinitely many grasps, which is not the\ncase when using other grasp representations such as predefined grasp points.\nFor instance, this property can be leveraged in motion optimization to define\ngoal sets as implicit surface constraints in the robot configuration space. In\nthis work, we restrict ourselves to the case of estimating possible\nend-effector positions directly from 2D camera images. To this extend, we\ndefine grasp manifolds via a set of key points and locate them in images using\na Mask R-CNN backbone. Using learned features allows generalizing to different\nview angles, with potentially noisy images, and objects that were not part of\nthe training set. We rely on simulation data only and perform experiments on\nsimple and complex objects, including unseen ones. Our framework achieves an\ninference speed of 11.5 fps on a GPU, an average precision for keypoint\nestimation of 94.5% and a mean pixel distance of only 1.29. This shows that we\ncan estimate the objects very well via bounding boxes and segmentation masks as\nwell as approximate the correct grasp manifold's keypoint coordinates.\n"}
+{"abstract": "  In LHC Run 3, the upgraded ALICE detector will record Pb-Pb collisions at a\nrate of 50 kHz usingcontinuous readout. The resulting stream of raw data at 3.5\nTB/s has to be processed with a setof lossy and lossless compression and data\nreduction techniques to a storage data rate of 90 GB/swhile preserving relevant\ndata for physics analysis. This contribution presents a custom losslessdata\ncompression scheme based on entropy coding as the final component in the data\nreductionchain which has to compress the data rate from 300 GB/s to 90 GB/s. A\nflexible, multi-processarchitecture for the data compression scheme is proposed\nthat seamlessly interfaces with the datareduction algorithms of earlier stages\nand allows to use parallel processing in order to keep therequired firm\nreal-time guarantees of the system. The data processed inside the\ncompressionprocess have a structure that allows the use of an rANS entropy\ncoder with more resource efficientstatic distribution tables. Extensions to the\nrANS entropy coder are introduced to efficientlywork with these static\ndistribution tables and large but sparse source alphabets consisting of upto 25\nBit per symbol. Preliminary performance results show compliance with the firm\nreal-timerequirements while offering close-to-optimal data compression.\n"}
+{"abstract": "  Matching and weighting methods for observational studies require the choice\nof an estimand, the causal effect with reference to a specific target\npopulation. Commonly used estimands include the average treatment effect in the\ntreated (ATT), the average treatment effect in the untreated (ATU), the average\ntreatment effect in the population (ATE), and the average treatment effect in\nthe overlap (i.e., equipoise population; ATO). Each estimand has its own\nassumptions, interpretation, and statistical methods that can be used to\nestimate it. This article provides guidance on selecting and interpreting an\nestimand to help medical researchers correctly implement statistical methods\nused to estimate causal effects in observational studies and to help audiences\ncorrectly interpret the results and limitations of these studies. The\ninterpretations of the estimands resulting from regression and instrumental\nvariable analyses are also discussed. Choosing an estimand carefully is\nessential for making valid inferences from the analysis of observational data\nand ensuring results are replicable and useful for practitioners.\n"}
+{"abstract": "  We present a unified description of spherical discontinuity surfaces in\nGeneral Relativity based on two parameters: mass function and surface\npermeability. The surfaces considered are: \\textit{Impulsive fronts}, massive\npermeable layer; \\textit{Surface layers}, massive impermeable layer;\n\\textit{Shock fronts}, massless permeable surface; and \\textit{Boundary\nsurfaces}, massless impermeable surface. We derive the exact jump conditions\nfor the physical variables across all these surfaces. Finally, we discuss the\nquasi-static approximation from studying slow hydrodynamic processes involving\ndiscontinuity surfaces.\n"}
+{"abstract": "  We study electron transport through a multichannel fractional quantum Hall\nedge in the presence of both interchannel interaction and random tunneling\nbetween channels, with emphasis on the role of contacts. The prime example in\nour discussion is the edge at filling factor 2/3 with two counterpropagating\nchannels. Having established a general framework to describe contacts to a\nmultichannel edge as thermal reservoirs, we particularly focus on the\nline-junction model for the contacts and investigate incoherent charge\ntransport for an arbitrary strength of interchannel interaction beneath the\ncontacts and, possibly different, outside them. We show that the conductance\ndoes not explicitly depend on the interaction strength either in or outside the\ncontact regions (implicitly, it only depends through renormalization of the\ntunneling rates). Rather, a long line-junction contact is characterized by a\nsingle parameter which defines the modes that are at thermal equilibrium with\nthe contact and is determined by the interplay of various types of scattering\nbeneath the contact. This parameter -- playing the role of an effective\ninteraction strength within an idealized model of thermal reservoirs -- is\ngenerically nonzero and affects the conductance. We formulate a framework of\nfractionalization-renormalized tunneling to describe the effect of disorder on\ntransport in the presence of interchannel interaction. Within this framework,\nwe give a detailed discussion of charge equilibration for arbitrarily strong\ninteraction in the bulk of the edge and arbitrary effective interaction\ncharacterizing the line-junction contacts.\n"}
+{"abstract": "  We consider random-access networks with nodes representing\ntransmitter-receiver pairs whose signals interfere with each other depending on\ntheir vicinity. Data packets arrive at the nodes over time and form queues. The\nnodes can be either active or inactive: a node deactivates at unit rate, while\nit activates at a rate that depends on its queue length, provided none of its\nneighbors is active. In order to model the effects of user mobility in wireless\nnetworks, we analyze dynamic interference graphs where the edges are allowed to\nappear and disappear over time. We focus on bipartite graphs, and study the\ntransition time between the two states where one part of the network is active\nand the other part is inactive, in the limit as the queue lengths become large.\nDepending on the speed of the dynamics, we are able to obtain a rough\nclassification of the effects of the dynamics on the transition time.\n"}
+{"abstract": "  Face recognition has achieved significant progress in deep-learning era due\nto the ultra-large-scale and well-labeled datasets.\n  However, training on ultra-large-scale datasets is time-consuming and takes\nup a lot of hardware resource.\n  Therefore, designing an efficient training approach is crucial and\nindispensable.\n  The heavy computational and memory costs mainly result from the high\ndimensionality of the Fully-Connected (FC) layer.\n  Specifically, the dimensionality is determined by the number of face\nidentities, which can be million-level or even more.\n  To this end, we propose a novel training approach for ultra-large-scale face\ndatasets, termed Faster Face Classification (F$^2$C).\n  In F$^2$C, we first define a Gallery Net and a Probe Net that are used to\ngenerate identities' centers and extract faces' features for face recognition,\nrespectively.\n  Gallery Net has the same structure as Probe Net and inherits the parameters\nfrom Probe Net with a moving average paradigm.\n  After that, to reduce the training time and hardware costs of the FC layer,\nwe propose a Dynamic Class Pool (DCP) that stores the features from Gallery Net\nand calculates the inner product (logits) with positive samples (whose\nidentities are in the DCP) in each mini-batch.\n  DCP can be regarded as a substitute for the FC layer but it is far smaller,\nthus greatly reducing the computational and memory costs.\n  For negative samples (whose identities are not in DCP), we minimize the\ncosine similarities between negative samples and those in DCP.\n  Then, to improve the update efficiency of DCP's parameters, we design a dual\ndata-loader including identity-based and instance-based loaders to generate a\ncertain of identities and samples in mini-batches.\n"}
+{"abstract": "  In the Wick-Cutkosky model, where two scalar massive constituents interact by\nmeans of the exchange of a scalar massless particle, the Bethe-Salpeter\nequation has solutions of two types, called \"normal\" and \"abnormal\". In the\nnon-relativistic limit, the normal solutions correspond to the usual Coulomb\nspectrum, whereas the abnormal ones do not have non-relativistic counterparts\n-- they are absent in the Schr\\\"odinger equation framework. We have studied, in\nthe formalism of the light-front dynamics, the Fock-space content of the\nabnormal solutions. It turns out that, in contrast to the normal ones, the\nabnormal states are dominated by the massless exchange particles (by 90 \\% or\nmore), what provides a natural explanation of their decoupling from the\ntwo-body Schr\\\"odinger equation. Assuming that one of the massive constituents\nis charged, we have calculated the electromagnetic elastic form factors of the\nnormal and abnormal states, as well as the transition form factors. The results\non form factors confirm the many-body nature of the abnormal states, as found\nfrom the Fock-space analysis. The abnormal solutions have thus properties\nsimilar to those of hybrid states, made here essentially of two massive\nconstituents and several or many massless exchange particles. They could also\nbe interpreted as the Abelian scalar analogs of the QCD hybrid states. The\nquestion of the validity of the ladder approximation of the model is also\nexamined.\n"}
+{"abstract": "  Strongly correlated electrons in layered perovskite structures have been the\nbirthplace of high-temperature superconductivity, spin liquid, and quantum\ncriticality. Specifically, the cuprate materials with layered structures made\nof corner sharing square planar CuO$_4$ units have been intensely studied due\nto their Mott insulating grounds state which leads to high-temperature\nsuperconductivity upon doping. Identifying new compounds with similar lattice\nand electronic structures has become a challenge in solid state chemistry.\nHere, we report the hydrothermal crystal growth of a new copper tellurite\nsulfate Cu$_3$(TeO$_4$)(SO$_4$)$\\cdot$H$_2$O, a promising alternative to\nlayered perovskites. The orthorhombic phase (space group $Pnma$) is made of\ncorrugated layers of corner-sharing CuO$_4$ square-planar units that are\nedge-shared with TeO$_4$ units. The layers are linked by slabs of\ncorner-sharing CuO$_4$ and SO$_4$. Using both the bond valence sum analysis and\nmagnetization data, we find purely Cu$^{2+}$ ions within the layers, but a\nmixed valence of Cu$^{2+}$/Cu${^+}$ between the layers.\nCu$_3$(TeO$_4$)(SO$_4$)$\\cdot$H$_2$O undergoes an antiferromagnetic transition\nat $T_N$=67 K marked by a peak in the magnetic susceptibility. Upon further\ncooling, a spin-canting transition occurs at $T^{\\star}$=12 K evidenced by a\nkink in the heat capacity. The spin-canting transition is explained based on a\n$J_1$-$J_2$ model of magnetic interactions, which is consistent with the\nslightly different in-plane super-exchange paths. We present\nCu$_3$(TeO$_4$)(SO$_4$)$\\cdot$H$_2$O as a promising platform for the future\ndoping and strain experiments that could tune the Mott insulating ground state\ninto superconducting or spin liquid states.\n"}
+{"abstract": "  We study the quasi-static limit for the $L^\\infty$ entropy weak solution of\nscalar one-dimensional hyperbolic equations with strictly concave or convex\nflux and time dependent boundary conditions. The quasi-stationary profile\nevolves with the quasi-static equation, whose entropy solution is determined by\nthe stationary profile corresponding to the boundary data at a given time.\n"}
+{"abstract": "  Let $\\mathcal{H}$ be a Coxeter hyperplane arrangement on $\\mathbb{R}^n$ with\nCoxeter group $W$. We assume that the map $-\\mathrm{id}$ is in the group $W$\nand that $\\mathcal{H}$ is not of type $A_1^n$. Let $K$ be a measurable subset\nof $\\mathbb{R}^n$ with finite volume such that $K$ is stable by $W$ and let\n$a\\in\\mathbb{R}^n$ such that $K$ contains the convex hull of $\\{w(a) : w\\in\nW\\}$. The authors proved in a previous article that the alternating sum over\nthe chambers $T$ of $\\mathcal{H}$ of the volumes of the sets $T\\cap(K+a)$ is\nzero. In this paper we give a dissection proof of this result. In fact, we lift\nthe identity to an abstract dissection group to obtain a similar identity with\nthe volume replaced by any valuation that is invariant under affine isometries.\nThis includes the cases of all intrinsic volumes. Apart from some basic\ngeometry, the main ingredient is a previous theorem of the authors (the proof\nof which uses only properties of Coxeter systems and closed convex polyhedral\ncones) where we relate the alternating sum of the values of certain valuations\nover the chambers of a Coxeter arrangement to similar alternating sums for\nsimpler subarrangements called $2$-structures.\n"}
+{"abstract": "  The topic is the average order of a connected induced subgraph of a graph.\nThis generalizes, to graphs in general, the average order of a subtree of a\ntree. In 1984, Jamison proved that the average order, over all trees of order\n$n$, is minimized by the path $P_n$. In 2018, Kroeker, Mol, and Oellermann\nconjectured that $P_n$ minimizes the average order over all connected graphs.\nThe main result of this paper confirms this conjecture.\n"}
+{"abstract": "  Variational representations of $f$-divergences are central to many machine\nlearning algorithms, with Lipschitz constrained variants recently gaining\nattention. Inspired by this, we define the Moreau-Yosida approximation of\n$f$-divergences with respect to the Wasserstein-$1$ metric. The corresponding\nvariational formulas provide a generalization of a number of recent results,\nnovel special cases of interest and a relaxation of the hard Lipschitz\nconstraint. Additionally, we prove that the so-called tight variational\nrepresentation of $f$-divergences can be to be taken over the quotient space of\nLipschitz functions, and give a characterization of functions achieving the\nsupremum in the variational representation. On the practical side, we propose\nan algorithm to calculate the tight convex conjugate of $f$-divergences\ncompatible with automatic differentiation frameworks. As an application of our\nresults, we propose the Moreau-Yosida $f$-GAN, providing an implementation of\nthe variational formulas for the Kullback-Leibler, reverse Kullback-Leibler,\n$\\chi^2$, reverse $\\chi^2$, squared Hellinger, Jensen-Shannon, Jeffreys,\ntriangular discrimination and total variation divergences as GANs trained on\nCIFAR-10, leading to competitive results and a simple solution to the problem\nof uniqueness of the optimal critic.\n"}
+{"abstract": "  We prove that small deformations of a projective variety of general type are\nalso projective varieties of general type, with the same plurigenera. Version\n2: small changes in first half. Improved version of the second half is now a\nseparate preprint (Seshadri's criterion and openness of projectivity).\n"}
+{"abstract": "  We consider the two-dimensional MHD Boundary layer system without\nhydrodynamic viscosity, and establish the existence and uniqueness of solutions\nin Sobolev spaces under the assumption that the tangential component of\nmagnetic fields dominates. This gives a complement to the previous works of\nLiu-Xie-Yang [Comm. Pure Appl. Math. 72 (2019)] and Liu-Wang-Xie-Yang [J.\nFunct. Anal. 279 (2020)], where the well-posedness theory was established for\nthe MHD boundary layer systems with both viscosity and resistivity and with\nviscosity only, respectively. We use the pseudo-differential calculation, to\novercome a new difficulty arising from the treatment of boundary integrals due\nto the absence of the diffusion property for the velocity.\n"}
+{"abstract": "  Weakly Interacting Massive Particles (WIMPs) are considered to be one of the\nfavoured dark matter candidates. Searching for any detectable signal due to the\nannihilation and decay of WIMPs over the entire electromagnetic spectrum has\nbecome a matter of interest for the last few decades. WIMP annihilation to\nStandard Model particles gives rise to a possibility of detection of this\nsignal at low radio frequencies via synchrotron radiation. Dwarf Spheroidal\nGalaxies (dSphs) are expected to contain a huge amount of dark matter which\nmakes them promising targets to search for such large scale diffuse radio\nemission. In this work, we present a stacking analysis of 23 dSph galaxies\nobserved at low frequency (147.5MHz) as part of the TIFR-GMRT Sky Survey\n(TGSS). The non-detection of any signal from these stacking exercises put very\ntight constraints on the dark matter parameters. The best limit comes from the\nnovel method of stacking after scaling the radio images of the individual dSph\ngalaxy fields after scaling them by the respective half-light radius. The\nconstraint on the thermally averaged cross-section is below the thermal relic\ncross-section value over a range of WIMP mass for reasonable choices of\nrelevant astrophysical parameters. Such analysis using future deeper\nobservation of individual targets as well as stacking can potentially reveal\nmore about the WIMP dark matter properties.\n"}
+{"abstract": "  We prove the Lieb-Schultz-Mattis (LSM) theorem on the energy spectrum of a\ngeneral two or three-dimensional quantum many-body system with the U(1)\nparticle number conservation and translation symmetry. Especially, it is\ndemonstrated that the theorem holds in a system with long-range interactions.\nTo this end, we introduce approximate magnetic translation symmetry under the\ntotal magnetic flux $\\Phi=2\\pi$ instead of the exact translation symmetry, and\nexplicitly construct low energy variational states. The energy spectrum at\n$\\Phi=2\\pi$ is shown to agree with that at $\\Phi=0$ in the thermodynamic limit,\nwhich concludes the LSM theorem.\n"}
+{"abstract": "  Context. As the importance of Gravitational Wave (GW) Astrophysics increases\nrapidly, astronomers in different fields and with different backgrounds can\nhave the need to get a quick idea of which GW source populations can be\ndetected by which detectors and with what measurement uncertainties.\n  Aims. The GW-Toolbox is an easy-to-use, flexible tool to simulate\nobservations on the GW universe with different detectors, including\nground-based interferometers (advanced LIGO, advanced VIRGO, KAGRA, Einstein\nTelescope, and also customised designs), space-borne interferometers (LISA and\na customised design), pulsar timing arrays mimicking the current working ones\n(EPTA, PPTA, NANOGrav, IPTA) and future ones. We include a broad range of\nsources such as mergers of stellar mass compact objects, namely black holes,\nneutron stars and black hole-neutron stars; and supermassive black hole\nbinaries mergers and inspirals, Galactic double white dwarfs in ultra-compact\norbit, extreme mass ratio inspirals and Stochastic GW backgrounds.\n  Methods. We collect methods to simulate source populations and determine\ntheir detectability with the various detectors. The paper aims at giving a\ncomprehensive description on the algorithm and functionality of the GW-Toolbox.\n  Results. The GW-Toolbox produces results that are consistent with more\ndetailed calculations of the different source classes and can be accessed with\na website interface (gw-universe.org) or as a python package\n(https://bitbucket.org/radboudradiolab/gwtoolbox). In the future, it will be\nupgraded with more functionality.\n"}
+{"abstract": "  With the recent advances in machine learning, problems that traditionally\nwould require accurate modeling to be solved analytically can now be\nsuccessfully approached with data-driven strategies. Among these, computing the\ninverse kinematics of a redundant robot arm poses a significant challenge due\nto the non-linear structure of the robot, the hard joint constraints and the\nnon-invertible kinematics map. Moreover, most learning algorithms consider a\ncompletely data-driven approach, while often useful information on the\nstructure of the robot is available and should be positively exploited. In this\nwork, we present a simple, yet effective, approach for learning the inverse\nkinematics. We introduce a structured prediction algorithm that combines a\ndata-driven strategy with the model provided by a forward kinematics function\n-- even when this function is misspecified -- to accurately solve the problem.\nThe proposed approach ensures that predicted joint configurations are well\nwithin the robot's constraints. We also provide statistical guarantees on the\ngeneralization properties of our estimator as well as an empirical evaluation\nof its performance on trajectory reconstruction tasks.\n"}
+{"abstract": "  We put forward a novel, twofold scheme that enables at the same time\nall-optical enantioselection and sorting of single multipolar chiral\nmicrospheres based on optical pulling forces exerted by two non-collinear,\nnonstructured, circularly-polarized light sources. Our chiral resolution method\ncan be externally controlled by varying the angle between their incident\nwavevectors, allowing for a fine-tuning of the range of chiral indices for\nenantioselection. Enantioselectivity is achieved by choosing angles such that\nonly particles with the same handedness of the light sources are pulled. This\nproposal allows one to achieve all-optical sorting of chiral microspheres with\narbitrarily small chiral parameters, thus outperforming current optical\nmethods.\n"}
+{"abstract": "  A widely held assumption on network dynamics is that similar components are\nmore likely to exhibit similar behavior than dissimilar ones and that generic\ndifferences among them are necessarily detrimental to synchronization. Here, we\nshow that this assumption does not generally hold in oscillator networks when\ncommunication delays are present. We demonstrate, in particular, that random\nparameter heterogeneity among oscillators can consistently rescue the system\nfrom losing synchrony. This finding is supported by electrochemical-oscillator\nexperiments performed on a multi-electrode array network. Remarkably, at\nintermediate levels of heterogeneity, random mismatches are more effective in\npromoting synchronization than parameter assignments specifically designed to\nfacilitate identical synchronization. Our results suggest that, rather than\nbeing eliminated or ignored, intrinsic disorder in technological and biological\nsystems can be harnessed to help maintain coherence required for function.\n"}
+{"abstract": "  X-ray scattering experiments using Free Electron Lasers (XFELs) are a\npowerful tool to determine the molecular structure and function of unknown\nsamples (such as COVID-19 viral proteins). XFEL experiments are a challenge to\ncomputing in two ways: i) due to the high cost of running XFELs, a fast\nturnaround time from data acquisition to data analysis is essential to make\ninformed decisions on experimental protocols; ii) data collection rates are\ngrowing exponentially, requiring new scalable algorithms. Here we report our\nexperiences analyzing data from two experiments at the Linac Coherent Light\nSource (LCLS) during September 2020. Raw data were analyzed on NERSC's Cori\nXC40 system, using the Superfacility paradigm: our workflow automatically moves\nraw data between LCLS and NERSC, where it is analyzed using the software\npackage CCTBX. We achieved real time data analysis with a turnaround time from\ndata acquisition to full molecular reconstruction in as little as 10 min --\nsufficient time for the experiment's operators to make informed decisions. By\nhosting the data analysis on Cori, and by automating LCLS-NERSC\ninteroperability, we achieved a data analysis rate which matches the data\nacquisition rate. Completing data analysis with 10 mins is a first for XFEL\nexperiments and an important milestone if we are to keep up with data\ncollection trends.\n"}
+{"abstract": "  A fully implicit phase field model has been implemented in Fortran for 1-3D\nsimulations. It is intended for simulations of diffusion and diffusion\ncontrolled transformations. Gibbs energy contributions from both gradients in\nconcentration and gradients in phase field variables are included. Orientation\ndependent interfacial energy is also supported. The implementation supports the\nuse of Calphad type databases as well as analytical Gibbs energy expressions,\nthough in the latter case phases may not have internal degrees of freedom.\n"}
+{"abstract": "  Low frequency ionization oscillations involving plasma and neutral density\n(breathing modes) are the most violent perturbations in Hall thrusters for\nelectric propulsion. Because of its simplicity, the zero-dimensional (0-D)\npredator-prey model of two nonlinearly coupled ordinary differential equations\nfor plasma and neutral density has been often used for the characterization of\nsuch oscillations and scaling estimates. We investigate the properties of its\ncontinuum analog, the one-dimensional (1-D) system of two nonlinearly coupled\nequations in partial derivatives (PDE) for plasma and neutral density. This is\na more general model, of which the standard 0-D predator-prey model is a\nspecial limit case. We show that the 1-D model is stable and does not show any\noscillations for the boundary conditions relevant to Hall thruster and the\nuniform ion velocity. We then propose a reduced 1-D model based on two coupled\nPDE for plasma and neutral densities that is unstable and exhibit oscillations\nif the ion velocity profile with the near the anode back-flow (toward the\nanode) region is used. Comparisons of the reduced model with the predictions of\nthe full model that takes into account the self-consistent plasma response show\nthat the main properties of the breathing mode are well captured. In\nparticular, it is shown that the frequency of the breathing mode oscillations\nis weakly dependent on the final ion velocity but shows a strong correlation\nwith the width of the ion back-flow region.\n"}
+{"abstract": "  This work deals with turbo coded single user massive multiple input multiple\noutput (SU-MMIMO) systems, with and without precoding. SU-MMIMO has a much\nhigher spectral efficiency compared to multi-user massive MIMO (MU-MMIMO) since\nindependent signals are transmitted from each of the antenna elements (spatial\nmultiplexing). MU-MMIMO that uses beamforming has a much lower spectral\nefficiency, since the same signal (with a delay) is transmitted from each of\nthe antenna elements. In this work, expressions for the upper bound on the\naverage signal-to-noise ratio (SNR) per bit and spectral efficiency are derived\nfor SU-MMIMO with and without precoding. We propose a performance index\n$f(N_t)$, which is a function of the number of transmit antennas $N_t$. Here\n$f(N_t)$ is the sum of the upper bound on the average SNR per bit and the\nspectral efficiency. We demonstrate that when the total number of antennas\n($N_{\\mathrm{tot}}$) in the transmitter and receiver is fixed, there exists a\nminimum value of $f(N_t)$, which has to be avoided. Computer simulations show\nthat the bit-error-rate (BER) is nearly insensitive to a wide range of the\nnumber of transmit antennas and re-transmissions, when $N_{\\mathrm{tot}}$ is\nlarge and kept constant. Thus, the spectral efficiency can be made as large as\npossible, for a given BER and $N_{\\mathrm{tot}}$.\n"}
+{"abstract": "  The electrohydrodynamic (EHD) force produced by ions ejected from a corona\nplasma is a solid state, silent mechanism for accelerating air, useful for\napplications ranging from electronics cooling to flying microrobots. This paper\npresents the theoretical motivation and the first implementation of a\nmulti-stage, highly miniaturized EHD device, which can provide both improved\nabsolute power output and power density as compared to single-stage devices. A\nlaser microfabricated, folded electrode design reduces component count and\nassembly time. Data from one, two, and three-stage devices demonstrates a near\nlinear scaling of output force with stage count, indicating inter-stage ducting\nsuccessfully reduces losses. Device lifetime is assessed to validate the use of\nstainless-steel emission electrodes. Areal thrust, force density, and\nvolumetric power density for the three-stage device are among the highest ever\nmeasured from an EHD actuator.\n"}
+{"abstract": "  We propose a general procedure for iterative inclusion of Stueckelberg fields\nto convert the theory into gauge system being equivalent to the original one.\nIn so doing, we admit reducibility of the Stueckelberg gauge symmetry. In this\ncase, no pairing exists between Stueckelberg fields and gauge parameters,\nunlike the irreducible Stueckelberg symmetry. The general procedure is\nexemplified by the case of Proca model, with the third order involutive closure\nchosen as the starting point. In this case, the set of Stueckelberg fields\nincludes, besides the scalar, also the second rank antisymmetric tensor. The\nreducible Stueckelberg gauge symmetry is shown to admit different gauge fixing\nconditions. One of the gauges reproduces the original Proca theory, while\nanother one excludes the original vector and the Stueckelberg scalar. In this\ngauge, the irreducible massive spin one is represented by antisymmetric second\nrank tensor obeying the third order field equations. Similar dual formulations\nare expected to exist for the fields of various spins.\n"}
+{"abstract": "  The accuracy and effectiveness of Hermite spectral methods for the numerical\ndiscretization of partial differential equations on unbounded domains, are\nstrongly affected by the amplitude of the Gaussian weight function employed to\ndescribe the approximation space. This is particularly true if the problem is\nunder-resolved, i.e., there are no enough degrees of freedom. The issue becomes\neven more crucial when the equation under study is time-dependent, forcing in\nthis way the choice of Hermite functions where the corresponding weight depends\non time. In order to adapt dynamically the approximation space, it is here\nproposed an automatic decision-making process that relies on machine learning\ntechniques, such as deep neural networks and support vector machines. The\nalgorithm is numerically tested with success on a simple 1D problem, but the\nmain goal is its exportability in the context of more serious applications.\n"}
+{"abstract": "  In this paper, we propose a physical layer security scheme that exploits a\nnovel index modulation (IM) technique for coordinate interleaved orthogonal\ndesigns (CIOD). Utilizing the diversity gain of CIOD transmission, the proposed\nscheme, named CIOD-IM, provides an improved spectral efficiency by means of IM.\nIn order to provide a satisfactory secrecy rate, we design a particular\nartificial noise matrix, which does not affect the performance of the\nlegitimate receiver, while deteriorating the performance of the eavesdropper.\nWe derive expressions of the ergodic secrecy rate and the theoretical bit error\nrate upper bound. In addition, we analyze the case of imperfect channel\nestimation by taking practical concerns into consideration. It is shown via\ncomputer simulations that the proposed scheme outperforms the existing IM-based\nschemes and might be a candidate for future secure communication systems.\n"}
+{"abstract": "  This paper proposes an additive phoneme-aware margin softmax (APM-Softmax)\nloss to train the multi-task learning network with phonetic information for\nlanguage recognition. In additive margin softmax (AM-Softmax) loss, the margin\nis set as a constant during the entire training for all training samples, and\nthat is a suboptimal method since the recognition difficulty varies in training\nsamples. In additive angular margin softmax (AAM-Softmax) loss, the additional\nangular margin is set as a costant as well. In this paper, we propose an\nAPM-Softmax loss for language recognition with phoneitc multi-task learning, in\nwhich the additive phoneme-aware margin is automatically tuned for different\ntraining samples. More specifically, the margin of language recognition is\nadjusted according to the results of phoneme recognition. Experiments are\nreported on Oriental Language Recognition (OLR) datasets, and the proposed\nmethod improves AM-Softmax loss and AAM-Softmax loss in different language\nrecognition testing conditions.\n"}
+{"abstract": "  The first computation of the compressible energy transfer rate from $\\sim$\n0.2 AU up to $\\sim$ 1.7 AU is obtained using PSP, THEMIS and MAVEN\nobservations. The compressible energy cascade rate $\\varepsilon_C$ is computed\nfor hundred of events at different heliocentric distances, for time intervals\nwhen the spacecraft were in the pristine solar wind. The observational results\nshow moderate increases of $\\varepsilon_C$ with respect to the incompressible\ncascade rate $\\varepsilon_I$. Depending on the level of compressibility in the\nplasma, which reach up to 25 $\\%$ in the PSP perihelion, the different terms in\nthe compressible exact relation are shown to have different impact in the total\ncascade rate $\\varepsilon_C$. Finally, the observational results are connected\nwith the local ion temperature and the solar wind heating problem.\n"}
+{"abstract": "  We consider $U(N)$ $\\mathcal N=4$ super Yang-Mills theory and discuss how to\nextract the strong coupling limit of non-planar corrections to observables\ninvolving the $\\frac{1}{2}$-BPS Wilson loop. Our approach is based on a\nsuitable saddle point treatment of the Eynard-Orantin topological recursion in\nthe Gaussian matrix model. Working directly at strong coupling we avoid the\nusual procedure of first computing observables at finite planar coupling\n$\\lambda$, order by order in $1/N$, and then taking the $\\lambda\\gg 1$ limit.\nIn the proposed approach, matrix model multi-point resolvents take a simplified\nform and some structures of the genus expansion, hardly visible at low order,\nmay be identified and rigorously proved. As a sample application, we consider\nthe expectation value of multiple coincident circular supersymmetric Wilson\nloops as well as their correlator with single trace chiral operators. For these\nquantities we provide novel results about the structure of their genus\nexpansion at large tension, generalising recent results in arXiv:2011.02885.\n"}
+{"abstract": "  We introduce trace definability, a weak notion of interpretability, and trace\nequivalence, a weak notion of equivalence for first order structures and\ntheories. In particular we get an interesting weak equivalence notion for\n$\\mathrm{NIP}$ theories. We describe a close connection to indiscernible\ncollapse. We also show that if $Q$ is a divisible subgroup of $(\\mathbb{R};+)$\nand $\\mathcal{Q}$ is a dp-rank one expansion of $(Q;+,<)$ then exactly one of\nthe following holds: $\\mathrm{Th}(\\mathcal{Q})$ trace defines $\\mathrm{RCF}$ or\n$\\mathcal{Q}$ is trace equivalent to a reduct of an ordered vector space.\n"}
+{"abstract": "  We show that in type-II superconductors a magnetic field applied transversely\nto correlated columnar disorder, drives a phase transition to a distinct\n\"smectic\" vortex glass (SmVG) state. SmVG is characterized by an infinitely\nanisotropic electrical transport, resistive (dissipationless) for current\nperpendicular to (along) columnar defects. Its positional order is also quite\nunusual, long-ranged with true Bragg peaks along columnar defects and\nlogarithmically rough vortex lattice distortions with quasi-Bragg peaks\ntransverse to columnar defects. For low temperatures and sufficiently weak\ncolumnar-only disorder, SmVG is a true topologically-ordered \"Bragg glass\",\ncharacterized by a vanishing dislocation density. At sufficiently long scales\nthe residual ever-present point disorder converts this state to a more\nstandard, but highly anisotropic vortex glass.\n"}
+{"abstract": "  This is an introductory textbook of the history of economics of inequality\nfor undergraduates and genreral readers. It begins with Adam Smith's critique\nof Rousseau. The first and second chapters focus on Smith and Karl Marx, in the\nbroad classical tradition of economics, where it is believed that there is an\ninseparable relationship between production and distribution, economic growth\nand inequality. Chapters 3 and 4 argue that despite the fact that the founders\nof the neoclassical school had shown an active interest in social issues,\nnamely worker poverty, the issues of production and distribution became\ndiscussed separately among neoclassicals. Toward the end of the 20th century,\nhowever, there was a renewed awareness within economics of the problem of the\nrelationship between production and distribution. The young Piketty's\nbeginnings as an economist are set against this backdrop. Chapters 5 to 8\nexplain the circumstances of the restoration of classical concerns within the\nneoclassical framework. Then, in chapters 9 and 10, I discuss the fact that\nThomas Piketty's seminal work is a new development in this \"inequality\nrenaissance,\" and try to gain a perspective on future trends in the debate.\nMathematical appendix presents simple models of growth and distribution.\n"}
+{"abstract": "  Twin neural network regression (TNNR) is a semi-supervised regression\nalgorithm, it can be trained on unlabelled data points as long as other,\nlabelled anchor data points, are present. TNNR is trained to predict\ndifferences between the target values of two different data points rather than\nthe targets themselves. By ensembling predicted differences between the targets\nof an unseen data point and all training data points, it is possible to obtain\na very accurate prediction for the original regression problem. Since any loop\nof predicted differences should sum to zero, loops can be supplied to the\ntraining data, even if the data points themselves within loops are unlabelled.\nSemi-supervised training improves TNNR performance, which is already state of\nthe art, significantly.\n"}
+{"abstract": "  Motivated by an imaging proteomics study for Alzheimer's disease (AD), in\nthis article, we propose a mediation analysis approach with high-dimensional\nexposures and high-dimensional mediators to integrate data collected from\nmultiple platforms. The proposed method combines principal component analysis\nwith penalized least squares estimation for a set of linear structural equation\nmodels. The former reduces the dimensionality and produces uncorrelated linear\ncombinations of the exposure variables, whereas the latter achieves\nsimultaneous path selection and effect estimation while allowing the mediators\nto be correlated. Applying the method to the AD data identifies numerous\ninteresting protein peptides, brain regions, and protein-structure-memory\npaths, which are in accordance with and also supplement existing findings of AD\nresearch. Additional simulations further demonstrate the effective empirical\nperformance of the method.\n"}
+{"abstract": "  We consider a gauge theory of vector fields in $3d$ Minkowski space. At the\nfree level, the dynamical variables are subjected to the extended Chern-Simons\n(ECS) equations with higher derivatives. If the color index takes $n$ values,\nthe third-order model admits a $2n$-parameter series of second-rank conserved\ntensors, which includes the canonical energy-momentum. Even though the\ncanonical energy is unbounded, the other representatives in the series can have\nbounded from below $00$-component. The theory admits consistent\nself-interactions with the Yang-Mills gauge symmetry. The Lagrangian couplings\npreserve the unbounded from below energy-momentum tensor, and they do not lead\nto a stable non-linear theory. The non-Lagrangian couplings are consistent with\nthe existence of conserved tensor with a bounded from below $00$-component.\nThese models are stable at the non-linear level. The dynamics of interacting\ntheory admits a constraint Hamiltonian form. The Hamiltonian density is given\nby the $00$-component of the conserved tensor. In the case of stable\ninteractions, the Poisson bracket and Hamiltonian do not follow from the\ncanonical Ostrogradski construction. The particular attention is paid to the\n\"triply massless\" ECS theory, which demonstrates instability already at the\nfree level. It is shown that the introduction of extra scalar field, serving as\nHiggs, can stabilize dynamics in the vicinity of the local minimum of energy.\nThe equations of motion of stable model are non-Lagrangian, but they admit the\nHamiltonian form of dynamics with a bounded from below Hamiltonian.\n"}
+{"abstract": "  An important goal in human-robot-interaction (HRI) is for machines to achieve\na close to human level of face perception. One of the important differences\nbetween machine learning and human intelligence is the lack of\ncompositionality. This paper introduces a new scheme to enable generative\nadversarial networks to learn the distribution of face images composed of\nsmaller parts. This results in a more flexible machine face perception and\neasier generalization to outside training examples. We demonstrate that this\nmodel is able to produce realistic high-quality face images by generating and\npiecing together the parts. Additionally, we demonstrate that this model learns\nthe relations between the facial parts and their distributions. Therefore, the\nspecific facial parts are interchangeable between generated face images.\n"}
+{"abstract": "  In this paper, we prove that it is always possible to define a realization of\nthe Laplacian $\\Delta_{\\kappa,\\theta}$ on $L^2(\\Omega)$ subject to nonlocal\nRobin boundary conditions with general jump measures on arbitrary open subsets\nof $\\mathbb R^N$. This is made possible by using a capacity approach to define\nadmissible pair of measures $(\\kappa,\\theta)$ that allows the associated form\n$\\mathcal E_{\\kappa,\\theta}$ to be closable. The nonlocal Robin Laplacian\n$\\Delta_{\\kappa,\\theta}$ generates a sub-Markovian $C_0-$semigroup on\n$L^2(\\Omega)$ which is not dominated by Neumann Laplacian semigroup unless the\njump measure $\\theta$ vanishes. Finally, the convergence of semigroups\nsequences $e^{-t\\Delta_{\\kappa_n,\\theta_n}}$ is investigated in the case of\nvague convergence and $\\gamma-$convergence of admissible pair of measures\n$(\\kappa_n,\\theta_n)$.\n"}
+{"abstract": "  In this paper, we study the framework of two-player Stackelberg games played\non graphs in which Player 0 announces a strategy and Player 1 responds\nrationally with a strategy that is an optimal response. While it is usually\nassumed that Player 1 has a single objective, we consider here the new setting\nwhere he has several. In this context, after responding with his strategy,\nPlayer 1 gets a payoff in the form of a vector of Booleans corresponding to his\nsatisfied objectives. Rationality of Player 1 is encoded by the fact that his\nresponse must produce a Pareto-optimal payoff given the strategy of Player 0.\nWe study the Stackelberg-Pareto Synthesis problem which asks whether Player 0\ncan announce a strategy which satisfies his objective, whatever the rational\nresponse of Player 1. For games in which objectives are either all parity or\nall reachability objectives, we show that this problem is fixed-parameter\ntractable and NEXPTIME-complete. This problem is already NP-complete in the\nsimple case of reachability objectives and graphs that are trees.\n"}
+{"abstract": "  The strong suppression of high-$p_T$ jets in heavy ion collisions is a result\nof elastic and inelastic energy loss suffered by the jet multi-prong collection\nof color charges that are resolved by medium interactions. Hence, quenching\neffects depend on the fluctuations of the jet substructure that are probed by\nthe cone size dependence of the spectrum. In this letter, we present the first\ncomplete, analytic calculation of the inclusive $R$-dependent jet spectrum in\nPbPb collisions at LHC energies, including resummation of energy loss effects\nfrom hard, vacuum-like emissions occurring in the medium and modeling of soft\nenergy flow and recovery at the jet cone. Both the geometry of the collision\nand the local medium properties, such as the temperature and fluid velocity,\nare given by a hydrodynamic evolution of the medium, leaving only the coupling\nconstant in the medium as a free parameter. The calculation yields a good\ndescription of the centrality and $p_T$ dependence of jet suppression for\n$R=0.4$ together with a mild cone size dependence, which is in agreement with\nrecent experimental results. Gauging the theoretical uncertainties, we find\nthat the largest sensitivity resides in the leading logarithmic approximation\nof the phase space of resolved splittings, which can be improved\nsystematically, while non-perturbative modeling of the soft-gluon sector is of\nrelatively minor importance up to large cone sizes.\n"}
+{"abstract": "  In this paper, we show that the permeability of porous media and that of a\nbubbly fluid are limiting cases of the complexified version of the two-fluid\nmodels posed in the 1990 paper by Lipton and Avellaneda. We assume the\nviscosity of the inclusion fluid is $z\\mu_1$ and the viscosity of the hosting\nfluid is $\\mu_1$, $z\\in\\mathbb{C}$. The proof is carried out by construction of\nsolutions for large $|z|$ and small $|z|$ by an iteration process similar with\nthe one used in the 1993 paper by Bruno and Leo. Moreover, we also show that\nfor a fixed microstructure, the permeabilities of these three cases share the\nsame integral representation formula (IRF) with different values of $s'$, as\nlong as the 'contrast parameter' $s':=\\frac{1}{z-1}$ is not in the interval\n$[-\\frac{2E_2^2}{1+2E_2^2},-\\frac{1}{1+2E_1^2}]$, where the constants $E_1$ and\n$E_2$ are the extension constants that depend on the geometry of $Q_1$, $Q_2$\nand $Q$.\n"}
+{"abstract": "  We study stochastic Nash equilibrium problems with expected valued cost\nfunctions whose pseudogradient satisfies restricted monotonicity properties\nwhich hold only with respect to the solution. We propose a forward-backward\nalgorithm and prove its convergence under restricted strong monotonicity,\nrestricted strict monotonicity and restricted cocoercivity of the\npseudogradient mapping. To approximate the expected value, we use either a\nfinite number of samples and a vanishing step size or an increasing number of\nsamples with a constant step. Numerical simulations show that our proposed\nalgorithm might be faster than the available algorithms.\n"}
+{"abstract": "  We investigated the ground state of monolayer 1T-XCl$_{2}$ (X: Fe, Co, and\nNi) using the generalized Bloch theorem, which can generate ferromagnetic,\nspiral, and antiferromagnetic states. Each state was represented by a unique\nspiral vector that arranges the magnetic moment of magnetic atom in the\nprimitive unit cell. We found the ferromagnetic ground state for the FeCl$_{2}$\nand NiCl$_{2}$ while the spiral ground state appears for the CoCl$_{2}$. We\nalso showed that the ground state depends sensitively on the lattice constant.\nWhen the hole-electron doping was taken into account, we found the phase\ntransition, which involves the ferromagnetic, spiral, and antiferromagnetic\nstates, for all the systems. Since the spin-spin interaction in the monolayer\nmetal dichlorides is influenced by the competition between the direct exchange\nand the superexchange, we justify that the carrier concentration determines\nwhich interaction should dominate.\n"}
+{"abstract": "  The paper considers subspaces of the strictly upper triangular matrices,\nwhich are stable under Lie bracket with any upper triangular matrix. These\nsubspaces are called ad-nilpotent ideals and there are Catalan number of such\nsubspaces. Each ad-nilpotent ideal meets a unique largest nilpotent orbit in\nthe Lie algebra of all matrices. The main result of the paper is that under an\nequivalence relation on ad-nilpotent ideals studied by Mizuno and others, the\nequivalence classes are the ad-nilpotent ideals with the same largest nilpotent\norbit. We include two applications of the result, one to the higher vanishing\nof cohomology groups of vector bundles on the flag variety and another to the\nKazhdan-Lusztig cells in the affine Weyl group of the symmetric group. Finally,\nsome combinatorial results are discussed.\n"}
+{"abstract": "  It is well-known that standard neural networks, even with a high\nclassification accuracy, are vulnerable to small $\\ell_\\infty$-norm bounded\nadversarial perturbations. Although many attempts have been made, most previous\nworks either can only provide empirical verification of the defense to a\nparticular attack method, or can only develop a certified guarantee of the\nmodel robustness in limited scenarios. In this paper, we seek for a new\napproach to develop a theoretically principled neural network that inherently\nresists $\\ell_\\infty$ perturbations. In particular, we design a novel neuron\nthat uses $\\ell_\\infty$-distance as its basic operation (which we call\n$\\ell_\\infty$-dist neuron), and show that any neural network constructed with\n$\\ell_\\infty$-dist neurons (called $\\ell_{\\infty}$-dist net) is naturally a\n1-Lipschitz function with respect to $\\ell_\\infty$-norm. This directly provides\na rigorous guarantee of the certified robustness based on the margin of\nprediction outputs. We then prove that such networks have enough expressive\npower to approximate any 1-Lipschitz function with robust generalization\nguarantee. We further provide a holistic training strategy that can greatly\nalleviate optimization difficulties. Experimental results show that using\n$\\ell_{\\infty}$-dist nets as basic building blocks, we consistently achieve\nstate-of-the-art performance on commonly used datasets: 93.09% certified\naccuracy on MNIST ($\\epsilon=0.3$), 35.42% on CIFAR-10 ($\\epsilon=8/255$) and\n16.31% on TinyImageNet ($\\epsilon=1/255$).\n"}
+{"abstract": "  How information is encoded in bio-molecular sequences is difficult to\nquantify since such an analysis usually requires sampling an exponentially\nlarge genetic space. Here we show how information theory reveals both robust\nand compressed encodings in the largest complete genotype-phenotype map (over 5\ntrillion sequences) obtained to date.\n"}
+{"abstract": "  Model parallelism has become a necessity for training modern large-scale deep\nlanguage models. In this work, we identify a new and orthogonal dimension from\nexisting model parallel approaches: it is possible to perform pipeline\nparallelism within a single training sequence for Transformer-based language\nmodels thanks to its autoregressive property. This enables a more fine-grained\npipeline compared with previous work. With this key idea, we design TeraPipe, a\nhigh-performance token-level pipeline parallel algorithm for synchronous\nmodel-parallel training of Transformer-based language models. We develop a\nnovel dynamic programming-based algorithm to calculate the optimal pipelining\nexecution scheme given a specific model and cluster configuration. We show that\nTeraPipe can speed up the training by 5.0x for the largest GPT-3 model with 175\nbillion parameters on an AWS cluster with 48 p3.16xlarge instances compared\nwith state-of-the-art model-parallel methods. The code for reproduction can be\nfound at https://github.com/zhuohan123/terapipe\n"}
+{"abstract": "  Growing interest in quantum computing for practical applications has led to a\nsurge in the availability of programmable machines for executing quantum\nalgorithms. Present day photonic quantum computers have been limited either to\nnon-deterministic operation, low photon numbers and rates, or fixed random gate\nsequences. Here we introduce a full-stack hardware-software system for\nexecuting many-photon quantum circuits using integrated nanophotonics: a\nprogrammable chip, operating at room temperature and interfaced with a fully\nautomated control system. It enables remote users to execute quantum algorithms\nrequiring up to eight modes of strongly squeezed vacuum initialized as two-mode\nsqueezed states in single temporal modes, a fully general and programmable\nfour-mode interferometer, and genuine photon number-resolving readout on all\noutputs. Multi-photon detection events with photon numbers and rates exceeding\nany previous quantum optical demonstration on a programmable device are made\npossible by strong squeezing and high sampling rates. We verify the\nnon-classicality of the device output, and use the platform to carry out\nproof-of-principle demonstrations of three quantum algorithms: Gaussian boson\nsampling, molecular vibronic spectra, and graph similarity.\n"}
+{"abstract": "  Elastic cross sections are calculated and compared with the data on\n$\\pi^{\\pm}$ scattering on $^{28}$Si, $^{40}$Ca, $^{58}$Ni, $^{208}$Pb nuclei in\nthe energy range from 130 to 290 MeV. To this end, both the folding optical\npotential (OP) and the local modified Kisslinger-type OP were calculated, and\nthen the $\\pi A$ cross sections were obtained by solving the Klein-Gordon\nequation to account for the relativization and distortion wave effects. In the\nfolding OPs, the parameters of the elementary $\\pi N$ amplitude were fitted\nwhen describing the $\\pi A$ scattering data, and thus the essential in-medium\neffect on the parameters of the $\\pi N$ amplitude was established since the\npion is scattered not on a free but on a bound nuclear nucleon. Fairly good\nagreement with experimental data was obtained for both models of optical\npotentials and their forms turn out to be in coincidence in the region of their\nsurfaces.\n"}
+{"abstract": "  In this paper, we present a novel approach of automated evaluation of\nprogramming assignments~(AEPA) the highlight of which is that it automatically\nidentifies multiple solution approaches to the programming question from the\nset of submitted solutions. Our approach does not require the instructor to\nforesee all the possible solution approaches and accomplishes this task with\nlittle or no human intervention. This paves the way to multiple fundamental\nimprovements in the way automated evaluation of programming assignments is done\ntoday. We have applied our method on multiple data sets of practical scale. In\nall cases, our method was able to detect the solution approaches employed by\nthe students.\n"}
+{"abstract": "  The main result of this article regards a small time approximation for the\nGirsanov's exponential. We prove that the latter is well described over short\ntime intervals by the solution of a deterministic partial differential\nequation.The rate of convergence of the approximation is of order one in the\nlength of the interval. As a possible application we show that our\napproximation can be used to obtain an estimation of the short-time transition\ndensity of a Langevin equation representing the dynamics of a Brownian particle\nmoving under the influence of an external, non-linear force. Using this\napproach is equivalent to consider a random ordinary differential equation,\nwhere the dynamics of the particle is deterministic and given by the\naforementioned force and the stochasticity enters through the initial\ncondition. The result is in accordance with those obtained by discretization\nmethods such as Euler-Maruyama, implicit Euler and by approximating the\nassociated Fokker-Planck-Smoluchowski equation.\n"}
+{"abstract": "  Two of the main challenges for cropland classification by satellite\ntime-series images are insufficient ground-truth data and inaccessibility of\nhigh-quality hyperspectral images for under-developed areas. Unlabeled\nmedium-resolution satellite images are abundant, but how to benefit from them\nis an open question. We will show how to leverage their potential for cropland\nclassification using self-supervised tasks. Self-supervision is an approach\nwhere we provide simple training signals for the samples, which are apparent\nfrom the data's structure. Hence, they are cheap to acquire and explain a\nsimple concept about the data. We introduce three self-supervised tasks for\ncropland classification. They reduce epistemic uncertainty, and the resulting\nmodel shows superior accuracy in a wide range of settings compared to SVM and\nRandom Forest. Subsequently, we use the self-supervised tasks to perform\nunsupervised domain adaptation and benefit from the labeled samples in other\nregions. It is crucial to know what information to transfer to avoid degrading\nthe performance. We show how to automate the information selection and transfer\nprocess in cropland classification even when the source and target areas have a\nvery different feature distribution. We improved the model by about 24%\ncompared to a baseline architecture without any labeled sample in the target\ndomain. Our method is amenable to gradual improvement, works with\nmedium-resolution satellite images, and does not require complicated models.\nCode and data are available.\n"}
+{"abstract": "  A high level of physical detail in a molecular model improves its ability to\nperform high accuracy simulations, but can also significantly affect its\ncomplexity and computational cost. In some situations, it is worthwhile to add\nadditional complexity to a model to capture properties of interest; in others,\nadditional complexity is unnecessary and can make simulations computationally\ninfeasible. In this work we demonstrate the use of Bayes factors for molecular\nmodel selection, using Monte Carlo sampling techniques to evaluate the evidence\nfor different levels of complexity in the two-centered Lennard-Jones +\nquadrupole (2CLJQ) fluid model. Examining three levels of nested model\ncomplexity, we demonstrate that the use of variable quadrupole and bond length\nparameters in this model framework is justified only sometimes. We also explore\nthe effect of the Bayesian prior distribution on the Bayes factors, as well as\nways to propose meaningful prior distributions. This Bayesian Markov Chain\nMonte Carlo (MCMC) process is enabled by the use of analytical surrogate models\nthat accurately approximate the physical properties of interest. This work\npaves the way for further atomistic model selection work via Bayesian inference\nand surrogate modeling\n"}
+{"abstract": "  Accurate prediction of the stability of molecular crystals is a longstanding\nchallenge, as often minuscule free energy differences between polymorphs are\nsensitively affected by the description of electronic structure, the\nstatistical mechanics of the nuclei and the cell, and thermal expansion. The\nimportance of these effects has been individually established, but rigorous\nfree energy calculations, which simultaneously account for all terms, have been\nprevented by prohibitive computational costs. Here we reproduce the\nexperimental stabilities of polymorphs of three prototypical compounds --\nbenzene, glycine, and succinic acid -- by computing rigorous ab initio Gibbs\nfree energies, at a fraction of the cost of conventional harmonic\napproximations. This is achieved by a bottom-up approach, which involves\ngenerating machine-learning potentials to calculate surrogate free energies and\nsubsequently calculating true ab initio free energies using inexpensive free\nenergy perturbations. Accounting for all relevant physical effects is no longer\na daunting task and provides the foundation for reliable structure predictions\nfor more complex systems of industrial importance.\n"}
+{"abstract": "  In this article, we introduce a numerical framework for quantum tomography\nand entanglement quantification of three-qubit generalized Werner states. The\nscheme involves the single-qubit SIC-POVM, which is then generalized to perform\nthree-qubit measurements. We introduce random errors into the scheme by\nimposing the Poisson noise on the measured photon counts. The precision of\nstate estimation is quantified and presented on graphs. As a special case, we\ncompare the efficiency of the framework for the pure three-qubit generalized\nWerner state and the W state. The reconstructed states are presented\ngraphically and discussed.\n"}
+{"abstract": "  The last experimental campaign of Tore Supra was also the last one with a set\nof plasma facing components mainly dating from the assembly of the machine in\n1988. This campaign was interrupted a few days ahead of schedule because of the\nrupture of one coolant tube of the Inner First Wall (IFW). The rupture was\nobserved by an abrupt increase of the pressure in the vessel. It was caused by\na water leak which occurred two seconds after the end of the lower hybrid\nheating pulse during the current ramp-down. Investigations show that the\naccident is not related to the operation of the machine or a weaker element but\nrather to a localised heat flux exceeding the heat removal capability of the\nIFW.\n"}
+{"abstract": "  Against the background of what has been termed a reproducibility crisis in\nscience, the NLP field is becoming increasingly interested in, and\nconscientious about, the reproducibility of its results. The past few years\nhave seen an impressive range of new initiatives, events and active research in\nthe area. However, the field is far from reaching a consensus about how\nreproducibility should be defined, measured and addressed, with diversity of\nviews currently increasing rather than converging. With this focused\ncontribution, we aim to provide a wide-angle, and as near as possible complete,\nsnapshot of current work on reproducibility in NLP, delineating differences and\nsimilarities, and providing pointers to common denominators.\n"}
+{"abstract": "  Modeling and managing portfolio risk is perhaps the most important step to\nachieve growing and preserving investment performance. Within the modern\nportfolio construction framework that built on Markowitz's theory, the\ncovariance matrix of stock returns is a required input to calculate portfolio\nrisk. Traditional approaches to estimate the covariance matrix are based on\nhuman-designed risk factors, which often require tremendous time and effort to\ndesign better risk factors to improve the covariance estimation. In this work,\nwe formulate the quest of mining risk factors as a learning problem and propose\na deep learning solution to effectively ``design'' risk factors with neural\nnetworks. The learning objective is also carefully set to ensure the learned\nrisk factors are effective in explaining the variance of stock returns as well\nas having desired orthogonality and stability. Our experiments on the stock\nmarket data demonstrate the effectiveness of the proposed solution: our method\ncan obtain $1.9\\%$ higher explained variance measured by $R^2$ and also reduce\nthe risk of a global minimum variance portfolio. The incremental analysis\nfurther supports our design of both the architecture and the learning\nobjective.\n"}
+{"abstract": "  Consider the collection of hyperplanes in $\\mathbb{R}^n$ whose defining\nequations are given by $\\{x_i + x_j = 0\\mid 1\\leq i<j\\leq n\\}$. This\narrangement is called the threshold arrangement since its regions are in\nbijection with labeled threshold graphs on $n$ vertices. Zaslavsky's theorem\nimplies that the number of regions of this arrangement is the sum of\ncoefficients of the characteristic polynomial of the arrangement. In the\npresent article we give a combinatorial meaning to these coefficients as the\nnumber of labeled threshold graphs with a certain property, thus answering a\nquestion posed by Stanley.\n"}
+{"abstract": "  We describe the phase space structures related to the semi-major axis of\nMolniya-like satellites subject to tesseral and lunisolar resonances. In\nparticular, the questions answered in this contribution are: (i) we study the\nindirect interplay of the critical inclination resonance on the\nsemi-geosynchronous resonance using a hierarchy of more realistic dynamical\nsystems, thus discussing the dynamics beyond the integrable approximation. By\nintroducing ad hoc tractable models averaged over fast angles, (ii) we\nnumerically demarcate the hyperbolic structures organising the long-term\ndynamics via Fast Lyapunov Indicators cartography. Based on the publicly\navailable two-line elements space orbital data, (iii) we identify two\nsatellites, namely Molniya 1-69 and Molniya 1-87, displaying fingerprints\nconsistent with the dynamics associated to the hyperbolic set. Finally, (iv)\nthe computations of their associated dynamical maps highlight that the\nspacecraft are trapped within the hyperbolic tangle. This research therefore\nreports evidence of actual artificial satellites in the near-Earth environment\nwhose dynamics are ruled by manifolds and resonant mechanisms. The tools,\nformalism and methodologies we present are exportable to other region of space\nsubject to similar commensurabilities as the geosynchronous region.\n"}
+{"abstract": "  Error mitigation is likely to be key in obtaining near term quantum\nadvantage. In this work we present one of the first implementations of several\nClifford data regression based methods which are used to mitigate the effect of\nnoise in real quantum data. We explore the dynamics of the 1-D Ising model with\ntransverse and longitudinal magnetic fields, highlighting signatures of\nconfinement. We find in general Clifford data regression based techniques are\nadvantageous in comparison with zero-noise extrapolation and obtain\nquantitative agreement with exact results for systems of 9 qubits with circuit\ndepths of up to 176, involving hundreds of CNOT gates. This is the largest\nsystems investigated so far in a study of this type. We also investigate the\ntwo-point correlation function and find the effect of noise on this more\ncomplicated observable can be mitigated using Clifford quantum circuit data\nhighlighting the utility of these methods.\n"}
+{"abstract": "  The rise of deep learning technologies has quickly advanced many fields,\nincluding that of generative music systems. There exist a number of systems\nthat allow for the generation of good sounding short snippets, yet, these\ngenerated snippets often lack an overarching, longer-term structure. In this\nwork, we propose CM-HRNN: a conditional melody generation model based on a\nhierarchical recurrent neural network. This model allows us to generate\nmelodies with long-term structures based on given chord accompaniments. We also\npropose a novel, concise event-based representation to encode musical lead\nsheets while retaining the notes' relative position within the bar with respect\nto the musical meter. With this new data representation, the proposed\narchitecture can simultaneously model the rhythmic, as well as the pitch\nstructures in an effective way. Melodies generated by the proposed model were\nextensively evaluated in quantitative experiments as well as a user study to\nensure the musical quality of the output as well as to evaluate if they contain\nrepeating patterns. We also compared the system with the state-of-the-art\nAttentionRNN. This comparison shows that melodies generated by CM-HRNN contain\nmore repeated patterns (i.e., higher compression ratio) and a lower tonal\ntension (i.e., more tonally concise). Results from our listening test indicate\nthat CM-HRNN outperforms AttentionRNN in terms of long-term structure and\noverall rating.\n"}
+{"abstract": "  We study the Gorenstein weak global dimension of associative rings and its\nrelation to the Gorenstein global dimension. In particular, we prove the\nconjecture that the Gorenstein weak global dimension is a left-right symmetric\ninvariant -- just like the (absolute) weak global dimension.\n"}
+{"abstract": "  We establish $L^2$ boundedness of all \"nice\" parabolic singular integrals on\n\"Good Parabolic Graphs\", aka {\\em regular} Lip(1,1/2) graphs. The novelty here\nis that we include non-homogeneous kernels, which are relevant to the theory of\nparabolic uniform rectifiability. Previously, the third named author had\ntreated the case of homogeneous kernels. The present proof combines the methods\nof that work (which in turn was based on methods described in Christ's CBMS\nlecture notes), with the techniques of Coifman-David-Meyer.\n  This is a very preliminary draft. Eventually, these results will be part of a\nmore extensive work on parabolic uniform rectifiability and singular integrals.\n"}
+{"abstract": "  The present contribution proposes a thermodynamically consistent model for\nthe simulation of the ductile damage. The model couples the phase field method\nof fracture to the Armstrong-Frederick plasticity model with kinematic\nhardening. The latter is particularly suitable for simulating the material\nbehavior under a cyclic load. The model relies on the minimum principle of the\ndissipation potential. However, the application of this approach is challenging\nsince potentials of coupled methods are defined in different spaces: The\ndissipation potential of the phase field model is expressed in terms of rates\nof internal variables, whereas the Armstrong-Frederick model proposes a\nformulation depending on thermodynamic forces. For this reason, a unique\nformulation requires the Legendre transformation of one of the potentials. The\npresent work performs the transformation of the Armstrong-Frederick potential,\nsuch that final formulation is only expressed in the space of rates of internal\nvariables. With the assumption for the free energy and the joint dissipation\npotential at hand, the derivation of evolution equations is straightforward.\nThe application of the model is illustrated by selected numerical examples\nstudying the material response for different load constellations and sample\ngeometries. The paper provides a comparison with the experimental results as\nwell.\n"}
+{"abstract": "  Epidemic spreading is well understood when a disease propagates around a\ncontact graph.\n  In a stochastic susceptible-infected-susceptible setting, spectral conditions\ncharacterise whether the disease vanishes. However, modelling human\ninteractions using a graph is a simplification which only considers pairwise\nrelationships.\n  This does not fully represent the more realistic case where people meet in\ngroups. Hyperedges can be used to record such group interactions, yielding more\nfaithful and flexible models, allowing for the rate of infection of a node to\nvary as a nonlinear function of the number of infectious neighbors. We discuss\ndifferent types of contagion models in this hypergraph setting, and derive\nspectral conditions that characterize whether the disease vanishes.\n  We study both the exact individual-level stochastic model and a deterministic\nmean field ODE approximation.\n  Numerical simulations are provided to illustrate the analysis. We also\ninterpret our results and show how the hypergraph model allows us to\ndistinguish between contributions to infectiousness that (a) are inherent in\nthe nature of the pathogen and (b) arise from behavioural choices (such as\nsocial distancing, increased hygiene and use of masks).\n  This raises the possibility of more accurately quantifying the effect of\ninterventions that are designed to contain the spread of a virus.\n"}
+{"abstract": "  Stress accumulation-relaxation meta-models of pulsar glitches make precise,\nmicrophysics-agnostic predictions of long-term glitch statistics, which can be\nfalsified by existing and future timing data. Previous meta-models assume that\nglitches are triggered by an avalanche process, e.g. involving superfluid\nvortices, and that the probability density function (PDF) of the avalanche\nsizes is history-independent and specified exogenously. Here a recipe is\nproposed to generate the avalanche sizes endogenously in a history-dependent\nmanner, by tracking the thresholds of occupied vortex pinning sites as a\nfunction of time. Vortices unpin spasmodically from sites with thresholds below\na global, time-dependent stress and repin at sites with thresholds above the\nglobal stress, imbuing the system with long-term memory. The meta-model\npredicts PDFs, auto- and cross-correlations for glitch sizes and waiting times,\nwhich are provisionally inconsistent with current observations, unlike some\nprevious meta-models (e.g. state dependent Poisson process), whose predictions\nare consistent. The theoretical implications are intriguing, albeit uncertain,\nbecause history-dependent avalanches embody faithfully the popular, idealized\nunderstanding in the literature of how vortex unpinning operates as a driven,\nstochastic process. The meta-model predicts aftershocks, which occur with\nlarger than average sizes and longer than average waiting times after the\nlargest, system-resetting glitches. This prediction will be tested, once more\ndata are generated by the next generation of pulsar timing campaigns.\n"}
+{"abstract": "  In this work, we extended our statistical model with charmed and bottomed\nhadrons, and fit the quark creational probabilities for the heavy quarks, using\nlow energy inclusive charmonium and bottomonium data. With the finalized fit\nfor all the relevant types of quarks (up, down, strange, charm, bottom) at the\nenergy range from a few GeV up to a few tens of GeV's, the model is now\nconsidered complete. Some examples are also given for proton-proton,\npion-proton, and proton-antiproton collisions with charmonium, bottomonium, and\nopen charm hadrons in the final state.\n"}
+{"abstract": "  We state conditions for which a definable local homomorphism between two\nlocally definable groups $\\mathcal{G}$, $\\mathcal{G^{\\prime}}$ can be uniquely\nextended when $\\mathcal{G}$ is simply connected (Theorem 2.1). As an\napplication of this result we obtain an easy proof of [3, Thm. 9.1] (see\nCorollary 2.2). We also prove that Theorem 10.2 in [3] also holds for any\ndefinably connected definably compact semialgebraic group $G$ not necessarily\nabelian over a sufficiently saturated real closed field $R$; namely, that the\no-minimal universal covering group $\\widetilde{G}$ of $G$ is an open locally\ndefinable subgroup of $\\widetilde{H\\left(R\\right)^{0}}$ for some $R$-algebraic\ngroup $H$ (Thm. 3.3). Finally, for an abelian definably connected semialgebraic\ngroup $G$ over $R$, we describe $\\widetilde{G}$ as a locally definable\nextension of subgroups of the o-minimal universal covering groups of\ncommutative $R$-algebraic groups (Theorem 3.4)\n"}
+{"abstract": "  Driving energy consumption plays a major role in the navigation of mobile\nrobots in challenging environments, especially if they are left to operate\nunattended under limited on-board power. This paper reports on first results of\nan energy-aware path planner, which can provide estimates of the driving energy\nconsumption and energy recovery of a robot traversing complex uneven terrains.\nEnergy is estimated over trajectories making use of a self-supervised learning\napproach, in which the robot autonomously learns how to correlate perceived\nterrain point clouds to energy consumption and recovery. A novel feature of the\nmethod is the use of 1D convolutional neural network to analyse the terrain\nsequentially in the same temporal order as it would be experienced by the robot\nwhen moving. The performance of the proposed approach is assessed in simulation\nover several digital terrain models collected from real natural scenarios, and\nis compared with a heuristic inclination-based energy model. We show evidence\nof the benefit of our method to increase the overall prediction r2 score by\n66.8% and to reduce the driving energy consumption over planned paths by 5.5%.\n"}
+{"abstract": "  Topological groupoids admit various types of morphisms. We push these notions\nto the level of continuous groupoid actions to obtain various types of groupoid\naction morphisms. Some dynamical properties and their relation to these\nmorphisms are studied. Among them are recurrence, various forms of\ntransitivity, minimality, limit, recurrent, periodic and almost periodic\npoints.\n"}
+{"abstract": "  Deep-learning models such as Convolutional Neural Networks (CNN) and Long\nShort-Term Memory (LSTM) have been successfully used for process-mining tasks.\nThey have achieved better performance for different predictive tasks than\ntraditional approaches. We extend the existing body of research by testing four\ndifferent variants of Graph Neural Networks (GNN) and a fully connected\nMulti-layer Perceptron (MLP) with dropout for the tasks of predicting the\nnature and timestamp of the next process activity. In contrast to existing\nstudies, we evaluate our models' performance at different stages of a process,\ndetermined by quartiles of the number of events and normalized quarters of the\ncase duration. This provides new insights into the performance of a prediction\nmodel, as they behave differently at different stages of a business-process.\nInterestingly, our experiments show that the simple MLP often outperforms more\nsophisticated deep-learning models in both prediction tasks. We argue that care\nneeds to be taken when applying automated process-prediction techniques at\ndifferent stages of a process. We further argue that researchers should reflect\ntheir results with strong baselines methods like simple MLPs.\n"}
+{"abstract": "  Image quality assessment (IQA) models aim to establish a quantitative\nrelationship between visual images and their perceptual quality by human\nobservers. IQA modeling plays a special bridging role between vision science\nand engineering practice, both as a test-bed for vision theories and\ncomputational biovision models, and as a powerful tool that could potentially\nmake profound impact on a broad range of image processing, computer vision, and\ncomputer graphics applications, for design, optimization, and evaluation\npurposes. IQA research has enjoyed an accelerated growth in the past two\ndecades. Here we present an overview of IQA methods from a Bayesian\nperspective, with the goals of unifying a wide spectrum of IQA approaches under\na common framework and providing useful references to fundamental concepts\naccessible to vision scientists and image processing practitioners. We discuss\nthe implications of the successes and limitations of modern IQA methods for\nbiological vision and the prospect for vision science to inform the design of\nfuture artificial vision systems.\n"}
+{"abstract": "  We prove Fermat's Last Theorem using the philosophical and mathematical\nknowledge of 1637, when French mathematician Pierre de Fermat claimed to have a\nproof of his conjecture. Our strategy consists in setting three variables of\nthe Fermat's equation as integers, and then we evaluate if the remaining\nvariable can be an integer as well. We assert that at least an irrational\nnumber is needed to satisfy Fermat's equation. We confirm that Fermat's Last\nTheorem is valid.\n"}
+{"abstract": "  Dynamical systems methods are used to investigate a cosmological model with\nnon-minimally coupled scalar field and asymptotically quadratic potential\nfunction. We found that for values of the non-minimal coupling constant\nparameter $\\frac{3}{16}<\\xi<\\frac{1}{4}$ there exists an unstable asymptotic de\nSitter state, free from a parallelly propagated singularity for\n$\\frac{5}{24}\\le\\xi<\\frac{1}{4}$, giving rise to non-singular beginning of the\nuniverse. The energy density associated with this state depends on value of the\nnon-minimal coupling constant and can be much smaller than the Planck energy\ndensity. For $\\xi=\\frac{1}{4}$ we found that the initial state is in form of\nthe static Einstein universe. Proposed evolutional model, contrary to the\nseminal Starobinsky's model, do not depend on the specific choice of initial\nconditions in phase space, moreover, a small change in the model parameters do\nnot change the evolution thus the model is generic and structurally stable. The\nvalues of the non-minimal coupling constant can indicate for a new fundamental\nsymmetry in the gravitational theory. We show that Jordan frame and Einstein\nframe formulation of the theory are physically nonequivalent.\n"}
+{"abstract": "  We propose Quantum Brain Networks (QBraiNs) as a new interdisciplinary field\nintegrating knowledge and methods from neurotechnology, artificial\nintelligence, and quantum computing. The objective is to develop an enhanced\nconnectivity between the human brain and quantum computers for a variety of\ndisruptive applications. We foresee the emergence of hybrid classical-quantum\nnetworks of wetware and hardware nodes, mediated by machine learning techniques\nand brain-machine interfaces. QBraiNs will harness and transform in\nunprecedented ways arts, science, technologies, and entrepreneurship, in\nparticular activities related to medicine, Internet of humans, intelligent\ndevices, sensorial experience, gaming, Internet of things, crypto trading, and\nbusiness.\n"}
+{"abstract": "  Sr$_2$CuO$_2$Cl$_2$ (SCOC) is a model undoped cuprate with $I4/mmm$\ncrystallographic symmetry, and a simple magnetic space group $C_Amca$ with\nassociated magnetic point group $mmm1'$. However, recent second harmonic\nspectroscopy in the antiferromagnetic phase has challenged this picture,\nsuggesting instead a magnetic point group $4/mm'm'$ that co-exists with the\nantiferromagnetism and breaks the two orthogonal mirror planes containing the\ntetragonal $c$-axis. Here, we analyze the symmetry of SCOC in light of the\nsecond harmonic results, and discuss possible ground states that are consistent\nwith the data.\n"}
+{"abstract": "  Excitonic resonance and binding energies can be altered by controlling the\nenvironmental screening of the attractive Coulomb potential. Although this\nscreening response is often assumed to be static, the time evolution of the\nexcitonic quasiparticles manifests a frequency-dependence in its Coulomb\nscreening efficacy. In this letter, we investigate a ground (1s) and first\nexcited exciton state (2s) in a multi-layered transition metal dichalcogenide\n(MoS$_2$) upon ultrafast photo-excitation. We explore the dynamic screening\neffects on the latter and show its resonance frequency is the relevant\nfrequency at which screening from the smaller-sized 1s counterparts is\neffective. Our finding sheds light on new avenues of external tuning on\nexcitonic properties.\n"}
+{"abstract": "  The relative entropy of entanglement $E_R$ is defined as the distance of a\nmulti-partite quantum state from the set of separable states as measured by the\nquantum relative entropy. We show that this optimisation is always achieved,\ni.e. any state admits a (unique) closest separable state, even in infinite\ndimension; also, $E_R$ is everywhere lower semi-continuous. These results,\nwhich seem to have gone unnoticed so far, hold not only for the relative\nentropy of entanglement and its multi-partite generalisations, but also for\nmany other similar resource quantifiers, such as the relative entropy of\nnon-Gaussianity, of non-classicality, of Wigner negativity -- more generally,\nall relative entropy distances from the sets of states with non-negative\n$\\lambda$-quasi-probability distribution. The crucial hypothesis underpinning\nall these applications is the weak*-closedness of the cone generated by free\nstates. We complement our findings by giving explicit and asymptotically tight\ncontinuity estimates for $E_R$ and closely related quantities in the presence\nof an energy constraint.\n"}
+{"abstract": "  We consider the incompressible 2D Navier-Stokes equations with periodic\nboundary conditions driven by a deterministic time periodic forcing and a\ndegenerate stochastic forcing. We show that the system possesses a unique\nergodic periodic invariant measure which is exponentially mixing under a\nWasserstein metric. We also prove the weak law of large numbers for the\ncontinuous time inhomogeneous solution process. In addition, we obtain the weak\nlaw of large numbers and central limit theorem by restricting the inhomogeneous\nsolution process to periodic times. The results are independent of the strength\nof the noise and hold true for any value of viscosity with a lower bound\n$\\nu_1$ characterized by the Grashof number $G_1$ associated with the\ndeterministic forcing. In the laminar case, there is a larger lower bound\n$\\nu_2$ of the viscosity characterized by the Grashof number $G_2$ associated\nwith both the deterministic and random forcing. We prove that in this laminar\ncase, the system has trivial dynamics for any viscosity larger than $\\nu_2$ by\ndemonstrating the existence of a unique globally exponentially stable random\nperiodic solution that supports the unique periodic invariant measure.\n"}
+{"abstract": "  Audio-visual event localization aims to localize an event that is both\naudible and visible in the wild, which is a widespread audio-visual scene\nanalysis task for unconstrained videos. To address this task, we propose a\nMultimodal Parallel Network (MPN), which can perceive global semantics and\nunmixed local information parallelly. Specifically, our MPN framework consists\nof a classification subnetwork to predict event categories and a localization\nsubnetwork to predict event boundaries. The classification subnetwork is\nconstructed by the Multimodal Co-attention Module (MCM) and obtains global\ncontexts. The localization subnetwork consists of Multimodal Bottleneck\nAttention Module (MBAM), which is designed to extract fine-grained\nsegment-level contents. Extensive experiments demonstrate that our framework\nachieves the state-of-the-art performance both in fully supervised and weakly\nsupervised settings on the Audio-Visual Event (AVE) dataset.\n"}
+{"abstract": "  We show that monopoles can be pair produced by cosmological magnetic fields\nin the early universe. The pair production gives rise to relic monopoles, and\nat the same time induces a self-screening of the magnetic fields. By studying\nthese effects we derive limits on the monopole mass, and also on the initial\namplitude of primordial magnetic fields. Monopoles of GUT scale mass can even\nbe produced if primordial magnetic fields exist at sufficiently high redshifts.\n"}
+{"abstract": "  Gaussian process optimization is a successful class of algorithms(e.g.\nGP-UCB) to optimize a black-box function through sequential evaluations.\nHowever, for functions with continuous domains, Gaussian process optimization\nhas to rely on either a fixed discretization of the space, or the solution of a\nnon-convex optimization subproblem at each evaluation. The first approach can\nnegatively affect performance, while the second approach puts requires a heavy\ncomputational burden. A third option, only recently theoretically studied, is\nto adaptively discretize the function domain. Even though this approach avoids\nthe extra non-convex optimization costs, the overall computational complexity\nis still prohibitive. An algorithm such as GP-UCB has a runtime of $O(T^4)$,\nwhere $T$ is the number of iterations. In this paper, we introduce Ada-BKB\n(Adaptive Budgeted Kernelized Bandit), a no-regret Gaussian process\noptimization algorithm for functions on continuous domains, that provably runs\nin $O(T^2 d_\\text{eff}^2)$, where $d_\\text{eff}$ is the effective dimension of\nthe explored space, and which is typically much smaller than $T$. We\ncorroborate our theoretical findings with experiments on synthetic non-convex\nfunctions and on the real-world problem of hyper-parameter optimization,\nconfirming the good practical performances of the proposed approach.\n"}
+{"abstract": "  We estimate galaxy clustering under a modified gravitational potential. In\nparticular, the modifications in gravitational potential energy occur due to a\npower-law and cosmological constant terms. We derive a canonical partition\nfunction for the system of galaxies interacting under such a modified\ngravitational potential. Moreover, we compute various thermodynamical equation\nof states for the system. We do comparative analysis in order to emphasize the\neffect of corrections on thermodynamics of the system. Interestingly, the\nmodifications in thermodynamical quantities are embedded in clustering\nparameter only.\n"}
+{"abstract": "  Organisations are currently lacking in developing and implementing business\nsystems in meaningful ways to motivate and engage their staff. This is\nparticularly salient as the average employee spends eleven cumulative years of\ntheir life at work, however less than one third of the workforce are actually\nengaged in their duties throughout their career. Such low levels of engagement\nare particularly prominent with younger employees, referred to as Generation Y\n(GenY), who are the least engaged of all groups at work. However, they will\ndedicate around five cumulative years of their life immersed playing video\ngames such as Clash of Clans, whether for social, competitive, extrinsic, or\nintrinsic motivational factors. Using behavioural concepts derived from video\ngames, and applying game design elements in business systems to motivate\nemployees in the digital economy, is a concept which has come to be recognised\nas Business Gamification. Thus, the purpose of this research paper is to\nfurther our understanding of game design elements for business, and investigate\ntheir properties from design to implementation in gamified systems. Following a\ntwo-year ethnographic style study with both a system development, and a\ncommunication agency largely staffed with GenY employees, findings suggest\nproperties in game design elements are emergent and temporal in their\ninstantiations.\n"}
+{"abstract": "  Lepton number is promoted to an $U(1)_L$ gauge symmetry in a simple extension\nof the standard model. The spontaneous breaking of $U(1)_L$ by three units\nallows a conserved $Z_3^L$ lepton symmetry to remain, guaranteeing that\nneutrinos are Dirac fermions, which acquire naturally small masses from a\npreviously proposed mechanism. Dark matter appears as a singlet scalar, with\ndark symmetry $Z_3^D$ derivable from $Z_3^L$. Muon $g-2$ may be explained.\n"}
+{"abstract": "  We propose a space mapping-based optimization algorithm for microscopic\ninteracting particle dynamics which are inappropriate for direct optimization.\nThis is of relevance for example in applications with bounded domains such that\nthe microscopic optimization is difficult. The space mapping algorithm exploits\nthe relationship of the microscopic description of the interacting particle\nsystem and the corresponding macroscopic description as partial differential\nequation in the \"many particle limit\". We validate the approach with the help\nof a toy problem that allows for direct optimization. Then we study the\nperformance of the algorithm in two applications. An evacuation dynamic is\nconsidered and the transportation of goods on a conveyor belt is optimized. The\nnumerical results underline the feasibility of the proposed approach.\n"}
+{"abstract": "  Game theory has been increasingly applied in settings where the game is not\nknown outright, but has to be estimated by sampling. For example, meta-games\nthat arise in multi-agent evaluation can only be accessed by running a\nsuccession of expensive experiments that may involve simultaneous deployment of\nseveral agents. In this paper, we focus on $\\alpha$-rank, a popular\ngame-theoretic solution concept designed to perform well in such scenarios. We\naim to estimate the $\\alpha$-rank of the game using as few samples as possible.\nOur algorithm maximizes information gain between an epistemic belief over the\n$\\alpha$-ranks and the observed payoff. This approach has two main benefits.\nFirst, it allows us to focus our sampling on the entries that matter the most\nfor identifying the $\\alpha$-rank. Second, the Bayesian formulation provides a\nfacility to build in modeling assumptions by using a prior over game payoffs.\nWe show the benefits of using information gain as compared to the confidence\ninterval criterion of ResponseGraphUCB (Rowland et al. 2019), and provide\ntheoretical results justifying our method.\n"}
+{"abstract": "  We study the existence and uniqueness of mild and strong solutions of\nnonlocal nonlinear diffusion problems of $p$-Laplacian type with nonlinear\nboundary conditions posed in metric random walk spaces. These spaces include,\namong others, weighted discrete graphs and $\\mathbb{R}^N$ with a random walk\ninduced by a nonsingular kernel. We also study the case of nonlinear dynamical\nboundary conditions. The generality of the nonlinearities considered allow us\nto cover the nonlocal counterparts of a large scope of local diffusion\nproblems: Stefan problems, Hele-Shaw problems, diffusion in porous media\nproblems, obstacle problems, and more. Nonlinear semigroup theory is the basis\nfor this study.\n"}
+{"abstract": "  An agent that is capable of predicting what happens next can perform a\nvariety of tasks through planning with no additional training. Furthermore,\nsuch an agent can internally represent the complex dynamics of the real-world\nand therefore can acquire a representation useful for a variety of visual\nperception tasks. This makes predicting the future frames of a video,\nconditioned on the observed past and potentially future actions, an interesting\ntask which remains exceptionally challenging despite many recent advances.\nExisting video prediction models have shown promising results on simple narrow\nbenchmarks but they generate low quality predictions on real-life datasets with\nmore complicated dynamics or broader domain. There is a growing body of\nevidence that underfitting on the training data is one of the primary causes\nfor the low quality predictions. In this paper, we argue that the inefficient\nuse of parameters in the current video models is the main reason for\nunderfitting. Therefore, we introduce a new architecture, named FitVid, which\nis capable of severe overfitting on the common benchmarks while having similar\nparameter count as the current state-of-the-art models. We analyze the\nconsequences of overfitting, illustrating how it can produce unexpected\noutcomes such as generating high quality output by repeating the training data,\nand how it can be mitigated using existing image augmentation techniques. As a\nresult, FitVid outperforms the current state-of-the-art models across four\ndifferent video prediction benchmarks on four different metrics.\n"}
+{"abstract": "  We derive a simple proof, based on information theoretic inequalities, of an\nupper bound on the largest rates of $q$-ary $\\overline{2}$-separable codes that\nimproves recent results of Wang for any $q\\geq 13$. For the case $q=2$, we\nrecover a result of Lindstr\\\"om, but with a much simpler derivation. The method\neasily extends to give bounds on $B_2$ codes which, although not improving on\nWang's results, use much simpler tools and might be useful for future\napplications.\n"}
+{"abstract": "  In this paper we consider the classical capacity problem for Gaussian\nmeasurement channels without imposing any kind of threshold condition. We prove\nGaussianity of the average state of the optimal ensemble in general and discuss\nthe Hypothesis of Gaussian Maximizers concerning the structure of the ensemble.\nThe proof uses an approach of Wolf, Giedke and Cirac adapted to the convex\nclosure of the output differential entropy. Then we discuss the case of one\nmode in detail, including the dual problem of accessible information of a\nGaussian ensemble.\n  In quantum communications there are several studies of the classical capacity\nin the transmission scheme where not only the Gaussian channel but also the\nreceiver is fixed, and the optimization is performed over certain set of the\ninput ensembles. These studies are practically important in view of the\ncomplexity of the optimal receiver in the Quantum Channel Coding (HSW) theorem.\nOur findings are relevant to such a situation where the receiver is Gaussian\nand concatenation of the channel and the receiver can be considered as one\nGaussian measurement channel. Our efforts in this and preceding papers are then\naimed at establishing full Gaussianity of the optimal ensemble (usually taken\nas an assumption) in such schemes.\n"}
+{"abstract": "  The mechanism behind the nematicity of FeSe is not known. Through\nelastoresitivity measurements it has been shown to be an electronic\ninstability. However, so far measurements have extended only to small strains,\nwhere the response is linear. Here, we apply large elastic strains to FeSe, and\nperform two types of measurements. (1) Using applied strain to control\ntwinning, the nematic resistive anisotropy at temperatures below the nematic\ntransition temperature Ts is determined. (2) Resistive anisotropy is measured\nas nematicity is induced through applied strain at fixed temperature above Ts.\nIn both cases, as nematicity strengthens the resistive anisotropy peaks about\nabout 7%, then decreases. Below ~40 K, the nematic resistive anisotropy changes\nsign. We discuss possible implications of this behaviour for theories of\nnematicity. We report in addition: (1) Under experimentally accessible\nconditions with bulk crystals, stress, rather than strain, is the conjugate\nfield to the nematicity of FeSe. (2) At low temperatures the twin boundary\nresistance is ~10% of the sample resistance, and must be properly subtracted to\nextract intrinsic resistivities. (3) Biaxial inplane compression increases both\nin-plane resistivity and the superconducting critical temperature Tc,\nconsistent with a strong role of the yz orbital in the electronic correlations.\n"}
+{"abstract": "  Let $(M,\\nabla,\\langle\\;,\\;\\rangle)$ be a manifold endowed with a flat\ntorsionless connection $\\nabla$ and a Riemannian metric $\\langle\\;,\\;\\rangle$\nand $(T^kM)_{k\\geq1}$ the sequence of tangent bundles given by\n$T^kM=T(T^{k-1}M)$ and $T^1M=TM$. We show that, for any $k\\geq1$, $T^kM$\ncarries a Hermitian structure $(J_k,g_k)$ and a flat torsionless connection\n$\\nabla^k$ and when $M$ is a Lie group and $(\\nabla,\\langle\\;,\\;\\rangle)$ are\nleft invariant there is a Lie group structure on each $T^kM$ such that\n$(J_k,g_k,\\nabla^k)$ are left invariant.\n  It is well-known that $(TM,J_1,g_1)$ is K\\\"ahler if and only if\n$\\langle\\;,\\;\\rangle$ is Hessian, i.e, in each system of affine coordinates\n$(x_1,\\ldots,x_n)$,\n$\\langle\\partial_{x_i},\\partial_{x_j}\\rangle=\\frac{\\partial^2\\phi}{\\partial_{x_i}\\partial_{x_j}}$.\n  Having in mind many generalizations of the K\\\"ahler condition introduced\nrecently, we give the conditions on $(\\nabla,\\langle\\;,\\;\\rangle)$ so that\n$(TM,J_1,g_1)$ is balanced, locally conformally balanced, locally conformally\nK\\\"ahler, pluriclosed, Gauduchon, Vaismann or Calabi-Yau with torsion.\nMoreover, we can control at the level of $(\\nabla,\\langle\\;,\\;\\rangle)$ the\nconditions insuring that some $(T^kM,J_k,g_k)$ or all of them satisfy a\ngeneralized K\\\"ahler condition.\n  For instance, we show that there are some classes of\n$(M,\\nabla,\\langle\\;,\\;\\rangle)$ such that, for any $k\\geq1$, $(T^kM,J_k,g_k)$\nis balanced non-K\\\"ahler and Calabi-Yau with torsion. By carefully studying the\ngeometry of $(M,\\nabla,\\langle\\;,\\;\\rangle)$, we develop a powerful machinery\nto build a large classes of generalized K\\\"ahler manifolds.\n"}
+{"abstract": "  Deep learning demonstrated major abilities in solving many kinds of different\nreal-world problems in computer vision literature. However, they are still\nstrained by simple reasoning tasks that humans consider easy to solve. In this\nwork, we probe current state-of-the-art convolutional neural networks on a\ndifficult set of tasks known as the same-different problems. All the problems\nrequire the same prerequisite to be solved correctly: understanding if two\nrandom shapes inside the same image are the same or not. With the experiments\ncarried out in this work, we demonstrate that residual connections, and more\ngenerally the skip connections, seem to have only a marginal impact on the\nlearning of the proposed problems. In particular, we experiment with DenseNets,\nand we examine the contribution of residual and recurrent connections in\nalready tested architectures, ResNet-18, and CorNet-S respectively. Our\nexperiments show that older feed-forward networks, AlexNet and VGG, are almost\nunable to learn the proposed problems, except in some specific scenarios. We\nshow that recently introduced architectures can converge even in the cases\nwhere the important parts of their architecture are removed. We finally carry\nout some zero-shot generalization tests, and we discover that in these\nscenarios residual and recurrent connections can have a stronger impact on the\noverall test accuracy. On four difficult problems from the SVRT dataset, we can\nreach state-of-the-art results with respect to the previous approaches,\nobtaining super-human performances on three of the four problems.\n"}
+{"abstract": "  A point process on a space is a random bag of elements of that space. In this\npaper we explore programming with point processes in a monadic style. To this\nend we identify point processes on a space X with probability measures of bags\nof elements in X. We describe this view of point processes using the\ncomposition of the Giry and bag monads on the category of measurable spaces and\nfunctions and prove that this composition also forms a monad using a\ndistributive law for monads. Finally, we define a morphism from a point process\nto its intensity measure, and show that this is a monad morphism. A special\ncase of this monad morphism gives us Wald's Lemma, an identity used to\ncalculate the expected value of the sum of a random number of random variables.\nUsing our monad we define a range of point processes and point process\noperations and compositionally compute their corresponding intensity measures\nusing the monad morphism.\n"}
+{"abstract": "  Generative Adversarial Networks (GANs) have recently achieved unprecedented\nsuccess in photo-realistic image synthesis from low-dimensional random noise.\nThe ability to synthesize high-quality content at a large scale brings\npotential risks as the generated samples may lead to misinformation that can\ncreate severe social, political, health, and business hazards. We propose\nSubsetGAN to identify generated content by detecting a subset of anomalous\nnode-activations in the inner layers of pre-trained neural networks. These\nnodes, as a group, maximize a non-parametric measure of divergence away from\nthe expected distribution of activations created from real data. This enable us\nto identify synthesised images without prior knowledge of their distribution.\nSubsetGAN efficiently scores subsets of nodes and returns the group of nodes\nwithin the pre-trained classifier that contributed to the maximum score. The\nclassifier can be a general fake classifier trained over samples from multiple\nsources or the discriminator network from different GANs. Our approach shows\nconsistently higher detection power than existing detection methods across\nseveral state-of-the-art GANs (PGGAN, StarGAN, and CycleGAN) and over different\nproportions of generated content.\n"}
+{"abstract": "  Assume $k$ is a field and let $F:C\\rightarrow Vect_{k}$ be a small $k$-linear\nfunctor from a $k$-linear abelian category $C$ to the category of vector spaces\nover the field $k$, the purpose of this note is to use a little knowledge of\nlinear algebra and category to give the description of $end(F)$ and $coend(F)$,\nand then we give the reconstruction theorem of bialgebras by using this\ndescription. We use a constructive approach to understand $end(F),coend(F)$ and\nwe describe the bialgebra structure of $coend(F)$ concretely when $F$ is a\ntensor functor.\n"}
+{"abstract": "  This paper investigates the dynamic voltage support (DVS) control of\ninverter-based resources (IBRs) under voltage sags to enhance the low-voltage\nride-through performance. We first revisit the prevalent droop control from an\noptimization perspective to elaborate on why it usually suffers from\nsuboptimality. Then, we formulate the DVS problem as an optimization program\nthat maximizes the positive-sequence voltage magnitude at the point of common\ncoupling (PCC) subject to the current, active power, and stability constraints.\nThe program is inherently nonconvex due to the active power limits, of which\nthe global optimality is not guaranteed by off-the-shelf solvers. In this\ncontext, we perform the optimality analysis to explore the global optimum\nanalytically. It is found that the unique global optimum has three\nscenarios/stages (S1--S3), which depends on the specific relationship among\ngrid voltage, grid strength, as well as physical limits of IBRs. The\nclosed-form solutions in S1 and S3 are derived and the optimality conditions\nfor S2 are provided, which guarantees the optimality and compatibility with the\nfast real-time control. We implement the optimum with a grid-connected\nphotovoltaic (PV) power plant by integrating a DVS controller. Dynamic\nsimulations are carried out under different scenarios to test our proposal and\ncompare it with other existing methods. Additionally, the robustness of\noptimality against model errors is discussed and numerically demonstrated.\n"}
+{"abstract": "  In 2019, the Event Horizon Telescope Collaboration (EHTC) has published the\nfirst image of a supermassive black hole (SMBH) obtained via the Very Large\nBaseline Interferometry (VLBI) technique. In the future, it is expected that\nadditional and more sensitive VLBI observations will be pursued for other\nnearby Active Galactic Nuclei (AGN), and it is therefore important to\nunderstand which possible features can be expected in such images. In this\npaper, we post-process General Relativistic Magneto-Hydrodynamical (GR-MHD)\nsimulations which include resistivity, thus providing a self-consistent jet\nformation model, including resistive mass loading of a wind launched from a\ndisc in Keplerian rotation. The ray-tracing is done using the General\nRelativistic Ray-Tracing code GRTRANS assuming synchrotron emission. We study\nthe appearance of the black hole environment including the accretion disc,\nwinds and jets under a large range of condition, varying black hole mass,\naccretion rate, spin, inclination angle, disc parameters and observed\nfrequency. When we adopt M87-like parameters, we show that we can reproduce a\nring-like feature (similar as observed by the EHT) for some of our simulations.\nThe latter suggests that such Keplerian disc models thus could be consistent\nwith the observed results. Depending on their masses, accretion rates, spin and\nthe sensitivity of the observation, we note that other SMBHs may show\nadditional features like winds and jets in the observations.\n"}
+{"abstract": "  The controlled functionalization of single-walled carbon nanotubes with\nluminescent sp3-defects has created the potential to employ them as\nquantum-light sources in the near-infrared. For that, it is crucial to control\ntheir spectral diversity. The emission wavelength is determined by the binding\nconfiguration of the defects rather than the molecular structure of the\nattached groups. However, current functionalization methods produce a variety\nof binding configurations and thus emission wavelengths. We introduce a simple\nreaction protocol for the creation of only one type of luminescent defect in\npolymer-sorted (6,5) nanotubes, which is more red-shifted and exhibits longer\nphotoluminescence lifetimes than the commonly obtained binding configurations.\nWe demonstrate single-photon emission at room temperature and expand this\nfunctionalization to other polymer-wrapped nanotubes with emission further in\nthe near-infrared. As the selectivity of the reaction with various aniline\nderivatives depends on the presence of an organic base we propose nucleophilic\naddition as the reaction mechanism.\n"}
+{"abstract": "  We determine theoretically the interaction between two magnetic impurities\nembedded in a spin-split $s$-wave superconductor. The spin-splitting in the\nsuperconductor gives rise to two different interaction types between the\nimpurity spins, depending on whether their spins lie in the plane perpendicular\nto the spin-splitting field (Heisenberg) or not (Ising). For impurity\nseparation distances exceeding $\\xi_S$, we find that the magnitude of the\nspin-splitting can determine whether an antiferromagnetic or ferromagnetic\nalignment of the impurity spins is preferred by the RKKY interaction. Moreover,\nthe Ising and Heisenberg terms of the RKKY interaction alternate on being the\ndominant term and their magnitudes oscillate as a function of distance between\nthe impurities.\n"}
+{"abstract": "  Learning to program an audio production VST synthesizer is a time consuming\nprocess, usually obtained through inefficient trial and error and only mastered\nafter years of experience. As an educational and creative tool for sound\ndesigners, we propose SerumRNN: a system that provides step-by-step\ninstructions for applying audio effects to change a user's input audio towards\na desired sound. We apply our system to Xfer Records Serum: currently one of\nthe most popular and complex VST synthesizers used by the audio production\ncommunity. Our results indicate that SerumRNN is consistently able to provide\nuseful feedback for a variety of different audio effects and synthesizer\npresets. We demonstrate the benefits of using an iterative system and show that\nSerumRNN learns to prioritize effects and can discover more efficient effect\norder sequences than a variety of baselines.\n"}
+{"abstract": "  We study modules over the commutative ring spectrum $H\\mathbb F_2\\wedge\nH\\mathbb F_2$, whose coefficient groups are quotients of the dual Steenrod\nalgebra by collections of the Milnor generators. We show that very few of these\nquotients admit algebra structures, but those that do can be constructed\nsimply: killing a generator $\\xi_k$ in the category of associative algebras\nfreely kills the higher generators $\\xi_{k+n}$. Using new information about the\nconjugation operation in the dual Steenrod algebra, we also consider quotients\nby families of Milnor generators and their conjugates. This allows us to\nproduce a family of associative $H\\mathbb F_2\\wedge H\\mathbb F_2$-algebras\nwhose coefficient rings are finite-dimensional and exhibit unexpected duality\nfeatures. We then use these algebras to give detailed computations of the\nhomotopy groups of several modules over this ring spectrum.\n"}
+{"abstract": "  In this paper the Feynman Green function for Maxwell's theory in curved\nspace-time is studied by using the Fock-Schwinger-DeWitt asymptotic expansion;\nthe point-splitting method is then applied, since it is a valuable tool for\nregularizing divergent observables. Among these, the stress-energy tensor is\nexpressed in terms of second covariant derivatives of the Hadamard Green\nfunction, which is also closely linked to the effective action; therefore one\nobtains a series expansion for the stress-energy tensor. Its divergent part can\nbe isolated, and a concise formula is here obtained: by dimensional analysis\nand combinatorics, there are two kinds of terms: quadratic in curvature tensors\n(Riemann, Ricci tensors and scalar curvature) and linear in their second\ncovariant derivatives. This formula holds for every space-time metric; it is\nmade even more explicit in the physically relevant particular cases of\nRicci-flat and maximally symmetric spaces, and fully evaluated for some\nexamples of physical interest: Kerr and Schwarzschild metrics and de Sitter\nspace-time.\n"}
+{"abstract": "  The purpose of the paper is to show that, in low dimensions, the WDVV\nequations are bi-Hamiltonian. The invariance of the bi-Hamiltonian formalism is\nproved for $N=3$. More examples in higher dimensions show that the result might\nhold in general. The invariance group of the bi-Hamiltonian pairs that we find\nfor WDVV equations is the group of projective transformations. The significance\nof projective invariance of WDVV equations is discussed in detail. The computer\nalgebra programs that were used for calculations throughout the paper are\nprovided in a GitHub repository.\n"}
+{"abstract": "  The Influenza type A virus can be considered as one of the most severe\nviruses that can infect multiple species with often fatal consequences to the\nhosts. The Haemagglutinin (HA) gene of the virus has the potential to be a\ntarget for antiviral drug development realised through accurate identification\nof its sub-types and possible the targeted hosts. In this paper, to accurately\npredict if an Influenza type A virus has the capability to infect human hosts,\nby using only the HA gene, is therefore developed and tested. The predictive\nmodel follows three main steps; (i) decoding the protein sequences into\nnumerical signals using EIIP amino acid scale, (ii) analysing these sequences\nby using Discrete Fourier Transform (DFT) and extracting DFT-based features,\n(iii) using a predictive model, based on Artificial Neural Networks and using\nthe features generated by DFT. In this analysis, from the Influenza Research\nDatabase, 30724, 18236 and 8157 HA protein sequences were collected for Human,\nAvian and Swine, respectively. Given this set of the proteins, the proposed\nmethod yielded 97.36% (+- 0.04%), 97.26% (+- 0.26%), 0.978 (+- 0.004), 0.963\n(+- 0.005) and 0.945 (+- 0.005) for the training accuracy validation accuracy,\nprecision, recall and Mathews Correlation Coefficient (MCC) respectively, based\non a 10-fold cross-validation. The classification model generated by using one\nof the largest dataset, if not the largest, yields promising results that could\nlead to early detection of such species and help develop precautionary\nmeasurements for possible human infections.\n"}
+{"abstract": "  The pre-training on the graph neural network model can learn the general\nfeatures of large-scale networks or networks of the same type by\nself-supervised methods, which allows the model to work even when node labels\nare missing. However, the existing pre-training methods do not take network\nevolution into consideration. This paper proposes a pre-training method on\ndynamic graph neural networks (PT-DGNN), which uses dynamic attributed graph\ngeneration tasks to simultaneously learn the structure, semantics, and\nevolution features of the graph. The method includes two steps: 1) dynamic\nsub-graph sampling, and 2) pre-training with dynamic attributed graph\ngeneration task. Comparative experiments on three realistic dynamic network\ndatasets show that the proposed method achieves the best results on the link\nprediction fine-tuning task.\n"}
+{"abstract": "  This work tackles the issue of fairness in the context of generative\nprocedures, such as image super-resolution, which entail different definitions\nfrom the standard classification setting. Moreover, while traditional group\nfairness definitions are typically defined with respect to specified protected\ngroups -- camouflaging the fact that these groupings are artificial and carry\nhistorical and political motivations -- we emphasize that there are no ground\ntruth identities. For instance, should South and East Asians be viewed as a\nsingle group or separate groups? Should we consider one race as a whole or\nfurther split by gender? Choosing which groups are valid and who belongs in\nthem is an impossible dilemma and being \"fair\" with respect to Asians may\nrequire being \"unfair\" with respect to South Asians. This motivates the\nintroduction of definitions that allow algorithms to be \\emph{oblivious} to the\nrelevant groupings.\n  We define several intuitive notions of group fairness and study their\nincompatibilities and trade-offs. We show that the natural extension of\ndemographic parity is strongly dependent on the grouping, and \\emph{impossible}\nto achieve obliviously. On the other hand, the conceptually new definition we\nintroduce, Conditional Proportional Representation, can be achieved obliviously\nthrough Posterior Sampling. Our experiments validate our theoretical results\nand achieve fair image reconstruction using state-of-the-art generative models.\n"}
+{"abstract": "  The complex-valued quantum mechanics considers quantum motion on the complex\nplane instead of on the real axis, and studies the variations of a particle\ncomplex position, momentum and energy along a complex trajectory. On the basis\nof quantum Hamilton-Jacobi formalism in the complex space, we point out that\nhaving complex-valued motion is a universal property of quantum systems,\nbecause every quantum system is actually accompanied with an intrinsic complex\nHamiltonian originating from the equation. It is revealed that the conventional\nreal-valued quantum mechanics is a special case of the complex-valued quantum\nmechanics in that the eigen-structures of real and complex quantum systems,\nsuch as their eigenvalues, eigenfunctions and eigen-trajectories, are invariant\nunder linear complex mapping. In other words, there is indeed no distinction\nbetween Hermitian systems, PT-symmetric systems, and non PT-symmetric systems\nwhen viewed from a complex domain. Their eigen-structures can be made\ncoincident through linear transformation of complex coordinates.\n"}
+{"abstract": "  Learned optimizers are increasingly effective, with performance exceeding\nthat of hand designed optimizers such as Adam~\\citep{kingma2014adam} on\nspecific tasks \\citep{metz2019understanding}. Despite the potential gains\navailable, in current work the meta-training (or `outer-training') of the\nlearned optimizer is performed by a hand-designed optimizer, or by an optimizer\ntrained by a hand-designed optimizer \\citep{metz2020tasks}. We show that a\npopulation of randomly initialized learned optimizers can be used to train\nthemselves from scratch in an online fashion, without resorting to a hand\ndesigned optimizer in any part of the process. A form of population based\ntraining is used to orchestrate this self-training. Although the randomly\ninitialized optimizers initially make slow progress, as they improve they\nexperience a positive feedback loop, and become rapidly more effective at\ntraining themselves. We believe feedback loops of this type, where an optimizer\nimproves itself, will be important and powerful in the future of machine\nlearning. These methods not only provide a path towards increased performance,\nbut more importantly relieve research and engineering effort.\n"}
+{"abstract": "  We conduct a randomized experiment that varies one-time health insurance\nsubsidy amounts (partial and full) in Ghana to study the impacts of subsidies\non insurance enrollment and health care utilization. We find that both partial\nand full subsidies promote insurance enrollment in the long run, even after the\nsubsidies expired. Although the long run enrollment rate and selective\nenrollment do not differ by subsidy level, long run health care utilization\nincreased only for the partial subsidy group. We show that this can plausibly\nbe explained by stronger learning-through-experience behavior in the partial\nthan in the full subsidy group.\n"}
+{"abstract": "  In task allocation for real-time domains, such as disaster response, a\nlimited number of agents is deployed across a large area to carry out numerous\ntasks, each with its prerequisites, profit, time window and workload. To\nmaximize profits while minimizing time penalties, agents need to cooperate by\nforming, disbanding and reforming coalitions. In this paper, we name this\nproblem Multi-Agent Routing and Scheduling through Coalition formation (MARSC)\nand show that it generalizes the important Team Orienteering Problem with Time\nWindows. We propose a binary integer program and an anytime and scalable\nheuristic to solve it. Using public London Fire Brigade records, we create a\ndataset with 347588 tasks and a test framework that simulates the mobilization\nof firefighters. In problems with up to 150 agents and 3000 tasks, our\nheuristic finds solutions up to 3.25 times better than the Earliest Deadline\nFirst approach commonly used in real-time systems. Our results constitute the\nfirst large-scale benchmark for the MARSC problem.\n"}
+{"abstract": "  This paper investigates the energy efficiency (EE) optimization in downlink\nmulti-cell massive multiple-input multiple-output (MIMO). In our research, the\nstatistical channel state information (CSI) is exploited to reduce the\nsignaling overhead. To maximize the minimum EE among the neighbouring cells, we\ndesign the transmit covariance matrices for each base station (BS).\nSpecifically, optimization schemes for this max-min EE problem are developed,\nin the centralized and distributed ways, respectively. To obtain the transmit\ncovariance matrices, we first find out the closed-form optimal transmit\neigenmatrices for the BS in each cell, and convert the original transmit\ncovariance matrices designing problem into a power allocation one. Then, to\nlower the computational complexity, we utilize an asymptotic approximation\nexpression for the problem objective. Moreover, for the power allocation\ndesign, we adopt the minorization maximization method to address the\nnon-convexity of the ergodic rate, and use Dinkelbach's transform to convert\nthe max-min fractional problem into a series of convex optimization\nsubproblems. To tackle the transformed subproblems, we propose a centralized\niterative water-filling scheme. For reducing the backhaul burden, we further\ndevelop a distributed algorithm for the power allocation problem, which\nrequires limited inter-cell information sharing. Finally, the performance of\nthe proposed algorithms are demonstrated by extensive numerical results.\n"}
+{"abstract": "  Optimal Mass Transport (OMT) is a well studied problem with a variety of\napplications in a diverse set of fields ranging from Physics to Computer Vision\nand in particular Statistics and Data Science. Since the original formulation\nof Monge in 1781 significant theoretical progress been made on the existence,\nuniqueness and properties of the optimal transport maps. The actual numerical\ncomputation of the transport maps, particularly in high dimensions, remains a\nchallenging problem. By Brenier's theorem, the continuous OMT problem can be\nreduced to that of solving a non-linear PDE of Monge-Ampere type whose solution\nis a convex function. In this paper, building on recent developments of input\nconvex neural networks and physics informed neural networks for solving PDE's,\nwe propose a Deep Learning approach to solve the continuous OMT problem.\n  To demonstrate the versatility of our framework we focus on the ubiquitous\ndensity estimation and generative modeling tasks in statistics and machine\nlearning. Finally as an example we show how our framework can be incorporated\nwith an autoencoder to estimate an effective probabilistic generative model.\n"}
+{"abstract": "  In this paper, we develop a numerical scheme to handle interfaces across\ncomputational domains in multi-block schemes for the approximation of systems\nof conservation laws. We are interested in transmitting shock discontinuities\nwithout lowering the overall precision of the method. We want to accomplish\nthis without using information from interior points of adjacent grids, that is,\nsharing only information from boundary points of those grids. To achieve this,\nwe choose to work with the second-order Kurganov-Tadmor (KT) method at interior\npoints, relaxing it to first order at interfaces. This allows us to keep\nsecond-order overall accuracy (in the relevant norm) and at the same time\npreserve the TVD property of the original scheme. After developing the method\nwe performed several standard one and two-dimensional tests. Among them, we\nused the one-dimensional advection and Burgers equations to verify the\nsecond-order convergence of the method. We also tested the two-dimensional\nEuler equations with an implosion and a Gresho vortex\\cite{liska2003}. In\nparticular, in the two-dimensional implosion test we can see that regardless of\nthe orientation of shocks with respect to the interface, they travel across\nthem without appreciable deformation both in amplitude and front direction.\n"}
+{"abstract": "  In this paper, we want to show the potential benefit of a dynamic auto-tuning\napproach for the inference process in the Deep Neural Network (DNN) context,\ntackling the object detection challenge. We benchmarked different neural\nnetworks to find the optimal detector for the well-known COCO 17 database, and\nwe demonstrate that even if we only consider the quality of the prediction\nthere is not a single optimal network. This is even more evident if we also\nconsider the time to solution as a metric to evaluate, and then select, the\nmost suitable network. This opens to the possibility for an adaptive\nmethodology to switch among different object detection networks according to\nrun-time requirements (e.g. maximum quality subject to a time-to-solution\nconstraint).\n  Moreover, we demonstrated by developing an ad hoc oracle, that an additional\nproactive methodology could provide even greater benefits, allowing us to\nselect the best network among the available ones given some characteristics of\nthe processed image. To exploit this method, we need to identify some image\nfeatures that can be used to steer the decision on the most promising network.\nDespite the optimization opportunity that has been identified, we were not able\nto identify a predictor function that validates this attempt neither adopting\nclassical image features nor by using a DNN classifier.\n"}
+{"abstract": "  Due to the lack of natural scene and haze prior information, it is greatly\nchallenging to completely remove the haze from single image without distorting\nits visual content. Fortunately, the real-world haze usually presents\nnon-homogeneous distribution, which provides us with many valuable clues in\npartial well-preserved regions. In this paper, we propose a Non-Homogeneous\nHaze Removal Network (NHRN) via artificial scene prior and bidimensional graph\nreasoning. Firstly, we employ the gamma correction iteratively to simulate\nartificial multiple shots under different exposure conditions, whose haze\ndegrees are different and enrich the underlying scene prior. Secondly, beyond\nutilizing the local neighboring relationship, we build a bidimensional graph\nreasoning module to conduct non-local filtering in the spatial and channel\ndimensions of feature maps, which models their long-range dependency and\npropagates the natural scene prior between the well-preserved nodes and the\nnodes contaminated by haze. We evaluate our method on different benchmark\ndatasets. The results demonstrate that our method achieves superior performance\nover many state-of-the-art algorithms for both the single image dehazing and\nhazy image understanding tasks.\n"}
+{"abstract": "  Let $H$ be a complex Hilbert space. Consider the ortho-Grassmann graph\n$\\Gamma^{\\perp}_{k}(H)$ whose vertices are $k$-dimensional subspaces of $H$\n(projections of rank $k$) and two subspaces are connected by an edge in this\ngraph if they are compatible and adjacent (the corresponding rank-$k$\nprojections commute and their difference is an operator of rank $2$). Our main\nresult is the following: if $\\dim H\\ne 2k$, then every automorphism of\n$\\Gamma^{\\perp}_{k}(H)$ is induced by a unitary or anti-unitary operator; if\n$\\dim H=2k\\ge 6$, then every automorphism of $\\Gamma^{\\perp}_{k}(H)$ is induced\nby a unitary or anti-unitary operator or it is the composition of such an\nautomorphism and the orthocomplementary map. For the case when $\\dim H=2k=4$\nthe statement fails. To prove this statement we compare geodesics of length two\nin ortho-Grassmann graphs and characterise compatibility (commutativity) in\nterms of geodesics in Grassmann and ortho-Grassmann graphs. At the end, we\nextend this result on generalised ortho-Grassmann graphs associated to\nconjugacy classes of finite-rank self-adjoint operators.\n"}
+{"abstract": "  We introduce an algebraic method to study local stability in the Newtonian\n$n$-body problem when certain symmetries are present. We use representation\ntheory of groups to simplify the calculations of certain eigenvalue problems.\nThe method should be applicable in many cases, we give two main examples here:\nthe square central configurations with four equal masses, and the equilateral\ntriangular configurations with three equal masses plus an additional mass of\narbitrary size at the center. We explicitly found the eigenvalues of certain\n8x8 Hessians in these examples, with only some simple calculations of traces.\nWe also studied the local stability properties of corresponding relative\nequilibria in the four-body problems.\n"}
+{"abstract": "  Spatial gaseous structures in the Beta Lyrae system have been studied with\nthe fact of change in the longitudinal component of the donor's magnetic field\nduring the orbital period in mind. The investigation was based primarily on the\nstudy of the dynamics of the circumstellar structures surrounding the binary\nsystem as a whole. The special emphasis was placed on the study of complex\nhelium lines, in particular those arising from metastable levels. A number of\ndifferent observable facts from the ultraviolet to the red spectral region were\nanalyzed. The configuration of the donor magnetic field is a factor that not\nonly enhances mass transfer and influences the formation of spatial gas\nstructures between stellar components but, to some extent, also affects the\noutflow of matter and the formation of external gas structures around this\ninteracting binary system. Together with previous articles\n\\citep{Skulskyy2020a, Skulskyy2020b}, the pieces of evidence of this work,\nconfirming the reflection of magnetically controlled matter in circumbinary\nstructures, define the basis for a coherent picture of the mass exchange\nbetween components and outflows of matter outwards\n"}
+{"abstract": "  In this article we consider a version of the challenging problem of learning\nfrom datasets whose size is too limited to allow generalisation beyond the\ntraining set. To address the challenge we propose to use a transfer learning\napproach whereby the model is first trained on a synthetic dataset replicating\nfeatures of the original objects. In this study the objects were smartphone\nphotographs of near-complete Roman terra sigillata pottery vessels from the\ncollection of the Museum of London. Taking the replicated features from\npublished profile drawings of pottery forms allowed the integration of expert\nknowledge into the process through our synthetic data generator. After this\nfirst initial training the model was fine-tuned with data from photographs of\nreal vessels. We show, through exhaustive experiments across several popular\ndeep learning architectures, different test priors, and considering the impact\nof the photograph viewpoint and excessive damage to the vessels, that the\nproposed hybrid approach enables the creation of classifiers with appropriate\ngeneralisation performance. This performance is significantly better than that\nof classifiers trained exclusively on the original data which shows the promise\nof the approach to alleviate the fundamental issue of learning from small\ndatasets.\n"}
+{"abstract": "  We calculate, for the first time, the NNLO QCD corrections to identified\nheavy hadron production at hadron colliders. The calculation is based on a\nflexible numeric framework which allows the calculation of any distribution of\na single identified heavy hadron plus jets and non-QCD particles. As a first\napplication we provide NNLO QCD predictions for several differential\ndistributions of $B$ hadrons in $t\\bar t$ events at the LHC. Among others,\nthese predictions are needed for the precise determination of the top quark\nmass. The extension of our results to other processes, like open or associated\n$B$ and charm production is straightforward. We also explore the prospects for\nextracting heavy flavor fragmentation functions from LHC data.\n"}
+{"abstract": "  Reward shaping (RS) is a powerful method in reinforcement learning (RL) for\novercoming the problem of sparse or uninformative rewards. However, RS\ntypically relies on manually engineered shaping-reward functions whose\nconstruction is time-consuming and error-prone. It also requires domain\nknowledge which runs contrary to the goal of autonomous learning. We introduce\nReinforcement Learning Optimising Shaping Algorithm (ROSA), an automated RS\nframework in which the shaping-reward function is constructed in a novel Markov\ngame between two agents. A reward-shaping agent (Shaper) uses switching\ncontrols to determine which states to add shaping rewards and their optimal\nvalues while the other agent (Controller) learns the optimal policy for the\ntask using these shaped rewards. We prove that ROSA, which easily adopts\nexisting RL algorithms, learns to construct a shaping-reward function that is\ntailored to the task thus ensuring efficient convergence to high performance\npolicies. We demonstrate ROSA's congenial properties in three carefully\ndesigned experiments and show its superior performance against state-of-the-art\nRS algorithms in challenging sparse reward environments.\n"}
+{"abstract": "  We study the problem of training a model that must obey demographic fairness\nconditions when the sensitive features are not available at training time -- in\nother words, how can we train a model to be fair by race when we don't have\ndata about race? We adopt a fairness pipeline perspective, in which an\n\"upstream\" learner that does have access to the sensitive features will learn a\nproxy model for these features from the other attributes. The goal of the proxy\nis to allow a general \"downstream\" learner -- with minimal assumptions on their\nprediction task -- to be able to use the proxy to train a model that is fair\nwith respect to the true sensitive features. We show that obeying multiaccuracy\nconstraints with respect to the downstream model class suffices for this\npurpose, and provide sample- and oracle efficient-algorithms and generalization\nbounds for learning such proxies. In general, multiaccuracy can be much easier\nto satisfy than classification accuracy, and can be satisfied even when the\nsensitive features are hard to predict.\n"}
+{"abstract": "  We consider learning Ising tree models when the observations from the nodes\nare corrupted by independent but non-identically distributed noise with unknown\nstatistics. Katiyar et al. (2020) showed that although the exact tree structure\ncannot be recovered, one can recover a partial tree structure; that is, a\nstructure belonging to the equivalence class containing the true tree. This\npaper presents a systematic improvement of Katiyar et al. (2020). First, we\npresent a novel impossibility result by deriving a bound on the necessary\nnumber of samples for partial recovery. Second, we derive a significantly\nimproved sample complexity result in which the dependence on the minimum\ncorrelation $\\rho_{\\min}$ is $\\rho_{\\min}^{-8}$ instead of $\\rho_{\\min}^{-24}$.\nFinally, we propose Symmetrized Geometric Averaging (SGA), a more statistically\nrobust algorithm for partial tree recovery. We provide error exponent analyses\nand extensive numerical results on a variety of trees to show that the sample\ncomplexity of SGA is significantly better than the algorithm of Katiyar et al.\n(2020). SGA can be readily extended to Gaussian models and is shown via\nnumerical experiments to be similarly superior.\n"}
+{"abstract": "  Carbon nanoscrolls (CNSs) are nanomaterials with geometry resembling graphene\nlayers rolled up into a spiral (papyrus-like) form. Effects of hydrogenation\nand temperature on the self-scrolling process of two nanoribbons interacting\nwith a carbon nanotube (CNT) have been studied by molecular dynamics\nsimulations for three configurations: (1) graphene/graphene/CNT; (2)\ngraphene/graphane/CNT, and (3) graphane/graphane/CNT. Graphane refers to a\nfully hydrogenated graphene nanoribbon. Nanoscroll formation is observed for\nconfiguration (1) and (2) for temperatures 300-1000 and 300-600 K,\nrespectively, while nanoribbons wrap CNT without nanoscroll formation for\nconfiguration (3).\n"}
+{"abstract": "  Convolutional video models have an order of magnitude larger computational\ncomplexity than their counterpart image-level models. Constrained by\ncomputational resources, there is no model or training method that can train\nlong video sequences end-to-end. Currently, the main-stream method is to split\na raw video into clips, leading to incomplete fragmentary temporal information\nflow. Inspired by natural language processing techniques dealing with long\nsentences, we propose to treat videos as serial fragments satisfying Markov\nproperty, and train it as a whole by progressively propagating information\nthrough the temporal dimension in multiple steps. This progressive training\n(PGT) method is able to train long videos end-to-end with limited resources and\nensures the effective transmission of information. As a general and robust\ntraining method, we empirically demonstrate that it yields significant\nperformance improvements on different models and datasets. As an illustrative\nexample, the proposed method improves SlowOnly network by 3.7 mAP on Charades\nand 1.9 top-1 accuracy on Kinetics with negligible parameter and computation\noverhead. Code is available at https://github.com/BoPang1996/PGT.\n"}
+{"abstract": "  We consider a sequence of finite quantum graphs with few loops, so that they\nconverge, in the sense of Benjamini-Schramm, to a random infinite quantum tree.\nWe assume these quantum trees are spectrally delocalized in some interval $I$,\nin the sense that their spectrum in $I$ is purely absolutely continuous and\ntheir Green's functions are well controlled near the real axis. We furthermore\nsuppose that the underlying sequence of discrete graphs is expanding. We deduce\na quantum ergodicity result, showing that the eigenfunctions with eigenvalues\nlying in $I$ are spatially delocalized.\n"}
+{"abstract": "  We propose a method to reconstruct the 3-D molecular structure from\nmicrographs collected at just one sample tilt angle in the random conical tilt\nscheme in cryo-electron microscopy. Our method uses autocorrelation analysis on\nthe micrographs to estimate features of the molecule which are invariant under\ncertain nuisance parameters such as the positions of molecular projections in\nthe micrographs. This enables us to reconstruct the molecular structure\ndirectly from micrographs, completely circumventing the need for particle\npicking. We demonstrate reconstructions with simulated data and investigate the\neffect of the missing-cone region. These results show promise to reduce the\nsize limit for single particle reconstruction in cryo-electron microscopy.\n"}
+{"abstract": "  Vehicular Ad Hoc Networks (VANETs) improves traffic management and reduce the\namount of road accidents by providing safety applications. However, VANETs are\nvulnerable to variety of security attacks from malicious entities. An\nauthentication is an integral a neighborhood of trust establishment and secure\ncommunications between vehicles. The Road-side Unit (RSU) evaluates trust-value\nand the Agent Trusted Authority (ATA) helps in computing the trust-value of\nauto supported its reward-points. The communication between nodes is enhanced,\nthis can reduce 50% of road accidents. The security of the VANET is improved.\nWe propose the utilization of Elliptic Curve Cryptography in the design of an\nefficient data encryption/decryption system for sensor nodes in a wireless\nnetwork. Elliptic Curve Cryptography can provide impressive levels of security\nstandards while keeping down the cost of certain issues, primarily storage\nspace. Sensors will benefit from having to store relatively smaller keys\ncoupled with increased computational capability and this will be a stronger\ndesign as the bit-level security is improved. Thus, reducing the time delay\nbetween the nodes and to provide better results between them we have made use\nof this method. The implementation of this work is done with NS2 software.\n"}
+{"abstract": "  With the remarkable success of representation learning for prediction\nproblems, we have witnessed a rapid expansion of the use of machine learning\nand deep learning for the analysis of digital pathology and biopsy image\npatches. However, learning over patch-wise features using convolutional neural\nnetworks limits the ability of the model to capture global contextual\ninformation and comprehensively model tissue composition. The phenotypical and\ntopological distribution of constituent histological entities play a critical\nrole in tissue diagnosis. As such, graph data representations and deep learning\nhave attracted significant attention for encoding tissue representations, and\ncapturing intra- and inter- entity level interactions. In this review, we\nprovide a conceptual grounding for graph analytics in digital pathology,\nincluding entity-graph construction and graph architectures, and present their\ncurrent success for tumor localization and classification, tumor invasion and\nstaging, image retrieval, and survival prediction. We provide an overview of\nthese methods in a systematic manner organized by the graph representation of\nthe input image, scale, and organ on which they operate. We also outline the\nlimitations of existing techniques, and suggest potential future research\ndirections in this domain.\n"}
+{"abstract": "  In this paper we consider the discounted 0-1 knapsack problem (DKP), which is\nan extension of the classical knapsack problem where a set of items is\ndecomposed into groups of three items. At most one item can be chosen from each\ngroup and the aim is to maximize the total profit of the selected items while\nrespecting the knapsack capacity constraint. The DKP is a relatively recent\nproblem in the literature. In this paper we propose a two-phase approach in\nwhich the problem is reduced by applying exact and / or heuristic fixation\nrules in a first phase that can be viewed as a preprocessing phase. The\nremaining problem can then be solved by dynamic programming. Experiments\nperformed on available instances in the literature show that the fixation\ntechniques are very useful to solve these instances. Indeed, the preprocessing\nphase greatly reduces the size of these instances, leading to a significant\nreduction in the time required for dynamic programming to provide an optimal\nsolution.\n"}
+{"abstract": "  In the paper, we study the moments $\\langle \\xi_{2;a_1}^{\\|;n}\\rangle$ of the\naxial-vector $a_1(1260)$-meson distribution amplitude by using the QCD sum\nrules approach under the background field theory. By considering the vacuum\ncondensates up to dimension-six and the perturbative part up to next-to-leading\norder QCD corrections, its first five moments at an initial scale $\\mu_0=1~{\\rm\nGeV}$ are $\\langle \\xi_{2;a_1}^{\\|;2}\\rangle|_{\\mu_0} = 0.210 \\pm 0.018$,\n$\\langle\\xi_{2;a_1}^{\\|;4}\\rangle|_{\\mu_0} = 0.091 \\pm 0.007$,\n$\\langle\\xi_{2;a_1}^{\\|;6}\\rangle |_{\\mu_0} = 0.052 \\pm 0.004$,\n$\\langle\\xi_{2;a_1}^{\\|;8}\\rangle |_{\\mu_0} = 0.036 \\pm 0.003$ and\n$\\langle\\xi_{2;a_1}^{\\|;10}\\rangle |_{\\mu_0} = 0.025 \\pm 0.002$, respectively.\nWe then construct a light-cone harmonic oscillator model for $a_1(1260)$-meson\nlongitudinal twist-2 distribution amplitude $\\phi_{2;a_1}^{\\|}(x,\\mu)$, whose\ninput parameters are fitted by using the least square method. As an application\nof $\\phi_{2;a_1}^\\|(x,\\mu)$, we calculate the transition form factors (TFFs) of\n$D\\to a_1(1260)$ in large and intermediate momentum transfers by using the QCD\nlight-cone sum rules approach. And at the largest recoil point ($q^2=0$), we\nobtain $ A(0) = 0.130_{ - 0.015}^{ + 0.013}$, $V_1(0) =\n1.900_{-0.125}^{+0.121}$, $V_2(0) = 0.210_{-0.020}^{ + 0.018}$, and $V_0(0) =\n0.236_{ - 0.024}^{ + 0.026}$. By applying the extrapolated TFFs to the\nsemi-leptonic decay $D^{0(+)} \\to a_1^{-(0)}(1260)\\ell^+\\nu_\\ell$, we obtain\n${\\cal B}(D^0\\to a_1^-(1260) e^+\\nu_e) = (5.458_{-0.681}^{+0.720}) \\times\n10^{-5}$, ${\\cal B}(D^+\\to a_1^0(1260) e^+\\nu_e) = (6.922_{-0.864}^{+0.913})\n\\times 10^{-5}$, ${\\cal B}(D^0\\to a_1^-(1260) \\mu^+\n\\nu_\\mu)=(4.897_{-0.627}^{+0.663}) \\times 10^{-5}$, ${\\cal B}(D^+ \\to\na_1^0(1260) \\mu^+ \\nu_\\mu)=(6.210_{-0.796}^{+0.767}) \\times 10^{-5}$.\n"}
+{"abstract": "  Thermal reverberation in accretion discs of active galactic nuclei is thought\nto be the reason of the continuum UV/optical time lags seen in these sources.\nRecently, we studied thermal reverberation of a standard Novikov-Thorne\naccretion disc illuminated by an X-ray point-like source, and we derived an\nanalytic prescription for the time lags as function of wavelength. In this\nwork, we use this analytic function to fit the time-lags spectra of seven\nSeyferts, that have been intensively monitored, in many wave-bands, in the last\nfew years. We find that thermal reverberation can explain the observed\nUV/optical time lags in all these sources. Contrary to previous claims, the\nmagnitude of the observed UV/optical time-lags is exactly as expected in the\ncase of a standard accretion disc in the lamp-post geometry, given the black\nhole mass and the accretion rate estimates for the objects we study. We derive\nestimates of the disc accretion rates and corona height for a non-spinning and\na maximally spinning black hole scenarios. We also find that the modelling of\nthe continuum optical/UV time-lags can be used to estimate the black hole spin,\nwhen combined with additional information. We also find that the model\nunder-predicts the observed X-ray to UV time-lags, but this difference is\nprobably due to the broad X-ray auto-correlation function of these sources.\n"}
+{"abstract": "  Knowledge distillation (KD) is commonly used to construct synthetic data for\ntraining non-autoregressive translation (NAT) models. However, there exists a\ndiscrepancy on low-frequency words between the distilled and the original data,\nleading to more errors on predicting low-frequency words. To alleviate the\nproblem, we directly expose the raw data into NAT by leveraging pretraining. By\nanalyzing directed alignments, we found that KD makes low-frequency source\nwords aligned with targets more deterministically but fails to align sufficient\nlow-frequency words from target to source. Accordingly, we propose reverse KD\nto rejuvenate more alignments for low-frequency target words. To make the most\nof authentic and synthetic data, we combine these complementary approaches as a\nnew training strategy for further boosting NAT performance. We conduct\nexperiments on five translation benchmarks over two advanced architectures.\nResults demonstrate that the proposed approach can significantly and\nuniversally improve translation quality by reducing translation errors on\nlow-frequency words. Encouragingly, our approach achieves 28.2 and 33.9 BLEU\npoints on the WMT14 English-German and WMT16 Romanian-English datasets,\nrespectively. Our code, data, and trained models are available at\n\\url{https://github.com/longyuewangdcu/RLFW-NAT}.\n"}
+{"abstract": "  We prove the existence of infinitely many solutions to an elliptic problem by\nborrowing the techniques from algebraic topology. The solution(s) thus obtained\nwill also be proved to be bounded.\n"}
+{"abstract": "  We analyzed Medical Subject Headings (MeSH) from 21.6 million research\narticles indexed by PubMed to map this vast space of entities and their\nrelations, providing insights into the origins and future of biomedical\nconvergence. Detailed analysis of MeSH co-occurrence networks identifies three\nrobust knowledge clusters: the vast universe of microscopic biological entities\nand structures; systems, disease and diagnostics; and emergent biological and\nsocial phenomena underlying the complex problems driving the health, behavioral\nand brain science frontiers. These domains integrated from the 1990s onward by\nway of technological and informatic capabilities that introduced highly\ncontrollable, scalable and permutable research processes and invaluable imaging\ntechniques for illuminating fundamental structure-function-behavior questions.\nArticle-level analysis confirms a positive relationship between team size and\ntopical diversity, and shows convergence to be increasing in prominence but\nwith recent saturation. Together, our results invite additional policy support\nfor cross-disciplinary team assembly to harness transdisciplinary convergence.\n"}
+{"abstract": "  We initiate the study of fairness for ordinal regression, or ordinal\nclassification. We adapt two fairness notions previously considered in fair\nranking and propose a strategy for training a predictor that is approximately\nfair according to either notion. Our predictor consists of a threshold model,\ncomposed of a scoring function and a set of thresholds, and our strategy is\nbased on a reduction to fair binary classification for learning the scoring\nfunction and local search for choosing the thresholds. We can control the\nextent to which we care about the accuracy vs the fairness of the predictor via\na parameter. In extensive experiments we show that our strategy allows us to\neffectively explore the accuracy-vs-fairness trade-off and that it often\ncompares favorably to \"unfair\" state-of-the-art methods for ordinal regression\nin that it yields predictors that are only slightly less accurate, but\nsignificantly more fair.\n"}
+{"abstract": "  We consider a sequential assortment selection problem where the user choice\nis given by a multinomial logit (MNL) choice model whose parameters are\nunknown. In each period, the learning agent observes a $d$-dimensional\ncontextual information about the user and the $N$ available items, and offers\nan assortment of size $K$ to the user, and observes the bandit feedback of the\nitem chosen from the assortment. We propose upper confidence bound based\nalgorithms for this MNL contextual bandit. The first algorithm is a simple and\npractical method which achieves an $\\tilde{\\mathcal{O}}(d\\sqrt{T})$ regret over\n$T$ rounds. Next, we propose a second algorithm which achieves a\n$\\tilde{\\mathcal{O}}(\\sqrt{dT})$ regret. This matches the lower bound for the\nMNL bandit problem, up to logarithmic terms, and improves on the best known\nresult by a $\\sqrt{d}$ factor. To establish this sharper regret bound, we\npresent a non-asymptotic confidence bound for the maximum likelihood estimator\nof the MNL model that may be of independent interest as its own theoretical\ncontribution. We then revisit the simpler, significantly more practical, first\nalgorithm and show that a simple variant of the algorithm achieves the optimal\nregret for a broad class of important applications.\n"}
+{"abstract": "  Manganese oxide-coated TiO2 nanotube array (NTA) was synthesized and applied\nfor the (electro)catalytic oxidation of sulfide. By setting the\nelectrodeposition parameters and calcination temperature, the coating obtained\nwas MnO2 or Mn2O3, with rod or needle like morphology. Excellent catalytic\nactivity of the Ti/TiO2 NTA/MnxOy anodes resulted in robust and selective\noxidation of sulfide to elemental sulfur via an inner sphere complex between\nthe sulfide ion and Mn-oxide. The penetration of the MnxOy inside the NTA\nimproved adhesion and enhanced the electron transfer, thus enabling complete\nand rapid electrocatalytic re-oxidation of the Mn-oxide coating. At pH 8, the\nfinal product of sulfide oxidation was colloidal sulfur, S8, which prevented\nanode passivation and enabled a stable (electro)catalytic activity in the\nsubsequent applications. This was observed in both synthetic electrolyte and\nreal sewage, thus demonstrating the efficiency of the Ti/TiO2 NTA/MnxOy anode\nfor sulfide oxidation in complex waste streams.\n"}
+{"abstract": "  In this paper, we present a field-trial C-band 72Gbit/s optical on-off keying\n(OOK) system over 18.8km dispersion-uncompensated submarine optical cable in\nthe South China Sea. Chromatic dispersion (CD) of 18.8km submarine optical\ncable causes four spectral nulls on the 36GHz bandwidth of 72Gbit/s OOK signal,\nwhich is the main obstacle for achieving an acceptable bit-error-rate (BER)\nperformance. Decision feedback equalizer (DFE) is effective to compensate for\nthe spectral nulls. However, DFE has a serious defect of burst-error\npropagation when the burst errors emerge due to the unstable submarine\nenvironment. Weighted DFE (WDFE) can be used to mitigate the burst-error\npropagation, but it cannot fully compensate for the spectral nulls because only\na part of feedback symbols is directly decided. Fortunately, maximum likelihood\nsequence estimation (MLSE) can be added after the WDFE to simultaneously\neliminate the resisting spectral distortions and implement optimal detection.\nCompared to the joint DFE and MLSE algorithm, the joint WDFE and MLSE algorithm\ncan effectively suppress the burst-error propagation to obtain a maximum 2.9dB\nimprovement of $\\boldsymbol{Q}$ factor and eliminate the phenomenon of BER\nfloor. In conclusion, the joint WDFE and MLSE algorithm can solve the\nburst-error propagation for the field-trial fiber-optic communications.\n"}
+{"abstract": "  More and more, optimization methods are used to find diverse solution sets.\nWe compare solution diversity in multi-objective optimization, multimodal\noptimization, and quality diversity in a simple domain. We show that\nmultiobjective optimization does not always produce much diversity, multimodal\noptimization produces higher fitness solutions, and quality diversity is not\nsensitive to genetic neutrality and creates the most diverse set of solutions.\nAn autoencoder is used to discover phenotypic features automatically, producing\nan even more diverse solution set with quality diversity. Finally, we make\nrecommendations about when to use which approach.\n"}
+{"abstract": "  The amount of CO$_2$ emitted per kilowatt-hour on an electricity grid varies\nby time of day and substantially varies by location due to the types of\ngeneration. Networked collections of warehouse scale computers, sometimes\ncalled Hyperscale Computing, emit more carbon than needed if operated without\nregard to these variations in carbon intensity. This paper introduces Google's\nsystem for Carbon-Intelligent Compute Management, which actively minimizes\nelectricity-based carbon footprint and power infrastructure costs by delaying\ntemporally flexible workloads. The core component of the system is a suite of\nanalytical pipelines used to gather the next day's carbon intensity forecasts,\ntrain day-ahead demand prediction models, and use risk-aware optimization to\ngenerate the next day's carbon-aware Virtual Capacity Curves (VCCs) for all\ndatacenter clusters across Google's fleet. VCCs impose hourly limits on\nresources available to temporally flexible workloads while preserving overall\ndaily capacity, enabling all such workloads to complete within a day. Data from\noperation shows that VCCs effectively limit hourly capacity when the grid's\nenergy supply mix is carbon intensive and delay the execution of temporally\nflexible workloads to \"greener\" times.\n"}
+{"abstract": "  We present an $(e^{O(p)} \\frac{\\log \\ell}{\\log\\log\\ell})$-approximation\nalgorithm for socially fair clustering with the $\\ell_p$-objective. In this\nproblem, we are given a set of points in a metric space. Each point belongs to\none (or several) of $\\ell$ groups. The goal is to find a $k$-medians,\n$k$-means, or, more generally, $\\ell_p$-clustering that is simultaneously good\nfor all of the groups. More precisely, we need to find a set of $k$ centers $C$\nso as to minimize the maximum over all groups $j$ of $\\sum_{u \\text{ in group\n}j} d(u,C)^p$. The socially fair clustering problem was independently proposed\nby Ghadiri, Samadi, and Vempala [2021] and Abbasi, Bhaskara, and\nVenkatasubramanian [2021]. Our algorithm improves and generalizes their\n$O(\\ell)$-approximation algorithms for the problem. The natural LP relaxation\nfor the problem has an integrality gap of $\\Omega(\\ell)$. In order to obtain\nour result, we introduce a strengthened LP relaxation and show that it has an\nintegrality gap of $\\Theta(\\frac{\\log \\ell}{\\log\\log\\ell})$ for a fixed $p$.\nAdditionally, we present a bicriteria approximation algorithm, which\ngeneralizes the bicriteria approximation of Abbasi et al. [2021].\n"}
+{"abstract": "  The large and diffuse galaxies NGC1052-DF2 and NGC1052-DF4 have been found to\nhave very low dark matter content and a population of luminous globular\nclusters. Accurate distance measurements are key to interpreting these\nobservations. Recently, the distance to NGC1052-DF4 was found to be $20.0\\pm\n1.6$ Mpc by identifying the tip of the red giant branch (TRGB) in 12 orbits of\nHubble Space Telescope (HST) Advanced Camera for Surveys (ACS) imaging. Here we\npresent 40 orbits of HST ACS data for NGC1052-DF2 and use these data to measure\nits TRGB. The TRGB is readily apparent in the color-magnitude diagram. Using a\nforward model that incorporates photometric uncertainties, we find a TRGB\nmagnitude of $m_{\\rm F814W, TRGB} = 27.67 \\pm 0.10$ mag. The inferred distance\nis $D_{\\rm TRGB} = 22.1 \\pm 1.2$ Mpc, consistent with the previous surface\nbrightness fluctuation distances to the bright elliptical galaxy NGC1052. The\nnew HST distance rules out the idea that some of NGC1052-DF2's unusual\nproperties can be explained if it were at $\\sim 13$ Mpc; instead, it implies\nthat the galaxy's globular clusters are even more luminous than had been\nderived using the previous distance of 20 Mpc. The distance from NGC1052-DF2 to\nNGC1052-DF4 is well-determined at $2.1\\pm 0.5$ Mpc, significantly larger than\nthe virial diameter of NGC1052. We discuss the implications for formation\nscenarios of the galaxies and for the external field effect, which has been\ninvoked to explain the intrinsic dynamics of these objects in the context of\nmodified Newtonian dynamics.\n"}
+{"abstract": "  In epidemiology, the effective reproduction number $R_e$ is used to\ncharacterize the growth rate of an epidemic outbreak. In this paper, we\ninvestigate properties of $R_e$ for a modified SEIR model of COVID-19 in the\ncity of Houston, TX USA, in which the population is divided into low-risk and\nhigh-risk subpopulations. The response of $R_e$ to two types of control\nmeasures (testing and distancing) applied to the two different subpopulations\nis characterized. A nonlinear cost model is used for control measures, to\ninclude the effects of diminishing returns. We propose three types of heuristic\nstrategies for mitigating COVID-19 that are targeted at reducing $R_e$, and we\nexhibit the tradeoffs between strategy implementation costs and number of\ndeaths. We also consider two variants of each type of strategy: basic\nstrategies, which consider only the effects of controls on $R_e$, without\nregard to subpopulation; and high-risk prioritizing strategies, which maximize\ncontrol of the high-risk subpopulation. Results showed that of the three\nheuristic strategy types, the most cost-effective involved setting a target\nvalue for $R_e$ and applying sufficient controls to attain that target value.\nThis heuristic led to strategies that begin with strict distancing of the\nentire population, later followed by increased testing. Strategies that\nmaximize control on high-risk individuals were less cost-effective than basic\nstrategies that emphasize reduction of the rate of spreading of the disease.\nThe model shows that delaying the start of control measures past a certain\npoint greatly worsens strategy outcomes. We conclude that the effective\nreproduction can be a valuable real-time indicator in determining\ncost-effective control strategies.\n"}
+{"abstract": "  We introduce the notion of squeezing function corresponding to $d$-balanced\ndomains motivated by the concept of generalized squeezing function given by\nRong and Yang. In this work we study some of its properties and its relation\nwith Fridman invariant.\n"}
+{"abstract": "  The past decade has witnessed a groundbreaking rise of machine learning for\nhuman language analysis, with current methods capable of automatically\naccurately recovering various aspects of syntax and semantics - including\nsentence structure and grounded word meaning - from large data collections.\nRecent research showed the promise of such tools for analyzing acoustic\ncommunication in nonhuman species. We posit that machine learning will be the\ncornerstone of future collection, processing, and analysis of multimodal\nstreams of data in animal communication studies, including bioacoustic,\nbehavioral, biological, and environmental data. Cetaceans are unique non-human\nmodel species as they possess sophisticated acoustic communications, but\nutilize a very different encoding system that evolved in an aquatic rather than\nterrestrial medium. Sperm whales, in particular, with their highly-developed\nneuroanatomical features, cognitive abilities, social structures, and discrete\nclick-based encoding make for an excellent starting point for advanced machine\nlearning tools that can be applied to other animals in the future. This paper\ndetails a roadmap toward this goal based on currently existing technology and\nmultidisciplinary scientific community effort. We outline the key elements\nrequired for the collection and processing of massive bioacoustic data of sperm\nwhales, detecting their basic communication units and language-like\nhigher-level structures, and validating these models through interactive\nplayback experiments. The technological capabilities developed by such an\nundertaking are likely to yield cross-applications and advancements in broader\ncommunities investigating non-human communication and animal behavioral\nresearch.\n"}
+{"abstract": "  We consider a single run-and-tumble particle (RTP) moving in one dimension.\nWe assume that the velocity of the particle is drawn independently at each\ntumbling from a zero-mean Gaussian distribution and that the run times are\nexponentially distributed. We investigate the probability distribution $P(X,N)$\nof the position $X$ of the particle after $N$ runs, with $N\\gg 1$. We show that\nin the regime $ X \\sim N^{3/4}$ the distribution $P(X,N)$ has a large deviation\nform with a rate function characterized by a discontinuous derivative at the\ncritical value $X=X_c>0$. The same is true for $X=-X_c$ due to the symmetry of\n$P(X,N)$. We show that this singularity corresponds to a first-order\ncondensation transition: for $X>X_c$ a single large jump dominates the RTP\ntrajectory. We consider the participation ratio of the single-run displacements\nas the order parameter of the system, showing that this quantity is\ndiscontinuous at $X=X_c$. Our results are supported by numerical simulations\nperformed with a constrained Markov chain Monte Carlo algorithm.\n"}
+{"abstract": "  The smartphone and laptop can be unlocked by face or fingerprint recognition,\nwhile neural networks which confront numerous requests every day have little\ncapability to distinguish between untrustworthy and credible users. It makes\nmodel risky to be traded as a commodity. Existed research either focuses on the\nintellectual property rights ownership of the commercialized model, or traces\nthe source of the leak after pirated models appear. Nevertheless, active\nidentifying users legitimacy before predicting output has not been considered\nyet. In this paper, we propose Model-Lock (M-LOCK) to realize an end-to-end\nneural network with local dynamic access control, which is similar to the\nautomatic locking function of the smartphone to prevent malicious attackers\nfrom obtaining available performance actively when you are away. Three kinds of\nmodel training strategy are essential to achieve the tremendous performance\ndivergence between certified and suspect input in one neural network. Extensive\nexperiments based on MNIST, FashionMNIST, CIFAR10, CIFAR100, SVHN and GTSRB\ndatasets demonstrated the feasibility and effectiveness of the proposed scheme.\n"}
+{"abstract": "  In many machine learning applications where massive and privacy-sensitive\ndata are generated on numerous mobile or IoT devices, collecting data in a\ncentralized location may be prohibitive. Thus, it is increasingly attractive to\nestimate parameters over mobile or IoT devices while keeping data localized.\nSuch learning setting is known as cross-device federated learning. In this\npaper, we propose the first theoretically guaranteed algorithms for general\nminimax problems in the cross-device federated learning setting. Our algorithms\nrequire only a fraction of devices in each round of training, which overcomes\nthe difficulty introduced by the low availability of devices. The communication\noverhead is further reduced by performing multiple local update steps on\nclients before communication with the server, and global gradient estimates are\nleveraged to correct the bias in local update directions introduced by data\nheterogeneity. By developing analyses based on novel potential functions, we\nestablish theoretical convergence guarantees for our algorithms. Experimental\nresults on AUC maximization, robust adversarial network training, and GAN\ntraining tasks demonstrate the efficiency of our algorithms.\n"}
+{"abstract": "  In this article we review the results of our recent work on the difference\nbetween the Borel representations of tau hadronic spectral function moments\nobtained with the CIPT and FOPT methods. For the presentation of the\ntheoretical results we focus on the large-$\\beta_0$ approximation, where all\nexpressions can be written down in closed form, and we comment on the\ngeneralization to full QCD. The results may explain the discrepancy in the\nbehavior of the FOPT and CIPT series that has been the topic of intense\ndiscussions in previous literature and which represents a major part of the\ntheoretical uncertainties in current strong coupling determinations from\nhadronic tau decays. The findings also imply that the OPE corrections for FOPT\nand CIPT differ and that the OPE corrections for CIPT do not have standard\nform.\n"}
+{"abstract": "  Collapsing supermassive stars ($M \\gtrsim 3 \\times 10^4 M_{\\odot}$) at high\nredshifts can naturally provide seeds and explain the origin of the\nsupermassive black holes observed in the centers of nearly all galaxies. During\nthe collapse of supermassive stars, a burst of non-thermal neutrinos is\ngenerated with a luminosity that could greatly exceed that of a conventional\ncore collapse supernova explosion. In this work, we investigate the extent to\nwhich the neutrinos produced in these explosions can be observed via coherent\nelastic neutrino-nucleus scattering (CE$\\nu$NS). Large scale direct dark matter\ndetection experiments provide particularly favorable targets. We find that\nupcoming $\\mathcal{O}(100)$ tonne-scale experiments will be sensitive to the\ncollapse of individual supermassive stars at distances as large as\n$\\mathcal{O}(10)$ Mpc.\n"}
+{"abstract": "  In this paper, we introduce a new perspective on training deep neural\nnetworks capable of state-of-the-art performance without the need for the\nexpensive over-parameterization by proposing the concept of In-Time\nOver-Parameterization (ITOP) in sparse training. By starting from a random\nsparse network and continuously exploring sparse connectivities during\ntraining, we can perform an Over-Parameterization in the space-time manifold,\nclosing the gap in the expressibility between sparse training and dense\ntraining. We further use ITOP to understand the underlying mechanism of Dynamic\nSparse Training (DST) and indicate that the benefits of DST come from its\nability to consider across time all possible parameters when searching for the\noptimal sparse connectivity. As long as there are sufficient parameters that\nhave been reliably explored during training, DST can outperform the dense\nneural network by a large margin. We present a series of experiments to support\nour conjecture and achieve the state-of-the-art sparse training performance\nwith ResNet-50 on ImageNet. More impressively, our method achieves dominant\nperformance over the overparameterization-based sparse methods at extreme\nsparsity levels. When trained on CIFAR-100, our method can match the\nperformance of the dense model even at an extreme sparsity (98%). Code can be\nfound https://github.com/Shiweiliuiiiiiii/In-Time-Over-Parameterization.\n"}
+{"abstract": "  We consider the isomonodromic formulation of the Calogero-Painlev\\'e\nmulti-particle systems and proceed to their canonical quantization. We then\nproceed to the quantum Hamiltonian reduction on a special representation to\nradial variables, in analogy with the classical case and also with the theory\nof quantum Calogero equations. This quantized version is compared to the\ngeneralization of a result of Nagoya on integral representations of certain\nsolutions of the quantum Painlev\\'e equations. We also provide multi-particle\ngeneralizations of these integral representations.\n"}
+{"abstract": "  We prove that, for every one-dimendional exponentially ergodic probabilistic\ncellular automaton with positive rates, there exists a locally defined\ncoupling-from-the-past flow whose coalescence time has a finite exponential\nmoment.\n"}
+{"abstract": "  Semantic code search is about finding semantically relevant code snippets for\na given natural language query. In the state-of-the-art approaches, the\nsemantic similarity between code and query is quantified as the distance of\ntheir representation in the shared vector space. In this paper, to improve the\nvector space, we introduce tree-serialization methods on a simplified form of\nAST and build the multimodal representation for the code data. We conduct\nextensive experiments using a single corpus that is large-scale and\nmulti-language: CodeSearchNet. Our results show that both our tree-serialized\nrepresentations and multimodal learning model improve the performance of code\nsearch. Last, we define intuitive quantification metrics oriented to the\ncompleteness of semantic and syntactic information of the code data, to help\nunderstand the experimental findings.\n"}
+{"abstract": "  In this paper, we present a theoretical analysis of separating images\nconsisting of pointlike and $C^{ \\beta}$-curvelike structures, where $\\beta \\in\n(1,2] $. Our approach is based on $l_1$-minimization, in which the sparsity of\nthe desired solution is exploited by two sparse representation systems. It is\nwell known that for such components wavelets provide an optimally sparse\nrepresentation for point singularities, whereas $\\alpha$-shearlet type with\n$\\alpha$=$\\frac{2}{\\beta}$ might be best adapted to the $C^{\\beta}$-curvilinear\nsingularities. In our analysis, we first propose a reconstruction framework\nwith a theoretical guarantee on convergence, which is extended to use general\nframes instead of Parseval frames. We then construct a dual pair of bandlimited\n$\\alpha$-shearlets which possesses a good time and frequency localization.\nFinally, we apply the result to derive an asymptotic accuracy of the\nreconstructions. In addition, we show that it is possible to separate these two\ncomponents as long as $\\alpha <2$, i.e., bandlimited $\\alpha$-shearlets which\nrange from wavelet to shearlet type do not coincide with wavelets in the sense\nof isotropic fashion.\n"}
+{"abstract": "  For the study of polarization reversal features in the structure of \"mixed\nionic-electronic conductor - on - ferroelectric\" type, the hydrogen bonded\nmolecular ferroelectric crystal triglycine sulphate (TGS) is used. The dynamic\ncurrant-voltage (I-V-) loops of thin TGS plates of polar (010) cut with\ndissimilar electrodes on the opposite surfaces (vacuum deposited Ag and\nAg-paste diluted with a water-ethanol mixture) are investigated. After\nelectroforming in the low-voltage vicinity of coercive voltage, during further\ncycling, a reversible transformation of I-V-loop shape occurs from typical of\nnon-linear dielectrics to typical of memristive systems and than to typical of\nferroelectrics (and vice versa) with a sequential increase (decrease) of\ndrawing voltage amplitude. Such \"awakening\" and \"falling asleep\" of I-V-loops\nof the memristor type is apparently associated with reversible electromigration\nand accumulation of charged species (e.g., protons), in the hydrated surface\nlayer of TGS as a component of electrochemical capacitor-on-ferroelectric\nstructure. The observed transformation can be explained by considering the\ncoupled ion transfer and polarization reversal taking into account the swinging\nof the boundary of the built-in chemical inhomogeneity under conditions of\nlinear and/or nonlinear dynamics of ion drift and ionic concentration\npolarization caused by charged species transfer under the applied voltage.\n"}
+{"abstract": "  We study heating dynamics in isolated quantum many-body systems driven\nperiodically at high frequency and large amplitude. Combining the\nhigh-frequency expansion for the Floquet Hamiltonian with Fermi's golden rule\n(FGR), we develop a master equation termed the Floquet FGR. Unlike the\nconventional one, the Floquet FGR correctly describes heating dynamics,\nincluding the prethermalization regime, even for strong drives, under which the\nFloquet Hamiltonian is significantly dressed, and nontrivial Floquet\nengineering is present. The Floquet FGR depends on system size only weakly,\nenabling us to analyze the thermodynamic limit with small-system calculations.\nOur results also indicate that, during heating, the system approximately stays\nin the thermal state for the Floquet Hamiltonian with a gradually rising\ntemperature.\n"}
+{"abstract": "  Osteoporosis is a common chronic metabolic bone disease that is often\nunder-diagnosed and under-treated due to the limited access to bone mineral\ndensity (BMD) examinations, Dual-energy X-ray Absorptiometry (DXA). In this\npaper, we propose a method to predict BMD from Chest X-ray (CXR), one of the\nmost common, accessible, and low-cost medical image examinations. Our method\nfirst automatically detects Regions of Interest (ROIs) of local and global bone\nstructures from the CXR. Then a multi-ROI model is developed to exploit both\nlocal and global information in the chest X-ray image for accurate BMD\nestimation. Our method is evaluated on 329 CXR cases with ground truth BMD\nmeasured by DXA. The model predicted BMD has a strong correlation with the gold\nstandard DXA BMD (Pearson correlation coefficient 0.840). When applied for\nosteoporosis screening, it achieves a high classification performance (AUC\n0.936). As the first effort in the field to use CXR scans to predict the spine\nBMD, the proposed algorithm holds strong potential in enabling early\nosteoporosis screening through routine chest X-rays and contributing to the\nenhancement of public health.\n"}
+{"abstract": "  Purpose: To develop a method for banding-free bSSFP cardiac cine with\nsubstantially reduced flow artifacts.\n  Methods: A projection-reconstruction (PR) trajectory is proposed for a\nfrequency-modulated cine sequence, facilitating reconstruction of three phase\ncycles and a field-map time series from a short, breath-held scan. Data is also\nacquired during the gradient rewinders to enable generation of field maps using\nBMART, B$_0$ mapping using rewinding trajectories, where the rewind data forms\nthe second TE image for calculating the field map. A field-map-based\ncombination method is developed which weights the phase-cycle component images\nto include only passband signal in the final cine images, and exclude stopband\nand near-band flow artifacts.\n  Results: The weights derived from the BMART-generated field maps mask out\nbanding and near-band flow artifacts in and around the heart. Therefore, the\nfield-map-based phase-cycle combination, which is facilitated by the PR\nacquisition with BMART, results in more homogeneous blood pools and reduced\nhyperintense regions than root-sum-of-squares.\n  Conclusion: With the proposed techniques, using a non-Cartesian trajectory\nfor a frequency-modulated cine sequence enables flow-artifact-reduced\nbanding-free cardiac imaging within a short breath-hold.\n"}
+{"abstract": "  We propose a means of omni-directional contact detection using accelerometers\ninstead of tactile sensors for object shape estimation using touch. Unlike\ntactile sensors, our contact-based detection method tends to induce a degree of\nuncertainty with false-positive contact data because the sensors may react not\nonly to actual contact but also to the unstable behavior of the robot.\nTherefore, it is crucial to consider a robust shape estimation method capable\nof handling such false-positive contact data. To realize this, we introduce the\nconcept of heteroscedasticity into the contact data and propose a robust shape\nestimation algorithm based on Gaussian process implicit surfaces (GPIS). We\nconfirmed that our algorithm not only reduces shape estimation errors caused by\nfalse-positive contact data but also distinguishes false-positive contact data\nmore clearly than the GPIS through simulations and actual experiments using a\nquadcopter.\n"}
+{"abstract": "  We address the estimation of conditional average treatment effects (CATEs)\nfor structured treatments (e.g., graphs, images, texts). Given a weak condition\non the effect, we propose the generalized Robinson decomposition, which (i)\nisolates the causal estimand (reducing regularization bias), (ii) allows one to\nplug in arbitrary models for learning, and (iii) possesses a quasi-oracle\nconvergence guarantee under mild assumptions. In experiments with small-world\nand molecular graphs we demonstrate that our approach outperforms prior work in\nCATE estimation.\n"}
+{"abstract": "  A Magnetic Resonance Imaging (MRI) exam typically consists of the acquisition\nof multiple MR pulse sequences, which are required for a reliable diagnosis.\nEach sequence can be parameterized through multiple acquisition parameters\naffecting MR image contrast, signal-to-noise ratio, resolution, or scan time.\nWith the rise of generative deep learning models, approaches for the synthesis\nof MR images are developed to either synthesize additional MR contrasts,\ngenerate synthetic data, or augment existing data for AI training. However,\ncurrent generative approaches for the synthesis of MR images are only trained\non images with a specific set of acquisition parameter values, limiting the\nclinical value of these methods as various sets of acquisition parameter\nsettings are used in clinical practice. Therefore, we trained a generative\nadversarial network (GAN) to generate synthetic MR knee images conditioned on\nvarious acquisition parameters (repetition time, echo time, image orientation).\nThis approach enables us to synthesize MR images with adjustable image\ncontrast. In a visual Turing test, two experts mislabeled 40.5% of real and\nsynthetic MR images, demonstrating that the image quality of the generated\nsynthetic and real MR images is comparable. This work can support radiologists\nand technologists during the parameterization of MR sequences by previewing the\nyielded MR contrast, can serve as a valuable tool for radiology training, and\ncan be used for customized data generation to support AI training.\n"}
+{"abstract": "  A nanoelectronic device made of twisted bilayer graphene (TBLG) is proposed\nto steer the direction of the current flow. The ballistic electron current,\ninjected at one edge of the bottom layer, can be guided predominantly to one of\nthe lateral edges of the top layer. The current is steered to the opposite\nlateral edge, if either the twist angle is reversed or the electrons are\ninjected in the valence band instead of the conduction band, making it possible\nto control the current flow by electric gates. When both graphene layers are\naligned, the current passes straight through the system without changing its\ninitial direction. The observed steering angle exceeds well the twist angle and\nemerges for a broad range of experimentally accessible parameters. It is\nexplained by the trigonal shape of the energy bands beyond the van Hove\nsingularity due to the Moir\\'e interference pattern. As the shape of the energy\nbands depends on the valley degree of freedom, the steered current is partially\nvalley polarized. Our findings show how to control and manipulate the current\nflow in TBLG. Technologically, they are of relevance for applications in\ntwistronics and valleytronics.\n"}
+{"abstract": "  Let $O$ be chosen uniformly at random from the group of $(N+L) \\times (N+L)$\northogonal matrices. Denote by $\\tilde{O}$ the upper-left $N \\times N$ corner\nof $O$, which we refer to as a truncation of $O$. In this paper we prove two\nconjectures of Forrester, Ipsen and Kumar (2020) on the number of real\neigenvalues $N^{(m)}_{\\mathbb{R}}$ of the product matrix $\\tilde{O}_{1}\\ldots\n\\tilde{O}_{m}$, where the matrices $\\{\\tilde{O}_{j}\\}_{j=1}^{m}$ are\nindependent copies of $\\tilde{O}$. When $L$ grows in proportion to $N$, we\nprove that $$ \\mathbb{E}(N^{(m)}_{\\mathbb{R}}) = \\sqrt{\\frac{2m\nL}{\\pi}}\\,\\mathrm{arctanh}\\left(\\sqrt{\\frac{N}{N+L}}\\right) + O(1), \\qquad N\n\\to \\infty. $$ We also prove the conjectured form of the limiting real\neigenvalue distribution of the product matrix. Finally, we consider the\nopposite regime where $L$ is fixed with respect to $N$, known as the regime of\nweak non-orthogonality. In this case each matrix in the product is very close\nto an orthogonal matrix. We show that $\\mathbb{E}(N^{(m)}_{\\mathbb{R}}) \\sim\nc_{L,m}\\,\\log(N)$ as $N \\to \\infty$ and compute the constant $c_{L,m}$\nexplicitly. These results generalise the known results in the one matrix case\ndue to Khoruzhenko, Sommers and \\.{Z}yczkowski (2010).\n"}
+{"abstract": "  The early diagnosis and screening of glaucoma are important for patients to\nreceive treatment in time and maintain eyesight. Nowadays, deep learning (DL)\nbased models have been successfully used for computer-aided diagnosis (CAD) of\nglaucoma from retina fundus images. However, a DL model pre-trained using a\ndataset from one hospital center may have poor performance on a dataset from\nanother new hospital center and therefore its applications in the real scene\nare limited. In this paper, we propose a self-adaptive transfer learning (SATL)\nstrategy to fill the domain gap between multicenter datasets. Specifically, the\nencoder of a DL model that is pre-trained on the source domain is used to\ninitialize the encoder of a reconstruction model. Then, the reconstruction\nmodel is trained using only unlabeled image data from the target domain, which\nmakes the encoder in the model adapt itself to extract useful high-level\nfeatures both for target domain images encoding and glaucoma classification,\nsimultaneously. Experimental results demonstrate that the proposed SATL\nstrategy is effective in the domain adaptation task between one private and two\npublic glaucoma diagnosis datasets, i.e. pri-RFG, REFUGE, and LAG. Moreover,\nthe proposed strategy is completely independent of the source domain data,\nwhich meets the real scene application and the privacy protection policy.\n"}
+{"abstract": "  Let $G=\\prod_{i=1}^{r} G_i$ be a product of simple real algebraic groups of\nrank one and $\\Gamma$ an Anosov subgroup of $G$ with respect to a minimal\nparabolic subgroup. For each $v$ in the interior of a positive Weyl chamber,\nlet $\\mathcal R_v\\subset\\Gamma\\backslash G$ denote the Borel subset of all\npoints with recurrent $\\exp (\\mathbb R_+ v)$-orbits. For a maximal\nhorospherical subgroup $N$ of $G$, we show that the $N$-action on ${\\mathcal\nR}_v$ is uniquely ergodic if $r={rank}(G)\\le 3$ and $v$ belongs to the interior\nof the limit cone of $\\Gamma$, and that there exists no $N$-invariant {Radon}\nmeasure on $\\mathcal R_v$ otherwise.\n"}
+{"abstract": "  Axion-like particles (ALPs) are predicted in some well-motivated theories\nbeyond the Standard Model. The TeV gamma-rays from active galactic nuclei (AGN)\nsuffer attenuation by the pair production interactions with the cosmic\nbackground light (EBL/CMB) during its travel to the earth. The attenuation can\nbe circumvented through photon-ALP conversions in the AGN and Galaxy\nmagnetic-field, and a flux enhancement is expected to arise in the observed\nspectrum. In this work, we study the potential of the AGN gamma-ray spectrum\nfor energy up to above 100\\,TeV to probe ALP-parameter space at around $\\mu$eV,\nwhere the coupling $g_{a\\gamma}$ is so far relatively weakly constrained.\n  We find the nearby and bright sources, Mrk\\,501, IC\\,310 and M\\,87, are\nsuitable for our objective. Assuming an intrinsic spectrum exponential cutoff\nenergy, we extrapolate the observed spectra of these sources up to above\n100\\,TeV by the models with/without ALPs. For $g_{a\\gamma}\\gtrsim\n2\\times$$10^{-11} \\rm GeV^{-1}$ with $m_{a}\\lesssim0.5\\,\\mu$eV, the flux at\naround 100\\,TeV predicted by the ALP model can be enhanced more than an order\nof magnitude than that from the standard absorption, and could be detected by\nLHAASO. Our result is subject to the uncertainty from the intrinsic cutoff\nenergy and the AGN lobe (or plume) magnetic-field. For an optimistic\nestimation, the constraint can be improved to $g_{a\\gamma}\\gtrsim\n2\\times$$10^{-11} \\rm GeV^{-1}$ with $m_{a}\\lesssim1\\,\\mu$eV. This require\nfurther observations on these sources by the forthcoming CTA, LHAASO, SWGO and\nso on.\n"}
+{"abstract": "  If the strange quark were lighter, QCD phase transition could have been first\norder. This implies that QCD may have quantum critical points as the Higgs vev\n$v_h$ is varied from its Standard Model value. We show that inflationary\nquantum evolution of $v_h$ with the relaxion can drive our universe toward\nthose critical points, realizing the weak scale close to the observed value\nwhile explaining its closeness to $\\Lambda_{\\rm QCD}$. We first explore quantum\ncritical points of $N_f=3$ QCD, parameterized by $v_h$ at $T=0$, and present a\nbasic model for the weak scale. It results in a sharply localized probability\ndistribution of the weak scale, which is critical not to the crossover at zero\nbut to the quantum transition at ${\\sim}\\Lambda_{\\rm QCD}$.\n"}
+{"abstract": "  Traffic control optimization is a challenging task for various traffic\ncenters around the world and the majority of existing approaches focus only on\ndeveloping adaptive methods under normal (recurrent) traffic conditions.\nOptimizing the control plans when severe incidents occur still remains an open\nproblem, especially when a high number of lanes or entire intersections are\naffected.\n  This paper aims at tackling this problem and presents a novel methodology for\noptimizing the traffic signal timings in signalized urban intersections, under\nnon-recurrent traffic incidents. With the purpose of producing fast and\nreliable decisions, we combine the fast running Machine Learning (ML)\nalgorithms and the reliable Genetic Algorithms (GA) into a single optimization\nframework. As a benchmark, we first start with deploying a typical GA algorithm\nby considering the phase duration as the decision variable and the objective\nfunction to minimize the total travel time in the network. We fine tune the GA\nfor crossover, mutation, fitness calculation and obtain the optimal parameters.\nSecondly, we train various machine learning regression models to predict the\ntotal travel time of the studied traffic network, and select the best\nperforming regressor which we further hyper-tune to find the optimal training\nparameters. Lastly, we propose a new algorithm BGA-ML combining the GA\nalgorithm and the extreme-gradient decision-tree, which is the best performing\nregressor, together in a single optimization framework. Comparison and results\nshow that the new BGA-ML is much faster than the original GA algorithm and can\nbe successfully applied under non-recurrent incident conditions.\n"}
+{"abstract": "  Machine learning classifiers rely on loss functions for performance\nevaluation, often on a private (hidden) dataset. In a recent line of research,\nlabel inference was introduced as the problem of reconstructing the ground\ntruth labels of this private dataset from just the (possibly perturbed)\ncross-entropy loss function values evaluated at chosen prediction vectors\n(without any other access to the hidden dataset). In this paper, we formally\nstudy the necessary and sufficient conditions under which label inference is\npossible from \\emph{any} (noisy) loss function value. Using tools from\nanalytical number theory, we show that a broad class of commonly used loss\nfunctions, including general Bregman divergence-based losses and multiclass\ncross-entropy with common activation functions like sigmoid and softmax, it is\npossible to design label inference attacks that succeed even for arbitrary\nnoise levels and using only a single query from the adversary. We formally\nstudy the computational complexity of label inference and show that while in\ngeneral, designing adversarial prediction vectors for these attacks is\nco-NP-hard, once we have these vectors, the attacks can also be carried out\nthrough a lightweight augmentation to any neural network model, making them\nlook benign and hard to detect. The observations in this paper provide a deeper\nunderstanding of the vulnerabilities inherent in modern machine learning and\ncould be used for designing future trustworthy ML.\n"}
+{"abstract": "  The stability of high-end piezo-acoustic drop-on-demand (DOD) inkjet printing\nis sometimes compromised by the entrainment of an air bubble inside the ink\nchannel. Here, bubble pinch-off from an acoustically driven meniscus is studied\nin an optically transparent DOD printhead as a function of the driving\nwaveform. We show that bubble pinch-off follows from low-amplitude higher-order\nmeniscus oscillations on top of the global high-amplitude meniscus motion that\ndrives droplet formation. In a certain window of control parameters, phase\ninversion between the low and high frequency components leads to the enclosure\nof an air cavity and bubble pinch-off. Although phenomenologically similar,\nbubble pinch-off is not a result of capillary wave interaction such as observed\nin drop impact on a liquid pool. Instead, we reveal geometrical flow focusing\nas the mechanism through which at first, an outward jet is formed on the\nretracted concave meniscus. When the subsequent high-frequency pressure wave\nhits the now toroidal-shaped meniscus, it accelerates the toroidal ring outward\nresulting in the formation of an air cavity that can pinch off. The critical\ncontrol parameters for pinch off are the pulse timing and amplitude. To cure\nthe bubble entrainment problem, the threshold for bubble pinch-off can be\nincreased by suppressing the high frequency acoustic waves through appropriate\nwaveform design. The present work therefore aids the improvement of the\nstability of inkjet printers through a physical understanding of meniscus\ninstabilities.\n"}
+{"abstract": "  Dirac/Weyl semimetals hosting linearly-dispersing bands have received recent\nattention for potential thermoelectric applications, since their\nultrahigh-mobility carriers could generate large thermoelectric and Nernst\npower factors. To optimize these efficiencies, the Fermi energy needs to be\nchemically controlled in a wide range, which is generally difficult in bulk\nmaterials because of disorder effects from the substituted ions. Here it is\nshown that the Fermi energy is tunable across the Dirac point for layered\nmagnet EuMnBi$_2$ by partially substituting Gd$^{3+}$ for Eu$^{2+}$ in the\ninsulating block layer, which dopes electrons into the Dirac fermion layer\nwithout degrading the mobility. Clear quantum oscillation observed even in the\ndoped samples allows us to quantitatively estimate the Fermi energy shift and\noptimize the power factor (exceeding 100 $\\mu$W/K$^2$cm at low temperatures) in\ncombination with the first-principles calculation. Furthermore, it is shown\nthat Nernst signal steeply increases with decreasing carrier density beyond a\nsimple theoretical prediction, which likely originates from the field-induced\ngap reduction of the Dirac band due to the exchange interaction with the Eu\nmoments. Thus, the magnetic block layer provides high controllability for the\nDirac fermions in EuMnBi$_2$, which would make this series of materials an\nappealing platform for novel transport phenomena.\n"}
+{"abstract": "  Infarction- or ischaemia-induced cardiac fibrosis can be arrythmogenic. We\nuse mathematcal models for diffuse fibrosis ($\\mathcal{DF}$), interstitial\nfibrosis ($\\mathcal{IF}$), patchy fibrosis ($\\mathcal{PF}$), and compact\nfibrosis ($\\mathcal{CF}$) to study patterns of fibrotic cardiac tissue that\nhave been generated by new mathematical algorithms. We show that the fractal\ndimension $\\mathbb{D}$, the lacunarity $\\mathcal{L}$, and the Betti numbers\n$\\beta_0$ and $\\beta_1$ of such patterns are \\textit{fibrotic-tissue markers}\nthat can be used to characterise the arrhythmogenicity of different types of\ncardiac fibrosis. We hypothesize, and then demonstrate by extensive \\textit{in\nsilico} studies of detailed mathematical models for cardiac tissue, that the\narrhytmogenicity of fibrotic tissue is high when $\\beta_0$ is large and the\nlacunarity parameter $b$ is small.\n"}
+{"abstract": "  Deep Neural Networks (DNNs) have been shown vulnerable to adversarial\n(Test-Time Evasion (TTE)) attacks which, by making small changes to the input,\nalter the DNN's decision. We propose an attack detector based on\nclass-conditional Generative Adversarial Networks (GANs). We model the\ndistribution of clean data conditioned on the predicted class label by an\nAuxiliary Classifier GAN (ACGAN). Given a test sample and its predicted class,\nthree detection statistics are calculated using the ACGAN Generator and\nDiscriminator. Experiments on image classification datasets under different TTE\nattack methods show that our method outperforms state-of-the-art detection\nmethods. We also investigate the effectiveness of anomaly detection using\ndifferent DNN layers (input features or internal-layer features) and\ndemonstrate that anomalies are harder to detect using features closer to the\nDNN's output layer.\n"}
+{"abstract": "  Using the standard fluctuational electrodynamics framework, we analytically\ncalculate the radiative heat current between two thin metallic layers,\nseparated by a vacuum gap. We analyse different contributions to the heat\ncurrent (travelling or evanescent waves, transverse electric or magnetic\npolarization) and reveal the crucial qualitative role played by the dc\nconductivity of the metals as compared to the speed of light. For poorly\nconducting metals, the heat current may be dominated by evanescent waves even\nwhen the separation between the layers greatly exceeds the thermal photon\nwavelength, and the coupling is of electrostatic nature. For well-conducting\nmetals, the evanescent contribution dominates at separations smaller than the\nthermal wavelength and is mainly due to magnetostatic coupling, in agreement\nwith earlier works on bulk metals.\n"}
+{"abstract": "  In this paper we present an asymptotically compatible meshfree method for\nsolving nonlocal equations with random coefficients, describing diffusion in\nheterogeneous media. In particular, the random diffusivity coefficient is\ndescribed by a finite-dimensional random variable or a truncated combination of\nrandom variables with the Karhunen-Lo\\`{e}ve decomposition, then a\nprobabilistic collocation method (PCM) with sparse grids is employed to sample\nthe stochastic process. On each sample, the deterministic nonlocal diffusion\nproblem is discretized with an optimization-based meshfree quadrature rule. We\npresent rigorous analysis for the proposed scheme and demonstrate convergence\nfor a number of benchmark problems, showing that it sustains the asymptotic\ncompatibility spatially and achieves an algebraic or sub-exponential\nconvergence rate in the random coefficients space as the number of collocation\npoints grows. Finally, to validate the applicability of this approach we\nconsider a randomly heterogeneous nonlocal problem with a given spatial\ncorrelation structure, demonstrating that the proposed PCM approach achieves\nsubstantial speed-up compared to conventional Monte Carlo simulations.\n"}
+{"abstract": "  Traffic congestion is a major challenge in modern urban settings. The\nindustry-wide development of autonomous and automated vehicles (AVs) motivates\nthe question of how can AVs contribute to congestion reduction. Past research\nhas shown that in small scale mixed traffic scenarios with both AVs and\nhuman-driven vehicles, a small fraction of AVs executing a controlled\nmultiagent driving policy can mitigate congestion. In this paper, we scale up\nexisting approaches and develop new multiagent driving policies for AVs in\nscenarios with greater complexity. We start by showing that a congestion metric\nused by past research is manipulable in open road network scenarios where\nvehicles dynamically join and leave the road. We then propose using a different\nmetric that is robust to manipulation and reflects open network traffic\nefficiency. Next, we propose a modular transfer reinforcement learning\napproach, and use it to scale up a multiagent driving policy to outperform\nhuman-like traffic and existing approaches in a simulated realistic scenario,\nwhich is an order of magnitude larger than past scenarios (hundreds instead of\ntens of vehicles). Additionally, our modular transfer learning approach saves\nup to 80% of the training time in our experiments, by focusing its data\ncollection on key locations in the network. Finally, we show for the first time\na distributed multiagent policy that improves congestion over human-driven\ntraffic. The distributed approach is more realistic and practical, as it relies\nsolely on existing sensing and actuation capabilities, and does not require\nadding new communication infrastructure.\n"}
+{"abstract": "  In this paper, a new asymptotic preserving (AP) scheme is proposed for the\nanisotropic elliptic equations. Different from previous AP schemes, the actual\none is based on first-order system least-squares for second-order partial\ndifferential equations, and it is uniformly well-posed with respect to\nanisotropic strength. The numerical computation is realized by a deep neural\nnetwork (DNN), where least-squares functionals are employed as loss functions\nto determine parameters of DNN. Numerical results show that the current AP\nscheme is easy for implementation and is robust to approximate solutions or to\nidentify anisotropic strength in various 2D and 3D tests.\n"}
+{"abstract": "  Battery energy storage systems (ESS) are widely used in microgrids to\ncomplement high renewables. However, the real-time energy management of\nmicrogrids with battery ESS is challenging in two aspects: 1) the evolution\nprocess of battery energy level is across-time coupled; 2) uncertainties\nunavoidably arise in the forecasting process for renewable generation. In this\npaper, a tube-based model predictive control (MPC) approach is innovatively\nproposed in accommodating the real-time energy management of microgrids with\nbattery ESS. Firstly, a real-time operation model of battery, including the\ndegradation cost and time-aware SoC range, is proposed for the battery ESS. In\nparticular, the battery feature shallower-cheaper is depicted and the terminal\nSoC requirement is achieved. Secondly, two cascaded MPC controllers are\ndesigned in the proposed tube-based MPC, in which reference trajectories are\ngenerated by the nominal MPC without uncertainties, and then the ancillary MPC\nsteers the actual trajectories to the nominal ones upon the realization of\nuncertainties. Specifically, in this paper, the battery SoC is viewed as the\nstate variable of the system, while the generator power output and exchange\npower with the utility are seen as control variables. Lastly, numerous case\nstudies demonstrate the effectiveness of the proposed approach, including both\nthe low and high penetration level of renewables. Additional Monte Carlo\nsimulations of consecutive 365 days show that the competitive ratio of the\nproposed approach is excellently below 1.10.\n"}
+{"abstract": "  The earth system is exceedingly complex and often chaotic in nature, making\nprediction incredibly challenging: we cannot expect to make perfect predictions\nall of the time. Instead, we look for specific states of the system that lead\nto more predictable behavior than others, often termed \"forecasts of\nopportunity\". When these opportunities are not present, scientists need\nprediction systems that are capable of saying \"I don't know.\" We introduce a\nnovel loss function, termed \"abstention loss\", that allows neural networks to\nidentify forecasts of opportunity for regression problems. The abstention loss\nworks by incorporating uncertainty in the network's prediction to identify the\nmore confident samples and abstain (say \"I don't know\") on the less confident\nsamples. The abstention loss is designed to determine the optimal abstention\nfraction, or abstain on a user-defined fraction via a PID controller. Unlike\nmany methods for attaching uncertainty to neural network predictions\npost-training, the abstention loss is applied during training to preferentially\nlearn from the more confident samples. The abstention loss is built upon a\nstandard computer science method. While the standard approach is itself a\nsimple yet powerful tool for incorporating uncertainty in regression problems,\nwe demonstrate that the abstention loss outperforms this more standard method\nfor the synthetic climate use cases explored here. The implementation of\nproposed loss function is straightforward in most network architectures\ndesigned for regression, as it only requires modification of the output layer\nand loss function.\n"}
+{"abstract": "  Active learning has proven to be useful for minimizing labeling costs by\nselecting the most informative samples. However, existing active learning\nmethods do not work well in realistic scenarios such as imbalance or rare\nclasses, out-of-distribution data in the unlabeled set, and redundancy. In this\nwork, we propose SIMILAR (Submodular Information Measures based actIve\nLeARning), a unified active learning framework using recently proposed\nsubmodular information measures (SIM) as acquisition functions. We argue that\nSIMILAR not only works in standard active learning, but also easily extends to\nthe realistic settings considered above and acts as a one-stop solution for\nactive learning that is scalable to large real-world datasets. Empirically, we\nshow that SIMILAR significantly outperforms existing active learning algorithms\nby as much as ~5% - 18% in the case of rare classes and ~5% - 10% in the case\nof out-of-distribution data on several image classification tasks like\nCIFAR-10, MNIST, and ImageNet. SIMILAR is available as a part of the DISTIL\ntoolkit: \"https://github.com/decile-team/distil\".\n"}
+{"abstract": "  Performance contracts used for servitized business models enable\nconsideration of overall life-cycle costs rather than just production costs.\nHowever, practical implementation of performance contracts has been limited due\nto challenges with performance evaluation, accountability, and financial\nconcepts. As a solution, this paper proposes the connection of the digital\nbuilding twin with blockchain-based smart contracts to execute\nperformance-based digital payments. First, we conceptualize a technical\narchitecture to connect blockchain to digital building twins. The digital\nbuilding twin stores and evaluates performance data in real-time while the\nblockchain ensures transparency and trusted execution of automatic performance\nevaluation and rewards through smart contracts. Next, we demonstrate the\nfeasibility of both the concept and technical architecture by integrating the\nEthereum blockchain with digital building models and sensors via the Siemens\nbuilding twin platform. The resulting prototype is the first full-stack\nimplementation of a performance-based smart contract in the built environment.\n"}
+{"abstract": "  A fundamental result of Springer says that a quadratic form over a field of\ncharacteristic not 2 is isotropic if it is so after an odd degree extension. In\nthis paper we generalize Springer's theorem as follows. Let R be a an arbitrary\nsemilocal ring, let S be a finite R-algebra of constant odd degree, which is\n{\\'e}tale or generated by one element, and let q be a nonsingular R-quadratic\nform whose base ring extension q S is isotropic. We show that then q is already\nisotropic.\n"}
+{"abstract": "  Researchers and robotic development groups have recently started paying\nspecial attention to autonomous mobile robot navigation in indoor environments\nusing vision sensors. The required data is provided for robot navigation and\nobject detection using a camera as a sensor. The aim of the project is to\nconstruct a mobile robot that has integrated vision system capability used by a\nwebcam to locate, track and follow a moving object. To achieve this task,\nmultiple image processing algorithms are implemented and processed in\nreal-time. A mini-laptop was used for collecting the necessary data to be sent\nto a PIC microcontroller that turns the processes of data obtained to provide\nthe robot's proper orientation. A vision system can be utilized in object\nrecognition for robot control applications. The results demonstrate that the\nproposed mobile robot can be successfully operated through a webcam that\ndetects the object and distinguishes a tennis ball based on its color and\nshape.\n"}
+{"abstract": "  Magnonics is now an attractive field which focuses on the dynamic\ncharacteristics of magnons, a kind of quasiparticles in magnetic media, and\nattempt to apply magnons for functional devices. In order to construct magnon\nbased devices, it is necessary to fabricate materials with specific and tunable\nmagnon bands and band gaps. Skyrmion based magnonic crystals is one of the most\nsuitable materials which possess periodical skyrmion structure and show\napplicative magnon bands and band gaps. In this review, we provide an overview\nover recent theoretical and experimental research on skyrmion based magnonic\ncrystals. We will firstly provide an introduction of magnonic crystals and\nmagnetic skyrmion. And then we will show the theoretical and experimental\nprogress on skyrmion based magnonic crystals and their magnon band\ncharacteristics. At the end, we will give an outlook and perspectives of new\nfascinating fields on topological nontrivial magnon modes, as well as hybrid\nand quantum magnonic phenomena of skyrmion based magnonic crystals.\n"}
+{"abstract": "  As a result of the rapidly advancing digital transformation of teaching,\nuniversities have started to face major competition from Massive Open Online\nCourses (MOOCs). Universities thus have to set themselves apart from MOOCs in\norder to justify the added value of three to five-year degree programs to\nprospective students. In this paper, we show how we address this challenge at\nDeggendorf Institute of Technology in ML and AI. We first share our best\npractices and present two concrete courses including their unique selling\npropositions: Computer Vision and Innovation Management for AI. We then\ndemonstrate how these courses contribute to Deggendorf Institute of\nTechnology's ability to differentiate itself from MOOCs (and other\nuniversities).\n"}
+{"abstract": "  Many recent AI architectures are inspired by zero-sum games, however, the\nbehavior of their dynamics is still not well understood. Inspired by this, we\nstudy standard gradient descent ascent (GDA) dynamics in a specific class of\nnon-convex non-concave zero-sum games, that we call hidden zero-sum games. In\nthis class, players control the inputs of smooth but possibly non-linear\nfunctions whose outputs are being applied as inputs to a convex-concave game.\nUnlike general zero-sum games, these games have a well-defined notion of\nsolution; outcomes that implement the von-Neumann equilibrium of the \"hidden\"\nconvex-concave game. We prove that if the hidden game is strictly\nconvex-concave then vanilla GDA converges not merely to local Nash, but\ntypically to the von-Neumann solution. If the game lacks strict convexity\nproperties, GDA may fail to converge to any equilibrium, however, by applying\nstandard regularization techniques we can prove convergence to a von-Neumann\nsolution of a slightly perturbed zero-sum game. Our convergence guarantees are\nnon-local, which as far as we know is a first-of-its-kind type of result in\nnon-convex non-concave games. Finally, we discuss connections of our framework\nwith generative adversarial networks.\n"}
+{"abstract": "  The HIBEAM/NNBAR program is a proposed two-stage experiment at the European\nSpallation Source focusing on searches for baryon number violation via\nprocesses in which neutrons convert to antineutrons. This paper outlines the\ncomputing and detector simulation framework for the HIBEAM/NNBAR program. The\nsimulation is based on predictions of neutron flux and neutronics together with\nsignal and background generation. A range of diverse simulation packages are\nincorporated, including Monte Carlo transport codes, neutron ray-tracing\nsimulation packages, and detector simulation software. The common simulation\npackage in which these elements are interfaced together is discussed. Data\nmanagement plans and triggers are also described.\n"}
+{"abstract": "  We propose a computationally feasible way of deriving the identified features\nof models with multiple equilibria in pure or mixed strategies. It is shown\nthat in the case of Shapley regular normal form games, the identified set is\ncharacterized by the inclusion of the true data distribution within the core of\na Choquet capacity, which is interpreted as the generalized likelihood of the\nmodel. In turn, this inclusion is characterized by a finite set of inequalities\nand efficient and easily implementable combinatorial methods are described to\ncheck them. In all normal form games, the identified set is characterized in\nterms of the value of a submodular or convex optimization program. Efficient\nalgorithms are then given and compared to check inclusion of a parameter in\nthis identified set. The latter are illustrated with family bargaining games\nand oligopoly entry games.\n"}
+{"abstract": "  We study the dominating set reconfiguration problem with the token sliding\nrule. It consists, given a graph G=(V,E) and two dominating sets D_s and D_t of\nG, in determining if there exists a sequence S=<D_1:=D_s,...,D_l:=D_t> of\ndominating sets of G such that for any two consecutive dominating sets D_r and\nD_{r+1} with r<t, D_{r+1}=(D_r\\ u) U v, where uv is an edge of G.\n  In a recent paper, Bonamy et al studied this problem and raised the following\nquestions: what is the complexity of this problem on circular arc graphs? On\ncircle graphs? In this paper, we answer both questions by proving that the\nproblem is polynomial on circular-arc graphs and PSPACE-complete on circle\ngraphs.\n"}
+{"abstract": "  Type I X-ray bursts are thermonuclear explosions on the neutron star (NS)\nsurface caused by mass accretion from a companion star. Observations of X-ray\nbursts provide valuable information on X-ray binary systems, e.g., binary\nparameters, the chemical composition of accreted matter, and the nuclear\nequation of state (EOS) of NSs. There have been several theoretical studies to\nconstrain the physics of X-ray bursters. However, they have mainly focused on\nthe burning layers above the solid crust of the NS, which brings up issues of\nthe treatment of NS gravitation and internal energy. In this study, focusing on\nthe microphysics inside NSs, we calculate a series of X-ray bursts using a\ngeneral-relativistic stellar-evolution code with several NS EOSs. We compare\nthe X-ray-burst models with the burst parameters of a clocked burster\nassociated with GS 1826-24. We find a monotonic correlation between the NS\nradius and the light-curve profile. A larger radius shows a higher recurrence\ntime and a large peak luminosity. In contrast, the dependence of light curves\non the NS mass becomes more complicated, where the neutrino cooling suppresses\nthe efficiency of nuclear ignition. We also constrain the EOS and mass of GS\n1826-24, i.e., stiffer EOSs, corresponding to larger NS radii, are not\npreferred due to too-high peak luminosity. The EOS and the cooling and heating\nof NSs are important to discuss the theoretical and observational properties of\nX-ray bursts.\n"}
+{"abstract": "  Horava gravity breaks Lorentz symmetry by introducing a dynamical timelike\nscalar field (the khronon), which can be used as a preferred time coordinate\n(thus selecting a preferred space-time foliation). Adopting the khronon as the\ntime coordinate, the theory is invariant only under time reparametrizations and\nspatial diffeomorphisms. In the infrared limit, this theory is sometimes\nreferred to as khronometric theory. Here, we explicitly construct a\ngeneralization of khronometric theory, which avoids the propagation of\nOstrogradski modes as a result of a suitable degeneracy condition (although\nstability of the latter under radiative corrections remains an open question).\nWhile this new theory does not have a general-relativistic limit and does not\nyield a Friedmann-Robertson-Walker-like cosmology on large scales, it still\npasses, for suitable choices of its coupling constants, local tests on Earth\nand in the solar system, as well as gravitational-wave tests. We also comment\non the possible usefulness of this theory as a toy model of quantum gravity, as\nit could be completed in the ultraviolet into a 'degenerate Horava gravity'\ntheory that could be perturbatively renormalizable without imposing any\nprojectability condition.\n"}
+{"abstract": "  With the recent development of deep convolutional neural networks and\nlarge-scale datasets, deep face recognition has made remarkable progress and\nbeen widely used in various applications. However, unlike the existing public\nface datasets, in many real-world scenarios of face recognition, the depth of\ntraining dataset is shallow, which means only two face images are available for\neach ID. With the non-uniform increase of samples, such issue is converted to a\nmore general case, a.k.a long-tail face learning, which suffers from data\nimbalance and intra-class diversity dearth simultaneously. These adverse\nconditions damage the training and result in the decline of model performance.\nBased on the Semi-Siamese Training (SST), we introduce an advanced solution,\nnamed Multi-Agent Semi-Siamese Training (MASST), to address these problems.\nMASST includes a probe network and multiple gallery agents, the former aims to\nencode the probe features, and the latter constitutes a stack of networks that\nencode the prototypes (gallery features). For each training iteration, the\ngallery network, which is sequentially rotated from the stack, and the probe\nnetwork form a pair of semi-siamese networks. We give theoretical and empirical\nanalysis that, given the long-tail (or shallow) data and training loss, MASST\nsmooths the loss landscape and satisfies the Lipschitz continuity with the help\nof multiple agents and the updating gallery queue. The proposed method is out\nof extra-dependency, thus can be easily integrated with the existing loss\nfunctions and network architectures. It is worth noting that, although multiple\ngallery agents are employed for training, only the probe network is needed for\ninference, without increasing the inference cost. Extensive experiments and\ncomparisons demonstrate the advantages of MASST for long-tail and shallow face\nlearning.\n"}
+{"abstract": "  We investigated the radio spectra of two magnetars, PSR J1622$-$4950 and 1E\n1547.0$-$5408, using observations from the Australia Telescope Compact Array\nand the Atacama Large Millimeter/submillimeter Array taken in 2017. Our\nobservations of PSR J1622$-$4950 show a steep spectrum with a spectral index of\n$-$1.3 $\\pm$ 0.2 in the range of 5.5-45 GHz during its re-activating X-ray\noutburst in 2017. By comparing the data taken at different epochs, we found\nsignificant enhancement in the radio flux density. The spectrum of 1E\n1547.0$-$5408 was inverted in the range of 43-95 GHz, suggesting a spectral\npeak at a few hundred gigahertz. Moreover, we obtained the X-ray and radio data\nof radio magnetars, PSR J1622$-$4950 and SGR J1745$-$2900, from literature and\nfound two interesting properties. First, radio emission is known to be\nassociated with X-ray outburst but has different evolution. We further found\nthat the rising time of the radio emission is much longer than that of the\nX-ray during the outburst. Second, the radio magnetars may have double peak\nspectra at a few GHz and a few hundred GHz. This could indicate that the\nemission mechanism is different in the cm and the sub-mm bands. These two\nphenomenons could provide a hint to understand the origin of radio emission and\nits connection with the X-ray properties.\n"}
+{"abstract": "  The origins of the Galilean satellites - namely Io, Europa, Ganymede, and\nCallisto - is not fully understood yet. Here we use N-body numerical\nsimulations to study the formation of Galilean satellites in a gaseous\ncircumplanetary disk around Jupiter. Our model includes the effects of pebble\naccretion, gas-driven migration, and gas tidal damping and drag.\nSatellitesimals in our simulations first grow via pebble accretion and start to\nmigrate inwards. When they reach the trap at the disk inner edge, scattering\nevents and collisions take place promoting additional growth. Growing\nsatellites eventually reach a multi-resonant configuration anchored at the disk\ninner edge. Our best match to the masses of the Galilean satellites is produced\nin simulations where the integrated pebble flux is 1e-3 MJ. These simulations\ntypically produce between 3 and 5 satellites. In our best analogues, adjacent\nsatellite pairs are all locked in 2:1 mean motion resonances. However, they\nhave also moderately eccentric orbits (0.1), unlike the current real\nsatellites. We propose that the Galilean satellites system is a primordial\nresonant chain, similar to exoplanet systems as TRAPPIST-1, Kepler-223, and\nTOI-178. Callisto was probably in resonance with Ganymede in the past but left\nthis configuration - without breaking the Laplacian resonance - via divergent\nmigration due to tidal planet-satellite interactions. These same effects\nfurther damped the orbital eccentricities of these satellites down to their\ncurrent values (0.001). Our results support the hypothesis that Io and Europa\nwere born with water-ice rich compositions and lost all/most of their water\nafterwards. Firmer constraints on the primordial compositions of the Galilean\nsatellites are crucial to distinguish formation models.\n"}
+{"abstract": "  An integrated clinical environment (ICE) enables the connection and\ncoordination of the internet of medical things around the care of patients in\nhospitals. However, ransomware attacks and their spread on hospital\ninfrastructures, including ICE, are rising. Often the adversaries are targeting\nmultiple hospitals with the same ransomware attacks. These attacks are detected\nby using machine learning algorithms. But the challenge is devising the\nanti-ransomware learning mechanisms and services under the following\nconditions: (1) provide immunity to other hospitals if one of them got the\nattack, (2) hospitals are usually distributed over geographical locations, and\n(3) direct data sharing is avoided due to privacy concerns. In this regard,\nthis paper presents a federated distributed integrated clinical environment,\naka. FedDICE. FedDICE integrates federated learning (FL), which is\nprivacy-preserving learning, to SDN-oriented security architecture to enable\ncollaborative learning, detection, and mitigation of ransomware attacks. We\ndemonstrate the importance of FedDICE in a collaborative environment with up to\nfour hospitals and four popular ransomware families, namely WannaCry, Petya,\nBadRabbit, and PowerGhost. Our results find that in both IID and non-IID data\nsetups, FedDICE achieves the centralized baseline performance that needs direct\ndata sharing for detection. However, as a trade-off to data privacy, FedDICE\nobserves overhead in the anti-ransomware model training, e.g., 28x for the\nlogistic regression model. Besides, FedDICE utilizes SDN's dynamic network\nprogrammability feature to remove the infected devices in ICE.\n"}
+{"abstract": "  Principal loading analysis is a dimension reduction method that discards\nvariables which have only a small distorting effect on the covariance matrix.\nPotentially, principal loading analysis and ordinary least squares regression\ncoincide by construction. We contribute conditions under which both methods\nintersect. Further, we provide bounds for the cut-off value in principal\nloading analysis for the case of intersection. This gives a choice for such a\nthreshold based on the perturbation matrices.\n"}
+{"abstract": "  The non-trivial zeros of the Riemann zeta function are central objects in\nnumber theory. In particular, they enable one to reproduce the prime numbers.\nThey have also attracted the attention of physicists working in Random Matrix\nTheory and Quantum Chaos for decades. Here we present an experimental\nobservation of the lowest non-trivial Riemann zeros by using a trapped ion\nqubit in a Paul trap, periodically driven with microwave fields. The waveform\nof the driving is engineered such that the dynamics of the ion is frozen when\nthe driving parameters coincide with a zero of the real component of the zeta\nfunction. Scanning over the driving amplitude thus enables the locations of the\nRiemann zeros to be measured experimentally to a high degree of accuracy,\nproviding a physical embodiment of these fascinating mathematical objects in\nthe quantum realm.\n"}
+{"abstract": "  Globular clusters can form inside their host galaxies at high redshift when\ngas densities were higher and gas-rich mergers were common. They can also form\ninside lower-mass galaxies that have since been accreted and tidally disrupted,\nleaving their globular cluster complement bound to higher-mass halos. We argue\nthat the age-metallicity-specific orbital energy relation in a galaxy's\nglobular cluster system can be used to identify its origin. Gas-rich mergers\nshould produce tightly bound systems in which metal-rich clusters are younger\nthan metal-poor clusters. Globular clusters formed in massive disks and then\nscattered into a halo should have no relationship between age and specific\norbital energy. Accreted globular clusters should produce weakly bound systems\nin which age and metallicity are correlated with each other but inversely\ncorrelated with specific orbital energy. We use precise relative ages,\nself-consistent metallicities, and space-based proper motion-informed orbits to\nshow that the Milky Way's metal-poor globular cluster system lies in a plane in\nage-metallicity-specific orbital energy space. We find that relatively young or\nmetal-poor globular clusters are weakly bound to the Milky Way, while\nrelatively old or metal-rich globular clusters are tightly bound to the Galaxy.\nWhile metal-rich globular clusters may be formed either in situ or ex situ, our\nresults suggest that metal-poor clusters formed outside of the Milky Way in\nnow-disrupted dwarf galaxies. We predict that this relationship between age,\nmetallicity, and specific orbital energy in a $L^{*}$ galaxy's globular cluster\nsystem is a natural outcome of galaxy formation in a $\\Lambda$CDM universe.\n"}
+{"abstract": "  Instrument-tissue interaction forces in Minimally Invasive Surgery (MIS)\nprovide valuable information that can be used to provide haptic perception,\nmonitor tissue trauma, develop training guidelines, and evaluate the skill\nlevel of novice and expert surgeons.Force and tactile sensing is lost in many\nRobot-Assisted Surgery (RAS) systems. Therefore, many researchers have focused\non recovering this information through sensing systems and estimation\nalgorithms.\n  This article provides a comprehensive systematic review of the current force\nsensing research aimed at RAS and, more generally, keyhole endoscopy, in which\ninstruments enter the body through small incisions. Articles published between\nJanuary 2011 and May 2020 are considered, following the Preferred Reporting\nItems for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The\nliterature search resulted in 110 papers on different force estimation\nalgorithms and sensing technologies, sensor design specifications, and\nfabrication techniques.\n"}
+{"abstract": "  A novel framework for statistical learning is introduced which combines ideas\nfrom regularization and ensembling. This framework is applied to learn an\nensemble of logistic regression models for high-dimensional binary\nclassification. In the new framework the models in the ensemble are learned\nsimultaneously by optimizing a multi-convex objective function. To enforce\ndiversity between the models the objective function penalizes overlap between\nthe models in the ensemble. Measures of diversity in classifier ensembles are\nused to show how our method learns the ensemble by exploiting the\naccuracy-diversity trade-off for ensemble models. In contrast to other\nensembling approaches, the resulting ensemble model is fully interpretable as a\nlogistic regression model, asymptotically consistent, and at the same time\nyields excellent prediction accuracy as demonstrated in an extensive simulation\nstudy and gene expression data applications. The models found by the proposed\nensemble methodology can also reveal alternative mechanisms that can explain\nthe relationship between the predictors and the response variable. An\nopen-source compiled software library implementing the proposed method is\nbriefly discussed.\n"}
+{"abstract": "  This paper studies a WiFi indoor localisation technique based on using a deep\nlearning model and its transfer strategies. We take CSI packets collected via\nthe WiFi standard channel sounding as the training dataset and verify the CNN\nmodel on the subsets collected in three experimental environments. We achieve a\nlocalisation accuracy of 46.55 cm in an ideal $(6.5m \\times 2.5m)$ office with\nno obstacles, 58.30 cm in an office with obstacles, and 102.8 cm in a sports\nhall $(40 \\times 35m)$. Then, we evaluate the transfer ability of the proposed\nmodel to different environments. The experimental results show that, for a\ntrained localisation model, feature extraction layers can be directly\ntransferred to other models and only the fully connected layers need to be\nretrained to achieve the same baseline accuracy with non-transferred base\nmodels. This can save 60% of the training parameters and reduce the training\ntime by more than half. Finally, an ablation study of the training dataset\nshows that, in both office and sport hall scenarios, after reusing the feature\nextraction layers of the base model, only 55% of the training data is required\nto obtain the models' accuracy similar to the base models.\n"}
+{"abstract": "  Detecting anomalies using deep learning has become a major challenge over the\nlast years, and is becoming increasingly promising in several fields. The\nintroduction of self-supervised learning has greatly helped many methods\nincluding anomaly detection where simple geometric transformation recognition\ntasks are used. However these methods do not perform well on fine-grained\nproblems since they lack finer features. By combining in a multi-task framework\nhigh-scale shape features oriented task with low-scale fine features oriented\ntask, our method greatly improves fine-grained anomaly detection. It\noutperforms state-of-the-art with up to 31% relative error reduction measured\nwith AUROC on various anomaly detection problems.\n"}
+{"abstract": "  A crucial part of the design of an FCC-ee detector is the minimisation of the\ndisruption of the beam due to the presence of a large and powerful detector\nmagnet. Indeed, the emittance blow-up of the few meters around the interaction\npoint (IP) at lower energies is comparable to the emittance introduced by the\nrest of the 100 km ring. Vertical emittance is the single most important factor\nin achieving high performance (luminosity, in this case) in a modern e^+ e^-\nstorage ring such as the FCC-ee. The design adopted is the simplest possible\narrangement that can nevertheless deliver high performance: two additional\ncoils per IP side. The performance achieved is such that vertical emittance\nblow-up will not be a limiting performance factor even in the case of a ring\nwith four experiments, and even in the most demanding energy regime, that of\nthe Z running (about 45 GeV beam energy).\n"}
+{"abstract": "  In their Letter[Phys. Rev. Lett. 126, 106803 (2021)], the authors found an\ninteresting reentrant localization phenomenon in a one-dimensional dimerized\nlattice with quasiperiodic disorder, i.e., the system undergoes a second\nlocalization transition at a higher disorder strength. They claimed that this\nreentrant feature would appear in the case of staggered disorder, but not for\nthe uniform ones. In this Comment, we show that such reentrant feature can also\nappear for the uniform disorder when larger system parameters are taken into\naccount.\n"}
+{"abstract": "  Our objective is to compute a textural loss that can be used to train texture\ngenerators with multiple material channels typically used for physically based\nrendering such as albedo, normal, roughness, metalness, ambient occlusion, etc.\nNeural textural losses often build on top of the feature spaces of pretrained\nconvolutional neural networks. Unfortunately, these pretrained models are only\navailable for 3-channel RGB data and hence limit neural textural losses to this\nformat. To overcome this limitation, we show that passing random triplets to a\n3-channel loss provides a multi-channel loss that can be used to generate\nhigh-quality material textures.\n"}
+{"abstract": "  We present a characterization of finite permutation groups which contain a\ntransitive dihedral subgroup.\n"}
+{"abstract": "  The number of relativistic species, $N_{\\rm eff}$, has been precisely\ncalculated in the standard model, and would be measured to the percent level by\nCMB-S4 in future. Neutral-current non-standard interactions would affect\nneutrino decoupling in the early Universe, thus modifying $N_{\\rm eff}$. We\nparameterize those operators up to dimension-7 in the effective field theory\nframework, and then provide a complete, generic and analytical dictionary for\nthe collision term integrals. From precision measurements of $N_{\\rm eff}$, the\nmost stringent constraint is obtained for the dimension-6 vector-type\nneutrino-electron operator, whose scale is constrained to be above about 195\n(331) GeV from Planck (CMB-S4). We find our results complementary to other\nexperiments like neutrino coherent scattering, neutrino oscillation, collider,\nand neutrino deep inelastic scattering experiments.\n"}
+{"abstract": "  We consider the problem of extending and avoiding partial edge colorings of\nhypercubes; that is, given a partial edge coloring $\\varphi$ of the\n$d$-dimensional hypercube $Q_d$, we are interested in whether there is a proper\n$d$-edge coloring of $Q_d$ that agrees with the coloring $\\varphi$ on every\nedge that is colored under $\\varphi$; or, similarly, if there is a proper\n$d$-edge coloring that disagrees with $\\varphi$ on every edge that is colored\nunder $\\varphi$. In particular, we prove that for any $d\\geq 1$, if $\\varphi$\nis a partial $d$-edge coloring of $Q_d$, then $\\varphi$ is avoidable if every\ncolor appears on at most $d/8$ edges and the coloring satisfies a relatively\nmild structural condition, or $\\varphi$ is proper and every color appears on at\nmost $d-2$ edges. We also show that the same conclusion holds if $d$ is\ndivisible by $3$ and every color class of $\\varphi$ is an induced matching.\nMoreover, for all $1 \\leq k \\leq d$, we characterize for which configurations\nconsisting of a partial coloring $\\varphi$ of $d-k$ edges and a partial\ncoloring $\\psi$ of $k$ edges, there is an extension of $\\varphi$ that avoids\n$\\psi$.\n"}
+{"abstract": "  Let $H_{s,k}$ be the graph defined by intersecting $s$ triangles and $k$\ncycles of odd lengths at least five in exactly one common vertex. Recently,\nHou, Qiu and Liu [Discrete Math. 341 (2018) 126--137], and Yuan [J. Graph\nTheory 89 (2018), no. 1, 26--39] determined independently the maximum number of\nedges in an $n$-vertex graph that does not contain $H_{s,k}$ as a subgraph. In\nthis paper, we determine the graphs of order $n$ that attain the maximum\nspectral radius among all graphs containing no $H_{s,k}$ for $n$ large enough.\n"}
+{"abstract": "  The interaction between equilibrium tides and convection in stellar envelopes\nis often considered important for tidal evolution in close binary and\nextrasolar planetary systems. Its efficiency for fast tides has however long\nbeen controversial, when the tidal frequency exceeds the turnover frequency of\nconvective eddies. Recent numerical simulations indicate that convection can\nact like an effective viscosity which decays quadratically with tidal frequency\nfor fast tides, resulting in inefficient dissipation in many applications\ninvolving pre- and main-sequence stars and giant planets. A new idea was\nhowever recently proposed by Terquem (2021), who suggested Reynolds stresses\ninvolving correlations between tidal flow components dominate the interaction\ninstead of correlations between convective flow components as usually assumed.\nThey further showed that this can potentially significantly enhance tidal\ndissipation for fast tides in many applications. Motivated by the importance of\nthis problem for tidal dissipation in stars and planets, we directly compute\nthis new term using analytical arguments and global spherical simulations using\nBoussinesq and anelastic hydrodynamic models. We demonstrate that the new term\nproposed by Terquem vanishes identically for equilibrium tides interacting with\nconvection in both Boussinesq and anelastic models; it is therefore unlikely to\ncontribute to tidal dissipation in stars and planets.\n"}
+{"abstract": "  We present results on the infection risk from SARS-CoV-2 under different\nscenarios based on measured particle size-dependent mask penetration, measured\ntotal inward leakage, measured human aerosol emission for sizes from 10nm to\n1mm, and re-hydration on inhalation. Well-mixed room models significantly\nunderestimate the risk of infection for short and direct exposure. To this end,\nwe estimate the upper bound for infection risk with the susceptible in the\ninfectious exhalation cloud or wearing masks by having the masked susceptible\ninhale the entire exhalation of a masked infectious. Social distances without a\nmask, even at 3m between speaking individuals results in an upper bound of 90\\%\nfor risk of infection after a few minutes. If both wear a surgical mask, the\nrisk of infection for the person speaking remains below 26\\% even after 60\nminutes. When both the infectious and susceptible wear a well-fitting FFP2\nmask, the upper bound for risk is reduced by a factor of 60 compared to\nsurgical masks. In both cases, face leakage is very important. For FFP2 masks,\nleakage is low in the nasal region and directed upward, which can be further\nreduced significantly by applying double-sided medical tape there. Considering\nthat the calculated upper bound greatly overestimates the risk of infection,\nand the fact that with a poorly worn mask even the upper bound we calculated is\nvery low, we conclude that wearing a mask, even with some leakage, provides\nexcellent third party and self-protection.\n"}
+{"abstract": "  Sixth generation (6G) mobile networks may include new passive technologies,\nsuch as ambient backscatter communication or the use of reconfigurable\nintelligent surfaces, to avoid the emission of waves and the corresponding\nenergy consumption. On the one hand, a reconfigurable intelligent surface\nimproves the network performance by adding electronically controlled reflected\npaths in the radio propagation channel. On the other hand, in an ambient\nbackscatter system, a device, named tag, communicates towards a reader by\nbackscattering the waves of an ambient source (such as a TV tower). However,\nthe tag's backscattered signal is weak and strongly interfered by the direct\nsignal from the ambient source. In this paper, we propose a new reconfigurable\nintelligent surface assisted ambient backscatter system. The proposed surface\nallows to control the reflection of an incident wave coming from the exact\nsource location towards the tag and reader locations (creating hot spots at\ntheir locations), thanks to passive reflected beams from a predefined codebook.\nA common phase-shift can also be applied to the beam. Thanks to these features,\nwe demonstrate experimentally that the performance of ambient backscatter\ncommunications can be significantly improved.\n"}
+{"abstract": "  BRITE-Constellation is devoted to high-precision optical photometric\nmonitoring of bright stars, distributed all over the Milky Way, in red and/or\nblue passbands. Photometry from space avoids the turbulent and absorbing\nterrestrial atmosphere and allows for very long and continuous observing runs\nwith high time resolution and thus provides the data necessary for\nunderstanding various processes inside stars (e.g., asteroseismology) and in\ntheir immediate environment. While the first astronomical observations from\nspace focused on the spectral regions not accessible from the ground it soon\nbecame obvious around 1970 that avoiding the turbulent terrestrial atmosphere\nsignificantly improved the accuracy of photometry and satellites explicitly\ndedicated to high-quality photometry were launched. A perfect example is\nBRITE-Constellation, which is the result of a very successful cooperation\nbetween Austria, Canada and Poland. Research highlights for targets distributed\nnearly over the entire HRD are presented, but focus primarily on massive and\nhot stars.\n"}
+{"abstract": "  The lensless endoscope (LE) is a promising device to acquire in vivo images\nat a cellular scale. The tiny size of the probe enables a deep exploration of\nthe tissues. Lensless endoscopy with a multicore fiber (MCF) commonly uses a\nspatial light modulator (SLM) to coherently combine, at the output of the MCF,\nfew hundreds of beamlets into a focus spot. This spot is subsequently scanned\nacross the sample to generate a fluorescent image. We propose here a novel\nscanning scheme, partial speckle scanning (PSS), inspired by compressive\nsensing theory, that avoids the use of an SLM to perform fluorescent imaging in\nLE with reduced acquisition time. Such a strategy avoids photo-bleaching while\nkeeping high reconstruction quality. We develop our approach on two key\nproperties of the LE: (i) the ability to easily generate speckles, and (ii) the\nmemory effect in MCF that allows to use fast scan mirrors to shift light\npatterns. First, we show that speckles are sub-exponential random fields.\nDespite their granular structure, an appropriate choice of the reconstruction\nparameters makes them good candidates to build efficient sensing matrices.\nThen, we numerically validate our approach and apply it on experimental data.\nThe proposed sensing technique outperforms conventional raster scanning: higher\nreconstruction quality is achieved with far fewer observations. For a fixed\nreconstruction quality, our speckle scanning approach is faster than\ncompressive sensing schemes which require to change the speckle pattern for\neach observation.\n"}
+{"abstract": "  For a set of positive integers $A$, let $p_A(n)$ denote the number of ways to\nwrite $n$ as a sum of integers from $A$, and let $p(n)$ denote the usual\npartition function. In the early 40s, Erd\\H{o}s extended the classical\nHardy--Ramanujan formula for $p(n)$ by showing that $A$ has density $\\alpha$ if\nand only if $\\log p_A(n) \\sim \\log p(\\alpha n)$. Nathanson asked if Erd\\H{o}s's\ntheorem holds also with respect to $A$'s lower density, namely, whether $A$ has\nlower-density $\\alpha$ if and only if $\\log p_A(n) / \\log p(\\alpha n)$ has\nlower limit $1$. We answer this question negatively by constructing, for every\n$\\alpha > 0$, a set of integers $A$ of lower density $\\alpha$, satisfying $$\n\\liminf_{n \\rightarrow \\infty} \\frac{\\log p_A(n)}{\\log p(\\alpha n)} \\geq\n\\left(\\frac{\\sqrt{6}}{\\pi}-o_{\\alpha}(1)\\right)\\log(1/\\alpha)\\;. $$ We further\nshow that the above bound is best possible (up to the $o_\\alpha(1)$ term), thus\ndetermining the exact extremal relation between the lower density of a set of\nintegers and the lower limit of its partition function. We also prove an\nanalogous theorem with respect to the upper density of a set of integers,\nanswering another question of Nathanson.\n"}
+{"abstract": "  We calculate the relevant Spencer cohomology of the minimal Poincar\\'e\nsuperalgebra in 5 spacetime dimensions and use it to define Killing spinors via\na connection on the spinor bundle of a 5-dimensional lorentzian spin manifold.\nWe give a definition of bosonic backgrounds in terms of this data. By imposing\nconstraints on the curvature of the spinor connection, we recover the field\nequations of minimal (ungauged) 5-dimensional supergravity, but also find a set\nof field equations for an $\\mathfrak{sp}(1)$-valued one-form which we interpret\nas the bosonic data of a class of rigid supersymmetric theories on curved\nbackgrounds. We define the Killing superalgebra of bosonic backgrounds and show\nthat their existence is implied by the field equations. The maximally\nsupersymmetric backgrounds are characterised and their Killing superalgebras\nare explicitly described as filtered deformations of the Poincar\\'e\nsuperalgebra.\n"}
+{"abstract": "  In this paper, we consider model order reduction for bilinear systems with\nnon-zero initial conditions. We discuss choices of Gramians for both the\nhomogeneous and the inhomogeneous parts of the system individually and prove\nhow these Gramians characterize the respective dominant subspaces of each of\nthe two subsystems. Proposing different, not necessarily structure preserving,\nreduced order methods for each subsystem, we establish several strategies to\nreduce the dimension of the full system. For all these approaches, error bounds\nare shown depending on the truncated Hankel singular values of the subsystems.\nBesides the error analysis, stability is discussed. In particular, a focus is\non a new criterion for the homogeneous subsystem guaranteeing the existence of\nthe associated Gramians and an asymptotically stable realization of the system.\n"}
+{"abstract": "  We prove sharp pointwise heat kernel estimates for symmetric Markov processes\nthat are generated by symmetric Dirichlet forms that are local with respect to\nsome coordinates and nonlocal with respect to the remaining coordinates. The\nmain theorem is a robustness result like the famous estimate for the\nfundamental solution of second order differential operators, obtained by Donald\nG. Aronson. Analogous to his result, we show that the corresponding\ntranslation-invariant process and the one given by the general Dirichlet form\nshare the same pointwise points.\n"}
+{"abstract": "  The suffix array, describing the lexicographic order of suffixes of a given\ntext, is the central data structure in string algorithms. The suffix array of a\nlength-$n$ text uses $\\Theta(n \\log n)$ bits, which is prohibitive in many\napplications. To address this, Grossi and Vitter [STOC 2000] and,\nindependently, Ferragina and Manzini [FOCS 2000] introduced space-efficient\nversions of the suffix array, known as the compressed suffix array (CSA) and\nthe FM-index. For a length-$n$ text over an alphabet of size $\\sigma$, these\ndata structures use only $O(n \\log \\sigma)$ bits. Immediately after their\ndiscovery, they almost completely replaced plain suffix arrays in practical\napplications, and a race started to develop efficient construction procedures.\nYet, after more than 20 years, even for $\\sigma=2$, the fastest algorithm\nremains stuck at $O(n)$ time [Hon et al., FOCS 2003], which is slower by a\n$\\Theta(\\log n)$ factor than the lower bound of $\\Omega(n / \\log n)$ (following\nsimply from the necessity to read the entire input). We break this\nlong-standing barrier with a new data structure that takes $O(n \\log \\sigma)$\nbits, answers suffix array queries in $O(\\log^{\\epsilon} n)$ time, and can be\nconstructed in $O(n\\log \\sigma / \\sqrt{\\log n})$ time using $O(n\\log \\sigma)$\nbits of space. Our result is based on several new insights into the recently\ndeveloped notion of string synchronizing sets [STOC 2019]. In particular,\ncompared to their previous applications, we eliminate orthogonal range queries,\nreplacing them with new queries that we dub prefix rank and prefix selection\nqueries. As a further demonstration of our techniques, we present a new\npattern-matching index that simultaneously minimizes the construction time and\nthe query time among all known compact indexes (i.e., those using $O(n \\log\n\\sigma)$ bits).\n"}
+{"abstract": "  Neural machine translation (NMT) architectures have achieved promising\nresults for automatic program repair. Yet, they have the limitation of\ngenerating low-quality patches(e.g., not compilable patches). This is because\nthe existing works only optimize a purely syntactic loss function based on\ncharacters and tokens without incorporating program-specific information during\nneural net weight optimization. In this paper, we proposea novel program repair\nmodel called RewardRepair. The core novelty of RewardRepair is to improve\nNMT-based program repair with a loss function based on program compilation and\ntest execution information, rewarding the network to produce patches that\ncompile and that do not overfit. We conduct several experiments to evaluate\nRewardRepair showing that it is feasible and effective to use compilation and\ntest execution results to optimize the underlying neural repair model.\n"}
+{"abstract": "  Flare spot is one type of flare artifact caused by a number of conditions,\nfrequently provoked by one or more high-luminance sources within or close to\nthe camera field of view. When light rays coming from a high-luminance source\nreach the front element of a camera, it can produce intra-reflections within\ncamera elements that emerge at the film plane forming non-image information or\nflare on the captured image. Even though preventive mechanisms are used,\nartifacts can appear. In this paper, we propose a robust computational method\nto automatically detect and remove flare spot artifacts. Our contribution is\nthreefold: firstly, we propose a characterization which is based on intrinsic\nproperties that a flare spot is likely to satisfy; secondly, we define a new\nconfidence measure able to select flare spots among the candidates; and,\nfinally, a method to accurately determine the flare region is given. Then, the\ndetected artifacts are removed by using exemplar-based inpainting. We show that\nour algorithm achieve top-tier quantitative and qualitative performance.\n"}
+{"abstract": "  We study several classes of processes associated with the tempered positive\nLinnik (TPL) distribution, in both the purely absolutely-continuous and mixed\nlaw regimes. We explore four main ramifications. Firstly, we analyze several\nsubordinated representations of TPL L\\'evy processes; in particular we\nestablish a stochastic self-similarity property of positive Linnik (PL) L\\'evy\nprocesses, connecting TPL processes with negative binomial subordination.\nSecondly, in finite activity regimes we show that the explicit compound Poisson\nrepresentations gives rise to innovations following novel Mittag-Leffler type\nlaws. Thirdly, we characterize two inhomogeneous TPL processes, namely the\nOrnstein-Uhlenbeck (OU) L\\'evy-driven processes with stationary distribution\nand the additive process generated by a TPL law. Finally, we propose a\nmultivariate TPL L\\'evy process based on a negative binomial mixing methodology\nof independent interest. Some potential applications of the considered\nprocesses are also outlined in the contexts of statistical anti-fraud and\nfinancial modelling.\n"}
+{"abstract": "  The von Neumann graph entropy is a measure of graph complexity based on the\nLaplacian spectrum. It has recently found applications in various learning\ntasks driven by networked data. However, it is computational demanding and hard\nto interpret using simple structural patterns. Due to the close relation\nbetween Lapalcian spectrum and degree sequence, we conjecture that the\nstructural information, defined as the Shannon entropy of the normalized degree\nsequence, might be a good approximation of the von Neumann graph entropy that\nis both scalable and interpretable.\n  In this work, we thereby study the difference between the structural\ninformation and von Neumann graph entropy named as {\\em entropy gap}. Based on\nthe knowledge that the degree sequence is majorized by the Laplacian spectrum,\nwe for the first time prove the entropy gap is between $0$ and $\\log_2 e$ in\nany undirected unweighted graphs. Consequently we certify that the structural\ninformation is a good approximation of the von Neumann graph entropy that\nachieves provable accuracy, scalability, and interpretability simultaneously.\nThis approximation is further applied to two entropy-related tasks: network\ndesign and graph similarity measure, where novel graph similarity measure and\nfast algorithms are proposed. Our experimental results on graphs of various\nscales and types show that the very small entropy gap readily applies to a wide\nrange of graphs and weighted graphs. As an approximation of the von Neumann\ngraph entropy, the structural information is the only one that achieves both\nhigh efficiency and high accuracy among the prominent methods. It is at least\ntwo orders of magnitude faster than SLaQ with comparable accuracy. Our\nstructural information based methods also exhibit superior performance in two\nentropy-related tasks.\n"}
+{"abstract": "  Transistors are key elements for enabling computational hardware in both\nclassical and quantum domains. Here, we propose a voltage-gated spin transistor\nusing itinerant electrons in the Hubbard model which acts at the level of\nsingle electron spins. Going beyond classical spintronics, it enables the\ncontrolling of the flow of quantum information between distant spin qubits. The\ntransistor has two modes of operation, open and closed, which are realized by\ntwo different charge configurations in the gate of the transistor. In the\nclosed mode, the spin information between source and drain is blocked while in\nthe open mode we have free spin information exchange. The switching between the\nmodes takes place within a fraction of the operation time which allows for\nseveral subsequent operations within the coherence time of the transistor. The\nsystem shows good resilience against several imperfections and opens up a\npractical application for quantum dot arrays.\n"}
+{"abstract": "  We consider a generalized riffle shuffle on the colored permutation group\n$G_{p, n}$ and derive a determinantal formula for the probability of finding\ndescents at given positions, proof of which is based on the bijection between\nthe set of shuffles in question and that of non-intersecting lattice paths.\n"}
+{"abstract": "  We present a thread-modular abstract interpretation(TMAI) technique to verify\nprograms under the release-acquire (RA) memory model for safety property\nviolations. The main contributions of our work are: we capture the execution\norder of program statements as an abstract domain, and propose a sound upper\napproximation over this domain to efficiently reason over RA concurrency. The\nproposed domain is general in its application and captures the ordering\nrelations as a first-class feature in the abstract interpretation theory. In\nparticular, the domain represents a set of sequences of modifications of a\nglobal variable in concurrent programs as a partially ordered set. Under this\napproximation, older sequenced-before stores of a global variable are forgotten\nand only the latest stores per variable are preserved. We establish the\nsoundness of our proposed abstractions and implement them in a prototype\nabstract interpreter called PRIORI. The evaluations of PRIORI on existing and\nchallenging RA benchmarks demonstrate that the proposed technique is not only\ncompetitive in refutation, but also in verification. PRIORI shows significantly\nfast analysis runtimes with higher precision compared to recent\nstate-of-the-art tools for RA concurrency.\n"}
+{"abstract": "  We construct a family of heptagon relations -- algebraic imitations of\nfive-dimensional Pachner move 3--4, parameterized by simplicial 3-cocycles.\n"}
+{"abstract": "  A quantum (e.g. noncommutative) version of the Sierpinski gasket is proposed,\non purely topological grounds. This is a $C^*$-algebra with a suitable form of\nself-similarity. Properties of this $C^*$-algebra are studied, in particular\nirreducible representations and extremal traces are fully described. A\nself-similar Dirichlet form together with a spectral triple are also\nconstructed, extending results already known for the classical gasket.\n"}
+{"abstract": "  Biophotonic techniques are growing in rapid rhythms enabling the monitoring\nof subcellular structures and non-invasive theranostic interventions in cancer\nand autoimmune diseases. The integration of Biophotonics with nanotechnology\nand biosensors brings a revolution in the micro- and nano-world with new\noptical tools. Among them, optical tweezers revive as a potential tool for\ntracking cells behavior and probing interactions forces between cells,\ncells-biomolecules and cells-nanoparticles. In this review we aim to exhibit\nthe state-of the art advances of the Biophotonics in the diagnostic and\ntherapeutic field, and the role of optical tweezers.\n"}
+{"abstract": "  Database fingerprinting have been widely adopted to prevent unauthorized\nsharing of data and identify the source of data leakages. Although existing\nschemes are robust against common attacks, like random bit flipping and subset\nattack, their robustness degrades significantly if attackers utilize the\ninherent correlations among database entries. In this paper, we first\ndemonstrate the vulnerability of existing database fingerprinting schemes by\nidentifying different correlation attacks: column-wise correlation attack,\nrow-wise correlation attack, and the integration of them. To provide robust\nfingerprinting against the identified correlation attacks, we then develop\nmitigation techniques, which can work as post-processing steps for any\noff-the-shelf database fingerprinting schemes. The proposed mitigation\ntechniques also preserve the utility of the fingerprinted database considering\ndifferent utility metrics. We empirically investigate the impact of the\nidentified correlation attacks and the performance of mitigation techniques\nusing real-world relational databases. Our results show (i) high success rates\nof the identified correlation attacks against existing fingerprinting schemes\n(e.g., the integrated correlation attack can distort 64.8\\% fingerprint bits by\njust modifying 14.2\\% entries in a fingerprinted database), and (ii) high\nrobustness of the proposed mitigation techniques (e.g., with the mitigation\ntechniques, the integrated correlation attack can only distort $3\\%$\nfingerprint bits).\n"}
+{"abstract": "  This contribution describes the concept, main structure and goals, and some\nhighlighted outcomes, of the AstroCamp -- an international academic excellence\nprogram in the field of astronomy and physics created in 2012 and organized by\nCentro de Astrof\\'{\\i}sica da Universidade do Porto (CAUP) together with the\nParedes de Coura municipality and several national and international partners.\n"}
+{"abstract": "  Word alignment over parallel corpora has a wide variety of applications,\nincluding learning translation lexicons, cross-lingual transfer of language\nprocessing tools, and automatic evaluation or analysis of translation outputs.\nThe great majority of past work on word alignment has worked by performing\nunsupervised learning on parallel texts. Recently, however, other work has\ndemonstrated that pre-trained contextualized word embeddings derived from\nmultilingually trained language models (LMs) prove an attractive alternative,\nachieving competitive results on the word alignment task even in the absence of\nexplicit training on parallel data. In this paper, we examine methods to marry\nthe two approaches: leveraging pre-trained LMs but fine-tuning them on parallel\ntext with objectives designed to improve alignment quality, and proposing\nmethods to effectively extract alignments from these fine-tuned models. We\nperform experiments on five language pairs and demonstrate that our model can\nconsistently outperform previous state-of-the-art models of all varieties. In\naddition, we demonstrate that we are able to train multilingual word aligners\nthat can obtain robust performance on different language pairs. Our aligner,\nAWESOME (Aligning Word Embedding Spaces of Multilingual Encoders), with\npre-trained models is available at https://github.com/neulab/awesome-align\n"}
+{"abstract": "  In recent years, there has been a massive increase in the amount of Internet\nof Things (IoT) devices as well as the data generated by such devices. The\nparticipating devices in IoT networks can be problematic due to their\nresource-constrained nature, and integrating security on these devices is often\noverlooked. This has resulted in attackers having an increased incentive to\ntarget IoT devices. As the number of attacks possible on a network increases,\nit becomes more difficult for traditional intrusion detection systems (IDS) to\ncope with these attacks efficiently. In this paper, we highlight several\nmachine learning (ML) methods such as k-nearest neighbour (KNN), support vector\nmachine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF),\nartificial neural network (ANN), and logistic regression (LR) that can be used\nin IDS. In this work, ML algorithms are compared for both binary and\nmulti-class classification on Bot-IoT dataset. Based on several parameters such\nas accuracy, precision, recall, F1 score, and log loss, we experimentally\ncompared the aforementioned ML algorithms. In the case of HTTP distributed\ndenial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other\nsimulation results-based precision, recall, F1 score, and log loss metric\nreveal that RF outperforms on all types of attacks in binary classification.\nHowever, in multi-class classification, KNN outperforms other ML algorithms\nwith an accuracy of 99%, which is 4% higher than RF.\n"}
+{"abstract": "  We study Hamilton circuits of the Cayley graphs of the Weyl groupoids of the\ngeneralized quantum groups, or the quantum double of the Nichols algebras of\ndiagonal-type, with finite root systems. We prove the existence of a Hamilton\ncircuit for any of them and explicitly draw one of them for rank 3 and 4 cases.\n"}
+{"abstract": "  In [ZY2], the second author proved Perelman's assertion, namely, for an\nancient Ricci flow with bounded and nonnegative curvature operator, bounded\nentropy is equivalent to noncollapsing on all scales. In this paper, we\ncontinue this discussion. It turns out that the curvature operator\nnonnegativity is not a necessary condition, and we need only to assume a\nconsequence of Hamilton's trace Harnack. Furthermore, we show that this\ncondition holds for steady Ricci solitons with nonnegative Ricci curvature.\n"}
+{"abstract": "  We report the direct imaging discovery of a low-mass companion to the nearby\naccelerating A star, HIP 109427, with the Subaru Coronagraphic Extreme Adaptive\nOptics (SCExAO) instrument coupled with the MKID Exoplanet Camera (MEC) and\nCHARIS integral field spectrograph. CHARIS data reduced with reference star PSF\nsubtraction yield 1.1-2.4 $\\mu$m spectra. MEC reveals the companion in $Y$ and\n$J$ band at a comparable signal-to-noise ratio using stochastic speckle\ndiscrimination, with no PSF subtraction techniques. Combined with complementary\nfollow-up $L_{\\rm p}$ photometry from Keck/NIRC2, the SCExAO data favors a\nspectral type, effective temperature, and luminosity of M4-M5.5, 3000-3200 $K$,\nand $\\log_{10}(L/L_{\\rm \\odot}) = -2.28^{+0.04}_{-0.04}$, respectively.\nRelative astrometry of HIP 109427 B from SCExAO/CHARIS and Keck/NIRC2, and\ncomplementary Gaia-Hipparcos absolute astrometry of the primary favor a\nsemimajor axis of $6.55^{+3.0}_{-0.48}$ au, an eccentricity of\n$0.54^{+0.28}_{-0.15}$, an inclination of $66.7^{+8.5}_{-14}$ degrees, and a\ndynamical mass of $0.280^{+0.18}_{-0.059}$ $M_{\\odot}$. This work shows the\npotential for extreme AO systems to utilize speckle statistics in addition to\nwidely-used post-processing methods to directly image faint companions to\nnearby stars near the telescope diffraction limit.\n"}
+{"abstract": "  We study the asymptotic dynamics of the lake equations in the following two\ncases, an island shrinking to a point and an emerging island. For both cases,\nwe derive an asymptotic lake-type equation. In the former case, the asymptotic\ndynamics includes an additional Dirac mass in the vorticity. The main\nmathematical difficulty is that the equations are singular when the water depth\nvanishes. We provide new uniform estimates in weighted spaces for the related\nstream functions which will imply the compactness result.\n"}
+{"abstract": "  In this article we explore the requirements for enabling high quality\noptically detected magnetic resonance (ODMR) spectroscopy in a conventional\ngradient force optical tweezers using nanodiamonds containing nitrogen-vacancy\n(NV$^{-}$) centres. We find that modulation of the infrared (1064 nm) trapping\nlaser during spectroscopic measurements substantially improves the ODMR\ncontrast compared with continuous wave trapping. The work is significant as it\nallows trapping and quantum sensing protocols to be performed in conditions\nwhere signal contrast is substantially reduced. We demonstrate the utility of\nthe technique by resolving NV$^{-}$ spin projections within the ODMR spectrum.\nManipulating the orientation of the nanodiamond via the trapping laser\npolarisation, we observe changes in spectral features. Theoretical modelling\nthen allows us to infer the crystallographic orientation of the NV$^{-}$. This\nis an essential capability for future magnetic sensing applications of\noptically trapped nanodiamonds.\n"}
+{"abstract": "  Very recently, a variety of vision transformer architectures for dense\nprediction tasks have been proposed and they show that the design of spatial\nattention is critical to their success in these tasks. In this work, we revisit\nthe design of the spatial attention and demonstrate that a carefully-devised\nyet simple spatial attention mechanism performs favourably against the\nstate-of-the-art schemes. As a result, we propose two vision transformer\narchitectures, namely, Twins-PCPVT and Twins-SVT. Our proposed architectures\nare highly-efficient and easy to implement, only involving matrix\nmultiplications that are highly optimized in modern deep learning frameworks.\nMore importantly, the proposed architectures achieve excellent performance on a\nwide range of visual tasks, including image level classification as well as\ndense detection and segmentation. The simplicity and strong performance suggest\nthat our proposed architectures may serve as stronger backbones for many vision\ntasks. Our code is released at https://github.com/Meituan-AutoML/Twins .\n"}
+{"abstract": "  Traditional computer vision models are trained to predict a fixed set of\npredefined categories. Recently, natural language has been shown to be a\nbroader and richer source of supervision that provides finer descriptions to\nvisual concepts than supervised \"gold\" labels. Previous works, such as CLIP,\nuse a simple pretraining task of predicting the pairings between images and\ntext captions. CLIP, however, is data hungry and requires more than 400M image\ntext pairs for training. We propose a data-efficient contrastive distillation\nmethod that uses soft labels to learn from noisy image-text pairs. Our model\ntransfers knowledge from pretrained image and sentence encoders and achieves\nstrong performance with only 3M image text pairs, 133x smaller than CLIP. Our\nmethod exceeds the previous SoTA of general zero-shot learning on ImageNet\n21k+1k by 73% relatively with a ResNet50 image encoder and DeCLUTR text\nencoder. We also beat CLIP by 10.5% relatively on zero-shot evaluation on\nGoogle Open Images (19,958 classes).\n"}
+{"abstract": "  In this paper an approximation of the image of the closed ball of the space\n$L_p$ $(p>1)$ centered at the origin with radius $r$ under Hilbert-Schmidt\nintegral operator $F(\\cdot):L_p\\rightarrow L_q$ $\\displaystyle\n\\left(\\frac{1}{p}+\\frac{1}{q}=1\\right)$ is presented. An error estimation for\ngiven approximation is obtained.\n"}
+{"abstract": "  We study the $\\delta$ Scuti -- red giant binary KIC9773821, the first\ndouble-pulsator binary of its kind. It was observed by \\textit{Kepler} during\nits four-year mission. Our aims are to ascertain whether the system is bound,\nrather than a chance alignment, and to identify the evolutionary state of the\nred giant via asteroseismology. An extension of these aims is to determine a\ndynamical mass and an age prior for a $\\delta$ Sct star, which may permit mode\nidentification via further asteroseismic modelling. We determine spectroscopic\nparameters and radial velocities (RVs) for the red giant component using\nHERMES@Mercator spectroscopy. Light arrival-time delays from the $\\delta$ Sct\npulsations are used with the red-giant RVs to determine that the system is\nbound and to infer its orbital parameters, including the binary mass ratio. We\nuse asteroseismology to model the individual frequencies of the red giant to\ngive a mass of $2.10^{+0.20}_{-0.10}$ M$_{\\odot}$ and an age of\n$1.08^{+0.06}_{-0.24}$ Gyr. We find that it is a helium-burning secondary clump\nstar, confirm that it follows the standard $\\nu_{\\rm max}$ scaling relation,\nand confirm its observed period spacings match their theoretical counterparts\nin the modelling code MESA. Our results also constrain the mass and age of the\n$\\delta$ Sct star. We leverage these constraints to construct $\\delta$ Sct\nmodels in a reduced parameter space and identify four of its five pulsation\nmodes.\n"}
+{"abstract": "  We propose a novel algorithm, based on physics-informed neural networks\n(PINNs) to efficiently approximate solutions of nonlinear dispersive PDEs such\nas the KdV-Kawahara, Camassa-Holm andBenjamin-Ono equations. The stability of\nsolutions of these dispersive PDEs is leveraged to prove rigorous bounds on the\nresulting error. We present several numerical experiments to demonstrate\nthatPINNs can approximate solutions of these dispersive PDEs very accurately\n"}
+{"abstract": "  Probabilistic cellular automata (CA) provides a classic framework for\nstudying non-equilibrium statistical physics on a lattices. A notable example\nis the Domany-Kinzel CA, which has been used to investigate the process of\ndirected percolation and the critical dynamics of the non-equilibrium phase\ntransition betweeen absorbing and percolating phases. In this work, we\nconstruct a non-unitary Quantum Cellular Automaton that generalises the\nDomany-Kinzel cellular automaton and study the resulting dynamical evolution\nusing the numerical simulations using the tensor network iTEBD algorithm. We\ndemonstrate the system undergoes the absorbing/percolating phase transition and\nthe addition of the Hamiltonian generates coherences, which are a distinct\nfeature of the quantum dynamics. A proposal for the implementation of the model\nwith Rydberg array is put forward, which does not require local addressing of\nindividual sites.\n"}
+{"abstract": "  The aim of this work is to create a long (410 years) series of average annual\ntotal sunspot areas AR - physically-based index of sunspot activity. We used\ntelescopic observations of the AR index in 1832-1868 and 1875-2020, as well as\nthe relationship between AR and long series of sunspot indices SN (ISN version\n2.0) and sunspot groups GN (Svalgaard and Schatten (2016) GSN version). The\nRoyal Greenwich observatory series after 1976 is extended by the Kislovodsk\nMountain Astronomical Station data. When reconstructing AR from SN, it is taken\ninto account that the function AR = f (SN) has a nonlinear systematic character\nand uncertainty associated with the heterogeneity of these indices. Therefore,\nin addition to modeling the most probable AR values, predictive limits of\nreconstruction uncertainty are determined. In the interval 1610-1699 the\nreconstruction we carried out on the basis of the GN series using the\npreviously proposed decomposition in pseudo-phase space method (DPS). The\nresulting series NO21y is freely available online. We show that for this series\nthe empirical Gnevyshev-Ohl rule and Waldmeier effect are fulfilled. Wavelet\nanalysis reveals periodicities of 8.4-13.8 years for the main cycle (with a\nsharp decrease of the period before the global Maunder and Dalton minima) and a\ntwo-component Gleissberg cycle with typical periods of 50-60 years and 90-110\nyears.\n"}
+{"abstract": "  Off-lattice SIR models are constructed on continuum percolation clusters\ndefined on the Ginibre point process (GPP) and on the Poisson point process\n(PPP). The static percolation transitions in the continuum percolation models\nas well as the infection-spreading transitions in the SIR models, which are\nregarded as the dynamic percolation transitions, are enhanced in the GPP-based\nmodel compared with the PPP-based model. This enhancement is caused by\nhyperuniformity of the GPP. On the other hand, in the extinction phase of\ninfection on the phase diagram, a wide parameter region is determined in which\nformation of infection clusters is suppressed in the GPP-based model compared\nwith the PPP-based model. We think that the PPP approximates a disordered\nconfiguration of individuals and the GPP does a configuration of citizens\nadopting a strategy to keep social distancing in a city in order to avoid\ncontagion. The suppression of infection clusters observed in the GPP-based\nmodel will prove that such a strategy is effective when the infectivity is\nrelatively small.\n"}
+{"abstract": "  In this work we demonstrate how to compute the one- and two-body reduced\ndensity matrices within the spin-adapted full configuration interaction quantum\nMonte Carlo (FCIQMC) method, which is based on the graphical unitary group\napproach (GUGA). This allows us to use GUGA-FCIQMC as a spin-pure configuration\ninteraction (CI) eigensolver within the complete active space self-consistent\nfield (CASSCF) procedure, and hence to stochastically treat active spaces far\nlarger than conventional CI solvers whilst variationally relaxing orbitals for\nspecific spin-pure states. We apply the method to investigate the spin-ladder\nin iron-sulfur dimer and tetramer model systems. We demonstrate the importance\nof the orbital relaxation by comparing the Heisenberg model magnetic coupling\nparameters from the CASSCF procedure to those from a CI-only procedure based on\nrestricted open-shell Hartree-Fock orbitals. We show that orbital relaxation\ndifferentially stabilizes the lower spin states, thus enlarging the coupling\nparameters with respect to the values predicted by ignoring orbital relaxation\neffects. Moreover, we find that while CI eigenvalues are well fit by a simple\nbilinear Heisenberg Hamiltonian, the CASSCF eigenvalues exhibits deviations\nthat necessitate the inclusion of biquadratic terms in the model Hamiltonian.\n"}
+{"abstract": "  Neural volume rendering became increasingly popular recently due to its\nsuccess in synthesizing novel views of a scene from a sparse set of input\nimages. So far, the geometry learned by neural volume rendering techniques was\nmodeled using a generic density function. Furthermore, the geometry itself was\nextracted using an arbitrary level set of the density function leading to a\nnoisy, often low fidelity reconstruction. The goal of this paper is to improve\ngeometry representation and reconstruction in neural volume rendering. We\nachieve that by modeling the volume density as a function of the geometry. This\nis in contrast to previous work modeling the geometry as a function of the\nvolume density. In more detail, we define the volume density function as\nLaplace's cumulative distribution function (CDF) applied to a signed distance\nfunction (SDF) representation. This simple density representation has three\nbenefits: (i) it provides a useful inductive bias to the geometry learned in\nthe neural volume rendering process; (ii) it facilitates a bound on the opacity\napproximation error, leading to an accurate sampling of the viewing ray.\nAccurate sampling is important to provide a precise coupling of geometry and\nradiance; and (iii) it allows efficient unsupervised disentanglement of shape\nand appearance in volume rendering. Applying this new density representation to\nchallenging scene multiview datasets produced high quality geometry\nreconstructions, outperforming relevant baselines. Furthermore, switching shape\nand appearance between scenes is possible due to the disentanglement of the\ntwo.\n"}
+{"abstract": "  Recently, V. Alexandrov proposed an intriguing sufficient condition for\nrigidity, which we will call \"transverse rigidity\". We show that transverse\nrigidity is actually equivalent to the known sufficient condition for rigidity\ncalled \"prestress stability\". Indeed this leads to a novel interpretation of\nthe prestress condition.\n"}
+{"abstract": "  Climate change perceptions are fundamental for adaptation and environmental\npolicy support. Although Africa is one of the most vulnerable regions to\nclimate change, little research has focused on how climate change is perceived\nin the continent. Using random forest methodology, we analyse Afrobarometer\ndata (N = 45,732), joint with climatic data, to explore what shapes climate\nchange perceptions in Africa. We include 5 different dimensions of climate\nchange perceptions: awareness, belief in its human cause, risk perception, need\nto stop it and self-efficacy. Results indicate that perceived agriculture\nconditions are crucial for perceiving climate change. Country-level factors and\nlong-term changes in local weather conditions are among the most important\npredictors. Moreover, education level, access to information, poverty,\nauthoritarian values, and trust in institutions shape individual climate change\nperceptions. Demographic effects -- including religion -- seem negligible.\nThese findings suggest policymakers and environmental communicators how to\nframe climate change in Africa to raise awareness, gather public support and\ninduce adaptation.\n"}
+{"abstract": "  Kernel-based feature selection is an important tool in nonparametric\nstatistics. Despite many practical applications of kernel-based feature\nselection, there is little statistical theory available to support the method.\nA core challenge is the objective function of the optimization problems used to\ndefine kernel-based feature selection are nonconvex. The literature has only\nstudied the statistical properties of the \\emph{global optima}, which is a\nmismatch, given that the gradient-based algorithms available for nonconvex\noptimization are only able to guarantee convergence to local minima. Studying\nthe full landscape associated with kernel-based methods, we show that feature\nselection objectives using the Laplace kernel (and other $\\ell_1$ kernels) come\nwith statistical guarantees that other kernels, including the ubiquitous\nGaussian kernel (or other $\\ell_2$ kernels) do not possess. Based on a sharp\ncharacterization of the gradient of the objective function, we show that\n$\\ell_1$ kernels eliminate unfavorable stationary points that appear when using\nan $\\ell_2$ kernel. Armed with this insight, we establish statistical\nguarantees for $\\ell_1$ kernel-based feature selection which do not require\nreaching the global minima. In particular, we establish model-selection\nconsistency of $\\ell_1$-kernel-based feature selection in recovering main\neffects and hierarchical interactions in the nonparametric setting with $n \\sim\n\\log p$ samples.\n"}
+{"abstract": "  Let $G=(V(G),E(G))$ be a simple graph. A set $S \\subseteq V(G)$ is a strong\nedge geodetic set if there exists an assignment of exactly one shortest path\nbetween each pair of vertices from $S$, such that these shortest paths cover\nall the edges $E(G)$. The cardinality of a smallest strong edge geodetic set is\nthe strong edge geodetic number $\\text{sge}(G)$ of $G$. In this paper, the\nstrong edge geodetic problem is studied on the Cartesian product of two paths.\nThe exact value of the strong edge geodetic number is computed for $P_n \\,\n\\square \\, P_2$, $P_n \\, \\square \\, P_3$ and $P_n \\, \\square \\, P_4$. Some\ngeneral upper bounds for $\\text{sge}(P_n \\, \\square \\, P_m)$ are also proved.\n"}
+{"abstract": "  We consider a two-way half-duplex decode-and-forward (DF) relaying system\nwith multiple pairs of single-antenna users assisted by a cell-free (CF)\nmassive multiple-input multiple-output (mMIMO) architecture with\nmultiple-antenna access points (APs). Under the practical constraint of\nimperfect channel state information (CSI), we derive the achievable sum\nspectral efficiency (SE) for a finite number of APs with maximum ratio (MR)\nlinear processing for both reception and transmission in closed-form. Notably,\nthe proposed CF mMIMO relaying architecture, exploiting the spatial diversity,\nand providing better coverage, outperforms the conventional collocated mMIMO\ndeployment. Moreover, we shed light on the power-scaling laws maintaining a\nspecific SE as the number of APs grows. A thorough examination of the interplay\nbetween the transmit powers per pilot symbol and user/APs takes place, and\nuseful conclusions are extracted. Finally, differently to the common approach\nfor power control in CF mMIMO systems, we design a power allocation scheme\nmaximizing the sum SE.\n"}
+{"abstract": "  In this article, we have theoretically studied the time averaged adiabatic\npotential (TAAP) scheme for realizing different atom trapping geometries. It is\nshown that by varying time orbiting potential (TOP) fields and radio frequency\n(rf) fields parameters, controlled manipulation of trapping potentials, and\nconversion from one trapping geometry to another, is possible. The proposed\ntrapping geometries can be useful for studying various atom-optic phenomena\nsuch as Bose-Einstein condensation (BEC) in low dimensions, super-fluidity,\ntunnelling, atom interferometry, etc.\n"}
+{"abstract": "  We propose an iterative algorithm for low-rank matrix completion that can be\ninterpreted as an iteratively reweighted least squares (IRLS) algorithm, a\nsaddle-escaping smoothing Newton method or a variable metric proximal gradient\nmethod applied to a non-convex rank surrogate. It combines the favorable\ndata-efficiency of previous IRLS approaches with an improved scalability by\nseveral orders of magnitude. We establish the first local convergence guarantee\nfrom a minimal number of samples for that class of algorithms, showing that the\nmethod attains a local quadratic convergence rate. Furthermore, we show that\nthe linear systems to be solved are well-conditioned even for very\nill-conditioned ground truth matrices. We provide extensive experiments,\nindicating that unlike many state-of-the-art approaches, our method is able to\ncomplete very ill-conditioned matrices with a condition number of up to\n$10^{10}$ from few samples, while being competitive in its scalability.\n"}
+{"abstract": "  Simulating electron-ion dynamics using time-dependent density functional\ntheory within an Ehrenfest dynamics scheme can be done in two ways that are in\nprinciple exact and identical: propagating time-dependent electronic Kohn-Sham\nequations or propagating electronic coefficients on surfaces obtained from\nlinear-response. We show here that using an approximate functional leads to\nqualitatively different dynamics in the two approaches. We argue that the\nlatter is more accurate because the functionals are evaluated on domains close\nto the ground-state where current approximations perform better. We demonstrate\nthis on an exactly-solvable model of charge-transfer, and discuss implications\nfor time-resolved spectroscopy.\n"}
+{"abstract": "  We perform for the first time a 3D hydrodynamics simulation of the evolution\nof the last minutes pre-collapse of the oxygen shell of a fast-rotating massive\nstar. This star has an initial mass of 38 M$_\\odot$, a metallicity of\n$\\sim$1/50 Z$_\\odot$, an initial rotational velocity of 600 km s$^{-1}$, and\nexperiences chemically homogeneous evolution. It has a silicon- and oxygen-rich\n(Si/O) convective layer at (4.7-17)$\\times 10^{8}$ cm, where oxygen-shell\nburning takes place. The power spectrum analysis of the turbulent velocity\nindicates the dominance of the large-scale mode ($\\ell \\sim 3$), which has also\nbeen seen in non-rotating stars that have a wide Si/O layer. Spiral arm\nstructures of density and silicon-enriched material produced by oxygen-shell\nburning appear in the equatorial plane of the Si/O shell. Non-axisymmetric,\nlarge-scale ($m \\le 3$) modes are dominant in these structures. The spiral arm\nstructures have not been identified in previous non-rotating 3D pre-supernova\nmodels. Governed by such a convection pattern, the angle-averaged specific\nangular momentum becomes constant in the Si/O convective layer, which is not\nconsidered in spherically symmetrical stellar evolution models. Such spiral\narms and constant specific angular momentum might affect the ensuing explosion\nor implosion of the star.\n"}
+{"abstract": "  The pion and kaon properties in a nuclear medium at nonvanishing temperature\nas well as the QCD chiral condensate in the presence of a magnetic field for\nvarious baryon densities are studied in the Nambu-Jona-Lasinio (NJL) model with\nthe help of the proper-time regularization (PTR) scheme, simulating a QCD\nconfinement. The density dependent of the quark mass in symmetric nuclear\nmatter is obtained from the quark-meson coupling (QMC) model, which shares the\nsame covariant feature with the NJL model, at quark level. We then analyze the\nQCD chiral condensates, and dynamical masses for various baryon densities at\nfinite temperature and magnetic field as well as the pion and kaon masses, pion\nand kaon weak-decay constants, pion-and kaon-quark coupling constants, and wave\nfunction renormalization factors for various baryon densities at finite\ntemperature. We find that the QCD chiral condensates suppress with increasing\ntemperature and baryon density and enhance under the presence of a magnetic\nfield, which are consistent with other model predictions. Interestingly, the\nwave function renormalization factors for the pion and kaon increase with\nrespect to temperature and reduce as the baryon density increases are found.\n"}
+{"abstract": "  The recent measurement of the muon anomalous magnetic moment by the Fermilab\nE989 experiment, when combined with the previous result at BNL, has confirmed\nthe tension with the SM prediction at $4.2\\,\\sigma$ CL, strengthening the\nmotivation for new physics in the leptonic sector. Among the different particle\nphysics models that could account for such an excess, a gauged\n$U(1)_{L_\\mu-L_{\\tau}}$ stands out for its simplicity. In this article, we\nexplore how the combination of data from different future probes can help\nidentify the nature of the new physics behind the muon anomalous magnetic\nmoment. In particular, we contrast $U(1)_{L_\\mu-L_{\\tau}}$ with an effective\n$U(1)_{L_\\mu}$-type model. We first show that muon fixed target experiments\n(such as NA64$\\mu$) will be able to measure the coupling of the hidden photon\nto the muon sector in the region compatible with $(g-2)_\\mu$, and will have\nsome sensitivity to the hidden photon's mass. We then study how experiments\nlooking for coherent elastic neutrino-nucleus scattering (CE$\\nu$NS) at\nspallation sources will provide crucial additional information on the kinetic\nmixing of the hidden photon. When combined with NA64$\\mu$ results, the\nexclusion limits (or reconstructed regions) of future CE$\\nu$NS detectors will\nalso allow for a better measurement of the mediator mass. Finally, the\nobservation of nuclear recoils from solar neutrinos in dark matter direct\ndetection experiments will provide unique information about the coupling of the\nhidden photon to the tau sector. The signal expected for\n$U(1)_{L_\\mu-L_{\\tau}}$ is larger than for $U(1)_{L_\\mu}$ with the same kinetic\nmixing, and future multi-ton liquid xenon proposals (such as DARWIN) have the\npotential to confirm the former over the latter.\n"}
+{"abstract": "  We model the distribution of the observed profiles of 21 cm line emission\nfrom neutral hydrogen (HI) in central galaxies selected from a statistically\nrepresentative mock catalog of the local Universe in the Lambda-cold dark\nmatter framework. The distribution of these HI velocity profiles (specifically,\ntheir widths $W_{50}$) has been observationally constrained, but has not been\nsystematically studied theoretically. Our model profiles derive from rotation\ncurves of realistically baryonified haloes in an N-body simulation, including\nthe quasi-adiabatic relaxation of the dark matter profile of each halo in\nresponse to its baryons. We study the predicted $W_{50}$ distribution using a\nrealistic pipeline applied to noisy profiles extracted from our\nluminosity-complete mock catalog with an ALFALFA-like survey geometry and\nredshift selection. Our default mock is in good agreement with observed ALFALFA\nresults for $W_{50}\\gtrsim700$ km/s, being incomplete at lower widths due to\nthe intrinsic threshold of $M_r\\leq-19$. Variations around the default model\nshow that the velocity width function at $W_{50}\\gtrsim300$ km/s is most\nsensitive to a possible correlation between galaxy inclination and host\nconcentration, followed by the physics of quasi-adiabatic relaxation. We also\nstudy the excess kurtosis of noiseless velocity profiles, obtaining a\ndistribution which tightly correlates with $W_{50}$, with a shape and scatter\nthat depend on the properties of the turbulent HI disk. Our results open the\ndoor towards using the shapes of HI velocity profiles as a novel statistical\nprobe of the baryon-dark matter connection.\n"}
+{"abstract": "  The autonomic nervous system (ANS) regulatory capacity begins before birth as\nthe sympathetic and parasympathetic activity contributes significantly to the\nfetus' development. Several studies have shown how vagus nerve is involved in\nmany vital processes during fetal, perinatal and postnatal life: from the\nregulation of inflammation through the anti-inflammatory cholinergic pathway,\nwhich may affect the functioning of each organ, to the production of hormones\ninvolved in bioenergetic metabolism. In addition, the vagus nerve has been\nrecognized as the primary afferent pathway capable of transmitting information\nto the brain from every organ of the body. Therefore, this hypothesis paper\naims to review the development of ANS during fetal and perinatal life, focusing\nparticularly on the vagus nerve, to identify possible \"critical windows\" that\ncould impact its maturation. These \"critical windows\" could help clinicians\nknow when to monitor fetuses to effectively assess the developmental status of\nboth ANS and specifically the vagus nerve. In addition, this paper will focus\non which factors (i.e. fetal characteristics and behaviors, maternal lifestyle\nand pathologies, placental health and dysfunction, labor, incubator conditions,\nand drug exposure) may have an impact on the development of the vagus during\nthe above-mentioned \"critical window\" and how. This analysis could help\nclinicians and stakeholders define precise guidelines for improving the\nmanagement of fetuses and newborns, particularly to reduce the potential\nadverse environmental impacts on ANS development that may lead to persistent\nlong-term consequences. Since the development of ANS and the vagus influence\nhave been shown to be reflected in cardiac variability, this paper will rely in\nparticular on studies using fetal heart rate variability (fHRV) to monitor the\ncontinued growth and health of both animal and human fetuses.\n"}
+{"abstract": "  Disfluency detection models now approach high accuracy on English text.\nHowever, little exploration has been done in improving the size and inference\ntime of the model. At the same time, automatic speech recognition (ASR) models\nare moving from server-side inference to local, on-device inference. Supporting\nmodels in the transcription pipeline (like disfluency detection) must follow\nsuit. In this work we concentrate on the disfluency detection task, focusing on\nsmall, fast, on-device models based on the BERT architecture. We demonstrate it\nis possible to train disfluency detection models as small as 1.3 MiB, while\nretaining high performance. We build on previous work that showed the benefit\nof data augmentation approaches such as self-training. Then, we evaluate the\neffect of domain mismatch between conversational and written text on model\nperformance. We find that domain adaptation and data augmentation strategies\nhave a more pronounced effect on these smaller models, as compared to\nconventional BERT models.\n"}
+{"abstract": "  Understanding and modeling the dynamics of human gaze behavior in 360$^\\circ$\nenvironments is a key challenge in computer vision and virtual reality.\nGenerative adversarial approaches could alleviate this challenge by generating\na large number of possible scanpaths for unseen images. Existing methods for\nscanpath generation, however, do not adequately predict realistic scanpaths for\n360$^\\circ$ images. We present ScanGAN360, a new generative adversarial\napproach to address this challenging problem. Our network generator is tailored\nto the specifics of 360$^\\circ$ images representing immersive environments.\nSpecifically, we accomplish this by leveraging the use of a spherical\nadaptation of dynamic-time warping as a loss function and proposing a novel\nparameterization of 360$^\\circ$ scanpaths. The quality of our scanpaths\noutperforms competing approaches by a large margin and is almost on par with\nthe human baseline. ScanGAN360 thus allows fast simulation of large numbers of\nvirtual observers, whose behavior mimics real users, enabling a better\nunderstanding of gaze behavior and novel applications in virtual scene design.\n"}
+{"abstract": "  We study the problem of maximizing the geometric mean of $d$ low-degree\nnon-negative forms on the real or complex sphere in $n$ variables. We show that\nthis highly non-convex problem is NP-hard even when the forms are quadratic and\nis equivalent to optimizing a homogeneous polynomial of degree $O(d)$ on the\nsphere. The standard Sum-of-Squares based convex relaxation for this polynomial\noptimization problem requires solving a semidefinite program (SDP) of size\n$n^{O(d)}$, with multiplicative approximation guarantees of\n$\\Omega(\\frac{1}{n})$. We exploit the compact representation of this polynomial\nto introduce a SDP relaxation of size polynomial in $n$ and $d$, and prove that\nit achieves a constant factor multiplicative approximation when maximizing the\ngeometric mean of non-negative quadratic forms. We also show that this analysis\nis asymptotically tight, with a sequence of instances where the gap between the\nrelaxation and true optimum approaches this constant factor as $d \\rightarrow\n\\infty$. Next we propose a series of intermediate relaxations of increasing\ncomplexity that interpolate to the full Sum-of-Squares relaxation, as well as a\nrounding algorithm that finds an approximate solution from the solution of any\nintermediate relaxation. Finally we show that this approach can be generalized\nfor relaxations of products of non-negative forms of any degree.\n"}
+{"abstract": "  In 1975, Don Zagier obtained a new version of the Kronecker limit formula for\na real quadratic field which involved an interesting function $F(x)$ which is\nnow known as the \\emph{Herglotz function}. As demonstrated by Zagier, and very\nrecently by Radchenko and Zagier, $F(x)$ satisfies beautiful properties which\nare of interest in both algebraic number theory as well as in analytic number\ntheory. In this paper, we study $\\mathscr{F}_{k,N}(x)$, an extension of the\nHerglotz function which also subsumes \\emph{higher Herglotz function} of\nVlasenko and Zagier. We call it the \\emph{extended higher Herglotz function}.\nIt is intimately connected with a certain generalized Lambert series. We derive\ntwo different kinds of functional equations satisfied by\n$\\mathscr{F}_{k,N}(x)$. Radchenko and Zagier gave a beautiful relation between\nthe integral $\\displaystyle\\int_{0}^{1}\\frac{\\log(1+t^x)}{1+t}\\, dt$ and $F(x)$\nand used it to evaluate this integral at various rational as well as irrational\narguments. We obtain a relation between $\\mathscr{F}_{k,N}(x)$ and a\ngeneralization of the above integral involving polylogarithm. The asymptotic\nexpansions of $\\mathscr{F}_{k, N}(x)$ and some generalized Lambert series are\nalso obtained along with other supplementary results.\n"}
+{"abstract": "  State of the art radial velocity (RV) exoplanet searches are limited by the\neffects of stellar magnetic activity. Magnetically active spots, plage, and\nnetwork regions each have different impacts on the observed spectral lines, and\ntherefore on the apparent stellar RV. Differentiating the relative coverage, or\nfilling factors, of these active regions is thus necessary to differentiate\nbetween activity-driven RV signatures and Doppler shifts due to planetary\norbits. In this work, we develop a technique to estimate feature-specific\nmagnetic filling factors on stellar targets using only spectroscopic and\nphotometric observations. We demonstrate linear and neural network\nimplementations of our technique using observations from the solar telescope at\nHARPS-N, the HK Project at the Mt. Wilson Observatory, and the Total Irradiance\nMonitor onboard SORCE. We then compare the results of each technique to direct\nobservations by the Solar Dynamics Observatory (SDO). Both implementations\nyield filling factor estimates that are highly correlated with the observed\nvalues. Modeling the solar RVs using these filling factors reproduces the\nexpected contributions of the suppression of convective blueshift and\nrotational imbalance due to brightness inhomogeneities. Both implementations of\nthis technique reduce the overall activity-driven RMS RVs from 1.64 m/s to 1.02\nm/s, corresponding to a 1.28 m/s reduction in the RMS variation. The technique\nprovides an additional 0.41 m/s reduction in the RMS variation compared to\ntraditional activity indicators.\n"}
+{"abstract": "  In meta-analysis of diagnostic test accuracy, summary receiver operating\ncharacteristic (SROC) is a recommended method to summarize the discriminant\ncapacity of a diagnostic test in the presence of study-specific cutoff values\nand the area under the SROC (SAUC) gives the aggregate measure of test\naccuracy. SROC or SAUC can be estimated by bivariate modelling of pairs of\nsensitivity and specificity over the primary diagnostic studies. However,\npublication bias is a major threat to the validity of estimates in\nmeta-analysis. To address this issue, we propose to adopt sensitivity analysis\nto make an objective inference for the impact of publication bias on SROC or\nSAUC. We extend Copas likelihood based sensitivity analysis to the bivariate\nnormal model used for meta-analysis of diagnostic test accuracy to evaluate how\nmuch SROC or SAUC would change with different selection probabilities under\nseveral selective publication mechanisms dependent on sensitivity and/or\nspecificity. The selection probability is modelled by a selection function on\n$t$-type statistic for the linear combination of logit-transformed sensitivity\nand specificity, allowing the selective publication of each study to be\ninfluenced by the cutoff-dependent $p$-value for sensitivity, specificity, or\ndiagnostic odds ratio. By embedding the selection function into the bivariate\nnormal model, the conditional likelihood is proposed and the bias-corrected\nSROC or SAUC can be estimated by maximizing the likelihood. We illustrate the\nproposed sensitivity analysis by reanalyzing a meta-analysis of test accuracy\nfor intravascular device related infection. Simulation studies are conducted to\ninvestigate the performance of proposed methods.\n"}
+{"abstract": "  Video compression is a critical component of Internet video delivery. Recent\nwork has shown that deep learning techniques can rival or outperform\nhuman-designed algorithms, but these methods are significantly less compute and\npower-efficient than existing codecs. This paper presents a new approach that\naugments existing codecs with a small, content-adaptive super-resolution model\nthat significantly boosts video quality. Our method, SRVC, encodes video into\ntwo bitstreams: (i) a content stream, produced by compressing downsampled\nlow-resolution video with the existing codec, (ii) a model stream, which\nencodes periodic updates to a lightweight super-resolution neural network\ncustomized for short segments of the video. SRVC decodes the video by passing\nthe decompressed low-resolution video frames through the (time-varying)\nsuper-resolution model to reconstruct high-resolution video frames. Our results\nshow that to achieve the same PSNR, SRVC requires 16% of the bits-per-pixel of\nH.265 in slow mode, and 2% of the bits-per-pixel of DVC, a recent deep\nlearning-based video compression scheme. SRVC runs at 90 frames per second on a\nNVIDIA V100 GPU.\n"}
+{"abstract": "  The success and superior performance of deep networks is spreading their\npopularity and use to an increasing number of applications. Very recent works,\nhowever, demonstrate that modern day deep networks suffer from\nirreproducibility (also referred to as nondeterminism or underspecification).\nTwo or more models that are identical in architecture, structure, training\nhyper-parameters, and parameters, and that are trained on exactly the same\ntraining data, yield different predictions on individual previously unseen\nexamples. Thus, a model that performs well on controlled test data, may perform\nin unexpected ways when deployed in the real world, whose data is expected to\nbe similar to the test data. We study simple synthetic models and data to\nunderstand the origins of these problems. We show that even with a single\nnonlinearity and for very simple data and models, irreproducibility occurs. Our\nstudy demonstrates the effects of randomness in initialization, training data\nshuffling window size, and activation functions on prediction\nirreproducibility, even under very controlled synthetic data. While, as one\nwould expect, randomness in initialization and in shuffling the training\nexamples exacerbates the phenomenon, we show that model complexity and the\nchoice of nonlinearity also play significant roles in making deep models\nirreproducible.\n"}
+{"abstract": "  We present our analysis of a significant data artifact in the official\n2019/2021 ASVspoof Challenge Dataset. We identify an uneven distribution of\nsilence duration in the training and test splits, which tends to correlate with\nthe target prediction label. Bonafide instances tend to have significantly\nlonger leading and trailing silences than spoofed instances. In this paper, we\nexplore this phenomenon and its impact in depth. We compare several types of\nmodels trained on a) only the duration of the leading silence and b) only on\nthe duration of leading and trailing silence. Results show that models trained\non only the duration of the leading silence perform particularly well, and\nachieve up to 85% percent accuracy and an equal error rate (EER) of 15.1%. At\nthe same time, we observe that trimming silence during pre-processing and then\ntraining established antispoofing models using signal-based features leads to\ncomparatively worse performance. In that case, EER increases from 3.6% (with\nsilence) to 15.5% (trimmed silence). Our findings suggest that previous work\nmay, in part, have inadvertently learned thespoof/bonafide distinction by\nrelying on the duration of silence as it appears in the official challenge\ndataset. We discuss the potential consequences that this has for interpreting\nsystem scores in the challenge and discuss how the ASV community may further\nconsider this issue.\n"}
+{"abstract": "  We present initial limit Datalog, a new extensible class of constrained Horn\nclauses for which the satisfiability problem is decidable. The class may be\nviewed as a generalisation to higher-order logic (with a simple restriction on\ntypes) of the first-order language limit Datalog$_Z$ (a fragment of Datalog\nmodulo linear integer arithmetic), but can be instantiated with any suitable\nbackground theory. For example, the fragment is decidable over any countable\nwell-quasi-order with a decidable first-order theory, such as natural number\nvectors under componentwise linear arithmetic, and words of a bounded,\ncontext-free language ordered by the subword relation. Formulas of initial\nlimit Datalog have the property that, under some assumptions on the background\ntheory, their satisfiability can be witnessed by a new kind of term model which\nwe call entwined structures. Whilst the set of all models is typically\nuncountable, the set of all entwined structures is recursively enumerable, and\nmodel checking is decidable.\n"}
+{"abstract": "  Accurate estimation of anthropometric body measurements from RGB images has\nmany potential applications in industrial design, online clothing, medical\ndiagnosis and ergonomics. Research on this topic is limited by the fact that\nthere exist only generated datasets which are based on fitting a 3D body mesh\nto 3D body scans in the commercial CAESAR dataset. For 2D only silhouettes are\ngenerated. To circumvent the data bottleneck, we introduce a new 3D scan\ndataset of 2,675 female and 1,474 male scans. We also introduce a small dataset\nof 200 RGB images and tape measured ground truth. With the help of the two new\ndatasets we propose a part-based shape model and a deep neural network for\nestimating anthropometric measurements from 2D images. All data will be made\npublicly available.\n"}
+{"abstract": "  In this letter, we present an update of a method for analysing arrival\ndirections of ultra-high-energy cosmic rays (UHECRs) above the\nGreisen--Zatsepin--Kuz'min cut-off with a deep convolutional neural network\ndeveloped originally in Kalashev, Pshirkov, Zotov (2020). Namely, we introduce\nenergy as another variable employed in the analysis. This allows us to take\ninto account the intrinsic uncertainties in energy of primary cosmic rays\npresent in any experiment, which were not taken into account in the previous\nstudy, without any loss of quality of the classifier. We present the\narchitecture of the new neural network, results of its application to mock maps\nof UHECR arrival directions and outline possible directions of a further\nimprovement of the method.\n"}
+{"abstract": "  An electron-positron pair ($e^- e^+$) is created in vacuum above a critical\nelectric field strength $E_{crt}$ which is quite large in the laboratory scale.\nThe photon, thus the field, annihilates in the pair creation process. Here, we\nquestion if the pair creation (at $E=E_{crt}$) introduces a boundary condition\nin the electromagnetic state, e.g., similar to the one in angular or linear\nmomentum. We show that introduction of such a reasonable condition yields\napproximately the well-known magnetic flux quanta which normally one obtains\nusing different arguments.\n"}
+{"abstract": "  Despite the immense popularity of the Automated Program Repair (APR) field,\nthe question of patch validation is still open. Most of the present-day\napproaches follow the so-called Generate-and-Validate approach, where first a\ncandidate solution is being generated and after validated against an oracle.\nThe latter, however, might not give a reliable result, because of the\nimperfections in such oracles; one of which is usually the test suite. Although\n(re-) running the test suite is right under one's nose, in real life\napplications the problem of over- and underfitting often occurs, resulting in\ninadequate patches. Efforts that have been made to tackle with this problem\ninclude patch filtering, test suite expansion, careful patch producing and many\nmore. Most approaches to date use post-filtering relying either on test\nexecution traces or make use of some similarity concept measured on the\ngenerated patches. Our goal is to investigate the nature of these\nsimilarity-based approaches. To do so, we trained a Doc2Vec model on an\nopen-source JavaScript project and generated 465 patches for 10 bugs in it.\nThese plausible patches alongside with the developer fix are then ranked based\non their similarity to the original program. We analyzed these similarity lists\nand found that plain document embeddings may lead to misclassification - it\nfails to capture nuanced code semantics. Nevertheless, in some cases it also\nprovided useful information, thus helping to better understand the area of\nAutomated Program Repair.\n"}
+{"abstract": "  In this work, we study the problem of clustering survival data $-$ a\nchallenging and so far under-explored task. We introduce a novel\nsemi-supervised probabilistic approach to cluster survival data by leveraging\nrecent advances in stochastic gradient variational inference. In contrast to\nprevious work, our proposed method employs a deep generative model to uncover\nthe underlying distribution of both the explanatory variables and censored\nsurvival times. We compare our model to the related work on clustering and\nmixture models for survival data in comprehensive experiments on a wide range\nof synthetic, semi-synthetic, and real-world datasets, including medical\nimaging data. Our method performs better at identifying clusters and is\ncompetitive at predicting survival times. Relying on novel generative\nassumptions, the proposed model offers a holistic perspective on clustering\nsurvival data and holds a promise of discovering subpopulations whose survival\nis regulated by different generative mechanisms.\n"}
+{"abstract": "  It has long been known that the coarse-grained approximation to the black\nhole density of states can be computed using classical Euclidean gravity. In\nthis work we argue for another entry in the dictionary between Euclidean\ngravity and black hole physics, namely that Euclidean wormholes describe a\ncoarse-grained approximation to the energy level statistics of black hole\nmicrostates. To do so we use the method of constrained instantons to obtain an\nintegral representation of wormhole amplitudes in Einstein gravity and in\nfull-fledged AdS/CFT. These amplitudes are non-perturbative corrections to the\ntwo-boundary problem in AdS quantum gravity. The full amplitude is likely UV\nsensitive, dominated by small wormholes, but we show it admits an integral\ntransformation with a macroscopic, weakly curved saddle-point approximation.\nThe saddle is the \"double cone\" geometry of Saad, Shenker, and Stanford, with\nfixed moduli. In the boundary description this saddle appears to dominate a\nsmeared version of the connected two-point function of the black hole density\nof states, and suggests level repulsion in the microstate spectrum. Using these\nmethods we further study Euclidean wormholes in pure Einstein gravity and in\nIIB supergravity on Euclidean AdS$_5\\times\\mathbb{S}^5$. We address the\nperturbative stability of these backgrounds and study brane nucleation\ninstabilities in 10d supergravity. In particular, brane nucleation\ninstabilities of the Euclidean wormholes are lifted by the analytic\ncontinuation required to obtain the Lorentzian spectral form factor from\ngravity. Our results indicate a factorization paradox in AdS/CFT.\n"}
+{"abstract": "  The availability of accurate computational tools for modeling and simulation\nis vital to accelerate the discovery of materials capable of storing hydrogen\n(H2) under given parameters of pressure swing and temperature. Previously, we\ncompiled the H2Bind275 dataset consisting of equilibrium geometries and\nassessed the performance of 55 density functionals over this dataset (Veccham,\nS. P.; Head-Gordon, M. J. Chem. Theory Comput., 2020, 16, 4963--4982). As it is\ncrucial for computational tools to accurately model the entire potential energy\ncurve (PEC), in addition to the equilibrium geometry, we have extended this\ndataset with 389 new data points to include two compressed and three elongated\ngeometries along 78 PECs for H2 binding, forming the H2Bind78x7 dataset.\nAssessing the performance of 55 density functionals on this significantly\nlarger and more comprehensive H2Bind78x7 dataset, we have identified the best\nperforming density functionals for H2 binding applications: PBE0-DH,\n$\\omega$B97X-V, $\\omega$B97M-V, and DSD-PBEPBE-D3(BJ). Addition of Hartree Fock\nexchange improves the performance of density functionals, albeit not uniformly\nthroughout the PEC. We recommend the usage of wB97X-V and wB97M-V density\nfunctionals as they give good performance for both geometries and energies. In\naddition, we have also identified B97M-V and B97M-rV as the best semi-local\ndensity functionals for predicting H2 binding energy at its equilibrium\ngeometry.\n"}
+{"abstract": "  We present an exact analytical solution to the problem of shear dispersion\ngiven a general initial condition. The solution is expressed as an infinite\nseries expansion involving Mathieu functions and their eigenvalues. The\neigenvalue system depends on the imaginary parameter $q=2ik$Pe, with $k$ the\nwavenumber that determines the tracer scale in the initial condition and Pe the\nP\\'{e}clet number. Solutions are valid for all Pe, $t>0$, and $k>0$ except at\nspecific values of $q=q_{\\ell}^{EP}$ called Exceptional Points (EPs), the first\noccurring at $q_{0}^{EP}\\approx1.468i$. For values of $q \\lessapprox 1.468i$,\nall the eigenvalues are real, different and eigenfunctions decay with time,\nthus shear dispersion can be represented as a diffusive process. For values of\n$q \\gtrapprox 1.468i$, pairs of eigenvalues coalesce at EPs becoming complex\nconjugates, the eigenfunctions propagate and decay with time, and so shear\ndispersion is no longer a purely diffusive process.\n  The limit $q\\rightarrow0$ is approached by the small P\\'{e}clet number limit\nfor all finite $k>0$, or equally by the large P\\'{e}clet number limit as long\nas $2k \\ll 1/$Pe. The latter implies $k\\rightarrow0$, strong separation of\nscales between the tracer and flow. The limit $q\\rightarrow\\infty$ results from\nlarge P\\'{e}clet number for any $k>0$, or from large $k$ and non-vanishing Pe.\n  We derive an exact closure that is continuous in wavenumber space. At small\n$q$, the closure approaches a diffusion operator with an effective diffusivity\nproportional to $U_0^2/\\kappa$, for flow speed $U_0$ and diffusivity $\\kappa$.\nAt large $q$, the closure approaches the sum of an advection operator plus a\nhalf-derivative operator (differential operator of fractional order), the\nlatter with coefficient proportional to $\\sqrt{\\kappa U_0}$.\n"}
+{"abstract": "  We give quantitative estimates of the Bergman distance through positivity of\ncapacity densities.\n"}
+{"abstract": "  Here we present the derivation, description and results of a Monte\nCarlo-based algorithm for simulating inelastic scattering of photo-electrons\nwhen passing through some scattering medium, such as a gas atmosphere or a\nsolid material. The code used to run these simulations was written in python\nand is freely available online\n(https://github.com/surfaceanalytics/montecarlo_electron_scattering).\n"}
+{"abstract": "  In this paper we present a benchmark solution with higher number of\ncontinuous and discrete states and control levers using validated powertrain\ncomponent models, where DP fails due to exponential rise in the computation\ntime. The problem involves 13 states and 4 control levers, with complex\ninteractions between multiple subsystems. Some of these variables are discrete\nwhile some are continuous. Some have slow dynamics while some have fast\ndynamics. A novel three step PS3 algorithm [1] which is presented in our\nprequel paper is used to obtain a near-optimal solution. PS3 algorithm makes\nuse of pseudo spectral method for accurate state estimations. We present three\nscenarios where only fuel is minimized, only emissions are minimized and,\nlastly a combination of both fuel and emissions are minimized. All three cases\nare analyzed for their performance and computation time. The optimal compromise\nbetween fuel consumption and emissions are analyzed using a Pareto-front study.\nThis large-scale powertrain optimization problem is solved for a P2 parallel\nhybrid architecture on a class 6 pick-up & delivery truck.\n"}
+{"abstract": "  We study arithmetic of the algebraic varieties defined over number fields by\napplying Lagrange interpolation to fibrations.\n  Assuming the finiteness of the Tate-Shafarevich group of a certain elliptic\ncurve, we show, for Ch\\^atelet surface bundles over curves, that the violation\nof Hasse principle being accounted for by the Brauer-Manin obstruction is not\ninvariant under an arbitrary finite extension of the ground field.\n"}
+{"abstract": "  Hand modeling is critical for immersive VR/AR, action understanding, or human\nhealthcare. Existing parametric models account only for hand shape, pose, or\ntexture, without modeling the anatomical attributes like bone, which is\nessential for realistic hand biomechanics analysis. In this paper, we present\nPIANO, the first parametric bone model of human hands from MRI data. Our PIANO\nmodel is biologically correct, simple to animate, and differentiable, achieving\nmore anatomically precise modeling of the inner hand kinematic structure in a\ndata-driven manner than the traditional hand models based on the outer surface\nonly. Furthermore, our PIANO model can be applied in neural network layers to\nenable training with a fine-grained semantic loss, which opens up the new task\nof data-driven fine-grained hand bone anatomic and semantic understanding from\nMRI or even RGB images. We make our model publicly available.\n"}
+{"abstract": "  We discuss new ideas for consideration of loop diagrams and angular integrals\nin $D$-dimensions in QCD. In case of loop diagrams, we propose the covariant\nformalism of expansion of tensorial loop integrals into the orthogonal basis of\nlinear combinations of external momenta. It gives a very simple presentation\nfor the final results and is more convenient for calculations on computer\nalgebra systems. In case of angular integrals we demonstrate how to simplify\nthe integration of differential cross sections over polar angles. Also we\nderive the recursion relations, which allow to reduce all occurring angular\nintegrals to a short set of basic scalar integrals. All order\n$\\epsilon$-expansion is given for all angular integrals with up to two\ndenominators based on the expansion of the basic integrals and using recursion\nrelations. A geometric picture for partial fractioning is developed which\nprovides a new rotational invariant algorithm to reduce the number of\ndenominators.\n"}
+{"abstract": "  Diverse promising datasets have been designed to hold back the development of\nfake audio detection, such as ASVspoof databases. However, previous datasets\nignore an attacking situation, in which the hacker hides some small fake clips\nin real speech audio. This poses a serious threat since that it is difficult to\ndistinguish the small fake clip from the whole speech utterance. Therefore,\nthis paper develops such a dataset for half-truth audio detection (HAD).\nPartially fake audio in the HAD dataset involves only changing a few words in\nan utterance.The audio of the words is generated with the very latest\nstate-of-the-art speech synthesis technology. We can not only detect fake\nuttrances but also localize manipulated regions in a speech using this dataset.\nSome benchmark results are presented on this dataset. The results show that\npartially fake audio presents much more challenging than fully fake audio for\nfake audio detection.\n"}
+{"abstract": "  Citizens' assemblies need to represent subpopulations according to their\nproportions in the general population. These large committees are often\nconstructed in an online fashion by contacting people, asking for the\ndemographic features of the volunteers, and deciding to include them or not.\nThis raises a trade-off between the number of people contacted (and the\nincurring cost) and the representativeness of the committee. We study three\nmethods, theoretically and experimentally: a greedy algorithm that includes\nvolunteers as long as proportionality is not violated; a non-adaptive method\nthat includes a volunteer with a probability depending only on their features,\nassuming that the joint feature distribution in the volunteer pool is known;\nand a reinforcement learning based approach when this distribution is not known\na priori but learnt online.\n"}
+{"abstract": "  We report our analyses of the multi-epoch (2015-2017) ALMA archival data of\nthe Class II binary system XZ Tau at Bands 3, 4 and 6. The millimeter dust\ncontinuum images show compact, unresolved (r <~ 15 au) circumstellar disks\n(CSDs) around the individual binary stars; XZ Tau A and B, with a projected\nseparation of ~ 39 au. The 12CO (2-1) emission associated with those CSDs\ntraces the Keplerian rotations, whose rotational axes are misaligned with each\nother (P.A. ~ -5 deg for XZ Tau A and ~ 130 deg for XZ Tau B). The similar\nsystemic velocities of the two CSDs (VLSR ~ 6.0 km s-1) suggest that the\norbital plane of the binary stars is close to the plane of the sky. From the\nmulti-epoch ALMA data, we have also identified the relative orbital motion of\nthe binary. Along with the previous NIR data, we found that the elliptical\norbit (e = 0.742+0.025-0.034, a = 0''.172+0''.002-0''.003, and {\\omega} =\n-54.2+2.0-4.7 deg) is preferable to the circular orbit. Our results suggest\nthat the two CSDs and the orbital plane of the XZ Tau system are all misaligned\nwith each other, and possible mechanisms to produce such a configuration are\ndiscussed. Our analyses of the multi-epoch ALMA archival data demonstrate the\nfeasibility of time-domain science with ALMA.\n"}
+{"abstract": "  Results for high multiplicity pp and p-Pb collisions at the LHC have revealed\nthat these small collision systems exhibit features of collectivity. To\nunderstand the origin of these unexpected phenomena, the relative transverse\nactivity classifier ($R_{\\rm{T}}$) can be exploited as a tool to disentangle\nsoft and hard particle production, by studying the yield of charged particles\nin different topological regions associated with transverse momentum trigger\nparticles. This allows to study system size dependence of charged particle\nproduction of different origins and in particular search for jet-quenching\neffects. Here, results on the system size and $R_{\\rm{T}}$ dependence of\ncharged particle production in pp, p-Pb and Pb-Pb collisions at $\\sqrt{s_{\\rm\nNN}}$ = 5.02 TeV are presented.\n"}
+{"abstract": "  This paper addresses unsupervised person re-identification (Re-ID) using\nmulti-label prediction and classification based on graph-structural insight.\nOur method extracts features from person images and produces a graph that\nconsists of the features and a pairwise similarity of them as nodes and edges,\nrespectively. Based on the graph, the proposed graph structure based\nmulti-label prediction (GSMLP) method predicts multi-labels by considering the\npairwise similarity and the adjacency node distribution of each node. The\nmulti-labels created by GSMLP are applied to the proposed selective multi-label\nclassification (SMLC) loss. SMLC integrates a hard-sample mining scheme and a\nmulti-label classification. The proposed GSMLP and SMLC boost the performance\nof unsupervised person Re-ID without any pre-labelled dataset. Experimental\nresults justify the superiority of the proposed method in unsupervised person\nRe-ID by producing state-of-the-art performance. The source code for this paper\nis publicly available on 'https://github.com/uknownpioneer/GSMLP-SMLC.git'.\n"}
+{"abstract": "  In this work, we present near-field image transmission and error vector\nmagnitude measurement in a rich scattering environment in a metal enclosure. We\ncheck the effect of loading metal enclosure on the performance of SDR based\nnear-field communication link. We focus on the key communication receiver\nparameters to observe the effect of near-field link in presence of\nrich-scattering and in presence of loading with RF absorber cones. The\nnear-field performance is measured by transmitting wideband OFDM-modulated\npackets containing image information. Our finding suggests that the performance\nof OFDM based wideband near-field communication improves when the metal\nenclosure is loaded with RF absorbers. Near-field EVM improves when the\nenclosure is loaded with RF absorber cones. Loading of the metal enclosure has\nthe effect of increased coherence bandwidth. Frequency selectivity was observed\nin an empty enclosure which suggests coherence bandwidth less than the signal\nbandwidth.\n"}
+{"abstract": "  Learning sensorimotor control policies from high-dimensional images crucially\nrelies on the quality of the underlying visual representations. Prior works\nshow that structured latent space such as visual keypoints often outperforms\nunstructured representations for robotic control. However, most of these\nrepresentations, whether structured or unstructured are learned in a 2D space\neven though the control tasks are usually performed in a 3D environment. In\nthis work, we propose a framework to learn such a 3D geometric structure\ndirectly from images in an end-to-end unsupervised manner. The input images are\nembedded into latent 3D keypoints via a differentiable encoder which is trained\nto optimize both a multi-view consistency loss and downstream task objective.\nThese discovered 3D keypoints tend to meaningfully capture robot joints as well\nas object movements in a consistent manner across both time and 3D space. The\nproposed approach outperforms prior state-of-art methods across a variety of\nreinforcement learning benchmarks. Code and videos at\nhttps://buoyancy99.github.io/unsup-3d-keypoints/\n"}
+{"abstract": "  Transistors operating at high frequencies are the basic building blocks of\nmillimeter-wave communication and sensor systems. The high velocity and\nmobility of carriers in graphene can open way for ultra-fast group IV\ntransistors with similar or even better performance than can be achieved with\nIII-V based semiconductors. However, the progress of high-speed graphene\ntransistors has been hampered due to fabrication issues, influence of adjacent\nmaterials, and self-heating effects. Here, we report a graphene field-effect\ntransistor (FET) on a diamond substrate, with a $f_{max}$ up to 54 GHz for a\ngate length of 500 nm. The high thermal conductivity of diamond provides an\nefficient heat-sink, and its relatively high optical-phonon energy improves\nsaturation velocity of carriers in the graphene channel. Moreover, we show that\ngraphene FETs on diamond, with different gate lengths, exhibit excellent\nscaling behavior. These results indicate that graphene FETs on diamond\ntechnology can reach sub-terahertz frequency performance.\n"}
+{"abstract": "  We introduce in this work an efficient numerical method for the simulation of\nthe quantum Liouville-BGK equation, which models the diffusive transport of\nquantum particles. The corner stone to the model is the BGK collision operator,\nobtained by minimizing the quantum free energy under the constraint that the\nlocal density of particles is conserved during collisions. This leads to a\nlarge system of coupled nonlinear nonlocal PDEs whose resolution is\nchallenging. We then define a splitting scheme that separates the transport and\nthe collision parts, which, exploiting the local conservation of particles,\nleads to a fully linear collision step. The latter involves the resolution of a\nconstrained optimization problem that is is handled with the nonlinear\nconjugate gradient algorithm. We prove that the time semi-discrete scheme is\nconvergent, and as an application of our numerical scheme, we validate the\nquantum drift-diffusion model that is obtained as the diffusive limit of the\nquantum Liouville-BGK equation.\n"}
+{"abstract": "  In this paper, we propose a new Fully Composite Formulation of convex\noptimization problems. It includes, as a particular case, the problems with\nfunctional constraints, max-type minimization problems, and problems of\nComposite Minimization, where the objective can have simple nondifferentiable\ncomponents. We treat all these formulations in a unified way, highlighting the\nexistence of very natural optimization schemes of different order. We prove the\nglobal convergence rates for our methods under the most general conditions.\nAssuming that the upper-level component of our objective function is\nsubhomogeneous, we develop efficient modification of the basic Fully Composite\nfirst-order and second-order Methods, and propose their accelerated variants.\n"}
+{"abstract": "  Analytic solutions to the nonlinear radiation diffusion equation with an\ninstantaneous point source for a non-homogeneous medium with a power law\nspatial density profile, are presented. The solutions are a generalization of\nthe well known solutions for a homogeneous medium. It is shown that the\nsolutions take various qualitatively different forms according to the value of\nthe spatial exponent. These different forms are studied in detail for linear\nand non linear heat conduction. In addition, by inspecting the generalized\nsolutions, we show that there exist values of the spatial exponent such the\nconduction front has constant speed or even accelerates. Finally, the various\nsolution forms are compared in detail to numerical simulations, and a good\nagreement is achieved.\n"}
+{"abstract": "  We performed integrated modelling of the chemical pathways of formation for\nboron nitride nanotube (BNNT) precursors during high-temperature synthesis in a\nB/N2 mixture. Modelling includes quantum chemistry, quantum-classical molecular\ndynamics, thermodynamic, and kinetic approaches. It is shown that BN compounds\nare formed in the interaction of N2 molecules with small boron clusters (N2\nmolecule fixation) rather than with less reactive liquid boron. We demonstrate\nthat the transformation and consumption of liquid boron proceeds through the\nevaporation of clusters, Bm with m less than or equal to 5 and their subsequent\nconversion into BmNn chains. The production of such chains is crucial to the\ngrowth of BNNTs because these chains form the building blocks of bigger and\nlonger BN chains and rings, which are themselves the building blocks of\nfullborenes and BNNTs. Moreover, kinetic modelling revealed that B4N4 and B5N4\nspecies play a major role in the N2 molecule fixation process. The formation of\nthese species via reactions with B4 and B5 clusters is not adequately described\nunder the assumption of thermodynamic equilibrium because the accumulation of\nboth B4N4 and B5N4 depends on the background gas pressure and the gas cooling\nrate. Long BN chains and rings, which are precursors of the fullborene and BNNT\ngrowth, form via self-assembly of component B4N4 and B5N4. Our modelling\nresults (particularly the increased densities of B4N4 and B5N4 species at\nhigher gas pressures) explain the experimentally observed effect of gas\npressure on the yield of high-quality BNNTs. The catalytic role of hydrogen was\nalso studied; it is shown that HBNH molecules can be the main precursor of BNNT\nsynthesis in the presence of hydrogen.\n"}
+{"abstract": "  The next-generation Versatile Video Coding (VVC) standard introduces a new\nMulti-Type Tree (MTT) block partitioning structure that supports Binary-Tree\n(BT) and Ternary-Tree (TT) splits in both vertical and horizontal directions.\nThis new approach leads to five possible splits at each block depth and thereby\nimproves the coding efficiency of VVC over that of the preceding High\nEfficiency Video Coding (HEVC) standard, which only supports Quad-Tree (QT)\npartitioning with a single split per block depth. However, MTT also has brought\na considerable impact on encoder computational complexity. In this paper, a\ntwo-stage learning-based technique is proposed to tackle the complexity\noverhead of MTT in VVC intra encoders. In our scheme, the input block is first\nprocessed by a Convolutional Neural Network (CNN) to predict its spatial\nfeatures through a vector of probabilities describing the partition at each 4x4\nedge. Subsequently, a Decision Tree (DT) model leverages this vector of spatial\nfeatures to predict the most likely splits at each block. Finally, based on\nthis prediction, only the N most likely splits are processed by the\nRate-Distortion (RD) process of the encoder. In order to train our CNN and DT\nmodels on a wide range of image contents, we also propose a public VVC frame\npartitioning dataset based on existing image dataset encoded with the VVC\nreference software encoder. Our proposal relying on the top-3 configuration\nreaches 46.6% complexity reduction for a negligible bitrate increase of 0.86%.\nA top-2 configuration enables a higher complexity reduction of 69.8% for 2.57%\nbitrate loss. These results emphasis a better trade-off between VTM intra\ncoding efficiency and complexity reduction compared to the state-of-the-art\nsolutions.\n"}
+{"abstract": "  Deep neural networks (DNNs) play an important role in machine learning due to\nits outstanding performance compared to other alternatives. However, DNNs are\nnot suitable for safety-critical applications since DNNs can be easily fooled\nby well-crafted adversarial examples. One promising strategy to counter\nadversarial attacks is to utilize spectral normalization, which ensures that\nthe trained model has low sensitivity towards the disturbance of input samples.\nUnfortunately, this strategy requires exact computation of spectral norm, which\nis computation intensive and impractical for large-scale networks. In this\npaper, we introduce an approximate algorithm for spectral normalization based\non Fourier transform and layer separation. The primary contribution of our work\nis to effectively combine the sparsity of weight matrix and decomposability of\nconvolution layers. Extensive experimental evaluation demonstrates that our\nframework is able to significantly improve both time efficiency (up to 60\\%)\nand model robustness (61\\% on average) compared with the state-of-the-art\nspectral normalization.\n"}
+{"abstract": "  Let $S_p(n)$ denote the sum of $p$th powers of the first $n$ positive\nintegers $1^p + 2^p + \\cdots + n^p$. In this paper, first we express $S_p(n)$\nin the so-called Faulhaber form, namely, as an even or odd polynomial in $(n +\n1/2)$, according as $p$ is odd or even. Then, using the relation $S_p(n) -\nS_p(n-1) = n^p$, we derive a recursive formula for the associated Faulhaber\ncoefficients. Applying Cramer's rule to the corresponding system of equations,\nwe obtain an explicit determinant formula for the said coefficients.\nFurthermore, we show how to convert the (even or odd) Faulhaber polynomials in\n$(n+ 1/2)$ into polynomials in $S_1(n)$ for any arbitrary $p$, and vice versa.\n"}
+{"abstract": "  We propose an uncertainty-aware service approach to provide drone-based\ndelivery services called Drone-as-a-Service (DaaS) effectively. Specifically,\nwe propose a service model of DaaS based on the dynamic spatiotemporal features\nof drones and their in-flight contexts. The proposed DaaS service approach\nconsists of three components: scheduling, route-planning, and composition.\nFirst, we develop a DaaS scheduling model to generate DaaS itineraries through\na Skyway network. Second, we propose an uncertainty-aware DaaS route-planning\nalgorithm that selects the optimal Skyways under weather uncertainties. Third,\nwe develop two DaaS composition techniques to select an optimal DaaS\ncomposition at each station of the planned route. A spatiotemporal DaaS\ncomposer first selects the optimal DaaSs based on their spatiotemporal\navailability and drone capabilities. A predictive DaaS composer then utilises\nthe outcome of the first composer to enable fast and accurate DaaS composition\nusing several Machine Learning classification methods. We train the classifiers\nusing a new set of spatiotemporal features which are in addition to other DaaS\nQoS properties. Our experiments results show the effectiveness and efficiency\nof the proposed approach.\n"}
+{"abstract": "  Feature selection identifies subsets of informative features and reduces\ndimensions in the original feature space, helping provide insights into data\ngeneration or a variety of domain problems. Existing methods mainly depend on\nfeature scoring functions or sparse regularizations; nonetheless, they have\nlimited ability to reconcile the representativeness and inter-correlations of\nfeatures. In this paper, we introduce a novel, simple yet effective\nregularization approach, named top-$k$ regularization, to supervised feature\nselection in regression and classification tasks. Structurally, the top-$k$\nregularization induces a sub-architecture on the architecture of a learning\nmodel to boost its ability to select the most informative features and model\ncomplex nonlinear relationships simultaneously. Theoretically, we derive and\nmathematically prove a uniform approximation error bound for using this\napproach to approximate high-dimensional sparse functions. Extensive\nexperiments on a wide variety of benchmarking datasets show that the top-$k$\nregularization is effective and stable for supervised feature selection.\n"}
+{"abstract": "  Low-light imaging on mobile devices is typically challenging due to\ninsufficient incident light coming through the relatively small aperture,\nresulting in a low signal-to-noise ratio. Most of the previous works on\nlow-light image processing focus either only on a single task such as\nillumination adjustment, color enhancement, or noise removal; or on a joint\nillumination adjustment and denoising task that heavily relies on short-long\nexposure image pairs collected from specific camera models, and thus these\napproaches are less practical and generalizable in real-world settings where\ncamera-specific joint enhancement and restoration is required. To tackle this\nproblem, in this paper, we propose a low-light image processing framework that\nperforms joint illumination adjustment, color enhancement, and denoising.\nConsidering the difficulty in model-specific data collection and the ultra-high\ndefinition of the captured images, we design two branches: a coefficient\nestimation branch as well as a joint enhancement and denoising branch. The\ncoefficient estimation branch works in a low-resolution space and predicts the\ncoefficients for enhancement via bilateral learning, whereas the joint\nenhancement and denoising branch works in a full-resolution space and\nprogressively performs joint enhancement and denoising. In contrast to existing\nmethods, our framework does not need to recollect massive data when being\nadapted to another camera model, which significantly reduces the efforts\nrequired to fine-tune our approach for practical usage. Through extensive\nexperiments, we demonstrate its great potential in real-world low-light imaging\napplications when compared with current state-of-the-art methods.\n"}
+{"abstract": "  Recent measurements of Galactic polarized dust emission have found a nonzero\n$TB$ signal, a correlation between the total intensity and the $B$-mode\npolarization component. We present evidence that this parity-odd signal is\ndriven by the relative geometry of the magnetic field and the filamentary\ninterstellar medium in projection. Using neutral hydrogen morphology and Planck\npolarization data, we find that the angle between intensity structures and the\nplane-of-sky magnetic field orientation is predictive of the signs of Galactic\n$TB$ and $EB$. Our results suggest that magnetically misaligned filamentary\ndust structures introduce nonzero $TB$ and $EB$ correlations in the dust\npolarization, and that the intrinsic dust $EB$ can be predicted from\nmeasurements of dust $TB$ and $TE$ over the same sky mask. We predict\ncorrelations between $TE$, $TB$, $EB$, and $EE/BB$, and confirm our predictions\nusing synthetic dust polarization maps from magnetohydrodynamic simulations. We\nintroduce and measure a scale-dependent effective magnetic misalignment angle,\n$\\psi_\\ell^{dust} \\sim 5^\\circ$ for $100 \\lesssim \\ell \\lesssim 500$, and\npredict a positive intrinsic dust $EB$ with amplitude $\\left<D_\\ell^{EB}\\right>\n\\lesssim 2.5~\\mu\\mathrm{K^2_{CMB}}$ for the same multipole range at 353 GHz\nover our sky mask. Both the sign and amplitude of the Galactic $EB$ signal can\nchange with the sky area considered. Our results imply that searches for parity\nviolation in the cosmic microwave background must account for the nonzero\nGalactic $EB$ and $TB$ signals, necessitating revision of existing analyses of\nthe evidence for cosmic birefringence.\n"}
+{"abstract": "  This paper presents new designs of graph convolutional neural networks (GCNs)\non 3D meshes for 3D object segmentation and classification. We use the faces of\nthe mesh as basic processing units and represent a 3D mesh as a graph where\neach node corresponds to a face. To enhance the descriptive power of the graph,\nwe introduce a 1-ring face neighbourhood structure to derive novel\nmulti-dimensional spatial and structure features to represent the graph nodes.\nBased on this new graph representation, we then design a densely connected\ngraph convolutional block which aggregates local and regional features as the\nkey construction component to build effective and efficient practical GCN\nmodels for 3D object classification and segmentation. We will present\nexperimental results to show that our new technique outperforms state of the\nart where our models are shown to have the smallest number of parameters and\nconsietently achieve the highest accuracies across a number of benchmark\ndatasets. We will also present ablation studies to demonstrate the soundness of\nour design principles and the effectiveness of our practical models.\n"}
+{"abstract": "  We consider the problem of goodness-of-fit testing for a model that has at\nleast one unknown parameter that cannot be eliminated by transformation.\nExamples of such problems can be as simple as testing whether a sample consists\nof independent Gamma observations, or whether a sample consists of independent\nGeneralised Pareto observations given a threshold. Over time the approach to\ndetermining the distribution of a test statistic for such a problem has moved\ntowards on-the-fly calculation post observing a sample. Modern approaches\ninclude the parametric bootstrap and posterior predictive checks. We argue that\nthese approaches are merely approximations to integrating over the posterior\npredictive distribution that flows naturally from a given model. Further, we\nattempt to demonstrate that shortcomings which may be present in the parametric\nbootstrap, especially in small samples, can be reduced through the use of\nobjective Bayes techniques, in order to more reliably produce a test with the\ncorrect size.\n"}
+{"abstract": "  We study experimentally and theoretically the temperature dependence of\ntransverse magnetic routing of light emission from hybrid\nplasmonic-semiconductor quantum well structures where the exciton emission from\nthe quantum well is routed into surface plasmon polaritons propagating along a\nnearby semiconductor-metal interface. In II-VI and III-V direct band\nsemiconductors the magnitude of routing is governed by the circular\npolarization of exciton optical transitions, that is induced by a magnetic\nfield. For structures comprising a (Cd,Mn)Te/(Cd,Mg)Te diluted magnetic\nsemiconductor quantum well we observe a strong directionality of the emission\nup to 15% at low temperature of 20 K and magnetic field of 485 mT due to giant\nZeeman splitting of holes mediated via the strong exchange interaction with\nMn$^{2+}$ ions. For increasing temperatures towards room-temperature the\nmagnetic susceptibility decreases and the directionality strongly decreases to\n4% at T = 45 K. We also propose an alternative design based on a non-magnetic\n(In,Ga)As/(In,Al)As quantum well structure, suitable for higher temperatures.\nAccording to our calculations, such structure can demonstrate emission\ndirectionality up to 5% for temperatures below 200 K and moderate magnetic\nfields of 1 T.\n"}
+{"abstract": "  Adapting optical microscopy methods for nanoscale characterization of defects\nin two-dimensional (2D) materials is a vital step for photonic on-chip devices.\nTo increase the analysis throughput, waveguide-based on-chip imaging platforms\nhave been recently developed. Their inherent disadvantage, however, is the\nnecessity to transfer the 2D material from the growth substrate to the imaging\nchip which introduces contamination, potentially altering the characterization\nresults. Here we present a unique approach to circumvent these shortfalls by\ndirectly growing a widely-used 2D material (hexagonal boron nitride, hBN) on\nsilicon nitride chips, and optically characterizing the defects in the intact\nas-grown material. We compare the direct growth approach to the standard wet\ntransfer method, and confirm the clear advantages of the direct growth. While\ndemonstrated with hBN in the current work, the method is easily extendable to\nother 2D materials.\n"}
+{"abstract": "  This research concerns the estimation of latent linear or polychoric\ncorrelations from fuzzy frequency tables. Fuzzy counts are of particular\ninterest to many disciplines including social and behavioral sciences, and are\nespecially relevant when observed data are classified using fuzzy categories -\nas for socio-economic studies, clinical evaluations, content analysis,\ninter-rater reliability analysis - or when imprecise observations are\nclassified into either precise or imprecise categories - as for the analysis of\nratings data or fuzzy coded variables. In these cases, the space of count\nmatrices is no longer defined over naturals and, consequently, the polychoric\nestimator cannot be used to accurately estimate latent linear correlations. The\naim of this contribution is twofold. First, we illustrate a computational\nprocedure based on generalized natural numbers for computing fuzzy frequencies.\nSecond, we reformulate the problem of estimating latent linear correlations\nfrom fuzzy counts in the context of Expectation-Maximization based maximum\nlikelihood estimation. A simulation study and two applications are used to\ninvestigate the characteristics of the proposed method. Overall, the results\nshow that the fuzzy EM-based polychoric estimator is more efficient to deal\nwith imprecise count data as opposed to standard polychoric estimators that may\nbe used in this context.\n"}
+{"abstract": "  This letter presents a combined measurement of the energy spectra of\natmospheric $\\nu_e$ and $\\nu_\\mu$ in the energy range between $\\sim$100 GeV and\n$\\sim$50 TeV with the ANTARES neutrino telescope. The analysis uses 3012 days\nof detector livetime in the period 2007--2017, and selects 1016 neutrinos\ninteracting in (or close to) the instrumented volume of the detector, yielding\nshower-like events (mainly from $\\nu_e+\\overline \\nu_e$ charged current plus\nall neutrino neutral current interactions) and starting track events (mainly\nfrom $\\nu_\\mu + \\overline \\nu_\\mu$ charged current interactions). The\ncontamination by atmospheric muons in the final sample is suppressed at the\nlevel of a few per mill by different steps in the selection analysis, including\na Boosted Decision Tree classifier. The distribution of reconstructed events is\nunfolded in terms of electron and muon neutrino fluxes. The derived energy\nspectra are compared with previous measurements that, above 100 GeV, are\nlimited to experiments in polar ice and, for $\\nu_\\mu$, to Super-Kamiokande.\n"}
+{"abstract": "  The Android operating system is the most spread mobile platform in the world.\nTherefor attackers are producing an incredible number of malware applications\nfor Android. Our aim is to detect Android's malware in order to protect the\nuser. To do so really good results are obtained by dynamic analysis of\nsoftware, but it requires complex environments. In order to achieve the same\nlevel of precision we analyze the machine code and investigate the frequencies\nof ngrams of opcodes in order to detect singular code blocks. This allow us to\nconstruct a database of infected code blocks. Then, because attacker may modify\nand organized differently the infected injected code in their new malware, we\nperform not only a semantic comparison of the tested software with the database\nof infected code blocks but also a structured comparison. To do such comparison\nwe compute subgraph isomorphism. It allows us to characterize precisely if the\ntested software is a malware and if so in witch family it belongs. Our method\nis tested both on a laboratory database and a set of real data. It achieves an\nalmost perfect detection rate.\n"}
+{"abstract": "  The emission of multi-MeV ($\\gamma$-ray) photons from the interaction of a\nhigh-powered laser pulse with a dense plasma target is studied using\nparticle-in-cell simulations. A new set of diagnostic techniques is presented\nand applied to analyze the intense field and ultra-relativistic electrons. Such\nmethods elucidate the dominant processes responsible for efficient\nlaser-to-$\\gamma$ energy conversion, which include nonlinear Compton scattering\nand magneto-bremsstrahlung radiation, and provide a clear picture of the\ninteraction on a microscopic level. We identify regions in the plasma target of\nhigh photon energy-density and obtain an energy conversion efficiency as high\nas 30%. The essential characteristics of the interaction are validated with\nfull-3D simulations.\n"}
+{"abstract": "  We study the problem of a planner who resolves risk-return trade-offs - like\nfinancial investment decisions - on behalf of a collective of agents with\nheterogeneous risk preferences. The planner's objective is a two-stage utility\nfunctional where an outer utility function is applied to the distribution of\nthe agents' certainty equivalents from a given decision. Assuming lognormal\nrisks and heterogeneous power utility preferences for the agents, we\ncharacterize optimal behavior in a setting where the planner can let each agent\nchoose between different options from a fixed menu of possible decisions,\nleading to a grouping of the agents by risk preferences. These optimal decision\nmenus are derived first for the case where the planner knows the distribution\nof preferences exactly and then for a case where he faces uncertainty about\nthis distribution, only having access to upper and lower bounds on agents'\nrelative risk aversion. Finally, we provide tight bounds on the welfare loss\nfrom offering a finite menu of choices rather than fully personalized\ndecisions.\n"}
+{"abstract": "  CPT invariance is one of the most fundamental symmetries in nature and it\nplays a major role in the formulation of Quantum Field Theory. Although no\ndefinitive signal of CPT violation has been observed so far, there are many\nreasons to carefully investigate various low-energy phenomena that can provide\nbetter probes to test CPT symmetry. In this context, neutrino experiments are\nexpected to provide more stringent bounds on CPT invariance violation when\ncompared to the existing bounds from the Kaon system. In this work, we\ninvestigate the sensitivity of the upcoming long-baseline experiments: Hyper\nKamiokande (T2HK, T2HKK), ESSnuSB and DUNE to constrain the CPT violating\nparameters $\\Delta(\\delta_{CP})$, $\\Delta(m^2_{31})$ and $\\Delta(\\sin^2\n\\theta_{23})$, which characterize the difference between neutrino and\nantineutrino oscillation parameters. Further, we analyse neutrino and\nantineutrino data independently and constrain the oscillation parameters\ngoverning them by considering the combination of these experiments (DUNE+T2HKK\nand DUNE+ESSnuSB). In addition, assuming CPT symmetry is violated in nature, we\nstudy the individual ability of the aforementioned experiments to establish CPT\nviolation. We found that the experiments Hyper-K (T2HK, T2HKK) and ESSnuSB,\nalong with DUNE, will be able to establish CPT violation in their proposed\nrun-times.\n"}
+{"abstract": "  Random walks are frequently used as a model for very diverse physical\nphenomena. The Monte Carlo method is a versatile tool for the study of the\nproperties of systems modelled as random walks. Often, each walker is\nassociated with a statistical weight, used in the estimation of observable\nquantities. Weights are typically assumed to be positive; nonetheless, some\napplications require the use of positive and negative weights or complex\nweights, and often pose particular challenges with convergence. In this paper,\nwe examine such a case from the field of nuclear reactor physics, where the\nnegative particle weights prevent the power iteration algorithm from converging\non the sought fundamental eigenstate of the Boltzmann transport equation. We\ndemonstrate how the use of weight cancellation allows convergence on the\nphysical eigenstate. To this end, we develop a novel method to perform weight\ncancellation in an exact manner, in three spatial dimensions. The viability of\nthis algorithm is then demonstrated on a reactor physics problem.\n"}
+{"abstract": "  In this paper, we present a maximum likelihood method for estimating the\nparameters of a univariate Hawkes process with self-excitation or inhibition.\nOur work generalizes techniques and results that were restricted to the\nself-exciting scenario. The proposed estimator is implemented for the classical\nexponential kernel and we show that, in the inhibition context, our procedure\nprovides more accurate estimations than current alternative approaches.\n"}
+{"abstract": "  I present an overview of some recent advancements on the empirical analysis\nand theoretical modeling of the process of price formation in financial markets\nas the result of the arrival of orders in a limit order book exchange. After\ndiscussing critically the possible modeling approaches and the observed\nstylized facts of order flow, I consider in detail market impact and\ntransaction cost of trades executed incrementally over an extended period of\ntime, by comparing model predictions and recent extensive empirical results. I\nalso discuss how the simultaneous presence of many algorithmic trading\nexecutions affects the quality and cost of trading.\n"}
+{"abstract": "  Fluid flows in coupled systems consisting of a free-flow region and the\nadjacent porous medium appear in a variety of environmental settings and\nindustrial applications. In many applications, fluid flow is non-parallel to\nthe fluid-porous interface that requires a generalisation of the Beavers-Joseph\ncoupling condition typically used for the Stokes-Darcy problem. Generalised\ncoupling conditions valid for arbitrary flow directions to the interface are\nrecently derived using the theory of homogenisation and boundary layers. The\naim of this work is the mathematical analysis of the Stokes-Darcy problem with\nthese generalised interface conditions. We prove the existence and uniqueness\nof the weak solution of the coupled problem. The well-posedness is guaranteed\nunder a suitable relationship between the permeability and the boundary layer\nconstants containing geometrical information about the porous medium and the\ninterface. We numerically study the validity of the obtained results for\nrealistic problems and provide a benchmark for numerical solution of the\nStokes-Darcy problem with generalised interface conditions.\n"}
+{"abstract": "  We formulate a data-driven, physics-constrained closure method for\ncoarse-scale numerical simulations of turbulent fluid flows. Our approach\ninvolves a closure scheme that is non-local both in space and time, i.e. the\nclosure terms are parametrized in terms of the spatial neighborhood of the\nresolved quantities but also their history. The data-driven scheme is\ncomplemented with a physical constrain expressing the energy conservation\nproperty of the nonlinear advection terms. We show that the adoption of this\nphysical constrain not only increases the accuracy of the closure scheme but\nalso improves the stability properties of the formulated coarse-scale model. We\ndemonstrate the presented scheme in fluid flows consisting of an incompressible\ntwo-dimensional turbulent jet. Specifically, we first develop one-dimensional\ncoarse-scale models describing the spatial profile of the jet. We then proceed\nto the computation of turbulent closures appropriate for two-dimensional\ncoarse-scale models. Training data are obtained through high-fidelity direct\nnumerical simulations (DNS). We also showcase how the developed scheme captures\nthe coarse-scale features of the concentration fields associated with inertial\ntracers, such as bubbles and particles, carried by the flow but not following\nthe flow. We thoroughly examine the generalizability properties of the trained\nclosure models for different Reynolds numbers, as well as, radically different\njet profiles from the ones used in the training phase. We also examine the\nrobustness of the derived closures with respect to the grid size. Overall the\nadoption of the constraint results in an average improvement of 26% for\none-dimensional closures and 29% for two-dimensional closures, being notably\nlarger for flows that were not used for training.\n"}
+{"abstract": "  End-to-end models have achieved impressive results on the task of automatic\nspeech recognition (ASR). For low-resource ASR tasks, however, labeled data can\nhardly satisfy the demand of end-to-end models. Self-supervised acoustic\npre-training has already shown its amazing ASR performance, while the\ntranscription is still inadequate for language modeling in end-to-end models.\nIn this work, we fuse a pre-trained acoustic encoder (wav2vec2.0) and a\npre-trained linguistic encoder (BERT) into an end-to-end ASR model. The fused\nmodel only needs to learn the transfer from speech to language during\nfine-tuning on limited labeled data. The length of the two modalities is\nmatched by a monotonic attention mechanism without additional parameters.\nBesides, a fully connected layer is introduced for the hidden mapping between\nmodalities. We further propose a scheduled fine-tuning strategy to preserve and\nutilize the text context modeling ability of the pre-trained linguistic\nencoder. Experiments show our effective utilizing of pre-trained modules. Our\nmodel achieves better recognition performance on CALLHOME corpus (15 hours)\nthan other end-to-end models.\n"}
+{"abstract": "  We investigate the stability of the wave equation with spatial dependent\ncoefficients on a bounded multidimensional domain. The system is stabilized via\na scattering passive feedback law. We formulate the wave equation in a\nport-Hamiltonian fashion and show that the system is semi-uniform stable, which\nis a stability concept between exponential stability and strong stability.\nHence, this also implies strong stability of the system. In particular,\nclassical solutions are uniformly stable. This will be achieved by showing that\nthe spectrum of the port-Hamiltonian operator is contained in the left half\nplane $\\mathbb{C}_{-}$ and the port-Hamiltonian operator generates a\ncontraction semigroup. Moreover, we show that the spectrum consists of\neigenvalues only and the port-Hamiltonian operator has a compact resolvent.\n"}
+{"abstract": "  In the physics of the Fractional Quantum Hall (FQH) effect, a zoo of Abelian\ntopological phases can be obtained by varying the magnetic field. Aiming to\nreach the same phenomenology in spin-like systems, we propose a family of\nSU($N$)-symmetric models in the fundamental representation, on the square\nlattice with short-range interactions restricted to triangular units, a natural\ngeneralization for arbitrary $N$ of an SU($3$) model studied previously where\ntime-reversal symmetry is broken explicitly. Guided by the recent discovery of\nSU($2$)$_1$ and SU($3$)$_1$ chiral spin liquids (CSL) on similar models we\nsearch for topological SU($N$)$_1$ CSL in some range of the Hamiltonian\nparameters via a combination of complementary numerical methods such as exact\ndiagonalizations (ED), infinite density matrix renormalization group (iDMRG)\nand infinite Projected Entangled Pair State (iPEPS). Extensive ED on small\n(periodic and open) clusters up to $N=10$ and an innovative SU($N$)-symmetric\nversion of iDMRG to compute entanglement spectra on (infinitely-long) cylinders\nin all topological sectors provide unambiguous signatures of the SU($N$)$_1$\ncharacter of the chiral liquids. An SU($4$)-symmetric chiral PEPS, constructed\nin a manner similar to its SU($2$) and SU($3$) analogs, is shown to give a good\nvariational ansatz of the $N=4$ ground state, with chiral edge modes\noriginating from the PEPS holographic bulk-edge correspondence. Finally, we\ndiscuss the possible observation of such Abelian CSL in ultracold atom setups\nwhere the possibility of varying $N$ provides a tuning parameter similar to the\nmagnetic field in the physics of the FQH effect.\n"}
+{"abstract": "  In 2006, Fowler and Foemmel defined ten core Continuous Integration (CI)\npractices that could increase the speed of software development feedback cycles\nand improve software quality. Since then, these practices have been widely\nadopted by industry and subsequent research has shown they improve software\nquality. However, there is poor understanding of how organizations implement\nthese practices, of the benefits developers perceive they bring, and of the\nchallenges developers and organizations experience in implementing them. In\nthis paper, we discuss a multiple-case study of three small- to medium-sized\ncompanies using the recommended suite of ten CI practices. Using interviews and\nactivity log mining, we learned that these practices are broadly implemented\nbut how they are implemented varies depending on their perceived benefits, the\ncontext of the project, and the CI tools used by the organization. We also\ndiscovered that CI practices can create new constraints on the software process\nthat hurt feedback cycle time. For researchers, we show that how CI is\nimplemented varies, and thus studying CI (for example, using data mining)\nrequires understanding these differences as important context for research\nstudies. For practitioners, our findings reveal in-depth insights on the\npossible benefits and challenges from using the ten practices, and how project\ncontext matters.\n"}
+{"abstract": "  Graph neural networks (GNNs) manifest pathologies including over-smoothing\nand limited discriminating power as a result of suboptimally expressive\naggregating mechanisms. We herein present a unifying framework for stochastic\naggregation (STAG) in GNNs, where noise is (adaptively) injected into the\naggregation process from the neighborhood to form node embeddings. We provide\ntheoretical arguments that STAG models, with little overhead, remedy both of\nthe aforementioned problems. In addition to fixed-noise models, we also propose\nprobabilistic versions of STAG models and a variational inference framework to\nlearn the noise posterior. We conduct illustrative experiments clearly\ntargeting oversmoothing and multiset aggregation limitations. Furthermore, STAG\nenhances general performance of GNNs demonstrated by competitive performance in\ncommon citation and molecule graph benchmark datasets.\n"}
+{"abstract": "  Online social media (OSM) has emerged as a prominent platform for debate on a\nwide range of issues. Even celebrities and public figures often share their\nopinions on a variety of topics through OSM platforms. One such subject that\nhas gained a lot of coverage on Twitter is the Novel Coronavirus, officially\nknown as COVID-19, which has become a pandemic and has sparked a crisis in\nhuman history. In this study, we examine 29 million tweets over three months to\nstudy highly influential users, whom we refer to as leaders. We recognize these\nleaders through social network techniques and analyze their tweets using text\nanalysis. Using a community detection algorithm, we categorize these leaders\ninto four clusters: research, news, health, and politics, with each cluster\ncontaining Twitter handles (accounts) of individual users or organizations.\nE.g., the health cluster includes the World Health Organization (@WHO), the\nDirector-General of WHO (@DrTedros), and so on. The emotion analysis reveals\nthat (i) all clusters show an equal amount of fear in their tweets, (ii)\nresearch and news clusters display more sadness than others, and (iii) health\nand politics clusters are attempting to win public trust. According to the text\nanalysis, the (i) research cluster is more concerned with recognizing symptoms\nand the development of vaccination; (ii) news and politics clusters are mostly\nconcerned with travel. We then show that we can use our findings to classify\ntweets into clusters with a score of 96% AUC ROC.\n"}
+{"abstract": "  This research focuses on the algorithms and approaches for learning Hidden\nMarkov Models (HMMs) and compares HMM learning methods and algorithms. HMM is a\nstatistical Markov model in which the system being modeled is assumed to be a\nMarkov process. One of the essential characteristics of HMMs is their learning\ncapabilities. Learning algorithms are introduced to overcome this\ninconvenience. One of the main problems of the newly proposed algorithms is\ntheir validation. This research aims by using the theoretical and experimental\nanalysis to 1) compare HMMs learning algorithms proposed in the literature, 2)\nprovide a validation tool for new HMM learning algorithms, and 3) present a new\nalgorithm called Asexual Reproduction Optimization (ARO) with one of its\nextensions - Modified ARO (MARO) - as a novel HMM learning algorithm to use the\nvalidation tool proposed. According to the literature findings, it seems that\npopulationbased algorithms perform better among HMMs learning approaches than\nother algorithms. Also, the testing was done in nine benchmark datasets. The\nresults show that MARO outperforms different algorithms in objective functions\nin terms of accuracy and robustness.\n"}
+{"abstract": "  How to learn long-range dependencies from 3D point clouds is a challenging\nproblem in 3D point cloud analysis. Addressing this problem, we propose a\nglobal attention network for point cloud semantic segmentation, named as\nGA-Net, consisting of a point-independent global attention module and a\npoint-dependent global attention module for obtaining contextual information of\n3D point clouds in this paper. The point-independent global attention module\nsimply shares a global attention map for all 3D points. In the point-dependent\nglobal attention module, for each point, a novel random cross attention block\nusing only two randomly sampled subsets is exploited to learn the contextual\ninformation of all the points. Additionally, we design a novel point-adaptive\naggregation block to replace linear skip connection for aggregating more\ndiscriminate features. Extensive experimental results on three 3D public\ndatasets demonstrate that our method outperforms state-of-the-art methods in\nmost cases.\n"}
+{"abstract": "  We prove that four different ways of defining Cartesian fibrations and the\nCartesian model structure are all Quillen equivalent: On marked simplicial\nsets, on bisimplicial spaces, on bisimplicial sets, on marked simplicial\nspaces. The main way to prove these equivalences is by using the Quillen\nequivalences between quasi-categories and complete Segal spaces as defined by\nJoyal-Tierney and the straightening construction due to Lurie.\n"}
+{"abstract": "  The fault diagnostic model trained for a laboratory case machine fails to\nperform well on the industrial machines running under variable operating\nconditions. For every new operating condition of such machines, a new\ndiagnostic model has to be trained which is a time-consuming and uneconomical\nprocess. Therefore, we propose a quick learning mechanism that can transform\nthe existing diagnostic model into a new model suitable for industrial machines\noperating in different conditions. The proposed method uses the Net2Net\ntransformation followed by a fine-tuning to cancel/minimize the maximum mean\ndiscrepancy between the new data and the previous one. The fine-tuning of the\nmodel requires a very less amount of labelled target samples and very few\niterations of training. Therefore, the proposed method is capable of learning\nthe new target data pattern quickly. The effectiveness of the proposed fault\ndiagnosis method has been demonstrated on the Case Western Reserve University\ndataset, Intelligent Maintenance Systems bearing dataset, and Paderborn\nuniversity dataset under the wide variations of the operating conditions. It\nhas been validated that the diagnostic model trained on artificially damaged\nfault datasets can be used to quickly train another model for a real damage\ndataset.\n"}
+{"abstract": "  Experiments by Chretien and co-workers suggest that mitochondria are 10oC\nhotter than their surroundings. Steady-state theoretical estimates place this\ndifference at a maximum of 10^-5 oC. This million-fold disagreement may be\ncalled the hot mitochondrion paradox. It is suggested that every proton\ntranslocated via ATP synthase sparks a picosecond temperature-difference spike\nof the order of magnitude measured by Chretien et al. Time-averaging of these\nspikes recovers the theoretical value. Further, a temporal and spatial\nsuperposition of the fluorescence intensity of a very large number of molecular\nthermometer molecules in the sample can give the appearance of a steady signal.\nThe inner mitochondrial membrane appears to be flanked by temperature\ndifferences fluctuating in time and along the membrane s surface, with hot and\ncold spots as ultrashort temperature spikes.\n"}
+{"abstract": "  Object classification is a significant task in computer vision. It has become\nan effective research area as an important aspect of image processing and the\nbuilding block of image localization, detection, and scene parsing. Object\nclassification from low-quality images is difficult for the variance of object\ncolors, aspect ratios, and cluttered backgrounds. The field of object\nclassification has seen remarkable advancements, with the development of deep\nconvolutional neural networks (DCNNs). Deep neural networks have been\ndemonstrated as very powerful systems for facing the challenge of object\nclassification from high-resolution images, but deploying such object\nclassification networks on the embedded device remains challenging due to the\nhigh computational and memory requirements. Using high-quality images often\ncauses high computational and memory complexity, whereas low-quality images can\nsolve this issue. Hence, in this paper, we investigate an optimal architecture\nthat accurately classifies low-quality images using DCNNs architectures. To\nvalidate different baselines on lowquality images, we perform experiments using\nwebcam captured image datasets of 10 different objects. In this research work,\nwe evaluate the proposed architecture by implementing popular CNN\narchitectures. The experimental results validate that the MobileNet\narchitecture delivers better than most of the available CNN architectures for\nlow-resolution webcam image datasets.\n"}
+{"abstract": "  The correction to the Coulomb energy due to virtual production of $e^+e^-$\npairs, which is on the order of one percent of the Coulomb energy at nuclear\nscales is discussed. The effects of including a pair-production term in the\nsemi-empirical mass formula and the correction to the Coulomb barrier for a\nhandful of nuclear collisions using the Bass and Coulomb potentials are\nstudied. With an eye toward future work using Constrained Molecular Dynamics\n(CoMD) model, we also calculate the correction to the Coulomb energy and force\nbetween protons after folding with a Gaussian spatial distribution.\n"}
+{"abstract": "  In this paper, we present in-depth transfer matrix analyses of the Anderson\ntransition in three non-Hermitian (NH) systems, NH Anderson, U(1) and Peierls\nmodels, that belong to NH class AI$^{\\dagger}$ or NH class A. We first argue a\ngeneral validity of the transfer matrix analysis, and clarify the symmetry\nproperties of the Lyapunov exponents, scattering ($S$) matrix and two-terminal\nconductance in these NH models. The unitarity of the $S$ matrix is violated in\nNH systems, where the two-terminal conductance can take arbitrarily large\nvalues. Nonetheless, we show that the transposition symmetry of a Hamiltonian\nleads to the symmetric nature of the $S$ matrix as well as the reciprocal\nsymmetries of the Lyapunov exponents and conductance in certain ways in these\nNH models. Finite size scaling data are fitted by polynomial functions, from\nwhich we determine the critical exponent $\\nu$ at different single-particle\nenergies and system parameters of the NH models. We conclude that the critical\nexponents of the NH class AI$^{\\dagger}$ and the NH class A are $\\nu=1.19 \\pm\n0.01$ and $\\nu=1.00 \\pm 0.04$, respectively. In the NH models, a distribution\nof the two-terminal conductance is not Gaussian. Instead, it contains small\nfractions of huge conductance values, which come from rare-event states with\nhuge transmissions amplified by on-site NH disorders. Nonetheless, a geometric\nmean of the conductance enables the finite-size scaling analysis. We show that\nthe critical exponents obtained from the conductance analysis are consistent\nwith those from the localization length in these three NH models.\n"}
+{"abstract": "  Nitrogen fertilizers have a detrimental effect on the environment, which can\nbe reduced by optimizing fertilizer management strategies. We implement an\nOpenAI Gym environment where a reinforcement learning agent can learn\nfertilization management policies using process-based crop growth models and\nidentify policies with reduced environmental impact. In our environment, an\nagent trained with the Proximal Policy Optimization algorithm is more\nsuccessful at reducing environmental impacts than the other baseline agents we\npresent.\n"}
+{"abstract": "  We study molecular emission in a massive condensation at the border of the\nHII region RCW 120, paying particular attention to the Core 1 and Core 2\nobjects, the most massive fragments of the condensation found previously by\nALMA. The latter fragment was previously suggested to host a high-mass analogue\nof Class 0 young stellar object. We present spectra of molecular emission in\nthe 1 mm range made with the APEX telescope. We detect CH$_3$OH and C$^{34}$S\nlines in Core 1 and Core 2. The CH$_3$CN series and the SO$_2$ lines are only\nfound in Core 2. We estimate gas physical parameters using methanol lines and\nobtain gas temperature less than 100 K in both regions. Molecular hydrogen\nnumber density in Core 2 is in the range of $10^5-10^7$ cm$^{-3}$ and is more\nuncertain in Core 1. However, the detection of the CH$_3$CN lines corresponding\nto highly excited transitions ($E_{\\rm u}> 400$~K) in Core~2 indicates that the\nregion contains hot gas, while the abundances of CH$_3$OH, CS, SO$_2$ and\nCH$_3$CN are quite low for a hot core stage. We propose that Core 2 is in the\nwarm-up phase prior to the establishing of the hot gas chemistry. We suggest\nthat Core 2 is in the beginning of the hot core stage. There are no detected\nCH$_3$CN lines in Core 1, therefore, it might be on an even less evolved\nevolutionary stage.\n"}
+{"abstract": "  We extend the results about Hensel minimality to include also the mixed\ncharacteristic case. This completes our axiomatic framework for tame\nnon-archimedean geometry over Henselian valued fields of characteristic zero.\nWe prove a Jacobian property, a strong form of Taylor approximations of\ndefinable functions, resplendency results and cell decomposition, all under\n1-h-minimality. We obtain a diophantine application of counting rational points\nof bounded height on Hensel minimal curves.\n"}
+{"abstract": "  The world is still fighting against COVID-19, which has been lasting for more\nthan a year. Till date, it has been a greatest challenge to human beings in\nfighting against COVID-19 since, the pathogen SARS-COV-2 that causes COVID-19\nhas significant biological and transmission characteristics when compared to\nSARS-COV and MERS-COV pathogens. In spite of many control strategies that are\nimplemented to reduce the disease spread, there is a rise in the number of\ninfected cases around the world. Hence, a mathematical model which can describe\nthe real nature and impact of COVID-19 is necessary for the better\nunderstanding of disease transmission dynamics of COVID-19. This article\nproposes a new compartmental SEIRUC mathematical model, which includes the new\nstate called convalesce (C). The basic reproduction number $\\mathcal{R}_0$ is\nidentified for the proposed model. The stability analysis are performed for the\ndisease free equilibrium ($\\mathcal{E}_0$) as well for the endemic equilibrium\n($\\mathcal{E}_*$) by using the Routh-Hurwitz criterion. The graphical\nillustrations of the proposed mathematical results are provided to validate the\ntheoretical results.\n"}
+{"abstract": "  The $SU(N)$ Yang-Mills matrix model admits self-dual and anti-self-dual\ninstantons. When coupled to $N_f$ flavors of massless quarks, the Euclidean\nDirac equation in an instanton background has $n_+$ positive and $n_-$ negative\nchirality zero modes. We show that the index $(n_+ - n_-)$ is equal to a\nsuitably defined instanton charge. Further, we show that the path integral\nmeasure is not invariant under a chiral rotation, and relate the non-invariance\nof the measure to the index of the Dirac operator. Axial symmetry is broken\nanomalously, with the residual symmetry being a finite group. For $N_f$\nfundamental fermions, this residual symmetry is $\\mathbb{Z}_{2N_f}$, whereas\nfor adjoint quarks it is $\\mathbb{Z}_{4N_f}$.\n"}
+{"abstract": "  In this paper, following the ideas in Marsden et al.[18], we set up the\nregular reduction theory of a regular controlled Lagrangian (RCL) system with\nsymmetry and momentum map, by using Legendre transformation and Euler-Lagrange\nvector field, and this reduction is an extension of symmetric reduction theory\nof a regular Lagrangian system under regular controlled Lagrangian equivalence\nconditions. Considering the completeness of reduction, in order to describe\nuniformly the RCL systems defined on a tangent bundle and on its regular\nreduced spaces, we first define a kind of RCL systems on a symplectic fiber\nbundle. Then we give a good expression of the dynamical vector field of the RCL\nsystem, such that we can describe the RCL-equivalence for the RCL systems.\nMoreover, we introduce regular point and regular orbit reducible RCL systems\nwith symmetries and momentum maps, by using the reduced Lagrange symplectic\nforms and the reduced Euler-Lagrange vector fields, and prove the regular point\nand regular orbit reduction theorems for the RCL systems and regular Lagrangian\nsystems, which explain the relationships between RpCL-equivalence,\nRoCL-equivalence for the reducible RCL systems with symmetries and\nRCL-equivalence for the associated reduced RCL systems, as well as the\nrelationship of equivalences of the regular reducible Lagrangian systems,\n$R_p$-reduced Lagrangian systems and $R_o$-reduced Lagrangian systems.\n"}
+{"abstract": "  We introduce the notion of real phase structure on rational polyhedral fans\nin Euclidean space. Such a structure consists of an assignment of affine spaces\nover $\\mathbb{Z}/2\\mathbb{Z}$ to each top dimensional face of the fan subject\nto two conditions. Given an oriented matroid we can construct a real phase\nstructure on the fan of the underlying matroid. Conversely, we show that from a\nreal phase structure on a matroid fan we can produce an orientation of the\nunderlying matroid. Thus real phase structures are cryptomorphic to matroid\norientations. The topes of the orientated matroid are recovered immediately\nfrom the real phase structure. We also provide a direct way to recover the\nsigned circuits of the oriented matroid from the real phase structure.\n"}
+{"abstract": "  We prove that a certain pair of isospectral planar sets are distinguished by\ntorsional rigidity.\n"}
+{"abstract": "  Hippocampal reverse replay is thought to contribute to learning, and\nparticularly reinforcement learning, in animals. We present a computational\nmodel of learning in the hippocampus that builds on a previous model of the\nhippocampal-striatal network viewed as implementing a three-factor\nreinforcement learning rule. To augment this model with hippocampal reverse\nreplay, a novel policy gradient learning rule is derived that associates place\ncell activity with responses in cells representing actions. This new model is\nevaluated using a simulated robot spatial navigation task inspired by the\nMorris water maze. Results show that reverse replay can accelerate learning\nfrom reinforcement, whilst improving stability and robustness over multiple\ntrials. As implied by the neurobiological data, our study implies that reverse\nreplay can make a significant positive contribution to reinforcement learning,\nalthough learning that is less efficient and less stable is possible in its\nabsence. We conclude that reverse replay may enhance reinforcement learning in\nthe mammalian hippocampal-striatal system rather than provide its core\nmechanism.\n"}
+{"abstract": "  Modern neural networks excel at image classification, yet they remain\nvulnerable to common image corruptions such as blur, speckle noise or fog.\nRecent methods that focus on this problem, such as AugMix and DeepAugment,\nintroduce defenses that operate in expectation over a distribution of image\ncorruptions. In contrast, the literature on $\\ell_p$-norm bounded perturbations\nfocuses on defenses against worst-case corruptions. In this work, we reconcile\nboth approaches by proposing AdversarialAugment, a technique which optimizes\nthe parameters of image-to-image models to generate adversarially corrupted\naugmented images. We theoretically motivate our method and give sufficient\nconditions for the consistency of its idealized version as well as that of\nDeepAugment. Our classifiers improve upon the state-of-the-art on common image\ncorruption benchmarks conducted in expectation on CIFAR-10-C and improve\nworst-case performance against $\\ell_p$-norm bounded perturbations on both\nCIFAR-10 and ImageNet.\n"}
+{"abstract": "  We study the Higgs mode in a Bardeen-Cooper-Schrieffer (BCS) superconductor.\nMotivated by the observation that U(1) symmetry of the BCS Hamiltonian is not\nessential for the Higgs mode, we study the Ising-like Hamiltonian in the\npseudospin representation. We show that the Higgs mode emerges as the lowest\nexcited state of the Ising-like Hamiltonian due to spontaneous breakdown of\n$\\mathbb{Z}_2$ symmetry under the time-reversal operation $\\mathcal T$ in the\npseudospin space. We further predict the existence of multiple Higgs modes that\nhave quantized energy $2(n+1)\\Delta_0$ ($0\\le n\\le N_{k_F}$), where $\\Delta_0$\nis the superconducting gap, $n$ is an integer, and $N_{k_F}$ is the number of\nstates on the Fermi surface.\n"}
+{"abstract": "  The island rule for the entanglement entropy is applied to an eternal\nReissner-Nordstr\\\"om black hole. The key ingredient is that the black hole is\nassumed to be in thermal equilibrium with a heat bath of an arbitrary\ntemperature and so the generalized entropy is treated as being off-shell.\nTaking the on-shell condition to the off-shell generalized entropy, we find the\ngeneralized entropy and then obtain the entanglement entropy following the\nisland rule. For the non-extremal black hole, the entanglement entropy grows\nlinearly in time and can be saturated after the Page time as expected. The\nentanglement entropy also has a well-defined Schwarzschild limit. In the\nextremal black hole, the island prescription provides a logarithmically growing\nentanglement entropy in time and a constant entanglement entropy after the Page\ntime. In the extremal black hole, the boundary of the island hits the curvature\nsingularity where the semi-classical approximations appear invalid. To avoid\nencountering the curvature singularity, we apply this procedure to the Hayward\nblack hole regular at the origin. Consequently, the presence of the island in\nextremal black holes can provide a finite entanglement entropy, which might\nimply non-trivial vacuum configurations of extremal black holes.\n"}
+{"abstract": "  A differentiable curve y = y(x) is determined by its tangent lines and is\nsaid to be the envelope of its tangent lines. The coefficients of the curve's\ntangent lines form a curve in another space, called the dual space. There is a\ntransformation between the original x,y-space and the dual space, such that\npoints on the original curve and the curve of the coefficients of the tangent\nlines are transformed into each other.The dual space and the transformation\ndepend upon the form that is used for the tangent lines. One choice is y = mx +\nb, so that the coordinates in the dual space are m and b, where the curve\nrepresenting the tangent lines is b = b(m). Each point of the curve b = b(m) in\nthe dual space corresponds to a tangent line to the curve y = y(x) in\nx,y-space. We present other choices of the form for the tangent lines and\nexplore techniques for finding a curve from the tangent lines, transformations\nbetween the original space and a dual space and among dual spaces, the\ndifferential equation, whose solutions are exactly the equations of the\nequations of the tangent lines and the original curve. Geometric constructions\nand other tools are used to find dual curves.\n"}
+{"abstract": "  Unsupervised domain adaptation aims to learn a task classifier that performs\nwell on the unlabeled target domain, by utilizing the labeled source domain.\nInspiring results have been acquired by learning domain-invariant deep features\nvia domain-adversarial training. However, its parallel design of task and\ndomain classifiers limits the ability to achieve a finer category-level domain\nalignment. To promote categorical domain adaptation (CatDA), based on a joint\ncategory-domain classifier, we propose novel losses of adversarial training at\nboth domain and category levels. Since the joint classifier can be regarded as\na concatenation of individual task classifiers respectively for the two\ndomains, our design principle is to enforce consistency of category predictions\nbetween the two task classifiers. Moreover, we propose a concept of vicinal\ndomains whose instances are produced by a convex combination of pairs of\ninstances respectively from the two domains. Intuitively, alignment of the\npossibly infinite number of vicinal domains enhances that of original domains.\nWe propose novel adversarial losses for vicinal domain adaptation (VicDA) based\non CatDA, leading to Vicinal and Categorical Domain Adaptation (ViCatDA). We\nalso propose Target Discriminative Structure Recovery (TDSR) to recover the\nintrinsic target discrimination damaged by adversarial feature alignment. We\nalso analyze the principles underlying the ability of our key designs to align\nthe joint distributions. Extensive experiments on several benchmark datasets\ndemonstrate that we achieve the new state of the art.\n"}
+{"abstract": "  Finite Sample Smeariness (FSS) has been recently discovered. It means that\nthe distribution of sample Fr\\'echet means of underlying rather unsuspicious\nrandom variables can behave as if it were smeary for quite large regimes of\nfinite sample sizes. In effect classical quantile-based statistical testing\nprocedures do not preserve nominal size, they reject too often under the null\nhypothesis. Suitably designed bootstrap tests, however, amend for FSS. On the\ncircle it has been known that arbitrarily sized FSS is possible, and that all\ndistributions with a nonvanishing density feature FSS. These results are\nextended to spheres of arbitrary dimension. In particular all rotationally\nsymmetric distributions, not necessarily supported on the entire sphere feature\nFSS of Type I. While on the circle there is also FSS of Type II it is\nconjectured that this is not possible on higher-dimensional spheres.\n"}
+{"abstract": "  The main purpose of this paper is a wide generalization of one of the results\nabstract algebraic geometry begins with, namely of the fact that the prime\nspectrum $\\mathrm{Spec}(R)$ of a unital commutative ring $R$ is always a\nspectral (=coherent) topological space. In this generalization, which includes\nseveral other known ones, the role of ideals of $R$ is played by elements of an\nabstract complete lattice $L$ equipped with binary multiplication with\n$xy\\leqslant x\\wedge y$ for all $x,y\\in L$. In fact when no further conditions\non $L$ are required, the resulting space can be and is only shown to be sober,\nand we discuss further conditions sufficient to make it spectral. This\ndiscussion involves establishing various comparison theorems on so-called\nprime, radical, solvable, and locally solvable elements of $L$; we also make\nshort additional remarks on semiprime elements. We consider categorical and\nuniversal-algebraic applications involving general theory of commutators, and\nan application to ideals in what we call the commutative world. The cases of\ngroups and of non-commutative rings are briefly considered separately.\n"}
+{"abstract": "  Most of state of the art methods applied on time series consist of deep\nlearning methods that are too complex to be interpreted. This lack of\ninterpretability is a major drawback, as several applications in the real world\nare critical tasks, such as the medical field or the autonomous driving field.\nThe explainability of models applied on time series has not gather much\nattention compared to the computer vision or the natural language processing\nfields. In this paper, we present an overview of existing explainable AI (XAI)\nmethods applied on time series and illustrate the type of explanations they\nproduce. We also provide a reflection on the impact of these explanation\nmethods to provide confidence and trust in the AI systems.\n"}
+{"abstract": "  Recommender systems rely on user behavior data like ratings and clicks to\nbuild personalization model. However, the collected data is observational\nrather than experimental, causing various biases in the data which\nsignificantly affect the learned model. Most existing work for recommendation\ndebiasing, such as the inverse propensity scoring and imputation approaches,\nfocuses on one or two specific biases, lacking the universal capacity that can\naccount for mixed or even unknown biases in the data. Towards this research\ngap, we first analyze the origin of biases from the perspective of \\textit{risk\ndiscrepancy} that represents the difference between the expectation empirical\nrisk and the true risk. Remarkably, we derive a general learning framework that\nwell summarizes most existing debiasing strategies by specifying some\nparameters of the general framework. This provides a valuable opportunity to\ndevelop a universal solution for debiasing, e.g., by learning the debiasing\nparameters from data. However, the training data lacks important signal of how\nthe data is biased and what the unbiased data looks like. To move this idea\nforward, we propose \\textit{AotoDebias} that leverages another (small) set of\nuniform data to optimize the debiasing parameters by solving the bi-level\noptimization problem with meta-learning. Through theoretical analyses, we\nderive the generalization bound for AutoDebias and prove its ability to acquire\nthe appropriate debiasing strategy. Extensive experiments on two real datasets\nand a simulated dataset demonstrated effectiveness of AutoDebias. The code is\navailable at \\url{https://github.com/DongHande/AutoDebias}.\n"}
+{"abstract": "  We consider Cauchy problem of the Hartree-type nonlinear Dirac equation with\npotentials given by $V_b(x) = \\frac1{4\\pi}\\frac{e^{-b|x|}}{|x|}\\, (b \\ge 0)$.\nIn previous works, a standard argument is to utilise null form estimates in\norder to prove global well-posedness for $H^s$-data, $s>0$. However, the null\nstructure inside the equations is not enough to attain the critical regularity.\nWe impose an extra regularity assumption with respect to the angular variable.\nFirstly, we prove global well-posedness and scattering of Dirac equations with\nHartree-type nonlinearity for $b>0$ for small $L^2_x$-data with additional\nangular regularity. We also show that only small amount of angular regularity\nis required to obtain global existence of solutions. Secondly, we obtain\nnon-scattering result for a certain class of solutions with the Coulomb\npotential $b=0$.\n"}
+{"abstract": "  A common approach for feature selection is to examine the variable importance\nscores for a machine learning model, as a way to understand which features are\nthe most relevant for making predictions. Given the significance of feature\nselection, it is crucial for the calculated importance scores to reflect\nreality. Falsely overestimating the importance of irrelevant features can lead\nto false discoveries, while underestimating importance of relevant features may\nlead us to discard important features, resulting in poor model performance.\nAdditionally, black-box models like XGBoost provide state-of-the art predictive\nperformance, but cannot be easily understood by humans, and thus we rely on\nvariable importance scores or methods for explainability like SHAP to offer\ninsight into their behavior.\n  In this paper, we investigate the performance of variable importance as a\nfeature selection method across various black-box and interpretable machine\nlearning methods. We compare the ability of CART, Optimal Trees, XGBoost and\nSHAP to correctly identify the relevant subset of variables across a number of\nexperiments. The results show that regardless of whether we use the native\nvariable importance method or SHAP, XGBoost fails to clearly distinguish\nbetween relevant and irrelevant features. On the other hand, the interpretable\nmethods are able to correctly and efficiently identify irrelevant features, and\nthus offer significantly better performance for feature selection.\n"}
+{"abstract": "  We study the effect of characteristic size variation on the phonon thermal\ntransport in crystalline GeTe for a wide range of temperatures using the\nfirst-principles density-functional method coupled with the kinetic collective\nmodel approach. The characteristic size dependence of phonon thermal transport\nreveals an intriguing collective phonon transport regime, located in between\nthe ballistic and the diffusive transport regimes. Therefore, systematic\ninvestigations have been carried out to describe the signatures of phonon\nhydrodynamics via the competitive effects between grain size and temperature. A\ncharacteristic nonlocal length associated with phonon hydrodynamics and a heat\nwave propagation length has been extracted. The connections between phonon\nhydrodynamics and these length scales are discussed in terms of the Knudsen\nnumber. Further, the scaling relation of thermal conductivity as a function of\ncharacteristic size in the intermediate size range emerges as a crucial\nindicator of the strength of the hydrodynamic behavior. A ratio concerning\nnormal and resistive scattering rates has been employed to understand these\ndifferent scaling relations, which seems to control the strength and prominent\nvisibility of the collective phonon transport in GeTe. This systematic\ninvestigation emphasizes the importance of the competitive effects between\ntemperature and characteristic size on phonon hydrodynamics in GeTe, which can\nlead to a better understanding of the generic behavior and consequences of the\nphonon hydrodynamics and its controlling parameters in low-thermal conductivity\nmaterials.\n"}
+{"abstract": "  We present a 5A-100um Spectral Energy Distribution (SED) of the ultracool\ndwarf star TRAPPIST-1, obtained as part of the Mega-MUSCLES Treasury Survey.\nThe SED combines ultraviolet and blue-optical spectroscopy obtained with the\nHubble Space Telescope, X-ray spectroscopy obtained with XMM-Newton, and models\nof the stellar photosphere, chromosphere, transition region and corona. A new\nDifferential Emission Measure model of the unobserved extreme-ultraviolet\nspectrum is provided, improving on the Lyman alpha to EUV relations often used\nto estimate the 100-911A flux from low-mass stars. We describe the observations\nand models used, as well as the recipe for combining them into an SED. We also\nprovide a semi-empirical, noise-free model of the stellar ultraviolet spectrum\nbased on our observations for use in atmospheric modelling of the TRAPPIST-1\nplanets.\n"}
+{"abstract": "  Artificial Intelligence (AI) covers a broad spectrum of computational\nproblems and use cases. Many of those implicate profound and sometimes\nintricate questions of how humans interact or should interact with AIs.\nMoreover, many users or future users do have abstract ideas of what AI is,\nsignificantly depending on the specific embodiment of AI applications.\nHuman-centered-design approaches would suggest evaluating the impact of\ndifferent embodiments on human perception of and interaction with AI. An\napproach that is difficult to realize due to the sheer complexity of\napplication fields and embodiments in reality. However, here XR opens new\npossibilities to research human-AI interactions. The article's contribution is\ntwofold: First, it provides a theoretical treatment and model of human-AI\ninteraction based on an XR-AI continuum as a framework for and a perspective of\ndifferent approaches of XR-AI combinations. It motivates XR-AI combinations as\na method to learn about the effects of prospective human-AI interfaces and\nshows why the combination of XR and AI fruitfully contributes to a valid and\nsystematic investigation of human-AI interactions and interfaces. Second, the\narticle provides two exemplary experiments investigating the aforementioned\napproach for two distinct AI-systems. The first experiment reveals an\ninteresting gender effect in human-robot interaction, while the second\nexperiment reveals an Eliza effect of a recommender system. Here the article\nintroduces two paradigmatic implementations of the proposed XR testbed for\nhuman-AI interactions and interfaces and shows how a valid and systematic\ninvestigation can be conducted. In sum, the article opens new perspectives on\nhow XR benefits human-centered AI design and development.\n"}
+{"abstract": "  In recent years, recommender systems play a pivotal role in helping users\nidentify the most suitable items that satisfy personal preferences. As\nuser-item interactions can be naturally modelled as graph-structured data,\nvariants of graph convolutional networks (GCNs) have become a well-established\nbuilding block in the latest recommenders. Due to the wide utilization of\nsensitive user profile data, existing recommendation paradigms are likely to\nexpose users to the threat of privacy breach, and GCN-based recommenders are no\nexception. Apart from the leakage of raw user data, the fragility of current\nrecommenders under inference attacks offers malicious attackers a backdoor to\nestimate users' private attributes via their behavioral footprints and the\nrecommendation results. However, little attention has been paid to developing\nrecommender systems that can defend such attribute inference attacks, and\nexisting works achieve attack resistance by either sacrificing considerable\nrecommendation accuracy or only covering specific attack models or protected\ninformation. In our paper, we propose GERAI, a novel differentially private\ngraph convolutional network to address such limitations. Specifically, in\nGERAI, we bind the information perturbation mechanism in differential privacy\nwith the recommendation capability of graph convolutional networks.\nFurthermore, based on local differential privacy and functional mechanism, we\ninnovatively devise a dual-stage encryption paradigm to simultaneously enforce\nprivacy guarantee on users' sensitive features and the model optimization\nprocess. Extensive experiments show the superiority of GERAI in terms of its\nresistance to attribute inference attacks and recommendation effectiveness.\n"}
+{"abstract": "  This paper deals with the scarcity of data for training optical flow\nnetworks, highlighting the limitations of existing sources such as labeled\nsynthetic datasets or unlabeled real videos. Specifically, we introduce a\nframework to generate accurate ground-truth optical flow annotations quickly\nand in large amounts from any readily available single real picture. Given an\nimage, we use an off-the-shelf monocular depth estimation network to build a\nplausible point cloud for the observed scene. Then, we virtually move the\ncamera in the reconstructed environment with known motion vectors and rotation\nangles, allowing us to synthesize both a novel view and the corresponding\noptical flow field connecting each pixel in the input image to the one in the\nnew frame. When trained with our data, state-of-the-art optical flow networks\nachieve superior generalization to unseen real data compared to the same models\ntrained either on annotated synthetic datasets or unlabeled videos, and better\nspecialization if combined with synthetic images.\n"}
+{"abstract": "  We introduce a protocol to transfer excitations between two noninteracting\nqubits via purely dissipative processes (i.e., in the Lindblad master equation\nthere is no coherent interaction between the qubits). The fundamental\ningredients are the presence of collective (i.e. nonlocal) dissipation and\nunbalanced local dissipation rates (the qubits dissipate at different rates).\nThe resulting quantum trajectories show that the measurement backaction changes\nthe system wave function and induces a passage of the excitation from one qubit\nto the other. While similar phenomena have been witnessed for a non-Markovian\nenvironment, here the dissipative quantum state transfer is induced by an\nupdate of the observer knowledge of the wave function in the presence of a\nMarkovian (memoryless) environment -- this is a single quantum trajectory\neffect. Beyond single quantum trajectories and postselection, such an effect\ncan be observed by histogramming the quantum jumps along several realizations\nat different times. By investigating the effect of the temperature in the\npresence of unbalanced local dissipation, we demonstrate that, if appropriately\nswitched on and off, the collective dissipator can act as a Maxwell's demon.\nThese effects are a generalized measure equivalent to the standard projective\nmeasure description of quantum teleportation and Maxwell's demon. They can be\nwitnessed in state-of-the-art setups given the extreme experimental control in,\ne.g., superconducting qubits or Rydberg atoms.\n"}
+{"abstract": "  The linear operator $c + (-\\Delta)^{\\alpha/2}$, where $c > 0$ and\n$(-\\Delta)^{\\alpha/2}$ is the fractional Laplacian on the periodic domain,\narises in the existence of periodic travelling waves in the fractional\nKorteweg--de Vries equation. We establish a relation of the Green's function of\nthis linear operator with the Mittag--Leffler function, which was previously\nused in the context of Riemann--Liouville's and Caputo's fractional\nderivatives. By using this relation, we prove that Green's function is strictly\npositive and single-lobe (monotonically decreasing away from the maximum point)\nfor every $c > 0$ and every $\\alpha \\in (0,2]$. On the other hand, we argue\nfrom numerical approximations that in the case of $\\alpha \\in (2,4]$, the\nGreen's function is positive and single-lobe for small $c$ and non-positive and\nnon-single lobe for large $c$.\n"}
+{"abstract": "  Combinatorial bandits with semi-bandit feedback generalize multi-armed\nbandits, where the agent chooses sets of arms and observes a noisy reward for\neach arm contained in the chosen set. The action set satisfies a given\nstructure such as forming a base of a matroid or a path in a graph. We focus on\nthe pure-exploration problem of identifying the best arm with fixed confidence,\nas well as a more general setting, where the structure of the answer set\ndiffers from the one of the action set. Using the recently popularized game\nframework, we interpret this problem as a sequential zero-sum game and develop\na CombGame meta-algorithm whose instances are asymptotically optimal algorithms\nwith finite time guarantees. In addition to comparing two families of learners\nto instantiate our meta-algorithm, the main contribution of our work is a\nspecific oracle efficient instance for best-arm identification with\ncombinatorial actions. Based on a projection-free online learning algorithm for\nconvex polytopes, it is the first computationally efficient algorithm which is\nasymptotically optimal and has competitive empirical performance.\n"}
+{"abstract": "  We present StyleTune, a mobile app for interactive multi-level control of\nneural style transfers that facilitates creative adjustments of style elements\nand enables high output fidelity. In contrast to current mobile neural style\ntransfer apps, StyleTune supports users to adjust both the size and orientation\nof style elements, such as brushstrokes and texture patches, on a global as\nwell as local level. To this end, we propose a novel stroke-adaptive\nfeed-forward style transfer network, that enables control over stroke size and\nintensity and allows a larger range of edits than current approaches. For\nadditional level-of-control, we propose a network agnostic method for\nstroke-orientation adjustment by utilizing the rotation-variance of CNNs. To\nachieve high output fidelity, we further add a patch-based style transfer\nmethod that enables users to obtain output resolutions of more than 20\nMegapixel. Our approach empowers users to create many novel results that are\nnot possible with current mobile neural style transfer apps.\n"}
+{"abstract": "  We consider theoretically the influence of the flexoelectric coupling on the\nspatial distribution and temperature behavior of spontaneous polarization for\nseveral types of stable domain structure in thin ferroelectric films, such as\nstripe domains and vortices.Finite element modelling (FEM) for BaTiO3 films and\nanalytical calculations within the Landau-Ginzburg-Devonshire approach reveals\nthat an out-of-plane polarization component can be very sensitive to the\nflexoelectric coupling for periodic quasi-2D stripe domains and 3D\nvortex-antivortex structures.However, the influence is rather different for\nthese structures. The flexoelectric coupling increases significantly the\namplitude of a small out-of-plane polarization component in the stripe domains,\nand the \"up\" or \"down\" direction of the component is defined by the sign of the\nflexoelectric coefficients.Concerning the vortex-antivortex pairs, their\nantivortices with in-plane anti-circulation have smooth wide dipolar cores\nthrough the entire film, whose shape and other features are almost insensitive\nto the coupling.The vortices with in-plane vorticity have spike-like cores with\nan out-of-plane quadrupolar moment induced by the flexoelectric coupling. The\ncores are located near the film-dead layer interfaces.FEM results corroborated\nby analytical calculations prove that a change of the flexoelectric coefficient\nsign leads to a reorientation of the core axial polarization, making the\nflexo-sensitive 3D vortices similar to the recently introduced \"flexons\" in\ncylindrical nanoparticles.The relatively wide temperature range (from 200 to\n400 K) of the flexo-sensitive vortices' existence gives us the hope that they\ncan be observed experimentally in thin ferroelectric films by scanning probe\nand nonlinear optical microscopy methods.\n"}
+{"abstract": "  Definitions of new symbols merely abbreviate expressions in logical\nframeworks, and no new facts (regarding previously defined symbols) should hold\nbecause of a new definition. In Isabelle/HOL, definable symbols are types and\nconstants. The latter may be ad-hoc overloaded, i.e. have different definitions\nfor non-overlapping types. We prove that symbols that are independent of a new\ndefinition may keep their interpretation in a model extension. This work\nrevises our earlier notion of model-theoretic conservative extension and\ngeneralises an earlier model construction. We obtain consistency of theories of\ndefinitions in higher-order logic (HOL) with ad-hoc overloading as a corollary.\nOur results are mechanised in the HOL4 theorem prover.\n"}
+{"abstract": "  In this paper, we first generalize to any hyper-K\\\"ahler manifold with\nnonzero third Betti number results proved by O'Grady for hyper-K\\\"ahler\nmanifolds of generalized Kummer type. In the second part, we restrict to\nhyper-K\\\"ahler manifolds of generalized Kummer type and prove, using results of\nMarkman, that their Kuga-Satake correspondence is algebraic.\n"}
+{"abstract": "  While classical forms of stochastic gradient descent algorithm treat the\ndifferent coordinates in the same way, a framework allowing for adaptive (non\nuniform) coordinate sampling is developed to leverage structure in data. In a\nnon-convex setting and including zeroth order gradient estimate, almost sure\nconvergence as well as non-asymptotic bounds are established. Within the\nproposed framework, we develop an algorithm, MUSKETEER, based on a\nreinforcement strategy: after collecting information on the noisy gradients, it\nsamples the most promising coordinate (all for one); then it moves along the\none direction yielding an important decrease of the objective (one for all).\nNumerical experiments on both synthetic and real data examples confirm the\neffectiveness of MUSKETEER in large scale problems.\n"}
+{"abstract": "  Evolutionary optimization algorithms, including particle swarm optimization\n(PSO), have been successfully applied in oil industry for production planning\nand control. Such optimization studies are quite challenging due to large\nnumber of decision variables, production scenarios, and subsurface\nuncertainties. In this work, a multi-stage, multi-swarm PSO (MS2PSO) is\nproposed to fix certain issues with canonical PSO algorithm such as premature\nconvergence, excessive influence of global best solution, and oscillation.\n  Multiple experiments are conducted using Olympus benchmark to compare the\nefficacy of algorithms. Canonical PSO hyperparameters are first tuned to\nprioritize exploration in early phase and exploitation in late phase. Next, a\ntwo-stage multi-swarm PSO (2SPSO) is used where multiple-swarms of the first\nstage collapse into a single swarm in the second stage. Finally, MS2PSO with\nmultiple stages and multiple swarms is used in which swarms recursively\ncollapse after each stage. Multiple swarm strategy ensures that diversity is\nretained within the population and multiple modes are explored. Staging ensures\nthat local optima found during initial stage does not lead to premature\nconvergence. Optimization test case comprises of 90 control variables and a\ntwenty year period of flow simulation. It is observed that different algorithm\ndesigns have their own benefits and drawbacks. Multiple swarms and stages help\nalgorithm to move away from local optima, but at the same time they may also\nnecessitate larger number of iterations for convergence. Both 2SPSO and MS2PSO\nare found to be helpful for problems with high dimensions and multiple modes\nwhere greater degree of exploration is desired.\n"}
+{"abstract": "  Linked Data Fragments (LDFs) refer to Web interfaces that allow for accessing\nand querying Knowledge Graphs on the Web. These interfaces, such as SPARQL\nendpoints or Triple Pattern Fragment servers, differ in the SPARQL expressions\nthey can evaluate and the metadata they provide. Client-side query processing\napproaches have been proposed and are tailored to evaluate queries over\nindividual interfaces. Moreover, federated query processing has focused on\nfederations with a single type of LDF interface, typically SPARQL endpoints. In\nthis work, we address the challenges of SPARQL query processing over\nfederations with heterogeneous LDF interfaces. To this end, we formalize the\nconcept of federations of Linked Data Fragment and propose a framework for\nfederated querying over heterogeneous federations with different LDF\ninterfaces. The framework comprises query decomposition, query planning, and\nphysical operators adapted to the particularities of different LDF interfaces.\nFurther, we propose an approach for each component of our framework and\nevaluate them in an experimental study on the well-known FedBench benchmark.\nThe results show a substantial improvement in performance achieved by devising\nthese interface-aware approaches exploiting the capabilities of heterogeneous\ninterfaces in federations.\n"}
+{"abstract": "  We demonstrate the use of Adaptive Stress Testing to detect and address\npotential vulnerabilities in a financial environment. We develop a simplified\nmodel for credit card fraud detection that utilizes a linear regression\nclassifier based on historical payment transaction data coupled with business\nrules. We then apply the reinforcement learning model known as Adaptive Stress\nTesting to train an agent, that can be thought of as a potential fraudster, to\nfind the most likely path to system failure -- successfully defrauding the\nsystem. We show the connection between this most likely failure path and the\nlimits of the classifier and discuss how the fraud detection system's business\nrules can be further augmented to mitigate these failure modes.\n"}
+{"abstract": "  This paper presents a physics-based constitutive equation to predict the\nthermo-chemical aging in elastomers. High-temperature oxidation in elastomers\nis a complex phenomenon. The macromolecular network of elastomers'\nmicrostructures undergoes chain scission and crosslinking under high\ntemperature and oxygen diffusion conditions. In this work, we modify the\nnetwork stiffness and the chain extensibility in the Arruda-Boyce well-known\neight-chain constitutive equation to incorporate the additional Helmholtz free\nenergy due to network changes in elastomers' microstructures. The effect of\nnetwork evolution due to aging in changing the shear modulus and the number of\nchain monomers is considered. The modification is based on chemical\ncharacterization tests, namely the equilibrium swelling experiment to measure\nthe crosslink density evolution. The developed constitutive equation predicts\nthe mechanical responses of thermo-chemically aged elastomers independent of\nany mechanical tests on aged samples. The proposed constitutive equation is\nvalidated with respect to a comprehensive set of experimental data available in\nthe literature that were designed to capture thermo-chemical aging effects in\nelastomers. The comparison showed that the constitutive equation can accurately\npredict the intermittent tensile tests based on crosslink density evolution\ninput. The developed constitutive equation is physics-based, simple, and\nincludes minimal material parameters.\n"}
+{"abstract": "  We are interested in the Klein-Gordon-Zakharov equations in\n$\\mathbb{R}^{1+3}$, and we aim to show that the energy for the global solution\nto the equations is uniformly bounded, and we do not require the compactness\nassumption on the initial data. To achieve these goals, the key is to apply\nAlinhac's ghost weight energy estimates adapted to the Klein-Gordon equations.\n"}
+{"abstract": "  In this paper we develop and significantly extend the thermal phase change\nmodel, introduced in [12], describing the process of paraffinic wax layer\nformation on the interior wall of a circular pipe transporting heated oil, when\nsubject to external cooling. In particular we allow for the natural dependence\nof the solidifying paraffinic wax conductivity on local temperature. We are\nable to develop a complete theory, and provide efficient numerical\ncomputations, for this extended model. Comparison with recent experimental\nobservations is made, and this, together with recent reviews of the physical\nmechanisms associated with wax layer formation, provide significant support for\nthe thermal model considered here.\n"}
+{"abstract": "  Biphoton frequency comb (BFC) having quantum entanglement in a high\ndimensional system is widely applicable to quantum communication and quantum\ncomputation. However, a dozen mode realized so far has not been enough to\nrealize its full potential. Here, we show a massive-mode BFC with polarization\nentanglement experimentally realized by a nonlinear optical waveguide\nresonator. The generated BFC at least 1400 modes is broad and dense, that\nstrongly enhances the advantage of BFC. We also demonstrated a versatile\nproperty of the present BFC, which enables us to prepare both the\nfrequency-multiplexed entangled photon pair and the high dimensional\nhyperentangled one. The versatile, stable and highly efficient system with the\nmassive-mode BFC will open up a large-scale photonic quantum information\nplatform.\n"}
+{"abstract": "  To unfold the tremendous amount of multimedia data uploaded daily to social\nmedia platforms, effective topic modeling techniques are needed. Existing work\ntends to apply topic models on written text datasets. In this paper, we propose\na topic extractor on video transcripts. Exploiting neural word embeddings\nthrough graph-based clustering, we aim to improve usability and semantic\ncoherence. Unlike most topic models, this approach works without knowing the\ntrue number of topics, which is important when no such assumption can or should\nbe made. Experimental results on the real-life multimodal dataset MuSe-CaR\ndemonstrates that our approach GraphTMT extracts coherent and meaningful topics\nand outperforms baseline methods. Furthermore, we successfully demonstrate the\napplicability of our approach on the popular Citysearch corpus.\n"}
+{"abstract": "  We present infrared spectral indices (1.0-2.3 um) of Galactic late-type\ngiants and red supergiants (RSGs). We used existing and new spectra obtained at\nresolution power R=2000 with SpeX on the IRTF telescope. While a large CO\nequivalent width (EW), at 2.29 um ([CO, 2.29]>45 AA) is a typical signature of\nRSGs later than spectral type M0, [CO] of K-type RSGs and giants are similar.\nIn the [CO, 2.29] versus [Mg I, 1.71] diagram, RSGs of all spectral types can\nbe distinguished from red giants, because the Mg I line weakens with increasing\ntemperature and decreasing gravity. We find several lines that vary with\nluminosity, but not temperature: Si I (1.59 um), Sr (1.033 um), Fe+Cr+Si+CN\n(1.16 um), Fe+Ti (1.185 um), Fe+Ti (1.196 um), Ti+Ca (1.28 um), and Mn (1.29\num). Good markers of CN enhancement are the Fe+Si+CN line at 1.087 um and CN\nline at 1.093 um. Using these lines, at the resolution of SpeX, it is possible\nto separate RSGs and giants. Contaminant O-rich Mira and S-type AGBs are\nrecognized by strong molecular features due to water vapor features, TiO band\nheads, and/or ZrO absorption. Among the 42 candidate RSGs that we observed, all\nbut one were found to be late-types. 21 have EWs consistent with those of RSGs,\n16 with those of O-rich Mira AGBs, and one with an S-type AGB. These infrared\nresults open new, unexplored, potential for searches at low-resolution of RSGs\nin the highly obscured innermost regions of the Milky Way.\n"}
+{"abstract": "  Games potentially provide a wealth of knowledge about our shared cultural\npast and the development of human civilisation, but our understanding of early\ngames is incomplete and often based on unreliable reconstructions. This paper\ndescribes the Digital Ludeme Project, a five-year research project currently\nunderway that aims to address such issues using modern computational\ntechniques.\n"}
+{"abstract": "  Images captured in snowy days suffer from noticeable degradation of scene\nvisibility, which degenerates the performance of current vision-based\nintelligent systems. Removing snow from images thus is an important topic in\ncomputer vision. In this paper, we propose a Deep Dense Multi-Scale Network\n(\\textbf{DDMSNet}) for snow removal by exploiting semantic and geometric\npriors. As images captured in outdoor often share similar scenes and their\nvisibility varies with depth from camera, such semantic and geometric\ninformation provides a strong prior for snowy image restoration. We incorporate\nthe semantic and geometric maps as input and learn the semantic-aware and\ngeometry-aware representation to remove snow. In particular, we first create a\ncoarse network to remove snow from the input images. Then, the coarsely\ndesnowed images are fed into another network to obtain the semantic and\ngeometric labels. Finally, we design a DDMSNet to learn semantic-aware and\ngeometry-aware representation via a self-attention mechanism to produce the\nfinal clean images. Experiments evaluated on public synthetic and real-world\nsnowy images verify the superiority of the proposed method, offering better\nresults both quantitatively and qualitatively.\n"}
+{"abstract": "  The advanced performance of depth estimation is achieved by the employment of\nlarge and complex neural networks. While the performance is still being\ncontinuously improved, we argue that the depth estimation has to be efficient\nas well since it is a preliminary requirement for real-world applications.\nHowever, fast depth estimation tends to lower the performance as the trade-off\nbetween the model's capacity and accuracy. In this paper, we aim to achieve\naccurate depth estimation with a light-weight network. To this end, we first\nintroduce a highly compact network that can estimate a depth map in real-time.\nWe then develop a knowledge distillation paradigm to further improve the\nperformance. We observe that many scenarios have the same scene scales in\nreal-world, yielding similar depth histograms, thus they are potentially\nvaluable and applicable to develop a better learning strategy. Therefore, we\npropose to employ auxiliary unlabeled/labeled data to improve knowledge\ndistillation. Through extensive and rigorous experiments, we show that our\nmethod can achieve comparable performance against state-of-the-of-art methods\nwith only 1% parameters, and outperforms previous light-weight methods in terms\nof inference accuracy, computational efficiency and generalizability.\n"}
+{"abstract": "  Insurers are increasingly adopting more demand-based strategies to\nincorporate the indirect effect of premium changes on their policyholders'\nwillingness to stay. However, since in practice both insurers' renewal premia\nand customers' responses to these premia typically depend on the customer's\nlevel of risk, it remains challenging in these strategies to determine how to\nproperly control for this confounding. We therefore consider a causal inference\napproach in this paper to account for customer price sensitivities and to\ndeduce optimal, multi-period profit maximizing premium renewal offers. More\nspecifically, we extend the discrete treatment framework of Guelman and\nGuill\\'en (2014) by Extreme Gradient Boosting, or XGBoost, and by multiple\nimputation to better account for the uncertainty in the counterfactual\nresponses. We additionally introduce the continuous treatment framework with\nXGBoost to the insurance literature to allow identification of the exact\noptimal renewal offers and account for any competition in the market by\nincluding competitor offers. The application of the two treatment frameworks to\na Dutch automobile insurance portfolio suggests that a policy's competitiveness\nin the market is crucial for a customer's price sensitivity and that XGBoost is\nmore appropriate to describe this than the traditional logistic regression.\nMoreover, an efficient frontier of both frameworks indicates that substantially\nmore profit can be gained on the portfolio than realized, also already with\nless churn and in particular if we allow for continuous rate changes. A\nmulti-period renewal optimization confirms these findings and demonstrates that\nthe competitiveness enables temporal feedback of previous rate changes on\nfuture demand.\n"}
+{"abstract": "  A high-quality factor microwave resonator in the presence of a strong\nmagnetic field could have a wide range of applications, such as axion dark\nmatter searches where the two aspects must coexist to enhance the experimental\nsensitivity. We introduce a polygon-shaped cavity design with bi-axially\ntextured YBa$_{2}$Cu$_{3}$O$_{7-x}$ superconducting tapes covering the entire\ninner wall. Using a 12-sided polygon cavity, we obtain substantially improved\nquality factors of the TM$_{010}$ mode at 6.9 GHz at 4 K with respect to a\ncopper cavity and observe no considerable degradation in the presence of\nmagnetic fields up to 8 T. This corresponds to the first demonstration of\npractical applications of superconducting radio frequency technology for axion\nand other research areas requiring low loss in a strong magnetic field. We\naddress the importance of the successful demonstration and discuss further\nimprovements.\n"}
+{"abstract": "  We propose a floating tunable coupler that does not rely on direct\nqubit-qubit coupling capacitances to achieve the zero-coupling condition. We\nshow that the polarity of the qubit-coupler couplings can be engineered to\noffset the otherwise constant qubit-qubit coupling and attain the zero-coupling\ncondition when the coupler frequency is above or below the qubit frequencies.\nWe experimentally demonstrate these two operating regimes of the tunable\ncoupler by implementing symmetric and asymmetric configurations of the\ncoupler's superconducting pads with respect to the qubits. Such a floating\ntunable coupler provides flexibility in designing large-scale quantum\nprocessors while reducing the always-on residual couplings.\n"}
+{"abstract": "  Objective: To investigate the impact of different logistic regression\nestimators applied to RDS samples obtained by simulation and real data.\nMethods: Four simulated populations were created combining different\nconnectivity models, levels of clusterization and infection processes. Each\nsubject in the population received two attributes, only one of them related to\nthe infection process. From each population, RDS samples with different sizes\nwere obtained. Similarly, RDS samples were obtained from a real-world dataset.\nThree logistic regression estimators were applied to assess the association\nbetween the attributes and the infection status, and subsequently the observed\ncoverage of each was measured. Results: The type of connectivity had more\nimpact on estimators performance than the clusterization level. In simulated\ndatasets, unweighted logistic regression estimators emerged as the best option,\nalthough all estimators showed a fairly good performance. In the real dataset,\nthe performance of weighted estimators presented some instabilities, making\nthem a risky option. Conclusion: An unweighted logistic regression estimator is\na reliable option to be applied to RDS samples, with similar performance to\nrandom samples and, therefore, should be the preferred option.\n"}
+{"abstract": "  In situ thermal transport measurement of flowing fluid could be useful for\nthe characterization and diagnosis of practical thermal systems such as fluid\nheat exchangers and thermal energy storage systems. Despite abundant reports on\nthe ex-situ thermal conductivity measurement of stagnant fluids, a suitable\ntechnique for the thermal conductivity measurement of flowing fluid has been\nrarely reported. This paper presents the thermal conductivity measurement of\nflowing fluid within a pipe using a non-contact modulated photothermal\nradiometry (MPR) technique, where the surface of the pipe is heated by an\nintensity-modulated laser and the heat diffuses into the fluid with suitable\nmodulation frequency. We design a tube section with small wall thickness\nsuitable for the MPR measurements to maximize the sensitivity of the thermal\nresponse to the fluid properties while minimizing the lateral heat spreading\neffect. Intrinsic thermal conductivity of different fluids was obtained within\na proper range of frequency and flow velocity where the forced convection\neffect is negligible. The forced convection effect became prominent at high\nflowing velocity and at low modulation frequency, leading to overestimated\nthermal conductivity of fluid. It is found that the intrinsic thermal\nconductivity could be obtained when the flow velocity is less than 100 mm/sec\nand ReD1/2Pr1/3 < 100 for DI water and Xceltherm oil under the specified\nexperimental conditions, where Re_D is the Reynolds number and Pr is the\nPrandtl number.\n"}
+{"abstract": "  Let $K$ be a field. The \\'etale open topology on the $K$-points $V(K)$ of a\n$K$-variety $V$ was introduced in our previous work. The \\'etale open topology\nis non-discrete if and only if $K$ is large. If $K$ is separably, real,\n$p$-adically closed then the \\'etale open topology agrees with the Zariski,\norder, valuation topology, respectively. We show that existentially definable\nsets in perfect large fields behave well with respect to this topology: such\nsets are finite unions of \\'etale open subsets of Zariski closed sets. This\nimplies that existentially definable sets in arbitrary perfect large fields\nenjoy some of the well-known topological properties of definable sets in\nalgebraically, real, and $p$-adically closed fields. We introduce and study the\nclass of \\'ez fields: $K$ is \\'ez if $K$ is large and every definable set is a\nfinite union of \\'etale open subsets of Zariski closed sets. This should be\nseen as a generalized notion of model completeness for large fields.\nAlgebraically closed, real closed, $p$-adically closed, and bounded\n$\\mathrm{PAC}$ fields are \\'ez. (In particular pseudofinite fields and infinite\nalgebraic extensions of finite fields are \\'ez.) We develop the basics of a\ntheory of definable sets in \\'ez fields. This gives a uniform approach to the\ntheory of definable sets across all characteristic zero local fields and a new\ntopological theory of definable sets in bounded $\\mathrm{PAC}$ fields. We also\nshow that some prominent examples of possibly non-model complete\nmodel-theoretically tame fields (characteristic zero Henselian fields and\nFrobenius fields) are \\'ez.\n"}
+{"abstract": "  Microwave pulse sequences are the basis of coherent manipulation of the\nelectronic spin ground state in nitrogen-vacancy (NV) centers. In this work we\ndemonstrate stimulated Raman transitions (SRT) and stimulated Raman adiabatic\npassage (STIRAP), two ways to drive the dipole-forbidden transition between two\nspin sublevels in the electronic triplet ground state of the NV center. This is\nachieved by a multitone Raman microwave pulse which simultaneously drives two\ndetuned transitions via a virtual level for SRT or via two adiabatic and\npartially overlapping resonant microwave pulses for STIRAP. We lay the\ntheoretical framework of SRT and STIRAP dynamics and verify experimentally the\ntheoretical predictions of population inversion by observing the\ndipole-forbidden transition in the ground state of a single NV center. A\ncomparison of the two schemes showed a better robustness and success of the\nspin swap for STIRAP as compared to SRT.\n"}
+{"abstract": "  Does engagement with opposing views help break down ideological `echo\nchambers'; or does it backfire and reinforce them? This question remains\ncritical as academics, policymakers and activists grapple with the question of\nhow to regulate political discussion on social media. In this study, we\ncontribute to the debate by examining the impact of opposing views within a\nmajor climate change skeptic online community on Reddit. A large sample of\nposts (N = 3000) was manually coded as either dissonant or consonant which\nallowed the automated classification of the full dataset of more than 50,000\nposts, with codes inferred from linked websites. We find that ideologically\ndissonant submissions act as a stimulant to activity in the community: they\nreceived more attention (comments) than consonant submissions, even though they\nreceived lower scores through up-voting and down-voting. Users who engaged with\ndissonant submissions were also more likely to return to the forum. Consistent\nwith identity theory, confrontation with opposing views triggered activity in\nthe forum, particularly among users that are highly engaged with the community.\nIn light of the findings, theory of social identity and echo chambers is\ndiscussed and enhanced.\n"}
+{"abstract": "  At present, more than a billion closed-circuit television (CCTV) cameras are\nwatching the world. These cameras garner a lot of visual information that is\noften processed and stored in remote and centralized cloud servers. Multiple\noccasions have revealed that this traditional approach is plagued with security\nand privacy breaches. The breaches could be the interception of raw videos\nwhile in transit to distant surveillance analytics centers (SAC), infiltration\nto cameras and network video records (NVR), or abuse of cameras and stored\nvideos. Hence, the traditional video surveillance system (VSS) cannot guarantee\nthe protection of the privacy of individuals caught on CCTV cameras. Wherefore,\nthis paper proposes a Secure and Privacy-preserving Stored surveillance video\nsharing (SePriS) mechanism for authorized users/nodes based on smart contracts,\nblockchain (BC), and the enciphering of video frames using DAB, a mechanism\ndeveloped based on discrete cosine transform (DCT), advanced encryption\nstandard (AES), and block shuffling (BS) algorithm. The BC-based solution\ncreates an environment auspicious for creating decentralized, reliable SACs and\nstorage sites with secure and privacy-aware sharing of stored surveillance\nvideos across SAC nodes and by law enforcers, police departments, and courts\nsecurely connected to the SAC nodes. The experiments and analyses validate that\nthe proposed BC-based SePriS solution achieves the design purpose.\n"}
+{"abstract": "  The growth of internet users in Indonesia gives an impact on many aspects of\ndaily life, including commerce. Indonesian small-medium enterprises took this\nadvantage of new media to derive their activity by the meaning of online\ncommerce. Until now, there is no known practical implementation of how to\npredict their sales and revenue using their historical transaction. In this\npaper, we build a sales prediction model on the Indonesian footwear industry\nusing real-life data crawled on Tokopedia, one of the biggest e-commerce\nproviders in Indonesia. Data mining is a discipline that can be used to gather\ninformation by processing the data. By using the method of classification in\ndata mining, this research will describe patterns of the market and predict the\npotential of the region in the national market commodities. Our approach is\nbased on the classification decision tree. We managed to determine predicted\nthe number of items sold by the viewers, price, and type of shoes.\n"}
+{"abstract": "  We shall prove a weak law of large numbers for the uncorrelated (see\nDefinition 3.1) fuzzy random variable sequence with respect to the uniform\nHausdorff metric $d_H^{\\infty}$, which is an extension of weak law of large\nnumbers for independent fuzzy random variables.\n"}
+{"abstract": "  We propose a robust universal approach to identify multiple dynamical states,\nincluding stationary and travelling chimera states based on an adaptive\ncoherence measure. Our approach allows automatic disambiguation of synchronized\nclusters, travelling waves, chimera states, and asynchronous regimes. In\naddition, our method can determine the number of clusters in the case of\ncluster synchronization. We further couple our approach with a new speed\ncalculation method for travelling chimeras. We validate our approach by an\nexample of a ring network of type II Morris-Lecar neurons with asymmetrical\nnonlocal inhibitory connections where we identify a rich repertoire of coherent\nand wave states. We propose that the method is robust for the networks of phase\noscillators and extends to a general class of relaxation oscillator networks.\n"}
+{"abstract": "  Predicting outcomes, such as survival or metastasis for individual cancer\npatients is a crucial component of precision oncology. Machine learning (ML)\noffers a promising way to exploit rich multi-modal data, including clinical\ninformation and imaging to learn predictors of disease trajectory and help\ninform clinical decision making. In this paper, we present a novel graph-based\napproach to incorporate imaging characteristics of existing cancer spread to\nlocal lymph nodes (LNs) as well as their connectivity patterns in a prognostic\nML model. We trained an edge-gated Graph Convolutional Network (Gated-GCN) to\naccurately predict the risk of distant metastasis (DM) by propagating\ninformation across the LN graph with the aid of soft edge attention mechanism.\nIn a cohort of 1570 head and neck cancer patients, the Gated-GCN achieves AUROC\nof 0.757 for 2-year DM classification and $C$-index of 0.725 for lifetime DM\nrisk prediction, outperforming current prognostic factors as well as previous\napproaches based on aggregated LN features. We also explored the importance of\ngraph structure and individual lymph nodes through ablation experiments and\ninterpretability studies, highlighting the importance of considering individual\nLN characteristics as well as the relationships between regions of cancer\nspread.\n"}
+{"abstract": "  Harnessing flexibility from distributed energy resources (DER) to participate\nin various markets while accounting for relevant technical and commercial\nconstraints is essential for the development of low-carbon grids. However,\nthere is no clear definition or even description of the salient features of\naggregated DER flexibility, including its steady-state and dynamic features and\nhow these are impacted by network constraints and market requirements. This\npaper proposes a comprehensive DER flexibility modelling and characterisation\nframework that is based on the concept of nodal operating envelope (NOE). In\nparticular, capacity, ramp, duration and cost are identified as key flexibility\nmetrics and associated with different but consistent NOEs describing\ncapability, feasibility, ramp, duration, economic, technical and commercial\nflexibility features. These NOEs, which conceptually arise from a Venn diagram,\ncan be built via optimal power flow (OPF) analysis, visualised in the\nactive-reactive power space, and used by different stakeholders. Results from a\ncanonical test system and a real distribution system illustrate the value and\napplicability of the proposed framework to model and characterise provision of\nflexibility and market services from DER for different use cases.\n"}
+{"abstract": "  We present a model for the configuration of noninteracting material that is\nejected in a continuous manner from an atmosphereless gravitating body for a\ngiven distribution of sources. The model is applicable to material on bound or\nunbound trajectories and to steady and nonsteady modes of ejection. For a jet\nthat is inclined to the surface normal, we related the distributions of\nejection direction, velocity, and size to the phase-space number density at the\ndistance from the source body. Integrating over velocity space, we obtained an\nexpression from which we inferred the density, flux, or optical depth of the\nejected material. As examples for the application of the code, we calculate\nprofiles of the dust density in the Enceladus plume, the pattern of mass\ndeposition rates around a plume on Europa, and images of optical depth\nfollowing the nonstationary emission of material from a volcano on Io. We make\nthe source code of a Fortran-95 implementation of the model freely available.\n"}
+{"abstract": "  We consider Takahashi's categorical interpretation of the Berglund-Hubsch\nmirror symmetry conjecture for invertible polynomials in the case of chain\npolynomials. Our strategy is based on a stronger claim that the relevant\ncategories satisfy a recursion of directed $A_{\\infty}$-categories, which may\nbe of independent interest. We give a full proof of this claim on the B-side.\nOn the A-side we give a detailed sketch of an argument, which falls short of a\nfull proof because of certain missing foundational results in Fukaya-Seidel\ncategories, most notably a generation statement.\n"}
+{"abstract": "  The changes of magnetic properties upon heat treatment of a metastable\nsupersaturated solid solution processed by severe plastic deformation are\ninvestigated by in-situ AC-hysteresis measurements. Data are analyzed in the\nframework of dynamic loss theory, with correlative investigations of the\nmicrostructural properties. The evolution of hysteresis upon annealing points\nout that the single-phase supersaturated solid solution remains stable up to\n400{\\deg}C, then hindering of domain wall motion sets in at this temperature.\nAt 600{\\deg}C, a multi phase microstructure is present, causing a significant\nincrease in coercivity.\n"}
+{"abstract": "  The dynamics of large systems of coupled oscillators is a subject of\nincreasing importance with prominent applications in several areas such as\nphysics and biology. The Kuramoto model, where a set of oscillators move around\na circle representing their phases, is a paradigm in this field, exhibiting a\ncontinuous transition between disordered and synchronous motion. Reinterpreting\nthe oscillators as rotating unit vectors, the model was extended to allow\nvectors to move on the surface of D-dimensional spheres, with $D=2$\ncorresponding to the original model. It was shown that the transition to\nsynchronous dynamics was discontinuous for odd D, raising a lot of interest.\nInspired by results in 2D, Ott et al proposed an ansatz for density function\ndescribing the oscillators and derived equations for the ansatz parameters,\neffectively reducing the dimensionality of the system. Here we take a different\napproach for the 3D system and construct an ansatz based on spherical harmonics\ndecomposition of the distribution function. Our result differs significantly\nfrom that proposed in Ott's work and leads to similar but simpler equations\ndetermining the dynamics of the order parameter. We derive the phase diagram of\nequilibrium solutions for several distributions of natural frequencies and find\nexcellent agreement with simulations. We also compare the dynamics of the order\nparameter with numerical simulations and with the previously derived equations,\nfinding good agreement in all cases. We believe our approach can be generalized\nto higher dimensions and help to achieve complexity reduction in other systems\nof equations.\n"}
+{"abstract": "  We study PBW bases of the untwisted quantum loop group $U_q(L\\mathfrak{g})$\n(in the Drinfeld new presentation) using the combinatorics of loop words, by\ngeneralizing the treatment of [26,27,40] in the finite type case. As an\napplication, we prove that Enriquez' homomorphism [10] from the positive half\nof the quantum loop group to the trigonometric degeneration of\nFeigin-Odesskii's elliptic algebra [13] associated to $\\mathfrak{g}$ is an\nisomorphism.\n"}
+{"abstract": "  A modular proof-theoretic framework was recently developed to prove Craig\ninterpolation for normal modal logics based on generalizations of sequent\ncalculi (e.g., nested sequents, hypersequents, and labelled sequents). In this\npaper, we turn to uniform interpolation, which is stronger than Craig\ninterpolation. We develop a constructive method for proving uniform\ninterpolation via nested sequents and apply it to reprove the uniform\ninterpolation property for normal modal logics $\\mathsf{K}$, $\\mathsf{D}$, and\n$\\mathsf{T}$. We then use the know-how developed for nested sequents to apply\nthe same method to hypersequents and obtain the first direct proof of uniform\ninterpolation for $\\mathsf{S5}$ via a cut-free sequent-like calculus. While our\nmethod is proof-theoretic, the definition of uniform interpolation for nested\nsequents and hypersequents also uses semantic notions, including bisimulation\nmodulo an atomic proposition.\n"}
+{"abstract": "  In this article, we particularly address the problem of assessing the impact\nof clinical stage and age on the specific survival times of men with breast\ncancer when cure is a possibility, where there is also the interest of\nexplaining this impact on different quantiles of the survival times. To this\nend, we developed a quantile regression model for survival data in the presence\nof long-term survivors based on the generalized distribution of Gompertz in a\ndefective version, which is conveniently reparametrized in terms of the q-th\nquantile and then linked to covariates via a logarithm link function. This\nproposal allows us to obtain how each variable affects the survival times in\ndifferent quantiles. In addition, we are able to study the effects of\ncovariates on the cure rate as well. We consider Markov Chain Monte Carlo\n(MCMC) methods to develop a Bayesian analysis in the proposed model and we\nevaluate its performance through a Monte Carlo simulation study. Finally, we\nillustrate the advantages of our model in a data set about male breast cancer\nfrom Brazil.\n"}
+{"abstract": "  Side-channel attacks extracting sensitive data from implementations have been\nconsidered a major threat to the security of cryptographic schemes. This has\nelevated the need for improved designs by embodying countermeasures, with\nmasking being the most prominent example. To formally verify the security of a\nmasking scheme, numerous attack models have been developed to capture the\nphysical properties of the information leakage as well as the capabilities of\nthe adversary. With regard to these models, extensive research has been\nperformed to realize masking schemes. These research efforts have led to\nsignificant progress in the development of security assessment methodologies\nand further initiated standardization activities. However, since the majority\nof this work is theoretical, it is challenging for the more practice-oriented\nhardware security community to fully grasp and contribute to. To bridge the\ngap, these advancements are reviewed and discussed in this survey, mainly from\nthe perspective of hardware security. In doing so, a clear taxonomy is provided\nthat is helpful for a systematic treatment of the masking-related topics. By\ngiving an extensive overview of the existing methods, this survey (1) provides\na research landscape of circuit masking for newcomers to the field, (2) offers\nguidelines on which attack model and verification tool to choose when designing\nmasking schemes, and (3) identifies interesting new research directions where\nmasking models and assessment tools can be applied. Thus, this survey serves as\nan essential reference for hardware security practitioners interested in the\ntheory behind masking techniques, the tools useful to verify the security of\nmasked circuits, and their potential applications.\n"}
+{"abstract": "  It was found that, although isovalent, Rh substituted for Ir in Sr$_2$IrO$_4$\nmay trap one electron inducing effective hole doping of Ir sites. Transport and\nthermoelectric measurements on Sr$_2$Ir$_{1-x}$Rh$_x$O$_4$ single crystals\npresented here reveal the existence of an electron-like contribution to\ntransport, in addition to the hole-doped one. As no electron band shows up in\nARPES measurements, this points to the possibility that this hidden electron\nmay delocalize in disordered clusters.\n"}
+{"abstract": "  Anticipating the maximum magnitude of injection-triggered earthquakes is\nhighly valuable for the safe and efficient exploitation of geoenergies. The\nrecent work by Li et al.(2021) reached the conclusion that unexpectedly large\ninjection-triggered earthquakes are primarily caused by large pre-existing\ncritical shear stresses on seismogenic faults. Also of great interest is the\nproposal of the ratio of fault slip to dilation as an index to anticipate the\nfault rupture energetics. However, their fluid injection experiments were\nconducted under fully drained conditions, where the fluid pressure distribution\non the fault plane is always uniform. As already pointed out by the authors,\nlocal fluid pressure perturbations inside faults under locally undrained\nconditions also have the potential to cause unexpectedly large seismic events.\nHere we add more datasets and possible explanations for this viewpoint, which\nhas not been explored in such detail by Li et al.(2021).\n"}
+{"abstract": "  We describe a congruence property of solvable polynomials over Q, based on\nthe irreducibility of cyclotomic polynomials over number fields that meet\ncertain conditions.\n"}
+{"abstract": "  We consider $\\beta$-smooth (satisfies the generalized Holder condition with\nparameter $\\beta > 2$) stochastic convex optimization problem with zero-order\none-point oracle. The best known result was arXiv:2006.07862: $\\mathbb{E}\n\\left[f(\\overline{x}_N) - f(x^*)\\right] = \\tilde{\\mathcal{O}}\n\\left(\\dfrac{n^{2}}{\\gamma N^{\\frac{\\beta-1}{\\beta}}} \\right)$ in\n$\\gamma$-strongly convex case, where $n$ is the dimension. In this paper we\nimprove this bound: $\\mathbb{E} \\left[f(\\overline{x}_N) - f(x^*)\\right] =\n\\tilde{\\mathcal{O}} \\left(\\dfrac{n^{2-\\frac{1}{\\beta}}}{\\gamma\nN^{\\frac{\\beta-1}{\\beta}}} \\right).$\n"}
+{"abstract": "  The beauty hadron decays is unique laboratory to study charmonium and\ncharmonium-like states, such as the $\\chi_{c1}(3872)$ meson, other exotic\nstates and the tensor D-wave $\\psi_2(3823$) states. However the nature of many\nexotic charmonium-like candidates are still unknown. The most recent LHCb\nresults related to b-hadron decays to charmonium states and obtained using\nlarge data samples collected during the Run 1 and Run 2 periods are presented.\nThis includes the most precise determination of the mass and width of the\n$\\chi_{c1}(3872)$ state using the $B^{+} \\to J/\\psi\\pi^{+}\\pi^{-}K^{+}$ decays,\nobservation of a resonant structure denoted as X(4740) in the $J/\\psi \\phi$\nmass spectrum from $B^{0}_{s} \\to J/\\psi\\pi^{+}\\pi^{-}K^{+}K^{-}$ decays and\nthe precise measurement of the $B^{0}_{s}$ meson mass.\n"}
+{"abstract": "  This paper provides a zeroth-order optimisation framework for non-smooth and\npossibly non-convex cost functions with matrix parameters that are real and\nsymmetric. We provide complexity bounds on the number of iterations required to\nensure a given accuracy level for both the convex and non-convex case. The\nderived complexity bounds for the convex case are less conservative than\navailable bounds in the literature since we exploit the symmetric structure of\nthe underlying matrix space. Moreover, the non-convex complexity bounds are\nnovel for the class of optimisation problems we consider. The utility of the\nframework is evident in the suite of applications that use symmetric matrices\nas tuning parameters. Of primary interest here is the challenge of tuning the\ngain matrices in model predictive controllers, as this is a challenge known to\nbe inhibiting industrial implementation of these architectures. To demonstrate\nthe framework we consider the problem of MIMO diesel air-path control, and\nconsider implementing the framework iteratively ``in-the-loop'' to reduce\ntracking error on the output channels. Both simulations and experimental\nresults are included to illustrate the effectiveness of the proposed framework\nover different engine drive cycles.\n"}
+{"abstract": "  We perform a general reduction of an M2-brane on a space-time that admits a\nnull Killing vector leading to fundamental string and D2-brane action in\nNewton-Cartan background.\n"}
+{"abstract": "  We propose a new framework, Translation between Augmented Natural Languages\n(TANL), to solve many structured prediction language tasks including joint\nentity and relation extraction, nested named entity recognition, relation\nclassification, semantic role labeling, event extraction, coreference\nresolution, and dialogue state tracking. Instead of tackling the problem by\ntraining task-specific discriminative classifiers, we frame it as a translation\ntask between augmented natural languages, from which the task-relevant\ninformation can be easily extracted. Our approach can match or outperform\ntask-specific models on all tasks, and in particular, achieves new\nstate-of-the-art results on joint entity and relation extraction (CoNLL04, ADE,\nNYT, and ACE2005 datasets), relation classification (FewRel and TACRED), and\nsemantic role labeling (CoNLL-2005 and CoNLL-2012). We accomplish this while\nusing the same architecture and hyperparameters for all tasks and even when\ntraining a single model to solve all tasks at the same time (multi-task\nlearning). Finally, we show that our framework can also significantly improve\nthe performance in a low-resource regime, thanks to better use of label\nsemantics.\n"}
+{"abstract": "  We consider the problem of optimal transportation with general cost between a\nempirical measure and a general target probability on R d , with d $\\ge$ 1. We\nextend results in [19] and prove asymptotic stability of both optimal transport\nmaps and potentials for a large class of costs in R d. We derive a central\nlimit theorem (CLT) towards a Gaussian distribution for the empirical\ntransportation cost under minimal assumptions, with a new proof based on the\nEfron-Stein inequality and on the sequential compactness of the closed unit\nball in L 2 (P) for the weak topology. We provide also CLTs for empirical\nWassertsein distances in the special case of potential costs | $\\bullet$ | p ,\np > 1.\n"}
+{"abstract": "  Physics-based character animation has seen significant advances in recent\nyears with the adoption of Deep Reinforcement Learning (DRL). However,\nDRL-based learning methods are usually computationally expensive and their\nperformance crucially depends on the choice of hyperparameters. Tuning\nhyperparameters for these methods often requires repetitive training of control\npolicies, which is even more computationally prohibitive. In this work, we\npropose a novel Curriculum-based Multi-Fidelity Bayesian Optimization framework\n(CMFBO) for efficient hyperparameter optimization of DRL-based character\ncontrol systems. Using curriculum-based task difficulty as fidelity criterion,\nour method improves searching efficiency by gradually pruning search space\nthrough evaluation on easier motor skill tasks. We evaluate our method on two\nphysics-based character control tasks: character morphology optimization and\nhyperparameter tuning of DeepMimic. Our algorithm significantly outperforms\nstate-of-the-art hyperparameter optimization methods applicable for\nphysics-based character animation. In particular, we show that hyperparameters\noptimized through our algorithm result in at least 5x efficiency gain comparing\nto author-released settings in DeepMimic.\n"}
+{"abstract": "  Atom probe tomography is a powerful tools in investigating nanostructures\nsuch as interfaces and nanoparticles in material science. Advanced analysis\ntools are particularly useful for analyzing these nanostructures characterized\nvery often by curved shapes. However, these tools are very limited for complex\nmaterials with non-negligible peak overlaps in their respective mass-to-charge\nratio spectra. Usually, an analyst solves peak overlaps in the bulk regions but\nthe behavior at interfaces is rarely considered. Therefore, in this work we\ndemonstrate how the proximity histogram generated for a specific interface can\nbe corrected by using the natural abundances of isotopes. This leads to\noverlap-solved proximity histograms with a resolution of up to 0.1 nm. This\nwork expands on previous work that showed the advantage of a maximum likelihood\npeak overlap solving. The corrected proximity histograms together with the\nmaximum likelihood peak overlap algorithm were implemented in an user-friendly\nsoftware suite called EPOSA.\n"}
+{"abstract": "  Instance-level image retrieval is the task of searching in a large database\nfor images that match an object in a query image. To address this task, systems\nusually rely on a retrieval step that uses global image descriptors, and a\nsubsequent step that performs domain-specific refinements or reranking by\nleveraging operations such as geometric verification based on local features.\nIn this work, we propose Reranking Transformers (RRTs) as a general model to\nincorporate both local and global features to rerank the matching images in a\nsupervised fashion and thus replace the relatively expensive process of\ngeometric verification. RRTs are lightweight and can be easily parallelized so\nthat reranking a set of top matching results can be performed in a single\nforward-pass. We perform extensive experiments on the Revisited Oxford and\nParis datasets, and the Google Landmarks v2 dataset, showing that RRTs\noutperform previous reranking approaches while using much fewer local\ndescriptors. Moreover, we demonstrate that, unlike existing approaches, RRTs\ncan be optimized jointly with the feature extractor, which can lead to feature\nrepresentations tailored to downstream tasks and further accuracy improvements.\nThe code and trained models are publicly available at\nhttps://github.com/uvavision/RerankingTransformer.\n"}
+{"abstract": "  There is a growing trend regarding perceiving personal data as a commodity.\nExisting studies have built frameworks and theories about how to determine an\narbitrage-free price of a given query according to the privacy loss quantified\nby differential privacy. However, those studies have assumed that data buyers\ncan purchase query answers with the arbitrary privacy loss of data owners,\nwhich may not be valid under strict privacy regulations and data owners'\nincreasing privacy concerns. In this paper, we study how to empower data owners\nto control privacy loss in data trading. First, we propose a framework for\ntrading personal data that enables data owners to bound their personalized\nprivacy losses. Second, since bounded privacy losses indicate bounded utilities\nof query answers, we propose a reasonable relaxation of arbitrage freeness\nnamed partial arbitrage freeness, i.e., the guarantee of arbitrage-free pricing\nonly for a limited range of utilities, which provides more possibilities for\nour market design. Third, to avoid arbitrage, we propose a general method for\nensuring arbitrage freeness under personalized differential privacy. Fourth, to\nfully utilize data owners' personalized privacy loss bounds, we propose privacy\nbudget allocation techniques to allocate privacy losses for queries under\narbitrage freeness. Finally, we conduct experiments to verify the effectiveness\nof our proposed trading protocols.\n"}
+{"abstract": "  This paper presents a new approach to statistical similarity assessment based\non sequence alignment. The algorithm performs mutual matching of two random\nsequences by successively searching for common elements and by applying\nsequence breaks to matchless elements in the function of exponential cost. As a\nresult, sequences varying significantly generate a high-cost alignment, while\nfor low-cost sequences the introduced interruptions allow inferring the nature\nof sequences dependence. The most important advantage of the algorithm is an\neasy interpretation of the obtained results based on two parameters: stretch\nratio and stretch cost. The operation of the method has been simulation tested\nand verified with the use of real data obtained from hardware random number\ngenerators. The proposed solution ensures simple implementation enabling the\nintegration of hardware solutions, and operation based on only two sequences of\nany length predisposes the method to online testing.\n"}
+{"abstract": "  Segmenting unseen object instances in cluttered environments is an important\ncapability that robots need when functioning in unstructured environments.\nWhile previous methods have exhibited promising results, they still tend to\nprovide incorrect results in highly cluttered scenes. We postulate that a\nnetwork architecture that encodes relations between objects at a high-level can\nbe beneficial. Thus, in this work, we propose a novel framework that refines\nthe output of such methods by utilizing a graph-based representation of\ninstance masks. We train deep networks capable of sampling smart perturbations\nto the segmentations, and a graph neural network, which can encode relations\nbetween objects, to evaluate the perturbed segmentations. Our proposed method\nis orthogonal to previous works and achieves state-of-the-art performance when\ncombined with them. We demonstrate an application that uses uncertainty\nestimates generated by our method to guide a manipulator, leading to efficient\nunderstanding of cluttered scenes. Code, models, and video can be found at\nhttps://github.com/chrisdxie/rice .\n"}
+{"abstract": "  Thermal emission is a universal phenomenon of stochastic electromagnetic\nemission from an object composed of arbitrary materials at elevated\ntemperatures. A defining feature of this emission is the monotonic and rapid\ngrowth of its intensity with the object's temperature for most known materials.\nThis growth originates from the Bose-Einstein statistics of the thermal\nphotonic field. The fact that the material's ability to absorb and emit light\nmay change with temperature, however, is often ignored. Here, we carry out a\ntheoretical study of thermal emission from structures incorporating ensembles\nof two-level media. We investigate this effect in a range of geometries\nincluding thin films and compact nanoparticles, and establish the general\ndependencies in the evolution of thermal emission from systems including\ntwo-level media. Thermal emission from such structures is essentially\nNon-Planckian and exhibits a universal asymptotic behavior in the limit of high\ntemperatures. These results might have important implications for the design of\nthermal energy harvesting and thermal vision systems.\n"}
+{"abstract": "  In this paper, we analyze multi-dimensional Weyl almost periodic type\nfunctions in Lebesgue spaces with variable exponents. The introduced classes\nseem to be new and not considered elsewhere even in the constant coefficient\ncase. We provide certain applications to the abstract Volterra\nintegro-differential equations in Banach spaces.\n"}
+{"abstract": "  Recently, various convolutions based on continuous or discrete kernels for\npoint cloud processing have been widely studied, and achieve impressive\nperformance in many applications, such as shape classification, scene\nsegmentation and so on. However, they still suffer from some drawbacks. For\ncontinuous kernels, the inaccurate estimation of the kernel weights constitutes\na bottleneck for further improving the performance; while for discrete ones,\nthe kernels represented as the points located in the 3D space are lack of rich\ngeometry information. In this work, rather than defining a continuous or\ndiscrete kernel, we directly embed convolutional kernels into the learnable\npotential fields, giving rise to potential convolution. It is convenient for us\nto define various potential functions for potential convolution which can\ngeneralize well to a wide range of tasks. Specifically, we provide two simple\nyet effective potential functions via point-wise convolution operations.\nComprehensive experiments demonstrate the effectiveness of our method, which\nachieves superior performance on the popular 3D shape classification and scene\nsegmentation benchmarks compared with other state-of-the-art point convolution\nmethods.\n"}
+{"abstract": "  The study of inner and cyclic functions in $\\ell^p_A$ spaces requires a\nbetter understanding of the zeros of the so-called optimal polynomial\napproximants.\n  We determine that a point of the complex plane is the zero of an optimal\npolynomial approximant for some element of $\\ell^p_A$ if and only if it lies\noutside of a closed disk (centered at the origin) of a particular radius which\ndepends on the value of $p$. We find the value of this radius for $p\\neq 2$. In\naddition, for each positive integer $d$ there is a polynomial $f_d$ of degree\nat most $d$ that minimizes the modulus of the root of its optimal linear\npolynomial approximant. We develop a method for finding these extremal\nfunctions $f_d$ and discuss their properties. The method involves the Lagrange\nmultiplier method and a resulting dynamical system.\n"}
+{"abstract": "  We propose and investigate the intrinsically thinnest transistor concept: a\nmonolayer ballistic heterojunction bipolar transistor based on a lateral\nheterostructure of transition metal dichalcogenides. The device is\nintrinsically thinner than a Field Effect Transistor because it does not need a\ntop or bottom gate, since transport is controlled by the electrochemical\npotential of the base electrode. As typical of bipolar transistors, the\ncollector current undergoes a tenfold increase for each 60 mV increase of the\nbase voltage over several orders of magnitude at room temperature, without\nsophisticated optimization of the electrostatics. We present a detailed\ninvestigation based on self-consistent simulations of electrostatics and\nquantum transport for both electron and holes of a pnp device using MoS$_2$ for\nthe 10-nm base and WSe$_2$ for emitter and collector. Our three-terminal device\nsimulations confirm the working principle and a large current modulation\nI$_\\text{ON}$/I$_\\text{OFF}\\sim 10^8$ for $\\Delta V_{\\rm EB}=0.5$ V. Assuming\nballistic transport, we are able to achieve a current gain $\\beta\\sim$ 10$^4$\nover several orders of magnitude of collector current and a cutoff frequency up\nto the THz range. Exploration of the rich world of bipolar nanoscale device\nconcepts in 2D materials is promising for their potential applications in\nelectronics and optoelectronics.\n"}
+{"abstract": "  Aeolian sediment transport is observed to occur on Mars as well as other\nextraterrestrial environments, generating ripples and dunes as on Earth. The\nsearch for terrestrial analogues of planetary bedforms, as well as\nenvironmental simulation experiments able to reproduce their formation in\nplanetary conditions, are powerful ways to question our understanding of\ngeomorphological processes towards unusual environmental conditions. Here, we\nperform sediment transport laboratory experiments in a closed-circuit wind\ntunnel placed in a vacuum chamber and operated at extremely low pressures to\nshow that Martian conditions belong to a previously unexplored saltation\nregime. The threshold wind speed required to initiate saltation is only\nquantitatively predicted by state-of-the art models up to a density ratio\nbetween grain and air of $4 \\times 10^5$, but unexpectedly falls to much lower\nvalues for higher density ratios. In contrast, impact ripples, whose emergence\nis continuously observed on the granular bed over the whole pressure range\ninvestigated, display a characteristic wavelength and propagation velocity\nessentially independent of pressure. A comparison of these findings with\nexisting models suggests that sediment transport at low Reynolds number but\nhigh grain-to-fluid density ratio may be dominated by collective effects\nassociated with grain inertia in the granular collisional layer.\n"}
+{"abstract": "  In this paper, we study the linear stability properties of perturbations\naround the homogeneous Couette flow for a 2D isentropic compressible fluid in\nthe domain $\\mathbb{T}\\times \\mathbb{R}$. In the inviscid case there is a\ngeneric Lyapunov type instability for the density and the irrotational\ncomponent of the velocity field. More precisely, we prove that their $L^2$ norm\ngrows as $t^{1/2}$ and this confirms previous observations in the physics\nliterature. Instead, the solenoidal component of the velocity field experience\ninviscid damping, meaning that it decays to zero even in the absence of\nviscosity. For a viscous compressible fluid, we show that the perturbations may\nhave a transient growth of order $\\nu^{-1/6}$ (with $\\nu^{-1}$ being\nproportional to the Reynolds number) on a time-scale $\\nu^{-1/3}$, after which\nit decays exponentially fast. This phenomenon is also called enhanced\ndissipation and our result appears to be the first to detect this mechanism for\na compressible fluid, where an exponential decay for the density is not a\npriori trivial given the absence of dissipation in the continuity equation.\n"}
+{"abstract": "  We present the task of differential face morph attack detection using a\nconditional generative network (cGAN). To determine whether a face image in an\nidentification document, such as a passport, is morphed or not, we propose an\nalgorithm that learns to implicitly disentangle identities from the morphed\nimage conditioned on the trusted reference image using the cGAN. Furthermore,\nthe proposed method can also recover some underlying information about the\nsecond subject used in generating the morph. We performed experiments on AMSL\nface morph, MorGAN, and EMorGAN datasets to demonstrate the effectiveness of\nthe proposed method. We also conducted cross-dataset and cross-attack detection\nexperiments. We obtained promising results of 3% BPCER @ 10% APCER on\nintra-dataset evaluation, which is comparable to existing methods; and 4.6%\nBPCER @ 10% APCER on cross-dataset evaluation, which outperforms\nstate-of-the-art methods by at least 13.9%.\n"}
+{"abstract": "  With the surge of online meetings, it has become more critical than ever to\nprovide high-quality speech audio and live captioning under various noise\nconditions. However, most monaural speech enhancement (SE) models introduce\nprocessing artifacts and thus degrade the performance of downstream tasks,\nincluding automatic speech recognition (ASR). This paper proposes a multi-task\ntraining framework to make the SE models unharmful to ASR. Because most ASR\ntraining samples do not have corresponding clean signal references, we\nalternately perform two model update steps called SE-step and ASR-step. The\nSE-step uses clean and noisy signal pairs and a signal-based loss function. The\nASR-step applies a pre-trained ASR model to training signals enhanced with the\nSE model. A cross-entropy loss between the ASR output and reference\ntranscriptions is calculated to update the SE model parameters. Experimental\nresults with realistic large-scale settings using ASR models trained on\n75,000-hour data show that the proposed framework improves the word error rate\nfor the SE output by 11.82% with little compromise in the SE quality.\nPerformance analysis is also carried out by changing the ASR model, the data\nused for the ASR-step, and the schedule of the two update steps.\n"}
+{"abstract": "  Road vehicles play an important role in the UK's energy systems and are a\ncritical component in reducing the reliance on fossil fuels and mitigating\nemissions. A dynamic model of vehicle fleet, based on predator-prey concepts,\nis presented. It allows to predict the evolution of the hydrogen based\nvehicle's role in the UK's vehicle fleet and the sensitivity of this growth to\nthe supply chain. In addition to this, this model is used to predict the demand\nof hydrogen for the passenger vehicle fleet for various scenarios. A key result\nis that the amount of hydrogen required to support a huge network of hydrogen\nbased vehicles is currently not practical, regarding the infrastructure\nrequired and the cost to build such an infrastructure. In order to mitigate\nthat, the policy focus should move primarily from hydrogen based vehicles to\nfirst encompass range extenders in the transport energy system.\n"}
+{"abstract": "  Chest X-rays are the most commonly available and affordable radiological\nexamination for screening thoracic diseases. According to the domain knowledge\nof screening chest X-rays, the pathological information usually lay on the lung\nand heart regions. However, it is costly to acquire region-level annotation in\npractice, and model training mainly relies on image-level class labels in a\nweakly supervised manner, which is highly challenging for computer-aided chest\nX-ray screening. To address this issue, some methods have been proposed\nrecently to identify local regions containing pathological information, which\nis vital for thoracic disease classification. Inspired by this, we propose a\nnovel deep learning framework to explore discriminative information from lung\nand heart regions. We design a feature extractor equipped with a multi-scale\nattention module to learn global attention maps from global images. To exploit\ndisease-specific cues effectively, we locate lung and heart regions containing\npathological information by a well-trained pixel-wise segmentation model to\ngenerate binarization masks. By introducing element-wise logical AND operator\non the learned global attention maps and the binarization masks, we obtain\nlocal attention maps in which pixels are $1$ for lung and heart region and $0$\nfor other regions. By zeroing features of non-lung and heart regions in\nattention maps, we can effectively exploit their disease-specific cues in lung\nand heart regions. Compared to existing methods fusing global and local\nfeatures, we adopt feature weighting to avoid weakening visual cues unique to\nlung and heart regions. Evaluated by the benchmark split on the publicly\navailable chest X-ray14 dataset, the comprehensive experiments show that our\nmethod achieves superior performance compared to the state-of-the-art methods.\n"}
+{"abstract": "  Artificial Intelligence (AI) and the regulation thereof is a topic that is\nincreasingly being discussed within various fora. Various proposals have been\nmade in literature for defining regulatory bodies and/or related regulation. In\nthis paper, we present a pragmatic approach for providing a technology\nassurance regulatory framework. To the best knowledge of the authors this work\npresents the first national AI technology assurance legal and regulatory\nframework that has been implemented by a national authority empowered through\nlaw to do so. In aim of both providing assurances where required and not\nstifling innovation yet supporting it, herein it is proposed that such\nregulation should not be mandated for all AI-based systems and that rather it\nshould primarily provide a voluntary framework and only be mandated in sectors\nand activities where required and as deemed necessary by other authorities for\nregulated and critical areas.\n"}
+{"abstract": "  Power grids play a very important role in delivering electrical energy to\nhomes, industries and other places that require it. Because of this increased\ndemand they are facing a great challenge of voltage variations. This happens\ndue to varied use of energy-consuming devices and appliances like electric\nvehicles, industrial consumption, occasional peak in energy demands etc. For\nthese fluctuations in demands, it becomes extremely important to monitor the\nconditions at which the power grid operates. Once these conditions are known,\nthe energy production can be manipulated to meet the demand. It has been found\nthat the existing Power System State Estimation (PSSE) techniques may not be\ngood in producing optimal Performance. Moreover, they are also expensive in\nterms of computational processing. To address this problem, this research\nproposes a state estimation method for power grids using Convolutional Neural\nNetworks (CNN). It was found that the model produced an RMSE of 2.57 x 10-4,\nwhich was comparatively accurate than one of previous studies involved in\nmaking the estimation using a Prox Linear Model (2.97 x 10-4).\n  Furthermore, the research also proposes Power System State Forecasting for\nimproving system awareness and resilience. The forecasting is carried out using\na model of Recurrent Neural Network (RNN). This model helps in accounting for\nlong-term nonlinear aspects present in data and based on that it does the\nforecasting. The proposed model forecasted with a RMSE of 2.53 x 10-3, which is\ncomparatively equal to the previous study mentioned above (2.59 x 10-3).\n"}
+{"abstract": "  We use X-ray Active Galactic Nuclei (AGN) observed by the Chandra X-ray\nObservatory within the 9.3 deg$^2$ Bo$\\rm \\ddot{o}$tes field of the NDWFS to\nstudy whether there is a correlation between X-ray luminosity (L$_X$) and star\nformation rate (SFR) of the host galaxy, at $\\rm 0.5<z<2.0$, with respect to\nthe position of the galaxy to the main sequence (SFR$_{norm}$). About half of\nthe sources in the X-ray sample have spectroscopic redshifts. We also construct\na reference galaxy catalogue. For both datasets, we use photometric data from\noptical to the far infrared, compiled by the HELP project and apply spectral\nenergy distribution (SED) fitting, using the X-CIGALE code. We exclude\nquiescent sources from both the X-ray and the reference samples. We also\naccount for the mass completeness of our dataset, in different redshifts bins.\nOur analysis highlights the importance of studying the SFR-L$_X$ relation, in a\nuniform manner, taking into account the systematics and selection effects. Our\nresults suggest that, in less massive galaxies ($\\rm log\\,[M_*(M_\\odot)] \\sim\n11$), AGN enhances the SFR of the host galaxy by $\\sim 50\\%$ compared to non\nAGN systems. A flat relation is observed for the most massive galaxies.\nSFR$_{norm}$ does not evolve with redshift. The results, although tentative,\nare consistent with a scenario in which, in less massive systems, both AGN and\nstar formation (SF) are fed by cold gas, supplied by a merger event. In more\nmassive galaxies, the flat relation could be explained by a different SMBH\nfuelling mechanism that is decoupled from the star formation of the host galaxy\n(e.g. hot diffuse gas). Finally, we compare the host galaxy properties of X-ray\nabsorbed and unabsorbed sources. Our results show no difference which suggests\nthat X-ray absorption is not linked with the properties of the galaxy.\n"}
+{"abstract": "  The abundance of semiconductors in our smartphones, computers, fiber optic\njunctions, cars, light sources, photovoltaic and thermoelectric cells results\nfrom the possibilities of controlling their properties through doping,\nlighting, and applying various fields. This paper, a part of the volume\ncelebrating 100 years of the Polish Physical Society, presents a biased\nselection of worthwhile results obtained by researchers at the Institute of\nPhysics, Polish Academy of Sciences relevant, as seen today, to topological\nmatter and spintronics. Comprehensive studies, combining materials development,\nexperimental investigations, and theoretical description of narrow-gap and\ndilute-magnetic semiconductors have been especially significant in this\ncontext. This survey also emphasizes, in an autobiographical tone, a half of a\ncentury of the author's intellectual emotions accompanying the rise of ideas\nand quantitative theories, allowing identifying the physics behind ongoing and\nfuture observations.\n"}
+{"abstract": "  The instability of financial system issues might trigger a bank failure,\nevoke spillovers, and generate contagion effects which negatively impacted the\nfinancial system, ultimately on the economy. This phenomenon is the result of\nthe highly interconnected banking transaction. The banking transactions network\nis considered as a financial architecture backbone. The strong\ninterconnectedness between banks escalates contagion disruption spreading over\nthe banking network and trigger the entire system collapse. This far, the\nfinancial instability is generally detected using macro approach mainly the\nuncontrolled transaction deficits amount and unpaid foreign debt. This research\nproposes financial instability detection in another point of view, through the\nmacro view where the banking network structure are explored globally and micro\nview where focuses on the detailed network patterns called motif. Network\ntriadic motif patterns used as a denomination to detect financial instability.\nThe most related network triadic motif changes related to the instability\nperiod are determined as a detector. We explore the banking network behavior\nunder financial instability phenomenon along with the major religious event in\nIndonesia, Eid al-Fitr. We discover one motif pattern as the financial\ninstability underlying detector. This research helps to support the financial\nsystem stability supervision.\n"}
+{"abstract": "  Multimodal VAEs seek to model the joint distribution over heterogeneous data\n(e.g.\\ vision, language), whilst also capturing a shared representation across\nsuch modalities. Prior work has typically combined information from the\nmodalities by reconciling idiosyncratic representations directly in the\nrecognition model through explicit products, mixtures, or other such\nfactorisations. Here we introduce a novel alternative, the MEME, that avoids\nsuch explicit combinations by repurposing semi-supervised VAEs to combine\ninformation between modalities implicitly through mutual supervision. This\nformulation naturally allows learning from partially-observed data where some\nmodalities can be entirely missing -- something that most existing approaches\neither cannot handle, or do so to a limited extent. We demonstrate that MEME\noutperforms baselines on standard metrics across both partial and complete\nobservation schemes on the MNIST-SVHN (image-image) and CUB (image-text)\ndatasets. We also contrast the quality of the representations learnt by mutual\nsupervision against standard approaches and observe interesting trends in its\nability to capture relatedness between data.\n"}
+{"abstract": "  Particle transport and localization phenomena in condensed-matter systems can\nbe modeled using a tight-binding lattice Hamiltonian. The ideal experimental\nemulation of such a model utilizes simultaneous, high-fidelity control and\nreadout of each lattice site in a highly coherent quantum system. Here, we\nexperimentally study quantum transport in one-dimensional and two-dimensional\ntight-binding lattices, emulated by a fully controllable $3 \\times 3$ array of\nsuperconducting qubits. We probe the propagation of entanglement throughout the\nlattice and extract the degree of localization in the Anderson and\nWannier-Stark regimes in the presence of site-tunable disorder strengths and\ngradients. Our results are in quantitative agreement with numerical simulations\nand match theoretical predictions based on the tight-binding model. The\ndemonstrated level of experimental control and accuracy in extracting the\nsystem observables of interest will enable the exploration of larger,\ninteracting lattices where numerical simulations become intractable.\n"}
+{"abstract": "  Typical user-friendly renderings of knowledge graphs are visualisations and\nnatural language text. Within the latter HCI solution approach, data-driven\nnatural language generation systems receive increased attention, but they are\noften outperformed by template-based systems due to suffering from errors such\nas content dropping, hallucination, or repetition. It is unknown which of those\nerrors are associated significantly with low quality judgements by humans who\nthe text is aimed for, which hampers addressing errors based on their impact on\nimproving human evaluations. We assessed their possible association with an\nexperiment availing of expert and crowdsourced evaluations of human authored\ntext, template generated text, and sequence-to-sequence model generated text.\nThe results showed that there was no significant association between human\nauthored texts with errors and the low human judgements of naturalness and\nquality. There was also no significant association between machine learning\ngenerated texts with dropped or hallucinated slots and the low human judgements\nof naturalness and quality. Thus, both approaches appear to be viable options\nfor designing a natural language interface for knowledge graphs.\n"}
+{"abstract": "  The stochastic frontier model with heterogeneous technical efficiency\nexplained by exoge-nous variables is augmented with a spatial-temporal\ncomponent, a generalization relaxing the panel independence assumption in a\npanel data. The estimation procedure takes advantage of additivity in the\nmodel, computational advantages over maximum likelihood estimation of\nparameters is exhibited. The spatial-temporal component can improve estimates\nof technical efficiency in a production frontier that is usually biased\ndownwards. We present a test to veri-fy model assumptions that facilitates\nestimation of parameters.\n"}
+{"abstract": "  As large Open Data are increasingly shared as RDF graphs today, there is a\ngrowing demand to help users discover the most interesting facets of a graph,\nwhich are often hard to grasp without automatic tools. We consider the problem\nof automatically identifying the k most interesting aggregate queries that can\nbe evaluated on an RDF graph, given an integer k and a user-specified\ninterestingness function. Our problem departs from analytics in relational data\nwarehouses in that (i) in an RDF graph we are not given but we must identify\nthe facts, dimensions, and measures of candidate aggregates; (ii) the classical\napproach to efficiently evaluating multiple aggregates breaks in the face of\nmulti-valued dimensions in RDF data. In this work, we propose an extensible\nend-to-end framework that enables the identification and evaluation of\ninteresting aggregates based on a new RDF-compatible one-pass algorithm for\nefficiently evaluating a lattice of aggregates and a novel early-stop technique\n(with probabilistic guarantees) that can prune uninteresting aggregates.\nExperiments using both real and synthetic graphs demonstrate the ability of our\nframework to find interesting aggregates in a large search space, the\nefficiency of our algorithms (with up to 2.9x speedup over a similar pipeline\nbased on existing algorithms), and scalability as the data size and complexity\ngrow.\n"}
+{"abstract": "  We discuss the emergence of black hole shadow and photon-sphere in the\ncontext of $f(R)$ gravity. It is shown that the shadow is exponentially\nsensitive to linear instabilities of metric coming from some $f(R)$ solutions.\nThus, the instabilities of photon circular trajectories, delimiting the black\nhole photon-sphere, are double exponentialized. Specifically we individuate two\nLyapunov exponents, rather than only one, related to two different sources of\nchaos in geodesic orbits as a sort of butterfly effect. Such a result violates\nthe black hole chaos bound proposed by Maldacena, Shenker and Stanford for\nGeneral Relativity. We also explore the impact of the black hole metric\ninstabilities in $f(R)$ gravity on the quasi-normal modes. In the framework of\nExtended Theories of Gravity, our analysis suggests a new paradigm to deal with\nblack hole shadow and gravitational waves observations coming from black hole\nmerging in the ringdown phase.\n"}
+{"abstract": "  Every convex homogeneous polynomial (or form) is nonnegative. Blekherman has\nshown that there exist convex forms that are not sums of squares via a\nnonconstructive argument. We provide an explicit example of a convex form of\ndegree four in 272 variables that is not a sum of squares. The form is related\nto the Cauchy-Schwarz inequality over the octonions. The proof uses symmetry\nreduction together with the fact (due to Blekherman) that forms of even degree,\nthat are near-constant on the unit sphere, are convex. Using this same\nconnection, we obtain improved bounds on the approximation quality achieved by\nthe basic sum-of-squares relaxation for optimizing quaternary quartic forms on\nthe sphere.\n"}
+{"abstract": "  Principled decision making in emergency response management necessitates the\nuse of statistical models that predict the spatial-temporal likelihood of\nincident occurrence. These statistical models are then used for proactive\nstationing which allocates first responders across the spatial area in order to\nreduce overall response time. Traditional methods that simply aggregate past\nincidents over space and time fail to make useful short-term predictions when\nthe spatial region is large and focused on fine-grained spatial entities like\ninterstate highway networks. This is partially due to the sparsity of incidents\nwith respect to the area in consideration. Further, accidents are affected by\nseveral covariates, and collecting, cleaning, and managing multiple streams of\ndata from various sources is challenging for large spatial areas. In this\npaper, we highlight how this problem is being solved for the state of\nTennessee, a state in the USA with a total area of over 100,000 sq. km. Our\npipeline, based on a combination of synthetic resampling, non-spatial\nclustering, and learning from data can efficiently forecast the spatial and\ntemporal dynamics of accident occurrence, even under sparse conditions. In the\npaper, we describe our pipeline that uses data related to roadway geometry,\nweather, historical accidents, and real-time traffic congestion to aid accident\nforecasting. To understand how our forecasting model can affect allocation and\ndispatch, we improve upon a classical resource allocation approach.\nExperimental results show that our approach can significantly reduce response\ntimes in the field in comparison with current approaches followed by first\nresponders.\n"}
+{"abstract": "  The posterior over Bayesian neural network (BNN) parameters is extremely\nhigh-dimensional and non-convex. For computational reasons, researchers\napproximate this posterior using inexpensive mini-batch methods such as\nmean-field variational inference or stochastic-gradient Markov chain Monte\nCarlo (SGMCMC). To investigate foundational questions in Bayesian deep\nlearning, we instead use full-batch Hamiltonian Monte Carlo (HMC) on modern\narchitectures. We show that (1) BNNs can achieve significant performance gains\nover standard training and deep ensembles; (2) a single long HMC chain can\nprovide a comparable representation of the posterior to multiple shorter\nchains; (3) in contrast to recent studies, we find posterior tempering is not\nneeded for near-optimal performance, with little evidence for a \"cold\nposterior\" effect, which we show is largely an artifact of data augmentation;\n(4) BMA performance is robust to the choice of prior scale, and relatively\nsimilar for diagonal Gaussian, mixture of Gaussian, and logistic priors; (5)\nBayesian neural networks show surprisingly poor generalization under domain\nshift; (6) while cheaper alternatives such as deep ensembles and SGMCMC methods\ncan provide good generalization, they provide distinct predictive distributions\nfrom HMC. Notably, deep ensemble predictive distributions are similarly close\nto HMC as standard SGLD, and closer than standard variational inference.\n"}
+{"abstract": "  Small to medium sized business enterprises (SMEs) generally thrive because\nthey have successfully done something unique within a niche market. For this\nreason, SMEs may seek to protect their competitive advantage by avoiding any\nstandardization encouraged by the use of packaged software (PS). Packaged\nsoftware implementation at SMEs therefore presents challenges relating to how\nbest to respond to misfits between the functionality offered by the packaged\nsoftware and each SME's business needs. An important question relates to which\nprocesses small software enterprises - or Small to Medium-Sized Software\nDevelopment Companies (SMSSDCs) - apply in order to identify and then deal with\nthese misfits. To explore the processes of packaged software (PS)\nimplementation, an ethnographic study was conducted to gain in-depth insights\ninto the roles played by analysts in two SMSSDCs. The purpose of the study was\nto understand PS implementation in terms of requirements engineering (or\n'PSIRE'). Data collected during the ethnographic study were analyzed using an\ninductive approach. Based on our analysis of the cases we constructed a\ntheoretical model explaining the requirements engineering process for PS\nimplementation, and named it the PSIRE Parallel Star Model. The Parallel Star\nModel shows that during PSIRE, more than one RE process can be carried out at\nthe same time. The Parallel Star Model has few constraints, because not only\ncan processes be carried out in parallel, but they do not always have to be\nfollowed in a particular order. This paper therefore offers a novel\ninvestigation and explanation of RE practices for packaged software\nimplementation, approaching the phenomenon from the viewpoint of the analysts,\nand offers the first extensive study of packaged software implementation RE\n(PSIRE) in SMSSDCs.\n"}
+{"abstract": "  We present a comprehensive study on the silver bismuthate Ag$_2$BiO$_3$,\nsynthesized under high-pressure high-temperature conditions, which has been the\nsubject of recent theoretical work on topologically complex electronic states.\nWe present X-ray photoelectron spectroscopy results showing two different\nbismuth states, and X-ray absorption spectroscopy results on the oxygen\n$K$-edge showing holes in the oxygen bands. These results support a bond\ndisproportionated state with holes on the oxygen atoms for Ag$_2$BiO$_3$. We\nestimate a band gap of $\\sim$1.25~eV for Ag$_2$BiO$_3$ from optical\nconductivity measurements, which matches the band gap in density functional\ncalculations of the electronic band structure in the non-symmorphic space group\n$Pnn2$, which supports two inequivalent Bi sites. In our band structure\ncalculations the disproportionated Ag$_2$BiO$_3$ is expected to host Weyl nodal\nchains, one of which is located $\\sim$0.5~eV below the Fermi level.\nFurthermore, we highlight similarities between Ag$_2$BiO$_3$ and the well-known\ndisproportionated bismuthate BaBiO$_3$, including breathing phonon modes with\nsimilar energy. In both compounds hybridization of Bi-$6s$ and O-$2p$ atomic\norbitals is important in shaping the band structure, but in contrast to the\nBa-$5p$ in BaBiO$_3$, the Ag-$4d$ bands in Ag$_2$BiO$_3$ extend up to the Fermi\nlevel.\n"}
+{"abstract": "  Both Earth and Mars host populations of co-orbital minor bodies. A large\nnumber of present-day Mars co-orbitals is probably associated with the fission\nof the parent body of Mars Trojan 5261 Eureka (1990 MB) during a\nrotation-induced YORP break-up event. Here, we use the statistical\ndistributions of the Tisserand parameter and the relative mean longitude of\nMars co-orbitals with eccentricity below 0.2 to estimate the importance of\nrotation-induced Yarkovsky-O'Keefe-Radzievskii-Paddack (YORP) break-up events\nin Martian co-orbital space. Machine-learning techniques (k-means++ and\nagglomerative hierarchical clustering algorithms) are applied to assess our\nfindings. Our statistical analysis identified three new Mars Trojans: 2009 SE,\n2018 EC4 and 2018 FC4. Two of them, 2018 EC4 and 2018 FC4, are probably linked\nto Eureka but we argue that 2009 SE may have been captured, so it is not\nrelated to Eureka. We also suggest that 2020 VT1, a recent discovery, is a\ntransient Martian co-orbital of the horseshoe type. When applied to Earth\nco-orbital candidates with eccentricity below 0.2, our approach led us to\nidentify some clustering, perhaps linked to fission events. The cluster with\nmost members could be associated with Earth quasi-satellite 469219 Kamo`oalewa\n(2016 HO3) that is a fast rotator. Our statistical analysis identified two new\nEarth co-orbitals: 2020 PN1, that follows a horseshoe path, and 2020 PP1, a\nquasi-satellite that is dynamically similar to Kamo`oalewa. For both Mars and\nEarth co-orbitals, we found pairs of objects whose values of the Tisserand\nparameter differ by very small amounts, perhaps hinting at recent disruption\nevents. Clustering algorithms and numerical simulations both suggest that 2020\nKZ2 and Kamo`oalewa could be related.\n"}
+{"abstract": "  Subjective evaluations are critical for assessing the perceptual realism of\nsounds in audio-synthesis driven technologies like augmented and virtual\nreality. However, they are challenging to set up, fatiguing for users, and\nexpensive. In this work, we tackle the problem of capturing the perceptual\ncharacteristics of localizing sounds. Specifically, we propose a framework for\nbuilding a general purpose quality metric to assess spatial localization\ndifferences between two binaural recordings. We model localization similarity\nby utilizing activation-level distances from deep networks trained for\ndirection of arrival (DOA) estimation. Our proposed metric (DPLM) outperforms\nbaseline metrics on correlation with subjective ratings on a diverse set of\ndatasets, even without the benefit of any human-labeled training data.\n"}
+{"abstract": "  Convolutional Neural Networks (CNNs) can play a key role in Medical Image\nAnalysis under large-scale annotated datasets. However, preparing such massive\ndataset is demanding. In this context, Generative Adversarial Networks (GANs)\ncan generate realistic but novel samples, and thus effectively cover the real\nimage distribution. In terms of interpolation, the GAN-based medical image\naugmentation is reliable because medical modalities can display the human\nbody's strong anatomical consistency at fixed position while clearly reflecting\ninter-subject variability; thus, we propose to use noise-to-image GANs (e.g.,\nrandom noise samples to diverse pathological images) for (i) medical Data\nAugmentation (DA) and (ii) physician training. Regarding the DA, the\nGAN-generated images can improve Computer-Aided Diagnosis based on supervised\nlearning. For the physician training, the GANs can display novel desired\npathological images and help train medical trainees despite\ninfrastructural/legal constraints. This thesis contains four GAN projects\naiming to present such novel applications' clinical relevance in collaboration\nwith physicians. Whereas the methods are more generally applicable, this thesis\nonly explores a few oncological applications.\n"}
+{"abstract": "  The problem of providing complete presentations of reduction algebras\nassociated to a pair of Lie algebras $(\\mathfrak{G},\\mathfrak{g})$ has\npreviously been considered by Khoroshkin and Ogievetsky in the case of the\ndiagonal reduction algebra for $\\mathfrak{gl}(n)$. In this paper we consider\nthe diagonal reduction algebra of the pair of Lie superalgebras\n$\\left(\\mathfrak{osp}(1|2) \\times \\mathfrak{osp}(1|2),\n\\mathfrak{osp}(1|2)\\right)$ as a double coset space having an associative\ndiamond product and give a complete presentation in terms of generators and\nrelations. We also provide a PBW basis for this reduction algebra along with\nCasimir-like elements and a subgroup of automorphisms.\n"}
+{"abstract": "  Can advanced deep learning technologies be applied to analyze some ancient\nhumanistic arts? Can deep learning technologies be directly applied to special\nscenes such as facial expression analysis of Terracotta Warriors? The big\nchallenging is that the facial features of the Terracotta Warriors are very\ndifferent from today's people. We found that it is very poor to directly use\nthe models that have been trained on other classic facial expression datasets\nto analyze the facial expressions of the Terracotta Warriors. At the same time,\nthe lack of public high-quality facial expression data of the Terracotta\nWarriors also limits the use of deep learning technologies. Therefore, we\nfirstly use Generative Adversarial Networks (GANs) to generate enough\nhigh-quality facial expression data for subsequent training and recognition. We\nalso verify the effectiveness of this approach. For the first time, this paper\nuses deep learning technologies to find common facial expressions of general\nand postured Terracotta Warriors. These results will provide an updated\ntechnical means for the research of art of the Terracotta Warriors and shine\nlights on the research of other ancient arts.\n"}
+{"abstract": "  A $k$-arc in PG($2,q$) is a set of $k$ points no three of which are\ncollinear. A hyperfocused $k$-arc is a $k$-arc in which the $k \\choose 2$\nsecants meet some external line in exactly $k-1$ points. Hyperfocused $k$-arcs\ncan be viewed as 1-factorizations of the complete graph $K_k$ that embed in\nPG($2,q$). We study the 526,915,620 1-factorizations of $K_{12}$, determine\nwhich are embeddable in PG($2,q$), and classify hyperfocused $12$-arcs.\nSpecifically we show if a $12$-arc $\\mathcal{K}$ is a hyperfocused arc in\nPG($2,q$) then $q = 2^{5k}$ and $\\mathcal{K}$ is a subset of a hyperconic\nincluding the nucleus.\n"}
+{"abstract": "  The known exact solutions of Einstein's vacuum field equations modeling the\ngravitational fields of pure gravitational radiation involve wave fronts which\nare either planar or roughly spherical. We describe a scheme designed to check\nexplicitly whether or not the wave fronts collide. From the spacetime point of\nview the scheme determines whether or not the null hypersurface histories of\nthe wave fronts intersect and, in particular, allows easy identification of the\ncases in which the null hypersurfaces do not intersect.\n"}
+{"abstract": "  Let $R$ be a commutative ring with identity and $S$ a multiplicative subset\nof $R$. First, we introduce and study the $S$-projective dimensions and\n$S$-injective dimensions of $R$-modules, and then explore the $S$-global\ndimension $S$-gl.dim$(R)$ of a commutative ring $R$ which is defined to be the\nsupremum of $S$-projective dimensions of all $R$-modules. Finally, we\ninvestigated the $S$-global dimension of factor rings and polynomial rings.\n"}
+{"abstract": "  We show that Vizing's Theorem holds in the Borel context for graphs induced\nby actions of 2-ended groups, and ask whether it holds more generally for\neverywhere two ended Borel graphs.\n"}
+{"abstract": "  The novel coronavirus disease 2019 (COVID-19) characterized by atypical\npneumonia has caused millions of deaths worldwide. Automatically segmenting\nlesions from chest Computed Tomography (CT) is a promising way to assist\ndoctors in COVID-19 screening, treatment planning, and follow-up monitoring.\nHowever, voxel-wise annotations are extremely expert-demanding and scarce,\nespecially when it comes to novel diseases, while an abundance of unlabeled\ndata could be available. To tackle the challenge of limited annotations, in\nthis paper, we propose an uncertainty-guided dual-consistency learning network\n(UDC-Net) for semi-supervised COVID-19 lesion segmentation from CT images.\nSpecifically, we present a dual-consistency learning scheme that simultaneously\nimposes image transformation equivalence and feature perturbation invariance to\neffectively harness the knowledge from unlabeled data. We then quantify the\nsegmentation uncertainty in two forms and employ them together to guide the\nconsistency regularization for more reliable unsupervised learning. Extensive\nexperiments showed that our proposed UDC-Net improves the fully supervised\nmethod by 6.3% in Dice and outperforms other competitive semi-supervised\napproaches by significant margins, demonstrating high potential in real-world\nclinical practice.\n"}
+{"abstract": "  Neutron capture-induced nuclear recoils have emerged as an important tool for\ndetector calibrations in direct dark matter detection and coherent elastic\nneutrino-nucleus scattering (CE${\\nu}$NS). $\\texttt{nrCascadeSim}$ is a\ncommand-line tool for generating simulation data for energy deposits resulting\nfrom neutron capture on pure materials. Presently, silicon, germanium, neon,\nand argon are supported. While the software was developed for solid state\ndetector calibration, it can be used for any application which requires\nsimulated neutron capture-induced nuclear recoil data.\n"}
+{"abstract": "  With growing access to versatile robotics, it is beneficial for end users to\nbe able to teach robots tasks without needing to code a control policy. One\npossibility is to teach the robot through successful task executions. However,\nnear-optimal demonstrations of a task can be difficult to provide and even\nsuccessful demonstrations can fail to capture task aspects key to robust skill\nreplication. Here, we propose a learning from demonstration (LfD) approach that\nenables learning of robust task definitions without the need for near-optimal\ndemonstrations. We present a novel algorithmic framework for learning tasks\nbased on the ergodic metric -- a measure of information content in motion.\nMoreover, we make use of negative demonstrations -- demonstrations of what not\nto do -- and show that they can help compensate for imperfect demonstrations,\nreduce the number of demonstrations needed, and highlight crucial task elements\nimproving robot performance. In a proof-of-concept example of cart-pole\ninversion, we show that negative demonstrations alone can be sufficient to\nsuccessfully learn and recreate a skill. Through a human subject study with 24\nparticipants, we show that consistently more information about a task can be\ncaptured from combined positive and negative (posneg) demonstrations than from\nthe same amount of just positive demonstrations. Finally, we demonstrate our\nlearning approach on simulated tasks of target reaching and table cleaning with\na 7-DoF Franka arm. Our results point towards a future with robust,\ndata-efficient LfD for novice users.\n"}
+{"abstract": "  Man-Computer Symbiosis (MCS) was originally envisioned by the famous computer\npioneer J.C.R. Licklider in 1960, as a logical evolution of the then inchoate\nrelationship between computer and humans. In his paper, Licklider provided a\nset of criteria by which to judge if a Man-Computer System is a symbiotic one,\nand also provided some predictions about such systems in the near and far\nfuture. Since then, innovations in computer networks and the invention of the\nInternet were major developments towards that end. However, with most systems\nbased on conventional logical algorithms, many aspects of Licklider's MCS\nremained unfulfilled. This paper explores the extent to which modern machine\nlearning systems in general, and deep learning ones in particular best\nexemplify MCS systems, and why they are the prime contenders to achieve a true\nMan-Computer Symbiosis as described by Licklider in his original paper in the\nfuture. The case for deep learning is built by illustrating each point of the\noriginal criteria as well as the criteria laid by subsequent research into MCS\nsystems, with specific examples and applications provided to strengthen the\narguments. The efficacy of deep neural networks in achieving Artificial General\nIntelligence, which would be the perfect version of an MCS system is also\nexplored.\n"}
+{"abstract": "  We present a new random approximation method that yields the existence of a\ndiscrete Beurling prime system $\\mathcal{P}=\\{p_{1}, p_{2}, \\dotso\\}$ which is\nvery close in a certain precise sense to a given non-decreasing,\nright-continuous, nonnegative, and unbounded function $F$. This discretization\nprocedure improves an earlier discrete random approximation method due to H.\nDiamond, H. Montgomery, and U. Vorhauer [Math. Ann. 334 (2006), 1-36], and\nrefined by W.-B. Zhang [Math. Ann. 337 (2007), 671-704].\n  We obtain several applications. Our new method is applied to a question posed\nby M. Balazard concerning Dirichlet series with a unique zero in their half\nplane of convergence, to construct examples of very well-behaved generalized\nnumber systems that solve a recent open question raised by T. Hilberdink and A.\nNeamah in [Int. J. Number Theory 16 05 (2020), 1005-1011], and to improve the\nmain result from [Adv. Math. 370 (2020), Article 107240], where a Beurling\nprime system with regular primes but extremely irregular integers was\nconstructed.\n"}
+{"abstract": "  Tidal interactions and planet evaporation processes impact the evolution of\nclose-in star-planet systems. We study the impact of stellar rotation on these\nprocesses. We compute the time evolution of star-planet systems consisting of a\nplanet with initial mass between 0.02 and 2.5 M$_{ Jup}$ (6 and 800 M$_{\nEarth}$), in a quasi-circular orbit with an initial orbital distance between\n0.01 and 0.10 au, around a solar-type star evolving from the Pre-Main-Sequence\n(PMS) until the end of the Main-Sequence (MS) phase. We account for the\nevolution of: the stellar structure, the stellar angular momentum due to tides\nand magnetic braking, the tidal interactions (equilibrium and dynamical tides\nin stellar convective zones), the mass-evaporation of the planet, and the\nsecular evolution of the planetary orbit. We consider that at the beginning of\nthe evolution, the proto-planetary disk has fully dissipated and planet\nformation is complete. Both a rapid initial stellar rotation, and a more\nefficient angular momentum transport inside the star, in general, contribute\ntoward the enlargement of the domain which is devoid of planets after the PMS\nphase, in the plane of planet mass vs. orbital distance. Comparisons with the\nobserved distribution of exoplanets orbiting solar mass stars, in the plane of\nplanet mass vs. orbital distance (addressing the \"Neptunian desert\" feature),\nshow an encouraging agreement with the present simulations, especially since no\nattempts have been made to fine-tune initial parameters of the models to fit\nthe observations. We also obtain an upper limit for the orbital period of\nbare-core planets, that agrees with observations of the \"radius valley\" feature\nin the plane of planet radius vs. orbital period. The two effects, tides and\nplanet evaporation, should be accounted for simultaneously and in a consistent\nway, with a detailed model for the evolution of the star.\n"}
+{"abstract": "  Symmetric Teleparallel Gravity is an exceptional theory of gravity that is\nconsistent with the vanishing affine connection. This theory is an alternative\nand a simpler geometrical formulation of general relativity, where the\nnon-metricity $Q$ drives the gravitational interaction. Our interest lies in\nexploring the cosmological bouncing scenarios in a flat\nFriedmann-Lima\\^itre-Robertson-Walker (FLRW) spacetime within this framework.\nWe explore bouncing scenarios with two different Lagrangian forms of $f(Q)$\nsuch as a linearly and non-linearly dependence on $Q$. We have successfully\nexamined all the energy conditions and stability analysis for both models to\npresent a matter bounce.\n"}
+{"abstract": "  We introduce the new concept of computation coding. Similar to how\nrate-distortion theory is concerned with the lossy compression of data,\ncomputation coding deals with the lossy computation of functions.\n  Particularizing to linear functions, we present an algorithm to reduce the\ncomputational cost of multiplying an arbitrary given matrix with an unknown\ncolumn vector. The algorithm decomposes the given matrix into the product of\ncodebook wiring matrices whose entries are either zero or signed integer powers\nof two.\n  For a typical implementation of deep neural networks, the proposed algorithm\nreduces the number of required addition units several times. To achieve the\naccuracy of 16-bit signed integer arithmetic for 4k-vectors, no multipliers and\nonly 1.5 adders per matrix entry are needed.\n"}
+{"abstract": "  In this article, the outlier production mechanism of the conventional\nMultiple Signal Classification (MUSIC) and the g-MUSIC Direction-of-Arrival\n(DoA) estimation technique is investigated using tools from Random Matrix\nTheory (RMT). A general Central Limit Theorem (CLT) is derived that allows to\nanalyze the asymptotic stochastic behavior of eigenvector-based cost functions\nin the asymptotic regime where the number of snapshots and the number of\nantennas increase without bound at the same rate. Furthermore, this CLT is used\nto provide an accurate prediction of the resolution capabilities of the MUSIC\nand the g-MUSIC DoA estimation method. The finite dimensional distribution of\nthe MUSIC and the g-MUSIC cost function is shown to be asymptotically jointly\nGaussian distributed in the asymptotic regime.\n"}
+{"abstract": "  Our aim is to find a complex continued fraction algorithm finding all the\nbest Diophantine approximations to a complex number. Using the sequence of\nminimal vectors in a two dimensional lattice over Gaussian integers, we obtain\nan algorithm defined on a submanifold of the space of unimodular two\ndimensional Gauss lattices. This submanifold is transverse to the diagonal\nflow. Thanks to the correspondence between minimal vectors and best Diophantine\napproximations, the algorithm finds all the best approximations to a complex\nnumber. A byproduct of the algorithm is the best constant for the complex\nversion of Dirichlet Theorem about approximations of complex numbers by\nquotients of Gaussian integers.\n"}
+{"abstract": "  Observations of galactic white dwarfs with Gaia have allowed for\nunprecedented modeling of white dwarf cooling, resolving core crystallization\nand sedimentary heating from neutron rich nuclei. These cooling sequences are\nsensitive to the diffusion coefficients of nuclei in Coulomb plasmas which have\norder 10\\% uncertainty and are often not valid across coupling regimes. Using\nlarge scale molecular dynamics simulations we calculate diffusion coefficients\nat high resolution in the regime relevant for white dwarf modeling. We present\na physically motivated law for diffusion with a semi-empirical correction which\nis accurate at the percent level. Implemented along with linear mixing in\nstellar evolution codes, this law should reduce the error from diffusion\ncoefficients by an order of magnitude.\n"}
+{"abstract": "  The author of this work proposes an overview of the recent semi-supervised\nlearning approaches and related works. Despite the remarkable success of neural\nnetworks in various applications, there exist few formidable constraints\nincluding the need for a large amount of labeled data. Therefore,\nsemi-supervised learning, which is a learning scheme in which the scarce labels\nand a larger amount of unlabeled data are utilized to train models (e.g., deep\nneural networks) is getting more important. Based on the key assumptions of\nsemi-supervised learning, which are the manifold assumption, cluster\nassumption, and continuity assumption, the work reviews the recent\nsemi-supervised learning approaches. In particular, the methods in regard to\nusing deep neural networks in a semi-supervised learning setting are primarily\ndiscussed. In addition, the existing works are first classified based on the\nunderlying idea and explained, and then the holistic approaches that unify the\naforementioned ideas are detailed.\n"}
+{"abstract": "  We study the existence of fully-heavy hidden-flavor $bc\\bar{b}\\bar{c}$\ntetraquark states with various $J^{PC}=0^{\\pm+}, 0^{--},1^{\\pm\\pm}, 2^{++}$, by\nusing the moment QCD sum rule method augmented by fundamental inequalities.\nUsing the moment sum rule analyses, our calculation shows that the masses for\nthe S-wave positive parity $bc\\bar{b}\\bar{c}$ tetraquark states are about\n$12.2-12.4$ GeV in both\n$[\\mathbf{\\bar{3}_c}]_{bc}\\otimes[\\mathbf{3_c}]_{\\bar{b}\\bar{c}}$ and\n$[\\mathbf{6_c}]_{bc}\\otimes[\\mathbf{\\bar{6}_c}]_{\\bar{b}\\bar{c}}$ color\nconfiguration channels. Except for two $0^{++}$ states, such results are below\nthe thresholds $T_{\\eta_c\\eta_b}/T_{\\Upsilon\\psi}$ and $T_{B_cB_c}$, implying\nthat these S-wave positive parity $bc\\bar{b}\\bar{c}$ tetraquark states are\nprobably stable against the strong interaction. For the P-wave negative parity\n$bc\\bar{b}\\bar{c}$ tetraquarks, their masses in the\n$[\\mathbf{\\bar{3}_c}]_{bc}\\otimes[\\mathbf{3_c}]_{\\bar{b}\\bar{c}}$ channel are\naround $12.9-13.2$ GeV, while a bit higher in the\n$[\\mathbf{6_c}]_{bc}\\otimes[\\mathbf{\\bar{6}_c}]_{\\bar{b}\\bar{c}}$ channel. They\ncan decay into the $c\\bar c+b\\bar b$ and $c\\bar b+b\\bar c$ final states via the\nspontaneous dissociation mechanism, including the $J/\\psi\\Upsilon$,\n$\\eta_c\\Upsilon$, $J/\\psi\\eta_b$, $B_c^+B_c^-$ channels.\n"}
+{"abstract": "  The adiabatic topological pumping is proposed by periodically modulating a\nsemiconductor nanowire double-quantum-dot chain. We demonstrate that the\nquantized charge transport can be achieved by a nontrivial modulation of the\nquantum-dot well and barrier potentials. When the quantum-dot well potential is\nreplaced by a time-dependent staggered magnetic field, the topological spin\npumping can be realized by periodically modulating the barrier potentials and\nmagnetic field. We also demonstrate that in the presence of Rashba spin-orbit\ninteraction, the double-quantum-dot chain can be used to implement the\ntopological spin pumping. However, the pumped spin in this case can have a\nquantization axis other than the applied magnetic field direction. Moreover, we\nshow that all the adiabatic topological pumping are manifested by the presence\nof gapless edge states traversing the band gap as a function of time.\n"}
+{"abstract": "  The large attention-based encoder-decoder network (Transformer) has become\nprevailing recently due to its effectiveness. But the high computation\ncomplexity of its decoder raises the inefficiency issue. By examining the\nmathematic formulation of the decoder, we show that under some mild conditions,\nthe architecture could be simplified by compressing its sub-layers, the basic\nbuilding block of Transformer, and achieves a higher parallelism. We thereby\npropose Compressed Attention Network, whose decoder layer consists of only one\nsub-layer instead of three. Extensive experiments on 14 WMT machine translation\ntasks show that our model is 1.42x faster with performance on par with a strong\nbaseline. This strong baseline is already 2x faster than the widely used\nstandard baseline without loss in performance.\n"}
+{"abstract": "  We demonstrate a smart laser-diffraction analysis technique for particle\nmixture identification. We retrieve information about the size, geometry, and\nratio concentration of two-component heterogeneous particle mixtures with an\nefficiency above 92%. In contrast to commonly-used laser diffraction schemes --\nin which a large number of detectors is needed -- our machine-learning-assisted\nprotocol makes use of a single far-field diffraction pattern, contained within\na small angle ($\\sim 0.26^{\\circ}$) around the light propagation axis. Because\nof its reliability and ease of implementation, our work may pave the way\ntowards the development of novel smart identification technologies for sample\nclassification and particle contamination monitoring in industrial\nmanufacturing processes.\n"}
+{"abstract": "  A critical subject in fully differential QED calculations originates from\nnumerical instabilities due to small fermion masses that act as regulators of\ncollinear singularities. At next-to-next-to-leading order (NNLO) a major\nchallenge is therefore to find a stable implementation of numerically delicate\nreal-virtual matrix elements. In the case of Bhabha scattering this has so far\nprevented the development of a fixed-order Monte Carlo at NNLO accuracy. In\nthis paper we present a new method for stabilising the real-virtual matrix\nelement. It is based on the expansion for soft photon energies including the\nnon-universal subleading term calculated with the method of regions. We have\napplied this method to Bhabha scattering to obtain a stable and efficient\nimplementation within the McMule framework. We therefore present for the first\ntime fully differential results for the photonic NNLO corrections to Bhabha\nscattering.\n"}
+{"abstract": "  As an attempt to tackle the low-data-rate issue of the conventional LoRa\nsystems, we propose two novel frequency-bin-index (FBI) LoRa schemes. In scheme\nI, the indices of starting frequency bins (SFBs) are utilized to carry the\ninformation bits. To facilitate the actual implementation, the SFBs of each\nLoRa signal are divided into several groups prior to the modulation process in\nthe proposed FBI-LoRa system. To further improve the system flexibility, we\nformulate a generalized modulation scheme and propose scheme II by treating the\nSFB groups as an additional type of transmission entity. In scheme II, the\ncombination of SFB indices and that of SFB group indices are both exploited to\ncarry the information bits. We derive the theoretical expressions for\nbit-error-rate (BER) and throughput of the proposed FBI-LoRa system with two\nmodulation schemes over additive white Gaussian noise (AWGN) and Rayleigh\nfading channels. Theoretical and simulation results show that the proposed\nFBI-LoRa schemes can significantly increases the transmission throughput\ncompared with the existing LoRa systems at the expense of a slight loss in BER\nperformance. Thanks to the appealing superiorities, the proposed FBI-LoRa\nsystem is a promising alternative for high-data-rate Internet of Things (IoT)\napplications.\n"}
+{"abstract": "  Palomar 5 is one of the sparsest star clusters in the Galactic halo and is\nbest-known for its spectacular tidal tails, spanning over 20 degrees across the\nsky. With N-body simulations we show that both distinguishing features can\nresult from a stellar-mass black hole population, comprising ~20% of the\npresent-day cluster mass. In this scenario, Palomar 5 formed with a `normal'\nblack hole mass fraction of a few per cent, but stars were lost at a higher\nrate than black holes, such that the black hole fraction gradually increased.\nThis inflated the cluster, enhancing tidal stripping and tail formation. A\ngigayear from now, the cluster will dissolve as a 100% black hole cluster.\nInitially denser clusters end up with lower black hole fractions, smaller\nsizes, and no observable tails. Black hole-dominated, extended star clusters\nare therefore the likely progenitors of the recently discovered thin stellar\nstreams in the Galactic halo.\n"}
+{"abstract": "  This paper investigates fractal dimension of linear combination of fractal\ncontinuous functions with the same or different fractal dimensions. It has been\nproved that: (1) $BV_{I}$ all fractal continuous functions with bounded\nvariation is fractal linear space; (2) ${}^{1}D_{I}$ all fractal continuous\nfunctions with Box dimension one is a fractal linear space; (3) ${}^{s}D_{I}$\nall fractal continuous functions with identical Box dimension $s(1<s\\leq 2)$ is\nsurprisingly a non-fractal linear space, even non-fractal linear manifold,\nbeyond our initial expectation, because the Box dimension of linear combination\nof fractal continuous functions can take any real number in $[1,s)$ if it\nexists, and some different upper and lower Box dimension if it does not exit.\nThis attracts our interests to probe into fractal characteristics of\n${}^{s}D_{I}$, and get some suggesting results.\n"}
+{"abstract": "  We present an engaging levitation experiment that students can perform at\nhome or in a simple laboratory using everyday objects. A cork, modified to be\nslightly denser than water, is placed in a jug containing tap water and coarse\nkitchen salt delivered at the bottom without stirring. The salt gradually\ndiffuses and determines a stable density stratification of water, the bottom\nlayers being denser than the top ones. During the dissolution of salt, the cork\nslowly rises at an increasing height, where at any instant its density is\nbalanced by that of the surrounding water. If the cork is gently pushed off its\ntemporary equilibrium position, it experiences a restoring force and starts to\noscillate. Students can perform many different measurements of the phenomena\ninvolved and tackle non-trivial physical issues related to the behaviour of a\nmacroscopic body immersed in a stratified fluid. Despite its simplicity, this\nexperiment allows to introduce various theoretical concepts of relevance for\nthe physics of the atmosphere and stars and offers students the opportunity of\ngetting acquainted with a simple system that can serve as a model to understand\ncomplex phenomena such as oscillations at the Brunt-V\\\"{a}is\\\"{a}l\\\"{a}\nfrequency and the propagation of internal gravity waves in a stratified medium.\n"}
+{"abstract": "  We show that every $r$-uniform hypergraph on $n$ vertices which does not\ncontain a tight cycle has at most $O(n^{r-1} (\\log n)^5)$ edges. This is an\nimprovement on the previously best-known bound, of $n^{r-1} e^{O(\\sqrt{\\log\nn})}$, due to Sudakov and Tomon, and our proof builds up on their work. A\nrecent construction of B. Janzer implies that our bound is tight up to an\n$O((\\log n)^4 \\log \\log n)$ factor.\n"}
+{"abstract": "  Gravitational wave observations of binary neutron star mergers provide\nvaluable information about neutron star structure and the equation of state of\ndense nuclear matter. Numerous methods have been proposed to analyze the\npopulation of observed neutron stars and previous work has demonstrated the\nnecessity of jointly fitting the astrophysical distribution and the equation of\nstate in order to accurately constrain the equation of state. In this work, we\nintroduce a new framework to simultaneously infer the distribution of binary\nneutron star masses and the nuclear equation of state using Gaussian mixture\nmodel density estimates which mitigates some of the limitations previously-used\nmethods suffer from. Using our method, we reproduce previous projections for\nthe expected precision of our joint mass distribution and equation of state\ninference with tens of observations. We also show that mismodeling the equation\nof state can bias our inference of the neutron star mass distribution. While we\nfocus on neutron star masses and matter effects, our method is widely\napplicable to population inference problems.\n"}
+{"abstract": "  A strong correlation between magnetic interaction and topological symmetries\nleads to unconventional magneto-transport behavior. Weyl fermions induce\ntopologically protected spin-momentum locking, which is closely related to\nspin-wave gap formation in magnetic crystals. Ferromagnetic SrRuO3, regarded as\na strong candidate for Weyl semimetal, inherently possesses a nonzero spin-wave\ngap owing to its strong magnetic anisotropy. In this paper, we propose a method\nto control the spin-wave dynamics by nanolayer designing of the SrRuO3/SrTiO3\nsuperlattices. In particular, the six-unit-cell-thick SrRuO3 layers within the\nsuperlattices undergo a phase transition in crystalline symmetry from\northorhombic to tetragonal, as the thickness of the SrTiO3 layers is modulated\nwith atomic-scale precision. Consequently, the magnetic anisotropy, anomalous\nHall conductivity, and spin-wave gap could be systematically manipulated. Such\ncustomization of magnetic anisotropy via nanoscale heterostructuring offers a\nnovel control knob to tailor the magnon excitation energy for future spintronic\napplications, including magnon waveguides and filters. Our nanolayer approach\nunveils the important correlation between the tunable lattice degrees of\nfreedom and spin dynamics in topologically non-trivial magnetic materials.\n"}
+{"abstract": "  The concept of Nature-Based Solutions (NBS) has emerged to foster sustainable\ndevelopment by transversally addressing social, economic, and environmental\nurban challenges. However, there is still a considerable lack of agreement on\nthe conceptualization of NBS, especially concerning typologies, nomenclature,\nand performance assessments in terms of ecosystem services (ES) and urban\nchallenges (UC). Therefore, this article consolidates the knowledge from 4\nEuropean projects to set a path for a common understanding of NBS and thus,\nfacilitate their mainstreaming.\n"}
+{"abstract": "  We study the merger rate of primordial black holes (PBHs) in the\nself-interacting dark matter (SIDM) halo models. To explore a numerical\ndescription for the density profile of the SIDM halo models, we use the result\nof a previously performed simulation for the SIDM halo models with\n$\\sigma/m=10~cm^{2}g^{-1}$. We also propose a concentration-mass-time relation\nthat can explain the evolution of the halo density profile related to the SIDM\nmodels. Furthermore, we investigate the encounter condition of PBHs that may\nhave been distributed in the medium of dark matter halos randomly. Under these\nassumptions, we calculate the merger rate of PBHs within each halo considering\nthe SIDM halo models and compare the results with the one obtained for the cold\ndark matter (CDM) halo models. We indicate that the merger rate of PBHs for the\nSIDM halo models during the first epoch should be lower than the corresponding\nresult for the CDM halo models, while by the time entering the second epoch\nsufficient PBH mergers in the SIDM halo models can be generated and even exceed\nthe one resulted from the CDM halo models. By considering the\nspherical-collapse halo mass function, we obtain similar results for the\ncumulative merger rate of PBHs. Moreover, we calculate the redshift evolution\nof the PBH total merger rate. To determine a constraint on the PBH abundance,\nwe study the merger rate of PBHs in terms of their fraction and masses and\ncompare those with the black hole merger rate estimated by the Advanced LIGO\n(aLIGO) detectors during the third observing run. The results demonstrate that\nwithin the context of the SIDM halo models during the second epoch, the merger\nrate of $10~M_{\\odot}-10~M_{\\odot}$ events falls within the aLIGO window. We\nalso estimate a relation between the fraction of PBHs and their masses, which\nis well consistent with our findings.\n"}
+{"abstract": "  The critical point equation arises as a critical point of the total scalar\ncurvature functional defined on the space of constant scalar curvature metrics\nof a unit volume on a compact manifold. In this equation, there exists a\nfunction $f$ on the manifold that satisfies the following\n  $$\n  (1+f){\\rm Ric} = Ddf + \\frac{nf +n-1}{n(n-1)}sg.\n  $$\n  It has been conjectured that if $(g, f)$ is a solution of the critical point\nequation, then $g$\n  is Einstein and so $(M, g)$ is isometric to a standard sphere.\n  In this paper, we show that this conjecture is true if the given Riemannian\nmetric has positive isotropic curvature.\n"}
+{"abstract": "  Numerous malware families rely on domain generation algorithms (DGAs) to\nestablish a connection to their command and control (C2) server. Counteracting\nDGAs, several machine learning classifiers have been proposed enabling the\nidentification of the DGA that generated a specific domain name and thus\ntriggering targeted remediation measures. However, the proposed\nstate-of-the-art classifiers are based on deep learning models. The black box\nnature of these makes it difficult to evaluate their reasoning. The resulting\nlack of confidence makes the utilization of such models impracticable. In this\npaper, we propose EXPLAIN, a feature-based and contextless DGA multiclass\nclassifier. We comparatively evaluate several combinations of feature sets and\nhyperparameters for our approach against several state-of-the-art classifiers\nin a unified setting on the same real-world data. Our classifier achieves\ncompetitive results, is real-time capable, and its predictions are easier to\ntrace back to features than the predictions made by the DGA multiclass\nclassifiers proposed in related work.\n"}
+{"abstract": "  The robust topology optimization formulation that introduces the eroded and\ndilated versions of the design has gained increasing popularity in recent\nyears, mainly because of its ability to produce designs satisfying a minimum\nlength scale. Despite its success in various topology optimization fields, the\nrobust formulation presents some drawbacks. This paper addresses one in\nparticular, which concerns the imposition of the minimum length scale. In the\ndensity framework, the minimum size of the solid and void phases must be\nimposed implicitly through the parameters that define the density filter and\nthe smoothed Heaviside projection. Finding these parameters can be time\nconsuming and cumbersome, hindering a general code implementation of the robust\nformulation. Motivated by this issue, in this article we provide analytical\nexpressions that explicitly relate the minimum length scale and the parameters\nthat define it. The expressions are validated on a density-based framework. To\nfacilitate the reproduction of results, MATLAB codes are provided.\n  As a side finding, this paper shows that to obtain simultaneous control over\nthe minimum size of the solid and void phases, it is necessary to involve the 3\nfields (eroded, intermediate and dilated) in the topology optimization problem.\nTherefore, for the compliance minimization problem subject to a volume\nrestriction, the intermediate and dilated designs can be excluded from the\nobjective function, but the volume restriction has to be applied to the dilated\ndesign in order to involve all 3 designs in the formulation.\n"}
+{"abstract": "  What is the computational model behind a Transformer? Where recurrent neural\nnetworks have direct parallels in finite state machines, allowing clear\ndiscussion and thought around architecture variants or trained models,\nTransformers have no such familiar parallel. In this paper we aim to change\nthat, proposing a computational model for the transformer-encoder in the form\nof a programming language. We map the basic components of a transformer-encoder\n-- attention and feed-forward computation -- into simple primitives, around\nwhich we form a programming language: the Restricted Access Sequence Processing\nLanguage (RASP). We show how RASP can be used to program solutions to tasks\nthat could conceivably be learned by a Transformer, and how a Transformer can\nbe trained to mimic a RASP solution. In particular, we provide RASP programs\nfor histograms, sorting, and Dyck-languages. We further use our model to relate\ntheir difficulty in terms of the number of required layers and attention heads:\nanalyzing a RASP program implies a maximum number of heads and layers necessary\nto encode a task in a transformer. Finally, we see how insights gained from our\nabstraction might be used to explain phenomena seen in recent works.\n"}
+{"abstract": "  The problem of designing NLP solvers for math word problems (MWP) has seen\nsustained research activity and steady gains in the test accuracy. Since\nexisting solvers achieve high performance on the benchmark datasets for\nelementary level MWPs containing one-unknown arithmetic word problems, such\nproblems are often considered \"solved\" with the bulk of research attention\nmoving to more complex MWPs. In this paper, we restrict our attention to\nEnglish MWPs taught in grades four and lower. We provide strong evidence that\nthe existing MWP solvers rely on shallow heuristics to achieve high performance\non the benchmark datasets. To this end, we show that MWP solvers that do not\nhave access to the question asked in the MWP can still solve a large fraction\nof MWPs. Similarly, models that treat MWPs as bag-of-words can also achieve\nsurprisingly high accuracy. Further, we introduce a challenge dataset, SVAMP,\ncreated by applying carefully chosen variations over examples sampled from\nexisting datasets. The best accuracy achieved by state-of-the-art models is\nsubstantially lower on SVAMP, thus showing that much remains to be done even\nfor the simplest of the MWPs.\n"}
+{"abstract": "  Terahertz time domain spectroscopy (THz TDS) is used to measure the melting\nkinetics of fructose molecular crystals. Combining single crystal anisotropy\nmeasurements with density functional calculations we assign the phonon\nfrequencies and interrogate how specific phonons behave with melting. While\nnearly all the low frequency phonons continuously red shift with heating and\nmelting, the lowest energy phonon polarized along the c-axis blue shifts at the\nmelting temperature, suggesting an initial structural change immediately before\nmelting. We find that the kinetics follow a 3D growth model with large\nactivation energies consistent with previous differential scanning calorimetry\n(DSC) measurements. The large activation energies indicate multiple H-bonds\nmust break collectively for the transition. The results suggest the generality\nof the kinetics for molecular crystals and that THz TDS with picosecond\nresolution could be used to measure ultra-fast kinetics.\n"}
+{"abstract": "  In this paper, we propose to enhance the pair-wise aspect and opinion terms\nextraction (PAOTE) task by incorporating rich syntactic knowledge. We first\nbuild a syntax fusion encoder for encoding syntactic features, including a\nlabel-aware graph convolutional network (LAGCN) for modeling the dependency\nedges and labels, as well as the POS tags unifiedly, and a local-attention\nmodule encoding POS tags for better term boundary detection. During pairing, we\nthen adopt Biaffine and Triaffine scoring for high-order aspect-opinion term\npairing, in the meantime re-harnessing the syntax-enriched representations in\nLAGCN for syntactic-aware scoring. Experimental results on four benchmark\ndatasets demonstrate that our model outperforms current state-of-the-art\nbaselines, meanwhile yielding explainable predictions with syntactic knowledge.\n"}
+{"abstract": "  Driven by the tremendous effort in researching novel deep learning (DL)\nalgorithms, the training cost of developing new models increases staggeringly\nin recent years. We analyze GPU cluster usage statistics from a top research\ninstitute for more insights into the hardware efficiency achieved by typical DL\ntraining jobs. Our study reveals that single-accelerator training jobs can\ndominate the cluster-wide resource consumption when launched repetitively\n(e.g., for hyper-parameter tuning) while severely under-utilizing the hardware.\nFortunately, we observe that such workloads have the following unique\ncharacteristics: (i) the models among jobs often have the same types of\noperators with the same shapes, and (ii) the inter-model horizontal fusion of\nsuch operators is mathematically equivalent to other already well-optimized\noperators. Thus, to help DL researchers and practitioners effectively improve\nthe hardware utilization of their novel DL training workloads, we propose\nHorizontally Fused Training Array (HFTA). HFTA is a new DL framework extension\nlibrary that horizontally fuses the models from different repetitive jobs\ndeeply down to operators and then trains them simultaneously on a shared\naccelerator. To show the generality of our solution, we apply HFTA to six DL\nmodels training on state-of-the-art accelerators (GPUs and TPUs). Our results\nindicate that HFTA is highly effective in improving hardware utilization and\nachieves up to $15.1 \\times$ higher training throughput vs. the standard\npractice of running each job on a separate accelerator.\n"}
+{"abstract": "  This paper considers grid formation of an unmanned aerial vehicle (UAV) swarm\nfor maximizing the secrecy rate in the presence of an unknown eavesdropper. In\nparticular, the UAV swarm performs coordinated beamforming onto the null space\nof the legitimate channel to jam the eavesdropper located at an unknown\nlocation. By nulling the channel between the legitimate receiver and the UAV\nswarm, we obtain an optimal trajectory and jamming power allocation for each\nUAV enabling wideband single ray beamforming to improve the secrecy rate.\nResults obtained demonstrate the effectiveness of the proposed UAV-aided\njamming scheme as well as the optimal number of UAVs in the swarm necessary to\nobserve a saturation effect in the secrecy rate. We also show the optimal\nradius of the unknown but constrained location of the eavesdropper.\n"}
+{"abstract": "  We show that if an exponential sum with multiplicative coefficients is large\nthen the associated multiplicative function is \"pretentious\". This leads to\napplications in the circle method, and a natural interpretation of the\nlocal-global principle.\n"}
+{"abstract": "  This paper presents a detailed computational protocol for atomistic\nsimulation of the formation and growth of metal-containing nanostructures\nduring the Focused Electron Beam Induced Deposition (FEBID) process. The\nprotocol is based upon the Irradiation-Driven Molecular Dynamics (IDMD) - a\nnovel and general methodology for computer simulations of irradiation-driven\ntransformations of complex molecular systems by means of the advanced software\npackages MBN Explorer and MBN Studio. Atomistic simulations performed following\nthe formulated protocol provide valuable insights into the fundamental\nmechanisms of electron-induced precursor fragmentation and the related\nmechanism of nanostructure formation and growth using FEBID, which are\nessential for the further advancement of FEBID-based nanofabrication. The\ndeveloped computational methodology is general and applicable to different\nprecursor molecules, substrate types, irradiation regimes, etc. The methodology\ncan also be adjusted to simulate the nanostructure formation by other\nnanofabrication techniques using electron beams, such as direct electron beam\nlithography. In the present study, the methodology is applied to the IDMD\nsimulation of the FEBID of Pt(PF$_3$)$_4$ - a widely studied precursor molecule\n- on a SiO$_2$ surface. The simulations reveal the processes driving the\ninitial phase of nanostructure formation during FEBID, including nucleation of\nPt atoms, formation of small metal clusters on the surface, followed by their\naggregation and the formation of dendritic platinum nanostructures. The\nanalysis of the simulation results provides space resolved relative metal\ncontent, height and the growth rate of the deposits which represent valuable\nreference data for the experimental characterization of the nanostructures\ngrown by FEBID.\n"}
+{"abstract": "  In this conference paper we summarise the findings of a recent study, where\nthe impact of the ultra-relativistic regime on the production of a feebly\ninteracting dark matter particle is considered. As its population accumulates\nover the thermal history, we inspected thoroughly the temperature window $T \\gg\nM$, which has been previously neglected in the context of dark matter models\nwith renormalizable operators. At high temperatures, and for the model\nconsidered in our work, the production rate of the feebly interacting particle\nis driven by multiple soft scatterings, as well as $2 \\to 2$ processes, that\ncan give a large contribution to the dark matter energy density.\n"}
+{"abstract": "  Advanced resuscitative technologies, such as Extra Corporeal Membrane\nOxygenation (ECMO) cannulation or Resuscitative Endovascular Balloon Occlusion\nof the Aorta (REBOA), are technically difficult even for skilled medical\npersonnel. This paper describes the core technologies that comprise a\nteleoperated system capable of granting femoral vascular access, which is an\nimportant step in both of these procedures and a major roadblock in their wider\nuse in the field. These technologies include a kinematic manipulator, various\nsensing modalities, and a user interface. In addition, we evaluate our system\non a surgical phantom as well as in-vivo porcine experiments. These resulted\nin, to the best of our knowledge, the first robot-assisted arterial\ncatheterizations; a major step towards our eventual goal of automatic catheter\ninsertion through the Seldinger technique.\n"}
+{"abstract": "  A diverse representation of different demographic groups in AI training data\nsets is important in ensuring that the models will work for a large range of\nusers. To this end, recent efforts in AI fairness and inclusion have advocated\nfor creating AI data sets that are well-balanced across race, gender,\nsocioeconomic status, and disability status. In this paper, we contribute to\nthis line of work by focusing on the representation of age by asking whether\nolder adults are represented proportionally to the population at large in AI\ndata sets. We examine publicly-available information about 92 face data sets to\nunderstand how they codify age as a case study to investigate how the subjects'\nages are recorded and whether older generations are represented. We find that\nolder adults are very under-represented; five data sets in the study that\nexplicitly documented the closed age intervals of their subjects included older\nadults (defined as older than 65 years), while only one included oldest-old\nadults (defined as older than 85 years). Additionally, we find that only 24 of\nthe data sets include any age-related information in their documentation or\nmetadata, and that there is no consistent method followed across these data\nsets to collect and record the subjects' ages. We recognize the unique\ndifficulties in creating representative data sets in terms of age, but raise it\nas an important dimension that researchers and engineers interested in\ninclusive AI should consider.\n"}
+{"abstract": "  As a sequel to \\cite{Licollapsing}, we study Calabi-Yau metrics collapsing\nalong a holomorphic fibration over a Riemann surface. Assuming at worst\ncanonical singular fibres, we prove a uniform diameter bound for all fibres in\nthe suitable rescaling. This has consequences on the geometry around the\nsingular fibres.\n"}
+{"abstract": "  Worldwide research shows that millions of lives lost per year because of\nheart disease. The healthcare sector produces massive volumes of data on heart\ndisease that are sadly not used to locate secret knowledge for successful\ndecision making. One of the most important aspects at this moment is detecting\nheart disease at an early stage. Researchers have applied distinct techniques\nto the UCI Machine Learning heart disease dataset. Many researchers have tried\nto apply some complex techniques to this dataset, where detailed studies are\nstill missing. In this paper, Principal Component Analysis (PCA) has been used\nto reduce attributes. Apart from a Hybrid genetic algorithm (HGA) with k-means\nused for final clustering. Typically, the k-means method is using for\nclustering the data. This type of clustering can get stuck in the local optima\nbecause this method is heuristic. We used the Hybrid Genetic Algorithm (HGA)\nfor data clustering to avoid this problem. Our proposed method can predict\nearly heart disease with an accuracy of 94.06%.\n"}
+{"abstract": "  We propose a novel method for finding the most innovative people in an\norganization, using email to analyze structure and dynamics of the\norganization's online communication. To illustrate our approach, we analyzed\nthe email archive of 2000 members of the R&D department of a US multinational\ncompany. We use metrics of social network analysis extended with meta-data of\ninteraction dynamics to calculate features for individual employees: their\nnetwork positions, messages sent and received, pings to others and response\ntimes. We find a distinction between innovation group leaders and subject\nmatter experts focused on publishing papers and patents. Innovation\nadministrators have a higher number of direct contacts, are more committed in\nconversations and receive more messages than they send. We also found\nsignificant differences between innovators oriented towards internal awards and\ninnovators more concerned with external recognition of their work.\n"}
+{"abstract": "  Containerization plays a crucial role in the de facto technology stack for\nimplementing microservices architecture (each microservice has its own database\nin most cases). Nevertheless, there are still fierce debates on containerizing\nproduction databases, mainly due to the data persistence issues and concerns.\nDriven by a project of refactoring an Automated Machine Learning system, this\nresearch proposes the container-native data persistence as a conditional\nsolution to running database containers in production. In essence, the proposed\nsolution distinguishes the stateless data access (i.e. reading) from the\nstateful data processing (i.e. creating, updating, and deleting) in databases.\nA master database handles the stateful data processing and dumps database\ncopies for building container images, while the database containers will keep\nstateless at runtime, based on the preloaded dump in the image. Although there\nare delays in the state/image update propagation, this solution is particularly\nsuitable for the read-only, the eventual consistency, and the asynchronous\nprocessing scenarios. Moreover, with optimal tuning (e.g., disabling locking),\nthe portability and performance gains of a read-only database container would\noutweigh the performance loss in accessing data across the underlying image\nlayers.\n"}
+{"abstract": "  In this paper, it is shown theoretically that spurious local minima are\ncommon for deep fully-connected networks and convolutional neural networks\n(CNNs) with piecewise linear activation functions and datasets that cannot be\nfitted by linear models. A motivating example is given to explain the reason\nfor the existence of spurious local minima: each output neuron of deep\nfully-connected networks and CNNs with piecewise linear activations produces a\ncontinuous piecewise linear (CPWL) output, and different pieces of CPWL output\ncan fit disjoint groups of data samples when minimizing the empirical risk.\nFitting data samples with different CPWL functions usually results in different\nlevels of empirical risk, leading to prevalence of spurious local minima. This\nresult is proved in general settings with any continuous loss function. The\nmain proof technique is to represent a CPWL function as a maximization over\nminimization of linear pieces. Deep ReLU networks are then constructed to\nproduce these linear pieces and implement maximization and minimization\noperations.\n"}
+{"abstract": "  The muon anomalous magnetic moment is discussed within the model based on the\n$SU(2)_L\\times SU(2)_R\\times U(1)_{B-L}$-gauge group. The contributions of the\nneutrinos having the dipole magnetic moment (DMM) are calculated. The cases of\nthe Majorana and Dirac neutrino are considered. It is shown that to account for\nthe $(g-2)_{\\mu}$ anomaly DMMs connected with heavy neutrinos should be on the\norder of $\\mbox{few}\\times10^{-8}\\mu_B$, that is at variance with the\ntheoretically predicted values.\n"}
+{"abstract": "  How does the accuracy of deep neural network models trained to classify\nclinical images of skin conditions vary across skin color? While recent studies\ndemonstrate computer vision models can serve as a useful decision support tool\nin healthcare and provide dermatologist-level classification on a number of\nspecific tasks, darker skin is underrepresented in the data. Most publicly\navailable data sets do not include Fitzpatrick skin type labels. We annotate\n16,577 clinical images sourced from two dermatology atlases with Fitzpatrick\nskin type labels and open-source these annotations. Based on these labels, we\nfind that there are significantly more images of light skin types than dark\nskin types in this dataset. We train a deep neural network model to classify\n114 skin conditions and find that the model is most accurate on skin types\nsimilar to those it was trained on. In addition, we evaluate how an algorithmic\napproach to identifying skin tones, individual typology angle, compares with\nFitzpatrick skin type labels annotated by a team of human labelers.\n"}
+{"abstract": "  To provide consistent emotional interaction with users, dialog systems should\nbe capable to automatically select appropriate emotions for responses like\nhumans. However, most existing works focus on rendering specified emotions in\nresponses or empathetically respond to the emotion of users, yet the individual\ndifference in emotion expression is overlooked. This may lead to inconsistent\nemotional expressions and disinterest users. To tackle this issue, we propose\nto equip the dialog system with personality and enable it to automatically\nselect emotions in responses by simulating the emotion transition of humans in\nconversation. In detail, the emotion of the dialog system is transitioned from\nits preceding emotion in context. The transition is triggered by the preceding\ndialog context and affected by the specified personality trait. To achieve\nthis, we first model the emotion transition in the dialog system as the\nvariation between the preceding emotion and the response emotion in the\nValence-Arousal-Dominance (VAD) emotion space. Then, we design neural networks\nto encode the preceding dialog context and the specified personality traits to\ncompose the variation. Finally, the emotion for response is selected from the\nsum of the preceding emotion and the variation. We construct a dialog dataset\nwith emotion and personality labels and conduct emotion prediction tasks for\nevaluation. Experimental results validate the effectiveness of the\npersonality-affected emotion transition.\n"}
+{"abstract": "  Alignment of non-spherical grains with magnetic fields is an important\nproblem as it lays the foundation of probing magnetic fields with polarized\ndust thermal emissions. In this paper, we investigate the feasibility of\nmagnetic alignment in protoplanetary disks (PPDs). We use an alignment\ncondition that Larmor precession should be fast compared with the damping\ntimescale. We first show that the Larmor precession timescale is some three\norders of magnitude longer than the damping time for millimeter-sized grains\nunder conditions typical of PPDs, making the magnetic alignment unlikely. The\nprecession time can be shortened by superparamagnetic inclusions (SPIs), but\nthe reduction factor strongly depends on the size of the SPI clusters, which we\nfind is limited by the so-called \"N\\'{e}el's relaxation process.\" In\nparticular, the size limit of SPIs is set by the so-called \"anisotropic energy\nconstant\" of the SPI material, which describes the energy barrier needed to\nchange the direction of the magnetic moment of an SPI. For the most common\niron-bearing materials, we find maximum SPI sizes corresponding to a reduction\nfactor of the Larmor precession timescale of order $10^3$. We also find that\nreaching this maximum reduction factor requires fine-tuning on the SPI sizes.\nLastly, we illustrate the effects of the SPI size limits on magnetic alignment\nof dust grains with a simple disk model, and we conclude that it is unlikely\nfor relatively large grains of order 100 $\\mu$m or more to be aligned with\nmagnetic fields even with SPIs.\n"}
+{"abstract": "  Finite-state Markov models are widely used for modeling wireless channels\naffected by a variety of non-idealities, ranging from shadowing to\ninterference. In an industrial environment, the derivation of a Markov model\nbased on the wireless communication physics can be prohibitive as it requires a\ncomplete knowledge of both the communication dynamics parameters and of the\ndisturbances/interferers. In this work, a novel methodology is proposed to\nlearn a Markov model of a fading channel via historical data of the\nsignal-to-interference-plus-noise-ratio (SINR). Such methodology can be used to\nderive a Markov jump model of a wireless control network, and thus to design a\nstochastic optimal controller that takes into account the interdependence\nbetween the plant and the wireless channel dynamics. The proposed method is\nvalidated by comparing its prediction accuracy and control performance with\nthose of a stationary finite-state Markov chain derived assuming perfect\nknowledge of the physical channel model and parameters of a WirelessHART\npoint-to-point communication based on the IEEE-802.15.4 standard.\n"}
+{"abstract": "  Recursive Neural Networks (RvNNs), which compose sequences according to their\nunderlying hierarchical syntactic structure, have performed well in several\nnatural language processing tasks compared to similar models without structural\nbiases. However, traditional RvNNs are incapable of inducing the latent\nstructure in a plain text sequence on their own. Several extensions have been\nproposed to overcome this limitation. Nevertheless, these extensions tend to\nrely on surrogate gradients or reinforcement learning at the cost of higher\nbias or variance. In this work, we propose Continuous Recursive Neural Network\n(CRvNN) as a backpropagation-friendly alternative to address the aforementioned\nlimitations. This is done by incorporating a continuous relaxation to the\ninduced structure. We demonstrate that CRvNN achieves strong performance in\nchallenging synthetic tasks such as logical inference and ListOps. We also show\nthat CRvNN performs comparably or better than prior latent structure models on\nreal-world tasks such as sentiment analysis and natural language inference.\n"}
+{"abstract": "  Fast-pairwise neutrino oscillations potentially affect many aspects of\ncore-collapse supernova (CCSN): the explosion mechanism, neutrino signals, and\nnucleosynthesis in the ejecta. This particular mode of collective neutrino\noscillations has a deep connection to the angular structure of neutrinos in\nmomentum space; for instance, the appearance of electron neutrinos lepton\nnumber (ELN) angular crossings in momentum space is a good indicator of\noccurrences of the flavor conversions. However, many multi-dimensional\n(multi-D) CCSN simulations are carried out with approximate neutrino transport\n(such as two-moment methods), which limits the access to the angular\ndistributions of neutrinos, i.e., inhibits ELN-crossing searches. In this\npaper, we develop a new method of ELN-crossing search in these CCSN\nsimulations. The required data is the zero-th and first angular moments of\nneutrinos and matter profile, all of which are available in CCSN models with\ntwo-moment method. One of the novelties of our new method is to use a\nray-tracing neutrino transport to determine ELNs in the direction of the\nstellar center. It is designed to compensate for shortcomings of the crossing\nsearches only with the two angular moments. We assess the capability of the\nmethod by carrying out a detailed comparison to results of full Boltzmann\nneutrino transport in 1D and 2D CCSN models. We find that the ray-tracing\nneutrino transport improves the accuracy of crossing searches; indeed, the\nappearance/disappearance of the crossings is accurately detected even in the\nregion of forward-peaked angular distributions. The new method is\ncomputationally cheap and has a benefit of efficient parallelization; hence, it\nwill be useful for ELN-crossing searches in any CCSN models employed two-moment\nneutrino transport.\n"}
+{"abstract": "  When stretched in one direction, most solids shrink in the transverse\ndirections. In soft silicone gels, however, we observe that small-scale\ntopographical features grow upon stretching. A quantitative analysis of the\ntopography shows that this counter-intuitive response is nearly linear,\nallowing us to tackle it through a small-strain analysis. We find that the\nsurprising increase of small-scale topography with stretch is due to a delicate\ninterplay of the bulk and surface responses to strain. Specifically, we find\nthat surface tension changes as the material is deformed. This response is\nexpected on general grounds for solid materials, but challenges the standard\ndescription of gel- and elastomer-surfaces.\n"}
+{"abstract": "  Propagation-invariant or non-diffracting optical beams have received\nconsiderable attention during the last two decades. However, the pulsed nature\nof light waves and the structured property of optical media like waveguides are\noften overlooked. We here present a four-dimensional spatiotemporal approach\nthat extends and unifies both concepts of conical waves and helicon beams,\nmainly studied in bulk media. By taking advantage of tight correlations between\nthe spatial modes, the topological charges, and the frequencies embedded in an\noptical field, we reveal propagation-invariant (dispersion- and\ndiffraction-free) space-time wavepackets carrying orbital angular momentum\n(OAM) that evolve on spiraling trajectories in both time and space in bulk\nmedia or multimode fibers. Besides their intrinsic linear nature, we show that\nsuch wave structures can spontaneously emerge when a rather intense ultrashort\npulse propagates nonlinearly in OAM modes. With emerging technologies of\npulse/beam shaping, multimode fibers and modal multiplexing, our proposed\nscheme to create OAM-carrying helicon wavepackets could find a plethora of\napplications.\n"}
+{"abstract": "  We present a thermodynamic model which explains the presence of a negative\nslope in the melt curve, as observed in systems as diverse as the alkali metals\nand molecular hydrogen at high pressure. We assume that components of the\nsystem can be in one of two well defined states - one associated with low\nenergy, the other with low volume.\n  The model exhibits a number of measurable features which are also observed in\nthese systems and are therefore expected to be associated with all negative\nClapeyron-slope systems: first order phase transitions, thermodynamic anomalies\nalong Widom lines.\n  The melt curve maximum is a feature of the model, but appears well below the\npressures where the change in state occurs in the solid: the solid-solid\ntransition is related to the melt line minimum. An example of the model fitted\nto the electride transition in potassium is discussed.\n"}
+{"abstract": "  For a graph $G$, $\\chi(G)$ will denote its chromatic number, and $\\omega(G)$\nits clique number. A graph $G$ is said to be perfectly divisible if for all\ninduced subgraphs $H$ of $G$, $V(H)$ can be partitioned into two sets $A$, $B$\nsuch that $H[A]$ is perfect and $\\omega(H[B]) < \\omega(H)$. An integer-valued\nfunction $f$ is called a $\\chi$-binding function for a hereditary class of\ngraphs $\\cal C$ if $\\chi(G) \\leq f(\\omega(G))$ for every graph $G\\in \\cal C$.\nThe fork is the graph obtained from the complete bipartite graph $K_{1,3}$ by\nsubdividing an edge once. The problem of finding a polynomial $\\chi$-binding\nfunction for the class of fork-free graphs is open. In this paper, we study the\nstructure of some classes of fork-free graphs; in particular, we study the\nclass of (fork,$F$)-free graphs $\\cal G$ in the context of perfect\ndivisibility, where $F$ is a graph on five vertices with a stable set of size\nthree, and show that every $G\\in \\cal G$ satisfies $\\chi(G)\\leq \\omega(G)^2$.\nWe also note that the class $\\cal G$ does not admit a linear $\\chi$-binding\nfunction.\n"}
+{"abstract": "  We introduce a Metropolis-Hastings Markov chain for Boltzmann distributions\nof classical spin systems. It relies on approximate tensor network contractions\nto propose correlated collective updates at each step of the evolution. We\npresent benchmarks for a wide variety of instances of the two-dimensional Ising\nmodel, including ferromagnetic, antiferromagnetic, (fully) frustrated and\nEdwards-Anderson spin glass cases, and we show that, with modest computational\neffort, our Markov chain achieves sizeable acceptance rates, even in the\nvicinity of critical points. In each of the situations we have considered, the\nMarkov chain compares well with other Monte Carlo schemes such as the\nMetropolis or Wolff algorithm: equilibration times appear to be reduced by a\nfactor that varies between 40 and 2000, depending on the model and the\nobservable being monitored. We also present an extension to three spatial\ndimensions, and demonstrate that it exhibits fast equilibration for finite\nferro and antiferromagnetic instances. Additionally, and although it is\noriginally designed for a square lattice of finite degrees of freedom with open\nboundary conditions, the proposed scheme can be used as such, or with slight\nmodifications, to study triangular lattices, systems with continuous degrees of\nfreedom, matrix models, a confined gas of hard spheres, or to deal with\narbitrary boundary conditions.\n"}
+{"abstract": "  Although instance segmentation has made considerable advancement over recent\nyears, it's still a challenge to design high accuracy algorithms with real-time\nperformance. In this paper, we propose a real-time instance segmentation\nframework termed OrienMask. Upon the one-stage object detector YOLOv3, a mask\nhead is added to predict some discriminative orientation maps, which are\nexplicitly defined as spatial offset vectors for both foreground and background\npixels. Thanks to the discrimination ability of orientation maps, masks can be\nrecovered without the need for extra foreground segmentation. All instances\nthat match with the same anchor size share a common orientation map. This\nspecial sharing strategy reduces the amortized memory utilization for mask\npredictions but without loss of mask granularity. Given the surviving box\npredictions after NMS, instance masks can be concurrently constructed from the\ncorresponding orientation maps with low complexity. Owing to the concise design\nfor mask representation and its effective integration with the anchor-based\nobject detector, our method is qualified under real-time conditions while\nmaintaining competitive accuracy. Experiments on COCO benchmark show that\nOrienMask achieves 34.8 mask AP at the speed of 42.7 fps evaluated with a\nsingle RTX 2080 Ti. The code is available at https://github.com/duwt/OrienMask.\n"}
+{"abstract": "  Keystone is a trusted execution environment, based on RISC-V architecture. It\ndivides the memory into a secure Keystone private memory and an unsecure\nnon-Keystone memory, and allows code that lies inside the Keystone private\nmemory to execute securely. Simple demand paging in Keystone ends up leaking\nsensitive access patterns of Keystone application to the Operating System(OS),\nthat is assumed to be malicious. This is because, to access the unsecure\nnon-Keystone memory, Keystone needs support of the OS. To mitigate this,\nKeystone needs to implement oblivious demand paging while obfuscating its page\naccess patterns by using Oblivious RAM(ORAM) techniques. This causes\nsubstantial slowdown in the application execution.\n  In this paper, we bridge the performance gap between application execution\ntime with unsecure and secure demand paging in Keystone by using Deterministic,\nstash free, Write only ORAM (DetWoORAM) for oblivious demand paging. We also\nshow why DetWoORAM, that is a write-only ORAM, is sufficient for oblivious\ndemand paging. DetWoORAM logically partitions the memory into a main area and a\nholding area. The actual pages are stored in main area. We propose two\nenhancements over DetWoORAM that improves the application execution slowdown.\nThe first enhancement, which we call the Eager DetWoORAM, involves page\npreloading that exploits the deterministic access pattern of DetWoORAM, and\ntries to hide the ORAM latency. The second enhancement, which we call the\nParallel DetWoORAM, involves spawning multiple threads and each thread performs\na part of the DetWoORAM memory access algorithm. Compared to DetWoORAM that\nshows slowdown of [1.4x, 2x, and 3.24x], Eager DetWoORAM and Parallel DetWoORAM\nprovide slowdown of [1.2x, 1.8x, and 3.2x] and [1.1x, 1.1x, and 1.4x], for k=\n3, 7, and 15, respectively.\n"}
+{"abstract": "  The Chekanov-Eliashberg dg-algebra is a holomorphic curve invariant\nassociated to Legendrian submanifolds of a contact manifold. We extend the\ndefinition to Legendrian embeddings of skeleta of Weinstein manifolds. Via\nLegendrian surgery, the new definition gives direct proofs of wrapped Floer\ncohomology push-out diagrams. It also leads to a proof of a conjectured\nisomorphism between partially wrapped Floer cohomology and Chekanov-Eliashberg\ndg-algebras with coefficients in chains on the based loop space.\n"}
+{"abstract": "  In the setting where we ask participants multiple similar possibly subjective\nmulti-choice questions (e.g. Do you like Bulbasaur? Y/N; do you like Squirtle?\nY/N), peer prediction aims to design mechanisms that encourage honest feedback\nwithout verification. A series of works have successfully designed multi-task\npeer prediction mechanisms where reporting truthfully is better than any other\nstrategy (dominantly truthful), while they require an infinite number of tasks.\nA recent work proposes the first multi-task peer prediction mechanism,\nDeterminant Mutual Information (DMI)-Mechanism, where not only is dominantly\ntruthful but also works for a finite number of tasks (practical). However, the\nexistence of other practical dominantly-truthful multi-task peer prediction\nmechanisms remains to be an open question. This work answers the above question\nby providing 1. a new family of information-monotone information measures:\nvolume mutual information (VMI), where DMI is a special case; 2. a new family\nof practical dominantly-truthful multi-task peer prediction mechanisms,\nVMI-Mechanisms. To illustrate the importance of VMI-Mechanisms, we also provide\na tractable effort incentive optimization goal. We show that DMI-Mechanism may\nnot be not optimal but we can construct a sequence of VMI-Mechanisms that are\napproximately optimal. The main technical highlight in this paper is a novel\ngeometric information measure, Volume Mutual Information, that is based on a\nsimple idea: we can measure an object A's information amount by the number of\nobjects that is less informative than A. Different densities over the object\nlead to different information measures. This also gives Determinant Mutual\nInformation a simple geometric interpretation.\n"}
+{"abstract": "  We use interface-resolved simulations to study near-wall turbulence\nmodulation by small inertial particles, much denser than the fluid, in\ndilute/semi-dilute conditions. We considered three bulk solid mass fractions,\n$\\Psi=0.34\\%$, $3.37\\%$ and $33.7\\%$, with only the latter two showing\nturbulence modulation. The increase of the drag is strong at $\\Psi=3.37\\%$, but\nmild in the densest case. Actually, two distinct regimes of turbulence\nmodulation emerge: for smaller mass fractions, the turbulence statistics are\nweakly affected and the near-wall particle accumulation increases the drag so\nthe flow appears as a single phase flow at slightly higher Reynolds number.\nConversely, at higher mass fractions, the particles modulate the turbulent\ndynamics over the entire flow, and the interphase coupling becomes more\ncomplex. In this case, fluid Reynolds stresses are attenuated, but the inertial\nparticle dynamics increases the drag via correlated velocity fluctuations\nleading to an overall drag increase. Hence, we conclude that, although\nparticles at high mass fractions reduce the fluid turbulent drag, the solid\nphase inertial dynamics may still increase the overall drag. However,\ninspection of the streamwise momentum budget in the two-way coupling limit of\nvanishing volume fraction, but finite mass fraction, indicates that this trend\ncould reverse at even higher particle load.\n"}
+{"abstract": "  A semi-supervised learning method for spiking neural networks is proposed.\nThe proposed method consists of supervised learning by backpropagation and\nsubsequent unsupervised learning by spike-timing-dependent plasticity (STDP),\nwhich is a biologically plausible learning rule. Numerical experiments show\nthat the proposed method improves the accuracy without additional labeling when\na small amount of labeled data is used. This feature has not been achieved by\nexisting semi-supervised learning methods of discriminative models. It is\npossible to implement the proposed learning method for event-driven systems.\nHence, it would be highly efficient in real-time problems if it were\nimplemented on neuromorphic hardware. The results suggest that STDP plays an\nimportant role other than self-organization when applied after supervised\nlearning, which differs from the previous method of using STDP as pre-training\ninterpreted as self-organization.\n"}
+{"abstract": "  The necessary and sufficient conditions for a three-dimensional Riemannian\nmetric to admit a group of isometries of dimension $r$ acting on s-dimensional\norbits are obtained. These conditions are Intrinsic, Deductive, Explicit and\nALgorithmic and they offer an IDEAL labeling that improves previously known\ninvariant studies.\n"}
+{"abstract": "  It is well known from universal algebra that, for every signature $\\Sigma$,\nthere exist algebras over $\\Sigma$ which are absolutely free, meaning that they\ndo not satisfy any identities or, alternatively, satisfy the universal mapping\nproperty for the class of $\\Sigma$-algebras. Furthermore, once we fix a\ncardinality of the generating set, they are, up to isomorphisms, unique, and\nequal to algebras of terms (or propositional formulas, in the context of\nlogic). Equivalently, the forgetful functor, from the category of\n$\\Sigma$-algebras to $\\textbf{Set}$, has a left adjoint. This result does not\nextend to multialgebras. Not only multialgebras satisfying the universal\nmapping property do not exist, but the forgetful functor $\\mathcal{U}$, from\nthe category of $\\Sigma$-multialgebras to $\\textbf{Set}$, does not have a left\nadjoint.\n  In this paper we generalize, in a natural way, algebras of terms to\nmultialgebras of terms, whose family of submultialgebras enjoys many properties\nof the former. One example is that, to every pair consisting of a function,\nfrom a multialgebra of terms to another multialgebra, and a collection of\nchoices (which selects how a homomorphism approaches indeterminacies), it\ncorresponds a unique homomorphism, which ressembles the universal mapping\nproperty. Another example is that the multialgebras of terms are generated by a\nset that may be viewed as a strong basis, which we call the ground of the\nmultialgebra. Submultialgebras of multialgebras of terms are what we call\nweakly free multialgebras. Finally, with these definitions at hand, we offer a\nsimple proof that multialgebras with the universal mapping property for the\nclass of all multialgebras do not exist and that $\\mathcal{U}$ does not have a\nleft adjoint.\n"}
+{"abstract": "  Quantum error mitigation techniques can reduce noise on current quantum\nhardware without the need for fault-tolerant quantum error correction. For\ninstance, the quasiprobability method simulates a noise-free quantum computer\nusing a noisy one, with the caveat of only producing the correct expected\nvalues of observables. The cost of this error mitigation technique manifests as\na sampling overhead which scales exponentially in the number of corrected\ngates. In this work, we present a new algorithm based on mathematical\noptimization that aims to choose the quasiprobability decomposition in a\nnoise-aware manner. This directly leads to a significantly lower basis of the\nsampling overhead compared to existing approaches. A key element of the novel\nalgorithm is a robust quasiprobability method that allows for a tradeoff\nbetween an approximation error and the sampling overhead via semidefinite\nprogramming.\n"}
+{"abstract": "  Low-energy magnon excitations in magnetoelectric LiFePO$_4$ have been\ninvestigated by high-frequency high-field electron spin resonance spectroscopy\nin magnetic fields up to B = 58 T and frequencies up to f = 745 GHz. For\nmagnetic fields applied along the easy magnetic axis, the excitation gap\nsoftens and vanishes at the spin-flop field of BSF = 32 T before hardening\nagain at higher fields. In addition, for B smaller than BSF we observe a\nresonance mode assigned to excitations due to Dzyaloshinskii-Moriya\n(DM)-interactions, thereby evidencing sizable DM interaction of approx 150\nmicro eV in LiFePO4. Both the magnetisation and the excitations up to high\nmagnetic fields are described in terms of a mean-field theory model which\nextends recent zero field inelastic neutron scattering results. Our results\nimply that magnetic interactions as well as magnetic anisotropy have a sizable\nquadratic field dependence which we attribute to significant magnetostriction.\n"}
+{"abstract": "  We dwell upon certain points concerning the meaning of quantum field theory,\namong these the problems with the perturbative approach, and the question\nraised by tHooft of the existence of the theory in a well defined mathematical\nsense, as well as some of the few existent mathematically precise results on\nfully quantized field theories. Emphasis is brought on how the mathematical\ncontributions help to elucidate or illuminate certain conceptual aspects of the\ntheory when applied to real physical phenomena, in particular, the singular\nnature of quantum fields. In a first part, we present a comprehensive review of\ndivergent versus asymptotic series, with qed as background example, as well as\na method due to Terence Tao which conveys mathematical sense to divergent\nseries. In a second part we apply the method of Tao to the Casimir effect in\nits simplest form, consisting of perfectly conducting parallel plates, arguing\nthat the usual theory, which makes use of the Euler-MacLaurin formula, still\ncontains a residual infinity, which is eliminated in our approach. In the third\npart, we revisit the general theory of nonperturbative quantum fields, in the\nform of newly proposed Wightman axioms for interacting field theories, together\nwith Christian Jaekel. Applications to dressed electrons in a theory with\nmassless particles, such as qed, as well as unstable particles, are included,\nand various problems (mostly open) are discussed in connection with concrete\nmodels.\n"}
+{"abstract": "  We propose that the symbol alphabet for classes of planar,\ndual-conformal-invariant Feynman integrals can be obtained as truncated cluster\nalgebras purely from their kinematics, which correspond to boundaries of\n(compactifications of) $G_+(4,n)/T$ for the $n$-particle massless kinematics.\nFor one-, two-, three-mass-easy hexagon kinematics with $n=7,8,9$, we find\nfinite cluster algebras $D_4$, $D_5$ and $D_6$ respectively, in accordance with\nprevious result on alphabets of these integrals. As the main example, we\nconsider hexagon kinematics with two massive corners on opposite sides and find\na truncated affine $D_4$ cluster algebra whose polytopal realization is a\nco-dimension 4 boundary of that of $G_+(4,8)/T$ with 39 facets; the normal\nvectors for 38 of them correspond to g-vectors and the remaining one gives a\nlimit ray, which yields an alphabet of $38$ rational letters and $5$ algebraic\nones with the unique four-mass-box square root. We construct the space of\nintegrable symbols with this alphabet and physical first-entry conditions,\nwhose dimension can be reduced using conditions from a truncated version of\ncluster adjacency. Already at weight $4$, by imposing last-entry conditions\ninspired by the $n=8$ double-pentagon integral, we are able to uniquely\ndetermine an integrable symbol that gives the algebraic part of the most\ngeneric double-pentagon integral. Finally, we locate in the space the $n=8$\ndouble-pentagon ladder integrals up to four loops using differential equations\nderived from Wilson-loop $d\\log$ forms, and we find a remarkable pattern about\nthe appearance of algebraic letters.\n"}
+{"abstract": "  It has recently been suggested that black hole remnants of primordial origin\nare not a viable dark matter candidate since they would have far too large a\nvelocity due to the recoil of Hawking radiation. We re-examined this\ninteresting claim in more details and found that it does not rule out such a\npossibility. On the contrary, for models based on non-commutativity of\nspacetime near the Planck scale, essentially the same argument can be used to\nestimate the scale at which non-commutativity effect becomes important. If dark\nmatter \"particles\" are non-commutative black holes that have passed the maximum\ntemperature, this implies that the non-commutative scale is about 100 times the\nPlanck length. The same analysis applies to other black hole remnants whose\ntemperature reaches a maximum before cooling off, for example, black holes in\nasymptotically safe gravity.\n"}
+{"abstract": "  During the past half century, a major approximation was natural in the field\nof light-matter interaction: the point-dipole model. It was assumed that the\nwavelength is much larger than the size of the emitting atom or molecule, so\nthat the emitter can be described as a single or a collection of elementary\ndipoles. As it is legitimate for visible light, the approximation does no\nlonger hold near plasmonic nanostructures, where the effective wavelength can\ndrop below 10 nm. In that case deviations arise from the approximate model.\nFirst, the emitter spatial extent influences the far-field spectrum. Second,\nhigh-order transitions beyond the dipolar ones are not forbidden anymore. Going\nbeyond the approximation requires intensive numerical efforts to compute the\nphotonic response over the spatial extent of the emitter, since the complete\nGreen's function is required. Here, we develop a general model that computes\nthe multipolar transition rates of a quantum emitter in a photonic environment,\nby computing the Green's function through an eigenpermittivity modal expansion.\nWe apply the method on graphene nanoislands, and we demonstrate a local\nbreakdown of the selection rules, with quadrupolar transition rates becoming\n100 times larger than dipolar ones.\n"}
+{"abstract": "  Dedicated to the memory of Prof. Veltman, one of the founding fathers of our\ndiscipline: his legacy lives on. Many times we have to turn back and follow his\nfootprints to find the right path. After reviewing general aspects of high\nenergy physics where he gave a seminal contribution we will introduce recent\ndevelopments in the standard model effective field theory, showing how the\nwhole movement from renormalization to predictions plays from Veltman to SMEFT.\n"}
+{"abstract": "  The smooth action of a compact Lie group on a compact manifold can be\nresolved to an iterated space, as made explicit by Pierre Albin and the second\nauthor. On the resolution the lifted action has fixed isotropy type\ncorresponding to the open stratum and also in an iterated sense, with\nconnecting equivariant fibrations over the boundary hypersurfaces covering the\nresolutions of the other strata. This structure descends to a resolution of the\nquotient as a stratified space. For an Abelian group action the equivariant\nK-theory can then be described in terms of bundles over the bases `dressed' by\nthe representations of the isotropy types with morphisms covering the\nconnecting maps. A similar model is given here covering the non-Abelian case.\nNow the reduced objects are torsion-twisted bundles over finite covers of the\nbases, corresponding to the projective action of the normalizers on the\nrepresentations of the isotropy groups, again with morphisms over all the\nboundaries. This leads to a closely related iterated deRham model for\nequivariant cohomology and, now with values in forms twisted by flat bundles of\nrepresentation rings over the bases, for delocalized equivariant cohomology. We\nshow, as envisioned by Baum, Brylinksi and MacPherson, that the usual\nequivariant Chern character, mapping to equivariant cohomology, factors through\na natural Chern character from equivariant K-theory to delocalized equivariant\ncohomology with the latter giving an Atiyah-Hirzebruch isomorphism.\n"}
+{"abstract": "  How should representations from complementary sensors be integrated for\nautonomous driving? Geometry-based sensor fusion has shown great promise for\nperception tasks such as object detection and motion forecasting. However, for\nthe actual driving task, the global context of the 3D scene is key, e.g. a\nchange in traffic light state can affect the behavior of a vehicle\ngeometrically distant from that traffic light. Geometry alone may therefore be\ninsufficient for effectively fusing representations in end-to-end driving\nmodels. In this work, we demonstrate that imitation learning policies based on\nexisting sensor fusion methods under-perform in the presence of a high density\nof dynamic agents and complex scenarios, which require global contextual\nreasoning, such as handling traffic oncoming from multiple directions at\nuncontrolled intersections. Therefore, we propose TransFuser, a novel\nMulti-Modal Fusion Transformer, to integrate image and LiDAR representations\nusing attention. We experimentally validate the efficacy of our approach in\nurban settings involving complex scenarios using the CARLA urban driving\nsimulator. Our approach achieves state-of-the-art driving performance while\nreducing collisions by 76% compared to geometry-based fusion.\n"}
+{"abstract": "  In this work, we investigate how flexible assets within a residential\nbuilding influence the long-term impact of operation. We use a measured-peak\ngrid tariff (MPGT) that puts a cost on the highest single-hour peak import over\nthe month. We apply a mathematical model of a Home Energy Management System\n(HEMS) together with Stochastic Dynamic Programming (SDP), which calculates the\nlong-term impact of operating as a non-linear expected future cost curve (EFCC)\nfrom the end of the scheduling period to the start. The proposed model is\napplied to a case study for a Norwegian building with smart control of a\nbattery energy storage system (BESS), Electric vehicle (EV) charging and space\nheating (SH). Each of the flexible assets are investigated individually with\nMPGT and for an energy-based grid tariff. The results showed that EV charging\nhas the highest peak-power impact in the system, decreasing the total\nelectricity cost by 14.6% with MPGT when controllable compared to a reference\ncase with passive charging. It is further shown how the EFCC helps achieve\noptimal timing and level of the peak demand, where it co-optimizes both\nreal-time pricing and the MPGT.\n"}
+{"abstract": "  Ahlgren and Samart relate three cusp forms with complex multiplication to\ncertain weakly holomorphic modular forms using $p$-adic bounds related to their\nFourier coefficients. In these three examples, their result strengthens a\ntheorem of Guerzhoy, Kent, and Ono which pairs certain CM forms with weakly\nholomorphic modular forms via $p$-adic limits. Ahlgren and Samart use only the\ntheory of modular forms and Hecke operators, whereas Guerzhoy, Kent, and Ono\nuse the theory of harmonic Maass forms. Here we extend Ahlgren and Samart's\nwork to all cases where the cusp form space is one-dimensional and has trivial\nNebentypus. Along the way, we obtain a duality result relating two families of\nmodular forms that arise naturally in each case.\n"}
+{"abstract": "  This paper targets to explore the inter-subject variations eliminated facial\nexpression representation in the compressed video domain. Most of the previous\nmethods process the RGB images of a sequence, while the off-the-shelf and\nvaluable expression-related muscle movement already embedded in the compression\nformat. In the up to two orders of magnitude compressed domain, we can\nexplicitly infer the expression from the residual frames and possible to\nextract identity factors from the I frame with a pre-trained face recognition\nnetwork. By enforcing the marginal independent of them, the expression feature\nis expected to be purer for the expression and be robust to identity shifts. We\ndo not need the identity label or multiple expression samples from the same\nperson for identity elimination. Moreover, when the apex frame is annotated in\nthe dataset, the complementary constraint can be further added to regularize\nthe feature-level game. In testing, only the compressed residual frames are\nrequired to achieve expression prediction. Our solution can achieve comparable\nor better performance than the recent decoded image based methods on the\ntypical FER benchmarks with about 3$\\times$ faster inference with compressed\ndata.\n"}
+{"abstract": "  This paper presents both a method and a result. The result presents a closed\nformula for the sum of the first $m+1,m \\ge 0,$ squares of the sequence\n$F^{(k)}$ where each member is the sum of the previous $k$ members and with\ninitial conditions of $k-1$ zeroes followed by a 1. The generalized result\nincludes the known result of sums of squares of the Fibonacci numbers and a\nrecent result of Schumaker on sums of squares of Tribonacci numbers. To prove\nthe identities uniformly for all $k,$ the Algebraic Verification method is\npresented which reduces proof of an identity to verification of the equality of\nfinitely many pairs of finite-degree polynomials, possibly in several\nvariables. Several other papers proving families of identities are examined,\nand it is suggested that the collection of the uniform proof methods used in\nthese papers could produce a new trend in stating and proving identities.\n"}
+{"abstract": "  Statistical modeling for massive spatial data sets has generated a\nsubstantial literature on scalable spatial processes based upon a likelihood\napproximation proposed by Vecchia in 1988. Vecchia's approximation for Gaussian\nprocess models enables fast evaluation of the likelihood by restricting\ndependencies at a location to its neighbors. We establish inferential\nproperties of microergodic spatial covariance parameters within the paradigm of\nfixed-domain asymptotics when they are estimated using Vecchia's approximation.\nThe conditions required to formally establish these properties are explored,\ntheoretically and empirically, and the effectiveness of Vecchia's approximation\nis further corroborated from the standpoint of fixed-domain asymptotics. These\nexplorations suggest some practical diagnostics for evaluating the quality of\nthe approximation.\n"}
+{"abstract": "  Fiber-based micro-endoscopes are a critically important tool for\nminimally-invasive deep-tissue imaging. However, the state-of-the-art\nmicro-endoscopes cannot perform three-dimensional imaging through\ndynamically-bent fibers without the use of bulky optical elements such as\nlenses and scanners at the distal end, increasing the footprint and\ntissue-damage. While great efforts have been invested in developing approaches\nthat avoid distal bulky optical elements, the fundamental barrier of dynamic\noptical wavefront-distortions in propagation through flexible fibers, limits\ncurrent approaches to nearly-static or non-flexible fibers. Here, we present an\napproach that allows holographic 3D bend-insensitive, coherence-gated,\nmicro-endoscopic imaging, using commercially available multi-core fibers\n(MCFs). We achieve this by adding a miniature partially-reflecting mirror to\nthe distal fiber-tip, allowing us to perform low-coherence full-field\nphase-shifting holography. We demonstrate widefield diffraction-limited\nreflection imaging of amplitude and phase targets through dynamically bent\nfibers at video-rates. Our approach holds great potential for label-free\ninvestigations of dynamic samples.\n"}
+{"abstract": "  Authentication of the quantum sources is a crucial task in building reliable\nand efficient protocols for quantum information processing. Steady progress\nvis-\\`{a}-vis verification of quantum devices in the scenario with fully\ncharacterised measurement devices has been observed in the last years. When it\ncomes to the scenario with uncharacterised measurements, the so-called\nblack-box scenario, practical verification methods are still rather scarce.\nDevelopment of self-testing methods is an important step forward, but these\nresults so far have been used for reliable verification only by considering the\nasymptotic behavior of large, identically and independently distributed (IID)\nsamples of a quantum resource. Such strong assumptions deprive the verification\nprocedure of its truly device-independent character. In this letter, we develop\na systematic approach to device-independent verification of quantum states free\nof IID assumptions in the finite copy regime. Remarkably, we show that\ndevice-independent verification can be performed with optimal sample\nefficiency. Furthermore, we develop device-independent protocols for quantum\nstate certification: a protocol in which a fragment of the resource copies is\nmeasured to warrant the rest of the copies to be close to some target state.\n"}
+{"abstract": "  The output of text-to-image synthesis systems should be coherent, clear,\nphoto-realistic scenes with high semantic fidelity to their conditioned text\ndescriptions. Our Cross-Modal Contrastive Generative Adversarial Network\n(XMC-GAN) addresses this challenge by maximizing the mutual information between\nimage and text. It does this via multiple contrastive losses which capture\ninter-modality and intra-modality correspondences. XMC-GAN uses an attentional\nself-modulation generator, which enforces strong text-image correspondence, and\na contrastive discriminator, which acts as a critic as well as a feature\nencoder for contrastive learning. The quality of XMC-GAN's output is a major\nstep up from previous models, as we show on three challenging datasets. On\nMS-COCO, not only does XMC-GAN improve state-of-the-art FID from 24.70 to 9.33,\nbut--more importantly--people prefer XMC-GAN by 77.3 for image quality and 74.1\nfor image-text alignment, compared to three other recent models. XMC-GAN also\ngeneralizes to the challenging Localized Narratives dataset (which has longer,\nmore detailed descriptions), improving state-of-the-art FID from 48.70 to\n14.12. Lastly, we train and evaluate XMC-GAN on the challenging Open Images\ndata, establishing a strong benchmark FID score of 26.91.\n"}
+{"abstract": "  Nonlinear responses give rise to various exciting phenomena, which are\nforbidden in linear responses. Among them, one of the most fascinating\nphenomena is the recently observed giant spontaneous Hall effect in\n$\\mathrm{Ce_{3}Bi_{4}Pd_{3}}$. This material is a promising candidate for a\nWeyl-Kondo semimetal, and this experiment implies that strong correlation\neffects can enhance the nonlinear Hall effect. However, most theoretical\nstudies on nonlinear responses have been limited to free systems, and the\nconnection between nonlinear responses and strong correlation effects is poorly\nunderstood. Motivated by these experiments and recent theoretical advances to\nanalyze strong correlation effects on the nonlinear response, we study a\nperiodic Anderson model describing $\\mathrm{Ce_{3}Bi_{4}Pd_{3}}$ using the\ndynamical mean-field theory. We calculate the nonlinear longitudinal\nconductivity and the nonlinear Hall conductivity using the Kubo formula\nextended to the nonlinear response regime and clarify their temperature\ndependences. We numerically show that strong correlations can enhance nonlinear\nconductivities, and we conclude that the magnitude of the experimentally\nobserved giant nonlinear Hall effect can be explained by strong correlation\neffects.\n"}
+{"abstract": "  We present a large-scale bibliometric analysis of gender differences in\nscientific careers, covering all scientific disciplines and a large number of\ncountries worldwide. We take a longitudinal perspective in which we trace the\npublication careers of almost six million male and female researchers in the\nperiod 1996-2018. Our analysis reveals an increasing trend in the percentage of\nwomen starting a career as publishing researcher, from 33% in 2000 to about 40%\nin recent years. Looking at cohorts of male and female researchers that started\ntheir publication career in the same year, we find that women seem to be\nsomewhat less likely to continue their career as publishing researcher than\nmen, but the difference is small. We also observe that men produce on average\nbetween 15% and 20% more publications than women. Moreover, in biomedical\ndisciplines, men are about 25% more likely than women to be last author of a\npublication, suggesting that men tend to have more senior roles than women.\nCompared with cross-sectional studies, our longitudinal analysis has the\nadvantage of providing a more in-depth understanding of gender imbalances among\nauthors of scientific publications.\n"}
+{"abstract": "  We report on a large-scale empirical study investigating the relevance of\nsocio-technical congruence over key basic software quality metrics, namely,\nbugs and churn. In particular, we explore whether alignment or misalignment of\nsocial communication structures and technical dependencies in large software\nprojects influences software quality. To this end, we have defined a\nquantitative and operational notion of socio-technical congruence, which we\ncall socio-technical motif congruence (STMC). STMC is a measure of the degree\nto which developers working on the same file or on two related files, need to\ncommunicate. As socio-technical congruence is a complex and multi-faceted\nphenomenon, the interpretability of the results is one of our main concerns, so\nwe have employed a careful mixed-methods statistical analysis. In particular,\nwe provide analyses with similar techniques as employed by seminal work in the\nfield to ensure comparability of our results with the existing body of work.\nThe major result of our study, based on an analysis of 25 large open-source\nprojects, is that STMC is not related to project quality measures -- software\nbugs and churn -- in any temporal scenario. That is, we find no statistical\nrelationship between the alignment of developer tasks and developer\ncommunications on the one hand, and project outcomes on the other hand. We\nconclude that, wherefore congruence does matter as literature shows, then its\nmeasurable effect lies elsewhere.\n"}
+{"abstract": "  This paper is devoted to the complete algebraic and geometric classification\nof complex $4$-dimensional nilpotent left-symmetric algebras. The corresponding\ngeometric variety has dimension $15$ and decomposes into $3$ irreducible\ncomponents determined by the Zariski closures of two one-parameter families of\nalgebras and a two-parameter family of algebras (see Theorem B). In particular,\nthere are no rigid $4$-dimensional complex nilpotent left symmetric algebras.\n"}
+{"abstract": "  We present a novel group collaborative learning framework (GCoNet) capable of\ndetecting co-salient objects in real time (16ms), by simultaneously mining\nconsensus representations at group level based on the two necessary criteria:\n1) intra-group compactness to better formulate the consistency among co-salient\nobjects by capturing their inherent shared attributes using our novel group\naffinity module; 2) inter-group separability to effectively suppress the\ninfluence of noisy objects on the output by introducing our new group\ncollaborating module conditioning the inconsistent consensus. To learn a better\nembedding space without extra computational overhead, we explicitly employ\nauxiliary classification supervision. Extensive experiments on three\nchallenging benchmarks, i.e., CoCA, CoSOD3k, and Cosal2015, demonstrate that\nour simple GCoNet outperforms 10 cutting-edge models and achieves the new\nstate-of-the-art. We demonstrate this paper's new technical contributions on a\nnumber of important downstream computer vision applications including content\naware co-segmentation, co-localization based automatic thumbnails, etc.\n"}
+{"abstract": "  We consider a loop representation of the $O(n)$ model at the critical point.\nWhen $n=0$ the model represents ensembles of self-avoiding loops (i.e., it\ncorresponds to SLE with $\\kappa=8/3$), and can be described by the logarithmic\nconformal field theory (LCFT) with central charge $c=0$. We focus on the\ncorrelation functions in the upper-half plane containing the twist operators in\nthe bulk, and a pair of the boundary one-leg operators. By using a Coulomb gas\nrepresentation for the correlation functions, we obtain explicit results for\nprobabilities of the SLE${}_{8/3}$ trace to wind in various ways about $N\\geq\n1$ marked points. When the points collapse pairwise the probabilities reduce to\nmulti-point Green's functions. We propose an explicit representation for the\nGreen's functions in terms of the correlation functions of the bulk 1/3-weight\noperators, and a pair of the boundary one-leg operators.\n"}
+{"abstract": "  Galaxy groups and poor clusters are more common than rich clusters, and host\nthe largest fraction of matter content in the Universe. Hence, their studies\nare key to understand the gravitational and thermal evolution of the bulk of\nthe cosmic matter. Moreover, because of their shallower gravitational\npotential, galaxy groups are systems where non-gravitational processes (e.g.,\ncooling, AGN feedback, star formation) are expected to have a higher impact on\nthe distribution of baryons, and on the general physical properties, than in\nmore massive objects, inducing systematic departures from the expected scaling\nrelations. Despite their paramount importance from the astrophysical and\ncosmological point of view, the challenges in their detection have limited the\nstudies of galaxy groups. Upcoming large surveys will change this picture,\nreassigning to galaxy groups their central role in studying the structure\nformation and evolution in the Universe, and in measuring the cosmic baryonic\ncontent. Here, we review the recent literature on various scaling relations\nbetween X-ray and optical properties of these systems, focusing on the\nobservational measurements, and the progress in our understanding of the\ndeviations from the self-similar expectations on groups' scales. We discuss\nsome of the sources of these deviations, and how feedback from supernovae\nand/or AGNs impacts the general properties and the reconstructed scaling laws.\nFinally, we discuss future prospects in the study of galaxy groups.\n"}
+{"abstract": "  The unpolarized and polarized Beam Char\\-ge Asymmetries (BCAs) of the\n$\\vv{e}^{\\pm}p \\to e^{\\pm}p \\gamma$ process off unpolarized hydrogen are\ndiscussed. The measurement of BCAs with the CLAS12 spectrometer at the Thomas\nJefferson National Accelerator Facility, using polarized positron and electron\nbeams at 10.6 GeV is investigated. This experimental configuration allows to\nmeasure azimuthal and $t$-dependences of the unpolarized and polarized BCAs\nover a large $(x_B,Q^2)$ phase space, providing a direct access to the real\npart of the Compton Form Factor (CFF) ${\\mathcal H}$. Additionally, these\nmeasurements confront the Bethe-Heitler dominance hypothesis and eventual\neffects beyond leading twist. The impact of potential positron beam data on the\ndetermination of CFFs is also investigated within a local fitting approach of\nexperimental observables. Positron data are shown to strongly reduce\ncorrelations between CFFs and consequently improve significantly the\ndetermination of $\\Re {\\rm e} [\\mathcal{H}]$.\n"}
+{"abstract": "  Tracking by natural language specification is a new rising research topic\nthat aims at locating the target object in the video sequence based on its\nlanguage description. Compared with traditional bounding box (BBox) based\ntracking, this setting guides object tracking with high-level semantic\ninformation, addresses the ambiguity of BBox, and links local and global search\norganically together. Those benefits may bring more flexible, robust and\naccurate tracking performance in practical scenarios. However, existing natural\nlanguage initialized trackers are developed and compared on benchmark datasets\nproposed for tracking-by-BBox, which can't reflect the true power of\ntracking-by-language. In this work, we propose a new benchmark specifically\ndedicated to the tracking-by-language, including a large scale dataset, strong\nand diverse baseline methods. Specifically, we collect 2k video sequences\n(contains a total of 1,244,340 frames, 663 words) and split 1300/700 for the\ntrain/testing respectively. We densely annotate one sentence in English and\ncorresponding bounding boxes of the target object for each video. We also\nintroduce two new challenges into TNL2K for the object tracking task, i.e.,\nadversarial samples and modality switch. A strong baseline method based on an\nadaptive local-global-search scheme is proposed for future works to compare. We\nbelieve this benchmark will greatly boost related researches on natural\nlanguage guided tracking.\n"}
+{"abstract": "  We describe certain Lagrangian fibrations by Prym varieties on holomorphic\nsymplectic varieties that deform to compactifications of Sp-Hitchin systems.\n"}
+{"abstract": "  We review and extend results on higher-curvature corrections to different\nconfigurations describing a superposition of heterotic strings, KK monopoles,\nsolitonic 5-branes and momentum waves. Depending on which sources are present,\nthe low-energy fields describe a black hole, a soliton or a naked singularity.\nWe show that this property is unaltered when perturbative higher-curvature\ncorrections are included, provided the sources are fixed. On the other hand,\nthis character may be changed by appropriate introduction (or removal) of\nsources regardless of the presence of curvature corrections, which constitutes\na non-perturbative modification of the departing system. The general system of\nmulticenter KK monopoles and their 5-brane charge induced by higher-curvature\ncorrections is discussed in some detail, with special attention paid to the\npossibility of merging monopoles. Our results are particularly relevant for\nsmall black holes (Dabholkar-Harvey states, DH), which remain singular after\nquadratic curvature corrections are taken into account. When there are four\nnon-compact dimensions, we notice the existence of a black hole with regular\nhorizon whose entropy coincides with that of the DH states, but the charges and\nsupersymmetry preserved by both configurations are different. A similar\nconstruction with five non-compact dimensions is possible, in this case with\nthe same charges as DH, although it fails to reproduce the DH entropy and\nsupersymmetry. No such configuration exists if $d>5$, which we interpret as\nreflecting the necessity of having a 5-brane wrapping the compact space.\n"}
+{"abstract": "  In this paper, we propose a novel conservative formulation for solving\nkinetic equations via neural networks. More precisely, we formulate the\nlearning problem as a constrained optimization problem with constraints that\nrepresent the physical conservation laws. The constraints are relaxed toward\nthe residual loss function by the Lagrangian duality. By imposing physical\nconservation properties of the solution as constraints of the learning problem,\nwe demonstrate far more accurate approximations of the solutions in terms of\nerrors and the conservation laws, for the kinetic Fokker-Planck equation and\nthe homogeneous Boltzmann equation.\n"}
+{"abstract": "  Astrophysical plasmas are often subject to both rotation and large-scale\nbackground magnetic fields. Individually, each is known to two-dimensionalize\nthe flow in the perpendicular plane. In realistic flows, both of these effects\nare simultaneously present and, importantly, need not be aligned. In this work,\nwe numerically investigate three-dimensional forced magnetohydrodynamic (MHD)\nturbulence subject to the competing effects of global rotation and a\nperpendicular background magnetic field. We focus on the case of a strong\nbackground field and find that increasing the rotation rate from zero produces\nsignificant changes in the structure of the turbulent flow. Starting with a\ntwo-dimensional inverse cascade at zero rotation, the flow first transitions to\na forward cascade of kinetic energy, then to a shear-layer dominated regime,\nand finally to a second shear-layer regime where the kinetic energy flux is\nstrongly suppressed and the energy transfer is mediated by the induced magnetic\nfield. We show that the first two transitions occur at distinct values of the\nRossby number, and the third occurs at a distinct value of the Lehnert number.\nMore generally, our results demonstrate that, when considering the simultaneous\nlimits of strong rotation and a strong guide field, the order in which those\nlimits are taken matters in the misaligned case.\n"}
+{"abstract": "  Software engineering researchers look for software artifacts to study their\ncharacteristics or to evaluate new techniques. In this paper, we introduce\nDUETS, a new dataset of software libraries and their clients. This dataset can\nbe exploited to gain many different insights, such as API usage, usage inputs,\nor novel observations about the test suites of clients and libraries. DUETS is\nmeant to support both static and dynamic analysis. This means that the\nlibraries and the clients compile correctly, they are executable and their test\nsuites pass. The dataset is composed of open-source projects that have more\nthan five stars on GitHub. The final dataset contains 395 libraries and 2,874\nclients. Additionally, we provide the raw data that we use to create this\ndataset, such as 34,560 pom.xml files or the complete file list from 34,560\nprojects. This dataset can be used to study how libraries are used by their\nclients or as a list of software projects that successfully build. The client's\ntest suite can be used as an additional verification step for code\ntransformation techniques that modify the libraries.\n"}
+{"abstract": "  The thermal equilibrium properties of physical systems can be described using\nGibbs states. It is therefore of great interest to know when such states allow\nfor an easy description. In particular, this is the case if correlations\nbetween distant regions are small. In this work, we consider 1D quantum spin\nsystems with local, finite-range, translation-invariant interactions at any\ntemperature. In this setting, we show that Gibbs states satisfy uniform\nexponential decay of correlations and, moreover, the mutual information between\ntwo regions decays exponentially with their distance, irrespective of the\ntemperature. The methods we use are based on the Belavkin-Staszewski relative\nentropy and on techniques developed by Araki. Moreover, we find that the Gibbs\nstates of the systems we consider are superexponentially close to saturating\nthe data-processing inequality for the Belavkin-Staszewski relative entropy.\n"}
+{"abstract": "  We discuss $(H_p,L_p)$ and $(H_p,\\text{weak}-L_p)$ type inequalities of\nweighted maximal operators of $T$ means with respect to the Vilenkin systems\nwith monotone coefficients, considered in \\cite{tut4} and prove that these\nresults are the best possible in a special sense. As applications, both some\nwell-known and new results are pointed out.\n"}
+{"abstract": "  Multi criteria decision analysis (MCDA) has been used to provide a holistic\nevaluation of the quality of 13 electricity generation technologies in use\ntoday. A group of 19 energy experts cast scores on a scale of 1 to 10 using 12\nquality criteria, based around the pillars of sustainability (society,\nenvironment and economy), with the aim of quantifying each criterion for each\ntechnology. The total mean score is employed as a holistic measure of system\nquality. The top three technologies to emerge in rank order are nuclear,\ncombined cycle gas and hydroelectric. The bottom three are solar PV, biomass\nand tidal lagoon. All seven new renewable technologies fared badly, perceived\nto be expensive, unreliable, and not as environmentally friendly as is often\nassumed. We validate our approach by 1) comparing scores for pairs of criteria\nwhere we expect a correlation to exist; 2) comparing our qualitative scores\nwith quantitative data; and; 3) comparing our qualitative scores with NEEDS\nproject baseline costs. In many cases, R2>0.8 suggests that the structured\nhierarchy of our approach has led to scores that may be used in a\nsemi-quantitative way. Adopting the results of this survey would lead to a very\ndifferent set of energy policy priorities in the OECD and throughout the world.\n"}
+{"abstract": "  Large amounts of threat intelligence information about malware attacks are\navailable in disparate, typically unstructured, formats. Knowledge graphs can\ncapture this information and its context using RDF triples represented by\nentities and relations. Sparse or inaccurate threat information, however, leads\nto challenges such as incomplete or erroneous triples. Generic information\nextraction (IE) models used to populate the knowledge graph cannot fully\nguarantee domain-specific context. This paper proposes a system to generate a\nMalware Knowledge Graph called MalKG, the first open-source automated knowledge\ngraph for malware threat intelligence. MalKG dataset (MT40K\\footnote{ Anonymous\nGitHub link: https://github.com/malkg-researcher/MalKG}) contains approximately\n40,000 triples generated from 27,354 unique entities and 34 relations. For\nground truth, we manually curate a knowledge graph called MT3K, with 3,027\ntriples generated from 5,741 unique entities and 22 relations. We demonstrate\nthe intelligence prediction of MalKG using two use cases. Predicting malware\nthreat information using the benchmark model achieves 80.4 for the hits@10\nmetric (predicts the top 10 options for an information class), and 0.75 for the\nMRR (mean reciprocal rank). We also propose an automated, contextual framework\nfor information extraction, both manually and automatically, at the sentence\nlevel from 1,100 malware threat reports and from the common vulnerabilities and\nexposures (CVE) database.\n"}
+{"abstract": "  Recent technological advancements have enabled detailed investigation of\nassociations between the molecular architecture and tumor heterogeneity,\nthrough multi-source integration of radiological imaging and genomic\n(radiogenomic) data. In this paper, we integrate and harness radiogenomic data\nin patients with lower grade gliomas (LGG), a type of brain cancer, in order to\ndevelop a regression framework called RADIOHEAD (RADIOgenomic analysis\nincorporating tumor HEterogeneity in imAging through Densities) to identify\nradiogenomic associations. Imaging data is represented through voxel intensity\nprobability density functions of tumor sub-regions obtained from multimodal\nmagnetic resonance imaging, and genomic data through molecular signatures in\nthe form of pathway enrichment scores corresponding to their gene expression\nprofiles. Employing a Riemannian-geometric framework for principal component\nanalysis on the set of probability densities functions, we map each probability\ndensity to a vector of principal component scores, which are then included as\npredictors in a Bayesian regression model with the pathway enrichment scores as\nthe response. Variable selection compatible with the grouping structure amongst\nthe predictors induced through the tumor sub-regions is carried out under a\ngroup spike-and-slab prior. A Bayesian false discovery rate mechanism is then\nused to infer significant associations based on the posterior distribution of\nthe regression coefficients. Our analyses reveal several pathways relevant to\nLGG etiology (such as synaptic transmission, nerve impulse and neurotransmitter\npathways), to have significant associations with the corresponding\nimaging-based predictors.\n"}
+{"abstract": "  Major theories of military innovation focus on relatively narrow\ntechnological developments, such as nuclear weapons or aircraft carriers.\nArguably the most profound military implications of technological change,\nhowever, come from more fundamental advances arising from general purpose\ntechnologies, such as the steam engine, electricity, and the computer. With few\nexceptions, political scientists have not theorized about GPTs. Drawing from\nthe economics literature on GPTs, we distill several propositions on how and\nwhen GPTs affect military affairs. We call these effects general-purpose\nmilitary transformations. In particular, we argue that the impacts of GMTs on\nmilitary effectiveness are broad, delayed, and shaped by indirect productivity\nspillovers. Additionally, GMTs differentially advantage those militaries that\ncan draw from a robust industrial base in the GPT. To illustrate the\nexplanatory value of our theory, we conduct a case study of the military\nconsequences of electricity, the prototypical GPT. Finally, we apply our\nfindings to artificial intelligence, which will plausibly cause a profound\ngeneral-purpose military transformation.\n"}
+{"abstract": "  Fiber-fed etalons are widely employed in advanced interferometric instruments\nsuch as gravitational-wave detectors, ultrastable lasers and calibration\nreference for high-precision spectrographs. We demonstrate that variation in\nnear-field distribution of the feeding fiber would deteriorate the spectrum\nprecision of the fiber-fed Fabry-Perot etalon, especially when precision at the\norder of 3 * 10-10 or higher is required. The octagonal fiber reinforced with\ndouble scrambler could greatly improve the steadiness and uniformness of the\nnear-field distribution. When building wavelength calibrators for sub-m s-1\nprecision radial-velocity instruments, the double scrambler should be\nconsidered meticulously.\n"}
+{"abstract": "  In the search for life in the cosmos, NASA's Transiting Exoplanet Survey\nSatellite (TESS) mission has already monitored about 74% of the sky for\ntransiting extrasolar planets, including potentially habitable worlds. However,\nTESS only observed a fraction of the stars long enough to be able to find\nplanets like Earth. We use the primary mission data - the first two years of\nobservations - and identify 4,239 stars within 210pc that TESS observed long\nenough to see 3 transits of an exoplanet that receives similar irradiation to\nEarth: 738 of these stars are located within 30pc. We provide reliable stellar\nparameters from the TESS Input Catalog that incorporates Gaia DR2 and also\ncalculate the transit depth and radial velocity semi-amplitude for an\nEarth-analog planet. Of the 4,239 stars in the Revised TESS HZ Catalog, 9 are\nknown exoplanet hosts - GJ 1061, GJ 1132, GJ 3512, GJ 685, Kepler-42, LHS 1815,\nL98-59, RR Cae, TOI 700 - around which TESS could identify additional\nEarth-like planetary companions. 37 additional stars host yet unconfirmed TESS\nObjects of Interest: three of these orbit in the habitable zone - TOI 203, TOI\n715, and TOI 2298. For a subset of 614 of the 4,239 stars, TESS has observed\nthe star long enough to be able to observe planets throughout the full\ntemperate, habitable zone out to the equivalent of Mars' orbit. Thus, the\nRevised TESS Habitable Zone Catalog provides a tool for observers to prioritize\nstars for follow-up observation to discover life in the cosmos. These stars are\nthe best path towards the discovery of habitable planets using the TESS mission\ndata.\n"}
+{"abstract": "  Applications of category theory often involve symmetric monoidal categories\n(SMCs), in which abstract processes or operations can be composed in series and\nparallel. However, in 2020 there remains a dearth of computational tools for\nworking with SMCs. We present an \"unbiased\" approach to implementing symmetric\nmonoidal categories, based on an operad of directed, acyclic wiring diagrams.\nBecause the interchange law and other laws of a SMC hold identically in a\nwiring diagram, no rewrite rules are needed to compare diagrams. We discuss the\nmathematics of the operad of wiring diagrams, as well as its implementation in\nthe software package Catlab.\n"}
+{"abstract": "  We study FO+, a fragment of first-order logic on finite words, where monadic\npredicates can only appear positively. We show that there is a FO-definable\nlanguage that is monotone in monadic predicates but not definable in FO+. This\nprovides a simple proof that Lyndon's preservation theorem fails on finite\nstructures. We additionally show that given a regular language, it is\nundecidable whether it is definable in FO+.\n"}
+{"abstract": "  New antibiotics are needed to battle growing antibiotic resistance, but the\ndevelopment process from hit, to lead, and ultimately to a useful drug, takes\ndecades. Although progress in molecular property prediction using\nmachine-learning methods has opened up new pathways for aiding the antibiotics\ndevelopment process, many existing solutions rely on large datasets and finding\nstructural similarities to existing antibiotics. Challenges remain in modelling\nof unconventional antibiotics classes that are drawing increasing research\nattention. In response, we developed an antimicrobial activity prediction model\nfor conjugated oligoelectrolyte molecules, a new class of antibiotics that\nlacks extensive prior structure-activity relationship studies. Our approach\nenables us to predict minimum inhibitory concentration for E. coli K12, with 21\nmolecular descriptors selected by recursive elimination from a set of 5,305\ndescriptors. This predictive model achieves an R2 of 0.65 with no prior\nknowledge of the underlying mechanism. We find the molecular representation\noptimum for the domain is the key to good predictions of antimicrobial\nactivity. In the case of conjugated oligoelectrolytes, a representation\nreflecting the 3-dimensional shape of the molecules is most critical. Although\nit is demonstrated with a specific example of conjugated oligoelectrolytes, our\nproposed approach for creating the predictive model can be readily adapted to\nother novel antibiotic candidate domains.\n"}
+{"abstract": "  We demonstrate that 1x1-convolutions in 1D time-channel separable\nconvolutions may be replaced by constant, sparse random ternary matrices with\nweights in $\\{-1,0,+1\\}$. Such layers do not perform any multiplications and do\nnot require training. Moreover, the matrices may be generated on the chip\nduring computation and therefore do not require any memory access. With the\nsame parameter budget, we can afford deeper and more expressive models,\nimproving the Pareto frontiers of existing models on several tasks. For command\nrecognition on Google Speech Commands v1, we improve the state-of-the-art\naccuracy from $97.21\\%$ to $97.41\\%$ at the same network size. Alternatively,\nwe can lower the cost of existing models. For speech recognition on\nLibrispeech, we half the number of weights to be trained while only sacrificing\nabout $1\\%$ of the floating-point baseline's word error rate.\n"}
+{"abstract": "  The ongoing global pandemic of Coronavirus Disease 2019 (COVID-19) poses a\nserious threat to public health and the economy. Rapid and accurate diagnosis\nof COVID-19 is crucial to prevent the further spread of the disease and reduce\nits mortality. Chest Computed tomography (CT) is an effective tool for the\nearly diagnosis of lung diseases including pneumonia. However, detecting\nCOVID-19 from CT is demanding and prone to human errors as some early-stage\npatients may have negative findings on images. Recently, many deep learning\nmethods have achieved impressive performance in this regard. Despite their\neffectiveness, most of these methods underestimate the rich spatial information\npreserved in the 3D structure or suffer from the propagation of errors. To\naddress this problem, we propose a Dual-Attention Residual Network (DARNet) to\nautomatically identify COVID-19 from other common pneumonia (CP) and healthy\npeople using 3D chest CT images. Specifically, we design a dual-attention\nmodule consisting of channel-wise attention and depth-wise attention\nmechanisms. The former is utilized to enhance channel independence, while the\nlatter is developed to recalibrate the depth-level features. Then, we integrate\nthem in a unified manner to extract and refine the features at different levels\nto further improve the diagnostic performance. We evaluate DARNet on a large\npublic CT dataset and obtain superior performance. Besides, the ablation study\nand visualization analysis prove the effectiveness and interpretability of the\nproposed method.\n"}
+{"abstract": "  The electrocardiogram (ECG) records electrical signals in a non-invasive way\nto observe the condition of the heart, typically looking at the heart from 12\ndifferent directions. Several types of the cardiac disease are diagnosed by\nusing 12-lead ECGs Recently, various wearable devices have enabled immediate\naccess to the ECG without the use of wieldy equipment. However, they only\nprovide ECGs with a couple of leads. This results in an inaccurate diagnosis of\ncardiac disease due to lacking of required leads. We propose a deep generative\nmodel for ECG synthesis from two asynchronous leads to ten leads. It first\nrepresents a heart condition referring to two leads, and then generates ten\nleads based on the represented heart condition. Both the rhythm and amplitude\nof leads generated resemble those of the original ones, while the technique\nremoves noise and the baseline wander appearing in the original leads. As a\ndata augmentation method, our model improves the classification performance of\nmodels compared with models using ECGs with only one or two leads.\n"}
+{"abstract": "  Recently directed acyclic graph (DAG) structure learning is formulated as a\nconstrained continuous optimization problem with continuous acyclicity\nconstraints and was solved iteratively through subproblem optimization. To\nfurther improve efficiency, we propose a novel learning framework to model and\nlearn the weighted adjacency matrices in the DAG space directly. Specifically,\nwe first show that the set of weighted adjacency matrices of DAGs are\nequivalent to the set of weighted gradients of graph potential functions, and\none may perform structure learning by searching in this equivalent set of DAGs.\nTo instantiate this idea, we propose a new algorithm, DAG-NoCurl, which solves\nthe optimization problem efficiently with a two-step procedure: 1) first we\nfind an initial cyclic solution to the optimization problem, and 2) then we\nemploy the Hodge decomposition of graphs and learn an acyclic graph by\nprojecting the cyclic graph to the gradient of a potential function.\nExperimental studies on benchmark datasets demonstrate that our method provides\ncomparable accuracy but better efficiency than baseline DAG structure learning\nmethods on both linear and generalized structural equation models, often by\nmore than one order of magnitude.\n"}
+{"abstract": "  Modeling media memorability has been a consistent challenge in the field of\nmachine learning. The Predicting Media Memorability task in MediaEval2020 is\nthe latest benchmark among similar challenges addressing this topic. Building\nupon techniques developed in previous iterations of the challenge, we developed\nensemble methods with the use of extracted video, image, text, and audio\nfeatures. Critically, in this work we introduce and demonstrate the efficacy\nand high generalizability of extracted audio embeddings as a feature for the\ntask of predicting media memorability.\n"}
+{"abstract": "  We investigated low-temperature dynamics in a charge density wave (CDW)\nconductor NbSe3, a widely studied representative of a class of systems of\ndriven periodic media with quenched disorder and relevant to a wider group of\nsystems exhibiting collective transport behaviors. To date, theoretical efforts\nhave not converged to produce a consistent description of the rich dynamics\nobserved in these systems, especially in the low temperature regime. We\ndeveloped modern sample preparation techniques and used frequency- and\ntime-domain transport measurements below the second characteristic Peierls CDW\ntransition to investigate the regime of temporally-ordered collective creep in\nNbSe3 samples in the low temperature regime between 15 K and 32 K. By measuring\nthe frequency of coherent oscillations between two characteristic threshold\nfields, ET and ET*, we show that in nine high-quality samples, pure, Ta-, or\nTi-doped, the current-field relation for the collective transport in this\nregime closely follows a modified Anderson-Kim form across five orders of\nmagnitude with thermally- and field-activated behavior above ET for a range of\ntemperatures. This study, combined with our transport relaxation measurements,\nprovides relevant length, energy, and time scales that set the dynamics in this\nregime and reveals that the collective dynamics, governed by large length and\nenergy scales, must be reconciled with microscopic local dynamics, with\nbarriers at orders of magnitude smaller scales. The interplay between the\ncollective and local mechanisms set the dynamics that is responsible for\nextremely slow (creep-like) collective, yet temporally-ordered behavior.\nCombined with the existing work, our results paint a consistent picture of a\ntransport phase diagram for CDWs, and density-wave systems in general, and\nprovide essential ingredients for a much-needed correct theoretical description\nof these systems.\n"}
+{"abstract": "  Both Stefan-Boltzmann law and the Casimir effect, in a universe described by\nthe FRW metric with zero curvature, are calculated. These effects are described\nby Thermo Field Dynamics (TFD). The gravitational energy-momentum tensor is\ndefined in the context of Teleparallel Equivalent to General Relativity (TEGR).\nEach of the two effects gives a consistent prediction with what is observed on\na cosmological scale. One of the effect establishes a minimum range for the\ndeceleration parameter. While another leads to the conclusion that a possible\ncosmological constant has a very small order of magnitude.\n"}
+{"abstract": "  This paper discusses a detailed design approach to determine the optimal\ninput impedance (Rin) and bandwidth (BW) for current preamplifiers in Front End\nElectronics (FEE) of high-accuracy time measurement systems used in particle\ndetection. Our study shows the effect of the input impedance Rin including the\nparasitic interconnection inductances of bonding wires between the detector and\nthe FEE. We explain also the development of a new mathematical model for the\nestimation of the timing jitter of the current preamplifier in the case of\nusing a low capacitor detector (Cd) as diamond. Different simulations based on\ndeveloped MATLAB Simulink behavioral models are done in addition to an electric\nsimulation of a transimpedance amplifier (TIA) designed in a 130 nm 1P8M CMOS\ntechnology which demonstrates the accuracy of the design approach and the\ntiming jitter estimation model.\n"}
+{"abstract": "  Behavioral software models play a key role in many software engineering\ntasks; unfortunately, these models either are not available during software\ndevelopment or, if available, quickly become outdated as implementations\nevolve. Model inference techniques have been proposed as a viable solution to\nextract finite state models from execution logs. However, existing techniques\ndo not scale well when processing very large logs that can be commonly found in\npractice.\n  In this paper, we address the scalability problem of inferring the model of a\ncomponent-based system from large system logs, without requiring any extra\ninformation. Our model inference technique, called PRINS, follows a divide and\nconquer approach. The idea is to first infer a model of each system component\nfrom the corresponding logs; then, the individual component models are merged\ntogether taking into account the flow of events across components, as reflected\nin the logs. We evaluated PRINS in terms of scalability and accuracy, using\nnine datasets composed of logs extracted from publicly available benchmarks and\na personal computer running desktop business applications. The results show\nthat PRINS can process large logs much faster than a publicly available and\nwell-known state-of-the-art tool, without significantly compromising the\naccuracy of inferred models.\n"}
+{"abstract": "  Accurate stellar ages are essential for our understanding of the star\nformation history of the Milky Way, Galactic chemical evolution, and to\nconstrain exoplanet formation models. Gyrochronology, an empirical relationship\nbetween stellar rotation and age, appears to be a reliable age indicator for\nmain sequence stars over the mass range 0.6 to 1.0 $M_\\odot$. Under the\nassumption that wide binaries contain two coeval stars, we selected pairs from\nthe $Kepler$, $K2$, and $TESS$ surveys in order to evaluate three\nGyrochronology models (Barnes 2007; Angus et al. 2015; Mamajek & Hillenbrand\n2008). In this first paper in a planned series, we describe a Monte Carlo\napproach to assess the precision and accuracy of age derived from these models.\nWe use this technique to demonstrate that current gyrochronology models achieve\n$\\sim$25 % precision in ages when $\\sigma_P$/P = 0.1 and $\\sigma_{B-V}$ = 0.02.\nWe also outline ways to improve this to $\\sim$10 %.\n"}
+{"abstract": "  A novel medium manganese steel named Novalloy with composition\nFe-8.3Mn-3.8Al-1.8Si-0.5C-0.06V-0.05Sn was developed and thermomechanically\nprocessed through hot rolling and intercritical annealing. The steel possessed\na yield strength of 1 GPa, tensile strength of 1.13 GPa and ductility of 41\\%.\nIn order to study the effect of cold rolling after intercritical annealing on\nsubsequent tensile properties, the steel was further cold rolled up to 20\\%\nreduction. After cold rolling, it was observed that the strain hardening rate\nincreased continuously with increasing cold rolling reduction but without a\nsignificant drop in ductility during subsequent tensile tests. The\nmicrostructural evolution with cold rolling reduction was analysed to\nunderstand the mechanisms behind this enhanced TRIP effect. It was found that\ncold rolling activated additional twinning systems which provided a large\nnumber of potent nucleation sites for strain induced martensite to form during\nsubsequent tensile tests.\n"}
+{"abstract": "  To the best of our knowledge, the application of deep learning in the field\nof quantitative risk management is still a relatively recent phenomenon. In\nthis article, we utilize techniques inspired by reinforcement learning in order\nto optimize the operation plans of underground natural gas storage facilities.\nWe provide a theoretical framework and assess the performance of the proposed\nmethod numerically in comparison to a state-of-the-art least-squares\nMonte-Carlo approach. Due to the inherent intricacy originating from the\nhigh-dimensional forward market as well as the numerous constraints and\nfrictions, the optimization exercise can hardly be tackled by means of\ntraditional techniques.\n"}
+{"abstract": "  We study theoretically spin pumping in bilayers consisting of superconductors\nand antiferromagnetic insulators. We consider both compensated and\nuncompensated interfaces and include both the regular scattering channel and\nthe Umklapp scattering channel. We find that at temperatures close to the\ncritical temperatures and precession frequencies much lower than the gap, the\nspin-current is enhanced in superconductors as compared to normal metals.\nOtherwise, the spin-current is suppressed. The relevant precession frequencies\nwhere the spin-current in SC/AFI is enhanced compared to NM/AFI is much lower\nthan the typical resonance frequencies of antiferromagnets, which makes the\ndetection of this effect experimentally challenging. A possible solution lies\nin the shifting of the resonance frequency by a static magnetic field.\n"}
+{"abstract": "  A corner is a set of three points in $\\mathbf{Z}^2$ of the form $(x, y), (x +\nd, y), (x, y + d)$ with $d \\neq 0$. We show that for infinitely many $N$ there\nis a set $A \\subset [N]^2$ of size $2^{-(c + o(1)) \\sqrt{\\log_2 N}} N^2$ not\ncontaining any corner, where $c = 2 \\sqrt{2 \\log_2 \\frac{4}{3}} \\approx\n1.822\\dots$.\n"}
+{"abstract": "  There has recently been growing interest in utilizing multimodal sensors to\nachieve robust lane line segmentation. In this paper, we introduce a novel\nmultimodal fusion architecture from an information theory perspective, and\ndemonstrate its practical utility using Light Detection and Ranging (LiDAR)\ncamera fusion networks. In particular, we develop, for the first time, a\nmultimodal fusion network as a joint coding model, where each single node,\nlayer, and pipeline is represented as a channel. The forward propagation is\nthus equal to the information transmission in the channels. Then, we can\nqualitatively and quantitatively analyze the effect of different fusion\napproaches. We argue the optimal fusion architecture is related to the\nessential capacity and its allocation based on the source and channel. To test\nthis multimodal fusion hypothesis, we progressively determine a series of\nmultimodal models based on the proposed fusion methods and evaluate them on the\nKITTI and the A2D2 datasets. Our optimal fusion network achieves 85%+ lane line\naccuracy and 98.7%+ overall. The performance gap among the models will inform\ncontinuing future research into development of optimal fusion algorithms for\nthe deep multimodal learning community.\n"}
+{"abstract": "  In this paper, we study the Hausdorff dimension of the generalized intrinsic\nlevel set with respect to the given ergodic meausre in a class of non-uniformly\nhyperbolic interval maps with finitely many branches.\n"}
+{"abstract": "  The generic Hidden Local Symmetry (HLS) model has given rise to the BHLS$_2$\nvariant which introduces symmetry breaking in the vector meson sector; the\ncentral mechanism is a modification of the covariant derivative at the root of\nthe HLS approach. However, the description of the Belle $\\tau$ dipion spectra\nis not satisfactory whereas the dealing with its annihilation sector ($e^+ e^-\n\\rightarrow \\pi^+ \\pi^-/\\pi^+ \\pi^-\\pi^0/ \\pi^0 \\gamma/\\eta \\gamma/K^+ K^-/K_L\nK_S$) is optimum. This issue is solved by an additional breaking term allowing\nto include the $[\\pi^0,\\eta,\\eta^\\prime]$ system. This extension of the usual\n't~Hooft determinant term only affects the BHLS$_2$ kinetic energy term. One\nobtains a fair account for the $\\tau$ dipion spectra as well as for the\nannihilation channels.\n  The Belle dipion spectrum provides evidence for a violation of CVC in the\n$\\tau$ lepton decay; this evidence is strengthened by imposing the conditions\n$<0|J_\\mu^q |[q^\\prime \\overline{q^\\prime}](p)>=ip_\\mu f_q \\delta_{q q^\\prime\n},\n  \\{ [q \\overline{q}], q=u,d,s\\}$ on BHLS$_2$ axial current matrix elements.\nBHLS$_2$ is found to recover the usual (completed) formulae for the\n[$\\pi^0,~\\eta,~\\eta^\\prime$] mixing parameters and the global fits return\nmixing parameter values in agreement with expectations and better\nuncertainties. Updating the muon HVP, one argues that the strong tension\nbetween the KLOE and BaBar pion form factors imposes to provide two solutions :\n$a_\\mu^{HVP-LO}({\\rm KLOE})=687.31 \\pm 2.93$ and $a_\\mu^{HVP-LO}({\\rm\nBaBar})=692.36 \\pm 2.95$, in units of $10^{-10}$, rather than some combination\nof these. Taking into account common systematics, their differences with the\nexperimental BNL-FNAL average value exhibit significances $> 5.4\\sigma$ (KLOE)\nand $> 4.1\\sigma$ (BaBar) with fit probabilities favoring the former.\n"}
+{"abstract": "  Thermoelectric technology is an alternate way to efficiently utilize the\nenergy by converting waste heat into electricity. Thermoelectric requires\nmaterial with low thermal conductivity to improves its thermoelectric\nperformance. In this work, by solving Boltzmann transport equation based on\nfirst principles calculations, we report an ultra-low room temperature thermal\nconductivity of 2.08 Wm$^{-1}$K$^{-1}$ and 2.9 Wm$^{-1}$K$^{-1}$ along c-axis\nand a-axis respectively for pure MgPb$_2$Te. To explain this ultra-low thermal\nconductivity, we analyzed the elastic constants, phonon group velocity,\nphonon-phonon scattering and contribution from transverse acoustic,\nlongitudinal acoustic and optical phonon branches. We also report the thermal\nconductivity of MgPb$_2$Te nanostructures. At 50 nm, the room temperature\nthermal conductivity of MgPb$_2$Te is 0.957 Wm$^{-1}$K$^{-1}$ and 1.459\nWm$^{-1}$K$^{-1}$ along c-axis and a-axis respectively. Ultra-low thermal\nconductivity unraveled in this work shows MgPb$_2$Te would a promising material\nfor thermoelectric applications.\n"}
+{"abstract": "  Deep reinforcement learning models are vulnerable to adversarial attacks that\ncan decrease a victim's cumulative expected reward by manipulating the victim's\nobservations. Despite the efficiency of previous optimization-based methods for\ngenerating adversarial noise in supervised learning, such methods might not be\nable to achieve the lowest cumulative reward since they do not explore the\nenvironmental dynamics in general. In this paper, we provide a framework to\nbetter understand the existing methods by reformulating the problem of\nadversarial attacks on reinforcement learning in the function space. Our\nreformulation generates an optimal adversary in the function space of the\ntargeted attacks, repelling them via a generic two-stage framework. In the\nfirst stage, we train a deceptive policy by hacking the environment, and\ndiscover a set of trajectories routing to the lowest reward or the worst-case\nperformance. Next, the adversary misleads the victim to imitate the deceptive\npolicy by perturbing the observations. Compared to existing approaches, we\ntheoretically show that our adversary is stronger under an appropriate noise\nlevel. Extensive experiments demonstrate our method's superiority in terms of\nefficiency and effectiveness, achieving the state-of-the-art performance in\nboth Atari and MuJoCo environments.\n"}
+{"abstract": "  This paper presents algorithms for computing the length of a sum of squares\nand a Pythagoras element in a global field $K$ of characteristic different from\n$2$. In the first part of the paper, we present algorithms for computing the\nlength in a non-dyadic and dyadic (if $K$ is a number field) completion of $K$.\nThese two algorithms serve as subsidiary steps for computing lengths in global\nfields. In the second part of the paper we present a procedure for constructing\nan element whose length equals the Pythagoras number of a global field, termed\na Pythagoras element.\n"}
+{"abstract": "  Vehicle re-identification (Re-ID) distinguishes between the same vehicle and\nother vehicles in images. It is challenging due to significant intra-instance\ndifferences between identical vehicles from different views and subtle\ninter-instance differences of similar vehicles. Researchers have tried to\naddress this problem by extracting features robust to variations of viewpoints\nand environments. More recently, they tried to improve performance by using\nadditional metadata such as key points, orientation, and temporal information.\nAlthough these attempts have been relatively successful, they all require\nexpensive annotations. Therefore, this paper proposes a novel deep neural\nnetwork called a multi-attention-based soft partition (MUSP) network to solve\nthis problem. This network does not use metadata and only uses multiple soft\nattentions to identify a specific vehicle area. This function was performed by\nmetadata in previous studies. Experiments verified that MUSP achieved\nstate-of-the-art (SOTA) performance for the VehicleID dataset without any\nadditional annotations and was comparable to VeRi-776 and VERI-Wild.\n"}
+{"abstract": "  Developing artificial intelligence based assistive systems to aid Persons\nwith Dementia (PwD) requires large amounts of training data. However, data\ncollection poses ethical, legal, economic, and logistic issues. Synthetic data\ngeneration tools, in this regard, provide a potential solution. However, we\nbelieve that already available such tools do not adequately reflect cognitive\ndeficiencies in behavior simulation. To counter these issues we propose a\nsimulation model (SimDem ) that primarily focuses on cognitive impairments\nsuffered by PwD and can be easily configured and adapted by the users to model\nand evaluate assistive solutions.\n"}
+{"abstract": "  Networks found in the real-world are numerous and varied. A common type of\nnetwork is the heterogeneous network, where the nodes (and edges) can be of\ndifferent types. Accordingly, there have been efforts at learning\nrepresentations of these heterogeneous networks in low-dimensional space.\nHowever, most of the existing heterogeneous network embedding methods suffer\nfrom the following two drawbacks: (1) The target space is usually Euclidean.\nConversely, many recent works have shown that complex networks may have\nhyperbolic latent anatomy, which is non-Euclidean. (2) These methods usually\nrely on meta-paths, which require domain-specific prior knowledge for meta-path\nselection. Additionally, different down-streaming tasks on the same network\nmight require different meta-paths in order to generate task-specific\nembeddings. In this paper, we propose a novel self-guided random walk method\nthat does not require meta-path for embedding heterogeneous networks into\nhyperbolic space. We conduct thorough experiments for the tasks of network\nreconstruction and link prediction on two public datasets, showing that our\nmodel outperforms a variety of well-known baselines across all tasks.\n"}
+{"abstract": "  In this paper, we develop a general framework to design differentially\nprivate expectation-maximization (EM) algorithms in high-dimensional latent\nvariable models, based on the noisy iterative hard-thresholding. We derive the\nstatistical guarantees of the proposed framework and apply it to three specific\nmodels: Gaussian mixture, mixture of regression, and regression with missing\ncovariates. In each model, we establish the near-optimal rate of convergence\nwith differential privacy constraints, and show the proposed algorithm is\nminimax rate optimal up to logarithm factors. The technical tools developed for\nthe high-dimensional setting are then extended to the classic low-dimensional\nlatent variable models, and we propose a near rate-optimal EM algorithm with\ndifferential privacy guarantees in this setting. Simulation studies and real\ndata analysis are conducted to support our results.\n"}
+{"abstract": "  In this paper, we consider and study a cell-free massive MIMO (CF-mMIMO)\nsystem aided with reconfigurable intelligent surfaces (RISs), where a large\nnumber of access points (APs) cooperate to serve a smaller number of users with\nthe help of RIS technology. We consider imperfect channel state information\n(CSI), where each AP uses the local channel estimates obtained from the uplink\npilots and applies conjugate beamforming for downlink data transmission.\nAdditionally, we consider random beamforming at the RIS during both training\nand data transmission phases. This allows us to eliminate the need of\nestimating each RIS assisted link, which has been proven to be a challenging\ntask in literature. We then derive a closed-form expression for the achievable\nrate and use it to evaluate the system's performance supported with numerical\nresults. We show that the RIS provided array gain improves the system's\ncoverage, and provides nearly a 2-fold increase in the minimum rate and a\n1.5-fold increase in the per-user throughput. We also use the results to\nprovide preliminary insights on the number of RISs that need to be used to\nreplace an AP, while achieving similar performance as a typical CF-mMIMO system\nwith dense AP deployment.\n"}
+{"abstract": "  We refine McMullen's construction of sequences of knots in $S^3$ obeying the\nChebotarev law in two ways. One is to extend his theorem for generalized\npseudo-Anosov flows, which may admit a finite number of 1-pronged singular\norbits. The other is to invoke the notion of rational Fried surgeries to\nproduce many pseudo-Anosov flows. These methods especially prove that modular\nknots around the missing trefoil in $S^3$ obey the Chebotarev law, yielding an\nequidistribution theorem for the Rademacher symbol.\n"}
+{"abstract": "  For machine agents to successfully interact with humans in real-world\nsettings, they will need to develop an understanding of human mental life.\nIntuitive psychology, the ability to reason about hidden mental variables that\ndrive observable actions, comes naturally to people: even pre-verbal infants\ncan tell agents from objects, expecting agents to act efficiently to achieve\ngoals given constraints. Despite recent interest in machine agents that reason\nabout other agents, it is not clear if such agents learn or hold the core\npsychology principles that drive human reasoning. Inspired by cognitive\ndevelopment studies on intuitive psychology, we present a benchmark consisting\nof a large dataset of procedurally generated 3D animations, AGENT (Action,\nGoal, Efficiency, coNstraint, uTility), structured around four scenarios (goal\npreferences, action efficiency, unobserved constraints, and cost-reward\ntrade-offs) that probe key concepts of core intuitive psychology. We validate\nAGENT with human-ratings, propose an evaluation protocol emphasizing\ngeneralization, and compare two strong baselines built on Bayesian inverse\nplanning and a Theory of Mind neural network. Our results suggest that to pass\nthe designed tests of core intuitive psychology at human levels, a model must\nacquire or have built-in representations of how agents plan, combining utility\ncomputations and core knowledge of objects and physics.\n"}
+{"abstract": "  Many online platforms are providing valuable real-time contents (e.g.,\ntraffic) by continuously acquiring the status of different Points of Interest\n(PoIs). In status acquisition, it is challenging to determine how frequently a\nPoI should upload its status to a platform, since they are self-interested with\nprivate and possibly time-varying preferences. This paper considers a general\nmulti-period status acquisition system, aiming to maximize the aggregate social\nwelfare and ensure the platform freshness. The freshness is measured by a\nmetric termed age of information. For this goal, we devise a long-term\ndecomposition (LtD) mechanism to resolve the time-varying information\nasymmetry. The key idea is to construct a virtual social welfare that only\ndepends on the current private information, and then decompose the per-period\noperation into multiple distributed bidding problems for the PoIs and\nplatforms. The LtD mechanism enables the platforms to achieve a tunable\ntrade-off between payoff maximization and freshness conditions. Moreover, the\nLtD mechanism retains the same social performance compared to the benchmark\nwith symmetric information and asymptotically ensures the platform freshness\nconditions. Numerical results based on real-world data show that when the\nplatforms pay more attention to payoff maximization, each PoI still obtains a\nnon-negative payoff in the long-term.\n"}
+{"abstract": "  In Polymer Quantum Mechanics, a quantization scheme that naturally emerges\nfrom Loop Quantum Gravity, position and momentum operators cannot be both\nwell-defined on the Hilbert space ( H_Poly ). It is henceforth deemed\nimpossible to define standard creation and annihilation operators. In this\nletter we show that a q-oscillator structure, and hence q-deformed\ncreation/annihilation operators, can be naturally defined on H_Poly , which is\nthen mapped into the sum of many copies of the q-oscillator Hilbert space. This\nshows that the q-calculus is a natural calculus for Polymer Quantum Mechanics.\nMoreover, we show that the inequivalence of different superselected sectors of\nH Poly is of topological nature.\n"}
+{"abstract": "  We present aperiodic sets of prototiles whose shapes are based on the\nwell-known Penrose rhomb tiling. Some decorated prototiles lead to an exact\nPenrose rhomb tiling without any matching rules. We also give an approximate\nsolution to an aperiodic monotile that tessellates the plane (including five\ntypes of gaps) only in a nonperiodic way.\n"}
+{"abstract": "  Few-shot learning has been proposed and rapidly emerging as a viable means\nfor completing various tasks. Many few-shot models have been widely used for\nrelation learning tasks. However, each of these models has a shortage of\ncapturing a certain aspect of semantic features, for example, CNN on long-range\ndependencies part, Transformer on local features. It is difficult for a single\nmodel to adapt to various relation learning, which results in the high variance\nproblem. Ensemble strategy could be competitive on improving the accuracy of\nfew-shot relation extraction and mitigating high variance risks. This paper\nexplores an ensemble approach to reduce the variance and introduces fine-tuning\nand feature attention strategies to calibrate relation-level features. Results\non several few-shot relation learning tasks show that our model significantly\noutperforms the previous state-of-the-art models.\n"}
+{"abstract": "  The purpose of this article is to investigate the structure of singular\nmeasures from a microlocal perspective. We introduce a notion of\n$L^1$-regularity wave front set for scalar and vector distributions. Our main\nresult is a proper microlocal characterisation of the support of the singular\npart of tempered Radon measures and of their polar functions at these points.\nWe deduce a sharp $L^1$ elliptic regularity result which appears to be new even\nfor scalar measures and which enlightens the interest of the techniques from\ngeometric measure theory to the study of harmonic analysis questions. We also\ndeduce several consequences including extensions of the results in [10] giving\nconstraints on the polar function at singular points for measures constrained\nby a PDE. Finally, we also illustrate the interest of this microlocal approach\nwith a result of propagation of singularities for constrained measures.\n"}
+{"abstract": "  We present a flexible, open-source Python package for the accurate simulation\nof the $z$-propagation dynamics of ultrashort optical pulses in nonlinear\nwaveguides, especially valid for few-cycle pulses and their interaction. The\nsimulation approach is based on unidirectional propagation equations for the\nanalytic signal. The provided software allows to account for dispersion,\nattenuation, four-wave mixing processes including, e.g., third-harmonic\ngeneration, and features various models for the Raman response. The propagation\nequations are solved on a periodic temporal domain. For $z$-propagation, a\nselection of pseudospectral methods is available. Propagation scenarios for a\ncustom propagation constant and initial field pulses can either be specified in\nterms of a HDF5 based input file format or by direct implementation using a\npython script. We demonstrate the functionality for a test-case for which an\nexact solution is available, by reproducing exemplary results documented in the\nscientific literature, and a complex propagation scenario involving multiple\npulses. The py-fmas code, its reference manual, an extended user guide, and\nfurther usage examples are available online at\nhttps://github.com/omelchert/py-fmas.\n"}
+{"abstract": "  We show that the centroid molecular dynamics (CMD) method provides a\nrealistic way to calculate the thermal diffusivity $a=\\lambda/\\rho c_{\\rm V}$\nof a quantum mechanical liquid such as para-hydrogen. Once $a$ has been\ncalculated, the thermal conductivity can be obtained from $\\lambda=\\rho c_{\\rm\nV}a$, where $\\rho$ is the density of the liquid and $c_{\\rm V}$ is the\nconstant-volume heat capacity. The use of this formula requires an accurate\nquantum mechanical heat capacity $c_{\\rm V}$, which can be obtained from a path\nintegral molecular dynamics simulation. The thermal diffusivity can be\ncalculated either from the decay of the equilibrium density fluctuations in the\nliquid or by using the Green-Kubo relation to calculate the CMD approximation\nto $\\lambda$ and then dividing this by the corresponding approximation to $\\rho\nc_{\\rm V}$. We show that both approaches give the same results for liquid\npara-hydrogen and that these results are in good agreement with experimental\nmeasurements of the thermal conductivity over a wide temperature range. In\nparticular, they correctly predict a decrease in the thermal conductivity at\nlow temperatures -- an effect that stems from the decrease in the quantum\nmechanical heat capacity and has eluded previous para-hydrogen simulations. We\nalso show that the method gives equally good agreement with experimental\nmeasurements for the thermal conductivity of normal liquid helium.\n"}
+{"abstract": "  In modern enterprises, Business Processes (BPs) are realized over a mix of\nworkflows, IT systems, Web services and direct collaborations of people.\nAccordingly, process data (i.e., BP execution data such as logs containing\nevents, interaction messages and other process artifacts) is scattered across\nseveral systems and data sources, and increasingly show all typical properties\nof the Big Data. Understanding the execution of process data is challenging as\nkey business insights remain hidden in the interactions among process entities:\nmost objects are interconnected, forming complex, heterogeneous but often\nsemi-structured networks. In the context of business processes, we consider the\nBig Data problem as a massive number of interconnected data islands from\npersonal, shared and business data. We present a framework to model process\ndata as graphs, i.e., Process Graph, and present abstractions to summarize the\nprocess graph and to discover concept hierarchies for entities based on both\ndata objects and their interactions in process graphs. We present a language,\nnamely BP-SPARQL, for the explorative querying and understanding of process\ngraphs from various user perspectives. We have implemented a scalable\narchitecture for querying, exploration and analysis of process graphs. We\nreport on experiments performed on both synthetic and real-world datasets that\nshow the viability and efficiency of the approach.\n"}
+{"abstract": "  Time-equispaced inertial measurements are practically used as inputs for\nmotion determination. Polynomial interpolation is a common technique of\nrecovering the gyroscope signal but is subject to a fundamentally numerical\nstability problem due to the Runge effect on equispaced samples. This paper\nreviews the theoretical results of Runge phenomenon in related areas and\nproposes a straightforward borrowing-and-cutting (BAC) strategy to depress it.\nIt employs the neighboring samples for higher-order polynomial interpolation\nbut only uses the middle polynomial segment in the actual time interval. The\nBAC strategy has been incorporated into attitude computation by functional\niteration, leading to accuracy benefit of several orders of magnitude under the\nclassical coning motion. It would potentially bring significant benefits to the\ninertial navigation computation under sustained dynamic motions.\n"}
+{"abstract": "  The past decade has witnessed the tremendous successes of machine learning\ntechniques in the supervised learning paradigm, where there is a clear\ndemarcation between training and testing. In the supervised learning paradigm,\nlearning is inherently passive, seeking to distill human-provided supervision\nin large-scale datasets into high capacity models. Following these successes,\nmachine learning researchers have looked beyond this paradigm and became\ninterested in tasks that are more dynamic. To them, robotics serve as an\nexcellent test-bed, for the challenges of robotics break many of the\nassumptions that made supervised learning successful. Out of the many different\nareas within robotics, robotic manipulation has become a favorite area for\nresearchers to demonstrate new algorithms because of the vast numbers of\npossible applications and its highly dynamical and complex nature. This\ndocument surveys recent robotics conferences and identifies the major trends\nwith which machine learning techniques have been applied to the challenges of\nrobotic manipulation.\n"}
+{"abstract": "  Background and purpose: Accelerator-Based Boron Neutron Capture Therapy is a\nradiotherapy based on compact accelerator neutron sources requiring an\nepithermal neutron field for tumour irradiations. Neutrons of 10 keV are\nconsidered as the maximum optimised energy to treat deep-seated tumours. We\ninvestigated, by means of Monte Carlo simulations, the epithermal range from 10\neV to 10 keV in order to optimise the maximum epithermal neutron energy as a\nfunction of the tumour depth.\n  Methods: A Snyder head phantom was simulated and mono-energetic neutrons with\n4 different incident energies were used: 10 eV, 100 eV, 1 keV and 10 keV.\n$^{10}$B capture rates and absorbed dose composition on every tissue were\ncalculated to describe and compare the effects of lowering the maximum\nepithermal energy. The Therapeutic Gain (TG) was estimated considering the\nwhole brain volume.\n  Results: For tumours seated at 4 cm depth, 10 eV, 100 eV and 1 keV neutrons\nprovided respectively 54 $\\%$, 36 $\\%$ and 18 $\\%$ increase on the TG compared\nto 10 keV neutrons. Neutrons with energies between 10 eV and 1 keV provided\nhigher TG than 10 keV neutrons for tumours seated up to 6.4 cm depth inside the\nhead. The size of the tumour does not change these results.\n  Conclusions: Using lower epithermal energy neutrons for AB-BNCT tumour\nirradiation could improve treatment efficacy, delivering more therapeutic dose\nwhile reducing the dose in healthy tissues. This could lead to new Beam Shape\nAssembly designs in order to optimise the BNCT irradiation.\n"}
+{"abstract": "  Recently proposed consistency-based Semi-Supervised Learning (SSL) methods\nsuch as the $\\Pi$-model, temporal ensembling, the mean teacher, or the virtual\nadversarial training, have advanced the state of the art in several SSL tasks.\nThese methods can typically reach performances that are comparable to their\nfully supervised counterparts while using only a fraction of labelled examples.\nDespite these methodological advances, the understanding of these methods is\nstill relatively limited. In this text, we analyse (variations of) the\n$\\Pi$-model in settings where analytically tractable results can be obtained.\nWe establish links with Manifold Tangent Classifiers and demonstrate that the\nquality of the perturbations is key to obtaining reasonable SSL performances.\nImportantly, we propose a simple extension of the Hidden Manifold Model that\nnaturally incorporates data-augmentation schemes and offers a framework for\nunderstanding and experimenting with SSL methods.\n"}
+{"abstract": "  In multi-center clinical trials, due to various reasons, the individual-level\ndata are strictly restricted to be assessed publicly. Instead, the summarized\ninformation is widely available from published results. With the advance of\ncomputational technology, it has become very common in data analyses to run on\nhundreds or thousands of machines simultaneous, with the data distributed\nacross those machines and no longer available in a single central location. How\nto effectively assemble the summarized clinical data information or information\nfrom each machine in parallel computation has become a challenging task for\nstatisticians and computer scientists. In this paper, we selectively review\nsome recently-developed statistical methods, including communication efficient\ndistributed statistical inference, and renewal estimation and incremental\ninference, which can be regarded as the latest development of calibration\ninformation methods in the era of big data. Even though those methods were\ndeveloped in different fields and in different statistical frameworks, in\nprinciple, they are asymptotically equivalent to those well known methods\ndeveloped in meta analysis. Almost no or little information is lost compared\nwith the case when full data are available. As a general tool to integrate\ninformation, we also review the generalized method of moments and estimating\nequations approach by using empirical likelihood method.\n"}
+{"abstract": "  This study considers modern surgical navigation systems based on augmented\nreality technologies. Augmented reality glasses are used to construct holograms\nof the patient's organs from MRI and CT data, subsequently transmitted to the\nglasses. This, in addition to seeing the actual patient, the surgeon gains\nvisualization inside the patient's body (bones, soft tissues, blood vessels,\netc.). The solutions developed at Peter the Great St. Petersburg Polytechnic\nUniversity allow reducing the invasiveness of the procedure and preserving\nhealthy tissues. This also improves the navigation process, making it easier to\nestimate the location and size of the tumor to be removed. We describe the\napplication of developed systems to different types of surgical operations\n(removal of a malignant brain tumor, removal of a cyst of the cervical spine).\nWe consider the specifics of novel navigation systems designed for anesthesia,\nfor endoscopic operations. Furthermore, we discuss the construction of novel\nvisualization systems for ultrasound machines. Our findings indicate that the\ntechnologies proposed show potential for telemedicine.\n"}
+{"abstract": "  A growing branch of computer vision is object detection. Object detection is\nused in many applications such as industrial process, medical imaging analysis,\nand autonomous vehicles. The ability to detect objects in videos is crucial.\nObject detection systems are trained on large image datasets. For applications\nsuch as autonomous vehicles, it is crucial that the object detection system can\nidentify objects through multiple frames in video. There are many problems with\napplying these systems to video. Shadows or changes in brightness that can\ncause the system to incorrectly identify objects frame to frame and cause an\nunintended system response. There are many neural networks that have been used\nfor object detection and if there was a way of connecting objects between\nframes then these problems could be eliminated. For these neural networks to\nget better at identifying objects in video, they need to be re-trained. A\ndataset must be created with images that represent consecutive video frames and\nhave matching ground-truth layers. A method is proposed that can generate these\ndatasets. The ground-truth layer contains only moving objects. To generate this\nlayer, FlowNet2-Pytorch was used to create the flow mask using the novel\nMagnitude Method. As well, a segmentation mask will be generated using networks\nsuch as Mask R-CNN or Refinenet. These segmentation masks will contain all\nobjects detected in a frame. By comparing this segmentation mask to the flow\nmask ground-truth layer, a loss function is generated. This loss function can\nbe used to train a neural network to be better at making consistent predictions\non video. The system was tested on multiple video samples and a loss was\ngenerated for each frame, proving the Magnitude Method's ability to be used to\ntrain object detection neural networks in future work.\n"}
+{"abstract": "  Machine Learning (ML) is widely used to automatically extract meaningful\ninformation from Electronic Health Records (EHR) to support operational,\nclinical, and financial decision-making. However, ML models require a large\nnumber of annotated examples to provide satisfactory results, which is not\npossible in most healthcare scenarios due to the high cost of clinician-labeled\ndata. Active Learning (AL) is a process of selecting the most informative\ninstances to be labeled by an expert to further train a supervised algorithm.\nWe demonstrate the effectiveness of AL in multi-label text classification in\nthe clinical domain. In this context, we apply a set of well-known AL methods\nto help automatically assign ICD-9 codes on the MIMIC-III dataset. Our results\nshow that the selection of informative instances provides satisfactory\nclassification with a significantly reduced training set (8.3\\% of the total\ninstances). We conclude that AL methods can significantly reduce the manual\nannotation cost while preserving model performance.\n"}
+{"abstract": "  The present work concerns the transferability of coarse-grained (CG) modeling\nin reproducing the dynamic properties of the reference atomistic systems across\na range of parameters. In particular, we focus on implicit-solvent CG modeling\nof polymer solutions. The CG model is based on the generalized Langevin\nequation, where the memory kernel plays the critical role in determining the\ndynamics in all time scales. Thus, we propose methods for transfer learning of\nmemory kernels. The key ingredient of our methods is Gaussian process\nregression. By integration with the model order reduction via proper orthogonal\ndecomposition and the active learning technique, the transfer learning can be\npractically efficient and requires minimum training data. Through two example\npolymer solution systems, we demonstrate the accuracy and efficiency of the\nproposed transfer learning methods in the construction of transferable memory\nkernels. The transferability allows for out-of-sample predictions, even in the\nextrapolated domain of parameters. Built on the transferable memory kernels,\nthe CG models can reproduce the dynamic properties of polymers in all time\nscales at different thermodynamic conditions (such as temperature and solvent\nviscosity) and for different systems with varying concentrations and lengths of\npolymers.\n"}
+{"abstract": "  We propose a quantization-based numerical scheme for a family of decoupled\nFBSDEs. We simplify the scheme for the control in Pag\\`es and Sagna (2018) so\nthat our approach is fully based on recursive marginal quantization and does\nnot involve any Monte Carlo simulation for the computation of conditional\nexpectations. We analyse in detail the numerical error of our scheme and we\nshow through some examples the performance of the whole procedure, which proves\nto be very effective in view of financial applications.\n"}
+{"abstract": "  This article is dedicated to the asymptotic geometry of wreath products $F\\wr\nH := \\left( \\bigoplus_H F \\right) \\rtimes H$ where $F$ is a finite group and\n$H$ a one-ended finitely presented group. Our main result is a complete\nclassification of these groups up to quasi-isometry. More precisely, given two\nfinite groups $F_1,F_2$ and two finitely presented one-ended groups $H_1,H_2$,\nwe show that $F_1 \\wr H_1$ and $F_2 \\wr H_2$ are quasi-isometric if and only if\neither (i) $H_1,H_2$ are non-amenable quasi-isometric groups and $|F_1|,|F_2|$\nhave the same prime divisors, or (ii) $H_1,H_2$ are amenable, $|F_1|=k^{n_1}$\nand $|F_2|=k^{n_2}$ for some $k,n_1,n_2 \\geq 1$, and there exists a\nquasi-$(n_2/n_1)$-to-one quasi-isometry $H_1 \\to H_2$. The article also\ncontains algebraic information on groups quasi-isometric to such wreath\nproducts. This can be seen as far reaching extension of a celebrated work of\nEskin-Fisher-Whyte who treated the case of $H=\\mathbb{Z}$. Our approach is\nhowever fundamentally different, as it crucially exploits the assumption that\n$H$ is one-ended. Our central tool is a new geometric interpretation of\nlamplighter groups involving natural families of quasi-median spaces.\n"}
+{"abstract": "  We consider distributed online learning for joint regret with communication\nconstraints. In this setting, there are multiple agents that are connected in a\ngraph. Each round, an adversary first activates one of the agents to issue a\nprediction and provides a corresponding gradient, and then the agents are\nallowed to send a $b$-bit message to their neighbors in the graph. All agents\ncooperate to control the joint regret, which is the sum of the losses of the\nactivated agents minus the losses evaluated at the best fixed common comparator\nparameters $u$. We observe that it is suboptimal for agents to wait for\ngradients that take too long to arrive. Instead, the graph should be\npartitioned into local clusters that communicate among themselves. Our main\nresult is a new method that can adapt to the optimal graph partition for the\nadversarial activations and gradients, where the graph partition is selected\nfrom a set of candidate partitions. A crucial building block along the way is a\nnew algorithm for online convex optimization with delayed gradient information\nthat is comparator-adaptive, meaning that its joint regret scales with the norm\nof the comparator $||u||$. We further provide near-optimal gradient compression\nschemes depending on the ratio of $b$ and the dimension times the diameter of\nthe graph.\n"}
+{"abstract": "  In the last decades the rapid development of technologies and methodologies\nin the field of digitization and 3D modelling has led to an increasing\nproliferation of 3D technologies in the Cultural Heritage domain. Despite the\ngreat potential of 3D digital heritage, the \"special effects\" of 3D may often\noverwhelm its importance in research. Projects and consortia of scholars have\ntried to put order in the different fields of application of these\ntechnologies, providing guidelines and proposing workflows. The use of computer\ngraphics as an effective methodology for CH research and communication\nhighlighted the need of transparent provenance data to properly document\ndigital assets and understand the degree of scientific quality and reliability\nof their outcomes. The building and release of provenance knowledge, consisting\nin the complete formal documentation of each phase of the process, is therefore\nof fundamental importance to ensure its repeatability and to guarantee the\nintegration and interoperability of the generated metadata on the Semantic Web.\nThis paper proposes a methodology for documenting the planning and creation of\n3D models used in archaeology and Cultural Heritage, by means of an application\nprofile based on the CIDOC CRM ecosystem and other international standards.\n"}
+{"abstract": "  The eROSITA Final Equatorial-Depth Survey has been carried out during the PV\nphase of the SRG/eROSITA telescope and completed in November 2019. This survey\nis designed to provide the first eROSITA-selected sample of galaxy clusters and\ngroups and to test the predictions for the all-sky survey in the context of\ncosmological studies with clusters. In the 140 deg$^2$ area covered by eFEDS,\n542 candidate clusters and groups are detected as extended X-ray sources, down\nto a flux of $\\sim10^{-14} $erg/s/cm$^2$ in the soft band (0.5-2 keV) within\n1'. In this work, we provide the catalog of candidate galaxy clusters and\ngroups in eFEDS. We perform imaging and spectral analysis on the eFEDS clusters\nwith eROSITA X-ray data, and study the properties of the sample. The clusters\nare distributed in the redshift range [0.01, 1.3], with the median redshift at\n0.35. We obtain the ICM temperature measurement with $>2\\sigma$ c.l. for\n$\\sim$1/5 (102/542) of the sample. The average temperature of these clusters is\n$\\sim$2 keV. Radial profiles of flux, luminosity, electron density, and gas\nmass are measured from the precise modeling of the imaging data. The selection\nfunction, the purity and completeness of the catalog are examined and discussed\nin detail. The contamination fraction is $\\sim1/5$ in this sample, dominated by\nmisidentified point sources. The X-ray Luminosity Function of the clusters\nagrees well with the results obtained from other recent X-ray surveys. We also\nfind 19 supercluster candidates in eFEDS, most of which are located at\nredshifts between 0.1 and 0.5. The eFEDS cluster and group catalog provides a\nbenchmark proof-of-concept for the eROSITA All-Sky Survey extended source\ndetection and characterization. We confirm the excellent performance of eROSITA\nfor cluster science and expect no significant deviations from our pre-launch\nexpectations for the final All-Sky Survey.\n"}
+{"abstract": "  The Strictly Correlated Electrons (SCE) limit of the Levy-Lieb functional in\nDensity Functional Theory (DFT) gives rise to a symmetric multi-marginal\noptimal transport problem with Coulomb cost, where the number of marginal laws\nis equal to the number of electrons in the system, which can be very large in\nrelevant applications. In this work, we design a numerical method, built upon\nconstrained overdamped Langevin processes to solve Moment Constrained Optimal\nTransport (MCOT) relaxations (introduced in A. Alfonsi, R. Coyaud, V. Ehrlacher\nand D. Lombardi, Math. Comp. 90, 2021, 689--737) of symmetric multi-marginal\noptimal transport problems with Coulomb cost. Some minimizers of such\nrelaxations can be written as discrete measures charging a low number of points\nbelonging to a space whose dimension, in the symmetrical case, scales linearly\nwith the number of marginal laws. We leverage the sparsity of those minimizers\nin the design of the numerical method and prove that any local minimizer to the\nresulting problem is actually a \\emph{global} one. We illustrate the\nperformance of the proposed method by numerical examples which solves MCOT\nrelaxations of 3D systems with up to 100 electrons.\n"}
+{"abstract": "  In this paper we present our submission for the EACL 2021 SRW; a methodology\nthat aims at bridging the gap between high and low-resource languages in the\ncontext of Open Information Extraction, showcasing it on the Greek language.\nThe goals of this paper are twofold: First, we build Neural Machine Translation\n(NMT) models for English-to-Greek and Greek-to-English based on the Transformer\narchitecture. Second, we leverage these NMT models to produce English\ntranslations of Greek text as input for our NLP pipeline, to which we apply a\nseries of pre-processing and triple extraction tasks. Finally, we\nback-translate the extracted triples to Greek. We conduct an evaluation of both\nour NMT and OIE methods on benchmark datasets and demonstrate that our approach\noutperforms the current state-of-the-art for the Greek natural language.\n"}
+{"abstract": "  We study the temporal evolution of the circuit complexity for a subsystem in\nharmonic lattices after a global quantum quench of the mass parameter, choosing\nthe initial reduced density matrix as the reference state. Upper and lower\nbounds are derived for the temporal evolution of the complexity for the entire\nsystem. The subsystem complexity is evaluated by employing the Fisher\ninformation geometry for the covariance matrices. We discuss numerical results\nfor the temporal evolutions of the subsystem complexity for a block of\nconsecutive sites in harmonic chains with either periodic or Dirichlet boundary\nconditions, comparing them with the temporal evolutions of the entanglement\nentropy. For infinite harmonic chains, the asymptotic value of the subsystem\ncomplexity is studied through the generalised Gibbs ensemble.\n"}
+{"abstract": "  Let $(W_1(s), W_2(t)), s,t\\ge 0$ be a bivariate Brownian motion with standard\nBrownian motion marginals and constant correlation $\\rho \\in (-1,1).$ Parisian\nruin is defined as a classical ruin that happens over an extended period of\ntime, the so-called time-in-red. We derive exact asymptotics for the\nnon-simultaneous Parisian ruin of the company conditioned on the event of\nnon-simultaneous ruin happening. We are interested in finding asymptotics of\nsuch problem as $u \\to \\infty$ and with the length of time-in-red being of\norder $\\frac{1}{u^2},$ where $u$ represents initial capital for the companies.\nApproximation of this problem is of interest for the analysis of Parisian ruin\nprobability in bivariate Brownian risk model, which is a standard way of\ndefining prolonged ruin models in the financial markets.\n"}
+{"abstract": "  We classify the GL(2,R)-invariant subvarieties M in strata of Abelian\ndifferentials for which any two M-parallel cylinders have homologous core\ncurves. This answers a question of Mirzakhani and Wright. As a corollary we\nshow that outside of an explicit list of exceptions, if M is a\nGL(2,R)-invariant subvariety, then the Kontsevich-Zorich cocycle has nonzero\nLyapunov exponents in the symplectic orthogonal of the projection of the\ntangent bundle of M to absolute cohomology.\n"}
+{"abstract": "  We present an easily defined countable family of permutations of the natural\nnumbers for which explicit rearrangements (i.e., the sums induced by the\npermutations) can be computed. The digamma function proves to be the key tool\nfor the computations found here for the alternating harmonic series. The\npermutations $\\phi$ under consideration are simple in a sense: they are\ninvolutions ($\\phi\\circ\\phi$ is the identity function). We show that the\ncountable set of rearrangements obtained from the simple involutions considered\nbelow are dense in the reals.\n"}
+{"abstract": "  Recently, pre-trained language models (LMs) have achieved strong performance\nwhen fine-tuned on difficult benchmarks like SuperGLUE. However, performance\ncan suffer when there are very few labeled examples available for fine-tuning.\nPattern Exploiting Training (PET) is a recent approach that leverages patterns\nfor few-shot learning. However, PET uses task-specific unlabeled data. In this\npaper, we focus on few-shot learning without any unlabeled data and introduce\nADAPET, which modifies PET's objective to provide denser supervision during\nfine-tuning. As a result, ADAPET outperforms PET on SuperGLUE without any\ntask-specific unlabeled data. Our code can be found at\nhttps://github.com/rrmenon10/ADAPET.\n"}
+{"abstract": "  Deep generative models, since their inception, have become increasingly more\ncapable of generating novel and perceptually realistic signals (e.g., images\nand sound waves). With the emergence of deep models for graph structured data,\nnatural interests seek extensions of these generative models for graphs.\nSuccessful extensions were seen recently in the case of learning from a\ncollection of graphs (e.g., protein data banks), but the learning from a single\ngraph has been largely under explored. The latter case, however, is important\nin practice. For example, graphs in financial and healthcare systems contain so\nmuch confidential information that their public accessibility is nearly\nimpossible, but open science in these fields can only advance when similar data\nare available for benchmarking.\n  In this work, we propose an approach to generating a doppelganger graph that\nresembles a given one in many graph properties but nonetheless can hardly be\nused to reverse engineer the original one, in the sense of a near zero edge\noverlap. The approach is an orchestration of graph representation learning,\ngenerative adversarial networks, and graph realization algorithms. Through\ncomparison with several graph generative models (either parameterized by neural\nnetworks or not), we demonstrate that our result barely reproduces the given\ngraph but closely matches its properties. We further show that downstream\ntasks, such as node classification, on the generated graphs reach similar\nperformance to the use of the original ones.\n"}
+{"abstract": "  Extensive work has argued in favour of paying crowd workers a wage that is at\nleast equivalent to the U.S. federal minimum wage. Meanwhile, research on\ncollecting high quality annotations suggests using a qualification that\nrequires workers to have previously completed a certain number of tasks. If\nmost requesters who pay fairly require workers to have completed a large number\nof tasks already then workers need to complete a substantial amount of poorly\npaid work before they can earn a fair wage. Through analysis of worker\ndiscussions and guidance for researchers, we estimate that workers spend\napproximately 2.25 months of full time effort on poorly paid tasks in order to\nget the qualifications needed for better paid tasks. We discuss alternatives to\nthis qualification and conduct a study of the correlation between\nqualifications and work quality on two NLP tasks. We find that it is possible\nto reduce the burden on workers while still collecting high quality data.\n"}
+{"abstract": "  In a previous paper (P. D. Mannheim, Phys. Rev. D 102, 123535 (2020)) we\nstudied cosmological perturbation theory in the cosmology associated with the\nfourth-order derivative conformal gravity theory, and provided an exact\nsolution to the theory in the recombination era. In this paper we present an\nexact solution that holds all the way from recombination until the current era.\n"}
+{"abstract": "  There is still a long way to go before artificial mini robots are really used\nfor search and rescue missions in disaster-hit areas due to hindrance in power\nconsumption, computation load of the locomotion, and obstacle-avoidance system.\nInsect-computer hybrid system, which is the fusion of living insect platform\nand microcontroller, emerges as an alternative solution. This study\ndemonstrates the first-ever insect-computer hybrid system conceived for search\nand rescue missions, which is capable of autonomous navigation and human\npresence detection in an unstructured environment. Customized navigation\ncontrol algorithm utilizing the insect's intrinsic navigation capability\nachieved exploration and negotiation of complex terrains. On-board\nhigh-accuracy human presence detection using infrared camera was achieved with\na custom machine learning model. Low power consumption suggests system\nsuitability for hour-long operations and its potential for realization in\nreal-life missions.\n"}
+{"abstract": "  It is becoming increasingly difficult for game developers to manage the cost\nof developing a game, while meeting the high expectations of gamers. One way to\nbalance the increasing gamer expectation and development stress is to build an\nactive modding community around the game. There exist several examples of games\nwith an extremely active and successful modding community, with the Minecraft\ngame being one of the most notable ones.\n  This paper reports on an empirical study of 1,114 popular and 1,114 unpopular\nMinecraft mods from the CurseForge mod distribution platform, one of the\nlargest distribution platforms for Minecraft mods. We analyzed the relationship\nbetween 33 features across 5 dimensions of mod characteristics and the\npopularity of mods (i.e., mod category, mod documentation, environmental\ncontext of the mod, remuneration for the mod, and community contribution for\nthe mod), to understand the characteristics of popular Minecraft mods. We\nfirstly verify that the studied dimensions have significant explanatory power\nin distinguishing the popularity of the studied mods. Then we evaluated the\ncontribution of each of the 33 features across the 5 dimensions. We observed\nthat popular mods tend to have a high quality description and promote community\ncontribution.\n"}
+{"abstract": "  A graph is maximal knotless if it is edge maximal for the property of\nknotless embedding in $R^3$. We show that such a graph has at least $\\frac74\n|V|$ edges, and construct an infinite family of maximal knotless graphs with\n$|E| < \\frac52|V|$. With the exception of $|E| = 22$, we show that for any $|E|\n\\geq 20$ there exists a maximal knotless graph of size $|E|$. We classify the\nmaximal knotless graphs through nine vertices and 20 edges. We determine which\nof these maxnik graphs are the clique sum of smaller graphs and construct an\ninfinite family of maxnik graphs that are not clique sums.\n"}
+{"abstract": "  The abundance of clouds, located both spatially and temporally, often makes\nremote sensing (RS) applications with optical images difficult or even\nimpossible to perform. Traditional cloud removing techniques have been studied\nfor years, and recently, Machine Learning (ML)-based approaches have also been\nconsidered. In this manuscript, a novel method for the restoration of\nclouds-corrupted optical images is presented, able to generate the whole\noptical scene of interest, not only the cloudy pixels, and based on a Joint\nData Fusion paradigm, where three deep neural networks are hierarchically\ncombined. Spatio-temporal features are separately extracted by a conditional\nGenerative Adversarial Network (cGAN) and by a Convolutional Long Short-Term\nMemory (ConvLSTM), from Synthetic Aperture Radar (SAR) data and optical\ntime-series of data respectively, and then combined with a U-shaped network.\nThe use of time-series of data has been rarely explored in the state of the art\nfor this peculiar objective, and moreover existing models do not combine both\nspatio-temporal domains and SAR-optical imagery. Quantitative and qualitative\nresults have shown a good ability of the proposed method in producing\ncloud-free images, by also preserving the details and outperforming the cGAN\nand the ConvLSTM when individually used. Both the code and the dataset have\nbeen implemented from scratch and made available to interested researchers for\nfurther analysis and investigation.\n"}
+{"abstract": "  For the Su-Schrieffer-Heeger (SSH) model on the two-dimensional square\nlattice, two third nearest neighbor hoppings which preserve chiral symmetry are\nintroduced. Like the case of one dimension, the longer-range hopping can drive\ntopological transitions and leads to the larger topological invariant (vectored\nBerry phase for the two dimensional SSH model). We obtain the phase boundaries\nand topological phase diagrams from the winding pattern of $\\rho(k_l)$ of the\nBloch Hamiltonian. Effects of correlations on the extended two dimensional SSH\nmodel are also investigated. The correlation shifts the phase boundaries and\nleads to topological transitions. Several special points in non-interacting\nphase diagrams are chosen to illustrate the different phase transitions and a\ncorrelations-driven topologically non-trivial phase from the trivial one is\nfound. In this work, the slave-rotor mean field method is applied to the\ninteracting model and we focus on the case of physical electrons, i.e. the Mott\ntransition of charge degree of freedom is avoided in our investigations.\n"}
+{"abstract": "  This article introduces a multiple classifier method to improve the\nperformance of concatenate-designed neural networks, such as ResNet and\nDenseNet, with the purpose to alleviate the pressure on the final classifier.\nWe give the design of the classifiers, which collects the features produced\nbetween the network sets, and present the constituent layers and the activation\nfunction for the classifiers, to calculate the classification score of each\nclassifier. We use the L2 normalization method to obtain the classifier score\ninstead of the Softmax normalization. We also determine the conditions that can\nenhance convergence. As a result, the proposed classifiers are able to improve\nthe accuracy in the experimental cases significantly, and show that the method\nnot only has better performance than the original models, but also produces\nfaster convergence. Moreover, our classifiers are general and can be applied to\nall classification related concatenate-designed network models.\n"}
+{"abstract": "  In this paper, we study the uniqueness of the difference of meromorphic\nfunctions. We give a new proof of the following result: Let $f$ be a\ntranscendental meromorphic function of hyper-order less than $1$, let $\\eta$ be\na non-zero complex number, $n\\geq1$, an integer, and let $a,b,c$ be three\ndistinct periodic small functions with period $\\eta$. If $f$ and\n$\\Delta_{\\eta}^{n}f$ share $a,b,c$ CM, then $f\\equiv\\Delta_{\\eta}^{n}f$, which\nusing a different method from \\cite{gkzz}.\n"}
+{"abstract": "  The exponential increase in CPU time taken to deterministically solve NP-Hard\nCombinatorial Optimisation Problems (COP), as the problem size scales, has\nresulted in a search for non-deterministic optimisation solution techniques to\nobtain solutions to COP efficiently. This paper juxtaposes classical and\nquantum optimisation algorithms' performance to solve two common COP, the\nTravelling Salesman Problem (TSP) and Quadratic Assignment Problem (QAP). The\ntwo classical optimisation techniques applied are Branch and Bound (BNB) and\nSimulated Annealing (SA), and the two quantum optimisation methods used are the\nVariational Quantum Eigensolver (VQE) algorithm and Quantum Approximate\nOptimisation Algorithm (QAOA). These algorithms are respectively executed on\nclassical and IBM's suite of Noisy Intermediate-Scale Quantum (NISQ) computers.\nOur experimental results resemble and extend on previously reported results in\nthe literature. Extensions include critically analysing, comparing and\ncommenting on the performance of quantum optimisation computing techniques to\nclassical techniques, with respect to computational time and additional metrics\nused to measure solution quality. Furthermore, a comparison of the impact of a\nnew set of basis gates on the quantum optimisation techniques was investigated;\nthe results did not reflect any consistent impact on results. The VQE algorithm\nand QAOA executed on state of the art IBM quantum devices are outperformed by\nclassical optimisation techniques, highlighting the shortcomings of NISQ\ndevices.\n"}
+{"abstract": "  We present the Colorization Transformer, a novel approach for diverse high\nfidelity image colorization based on self-attention. Given a grayscale image,\nthe colorization proceeds in three steps. We first use a conditional\nautoregressive transformer to produce a low resolution coarse coloring of the\ngrayscale image. Our architecture adopts conditional transformer layers to\neffectively condition grayscale input. Two subsequent fully parallel networks\nupsample the coarse colored low resolution image into a finely colored high\nresolution image. Sampling from the Colorization Transformer produces diverse\ncolorings whose fidelity outperforms the previous state-of-the-art on\ncolorising ImageNet based on FID results and based on a human evaluation in a\nMechanical Turk test. Remarkably, in more than 60% of cases human evaluators\nprefer the highest rated among three generated colorings over the ground truth.\nThe code and pre-trained checkpoints for Colorization Transformer are publicly\navailable at\nhttps://github.com/google-research/google-research/tree/master/coltran\n"}
+{"abstract": "  Automatically evaluating the quality of image captions can be very\nchallenging since human language is quite flexible that there can be various\nexpressions for the same meaning. Most of the current captioning metrics rely\non token level matching between candidate caption and the ground truth label\nsentences. It usually neglects the sentence-level information. Motivated by the\nauto-encoder mechanism and contrastive representation learning advances, we\npropose a learning-based metric for image captioning, which we call Intrinsic\nImage Captioning Evaluation($I^2CE$). We develop three progressive model\nstructures to learn the sentence level representations--single branch model,\ndual branches model, and triple branches model. Our empirical tests show that\n$I^2CE$ trained with dual branches structure achieves better consistency with\nhuman judgments to contemporary image captioning evaluation metrics.\nFurthermore, We select several state-of-the-art image captioning models and\ntest their performances on the MS COCO dataset concerning both contemporary\nmetrics and the proposed $I^2CE$. Experiment results show that our proposed\nmethod can align well with the scores generated from other contemporary\nmetrics. On this concern, the proposed metric could serve as a novel indicator\nof the intrinsic information between captions, which may be complementary to\nthe existing ones.\n"}
+{"abstract": "  The idea of intersectionality has become a frequent topic of discussion both\nin academic sociology, as well as among popular movements for social justice\nsuch as Black Lives Matter, intersectional feminism, and LGBT rights.\nIntersectionality proposes that an individual's experience of society has\naspects that are irreducible to the sum of one's various identities considered\nindividually, but are \"greater than the sum of their parts.\" In this work, we\nshow that the effects of intersectional identities can be statistically\nobserved in empirical data using information theory. We show that, when\nconsidering the predictive relationship between various identities categories\nsuch as race, sex, and income (as a proxy for class) on outcomes such as health\nand wellness, robust statistical synergies appear. These synergies show that\nthere are joint-effects of identities on outcomes that are irreducible to any\nidentity considered individually and only appear when specific categories are\nconsidered together (for example, there is a large, synergistic effect of race\nand sex considered jointly on income irreducible to either race or sex). We\nthen show using synthetic data that the current gold-standard method of\nassessing intersectionalities in data (linear regression with multiplicative\ninteraction coefficients) fails to disambiguate between truly synergistic,\ngreater-than-the-sum-of-their-parts interactions, and redundant interactions.\nWe explore the significance of these two distinct types of interactions in the\ncontext of making inferences about intersectional relationships in data and the\nimportance of being able to reliably differentiate the two. Finally, we\nconclude that information theory, as a model-free framework sensitive to\nnonlinearities and synergies in data, is a natural method by which to explore\nthe space of higher-order social dynamics.\n"}
+{"abstract": "  Collecting annotated data for semantic segmentation is time-consuming and\nhard to scale up. In this paper, we for the first time propose a unified\nframework, termed as Multi-Dataset Pretraining, to take full advantage of the\nfragmented annotations of different datasets. The highlight is that the\nannotations from different domains can be efficiently reused and consistently\nboost performance for each specific domain. This is achieved by first\npretraining the network via the proposed pixel-to-prototype contrastive loss\nover multiple datasets regardless of their taxonomy labels, and followed by\nfine-tuning the pretrained model over specific dataset as usual. In order to\nbetter model the relationship among images and classes from different datasets,\nwe extend the pixel level embeddings via cross dataset mixing and propose a\npixel-to-class sparse coding strategy that explicitly models the pixel-class\nsimilarity over the manifold embedding space. In this way, we are able to\nincrease intra-class compactness and inter-class separability, as well as\nconsidering inter-class similarity across different datasets for better\ntransferability. Experiments conducted on several benchmarks demonstrate its\nsuperior performance. Notably, MDP consistently outperforms the pretrained\nmodels over ImageNet by a considerable margin, while only using less than 10%\nsamples for pretraining.\n"}
+{"abstract": "  We introduce a new Legendrian isotopy invariant for any closed orientable\nLegendrian surface $L$ embedded in a closed contact $5$-manifold $(M, \\xi)$\nwhich admits an \"admissable\" open book $(B, f)$ (supporting $\\xi$) for $L$. We\nshow that to any such $L$ and a fixed page $X$, one can assign an integer\n$M\\mathcal{P}_{X}(L)$, called \"Relative Maximal Page Crossing Number of $L$\nwith respect to $X$\", which is invariant under Legendrian isotopies of $L$. We\nalso show that one can extend this to a page-free invariant, i.e., one can\nassign an integer $M\\mathcal{P}_{(B,f)}(L)$, called \"Absolute Maximal Page\nCrossing Number of $L$ with respect to $(B, f)$\", which is invariant under\nLegendrian isotopies of $L$. In particular, this new invariant distinguishes\nLegendrian surfaces in the standard five-sphere which can not be distinguished\nby Thurston-Bennequin invariant.\n"}
+{"abstract": "  This paper investigates the transition from abrasive to adhesive wear in\ngross-slip fretting assuming contact oxygenation concept which suggests that\nadhesion appears in the inner part of the interface if the di-oxygen partial\npressure is below a threshold value. In the lateral sides, where di-oxygen\nmolecules are sufficient, oxidation and abrasion prevail. To assess this\nphenomenon, 34NiCrMo16 flat-on-flat contacts are tested. Contact oxygenation is\nquantified using the ''oxygen-distance, '' parameter defined as the averaged\nwidth of the external abrasion corona. Confirming this concept, decreases with\ncontact pressure and frequency but remains constant versus sliding amplitude,\nfretting cycles and contact area. evolution is formalized using a power law\nformulation which allowed predicting wear transitions for plain and\nmacro-textured surfaces.\n"}
+{"abstract": "  The European Union Emission Trading Scheme (EU ETS) is a cornerstone of the\nEU's strategy to fight climate change and an important device for plummeting\ngreenhouse gas (GHG) emissions in an economically efficient manner. The power\nindustry has switched to an auction-based allocation system at the onset of\nPhase III of the EU ETS to bring economic efficiency by negating windfall\nprofits that have been resulted from grandfathered allocation of allowances in\nthe previous phases. In this work, we analyze and simulate the interaction of\noligopolistic generators in an electricity market with a game-theoretical\nframework where the electricity and the emissions markets interact in a\ntwo-stage electricity market. For analytical simplicity, we assume a single\nfutures market where the electricity is committed at the futures price, and the\nemissions allowance is contracted in advance, prior to a spot market where the\nenergy and allowances delivery takes place. Moreover, a coherent risk measure\nis applied (Conditional Value at Risk) to model both risk averse and risk\nneutral generators and a two-stage stochastic optimization setting is\nintroduced to deal with the uncertainty of renewable capacity, demand,\ngeneration, and emission costs. The performance of the proposed equilibrium\nmodel and its main properties are examined through realistic numerical\nsimulations. Our results show that renewable generators are surging and\nsubstituting conventional generators without compromising social welfare.\nHence, both renewable deployment and emission allowance auctioning are\neffectively reducing GHG emissions and promoting low-carbon economic path.\n"}
+{"abstract": "  The self-adjoint angular flux and streamline-upwind Petrov-Galerkin transport\nequations are discretized using reproducing kernels with the collocation method\nto produce a discretization that is compatible with conservative reproducing\nkernel smoothed particle hydrodynamics. A novel second derivative is derived\nfor the diffusion-like term in the self-adjoint angular flux equation. The\nresulting equations involve only evaluations of kernels and physical data at\nthe nodal centers.\n"}
+{"abstract": "  VdW materials are a family of materials ranging from semimetal, semiconductor\nto insulator, and their common characteristic is the layered structure. These\nfeatures make them widely applied in the fabrication of nano-photonic and\nelectronic devices, particularly, vdW heterojunctions. HBN is the only layered\nmaterial to date that is demonstrated to contain optically-detected electronic\nspins, which can benefit the construction of solid qubit and quantum sensor,\netc., especially embedded in the nano-layered-devices. To realize this, Rabi\noscillation is a crucial step. Here, we demonstrate the Rabi oscillation of\nV$_\\text{B}^-$ spins in hBN. Interestingly, we find the behaviors of the spins\nare completely different under the conditions of weak and relatively higher\nmagnetic field. The former behaves like a single wide peak, but the latter\nbehaves like multiple narrower peaks (e.g., a clear beat in Ramsey fringes). We\nconjecture a strong coupling of the spin with the surrounding nuclear spins,\nand the magnetic field can control the nuclear spin bath.\n"}
+{"abstract": "  This paper investigates the problem to determine whether a given stochastic\nprocess generates a sampled Brownian filtration. A fairly general sufficient\ncondition is obtained by applying the Frank H. Clarke contraction criteria to a\nfunctional whose construction relies on the quasi-invariance properties of a\nsampled Wiener measure. The latter are investigated from the precise analytic\nstructure underlying this sampled Brownian motion. In particular, the\nCameron-Martin space is identified from usual Lebesgue integrals over a\nGuseinov measure. As an application we obtain sufficient conditions for the\nexistence of a solution to a class of not necessarily Markovian stochastic\ndifferential equations driven by a sampled Brownian motion. By taking a\nsampling times set which coincides with the unit interval, the results apply to\ncontinuous time models.\n"}
+{"abstract": "  The detection of nuclei and cells in histology images is of great value in\nboth clinical practice and pathological studies. However, multiple reasons such\nas morphological variations of nuclei or cells make it a challenging task where\nconventional object detection methods cannot obtain satisfactory performance in\nmany cases. A detection task consists of two sub-tasks, classification and\nlocalization. Under the condition of dense object detection, classification is\na key to boost the detection performance. Considering this, we propose\nsimilarity based region proposal networks (SRPN) for nuclei and cells detection\nin histology images. In particular, a customized convolution layer termed as\nembedding layer is designed for network building. The embedding layer is added\ninto the region proposal networks, enabling the networks to learn\ndiscriminative features based on similarity learning. Features obtained by\nsimilarity learning can significantly boost the classification performance\ncompared to conventional methods. SRPN can be easily integrated into standard\nconvolutional neural networks architectures such as the Faster R-CNN and\nRetinaNet. We test the proposed approach on tasks of multi-organ nuclei\ndetection and signet ring cells detection in histological images. Experimental\nresults show that networks applying similarity learning achieved superior\nperformance on both tasks when compared to their counterparts. In particular,\nthe proposed SRPN achieve state-of-the-art performance on the MoNuSeg benchmark\nfor nuclei segmentation and detection while compared to previous methods, and\non the signet ring cell detection benchmark when compared with baselines. The\nsourcecode is publicly available at:\nhttps://github.com/sigma10010/nuclei_cells_det.\n"}
+{"abstract": "  Perhaps the largest debate in network Ecology, the emergence of structural\npatterns stands out as a multifaceted problem. To the methodological challenges\n-- pattern identification, statistical significance -- one has to add the\nrelationship between candidate architectures and dynamical performance. In the\ncase of mutualistic communities, the debate revolves mostly around two\nstructural arrangements (nestedness and modularity) and two requirements for\npersistence, namely feasibility and stability. So far, it is clear that the\nformer is strongly related to nestedness, while the latter is enhanced in\nmodular systems. Adding to this, it has recently become clear that nestedness\nand modularity are antagonistic patterns -- or, at the very least, their\ncoexistence in a single system is problematic. In this context, this work\naddresses the role of the interaction architecture in the emergence and\nmaintenance of both properties, introducing the idea of hybrid architectural\nconfigurations. Specifically, we examine in-block nestedness, compound by\ndisjoint subsets of species (modules) with internal nested organization, and\nprove that it grants a balanced trade-off between stability and feasibility.\nRemarkably, we analyze a large amount of empirical communities and find that a\nrelevant fraction of them exhibits a marked in-block nested structure. We\nelaborate on the implications of these results, arguing that they provide new\ninsights about the key properties ruling community assembly.\n"}
+{"abstract": "  Event-triggered control (ETC) holds the potential to significantly improve\nthe efficiency of wireless networked control systems. Unfortunately, its\nreal-world impact has hitherto been hampered by the lack of a network stack\nable to transfer its benefits from theory to practice specifically by\nsupporting the latency and reliability requirements of the aperiodic\ncommunication ETC induces. This is precisely the contribution of this paper.\nOur Wireless Control Bus (WCB) exploits carefully orchestrated network-wide\nfloods of concurrent transmissions to minimize overhead during quiescent,\nsteady-state periods, and ensures timely and reliable collection of sensor\nreadings and dissemination of actuation commands when an ETC triggering\ncondition is violated. Using a cyber-physical testbed emulating a water\ndistribution system controlled over a real-world multi-hop wireless network, we\nshow that ETC over WCB achieves the same quality of periodic control at a\nfraction of the energy costs, therefore unleashing and concretely demonstrating\nits full potential for the first time.\n"}
+{"abstract": "  In this paper, we investigate the feasibility and physical consequences of\ncyber attacks against energy management systems (EMS). Within this framework,\nwe have designed a complete simulation platform to emulate realistic EMS\noperations: it includes state estimation (SE), real-time contingency analysis\n(RTCA), and security constrained economic dispatch (SCED). This software\nplatform allowed us to achieve two main objectives: 1) to study the cyber\nvulnerabilities of an EMS and understand their consequences on the system, and\n2) to formulate and implement countermeasures against cyber-attacks exploiting\nthese vulnerabilities. Our results show that the false data injection attacks\nagainst state estimation described in the literature do not easily cause\nbase-case overflows because of the conservatism introduced by RTCA. For a\nsuccessful attack, a more sophisticated model that includes all of the EMS\nblocks is needed; even in this scenario, only post-contingency violations can\nbe achieved. Nonetheless, we propose several countermeasures that can detect\nchanges due to cyber-attacks and limit their impact on the system.\n"}
+{"abstract": "  People navigating in unfamiliar buildings take advantage of myriad visual,\nspatial and semantic cues to efficiently achieve their navigation goals.\nTowards equipping computational agents with similar capabilities, we introduce\nPathdreamer, a visual world model for agents navigating in novel indoor\nenvironments. Given one or more previous visual observations, Pathdreamer\ngenerates plausible high-resolution 360 visual observations (RGB, semantic\nsegmentation and depth) for viewpoints that have not been visited, in buildings\nnot seen during training. In regions of high uncertainty (e.g. predicting\naround corners, imagining the contents of an unseen room), Pathdreamer can\npredict diverse scenes, allowing an agent to sample multiple realistic outcomes\nfor a given trajectory. We demonstrate that Pathdreamer encodes useful and\naccessible visual, spatial and semantic knowledge about human environments by\nusing it in the downstream task of Vision-and-Language Navigation (VLN).\nSpecifically, we show that planning ahead with Pathdreamer brings about half\nthe benefit of looking ahead at actual observations from unobserved parts of\nthe environment. We hope that Pathdreamer will help unlock model-based\napproaches to challenging embodied navigation tasks such as navigating to\nspecified objects and VLN.\n"}
+{"abstract": "  Using a 3D global solver of the linearized Euler equations, we model acoustic\noscillations over background velocity flow fields of single-cell meridional\ncirculation with deep and shallow return flows as well as a double-cell\nmeridional circulation profile. The velocities are generated using a mean-field\nhydrodynamic and dynamo model -- moving through the regimes with minimal\nparameter changes; counter-rotation near the base of the tachocline is induced\nby sign inversion of the non-diffusive action of turbulent Reynolds stresses\n($\\Lambda$-effect) due to the radial inhomogeneity of the Coriolis number. By\nmimicking the stochastic excitation of resonant modes in the convective\ninterior, we simulate realization noise present in solar observations. Using\ndeep-focusing to analyze differences in travel-time signatures between the\nthree regimes, as well as comparing to solar observations, we show that current\nhelioseismology techniques may offer important insights about the location of\nthe return flow, however, that it may not be possible to definitively\ndistinguish between profiles of single-cell or double-cell meridional\ncirculation.\n"}
+{"abstract": "  Inferring high-fidelity constraints on the spatial curvature parameter,\n$\\Omega_{\\rm K}$, under as few assumptions as possible, is of fundamental\nimportance in cosmology. We propose a method to non-parametrically infer\n$\\Omega_{\\rm K}$ from late-Universe probes alone. Using Gaussian Processes (GP)\nto reconstruct the expansion history, we combine Cosmic Chronometers (CC) and\nType Ia Supernovae (SNe~Ia) data to infer constraints on curvature,\nmarginalized over the expansion history, calibration of the CC and SNe~Ia data,\nand the GP hyper-parameters. The obtained constraints on $\\Omega_{\\rm K}$ are\nfree from parametric model assumptions for the expansion history, and are\ninsensitive to the overall calibration of both the CC and SNe~Ia data (being\nsensitive only to relative distances and expansion rates). Applying this method\nto \\textit{Pantheon} SNe~Ia and the latest compilation of CCs, we find\n$\\Omega_{\\rm K} = -0.03 \\pm 0.26$, consistent with spatial flatness at the\n$\\mathcal{O}(10^{-1})$ level, and independent of any early-Universe probes.\nApplying our methodology to future Baryon Acoustic Oscillations and SNe~Ia data\nfrom upcoming Stage IV surveys, we forecast the ability to constrain\n$\\Omega_{\\rm K}$ at the $\\mathcal{O}(10^{-2})$ level.\n"}
+{"abstract": "  Light detection and ranging (LiDAR) has been widely used in autonomous\ndriving and large-scale manufacturing. Although state-of-the-art scanning LiDAR\ncan perform long-range three-dimensional imaging, the frame rate is limited by\nboth round-trip delay and the beam steering speed, hindering the development of\nhigh-speed autonomous vehicles. For hundred-meter level ranging applications, a\nseveral-time speedup is highly desirable. Here, we uniquely combine fiber-based\nencoders with wavelength-division multiplexing devices to implement all-optical\ntime-encoding on the illumination light. Using this method, parallel detection\nand fast inertia-free spectral scanning can be achieved simultaneously with\nsingle-pixel detection. As a result, the frame rate of a scanning LiDAR can be\nmultiplied with scalability. We demonstrate a 4.4-fold speedup for a maximum\n75-m detection range, compared with a time-of-flight-limited laser ranging\nsystem. This approach has the potential to improve the velocity of LiDAR-based\nautonomous vehicles to the regime of hundred kilometers per hour and open up a\nnew paradigm for ultrafast-frame-rate LiDAR imaging.\n"}
+{"abstract": "  Nonlocality in time is an important property of systems in which their\npresent state depends on the history of the whole evolution. Combined with the\nnonlinearity of the process it poses serious difficulties in both analytical\nand numerical treatment.\n  We investigate a time-fractional porous medium equation that has proved to be\nimportant in many applications, notably in hydrology and material sciences. We\nshow that the solution of both free boundary Dirichlet, Neumann, and Robin\nproblems on the half-line satisfies a Volterra integral equation with\nnon-Lipschitz nonlinearity. Based on this result we prove existence,\nuniqueness, and construct a family of numerical methods that solve these\nequations outperforming the usual na\\\"ive finite difference approach. Moreover,\nwe prove the convergence of these methods and illustrate the theory with\nseveral numerical examples.\n"}
+{"abstract": "  Lat-Igusa-Todorov algebras are a natural generalization of Igusa-Todorov\nalgebras. They are defined using the generalized Igusa-Todorov functions given\nin \\cite{BLMV} and also verify the finitistic dimension conjecture. In this\narticle we give new ways to construct examples of Lat-Igusa-Todorov algebras.\nOn the other hand we show an example of a family of algebras that are not\nLat-Igusa-Todorov.\n"}
+{"abstract": "  The unexpectedly large radii of hot Jupiters are a longstanding mystery whose\nsolution will provide important insights into their interior physics. Many\npotential solutions have been suggested, which make diverse predictions about\nthe details of inflation. In particular, although any valid model must allow\nfor maintaining large planetary radii, only some allow for radii to increase\nwith time. This reinflation process would potentially occur when the incident\nflux on the planet is increased. In this work, we examine the observed\npopulation of hot Jupiters to see if they grow as their parent stars brighten\nalong the main sequence. We consider the relation between radius and other\nobservables, including mass, incident flux, age, and fractional age (age over\nmain sequence lifetime), and show that main sequence brightening is often\nsufficient to produce detectable reinflation. We further argue that these\nprovide strong evidence for the relatively rapid reinflation of giant planets,\nand discuss the implications for proposed heating mechanisms. In our population\nanalysis we also find evidence for a \"delayed-cooling effect\", wherein planets\ncool and contract far more slowly than expected. While not capable of\nexplaining the observed radii alone, it may represent an important component of\nthe effect. Finally, we identify a weak negative relationship between stellar\nmetallicity and planet radius which is presumably the result of enhanced\nplanetary bulk metallicity around metal-rich stars and has important\nimplications for planet formation theory.\n"}
+{"abstract": "  Searches for new resonances in the diphoton final state, with spin 0 as\npredicted by theories with an extended Higgs sector and with spin 2 using a\nwarped extra-dimension benchmark model, are presented using 139 fb$^{-1}$ of\n$\\sqrt{s} = $ 13 TeV $pp$ collision data collected by the ATLAS experiment at\nthe LHC. No significant deviation from the Standard Model is observed and upper\nlimits are placed on the production cross-section times branching ratio to two\nphotons as a function of the resonance mass.\n"}
+{"abstract": "  We report on hollow-core fibers displaying rms core roughness under the\nsurface capillary waves thermodynamic limit and record loss in the\nshort-wavelength range (50.0 dB/km at 290 nm, 9.7 dB/km at 369 nm, 5.0 dB/km at\n480 nm, 0.9 dB/km at 558 nm, 1.8 dB/km at 719 nm).\n"}
+{"abstract": "  The ferromagnetic (FM) nature of the metallic LaCo$_2$P$_2$ was investigated\nwith the positive muon spin rotation, relaxation and resonance ($\\mu^+$SR)\ntechnique. Transverse and zero field $\\mu^+$SR measurements revealed that the\ncompound enters a long range FM ground state at $T_{\\rm C}=130.91(65)$~K,\nconsistent with previous studies. Based on the reported FM structure, the\ninternal magnetic field was computed at the muon sites, which were predicted\nwith first principles calculations. The computed result agree well with the\nexperimental data. Moreover, although LaCo$_2$P$_2$ is a paramagnet at higher\ntemperatures $T>160$~K, it enters a short range ordered (SRO) magnetic phase\nfor $T_{\\rm C}<T\\leq160$~K. Measurements below the vicinity of $T_{\\rm C}$\nrevealed that the SRO phase co-exists with the long range FM order at\ntemperatures $124\\leq T\\leq T_{\\rm C}$. Such co-existence is an intrinsic\nproperty and stems from competition between the 2D and 3D\ninteractions/fluctuations.\n"}
+{"abstract": "  Motivated by a famous question of Lehmer about the Mahler measure we study\nand solve its analytic analogue.\n"}
+{"abstract": "  L0-regularization-based compressed sensing (L0-RBCS) has the potential to\noutperform L1-regularization-based compressed sensing (L1-RBCS), but the\noptimization in L0-RBCS is difficult because it is a combinatorial optimization\nproblem. To perform optimization in L0-RBCS, we propose a quantum-classical\nhybrid system consisting of a quantum machine and a classical digital\nprocessor. The coherent Ising machine (CIM) is a suitable quantum machine for\nthis system because this optimization problem can only be solved with a densely\nconnected network. To evaluate the performance of the CIM-classical hybrid\nsystem theoretically, a truncated Wigner stochastic differential equation\n(W-SDE) is obtained from the master equation for the density operator of the\nnetwork of degenerate optical parametric oscillators, and macroscopic equations\nare derived by applying statistical mechanics to the W-SDE. We show that the\nsystem performance in principle approaches the theoretical limit of compressed\nsensing and this hybrid system may exceed the estimation accuracy of L1-RBCS in\nactual situations, such as magnetic resonance imaging data analysis.\n"}
+{"abstract": "  This paper explores two topics at once: the use of denotational semantics to\nbound the evaluation length of functional programs, and the semantics of strong\n(that is, possibly under abstractions) call-by-value evaluation.\n  About the first, we analyze de Carvalho's seminal use of relational semantics\nfor bounding the evaluation length of lambda-terms, starting from the\npresentation of the semantics as an intersection types system. We focus on the\npart of his work which is usually neglected in its many recent adaptations,\ndespite being probably the conceptually deeper one: how to transfer the\nbounding power from the type system to the relational semantics itself. We\ndissect this result and re-understand it via the isolation of a simpler size\nrepresentation property.\n  About the second, we use relational semantics to develop a semantical study\nof strong call-by-value evaluation, which is both a delicate and neglected\ntopic. We give a semantic characterization of terms normalizable with respect\nto strong evaluation, providing in particular the first result of adequacy with\nrespect to strong call-by-value. Moreover, we extract bounds about strong\nevaluation from both the type systems and the relational semantics.\n  Essentially, we use strong call-by-value to revisit de Carvalho's semantic\nbounds, and de Carvalho's technique to provide semantical foundations for\nstrong call-by-value.\n"}
+{"abstract": "  Conditional disclosure of secrets (CDS) is the problem of disclosing as\nefficiently as possible, one secret from Alice and Bob to Carol if and only if\nthe inputs at Alice and Bob satisfy some function $f$. The information\ntheoretic capacity of CDS is the maximum number of bits of the secret that can\nbe securely disclosed per bit of total communication. All CDS instances, where\nthe capacity is the highest and is equal to $1/2$, are recently characterized\nthrough a noise and signal alignment approach and are described using a graph\nrepresentation of the function $f$. In this work, we go beyond the best case\nscenarios and further develop the alignment approach to characterize the linear\ncapacity of a class of CDS instances to be $(\\rho-1)/(2\\rho)$, where $\\rho$ is\na covering parameter of the graph representation of $f$.\n"}
+{"abstract": "  While most strong-gravitational-lensing systems may be roughly modelled by a\nsingle massive object between the source and the observer, in the details all\nthe structures near the light path contribute to the observed images. These\nadditional contributions, known as line-of-sight effects, are non-negligible in\npractice. This article proposes a new theoretical framework to model the\nline-of-sight effects, together with very promising applications at the\ninterface of weak and strong lensing. Our approach relies on the dominant-lens\napproximation, where one deflector is treated as the main lens while the others\nare treated as perturbations. The resulting framework is technically simpler to\nhandle than the multi-plane lensing formalism, while allowing one to\nconsistently model any sub-critical perturbation. In particular, it is not\nlimited to the usual external-convergence and external-shear parameterisation.\nAs a first application, we identify a specific notion of line-of-sight shear\nthat is not degenerate with the ellipticity of the main lens, and which could\nthus be extracted from strong-lensing images. This result supports and improves\nthe recent proposal that Einstein rings might be powerful probes of cosmic\nshear. As a second application, we investigate the distortions of\nstrong-lensing critical curves under line-of-sight effects, and more\nparticularly their correlations across the sky. We find that such correlations\nmay be used to probe, not only the large-scale structure of the Universe, but\nalso the dark-matter halo profiles of strong lenses. This last possibility\nwould be a key asset to improve the accuracy of the measurement of the\nHubble-Lema\\^itre constant via time-delay cosmography.\n"}
+{"abstract": "  The inflow of cosmological gas onto haloes, while challenging to directly\nobserve and quantify, plays a fundamental role in the baryon cycle of galaxies.\nUsing the EAGLE suite of hydrodynamical simulations, we present a thorough\nexploration of the physical properties of gas accreting onto haloes -- namely,\nits spatial characteristics, density, temperature, and metallicity. Classifying\naccretion as ``hot'' or `` cold'' based on a temperature cut of $10^{5.5}{\\rm\nK}$, we find that the covering fraction ($f_{\\rm cov}$) of cold-mode accreting\ngas is significantly lower than the hot-mode, with $z=0$ $f_{\\rm cov}$ values\nof $\\approx 50\\%$ and $\\approx 80\\%$ respectively. Active Galactic Nuclei (AGN)\nfeedback in EAGLE reduces inflow $f_{\\rm cov}$ values by $\\approx 10\\%$, with\noutflows decreasing the solid angle available for accretion flows. Classifying\ninflow by particle history, we find that gas on first-infall onto a halo is\nmetal-depleted by $\\approx 2$~dex compared to pre-processed gas, which we find\nto mimic the circum-galactic medium (CGM) in terms of metal content. We also\nshow that high (low) halo-scale gas accretion rates are associated with\nmetal-poor (rich) CGM in haloes below $10^{12}M_{\\odot}$, and that variation in\nhalo-scale gas accretion rates may offer a physical explanation for the\nenhanced scatter in the star-forming main sequence at low\n($\\lesssim10^{9}M_{\\odot}$) and high ($\\gtrsim10^{10}M_{\\odot}$) stellar\nmasses. Our results highlight how gas inflow influences several halo- and\ngalaxy-scale properties, and the need to combine kinematic and chemical data in\norder to confidently break the degeneracy between accreting and outgoing gas in\nCGM observations.\n"}
+{"abstract": "  Enriched versions of type A Schubert polynomials are constructed with\ncoefficients in a polynomial ring in variables $c_1, c_2, \\ldots$. Specializing\nthese variables to $0$ recovers the double Schubert polynomials of Lascoux and\nSch\\\"utzenberger; specializing them to certain power series recovers the\nback-stable double Schubert polynomials of Lam, Lee, and Shimozono;\nspecializing them to Schur Q-polynomials relates them to the type C double\nSchubert polynomials of Ikeda, Mihalcea, and Naruse. Many formulas for\nclassical Schubert polynomials generalize to this setting. They give, and are\ncharacterized by, formulas for degeneracy loci.\n"}
+{"abstract": "  Fine-tuning pre-trained language models (PLMs) has demonstrated its\neffectiveness on various downstream NLP tasks recently. However, in many\nlow-resource scenarios, the conventional fine-tuning strategies cannot\nsufficiently capture the important semantic features for downstream tasks. To\naddress this issue, we introduce a novel framework (named \"CSS-LM\") to improve\nthe fine-tuning phase of PLMs via contrastive semi-supervised learning.\nSpecifically, given a specific task, we retrieve positive and negative\ninstances from large-scale unlabeled corpora according to their domain-level\nand class-level semantic relatedness to the task. We then perform contrastive\nsemi-supervised learning on both the retrieved unlabeled and original labeled\ninstances to help PLMs capture crucial task-related semantic features. The\nexperimental results show that CSS-LM achieves better results than the\nconventional fine-tuning strategy on a series of downstream tasks with few-shot\nsettings, and outperforms the latest supervised contrastive fine-tuning\nstrategies. Our datasets and source code will be available to provide more\ndetails.\n"}
+{"abstract": "  We push the boundaries of electronic structure-based \\textit{ab-initio}\nmolecular dynamics (AIMD) beyond 100 million atoms. This scale is otherwise\nbarely reachable with classical force-field methods or novel neural network and\nmachine learning potentials. We achieve this breakthrough by combining\ninnovations in linear-scaling AIMD, efficient and approximate sparse linear\nalgebra, low and mixed-precision floating-point computation on GPUs, and a\ncompensation scheme for the errors introduced by numerical approximations.\n  The core of our work is the non-orthogonalized local submatrix (NOLSM)\nmethod, which scales very favorably to massively parallel computing systems and\ntranslates large sparse matrix operations into highly parallel, dense matrix\noperations that are ideally suited to hardware accelerators. We demonstrate\nthat the NOLSM method, which is at the center point of each AIMD step, is able\nto achieve a sustained performance of 324 PFLOP/s in mixed FP16/FP32 precision\ncorresponding to an efficiency of 67.7\\% when running on 1536 NVIDIA A100 GPUs.\n"}
+{"abstract": "  Koplienko \\cite{Ko} found a trace formula for perturbations of self-adjoint\noperators by operators of Hilbert-Schmidt class $\\mathcal{B}_2(\\mathcal{H})$.\nIn 2012, Potapov and Sukochev \\cite{PoSu} obtained a similar formula for pairs\nof contractions by answering an open question posed by Gesztesy, Pushnitski,\nand Simon in \\cite[Open Question 11.2]{GePu}. In this article, we give a still\nanother proof of the Koplienko trace formula in the case of unitary operators\nvia linear path by reducing the problem to a finite-dimensional one as in the\nproof of Krein's trace formula by Voiculescu \\cite{Voi}, Sinha and Mohapatra\n\\cite{MoSi94,MoSi96}. Consequently, we obtain the Koplienko trace formula for a\nclass of pairs of contractions using the Sch$\\ddot{a}$ffer matrix unitary\ndilation. Moreover, we also obtain the Koplienko trace formula for a pair of\nself-adjoint operators and maximal dissipative operators using the Cayley\ntransform.\n"}
+{"abstract": "  Generating long and coherent text is an important but challenging task,\nparticularly for open-ended language generation tasks such as story generation.\nDespite the success in modeling intra-sentence coherence, existing generation\nmodels (e.g., BART) still struggle to maintain a coherent event sequence\nthroughout the generated text. We conjecture that this is because of the\ndifficulty for the decoder to capture the high-level semantics and discourse\nstructures in the context beyond token-level co-occurrence. In this paper, we\npropose a long text generation model, which can represent the prefix sentences\nat sentence level and discourse level in the decoding process. To this end, we\npropose two pretraining objectives to learn the representations by predicting\ninter-sentence semantic similarity and distinguishing between normal and\nshuffled sentence orders. Extensive experiments show that our model can\ngenerate more coherent texts than state-of-the-art baselines.\n"}
+{"abstract": "  Some recent papers have argued that frequency should have the dimensions of\nangle/time, with the consequences that 1 Hz = $2\\pi$ rad/s instead of 1\ns$^{-1}$, and also that $\\nu = \\omega$ and $h = \\hbar$. This letter puts the\ncase that this argument redefines the quantity frequency and then draws\nconclusions from equations that rely on the standard definition being used. The\nproblems that this redefinition is designed to address arise from the\nwidespread unstated adoption of the Radian Convention, which treats the radian\nas a dimensionless quantity equal to the number 1, in effect making the radian\na hidden unit. This convention is currently built-in to the SI, when it should\nbe separable from it. The unhelpful status of angles in the SI can be remedied\nwith minimal disruption by (1) changing the definition of the radian from a\nradian equals 1 m/m to e.g. a right angle equals $\\pi/2$ radians, and (2)\nacknowledging that the adoption of the Radian Convention is acceptable, when\nmade explicit. The standard definitions of frequency and the hertz should\nremain unchanged.\n"}
+{"abstract": "  Page 332 of Ramanujan's Lost Notebook contains a compelling identity for\n$\\zeta(1/2)$, which has been studied by many mathematicians over the years. On\nthe same page, Ramanujan also recorded the series, \\begin{align*}\n\\frac{1^r}{\\exp(1^s x) - 1} + \\frac{2^r}{\\exp(2^s x) - 1} + \\frac{3^r}{\\exp(3^s\nx) - 1} + \\cdots, \\end{align*} where $s$ is a positive integer and $r-s$ is any\neven integer. Unfortunately, Ramanujan doesn't give any formula for it. This\nseries was rediscovered by Kanemitsu, Tanigawa, and Yoshimoto, although they\nstudied it only when $r-s$ is a negative even integer. Recently, Dixit and the\nsecond author generalized the work of Kanemitsu et al. and obtained a\ntransformation formula for the aforementioned series with $r-s$ is any even\ninteger. While extending the work of Kanemitsu et al., Dixit and the second\nauthor obtained a beautiful generalization of Ramanujan's formula for odd zeta\nvalues. In the current paper, we investigate transformation formulas for an\ninfinite series, and interestingly, we derive Ramanujan's formula for\n$\\zeta(1/2)$, Wigert's formula for $\\zeta(1/k)$ as well as Ramanujan's formula\nfor $\\zeta(2m+1)$. Furthermore, we obtain a new identity for $\\zeta(-1/2)$ in\nthe spirit of Ramanujan.\n"}
+{"abstract": "  In this paper, we propose new learning algorithms for approximating\nhigh-dimensional functions using tree tensor networks in a least-squares\nsetting. Given a dimension tree or architecture of the tensor network, we\nprovide an algorithm that generates a sequence of nested tensor subspaces based\non a generalization of principal component analysis for multivariate functions.\nAn optimal least-squares method is used for computing projections onto the\ngenerated tensor subspaces, using samples generated from a distribution\ndepending on the previously generated subspaces. We provide an error bound in\nexpectation for the obtained approximation. Practical strategies are proposed\nfor adapting the feature spaces and ranks to achieve a prescribed error. Also,\nwe propose an algorithm that progressively constructs the dimension tree by\nsuitable pairings of variables, that allows to further reduce the number of\nsamples necessary to reach that error. Numerical examples illustrate the\nperformance of the proposed algorithms and show that stable approximations are\nobtained with a number of samples close to the number of free parameters of the\nestimated tensor networks.\n"}
+{"abstract": "  We consider online convex optimization (OCO) over a heterogeneous network\nwith communication delay, where multiple workers together with a master execute\na sequence of decisions to minimize the accumulation of time-varying global\ncosts. The local data may not be independent or identically distributed, and\nthe global cost functions may not be locally separable. Due to communication\ndelay, neither the master nor the workers have in-time information about the\ncurrent global cost function. We propose a new algorithm, termed Hierarchical\nOCO (HiOCO), which takes full advantage of the network heterogeneity in\ninformation timeliness and computation capacity to enable multi-step gradient\ndescent at both the workers and the master. We analyze the impacts of the\nunique hierarchical architecture, multi-slot delay, and gradient estimation\nerror to derive upper bounds on the dynamic regret of HiOCO, which measures the\ngap of costs between HiOCO and an offline globally optimal performance\nbenchmark.\n"}
+{"abstract": "  In this paper, we propose a new challenging task named as \\textbf{partial\nmulti-view few-shot learning}, which unifies two tasks, i.e. few-shot learning\nand partial multi-view learning, together. Different from the traditional\nfew-shot learning, this task aims to solve the few-shot learning problem given\nthe incomplete multi-view prior knowledge, which conforms more with the\nreal-world applications. However, this brings about two difficulties within\nthis task. First, the gaps among different views can be large and hard to\nreduce, especially with sample scarcity. Second, due to the incomplete view\ninformation, few-shot learning becomes more challenging than the traditional\none. To deal with the above issues, we propose a new \\textbf{Meta-alignment and\nContext Gated-aggregation Network} by equipping meta-alignment and context\ngated-aggregation with partial multi-view GNNs. Specifically, the\nmeta-alignment effectively maps the features from different views into a more\ncompact latent space, thereby reducing the view gaps. Moreover, the context\ngated-aggregation alleviates the view-missing influence by leveraging the\ncross-view context. Extensive experiments are conducted on the PIE and ORL\ndataset for evaluating our proposed method. By comparing with other few-shot\nlearning methods, our method obtains the state-of-the-art performance\nespecially with heavily-missing views.\n"}
+{"abstract": "  Optical frequency transfer provides the means for high-fidelity frequency\ntransfer across thousands of kilometers. A critical step in the further\ndeveloping optical frequency transfer is its capability to transfer a high\nspectral purity feature from ultrastable lasers or optical clocks to any remote\nlocations and, at the same time, its adaptability to incorporate the optical\nfrequency transfer technique into any existing communication networks with\ndifferent topologies. Here we for the first time report a technique that\ndelivers optical-frequency signals to multiple independent remote hubs along a\nring optical-fiber network with passive phase stabilization. The technique\nautomatically corrects optical-fiber length fluctuations of arbitrary hubs\nalong the loop by mixing and shifting optical signals. Without the help of an\nactive phase tracker and a compensator, it could significantly mitigate some\ntechnical problems such as the limited compensation speed and phase recovery\ntime, the phase jitter contamination caused by the servo bump in conventional\nphase noise cancellation. Moreover, by transmitting optical signals along both\ndirections using the same optical source, it can improve the signal-to-noise\nratio at each hub. This technique maintains the same delay-limited phase noise\ncorrection capability as in conventional techniques and, furthermore, improves\nthe phase jitter by a factor of 3, opening a way to a broad distribution of an\nultrastable frequency reference with high spectral purity and enabling a wide\nrange of applications beyond metrology over a ring fiber network with the\nnaturally impressive reliability and scalability.\n"}
+{"abstract": "  The controlled design of martensitic transformation (MT) to achieve specific\nproperties is crucial for the innovative application of shape memory alloys\n(SMAs) in advanced technologies. Herein, we explore and design the MT behaviors\nand the mechanical properties of Ti-Nb nanocomposites by combining\nhigh-throughput phase-field simulations and machine learning (ML) approaches.\nBased on the systematic phase-field simulations, we obtain data sets of the\nmechanical properties for various nanocomposites constructed by four\nmacroscopic degrees of freedom, which can be employed to design and optimize\nthe microstructures for different applications. To accelerate the phase-field\nscreening of the desired metallic biomaterials, a ML assisted strategy is\nadopted to perform multi-objective optimization of the mechanical properties,\nthrough which promising nanocomposite configurations are pre-screened for the\nnext set of phase-field simulations. With the ML guided simulations, an\noptimized candidate composed of Nb-rich matrix and Nb-lean nanofillers that\nexhibits a combination of unprecedented mechanical properties, including\nultra-low modulus, linear super-elasticity, and near-hysteresis-free is\ndesigned. The exceptional mechanical properties in the nanocomposite originate\nfrom optimized continuous MT rather than a sharp first-order transition, which\nis common in typical SMAs. This work provides a new computational approach and\ndesign concept for developing novel functional materials with extraordinary\nproperties.\n"}
+{"abstract": "  Spintronic diodes are emerging as disruptive candidates for impacting several\ntechnological applications ranging from the Internet of Things to Artificial\nIntelligence. In this letter, an overview of the recent achievements on\nspintronic diodes is briefly presented, underling the major breakthroughs that\nhave led these devices to have the largest sensitivity measured up to date for\na diode. For each class of spintronic diodes (passive, active, resonant,\nnon-resonant), we indicate the remaining developments to improve the\nperformances as well as the future directions. We also dedicate the last part\nof this perspective to new ideas for developing spintronic diodes in\nmultiphysics systems by combining 2-dimensional materials and antiferromagnets.\n"}
+{"abstract": "  We propose a double bootstrap procedure for reducing coverage error in the\nconfidence intervals of descriptive statistics for independent and identically\ndistributed functional data. Through a series of Monte Carlo simulations, we\ncompare the finite sample performance of single and double bootstrap procedures\nfor estimating the distribution of descriptive statistics for independent and\nidentically distributed functional data. At the cost of longer computational\ntime, the double bootstrap with the same bootstrap method reduces confidence\nlevel error and provides improved coverage accuracy than the single bootstrap.\nIllustrated by a Canadian weather station data set, the double bootstrap\nprocedure presents a tool for visualising the distribution of the descriptive\nstatistics for the functional data.\n"}
+{"abstract": "  We report on the X-ray properties of the new transient Swift J0840.7$-$3516,\ndiscovered with Swift/BAT in 2020 February, using extensive data of Swift,\nMAXI, NICER, and NuSTAR. The source flux increased for $\\sim 10^3$ s after the\ndiscovery, decayed rapidly over $\\sim$ 5 orders of magnitude in 5 days, and\nthen remained almost constant over 9 months. Large-amplitude short-term\nvariations on time scales of 1--$10^4$ s were observed throughout the decay. In\nthe initial flux rise, the source showed a hard power-law shaped spectrum with\na photon index of $\\sim 1.0$ extending up to $\\sim 30$ keV, above which an\nexponential cutoff was present. The photon index increased in the following\nrapid decay and became $\\sim 2$ at the end of the decay. A spectral absorption\nfeature at 3--4 keV was detected in the decay. It is not straightforward to\nexplain all the observed properties by any known class of X-ray sources. We\ndiscuss the possible nature of the source, including a Galactic low mass X-ray\nbinary with multiple extreme properties and a tidal disruption event by a\nsupermassive black hole or a Galactic neutron star.\n"}
+{"abstract": "  Currently available quantum hardware allows for small scale implementations\nof quantum machine learning algorithms. Such experiments aid the search for\napplications of quantum computers by benchmarking the near-term feasibility of\ncandidate algorithms. Here we demonstrate the quantum learning of a two-qubit\nunitary by a sequence of three parameterized quantum circuits containing a\ntotal of 21 variational parameters. Moreover, we variationally diagonalize the\nunitary to learn its spectral decomposition, i.e., its eigenvalues and\neigenvectors. We illustrate how this can be used as a subroutine to compress\nthe depth of dynamical quantum simulations. One can view our implementation as\na demonstration of entanglement-enhanced machine learning, as only a single\n(entangled) training data pair is required to learn a 4x4 unitary matrix.\n"}
+{"abstract": "  In the present paper, we first give a characterization for Bongartz\ncompletion in $\\tau$-tilting theory via $c$-vectors. Motivated by this\ncharacterization, we give the definition of Bongartz completion in cluster\nalgebras using $c$-vectors. Then we prove the existence and uniqueness of\nBongartz completion in cluster algebras. We also prove that Bongartz completion\nadmits certain commutativity. We give two applications for Bongartz completion\nin cluster algebras. As the first application, we prove the full subquiver of\nthe exchange quiver (or known as oriented exchange graph) of a cluster algebra\n$\\mathcal A$ whose vertices consist of seeds of $\\mathcal A$ containing\nparticular cluster variables is isomorphic to the exchange quiver of another\ncluster algebra. As the second application, we prove that in a cluster Poisson\nalgebra $\\mathcal X_\\bullet$, each cluster Poisson seed (up to seed\nequivalence) of $\\mathcal X_\\bullet$ is uniquely determined by its negative\ncluster Poisson variables.\n"}
+{"abstract": "  This paper studies the problem of autonomous exploration under localization\nuncertainty for a mobile robot with 3D range sensing. We present a framework\nfor self-learning a high-performance exploration policy in a single simulation\nenvironment, and transferring it to other environments, which may be physical\nor virtual. Recent work in transfer learning achieves encouraging performance\nby domain adaptation and domain randomization to expose an agent to scenarios\nthat fill the inherent gaps in sim2sim and sim2real approaches. However, it is\ninefficient to train an agent in environments with randomized conditions to\nlearn the important features of its current state. An agent can use domain\nknowledge provided by human experts to learn efficiently. We propose a novel\napproach that uses graph neural networks in conjunction with deep reinforcement\nlearning, enabling decision-making over graphs containing relevant exploration\ninformation provided by human experts to predict a robot's optimal sensing\naction in belief space. The policy, which is trained only in a single\nsimulation environment, offers a real-time, scalable, and transferable\ndecision-making strategy, resulting in zero-shot transfer to other simulation\nenvironments and even real-world environments.\n"}
+{"abstract": "  We present an approach for state and parameter estimation in retinal laser\ntreatment by a novel setup where both measurement and heating is performed by a\nsingle laser. In this medical application, the temperature that is induced by\nthe laser in the patient's eye is critical for a successful and safe treatment.\nTo this end, we pursue a model-based approach using a model given by a heat\ndiffusion equation on a cylindrical domain, where the source term is given by\nthe absorbed laser power. The model is parametric in the sense that it involves\nan absorption coefficient, which depends on the treatment spot and plays a\ncentral role in the input-output behavior of the system. After discretization,\nwe apply a particularly suited parametric model order reduction to ensure\nreal-time tractability while retaining parameter dependence. We augment known\nstate estimation techniques, i.e., extended Kalman filtering and moving horizon\nestimation, with parameter estimation to estimate the absorption coefficient\nand the current state of the system. Eventually, we show first results for\nsimulated and experimental data from porcine eyes. We find that, regarding\nconvergence speed, the moving horizon estimation slightly outperforms the\nextended Kalman filter on measurement data in terms of parameter and state\nestimation, however, on simulated data the results are very similar.\n"}
+{"abstract": "  Smart environments powered by the Internet of Things aim at improving our\ndaily lives by automatically tuning ambient parameters (e.g. temperature,\ninterior light) and by achieving energy savings through self-managing\ncyber-physical systems. Commercial solutions, however, only permit setting\nsimple target goals on those parameters and do not consider mediating\nconflicting goals among different users and/or system administrators, and\nfeature limited compatibility across different IoT verticals. In this article,\nwe propose a declarative framework to represent smart environments, user-set\ngoals and customisable mediation policies to reconcile contrasting goals\nencompassing multiple IoT systems. An open-source Prolog prototype of the\nframework is showcased over two lifelike motivating examples.\n"}
+{"abstract": "  In this short note we observe that the boundary of a properly embedded\ncompact exact Lagrangian sub-manifolds in a subcritical Weinstein domain $X$\nnecessarily admits Reeb chords. The existence of this Reeb chords either\nfollows from an obstruction to the deformation of the boundary to a cylinder\nover a Legendrian sub-manifold or from the fact that the wrapped Floer homology\nof the Lagrangian vanishes once this boundary have been \"collared\".\n"}
+{"abstract": "  Honeypots are a well-studied defensive measure in network security. This work\nproposes an effective low-cost honeypot that is easy to deploy and maintain.\nThe honeypot introduced in this work is able to handle commands in a\nnon-standard way by blocking them or replying with an insult to the attacker.\nTo determine the most efficient defense strategy, the interaction between\nattacker and defender is modeled as a Bayesian two-player game. For the\nempirical analysis, three honeypot instances were deployed, each with a slight\nvariation in its configuration. In total, over 200 distinct sessions were\ncaptured, which allows for qualitative evaluation of post-exploitation\nbehavior. The findings show that attackers react to insults and blocked\ncommands in different ways, ranging from ignoring to sending insults\nthemselves. The main contribution of this work lies in the proposed framework,\nwhich offers a low-cost alternative to more technically sophisticated and\nresource-intensive approaches.\n"}
+{"abstract": "  For $0<\\alpha<\\frac{1}{3}$ we construct unique solutions to the fractal\nBurgers equation $\\partial_t u + u\\partial_xu + (-\\Delta)^\\alpha u = 0$ which\ndevelop a first shock in finite time, starting from smooth generic initial\ndata. This first singularity is an asymptotically self-similar, stable $H^6$\nperturbation of a stable, self-similar Burgers shock profile. Furthermore, we\nare able to compute the spatio-temporal location and H\\\"older regularity for\nthe first singularity. There are many results showing that gradient blowup\noccurs in finite time for the supercritical range, but the present result is\nthe first example where singular solutions have been explicitly constructed and\nso precisely characterized.\n"}
+{"abstract": "  We propose and demonstrate an experimental method to measure by absorption\nimaging the size and local column density of a cloud of atoms, even when its\nsmallest dimension is smaller than the resolution of the imaging system. To do\nthis, we take advantage of the fact that, for a given total number of atoms, a\nsmaller and denser cloud scatters less photons when the gas is optically thick.\nThe method relies on making an ansatz on the cloud shape along the unresolved\ndimension(s), and on providing an additional information such as the total\nnumber of atoms. We demonstrate the method on \\textit{in-situ} absorption\nimages of elongated 87Sr Fermi gases. We find significant non-linear\ncorrections to the estimated size and local density of the cloud compared to a\nstandard analysis. This allows us to recover an un-distorted longitudinal\ndensity profile, and to measure transverse sizes as small as one fourth of our\nimaging resolution. The ultimate limit of our method is the wavelength that is\nused for imaging.\n"}
+{"abstract": "  In this paper, the interplay of the non-Herimiticity and the cascade of\ndelocalization transition in the quasi-periodic chain is studied. The study is\napplied in a non-Hermitian interpolating Aubry-Andr{\\'e}-Fibonacci (IAAF)\nmodel, which combines the non-Hermitian Aubry-Andr{\\'e} (AA) model and the\nnon-Hermitian Fibonacci model through a varying parameter, and the\nnon-Hermiticity in this model is introduced by the non-reciprocal hopping. In\nthe non-Hermitian AA limit, the system undergoes a delocalization transition by\ntuning the potential strength. At the critical point, the spatial distribution\nof the critical state shows a self-similar structure with the relative distance\nbetween the peaks being the Fibonacci sequence, and the finite-size scaling of\nthe inverse participation ratios $({\\rm IPRs})$ of the critical ground state\nwith lattice size $L$ shows that ${\\rm IPR}_g\\propto L^{-0.1189}$. In the\nnon-Hermitian Fibonacci limit, we find that the system is always in the\nextended phase. Along the continuous deformation from the non-Hermitian AA\nmodel into the non-Hermitian Fibonacci model in the IAAF model, the cascade of\nthe delocalization transition is found, but only a few plateaux appear.\nMoreover, the self-similar structure of spatial distribution for the critical\nmodes along the cascade transition is also found. In addition, we find that the\ndelocalization transition and the real-complex transition for the excited\nstates happen at almost the same parameter. Our results show that the\nnon-Hermiticity provides an additional knob to control the cascade of the\ndelocalization transition besides the on-site potential.\n"}
+{"abstract": "  Whitney proved in 1931 that 4-connected planar triangulations are\nHamiltonian. Hakimi, Schmeichel, and Thomassen conjectured in 1979 that if $G$\nis a 4-connected planar triangulation with $n$ vertices then $G$ contains at\nleast $2(n-2)(n-4)$ Hamiltonian cycles, with equality if and only if $G$ is a\ndouble wheel. On the other hand, a recent result of Alahmadi, Aldred, and\nThomassen states that there are exponentially many Hamiltonian cycles in\n5-connected planar triangulations. In this paper, we consider 4-connected\nplanar $n$-vertex triangulations $G$ that do not have too many separating\n4-cycles or have minimum degree 5. We show that if $G$ has $O(n/{\\log}_2 n)$\nseparating 4-cycles then $G$ has $\\Omega(n^2)$ Hamiltonian cycles, and if\n$\\delta(G)\\ge 5$ then $G$ has $2^{\\Omega(n^{1/4})}$ Hamiltonian cycles. Both\nresults improve previous work. Moreover, the proofs involve a \"double wheel\"\nstructure, providing further evidence to the above conjecture.\n"}
+{"abstract": "  The astrobiology is an interdisciplinary science, combining the methods and\nthe means of physics, biology, chemistry and astronomy. Its main purpose is to\nfind out if the exoplanets are habitable and if so, to confirm life on them.\nThe basic conditions for habitability are the essential ones, like these on the\nEarth. But additional, essential as well, exist, like the mass of the\nexoplanet, the atmospheric composition and its location. To find the answer we\nused basic molecular physics and classical mechanics knowledge. We also\nproposed a method for exoplanet hunting and conformation and exploration of\ntheir atmospheres. With the constantly improving techniques and apparatuses,\nthe answer converts from if to when are we going to find extraterrestrial life,\nincluding microbial one.\n"}
+{"abstract": "  In a recent article M. Baggioli and A. Zaccone (Phys. Rev. Lett. {\\bf 112},\n145501 (2019)) claimed that an anharmonic damping, leading to a sound\nattenuation proportional to $\\omega^2$ (Akhiezer-type damping) would imply a\nboson peak, i.e.\\ a maximum in the vibrational density of states, divided by\nthe frequency squared (reduced density of states). This would apply both to\nglasses and crystals.Here we show that this is not the case. In a\nmathematically correct treatment of the model the reduced density of states\nmonotonously decreases, i.e.\\ there is no boson peak. We further show that the\nformula for the would-be boson peak, presented by the authors, corresponds to a\nvery short one-dimensional damped oscillator system. The peaks they show\ncorrespond to resonances, which vanish in the thermodynamic limit.\n"}
+{"abstract": "  This work presents a novel version of recently developed Gauss-Newton method\nfor solving systems of nonlinear equations, based on upper bound of solution\nresidual and quadratic regularization ideas. We obtained for such method global\nconvergence bounds and under natural non-degeneracy assumptions we present\nlocal quadratic convergence results. We developed stochastic optimization\nalgorithms for presented Gauss-Newton method and justified sub-linear and\nlinear convergence rates for these algorithms using weak growth condition (WGC)\nand Polyak-Lojasiewicz (PL) inequality. We show that Gauss-Newton method in\nstochastic setting can effectively find solution under WGC and PL condition\nmatching convergence rate of the deterministic optimization method. The\nsuggested method unifies most practically used Gauss-Newton method\nmodifications and can easily interpolate between them providing flexible and\nconvenient method easily implementable using standard techniques of convex\noptimization.\n"}
+{"abstract": "  The element beryllium is detected for the first time in white dwarf stars.\nThis discovery in the spectra of two helium-atmosphere white dwarfs was made\npossible only because of the remarkable overabundance of Be relative to all\nother elements, heavier than He, observed in these stars. The measured Be\nabundances, relative to chondritic, are by far the largest ever seen in any\nastronomical object. We anticipate that the Be in these accreted planetary\nbodies was produced by spallation of one or more of O, C, and N in a region of\nhigh fluence of particles of MeV or greater energy.\n"}
+{"abstract": "  The addition of co-solutes to colloidal suspensions is often employed to\ninduce tunable depletion interactions. In this work we investigate effective\ncolloidal interactions arising from binary co-solute mixtures of hard spheres\nand patchy particles. By changing the relative concentration of the two\nspecies, we show that the resulting effective potential $V_\\text{eff}$\ncontinuously changes from the one obtained for a single-component hard sphere\nco-solute to that mediated by the single-component patchy particle co-solute.\nInterestingly, we find that, independently of the relative concentration of the\ntwo components, the resulting $V_\\text{eff}$ is additive, i.e., it is\nwell-described by the linear combination of the effective interactions mediated\nby respective pure co-solutes. However, a breakdown of the additivity occurs\nwhen the co-solute mixture is close to the onset of a demixing transition.\nThese results represent a step forward in understanding and predicting\ncolloidal behaviour in complex and crowded environments and for exploiting this\nknowledge to design targeted colloidal superstructures.\n"}
+{"abstract": "  We investigate the $H_0$ tension in a range of extended model frameworks\nbeyond the standard $\\Lambda$CDM without the data from cosmic microwave\nbackground (CMB). Specifically, we adopt the data from baryon acoustic\noscillation, big bang nucleosynthesis and type Ia supernovae as indirect\nmeasurements of $H_0$ to study the tension. We show that the estimated value of\n$H_0$ from indirect measurements is overall lower than that from direct local\nones regardless of the data sets and a range of extended models to be analyzed,\nwhich indicates that, although the significance of the tension varies depending\non models, the $H_0$ tension persists in a broad framework beyond the standard\n$\\Lambda$CDM model even without CMB data.\n"}
+{"abstract": "  Background: Trends in hospitalised case-fatality risk (HFR), risk of\nintensive care unit (ICU) admission and lengths of stay for patients\nhospitalised for COVID-19 in England over the pre-vaccination era are unknown.\n  Methods: Data on hospital and ICU admissions with COVID-19 at 31 NHS trusts\nin England were collected by Public Health England's Severe Acute Respiratory\nInfections surveillance system and linked to death information. We applied\nparametric multi-state mixture models, accounting for censored outcomes and\nregressing risks and times between events on month of admission, geography, and\nbaseline characteristics.\n  Findings: 20,785 adults were admitted with COVID-19 in 2020. Between March\nand June/July/August estimated HFR reduced from 31.9% (95% confidence interval\n30.3-33.5%) to 10.9% (9.4-12.7%), then rose steadily from 21.6% (18.4-25.5%) in\nSeptember to 25.7% (23.0-29.2%) in December, with steeper increases among older\npatients, those with multi-morbidity and outside London/South of England. ICU\nadmission risk reduced from 13.9% (12.8-15.2%) in March to 6.2% (5.3-7.1%) in\nMay, rising to a high of 14.2% (11.1-17.2%) in September. Median length of stay\nin non-critical care increased during 2020, from 6.6 to 12.3 days for those\ndying, and from 6.1 to 9.3 days for those discharged.\n  Interpretation: Initial improvements in patient outcomes, corresponding to\ndevelopments in clinical practice, were not sustained throughout 2020, with HFR\nin December approaching the levels seen at the start of the pandemic, whilst\nmedian hospital stays have lengthened. The role of increased transmission, new\nvariants, case-mix and hospital pressures in increasing COVID-19 severity\nrequires urgent further investigation.\n"}
+{"abstract": "  It is a classical result that configuration spaces of labelled particles in\n$\\mathbb{R}^d$ are free algebras over the little $d$-cubes operad\n$\\mathscr{C}_d$, and their $d$-fold bar construction is equivalent to the\n$d$-fold suspension of the labelling space. The aim of this paper is to study a\nvariation of these spaces, namely the configuration space of labelled clusters\nof points in $\\mathbb{R}^d$. This configuration space is again an\n$E_d$-algebra, but in general not a free one. We give geometric models for\ntheir iterated bar construction in two different ways: one uses an additional\nverticality constraint, and the other one uses a description of these clustered\nconfiguration spaces as cellular $E_1$-algebras. In the last section, we show a\nstable splitting result and present some applications.\n"}
+{"abstract": "  Business success in e-commerce depends on customer perceived value. A\ncustomer with high perceived value buys, returns, and recommends items. The\nperceived value is at risk whenever the information load harms users' shopping\nexperience. In e-grocery, shoppers face an overwhelming number of items, the\nmajority of which is irrelevant for the shopper. Recommender systems (RS)\nenable businesses to master information overload (IO) by providing users with\nan item ranking by relevance. Prior work proposes RS with short personalized\nrankings (top-k). Given large order sizes and high user heterogeneity in\ne-grocery, top-k RS are insufficient to diminish IO in this domain. To fill\nthis gap and raise business performance, this paper introduces an RS with a\npersonalized long ranking (top-N). Undertaking a randomized field experiment,\nthe paper establishes the merit of shifting from top-k to top-N rankings.\nSpecifically, the proposed RS reduces IO by 29.4% and lowers users' search time\nby 3.3 seconds per item. The field experiment also reveals a 7% uplift in\nrevenue due to the top-N ranking. Substantial benefits for the customer and the\ncompany highlight the business value of top-N rankings as a new design\nrequirement for recommender systems in e-grocery.\n"}
+{"abstract": "  COVID-19 has led to school closures in Japan to cope with the pandemic. Under\nthe state of emergency, in addition to school closure, after-school care has\nnot been sufficiently supplied. We independently collected individual level\ndata through internet surveys to construct short panel data from mid-March to\nmid-June 2020, which covered before and after the state of emergency. We\nanalyze how the presence of school-aged children influences their parents views\nabout working from home. After controlling for various factors using a fixed\neffects model, we find that in cases where parents were workers, and the\nchildren are (1) in primary school, parents are willing to promote working from\nhome. If children are (2) in junior high school, the parents view is hardly\naffected. (3) Surprisingly, workers whose children are primary school pupils\nare most likely to support promotion of working from home after schools reopen.\nDue to school closure and a lack of after-school care, parents need to work\nfrom home, and this experience motivated workers with small children to\ncontinue doing so to improve work-life balance even after schools reopen.\n"}
+{"abstract": "  This document discusses the curriculum of the business informatics program of\nthe Nijmegen Institute for Informatics and business informatics (NIII). The aim\nis to provide a 'repository' with regard to the structure of the curriculum,\nwhich will apply from 2003.\n  In the past three years, the image of information science as a discipline has\nbeen further concretised at both national and Nijmegen level. Curriculum 2003\nis on the one hand the result of this concretization and on the other hand of\nthe three years of experience that has now been built up within the NIII with\nthe information science training. In this document, therefore, explicit\nattention will also be paid to the 'migration' from the existing 'start-up'\ncurricula: 2000, 2001 and 2002. It should be noted here that the students of\ncohort 2000 will in principle have completed the bachelor's phase of the\nprogram this year (2003). Complete information science education. No specific\n`migration 'is therefore necessary for this group of students.\n"}
+{"abstract": "  Learning to interact with the environment not only empowers the agent with\nmanipulation capability but also generates information to facilitate building\nof action understanding and imitation capabilities. This seems to be a strategy\nadopted by biological systems, in particular primates, as evidenced by the\nexistence of mirror neurons that seem to be involved in multi-modal action\nunderstanding. How to benefit from the interaction experience of the robots to\nenable understanding actions and goals of other agents is still a challenging\nquestion. In this study, we propose a novel method, deep modality blending\nnetworks (DMBN), that creates a common latent space from multi-modal experience\nof a robot by blending multi-modal signals with a stochastic weighting\nmechanism. We show for the first time that deep learning, when combined with a\nnovel modality blending scheme, can facilitate action recognition and produce\nstructures to sustain anatomical and effect-based imitation capabilities. Our\nproposed system, can be conditioned on any desired sensory/motor value at any\ntime-step, and can generate a complete multi-modal trajectory consistent with\nthe desired conditioning in parallel avoiding accumulation of prediction\nerrors. We further showed that given desired images from different\nperspectives, i.e. images generated by the observation of other robots placed\non different sides of the table, our system could generate image and joint\nangle sequences that correspond to either anatomical or effect based imitation\nbehavior. Overall, the proposed DMBN architecture not only serves as a\ncomputational model for sustaining mirror neuron-like capabilities, but also\nstands as a powerful machine learning architecture for high-dimensional\nmulti-modal temporal data with robust retrieval capabilities operating with\npartial information in one or multiple modalities.\n"}
+{"abstract": "  Light-in-flight (LIF) imaging is the measurement and reconstruction of\nlight's path as it moves and interacts with objects. It is well known that\nrelativistic effects can result in apparent velocities that differ\nsignificantly from the speed of light. However, less well known is that\nRayleigh scattering and the effects of imaging optics can lead to observed\nintensities changing by several orders of magnitude along light's path. We\ndevelop a model that enables us to correct for all of these effects, thus we\ncan accurately invert the observed data and reconstruct the true\nintensity-corrected optical path of a laser pulse as it travels in air. We\ndemonstrate the validity of our model by observing the photon arrival time and\nintensity distribution obtained from single-photon avalanche detector (SPAD)\narray data for a laser pulse propagating towards and away from the camera. We\ncan then reconstruct the true intensity-corrected path of the light in four\ndimensions (three spatial dimensions and time).\n"}
+{"abstract": "  Neural networks (NNs) have been extremely successful across many tasks in\nmachine learning. Quantization of NN weights has become an important topic due\nto its impact on their energy efficiency, inference time and deployment on\nhardware. Although post-training quantization is well-studied, training optimal\nquantized NNs involves combinatorial non-convex optimization problems which\nappear intractable. In this work, we introduce a convex optimization strategy\nto train quantized NNs with polynomial activations. Our method leverages hidden\nconvexity in two-layer neural networks from the recent literature, semidefinite\nlifting, and Grothendieck's identity. Surprisingly, we show that certain\nquantized NN problems can be solved to global optimality in polynomial-time in\nall relevant parameters via semidefinite relaxations. We present numerical\nexamples to illustrate the effectiveness of our method.\n"}
+{"abstract": "  In this paper we continue the programme initiated in Part I, that is the\nstudy of entanglement measures in the sine-Gordon model. In both parts, we have\nfocussed on one specific technique, that is the well-known connection between\nbranch point twist field correlators and measures of entanglement in 1+1D\nintegrable quantum field theory. Our papers apply this technique for the first\ntime to a non-diagonal theory with an involved particle spectrum, the\nsine-Gordon model. In this Part II we focus on a different entanglement\nmeasure, the symmetry resolved entanglement, and develop its associated twist\nfield description, exploiting the underlying U(1) symmetry of the theory. In\nthis context, conventional branch point twist fields are no longer the fields\nrequired, but instead we must work with one of their composite generalisations,\nwhich can be understood as the field resulting from the fusion of a standard\nbranch point twist field and the sine-Gordon exponential field associated with\nU(1) symmetry. The resulting composite twist field has correlators which as\nusual admit a form factor expansion. In this paper we write the associated form\nfactor equations and solve them for various examples in the breather sector by\nusing the method of angular quantisation. We show that, in the attractive\nregime, this is the sector which provides the leading contribution to the\nsymmetry resolved entropies, both Renyi and von Neumann. We compute the latter\nin the limit of a large region size and show that they satisfy the property of\nequipartition, that is the leading contribution to the symmetry resolved\nentanglement is independent of the symmetry sector.\n"}
+{"abstract": "  Given an undirected graph, the Densest-k-Subgraph problem (DkS) seeks to find\na subset of k vertices such that the sum of the edge weights in the\ncorresponding subgraph is maximized. The problem is known to be NP-hard, and is\nalso very difficult to approximate, in the worst-case. In this paper, we\npresent a new convex relaxation for the problem. Our key idea is to reformulate\nDkS as minimizing a submodular function subject to a cardinality constraint.\nExploiting the fact that submodular functions possess a convex, continuous\nextension (known as the Lov\\'asz extension), we propose to minimize the\nLov\\'asz extension over the convex hull of the cardinality constraints.\nAlthough the Lov\\'asz extension of a submodular function does not admit an\nanalytical form in general, for DkS we show that it does. We leverage this\nresult to develop a highly scalable algorithm based on the Alternating\nDirection Method of Multipliers (ADMM) for solving the relaxed problem. Coupled\nwith a pair of fortuitously simple rounding schemes, we demonstrate that our\napproach outperforms existing baselines on real-world graphs and can yield high\nquality sub-optimal solutions which typically are a posteriori no worse than\n65-80\\% of the optimal density.\n"}
+{"abstract": "  Photo-realistic re-rendering of a human from a single image with explicit\ncontrol over body pose, shape and appearance enables a wide range of\napplications, such as human appearance transfer, virtual try-on, motion\nimitation, and novel view synthesis. While significant progress has been made\nin this direction using learning-based image generation tools, such as GANs,\nexisting approaches yield noticeable artefacts such as blurring of fine\ndetails, unrealistic distortions of the body parts and garments as well as\nsevere changes of the textures. We, therefore, propose a new method for\nsynthesising photo-realistic human images with explicit control over pose and\npart-based appearance, i.e., StylePoseGAN, where we extend a non-controllable\ngenerator to accept conditioning of pose and appearance separately. Our network\ncan be trained in a fully supervised way with human images to disentangle pose,\nappearance and body parts, and it significantly outperforms existing single\nimage re-rendering methods. Our disentangled representation opens up further\napplications such as garment transfer, motion transfer, virtual try-on, head\n(identity) swap and appearance interpolation. StylePoseGAN achieves\nstate-of-the-art image generation fidelity on common perceptual metrics\ncompared to the current best-performing methods and convinces in a\ncomprehensive user study.\n"}
+{"abstract": "  Stifle joint issues are a major cause of lameness in dogs and it can be a\nsignificant marker for various forms of diseases or injuries. A known Tibial\nPlateau Angle (TPA) helps in the reduction of the diagnosis time of the cause.\nWith the state of the art object detection algorithm YOLO, and its variants,\nthis paper delves into identifying joints, their centroids and other regions of\ninterest to draw multiple line axes and finally calculating the TPA. The\nmethods investigated predicts successfully the TPA within the normal range for\n80 percent of the images.\n"}
+{"abstract": "  In this paper we consider minimization of a difference-of-convex (DC)\nfunction with and without linear constraints. We first study a smooth\napproximation of a generic DC function, termed difference-of-Moreau-envelopes\n(DME) smoothing, where both components of the DC function are replaced by their\nrespective Moreau envelopes. The resulting smooth approximation is shown to be\nLipschitz differentiable, capture stationary points, local, and global minima\nof the original DC function, and enjoy some growth conditions, such as\nlevel-boundedness and coercivity, for broad classes of DC functions. We then\ndevelop four algorithms for solving DC programs with and without linear\nconstraints based on the DME smoothing. In particular, for a smoothed DC\nprogram without linear constraints, we show that the classic gradient descent\nmethod as well as an inexact variant can obtain a stationary solution in the\nlimit with a convergence rate of $\\mathcal{O}(K^{-1/2})$, where $K$ is the\nnumber of proximal evaluations of both components. Furthermore, when the DC\nprogram is explicitly constrained in an affine subspace, we combine the\nsmoothing technique with the augmented Lagrangian function and derive two\nvariants of the augmented Lagrangian method (ALM), named LCDC-ALM and composite\nLCDC-ALM, focusing on different structures of the DC objective function. We\nshow that both algorithms find an $\\epsilon$-approximate stationary solution of\nthe original DC program in $\\mathcal{O}(\\epsilon^{-2})$ iterations. Comparing\nto existing methods designed for linearly constrained weakly convex\nminimization, the proposed ALM-based algorithms can be applied to a broader\nclass of problems, where the objective contains a nonsmooth concave component.\nFinally, numerical experiments are presented to demonstrate the performance of\nthe proposed algorithms.\n"}
+{"abstract": "  Given a finite group $G$ acting on a ring $R$, Merling constructed an\nequivariant algebraic $K$-theory $G$-spectrum, and work of Malkiewich and\nMerling, as well as work of Barwick, provides an interpretation of this\nconstruction as a spectral Mackey functor. This construction is powerful, but\nhighly categorical; as a result the Mackey functors comprising the homotopy are\nnot obvious from the construction and have therefore not yet been calculated.\nIn this work, we provide a computation of the homotopy Mackey functors of\nequivariant algebraic $K$-theory in terms of a purely algebraic construction.\nIn particular, we construct Mackey functors out of the $n$th algebraic\n$K$-groups of group rings whose multiplication is twisted by the group action.\nRestrictions and transfers for these functors admit a tractable algebraic\ndescription in that they arise from restriction and extension of scalars along\nmodule categories of twisted group rings. In the case where the group action is\ntrivial, our construction recovers work of Dress and Kuku from the 1980's which\nconstructs Mackey functors out of the algebraic $K$-theory of group rings. We\ndevelop many families of examples of Mackey functors, both new and old,\nincluding $K$-theory of endomorphism rings, the $K$-theory of fixed subrings of\nGalois extensions, and (topological) Hochschild homology of twisted group\nrings.\n"}
+{"abstract": "  A significant challenge in seasonal climate prediction is whether a\nprediction can beat climatology. We hereby present results from two data-driven\nmodels - a convolutional (CNN) and a recurrent (RNN) neural network - that\npredict 2 m temperature out to 52 weeks for six geographically-diverse\nlocations. The motivation for testing the two classes of ML models is to allow\nthe CNN to leverage information related to teleconnections and the RNN to\nleverage long-term historical temporal signals. The ML models boast improved\naccuracy of long-range temperature forecasts up to a lead time of 30 weeks for\nPCC and up 52 weeks for RMSESS, however only for select locations. Further\niteration is required to ensure the ML models have value beyond regions where\nthe climatology has a noticeably reduced correlation skill, namely the tropics.\n"}
+{"abstract": "  We propose a fully differentiable architecture for simultaneous semantic and\ninstance segmentation (a.k.a. panoptic segmentation) consisting of a\nconvolutional neural network and an asymmetric multiway cut problem solver. The\nlatter solves a combinatorial optimization problem that elegantly incorporates\nsemantic and boundary predictions to produce a panoptic labeling. Our\nformulation allows to directly maximize a smooth surrogate of the panoptic\nquality metric by backpropagating the gradient through the optimization\nproblem. Experimental evaluation shows improvement by backpropagating through\nthe optimization problem w.r.t. comparable approaches on Cityscapes and COCO\ndatasets. Overall, our approach shows the utility of using combinatorial\noptimization in tandem with deep learning in a challenging large scale\nreal-world problem and showcases benefits and insights into training such an\narchitecture.\n"}
+{"abstract": "  This article provides a link diagram to visualize relations between two\nordered sets representing precedences on decision-making options or solutions\nto strategic form games. The diagram consists of floating loops whose any two\nloops cross just twice each other. As problems formulated by relations between\ntwo ordered sets, I give two examples: visualizing rankings from pairwise\ncomparisons on the diagram and finding Pareto optimal solutions to a game of\nprisoners' dilemma. At visualizing rankings, we can see whether a ranking\nsatisfies Condorcet's principle or not by checking whether the top loop is\nsplittable or not. And at finding solutions to the game, when a solution of the\ngame of prisoners' dilemma is Pareto optimal, the loop corresponding to the\nsolution has no splittable loop above it. Throughout the article, I point out\nthat checking the splittability of loops is an essence. I also mention that the\ndiagram can visualize natural transformations between two functors on free\nconstruction categories.\n"}
+{"abstract": "  Quantum noise limits the sensitivity of precision measurement devices, such\nas laser interferometer gravitational-wave observatories and axion detectors.\nIn the shot-noise-limited regime, these resonant detectors are subject to a\ntrade-off between the peak sensitivity and bandwidth. One approach to\ncircumvent this limitation in gravitational-wave detectors is to embed an\nanomalous-dispersion optomechanical filter to broaden the bandwidth. The\noriginal filter cavity design, however, makes the entire system unstable.\nRecently, we proposed the coherent feedback between the arm cavity and the\noptomechanical filter to eliminate the instability via PT-symmetry. The\noriginal analysis based upon the Hamiltonian formalism adopted the single-mode\nand resolved-sideband approximations. In this paper, we go beyond these\napproximations and consider realistic parameters. We show that the main\nconclusion concerning stability remains intact, with both Nyquist analysis and\na detailed time-domain simulation.\n"}
+{"abstract": "  In Part 1, we introduced a stochastic model of an infectious disease, based\non the BDI (birth and death with immigration) process. We showed that random\nprocesses defined by this model can capture the essence of the stochastic,\noften erratic, behavior of the infection process. The most significant finding\nwas that it is negative binomial distributed with small r, hence it is\ngeometrically distributed with an exceedingly long tail. This leads to a much\nlarger disparity in the epidemic patterns than has been known to the modeling\ncommunity. In Part 2 we conduct simulation experiments by implementing an\nevent-driven simulator. Several independent runs are presented to verify the\nfindings reported in Part 1. The enormous variations among the sample paths\nobtained from several consecutive and independent runs with statistically\nidentical conditions confirm our analysis. Note that the epidemic pattern that\nwe observe is merely one sample path taken out of infinitely many paths. Thus,\none such path cannot represent the ensemble of the underlying random process.\nBy plotting the simulation runs in the semi-log scale, we can see that the\nprobabilistic chances in the early phase of the infection process determine the\nbehavior of the process. Once the process has reached a considerable number,\nthe weak law of large numbers sets in, and the process behaves less erratically\nthan in the early phase. One important implication of our findings is that it\nwould be a futile effort to attempt to identify all plausible causes of the\nepidemic patterns. Mere luck may play a more significant role than most people\nbelieve. It would be worth remarking that the BDI process might be applicable\nto explain the disparity in wealth among individuals of similar earning power,\nexpenditure, and investment portfolio.\n"}
+{"abstract": "  The emerging data-intensive applications are increasingly dependent on\ndata-intensive scalable computing (DISC) systems, such as Apache Spark, to\nprocess large data. Despite their popularity, DISC applications are hard to\ntest. In recent years, fuzz testing has been remarkably successful; however, it\nis nontrivial to apply such traditional fuzzing to big data analytics directly\nbecause: (1) the long latency of DISC systems prohibits the applicability of\nfuzzing, and (2) conventional branch coverage is unlikely to identify\napplication logic from the DISC framework implementation. We devise a novel\nfuzz testing tool called BigFuzz that automatically generates concrete data for\nan input Apache Spark program. The key essence of our approach is that we\nabstract the dataflow behavior of the DISC framework with executable\nspecifications and we design schema-aware mutations based on common error types\nin DISC applications. Our experiments show that compared to random fuzzing,\nBigFuzz is able to speed up the fuzzing time by 1477X, improves application\ncode coverage by 271%, and achieves 157% improvement in detecting application\nerrors. The demonstration video of BigFuzz is available at\nhttps://www.youtube.com/watch?v=YvYQISILQHs&feature=youtu.be.\n"}
+{"abstract": "  We attempt to identify RR Lyrae (RRL) stars in stellar streams that might\nhave escaped from seven globular clusters (GCs) based on proper motions,\ndistances, color-magnitude diagrams, and other properties extracted from the\nGaia Early Data Release 3 (EDR3) database. Specifically, we cross-match two\nlarge RRL stars catalogs (from Gaia DR2 and Catalina Sky Survey) with each\nother and with the EDR3 database and we end up with a sample of ~ 150,000\nunique RRL stars. We calculate distances to RRL stars using the (M_G-[Fe/H])\nand (M_V-[Fe/H]) absolute magnitude-metallicity relations and adopt [Fe/H]\nvalues for the GCs from different spectroscopic studies. We also constrain our\nsearch in areas where stellar streams associated with GCs were previously\nsuggested or identified in other studies. We identify 24 RRL stars that might\nhave escaped from the following seven GCs: Palomar 13 (Pal 13), NGC 6341 (M92),\nNGC 5904 (M5), NGC 5466, NGC 1261, NGC 288, and NGC 1851. We list our findings\nin Table 2.\n"}
+{"abstract": "  Hot, dust-obscured galaxies (Hot DOGs) are a population of hyper-luminous\nobscured quasars identified by WISE. We present ALMA observations of the [CII]\nfine-structure line and underlying dust continuum emission in a sample of seven\nof the most extremely luminous (EL; L$_{\\rm bol}$ $\\ge$ 10$^{14}$ L$_\\odot$)\nHot DOGs, at redshifts z ~ 3.0-4.6. The [CII] line is robustly detected in four\nobjects, tentatively in one, and likely red-shifted out of the spectral window\nin the remaining two based on additional data. On average, [CII] is red-shifted\nby ~ 780 km/s from rest-frame ultraviolet emission lines. EL Hot DOGs exhibit\nconsistently very high ionized gas surface densities, with $\\Sigma_{\\rm [CII]}$\n~ 1-2 x 10$^{9}$ L$_\\odot$ kpc$^{-2}$; as high as the most extreme cases seen\nin other high-redshift quasars. As a population, EL Hot DOG hosts seem to be\nroughly centered on the main-sequence of star forming galaxies, but the\nuncertainties are substantial and individual sources can fall above and below.\nThe average, intrinsic [CII] and dust continuum sizes (FWHMs) are ~ 2.1 kpc and\n~ 1.6 kpc, respectively, with a very narrow range of line-to-continuum size\nratios, 1.61 $\\pm$ 0.10, suggesting they could be linearly proportional. The\n[CII] velocity fields of EL Hot DOGs are diverse: from barely rotating\nstructures, to resolved hosts with ordered, circular motions, to complex,\ndisturbed systems that are likely the result of ongoing mergers. In contrast,\nall sources display large line-velocity dispersions, FWHM $\\gtrsim$ 500 km/s,\nwhich on average are larger than optically and IR-selected quasars at similar\nor higher redshifts. We argue that one possible hypothesis for the lack of a\ncommon velocity structure, the systematically large dispersion of the ionized\ngas, and the presence of nearby companion galaxies may be that, rather than a\nsingle event, the EL Hot DOG phase could be recurrent.\n"}
+{"abstract": "  Ubiquitous mobile computing have enabled ride-hailing services to collect\nvast amounts of behavioral data of riders and drivers and optimize supply and\ndemand matching in real time. While these mobility service providers have some\ndegree of control over the market by assigning vehicles to requests, they need\nto deal with the uncertainty arising from self-interested driver behavior since\nworkers are usually free to drive when they are not assigned tasks. In this\nwork, we formulate the problem of passenger-vehicle matching in a sparsely\nconnected graph and proposed an algorithm to derive an equilibrium policy in a\nmulti-agent environment. Our framework combines value iteration methods to\nestimate the optimal policy given expected state visitation and policy\npropagation to compute multi-agent state visitation frequencies. Furthermore,\nwe developed a method to learn the driver's reward function transferable to an\nenvironment with significantly different dynamics from training data. We\nevaluated the robustness to changes in spatio-temporal supply-demand\ndistributions and deterioration in data quality using a real-world taxi\ntrajectory dataset; our approach significantly outperforms several baselines in\nterms of imitation accuracy. The computational time required to obtain an\nequilibrium policy shared by all vehicles does not depend on the number of\nagents, and even on the scale of real-world services, it takes only a few\nseconds on a single CPU.\n"}
+{"abstract": "  Cancer invasion of the surrounding tissue is a multiscale process that\ninvolves not only tumour cells but also other immune cells in the environment,\nsuch as the tumour-associated macrophages (TAMs). The heterogeneity of these\nimmune cells, with the two extremes being the pro-inflammatory and anti-tumour\nM1 cells, and the anti-inflammatory and pro-tumour M2 cells, has a significant\nimpact on cancer invasion as these cell interact in different ways with the\ntumour cells and with the ExtraCellular Matrix (ECM). Experimental studies have\nshown that cancer cells co-migrate with TAMs, but the impact of these different\nTAM sub-populations (which can change their phenotype and re-polarise depending\non the microenvironment) on this co-migration is not fully understood. In this\nstudy, we extend a previous multi-scale moving boundary mathematical model, by\nintroducing the M1-like macrophages alongside with their exerted multi-scale\neffects on the tumour invasion process. With the help of this model we\ninvestigate numerically the impact of re-polarising the M2 TAMs into the\nanti-tumoral M1 phenotype and how such a strategy affects the overall tumour\nprogression. In particular, we investigate numerically whether the M2-->M1\nre-polarisation could depend on time and/or space, and what would be the\nmacroscopic effects of this spatial- and temporal-dependent re-polarisation on\ntumour invasion.\n"}
+{"abstract": "  We study the effect of the cubic torus topology of the Universe on scalar\ncosmological perturbations which define the gravitational potential. We obtain\nthree alternative forms of the solution for both the gravitational potential\nproduced by point-like masses, and the corresponding force. The first solution\nincludes the expansion of delta-functions into Fourier series, exploiting\nperiodic boundary conditions. The second one is composed of summed solutions of\nthe Helmholtz equation for the original mass and its images. Each of these\nsummed solutions is the Yukawa potential. In the third formula, we express the\nYukawa potentials via Ewald sums. We show that for the present Universe, both\nthe bare summation of Yukawa potentials and the Yukawa-Ewald sums require\nsmaller numbers of terms to yield the numerical values of the potential and the\nforce up to desired accuracy. Nevertheless, the Yukawa formula is yet\npreferable owing to its much simpler structure.\n"}
+{"abstract": "  Two classes of X-ray/$\\gamma$-ray sources, the Soft Gamma Repeaters and the\nAnomalous X-ray Pulsars have been identified with isolated, slowly spinning\nmagnetars, neutron stars whose emission draws energy from their extremely\nstrong magnetic field ($\\sim 10^{15}-10^{16}$ G). Magnetars are believed to\nform with millisecond spin period and to represent an important fraction of the\nwhole population of young neutron stars. Newborn magnetars can convert very\nquickly their rotational energy into electromagnetic and/or gravitational\nwaves, by virtue of their strong magnetic fields and fast spins. This chapter\nprovides a brief summary of astrophysical problems and scenarios in which\nmillisecond magnetars are believed to play a key role: these include Gamma Ray\nBursts, Supernovae, Gravitational Wave events and Fast Radio Bursts.\n"}
+{"abstract": "  We investigate the interplay of electronic correlations and spin-orbit\ncoupling (SOC) for a one-band and a two-band honeycomb lattice model. The main\ndifference between the two models concerning SOC is that in the one-band case\nthe SOC is a purely non-local term in the basis of the $p_z$ orbitals, whereas\nin the two-band case with $p_x$ and $p_y$ as basis functions it is purely\nlocal. In order to grasp the correlation effects on non-local spin-orbit\ncoupling, we apply the TRILEX approach that allows to calculate non-local\ncontributions to the self-energy approximatively. For the two-band case we\napply dynamical mean-field theory. In agreement with previous studies, we find\nthat for all parameter values in our study, the effect of correlations on the\nspin-orbit coupling strength is that the bare effective SOC parameter is\nincreased. However, this increase is much weaker in the non-local than in the\nlocal SOC case. Concerning the TRILEX method, we introduce the necessary\nformulas for calculations with broken SU(2) symmetry.\n"}
+{"abstract": "  In this work we consider the problem of identification and reconstruction of\ndoubly-dispersive channel operators which are given by finite linear\ncombinations of time-frequency shifts. Such operators arise as time-varying\nlinear systems for example in radar and wireless communications. In particular,\nfor information transmission in highly non-stationary environments the channel\nneeds to be estimated quickly with identification signals of short duration and\nfor vehicular application simultaneous high-resolution radar is desired as\nwell. We consider the time-continuous setting and prove an exact resampling\nreformulation of the involved channel operator when applied to a trigonometric\npolynomial as identifier in terms of sparse linear combinations of real-valued\natoms. Motivated by recent works of Heckel et al. we present an exact approach\nfor off-the-grid superresolution which allows to perform the identification\nwith realizable signals having compact support. Then we show how an alternating\ndescent conditional gradient algorithm can be adapted to solve the reformulated\nproblem. Numerical examples demonstrate the performance of this algorithm, in\nparticular in comparison with a simple adaptive grid refinement strategy and an\northogonal matching pursuit algorithm.\n"}
+{"abstract": "  In the study of correlated systems, approximations based on the dynamical\nmean-field theory (DMFT) provide a practical way to take local vertex\ncorrections into account, which capture, respectively, particle-particle\nscreening at weak coupling and the formation of the local moment at strong\ncoupling. We show that in both limits the local vertex corrections can be\nefficiently parametrized in terms of single-boson exchange, such that the\ntwo-particle physics described by DMFT and its diagrammatic extensions is\nrecovered to good approximation and at a reduced computational cost. Our\ninvestigation highlights the importance of the frequency-dependent\nfermion-boson coupling (Hedin vertex) for local vertex corrections. Namely, at\nweak coupling the fermion-spin-boson coupling suppresses the N\\'eel temperature\nof the DMFT approximation compared to the static mean-field, whereas for large\ninteraction it facilitates a huge enhancement of local spin-fluctuation\nexchange, giving rise to the effective-exchange energy scale $4t^2/U$. We find\nthat parametrizations of the vertex which neglect the nontrivial part of the\nfermion-boson coupling fail qualitatively at strong coupling.\n"}
+{"abstract": "  Missing data is frequently encountered in practice. Propensity score\nestimation is a popular tool for handling such missingness. The propensity\nscore is often developed using the model for the response probability, which\ncan be subject to model misspecification. In this paper, we consider an\nalternative approach of estimating the inverse of the propensity scores using\nthe density ratio function. By partitioning the sample into two groups based on\nthe response status of the elements, we can apply the density ratio function\nestimation method and obtain the inverse propensity scores for nonresponse\nadjustment. Density ratio estimation can be obtained by applying the so-called\nmaximum entropy method, which uses the Kullback-Leibler divergence measure\nunder calibration constraints. By including the covariates for the outcome\nregression models only into the density ratio model, we can achieve efficient\npropensity score estimation. We further extend the proposed approach to the\nmultivariate missing case. Some limited simulation studies are presented to\ncompare with the existing methods.\n"}
+{"abstract": "  We introduce a new measure on regular languages: their nondeterministic\nsyntactic complexity. It is the least degree of any extension of the `canonical\nboolean representation' of the syntactic monoid. Equivalently, it is the least\nnumber of states of any subatomic nondeterministic acceptor. It turns out that\nessentially all previous structural work on nondeterministic state-minimality\ncomputes this measure. Our approach rests on an algebraic interpretation of\nnondeterministic finite automata as deterministic finite automata endowed with\nsemilattice structure. Crucially, the latter form a self-dual category.\n"}
+{"abstract": "  We propose a new classifier based on Dempster-Shafer (DS) theory and a\nconvolutional neural network (CNN) architecture for set-valued classification.\nIn this classifier, called the evidential deep-learning classifier,\nconvolutional and pooling layers first extract high-dimensional features from\ninput data. The features are then converted into mass functions and aggregated\nby Dempster's rule in a DS layer. Finally, an expected utility layer performs\nset-valued classification based on mass functions. We propose an end-to-end\nlearning strategy for jointly updating the network parameters. Additionally, an\napproach for selecting partial multi-class acts is proposed. Experiments on\nimage recognition, signal processing, and semantic-relationship classification\ntasks demonstrate that the proposed combination of deep CNN, DS layer, and\nexpected utility layer makes it possible to improve classification accuracy and\nto make cautious decisions by assigning confusing patterns to multi-class sets.\n"}
+{"abstract": "  Using the framework of higher-form global symmetries, we examine the regime\nof validity of force-free electrodynamics by evaluating the lifetime of the\nelectric field operator, which is non-conserved due to screening effects. We\nfocus on a holographic model which has the same global symmetry as that of low\nenergy plasma and obtain the lifetime of (non-conserved) electric flux in a\nstrong magnetic field regime. The lifetime is inversely correlated to the\nmagnetic field strength and thus suppressed in the strong field regime.\n"}
+{"abstract": "  In this paper, we prove the compressible Euler limit from Boltzmann equation\nwith complete diffusive boundary condition in half-space by employing the\nHilbert expansion which includes interior and Knudsen layers. This rigorously\njustifies the corresponding formal analysis in Sone's book\n\\cite{Sone-2007-Book} in the context of short time smooth solutions. In\nparticular, different with previous works in this direction, no Prandtl layers\nare needed.\n"}
+{"abstract": "  Deep learning has become an increasingly popular and powerful option for\nmodern pattern recognition systems. However, many deep neural networks have\nmillions to billions of parameters, making them untenable for real-world\napplications with constraints on memory or latency. As a result, powerful\nnetwork compression techniques are a must for the widespread adoption of deep\nlearning. We present DECORE, a reinforcement learning approach to automate the\nnetwork compression process. Using a simple policy gradient method to learn\nwhich neurons or channels to keep or remove, we are able to achieve compression\nrates 3x to 5x greater than contemporary approaches. In contrast with other\narchitecture search methods, DECORE is simple and quick to train, requiring\nonly a few hours of training on 1 GPU. When applied to standard network\narchitectures on different datasets, our approach achieves 11x to 103x\ncompression on different architectures while maintaining accuracies similar to\nthose of the original, large networks.\n"}
+{"abstract": "  We study when the twisted groupoid Banach $*$-algebra\n$L^1(\\mathcal{G},\\sigma)$ is Hermitian. In particular, we prove that Hermitian\ngroupoids satisfy the weak containment property. Furthermore, we find that for\n$L^1(\\mathcal{G},\\sigma)$ to be Hermitian it is sufficient that $L^1\n(\\mathcal{G}_\\sigma)$ is Hermitian. Moreover, if $\\mathcal{G}$ is ample, we\nfind necessary conditions for $L^1(\\mathcal{G},\\sigma)$ to be Hermitian in\nterms of the fibers $\\mathcal{G}^x_x$.\n"}
+{"abstract": "  In this article we extend a previous discussion about the influence of an\nexternal magnetic field on renormalons in a self interacting scalar theory by\nincluding now temperature effects, in the imaginary formalism, together with an\nexternal weak external magnetic field. We show that the location of poles in\nthe Borel plane does not change, getting their residues, however, a dependence\non temperature and on the magnetic field.The effects of temperature and the\nmagnetic field strength on the residues turn out to be opposite. We present a\ndetailed discussion about the evolution of these residues, showing technical\ndetails involved in the calculation.\n"}
+{"abstract": "  Communication using orbital angular momentum (OAM) modes has recently\nreceived a considerable interest in free space optical (FSO) communications.\nPropagating OAM modes through free space may be subject to atmospheric\nturbulence (AT) distortions that cause signal attenuation and crosstalk which\ndegrades the system capacity and increases the error probability. In this\npaper, we propose to enhance the OAM FSO communications in terms of bit error\nrate and spectral efficiency, for different levels of AT regimes. The\nperformance gain is achieved by introducing orthogonal frequency division\nmultiplexing (OFDM) with index modulation technique to the OAM FSO system.\n"}
+{"abstract": "  Classical black holes shield us from the singularities that inevitably appear\nin general relativity. Being singularity regularization one of the main\nlandmarks for a successful theory of quantum gravity, quantum black holes are\nnot obliged to hide their inner core from the outside world. Notwithstanding\nthe aforesaid, it is often implicitly assumed that quantum gravity effects must\nremain confined to black hole interiors. In this essay we argue in the opposite\ndirection, discussing theoretical evidence for the existence of strong\ncorrelations between the physics inside and outside non-singular black holes.\nWe conclude that astronomical tests of the surroundings of black holes can\nprovide invaluable information about their so-far unexplored interiors.\n"}
+{"abstract": "  By studying the 2-dimensional Su-Schrieffer-Heeger-Bose-Hubbard model, we\nshow the existence of topological Higgs amplitude modes in the strongly\ninteracting superfluid phase. Using the slave boson approach, we find that, in\nthe large filling limit, the Higgs excitations and the Goldstone excitations\nabove the ground state are well decoupled, and both of them exhibit nontrivial\ntopology inherited from the underlying noninteracting bands. At finite\nfillings, they become coupled at high energies; nevertheless, the topology of\nthese modes are unchanged. Moreover, based on an effective action analysis, we\nfurther provide a universal physical picture for the topological character of\nHiggs and Goldstone modes. Our discovery of the first realization of the\ntopological Higgs mode opens the path to novel investigations in various\nsystems such as superconductors and quantum magnetism.\n"}
+{"abstract": "  PoseNet can map a photo to the position where it is taken, which is appealing\nin robotics. However, training PoseNet requires full supervision, where ground\ntruth positions are non-trivial to obtain. Can we train PoseNet without knowing\nthe ground truth positions for each observation? We show that this is possible\nvia constraint-based weak-supervision, leading to the proposed framework:\nDeepGPS. Particularly, using wheel-encoder-estimated distances traveled by a\nrobot along random straight line segments as constraints between PoseNet\noutputs, DeepGPS can achieve a relative positioning error of less than 2%.\nMoreover, training DeepGPS can be done as auto-calibration with almost no human\nattendance, which is more attractive than its competing methods that typically\nrequire careful and expert-level manual calibration. We conduct various\nexperiments on simulated and real datasets to demonstrate the general\napplicability, effectiveness, and accuracy of DeepGPS, and perform a\ncomprehensive analysis of its robustness. Our code is available at\nhttps://ai4ce.github.io/DeepGPS/.\n"}
+{"abstract": "  Mild Traumatic Brain Injuries (mTBI) are caused by violent head motions or\nimpacts. Most mTBI prevention strategies explicitly or implicitly rely on a\n\"brain injury criterion\". A brain injury criterion takes some descriptor of the\nhead's motion as input and yields a prediction for that motion's potential for\ncausing mTBI as the output. The inputs are descriptors of the head's motion\nthat are usually synthesized from accelerometer and gyroscope data. In the\ncontext of brain injury criterion the head is modeled as a rigid body. We\npresent an algorithm for determining the complete motion of the head using data\nfrom only four head mounted tri-axial accelerometers. In contrast to inertial\nmeasurement unit based algorithms for determining rigid body motion the\npresented algorithm does not depend on data from gyroscopes; which consume much\nmore power than accelerometers. Several algorithms that also make use of data\nfrom only accelerometers already exist. However, those algorithms, except for\nthe recently presented AO-algorithm [Rahaman MM, Fang W, Fawzi AL, Wan Y,\nKesari H (2020): J Mech Phys Solids 104014], give the rigid body's acceleration\nfield in terms of the body frame, which in general is unknown. Compared to the\nAO-algorithm the presented algorithm is much more insensitive to bias type\nerrors, such as those that arise from inaccurate measurement of sensor\npositions and orientations.\n"}
+{"abstract": "  For steady-state autonomous absorption refrigerators operating in the linear\nresponse regime, we show that there exists a hierarchy between the relative\nfluctuation of currents for cold, hot, and work terminals. Our proof requires\nthe Onsager's reciprocity relation along with the refrigeration condition that\nsets the direction of the mean currents for each terminal. As a consequence,\nthe universal bounds on the mean cooling power, obtained following the\nthermodynamic uncertainty relations, receive a hierarchy. Interestingly, within\nthis hierarchy, the tightest bound is given in terms of the work current\nfluctuation. Furthermore, the relative uncertainty hierarchy hands over\nadditional bounds that can be tighter than the bounds obtained from the\nthermodynamic uncertainty relations. Interestingly, all of these bounds\nsaturate in the tight-coupling limit. We test the validity of our results for\ntwo paradigmatic absorption refrigerator models: (i) a four-level working fluid\nand (ii) a two-level working fluid, operating, respectively, in the weak\n(additive) and in the strong (multiplicative) system-bath interaction regime.\n"}
+{"abstract": "  Deep learning models are known to be vulnerable to adversarial examples\ncrafted by adding human-imperceptible perturbations on benign images. Many\nexisting adversarial attack methods have achieved great white-box attack\nperformance, but exhibit low transferability when attacking other models.\nVarious momentum iterative gradient-based methods are shown to be effective to\nimprove the adversarial transferability. In what follows, we propose an\nenhanced momentum iterative gradient-based method to further enhance the\nadversarial transferability. Specifically, instead of only accumulating the\ngradient during the iterative process, we additionally accumulate the average\ngradient of the data points sampled in the gradient direction of the previous\niteration so as to stabilize the update direction and escape from poor local\nmaxima. Extensive experiments on the standard ImageNet dataset demonstrate that\nour method could improve the adversarial transferability of momentum-based\nmethods by a large margin of 11.1% on average. Moreover, by incorporating with\nvarious input transformation methods, the adversarial transferability could be\nfurther improved significantly. We also attack several extra advanced defense\nmodels under the ensemble-model setting, and the enhancements are remarkable\nwith at least 7.8% on average.\n"}
+{"abstract": "  Artificial spin ices are magnetic metamaterials comprising\ngeometrically-tiled interacting nanomagnets. There is significant interest in\nthese systems for reconfigurable magnonics due to their vast microstate\nlandscape. Studies to-date have focused on the in-field GHz spin-wave response,\nconvoluting effects from applied field, nanofabrication-imperfections\n('quenched disorder') and microstate-dependent dipolar field landscapes. Here,\nwe study artificial spin ices in pure and disordered microstates and evaluate\ntheir zero-field spectra. Removing the applied field allows us to deconvolute\ncontributions to reversal dynamics and spin-wave spectra, directly measuring\nthe dipolar field landscape and quenched disorder. Mode-amplitude provides\npopulation readout of nanomagnet magnetisation direction, and hence net\nmagnetisation as well as local vertex populations. We demonstrate\nmicrostate-fingerprinting via distinct spectral-readout of three microstates\nwith identical (zero) magnetisation, supported by simulation. These results\nestablish remanence 'spectral-fingerprinting' as a rapid, scalable on-chip\nreadout of both magnetic state and nanoscale dipolar field texture, a critical\nstep in realising next-generation functional magnonic devices.\n"}
+{"abstract": "  Bochi-Katok-Rodriguez Hertz proposed in [BKH21] a program on the flexibility\nof Lyapunov exponents for conservative Anosov diffeomorphisms, and obtained\npartial results in this direction. For conservative Anosov diffeomorphisms with\nstrong hyperbolic properties we establish extended flexibility results for\ntheir Lyapunov exponents. We give examples of Anosov diffeomorphisms with the\nstrong unstable exponent larger than the strong unstable exponent of the linear\npart. We also give examples of derived from Anosov diffeomorphisms with the\nmetric entropy entropy larger than the entropy of the linear part.\n  These results rely on a new type of deformation which goes beyond the\nprevious Shub-Wilkinson and Baraviera-Bonatti techniques for conservative\nsystems having some invariant directions. In order to estimate the Lyapunov\nexponents even after breaking the invariant bundles, we obtain an abstract\nresult (Theorem 3.1) which gives bounds on exponents of some specific cocycles\nand which can be applied in various other settings. We also include various\ninteresting comments in the appendices: our examples are $\\mathcal{C}^2$\nrobust, the Lyapunov exponents are continuous (with respect to the map) even\nafter breaking the invariant bundles, a similar construction can be obtained\nfor the case of multiple eigenvalues.\n"}
+{"abstract": "  In supervised classification problems, the test set may contain data points\nbelonging to classes not observed in the learning phase. Moreover, the same\nunits in the test data may be measured on a set of additional variables\nrecorded at a subsequent stage with respect to when the learning sample was\ncollected. In this situation, the classifier built in the learning phase needs\nto adapt to handle potential unknown classes and the extra dimensions. We\nintroduce a model-based discriminant approach, Dimension-Adaptive Mixture\nDiscriminant Analysis (D-AMDA), which can detect unobserved classes and adapt\nto the increasing dimensionality. Model estimation is carried out via a full\ninductive approach based on an EM algorithm. The method is then embedded in a\nmore general framework for adaptive variable selection and classification\nsuitable for data of large dimensions. A simulation study and an artificial\nexperiment related to classification of adulterated honey samples are used to\nvalidate the ability of the proposed framework to deal with complex situations.\n"}
+{"abstract": "  Deep neural networks (DNNs) are vulnerable to adversarial noise.\nPreprocessing based defenses could largely remove adversarial noise by\nprocessing inputs. However, they are typically affected by the error\namplification effect, especially in the front of continuously evolving attacks.\nTo solve this problem, in this paper, we propose to remove adversarial noise by\nimplementing a self-supervised adversarial training mechanism in a class\nactivation feature space. To be specific, we first maximize the disruptions to\nclass activation features of natural examples to craft adversarial examples.\nThen, we train a denoising model to minimize the distances between the\nadversarial examples and the natural examples in the class activation feature\nspace. Empirical evaluations demonstrate that our method could significantly\nenhance adversarial robustness in comparison to previous state-of-the-art\napproaches, especially against unseen adversarial attacks and adaptive attacks.\n"}
+{"abstract": "  We will construct a loop space formalism for general relativity, and\nconstruct the Polyakov variables as connections for such a loop space. We will\nuse these Polyakov variables to construct a dual theory of gravity beyond\nlinear approximation. It will be demonstrated that this loop space duality\nreduces to the Hodge duality for linearized gravity. Furthermore, a loop space\ncurvature will be constructed from this Polyakov variable. It will be shown\nthat this loop space curvature vanishes in the absence of topological defects,\nand so it can be used to investigate gravitational monopoles. We will also\nconstruct the suitable monopole charge for such gravitational monopoles.\n"}
+{"abstract": "  Eigenspectra of a spinless quantum particle trapped inside a rigid,\nrectangular, two-dimensional (2D) box subject to diverse inner potential\ndistributions are investigated under hermitian, as well as non-hermitian\nantiunitary $\\mathcal{PT}$ (composite parity and time-reversal) symmetric\nregimes. Four sectors or \"stripes\" inscribed in the rigid box comprising\ncontiguously conjoined parallel rectangular segments with one side equaling the\nentire width of the box are studied. The stripes encompass piecewise constant\npotentials whose exact, complete energy eigenspectrum is obtained employing\nmatrix mechanics. Various striped potential compositions, viz. real valued ones\nin the hermitian regime as well as complex, non-hermitian but $\\mathcal{PT}$\nsymmetric ones are considered separately and in conjunction, unraveling among\ntypical lowest lying eigenvalues, retention and breakdown scenarios engendered\nby the $\\mathcal{PT}$ symmetry, bearing upon the strength of non-hermitian\nsectors. Some states exhibit a remarkable crossover of symmetry `making' and\n`breaking': while a broken $\\mathcal{PT}$ gets reinstated for an energy level,\nhigher levels may couple to continue with symmetry breaking. Further, for a\ncharged quantum particle a $\\mathcal{PT}$ symmetric electric field, furnished\nwith a striped potential backdrop, also reveals peculiar retention and\nbreakdown $\\mathcal{PT}$ scenarios. Depictions of prominent probability\nredistributions relating to various potential distributions both under\nnorm-conserving unitary regime for hermitian Hamiltonians and non-conserving\nones post $\\mathcal{PT}$ breakdown are presented.\n"}
+{"abstract": "  In this thesis we investigate some aspects of quantum field theories from a\nholographic perspective. In the first chapters we examine in detail a\none-paremeter family of three-dimensional gauge theories by means of their type\nIIA gravity duals. We analyse features such as their confinement nature,\nspectrum, entanglement properties or thermal phase transitions. This family\ninterpolates between quasi-conformal and quasi-confining physics. In the last\ntwo chapters, we use bottom-up models to study complex conformal field theories\nand transport properties of dense QCD respectively.\n"}
+{"abstract": "  Refactorings must not alter the program's functionality. However, not all\nrefactorings fulfill this requirement. Hence, one must explicitly check that a\nrefactoring does not alter the functionality. Since one rarely has a formal\nspecification of the program's behavior, we utilize the original program as\nfunctional specification. Then, we check whether the original and refactored\nprogram are functionally equivalent. To this end, we apply a common idea and\nreduce equivalence checking to program verification. To increase efficiency,\nour equivalence checker PEQcheck constructs one verification task per\nrefactored code segment instead of one per function as typically done by prior\nwork. In addition, PEQcheck considers the context of the code segments. For\ninstance, only variables that are modified and live are required to be\nequivalent and read-only variables may be shared between original and\nrefactored code segments. We show that PEQcheck is sound.Moreover, our\nevaluation testifies that the localized and context-aware checking performed by\n\\peqcheck can indeed be beneficial.\n"}
+{"abstract": "  Recent studies have suggested a new phase in the extended Bose-Hubbard model\nin one dimension at integer filling [1,2]. In this work, we show that this new\nphase is phase-separated into a supersolid and superfluid part, generated by\nmechanical instability. Numerical simulations are performed by means of the\ndensity matrix renormalization group algorithm in terms of matrix product\nstates. In the phase-separated phase and the adjacent homogeneous superfluid\nand supersolid phases, we find peculiar spatial patterns in the entanglement\nspectrum and string-order correlation functions and show that they survive in\nthe thermodynamic limit. In particular, we demonstrate that the elementary\nexcitations of the homogeneous superfluid with enhanced periodic modulations\nare phonons, find the central charge to be $c=1$, and show that the velocity of\nsound, extracted from the intrinsic level splitting for finite systems, matches\nwith the propagation velocity of local excitations in dynamical simulations.\nThis suggests that the low-energy spectrum of the phase under investigation is\neffectively captured by a spinless Luttinger liquid, for which we find\nconsistent results between the Luttinger parameter obtained from the linear\ndependence of the structure factor and the algebraic decay of the one-body\ndensity matrix.\n"}
+{"abstract": "  In this article, formulas for the calculation of force and torque between two\ncircular filaments arbitrarily oriented in space were derived by using\nKalantarov-Zeitlin's method. Formulas are presented in an analytical form\nthrough integral expressions, whose kernel function is expressed in terms of\nthe elliptic integrals of the first and second kinds, and provide an\nalternative formulation to Babic's expressions. The derived new formulas were\nvalidated via comparison with a series of reference examples. Also, we obtained\nadditional expressions for the calculation of force and torque between two\ncircular filaments by means of differentiation of Grover's formula for the\nmutual inductance between two circular filaments with respect to appropriate\ncoordinates. These additional expressions allow to verifying comprehensively\nand independently derived new formulas.\n"}
+{"abstract": "  A fundamental problem in spacecraft mission design is to find a free flight\npath from one place to another with a given transfer time. This problem for\npaths in a central force field is known as Lambert's problem. Although this is\nan old problem, we take a new approach. Given two points in the plane, we\nproduce the conic parameters for all conic paths between these points. For a\ngiven central force gravitational parameter, the travel time between the launch\nand destination points is computed along with the initial and final velocities\nfor each transfer conic. For a given travel time, we calculate the parameters\nfor a transfer conic having that travel time.\n"}
+{"abstract": "  In this letter we present the first multiparticle solutions to Einstein's\nfield equations in the presence of matter. These solutions are iteratively\nobtained via the perturbiner method, which can circumvent gravity's infinite\nnumber of vertices with the definition of a multiparticle expansion for the\ninverse spacetime metric as well. Our construction provides a simple layout for\nthe computation of tree level field theory amplitudes in $D$ spacetime\ndimensions involving any number of gravitons and matter fields, with or without\nsupersymmetry.\n"}
+{"abstract": "  We present a new library of semi-empirical stellar spectra that is based on\nthe empirical MILES library. A new, high resolution library of theoretical\nstellar spectra is generated that is specifically designed for use in stellar\npopulation studies. We test these models across their full wavelength range\nagainst other model libraries and find reasonable agreement in their\npredictions of spectral changes due to atmospheric $\\alpha$-element variations,\nknown as differential corrections. We also test the models against the MILES\nand MaStar libraries of empirical stellar spectra and also find reasonable\nagreements, as expected from previous work. We then use the abundance pattern\npredictions of the new theoretical stellar spectra to differentially correct\nMILES spectra to create semi-empirical MILES (sMILES) star spectra with\nabundance patterns that differ from those present in the Milky Way. The final\nresult is 5 families of 801 sMILES stars with [$\\alpha$/Fe] abundances ranging\nfrom $-$0.20 to 0.60 dex at MILES resolution (FWHM=$2.5\\,${\\AA}) and wavelength\ncoverage ($3540.5-7409.6\\,${\\AA}). We make the sMILES library publicly\navailable.\n"}
+{"abstract": "  In this paper we introduce our vision of a Cognitive Computing Continuum to\naddress the changing IT service provisioning towards a distributed,\nopportunistic, self-managed collaboration between heterogeneous devices outside\nthe traditional data center boundaries. The focal point of this continuum are\ncognitive devices, which have to make decisions autonomously using their\non-board computation and storage capacity based on information sensed from\ntheir environment. Such devices are moving and cannot rely on fixed\ninfrastructure elements, but instead realise on-the-fly networking and thus\nfrequently join and leave temporal swarms. All this creates novel demands for\nthe underlying architecture and resource management, which must bridge the gap\nfrom edge to cloud environments, while keeping the QoS parameters within\nrequired boundaries. The paper presents an initial architecture and a resource\nmanagement framework for the implementation of this type of IT service\nprovisioning.\n"}
+{"abstract": "  We show that the number of configurations of the 20 Vertex model on certain\ndomains with domain wall type boundary conditions is equal to the number of\ndomino tilings of Aztec-like triangles, proving a conjecture from [P. Di\nFrancesco and E. Guitter, Twenty-Vertex Model with Domain Wall Boundaries and\nDomino Tilings, Elec. Jour. of Combinatorics 27 (2020), no. 2, P2.13]. The\nresult is based on the integrability of the 20 Vertex model and uses a\nconnection to the U-turn boundary 6 Vertex model to re-express the number of 20\nVertex configurations as a simple determinant, which is then related to a\nLindstr\\\"om-Gessel-Viennot determinant for the domino tiling problem. The\ncommon number of configurations is conjectured to be\n$2^{n(n-1)/2}\\prod_{j=0}^{n-1}\\frac{(4j+2)!}{(n+2j+1)!}=1, 4, 60, 3328,\n678912...$ The enumeration result is extended to include refinements of both\nnumbers.\n"}
+{"abstract": "  The paper develops a general methodology for analyzing policies with\npath-dependency (hysteresis) in stochastic models with forward looking\noptimizing agents. Our main application is a macro-climate model with a\npath-dependent climate externality. We derive in closed form the dynamics of\nthe optimal Pigouvian tax, that is, its drift and diffusion coefficients. The\ndynamics of the present marginal damages is given by the recently developed\nfunctional It\\^o formula. The dynamics of the conditional expectation process\nof the future marginal damages is given by a new total derivative formula that\nwe prove. The total derivative formula represents the evolution of the\nconditional expectation process as a sum of the expected dynamics of hysteresis\nwith respect to time, a form of a time derivative, and the expected dynamics of\nhysteresis with the shocks to the trajectory of the stochastic process, a form\nof a stochastic derivative. We then generalize the results. First, we propose a\ngeneral class of hysteresis functionals that permits significant tractability.\nSecond, we characterize in closed form the dynamics of the stochastic\nhysteresis elasticity that represents the change in the whole optimal policy\nprocess with an introduction of small hysteresis effects. Third, we determine\nthe optimal policy process.\n"}
+{"abstract": "  A theoretical investigation associated with obliquely propagating\nion-acoustic shock waves (IASHWs) in a three-component magnetized plasma having\ninertialess non-extensive electrons, inertial warm positive and negative ions\nhas been performed. A Burgers equation is derived by employing the reductive\nperturbation method. Our plasma model supports both positive and negative shock\nstructures under the consideration of non-extensive electrons. It is found that\nthe positive and negative shock wave potentials increase with the oblique angle\n($\\delta$) which arises due to the external magnetic field. It is also observed\nthat the magnitude of the amplitude of positive and negative shock waves is not\neffected by the variation of the ion kinematic viscosity but the steepness of\nthe positive and negative shock waves decreases with ion kinematic viscosity.\nThe implications of our findings in space and laboratory plasmas are briefly\ndiscussed.\n"}
+{"abstract": "  Let $\\ell$ be a rational prime. Previously, abelian $\\ell$-towers of\nmultigraphs were introduced which are analogous to\n$\\mathbb{Z}_{\\ell}$-extensions of number fields. It was shown that for a\ncertain class of towers of bouquets, the growth of the $\\ell$-part of the\nnumber of spanning trees behaves in a predictable manner (analogous to a\nwell-known theorem of Iwasawa for $\\mathbb{Z}_{\\ell}$-extensions of number\nfields). In this paper, we give a generalization to a broader class of regular\nabelian $\\ell$-towers of bouquets than was originally considered. To carry this\nout, we observe that certain shifted Chebyshev polynomials are members of a\ncontinuously parametrized family of power series with coefficients in\n$\\mathbb{Z}_{\\ell}$ and then study the special value at $s=1$ of the\nArtin-Ihara $L$-function $\\ell$-adically.\n"}
+{"abstract": "  The muon $g-2$ anomaly was strengthened by recent experimental results at\nFermilab, which may be signatures of new physics. A scenario of leptophilic\nscalar $\\phi_L$ accounting for the muon $g-2$ anomaly is investigated in this\npaper. Though a light $\\phi_L$ mainly decaying into standard model (SM)\nparticles has been excluded by experiments, a dark leptophilic scalar $\\phi_L$\npredominantly decaying into invisible fermionic dark matter (DM) $\\chi\n\\bar{\\chi}$ is still allowed. Considering the decay mode $\\phi_L \\to \\chi\n\\bar{\\chi}$ opened (here $m_{\\phi_L} = 3 m_\\chi$), the coupling preferred by\nthe muon $g-2$ and the $\\phi_L-\\chi$ coupling are derived. The light/heavy\n$\\phi_L$ (roughly 1 GeV as a benchmark value) can be tested by future\nexperiments via DM/SM decay modes. The search of $\\chi$ via $\\chi-$electron\nscattering in DM direct detection is not sensitive due to a tiny\n$\\phi_L-$electron coupling, especially for $m_\\chi \\gtrsim m_\\mu$.\n"}
+{"abstract": "  We investigate the in-medium properties of hyperons and anti-hyperons in the\nframework of the Non-Linear Derivative (NLD) model. We focus on the momentum\ndependence of in-medium strangeness optical potentials. The NLD model is based\non the simplicity of the well-established Relativistic Mean-Field (RMF)\napproximation, but it incorporates an explicit momentum dependence on a\nfield-theoretical level. The extension of the NLD model to the\n(anti)baryon-octet is formulated in the spirit of SU(6) and G-parity arguments.\nIt is shown that with an appropriate choice of momentum cut-offs the $\\Lambda$,\n$\\Sigma$ and $\\Xi$ optical potentials are consistent with recent studies of the\nchiral effective field theory and Lattice-QCD calculations over a wide momentum\nregion. In addition, we present NLD predictions for the in-medium momentum\ndependence of $\\overline{\\Lambda}$-, $\\overline{\\Sigma}$- and\n$\\overline{\\Xi}$-hyperons. This work is important for future experimental\nstudies such as CBM, PANDA at the Facility for Antiproton and Ion Research\n(FAIR). It is relevant for nuclear astrophysics too.\n"}
+{"abstract": "  In the field of differential equations, particularly fluid dynamics, many\nresearchers have shown an interest in the behavior of time periodic solutions.\nIn this paper, we study isentropic gas flow in a bounded interval and apply a\ntime periodic outer force. This motion is described by the compressible Euler\nequation with the outer force. Our purpose in this paper is to prove the\nexistence of a time periodic solutions. Unfortunately, little is known for the\nsystem of conservation laws until now. The problem seems to lie in fact that\nthe equation does not possesses appropriate decay estimates.\n"}
+{"abstract": "  We obtain an exact spherically symmetric and magnetically charged black hole\nsolution in 4D Einstein-Gauss-Bonnet gravity coupled with nonlinear\nelectrodynamics with the Lagrangian ${\\cal L}_{NED} = -{\\cal\nF}/(1+\\sqrt{2\\mid\\beta{\\cal F}\\mid})$ (${\\cal F}$ is the field strength\ninvariant). The thermodynamics of the black hole is studied within our model.\nWe calculate the Hawking temperature and the heat capacity of the black hole.\nThe phase transitions occur in the point where the Hawking temperature\npossesses an extremum and the heat capacity diverges. It was shown that black\nholes are thermodynamically stable in some range of event horizon radii when\nthe heat capacity and Hawking temperature are positive. The logarithmic\ncorrection to the Bekenstein - Hawking BH entropy is obtained which mimics a\nquantum correction. The dependence of the BH shadow radius on the model\nparameters is studied. We also investigate the particle emission rate of the\nBH. The stability of the BH is studied by analysing the quasinormal modes.\n"}
+{"abstract": "  We define an exactly solvable model for 2+1D topological phases of matter on\na triangulated surface derived from a crossed module of semisimple\nfinite-dimensional Hopf algebras, the \"Hopf-algebraic higher Kitaev model\".\nThis model generalizes both the Kitaev quantum double model for a semisimple\nHopf algebra and the full higher Kitaev model derived from a 2-group, and can\nhence be interpreted as a Hopf-algebraic discrete higher gauge theory.\n  We construct a family of crossed modules of semisimple Hopf algebras\n$(\\mathscr{F}_{\\mathbb{C}}(X) \\otimes \\mathbb{C}E \\xrightarrow{\\partial}\n\\mathscr{F}_{\\mathbb{C}}(Y) \\rtimes \\mathbb{C} G, \\triangleright)$ that depends\non four finite groups $E,G,X$ and $Y$. We calculate the ground-state spaces of\nthe resulting model when $G=E=\\{1\\}$ and when $Y=\\{1\\}$, both properly\nHopf-algebraic constructions; prove that they are canonically independent of\nthe triangulations; and find a 2+1D TQFT whose state spaces on surfaces give\nthe ground-state spaces. These TQFTs are particular cases of Quinn's finite\ntotal homotopy TQFT and hence the state spaces assigned to surfaces are free\nvector spaces on sets of homotopy classes of maps from a surface to homotopy\nfinite spaces, in this case obtained as classifying spaces of finite groupoids\nand finite crossed modules of groupoids.\n  We leave it as an open problem whether the ground-state space of the\nHopf-algebraic higher Kitaev model on a triangulated surface is independent of\nthe triangulation for general crossed modules of semisimple Hopf algebras,\nwhether a TQFT always exists whose state space on a surface gives the\nground-state space of the model, and whether the ground-state space of the\nmodel obtained from $E,G,X,Y$ can always be given a homotopical explanation.\n"}
+{"abstract": "  The precise calibration of the strain readout of the LIGO gravitational wave\nobservatories is paramount to the accurate interpretation of gravitational wave\nevents. This calibration is traditionally done by imparting a known force on\nthe test masses of the observatory via radiation pressure. Here we describe the\nimplementation of an alternative calibration scheme: the Newtonian Calibrator.\nThis system uses a rotor consisting of both quadrupole and hexapole mass\ndistributions to apply a time-varying gravitational force on one of the\nobservatory's test masses. The force produced by this rotor can be predicted to\n$<1\\%$ relative uncertainty and is well-resolved in the readout of the\nobservatory. This system currently acts as a cross-check of the existing\nabsolute calibration system.\n"}
+{"abstract": "  This paper considers the design of On-Demand Multimodal Transit Systems\n(ODMTS) that combine fixed bus/rail routes between transit hubs with on-demand\nshuttles to serve the first/last miles to/from the hubs. The design problem\naims at finding a network design for the fixed routes to allow a set of riders\nto travel from their origins to their destinations, while minimizing the sum of\nthe travel costs, the bus operating costs, and rider travel times. The paper\naddresses two gaps in existing tools for designing ODMTS. First, it generalizes\nprior work by including ridesharing in the shuttle rides. Second, it proposes\nnovel fleet-sizing algorithms for determining the number of shuttles needed to\nmeet the performance metrics of the ODMTS design. Both contributions are based\non Mixed-Integer Programs (MIP). For the ODMTS design, the MIP reasons about\npickup and dropoff routes in order to capture ridesharing, grouping riders who\ntravel to/from the same hub. The fleet-sizing optimization is modeled as a\nminimum flow problem with covering constraints. The natural formulation leads\nto a dense graph and computational issues, and is refined in an optimized model\nworking on a sparse graph. The methodological contributions are evaluated on a\nreal case study: the public transit system of the broader Ann Arbor and\nYpsilanti region in Michigan. The results demonstrate the substantial potential\nof ridesharing for ODMTS, as costs are reduced by about 26% with minimal\nincrease in transit times. Compared to the existing system, the designed ODMTS\nalso cuts down costs by 35% and reduces transit times by 38%.\n"}
+{"abstract": "  Let $A$ be a right linear operator on a two-sided quaternionic Banach space\n$X$. The paper studies the Drazin inverse for right linear operators on a\nquaternionic Banach space. It is shown that if $A$ is Drazin invertible then\nthe Drazin inverse of $A$ is given by $f(A)$ where $f$ is $0$ in an axially\nsymmetric neighborhood of $0$ and $f(q) = q^{-1}$ in an axially symmetric\nneighborhood of the nonzero spherical spectrum of $A$. Some results analogous\nto the ones concerning the Drazin inverse of operators on complex Banach spaces\nare proved in the quaternionic context.\n"}
+{"abstract": "  The space-weather conditions that result from stellar winds significantly\nimpact the habitability of exoplanets. The conditions can be calculated from\nfirst principles if the necessary boundary conditions -- namely on the plasma\ndensity in the outer corona and the radial distance at which the plasma forces\nthe closed magnetic field into an open geometry -- are specified. Low frequency\nradio observations ($\\nu \\lesssim 200$ MHz) of plasma and cyclotron emission\nfrom stars probe these magneto-ionic conditions. Here we report the detection\nof low-frequency ($120-167\\,{\\rm MHz}$) radio emission associated with the dMe6\nstar WX UMa. If the emission originates in WX UMa's corona, we show that the\nclosed field regions extends to at least $\\approx 10$ stellar radii, that is\nabout a factor of a few larger than the solar value, and possibly to $\\gtrsim\n20$ stellar radii. Our results suggest that the magnetic-field structure of M\ndwarfs is in between Sun-like and planet-like configurations, where compact\nover-dense coronal loops with X-ray emitting plasma co-exist with a large-scale\nmagnetosphere with lower plasma density and closed magnetic geometry.\n"}
+{"abstract": "  The Ensemble Empirical Mode Decomposition (EEMD) has become a preferred\ntechnique to decompose nonlinear and non-stationary signals due to its ability\nto create time-varying basis functions. However, current EEMD signal cleaning\ntechniques are unable to deal with situations where a signal only occurs for a\nportion of the entire recording length. By combining change point detection and\nstatistical hypothesis testing, we demonstrate how to clean a signal to\nemphasize unique local changes within each basis function. This not only allows\nus to observe which frequency bands are undergoing a change, but also leads to\nimproved recovery of the underlying information. Using this technique, we\ndemonstrate improved signal cleaning performance for acoustic shockwave signal\ndetection. The technique is implemented in R via the LCDSC package.\n"}
+{"abstract": "  A search for a heavy resonance decaying to a top quark and a W boson in the\nfully hadronic final state is presented. The analysis is performed using data\nfrom proton-proton collisions at a center-of-mass energy of 13 TeV,\ncorresponding to an integrated luminosity of 137 fb$^{-1}$ recorded by the CMS\nexperiment at the LHC. The search is focused on heavy resonances, where the\ndecay products of each top quark or W boson are expected to be reconstructed as\na single, large-radius jet with a distinct substructure. The production of an\nexcited bottom quark, b*, is used as a benchmark when setting limits on the\ncross section for a heavy resonance decaying to a top quark and a W boson. The\nhypotheses of b* quarks with left-handed, right-handed, and vector-like\nchiralities are excluded at 95% confidence level for masses below 2.6, 2.8, and\n3.1 TeV, respectively. These are the most stringent limits on the b* quark mass\nto date, extending the previous best limits by almost a factor of two.\n"}
+{"abstract": "  Injecting frozen deuterium pellets into an ELMy H-mode plasma is a well\nestablished scheme for triggering edge localized modes (ELMs) before they\nnaturally occur. Based on an ASDEX Upgrade H-mode plasma, this article presents\na comparison of extended MHD simulations of spontaneous type-I ELMs and\npellet-triggered ELMs allowing to study their non-linear dynamics in detail. In\nparticular, pellet-triggered ELMs are simulated by injecting deuterium pellets\ninto different time points during the pedestal build-up described in [A. Cathey\net al. Nuclear Fusion 60, 124007 (2020)]. Realistic ExB and diamagnetic\nbackground plasma flows as well as the time dependent bootstrap current\nevolution are included during the build-up to capture the balance between\nstabilising and destabilising terms for the edge instabilities accurately.\nDependencies on the pellet size and injection times are studied. The\nspatio-temporal structures of the modes and the resulting divertor heat fluxes\nare compared in detail between spontaneous and triggered ELMs. We observe that\nthe premature excitation of ELMs by means of pellet injection is caused by a\nhelical perturbation described by a toroidal mode number of n = 1. In\naccordance with experimental observations, the pellet-triggered ELMs show\nreduced thermal energy losses and narrower divertor wetted area with respect to\nspontaneous ELMs. The peak divertor energy fluency is seen to decrease when\nELMs are triggered by pellets injected earlier during the pedestal build-up.\n"}
+{"abstract": "  We present the discovery of 24 pulsars in 15 Globular Clusters (GCs) using\nthe Five-hundred-meter Aperture Spherical radio Telescope (FAST). These include\nthe first pulsar discoveries in M2, M10, and M14. Most of the new systems are\neither confirmed or likely members of binary systems. M53C, NGC6517H and I are\nthe only three pulsars confirmed to be isolated. M14A is a black widow pulsar\nwith an orbital period of 5.5 hours and a minimum companion mass of 0.016 \\Ms.\nM14E is an eclipsing binary pulsar with an orbital period of 20.3 hours. With\nthe other 8 discoveries that have been reported elsewhere, in total 32 GC\npulsars have been discovered by FAST so far. In addition, We detected M3A\ntwice. This was enough to determine that it is a black widow pulsar with an\norbital period of 3.3 hours and a minimum companion mass of 0.0125 \\Ms.\n"}
+{"abstract": "  We report the detection of a short-lived narrow quasi-periodic oscillation\n(QPO) at ~88 mHz in an Insight-HXMT observation during the soft state of the\npersistent black hole high mass X-ray binary Cygnus X-1. This QPO is\nsignificantly detected in all the three instruments of Insight-HXMT, so in the\nbroad energy range 1-250 keV. The fractional rms of the QPO does not show\nsignificant variations above 3 keV (~5%) while decreases at lower energy (~2%).\nWe show that this QPO is different from the type-A, -B, and -C QPOs usually\nobserved in black hole X-ray binaries. We compare QPOs at similar frequencies\nthat have been previously detected in other persistent high-mass X-ray binaries\nin the soft state; we speculate that such QPOs might relate to some local\ninhomogeneity rarely formed in the accretion flow of wind-fed accretion\nsystems.\n"}
+{"abstract": "  Low resource Handwritten Text Recognition (HTR) is a hard problem due to the\nscarce annotated data and the very limited linguistic information (dictionaries\nand language models). For example, in the case of historical ciphered\nmanuscripts, which are usually written with invented alphabets to hide the\nmessage contents. Thus, in this paper we address this problem through a data\ngeneration technique based on Bayesian Program Learning (BPL). Contrary to\ntraditional generation approaches, which require a huge amount of annotated\nimages, our method is able to generate human-like handwriting using only one\nsample of each symbol in the alphabet. After generating symbols, we create\nsynthetic lines to train state-of-the-art HTR architectures in a segmentation\nfree fashion. Quantitative and qualitative analyses were carried out and\nconfirm the effectiveness of the proposed method.\n"}
+{"abstract": "  In this short note, we compute the rational $C_{2^n}$-equivariant stable\nstems and give minimal presentations for the $RO(C_{2^n})$-graded Bredon\ncohomology of the equivariant classifying spaces $B_{C_{2^n}}S^1$ and\n$B_{C_{2^n}}\\Sigma_2$ over the rational Burnside functor $A_{\\mathbf Q}$. We\nalso examine for which compact Lie groups $L$ the maximal torus inclusion $T\\to\nL$ induces an isomorphism from $H^*_{C_{2^n}}(B_{C_{2^n}}L;A_{\\mathbf Q})$ onto\nthe fixed points of $H^*_{C_{2^n}}(B_{C_{2^n}}T;A_{\\mathbf Q})$ under the Weyl\ngroup action. We prove that this holds for $L=U(m)$ and any $n,m\\ge 1$ but does\nnot hold for $L=SU(2)$ and $n>1$.\n"}
+{"abstract": "  Silicon microstrip detectors are widely used in experiments for space\nastronomy. Before the detector is assembled, extensive characterization of the\nsilicon microstrip sensors is indispensable and challenging. This work\nelectrically evaluates a series of sensor parameters, including the depletion\nvoltage, bias resistance, metal strip resistance, total leakage current, strip\nleakage current, coupling capacitance, and interstrip capacitance. Two methods\nare used to accurately measure the strip leakage current, and the test results\nmatch each other well. In measuring the coupling capacitance, we extract the\ncorrect value based on a SPICE model and two-port network analysis. In\naddition, the expression of the measured bias resistance is deduced based on\nthe SPICE model.\n"}
+{"abstract": "  Ranked list truncation is of critical importance in a variety of professional\ninformation retrieval applications such as patent search or legal search. The\ngoal is to dynamically determine the number of returned documents according to\nsome user-defined objectives, in order to reach a balance between the overall\nutility of the results and user efforts. Existing methods formulate this task\nas a sequential decision problem and take some pre-defined loss as a proxy\nobjective, which suffers from the limitation of local decision and non-direct\noptimization. In this work, we propose a global decision based truncation model\nnamed AttnCut, which directly optimizes user-defined objectives for the ranked\nlist truncation. Specifically, we take the successful transformer architecture\nto capture the global dependency within the ranked list for truncation\ndecision, and employ the reward augmented maximum likelihood (RAML) for direct\noptimization. We consider two types of user-defined objectives which are of\npractical usage. One is the widely adopted metric such as F1 which acts as a\nbalanced objective, and the other is the best F1 under some minimal recall\nconstraint which represents a typical objective in professional search.\nEmpirical results over the Robust04 and MQ2007 datasets demonstrate the\neffectiveness of our approach as compared with the state-of-the-art baselines.\n"}
+{"abstract": "  With the expressed goal of improving system transparency and visual grounding\nin the reasoning process in VQA, we present a modular system for the task of\ncompositional VQA based on scene graphs. Our system is called \"Adventurer's\nTreasure Hunt\" (or ATH), named after an analogy we draw between our model's\nsearch procedure for an answer and an adventurer's search for treasure. We\ndeveloped ATH with three characteristic features in mind: 1. By design, ATH\nallows us to explicitly quantify the impact of each of the sub-components on\noverall VQA performance, as well as their performance on their individual\nsub-task. 2. By modeling the search task after a treasure hunt, ATH inherently\nproduces an explicit, visually grounded inference path for the processed\nquestion. 3. ATH is the first GQA-trained VQA system that dynamically extracts\nanswers by querying the visual knowledge base directly, instead of selecting\none from a specially learned classifier's output distribution over a pre-fixed\nanswer vocabulary. We report detailed results on all components and their\ncontributions to overall VQA performance on the GQA dataset and show that ATH\nachieves the highest visual grounding score among all examined systems.\n"}
+{"abstract": "  The vertices of the Cayley graph of a finitely generated semigroup form a set\nof sites which can be labeled by elements of a finite alphabet in a manner\ngoverned by a nonnegative real interaction matrix, respecting nearest neighbor\nadjacency restrictions. To the set of these configurations one can associate a\npressure, which is defined as the limit, when it exists, of averages of the\nlogarithm of the partition function over certain finite subgraphs. We prove\nthat for shifts of finite type on generalized Fibonacci trees, under an added\ncondition, the limit exists and is given by an infinite series. We also show\nthat the limit of any cluster points of the pressure on finite subtrees as the\nnumber of generators grows without bound, which we call the asymptotic\npressure, equals the logarithm of the maximum row sum of the interaction\nmatrix.\n"}
+{"abstract": "  Data hiding is the art of hiding secret data into a cover object such as\ndigital image for covert communication. In this paper, we make the first step\ntowards hiding ``data hiding'', which is totally different from many\nconventional works that directly embed secret data in a given cover object. In\ndetail, we propose a novel method to disguise data hiding tools, including a\ndata embedding tool and a data extraction tool, as a deep neural network (DNN)\nwith an ordinary task (i.e., style transfer). After training the DNN for both\nstyle transfer and data hiding, while the DNN can transfer the style of an\nimage to the target one, it can also hide secret data into a cover image or\nextract secret data from a stego image. In other words, the tools of data\nhiding are hidden to avoid arousing suspicion. Experimental results and\nanalysis have shown the feasibility, applicability and superiority of the\nproposed method.\n"}
+{"abstract": "  Disordered quantum systems feature an energy scale know as the Thouless\nenergy. For energy ranges below this scale, the properties of the energy\nspectrum can be described by random matrix theory. Above this scale a different\nbehavior sets in. For a metallic system it has been long ago shown by Altshuler\nand Shklovskii that the number variance should increase as a power law with a\npower dependent only on the dimensionality of the system. Although tantalizing\nhints for this behavior have been seen in previous numerical studies, it is\nquite difficult to verify this prediction using the standard local unfolding\nmethods. Here we use a different unfolding method, i.e., the singular value\ndecomposition, and establish a connection between the power law behavior of the\nscree plot (the singular values ranked by their amplitude) and the power law\nbehavior of the number variance. Thus we are able to numerically verify the\nAltshuler and Shklovskii's prediction for disordered $3D$, $4D$, and $5D$\nsingle-electron Anderson models on square lattices in the metallic regime. The\nsame method could be applied to systems such as the Sachdev-Ye-Kitaev model and\nvarious interacting many body models for which the many body localization\noccurs. It has been recently reported that such systems exhibit a Thouless\nenergy and analyzing the spectrum's behavior on larger scales is of much\ncurrent interest.\n"}
+{"abstract": "  The microscopic processes underlying electro-optic sampling of quantum-vacuum\nfluctuations are discussed, leading to the interpretation of these experiments\nin terms of an exchange of virtual photons. With this in mind it is shown how\none can directly study the Purcell effect, i.e. the changes induced by cavities\nupon the quantum vacuum, in the frequency and time domains. This forges a link\nbetween electro-optic sampling of the quantum vacuum and geometry-induced\nvacuum effects.\n"}
+{"abstract": "  Nonzero neutrino masses imply the existence of degrees of freedom and\ninteractions beyond those in the Standard Model. A powerful indicator of what\nthese might be is the nature of the massive neutrinos: Dirac fermions versus\nMajorana fermions. While addressing the nature of neutrinos is often associated\nwith searches for lepton-number violation, there are several other features\nthat distinguish Majorana from Dirac fermions. Here, we compute in great detail\nthe kinematics of the daughters of the decays into charged-leptons and\nneutrinos of hypothetical heavy neutral leptons at rest. We allow for the decay\nto be mediated by the most general four-fermion interaction Lagrangian. We\ndemonstrate, for example, that when the daughter charged-leptons have the same\nflavor or the detector is insensitive to their charges, polarized\nMajorana-fermion decays have zero forward/backward asymmetry in the direction\nof the outgoing neutrino (relative to the parent spin), whereas Dirac-fermion\ndecays can have large asymmetries. Going beyond studying forward/backward\nasymmetries, we also explore the fully-differential width of the three-body\ndecays. It contains a wealth of information not only about the nature of the\nnew fermions but also the nature of the interactions behind their decays.\n"}
+{"abstract": "  Line segmentation from handwritten text images is one of the challenging task\ndue to diversity and unknown variations as undefined spaces, styles,\norientations, stroke heights, overlapping, and alignments. Though abundant\nresearches, there is a need of improvement to achieve robustness and higher\nsegmentation rates. In the present work, an adaptive approach is used for the\nline segmentation from handwritten text images merging the alignment of\nconnected component coordinates and text height. The mathematical justification\nis provided for measuring the text height respective to the image size. The\nnovelty of the work lies in the text height calculation dynamically. The\nexperiments are tested on the dataset provided by the Chinese company for the\nproject. The proposed scheme is tested on two different type of datasets;\ndocument pages having base lines and plain pages. Dataset is highly complex and\nconsists of abundant and uncommon variations in handwriting patterns. The\nperformance of the proposed method is tested on our datasets as well as\nbenchmark datasets, namely IAM and ICDAR09 to achieve 98.01% detection rate on\naverage. The performance is examined on the above said datasets to observe\n91.99% and 96% detection rates, respectively.\n"}
+{"abstract": "  Using {\\it Gaia} Early Data Release 3 (EDR3) parallaxes and Bayesian\ninference, we infer a parallax of the Westerlund 1 (Wd1) cluster. We find a\nparallax of $0.34\\pm{0.05}$ mas corresponding to a distance of\n$2.8^{+0.7}_{-0.6}$ kpc. The new {\\it Gaia} EDR3 distance is consistent with\nour previous result using {\\it Gaia} DR2 parallaxes. This confirms that Wd1 is\nless massive and older than previously assumed. Compared to DR2, the EDR3\nindividual parallax uncertainties for each star decreased by 30\\%. However, the\naggregate parallax uncertainty for the cluster remained the same. This suggests\nthat the uncertainty is dominated by systematics, which is possibly due to\ncrowding, motions within the cluster, or motions due to binary orbits.\n"}
+{"abstract": "  Very recently, To et al.~have experimentally explored granular flow in a\ncylindrical silo, with a bottom wall that rotates horizontally with respect to\nthe lateral wall \\cite{Kiwing2019}. Here, we numerically reproduce their\nexperimental findings, in particular, the peculiar behavior of the mass flow\nrate $Q$ as a function of the frequency of rotation $f$. Namely, we find that\nfor small outlet diameters $D$ the flow rate increased with $f$, while for\nlarger $D$ a non-monotonic behavior is confirmed. Furthermore, using a\ncoarse-graining technique, we compute the macroscopic density, momentum, and\nthe stress tensor fields. These results show conclusively that changes in the\ndischarge process are directly related to changes in the flow pattern from\nfunnel flow to mass flow. Moreover, by decomposing the mass flux (linear\nmomentum field) at the orifice into two main factors: macroscopic velocity and\ndensity fields, we obtain that the non-monotonic behavior of the linear\nmomentum is caused by density changes rather than by changes in the macroscopic\nvelocity. In addition, by analyzing the spatial distribution of the kinetic\nstress, we find that for small orifices increasing rotational shear enhances\nthe mean kinetic pressure $\\langle p^k \\rangle$ and the system dilatancy. This\nreduces the stability of the arches, and, consequently, the volumetric flow\nrate increases monotonically. For large orifices, however, we detected that\n$\\langle p^k \\rangle$ changes non-monotonically, which might explain the\nnon-monotonic behavior of $Q$ when varying the rotational shear.\n"}
+{"abstract": "  In this short note, we observe that Theorem 3.1 in arXiv:1508.00682 for\nsemiorthogonal indecomposability of the derived category of smooth DM stacks\nbased on the canonical bundle can be extended to the case of projective\nvarieties with Cohen-Macaulay singularities. As a consequence, all projective\ncurves of positive arithmetic genus have weakly indecomposable bounded derived\ncategories and indecomposable categories of perfect complexes. Here weak\nindecomposablility refers to the admissibility of components.\n"}
+{"abstract": "  We present an audio-visual speech separation learning method that considers\nthe correspondence between the separated signals and the visual signals to\nreflect the speech characteristics during training. Audio-visual speech\nseparation is a technique to estimate the individual speech signals from a\nmixture using the visual signals of the speakers. Conventional studies on\naudio-visual speech separation mainly train the separation model on the\naudio-only loss, which reflects the distance between the source signals and the\nseparated signals. However, conventional losses do not reflect the\ncharacteristics of the speech signals, including the speaker's characteristics\nand phonetic information, which leads to distortion or remaining noise. To\naddress this problem, we propose the cross-modal correspondence (CMC) loss,\nwhich is based on the cooccurrence of the speech signal and the visual signal.\nSince the visual signal is not affected by background noise and contains\nspeaker and phonetic information, using the CMC loss enables the audio-visual\nspeech separation model to remove noise while preserving the speech\ncharacteristics. Experimental results demonstrate that the proposed method\nlearns the cooccurrence on the basis of CMC loss, which improves separation\nperformance.\n"}
+{"abstract": "  This paper focuses on inverse reinforcement learning for autonomous\nnavigation using distance and semantic category observations. The objective is\nto infer a cost function that explains demonstrated behavior while relying only\non the expert's observations and state-control trajectory. We develop a map\nencoder, that infers semantic category probabilities from the observation\nsequence, and a cost encoder, defined as a deep neural network over the\nsemantic features. Since the expert cost is not directly observable, the model\nparameters can only be optimized by differentiating the error between\ndemonstrated controls and a control policy computed from the cost estimate. We\npropose a new model of expert behavior that enables error minimization using a\nclosed-form subgradient computed only over a subset of promising states via a\nmotion planning algorithm. Our approach allows generalizing the learned\nbehavior to new environments with new spatial configurations of the semantic\ncategories. We analyze the different components of our model in a minigrid\nenvironment. We also demonstrate that our approach learns to follow traffic\nrules in the autonomous driving CARLA simulator by relying on semantic\nobservations of buildings, sidewalks, and road lanes.\n"}
+{"abstract": "  Let $\\Delta+V$ be the discrete Schr\\\"odinger operator,\n  where $\\Delta$ is the discrete Laplacian on $\\Z^d$ and the potential\n$V:\\Z^d\\to \\R$ is periodic.\n  We prove three rigidity theorems for discrete periodic Schr\\\"odinger\noperators in any dimension $d\\geq 3$:\n  \\begin{enumerate}\n  \\item if at some energy level, Fermi varieties of the $\\Gamma$-periodic\npotential $V$ and the $\\Gamma$-periodic potential $Y$ are the same (referred to\nas {\\it Fermi isospectrality} of $V$ and $Y$), and $Y $ is a separable\nfunction, then $V$ is separable;\n  \\item if potentials $V$ and $Y$ are {\\it Fermi isospectral} and both\n$V=\\bigoplus_{j=1}^rV_j$ and $Y=\\bigoplus_{j=1}^r Y_j$ are separable functions,\nthen, up to a constant, lower dimensional decompositions $V_j$ and $Y_j$ are\nFloquet isospectral, $j=1,2,\\cdots,r$;\n  \\item if a potential $V$ and the zero potential are {\\it Fermi isospectral},\nthen $V$ is zero.\n  \\end{enumerate} In particular, all conclusions in (1), (2) and (3) hold if we\nreplace the assumption ``Fermi isospectrality\" with a stronger assumption\n``Floquet isospectrality\".\n"}
+{"abstract": "  Readout errors on near-term quantum computers can introduce significant error\nto the empirical probability distribution sampled from the output of a quantum\ncircuit. These errors can be mitigated by classical postprocessing given the\naccess of an experimental \\emph{response matrix} that describes the error\nassociated with measurement of each computational basis state. However, the\nresources required to characterize a complete response matrix and to compute\nthe corrected probability distribution scale exponentially in the number of\nqubits $n$. In this work, we modify standard matrix inversion techniques using\ntwo perturbative approximations with significantly reduced complexity and\nbounded error when the likelihood of high order bitflip events is strongly\nsuppressed. Given a characteristic error rate $q$, our first method recovers\nthe probability of the all-zeros bitstring $p_0$ by sampling only a small\nsubspace of the response matrix before inverting readout error resulting in a\nrelative speedup of $\\text{poly}\\left(2^{n} / \\big(\\begin{smallmatrix} n \\\\ w\n\\end{smallmatrix}\\big)\\right)$, which we motivate using a simplified error\nmodel for which the approximation incurs only $O(q^w)$ error for some integer\n$w$. We then provide a generalized technique to efficiently recover full output\ndistributions with $O(q^w)$ error in the perturbative limit. These approximate\ntechniques for readout error correction may greatly accelerate near term\nquantum computing applications.\n"}
+{"abstract": "  A positive integer $n$ is called perfect if $ \\sigma(n)=2n$, where\n$\\sigma(n)$ denote the sum of divisors of $n$. In this paper we study the ratio\n$\\frac{\\sigma(n)}{n}$. We define the function Abundancy Index $I:\\mathbb{N} \\to\n\\mathbb{Q}$ with $I(n)=\\frac{\\sigma(n)}{n}$. Then we study different properties\nof the Abundancy Index and discuss the set of Abundancy Index. Using this\nfunction we define a new class of numbers known as superabundant numbers.\nFinally, we study superabundant numbers and their connection with Riemann\nHypothesis.\n"}
+{"abstract": "  Can a dynamically robust bosonic star (BS) produce an (effective) shadow that\nmimics that of a black hole (BH)? The BH shadow is linked to the existence of\nlight rings (LRs). For free bosonic fields, yielding mini-BSs, it is known that\nthese stars can become ultra-compact - i.e., possess LRs - but only for\nperturbatively unstable solutions. We show this remains the case even when\ndifferent self-interactions are considered. However, an effective shadow can\narise in a different way: if BSs reproduce the existence of an innermost stable\ncircular orbit (ISCO) for timelike geodesics (located at $r_{\\rm ISCO}=6M$ for\na Schwarzschild BH of mass M), the accretion flow morphology around BHs is\nmimicked and an effective shadow arises in an astrophysical environment. Even\nthough spherical BSs may accommodate stable timelike circular orbits all the\nway down to their centre, we show the angular velocity along such orbits may\nhave a maximum away from the origin, at $R_{\\Omega}$; this scale was recently\nobserved to mimic the BH's ISCO in some scenarios of accretion flow. Then: (i)\nfor free scalar fields or with quartic self-interactions, $R_{\\Omega}\\neq 0$\nonly for perturbatively unstable BSs; (ii) for higher scalar self-interactions,\ne.g. axionic, $R_{\\Omega}\\neq 0$ is possible for perturbatively stable BSs, but\nno solution with $R_{\\Omega}=6M$ was found in the parameter space explored;\n(iii) but for free vector fields, yielding Proca stars (PSs), perturbatively\nstable solutions with $R_{\\Omega}\\neq 0$ exist, and indeed $R_{\\Omega}=6M$ for\na particular solution. Thus, dynamically robust spherical PSs can mimic the\nshadow of a (near-)equilibrium Schwarzschild BH with the same M, in an\nastrophysical environment, despite the absence of a LR, at least under some\nobservation conditions, as we confirm by comparing the lensing of such PSs and\nSchwarzschild BHs.\n"}
+{"abstract": "  Cell-free massive MIMO (CF-mMIMO) systems represent a promising approach to\nincrease the spectral efficiency of wireless communication systems. However,\nnear-optimal beamforming solutions require a large amount of signaling exchange\nbetween access points (APs) and the network controller (NC). In this letter, we\npropose two unsupervised deep neural networks (DNN) architectures, fully and\npartially distributed, that can perform decentralized coordinated beamforming\nwith zero or limited communication overhead between APs and NC, for both fully\ndigital and hybrid precoding. The proposed DNNs achieve near-optimal sum-rate\nwhile also reducing computational complexity by 10-24x compared to conventional\nnear-optimal solutions.\n"}
+{"abstract": "  Medical image segmentation, or computing voxelwise semantic masks, is a\nfundamental yet challenging task to compute a voxel-level semantic mask. To\nincrease the ability of encoder-decoder neural networks to perform this task\nacross large clinical cohorts, contrastive learning provides an opportunity to\nstabilize model initialization and enhance encoders without labels. However,\nmultiple target objects (with different semantic meanings) may exist in a\nsingle image, which poses a problem for adapting traditional contrastive\nlearning methods from prevalent 'image-level classification' to 'pixel-level\nsegmentation'. In this paper, we propose a simple semantic-aware contrastive\nlearning approach leveraging attention masks to advance multi-object semantic\nsegmentation. Briefly, we embed different semantic objects to different\nclusters rather than the traditional image-level embeddings. We evaluate our\nproposed method on a multi-organ medical image segmentation task with both\nin-house data and MICCAI Challenge 2015 BTCV datasets. Compared with current\nstate-of-the-art training strategies, our proposed pipeline yields a\nsubstantial improvement of 5.53% and 6.09% on Dice score for both medical image\nsegmentation cohorts respectively (p-value<0.01). The performance of the\nproposed method is further assessed on natural images via the PASCAL VOC 2012\ndataset, and achieves a substantial improvement of 2.75% on mIoU\n(p-value<0.01).\n"}
+{"abstract": "  In \\cite{NNM} the author with A. N\\'emethi computed the multiplicity of\ngeneric surface singularities, the formula is purely topological computable\nfrom the resolution graph of the surface singularity. In the present paper we\nextend the results partly to the relative case, when there is a pair of\nresolution graphs $\\mathcal{T}_1 \\subset \\mathcal{T}$, a fixed singularity\n$\\tX_1$ with resolution graph $\\mathcal{T}_1$, a relatively generic singularity\n$\\tX$ corresponding to the subsingularity $\\tX_1$ with resolution graph\n$\\mathcal{T}$.\n  We determine the base points of the natural line bundles (under some mild\nconditions) on $\\tX$ from the resolution graph $\\mathcal{T}$ and the analytic\ninvariants of the subsingularity $\\tX_1$. For each base point $p$ we determine\na lower bound for the number $t_p$ such that $p$ is $t_p$-simple and we compute\nfrom it a lower bound of the multiplicity of $\\tX$, which is sharp in all known\ncases.\n"}
+{"abstract": "  For a compact convex set F in R^n, with the origin in its interior, we\npresent a formula to compute the curvature at a fixed point on its boundary, in\nthe direction of any tangent vector. This formula is equivalent to the existing\nones, but it is easier to apply.\n"}
+{"abstract": "  The organising principles underlying the structure of phenomenologically\nviable string vacua can be accessed by sampling such vacua. In many cases this\nis prohibited by the computational cost of standard sampling methods in the\nhigh dimensional model space. Here we show how this problem can be alleviated\nusing reinforcement learning techniques to explore string flux vacua. We\ndemonstrate in the case of the type IIB flux landscape that vacua with\nrequirements on the expectation value of the superpotential and the string\ncoupling can be sampled significantly faster by using reinforcement learning\nthan by using metropolis or random sampling. Our analysis is on conifold and\nsymmetric torus background geometries. We show that reinforcement learning is\nable to exploit successful strategies for identifying such phenomenologically\ninteresting vacua. The strategies are interpretable and reveal previously\nunknown correlations in the flux landscape.\n"}
+{"abstract": "  Cybersecurity is a very emerging field that protects systems, networks, and\ndata from digital attacks. With the increase in the scale of the Internet and\nthe evolution of cyber attacks, developing novel cybersecurity tools has become\nimportant, particularly for Internet of things (IoT) networks. This paper\nprovides a systematic review of the application of deep learning (DL)\napproaches for cybersecurity. This paper provides a short description of DL\nmethods which is used in cybersecurity, including deep belief networks,\ngenerative adversarial networks, recurrent neural networks, and others. Next,\nwe illustrate the differences between shallow learning and DL. Moreover, a\ndiscussion is provided on the currently prevailing cyber-attacks in IoT and\nother networks, and the effectiveness of DL methods to manage these attacks.\nBesides, this paper describes studies that highlight the DL technique,\ncybersecurity applications, and the source of datasets. Next, a discussion is\nprovided on the feasibility of DL systems for malware detection and\nclassification, intrusion detection, and other frequent cyber-attacks,\nincluding identifying file type, spam, and network traffic. Our review\nindicates that high classification accuracy of 99.72% is obtained by restricted\nBoltzmann machine (RBM) when applied to a custom dataset, while long short-term\nmemory (LSTM) achieves an accuracy of 99.80% for KDD Cup 99 dataset. Finally,\nthis article discusses the importance of cybersecurity for reliable and\npracticable IoT-driven healthcare systems.\n"}
+{"abstract": "  This paper presents a general procedure based on using the method of types to\ncalculate the box dimension of sets. The approach unifies and simplifies\nmultiple box counting arguments. In particular, we use it to generalize the\nformula for the box dimension of self-affine carpets of Gatzouras-Lalley and of\nBara\\'nski type to their higher dimensional sponge analogues. In addition to a\nclosed form, we also obtain a variational formula which resembles the\nLedrappier-Young formula for Hausdorff dimension.\n"}
+{"abstract": "  In quantum fluids, the quantisation of circulation forbids the diffusion of a\nvortex swirling flow seen in classical viscous fluids. Yet, a quantum vortex\naccelerating in a superfluid may lose its energy into acoustic radiation, in a\nsimilar way an electric charge decelerates upon emitting photons. The\ndissipation of vortex energy underlies central problems in quantum\nhydrodynamics, such as the decay of quantum turbulence, highly relevant to\nsystems as varied as neutron stars, superfluid helium and atomic condensates. A\ndeep understanding of the elementary mechanisms behind irreversible vortex\ndynamics has been a goal for decades, but it is complicated by the shortage of\nconclusive experimental signatures. Here, we address this challenge by\nrealising a programmable quantum vortex collider in a planar, homogeneous\natomic Fermi superfluid with tunable inter-particle interactions. We create\non-demand vortex configurations and monitor their evolution, taking advantage\nof the accessible time and length scales of our ultracold Fermi gas.\nEngineering collisions within and between vortex-antivortex pairs allows us to\ndecouple relaxation of the vortex energy due to sound emission and interactions\nwith normal fluid, i.e. mutual friction. We directly visualise how the\nannihilation of vortex dipoles radiates a sound pulse in the superfluid.\nFurther, our few-vortex experiments extending across different superfluid\nregimes suggest that fermionic quasiparticles localised inside the vortex core\ncontribute significantly to dissipation, opening the route to exploring new\npathways for quantum turbulence decay, vortex by vortex.\n"}
+{"abstract": "  The destructive interference of the neighbouring field configurations with\ninfinite classical action in the gravitational path integral approach serves as\na dynamical mechanism resolving the black hole singularity problem. It also\nprovides an isotropic and homogeneous early universe without the need of\ninflation. In this work, we elaborate on the finite action in the framework of\nHorava-Lifshitz gravity -- a ghost-free QFT. Assuming the mixmaster chaotic\nsolutions in the projectable H-L theory, we show that the beginning of the\nuniverse is homogeneous and isotropic. Furthermore, we show that the H-L\ngravity action selects only the regular black-hole spacetimes. We also comment\non possibility of traversable wormholes in theories with higher curvature\ninvariants.\n"}
+{"abstract": "  Owing to the discrete frequency spectrum of whispering gallery resonators\n(WGRs), the resonance and phase-matching conditions for the interacting waves\nin the case of second-harmonic generation (SHG) cannot generally be fulfilled\nsimultaneously. To account for this, we develop a model describing SHG in WGRs\nwith non-zero frequency detunings at both the pump and second-harmonic\nfrequencies. Our model predicts strong distortions of the line shape of pump\nand second-harmonic resonances for similar linewidths at both frequencies; for\nmuch larger linewidths at the second-harmonic frequency, this behavior is\nabsent. Furthermore, it describes the SHG efficiency as a function of detuning.\nExperimentally, one can change the WGR eigenfrequencies, and thus the relative\ndetuning between pump and second-harmonic waves by a number of means, for\nexample electro-optically and thermally. Using a lithium niobate WGR, we show\nan excellent quantitative agreement for the SHG efficiency between our\nexperimental results and the model. Also, we show the predicted distortions of\nthe pump and second-harmonic resonances to be absent in the lithium niobate\nWGR, but present in a cadmium silicon phosphide WGR, as expected from the\nlinewidths of the resonances involved.\n"}
+{"abstract": "  The empirical success of deep convolutional networks on tasks involving\nhigh-dimensional data such as images or audio suggests that they can\nefficiently approximate certain functions that are well-suited for such tasks.\nIn this paper, we study this through the lens of kernel methods, by considering\nsimple hierarchical kernels with two or three convolution and pooling layers,\ninspired by convolutional kernel networks. These achieve good empirical\nperformance on standard vision datasets, while providing a simple enough\ndescription of the functional space to shed light on their inductive bias. We\nshow that the RKHS consists of additive models of interaction terms between\npatches, and that its norm encourages structured spatial similarities between\nthese terms through pooling layers. We then provide generalization bounds which\nillustrate how pooling yields improved sample complexity guarantees when the\ntarget function presents such regularities.\n"}
+{"abstract": "  Orbits of automorphism groups of partially ordered sets are not necessarily\ncongruence classes, i.e. images of an order homomorphism. Based on so-called\norbit categories a framework of factorisations and unfoldings is developed that\npreserves the antisymmetry of the order Relation. Finally some suggestions are\ngiven, how the orbit categories can be represented by simple directed and\nannotated graphs and annotated binary relations. These relations are reflexive,\nand, in many cases, they can be chosen to be antisymmetric. From these\nconstructions arise different suggestions for fundamental systems of partially\nordered sets and reconstruction data which are illustrated by examples from\nmathematical music theory.\n"}
+{"abstract": "  We prove that two analytic branches in $(\\mathbb{C}^n,0)$ whose dual\nresolution graph is the same admit an ambient isotopy which is smooth outside\nthe origin. A weaker version of the converse is also proved.\n"}
+{"abstract": "  The need to evaluate treatment effectiveness is ubiquitous in most of\nempirical science, and interest in flexibly investigating effect heterogeneity\nis growing rapidly. To do so, a multitude of model-agnostic, nonparametric\nmeta-learners have been proposed in recent years. Such learners decompose the\ntreatment effect estimation problem into separate sub-problems, each solvable\nusing standard supervised learning methods. Choosing between different\nmeta-learners in a data-driven manner is difficult, as it requires access to\ncounterfactual information. Therefore, with the ultimate goal of building\nbetter understanding of the conditions under which some learners can be\nexpected to perform better than others a priori, we theoretically analyze four\nbroad meta-learning strategies which rely on plug-in estimation and\npseudo-outcome regression. We highlight how this theoretical reasoning can be\nused to guide principled algorithm design and translate our analyses into\npractice by considering a variety of neural network architectures as\nbase-learners for the discussed meta-learning strategies. In a simulation\nstudy, we showcase the relative strengths of the learners under different\ndata-generating processes.\n"}
+{"abstract": "  We consider the infrared factorisation of non-abelian multi-particle\nscattering amplitudes, and we study the form of the universal colour operator\nresponsible for infrared divergences, when expressed in terms of coordinates on\nthe `celestial sphere' intersecting the future light-cone at asymptotic\ndistances. We find that colour-dipole contributions to the infrared operator,\nto all orders in perturbation theory, have a remarkably simple expression in\nthese coordinates, with scale and coupling dependence factorised from\nkinematics and colour. Generalising earlier suggestions in the abelian theory,\nwe then show that the infrared operator can be computed as a correlator of\nvertex operators in a conformal field theory of Lie-algebra-valued free bosons\non the celestial sphere. We verify by means of the OPE that the theory\ncorrectly predicts the all-order structure of collinear limits, and the\ntree-level factorisation of soft real radiation.\n"}
+{"abstract": "  First-order convergence in time and space is proved for a fully discrete\nsemi-implicit finite element method for the two-dimensional Navier--Stokes\nequations with $L^2$ initial data in convex polygonal domains, without extra\nregularity assumptions or grid-ratio conditions. The proof utilises the\nsmoothing properties of the Navier--Stokes equations, an appropriate duality\nargument, and the smallness of the numerical solution in the discrete\n$L^2(0,t_m;H^1)$ norm when $t_m$ is smaller than some constant. Numerical\nexamples are provided to support the theoretical analysis.\n"}
+{"abstract": "  Improvements in rechargeable batteries are enabling several electric urban\nair mobility (UAM) aircraft designs with up to 300 miles of range with payload\nequivalents of up to 7 passengers. We find that novel UAM aircraft consume\nbetween 130 Wh/passenger-mile up to ~1,200 Wh/passenger-mile depending on the\ndesign and utilization, relative to an expected consumption of over 220\nWh/passenger-mi for terrestrial electric vehicles and 1,000 Wh/passenger-mile\nfor combustion engine vehicles. We also find that several UAM aircraft designs\nare approaching technological viability with current Li-ion batteries, based on\nthe specific power-and-energy while rechargeability and lifetime performance\nremain uncertain. These aspects highlight the technological readiness of a new\nsegment of transportation.\n"}
+{"abstract": "  Ivrii's conjecture asserts that the Cauchy problem is $C^{\\infty}$ well-posed\nfor any lower order term if every critical point of the principal symbol is\neffectively hyperbolic. Effectively hyperbolic critical point is at most triple\ncharacteristic. If every characteristic is at most double this conjecture has\nbeen proved in 1980's. In this paper we prove the conjecture for the remaining\ncases, that is for operators with triple effectively hyperbolic\ncharacteristics.\n"}
+{"abstract": "  Presentation slides have become a common addition to the teaching material.\nEmphasizing strong leading words in presentation slides can allow the audience\nto direct the eye to certain focal points instead of reading the entire slide,\nretaining the attention to the speaker during the presentation. Despite a large\nvolume of studies on automatic slide generation, few studies have addressed the\nautomation of design assistance during the creation process. Motivated by this\ndemand, we study the problem of Emphasis Selection (ES) in presentation slides,\ni.e., choosing candidates for emphasis, by introducing a new dataset containing\npresentation slides with a wide variety of topics, each is annotated with\nemphasis words in a crowdsourced setting. We evaluate a range of\nstate-of-the-art models on this novel dataset by organizing a shared task and\ninviting multiple researchers to model emphasis in this new domain. We present\nthe main findings and compare the results of these models, and by examining the\nchallenges of the dataset, we provide different analysis components.\n"}
+{"abstract": "  The baryon acoustic oscillations (BAO) have proven to be an invaluable tool\nin constraining the expansion history of the Universe at late times and are\ncharacterized by the comoving sound horizon at the baryon drag epoch\n$r_\\mathrm{s}(z_\\mathrm{d})$. The latter quantity can be calculated either\nnumerically using recombination codes or via fitting functions, such as the one\nby Eisenstein and Hu (EH), made via grids of parameters of the recombination\nhistory. Here we quantify the accuracy of these expressions and show that they\ncan strongly bias the derived constraints on the cosmological parameters using\nBAO data. Then, using a machine learning approach, called the genetic\nalgorithms, we proceed to derive new analytic expressions for\n$r_\\mathrm{s}(z_\\mathrm{d})$ which are accurate at the $\\sim0.003\\%$ level in a\nrange of $10\\sigma$ around the Planck 2018 best-fit or $\\sim0.018\\%$ in a much\nbroader range, compared to $\\sim 2-4\\%$ for the EH expression, thus obtaining\nan improvement of two to three orders of magnitude. Moreover, we also provide\nfits that include the effects of massive neutrinos and an extension to the\nconcordance cosmological model assuming variations of the fine structure\nconstant. Finally, we note that our expressions can be used to ease the\ncomputational cost required to compute $r_\\mathrm{s}(z_\\mathrm{d})$ with a\nBoltzmann code when deriving cosmological constraints using BAO data from\ncurrent and upcoming surveys.\n"}
+{"abstract": "  Predicting accurate future trajectories of multiple agents is essential for\nautonomous systems, but is challenging due to the complex agent interaction and\nthe uncertainty in each agent's future behavior. Forecasting multi-agent\ntrajectories requires modeling two key dimensions: (1) time dimension, where we\nmodel the influence of past agent states over future states; (2) social\ndimension, where we model how the state of each agent affects others. Most\nprior methods model these two dimensions separately, e.g., first using a\ntemporal model to summarize features over time for each agent independently and\nthen modeling the interaction of the summarized features with a social model.\nThis approach is suboptimal since independent feature encoding over either the\ntime or social dimension can result in a loss of information. Instead, we would\nprefer a method that allows an agent's state at one time to directly affect\nanother agent's state at a future time. To this end, we propose a new\nTransformer, AgentFormer, that jointly models the time and social dimensions.\nThe model leverages a sequence representation of multi-agent trajectories by\nflattening trajectory features across time and agents. Since standard attention\noperations disregard the agent identity of each element in the sequence,\nAgentFormer uses a novel agent-aware attention mechanism that preserves agent\nidentities by attending to elements of the same agent differently than elements\nof other agents. Based on AgentFormer, we propose a stochastic multi-agent\ntrajectory prediction model that can attend to features of any agent at any\nprevious timestep when inferring an agent's future position. The latent intent\nof all agents is also jointly modeled, allowing the stochasticity in one\nagent's behavior to affect other agents. Our method substantially improves the\nstate of the art on well-established pedestrian and autonomous driving\ndatasets.\n"}
+{"abstract": "  I am considering Veltman's \"The Infrared - Ultraviolet Connection\" addressing\nthe issue of quadratic divergences and the related huge radiative correction\npredicted by the electroweak Standard Model (SM) in the relationship between\nthe bare and the renormalized theory, commonly called \"the hierarchy problem\"\nwhich usually is claimed that this has to be cured. After the discovery of the\nHiggs particle at CERN, which essentially completed the SM, an amazing\ninterrelation of the leading interaction strengths of the gauge bosons, the\ntop-quark and the Higgs boson showed up amounting that the SM allows for a\nperturbative extrapolation of the running couplings up to the Planck scale. The\ncentral question concerns the stability of the electroweak vacuum, which\nrequires that the running Higgs self-coupling stays positive. Although several\nevaluations seem to favor the meta-stability within the experimental and\ntheoretical parameter-uncertainties, one should not exclude the possibility\nthat other experiments and improved matching conditions will be able to\nestablish the absolute stability of the SM vacuum in the future. I will discuss\nthe stable vacuum scenario and its impact on early cosmology, revealing the\nHiggs boson as the inflaton. It turns out that the Standard Model's presumed\n\"hierarchy problem\" and similarly the \"cosmological constant problem\" resolve\nthemselves when we understand the SM as a low energy effective tail that is\nemergent from a cutoff-medium at the Planck scale. \"The Infrared - Ultraviolet\nConnection\" conveyed by the Higgs boson mass renormalization appears in a new\nlight when the energy dependence of the SM couplings is taken into account. The\nbare Higgs boson mass square then changes sign below the Planck scale where it\nis activating the Higgs mechanism.\n"}
+{"abstract": "  We present a method to construct \"native\" type systems for a broad class of\nlanguages, in which types are built from term constructors by predicate logic\nand dependent types. Many languages can be modelled as structured\nlambda-theories, and the internal language of their presheaf toposes provides\ntotal specification the structure and behavior of programs. The construction is\nfunctorial, thereby providing a shared framework of higher-order reasoning for\nmost existing programming languages.\n"}
+{"abstract": "  The main object of investigation in this paper is a very general regression\nmodel in optional setting - when an observed process is an optional\nsemimartingale depending on an unknown parameter. It is well-known that\nstatistical data may present an information flow/filtration without usual\nconditions. The estimation problem is achieved by means of structural least\nsquares (LS) estimates and their sequential versions. The main results of the\npaper are devoted to the strong consistency of such LS-estimates. For\nsequential LS-estimates the property of fixed accuracy is proved.\n"}
+{"abstract": "  Using the Purple Mountain Observatory 13.7-m millimeter telescope at Delingha\nin China, we have conducted a large-scale simultaneous survey of $^{12}$CO,\n$^{13}$CO, and \\xxco ($J$ = 1--0) toward the CMa OB1 complex with a sky\ncoverage of 16.5 deg$^{\\rm 2}$ ($221^{\\circ}.5\\leq l\\leq227^{\\circ}$,\n$-2^{\\circ}.5\\leq b\\leq0^{\\circ}.5$). Emission from the CMa OB1 complex is\nfound in the range 7 km s$^{-1}\\ \\leq$ V$_\\mathrm{LSR}\\ \\leq$ 25 km s$^{-1}$.\nThe large-scale structure, physical properties and chemical abundances of the\nmolecular clouds are presented. A total of 83 C$^{18}$O molecular clumps are\nidentified with GaussClumps algorithm within the mapped region. We find that\n94% of these C$^{18}$O molecular clumps are gravitationally bound. The\nrelationship between their size and mass indicates that none of the C$^{18}$O\nclump has the potential to form high-mass stars. Using semi-automatic IDL\nalgorithm, we newly discover 85 CO outflow candidates in the mapped area,\nincluding 23 bipolar outflows candidates. Additionally, a comparative study\nreveals evidence for significant variety of physical properties, evolutionary\nstages, and levels of star formation activity in different sub-regions of the\nCMa OB1 complex.\n"}
+{"abstract": "  I consider a spin chain with nearest-neighbor and next nearest-neighbor\ninteractions. I show that the Hamiltonian obtained by specifying the desired\ncombination of symmetry blocks is a fast scrambler. This is a smaller model\nthan the \"minimal model\" by Belyansky et al..\n"}
+{"abstract": "  In this paper, we recall hypergeometric functions $\\mathscr{F}^{\\rm\nDw}_{a_1,\\cdots,a_s}(t),$ $\\mathscr{F}^{(\\sigma)}_{a_1,\\cdots,a_s}(t)$,\n$\\widehat{\\mathscr{F}}^{(\\sigma)}_{a,\\cdots,a}(t)$ and their transformation\nformulas. Then we prove that one of transformation formula implies another.\n"}
+{"abstract": "  This paper presents a methodology for freight traffic assignment in a\nlarge-scale road-rail intermodal network under uncertainty. Network\nuncertainties caused by natural disasters have dramatically increased in recent\nyears. Several of these disasters (e.g., Hurricane Sandy, Mississippi River\nFlooding, Hurricane Harvey) severely disrupted the U.S. freight transport\nnetwork, and consequently, the supply chain. To account for these network\nuncertainties, a stochastic freight traffic assignment model is formulated. An\nalgorithmic framework, involving the sample average approximation and gradient\nprojection algorithm, is proposed to solve this challenging problem. The\ndeveloped methodology is tested on the U.S. intermodal network with freight\nflow data from the Freight Analysis Framework. The experiments consider four\ntypes of natural disasters that have different risks and impacts on the\ntransportation network: earthquake, hurricane, tornado, and flood. The results\ndemonstrate the feasibility of the model and algorithmic framework to obtain\nfreight flows for a realistic-sized network in reasonable time (between 417 and\n716 minutes). It is found that for all disaster scenarios the freight ton-miles\nare higher compared to the base case without uncertainty. The increase in\nfreight ton-miles is the highest under the flooding scenario; this is due to\nthe fact that there are more states in the flood-risk areas and they are\nscattered throughout the U.S.\n"}
+{"abstract": "  It has been shown several times in the literature that telematics data\ncollected in motor insurance help to better understand an insured's driving\nrisk. Insurers that use this data reap several benefits, such as a better\nestimate of the pure premium, more segmented pricing and less adverse\nselection. The flip side of the coin is that collected telematics information\nis often sensitive and can therefore compromise policyholders' privacy.\nMoreover, due to their large volume, this type of data is costly to store and\nhard to manipulate. These factors, combined with the fact that insurance\nregulators tend to issue more and more recommendations regarding the collection\nand use of telematics data, make it important for an insurer to determine the\nright amount of telematics information to collect. In addition to traditional\ncontract information such as the age and gender of the insured, we have access\nto a telematics dataset where information is summarized by trip. We first\nderive several features of interest from these trip summaries before building a\nclaim classification model using both traditional and telematics features. By\ncomparing a few classification algorithms, we find that logistic regression\nwith lasso penalty is the most suitable for our problem. Using this model, we\ndevelop a method to determine how much information about policyholders' driving\nshould be kept by an insurer. Using real data from a North American insurance\ncompany, we find that telematics data become redundant after about 3 months or\n4,000 kilometers of observation, at least from a claim classification\nperspective.\n"}
+{"abstract": "  Southern Spectroscopic Survey Telescope (SSST) is a wide-field spectroscopic\nsurvey telescope that China plans to build in Chile in the next few years. As\nan instrument for astronomical spectroscopic survey, the multi-object and\nfiber-fed spectrograph (MOFFS) is one of the most important scientific\ninstruments for SSST. In this paper, we present a recommended optical design\nfor the MOFFS system based on the Volume Phase Holographic Gratings (VPHG). The\nwhole design philosophy and procedure, including the analytic method to\ndetermine the initial structure, optimization procedures of the VPHG and the\ncamera groups, are demonstrated in detail. The numerical results of the final\nobtained spectrograph show a superior imaging quality and a relatively high\ntransmittance for the whole working waveband and the field of view. The design\nmethod proposed in this paper can provide a reference for the design of MOFFS\naccommodated in spectroscopic survey telescopes.\n"}
+{"abstract": "  Traditional supervised learning methods are hitting a bottleneck because of\ntheir dependency on expensive manually labeled data and their weaknesses such\nas limited generalization ability and vulnerability to adversarial attacks. A\npromising alternative, self-supervised learning, as a type of unsupervised\nlearning, has gained popularity because of its potential to learn effective\ndata representations without manual labeling. Among self-supervised learning\nalgorithms, contrastive learning has achieved state-of-the-art performance in\nseveral fields of research. This literature review aims to provide an\nup-to-date analysis of the efforts of researchers to understand the key\ncomponents and the limitations of self-supervised learning.\n"}
+{"abstract": "  In this work, we present a modification of explicit Runge-Kutta temporal\nintegration schemes that guarantees the preservation of any locally-defined\nquasiconvex set of bounds for the solution. These schemes operate on the basis\nof a bijective mapping between an admissible set of solutions and the real\ndomain to strictly enforce bounds. Within this framework, we show that it is\npossible to recover a wide range of methods independently of the spatial\ndiscretization, including positivity preserving, discrete maximum principle\nsatisfying, entropy dissipative, and invariant domain preserving schemes.\nFurthermore, these schemes are proven to recover the order of accuracy of the\nunderlying Runge-Kutta method upon which they are built. The additional\ncomputational cost is the evaluation of two nonlinear mappings which generally\nhave closed-form solutions. We show the utility of this approach in numerical\nexperiments using a pseudospectral spatial discretization without any explicit\nshock capturing schemes for nonlinear hyperbolic problems with discontinuities.\n"}
+{"abstract": "  A theory of spline quadrature rules for arbitrary continuity class in a\nclosed interval $[a, b]$ with arbitrary nonuniform subintervals based on\nsemi-classical orthogonal Jacobi polynomials is proposed. For continuity class\n$c \\ge 2$ this theory depends on a conjecture.\n"}
+{"abstract": "  The limited memory BFGS (L-BFGS) method is one of the popular methods for\nsolving large-scale unconstrained optimization. Since the standard L-BFGS\nmethod uses a line search to guarantee its global convergence, it sometimes\nrequires a large number of function evaluations. To overcome the difficulty, we\npropose a new L-BFGS with a certain regularization technique. We show its\nglobal convergence under the usual assumptions. In order to make the method\nmore robust and efficient, we also extend it with several techniques such as\nnonmonotone technique and simultaneous use of the Wolfe line search. Finally,\nwe present some numerical results for test problems in CUTEst, which show that\nthe proposed method is robust in terms of solving number of problems.\n"}
+{"abstract": "  Existence of mild solutions for the 3D MHD system in bounded Lipschitz\ndomains is established in critical spaces with the absolute boundary\nconditions.\n"}
+{"abstract": "  New CCD photometric observations and their investigation of the W UMa-type\nbinary, V870 Ara, are presented. Light curves of the system were taken through\nBVI filters from the Congarinni Observatory in Australia. The new ephemeris is\ncalculated based on seven new determined minimum times, together with the TESS\ndata and others compiled from the literature. Photometric solutions determined\nby the Wilson-Devinney (W-D) code are combined with the Monte Carlo simulation\nto determine the adjustable parameters' uncertainties. These solutions suggest\nthat V870 Ara is a contact binary system with a mass ratio of 0.082, a fillout\nfactor of 96+-4 percent, and an inclination of 73.60+-0.64 degrees. The\nabsolute parameters of V870 Ara were determined by combining the Gaia EDR3\nparallax and photometric elements.\n"}
+{"abstract": "  We introduce the Colonel Blotto game with favoritism, an extension of the\nfamous Colonel Blotto game where the winner-determination rule is generalized\nto include pre-allocations and asymmetry of the players' resources\neffectiveness on each battlefield. Such favoritism is found in many classical\napplications of the Colonel Blotto game. We focus on the Nash equilibrium.\nFirst, we consider the closely related model of all-pay auctions with\nfavoritism and completely characterize its equilibrium. Based on this result,\nwe prove the existence of a set of optimal univariate distributions -- which\nserve as candidate marginals for an equilibrium -- of the Colonel Blotto game\nwith favoritism and show an explicit construction thereof. In several\nparticular cases, this directly leads to an equilibrium of the Colonel Blotto\ngame with favoritism. In other cases, we use these optimal univariate\ndistributions to derive an approximate equilibrium with well-controlled\napproximation error. Finally, we propose an algorithm -- based on the notion of\nwinding number in parametric curves -- to efficiently compute an approximation\nof the proposed optimal univariate distributions with arbitrarily small error.\n"}
+{"abstract": "  There is a vast literature showing the connection between a deformed\nrelativistic kinematics and a curved momentum space, and, in particular, how\nthe former can be obtained from the geometrical properties of the latter.\nHowever, there is not any mention about the geometry of a multi-particle system\nmaking manifest a possible modification in the metric of one particle due to\nthe presence of others. In this work we explore how a curved momentum metric\ndepending on the particles involved in an interaction arises when considering a\nprocess. We also show that the principle of relative locality obtained in\ndoubly special relativity from an action is achieved in this geometrical\nframework in a direct fashion. Moreover, this formalism allows us to generalize\nthis principle when a curvature of spacetime is present in a natural way.\nFurthermore this geometrical setup allows us to define a new momentum dependent\nspace-time coordinates for a multi-particle system in which locality of\ninteractions is recovered.\n"}
+{"abstract": "  We study the dynamics of the quenched Anderson model at finite temperature\nusing matrix product states. Exploiting a chain mapping for the electron bath,\nwe investigate the entanglement structure in the MPS for various orderings of\nthe two chains, which emerge from the thermofield transformation employed to\ndeal with nonzero temperature. We show that merging both chains can\nsignificantly lower the entanglement at finite temperatures as compared to an\nintuitive nearest-neighbor implementation of the Hamiltonian. Analyzing the\npopulation of the free bath modes -- possible when simulating the full dynamics\nof impurity plus bath -- we find clear signatures of the Kondo effect in the\nquench dynamics.\n"}
+{"abstract": "  A modest extension of the Standard Model by two additional Higgs doublets -\nthe Higgs Troika Model - can provide a well-motivated scenario for successful\nbaryogenesis if neutrinos are Dirac fermions. Adapting the \"Spontaneous Flavor\nViolation\" framework, we consider a version of the Troika model where light\nquarks have significant couplings to the new multi-TeV Higgs states. Resonant\nproduction of new scalars leading to di-jet or top-pair signals are typical\npredictions of this setup. The initial and final state quarks relevant to the\ncollider phenomenology also play a key role in baryogenesis, potentially\nproviding direct access to the relevant early Universe physics in high energy\nexperiments. Viable baryogenesis generally prefers some hierarchy of masses\nbetween the observed and the postulated Higgs states. We show that there is a\ncomplementarity between direct searches at a future 100 TeV $pp$ collider and\nindirect searches at flavor experiments, with both sensitive to different\nregions of parameter space relevant for baryogenesis. In particular,\nmeasurements of $D-\\bar{D}$ mixing at LHCb probe much of the interesting\nparameter space. Direct and indirect searches can uncover the new Higgs states\nup to masses of $\\mathcal{O}(10)$ TeV, thereby providing an impressive reach to\ninvestigate this model.\n"}
+{"abstract": "  Origami has recently emerged as a platform for building functional\nengineering systems with versatile characteristics that targeted niche\napplications. One widely utilized origami-based structure is known as the\nKresling origami spring (KOS), which inspired, among many other things, the\ndesign of vibration isolators, fluidic muscles, and mechanical bit memory\nswitches. Previous demonstrations of such concepts were carried out using\npaper-based KOSs, which are suitable for conceptual illustrations but not for\nimplementation in a real environment. In addition to the very low durability\nresulting from the high plastic deformations at the paper folds; lack of\nrepeatability, and high variation of performance among similar samples are also\ninevitable. To circumvent these issues, this paper presents a novel approach\nfor the design of a non-paper based KOS, which mimics the qualitative behavior\nof the paper-based KOS without compromising on durability, repeatability, and\nfunctionality. In the new design, each fundamental triangle in the paper-based\nKOS is replaced by an inner central rigid core and an outer flexible\nrubber-like frame, which are fabricated out of different visco-elastic\nmaterials using advanced 3-D printing technologies. The quasi-static behavior\nof the fabricated springs was assessed under both compressive and tensile\nloads. It is shown that KOSs with linear, softening, hardening, mono- and\nbi-stable restoring force behavior can be fabricated using the proposed design\nby simple changes to the geometric design parameters, further pointing to the\nvast range of potential applications of such springs.\n"}
+{"abstract": "  We analyze the rainbow tensor model and present the Virasoro constraints,\nwhere the constraint operators obey the Witt algebra and null 3-algebra. We\ngeneralize the method of W-representation in matrix model to the rainbow tensor\nmodel, where the operators preserving and increasing the grading play a crucial\nrole. It is shown that the rainbow tensor model can be realized by acting on\nelementary function with exponent of the operator increasing the grading. We\nderive the compact expression of correlators and apply it to several models,\ni.e., the red tensor model, Aristotelian tensor model and r=4 rainbow tensor\nmodel. Furthermore, we discuss the case of the non-Gaussian red tensor model\nand present a dual expression for partition function through differentiation.\n"}
+{"abstract": "  Data-driven sensor interpretation in autonomous driving can lead to highly\nimplausible predictions as can most of the time be verified with common-sense\nknowledge. However, learning common knowledge only from data is hard and\napproaches for knowledge integration are an active research area. We propose to\nuse a partly human-designed, partly learned set of rules to describe relations\nbetween objects of a traffic scene on a high level of abstraction. In doing so,\nwe improve and robustify existing deep neural networks consuming low-level\nsensor information. We present an initial study adapting the well-established\nProbabilistic Soft Logic (PSL) framework to validate and improve on the problem\nof semantic segmentation. We describe in detail how we integrate common\nknowledge into the segmentation pipeline using PSL and verify our approach in a\nset of experiments demonstrating the increase in robustness against several\nsevere image distortions applied to the A2D2 autonomous driving data set.\n"}
+{"abstract": "  We further investigate the weak topology generated by the irreducible unitary\nrepresentations of a group $G$. A deep result due to Ernest \\cite{Ernest1971}\nand Hughes \\cite{Hughes1973} asserts that every weakly compact subset of a\nlocally compact (LC) group $G$ is compact in the LC-topology, generalizing\nthereby a previous result of Glicksberg \\cite{glicks1962} for abelian locally\ncompact (LCA) groups. Here, we first survey some recent findings on the weak\ntopology and establish some new results about the preservation of several\ncompact-like properties when going from the weak topology to the original\ntopology of LC groups. Among others, we deal with the preservation of countably\ncompactness, pseudocompactness and functional boundedness.\n"}
+{"abstract": "  The accurate prognosis of Glioblastoma Multiforme (GBM) plays an essential\nrole in planning correlated surgeries and treatments. The conventional models\nof survival prediction rely on radiomic features using magnetic resonance\nimaging (MRI). In this paper, we propose a radiogenomic overall survival (OS)\nprediction approach by incorporating gene expression data with radiomic\nfeatures such as shape, geometry, and clinical information. We exploit TCGA\n(The Cancer Genomic Atlas) dataset and synthesize the missing MRI modalities\nusing a fully convolutional network (FCN) in a conditional Generative\nAdversarial Network (cGAN). Meanwhile, the same FCN architecture enables the\ntumor segmentation from the available and the synthesized MRI modalities. The\nproposed FCN architecture comprises octave convolution (OctConv) and a novel\ndecoder, with skip connections in spatial and channel squeeze & excitation\n(skip-scSE) block. The OctConv can process low and high-frequency features\nindividually and improve model efficiency by reducing channel-wise redundancy.\nSkip-scSE applies spatial and channel-wise excitation to signify the essential\nfeatures and reduces the sparsity in deeper layers learning parameters using\nskip connections. The proposed approaches are evaluated by comparative\nexperiments with state-of-the-art models in synthesis, segmentation, and\noverall survival (OS) prediction. We observe that adding missing MRI modality\nimproves the segmentation prediction, and expression levels of gene markers\nhave a high contribution in the GBM prognosis prediction, and fused\nradiogenomic features boost the OS estimation.\n"}
+{"abstract": "  Natural image matting separates the foreground from background in fractional\noccupancy which can be caused by highly transparent objects, complex foreground\n(e.g., net or tree), and/or objects containing very fine details (e.g., hairs).\nAlthough conventional matting formulation can be applied to all of the above\ncases, no previous work has attempted to reason the underlying causes of\nmatting due to various foreground semantics.\n  We show how to obtain better alpha mattes by incorporating into our framework\nsemantic classification of matting regions. Specifically, we consider and learn\n20 classes of matting patterns, and propose to extend the conventional trimap\nto semantic trimap. The proposed semantic trimap can be obtained automatically\nthrough patch structure analysis within trimap regions. Meanwhile, we learn a\nmulti-class discriminator to regularize the alpha prediction at semantic level,\nand content-sensitive weights to balance different regularization losses.\nExperiments on multiple benchmarks show that our method outperforms other\nmethods and has achieved the most competitive state-of-the-art performance.\nFinally, we contribute a large-scale Semantic Image Matting Dataset with\ncareful consideration of data balancing across different semantic classes.\n"}
+{"abstract": "  The search of an unstructured database amounts to finding one element having\na certain property out of $N$ elements. The classical search with an oracle\nchecking one element at a time requires on average $N/2$ steps. The Grover\nalgorithm for the quantum search, and its unitary Hamiltonian evolution\nanalogue, accomplish the search asymptotically optimally in $\\mathcal{O}\n(\\sqrt{N})$ time steps. We reformulate the search problem as a dissipative\nMarkov process acting on an $N$-level system weakly coupled to a thermal bath.\nAssuming that the energy levels of the system represent the database elements,\nwe show that, with a proper choice of the spectrum and physically admissible,\nlong-range transition rates between the energy levels, the system relaxes to\nthe ground state, corresponding to the sought element, in time $\\mathcal{O}\n(\\ln N)$.\n"}
+{"abstract": "  This demo abstract presents the visualization of deep reinforcement learning\n(DRL)-based autonomous aerial mobility simulations. In order to implement the\nsoftware, Unity-RL is used and additional buildings are introduced for urban\nenvironment. On top of the implementation, DRL algorithms are used and we\nconfirm it works well in terms of trajectory and 3D visualization.\n"}
+{"abstract": "  We introduce a non-parametric approach for infinite video texture synthesis\nusing a representation learned via contrastive learning. We take inspiration\nfrom Video Textures, which showed that plausible new videos could be generated\nfrom a single one by stitching its frames together in a novel yet consistent\norder. This classic work, however, was constrained by its use of hand-designed\ndistance metrics, limiting its use to simple, repetitive videos. We draw on\nrecent techniques from self-supervised learning to learn this distance metric,\nallowing us to compare frames in a manner that scales to more challenging\ndynamics, and to condition on other data, such as audio. We learn\nrepresentations for video frames and frame-to-frame transition probabilities by\nfitting a video-specific model trained using contrastive learning. To\nsynthesize a texture, we randomly sample frames with high transition\nprobabilities to generate diverse temporally smooth videos with novel sequences\nand transitions. The model naturally extends to an audio-conditioned setting\nwithout requiring any finetuning. Our model outperforms baselines on human\nperceptual scores, can handle a diverse range of input videos, and can combine\nsemantic and audio-visual cues in order to synthesize videos that synchronize\nwell with an audio signal.\n"}
+{"abstract": "  To meet the ever increasing mobile traffic demand in 5G era, base stations\n(BSs) have been densely deployed in radio access networks (RANs) to increase\nthe network coverage and capacity. However, as the high density of BSs is\ndesigned to accommodate peak traffic, it would consume an unnecessarily large\namount of energy if BSs are on during off-peak time. To save the energy\nconsumption of cellular networks, an effective way is to deactivate some idle\nbase stations that do not serve any traffic demand. In this paper, we develop a\ntraffic-aware dynamic BS sleep control framework, named DeepBSC, which presents\na novel data-driven learning approach to determine the BS active/sleep modes\nwhile meeting lower energy consumption and satisfactory Quality of Service\n(QoS) requirements. Specifically, the traffic demands are predicted by the\nproposed GS-STN model, which leverages the geographical and semantic\nspatial-temporal correlations of mobile traffic. With accurate mobile traffic\nforecasting, the BS sleep control problem is cast as a Markov Decision Process\nthat is solved by Actor-Critic reinforcement learning methods. To reduce the\nvariance of cost estimation in the dynamic environment, we propose a benchmark\ntransformation method that provides robust performance indicator for policy\nupdate. To expedite the training process, we adopt a Deep Deterministic Policy\nGradient (DDPG) approach, together with an explorer network, which can\nstrengthen the exploration further. Extensive experiments with a real-world\ndataset corroborate that our proposed framework significantly outperforms the\nexisting methods.\n"}
+{"abstract": "  In this article we study a class of prescribed curvature problems on complete\nnoncompact Riemannian manifolds. To be precise, we derive local $C^0$-estimate\nunder an asymptotic condition which is in effect optimal, and prove the\nexistence of complete conformal metrics with prescribed curvature functions. A\nkey ingredient of our strategy is Aviles-McOwen's result or its fully nonlinear\nversion on the existence of complete conformal metrics with prescribed\ncurvature functions on manifolds with boundary.\n"}
+{"abstract": "  We prove an abstract critical point theorem based on a cohomological index\ntheory that produces pairs of nontrivial critical points with nontrivial higher\ncritical groups. This theorem yields pairs of nontrivial solutions that are\nneither local minimizers nor of mountain pass type for problems with combined\nnonlinearities. Applications are given to subcritical and critical\n$p$-Laplacian problems, Kirchhoff type nonlocal problems, and critical\nfractional $p$-Laplacian problems.\n"}
+{"abstract": "  In this paper, a novel method for detecting transformer winding axial\ndisplacement has been presented. In this method, which is based on UWB radar\nimaging, a UWB pulse is transmitted to the transformer winding and the\nreflection from it is received and recorded. By changing the antenna position\nthis process is repeated on several points along the axis of the winding. The\nmeasured signals are mapped to a 2-D image of the winding. By analyzing this\nimage, the axial displacement of the winding can detected and the magnitude of\nit is determined with an acceptable precision. Simulation results are provided\nto verify the proposed method.\n"}
+{"abstract": "  Visual script languages with a node-based interface have commonly been used\nin the video game industry. We examined the bug database obtained in the\ndevelopment of FINAL FANTASY XV (FFXV), and noticed that several types of bugs\nwere caused by simple mis-descriptions of visual scripts and could therefore be\nmechanically detected. We propose a method for the automatic verification of\nvisual scripts in order to improve productivity of video game development. Our\nmethod can automatically detect those bugs by using symbolic model checking. We\nshow a translation algorithm which can automatically convert a visual script to\nan input model for NuSMV that is an implementation of symbolic model checking.\nFor a preliminary evaluation, we applied our method to visual scripts used in\nthe production for FFXV. The evaluation results demonstrate that our method can\ndetect bugs of scripts and works well in a reasonable time.\n"}
+{"abstract": "  We consider the problem of training User Verification (UV) models in\nfederated setting, where each user has access to the data of only one class and\nuser embeddings cannot be shared with the server or other users. To address\nthis problem, we propose Federated User Verification (FedUV), a framework in\nwhich users jointly learn a set of vectors and maximize the correlation of\ntheir instance embeddings with a secret linear combination of those vectors. We\nshow that choosing the linear combinations from the codewords of an\nerror-correcting code allows users to collaboratively train the model without\nrevealing their embedding vectors. We present the experimental results for user\nverification with voice, face, and handwriting data and show that FedUV is on\npar with existing approaches, while not sharing the embeddings with other users\nor the server.\n"}
+{"abstract": "  To apply neural sequence models such as the Transformers to music generation\ntasks, one has to represent a piece of music by a sequence of tokens drawn from\na finite set of pre-defined vocabulary. Such a vocabulary usually involves\ntokens of various types. For example, to describe a musical note, one needs\nseparate tokens to indicate the note's pitch, duration, velocity (dynamics),\nand placement (onset time) along the time grid. While different types of tokens\nmay possess different properties, existing models usually treat them equally,\nin the same way as modeling words in natural languages. In this paper, we\npresent a conceptually different approach that explicitly takes into account\nthe type of the tokens, such as note types and metric types. And, we propose a\nnew Transformer decoder architecture that uses different feed-forward heads to\nmodel tokens of different types. With an expansion-compression trick, we\nconvert a piece of music to a sequence of compound words by grouping\nneighboring tokens, greatly reducing the length of the token sequences. We show\nthat the resulting model can be viewed as a learner over dynamic directed\nhypergraphs. And, we employ it to learn to compose expressive Pop piano music\nof full-song length (involving up to 10K individual tokens per song), both\nconditionally and unconditionally. Our experiment shows that, compared to\nstate-of-the-art models, the proposed model converges 5--10 times faster at\ntraining (i.e., within a day on a single GPU with 11 GB memory), and with\ncomparable quality in the generated music.\n"}
+{"abstract": "  Service manual documents are crucial to the engineering company as they\nprovide guidelines and knowledge to service engineers. However, it has become\ninconvenient and inefficient for service engineers to retrieve specific\nknowledge from documents due to the complexity of resources. In this research,\nwe propose an automated knowledge mining and document classification system\nwith novel multi-model transfer learning approaches. Particularly, the\nclassification performance of the system has been improved with three effective\ntechniques: fine-tuning, pruning, and multi-model method. The fine-tuning\ntechnique optimizes a pre-trained BERT model by adding a feed-forward neural\nnetwork layer and the pruning technique is used to retrain the BERT model with\nnew data. The multi-model method initializes and trains multiple BERT models to\novercome the randomness of data ordering during the fine-tuning process. In the\nfirst iteration of the training process, multiple BERT models are being trained\nsimultaneously. The best model is then selected for the next phase of the\ntraining process with another two iterations and the training processes for\nother BERT models will be terminated. The performance of the proposed system\nhas been evaluated by comparing with two robust baseline methods, BERT and\nBERT-CNN. Experimental results on a widely used Corpus of Linguistic\nAcceptability (CoLA) dataset have shown that the proposed techniques perform\nbetter than these baseline methods in terms of accuracy and MCC score.\n"}
+{"abstract": "  In this work, we investigate the influence of planetary tidal interactions on\nthe transit-timing variations of short-period low-mass rocky exoplanets. For\nsuch purpose, we employ the recently-developed creep tide theory to compute\ntidally-induced TTVs. We implement the creep tide in the recently-developed\nPosidonius N-body code, thus allowing for a high-precision evolution of the\ncoupled spin-orbit dynamics of planetary systems. As a working example for the\nanalyses of tidally-induced TTVs, we apply our version of the code to the\nK2-265 b planet. We analyse the dependence of tidally-induced TTVs with the\nplanetary rotation rate, uniform viscosity coefficient and eccentricity. Our\nresults show that the tidally-induced TTVs are more significant in the case\nwhere the planet is trapped in non-synchronous spin-orbit resonances, in\nparticular the 3/2 and 2/1 spin-orbit resonant states. An analysis of the TTVs\ninduced separately by apsidal precession and tidally-induced orbital decay has\nallowed for the conclusion that the latter effect is much more efficient at\ncausing high-amplitude TTVs than the former effect by 2 - 3 orders of\nmagnitude. We compare our findings for the tidally-induced TTVs obtained with\nPosidonius with analytical formulations for the transit timings used in\nprevious works, and verified that the results for the TTVs coming from\nPosidonius are in excellent agreement with the analytical formulations. These\nresults show that the new version of Posidonius containing the creep tide\ntheory implementation can be used to study more complex cases in the future.\nFor instance, the code can be used to study multiplanetary systems, in which\ncase planet-planet gravitational perturbations must be taken into account\nadditionally to tidal interactions to obtain the TTVs.\n"}
+{"abstract": "  While deep learning reshaped the classical motion capture pipeline with\nfeed-forward networks, generative models are required to recover fine alignment\nvia iterative refinement. Unfortunately, the existing models are usually\nhand-crafted or learned in controlled conditions, only applicable to limited\ndomains. We propose a method to learn a generative neural body model from\nunlabelled monocular videos by extending Neural Radiance Fields (NeRFs). We\nequip them with a skeleton to apply to time-varying and articulated motion. A\nkey insight is that implicit models require the inverse of the forward\nkinematics used in explicit surface models. Our reparameterization defines\nspatial latent variables relative to the pose of body parts and thereby\novercomes ill-posed inverse operations with an overparameterization. This\nenables learning volumetric body shape and appearance from scratch while\njointly refining the articulated pose; all without ground truth labels for\nappearance, pose, or 3D shape on the input videos. When used for\nnovel-view-synthesis and motion capture, our neural model improves accuracy on\ndiverse datasets. Project website: https://lemonatsu.github.io/anerf/ .\n"}
+{"abstract": "  The exact factorization of the time-dependent electron-nuclear wavefunction\nhas been employed successfully in the field of quantum molecular dynamics\nsimulations for interpreting and simulating light-induced ultrafast processes.\nIn this work, we summarize the major developments leading to the formulation of\na trajectory-based approach, derived from the exact factorization equations,\ncapable of dealing with nonadiabatic electronic processes, and including\nspin-orbit coupling and the non-perturbative effect of an external\ntime-dependent field. This trajectory-based quantum-classical approach has been\ndubbed coupled-trajectory mixed quantum-classical (CT-MQC) algorithm, whose\nperformance is tested here to study the photo-dissociation dynamics of IBr.\n"}
+{"abstract": "  Core-collapse supernovae are fascinating astrophysical objects for\nmultimessenger studies. Gravitational waves (GWs) are expected to play a role\nin the supernova explosion mechanism, but their modelling is also challenging\ndue to the stochastic nature of the dynamics and the vast possible progenitors,\nand moreover, the GW detection from these objects is still elusive with the\nalready advanced detectors. Low-energy neutrinos will be emitted enormously\nduring the core-collapse explosion and can help for the gravitational wave\ncounterpart search. In this work we develop a multi-messenger strategy to\nsearch for such astrophysical objects by exploiting a global network of both\nlow-energy neutrino and gravitational wave detectors. First, we discuss how to\nimprove the detection potential of the neutrino sub-network by exploiting the\ntemporal behaviour of a neutrino burst from a core-collapse supernova. We show\nthat with the proposed approach neutrino detectors can gain at least $10\\%$ of\ndetection efficiency at the distance where their efficiency drops. Then, we\ncombine the information provided by GW and neutrino in a multimessenger\nstrategy. In particular, we obtain an increase of the probability to detect the\nGW signal from a CCSN at $60$ kpc from zero when using only GW analysis to\n$\\sim 33\\%$ with our combined GW-$\\nu$ approach.\n  Keywords: multimessenger, supernova, core-collapse, low-energy neutrino,\ngravitational wave.\n"}
+{"abstract": "  In this note, we prove the existence of an equilibrium concept, dubbed\nconditional strategy equilibrium, for non-cooperative games in which a strategy\nof a player is a function from the other players' actions to her own actions.\nWe also study the properties of conditional strategy equilibrium.\n"}
+{"abstract": "  Experimental research has shown that the brain's fast electrochemical\ndynamics, or neurodynamics (ND), is strongly stochastic, chaotic, and instanton\n(neuroavalanche)-dominated. It is also partly scale-invariant which has been\nloosely associated with critical phenomena. It has been recently demonstrated\nthat the supersymmetric theory of stochastics (STS) offers a theoretical\nframework that can explain all of the above ND features. In the STS, all\nstochastic models possess a topological supersymmetry (TS), and the\n\"criticality\" of ND and similar stochastic processes is associated with\nnoise-induced, spontaneous breakdown of this TS (due to instanton condensation\nnear the border with ordinary chaos in which TS is broken by\nnon-integrability). Here, we propose a new approach that may be useful for the\nconstruction of low-energy effective theories of ND. Its centerpiece is a\ncoarse-graining procedure of neural networks based on simplicial complexes and\nthe concept of the \"enveloping lattice.\" It represents a neural network as a\ncontinuous, high-dimensional base space whose rich topology reflects that of\nthe original network. The reduced one-instanton state space is determined by\nthe de Rham cohomology classes of this base space, and the effective ND\ndynamics can be recognized as interactions of the instantons in the spirit of\nthe Segal-Atiyah formalism.\n"}
+{"abstract": "  Recent theoretical studies inspired by experiments on the Kitaev magnet\n$\\alpha$-RuCl$_3$ highlight the nontrivial impact of phonons on the thermal\nHall conductivity of chiral topological phases. Here we introduce mixed\nmesoscopic-macroscopic devices that allow refined thermal-transport probes of\nnon-Abelian spin liquids with Ising topological order. These devices feature a\nquantum-coherent mesoscopic region with negligible phonon conductance, flanked\nby macroscopic lobes that facilitate efficient thermalization between chiral\nMajorana edge modes and bulk phonons. We show that our devices enable $(i)$\naccurate determination of the quantized thermal Hall conductivity, $(ii)$\nidentification of non-Abelian Ising anyons via the temperature dependence of\nthe thermal conductance, and most interestingly $(iii)$ single-anyon detection\nthrough heat-based anyon interferometry. Analogous results apply broadly to\nphonon-coupled chiral topological orders.\n"}
+{"abstract": "  There are six different mathematical formulations of the symmetry group in\nquantum mechanics, among them the set of pure states $\\mathbf{P}$ -- i.e., the\nset of one-dimensional projections on a complex Hilbert space $H$ -- and the\northomodular lattice $\\mathbf{L}$ of closed subspaces of $H$. These six groups\nare isomorphic when the dimension of $H$ is $\\geq 3$. Despite of the\ndifficulties caused by $M_2(\\mathbb{C})$, rank two algebras are used for\nquantum mechanics description of the spin state of spin-$\\frac12$ particles,\nthere is a counterexample for Uhlhorn's version of Wigner's theorem for such\nstate space.\n  In this note we prove that in order that the description of the spin will be\nrelativistic, it is not enough to preserve the projection lattice equipped with\nits natural partial order and orthogonality, but we also need to preserve the\npartial order set of all tripotents and orthogonality among them (a set which\nstrictly enlarges the lattice of projections). Concretely, let $M$ and $N$ be\ntwo atomic JBW$^*$-triples not containing rank-one Cartan factors, and let\n$\\mathcal{U} (M)$ and $\\mathcal{U} (N)$ denote the set of all tripotents in $M$\nand $N$, respectively. We show that each bijection $\\Phi: \\mathcal{U} (M)\\to\n\\mathcal{U} (N)$, preserving the partial ordering in both directions,\northogonality in one direction and satisfying some mild continuity hypothesis\ncan be extended to a real linear triple automorphism. This, in particular,\nextends a result of Moln{\\'a}r to the wider setting of atomic JBW$^*$-triples\nnot containing rank-one Cartan factors, and provides new models to present\nquantum behavior.\n"}
+{"abstract": "  Speech recognition systems for the Spanish language, such as Google's,\nproduce errors quite frequently when used in applications of a specific domain.\nThese errors mostly occur when recognizing words new to the recognizer's\nlanguage model or ad hoc to the domain. This article presents an algorithm that\nuses Levenshtein distance on phonemes to reduce the speech recognizer's errors.\nThe preliminary results show that it is possible to correct the recognizer's\nerrors significantly by using this metric and using a dictionary of specific\nphrases from the domain of the application. Despite being designed for\nparticular domains, the algorithm proposed here is of general application. The\nphrases that must be recognized can be explicitly defined for each application,\nwithout the algorithm having to be modified. It is enough to indicate to the\nalgorithm the set of sentences on which it must work. The algorithm's\ncomplexity is $O(tn)$, where $t$ is the number of words in the transcript to be\ncorrected, and $n$ is the number of phrases specific to the domain.\n"}
+{"abstract": "  We investigated coherent betatron oscillations of a deuteron beam in the\nstorage ring COSY, excited by a detuned radio-frequency Wien filter. The beam\noscillations were detected by conventional beam position monitors. With the\ncurrently available apparatus, we show that oscillation amplitudes down to\n\\SI{1}{\\micro \\meter} can be detected. The interpretation of the response of\nthe stored beam to the detuned radio-frequency Wien filter is based on\nsimulations of the beam evolution in the lattice of the ring and realistic\ntime-dependent 3D field maps of the Wien filter. Future measurements of the\nelectric dipole moment of protons will, however, require control of the\nrelative position of counter-propagating beams in the sub-picometer range.\nSince here the stored beam can be considered as a rarefied gas of uncorrelated\nparticles, we moreover demonstrate that the amplitudes of the zero-point\n(ground state) betatron oscillations of individual particles are only a factor\nof about 10 larger than the Heisenberg uncertainty limit. As a consequence of\nthis, we conclude that quantum mechanics does not preclude the control of the\nbeam centroids to sub-picometer accuracy. The smallest Lorentz force exerted on\na single particle that we have been able to determine is \\SI{10}{aN}.\n"}
+{"abstract": "  In this paper, we have constructed a bottom-up holographic model for the\ncolor superconductivity (CSC) of the Yang-Mills theory with including the\nhigher derivative corrections which allow to study the CSC phase with the color\nnumber $N_c\\geq2$. First, we consider the CSC phase transition in the context\nof Einstein-Gauss-Bonnet (EGB) gravity. We analyze the Cooper pair condensate\nin the deconfinement and confinement phases which are dual to the planar\nGB-RN-AdS black hole and GB-AdS soliton, respectively, where the backreaction\nof the matter part is taken into account. By examining the breakdown of the\nBreitenlohner-Freedman bound in the background of the planar GB-RN-AdS black\nhole, we find that the positive GB coupling parameter $\\alpha>0$ leads to a\nlower upper bound of the color number in comparison to Einstein gravity where\nthe CSC phase for $N_c\\geq2$ is not realized. But, with the $\\alpha<0$ case it\nis possible to observe the Cooper pair condensate for $N_c\\geq2$ with the\nreasonable magnitude of $\\alpha$. This is confirmed and the corresponding phase\ndiagram is found by solving numerically the equations of motion for the\ngravitational system. In addition, we show that the CSC phase disappears in the\nconfinement phase for the magnitude of $\\alpha$ below a certain value which\nmeans that beyond that value it might lead to the breakdown region of the EGB\ngravity in investigating the CSC phase. However, the CSC phase transition\noccurring with $N_c\\geq2$ requires the magnitude of the GB coupling parameter\nto be rather large. As a result, the GB term would no longer be considered as\nthe correction and it also violates the boundary causality bound. We resolve\nthis problem by including additionally the higher derivative correction for the\nMaxwell electrodynamics and the non-minimal coupled Maxwell field.\n"}
+{"abstract": "  Deep learning-based image super-resolution (DL-SR) has shown great promise in\nmedical imaging applications. To date, most of the proposed methods for DL-SR\nhave only been assessed by use of traditional measures of image quality (IQ)\nthat are commonly employed in the field of computer vision. However, the impact\nof these methods on objective measures of image quality that are relevant to\nmedical imaging tasks remains largely unexplored. In this study, we investigate\nthe impact of DL-SR methods on binary signal detection performance. Two popular\nDL-SR methods, the super-resolution convolutional neural network (SRCNN) and\nthe super-resolution generative adversarial network (SRGAN), were trained by\nuse of simulated medical image data. Binary signal-known-exactly with\nbackground-known-statistically (SKE/BKS) and signal-known-statistically with\nbackground-known-statistically (SKS/BKS) detection tasks were formulated.\nNumerical observers, which included a neural network-approximated ideal\nobserver and common linear numerical observers, were employed to assess the\nimpact of DL-SR on task performance. The impact of the complexity of the DL-SR\nnetwork architectures on task-performance was quantified. In addition, the\nutility of DL-SR for improving the task-performance of sub-optimal observers\nwas investigated. Our numerical experiments confirmed that, as expected, DL-SR\ncould improve traditional measures of IQ. However, for many of the study\ndesigns considered, the DL-SR methods provided little or no improvement in task\nperformance and could even degrade it. It was observed that DL-SR could improve\nthe task-performance of sub-optimal observers under certain conditions. The\npresented study highlights the urgent need for the objective assessment of\nDL-SR methods and suggests avenues for improving their efficacy in medical\nimaging applications.\n"}
+{"abstract": "  General solutions to the quantum Rabi model involve subspaces with unbounded\nnumber of photons. However, for the multiqubit multimode case, we find special\nsolutions with at most one photon for arbitrary number of qubits and photon\nmodes. Unlike the Juddian solution, ours exists for arbitrary single\nqubit-photon coupling strength with constant eigenenergy. This corresponds to a\nhorizontal line in the spectrum, while still being a qubit-photon entangled\nstate. As a possible application, we propose an adiabatic scheme for the fast\ngeneration of arbitrary single-photon multimode W states with nonadiabatic\nerror less than 1%. Finally, we propose a superconducting circuit design,\nshowing the experimental feasibility of the multimode multiqubit Rabi model.\n"}
+{"abstract": "  As first shown by H. S. Green in 1952, the entropy of a classical fluid of\nidentical particles can be written as a sum of many-particle contributions,\neach of them being a distinctive functional of all spatial distribution\nfunctions up to a given order. By revisiting the combinatorial derivation of\nthe entropy formula, we argue that a similar correlation expansion holds for\nthe entropy of a crystalline system. We discuss how one- and two-body entropies\nscale with the size of the crystal, and provide fresh numerical data to check\nthe expectation, grounded on theoretical arguments, that both entropies are\nextensive quantities.\n"}
+{"abstract": "  Studying the changed water dynamics in the hydration layers of biomolecules\nis an important step towards fuller understanding of their function and\nmechanisms, but has shown to be quite difficult. The measurement of the\ntime-dependent Stokes shift of a chromophore attached to the biomolecule is a\npromising method to achieve this goal, as published in J. Phys. Chem. Lett. 5\n(2014), 1845, where trehalose was used as biomolecule,\n1-methyl-6-oxyquinolinium betaine as chromophore and water as solvent. An\noverall retardation of solvent molecules is then obtained by comparison of the\nlinked system to the same system without trehalose, but contributions from\ndifferent subgroups of solvent molecules, for example molecules close to or far\nfrom trehalose, are unknown. The difficulty arising from these unknown\ncontributions of retarded and possibly unretarded solvent molecules is overcome\nin this work by conducting computer simulations on this system and decomposing\nthe overall signal into contributions from various molecules at different\nlocations. We performed non-equilibrium molecular dynamics simulation using a\npolarizable water model and a non-polarizable solute model and could reproduce\nthe experimental time-dependent Stokes shift accurately for the linked\ntrehalose-oxyquinolinium and the pure oxyquinolinium over a wide temperature\nrange, indicating the correctness of our employed models. Decomposition of the\nshift into contributions from different solvent subgroups showed that the\namplitude of the measured shift is made up only half by the desired retarded\nsolvent molecules in the hydration layer, but to another half by unretarded\nbulk water, so that measured relaxation times of the overall Stokes shift are\nonly a lower boundary for the true relaxation times in the hydration layer of\ntrehalose.\n"}
+{"abstract": "  Exciting contemporary machine learning problems have recently been phrased in\nthe classic formalism of tree search -- most famously, the game of Go.\nInterestingly, the state-space underlying these sequential decision-making\nproblems often posses a more general latent structure than can be captured by a\ntree. In this work, we develop a probabilistic framework to exploit a search\nspace's latent structure and thereby share information across the search tree.\nThe method is based on a combination of approximate inference in jointly\nGaussian models for the explored part of the problem, and an abstraction for\nthe unexplored part that imposes a reduction of complexity ad hoc. We\nempirically find our algorithm to compare favorably to existing\nnon-probabilistic alternatives in Tic-Tac-Toe and a feature selection\napplication.\n"}
+{"abstract": "  We present in this document some essential properties of solutions to the\nhomogeneous Landau-Fermi-Dirac equation for moderately soft potentials. Uniform\nin time estimates for statistical moments, $L^{p}$-norm generation and Sobolev\nregularity are shown using a combination of techniques that include recent\ndevelopments concerning level set analysis in the spirit of De Giorgi and\nrefined entropy-entropy dissipation functional inequalities for the Landau\ncollision operator which are extended to the case in question here. As a\nconsequence of the analysis, we prove algebraic relaxation of non degenerate\ndistributions towards the Fermi-Dirac statistics under a weak non saturation\ncondition for the initial datum. All quantitative estimates are uniform with\nrespect to the quantum parameter. They therefore also hold for the classical\nlimit, that is the Landau equation.\n"}
+{"abstract": "  Within the de Broglie-Bohm (dBB) theory, the measurement process and the\ndetermination of its outcome are usually discussed in terms of the effect of\nthe Bohmian positions of the measured system S. %} This article shows that the\nBohmian positions associated with the measurement apparatus M can actually play\na crucial role in the determination of the result of measurement. Indeed, in\nmany cases, the result is practically independent of the initial value of a\nBohmian position associated with S, and determined only by those of M. The\nmeasurement then does not reveal the value of any pre-existing variable\nattached to S, but just the initial state of the measurement apparatus. Quantum\ncontextuality then appears with particular clarity as a consequence of the dBB\ndynamics for entangled systems.\n"}
+{"abstract": "  In this paper, we de\\\"One a new type curve as V-Mannheim curve, V Mannheim\npartner curve and generating curve of Mannheim curve. We give characterization\nof these curve. In addition, we study a relation between Mannheim curve and\nspherical curve. Eventually, with Salkowski method, we give an example of the\nMannheim curve.\n"}
+{"abstract": "  In recent years, researchers have proposed many deep learning (DL) methods\nfor various tasks, and particularly face recognition (FR) made an enormous leap\nusing these techniques. Deep FR systems benefit from the hierarchical\narchitecture of the DL methods to learn discriminative face representation.\nTherefore, DL techniques significantly improve state-of-the-art performance on\nFR systems and encourage diverse and efficient real-world applications. In this\npaper, we present a comprehensive analysis of various FR systems that leverage\nthe different types of DL techniques, and for the study, we summarize 168\nrecent contributions from this area. We discuss the papers related to different\nalgorithms, architectures, loss functions, activation functions, datasets,\nchallenges, improvement ideas, current and future trends of DL-based FR\nsystems. We provide a detailed discussion of various DL methods to understand\nthe current state-of-the-art, and then we discuss various activation and loss\nfunctions for the methods. Additionally, we summarize different datasets used\nwidely for FR tasks and discuss challenges related to illumination, expression,\npose variations, and occlusion. Finally, we discuss improvement ideas, current\nand future trends of FR tasks.\n"}
+{"abstract": "  We present a comprehensive study of the low-energy band structure and Fermi\nsurface (FS) topology of $A$Co$_2$As$_2$ ($A=$ Ca, Sr, Ba, Eu) using\nhigh-resolution angle-resolved photoemission spectroscopy. The experimental FS\ntopology and band dispersion data are compared with theoretical full-potential\nlinearized augmented-plane-wave (FP-LAPW) calculations, which yielded\nreasonably good agreement. We demonstrate that the FS maps of $A$Co$_2$As$_2$\nare significantly different from those of the parent compounds of Fe-based\nhigh-temperature superconductors. Further, the FSs of CaCo$_2$As$_2$ do not\nshow significant changes across its antiferromagnetic transition temperature.\nThe band dispersions extracted in different momentum $(k_{\\it x}, k_{\\it y})$\ndirections show a small electron pocket at the center and a large electron\npocket at the corner of the Brillouin zone (BZ). The absence of the hole FS in\nthese compounds does not allow nesting between pockets at the Fermi energy\n({\\it E}$_{\\rm F}$), which is in contrast to $A$Fe$_2$As$_2$-type parent\ncompounds of the iron-based superconductors. Interestingly, we find that the\nhole bands are moved 300--400~meV below $E_{\\rm F}$ depending on the $A$\nelement. Moreover, the existence of nearly flat bands in the vicinity of\n$E_{\\rm F}$ are consistent with the large density of states at $E_{\\rm F}$.\nThese results are important to understand the physical properties as well as\nthe possibility of the emergence of superconductivity in related materials.\n"}
+{"abstract": "  Outflows and feedback are key ingredients of galaxy evolution. Evidence for\nan outflow arising from the Galactic center (GC) has recently been discovered\nat different wavelength. We show that the X-ray, radio, and infrared emissions\nare deeply interconnected, affecting one another and forming coherent features\non scales of hundreds of parsecs, therefore indicating a common physical link\nassociated with the GC outflow. We debate the location of the northern chimney\nand suggest that it might be located on the front side of the GC because of a\nsignificant tilt of the chimneys toward us. We report the presence of strong\nshocks at the interface between the chimneys and the interstellar medium, which\nare traced by radio and warm dust emission. We observe entrained molecular gas\noutflowing within the chimneys, revealing the multiphase nature of the outflow.\nIn particular, the molecular outflow produces a long, strong, and structured\nshock along the northwestern wall of the chimney. Because of the different\ndynamical times of the various components of the outflow, the chimneys appear\nto be shaped by directed large-scale winds launched at different epochs. The\ndata support the idea that the chimneys are embedded in an (often dominant)\nvertical magnetic field, which likely diverges with increasing latitude. We\nobserve that the thermal pressure associated with the hot plasma appears to be\nsmaller than the ram pressure of the molecular outflow and the magnetic\npressure. This leaves open the possibility that either the main driver of the\noutflow is more powerful than the observed hot plasma, or the chimneys\nrepresent a \"relic\" of past and more powerful activity. These multiwavelength\nobservations corroborate the idea that the chimneys represent the channel\nconnecting the quasi-continuous, but intermittent, activity at the GC with the\nbase of the Fermi bubbles.\n"}
+{"abstract": "  The computing continuum extends the high-performance cloud data centers with\nenergy-efficient and low-latency devices close to the data sources located at\nthe edge of the network. However, the heterogeneity of the computing continuum\nraises multiple challenges related to application management. These include\nwhere to offload an application - from the cloud to the edge - to meet its\ncomputation and communication requirements. To support these decisions, we\nprovide in this article a detailed performance and carbon footprint analysis of\na selection of use case applications with complementary resource requirements\nacross the computing continuum over a real-life evaluation testbed.\n"}
+{"abstract": "  Writing code is hard; proving it correct is even harder. As the scale of\nverified software projects reaches new heights, the problem of efficiently\nverifying large amounts of software becomes more and more salient. Nowhere is\nthis issue more evident than in the context of verified cryptographic\nlibraries. To achieve feature-parity and be competitive with unverified\ncryptographic libraries, a very large number of algorithms and APIs need to be\nverified. However, the task is oftentimes repetitive, and factoring out\ncommonality between algorithms is fraught with difficulties, requiring until\nnow a significant amount of manual effort.\n  This paper shows how a judicious combination of known functional programming\ntechniques leads to an order-of-magnitude improvement in the amount of verified\ncode produced by the popular HACL* cryptographic library, without compromising\nperformance. We review three techniques that build upon each other, in order of\nincreasing sophistication. First, we use dependent types to crisply capture the\nspecification and state machine of a block algorithm, a cryptographic notion\nthat was until now only informally and imprecisely specified. Next, we rely on\npartial evaluation to author a higher-order, stateful functor that transforms\nany unsafe block API into a safe counterpart. Finally, we rely on elaborator\nreflection to automate the very process of authoring a functor, using a\ncode-rewriting tactic. This culminates in a style akin to templatized C++ code,\nbut relying on a userland tactic and partial evaluation, rather than built-in\ncompiler support.\n"}
+{"abstract": "  We present experimental evidence of incoherent Kondo scattering as the source\nof resistivity minima in bulk polycrystalline and nanocrystalline\nPr$_2$Ir$_2$O$_7$. The temperature dependence of thermopower shows a positive\nmaximum at high temperature followed by a negative minimum at low temperature,\nwith the sign inversion occurring at a much higher temperature than $T_K$.\nMoreover, we observe little correlation between TK and intersite coupling\nstrength given by $\\theta_{CW}$. We describe the temperature dependence of\nthermopower and resistivity within the framework of crystal field excitation in\na Kondo lattice.\n"}
+{"abstract": "  Mapping a single exposure low dynamic range (LDR) image into a high dynamic\nrange (HDR) is considered among the most strenuous image to image translation\ntasks due to exposure-related missing information. This study tackles the\nchallenges of single-shot LDR to HDR mapping by proposing a novel two-stage\ndeep network. Notably, our proposed method aims to reconstruct an HDR image\nwithout knowing hardware information, including camera response function (CRF)\nand exposure settings. Therefore, we aim to perform image enhancement task like\ndenoising, exposure correction, etc., in the first stage. Additionally, the\nsecond stage of our deep network learns tone mapping and bit-expansion from a\nconvex set of data samples. The qualitative and quantitative comparisons\ndemonstrate that the proposed method can outperform the existing LDR to HDR\nworks with a marginal difference. Apart from that, we collected an LDR image\ndataset incorporating different camera systems. The evaluation with our\ncollected real-world LDR images illustrates that the proposed method can\nreconstruct plausible HDR images without presenting any visual artefacts. Code\navailable: https://github. com/sharif-apu/twostageHDR_NTIRE21.\n"}
+{"abstract": "  We consider the optimal control problem of a general nonlinear\nspatio-temporal system described by Partial Differential Equations (PDEs).\nTheory and algorithms for control of spatio-temporal systems are of rising\ninterest among the automatic control community and exhibit numerous challenging\ncharacteristic from a control standpoint. Recent methods focus on\nfinite-dimensional optimization techniques of a discretized finite dimensional\nODE approximation of the infinite dimensional PDE system. In this paper, we\nderive a differential dynamic programming (DDP) framework for distributed and\nboundary control of spatio-temporal systems in infinite dimensions that is\nshown to generalize both the spatio-temporal LQR solution, and modern finite\ndimensional DDP frameworks. We analyze the convergence behavior and provide a\nproof of global convergence for the resulting system of continuous-time\nforward-backward equations. We explore and develop numerical approaches to\nhandle sensitivities that arise during implementation, and apply the resulting\nSTDDP algorithm to a linear and nonlinear spatio-temporal PDE system. Our\nframework is derived in infinite dimensional Hilbert spaces, and represents a\ndiscretization-agnostic framework for control of nonlinear spatio-temporal PDE\nsystems.\n"}
+{"abstract": "  Visual engagement in social media platforms comprises interactions with photo\nposts including comments, shares, and likes. In this paper, we leverage such\nvisual engagement clues as supervisory signals for representation learning.\nHowever, learning from engagement signals is non-trivial as it is not clear how\nto bridge the gap between low-level visual information and high-level social\ninteractions. We present VisE, a weakly supervised learning approach, which\nmaps social images to pseudo labels derived by clustered engagement signals. We\nthen study how models trained in this way benefit subjective downstream\ncomputer vision tasks such as emotion recognition or political bias detection.\nThrough extensive studies, we empirically demonstrate the effectiveness of VisE\nacross a diverse set of classification tasks beyond the scope of conventional\nrecognition.\n"}
+{"abstract": "  Nonlinear optical properties, such as bulk photovoltaic effects, possess\ngreat potential in energy harvesting, photodetection, rectification, etc. To\nenable efficient light-current conversion, materials with strong\nphoto-responsivity are highly desirable. In this work, we predict that\nmonolayer Janus transition metal dichalcogenides (JTMDs) in the 1T' phase\npossess colossal nonlinear photoconductivity owing to their topological band\nmixing, strong inversion symmetry breaking, and small electronic bandgap. 1T'\nJTMDs have inverted bandgaps on the order of 10 meV and are exceptionally\nresponsive to light in the terahertz (THz) range. By first-principles\ncalculations, we reveal that 1T' JTMDs possess shift current (SC) conductivity\nas large as $2300 ~\\rm nm \\cdot \\mu A / V^2$, equivalent to a\nphoto-responsivity of $2800 ~\\rm mA/W$. The circular current (CC) conductivity\nof 1T' JTMDs is as large as $10^4~ \\rm nm \\cdot \\mu A / V^2$. These remarkable\nphoto-responsivities indicate that the 1T' JTMDs can serve as efficient\nphotodetectors in the THz range. We also find that external stimuli such as the\nin-plane strain and out-of-plane electric field can induce topological phase\ntransitions in 1T' JTMDs and that the SC can abruptly flip their directions.\nThe abrupt change of the nonlinear photocurrent can be used to characterize the\ntopological transition and has potential applications in 2D optomechanics and\nnonlinear optoelectronics.\n"}
+{"abstract": "  Ultrafast imaging is essential in physics and chemistry to investigate the\nfemtosecond dynamics of nonuniform samples or of phenomena with strong spatial\nvariations. It relies on observing the phenomena induced by an ultrashort laser\npump pulse using an ultrashort probe pulse at a later time. Recent years have\nseen the emergence of very successful ultrafast imaging techniques of single\nnon-reproducible events with extremely high frame rate, based on wavelength or\nspatial frequency encoding. However, further progress in ultrafast imaging\ntowards high spatial resolution is hampered by the lack of characterization of\nweak probe beams. Because of the difference in group velocities between pump\nand probe in the bulk of the material, the determination of the absolute\npump-probe delay depends on the sample position. In addition, pulse-front tilt\nis a widespread issue, unacceptable for ultrafast imaging, but which is\nconventionally very difficult to evaluate for the low-intensity probe pulses.\nHere we show that a pump-induced micro-grating generated from the electronic\nKerr effect provides a detailed in-situ characterization of a weak probe pulse.\nIt allows solving the two issues. Our approach is valid whatever the\ntransparent medium, whatever the probe pulse polarization and wavelength.\nBecause it is nondestructive and fast to implement, this in-situ probe\ndiagnostic can be repeated to calibrate experimental conditions, particularly\nin the case where complex wavelength, spatial frequency or polarization\nencoding is used. We anticipate that this technique will enable previously\ninaccessible spatiotemporal imaging in all fields of ultrafast science and high\nfield physics at the micro- and nanoscale.\n"}
+{"abstract": "  A deterministic finite automaton is directable if it has a directing word\nwhich takes the automaton from every state to the same state. These notions\nhave been extended also to other kinds of automata. Thus, B.~Imreh and\nM.~Steinby (1999) identified three natural types of directing words, called\nD1-, D2- and D3-directing words, for nondeterministic finite automata (NFAs).\nHere we adapt these notions for fuzzy finite automata (FFAs). The D3-directing\nwords obtained this way are precisely the directing words introduced by\nV.~Karthikeyan and M. Rajasekar (2015). With any FFA F we associate an NFA Fnd\nwhich has the same Di-directing words as F. Thus, if these definitions are\nused, the theory of directable FFAs reduces to that of NFAs.\n  We also introduce three new kinds of directing words of fuzzy automata that\nwe call DD1-, DD2- and DD3-directing words, respectively which depend more on\nthe fuzzy transition degrees between states. We establish some basic properties\nof the sets $DDi(F)$ of DDi-directing words of any given FFA F. In particular,\nit is shown that these languages are regular, and that DDi-directability is\ndecidable. For so-called normal FFAs the languages DDi(F) are shown to have\nsome special properties. Several relationships between the families of the\ncorresponding sets DDi(F) of DDi-directing words are presented. We also\ndetermine the complete meet-semilattice of the various classes of\nDDi-directable FFAs and normal FFAs and their intersections.\n"}
+{"abstract": "  In this article, we investigate some standard geometric properties of the\nintegral operator $$\nC_{\\alpha,\\beta}[f,g](z)=\\int_{0}^{z}\\bigg(\\frac{f(w)}{w}\\bigg)^\\alpha\n(g'(w))^\\beta dw, \\,\\,\\, \\alpha,\\beta \\in \\mathbb{R} \\text{ and } |z|<1, $$\nwhere $f$ and $g$ are elements of certain classical families of normalized\nanalytic functions defined on the unit disk. Here, the exponents are chosen in\nsuch a way that the respective functions are analytic in suitable branches.\n"}
+{"abstract": "  Medical systems in general, and patient treatment decisions and outcomes in\nparticular, are affected by bias based on gender and other demographic\nelements. As language models are increasingly applied to medicine, there is a\ngrowing interest in building algorithmic fairness into processes impacting\npatient care. Much of the work addressing this question has focused on biases\nencoded in language models -- statistical estimates of the relationships\nbetween concepts derived from distant reading of corpora. Building on this\nwork, we investigate how word choices made by healthcare practitioners and\nlanguage models interact with regards to bias. We identify and remove gendered\nlanguage from two clinical-note datasets and describe a new debiasing procedure\nusing BERT-based gender classifiers. We show minimal degradation in health\ncondition classification tasks for low- to medium-levels of bias removal via\ndata augmentation. Finally, we compare the bias semantically encoded in the\nlanguage models with the bias empirically observed in health records. This work\noutlines an interpretable approach for using data augmentation to identify and\nreduce the potential for bias in natural language processing pipelines.\n"}
+{"abstract": "  This paper details an accessible geometric derivation of the forward and\ninverse kinematics of a parallel robotic linkage known as the Canfield joint,\nwhich can be used for pointing applications. The original purpose of the\nCanfield joint was to serve as a human wrist replacement, and it can be\nutilized for other purposes such as the precision pointing and tracking of\nantennas, telescopes, and thrusters. We build upon previous analyses, and\ngeneralize them to include the situation where one of the three legs freezes;\nthe kinematics are also substantially generalized beyond failure modes,\ndetailed within. The core of this work states and clarifies the assumptions\nnecessary to analyze this type of parallel robotic linkage. Specific guidance\nis included for engineering use cases.\n"}
+{"abstract": "  Let g be a G-invariant Einstein metric on a compact homogeneous space M=G/K.\nWe use a formula for the Lichnerowicz Laplacian of g at G-invariant TT-tensors\nto study the stability type of g as a critical point of the scalar curvature\nfunction. The case when g is naturally reductive is studied in special detail.\n"}
+{"abstract": "  We present the B-BOP instrument, a polarimetric camera on board the future\nESA-JAXA SPICA far-infrared space observatory. B-BOP will allow the study of\nthe magnetic field in various astrophysical environments thanks to its\nunprecedented ability to measure the linear polarization of the submillimeter\nlight. The maps produced by B-BOP will contain not only information on total\npower, but also on the degree and the angle of polarization, simultaneously in\nthree spectral bands (70, 200 and 350 microns). The B-BOP detectors are\nultra-sensitive silicon bolometers that are intrinsically sensitive to\npolarization. Their NEP is close to 10E-18 W/sqrt(Hz). We will present the\noptical and thermal architectures of the instrument, we will detail the\nbolometer design and we will show the expected performances of the instrument\nbased on preliminary lab work.\n"}
+{"abstract": "  While showing great promise, circuit synthesis techniques that combine\nnumerical optimization with search over circuit structures face scalability\nchallenges due to a large number of parameters, exponential search spaces, and\ncomplex objective functions. The LEAP algorithm improves scaling across these\ndimensions using iterative circuit synthesis, incremental re-optimization,\ndimensionality reduction, and improved numerical optimization. LEAP draws on\nthe design of the optimal synthesis algorithm QSearch by extending it with an\nincremental approach to determine constant prefix solutions for a circuit. By\nnarrowing the search space, LEAP improves scalability from four to six qubit\ncircuits. LEAP was evaluated with known quantum circuits such as QFT and\nphysical simulation circuits like the VQE, TFIM, and QITE. LEAP can compile\nfour qubit unitaries up to $59\\times$ faster than QSearch and five and six\nqubit unitaries with up to $1.2\\times$ fewer CNOTs compared to the QFAST\npackage. LEAP can reduce the CNOT count by up to $36\\times$, or $7\\times$ on\naverage, compared to the CQC Tket compiler. Despite its heuristics, LEAP has\ngenerated optimal circuits for many test cases with a priori known solutions.\nThe techniques introduced by LEAP are applicable to other\nnumerical-optimization-based synthesis approaches.\n"}
+{"abstract": "  For nonlinear hyperbolic systems of conservation laws in one space variable,\nwe establish the existence of nonclassical entropy solutions exhibiting\nnonlinear interactions between shock waves with strong strength. The proposed\ntheory is relevant in the theory of phase transition dynamics, and the\nsolutions under consideration enjoy a splitting--merging pattern, consisting of\n(compressive) classical and (undercompressive) nonclassical waves, interacting\ntogether as well as with classical waves of smaller strength. Our analysis is\nbased on three novel ideas. First, a generalization of Hayes--LeFloch's\nnonclassical Riemann solver is introduced for systems and is based on\nprescribing, on one hand, a kinetic relation for the propagation of\nnonclassical undercompressive shocks and, on the other hand, a nucleation\ncriterion that selects between classical and nonclassical behavior. Second, we\nextend LeFloch-Shearer's theorem to systems and we prove that the presence of a\nnucleation condition implies that only a finite number of splitting and merging\ncycles can occur. Third, our arguments of nonlinear stability build upon recent\nwork by the last two authors who identified a natural total variation\nfunctional for scalar conservation laws and, specifically, for systems of\nconservation laws we introduce here novel functionals which measure the total\nvariation and wave interaction of nonclassical and classical waves.\n"}
+{"abstract": "  Many trajectory forecasting methods, implementing deterministic and\nstochastic models, have been presented in the last decade for automotive\napplications. In this work, a deep-learning framework is proposed to model and\npredict the evolution of the coupled driver-vehicle system dynamics.\nParticularly, we aim to describe how the road geometry affects the actions\nperformed by the driver. Differently from other works, the problem is\nformulated in such a way that the user may specify the features of interest.\nNonetheless, we propose a set of features that is commonly used for automotive\ncontrol applications to practically show the functioning of the algorithm. To\nsolve the prediction problem, a deep recurrent neural network based on Long\nShort-Term Memory autoencoders is designed. It fuses the information on the\nroad geometry and the past driver-vehicle system dynamics to produce\ncontext-aware predictions. Also, the complexity of the neural network is\nconstrained to favour its use in online control tasks. The efficacy of the\nproposed approach was verified in a case study centered on motion cueing\nalgorithms, using a dataset collected during test sessions of a\nnon-professional driver on a dynamic driving simulator. A 3D track with complex\ngeometry was employed as driving environment to render the prediction task\nchallenging. Finally, the robustness of the neural network to changes in the\ndriver and track was investigated to set guidelines for future works.\n"}
+{"abstract": "  The discrete fracture model (DFM) has been widely used in the simulation of\nfluid flow in fractured porous media. Traditional DFM uses the so-called\nhybrid-dimensional approach to treat fractures explicitly as low-dimensional\nentries (e.g. line entries in 2D media and face entries in 3D media) on the\ninterfaces of matrix cells and then couple the matrix and fracture flow systems\ntogether based on the principle of superposition with the fracture thickness\nused as the dimensional homogeneity factor. Because of this methodology, DFM is\nconsidered to be limited on conforming meshes and thus may raise difficulties\nin generating high quality unstructured meshes due to the complexity of\nfracture's geometrical morphology. In this paper, we clarify that the DFM\nactually can be extended to non-conforming meshes without any essential\nchanges. To show it clearly, we provide another perspective for DFM based on\nhybrid-dimensional representation of permeability tensor to describe fractures\nas one-dimensional line Dirac delta functions contained in permeability tensor.\nA finite element DFM scheme for single-phase flow on non-conforming meshes is\nthen derived by applying Galerkin finite element method to it. Analytical\nanalysis and numerical experiments show that our DFM automatically degenerates\nto the classical finite element DFM when the mesh is conforming with fractures.\nMoreover, the accuracy and efficiency of the model on non-conforming meshes are\ndemonstrated by testing several benchmark problems. This model is also\napplicable to curved fracture with variable thickness.\n"}
+{"abstract": "  By learning Variable Impedance Control policy, robot assistants can\nintelligently adapt their manipulation compliance to ensure both safe\ninteraction and proper task completion when operating in human-robot\ninteraction environments. In this paper, we propose a DMP-based framework that\nlearns and generalizes variable impedance manipulation skills from human\ndemonstrations. This framework improves robots$'$ adaptability to environment\nchanges(i.e. the weight and shape changes of grasping object at the robot\nend-effector) and inherits the efficiency of demonstration-variance-based\nstiffness estimation methods. Besides, with our stiffness estimation method, we\ngenerate not only translational stiffness profiles but also rotational\nstiffness profiles that are ignored or incomplete in most learning Variable\nImpedance Control papers. Real-world experiments on a 7 DoF redundant robot\nmanipulator have been conducted to validate the effectiveness of our framework.\n"}
+{"abstract": "  Overlay measurements are a critical part of modern semiconductor fabrication,\nbut overlay targets have not scaled down in the way devices have. In this work,\nwe produce overlay targets with very small footprint, consisting of just a few\nscattering nanoparticles in two separate device layers. Using moir\\'e patterns\nto deterministically generate many overlay errors on a single chip, we\ndemonstrate successful readout of the relative displacement between the two\nlayers and show that calibration on one realization of the targets can be used\nfor overlay measurements on subsequent instances. Our results suggest using\ngreater quantities of smaller overlay targets may benefit performance both\ndirectly and through finer sampling of deformation.\n"}
+{"abstract": "  A function on an algebra is congruence preserving if, for any congruence, it\nmaps pairs of congruent elements onto pairs of congruent elements. An algebra\n  is said to be affine complete if every congruence preserving function is a\npolynomial function. We show that the algebra of (possibly empty) binary trees\nwhose leaves are labeled by letters of an alphabet containing at least one\nletter, and the free monoid on an alphabet containing at least two letters are\naffine complete.\n"}
+{"abstract": "  From a diabatic bound state approach to $J^{PC}=1^{--}$ and $(0,1,2)^{++}$\ncharmoniumlike resonances below $4.1$ GeV, formulated in terms of\n${c\\overline{c}}$ and closed meson-meson channels, we calculate mass shifts and\nwidths due to open meson-meson channels. This calculation does not involve any\nnew free parameter, so comparison of our predictions with existing data\nprovides a direct test of our approach. Further mass corrections are also\nestimated and good agreement with the measured masses comes out. As for the\ncalculated widths, overall reasonable, they point out to the need of some\nrefinement of our current bound state approximation for an accurate description\nof data. These results give additional support to the diabatic approach in QCD\nas an adequate framework for a complete unified description of conventional and\nunconventional charmoniumlike resonances. In this respect, the experimental\ndiscovery of a predicted $2^{++}$ resonance with a mass around $4$ GeV would be\nof special relevance.\n"}
+{"abstract": "  Classical distributed estimation scenarios typically assume timely and\nreliable exchanges of information over the sensor network. This paper, in\ncontrast, considers single time-scale distributed estimation via a sensor\nnetwork subject to transmission time-delays. The proposed discrete-time\nnetworked estimator consists of two steps: (i) consensus on (delayed) a-priori\nestimates, and (ii) measurement update. The sensors only share their a-priori\nestimates with their out-neighbors over (possibly) time-delayed transmission\nlinks. The delays are assumed to be fixed over time, heterogeneous, and known.\nWe assume distributed observability instead of local observability, which\nsignificantly reduces the communication/sensing loads on sensors. Using the\nnotions of augmented matrices and Kronecker product, the convergence of the\nproposed estimator over strongly-connected networks is proved for a specific\nupper-bound on the time-delay.\n"}
+{"abstract": "  Colorectal cancer is a leading cause of death worldwide. However, early\ndiagnosis dramatically increases the chances of survival, for which it is\ncrucial to identify the tumor in the body. Since its imaging uses\nhigh-resolution techniques, annotating the tumor is time-consuming and requires\nparticular expertise. Lately, methods built upon Convolutional Neural\nNetworks(CNNs) have proven to be at par, if not better in many biomedical\nsegmentation tasks. For the task at hand, we propose another CNN-based\napproach, which uses atrous convolutions and residual connections besides the\nconventional filters. The training and inference were made using an efficient\npatch-based approach, which significantly reduced unnecessary computations. The\nproposed AtResUNet was trained on the DigestPath 2019 Challenge dataset for\ncolorectal cancer segmentation with results having a Dice Coefficient of 0.748.\n"}
+{"abstract": "  We develop a distribution-free, unsupervised anomaly detection method called\nECAD, which wraps around any regression algorithm and sequentially detects\nanomalies. Rooted in conformal prediction, ECAD does not require data\nexchangeability but approximately controls the Type-I error when data are\nnormal. Computationally, it involves no data-splitting and efficiently trains\nensemble predictors to increase statistical power. We demonstrate the superior\nperformance of ECAD on detecting anomalous spatio-temporal traffic flow.\n"}
+{"abstract": "  We show that the weak disorder phase for the directed polymer model in a\nbounded random environment is characterized by the integrability of the running\nsupremum $\\sup_{n\\in \\mathbb N}W_n^\\beta$ of the associated martingale\n$(W_n^\\beta)_{n\\in \\mathbb N}$. Using this characterization, we prove that\n$(W_n^\\beta)_{n\\in \\mathbb N}$ is $L^p$-bounded in the whole weak disorder\nphase, for some $p>1$. The argument generalizes to non-negative martingales\nwith a certain product structure.\n"}
+{"abstract": "  Bronstein's lazy Hermite reduction is a symbolic integration technique that\nreduces algebraic functions to integrands with only simple poles without the\nprior computation of an integral basis. We sharpen the lazy Hermite reduction\nby combining it with the polynomial reduction to solve the decomposition\nproblem of algebraic functions. The sharpened reduction is then used to design\na reduction-based telescoping algorithm for algebraic functions in two\nvariables.\n"}
+{"abstract": "  In this paper, we study the connection between the Ericksen-Leslie equations\nand the Beris-Edwards equations in dimension two. It is shown that the weak\nsolutions to the Beris-Edwards equations converge to the one to the\nEricksen-Leslie equations as the elastic coefficient tends to zero. Moreover,\nthe limiting weak solutions to the Ericksen-Leslie equations may have singular\npoints.\n"}
+{"abstract": "  One-dimensional diffusion of Co ad-atoms on graphene nanoribbons has been\ninduced and investigated by means of scanning tunnelling microscopy (STM). To\nthis end, the nanoribbons and the Co ad-atoms have been imaged before and after\ninjecting current pulses into the nanoribbons, with the STM tip in direct\ncontact with the ribbon. We observe current-induced motion of the Co atoms\nalong the nanoribbons, which is approximately described by a distribution\nexpected for a thermally activated one-dimensional random walk. This indicates\nthat the nanoribbons reach temperatures far beyond 100 K, which is well above\nthe temperature of the underlying Au substrate. This model system can be\ndeveloped further for the study of electromigration at the single-atom level.\n"}
+{"abstract": "  It is very important which the Hamiltonian of the quantum system is time\nchanging, especially the potential well that its width can change, the\nschrodinger equation and klein Gordon equation of this kind of circumstance are\nsolved by some studies, but the Dirac equation haven't be solved, so this\narticle discussed the solution of the Dirac equation in this kind of\ncircumstance.\n"}
+{"abstract": "  This paper introduced the space mission DikpolaSat Mission, how this research\nfits into the mission, and the importance of having a trained DNN model instead\nof the usual GN&C functionality. This paper shows how the controller\ndemonstration is carried out by having the spacecraft follow a desired path,\nspecified in the referenced model. Increases can be made by examining the route\nused to construct a DNN and understanding the effects of various activating\nfunctions on system efficiency. The obstacle avoidance algorithm is built into\nthe control features to respond spontaneously using inputs from the neural\nnetwork for collision avoidance while optimizing the modified trajectory. The\naction of a neural network to control the adaptive nature of the nonlinear\nmechanisms in the controller will make the control system capable of handling\nmultiple nonlinear events and also uncertainties that have not been induced in\nthe control algorithm. Multiple algorithms for optimizing flight controls and\nfuel consumption can be implemented using knowledge of flight dynamics in\ntrajectory and also in the event of obstacle avoidance. This paper also\nexplains how a DNN can learn to control the flight path and make the system\nmore reliable with each launch, thereby improving the chances of predicting\ncollisions of space objects. The data released from this research is used to\ndesign more advanced DNN model capable of predicting other orbital events as\nwell.\n"}
+{"abstract": "  The collective attention on online items such as web pages, search terms, and\nvideos reflects trends that are of social, cultural, and economic interest.\nMoreover, attention trends of different items exhibit mutual influence via\nmechanisms such as hyperlinks or recommendations. Many visualisation tools\nexist for time series, network evolution, or network influence; however, few\nsystems connect all three. In this work, we present AttentionFlow, a new system\nto visualise networks of time series and the dynamic influence they have on one\nanother. Centred around an ego node, our system simultaneously presents the\ntime series on each node using two visual encodings: a tree ring for an\noverview and a line chart for details. AttentionFlow supports interactions such\nas overlaying time series of influence and filtering neighbours by time or\nflux. We demonstrate AttentionFlow using two real-world datasets, VevoMusic and\nWikiTraffic. We show that attention spikes in songs can be explained by\nexternal events such as major awards, or changes in the network such as the\nrelease of a new song. Separate case studies also demonstrate how an artist's\ninfluence changes over their career, and that correlated Wikipedia traffic is\ndriven by cultural interests. More broadly, AttentionFlow can be generalised to\nvisualise networks of time series on physical infrastructures such as road\nnetworks, or natural phenomena such as weather and geological measurements.\n"}
+{"abstract": "  In recent years there has been a special interest in word embeddings as a new\napproach to convert words to vectors. It has been a focal point to understand\nhow much of the semantics of the the words has been transferred into embedding\nvectors. This is important as the embedding is going to be used as the basis\nfor downstream NLP applications and it will be costly to evaluate the\napplication end-to-end in order to identify quality of the used embedding\nmodel. Generally the word embeddings are evaluated through a number of tests,\nincluding analogy test. In this paper we propose a test framework for Persian\nembedding models. Persian is a low resource language and there is no rich\nsemantic benchmark to evaluate word embedding models for this language. In this\npaper we introduce an evaluation framework including a hand crafted Persian SAT\nbased analogy dataset, a colliquial test set (specific to Persian) and a\nbenchmark to study the impact of various parameters on the semantic evaluation\ntask.\n"}
+{"abstract": "  The Shuffle Test is the most common task to evaluate whether NLP models can\nmeasure coherence in text. Most recent work uses direct supervision on the\ntask; we show that by simply finetuning a RoBERTa model, we can achieve a near\nperfect accuracy of 97.8%, a state-of-the-art. We argue that this outstanding\nperformance is unlikely to lead to a good model of text coherence, and suggest\nthat the Shuffle Test should be approached in a Zero-Shot setting: models\nshould be evaluated without being trained on the task itself. We evaluate\ncommon models in this setting, such as Generative and Bi-directional\nTransformers, and find that larger architectures achieve high-performance\nout-of-the-box. Finally, we suggest the k-Block Shuffle Test, a modification of\nthe original by increasing the size of blocks shuffled. Even though human\nreader performance remains high (around 95% accuracy), model performance drops\nfrom 94% to 78% as block size increases, creating a conceptually simple\nchallenge to benchmark NLP models. Code available:\nhttps://github.com/tingofurro/shuffle_test/\n"}
+{"abstract": "  We study massive scalar field perturbation on Kerr black holes in dynamical\nChern-Simons gravity by performing a $(2+1)$-dimensional simulation. Object\npictures of the wave dynamics in time domain are obtained. The tachyonic\ninstability is found to always occur for any nonzero black hole spin and any\nscalar field mass as long as the coupling constant exceeds a critical value.\nThe presence of the mass term suppresses or even quenches the instability. The\nquantitative dependence of the onset of the tachyonic instability on the\ncoupling constant, the scalar field mass and the black hole spin is given\nnumerically.\n"}
+{"abstract": "  We revisit the conserved quantities of the Mathisson-Papapetrou-Tulczyjew\nequations describing the motion of spinning particles on a fixed background.\nAssuming Ricci-flatness and the existence of a Killing-Yano tensor, we\ndemonstrate that besides the two non-trivial quasi-conserved quantities, i.e.\nconserved at linear order in the spin, found by R\\\"udiger, non-trivial\nquasi-conserved quantities are in one-to-one correspondence with non-trivial\nmixed-symmetry Killing tensors. We prove that no such stationary and\naxisymmetric mixed-symmetry Killing tensor exists on the Kerr geometry. We\ndiscuss the implications for the motion of spinning particles on Kerr spacetime\nwhere the quasi-constants of motion are shown not to be in complete involution.\n"}
+{"abstract": "  A predicate head is a verbal expression that plays a role as the structural\ncenter of a sentence. Identifying predicate heads is critical to understanding\na sentence. It plays the leading role in organizing the relevant syntactic\nelements in a sentence, including subject elements, adverbial elements, etc.\nFor some languages, such as English, word morphologies are valuable for\nidentifying predicate heads. However, Chinese offers no morphological\ninformation to indicate words` grammatical roles. A Chinese sentence often\ncontains several verbal expressions; identifying the expression that plays the\nrole of the predicate head is not an easy task. Furthermore, Chinese sentences\nare inattentive to structure and provide no delimitation between words.\nTherefore, identifying Chinese predicate heads involves significant challenges.\nIn Chinese information extraction, little work has been performed in predicate\nhead recognition. No generally accepted evaluation dataset supports work in\nthis important area. This paper presents the first attempt to develop an\nannotation guideline for Chinese predicate heads and their relevant syntactic\nelements. This annotation guideline emphasizes the role of the predicate as the\nstructural center of a sentence. The design of relevant syntactic element\nannotation also follows this principle. Many considerations are proposed to\nachieve this goal, e.g., patterns of predicate heads, a flattened annotation\nstructure, and a simpler syntactic unit type. Based on the proposed annotation\nguideline, more than 1,500 documents were manually annotated. The corpus will\nbe available online for public access. With this guideline and annotated\ncorpus, our goal is to broadly impact and advance the research in the area of\nChinese information extraction and to provide the research community with a\ncritical resource that has been lacking for a long time.\n"}
+{"abstract": "  Federated learning has emerged as a popular paradigm for collaboratively\ntraining a model from data distributed among a set of clients. This learning\nsetting presents, among others, two unique challenges: how to protect privacy\nof the clients' data during training, and how to ensure integrity of the\ntrained model. We propose a two-pronged solution that aims to address both\nchallenges under a single framework. First, we propose to create secure\nenclaves using a trusted execution environment (TEE) within the server. Each\nclient can then encrypt their gradients and send them to verifiable enclaves.\nThe gradients are decrypted within the enclave without the fear of privacy\nbreaches. However, robustness check computations in a TEE are computationally\nprohibitive. Hence, in the second step, we perform a novel gradient encoding\nthat enables TEEs to encode the gradients and then offloading Byzantine check\ncomputations to accelerators such as GPUs. Our proposed approach provides\ntheoretical bounds on information leakage and offers a significant speed-up\nover the baseline in empirical evaluation.\n"}
+{"abstract": "  We investigate the problem of exact cluster recovery using oracle queries.\nPrevious results show that clusters in Euclidean spaces that are convex and\nseparated with a margin can be reconstructed exactly using only $O(\\log n)$\nsame-cluster queries, where $n$ is the number of input points. In this work, we\nstudy this problem in the more challenging non-convex setting. We introduce a\nstructural characterization of clusters, called $(\\beta,\\gamma)$-convexity,\nthat can be applied to any finite set of points equipped with a metric (or even\na semimetric, as the triangle inequality is not needed). Using\n$(\\beta,\\gamma)$-convexity, we can translate natural density properties of\nclusters (which include, for instance, clusters that are strongly non-convex in\n$\\mathbb{R}^d$) into a graph-theoretic notion of convexity. By exploiting this\nconvexity notion, we design a deterministic algorithm that recovers\n$(\\beta,\\gamma)$-convex clusters using $O(k^2 \\log n + k^2\n(6/\\beta\\gamma)^{dens(X)})$ same-cluster queries, where $k$ is the number of\nclusters and $dens(X)$ is the density dimension of the semimetric. We show that\nan exponential dependence on the density dimension is necessary, and we also\nshow that, if we are allowed to make $O(k^2 + k\\log n)$ additional queries to a\n\"cluster separation\" oracle, then we can recover clusters that have different\nand arbitrary scales, even when the scale of each cluster is unknown.\n"}
+{"abstract": "  The blooming of fake news on social networks has devastating impacts on\nsociety, economy, and public security. Although numerous studies are conducted\nfor the automatic detection of fake news, the majority tend to utilize deep\nneural networks to learn event-specific features for superior detection\nperformance on specific datasets. However, the trained models heavily rely on\nthe training datasets and are infeasible to apply to upcoming events due to the\ndiscrepancy between event distributions. Inspired by domain adaptation\ntheories, we propose an end-to-end adversarial adaptation network, dubbed as\nMetaDetector, to transfer meta knowledge (event-shared features) between\ndifferent events. Specifically, MetaDetector pushes the feature extractor and\nevent discriminator to eliminate event-specific features and preserve required\nevent-shared features by adversarial training. Furthermore, the pseudo-event\ndiscriminator is utilized to evaluate the importance of historical event posts\nto obtain partial shared features that are discriminative for detecting fake\nnews. Under the coordinated optimization among the four submodules,\nMetaDetector accurately transfers the meta-knowledge of historical events to\nthe upcoming event for fact checking. We conduct extensive experiments on two\nlarge-scale datasets collected from Weibo and Twitter. The experimental results\ndemonstrate that MetaDetector outperforms the state-of-the-art methods,\nespecially when the distribution shift between events is significant.\nFurthermore, we find that MetaDetector is able to learn the event-shared\nfeatures, and alleviate the negative transfer caused by the large distribution\nshift between events.\n"}
+{"abstract": "  Ultra-hot Jupiters are tidally locked gas giants with dayside temperatures\nhigh enough to dissociate hydrogen and other molecules. Their atmospheres are\nvastly non-uniform in terms of chemistry, temperature and dynamics, and this\nmakes their high-resolution transmission spectra and cross-correlation signal\ndifficult to interpret. In this work, we use the SPARC/MITgcm global\ncirculation model to simulate the atmosphere of the ultra-hot Jupiter WASP-76b\nunder different conditions, such as atmospheric drag and the absence of TiO and\nVO. We then employ a 3D Monte-Carlo radiative transfer code, HIRES-MCRT, to\nself-consistently model high-resolution transmission spectra with iron (Fe I)\nlines at different phases during the transit. To untangle the structure of the\nresulting cross-correlation map, we decompose the limb of the planet into four\nsectors, and we analyse each of their contributions separately. Our experiments\ndemonstrate that the cross-correlation signal of an ultra-hot Jupiter is\nprimarily driven by its temperature structure, rotation and dynamics, while\nbeing less sensitive to the precise distribution of iron across the atmosphere.\nWe also show that the previously published iron signal of WASP-76b can be\nreproduced by a model featuring iron condensation on the leading limb.\nAlternatively, the signal may be explained by a substantial temperature\nasymmetry between the trailing and leading limb, where iron condensation is not\nstrictly required to match the data. Finally, we compute the $K_{p}-V_{sys}$\nmaps of the simulated WASP-76b atmospheres, and we show that rotation and\ndynamics can lead to multiple peaks that are displaced from zero in the\nplanetary rest frame.\n"}
+{"abstract": "  Single layer hexagonal boron nitride is produced on 2 inch Pt(111)/sapphire\nwafers. The growth with borazine vapour deposition at process temperatures\nbetween 1000 and 1300 K is in-situ investigated by photoelectron yield\nmeasurements. The growth kinetics is slower at higher temperatures and follows\na tanh$^2$ law which better fits for higher temperatures. The crystal-quality\nof h-BN/Pt(111) is inferred from scanning low energy electron diffraction (x-y\nLEED). The data indicate a strong dependence of the epitaxy on the growth\ntemperature. The dominant structure is an aligned coincidence lattice with 10\nh-BN on 9 Pt(1$\\times$1) unit cells and follows the substrate twinning at the\nmillimeter scale.\n"}
+{"abstract": "  The goal of our present paper is to deliberate $*$-conformal $\\eta$-Ricci\nsoliton within the framework of Kenmotsu manifolds. Here we have shown that a\nKenmotsu metric as a $*$-conformal $\\eta$-Ricci soliton is Einstein metric if\nthe soliton vector field is contact. Further, we have evolved the\ncharacterization of the Kenmotsu manifold or the nature of the potential vector\nfield when the manifold satisfies gradient almost $*$-conformal $\\eta$-Ricci\nsoliton. Next, we have contrived $*$-conformal $\\eta$-Ricci soliton admitting\n$(\\kappa,\\mu)^\\prime$-almost Kenmotsu manifold and proved that the manifold is\nRicci flat and is locally isometric to\n$\\mathbb{H}^{n+1}(-4)\\times\\mathbb{R}^n$. Finally we have constructed some\nexamples to illustrate the existence of $*$-conformal $\\eta$-Ricci soliton,\ngradient almost $*$-conformal $\\eta$-Ricci soliton on Kenmotsu manifold and\n$(\\kappa,\\mu)^\\prime$-almost Kenmotsu manifolds.\n"}
+{"abstract": "  In most real-world large-scale online applications (e.g., e-commerce or\nfinance), customer acquisition is usually a multi-step conversion process of\naudiences. For example, an impression->click->purchase process is usually\nperformed of audiences for e-commerce platforms. However, it is more difficult\nto acquire customers in financial advertising (e.g., credit card advertising)\nthan in traditional advertising. On the one hand, the audience multi-step\nconversion path is longer. On the other hand, the positive feedback is sparser\n(class imbalance) step by step, and it is difficult to obtain the final\npositive feedback due to the delayed feedback of activation. Multi-task\nlearning is a typical solution in this direction. While considerable multi-task\nefforts have been made in this direction, a long-standing challenge is how to\nexplicitly model the long-path sequential dependence among audience multi-step\nconversions for improving the end-to-end conversion. In this paper, we propose\nan Adaptive Information Transfer Multi-task (AITM) framework, which models the\nsequential dependence among audience multi-step conversions via the Adaptive\nInformation Transfer (AIT) module. The AIT module can adaptively learn what and\nhow much information to transfer for different conversion stages. Besides, by\ncombining the Behavioral Expectation Calibrator in the loss function, the AITM\nframework can yield more accurate end-to-end conversion identification. The\nproposed framework is deployed in Meituan app, which utilizes it to real-timely\nshow a banner to the audience with a high end-to-end conversion rate for\nMeituan Co-Branded Credit Cards. Offline experimental results on both\nindustrial and public real-world datasets clearly demonstrate that the proposed\nframework achieves significantly better performance compared with\nstate-of-the-art baselines.\n"}
+{"abstract": "  We prove a result on the fractional Sobolev regularity of composition of\npaths of low fractional Sobolev regularity with functions of bounded variation.\nThe result relies on the notion of variability, proposed by us in the previous\narticle [43, arXiv:2003.11698]. Here we work under relaxed hypotheses,\nformulated in terms of Sobolev norms, and we can allow discontinuous paths,\nwhich is new. The result applies to typical realizations of certain Gaussian or\nL\\'evy processes, and we use it to show the existence of Stieltjes type\nintegrals involving compositions.\n"}
+{"abstract": "  The COVID-19 pandemic has spread globally for several months. Because its\ntransmissibility and high pathogenicity seriously threaten people's lives, it\nis crucial to accurately and quickly detect COVID-19 infection. Many recent\nstudies have shown that deep learning (DL) based solutions can help detect\nCOVID-19 based on chest CT scans. However, most existing work focuses on 2D\ndatasets, which may result in low quality models as the real CT scans are 3D\nimages. Besides, the reported results span a broad spectrum on different\ndatasets with a relatively unfair comparison. In this paper, we first use three\nstate-of-the-art 3D models (ResNet3D101, DenseNet3D121, and MC3\\_18) to\nestablish the baseline performance on the three publicly available chest CT\nscan datasets. Then we propose a differentiable neural architecture search\n(DNAS) framework to automatically search for the 3D DL models for 3D chest CT\nscans classification with the Gumbel Softmax technique to improve the searching\nefficiency. We further exploit the Class Activation Mapping (CAM) technique on\nour models to provide the interpretability of the results. The experimental\nresults show that our automatically searched models (CovidNet3D) outperform the\nbaseline human-designed models on the three datasets with tens of times smaller\nmodel size and higher accuracy. Furthermore, the results also verify that CAM\ncan be well applied in CovidNet3D for COVID-19 datasets to provide\ninterpretability for medical diagnosis.\n"}
+{"abstract": "  Object localization has a vital role in any object detector, and therefore,\nhas been the focus of attention by many researchers. In this article, a special\ntraining approach is proposed for a light convolutional neural network (CNN) to\ndetermine the region of interest (ROI) in an image while effectively reducing\nthe number of probable anchor boxes. Almost all CNN-based detectors utilize a\nfixed input size image, which may yield poor performance when dealing with\nvarious object sizes. In this paper, a different CNN structure is proposed\ntaking three different input sizes, to enhance the performance. In order to\ndemonstrate the effectiveness of the proposed method, two common data set are\nused for training while tracking by localization application is considered to\ndemonstrate its final performance. The promising results indicate the\napplicability of the presented structure and the training method in practice.\n"}
+{"abstract": "  In this paper, we investigate the random access problem for a\ndelay-constrained heterogeneous wireless network. As a first attempt to study\nthis new problem, we consider a network with two users who deliver\ndelay-constrained traffic to an access point (AP) via a common unreliable\ncollision wireless channel. We assume that one user (called user 1) adopts\nALOHA and we optimize the random access scheme of the other user (called user\n2). The most intriguing part of this problem is that user 2 does not know the\ninformation of user 1 but needs to maximize the system timely throughput. Such\na paradigm of collaboratively sharing spectrum is envisioned by DARPA to better\ndynamically match the supply and demand in the future [1], [2]. We first\npropose a Markov Decision Process (MDP) formulation to derive a modelbased\nupper bound, which can quantify the performance gap of any designed schemes. We\nthen utilize reinforcement learning (RL) to design an R-learning-based [3]-[5]\nrandom access scheme, called TSRA. We finally carry out extensive simulations\nto show that TSRA achieves close-to-upper-bound performance and better\nperformance than the existing baseline DLMA [6], which is our counterpart\nscheme for delay-unconstrained heterogeneous wireless network. All source code\nis publicly available in https://github.com/DanzhouWu/TSRA.\n"}
+{"abstract": "  Cosmic rays (CRs) have critical impacts in the multiphase interstellar medium\n(ISM), driving dynamical motions in low-density plasma and modifying the\nionization state, temperature, and chemical composition of higher-density\natomic and molecular gas. We present a study of CR propagation between the\nionized ISM and a neutral cloud. Using one-dimensional magnetohydrodynamic\nparticle-in-cell simulations which include ion-neutral drag to damp\nAlfv$\\acute{\\text{e}}$n waves in the cloud, we self-consistently evolve the\nkinetic physics of CRs and fluid dynamics of the multiphase gas. By introducing\nthe cloud in our periodic domain, our simulations break translational symmetry\nand allow the emergence of spatial structure in the CR distribution function. A\nnegative spatial gradient forms across the fully-ionized ISM region while a\npositive gradient forms across the neutral cloud. We connect our results with\nCR hydrodynamics formulations by computing the wave-particle scattering rates\nas predicted by quasilinear, fluid, and Fokker-Planck theory. For momenta where\nthe mean free path is short relative to the box size, we find excellent\nagreement among all scattering rates. By exploring different cloud sizes and\nion-neutral collision rates, we show that our results are robust. Our work\nprovides a first-principles verification of CR hydrodynamics when particles\nstream down their pressure gradient, and opens a pathway toward comprehensive\ncalibrations of transport coefficients from self-generated\nAlfv$\\acute{\\text{e}}$n wave scattering with CRs.\n"}
+{"abstract": "  A word $w$ is said to be closed if it has a proper factor $x$ which occurs\nexactly twice in $w$, as a prefix and as a suffix of $w$. Based on the concept\nof Ziv-Lempel factorization, we define the closed $z$-factorization of finite\nand infinite words. Then we find the closed $z$-factorization of the infinite\n$m$-bonacci words for all $m \\geq 2$. We also classify closed prefixes of the\ninfinite $m$-bonacci words.\n"}
+{"abstract": "  In the presence of certain symmetries, three-dimensional Dirac semimetals can\nharbor not only surface Fermi arcs, but also surface Dirac cones. Motivated by\nthe experimental observation of rotation-symmetry-protected Dirac semimetal\nstates in iron-based superconductors, we investigate the potential intrinsic\ntopological phases in a $C_{4z}$-rotational invariant superconducting Dirac\nsemimetal with $s_{\\pm}$-wave pairing. When the normal state harbors only\nsurface Fermi arcs on the side surfaces, we find that an interesting gapped\nsuperconducting state with a quartet of Majorana cones on each side surface can\nbe realized, even though the first-order topology of its bulk is trivial. When\nthe normal state simultaneously harbors surface Fermi arcs and surface Dirac\ncones, we find that a second-order time-reversal invariant topological\nsuperconductor with helical Majorana hinge states can be realized. The criteria\nfor these two distinct topological phases have a simple geometric\ninterpretation in terms of three characteristic surfaces in momentum space. By\nreducing the bulk material to a thin film normal to the axis of rotation\nsymmetry, we further find that a two-dimensional first-order time-reversal\ninvariant topological superconductor can be realized if the inversion symmetry\nis broken by applying a gate voltage. Our work reveals that diverse topological\nsuperconducting phases and types of Majorana modes can be realized in\nsuperconducting Dirac semimetals.\n"}
+{"abstract": "  An example of an activation function $\\sigma$ is given such that networks\nwith activations $\\{\\sigma, \\lfloor\\cdot\\rfloor\\}$, integer weights and a fixed\narchitecture depending on $d$ approximate continuous functions on $[0,1]^d$.\nThe range of integer weights required for $\\varepsilon$-approximation of\nH\\\"older continuous functions is derived, which leads to a convergence rate of\norder $n^{\\frac{-2\\beta}{2\\beta+d}}\\log_2n$ for neural network regression\nestimation of unknown $\\beta$-H\\\"older continuous function with given $n$\nsamples.\n"}
+{"abstract": "  We consider the problem of principal component analysis from a data matrix\nwhere the entries of each column have undergone some unknown permutation,\ntermed Unlabeled Principal Component Analysis (UPCA). Using algebraic geometry,\nwe establish that for generic enough data, and up to a permutation of the\ncoordinates of the ambient space, there is a unique subspace of minimal\ndimension that explains the data. We show that a permutation-invariant system\nof polynomial equations has finitely many solutions, with each solution\ncorresponding to a row permutation of the ground-truth data matrix. Allowing\nfor missing entries on top of permutations leads to the problem of unlabeled\nmatrix completion, for which we give theoretical results of similar flavor. We\nalso propose a two-stage algorithmic pipeline for UPCA suitable for the\npractically relevant case where only a fraction of the data has been permuted.\nStage-I of this pipeline employs robust-PCA methods to estimate the\nground-truth column-space. Equipped with the column-space, stage-II applies\nmethods for linear regression without correspondences to restore the permuted\ndata. A computational study reveals encouraging findings, including the ability\nof UPCA to handle face images from the Extended Yale-B database with\narbitrarily permuted patches of arbitrary size in $0.3$ seconds on a standard\ndesktop computer.\n"}
+{"abstract": "  In this paper, we study the geometry of $GT-$varieties $X_{d}$ with group a\nfinite cyclic group $\\Gamma \\subset \\mathrm{GL}(n+1,\\mathbb{K})$ of order $d$.\nWe prove that the homogeneous ideal $\\mathrm{I}(X_{d})$ of $X_{d}$ is generated\nby binomials of degree at most $3$ and we provide examples reaching this bound.\nWe give a combinatorial description of the canonical module of the homogeneous\ncoordinate ring of $X_{d}$ and we show that it is generated by monomial\ninvariants of $\\Gamma$ of degree $d$ and $2d$. This allows us to characterize\nthe Castelnuovo-Mumford regularity of the homogeneous coordinate ring of $X_d$.\nFinally, we compute the cohomology table of the normal bundle of the so called\n$RL-$varieties. They are projections of the Veronese variety\n$\\nu_{d}(\\mathbb{P}^{n}) \\subset \\mathbb{P}^{\\binom{n+d}{n}-1}$ which naturally\narise from level $GT-$varieties.\n"}
+{"abstract": "  Conditioning a branching Brownian motion to have an atypically low maximum\nleads to a suppression of the branching mechanism. In this note, we consider a\nbranching Brownian motion conditioned to have a maximum below $\\sqrt{2}\\alpha\nt$ ($\\alpha<1$). We study the precise effects of an early/late first branching\ntime and a low/high first branching location under this condition. We do so by\nimposing additional constraints on the first branching time and location. We\nobtain large deviation estimates, as well as the optimal first branching time\nand location given the additional constraints.\n"}
+{"abstract": "  The correct modeling of $e^+e^-$ collision events at the International Linear\nCollider (ILC), as well as the response of a collider detector like the Silicon\nDetector (SiD), is crucial to evaluating the expected sensitivity to key\nproperties of the Higgs boson. In this document we describe the event\ngeneration and detector simulation in use for the SiD Letters of Interest\nsubmitted for the 2021 Snowmass community planning exercise.\n"}
+{"abstract": "  The occurrence of a planet transiting in front of its host star offers the\nopportunity to observe the planet's atmosphere filtering starlight. The\nfraction of occulted stellar flux is roughly proportional to the optically\nthick area of the planet, the extent of which depends on the opacity of the\nplanet's gaseous envelope at the observed wavelengths. Chemical species, haze,\nand clouds are now routinely detected in exoplanet atmospheres through rather\nsmall features in transmission spectra, i.e., collections of planet-to-star\narea ratios across multiple spectral bins and/or photometric bands.\nTechnological advances have led to a shrinking of the error bars down to a few\ntens of parts per million (ppm) per spectral point for the brightest targets.\nThe upcoming James Webb Space Telescope (JWST) is anticipated to deliver\ntransmission spectra with precision down to 10 ppm. The increasing precision of\nmeasurements requires a reassessment of the approximations hitherto adopted in\nastrophysical models, including transit light curve models. Recently, it has\nbeen shown that neglecting the planet's thermal emission can introduce\nsignificant biases in the transit depth measured with the JWST/Mid-InfraRed\nInstrument, integrated between 5 and 12 $\\mu$m. In this paper, we take a step\nforward by analyzing the effects of the approximation on transmission spectra\nover the 0.6-12 $\\mu$m wavelength range covered by various JWST instruments. We\npresent open source software to predict the spectral bias, showing that, if not\ncorrected, it may affect the inferred molecular abundances and thermal\nstructure of some exoplanet atmospheres.\n"}
+{"abstract": "  Alkaline-earth-like~(AEL) atoms with two valence electrons and a nonzero\nnuclear spin can be excited to Rydberg state for quantum computing. Typical AEL\nground states possess no hyperfine splitting, but unfortunately a GHz-scale\nsplitting seems necessary for Rydberg excitation. Though strong magnetic fields\ncan induce a GHz-scale splitting, weak fields are desirable to avoid noise in\nexperiments. Here, we provide two solutions to this outstanding challenge with\nrealistic data of well-studied AEL isotopes. In the first theory, the two\nnuclear spin qubit states $|0\\rangle$ and $|1\\rangle$ are excited to Rydberg\nstates $|r\\rangle$ with detuning $\\Delta$ and 0, respectively, where a\nMHz-scale detuning $\\Delta$ arises from a weak magnetic field on the order of\n1~G. With a proper ratio between $\\Delta$ and $\\Omega$, the qubit state\n$|1\\rangle$ can be fully excited to the Rydberg state while $|0\\rangle$ remains\nthere. In the second theory, we show that by choosing appropriate intermediate\nstates a two-photon Rydberg excitation can proceed with only one nuclear spin\nqubit state. The second theory is applicable whatever the magnitude of the\nmagnetic field is. These theories bring a versatile means for quantum\ncomputation by combining the broad applicability of Rydberg blockade and the\nincomparable advantages of nuclear-spin quantum memory in two-electron neutral\natoms.\n"}
+{"abstract": "  In this paper, an improvement has been made on the variation after projection\n(VAP) method, which is crucial in the calculations of high-spin states. It\nturns out that, the form of the trial VAP wave function with spin $J$ can be\nsimplified by adopting just one projected state rather than previously adopting\nall $(2J+1)$ angular momentum projected states for each selected reference\nstate, $|\\Phi\\rangle$. The present calculations show that such simplification\nstill minimizes the angular momentum projected energy up to a very good\napproximation. In this simplified VAP scheme, one can obtain almost equivalent\nVAP wave functions starting from different sets of the projected basis states.\nThis clearly shows that a nuclear state can not be identified with a single\nintrinsic state, while in the traditional nuclear collective models, an\nintrinsic state is usually assigned to a well deformed rotational band.\n"}
+{"abstract": "  Great research interests have been attracted to devise AI services that are\nable to provide mental health support. However, the lack of corpora is a main\nobstacle to this research, particularly in Chinese language. In this paper, we\npropose PsyQA, a Chinese dataset of psychological health support in the form of\nquestion and answer pair. PsyQA is crawled from a Chinese mental health service\nplatform, and contains 22K questions and 56K long and well-structured answers.\nBased on the psychological counseling theories, we annotate a portion of answer\ntexts with typical strategies for providing support, and further present\nin-depth analysis of both lexical features and strategy patterns in the\ncounseling answers. We also evaluate the performance of generating counseling\nanswers with the generative pretrained models. Results show that utilizing\nstrategies enhances the fluency and helpfulness of generated answers, but there\nis still a large space for future research.\n"}
+{"abstract": "  The task of calculating similarities between strings held by different\norganizations without revealing these strings is an increasingly important\nproblem in areas such as health informatics, national censuses, genomics, and\nfraud detection. Most existing privacy-preserving string comparison functions\nare either based on comparing sets of encoded character q-grams, allow only\nexact matching of encrypted strings, or they are aimed at long genomic\nsequences that have a small alphabet. The set-based privacy-preserving\nsimilarity functions commonly used to compare name and address strings in the\ncontext of privacy-preserving record linkage do not take the positions of\nsub-strings into account. As a result, two very different strings can\npotentially be considered as an exact match leading to wrongly linked records.\nExisting set-based techniques also cannot identify the length of the longest\ncommon sub-string across two strings. In this paper we propose a novel approach\nfor accurate and efficient privacy-preserving string matching based on suffix\ntrees that are encoded using chained hashing. We incorporate a hashing based\nencoding technique upon the encoded suffixes to improve privacy against\nfrequency attacks such as those exploiting Benford's law. Our approach allows\nvarious operations to be performed without the strings to be compared being\nrevealed: the length of the longest common sub-string, do two strings have the\nsame beginning, middle or end, and the longest common sub-string similarity\nbetween two strings. These functions allow a more accurate comparison of, for\nexample, bank account, credit card, or telephone numbers, which cannot be\ncompared appropriately with existing privacy-preserving string matching\ntechniques. Our evaluation on several data sets with different types of strings\nvalidates the privacy and accuracy of our proposed approach.\n"}
+{"abstract": "  We establish large sieve inequalities for power moduli in imaginary quadratic\nnumber fields, extending earlier work of Baier and Bansal for the Gaussian\nfield.\n"}
+{"abstract": "  We have fit the far-ultraviolet (FUV) to mid-infrared (MIR) spectral energy\ndistributions (SEDs) for several nearby galaxies ($<$ 20 Mpc). Global, radial,\nand local photometric measurements are explored to better understand how\nSED-derived star formation histories (SFHs) and classic star formation rate\n(SFR) tracers manifest at different scales. Surface brightness profiles and\nradial SED fitting provide insight into stellar population gradients in stellar\ndiscs and haloes. A double exponential SFH model is used in the SED fitting to\nbetter understand the distributions of young vs. old populations throughout\nthese galaxies. Different regions of a galaxy often have undergone very\ndifferent SFHs, either in strength, rate, timing, or some combination of all\nthese factors. An analysis of individual stellar complexes within these\ngalaxies shows a relationship between the ages of stellar clusters and how\nthese clusters are distributed throughout the galaxy. These star formation\nproperties are presented alongside previously published HI observations to\nprovide a holistic picture of a small sample of nearby star-forming galaxies.\nThe results presented here show that there is a wide variety of star formation\ngradients and average stellar age distributions that can manifest in a\n$\\Lambda$CDM universe.\n"}
+{"abstract": "  Convolutional neural networks, the state of the art for image segmentation,\nhave been successfully applied to histology images by many computational\nresearchers. However, the translatability of this technology to clinicians and\nbiological researchers is limited due to the complex and undeveloped user\ninterface of the code, as well as the extensive computer setup required. As an\nextension of our previous work (arXiv:1812.07509), we have developed a tool for\nsegmentation of whole slide images (WSIs) with an easy to use graphical user\ninterface. Our tool runs a state-of-the-art convolutional neural network for\nsegmentation of WSIs in the cloud. Our plugin is built on the open source tool\nHistomicsTK by Kitware Inc. (Clifton Park, NY), which provides remote data\nmanagement and viewing abilities for WSI datasets. The ability to access this\ntool over the internet will facilitate widespread use by computational\nnon-experts. Users can easily upload slides to a server where our plugin is\ninstalled and perform human in the loop segmentation analysis remotely. This\ntool is open source, and has the ability to be adapted to segment of any\npathological structure. For a proof of concept, we have trained it to segment\nglomeruli from renal tissue images, achieving an F-score > 0.97 on holdout\ntissue slides.\n"}
+{"abstract": "  Distributed word representations are popularly used in many tasks in natural\nlanguage processing, adding that pre-trained word vectors on huge text corpus\nachieved high performance in many different NLP tasks. This paper introduces\nmultiple high quality word vectors for the French language where two of them\nare trained on huge crawled French data and the others are trained on an\nalready existing French corpus. We also evaluate the quality of our proposed\nword vectors and the existing French word vectors on the French word analogy\ntask. In addition, we do the evaluation on multiple real NLP tasks that show\nthe important performance enhancement of the pre-trained word vectors compared\nto the existing and random ones. Finally, we created a demo web application to\ntest and visualize the obtained word embeddings. The produced French word\nembeddings are available to the public, along with the fine-tuning code on the\nNLU tasks and the demo code.\n"}
+{"abstract": "  To connect structure, dynamics and function in systems with multibody\ninteractions, network scientists model random walks on hypergraphs and identify\ncommunities that confine the walks for a long time. The two flow-based\ncommunity-detection methods Markov stability and the map equation identify such\ncommunities based on different principles and search algorithms. But how\nsimilar are the resulting communities? We explain both methods' machinery\napplied to hypergraphs and compare them on synthetic and real-world hypergraphs\nusing various hyperedge-size biased random walks and time scales. We find that\nthe map equation is more sensitive to time-scale changes and that Markov\nstability is more sensitive to hyperedge-size biases.\n"}
+{"abstract": "  We investigate accretion of dark matter onto a moving Schwarzschild black\nhole. The dark matter is modeled by the collisionless Vlasov gas, assumed to be\nin thermal equilibrium at infinity. We derive an exact stationary solution and\nprovide a compact formula for the mass accretion rate. In general, the mass\naccretion rate is a nonmonotonic function of the black hole velocity. A\nmonotonic relation (the accretion rate proportional to the Lorentz factor\nassociated with the velocity of the black hole) is obtained for high asymptotic\ntemperatures of the gas. The derived accretion rates are relevant for the\ngrowth of primordial black holes in the early Universe.\n"}
+{"abstract": "  I use a direct method to extract the longitudinal structure function in the\nnext-to-leading order approximation with respect to the number of active flavor\nfrom the parametrization of parton distributions. The contribution of charm and\nbottom quarks corresponding to the gluon distributions (i.e.,\n$G_{n_{f}=3}(x,Q^{2})$ and $G_{n_{f}=5}(x,Q^{2})$) is considered. I compare the\nobtained longitudinal structure function at $n_{f}=4$ with the H1 data\n[Eur.Phys.J.C{\\bf74}, 2814(2014) and Eur.Phys.J.C{\\bf71}, 1579 (2011)] and the\nresult L.P.Kaptari et al.[ Phys.Rev.D{\\bf 99}, 096019(2019)] which is based on\nthe Mellin transforms. These calculations compared with the results\n  from CT18 [Phys.Rev.D103, 014013(2021)] parametrization model. The nonlinear\neffects on the gluon distribution improve the behavior of the longitudinal\nstructure function in comparison with the H1 data and CT18 at low values of\n$Q^{2}$.\n"}
+{"abstract": "  The transfer function of the baryon power spectrum from redshift $z\\approx\n1100$ to today has recently been, for the first time, determined from data by\nPardo and Spergel. We observe a remarkable coincidence between this function\nand the transport function of a cold ideal Fermi gas at different redshifts.\nGuided by this, we unveil an infinite set of critical temperatures of the\nrelativistic ideal Fermi gas which depend on a very finely quantized\nlong-distance cutoff. The sound horizon scale of Baryon Acoustic Oscillations\n(BAO) seems set such a cutoff, which dials a critical temperature that is\nsubsequently reached during redshift. At the critical point the Fermi gas\nbecomes scale invariant and may condense to subsequently undergo gravitational\ncollapse, seeding small scale structure. We mention some profound implications\nincluding the apparent quantization of Fermi momentum conjugate to the cutoff\nand the corresponding \"gapping\" of temperature.\n"}
+{"abstract": "  This is a commentary on two papers (PNAS 117 (51) 32244-32250 (2020) and\narXiv:2011.06721), which observed a series of ordering transitions in a\nstrongly correlated two-dimensional electron system confined to a AlAs quantum\nwell. We summarize the main discoveries (electron nematicity, insulating\nferromagnetism, etc.) of them and speculate on $T-r_s$ phase diagram based on\ntheoretical considerations.\n"}
+{"abstract": "  The design, fabrication, and characterization of low loss ultra-compact bends\nin high index (n = 3.1 at {\\lambda} = 1550 nm) PECVD silicon rich silicon\nnitride (SRN) is demonstrated and utilized to realize efficient, small\nfootprint thermo-optic phase shifter. Compact bends are structured into a\nfolded waveguide geometry to form a rectangular spiral within an area of 65 x\n65 \\mum2 possessing a total active waveguide length of 1.2 mm. The device\nfeatures a phase shifting efficiency of 8 mW/{\\pi} and a 3 dB switching\nbandwidth of 15 KHz. We propose SRN as a promising thermo-optic platform\ncombining the properties of silicon and silicon stoichiometric nitride\n"}
+{"abstract": "  Conformational isomers or conformers of molecules play a decisive role in\nchemistry and biology. However, experimental methods to investigate chemical\nreaction dynamics are typically not conformer-sensitive. Here, we report on a\ngas-phase megaelectronvolt ultrafast electron diffraction investigation of\n{\\alpha}-phellandrene undergoing an electrocyclic ring-opening reaction. We\ndirectly image the evolution of a specific set of {\\alpha}-phellandrene\nconformers into the product isomer predicted by the Woodward-Hoffmann rules in\nreal space and time. Our experimental results are in quantitative agreement\nwith nonadiabatic quantum molecular dynamics simulations, which provide\nunprecedented detail of how conformation influences time scale and quantum\nefficiency of photoinduced ring-opening reactions. Due to the prevalence of\nlarge numbers of conformers in organic chemistry, our findings impact our\ngeneral understanding of reaction dynamics in chemistry and biology.\n"}
+{"abstract": "  We give a short introduction to discrete flat fronts in hyperbolic space and\nprove that any discrete flat front in the mixed area sense admits a Weierstrass\nrepresentation.\n"}
+{"abstract": "  We simulate numerically the formation of spherically symmetric primordial\nblack holes (PBHs) seeded by different families of primordial curvature\nperturbations profiles on a Friedman-Robertson-Walker (FRW) Universe filled by\nradiation fluid. We have studied the dependency on the curvature profile of the\ninitial mass $M_{\\rm BH,i}$ of the PBHs at the time of apparent horizon\nformation $t_{AH}$, and the final mass $M_{\\rm BH,f}$ after the accretion\nprocess, using an excision technique, comparing $M_{\\rm BH,i}$ to previous\nanalytical estimations obtained using compensated PBHs model approach. The\nanalytical estimations are in agreement with numerical results, except for\nlarge values of the initial perturbation amplitude, when the compensated model\nis less accurate. The masses $M_{\\rm BH,f}$ and $M_{\\rm BH,i}$ do not depend\nonly on the shape around the compaction function peak, but on the full profile\nof the initial curvature perturbation. We also estimate the accretion effects,\nand for those PBHs with masses relevant for the dark matter abundance, with a\nfinal mass equal to the horizon crossing mass, we find $M_{\\rm BH,f}\\approx 3\nM_{\\rm BH,i} $.\n"}
+{"abstract": "  This paper deals with large-scale composite optimization problems having the\nobjective function formed as a sum of two terms, one has Lipschitz continuous\ngradient along random subspaces and may be nonconvex and the second term is\nsimple, but possibly nonconvex and nonseparable. Under these settings we design\na stochastic coordinate proximal gradient method which takes into account the\nnonseparable composite form of the objective function. This algorithm achieves\nscalability by constructing at each iteration a local approximation model of\nthe nonseparable objective function along a random subspace with\nuser-determined dimension. We outline efficient techniques for selecting the\nrandom subspace, yielding an implementation that has low cost per-iteration\nwhile also achieving fast convergence rates. We present a probabilistic\nworst-case complexity analysis for our stochastic coordinate proximal gradient\nmethod in convex and nonconvex settings, in particular we prove\nhigh-probability bounds on the number of iterations before a given optimality\nis achieved. To the best of our knowledge, this work is the first proposing a\npure stochastic coordinate descent algorithm which is supported by global\nefficiency estimates for general nonseparable composite optimization problems.\nPreliminary numerical results also confirm the efficiency of our algorithm.\n"}
+{"abstract": "  The evolution of galaxies depends on their environments. In this work, active\ngalactic nucleus (AGN) activity in different environments has been studied. The\nfractions of radio and optical AGN in four different environments have been\ncompared using samples of void, isolated, group member, and the brightest group\ngalaxies (BGGs). Galaxies in voids show significantly lower stellar ages,\nconcentrations, colours and surface mass densities, and they experience more\none-on-one interactions compared to the isolated galaxies and galaxies in\ngroups. In order to study pure environmental effects, the biases caused by the\nstellar mass and galaxy type quantified by 4000$\\AA$ break strength have been\nremoved. While the results confirm no dependence of the optical AGN activity on\nenvironment in blue galaxies and with lower significance in green galaxies, a\nhigher fraction of optical AGN has been observed for the massive red galaxies\nin voids compared to the galaxies in dense environments. This may be related to\nthe higher amount of one-on-one interaction observed in the void galaxies, or\nit may reflect more fundamental differences in the host galaxies or\nenvironments of the voids. The radio-mode AGN activity increases in dense\nenvironment for red galaxies. No changes in the radio-loud AGN fraction have\nbeen observed for the blue and green galaxies. This shows that the effect of\nenvironment on AGN activity is not significant in the presence of cold gas in\ngalaxies. We also discuss whether the efficiency of gas accretion depends on\nthe properties of the host galaxy.\n"}
+{"abstract": "  While medical image segmentation is an important task for computer aided\ndiagnosis, the high expertise requirement for pixelwise manual annotations\nmakes it a challenging and time consuming task. Since conventional data\naugmentations do not fully represent the underlying distribution of the\ntraining set, the trained models have varying performance when tested on images\ncaptured from different sources. Most prior work on image synthesis for data\naugmentation ignore the interleaved geometric relationship between different\nanatomical labels. We propose improvements over previous GAN-based medical\nimage synthesis methods by learning the relationship between different\nanatomical labels. We use a weakly supervised segmentation method to obtain\npixel level semantic label map of images which is used learn the intrinsic\nrelationship of geometry and shape across semantic labels. Latent space\nvariable sampling results in diverse generated images from a base image and\nimproves robustness. We use the synthetic images from our method to train\nnetworks for segmenting COVID-19 infected areas from lung CT images. The\nproposed method outperforms state-of-the-art segmentation methods on a public\ndataset. Ablation studies also demonstrate benefits of integrating geometry and\ndiversity.\n"}
+{"abstract": "  At the quantum many-body level, atom-light interfaces generally remain\nchallenging to solve for or understand in a non-perturbative fashion. Here, we\nconsider a waveguide quantum electrodynamics model, where two-level atoms\ninteract with and via propagating photons in a one-dimensional waveguide, and\nspecifically investigate the interplay of atomic position disorder, multiple\nscattering of light, quantum nonlinear interactions and dissipation. We develop\nqualitative arguments and present numerical evidence that such a system\nexhibits a many-body localized~(MBL) phase, provided that atoms are less than\nhalf excited. Interestingly, while MBL is usually formulated with respect to\nclosed systems, this system is intrinsically open. However, as dissipation\noriginates from transport of energy to the system boundaries and the subsequent\nradiative loss, the lack of transport in the MBL phase makes the waveguide QED\nsystem look essentially closed and makes applicable the notions of MBL.\nConversely, we show that if the system is initially in a delocalized phase due\nto a large excitation density, rapid initial dissipation can leave the system\nunable to efficiently transport energy at later times, resulting in a dynamical\ntransition to an MBL phase. These phenomena can be feasibly realized in\nstate-of-the-art experimental setups.\n"}
+{"abstract": "  We compare the accuracy, convergence rate and computational cost of\neigenerosion (EE) and phase-field (PF) methods. For purposes of comparison, we\nspecifically consider the standard test case of a center-crack panel loaded in\nbiaxial tension and assess the convergence of the energy error as the length\nscale parameter and mesh size tend to zero simultaneously. The panel is\ndiscretized by means of a regular mesh consisting of standard bilinear or Q1\nelements. The exact stresses from the known analytical linear elastic solution\nare applied to the boundary. All element integrals over the interior and the\nboundary of the domain are evaluated exactly using the symbolic computation\nprogram Mathematica. When the EE inelastic energy is enhanced by means of\nRichardson extrapolation, EE is found to converge at twice the rate of PF and\nto exhibit much better accuracy. In addition, EE affords a\none-order-of-magnitude computational speed-up over PF.\n"}
+{"abstract": "  Language models can generate harmful and biased outputs and exhibit\nundesirable behavior according to a given cultural context. We propose a\nProcess for Adapting Language Models to Society (PALMS) with Values-Targeted\nDatasets, an iterative process to significantly change model behavior by\ncrafting and fine-tuning on a dataset that reflects a predetermined set of\ntarget values. We evaluate our process using three metrics: quantitative\nmetrics with human evaluations that score output adherence to a target value,\ntoxicity scoring on outputs; and qualitative metrics analyzing the most common\nword associated with a given social category. Through each iteration, we add\nadditional training dataset examples based on observed shortcomings from\nevaluations. PALMS performs significantly better on all metrics compared to\nbaseline and control models for a broad range of GPT-3 language model sizes\nwithout compromising capability integrity. We find that the effectiveness of\nPALMS increases with model size. We show that significantly adjusting language\nmodel behavior is feasible with a small, hand-curated dataset.\n"}
+{"abstract": "  Superposition of two independent orthogonally polarized beams is a\nconventional principle of creating a new light beam. Here, we intend to achieve\nthe inverse process, namely, extracting inherent polarization modes from a\nsingle light beam. However, inherent polarization modes within a light beam are\nalways entangled so that a stable polarization is maintained during propagation\nin free space. To overcome this limitation, we report an approach that breaks\nthe modulation symmetry of a light beam, thereby disentangling the inherent\npolarization modes. Using polarization mode competition along with an optical\npen, polarization modes are extracted at will in the focal region of an\nobjective lens. This work demonstrates polarization mode extraction from a\nlight beam, which will not only provide an entirely new principle of\npolarization modulation but also pave the way for multidimensional manipulation\nof light fields, thereby facilitating extensive developments in optics.\n"}
+{"abstract": "  To perform their daily tasks, developers intensively make use of existing\nresources by consulting open-source software (OSS) repositories. Such platforms\ncontain rich data sources, e.g., code snippets, documentation, and user\ndiscussions, that can be useful for supporting development activities. Over the\nlast decades, several techniques and tools have been promoted to provide\ndevelopers with innovative features, aiming to bring in improvements in terms\nof development effort, cost savings, and productivity. In the context of the EU\nH2020 CROSSMINER project, a set of recommendation systems has been conceived to\nassist software programmers in different phases of the development process. The\nsystems provide developers with various artifacts, such as third-party\nlibraries, documentation about how to use the APIs being adopted, or relevant\nAPI function calls. To develop such recommendations, various technical choices\nhave been made to overcome issues related to several aspects including the lack\nof baselines, limited data availability, decisions about the performance\nmeasures, and evaluation approaches. This paper is an experience report to\npresent the knowledge pertinent to the set of recommendation systems developed\nthrough the CROSSMINER project. We explain in detail the challenges we had to\ndeal with, together with the related lessons learned when developing and\nevaluating these systems. Our aim is to provide the research community with\nconcrete takeaway messages that are expected to be useful for those who want to\ndevelop or customize their own recommendation systems. The reported experiences\ncan facilitate interesting discussions and research work, which in the end\ncontribute to the advancement of recommendation systems applied to solve\ndifferent issues in Software Engineering.\n"}
+{"abstract": "  Urban analytics combines spatial analysis, statistics, computer science, and\nurban planning to understand and shape city futures. While it promises better\npolicymaking insights, concerns exist around its epistemological scope and\nimpacts on privacy, ethics, and social control. This chapter reflects on the\nhistory and trajectory of urban analytics as a scholarly and professional\ndiscipline. In particular, it considers the direction in which this field is\ngoing and whether it improves our collective and individual welfare. It first\nintroduces early theories, models, and deductive methods from which the field\noriginated before shifting toward induction. It then explores urban network\nanalytics that enrich traditional representations of spatial interaction and\nstructure. Next it discusses urban applications of spatiotemporal big data and\nmachine learning. Finally, it argues that privacy and ethical concerns are too\noften ignored as ubiquitous monitoring and analytics can empower social\nrepression. It concludes with a call for a more critical urban analytics that\nrecognizes its epistemological limits, emphasizes human dignity, and learns\nfrom and supports marginalized communities.\n"}
+{"abstract": "  Deep learning has led to unprecedented successes in solving some very\ndifficult problems in domains such as computer vision, natural language\nprocessing, and general pattern recognition. These achievements are the\nculmination of decades-long research into better training techniques and deeper\nneural network models, as well as improvements in hardware platforms that are\nused to train and execute the deep neural network models. Many\napplication-specific integrated circuit (ASIC) hardware accelerators for deep\nlearning have garnered interest in recent years due to their improved\nperformance and energy-efficiency over conventional CPU and GPU architectures.\nHowever, these accelerators are constrained by fundamental bottlenecks due to\n1) the slowdown in CMOS scaling, which has limited computational and\nperformance-per-watt capabilities of emerging electronic processors, and 2) the\nuse of metallic interconnects for data movement, which do not scale well and\nare a major cause of bandwidth, latency, and energy inefficiencies in almost\nevery contemporary processor. Silicon photonics has emerged as a promising\nCMOS-compatible alternative to realize a new generation of deep learning\naccelerators that can use light for both communication and computation. This\narticle surveys the landscape of silicon photonics to accelerate deep learning,\nwith a coverage of developments across design abstractions in a bottom-up\nmanner, to convey both the capabilities and limitations of the silicon\nphotonics paradigm in the context of deep learning acceleration.\n"}
+{"abstract": "  With a key improvement, the auxiliary mass flow method is now able to compute\nmany badly-needed Feynman integrals encountered in cutting-edge collider\nprocesses. We have successfully applied it to two-loop electroweak correction\nto $e^+e^-\\to HZ$, two-loop QCD corrections to $3j$, $W/Z/H+2j$, $t\\bar{t}H$\nand $4j$ production at hadron colliders, and three-loop QCD correction to\n$t\\bar{t}$ production at hadron colliders, all of which are crucial for\nprecision test in the following decade. Our results are important building\nblocks and benchmarks for future study of these processes.\n"}
+{"abstract": "  The tiny modification of dispersion relation induced by Lorentz violation\n(LV) is an essential issue in quantum gravity (QG) theories, which can be\nmagnified into significant effects when dealing with astrophysical observations\nat high energy and long propagation distance. LV would lead to photon decay at\nhigh energies, therefore, observations of high-energy photons could constrain\nLV or even QG theories. The Large High Altitude Air Shower Observatory (LHAASO)\nis the most sensitive gamma-array instrument currently operating above 100 TeV.\nRecently, LHAASO reported the detection of 12 sources above 100 TeV with\nmaximum photon energy exceeding 1 PeV. According to these observations, the\nmost stringent restriction is achieved in this paper, i.e., limiting the LV\nenergy scale to $1.7\\times10^{33} $ eV, which is over 139,000 times that of the\nPlanck energy and achieve an improvement of about 1.9 orders of magnitude over\nprevious limits.\n"}
+{"abstract": "  In this work, we propose an abstraction and refinement methodology for the\ncontroller synthesis of discrete-time stochastic systems to enforce complex\nlogical properties expressed by deterministic finite automata (a.k.a. DFA). Our\nproposed scheme is based on a notion of so-called\n$(\\epsilon,\\delta)$-approximate probabilistic relations, allowing one to\nquantify the similarity between stochastic systems modeled by discrete-time\nstochastic games and their corresponding finite abstractions. Leveraging this\ntype of relations, the lower bound for the probability of satisfying the\ndesired specifications can be well ensured by refining controllers synthesized\nover abstract systems to the original games. Moreover, we propose an\nalgorithmic procedure to construct such a relation for a particular class of\nnonlinear stochastic systems with slope restrictions on the nonlinearity. The\nproposed methods are demonstrated on a quadrotor example, and the results\nindicate that the desired lower bound for the probability of satisfaction is\nguaranteed.\n"}
+{"abstract": "  The climate is a non-equilibrium system undergoing the continuous action of\nforcing and dissipation. Under the effect of a spatially inhomogeneous\nabsorption of solar energy, all the climate components dynamically respond by\nredistributing energy until an approximate steady state is reached. In order to\nimprove the skill of climate models and correct their biases, it is essential\nto investigate how such dynamical balance is reached. In general, the climate\nsystem features multiple steady states for a given set of boundary conditions.\nHere, we apply the Thermodynamic Diagnostic Tool (TheDiaTo) to investigate the\nstatistical properties of the five co-existing climates, ranging from a\nsnowball to an ice-free aquaplanet, obtained in MITgcm coupled simulations\nunder the same boundary conditions. The aim is to explore the multistability of\nthe climate by highlighting differences in competing steady states and their\ncharacteristic signatures regarding the meridional transport of heat and water\nmass, the Lorenz energy cycle and the material entropy production. Alternative\ncloud parametrizations and descriptions of energy exchange are also used to\ninvestigate how robust such signatures are and, at the same time, how the\nstatistical properties can be improved in the simulated climatic states. Thus\nwe show how the diagnostic tool can help in identifying strengths and\nweaknesses of a model configuration.\n"}
+{"abstract": "  With the introduction of educational robotics (ER) and computational thinking\n(CT) in classrooms, there is a rising need for operational models that help\nensure that CT skills are adequately developed. One such model is the Creative\nComputational Problem Solving Model (CCPS) which can be employed to improve the\ndesign of ER learning activities. Following the first validation with students,\nthe objective of the present study is to validate the model with teachers,\nspecifically considering how they may employ the model in their own practices.\nThe Utility, Usability and Acceptability framework was leveraged for the\nevaluation through a survey analysis with 334 teachers. Teachers found the CCPS\nmodel useful to foster transversal skills but could not recognise the impact of\nspecific intervention methods on CT-related cognitive processes. Similarly,\nteachers perceived the model to be usable for activity design and intervention,\nalthough felt unsure about how to use it to assess student learning and adapt\ntheir teaching accordingly. Finally, the teachers accepted the model, as shown\nby their intent to replicate the activity in their classrooms, but were less\nwilling to modify it or create their own activities, suggesting that they need\ntime to appropriate the model and underlying tenets.\n"}
+{"abstract": "  An oscillating spanwise pressure gradient is imposed numerically to control\nthe flow separation and reduce the drag of a turbulent flow in a channel with\nsquare bars. The transverse flow produces a maximum drag reduction of 25\\%, due\nto lower pressure and skin-friction drag forces. The pressure drag reduction\nreaches a maximum of 22\\% and is due to a decrease of the positive high\npressure in front of the bars and an increase of the low negative pressure\nbehind the bars. The skin-friction drag reduction is caused by a lower\nwall-shear stress along the cavity between the bars where the flow is fully\nattached, while the wall-shear stress on the crest of the bars and in the\nseparated region behind the bars is unaffected. The spanwise laminar flow\nobtained by neglecting the nonlinear terms involving the turbulent velocity\nfluctuations is used to compute the power spent for oscillating the fluid along\nthe spanwise direction and an excellent agreement is found with the power spent\nobtained by the averaged turbulent flow. A marginal or negative net power saved\nis found by subtracting the power employed for controlling the flow from the\npower saved thanks to the transverse flow. The control reduces the total drag\nas the integral of the Reynolds stresses along the horizontal line connecting\nthe corners of two consecutive bars is decreased, which in turn impacts on the\npressure and wall-shear stress reductions.\n"}
+{"abstract": "  We aim to cluster financial assets in order to identify a small set of stocks\nto approximate the level of diversification of the whole universe of stocks. We\ndevelop a data-driven approach to clustering based on a correlation blockmodel\nin which assets in the same cluster are highly correlated with each other and,\nat the same time, have the same correlations with all other assets. We devise\nan algorithm to detect the clusters, with theoretical analysis and practical\nguidance. Finally, we conduct an empirical analysis to verify the performance\nof the algorithm.\n"}
+{"abstract": "  In this paper, we study some controllability and observability properties for\na coupled system of time-discrete fourth- and second-order parabolic equations.\nThis system can be regarded as a simplification of the well-known stabilized\nKumamoto-Sivashinsky equation. Unlike the continuous case, we can prove only a\nrelaxed observability inequality which yields a $\\phi(\\triangle\nt)$-controllability result. This result tells that we cannot reach exactly zero\nbut rather a small target whose size goes to 0 as the discretization parameter\n$\\triangle t$ goes to 0. The proof relies on a known Carleman estimate for\nsecond-order time-discrete parabolic operators and a new Carleman estimate for\nthe time-discrete fourth-order equation.\n"}
+{"abstract": "  We investigate ytterbium gallium garnet Yb$_{3}$Ga$_{5}$O$_{12}$ in the\nparamagnetic phase above the supposed magnetic transition at $T_{\\lambda}\n\\approx 54$ mK. Our study combines susceptibility and specific heat\nmeasurements with neutron scattering experiments and theoretical calculations.\nBelow 500 mK, the elastic neutron response is strongly peaked in the momentum\nspace. Along with that the inelastic spectrum develops flat excitation modes.\nIn magnetic field, the lowest energy branch follows a Zeeman shift in\naccordance with the field-dependent specific heat data. An intermediate state\nwith spin canting away from the field direction is evidenced in small magnetic\nfields. In the field of 2 T, the total magnetization almost saturates and the\nmeasured excitation spectrum is well reproduced by the spin-wave calculations\ntaking into account solely the dipole-dipole interactions. The small positive\nCurie-Weiss temperature derived from the susceptibility measurements is also\naccounted for by the dipole spin model. Altogether, our results suggest that\nYb$_{3}$Ga$_{5}$O$_{12}$ is a quantum dipolar magnet.\n"}
+{"abstract": "  We begin with the theoretical study of spectral energy cascade due to the\npropagation of high amplitude sound in the absence of thermal sources. To this\nend, a first-principles-based system of governing equations, correct up to\nsecond order in perturbation variables is derived. The exact energy corollary\nof such second-order system of equations is then formulated and used to\nelucidate the spectral energy dynamics of nonlinear acoustic waves. We then\nextend this analysis to thermoacoustically unstable waves -- i.e. amplified as\na result of thermoacoustic instability. We drive such instability up until the\ngeneration of shock waves. We further study the nonlinear wave propagation in\ngeometrically complex case of waves induced by the spark plasma between the\nelectrodes. This case adds the geometrical complexity of a curved,\nthree-dimensional shock, yielding vorticity production due to baroclinic\ntorque. Finally, detonation waves are simulated by using a low-order approach,\nin a periodic setup subjected to high pressure inlet and exhaust of combustible\ngaseous mixture. An order adaptive fully compressible and unstructured Navier\nStokes solver is currently under development to enable higher fidelity studies\nof both the spark plasma and detonation wave problem in the future.\n"}
+{"abstract": "  The capability of the human to pay attention to both coarse and fine-grained\nregions has been applied to computer vision tasks. Motivated by that, we\npropose a collaborative learning framework in the complex domain for monaural\nnoise suppression. The proposed system consists of two principal modules,\nnamely spectral feature extraction module (FEM) and stacked glance-gaze modules\n(GGMs). In FEM, the UNet-block is introduced after each convolution layer,\nenabling the feature recalibration from multiple scales. In each GGM, we\ndecompose the multi-target optimization in the complex spectrum into two\nsub-tasks. Specifically, the glance path aims to suppress the noise in the\nmagnitude domain to obtain a coarse estimation, and meanwhile, the gaze path\nattempts to compensate for the lost spectral detail in the complex domain. The\ntwo paths work collaboratively and facilitate spectral estimation from\ncomplementary perspectives. Besides, by repeatedly unfolding the GGMs, the\nintermediate result can be iteratively refined across stages and lead to the\nultimate estimation of the spectrum. The experiments are conducted on the\nWSJ0-SI84, DNS-Challenge dataset, and Voicebank+Demand dataset. Results show\nthat the proposed approach achieves state-of-the-art performance over previous\nadvanced systems on the WSJ0-SI84 and DNS-Challenge dataset, and meanwhile,\ncompetitive performance is achieved on the Voicebank+Demand corpus.\n"}
+{"abstract": "  The excess work performed in a heat-engine process with given finite\noperation time \\tau is bounded by the thermodynamic length, which measures the\ndistance during the relaxation along a path in the space of the thermodynamic\nstate. Unfortunately, the thermodynamic length, as a guidance for the heat\nengine optimization, is beyond the experimental measurement. We propose to\nmeasure the thermodynamic length \\mathcal{L} through the extrapolation of\nfinite-time measurements\n\\mathcal{L}(\\tau)=\\int_{0}^{\\tau}[P_{\\mathrm{ex}}(t)]^{1/2}dt via the excess\npower P_{\\mathrm{ex}}(t). The current proposal allows to measure the\nthermodynamic length for a single control parameter without requiring extra\neffort to find the optimal control scheme. We illustrate the measurement\nstrategy via examples of the quantum harmonic oscillator with tuning frequency\nand the classical ideal gas with changing volume.\n"}
+{"abstract": "  Chern-Simons topological quantum computer is a device that can be effectively\ndescribed by the Chern-Simons topological quantum field theory and used for\nquantum computations. Quantum qudit gates of this quantum computer are\nrepresented by sequences of quantum $\\mathcal{R}$-matrices. Its dimension and\nexplicit form depend on the parameters of the Chern-Simons theory -- level $k$,\ngauge group $SU(N)$, and representation, which is chosen to be symmetric\nrepresentation $[r]$. In this paper, we examine the universality of such a\nquantum computer. We prove that for sufficiently large $k$ it is universal, and\nthe minimum allowed value of $k$ depends on the remaining parameters $r$ and\n$N$.\n"}
+{"abstract": "  Online hate speech is an important issue that breaks the cohesiveness of\nonline social communities and even raises public safety concerns in our\nsocieties. Motivated by this rising issue, researchers have developed many\ntraditional machine learning and deep learning methods to detect hate speech in\nonline social platforms automatically. However, most of these methods have only\nconsidered single type textual feature, e.g., term frequency, or using word\nembeddings. Such approaches neglect the other rich textual information that\ncould be utilized to improve hate speech detection. In this paper, we propose\nDeepHate, a novel deep learning model that combines multi-faceted text\nrepresentations such as word embeddings, sentiments, and topical information,\nto detect hate speech in online social platforms. We conduct extensive\nexperiments and evaluate DeepHate on three large publicly available real-world\ndatasets. Our experiment results show that DeepHate outperforms the\nstate-of-the-art baselines on the hate speech detection task. We also perform\ncase studies to provide insights into the salient features that best aid in\ndetecting hate speech in online social platforms.\n"}
+{"abstract": "  With the relaxation of the containment measurements around the globe,\nmonitoring the social distancing in crowded public places is of grate\nimportance to prevent a new massive wave of COVID-19 infections. Recent works\nin that matter have limited themselves by detecting social distancing in\ncorridors up to small crowds by detecting each person individually considering\nthe full body in the image. In this work, we propose a new framework for\nmonitoring the social-distance using end-to-end Deep Learning, to detect crowds\nviolating the social-distance in wide areas where important occlusions may be\npresent. Our framework consists in the creation of a new ground truth based on\nthe ground truth density maps and the proposal of two different solutions, a\ndensity-map-based and a segmentation-based, to detect the crowds violating the\nsocial-distance constrain. We assess the results of both approaches by using\nthe generated ground truth from the PET2009 and CityStreet datasets. We show\nthat our framework performs well at providing the zones where people are not\nfollowing the social-distance even when heavily occluded or far away from one\ncamera.\n"}
+{"abstract": "  Let $p$ and $q$ be positive integers such that the $(p, q)$-leaper $L$ is\nfree. We prove that $L$ tours the board of size $4pq \\times n$ for all\nsufficiently large positive integers $n$. Combining this with the recently\nestablished conjecture of Willcocks which states that $L$ tours the square\nboard of side $2(p + q)$, we conclude that furthermore $L$ tours all boards\nboth of whose sides are even and sufficiently large. This, in particular,\ncompletely resolves the question of the Hamiltonicity of leaper graphs on\nsufficiently large square boards.\n"}
+{"abstract": "  Given the abundance of applications of ranking in recent years, addressing\nfairness concerns around automated ranking systems becomes necessary for\nincreasing the trust among end-users. Previous work on fair ranking has mostly\nfocused on application-specific fairness notions, often tailored to online\nadvertising, and it rarely considers learning as part of the process. In this\nwork, we show how to transfer numerous fairness notions from binary\nclassification to a learning to rank setting. Our formalism allows us to design\nmethods for incorporating fairness objectives with provable generalization\nguarantees. An extensive experimental evaluation shows that our method can\nimprove ranking fairness substantially with no or only little loss of model\nquality.\n"}
+{"abstract": "  Microprocessor design, debug, and validation research and development are\nincreasingly based on modeling and simulation at different abstraction layers.\nMicroarchitecture-level simulators have become the most commonly used tools for\nperformance evaluation, due to their high simulation throughput, compared to\nlower levels of abstraction, but usually come at the cost of loss of hardware\naccuracy. As a result, the implementation, speed, and accuracy of\nmicroarchitectural simulators are becoming more and more crucial for\nresearchers and microprocessor architects. One of the most critical aspects of\na microarchitectural simulator is its ability to accurately express design\nstandards as various aspects of the microarchitecture change during design\nrefinement. On the other hand, modern microprocessor models rely on dedicated\nhardware implementations, making the design space exploration a time-consuming\nprocess that can be performed using a variety of methods, ranging from\nhigh-level models to hardware prototyping. Therefore, the tradeoff between\nsimulation speed and accuracy, can be significantly varied, and an\napplication's performance measurements uncertain. In this paper, we present a\nmicroarchitecture-level simulation modeling study, which enables as accurate as\npossible performance modeling of a RISC-V out-of-order superscalar\nmicroprocessor core. By diligently adjusting several important\nmicroarchitectural parameters of the widely used gem5 simulator, we investigate\nthe challenges of accurate performance modeling on microarchitecture-level\nsimulation compared to accuracy and low simulation throughput of RTL simulation\nof the target design. Further, we demonstrate the main sources of errors that\nprevent high accuracy levels of the microarchitecture-level modeling.\n"}
+{"abstract": "  While many defences against adversarial examples have been proposed, finding\nrobust machine learning models is still an open problem. The most compelling\ndefence to date is adversarial training and consists of complementing the\ntraining data set with adversarial examples. Yet adversarial training severely\nimpacts training time and depends on finding representative adversarial\nsamples. In this paper we propose to train models on output spaces with large\nclass separation in order to gain robustness without adversarial training. We\nintroduce a method to partition the output space into class prototypes with\nlarge separation and train models to preserve it. Experimental results shows\nthat models trained with these prototypes -- which we call deep repulsive\nprototypes -- gain robustness competitive with adversarial training, while also\npreserving more accuracy on natural samples. Moreover, the models are more\nresilient to large perturbation sizes. For example, we obtained over 50%\nrobustness for CIFAR-10, with 92% accuracy on natural samples and over 20%\nrobustness for CIFAR-100, with 71% accuracy on natural samples without\nadversarial training. For both data sets, the models preserved robustness\nagainst large perturbations better than adversarially trained models.\n"}
+{"abstract": "  Structured pruning methods are among the effective strategies for extracting\nsmall resource-efficient convolutional neural networks from their dense\ncounterparts with minimal loss in accuracy. However, most existing methods\nstill suffer from one or more limitations, that include 1) the need for\ntraining the dense model from scratch with pruning-related parameters embedded\nin the architecture, 2) requiring model-specific hyperparameter settings, 3)\ninability to include budget-related constraint in the training process, and 4)\ninstability under scenarios of extreme pruning. In this paper, we present\nChipNet, a deterministic pruning strategy that employs continuous Heaviside\nfunction and a novel crispness loss to identify a highly sparse network out of\nan existing dense network. Our choice of continuous Heaviside function is\ninspired by the field of design optimization, where the material distribution\ntask is posed as a continuous optimization problem, but only discrete values (0\nor 1) are practically feasible and expected as final outcomes. Our approach's\nflexible design facilitates its use with different choices of budget\nconstraints while maintaining stability for very low target budgets.\nExperimental results show that ChipNet outperforms state-of-the-art structured\npruning methods by remarkable margins of up to 16.1% in terms of accuracy.\nFurther, we show that the masks obtained with ChipNet are transferable across\ndatasets. For certain cases, it was observed that masks transferred from a\nmodel trained on feature-rich teacher dataset provide better performance on the\nstudent dataset than those obtained by directly pruning on the student data\nitself.\n"}
+{"abstract": "  In the last years, a new generation of large-scale imaging surveys have\nprobed wide field regions around some nearby galaxies at unprecedented low\nsurface brightness regime (~28.0-29.0 mag arcsec^-2). This offers a chance of\ndiscovering very faint dwarf satellites by means of visual inspection of these\npublic deep images. We report the first results of a systematic survey of faint\ndwarf spheroidal galaxies in the vicinity of the bright late-type spiral NGC\n253 galaxy by means of a visual inspection of the images taken by the Dark\nEnergy Survey. Three new dwarf galaxies have been discovered in the vicinity of\nthe brightest member of the Sculptor filament, the late-type spiral NGC 253.\nAssuming they are companions of NGC 253, their total absolute V-magnitudes fall\nin the -7 to -9 mag range, which is typical for dwarf satellites in the local\nUniverse. The central surface brightness tend to be extremely low for all the\ndiscovered dwarfs and fall roughly in the range of 25-26 mag arcsec^-2 in\ng-band. Using known data on distances and velocities of galaxies, we estimate\nthe total virial mass of the NGC 253 group to be 8 x 10^11 Mo, which gives the\nvirial radius R_200 = 186 kpc and the turn-around radius of 706 kpc. We also\ndiscuss the possible existence of a spatially flattened and velocity-correlated\nsatellite system around NGC 253. This large-scale structure is orientated\nalmost edge-on to line of sight. The possible plane of satellites is only 31\nkpc thick with the minor-to-major axis ratio of 0.14. Four out of five galaxies\nwith measured velocities follow a common velocity trend similar to those\nobserved in the planes of satellites around the Andromeda and Centaurus A\ngalaxies. However, the small number of galaxies with known velocities prevents\nto reach a definitive conclusion about the formation scenario of the structure\nand its possible relation to the surrounding cosmic web.\n"}
+{"abstract": "  The recent publication by Siraj & Loeb (2021; Nature Scientific Reports 11,\n3803) attempts to revive the debate over whether the Chicxulub impactor was a\ncomet or an asteroid. They calculate that ~20% of long-period comets impacting\nEarth will have first been disrupted by passage inside the Sun's Roche limit,\ngenerating thousands of fragments, each the needed size of the Chicxulub\nimpactor. This would increase the impact rate of comets by a factor ~15, making\nthem as likely to hit the Earth as an asteroid. They also argue that a comet\nwould be a factor of 10 more likely to match the geochemical constraints, which\nindicate the Chicxulub impactor was carbonaceous chondrite-like. These\nconclusions are based on misinterpretations of the literature. Siraj & Loeb [1]\noverestimate the number of fragments produced during tidal disruption of a\ncomet: tens of fragments are produced, not thousands. They also conflate\n'carbonaceous chondrite' with specific types of carbonaceous chondrite, and\nignore the evidence of iridium, making comets seem more likely than asteroids\nto match the Chicxulub impactor, when in fact they likely can be ruled out.\nRather than a comet, an asteroidal impactor similar to CM or CR carbonaceous\nchondrites is strongly favored.\n"}
+{"abstract": "  Quantum computers are invaluable tools to explore the properties of complex\nquantum systems. We show that dynamical localization of the quantum sawtooth\nmap, a highly sensitive quantum coherent phenomenon, can be simulated on\nactual, small-scale quantum processors. Our results demonstrate that quantum\ncomputing of dynamical localization may become a convenient tool for evaluating\nadvances in quantum hardware performances.\n"}
+{"abstract": "  We have calculated the thermal partition function for the thermodynamical\nsystem of massless free bosonic higher spin fields (Fronsdal theory) by using\nFeynman path integral formalism. However, we have established a connection\nbetween the microscopic world (Quantum statistical) of the thermodynamical\nsystem of massless free bosonic HS fields to the thermodynamics of the\nmacroscopic world its and also we have observed the duality between the\nthermodynamical system of massless free HS fields on $d$-dimensional Minkowski\nspacetime and thermodynamical system of Klein-Gordon scalar fields on\n4-dimensional Minkowski spacetime at the thermal equilibrium condition. The\naverage energy of the canonical ensemble of the thermodynamical system of\nmassless free bosonic HS fields is not in agreement with the equipartition\ntheorem, hence it deviates from the classical behaviour of the system and it\nshows that the true signature of the quantum thermodynamical system.\n"}
+{"abstract": "  Motive and electrical energy has played a crucial role in human civilization.\nSince Ancient times, motive energy played a primary role in agricultural and\nindustrial production as well as transportation. At that time, motive energy\nwas provided by work of humans and draft animals. Later, work of water and wind\npower was harnessed. During the 19$^{\\text{th}}$ century, steam power became\nthe main source of motive energy in USA and Britain. Modern transportation and\nindustry depend on the work of heat engines that use fossil fuel. A brief\nhistory of different sources of energy is presented in this work. The energy\nconsumptions in pre-industrial and industrial societies are calculated. The\nlost opportunities for the Second Industrial Revolution (such as fast breeder\nreactors and thermonuclear power stations) are discussed. The case that the\nSolar Power will become the main source of energy by the second half of this\ncentury is presented. It is calculated that the Solar Power has the potential\nto bring about the new Industrial Revolution. Based on material and energy\nresources available in the Solar System, it is demonstrated that the Solar\nSystem Civilization supporting a population of 10 Quadrillion with a high\nstandard of living is possible.\n"}
+{"abstract": "  We review our recent work in the area of autonomous materials research,\nhighlighting the coupling of machine learning methods and models and more\nproblem-aware modeling. We review the general Bayesian framework for\nclosed-loop design employed by many autonomous materials platforms. We then\nprovide examples of our work on such platforms. We finally review our\napproaches to extend current statistical and ML models to better reflect\nproblem-specific structure including the use of physics-based models and\nincorporation of operational considerations into the decision-making procedure.\n"}
+{"abstract": "  In our recent work [8], we have studied the homogenization of the Poisson\nequation in a class of non periodically perforated domains. In this paper, we\nexamine the case of the Stokes system. We consider a porous medium in which the\ncharacteristic distance between two holes, denoted by $\\epsilon$, is\nproportional to the characteristic size of the holes. It is well known (see\n[1],[17] and [19]) that, when the holes are periodically distributed in space,\nthe velocity converges to a limit given by the Darcy's law when the size of the\nholes tends to zero. We generalize these results to the setting of [8]. The\nnon-periodic domains are defined as a local perturbation of a periodic\ndistribution of holes. We obtain classical results of the homogenization theory\nin perforated domains (existence of correctors and regularity estimates uniform\nin $\\epsilon$) and we prove $H^2$--convergence estimates for particular force\nfields.\n"}
+{"abstract": "  In this paper, we introduce a definition of phase response for a class of\nmulti-input multi-output (MIMO) linear time-invariant (LTI) systems whose\nfrequency responses are (semi-)sectorial at all frequencies. The newly defined\nphase concept subsumes the well-known notions of positive real systems and\nnegative imaginary systems. We formulate a small phase theorem for feedback\nstability, which complements the celebrated small gain theorem. The small phase\ntheorem lays the foundation of a phase theory of MIMO systems. We also discuss\ntime-domain interpretations of phase-bounded systems via both energy signal\nanalysis and power signal analysis. In addition, a sectored real lemma is\nderived for the computation of MIMO phases, which serves as a natural\ncounterpart of the bounded real lemma.\n"}
+{"abstract": "  Empirical studies are fundamental in assessing the effectiveness of\nimplementations of branch-and-bound algorithms. The complexity of such\nimplementations makes empirical study difficult for a wide variety of reasons.\nVarious attempts have been made to develop and codify a set of standard\ntechniques for the assessment of optimization algorithms and their software\nimplementations; however, most previous work has been focused on classical\nsequential algorithms. Since parallel computation has become increasingly\nmainstream, it is necessary to re-examine and modernize these practices. In\nthis paper, we propose a framework for assessment based on the notion that\n\\emph{resource consumption} is at the heart of what we generally refer to as\nthe \"effectiveness\" of an implementation. The proposed framework carefully\ndistinguishes between an implementation's baseline efficiency, the efficacy\nwith which it utilizes a fixed allocation of resources, and its scalability, a\nmeasure of how the efficiency changes as resources (typically additional\ncomputing cores) are added or removed. Efficiency is typically applied to\nsequential implementations, whereas scalability is applied to parallel\nimplementations. Efficiency and scalability are both important contributors in\ndetermining the overall effectiveness of a given parallel implementation, but\nthe goal of improved efficiency is often at odds with the goal of improved\nscalability. Within the proposed framework, we review the challenges to\neffective evaluation and discuss the strengths and weaknesses of existing\nmethods of assessment.\n"}
+{"abstract": "  Learning based video compression attracts increasing attention in the past\nfew years. The previous hybrid coding approaches rely on pixel space operations\nto reduce spatial and temporal redundancy, which may suffer from inaccurate\nmotion estimation or less effective motion compensation. In this work, we\npropose a feature-space video coding network (FVC) by performing all major\noperations (i.e., motion estimation, motion compression, motion compensation\nand residual compression) in the feature space. Specifically, in the proposed\ndeformable compensation module, we first apply motion estimation in the feature\nspace to produce motion information (i.e., the offset maps), which will be\ncompressed by using the auto-encoder style network. Then we perform motion\ncompensation by using deformable convolution and generate the predicted\nfeature. After that, we compress the residual feature between the feature from\nthe current frame and the predicted feature from our deformable compensation\nmodule. For better frame reconstruction, the reference features from multiple\nprevious reconstructed frames are also fused by using the non-local attention\nmechanism in the multi-frame feature fusion module. Comprehensive experimental\nresults demonstrate that the proposed framework achieves the state-of-the-art\nperformance on four benchmark datasets including HEVC, UVG, VTL and MCL-JCV.\n"}
+{"abstract": "  For a given Jacobi-Jordan algebra $A$ and a vector space $V$ over a field\n$k$, a non-abelian cohomological type object ${\\mathcal H}^{2}_{A} \\, (V, \\,\nA)$ is constructed: it classifies all Jacobi-Jordan algebras containing $A$ as\na subalgebra of codimension equal to ${\\rm dim}_k (V)$. Any such algebra is\nisomorphic to a so-called \\emph{unified product} $A \\, \\natural \\, V$.\nFurthermore, we introduce the bicrossed (semi-direct, crossed, or skew crossed)\nproduct $A \\bowtie V$ associated to two Jacobi-Jordan algebras as a special\ncase of the unified product. Several examples and applications are provided:\nthe Galois group of the extension $A \\subseteq A \\bowtie V$ is described as a\nsubgroup of the semidirect product of groups ${\\rm GL}_k (V) \\rtimes {\\rm\nHom}_k (V, \\, A)$ and an Artin type theorem for Jacobi-Jordan algebra is\nproven. The key tools for classifying supersolvable and flag Jacobi-Jordan\nalgebras are introduced.\n"}
+{"abstract": "  This manuscript presents a model of compliant contacts for time-critical\nhumanoid robot motion control. The proposed model considers the environment as\na continuum of spring-damper systems, which allows us to compute the equivalent\ncontact force and torque that the environment exerts on the contact surface. We\nshow that the proposed model extends the linear and rotational springs and\ndampers - classically used to characterize soft terrains - to the case of large\ncontact surface orientations. The contact model is then used for the real-time\nwhole-body control of humanoid robots walking on visco-elastic environments.\nThe overall approach is validated by simulating walking motions of the iCub\nhumanoid robot. Furthermore, the paper compares the proposed whole-body control\nstrategy and state of the art approaches. In this respect, we investigate the\nterrain compliance that makes the classical approaches assuming rigid contacts\nfail. We finally analyze the robustness of the presented control design with\nrespect to non-parametric uncertainty in the contact-model.\n"}
+{"abstract": "  We find geometric conditions on a four-dimensional Hermitian manifold endowed\nwith a metric connection with totally skew-symmetric torsion under which the\ncomplex structure is a harmonic map from the manifold into its twistor space\nconsidered with a natural family of Riemannian metrics defined by means of the\nmetric and the given connection on the base manifold.\n"}
+{"abstract": "  Employing recent advances in response theory and nonequilibrium ensemble\nreweighting, we study the dynamic and static correlations that give rise to an\nelectric field-dependent ionic conductivity in electrolyte solutions. We\nconsider solutions modeled with both implicit and explicit solvents, with\ndifferent dielectric properties, and at multiple concentrations. Implicit\nsolvent models at low concentrations and small dielectric constants exhibit\nstrongly field-dependent conductivities. We compared these results to the\nOnsager-Wilson theory of the Wien effect, which provides a qualitatively\nconsistent prediction at low concentrations and high static dielectric\nconstants, but is inconsistent away from these regimes. The origin of the\ndiscrepancy is found to be increased ion correlations under these conditions.\nExplicit solvent effects act to suppress nonlinear responses, yielding a weakly\nfield-dependent conductivity over the range of physically realizable field\nstrengths. By decomposing the relevant time correlation functions, we find that\nthe insensitivity of the conductivity to the field results from the persistent\nfrictional forces on the ions from the solvent. Our findings illustrate the\nutility of nonequilibrium response theory in rationalizing nonlinear transport\nbehavior.\n"}
+{"abstract": "  The creep motion of domain walls driven by external fields in magnetic thin\nfilms is described by universal features related to the underlying depinning\ntransition. One key parameter in this description is the roughness exponent\ncharacterizing the growth of fluctuations of the domain wall position with its\nlongitudinal length scale. The roughness amplitude, which gives information\nabout the scale of fluctuations, however, has received less attention. Albeit\ntheir relevance, experimental reports of the roughness parameters, both\nexponent and amplitude, are scarce. We report here experimental values of the\nroughness parameters for different magnetic field intensities in the creep\nregime at room temperature for a Pt/Co/Pt thin film. The mean value of the\nroughness exponent is $\\zeta = 0.74$, and we show that it can be rationalized\nas an effective value in terms of the known universal values corresponding to\nthe depinning and thermal cases. In addition, it is shown that the roughness\namplitude presents a significant increase with decreasing field. These results\ncontribute to the description of domain wall motion in disordered thin magnetic\nsystems.\n"}
+{"abstract": "  Many Beyond-Standard Model physics signatures are enhanced in high-energy\nneutrino interactions. To explore these signatures, ultra-large Cherenkov\ndetectors such as IceCube exploit event samples with charged current muon\nneutrino interactions > 1 TeV. Most of these interactions occur below the\ndetector volume, and produce muons that enter the detector. However, the large\nspacing between detectors leads to inefficiency for measuring muons with\nenergies below or near the critical energy of 400 GeV. In response, IceCube has\nbuilt a densely instrumented region within the larger detector. This provides\nlarge samples of well-reconstructed interactions that are contained within the\ndensely instrumented region, extending up to energies of ~50 GeV. This leaves a\ngap of relatively unexplored atmospheric-neutrino events with energies between\n50 GeV and 1 TeV in the ultra-large detectors. In this paper we point out that\ninteresting Beyond Standard Model signatures may appear in this energy window,\nand that early running of the DUNE far detectors can give insight into new\nphysics that may appear in this range.\n"}
+{"abstract": "  Quantum simulation represents the most promising quantum application to\ndemonstrate quantum advantage on near-term noisy intermediate-scale quantum\n(NISQ) computers, yet available quantum simulation algorithms are prone to\nerrors and thus difficult to be realized. Herein, we propose a novel scheme to\nutilize intrinsic gate errors of NISQ devices to enable controllable simulation\nof open quantum system dynamics without ancillary qubits or explicit bath\nengineering, thus turning unwanted quantum noises into useful quantum\nresources. Specifically, we simulate energy transfer process in a\nphotosynthetic dimer system on IBM-Q cloud. By employing designed\ndecoherence-inducing gates, we show that quantum dissipative dynamics can be\nsimulated efficiently across coherent-to-incoherent regimes with results\ncomparable to those of the numerically-exact classical method. Moreover, we\ndemonstrate a calibration routine that enables consistent and predictive\nsimulations of open-quantum system dynamics in the intermediate coupling\nregime. This work provides a new direction for quantum advantage in the NISQ\nera.\n"}
+{"abstract": "  Blind Descent uses constrained but, guided approach to learn the weights. The\nprobability density function is non-zero in the infinite space of the dimension\n(case in point: Gaussians and normal probability distribution functions). In\nBlind Descent paper, some of the implicit ideas involving layer by layer\ntraining and filter by filter training (with different batch sizes) were\nproposed as probable greedy solutions. The results of similar experiments are\ndiscussed. Octave (and proposed PyTorch variants) source code of the\nexperiments of this paper can be found at\nhttps://github.com/PrasadNR/Attempted-Blind-Constrained-Descent-Experiments-ABCDE-\n. This is compared against the ABCDE derivatives of the original PyTorch source\ncode of https://github.com/akshat57/Blind-Descent .\n"}
+{"abstract": "  The first full angular analysis of the $B^0 \\to D^{*-} D_s^{*+}$ decay is\nperformed using 6 fb$^{-1}$ of $pp$ collision data collected with the LHCb\nexperiment at a centre-of-mass energy of 13 TeV. The $D_s^{*+} \\to D_s^+\n\\gamma$ and $D^{*-} \\to \\bar{D}^0 \\pi^-$ vector meson decays are used with the\nsubsequent $D_s^+ \\to K^+ K^- \\pi^+$ and $\\bar{D}^0 \\to K^+ \\pi^-$ decays. All\nhelicity amplitudes and phases are measured, and the longitudinal polarisation\nfraction is determined to be $f_{\\rm L} = 0.578 \\pm 0.010 \\pm 0.011$ with\nworld-best precision, where the first uncertainty is statistical and the second\nis systematic. The pattern of helicity amplitude magnitudes is found to align\nwith expectations from quark-helicity conservation in $B$ decays. The ratio of\nbranching fractions $[\\mathcal{B}(B^0 \\to D^{*-} D_s^{*+}) \\times\n\\mathcal{B}(D_s^{*+} \\to D_s^+ \\gamma)]/\\mathcal{B}(B^0 \\to D^{*-} D_s^+)$ is\nmeasured to be $2.045 \\pm 0.022 \\pm 0.071$ with world-best precision. In\naddition, the first observation of the Cabibbo-suppressed $B_s^0 \\to D^{*-}\nD_s^+$ decay is made with a significance of seven standard deviations. The\nbranching fraction ratio $\\mathcal{B}(B_s^0 \\to D^{*-} D_s^+)/\\mathcal{B}(B^0\n\\to D^{*-} D_s^+)$ is measured to be $0.049 \\pm 0.006 \\pm 0.003 \\pm 0.002$,\nwhere the third uncertainty is due to limited knowledge of the ratio of\nfragmentation fractions.\n"}
+{"abstract": "  We present an architecture that is effective for continual learning in an\nespecially demanding setting, where task boundaries do not exist or are\nunknown, and where classes have to be learned online (with each example\npresented only once). To obtain good performance under these constraints, while\nmitigating catastrophic forgetting, we exploit recent advances in contrastive,\nself-supervised learning, allowing us to use a pre-trained, general purpose\nimage encoder whose weights can be frozen, which precludes forgetting. The\npre-trained encoder also greatly simplifies the downstream task of\nclassification, which we solve with an ensemble of very simple classifiers.\nCollectively, the ensemble exhibits much better performance than any individual\nclassifier, an effect which is amplified through specialisation and competitive\nselection. We assess the performance of the encoders-and-ensembles architecture\non standard continual learning benchmarks, where it outperforms prior\nstate-of-the-art by a large margin on the hardest problems, as well as in less\nfamiliar settings where the data distribution changes gradually or the classes\nare presented one at a time.\n"}
+{"abstract": "  Emotional support is a crucial ability for many conversation scenarios,\nincluding social interactions, mental health support, and customer service\nchats. Following reasonable procedures and using various support skills can\nhelp to effectively provide support. However, due to the lack of a\nwell-designed task and corpora of effective emotional support conversations,\nresearch on building emotional support into dialog systems remains untouched.\nIn this paper, we define the Emotional Support Conversation (ESC) task and\npropose an ESC Framework, which is grounded on the Helping Skills Theory. We\nconstruct an Emotion Support Conversation dataset (ESConv) with rich annotation\n(especially support strategy) in a help-seeker and supporter mode. To ensure a\ncorpus of high-quality conversations that provide examples of effective\nemotional support, we take extensive effort to design training tutorials for\nsupporters and several mechanisms for quality control during data collection.\nFinally, we evaluate state-of-the-art dialog models with respect to the ability\nto provide emotional support. Our results show the importance of support\nstrategies in providing effective emotional support and the utility of ESConv\nin training more emotional support systems.\n"}
+{"abstract": "  To remain adaptable to a dynamic environment, coordinated neural activity in\nthe brain must be simultaneously flexible and reliable. There is evidence that\nthe brain facilitates this adaptive response using highly-conserved\nmetabotropic neuromodulatory neurotransmitters, such as noradrenaline and\nacetylcholine. While we understand how these neuromodulators alter individual\nneuronal dynamics, precisely how neuromodulation operates in networks of\nneurons to give rise to observed large-scale dynamics remains unknown. Here, we\ninvestigate this disparity by demonstrating correspondence between adaptive\ninformation processing modes - calculated on in vivo electrophysiological\nrecordings of bursting layer V neurons in awake mice - and fluctuations in\nneuromodulatory tone - assessed by dynamic changes in pupil diameter. We\ntheoretically validate these results by creating a novel, biologically\nplausible dual-compartment model of nonlinear layer V pyramidal neurons -\ncapable of both regular spike and bursting modes - that reproduce our main\nempirical findings. We then demonstrate that the adrenergic and cholinergic\nneuromodulatory systems shift the brain into a neuroprotective (i.e., slightly\nsubcritical) regime - assessed by the branching parameter - while facilitating\nflexible and reliable dynamics, respectively. This unexpected result\ndemonstrates that the brain has circumvented the necessity to shift the system\ncloser to criticality for variability by differentially augmenting an intrinsic\nneuronal nonlinearity. Our analyses establish that these distinct arms of the\nascending arousal system modulate ensembles of layer V pyramidal neurons to\naugment critical dynamics and facilitate distinct adaptive information\nprocessing modes within the brain.\n"}
+{"abstract": "  We give the geometric version of a construction of Colmez-Niziol which\nestablishes a comparison theorem between arithmetic p-adic nearby cycles and\nsyntomic sheaves. The local construction of the period isomorphism uses\n$(\\phi,\\Gamma)$-modules theory and is obtained by reducing the period\nisomorphism to a comparison theorem between cohomologies of Lie algebras. By\napplying the method of \"more general coordinates\" used by Bhatt-Morrow-Scholze,\nwe construct a global isomorphism. In particular, we deduce the semi-stable\nconjecture of Fontaine-Jannsen. This result was also proved by (among others)\nTsuji, via the Fontaine-Messing map, and by Cesnavicius and Koshikawa, which\ngeneralized the proof of the crystalline conjecture by Bhatt, Morrow and\nScholze. We use the previous map to show that the period morphism of Tsuji and\nthe one of Cesnavicius-Koshikawa are the same.\n"}
+{"abstract": "  The blast wave of machine learning and artificial intelligence has also\nreached the power systems community, and amid the frenzy of methods and\nblack-box tools that have been left in its wake, it is sometimes difficult to\nperceive a glimmer of Occam's razor principle. In this letter, we use the unit\ncommitment problem (UCP), an NP-hard mathematical program that is fundamental\nto power system operations, to show that simplicity must guide any strategy to\nsolve it, in particular those that are based on learning from past UCP\ninstances. To this end, we apply a naive algorithm to produce candidate\nsolutions to the UCP and show, using a variety of realistically sized power\nsystems, that we are able to find optimal or quasi-optimal solutions with\nremarkable speedups. Our claim is thus that any sophistication of the learning\nmethod must be backed up with a statistically significant improvement of the\nresults in this letter.\n"}
+{"abstract": "  Craniomaxillofacial reconstruction with patient-specific customized\ncraniofacial implants (CCIs) is most commonly performed for large-sized\nskeletal defects. Because the exact size of skull resection may not be known\nprior to the surgery, in the single-stage cranioplasty, a large CCI is\nprefabricated and resized intraoperatively with a manual-cutting process\nprovided by a surgeon. The manual resizing, however, may be inaccurate and\nsignificantly add to the operating time. This paper introduces a fast and\nnon-contact approach for intraoperatively determining the exact contour of the\nskull resection and automatically resizing the implant to fit the resection\narea. Our approach includes four steps: First, a patient's defect information\nis acquired by a 3D scanner. Second, the scanned defect is aligned to the CCI\nby registering the scanned defect to the reconstructed CT model. Third, a\ncutting toolpath is generated from the contour of the scanned defect. Lastly,\nthe large CCI is resized by a cutting robot to fit the resection area according\nto the given toolpath. To evaluate the resizing performance of our method, six\ndifferent resection shapes were used in the cutting experiments. We compared\nthe performance of our method to the performances of surgeon's manual resizing\nand an existing technique which collects the defect contour with an optical\ntracking system and projects the contour on the CCI to guide the manual\nmodification. The results show that our proposed method improves the resizing\naccuracy by 56% compared to the surgeon's manual modification and 42% compared\nto the projection method.\n"}
+{"abstract": "  Minimal linear codes are in one-to-one correspondence with special types of\nblocking sets of projective spaces over a finite field, which are called strong\nor cutting blocking sets. In this paper we prove an upper bound on the minimal\nlength of minimal codes of dimension $k$ over the $q$-element Galois field\nwhich is linear in both $q$ and $k$, hence improve the previous superlinear\nbounds. This result determines the minimal length up to a small constant\nfactor. We also improve the lower and upper bounds on the size of so called\nhiggledy-piggledy line sets in projective spaces and apply these results to\npresent improved bounds on the size of covering codes and saturating sets in\nprojective spaces as well. The contributions rely on geometric and\nprobabilistic arguments.\n"}
+{"abstract": "  We propose methods to infer properties of the execution environment of\nmachine learning pipelines by tracing characteristic numerical deviations in\nobservable outputs. Results from a series of proof-of-concept experiments\nobtained on local and cloud-hosted machines give rise to possible forensic\napplications, such as the identification of the hardware platform used to\nproduce deep neural network predictions. Finally, we introduce boundary samples\nthat amplify the numerical deviations in order to distinguish machines by their\npredicted label only.\n"}
+{"abstract": "  Fast and affordable solutions for COVID-19 testing are necessary to contain\nthe spread of the global pandemic and help relieve the burden on medical\nfacilities. Currently, limited testing locations and expensive equipment pose\ndifficulties for individuals trying to be tested, especially in low-resource\nsettings. Researchers have successfully presented models for detecting COVID-19\ninfection status using audio samples recorded in clinical settings [5, 15],\nsuggesting that audio-based Artificial Intelligence models can be used to\nidentify COVID-19. Such models have the potential to be deployed on smartphones\nfor fast, widespread, and low-resource testing. However, while previous studies\nhave trained models on cleaned audio samples collected mainly from clinical\nsettings, audio samples collected from average smartphones may yield suboptimal\nquality data that is different from the clean data that models were trained on.\nThis discrepancy may add a bias that affects COVID-19 status predictions. To\ntackle this issue, we propose a multi-branch deep learning network that is\ntrained and tested on crowdsourced data where most of the data has not been\nmanually processed and cleaned. Furthermore, the model achieves state-of-art\nresults for the COUGHVID dataset [16]. After breaking down results for each\ncategory, we have shown an AUC of 0.99 for audio samples with COVID-19 positive\nlabels.\n"}
+{"abstract": "  The unsteady, lineal translation of a solid spherical particle through\nviscoelastic fluids described by the Johnson-Segalman and Giesekus models is\nstudied analytically. Solutions for the pressure and velocity fields as well as\nthe force on the particle are expanded as a power series in the Weissenberg\nnumber. The momentum balance and constitutive equation are solved\nasymptotically for a steadily translating particle up to second order in the\nparticle velocity, and rescaling of the pressure and velocity in the frequency\ndomain is used to relate the solutions for steady lineal translation to those\nfor unsteady lineal translation. The unsteady force at third order in the\nparticle velocity is then calculated through application of the Lorentz\nreciprocal theorem, and it is shown that this weakly nonlinear contribution to\nthe force can be expressed as part of a Volterra series. Through a series of\nexamples, it is shown that a truncated representation of this Volterra series,\nwhich can be manipulated to describe the velocity in terms of an imposed force,\nis useful for analyzing specific time-dependent particle motions. Two examples\nstudied using this relationship are the force on a particle suddenly set into\nmotion and the velocity of a particle in response to a suddenly imposed steady\nforce. Additionally, the weakly nonlinear response of particle captured by a\nharmonic trap moving lineally through the fluid is computed. This is an analog\nto active microrheology experiments, and can be used to explain how weakly\nnonlinear responses manifest in active microrheology experiments with spherical\nprobes.\n"}
+{"abstract": "  In a recent paper by Casado-Pascual [Phys. Rev. E \\textbf{97}, 032219\n(2018)], directed motion of a sphere immersed in a viscous fluid and subjected\nto zero-average biharmonic forces is studied. The author explains the\ndependence on the relative amplitude of the two harmonic components of the\naverage terminal velocity from the perspective of a general formalism. In this\nComment, this explanation is shown to be in general incorrect, while the theory\nof ratchet universality together with the vibrational mechanics approach\nprovide a satisfactory explanation of major aspects of the observed phenomena.\n"}
+{"abstract": "  Mobile edge computing provides users with a cloud environment close to the\nedge of the wireless network, supporting the computing intensive applications\nthat have low latency requirements. The combination of offloading with the\nwireless communication brings new challenges. This paper investigates the\nservice caching problem during the long-term service offloading in the\nuser-centric wireless network. To meet the time-varying service demands of a\ntypical user, a cooperative service caching strategy in the unit of the base\nstation (BS) cluster is proposed. We formulate the caching problem as a\ntime-averaged completion delay minimization problem and transform it into\ntime-decoupled instantaneous problems with a virtual caching cost queue at\nfirst. Then we propose a distributed algorithm which is based on the\nconsensus-sharing alternating direction method of multipliers to solve each\ninstantaneous problem. The simulations validate that the proposed online\ndistributed service caching algorithm can achieve the optimal time-averaged\ncompletion delay of offloading tasks with the smallest caching cost in the unit\nof a BS cluster.\n"}
+{"abstract": "  In this paper we introduce and study double tails of multiple zeta values. We\nshow, in particular, that they satisfy certain recurrence relations and deduce\nfrom this a generalization of Euler's classical formula\n$\\zeta(2)=3\\sum_{m=1}^\\infty m^{-2}\\left({2m\\atop m}\\right)^{-1}$ to all\nmultiple zeta values, as well as a new and very efficient algorithm for\ncomputing these values.\n"}
+{"abstract": "  Experimental studies are prevalent in Evolutionary Computation (EC), and\nconcerns about the reproducibility and replicability of such studies have\nincreased in recent times, reflecting similar concerns in other scientific\nfields. In this article, we discuss, within the context of EC, the different\ntypes of reproducibility and suggest a classification that refines the badge\nsystem of the Association of Computing Machinery (ACM) adopted by ACM\nTransactions on Evolutionary Learning and Optimization\n(https://dlnext.acm.org/journal/telo). We identify cultural and technical\nobstacles to reproducibility in the EC field. Finally, we provide guidelines\nand suggest tools that may help to overcome some of these reproducibility\nobstacles.\n"}
+{"abstract": "  Most artificial neural networks used for object detection and recognition are\ntrained in a fully supervised setup. This is not only very resource consuming\nas it requires large data sets of labeled examples but also very different from\nhow humans learn. We introduce a setup in which an artificial agent first\nlearns in a simulated world through self-supervised exploration. Following\nthis, the representations learned through interaction with the world can be\nused to associate semantic concepts such as different types of doors. To do\nthis, we use a method we call fast concept mapping which uses correlated firing\npatterns of neurons to define and detect semantic concepts. This association\nworks instantaneous with very few labeled examples, similar to what we observe\nin humans in a phenomenon called fast mapping. Strikingly, this method already\nidentifies objects with as little as one labeled example which highlights the\nquality of the encoding learned self-supervised through embodiment using\ncuriosity-driven exploration. It therefor presents a feasible strategy for\nlearning concepts without much supervision and shows that through pure\ninteraction with the world meaningful representations of an environment can be\nlearned.\n"}
+{"abstract": "  The emergence and continued reliance on the Internet and related technologies\nhas resulted in the generation of large amounts of data that can be made\navailable for analyses. However, humans do not possess the cognitive\ncapabilities to understand such large amounts of data. Machine learning (ML)\nprovides a mechanism for humans to process large amounts of data, gain insights\nabout the behavior of the data, and make more informed decision based on the\nresulting analysis. ML has applications in various fields. This review focuses\non some of the fields and applications such as education, healthcare, network\nsecurity, banking and finance, and social media. Within these fields, there are\nmultiple unique challenges that exist. However, ML can provide solutions to\nthese challenges, as well as create further research opportunities.\nAccordingly, this work surveys some of the challenges facing the aforementioned\nfields and presents some of the previous literature works that tackled them.\nMoreover, it suggests several research opportunities that benefit from the use\nof ML to address these challenges.\n"}
+{"abstract": "  We show that object detectors can hallucinate and detect missing objects;\npotentially even accurately localized at their expected, but non-existing,\nposition. This is particularly problematic for applications that rely on visual\npart verification: detecting if an object part is present or absent. We show\nhow popular object detectors hallucinate objects in a visual part verification\ntask and introduce the first visual part verification dataset: DelftBikes,\nwhich has 10,000 bike photographs, with 22 densely annotated parts per image,\nwhere some parts may be missing. We explicitly annotated an extra object state\nlabel for each part to reflect if a part is missing or intact. We propose to\nevaluate visual part verification by relying on recall and compare popular\nobject detectors on DelftBikes.\n"}
+{"abstract": "  Dark matter particles may interact with other dark matter particles via a new\nforce mediated by a dark photon, $A^{\\prime}$, which would be the dark-sector\nanalog to the ordinary photon of electromagnetism. The dark photon can obtain a\nhighly suppressed mixing-induced coupling to the electromagnetic current,\nproviding a portal through which dark photons can interact with ordinary\nmatter. This review focuses on $A^{\\prime}$ scenarios that are potentially\naccessible to accelerator-based experiments. We summarize the existing\nconstraints placed by such experiments on dark photons, highlight what could be\nobserved in the near future, and discuss the major experimental challenges that\nmust be overcome to improve sensitivities.\n"}
+{"abstract": "  We introduce MADGRAD, a novel optimization method in the family of AdaGrad\nadaptive gradient methods. MADGRAD shows excellent performance on deep learning\noptimization problems from multiple fields, including classification and\nimage-to-image tasks in vision, and recurrent and bidirectionally-masked models\nin natural language processing. For each of these tasks, MADGRAD matches or\noutperforms both SGD and ADAM in test set performance, even on problems for\nwhich adaptive methods normally perform poorly.\n"}
+{"abstract": "  We investigate the structure, dynamics, and chemistry of the molecule-rich\nnebula around the stellar symbiotic system R Aqr, which is significantly\naffected by the presence of a white dwarf (WD) companion. We study the effects\nof the strong dynamical interaction between the AGB wind and the WD and of\nphotodissociation by the WD UV radiation on the circumstellar shells.\n  We obtained high-quality ALMA maps of the 12CO J=2-1, J=3-2, and J=6-5 lines\nand of 13CO J=3-2. The maps were analyzed by means of a heuristic 3D model that\nis able to reproduce the observations. In order to interpret this description\nof the molecule-rich nebula, we performed sophisticated calculations of\nhydrodynamical interaction and photoinduced chemistry.\n  We find that the CO-emitting gas is distributed within a relatively small\nregion <~ 1.5\". Its structure consists of a central dense component plus\nstrongly disrupted outer regions, which seem to be parts of spiral arms that\nare highly focused on the orbital plane. The structure and dynamics of these\nspiral arms are compatible with our hydrodynamical calculations. We argue that\nthe observed nebula is the result of the dynamical interaction between the wind\nand the gravitational attraction of the WD. We also find that UV emission from\nthe hot companion efficiently photodissociates molecules except in the densest\nand best-shielded regions, that is, in the close surroundings of the AGB star\nand some shreds of the spiral arms from which the detected lines come. We can\noffer a faithful description of the distribution of nebular gas in this\nprototypical source, which will be a useful template for studying material\naround other tight binary systems.\n"}
+{"abstract": "  It is common for video-on-demand and music streaming services to adopt a user\ninterface composed of several recommendation lists, i.e. widgets or swipeable\ncarousels, each generated according to a specific criterion or algorithm (e.g.\nmost recent, top popular, recommended for you, editors' choice, etc.).\nSelecting the appropriate combination of carousel has significant impact on\nuser satisfaction. A crucial aspect of this user interface is that to measure\nthe relevance a new carousel for the user it is not sufficient to account\nsolely for its individual quality. Instead, it should be considered that other\ncarousels will already be present in the interface. This is not considered by\ntraditional evaluation protocols for recommenders systems, in which each\ncarousel is evaluated in isolation, regardless of (i) which other carousels are\ndisplayed to the user and (ii) the relative position of the carousel with\nrespect to other carousels. Hence, we propose a two-dimensional evaluation\nprotocol for a carousel setting that will measure the quality of a\nrecommendation carousel based on how much it improves upon the quality of an\nalready available set of carousels. Our evaluation protocol takes into account\nalso the position bias, i.e. users do not explore the carousels sequentially,\nbut rather concentrate on the top-left corner of the screen.\n  We report experiments on the movie domain and notice that under a carousel\nsetting the definition of which criteria has to be preferred to generate a list\nof recommended items changes with respect to what is commonly understood.\n"}
+{"abstract": "  Multi-label text classification refers to the problem of assigning each given\ndocument its most relevant labels from the label set. Commonly, the metadata of\nthe given documents and the hierarchy of the labels are available in real-world\napplications. However, most existing studies focus on only modeling the text\ninformation, with a few attempts to utilize either metadata or hierarchy\nsignals, but not both of them. In this paper, we bridge the gap by formalizing\nthe problem of metadata-aware text classification in a large label hierarchy\n(e.g., with tens of thousands of labels). To address this problem, we present\nthe MATCH solution -- an end-to-end framework that leverages both metadata and\nhierarchy information. To incorporate metadata, we pre-train the embeddings of\ntext and metadata in the same space and also leverage the fully-connected\nattentions to capture the interrelations between them. To leverage the label\nhierarchy, we propose different ways to regularize the parameters and output\nprobability of each child label by its parents. Extensive experiments on two\nmassive text datasets with large-scale label hierarchies demonstrate the\neffectiveness of MATCH over state-of-the-art deep learning baselines.\n"}
+{"abstract": "  Convolutional neural networks (CNNs) have achieved beyond human-level\naccuracy in the image classification task and are widely deployed in real-world\nenvironments. However, CNNs show vulnerability to adversarial perturbations\nthat are well-designed noises aiming to mislead the classification models. In\norder to defend against the adversarial perturbations, adversarially trained\nGAN (ATGAN) is proposed to improve the adversarial robustness generalization of\nthe state-of-the-art CNNs trained by adversarial training. ATGAN incorporates\nadversarial training into standard GAN training procedure to remove obfuscated\ngradients which can lead to a false sense in defending against the adversarial\nperturbations and are commonly observed in existing GANs-based adversarial\ndefense methods. Moreover, ATGAN adopts the image-to-image generator as data\naugmentation to increase the sample complexity needed for adversarial\nrobustness generalization in adversarial training. Experimental results in\nMNIST SVHN and CIFAR-10 datasets show that the proposed method doesn't rely on\nobfuscated gradients and achieves better global adversarial robustness\ngeneralization performance than the adversarially trained state-of-the-art\nCNNs.\n"}
+{"abstract": "  It is known that every surface-link, which is a closed surface embedded in\n$4$-space, can be described as the closure of a $2$-dimensional braid providing\nit is orientable. In this paper, we introduce a new method of describing a\nsurface-link using a braided surface, which we call a plat form. We prove that\nevery surface-link, not necessarily orientable, can be described in a plat\nform. The plat index for a surface-link is defined. In classical knot theory,\nthe plat index of a link coincides with the bridge index. The plat index of a\nsurface-link we introduce here is an analogy of them.\n"}
+{"abstract": "  To determine whether some often-used lexical association measures assign high\nscores to n-grams that chance could have produced as frequently as observed, we\nused an extension of Fisher's exact test to sequences longer than two words to\nanalyse a corpus of four million words. The results, based on the\nprecision-recall curve and a new index called chance-corrected average\nprecision, show that, as expected, simple-ll is extremely effective. They also\nshow, however, that MI3 is more efficient than the other hypothesis tests-based\nmeasures and even reaches a performance level almost equal to simple-ll for\n3-grams. It is additionally observed that some measures are more efficient for\n3-grams than for 2-grams, while others stagnate.\n"}
+{"abstract": "  Inertial sensor calibration plays a progressively important role in many\nareas of research among which navigation engineering. By performing this task\naccurately, it is possible to significantly increase general navigation\nperformance by correctly filtering out the deterministic and stochastic\nmeasurement errors that characterize such devices. While different techniques\nare available to model and remove the deterministic errors, there has been\nconsiderable research over the past years with respect to modelling the\nstochastic errors which have complex structures. In order to do the latter,\ndifferent replicates of these error signals are collected and a model is\nidentified and estimated based on one of these replicates. While this procedure\nhas allowed to improve navigation performance, it has not yet taken advantage\nof the information coming from all the other replicates collected on the same\nsensor. However, it has been observed that there is often a change of error\nbehaviour between replicates which can also be explained by different\n(constant) external conditions under which each replicate was taken. Whatever\nthe reason for the difference between replicates, it appears that the model\nstructure remains the same between replicates but the parameter values vary. In\nthis work we therefore consider and study the properties of different\napproaches that allow to combine the information from all replicates\nconsidering this phenomenon, confirming their validity both in simulation\nsettings and also when applied to real inertial sensor error signals. By taking\ninto account parameter variation between replicates, this work highlights how\nthese approaches can improve the average navigation precision as well as obtain\nreliable estimates of the uncertainty of the navigation solution.\n"}
+{"abstract": "  We propose a robust calibration pipeline that optimises the selection of\ncalibration samples for the estimation of calibration parameters that fit the\nentire scene. We minimise user error by automating the data selection process\naccording to a metric, called Variability of Quality (VOQ) that gives a score\nto each calibration set of samples. We show that this VOQ score is correlated\nwith the estimated calibration parameter's ability to generalise well to the\nentire scene, thereby overcoming the overfitting problems of existing\ncalibration algorithms. Our approach has the benefits of simplifying the\ncalibration process for practitioners of any calibration expertise level and\nproviding an objective measure of the quality for our calibration pipeline's\ninput and output data. We additionally use a novel method of assessing the\naccuracy of the calibration parameters. It involves computing reprojection\nerrors for the entire scene to ensure that the parameters are well fitted to\nall features in the scene. Our proposed calibration pipeline takes 90s, and\nobtains an average reprojection error of 1-1.2cm, with standard deviation of\n0.4-0.5cm over 46 poses evenly distributed in a scene. This process has been\nvalidated by experimentation on a high resolution, software definable lidar,\nBaraja Spectrum-Scan; and a low, fixed resolution lidar, Velodyne VLP-16. We\nhave shown that despite the vast differences in lidar technologies, our\nproposed approach manages to estimate robust calibration parameters for both.\nOur code and data set used for this paper are made available as open-source.\n"}
+{"abstract": "  We study the homotopy type of the space of the unitary group\n$\\operatorname{U}_1(C^\\ast_u(|\\mathbb{Z}^n|))$ of the uniform Roe algebra\n$C^\\ast_u(|\\mathbb{Z}^n|)$ of $\\mathbb{Z}^n$. We show that the stabilizing map\n$\\operatorname{U}_1(C^\\ast_u(|\\mathbb{Z}^n|))\\to\\operatorname{U}_\\infty(C^\\ast_u(|\\mathbb{Z}^n|))$\nis a homotopy equivalence. Moreover, when $n=1,2$, we determine the homotopy\ntype of $\\operatorname{U}_1(C^\\ast_u(|\\mathbb{Z}^n|))$, which is the product of\nthe unitary group $\\operatorname{U}_1(C^\\ast(|\\mathbb{Z}^n|))$ (having the\nhomotopy type of $\\operatorname{U}_\\infty(\\mathbb{C})$ or $\\mathbb{Z}\\times\nB\\operatorname{U}_\\infty(\\mathbb{C})$ depending on the parity of $n$) of the\nRoe algebra $C^\\ast(|\\mathbb{Z}^n|)$ and rational Eilenberg--MacLane spaces.\n"}
+{"abstract": "  We analyze the effect of polarized diffuse emission in the calibration of\nwide-beam mm-wave polarimeters, when using the Crab Nebula as a reference\nsource for both polarized brightness and polarization angle. We show that, for\nCMB polarization experiments aiming at detecting B-mode in a scenario with a\ntensor to scalar ratio $r \\sim 0.001$, wide (a few degrees in diameter),\nprecise ($\\sigma_Q$ , $\\sigma_U$ $\\sim$ 20 $\\mu$$K_{CMB}$ arcmin), high angular\nresolution ($< \\mathrm{FWHM}$) reference maps are needed to properly take into\naccount the effects of diffuse polarized emission and avoid significant bias in\nthe calibration.\n"}
+{"abstract": "  Enzyme is the major workhorse to carry out the diverse cellular functions. It\ncatalyzes the biological reactions with a high specificity, with its topology\nplaying a crucial role. For ecologically safe production of numerous\nbioproducts including drugs and chemicals, we have been striving to design the\nindustrially useful enzyme molecules with highly improved catalytic capability.\nAs the sequence space is enormous for an enzyme, its quick and effective\nexploration is quite improbable for the mutagenesis studies whose accuracy is\ngreatly reliant on the prior information of the mutated sites and the extent of\nrigorous screening of the mutant libraries. Although directed evolution methods\nsignificantly aid the construction of a functionally improved molecule, their\ncredibility depends on the successful excavation of the functionally similar\nsequence space in the available databases, encompassing billions of proteins.\nAs deep learning methods aid us to extensively uncover the underlying network\nof all the key catalytic positions without any experimental data, their\nimplementation has reliably increased the accuracy of directed evolution. The\nchapter comprehensively explains data mining and deep learning methods to\nfurther showcase their importance in enzyme engineering methods. The key\nbiological and algorithmic limitations of these deep learning methodologies are\nlastly highlighted.\n"}
+{"abstract": "  The time dependence of the temperature during the reheating process is\nstudied. We consider the thermal feedback effects of the produced particles on\nthe effective dissipation rate of the inflaton field, which can lead to\nenhanced production of particles. We parameterize the temperature dependence of\nthe dissipation rate in terms of a Taylor expansion containing the vacuum decay\nrate and the thermal terms. By solving the Boltzmann equations for the energy\ndensities of the inflaton and radiation, we provide analytic estimates for a\ngeneral power law dependence on the temperature. In this way we describe the\nentire reheating process. The maximum temperature of the reheating process and\nits dependence on model parameters are studied in different cases. The impact\nof the thermal feedback effects on the expansion history of the universe and\nthe cosmic microwave background (CMB) is discussed. We also discuss the range\nof validity of our approach.\n"}
+{"abstract": "  In spite of much recent research in the area, it is still unclear whether\nsubject-area question-answering data is useful for machine reading\ncomprehension (MRC) tasks. In this paper, we investigate this question. We\ncollect a large-scale multi-subject multiple-choice question-answering dataset,\nExamQA, and use incomplete and noisy snippets returned by a web search engine\nas the relevant context for each question-answering instance to convert it into\na weakly-labeled MRC instance. We then propose a self-teaching paradigm to\nbetter use the generated weakly-labeled MRC instances to improve a target MRC\ntask. Experimental results show that we can obtain +5.1% in accuracy on a\nmultiple-choice MRC dataset, C^3, and +3.8% in exact match on an extractive MRC\ndataset, CMRC 2018 over state-of-the-art MRC baselines, demonstrating the\neffectiveness of our framework and the usefulness of large-scale subject-area\nquestion-answering data for different types of machine reading comprehension\ntasks.\n"}
+{"abstract": "  A fully implicit particle-in-cell method for handling the\n$v_\\parallel$-formalism of electromagnetic gyrokinetics has been implemented in\nXGC. By choosing the $v_\\parallel$-formalism, we avoid introducing the\nnon-physical skin terms in Amp\\`{e}re's law, which are responsible for the\nwell-known ``cancellation problem\" in the $p_\\parallel$-formalism. The\n$v_\\parallel$-formalism, however, is known to suffer from a numerical\ninstability when explicit time integration schemes are used due to the\nappearance of a time derivative in the particle equations of motion from the\ninductive component of the electric field. Here, using the conventional $\\delta\nf$ scheme, we demonstrate that our implicitly discretized algorithm can provide\nnumerically stable simulation results with accurate dispersive properties. We\nverify the algorithm using a test case for shear Alfv\\'{e}n wave propagation in\naddition to a case demonstrating the ITG-KBM transition. The ITG-KBM transition\ncase is compared to results obtained from other $\\delta f$ gyrokinetic\ncodes/schemes, whose verification has already been archived in the literature.\n"}
+{"abstract": "  An important problem in causal inference is to break down the total effect of\na treatment on an outcome into different causal pathways and to quantify the\ncausal effect in each pathway. For instance, in causal fairness, the total\neffect of being a male employee (i.e., treatment) constitutes its direct effect\non annual income (i.e., outcome) and the indirect effect via the employee's\noccupation (i.e., mediator). Causal mediation analysis (CMA) is a formal\nstatistical framework commonly used to reveal such underlying causal\nmechanisms. One major challenge of CMA in observational studies is handling\nconfounders, variables that cause spurious causal relationships among\ntreatment, mediator, and outcome. Conventional methods assume sequential\nignorability that implies all confounders can be measured, which is often\nunverifiable in practice. This work aims to circumvent the stringent sequential\nignorability assumptions and consider hidden confounders. Drawing upon proxy\nstrategies and recent advances in deep learning, we propose to simultaneously\nuncover the latent variables that characterize hidden confounders and estimate\nthe causal effects. Empirical evaluations using both synthetic and\nsemi-synthetic datasets validate the effectiveness of the proposed method. We\nfurther show the potentials of our approach for causal fairness analysis.\n"}
+{"abstract": "  Rapid rise in income inequality in India is a serious concern. While the\nemphasis is on inclusive growth, it seems difficult to tackle the problem\nwithout looking at the intricacies of the problem. Social mobility is one such\nimportant tool which helps in reaching the cause of the problem and focuses on\nbringing long term equality in the country. The purpose of this study is to\nexamine the role of social background and education attainment in generating\noccupation mobility in the country. By applying an extended version of the RC\nassociation model to 68th round (2011-12) of the Employment and Unemployment\nSurvey by the National Sample Survey Office of India, we found that the role of\neducation is not important in generating occupation mobility in India, while\nsocial background plays a critical role in determining one's occupation. This\nstudy successfully highlights the strong intergenerational occupation\nimmobility in the country and also the need to focus on education. In this\nregard, further studies are needed to uncover other crucial factors limiting\nthe growth of individuals in the country.\n"}
+{"abstract": "  The parameterisation proposed by Casas and Ibarra in the year 2001 have shown\npromising role in the extraction of neutrino Yukawa coupling which is a basic\ningredient of the seesaw mechanism generating neutrino mass. We pay special\nattention in establishing the crucial role of the Casas-Ibarra (CI)\nparameterisation in presence of two different orthogonal matrices, $R=\n\\textbf{O} \\,\\rm e^{i {\\bf A}}$ and $R= \\textbf{O} \\,\\rm e^{{\\bf A}}$ in order\nto investigate flavored leptogenesis. In the light of these two choices of the\northogonal matrix we examine the connection between the low energy and high\nenergy CP violations along with certain interesting predictions on the low\nenergy parameters namely, the lightest neutrino mass and the Dirac CP phase\n($\\delta$). Considering the right handed neutrino (RHN) mass window to be\n$10^8$ GeV, we show that Dirac phase leptogenesis is possible with the choices\nof these two orthogonal matrices. We choose a nearly degenerate spectrum for\nthe RHN masses for having a successful leptogenesis. We also emphasize on\npresenting a range of the matrix elements of the skew symmetric matrix $\n\\textbf{A}$. The results obtained in the present analysis underline the\nimportance of understanding the status of CP violation in the low energy\nsector. We also discuss the phenomenological implications of these two case\nstudies in the context of LFV considering the $\\mu \\rightarrow e\\gamma$ decay\nprocess.\n"}
+{"abstract": "  With the development of medical computer-aided diagnostic systems, pulmonary\nartery-vein(A/V) separation plays a crucial role in assisting doctors in\npreoperative planning for lung cancer surgery. However, distinguishing arterial\nfrom venous irrigation in chest CT images remains a challenge due to the\nsimilarity and complex structure of the arteries and veins. We propose a novel\nmethod for automatic separation of pulmonary arteries and veins from chest CT\nimages. The method consists of three parts. First, global connection\ninformation and local feature information are used to construct a complete\ntopological tree and ensure the continuity of vessel reconstruction. Second,\nthe Twin-Pipe network proposed can automatically learn the differences between\narteries and veins at different levels to reduce classification errors caused\nby changes in terminal vessel characteristics. Finally, the topology optimizer\nconsiders interbranch and intrabranch topological relationships to maintain\nspatial consistency to avoid the misclassification of A/V irrigations. We\nvalidate the performance of the method on chest CT images. Compared with manual\nclassification, the proposed method achieves an average accuracy of 96.2% on\nnoncontrast chest CT. In addition, the method has been proven to have good\ngeneralization, that is, the accuracies of 93.8% and 94.8% are obtained for CT\nscans from other devices and other modes, respectively. The result of pulmonary\nartery-vein obtained by the proposed method can provide better assistance for\npreoperative planning of lung cancer surgery.\n"}
+{"abstract": "  In early January 2020, after China reported the first cases of the new\ncoronavirus (SARS-CoV-2) in the city of Wuhan, unreliable and not fully\naccurate information has started spreading faster than the virus itself.\nAlongside this pandemic, people have experienced a parallel infodemic, i.e., an\noverabundance of information, some of which misleading or even harmful, that\nhas widely spread around the globe. Although Social Media are increasingly\nbeing used as information source, Web Search Engines, like Google or Yahoo!,\nstill represent a powerful and trustworthy resource for finding information on\nthe Web. This is due to their capability to capture the largest amount of\ninformation, helping users quickly identify the most relevant, useful, although\nnot always the most reliable, results for their search queries. This study aims\nto detect potential misleading and fake contents by capturing and analysing\ntextual information, which flow through Search Engines. By using a real-world\ndataset associated with recent CoViD-19 pandemic, we first apply re-sampling\ntechniques for class imbalance, then we use existing Machine Learning\nalgorithms for classification of not reliable news. By extracting lexical and\nhost-based features of associated Uniform Resource Locators (URLs) for news\narticles, we show that the proposed methods, so common in phishing and\nmalicious URLs detection, can improve the efficiency and performance of\nclassifiers. Based on these findings, we suggest that the use of both textual\nand URLs features can improve the effectiveness of fake news detection methods.\n"}
+{"abstract": "  The iron-based superconductor FeTe$_{1-x}$Se$_{x}$ has attracted considerable\nattention as a candidate topological superconductor owing to a unique\ncombination of topological surface states and bulk high-temperature\nsuperconductivity. The superconducting properties of as-grown single crystals,\nhowever, are highly variable and synthesis dependent due to excess interstitial\niron impurities incorporated during growth. Here we report a novel\nphysicochemical process for pumping this interstitial iron out of the\nFeTe$_{1-x}$Se$_{x}$ matrix and achieving bulk superconductivity. Our method\nshould have significant value for the synthesis of high-quality single crystals\nof FeTe$_{1-x}$Se$_{x}$ with large superconducting volume fractions.\n"}
+{"abstract": "  Algorithms for exchange of kidneys is one of the key successful applications\nin market design, artificial intelligence, and operations research. Potent\nimmunosuppressant drugs suppress the body's ability to reject a transplanted\norgan up to the point that a transplant across blood- or tissue-type\nincompatibility becomes possible. In contrast to the standard kidney exchange\nproblem, we consider a setting that also involves the decision about which\nrecipients receive from the limited supply of immunosuppressants that make them\ncompatible with originally incompatible kidneys. We firstly present a general\ncomputational framework to model this problem. Our main contribution is a range\nof efficient algorithms that provide flexibility in terms of meeting meaningful\nobjectives. Motivated by the current reality of kidney exchanges using\nsophisticated mathematical-programming-based clearing algorithms, we then\npresent a general but scalable approach to optimal clearing with\nimmunosuppression; we validate our approach on realistic data from a large\nfielded exchange.\n"}
+{"abstract": "  The thermodynamic and physical properties of the Laves phase Fe2Mo have been\ninvestigated using the finite-temperature quantum mechanical calculations\nwithin the frame of the density functional theory (DFT). All relevant free\nenergy contributions including electronic, vibrational and magnetic excitations\nare considered. The quasi-harmonic Debye - Gruneisen theory is used. In this\nwork, a new method of searching a thermal expansion path of compounds is\nproposed. It allows to reduce the problem to a one-dimensional case and\nminimize the free energy in one variable, in volume. The heat capacity, thermal\nexpansion, elastic constants and bulk modulus are modelled. The calculated\nresults analyzed and are in an agreement with the available experimental data.\nIt is shown that magnetic entropy must be considered on equal footing with\nvibrational and electronic energies to reliably predict stability of Fe2Mo.\n"}
+{"abstract": "  The bulk band topology of symmetry invariant adiabatic systems in the\nthermodynamic limit are considered to be determined by the hopping energy. In\nthis work, we present that in closed classical systems, due to generalized\nchiral symmetry broken, the on-site energy cannot always be regarded as\nidentical and can crucially impact the topological properties of the systems.\nBased on a finite one-dimensional chain, we demonstrate that the non-equivalent\non-site energy of bulk lattices affects the topological phases of the bands,\nand the on-site energy of end lattices affects the existence of the topological\nstates. Along these lines, the correspondence with generalized chiral symmetry\nin acoustic system is rigorously proposed. Our work provides a new degree of\nfreedom for topological classical systems, and can be generalized to\nhigher-dimensions and non-Hermitian conditions.\n"}
+{"abstract": "  Quasi-Periodic Eruptions (QPEs) are extreme high-amplitude bursts of X-ray\nradiation recurring every few hours and originating near the central\nsupermassive black holes in galactic nuclei. It is currently unknown what\ntriggers these events, how long they last and how they are connected to the\nphysical properties of the inner accretion flows. Previously, only two such\nsources were known, found either serendipitously or in archival data, with\nemission lines in their optical spectra classifying their nuclei as hosting an\nactively accreting supermassive black hole. Here we present the detection of\nQPEs in two further galaxies, obtained with a blind and systematic search over\nhalf of the X-ray sky. The optical spectra of these galaxies show no signature\nof black hole activity, indicating that a pre-existing accretion flow typical\nof active nuclei is not required to trigger these events. Indeed, the periods,\namplitudes and profiles of the newly discovered QPEs are inconsistent with\ncurrent models that invoke radiation-pressure driven accretion disk\ninstabilities. Instead, QPEs might be driven by an orbiting compact object.\nFurthermore, their observed properties require the mass of the secondary object\nto be much smaller than the main body and future X-ray observations may\nconstrain possible changes in the period due to orbital evolution. This\nscenario could make QPEs a viable candidate for the electromagnetic\ncounterparts of the so-called extreme mass ratio inspirals, with considerable\nimplications for multi-messenger astrophysics and cosmology.\n"}
+{"abstract": "  Using 6.32 fb$^{-1}$ of electron-positron collision data recorded by the\nBESIII detector at center-of-mass energies between 4.178 and 4.226~GeV, we\npresent the first search for the decay $D_s^+\\to a_0(980)^0\ne^+\\nu_e,\\,a_0(980)^0\\rightarrow \\pi^0\\eta$, which could proceed via\n$a_0(980)$-$f_0(980)$ mixing. No significant signal is observed. An upper limit\nof $1.2 \\times 10^{-4}$ at the $90\\%$ confidence level is set on the product of\nthe branching fractions of $D_{s}^{+}\\to a_0(980)^0 e^+\\nu_e$ and\n$a_0(980)^0\\rightarrow \\pi^0\\eta$ decays.\n"}
+{"abstract": "  Disease transmission is studied through disciplines like epidemiology,\napplied mathematics, and statistics. Mathematical simulation models for\ntransmission have implications in solving public and personal health\nchallenges. The SIR model uses a compartmental approach including dynamic and\nnonlinear behavior of transmission through three factors: susceptible,\ninfected, and removed (recovered and deceased) individuals. Using the Lambert W\nFunction, we propose a framework to study solutions of the SIR model. This\ndemonstrates the applications of COVID-19 transmission data to model the spread\nof a real-world disease. Different models of disease including the SIR, SIRmp\nand SEIRpqr model are compared with respect to their ability to predict disease\nspread. Physical distancing impacts and personal protection equipment use are\ndiscussed with relevance to the COVID-19 spread.\n"}
+{"abstract": "  One fundamental limitation of blockchain-based smart contracts is that they\nexecute in a closed environment. Thus, they only have access to data and\nfunctionality that is already on the blockchain, or is fed into the blockchain.\nAny interactions with the real world need to be mediated by a bridge service,\nwhich is called an oracle. As decentralized applications mature, oracles are\nplaying an increasingly prominent role. With their evolution comes more\nattacks, necessitating greater attention to their trust model. In this\nsystemization of knowledge paper (SoK), we dissect the design alternatives for\noracles, showcase attacks, and discuss attack mitigation strategies.\n"}
+{"abstract": "  The scope of automotive functions has grown from a single-vehicle as an\nentity to multiple vehicles working together as an entity, referred to as\ncooperative driving. The current automotive safety standard, ISO 26262, is\ndesigned for single vehicles. With the increasing number of cooperative driving\ncapable vehicles on the road, it is now imperative to systematically assess the\nfunctional safety of architectures of these vehicles. Many methods are proposed\nto assess architectures with respect to different quality attributes in the\nsoftware architecture domain, but to the best of our knowledge, functional\nsafety assessment of automotive architectures is not explored in the\nliterature. We present a method, that leverages existing research in software\narchitecture and safety engineering domains, to check whether the functional\nsafety requirements for a cooperative driving scenario are fulfilled in the\ntechnical architecture of a vehicle. We apply our method on a real-life\nacademic prototype for a cooperative driving scenario, platooning, and discuss\nour insights.\n"}
+{"abstract": "  Local distinguishability of sets of generalized Bell states (GBSs) is\ninvestigated. We first clarify the conditions such that a set of GBSs can be\nlocally transformed to a certain type of GBS set that is easily distinguishable\nwithin local operations and one-way classical communication. We then show that,\nif the space dimension $d$ is a prime, these conditions are necessary and\nsufficient for sets of $d$ GBSs in $\\mathbb{C}^d \\otimes \\mathbb{C}^d$ to be\nlocally distinguishable. Thus we obtain a simple computable criterion for local\ndistinguishability of sets of $d$ GBSs in prime dimension $d$.\n"}
+{"abstract": "  In 2017, the Event Horizon Telescope (EHT) Collaboration succeeded in\ncapturing the first direct image of the center of the M87 galaxy. The\nasymmetric ring morphology and size are consistent with theoretical\nexpectations for a weakly accreting supermassive black hole of mass\napproximately 6.5 x 10^9 M_solar. The EHTC also partnered with several\ninternational facilities in space and on the ground, to arrange an extensive,\nquasi-simultaneous multi-wavelength campaign. This Letter presents the results\nand analysis of this campaign, as well as the multi-wavelength data as a legacy\ndata repository. We captured M87 in a historically low state, and the core flux\ndominates over HST-1 at high energies, making it possible to combine core flux\nconstraints with the more spatially precise very long baseline interferometry\ndata. We present the most complete simultaneous multi-wavelength spectrum of\nthe active nucleus to date, and discuss the complexity and caveats of combining\ndata from different spatial scales into one broadband spectrum. We apply two\nheuristic, isotropic leptonic single-zone models to provide insight into the\nbasic source properties, but conclude that a structured jet is necessary to\nexplain M87's spectrum. We can exclude that the simultaneous gamma-ray emission\nis produced via inverse Compton emission in the same region producing the EHT\nmm-band emission, and further conclude that the gamma-rays can only be produced\nin the inner jets (inward of HST-1) if there are strongly particle-dominated\nregions. Direct synchrotron emission from accelerated protons and secondaries\ncannot yet be excluded.\n"}
+{"abstract": "  Availability of domain-specific datasets is an essential problem in object\ndetection. Maritime vessel detection of inshore and offshore datasets is no\nexception, there is a limited number of studies addressing this need. For that\nreason, we collected a dataset of images of maritime vessels taking into\naccount different factors: background variation, atmospheric conditions,\nillumination, visible proportion, occlusion and scale variation. Vessel\ninstances (including 9 types of vessels), seamarks and miscellaneous floaters\nwere precisely annotated: we employed a first round of labelling and\nsubsequently, we used the CSRT [1] tracker to trace inconsistencies and relabel\ninadequate label instances. Moreover, we evaluated the the out-of-the-box\nperformance of four prevalent object detection algorithms (Faster R-CNN [2],\nR-FCN [3], SSD [4] and EfficientDet [5]). The algorithms were previously\ntrained on the Microsoft COCO dataset. We compare their accuracy based on\nfeature extractor and object size. Our experiments show that Faster R-CNN with\nInception-Resnet v2 outperforms the other algorithms, except in the large\nobject category where EfficientDet surpasses the latter.\n"}
+{"abstract": "  This paper presents a computational framework that generates ensemble\npredictive mechanics models with uncertainty quantification (UQ). We first\ndevelop a causal discovery algorithm to infer causal relations among\ntime-history data measured during each representative volume element (RVE)\nsimulation through a directed acyclic graph (DAG). With multiple plausible sets\nof causal relationships estimated from multiple RVE simulations, the\npredictions are propagated in the derived causal graph while using a deep\nneural network equipped with dropout layers as a Bayesian approximation for\nuncertainty quantification. We select two representative numerical examples\n(traction-separation laws for frictional interfaces, elastoplasticity models\nfor granular assembles) to examine the accuracy and robustness of the proposed\ncausal discovery method for the common material law predictions in civil\nengineering applications.\n"}
+{"abstract": "  Super-resolution (SR) methods have seen significant advances thanks to the\ndevelopment of convolutional neural networks (CNNs). CNNs have been\nsuccessfully employed to improve the quality of endomicroscopy imaging. Yet,\nthe inherent limitation of research on SR in endomicroscopy remains the lack of\nground truth high-resolution (HR) images, commonly used for both supervised\ntraining and reference-based image quality assessment (IQA). Therefore,\nalternative methods, such as unsupervised SR are being explored. To address the\nneed for non-reference image quality improvement, we designed a novel zero-shot\nsuper-resolution (ZSSR) approach that relies only on the endomicroscopy data to\nbe processed in a self-supervised manner without the need for ground-truth HR\nimages. We tailored the proposed pipeline to the idiosyncrasies of\nendomicroscopy by introducing both: a physically-motivated Voronoi downscaling\nkernel accounting for the endomicroscope's irregular fibre-based sampling\npattern, and realistic noise patterns. We also took advantage of video\nsequences to exploit a sequence of images for self-supervised zero-shot image\nquality improvement. We run ablation studies to assess our contribution in\nregards to the downscaling kernel and noise simulation. We validate our\nmethodology on both synthetic and original data. Synthetic experiments were\nassessed with reference-based IQA, while our results for original images were\nevaluated in a user study conducted with both expert and non-expert observers.\nThe results demonstrated superior performance in image quality of ZSSR\nreconstructions in comparison to the baseline method. The ZSSR is also\ncompetitive when compared to supervised single-image SR, especially being the\npreferred reconstruction technique by experts.\n"}
+{"abstract": "  We present a modified version of the Judd-Ofelt theory, which describes the\nintensities of f-f transitions by trivalent lanthanide ions (Ln$^{3+}$) in\nsolids. In our model, the properties of the dopant are calculated with\nwell-established atomic-structure techniques, while the influence of the\ncrystal-field potential is described by three adjustable parameters. By\napplying our model to europium (Eu$^{3+}$), well-known to challenge the\nstandard Judd-Ofelt theory, we are able to give a physical insight into all the\ntransitions within the ground electronic configuration, and also to reproduce\nquantitatively experimental absorption oscillator strengths. Our model opens\nthe possibility to interpret polarized-light transitions between individual\nlevels of the ion-crystal system.\n"}
+{"abstract": "  The interfacial structures and interactions of two-dimensional (2D) materials\non solid substrates are of fundamental importance for the fabrication and\napplication of 2D materials. However, selection of a suitable solid substrate\nto grow 2D material, determination and control of the 2D material-substrate\ninterface remain a big challenge due to the large diversity of possible\nconfigurations. Here, we propose a computational framework to select an\nappropriate substrate for epitaxial growth of 2D material and to predict\npossible 2D material-substrate interface structures and orientations using\ndensity functional theory calculations performed for all non-equivalent atomic\nstructures satisfying the symmetry constraints. The approach was validated by\nthe correct prediction of three experimentally reported 2D material-substrate\ninterface systems with only the given information of two parent materials.\nSeveral possible interface configurations are also proposed based on this\napproach. We therefore construct a database that contains these interface\nsystems and has been continuously expanding. This database serves as\npreliminary guidance for epitaxial growth and stabilization of new materials in\nexperiments.\n"}
+{"abstract": "  We study the sensitivity of the Fermilab Short-Baseline Neutrino (SBN)\nexperiments, MicroBooNE, ICARUS, and SBND, to MeV- to GeV-scale inelastic dark\nmatter interacting through a dark photon mediator. These models provide\ninteresting scenarios of light thermal dark matter, which, while challenging to\nprobe with direct and indirect detection experiments, are amenable to\naccelerator-based searches. We consider production of the dark sector states\nwith both the Fermilab Booster 8 GeV and NuMI 120 GeV proton beams and study\nthe signatures of scattering and decay of the heavy excited dark state in the\nSBN detectors. These distinct signatures probe complementary regions of\nparameter space. All three experiments will be able to cover new ground, with\nan excellent near-term opportunity to search for cosmologically motivated\ntargets explaining the observed dark matter abundance.\n"}
+{"abstract": "  Modern logics of dependence and independence are based on team semantics,\nwhich means that formulae are evaluated not on a single assignment of values to\nvariables, but on a set of such assignments, called a team. This leads to high\nexpressive power, on the level of existential second-order logic. As an\nalternative, Baltag and van Benthem have proposed a local variant of dependence\nlogic, called logic of functional dependence (LFD). While its semantics is also\nbased on a team, the formulae are evaluated locally on just one of its\nassignments, and the team just serves as the supply of the possible assignments\nthat are taken into account in the evaluation process. This logic thus relies\non the modal perspective of generalized assignments semantics, and can be seen\nas a fragment of first-order logic. For the variant of LFD without equality,\nthe satisfiability problem is decidable.\n  We extend the idea of localising logics of dependence and independence in a\nsystematic way, taking into account local variants of standard atomic\ndependency properties: besides dependence and independence, also inclusion,\nexclusion, and anonymity. We study model-theoretic and algorithmic questions of\nthe localised logics, and also resolve some of the questions that had been left\nopen by Baltag and van Benthem. In particular, we study decidability issues of\nthe local logics, and prove that satisfiability of LFD with equality is\nundecidable. Further, we establish characterisation theorems via appropriate\nnotions of bisimulation and study the complexity of model checking problems for\nthese logics.\n"}
+{"abstract": "  Liouville closed $H$-fields are ordered differential fields whose ordering\nand derivation interact in a natural way and where every linear differential\nequation of order $1$ has a nontrivial solution. (The introduction gives a\nprecise definition.) For a Liouville closed $H$-field $K$ with small derivation\nwe show: $K$ has the Intermediate Value Property for differential polynomials\niff $K$ is elementarily equivalent to the ordered differential field of\ntransseries. We also indicate how this applies to Hardy fields.\n"}
+{"abstract": "  Spectral CT offers enhanced material discrimination over single-energy\nsystems and enables quantitative estimation of basis material density images.\nWater/iodine decomposition in contrast-enhanced CT is one of the most\nwidespread applications of this technology in the clinic. However, low\nconcentrations of iodine can be difficult to estimate accurately, limiting\npotential clinical applications and/or raising injected contrast agent\nrequirements. We seek high-sensitivity spectral CT system designs which\nminimize noise in water/iodine density estimates. In this work, we present a\nmodel-driven framework for spectral CT system design optimization to maximize\nmaterial separability. We apply this tool to optimize the sensitivity spectra\non a spectral CT test bench using a hybrid design which combines source kVp\ncontrol and k-edge filtration. Following design optimization, we scanned a\nwater/iodine phantom with the hybrid spectral CT system and performed\ndose-normalized comparisons to two single-technique designs which use only kVp\ncontrol or only kedge filtration. The material decomposition results show that\nthe hybrid system reduces both standard deviation and crossmaterial noise\ncorrelations compared to the designs where the constituent technologies are\nused individually.\n"}
+{"abstract": "  The ability to detect and segment moving objects in a scene is essential for\nbuilding consistent maps, making future state predictions, avoiding collisions,\nand planning. In this paper, we address the problem of moving object\nsegmentation from 3D LiDAR scans. We propose a novel approach that pushes the\ncurrent state of the art in LiDAR-only moving object segmentation forward to\nprovide relevant information for autonomous robots and other vehicles. Instead\nof segmenting the point cloud semantically, i.e., predicting the semantic\nclasses such as vehicles, pedestrians, roads, etc., our approach accurately\nsegments the scene into moving and static objects, i.e., also distinguishing\nbetween moving cars vs. parked cars. Our proposed approach exploits sequential\nrange images from a rotating 3D LiDAR sensor as an intermediate representation\ncombined with a convolutional neural network and runs faster than the frame\nrate of the sensor. We compare our approach to several other state-of-the-art\nmethods showing superior segmentation quality in urban environments.\nAdditionally, we created a new benchmark for LiDAR-based moving object\nsegmentation based on SemanticKITTI. We published it to allow other researchers\nto compare their approaches transparently and we furthermore published our\ncode.\n"}
+{"abstract": "  When applying machine learning in safety-critical systems, a reliable\nassessment of the uncertainy of a classifier is required. However, deep neural\nnetworks are known to produce highly overconfident predictions on\nout-of-distribution (OOD) data and even if trained to be non-confident on OOD\ndata one can still adversarially manipulate OOD data so that the classifer\nagain assigns high confidence to the manipulated samples. In this paper we\npropose a novel method where from first principles we combine a certifiable OOD\ndetector with a standard classifier into an OOD aware classifier. In this way\nwe achieve the best of two worlds: certifiably adversarially robust OOD\ndetection, even for OOD samples close to the in-distribution, without loss in\nprediction accuracy and close to state-of-the-art OOD detection performance for\nnon-manipulated OOD data. Moreover, due to the particular construction our\nclassifier provably avoids the asymptotic overconfidence problem of standard\nneural networks.\n"}
+{"abstract": "  In the cases where there is no Sobolev-type or Gagliardo-Nirenberg-type\nfractional estimate involving $\\lvert u\\rvert_{W^{s,p}}$, we establish\nalternative estimates where the strong $L^p$ norms are replaced by Lorentz\nnorms.\n"}
+{"abstract": "  Large language models have led to remarkable progress on many NLP tasks, and\nresearchers are turning to ever-larger text corpora to train them. Some of the\nlargest corpora available are made by scraping significant portions of the\ninternet, and are frequently introduced with only minimal documentation. In\nthis work we provide some of the first documentation for the Colossal Clean\nCrawled Corpus (C4; Raffel et al., 2020), a dataset created by applying a set\nof filters to a single snapshot of Common Crawl. We begin by investigating\nwhere the data came from, and find a significant amount of text from unexpected\nsources like patents and US military websites. Then we explore the content of\nthe text itself, and find machine-generated text (e.g., from machine\ntranslation systems) and evaluation examples from other benchmark NLP datasets.\nTo understand the impact of the filters applied to create this dataset, we\nevaluate the text that was removed, and show that blocklist filtering\ndisproportionately removes text from and about minority individuals. Finally,\nwe conclude with some recommendations for how to created and document web-scale\ndatasets from a scrape of the internet.\n"}
+{"abstract": "  In the Gaia era, the membership analysis and parameter determination of open\nclusters (OCs) are more accurate. We performed a census of OC's classical\nCepheids based on Gaia Early Data Release 3 (EDR3) and obtained a sample of 33\nOC Cepheids fulfilling the constraints of the spatial position, proper motion,\nparallax and evolution state. 13 of 33 OC Cepheids are newly discovered. Among\nthem, CM Sct is the first first-crossing Cepheids with direct evidence of\nevolution. DP Vel is likely a fourth- or fifth-crossing Cepheids. Based on\nindependent distances from OCs, W_1-band period-luminosity relation of Cepheids\nis determined with a 3.5% accuracy: <MW1> = -(3.274 +- 0.090) log P - (-2.567\n+- 0.080). The Gaia-band period-Wesenheit relation agrees well with Ripepi et\nal. (2019). A direct period-age relation for fundamental Cepheids are also\ndetermined based on OC's age, that is log t = -(0.638 +- 0.063) log P + (8.569\n+- 0.057).\n"}
+{"abstract": "  We propose an ultracold-atom setting where a fermionic superfluidity with\nattractive s-wave interaction is uploaded in a non-Hermitian Lieb optical\nlattice. The existence of a real-energy flat band solution is revealed. We show\nthat the interplay between the skin effect and flat-band localization leads to\nexotic localization properties. We develop a multiband mean-field description\nof this system and use both order parameters and superfluid weight to describe\nthe phase transition. A relation between the superfluid weight and\nnon-Hermitian quantum metric of the quantum states manifold is built. We find\nnon-monotone criticality depending on the non-Hermiticity, and the\nnon-reciprocity prominently enhances the phase coherence of the pairing field,\nsuggesting ubiquitous critical behavior of the non-Hermitian fermionic\nsuperfluidity.\n"}
+{"abstract": "  We study the large time behaviour of the reaction-diffsuion equation\n$\\partial_t u=\\Delta u +f(u)$ in spatial dimension $N$, when the nonlinear term\nis bistable and the initial datum is compactly supported. We prove the\nexistence of a Lipschitz function $s^\\infty$ of the unit sphere, such that\n$u(t,x)$ converges uniformly in $\\mathbb{R}^N$, as $t$ goes to infinity, to\n  $U_{c_*}\\bigg(|x|-c_*t + \\frac{N-1}{c_*} \\mathrm{ln}t +\ns^\\infty\\Big(\\frac{x}{|x|}\\Big)\\bigg)$, where $U_{c*}$ is the unique 1D\ntravelling profile. This extends earlier results that identified the locations\nof the level sets of the solutions with $o_{t\\to+\\infty}(t)$ precision, or\nidentified precisely the level sets locations for almost radial initial data.\n"}
+{"abstract": "  This document provides recommendations to the Government of Canada and the\nCanadian Space Agency in response to their call for feedback on the future of\nCanadian space exploration. The report focuses on how the construction and\nlong-term placement of mega-constellations of satellites into Earth orbit will\naffect astronomy and the view of the night sky by all peoples, with attention\nto all Canadians. The broader discussion highlights several environmental\nconcerns associated with the construction and maintenance of these\nmega-constellations. The eight recommendations here address ways that Canada\ncan play a role in mitigating some of these negative effects through national\nand international initiatives. In drafting the recommendations, we take the\napproach that space needs to be developed sustainably. In this regard, we use\nthe Brundtland Report's definition: \"Sustainable development is the development\nthat meets the needs of the present without compromising the ability of future\ngenerations to meet their own needs.\" Thus, all recommendations here are made\nwith the intent of minimizing the negative consequences of mega-constellations,\nwhile also recognizing that their development will continue.\n"}
+{"abstract": "  We study the online stochastic matching problem. Consider a bipartite graph\nwith offline vertices on one side, and with i.i.d.online vertices on the other\nside. The offline vertices and the distribution of online vertices are known to\nthe algorithm beforehand. The realization of the online vertices, however, is\nrevealed one at a time, upon which the algorithm immediately decides how to\nmatch it. For maximizing the cardinality of the matching, we give a\n$0.711$-competitive online algorithm, which improves the best previous ratio of\n$0.706$. When the offline vertices are weighted, we introduce a\n$0.7009$-competitive online algorithm for maximizing the total weight of the\nmatched offline vertices, which improves the best previous ratio of $0.662$.\n  Conceptually, we find that the analysis of online algorithms simplifies if\nthe online vertices follow a Poisson process, and establish an approximate\nequivalence between this Poisson arrival model and online stochstic matching.\nTechnically, we propose a natural linear program for the Poisson arrival model,\nand demonstrate how to exploit its structure by introducing a converse of\nJensen's inequality. Moreover, we design an algorithmic amortization to replace\nthe analytic one in previous work, and as a result get the first\nvertex-weighted online stochastic matching algorithm that improves the results\nin the weaker random arrival model.\n"}
+{"abstract": "  Growing advancements in reinforcement learning has led to advancements in\ncontrol theory. Reinforcement learning has effectively solved the inverted\npendulum problem and more recently the double inverted pendulum problem. In\nreinforcement learning, our agents learn by interacting with the control system\nwith the goal of maximizing rewards. In this paper, we explore three such\nreward functions in the cart position problem. This paper concludes that a\ndiscontinuous reward function that gives non-zero rewards to agents only if\nthey are within a given distance from the desired position gives the best\nresults.\n"}
+{"abstract": "  We propose a data-driven method for synthesizing a static analyzer to detect\nside-channel information leaks in cryptographic software. Compared to the\nconventional way of manually crafting such a static analyzer, which can be\nlabor intensive, error prone and suboptimal, our learning-based technique is\nnot only automated but also provably sound. Our analyzer consists of a set of\ntype-inference rules learned from the training data, i.e., example code\nsnippets annotated with ground truth. Internally, we use syntax-guided\nsynthesis (SyGuS) to generate new features and decision tree learning (DTL) to\ngenerate type-inference rules based on these features. We guarantee soundness\nby formally proving each learned rule via a technique called Datalog query\ncontainment checking. We have implemented our technique in the LLVM compiler\nand used it to detect power side channels in C programs. Our results show that,\nin addition to being automated and provably sound during synthesis, the learned\nanalyzer also has the same empirical accuracy as two state-of-the-art, manually\ncrafted analyzers while being 300X and 900X faster, respectively.\n"}
+{"abstract": "  In combinatorics on words, a word $w$ over an alphabet $\\Sigma$ is said to\navoid a pattern $p$ over an alphabet $\\Delta$ if there is no factor $f$ of $w$\nsuch that $f=h(p)$ where $h:\\Delta^*\\to\\Sigma^*$ is a non-erasing morphism. A\npattern $p$ is said to be $k$-avoidable if there exists an infinite word over a\n$k$-letter alphabet that avoids $p$. A pattern is \\emph{doubled} if every\nvariable occurs at least twice. Doubled patterns are known to be $3$-avoidable.\nCurrie, Mol, and Rampersad have considered a generalized notion which allows\nvariable occurrences to be reversed. That is, $h(V^R)$ is the mirror image of\n$h(V)$ for every $V\\in\\Delta$. We show that doubled patterns with reversal are\n$3$-avoidable. We also show that for every doubled pattern $p$, the growth rate\nof ternary words avoiding $p$ is at least the growth rate of ternary\nsquare-free words. A previous version of this paper containing only the first\nresult has been presented at WORDS 2021.\n"}
+{"abstract": "  In humans and animals, curriculum learning -- presenting data in a curated\norder - is critical to rapid learning and effective pedagogy. Yet in machine\nlearning, curricula are not widely used and empirically often yield only\nmoderate benefits. This stark difference in the importance of curriculum raises\na fundamental theoretical question: when and why does curriculum learning help?\n  In this work, we analyse a prototypical neural network model of curriculum\nlearning in the high-dimensional limit, employing statistical physics methods.\nCurricula could in principle change both the learning speed and asymptotic\nperformance of a model. To study the former, we provide an exact description of\nthe online learning setting, confirming the long-standing experimental\nobservation that curricula can modestly speed up learning. To study the latter,\nwe derive performance in a batch learning setting, in which a network trains to\nconvergence in successive phases of learning on dataset slices of varying\ndifficulty. With standard training losses, curriculum does not provide\ngeneralisation benefit, in line with empirical observations. However, we show\nthat by connecting different learning phases through simple Gaussian priors,\ncurriculum can yield a large improvement in test performance. Taken together,\nour reduced analytical descriptions help reconcile apparently conflicting\nempirical results and trace regimes where curriculum learning yields the\nlargest gains. More broadly, our results suggest that fully exploiting a\ncurriculum may require explicit changes to the loss function at curriculum\nboundaries.\n"}
+{"abstract": "  This paper proposes a novel online measurement-based Wide-Area Voltage\nControl (WAVC) method using Phasor Measurement Unit (PMU) data in power systems\nwith Flexible AC Transmission System (FACTS) devices. As opposed to previous\nWAVC methods, the proposed WAVC does not require any model knowledge or the\nparticipation of all buses and considers both active and reactive power\nperturbations. Specifically, the proposed WAVC method exploits the regression\ntheorem of the Ornstein-Uhlenbeck process to estimate the sensitivity matrices\nthrough PMU data online, which are further used to design and apply the voltage\nregulation by updating the reference points of FACTS devices. Numerical results\non the IEEE 39- Bus and IEEE 68-Bus systems demonstrate that the proposed\nmodel-free WAVC can provide effective voltage control in various network\ntopologies, different combinations of voltage-controlled and\nvoltage-uncontrolled buses, under measurement noise, and in case of missing\nPMUs. Particularly, the proposed WAVC algorithm may outperform the model-based\nWAVC when an undetected topology change happens.\n"}
+{"abstract": "  We propose a method of Category-level 6D Object Pose and Size Estimation\n(COPSE) from a single depth image, without external pose-annotated real-world\ntraining data. While previous works exploit visual cues in RGB(D) images, our\nmethod makes inferences based on the rich geometric information of the object\nin the depth channel alone. Essentially, our framework explores such geometric\ninformation by learning the unified 3D Orientation-Consistent Representations\n(3D-OCR) module, and further enforced by the property of Geometry-constrained\nReflection Symmetry (GeoReS) module. The magnitude information of object size\nand the center point is finally estimated by Mirror-Paired Dimensional\nEstimation (MPDE) module. Extensive experiments on the category-level NOCS\nbenchmark demonstrate that our framework competes with state-of-the-art\napproaches that require labeled real-world images. We also deploy our approach\nto a physical Baxter robot to perform manipulation tasks on unseen but\ncategory-known instances, and the results further validate the efficacy of our\nproposed model. Our videos are available in the supplementary material.\n"}
+{"abstract": "  The use of natural language interfaces in the field of human-computer\ninteraction is undergoing intense study through dedicated scientific and\nindustrial research. The latest contributions in the field, including deep\nlearning approaches like recurrent neural networks, the potential of\ncontext-aware strategies and user-centred design approaches, have brought back\nthe attention of the community to software-based dialogue systems, generally\nknown as conversational agents or chatbots. Nonetheless, and given the novelty\nof the field, a generic, context-independent overview on the current state of\nresearch of conversational agents covering all research perspectives involved\nis missing. Motivated by this context, this paper reports a survey of the\ncurrent state of research of conversational agents through a systematic\nliterature review of secondary studies. The conducted research is designed to\ndevelop an exhaustive perspective through a clear presentation of the\naggregated knowledge published by recent literature within a variety of\ndomains, research focuses and contexts. As a result, this research proposes a\nholistic taxonomy of the different dimensions involved in the conversational\nagents' field, which is expected to help researchers and to lay the groundwork\nfor future research in the field of natural language interfaces.\n"}
+{"abstract": "  We study the large $N$ matrix model for the index of 4d $\\mathcal{N}=4$\nYang-Mills theory and its truncations to understand the dual AdS$_5$ black\nholes. Numerical studies of the truncated models provide insights on the black\nhole physics, some of which we investigate analytically with the full\nYang-Mills matrix model. In particular, we find many branches of saddle points\nwhich describe the known black hole solutions. We analytically construct the\nsaddle points dual to the small black holes whose sizes are much smaller than\nthe AdS radius. They include the asymptotically flat BMPV black holes embedded\nin large AdS with novel thermodynamic instabilities.\n"}
+{"abstract": "  In this paper, we propose a Thompson Sampling algorithm for \\emph{unimodal}\nbandits, where the expected reward is unimodal over the partially ordered arms.\nTo exploit the unimodal structure better, at each step, instead of exploration\nfrom the entire decision space, our algorithm makes decision according to\nposterior distribution only in the neighborhood of the arm that has the highest\nempirical mean estimate. We theoretically prove that, for Bernoulli rewards,\nthe regret of our algorithm reaches the lower bound of unimodal bandits, thus\nit is asymptotically optimal. For Gaussian rewards, the regret of our algorithm\nis $\\mathcal{O}(\\log T)$, which is far better than standard Thompson Sampling\nalgorithms. Extensive experiments demonstrate the effectiveness of the proposed\nalgorithm on both synthetic data sets and the real-world applications.\n"}
+{"abstract": "  Designing flat sheets that can be made to deform into 3D shapes is an area of\nintense research with applications in micromachines, soft robotics, and medical\nimplants. Thus far, such sheets were designed to adopt a single target shape.\nHere, we show that through anisotropic deformation applied inhomogenously\nthroughout a sheet, it is possible to design a single sheet that can deform\ninto multiple surface geometries upon different actuations. The key to our\napproach is development of an analytical method for solving this multi-valued\ninverse problem. Such sheets open the door to fabricating machines that can\nperform complex tasks through cyclic transitions between multiple shapes. As a\nproof of concept we design a simple swimmer capable of moving through a fluid\nat low Reynolds numbers.\n"}
+{"abstract": "  High throughput sequencing (HTS)-based technology enables identifying and\nquantifying non-culturable microbial organisms in all environments. Microbial\nsequences have enhanced our understanding of the human microbiome, the soil and\nplant environment, and the marine environment. All molecular microbial data\npose statistical challenges due to contamination sequences from reagents, batch\neffects, unequal sampling, and undetected taxa. Technical biases and\nheteroscedasticity have the strongest effects, but different strains across\nsubjects and environments also make direct differential abundance testing\nunwieldy. We provide an introduction to a few statistical tools that can\novercome some of these difficulties and demonstrate those tools on an example.\nWe show how standard statistical methods, such as simple hierarchical mixture\nand topic models, can facilitate inferences on latent microbial communities. We\nalso review some nonparametric Bayesian approaches that combine visualization\nand uncertainty quantification. The intersection of molecular microbial biology\nand statistics is an exciting new venue. Finally, we list some of the important\nopen problems that would benefit from more careful statistical method\ndevelopment.\n"}
+{"abstract": "  Dimensions was introduced as an alternative bibliometric database to the\nwell-established Web of Science (WoS) and Scopus, however all three databases\nhave fundamental differences in coverage and content, resultant from their\nowners' indexation philosophies. In light of these differences, we explore\nhere, using a citation network analysis and assessment of normalised citation\nimpact of \"duplicate\" publications, whether the three databases offer\nstructurally different perspectives of the bibliometric landscape or if they\nare essentially homogenous substitutes. Our citation network analysis of core\nand exclusive 2016-2018 publications revealed a large set of core publications\nindexed in all three databases that are highly self-referential. In comparison,\neach database selected a set of exclusive publications that appeared to hold\nsimilarly low levels of relevance to the core set and to one another, with\nslightly more internal communication between exclusive publications in Scopus\nand Dimensions than WoS. Our comparison of normalised citations for 41,848\npublications indexed in all three databases found that German sectors were\nvaluated as more impactful in Scopus and Dimensions compared to WoS,\nparticularly for sectors with an applied research focus. We conclude that the\ndatabases do present structurally different perspectives, although Scopus and\nDimensions with their additional circle of applied research vary more from the\nmore base research-focused WoS than they do from one another.\n"}
+{"abstract": "  We consider the $B^0_s \\to \\mu^{+} \\mu^{-} \\gamma$ effective lifetime, and\nthe related CP-phase sensitive quantity $A_{\\Delta\\Gamma_s}^{\\mu \\mu \\gamma}$,\nas a way to obtain qualitatively new insights on the current $B$-decay\ndiscrepancies. Through a fit comparing pre- to post-Moriond-2021 data we\nidentify a few theory benchmark scenarios addressing these discrepancies, and\nfeaturing large CP violation in addition. We then explore the possibility of\ntelling apart these scenarios with $A_{\\Delta\\Gamma_s}^{\\mu \\mu \\gamma}$, once\nresonance-modeling and form-factor uncertainties are taken into account. We do\nso in both regions of low and high invariant di-lepton mass-squared $q^2$. For\nlow $q^2$, we show how to shape the integration range in order to reduce the\nimpact of the $\\phi$-resonance modelling on the $A_{\\Delta\\Gamma_s}^{\\mu \\mu\n\\gamma}$ prediction. For high $q^2$, we find that the corresponding pollution\nfrom broad-charmonium resonances has a surprisingly small effect on\n$A_{\\Delta\\Gamma_s}^{\\mu \\mu \\gamma}$. This is due to a number of\ncancellations, that can be traced back to the complete dominance of\nsemi-leptonic operator contributions for high $q^2$ -- at variance with low\n$q^2$ -- and to $A_{\\Delta\\Gamma_s}^{\\mu \\mu \\gamma}$ behaving like a\nratio-of-amplitudes observable. Our study suggests that\n$A_{\\Delta\\Gamma_s}^{\\mu \\mu \\gamma}$ is -- especially at high $q^2$ -- a\npotentially valuable probe of short-distance CP-violating effects in the very\nsame Wilson coefficients that are associated to current $b \\to s$\ndiscrepancies. Its discriminating power, however, relies on progress in\nform-factor uncertainties. Interestingly, high $q^2$ is the region where $B^0_s\n\\to \\mu^{+} \\mu^{-} \\gamma$ is already being accessed experimentally, and the\nregion where form factors are more accessible through non-perturbative QCD\nmethods.\n"}
+{"abstract": "  The electromagnetic process $e^{+}e^{-}\\to p\\bar{p}$ is studied with the\ninitial-state-radiation technique using 7.5 fb$^{-1}$ of data collected by the\nBESIII experiment at seven energy points from 3.773 to 4.600 GeV. The Born\ncross section and the effective form factor of the proton are measured from the\nproduction threshold to 3.0 GeV/$c^{2}$ using the $p\\bar{p}$ invariant-mass\nspectrum. The ratio of electric and magnetic form factors of the proton is\ndetermined from the analysis of the proton-helicity angular distribution.\n"}
+{"abstract": "  Past and current research has typically focused on ensuring that search\ntechnology for the classroom serves children. In this paper, we argue for the\nneed to broaden the research focus to include teachers and how search\ntechnology can aid them. In particular, we share how furnishing a\nbehind-the-scenes portal for teachers can empower them by providing a window\ninto the spelling, writing, and concept connection skills of their students.\n"}
+{"abstract": "  In this paper, we argue that modern pre-integration methods for inertial\nmeasurement units (IMUs) are accurate enough to ignore the drift for short time\nintervals. This allows us to consider a simplified camera model, which in turn\nadmits further intrinsic calibration. We develop the first-ever solver to\njointly solve the relative pose problem with unknown and equal focal length and\nradial distortion profile while utilizing the IMU data. Furthermore, we show\nsignificant speed-up compared to state-of-the-art algorithms, with small or\nnegligible loss in accuracy for partially calibrated setups. The proposed\nalgorithms are tested on both synthetic and real data, where the latter is\nfocused on navigation using unmanned aerial vehicles (UAVs). We evaluate the\nproposed solvers on different commercially available low-cost UAVs, and\ndemonstrate that the novel assumption on IMU drift is feasible in real-life\napplications. The extended intrinsic auto-calibration enables us to use\ndistorted input images, making tedious calibration processes obsolete, compared\nto current state-of-the-art methods.\n"}
+{"abstract": "  Routine ultraviolet imaging of the Sun's upper atmosphere shows the\nspectacular manifestation of solar activity; yet we remain blind to its main\ndriver, the magnetic field. Here we report unprecedented spectropolarimetric\nobservations of an active region plage and its surrounding enhanced network,\nshowing circular polarization in ultraviolet (Mg II $h$ & $k$ and Mn I) and\nvisible (Fe I) lines. We infer the longitudinal magnetic field from the\nphotosphere to the very upper chromosphere. At the top of the plage\nchromosphere the field strengths reach more than 300 gauss, strongly correlated\nwith the Mg II $k$ line core intensity and the electron pressure. This unique\nmapping shows how the magnetic field couples the different atmospheric layers\nand reveals the magnetic origin of the heating in the plage chromosphere.\n"}
+{"abstract": "  We present a theoretical investigation of electron states hosted by magnetic\ndomain walls on the 3D topological insulator surface. The consideration\nincludes the domain walls with distinct vectorial and spatial textures. The\nstudy is carried out on the basis of the Hamiltonian for quasi-relativistic\nfermions by using a continual approach and tight-binding calculations. We\nderive the spectral characteristics and spatial localization of the the\none-dimensional low-energy states appearing at the domain walls. The antiphase\ndomain walls are shown to generate the topologically protected chiral states\nwith linear dispersion, the group velocity and spin-polarization direction of\nwhich depend on an easy axis orientation. In the case of an easy plane\nanisotropy, we predict a realization of a dispersionless state, flat band in\nthe energy spectrum, that is spin-polarized along the surface normal.\nModification of the surface states in the multi-domain case, which is\napproximated by a periodic set of domain walls, is described as well. We find\nthat the magnetic domain walls with complex internal texture, such as\nN\\'eel-like or Bloch-like walls, also host the topological states, although\ntheir spectrum and spin structure can be changed compared with the sharp wall\ncase.\n"}
+{"abstract": "  We present the discovery of J22564-5910, a new type of hot subdwarf (sdB)\nwhich shows evidence of gas present in the system and has shallow, multi-peaked\nhydrogen and helium lines which vary in shape over time. All observational\nevidence points towards J22564-5910 being observed very shortly after the\nmerger phase that formed it. Using high-resolution, high signal-to-noise\nspectroscopy, combined with multi-band photometry, Gaia astrometry, and TESS\nlight curves, we aim to interpret these unusual spectral features. The\nphotometry, spectra and light curves are all analyzed, and their results are\ncombined in order to support our interpretation of the observations: the likely\npresence of a magnetic field combined with gas features around the sdB. Based\non the triple-peaked H lines, the magnetic field strength is estimated and, by\nusing the shellspec code, qualitative models of gas configurations are fitted\nto the observations. All observations can either be explained by a magnetic\nfield of ~650 kG which enables the formation of a centrifugal magnetosphere, or\na non-magnetic hot subdwarf surrounded by a circumstellar gas disk/torus. Both\nscenarios are not mutually exclusive and both can be explained by a recent\nmerger. J22564-5910 is the first object of its kind. It is a rapidly spinning\nsdB with gas still present in the system. It is the first post-merger star\nobserved this early after the merger event, and as such is very valuable system\nto test merger theories. If the magnetic field can be confirmed, it is not only\nthe first magnetic sdB, but it hosts the strongest magnetic field ever found in\na pre-white dwarf object. Thus, it could represent the long-sought for\nimmediate ancestor of strongly magnetic WDs.\n"}
+{"abstract": "  In this paper we study the role of planarity in generalized scattering\namplitudes, through several closely interacting structures in combinatorics,\nalgebraic and tropical geometry.\n  The generalized biadjoint scalar amplitude, introduced recently by\nCachazo-Early-Guevara-Mizera (CEGM), is a rational function of homogeneous\ndegree $-(k-1)(n-k-1)$ in $\\binom{n}{k}-n$ independent variables; its poles can\nbe constructed directly from the rays of the positive tropical Grassmannian.\n  We introduce for each pair of integers $(k,n)$ with $2\\le k\\le n-2$ a system\nof generalized positive roots which arises as a specialization of the planar\nbasis of kinematic invariants. We prove that the higher root polytope\n$\\mathcal{R}^{(k)}_{n-k}$ has volume the k-dimensional Catalan number\n$C^{(k)}_{n-k}$, via a flag unimodular triangulation into simplices, in\nbijection with noncrossing collections of $k$-element subsets. We also give a\nbijection between certain positroidal subdivisions, called tripods, of the\nhypersimplex $\\Delta_{3,n}$ and noncrossing pairs of 3-element subsets that are\nnot weakly separated.\n  We show that the facets of the Planar Kinematics (PK) polytope, introduced\nrecently by Cachazo and the author, are exactly the $\\binom{n}{k}-n$\ngeneralized positive roots. We show that the PK specialization of the\ngeneralized biadjoint amplitude evaluates to $C^{(k)}_{n-k}$.\n  Looking forward, we give defining equations and conjecture explicit solutions\nusing $(\\mathbb{CP}^{n-k-1})^{\\times (k-1)}$ via a notion of compatibility\ndegree for noncrossing collections, for a two parameter family of generalized\nworldsheet associahedra $\\mathcal{W}^+_{k,n}$. These specialize when $k=2$ to a\ncertain dihedrally invariant partial compactification of the configuration\nspace $M_{0,n}$ of $n$ distinct points in $\\mathbb{CP}^{1}$. Many detailed\nexamples are given throughout to motivate future work.\n"}
+{"abstract": "  We study the stationary reflected Brownian motion in a non-convex wedge,\nwhich, compared to its convex analogue model, has been much rarely analyzed in\nthe probabilistic literature. We prove that its stationary distribution can be\nfound by solving a two dimensional vector boundary value problem (BVP) on a\nsingle curve for the associated Laplace transforms. The reduction to this kind\nof vector BVP seems to be new in the literature. As a matter of comparison, one\nsingle boundary condition is sufficient in the convex case. When the parameters\nof the model (drift, reflection angles and covariance matrix) are symmetric\nwith respect to the bisector line of the cone, the model is reducible to a\nstandard reflected Brownian motion in a convex cone. Finally, we construct a\none-parameter family of distributions, which surprisingly provides, for any\nwedge (convex or not), one particular example of stationary distribution of a\nreflected Brownian motion.\n"}
+{"abstract": "  Multicopter swarms with decentralized structure possess the nature of\nflexibility and robustness, while efficient spatial-temporal trajectory\nplanning still remains a challenge. This report introduces decentralized\nspatial-temporal trajectory planning, which puts a well-formed trajectory\nrepresentation named MINCO into multi-agent scenarios. Our method ensures\nhigh-quality local planning for each agent subject to any constraint from\neither the coordination of the swarm or safety requirements in cluttered\nenvironments. Then, the local trajectory generation is formulated as an\nunconstrained optimization problem that is efficiently solved in milliseconds.\nMoreover, a decentralized asynchronous mechanism is designed to trigger the\nlocal planning for each agent. A systematic solution is presented with detailed\ndescriptions of careful engineering considerations. Extensive benchmarks and\nindoor/outdoor experiments validate its wide applicability and high quality.\nOur software will be released for the reference of the community.\n"}
+{"abstract": "  We report on detailed in-situ distributed temperature measurements inside a\nhigh power fiber amplifier. The deducted thermal load and the TMI-threshold of\na commercial LMA fiber with 25 micron core and 400 micron cladding was measured\nat various seed wavelengths. By matching these results with detailed\nsimulations we show that photodarkening has a negligible impact on the thermal\nload and, therefore, on the TMI threshold in this fiber.\n"}
+{"abstract": "  Images captured under low-light conditions manifest poor visibility, lack\ncontrast and color vividness. Compared to conventional approaches, deep\nconvolutional neural networks (CNNs) perform well in enhancing images. However,\nbeing solely reliant on confined fixed primitives to model dependencies,\nexisting data-driven deep models do not exploit the contexts at various spatial\nscales to address low-light image enhancement. These contexts can be crucial\ntowards inferring several image enhancement tasks, e.g., local and global\ncontrast, brightness and color corrections; which requires cues from both local\nand global spatial extent. To this end, we introduce a context-aware deep\nnetwork for low-light image enhancement. First, it features a global context\nmodule that models spatial correlations to find complementary cues over full\nspatial domain. Second, it introduces a dense residual block that captures\nlocal context with a relatively large receptive field. We evaluate the proposed\napproach using three challenging datasets: MIT-Adobe FiveK, LoL, and SID. On\nall these datasets, our method performs favorably against the state-of-the-arts\nin terms of standard image fidelity metrics. In particular, compared to the\nbest performing method on the MIT-Adobe FiveK dataset, our algorithm improves\nPSNR from 23.04 dB to 24.45 dB.\n"}
+{"abstract": "  In this paper, a new randomized response technique aimed at protecting\nrespondents' privacy is proposed. It is designed for estimating the population\ntotal, or the population mean, of a quantitative characteristic. It provides\na~high degree of protection to the interviewed individuals, hence it may be\nfavorably perceived by them and increase their willingness to cooperate.\nInstead of revealing the true value of the characteristic under investigation,\na respondent only states whether the value is greater (or smaller) than\na~number which is selected by him/her at random, and is unknown to the\ninterviewer. For each respondent this number, a sort of individual threshold,\nis generated as a pseudorandom number from the uniform distribution. Further,\ntwo modifications of the proposed technique are presented. The first\nmodification assumes that the interviewer also knows the generated random\nnumber. The second modification deals with the issue that, for certain\nvariables, such as income, it may be embarrassing for the respondents to report\neither high or low values. Thus, depending on the value of the pseudorandom\nlower bound, the respondent is asked different questions to avoid being\nembarrassed. The suggested approach is applied in detail to the simple random\nsampling without replacement, but it can also be applied to many currently used\nsampling schemes, including cluster sampling, two-stage sampling, etc. Results\nof simulations illustrate the behavior of the proposed procedure.\n"}
+{"abstract": "  We present a slowly rotating generalization of a black hole modeled by a Solv\nhorizon geometry, in five dimensional General Relativity. A separable ansatz\ncompatible with Einstein equations is proposed, which is integrated in terms of\nhypergeometric and exponential functions. The solution highly simplifies after\nrequiring to maintain the same asymptotic behaviour as the non-rotating black\nhole and to have finite, non-vanishing charges. We compute the charges of the\nsolution and also comment on its importance for studying the phase space of the\ntheory in this sector.\n"}
+{"abstract": "  We present results for the electronic and magnetic structure of Mn and Fe\nclusters on Nb(110) surface, focusing on building blocks of atomic chains as\npossible realizations of topological superconductivity. The magnetic ground\nstates of the atomic dimers and most of the monatomic chains are determined by\nthe nearest-neighbor isotropic interaction. The dependence on the\ncrystallographic direction as well as on the atomic coordination number is\nanalyzed via an orbital decomposition of this isotropic interaction based on\nthe spin-cluster expansion and the difference in the local density of states\nbetween ferromagnetic and antiferromagnetic configurations. A spin-spiral\nground state is obtained for Fe chains along the [$1\\overline{1}0$] direction\nas a consequence of the frustration of the isotropic interactions. Here, a flat\nspin-spiral dispersion relation is identified, which can stabilize spin spirals\nwith various wave vectors together with the magnetic anisotropy. This may lead\nto the observation of spin spirals of different wave vectors and chiralities in\nlonger chains instead of a unique ground state.\n"}
+{"abstract": "  Highly symmetric networks can exhibit partly symmetry-broken states,\nincluding clusters and chimera states, i.e., states of coexisting synchronized\nand unsynchronized elements. We address the $\\mathbb{S}_4$ permutation symmetry\nof four globally coupled Stuart-Landau oscillators and uncover an\ninterconnected web of differently symmetric solutions. Among these are chaotic\n$2\\!-\\!1\\!-\\!1$ minimal chimeras that arise from $2\\!-\\!1\\!-\\!1$ periodic\nsolutions in a period-doubling cascade, as well as fully asymmetric chaotic\nstates arising similarly from periodic $1\\!-\\!1\\!-\\!1\\!-\\!1$ solutions. A\nbackbone of equivariant pitchfork bifurcations mediate between the two\ncascades, culminating in equivariant pitchforks of chaotic attractors.\n"}
+{"abstract": "  This paper presents a detailed quantitative evaluation of standardised\nDecentralised Congestion Control (DCC) and packet dropping mechanisms for\nCellular V2X (C-V2X). Based on the identified shortcomings, an Access layer DCC\nscheme, RRI adaptive, is then proposed. RRI adaptive accommodates the sidelink\nscheduling mechanism Sensing Based Semi-Persistent Scheduling (SB-SPS),\neliminating incompatibilities between current standards and the scheduling\nmechanism, to avoid unnecessary and reoccurring collisions. Two variants are\nproposed; one is an evolution of the ETSI Reactive DCC mechanism and the other\naligns with the 3GPP approach based on channel occupancy ratio (CR). Both\napproaches are compared with current ETSI and 3GPP standards and exhibit\nimproved performance. An evaluation of existing DCC standards and RRI Adaptive\nto meet the Quality of Service (QoS) requirements of vehicular cooperative\nawareness applications is also conducted.\n"}
+{"abstract": "  Extropy was introduced as a dual complement of the Shannon entropy. In this\ninvestigation, we consider failure extropy and its dynamic version. Various\nbasic properties of these measures are presented. It is shown that the dynamic\nfailure extropy characterizes the distribution function uniquely. We also\nconsider weighted versions of these measures. Several virtues of the weighted\nmeasures are explored. Finally, nonparametric estimators are introduced based\non the empirical distribution function.\n"}
+{"abstract": "  Context. Hot subdwarfs experienced strong mass loss on the Red Giant Branch\n(RGB) and are now hot and small He-burning objects. Aims. In this project we\naim to perform a transit survey in all available light curves of hot subdwarfs\nfrom space-based telescopes (Kepler, K2, TESS, and CHEOPS), with our\ncustom-made pipeline SHERLOCK, in order to determine the occurrence rate of\nplanets around these stars, as a function of orbital period and planetary\nradius. Methods. In this first paper, we perform injection-and-recovery tests\nof synthetic transits for a selection of representative Kepler, K2 and TESS\nlight curves, to determine which transiting bodies, in terms of object radius\nand orbital period, we will be able to detect with our tools. We also provide\nsuch estimates for CHEOPS data, which we analyze with the pycheops package.\nResults. Transiting objects with a radius $\\lesssim$ 1.0 $R_{\\Earth}$ can be\ndetected in most of Kepler, K2 and CHEOPS targets for the shortest orbital\nperiods (1 d and below), reaching values as small as $\\sim$0.3 $R_{\\Earth}$ in\nthe best cases. Reaching sub-Earth-sized bodies is achieved only for the\nbrightest TESS targets, and the ones observed during a significant number of\nsectors. We also give a series of representative results for farther and bigger\nplanets, for which the performances strongly depend on the target magnitude,\nthe length and the quality of the data. Conclusions. The TESS sample will\nprovide the most important statistics for the global aim of measuring the\nplanet occurrence rate around hot subdwarfs. The Kepler, K2 and CHEOPS data\nwill allow us to search for planetary remnants, i.e. very close and small\n(possibly disintegrating) objects, which would have partly survived the\nengulfment in their red giant host.\n"}
+{"abstract": "  We address the problem of inferring descriptions of system behavior using\nLinear Temporal Logic (LTL) from a finite set of positive and negative\nexamples. Most of the existing approaches for solving such a task rely on\npredefined templates for guiding the structure of the inferred formula. The\napproaches that can infer arbitrary LTL formulas, on the other hand, are not\nrobust to noise in the data. To alleviate such limitations, we devise two\nalgorithms for inferring concise LTL formulas even in the presence of noise.\nOur first algorithm infers minimal LTL formulas by reducing the inference\nproblem to a problem in maximum satisfiability and then using off-the-shelf\nMaxSAT solvers to find a solution. To the best of our knowledge, we are the\nfirst to incorporate the usage of MaxSAT solvers for inferring formulas in LTL.\nOur second learning algorithm relies on the first algorithm to derive a\ndecision tree over LTL formulas based on a decision tree learning algorithm. We\nhave implemented both our algorithms and verified that our algorithms are\nefficient in extracting concise LTL descriptions even in the presence of noise.\n"}
+{"abstract": "  We show that any surface of infinite type admits an ideal triangulation.\nFurthermore, we show that a set of disjoint arcs can be completed into a\ntriangulation if and only if, as a set, they intersect every simple closed\ncurve a finite number of times.\n"}
+{"abstract": "  We show that, for any fixed weight, there is a natural system of Hodge\nsheaves, whose Higgs field has no poles, arising from a flat projective family\nof varieties parametrized by a regular complex base scheme, extending the\nanalogous classical result for smooth projective families due to Griffiths. As\nan application, based on positivity of direct image sheaves, we establish a\ncriterion for base spaces of rational Gorenstein families to be of general\ntype. A key component of our arguments is centered around the construction of\nderived categorical objects generalizing relative logarithmic forms for smooth\nmaps and their functorial properties.\n"}
+{"abstract": "  The present paper shows how one might model Everettian quantum mechanics\nusing hyperfinitely many worlds. A hyperfinite model allows one to consider\nidealized measurements of observables with continuous-valued spectra where\ndifferent outcomes are associated with possibly infinitesimal probabilities.\nOne can also prove hyperfinite formulations of Everett's limiting\nrelative-frequency and randomness properties, theorems he considered central to\nhis formulation of quantum mechanics. This approach also provides a more\ngeneral framework in which to consider no-collapse formulations of quantum\nmechanics more generally.\n"}
+{"abstract": "  Light field (LF) cameras provide rich spatio-angular visual representations\nby sensing the visual scene from multiple perspectives and have recently\nemerged as a promising technology to boost the performance of human-machine\nsystems such as biometrics and affective computing. Despite the significant\nsuccess of LF representation for constrained facial image analysis, this\ntechnology has never been used for face and expression recognition in the wild.\nIn this context, this paper proposes a new deep face and expression recognition\nsolution, called CapsField, based on a convolutional neural network and an\nadditional capsule network that utilizes dynamic routing to learn hierarchical\nrelations between capsules. CapsField extracts the spatial features from facial\nimages and learns the angular part-whole relations for a selected set of 2D\nsub-aperture images rendered from each LF image. To analyze the performance of\nthe proposed solution in the wild, the first in the wild LF face dataset, along\nwith a new complementary constrained face dataset captured from the same\nsubjects recorded earlier have been captured and are made available. A subset\nof the in the wild dataset contains facial images with different expressions,\nannotated for usage in the context of face expression recognition tests. An\nextensive performance assessment study using the new datasets has been\nconducted for the proposed and relevant prior solutions, showing that the\nCapsField proposed solution achieves superior performance for both face and\nexpression recognition tasks when compared to the state-of-the-art.\n"}
+{"abstract": "  The development of large-scale quantum networks promises to bring a multitude\nof technological applications as well as shed light on foundational topics,\nsuch as quantum nonlocality. It is particularly interesting to consider\nscenarios where sources within the network are statistically independent, which\nleads to so-called network nonlocality, even when parties perform fixed\nmeasurements. Here we promote certain parties to be trusted and introduce the\nnotion of network steering and network local hidden state (NLHS) models within\nthis paradigm of independent sources. In one direction, we show how results\nfrom Bell nonlocality and quantum steering can be used to demonstrate network\nsteering. We further show that it is a genuinely novel effect, by exhibiting\nunsteerable states that nevertheless demonstrate network steering, based upon\nentanglement swapping, yielding a form of activation. On the other hand, we\nprovide no-go results for network steering in a large class of scenarios, by\nexplicitly constructing NLHS models.\n"}
+{"abstract": "  When observing task demonstrations, human apprentices are able to identify\nwhether a given task is executed correctly long before they gain expertise in\nactually performing that task. Prior research into learning from demonstrations\n(LfD) has failed to capture this notion of the acceptability of a task's\nexecution; meanwhile, temporal logics provide a flexible language for\nexpressing task specifications. Inspired by this, we present Bayesian\nspecification inference, a probabilistic model for inferring task specification\nas a temporal logic formula. We incorporate methods from probabilistic\nprogramming to define our priors, along with a domain-independent likelihood\nfunction to enable sampling-based inference. We demonstrate the efficacy of our\nmodel for inferring specifications, with over 90% similarity observed between\nthe inferred specification and the ground truth, both within a synthetic domain\nand during a real-world table setting task.\n"}
+{"abstract": "  Telescopers for a function are linear differential (resp. difference)\noperators annihilated by the definite integral (resp. definite sum) of this\nfunction. They play a key role in Wilf-Zeilberger theory and algorithms for\ncomputing them have been extensively studied in the past thirty years. In this\npaper, we introduce the notion of telescopers for differential forms with\n$D$-finite function coefficients. These telescopers appear in several areas of\nmathematics, for instance parametrized differential Galois theory and mirror\nsymmetry. We give a sufficient and necessary condition for the existence of\ntelescopers for a differential form and describe a method to compute them if\nthey exist. Algorithms for verifying this condition are also given.\n"}
+{"abstract": "  The Cabibbo-suppressed decay $\\Lambda_b^0\\rightarrow\\chi_{c1}p\\pi^-$ is\nobserved for the first time using data from proton-proton collisions\ncorresponding to an integrated luminosity of 6fb$^{-1}$, collected with the\nLHCb detector at a centre-of-mass energy of 13TeV. Evidence for the\n$\\Lambda_b^0\\rightarrow\\chi_{c2}p\\pi^-$ decay is also found. Using the\n$\\Lambda_b^0\\rightarrow\\chi_{c1}pK^-$ decay as normalisation channel, the\nratios of branching fractions are measured to be $$\\begin{array}{rcl} \\frac{\n\\mathcal{B} (\\Lambda_b^0\\rightarrow\\chi_{c1}p\\pi^-)}{\\mathcal{B}\n(\\Lambda_b^0\\rightarrow\\chi_{c1}pK^-)} & = & (6.59 \\pm 1.01 \\pm 0.22 ) \\times\n10^{-2} \\,,\n  \\frac{\\mathcal{B} (\\Lambda_b^0\\rightarrow\\chi_{c2}p\\pi^-)}{\\mathcal{B}\n(\\Lambda_b^0\\rightarrow\\chi_{c1}p\\pi^-)} & = & 0.95 \\pm 0.30 \\pm 0.04 \\pm 0.04\n\\,,\n  \\frac{\\mathcal{B} (\\Lambda_b^0\\rightarrow\\chi_{c2}pK^-)}{\\mathcal{B}\n(\\Lambda_b^0\\rightarrow\\chi_{c1}pK^-)} & = & 1.06 \\pm 0.05 \\pm 0.04 \\pm 0.04\n\\,,\\end{array}$$ where the first uncertainty is statistical, the second is\nsystematic and the third is due to the uncertainties in the branching fractions\nof $\\chi_{c1,2}\\rightarrow J/\\psi\\gamma$ decays.\n"}
+{"abstract": "  A novel mechanism of boosting dark matter by cosmic neutrinos is proposed.\nThe new mechanism is so significant that the arriving flux of dark matter in\nthe mass window $1~{\\rm keV} \\lesssim m_{\\rm DM} \\lesssim 1~{\\rm MeV}$ on Earth\ncan be enhanced by two to four orders of magnitude compared to one only by\ncosmic electrons. Thereby we firstly derive conservative but still stringent\nbounds and future sensitivity limits for such cosmic-neutrino-boosted dark\nmatter ($\\nu$BDM) from advanced underground experiments such as Borexino,\nPandaX, XENON1T, and JUNO.\n"}
+{"abstract": "  We study the continuity of half-plane capacity as a function of boundary\nhulls with respect to the Carath\\'eodory convergence. In particular, our\ninterest lies in the case that hulls are unbounded. Under the assumption that\nevery hull is contained in a fixed hull with finite imaginary part and finite\nhalf-plane capacity, we show that the half-plane capacity is indeed continuous.\nWe also discuss the extension of this result to the case that the underlying\ndomain is finitely connected.\n"}
+{"abstract": "  Persuasion games are fundamental in economics and AI research and serve as\nthe basis for important applications. However, work on this setup assumes\ncommunication with stylized messages that do not consist of rich human\nlanguage. In this paper we consider a repeated sender (expert) -- receiver\n(decision maker) game, where the sender is fully informed about the state of\nthe world and aims to persuade the receiver to accept a deal by sending one of\nseveral possible natural language reviews. We design an automatic expert that\nplays this repeated game, aiming to achieve the maximal payoff. Our expert is\nimplemented within the Monte Carlo Tree Search (MCTS) algorithm, with deep\nlearning models that exploit behavioral and linguistic signals in order to\npredict the next action of the decision maker, and the future payoff of the\nexpert given the state of the game and a candidate review. We demonstrate the\nsuperiority of our expert over strong baselines, its adaptability to different\ndecision makers, and that its selected reviews are nicely adapted to the\nproposed deal.\n"}
+{"abstract": "  Machine scheduling problems involving conflict jobs can be seen as a\nconstrained version of the classical scheduling problem, in which some jobs are\nconflict in the sense that they cannot be proceeded simultaneously on different\nmachines. This conflict constraint naturally arises in several practical\napplications and has recently received considerable attentions in the research\ncommunity. In fact, the problem is typically NP-hard (even for approximation)\nand most of algorithmic results achieved so far have heavily relied on special\nstructures of the underlying graph used to model the conflict-job relation. Our\nfocus is on three objective functions: minimizing the makespan, minimizing the\nweighted summation of the jobs' completion time, and maximizing the total\nweights of completed jobs; the first two of which have been intensively studied\nin the literature. For each objective function considered, we present several\nmixed integer linear programming models and a constraint programming model,\nfrom which we can solve the problems to optimality using dedicated solvers.\nBinary search-based algorithms are also proposed to solve the makespan problem.\nThe results of numerical experiments performed on randomly generated data sets\nwith up to 32 jobs and 6 machines are reported and analysed to verify the\nperformance of the proposed methods.\n"}
+{"abstract": "  We consider Taylor dispersion for tracer particles in micro-fluidic planar\nchannels with strong confinement. In this context, the channel walls modify the\nlocal diffusivity tensor and also interactions between the tracer particles and\nthe walls become important. We provide a simple and general formula for the\neffective diffusion constant along the channel as well as the first non-trivial\nfinite time correction for arbitrary flows along the channel, arbitrary\ninteraction potentials with the walls and arbitrary expressions for the\ndiffusion tensor. The formula are in particular amenable to a straightforward\nnumerical implementation, rendering them extremely useful for comparison with\nexperiments. We present a number of applications, notably for systems which\nhave parabolically varying diffusivity profiles, to systems with attractive\ninteractions with the walls as well as electroosmotic flows between plates with\ndiffering surface charges within the Debye-H\\\"uckel approximation.\n"}
+{"abstract": "  The oscillation model has been proposed as a theoretical framework for\ndescribing user dynamics in online social networks. This model can model the\nuser dynamics generated by a particular network structure and allow its causal\nrelationships to be explicitly described. In this paper, by applying\nperturbation theory to the fundamental equation of the oscillation model, we\nconfirm that we can explicitly trace, at least in principle, the changes in\nuser dynamics associated with changes in the network structure. Specifically,\nwe formulate perturbative expansions up to infinite order, by drawing on\ninferences from regularities found in perturbative expansions; the accuracy of\nperturbative expansions of finite order is evaluated by numerical experiments.\n"}
+{"abstract": "  The reflected entropy $S_R(A:B)$ of a density matrix $\\rho_{AB}$ is a\nbipartite correlation measure lower-bounded by the quantum mutual information\n$I(A:B)$. In holographic states satisfying the quantum extremal surface\nformula, where the reflected entropy is related to the area of the entanglement\nwedge cross-section, there is often an order-$N^2$ gap between $S_R$ and $I$.\nWe provide an information-theoretic interpretation of this gap by observing\nthat $S_R - I$ is related to the fidelity of a particular Markov recovery\nproblem that is impossible in any state whose entanglement wedge cross-section\nhas a nonempty boundary; for this reason, we call the quantity $S_R - I$ the\nMarkov gap. We then prove that for time-symmetric states in pure AdS$_3$\ngravity, the Markov gap is universally lower bounded by $\\log(2)\n\\ell_{\\text{AdS}}/2 G_N$ times the number of endpoints of the cross-section. We\nprovide evidence that this lower bound continues to hold in the presence of\nbulk matter, and comment on how it might generalize above three bulk\ndimensions. Finally, we explore the Markov recovery problem controlling $S_R -\nI$ using fixed area states. This analysis involves deriving a formula for the\nquantum fidelity -- in fact, for all the sandwiched R\\'enyi relative entropies\n-- between fixed area states with one versus two fixed areas, which may be of\nindependent interest. We discuss, throughout the paper, connections to the\ngeneral theory of multipartite entanglement in holography.\n"}
+{"abstract": "  Classification of features in a scene typically requires conversion of the\nincoming photonic field into the electronic domain. Recently, an alternative\napproach has emerged whereby passive structured materials can perform\nclassification tasks by directly using free-space propagation and diffraction\nof light. In this manuscript, we present a theoretical and computational study\nof such systems and establish the basic features that govern their performance.\nWe show that system architecture, material structure, and input light field are\nintertwined and need to be co-designed to maximize classification accuracy. Our\nsimulations show that a single layer metasurface can achieve classification\naccuracy better than conventional linear classifiers, with an order of\nmagnitude fewer diffractive features than previously reported. For a wavelength\n{\\lambda}, single layer metasurfaces of size with aperture density achieve ~96%\ntesting accuracy on the MNIST dataset, for an optimized distance ~ to the\noutput plane. This is enabled by an intrinsic nonlinearity in photodetection,\ndespite the use of linear optical metamaterials. Furthermore, we find that once\nthe system is optimized, the number of diffractive features is the main\ndeterminant of classification performance. The slow asymptotic scaling with the\nnumber of apertures suggests a reason why such systems may benefit from\nmultiple layer designs. Finally, we show a trade-off between the number of\napertures and fabrication noise.\n"}
+{"abstract": "  Einstein based his special theory of relativity on two postulates: (a)\nphysical laws appear the same in all inertial frames, and (b) the speed of\nlight in vacuum is an observer-independent constant. However, it is already\nknown that the principle of the constancy of the speed of light is redundant in\nthe construction of the special theory of relativity. Adhering to this idea, we\nshow here that the form of the Lagrangian for a free relativistic particle can\nbe derived if we consider a class of theories that remain invariant under a\nduality transformation between space and time intervals. Therefore, the special\ntheory of relativity is interpreted as a theory that exhibits a duality\nsymmetry with space intervals and time intervals being dual to each other. The\nLorentz transformations then are simply deduced as the linear transformation\nbetween inertial observers that leaves the (infinitesimal) action of the\nrelativistic particles unchanged.\n"}
+{"abstract": "  The remarkable recent progress in the precision of Lattice QCD computations\nfor a number of physical quantities relevant for flavour physics has motivated\nthe introduction of isospin-breaking effects, including in particular\nelectromagnetic corrections, to the computations. The isospin breaking\ncorrections are necessary to fully exploit this improved precision for the\ndetermination of the fundamental parameters of the Standard Model, including\nthe CKM matrix elements, and to look for deviations from experimental\nmeasurements which might signal the presence of new physics. Together with\ncolleagues from Rome, we have developed and implemented a framework for\nincluding isospin-breaking corrections in leptonic decays\n$P\\to\\ell\\bar\\nu_\\ell(\\gamma)$, where $P$ is a pseudoscalar meson and $\\ell$ a\ncharged lepton, and the theoretical framework and numerical results are\nreviewed below. The status of our studies to extend this framework to\nsemileptonic decays $P_1\\to P_2\\ell\\bar\\nu_\\ell(\\gamma)$, where $P_{1,2}$ are\npseudoscalar mesons, is also presented.\n"}
+{"abstract": "  Human motion prediction from historical pose sequence is at the core of many\napplications in machine intelligence. However, in current state-of-the-art\nmethods, the predicted future motion is confined within the same activity. One\ncan neither generate predictions that differ from the current activity, nor\nmanipulate the body parts to explore various future possibilities. Undoubtedly,\nthis greatly limits the usefulness and applicability of motion prediction. In\nthis paper, we propose a generalization of the human motion prediction task in\nwhich control parameters can be readily incorporated to adjust the forecasted\nmotion. Our method is compelling in that it enables manipulable motion\nprediction across activity types and allows customization of the human movement\nin a variety of fine-grained ways. To this aim, a simple yet effective\ncomposite GAN structure, consisting of local GANs for different body parts and\naggregated via a global GAN is presented. The local GANs game in lower\ndimensions, while the global GAN adjusts in high dimensional space to avoid\nmode collapse. Extensive experiments show that our method outperforms\nstate-of-the-art. The codes are available at\nhttps://github.com/herolvkd/AM-GAN.\n"}
+{"abstract": "  Recently, we systematically studied the basic theory of Bregman circumcenters\nin another paper. In this work, we aim to investigate the application of\nBregman circumcenters.\n  Here, we propose the forward Bregman monotonicity which is a generalization\nof the powerful Fej\\'er monotonicity, and show a weak convergence result of the\nforward Bregman monotone sequence. We also naturally introduce the Bregman\ncircumcenter mappings associated with a finite set of operators. Then we\nprovide sufficient conditions for the sequence of iterations of the forward\nBregman circumcenter mapping to be forward Bregman monotone. Furthermore, we\nprove that the sequence of iterations of the forward Bregman circumcenter\nmapping weakly converges to a point in the intersection of the fixed point sets\nof relevant operators, which reduces to the known weak convergence result of\nthe circumcentered method under the Euclidean distance. In addition, particular\nexamples are provided to illustrate the Bregman isometry and Browder's\ndemiclosedness principle, and our convergence result.\n"}
+{"abstract": "  In this paper, we consider the problem of determining the presence of a given\nsignal in a high-dimensional observation with unknown covariance matrix by\nusing an adaptive matched filter. Traditionally such filters are formed from\nthe sample covariance matrix of some given training data, but, as is\nwell-known, the performance of such filters is poor when the number of training\ndata $n$ is not much larger than the data dimension $p$. We thus seek a\ncovariance estimator to replace sample covariance. To account for the fact that\n$n$ and $p$ may be of comparable size, we adopt the \"large-dimensional\nasymptotic model\" in which $n$ and $p$ go to infinity in a fixed ratio. Under\nthis assumption, we identify a covariance estimator that is asymptotically\ndetection-theoretic optimal within a general shrinkage class inspired by C.\nStein, and we give consistent estimates for conditional false-alarm and\ndetection rate of the corresponding adaptive matched filter.\n"}
+{"abstract": "  In this paper, a novel and innovative methodology for feasible motion\nplanning in the multi-agent system is developed. On the basis of velocity\nobstacles characteristics, the chance constraints are formulated in the\nreceding horizon control (RHC) problem, and geometric information of collision\ncones is used to generate the feasible regions of velocities for the host\nagent. By this approach, the motion planning is conducted at the velocity level\ninstead of the position level. Thus, it guarantees a safer collision-free\ntrajectory for the multi-agent system, especially for the systems with\nhigh-speed moving agents. Moreover, a probability threshold of potential\ncollisions can be satisfied during the motion planning process. In order to\nvalidate the effectiveness of the methodology, different scenarios for multiple\nagents are investigated, and the simulation results clearly show that the\nproposed approach can effectively avoid potential collisions with a collision\nprobability less than a specific threshold.\n"}
+{"abstract": "  We obtain the quadratic-in-spin terms of the conservative Hamiltonian\ndescribing the interactions of a binary of spinning bodies in General\nRelativity through $\\mathcal{O}(G^2)$ and to all orders in velocity. Our\ncalculation extends a recently-introduced framework based on scattering\namplitudes and effective field theory to consider non-minimal coupling of the\nspinning objects to gravity. At the order that we consider, we establish the\nvalidity of the formula proposed in \\cite{Bern:2020buy} that relates the\nimpulse and spin kick in a scattering event to the eikonal phase.\n"}
+{"abstract": "  The task of compression of data -- as stated by the source coding theorem --\nis one of the cornerstones of information theory. Data compression usually\nexploits statistical redundancies in the data according to its prior\ndistribution. Motivated by situations where one does not have access to the\nstatistics of data, but has some information about a transformation that is\ngoing to be applied to it, we propose a novel method for data compression\ncalled reverse compression. It is defined in such a way that works for both\nclassical and quantum information processes, and furthermore relies exclusively\non the channel to be used: all input data leading to indistinguishable outputs\nis compressed to the same state, regardless of their prior distribution.\nMoreover, this process can be characterized as a higher order operation within\nthe type of preprocessing. We also consider as an example the application of\nthe method to the classical and quantum erasure channel. The examples suggest\nthat noiseless reverse compression can take place only in trivial cases,\nalthough meaningful instances of noisy reverse compression can exist.\n"}
+{"abstract": "  In this paper we consider a small-$x$ model for gluon GTMDs that we fit to\ndata on diffractive dijet production in electron-proton collisions obtained by\nHERA's H1 Collaboration. Assuming a small number of free parameters, each with\na physical motivation, we are able to describe those data fairly well and with\nthis model we obtain predictions for the EIC for both electroproduction and\nphotoproduction which may allow to further test the underlying GTMD\ndescription. In the general discussion of the impact parameter dependence we\nrecall some subtle issues related to localization of states, choice of frames,\nand discuss what these aspects imply for the range of applicability of the\nmodel.\n"}
+{"abstract": "  We analyse recently measured nonlinear photoemission spectra from Ag surfaces\nthat reveal resonances whose energies do not scale with the applied photon\nenergy but stay pinned to multiples of bulk plasmon energy $\\hbar\\omega_p$\nabove the Fermi level. To elucidate these unexpected and peculiar features we\ninvestigate the spectra of plasmons generated in a solid by the optically\npumped electronic polarization and their effect on photoemission. By combining\nquadratic response formalism for calculations of photoemission yield, a\nnonperturbative approach to inelastic electron scattering, and first-principles\ncalculations for the electronic structure, we demonstrate the dependence of\nprobability amplitude for single- and multiplasmon excitations on the basic\nparameters characterizing the photon pulse and the system. The resulting\nmultiexcitation spectrum evolves towards a truncated plasmonic coherent state.\nAnalogous concept is extrapolated to interpret plasmon generation by\nmultiphoton excited electronic polarization. Based on this we elaborate a\nscenario that the thus created real plasmons act as supplementary\nfrequency-locked pump field for non-Einsteinian plasmonically assisted channels\nof photoemission from metals. The established paradigm enables assignment and\nassessment of the observed linear $\\hbar\\omega_p$- and nonlinear\n$2\\hbar\\omega_p$-electron yields from Ag. Such effects may be exploited for\nselective filtering of optical energy conversion in electronic systems.\n"}
+{"abstract": "  Malicious activities in cyberspace have gone further than simply hacking\nmachines and spreading viruses. It has become a challenge for a nations\nsurvival and hence has evolved to cyber warfare. Malware is a key component of\ncyber-crime, and its analysis is the first line of defence against attack. This\nwork proposes a novel deep boosted hybrid learning-based malware classification\nframework and named as Deep boosted Feature Space-based Malware classification\n(DFS-MC). In the proposed framework, the discrimination power is enhanced by\nfusing the feature spaces of the best performing customized CNN architectures\nmodels and its discrimination by an SVM for classification. The discrimination\ncapacity of the proposed classification framework is assessed by comparing it\nagainst the standard customized CNNs. The customized CNN models are implemented\nin two ways: softmax classifier and deep hybrid learning-based malware\nclassification. In the hybrid learning, Deep features are extracted from\ncustomized CNN architectures and fed into the conventional machine learning\nclassifier to improve the classification performance. We also introduced the\nconcept of transfer learning in a customized CNN architecture based malware\nclassification framework through fine-tuning. The performance of the proposed\nmalware classification approaches are validated on the MalImg malware dataset\nusing the hold-out cross-validation technique. Experimental comparisons were\nconducted by employing innovative, customized CNN, trained from scratch and\nfine-tuning the customized CNN using transfer learning. The proposed\nclassification framework DFS-MC showed improved results, Accuracy: 98.61%,\nF-score: 0.96, Precision: 0.96, and Recall: 0.96.\n"}
+{"abstract": "  Kidney transplantation can significantly enhance living standards for people\nsuffering from end-stage renal disease. A significant factor that affects graft\nsurvival time (the time until the transplant fails and the patient requires\nanother transplant) for kidney transplantation is the compatibility of the\nHuman Leukocyte Antigens (HLAs) between the donor and recipient. In this paper,\nwe propose new biologically-relevant feature representations for incorporating\nHLA information into machine learning-based survival analysis algorithms. We\nevaluate our proposed HLA feature representations on a database of over 100,000\ntransplants and find that they improve prediction accuracy by about 1%, modest\nat the patient level but potentially significant at a societal level. Accurate\nprediction of survival times can improve transplant survival outcomes, enabling\nbetter allocation of donors to recipients and reducing the number of\nre-transplants due to graft failure with poorly matched donors.\n"}
+{"abstract": "  We report the discovery of 34 new open clusters and candidates as a result of\na systematic search carried out in 200 adjacent fields of 1x1 square degrees\narea projected towards the Galactic bulge, using Gaia DR2 data. The objects\nwere identified and characterized by a joint analysis of their photometric,\nkinematic and spatial distribution, which has been consistently used and proved\nto be effective in our previous works. The discoveries were validated by\ncross-referencing the objects position and astrometric parameters with the\navailable literature. Besides their coordinates and astrometric parameters, we\nalso provide sizes, ages, distances and reddening for the discovered objects.\nIn particular, 32 clusters are closer than 2 kpc from the Sun, which represents\nan increment of nearly 39% of objects with astrophysical parameters determined\nin the nearby inner disk. Although these objects fill an important gap in the\nopen clusters distribution along the Sagittarius arm, this arm, traced by known\nclusters, appears to be interrupted, which may be an artifact due to the\nincompleteness of the cluster census.\n"}
+{"abstract": "  This paper presents algorithms for the included-sums and excluded-sums\nproblems used by scientific computing applications such as the fast multipole\nmethod. These problems are defined in terms of a $d$-dimensional array of $N$\nelements and a binary associative operator~$\\oplus$ on the elements. The\nincluded-sum problem requires that the elements within overlapping boxes\ncornered at each element within the array be reduced using $\\oplus$. The\nexcluded-sum problem reduces the elements outside each box. The weak versions\nof these problems assume that the operator $\\oplus$ has an inverse $\\ominus$,\nwhereas the strong versions do not require this assumption. In addition to\nstudying existing algorithms to solve these problems, we introduce three new\nalgorithms.\n  The bidirectional box-sum (BDBS) algorithm solves the strong included-sums\nproblem in $\\Theta(d N)$ time, asymptotically beating the classical summed-area\ntable (SAT) algorithm, which runs in $\\Theta(2^d N)$ and which only solves the\nweak version of the problem. Empirically, the BDBS algorithm outperforms the\nSAT algorithm in higher dimensions by up to $17.1\\times$.\n  The \\defn{box-complement} algorithm can solve the strong excluded-sums\nproblem in $\\Theta(d N)$ time, asymptotically beating the state-of-the-art\ncorners algorithm by Demaine et al., which runs in $\\Omega(2^d N)$ time. In 3\ndimensions the box-complement algorithm empirically outperforms the corners\nalgorithm by about $1.4\\times$ given similar amounts of space.\n  The weak excluded-sums problem can be solved in $\\Theta(d N)$ time by the\nbidirectional box-sum complement (BDBSC) algorithm, which is a trivial\nextension of the BDBS algorithm. Given an operator inverse $\\ominus$, BDBSC can\nbeat box-complement by up to a factor of $4$.\n"}
+{"abstract": "  Brain networks are adaptively rewired continually, adjusting their topology\nto bring about functionality and efficiency in sensory, motor and cognitive\ntasks. In model neural network architectures, adaptive rewiring generates\ncomplex, brain-like topologies. Present models, however, cannot account for the\nemergence of complex directed connectivity structures. We tested a biologically\nplausible model of adaptive rewiring in directed networks, based on two\nalgorithms widely used in distributed computing: advection and consensus. When\nboth are used in combination as rewiring criteria, adaptive rewiring shortens\npath length and enhances connectivity. When keeping a balance between advection\nand consensus, adaptive rewiring produces convergent-divergent units consisting\nof convergent hub nodes, which collect inputs from pools of sparsely connected,\nor local, nodes and project them via densely interconnected processing nodes\nonto divergent hubs that broadcast output back to the local pools.\nConvergent-divergent units operate within and between sensory, motor, and\ncognitive brain regions as their connective core, mediating context-sensitivity\nto local network units. By showing how these structures emerge spontaneously in\ndirected networks models, adaptive rewiring offers self-organization as a\nprinciple for efficient information propagation and integration in the brain.\n"}
+{"abstract": "  Commonsense reasoning research has so far been limited to English. We aim to\nevaluate and improve popular multilingual language models (ML-LMs) to help\nadvance commonsense reasoning (CSR) beyond English. We collect the Mickey\nCorpus, consisting of 561k sentences in 11 different languages, which can be\nused for analyzing and improving ML-LMs. We propose Mickey Probe, a\nlanguage-agnostic probing task for fairly evaluating the common sense of\npopular ML-LMs across different languages. In addition, we also create two new\ndatasets, X-CSQA and X-CODAH, by translating their English versions to 15 other\nlanguages, so that we can evaluate popular ML-LMs for cross-lingual commonsense\nreasoning. To improve the performance beyond English, we propose a simple yet\neffective method -- multilingual contrastive pre-training (MCP). It\nsignificantly enhances sentence representations, yielding a large performance\ngain on both benchmarks.\n"}
+{"abstract": "  For a conformally compact Poincar\\'{e}-Einstein manifold $(X,g_+)$, we\nconsider two types of compactifications for it. One is $\\bar{g}=\\rho^2g_+$,\nwhere $\\rho$ is a fixed smooth defining function; the other is the adapted\n(including Fefferman-Graham) compactification $\\bar{g}_s=\\rho^2_sg_+$ with a\ncontinuous parameter $s>\\frac{n}{2}$. In this paper, we mainly prove that for a\nset of conformally compact Poincar\\'{e}-Einstein manifolds $\\{(X,\ng_{+}^{(i)})\\}$ with conformal infinity of positive Yamabe type,\n$\\{\\bar{g}^{(i)}\\}$ is compact in $C^{k,\\alpha}(\\overline{X})$ topology if and\nonly if $\\{\\bar{g}_s^{(i)}\\}$ is compact in some $C^{l,\\beta}(\\overline{X})$\ntopology, provided that\n$\\bar{g}^{(i)}|_{TM}=\\bar{g}_s^{(i)}|_{TM}=\\hat{g}^{(i)}$ and $\\hat{g}^{(i)}$\nhas positive scalar curvature for each $i$. See Theorem 1.1 and Corollary 1.1\nfor the exact relation of $(k,\\alpha)$ and $(l,\\beta)$.\n"}
+{"abstract": "  The present work constitutes a first step towards establishing a systematic\nframework for treating variational problems that depend on a given input\nfunction through a mixture of its derivatives of different orders in different\ndirections. For a fixed vector $\\mathbf{a} := (a_1, \\ldots, a_N) \\in\n\\mathbb{N}^N$ and $u \\colon \\mathbb{R}^N \\supset \\Omega \\to \\mathbb{R}^n$ we\ndenote by $\\nabla_{\\mathbf{a}} u := (\\partial^{\\alpha} u)_{\\langle \\alpha,\n\\mathbf{a}^{-1} \\rangle = 1}$ the matrix whose $i$-th row is composed of\nderivatives $\\partial^\\alpha u^i$ of the $i$-th component of the map $u$, and\nwhere the multi-indices $\\alpha$ satisfy $\\langle \\alpha, \\mathbf{a}^{-1}\n\\rangle = \\sum_{j=1}^N \\frac{\\alpha_j}{a_j} = 1$. We study functionals of the\nform $$ \\mathrm{W}^{\\mathbf{a},p}(\\Omega;\\mathbb{R}^n) \\ni u \\mapsto\n\\int_\\Omega F(\\nabla_{\\mathbf{a}} u(x)) \\, \\mathrm{d} x,$$ where\n$\\mathrm{W}^{\\mathbf{a},p}(\\Omega; \\mathbb{R}^n)$ is an appropriate Sobolev\nspace of mixed smoothness and $F$ is the integrand. We study existence of\nminimisers of such functionals under prescribed Dirichlet boundary conditions.\nWe characterise coercivity, lower semicontiuity, and envelopes of relaxation of\nsuch functionals, in terms of an appropriate generalisation of Morrey's\nquasiconvexity.\n"}
+{"abstract": "  In this work, we propose a novel method for performing inertial aided\nnavigation, by using deep neural networks (DNNs). To date, most DNN inertial\nnavigation methods focus on the task of inertial odometry, by taking gyroscope\nand accelerometer readings as input and regressing for integrated IMU poses\n(i.e., position and orientation). While this design has been successfully\napplied on a number of applications, it is not of theoretical performance\nguarantee unless patterned motion is involved. This inevitably leads to\nsignificantly reduced accuracy and robustness in certain use cases. To solve\nthis problem, we design a framework to compute observable IMU integration terms\nusing DNNs, followed by the numerical pose integration and sensor fusion to\nachieve the performance gain. Specifically, we perform detailed analysis on the\nmotion terms in IMU kinematic equations, propose a dedicated network design,\nloss functions, and training strategies for the IMU data processing, and\nconduct extensive experiments. The results show that our method is generally\napplicable and outperforms both traditional and DNN methods by wide margins.\n"}
+{"abstract": "  We use the wealth of deep archival optical spectroscopy on the GOODS-South\nfield from Keck, the VLT, and other facilities to select candidate\nhigh-redshift Lyman continuum (LyC) leakers in the Hubble Deep UV Legacy Survey\n(HDUV) dataset. We select sources at $2.35 < z < 3.05$, where the HST/WFC3\nF275W filter probes only the redshifted LyC. We find five moderately\nF275W-bright sources (four detected at $\\gtrsim3\\sigma$ significance) in this\nredshift range. However, two of these show evidence in their optical spectra\nfor contamination by foreground galaxies along the line-of-sight. We then\nperform an F275W error-weighted sum of the fluxes of all 129 galaxies at $2.35\n< z < 3.05$ in both the GOODS-N and GOODS-S HDUV areas to estimate the total\nionizing flux. The result is dominated by just five candidate F275W-bright LyC\nsources. Lastly, we examine the contributions to the metagalactic ionizing\nbackground, finding that, at the sensitivity of the HDUV F275W data and\nallowing for the effects of LyC transmission in the intergalactic medium (IGM),\nstar-forming galaxies can match the UV flux required to maintain an ionized IGM\nat $z \\sim 2.5$.\n"}
+{"abstract": "  We explore the chaotic dynamics and complexity of a neuro-system with respect\nto variable synaptic weights in both noise free and noisy conditions. The\nchaotic dynamics of the system is investigated by bifurcation analysis and 0-1\ntest. A multiscale complexity of the system is proposed based on the notion of\nrecurrence plot density entropy. Numerical results support the proposed\nanalysis. Impact of music on the aforesaid neuro-system has also been studied.\nThe analysis shows that inclusion of white noise even with a minimal strength\nmakes the neuro dynamics more complex, where as music signal keeps the dynamics\nalmost similar to that of the original system. This is properly interpreted by\nthe proposed multiscale complexity measure.\n"}
+{"abstract": "  Physics-based simulation for fluid flow in porous media is a computational\ntechnology to predict the temporal-spatial evolution of state variables (e.g.\npressure) in porous media, and usually requires high computational expense due\nto its nonlinearity and the scale of the study domain. This letter describes a\ndeep learning (DL) workflow to predict the pressure evolution as fluid flows in\nlarge-scale 3D heterogeneous porous media. In particular, we apply feature\ncoarsening technique to extract the most representative information and perform\nthe training and prediction of DL at the coarse scale, and further recover the\nresolution at the fine scale by 2D piecewise cubic interpolation. We validate\nthe DL approach that is trained from physics-based simulation data to predict\npressure field in a field-scale 3D geologic CO_2 storage reservoir. We evaluate\nthe impact of feature coarsening on DL performance, and observe that the\nfeature coarsening can not only decrease training time by >74% and reduce\nmemory consumption by >75%, but also maintains temporal error <1.5%. Besides,\nthe DL workflow provides predictive efficiency with ~1400 times speedup\ncompared to physics-based simulation.\n"}
+{"abstract": "  In this work, we present a learning-based pipeline to realise local\nnavigation with a quadrupedal robot in cluttered environments with static and\ndynamic obstacles. Given high-level navigation commands, the robot is able to\nsafely locomote to a target location based on frames from a depth camera\nwithout any explicit mapping of the environment. First, the sequence of images\nand the current trajectory of the camera are fused to form a model of the world\nusing state representation learning. The output of this lightweight module is\nthen directly fed into a target-reaching and obstacle-avoiding policy trained\nwith reinforcement learning. We show that decoupling the pipeline into these\ncomponents results in a sample efficient policy learning stage that can be\nfully trained in simulation in just a dozen minutes. The key part is the state\nrepresentation, which is trained to not only estimate the hidden state of the\nworld in an unsupervised fashion, but also helps bridging the reality gap,\nenabling successful sim-to-real transfer. In our experiments with the\nquadrupedal robot ANYmal in simulation and in reality, we show that our system\ncan handle noisy depth images, avoid dynamic obstacles unseen during training,\nand is endowed with local spatial awareness.\n"}
+{"abstract": "  We introduce generalized hypergeometric Bernoulli numbers for Dirichlet\ncharacters. We study their properties, including relations, expressions and\ndeterminants. At the end in Appendix we derive first few expressions of these\nnumbers.\n"}
+{"abstract": "  The dependence of the fractal dimension of global attractors for the damped\n3D Euler--Bardina equations on the regularization parameter $\\alpha>0$ and\nEkman damping coefficient $\\gamma>0$ is studied. We present explicit upper\nbounds for this dimension for the case of the whole space, periodic boundary\nconditions, and the case of bounded domain with Dirichlet boundary conditions.\nThe sharpness of these estimates when $\\alpha\\to0$ and $\\gamma\\to0$ (which\ncorresponds in the limit to the classical Euler equations) is demonstrated on\nthe 3D Kolmogorov flows on a torus.\n"}
+{"abstract": "  Boundary time crystals (BTC's) are non-equilibrium phases of matter occurring\nin quantum systems in contact to an environment, for which a macroscopic\nfraction of the many body system breaks time translation symmetry. We study\nBTC's in collective $d$-level systems, focusing in the cases with $d=2$, $3$\nand $4$. We find that BTC's appear in different forms for the different cases.\nWe first consider the model with collective $d=2$-level systems [presented in\nPhys. Rev. Lett. $121$, $035301$ ($2018$)], whose dynamics is described by a\nLindblad master equation, and perform a throughout analysis of its phase\ndiagram and Jacobian stability for different interacting terms in the coherent\nHamiltonian. In particular, using perturbation theory for general (non\nHermitian) matrices we obtain analytically how a specific $\\mathbb{Z}_2$\nsymmetry breaking Hamiltonian term destroys the BTC phase in the model. Based\non these results we define a $d=4$ model composed of a pair of collective\n$2$-level systems interacting with each other. We show that this model support\nricher dynamical phases, ranging from limit-cycles, period-doubling\nbifurcations and a route to chaotic dynamics. The BTC phase is more robust in\nthis case, not annihilated by the former symmetry breaking Hamiltonian terms.\nThe model with collective $d=3$-level systems is defined similarly, as\ncompeting pairs of levels, but sharing a common collective level. The dynamics\ncan deviate significantly from the previous cases, supporting phases with the\ncoexistence of multiple limit-cycles, closed orbits and a full degeneracy of\nzero Lyapunov exponents.\n"}
+{"abstract": "  Potential applications of Zr/Al ODS alloys vests on the irradiation stability\nof the Y4Zr3O12 dispersoids. Fundamental studies to identify the type of\ndefects are important in order to recognize pathways for damage alleviation. In\nthis context, studies relating to identification of point defects and their\nclusters by in-situ ionoluminescence spectroscopy were taken up. The\nionoluminescence spectrum acquired during 100 keV He+ ion irradiation shows two\nprominent bands, at 330 nm and 415 nm. Using density functional theory\ncalculations with HSE06 hybrid exchange correlation functional, the luminescent\nbands have been identified to be arising due to native and irradiation induced\noxygen vacancy defects in charged and neutral configurations.\n"}
+{"abstract": "  Automatically generating medical reports for retinal images is one of the\npromising ways to help ophthalmologists reduce their workload and improve work\nefficiency. In this work, we propose a new context-driven encoding network to\nautomatically generate medical reports for retinal images. The proposed model\nis mainly composed of a multi-modal input encoder and a fused-feature decoder.\nOur experimental results show that our proposed method is capable of\neffectively leveraging the interactive information between the input image and\ncontext, i.e., keywords in our case. The proposed method creates more accurate\nand meaningful reports for retinal images than baseline models and achieves\nstate-of-the-art performance. This performance is shown in several commonly\nused metrics for the medical report generation task: BLEU-avg (+16%), CIDEr\n(+10.2%), and ROUGE (+8.6%).\n"}
+{"abstract": "  We generalise the form factor bootstrap approach to integrable field theories\nwith U(1) symmetry to derive matrix elements of composite branch-point twist\nfields associated with symmetry resolved entanglement entropies. The bootstrap\nequations are solved for the free massive Dirac and complex boson theories,\nwhich are the simplest theories with U(1) symmetry. We present the exact and\ncomplete solution for the bootstrap, including vacuum expectation values and\nform factors involving any type and arbitrarily number of particles. The\nnon-trivial solutions are carefully cross-checked by performing various limits\nand by the application of the Delta-theorem. An alternative and compact\ndetermination of the novel form factors is also presented. Based on the form\nfactors of the U(1) composite branch-point twist fields, we re-derive earlier\nresults showing entanglement equipartition for an interval in the ground state\nof the two models.\n"}
+{"abstract": "  The detection and estimation of sinusoids is a fundamental signal processing\ntask for many applications related to sensing and communications. While\nalgorithms have been proposed for this setting, quantization is a critical, but\noften ignored modeling effect. In wireless communications, estimation with low\nresolution data converters is relevant for reduced power consumption in\nwideband receivers. Similarly, low resolution sampling in imaging and spectrum\nsensing allows for efficient data collection. In this work, we propose\nSignalNet, a neural network architecture that detects the number of sinusoids\nand estimates their parameters from quantized in-phase and quadrature samples.\nWe incorporate signal reconstruction internally as domain knowledge within the\nnetwork to enhance learning and surpass traditional algorithms in mean squared\nerror and Chamfer error. We introduce a worst-case learning threshold for\ncomparing the results of our network relative to the underlying data\ndistributions. This threshold provides insight into why neural networks tend to\noutperform traditional methods and into the learned relationships between the\ninput and output distributions. In simulation, we find that our algorithm is\nalways able to surpass the threshold for three-bit data but often cannot exceed\nthe threshold for one-bit data. We use the learning threshold to explain, in\nthe one-bit case, how our estimators learn to minimize the distributional loss,\nrather than learn features from the data.\n"}
+{"abstract": "  With the successful adoption of machine learning on electronic health records\n(EHRs), numerous computational models have been deployed to address a variety\nof clinical problems. However, due to the heterogeneity of EHRs, models trained\non different patient groups suffer from poor generalizability. How to mitigate\ndomain shifts between the source patient group where the model is built upon\nand the target one where the model will be deployed becomes a critical issue.\nIn this paper, we propose a data augmentation method to facilitate domain\nadaptation, which leverages knowledge from the source patient group when\ntraining model on the target one. Specifically, adversarially generated samples\nare used during domain adaptation to fill the generalization gap between the\ntwo patient groups. The proposed method is evaluated by a case study on\ndifferent predictive modeling tasks on MIMIC-III EHR dataset. Results confirm\nthe effectiveness of our method and the generality on different tasks.\n"}
+{"abstract": "  We develop efficient, accurate, transferable, and interpretable machine\nlearning force fields for Au nanoparticles, based on data gathered from Density\nFunctional Theory calculations. We then use them to investigate the\nthermodynamic stability of Au nanoparticles of different sizes (1 to 6 nm),\ncontaining up to 6266 atoms, with respect to a solid-liquid phase change\nthrough molecular dynamics simulations. We predict nanoparticle melting\ntemperatures in good agreement with respect to available experimental data.\nFurthermore, we characterize in detail the solid to liquid phase change\nmechanism employing an unsupervised learning scheme to categorize local atomic\nenvironments. We thus provide a rigorous and data-driven definition of liquid\natomic arrangements in the inner and surface regions of a nanoparticle, and\nemploy it to show that melting initiates at the outer layers.\n"}
+{"abstract": "  SOTA coreference resolution produces increasingly impressive scores on the\nOntoNotes benchmark. However lack of comparable data following the same scheme\nfor more genres makes it difficult to evaluate generalizability to open domain\ndata. This paper provides a dataset and comprehensive evaluation showing that\nthe latest neural LM based end-to-end systems degrade very substantially out of\ndomain. We make an OntoNotes-like coreference dataset called OntoGUM publicly\navailable, converted from GUM, an English corpus covering 12 genres, using\ndeterministic rules, which we evaluate. Thanks to the rich syntactic and\ndiscourse annotations in GUM, we are able to create the largest human-annotated\ncoreference corpus following the OntoNotes guidelines, and the first to be\nevaluated for consistency with the OntoNotes scheme. Out-of-domain evaluation\nacross 12 genres shows nearly 15-20% degradation for both deterministic and\ndeep learning systems, indicating a lack of generalizability or covert\noverfitting in existing coreference resolution models.\n"}
+{"abstract": "  Let $s,t$ be natural numbers, and fix an $s$-core partition $\\sigma$ and a\n$t$-core partition $\\tau$. Put $d=\\gcd(s,t)$ and $m= lcm(s,t)$, and write\n$N_{\\sigma, \\tau}(k)$ for the number of $m$-core partitions of length no\ngreater than $k$ whose $s$-core is $\\sigma$ and $t$-core is $\\tau$. We prove\nthat for $k$ large, $N_{\\sigma, \\tau}(k)$ is a quasipolynomial of period $m$\nand degree $\\frac{1}{d}(s-d)(t-d)$.\n"}
+{"abstract": "  This paper presents the beam dynamics systematic corrections and their\nuncertainties for the Run-1 data set of the Fermilab Muon g-2 Experiment. Two\ncorrections to the measured muon precession frequency $\\omega_a^m$ are\nassociated with well-known effects owing to the use of electrostatic quadrupole\n(ESQ) vertical focusing in the storage ring. An average vertically oriented\nmotional magnetic field is felt by relativistic muons passing transversely\nthrough the radial electric field components created by the ESQ system. The\ncorrection depends on the stored momentum distribution and the tunes of the\nring, which has relatively weak vertical focusing. Vertical betatron motions\nimply that the muons do not orbit the ring in a plane exactly orthogonal to the\nvertical magnetic field direction. A correction is necessary to account for an\naverage pitch angle associated with their trajectories. A third small\ncorrection is necessary because muons that escape the ring during the storage\ntime are slightly biased in initial spin phase compared to the parent\ndistribution. Finally, because two high-voltage resistors in the ESQ network\nhad longer than designed RC time constants, the vertical and horizontal\ncentroids and envelopes of the stored muon beam drifted slightly, but\ncoherently, during each storage ring fill. This led to the discovery of an\nimportant phase-acceptance relationship that requires a correction. The sum of\nthe corrections to $\\omega_a^m$ is 0.50 $\\pm$ 0.09 ppm; the uncertainty is\nsmall compared to the 0.43 ppm statistical precision of $\\omega_a^m$.\n"}
+{"abstract": "  We study branching problem of the metaplectic representation of $Sp(2,\n\\mathbb R)$ under its principle subgroup $SL(2, \\mathbb R)$. We find the\ncomplete decomposition.\n"}
+{"abstract": "  Weighted Gaussian Curvature is an important measurement for images. However,\nits conventional computation scheme has low performance, low accuracy and\nrequires that the input image must be second order differentiable. To tackle\nthese three issues, we propose a novel discrete computation scheme for the\nweighted Gaussian curvature. Our scheme does not require the second order\ndifferentiability. Moreover, our scheme is more accurate, has smaller support\nregion and computationally more efficient than the conventional schemes.\nTherefore, our scheme holds promise for a large range of applications where the\nweighted Gaussian curvature is needed, for example, image smoothing, cartoon\ntexture decomposition, optical flow estimation, etc.\n"}
+{"abstract": "  Superpixels are a useful representation to reduce the complexity of image\ndata. However, to combine superpixels with convolutional neural networks (CNNs)\nin an end-to-end fashion, one requires extra models to generate superpixels and\nspecial operations such as graph convolution. In this paper, we propose a way\nto implicitly integrate a superpixel scheme into CNNs, which makes it easy to\nuse superpixels with CNNs in an end-to-end fashion. Our proposed method\nhierarchically groups pixels at downsampling layers and generates superpixels.\nOur method can be plugged into many existing architectures without a change in\ntheir feed-forward path because our method does not use superpixels in the\nfeed-forward path but use them to recover the lost resolution instead of\nbilinear upsampling. As a result, our method preserves detailed information\nsuch as object boundaries in the form of superpixels even when the model\ncontains downsampling layers. We evaluate our method on several tasks such as\nsemantic segmentation, superpixel segmentation, and monocular depth estimation,\nand confirm that it speeds up modern architectures and/or improves their\nprediction accuracy in these tasks.\n"}
+{"abstract": "  Since its introduction over 50 years ago, the concept of Mosco convergence\nhas permeated through diverse areas of mathematics and applied sciences. These\ninclude applied analysis, the theory of partial differential equations,\nnumerical analysis, and infinite dimensional constrained optimization, among\nothers. In this paper we explore some of the consequences of Mosco convergence\non applied problems that involve moving sets, with some historical accounts,\nand modern trends and features. In particular, we focus on connections with\ndensity of convex intersections, finite element approximations,\nquasi-variational inequalities, and impulse problems.\n"}
+{"abstract": "  Machine Learning models should ideally be compact and robust. Compactness\nprovides efficiency and comprehensibility whereas robustness provides\nresilience. Both topics have been studied in recent years but in isolation.\nHere we present a robust model compression scheme which is independent of model\ntypes: it can compress ensembles, neural networks and other types of models\ninto diverse types of small models. The main building block is the notion of\ndepth derived from robust statistics. Originally, depth was introduced as a\nmeasure of the centrality of a point in a sample such that the median is the\ndeepest point. This concept was extended to classification functions which\nmakes it possible to define the depth of a hypothesis and the median\nhypothesis. Algorithms have been suggested to approximate the median but they\nhave been limited to binary classification. In this study, we present a new\nalgorithm, the Multiclass Empirical Median Optimization (MEMO) algorithm that\nfinds a deep hypothesis in multi-class tasks, and prove its correctness. This\nleads to our Compact Robust Estimated Median Belief Optimization (CREMBO)\nalgorithm for robust model compression. We demonstrate the success of this\nalgorithm empirically by compressing neural networks and random forests into\nsmall decision trees, which are interpretable models, and show that they are\nmore accurate and robust than other comparable methods. In addition, our\nempirical study shows that our method outperforms Knowledge Distillation on DNN\nto DNN compression.\n"}
+{"abstract": "  The charge states of ions in dense plasmas fluctuate due to collisional\nionization and recombination. Here we show how, by modifying the ion\ninteraction potential, these fluctuations can mediate energy exchange between\nthe plasma electrons and ions. Moreover, we develop a theory for this novel\nelectron-ion energy transfer mechanism. Calculations using a random walk\napproach for the fluctuations suggest that the energy exchange rate from charge\nstate fluctuations could be comparable to direct electron-ion collisions. This\nmechanism is, however, predicted to exhibit a complex dependence on the\ntemperature and ionization state of the plasma, which could contribute to our\nunderstanding of significant variation in experimental measurements of\nequilibration times.\n"}
+{"abstract": "  We consider GMRES applied to discretisations of the high-frequency Helmholtz\nequation with strong trapping; recall that in this situation the problem is\nexponentially ill-conditioned through an increasing sequence of frequencies.\nUnder certain assumptions about the distribution of the eigenvalues, we prove\nupper bounds on how the number of GMRES iterations grows with the frequency.\nOur main focus is on boundary-integral-equation formulations of the exterior\nDirichlet and Neumann obstacle problems in 2- and 3-d; for these problems, we\ninvestigate numerically the sharpness (in terms of dependence on frequency) of\nboth our bounds and various quantities entering our bounds. This paper is\ntherefore the first comprehensive study of the frequency-dependence of the\nnumber of GMRES iterations for Helmholtz boundary-integral equations under\ntrapping.\n"}
+{"abstract": "  Regulating the activity of matrix metalloproteinases (MMPs) is a potential\nstrategy for osteoarthritis (OA) therapy, although delivering this effect in a\nspatially and temporally localised fashion remains a challenge. Here, we report\nan injectable and self-healing hydrogel enabling factor-free MMP regulation and\nbiomechanical competence in situ. The hydrogel is realised within one minute\nupon room temperature coordination between hyaluronic acid (HA) and a\ncell-friendly iron-glutathione complex in aqueous environment. The resultant\ngel displayed up to 300% in shear strain and tolerance towards ATDC 5\nchondrocytes, in line with the elasticity and biocompatibility requirements for\nconnective tissue application. Significantly enhanced inhibition of MMP-13\nactivity was achieved after 12 hours in vitro, compared with a commercial HA\ninjection (OSTENIL PLUS). Noteworthy, 24-hour incubation of a clinical synovial\nfluid sample collected from a late-stage OA patient with the reported hydrogel\nwas still shown to downregulate synovial fluid MMP activity (100.0 +/- 17.6 %\n--> 81.0 +/- 7.5 %), with at least comparable extent to the case of the OSTENIL\nPLUS-treated SF group (100.0 +/- 17.6 % --> 92.3 +/- 27.3 %). These results\ntherefore open up new possibilities in the use of HA as both\nmechanically-competent hydrogel as well as a mediator of MMP regulation for OA\ntherapy.\n"}
+{"abstract": "  We construct for every proper algebraic space over a ground field an Albanese\nmap to a para-abelian variety, which is unique up to unique isomorphism. This\nholds in the absence of rational points or ample sheaves, and also for\nreducible or non-reduced spaces, under the mere assumption that the structure\nmorphism is in Stein factorization. It also works under suitable assumptions in\nfamilies. In fact the treatment of the relative setting is crucial, even to\nunderstand the situation over ground fields. This also ensures that Albanese\nmaps are equivariant with respect to actions of group schemes. Our approach\ndepends on the notion of families of para-abelian varieties, where each\ngeometric fiber admits the structure of an abelian variety, and\nrepresentability of tau-parts in relative Picard groups, together with\nstructure results on algebraic groups.\n"}
+{"abstract": "  We investigate the buckling and post-buckling properties of a hyperelastic\nhalf-space coated by two hyperelastic layers when the composite structure is\nsubjected to a uniaxial compression. In the case of a half-space coated with a\n{\\it single} layer, it is known that when the shear modulus $\\mu_f$ of the\nlayer is larger than the shear modulus $\\mu_s$ of the half-space, a linear\nanalysis predicts the existence of a critical stretch and wave number, whereas\na weakly nonlinear analysis predicts the existence of a threshold value of the\nmodulus ratio $\\mu_s/\\mu_f\\approx 0.57$ below which the buckling is\nsuper-critical and above which the buckling is sub-critical. It is shown in\nthis paper that when another layer is added, a larger variety of behaviour can\nbe observed. For instance, buckling can occur at a preferred wavenumber\nsuper-critically even if both layers are softer than the half-space although\nthe top layer would need to be harder than the bottom layer. When the shear\nmodulus of the bottom layer lies in a certain interval, the super-critical to\nsub-critical transition can happen a number of times as the shear modulus of\nthe top layer is increased gradually. Thus, an extra layer imparts more\nflexibility in producing wrinkling patterns with desired properties, and our\nweakly nonlinear analysis provides a road map on the parameter regimes where\nthis can be achieved.\n"}
+{"abstract": "  In quantum communication systems, the precise estimation of the detector's\nresponse to the incoming light is necessary to avoid security breaches. The\ntypical working regime uses a free-running single-photon avalanche diode in\ncombination with attenuated laser pulses at telecom wavelength for encoding\ninformation. We demonstrate the validity of an analytical model for this regime\nwhich considers the effects of dark counts and dead time on the measured count\nrate. For the purpose of gaining a better understanding of these effects, the\nphoton detections were separated from the dark counts via a software-induced\ngating mechanism. The model was verified by experimental data for mean photon\nnumbers covering three orders of magnitude as well as for laser repetition\nfrequencies below and above the inverse dead time. Consequently, our model\nwould be of interest for predicting the detector response not only in the field\nof quantum communications, but also in any other quantum physics experiment\nwhere high detection rates are needed.\n"}
+{"abstract": "  Isomorphs are curves in the phase diagram along which both structure and\ndynamics to a good approximation are invariant. There are two main methods to\ntrace out isomorphs in both atomic and molecular systems, the configurational\nadiabat method and the direct isomorph check method. We introduce and test a\nnew family of force based methods on three molecular models; the asymmetric\ndumbbell model, the symmetric inverse power law dumbbell model, and the\nLewis-Wahnstr\\\"{o}m model of o-terphenyl. A unique feature of the force based\nmethods is that they only require a single configuration to trace out an\nisomorph. The atomic force method was previously shown to work very well for\nthe Kob-Andersen binary Lennard-Jones mixture, but we show that it does not\nwork for molecular models. In contrast, we find that a new method based on\nmolecular forces works well for all three molecular models\n"}
+{"abstract": "  An approach is presented for the inversion of experimental in-plane,\nco-polarized light scattering data in p and s polarization to obtain the\nnormalized surface-height autocorrelation function and the rms-roughness of a\ntwo-dimensional randomly rough metal surface. The approach is based on an\nexpression, obtained on the basis of second-order phase perturbation theory,\nfor the contribution to the mean differential reflection coefficient from the\nlight scattered diffusely by the rough surface. The inversion scheme is\nvalidated by using several sets of computer generated scattering data for rough\nsilver surfaces defined by Gaussian surface height correlation functions. The\nreconstructions obtained by this approach are found to be rather accurate for\nweakly rough surfaces illuminated by p- and s-polarized incident light; this is\nalso true in cases where the contributions to the input data from multiple\nscattering of surface plasmon polaritons is not insignificant. Finally, the\ninversion scheme is applied to experimental scattering data obtained for\ncharacterized two-dimensional randomly rough gold surfaces, and the results\nobtained in this way, compare favorably to what is obtained by directly\nanalyzing the surface morphology. Such results testify to the attractiveness of\nthe computationally efficient inversion scheme that we propose.\n"}
+{"abstract": "  Light bosonic scalars (e.g. axions) may form clouds around black holes via\nsuperradiant instabilities, or via accretion if they form some component of the\ndark matter. It has been suggested that their presence may lead to a\ndistinctive dephasing of the gravitational wave signal when a small compact\nobject spirals into a larger black hole. Motivated by this, we study\nnumerically the dynamical friction force on a black hole moving at relativistic\nvelocities in a background scalar field with an asymptotically homogeneous\nenergy density. We show that the relativistic scaling is analogous to that\nfound for supersonic collisional fluids, assuming an approximate expression for\nthe pressure correction which depends on the velocity and scalar mass. While we\nfocus on a complex scalar field, our results confirm the expectation that real\nscalars would exert a force which oscillates between positive and negative\nvalues in time with a frequency set by the scalar mass. The complex field\ndescribes the time averaged value of this force, but in a real scalar the rapid\nforce oscillations could in principle leave an imprint on the trajectory. The\napproximation we obtain can be used to inform estimates of dephasing in the\nfinal stages of an extreme mass ratio inspiral.\n"}
+{"abstract": "  We promote the teaching of mass functions as an integral part of an\ninterdisciplinary science education. Mass functions characterize the frequency\ndistributions of objects with different masses on all cosmic scales. We intend\nto enhance experiential learning of this concept with a creative LEGO brick\nexperiment for a diverse student audience. To our surprise, the LEGO mass\nfunction is not only qualitatively but also quantitatively comparable to mass\nfunctions found across the Universe. We also discuss the relation between\ngravitation and mass distributions as a possible explanation for the continuity\nof the universal mass function.\n"}
+{"abstract": "  In 2002 Robert Solovay proved that a subsystem BI of classical second order\narithmetic, with bar induction and arithmetical countable choice, can be\nnegatively interpreted in the neutral subsystem BSK of Kleene's intuitionistic\nanalysis FIM using Markov's Principle MP. Combining this result with Kleene's\nformalized recursive realizability, he established (in primitive recursive\narithmetic PRA) that FIM + MP and BI have the same consistency strength.\n  This historical note includes Solovay's original proof, with his permission,\nand the additional observation that Markov's Principle can be weakened to a\ndouble negation shift axiom consistent with Brouwer's creating subject\ncounterexamples.\n"}
+{"abstract": "  We present a new sufficient criterion to prove that a non-sofic\nhalf-synchronized subshift is direct prime. The criterion is based on conjugacy\ninvariant properties of Fischer graphs of half-synchronized shifts. We use this\ncriterion to show as a new result that all n-Dyck shifts are direct prime, and\nwe also give new proofs of direct primeness of non-sofic beta-shifts and\nnon-sofic S-gap shifts. We also construct a class of non-sofic synchronized\ndirect prime subshifts which additionally admit reversible cellular automata\nwith all directions sensitive.\n"}
+{"abstract": "  Continuous biomarkers are common for disease screening and diagnosis. To\nreach a dichotomous clinical decision, a threshold would be imposed to\ndistinguish subjects with disease from non-diseased individuals. Among various\nperformance metrics for a continuous biomarker, specificity at a controlled\nsensitivity level (or vice versa) is often desirable for clinical utility since\nit directly targets where the clinical test is intended to operate. Covariates,\nsuch as age, race, and sample collection, could impact the controlled\nsensitivity level in subpopulations and may also confound the association\nbetween biomarker and disease status. Therefore, covariate adjustment is\nimportant in such biomarker evaluation. In this paper, we suggest to adopt a\nparsimonious quantile regression model for the diseased population, locally at\nthe controlled sensitivity level, and assess specificity with\ncovariate-specific control of the sensitivity. Variance estimates are obtained\nfrom a sample-based approach and bootstrap. Furthermore, our proposed local\nmodel extends readily to a global one for covariate adjustment for the receiver\noperating characteristic (ROC) curve over the sensitivity continuum. We\ndemonstrate computational efficiency of this proposed method and restore the\ninherent monotonicity in the estimated covariate-adjusted ROC curve. The\nasymptotic properties of the proposed estimators are established. Simulation\nstudies show favorable performance of the proposal. Finally, we illustrate our\nmethod in biomarker evaluation for aggressive prostate cancer.\n"}
+{"abstract": "  Blockchain has been praised for its capacity to hold data in a decentralized\nand tamper-proof way. It also supports the execution of code through\nblockchain's smart contracts, adding automation of actions to the network with\nhigh trustability. However, as smart contracts are visible by anybody on the\nnetwork, the business data and logic may be at risk, thus companies could be\nreluctant to use such technology. This paper aims to propose a pattern that\nallows the execution of automatable legal contract clauses, where its execution\nstates are stored in an on-chain smart-contract and the logic needed to enforce\nit wraps it off-chain. An engine completes this pattern by running a business\nprocess that corresponds to the legal contract. We then propose a pattern-based\nsolution based on a real-life use case: transportation of refrigerated goods.\nWe argue that this pattern guarantees companies pseudonymity and data\nconfidentiality while ensuring that an audit trail can be reconstituted through\nthe blockchain smart-contract to identify misbehavior or errors. This paper\npaves the way for a future possible implementation of the solution described,\nas well as its evaluation.\n"}
+{"abstract": "  We introduce reflection properties of cardinals in which the attributes that\nreflect are expressible by infinitary formulas whose lengths can be strictly\nlarger than the cardinal under consideration. This kind of generalized\nreflection principle leads to the definitions of\n$L_{\\kappa^+,\\kappa^+}$-indescribability and $\\Pi^1_\\xi$-indescribability of a\ncardinal $\\kappa$ for all $\\xi<\\kappa^+$. In this context, universal\n$\\Pi^1_\\xi$ formulas exist, there is a normal ideal associated to\n$\\Pi^1_\\xi$-indescribability and the notions of $\\Pi^1_\\xi$-indescribability\nyield a strict hierarchy below a measurable cardinal. Additionally, given a\nregular cardinal $\\mu$, we introduce a diagonal version of Cantor's derivative\noperator and use it to extend Bagaria's \\cite{MR3894041} sequence\n$langle\\tau_\\xi:\\xi<\\mu\\rangle$ of derived topologies on $\\mu$ to\n$\\langle\\tau_\\xi:\\xi<\\mu^+\\rangle$. Finally, we prove that for all $\\xi<\\mu^+$,\nif there is a stationary set of $\\alpha<\\mu$ that have a high enough degree of\nindescribability, then there are stationarily-many $\\alpha<\\mu$ that are\nnonisolated points in the space $(\\mu,\\tau_{\\xi+1})$.\n"}
+{"abstract": "  We study the robust mean estimation problem in high dimensions, where less\nthan half of the datapoints can be arbitrarily corrupted. Motivated by\ncompressive sensing, we formulate the robust mean estimation problem as the\nminimization of the $\\ell_0$-`norm' of an \\emph{outlier indicator vector},\nunder a second moment constraint on the datapoints. We further relax the\n$\\ell_0$-`norm' to the $\\ell_p$-norm ($0<p\\leq 1$) in the objective and prove\nthat the global minima for each of these objectives are order-optimal for the\nrobust mean estimation problem. Then we propose a computationally tractable\niterative $\\ell_p$-minimization and hard thresholding algorithm that outputs an\norder-optimal robust estimate of the population mean. Both synthetic and real\ndata experiments demonstrate that the proposed algorithm outperforms\nstate-of-the-art robust mean estimation methods. The source code will be made\navailable at GitHub.\n"}
+{"abstract": "  Objective: The purpose of this paper is to identify the largest cognitive\nchallenges faced by novices developing software in teams.\n  Method: Using grounded theory, we conducted an ethnographic study for two\nmonths following four ten person novice teams, consisting of computer science\nstudents, developing software systems.\n  Result: This paper identifies version control and merge operations as the\nlargest challenge faced by the novices. The literature studies reveal that\nlittle research appears to have been carried out in the area of version control\nfrom a user perspective.\n  Limitations: A qualitative study on students is not applicable in all\ncontexts, but the result is credible and grounded in data and substantiated by\nextant literature.\n  Conclusion: We conclude that our findings motivate further research on\ncognitive perspectives to guide improvement of software engineering and its\ntools.\n"}
+{"abstract": "  In the light of recent experimental progress in determining the pressure and\nshear distributions in the proton, these quantities are calculated in a model\nwith confined quarks supplemented by the pion field required by chiral\nsymmetry. The incorporation of the pion contributions is shown to account for\nthe long-range distributions, in general agreement with the experimentally\nextracted quark contributions. The results of the model are also compared with\nlattice QCD results at unphysically large quark mass.\n"}
+{"abstract": "  This study provides an efficient approach for using text data to calculate\npatent-to-patent (p2p) technological similarity, and presents a hybrid\nframework for leveraging the resulting p2p similarity for applications such as\nsemantic search and automated patent classification. We create embeddings using\nSentence-BERT (SBERT) based on patent claims. We leverage SBERTs efficiency in\ncreating embedding distance measures to map p2p similarity in large sets of\npatent data. We deploy our framework for classification with a simple Nearest\nNeighbors (KNN) model that predicts Cooperative Patent Classification (CPC) of\na patent based on the class assignment of the K patents with the highest p2p\nsimilarity. We thereby validate that the p2p similarity captures their\ntechnological features in terms of CPC overlap, and at the same demonstrate the\nusefulness of this approach for automatic patent classification based on text\ndata. Furthermore, the presented classification framework is simple and the\nresults easy to interpret and evaluate by end-users. In the out-of-sample model\nvalidation, we are able to perform a multi-label prediction of all assigned CPC\nclasses on the subclass (663) level on 1,492,294 patents with an accuracy of\n54% and F1 score > 66%, which suggests that our model outperforms the current\nstate-of-the-art in text-based multi-label and multi-class patent\nclassification. We furthermore discuss the applicability of the presented\nframework for semantic IP search, patent landscaping, and technology\nintelligence. We finally point towards a future research agenda for leveraging\nmulti-source patent embeddings, their appropriateness across applications, as\nwell as to improve and validate patent embeddings by creating domain-expert\ncurated Semantic Textual Similarity (STS) benchmark datasets.\n"}
+{"abstract": "  It was shown recently that the heart of a twin cotorsion pair on an\nextriangulated category is semi-abelian. In this article, we consider a special\nkind of hearts of twin cotorsion pairs induced by $d$-cluster tilting\nsubcategories in extriangulated categories. We give a necessary and sufficient\ncondition for such hearts to be abelian. In particular, we also can see that\nsuch hearts are hereditary. As an application, this generalizes the work by Liu\nin an exact case, thereby providing new insights in a triangulated case.\n"}
+{"abstract": "  We establish enhanced bounds on Cheeger-Gromov rho-invariants for general\n3-manifolds and yet stronger bounds for special classes of 3-manifold. As key\ningredients, we construct chain null-homotopies whose complexity is linearly\nbounded by its boundary's. This result can be regarded as an algebraic\ntopological analogue of Gromov's conjecture for quantitative topology. The\nauthor hopes for applications to various fields including the smooth knot\nconcordance group, quantitative topology, and complexity theory.\n"}
+{"abstract": "  The formation of positive ions of antihydrogen $\\bar{\\rm{H}}^+$ via the three\nbody reaction (i) $\\rm{e}^+ + \\rm{e}^- + \\bar{\\rm{H}} \\rightarrow \\rm{e}^- +\n\\bar{\\rm{H}}^+$ is considered. In reaction (i), free positrons $\\rm{e}^+$ are\nincident on antihydrogen $\\bar{\\rm{H}}$ embedded in a gas of low-energy ($\\sim\n$ meV) electrons and, due to the positron-electron interaction, a positron is\nattached to $\\bar{\\rm{H}}$ whereas an electron carries away the energy excess.\nWe compare reaction (i) with two radiative attachment mechanisms. One of them\nis (ii) spontaneous radiative attachment, in which the ion is formed due to\nspontaneous emission of a photon by a positron incident on $\\bar{\\rm{H}}$. The\nother is (iii) two-center dileptonic attachment which takes place in the\npresence of a neighboring atom B and in which an incident positron is attached\nto $\\bar{\\rm{H}}$ via resonant transfer of energy to B with its subsequent\nrelaxation through spontaneous radiative decay. It is shown that reaction (i)\ncan strongly dominate over mechanisms (ii) and (iii) for positron energies\nbelow $0.1$ eV. It is also shown that at the energies considered reaction (i)\nwill not be influenced by annihilation and that the reaction $\\rm{e}^+ +\n\\rm{e}^+ + \\bar{\\rm{H}} \\rightarrow \\rm{e}^+ + \\bar{\\rm{H}}^+$ has a\nvanishingly small rate compared to reaction (i).\n"}
+{"abstract": "  The eROSITA Final Equatorial-Depth Survey (eFEDS), covering ~140 square\ndegrees, was performed as part of the performance verification phase of the\neROSITA telescope on board of the Russian-German satellite\nSpectrum-Roentgen-Gamma (SRG). In this paper we present the optical follow-up\nof 542 X-ray extent selected galaxy group and cluster candidates providing\nredshifts and cluster confirmation for the full sample. We use optical imaging\ndata from the Hyper Suprime-Cam Subaru Strategic Program and from the Legacy\nSurvey to run the cluster redshift and confirmation tool MCMF as well as the\noptical cluster finder CAMIRA at the location of the X-ray candidates. While\nproviding redshift estimates for all 542 candidates, we construct an optically\nconfirmed sample of 477 clusters and groups with a residual contamination of\n6%. Of these, 470 (98.5%) are confirmed using MCMF and 7 systems are added\nthrough cross matching with spectroscopic group catalogs. Using observable to\nobservable scaling and the applied confirmation threshold, we predict 8 +/- 2\nreal systems have been excluded with the MCMF cut required to build this low\ncontamination sample. This number is in good agreement with the 7 systems\nrecovered through cross matching. Thus, we expect those 477 systems to include\n>99% of all true clusters in the candidate list. Using an MCMF independent\nmethod, we confirm the catalog contamination of the confirmed subsample to be 6\n+/- 3% and find 17 +/- 3% contamination for the full X-ray sample. The\nestimated contamination of the fulls sample is in agreement with MCMF dependent\nestimate of ~17% and the expectation from dedicated X-ray simulations of ~20%.\nWe further present a sample of optically selected merging cluster candidates.\n"}
+{"abstract": "  The soft X-rays (SXRs: 90--150 $\\r{A}$) are among the most interesting\nspectral ranges to be investigated in the next generation of solar missions due\nto their unique capability of diagnosing phenomena involving hot plasma with\ntemperatures up to 15~MK. Multilayer (ML) coatings are crucial for developing\nSXR instrumentation, as so far they represent the only viable option for the\ndevelopment of high-efficiency mirrors in this spectral range. However, the\ncurrent standard MLs are characterized by a very narrow spectral band which is\nincompatible with the science requirements expected for a SXR spectrometer.\nNevertheless, recent advancement in the ML technology has made the development\nof non-periodic stacks repeatable and reliable, enabling the manufacturing of\nSXR mirrors with a valuable efficiency over a large range of wavelengths.\n  In this work, after reviewing the state-of-the-art ML coatings for the SXR\nrange, we investigate the possibility of using M-fold and aperiodic stacks for\nthe development of multiband SXR spectrometers. After selecting a possible\nchoice of key spectral lines, some trade-off studies for an eight-bands\nspectrometer are also presented and discussed, giving an evaluation of their\nfeasibility and potential performance.\n"}
+{"abstract": "  Tunneling atomic force microscopy (TUNA) was used at ambient conditions to\nmeasure the current-voltage ($I$-$V$) characteristics at clean surfaces of\nhighly oriented graphite samples with Bernal and rhombohedral stacking orders.\nThe characteristic curves measured on Bernal-stacked graphite surfaces can be\nunderstood with an ordinary self-consistent semiconductor modeling and quantum\nmechanical tunneling current derivations. We show that the absence of a voltage\nregion without measurable current in the $I$-$V$ spectra is not a proof of the\nlack of an energy band gap. It can be induced by a surface band bending due to\na finite contact potential between tip and sample surface. Taking this into\naccount in the model, we succeed to obtain a quantitative agreement between\nsimulated and measured tunnel spectra for band gaps $(12 \\ldots 37)$\\,meV, in\nagreement to those extracted from the exponential temperature decrease of the\nlongitudinal resistance measured in graphite samples with Bernal stacking\norder. In contrast, the surface of relatively thick graphite samples with\nrhombohedral stacking reveals the existence of a maximum in the first\nderivative $dI/dV$, a behavior compatible with the existence of a flat band.\nThe characteristics of this maximum are comparable to those obtained at low\ntemperatures with similar techniques.\n"}
+{"abstract": "  Notably innovative technologies will permit compact and affordable advanced\naccelerators as the linear collider and X-ray free-electron lasers (XFELs) with\naccelerating gradients over twice the value achieved with current technologies.\nIn particular XFEL is able to produce coherent X-ray pulses with peak\nbrightness 10 orders of magnitude greater than preceding approaches, which has\nrevolutionized numerous fields through imaging of the nanoscopic world at the\ntime and length scale of atom-based systems, that is of femtosecond and\nAngstrom. There is a strong interest for combining these two fields, to form a\nproper tool with the goal of producing a very compact XFEL in order to\ninvestigate multi-disciplinary researches in chemistry, biology, materials\nscience, medicine and physics.\n  In the framework of the Ultra -Compact XFEL project (UC-XFEL) under study at\nthe UCLA, an ultra high gradient higher harmonic RF accelerating structure for\nthe longitudinal space phase linearization is foreseen. To this aim, a Ka-Band\nlinearizer (34.2 GHz) with an integrated voltage of at least 15 MV working on\n6th harmonic with respect to the main Linac frequency (5.712 GHz) is required.\nWe here present the electromagnetic design of a cold ultra compact Ka-band SW\nlinearizer, 8 cm long, working on pi mode with an ultra high accelerating\ngradient (beyond 100 MV/m) and minimum surface electric field for minimizing\nthe probability of RF breakdown. Moreover, we discuss a TW option and compare\nit with the initial SW structure, by means of main RF parameters as well as\nbeam-dynamics considerations. The numerical electromagnetic studies have been\nperformed by using the well known SuperFish, HFSS and CST.\n"}
+{"abstract": "  Modeling and predicting solar events, in particular, the solar ramping event\nis critical for improving situational awareness for solar power generation\nsystems. Solar ramping events are significantly impacted by weather conditions\nsuch as temperature, humidity, and cloud density. Discovering the correlation\nbetween different locations and times is a highly challenging task since the\nsystem is complex and noisy. We propose a novel method to model and predict\nramping events from spatial-temporal sequential solar radiation data based on a\nspatio-temporal interactive Bernoulli process. We demonstrate the good\nperformance of our approach on real solar radiation datasets.\n"}
+{"abstract": "  Gas has been detected in many exoplanetary systems ($>$10 Myr), thought to be\nreleased in the destruction of volatile-rich planetesimals orbiting in\nexo-Kuiper belts. In this letter, we aim to explore whether gas is also\nexpected in the Kuiper belt (KB) in our Solar System. To quantify the gas\nrelease in our Solar System, we use models for gas release that have been\napplied to extrasolar planetary systems, as well as a physical model that\naccounts for gas released due to the progressive internal warming of large\nplanetesimals. We find that only bodies larger than about 4 km can still\ncontain CO ice after 4.6 Gyr of evolution. This finding may provide a clue as\nto why Jupiter-family comets, thought to originate in the Kuiper belt, are\ndeficient in CO compared to Oort-clouds comets. We predict that gas is still\nproduced in the KB right now at a rate of $2 \\times 10^{-8}$ M$_\\oplus$/Myr for\nCO and orders of magnitude more when the Sun was younger. Once released, the\ngas is quickly pushed out by the Solar wind. Therefore, we predict a gas wind\nin our Solar System starting at the KB location and extending far beyond with\nregards to the heliosphere with a current total CO mass of $\\sim 2 \\times\n10^{-12}$ M$_\\oplus$. We also predict the existence of a slightly more massive\natomic gas wind made of carbon and oxygen (neutral and ionized) with a mass of\n$\\sim 10^{-11}$ M$_\\oplus$. We predict that gas is currently present in our\nSolar System beyond the Kuiper belt and that although it cannot be detected\nwith current instrumentation, it could be observed in the future with an in\nsitu mission using an instrument similar to Alice on New Horizons with larger\ndetectors. Our model of gas release due to slow heating may also work for\nexoplanetary systems and provide the first real physical mechanism for the gas\nobservations.\n"}
+{"abstract": "  We advance the theory of strong colorings over partitions, studying both\npositive and negative Ramsey relations at the level of the uncountable.\n  A correspondence between combinatorial properties of partitions and chain\nconditions of natural forcing notions for destroying strong colorings over them\nis uncovered and enables us to prove positive Ramsey relations for $\\aleph_1$\nfrom weak forms of Martin's Axiom, thereby answering two questions from\n[CKS21]. Positive Ramsey relations for $\\aleph_2$ and higher cardinals are\nestablished as well and without making use of large cardinals.\n  We also provide a group of pump-up theorems for strong colorings over\npartitions. Some of them solve more problems from [CKS21].\n"}
+{"abstract": "  We report results of large scale ground state density matrix renormalization\ngroup(DMRG) calculations on $t$-$t'$-$J$ cylinders with circumferences 6 and 8.\nWe determine a rough phase diagram which appears to approximate the 2D system.\nWhile for many properties, positive and negative $t'$ values ($t'/t = \\pm 0.2$)\nappear to correspond to electron and hole doped cuprate systems, respectively,\nthe behavior of superconductivity itself shows an inconsistency between the\nmodel and the materials. The $t'<0$ (hole doped) region shows\nantiferromagnetism limited to very low doping, stripes more generally, and the\nfamiliar Fermi-surface of the hole doped cuprates. However, we find $t'<0$\nstrongly suppresses superconductivity. The $t'>0$ (electron doped) region shows\nthe expected circular Fermi pocket of holes around the $(\\pi,\\pi)$ point and a\nbroad low-doped region of coexisting antiferromagnetism and $d$-wave pairing\nwith a strong triplet $p$ component at wave-vector $(\\pi,\\pi)$ induced by the\nantiferromagnetism and $d$-wave pairing. The pairing for the electron low-doped\nsystem with $t'>0$ is strong and unambiguous in the DMRG simulations. At larger\ndoping another broad region with stripes in addition to weaker $d$-wave pairing\nand striped $p$-wave pairing appears. In a small doping region near $x=0.08$\nfor $t'\\sim-0.2$, we find a new type of stripe involving unpaired holes located\npredominantly on chains spaced three lattice spacings apart. The undoped\ntwo-leg ladder regions in between mimic the short-ranged spin correlations seen\nin two-leg Heisenberg ladders.\n"}
+{"abstract": "  Let $K_n$ denote the number of distinct values among the first $n$ terms of\nan infinite exchangeable sequence of random variables $(X_1,X_2,\\ldots)$. We\nprove for $n=3$ that the extreme points of the convex set of all possible laws\nof $K_3$ are those derived from i.i.d. sampling from discrete uniform\ndistributions and the limit case with $P(K_3=3)=1$, and offer a conjecture for\nlarger $n$. We also consider variants of the problem for finite exchangeable\nsequences and exchangeable random partitions.\n"}
+{"abstract": "  We study the phase diagram of an extended parafermion chain, which, in\naddition to terms coupling parafermions on neighbouring sites, also possesses\nterms involving four sites. Via a Fradkin--Kadanoff transformation the\nparafermion chain is shown to be equivalent to the non-chiral $\\mathbb{Z}_3$\naxial next-nearest neighbour Potts model. We discuss a possible experimental\nrealisation using hetero-nanostructures. The phase diagram contains several\ngapped phases, including a topological phase where the system possesses three\n(nearly) degenerate ground states, and a gapless Luttinger-liquid phase.\n"}
+{"abstract": "  Robots need to be able to work in multiple different environments. Even when\nperforming similar tasks, different behaviour should be deployed to best fit\nthe current environment. In this paper, We propose a new approach to\nnavigation, where it is treated as a multi-task learning problem. This enables\nthe robot to learn to behave differently in visual navigation tasks for\ndifferent environments while also learning shared expertise across\nenvironments. We evaluated our approach in both simulated environments as well\nas real-world data. Our method allows our system to converge with a 26%\nreduction in training time, while also increasing accuracy.\n"}
+{"abstract": "  News recommendation is important for online news services. Precise user\ninterest modeling is critical for personalized news recommendation. Existing\nnews recommendation methods usually rely on the implicit feedback of users like\nnews clicks to model user interest. However, news click may not necessarily\nreflect user interests because users may click a news due to the attraction of\nits title but feel disappointed at its content. The dwell time of news reading\nis an important clue for user interest modeling, since short reading dwell time\nusually indicates low and even negative interest. Thus, incorporating the\nnegative feedback inferred from the dwell time of news reading can improve the\nquality of user modeling. In this paper, we propose a neural news\nrecommendation approach which can incorporate the implicit negative user\nfeedback. We propose to distinguish positive and negative news clicks according\nto their reading dwell time, and respectively learn user representations from\npositive and negative news clicks via a combination of Transformer and additive\nattention network. In addition, we propose to compute a positive click score\nand a negative click score based on the relevance between candidate news\nrepresentations and the user representations learned from the positive and\nnegative news clicks. The final click score is a combination of positive and\nnegative click scores. Besides, we propose an interactive news modeling method\nto consider the relatedness between title and body in news modeling. Extensive\nexperiments on real-world dataset validate that our approach can achieve more\naccurate user interest modeling for news recommendation.\n"}
+{"abstract": "  The hunt for exotic quantum phase transitions described by emergent\nfractionalized degrees of freedom coupled to gauge fields requires a precise\ndetermination of the fixed point structure from the field theoretical side, and\nan extreme sensitivity to weak first-order transitions from the numerical side.\nAddressing the latter, we revive the classic definition of the order parameter\nin the limit of a vanishing external field at the transition. We demonstrate\nthat this widely understood, yet so far unused approach provides a diagnostic\ntest for first-order versus continuous behavior that is distinctly more\nsensitive than current methods. We first apply it to the family of $Q$-state\nPotts models, where the nature of the transition is continuous for $Q\\leq4$ and\nturns (weakly) first order for $Q>4$, using an infinite system matrix product\nstate implementation. We then employ this new approach to address the unsettled\nquestion of deconfined quantum criticality in the $S=1/2$ N\\'eel to valence\nbond solid transition in two dimensions, focusing on the square lattice $J$-$Q$\nmodel. Our quantum Monte Carlo simulations reveal that both order parameters\nremain finite at the transition, directly confirming a first-order scenario\nwith wide reaching implications in condensed matter and quantum field theory.\n"}
+{"abstract": "  Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of\ncancer death in the United States. Predicting tumors like PDACs (including both\nclassification and segmentation) from medical images by deep learning is\nbecoming a growing trend, but usually a large number of annotated data are\nrequired for training, which is very labor-intensive and time-consuming. In\nthis paper, we consider a partially supervised setting, where cheap image-level\nannotations are provided for all the training data, and the costly per-voxel\nannotations are only available for a subset of them. We propose an Inductive\nAttention Guidance Network (IAG-Net) to jointly learn a global image-level\nclassifier for normal/PDAC classification and a local voxel-level classifier\nfor semi-supervised PDAC segmentation. We instantiate both the global and the\nlocal classifiers by multiple instance learning (MIL), where the attention\nguidance, indicating roughly where the PDAC regions are, is the key to bridging\nthem: For global MIL based normal/PDAC classification, attention serves as a\nweight for each instance (voxel) during MIL pooling, which eliminates the\ndistraction from the background; For local MIL based semi-supervised PDAC\nsegmentation, the attention guidance is inductive, which not only provides\nbag-level pseudo-labels to training data without per-voxel annotations for MIL\ntraining, but also acts as a proxy of an instance-level classifier.\nExperimental results show that our IAG-Net boosts PDAC segmentation accuracy by\nmore than 5% compared with the state-of-the-arts.\n"}
+{"abstract": "  The Bluetooth Special Interest Group (Bluetooth SIG) introduced a new feature\nfor highly accurate localization called the Direction Finding in the Bluetooth\nCore Specification 5.1. Since this new localization feature is relatively new,\ndespite the significant interest of industry and academia in the accurate\npositioning of Bluetooth devices/tags, there are only a handful of experimental\nstudies conducted to evaluate the performance of the new technology.\nFurthermore, these experimental works are constrained to only indoor\nenvironments or performed with hardware emulation of Bluetooth 5.1 via\nUniversal Software Radio Peripherals (USRPs). In this paper, we perform an\nexperimental study on the positioning accuracy of the direction finding using\nCOTS Bluetooth 5.1 devices in booth indoor and outdoor environments to provide\ninsights on the performance gap under these different experimental settings.\nOur results demonstrate that the average angular error in an outdoor\nenvironment is 0.28 degrees, significantly improving the angular error measured\nin an indoor environment by 73%. It is also demonstrated that the average\npositioning accuracy measured in an outdoor environment is 22cm which is 39.7%\nsmaller than that measured in an indoor environment.\n"}
+{"abstract": "  Silicon-substituted hydroxyapatite (SiHA) macroporous scaffolds have been\nprepared by robocasting. In order to optimize their bone regeneration\nproperties, we have manufactured these scaffolds presenting different\nmicrostructures: nanocrystalline and crystalline. Moreover, their surfaces have\nbeen decorated with vascular endothelial growth factor (VEGF) to evaluate the\npotential coupling between vascularization and bone regeneration. In vitro cell\nculture tests evidence that nanocrystalline SiHA hinders pre-osteblast\nproliferation, whereas the presence of VEGF enhances the biological functions\nof both endothelial cells and pre-osteoblasts. The bone regeneration capability\nhas been evaluated using an osteoporotic sheep model. In vivo observations\nstrongly correlate with in vitro cell culture tests. Those scaffolds made of\nnanocrystalline SiHA were colonized by fibrous tissue, promoted inflammatory\nresponse and fostered osteoclast recruitment. These observations discard\nnanocystalline SiHA as a suitable material for bone regeneration purposes. On\nthe contrary, those scaffolds made of crystalline SiHA and decorated with VEGF\nexhibited bone regeneration properties, with high ossification degree, thicker\ntrabeculae and higher presence of osteoblasts and blood vessels. Considering\nthese results, macroporous scaffolds made of SiHA and decorated with VEGF are\nsuitable bone grafts for regeneration purposes, even in adverse pathological\nscenarios such as osteoporosis.\n"}
+{"abstract": "  The narrow-line region (NLR) consists of gas clouds ionized by the strong\nradiation from the active galactic nucleus (AGN), distributed in the spatial\nscale of AGN host galaxies. The strong emission lines from the NLR are useful\nto diagnose physical and chemical properties of the interstellar medium in AGN\nhost galaxies. However, the origin of the NLR is unclear; the gas clouds in\nNLRs may be originally in the host and photoionized by the AGN radiation, or\nthey may be transferred from the nucleus with AGN-driven outflows. For studying\nthe origin of the NLR, we systematically investigate the gas density and\nvelocity dispersion of NLR gas clouds using a large spectroscopic data set\ntaken from the Sloan Digital Sky Survey. The [S II] emission-line flux ratio\nand [O III] velocity width of 9,571 type-2 Seyfert galaxies and 110,041\nstar-forming galaxies suggest that the gas density and velocity dispersion of\nNLR clouds in Seyfert galaxies (ne ~ 194 cm-3 and sigma([O III]) ~ 147 km s-1)\nare systematically larger than those of clouds in H II regions of star-forming\ngalaxies (ne ~ 29 cm-3 and sigma([O III]) ~ 58 km s-1). Interestingly, the\nelectron density and velocity dispersion of NLR gas clouds are larger for\nSeyfert galaxies with a higher [O III]/Hbeta flux ratio, i.e., with a more\nactive AGN. We also investigate the spatially-resolved kinematics of ionized\ngas clouds using the Mapping Nearby Galaxies at the Apache Point Observatory\n(MaNGA) survey data for 90 Seyfert galaxies and 801 star-forming galaxies. We\nfind that the velocity dispersion of NLR gas clouds in Seyfert galaxies is\nlarger than that in star-forming galaxies at a fixed stellar mass, at both\ncentral and off-central regions. These results suggest that gas clouds in NLRs\ncome from the nucleus, probably through AGN outflows.\n"}
+{"abstract": "  In this work, we investigate the mean-field limit of a model consisting in $m\n\\geq 1 $ tracer particles, coupled to an interacting boson field. We assume the\nmass of the tracer particles and the expected number of bosons to be of the\nsame order of magnitude $N \\geq 1 $ and we investigate the $N\\rightarrow \\infty\n$ limit. In particular, we show that the limiting system can be effectively\ndescribed by a pair of variables $( \\textbf{X}_t ,\\varphi_t ) \\in\n\\mathbb{R}^{3m} \\times H^1(\\mathbb{R}^3)$ that solve a mean-field equation. Our\nmethods are based on proving estimates for the number of bosonic particles in a\nsuitable \\textit{fluctuation state} $\\Omega_{N,t }$. The main diffculty of the\nproblem comes from the fact that the interaction with the tracer particles can\ncreate or destroy bosons for states close to the vacuum.\n"}
+{"abstract": "  In 1869, Jordan proved that the set $\\mathcal{T}$ of all finite group that\ncan be represented as the automorphism group of a tree is containing the\ntrivial group and it is closed under taken direct product of groups of lower\norder in $\\mathcal{T}$ and wreath product of a member in $\\mathcal{T}$ and the\nsymmetric group on $n$ symbols. The aim of this paper is to continue this work\nand another works by Klav$\\acute{\\rm i}$k and Zeman in 2017 to present a class\n$\\mathcal{S}$ of finite groups for which the automorphism group of each\nbicyclic graph is a member of $\\mathcal{S}$ and this class is minimal with this\nproperty.\n"}
+{"abstract": "  We consider here an elliptic coupled system describing the dynamics of liquid\ncrystals flows. This system is posed on the whole n-dimensional space. We\nintroduce first the notion of very weak solutions for this system. Then, within\nthe fairly general framework of the Morrey spaces, we derive some sufficient\nconditions on the very weak solutions which improve their regularity. As a\nbi-product, we also prove a new regularity criterium for the time-independing\nNavier-Stokes equations.\n"}
+{"abstract": "  Let $S$ be a set of $n$ sites, each associated with a point in $\\mathbb{R}^2$\nand a radius $r_s$ and let $\\mathcal{D}(S)$ be the disk graph on $S$. We\nconsider the problem of designing data structures that maintain the\nconnectivity structure of $\\mathcal{D}(S)$ while allowing the insertion and\ndeletion of sites. For unit disk graphs we describe a data structure that has\n$O(\\log^2n)$ amortized update time and $O((\\log n)/(\\log\\log n))$ amortized\nquery time.\n  For disk graphs where the ratio $\\Psi$ between the largest and smallest\nradius is bounded, we consider the decremental and the incremental case\nseparately, in addition to the fully dynamic case. In the fully dynamic case we\nachieve amortized $O(\\Psi \\lambda_6(\\log n) \\log^{9}n)$ update time and $O(\\log\nn)$ query time, where $\\lambda_s(n)$ is the maximum length of a\nDavenport-Schinzel sequence of order $s$ on $n$ symbols. This improves the\nupdate time of the currently best known data structure by a factor of $\\Psi$ at\nthe cost of an additional $O(\\log \\log n)$ factor in the query time. In the\nincremental case we manage to achieve a logarithmic dependency on $\\Psi$ with a\ndata structure with $O(\\alpha(n))$ query and $O(\\log\\Psi \\lambda_6(\\log n)\n\\log^{9}n)$ update time.\n  For the decremental setting we first develop a new dynamic data structure\nthat allows us to maintain two sets $B$ and $P$ of disks, such than at a\ndeletion of a disk from $B$ we can efficiently report all disks in $P$ that no\nlonger intersect any disk of $B$. Having this data structure at hand, we get\ndecremental data structures with an amortized query time of $O((\\log n)/(\\log\n\\log n))$ supporting $m$ deletions in $O((n\\log^{5}n + m \\log^{9}n)\n\\lambda_6(\\log n) + n\\log\\Psi\\log^4n)$ overall time for bounded radius ratio\n$\\Psi$ and $O(( n\\log^{6} n + m \\log^{10}n) \\lambda_6(\\log n))$ for general\ndisk graphs.\n"}
+{"abstract": "  Accidents that vehicles cross median and enter opposite lane happen\nfrequently, and the existing median barrier has weak anti-collision strength. A\nnew combined median barrier (NCMB) consisted of W-beam guardrail and concrete\nstructure was proposed to decrease deformation and enhance anti-collision\nstrength in this paper. However, there were some uncertainties in the initial\ndesign of the NCMB. If the uncertainties were not considered in the design\nprocess, the optimization objectives were especially sensitive to the small\nfluctuation of the variables, and it might result in design failure. For this\npurpose, the acceleration and deflection were taken as objectives; post\nthickness, W-beam thickness and post spacing were chosen as design variables;\nthe velocity, mass of vehicle and the yield stress of barrier components were\ntaken as noise factors, a multi-objective robust optimization is carried out\nfor the NCMB based on Taguchi and grey relational analysis (GRA). The results\nindicate that the acceleration and deflection after optimization are reduced by\n47.3% and 76.7% respectively; Signal-to-noise ratio (SNR) of objectives after\noptimization are increased, it greatly enhances the robustness of the NCMB. The\nresults demonstrate that the effectiveness of the methodology that based on\nTaguchi method and grey relational analysis.\n"}
+{"abstract": "  We perform a numerical analysis of mid-infrared photoluminescence emitted by\npraseodymium (III) doped chalcogenide selenide glass pumped at near-infrared\nwavelengths. The results obtained show that an effective inversion of level\npopulations can be achieved using both 1480 nm and 1595 nm laser diodes. The\nrate of the spontaneous emission achieved when pumping at 1480 nm and 1595 nm\nis comparable to this achieved using the standard pumping wavelength of 2040\nnm.\n"}
+{"abstract": "  The theory of magnetoresistance of the edge state of a two-dimensional\ntopological insulator is developed. The magnetic field violates the\ntime-reversal invariance. Magnetoresistance arises due to the energy gap opened\nby a magnetic field parallel to the sample surface. The combined action of\nimpurities and the magnetic field causes backscattering of edge electrons.\nAlthough impurities are necessary for scattering, sufficiently strong\ninteraction with impurities leads to the suppression of backscattering.\n"}
+{"abstract": "  The H.E.S.S. Galactic Plane Survey (HGPS) revealed 78 TeV sources among which\n47 are not clearly associated with a known object. We present a multiwavelength\napproach to constrain the origin of the emission from unidentified HGPS\nsources. We present a generic pipeline that explores a large database of\nmultiwavelength archival data toward any region in the Galactic plane. Along\nwith a visual inspection of the retrieved multiwavelength observations to\nsearch for faint and uncataloged counterparts, we derive a radio spectral index\nthat helps disentangle thermal from nonthermal emission and a mean magnetic\nfield through X-ray and TeV data in case of a leptonic scenario. We also search\nfor a spectral connection between the GeV and the TeV regimes with the\nFermi-LAT cataloged sources that may be associated with the unidentified HGPS\nsource. We complete the association procedure with catalogs of known objects\nand with the source catalogs from instruments whose data are retrieved. The\nmethod is applied on two unidentified sources, namely HESS J1427$-$608 and HESS\nJ1458$-$608, for which the multiwavelength constraints favor the pulsar wind\nnebula (PWN) scenario. We model their broadband nonthermal spectra in a\nleptonic scenario with a magnetic field $B \\lesssim 10$ $\\mu$G, which is\nconsistent with that obtained from ancient PWNe. We place both sources within\nthe context of the TeV PWN population to estimate the spin-down power and the\ncharacteristic age of the putative pulsar. We also shed light on two possibly\nsignificant $\\gamma$-ray excesses in the HGPS: the first is located in the\nsouth of the unidentified source HESS J1632$-$478 and the second is spatially\ncoincident with the synchrotron-emitting supernova remnant G28.6$-$0.1. The\nmultiwavelength counterparts found toward both $\\gamma$-ray excesses make these\npromising candidates for being new very-high energy $\\gamma$-ray sources.\n"}
+{"abstract": "  Human volumetric capture is a long-standing topic in computer vision and\ncomputer graphics. Although high-quality results can be achieved using\nsophisticated off-line systems, real-time human volumetric capture of complex\nscenarios, especially using light-weight setups, remains challenging. In this\npaper, we propose a human volumetric capture method that combines temporal\nvolumetric fusion and deep implicit functions. To achieve high-quality and\ntemporal-continuous reconstruction, we propose dynamic sliding fusion to fuse\nneighboring depth observations together with topology consistency. Moreover,\nfor detailed and complete surface generation, we propose detail-preserving deep\nimplicit functions for RGBD input which can not only preserve the geometric\ndetails on the depth inputs but also generate more plausible texturing results.\nResults and experiments show that our method outperforms existing methods in\nterms of view sparsity, generalization capacity, reconstruction quality, and\nrun-time efficiency.\n"}
+{"abstract": "  The goal of this paper is to test solids4Foam, the fluid-structure\ninteraction (FSI) toolbox developed for foam-extend (a branch of OpenFOAM), and\nassess its flexibility in handling more complex flows. For this purpose, we\nconsider the interaction of an incompressible fluid described by a Leray model\nwith a hyperelastic structure modeled as a Saint Venant-Kirchhoff material. We\nfocus on a strongly coupled, partitioned fluid-structure interaction (FSI)\nsolver in a finite volume environment, combined with an arbitrary\nLagrangian-Eulerian approach to deal with the motion of the fluid domain. For\nthe implementation of the Leray model, which features a nonlinear differential\nlow-pass filter, we adopt a three-step algorithm called Evolve-Filter-Relax. We\nvalidate our approach against numerical data available in the literature for\nthe 3D cross flow past a cantilever beam at Reynolds number 100 and 400.\n"}
+{"abstract": "  We analyze the electromagnetic field near a plane interface between a\nconductive and a dielectric media, under conditions supporting surface\nplasmon-polariton (SPP) propagation. The conductive medium is described by the\nhydrodynamic electron-gas model that enables a consistent analysis of the\nfield-induced variations of the electron density and velocity at the interface\nand its nearest vicinity. The distributions of electromagnetic dynamical\ncharacteristics: energy, energy flow, spin and momentum are calculated\nanalytically and illustrated numerically, employing silver-vacuum interface as\nan example. A set of the \"field\" and material contributions to the energy, spin\nand momentum are explicitly identified and classified with respect to their\nphysical origins and properties, and the orbital (canonical) and spin\n(Belinfante) momentum constituents are separately examined. In this context, a\nprocedure for the spin-orbital momentum decomposition in the presence of free\ncharges is proposed and substantiated. The microscopic results agree with the\nknown phenomenological data but additionally show specific nanoscale structures\nin the near-interface behavior of the SPP energy and momentum, which can be\ndeliberately created, controlled and used in nanotechnology applications.\n"}
+{"abstract": "  In this paper, a new $P_{2}-P_{1}$ finite element pair is proposed for\nincompressible fluid. For this pair, the discrete inf-sup condition and the\ndiscrete Korn's inequality hold on general triangulations. It yields exactly\ndivergence-free velocity approximations when applied to models of\nincompressible flows. The robust capacity of the pair for incompressible flows\nare verified theoretically and numerically.\n"}
+{"abstract": "  In this paper, we study PAC learnability and certification of predictions\nunder instance-targeted poisoning attacks, where the adversary who knows the\ntest instance may change a fraction of the training set with the goal of\nfooling the learner at the test instance. Our first contribution is to\nformalize the problem in various settings and to explicitly model subtle\naspects such as the proper or improper nature of the learning, learner's\nrandomness, and whether (or not) adversary's attack can depend on it. Our main\nresult shows that when the budget of the adversary scales sublinearly with the\nsample complexity, (improper) PAC learnability and certification are\nachievable; in contrast, when the adversary's budget grows linearly with the\nsample complexity, the adversary can potentially drive up the expected 0-1 loss\nto one. We also study distribution-specific PAC learning in the same attack\nmodel and show that proper learning with certification is possible for learning\nhalf spaces under natural distributions. Finally, we empirically study the\nrobustness of K nearest neighbour, logistic regression, multi-layer perceptron,\nand convolutional neural network on real data sets against targeted-poisoning\nattacks. Our experimental results show that many models, especially\nstate-of-the-art neural networks, are indeed vulnerable to these strong\nattacks. Interestingly, we observe that methods with high standard accuracy\nmight be more vulnerable to instance-targeted poisoning attacks.\n"}
+{"abstract": "  We apply reinforcement learning to video compressive sensing to adapt the\ncompression ratio. Specifically, video snapshot compressive imaging (SCI),\nwhich captures high-speed video using a low-speed camera is considered in this\nwork, in which multiple (B) video frames can be reconstructed from a snapshot\nmeasurement. One research gap in previous studies is how to adapt B in the\nvideo SCI system for different scenes. In this paper, we fill this gap\nutilizing reinforcement learning (RL). An RL model, as well as various\nconvolutional neural networks for reconstruction, are learned to achieve\nadaptive sensing of video SCI systems. Furthermore, the performance of an\nobject detection network using directly the video SCI measurements without\nreconstruction is also used to perform RL-based adaptive video compressive\nsensing. Our proposed adaptive SCI method can thus be implemented in low cost\nand real time. Our work takes the technology one step further towards real\napplications of video SCI.\n"}
+{"abstract": "  In the modern search for life elsewhere in the Universe, we are broadly\nlooking for the following: the planets similar to Earth - physical indicators\nof habitability, and the manifestation of life - the biological signatures. A\nbiosignature is a measured parameter that has a high probability of being\ncaused by the living organisms, either atmospheric gas species or some surface\nfeatures. Therefore, the focus of a search is on a product or phenomena\nproduced by the living systems, mostly by microorganisms as these are the most\nabundant on our planet like, say, methane. However, we may need to distinguish\nthe terms `biosignature' and `bioindicator'. A biosignature is what living\norganisms produce - a bioproduct, while a bioindicator may be anything\nnecessary for life as we know it, such as water or a rocky planet. Oxygen in\nthis case is a double biomarker; first, it is a byproduct of oxygenic\nphotosynthesis and, second, it is a signature of a complex life, because\ncomplex highly organized life requires high levels of oxygen. It is possible\nthat there are other such bioindicators. For example, in the atmospheric\ncompositions of terrestrial planets in our Solar System (including Titan),\nargon is one of the major constituents, moreover it was recently acknowledged\nto be a `biologically' active gas, exhibiting organprotective and\nneuroprotective properties, especially under hypoxic conditions. Here we\npropose that argon in the atmosphere of a rocky planet is a bioindicator of a\nhighly organized life, provided that the planet is already deemed potentially\nhabitable: with water, atmosphere, and of a certain age allowing for the\ncomplex life to evolve. We also delineate its possible detection methods.\n"}
+{"abstract": "  Let $\\Omega \\subset \\mathbb{R}^d$ be a set with finite Lebesgue measure such\nthat, for a fixed radius $r>0$, the Lebesgue measure of $\\Omega \\cap B_r (x)$\nis equal to a positive constant when $x$ varies in the essential boundary of\n$\\Omega$. We prove that $\\Omega$ is a ball (or a finite union of equal balls)\nprovided it satisfies a nondegeneracy condition, which holds in particular for\nany set of diameter larger than $r$ which is either open and connected, or of\nfinite perimeter and indecomposable. The proof requires reinventing each step\nof the moving planes method by Alexandrov in the framework of measurable sets.\n"}
+{"abstract": "  In this paper we introduce and study a type of Cayley graph -- subnormal\nCayley graph. We prove that a subnormal 2-arc transitive Cayley graph is a\nnormal Cayley graph or a normal cover of a complete bipartite graph\n$K_{p^d,p^d}$ with $p$ prime. Then we obtain a generic method for constructing\nhalf-symmetric (namely edge transitive but not arc transitive) Cayley graphs.\n"}
+{"abstract": "  A novel approach to simulate simple protein-ligand systems at large time- and\nlength-scales is to couple Markov state models (MSMs) of molecular kinetics\nwith particle-based reaction-diffusion (RD) simulations, MSM/RD. Currently,\nMSM/RD lacks a mathematical framework to derive coupling schemes; is limited to\nisotropic ligands in a single conformational state, and is lacking a\nmulti-particle extensions. In this work, we address these needs by developing a\ngeneral MSM/RD framework by coarse-graining molecular dynamics into hybrid\nswitching diffusion processes. Given enough data to parametrize the model, it\nis capable of modeling protein-protein interactions over large time- and\nlength-scales, and it can be extended to handle multiple molecules. We derive\nthe MSM/RD framework, and we implement and verify it for two protein-protein\nbenchmark systems and one multiparticle implementation to model the formation\nof pentameric ring molecules. To enable reproducibility, we have published our\ncode in the MSM/RD software package.\n"}
+{"abstract": "  We show that any conformal vector field on a compact lcK manifold is Killing\nwith respect to the Gauduchon metric. Furthermore, we prove that any conformal\nvector field on a compact lcK manifold whose K\\\"ahler cover is neither flat,\nnor hyperk\\\"ahler, is holomorphic.\n"}
+{"abstract": "  Importance: An artificial intelligence (AI)-based model to predict COVID-19\nlikelihood from chest x-ray (CXR) findings can serve as an important adjunct to\naccelerate immediate clinical decision making and improve clinical decision\nmaking. Despite significant efforts, many limitations and biases exist in\npreviously developed AI diagnostic models for COVID-19. Utilizing a large set\nof local and international CXR images, we developed an AI model with high\nperformance on temporal and external validation.\n  Conclusions and Relevance: AI-based diagnostic tools may serve as an adjunct,\nbut not replacement, for clinical decision support of COVID-19 diagnosis, which\nlargely hinges on exposure history, signs, and symptoms. While AI-based tools\nhave not yet reached full diagnostic potential in COVID-19, they may still\noffer valuable information to clinicians taken into consideration along with\nclinical signs and symptoms.\n"}
+{"abstract": "  User engagement is crucial to the long-term success of a mobile app. Several\nmetrics, such as dwell time, have been used for measuring user engagement.\nHowever, how to effectively predict user engagement in the context of mobile\napps is still an open research question. For example, do the mobile usage\ncontexts (e.g.,~time of day) in which users access mobile apps impact their\ndwell time? Answers to such questions could help mobile operating system and\npublishers to optimize advertising and service placement. In this paper, we\nfirst conduct an empirical study for assessing how user characteristics,\ntemporal features, and the short/long-term contexts contribute to gains in\npredicting users' app dwell time on the population level. The comprehensive\nanalysis is conducted on large app usage logs collected through a mobile\nadvertising company. The dataset covers more than 12K anonymous users and 1.3\nmillion log events. Based on the analysis, we further investigate a novel\nmobile app engagement prediction problem -- can we predict simultaneously what\napp the user will use next and how long he/she will stay on that app? We\npropose several strategies for this joint prediction problem and demonstrate\nthat our model can improve the performance significantly when compared with the\nstate-of-the-art baselines. Our work can help mobile system developers in\ndesigning a better and more engagement-aware mobile app user experience.\n"}
+{"abstract": "  We propose to examine the predictability and the complexity characteristics\nof the Standard&Poor500 dynamics behaviors in a coarse-grained way using the\nsymbolic dynamics method and under the prism of the Information theory through\nthe concept of entropy and uncertainty. We believe that experimental\nmeasurement of entropy as a way of examining the complexity of a system is more\nrelevant than more common tests of universality in the transition to chaos\nbecause it does not make any prior prejudices on the underlying causes\nassociated with the system dynamics, whether deterministic or stochastic. We\nregard the studied economic time series as being complex and propose to express\nit in terms of the amount of information this last is producing on different\ntime scales and according to various scaling parameters.\n"}
+{"abstract": "  MnBi$_2$Te$_4$ (MBT) materials are promising antiferromagnetic topological\ninsulators where field driven ferromagnetism is predicted to cause a transition\nbetween axion insulator and Weyl semimetallic states. However, the presence of\nantiferromagnetic coupling between Mn/Bi antisite defects and the main Mn layer\ncan reduce the low-field magnetization, and it has been shown that such defects\nare more prevalent in the structurally identical trivial magnetic insulator\nMnSb$_2$Te$_4$ (MST). We use high-field magnetization measurements to show that\nthe magnetization of MBT and MST occur in stages and full saturation requires\nfields of~$\\sim$~60 Tesla. As a consequence, the low-field magnetization\nplateau state in MBT, where many determinations of quantum anomalous Hall state\nare studied, actually consists of ferrimagnetic septuple blocks containing both\na uniform and staggered magnetization component.\n"}
+{"abstract": "  Model deficiency that results from incomplete training data is a form of\nstructural blindness that leads to costly errors, oftentimes with high\nconfidence. During the training of classification tasks, underrepresented\nclass-conditional distributions that a given hypothesis space can recognize\nresults in a mismatch between the model and the target space. To mitigate the\nconsequences of this discrepancy, we propose Random Test Sampling and\nCross-Validation (RTSCV) as a general algorithmic framework that aims to\nperform a post-training model rectification at deployment time in a supervised\nway. RTSCV extracts unknown unknowns (u.u.s), i.e., examples from the\nclass-conditional distributions that a classifier is oblivious to, and works in\ncombination with a diverse family of modern prediction models. RTSCV augments\nthe training set with a sample of the test set (or deployment data) and uses\nthis redefined class layout to discover u.u.s via cross-validation, without\nrelying on active learning or budgeted queries to an oracle. We contribute a\ntheoretical analysis that establishes performance guarantees based on the\ndesign bases of modern classifiers. Our experimental evaluation demonstrates\nRTSCV's effectiveness, using 7 benchmark tabular and computer vision datasets,\nby reducing a performance gap as large as 41% from the respective\npre-rectification models. Last we show that RTSCV consistently outperforms\nstate-of-the-art approaches.\n"}
+{"abstract": "  Decentralized conflict resolution for autonomous vehicles is needed in many\nplaces where a centralized method is not feasible, e.g., parking lots, rural\nroads, merge lanes, etc. However, existing methods generally do not fully\nutilize optimization in decentralized conflict resolution. We propose a\ndecentralized conflict resolution method for autonomous vehicles based on a\nnovel extension to the Alternating Directions Method of Multipliers (ADMM),\ncalled Online Adaptive ADMM (OA-ADMM), and on Model Predictive Control (MPC).\nOA-ADMM is tailored to online systems, where fast and adaptive real-time\noptimization is crucial, and allows the use of safety information about the\nphysical system to improve safety in real-time control. We prove convergence in\nthe static case and give requirements for online convergence. Combining OA-ADMM\nand MPC allows for robust decentralized motion planning and control that\nseamlessly integrates decentralized conflict resolution. The effectiveness of\nour proposed method is shown through simulations in CARLA, an open-source\nvehicle simulator, resulting in a reduction of 47.93% in mean added delay\ncompared with the next best method.\n"}
+{"abstract": "  The dynamics of suspensions plays a crucial role on the evolution of\ngeophysical systems such as lava lakes, magma chambers and magma oceans. During\ntheir cooling and solidification, these magmatic bodies involve convective\nviscous fluids and dispersed solid crystals that can form either a cumulate or\na floating lid by sedimentation. We study such systems based on internal\nheating convection experiments in high Prandtl fluids bearing plastic beads. We\naim to determine the conditions required to produce a floating lid or a\nsedimented deposit. We show that although the sign of particles buoyancy is the\nkey parameter, it is not sufficient to predict the particles fate. To\ncomplement the model we introduce the Shields formalism and couple it with\nscaling laws describing convection. We propose a generalised Shields number\nthat enables a self-consistent description of the fate of particles in the\nsystem, especially the possibility to segregate from the convective bulk. We\nprovide a quantification of the partition of the mass of particles in the\ndifferent potential reservoirs (bulk suspension, floating lid, settled\ncumulate) through reconciling the suspension stability framework with the\nShields formalism. We illustrate the geophysical implications of the model by\nrevisiting the problem of the stability of flotation crusts on solidifying\nrocky bodies.\n"}
+{"abstract": "  In this paper we introduce refined canonical stable Grothendieck polynomials\nand their duals with two infinite sequences of parameters. These polynomials\nunify several generalizations of Grothendieck polynomials including canonical\nstable Grothendieck polynomials due to Yeliussizov, refined Grothendieck\npolynomials due to Chan and Pflueger, and refined dual Grothendieck polynomials\ndue to Galashin, Liu, and Grinberg. We give Jacobi--Trudi-type formulas,\ncombinatorial models, Schur expansions, Schur positivity, and dualities of\nthese polynomials. We also consider flagged versions of Grothendieck\npolynomials and their duals with skew shapes.\n"}
+{"abstract": "  We investigate the massive Schwinger model in $d = 1 + 1$ dimensions using\nbosonization and the non-perturbative functional renormalization group. In\nagreement with previous studies we find that the phase transition, driven by a\nchange of the ratio $m/e$ between the mass and the charge of the fermions,\nbelongs to the two-dimensional Ising universality class. The temperature and\nvacuum angle dependence of various physical quantities (chiral density,\nelectric field, entropy density) are also determined and agree with results\nobtained from density matrix renormalization group studies. Screening of\nfractional charges and deconfinement occur only at infinite temperature.\n"}
+{"abstract": "  In the past decade, contextual bandit and reinforcement learning algorithms\nhave been successfully used in various interactive learning systems such as\nonline advertising, recommender systems, and dynamic pricing. However, they\nhave yet to be widely adopted in high-stakes application domains, such as\nhealthcare. One reason may be that existing approaches assume that the\nunderlying mechanisms are static in the sense that they do not change over\ndifferent environments. In many real world systems, however, the mechanisms are\nsubject to shifts across environments which may invalidate the static\nenvironment assumption. In this paper, we tackle the problem of environmental\nshifts under the framework of offline contextual bandits. We view the\nenvironmental shift problem through the lens of causality and propose\nmulti-environment contextual bandits that allow for changes in the underlying\nmechanisms. We adopt the concept of invariance from the causality literature\nand introduce the notion of policy invariance. We argue that policy invariance\nis only relevant if unobserved confounders are present and show that, in that\ncase, an optimal invariant policy is guaranteed to generalize across\nenvironments under suitable assumptions. Our results may be a first step\ntowards solving the environmental shift problem. They also establish concrete\nconnections among causality, invariance and contextual bandits.\n"}
+{"abstract": "  Given a prime power $q$ and $n \\gg 1$, we prove that every integer in a large\nsubinterval of the Hasse--Weil interval $[(\\sqrt{q}-1)^{2n},(\\sqrt{q}+1)^{2n}]$\nis $#A(\\mathbb{F}_q)$ for some geometrically simple ordinary principally\npolarized abelian variety $A$ of dimension $n$ over $\\mathbb{F}_q$. As a\nconsequence, we generalize a result of Howe and Kedlaya for $\\mathbb{F}_2$ to\nshow that for each prime power $q$, every sufficiently large positive integer\nis realizable, i.e., $#A(\\mathbb{F}_q)$ for some abelian variety $A$ over\n$\\mathbb{F}_q$. Our result also improves upon the best known constructions of\nsequences of simple abelian varieties with point counts towards the extremes of\nthe Hasse--Weil interval. A separate argument determines, for fixed $n$, the\nlargest subinterval of the Hasse--Weil interval consisting of realizable\nintegers, asymptotically as $q \\to \\infty$; this gives an asymptotically\noptimal improvement of a 1998 theorem of DiPippo and Howe. Our methods are\neffective: We prove that if $q \\le 5$, then every positive integer is\nrealizable, and for arbitrary $q$, every positive integer $\\ge q^{3 \\sqrt{q}\n\\log q}$ is realizable.\n"}
+{"abstract": "  We present a formal language for specifying qualitative preferences over\ntemporal goals and a preference-based planning method in stochastic systems.\nUsing automata-theoretic modeling, the proposed specification allows us to\nexpress preferences over different sets of outcomes, where each outcome\ndescribes a set of temporal sequences of subgoals. We define the value of\npreference satisfaction given a stochastic process over possible outcomes and\ndevelop an algorithm for time-constrained probabilistic planning in labeled\nMarkov decision processes where an agent aims to maximally satisfy its\npreference formula within a pre-defined finite time duration. We present\nexperimental results using a stochastic gridworld example and discuss possible\nextensions of the proposed preference model.\n"}
+{"abstract": "  The skip-gram (SG) model learns word representation by predicting the words\nsurrounding a center word from unstructured text data. However, not all words\nin the context window contribute to the meaning of the center word. For\nexample, less relevant words could be in the context window, hindering the SG\nmodel from learning a better quality representation. In this paper, we propose\nan enhanced version of the SG that leverages context information to produce\nword representation. The proposed model, Contextual Skip-gram, is designed to\npredict contextual words with both the center words and the context\ninformation. This simple idea helps to reduce the impact of irrelevant words on\nthe training process, thus enhancing the final performance\n"}
+{"abstract": "  We propose quadratic residual networks (QRes) as a new type of\nparameter-efficient neural network architecture, by adding a quadratic residual\nterm to the weighted sum of inputs before applying activation functions. With\nsufficiently high functional capacity (or expressive power), we show that it is\nespecially powerful for solving forward and inverse physics problems involving\npartial differential equations (PDEs). Using tools from algebraic geometry, we\ntheoretically demonstrate that, in contrast to plain neural networks, QRes\nshows better parameter efficiency in terms of network width and depth thanks to\nhigher non-linearity in every neuron. Finally, we empirically show that QRes\nshows faster convergence speed in terms of number of training epochs especially\nin learning complex patterns.\n"}
+{"abstract": "  We consider a class of random banded Hessenberg matrices with independent\nentries having identical distributions along diagonals. The distributions may\nbe different for entries belonging to different diagonals. For a sequence of\n$n\\times n$ matrices in the class considered, we investigate the asymptotic\nbehavior of their empirical spectral distribution as $n$ tends to infinity.\n"}
+{"abstract": "  The polar covalent bond between a single Au atom terminating the apex of an\natomic force microscope tip and a C atom of graphene on SiC(0001) is exposed to\nan external electric field. For one field orientation the Au-C bond is strong\nenough to sustain the mechanical load of partially detached graphene, whilst\nfor the opposite orientation the bond breaks easily. Calculations based on\ndensity functional theory and nonequilibrium Green's function methods support\nthe experimental observations by unveiling bond forces that reflect the polar\ncharacter of the bond. Field-induced charge transfer between the atomic\norbitals modifies the polarity of the different electronegative reaction\npartners and the Au-C bond strength.\n"}
+{"abstract": "  Models for question answering, dialogue agents, and summarization often\ninterpret the meaning of a sentence in a rich context and use that meaning in a\nnew context. Taking excerpts of text can be problematic, as key pieces may not\nbe explicit in a local window. We isolate and define the problem of sentence\ndecontextualization: taking a sentence together with its context and rewriting\nit to be interpretable out of context, while preserving its meaning. We\ndescribe an annotation procedure, collect data on the Wikipedia corpus, and use\nthe data to train models to automatically decontextualize sentences. We present\npreliminary studies that show the value of sentence decontextualization in a\nuser facing task, and as preprocessing for systems that perform document\nunderstanding. We argue that decontextualization is an important subtask in\nmany downstream applications, and that the definitions and resources provided\ncan benefit tasks that operate on sentences that occur in a richer context.\n"}
+{"abstract": "  Detecting objects of interest in images was always a compelling task to\nautomate. In recent years this task was more and more explored using deep\nlearning techniques, mostly using region-based convolutional networks. In this\nproject we propose an alternative semantic segmentation technique making use of\nGenerative Adversarial Networks. We consider semantic segmentation to be a\ndomain transfer problem. Thus, we train a feed forward network (FFNN) to\nreceive as input a seed real image and generate as output its segmentation\nmask.\n"}
+{"abstract": "  In stochastic optimisation, the large number of scenarios required to\nfaithfully represent the underlying uncertainty is often a barrier to finding\nefficient numerical solutions. This motivates the scenario reduction problem:\nby find a smaller subset of scenarios, reduce the numerical complexity while\nkeeping the error at an acceptable level.\n  In this paper we propose a novel and computationally efficient methodology to\ntackle the scenario reduction problem when the error to be minimised is the\nimplementation error, i.e. the error incurred by implementing the solution of\nthe reduced problem in the original problem. Specifically, we develop a\nproblem-driven scenario clustering method that produces a partition of the\nscenario set. Each cluster contains a representative scenario that best\nreflects the conditional objective values in each cluster of the partition to\nbe identified.\n  We demonstrate the efficiency of our method by applying it to two challenging\nstochastic combinatorial optimization problems: the two-stage stochastic\nnetwork design problem and the two-stage facility location problem. When\ncompared to alternative clustering methods and Monte Carlo sampling, our method\nis shown to clearly outperform all other methods.\n"}
+{"abstract": "  Recent studies on unsupervised object detection based on spatial attention\nhave achieved promising results. Models, such as AIR and SPAIR, output \"what\"\nand \"where\" latent variables that represent the attributes and locations of\nobjects in a scene, respectively. Most of the previous studies concentrate on\nthe \"where\" localization performance; however, we claim that acquiring \"what\"\nobject attributes is also essential for representation learning. This paper\npresents a framework, GMAIR, for unsupervised object detection. It incorporates\nspatial attention and a Gaussian mixture in a unified deep generative model.\nGMAIR can locate objects in a scene and simultaneously cluster them without\nsupervision. Furthermore, we analyze the \"what\" latent variables and clustering\nprocess. Finally, we evaluate our model on MultiMNIST and Fruit2D datasets and\nshow that GMAIR achieves competitive results on localization and clustering\ncompared to state-of-the-art methods.\n"}
+{"abstract": "  Baryogenesis in the context of the compact $SU\\left( 3\\right)_C\\otimes\nSU\\left( 4\\right)_{L}\\otimes U\\left( 1\\right)_X$ model is investigated. Using\nthe finite temperature effective potential approach together with unitarity,\nstability and no ghost masses constraints, the existence of a strong first\norder electroweak phase transition (EWPT) was shown and checked numerically\nduring all steps of the spontaneous breakdown of the gauge symmetry of the\nmodel. Higgs masses regions fulfilling the EWPT criteria are also discussed.\nMoreover, and as a byproduct of our study, the B-violation via sphaleron was\nalso emphasized.\n"}
+{"abstract": "  Einstein-Podolsky-Rosen steering describes a quantum correlation in addition\nto entanglement and Bell nonlocality. However, conceptually different from\nentanglement and Bell nonlocality, quantum steering has an asymmetric\ndefinition. Motivated by the asymmetric definition of quantum steering, we\nstudy the steerability of two-interacting qubits, which have asymmetric energy\nlevels, coupled with asymmetric environments. The asymmetric (nonequilibrium)\nenvironments are two environments with different temperatures or chemical\npotentials. The Bloch-Redfield equation is applied to study the dynamics of two\nqubits and its long-time behavior. In our study, the steady-state steerability\nis determined by an experimentally friendly steering criteria, which\ndemonstrates steering through the entanglement detection. Our results show that\nthe steady states of two asymmetric qubits have the advantage for one direction\nof steering, compared to the symmetric setup. We also provide analytical\nresults on the minimal coupling strength between the two qubits in order to be\nsteerable. The asymmetric steerability is collectively determined by the nature\nof the two qubits and the influence from equilibrium or nonequilibrium\nenvironments. Nonequilibrium environments with the cost of nonzero entropy\nproduction can enhance the steerability in one direction. We also show the\nstrict hierarchy of entanglement, steering and Bell nonlocality of the\nnonequilibrium steady states, which shows a richer structure of steering than\nentanglement and Bell nonlocality.\n"}
+{"abstract": "  We discuss the evolution from BCS to Bose superconductivity versus carrier\ndensity in gated two-dimensional s-wave superconductors. We show that the\ntransition from the normal to the superconducting state is controlled by the\nBerezinskii-Kosterlitz-Thouless vortex-antivortex pairing mechanism, and that\nthe evolution from high carrier density (BCS pairing) to low carrier density\n(Bose pairing) is just a crossover in s-wave systems. We compare our results to\nrecent experiments on the superconductor LixZrNCl, a lithium intercalated\nlayered nitride, and obtain very good quantitative agreement at low and\nintermediate densities.\n"}
+{"abstract": "  In this article, we consider numerical schemes for polynomial diffusions on\nthe unit ball $\\mathscr{B}^{d}$, which are solutions of stochastic differential\nequations with a diffusion coefficient of the form $\\sqrt{1-|x|^{2}}$. We\nintroduce a projection scheme on the unit ball $\\mathscr{B}^{d}$ based on a\nbackward Euler--Maruyama scheme and provide the $L^{2}$-rate of convergence.\nThe main idea to consider the numerical scheme is the transformation argument\nintroduced by Swart [29] for proving the pathwise uniqueness for some\nstochastic differential equation with a non-Lipschitz diffusion coefficient.\n"}
+{"abstract": "  We show that for any permutation $w$ that avoids a certain set of 13 patterns\nof lengths 5 and 6, the Schubert polynomial $\\mathfrak S_w$ can be expressed as\nthe determinant of a matrix of elementary symmetric polynomials in a manner\nsimilar to the Jacobi-Trudi identity. For such $w$, this determinantal formula\nis equivalent to a (signed) subtraction-free expansion of $\\mathfrak S_w$ in\nthe basis of standard elementary monomials.\n"}
+{"abstract": "  A set $E$ in a Banach space $X$ is compactivorous if for every compact set\n$K$ in $X$ there is a nonempty, (relatively) open subset of $K$ which can be\ntranslated into $E$. In a separable Banach space, this is a sufficient\ncondition which guarantees the Haar nonnegligibility of Borel subsets. We give\nsome characterisations of this property in both separable and nonseparable\nBanach spaces and prove an extension of the main theorem to countable products\nof locally compact Polish groups.\n"}
+{"abstract": "  We study a problem of an online retailer who observes the unit sales of a\nproduct, and dynamically changes the retail price, in order to maximize the\nexpected revenue. Assuming the demand of the product is price sensitive, we are\ninterested in the optimal pricing policy when future demand is uncertain. We\nbuild a system to investigate the relationship between retail price and demand\nand estimate the demand function. The system predicts demand and revenue at a\ngiven retail price. We formulate a revenue maximization problem over a discrete\nfinite time horizon with discrete retail price. The optimal pricing policy is\nsolved based on the predicted demand and revenue values. With computational\nexperiments, we investigate the effect of optimal pricing policy to inventory\nmanagement.\n"}
+{"abstract": "  We propose a new hyper-reduction method for a recently introduced nonlinear\nmodel reduction framework based on dynamically transformed basis functions and\nespecially well-suited for advection-dominated systems. Furthermore, we discuss\napplying this new method to a wildland fire model whose dynamics feature\ntraveling combustion waves and local ignition and is thus challenging for\nclassical model reduction schemes based on linear subspaces. The new\nhyper-reduction framework allows us to construct parameter-dependent\nreduced-order models (ROMs) with efficient offline/online decomposition. The\nnumerical experiments demonstrate that the ROMs obtained by the novel method\noutperform those obtained by a classical approach using the proper orthogonal\ndecomposition and the discrete empirical interpolation method in terms of run\ntime and accuracy.\n"}
+{"abstract": "  Methods are described for the solution of linear inference problems subject\nto deterministic constraints. The approach builds on work by Backus (1970a,b,c)\nand Parker (1977), but a range useful advances are suggested to address both\nconceptual and practical issues. The theory is motivated by, and illustrated\nwith, the estimation of a finite number of a function's spherical harmonic\ncoefficients from a finite set of its point values. Numerical examples are\nincluded to demonstrate that the methods can be efficiently applied to\nrealistic problems.\n"}
+{"abstract": "  We propose a unified framework that extends the inference methods for\nclassical hidden Markov models to continuous settings, where both the hidden\nstates and observations occur in continuous time. Two different settings are\nanalyzed: hidden jump process with a finite state space, and hidden diffusion\nprocess with a continuous state space. For each setting, we first estimate the\nhidden states given the observations and model parameters, showing that the\nposterior distribution of the hidden states can be described by differential\nequations in continuous time. We then consider the estimation of unknown model\nparameters, deriving the continuous-time formulas for the\nexpectation-maximization algorithm. We also propose a Monte Carlo method based\non the continuous formulation, sampling the posterior distribution of the\nhidden states and updating the parameter estimation.\n"}
+{"abstract": "  This paper studies regret minimization in multi-armed bandits, a classical\nonline learning problem. To develop more statistically-efficient algorithms, we\npropose to use the assumption of a random-effect model. In this model, the mean\nrewards of arms are drawn independently from an unknown distribution, whose\nparameters we estimate. We provide an estimator of the arm means in this model\nand also analyze its uncertainty. Based on these results, we design a UCB\nalgorithm, which we call ReUCB. We analyze ReUCB and prove a Bayes regret bound\non its $n$-round regret, which matches an existing lower bound. Our experiments\nshow that ReUCB can outperform Thompson sampling in various scenarios, without\nassuming that the prior distribution of arm means is known.\n"}
+{"abstract": "  The mergers of double helium white dwarfs are believed to form isolated\nhelium-rich hot subdwarfs. Observation shows that the helium-rich hot subdwarfs\ncan be divided into two subgroups based on whether the surface is carbon-rich\nor carbon-normal. But it is not clear whether this distribution directly comes\nfrom binary evolution. We adopt the binary population synthesis (BPS) to obtain\nthe population of single helium-rich hot subdwarfs according to the channel of\ndouble helium white dwarfs merger. We find that the merger channel can\nrepresent the two subgroups in the $T_{\\rm{eff}}-\\log g$ plane related to\ndifferent masses of progenitor helium white dwarfs. For $Z$ = 0.02, the birth\nrates and local density of helium-rich hot subdwarf stars by the mergers of two\nhelium white dwarfs is $\\sim 4.82 \\times 10^{-3}$ $\\rm yr^{-1}$ and $\\sim$\n290.0 $\\rm kpc^{-3}$ at 13.7 Gyr in our Galaxy, respectively. The proportion of\ncarbon-rich and carbon-normal helium-rich hot subdwarfs are 32$\\%$ and 68$\\%$,\nrespectively.\n"}
+{"abstract": "  Meta-learning has been sufficiently validated to be beneficial for\nlow-resource neural machine translation (NMT). However, we find that\nmeta-trained NMT fails to improve the translation performance of the domain\nunseen at the meta-training stage. In this paper, we aim to alleviate this\nissue by proposing a novel meta-curriculum learning for domain adaptation in\nNMT. During meta-training, the NMT first learns the similar curricula from each\ndomain to avoid falling into a bad local optimum early, and finally learns the\ncurricula of individualities to improve the model robustness for learning\ndomain-specific knowledge. Experimental results on 10 different low-resource\ndomains show that meta-curriculum learning can improve the translation\nperformance of both familiar and unfamiliar domains. All the codes and data are\nfreely available at https://github.com/NLP2CT/Meta-Curriculum.\n"}
+{"abstract": "  Spectral imaging is an umbrella term for energy-resolved x-ray imaging in\nmedicine. The technique makes use of the energy dependence of x-ray attenuation\nto either increase the contrast-to-noise ratio, or to provide quantitative\nimage data and reduce image artefacts by so-called material decomposition.\nSpectral imaging is not new, but has gained interest in recent years because of\nrapidly increasing availability of spectral and dual-energy CT and the dawn of\nenergy-resolved photon-counting detectors. This review examines the current\ntechnological status of spectral and dual-energy imaging and a number of\npractical applications of the technology.\n"}
+{"abstract": "  This document presents implementations of fundamental convolutional neural\nnetwork (CNN) layers on the Manticore cluster-based many-core architecture and\ndiscusses their characteristics and trade-offs.\n"}
+{"abstract": "  Based on the Gale-Ryser theorem for the existence of suitable\n$(0,1)$-matrices for different partitions of a natural number, we revisit the\nclassical result of G. G. Lorentz regarding the characterization of a plane\nmeasurable set, in terms of its cross sections, and extend it to general\nmeasure spaces.\n"}
+{"abstract": "  Modern visual-inertial navigation systems (VINS) are faced with a critical\nchallenge in real-world deployment: they need to operate reliably and robustly\nin highly dynamic environments. Current best solutions merely filter dynamic\nobjects as outliers based on the semantics of the object category. Such an\napproach does not scale as it requires semantic classifiers to encompass all\npossibly-moving object classes; this is hard to define, let alone deploy. On\nthe other hand, many real-world environments exhibit strong structural\nregularities in the form of planes such as walls and ground surfaces, which are\nalso crucially static. We present RP-VIO, a monocular visual-inertial odometry\nsystem that leverages the simple geometry of these planes for improved\nrobustness and accuracy in challenging dynamic environments. Since existing\ndatasets have a limited number of dynamic elements, we also present a\nhighly-dynamic, photorealistic synthetic dataset for a more effective\nevaluation of the capabilities of modern VINS systems. We evaluate our approach\non this dataset, and three diverse sequences from standard datasets including\ntwo real-world dynamic sequences and show a significant improvement in\nrobustness and accuracy over a state-of-the-art monocular visual-inertial\nodometry system. We also show in simulation an improvement over a simple\ndynamic-features masking approach. Our code and dataset are publicly available.\n"}
+{"abstract": "  Estimation of parameters in differential equation models can be achieved by\napplying learning algorithms to quantitative time-series data. However,\nsometimes it is only possible to measure qualitative changes of a system in\nresponse to a controlled condition. In dynamical systems theory, such change\npoints are known as bifurcations and lie on a function of the controlled\ncondition called the bifurcation diagram. In this work, we propose a\ngradient-based approach for inferring the parameters of differential equations\nthat produce a user-specified bifurcation diagram. The cost function contains\nan error term that is minimal when the model bifurcations match the specified\ntargets and a bifurcation measure which has gradients that push optimisers\ntowards bifurcating parameter regimes. The gradients can be computed without\nthe need to differentiate through the operations of the solver that was used to\ncompute the diagram. We demonstrate parameter inference with minimal models\nwhich explore the space of saddle-node and pitchfork diagrams and the genetic\ntoggle switch from synthetic biology. Furthermore, the cost landscape allows us\nto organise models in terms of topological and geometric equivalence.\n"}
+{"abstract": "  The ability to accurately extract low-amplitude voltage signals is crucial in\nseveral fields, ranging from single-use diagnostics and medical technology to\nrobotics and the Internet of Things. The organic electrochemical transistor,\nwhich features large transconductance values at low operation voltages, is\nideal for monitoring small signals. Its large transconductance translates small\ngate voltage variations into significant changes in the drain current. However,\na current-to-voltage conversion is further needed to allow proper data\nacquisition and signal processing. Low power consumption, high amplification,\nand manufacturability on flexible and low-cost carriers are also crucial and\nhighly anticipated for targeted applications. Here, we report low-power and\nhigh-gain flexible circuits based on printed complementary organic\nelectrochemical transistors (OECTs). We leverage the low threshold voltage of\nboth p-type and n-type enhancement-mode OECTs to develop complementary voltage\namplifiers that can sense voltages as low as 100 $\\mu$V, with gains of 30.4 dB\nand at a power consumption < 2.7 $\\mu$W (single-stage amplifier). At the\noptimal operating conditions, the voltage gain normalized to power consumption\nreaches 169 dB/$\\mu$W, which is > 50 times larger than state-of-the-art\nOECT-based amplifiers. In a two-stage configuration, the complementary voltage\namplifiers reach a DC voltage gain of 193 V/V, which is the highest among\nemerging CMOS-like technologies operating at supply voltages below 1 volt. Our\nfindings demonstrate that flexible complementary circuits based on printed\nOECTs define a power-efficient platform for sensing and amplifying\nlow-amplitude voltage signals in several emerging beyond-silicon applications.\n"}
+{"abstract": "  Photothermal spectroscopy and microscopy provides a route to measure the\nspectral and spatial properties of individual nanoscopic absorbers, independent\nfrom scattering, extinction, and emission. The approach relies upon use of two\nlight sources, one that resonantly excites and heats the target and its\nsurrounding environment and a second off-resonant probe that scatters from the\nresulting volume of thermally modified refractive index. Over the past twenty\nyears, considerable effort has been extended to apply photothermal methods to\ndetect, spatially resolve, and perform absorption spectroscopy on single\nnon-emissive molecules and other absorbers like plasmonic nanoparticles at room\ntemperature conditions. Companion theoretical models have been developed to\ninterpret these experimental advances, yet it is not clear how they are related\nto each other nor how the effects of lock-in detection modify the theory. For\nlarger target systems that host their own intrinsic scattering resonances as\nwell as for background media that do not instantaneously thermalize with the\nabsorbing target, additional dependencies arise that are yet to be explored\ntheoretically. The aim of this Perspective is to overview the theory of\nphotothermal spectroscopy and microscopy and present a unifying theoretical\napproach that recovers past models in certain limits while explicitly including\nthe effects of target scattering resonances, thermal and optical retardation,\nand lock-in detection. Focus is made on plasmonic particles to interpret the\nphotothermal signal, yet all results are applicable equally to individual\nmolecules or nanoparticle absorbers. Consequently, we expect this review to\nprovide a useful foundation for the understanding of photothermal measurements\nindependent of target identity.\n"}
+{"abstract": "  Contamination by polarized foregrounds is one of the biggest challenges for\nfuture polarized cosmic microwave background (CMB) surveys and the potential\ndetection of primordial $B$-modes. Future experiments, such as Simons\nObservatory (SO) and CMB-S4, will aim at very deep observations in relatively\nsmall ($f_{\\rm sky} \\sim 0.1$) areas of the sky. In this work, we investigate\nthe forecasted performance, as a function of the survey field location on the\nsky, for regions over the full sky, balancing between polarized foreground\navoidance and foreground component separation modeling needs. To do this, we\nsimulate observations by an SO-like experiment and measure the error bar on the\ndetection of the tensor-to-scalar ratio, $\\sigma(r)$, with a pipeline that\nincludes a parametric component separation method, the Correlated Component\nAnalysis, and the use of the Fisher information matrix. We forecast the\nperformance over 192 survey areas covering the full sky and also for optimized\nlow-foreground regions. We find that modeling the spectral energy distribution\nof foregrounds is the most important factor, and any mismatch will result in\nresiduals and bias in the primordial $B$-modes. At these noise levels,\n$\\sigma(r)$ is not especially sensitive to the level of foreground\ncontamination, provided the survey targets the least-contaminated regions of\nthe sky close to the Galactic poles.\n"}
+{"abstract": "  In this paper, we propose a new Sound Event Classification (SEC) method which\nis inspired in recent works for out-of-distribution detection. In our method,\nwe analyse all the activations of a generic CNN in order to produce feature\nrepresentations using Gram Matrices. The similarity metrics are evaluated\nconsidering all possible classes, and the final prediction is defined as the\nclass that minimizes the deviation with respect to the features seeing during\ntraining. The proposed approach can be applied to any CNN and our experimental\nevaluation of four different architectures on two datasets demonstrated that\nour method consistently improves the baseline models.\n"}
+{"abstract": "  A 10\\% difference in the scale for the Hubble parameter constitutes a clear\nproblem for cosmology. Here, considering angular distribution of Type Ia\nsupernovae (SN) within the Pantheon compilation and working within flat\n$\\Lambda$CDM cosmology, we observe that the correlation between higher $H_0$\nand the CMB dipole direction \\cite{Krishnan:2021dyb}, can be extended from\nstrongly lensed quasars to SN. Concretely, we record a $\\sim 1$ km/s/Mpc\nvariation in $H_0$ at antipodal points on the sky within the Pantheon sample,\nwhich is evident in the Low $z$ subsample ($z \\lesssim 0.075$) and gets\nenhanced by higher redshift SN. Our work raises the possibility that we may be\nat the precision required to probe anisotropic Hubble expansions, while\nproviding a concrete prediction for future inferences of $H_0$.\n"}
+{"abstract": "  The problem of efficiently generating random samples from high-dimensional\nand non-log-concave posterior measures arising from nonlinear regression\nproblems is considered. Extending investigations from arXiv:2009.05298, local\nand global stability properties of the model are identified under which such\nposterior distributions can be approximated in Wasserstein distance by suitable\nlog-concave measures. This allows the use of fast gradient based sampling\nalgorithms, for which convergence guarantees are established that scale\npolynomially in all relevant quantities (assuming `warm' initialisation). The\nscope of the general theory is illustrated in a non-linear inverse problem from\nintegral geometry for which new stability results are derived.\n"}
+{"abstract": "  In data analysis problems where we are not able to rely on distributional\nassumptions, what types of inference guarantees can still be obtained? Many\npopular methods, such as holdout methods, cross-validation methods, and\nconformal prediction, are able to provide distribution-free guarantees for\npredictive inference, but the problem of providing inference for the underlying\nregression function (for example, inference on the conditional mean\n$\\mathbb{E}[Y|X]$) is more challenging. In the setting where the features $X$\nare continuously distributed, recent work has established that any confidence\ninterval for $\\mathbb{E}[Y|X]$ must have non-vanishing width, even as sample\nsize tends to infinity. At the other extreme, if $X$ takes only a small number\nof possible values, then inference on $\\mathbb{E}[Y|X]$ is trivial to achieve.\nIn this work, we study the problem in settings in between these two extremes.\nWe find that there are several distinct regimes in between the finite setting\nand the continuous setting, where vanishing-width confidence intervals are\nachievable if and only if the effective support size of the distribution of $X$\nis smaller than the square of the sample size.\n"}
+{"abstract": "  The device used in this work detects the objects over the surface of the\nwater using two thermal cameras which aid the users to detect and avoid the\nobjects in scenarios where the human eyes cannot (night, fog, etc.). To avoid\nthe obstacle collision autonomously, it is required to track the objects in\nreal-time and assign a specific identity to each object to determine its\ndynamics (trajectory, velocity, etc.) for making estimated collision\npredictions. In the following work, a Machine Learning (ML) approach for\nComputer Vision (CV) called Convolutional Neural Network (CNN) was used using\nTensorFlow as the high-level programming environment in Python. To validate the\nalgorithm a test set was generated using an annotation tool that was created\nduring the work for proper evaluation. Once validated, the algorithm was\ndeployed on the platform and tested with the sequence generated by the test\nboat.\n"}
+{"abstract": "  Utilizing review information to enhance recommendation, the de facto\nreview-involved recommender systems, have received increasing interests over\nthe past few years. Thereinto, one advanced branch is to extract salient\naspects from textual reviews (i.e., the item attributes that users express) and\ncombine them with the matrix factorization technique. However, existing\napproaches all ignore the fact that semantically different reviews often\ninclude opposite aspect information. In particular, positive reviews usually\nexpress aspects that users prefer, while negative ones describe aspects that\nusers reject. As a result, it may mislead the recommender systems into making\nincorrect decisions pertaining to user preference modeling. Towards this end,\nin this paper, we propose a Review Polarity-wise Recommender model, dubbed as\nRPR, to discriminately treat reviews with different polarities. To be specific,\nin this model, positive and negative reviews are separately gathered and\nutilized to model the user-preferred and user-rejected aspects, respectively.\nBesides, in order to overcome the imbalance problem of semantically different\nreviews, we also develop an aspect-aware importance weighting approach to align\nthe aspect importance for these two kinds of reviews. Extensive experiments\nconducted on eight benchmark datasets have demonstrated the superiority of our\nmodel as compared to a series of state-of-the-art review-involved baselines.\nMoreover, our method can provide certain explanations to the real-world rating\nprediction scenarios.\n"}
+{"abstract": "  Asymptotic stationarity and regularity conditions turned out to be quite\nuseful to study the qualitative properties of numerical solution methods for\nstandard nonlinear and complementarity-constrained programs. In this paper, we\nfirst extend these notions to nonlinear optimization problems with nonsmooth\nbut Lipschitzian data functions in order to find reasonable notions of\nasymptotic stationarity and regularity in terms of Clarke's and Mordukhovich's\nsubdifferential construction. Particularly, we compare the associated novel\nasymptotic constraint qualifications with already existing ones. The second\npart of the paper presents two applications of the obtained theory. On the one\nhand, we specify our findings for complementarity-constrained optimization\nproblems and recover recent results from the literature which demonstrates the\npower of the approach. Furthermore, we hint at potential extensions to or- and\nvanishing-constrained optimization. On the other hand, we demonstrate the\nusefulness of asymptotic regularity in the context of bilevel optimization.\nMore precisely, we justify a well-known stationarity system for affinely\nconstrained bilevel optimization problems in a novel way. Afterwards, we\nsuggest a solution algorithm for this class of bilevel optimization problems\nwhich combines a penalty method with ideas from DC-programming. After a brief\nconvergence analysis, we present results of some numerical experiments.\n"}
+{"abstract": "  The anticorrelation between quantum efficiency (QE) and electron spin\npolarization (ESP) from a p-doped GaAs activated to negative electron affinity\n(NEA) is studied in detail using an ensemble Monte Carlo approach. The\nphotoabsorption, momentum and spin relaxation during transport, and tunnelling\nof electrons through the surface potential barrier are modeled to identify\nfundamental mechanisms, which limit the efficiency of GaAs spin-polarized\nelectron sources. In particular, we study the response of QE and ESP to various\nparameters such as the photoexcitation energy, doping density, and electron\naffinity level. Our modeling results for various transport and emission\ncharacteristics are in a good agreement with available experimental data. Our\nfindings show that the behaviour of both QE and ESP at room temperature can be\nfully explained by the bulk relaxation mechanisms and the time which electrons\nspend in the material before being emitted.\n"}
+{"abstract": "  We call an investment strategy survival, if an agent who uses it maintains a\nnon-vanishing share of market wealth over the infinite time horizon. In a\ndiscrete-time multi-agent model with endogenous asset prices determined through\na short-run equilibrium of supply and demand, we show that a survival strategy\ncan be constructed as follows: an agent should assume that only their actions\ndetermine the prices and use a growth optimal (log-optimal) strategy with\nrespect to these prices, disregarding the actual prices. Then any survival\nstrategy turns out to be close to this strategy asymptotically. The main\nresults are obtained under the assumption that the assets are short-lived.\n"}
+{"abstract": "  Machine learning (ML) currently exerts an outsized influence on the world,\nincreasingly affecting communities and institutional practices. It is therefore\ncritical that we question vague conceptions of the field as value-neutral or\nuniversally beneficial, and investigate what specific values the field is\nadvancing. In this paper, we present a rigorous examination of the values of\nthe field by quantitatively and qualitatively analyzing 100 highly cited ML\npapers published at premier ML conferences, ICML and NeurIPS. We annotate key\nfeatures of papers which reveal their values: how they justify their choice of\nproject, which aspects they uplift, their consideration of potential negative\nconsequences, and their institutional affiliations and funding sources. We find\nthat societal needs are typically very loosely connected to the choice of\nproject, if mentioned at all, and that consideration of negative consequences\nis extremely rare. We identify 67 values that are uplifted in machine learning\nresearch, and, of these, we find that papers most frequently justify and assess\nthemselves based on performance, generalization, efficiency, researcher\nunderstanding, novelty, and building on previous work. We present extensive\ntextual evidence and analysis of how these values are operationalized. Notably,\nwe find that each of these top values is currently being defined and applied\nwith assumptions and implications generally supporting the centralization of\npower. Finally, we find increasingly close ties between these highly cited\npapers and tech companies and elite universities.\n"}
+{"abstract": "  One strategy for adversarially training a robust model is to maximize its\ncertified radius -- the neighborhood around a given training sample for which\nthe model's prediction remains unchanged. The scheme typically involves\nanalyzing a \"smoothed\" classifier where one estimates the prediction\ncorresponding to Gaussian samples in the neighborhood of each sample in the\nmini-batch, accomplished in practice by Monte Carlo sampling. In this paper, we\ninvestigate the hypothesis that this sampling bottleneck can potentially be\nmitigated by identifying ways to directly propagate the covariance matrix of\nthe smoothed distribution through the network. To this end, we find that other\nthan certain adjustments to the network, propagating the covariances must also\nbe accompanied by additional accounting that keeps track of how the\ndistributional moments transform and interact at each stage in the network. We\nshow how satisfying these criteria yields an algorithm for maximizing the\ncertified radius on datasets including Cifar-10, ImageNet, and Places365 while\noffering runtime savings on networks with moderate depth, with a small\ncompromise in overall accuracy. We describe the details of the key\nmodifications that enable practical use. Via various experiments, we evaluate\nwhen our simplifications are sensible, and what the key benefits and\nlimitations are.\n"}
+{"abstract": "  Climate models project an uncertainty range of possible warming scenarios\nfrom 1.5 to 5 degree Celsius global temperature increase until 2100, according\nto the CMIP6 model ensemble. Climate risk management and infrastructure\nadaptation requires the accurate quantification of the uncertainties at the\nlocal level. Ensembles of high-resolution climate models could accurately\nquantify the uncertainties, but most physics-based climate models are\ncomputationally too expensive to run as ensemble. Recent works in\nphysics-informed neural networks (PINNs) have combined deep learning and the\nphysical sciences to learn up to 15k faster copies of climate submodels.\nHowever, the application of PINNs in climate modeling has so far been mostly\nlimited to deterministic models. We leverage a novel method that combines\npolynomial chaos expansion (PCE), a classic technique for uncertainty\npropagation, with PINNs. The PCE-PINNs learn a fast surrogate model that is\ndemonstrated for uncertainty propagation of known parameter uncertainties. We\nshowcase the effectiveness in ocean modeling by using the local\nadvection-diffusion equation.\n"}
+{"abstract": "  M. Kruskal showed that each nearly-periodic dynamical system admits a formal\n$U(1)$ symmetry, generated by the so-called roto-rate. We prove that such\nsystems also admit nearly-invariant manifolds of each order, near which rapid\noscillations are suppressed. We study the nonlinear normal stability of these\nslow manifolds for nearly-periodic Hamiltonian systems on barely symplectic\nmanifolds -- manifolds equipped with closed, non-degenerate $2$-forms that may\nbe degenerate to leading order. In particular, we establish a sufficient\ncondition for long-term normal stability based on second derivatives of the\nwell-known adiabatic invariant. We use these results to investigate the problem\nof embedding guiding center dynamics of a magnetized charged particle as a slow\nmanifold in a nearly-periodic system. We prove that one previous embedding, and\ntwo new embeddings enjoy long-term normal stability, and thereby strengthen the\ntheoretical justification for these models.\n"}
+{"abstract": "  The emerging paradigm of the Social Internet of Things (SIoT) has transformed\nthe traditional notion of the Internet of Things (IoT) into a social network of\nbillions of interconnected smart objects by integrating social networking\nfacets into the same. In SIoT, objects can establish social relationships in an\nautonomous manner and interact with the other objects in the network based on\ntheir social behaviour. A fundamental problem that needs attention is\nestablishing of these relationships in a reliable and trusted way, i.e.,\nestablishing trustworthy relationships and building trust amongst objects. In\naddition, it is also indispensable to ascertain and predict an object's\nbehaviour in the SIoT network over a period of time. Accordingly, in this\npaper, we have proposed an efficient time-aware machine learning-driven trust\nevaluation model to address this particular issue. The envisaged model\ndeliberates social relationships in terms of friendship and community-interest,\nand further takes into consideration the working relationships and\ncooperativeness (object-object interactions) as trust parameters to quantify\nthe trustworthiness of an object. Subsequently, in contrast to the traditional\nweighted sum heuristics, a machine learning-driven aggregation scheme is\ndelineated to synthesize these trust parameters to ascertain a single trust\nscore. The experimental results demonstrate that the proposed model can\nefficiently segregates the trustworthy and untrustworthy objects within a\nnetwork, and further provides the insight on how the trust of an object varies\nwith time along with depicting the effect of each trust parameter on a trust\nscore.\n"}
+{"abstract": "  Exploring the possible consequences of spatial reciprocity on the evolution\nof cooperation is an intensively studied research avenue. Related works assumed\na certain interaction graph of competing players and studied how particular\ntopologies may influence the dynamical behavior. In this paper we apply a\nnumerically more demanding off-lattice population approach which could be\npotentially relevant especially in microbiological environments. As expected,\nresults are conceptually similar to those which were obtained for lattice-type\ninteraction graphs, but some spectacular differences can also be revealed. On\none hand, in off-lattice populations spatial reciprocity may work more\nefficiently than for a lattice-based system. On the other hand, competing\nstrategies may separate from each other in the continuous space concept, which\ngives a chance for cooperators to survive even at relatively high temptation\nvalues. Furthermore, the lack of strict neighborhood results in soft borders\nbetween competing patches which jeopardizes the long term stability of\nhomogeneous domains. We survey the major social dilemma games based on pair\ninteractions of players and reveal all analogies and differences compared to\non-lattice simulations.\n"}
+{"abstract": "  The hierarchical overlap graph (HOG) is a graph that encodes overlaps from a\ngiven set P of n strings, as the overlap graph does. A best known algorithm\nconstructs HOG in O(||P|| log n) time and O(||P||) space, where ||P|| is the\nsum of lengths of the strings in P. In this paper we present a new algorithm to\nconstruct HOG in O(||P||) time and space. Hence, the construction time and\nspace of HOG are better than those of the overlap graph, which are O(||P|| +\nn^2).\n"}
+{"abstract": "  Of all the characteristics of a violin, those that concern its shape are\nprobably the most important ones, as the violin maker has complete control over\nthem. Contemporary violin making, however, is still based more on tradition\nthan understanding, and a definitive scientific study of the specific relations\nthat exist between shape and vibrational properties is yet to come and sorely\nmissed. In this article, using standard statistical learning tools, we show\nthat the modal frequencies of violin tops can, in fact, be predicted from\ngeometric parameters, and that artificial intelligence can be successfully\napplied to traditional violin making. We also study how modal frequencies vary\nwith the thicknesses of the plate (a process often referred to as {\\em plate\ntuning}) and discuss the complexity of this dependency. Finally, we propose a\npredictive tool for plate tuning, which takes into account material and\ngeometric parameters.\n"}
+{"abstract": "  The popularity of the online media-driven social network relation is proven\nin today's digital era. The many challenges that these emergence has created\ninclude a huge growing network of social relations, and the large amount of\ndata which is continuously been generated via the different platform of social\nnetworking sites, viz. Facebook, Twitter, LinkedIn, Instagram, etc. These data\nare Personally Identifiable Information (PII) of the users which are also\npublicly available for some platform, and others allow with some restricted\npermission to download it for research purposes. The users' accessible data\nhelp in providing with better recommendation services to users, however, the\nPII can be used to embezzle the users and cause severe detriment to them.\nHence, it is crucial to maintain the users' privacy while providing their PII\naccessible for various services. Therefore, it is a burning issue to come up\nwith an approach that can help the users in getting better recommendation\nservices without their privacy being harmed. In this paper, a framework is\nsuggested for the same. Further, how data through Twitter API can be crawled\nand used has been extensively discussed. In addition to this, various security\nand legal perspectives regarding PII while crawling the data is highlighted. We\nbelieve the presented approach in this paper can serve as a benchmark for\nfuture research in the field of data privacy.\n"}
+{"abstract": "  LiteBIRD, the Lite (Light) satellite for the study of B-mode polarization and\nInflation from cosmic background Radiation Detection, is a space mission for\nprimordial cosmology and fundamental physics. JAXA selected LiteBIRD in May\n2019 as a strategic large-class (L-class) mission, with its expected launch in\nthe late 2020s using JAXA's H3 rocket. LiteBIRD plans to map the cosmic\nmicrowave background (CMB) polarization over the full sky with unprecedented\nprecision. Its main scientific objective is to carry out a definitive search\nfor the signal from cosmic inflation, either making a discovery or ruling out\nwell-motivated inflationary models. The measurements of LiteBIRD will also\nprovide us with an insight into the quantum nature of gravity and other new\nphysics beyond the standard models of particle physics and cosmology. To this\nend, LiteBIRD will perform full-sky surveys for three years at the Sun-Earth\nLagrangian point L2 for 15 frequency bands between 34 and 448 GHz with three\ntelescopes, to achieve a total sensitivity of 2.16 micro K-arcmin with a\ntypical angular resolution of 0.5 deg. at 100GHz. We provide an overview of the\nLiteBIRD project, including scientific objectives, mission requirements,\ntop-level system requirements, operation concept, and expected scientific\noutcomes.\n"}
+{"abstract": "  Motivated by questions originating from the study of a class of shallow\nstudent-teacher neural networks, methods are developed for the analysis of\nspurious minima in classes of gradient equivariant dynamics related to neural\nnets. In the symmetric case, methods depend on the generic equivariant\nbifurcation theory of irreducible representations of the symmetric group on $n$\nsymbols, $S_n$; in particular, the standard representation of $S_n$. It is\nshown that spurious minima do not arise from spontaneous symmetry breaking but\nrather through a complex deformation of the landscape geometry that can be\nencoded by a generic $S_n$-equivariant bifurcation. We describe minimal models\nfor forced symmetry breaking that give a lower bound on the dynamic complexity\ninvolved in the creation of spurious minima when there is no symmetry. Results\non generic bifurcation when there are quadratic equivariants are also proved;\nthis work extends and clarifies results of Ihrig & Golubitsky and Chossat,\nLauterback & Melbourne on the instability of solutions when there are quadratic\nequivariants.\n"}
+{"abstract": "  Cryo-electron microscopy (cryo-EM) has revolutionized experimental protein\nstructure determination. Despite advances in high resolution reconstruction, a\nmajority of cryo-EM experiments provide either a single state of the studied\nmacromolecule, or a relatively small number of its conformations. This reduces\nthe effectiveness of the technique for proteins with flexible regions, which\nare known to play a key role in protein function. Recent methods for capturing\nconformational heterogeneity in cryo-EM data model it in volume space, making\nrecovery of continuous atomic structures challenging. Here we present a fully\ndeep-learning-based approach using variational auto-encoders (VAEs) to recover\na continuous distribution of atomic protein structures and poses directly from\npicked particle images and demonstrate its efficacy on realistic simulated\ndata. We hope that methods built on this work will allow incorporation of\nstronger prior information about protein structure and enable better\nunderstanding of non-rigid protein structures.\n"}
+{"abstract": "  We provide a generalization of pseudo-Frobenius numbers of numerical\nsemigroups to the context of the simplicial affine semigroups. In this way, we\ncharacterize the Cohen-Macaulay type of the simplicial affine semigroup ring\n$\\mathbb{K}[S]$.\n  We define the type of $S$, $\\operatorname{type}$, in terms of some Ap\\'ery\nsets of $S$ and show that it coincides with the Cohen-Macaulay type of the\nsemigroup ring, when $\\mathbb{K}[S]$ is Cohen-Macaulay. If $\\mathbb{K}[S]$ is a\n$d$-dimensional Cohen-Macaulay ring of embedding dimension at most $d+2$, then\n$\\operatorname{type}\\leq 2$. Otherwise, $\\operatorname{type}$ might be\narbitrary large and it has no upper bound in terms of the embedding dimension.\nFinally, we present a generating set for the conductor of $S$ as an ideal of\nits normalization.\n"}
+{"abstract": "  We develop the protocol for structural restoring of multi-quantum coherence\nmatrices of the multi-qubit quantum state transferred from the sender to the\nreceiver along a spin-1/2 chain. We also propose a protocol for constructing\nsuch 0-order coherence matrix that can be perfectly transferred in this\nprocess. The restoring protocol is based on the specially constructed unitary\ntransformation of the extended receiver.{This transformation for a given length\nparameters of the chain is universally optimal in the sense that ones\nconstructed it can be applied to optimally restore any higher-order coherence\nmatrices.\n"}
+{"abstract": "  This paper presents an overview of the emerging area of collaborative\nintelligence (CI). Our goal is to raise awareness in the signal processing\ncommunity of the challenges and opportunities in this area of growing\nimportance, where key developments are expected to come from signal processing\nand related disciplines. The paper surveys the current state of the art in CI,\nwith special emphasis on signal processing-related challenges in feature\ncompression, error resilience, privacy, and system-level design.\n"}
+{"abstract": "  We gather and illustrate some functions that we wrote in the Magma computer\nalgebra system for curves of genus $2$ and $3$. In genus $3$, we furnish\nfunctions both for non-hyperelliptic and for hyperelliptic curves. A fair bit\nof the functionality in the latter case extends to hyperelliptic curves of\narbitrary genus.\n"}
+{"abstract": "  In this Letter we present strong arguments in favour of thoroughly revising\nthe negative mass cosmology (NMC), which has been proposed as a simple\nalternative explanation of dark energy and dark matter effects, within the\nframework of General Relativity. We show that there are various physical\npredictions in this model which require fine-tuning in order to make them\ncompatible with current cosmological surveys. In this way, the original\nmotivation of the NMC model becomes obscured due to the imposition of\nfine-tuned unknown variables. We conclude that a more rigorous theoretical\ntreatment is needed in order to make the NMC a viable cosmological model.\n"}
+{"abstract": "  We show a simple reduction which demonstrates the cryptographic hardness of\nlearning a single periodic neuron over isotropic Gaussian distributions in the\npresence of noise. More precisely, our reduction shows that any polynomial-time\nalgorithm (not necessarily gradient-based) for learning such functions under\nsmall noise implies a polynomial-time quantum algorithm for solving worst-case\nlattice problems, whose hardness form the foundation of lattice-based\ncryptography. Our core hard family of functions, which are well-approximated by\none-layer neural networks, take the general form of a univariate periodic\nfunction applied to an affine projection of the data. These functions have\nappeared in previous seminal works which demonstrate their hardness against\ngradient-based (Shamir'18), and Statistical Query (SQ) algorithms (Song et\nal.'17). We show that if (polynomially) small noise is added to the labels, the\nintractability of learning these functions applies to all polynomial-time\nalgorithms, beyond gradient-based and SQ algorithms, under the aforementioned\ncryptographic assumptions. Moreover, we demonstrate the necessity of noise in\nthe hardness result by designing a polynomial-time algorithm for learning\ncertain families of such functions under exponentially small adversarial noise.\nOur proposed algorithm is not a gradient-based or an SQ algorithm, but is\nrather based on the celebrated Lenstra-Lenstra-Lov\\'asz (LLL) lattice basis\nreduction algorithm. Furthermore, in the absence of noise, this algorithm can\nbe directly applied to solve CLWE detection (Bruna et al.'21) and phase\nretrieval with an optimal sample complexity of $d+1$ samples. In the former\ncase, this improves upon the quadratic-in-$d$ sample complexity required in\n(Bruna et al.'21).\n"}
+{"abstract": "  In recent years we have witnessed a boom in Internet of Things (IoT) device\ndeployments, which has resulted in big data and demand for low-latency\ncommunication. This shift in the demand for infrastructure is also enabling\nreal-time decision making using artificial intelligence for IoT applications.\nArtificial Intelligence of Things (AIoT) is the combination of Artificial\nIntelligence (AI) technologies and the IoT infrastructure to provide robust and\nefficient operations and decision making. Edge computing is emerging to enable\nAIoT applications. Edge computing enables generating insights and making\ndecisions at or near the data source, reducing the amount of data sent to the\ncloud or a central repository. In this paper, we propose a framework for\nfacilitating machine learning at the edge for AIoT applications, to enable\ncontinuous delivery, deployment, and monitoring of machine learning models at\nthe edge (Edge MLOps). The contribution is an architecture that includes\nservices, tools, and methods for delivering fleet analytics at scale. We\npresent a preliminary validation of the framework by performing experiments\nwith IoT devices on a university campus's rooms. For the machine learning\nexperiments, we forecast multivariate time series for predicting air quality in\nthe respective rooms by using the models deployed in respective edge devices.\nBy these experiments, we validate the proposed fleet analytics framework for\nefficiency and robustness.\n"}
+{"abstract": "  Reiher, R\\\"odl and Schacht [J. London Math. Soc. 97 (2018), 77--97] showed\nthat the uniform Tur\\'an density of every $3$-uniform hypergraph is either $0$\nor at least $1/27$, and asked whether there exist $3$-uniform hypergraphs with\nuniform Tur\\'an density equal or arbitrarily close to $1/27$. We construct\n$3$-uniform hypergraphs with uniform Tur\\'an density equal to $1/27$.\n"}
+{"abstract": "  This paper explores self-supervised learning of amodal 3D feature\nrepresentations from RGB and RGB-D posed images and videos, agnostic to object\nand scene semantic content, and evaluates the resulting scene representations\nin the downstream tasks of visual correspondence, object tracking, and object\ndetection. The model infers a latent3D representation of the scene in the form\nof 3D feature points, where each continuous world 3D point is mapped to its\ncorresponding feature vector. The model is trained for contrastive view\nprediction by rendering 3D feature clouds in queried viewpoints and matching\nagainst the 3D feature point cloud predicted from the query view. Notably, the\nrepresentation can be queried for any 3D location, even if it is not visible\nfrom the input view. Our model brings together three powerful ideas of recent\nexciting research work: 3D feature grids as a neural bottleneck for view\nprediction, implicit functions for handling resolution limitations of 3D grids,\nand contrastive learning for unsupervised training of feature representations.\nWe show the resulting 3D visual feature representations effectively scale\nacross objects and scenes, imagine information occluded or missing from the\ninput viewpoints, track objects over time, align semantically related objects\nin 3D, and improve 3D object detection. We outperform many existing\nstate-of-the-art methods for 3D feature learning and view prediction, which are\neither limited by 3D grid spatial resolution, do not attempt to build amodal 3D\nrepresentations, or do not handle combinatorial scene variability due to their\nnon-convolutional bottlenecks.\n"}
+{"abstract": "  We consider remote state estimation of multiple discrete-time linear\ntime-invariant (LTI) systems over multiple wireless time-varying communication\nchannels. Each system state is measured by a sensor, and the measurements from\nsensors are sent to a remote estimator over the shared wireless channels in a\nscheduled manner. We answer the following open problem: what is the fundamental\nrequirement on the multi-sensor-multi-channel system to guarantee the existence\nof a sensor scheduling policy that can stabilize the remote estimation system?\nTo tackle the problem, we propose a novel policy construction method, and\ndevelop a new analytical approach by applying the asymptotic theory of spectral\nradii of products of non-negative matrices. A necessary and sufficient\nstability condition is derived in terms of the LTI system parameters and the\nchannel statistics, which is more effective than existing sufficient conditions\navailable in the literature. Explicit scheduling policies with stability\nguarantees are presented as well. We further extend the analytical framework to\ncover remote estimation with four alternative network setups and obtain\ncorresponding necessary and sufficient stability conditions.\n"}
+{"abstract": "  We consider the mixed problem on the exterior of the unit ball in\n$\\mathbb{R}^{n}$, $n\\ge2$, for a defocusing Schr\\\"{o}dinger equation with a\npower nonlinearity $|u|^{p-1}u$, with zero boundary data. Assuming that the\ninitial data are non radial, sufficiently small perturbations of \\emph{large}\nradial initial data, we prove that for all powers $p>n+6$ the solution exists\nfor all times, its Sobolev norms do not inflate, and the solution is unique in\nthe energy class.\n"}
+{"abstract": "  Network appliances continue to offer novel opportunities to offload\nprocessing from computing nodes directly into the data plane. One popular\nconcern of network operators and their customers is to move data increasingly\nfaster. A common technique to increase data throughput is to compress it before\nits transmission. However, this requires compression of the data -- a time and\nenergy demanding pre-processing phase -- and decompression upon reception -- a\nsimilarly resource consuming operation. Moreover, if multiple nodes transfer\nsimilar data chunks across the network hop (e.g., a given pair of switches),\neach node effectively wastes resources by executing similar steps. This paper\nproposes ZipLine, an approach to design and implement (de)compression at line\nspeed leveraging the Tofino hardware platform which is programmable using the\nP4_16 language. We report on lessons learned while building the system and show\nthroughput, latency and compression measurements on synthetic and real-world\ntraces, showcasing the benefits and trade-offs of our design.\n"}
+{"abstract": "  In this paper, we prove a maximum principle for the general multi-term\nspace-time-fractional transport equation and apply it for establishing\nuniqueness of solution to an initial-boundary-value problem for this equation.\nWe also derive some comparison principles for solutions to the\ninitial-boundary-value problems with different problem data. Finally, we\npresent a maximum principle for the Cauchy problem for a time-fractional\ntransport equation on an unbounded domain.\n"}
+{"abstract": "  Kirigami, an ancient paper cutting art, offers a promising strategy for\n2D-to-3D shape morphing through cut-guided deformation. Existing kirigami\ndesigns for target 3D curved shapes rely on intricate cut patterns in thin\nsheets, making the inverse design challenging. Motivated by the Gauss-Bonnet\ntheorem that correlates the geodesic curvature along the boundary with the\ntopological Gaussian curvature, here, we exploit programming the curvature of\ncut boundaries rather than complex cut patterns in kirigami sheets for target\n3D curved topologies through both forward and inverse designs. Such a new\nstrategy largely simplifies the inverse design. We demonstrate the achievement\nof varieties of dynamic 3D shape shifting under both mechanical stretching and\nremote magnetic actuation, and its potential application as an untethered\npredator-like kirigami soft robot. This study opens a new avenue to encode\nboundary curvatures for shape-programing materials with potential applications\nin shape-morphing structures, soft robots, and multifunctional devices.\n"}
+{"abstract": "  Boltzmann transport equation (BTE) is an ideal tool to describe the\nmultiscale phonon transport phenomena, which are critical to applications like\nmicroelectronics cooling. Numerically solving phonon BTE is extremely\ncomputationally challenging due to the high dimensionality of such problems,\nespecially when mode-resolved properties are considered. In this work, we\ndemonstrate the use of physics-informed neural networks (PINNs) to efficiently\nsolve phonon BTE for multiscale thermal transport problems with the\nconsideration of phonon dispersion and polarization. In particular, a PINN\nframework is devised to predict the phonon energy distribution by minimizing\nthe residuals of governing equations and boundary conditions, without the need\nfor any labeled training data. Moreover, geometric parameters, such as the\ncharacteristic length scale, are included as a part of the input to PINN, which\nenables learning BTE solutions in a parametric setting. The effectiveness of\nthe present scheme is demonstrated by solving a number of phonon transport\nproblems in different spatial dimensions (from 1D to 3D). Compared to existing\nnumerical BTE solvers, the proposed method exhibits superiority in efficiency\nand accuracy, showing great promises for practical applications, such as the\nthermal design of electronic devices.\n"}
+{"abstract": "  Infrared small target detection is an important fundamental task in the\ninfrared system. Therefore, many infrared small target detection methods have\nbeen proposed, in which the low-rank model has been used as a powerful tool.\nHowever, most low-rank-based methods assign the same weights for different\nsingular values, which will lead to inaccurate background estimation.\nConsidering that different singular values have different importance and should\nbe treated discriminatively, in this paper, we propose a non-convex tensor\nlow-rank approximation (NTLA) method for infrared small target detection. In\nour method, NTLA regularization adaptively assigns different weights to\ndifferent singular values for accurate background estimation. Based on the\nproposed NTLA, we propose asymmetric spatial-temporal total variation (ASTTV)\nregularization to achieve more accurate background estimation in complex\nscenes. Compared with the traditional total variation approach, ASTTV exploits\ndifferent smoothness intensities for spatial and temporal regularization. We\ndesign an efficient algorithm to find the optimal solution of our method.\nCompared with some state-of-the-art methods, the proposed method achieves an\nimprovement in terms of various evaluation metrics. Extensive experimental\nresults in various complex scenes demonstrate that our method has strong\nrobustness and low false-alarm rate. Code is available at\nhttps://github.com/LiuTing20a/ASTTV-NTLA.\n"}
+{"abstract": "  We present a measurement of the combined $\\nu_e$ + $\\bar{\\nu}_e$\nflux-averaged charged-current inclusive cross section on argon using data from\nthe MicroBooNE liquid argon time projection chamber (LArTPC) at Fermilab. Using\nthe off-axis flux from the NuMI beam, MicroBooNE has reconstructed 214\ncandidate $\\nu_e$ + $\\bar{\\nu}_e$ interactions with an estimated exposure of\n2.4$\\times10^{20}$ protons on target. Given the estimated purity of 38.6\\%,\nthis implies the observation of 80 $\\nu_e$ + $\\bar{\\nu}_e$ events in argon, the\nlargest such sample to date. The analysis includes the first demonstration of a\nfully automated application of a dE/dx-based particle discrimination technique\nof electron and photon induced showers in a LArTPC neutrino detector. We\nmeasure the $\\nu_e + \\bar{\\nu}_e$ flux-averaged charged-current total cross\nsection to be\n${6.84\\pm\\!1.51~\\textrm{(stat.)}\\pm\\!2.33~\\textrm{(sys.)}\\!\\times\\!10^{-39}~\\textrm{cm}^{2}/~\\textrm{nucleon}}$,\nfor neutrino energies above 250 MeV and an average neutrino flux energy of 905\nMeV when this threshold is applied. The measurement is sensitive to neutrino\nevents where the final state electron momentum is above 48 MeV/c, includes the\nentire angular phase space of the electron, and is in agreement with the\ntheoretical predictions from \\texttt{GENIE} and \\texttt{NuWro}. This\nmeasurement is also the first demonstration of electron neutrino reconstruction\nin a surface LArTPC in the presence of cosmic ray backgrounds, which will be a\ncrucial task for surface experiments like those that comprise the\nShort-Baseline Neutrino (SBN) Program at Fermilab.\n"}
+{"abstract": "  Recognizing Activities of Daily Living (ADL) is a vital process for\nintelligent assistive robots, but collecting large annotated datasets requires\ntime-consuming temporal labeling and raises privacy concerns, e.g., if the data\nis collected in a real household. In this work, we explore the concept of\nconstructing training examples for ADL recognition by playing life simulation\nvideo games and introduce the SIMS4ACTION dataset created with the popular\ncommercial game THE SIMS 4. We build Sims4Action by specifically executing\nactions-of-interest in a \"top-down\" manner, while the gaming circumstances\nallow us to freely switch between environments, camera angles and subject\nappearances. While ADL recognition on gaming data is interesting from the\ntheoretical perspective, the key challenge arises from transferring it to the\nreal-world applications, such as smart-homes or assistive robotics. To meet\nthis requirement, Sims4Action is accompanied with a GamingToReal benchmark,\nwhere the models are evaluated on real videos derived from an existing ADL\ndataset. We integrate two modern algorithms for video-based activity\nrecognition in our framework, revealing the value of life simulation video\ngames as an inexpensive and far less intrusive source of training data.\nHowever, our results also indicate that tasks involving a mixture of gaming and\nreal data are challenging, opening a new research direction. We will make our\ndataset publicly available at https://github.com/aroitberg/sims4action.\n"}
+{"abstract": "  Since its advent in 2009, Bitcoin, a cryptography-enabled peer-to-peer\ndigital payment system, has been gaining increasing attention from both\nacademia and industry. An effort designed to overcome a cluster of bottlenecks\ninherent in existing centralized financial systems, Bitcoin has always been\nchampioned by the crypto community as an example of the spirit of\ndecentralization. While the decentralized nature of Bitcoin's Proof-of-Work\nconsensus algorithm has often been discussed in great detail, no systematic\nstudy has so far been conducted to quantitatively measure the degree of\ndecentralization of Bitcoin from an asset perspective -- How decentralized is\nBitcoin as a financial asset? We present in this paper the first systematic\ninvestigation of the degree of decentralization for Bitcoin based on its entire\ntransaction history. We proposed both static and dynamic analysis of Bitcoin\ntransaction network with quantifiable decentralization measures developed based\non network analysis and market efficiency study. Case studies are also\nconducted to demonstrate the effectiveness of our proposed metrics.\n"}
+{"abstract": "  Dialogue state tracking is an essential part of goal-oriented dialogue\nsystems, while most of these state tracking models often fail to handle unseen\nservices. In this paper, we propose SGD-QA, a simple and extensible model for\nschema-guided dialogue state tracking based on a question answering approach.\nThe proposed multi-pass model shares a single encoder between the domain\ninformation and dialogue utterance. The domain's description represents the\nquery and the dialogue utterance serves as the context. The model improves\nperformance on unseen services by at least 1.6x compared to single-pass\nbaseline models on the SGD dataset. SGD-QA shows competitive performance\ncompared to state-of-the-art multi-pass models while being significantly more\nefficient in terms of memory consumption and training performance. We provide a\nthorough discussion on the model with ablation study and error analysis.\n"}
+{"abstract": "  Recent advances in Reinforcement Learning (RL) have surpassed human-level\nperformance in many simulated environments. However, existing reinforcement\nlearning techniques are incapable of explicitly incorporating already known\ndomain-specific knowledge into the learning process. Therefore, the agents have\nto explore and learn the domain knowledge independently through a trial and\nerror approach, which consumes both time and resources to make valid responses.\nHence, we adapt the Deep Deterministic Policy Gradient (DDPG) algorithm to\nincorporate an adviser, which allows integrating domain knowledge in the form\nof pre-learned policies or pre-defined relationships to enhance the agent's\nlearning process. Our experiments on OpenAi Gym benchmark tasks show that\nintegrating domain knowledge through advisers expedites the learning and\nimproves the policy towards better optima.\n"}
+{"abstract": "  Surgical skills have a great influence on surgical safety and patients'\nwell-being. Traditional assessment of surgical skills involves strenuous manual\nefforts, which lacks efficiency and repeatability. Therefore, we attempt to\nautomatically predict how well the surgery is performed using the surgical\nvideo. In this paper, a unified multi-path framework for automatic surgical\nskill assessment is proposed, which takes care of multiple composing aspects of\nsurgical skills, including surgical tool usage, intraoperative event pattern,\nand other skill proxies. The dependency relationships among these different\naspects are specially modeled by a path dependency module in the framework. We\nconduct extensive experiments on the JIGSAWS dataset of simulated surgical\ntasks, and a new clinical dataset of real laparoscopic surgeries. The proposed\nframework achieves promising results on both datasets, with the\nstate-of-the-art on the simulated dataset advanced from 0.71 Spearman's\ncorrelation to 0.80. It is also shown that combining multiple skill aspects\nyields better performance than relying on a single aspect.\n"}
+{"abstract": "  Long-range spatiotemporal correlations may play important roles in\nnonequilibrium surface growth process. In order to investigate the effects of\nlong-range temporal correlation on dynamic scaling of growing surfaces, we\nperform extensive numerical simulations of the (1+1)- and (2+1)-dimensional\nKardar-Parisi-Zhang (KPZ) growth system in the presence of temporally\ncorrelated noise, and compare our results with previous theoretical predictions\nand numerical simulations. We find that surface morphologies are obviously\naffected with long-range temporal correlations, and as the temporal correlation\nexponent increases, the KPZ surfaces develop gradually faceted patterns in the\nsaturated growth regimes. Our results show that the temporal correlated KPZ\nsystem displays evidently nontrivial dynamic properties when $0<\\theta<0.5$,\nthe characteristic roughness exponents satisfy $\\alpha<\\alpha_s$, and\n$\\alpha_{loc}$ exhibiting non-universal scaling within local window sizes,\nwhich differs with the existing dynamic scaling classifications, both in the\n(1+1)- and (2+1)-dimensions.\n"}
+{"abstract": "  A novel concept for dynamic focus shaping based on highly efficient coherent\nbeam combining with microlens arrays (MLA) as combining element is presented.\nThis concept allows to control the power weights of diffraction orders by\nvarying the absolute phases of an array of input beams. A proof-of-principle\nexperiment is supported by simulations. For this, an input beam matrix of 5 x 5\nbeams is combined proving both the ability for further power scaling and\ndynamic focus shaping.\n"}
+{"abstract": "  We introduce a neural network architecture to solve inverse problems linked\nto a one-dimensional integral operator. This architecture is built by unfolding\na forward-backward algorithm derived from the minimization of an objective\nfunction which consists of the sum of a data-fidelity function and a\nTikhonov-type regularization function. The robustness of this inversion method\nwith respect to a perturbation of the input is theoretically analyzed. Ensuring\nrobustness is consistent with inverse problem theory since it guarantees both\nthe continuity of the inversion method and its insensitivity to small noise.\nThe latter is a critical property as deep neural networks have been shown to be\nvulnerable to adversarial perturbations. One of the main novelties of our work\nis to show that the proposed network is also robust to perturbations of its\nbias. In our architecture, the bias accounts for the observed data in the\ninverse problem. We apply our method to the inversion of Abel integral\noperators, which define a fractional integration involved in wide range of\nphysical processes. The neural network is numerically implemented and tested to\nillustrate the efficiency of the method. Lipschitz constants after training are\ncomputed to measure the robustness of the neural networks.\n"}
+{"abstract": "  We describe the application of Semantic Segmentation by using the Self\nOrganizing Map technique to an high spatial and spectral resolution dataset\nacquired along the H$\\alpha$ line at 656.28 nm by the Interferometric\nBi-dimensional Spectrometer installed at the focus plane of the Dunn Solar\nTelescope. This machine learning approach allowed us to identify several\nfeatures corresponding to the main structures of the solar photosphere and\nchromosphere. The obtained results show the capability and flexibility of this\nmethod to identifying and analyzing the fine structures which characterize the\nsolar activity in the low atmosphere. This is a first successful application of\nthe SOM technique to astrophysical data sets.\n"}
+{"abstract": "  This paper presents a novel intrinsic image transfer (IIT) algorithm for\nillumination manipulation, which creates a local image translation between two\nillumination surfaces. This model is built on an optimization-based framework\nconsisting of three photo-realistic losses defined on the sub-layers factorized\nby an intrinsic image decomposition. We illustrate that all losses can be\nreduced without the necessity of taking an intrinsic image decomposition under\nthe well-known spatial-varying illumination illumination-invariant reflectance\nprior knowledge. Moreover, with a series of relaxations, all of them can be\ndirectly defined on images, giving a closed-form solution for image\nillumination manipulation. This new paradigm differs from the prevailing\nRetinex-based algorithms, as it provides an implicit way to deal with the\nper-pixel image illumination. We finally demonstrate its versatility and\nbenefits to the illumination-related tasks such as illumination compensation,\nimage enhancement, and high dynamic range (HDR) image compression, and show the\nhigh-quality results on natural image datasets.\n"}
+{"abstract": "  In the era of Cyber Physical Systems, designers need to offer support for\nrun-time adaptivity considering different constraints, including the internal\nstatus of the system. This work presents a run-time monitoring approach, based\non the Performance Application Programming Interface, that offers a unified\ninterface to transparently access both the standard Performance Monitoring\nCounters (PMCs) in the CPUs and the custom ones integrated into hardware\naccelerators. Automatic tools offer to Sw programmers the support to design and\nimplement Coarse-Grain Virtual Reconfigurable Circuits, instrumented with\ncustom PMCs. This approach has been validated on a heterogeneous application\nfor image/video processing with an overhead of 6% of the execution time.\n"}
+{"abstract": "  We demonstrate a cost-effective, fully software-based lock-in amplifier (LIA)\nimplemented on a commercial computer. The device is designed for application in\nnonlinear spectroscopy, such as transient absorption and coherent\nmultidi-mensional spectroscopy, but may be also used in any other application.\nThe performance of our device is compared to a state-of-the-art commercial LIA\nwith nearly identical results for both devices. Advantages of our device over\ncommercial hardwired electronic lock-in amplifiers is the improved flexibility\nin the data analysis and the possibility of arbitrary up-scaling of the number\nof LIA channels.\n"}
+{"abstract": "  Let $(M,g)$ be a smooth compact Riemann surface with the multicomponent\nboundary\n$\\Gamma=\\Gamma_0\\cup\\Gamma_1\\cup\\dots\\cup\\Gamma_m=:\\Gamma_0\\cup\\tilde\\Gamma$.\nLet $u=u^f$ obey $\\Delta u=0$ in $M$, $u|_{\\Gamma_0}=f,\\,\\,u|_{\\tilde\\Gamma}=0$\n(the grounded holes) and $v=v^h$ obey $\\Delta v=0$ in $M$,\n$v|_{\\Gamma_0}=h,\\,\\,\\partial_\\nu v|_{\\tilde\\Gamma}=0$ (the isolated holes).\nLet $\\Lambda_{g}^{\\rm gr}: f\\mapsto\\partial_\\nu u^f|_{\\Gamma_{0}}$ and\n$\\Lambda_{g}^{\\rm is}: h\\mapsto\\partial_\\nu v^h|_{\\Gamma_{0}}$ be the\ncorresponding DN-maps. The EIT problem is to determine $M$ from\n$\\Lambda_{g}^{\\rm gr}$ or $\\Lambda_{g}^{\\rm is}$.\n  To solve it, an algebraic version of the BC-method is applied. The main\ninstrument is the algebra of holomorphic functions on the ma\\-ni\\-fold\n${\\mathbb M}$, which is obtained by gluing two examples of $M$ along\n$\\tilde{\\Gamma}$. We show that this algebra is determined by $\\Lambda_{g}^{\\rm\ngr}$ (or $\\Lambda_{g}^{\\rm is}$) up to isometric isomorphism. Its Gelfand\nspectrum (the set of characters) plays the role of the material for\nconstructing a relevant copy $(M',g',\\Gamma_{0}')$ of $(M,g,\\Gamma_{0})$. This\ncopy is conformally equivalent to the original, provides\n$\\Gamma_{0}'=\\Gamma_{0},\\,\\,\\Lambda_{g'}^{\\rm gr}=\\Lambda_{g}^{\\rm\ngr},\\,\\,\\Lambda_{g'}^{\\rm is}=\\Lambda_{g}^{\\rm is}$, and thus solves the\nproblem.\n"}
+{"abstract": "  Fusion energy is often regarded as a long-term solution to the world's energy\nneeds. However, even after solving the critical research challenges,\nengineering and materials science will still impose significant constraints on\nthe characteristics of a fusion power plant. Meanwhile, the global energy grid\nmust transition to low-carbon sources by 2050 to prevent the worst effects of\nclimate change. We review three factors affecting fusion's future trajectory:\n(1) the significant drop in the price of renewable energy, (2) the\nintermittency of renewable sources and implications for future energy grids,\nand (3) the recent proposition of intermediate-level nuclear waste as a product\nof fusion. Within the scenario assumed by our premises, we find that while\nthere remains a clear motivation to develop fusion power plants, this\nmotivation is likely weakened by the time they become available. We also\nconclude that most current fusion reactor designs do not take these factors\ninto account and, to increase market penetration, fusion research should\nconsider relaxed nuclear waste design criteria, raw material availability\nconstraints and load-following designs with pulsed operation.\n"}
+{"abstract": "  Chinese character decomposition has been used as a feature to enhance Machine\nTranslation (MT) models, combining radicals into character and word level\nmodels. Recent work has investigated ideograph or stroke level embedding.\nHowever, questions remain about different decomposition levels of Chinese\ncharacter representations, radical and strokes, best suited for MT. To\ninvestigate the impact of Chinese decomposition embedding in detail, i.e.,\nradical, stroke, and intermediate levels, and how well these decompositions\nrepresent the meaning of the original character sequences, we carry out\nanalysis with both automated and human evaluation of MT. Furthermore, we\ninvestigate if the combination of decomposed Multiword Expressions (MWEs) can\nenhance the model learning. MWE integration into MT has seen more than a decade\nof exploration. However, decomposed MWEs has not previously been explored.\n"}
+{"abstract": "  We present some results that complement our prequels\n[arXiv:1809.08425,arXiv:1907.05770] on holomorphic vector bundles. We apply the\nmethod of the Quot-scheme limit of Fubini-Study metrics developed therein to\nprovide a generalisation to the singular case of the result originally obtained\nby X.W. Wang for the smooth case, which states that the existence of balanced\nmetrics is equivalent to the Gieseker stability of the vector bundle. We also\nprove that the Bergman 1-parameter subgroups form subgeodesics in the space of\nhermitian metrics. This paper also contains a review of techniques developed in\n[arXiv:1809.08425,arXiv:1907.05770] and how they correspond to their\ncounterparts developed in the study of the Yau-Tian-Donaldson conjecture.\n"}
+{"abstract": "  Everything we know about the environment around us is thanks to light, a kind\nof electromagnetic radiation. Astronomy takes advantage of it and all the\nelectromagnetic spectrum with the help of many devices to record them and\ndetermine from which places in our Universe they come. These signals must be\nprocessed to obtain the images that are will be then exposed to the public.\nAstronomers know that these images will inspire and generate curiosity in each\nperson who sees them. This Science is inclusive and wants to transmit the\nknowledge and the beautiful events that happen in the universe to all people.\nWe try to do that by developing and showing other forms of teaching, taking\nadvantage of new technologies that are available today to bring this knowledge\ncloser to the minorities in our country like visually impaired people.\n  Because of this problem, the AstroBVI project arrived in Peru for the first\ntime, thanks to the distribution of tactile images of 3D galaxies that were\ndelivered to us from the Centro de Astronom\\'ia de la Universidad de\nAntofagasta of the and financed by the International Astronomical Union Office\nof Astronomy for Development (IAU-OAD). This allowed the holding of seven\nworkshops during 2019, visiting various institutions and benefiting more than\n160 participants with blindness and low vision, identifying in them a lot of\ninterest, which shows the enormous potential presented by these 3D tactile\nmaterials.\n"}
+{"abstract": "  For a large liquid argon time projection chamber (LArTPC) operating on or\nnear the Earth's surface to detect neutrino interactions, the rejection of\ncosmogenic background is a critical and challenging task because of the large\ncosmic ray flux and the long drift time of the TPC. We introduce a superior\ncosmic background rejection procedure based on the Wire-Cell three-dimensional\n(3D) event reconstruction for LArTPCs. From an initial 1:20,000 neutrino to\ncosmic-ray background ratio, we demonstrate these tools on data from the\nMicroBooNE experiment and create a high performance generic neutrino event\nselection with a cosmic contamination of 14.9\\% (9.7\\%) for a visible energy\nregion greater than O(200)~MeV. The neutrino interaction selection efficiency\nis 80.4\\% and 87.6\\% for inclusive $\\nu_\\mu$ charged-current and $\\nu_e$\ncharged-current interactions, respectively. This significantly improved\nperformance compared to existing reconstruction algorithms, marks a major\nmilestone toward reaching the scientific goals of LArTPC neutrino oscillation\nexperiments operating near the Earth's surface.\n"}
+{"abstract": "  We present optical follow-up observation results of three binary black hole\nmerger (BBH) events, GW190408_181802, GW190412, and GW190503_185404, which were\ndetected by the Advanced LIGO and Virgo gravitational wave (GW) detectors.\nElectromagnetic (EM) counterparts are generally not expected for BBH merger\nevents. However, some theoretical models suggest that EM counterparts of BBH\ncan possibly arise in special environments, prompting motivation to search for\nEM counterparts for such events. We observed high-credibility regions of the\nsky for the three BBH merger events with telescopes of the Gravitational-wave\nEM Counterpart Korean Observatory (GECKO), including the KMTNet. Our\nobservation started as soon as 100 minutes after the GW event alerts and\ncovered 29 - 63 deg$^2$ for each event with a depth of $\\sim$ 22.5 mag in\n$R$-band within hours of observation. No plausible EM counterparts were found\nfor these events, but from no detection in the GW190503_185404 event, for which\nwe covered 69% credibility region, we place the BBH merger EM counterpart\nsignal to be $M_{g}$ > -18.0 AB mag within about 1 day of the GW event. The\ncomparison of our detection limits with light curves of several kilonova models\nsuggests that a kilonova event could have been identified within hours from GW\nalert with GECKO observations if the compact merger happened at < 400 Mpc and\nthe localization accuracy was of order of 50 deg$^2$. Our result gives a great\npromise for the GECKO facilities to find EM counterparts within a few hours\nfrom GW detection in future GW observation runs.\n"}
+{"abstract": "  In smoking cessation clinical research and practice, objective validation of\nself-reported smoking status is crucial for ensuring the reliability of the\nprimary outcome, that is, smoking abstinence. Speech signals convey important\ninformation about a speaker, such as age, gender, body size, emotional state,\nand health state. We investigated (1) if smoking could measurably alter voice\nfeatures, (2) if smoking cessation could lead to changes in voice, and\ntherefore (3) if the voice-based smoking status assessment has the potential to\nbe used as an objective smoking cessation validation method.\n"}
+{"abstract": "  In recent times, deep learning-based steganalysis classifiers became popular\ndue to their state-of-the-art performance. Most deep steganalysis classifiers\nusually extract noise residuals using high-pass filters as preprocessing steps\nand feed them to their deep model for classification. It is observed that\nrecent steganographic embedding does not always restrict their embedding in the\nhigh-frequency zone; instead, they distribute it as per embedding policy.\nTherefore, besides noise residual, learning the embedding zone is another\nchallenging task. In this work, unlike the conventional approaches, the\nproposed model first extracts the noise residual using learned denoising\nkernels to boost the signal-to-noise ratio. After preprocessing, the sparse\nnoise residuals are fed to a novel Multi-Contextual Convolutional Neural\nNetwork (M-CNET) that uses heterogeneous context size to learn the sparse and\nlow-amplitude representation of noise residuals. The model performance is\nfurther improved by incorporating the Self-Attention module to focus on the\nareas prone to steganalytic embedding. A set of comprehensive experiments is\nperformed to show the proposed scheme's efficacy over the prior arts. Besides,\nan ablation study is given to justify the contribution of various modules of\nthe proposed architecture.\n"}
+{"abstract": "  Executing a Golog program on an actual robot typically requires additional\nsteps to account for hardware or software details of the robot platform, which\ncan be formulated as constraints on the program. Such constraints are often\ntemporal, refer to metric time, and require modifications to the abstract Golog\nprogram. We describe how to formulate such constraints based on a modal variant\nof the Situation Calculus. These constraints connect the abstract program with\nthe platform models, which we describe using timed automata. We show that for\nprograms over finite domains and with fully known initial state, the problem of\nsynthesizing a controller that satisfies the constraints while preserving the\neffects of the original program can be reduced to MTL synthesis. We do this by\nconstructing a timed automaton from the abstract program and synthesizing an\nMTL controller from this automaton, the platform models, and the constraints.\nWe prove that the synthesized controller results in execution traces which are\nthe same as those of the original program, possibly interleaved with\nplatform-dependent actions, that they satisfy all constraints, and that they\nhave the same effects as the traces of the original program. By doing so, we\nobtain a decidable procedure to synthesize a controller that satisfies the\nspecification while preserving the original program.\n"}
+{"abstract": "  The $s$-colour size-Ramsey number of a hypergraph $H$ is the minimum number\nof edges in a hypergraph $G$ whose every $s$-edge-colouring contains a\nmonochromatic copy of $H$. We show that the $s$-colour size-Ramsey number of\nthe $t$-power of the $r$-uniform tight path on $n$ vertices is linear in $n$,\nfor every fixed $r, s, t$, thus answering a question of Dudek, La Fleur,\nMubayi, and R\\\"odl (2017).\n  In fact, we prove a stronger result that allows us to deduce that powers of\nbounded degree hypergraph trees and powers of `long subdivisions' of bounded\ndegree hypergraphs have size-Ramsey numbers that are linear in the number of\nvertices. This extends and strongly generalises recent results about the\nlinearity of size-Ramsey numbers of powers of bounded degree trees and of long\nsubdivisions of bounded degree graphs.\n"}
+{"abstract": "  The canonical forms associated to scattering amplitudes of planar Feynman\ndiagrams are interpreted in terms of masses of projectives, defined as the\nmodulus of their central charges, in the hearts of certain $t$-structures of\nderived categories of quiver representations and, equivalently, in terms of\ncluster tilting objects of the corresponding cluster categories.\n"}
+{"abstract": "  Learning meaningful representations of data that can address challenges such\nas batch effect correction, data integration and counterfactual inference is a\ncentral problem in many domains including computational biology. Adopting a\nConditional VAE framework, we identify the mathematical principle that unites\nthese challenges: learning a representation that is marginally independent of a\ncondition variable. We therefore propose the Contrastive Mixture of Posteriors\n(CoMP) method that uses a novel misalignment penalty to enforce this\nindependence. This penalty is defined in terms of mixtures of the variational\nposteriors themselves, unlike prior work which uses external discrepancy\nmeasures such as MMD to ensure independence in latent space. We show that CoMP\nhas attractive theoretical properties compared to previous approaches,\nespecially when there is complex global structure in latent space. We further\ndemonstrate state of the art performance on a number of real-world problems,\nincluding the challenging tasks of aligning human tumour samples with cancer\ncell-lines and performing counterfactual inference on single-cell RNA\nsequencing data. Incidentally, we find parallels with the fair representation\nlearning literature, and demonstrate CoMP has competitive performance in\nlearning fair yet expressive latent representations.\n"}
+{"abstract": "  In this paper, we propose a Zero-Touch, deep reinforcement learning\n(DRL)-based Proactive Failure Recovery framework called ZT-PFR for stateful\nnetwork function virtualization (NFV)-enabled networks. To this end, we\nformulate a resource-efficient optimization problem minimizing the network cost\nfunction including resource cost and wrong decision penalty. As a solution, we\npropose state-of-the-art DRL-based methods such as soft-actor-critic (SAC) and\nproximal-policy-optimization (PPO). In addition, to train and test our DRL\nagents, we propose a novel impending-failure model. Moreover, to keep network\nstatus information at an acceptable freshness level for appropriate\ndecision-making, we apply the concept of age of information to strike a balance\nbetween the event and scheduling based monitoring. Several key systems and DRL\nalgorithm design insights for ZT-PFR are drawn from our analysis and simulation\nresults. For example, we use a hybrid neural network, consisting long\nshort-term memory layers in the DRL agents structure, to capture\nimpending-failures time dependency.\n"}
+{"abstract": "  The contribution contains the preface to the Proceedings to the 23rd\nInternational Workshop \"What Comes Beyond the Standard Models\", July 04 -- July\n12, 2020, Bled, Slovenia, [Virtual Workshop -- July 6.--10. 2020], Volume 1:\nInvited Talks and Volume 2: Further Talks And Scientific Debuts, published in\nBled workshops in physics, Vol.21, No. 1 and 2, DMFA-Zalo\\v{z}nistvo,\nLjubljana, Dec. 2020, links to (most of) the published contributions, section\n(by M.Yu. Khlopov) on VIA and virtual conference at Bled 2020, and two poems by\nAstri Kleppe.\n"}
+{"abstract": "  Pre-trained models have achieved state-of-the-art results in various Natural\nLanguage Processing (NLP) tasks. Recent works such as T5 and GPT-3 have shown\nthat scaling up pre-trained language models can improve their generalization\nabilities. Particularly, the GPT-3 model with 175 billion parameters shows its\nstrong task-agnostic zero-shot/few-shot learning capabilities. Despite their\nsuccess, these large-scale models are trained on plain texts without\nintroducing knowledge such as linguistic knowledge and world knowledge. In\naddition, most large-scale models are trained in an auto-regressive way. As a\nresult, this kind of traditional fine-tuning approach demonstrates relatively\nweak performance when solving downstream language understanding tasks. In order\nto solve the above problems, we propose a unified framework named ERNIE 3.0 for\npre-training large-scale knowledge enhanced models. It fuses auto-regressive\nnetwork and auto-encoding network, so that the trained model can be easily\ntailored for both natural language understanding and generation tasks with\nzero-shot learning, few-shot learning or fine-tuning. We trained the model with\n10 billion parameters on a 4TB corpus consisting of plain texts and a\nlarge-scale knowledge graph. Empirical results show that the model outperforms\nthe state-of-the-art models on 54 Chinese NLP tasks, and its English version\nachieves the first place on the SuperGLUE benchmark (July 3, 2021), surpassing\nthe human performance by +0.8% (90.6% vs. 89.8%).\n"}
+{"abstract": "  Though pre-trained language models such as Bert and XLNet, have rapidly\nadvanced the state-of-the-art on many NLP tasks, they implicit semantics only\nrelying on surface information between words in corpus. Intuitively, background\nknowledge influences the efficacy of understanding. Inspired by this common\nsense, we focus on improving model pretraining by leveraging explicit\nknowledge. Different from recent research that optimize pretraining model by\nknowledge masking strategies, we propose a simple but general method to combine\nexplicit knowledge with pretraining. To be specific, we first match knowledge\nfacts from knowledge graph (KG) and then add a knowledge injunction layer to\ntransformer directly without changing its architecture. The present study seeks\nto find the direct impact of explicit knowledge on transformer per-training. We\nconduct experiments on various datasets for different downstream tasks. The\nexperimental results show that solely by adding external knowledge to\ntransformer can improve the learning performance on many NLP tasks.\n"}
+{"abstract": "  Given a stochastic dynamical system modelled via stochastic differential\nequations (SDEs), we evaluate the safety of the system through\ncharacterisations of its exit time moments. We lift the (possibly nonlinear)\ndynamics into the space of the occupation and exit measures to obtain a set of\nlinear evolution equations which depend on the infinitesimal generator of the\nSDE. Coupled with appropriate semidefinite positive matrix constraints, this\nyields a moment-based approach for the computation of exit time moments of SDEs\nwith polynomial drift and diffusion dynamics. To extend the capability of the\nmoment approach, we propose a state augmentation method which allows us to\ngenerate the evolution equations for a broader class of nonlinear stochastic\nsystems and apply the moment method to previously unsupported dynamics. In\nparticular, we show a general augmentation strategy for sinusoidal dynamics\nwhich can be found in most physical systems. We employ the methodology on an\nOrnstein-Uhlenbeck process and stochastic spring-mass-damper model to\ncharacterise their safety via their expected exit times and show the additional\nexit distribution insights that are afforded through higher order moments.\n"}
+{"abstract": "  The large imbalance in the neutron and proton densities in very neutron rich\nsystems increases the nuclear symmetry energy so that it governs many aspects\nof neutron stars and their mergers. Extracting the density dependence of the\nsymmetry energy therefore constitutes an important scientific objective. Many\nanalyses have been limited to extracting values for the symmetry energy, $S_0$,\nand its ``derivative'', $L$, at saturation density $\\rho_0 \\approx 2.6 \\times\n10^{14}~\\mathrm{g/cm^3}$ $\\approx 0.16~\\mathrm{nucleons/fm^{3}}$, resulting in\nconstraints that appear contradictory. We show that most experimental\nobservables actually probe the symmetry energy at densities far from $\\rho_0$,\nmaking the extracted values of $S_0$ or $L$ imprecise. By focusing on the\ndensities these observables actually probe, we obtain a detailed picture of the\ndensity dependence of the symmetry energy from $0.25\\rho_0$ to $1.5\\rho_0$.\nFrom this experimentally derived density functional, we extract\n$L_{01}=53.1\\pm6.1 MeV$ at $\\rho \\approx 0.10~\\mathrm{fm^{-3}}$, a neutron skin\nthickness for $^{208}Pb$ of $R_{np} =$ $0.23\\pm0.04$ fm, a symmetry pressure at\nsaturation density of $P_0=3.2\\pm1.2 MeV/fm^3$ and suggests a radius for a 1.4\nsolar mass neutron star of $13.1\\pm0.6$ km.\n"}
+{"abstract": "  Analyzing the financial benefit of marketing is still a critical topic for\nboth practitioners and researchers. Companies consider marketing costs as a\ntype of investment and expect this investment to be returned to the company in\nthe form of profit. On the other hand, companies adopt different innovative\nstrategies to increase their value. Therefore, this study aims to test the\nimpact of marketing investment on firm value and systematic risk. To do so,\ndata related to four Arabic emerging markets during the period 2010-2019 are\nconsidered, and firm share price and beta share are considered to measure firm\nvalue and systematic risk, respectively. Since a firm's ownership concentration\nis a determinant factor in firm value and systematic risk, this variable is\nconsidered a moderated variable in the relationship between marketing\ninvestment and firm value and systematic risk. The findings of the study, using\npanel data regression, indicate that increasing investment in marketing has a\npositive effect on the firm value valuation model. It is also found that the\nownership concentration variable has a reinforcing role in the relationship\nbetween marketing investment and firm value. It is also disclosed that it\nmoderates the systematic risk aligned with the monitoring impact of controlling\nshareholders. This study provides a logical combination of governance-marketing\ndimensions to interpret performance indicators in the capital market.\n"}
+{"abstract": "  Multi-component dark matter scenarios are studied in the model with $U(1)_X$\ndark gauge symmetry that is broken into its product subgroup $Z_2 \\times Z_3$\n\\'{a} la Krauss-Wilczek mechanism. In this setup, there exist two types of dark\nmatter fields, $X$ and $Y$, distinguished by different $Z_2 \\times Z_3$\ncharges. The real and imaginary parts of the $Z_2$-charged field, $X_R$ and\n$X_I$, get different masses from the $U(1)_X$ symmetry breaking. The field $Y$,\nwhich is another dark matter candidate due to the unbroken $Z_3$ symmetry,\nbelongs to the Strongly Interacting Massive Particle (SIMP)-type dark matter.\nBoth $X_I$ and $X_R$ may contribute to $Y$'s $3\\rightarrow 2$ annihilation\nprocesses, opening a new class of SIMP models with a local dark gauge symmetry.\nDepending on the mass difference between $X_I$ and $X_R$, we have either\ntwo-component or three-component dark matter scenarios. In particular two- or\nthree-component SIMP scenarios can be realised not only for small mass\ndifference between $X$ and $Y$, but also for large mass hierarchy between them,\nwhich is a new and unique feature of the present model. We consider both\ntheoretical and experimental constraints, and present four case studies of the\nmulti-component dark matter scenarios.\n"}
+{"abstract": "  Over the past 70 years, the number of international environmental agreements\n(IEAs) has increased substantially, highlighting their prominent role in\nenvironmental governance. This paper applies the toolkit of network analysis to\nidentify the network properties of international environmental cooperation\nbased on 546 IEAs signed between 1948 and 2015. We identify four stylised facts\nthat offer topological corroboration for some key themes in the IEA literature.\nFirst, we find that a statistically significant cooperation network did not\nemerge until early 1970, but since then the network has grown continuously in\nstrength, resulting in higher connectivity and intensity of cooperation between\nsignatory countries. Second, over time the network has become closer, denser\nand more cohesive, allowing more effective policy coordination and knowledge\ndiffusion. Third, the network, while global, has a noticeable European imprint:\ninitially the United Kingdom and more recently France and Germany have been the\nmost strategic players to broker environmental cooperation. Fourth,\ninternational environmental coordination started with the management of\nfisheries and the sea, but is now most intense on waste and hazardous\nsubstances. The network of air and atmosphere treaties is weaker on a number of\nmetrics and lacks the hierarchical structure found in other networks. It is the\nonly network whose topological properties are shaped significantly by\nUN-sponsored treaties.\n"}
+{"abstract": "  With emerging thin film PIN based optoelectronics devices, a significant\nresearch thrust is focused on the passivation of trap states for performance\nenhancement. Among various methods, capacitance frequency technique (CFT) is\noften employed to quantify trap state parameters, however, the trapped charge\ninduced electrostatic effect on the same is not yet established for such\ndevices. Herein, we present a theoretical methodology to incorporate such\neffects in the CF characteristics of well-established carrier selective\nperovskite-based PIN devices. We show that the electrostatic effect of trapped\ncharges leads to non-linear energy bands in perovskite layer which results in\nthe underestimation of trap density from existing models of CFT. Consequently,\na parabolic band approximation with effective length PBAEL model is developed\nwhich accurately predicts the trap density for shallow or deep states from CFT\nanalysis. In addition, we demonstrate that the attempt to escape frequency,\ncrucial for trapped charge dynamics with continuum energy bands, can be well\nextracted by eliminating non-linear effects at reduced perovskite thickness. We\nbelieve that our work provides a unified theoretical platform for CFT to\nextract trap state parameters for a broad class of organic and hybrid\nmaterials-based thin film devices for energy conversion applications such as\nsolar cells, LEDs, etc.\n"}
+{"abstract": "  In educational applications, Knowledge Tracing (KT), the problem of\naccurately predicting students' responses to future questions by summarizing\ntheir knowledge states, has been widely studied for decades as it is considered\na fundamental task towards adaptive online learning. Among all the proposed KT\nmethods, Deep Knowledge Tracing (DKT) and its variants are by far the most\neffective ones due to the high flexibility of the neural network. However, DKT\noften ignores the inherent differences between students (e.g. memory skills,\nreasoning skills, ...), averaging the performances of all students, leading to\nthe lack of personalization, and therefore was considered insufficient for\nadaptive learning. To alleviate this problem, in this paper, we proposed\nLeveled Attentive KNowledge TrAcing (LANA), which firstly uses a novel\nstudent-related features extractor (SRFE) to distill students' unique inherent\nproperties from their respective interactive sequences. Secondly, the pivot\nmodule was utilized to dynamically reconstruct the decoder of the neural\nnetwork on attention of the extracted features, successfully distinguishing the\nperformance between students over time. Moreover, inspired by Item Response\nTheory (IRT), the interpretable Rasch model was used to cluster students by\ntheir ability levels, and thereby utilizing leveled learning to assign\ndifferent encoders to different groups of students. With pivot module\nreconstructed the decoder for individual students and leveled learning\nspecialized encoders for groups, personalized DKT was achieved. Extensive\nexperiments conducted on two real-world large-scale datasets demonstrated that\nour proposed LANA improves the AUC score by at least 1.00% (i.e. EdNet 1.46%\nand RAIEd2020 1.00%), substantially surpassing the other State-Of-The-Art KT\nmethods.\n"}
+{"abstract": "  Chest X-rays are the most commonly performed diagnostic examination to detect\ncardiopulmonary abnormalities. However, the presence of bony structures such as\nribs and clavicles can obscure subtle abnormalities, resulting in diagnostic\nerrors. This study aims to build a deep learning-based bone suppression model\nthat identifies and removes these occluding bony structures in frontal CXRs to\nassist in reducing errors in radiological interpretation, including DL\nworkflows, related to detecting manifestations consistent with tuberculosis\n(TB). Several bone suppression models with various deep architectures are\ntrained and optimized using the proposed combined loss function and their\nperformances are evaluated in a cross-institutional test setting. The\nbest-performing model is used to suppress bones in the publicly available\nShenzhen and Montgomery TB CXR collections. A VGG-16 model is pretrained on a\nlarge collection of publicly available CXRs. The CXR-pretrained model is then\nfine-tuned individually on the non-bone-suppressed and bone-suppressed CXRs of\nShenzhen and Montgomery TB CXR collections to classify them as showing normal\nlungs or TB manifestations. The performances of these models are compared using\nseveral performance metrics, analyzed for statistical significance, and their\npredictions are qualitatively interpreted through class-selective relevance\nmaps. It is observed that the models trained on bone-suppressed CXRs\nsignificantly outperformed (p<0.05) the models trained on the\nnon-bone-suppressed CXRs. Models trained on bone-suppressed CXRs improved\ndetection of TB-consistent findings and resulted in compact clustering of the\ndata points in the feature space signifying that bone suppression improved the\nmodel sensitivity toward TB classification.\n"}
+{"abstract": "  We present new numerical schemes for pricing perpetual Bermudan and American\noptions as well as $\\alpha$-quantile options. This includes a new direct\ncalculation of the optimal exercise barrier for early-exercise options. Our\napproach is based on the Spitzer identities for general L\\'evy processes and on\nthe Wiener-Hopf method. Our direct calculation of the price of\n$\\alpha$-quantile options combines for the first time the Dassios-Port-Wendel\nidentity and the Spitzer identities for the extrema of processes. Our results\nshow that the new pricing methods provide excellent error convergence with\nrespect to computational time when implemented with a range of L\\'evy\nprocesses.\n"}
+{"abstract": "  The problem of repairing linear codes and, in particular, Reed Solomon (RS)\ncodes has attracted a lot of attention in recent years due to their extreme\nimportance to distributed storage systems. In this problem, a failed code\nsymbol (node) needs to be repaired by downloading as little information as\npossible from a subset of the remaining nodes. By now, there are examples of RS\ncodes that have efficient repair schemes, and some even attain the cut-set\nbound. However, these schemes fall short in several aspects; they require a\nconsiderable field extension degree. They do not provide any nontrivial repair\nscheme over prime fields. Lastly, they are all linear repairs, i.e., the\ncomputed functions are linear over the base field. Motivated by these and by a\nquestion raised in [GW17] on the power of nonlinear repair schemes, we study\nthe problem of nonlinear repair schemes of RS codes.\n  Our main results are the first nonlinear repair scheme of RS codes with\nasymptotically optimal repair bandwidth (asymptotically matching the cut-set\nbound). This is the first example of a nonlinear repair scheme of any code and\nalso the first example that a nonlinear repair scheme can outperform all linear\nones. Lastly, we show that the cut-set bound for RS codes is not tight over\nprime fields by proving a tighter bound, using additive combinatorics ideas.\n"}
+{"abstract": "  The fine-grained relationship between form and function with respect to deep\nneural network architecture design and hardware-specific acceleration is one\narea that is not well studied in the research literature, with form often\ndictated by accuracy as opposed to hardware function. In this study, a\ncomprehensive empirical exploration is conducted to investigate the impact of\ndeep neural network architecture design on the degree of inference speedup that\ncan be achieved via hardware-specific acceleration. More specifically, we\nempirically study the impact of a variety of commonly used macro-architecture\ndesign patterns across different architectural depths through the lens of\nOpenVINO microprocessor-specific and GPU-specific acceleration. Experimental\nresults showed that while leveraging hardware-specific acceleration achieved an\naverage inference speed-up of 380%, the degree of inference speed-up varied\ndrastically depending on the macro-architecture design pattern, with the\ngreatest speedup achieved on the depthwise bottleneck convolution design\npattern at 550%. Furthermore, we conduct an in-depth exploration of the\ncorrelation between FLOPs requirement, level 3 cache efficacy, and network\nlatency with increasing architectural depth and width. Finally, we analyze the\ninference time reductions using hardware-specific acceleration when compared to\nnative deep learning frameworks across a wide variety of hand-crafted deep\nconvolutional neural network architecture designs as well as ones found via\nneural architecture search strategies. We found that the DARTS-derived\narchitecture to benefit from the greatest improvement from hardware-specific\nsoftware acceleration (1200%) while the depthwise bottleneck convolution-based\nMobileNet-V2 to have the lowest overall inference time of around 2.4 ms.\n"}
+{"abstract": "  Semantic segmentation arises as the backbone of many vision systems, spanning\nfrom self-driving cars and robot navigation to augmented reality and\nteleconferencing. Frequently operating under stringent latency constraints\nwithin a limited resource envelope, optimising for efficient execution becomes\nimportant. To this end, we propose a framework for converting state-of-the-art\nsegmentation models to MESS networks; specially trained CNNs that employ\nparametrised early exits along their depth to save computation during inference\non easier samples. Designing and training such networks naively can hurt\nperformance. Thus, we propose a two-staged training process that pushes\nsemantically important features early in the network. We co-optimise the\nnumber, placement and architecture of the attached segmentation heads, along\nwith the exit policy, to adapt to the device capabilities and\napplication-specific requirements. Optimising for speed, MESS networks can\nachieve latency gains of up to 2.83x over state-of-the-art methods with no\naccuracy degradation. Accordingly, optimising for accuracy, we achieve an\nimprovement of up to 5.33 pp, under the same computational budget.\n"}
+{"abstract": "  Causal Learner is a toolbox for learning causal structure and Markov blanket\n(MB) from data. It integrates functions for generating simulated Bayesian\nnetwork data, a set of state-of-the-art global causal structure learning\nalgorithms, a set of state-of-the-art local causal structure learning\nalgorithms, a set of state-of-the-art MB learning algorithms, and functions for\nevaluating algorithms. The data generation part of Causal Learner is written in\nR, and the rest of Causal Learner is written in MATLAB. Causal Learner aims to\nprovide researchers and practitioners with an open-source platform for causal\nlearning from data and for the development and evaluation of new causal\nlearning algorithms. The Causal Learner project is available at\nhttp://bigdata.ahu.edu.cn/causal-learner.\n"}
+{"abstract": "  A \\emph{domatic} (\\emph{total domatic}) \\emph{$k$-coloring} of a graph $G$ is\nan assignment of $k$ colors to the vertices of $G$ such that each vertex\ncontains vertices of all $k$ colors in its closed neighborhood (neighborhood).\nThe \\emph{domatic} (\\emph{total domatic}) \\emph{number} of $G$, denoted $d(G)$\n($d_t (G)$), is the maximum $k$ for which $G$ has a domatic (total domatic)\n$k$-coloring. In this paper, we show that for two non-trivial graphs $G$ and\n$H$, the domatic and total domatic numbers of their Cartesian product $G \\cart\nH$ is bounded above by $\\max\\{|V(G)|, |V(H)|\\}$ and below by $\\max\\{d(G),\nd(H)\\}$. Both these bounds are tight for an infinite family of graphs. Further,\nwe show that if $H$ is bipartite, then $d_t(G \\cart H)$ is bounded below by\n$2\\min\\{d_t(G),d_t(H)\\}$ and $d(G \\cart H)$ is bounded below by\n$2\\min\\{d(G),d_t(H)\\}$. These bounds give easy proofs for many of the known\nbounds on the domatic and total domatic numbers of hypercubes \\cite{chen,zel4}\nand the domination and total domination numbers of hypercubes \\cite{har,joh}\nand also give new bounds for Hamming graphs. We also obtain the domatic (total\ndomatic) number and domination (total domination) number of $n$-dimensional\ntorus $\\mathop{\\cart}\\limits_{i=1}^{n} C_{k_i}$ with some suitable conditions\nto each $k_i$, which turns out to be a generalization of a result due to\nGravier \\cite{grav2} %[\\emph{Total domination number of grid graphs}, Discrete\nAppl. Math. 121 (2002) 119-128] and give easy proof of a result due to\nKlav\\v{z}ar and Seifter \\cite{sand}.\n"}
+{"abstract": "  High-harmonic generation (HHG) provides short-wavelength light that is useful\nfor precision spectroscopy and probing ultrafast dynamics. We report efficient,\nphase-coherent harmonic generation up to 9th-order (333 nm) in chirped\nperiodically poled lithium niobate waveguides driven by phase-stable\n$\\leq$12-nJ, 100 fs pulses at 3 $\\mu$m with 100 MHz repetition rate. A\nmid-infrared to ultraviolet-visible conversion efficiency as high as 10% is\nobserved, amongst an overall 23% conversion of the fundamental to all\nharmonics. We verify the coherence of the harmonic frequency combs despite the\ncomplex highly nonlinear process. Numerical simulations based on a single\nbroadband envelope equation with quadratic nonlinearity give estimates for the\nconversion efficiency within approximately 1 order of magnitude over a wide\nrange of experimental parameters. From this comparison we identify a\ndimensionless parameter capturing the competition between three-wave mixing and\ngroup-velocity walk-off of the harmonics that governs the cascaded HHG physics.\nThese results can inform cascaded HHG in a range of different platforms.\n"}
+{"abstract": "  Transformer model with multi-head attention requires caching intermediate\nresults for efficient inference in generation tasks. However, cache brings new\nmemory-related costs and prevents leveraging larger batch size for faster\nspeed. We propose memory-efficient lossless attention (called EL-attention) to\naddress this issue. It avoids heavy operations for building multi-head keys and\nvalues, cache for them is not needed. EL-attention constructs an ensemble of\nattention results by expanding query while keeping key and value shared. It\nproduces the same result as multi-head attention with less GPU memory and\nfaster inference speed. We conduct extensive experiments on Transformer, BART,\nand GPT-2 for summarization and question generation tasks. The results show\nEL-attention speeds up existing models by 1.6x to 5.3x without accuracy loss.\n"}
+{"abstract": "  Nowadays, as edge devices such as smartphones become prevalent, there are\nincreasing demands for personalized services. However, traditional\npersonalization methods are not suitable for edge devices because retraining or\nfinetuning is needed with limited personal data. Also, a full model might be\ntoo heavy for edge devices with limited resources. Unfortunately, model\ncompression methods which can handle the model complexity issue also require\nthe retraining phase. These multiple training phases generally need huge\ncomputational cost during on-device learning which can be a burden to edge\ndevices. In this work, we propose a dynamic personalization method called\nprototype-based personalized pruning (PPP). PPP considers both ends of\npersonalization and model efficiency. After training a network, PPP can easily\nprune the network with a prototype representing the characteristics of personal\ndata and it performs well without retraining or finetuning. We verify the\nusefulness of PPP on a couple of tasks in computer vision and Keyword spotting.\n"}
+{"abstract": "  Randomized algorithms have propelled advances in artificial intelligence and\nrepresent a foundational research area in advancing AI for Science. Future\nadvancements in DOE Office of Science priority areas such as climate science,\nastrophysics, fusion, advanced materials, combustion, and quantum computing all\nrequire randomized algorithms for surmounting challenges of complexity,\nrobustness, and scalability. This report summarizes the outcomes of that\nworkshop, \"Randomized Algorithms for Scientific Computing (RASC),\" held\nvirtually across four days in December 2020 and January 2021.\n"}
+{"abstract": "  Learning from visual data opens the potential to accrue a large range of\nmanipulation behaviors by leveraging human demonstrations without specifying\neach of them mathematically, but rather through natural task specification. In\nthis paper, we present Learning by Watching (LbW), an algorithmic framework for\npolicy learning through imitation from a single video specifying the task. The\nkey insights of our method are two-fold. First, since the human arms may not\nhave the same morphology as robot arms, our framework learns unsupervised human\nto robot translation to overcome the morphology mismatch issue. Second, to\ncapture the details in salient regions that are crucial for learning state\nrepresentations, our model performs unsupervised keypoint detection on the\ntranslated robot videos. The detected keypoints form a structured\nrepresentation that contains semantically meaningful information and can be\nused directly for computing reward and policy learning. We evaluate the\neffectiveness of our LbW framework on five robot manipulation tasks, including\nreaching, pushing, sliding, coffee making, and drawer closing. Extensive\nexperimental evaluations demonstrate that our method performs favorably against\nthe state-of-the-art approaches.\n"}
+{"abstract": "  The interface of Pt/TiO$_2$ plays an essential role in device engineering and\nchemical reactions. Here, we report the electrostatic potential distribution of\na Pt/TiO$_2$ interface by electron holography. The decrease of the\nelectrostatic potential exists at TiO$_2$ in the vicinity of the interface,\nindicating the presence of negative charge due to electron transfer from\nTiO$_2$ and Pt. The decrease of the electrostatic potential can be understood\nin the difference in work functions between Pt and TiO$_2$. This study reveals\nthe interplay between Pt and TiO$_2$ and the usefulness of electron holography\nfor probing the potential in nanoscale interfaces.\n"}
+{"abstract": "  Receding horizon control (RHC) is a popular procedure to deal with optimal\ncontrol problems. Due to the existence of state constraints, optimization-based\nRHC often suffers the notorious issue of infeasibility, which strongly shrinks\nthe region of controllable state. This paper proposes a generalized control\nbarrier function (CBF) to enlarge the feasible region of constrained RHC with\nonly a one-step constraint on the prediction horizon. This design can reduce\nthe constrained steps by penalizing the tendency to move towards the constraint\nboundary. Additionally, generalized CBF is able to handle high-order equality\nor inequality constraints through extending the constrained step to nonadjacent\nnodes. We apply this technique on an automated vehicle control task. The\nresults show that compared to multi-step pointwise constraints, generalized CBF\ncan effectively avoid the infeasibility issue in a larger partition of the\nstate space, and the computing efficiency is also improved by 14%-23%.\n"}
+{"abstract": "  The realization of a quantum network node of matter-based qubits compatible\nwith telecom-band operation and large-scale quantum information processing is\nan outstanding challenge that has limited the potential of elementary quantum\nnetworks. We propose a platform for interfacing quantum processors comprising\nneutral atom arrays with telecom-band photons in a multiplexed network\narchitecture. The use of a large atom array instead of a single atom mitigates\nthe deleterious effects of two-way communication and improves the entanglement\nrate between two nodes by nearly two orders of magnitude. Further, this system\nsimultaneously provides the ability to perform high-fidelity deterministic\ngates and readout within each node, opening the door to quantum repeater and\npurification protocols to enhance the length and fidelity of the network,\nrespectively. Using intermediate nodes as quantum repeaters, we demonstrate the\nfeasibility of entanglement distribution over approximately 1500 km based on\nrealistic assumptions, providing a blueprint for a transcontinental network.\nFinally, we demonstrate that our platform can distribute approximately 25 Bell\npairs over metropolitan distances, which could serve as the backbone of a\ndistributed fault-tolerant quantum computer.\n"}
+{"abstract": "  We present new constrained and free-swimming experiments and simulations of a\npair of pitching hydrofoils interacting in a minimal school. The hydrofoils\nhave an out-of-phase synchronization and they are varied through in-line,\nstaggered, and side-by-side arrangements within the two-dimensional interaction\nplane. It is discovered that there is a \\textit{two-dimensionally} stable\nequilibrium point for a side-by-side arrangement. In fact, this arrangement is\nsuper-stable, meaning that hydrodynamic forces will passively maintain this\narrangement even under external perturbations and the school as a whole has no\nnet forces acting on it, causing it to drift to one side or the other.\nMoreover, previously discovered \\textit{one-dimensionally} stable equilibria\ndriven by wake vortex interactions are shown to be, in fact, two-dimensionally\n\\textit{unstable}, at least for an out-of-phase synchronization. Additionally,\nit is discovered that a trailing-edge vortex mechanism provides the restorative\nforce to stabilize a side-by-side arrangement and the stable equilibrium is\nfurther verified for freely-swimming foils where dynamic recoil motions are\npresent. When constrained, the swimmers experience a collective thrust and\nefficiency increase up to 100\\% and 40\\%, respectively, in a side-by-side\narrangement, whereas the staggered arrangements output an even higher\nefficiency improvement of 87\\% with a 94\\% increase in thrust. For\nfreely-swimming foils, the recoil motion attenuates the improvements at the\nstable equilibrium, showing a more modest speed and efficiency enhancement of\nup to 9\\% and 6\\%, respectively. These newfound schooling performance and\nstability characteristics suggest that fluid-mediated equilibria may play a\nrole in the control strategies of schooling fish and fish-inspired robots.\n"}
+{"abstract": "  The fermionic and bosonic zero modes of the 1D interacting Kitaev chain at\nthe symmetric point are unveiled. The many-body structures of the Majorana zero\nmodes in the topological region are given explicitly by carrying out\nperturbation expansion up to infinite order. We also give the analytic\nexpressions of the bosonic zero modes in the topologically trivial phase. Our\nresults are generalized to the hybrid fermion system comprised of the\ninteracting Kitaev model and the Su-Schrieffer-Heeger model, in which we show\nthat these two types of zero modes can coexist in certain region of its phase\ndiagram.\n"}
+{"abstract": "  Parker Solar Probe (PSP) routinely observes magnetic field deflections in the\nsolar wind at distances less than 0.3 au from the Sun. These deflections are\nrelated to structures commonly called 'switchbacks' (SBs), whose origins and\ncharacteristic properties are currently debated. Here, we use a database of\nvisually selected SB intervals - and regions of solar wind plasma measured just\nbefore and after each SB - to examine plasma parameters, turbulent spectra from\ninertial to dissipation scales, and intermittency effects in these intervals.\nWe find that many features, such as perpendicular stochastic heating rates and\nturbulence spectral slopes are fairly similar inside and outside of SBs.\nHowever, important kinetic properties, such as the characteristic break scale\nbetween the inertial to dissipation ranges differ inside and outside these\nintervals, as does the level of intermittency, which is notably enhanced inside\nSBs and in their close proximity, most likely due to magnetic field and\nvelocity shears observed at the edges. We conclude that the plasma inside and\noutside of a SB, in most of the observed cases, belongs to the same stream, and\nthat the evolution of these structures is most likely regulated by kinetic\nprocesses, which dominate small scale structures at the SB edges.\n"}
+{"abstract": "  Recently, many cooperative distributed multi-agent reinforcement learning\n(MARL) algorithms have been proposed in the literature. In this work, we study\nthe effect of adversarial attacks on a network that employs a consensus-based\nMARL algorithm. We show that an adversarial agent can persuade all the other\nagents in the network to implement policies that optimize an objective that it\ndesires. In this sense, the standard consensus-based MARL algorithms are\nfragile to attacks.\n"}
+{"abstract": "  The LIBOR rate is currently scheduled for discontinuation, and the\nreplacement advocated by regulators in the US is the Secured Overnight\nFinancing Rate (SOFR). The change has the potential to disrupt the $200\ntrillion market of derivatives and debt tied to the LIBOR. The only SOFR linked\nderivative with any significant liquidity and trading history is the SOFR\nfutures contract, traded at the CME since 2018. We use the historical record of\nfutures prices to construct dynamic arbitrage-free models for the SOFR term\nstructure. The models allow you to construct forward-looking SOFR term rates,\nimply a SOFR discounting curve and price and risk and risk manage SOFR\nderivatives, not yet liquidly traded in the market. We find that a standard\nthree-factor Gaussian arbitrage-free Nelson-Siegel model describes term rates\nvery well but a shadow-rate extension is needed to describe the behaviour near\nthe zero-boundary. We also find that the jumps and seasonal effects observed in\nSOFR, do not need to be specifically accounted for in a model for the futures\nprices. Finally we study the so-called convexity correction and find that it\nbecomes significant beyond the 2 year maturity. For validation purposes we\ndemonstrate that our model aligns closely with the methodology used by the\nFederal Reserve to publish indicative SOFR term rates.\n"}
+{"abstract": "  The accuracy of theoretical mass, radius and effective temperature values for\nM-dwarf stars is an active topic of debate. Differences between observed and\ntheoretical values have raised the possibility that current theoretical stellar\nstructure and evolution models are inaccurate towards the low-mass end of the\nmain sequence. To explore this issue we use the CHEOPS satellite to obtain\nhigh-precision light curves of eclipsing binaries with low mass stellar\ncompanions. We use these light curves combined with the spectroscopic orbit for\nthe solar-type companion to measure the mass, radius and effective temperature\nof the M-dwarf star. Here we present the analysis of three eclipsing binaries.\nWe use the pycheops data analysis software to fit the observed transit and\neclipse events of each system. Two of our systems were also observed by the\nTESS satellite -- we similarly analyse these light curves for comparison. We\nfind consistent results between CHEOPS and TESS, presenting three stellar radii\nand two stellar effective temperature values of low-mass stellar objects. These\ninitial results from our on-going observing programme with CHEOPS show that we\ncan expect to have ~24 new mass, radius and effective temperature measurements\nfor very low mass stars within the next few years.\n"}
+{"abstract": "  The time frame in which hydrogen reionization occurred is highly uncertain,\nbut can be constrained by observations of Lyman-alpha (Ly$\\alpha$) emission\nfrom distant sources. Neutral hydrogen in the intergalactic medium (IGM)\nattenuates Ly$\\alpha$~photons emitted by galaxies. As reionization progressed\nthe IGM opacity decreased, increasing Ly$\\alpha$~visibility. The galaxy\nLy$\\alpha$~luminosity function (LF) is thus a useful tool to constrain the\ntimeline of reionization. In this work, we model the Ly$\\alpha$~LF as a\nfunction of redshift, $z=5-10$, and average IGM neutral hydrogen fraction,\n$\\overline{x}_\\textsc{hi}$. We combine the Ly$\\alpha$~luminosity probability\ndistribution obtained from inhomogeneous reionization simulations with a model\nfor the UV LF to model the Ly$\\alpha$~LF. As the neutral fraction increases,\nthe average number density of Ly$\\alpha$~emitting galaxies decreases, and are\nless luminous, though for $\\overline{x}_\\textsc{hi} \\lesssim 0.4$ there is only\na small decrease of the Ly$\\alpha$~LF. We use our model to infer the IGM\nneutral fraction at $z=6.6, 7.0, 7.3$ from observed Ly$\\alpha$~LFs. We conclude\nthat there is a significant increase in the neutral fraction with increasing\nredshift: $\\overline{x}_\\textsc{hi}(z=6.6)=0.08^{+ 0.08}_{- 0.05}, \\,\n\\overline{x}_\\textsc{hi}(z=7.0)=0.28 \\pm 0.05$ and\n$\\overline{x}_\\textsc{hi}(z=7.3)=0.83^{+ 0.06}_{- 0.07}$. We predict trends in\nthe Ly$\\alpha$~luminosity density and Schechter parameters as a function of\nredshift and the neutral fraction. We find that the Ly$\\alpha$~luminosity\ndensity decreases as the universe becomes more neutral. Furthermore, as the\nneutral fraction increases, the faint-end slope of the Ly$\\alpha$~LF steepens,\nand the characteristic Ly$\\alpha$~luminosity shifts to lower values, concluding\nthat the evolving shape of the Ly$\\alpha$~LF -- not just its integral -- is an\nimportant tool to study reionization.\n"}
+{"abstract": "  We develop an energy-based finite difference method for the wave equation in\nsecond order form. The spatial discretization satisfies a summation-by-parts\n(SBP) property. With boundary conditions and material interface conditions\nimposed weakly by the simultaneous-approximation-term (SAT) method, we derive\nenergy estimates for the semi-discretization. In addition, error estimates are\nderived by the normal mode analysis. The energy-based discretization does not\nuse any mesh-dependent parameter, even in the presence of Dirichlet boundary\nconditions and material interfaces. Furthermore, similar to upwind\ndiscontinuous Galerkin methods, numerical dissipation can be added to the\ndiscretization through the boundary conditions. We present numerical\nexperiments that verify convergence and robustness of the proposed method.\n"}
+{"abstract": "  State-of-the-art topological devices require the use topological surface\nstates to drive electronic transport. In this study, we examine a tunable\ntopological system, $Ge(Bi_{x}Sb_{1-x})_{2}Te_{4}$, for a range of 'x' values\nfrom 0 to 1, using a combination of Fourier Transform Scanning Tunneling\nSpectroscopy (FT-STS) and Angle-Resolved Photoemission Spectroscopy (ARPES).\nOur results show that the Dirac point shifts linearly with 'x', crossing the\nFermi energy near x = 0.7. This novel observation of a smoothly tunable,\nisolated Dirac point crossing through the topological transport regime and\nhaving strong linear dependence with substitution can be critical for future\ntopological spintronics applications.\n"}
+{"abstract": "  Code summarization is the task of generating readable summaries that are\nsemantically meaningful and can accurately describe the presumed task of a\nsoftware. Program comprehension has become one of the most tedious tasks for\nknowledge transfer. As the codebase evolves over time, the description needs to\nbe manually updated each time with the changes made. An automatic approach is\nproposed to infer such captions based on benchmarked and custom datasets with\ncomparison between the original and generated results.\n"}
+{"abstract": "  Specific developmental and operational characteristics of machine learning\n(ML) components, as well as their inherent uncertainty, demand robust\nengineering principles are used to ensure their quality. We aim to determine\nhow software systems can be (re-) architected to enable robust integration of\nML components. Towards this goal, we conducted a mixed-methods empirical study\nconsisting of (i) a systematic literature review to identify the challenges and\ntheir solutions in software architecture forML, (ii) semi-structured interviews\nwith practitioners to qualitatively complement the initial findings, and (iii)\na survey to quantitatively validate the challenges and their solutions. In\ntotal, we compiled and validated twenty challenges and solutions for (re-)\narchitecting systems with ML components. Our results indicate, for example,\nthat traditional software architecture challenges (e.g., component coupling)\nalso play an important role when using ML components; along new ML specific\nchallenges (e.g., the need for continuous retraining). Moreover, the results\nindicate that ML heightened decision drivers, such as privacy, play a marginal\nrole compared to traditional decision drivers, such as scalability or\ninteroperability. Using the survey, we were able to establish a link between\narchitectural solutions and software quality attributes; which enabled us to\nprovide twenty architectural tactics used for satisfying individual quality\nrequirements of systems with ML components. Altogether, the results can be\ninterpreted as an empirical framework that supports the process of (re-)\narchitecting software systems with ML components.\n"}
+{"abstract": "  In this short note, we investigate the feedback control of relativistic\ndynamics propelled by mass ejection, modeling, e.g., the relativistic rocket\ncontrol or the relativistic (space-travel) flight control. As an extreme case,\nwe also examine the control of relativistic photon rockets which are propelled\nby ejecting photons.\n"}
+{"abstract": "  Using M-theory in physics, Cho, Gang, and Kim (JHEP 2020, 115 (2020) )\nrecently outlined a program that connects two parallel subjects of three\ndimensional manifolds, namely, geometric topology and quantum topology. They\nsuggest that classical topological invariants such as Chern-Simons invariants\nof $\\text{SL}(2,\\mathbb{C})$-flat connections and adjoint Reidemeister torsions\nof a three manifold can be packaged together to produce a $(2+1)$-topological\nquantum field theory, which is essentially equivalent to a modular tensor\ncategory. It is further conjectured that every modular tensor category can be\nobtained from a three manifold and a semi-simple Lie group. In this paper, we\nstudy this program mathematically, and provide strong support for the\nfeasibility of such a program. The program produces an algorithm to generate\nthe potential modular $T$-matrix and the quantum dimensions of a candidate\nmodular data. The modular $S$-matrix follows from essentially a trial-and-error\nprocedure. We find modular tensor categories that realize candidate modular\ndata constructed from Seifert fibered spaces and torus bundles over the circle\nthat reveal many subtleties in the program. We make a number of improvements to\nthe program based on our computations. Our main result is a mathematical\nconstruction of a premodular category from each Seifert fibered space with\nthree singular fibers and a family of torus bundles over the circle with\nThurston SOL geometry. The premodular categories from Seifert fibered spaces\nare related to Temperley-Lieb-Jones categories and the ones from torus bundles\nover the circle are related to metaplectic categories. We conjecture that a\nresulting premodular category is modular if and only if the three manifold is a\n$\\mathbb{Z}_2$-homology sphere and condensation of bosons in premodular\ncategories leads to either modular or super-modular categories.\n"}
+{"abstract": "  Inferring 3D structure of a generic object from a 2D image is a long-standing\nobjective of computer vision. Conventional approaches either learn completely\nfrom CAD-generated synthetic data, which have difficulty in inference from real\nimages, or generate 2.5D depth image via intrinsic decomposition, which is\nlimited compared to the full 3D reconstruction. One fundamental challenge lies\nin how to leverage numerous real 2D images without any 3D ground truth. To\naddress this issue, we take an alternative approach with semi-supervised\nlearning. That is, for a 2D image of a generic object, we decompose it into\nlatent representations of category, shape and albedo, lighting and camera\nprojection matrix, decode the representations to segmented 3D shape and albedo\nrespectively, and fuse these components to render an image well approximating\nthe input image. Using a category-adaptive 3D joint occupancy field (JOF), we\nshow that the complete shape and albedo modeling enables us to leverage real 2D\nimages in both modeling and model fitting. The effectiveness of our approach is\ndemonstrated through superior 3D reconstruction from a single image, being\neither synthetic or real, and shape segmentation.\n"}
+{"abstract": "  In this paper, we provide a generalized framework for Variational\nInference-Stochastic Optimal Control by using thenon-extensive Tsallis\ndivergence. By incorporating the deformed exponential function into the\noptimality likelihood function, a novel Tsallis Variational Inference-Model\nPredictive Control algorithm is derived, which includes prior works such as\nVariational Inference-Model Predictive Control, Model Predictive PathIntegral\nControl, Cross Entropy Method, and Stein VariationalInference Model Predictive\nControl as special cases. The proposed algorithm allows for effective control\nof the cost/reward transform and is characterized by superior performance in\nterms of mean and variance reduction of the associated cost. The aforementioned\nfeatures are supported by a theoretical and numerical analysis on the level of\nrisk sensitivity of the proposed algorithm as well as simulation experiments on\n5 different robotic systems with 3 different policy parameterizations.\n"}
+{"abstract": "  The Gleason scoring system is the primary diagnostic and prognostic tool for\nprostate cancer. In recent years, with the development of digitisation devices,\nthe use of computer vision techniques for the analysis of biopsies has\nincreased. However, to the best of the authors' knowledge, the development of\nalgorithms to automatically detect individual cribriform patterns belonging to\nGleason grade 4 has not yet been studied in the literature. The objective of\nthe work presented in this paper is to develop a deep-learning-based system\nable to support pathologists in the daily analysis of prostate biopsies. The\nmethodological core of this work is a patch-wise predictive model based on\nconvolutional neural networks able to determine the presence of cancerous\npatterns. In particular, we train from scratch a simple self-design\narchitecture. The cribriform pattern is detected by retraining the set of\nfilters of the last convolutional layer in the network. From the reconstructed\nprediction map, we compute the percentage of each Gleason grade in the tissue\nto feed a multi-layer perceptron which provides a biopsy-level score.mIn our\nSICAPv2 database, composed of 182 annotated whole slide images, we obtained a\nCohen's quadratic kappa of 0.77 in the test set for the patch-level Gleason\ngrading with the proposed architecture trained from scratch. Our results\noutperform previous ones reported in the literature. Furthermore, this model\nreaches the level of fine-tuned state-of-the-art architectures in a\npatient-based four groups cross validation. In the cribriform pattern detection\ntask, we obtained an area under ROC curve of 0.82. Regarding the biopsy Gleason\nscoring, we achieved a quadratic Cohen's Kappa of 0.81 in the test subset.\nShallow CNN architectures trained from scratch outperform current\nstate-of-the-art methods for Gleason grades classification.\n"}
+{"abstract": "  Reconfigurable intelligent surfaces (RISs), which enable tunable anomalous\nreflection, have appeared as a promising method to enhance wireless systems. In\nthis paper, we propose to use an RIS as a spatial equalizer to address the\nwell-known multi-path fading phenomenon. By introducing some controllable paths\nartificially against the multi-path fading through the RIS, we can perform\nequalization during the transmission process instead of at the receiver, and\nthus all the users can share the same equalizer. Unlike the beamforming\napplication of the RIS, which aims to maximize the received energy at\nreceivers, the objective of the equalization application is to reduce the\ninter-symbol interference (ISI), which makes phase shifts at the RIS different.\nTo this end, we formulate the phase shift optimization problem and propose an\niterative algorithm to solve it. Simulation results show that the multi-path\nfading effect can be eliminated effectively compared to benchmark schemes.\n"}
+{"abstract": "  The famous theorem of Conway and Coxeter on frieze patterns gave a geometric\ninterpretation to integral friezes via triangulations of polygons. In this\narticle, we review this result and show some of the development it has led to.\nThe last decade has seen a lot of activities on friezes. One reason behind this\nis the connection to cluster combinatorics.\n"}
+{"abstract": "  Many AI-related tasks involve the interactions of data in multiple\nmodalities. It has been a new trend to merge multi-modal information into\nknowledge graph(KG), resulting in multi-modal knowledge graphs (MMKG). However,\nMMKGs usually suffer from low coverage and incompleteness. To mitigate this\nproblem, a viable approach is to integrate complementary knowledge from other\nMMKGs. To this end, although existing entity alignment approaches could be\nadopted, they operate in the Euclidean space, and the resulting Euclidean\nentity representations can lead to large distortion of KG's hierarchical\nstructure. Besides, the visual information has yet not been well exploited. In\nresponse to these issues, in this work, we propose a novel multi-modal entity\nalignment approach, Hyperbolic multi-modal entity alignment(HMEA), which\nextends the Euclidean representation to hyperboloid manifold. We first adopt\nthe Hyperbolic Graph Convolutional Networks (HGCNs) to learn structural\nrepresentations of entities. Regarding the visual information, we generate\nimage embeddings using the densenet model, which are also projected into the\nhyperbolic space using HGCNs. Finally, we combine the structure and visual\nrepresentations in the hyperbolic space and use the aggregated embeddings to\npredict potential alignment results. Extensive experiments and ablation studies\ndemonstrate the effectiveness of our proposed model and its components.\n"}
+{"abstract": "  Using the framework of operator or Calder\\'on preconditioning, uniform\npreconditioners are constructed for elliptic operators discretized with\ncontinuous finite (or boundary) elements. The preconditioners are constructed\nas the composition of an opposite order operator, discretized on the same\nansatz space, and two diagonal scaling operators.\n"}
+{"abstract": "  Adopting the pullback formalism, a new linear connection in Finsler geometry\nhas been introduced and investigated. Such connection unifies all formerly\nknown Finsler connections and some other connections not introduced so far.\nAlso, our connection is a Finslerian version of the Tripathi connection\nintroduced in Riemannian geometry. The existence and uniqueness of such\nconnection is proved intrinsically. An explicit intrinsic expression relating\nthis connection to Cartan connection is obtained. Some generalized Finsler\nconnections are constructed from Tripathi Finsler connection, by applying the\nP1-process and C-process introduced by Matsumoto. Finally, under certain\nconditions, many special Finsler connections are given.\n"}
+{"abstract": "  We look for ground state solutions to the Schr\\\"odinger-type system \\[\n\\begin{cases} -\\Delta u_j + \\lambda_j u_j = \\partial_jF(u)\\\\ \\int_{\\rn} u_j^2\n\\, dx = a_j^2\\\\ (\\lambda_j,u_j) \\in \\mathbb{R} \\times H^1(\\mathbb{R}^N)\n\\end{cases} j \\in \\{1,\\dots,M\\} \\] with $N,M\\ge1$, where $a=(a_1,\\dots,a_M) \\in\n]0,\\infty[^M$ is prescribed and $(\\lambda,u) =\n(\\lambda_1,\\dots,\\lambda_M,u_1,\\dots u_M)$ is the unknown. We provide generic\nassumptions about the nonlinearity $F$ which correspond to the\n$L^2$-subcritical and $L^2$-critical cases, i.e., when the energy is bounded\nfrom below for all or some values of $a$. Making use of a recent idea, we\nminimize the energy over the constraint $\\Set{\\left|u_j\\right|_{L^2}\\le a_j\n\\text{ for all } j}$ and then provide further assumptions that ensure\n$|u_j|_{L^2}=a_j$.\n"}
+{"abstract": "  Domain-specific dialogue systems generally determine user intents by relying\non sentence-level classifiers which mainly focus on single action sentences.\nSuch classifiers are not designed to effectively handle complex queries\ncomposed of conditional and sequential clauses that represent multiple actions.\nWe attempt to decompose such queries into smaller single-action sub-queries\nthat are reasonable for intent classifiers to understand in a dialogue\npipeline. We release CANDLE (Conditional & AND type Expressions), a dataset\nconsisting of 3124 utterances manually tagged with conditional and sequential\nlabels and demonstrates this decomposition by training two baseline taggers.\n"}
+{"abstract": "  The mechanochemistry of ring-opening reactions of cyclopropane derivatives\nturns out to be unexpectedly rich and puzzling. After showing that a rare\nso-called uphill bifurcation in the case of trans-gem-difluorocyclopropane\nturns into a downhill bifurcation upon substitution of fluorine by chlorine,\nbromine and iodine in the thermal activation limit, the dichloro derivative is\nstudied systematically in the realm of mechanochemical activation. Detailed\nexploration of the force-transformed potential energy surface of\ntrans-gem-dichlorocyclopropane in terms of Dijkstra path analysis unveils a\nhitherto unknown topological catastrophe where the global shape of the energy\nlandscape is fundamentally changed. From thermal activation up to moderately\nlarge forces, it is an uphill bifurcation that decides about dis-versus\nconrotatory ring-opening followed by separate transition states along both\npathways. Above a critical force, the two distinct transition states merge to\nyield a single transition state such that the decision about the dis-versus\nconrotatory ring-opening process is taken at a newly established downhill\nbifurcation. The discovery of a force-induced qualitative change of the\ntopology of a reaction network vastly transcends the previous understanding of\nthe ring-opening reaction of this species. It would be astonishing to not\ndiscover a wealth of such catastrophes for mechanochemically activated\nreactions which will greatly extend the known opportunities to manipulate\nchemical reaction networks.\n"}
+{"abstract": "  Prototype learning is extensively used for few-shot segmentation. Typically,\na single prototype is obtained from the support feature by averaging the global\nobject information. However, using one prototype to represent all the\ninformation may lead to ambiguities. In this paper, we propose two novel\nmodules, named superpixel-guided clustering (SGC) and guided prototype\nallocation (GPA), for multiple prototype extraction and allocation.\nSpecifically, SGC is a parameter-free and training-free approach, which\nextracts more representative prototypes by aggregating similar feature vectors,\nwhile GPA is able to select matched prototypes to provide more accurate\nguidance. By integrating the SGC and GPA together, we propose the Adaptive\nSuperpixel-guided Network (ASGNet), which is a lightweight model and adapts to\nobject scale and shape variation. In addition, our network can easily\ngeneralize to k-shot segmentation with substantial improvement and no\nadditional computational cost. In particular, our evaluations on COCO\ndemonstrate that ASGNet surpasses the state-of-the-art method by 5% in 5-shot\nsegmentation.\n"}
+{"abstract": "  Mean-variance portfolio optimization problems often involve separable\nnonconvex terms, including penalties on capital gains, integer share\nconstraints, and minimum position and trade sizes. We propose a heuristic\nalgorithm for this problem based on the alternating direction method of\nmultipliers (ADMM). This method allows for solve times in tens to hundreds of\nmilliseconds with around 1000 securities and 100 risk factors. We also obtain a\nbound on the achievable performance. Our heuristic and bound are both derived\nfrom similar results for other optimization problems with a separable objective\nand affine equality constraints. We discuss a concrete implementation in the\ncase where the separable terms in the objective are piecewise-quadratic, and we\ndemonstrate their effectiveness empirically in realistic tax-aware portfolio\nconstruction problems.\n"}
+{"abstract": "  The vast quantity of strong galaxy-galaxy gravitational lenses expected by\nfuture large-scale surveys necessitates the development of automated methods to\nefficiently model their mass profiles. For this purpose, we train an\napproximate Bayesian convolutional neural network (CNN) to predict mass profile\nparameters and associated uncertainties, and compare its accuracy to that of\nconventional parametric modelling for a range of increasingly complex lensing\nsystems. These include standard smooth parametric density profiles,\nhydrodynamical EAGLE galaxies and the inclusion of foreground mass structures,\ncombined with parametric sources and sources extracted from the Hubble Ultra\nDeep Field. In addition, we also present a method for combining the CNN with\ntraditional parametric density profile fitting in an automated fashion, where\nthe CNN provides initial priors on the latter's parameters. On average, the CNN\nachieved errors 19 $\\pm$ 22 per cent lower than the traditional method's blind\nmodelling. The combination method instead achieved 27 $\\pm$ 11 per cent lower\nerrors over the blind modelling, reduced further to 37 $\\pm$ 11 per cent when\nthe priors also incorporated the CNN-predicted uncertainties, with errors also\n17 $\\pm$ 21 per cent lower than the CNN by itself. While the CNN is undoubtedly\nthe fastest modelling method, the combination of the two increases the speed of\nconventional fitting alone by factors of 1.73 and 1.19 with and without\nCNN-predicted uncertainties, respectively. This, combined with greatly improved\naccuracy, highlights the benefits one can obtain through combining neural\nnetworks with conventional techniques in order to achieve an efficient\nautomated modelling approach.\n"}
+{"abstract": "  In this paper, a 3D-RegNet-based neural network is proposed for diagnosing\nthe physical condition of patients with coronavirus (Covid-19) infection. In\nthe application of clinical medicine, lung CT images are utilized by\npractitioners to determine whether a patient is infected with coronavirus.\nHowever, there are some laybacks can be considered regarding to this diagnostic\nmethod, such as time consuming and low accuracy. As a relatively large organ of\nhuman body, important spatial features would be lost if the lungs were\ndiagnosed utilizing two dimensional slice image. Therefore, in this paper, a\ndeep learning model with 3D image was designed. The 3D image as input data was\ncomprised of two-dimensional pulmonary image sequence and from which relevant\ncoronavirus infection 3D features were extracted and classified. The results\nshow that the test set of the 3D model, the result: f1 score of 0.8379 and AUC\nvalue of 0.8807 have been achieved.\n"}
+{"abstract": "  We study the motion of dispersed nanoprobes in entangled active-passive\npolymer mixtures. By comparing the two architectures of linear vs.\nunconcatenated and unknotted circular polymers, we demonstrate that novel, rich\nphysics emerge. For both polymer architectures, nanoprobes of size smaller than\nthe entanglement threshold of the solution move faster as activity is increased\nand more energy is pumped in the system. For larger nanoprobes, a surprising\nphenomenon occurs: while in linear solutions they move qualitatively as before,\nin active-passive ring solutions nanoprobes {\\it decelerate} with respect to\nthe purely passive conditions. We rationalize this effect in terms of the\nnon-equilibrium, topology-dependent association (clustering) of nanoprobes to\nthe cold component of the ring mixture reminiscent of the recently discovered\n[Weber et al., Phys. Rev. Lett. 116, 058301 (2016)] phase separation in scalar\nactive-passive mixtures. We conclude with a potential connection to the\nmicrorheology of the chromatin in the nuclei of the cells.\n"}
+{"abstract": "  In this paper, we propose a lightweight yet powerful dynamic epistemic logic\nthat captures not only the distinction between de dicto and de re knowledge but\nalso the distinction between de dicto and de re updates. The logic is based on\nthe dynamified version of an epistemic language extended with the assignment\noperator borrowed from dynamic logic, following the work of Wang and Seligman\n(Proc. AiML 2018). We obtain complete axiomatizations for the counterparts of\npublic announcement logic and event-model-based DEL based on new reduction\naxioms taking care of the interactions between dynamics and assignments.\n"}
+{"abstract": "  Emotional voice conversion models adapt the emotion in speech without\nchanging the speaker identity or linguistic content. They are less data hungry\nthan text-to-speech models and allow to generate large amounts of emotional\ndata for downstream tasks. In this work we propose EmoCat, a language-agnostic\nemotional voice conversion model. It achieves high-quality emotion conversion\nin German with less than 45 minutes of German emotional recordings by\nexploiting large amounts of emotional data in US English. EmoCat is an\nencoder-decoder model based on CopyCat, a voice conversion system which\ntransfers prosody. We use adversarial training to remove emotion leakage from\nthe encoder to the decoder. The adversarial training is improved by a novel\ncontribution to gradient reversal to truly reverse gradients. This allows to\nremove only the leaking information and to converge to better optima with\nhigher conversion performance. Evaluations show that Emocat can convert to\ndifferent emotions but misses on emotion intensity compared to the recordings,\nespecially for very expressive emotions. EmoCat is able to achieve audio\nquality on par with the recordings for five out of six tested emotion\nintensities.\n"}
+{"abstract": "  Dust grains play a central role in the physics and chemistry of cosmic\nenvironments. They influence the optical and thermal properties of the medium\ndue to their interaction with stellar radiation; provide surfaces for the\nchemical reactions that are responsible for the synthesis of a significant\nfraction of key astronomical molecules; and they are building blocks of\npebbles, comets, asteroids, planetesimals, and planets. In this paper, we\nreview experimental studies of physical and chemical processes, such as\nadsorption, desorption, diffusion, and reactions forming molecules, on the\nsurface of reliable cosmic dust grain analogues as related to processes in\ndiffuse, translucent, and dense interstellar clouds, protostellar envelopes,\nplanet-forming disks, and planetary atmospheres. The information that such\nexperiments reveal should be flexible enough to be used in many different\nenvironments. In addition, we provide a forward look discussing new ideas,\nexperimental approaches, and research directions.\n"}
+{"abstract": "  In this study, we propose a decision-making strategy for pinning-based\ndistributed multi-agent (PDMA) automatic generation control (AGC) in islanded\nmicrogrids against stochastic communication disruptions. The target microgrid\nis construed as a cyber-physical system, wherein the physical microgrid is\nmodeled as an inverter-interfaced autonomous grid with detailed system dynamic\nformulation, and the communication network topology is regarded as a\ncyber-system independent of its physical connection. The primal goal of the\nproposed method is to decide the minimum number of generators to be pinned and\ntheir identities amongst all distributed generators (DGs). The pinningdecisions\nare made based on complex network theories using the genetic algorithm (GA),\nfor the purpose of synchronizing and regulating the frequencies and voltages of\nall generator busbars in a PDMA control structure, i.e., without resorting to a\ncentral AGC agent. Thereafter, the mapping of cyber-system topology and the\npinning decision is constructed using deeplearning (DL) technique, so that the\npinning-decision can be made nearly instantly upon detecting a new cyber-system\ntopology after stochastic communication disruptions. The proposed\ndecision-making approach is verified using a 10-generator, 38-bus microgrid\nthrough time-domain simulation for transient stability analysis.\n"}
+{"abstract": "  Consider the problem of finding the maximal nonpositive solvent $\\Phi$ of the\nquadratic matrix equation (QME) $X^2 + BX + C =0$ with $B$ being a nonsingular\n$M$-matrix and $C$ an $M$-matrix such that $B^{-1}C\\ge 0$, and $B - C - I$ a\nnonsingular $M$-matrix. Such QME arises from an overdamped vibrating system.\nRecently, Yu et al. ({\\em Appl. Math. Comput.}, 218: 3303--3310, 2011) proved\nthat $\\rho(\\Phi)\\le 1$ for this QME. In this paper, we slightly improve their\nresult and prove $\\rho(\\Phi)< 1$, which is important for the quadratic\nconvergence of the structure-preserving doubling algorithm. Then, a new\nglobally monotonically and quadratically convergent structure-preserving\ndoubling algorithm to solve the QME is developed. Numerical examples are\npresented to demonstrate the feasibility and effectiveness of our method.\n"}
+{"abstract": "  Low lattice thermal conductivity is essential for high thermoelectric\nperformance of a material. Lattice thermal conductivity is often computed using\ndensity functional theory (DFT), typically at a high computational cost.\nTraining machine learning models to predict lattice thermal conductivity could\noffer an effective procedure to identify low lattice thermal conductivity\ncompounds. However, in doing so, we must face the fact that such compounds can\nbe quite rare and distinct from those in a typical training set. This\ndistinctness can be problematic as standard machine learning methods are\ninaccurate when predicting properties of compounds with features differing\nsignificantly from those in the training set. By computing the lattice thermal\nconductivity of 122 half-Heusler compounds, using the temperature-dependent\neffective potential method, we generate a data set to explore this issue. We\nfirst show how random forest regression can fail to identify low lattice\nthermal conductivity compounds with random selection of training data. Next, we\nshow how active selection of training data using feature and principal\ncomponent analysis can be used to improve model performance and the ability to\nidentify low lattice thermal conductivity compounds. Lastly, we find that\nactive learning without the use of DFT-based features can be viable as a\nquicker way of selecting samples.\n"}
+{"abstract": "  Ridesharing has great potential to improve transportation efficiency while\nreducing congestion and pollution. To realize this potential, mechanisms are\nneeded that allocate vehicles optimally and provide the right incentives to\nriders. However, many existing approaches consider restricted settings (e.g.,\nonly one rider per vehicle or a common origin for all riders). Moreover, naive\napplications of standard approaches, such as the Vickrey-Clarke-Groves or\ngreedy mechanisms, cannot achieve a polynomial-time, truthful, individually\nrational and budget balanced mechanism. To address this, we formulate a general\nridesharing problem and apply mechanism design to develop a novel mechanism\nwhich satisfies all four properties and whose social cost is within 8.6% of the\noptimal on average.\n"}
+{"abstract": "  Agents trained by reinforcement learning (RL) often fail to generalize beyond\nthe environment they were trained in, even when presented with new scenarios\nthat seem similar to the training environment. We study the query complexity\nrequired to train RL agents that generalize to multiple environments.\nIntuitively, tractable generalization is only possible when the environments\nare similar or close in some sense. To capture this, we introduce Weak\nProximity, a natural structural condition that requires the environments to\nhave highly similar transition and reward functions and share a policy\nproviding optimal value. Despite such shared structure, we prove that tractable\ngeneralization is impossible in the worst case. This holds even when each\nindividual environment can be efficiently solved to obtain an optimal linear\npolicy, and when the agent possesses a generative model. Our lower bound\napplies to the more complex task of representation learning for the purpose of\nefficient generalization to multiple environments. On the positive side, we\nintroduce Strong Proximity, a strengthened condition which we prove is\nsufficient for efficient generalization.\n"}
+{"abstract": "  Finite volume schemes often have difficulties to resolve the low Mach number\n(incompressible) limit of the Euler equations. Incompressibility is only\nnon-trivial in multiple spatial dimensions. Low Mach fixes, however generally\nare applied to the one-dimensional method and the method is then used in a\ndimensionally split way. This often reduces its stability. Here, it is\nsuggested to keep the one-dimensional method as it is, and only to extend it to\nmultiple dimensions in a particular, all-speed way. This strategy is found to\nlead to much more stable numerical methods. Apart from the conceptually\npleasing property of modifying the scheme only when it becomes necessary, the\nmulti-dimensional all-speed extension also does not include any free parameters\nor arbitrary functions, which generally are difficult to choose, or might be\nproblem dependent. The strategy is exemplified on a Lagrange Projection method\nand on a relaxation solver.\n"}
+{"abstract": "  A trace ratio optimization problem over the Stiefel manifold is investigated\nfrom the perspectives of both theory and numerical computations. At least three\nspecial cases of the problem have arisen from Fisher linear discriminant\nanalysis, canonical correlation analysis, and unbalanced Procrustes problem,\nrespectively. Necessary conditions in the form of nonlinear eigenvalue problem\nwith eigenvector dependency are established and a numerical method based on the\nself-consistent field (SCF) iteration is designed and proved to be always\nconvergent. As an application to multi-view subspace learning, a new framework\nand its instantiated concrete models are proposed and demonstrated on real\nworld data sets. Numerical results show that the efficiency of the proposed\nnumerical methods and effectiveness of the new multi-view subspace learning\nmodels.\n"}
+{"abstract": "  We present a systematic investigation of muon-stopping states in\nsuperconductors that reportedly exhibit spontaneous magnetic fields below their\ntransition temperatures due to time-reversal symmetry breaking. These materials\ninclude elemental rhenium, several intermetallic systems and Sr$_2$RuO$_4$. We\ndemonstrate that the presence of the muon leads to only a limited and\nrelatively localized perturbation to the local crystal structure, while any\nsmall changes to the electronic structure occur several electron volts below\nthe Fermi energy leading to only minimal changes in the charge density on ions\nclose to the muon. Our results imply that the muon-induced perturbation alone\nis unlikely to lead to the observed spontaneous fields in these materials,\nwhose origin is more likely intrinsic to the time-reversal symmetry broken\nsuperconducting state.\n"}
+{"abstract": "  We present a pulsar candidate identification and confirmation procedure based\non a position-switch mode during the pulsar search observations. This method\nenables the simultaneous search and confirmation of a pulsar in a single\nobservation, by utilizing the different spatial features of a pulsar signal and\na radio frequency interference (RFI). Based on this method, we performed test\npulsar search observations in globular clusters M3, M15, and M92. We discovered\nand confirmed a new pulsar, M3F, and detected the known pulsars M3B, M15 A to G\n(except C), and M92A.\n"}
+{"abstract": "  Detecting groups of people who are jointly deceptive in video conversations\nis crucial in settings such as meetings, sales pitches, and negotiations. Past\nwork on deception in videos focuses on detecting a single deceiver and uses\nfacial or visual features only. In this paper, we propose the concept of\nFace-to-Face Dynamic Interaction Networks (FFDINs) to model the interpersonal\ninteractions within a group of people. The use of FFDINs enables us to leverage\nnetwork relations in detecting group deception in video conversations for the\nfirst time. We use a dataset of 185 videos from a deception-based game called\nResistance. We first characterize the behavior of individual, pairs, and groups\nof deceptive participants and compare them to non-deceptive participants. Our\nanalysis reveals that pairs of deceivers tend to avoid mutual interaction and\nfocus their attention on non-deceivers. In contrast, non-deceivers interact\nwith everyone equally. We propose Negative Dynamic Interaction Networks to\ncapture the notion of missing interactions. We create the DeceptionRank\nalgorithm to detect deceivers from NDINs extracted from videos that are just\none minute long. We show that our method outperforms recent state-of-the-art\ncomputer vision, graph embedding, and ensemble methods by at least 20.9% AUROC\nin identifying deception from videos.\n"}
+{"abstract": "  Browser fingerprinting is a stateless identification technique based on\nbrowser properties. Together, they form an identifier that can be collected\nwithout users' notice and has been studied to be unique and stable. As this\ntechnique relies on browser properties that serve legitimate purposes, the\ndetection of this technique is challenging. While several studies propose\nclassification techniques, none of these are publicly available, making them\ndifficult to reproduce. This paper proposes a new browser fingerprinting\ndetection technique. Based on an incremental process, it relies on both\nautomatic and manual decisions to be both reliable and fast. The automatic step\nmatches API calls similarities between scripts while the manual step is\nrequired to classify a script with different calls. We publicly share our\nalgorithm and implementation to improve the general knowledge on the subject.\n"}
+{"abstract": "  Linear stability analysis is performed using a combination of two-dimensional\nDirect Simulation Monte Carlo (DSMC) method for the computation of the basic\nstate and solution of the pertinent eigenvalue problem, as applied to the\ncanonical boundary layer on a semi-infinite flat plate. Three different gases\nare monitored, namely nitrogen, argon and air, the latter as a mixture of 79\\%\nNitrogen and 21\\% Oxygen at a range of free-stream Mach numbers corresponding\nto flight at an altitude of 55km. A neural network has been utilised to predict\nand smooth the raw DSMC data; the steady laminar profiles obtained are in very\ngood agreement with those computed by (self-similar) boundary layer theory,\nunder isothermal or adiabatic wall conditions, subject to the appropriate slip\ncorrections computed in the DSMC method.\n  The leading eigenmode results pertaining to the unsmoothed DSMC profiles are\ncompared against those of the classic boundary layer theory. Small\nquantitative, but no significant qualitative differences between the results of\nthe two classes of steady base flows have been found at all parameters\nexamined. The frequencies of the leading eigenmodes at all conditions examined\nare practically identical, while perturbations corresponding to the DSMC\nprofiles are found to be systematically more damped than their counterparts\narising in the boundary layer at the conditions examined, when the correct\nvelocity slip and temperature jump boundary conditions are imposed in the base\nflow profiles; by contrast, when the classic no-slip boundary conditions are\nused, less damped/more unstable profiles are obtained, which would lead the\nflow to earlier transition. On the other hand, the DSMC profiles smoothed by\nthe neural network are marginally more stable than their unsmoothed\ncounterparts.\n"}
+{"abstract": "  Abbe's resolution limit, one of the best-known physical limitations, poses a\ngreat challenge for any wave systems in imaging, wave transport, and dynamics.\nOriginally formulated in linear optics, this Abbe's limit can be broken using\nnonlinear optical interactions. Here we extend the Abbe theory into a nonlinear\nregime and experimentally demonstrate a far-field, label-free, and scan-free\nsuper-resolution imaging technique based on nonlinear four-wave mixing to\nretrieve near-field scattered evanescent waves, achieving sub-wavelength\nresolution of $\\lambda/15.6$. This method paves the way for application in\nbiomedical imaging, semiconductor metrology, and photolithography.\n"}
+{"abstract": "  Despite the rapid development and great success of machine learning models,\nextensive studies have exposed their disadvantage of inheriting latent\ndiscrimination and societal bias from the training data. This phenomenon\nhinders their adoption on high-stake applications. Thus, many efforts have been\ntaken for developing fair machine learning models. Most of them require that\nsensitive attributes are available during training to learn fair models.\nHowever, in many real-world applications, it is usually infeasible to obtain\nthe sensitive attributes due to privacy or legal issues, which challenges\nexisting fair-ensuring strategies. Though the sensitive attribute of each data\nsample is unknown, we observe that there are usually some non-sensitive\nfeatures in the training data that are highly correlated with sensitive\nattributes, which can be used to alleviate the bias. Therefore, in this paper,\nwe study a novel problem of exploring features that are highly correlated with\nsensitive attributes for learning fair and accurate classifiers. We\ntheoretically show that by minimizing the correlation between these related\nfeatures and model prediction, we can learn a fair classifier. Based on this\nmotivation, we propose a novel framework which simultaneously uses these\nrelated features for accurate prediction and enforces fairness. In addition,\nthe model can dynamically adjust the regularization weight of each related\nfeature to balance its contribution on model classification and fairness.\nExperimental results on real-world datasets demonstrate the effectiveness of\nthe proposed model for learning fair models with high classification accuracy.\n"}
+{"abstract": "  Neural networks are used for many real world applications, but often they\nhave problems estimating their own confidence. This is particularly problematic\nfor computer vision applications aimed at making high stakes decisions with\nhumans and their lives. In this paper we make a meta-analysis of the\nliterature, showing that most if not all computer vision applications do not\nuse proper epistemic uncertainty quantification, which means that these models\nignore their own limitations. We describe the consequences of using models\nwithout proper uncertainty quantification, and motivate the community to adopt\nversions of the models they use that have proper calibrated epistemic\nuncertainty, in order to enable out of distribution detection. We close the\npaper with a summary of challenges on estimating uncertainty for computer\nvision applications and recommendations.\n"}
+{"abstract": "  Purcell effect predicts that spontaneous radiation is not an intrinsic\nproperty of matter, but is affected by the environment in which it is located,\nand is the result of the interaction of matter and field. Purcell effect can be\ninferred from Fermi Gold rule through strict quantum electrodynamics (QED), and\nthrough it can achieve the enhancement or suppression of radiation. We suggest\nthat, in principle, the Purcell effect can be detected at the percentage level\nof neutron decay in experiments with trapped ultra-cold neutrons. As a test of\nour claim, we propose a currently achievable experimental protocol that can\ndetect whether Purcell effect has occurred in an trapped ultra-cold neutron\nlifetime measurement experiment. Finally, we discuss the discrepancy in current\nmethods of measuring neutron lifetime, which may be caused by different\nexperimental setups.\n"}
+{"abstract": "  In Federated Learning (FL), a strong global model is collaboratively learned\nby aggregating the clients' locally trained models. Although this allows no\nneed to access clients' data directly, the global model's convergence often\nsuffers from data heterogeneity. This paper suggests that forgetting could be\nthe bottleneck of global convergence. We observe that fitting on biased local\ndistribution shifts the feature on global distribution and results in\nforgetting of global knowledge. We consider this phenomenon as an analogy to\nContinual Learning, which also faces catastrophic forgetting when fitted on the\nnew task distribution. Based on our findings, we hypothesize that tackling down\nthe forgetting in local training relives the data heterogeneity problem. To\nthis end, we propose a simple yet effective framework Federated Local\nSelf-Distillation (FedLSD), which utilizes the global knowledge on locally\navailable data. By following the global perspective on local data, FedLSD\nencourages the learned features to preserve global knowledge and have\nconsistent views across local models, thus improving convergence without\ncompromising data privacy. Under our framework, we further extend FedLSD to\nFedLS-NTD, which only considers the not-true class signals to compensate noisy\nprediction of the global model. We validate that both FedLSD and FedLS-NTD\nsignificantly improve the performance in standard FL benchmarks in various\nsetups, especially in the extreme data heterogeneity cases.\n"}
+{"abstract": "  Given a permutation $f$, we study the positroid Catalan number $C_f$ defined\nto be the torus-equivariant Euler characteristic of the associated open\npositroid variety. We introduce a class of repetition-free permutations and\nshow that the corresponding positroid Catalan numbers count Dyck paths avoiding\na convex subset of the rectangle. We show that any convex subset appears in\nthis way. Conjecturally, the associated $q,t$-polynomials coincide with the\ngeneralized $q,t$-Catalan numbers that recently appeared in relation to the\nshuffle conjecture, flag Hilbert schemes, and Khovanov-Rozansky homology of\nCoxeter links.\n"}
+{"abstract": "  We study the holomorphic symplectic geometry of (the smooth locus of) the\nspace of holomorphic sections of a twistor space with rotating circle action.\nThe twistor space has a meromorphic connection constructed by Hitchin. We give\nan interpretation of Hitchin's meromorphic connection in the context of the\nAtiyah--Ward transform of the corresponding hyperholomorphic line bundle.\n  It is shown that the residue of the meromorphic connection serves as a moment\nmap for the induced circle action, and its critical points are studied.\nParticular emphasis is given to the example of Deligne--Hitchin moduli spaces.\n"}
+{"abstract": "  The interaction between shear driven turbulence and stratification is a key\nprocess in a wide array of geophysical flows with spatio-temporal scales that\nspan many orders of magnitude. A quick numerical model prediction based on\nexternal parameters of stratified boundary layers could greatly benefit the\nunderstanding of the interaction between velocity and scalar flux at varying\nscales. For these reasons, here, we use the resolvent framework to investigate\nthe effects of an active scalar on incompressible wall-bounded turbulence. We\nobtain the state of the flow system by applying the linear resolvent operator\nto the nonlinear terms in the governing Navier-Stokes equations with the\nBoussinesq approximation. This extends the formulation to include the scalar\nadvection equation with the scalar component acting in the wall-normal\ndirection in the momentum equations. We use the mean velocity profiles from a\nDNS of a stably-stratified turbulent channel flow at varying friction\nRichardson number. The results obtained from the resolvent analysis are\ncompared to the premultiplied energy spectra, auto-correlation coefficient, and\nthe energy budget terms obtained from the DNS. It is shown that despite using\nonly a very limited range of representative scales, the resolvent model is able\nto reproduce the balance of energy budget terms as well as provide meaningful\ninsight of coherent structures occurring in the flow. Computation of the\nleading resolvent models, despite considering a limited range of scales,\nreproduces the balance of energy budget terms, provides meaningful predictions\nof coherent structures in the flow, and is more cost-effective than performing\nfull-scale simulations. This quick model can provide further understanding of\nstratified flows with only information about the mean profile and prior\nknowledge of energetic scales of motion in the neutrally-buoyant boundary\nlayers.\n"}
+{"abstract": "  Healthcare blockchains provide an innovative way to store healthcare\ninformation, execute healthcare transactions, and build trust for healthcare\ndata sharing and data integration in a decentralized open healthcare network\nenvironment. Although the healthcare blockchain technology has attracted broad\ninterests and attention in industry, government and academia, the security and\nprivacy concerns remain the focus of debate when deploying blockchains for\ninformation sharing in the healthcare sector from business operation to\nresearch collaboration. This paper focuses on the security and privacy\nrequirements for medical data sharing using blockchain, and provides a\ncomprehensive analysis of the security and privacy risks and requirements,\naccompanied by technical solution techniques and strategies. First, we discuss\nthe security and privacy requirements and attributes required for electronic\nmedical data sharing by deploying the healthcare blockchain. Second, we\ncategorize existing efforts into three reference blockchain usage scenarios for\nelectronic medical data sharing, and discuss the technologies for implementing\nthese security and privacy properties in the three categories of usage\nscenarios for healthcare blockchain, such as anonymous signatures,\nattribute-based encryption, zero-knowledge proofs, verification techniques for\nsmart contract security. Finally, we discuss other potential blockchain\napplication scenarios in healthcare sector. We conjecture that this survey will\nhelp healthcare professionals, decision makers, and healthcare service\ndevelopers to gain technical and intuitive insights into the security and\nprivacy of healthcare blockchains in terms of concepts, risks, requirements,\ndevelopment and deployment technologies and systems.\n"}
+{"abstract": "  The recent popularity growth of Deep Learning (DL) re-ignited the interest\ntowards traffic classification, with several studies demonstrating the accuracy\nof DL-based classifiers to identify Internet applications' traffic. Even with\nthe aid of hardware accelerators (GPUs, TPUs), DL model training remains\nexpensive, and limits the ability to operate frequent model updates necessary\nto fit to the ever evolving nature of Internet traffic, and mobile traffic in\nparticular. To address this pain point, in this work we explore Incremental\nLearning (IL) techniques to add new classes to models without a full\nretraining, hence speeding up model's updates cycle. We consider iCarl, a state\nof the art IL method, and MIRAGE-2019, a public dataset with traffic from 40\nAndroid apps, aiming to understand \"if there is a case for incremental learning\nin traffic classification\". By dissecting iCarl internals, we discuss ways to\nimprove its design, contributing a revised version, namely iCarl+. Despite our\nanalysis reveals their infancy, IL techniques are a promising research area on\nthe roadmap towards automated DL-based traffic analysis systems.\n"}
+{"abstract": "  The optimal gain matrix of the Kalman filter is often derived by minimizing\nthe trace of the posterior covariance matrix. Here, I show that the Kalman gain\nalso minimizes the determinant of the covariance matrix, a quantity known as\nthe generalized variance. When the error distributions are Gaussian, the\ndifferential entropy is also minimized.\n"}
+{"abstract": "  We present the latest results of our ongoing multiplicity study of\n(Community) TESS Objects of Interest, using astro- and photometric data from\nthe ESA-Gaia mission, to detect stellar companions of these stars and to\ncharacterize their properties. In total, 113 binary, 5 hierarchical triple star\nsystems, as well as one quadruple system were detected among 585 targets\nsurveyed, which are all located at distances closer than about 500pc around the\nSun. As proven with their accurate Gaia EDR3 astrometry the companions and the\ntargets are located at the same distance and share a common proper motion, as\nit is expected for components of gravitationally bound stellar systems. The\ncompanions exhibit masses in the range between about 0.09$M_\\odot$ and\n4.5$M_\\odot$ and are most frequently found in the mass range between 0.15 and\n0.6$M_{\\odot}$. The companions are separated from the targets by about 120 up\nto 9500au and their frequency is the highest and constant within about 500au\nwhile it continually decreases for larger separations. Beside mainly early to\nmid M dwarfs, also 5 white dwarf companions were identified in this survey,\nwhose true nature was revealed by their photometric properties.\n"}
+{"abstract": "  Place recognition is critical for both offline mapping and online\nlocalization. However, current single-sensor based place recognition still\nremains challenging in adverse conditions. In this paper, a heterogeneous\nmeasurements based framework is proposed for long-term place recognition, which\nretrieves the query radar scans from the existing lidar maps. To achieve this,\na deep neural network is built with joint training in the learning stage, and\nthen in the testing stage, shared embeddings of radar and lidar are extracted\nfor heterogeneous place recognition. To validate the effectiveness of the\nproposed method, we conduct tests and generalization experiments on the\nmulti-session public datasets compared to other competitive methods. The\nexperimental results indicate that our model is able to perform multiple place\nrecognitions: lidar-to-lidar, radar-to-radar and radar-to-lidar, while the\nlearned model is trained only once. We also release the source code publicly:\nhttps://github.com/ZJUYH/radar-to-lidar-place-recognition.\n"}
+{"abstract": "  This paper analyzes a discretization of a stochastic parabolic optimal\ncontrol problem, where the diffusion term contains the control variable. With\nrough data, the convergence of the discretization is derived. In addition, a\nMonte-Carlo method is presented.\n"}
+{"abstract": "  We present OffRoadTranSeg, the first end-to-end framework for semi-supervised\nsegmentation in unstructured outdoor environment using transformers and\nautomatic data selection for labelling. The offroad segmentation is a scene\nunderstanding approach that is widely used in autonomous driving. The popular\noffroad segmentation method is to use fully connected convolution layers and\nlarge labelled data, however, due to class imbalance, there will be several\nmismatches and also some classes may not be detected. Our approach is to do the\ntask of offroad segmentation in a semi-supervised manner. The aim is to provide\na model where self supervised vision transformer is used to fine-tune offroad\ndatasets with self-supervised data collection for labelling using depth\nestimation. The proposed method is validated on RELLIS-3D and RUGD offroad\ndatasets. The experiments show that OffRoadTranSeg outperformed other state of\nthe art models, and also solves the RELLIS-3D class imbalance problem.\n"}
+{"abstract": "  Modern programming languages (e.g., Java and C#) provide features to separate\nerror-handling code from regular code, seeking to enhance software\ncomprehensibility and maintainability. Nevertheless, the way exception handling\n(EH) code is structured in such languages may lead to multiple, different, and\ncomplex control flows, which may affect the software testability. Previous\nstudies have reported that EH code is typically neglected, not well tested, and\nits misuse can lead to reliability degradation and catastrophic failures.\nHowever, little is known about the relationship between testing practices and\nEH testing effectiveness. In this exploratory study, we (i) measured the\nadequacy degree of EH testing concerning code coverage (instruction, branch,\nand method) criteria; and (ii) evaluated the effectiveness of the EH testing by\nmeasuring its capability to detect artificially injected faults (i.e., mutants)\nusing 7 EH mutation operators. Our study was performed using test suites of 27\nlong-lived Java libraries from open-source ecosystems. Our results show that\ninstructions and branches within $\\mathtt{catch}$ blocks and $\\mathtt{throw}$\ninstructions are less covered, with statistical significance than the overall\ninstructions and branches. Nevertheless, most of the studied libraries\npresented test suites capable of detecting more than 70% of the injected\nfaults. From a total of 12,331 mutants created in this study, the test suites\nwere able to detect 68% of them.\n"}
+{"abstract": "  Deep learning technology promotes the rapid development of person\nre-identifica-tion (re-ID). However, some challenges are still existing in the\nopen-world. First, the existing re-ID research usually assumes only one factor\nvariable (view, clothing, pedestrian pose, pedestrian occlusion, image\nresolution, RGB/IR modality) changing, ignoring the complexity of multi-factor\nvariables in the open-world. Second, the existing re-ID methods are over depend\non clothing color and other apparent features of pedestrian, which are easily\ndisguised or changed. In addition, the lack of benchmark datasets containing\nmulti-factor variables is also hindering the practically application of re-ID\nin the open-world. In this paper, we propose a low-cost and high-efficiency\nmethod to solve shortcomings of the existing re-ID research, such as unreliable\nfeature selection, low efficiency of feature extraction, single research\nvariable, etc. Our approach based on pose estimation model improved by group\nconvolution to obtain the continuous key points of pedestrian, and utilize\ndynamic time warping (DTW) to measure the similarity of features between\ndifferent pedestrians. At the same time, to verify the effectiveness of our\nmethod, we provide a miniature dataset which is closer to the real world and\nincludes pedestrian changing clothes and cross-modality factor variables\nfusion. Extensive experiments are conducted and the results show that our\nmethod achieves Rank-1: 60.9%, Rank-5: 78.1%, and mAP: 49.2% on this dataset,\nwhich exceeds most existing state-of-art re-ID models.\n"}
+{"abstract": "  Though nearest neighbor Machine Translation ($k$NN-MT)\n\\cite{khandelwal2020nearest} has proved to introduce significant performance\nboosts over standard neural MT systems, it is prohibitively slow since it uses\nthe entire reference corpus as the datastore for the nearest neighbor search.\nThis means each step for each beam in the beam search has to search over the\nentire reference corpus. $k$NN-MT is thus two-order slower than vanilla MT\nmodels, making it hard to be applied to real-world applications, especially\nonline services. In this work, we propose Fast $k$NN-MT to address this issue.\nFast $k$NN-MT constructs a significantly smaller datastore for the nearest\nneighbor search: for each word in a source sentence, Fast $k$NN-MT first\nselects its nearest token-level neighbors, which is limited to tokens that are\nthe same as the query token. Then at each decoding step, in contrast to using\nthe entire corpus as the datastore, the search space is limited to target\ntokens corresponding to the previously selected reference source tokens. This\nstrategy avoids search through the whole datastore for nearest neighbors and\ndrastically improves decoding efficiency. Without loss of performance, Fast\n$k$NN-MT is two-order faster than $k$NN-MT, and is only two times slower than\nthe standard NMT model. Fast $k$NN-MT enables the practical use of $k$NN-MT\nsystems in real-world MT applications.\\footnote{Code is available at\n\\url{https://github.com/ShannonAI/fast-knn-nmt.}}\n"}
+{"abstract": "  We study the cosmological evolution for a scalar field dark matter model, by\nconsidering a parameterization of the evolution equations that allow us to\nunify in a single parameter a family of potentials: quadratic (free case),\ntrigonometric (Axion-like case), and hyperbolic. After exploring the\ncosmological dynamics of this model, we perform a statistical analysis to study\nthe viability of such model in comparison with the standard Cold Dark Matter\nmodel. We found that the free case is preferred over the other scalar field\npotentials, but in any case all of them are disfavored by the cosmological\nobservations with respect to the standard model.\n"}
+{"abstract": "  Generative adversarial networks built from deep convolutional neural networks\n(GANs) lack the ability to exactly replicate the high-frequency components of\nnatural images. To alleviate this issue, we introduce two novel training\ntechniques called frequency dropping (F-Drop) and frequency matching (F-Match).\nThe key idea of F-Drop is to filter out unnecessary high-frequency components\nfrom the input images of the discriminators. This simple modification prevents\nthe discriminators from being confused by perturbations of the high-frequency\ncomponents. In addition, F-Drop makes the GANs focus on fitting in the\nlow-frequency domain, in which there are the dominant components of natural\nimages. F-Match minimizes the difference between real and fake images in the\nfrequency domain for generating more realistic images. F-Match is implemented\nas a regularization term in the objective functions of the generators; it\npenalizes the batch mean error in the frequency domain. F-Match helps the\ngenerators to fit in the high-frequency domain filtered out by F-Drop to the\nreal image. We experimentally demonstrate that the combination of F-Drop and\nF-Match improves the generative performance of GANs in both the frequency and\nspatial domain on multiple image benchmarks.\n"}
+{"abstract": "  This paper describes our submission for the End-to-end Multi-domain Task\nCompletion Dialog shared task at the 9th Dialog System Technology Challenge\n(DSTC-9). Participants in the shared task build an end-to-end task completion\ndialog system which is evaluated by human evaluation and a user simulator based\nautomatic evaluation. Different from traditional pipelined approaches where\nmodules are optimized individually and suffer from cascading failure, we\npropose an end-to-end dialog system that 1) uses Generative Pretraining 2\n(GPT-2) as the backbone to jointly solve Natural Language Understanding, Dialog\nState Tracking, and Natural Language Generation tasks, 2) adopts Domain and\nTask Adaptive Pretraining to tailor GPT-2 to the dialog domain before\nfinetuning, 3) utilizes heuristic pre/post-processing rules that greatly\nsimplify the prediction tasks and improve generalizability, and 4) equips a\nfault tolerance module to correct errors and inappropriate responses. Our\nproposed method significantly outperforms baselines and ties for first place in\nthe official evaluation. We make our source code publicly available.\n"}
+{"abstract": "  Non-perturbatively computing the hadronic vacuum polarization at large photon\nvirtualities and making contact with perturbation theory enables a precision\ndetermination of the electromagnetic coupling at the $Z$ pole, which enters\nglobal electroweak fits. In order to achieve this goal ab initio using lattice\nQCD, one faces the challenge that, at the short distances which dominate the\nobservable, discretization errors are hard to control. Here we address\nchallenges of this type with the help of static screening correlators in the\nhigh-temperature phase of QCD, yet without incurring any bias. The idea is\nmotivated by the observations that (a) the cost of high-temperature simulations\nis typically much lower than their vacuum counterpart, and (b) at distances\n$x_3$ far below the inverse temperature $1/T$, the operator-product expansion\nguarantees the thermal correlator of two local currents to deviate from the\nvacuum correlator by a relative amount that is power-suppressed in $(x_3\\:T)$.\nThe method is first investigated in lattice perturbation theory, where we point\nout the appearance of an O$(a^2 \\log(1/a))$ lattice artifact in the vacuum\npolarization with a prefactor that we calculate. It is then applied to\nnon-perturbative lattice QCD data with two dynamical flavors of quarks. Our\nlattice spacings range down to 0.049 fm for the vacuum simulations and down to\n0.033 fm for the simulations performed at a temperature of 250 MeV.\n"}
+{"abstract": "  We use the Keck Cosmic Web Imager integral-field unit spectrograph to: 1)\nmeasure the global stellar population parameters for the ultra-diffuse galaxy\n(UDG) Dragonfly 44 (DF44) to much higher precision than previously possible for\nany UDG, and 2) for the first time measure spatially-resolved stellar\npopulation parameters of a UDG. We find that DF44 falls below the\nmass--metallicity relation established by canonical dwarf galaxies both in and\nbeyond the Local Group. We measure a flat radial age gradient ($m_{\\rm age}\n\\sim +0.01_{-0.08}^{+0.07}$ log Gyr kpc$^{-1}$) and a flat-to-positive\nmetallicity gradient ($m_{\\rm [Fe/H]} \\sim +0.08_{-0.11}^{+0.11}$ dex\nkpc$^{-1}$), which are inconsistent with the gradients measured in similarly\npressure-supported dwarf galaxies. We also measure a flat-to-negative [Mg/Fe]\ngradient ($m_{\\rm [Mg/Fe]} \\sim -0.18_{-0.17}^{+0.17}$ dex kpc$^{-1}$) such\nthat the central $1.5$ kpc of DF44 has stellar population parameters comparable\nto metal-poor globular clusters. Overall, DF44 does not have internal\nproperties similar to other dwarf galaxies and is inconsistent with it having\nbeen puffed up through a prolonged, bursty star-formation history, as suggested\nby some simulations. Rather, the evidence indicates that DF44 experienced an\nintense epoch of \"inside-out\" star formation and then quenched early and\ncatastrophically, such that star-formation was cut off more quickly than in\ncanonical dwarf galaxies.\n"}
+{"abstract": "  In spite of considerable practical importance, current algorithmic fairness\nliterature lacks in technical methods to account for underlying geographic\ndependency while evaluating or mitigating fairness issues for spatial data. We\ninitiate the study of spatial fairness in this paper, taking the first step\ntowards formalizing this line of quantitative methods. Fairness considerations\nfor spatial data often get confounded by the underlying spatial\nautocorrelation. We propose hypothesis testing methodology to detect the\npresence and strength of this effect, then mitigate it using a spatial\nfiltering-based approach -- in order to enable application of existing bias\ndetection metrics. We evaluate our proposed methodology through numerical\nexperiments on real and synthetic datasets, demonstrating that in presence of\nseveral types of confounding effects due to the underlying spatial structure\nour testing methods perform well in maintaining low type-II errors and nominal\ntype-I errors.\n"}
+{"abstract": "  We present the first numerical solutions of the causal, stable relativistic\nNavier-Stokes equations as formulated by Bemfica, Disconzi, Noronha, and Kovtun\n(BDNK). For this initial investigation we restrict to plane-symmetric\nconfigurations of a conformal fluid in Minkowski spacetime. We consider\nevolution of three classes of initial data: a smooth (initially) stationary\nconcentration of energy, a standard shock tube setup, and a smooth shockwave\nsetup. We compare these solutions to those obtained with the\nMuller-Israel-Stewart (MIS) formalism, variants of which are the common tools\nused to model relativistic, viscous fluids. We find that for the two smooth\ninitial data cases, simple finite difference methods are adequate to obtain\nstable, convergent solutions to the BDNK equations. For low viscosity, the MIS\nand BDNK evolutions show good agreement. At high viscosity the solutions begin\nto differ in regions with large gradients, and there the BDNK solutions can (as\nexpected) exhibit violation of the weak energy condition. This behavior is\ntransient, and the solutions evolve toward a hydrodynamic regime in a way\nreminiscent of an approach to a universal attractor. For the shockwave problem,\nwe give evidence that if a hydrodynamic frame is chosen so that the maximum\ncharacteristic speed of the BDNK system is the speed of light (or larger),\narbitrarily strong shockwaves are smoothly resolved. Regarding the shock tube\nproblem, it is unclear whether discontinuous initial data is mathematically\nwell-posed for the BDNK system, even in a weak sense. Nevertheless we attempt\nnumerical solution, and then need to treat the perfect fluid terms using\nhigh-resolution shock-capturing methods. When such methods can successfully\nevolve the solution beyond the initial time, subsequent evolution agrees with\ncorresponding MIS solutions, as well as the perfect fluid solution in the limit\nof zero viscosity.\n"}
+{"abstract": "  We develop an analytical ringdown waveform model, including both the\nfundamental and the overtone quasinormal modes, for charged black holes and\nshow that it is precise enough to analyze current gravitational wave data.\nApplying this waveform model to GW150914, the charge to mass ratio of the\nremnant black hole ($\\lambda_f$) has been constrained with the sole ringdown\ngravitational wave data for the first time and the $90\\%$ upper limit is\n$\\lambda_f\\leq 0.38$. Correspondingly, the deviation parameter of the\nscalar-tensor vector gravity ($\\alpha_{\\rm s}$) is limited to be $\\alpha_{\\rm\ns}\\leq 0.17$. Our approach can be directly applied to other binary black hole\nmergers with loud ringdown radiation.\n"}
+{"abstract": "  Although cyberattacks on machine learning (ML) production systems can be\nharmful, today, security practitioners are ill equipped, lacking methodologies\nand tactical tools that would allow them to analyze the security risks of their\nML-based systems. In this paper, we performed a comprehensive threat analysis\nof ML production systems. In this analysis, we follow the ontology presented by\nNIST for evaluating enterprise network security risk and apply it to ML-based\nproduction systems. Specifically, we (1) enumerate the assets of a typical ML\nproduction system, (2) describe the threat model (i.e., potential adversaries,\ntheir capabilities, and their main goal), (3) identify the various threats to\nML systems, and (4) review a large number of attacks, demonstrated in previous\nstudies, which can realize these threats. In addition, to quantify the risk of\nadversarial machine learning (AML) threat, we introduce a novel scoring system,\nwhich assign a severity score to different AML attacks. The proposed scoring\nsystem utilizes the analytic hierarchy process (AHP) for ranking, with the\nassistance of security experts, various attributes of the attacks. Finally, we\ndeveloped an extension to the MulVAL attack graph generation and analysis\nframework to incorporate cyberattacks on ML production systems. Using the\nextension, security practitioners can apply attack graph analysis methods in\nenvironments that include ML components; thus, providing security practitioners\nwith a methodological and practical tool for evaluating the impact and\nquantifying the risk of a cyberattack targeting an ML production system.\n"}
+{"abstract": "  A framework is presented for fitting inverse problem models via variational\nBayes approximations. This methodology guarantees flexibility to statistical\nmodel specification for a broad range of applications, good accuracy\nperformances and reduced model fitting times, when compared with standard\nMarkov chain Monte Carlo methods. The message passing and factor graph fragment\napproach to variational Bayes we describe facilitates streamlined\nimplementation of approximate inference algorithms and forms the basis to\nsoftware development. Such approach allows for supple inclusion of numerous\nresponse distributions and penalizations into the inverse problem model. Even\nthough our work is circumscribed to one- and two-dimensional response\nvariables, we lay down an infrastructure where efficient algorithm updates\nbased on nullifying weak interactions between variables can also be derived for\ninverse problems in higher dimensions. Image processing applications motivated\nby biomedical and archaeological problems are included as illustrations.\n"}
+{"abstract": "  Recently deep neural networks have been successfully applied in channel\ncoding to improve the decoding performance. However, the state-of-the-art\nneural channel decoders cannot achieve high decoding performance and low\ncomplexity simultaneously. To overcome this challenge, in this paper we propose\ndoubly residual neural (DRN) decoder. By integrating both the residual input\nand residual learning to the design of neural channel decoder, DRN enables\nsignificant decoding performance improvement while maintaining low complexity.\nExtensive experiment results show that on different types of channel codes, our\nDRN decoder consistently outperform the state-of-the-art decoders in terms of\ndecoding performance, model sizes and computational cost.\n"}
+{"abstract": "  We propose a universal and physically realizable adversarial attack on a\ncascaded multi-modal deep learning network (DNN), in the context of\nself-driving cars. DNNs have achieved high performance in 3D object detection,\nbut they are known to be vulnerable to adversarial attacks. These attacks have\nbeen heavily investigated in the RGB image domain and more recently in the\npoint cloud domain, but rarely in both domains simultaneously - a gap to be\nfilled in this paper. We use a single 3D mesh and differentiable rendering to\nexplore how perturbing the mesh's geometry and texture can reduce the\nrobustness of DNNs to adversarial attacks. We attack a prominent cascaded\nmulti-modal DNN, the Frustum-Pointnet model. Using the popular KITTI benchmark,\nwe showed that the proposed universal multi-modal attack was successful in\nreducing the model's ability to detect a car by nearly 73%. This work can aid\nin the understanding of what the cascaded RGB-point cloud DNN learns and its\nvulnerability to adversarial attacks.\n"}
+{"abstract": "  Autonomous vehicle following systems are playing a decisive role to increase\nvehicle density on roads by shortening inter-vehicle time gaps. However,\ndisturbance attenuation along a platoon of vehicles, i.e., string stability, is\nbeing a challenging task while time gap is getting shorter. In order to\nguarantee the string stability of a vehicle platoon, a multi-objective\n$H_{\\infty}$ control formulation for adaptive cruise control and cooperative\nadaptive cruise control structures has been investigated in this paper. The\nproposed control method solves an optimization problem and achieves a\ncontroller that is able to provide not only the system stability, but also the\nstring stability as distinct from the traditional $H_{\\infty}$ control.\n"}
+{"abstract": "  StyleGAN is one of the state-of-the-art image generators which is well-known\nfor synthesizing high-resolution and hyper-realistic face images. Though images\ngenerated by vanilla StyleGAN model are visually appealing, they sometimes\ncontain prominent circular artifacts which severely degrade the quality of\ngenerated images. In this work, we provide a systematic investigation on how\nthose circular artifacts are formed by studying the functionalities of\ndifferent stages of vanilla StyleGAN architecture, with both mechanism analysis\nand extensive experiments. The key modules of vanilla StyleGAN that promote\nsuch undesired artifacts are highlighted. Our investigation also explains why\nthe artifacts are usually circular, relatively small and rarely split into 2 or\nmore parts. Besides, we propose a simple yet effective solution to remove the\nprominent circular artifacts for vanilla StyleGAN, by applying a novel\npixel-instance normalization (PIN) layer.\n"}
+{"abstract": "  We study the time-dependent neutralization of a slow highly charged ion that\npenetrates a hexagonal hollow-centred graphene nanoflake. To compute the\nultrafast charge transfer dynamics, we apply an effective Hubbard nanocluster\nmodel and use the method of nonequilibrium Green functions (NEGF) in\nconjunction with an embedding self-energy scheme which allows one to follow the\ntemporal changes of the number of electrons in the nanoflake. We perform\nextensive simulations of the charge transfer dynamics for a broad range of ion\ncharge states and impact velocities. The results are used to put forward a\nsimple semi-analytical model of the neutralization dynamics that is in very\ngood agreement with transmission experiments, in which highly charged xenon\nions pass through sheets of single-layer graphene.\n"}
+{"abstract": "  We use the general notion of 2-dimensional adjunction with given coherence\nequations as introduced by MacDonald-Stone, building on earlier work by Gray,\nto derive coherence equations for a general 2-monad, which we refer to as a\nlax-Gray monad. The free lax-Gray 2-monad on one object may be regarded as the\nsuspension of a lax 2-dimensional analogue of the simplicial category Delta. We\ncall this analogue Delta LG for lax-Gray Delta. This is analogous to the way\nthat the free 1-monad Mnd (as presented in Schanuel-Street) is a concrete\nexample of the suspension of the simplicial category Delta, described by Mac\nLane.\n"}
+{"abstract": "  We study the local analytic classification of affine structures with\nlogarithmic pole on complex surfaces. With this result in hand, we can get the\nlocal classification of the logarithmic parallelizable d-webs, d $\\ge$ 3.\n"}
+{"abstract": "  Extra-large datasets are becoming increasingly accessible, and computing\ntools designed to handle huge amount of data efficiently are democratizing\nrapidly. However, conventional statistical and econometric tools are still\nlacking fluency when dealing with such large datasets. This paper dives into\neconometrics on big datasets, specifically focusing on the logistic regression\non Spark. We review the robustness of the functions available in Spark to fit\nlogistic regression and introduce a package that we developed in PySpark which\nreturns the statistical summary of the logistic regression, necessary for\nstatistical inference.\n"}
+{"abstract": "  A search for neutrinos produced in coincidence with Gamma-Ray Bursts(GRB) was\nconducted with the Super-Kamiokande (SK) detector. Between December 2008 and\nMarch 2017, the Gamma-ray Coordinates Network recorded 2208 GRBs that occurred\nduring normal SK operation. Several time windows around each GRB were used to\nsearch for coincident neutrino events. No statistically significant signal in\nexcess of the estimated backgrounds was detected. The $\\bar\\nu_e$ fluence in\nthe range from 8 MeV to 100 MeV in positron total energy for\n$\\bar\\nu_e+p\\rightarrow e^{+}+n$ was found to be less than $\\rm 5.07\\times10^5$\ncm$^{-2}$ per GRB in 90\\% C.L. Upper bounds on the fluence as a function of\nneutrino energy were also obtained.\n"}
+{"abstract": "  In the race to bring Artificial Intelligence (AI) to the edge, collaborative\nintelligence has emerged as a promising way to lighten the computation load on\nedge devices that run applications based on Deep Neural Networks (DNNs).\nTypically, a deep model is split at a certain layer into edge and cloud\nsub-models. The deep feature tensor produced by the edge sub-model is\ntransmitted to the cloud, where the remaining computationally intensive\nworkload is performed by the cloud sub-model. The communication channel between\nthe edge and cloud is imperfect, which will result in missing data in the deep\nfeature tensor received at the cloud side. In this study, we examine the\neffectiveness of four low-rank tensor completion methods in recovering missing\ndata in the deep feature tensor. We consider both sparse tensors, such as those\nproduced by the VGG16 model, as well as non-sparse tensors, such as those\nproduced by ResNet34 model. We study tensor completion effectiveness in both\nconplexity-constrained and unconstrained scenario.\n"}
+{"abstract": "  We study the finite groups $G$ with the curious property that there exists an\nelement $k\\in G$ and a function $f\\colon G\\to G$ such that $f(xk)=xf(x)$ holds\nfor all $x\\in G$. This property arose from the study of near-rings and\ninput-output automata on groups. We call a group with this property a\n$J$-group. Finite $J$-groups must have odd order, and hence are solvable. We\nprove that every finite nilpotent group of odd order is a $J$-group if its\nnilpotency class $c$ satisfies $c\\le6$. If $G$ is a finite $p$-group, with\n$p>2$ and $p^2>2c-1$, then we prove that $G$ is $J$-group. Finally, if $p>2$\nand $G$ is a regular $p$-group or, more generally, a power-closed one (i.e., in\neach section and for each $m\\geq1$ the subset of $p^m$-th powers is a\nsubgroup), then we prove that $G$ is a $J$-group.\n"}
+{"abstract": "  We consider the stochastic combinatorial semi-bandit problem with adversarial\ncorruptions. We provide a simple combinatorial algorithm that can achieve a\nregret of $\\tilde{O}\\left(C+d^2K/\\Delta_{min}\\right)$ where $C$ is the total\namount of corruptions, $d$ is the maximal number of arms one can play in each\nround, $K$ is the number of arms. If one selects only one arm in each round, we\nachieves a regret of $\\tilde{O}\\left(C+\\sum_{\\Delta_i>0}(1/\\Delta_i)\\right)$.\nOur algorithm is combinatorial and improves on the previous combinatorial\nalgorithm by [Gupta et al., COLT2019] (their bound is\n$\\tilde{O}\\left(KC+\\sum_{\\Delta_i>0}(1/\\Delta_i)\\right)$), and almost matches\nthe best known bounds obtained by [Zimmert et al., ICML2019] and [Zimmert and\nSeldin, AISTATS2019] (up to logarithmic factor). Note that the algorithms in\n[Zimmert et al., ICML2019] and [Zimmert and Seldin, AISTATS2019] require one to\nsolve complex convex programs while our algorithm is combinatorial, very easy\nto implement, requires weaker assumptions and has very low oracle complexity\nand running time. We also study the setting where we only get access to an\napproximation oracle for the stochastic combinatorial semi-bandit problem. Our\nalgorithm achieves an (approximation) regret bound of\n$\\tilde{O}\\left(d\\sqrt{KT}\\right)$. Our algorithm is very simple, only worse\nthan the best known regret bound by $\\sqrt{d}$, and has much lower oracle\ncomplexity than previous work.\n"}
+{"abstract": "  A new kinematic condition for soliton motions of an $n$-dimensional continuum\nin $R^{n+m}$, independent of the underlying physics, is proven. The condition\nand its consequences for different cases are demonstrated. A soliton in a 1D\nstring that rocks back and forth, a rotating soliton in a 2D membrane, and\nvarious other cases are presented as examples. It is shown that traveling knots\nbased on classical wave equation are plausible. Cases in which all the motions\nare solitons are also presented. Compatibility of equations of motion with the\nkinematic constraint is explored and demonstrated.\n"}
+{"abstract": "  We prove that both local Galois representations and\n$(\\varphi,\\Gamma)$-modules can be recovered from prismatic F-crystals, from\nwhich we obtain a new proof of the equivalence of Galois representations and\n$(\\varphi,\\Gamma)$-modules.\n"}
+{"abstract": "  A signification fraction of Galactic massive stars (> 8Mo) are ejected from\ntheir parent cluster and supersonically sail away through the interstellar\nmedium (ISM). The winds of these fast-moving stars blow asymmetric bubbles thus\ncreating a circumstellar environment in which stars eventually die with a\nsupernova explosion. The morphology of the resulting remnant is largely\ngoverned by the circumstellar medium of the defunct progenitor star. In this\npaper, we present 2D magneto-hydrodynamical simulations investigating the\neffect of the ISM magnetic field on the shape of the supernova remnants of a\n35Mo star evolving through a Wolf-Rayet phase and running with velocity 20 and\n40 km/s, respectively. A 7 microG ambient magnetic field is sufficient to\nmodify the properties of the expanding supernova shock front and in particular\nto prevent the formation of filamentary structures. Prior to the supernova\nexplosion, the compressed magnetic field in the circumstellar medium stabilises\nthe wind/ISM contact discontinuity in the tail of the wind bubble. A\nconsequence is a reduced mixing efficiency of ejecta and wind materials in the\ninner region of the remnant, where the supernova shock wave propagates.\nRadiative transfer calculations for synchrotron emission reveal that the\nnon-thermal radio emission has characteristic features reflecting the asymmetry\nof exiled core-collapse supernova remnants from Wolf-Rayet progenitors. Our\nmodels are qualitatively consistent with the radio appearance of several\nremnants of high-mass progenitors, namely the bilateral G296.5+10.0 and the\nshell-type remnants CTB109 and Kes 17, respectively.\n"}
+{"abstract": "  The density fluctuations of quantum vortex lines are measured in a turbulent\nflow of superfluid He, at temperatures corresponding to superfluid fraction of\n16%, 47% and 81%. The probe is a micro-fabricated second sound resonator that\nallows for local and small-scale measurements in the core of the flow, at a\n10-mesh-size behind a grid. Remarkably, all the vortex power spectra collapse\non a single master curve, independently from the superfluid fraction and the\nmean velocity. By contrast with previous measurements, we report an peculiar\nshape of the power spectra. The vortex density probability distributions are\nfound to be strongly skewed, similarly to the vorticity distributions observed\nin classical turbulence. Implications of those results are discussed.\n"}
+{"abstract": "  Distributed arithmetic coding (DAC) has been shown to be effective for\nSlepian-Wolf coding, especially for short data blocks. In this letter, we\npropose to use the DAC to compress momery-correlated sources. More\nspecifically, the correlation between sources is modeled as a hidden Markov\nprocess. Experimental results show that the performance is close to the\ntheoretical Slepian-Wolf limit.\n"}
+{"abstract": "  High levels of missing data and strong class imbalance are ubiquitous\nchallenges that are often presented simultaneously in real-world time series\ndata. Existing methods approach these problems separately, frequently making\nsignificant assumptions about the underlying data generation process in order\nto lessen the impact of missing information. In this work, we instead\ndemonstrate how a general self-supervised training method, namely\nAutoregressive Predictive Coding (APC), can be leveraged to overcome both\nmissing data and class imbalance simultaneously without strong assumptions.\nSpecifically, on a synthetic dataset, we show that standard baselines are\nsubstantially improved upon through the use of APC, yielding the greatest gains\nin the combined setting of high missingness and severe class imbalance. We\nfurther apply APC on two real-world medical time-series datasets, and show that\nAPC improves the classification performance in all settings, ultimately\nachieving state-of-the-art AUPRC results on the Physionet benchmark.\n"}
+{"abstract": "  We study the BCS energy gap $\\Xi$ in the high-density limit and derive an\nasymptotic formula, which strongly depends on the strength of the interaction\npotential $V$ on the Fermi surface. In combination with the recent result by\none of us (arXiv:2106.02015) on the critical temperature $T_c$ at high\ndensities, we prove the universality of the ratio of the energy gap and the\ncritical temperature.\n"}
+{"abstract": "  Semiconductors with very low lattice thermal conductivities are highly\ndesired for applications relevant to thermal energy conversion and management,\nsuch as thermoelectrics and thermal barrier coatings. Although the crystal\nstructure and chemical bonding are known to play vital roles in shaping heat\ntransfer behavior, material design approaches of lowering lattice thermal\nconductivity using chemical bonding principles are uncommon. In this work, we\npresent an effective strategy of weakening interatomic interactions and\ntherefore suppressing lattice thermal conductivity based on chemical bonding\nprinciples and develop a high-efficiency approach of discovering low\n$\\kappa_{\\rm L}$ materials by screening the local coordination environments of\ncrystalline compounds. The followed first-principles calculations uncover 30\nhitherto unexplored compounds with (ultra)low lattice thermal conductivities\nfrom thirteen prototype crystal structures contained in the inorganic crystal\nstructure database. Furthermore, we demonstrate an approach of rationally\ndesigning high-performance thermoelectrics by additionally incorporating\ncations with stereochemically active lone-pair electrons. Our results not only\nprovide fundamental insights into the physical origin of the low lattice\nthermal conductivity in a large family of copper-based compounds but also offer\nan efficient approach to discovery and design materials with targeted thermal\ntransport properties.\n"}
+{"abstract": "  We introduce a new renormalization procedure on double rotations, which is\nreminiscent of the classical Rauzy induction. Using this renormalization we\nprove that the set of parameters which induce infinite type double rotations\nhas Hausdorff dimension strictly smaller than 3. Moreover, we construct a\nnatural invariant measure supported on these parameters and show that, with\nrespect to this measure, almost all double rotations are uniquely ergodic.\n"}
+{"abstract": "  Context. The Small Magellanic Cloud (SMC) hosts many known high-mass X-ray\nbinaries (HMXBs), and all but one (SMC X-1) have a Be companion star. Through\nthe calibration and verification phase of eROSITA on board the\nSpektrum-Roentgen-Gamma (SRG) spacecraft, the Be/X-ray binary XMMU\nJ010429.4-723136 was in the field of view during observations of the supernova\nremnant, 1E0102.2-7219, used as a calibration standard.\n  Aims. We report timing and spectral analyses of XMMU J010429.4-723136 based\non three eROSITA observations of the field, two of which were performed on 2019\nNovember 7-9, with the third on 2020 June 18-19. We also reanalyse the OGLE-IV\nlight curve for that source in order to determine the orbital period.\n  Methods. We performed a Lomb-Scargle periodogram analysis to search for\npulsations (from the X-ray data) and for the orbital period (from the OGLE\ndata). X-ray spectral parameters and fluxes were retrieved from the best-fit\nmodel.\n  Results. We detect, for the first time, the pulsations of XMMU\nJ010429.4-723136 at a period of ~164 s, and therefore designate the source as\nSXP 164. From the spectral fitting, we derive a source flux of ~1x10e-12 erg\ns-1 cm-2 for all three observations, corresponding to a luminosity of ~4x10e35\nerg s-1 at the distance of the SMC. Furthermore, reanalysing the OGLE light\ncurve, including the latest observations, we find a significant periodic signal\nthat we believe is likely be the orbital period; at 22.3d, this is shorter than\nthe previously reported values. The Swift/XRT light curve, extracted from two\nlong monitorings of the field and folded at the same period, suggests that a\nmodulation is also present in the X-ray data.\n"}
+{"abstract": "  Dense granular systems subjected to an imposed shear stress undergo\nstick-slip dynamics with systematic patterns of dilation-compaction. During\neach stick phase, as the frictional strength builds up, the granular system\ndilates to accommodate shear strain, developing stronger force networks. During\neach slip event, when the stored energy is released, particles experience large\nrearrangements and the granular network can significantly change. Here, we use\nnumerical simulations of 3D, sheared frictional packings to show that the mean\nbetweenness centrality -- a property of network of interparticle connections --\nfollows consistent patterns during the stick-slip dynamics, showing sharp\nspikes at each slip event. We identify the source of this behavior as arising\nfrom the connectivity and contact arrangements of granular network during\ndilation-compaction cycles, and find that a lower potential for connection\nbetween particles leads to an increase of mean betweenness centrality in the\nsystem. Furthermore, we show that at high confinements, few particles lose\ncontact during slip events, leading to a smaller change in granular\nconnectivity and betweenness centrality.\n"}
+{"abstract": "  In this paper we consider a combination of the joint replenishment problem\n(JRP) and single machine scheduling with release dates. There is a single\nmachine and one or more item types. Each job has a release date, a positive\nprocessing time, and it requires a subset of items. A job can be started at\ntime $t$ only if all the required item types were replenished between the\nrelease date of the job and time point $t$. The ordering of item types for\ndistinct jobs can be combined. The objective is to minimize the total ordering\ncost plus a scheduling criterion, such as total weighted completion time or\nmaximum flow time, where the cost of ordering a subset of items simultaneously\nis the sum of a joint ordering cost, and an additional item ordering cost for\neach item type in the subset. We provide several complexity results for the\noffline problem, and competitive analysis for online variants with min-sum and\nmin-max criteria, respectively.\n"}
+{"abstract": "  The Clifford group is a finite subgroup of the unitary group generated by the\nHadamard, the CNOT, and the Phase gates. This group plays a prominent role in\nquantum error correction, randomized benchmarking protocols, and the study of\nentanglement. Here we consider the problem of finding a short quantum circuit\nimplementing a given Clifford group element. Our methods aim to minimize the\nentangling gate count assuming all-to-all qubit connectivity. First, we\nconsider circuit optimization based on template matching and design\nClifford-specific templates that leverage the ability to factor out Pauli and\nSWAP gates. Second, we introduce a symbolic peephole optimization method. It\nworks by projecting the full circuit onto a small subset of qubits and\noptimally recompiling the projected subcircuit via dynamic programming. CNOT\ngates coupling the chosen subset of qubits with the remaining qubits are\nexpressed using symbolic Pauli gates. Software implementation of these methods\nfinds circuits that are only 0.2% away from optimal for 6 qubits and reduces\nthe two-qubit gate count in circuits with up to 64 qubits by 64.7% on average,\ncompared with the Aaronson-Gottesman canonical form.\n"}
+{"abstract": "  We analyze the fine structure of Clark measures and Clark isometries\nassociated with two-variable rational inner functions on the bidisk. In the\ndegree (n,1) case, we give a complete description of supports and weights for\nboth generic and exceptional Clark measures, characterize when the associated\nembedding operators are unitary, and give a formula for those embedding\noperators. We also highlight connections between our results and both the\nstructure of Agler decompositions and study of extreme points for the set of\npositive pluriharmonic measures on 2-torus.\n"}
+{"abstract": "  We study a time minimization problem on the group of motions of a plane with\nadmissible control in a half-disk. The considered control system describes a\nmodel of a car that can move forward on a plane and turn in place. Optimal\ntrajectories of this system are used in image processing for the detection of\nsalient lines. In particular, such trajectories are used in the analysis of\nmedical images when searching for blood vessels in photos of the human retina.\nThe problem is of interest in geometric control theory as a model example in\nwhich the set of admissible controls contains zero at the boundary. We prove\ncomplete controllability and the existence of optimal trajectories. By\nanalyzing the Hamiltonian system of Pontryagin maximum principle we derive\nexplicit formulas for extremal controls and trajectories. The optimality of\nextremals is partially investigated. The structure of optimal synthesis is\ndescribed.\n"}
+{"abstract": "  Dynamical two-particle susceptibilites are important for a wide range of\ndifferent experiments in condensed-matter physics and beyond. Nevertheless,\nmost textbooks avoid describing how to derive such response functions, perhaps\nbecause they are viewed as too complex. In the literature, most derivations\nwork with generalized susceptibilities, which are more general, but require an\neven higher layer of complexity. In this work, we show a more direct derivation\nin the context of model Hamiltonians which can be mapped directly onto an\nimpurity model. We also present an alternative derivation for the irreducible\nvertex in the context of the Falicov-Kimball model.\n"}
+{"abstract": "  Emerging hybrid memory systems that comprise technologies such as Intel's\nOptane DC Persistent Memory, exhibit disparities in the access speeds and\ncapacity ratios of their heterogeneous memory components. This breaks many\nassumptions and heuristics designed for traditional DRAM-only platforms. High\napplication performance is feasible via dynamic data movement across memory\nunits, which maximizes the capacity use of DRAM while ensuring efficient use of\nthe aggregate system resources. Newly proposed solutions use performance models\nand machine intelligence to optimize which and how much data to move\ndynamically; however, the decision of when to move this data is based on\nempirical selection of time intervals, or left to the applications. Our\nexperimental evaluation shows that failure to properly configure the data\nmovement frequency can lead to 10%-100% slowdown for a given data movement\npolicy; yet, there is no established methodology on how to properly configure\nthis value for a given workload, platform and policy. We propose Cori, a\nsystem-level tuning solution that identifies and extracts the necessary\napplication-level data reuse information, and guides the selection of data\nmovement frequency to deliver gains in application performance and system\nresource efficiency. Experimental evaluation shows that Cori configures data\nmovement frequencies that provide application performance within 3% of the\noptimal one, and that it can achieve this up to 5x more quickly than random or\nbrute-force approaches. System-level validation of Cori on a platform with DRAM\nand Intel's Optane DC PMEM confirms its practicality and tuning efficiency.\n"}
+{"abstract": "  When the Stable Unit Treatment Value Assumption (SUTVA) is violated and there\nis interference among units, there is not a uniquely defined Average Treatment\nEffect (ATE), and alternative estimands may be of interest, among them average\nunit-level differences in outcomes under different homogeneous treatment\npolicies. We term this target the Homogeneous Assignment Average Treatment\nEffect (HAATE). We consider approaches to experimental design with multiple\ntreatment conditions under partial interference and, given the estimand of\ninterest, we show that difference-in-means estimators may perform better than\ncorrectly specified regression models in finite samples on root mean squared\nerror (RMSE). With errors correlated at the cluster level, we demonstrate that\ntwo-stage randomization procedures with intra-cluster correlation of treatment\nstrictly between zero and one may dominate one-stage randomization designs on\nthe same metric. Simulations demonstrate performance of this approach; an\napplication to online experiments at Facebook is discussed.\n"}
+{"abstract": "  First-order phase transitions can leave relic pockets of false vacua and\ntheir particles, that manifest as macroscopic Dark Matter. We compute one\npredictive model: a gauge theory with a dark quark relic heavier than the\nconfinement scale. During the first-order phase transition to confinement, dark\nquarks remain in the false vacuum and get compressed, forming Fermi balls that\ncan undergo gravitational collapse to stable dark dwarfs (bound states\nanalogous to white dwarfs) near the Chandrasekhar limit, or to primordial black\nholes.\n"}
+{"abstract": "  We describe SuperIso v4.1, a public code for calculation of flavour\nobservables, in the Standard Model and for New Physics scenarios in a\nmodel-independent way as well as for specific beyond the SM models such as two\nHiggs doublet models (2HDM), minimal supersymmetric Standard Model (MSSM) and\nnext-to-minimal supersymmetric Standard Model (NMSSM).\n"}
+{"abstract": "  The $A$MnO$_{2}$ delafossites ($A$=Na, Cu), are model frustrated\nantiferromagnets, with triangular layers of Mn$^{3+}$~spins. At low\ntemperatures ($T_{N}$=65 K), a $C2/m \\rightarrow P\\overline{1}$ transition is\nfound in CuMnO$_2$, which breaks frustration and establishes magnetic order. In\ncontrast to this clean transition, $A$=Na only shows short-range distortions at\n$T_N$. Here we report a systematic crystallographic, spectroscopic, and\ntheoretical investigation of CuMnO$_2$. We show that, even in stoichiometric\nsamples, non-zero anisotropic Cu displacements co-exist with magnetic order.\nUsing X-ray/neutron diffraction and Raman scattering, we show that high\npressures acts to decouple these degrees of freedom. This manifests as an\nisostuctural phase transition at $\\sim$10 GPa, with a reversible collapse of\nthe $c$-axis. This is shown to be the high pressure analog of the $c$-axis\nnegative thermal expansion seen at ambient pressure. DFT simulations confirm\nthat dynamical instabilities of the Cu$^{+}$ cations and edge-shared MnO$_{6}$\nlayers are intertwined at ambient pressure. However, high pressure selectively\nactivates the former, before an eventual predicted re-emergence of magnetism at\nthe highest pressures. Our results show that the lattice dynamics and local\nstructure of CuMnO$_2$ are quantitatively different to non-magnetic Cu\ndelafossites, and raise questions about the role of intrinsic inhomogeniety in\nfrustrated antiferromagnets.\n"}
+{"abstract": "  One of the main challenges for arbitrary-shaped text detection is to design a\ngood text instance representation that allows networks to learn diverse text\ngeometry variances. Most of existing methods model text instances in image\nspatial domain via masks or contour point sequences in the Cartesian or the\npolar coordinate system. However, the mask representation might lead to\nexpensive post-processing, while the point sequence one may have limited\ncapability to model texts with highly-curved shapes. To tackle these problems,\nwe model text instances in the Fourier domain and propose one novel Fourier\nContour Embedding (FCE) method to represent arbitrary shaped text contours as\ncompact signatures. We further construct FCENet with a backbone, feature\npyramid networks (FPN) and a simple post-processing with the Inverse Fourier\nTransformation (IFT) and Non-Maximum Suppression (NMS). Different from previous\nmethods, FCENet first predicts compact Fourier signatures of text instances,\nand then reconstructs text contours via IFT and NMS during test. Extensive\nexperiments demonstrate that FCE is accurate and robust to fit contours of\nscene texts even with highly-curved shapes, and also validate the effectiveness\nand the good generalization of FCENet for arbitrary-shaped text detection.\nFurthermore, experimental results show that our FCENet is superior to the\nstate-of-the-art (SOTA) methods on CTW1500 and Total-Text, especially on\nchallenging highly-curved text subset.\n"}
+{"abstract": "  We study celestial amplitudes in (super) Yang-Mills theory using a\nparameterisation of the spinor helicity variables where their overall phase is\nnot fixed by the little group action. In this approach the spin constraint\n$h-\\bar{h}=J$ for celestial conformal primaries emerges naturally from a new\nMellin transform, and the action of conformal transformations on celestial\namplitudes is derived. Applying this approach to $\\mathcal{N}\\!=\\!4$ super\nYang-Mills, we show how the appropriate definition of on-shell superspace\ncoordinates leads naturally to a formulation of chiral celestial\nsuperamplitudes and a representation of the generators of the four-dimensional\nsuperconformal algebra on the celestial sphere, which by construction\nannihilate all tree-level celestial superamplitudes.\n"}
+{"abstract": "  Available data on the polarization of the secondary proton (as a function of\nits momentum $K$) in the inelastic ({\\it p, p$'$}) reactions with the $^{9}$Be,\n$^{12}$C, and $^{40}$Ca nuclei and differential cross section data (the\nmomentum distributions) for the reactions at the initial proton energy 1 GeV\nand scattering angles $\\Theta$=21$^\\circ$ and $\\Theta$=24.5$^\\circ$ were\nanalysed in a range of the high momenta $K$ close to the momentum corresponding\nto the proton elastic scattering off the investigated nucleus. A structure in\nthe polarization and momentum distribution data at the high momenum $K$,\nrelated probably to quasi-elastic scattering off a $^{8}$Be-like nucleon\ncluster inside the nuclei, was observed.\n"}
+{"abstract": "  With smart devices being an essential part of our everyday lives,\nunsupervised access to the mobile sensors' data can result in a multitude of\nside-channel attacks. In this paper, we study potential data leaks from Apple\nPencil (2nd generation) supported by the Apple iPad Pro, the latest stylus pen\nwhich attaches to the iPad body magnetically for charging. We observe that the\nPencil's body affects the magnetic readings sensed by the iPad's magnetometer\nwhen a user is using the Pencil. Therefore, we ask: Can we infer what a user is\nwriting on the iPad screen with the Apple Pencil, given access to only the\niPad's motion sensors' data? To answer this question, we present Side-channel\nattack on Stylus pencil through Sensors (S3), a system that identifies what a\nuser is writing from motion sensor readings. We first use the sharp\nfluctuations in the motion sensors' data to determine when a user is writing on\nthe iPad. We then introduce a high-dimensional particle filter to track the\nlocation and orientation of the Pencil during usage. Lastly, to guide\nparticles, we build the Pencil's magnetic map serving as a bridge between the\nmeasured magnetic data and the Pencil location and orientation. We evaluate S3\nwith 10 subjects and demonstrate that we correctly identify 93.9%, 96%, 97.9%,\nand 93.33% of the letters, numbers, shapes, and words by only having access to\nthe motion sensors' data.\n"}
+{"abstract": "  In the supervisory control framework of discrete-event systems (DES) with\ninfinite behavior initiated by Thistle and Wonham, a supervisor satisfying the\nminimal acceptable specification and the maximal legal specification is\nsynthesized. However, this supervisor may incur livelocks as it cannot ensure\nthat the infinite behavior under supervision will always visit some marker\nstates. To tackle this problem, we propose the definition of markability by\nrequiring that all infinite cycles include at least one marker state. Then we\nformulate the problem of $\\omega-$nonblocking supervisory control of DES with\ninfinite behavior to synthesize an $\\omega-$nonblocking (i.e. nonblocking,\ndeadlock-free and livelock-free) supervisor. An algorithm is proposed to\nachieve $\\omega-$nonblockingness by computing the supremal $*-$controllable,\n$*-$closed, $\\omega-$controllable and markable sublanguage. We utilize the\nexample of a robot as a running example.\n"}
+{"abstract": "  For many, Graphics Processing Units (GPUs) provides a source of reliable\ncomputing power. Recently, Nvidia introduced its 9th generation HPC-grade GPUs,\nthe Ampere 100, claiming significant performance improvements over previous\ngenerations, particularly for AI-workloads, as well as introducing new\narchitectural features such as asynchronous data movement. But how well does\nthe A100 perform on non-AI benchmarks, and can we expect the A100 to deliver\nthe application improvements we have grown used to with previous GPU\ngenerations? In this paper, we benchmark the A100 GPU and compare it to four\nprevious generations of GPUs, with particular focus on empirically quantifying\nour derived performance expectations, and -- should those expectations be\nundelivered -- investigate whether the introduced data-movement features can\noffset any eventual loss in performance? We find that the A100 delivers less\nperformance increase than previous generations for the well-known Rodinia\nbenchmark suite; we show that some of these performance anomalies can be\nremedied through clever use of the new data-movement features, which we\nmicrobenchmark and demonstrate where (and more importantly, how) they should be\nused.\n"}
+{"abstract": "  The automation of decision procedures makes certification essential. We\nsuggest to use determinacy of turn-based two-player games with regular winning\nconditions in order to generate certificates for the number of states that a\ndeterministic finite automaton (DFA) needs in order to recognize a given\nlanguage. Given a language $L$ and a bound $k$, recognizability of $L$ by a DFA\nwith $k$ states is reduced to a game between Prover and Refuter. The\ninteraction along the game then serves as a certificate. Certificates generated\nby Prover are minimal DFAs. Certificates generated by Refuter are faulty\nattempts to define the required DFA. We compare the length of offline\ncertificates, which are generated with no interaction between Prover and\nRefuter, and online certificates, which are based on such an interaction, and\nare thus shorter. We show that our approach is useful also for certification of\nseparability of regular languages by a DFA of a given size. Unlike DFA\nminimization, which can be solved in polynomial time, separation is\nNP-complete, and thus the certification approach is essential. In addition, we\nprove NP-completeness of a strict version of separation.\n"}
+{"abstract": "  The recent outbreak of the COVID-19 led to the death of millions of people\nworldwide. To stave off the spread of the virus, the authorities in the US\nemployed different strategies, including the mask mandate order issued by the\nstates' governors. In the current work, we defined a parameter called the\naverage death ratio as the monthly average of the number of daily deaths to the\nmonthly average number of daily cases. We utilized survey data to quantify\npeople's abidance by the mask mandate order. Additionally, we implicitly\naddressed the extent to which people abide by the mask mandate order that may\ndepend on some parameters like population, income, and education level. Using\ndifferent machine learning classification algorithms, we investigated how the\ndecrease or increase in death ratio for the counties in the US West Coast\ncorrelates with the input parameters. The results showed that for most counties\nthere, the mask mandate order decreased the death ratio reflecting the\neffectiveness of this preventive measure on the West Coast. Additionally, the\nchanges in the death ratio demonstrated a noticeable correlation with the\nsocio-economic condition of each county. Moreover, the results showed a\npromising classification accuracy score as high as around 90%.\n"}
+{"abstract": "  Question Answering (QA) tasks requiring information from multiple documents\noften rely on a retrieval model to identify relevant information for reasoning.\nThe retrieval model is typically trained to maximize the likelihood of the\nlabeled supporting evidence. However, when retrieving from large text corpora\nsuch as Wikipedia, the correct answer can often be obtained from multiple\nevidence candidates. Moreover, not all such candidates are labeled as positive\nduring annotation, rendering the training signal weak and noisy. This problem\nis exacerbated when the questions are unanswerable or when the answers are\nBoolean, since the model cannot rely on lexical overlap to make a connection\nbetween the answer and supporting evidence. We develop a new parameterization\nof set-valued retrieval that handles unanswerable queries, and we show that\nmarginalizing over this set during training allows a model to mitigate false\nnegatives in supporting evidence annotations. We test our method on two\nmulti-document QA datasets, IIRC and HotpotQA. On IIRC, we show that joint\nmodeling with marginalization improves model performance by 5.5 F1 points and\nachieves a new state-of-the-art performance of 50.5 F1. We also show that\nretrieval marginalization results in 4.1 QA F1 improvement over a\nnon-marginalized baseline on HotpotQA in the fullwiki setting.\n"}
+{"abstract": "  Let $m\\in\\mathbb N_{\\ge 2}$, and let $\\mathcal K=\\{K_\\lambda: \\lambda\\in(0,\n1/m]\\}$ be a class of Cantor sets, where $K_{\\lambda}=\\{\\sum_{i=1}^\\infty\nd_i\\lambda^i: d_i\\in\\{0,1,\\ldots, m-1\\}, i\\ge 1\\}$. We investigate in this\npaper the likelyhood of a fixed point in the Cantor sets of $\\mathcal K$. More\nprecisely, for a fixed point $x\\in(0,1)$ we consider the parameter set\n$\\Lambda(x)=\\{\\lambda\\in(0,1/m]: x\\in K_\\lambda\\}$, and show that $\\Lambda(x)$\nis a topological Cantor set having zero Lebesgue measure and full Hausdorff\ndimension. Furthermore, by constructing a sequence of Cantor subsets with large\nthickness in $\\Lambda(x)$ we prove that the intersection\n$\\Lambda(x)\\cap\\Lambda(y)$ also has full Hausdorff dimension for any $x,\ny\\in(0,1)$.\n"}
+{"abstract": "  Although true metal-free \"Population III\" stars have so-far escaped\ndiscovery, their nature, and that of their supernovae, is revealed in the\nchemical products left behind in the next generations of stars. Here we report\nthe detection of an ultra-metal poor star in the Sculptor dwarf spheroidal\ngalaxy, AS0039. With [Fe/H]$_{\\rm LTE}=-4.11$, it is the most metal-poor star\nso far discovered in any external galaxy. Contrary to the majority of Milky Way\nstars at this metallicity, AS0039 is clearly not enhanced in carbon, with\n[C/Fe]$_{\\rm LTE}=-0.75$ and A(C)=+3.60, making it the lowest detected carbon\nabundance in any star to date. It furthermore lacks $\\alpha$-element\nuniformity, having extremely low [Mg/Ca]$_{\\rm NLTE}=-0.60$ and [Mg/Ti]$_{\\rm\nNLTE}=-0.86$, in stark contrast with the near solar ratios observed in C-normal\nstars within the Milky Way halo. The unique abundance pattern indicates that\nAS0039 formed out of material that was predominantly enriched by a $\\sim$20$\nM_\\odot$ progenitor star with an unusually high explosion energy\n$E=10\\times10^{51}$ erg. The star AS0039 is thus one of the first observational\nevidence for zero-metallicity hypernovae and provides a unique opportunity to\ninvestigate the diverse nature of Population III stars.\n"}
+{"abstract": "  Timely and reliable sensing of infrastructure conditions is critical in\ndisaster management for planning effective infrastructure restorations. Social\nmedia, a near real-time information source, has been widely used in disasters\nfor forming timely situational awareness. Yet, using social media to sense\nelectricity infrastructure conditions has not been explored. This study aims to\naddress the research gap through mining public topics from social media. To\nachieve this purpose, we proposed a systematic and customized approach wherein\n(1) electricity-related social media data is extracted by the classifier\ndeveloped based on Bidirectional Encoder Representations from Transformers\n(BERT); and (2) public topics are modeled with unigrams, bigrams, and trigrams\nto incorporate the formulaic expressions of infrastructure conditions in social\nmedia. Electricity infrastructures in Florida impacted by Hurricane Irma are\nstudied for illustration and demonstration. Results show that the proposed\napproach is capable of sensing the temporal evolutions and geographic\ndifferences of electricity infrastructure conditions.\n"}
+{"abstract": "  We observe a system of filaments and clusters around Cl0016+1609 and\nMACSJ1621.4+3810 using the SITELLE Fourier transform spectrograph at the Canada\nFrance Hawaii Telescope. For Cl0016+1609 (z=0.546), the observations span an\n11.8 Mpc x 4.3 Mpc region along an eastern filament which covers the main\ncluster core, as well as two 4.3 Mpc x 4.3 Mpc regions which each cover\nsouthern subclumps. For MACSJ1621.4+3810 (z= 0.465), 3.9 Mpc x 3.9 Mpc around\nthe main cluster core is covered. We present the frequency and location of the\nemission line galaxies, their emission line images, and calculate the star\nformation rates, specific star formation rates and merger statistics. In\nCl0016+1609, we find thirteen [OII]~3727 Angstrom emitting galaxies with star\nformation rates between 0.2 and 14.0 M$_{\\odot}$ yr$^{-1}$. 91$^{+3}_{-10}$%\nare found in regions with moderate local galaxy density, avoiding the dense\ncluster cores. These galaxies follow the main filament of the superstructure,\nand are mostly blue and disky, with several showing close companions and\nmerging morphologies. In MACSJ1621.4+3810, we find ten emission line sources.\nAll are blue (100$^{+0}_{-15}$%), with 40$^{+16}_{-12}$% classified as disky\nand 60$^{+12}_{-16}$% as merging systems. Eight avoid the cluster core\n(80$^{+7}_{-17}$%), but two (20$^{+17}_{-7}$%) are found near high density\nregions, including the brightest cluster galaxy (BCG). These observations push\nthe spectroscopic study of galaxies in filaments beyond z~ 0.3 to z~ 0.5. Their\nefficient confirmation is paramount to their usefulness as more galaxy surveys\ncome online.\n"}
+{"abstract": "  As the convolutional neural network (CNN) gets deeper and wider in recent\nyears, the requirements for the amount of data and hardware resources have\ngradually increased. Meanwhile, CNN also reveals salient redundancy in several\ntasks. The existing magnitude-based pruning methods are efficient, but the\nperformance of the compressed network is unpredictable. While the accuracy loss\nafter pruning based on the structure sensitivity is relatively slight, the\nprocess is time-consuming and the algorithm complexity is notable. In this\narticle, we propose a novel automatic channel pruning method (ACP).\nSpecifically, we firstly perform layer-wise channel clustering via the\nsimilarity of the feature maps to perform preliminary pruning on the network.\nThen a population initialization method is introduced to transform the pruned\nstructure into a candidate population. Finally, we conduct searching and\noptimizing iteratively based on the particle swarm optimization (PSO) to find\nthe optimal compressed structure. The compact network is then retrained to\nmitigate the accuracy loss from pruning. Our method is evaluated against\nseveral state-of-the-art CNNs on three different classification datasets\nCIFAR-10/100 and ILSVRC-2012. On the ILSVRC-2012, when removing 64.36%\nparameters and 63.34% floating-point operations (FLOPs) of ResNet-50, the Top-1\nand Top-5 accuracy drop are less than 0.9%. Moreover, we demonstrate that\nwithout harming overall performance it is possible to compress SSD by more than\n50% on the target detection dataset PASCAL VOC. It further verifies that the\nproposed method can also be applied to other CNNs and application scenarios.\n"}
+{"abstract": "  Graph associahedra are generalized permutohedra arising as special cases of\nnestohedra and hypergraphic polytopes. The graph associahedron of a graph $G$\nencodes the combinatorics of search trees on $G$, defined recursively by a root\n$r$ together with search trees on each of the connected components of $G-r$. In\nparticular, the skeleton of the graph associahedron is the rotation graph of\nthose search trees. We investigate the diameter of graph associahedra as a\nfunction of some graph parameters. We give a tight bound of $\\Theta(m)$ on the\ndiameter of trivially perfect graph associahedra on $m$ edges. We consider the\nmaximum diameter of associahedra of graphs on $n$ vertices and of given\ntree-depth, treewidth, or pathwidth, and give lower and upper bounds as a\nfunction of these parameters. We also prove that the maximum diameter of\nassociahedra of graphs of pathwidth two is $\\Theta (n\\log n)$. Finally, we give\nthe exact diameter of the associahedra of complete split and of unbalanced\ncomplete bipartite graphs.\n"}
+{"abstract": "  Complex machine learning (ML) inference algorithms like recurrent neural\nnetworks (RNNs) use standard functions from math libraries like exponentiation,\nsigmoid, tanh, and reciprocal of square root. Although prior work on secure\n2-party inference provides specialized protocols for convolutional neural\nnetworks (CNNs), existing secure implementations of these math operators rely\non generic 2-party computation (2PC) protocols that suffer from high\ncommunication. We provide new specialized 2PC protocols for math functions that\ncrucially rely on lookup-tables and mixed-bitwidths to address this performance\noverhead; our protocols for math functions communicate up to 423x less data\nthan prior work. Some of the mixed bitwidth operations used by our math\nimplementations are (zero and signed) extensions, different forms of\ntruncations, multiplication of operands of mixed-bitwidths, and digit\ndecomposition (a generalization of bit decomposition to larger digits). For\neach of these primitive operations, we construct specialized 2PC protocols that\nare more communication efficient than generic 2PC, and can be of independent\ninterest. Furthermore, our math implementations are numerically precise, which\nensures that the secure implementations preserve model accuracy of cleartext.\nWe build on top of our novel protocols to build SIRNN, a library for end-to-end\nsecure 2-party DNN inference, that provides the first secure implementations of\nan RNN operating on time series sensor data, an RNN operating on speech data,\nand a state-of-the-art ML architecture that combines CNNs and RNNs for\nidentifying all heads present in images. Our evaluation shows that SIRNN\nachieves up to three orders of magnitude of performance improvement when\ncompared to inference of these models using an existing state-of-the-art 2PC\nframework.\n"}
+{"abstract": "  The cluster Westerlund~1 (Wd1) is host to a large variety of post\nmain-sequence (MS) massive stars. The simultaneous presence of these stars can\nonly be explained by stellar models if the cluster has a finely-tuned age of\n4-5Myr, with several published studies independently claiming ages within this\nrange. At this age, stellar models predict that the cool supergiants (CSGs)\nshould have luminosities of $\\log(L/L_\\odot) \\approx 5.5$, close to the\nempirical luminosity limit. Here, we test that prediction using archival data\nand new photometry from SOFIA to estimate bolometric luminosities for the CSGs.\nWe find that these stars are on average 0.4dex too faint to be 5Myr old,\nregardless of which stellar evolution model is used, and instead are indicative\nof a much older age of $10.4^{+1.3}_{-1.2}$Myr. We argue that neither\nsystematic uncertainties in the extinction law nor stellar variability can\nexplain this discrepancy. In reviewing various independent age estimates of Wd1\nin the literature, we firstly show that those based on stellar diversity are\nunreliable. Secondly, we re-analyse Wd1's pre-MS stars employing the Damineli\nextinction law, finding an age of $7.2^{+1.1}_{-2.3}$Myr; older than that of\nprevious studies, but which is vulnerable to systematic errors that could push\nthe age close to 10Myr. However, there remains significant tension between the\nCSG age and that inferred from the eclipsing binary W13. We conclude that\nstellar evolution models cannot explain Wd1 under the single age paradigm.\nInstead, we propose that the stars in the Wd1 region formed over a period of\nseveral Myr.\n"}
+{"abstract": "  Explaining Graph Neural Networks predictions to end users of AI applications\nin easily understandable terms remains an unsolved problem. In particular, we\ndo not have well developed methods for automatically evaluating explanations,\nin ways that are closer to how users consume those explanations. Based on\nrecent application trends and our own experiences in real world problems, we\npropose automatic evaluation approaches for GNN Explanations.\n"}
+{"abstract": "  Stellar streams from globular clusters (GCs) offer constraints on the nature\nof dark matter and have been used to explore the dark matter halo structure and\nsubstructure of our Galaxy. Detection of GC streams in other galaxies would\nbroaden this endeavor to a cosmological context, yet no such streams have been\ndetected to date. To enable such exploration, we develop the Hough Stream\nSpotter (HSS), and apply it to the Pan-Andromeda Archaeological Survey (PAndAS)\nphotometric data of resolved stars in M31's stellar halo. We first demonstrate\nthat our code can re-discover known dwarf streams in M31. We then use the HSS\nto blindly identify 27 linear GC stream-like structures in the PAndAS data. For\neach HSS GC stream candidate, we investigate the morphologies of the streams\nand the colors and magnitudes of all stars in the candidate streams. We find\nthat the five most significant detections show a stronger signal along the red\ngiant branch in color-magnitude diagrams (CMDs) than spurious non-stream\ndetections. Lastly, we demonstrate that the HSS will easily detect globular\ncluster streams in future Nancy Grace Roman Space Telescope data of nearby\ngalaxies. This has the potential to open up a new discovery space for GC stream\nstudies, GC stream gap searches, and for GC stream-based constraints on the\nnature of dark matter.\n"}
+{"abstract": "  We propose a method for object-aware 3D egocentric pose estimation that\ntightly integrates kinematics modeling, dynamics modeling, and scene object\ninformation. Unlike prior kinematics or dynamics-based approaches where the two\ncomponents are used disjointly, we synergize the two approaches via\ndynamics-regulated training. At each timestep, a kinematic model is used to\nprovide a target pose using video evidence and simulation state. Then, a\nprelearned dynamics model attempts to mimic the kinematic pose in a physics\nsimulator. By comparing the pose instructed by the kinematic model against the\npose generated by the dynamics model, we can use their misalignment to further\nimprove the kinematic model. By factoring in the 6DoF pose of objects (e.g.,\nchairs, boxes) in the scene, we demonstrate for the first time, the ability to\nestimate physically-plausible 3D human-object interactions using a single\nwearable camera. We evaluate our egocentric pose estimation method in both\ncontrolled laboratory settings and real-world scenarios.\n"}
+{"abstract": "  A near-infrared (NIR) enhanced silicon single-photon avalanche diode (SPAD)\nfabricated in a customized 0.13 $\\mu$m CMOS technology is presented. The SPAD\nhas a depleted absorption volume of approximately 15 $\\mu$m x 15 $\\mu$m x 18\n$\\mu$m. Electrons generated in the absorption region are efficiently\ntransported by drift to a central active avalanche region with a diameter of 2\n$\\mu$m. At the operating voltage, the active region contains a spherically\nuniform field peak, enabling the multiplication of electrons originating from\nall corners of the device. The advantages of the SPAD architecture include high\nNIR photon detection efficiency (PDE), drift-based transport, low afterpulsing,\nand compatibility with an integrated CMOS readout. A front-side illuminated\ndevice is fabricated and characterized. The SPAD has a PDE of 13% at wavelength\n905 nm, an afterpulsing probability < 0.1% for a dead time of 13 ns, and a\nmedian dark count rate (DCR) of 840 Hz at room temperature. The device shows\npromising performance for time-of-flight applications that benefit from uniform\nNIR-sensitive SPAD arrays.\n"}
+{"abstract": "  The fluidic pinball is a geometrically simple flow configuration with three\nrotating cylinders on the vertex of an equilateral triangle. Yet, it remains\nphysically rich enough to host a range of interacting frequencies and to allow\ntesting of control laws within minutes on a laptop. The system has multiple\ninputs (the three cylinders can independently rotate around their axis) and\nmultiple outputs (downstream velocity sensors). Investigating the natural flow\ndynamics, we found that the first unsteady transition undergone by the wake\nflow, when increasing the Reynolds number, is a Hopf bifurcation leading to the\nusual time-periodic vortex shedding phenomenon, typical of cylinder wake flows,\nin which the mean flow field preserves axial symmetry. We extract dynamically\nconsistent modes from the flow data in order to built a reduced-order model\n(ROM) of this flow regime. We show that the main dynamical features of the\nprimary Hopf bifurcation can be described by a non-trivial lowest-order model\nmade of three degrees of freedom.\n"}
+{"abstract": "  In this paper, we propose a novel reference based image super-resolution\napproach via Variational AutoEncoder (RefVAE). Existing state-of-the-art\nmethods mainly focus on single image super-resolution which cannot perform well\non large upsampling factors, e.g., 8$\\times$. We propose a reference based\nimage super-resolution, for which any arbitrary image can act as a reference\nfor super-resolution. Even using random map or low-resolution image itself, the\nproposed RefVAE can transfer the knowledge from the reference to the\nsuper-resolved images. Depending upon different references, the proposed method\ncan generate different versions of super-resolved images from a hidden\nsuper-resolution space. Besides using different datasets for some standard\nevaluations with PSNR and SSIM, we also took part in the NTIRE2021 SR Space\nchallenge and have provided results of the randomness evaluation of our\napproach. Compared to other state-of-the-art methods, our approach achieves\nhigher diverse scores.\n"}
+{"abstract": "  We consider zero sets of entire functions belonging to the Schwartz algebra.\nThis algebra is defined as the Fourier-Laplace transform image of the space of\nall distributions compactly supported on the real line. We study the conditions\nunder which given complex sequence forms zero set of some invertible in the\nsense of Ehrenpreis element of the Schwartz algebra (slowly decreasing\nfunction).\n"}
+{"abstract": "  Smart cities will be characterized by a variety of intelligent and networked\nservices, each with specific requirements for the underlying network\ninfrastructure. While smart city architectures and services have been studied\nextensively, little attention has been paid to the network technology. The\nKIGLIS research project, consisting of a consortium of companies, universities\nand research institutions, focuses on artificial intelligence for optimizing\nfiber-optic networks of a smart city, with a special focus on future mobility\napplications, such as automated driving. In this paper, we present early\nresults on our process of collecting smart city requirements for communication\nnetworks, which will lead towards reference infrastructure and architecture\nsolutions. Finally, we suggest directions in which artificial intelligence will\nimprove smart city networks.\n"}
+{"abstract": "  Terahertz (THz) imaging has recently attracted significant attention thanks\nto its non-invasive, non-destructive, non-ionizing, material-classification,\nand ultra-fast nature for object exploration and inspection. However, its\nstrong water absorption nature and low noise tolerance lead to undesired blurs\nand distortions of reconstructed THz images. The performances of existing\nrestoration methods are highly constrained by the diffraction-limited THz\nsignals. To address the problem, we propose a novel\nSubspace-and-Attention-guided Restoration Network (SARNet) that fuses\nmulti-spectral features of a THz image for effective restoration. To this end,\nSARNet uses multi-scale branches to extract spatio-spectral features of\namplitude and phase which are then fused via shared subspace projection and\nattention guidance. Here, we experimentally construct ultra-fast THz\ntime-domain spectroscopy system covering a broad frequency range from 0.1 THz\nto 4 THz for building up temporal/spectral/spatial/phase/material THz database\nof hidden 3D objects. Complementary to a quantitative evaluation, we\ndemonstrate the effectiveness of our SARNet model on 3D THz tomographic\nreconstruction\n"}
+{"abstract": "  The solar energy is clean and future energy for electricity generation. Its\nenergy has enormous potential but do not optimal to utilized caused by\nintermittent energy. The intermittent radiance and ambient temperature\ninfluence the energy produced fluctuates and unstable. These power fluctuations\naffect system stability and frequency. To overcome this problem, several\nmethods for PV power stabilization have been developed. One of them is the\nStabilized Power Generation where PV output power is to a certain power value.\nThe result of SPG method is reducing in PV power fluctuations but still\nunstable. For reaching the stable condition of PV Power output, the Dynamic\nPower Insertion method is proposed which is a modification of the SPG method\nwith energy storage batteries. DPI improve the SPG method and make PV power\nstable with active power management with charge and discharge action. The\nbattery is using as energy storage when PV power is larger than Power limit. On\nthe other hand battery as an energy source for active power insertion at PV\npower is smaller than Power limit. Thus the output power in each condition can\nbe maintained as Power PV equals to P limit. For test this method, DPI modules\nare built on ARM lpc1768 NXP and monitored with the Thing speak webserver\n(simulated with Simulink MatLab Software). The experimental results of DPI can\nstabilize PV power fluctuation at its setting power with an error of 5 percent.\n"}
+{"abstract": "  Image relighting is attracting increasing interest due to its various\napplications. From a research perspective, image relighting can be exploited to\nconduct both image normalization for domain adaptation, and also for data\naugmentation. It also has multiple direct uses for photo montage and aesthetic\nenhancement. In this paper, we review the NTIRE 2021 depth guided image\nrelighting challenge.\n  We rely on the VIDIT dataset for each of our two challenge tracks, including\ndepth information. The first track is on one-to-one relighting where the goal\nis to transform the illumination setup of an input image (color temperature and\nlight source position) to the target illumination setup. In the second track,\nthe any-to-any relighting challenge, the objective is to transform the\nillumination settings of the input image to match those of another guide image,\nsimilar to style transfer. In both tracks, participants were given depth\ninformation about the captured scenes. We had nearly 250 registered\nparticipants, leading to 18 confirmed team submissions in the final competition\nstage. The competitions, methods, and final results are presented in this\npaper.\n"}
+{"abstract": "  The synthesis problem asks to automatically generate, if it exists, an\nalgorithm from a specification of correct input-output pairs. In this paper, we\nconsider the synthesis of computable functions of infinite words, for a\nclassical Turing computability notion over infinite inputs. We consider\nspecifications which are rational relations of infinite words, i.e.,\nspecifications defined non-deterministic parity transducers. We prove that the\nsynthesis problem of computable functions from rational specifications is\nundecidable. We provide an incomplete but sound reduction to some parity game,\nsuch that if Eve wins the game, then the rational specification is realizable\nby a computable function. We prove that this function is even computable by a\ndeterministic two-way transducer. We provide a sufficient condition under which\nthe latter game reduction is complete. This entails the decidability of the\nsynthesis problem of computable functions, which we prove to be\nExpTime-complete, for a large subclass of rational specifications, namely\ndeterministic rational specifications. This subclass contains the class of\nautomatic relations over infinite words, a yardstick in reactive synthesis.\n"}
+{"abstract": "  Great progress has been made in unsupervised bilingual lexicon induction\n(UBLI) by aligning the source and target word embeddings independently trained\non monolingual corpora. The common assumption of most UBLI models is that the\nembedding spaces of two languages are approximately isomorphic. Therefore the\nperformance is bound by the degree of isomorphism, especially on etymologically\nand typologically distant languages. To address this problem, we propose a\ntransformation-based method to increase the isomorphism. Embeddings of two\nlanguages are made to match with each other by rotating and scaling. The method\ndoes not require any form of supervision and can be applied to any language\npair. On a benchmark data set of bilingual lexicon induction, our approach can\nachieve competitive or superior performance compared to state-of-the-art\nmethods, with particularly strong results being found on distant languages.\n"}
+{"abstract": "  We present the results of photometric and spectroscopic monitoring campaigns\nof the changing look AGN NGC 3516 carried out in 2018 to 2020 covering the\nwavelength range from the X-ray to the optical. The facilities included the\ntelescopes of the CMO SAI MSU, the 2.3-m WIRO telescope, and the XRT and UVOT\nof Swift. We found that NGC 3516 brightened to a high state and could be\nclassified as Sy1.5 during the late spring of 2020. We have measured time\ndelays in the responses of the Balmer and He II 4686 lines to continuum\nvariations. In the case of the best-characterized broad H-beta line, the delay\nto continuum variability is about 17 days in the blue wing and is clearly\nshorter, 9 days, in the red, which is suggestive of inflow. As the broad lines\nstrengthened, the blue side came to dominate the Balmer lines, resulting in\nvery asymmetric profiles with blueshifted peaks during this high state. During\nthe outburst the X-ray flux reached its maximum on 1 April 2020 and it was the\nhighest value ever observed for NGC 3516 by the Swift observatory. The X-ray\nhard photon index became softer, about 1.8 in the maximum on 21 Apr 2020\ncompared to the mean about 0.7 during earlier epochs before 2020. We have found\nthat the UV and optical variations correlated well (with a small time delay of\n1-2 days) with the X-ray until the beginning of April 2020, but later, until\nthe end of Jun. 2020, these variations were not correlated. We suggest that\nthis fact may be a consequence of partial obscuration by Compton-thick clouds\ncrossing the line of sight.\n"}
+{"abstract": "  Image augmentation techniques apply transformation functions such as\nrotation, shearing, or color distortion on an input image. These augmentations\nwere proven useful in improving neural networks' generalization ability. In\nthis paper, we present a novel augmentation operation, InAugment, that exploits\nimage internal statistics. The key idea is to copy patches from the image\nitself, apply augmentation operations on them, and paste them back at random\npositions on the same image. This method is simple and easy to implement and\ncan be incorporated with existing augmentation techniques. We test InAugment on\ntwo popular datasets -- CIFAR and ImageNet. We show improvement over\nstate-of-the-art augmentation techniques. Incorporating InAugment with Auto\nAugment yields a significant improvement over other augmentation techniques\n(e.g., +1% improvement over multiple architectures trained on the CIFAR\ndataset). We also demonstrate an increase for ResNet50 and EfficientNet-B3\ntop-1's accuracy on the ImageNet dataset compared to prior augmentation\nmethods. Finally, our experiments suggest that training convolutional neural\nnetwork using InAugment not only improves the model's accuracy and confidence\nbut its performance on out-of-distribution images.\n"}
+{"abstract": "  This study presents experimental and computational analyses of the phase\ncomposition and mechanical behavior of a functionally graded material (FGM)\ngrading from 100 vol% 304L stainless steel (SS304L) to 100 vol% Ni-20Cr (NiCr)\nin 10 vol% increments. The mechanical behavior of the FGM was evaluated by\nextracting tensile specimens from SS304L-rich and NiCr-rich regions of the FGM,\nwith the former behaving in a ductile manner and the latter being relatively\nbrittle. The brittle response of the NiCr-rich sample was explained by the\npresence of the eutectic FCC+BCC phase in this sample, as identified by\nScheil-Gulliver simulations along with experimental phase and composition\nanalyses. The spatially varying yield and flow behavior of the FGM was\ndetermined through comparison of finite element analysis simulations with\nexperimentally measured stress-strain curves and surface strain contours. The\nresults highlight that the overall ductile behavior of SS304L and brittle\nbehavior of NiCr are preserved in the SS304L/NiCr FGM but the formation of the\nbrittle eutectic phase further reduced the ductility within the regions where\nit formed (90 vol% NiCr/10 vol% SS304L).\n"}
+{"abstract": "  The generalized fractional Brownian motion (GFBM) $X:=\\{X(t)\\}_{t\\ge0}$ with\nparameters $\\gamma \\in [0, 1)$ and $\\alpha\\in \\left(-\\frac12+\\frac{\\gamma}{2},\n\\, \\frac12+\\frac{\\gamma}{2} \\right)$ is a centered Gaussian $H$-self-similar\nprocess introduced by Pang and Taqqu (2019) as the scaling limit of power-law\nshot noise processes, where $H = \\alpha-\\frac{\\gamma}{2}+\\frac12 \\in(0,1)$.\nWhen $\\gamma = 0$, $X$ is the ordinary fractional Brownian motion. For $\\gamma\n\\in (0, 1)$, GFBM $X$ does not have stationary increments, and its sample path\nproperties such as H\\\"older continuity, path\ndifferentiability/non-differentiability, and the functional law of the iterated\nlogarithm have been investigated recently by Ichiba, Pang and Taqqu (2020).\nThey mainly focused on sample path properties that are described in terms of\nthe self-similarity index $H$ (e.g., LILs at the origin and at infinity).\n  In this paper, we further study the sample path properties of GFBM $X$ and\nestablish the exact uniform modulus of continuity, small ball probabilities,\nand Chung's law of iterated logarithm. Our results show that the local\nregularity properties of GFBM $X$ away from the origin are determined by the\nindex $\\alpha +\\frac1 2$, instead of the self-similarity index $H$. This is in\ncontrast with the properties of ordinary fractional Brownian motion whose local\nand asymptotic properties are determined by the single index $H$.\n"}
+{"abstract": "  This paper solves the rational noncommutative analog of Hilbert's 17th\nproblem: if a noncommutative rational function is positive semidefinite on all\ntuples of hermitian matrices in its domain, then it is a sum of hermitian\nsquares of noncommutative rational functions. This result is a generalization\nand culmination of earlier positivity certificates for noncommutative\npolynomials or rational functions without hermitian singularities. More\ngenerally, a rational Positivstellensatz for free spectrahedra is given: a\nnoncommutative rational function is positive semidefinite or undefined at every\nmatricial solution of a linear matrix inequality $L\\succeq0$ if and only if it\nbelongs to the rational quadratic module generated by $L$. The essential\nintermediate step towards this Positivstellensatz for functions with\nsingularities is an extension theorem for invertible evaluations of linear\nmatrix pencils.\n"}
+{"abstract": "  The DerSimonian-Laird (DL) weighted average method has been widely used for\nestimation of a pooled effect size from an aggregated data meta-analysis study.\nIt is mainly criticized for its underestimation of the standard error of the\npooled effect size in the presence of heterogeneous study effect sizes. The\nuncertainty in the estimation of the between-study variance is not accounted\nfor in the calculation of this standard error. Due to this negative property,\nmany alternative estimation approaches have been proposed in literature. One\napproach was developed by Hardy and Thompson (HT), who implemented a profile\nlikelihood approach instead of the moment-based approach of DL. Others have\nfurther extended the likelihood approach and proposed higher-order likelihood\ninferences (e.g., Bartlett-type corrections). Likelihood-based methods better\naddress the uncertainty in estimating the between-study variance than the DL\nmethod, but all these methods assume that the within-study standard deviations\nare known and equal to the observed standard error of the study effect sizes.\nHere we treat the observed standard errors as estimators for the within-study\nvariability and we propose a bivariate likelihood approach that jointly\nestimates the pooled effect size, the between-study variance, and the\npotentially heteroscedastic within-study variances. We study the performance of\nthe proposed method by means of simulation, and compare it to DL, HT, and the\nhigher-order likelihood methods. Our proposed approach appears to be less\nsensitive to the number of studies, and less biased in case of\nheteroscedasticity.\n"}
+{"abstract": "  For an algebraic number $\\alpha$ we consider the orders of the reductions of\n$\\alpha$ in finite fields. In the case where $\\alpha$ is an integer, it is\nknown by the work on Artin's primitive root conjecture that the order is\n\"almost always almost maximal\" assuming the generalized Riemann hypothesis, but\nunconditional results remain modest. We consider higher degree variants under\nGRH. First, we modify an argument of Roskam to settle the case where $\\alpha$\nand the reduction have degree two. Second, we give a positive lower density\nresult when $\\alpha$ is of degree three and the reduction is of degree two.\nThird, we give higher rank results in situations where the reductions are of\ndegree two, three, four or six. As an application we give an almost\nequidistribution result for linear recurrences modulo primes. Finally, we\npresent a general result conditional to GRH and a hypothesis on smooth values\nof polynomials at prime arguments.\n"}
+{"abstract": "  This is an article or technical note which is intended to provides an insight\njourney of Machine Learning Systems (MLS) testing, its evolution, current\nparadigm and future work. Machine Learning Models, used in critical\napplications such as healthcare industry, Automobile, and Air Traffic control,\nShare Trading etc., and failure of ML Model can lead to severe consequences in\nterms of loss of life or property. To remediate this, developers, scientists,\nand ML community around the world, must build a highly reliable test\narchitecture for critical ML application. At the very foundation layer, any\ntest model must satisfy the core testing attributes such as test properties and\nits components. This attribute comes from the software engineering, but the\nsame cannot be applied in as-is form to the ML testing and we will tell you\nwhy.\n"}
+{"abstract": "  Facial expressions play a fundamental role in human communication. Indeed,\nthey typically reveal the real emotional status of people beyond the spoken\nlanguage. Moreover, the comprehension of human affect based on visual patterns\nis a key ingredient for any human-machine interaction system and, for such\nreasons, the task of Facial Expression Recognition (FER) draws both scientific\nand industrial interest. In the recent years, Deep Learning techniques reached\nvery high performance on FER by exploiting different architectures and learning\nparadigms. In such a context, we propose a multi-resolution approach to solve\nthe FER task. We ground our intuition on the observation that often faces\nimages are acquired at different resolutions. Thus, directly considering such\nproperty while training a model can help achieve higher performance on\nrecognizing facial expressions. To our aim, we use a ResNet-like architecture,\nequipped with Squeeze-and-Excitation blocks, trained on the Affect-in-the-Wild\n2 dataset. Not being available a test set, we conduct tests and models\nselection by employing the validation set only on which we achieve more than\n90\\% accuracy on classifying the seven expressions that the dataset comprises.\n"}
+{"abstract": "  This paper studies average-cost Markov decision processes with semi-uniform\nFeller transition probabilities. This class of MDPs was recently introduced by\nthe authors to study MDPs with incomplete information. This paper studies the\nvalidity of optimality inequalities, the existence of optimal policies, and the\napproximations of optimal policies by policies optimizing total discounted\ncosts.\n"}
+{"abstract": "  Voluntary shelter-in-place directives and lockdowns are the main\nnon-pharmaceutical interventions that governments around the globe have used to\ncontain the Covid-19 pandemic. In this paper we study the impact of such\ninterventions in the capital of a developing country, Santiago, Chile, that\nexhibits large socioeconomic inequality. A distinctive feature of our study is\nthat we use granular geolocated mobile phone data to construct mobility\nmeasures that capture (1) shelter-in-place behavior, and (2) trips within the\ncity to destinations with potentially different risk profiles. Using panel data\nlinear regression models we first show that the impact of social distancing\nmeasures and lockdowns on mobility is highly heterogeneous and dependent on\nsocioeconomic levels. More specifically, our estimates indicate that while\nzones of high socioeconomic levels can exhibit reductions in mobility of around\n50\\% to 90\\% depending on the specific mobility metric used, these reductions\nare only 20\\% to 50\\% for lower-income communities. The large reductions in\nhigher-income communities are significantly driven by voluntary\nshelter-in-place behavior. Second, also using panel data methods we show that\nour mobility measures are important predictors of infections: roughly, a 10\\%\nincrease in mobility correlates with a 5\\% increase in the rate of infection.\nOur results suggest that mobility is an important factor explaining differences\nin infections rates between high and low incomes areas within the city.\nFurther, they confirm the challenges of reducing mobility in lower-income\ncommunities, where people generate their income from their daily work. To be\neffective, shelter-in-place restrictions in municipalities of low socioeconomic\nlevels may need to be complemented by other supporting measures that enable\ntheir inhabitants to increase compliance.\n"}
+{"abstract": "  Robotic assembly planning has the potential to profoundly change how\nbuildings can be designed and created. It enables architects to explicitly\naccount for the assembly process already during the design phase, and enables\nefficient building methods that profit from the robots' different capabilities.\nPrevious work has addressed planning of robot assembly sequences and\nidentifying the feasibility of architectural designs. This paper extends\nprevious work by enabling assembly planning with large, heterogeneous teams of\nrobots. We present a scalable planning system which enables parallelization of\ncomplex task and motion planning problems by iteratively solving smaller\nsub-problems. Combining optimization methods to solve for manipulation\nconstraints with a sampling-based bi-directional space-time path planner\nenables us to plan cooperative multi-robot manipulation with unknown\narrival-times. Thus, our solver allows for completing sub-problems and tasks\nwith differing timescales and synchronizes them effectively. We demonstrate the\napproach on multiple case-studies and on two long-horizon building assembly\nscenarios to show the robustness and scalability of our algorithm.\n"}
+{"abstract": "  To cater the rapidly growing demand for electricity leading to the\nintegration of renewable energy sources in power system. Due to intermittent\nnature of renewables, it also brings challenges for research community during\nthe planning and operation stage in power system. Therefore it is primary\nnecessity of the community to develop an accurate forecasting technique to\nsolve the intermittency problem. In this report, A forecasting technique is\nproposed based on ensemble of state of the art forecasting techniques. For\nperformance comparison among the techniques, GEFCom2014 meteorological data are\nused to predict the photovoltaic power, and the obtained results are included\nin this report.\n"}
+{"abstract": "  Compliant mechanisms are alternatives to conventional mechanisms which\nexploit elastic strain to produce desired deformations instead of using\nmoveable parts. They are designed for a kinematic task (providing desired\ndeformations) but do not possess a kinematics in the strict sense. This leads\nto difficulties while assessing the quality of a compliant mechanism's design.\nThe kinematics of a compliant mechanism can be seen as a fuzzy property. There\nis no unique kinematics, since every deformation need a particular force system\nto act; however, certain deformations are easier to obtain than others. A\nparallel can be made with measurement theory: the measured value of a quantity\nis not unique, but exists as statistic distribution of measures. A\nrepresentative measure of this distribution can be chosen to evaluate how far\nthe measures divert from a reference value. Based on this analogy, the concept\nof accuracy and precision of compliant systems are introduced and discussed in\nthis paper. A quantitative determination of these qualities based on the\neigenvalue analysis of the hinge's stiffness is proposed. This new approach is\ncapable of removing most of the ambiguities included in the state-of-the-art\nassessment criteria (usually based on the concepts of path deviation and\nparasitic motion).\n"}
+{"abstract": "  The paper describes the usage of intelligent approaches for field development\ntasks that may assist a decision-making process. We focused on the problem of\nwells location optimization and two tasks within it: improving the quality of\noil production estimation and estimation of reservoir characteristics for\nappropriate wells allocation and parametrization, using machine learning\nmethods. For oil production estimation, we implemented and investigated the\nquality of forecasting models: physics-based, pure data-driven, and hybrid one.\nThe CRMIP model was chosen as a physics-based approach. We compare it with the\nmachine learning and hybrid methods in a frame of oil production forecasting\ntask. In the investigation of reservoir characteristics for wells location\nchoice, we automated the seismic analysis using evolutionary identification of\nconvolutional neural network for the reservoir detection. The Volve oil field\ndataset was used as a case study to conduct the experiments. The implemented\napproaches can be used to analyze different oil fields or adapted to similar\nphysics-related problems.\n"}
+{"abstract": "  Binomial coefficients have been used for centuries in a variety of fields and\nhave accumulated numerous definitions. In this paper, we introduce a new way of\ndefining binomial coefficients as repeated sums of ones. A multitude of\nbinomial coefficient identities will be shown in order to prove this\ndefinition. Using this new definition, we simplify some particular sums such as\nthe repeated Harmonic sum and the repeated Binomial-Harmonic sum. We derive\nformulae for simplifying general repeated sums as well as a variant containing\nbinomial coefficients. Additionally, we study the $m$-th difference of a\nsequence and show how sequences whose $m$-th difference is constant can be\nrelated to binomial coefficients.\n"}
+{"abstract": "  A crucial subroutine in quantum computing is to load the classical data of\n$N$ complex numbers into the amplitude of a superposed $n=\\lceil\n\\log_2N\\rceil$-qubit state. It has been proven that any algorithm universally\nimplementing this subroutine would need at least $\\mathcal O(N)$ constant\nweight operations. However, the proof assumes that only $n$ qubits are used,\nwhereas the circuit depth could be reduced by extending the space and allowing\nancillary qubits. Here we investigate this space-time tradeoff in quantum state\npreparation with classical data. We propose quantum algorithms with $\\mathcal\nO(n^2)$ circuit depth to encode any $N$ complex numbers using only single-,\ntwo-qubit gates and local measurements with ancillary qubits. Different\nvariances of the algorithm are proposed with different space and runtime. In\nparticular, we present a scheme with $\\mathcal O(N^2)$ ancillary qubits,\n$\\mathcal O(n^2)$ circuit depth, and $\\mathcal O(n^2)$ average runtime, which\nexponentially improves the conventional bound. While the algorithm requires\nmore ancillary qubits, it consists of quantum circuit blocks that only\nsimultaneously act on a constant number of qubits and at most $\\mathcal O(n)$\nqubits are entangled. We also prove a fundamental lower bound $\\mit\\Omega(n)$\nfor the minimum circuit depth and runtime with arbitrary number of ancillary\nqubits, aligning with our scheme with $\\mathcal O(n^2)$. The algorithms are\nexpected to have wide applications in both near-term and universal quantum\ncomputing.\n"}
+{"abstract": "  In this note, we show that one can use average embeddings, introduced\nrecently in [Naor'20, arXiv:1905.01280], to obtain efficient algorithms for\napproximate nearest neighbor search. In particular, a metric $X$ embeds into\n$\\ell_2$ on average, with distortion $D$, if, for any distribution $\\mu$ on\n$X$, the embedding is $D$ Lipschitz and the (square of) distance does not\ndecrease on average (wrt $\\mu$). In particular existence of such an embedding\n(assuming it is efficient) implies a $O(D^3)$ approximate nearest neighbor\nsearch under $X$. This can be seen as a strengthening of the classic\n(bi-Lipschitz) embedding approach to nearest neighbor search, and is another\napplication of data-dependent hashing paradigm.\n"}
+{"abstract": "  In this study, we propose a novel approach to predict the distances of the\ndetected objects in an observed scene. The proposed approach modifies the\nrecently proposed Convolutional Support Estimator Networks (CSENs). CSENs are\ndesigned to compute a direct mapping for the Support Estimation (SE) task in a\nrepresentation-based classification problem. We further propose and demonstrate\nthat representation-based methods (sparse or collaborative representation) can\nbe used in well-designed regression problems. To the best of our knowledge,\nthis is the first representation-based method proposed for performing a\nregression task by utilizing the modified CSENs; and hence, we name this novel\napproach as Representation-based Regression (RbR). The initial version of CSENs\nhas a proxy mapping stage (i.e., a coarse estimation for the support set) that\nis required for the input. In this study, we improve the CSEN model by\nproposing Compressive Learning CSEN (CL-CSEN) that has the ability to jointly\noptimize the so-called proxy mapping stage along with convolutional layers. The\nexperimental evaluations using the KITTI 3D Object Detection distance\nestimation dataset show that the proposed method can achieve a significantly\nimproved distance estimation performance over all competing methods. Finally,\nthe software implementations of the methods are publicly shared at\nhttps://github.com/meteahishali/CSENDistance.\n"}
+{"abstract": "  3D convolution is powerful for video classification but often computationally\nexpensive, recent studies mainly focus on decomposing it on spatial-temporal\nand/or channel dimensions. Unfortunately, most approaches fail to achieve a\npreferable balance between convolutional efficiency and feature-interaction\nsufficiency. For this reason, we propose a concise and novel Channel\nTensorization Network (CT-Net), by treating the channel dimension of input\nfeature as a multiplication of K sub-dimensions. On one hand, it naturally\nfactorizes convolution in a multiple dimension way, leading to a light\ncomputation burden. On the other hand, it can effectively enhance feature\ninteraction from different channels, and progressively enlarge the 3D receptive\nfield of such interaction to boost classification accuracy. Furthermore, we\nequip our CT-Module with a Tensor Excitation (TE) mechanism. It can learn to\nexploit spatial, temporal and channel attention in a high-dimensional manner,\nto improve the cooperative power of all the feature dimensions in our\nCT-Module. Finally, we flexibly adapt ResNet as our CT-Net. Extensive\nexperiments are conducted on several challenging video benchmarks, e.g.,\nKinetics-400, Something-Something V1 and V2. Our CT-Net outperforms a number of\nrecent SOTA approaches, in terms of accuracy and/or efficiency. The codes and\nmodels will be available on https://github.com/Andy1621/CT-Net.\n"}
+{"abstract": "  In today's business marketplace, many high-tech Internet enterprises\nconstantly explore innovative ways to provide optimal online user experiences\nfor gaining competitive advantages. The great needs of developing intelligent\ninteractive recommendation systems are indicated, which could sequentially\nsuggest users the most proper items by accurately predicting their preferences,\nwhile receiving the up-to-date feedback to refine the recommendation results,\ncontinuously. Multi-armed bandit algorithms, which have been widely applied\ninto various online systems, are quite capable of delivering such efficient\nrecommendation services. However, few existing bandit models are able to adapt\nto new changes introduced by the modern recommender systems.\n"}
+{"abstract": "  During the pandemic of COVID-19, the demand of the transportation systems are\ndrastically changed both qualitatively and quantitatively and the network has\nbecome obsolete. In this article, we study the problem of finding an optimal\nsubnetwork that guarantee that (i) the minimal access time from any node of the\nurban network to the new network is not {\\em too large} compared to the\noriginal transportation network; (ii) for any itinerary, the delay caused by\nthe deletion of nodes of the transportation network is not {\\em too big}; and\n(iii) the number of nodes of the transportation network has been reduced at\nleast by a known factor. A solution is optimal if it induces a minimal global\ndelay. We model this problem as a Mixed Integer Linear Program before applying\nthe model on a real-case application on the Lyon's buses transportation\nnetwork.\n"}
+{"abstract": "  In recent years, ransomware has been one of the most notorious malware\ntargeting end users, governments, and business organizations. It has become a\nvery profitable business for cybercriminals with revenues of millions of\ndollars, and a very serious threat to organizations with financial loss of\nbillions of dollars. Numerous studies were proposed to address the ransomware\nthreat, including surveys that cover certain aspects of ransomware research.\nHowever, no study exists in the literature that gives the complete picture on\nransomware and ransomware defense research with respect to the diversity of\ntargeted platforms. Since ransomware is already prevalent in\nPCs/workstations/desktops/laptops, is becoming more prevalent in mobile\ndevices, and has already hit IoT/CPS recently, and will likely grow further in\nthe IoT/CPS domain very soon, understanding ransomware and analyzing defense\nmechanisms with respect to target platforms is becoming more imperative. In\norder to fill this gap and motivate further research, in this paper, we present\na comprehensive survey on ransomware and ransomware defense research with\nrespect to PCs/workstations, mobile devices, and IoT/CPS platforms.\nSpecifically, covering 137 studies over the period of 1990-2020, we give a\ndetailed overview of ransomware evolution, comprehensively analyze the key\nbuilding blocks of ransomware, present a taxonomy of notable ransomware\nfamilies, and provide an extensive overview of ransomware defense research\n(i.e., analysis, detection, and recovery) with respect to platforms of\nPCs/workstations, mobile devices, and IoT/CPS. Moreover, we derive an extensive\nlist of open issues for future ransomware research. We believe this survey will\nmotivate further research by giving a complete picture on state-of-the-art\nransomware research.\n"}
+{"abstract": "  Deeply learned representations are the state-of-the-art descriptors for face\nrecognition methods. These representations encode latent features that are\ndifficult to explain, compromising the confidence and interpretability of their\npredictions. Most attempts to explain deep features are visualization\ntechniques that are often open to interpretation. Instead of relying only on\nvisualizations, we use the outputs of hidden layers to predict face attributes.\nThe obtained performance is an indicator of how well the attribute is\nimplicitly learned in that layer of the network. Using a variable selection\ntechnique, we also analyze how these semantic concepts are distributed inside\neach layer, establishing the precise location of relevant neurons for each\nattribute. According to our experiments, gender, eyeglasses and hat usage can\nbe predicted with over 96% accuracy even when only a single neural output is\nused to predict each attribute. These performances are less than 3 percentage\npoints lower than the ones achieved by deep supervised face attribute networks.\nIn summary, our experiments show that, inside DCNNs optimized for face\nidentification, there exists latent neurons encoding face attributes almost as\naccurately as DCNNs optimized for these attributes.\n"}
+{"abstract": "  The growing popularity of wearable sensors has generated large quantities of\ntemporal physiological and activity data. Ability to analyze this data offers\nnew opportunities for real-time health monitoring and forecasting. However,\ntemporal physiological data presents many analytic challenges: the data is\nnoisy, contains many missing values, and each series has a different length.\nMost methods proposed for time series analysis and classification do not handle\ndatasets with these characteristics nor do they offer interpretability and\nexplainability, a critical requirement in the health domain. We propose an\nunsupervised method for learning representations of time series based on common\npatterns identified within them. The patterns are, interpretable, variable in\nlength, and extracted using Byte Pair Encoding compression technique. In this\nway the method can capture both long-term and short-term dependencies present\nin the data. We show that this method applies to both univariate and\nmultivariate time series and beats state-of-the-art approaches on a real world\ndataset collected from wearable sensors.\n"}
+{"abstract": "  We consider the problem of computing the topology and describing the geometry\nof a parametric curve in $\\mathbb{R}^n$. We present an algorithm, PTOPO, that\nconstructs an abstract graph that is isotopic to the curve in the embedding\nspace. Our method exploits the benefits of the parametric representation and\ndoes not resort to implicitization.\n  Most importantly, we perform all computations in the parameter space and not\nin the implicit space. When the parametrization involves polynomials of degree\nat most $d$ and maximum bitsize of coefficients $\\tau$, then the worst case bit\ncomplexity of PTOPO is $\n\\widetilde{\\mathcal{O}}_B(nd^6+nd^5\\tau+d^4(n^2+n\\tau)+d^3(n^2\\tau+\nn^3)+n^3d^2\\tau)$. This bound matches the current record bound\n$\\widetilde{\\mathcal{O}}_B(d^6+d^5\\tau)$ for the problem of computing the\ntopology of a plane algebraic curve given in implicit form. For plane and space\ncurves, if $N = \\max\\{d, \\tau \\}$, the complexity of PTOPO becomes\n$\\widetilde{\\mathcal{O}}_B(N^6)$, which improves the state-of-the-art result,\ndue to Alc\\'azar and D\\'iaz-Toca [CAGD'10], by a factor of $N^{10}$. In the\nsame time complexity, we obtain a graph whose straight-line embedding is\nisotopic to the curve. However, visualizing the curve on top of the abstract\ngraph construction, increases the bound to $\\widetilde{\\mathcal{O}}_B(N^7)$.\nFor curves of general dimension, we can also distinguish between ordinary and\nnon-ordinary real singularities and determine their multiplicities in the same\nexpected complexity of PTOPO by employing the algorithm of Blasco and\nP\\'erez-D\\'iaz [CAGD'19]. We have implemented PTOPO in Maple for the case of\nplane and space curves. Our experiments illustrate its practical nature.\n"}
+{"abstract": "  Let $f$ and $g$ be two circle endomorphisms of degree $d\\geq 2$ such that\neach has bounded geometry, preserves the Lebesgue measure, and fixes $1$. Let\n$h$ fixing $1$ be the topological conjugacy from $f$ to $g$. That is, $h\\circ\nf=g\\circ h$. We prove that $h$ is a symmetric circle homeomorphism if and only\nif $h=Id$. Many other rigidity results in circle dynamics follow from this very\ngeneral symmetric rigidity result.\n"}
+{"abstract": "  To describe the double-charge-exchange (DCE) processes, we have designed\nrecently the $(pn,2p2n)$-QTDA model which fully includes the pairing\ncorrelations and four quasiparticle excitations. It has been applied in $2\\nu$\ndouble beta decays (DBDs), and the double charge-exchange resonances (DCERs).\nHere we extend it to $ 0\\nu $ DBD and discuss the relationship between the\nnuclear matrix elements (NMEs), and the DCE reaction matrix elements (RMEs)\nwith the same spin-isospin structure. We do it for all final $0^+$ states, even\nin the region of DCERs, where the DBD is energetically forbidden.\n  As an example, we evaluate the DBD $^{76}$Ge $\\rightarrow ^{76}$Se, both for\n$2\\nu$ and $0\\nu$ modes, as well as the associated DCE sum rules, excitation\nenergies within the $Q$-value window for DBD, and the $Q$-value itself. We find\nthat the $0\\nu$ NMEs are correlated with the RMEs, both at low energy, and in\nthe DCER region where most of the transition strength is concentrated. These\nfindings occur in other nuclei as well and suggest that measurements of $0^+$\nDCERs could provide useful information regarding the $ 0\\nu $ DBD. An analogous\ncomparison and conclusion cannot be made for the $2^+$ states, since the $0\\nu$\nNMEs and RMEs transition operators are not similar to each other in this case.\n"}
+{"abstract": "  Let $S_{g,n}$ be an oriented surface of genus $g$ with $n$ punctures, where\n$2g-2+n>0$ and $n>0$. Any ideal triangulation of $S_{g,n}$ induces a global\nparametrization of the Teichm\\\"uller space $\\mathcal{T}_{g,n}$ called the\nshearing coordinates. We study the asymptotics of the number of the mapping\nclass group orbits with respect to the standard Euclidean norm of the shearing\ncoordinates. The result is based on the works of Mirzakhani.\n"}
+{"abstract": "  We obtain high energy asymptotics of Titchmarsh-Weyl functions of the\ngeneralised canonical systems generalising in this way a seminal Gesztesy-Simon\nresult. The matrix valued analog of the amplitude function satisfies in this\ncase an interesting new identity. The corresponding structured operators are\nstudied as well.\n"}
+{"abstract": "  Traditional video summarization methods generate fixed video representations\nregardless of user interest. Therefore such methods limit users' expectations\nin content search and exploration scenarios. Multi-modal video summarization is\none of the methods utilized to address this problem. When multi-modal video\nsummarization is used to help video exploration, a text-based query is\nconsidered as one of the main drivers of video summary generation, as it is\nuser-defined. Thus, encoding the text-based query and the video effectively are\nboth important for the task of multi-modal video summarization. In this work, a\nnew method is proposed that uses a specialized attention network and\ncontextualized word representations to tackle this task. The proposed model\nconsists of a contextualized video summary controller, multi-modal attention\nmechanisms, an interactive attention network, and a video summary generator.\nBased on the evaluation of the existing multi-modal video summarization\nbenchmark, experimental results show that the proposed model is effective with\nthe increase of +5.88% in accuracy and +4.06% increase of F1-score, compared\nwith the state-of-the-art method.\n"}
+{"abstract": "  The nucleation of crystals from the liquid melt is often characterized by a\ncompetition between different crystalline structures or polymorphs, and can\nresult in nuclei with heterogeneous compositions. These mixed-phase nuclei can\ndisplay nontrivial spatial arrangements, such as layered and onion-like\nstructures, whose composition varies according to the radial distance, and\nwhich so far have been explained on the basis of bulk and surface free-energy\ndifferences between the competing phases. Here we extend the generality of\nthese non-classical nucleation processes, showing that layered and onion-like\nstructures can emerge solely based on structural fluctuations even in absence\nof free-energy differences. We consider two examples of competing crystalline\nstructures, hcp and fcc forming in hard spheres, relevant for repulsive\ncolloids and dense liquids, and the cubic and hexagonal diamond forming in\nwater, relevant also for other group 14 elements such as carbon and silicon. We\nintroduce a novel structural order parameter that combined with a neural\nnetwork classification scheme allows us to study the properties of the growing\nnucleus from the early stages of nucleation. We find that small nuclei have\ndistinct size fluctuations and compositions from the nuclei that emerge from\nthe growth stage. The transition between these two regimes is characterized by\nthe formation of onion-like structures, in which the composition changes with\nthe distance from the center of the nucleus, similarly to what seen in two-step\nnucleation process.\n"}
+{"abstract": "  Natural and resonant oscillations of suspended circular graphene and\nhexagonal boron nitride (h-BN) membranes (single-layer sheets lying on a flat\nsubstrate having a circular hole of radius $R$) have been simulated using\nfull-atomic models. Substrates formed by flat surfaces of graphite and h-BN\ncrystal, hexagonal ice, silicon carbide 6H-SiC and nickel surface (111) have\nbeen used. The presence of the substrate leads to the forming of a gap at the\nbottom of the frequency spectrum of transversal vibrations of the sheet. The\nfrequencies of natural oscillations of the membrane (oscillations localized on\nthe suspended section of the sheet) always lie in this gap, and the frequencies\nof oscillations decrease by increasing radius of the membrane as $(R+R_i)^{-2}$\nwith nonezero effective increase of radius $R_i>0$. The modeling of the sheet\ndynamics has shown that small periodic transversal displacements of the\nsubstrate lead to resonant vibrations of the membranes at frequencies close to\neigenfrequencies of nodeless vibrations of membranes with a circular symmetry.\nThe energy distribution of resonant vibrations of the membrane has a circular\nsymmetry and several nodal circles, whose number $i$ coincides with the number\nof the resonant frequency. The frequencies of the resonances decrease by\nincreasing the radius of the membrane as $(R+R_i)^{\\alpha_i}$ with exponent\n$\\alpha_i<2$. The lower rate of resonance frequency decrease is caused by the\nanharmonicity of membrane vibrations.\n"}
+{"abstract": "  Nowadays, users are encouraged to activate across multiple online social\nnetworks simultaneously. Anchor link prediction, which aims to reveal the\ncorrespondence among different accounts of the same user across networks, has\nbeen regarded as a fundamental problem for user profiling, marketing,\ncybersecurity, and recommendation. Existing methods mainly address the\nprediction problem by utilizing profile, content, or structural features of\nusers in symmetric ways. However, encouraged by online services, users would\nalso post asymmetric information across networks, such as geo-locations and\ntexts. It leads to an emerged challenge in aligning users with asymmetric\ninformation across networks. Instead of similarity evaluation applied in\nprevious works, we formalize correlation between geo-locations and texts and\npropose a novel anchor link prediction framework for matching users across\nnetworks. Moreover, our model can alleviate the label scarcity problem by\nintroducing external data. Experimental results on real-world datasets show\nthat our approach outperforms existing methods and achieves state-of-the-art\nresults.\n"}
+{"abstract": "  We consider a system of two singularly perturbed Boundary Value Problems\n(BVPs) of convection-diffusion type with discontinuous source terms and a small\npositive parameter multiplying the highest derivatives. Then their solutions\nexhibit boundary layers as well as weak interior layers. A numerical method\nbased on finite element method (Shishkin and Bakhvalov-Shishkin meshes) is\npresented. We derive an error estimate of order $O(N^{-1}\\ln^{3/2}{N})$ in the\nenergy norm with respect to the perturbation parameter. Numerical experiments\nare also presented to support our theoretical results.\n"}
+{"abstract": "  Vulnerable individuals have a limited ability to make reasonable financial\ndecisions and choices and, thus, the level of care that is appropriate to be\nprovided to them by financial institutions may be different from that required\nfor other consumers. Therefore, identifying vulnerability is of central\nimportance for the design and effective provision of financial services and\nproducts. However, validating the information that customers share and\nrespecting their privacy are both particularly important in finance and this\nposes a challenge for identifying and caring for vulnerable populations. This\nposition paper examines the potential of the combination of two emerging\ntechnologies, Decentralized Identifiers (DIDs) and Verifiable Credentials\n(VCs), for the identification of vulnerable consumers in finance in an\nefficient and privacy-preserving manner.\n"}
+{"abstract": "  Different from general photo retouching tasks, portrait photo retouching\n(PPR), which aims to enhance the visual quality of a collection of flat-looking\nportrait photos, has its special and practical requirements such as\nhuman-region priority (HRP) and group-level consistency (GLC). HRP requires\nthat more attention should be paid to human regions, while GLC requires that a\ngroup of portrait photos should be retouched to a consistent tone. Models\ntrained on existing general photo retouching datasets, however, can hardly meet\nthese requirements of PPR. To facilitate the research on this high-frequency\ntask, we construct a large-scale PPR dataset, namely PPR10K, which is the first\nof its kind to our best knowledge. PPR10K contains $1, 681$ groups and $11,\n161$ high-quality raw portrait photos in total. High-resolution segmentation\nmasks of human regions are provided. Each raw photo is retouched by three\nexperts, while they elaborately adjust each group of photos to have consistent\ntones. We define a set of objective measures to evaluate the performance of PPR\nand propose strategies to learn PPR models with good HRP and GLC performance.\nThe constructed PPR10K dataset provides a good benchmark for studying automatic\nPPR methods, and experiments demonstrate that the proposed learning strategies\nare effective to improve the retouching performance. Datasets and codes are\navailable: https://github.com/csjliang/PPR10K.\n"}
+{"abstract": "  Gioseffo Zarlino reintroduced the Pythagorean paradigm into Renaissance\nmusical theory. In a similar fashion, Nicolaus Copernicus, Galileo Galilei,\nJohannes Kepler, and Isaac Newton reinvigorated Pythagorean ideas in celestial\nmechanics; Kepler and Newton explicitly invoked musical principles. Today, the\ntheory of dynamical systems allows us to describe very different applications\nof physics, from the orbits of asteroids in the Solar System to the pitch of\ncomplex sounds. Our aim in this text is to review the overarching aims of our\nresearch in this field over the past quarter of a century. We demonstrate with\na combination of dynamical systems theory and music theory the thread running\nfrom Pythagoras to Zarlino that allowed the latter to construct musical scales\nusing the ideas of proportion known to the former, and we discuss how the\nmodern theory of dynamical systems, with the study of resonances in nonlinear\nsystems, returns to Pythagorean ideas of a Musica Universalis.\n"}
+{"abstract": "  We study nonparametric dependence detection with the proposed binary\nexpansion approximation of uniformity (BEAUTY) approach, which generalizes the\ncelebrated Euler's formula, and approximates the characteristic function of any\ncopula with a linear combination of expectations of binary interactions from\nmarginal binary expansions. This novel theory enables a unification of many\nimportant tests through approximations from some quadratic forms of symmetry\nstatistics, where the deterministic weight matrix characterizes the power\nproperties of each test. To achieve a robust power, we study test statistics\nwith data-adaptive weights, referred to as the binary expansion adaptive\nsymmetry test (BEAST). By utilizing the properties of the binary expansion\nfiltration, we show that the Neyman-Pearson test of uniformity can be\napproximated by an oracle weighted sum of symmetry statistics. The BEAST with\nthis oracle provides a benchmark of feasible power against any alternative by\nleading all existing tests with a substantial margin. To approach this oracle\npower, we develop the BEAST through a regularized resampling approximation of\nthe oracle test. The BEAST improves the empirical power of many existing tests\nagainst a wide spectrum of common alternatives and provides clear\ninterpretation of the form of dependency when significant.\n"}
+{"abstract": "  Precise real time estimates of earthquake magnitude and location are\nessential for early warning and rapid response. While recently multiple deep\nlearning approaches for fast assessment of earthquakes have been proposed, they\nusually rely on either seismic records from a single station or from a fixed\nset of seismic stations. Here we introduce a new model for real-time magnitude\nand location estimation using the attention based transformer networks. Our\napproach incorporates waveforms from a dynamically varying set of stations and\noutperforms deep learning baselines in both magnitude and location estimation\nperformance. Furthermore, it outperforms a classical magnitude estimation\nalgorithm considerably and shows promising performance in comparison to a\nclassical localization algorithm. In this work, we furthermore conduct a\ncomprehensive study of the requirements on training data, the training\nprocedures and the typical failure modes using three diverse and large scale\ndata sets. Our analysis gives several key insights. First, we can precisely\npinpoint the effect of large training data; for example, a four times larger\ntraining set reduces the required time for real time assessment by a factor of\nfour. Second, the basic model systematically underestimates large magnitude\nevents. This issue can be mitigated by incorporating events from other regions\ninto the training through transfer learning. Third, location estimation is\nhighly precise in areas with sufficient training data, but is strongly degraded\nfor events outside the training distribution. Our analysis suggests that these\ncharacteristics are not only present for our model, but for most deep learning\nmodels for fast assessment published so far. They result from the black box\nmodeling and their mitigation will likely require imposing physics derived\nconstraints on the neural network.\n"}
+{"abstract": "  The recent TASK meta-GGA density functional [Phys. Rev. Research, 1, 033082\n(2019)] is constructed with an enhanced nonlocality in the generalized\nKohn-Sham scheme, and therefore harbors great opportunities for band gap\nprediction. Although this approximation was found to yield excellent band gaps\nof bulk solids, this accuracy cannot be straightforwardly transferred to\nlow-dimensional materials. The reduced screening of these materials results in\nlarger band gaps compared to their bulk counterparts, as an additional barrier\nto overcome. In this work, we demonstrate how the alteration of exact physical\nconstraints in this functional affects the band gaps of monolayers and\nnanoribbons, and present accurate band gaps competing with the HSE06\napproximation. In order to achieve this goal, we have modified the TASK\nfunctional (a) by changing the tight upper-bound for one or two-electron\nsystems ($h_X^0$) from 1.174 to 1.29 (b) by changing the limit of interpolation\nfunction $f_X (\\alpha \\rightarrow \\infty$) of the TASK functional that\ninterpolates the exchange enhancement factor $F_X (s,\\alpha)$ from $\\alpha=$ 0\nto 1. The resulting modified TASK (mTASK) was tested for various materials from\n3D to 2D to 1D (nanoribbons), and was compared with the results of the\nhigher-level hybrid functional HSE06 or with the G$_0$W$_0$ approximation\nwithin many-body perturbation theory. We find that mTASK greatly improves the\nband gaps and band structures of 2D and 1D systems, without significantly\naffecting the accuracy of the original TASK for the bulk 3D materials, when\ncompared to the PBE-GGA and SCAN meta-GGA. We further demonstrate the\napplicability of mTASK by assessing the band structures of TMD nanoribbons with\nrespect to various bending curvatures.\n"}
+{"abstract": "  The problem of measuring sentence similarity is an essential issue in the\nnatural language processing (NLP) area. It is necessary to measure the\nsimilarity between sentences accurately. There are many approaches to measuring\nsentence similarity. Deep learning methodology shows a state-of-the-art\nperformance in many natural language processing fields and is used a lot in\nsentence similarity measurement methods. However, in the natural language\nprocessing field, considering the structure of the sentence or the word\nstructure that makes up the sentence is also important. In this study, we\npropose a methodology combined with both deep learning methodology and a method\nconsidering lexical relationships. Our evaluation metric is the Pearson\ncorrelation coefficient and Spearman correlation coefficient. As a result, the\nproposed method outperforms the current approaches on a KorSTS standard\nbenchmark Korean dataset. Moreover, it performs a maximum of 65% increase than\nonly using deep learning methodology. Experiments show that our proposed method\ngenerally results in better performance than those with only a deep learning\nmodel.\n"}
+{"abstract": "  The 1-2-3 Conjecture asks whether almost all graphs can be (edge-)labelled\nwith $1,2,3$ so that no two adjacent vertices are incident to the same sum of\nlabels. In the last decades, several aspects of this problem have been studied\nin literature, including more general versions and slight variations. Notable\nsuch variations include the List 1-2-3 Conjecture variant, in which edges must\nbe assigned labels from dedicated lists of three labels, and the Multiplicative\n1-2-3 Conjecture variant, in which labels~$1,2,3$ must be assigned to the edges\nso that adjacent vertices are incident to different products of labels. Several\nresults obtained towards these two variants led to observe some behaviours that\nare distant from those of the original conjecture.\n  In this work, we consider the list version of the Multiplicative 1-2-3\nConjecture, proposing the first study dedicated to this very problem. In\nparticular, given any graph $G$, we wonder about the minimum~$k$ such that $G$\ncan be labelled as desired when its edges must be assigned labels from\ndedicated lists of size~$k$. Exploiting a relationship between our problem and\nthe List 1-2-3 Conjecture, we provide upper bounds on~$k$ when $G$ belongs to\nparticular classes of graphs. We further improve some of these bounds through\ndedicated arguments.\n"}
+{"abstract": "  This paper establishes asymptotic results for the maximum likelihood and\nrestricted maximum likelihood (REML) estimators of the parameters in the nested\nerror regression model for clustered data when both of the number of\nindependent clusters and the cluster sizes (the number of observations in each\ncluster) go to infinity. Under very mild conditions, the estimators are shown\nto be asymptotically normal with an elegantly structured covariance matrix.\nThere are no restrictions on the rate at which the cluster size tends to\ninfinity but it turns out that we need to treat within cluster parameters (i.e.\ncoefficients of unit-level covariates that vary within clusters and the within\ncluster variance) differently from between cluster parameters (i.e.\ncoefficients of cluster-level covariates that are constant within clusters and\nthe between cluster variance) because they require different normalisations and\nare asymptotically independent.\n"}
+{"abstract": "  We study a one-dimensional array of bosons with infinite-range interactions\nmediated by a laser-driven dissipative optical cavity. The cavity-mediated\ninfinite-range interactions open up a new pathway to fermionization, hitherto\nonly known for dipolar bosons due to their long-range interactions. In\nparameter ranges attainable in state-of-the-art experiments, we systematically\ncompare observables for bosons and fermions with infinite-range interactions.\nAt large enough laser pump power, many observables, including density\ndistributions in real and momentum space, correlation functions, eigenvalues of\nthe one-body density matrix, and superradiance order parameter, become\nidentical for bosons and fermions. We map out the emergence of this\ncavity-induced fermionization as a function of pump power and contact\ninteractions. We discover that cavity-mediated interactions can compensate a\nreduction by several orders of magnitude in the strength of the contact\ninteractions needed to trigger fermionization.\n"}
+{"abstract": "  Structural health monitoring (SHM) using self-sensing cement-based materials\nhas been reported before, where nano-fillers have been incorporated in\ncementitious matrices as functional sensing elements. A percolation threshold\nis always required in order for conductive nano-fillers modified concrete to be\nuseful for SHM. Nonetheless, the best pressure/strain sensitivity results\nachieved for any self-sensing cementitious matrix are <0.01 MPa-1. In this\nwork, we introduce novel reduced graphene oxide (RGO) based electronic textile\n(e-textile) embedded in plain and polymer-binder-modified cementitious matrix\nfor SHM applications. As a proof of concept, it was demonstrated that these\ncoated fabric-based sensors can be successfully embedded within the\ncement-based structures, which are independent of any percolation threshold due\nto the interconnected fabric inside the host matrix. The piezo-resistive\nresponse was measured by applying direct and cyclic compressive loads (0.1 to\n3.9 MPa). A pressure sensitivity of 1.5 MPa-1 and an ultra-high gauge factor of\n2000 was obtained for the system of the self-sensing cementitious structure\nwith embedded e-textiles. The sensitivity of this new system with embedded\ne-textile is many orders of magnitude higher than nanoparticle based\nself-sensing of cementitious composites. The manufactured e-textile sensors\nshowed mechanical stability and functional durability over long-term cyclic\ncompression tests of 1000 cycles.\n"}
+{"abstract": "  X-ray bursts have recently been discovered in the Cepheids $\\delta$ Cep and\n$\\beta$ Dor modulated by the pulsation cycle. We have obtained an observation\nof the Cepheid $\\eta$ Aql with the XMM-Newton satellite at the phase of maximum\nradius, the phase at which there is a burst of X-rays in $\\delta$ Cep. No\nX-rays were seen from the Cepheid $\\eta$ Aql at this phase, and the\nimplications for Cepheid upper atmospheres are discussed. We have also used the\ncombination of X-ray sources and Gaia and 2MASS data to search for a possible\ngrouping around the young intermediate mass Cepheid. No indication of such a\ngroup was found.\n"}
+{"abstract": "  Source-free so-called ModMax theories of nonlinear electrodynamics in the\nfour dimensional Minkowski spacetime vacuum are the only possible continuous\ndeformations -- and as a function of a single real and positive parameter -- of\nsource-free Maxwell linear electrodynamics in the same vacuum, which preserve\nall the same Poincar\\'e and conformal spacetime symmetries as well as the\ncontinuous duality invariance of Maxwell's theory. Null field configurations of\nthe latter however, including null electromagnetic knots, are singular for the\nLagrangian formulation of any spacetime Poincar\\'e and conformal invariant\ntheory of nonlinear electrodynamics. In particular null hopfion-Ra\\~nada knots\nare a distinguished and fascinating class on their own of topologically\nnontrivial solutions to Maxwell's equations. This work addresses the fate of\nthese configurations within ModMax theories. A doubled class of ModMax deformed\nhopfion-Ra\\~nada knots is thereby identified, each of which coalescing back in\na continuous fashion to the original hopfion-Ra\\~nada knot when the nonlinear\ndeformation parameter is turned off.\n"}
+{"abstract": "  Consider a channel ${\\bf Y}={\\bf X}+ {\\bf N}$ where ${\\bf X}$ is an\n$n$-dimensional random vector, and ${\\bf N}$ is a Gaussian vector with a\ncovariance matrix ${\\bf \\mathsf{K}}_{\\bf N}$. The object under consideration in\nthis paper is the conditional mean of ${\\bf X}$ given ${\\bf Y}={\\bf y}$, that\nis ${\\bf y} \\to E[{\\bf X}|{\\bf Y}={\\bf y}]$. Several identities in the\nliterature connect $E[{\\bf X}|{\\bf Y}={\\bf y}]$ to other quantities such as the\nconditional variance, score functions, and higher-order conditional moments.\nThe objective of this paper is to provide a unifying view of these identities.\n  In the first part of the paper, a general derivative identity for the\nconditional mean is derived. Specifically, for the Markov chain ${\\bf U}\n\\leftrightarrow {\\bf X} \\leftrightarrow {\\bf Y}$, it is shown that the Jacobian\nof $E[{\\bf U}|{\\bf Y}={\\bf y}]$ is given by ${\\bf \\mathsf{K}}_{{\\bf N}}^{-1}\n{\\bf Cov} ( {\\bf X}, {\\bf U} | {\\bf Y}={\\bf y})$.\n  In the second part of the paper, via various choices of ${\\bf U}$, the new\nidentity is used to generalize many of the known identities and derive some new\nones. First, a simple proof of the Hatsel and Nolte identity for the\nconditional variance is shown. Second, a simple proof of the recursive identity\ndue to Jaffer is provided. Third, a new connection between the conditional\ncumulants and the conditional expectation is shown. In particular, it is shown\nthat the $k$-th derivative of $E[X|Y=y]$ is the $(k+1)$-th conditional\ncumulant.\n  The third part of the paper considers some applications. In a first\napplication, the power series and the compositional inverse of $E[X|Y=y]$ are\nderived. In a second application, the distribution of the estimator error\n$(X-E[X|Y])$ is derived. In a third application, we construct consistent\nestimators (empirical Bayes estimators) of the conditional cumulants from an\ni.i.d. sequence $Y_1,...,Y_n$.\n"}
+{"abstract": "  Based on PixelHop and PixelHop++, which are recently developed using the\nsuccessive subspace learning (SSL) framework, we propose an enhanced solution\nfor object classification, called E-PixelHop, in this work. E-PixelHop consists\nof the following steps. First, to decouple the color channels for a color\nimage, we apply principle component analysis and project RGB three color\nchannels onto two principle subspaces which are processed separately for\nclassification. Second, to address the importance of multi-scale features, we\nconduct pixel-level classification at each hop with various receptive fields.\nThird, to further improve pixel-level classification accuracy, we develop a\nsupervised label smoothing (SLS) scheme to ensure prediction consistency.\nForth, pixel-level decisions from each hop and from each color subspace are\nfused together for image-level decision. Fifth, to resolve confusing classes\nfor further performance boosting, we formulate E-PixelHop as a two-stage\npipeline. In the first stage, multi-class classification is performed to get a\nsoft decision for each class, where the top 2 classes with the highest\nprobabilities are called confusing classes. Then,we conduct a binary\nclassification in the second stage. The main contributions lie in Steps 1, 3\nand 5.We use the classification of the CIFAR-10 dataset as an example to\ndemonstrate the effectiveness of the above-mentioned key components of\nE-PixelHop.\n"}
+{"abstract": "  A popular way to accelerate the sampling of rare events in molecular dynamics\nsimulations is to introduce a potential that increases the fluctuations of\nselected collective variables. For this strategy to be successful, it is\ncritical to choose appropriate variables. Here we review some recent\ndevelopments in the data-driven design of collective variables, with a focus on\nthe combination of Fisher's discriminant analysis and neural networks. This\napproach allows to compress the fluctuations of metastable states into a\nlow-dimensional representation. We illustrate through several examples the\neffectiveness of this method in accelerating the sampling, while also\nidentifying the physical descriptors that undergo the most significant changes\nin the process.\n"}
+{"abstract": "  Motivated by the need for fair algorithmic decision making in the age of\nautomation and artificially-intelligent technology, this technical report\nprovides a theoretical insight into adversarial training for fairness in deep\nlearning. We build upon previous work in adversarial fairness, show the\npersistent tradeoff between fair predictions and model performance, and explore\nfurther mechanisms that help in offsetting this tradeoff.\n"}
+{"abstract": "  Reconfigurable intelligent surface (RIS) technology has recently emerged as a\nspectral- and cost-efficient approach for wireless communications systems.\nHowever, existing hand-engineered schemes for passive beamforming design and\noptimization of RIS, such as the alternating optimization (AO) approaches,\nrequire a high computational complexity, especially for\nmultiple-input-multiple-output (MIMO) systems. To overcome this challenge, we\npropose a low-complexity unsupervised learning scheme, referred to as\nlearning-phase-shift neural network (LPSNet), to efficiently find the solution\nto the spectral efficiency maximization problem in RIS-aided MIMO systems. In\nparticular, the proposed LPSNet has an optimized input structure and requires a\nsmall number of layers and nodes to produce efficient phase shifts for the RIS.\nSimulation results for a 16x2 MIMO system assisted by an RIS with 40 elements\nshow that the LPSNet achieves 97.25% of the SE provided by the AO counterpart\nwith more than a 95% reduction in complexity.\n"}
+{"abstract": "  Industrial Internet of Things (IIoT) revolutionizes the future manufacturing\nfacilities by integrating the Internet of Things technologies into industrial\nsettings. With the deployment of massive IIoT devices, it is difficult for the\nwireless network to support the ubiquitous connections with diverse\nquality-of-service (QoS) requirements. Although machine learning is regarded as\na powerful data-driven tool to optimize wireless network, how to apply machine\nlearning to deal with the massive IIoT problems with unique characteristics\nremains unsolved. In this paper, we first summarize the QoS requirements of the\ntypical massive non-critical and critical IIoT use cases. We then identify\nunique characteristics in the massive IIoT scenario, and the corresponding\nmachine learning solutions with its limitations and potential research\ndirections. We further present the existing machine learning solutions for\nindividual layer and cross-layer problems in massive IIoT. Last but not the\nleast, we present a case study of massive access problem based on deep neural\nnetwork and deep reinforcement learning techniques, respectively, to validate\nthe effectiveness of machine learning in massive IIoT scenario.\n"}
+{"abstract": "  The appearance of a light composite $0^+$ scalar resonance in nearly\nconformal gauge-fermion theories motivates further study of the low energy\nstructure of these theories. To this end, we present a nonperturbative lattice\ncalculation of s-wave scattering of Goldstone bosons in the maximal-isospin\nchannel in SU(3) gauge theory with $N_f=8$ light, degenerate flavors. The\nscattering phase shift is measured both for different values of the underlying\nfermion mass and for different values of the scattering momentum. We examine\nthe effect of a light flavor-singlet scalar (reported in earlier studies) on\nGoldstone boson scattering, employing a dilaton effective field theory (EFT) at\nthe tree level. The EFT gives a good description of the scattering data,\ninsofar as the magnitude of deviations between EFT and lattice data are no\nlarger than the expected size of next-to-leading order corrections in the EFT.\n"}
+{"abstract": "  Determining whether nodes can be uniquely localized, called localizability\ndetection, is a concomitant problem of network localization. Localizability\nunder traditional Non-Linear Localization (NLL) schema has been well explored,\nwhereas localizability under the emerging Barycentric coordinate-based Linear\nLocalization (BLL) schema has not been well touched. In this paper, we\ninvestigate the deficiency of existing localizability theories and algorithms\nin BLL, and then propose a necessary condition and a sufficient condition for\nBLL node localizability. Based on these two conditions, an efficient iterative\nmaximum flow (IMF) algorithm is designed to identify BLL localizable nodes.\nFinally, our algorithms are validated by both theoretical analysis and\nexperimental evaluations.\n"}
+{"abstract": "  Most commercially available optical see-through head-mounted displays\n(OST-HMDs) utilize optical combiners to simultaneously visualize the physical\nbackground and virtual objects. The displayed images perceived by users are a\nblend of rendered pixels and background colors. Enabling high fidelity color\nperception in mixed reality (MR) scenarios using OST-HMDs is an important but\nchallenging task. We propose a real-time rendering scheme to enhance the color\ncontrast between virtual objects and the surrounding background for OST-HMDs.\nInspired by the discovery of color perception in psychophysics, we first\nformulate the color contrast enhancement as a constrained optimization problem.\nWe then design an end-to-end algorithm to search the optimal complementary\nshift in both chromaticity and luminance of the displayed color. This aims at\nenhancing the contrast between virtual objects and the real background as well\nas keeping the consistency with the original color. We assess the performance\nof our approach using a simulated OST-HMD environment and an off-the-shelf\nOST-HMD. Experimental results from objective evaluations and subjective user\nstudies demonstrate that the proposed approach makes rendered virtual objects\nmore distinguishable from the surrounding background, thereby bringing a better\nvisual experience.\n"}
+{"abstract": "  Massive Klein-Gordon theory is quantized on the timelike hypercylinder in\nMinkowski space. Crucially, not only the propagating, but also the evanescent\nsector of phase space is included, laying in this way foundations for a quantum\nscattering theory of fields at finite distance. To achieve this, the novel\n$\\alpha$-K\\\"ahler quantization scheme is employed in the framework of general\nboundary quantum field theory. A potential quantization ambiguity is fixed by\nstringent requirements, leading to a unitary radial evolution. Formulas for\nbuilding scattering amplitudes and correlation functions are exhibited.\n"}
+{"abstract": "  Digital twins are emerging in many industries, typically consisting of\nsimulation models and data associated with a specific physical system. One of\nthe main reasons for developing a digital twin, is to enable the simulation of\npossible consequences of a given action, without the need to interfere with the\nphysical system itself. Physical systems of interest, and the environments they\noperate in, do not always behave deterministically. Moreover, information about\nthe system and its environment is typically incomplete or imperfect.\nProbabilistic representations of systems and environments may therefore be\ncalled for, especially to support decisions in application areas where actions\nmay have severe consequences.\n  In this paper we introduce the probabilistic digital twin (PDT). We will\nstart by discussing how epistemic uncertainty can be treated using measure\ntheory, by modelling epistemic information via $\\sigma$-algebras. Based on\nthis, we give a formal definition of how epistemic uncertainty can be updated\nin a PDT. We then study the problem of optimal sequential decision making. That\nis, we consider the case where the outcome of each decision may inform the\nnext. Within the PDT framework, we formulate this optimization problem. We\ndiscuss how this problem may be solved (at least in theory) via the maximum\nprinciple method or the dynamic programming principle. However, due to the\ncurse of dimensionality, these methods are often not tractable in practice. To\nmend this, we propose a generic approximate solution using deep reinforcement\nlearning together with neural networks defined on sets. We illustrate the\nmethod on a practical problem, considering optimal information gathering for\nthe estimation of a failure probability.\n"}
+{"abstract": "  Route controlled autonomous vehicles could have a significant impact in\nreducing congestion in the future. Before applying multi-agent reinforcement\nlearning algorithms to route control, we can model the system using a\ncongestion game to predict and mitigate potential issues. We consider the\nproblem of distributed operating systems in a transportation network that\ncontrol the routing choices of their assigned vehicles. We formulate an\nassociated network control game, consisting of multiple actors seeking to\noptimise the social welfare of their assigned subpopulations in an underlying\nnonatomic congestion game. Then we find the inefficiency of the routing\nequilibria by calculating the Price of Anarchy for polynomial cost functions.\nFinally, we extend the analysis to allow vehicles to choose their operating\nsystem.\n"}
+{"abstract": "  Semiconducting transition-metal dichalcogenides (TMDCs) provide a fascinating\ndiscovery platform for strong light-matter interaction effects in the visible\nspectrum at ambient conditions. While most of the work has focused on\nhybridizing excitons with resonant photonic modes of external mirrors,\ncavities, or nanostructures, intriguingly, TMDC flakes of sub-wavelength\nthickness can themselves act as nanocavities. Here, we determine the optical\nresponse of such freestanding planar waveguides of WSe$_2$, by means of\ncathodoluminescence spectroscopy. We reveal strong exciton-photon interaction\neffects that foster long-range propagating exciton-polaritons and enable direct\nimaging of the energy transfer dynamics originating from cavity-like\nFabry-Perot resonances. Furthermore, confinement effects due to discontinuities\nin the flakes are demonstrated as an efficient means to tailor the\nexciton-photon coupling strength, along the edges of natural flakes. Our\ncombined experimental and theoretical results provide a deeper understanding of\nexciton-photon self-hybridization in semiconducting TMDCs and may pave the way\nto optoelectronic nanocircuits exploiting exciton-photon interaction.\n"}
+{"abstract": "  Bose-Einstein condensates (BECs) in free fall constitute a promising source\nfor space-borne matter-wave interferometry. Indeed, BECs enjoy a slowly\nexpanding wave function, display a large spatial coherence and can be\nengineered and probed by optical techniques. On a sounding rocket, we explore\nmatter-wave fringes of multiple spinor components of a BEC released in free\nfall employing light-pulses to drive Bragg processes and induce phase\nimprinting. The prevailing microgravity played a crucial role in the\nobservation of these interferences which not only reveal the spatial coherence\nof the condensates but also allow us to measure differential forces. Our work\nestablishes matter-wave interferometry in space with future applications in\nfundamental physics, navigation and Earth observation.\n"}
+{"abstract": "  In this paper we present an experience report for the RMQFMU, a plug and play\ntool, that enables feeding data to/from an FMI2-based co-simulation environment\nbased on the AMQP protocol. Bridging the co-simulation to an external\nenvironment allows on one side to feed historical data to the co-simulation,\nserving different purposes, such as visualisation and/or data analysis. On the\nother side, such a tool facilitates the realisation of the digital twin concept\nby coupling co-simulation and hardware/robots close to real-time. In the paper\nwe present limitations of the initial version of the RMQFMU with respect to the\ncapability of bridging co-simulation with the real world. To provide the\ndesired functionality of the tool, we present in a step-by-step fashion how\nthese limitations, and subsequent limitations, are alleviated. We perform\nvarious experiments in order to give reason to the modifications carried out.\nFinally, we report on two case-studies where we have adopted the RMQFMU, and\nprovide guidelines meant to aid practitioners in its use.\n"}
+{"abstract": "  We consider Markov Decision Processes (MDPs) where distributional parameters,\nsuch as transition probabilities, are unknown and estimated from data. The\npopular distributionally robust approach to addressing the parameter\nuncertainty can sometimes be overly conservative. In this paper, we propose a\nBayesian risk approach to MDPs with parameter uncertainty, where a risk\nfunctional is applied in nested form to the expected discounted total cost with\nrespect to the Bayesian posterior distributions of the unknown parameters in\neach time stage. The proposed approach provides more flexibility of risk\nattitudes towards parameter uncertainty and takes into account the availability\nof data in future time stages. For the finite-horizon MDPs, we show the dynamic\nprogramming equations can be solved efficiently with an upper confidence bound\n(UCB) based adaptive sampling algorithm. For the infinite-horizon MDPs, we\npropose a risk-adjusted Bellman operator and show the proposed operator is a\ncontraction mapping that leads to the optimal value function to the Bayesian\nrisk formulation. We demonstrate the empirical performance of our proposed\nalgorithms in the finite-horizon case on an inventory control problem and a\npath planning problem.\n"}
+{"abstract": "  Information technology demands continuous increase of data-storage density.\nIn high-density magnetic recording media, the large magneto-crystalline\nanisotropy (MCA) stabilizes the stored information against decay through\nthermal fluctuations. In the latest generation storage media, MCA is so large\nthat magnetic order needs to be transiently destroyed by heat to enable bit\nwriting. Here we show an alternative approach to control high-anisotropy\nmagnets: With ultrashort laser pulses the anisotropy itself can be manipulated\nvia electronic state excitations. In rare-earth materials like terbium metal,\nmagnetic moment and high MCA both originate from the 4f electronic state.\nFollowing infrared laser excitation 5d-4f electron-electron scattering\nprocesses lead to selective orbital excitations that change the 4f orbital\noccupation and significantly alter the MCA. Besides these excitations within\nthe 4f multiplet, 5d-4f electron transfer causes a transient change of the 4f\noccupation number, which, too, strongly alters the MCA. Such MCA change cannot\nbe achieved by heating: The material would rather be damaged than the 4f\nconfiguration modified. Our results show a way to overcome this limitation for\na new type of efficient magnetic storage medium. Besides potential\ntechnological relevance, the observation of MCA-changing excitations also has\nimplications for a general understanding of magnetic dynamics processes on\nultrashort time scales, where the 4f electronic state affects the angular\nmomentum transfer between spin system and lattice.\n"}
+{"abstract": "  The concept of zero forcing was introduced in the context of linear algebra,\nand was further studied by both graph theorists and linear algebraists. It is\nbased on the process of activating vertices of a graph $G$ starting from a set\nof vertices that are already active, and applying the rule that an active\nvertex with exactly one non-active neighbor forces that neighbor to become\nactive. A set $S\\subset V(G)$ is called a zero forcing set of $G$ if initially\nonly vertices of $S$ are active and the described process enforces all vertices\nof $G$ to become active. The size of a minimum zero forcing set in $G$ is\ncalled the zero forcing number of $G$. While a minimum zero forcing set can\nonly be unique in edgeless graphs, we consider the weaker uniqueness condition,\nnotably that for every two minimum zero forcing sets in a graph $G$ there is an\nautomorphism that maps one to the other. We characterize the class of trees\nthat enjoy this condition by using properties of minimum path covers of trees.\nIn addition, we investigate both variations of uniqueness for several concepts\nof Grundy domination, which first appeared in the context of domination games,\nyet they are also closely related to zero forcing. For each of the four\nvariations of Grundy domination we characterize the graphs that have only one\nGrundy dominating set of the given type, and characterize those forests that\nenjoy the weaker (isomorphism based) condition of uniqueness. The latter\ncharacterizations lead to efficient algorithms for recognizing the\ncorresponding classes of forests.\n"}
+{"abstract": "  A new protomodular analog of the classical criterion for the existence of a\ngroup term in the algebraic theory of a variety of universal algebras is given.\nTo this end, the notion of a right-cancellable protomodular algebra is\nintroduced. It is proved that the algebraic theory of a variety of universal\nalgebras contains a group term if and only if it contains protomodular terms\nwith respect to which all algebras from the variety are right-cancellable.\nThis, in particular, gives a partial answer to the extended version of an open\nproblem from loop theory whether any Hausdorff topological (semi-)loop is\ncompletely regular. Moreover, the right-cancellable algebras from the simplest\nprotomodular varieties are characterized as sets with principal group actions\nas well as groups with simple additional structures.\n"}
+{"abstract": "  Till now geometric structures don't play a major role in cryptography.\nGilbert, MacWilliams and Sloane introduced in 1974 an authentication scheme in\nthe projective plane and showed its perfectness in the sense of the definition\nof Shannon. In this paper we will show that this authentication scheme also\nfulfills the requirement of completeness according to Kam and Davida and we\nwill extend the application of geometric structures in cryptography by\nintroducing an encryption scheme in the M\\\"obius plane. We will further examine\nits properties, showing that it also fulfills the requirement of completeness\nand Shannon's requirement of perfectness in first approximation. The results of\nthis paper can be used to define similar encryption schemes in the circle\ngeometries of Laguerre and Minkowski.\n"}
+{"abstract": "  For an arranged subset $Q = \\{q_1, q_2, ..., q_k\\}$ of vertices in a\nconnected graph $G$ the metric representation of a vertex $v$ in $G$, is the\n$k$-vector $r(v | Q) = (d(v, q_1), d(v, q_2), ..., d(v, q_k ))$ relative to\n$Q$. Also, the subset $Q$ is considered as resolving set for $G$ if any pair of\nvertices of $G$ is distinguished by some vertices of $Q$. In the present\narticle, we study the minimum size of resolving set, and doubly resolving set\nfor the graph $H(n)$, and the line graph of the graph $H(n)$ is denoted by\n$L(n)$. It is well known that these problems are NP hard.\n"}
+{"abstract": "  In this article, we study geometric properties of nilpotent groups. We find a\ngeometric criterion for the word problem for the finitely generated free\nnilpotent groups. By geometric criterion, we mean a way to determine whether\ntwo words represent the same element in a free nilpotent group of rank $r$ and\nclass $k$ by analyzing their behavior on the Cayley graph of the free nilpotent\ngroup of rank $r$ and class $k-1$.\n"}
+{"abstract": "  An approach is proposed for inferring Granger causality between jointly\nstationary, Gaussian signals from quantized data. First, a necessary and\nsufficient rank criterion for the equality of two conditional Gaussian\ndistributions is proved. Assuming a partial finite-order Markov property,\nconditions are then derived under which Granger causality between them can be\nreliably inferred from the second order moments of the quantized processes. A\nnecessary and sufficient condition is proposed for Granger causality inference\nunder binary quantization. Furthermore, sufficient conditions are introduced to\ninfer Granger causality between jointly Gaussian signals through measurements\nquantized via non-uniform, uniform or high resolution quantizers. This approach\ndoes not require the statistics of the underlying Gaussian signals to be\nestimated, or a system model to be identified. No assumptions are made on the\nidentifiability of the jointly Gaussian random processes through the quantized\nobservations. The effectiveness of the proposed method is illustrated by\nsimulation results.\n"}
+{"abstract": "  We consider online convex optimization when a number k of data points are\noutliers that may be corrupted. We model this by introducing the notion of\nrobust regret, which measures the regret only on rounds that are not outliers.\nThe aim for the learner is to achieve small robust regret, without knowing\nwhere the outliers are. If the outliers are chosen adversarially, we show that\na simple filtering strategy on extreme gradients incurs O(k) additive overhead\ncompared to the usual regret bounds, and that this is unimprovable, which means\nthat k needs to be sublinear in the number of rounds. We further ask which\nadditional assumptions would allow for a linear number of outliers. It turns\nout that the usual benign cases of independently, identically distributed\n(i.i.d.) observations or strongly convex losses are not sufficient. However,\ncombining i.i.d. observations with the assumption that outliers are those\nobservations that are in an extreme quantile of the distribution, does lead to\nsublinear robust regret, even though the expected number of outliers is linear.\n"}
+{"abstract": "  High-order perturbative calculations for thermodynamic quantities in QCD are\ncomplicated by the physics of dynamical screening that affects the soft,\nlong-wavelength modes of the system. Here, we provide details for the\nevaluation of this soft contribution to the next-to-next-to-next-to-leading\norder (NNNLO) pressure of high-density, zero-temperature quark matter (QM),\ncomplementing our accompanying paper in arXiv:2103.05658. Our calculation\nrequires the determination of the pressure of the hard-thermal-loop (HTL)\neffective theory to full two-loop order at zero temperature, which we go\nthrough in considerable detail. In addition to this, we comprehensively discuss\nthe structure of the weak-coupling expansion of the QM pressure, and lay out a\nroadmap towards the evaluation of the contributions missing from a full NNNLO\nresult for this quantity.\n"}
+{"abstract": "  Tidal disruption events are an excellent probe for supermassive black holes\nin distant inactive galaxies because they show bright multi-wavelength flares\nlasting several months to years. AT2019dsg presents the first potential\nassociation with neutrino emission from such an explosive event.\n"}
+{"abstract": "  We design a low complexity distributed compression scheme for computing\narbitrary functions of sources with discrete alphabets. We use a helper-based\nmethod that extends the definition of the G{\\'a}cs-K{\\\"o}rner-Witsenhausen\n(GKW) common information to functional common information. The helper relaxes\nthe combinatorial structure of GKW by partitioning the joint source\ndistribution into nests imposed by the function, which ensures hierarchical\ncooperation between the sources for effectively distributed computing. By\ncontrasting our approach's performance with existing efficient techniques, we\ndemonstrate the rate savings in recovering function and source data.\n  Permutation invariant functions are prevalent in learning and combinatorial\noptimization fields and most recently applied to graph neural networks. We\nconsider the efficient compression for computing permutation invariant\nfunctions in a network with two sources and one decoder. We use a bipartite\ngraph representation, where two disjoint sets of vertices (parts) denote the\nindividual source graphs and the edge weights capture the joint source\ndistribution. We compress bipartite graphs by creating connected components\ndetermined by the function's distribution, accounting for the edge symmetries,\nand eliminating the low probability edges. We separately encode those edges and\nsend them as refinements. Our approach can substitute high complexity joint\ndecoding techniques and inform neural networks to reduce the computing time and\nreduce complexity.\n"}
+{"abstract": "  In this work, we investigate a novel simultaneous transmission and reflection\nreconfigurable intelligent surface (RIS)-assisted multiple-input\nmultiple-output downlink system, where three practical transmission protocols,\nnamely, energy splitting (ES), mode selection (MS), and time splitting (TS),\nare studied. For the system under consideration, we maximize the weighted sum\nrate with multiple coupled variables. To solve this optimization problem, a\nblock coordinate descent algorithm is proposed to reformulate this problem and\ndesign the precoding matrices and the transmitting and reflecting coefficients\n(TARCs) in an alternate manner. Specifically, for the ES scheme, the precoding\nmatrices are solved using the Lagrange dual method, while the TARCs are\nobtained using the penalty concave-convex method. Additionally, the proposed\nmethod is extended to the MS scheme by solving a mixed-integer problem.\nMoreover, we solve the formulated problem for the TS scheme using a\none-dimensional search and the Majorization-Minimization technique. Our\nsimulation results reveal that: 1) Simultaneous transmission and reflection RIS\n(STAR-RIS) can achieve better performance than reflecting-only RIS; 2) In\nunicast communication, TS scheme outperforms the ES and MS schemes, while in\nbroadcast communication, ES scheme outperforms the TS and MS schemes.\n"}
+{"abstract": "  The smoothing procedure known as the gradient flow that suppresses\nultraviolet fluctuations of gauge fields plays an important role in lattice\ngauge theory calculations. In particular, this procedure is often used for\nhigh-precision scale setting and renormalization of operators. The gradient\nflow equation is defined on the SU(3) manifold and therefore requires\ngeometric, or structure-preserving, integration methods to obtain its numerical\nsolutions. We examine the properties and origins of the three-stage third-order\nexplicit Runge-Kutta Lie group integrator commonly used in the lattice gauge\ntheory community, demonstrate its relation to 2N-storage classical Runge-Kutta\nmethods and explore how its coefficients can be tuned for optimal performance\nin integrating the gradient flow. We also compare the performance of the tuned\nmethod with two third-order variable step size methods. Next, based on the\nrecently established connection between low-storage Lie group integrators and\nclassical 2N-storage Runge-Kutta methods, we study two fourth-order low-storage\nmethods that provide a computationally efficient alternative to the commonly\nused third-order method while retaining the convenient iterative property of\nthe latter. Finally, we demonstrate that almost no coding effort is needed to\nimplement the low-storage Lie group methods into existing gradient flow codes.\n"}
+{"abstract": "  Ultralight dark photons predicted in several Standard Model extensions can\ntrigger the superradiant instability around rotating black holes if their\nCompton wavelength is comparable to the Blackhole radius. Consequently, the\nangular momentum of the black hole is reduced to a value which depends upon the\nmass and spin of the black hole as well as the mass of the dark photon. We use\nthe mass and spin measurements of the primary black holes in two recently\nobserved binary black hole systems: GW190517 and GW190426_152155 to constrain\ndark photon mass in the ranges $1.7\\times 10^{-14}{\\rm\\ eV}<m_{A'}<7.6\\times\n10^{-13}{\\rm\\ eV}$ and $1.3\\times 10^{-13}{\\rm\\ eV}<m_{A'}<4.2\\times\n10^{-12}{\\rm\\ eV}$ respectively, assuming a timescale of a few million years\nfrom the time of formation of the binary black hole system to the time of their\nmerger. We also discuss an interesting X-ray binary system, MAXI J1820_070,\nalbeit with a relatively small value of the spin parameter.\n"}
+{"abstract": "  Distributed Feedback Laser plays a key role as a light source component in\noptical fiber communication systems ranging from metro, long-haul to submarine\none thanks to its competitive features of superior narrow spectral width and\nwavelength cohesion. Characterizing such lasers via obtaining their electrical\nand spectral data and extracting their internal parameters therefore remains a\ncritical task in designing and troubleshooting optical fiber systems. This\npaper presents first an agile framework for a rapid collection of laser data\nvia automatic measurement and second an efficient approach for extracting laser\ninternal parameters.\n"}
+{"abstract": "  In this paper we study test time decoding; an ubiquitous step in almost all\nsequential text generation task spanning across a wide array of natural\nlanguage processing (NLP) problems. Our main contribution is to develop a\ncontinuous relaxation framework for the combinatorial NP-hard decoding problem\nand propose Disco - an efficient algorithm based on standard first order\ngradient based. We provide tight analysis and show that our proposed algorithm\nlinearly converges to within $\\epsilon$ neighborhood of the optima. Finally, we\nperform preliminary experiments on the task of adversarial text generation and\nshow superior performance of Disco over several popular decoding approaches.\n"}
+{"abstract": "  The lattice Boltzmann method, now widely used for a variety of applications,\nhas also been extended to model multi-phase flows through different\nformulations. While already applied to many different configurations in the low\nWeber and Reynolds number regimes, applications to higher Weber/Reynolds\nnumbers or larger density/viscosity ratios are still the topic of active\nresearch. In this study, through a combination of the decoupled phase-field\nformulation -- conservative Allen-Cahn equation -- and a cumulants-based\ncollision operator for a low-Mach pressure-based flow solver, we present an\nalgorithm that can be used for higher Reynolds/Weber numbers. The algorithm is\nvalidated through a variety of test-cases, starting with the Rayleigh-Taylor\ninstability both in 2-D and 3-D, followed by the impact of a droplet on a\nliquid sheet. In all simulations, the solver is shown to correctly capture the\ndynamics of the flow and match reference results very well. As the final\ntest-case, the solver is used to model droplet splashing on a thin liquid sheet\nin 3-D with a density ratio of 1000 and kinematic viscosity ratio of 15 --\nmatching the water/air system -- at We=8000 and Re=1000. The results show that\nthe solver correctly captures the fingering instabilities at the crown rim and\ntheir subsequent breakup, in agreement with experimental and numerical\nobservations reported in the literature.\n"}
+{"abstract": "  From 22 to 26 June 2020, we hosted ESO's first live e-conference, #H02020,\nfrom within ESO headquarters in Garching, Germany. Every day, between 200 and\n320 researchers around the globe tuned in to discuss the nature and\nimplications of the discord between precise determinations of the Universe's\nexpansion rate, H0. Originally planned as an in-person meeting, we moved to the\nvirtual domain to maintain strong scientific discourse despite the SARS-CoV-2\n(COVID-19) pandemic. Here, we describe our conference setup, participants\nfeedback gathered before and after the meeting, and lessons learned from this\nunexpected exercise. As e-conferencing will become increasingly common in the\nfuture, we provide our perspective on how e-conferences can make scientific\nexchange more effective and inclusive, in addition to climate friendly.\n"}
+{"abstract": "  We show that the Freiman--Ruzsa theorem, characterising finite sets with\nbounded doubling, leads to an alternative proof of a characterisation of Meyer\nsets, that is, relatively dense subsets of Euclidean spaces whose difference\nsets are uniformly discrete.\n"}
+{"abstract": "  We studied the absorption features of CO lines against the continuum\noriginating from the heated dust in the obscuring tori around active galactic\nnuclei (AGNs). We investigated the formation of absorption lines corresponding\nto the CO rotational transitions using three-dimensional non-LTE line transfer\nsimulations considering the dust thermal emission. As in Papers I--III of this\nseries, we performed post-processed radiative transfer calculations using the\n\"radiation-driven fountain model\" (wada2016}, which yields a geometrically\nthick obscuring structure around the nucleus. This model is consistent with the\nspectral energy distribution of the nearest type-2 Seyfert galaxy, the Circinus\ngalaxy. We found that the continuum-subtracted channel maps of $J = 4-3$ and\nhigher transitions show absorption regions along the disk mid-plane for an\nedge-on viewing angle. The spectra consist of multiple absorption and emission\nfeatures, reflecting the internal inhomogeneous and turbulent structure of the\ntorus. The deepest absorption feature is caused by the gas on the near-side of\nthe torus between $r =10$ and 15 pc, which is located in front of the\nAGN-heated dust inside $r \\simeq 5$ pc. We also found that a spatial resolution\nof 0.5--1.0 pc is necessary to resolve the absorption features. Moreover, the\ninclination angle must be close to the edge-on angle (i.e., $\\sim 85^\\circ$) to\nobserve the absorption features. The findings of the present study imply that\ncombining our radiation-hydrodynamic model with high-resolution observations of\nCO (7-6) by ALMA can provide new information about the internal structure of\nthe molecular tori in nearby AGNs.\n"}
+{"abstract": "  We present the results from the multi-wavelength monitoring observations of\nthe Narrow-Line Seyfert 1 galaxy Mrk 335 with AstroSat. We analysed both the\nX-ray (SXT & LAXPC) and UV (UVIT) data of the source at two epochs, separated\nby ~18 days. The source was in a low flux state during the observations, and\nthe X-ray spectra were found to be harder than usual. The presence of soft\nX-ray excess was identified in the observations, and the broadband X-ray\ncontinuum was modelled with power-law and blackbody (modified by intrinsic\nabsorption) and a distant neutral reflection component. We did not find any\nvariability in the X-ray spectral shape or the flux over this period. However,\nthe UV flux is found to be variable between the observations. The obtained\nresults from the X-ray analysis point to a scenario where the primary emission\nis suppressed and the component due to distant reflection dominates the\nobserved spectrum.\n"}
+{"abstract": "  Li and Panigrahi, in recent work, obtained the first deterministic algorithm\nfor the global minimum cut of a weighted undirected graph that runs in time\n$o(mn)$. They introduced an elegant and powerful technique to find isolating\ncuts for a terminal set in a graph via a small number of $s$-$t$ minimum cut\ncomputations.\n  In this paper we generalize their isolating cut approach to the abstract\nsetting of symmetric bisubmodular functions (which also capture symmetric\nsubmodular functions). Our generalization to bisubmodularity is motivated by\napplications to element connectivity and vertex connectivity. Utilizing the\ngeneral framework and other ideas we obtain significantly faster randomized\nalgorithms for computing global (and subset) connectivity in a number of\nsettings including hypergraphs, element connectivity and vertex connectivity in\ngraphs, and for symmetric submodular functions.\n"}
+{"abstract": "  We propose a new approach for defining and searching clusters in graphs that\nrepresent real technological or transaction networks. In contrast to the\nstandard way of finding dense parts of a graph, we concentrate on the structure\nof edges between the clusters, as it is motivated by some earlier observations,\ne.g. in the structure of networks in ecology and economics and by applications\nof discrete tomography. Mathematically special colorings and chromatic numbers\nof graphs are studied.\n"}
+{"abstract": "  Computation of gas dispersal in urban places or hilly grounds requires a\nlarge amount of computer time when addressed with conventional multidimensional\nmodels. Those are usually based on two-phase flow or Navier-Stokes equations.\nDifferent classes of simplified models exist. Among them, two-layer shallow\nwater models are interesting to address large-scale dispersion. Indeed,\ncompared to conventional multidimensional approaches, 2D simulations are\ncarried out to mimic 3D effects. The computational gain in CPU time is\nconsequently expected to be tremendous. However, such models involve at least\nthree fundamental difficulties. The first one is related to the lack of\nhyperbolicity of most existing formulations, yielding serious consequences\nregarding wave propagation. The second is related to the non-conservative terms\nin the momentum equations. Those terms account for interactions between fluid\nlayers. Recently, these two difficulties have been addressed in Chiapolino and\nSaurel (2018) and an unconditional hyperbolic model has been proposed along\nwith a Harten-Lax-van Leer (HLL) type Riemann solver dealing with the\nnon-conservative terms. In the same reference, numerical experiments showed\nrobustness and accuracy of the formulation. In the present paper, a third\ndifficulty is addressed. It consists of the determination of appropriate drag\neffect formulation. Such effects also account for interactions between fluid\nlayers and become of particular importance when dealing with heavy-gas\ndispersion. With this aim, the model is compared to laboratory experiments in\nthe context of heavy gas dispersal in quiescent air. It is shown that the model\naccurately reproduces experimental results thanks to an appropriate drag force\ncorrelation. This function expresses drag effects between the heavy and light\ngas layers. It is determined thanks to various experimental configurations of\ndam-break test problems.\n"}
+{"abstract": "  Discoveries of extended rotation curves have suggested the presence of dark\nmatter in spiral galaxy haloes. It has led to many studies that estimated the\ngalaxy total mass, mostly by using the Navarro Frenk and White (NFW) density\nprofile. We aim at verifying how the choice of the dark-matter profile may\naffect the predicted values of extrapolated total masses. We have considered\nthe recent Milky Way (MW) rotation curve, firstly because of its unprecedented\naccuracy, and secondly because the Galactic disk is amongst the least affected\nby past major mergers having fully reshaped the initial disk. We find that the\nuse of NFW profile (or its generalized form, gNFW) for calculating the\ndark-matter contribution to the MW rotation curve generates apparently\ninconsistent results, e.g., an increase of the baryonic mass leads to increase\nof the dark matter mass. Furthermore we find that NFW and gNFW profile narrow\nthe total mass range, leading to a possible methodological bias particularly\nagainst small MW masses. By using the Einasto profile that is more appropriate\nto represent cold dark matter haloes, we finally find that the Milky Way\nslightly decreasing rotation curve favors total mass that can be as small as\n2.6 $\\times 10^{11}$ $M_{\\odot}$, disregarding any other dynamical tracers\nfurther out in the MW. It is inconsistent with values larger than 18 $\\times\n10^{11}$ $M_{\\odot}$ for any kind of CDM dark-matter halo profiles, under the\nassumption that stars and gas do not influence the predicted dark matter\ndistribution in the MW. This methodological paper encourages the use of the\nEinasto profile for characterizing rotation curves with the aim of evaluating\ntheir total masses.\n"}
+{"abstract": "  We introduced the contemporary Amharic corpus, which is automatically tagged\nfor morpho-syntactic information. Texts are collected from 25,199 documents\nfrom different domains and about 24 million orthographic words are tokenized.\nSince it is partly a web corpus, we made some automatic spelling error\ncorrection. We have also modified the existing morphological analyzer,\nHornMorpho, to use it for the automatic tagging.\n"}
+{"abstract": "  Nuclear magnetism in n-doped semiconductors with positive hyperfine constant\nis revisited. Two kinds of nuclear magnetic ordering can be induced by resident\nelectrons in a deeply cooled nuclear spin system. At positive nuclear spin\ntemperature below a critical value, randomly oriented nuclear spin polarons\nsimilar to that predicted by I. Merkulov [I. Merkulov, Physics of the Solid\nState 40, 930 (1998)] should emerge. These polarons are oriented randomly and\nwithin each polaron nuclear and electron spins are aligned\nantiferromagnetically. At negative nuclear spin temperature below a critical\nvalue we predict another type of magnetic ordering - dynamically induced\nnuclear ferromagnet. This is a long-range ferromagnetically ordered state\ninvolving both electrons and nuclei. It can form if electron spin relaxation is\ndominated by the hyperfine coupling, rather than by the spin-orbit interaction.\nApplication of the theory to the n-doped GaAs suggests that the ferromagnetic\norder may be reached at experimentally achievable nuclear spin temperature\n$\\Theta_N \\approx 0.5$ $\\mu$K and lattice temperature $T_L \\approx 5$ K.\n"}
+{"abstract": "  The electronic properties of bilayer graphene with a magnetic quantum dot and\na magnetic quantum ring are investigated. The eigenenergies and wavefunctions\nof quasiparticle states are calculated analytically by solving decoupled\nfourth-order differential equations. For the magnetic quantum dot, in the case\nof a negative inner magnetic field, two peculiar characteristics of the\neigenenergy evolution are found: (i) the energy eigenstates change in a\nstepwise manner owing to energy anticrossing and (ii) the quantum states\napproach zero energy. For the magnetic quantum ring, there is an angular\nmomentum transition of eigenenergy as the inner radius of the ring varies, and\nthe Aharonov--Bohm effect is observed in the eigenenergy spectra for both\npositive and negative magnetic fields inside the inner radius.\n"}
+{"abstract": "  Let (X, d) be a bounded metric space with a base point 0 X , (Y, $\\bullet$)\nbe a Banach space and Lip $\\alpha$ 0 (X, Y) be the space of all\n$\\alpha$-H{\\\"o}lderfunctions that vanish at 0 X , equipped with its natural\nnorm (0 < $\\alpha$ $\\le$ 1). Let 0 < $\\alpha$ < $\\beta$ $\\le$ 1. We prove that\nLip $\\beta$ 0 (X, Y) is a $\\sigma$-porous subset of Lip $\\alpha$ 0 (X, Y), if\n(and only if) inf{d(x, x ') : x, x ' $\\in$ X; x = x ' } = 0 (i.e. d is\nnon-uniformly discrete). A more general result will be given.\n"}
+{"abstract": "  Manipulation tasks like loading a dishwasher can be seen as a sequence of\nspatial constraints and relationships between different objects. For example, a\nplate can be placed in a tray only if the tray is open. We aim to discover such\ntask-specific rules from demonstrations. We pose manipulation as a\nclassification problem over a graph, whose nodes represent task relevant\nentities like objects and goals, transform the environment scene into a graph\nand learn a graph neural network (GNN) policy using imitation learning. In our\nexperiments, a single learned GNN policy, trained using 20 expert\ndemonstrations, can solve multiple blockstacking and rearrangement tasks in\nboth simulation and on hardware, without any task description. The policy\nsuccessfully generalizes over the number of objects in the environment, their\npositions, and goal configurations (trained on single stacks, generalizes to\npyramids and multiple stacks). We also apply our approach to a complex\nsimulated dishwasher environment, where a robot learns to load a dishwasher\nfrom only 5 high-level human demonstrations. These experiments show that\nimitation learning on a graphical state and policy is a simple, yet powerful\ntool for solving complex long-horizon manipulation problems, without requiring\ndetailed task descriptions. Videos can be found at:\nhttps://youtu.be/x9hcKBh6K0A.\n"}
+{"abstract": "  An aggregated model is proposed, of which the partial-sum process scales to\nthe Karlin stable processes recently investigated in the literature. The limit\nextremes of the proposed model, when having regularly-varying tails, are\ncharacterized by the convergence of the corresponding point processes. The\nproposed model is an extension of an aggregated model proposed by Enriquez\n(2004) in order to approximate fractional Brownian motions with Hurst index\n$H\\in(0,1/2)$, and is of a different nature of the other recently investigated\nKarlin models which are essentially based on infinite urn schemes.\n"}
+{"abstract": "  We experimentally investigated the magnetic properties of NiCo$_2$O$_4$\nepitaxial films known to be conductive oxides with perpendicular magnetic\nanisotropy (PMA) at room temperature. Both magneto-torque and magnetization\nmeasurements at various temperatures provide clear experimental evidence of the\nspin reorientation transition at which the MA changes from PMA to easy-cone\nmagnetic anisotropy (ECMA) at a certain temperature ($T_{\\rm{SR}}$). ECMA was\ncommonly observed in films grown by pulsed laser deposition and reactive radio\nfrequency magnetron sputtering, although $T_{\\mathrm{SR}}$ is dependent on the\ngrowth method as well as the conditions. The cone angles measured from the\n$c$-axis increased successively at $T_{\\mathrm{SR}}$ and approached a maximum\nof 45-50 degrees at the lowest measurement temperature of 5 K. Calculation with\nthe cluster model suggests that the Ni$^{3+}$ ions occupying the $T_d$ site\ncould be the origin of the ECMA. Both the magnetic properties and the results\nof the calculation based on the cluster model indicate that the ECMA is\nattributable to the cation anti-site distribution of Ni$^{3+}$, which is\npossibly formed during the growth process of the thin films.\n"}
+{"abstract": "  Assessing the complexity of functions computed by a neural network helps us\nunderstand how the network will learn and generalize. One natural measure of\ncomplexity is how the network distorts length - if the network takes a\nunit-length curve as input, what is the length of the resulting curve of\noutputs? It has been widely believed that this length grows exponentially in\nnetwork depth. We prove that in fact this is not the case: the expected length\ndistortion does not grow with depth, and indeed shrinks slightly, for ReLU\nnetworks with standard random initialization. We also generalize this result by\nproving upper bounds both for higher moments of the length distortion and for\nthe distortion of higher-dimensional volumes. These theoretical results are\ncorroborated by our experiments.\n"}
+{"abstract": "  The smallest open case for classifying Steiner triple systems is order 21. A\nSteiner triple system of order 21, an STS(21), can have subsystems of orders 7\nand 9, and it is known that there are 12,661,527,336 isomorphism classes of\nSTS(21)s with sub-STS(9)s. The classification of STS(21)s with subsystems is\nhere completed by settling the case of STS(21)s with sub-STS(7)s; there are\n116,635,963,205,551 isomorphism classes of such systems. An estimation of the\nnumber of isomorphism classes of STS(21)s is given.\n"}
+{"abstract": "  Single file diffusion (SFD) exhibits anomalously slow collective transport\nwhen particles are able to immobilize by binding and unbinding to the\none-dimensional channel within which the particles diffuse. We have explored\nthis system for short pore-like channels using a symmetric exclusion process\n(SEP) with fully stochastic dynamics. We find that for shorter channels, a\nnon-Fickian regime emerges for slow binding kinetics. In this regime the\naverage flux $\\langle \\Phi \\rangle \\sim 1/L^3$, where $L$ is the channel length\nin units of the particle size. We find that a two-state model describes this\nbehavior well for sufficiently slow binding rates, where the binding rates\ndetermine the switching time between high-flux bursts of directed transport and\nlow-flux leaky states. Each high-flux burst is Fickian with $\\langle \\Phi\n\\rangle \\sim 1/L$. Longer systems are more often in a low flux state, leading\nto the non-Fickian behavior.\n"}
+{"abstract": "  In extreme pressures and temperature gradients, heat flow and magnetic fields\nare strongly coupled, but although theoretical work shows that strong heat\nflows can cause significant changes in the magnetic field, it has long proven\ndifficult to measure these changes experimentally. Here we describe the first\ndirect measurement of Nernst-driven magnetic cavitation, in which heat flow\ncauses expulsion of the magnetic field from the hottest regions of a plasma.\nUsing laser-driven proton radiography, we demonstrate that Nernst advection\ndominates changes to the magnetic field in underdense plasmas on these\nnanosecond timescales. Due to the increased magnetic field strength at the edge\nof the hot plasma, the observed magnetic cavitation can be accurately described\nby fluid models of heat flow, despite mean free paths much longer than the\ntemperature gradient. Expulsion of the magnetic field from hot regions of the\nplasma reduces the effectiveness of magnetised fusion techniques and disrupts\nmagnetised plasma experiments.\n"}
+{"abstract": "  We examine the role of trustworthiness and trust in statistical inference,\narguing that it is the extent of trustworthiness in inferential statistical\ntools which enables trust in the conclusions. Certain tools, such as the\np-value and significance test, have recently come under renewed criticism, with\nsome arguing that they damage trust in statistics. We argue the contrary,\nbeginning from the position that the central role of these methods is to form\nthe basis for trusted conclusions in the face of uncertainty in the data, and\nnoting that it is the misuse and misunderstanding of these tools which damages\ntrustworthiness and hence trust. We go on to argue that recent calls to ban\nthese tools would tackle the symptom, not the cause, and themselves risk\ndamaging the capability of science to advance, and feeding into public\nsuspicion of the discipline of statistics. The consequence could be aggravated\nmistrust of our discipline and of science more generally. In short, the very\nproposals could work in quite the contrary direction from that intended. We\nmake some alternative proposals for tackling the misuse and misunderstanding of\nthese methods, and for how trust in our discipline might be promoted.\n"}
+{"abstract": "  Orthogonal Cellular Automata (OCA) have been recently investigated in the\nliterature as a new approach to construct orthogonal Latin squares for\ncryptographic applications such as secret sharing schemes. In this paper, we\nconsider OCA for a different cryptographic task, namely the generation of\npseudorandom sequences. The idea is to iterate a dynamical system where the\noutput of an OCA pair is fed back as a new set of coordinates on the superposed\nsquares. The main advantage is that OCA ensure a certain amount of diffusion in\nthe generated sequences, a property which is usually missing from traditional\nCA-based pseudorandom number generators. We study the problem of finding OCA\npairs with maximal period by first performing an exhaustive search up to local\nrules of diameter $d=5$, and then focusing on the subclass of linear\nbipermutive rules. In this case, we characterize the periods of the sequences\nin terms of the order of the subgroup generated by an invertible Sylvester\nmatrix. We finally devise an algorithm based on Lagrange's theorem to\nefficiently enumerate all linear OCA pairs of maximal period up to diameter\n$d=11$.\n"}
+{"abstract": "  In their comment, Angelini et al. object to the conclusion of [J. Phys. A:\nMath. Theor., 52:445002, 2019] (1), where we show that in [Phys. Rev. B,\n87:134201, 2013] the exponent $\\nu$ has been obtained by applying a\nmathematical relation in a regime where this relation is not valid. We observe\nthat the criticism above on the mathematical validity of such relation has not\nbeen addressed in the comment. Our criticism thus remains valid, and disproves\nthe conclusions of the comment. This constitutes the main point of this reply.\n  We also provide a point-by-point response and discussion of Angelini et al.'s\nclaims. First, Angelini et al. claim that the prediction $2^{1/\\nu}=1$ of [1]\nis incorrect, because it results from the relation $\\lambda_{\\rm\nmax}=2^{1/\\nu}$ between the largest eigenvalue of the linearized\nrenormalization-group (RG) transformation and $\\nu$, which cannot be applied to\nthe ensemble renormalization group (ERG) method, because for the ERG\n$\\lambda_{\\rm max} =1 $. However, the feature $\\lambda_{\\rm max}=1$ is specific\nto the ERG transformation and it does not give any grounds for questioning the\nvalidity of the general relation $\\lambda_{\\rm max}=2^{1/\\nu}$ specifically for\nthe ERG transformation. Second, Angelini et al. claim that $\\nu$ should be\nextracted from an early RG regime (A), as opposed to the asymptotic regime (B)\nused to estimate $\\nu$ in [1] and that (B) is dominated by finite-size effects.\nStill, (A) is a small-wavelength, non-critical regime, which cannot\ncharacterize the critical exponent $\\nu$ related to the divergence of the\ncorrelation length. Also, the fact that (B) involves finite-size effects is a\nfeature specific to the ERG, and gives no rationale for extracting $\\nu$ from\n(A).\n  Finally, we refute the remaining claims made by Angelini et al., and thus\nstand by our assertion that the ERG method yields a prediction given by\n$2^{1/\\nu}=1$.\n"}
+{"abstract": "  Hubble tension and the search for preferred direction are two crucial\nunresolved issues in modern cosmology. Different measurements of the Hubble\nconstant provide significantly different values, and this is known as the\nHubble tension. The cosmological principle assumes that the universe is\nhomogeneous and isotropic; however, deviations from the isotropy have often\nbeen observed.\n  We apply the Bayesian tools and the Extreme Value theory dependent statistic\nto address the above issues. These techniques have been applied to the\nPanstarrs1 type Ia supernovae data. Our analysis for Hubble constant does not\nreject the Hubble tension. However, our value is smaller than that of the SHoES\nprogram and agrees with the CCHP value. Extreme value theory-based analysis\nindicates that the data does not show direction dependence. As a byproduct of\nour technique, we show that the errors in the data are non-Gaussian in nature.\n"}
+{"abstract": "  Background: The $K^\\pi=2^-$ excited band emerges systematically in $N=150$\nisotones raging from Pu to No with even-$Z$ numbers, and a sharp drop in\nenergies was observed in Cf. Purpose: I attempt to uncover the microscopic\nmechanism for the appearance of such a low-energy $2^-$ state in $^{248}$Cf.\nFurthermore, I investigate the possible occurrence of the low-energy\n$K^\\pi=2^+$ state to elucidate the mechanism that prefers the simultaneous\nbreaking of the reflection and axial symmetry to the breaking of the axial\nsymmetry alone in this mass region. Method: I employ a nuclear EDF method: the\nSkyrme-Kohn-Sham-Bogoliubov and the quasiparticle random-phase approximation\nare used to describe the ground state and the transition to excited states.\nResults: The Skyrme-type SkM* and SLy4 functionals reproduce the fall in\nenergy, but not the absolute value, of the $K^\\pi=2^-$ state at $Z=98$, where\nthe proton 2qp excitation $[633]7/2 \\otimes [521]3/2$ plays a decisive role for\nthe peculiar isotonic dependence. I find interweaving roles by the pairing\ncorrelation of protons and the deformed shell closure at $Z=98$. The SkM* model\npredicts the $K^\\pi=2^-$ state appears lower in energy in $^{246}$Cf than in\n$^{248}$Cf as the Fermi level of neutrons is located in between the $[622]5/2$\nand $[734]9/2$ orbitals. Except for $^{250}$Fm in the SkM* calculation, the\n$K^\\pi=2^+$ state is predicted to appear higher in energy than the $K^\\pi=2^-$\nstate because the quasi-proton $[521]1/2$ orbital is located above the\n$[633]7/2$ orbital. Conclusions: A systematic study of low-lying collective\nstates in heavy actinide nuclei provides a rigorous testing ground for\nmicroscopic nuclear models. The present study shows a need for improvements in\nthe EDFs to describe pairing correlations and shell structures in heavy nuclei,\nthat are indispensable in predicting the heaviest nuclei.\n"}
+{"abstract": "  Requiring the effective mass term for a category of fundamental Dirac or\nMajorana fermions of the same electric charge to be invariant under the\ntranslational transformations $\\psi^{}_{\\alpha \\rm L (R)} \\to \\psi^{}_{\\alpha\n\\rm L (R)} + n^{}_{\\alpha} z^{}_{\\psi \\rm L(R)}$ in the flavor space, where\n$n^{}_\\alpha$ and $z^{}_{\\psi \\rm L(R)}$ stand respectively for the\nflavor-dependent complex numbers and a constant spinor field anticommuting with\nthe fermion fields, we show that $n^{}_\\alpha$ can be identified as the\nelements $U^{}_{\\alpha i}$ in the $i$-th column of the unitary matrix $U$ used\nto diagonalize the corresponding Hermitian or symmetric fermion mass matrix\n$M^{}_\\psi$, and $m^{}_i = 0$ holds accordingly. We find that the reverse is\nalso true. Now that the mass spectra of charged leptons, up- and down-type\nquarks are all strongly hierarchical and current experimental data allow the\nlightest neutrino to be massless, we argue that the zero mass limit for the\nfirst-family fermions and the translational flavor symmetry behind it should be\na natural starting point for building viable fermion mass models.\n"}
+{"abstract": "  The Hamming distance is ubiquitous in computing. Its computation gets\nexpensive when one needs to compare a string against many strings. Quantum\ncomputers (QCs) may speed up the comparison.\n  In this paper, we extend an existing algorithm for computing the Hamming\ndistance. The extension can compare strings with symbols drawn from an\narbitrary-long alphabet (which the original algorithm could not). We implement\nour extended algorithm using the QisKit framework to be executed by a\nprogrammer without the knowledge of a QC (the code is publicly available). We\nthen provide four pedagogical examples: two from the field of bioinformatics\nand two from the field of software engineering. We finish by discussing\nresource requirements and the time horizon of the QCs becoming practical for\nstring comparison.\n"}
+{"abstract": "  All higher-spin s >= 1/2 Ising spin glasses are studied by\nrenormalization-group theory in spatial dimension d=3. The s-sequence of global\nphase diagrams, the chaos Lyapunov exponent, and the spin-glass runaway\nexponent are calculated. It is found that, in d=3, a finite-temperature\nspin-glass phase occurs for all spin values, including the continuum limit of s\n\\rightarrow \\infty. The phase diagrams, with increasing spin s, saturate to a\nlimit value. The spin-glass phase, for all s, exhibits chaotic behavior under\nrescalings, with the calculated Lyapunov exponent of \\lambda = 1.93 and runaway\nexponent of y_R=0.24, showing simultaneous strong-chaos and strong-coupling\nbehaviors. The ferromagnetic-spinglass-antiferromagnetic phase transitions\noccurring around p_t = 0.37 and 0.63 are unaffected by s, confirming the\npercolative nature of this phase transition.\n"}
+{"abstract": "  Airborne viruses, such as influenza, tuberculosis, and SARS-CoV-2, are\ntransmitted through virus-laden particles expelled when an infectious person\nsneezes, coughs, talks, or breathes. These virus-laden particles are more\nhighly concentrated in the expiratory jet of an infectious person than in a\nwell-mixed room, but this near-field enhancement in virion exposure has not\nbeen well quantified. Transmission of airborne viruses depends on factors that\nare inherently variable and, in many cases, poorly constrained, and quantifying\nthis uncertainty requires large ensembles of model simulations that span the\nvariability in input parameters. However, models that are well-suited to\nsimulate the near-field evolution of respiratory particles are also\ncomputationally expensive, which limits the exploration of parametric\nuncertainty. In order to perform many simulations that span the wide\nvariability in factors governing transmission, we developed the\nQuadrature-based model of Respiratory Aerosol and Droplets (QuaRAD). QuaRAD is\nan efficient framework for simulating the evolution of virus-laden particles\nafter they are expelled from an infectious person, their deposition to the\nnasal cavity of a susceptible person, and the subsequent risk of initial\ninfection. We simulated 10,000 scenarios to quantify the risk of initial\ninfection by a particular virus, SARS-CoV-2. The predicted risk of infection\nwas highly variable among scenarios and, in each scenario, was strongly\nenhanced near the infectious individual. In more than 50% of scenarios, the\nphysical distancing needed to avoid near-field enhancements in airborne\ntransmission was beyond the recommended safe distance of two meters (six feet)\nif the infectious person is not wearing a mask, though this distance defining\nthe near-field extent was also highly variable among scenarios.\n"}
+{"abstract": "  Salicylic acid (SA) is a primary phytohormone released in response to stress,\nparticularly biotic infections in plants. Monitoring SA levels may provide a\nway for early disease detection in crops providing a way for applying effective\nmeasures for reducing agricultural losses while increase our agricultural\nefficiency. Additionally, SA is an important chemical used extensively in the\npharmaceutical and healthcare industry due to its analgesic and\nanti-inflammatory properties. Developing a fast and accurate way for monitoring\nSA levels in human serum can have a life-saving impact for patients suffering\nfrom overdosing and/or mis-dosing. In this work, we present a low-cost,\nportable, and field-deployable electrochemical SA sensing system aimed towards\nachieving the above-mentioned goals. The developed sensor consists of a\nplug-and-play type device equipped with specialized designed high accuracy\nsensing electronics and a novel procedure for robust data analysis. The\ndeveloped sensor exhibits excellent linearity and sensitivity and selectivity.\nThe practical applicability of the developed sensor was also demonstrated by\nmeasuring SA levels in real samples with good accuracy.\n"}
+{"abstract": "  First introduced in 1954, linear probing is one of the oldest data structures\nin computer science, and due to its unrivaled data locality, it continues to be\none of the fastest hash tables in practice. It is widely believed and taught,\nhowever, that linear probing should never be used at high load factors; this is\nbecause primary-clustering effects cause insertions at load factor $1 - 1 /x$\nto take expected time $\\Theta(x^2)$ (rather than the ideal $\\Theta(x)$). The\ndangers of primary clustering, first discovered by Knuth in 1963, have been\ntaught to generations of computer scientists, and have influenced the design of\nsome of many widely used hash tables.\n  We show that primary clustering is not a foregone conclusion. We demonstrate\nthat small design decisions in how deletions are implemented have dramatic\neffects on the asymptotic performance of insertions, so that, even if a hash\ntable operates continuously at a load factor $1 - \\Theta(1/x)$, the expected\namortized cost per operation is $\\tilde{O}(x)$. This is because tombstones\ncreated by deletions actually cause an anti-clustering effect that combats\nprimary clustering.\n  We also present a new variant of linear probing (which we call graveyard\nhashing) that completely eliminates primary clustering on \\emph{any} sequence\nof operations: if, when an operation is performed, the current load factor is\n$1 - 1/x$ for some $x$, then the expected cost of the operation is $O(x)$. One\ncorollary is that, in the external-memory model with a data blocks of size $B$,\ngraveyard hashing offers the following remarkable guarantee: at any load factor\n$1 - 1/x$ satisfying $x = o(B)$, graveyard hashing achieves $1 + o(1)$ expected\nblock transfers per operation. Past external-memory hash tables have only been\nable to offer a $1 + o(1)$ guarantee when the block size $B$ is at least\n$\\Omega(x^2)$.\n"}
+{"abstract": "  Inhomogeneous phase-type distributions (IPH) are a broad class of laws which\narise from the absorption times of Markov jump processes. In the\ntime-homogeneous particular case, we recover phase-type (PH) distributions. In\nmatrix notation, various functionals corresponding to their distributional\nproperties are explicitly available and succinctly described. As the number of\nparameters increases, IPH distributions may converge weakly to any probability\nmeasure on the positive real line, making them particularly attractive\ncandidates for statistical modelling purposes. Contrary to PH distributions,\nthe IPH class allows for a wide range of tail behaviours, which often leads to\nadequate estimation with a moderate number of parameters. One of the main\ndifficulties in estimating PH and IPH distributions is their large number of\nmatrix parameters. This drawback is best handled through the\nexpectation-maximisation (EM) algorithm, exploiting the underlying and\nunobserved Markov structure. The matrixdist package presents tools for IPH\ndistributions to efficiently evaluate functionals, simulate, and carry out\nmaximum likelihood estimation through a three-step EM algorithm. Aggregated and\nright-censored data are supported by the fitting routines, and in particular,\none may estimate time-to-event data, histograms, or discretised theoretical\ndistributions.\n"}
+{"abstract": "  Recently, Self-supervised learning methods able to perform image denoising\nwithout ground truth labels have been proposed. These methods create\nlow-quality images by adding random or Gaussian noise to images and then train\na model for denoising. Ideally, it would be beneficial if one can generate\nhigh-quality CT images with only a few training samples via self-supervision.\nHowever, the performance of CT denoising is generally limited due to the\ncomplexity of CT noise. To address this problem, we propose a novel\nself-supervised learning-based CT denoising method. In particular, we train\npre-train CT denoising and noise models that can predict CT noise from Low-dose\nCT (LDCT) using available LDCT and Normal-dose CT (NDCT) pairs. For a given\ntest LDCT, we generate Pseudo-LDCT and NDCT pairs using the pre-trained\ndenoising and noise models and then update the parameters of the denoising\nmodel using these pairs to remove noise in the test LDCT. To make realistic\nPseudo LDCT, we train multiple noise models from individual images and generate\nthe noise using the ensemble of noise models. We evaluate our method on the\n2016 AAPM Low-Dose CT Grand Challenge dataset. The proposed ensemble noise\nmodel can generate realistic CT noise, and thus our method significantly\nimproves the denoising performance existing denoising models trained by\nsupervised- and self-supervised learning.\n"}
+{"abstract": "  In this paper, we study a colorful, impartial combinatorial game played on a\ntwo-dimensional grid, Transverse Wave. We are drawn to this game because of its\napparent simplicity, contrasting intractability, and intrinsic connection to\ntwo other combinatorial games, one inspired by social influence and another\ninspired by quantum superpositions.\n  More precisely, we show that Transverse Wave is at the intersection of\nsocial-influence-inspired Friend Circle and superposition-based Demi-Quantum\nNim. Transverse Wave is also connected with Schaefer's logic game Avoid True.\nIn addition to analyzing the mathematical structures and computational\ncomplexity of Transverse Wave, we provide a web-based version of the game,\nplayable at\nhttps://turing.plymouth.edu/~kgb1013/DB/combGames/transverseWave.html.\nFurthermore, we formulate a basic network-influence inspired game, called\nDemographic Influence, which simultaneously generalizes Node-Kyles and\nDemi-Quantum Nim (which in turn contains as special cases Nim, Avoid True, and\nTransverse Wave). These connections illuminate the lattice order, induced by\nspecial-case/generalization relationships over mathematical games, fundamental\nto both the design and comparative analyses of combinatorial games.\n"}
+{"abstract": "  Spin and spatial dimensionalities are universal concepts, essential for\ndescribing both phase transitions and dynamics in magnetic materials. Lately,\nthese ideas have been adopted to describe magnetic properties of metamaterials,\nas well as to replicate and explore ensembles of mesospins belonging to\ndifferent universality classes. Here, we take the next step by investigating\nmagnetic metamaterials not conforming to the conventional framework of\ncontinuous phase transitions. Instead of a continuous decrease in the moment\nwith temperature, discrete steps are possible, resulting in a binary transition\nin the interactions of the elements. The transition is enabled by nucleation\nand annihilation of vortex cores, shifting topological charges between the\ninterior and the edges of the elements. Consequently, the mesospins can be\nviewed as shifting their spin dimensionality, from 2 (XY-like) to 0, (vortices)\nat the transition. The results provide insight into how dynamics at different\nlength-scales couple, which can lead to thermally driven topological\ntransitions in magnetic metamaterials.\n"}
+{"abstract": "  Phase transitions in metal halide perovskites triggered by external\nprovocations produce significantly different material properties, providing a\nprodigious opportunity for a comprehensive applications. In the present study,\nthe first principles calculation has been performed with the help of density\nfunctional theory (DFT) using CASTEP code to investigate the physical\nproperties of lead-free CsSnBr3 metal halide under various hydrostatic\npressures. The pressure effect is determined in the range of 0-16 GPa.\nSubsequently, a significant change is observed in lattice constant and volume\nwith increasing pressure. The electronic band structure show semiconductor to\nmetal phase transition under elevated pressure. The investigation of optical\nfunctions displays that the absorption edge of CsSnBr3 perovskite is shifted\nremarkably toward the low energy region (red shift) with improved pressure up\nto 16 GPa. In addition, the absorptivity and dielectric constant also upsurges\nwith the applied hydrostatic pressure. Finally, the mechanical properties\nreveal that CsSnBr3 perovskite is mechanically stable and highly ductile; the\nductility is increased with raising pressure. This type of semiconductor to\nmetal phase transition may inspire a wide range of potential applications.\n"}
+{"abstract": "  This paper presents a wearable electrotactile feedback system to enhance\ncontact information for mid-air interactions with virtual objects. In\nparticular, we propose the use of electrotactile feedback to render the\ninterpenetration distance between the user's finger and the virtual content is\ntouched. Our approach consists of modulating the perceived intensity (frequency\nand pulse width modulation) of the electrotactile stimuli according to the\nregistered interpenetration distance. In a user study (N=21), we assessed the\nperformance of four different interpenetration feedback approaches:\nelectrotactile-only, visual-only, electrotactile and visual, and no\ninterpenetration feedback. First, the results showed that contact precision and\naccuracy were significantly improved when using interpenetration feedback.\nSecond, and more interestingly, there were no significant differences between\nvisual and electrotactile feedback when the calibration was optimized and the\nuser was familiarized with electrotactile feedback. Taken together, these\nresults suggest that electrotactile feedback could be an efficient replacement\nof visual feedback for enhancing contact information in virtual reality\navoiding the need of active visual focus and the rendering of additional visual\nartefacts.\n"}
+{"abstract": "  Topic modelling has been a successful technique for text analysis for almost\ntwenty years. When topic modelling met deep neural networks, there emerged a\nnew and increasingly popular research area, neural topic models, with over a\nhundred models developed and a wide range of applications in neural language\nunderstanding such as text generation, summarisation and language models. There\nis a need to summarise research developments and discuss open problems and\nfuture directions. In this paper, we provide a focused yet comprehensive\noverview of neural topic models for interested researchers in the AI community,\nso as to facilitate them to navigate and innovate in this fast-growing research\narea. To the best of our knowledge, ours is the first review focusing on this\nspecific topic.\n"}
+{"abstract": "  We study a single field inflationary model modified by a non-minimal coupling\nterm between the Ricci scalar $R$ and the scalar field $\\varphi$ in the context\nof constant-roll inflation. The first-order formalism is used to analyse the\nconstant-roll inflation instead of the standard methods used in the literature.\nIn principle, the formalism considers two functions of the scalar field,\n$W=W(\\varphi)$ and $Z=Z(\\varphi)$, which lead to the reduction of the equations\nof motion to first-order differential equations. The approach can be applied to\na wide range of cosmological situations since it directly relates the function\n$W$ with Hubble parameter $H$. We perform the inflationary analysis for\npower-law and exponential couplings, separately. Then, we investigate the\nfeatures of constant-roll potentials as inflationary potentials. Finally, we\ncompare the inflationary parameters of the models with the observations of CMB\nanisotropies in view of realize a physically motivated model.\n"}
+{"abstract": "  Quantum field theory of equilibrium and nonequilibrium Bose-Einstein\ncondensates is formulated so as to satisfy three basic requirements: the\nHugenholtz-Pines relation; conservation laws; identities among vertices\noriginating from Goldstone's theorem I. The key inputs are irreducible\nfour-point vertices, in terms of which we derive a closed system of equations\nfor Green's functions, three- and four-point vertices, and two-particle Green's\nfunctions. It enables us to study correlated Bose-Einstein condensates with a\ngapless branch of single-particle excitations without encountering any infrared\ndivergence. The single- and two-particle Green's functions are found to share\npoles, i.e., the structure of the two-particle Green's functions predicted by\nGavoret and Nozi\\`eres for a homogeneous condensate at $T=0$ is also shown to\npersist at finite temperatures, in the presence of inhomogeneity, and also in\nnonequilibrium situations.\n"}
+{"abstract": "  A sizable amount of goodness-of-fit tests involving functional data have\nappeared in the last decade. We provide a relatively compact revision of most\nof these contributions, within the independent and identically distributed\nframework, by reviewing goodness-of-fit tests for distribution and regression\nmodels with functional predictor and either scalar or functional response.\n"}
+{"abstract": "  This paper introduces the notion of exponential arcs in Hilbert space and of\nexponential arcs connecting vector states on a sigma-finite von Neumann algebra\nin its standard representation. Results from Tomita-Takesaki theory form an\nessential ingredient. Starting point is a non-commutative Radon-Nikodym theorem\nthat involves positive operators affiliated with the commutant algebra. It is\nshown that exponential arcs are differentiable and that parts of an exponential\narc are again exponential arcs. Special cases of probability theory and of\nquantum probability are used to illustrate the approach.\n"}
+{"abstract": "  Employing very simple electro-mechanical principles known from classical\nphysics, the Kibble balance establishes a very precise and absolute link\nbetween quantum electrical standards and macroscopic mass or force\nmeasurements. The success of the Kibble balance, in both determining\nfundamental constants ($h$, $N_A$, $e$) and realizing a quasi-quantum mass in\nthe 2019 newly revised International System of Units, relies on the perfection\nof Maxwell's equations and the symmetry they describe between Lorentz's force\nand Faraday's induction, a principle and a symmetry stunningly demonstrated in\nthe weighing and velocity modes of Kibble balances to within $1\\times10^{-8}$,\nwith nothing but imperfect wires and magnets. However, recent advances in the\nunderstanding of the current effect in Kibble balances reveal a troubling\nparadox. A diamagnetic effect, a force that does not cancel between mass-on and\nmass-off measurement, is challenging balance maker's assumptions of symmetry at\nlevels that are almost two orders of magnitude larger than the reported\nuncertainties. The diamagnetic effect, if it exists, shows up in weighing mode\nwithout a readily apparent reciprocal effect in the velocity mode, begging\nquestions about systematic errors at the very foundation of the new measurement\nsystem. The hypothetical force is caused by the coil current changing the\nmagnetic field, producing an unaccounted force that is systematically modulated\nwith the weighing current. Here we show that this diamagnetic force exists, but\nthe additional force does not change the equivalence between weighing and\nvelocity measurements. We reveal the unexpected way that symmetry is preserved\nand show that for typical materials and geometries the total relative effect on\nthe measurement is $\\approx 1\\times10^{-9}$.\n"}
+{"abstract": "  We propose an algorithm to obtain Green's functions as a continued fraction\non quantum computers, which is based on the construction of the Krylov basis\nusing variational quantum algorithms, and included in a Lanczos iterative\nscheme. This allows the integration of quantum algorithms with first principles\nmaterial science simulations, as we demonstrate within the dynamical mean-field\ntheory (DMFT) framework. DMFT enables quantitative predictions for strongly\ncorrelated materials, and relies on the calculation of Green's functions. On\nconventional computers the exponential growth of the Hilbert space with the\nnumber of orbitals limits DMFT to small systems. Quantum computers open new\navenues and can lead to a significant speedup in the computation of expectation\nvalues required to obtain the Green's function. We apply our Krylov variational\nquantum algorithm combined with DMFT to the charge transfer insulator\nLa$_{2}$CuO$_4$ using a quantum computing emulator, and show that with 8 qubits\nit predicts the correct insulating material properties for the paramagnetic\nphase. We therefore expect that the method is ideally suited to perform\nsimulations for real materials on near term quantum hardware.\n"}
+{"abstract": "  Here, we present a micro-electromechanical system (MEMS) for the\ninvestigation of the electromechanical coupling in graphene and potentially\nrelated 2D materials. Key innovations of our technique include: (1) the\nintegration of graphene into silicon-MEMS technology; (2) full control over\ninduced strain fields and doping levels within the graphene membrane and their\ncharacterization via spatially resolved confocal Raman spectroscopy; and (3)\nthe ability to detect the mechanical coupling of the graphene sheet to the MEMS\ndevice with via their mechanical resonator eigenfrequencies.\n"}
+{"abstract": "  Dynamic jamming is a phenomenon whereby a dense suspension switches from a\nfluid-like to a solid-like state when subjected to sufficient stress and\ndeformation. Large enough systems show that this transition is accompanied by a\ndistinct jamming front. We present an experimental study where two jamming\nfronts are created simultaneously using two cylinders moving in parallel. We\nfocus our observations on the collision of the jammed regions when the two\nfronts meet. Surprisingly, our measurements, combining surface texture\nvisualization and time-resolved particle image velocimetry, show the formation\nof an unjammed region contained within the otherwise jammed suspension.\n"}
+{"abstract": "  In this paper we consider the space-fractional Schr\\\"odinger equation with a\nsingular potential for a wide class of fractional hypoelliptic operators. Such\nanalysis can be conveniently realised in the setting of graded Lie groups. The\npaper is a continuation and extension of a previous one where the classical\nSchr\\\"odinger equation on $\\mathbb R^n$ with singular potentials was\nconsidered.\n"}
+{"abstract": "  In many multi-agent spatiotemporal systems, agents operate under the\ninfluence of shared, unobserved variables (e.g., the play a team is executing\nin a game of basketball). As a result, the trajectories of the agents are often\nstatistically dependent at any given time step; however, almost universally,\nmulti-agent models implicitly assume the agents' trajectories are statistically\nindependent at each time step. In this paper, we introduce baller2vec++, a\nmulti-entity Transformer that can effectively model coordinated agents.\nSpecifically, baller2vec++ applies a specially designed self-attention mask to\na mixture of location and \"look-ahead\" trajectory sequences to learn the\ndistributions of statistically dependent agent trajectories. We show that,\nunlike baller2vec (baller2vec++'s predecessor), baller2vec++ can learn to\nemulate the behavior of perfectly coordinated agents in a simulated toy\ndataset. Additionally, when modeling the trajectories of professional\nbasketball players, baller2vec++ outperforms baller2vec by a wide margin.\n"}
+{"abstract": "  This paper describes a quality assessment model for perceptual video\ncompression applications (PVM), which stimulates visual masking and\ndistortion-artefact perception using an adaptive combination of noticeable\ndistortions and blurring artefacts. The method shows significant improvement\nover existing quality metrics based on the VQEG database, and provides\ncompatibility with in-loop rate-quality optimisation for next generation video\ncodecs due to its latency and complexity attributes. Performance comparison are\nvalidated against a range of different distortion types.\n"}
+{"abstract": "  The study of thermal heat engines was pivotal to establishing the principles\nof equilibrium thermodynamics, with implications far wider than only engine\noptimization. For nonequilibrium systems, which by definition dissipate energy\neven at rest, how to best convert such dissipation into useful work is still\nlargely an outstanding question, with similar potential to illuminate general\nphysical principles. We review recent theoretical progress in studying the\nperformances of engines operating with active matter, where particles are\ndriven by individual self-propulsion. We distinguish two main classes, either\nautonomous engines exploiting a particle current, or cyclic engines applying\nperiodic transformation to the system, and present the strategies put forward\nso far for optimization. We delineate the limitations of previous studies, and\npropose some futures perspectives, with a view to building a consistent\nthermodynamic framework far from equilibrium.\n"}
+{"abstract": "  Being able to automatically detect the performance issues in apps can\nsignificantly improve apps' quality as well as having a positive influence on\nuser satisfaction. Application Performance Management (APM) libraries are used\nto locate the apps' performance bottleneck, monitor their behaviors at runtime,\nand identify potential security risks. Although app developers have been\nexploiting application performance management (APM) tools to capture these\npotential performance issues, most of them do not fully understand the\ninternals of these APM tools and the effect on their apps. To fill this gap, in\nthis paper, we conduct the first systematic study on APMs for apps by\nscrutinizing 25 widely-used APMs for Android apps and develop a framework named\nAPMHunter for exploring the usage of APMs in Android apps. Using APMHunter, we\nconduct a large-scale empirical study on 500,000 Android apps to explore the\nusage patterns of APMs and discover the potential misuses of APMs. We obtain\ntwo major findings: 1) some APMs still employ deprecated permissions and\napproaches, which makes APMs fail to perform as expected; 2) inappropriate use\nof APMs can cause privacy leaks. Thus, our study suggests that both APM vendors\nand developers should design and use APMs scrupulously.\n"}
+{"abstract": "  The nearby young star beta Pictoris hosts a rich and complex planetary\nsystem, with at least two giant planets and a nearly edge-on debris disk that\ncontains several dynamical subpopulations of planetesimals. While the inner\nranges of the debris disk have been studied extensively, less information is\nknown about the outer, fainter parts of the disk. Here we present an analysis\nof archival FORS V-band imaging data from 2003-2004, which have previously not\nbeen explored scientifically because the halo substructure of the bright\nstellar point spread function is complex. Through a high-contrast scheme based\non angular differential imaging, with a forward-modelling approach to mitigate\nself-subtraction, we produced the deepest imaging yet for the outer range of\nthe beta Pic disk, and extracted its morphological characteristics. A\nbrightness asymmetry between the two arms of the edge-on disk, which was\npreviously noted in the inner disk, is even more pronounced at larger angular\nseparations, reaching a factor ~10 around 1000 AU. Approaching 2000 AU, the\nbrighter arm is visible at a surface brightness of 27-28 mag/arcsec^2. Much\nlike for the brightness asymmetry, a tilt angle asymmetry exists between the\ntwo arms that becomes increasingly extreme at large separations. The outer tilt\nangle of 7.2 deg can only be explained if the outer disk is farther from an\nedge-on inclination than the inner disk, or if its dust has a stronger\nscattering anisotropy, or (most likely) both. The strong asymmetries imply the\npresence of a highly eccentric kinematic disk component, which may have been\ncaused by a disruptive event thought to have taken place at a closer-in\nlocation in the disk.\n"}
+{"abstract": "  We achieve two primary goals in this work. First, we propose a flexible\nalgorithm that can simulate various scenarios of state/government intervention.\nSecondly, we analyze the scenario exhibiting the critical behavior of the\nmarket of competing firms, which depends on the degree of government\nintervention and the activity level of the firms. Thus, we have analyzed the\nsecond-order phase transition series, finding the levels of critical\nintervention and the critical exponent values. As a result, we have observed a\nsharp increase of fluctuations at a critical intervention level and the local\nbreakdown of the average market technology concerning the frontier technology.\n"}
+{"abstract": "  We give a modification of Yu's construction of supercuspidal representations\nof a connected reductive group over a non-archimedean local field. This\nmodification restores the validity of certain key intertwining property claims\nmade by Yu, which were recently proven to be false for the original\nconstruction.\n  This modification is also an essential ingredient in the explicit\nconstruction of supercuspidal L-packets. As further applications, we prove the\nstability and many instances of endoscopic character identities of these\nsupercuspidal L-packets, subject to some conditions on the base field. In\nparticular, for regular supercuspidal parameters we prove all instances of\nstandard endoscopy. In addition, we prove that these supercuspidal L-packets\nsatisfy a certain property, which, together with standard endoscopy, uniquely\ncharacterizes the local Langlands correspondence for supercuspidal L-packets\n(again subject to the above mentioned conditions on the base field). These\nresults are based on a statement of the Harish-Chandra character formula for\nthe supercuspidal representations arising from the twisted Yu construction.\n"}
+{"abstract": "  Background: Floods are the most common natural disaster in the world,\naffecting the lives of hundreds of millions. Flood forecasting is therefore a\nvitally important endeavor, typically achieved using physical water flow\nsimulations, which rely on accurate terrain elevation maps. However, such\nsimulations, based on solving partial differential equations, are\ncomputationally prohibitive on a large scale. This scalability issue is\ncommonly alleviated using a coarse grid representation of the elevation map,\nthough this representation may distort crucial terrain details, leading to\nsignificant inaccuracies in the simulation. Contributions: We train a deep\nneural network to perform physics-informed downsampling of the terrain map: we\noptimize the coarse grid representation of the terrain maps, so that the flood\nprediction will match the fine grid solution. For the learning process to\nsucceed, we configure a dataset specifically for this task. We demonstrate that\nwith this method, it is possible to achieve a significant reduction in\ncomputational cost, while maintaining an accurate solution. A reference\nimplementation accompanies the paper as well as documentation and code for\ndataset reproduction.\n"}
+{"abstract": "  Given smooth step-like initial data $V(0,x)$ on the real line, we show that\nthe Korteweg--de Vries equation is globally well-posed for initial data $u(0,x)\n\\in V(0,x) + H^{-1}(\\mathbb{R})$. The proof uses our general well-posedness\nresult for exotic spatial asymptotics.\n  As a prerequisite, we show that KdV is globally well-posed for\n$H^3(\\mathbb{R})$ perturbations of step-like initial data. In the case $V\n\\equiv 0$, we obtain a new proof of the Bona--Smith theorem using the\nlow-regularity methods that established the sharp well-posedness of KdV in\n$H^{-1}$.\n"}
+{"abstract": "  Semantic Scene Completion (SSC) aims to jointly estimate the complete\ngeometry and semantics of a scene, assuming partial sparse input. In the last\nyears following the multiplication of large-scale 3D datasets, SSC has gained\nsignificant momentum in the research community because it holds unresolved\nchallenges. Specifically, SSC lies in the ambiguous completion of large\nunobserved areas and the weak supervision signal of the ground truth. This led\nto a substantially increasing number of papers on the matter. This survey aims\nto identify, compare and analyze the techniques providing a critical analysis\nof the SSC literature on both methods and datasets. Throughout the paper, we\nprovide an in-depth analysis of the existing works covering all choices made by\nthe authors while highlighting the remaining avenues of research. SSC\nperformance of the SoA on the most popular datasets is also evaluated and\nanalyzed.\n"}
+{"abstract": "  We determine the asymptotics for the variance of the number of zeros of\nrandom linear combinations of orthogonal polynomials of degree $\\leq n$ in\nsubintervals $\\left [ a,b\\right ] $ of the support of the underlying\northogonality measure $\\mu $. We show that, as $n\\rightarrow \\infty $, this\nvariance is asymptotic to $cn$, for some explicit constant $c>0$.\n"}
+{"abstract": "  We report numerical simulations of a trapped elastic vortex driven by a\nstrong ac magnetic field $H(t)=H\\sin\\omega t$ parallel to the surface of a\nsuperconducting film. The surface resistance and the power dissipated by an\noscillating vortex perpendicular to the film surface were calculated as\nfunctions of $H$ and $\\omega$ for different spatial distributions, densities,\nand strengths of pinning centers, including bulk pinning, surface pinning, and\ncluster pinning. Our simulations were performed for both the Bardeen-Stephen\nviscous vortex drag and the Larkin-Ovchinnikov (LO) drag coefficient $\\eta(v)$\ndecreasing with the vortex velocity $v$. The local residual surface resistance\n$R_i(H)$ calculated for different statistical realizations of the pinning\npotential exhibits strong mesoscopic fluctuations caused by local depinning\njumps of a vortex segment as $H$ increases, but the global surface resistance\n$\\bar{R}_i(H)$ obtained by averaging $R_i(H)$ over different pin configurations\nincreases smoothly with the field amplitude at small $H$ and levels off at\nhigher fields. For strong pinning, the LO decrease of $\\eta(v)$ with $v$ can\nresult in a nonmonotonic field dependence of $R_i(H)$ which decreases with $H$\nat higher fields, but cause a runaway instability of the vortex in a thick film\nfor weak pinning. It is shown that overheating of a single moving vortex can\nproduce the LO-like velocity dependence of $\\eta(v)$, but can mask the decrease\nof the surface resistance with $H$ at a higher density of trapped vortices.\n"}
+{"abstract": "  Academic research projects receive hundreds of billions of dollars of\ngovernment investment each year. They complement business research projects by\nfocusing on the generation of new foundational knowledge and addressing\nsocietal challenges. Despite the importance of academic research, the\nmanagement of it is often undisciplined and ad hoc. It has been postulated that\nthe inherent uncertainty and complexity of academic research projects make them\nchallenging to manage. However, based on this study's analysis of input and\nvoting from more than 500 academic research team members in facilitated risk\nmanagement sessions, the most important perceived risks are general, as opposed\nto being research specific. Overall participants' top risks related to funding,\nteam instability, unreliable partners, study participant recruitment, and data\naccess. Many of these risks would require system- or organization-level\nresponses that are beyond the scope of individual academic research teams.\n"}
+{"abstract": "  Electromagnetic waves in a system with a space and time dependent boundary\nexperience both diffraction and Doppler-like frequency conversion. In order to\nanalyse such situations, conventional methods call for either the eigenmodes or\nthe dyadic Green's function in space and time dependent media. Here, we propose\na dynamical differential method which does not require either of them. Our\nmethod utilises a dynamical coordinate transformation in order to simplify the\ncalculation of the optical response of the space and time dependent system. We\nreveal that the diffraction symmetry is broken in the presence of\ntraveling-wave type spatiotemporal modulation.\n"}
+{"abstract": "  Federated learning (FL) enables multiple clients to collaboratively train a\nshared model without disclosing their local datasets. This is achieved by\nexchanging local model updates with the help of a parameter server (PS).\nHowever, due to the increasing size of the trained models, the communication\nload due to the iterative exchanges between the clients and the PS often\nbecomes a bottleneck in the performance. Sparse communication is often employed\nto reduce the communication load, where only a small subset of the model\nupdates are communicated from the clients to the PS. In this paper, we\nintroduce a novel time-correlated sparsification (TCS) scheme, which builds\nupon the notion that sparse communication framework can be considered as\nidentifying the most significant elements of the underlying model. Hence, TCS\nseeks a certain correlation between the sparse representations used at\nconsecutive iterations in FL, so that the overhead due to encoding and\ntransmission of the sparse representation can be significantly reduced without\ncompromising the test accuracy. Through extensive simulations on the CIFAR-10\ndataset, we show that TCS can achieve centralized training accuracy with 100\ntimes sparsification, and up to 2000 times reduction in the communication load\nwhen employed together with quantization.\n"}
+{"abstract": "  We present results from parallelizing the unpacking and clustering steps of\nthe raw data from the silicon strip modules for reconstruction of charged\nparticle tracks. Throughput is further improved by concurrently processing\nmultiple events using nested OpenMP parallelism on CPU or CUDA streams on GPU.\nThe new implementation along with earlier work in developing a parallelized and\nvectorized implementation of the combinatoric Kalman filter algorithm has\nenabled efficient global reconstruction of the entire event on modern computer\narchitectures. We demonstrate the performance of the new implementation on\nIntel Xeon and NVIDIA GPU architectures.\n"}
+{"abstract": "  In this paper, we tackle two important problems in low-rank learning, which\nare partial singular value decomposition and numerical rank estimation of huge\nmatrices. By using the concepts of Krylov subspaces such as Golub-Kahan\nbidiagonalization (GK-bidiagonalization) as well as Ritz vectors, we propose\ntwo methods for solving these problems in a fast and accurate way. Our\nexperiments show the advantages of the proposed methods compared to the\ntraditional and randomized singular value decomposition methods. The proposed\nmethods are appropriate for applications involving huge matrices where the\naccuracy of the desired singular values and also all of their corresponding\nsingular vectors are essential. As a real application, we evaluate the\nperformance of our methods on the problem of Riemannian similarity learning\nbetween two various image datasets of MNIST and USPS.\n"}
+{"abstract": "  Inhibitory neurons play a crucial role in maintaining persistent neuronal\nactivity. Although connected extensively through electrical synapses\n(gap-junctions), these neurons also exhibit interactions through chemical\nsynapses in certain regions of the brain. When the coupling is sufficiently\nstrong, the effects of these two synaptic modalities combine in a nonlinear\nway. Hence, in this work, we focus on the strong inhibition regime and identify\nthe parametric conditions that result in the emergence of self-sustained\noscillations in systems of coupled excitable neurons, in the presence of a\nbrief sub-threshold stimulus. Our investigation on the dynamics in a minimal\nnetwork of two neurons reveals a rich set of dynamical behaviors viz., periodic\nand various complex oscillations including period-n (n=2,4,8...) dynamics and\nchaos. We further extend our study by considering a system of inhibitory\nneurons arranged in a one-dimensional ring topology and determine the optimal\nconditions for sustained activity. Our work highlights the nonlinear dynamical\nbehavior arising due to the combined effects of gap-junctions and strong\nsynaptic inhibition, which can have potential implications in maintaining\nrobust memory patterns.\n"}
+{"abstract": "  Given a sequence of Borel probability measures on a Hausdorff space which\nsatisfy a large deviation principle, we consider the corresponding sequence of\nmeasures formed by conditioning on a set $B$. If the large deviation rate\nfunction $I$ is good and effectively continuous and the conditioning set has\nthe property that (1) $\\overline{B^\\circ} = \\overline{B}$ and (2) $I(x) <\n\\infty$ for all $x \\in \\overline{B}$, then the sequence of conditional measures\nsatisfies a large deviation principle with the good, effectively continuous\nrate function $I_B$, where $I_B(x) = I(x)-\\inf I(B)$ if $x\\in\\overline{B}$ and\n$I_B(x) = \\infty$ otherwise.\n"}
+{"abstract": "  Constructing quantum Hamiltonians for simulating and controlling the exotic\nphysics of many-body systems belongs to the most important topics of condensed\nmatter physics and quantum technologies. The main challenge that hinders the\nfuture investigations is the extremely high complexity for either their\nnumerical simulations or experimental realizations. In this work, we propose\nthe temperature-adaptive entanglement simulator (TAES) that mimics and tunes\nthe thermodynamics of the one-dimensional (1D) many-body system by embedding a\nsmall-size model in an entanglement bath. The entanglement bath is described by\nthe interactions located at the boundaries of the small-size model, whose\ncoupling constants are optimized by means of differentiable tensor network at\ntarget temperatures. With the benchmark on 1D spin chains, TAES surpasses the\nstate-of-the-art accuracy compared with the existing finite-temperature\napproaches such as linearized and differential tensor renormalization group\nalgorithms. By tuning the couplings of the entanglement bath with the\ntemperature fixed, the bulk entropy exhibits similar behavior compared to that\nobtained by tuning the temperature. Our work provides novel opportunities of\nengineering the distribution of fluctuations and mimicking the non-equilibrium\nphenomena in a uniform temperature within the canonical ensemble framework\nusing the optimized boundary interactions.\n"}
+{"abstract": "  A first principles approach to the theoretical description of the development\nof biological forms, from a fertilized egg to a functioning embryo, remains a\ncentral challenge to applied physics and theoretical biology. Rather than refer\nto principles of self-organization and non-equilibrium statistical mechanics to\ndescribe a developing embryo from its active cellular constituents, a purely\ngeometric theory is constructed that references the properties of the ambient\nspace that the embryo occupies. In 1975 the Fields laureate Ren\\'{e} Thom\ndeveloped a system of techniques and local dynamical models that are capable of\nreconstructing the local dynamic of an embryo at each new growth event of the\nsystem. Each new growth event (the development of a limb, for example) is a\ntopological change in the dynamic of the system that can be classified only\naccording to the properties of space. The local models can be non-conservative\nflows with robust attractor behavior that serve as organizing centers for\nsystems development. Hamiltonian flows can also be considered with novel,\nself-reproducing vague attractor behavior. The set of growth events become an\nunfolding space related to the differentiable manifold of states. The set of\ngrowth events, which Thom refers to as the catastrophe set, has special\nalgebraic properties which permit these models to be low dimensional--the local\nmodel contains few parameters. We present Thom's work as a research program\noutlining a framework for the construction of these local models, and,\nultimately, the synthesis of these models into a full theoretical description\nof a developing biological organism. We give examples of the application of\nselected models to key growth events in the process of gastrulation.\n"}
+{"abstract": "  The optimal dynamic treatment rule (ODTR) framework offers an approach for\nunderstanding which kinds of patients respond best to specific treatments -- in\nother words, treatment effect heterogeneity. Recently, there has been a\nproliferation of methods for estimating the ODTR. One such method is an\nextension of the SuperLearner algorithm -- an ensemble method to optimally\ncombine candidate algorithms extensively used in prediction problems -- to\nODTRs. Following the \"causal roadmap,\" we causally and statistically define the\nODTR and provide an introduction to estimating it using the ODTR SuperLearner.\nAdditionally, we highlight practical choices when implementing the algorithm,\nincluding choice of candidate algorithms, metalearners to combine the\ncandidates, and risk functions to select the best combination of algorithms.\nUsing simulations, we illustrate how estimating the ODTR using this\nSuperLearner approach can uncover treatment effect heterogeneity more\neffectively than traditional approaches based on fitting a parametric\nregression of the outcome on the treatment, covariates and treatment-covariate\ninteractions. We investigate the implications of choices in implementing an\nODTR SuperLearner at various sample sizes. Our results show the advantages of:\n(1) including a combination of both flexible machine learning algorithms and\nsimple parametric estimators in the library of candidate algorithms; (2) using\nan ensemble metalearner to combine candidates rather than selecting only the\nbest-performing candidate; (3) using the mean outcome under the rule as a risk\nfunction. Finally, we apply the ODTR SuperLearner to the \"Interventions\" study,\nan ongoing randomized controlled trial, to identify which justice-involved\nadults with mental illness benefit most from cognitive behavioral therapy (CBT)\nto reduce criminal re-offending.\n"}
+{"abstract": "  This paper describes the automatic measurement of the spatial organization of\nthe visual axes of insect compound eyes, which consist of several thousands of\nvisual units called ommatidia. Each ommatidium samples the optical information\nfrom a small solid angle, with an approximately Gaussian-distributed\nsensitivity (half-width of the order of one degree) centered around a visual\naxis. Together, the ommatidia gather the optical information from virtually the\nfull surroundings. The spatial distribution of the visual axes thus determines\nthe eye's spatial resolution. Knowledge of the optical organization of a\ncompound eye and its visual acuity is crucial for quantitative studies of the\nneural processing of visual information. Here we present an automated procedure\nfor mapping a compound eye's visual axes, using an intrinsic optical\nphenomenon, the pseudopupil. We outline the optomechanical setup for scanning\ninsect eyes, and use experimental results obtained from a housefly, Musca\ndomestica, to illustrate the steps in the measurement procedure.\n"}
+{"abstract": "  The purpose of this article is to show a close relationship between the\ngeneralized central series of Leibniz algebras. Some analogues of the classical\ngroup-theoretical theorems of Schur and Baer for Leibniz algebras are proved.\n"}
+{"abstract": "  Quantum channel discrimination is a fundamental problem in quantum\ninformation science. In this study, we consider general quantum channel\ndiscrimination problems, and derive the lower bounds of the error probability.\nOur lower bounds are based on the triangle inequalities of the Bures angle and\nthe trace distance. As a consequence of the lower bound based on the Bures\nangle, we prove the optimality of Grover's search if the number of marked\nelements is fixed to some integer $k$. This result generalizes Zalka's result\nfor $k=1$. We also present several numerical results in which our lower bounds\nbased on the trace distance outperform recently obtained lower bounds.\n"}
+{"abstract": "  Affective Analysis is not a single task, and the valence-arousal value,\nexpression class, and action unit can be predicted at the same time. Previous\nresearches did not pay enough attention to the entanglement and hierarchical\nrelation of these three facial attributes. We propose a novel model named\nfeature pyramid networks for multi-task affect analysis. The hierarchical\nfeatures are extracted to predict three labels and we apply a teacher-student\ntraining strategy to learn from pretrained single-task models. Extensive\nexperiment results demonstrate the proposed model outperforms other models.\nThis is a submission to The 2nd Workshop and Competition on Affective Behavior\nAnalysis in the wild (ABAW). The code and model are available for research\npurposes at https://github.com/ryanhe312/ABAW2-FPNMAA.\n"}
+{"abstract": "  Fractional equations have become the model of choice in several applications\nwhere heterogeneities at the microstructure result in anomalous diffusive\nbehavior at the macroscale. In this work we introduce a new fractional operator\ncharacterized by a doubly-variable fractional order and possibly truncated\ninteractions. Under certain conditions on the model parameters and on the\nregularity of the fractional order we show that the corresponding Poisson\nproblem is well-posed. We also introduce a finite element discretization and\ndescribe an efficient implementation of the finite-element matrix assembly in\nthe case of piecewise constant fractional order. Through several numerical\ntests, we illustrate the improved descriptive power of this new operator across\nmedia interfaces. Furthermore, we present one-dimensional and two-dimensional\n$h$-convergence results that show that the variable-order model has the same\nconvergence behavior as the constant-order model.\n"}
+{"abstract": "  Secure Device-to-Device (D2D) communication is becoming increasingly\nimportant with the ever-growing number of Internet-of-Things (IoT) devices in\nour daily life. To achieve secure D2D communication, the key agreement between\ndifferent IoT devices without any prior knowledge is becoming desirable.\nAlthough various approaches have been proposed in the literature, they suffer\nfrom a number of limitations, such as low key generation rate and short pairing\ndistance. In this paper, we present InaudibleKey, an inaudible acoustic\nsignal-based key generation protocol for mobile devices. Based on acoustic\nchannel reciprocity, InaudibleKey exploits the acoustic channel frequency\nresponse of two legitimate devices as a common secret to generating keys.\nInaudibleKey employs several novel technologies to significantly improve its\nperformance. We conduct extensive experiments to evaluate the proposed system\nin different real environments. Compared to state-of-the-art works,\nInaudibleKey improves key generation rate by 3-145 times, extends pairing\ndistance by 3.2-44 times, and reduces information reconciliation counts by\n2.5-16 times. Security analysis demonstrates that InaudibleKey is resilient to\na number of malicious attacks. We also implement InaudibleKey on modern\nsmartphones and resource-limited IoT devices. Results show that it is\nenergy-efficient and can run on both powerful and resource-limited IoT devices\nwithout incurring excessive resource consumption.\n"}
+{"abstract": "  Modern and emerging architectures demand increasingly complex compiler\nanalyses and transformations. As the emphasis on compiler infrastructure moves\nbeyond support for peephole optimizations and the extraction of\ninstruction-level parallelism, they should support custom tools designed to\nmeet these demands with higher-level analysis-powered abstractions of wider\nprogram scope. This paper introduces NOELLE, a robust open-source\ndomain-independent compilation layer built upon LLVM providing this support.\nNOELLE is modular and demand-driven, making it easy-to-extend and adaptable to\ncustom-tool-specific needs without unduly wasting compile time and memory. This\npaper shows the power of NOELLE by presenting a diverse set of ten custom tools\nbuilt upon it, with a 33.2% to 99.2% reduction in code size (LoC) compared to\ntheir counterparts without NOELLE.\n"}
+{"abstract": "  Cardiac segmentation of atriums, ventricles, and myocardium in computed\ntomography (CT) images is an important first-line task for presymptomatic\ncardiovascular disease diagnosis. In several recent studies, deep learning\nmodels have shown significant breakthroughs in medical image segmentation\ntasks. Unlike other organs such as the lungs and liver, the cardiac organ\nconsists of multiple substructures, i.e., ventricles, atriums, aortas,\narteries, veins, and myocardium. These cardiac substructures are proximate to\neach other and have indiscernible boundaries (i.e., homogeneous intensity\nvalues), making it difficult for the segmentation network focus on the\nboundaries between the substructures. In this paper, to improve the\nsegmentation accuracy between proximate organs, we introduce a novel model to\nexploit shape and boundary-aware features. We primarily propose a shape-aware\nattention module, that exploits distance regression, which can guide the model\nto focus on the edges between substructures so that it can outperform the\nconventional contour-based attention method. In the experiments, we used the\nMulti-Modality Whole Heart Segmentation dataset that has 20 CT cardiac images\nfor training and validation, and 40 CT cardiac images for testing. The\nexperimental results show that the proposed network produces more accurate\nresults than state-of-the-art networks by improving the Dice similarity\ncoefficient score by 4.97%. Our proposed shape-aware contour attention\nmechanism demonstrates that distance transformation and boundary features\nimprove the actual attention map to strengthen the responses in the boundary\narea. Moreover, our proposed method significantly reduces the false-positive\nresponses of the final output, resulting in accurate segmentation.\n"}
+{"abstract": "  We consider $W^+W^-$ production in hadronic collisions and present the\ncomputation of next-to-next-to-leading order accurate predictions consistently\nmatched to parton showers (NNLO+PS) using the MiNNLO$_{\\rm PS}$ method. Spin\ncorrelations, interferences and off-shell effects are included by calculating\nthe full process $pp \\to e^+\\nu_e \\mu^-\\bar{\\nu}_\\mu$. This is the first\nNNLO+PS calculation for $W^+W^-$ production that does not require an\na-posteriori multi-differential reweighting. The evaluation time of the\ntwo-loop contribution has been reduced by more than one order of magnitude\nthrough a four-dimensional cubic spline interpolation. We find good agreement\nwith the inclusive and fiducial cross sections measured by ATLAS and CMS. Both\nNNLO corrections and matching to parton showers are important for an accurate\nsimulation of the $W^+W^-$ signal, and their matching provides the best\ndescription of fully exclusive $W^+W^-$ events to date.\n"}
+{"abstract": "  Motivated by [G. Cannizzaro, M. Hairer, arXiv:2010.02766], we provide an\nalternative characterisation of the Brownian Web (see [T\\'oth B., Werner W.,\nProbab. Theory Related Fields, '98] and [L. R. G. Fontes, M. Isopi, C. M.\nNewman, and K. Ravishankar, Ann. Probab., '04]), i.e. a family of coalescing\nBrownian motions starting from every point in $\\mathbb R^2$ simultaneously, and\nfit it into the wider framework of random (spatial) $\\mathbb R$-trees. We\ndetermine some of its properties (e.g. its box-counting dimension) and recover\nsome which were determined in earlier works, such as duality, special points\nand convergence of the graphical representation of coalescing random walks.\nAlong the way, we introduce a modification of the topology of spatial $\\mathbb\nR$-trees in [T. Duquesne, J.-F. Le Gall, Probab. Theory Related Fields, '05]\nand [M. T. Barlow, D. A. Croydon, T. Kumagai, Ann. Probab. '17] which makes it\nPolish and could be of independent interest.\n"}
+{"abstract": "  An accurate and reliable technique for predicting Remaining Useful Life (RUL)\nfor battery cells proves helpful in battery-operated IoT devices, especially in\nremotely operated sensor nodes. Data-driven methods have proved to be the most\neffective methods until now. These IoT devices have low computational\ncapabilities to save costs, but Data-Driven battery health techniques often\nrequire a comparatively large amount of computational power to predict SOH and\nRUL due to most methods being feature-heavy. This issue calls for ways to\npredict RUL with the least amount of calculations and memory. This paper\nproposes an effective and novel peak extraction method to reduce computation\nand memory needs and provide accurate prediction methods using the least number\nof features while performing all calculations on-board. The model can\nself-sustain, requires minimal external interference, and hence operate\nremotely much longer. Experimental results prove the accuracy and reliability\nof this method. The Absolute Error (AE), Relative error (RE), and Root Mean\nSquare Error (RMSE) are calculated to compare effectiveness. The training of\nthe GPR model takes less than 2 seconds, and the correlation between SOH from\npeak extraction and RUL is 0.97.\n"}
+{"abstract": "  In this paper we show that the closure of the numerical range of an\n$n+1$-periodic tridiagonal operator is equal to the numerical range of a\n$2(n+1)\\times 2(n+1)$ complex matrix.\n"}
+{"abstract": "  To highlight the challenges of achieving representation disentanglement for\ntext domain in an unsupervised setting, in this paper we select a\nrepresentative set of successfully applied models from the image domain. We\nevaluate these models on 6 disentanglement metrics, as well as on downstream\nclassification tasks and homotopy. To facilitate the evaluation, we propose two\nsynthetic datasets with known generative factors. Our experiments highlight the\nexisting gap in the text domain and illustrate that certain elements such as\nrepresentation sparsity (as an inductive bias), or representation coupling with\nthe decoder could impact disentanglement. To the best of our knowledge, our\nwork is the first attempt on the intersection of unsupervised representation\ndisentanglement and text, and provides the experimental framework and datasets\nfor examining future developments in this direction.\n"}
+{"abstract": "  In recent work, the first two authors constructed a generalized continued\nfraction called the $p$-continued fraction, characterized by the property that\nits convergents (a subsequence of the regular convergents) are best\napproximations with respect to the $L^p$ norm, where $p\\geq 1$. We extend this\nconstruction to the region $0<p<1$, where now the $L^p$ quasinorm is\nnon-convex. We prove that the approximation coefficients of the $p$-continued\nfraction are bounded above by $1/\\sqrt{5}+\\varepsilon_p$, where\n$\\varepsilon_p\\to 0$ as $p\\to 0$. In light of Hurwitz's theorem, this upper\nbound is sharp, in the limit. We also measure the maximum number of consecutive\nregular convergents that are skipped by the $p$-continued fraction.\n"}
+{"abstract": "  Hate Speech has become a major content moderation issue for online social\nmedia platforms. Given the volume and velocity of online content production, it\nis impossible to manually moderate hate speech related content on any platform.\nIn this paper we utilize a multi-task and multi-lingual approach based on\nrecently proposed Transformer Neural Networks to solve three sub-tasks for hate\nspeech. These sub-tasks were part of the 2019 shared task on hate speech and\noffensive content (HASOC) identification in Indo-European languages. We expand\non our submission to that competition by utilizing multi-task models which are\ntrained using three approaches, a) multi-task learning with separate task\nheads, b) back-translation, and c) multi-lingual training. Finally, we\ninvestigate the performance of various models and identify instances where the\nTransformer based models perform differently and better. We show that it is\npossible to to utilize different combined approaches to obtain models that can\ngeneralize easily on different languages and tasks, while trading off slight\naccuracy (in some cases) for a much reduced inference time compute cost. We\nopen source an updated version of our HASOC 2019 code with the new improvements\nat https://github.com/socialmediaie/MTML_HateSpeech.\n"}
+{"abstract": "  A smart meta-superconductor Bi(Pb)SrCaCuO (B(P)SCCO) may increase the\ncritical transition temperature (TC) of B(P)SCCO by electroluminescence (EL)\nenergy injection of inhomogeneous phases. However, the increase amplitude\n{\\Delta}TC ({\\Delta}TC=TC-T(C,pure)) of TC is relatively small. In this study,\na smart meta-superconductor B(P)SCCO with different matrix sizes was designed.\nThree kinds of raw materials with different particle sizes were used, and\ndifferent series of Y2O3:Sm3+, Y2O3, Y2O3:Eu3+, and Y2O3:Eu3++Ag doped samples\nand pure B(P)SCCO were prepared. Results indicated that the TC of the Y2O3 or\nY2O3:Sm3+ non-luminescent dopant doping sample is lower than that of pure\nB(P)SCCO. However, the TC of the Y2O3:Eu3++Ag or Y2O3:Eu3+ luminescent\ninhomogeneous phase doping sample is higher than that of pure B(P)SCCO. With\nthe decrease of the raw material particle size from 30 to 5 {\\mu}m, the\nparticle size of the B(P)SCCO superconducting matrix in the prepared samples\ndecreases, and the doping content of the Y2O3:Eu3++Ag or Y2O3:Eu3+ increases\nfrom 0.2% to 0.4%. Meanwhile, the increase of the inhomogeneous phase content\nenhances the {\\Delta}TC. When the particle size of raw material is 5 {\\mu}m,\nthe doping concentration of the luminescent inhomogeneous phase can be\nincreased to 0.4%. At this time, the zero-resistance temperature and onset\ntransition temperature of the Y2O3:Eu3++Ag doped sample are 4 and 6.3 K higher\nthan those of pure B(P)SCCO, respectively.\n"}
+{"abstract": "  Let $M$ be the maximal operator associated to a smooth curve in $\\mathbb R^3$\nwhich has nonvanishing curvature and torsion. We prove that $M$ is bounded on\n$L^p$ if and only if $p>3$.\n"}
+{"abstract": "  Consider an irreducible Markov chain which satisfies a ratio limit theorem,\nand let $\\rho$ be the spectral radius of the chain. We investigate the relation\nof the the $\\rho\\,$-Martin boundary with the boundary induced by the\n$\\rho\\,$-harmonic kernel which appears in the ratio limit. Special emphasis is\non random walks on non-amenable groups, specifically, free groups and\nhyperbolic groups.\n"}
+{"abstract": "  Constrained reinforcement learning involves multiple rewards that must\nindividually accumulate to given thresholds. In this class of problems, we show\na simple example in which the desired optimal policy cannot be induced by any\nlinear combination of rewards. Hence, there exist constrained reinforcement\nlearning problems for which neither regularized nor classical primal-dual\nmethods yield optimal policies. This work addresses this shortcoming by\naugmenting the state with Lagrange multipliers and reinterpreting primal-dual\nmethods as the portion of the dynamics that drives the multipliers evolution.\nThis approach provides a systematic state augmentation procedure that is\nguaranteed to solve reinforcement learning problems with constraints. Thus,\nwhile primal-dual methods can fail at finding optimal policies, running the\ndual dynamics while executing the augmented policy yields an algorithm that\nprovably samples actions from the optimal policy.\n"}
+{"abstract": "  A sweep of a point configuration is any ordered partition induced by a linear\nfunctional. Posets of sweeps of planar point configurations were formalized and\nabstracted by Goodman and Pollack under the theory of allowable sequences of\npermutations. We introduce two generalizations that model posets of sweeps of\nhigher dimensional configurations.\n  Mimicking the fact that sweep polytopes of point configurations (the monotone\npath polytopes of the associated zonotopes) are projections of permutahedra, we\ndefine sweep oriented matroids as strong maps of the braid oriented matroid.\nAllowable sequences are then the sweep oriented matroids of rank 2, and many of\ntheir properties extend to higher rank. We show strong ties between sweep\noriented matroids and both modular hyperplanes and Dilworth truncations from\n(unoriented) matroid theory. We also explore their connection with the\ngeneralized Baues problem for cellular strings, where sweep oriented matroids\ncan play the role of monotone path polytopes, even for non-realizable oriented\nmatroids. In particular, we show that for oriented matroids that admit a sweep\noriented matroid, their poset of pseudo-sweeps deformation retracts to a sphere\nof the appropriate dimension.\n  A second generalization are allowable graphs of permutations: symmetric sets\nof permutations pairwise connected by allowable sequences. They have the\nstructure of acycloids and include sweep oriented matroids.\n"}
+{"abstract": "  We propose a worldsheet description for the ${\\rm AdS}_5\\times {\\rm S}^5$\nstring theory dual to large $N$, free ${\\cal N}=4$ super Yang-Mills in four\ndimensions. The worldsheet theory is a natural generalisation of the recently\ninvestigated tensionless string on ${\\rm AdS}_3\\times {\\rm S}^3\\times\n\\mathbb{T}^4$. As in the case of ${\\rm AdS}_3$ it has a free field description,\nwith spectrally flowed sectors, and is closely related to an (ambi-)twistor\nstring theory. Here, however, we view it as a critical $N=4$ (closed) string\nbackground. We argue that the corresponding worldsheet gauge constraints reduce\nthe degrees of freedom to a finite number of oscillators (string bits) in each\nspectrally flowed sector. Imposing a set of residual gauge constraints on this\nreduced oscillator Fock space then determines the physical spectrum of the\nstring theory. Quite remarkably, we find compelling evidence that this\nprescription reproduces precisely the entire planar spectrum - of single trace\noperators - of the free super Yang-Mills theory.\n"}
+{"abstract": "  We investigate an interpolation/extrapolation method that, given scattered\nobservations of the Fourier transform, approximates its inverse. The\ninterpolation algorithm takes advantage of modelling the available data via a\nshape-driven interpolation based on Variably Scaled Kernels (VSKs), whose\nimplementation is here tailored for inverse problems. The so-constructed\ninterpolants are used as inputs for a standard iterative inversion scheme.\nAfter providing theoretical results concerning the spectrum of the VSK\ncollocation matrix, we test the method on astrophysical imaging benchmarks.\n"}
+{"abstract": "  Intelligent diagnosis method based on data-driven and deep learning is an\nattractive and meaningful field in recent years. However, in practical\napplication scenarios, the imbalance of time-series fault is an urgent problem\nto be solved. This paper proposes a novel deep metric learning model, where\nimbalanced fault data and a quadruplet data pair design manner are considered.\nBased on such data pair, a quadruplet loss function which takes into account\nthe inter-class distance and the intra-class data distribution are proposed.\nThis quadruplet loss pays special attention to imbalanced sample pair. The\nreasonable combination of quadruplet loss and softmax loss function can reduce\nthe impact of imbalance. Experiment results on two open-source datasets show\nthat the proposed method can effectively and robustly improve the performance\nof imbalanced fault diagnosis.\n"}
+{"abstract": "  We study the Lipschitz stability in time for $\\alpha$-dissipative solutions\nto the Hunter-Saxton equation, where $\\alpha \\in [0,1]$ is a constant. We\ndefine metrics in both Lagrangian and Eulerian coordinates, and establish\nLipschitz stability for those metrics.\n"}
+{"abstract": "  Edge intelligence, which is a new paradigm to accelerate artificial\nintelligence (AI) applications by leveraging computing resources on the network\nedge, can be used to improve intelligent transportation systems (ITS). However,\ndue to physical limitations and energy-supply constraints, the computing powers\nof edge equipment are usually limited. High altitude platform station (HAPS)\ncomputing can be considered as a promising extension of edge computing. HAPS is\ndeployed in the stratosphere to provide wide coverage and strong computational\ncapabilities. It is suitable to coordinate terrestrial resources and store the\nfundamental data associated with ITS-based applications. In this work, three\ncomputing layers,i.e., vehicles, terrestrial network edges, and HAPS, are\nintegrated to build a computation framework for ITS, where the HAPS data\nlibrary stores the fundamental data needed for the applications. In addition,\nthe caching technique is introduced for network edges to store some of the\nfundamental data from the HAPS so that large propagation delays can be reduced.\nWe aim to minimize the delay of the system by optimizing computation offloading\nand caching decisions as well as bandwidth and computing resource allocations.\nThe simulation results highlight the benefits of HAPS computing for mitigating\ndelays and the significance of caching at network edges.\n"}
+{"abstract": "  We develop a general theory of categories that admit a functorial invariant\n(the triangulation functor) which generalizes the tree-width of graphs. Our\ntriangulation functor provides a uniform construction for various\ntree-width-like invariants including hypergraph tree-width, and the tree-width\nof the modular quotient in the category of modular partition functions.\n"}
+{"abstract": "  Epigenetic alterations have an important role in the development of several\ntypes of cancer. Epigenetic studies generate a large amount of data, which\nmakes it essential to develop novel models capable of dealing with large-scale\ndata. In this work, we propose a deep embedded refined clustering method for\nbreast cancer differentiation based on DNA methylation. In concrete, the deep\nlearning system presented here uses the levels of CpG island methylation\nbetween 0 and 1. The proposed approach is composed of two main stages. The\nfirst stage consists in the dimensionality reduction of the methylation data\nbased on an autoencoder. The second stage is a clustering algorithm based on\nthe soft-assignment of the latent space provided by the autoencoder. The whole\nmethod is optimized through a weighted loss function composed of two terms:\nreconstruction and classification terms. To the best of the authors' knowledge,\nno previous studies have focused on the dimensionality reduction algorithms\nlinked to classification trained end-to-end for DNA methylation analysis. The\nproposed method achieves an unsupervised clustering accuracy of 0.9927 and an\nerror rate (%) of 0.73 on 137 breast tissue samples. After a second test of the\ndeep-learning-based method using a different methylation database, an accuracy\nof 0.9343 and an error rate (%) of 6.57 on 45 breast tissue samples is\nobtained. Based on these results, the proposed algorithm outperforms other\nstate-of-the-art methods evaluated under the same conditions for breast cancer\nclassification based on DNA methylation data.\n"}
+{"abstract": "  Carbon contamination is a significant concern for proton-conducting oxides in\nthe cerate and zirconate family, particularly for BaCeO$_3$. Here, we use\nfirst-principles calculations to evaluate carbon stability in SrCeO$_3$,\nBaCeO$_3$, SrZrO$_3$, and BaZrO$_3$. The cerates require more carbon-poor\nenvironments to prevent carbonate formation, though this requirement can be\nloosened through the use of more oxygen-poor growth conditions. Carbonate\nformation is not the only concern, however. We find that interstitial carbon\nhas lower formation energies in the cerates relative to the zirconates, leading\nto higher carbon concentrations that compete with the desired oxygen vacancy\nformation. We also examine the mobility of carbon interstitials, finding that\nboth migration barriers and binding energies to acceptors are lower in the\ncerates. As a result, the cerates are likely to degrade when exposed to carbon\nat operating temperatures. Our results show definitively why the cerates are\nless stable than the zirconates with respect to carbon and elucidate the\nmechanisms contributing to their instability, thereby helping to explain why\nalloying with zirconium will enhance their operational efficiency.\n"}
+{"abstract": "  Mass number dependence of the nuclear radii is closely related to the nuclear\nmatter properties. It is known that the most of nuclei exhibit some\ndeformation. We discuss how the nuclear density profile is modified by the\nnuclear deformation to elucidate the enhancement mechanism of the nuclear radii\nthrough a systematic investigation of neutron-rich Ne, Mg, Si, S, Ar, Ti, Cr,\nand Fe isotopes. Skyrme-Hartree-Fock calculations are performed in a\nthree-dimensional Cartesian grid to describe the nuclear deformation in a\nnon-empirical way. The role of the nuclear deformation on the nuclear density\nprofiles is explored in comparison to calculations with spherical limit. We\nfind correlations between the nuclear deformation and the internal nuclear\ndensity. The evolution of the nuclear radii appears to follow the core swelling\nmechanism recently proposed in spherical nuclei [Phys. Rev. C 101, 061301(R)\n(2020)], and the radius is further enhanced by the nuclear deformation. This\nstudy demands further theoretical and experimental investigations for the\ninternal density.\n"}
+{"abstract": "  Our purpose of this paper is to study isolated singular solutions of\nsemilinear Helmholtz equation $$ -\\Delta u-u=Q|u|^{p-1}u \\quad{\\rm in}\\ \\\n\\mathbb{R}^N\\setminus\\{0\\},\\ \\qquad\\lim_{|x|\\to0}u(x)=+\\infty, $$ where $N\\geq\n2$, $p>1$ and the potential $Q: \\mathbb{R}^N\\to (0,+\\infty)$ is a H\\\"older\ncontinuous function satisfying extra decaying conditions at infinity. We give\nthe classification of the isolated singularity in the Serrin's subcritical case\nand then isolated singular solutions is derived with the form $u_k=k\\Phi+v_k$\nvia the Schauder fixed point theorem for the integral equation\n$$v_k=\\Phi\\ast\\big(Q|kw_\\sigma+v_k|^{p-1}(kw_\\sigma+v_k)\\big)\\quad{\\rm in}\\ \\,\n\\mathbb{R}^N,$$ where $\\Phi$ is the real valued fundamental solution\n$-\\Delta-1$ and $w_\\sigma$ is a also a real valued solution\n$(-\\Delta-1)w_\\sigma=\\delta_0$ with the asymptotic behavior at infinity\ncontrolled by $|x|^{-\\sigma}$ for some $\\sigma\\leq \\frac{N-1}{2}$.\n"}
+{"abstract": "  We study the dissipative dynamics of a single quantum harmonic oscillator\nsubjected to a parametric driving with in an effective Hamiltonian approach.\nUsing Liouville von Neumann approach, we show that the time evolution of a\nparametrically driven dissipative quantum oscillator has a strong connection\nwith the classical damped Mathieu equation. Based on the numerical analysis of\nthe Monodromy matrix, we demonstrate that the dynamical instability generated\nby the parametric driving are reduced by the effect of dissipation. Further, we\nobtain a closed relationship between the localization of the Wigner function\nand the stability of the damped Mathieu equation.\n"}
+{"abstract": "  In this paper, we propose a general scheme to analyze the gradient vanishing\nphenomenon, also known as the barren plateau phenomenon, in training quantum\nneural networks with the ZX-calculus. More precisely, we extend the barren\nplateaus theorem from unitary 2-design circuits to any parameterized quantum\ncircuits under certain reasonable assumptions. The main technical contribution\nof this paper is representing certain integrations as ZX-diagrams and computing\nthem with the ZX-calculus. The method is used to analyze four concrete quantum\nneural networks with different structures. It is shown that, for the hardware\nefficient ansatz and the MPS-inspired ansatz, there exist barren plateaus,\nwhile for the QCNN ansatz and the tree tensor network ansatz, there exists no\nbarren plateau.\n"}
+{"abstract": "  Resolvent analysis has demonstrated encouraging results for modeling coherent\nstructures in jets when compared against their data-educed counterparts from\nhigh-fidelity large-eddy simulations (LES). We formulate resolvent analysis as\nan acoustic analogy that relates the near-field resolvent forcing to the near-\nand far-field pressure. We use an LES database of round, isothermal, Mach 0.9\nand 1.5 jets to produce an ensemble of realizations for the acoustic field that\nwe project onto a limited set of resolvent modes. In the near-field, we perform\nprojections on a restricted acoustic output domain, $r/D = [5,6]$, while the\nfar-field projections are performed on a Kirchhoff surface comprising a\n100-diameter arc centered at the nozzle. This allows the LES realizations to be\nexpressed in the resolvent basis via a data-deduced, low-rank, cross-spectral\ndensity matrix. We find that a single resolvent mode reconstructs the most\nenergetic regions of the acoustic field across Strouhal numbers, $St = [0-1]$,\nand azimuthal wavenumbers, $m=[0,2]$. Finally, we present a simple function\nthat results in a rank-1 resolvent model agreeing within 2dB of the peak noise\nfor both jets.\n"}
+{"abstract": "  In this paper, we first generalize a result of Bishop and Steger\n[Representation theoretic rigidity in PSL(2, R). Acta Math., 170, (1993),\n121-149] by proving that for a Fuchsian group $G$ of divergence type and\nnon-lattice, if $h$ is a quasi-symmetric homeomorphism of the real axis\n$\\mathbb{R}$ corresponding to a quasi-conformal compact deformation of $G$.\nThen for any $E\\subset \\mathbb{R}$, we have max(dim$E$,\ndim$h(\\mathbb{R}\\setminus E))=1$. Furthermore, we showed that Bishop and\nsteger's result does not hold for the covering groups of all '$d$-dimensional\njungle gym' (d is any positive integer) which generalizes G\\\"onye's results [\nDifferentiability of quasi-conformal maps on the jungle gym. Trans. Amer. Math.\nSoc. Vol 359 (2007), 9-32] where the author discussed the case of\n'$1$-dimensional jungle gym'.\n"}
+{"abstract": "  Given N data points drawn from a chi-square distribution, we use Bayesian\ninference to determine most likely values and N-dependent confidence intervals\nfor the width sigma and the number k of degrees of freedom of that\ndistribution. Using reduced partial neutron widths measured in a number of\nnuclei, a guessed value of sigma, and a maximum-likelihood approach (different\nfrom Bayesian inference), Koehler et al. and Koehler have determined the most\nlikely k-values of chi-square distributions that fit the data. In all cases\nthey find values for k that differ substantially from k = 1 (the value\ncharacterizing the Porter-Thomas distribution (PTD) predicted by random-matrix\ntheory). The authors conclude that the validity of the PTD must be rejected\nwith considerable statistical significance. We show that the value of sigma\nguessed in these papers lies far outside the Bayesian confidence interval for\nsigma, casting serious doubt on the results of and the conclusions drawn there.\nWe also show that sigma and k must both be determined from the data. Comparison\nof the results with the Bayesian confidence intervals would then decide on\nacceptance or rejection of the PTD.\n"}
+{"abstract": "  In this paper, we propose a new technique based on program synthesis for\nextracting information from webpages. Given a natural language query and a few\nlabeled webpages, our method synthesizes a program that can be used to extract\nsimilar types of information from other unlabeled webpages. To handle websites\nwith diverse structure, our approach employs a neurosymbolic DSL that\nincorporates both neural NLP models as well as standard language constructs for\ntree navigation and string manipulation. We also propose an optimal synthesis\nalgorithm that generates all DSL programs that achieve optimal F1 score on the\ntraining examples. Our synthesis technique is compositional, prunes the search\nspace by exploiting a monotonicity property of the DSL, and uses transductive\nlearning to select programs with good generalization power. We have implemented\nthese ideas in a new tool called WebQA and evaluate it on 25 different tasks\nacross multiple domains. Our experiments show that WebQA significantly\noutperforms existing tools such as state-of-the-art question answering models\nand wrapper induction systems.\n"}
+{"abstract": "  Dealing with quadratic payments, marginal probability is usually considered\nideally constant, maybe for the sake of initial simplicity. Considering the\nvoting scenario depicted in \"Quadratic Payments: A Primer\" by Vitalik Buterin,\nfirstly its math foundations are made explicit. Developing a simple referendum\nmodel, more realistic outcome probability and marginal probability qualitative\nshapes are introduced. Enforcing seemingly reasonable assumptions, quadratic\npayments are then generalized to take into account these new functions shapes,\nand the way they are still quadratic is discussed. Closing remarks underline\nthe emerging of trade-off constraints not existing in ideal case.\n"}
+{"abstract": "  Neural Architecture Search (NAS) has shown excellent results in designing\narchitectures for computer vision problems. NAS alleviates the need for\nhuman-defined settings by automating architecture design and engineering.\nHowever, NAS methods tend to be slow, as they require large amounts of GPU\ncomputation. This bottleneck is mainly due to the performance estimation\nstrategy, which requires the evaluation of the generated architectures, mainly\nby training them, to update the sampler method. In this paper, we propose\nEPE-NAS, an efficient performance estimation strategy, that mitigates the\nproblem of evaluating networks, by scoring untrained networks and creating a\ncorrelation with their trained performance. We perform this process by looking\nat intra and inter-class correlations of an untrained network. We show that\nEPE-NAS can produce a robust correlation and that by incorporating it into a\nsimple random sampling strategy, we are able to search for competitive\nnetworks, without requiring any training, in a matter of seconds using a single\nGPU. Moreover, EPE-NAS is agnostic to the search method, since it focuses on\nthe evaluation of untrained networks, making it easy to integrate into almost\nany NAS method.\n"}
+{"abstract": "  Based on decision trees, many fields have arguably made tremendous progress\nin recent years. In simple words, decision trees use the strategy of\n\"divide-and-conquer\" to divide the complex problem on the dependency between\ninput features and labels into smaller ones. While decision trees have a long\nhistory, recent advances have greatly improved their performance in\ncomputational advertising, recommender system, information retrieval, etc. We\nintroduce common tree-based models (e.g., Bayesian CART, Bayesian regression\nsplines) and training techniques (e.g., mixed integer programming, alternating\noptimization, gradient descent). Along the way, we highlight probabilistic\ncharacteristics of tree-based models and explain their practical and\ntheoretical benefits. Except machine learning and data mining, we try to show\ntheoretical advances on tree-based models from other fields such as statistics\nand operation research. We list the reproducible resource at the end of each\nmethod.\n"}
+{"abstract": "  For many psychiatric disorders, neuroimaging offers a potential for\nrevolutionizing diagnosis, and potentially treatment, by providing access to\npreverbal mental processes. In their study \"Machine learning of neural\nrepresentations of suicide and emotion concepts identifies suicidal youth.\"1,\nJust and colleagues report that a Naive Bayes classifier, trained on voxelwise\nfMRI responses in human participants during the presentation of words and\nconcepts related to mortality, can predict whether an individual had reported\nhaving suicidal ideations with a classification accuracy of 91%. Here we report\na reappraisal of the methods employed by the authors, including re-analysis of\nthe same data set, that calls into question the accuracy of the authors\nfindings. The analysis is a case study in the dangers of overfitting in machine\nlearning.\n"}
+{"abstract": "  Galaxy clusters exhibit a rich morphology during the early and intermediate\nstages of mass assembly, especially beyond their boundary. A classification\nscheme based on shapefinders deduced from the Minkowski functionals is examined\nto fully account for the morphological diversity of galaxy clusters, including\nrelaxed and merging clusters, clusters fed by filamentary structures, and\ncluster-pair bridges. These configurations are conveniently treated with\nidealised geometric models and analytical formulae, some of which are novel.\nExamples from CLASH and LC$^2$ clusters and observed cluster-pair bridges are\ndiscussed.\n"}
+{"abstract": "  Human-AI collaborative policy synthesis is a procedure in which (1) a human\ninitializes an autonomous agent's behavior, (2) Reinforcement Learning improves\nthe human specified behavior, and (3) the agent can explain the final optimized\npolicy to the user. This paradigm leverages human expertise and facilitates a\ngreater insight into the learned behaviors of an agent. Existing approaches to\nenabling collaborative policy specification involve black box methods which are\nunintelligible and are not catered towards non-expert end-users. In this paper,\nwe develop a novel collaborative framework to enable humans to initialize and\ninterpret an autonomous agent's behavior, rooted in principles of\nhuman-centered design. Through our framework, we enable humans to specify an\ninitial behavior model in the form of unstructured, natural language, which we\nthen convert to lexical decision trees. Next, we are able to leverage these\nhuman-specified policies, to warm-start reinforcement learning and further\nallow the agent to optimize the policies through reinforcement learning.\nFinally, to close the loop on human-specification, we produce explanations of\nthe final learned policy, in multiple modalities, to provide the user a final\ndepiction about the learned policy of the agent. We validate our approach by\nshowing that our model can produce >80% accuracy, and that human-initialized\npolicies are able to successfully warm-start RL. We then conduct a novel\nhuman-subjects study quantifying the relative subjective and objective benefits\nof varying XAI modalities(e.g., Tree, Language, and Program) for explaining\nlearned policies to end-users, in terms of usability and interpretability and\nidentify the circumstances that influence these measures. Our findings\nemphasize the need for personalized explainable systems that can facilitate\nuser-centric policy explanations for a variety of end-users.\n"}
+{"abstract": "  High harmonic generation (HHG) in crystals has revealed a wealth of\nperspectives such as all-optical mapping of the electronic band structure,\nultrafast quantum information and the creation of novel all-solid-state\nattosecond sources. Significant efforts have been made to understand the\nmicroscopic aspects of HHG in crystals, whereas the macroscopic effects, such\nas non-linear propagation effects of the driving pulse inside the dense solid\nmedia and its impact on the HHG process is often overlooked. In this work, we\nstudy macroscopic effects by comparing two materials with distinct optical\nproperties, silicon (Si) and zinc oxide (ZnO). By scanning the focal position\nof 85 fs, 2.123 $\\mu$m wavelength pulses inside the crystals (Z-scan) we reveal\nspectral shifts in the generated harmonics. We interpret the overall blueshift\nof the emitted harmonic spectrum as an imprint of the driving field spectral\nmodulation occurring during the propagation inside the crystal. This is\nsupported with numerical simulations. This study demonstrates that through\nmanipulation of the fundamental driving field through non-linear propagation\neffects, precise control of the emitted HHG spectrum in solids can be realised.\nThis method could offer a robust way to tailor HHG spectra for a range of\nspectroscopic applications.\n"}
+{"abstract": "  In this paper, we present a novel passive single Degree-of-Freedom (DoF)\nmanipulator design and its integration on an autonomous drone to capture a\nmoving target. The end-effector is designed to be passive, to disengage the\nmoving target from a flying UAV and capture it efficiently in the presence of\ndisturbances, with minimal energy usage. It is also designed to handle target\nsway and the effect of downwash. The passive manipulator is integrated with the\ndrone through a single Degree of Freedom (DoF) arm, and experiments are carried\nout in an outdoor environment. The rack-and-pinion mechanism incorporated for\nthis manipulator ensures safety by extending the manipulator beyond the body of\nthe drone to capture the target. The autonomous capturing experiments are\nconducted using a red ball hanging from a stationary drone and subsequently\nfrom a moving drone. The experiments show that the manipulator captures the\ntarget with a success rate of 70\\% even under environmental/measurement\nuncertainties and errors.\n"}
+{"abstract": "  Orientation is a crucial skill for football players that becomes a\ndifferential factor in a large set of events, especially the ones involving\npasses. However, existing orientation estimation methods, which are based on\ncomputer-vision techniques, still have a lot of room for improvement. To the\nbest of our knowledge, this article presents the first deep learning model for\nestimating orientation directly from video footage. By approaching this\nchallenge as a classification problem where classes correspond to orientation\nbins, and by introducing a cyclic loss function, a well-known convolutional\nnetwork is refined to provide player orientation data. The model is trained by\nusing ground-truth orientation data obtained from wearable EPTS devices, which\nare individually compensated with respect to the perceived orientation in the\ncurrent frame. The obtained results outperform previous methods; in particular,\nthe absolute median error is less than 12 degrees per player. An ablation study\nis included in order to show the potential generalization to any kind of\nfootball video footage.\n"}
+{"abstract": "  Weakly supervised object detection (WSOD) has recently attracted much\nattention. However, the method, performance and speed gaps between WSOD and\nfully supervised detection prevent WSOD from being applied in real-world tasks.\nTo bridge the gaps, this paper proposes a new framework, Salvage of Supervision\n(SoS), with the key idea being to harness every potentially useful supervisory\nsignal in WSOD: the weak image-level labels, the pseudo-labels, and the power\nof semi-supervised object detection. This paper shows that each type of\nsupervisory signal brings in notable improvements, outperforms existing WSOD\nmethods (which mainly use only the weak labels) by large margins. The proposed\nSoS-WSOD method achieves 64.4 $m\\text{AP}_{50}$ on VOC2007, 61.9\n$m\\text{AP}_{50}$ on VOC2012 and 16.4 $m\\text{AP}_{50:95}$ on MS-COCO, and also\nhas fast inference speed. Ablations and visualization further verify the\neffectiveness of SoS.\n"}
+{"abstract": "  We present geometric optics expansion in the subleading order for the\ncircularly polarized gravitational waves on curved spacetimes. We call spin\noptics to the subleading order geometric optics expansion. Spin optics involves\nthe modification of the standard eikonal function by including a specially\nchosen helicity dependent correction. We show that the techniques developed for\nthe propagation of electromagnetic waves could be applied to gravitational\nwaves, in the limit of spin optics also. However, we should account for the\ndifference in their helicity.\n"}
+{"abstract": "  We present a detailed analysis for a subset of the high resolution (~35 mas,\nor 5 au) ALMA observations from the Disk Substructures at High Angular\nResolution Project (DSHARP) to search for faint 1.3 mm continuum emission\nassociated with dusty circumplanetary material located within the narrow annuli\nof depleted emission (gaps) in circumstellar disks. This search used the\nJennings et al. (2020) $\\tt{frank}$ modeling methodology to mitigate\ncontamination from the local disk emission, and then deployed a suite of\ninjection-recovery experiments to statistically characterize point-like\ncircumplanetary disks in residual images. While there are a few putative\ncandidates in this sample, they have only marginal local signal-to-noise ratios\nand would require deeper measurements to confirm. Associating a 50% recovery\nfraction with an upper limit, we find these data are sensitive to\ncircumplanetary disks with flux densities $\\gtrsim 50-70$ $\\mu$Jy in most\ncases. There are a few examples where those limits are inflated ($\\gtrsim 110$\n$\\mu$Jy) due to lingering non-axisymmetric structures in their host\ncircumstellar disks, most notably for a newly identified faint spiral in the HD\n143006 disk. For standard assumptions, this analysis suggests that these data\nshould be sensitive to circumplanetary disks with dust masses $\\gtrsim\n0.001-0.2$ M$_\\oplus$. While those bounds are comparable to some theoretical\nexpectations for young giant planets, we discuss how plausible system\nproperties (e.g., relatively low host planet masses or the efficient radial\ndrift of solids) could require much deeper observations to achieve robust\ndetections.\n"}
+{"abstract": "  A posteriori probability (APP) and max-log APP detection is widely used in\nsoft-input soft-output detection. In contrast to bijective modulation schemes,\nthere are important differences when applying these algorithms to non-bijective\nsymbol constellations. In this letter the main differences are highlighted.\n"}
+{"abstract": "  Face clustering is a useful tool for applications like automatic face\nannotation and retrieval. The main challenge is that it is difficult to cluster\nimages from the same identity with different face poses, occlusions, and image\nquality. Traditional clustering methods usually ignore the relationship between\nindividual images and their neighbors which may contain useful context\ninformation. In this paper, we repurpose the well-known Transformer and\nintroduce a Face Transformer for supervised face clustering. In Face\nTransformer, we decompose the face clustering into two steps: relation encoding\nand linkage predicting. Specifically, given a face image, a \\textbf{relation\nencoder} module aggregates local context information from its neighbors and a\n\\textbf{linkage predictor} module judges whether a pair of images belong to the\nsame cluster or not. In the local linkage graph view, Face Transformer can\ngenerate more robust node and edge representations compared to existing\nmethods. Experiments on both MS-Celeb-1M and DeepFashion show that our method\nachieves state-of-the-art performance, e.g., 91.12\\% in pairwise F-score on\nMS-Celeb-1M.\n"}
+{"abstract": "  We consider smooth families of Lie groups (group bundles) and connections\nthat are compatible with the group operation. We characterize the space of\ngroup connections on a group bundle as an affine space modeled over the vector\nspace of $1$-forms with values cocycles in the Lie algebra bundle of the\naforementioned group bundle. We show that group connections satisfy the\nAmbrose-Singer theorem and that group bundles can be seen as a particular case\nof associated bundles realizing group connections as associated connections. We\ngive a construction of the Moduli space of group connections with fixed base\nand fiber, as an space of representations of the fundamental group of the base.\n"}
+{"abstract": "  The potential of the process $\\gamma \\gamma \\rightarrow \\tau\n\\bar{\\tau}\\gamma$ is examined in a model-independent way using the effective\nLagrangian approach for the ILC which is designed with standard configurations\nof 0.25 TeV/2000 fb$^{-1}$, 0.35 TeV/200 fb$^{-1}$ and 0.5 TeV/4000 fb$^{-1}$.\nThe limits obtained for $\\tilde{a}_{\\tau}$ and $\\tilde{{d}_{\\tau}}$ parameters\ndefining the anomalous $\\tau \\bar{\\tau}\\gamma$ couplings at $95\\%$ confidence\nlevel and systematic uncertainties of $\\delta_{sys}=0,5,10 \\%$ are compared\nwith the experimental results. The best limits obtained without systematic\nerror on the anomalous couplings are $-0.00082<\\tilde{a}_{\\tau}<0.00050$ and\n$|\\tilde{{d}_{\\tau}}|<3.5969 \\times 10^{-18}$ $e\\,cm$, respectively. Thus, our\nresults show that $\\gamma \\gamma$ collisions at the ILC lead to a remarkable\nimprovement in the existing experimental limits on the anomalous magnetic and\nelectric dipole moments of the tau lepton.\n"}
+{"abstract": "  Chromatic properties of the multi-prism and prism-array X-ray lenses (MPL and\nPAL) can potentially be utilized for efficient energy filtering and dose\nreduction in mammography. The line-shaped foci of the lenses are optimal for\ncoupling to photon-counting silicon strip detectors in a scanning system. A\ntheoretical model was developed and used to investigate the benefit of two\nlenses compared with an absorption-filtered reference system. The dose\nreduction of the MPL filter was ~15% compared with the reference system at\nmatching scan time, and the spatial resolution was higher. The dose of the\nPAL-filtered system was found to be ~20% lower than for the reference system at\nequal scan time and resolution, and only ~20% higher than for a monochromatic\nbeam. An investigation of some practical issues remains, including the\nfeasibility of brilliant-enough X-ray sources and manufacturing of a polymer\nPAL.\n"}
+{"abstract": "  Uniqueness theorems are considered for various types of almost periodic\nobjects: functions, measures, distributions, multisets, holomorphic and\nmeromorphic functions.\n"}
+{"abstract": "  Alweiss, Lovett, Wu, and Zhang introduced $q$-spread hypergraphs in their\nbreakthrough work regarding the sunflower conjecture, and since then $q$-spread\nhypergraphs have been used to give short proofs of several outstanding problems\nin probabilistic combinatorics. A variant of $q$-spread hypergraphs was\nimplicitly used by Kahn, Narayanan, and Park to determine the threshold for\nwhen a square of a Hamiltonian cycle appears in the random graph $G_{n,p}$. In\nthis paper we give a common generalization of the original notion of $q$-spread\nhypergraphs and the variant used by Kahn et al.\n"}
+{"abstract": "  Let $F$ be a number field and $p$ an odd prime. We estimate the kernels and\ncokernels of the codescent maps of the \\'etale wild kernels over various\n$p$-adic Lie extensions. For this, we propose a novel approach of viewing the\n\\'etale wild kernel as an appropriate fine Selmer group in the sense of\nCoates-Sujatha. This viewpoint reduces the problem to a control theorem of the\nsaid fine Selmer groups, which in turn allows us to employ the strategies\ndeveloped by Mazur and Greenberg. As applications of our estimates on the\nkernels and cokernels of the codescent maps, we establish asymptotic growth\nformulas for the \\'etale wild kernels in the various said $p$-adic Lie\nextensions. We then relate these growth formulas to the Greenberg's conjecture\n(and its noncommutative analogue). Finally, we shall give some examples to\nillustrate our results.\n"}
+{"abstract": "  Objective: In this work, we set out to investigate the accuracy of direct\nattenuation correction (AC) in the image domain for the myocardial perfusion\nSPECT imaging (MPI-SPECT) using two residual (ResNet) and UNet deep\nconvolutional neural networks. Methods: The MPI-SPECT 99mTc-sestamibi images of\n99 participants were retrospectively examined. UNet and ResNet networks were\ntrained using SPECT non-attenuation corrected images as input and CT-based\nattenuation corrected SPECT images (CT-AC) as reference. The Chang AC approach,\nconsidering a uniform attenuation coefficient within the body contour, was also\nimplemented. Quantitative and clinical evaluation of the proposed methods were\nperformed considering SPECT CT-AC images of 19 subjects as reference using the\nmean absolute error (MAE), structural similarity index (SSIM) metrics, as well\nas relevant clinical indices such as perfusion deficit (TPD). Results: Overall,\nthe deep learning solution exhibited good agreement with the CT-based AC,\nnoticeably outperforming the Chang method. The ResNet and UNet models resulted\nin the ME (count) of ${-6.99\\pm16.72}$ and ${-4.41\\pm11.8}$ and SSIM of\n${0.99\\pm0.04}$ and ${0.98\\pm0.05}$, respectively. While the Change approach\nled to ME and SSIM of ${25.52\\pm33.98}$ and ${0.93\\pm0.09}$, respectively.\nSimilarly, the clinical evaluation revealed a mean TPD of ${12.78\\pm9.22}$ and\n${12.57\\pm8.93}$ for the ResNet and UNet models, respectively, compared to\n${12.84\\pm8.63}$ obtained from the reference SPECT CT-AC images. On the other\nhand, the Chang approach led to a mean TPD of ${16.68\\pm11.24}$. Conclusion: We\nevaluated two deep convolutional neural networks to estimate SPECT-AC images\ndirectly from the non-attenuation corrected images. The deep learning solutions\nexhibited the promising potential to generate reliable attenuation corrected\nSPECT images without the use of transmission scanning.\n"}
+{"abstract": "  Backdoor attack intends to inject hidden backdoor into the deep neural\nnetworks (DNNs), such that the prediction of infected models will be\nmaliciously changed if the hidden backdoor is activated by the attacker-defined\ntrigger. Currently, most existing backdoor attacks adopted the setting of\nstatic trigger, $i.e.,$ triggers across the training and testing images follow\nthe same appearance and are located in the same area. In this paper, we revisit\nthis attack paradigm by analyzing trigger characteristics. We demonstrate that\nthis attack paradigm is vulnerable when the trigger in testing images is not\nconsistent with the one used for training. As such, those attacks are far less\neffective in the physical world, where the location and appearance of the\ntrigger in the digitized image may be different from that of the one used for\ntraining. Moreover, we also discuss how to alleviate such vulnerability. We\nhope that this work could inspire more explorations on backdoor properties, to\nhelp the design of more advanced backdoor attack and defense methods.\n"}
+{"abstract": "  In the first part of this dissertation research, we develop a modular\nframework that can serve as a recipe for constructing and analyzing iterative\nalgorithms for convex optimization. Specifically, our work casts optimization\nas iteratively playing a two-player zero-sum game. Many existing optimization\nalgorithms including Frank-Wolfe and Nesterov's acceleration methods can be\nrecovered from the game by pitting two online learners with appropriate\nstrategies against each other. Furthermore, the sum of the weighted average\nregrets of the players in the game implies the convergence rate. As a result,\nour approach provides simple alternative proofs to these algorithms. Moreover,\nwe demonstrate that our approach of optimization as iteratively playing a game\nleads to three new fast Frank-Wolfe-like algorithms for some constraint sets,\nwhich further shows that our framework is indeed generic, modular, and\neasy-to-use.\n  In the second part, we develop a modular analysis of provable acceleration\nvia Polyak's momentum for certain problems, which include solving the classical\nstrongly quadratic convex problems, training a wide ReLU network under the\nneural tangent kernel regime, and training a deep linear network with an\northogonal initialization. We develop a meta theorem and show that when\napplying Polyak's momentum for these problems, the induced dynamics exhibit a\nform where we can directly apply our meta theorem.\n  In the last part of the dissertation, we show another advantage of the use of\nPolyak's momentum -- it facilitates fast saddle point escape in smooth\nnon-convex optimization. This result, together with those of the second part,\nsheds new light on Polyak's momentum in modern non-convex optimization and deep\nlearning.\n"}
+{"abstract": "  Spin-1/2 Heisenberg model on the Shastry-Sutherland lattice is considered\nwithin the many-body perturbation theory developed from the exactly solved\nspin-1/2 Ising-Heisenberg model with the Heisenberg intradimer and Ising\ninterdimer interactions. The former model is widely used for a description of\nmagnetic properties of the layered compound SrCu$_2$(BO$_3$)$_2$, which\nexhibits a series of fractional magnetization plateaux at sufficiently low\ntemperatures. Using the novel type of many-body perturbation theory we have\nfound the effective model of interacting triplet excitations with the extended\nhard-core repulsion, which accurately recovers 1/8, 1/6 and 1/4 magnetization\nplateaux for moderate values of the interdimer coupling. A possible existence\nof a striking quantum phase of bound triplons is also revealed at low enough\nmagnetic fields.\n"}
+{"abstract": "  We prove orbital stability result for physical ground states of a nonlinear\nSchr\\\"{o}dinger (NLS) equation in the sense that the set of these ground states\nis contained in the set of prescribed mass solutions which is orbital stable by\nthe Cazenave-Lions theorem. We apply the nonlinear generalized Rayleigh\nquotients method which allows establishing a one-to-one correspondence between\nthe values of the mass $m$, the frequency $\\lambda$, and the action level $S$\nof the physical ground states.\n"}
+{"abstract": "  We study the formation of over 6000 compact groups (CGs) of galaxies\nidentified in mock redshift-space galaxy catalogues built from semi-analytical\nmodels of galaxy formation (SAMs) run on the Millennium Simulations. We select\nCGs of 4 members in our mock SDSS galaxy catalogues and, for each CG, we trace\nback in time the real-space positions of the most massive progenitors of their\n4 galaxies. By analysing the evolution of the distance of the galaxy members to\nthe centre of mass of the group, we identify 4 channels of CG formation. The\nclassification of these assembly channels is performed with an automatic recipe\ninferred from a preliminary visual inspection and based on the orbit of the\ngalaxy with the fewest number of orbits. Most CGs show late assembly, with the\nlast galaxy arriving on its first or second passage, while only 10-20 per cent\nform by the gradual contraction of their orbits by dynamical friction, and only\na few per cent forming early with little subsequent contraction. However, a SAM\nfrom a higher resolution simulation leads to earlier assembly. Assembly\nhistories of CGs also depend on cosmological parameters. At similar resolution,\nCGs assemble later in SAMs built on parent cosmological simulations of high\ndensity parameter. Several observed properties of mock CGs correlate with their\nassembly history: early-assembling CGs are smaller, with shorter crossing\ntimes, and greater magnitude gaps between their brightest two members, and\ntheir brightest galaxies have smaller spatial offsets and are more passive.\n"}
+{"abstract": "  We establish a series of results showing that the Jacobian ideal is contained\nin the test ideal. We first prove a new result in characteristic $p$ for\ncomplete rings over a field $K$. Then we prove some results showing that\nJacobian ideals are contained in test ideals in equal characteristic 0 for\naffine, analytic, affine-analytic and semianalytic $K$-algebras.\n"}
+{"abstract": "  In this paper we obtain the genus field of a general Kummer extension of a\nglobal rational function field. We study first the case of a general Kummer\nextension of degree a power of a prime. Then we prove that the genus field of a\ncomposite of two abelian extensions of a global rational function field with\nrelatively prime degrees is equal to the composite of their respective genus\nfields. Our main result, the genus of a general Kummer extension of a global\nrational function field, is a direct consequence of this fact.\n"}
+{"abstract": "  In this paper, the notions of integral closure of hyperrings and hyperideals\nin a Krasner hyperring $(R, +, \\cdot)$ are defined and some basics properties\nof them are studied. We define also the notion of hypervaluation hyperideals\nand then a relations between hypervaluations, integral closure of hyperideals\nand primary hyperideals are studied. In fact it is shown that the integral\nclosure of a hyperideal is determined by the hypervaluation Krasner hyperrings.\n"}
+{"abstract": "  We discuss the applicability of elementary band representations (EBRs) to\ndiagnose spatial- and time-reversal-symmetry protected topological phases in\ninteracting insulators in terms of their single-particle Green's functions. We\ndo so by considering an auxiliary non-interacting system\n$H_{\\textrm{T}}(\\mathbf{k}) = -G^{-1}(0, \\mathbf{k})$, known as the topological\nHamiltonian, whose bands can be labeled by EBRs. This labeling is stable if\nneither (i) the gap in the spectral function at zero frequency closes, (ii) the\nGreen's function has a zero at zero frequency or (iii) the Green's function\nbreaks a protecting symmetry. We demonstrate the use of EBRs applied to the\nGreen's function on the one-dimensional Su-Schrieffer-Heeger model with Hubbard\ninteractions, which we solve by exact diagonalization for a finite number of\nunit cells. Finally, the use of EBRs for the Green's function to diagnose\nso-called symmetry protected topological phases is discussed, but remains an\nopen question.\n"}
+{"abstract": "  We give examples of birational selfmaps of $\\mathbb{P}^d, d \\geq 3,$ whose\ndynamical degree is a transcendental number. This contradicts a conjecture by\nBellon and Viallet. The proof uses a combination of techniques from algebraic\ndynamics and diophantine approximation.\n"}
+{"abstract": "  We propose a method for generating nodes for kernel quadrature by a\npoint-wise gradient descent method. For kernel quadrature, most methods for\ngenerating nodes are based on the worst case error of a quadrature formula in a\nreproducing kernel Hilbert space corresponding to the kernel. In typical ones\namong those methods, a new node is chosen among a candidate set of points in\neach step by an optimization problem with respect to a new node. Although such\nsequential methods are appropriate for adaptive quadrature, it is difficult to\napply standard routines for mathematical optimization to the problem. In this\npaper, we propose a method that updates a set of points one by one with a\nsimple gradient descent method. To this end, we provide an upper bound of the\nworst case error by using the fundamental solution of the Laplacian on\n$\\mathbf{R}^{d}$. We observe the good performance of the proposed method by\nnumerical experiments.\n"}
+{"abstract": "  A graph whose nodes have degree 1 or 3 is called a $\\{1,3\\}$-graph. Liu and\nOsserman associated a polytope to each $\\{1,3\\}$-graph and studied the Ehrhart\nquasi-polynomials of these polytopes. They showed that the vertices of these\npolytopes have coordinates in the set $\\{0,\\frac14,\\frac12,1\\}$, which implies\nthat the period of their Ehrhart quasi-polynomials is either 1, 2, or 4. We\nshow that the period of the Ehrhart quasi-polynomial of these polytopes is at\nmost 2 if the graph is a tree or a cubic graph, and it is equal to 4 otherwise.\n  In the process of proving this theorem, several interesting combinatorial and\ngeometric properties of these polytopes were uncovered, arising from the\nstructure of their associated graphs. The tools developed here may find other\napplications in the study of Ehrhart quasi-polynomials and enumeration problems\nfor other polytopes that arise from graphs. Additionally, we have identified\nsome interesting connections with triangulations of 3-manifolds.\n"}
+{"abstract": "  This paper presents Poisoning MorphNet, the first backdoor attack method on\npoint clouds. Conventional adversarial attack takes place in the inference\nstage, often fooling a model by perturbing samples. In contrast, backdoor\nattack aims to implant triggers into a model during the training stage, such\nthat the victim model acts normally on the clean data unless a trigger is\npresent in a sample. This work follows a typical setting of clean-label\nbackdoor attack, where a few poisoned samples (with their content tampered yet\nlabels unchanged) are injected into the training set. The unique contributions\nof MorphNet are two-fold. First, it is key to ensure the implanted triggers\nboth visually imperceptible to humans and lead to high attack success rate on\nthe point clouds. To this end, MorphNet jointly optimizes two objectives for\nsample-adaptive poisoning: a reconstruction loss that preserves the visual\nsimilarity between benign / poisoned point clouds, and a classification loss\nthat enforces a modern recognition model of point clouds tends to mis-classify\nthe poisoned sample to a pre-specified target category. This implicitly\nconducts spectral separation over point clouds, hiding sample-adaptive triggers\nin fine-grained high-frequency details. Secondly, existing backdoor attack\nmethods are mainly designed for image data, easily defended by some point cloud\nspecific operations (such as denoising). We propose a third loss in MorphNet\nfor suppressing isolated points, leading to improved resistance to\ndenoising-based defense. Comprehensive evaluations are conducted on ModelNet40\nand ShapeNetcorev2. Our proposed Poisoning MorphNet outstrips all previous\nmethods with clear margins.\n"}
+{"abstract": "  The paper deals with the fast-slow motions setups in the continuous time\n$\\frac {dX^\\ve(t)}{dt}=\\frac 1\\ve\nB(X^\\ve(t),\\xi(t/\\ve^2))+b(X^\\ve(t),\\,\\xi(t/\\ve^2)),\\, t\\in [0,T]$ and the\ndiscrete time $X^\\ve((n+1)\\ve^2)=X^\\ve(n\\ve^2)+\\ve B(X^\\ve(n\\ve^2),\\xi(n))\n+\\ve^2b(X^\\ve(n\\ve^2),\\xi(n))$, $n=0,1,...,[T/\\ve^2]$ where $B$ and $b$ are\nsmooth vector functions and $\\xi$ is a stationary vector stochastic process\nsuch that $EB(x,\\xi(0))=0$ for all $x\\in\\bbR^d$. Unlike \\cite{Ki20} the\nassumptions imposed on the process $\\xi$ allow applications to a wide class of\nobservables $g$ in the dynamical systems setup so that $\\xi$ can be taken in\nthe form $\\xi(t)=g(F^t\\xi(0))$ or $\\xi(n)=g(F^n\\xi(0))$ where $F$ is either a\nflow or a diffeomorphism with some hyperbolicity and $g$ is a vector function.\nIn this paper we show that both $X^\\ve$ and a family of diffusions $\\Xi^\\ve$\ncan be redefined on a common sufficiently rich probability space so that\n$E\\sup_{0\\leq t\\leq T}|X^\\ve(t)-\\Xi^\\ve(t)|^{p}\\leq C\\ve^\\del,\\, p\\geq 1$ for\nsome $C,\\del>0$ and all $\\ve>0$, where all $\\Xi^\\ve,\\, \\ve>0$ have the same\ndiffusion coefficients but underlying Brownian motions may change with $\\ve$.\n"}
+{"abstract": "  We present the structural and initial magnetic characterization of the\npreviously unreported materials NdPd5Ge3 and NdPd5Ge3. These materials, with\n1-dimensional Nd chains, crystallize in the orthorhombic YNi5Si3-type structure\nin space group Pnma. Magnetic ordering is observed for both compounds at ~ 2.2\nK for NdPd5Ge3 and ~ 3.0 K for NdPt5Ge3. A magnetic-field-induced transition is\nclearly observed for NdPd5Ge3 below 2K, at an applied magnetic field of around\n1.6 Tesla. The heat capacity data reveals that essentially all the available\nmagnetic entropy is released at the magnetic ordering transition for NdPd5Ge3\nbut that only 29% is seen for NdPt5Ge3 at temperatures down to 1.5 K, the data\nimplying that a second magnetic transition can be expected at lower\ntemperatures.\n"}
+{"abstract": "  A source node updates its status as a point process and also forwards its\nupdates to a network of observer nodes. Within the network of observers, these\nupdates are forwarded as point processes from node to node. Each node wishes\nits knowledge of the source to be as timely as possible. In this network,\ntimeliness is measured by a discrete form of age of information: each status\nchange at the source is referred to as a version and the age at a node is how\nmany versions out of date is its most recent update from the source. This work\nintroduces a method for evaluating the average version age at each node in the\nnetwork when nodes forward updates using a memoryless gossip protocol. This\nmethod is then demonstrated by version age analysis for a collection of simple\nnetworks. For gossip on a complete graph with symmetric updating rates, it is\nshown that each node has average age that grows as the logarithm of the network\nsize.\n"}
+{"abstract": "  We report on temperature-dependent soft X-ray absorption spectroscopy (XAS)\nmeasurements utilizing linearly polarized synchrotron radiation to probe\nmagnetic phase transitions in iron-rich Fe1+yTe. X-ray magnetic linear\ndichroism (XMLD) signals, which sense magnetic ordering processes at surfaces,\nstart to increase monotonically below the N\\'eel temperature TN = 57 K. This\nincrease is due to a progressive bicollinear antiferromagnetic (AFM) alignment\nof Fe spins of the monoclinic Fe1+yTe parent phase. This AFM alignment was\nachieved by a [100]-oriented biasing field favoring a single-domain state\nduring cooling across TN. Our specific heat and magnetization measurements\nconfirm the bulk character of this AFM phase transition. On longer time scales,\nhowever, we observe that the field-biased AFM state is highly unstable even at\nthe lowest temperature of T = 3 K. After switching off the biasing field, the\nXMLD signal decays exponentially with a time constant {\\tau} = 1506 s. The\ninitial XMLD signal is restored only upon repeating a cycle consisting of\nheating and field-cooling through TN. We explain the time effect by a gradual\nformation of a multi-domain state with 90 deg rotated AFM domains, promoted by\nstructural disorder, facilitating the motion of twin-domains. Significant\ndisorder in our Fe1+yTe sample is evident from our X-ray diffraction and\nspecific heat data. The stability of magnetic phases in Fe-chalcogenides is an\nimportant material property, since the Fe(Te1-xSex) phase diagram shows\nmagnetism intimately connected with superconductivity.\n"}
+{"abstract": "  Many ecological studies and conservation policies are based on field\nobservations of species, which can be affected by systematic variability\nintroduced by the observation process. A recently introduced causal modeling\ntechnique called 'half-sibling regression' can detect and correct for\nsystematic errors in measurements of multiple independent random variables.\nHowever, it will remove intrinsic variability if the variables are dependent,\nand therefore does not apply to many situations, including modeling of species\ncounts that are controlled by common causes. We present a technique called\n'three-quarter sibling regression' to partially overcome this limitation. It\ncan filter the effect of systematic noise when the latent variables have\nobserved common causes. We provide theoretical justification of this approach,\ndemonstrate its effectiveness on synthetic data, and show that it reduces\nsystematic detection variability due to moon brightness in moth surveys.\n"}
+{"abstract": "  Summary statistics of the likelihood, such as the Bayesian evidence, offer a\nprincipled way of comparing models and assessing tension between, or within,\nthe results of physical experiments. Noisy realisations of the data induce\nscatter in these model comparison statistics. For a realistic case of\ncosmological inference from large-scale structure we show that the logarithm of\nthe Bayes factor attains scatter of order unity, increasing significantly with\nstronger tension between the models under comparison. We develop an approximate\nprocedure that quantifies the sampling distribution of the evidence at small\nadditional computational cost and apply it to real data to demonstrate the\nimpact of the scatter, which acts to reduce the significance of any model\ndiscrepancies. Data compression is highlighted as a potential avenue to\nsuppressing noise in the evidence to negligible levels, with a proof of concept\non Planck cosmic microwave background data.\n"}
+{"abstract": "  We will describe several pioneering efforts in the study of electromagnetic\nradiative corrections to semileptonic decay processes, with particular emphasis\non the role of lattice QCD. These studies are essential for the precise\nextraction of the matrix element $V_{ud}$ from beta decays of pion, free\nneutron and $J^P=0^+$ nuclei, and are crucial to address several\nrecently-emerged anomalies involving $V_{ud}$ and $V_{us}$, which may provide\nhints for physics beyond the Standard Model.\n"}
+{"abstract": "  We present a study of fixed and partial-node approximations in Slater\ndeterminant basis sets, using full configuration interaction quantum Monte\nCarlo (FCIQMC) to perform sampling. Walker annihilation in the FCIQMC method\nallows partial-node simulations to be performed, relaxing the nodal constraint\nto converge to the FCI solution. This is applied to ab initio molecular\nsystems, using symmetry-projected Jastrow mean-field wave functions for\ncomplete active space (CAS) problems. Convergence and the sign problem within\nthe partial-node approximation are studied, which is shown to eventually be\nlimited in its use due to the large walker populations required. However the\nfixed-node approximation results in an accurate and practical method. We apply\nthese approaches to various molecular systems and active spaces, including\nferrocene and acenes. This also provides a test of symmetry-projected Jastrow\nmean-field wave functions in variational Monte Carlo (VMC) for a new set of\nproblems. For trans-polyacetylene molecules and acenes we find that the time to\nperform a constant number of fixed-node FCIQMC iterations scales as O(N^{1.44})\nand O(N^{1.75}) respectively, resulting in an efficient method for CAS-based\nproblems that can be applied accurately to large active spaces.\n"}
+{"abstract": "  A Type IV-II Z4-code is a self-dual code over Z4 with the property that all\nEuclidean weights are divisible by eight and all codewords have even Hamming\nweight. In this paper we use generalized bent functions for a construction of\nself-orthogonal codes over Z4 of length $2^m$, for $m$ odd, $m \\geq 3$, and\nprove that for $m \\geq 5$ those codes can be extended to Type IV-II Z4-codes.\nFrom that family of Type IV-II Z4-codes, we obtain a family of self-dual Type\nII binary codes by using Gray map. We also consider the weight distributions of\nthe obtained codes and the structure of the supports of the minimum weight\ncodewords.\n"}
+{"abstract": "  Sentiment analysis is a research topic focused on analysing data to extract\ninformation related to the sentiment that it causes. Applications of sentiment\nanalysis are wide, ranging from recommendation systems, and marketing to\ncustomer satisfaction. Recent approaches evaluate textual content using Machine\nLearning techniques that are trained over large corpora. However, as social\nmedia grown, other data types emerged in large quantities, such as images.\nSentiment analysis in images has shown to be a valuable complement to textual\ndata since it enables the inference of the underlying message polarity by\ncreating context and connections. Multimodal sentiment analysis approaches\nintend to leverage information of both textual and image content to perform an\nevaluation. Despite recent advances, current solutions still flounder in\ncombining both image and textual information to classify social media data,\nmainly due to subjectivity, inter-class homogeneity and fusion data\ndifferences. In this paper, we propose a method that combines both textual and\nimage individual sentiment analysis into a final fused classification based on\nAutoML, that performs a random search to find the best model. Our method\nachieved state-of-the-art performance in the B-T4SA dataset, with 95.19%\naccuracy.\n"}
+{"abstract": "  For every fixed class of regular languages, there is a natural hierarchy of\nincreasingly more general problems: Firstly, the membership problem asks\nwhether a given language belongs to the fixed class of languages. Secondly, the\nseparation problem asks for two given languages whether they can be separated\nby a language from the fixed class. And thirdly, the covering problem is a\ngeneralization of separation problem to more than two given languages. Most\ninstances of such problems were solved by the connection of regular languages\nand finite monoids. Both the membership problem and the separation problem were\nalso extended to ordered monoids. The computation of pointlikes can be\ninterpreted as the algebraic counterpart of the covering problem. In this\npaper, we consider the extension the computation of pointlikes to ordered\nmonoids. This leads to the notion of conelikes for the corresponding algebraic\nframework.\n  We apply this framework to the Trotter-Weil hierarchy and both the full and\nthe half levels of the $\\text{FO}^2$ quantifier alternation hierarchy. As a\nconsequence, we solve the covering problem for the resulting subvarieties of\n$\\mathbf{DA}$. An important combinatorial tool are uniform ranker\ncharacterizations for all subvarieties under consideration; these\ncharacterizations stem from order comparisons of ranker positions.\n"}
+{"abstract": "  The partition function for a system of non-interacting $N-$particles can be\nfound by summing over all the states of the system. The classical partition\nfunction for an ideal gas differs from Bosonic or Fermionic partition function\nin the classical regime. Students find it difficult to follow the differences\narising out of incorrect counting by the classical partition function by\nmissing out on the indistinguishability of particles and Fermi-Bose statistics.\nWe present a pedagogical computer-based experiment to probe and demonstrate the\nkey differences in the partition functions (i) $Q$, for the system of\ndistinguishable and indistinguishable particles (ii) $B$, for Bosons and (ii)\n$F$, for Fermions without formally using the single-particle partition\nfunction.\n"}
+{"abstract": "  The current increasing need for privacy-preserving voice communications is\nleading to new ideas for securing voice transmission. This paper refers to a\nrelatively new concept of sending encrypted data or speech as pseudo-speech in\nthe audio domain over existing voice communication infrastructures, like 3G\ncellular network and Voice over IP (VoIP). The distinctive characteristic of\nsuch a communication system is that it relies on the robust transmission of\nbinary information in the form of audio signal.\n  This work presents a novel Data over Voice (DoV) technique based on codebooks\nof short harmonic waveforms. The technique provides a sufficiently fast and\nreliable data rate over cellular networks and many VoIP applications. The new\nmethod relies on general principles of Linear Predictive Coding for voice\ncompression (LPC voice coding) and is more versatile compared to solutions\ntrained on exact channel models. The technique gives by design a high control\nover the desired rate of transmission and provides robustness to channel\ndistortion. In addition, an efficient codebook design approach inspired by\nquaternary error correcting codes is proposed.\n  The usability of the proposed DoV technique for secure voice communication\nover cellular networks and VoIP has been successfully validated by empirical\nexperiments. The paper details the system parameters, putting a special\nemphasis on system's security and technical challenges.\n"}
+{"abstract": "  We propose a curriculum-driven learning strategy for solving difficult\nmulti-agent coordination tasks. Our method is inspired by a study of animal\ncommunication, which shows that two straightforward design features (mutual\nreward and decentralization) support a vast spectrum of communication protocols\nin nature. We highlight the importance of similarly interpreting emergent\ncommunication as a spectrum. We introduce a toroidal, continuous-space\npursuit-evasion environment and show that naive decentralized learning does not\nperform well. We then propose a novel curriculum-driven strategy for\nmulti-agent learning. Experiments with pursuit-evasion show that our approach\nenables decentralized pursuers to learn to coordinate and capture a superior\nevader, significantly outperforming sophisticated analytical policies. We argue\nthrough additional quantitative analysis -- including influence-based measures\nsuch as Instantaneous Coordination -- that emergent implicit communication\nplays a large role in enabling superior levels of coordination.\n"}
+{"abstract": "  In this work, we present polarization profiles for 23 millisecond pulsars\nobserved at 820 MHz and 1500 MHz with the Green Bank Telescope as part of the\nNANOGrav pulsar timing array. We calibrate the data using Mueller matrix\nsolutions calculated from observations of PSRs B1929+10 and J1022+1001. We\ndiscuss the polarization profiles, which can be used to constrain pulsar\nemission geometry, and present both the first published radio polarization\nprofiles for nine pulsars and the discovery of very low intensity average\nprofile components (\"microcomponents\") in four pulsars. Using the Faraday\nrotation measures, we measure for each pulsar and use it to calculate the\nGalactic magnetic field parallel to the line of sight for different lines of\nsight through the interstellar medium. We fit for linear and sinusoidal trends\nin time in the dispersion measure and Galactic magnetic field and detect\nmagnetic field variations with a period of one year in some pulsars, but\noverall find that the variations in these parameters are more consistent with a\nstochastic origin.\n"}
+{"abstract": "  Quantum features of the baryon number fluctuations in subsystems of a hot and\ndense relativistic gas of fermions are analyzed. We find that the fluctuations\nin small systems are significantly increased compared to their values known\nfrom the statistical physics, and diverge in the limit where the system size\ngoes to zero. The numerical results obtained for a broad range of the\nthermodynamic parameters expected in heavy-ion collisions are presented. They\ncan be helpful to interpret and shed new light on the experimental data.\n"}
+{"abstract": "  Background: One-neutron removal reactions are used to study the\nsingle-particle structure of unstable nuclei, and in particular the exotic halo\nnuclei. The Eikonal Reaction Theory (ERT) has been developed by Yahiro, Ogata\nand Minomo in Prog. Theor. Phys. 126, 167 (2011) to include dynamical effects,\nwhich are missing in the usual eikonal description of these reactions.\nEncouraging results have been obtained for total breakup cross sections in\ncomparison to more elaborate reaction models.\n  Purpose: We extend these comparisons to more differential breakup cross\nsections expressed as functions of the relative energy or parallel momentum\nbetween the core and halo neutron.\n  Method: ERT predictions of these cross sections are compared to\nstate-of-the-art calculations.\n  Results: The hypotheses upon which the ERT is based are confirmed and their\nrange of validity is made clearer. The actual ordering of the evolution\noperators {affects ERT differential} cross sections and a specific choice leads\nto excellent agreement with the reference calculation. Dynamical effects in the\ntreatment of the neutron-target interaction can be significant in the\nparallel-momentum observable.\n  Conclusions: The role of the different interactions in the dynamics of\nbreakup reactions of one-neutron halo nuclei are better understood and\nimprovements to the ERT are suggested.\n"}
+{"abstract": "  Much has been learned about universal properties of the eigenstate\nentanglement entropy for one-dimensional lattice models, which is described by\na Hermitian Hamiltonian. While very less of it has been understood for\nnon-Hermitian systems. In the present work we study a non-Hermitian,\nnon-interacting model of fermions which is invariant under combined $PT$\ntransformation. Our models show a phase transition from $PT$ unbroken phase to\nbroken phase as we tune the hermiticity breaking parameter. Entanglement\nentropy of such systems can be defined in two different ways, depending on\nwhether we consider only right (or equivalently only left) eigenstates or a\ncombination of both left and right eigenstates which form a complete set of\nbi-orthonormal eigenstates. We demonstrate that the entanglement entropy of the\nground state and also of the typical excited states show some unique features\nin both of these phases of the system. Most strikingly, entanglement entropy\nobtained taking a combination of both left and right eigenstates shows an\nexponential divergence with system size at the transition point. While in the\n$PT$-unbroken phase, the entanglement entropy obtained from only the right (or\nequivalently left) eigenstates shows identical behavior as of an equivalent\nHermitian system which is connected to the non-Hermitian system by a similarity\ntransformation.\n"}
+{"abstract": "  We report the detection of a single transit-like signal in the Kepler data of\nthe slightly evolved F star KIC4918810. The transit duration is ~45 hours, and\nwhile the orbital period ($P\\sim10$ years) is not well constrained, it is one\nof the longest among companions known to transit. We calculate the size of the\ntransiting object to be $R_P = 0.910$ $R_J$. Objects of this size vary by\norders of magnitude in their densities, encompassing masses between that of\nSaturn ($0.3$ $M_J$) and stars above the hydrogen-burning limit (~80 $M_J$).\nRadial-velocity observations reveal that the companion is unlikely to be a\nstar. The mass posterior is bimodal, indicating a mass of either ~0.24 $M_J$ or\n~26 $M_J$. Continued spectroscopic monitoring should either constrain the mass\nto be planetary or detect the orbital motion, the latter of which would yield a\nbenchmark long-period brown dwarf with a measured mass, radius, and age.\n"}
+{"abstract": "  This paper is devoted to the development of algorithms for finding unit\ndistance graphs with chromatic number greater than 4, embedded in a\ntwo-dimensional sphere or plane. Such graphs provide a lower bound for the\nNelson-Hadwiger problem on the chromatic number of the plane and its\ngeneralizations to the case of the sphere. A series of 5-chromatic unit\ndistance graphs on 64513 vertices embedded into the plane is constructed.\nUnlike previously known examples, these graphs do not contain the Moser spindle\nas a subgraph. The construction of 5-chromatic graphs embedded in a sphere at\ntwo values of the radius is given. Namely, the 5-chromatic unit distance graph\non 372 vertices embedded into the circumsphere of an icosahedron with a unit\nedge length, and the 5-chromatic graph on 972 vertices embedded into the\ncircumsphere of a great icosahedron are constructed.\n"}
+{"abstract": "  In a graph $G$, a vertex dominates itself and its neighbors. A subset\n$S\\subseteq V(G)$ is said to be a double dominating set of $G$ if $S$ dominates\nevery vertex of $G$ at least twice. The double domination number\n$\\gamma_{\\times 2}(G)$ is the minimum cardinality of a double dominating set of\n$G$. We show that if $G$ is a maximal outerplanar graph on $n\\geq 3$ vertices,\nthen $\\gamma_{\\times 2}(G)\\leq \\lfloor \\frac{2n}{3}\\rfloor$. Further, if $n\\geq\n4$, then $\\gamma_{\\times 2}(G)\\leq \\min \\{\\lfloor \\frac{n+t}{2}\\rfloor, n-t\\}$,\nwhere $t$ is the number of vertices of degree $2$ in $G$. These bounds are\nshown to be tight. In addition, we also study the case that $G$ is a striped\nmaximal outerplanar graph.\n"}
+{"abstract": "  The advent of the big data era drives the media data owner to seek help from\nthe cloud platform for data hosting and sharing. Sharing media data through the\ncloud suffers three key security/privacy problems including the leakage of data\nprivacy, the infringement on the data owner's copyright, and the infringement\non the user's right. Existing techniques such as attribute-based encryption,\nproxy re-encryption, and asymmetric fingerprinting are insufficient to solve\nall three problems. In this work, we consider the scheme design of being\ncapable of addressing these three problems simultaneously. Associating the\nadditive homomorphic proxy re-encryption technique with the asymmetric\nfingerprinting based on user-side embedding, we bring forward two novel cloud\nmedia sharing schemes: CREAMS-I and CREAMS-II. Among them, CREAMS-II has better\nsecurity performance, while CREAMS-I has more outstanding cloud-side\nefficiency. It is demonstrated that both proposed schemes can solve the\nexisting three problems well and have advantages over existing peers. In\naddition, these two schemes can also be seen as an instantiation of\nprivacy-preserving outsourcing of asymmetric fingerprinting, from which the\nowner can reap substantial savings in local storage, communication, and\ncomputing resources. The feasibility of CREAMS-I and CREAMS-II is also verified\nby simulation.\n"}
+{"abstract": "  Legged robots can traverse challenging terrain, use perception to plan their\nsafe foothold positions, and navigate the environment. Such unique mobility\ncapabilities make these platforms a perfect candidate for scenarios such as\nsearch and rescue, inspection, and exploration tasks. While traversing through\nsuch terrains, the robot's instability is a significant concern. Many times the\nrobot needs to switch gaits depending on its environment. Due to the complex\ndynamics of quadruped robots, classical PID control fails to provide high\nstability. Thus, there is a need for advanced control methods like the Model\nPredictive Control (MPC) which uses the system model and the nature of the\nterrain in order to predict the stable body pose of the robot. The controller\nalso provides correction to any external disturbances that result in a change\nin the desired behavior of the robot. The MPC controller is designed in MATLAB,\nfor full body torque control. The controller performance was verified on Boston\nDynamics Spot in Webots simulator. The robot is able to provide correction for\nexternal perturbations up to 150 N and also resist falls till 80 cm.\n"}
+{"abstract": "  Exotic phases of matter emerge from the interplay between strong electron\ninteractions and non-trivial topology. Owing to their lack of dispersion at the\nsingle-particle level, systems harboring flat bands are excellent testbeds for\nstrongly interacting physics, with twisted bilayer graphene serving as a prime\nexample. On the other hand, existing theoretical models for obtaining flat\nbands in crystalline materials, such as the line-graph formalism, are often too\nrestrictive for real-life material realizations. Here we present a generic\ntechnique for constructing perfectly flat bands from bipartite crystalline\nlattices. Our prescription encapsulates and generalizes the various flat band\nmodels in the literature, being applicable to systems with any orbital content,\nwith or without spin-orbit coupling. Using Topological Quantum Chemistry, we\nbuild a complete topological classification in terms of symmetry eigenvalues of\nall the gapped and gapless flat bands, for all 1651 Magnetic Space Groups. In\naddition, we derive criteria for the existence of symmetry-protected band\ntouching points between the flat and dispersive bands, and we identify the\ngapped flat bands as prime candidates for fragile topological phases. Finally,\nwe show that the set of all (gapped and gapless) perfectly flat bands is\nfinitely generated and construct the corresponding bases for all 1651 Shubnikov\nSpace Groups.\n"}
+{"abstract": "  The Seebeck coefficient and electrical conductivity are two critical\nquantities to optimize simultaneously in designing thermoelectric materials,\nand they are determined by the dynamics of carrier scattering. We uncover a new\nregime where the co-existence at the Fermi level of multiple bands with\ndifferent effective masses leads to strongly energy-dependent carrier lifetimes\ndue to intrinsic electron-phonon scattering. In this anomalous regime,\nelectrical conductivity decreases with carrier concentration, Seebeck\ncoefficient reverses sign even at high doping, and power factor exhibits an\nunusual second peak. We discuss the origin and magnitude of this effect using\nfirst-principles Boltzmann transport calculations and simplified models. We\nalso identify general design rules for using this paradigm to engineer enhanced\nperformance in thermoelectric materials.\n"}
+{"abstract": "  An element $g$ of a free group $F$ depends on a subgroup $H < F$, or is\nalgebraic over $H$, if it satisfies a univariate equation over $H$.\nEquivalently, when $H$ is finitely-generated, the rank of $\\gen{H,g}$ is at\nmost the rank of $H$. We study the elements that depend on subgroups of $F$,\nthe subgroups they generate and the equations that they satisfy.\n"}
+{"abstract": "  Macroscopic, phenomenological models have proven useful as concise framings\nof our understandings in fields from statistical physics to economics to\nbiology. Constructing a phenomenological model for development would provide a\nframework for understanding the complicated, regulatory nature of oogenesis and\nembryogenesis. Here, we use a data-driven approach to infer quantitative,\nprecise models of human oocyte maturation and pre-implantation embryo\ndevelopment, by analyzing existing clinical In-Vitro Fertilization (IVF) data\non 7,399 IVF cycles resulting in 57,827 embryos. Surprisingly, we find that\nboth oocyte maturation and early embryo development are quantitatively\ndescribed by simple models with minimal interactions. This simplicity suggests\nthat oogenesis and embryogenesis are composed of modular processes that are\nrelatively siloed from one another. In particular, our analysis provides strong\nevidence that (i) pre-antral follicles produce anti-M{\\\"u}llerian hormone\nindependently of effects from other follicles, (ii) oocytes mature to\nmetaphase-II independently of the woman's age, her BMI, and other factors,\n(iii) early embryo development is memoryless for the variables assessed here,\nin that the probability of an embryo transitioning from its current\ndevelopmental stage to the next is independent of its previous stage. Our\nresults both provide insight into the fundamentals of oogenesis and\nembryogenesis and have implications for the clinical practice of IVF.\n"}
+{"abstract": "  We investigate features of the deconfinement phase transition in an $SU(N_c)$\ngauge theory as revealed by fluctuations of the order parameter. The tool of\nchoice is an effective model built from one-loop expressions of the field\ndeterminants of gluon and ghost, in the presence of a Polyakov loop background\nfield. We show that the curvature masses associated with the Cartan angles,\nwhich serve as a proxy to study the $A_0$-gluon screening mass, show a\ncharacteristic dip in the vicinity of the transition temperature. The strength\nof the observables, which reflects a competition between the confining and the\ndeconfining forces, is sensitive to assumptions of dynamics, thus provides an\ninteresting link between the $Z(N_c)$ vacuum structure and the properties of\ngluon and ghost propagators.\n"}
+{"abstract": "  This paper evaluates the heterogenous impacts of the America Invents Act of\n2011 (AIA) on patent applications for small and large businesses. Using data\ncollected from the United States Patent and Trademark Office and Google\nPatents, I compare how the probability of successfully overcoming an initial\nrejection is affected by the AIA for small- and large-business applicants,\nrespectively. This comparison is achieved by analyzing the data using a\ndifference-in-differences approach. Results suggest that after the enactment of\nthe AIA, small-business applicants were relatively favored when compared\nagainst large-business applicants. This effect is statistically significant and\nalso practically large.\n"}
+{"abstract": "  Phenomena in chemistry, biology and neuroscience are often modelled using\nordinary differential equations (ODEs) in which the right-hand-side is\ncomprised of terms which correspond to individual 'processes' or 'fluxes'.\nFrequently, these ODEs are characterised by multiple time-scale phenomena due\nto order of magnitude differences between contributing processes and the\npresence of switching, i.e., dominance or sub-dominance of particular terms as\na function of state variables. We outline a heuristic procedure for the\nidentification of small parameters in ODE models of this kind, with a\nparticular emphasis on the identification of small parameters relating to\nswitching behaviours. This procedure is outlined informally in generality, and\napplied in detail to a model for intracellular calcium dynamics characterised\nby switching and multiple (more than two) time-scale dynamics. A total of five\nsmall parameters are identified, and related to a single perturbation parameter\nby a polynomial scaling law based on order of magnitude comparisons. The\nresulting singular perturbation problem has a time-scale separation which\ndepends on the region of state space. We prove the existence and uniqueness of\nstable relaxation oscillations with three distinct time-scales using a\ncoordinate-independent formulation of GSPT in combination with the blow-up\nmethod. We also provide an estimate for the period of the oscillations, and\nconsider a number of possibilities for their onset under parameter variation.\n"}
+{"abstract": "  The reconstruction of accurate three-dimensional environment models is one of\nthe most fundamental goals in the field of photogrammetry. Since satellite\nimages provide suitable properties for obtaining large-scale environment\nreconstructions, there exist a variety of Stereo Matching based methods to\nreconstruct point clouds for satellite image pairs. Recently, the first\nStructure from Motion (SfM) based approach has been proposed, which allows to\nreconstruct point clouds from multiple satellite images. In this work, we\npropose an extension of this SfM based pipeline that allows us to reconstruct\nnot only point clouds but watertight meshes including texture information. We\nprovide a detailed description of several steps that are mandatory to exploit\nstate-of-the-art mesh reconstruction algorithms in the context of satellite\nimagery. This includes a decomposition of finite projective camera calibration\nmatrices, a skew correction of corresponding depth maps and input images as\nwell as the recovery of real-world depth maps from reparameterized depth\nvalues. The paper presents an extensive quantitative evaluation on multi-date\nsatellite images demonstrating that the proposed pipeline combined with current\nmeshing algorithms outperforms state-of-the-art point cloud reconstruction\nalgorithms in terms of completeness and median error. We make the source code\nof our pipeline publicly available.\n"}
+{"abstract": "  The World Wide Web is not only one of the most important platforms of\ncommunication and information at present, but also an area of growing interest\nfor scientific research. This motivates a lot of work and projects that require\nlarge amounts of data. However, there is no dataset that integrates the\nparameters and visual appearance of Web pages, because its collection is a\ncostly task in terms of time and effort. With the support of various computer\ntools and programming scripts, we have created a large dataset of 49,438 Web\npages. It consists of visual, textual and numerical data types, includes all\ncountries worldwide, and considers a broad range of topics such as art,\nentertainment, economy, business, education, government, news, media, science,\nand environment, covering different cultural characteristics and varied design\npreferences. In this paper, we describe the process of collecting, debugging\nand publishing the final product, which is freely available. To demonstrate the\nusefulness of our dataset, we expose a binary classification model for\ndetecting error Web pages, and a multi-class Web subject-based categorization,\nboth problems using convolutional neural networks.\n"}
+{"abstract": "  In this paper, we give some properties of fuzzy Riesz homomorphism on fuzzy\nRiesz space. We give definitions of fuzzy quotient spaces, and some\ncharacterizations of fuzzy Archimedean quotient spaces. We prove the properties\nwhich fuzzy Riesz homomorphism is fuzzy order continuous, and study the\nextension of fuzzy lattice homomorphism and the extension of fuzzy order\ncontinuous lattice homomorphism. Finally, we investigate the factorization by\nfuzzy Riesz homomorphisms.\n"}
+{"abstract": "  Participatory budgeting (PB) is a democratic process where citizens jointly\ndecide on how to allocate public funds to indivisible projects. This paper\nfocuses on PB processes where citizens may give additional money to projects\nthey want to see funded. We introduce a formal framework for this kind of PB\nwith donations. Our framework also allows for diversity constraints, meaning\nthat each project belongs to one or more types, and there are lower and upper\nbounds on the number of projects of the same type that can be funded. We\npropose three general classes of methods for aggregating the citizens'\npreferences in the presence of donations and analyze their axiomatic\nproperties. Furthermore, we investigate the computational complexity of\ndetermining the outcome of a PB process with donations and of finding a\ncitizen's optimal donation strategy.\n"}
+{"abstract": "  Materials with exceptional magnetism and superconductivity usually conceive\nemergent physical phenomena. Here, we investigate the physical properties of\nthe (Eu,La)FeAs2 system with double magnetic sublattices. The parent EuFeAs2\nshows anisotropy-associated magnetic behaviors, such as Eu-related moment\ncanting and exchange bias. Through La doping, the magnetic anisotropy is\nenhanced with ferromagnetism of Eu2+ realized in the overdoped region, and a\nspecial exchange bias of the superposed ferromagnetic/superconducting loop\nrevealed in Eu0.8La0.2FeAs2. Meanwhile, the Fe-related antiferromagnetism shows\nunusual robustness against La doping. Theoretical calculation and 57Fe\nM\\\"ossbauer spectroscopy investigation reveal a doping-tunable dual\nitinerant/localized nature of the Fe-related antiferromagnetism. Coexistence of\nthe Eu-related ferromagnetism, Fe-related robust antiferromagnetism, and\nsuperconductivity is further revealed in Eu0.8La0.2FeAs2, providing a platform\nfor further exploration of potential applications and emergent physics.\nFinally, an electronic phase diagram is established for (Eu,La)FeAs2 with the\nwhole superconducting dome adjacent to the Fe-related antiferromagnetic phase,\nwhich is of benefit for seeking underlying clues to high-temperature\nsuperconductivity.\n"}
+{"abstract": "  Louveau showed that if a Borel set in a Polish space happens to be in a Borel\nWadge class $\\Gamma$, then its $\\Gamma$-code can be obtained from its Borel\ncode in a hyperarithmetical manner. We extend Louveau's theorem to Borel\nfunctions: If a Borel function on a Polish space happens to be a\n$\\Sigma_t$-function, then one can effectively find its $\\Sigma_t$-code\nhyperarithmetically relative to its Borel code. More generally, we prove\nextension-type, domination-type, and decomposition-type variants of Louveau's\ntheorem for Borel functions.\n"}
+{"abstract": "  Augmented Reality is the fusion of virtual components and our real\nsurroundings. The simultaneous visibility of generated and natural objects\noften requires users to direct their selective attention to a specific target\nthat is either real or virtual. In this study, we investigated whether this\ntarget is real or virtual by using machine learning techniques to classify\nelectroencephalographic (EEG) data collected in Augmented Reality scenarios. A\nshallow convolutional neural net classified 3 second data windows from 20\nparticipants in a person-dependent manner with an average accuracy above 70\\%\nif the testing data and training data came from different trials.\nPerson-independent classification was possible above chance level for 6 out of\n20 participants. Thus, the reliability of such a Brain-Computer Interface is\nhigh enough for it to be treated as a useful input mechanism for Augmented\nReality applications.\n"}
+{"abstract": "  We study linear peer effects models where peers interact in groups,\nindividual's outcomes are linear in the group mean outcome and characteristics,\nand group effects are random. Our specification is motivated by the moment\nconditions imposed in Graham 2008. We show that these moment conditions can be\ncast in terms of a linear random group effects model and lead to a class of GMM\nestimators that are generally identified as long as there is sufficient\nvariation in group size. We also show that our class of GMM estimators contains\na Quasi Maximum Likelihood estimator (QMLE) for the random group effects model,\nas well as the Wald estimator of Graham 2008 and the within estimator of Lee\n2007 as special cases. Our identification results extend insights in Graham\n2008 that show how assumptions about random group effects as well as variation\nin group size can be used to overcome the reflection problem in identifying\npeer effects. Our QMLE and GMM estimators can easily be augmented with\nadditional covariates and are valid in situations with a large but finite\nnumber of different group sizes. Because our estimators are general moment\nbased procedures, using instruments other than binary group indicators in\nestimation is straight forward. Monte-Carlo simulations show that the bias of\nthe QMLE estimator decreases with the number of groups and the variation in\ngroup size, and increases with group size. We also prove the consistency and\nasymptotic normality of the estimator under reasonable assumptions.\n"}
+{"abstract": "  In this paper, we consider an extension of cycle-complete graph Ramsey\nnumbers to Berge cycles in hypergraphs: for $k \\geq 2$, a {\\em non-trivial\nBerge $k$-cycle} is a family of sets $e_1,e_2,\\dots,e_k$ such that $e_1 \\cap\ne_2, e_2 \\cap e_3,\\dots,e_k \\cap e_1$ has a system of distinct representatives\nand $e_1 \\cap e_2 \\cap \\dots \\cap e_k = \\emptyset$. In the case that all the\nsets $e_i$ have size three, let $\\mathcal{B}_k$ denotes the family of all\nnon-trivial Berge $k$-cycles. The {\\em Ramsey numbers} $R(t,\\mathcal{B}_k)$\ndenote the minimum $n$ such that every $n$-vertex $3$-uniform hypergraph\ncontains either a non-trivial Berge $k$-cycle or an independent set of size\n$t$. We prove \\[ R(t, \\mathcal{B}_{2k}) \\leq t^{1 + \\frac{1}{2k-1} +\n\\frac{4}{\\sqrt{\\log t}}}\\] and moreover, we show that if a conjecture of\nErd\\H{o}s and Simonovits \\cite{ES} on girth in graphs is true, then this is\ntight up to a factor $t^{o(1)}$ as $t \\rightarrow \\infty$.\n"}
+{"abstract": "  We study variance estimation and associated confidence intervals for\nparameters characterizing genetic effects from genome-wide association studies\n(GWAS) misspecified mixed model analysis. Previous studies have shown that, in\nspite of the model misspecification, certain quantities of genetic interests\nare estimable, and consistent estimators of these quantities can be obtained\nusing the restricted maximum likelihood (REML) method under a misspecified\nlinear mixed model. However, the asymptotic variance of such a REML estimator\nis complicated and not ready to be implemented for practical use. In this\npaper, we develop practical and computationally convenient methods for\nestimating such asymptotic variances and constructing the associated confidence\nintervals. Performance of the proposed methods is evaluated empirically based\non Monte-Carlo simulations and real-data application.\n"}
+{"abstract": "  Evolving attacks on the vulnerabilities of the computing systems demand novel\ndefense strategies to keep pace with newer attacks. This report discusses\nprevious works on side-channel attacks (SCAs) and defenses for cache-targeted\nand physical proximity attacks. We then discuss the proposed Entropy-Shield as\na defense against timing SCAs, and explain how we can extend the same to\nhardware-based implementations of crypto applications as \"Entropy-Shield for\nFPGA\". We then discuss why we want to build newer attacks with the hope of\ncoming up with better defense strategies.\n"}
+{"abstract": "  Functional near-infrared spectroscopy (fNIRS) is a non-invasive, low-cost\nmethod used to study the brain's blood flow pattern. Such patterns can enable\nus to classify performed by a subject. In recent research, most classification\nsystems use traditional machine learning algorithms for the classification of\ntasks. These methods, which are easier to implement, usually suffer from low\naccuracy. Further, a complex pre-processing phase is required for data\npreparation before implementing traditional machine learning methods. The\nproposed system uses a Bi-Directional LSTM based deep learning architecture for\ntask classification, including mental arithmetic, motor imagery, and idle state\nusing fNIRS data. Further, this system will require less pre-processing than\nthe traditional approach, saving time and computational resources while\nobtaining an accuracy of 81.48\\%, which is considerably higher than the\naccuracy obtained using conventional machine learning algorithms for the same\ndata set.\n"}
+{"abstract": "  We present a method to estimate dense depth by optimizing a sparse set of\npoints such that their diffusion into a depth map minimizes a multi-view\nreprojection error from RGB supervision. We optimize point positions, depths,\nand weights with respect to the loss by differential splatting that models\npoints as Gaussians with analytic transmittance. Further, we develop an\nefficient optimization routine that can simultaneously optimize the 50k+ points\nrequired for complex scene reconstruction. We validate our routine using ground\ntruth data and show high reconstruction quality. Then, we apply this to light\nfield and wider baseline images via self supervision, and show improvements in\nboth average and outlier error for depth maps diffused from inaccurate sparse\npoints. Finally, we compare qualitative and quantitative results to image\nprocessing and deep learning methods. http://visual.cs.brown.edu/diffdiffdepth\n"}
+{"abstract": "  We introduce a new class of non-linear models for functional data based on\nneural networks. Deep learning has been very successful in non-linear modeling,\nbut there has been little work done in the functional data setting. We propose\ntwo variations of our framework: a functional neural network with continuous\nhidden layers, called the Functional Direct Neural Network (FDNN), and a second\nversion that utilizes basis expansions and continuous hidden layers, called the\nFunctional Basis Neural Network (FBNN). Both are designed explicitly to exploit\nthe structure inherent in functional data. To fit these models we derive a\nfunctional gradient based optimization algorithm. The effectiveness of the\nproposed methods in handling complex functional models is demonstrated by\ncomprehensive simulation studies and real data examples.\n"}
+{"abstract": "  As HPC systems grow in complexity, efficient and manageable operation is\nincreasingly critical. Many centers are thus starting to explore the use of\nOperational Data Analytics (ODA) techniques, which extract knowledge from\nmassive amounts of monitoring data and use it for control and visualization\npurposes. As ODA is a multi-faceted problem, much effort has gone into\nresearching its separate aspects: however, accounts of production ODA\nexperiences are still hard to come across.\n  In this work we aim to bridge the gap between ODA research and production use\nby presenting our experiences with ODA in production, involving in particular\nthe control of cooling infrastructures and visualization of job data on two HPC\nsystems. We cover the entire development process, from design to deployment,\nhighlighting our insights in an effort to drive the community forward. We rely\non open-source tools, which make for a generic ODA framework suitable for most\nscenarios.\n"}
+{"abstract": "  Everyday thousands of meteoroids enter the Earth's atmosphere. The vast\nmajority burn up harmlessly during the descent, but the larger objects survive,\noccasionally experiencing intense fragmentation events, and reach the ground.\nThese events can pose a threat for a village or a small city; therefore, models\nof asteroid fragmentation, along with accurate post-breakup trajectory and\nstrewn field estimation, are needed to enable a reliable risk assessment. In\nthis work, a methodology to describe meteoroids entry, fragmentation, descent,\nand strewn field is presented by means of a continuum approach. At breakup, a\nmodified version of the NASA Standard Breakup Model is used to generate the\ndistribution of the fragments in terms of their area-to-mass ratio and ejection\nvelocity. This distribution, combined with the meteoroid state, is directly\npropagated using the continuity equation coupled with the non-linear entry\ndynamics. At each time step, the probability density evolution of the fragments\nis reconstructed using GMM interpolation. Using this information is then\npossible to estimate the meteoroid's ground impact probability. This approach\ndeparts from the current state-of-the-art models: it has the flexibility to\ninclude large fragmentation events while maintaining a continuum formulation\nfor a better physical representation of the phenomenon. The methodology is also\ncharacterised by a modular structure, so that updated asteroids fragmentation\nmodels can be readily integrated into the framework, allowing a continuously\nimproving prediction of re-entry and fragmentation events. The propagation of\nthe fragments' density and its reconstruction, currently considering only one\nfragmentation point, is first compared against Monte Carlo simulations, and\nthen against real observations. Both deceleration due to atmospheric drag and\nablation due to aerothermodynamics effects have been considered.\n"}
+{"abstract": "  Machine learning (ML) and AI toolboxes such as scikit-learn or Weka are\nworkhorses of contemporary data scientific practice -- their central role being\nenabled by usable yet powerful designs that allow to easily specify, train and\nvalidate complex modeling pipelines. However, despite their universal success,\nthe key design principles in their construction have never been fully analyzed.\nIn this paper, we attempt to provide an overview of key patterns in the design\nof AI modeling toolboxes, taking inspiration, in equal parts, from the field of\nsoftware engineering, implementation patterns found in contemporary toolboxes,\nand our own experience from developing ML toolboxes. In particular, we develop\na conceptual model for the AI/ML domain, with a new type system, called\nscientific types, at its core. Scientific types capture the scientific meaning\nof common elements in ML workflows based on the set of operations that we\nusually perform with them (i.e. their interface) and their statistical\nproperties. From our conceptual analysis, we derive a set of design principles\nand patterns. We illustrate that our analysis can not only explain the design\nof existing toolboxes, but also guide the development of new ones. We intend\nour contribution to be a state-of-art reference for future toolbox engineers, a\nsummary of best practices, a collection of ML design patterns which may become\nuseful for future research, and, potentially, the first steps towards a\nhigher-level programming paradigm for constructing AI.\n"}
+{"abstract": "  Determining the trajectories of cells and their lineages or ancestries in\nlive-cell experiments are fundamental to the understanding of how cells behave\nand divide. This paper proposes novel online algorithms for jointly tracking\nand resolving lineages of an unknown and time-varying number of cells from\ntime-lapse video data. Our approach involves modeling the cell ensemble as a\nlabeled random finite set with labels representing cell identities and\nlineages. A spawning model is developed to take into account cell lineages and\nchanges in cell appearance prior to division. We then derive analytic filters\nto propagate multi-object distributions that contain information on the current\ncell ensemble including their lineages. We also develop numerical\nimplementations of the resulting multi-object filters. Experiments using\nsimulation, synthetic cell migration video, and real time-lapse sequence, are\npresented to demonstrate the capability of the solutions.\n"}
+{"abstract": "  A regularized minimization model with $l_1$-norm penalty (RP) is introduced\nfor training the autoencoders that belong to a class of two-layer neural\nnetworks. We show that the RP can act as an exact penalty model which shares\nthe same global minimizers, local minimizers, and d(irectional)-stationary\npoints with the original regularized model under mild conditions. We construct\na bounded box region that contains at least one global minimizer of the RP, and\npropose a linearly constrained regularized minimization model with $l_1$-norm\npenalty (LRP) for training autoencoders. A smoothing proximal gradient\nalgorithm is designed to solve the LRP. Convergence of the algorithm to a\ngeneralized d-stationary point of the RP and LRP is delivered. Comprehensive\nnumerical experiments convincingly illustrate the efficiency as well as the\nrobustness of the proposed algorithm.\n"}
+{"abstract": "  Riemannian manifold Hamiltonian Monte Carlo is traditionally carried out\nusing the generalized leapfrog integrator. However, this integrator is not the\nonly choice and other integrators yielding valid Markov chain transition\noperators may be considered. In this work, we examine the implicit midpoint\nintegrator as an alternative to the generalized leapfrog integrator. We discuss\nadvantages and disadvantages of the implicit midpoint integrator for\nHamiltonian Monte Carlo, its theoretical properties, and an empirical\nassessment of the critical attributes of such an integrator for Hamiltonian\nMonte Carlo: energy conservation, volume preservation, and reversibility.\nEmpirically, we find that while leapfrog iterations are faster, the implicit\nmidpoint integrator has better energy conservation, leading to higher\nacceptance rates, as well as better conservation of volume and better\nreversibility, arguably yielding a more accurate sampling procedure.\n"}
+{"abstract": "  Understanding the dynamics of charge exchange between a solid surface and a\nliquid is fundamental to various situations, ranging from nanofiltration to\ncatalysis and electrochemistry. Charge transfer is ultimately determined by\nphysicochemical processes (surface group dissociation, ion adsorption, etc...)\noccurring in the few layers of molecules at the interface between the solid and\nthe liquid. Unfortunately, these processes remain largely uncharted due to the\nexperimental challenges in probing interfacial charge dynamics with\nsufficiently high spatial and temporal resolution. Here, we resolve at the\nsingle-charge scale, the dynamics of proton charges at the interface between an\nhBN crystal and binary mixtures of water and organic amphiphilic solvents (e.g.\nalcohol), evidencing a dramatic influence of solvation on interfacial dynamics.\nOur observations rely on the application of spectral Single Molecule\nLocalization Microscopy (sSMLM) to two types of optically active defects at the\nhBN surface, which act as intrinsic optical markers for both surface\nprotonation and interaction with apolar alkyl groups of the organic solvent. We\nuse sSMLM to reveal interfacial proton charge transport as a succession of\njumps between the titratable surface defects, mediated by the transport of the\nsolvated proton charge along the solid/liquid interface. By changing the\nrelative concentration of water in binary mixtures, we evidence a non-trivial\neffect on interfacial proton charge dynamics, leading at intermediate water\nconcentration to an increased affinity of the proton charge to the solid\nsurface, accompanied by an increased surface diffusivity. These measurements\nconfirm the strong role of solvation on interfacial proton charge transport and\nestablish the potential of single-molecule localization techniques to probe a\nwide range of dynamic processes at solid/liquid interfaces.\n"}
+{"abstract": "  Avoiding collisions is the core problem in multi-agent navigation. In\ndecentralized settings, when agents have limited communication and sensory\ncapabilities, collisions are typically avoided in a reactive fashion, relying\non local observations/communications. Prominent collision avoidance techniques,\ne.g. ORCA, are computationally efficient and scale well to a large number of\nagents. However, in numerous scenarios, involving navigation through the tight\npassages or confined spaces, deadlocks are likely to occur due to the egoistic\nbehaviour of the agents and as a result, the latter can not achieve their\ngoals. To this end, we suggest an application of the locally confined\nmulti-agent path finding (MAPF) solvers that coordinate sub-groups of the\nagents that appear to be in a deadlock (to detect the latter we suggest a\nsimple, yet efficient ad-hoc routine). We present a way to build a grid-based\nMAPF instance, typically required by modern MAPF solvers. We evaluate two of\nthem in our experiments, i.e. Push and Rotate and a bounded-suboptimal version\nof Conflict Based Search (ECBS), and show that their inclusion into the\nnavigation pipeline significantly increases the success rate, from 15% to 99%\nin certain cases.\n"}
+{"abstract": "  Monocular depth prediction plays a crucial role in understanding 3D scene\ngeometry. Although recent methods have achieved impressive progress in terms of\nevaluation metrics such as the pixel-wise relative error, most methods neglect\nthe geometric constraints in the 3D space. In this work, we show the importance\nof the high-order 3D geometric constraints for depth prediction. By designing a\nloss term that enforces a simple geometric constraint, namely, virtual normal\ndirections determined by randomly sampled three points in the reconstructed 3D\nspace, we significantly improve the accuracy and robustness of monocular depth\nestimation. Significantly, the virtual normal loss can not only improve the\nperformance of learning metric depth, but also disentangle the scale\ninformation and enrich the model with better shape information. Therefore, when\nnot having access to absolute metric depth training data, we can use virtual\nnormal to learn a robust affine-invariant depth generated on diverse scenes. In\nexperiments, We show state-of-the-art results of learning metric depth on NYU\nDepth-V2 and KITTI. From the high-quality predicted depth, we are now able to\nrecover good 3D structures of the scene such as the point cloud and surface\nnormal directly, eliminating the necessity of relying on additional models as\nwas previously done. To demonstrate the excellent generalizability of learning\naffine-invariant depth on diverse data with the virtual normal loss, we\nconstruct a large-scale and diverse dataset for training affine-invariant\ndepth, termed Diverse Scene Depth dataset (DiverseDepth), and test on five\ndatasets with the zero-shot test setting. Code is available at:\nhttps://git.io/Depth\n"}
+{"abstract": "  In this paper, a computational resources-aware parameter adaptation method\nfor visual-inertial navigation systems is proposed with the goal of enabling\nthe improved deployment of such algorithms on computationally constrained\nsystems. Such a capacity can prove critical when employed on ultra-lightweight\nsystems or alongside mission critical computationally expensive processes. To\nachieve this objective, the algorithm proposes selected changes in the vision\nfront-end and optimization back-end of visual-inertial odometry algorithms,\nboth prior to execution and in real-time based on an online profiling of\navailable resources. The method also utilizes information from the motion\ndynamics experienced by the system to manipulate parameters online. The general\npolicy is demonstrated on three established algorithms, namely S-MSCKF,\nVINS-Mono and OKVIS and has been verified experimentally on the EuRoC dataset.\nThe proposed approach achieved comparable performance at a fraction of the\noriginal computational cost.\n"}
+{"abstract": "  In this work we computationally study the effect of microstructure on\nsaturation magnetostriction of Terfenol-D (Tb$_{0.27}$Dy$_{0.73}$Fe$_{2}$) by\nmeans of Finite Element Method. The model is based on the equilibrium\nmagnetoelastic strain tensor at magnetic saturation, and shows that the crystal\norientation might play a more significant role on saturation magnetostriction\nthan the morphology of the grains. We also calculate the dependence of\nsaturation magnetostriction on the dispersion angle of the distribution of\ngrains in the oriented growth crystal directions $<011>$ and $<111>$, finding\nthat not highly oriented grain distributions reduce saturation magnetostriction\nsignificantly. This result evinces the importance of high-quality control of\ngrain orientation in the synthesis of grain-aligned polycrystalline Terfenol-D,\nand provides a quantitative estimation for the range of acceptable values for\nthe dispersion angle of the distribution of the oriented grains.\n"}
+{"abstract": "  Virtual reality (VR) is promising to fundamentally transform a broad spectrum\nof industry sectors and the way humans interact with virtual content. However,\ndespite unprecedented progress, current networking and computing\ninfrastructures are incompetent to unlock VR's full potential. In this paper,\nwe consider delivering the wireless multi-tile VR video service over a mobile\nedge computing (MEC) network. The primary goal is to minimize the system\nlatency/energy consumption and to arrive at a tradeoff thereof. To this end, we\nfirst cast the time-varying view popularity as a model-free Markov chain to\neffectively capture its dynamic characteristics. After jointly assessing the\ncaching and computing capacities on both the MEC server and the VR playback\ndevice, a hybrid policy is then implemented to coordinate the dynamic caching\nreplacement and the deterministic offloading, so as to fully utilize the system\nresources. The underlying multi-objective problem is reformulated as a\npartially observable Markov decision process, and a deep deterministic policy\ngradient algorithm is proposed to iteratively learn its solution, where a long\nshort-term memory neural network is embedded to continuously predict the\ndynamics of the unobservable popularity. Simulation results demonstrate the\nsuperiority of the proposed scheme in achieving a trade-off between the energy\nefficiency and the latency reduction over the baseline methods.\n"}
+{"abstract": "  The rigidity and flexibility of homologous psychrophilic(P), mesophilic(M)\nand thermophilic(T) proteins have been investigated at the global and local\nlevels in terms of packing factor and atomic fluctuations obtained from\nB-factors. For comparison of atomic fluctuations, correction of errors by\nconsidering errors in B-factors from all sources in a consolidated manner and\nconversion of the fluctuations to the same temperature have been suggested and\nvalidated. Results indicate no differences in the global values like average\npacking factor among the three classes of protein homologs but at local levels\nthere are differences. Comparison of homologous proteins triplets show that the\naverage atomic fluctuations at a given temperature obey the order P>M>T.\nPacking factors and the atomic fluctuations are anti-correlated suggesting that\naltering the rigidity of the active site might be a potential strategy to make\ntailor made psychrophilic or thermophilic proteins from their mesophilic\nhomologs.\n"}
+{"abstract": "  We report Brownian Dynamics simulation results for the relative permittivity\nof electrorheological (ER) fluids in an applied electric field. The relative\npermittivity of an ER fluid can be calculated from the Clausius-Mosotti (CM)\nequation in the small applied field limit. When a strong field is applied,\nhowever, the ER spheres are organized into chains and assemblies of chains in\nwhich case the ER spheres are polarized not only by the external field but by\neach other. This manifests itself in an enhanced dielectric response, e.g., in\nan increase in the relative permittivity. The correction to the relative\npermittivity and the time dependence of this correction is simulated on the\nbasis of a model in which the ER particles are represented as polarizable\nspheres. In this model, the spheres are also polarized by each other in\naddition to the applied field. Our results are qualitatively similar to those\nobtained by Horv\\'ath and Szalai experimentally (\\textit{Phys. Rev. E},\n\\textbf{86}, 061403, 2012). We report characteristic time constants obtained\nfrom bi-exponential fits that can be associated with formation of pairs and\nshort chains as well as with aggregation of chains. The electric field\ndependence of the induced dielectric increment reveals the same qualitative\nbehavior that experiments did: three regions with different slopes\ncorresponding to different aggregation processes are identified.\n"}
+{"abstract": "  Meta-learning algorithms leverage regularities that are present on a set of\ntasks to speed up and improve the performance of a subsidiary learning process.\nRecent work on deep neural networks has shown that prior gradient-based\nlearning of meta-parameters can greatly improve the efficiency of subsequent\nlearning. Here, we present a biologically plausible meta-learning algorithm\nbased on equilibrium propagation. Instead of explicitly differentiating the\nlearning process, our contrastive meta-learning rule estimates meta-parameter\ngradients by executing the subsidiary process more than once. This avoids\nreversing the learning dynamics in time and computing second-order derivatives.\nIn spite of this, and unlike previous first-order methods, our rule recovers an\narbitrarily accurate meta-parameter update given enough compute. We establish\ntheoretical bounds on its performance and present experiments on a set of\nstandard benchmarks and neural network architectures.\n"}
+{"abstract": "  In this work, we study the behavior of standard models for community\ndetection under spectral manipulations. Through various ablation experiments,\nwe evaluate the impact of bandpass filtering on the performance of a GCN: we\nempirically show that most of the necessary and used information for nodes\nclassification is contained in the low-frequency domain, and thus contrary to\nimages, high frequencies are less crucial to community detection. In\nparticular, it is sometimes possible to obtain accuracies at a state-of-the-art\nlevel with simple classifiers that rely only on a few low frequencies.\n"}
+{"abstract": "  Disturbance observer is an inner-loop output-feedback controller whose role\nis to reject external disturbances and to make the outer-loop baseline\ncontroller robust against plant's uncertainties. Therefore, the closed-loop\nsystem with the DOB approximates the nominal closed-loop by the baseline\ncontroller and the nominal plant model with no disturbances. This article\npresents how the disturbance observer works under what conditions, and how one\ncan design a disturbance observer to guarantee robust stability and to recover\nthe nominal performance not only in the steady-state but also for the transient\nresponse under large uncertainty and disturbance.\n"}
+{"abstract": "  We approach the task of network congestion control in datacenters using\nReinforcement Learning (RL). Successful congestion control algorithms can\ndramatically improve latency and overall network throughput. Until today, no\nsuch learning-based algorithms have shown practical potential in this domain.\nEvidently, the most popular recent deployments rely on rule-based heuristics\nthat are tested on a predetermined set of benchmarks. Consequently, these\nheuristics do not generalize well to newly-seen scenarios. Contrarily, we\ndevise an RL-based algorithm with the aim of generalizing to different\nconfigurations of real-world datacenter networks. We overcome challenges such\nas partial-observability, non-stationarity, and multi-objectiveness. We further\npropose a policy gradient algorithm that leverages the analytical structure of\nthe reward function to approximate its derivative and improve stability. We\nshow that this scheme outperforms alternative popular RL approaches, and\ngeneralizes to scenarios that were not seen during training. Our experiments,\nconducted on a realistic simulator that emulates communication networks'\nbehavior, exhibit improved performance concurrently on the multiple considered\nmetrics compared to the popular algorithms deployed today in real datacenters.\nOur algorithm is being productized to replace heuristics in some of the largest\ndatacenters in the world.\n"}
+{"abstract": "  BACKGROUND: Meniere's Disease (MD) is a condition of the inner ear with\nsymptoms affecting both vestibular and hearing functions. Some patients with MD\nexperience vestibular drop attacks (VDAs), which are violent falls caused by\nspurious vestibular signals from the utricle and/or saccule. Recent surgical\nwork has shown that patients who experience VDAs also show distrupted utricular\notolithic membranes. OBJECTIVE: The objective of this study is to determine if\notolithic membrane damage alone is sufficient to induce spurious vestibular\nsignals, thus potentially eliciting VDAs and the vestibular dysfunction seen in\npatients with MD. METHODS: We use a previously developed numerical model to\ndescribe the nonlinear dynamics of an array of active, elastically coupled hair\ncells. We then reduce the coupling strength of a selected region of the\nmembrane to model the effects of tissue damage. RESULTS: As we reduce the\ncoupling strength, we observe large and abrupt spikes in hair bundle position.\nAs bundle displacements from the equilibrium position have been shown to lead\nto depolarization of the hair-cell soma and hence trigger neural activity, this\nspontaneous activity could elicit false detection of a vestibular signal.\nCONCLUSIONS: The results of this numerical model suggest that otolithic\nmembrane damage alone may be sufficient to induce VDAs and the vestibular\ndysfunction seen in patients with MD. Future experimental work is needed to\nconfirm these results in vitro.\n"}
+{"abstract": "  In this paper, the authors consider an initial boundary value problem for the\nheat flow of equation of surfaces with constant mean curvatures, which was\ninvestigated in [On the heat flow of equation of surfaces of constant mean\ncurvatures, Manuscripta Mathematica, 2011, 134: 259-271] by Huang et al., where\nglobal well-posedness and finite time blow-up of regular solutions were\nobtained. Their results are complemented in this paper in the sense that some\nnew conditions on the initial data are provided for the solutions to develop\nfinite time singularity.\n"}
+{"abstract": "  We study the linear stability of entire radial solutions\n$u(re^{i\\theta})=f(r)e^{i\\theta}$, with positive increasing profile $f(r)$, to\nthe anisotropic Ginzburg-Landau equation \\[ -\\Delta u -\\delta\n(\\partial_x+i\\partial_y)^2\\bar u =(1-|u|^2)u,\\quad -1<\\delta <1, \\] which\narises in various liquid crystal models. In the isotropic case $\\delta=0$,\nMironescu showed that such solution is nondegenerately stable. We prove\nstability of this radial solution in the range $\\delta\\in (\\delta_1,0]$ for\nsome $-1<\\delta_1<0$, and instability outside this range. In strong contrast\nwith the isotropic case, stability with respect to higher Fourier modes is\n\\emph{not} a direct consequence of stability with respect to lower Fourier\nmodes. In particular, in the case where $\\delta\\approx -1$, lower modes are\nstable and yet higher modes are unstable.\n"}
+{"abstract": "  The nano-particle systems under theoretically and experimentally\ninvestigation because of the potential applications in the nano-technology such\nas drug delivery, ferrofluids mechanics, magnetic data storage, sensors,\nmagnetic resonance imaging, and cancer therapy. Recently, it is reported that\ninteracting nano-particles behave as spin-glasses and experimentally show that\nthe relaxation of these systems obeys stretched exponential i.e., KWW\nrelaxation. Therefore, in this study, considering the interacting nano-particle\nsystems we model the relaxation and investigate frequency and temperature\nbehaviour depends on slow relaxation by using a simple operator formalism. We\nshow that relaxation deviates from Debye and obeys to KWW in the presence of\nthe memory effects in the system. Furthermore, we obtain the frequency and\ntemperature behaviour depend on KWW relaxation. We conclude that the obtained\nresults are consistent with experimental results and the simple model,\npresented here, is very useful and pedagogical to discuss the slow relaxation\nof the interacting nano-particles.\n"}
+{"abstract": "  In this paper, we study forward problem and inverse problem for the\nfractional magnetic Schrodinger equation with nonlinear electric potential. We\nfirst investigate the maximum principle for the linearized equation and apply\nit to show that the problem is well-posed under suitable assumptions on the\nexterior data. Moreover, we explore uniqueness of recovery of both magnetic and\nelectric potentials.\n"}
+{"abstract": "  Spin-momentum locking of evanescent waves describes the relationship between\nthe propagation constant of an evanescent mode and the polarization of its\nelectromagnetic field, giving rise to applications in light nano-routing and\npolarimetry among many others. The use of complex numbers in physics is a\npowerful representation in areas such as quantum mechanics or electromagnetism;\nit is well known that a lossy waveguide can be modelled with the addition of an\nimaginary part to the propagation constant. Here we explore how these losses\nare entangled with the polarization of the associated evanescent tails for the\nwaveguide, revealing a well-defined mapping between waveguide losses and the\nPoincar\\'e sphere of polarizations, in what could be understood as a\n\"polarization-loss locking\" of evanescent waves. We analyse the implications\nfor near-field directional coupling of sources to waveguides, as optimized\ndipoles must take into account the losses for a perfectly unidirectional\nexcitation. We also reveal the potential advantage of calculating the angular\nspectrum of a source defined in a complex, rather than the traditionally purely\nreal, transverse wavevector space formalism.\n"}
+{"abstract": "  We introduce the problem of weakly supervised Multi-Object Tracking and\nSegmentation, i.e. joint weakly supervised instance segmentation and\nmulti-object tracking, in which we do not provide any kind of mask annotation.\nTo address it, we design a novel synergistic training strategy by taking\nadvantage of multi-task learning, i.e. classification and tracking tasks guide\nthe training of the unsupervised instance segmentation. For that purpose, we\nextract weak foreground localization information, provided by Grad-CAM\nheatmaps, to generate a partial ground truth to learn from. Additionally, RGB\nimage level information is employed to refine the mask prediction at the edges\nof the objects. We evaluate our method on KITTI MOTS, the most representative\nbenchmark for this task, reducing the performance gap on the MOTSP metric\nbetween the fully supervised and weakly supervised approach to just 12% and\n12.7% for cars and pedestrians, respectively.\n"}
+{"abstract": "  In the present paper we introduce a semi-quasi-Einstein manifold from a semi\nsymmetric metric connection. Among others, the popular Schwarzschild and\nKottler spacetimes are shown to possess this structure. Certain curvature\nconditions are studied in such a manifold with a Killing generator.\n"}
+{"abstract": "  The excited state population of single solid-state emitters is subjected to\nenergy fluctuations around the equilibrium driven by the bath and relaxation\nthrough the emission of phonons or photons. Simultaneous measurement of the\nassociated spectral dynamics requires a technique with a high spectral and\ntemporal resolution with an additionally high temporal dynamic range. We\npropose a pulsed excitation-laser analog of Photon-Correlation Fourier\nSpectroscopy (PCFS), which extracts the lineshape and spectral diffusion\ndynamics along the emission lifetime trajectory of the emitter, effectively\ndiscriminating spectral dynamics from relaxation and bath fluctuations. This\nlifetime-resolved PCFS correlates photon-pairs at the output arm of a Michelson\ninterferometer in both their time-delay between laser-excitation and\nphoton-detection and the time-delay between two photons. We propose the utility\nof the technique for systems with changing relative contributions to the\nemission from multiple states, for example, quantum emitters exhibiting\nphonon-mediated exchange between different fine-structure states.\n"}
+{"abstract": "  The aim of this paper is to introduce Bell polynomials and numbers of the\nsecond kind and poly-Bell polynomials and numbers of the second kind, and to\nderive their explicit expressions, recurrence relations and some identities\ninvolving those polynomials and numbers. We also consider degenerate versions\nof those polynomials and numbers, namely degenerate Bell polynomials and\nnumbers of the second kind and degenerate poly-Bell polynomials and numbers of\nthe second kind, and deduce their similar results.\n"}
+{"abstract": "  Recently published ALMA observations suggest the presence of 20 ppb PH$_3$ in\nthe upper clouds of Venus. This is an unexpected result, as PH$_3$ does not\nhave a readily apparent source and should be rapidly photochemically destroyed\naccording to our current understanding of Venus atmospheric chemistry. While\nthe reported PH$_3$ spectral line at 266.94 GHz is nearly co-located with an\nSO$_2$ spectral line, the non-detection of stronger SO$_2$ lines in the\nwideband ALMA data is used to rule out SO$_2$ as the origin of the feature. We\npresent a reassessment of wideband and narrowband datasets derived from these\nALMA observations. The ALMA observations are re-reduced following both the\ninitial and revised calibration procedures discussed by the authors of the\noriginal study. We also investigate the phenomenon of apparent spectral line\ndilution over varying spatial scales resulting from the ALMA antenna\nconfiguration. A 266.94 GHz spectral feature is apparent in the narrowband data\nusing the initial calibration procedures, but this same feature can not be\nidentified following calibration revisions. The feature is also not reproduced\nin the wideband data. While the SO$_2$ spectral line is not observed at 257.54\nGHz in the ALMA wideband data, our dilution simulations suggest that SO$_2$\nabundances greater than the previously suggested 10 ppb limit would also not be\ndetected by ALMA. Additional millimeter, sub-millimeter, and infrared\nobservations of Venus should be undertaken to further investigate the\npossibility of PH$_3$ in the Venus atmosphere.\n"}
+{"abstract": "  We demonstrate that dislocations in two-dimensional non-Hermitian systems can\ngive rise to density accumulation or depletion through the localization of an\nextensive number of states. These effects are shown by numerical simulations in\na prototype lattice model and expose a different face of non-Hermitian skin\neffect, by disentangling it from the need for boundaries. We identify a\ntopological invariant responsible for the dislocation skin effect, which takes\nthe form of a ${\\mathbb Z}_2$ Hopf index that depends on the Burgers vector\ncharacterizing the dislocations. Remarkably, we find that this effect and its\ncorresponding signature for defects in Hermitian systems falls outside of the\nknown topological classification based on bulk-defect correspondence.\n"}
+{"abstract": "  In real-time strategy (RTS) game artificial intelligence research, various\nmulti-agent deep reinforcement learning (MADRL) algorithms are widely and\nactively used nowadays. Most of the research is based on StarCraft II\nenvironment because it is the most well-known RTS games in world-wide. In our\nproposed MADRL-based algorithm, distributed MADRL is fundamentally used that is\ncalled QMIX. In addition to QMIX-based distributed computation, we consider\nstate categorization which can reduce computational complexity significantly.\nFurthermore, self-attention mechanisms are used for identifying the\nrelationship among agents in the form of graphs. Based on these approaches, we\npropose a categorized state graph attention policy (CSGA-policy). As observed\nin the performance evaluation of our proposed CSGA-policy with the most\nwell-known StarCraft II simulation environment, our proposed algorithm works\nwell in various settings, as expected.\n"}
+{"abstract": "  Language-queried video actor segmentation aims to predict the pixel-level\nmask of the actor which performs the actions described by a natural language\nquery in the target frames. Existing methods adopt 3D CNNs over the video clip\nas a general encoder to extract a mixed spatio-temporal feature for the target\nframe. Though 3D convolutions are amenable to recognizing which actor is\nperforming the queried actions, it also inevitably introduces misaligned\nspatial information from adjacent frames, which confuses features of the target\nframe and yields inaccurate segmentation. Therefore, we propose a collaborative\nspatial-temporal encoder-decoder framework which contains a 3D temporal encoder\nover the video clip to recognize the queried actions, and a 2D spatial encoder\nover the target frame to accurately segment the queried actors. In the decoder,\na Language-Guided Feature Selection (LGFS) module is proposed to flexibly\nintegrate spatial and temporal features from the two encoders. We also propose\na Cross-Modal Adaptive Modulation (CMAM) module to dynamically recombine\nspatial- and temporal-relevant linguistic features for multimodal feature\ninteraction in each stage of the two encoders. Our method achieves new\nstate-of-the-art performance on two popular benchmarks with less computational\noverhead than previous approaches.\n"}
+{"abstract": "  Animals are known to make efficient probabilistic inferences based on\nuncertain and noisy information from the outside world. Although it is known\nthat generic neural networks can perform near-optimal point estimation by\nprobabilistic population codes which has been proposed as a neural basis for\nencoding of probability distribution, the mechanisms of sampling-based\ninference has not been clarified. In this study, we trained two types of\nartificial neural networks: feedforward neural networks (FFNNs) and recurrent\nneural networks (RNNs) to perform sampling based probabilistic inference. Then,\nwe analyzed and compared the mechanisms of sampling in the RNN with those in\nthe FFNN. As a result, it was found that sampling in RNN is performed by a\nmechanism that efficiently utilizes the properties of dynamical systems, unlike\nFFNN. It was also found that sampling in RNNs acts as an inductive bias,\nenabling more accurate estimation than in MAP estimation. These results will\nprovide important implications for the discussion of the relationship between\ndynamical systems and information processing in neural networks.\n"}
+{"abstract": "  Weakly supervised question answering usually has only the final answers as\nsupervision signals while the correct solutions to derive the answers are not\nprovided. This setting gives rise to the spurious solution problem: there may\nexist many spurious solutions that coincidentally derive the correct answer,\nbut training on such solutions can hurt model performance (e.g., producing\nwrong solutions or answers). For example, for discrete reasoning tasks as on\nDROP, there may exist many equations to derive a numeric answer, and typically\nonly one of them is correct. Previous learning methods mostly filter out\nspurious solutions with heuristics or using model confidence, but do not\nexplicitly exploit the semantic correlations between a question and its\nsolution. In this paper, to alleviate the spurious solution problem, we propose\nto explicitly exploit such semantic correlations by maximizing the mutual\ninformation between question-answer pairs and predicted solutions. Extensive\nexperiments on four question answering datasets show that our method\nsignificantly outperforms previous learning methods in terms of task\nperformance and is more effective in training models to produce correct\nsolutions.\n"}
+{"abstract": "  We present a guide to compute the phase-boundaries of classical systems using\na dynamic Clausius-Clapeyron integration (dCCI) method within the LAMMPS\n(Large-scale Atomic/Molecular Massively Parallel Simulator) code. The advantage\nof the dCCI method is because it provides coexistence curves spanning a wide\nrange of thermodynamic states using relatively short single non-equilibrium\nsimulations. We describe the state-of-the-art of non-equilibrium free-energy\nmethods that allow us to compute the Gibbs free-energy in a wide interval of\npressure and/or temperature. We present the dCCI method in details, discuss its\nimplementation in the LAMMPS package and make available source code, scripts,\nas well as auxiliary files. As an illustrative example, we determine the phase\ndiagram of silicon in a range of pressures covering from 0 to 15 GPa and\ntemperatures as low as 400 K up to the liquid phase, in order to obtain the\nphase boundaries and triple point between diamond, liquid and beta-Sn phases.\n"}
+{"abstract": "  Classification, recommendation, and ranking problems often involve competing\ngoals with additional constraints (e.g., to satisfy fairness or diversity\ncriteria). Such optimization problems are quite challenging, often involving\nnon-convex functions along with considerations of user preferences in balancing\ntrade-offs. Pareto solutions represent optimal frontiers for jointly optimizing\nmultiple competing objectives. A major obstacle for frequently used\nlinear-scalarization strategies is that the resulting optimization problem\nmight not always converge to a global optimum. Furthermore, such methods only\nreturn one solution point per run. A Pareto solution set is a subset of all\nsuch global optima over multiple runs for different trade-off choices.\nTherefore, a Pareto front can only be guaranteed with multiple runs of the\nlinear-scalarization problem, where all runs converge to their respective\nglobal optima. Consequently, extracting a Pareto front for practical problems\nis computationally intractable with substantial computational overheads,\nlimited scalability, and reduced accuracy. We propose a robust, low cost,\ntwo-stage, hybrid neural Pareto optimization approach that is accurate and\nscales (compute space and time) with data dimensions, as well as number of\nfunctions and constraints. The first stage (neural network) efficiently\nextracts a weak Pareto front, using Fritz-John conditions as the discriminator,\nwith no assumptions of convexity on the objectives or constraints. The second\nstage (efficient Pareto filter) extracts the strong Pareto optimal subset given\nthe weak front from stage 1. Fritz-John conditions provide us with theoretical\nbounds on approximation error between the true and network extracted weak\nPareto front. Numerical experiments demonstrates the accuracy and efficiency on\na canonical set of benchmark problems and a fairness optimization task from\nprior works.\n"}
+{"abstract": "  We propose a model for a thermally driven microswimmer in which three spheres\nare connected by two springs with odd elasticity. We demonstrate that the\npresence of odd elasticity leads to the directional locomotion of the\nstochastic microswimmer.\n"}
+{"abstract": "  We consider a class (convex set) of quantum states containing all finite rank\nstates and infinite rank states with the sufficient rate of decreasing of\neigenvalues (in particular, all Gaussian states). Quantum states from this\nclass are characterized by the property (called the FA-property) that allows to\nobtain several results concerning finite-dimensional approximation of basic\nentropic and information characteristics of quantum systems and channels.\n  We obtain a simple sufficient condition of the FA-property. We show that this\nproperty implies finiteness of the von Neumann entropy, but leave unsolved the\nquestion concerning the converse implication.\n  We obtain uniform approximation results for characteristics depending on a\npair (channel, input state) and for characteristics depending on a pair\n(channel, input ensemble). We establish the uniform continuity of the above\ncharacteristics as functions of a channel w.r.t. the strong convergence\nprovided that the FA-property holds either for the input state or for the\naverage state of input ensemble.\n"}
+{"abstract": "  Session-based recommendation is an important task for e-commerce services,\nwhere a large number of users browse anonymously or may have very distinct\ninterests for different sessions. In this paper we present one of the winning\nsolutions for the Recommendation task of the SIGIR 2021 Workshop on E-commerce\nData Challenge. Our solution was inspired by NLP techniques and consists of an\nensemble of two Transformer architectures - Transformer-XL and XLNet - trained\nwith autoregressive and autoencoding approaches. To leverage most of the rich\ndataset made available for the competition, we describe how we prepared\nmulti-model features by combining tabular events with textual and image\nvectors. We also present a model prediction analysis to better understand the\neffectiveness of our architectures for the session-based recommendation.\n"}
+{"abstract": "  Generation of maps from satellite images is conventionally done by a range of\ntools. Maps became an important part of life whose conversion from satellite\nimages may be a bit expensive but Generative models can pander to this\nchallenge. These models aims at finding the patterns between the input and\noutput image. Image to image translation is employed to convert satellite image\nto corresponding map. Different techniques for image to image translations like\nGenerative adversarial network, Conditional adversarial networks and\nCo-Variational Auto encoders are used to generate the corresponding\nhuman-readable maps for that region, which takes a satellite image at a given\nzoom level as its input. We are training our model on Conditional Generative\nAdversarial Network which comprises of Generator model which which generates\nfake images while the discriminator tries to classify the image as real or fake\nand both these models are trained synchronously in adversarial manner where\nboth try to fool each other and result in enhancing model performance.\n"}
+{"abstract": "  There have been numerous studies on mining temporal specifications from\nexecution traces. These approaches learn finite-state automata (FSA) from\nexecution traces when running tests. To learn accurate specifications of a\nsoftware system, many tests are required. Existing approaches generalize from a\nlimited number of traces or use simple test generation strategies.\nUnfortunately, these strategies may not exercise uncommon usage patterns of a\nsoftware system. To address this problem, we propose a new approach,\nadversarial specification mining, and develop a prototype, DICE (Diversity\nthrough Counter-Examples). DICE has two components: DICE-Tester and DICE-Miner.\nAfter mining Linear Temporal Logic specifications from an input test suite,\nDICE-Tester adversarially guides test generation, searching for counterexamples\nto these specifications to invalidate spurious properties. These\ncounterexamples represent gaps in the diversity of the input test suite. This\nprocess produces execution traces of usage patterns that were unrepresented in\nthe input test suite. Next, we propose a new specification inference algorithm,\nDICE-Miner, to infer FSAs using the traces, guided by the temporal\nspecifications. We find that the inferred specifications are of higher quality\nthan those produced by existing state-of-the-art specification miners. Finally,\nwe use the FSAs in a fuzzer for servers of stateful protocols, increasing its\ncoverage.\n"}
+{"abstract": "  RISC-V is a promising open-source architecture primarily targeted for\nembedded systems. Programs compiled using the RISC-V toolchain can run\nbare-metal on the system, and, as such, can be vulnerable to several memory\ncorruption vulnerabilities. In this work, we present HeapSafe, a lightweight\nhardware assisted heap-buffer protection scheme to mitigate heap overflow and\nuse-after-free vulnerabilities in a RISC-V SoC. The proposed scheme tags\npointers associated with heap buffers with metadata indices and enforces tag\npropagation for commonly used pointer operations. The HeapSafe hardware is\ndecoupled from the core and is designed as a configurable coprocessor and is\nresponsible for validating the heap buffer accesses. Benchmark results show a\n1.5X performance overhead and 1.59% area overhead, while being 22% faster than\na software protection. We further implemented a HeapSafe-nb, an asynchronous\nvalidation design, which improves performance by 27% over the synchronous\nHeapSafe.\n"}
+{"abstract": "  How can we explain the predictions of a machine learning model? When the data\nis structured as a multivariate time series, this question induces additional\ndifficulties such as the necessity for the explanation to embody the time\ndependency and the large number of inputs. To address these challenges, we\npropose dynamic masks (Dynamask). This method produces instance-wise importance\nscores for each feature at each time step by fitting a perturbation mask to the\ninput sequence. In order to incorporate the time dependency of the data,\nDynamask studies the effects of dynamic perturbation operators. In order to\ntackle the large number of inputs, we propose a scheme to make the feature\nselection parsimonious (to select no more feature than necessary) and legible\n(a notion that we detail by making a parallel with information theory). With\nsynthetic and real-world data, we demonstrate that the dynamic underpinning of\nDynamask, together with its parsimony, offer a neat improvement in the\nidentification of feature importance over time. The modularity of Dynamask\nmakes it ideal as a plug-in to increase the transparency of a wide range of\nmachine learning models in areas such as medicine and finance, where time\nseries are abundant.\n"}
+{"abstract": "  Let X be a minimal complex surface of general type such that its image via\nthe canonical map is a surface; we denote by d the degree of the canonical map.\n  In this expository work, first of all we recall the known possibilities for\nthe canonical image and for d when the canonical map is not birational, which\nare quite a few, and then we consider the question of producing concrete\nexamples for all of them.\n  We present the two main methods of construction of such examples and we give\nseveral instances of their application. We end the paper outlining the state of\nthe art on this topic and raising several questions.\n"}
+{"abstract": "  The performance of a reinforcement learning (RL) system depends on the\ncomputational architecture used to approximate a value function. Deep learning\nmethods provide both optimization techniques and architectures for\napproximating nonlinear functions from noisy, high-dimensional observations.\nHowever, prevailing optimization techniques are not designed for\nstrictly-incremental online updates. Nor are standard architectures designed\nfor observations with an a priori unknown structure: for example, light sensors\nrandomly dispersed in space. This paper proposes an online RL prediction\nalgorithm with an adaptive architecture that efficiently finds useful nonlinear\nfeatures. The algorithm is evaluated in a spatial domain with high-dimensional,\nstochastic observations. The algorithm outperforms non-adaptive baseline\narchitectures and approaches the performance of an architecture given\nside-channel information. These results are a step towards scalable RL\nalgorithms for more general problems, where the observation structure is not\navailable.\n"}
+{"abstract": "  The Gaussian Orthogonal Ensemble (GOE) of random matrices has been widely\nemployed to describe diverse phenomena in strongly coupled quantum systems. An\nimportant prediction is that the decay rates of the GOE eigenstates fluctuate\naccording to the distribution for one degree of freedom, as derived by Brink\nand by Porter and Thomas. However, we find that the coupling to the decay\nchannels can change the effective number of degrees of freedom from $\\nu = 1$\nto $\\nu = 2$. Our conclusions are based on a configuration-interaction\nHamiltonian originally constructed to test the validity of transition-state\ntheory, also known as Rice-Ramsperger-Kassel-Marcus (RRKM) theory in chemistry.\nThe internal Hamiltonian consists of two sets of GOE reservoirs connected by an\ninternal channel. We find that the effective number of degrees of freedom $\\nu$\ncan vary from one to two depending on the control parameter $\\rho \\Gamma$,\nwhere $\\rho$ is the level density in the first reservoir and $\\Gamma$ is the\nlevel decay width. The $\\nu = 2$ distribution is a well-known property of the\nGaussian Unitary ensemble (GUE); our model demonstrates that the GUE\nfluctuations can be present under much milder conditions. Our treatment of the\nmodel permits an analytic derivation for $\\rho\\Gamma \\gtrsim 1$.\n"}
+{"abstract": "  Remarkable performance from Transformer networks in Natural Language\nProcessing promote the development of these models in dealing with computer\nvision tasks such as image recognition and segmentation. In this paper, we\nintroduce a novel framework, called Multi-level Multi-scale Point Transformer\n(MLMSPT) that works directly on the irregular point clouds for representation\nlearning. Specifically, a point pyramid transformer is investigated to model\nfeatures with diverse resolutions or scales we defined, followed by a\nmulti-level transformer module to aggregate contextual information from\ndifferent levels of each scale and enhance their interactions. While a\nmulti-scale transformer module is designed to capture the dependencies among\nrepresentations across different scales. Extensive evaluation on public\nbenchmark datasets demonstrate the effectiveness and the competitive\nperformance of our methods on 3D shape classification, part segmentation and\nsemantic segmentation tasks.\n"}
+{"abstract": "  Likelihood-free methods are useful for parameter estimation of complex models\nwith intractable likelihood functions for which it is easy to simulate data.\nSuch models are prevalent in many disciplines including genetics, biology,\necology and cosmology. Likelihood-free methods avoid explicit likelihood\nevaluation by finding parameter values of the model that generate data close to\nthe observed data. The general consensus has been that it is most efficient to\ncompare datasets on the basis of a low dimensional informative summary\nstatistic, incurring information loss in favour of reduced dimensionality. More\nrecently, researchers have explored various approaches for efficiently\ncomparing empirical distributions in the likelihood-free context in an effort\nto avoid data summarisation. This article provides a review of these full data\ndistance based approaches, and conducts the first comprehensive comparison of\nsuch methods, both qualitatively and empirically. We also conduct a substantive\nempirical comparison with summary statistic based likelihood-free methods. The\ndiscussion and results offer guidance to practitioners considering a\nlikelihood-free approach. Whilst we find the best approach to be problem\ndependent, we also find that the full data distance based approaches are\npromising and warrant further development. We discuss some opportunities for\nfuture research in this space.\n"}
+{"abstract": "  Millions of repetitive code snippets are submitted to code repositories every\nday. To search from these large codebases using simple natural language queries\nwould allow programmers to ideate, prototype, and develop easier and faster.\nAlthough the existing methods have shown good performance in searching codes\nwhen the natural language description contains keywords from the code, they are\nstill far behind in searching codes based on the semantic meaning of the\nnatural language query and semantic structure of the code. In recent years,\nboth natural language and programming language research communities have\ncreated techniques to embed them in vector spaces. In this work, we leverage\nthe efficacy of these embedding models using a simple, lightweight 2-layer\nneural network in the task of semantic code search. We show that our model\nlearns the inherent relationship between the embedding spaces and further\nprobes into the scope of improvement by empirically analyzing the embedding\nmethods. In this analysis, we show that the quality of the code embedding model\nis the bottleneck for our model's performance, and discuss future directions of\nstudy in this area.\n"}
+{"abstract": "  Let $Mat_{\\mathbb{C}}(K,N)$ be the space of $K\\times N$ complex matrices. Let\n$\\mathbf{B}_t$ be Brownian motion on $Mat_{\\mathbb{C}}(K,N)$ starting from the\nzero matrix and $\\mathbf{M}\\in Mat_{\\mathbb{C}}(K,N)$. We prove that, with\n$K\\ge N$, the $N$ eigenvalues of\n$\\left(\\mathbf{B}_t+t\\mathbf{M}\\right)^*\\left(\\mathbf{B}_t+t\\mathbf{M}\\right)$\nform a Markov process with an explicit transition kernel. This generalizes a\nclassical result of Rogers and Pitman for multidimensional Brownian motion with\ndrift which corresponds to $N=1$. We then give two more descriptions for this\nMarkov process. First, as independent squared Bessel diffusion processes in the\nwide sense, introduced by Watanabe and studied by Pitman and Yor, conditioned\nto never intersect. Second, as the distribution of the top row of interacting\nsquared Bessel type diffusions in some interlacting array. The last two\ndescriptions also extend to a general class of one-dimensional diffusions.\n"}
+{"abstract": "  In this article we study the connected components of moduli spaces of p-adic\nshtukas and local Shimura varieties. Given local shtuka datum (G, b, mu) with G\nan unramified reductive group over Qp and (b, mu) HN-irreducible, we determine\nthe locally profinite set of geometric connected components\npi0(Sht(G,b,[mu],infty) x Cp) and describe explicitly the continuous right\naction of the group G(Qp)x Jb(Qp)x WE on this space. This confirms and\ngeneralizes conjecture 4.26 of [30] in the unramified case. This work builds on\nand generalizes M. Chens Theorem B of [4].\n"}
+{"abstract": "  The traditional process of building interactive machine learning systems can\nbe viewed as a teacher-learner interaction scenario where the machine-learners\nare trained by one or more human-teachers. In this work, we explore the idea of\nusing a conversational interface to investigate the interaction between\nhuman-teachers and interactive machine-learners. Specifically, we examine\nwhether teachable AI agents can reliably learn from human-teachers through\nconversational interactions, and how this learning compare with traditional\nsupervised learning algorithms. Results validate the concept of teachable\nconversational agents and highlight the factors relevant for the development of\nmachine learning systems that intend to learn from conversational interactions.\n"}
+{"abstract": "  This thesis pretends to be another step in the development of numerical\nresearch in disordered systems. Specifically, we will focus on spin glasses\nwhich have demonstrated to be a fertile field from both, experimental and\ntheoretical approaches. Throughout this thesis, we will discuss a variety of\ninteresting phenomenons and we will also open new avenues to previously\nunexplored effects in the context of spin glasses. However, without a doubt,\nthe leitmotiv conducting this thesis is the role of numerical simulations as a\nvaluable tool to explore spin-glass physics.\n  This thesis is organized into five different parts. The first part,\ncontaining the Chapter 1, is focused on introducing the spin glasses to the\nreader.\n  The second part, containing the Chapter 2, is dedicated to discussing the\nmetastate.\n  The third part, shaped by the Chapter 3 and Chapter 4, is devoted to studying\nthe off-equilibrium dynamics in spin glasses. Specifically, Chapter 3 is\nfocused on discussing the growth of the coherence length in spin glasses, a key\nquantity that characterizes the off-equilibrium evolution of those systems. In\nChapter 4 we will discuss an interesting phenomenon: the Mpemba effect.\n  The fourth part, containing the Chapter 5, Chapter 6 and Chapter 7 is devoted\nto study the Temperature Chaos phenomenon in spin glasses. In Chapter 5 we\nintroduce the main historical results on Temperature Chaos, from its origins to\nthe last steps. In Chapter 6 we study equilibrated spin glasses and we\ncharacterize the Temperature Chaos from a static and a dynamical point of view.\nIn Chapter 7 we tackle the problem of characterizing Temperature Chaos in\noff-equilibrium dynamics.\n  The fifth and last part of the main body of the thesis corresponds to the\nconclusions.\n"}
+{"abstract": "  We report on the first measurements of branching fractions, CP-violating\ncharge asymmetries, and longitudinal polarization fractions in charmless $B$\ndecays at the Belle II experiment. We use a sample of electron-positron\ncollisions collected in 2019 and 2020 at the $\\Upsilon(4S)$ resonance that\ncorresponds to 34.6 fb$^{-1}$ of integrated luminosity. The results are\ncompatible with the known values, which indicates a good understanding of\ndetector performance.\n"}
+{"abstract": "  Given a global field $F$ with absolute Galois group $G_F$, we define a\ncategory $SMod_F$ whose objects are finite $G_F$-modules decorated with local\nconditions. We define this category so that `taking the Selmer group' defines a\nfunctor $Sel$ from $SMod_F$ to $Ab$. After defining a duality functor $\\vee$ on\n$SMod_F$, we show that every short exact sequence $0 \\to M_1 \\to M \\to M_2 \\to\n0$ in $SMod_F$ gives rise to a natural bilinear pairing $$Sel (M_2) \\times Sel\n(M_1^{\\vee}) \\to \\mathbb{Q}/\\mathbb{Z}$$ whose left and right kernels are the\nimages of $Sel (M)$ and $Sel (M^{\\vee})$, respectively. This construction\ngeneralizes the Cassels-Tate pairing defined on Shafarevich-Tate groups of\nabelian varieties over global fields. Our main results generalize the fact that\nthe classical pairing is antisymmetric for principally polarized abelian\nvarieties but, by work of Poonen and Stoll, not necessarily alternating.\n  We can express many previously-defined class field theoretic pairings as\ninstances of this construction, giving the study of these pairings access to\nour general results for $SMod_F$. As one application, we reprove and extend\nrecent results about the class groups of number fields containing many roots of\nunity.\n"}
+{"abstract": "  Designing efficient exploration is central to Reinforcement Learning due to\nthe fundamental problem posed by the exploration-exploitation dilemma. Bayesian\nexploration strategies like Thompson Sampling resolve this trade-off in a\nprincipled way by modeling and updating the distribution of the parameters of\nthe action-value function, the outcome model of the environment. However, this\ntechnique becomes infeasible for complex environments due to the computational\nintractability of maintaining probability distributions over parameters of\noutcome models of corresponding complexity. Moreover, the approximation\ntechniques introduced to mitigate this issue typically result in poor\nexploration-exploitation trade-offs, as observed in the case of deep neural\nnetwork models with approximate posterior methods that have been shown to\nunderperform in the deep bandit scenario. In this paper we introduce Sample\nAverage Uncertainty (SAU), a simple and efficient uncertainty measure for\ncontextual bandits. While Bayesian approaches like Thompson Sampling estimate\noutcomes uncertainty indirectly by first quantifying the variability over the\nparameters of the outcome model, SAU is a frequentist approach that directly\nestimates the uncertainty of the outcomes based on the value predictions.\nImportantly, we show theoretically that the uncertainty measure estimated by\nSAU asymptotically matches the uncertainty provided by Thompson Sampling, as\nwell as its regret bounds. Because of its simplicity SAU can be seamlessly\napplied to deep contextual bandits as a very scalable drop-in replacement for\nepsilon-greedy exploration. We confirm empirically our theory by showing that\nSAU-based exploration outperforms current state-of-the-art deep Bayesian bandit\nmethods on several real-world datasets at modest computation cost. Code is\navailable at \\url{https://github.com/ibm/sau-explore}.\n"}
+{"abstract": "  Personalized federated learning is tasked with training machine learning\nmodels for multiple clients, each with its own data distribution. The goal is\nto train personalized models in a collaborative way while accounting for data\ndisparities across clients and reducing communication costs. We propose a novel\napproach to this problem using hypernetworks, termed pFedHN for personalized\nFederated HyperNetworks. In this approach, a central hypernetwork model is\ntrained to generate a set of models, one model for each client. This\narchitecture provides effective parameter sharing across clients, while\nmaintaining the capacity to generate unique and diverse personal models.\nFurthermore, since hypernetwork parameters are never transmitted, this approach\ndecouples the communication cost from the trainable model size. We test pFedHN\nempirically in several personalized federated learning challenges and find that\nit outperforms previous methods. Finally, since hypernetworks share information\nacross clients we show that pFedHN can generalize better to new clients whose\ndistributions differ from any client observed during training.\n"}
+{"abstract": "  Networks have advanced the study of nonlocality beyond Bell's theorem. Here,\nwe introduce the concept of full network nonlocality, which describes\ncorrelations that necessitate all links in a network to distribute nonlocal\nresources. Showcasing that this notion is stronger than standard network\nnonlocality, we prove that the most well-known network Bell test does not\nwitness full network nonlocality. In contrast, we demonstrate that its\ngeneralisation to star networks is capable of detecting full network\nnonlocality in quantum theory. More generally, we point out that established\nmethods for analysing local and theory-independent correlations in networks can\nbe combined in order to systematically deduce sufficient conditions for full\nnetwork nonlocality in any network and input/output scenario. We demonstrate\nthe usefulness of these methods by constructing polynomial witnesses of full\nnetwork nonlocality for the bilocal scenario. Then, we show that these\ninequalities can be violated via quantum Elegant Joint Measurements.\n"}
+{"abstract": "  We investigate the renormalization group theory of generalized multi-vertex\nsine-Gordon model by employing the dimensional regularization method and also\nthe Wilson renormalization group method. The vertex interaction is given by\n$\\cos(k_j\\cdot \\phi)$ where $k_j$ ($j=1,2,\\cdots,M$) are momentum vectors and\n$\\phi$ is an $N$-component scalar field. The beta functions are calculated for\nthe sine-Gordon model with multi cosine interactions. The second-order\ncorrection in the renormalization procedure is given by the two-point\nscattering amplitude for tachyon scattering. We show that new vertex\ninteraction with momentum vector $k_{\\ell}$ is generated from two vertex\ninteractions with vectors $k_i$ and $k_j$ when $k_i$ and $k_j$ meet the\ncondition $k_{\\ell}=k_i\\pm k_j$ called the triangle condition. Further\ncondition $k_i\\cdot k_j=\\pm 1/2$ is required within the dimensional\nregularization method. The renormalization group equations form a set of closed\nequations when $\\{k_j\\}$ form an equilateral triangle for $N=2$ or a regular\ntetrahedron for $N=3$. The Wilsonian renormalization group method gives\nqualitatively the same result for beta functions.\n"}
+{"abstract": "  A triangle with rational sides and rational area is called a rational\ntriangle. In this paper we consider three problems of finding pairs of rational\ntriangles which have a common circumradius as well as either a common perimeter\nor a common inradius or a common area. While several similar problems\nconcerning pairs of rational triangles have been considered in the existing\nliterature, these three problems have not been studied till now. For each of\nthese problems, we give a parametric solution and we also indicate how\nadditional parametric solutions of these problems may be obtained.\n"}
+{"abstract": "  A Kahler-type form is a symplectic form compatible with an integrable complex\nstructure. Let M be a either a torus or a K3-surface equipped with a\nKahler-type form. We show that the homology class of any Maslov-zero Lagrangian\ntorus in M has to be non-zero and primitive. This extends previous results of\nAbouzaid-Smith (for tori) and Sheridan-Smith (for K3-surfaces) who proved it\nfor particular Kahler-type forms on M. In the K3 case our proof uses dynamical\nproperties of the action of the diffeomorphism group of M on the space of the\nKahler-type forms. These properties are obtained using Shah's arithmetic\nversion of Ratner's orbit closure theorem.\n"}
+{"abstract": "  Curiosity is a general method for augmenting an environment reward with an\nintrinsic reward, which encourages exploration and is especially useful in\nsparse reward settings. As curiosity is calculated using next state prediction\nerror, the type of state encoding used has a large impact on performance.\nRandom features and inverse-dynamics features are generally preferred over VAEs\nbased on previous results from Atari and other mostly 2D environments. However,\nunlike VAEs, they may not encode sufficient information for optimal behaviour,\nwhich becomes increasingly important as environments become more complex. In\nthis paper, we use the sparse reward 3D physics environment Animal-AI, to\ndemonstrate how a fixed $\\beta$-VAE encoding can be used effectively with\ncuriosity. We combine this with curriculum learning to solve the previously\nunsolved exploration intensive detour tasks while achieving 22\\% gain in sample\nefficiency on the training curriculum against the next best encoding. We also\ncorroborate the results on Atari Breakout, with our custom encoding\noutperforming random features and inverse-dynamics features.\n"}
+{"abstract": "  Iron sand is one of the abundant natural resources in Indonesia, especially\non the south coast of Cidaun; West Java which is the basic material for\nbuilding and metal industry. Iron mineral content is generally metal oxide such\nas magnetite, hematite and silica/quartz. Sand with iron content used in this\nstudy is derived from beach sand Desa Kertajadi, Kecamatan Cidaun, Kabupaten\nCianjur, Jawa Barat. Then mass of 2 kg sand was separated using a magnetic\nseparator in order to obtain magnetic and nonmagnetic mineral content. After\nnine rounds of separation takes two different types of samples that are no\nseparation sand (TS) sample and concentrate in the third separation (S3)\nsample. The sample is then examined by X-Ray Diffraction (XRD) measurement and\nanalyzed quantitatively using MAUD software to determine the content of Fe3O4\n(magnetite) by using the Rietveld refinement method from XRD data. As the\nanalysis result, the magnetite content contained in iron sand is counted\nquantitatively for each different sample. For iron sand samples (TS) yielding a\n24.27 percent of magnetite and a third concentrate separation sample (S3)\nyields 61.98 percent.\n"}
+{"abstract": "  In this paper, by introducing the degenerate Fubini-type polynomials, we give\nseveral relations with the help of the Fa\\`a di Bruno formula and some\nproperties of Bell polynomials, and generating function methods. Also, we\nderive some new explicit formulas and recurrence relations for Fubini-type\npolynomials and numbers. Associating the degenerate Fubini-type polynomials\nnewly defined here with degenerate Apostol-Bernoulli polynomials and degenerate\nApostol-Euler polynomials of order {\\alpha} enables us to present additional\nrelations for some degenerate special polynomials and numbers.\n"}
+{"abstract": "  Citation context analysis (CCA) is an important task in natural language\nprocessing that studies how and why scholars discuss each others' work. Despite\ndecades of study, traditional frameworks for CCA have largely relied on\noverly-simplistic assumptions of how authors cite, which ignore several\nimportant phenomena. For instance, scholarly papers often contain rich\ndiscussions of cited work that span multiple sentences and express multiple\nintents concurrently. Yet, CCA is typically approached as a single-sentence,\nsingle-label classification task, and thus existing datasets fail to capture\nthis interesting discourse. In our work, we address this research gap by\nproposing a novel framework for CCA as a document-level context extraction and\nlabeling task. We release MultiCite, a new dataset of 12,653 citation contexts\nfrom over 1,200 computational linguistics papers. Not only is it the largest\ncollection of expert-annotated citation contexts to-date, MultiCite contains\nmulti-sentence, multi-label citation contexts within full paper texts. Finally,\nwe demonstrate how our dataset, while still usable for training classic CCA\nmodels, also supports the development of new types of models for CCA beyond\nfixed-width text classification. We release our code and dataset at\nhttps://github.com/allenai/multicite.\n"}
+{"abstract": "  Despite advances in the programmable logic capabilities of modern trigger\nsystems, a significant bottleneck remains in the amount of data to be\ntransported from the detector to off-detector logic where trigger decisions are\nmade. We demonstrate that a neural network autoencoder model can be implemented\nin a radiation tolerant ASIC to perform lossy data compression alleviating the\ndata transmission problem while preserving critical information of the detector\nenergy profile. For our application, we consider the high-granularity\ncalorimeter from the CMS experiment at the CERN Large Hadron Collider. The\nadvantage of the machine learning approach is in the flexibility and\nconfigurability of the algorithm. By changing the neural network weights, a\nunique data compression algorithm can be deployed for each sensor in different\ndetector regions, and changing detector or collider conditions. To meet area,\nperformance, and power constraints, we perform a quantization-aware training to\ncreate an optimized neural network hardware implementation. The design is\nachieved through the use of high-level synthesis tools and the hls4ml\nframework, and was processed through synthesis and physical layout flows based\non a LP CMOS 65 nm technology node. The flow anticipates 200 Mrad of ionizing\nradiation to select gates, and reports a total area of 3.6 mm^2 and consumes 95\nmW of power. The simulated energy consumption per inference is 2.4 nJ. This is\nthe first radiation tolerant on-detector ASIC implementation of a neural\nnetwork that has been designed for particle physics applications.\n"}
+{"abstract": "  A crucial aspect in reliable machine learning is to design a deployable\nsystem in generalizing new related but unobserved environments. Domain\ngeneralization aims to alleviate such a prediction gap between the observed and\nunseen environments. Previous approaches commonly incorporated learning\ninvariant representation for achieving good empirical performance. In this\npaper, we reveal that merely learning invariant representation is vulnerable to\nthe unseen environment. To this end, we derive novel theoretical analysis to\ncontrol the unseen test environment error in the representation learning, which\nhighlights the importance of controlling the smoothness of representation. In\npractice, our analysis further inspires an efficient regularization method to\nimprove the robustness in domain generalization. Our regularization is\northogonal to and can be straightforwardly adopted in existing domain\ngeneralization algorithms for invariant representation learning. Empirical\nresults show that our algorithm outperforms the base versions in various\ndataset and invariance criteria.\n"}
+{"abstract": "  The aim of this sequel to arXiv:1812.02935 is to set up the cornerstones of\nKoszul duality and Koszulity in the context of a large class of operadic\ncategories. In particular, we will prove that operads, in the generalized sense\nof Batanin-Markl, governing important operad- and/or PROP-like structures such\nas the classical operads, their variants such as cyclic, modular or wheeled\noperads, and also diverse versions of PROPs such as properads, dioperads,\n1/2PROPs, and still more exotic objects such as permutads and pre-permutads are\nbinary quadratic, and describe their Koszul duals. We then prove, using the\nresults of arXiv:2002.06640, that operads governing the most common structures\nare Koszul.\n"}
+{"abstract": "  The article reveals the main theoretical approaches to the analysis and study\nof the phenomenon of corruption. Special attention is paid to the consideration\nof the index approach to the analysis of corruption.\n"}
+{"abstract": "  Advances in the realm of Generative Adversarial Networks (GANs) have led to\narchitectures capable of producing amazingly realistic images such as\nStyleGAN2, which, when trained on the FFHQ dataset, generates images of human\nfaces from random vectors in a lower-dimensional latent space. Unfortunately,\nthis space is entangled - translating a latent vector along its axes does not\ncorrespond to a meaningful transformation in the output space (e.g., smiling\nmouth, squinting eyes). The model behaves as a black box, providing neither\ncontrol over its output nor insight into the structures it has learned from the\ndata. We present a method to explore the manifolds of changes of spatially\nlocalized regions of the face. Our method discovers smoothly varying sequences\nof latent vectors along these manifolds suitable for creating animations.\nUnlike existing disentanglement methods that either require labelled data or\nexplicitly alter internal model parameters, our method is an optimization-based\napproach guided by a custom loss function and manually defined region of\nchange. Our code is open-sourced, which can be found, along with supplementary\nresults, on our project page: https://github.com/bmolab/masked-gan-manifold\n"}
+{"abstract": "  Single crystalline samples of the van der Waals antiferromagnet CrPS4 were\nstudied by measurements of specific heat and comprehensive anisotropic\ntemperature- and magnetic field-dependent magnetization. In addition,\nmeasurements of the heat capacity and magnetization were performed under\npressures of up to ~21 kbar and ~14 kbar respectively. At ambient pressure, two\nmagnetic transitions are observed, second order from a paramagnetic to an\nantiferromagnetic state at TN ~ 37 K, and a first-order spin reorientation\ntransition at T* ~ 34 K. Anisotropic H - T phase diagrams were constructed\nusing the M(T,H) data. As pressure is increased, TN is weakly suppressed with\ndTN/dP ~ -0.1 K/kbar. T*, on the other hand, is suppressed quite rapidly, with\ndT*/dP ~ -2 K/kbar, extrapolating to a possible quantum phase transition at Pc\n~ 15 kbar.\n"}
+{"abstract": "  Cryptocurrencies (CCs) become more interesting for institutional investors'\nstrategic asset allocation and will be a fixed component of professional\nportfolios in future. This asset class differs from established assets\nespecially in terms of the severe manifestation of statistical parameters. The\nquestion arises whether CCs with similar statistical key figures exist. On this\nbasis, a core market incorporating CCs with comparable properties enables the\nimplementation of a tracking error approach. A prerequisite for this is the\nsegmentation of the CC market into a core and a satellite, the latter\ncomprising the accumulation of the residual CCs remaining in the complement.\nUsing a concrete example, we segment the CC market into these components, based\non modern methods from image / pattern recognition.\n"}
+{"abstract": "  Let $\\Gamma$ be a finitely generated group and let $(X,\\mu_X)$ be an ergodic\nstandard Borel probability $\\Gamma$-space. Suppose that $G$ is the connected\ncomponent of the identity of the isometry group of a Hermitian symmetric space.\nGiven a Zariski dense measurable cocycle $\\sigma:\\Gamma\\times X\\rightarrow G$,\nwe define the notion of parametrized K\\\"{a}hler class and we show that it\ncompletely determines the cocycle up to cohomology.\n"}
+{"abstract": "  We consider the algorithmic question of choosing a subset of candidates of a\ngiven size $k$ from a set of $m$ candidates, with knowledge of voters' ordinal\nrankings over all candidates. We consider the well-known and classic scoring\nrule for achieving diverse representation: the Chamberlin-Courant (CC) or\n$1$-Borda rule, where the score of a committee is the average over the voters,\nof the rank of the best candidate in the committee for that voter; and its\ngeneralization to the average of the top $s$ best candidates, called the\n$s$-Borda rule.\n  Our first result is an improved analysis of the natural and well-studied\ngreedy heuristic. We show that greedy achieves a $\\left(1 -\n\\frac{2}{k+1}\\right)$-approximation to the maximization (or satisfaction)\nversion of CC rule, and a $\\left(1 - \\frac{2s}{k+1}\\right)$-approximation to\nthe $s$-Borda score. Our result improves on the best known approximation\nalgorithm for this problem. We show that these bounds are almost tight.\n  For the dissatisfaction (or minimization) version of the problem, we show\nthat the score of $\\frac{m+1}{k+1}$ can be viewed as an optimal benchmark for\nthe CC rule, as it is essentially the best achievable score of any\npolynomial-time algorithm even when the optimal score is a polynomial factor\nsmaller (under standard computational complexity assumptions). We show that\nanother well-studied algorithm for this problem, called the Banzhaf rule,\nattains this benchmark.\n  We finally show that for the $s$-Borda rule, when the optimal value is small,\nthese algorithms can be improved by a factor of $\\tilde \\Omega(\\sqrt{s})$ via\nLP rounding. Our upper and lower bounds are a significant improvement over\nprevious results, and taken together, not only enable us to perform a finer\ncomparison of greedy algorithms for these problems, but also provide analytic\njustification for using such algorithms in practice.\n"}
+{"abstract": "  New methods for computing parametric local $b$-functions are introduced for\n$\\mu$-constant deformations of semi-weighted homogeneous singularities. The\nkeys of the methods are comprehensive Gr\\\"obner systems in\nPoincar\\'e-Birkhoff-Witt algebra and holonomic ${\\mathcal D}$-modules. It is\nshown that the use of semi-weighted homogeneity reduces the computational\ncomplexity of $b$-functions associated with $\\mu$-constant deformations. In the\ncase of inner modality 2, local $b$-functions associated with $\\mu$-constant\ndeformations are obtained by the resulting method and given the list of\nparametric local $b$-functions.\n"}
+{"abstract": "  Neural Networks are sensitive to various corruptions that usually occur in\nreal-world applications such as blurs, noises, low-lighting conditions, etc. To\nestimate the robustness of neural networks to these common corruptions, we\ngenerally use a group of modeled corruptions gathered into a benchmark.\nUnfortunately, no objective criterion exists to determine whether a benchmark\nis representative of a large diversity of independent corruptions. In this\npaper, we propose a metric called corruption overlapping score, which can be\nused to reveal flaws in corruption benchmarks. Two corruptions overlap when the\nrobustnesses of neural networks to these corruptions are correlated. We argue\nthat taking into account overlappings between corruptions can help to improve\nexisting benchmarks or build better ones.\n"}
+{"abstract": "  This paper presents a matching network to establish point correspondence\nbetween images. We propose a Multi-Arm Network (MAN) to learn region overlap\nand depth, which can greatly improve the keypoint matching robustness while\nbringing little computational cost during the inference stage. Another design\nthat makes this framework different from many existing learning based pipelines\nthat require re-training when a different keypoint detector is adopted, our\nnetwork can directly work with different keypoint detectors without such a\ntime-consuming re-training process. Comprehensive experiments conducted on\noutdoor and indoor datasets demonstrated that our proposed MAN outperforms\nstate-of-the-art methods. Code will be made publicly available.\n"}
+{"abstract": "  Wireless Capsule Endoscopy (WCE) helps physicians examine the\ngastrointestinal (GI) tract noninvasively. There are few studies that address\npathological assessment of endoscopy images in multiclass classification and\nmost of them are based on binary anomaly detection or aim to detect a specific\ntype of anomaly. Multiclass anomaly detection is challenging, especially when\nthe dataset is poorly sampled or imbalanced. Many available datasets in\nendoscopy field, such as KID2, suffer from an imbalance issue, which makes it\ndifficult to train a high-performance model. Additionally, increasing the\nnumber of classes makes classification more difficult. We proposed a multiclass\nclassification algorithm that is extensible to any number of classes and can\nhandle an imbalance issue. The proposed method uses multiple autoencoders where\neach one is trained on one class to extract features with the most\ndiscrimination from other classes. The loss function of autoencoders is set\nbased on reconstruction, compactness, distance from other classes, and\nKullback-Leibler (KL) divergence. The extracted features are clustered and then\nclassified using an ensemble of support vector data descriptors. A total of\n1,778 normal, 227 inflammation, 303 vascular, and 44 polyp images from the KID2\ndataset are used for evaluation. The entire algorithm ran 5 times and achieved\nF1-score of 96.3 +- 0.2% and 85.0 +- 0.4% on the test set for binary and\nmulticlass anomaly detection, respectively. The impact of each step of the\nalgorithm was investigated by various ablation studies and the results were\ncompared with published works. The suggested approach is a competitive option\nfor detecting multiclass anomalies in the GI field.\n"}
+{"abstract": "  The recent return of the US to the Paris Climate Accord, massive increase in\nsolar panel production and energy storage solutions has resulted in pressure on\nsupply for solar cell materials and recycling of panels installed in the 90's\nand beginning of the 2000's which have reached their end of life. In this work\nwe focus on recycling silicon wafers and dies by stripping previous structures\nfrom the die using potent acids after which its base material is characterized\nand binned. We demonstrate the process for silicon p-type substrates where\nn-type doping is attained by using a simple solution of phosphoric acid, which\nis diffused into the substrate using a furnace thus creating a PN junction. In\ncase the substrate is n-type it could be replaced by boric acid. This is\nfollowed by deposition of a conductive antireflective coating, bus bars and\nrear wafer metal coating. The initial demonstrated laboratory results indicate\nthe feasibility of recycling wafers using simple low cost standard industrial\nmethods.\n"}
+{"abstract": "  Conversion Rate (\\emph{CVR}) prediction in modern industrial e-commerce\nplatforms is becoming increasingly important, which directly contributes to the\nfinal revenue. In order to address the well-known sample selection bias\n(\\emph{SSB}) and data sparsity (\\emph{DS}) issues encountered during CVR\nmodeling, the abundant labeled macro behaviors ($i.e.$, user's interactions\nwith items) are used. Nonetheless, we observe that several purchase-related\nmicro behaviors ($i.e.$, user's interactions with specific components on the\nitem detail page) can supplement fine-grained cues for \\emph{CVR} prediction.\nMotivated by this observation, we propose a novel \\emph{CVR} prediction method\nby Hierarchically Modeling both Micro and Macro behaviors ($HM^3$).\nSpecifically, we first construct a complete user sequential behavior graph to\nhierarchically represent micro behaviors and macro behaviors as one-hop and\ntwo-hop post-click nodes. Then, we embody $HM^3$ as a multi-head deep neural\nnetwork, which predicts six probability variables corresponding to explicit\nsub-paths in the graph. They are further combined into the prediction targets\nof four auxiliary tasks as well as the final $CVR$ according to the conditional\nprobability rule defined on the graph. By employing multi-task learning and\nleveraging the abundant supervisory labels from micro and macro behaviors,\n$HM^3$ can be trained end-to-end and address the \\emph{SSB} and \\emph{DS}\nissues. Extensive experiments on both offline and online settings demonstrate\nthe superiority of the proposed $HM^3$ over representative state-of-the-art\nmethods.\n"}
+{"abstract": "  The basis generation in reduced order modeling usually requires multiple\nhigh-fidelity large-scale simulations that could take a huge computational\ncost. In order to accelerate these numerical simulations, we introduce a\nFOM/ROM hybrid approach in this paper. It is developed based on an a posteriori\nerror estimation for the output approximation of the dynamical system. By\ncontrolling the estimated error, the method dynamically switches between the\nfull-order model and the reduced-oder model generated on the fly. Therefore, it\nreduces the computational cost of a high-fidelity simulation while achieving a\nprescribed accuracy level. Numerical tests on the non-parametric and parametric\nPDEs illustrate the efficacy of the proposed approach.\n"}
+{"abstract": "  We address the use of selfie ocular images captured with smartphones to\nestimate age and gender. Partial face occlusion has become an issue due to the\nmandatory use of face masks. Also, the use of mobile devices has exploded, with\nthe pandemic further accelerating the migration to digital services. However,\nstate-of-the-art solutions in related tasks such as identity or expression\nrecognition employ large Convolutional Neural Networks, whose use in mobile\ndevices is infeasible due to hardware limitations and size restrictions of\ndownloadable applications. To counteract this, we adapt two existing\nlightweight CNNs proposed in the context of the ImageNet Challenge, and two\nadditional architectures proposed for mobile face recognition. Since datasets\nfor soft-biometrics prediction using selfie images are limited, we counteract\nover-fitting by using networks pre-trained on ImageNet. Furthermore, some\nnetworks are further pre-trained for face recognition, for which very large\ntraining databases are available. Since both tasks employ similar input data,\nwe hypothesize that such strategy can be beneficial for soft-biometrics\nestimation. A comprehensive study of the effects of different pre-training over\nthe employed architectures is carried out, showing that, in most cases, a\nbetter accuracy is obtained after the networks have been fine-tuned for face\nrecognition.\n"}
+{"abstract": "  This paper proposes various new analysis techniques for Bayes networks in\nwhich conditional probability tables (CPTs) may contain symbolic variables. The\nkey idea is to exploit scalable and powerful techniques for synthesis problems\nin parametric Markov chains. Our techniques are applicable to arbitrarily many,\npossibly dependent parameters that may occur in various CPTs. This lifts the\nsevere restrictions on parameters, e.g., by restricting the number of\nparametrized CPTs to one or two, or by avoiding parameter dependencies between\nseveral CPTs, in existing works for parametric Bayes networks (pBNs). We\ndescribe how our techniques can be used for various pBN synthesis problems\nstudied in the literature such as computing sensitivity functions (and values),\nsimple and difference parameter tuning, ratio parameter tuning, and minimal\nchange tuning. Experiments on several benchmarks show that our prototypical\ntool built on top of the probabilistic model checker Storm can handle several\nhundreds of parameters.\n"}
+{"abstract": "  Particle methods can provide detailed descriptions of sea ice dynamics that\nexplicitly model fracture and discontinuities in the ice, which are difficult\nto capture with traditional continuum approaches. We use the ParticLS software\nlibrary to develop a discrete element method (DEM) model for sea ice dynamics\nat regional scales and smaller (<100 km). We model the sea ice as a collection\nof discrete rigid particles that are initially bonded together using a cohesive\nbeam model that approximates the response of an Euler-Bernoulli beam located\nbetween particle centroids. Ice fracture and lead formation are determined\nbased on the value of a non-local stress state around each particle and a\nMohr-Coulomb fracture model. Therefore, large ice floes are modeled as\ncontinuous objects made up of many bonded particles that can interact with each\nother, deform, and fracture. We generate realistic particle configurations by\ndiscretizing the ice in MODIS satellite imagery into polygonal floes that fill\nthe ice shape and extent that occurred in nature. The model is tested on ice\nadvecting through an idealized channel and through Nares Strait. The results\nindicate that the bonded DEM model is capable of capturing the behavior of sea\nice over a wide range of spatial scales, as well as the dynamic sea ice\npatterns through constrictions (arching, lead formation).\n"}
+{"abstract": "  Renewable energy forecasting is attaining greater importance due to its\nconstant increase in contribution to the electrical power grids. Solar energy\nis one of the most significant contributors to renewable energy and is\ndependent on solar irradiation. For the effective management of electrical\npower grids, forecasting models that predict solar irradiation, with high\naccuracy, are needed. In the current study, Machine Learning techniques such as\nLinear Regression, Extreme Gradient Boosting and Genetic Algorithm Optimization\nare used to forecast solar irradiation. The data used for training and\nvalidation is recorded from across three different geographical stations in the\nUnited States that are part of the SURFRAD network. A Global Horizontal Index\n(GHI) is predicted for the models built and compared. Genetic Algorithm\nOptimization is applied to XGB to further improve the accuracy of solar\nirradiation prediction.\n"}
+{"abstract": "  Angle disorder is an intrinsic feature of twisted bilayer graphene and other\nmoir\\'e materials. Here, we discuss electron transport in twisted bilayer\ngraphene in the presence of angle disorder. We compute the local density of\nstates and the Landauer-B\\\"uttiker transmission through an angle disorder\nbarrier of width comparable to the moir\\'e period, using a decimation technique\nbased on a real space description. We find that barriers which separate regions\nwhere the width of the bands differ by 50% or more lead to a minor suppression\nof the transmission, and that the transmission is close to one for normal\nincidence, which is reminiscent of Klein tunneling. These results suggest that\ntransport in twisted bilayer graphene is weakly affected by twist angle\ndisorder.\n"}
+{"abstract": "  We consider the setting of vector valued non-linear dynamical systems\n$X_{t+1} = \\phi(A^* X_t) + \\eta_t$, where $\\eta_t$ is unbiased noise and $\\phi\n: \\mathbb{R} \\to \\mathbb{R}$ is a known link function that satisfies certain\n{\\em expansivity property}. The goal is to learn $A^*$ from a single trajectory\n$X_1,\\cdots,X_T$ of {\\em dependent or correlated} samples. While the problem is\nwell-studied in the linear case, where $\\phi$ is identity, with optimal error\nrates even for non-mixing systems, existing results in the non-linear case hold\nonly for mixing systems. In this work, we improve existing results for learning\nnonlinear systems in a number of ways: a) we provide the first offline\nalgorithm that can learn non-linear dynamical systems without the mixing\nassumption, b) we significantly improve upon the sample complexity of existing\nresults for mixing systems, c) in the much harder one-pass, streaming setting\nwe study a SGD with Reverse Experience Replay ($\\mathsf{SGD-RER}$) method, and\ndemonstrate that for mixing systems, it achieves the same sample complexity as\nour offline algorithm, d) we justify the expansivity assumption by showing that\nfor the popular ReLU link function -- a non-expansive but easy to learn link\nfunction with i.i.d. samples -- any method would require exponentially many\nsamples (with respect to dimension of $X_t$) from the dynamical system. We\nvalidate our results via. simulations and demonstrate that a naive application\nof SGD can be highly sub-optimal. Indeed, our work demonstrates that for\ncorrelated data, specialized methods designed for the dependency structure in\ndata can significantly outperform standard SGD based methods.\n"}
+{"abstract": "  This paper presents a novel unmanned aerial vehicle (UAV) aided mobile edge\ncomputing (MEC) architecture for vehicular networks. It is considered that the\nvehicles should complete latency critical computation intensive tasks either\nlocally with on-board computation units or by offloading part of their tasks to\nroad side units (RSUs) with collocated MEC servers. In this direction, a\nhovering UAV can serve as an aerial RSU (ARSU) for task processing or act as an\naerial relay and further offload the computation tasks to a ground RSU (GRSU).\nIn order to significantly reduce the delay during data offloading and\ndownloading, this architecture relies on the benefits of massive multiple input\nmultiple output (MIMO). Therefore, it is considered that the vehicles, the\nARSU, and the GRSU employ large scale antennas. A three dimensional (3D)\ngeometrical representation of the MEC enabled network is introduced and an\noptimization method is proposed that minimizes the weighted total energy\nconsumption (WTEC) of the vehicles and ARSU subject to transmit power\nallocation, task allocation, and timeslot scheduling. The numerical results\nverify the theoretical derivations, emphasize on the effectiveness of the\nmassive MIMO transmission, and provide useful engineering insights.\n"}
+{"abstract": "  In this paper, the non-BPS amplitudes ($Z < 2m$) are considered. Utilizing\non-shell methods, the three point amplitudes of two equal-mass particles and\none massless particle were constructed, where the two massive particles are\nnon-BPS states. We verify the result by matching the $Z = 0$ and BPS limit with\n$N = 2$ supersymmetry. As an application we derive the non-BPS coupling for $N\n= 4$ super-Maxwell and supergravity.\n"}
+{"abstract": "  Testing 3D hydrodynamic models of stellar atmospheres is feasible by\nretrieving spectral line shapes across stellar disks, using differential\nspectroscopy during exoplanet transits. From synthetic data at hyper-high\nspectral resolution, characteristic patterns for FeI and FeII lines were\nidentified in Paper IV from 3D models spanning T=3964-6726K (spectral types\napprox. K8V-F3V). The observability of patterns among lines of different\nstrength, excitation potential and ionization level are now examined, as\nobserved at ordinary spectral resolutions and in the presence of noise. Time\nvariability in 3D atmospheres induces changes in spectral-line parameters, some\nof which are correlated. An adequate calibration could identify proxies for the\njitter in apparent radial velocity to enable adjustments to actual stellar\nradial motion. We also examined the center-to-limb temporal variability.\nRecovery of spatially resolved line profiles with fitted widths and depths is\nshown for various noise levels. Signals during exoplanet transit are simulated.\nIn addition to Rossiter-McLaughlin type signatures in apparent radial velocity,\nanalogous effects are shown for line depths and widths. From exoplanet\ntransits, overall stellar line parameters of width, depth and wavelength\nposition can be retrieved already with moderate efforts, but a very good\nsignal-to-noise ratio is required to reveal the more subtle signatures between\nsubgroups of spectral lines, where finer details of atmospheric structure are\nencoded. In a solar model, temporal variability shows correlations between\njittering in apparent radial velocity and fluctuations in line depth. Since\nboth fluctuations in line depth and jittering in wavelength can be measured\nfrom the ground, searches for low-mass exoplanets should explore these to\nadjust apparent radial velocities to actual stellar motion.\n"}
+{"abstract": "  We report on the first measurement of the direct $CP$-violating asymmetry\n($\\mathcal{A}$) in the charmless decay $B^0 \\to K^0\\pi^0$ at Belle II and an\nupdated measurement of its branching fraction ($\\mathcal{B}$). We use a sample\nof electron-positron collisions collected in 2019 and 2020 at the\n$\\Upsilon(4S)$ resonance and corresponding to $62.8$ $\\text{fb}^{-1}$ of\nintegrated luminosity. We reconstruct and select about $50$ $B^0 \\to K_S^0\n\\pi^0$ candidates, and we measure $\\mathcal{A}_{{K^0\\pi^0}} =\n-0.40_{-0.44}^{+0.46} (\\text{stat}) \\pm 0.04 (\\text{syst})$ and\n$\\mathcal{B}(B^0 \\to K^0 \\pi^0) = [8.5_{-1.6}^{+1.7} (\\text{stat}) \\pm 1.2\n(\\text{syst})] \\times 10^{-6}$. This is the first measurement of $CP$ violation\nin $B^0 \\to K^0\\pi^0$ decays reported by Belle II. The results agree with\nprevious determinations and show a detector performance comparable with the\nbest Belle results.\n"}
+{"abstract": "  We present a basis set correction scheme for the coupled-cluster singles and\ndoubles (CCSD) method. The scheme is based on employing frozen natural orbitals\n(FNOs) and diagrammatically decomposed contributions to the electronic\ncorrelation energy that dominate the basis set incompleteness error (BSIE). As\nrecently discussed in [https://doi.org/10.1103/PhysRevLett.123.156401], the\nBSIE of the CCSD correlation energy is dominated by the second-order\nM{\\o}ller-Plesset (MP2) perturbation energy and the particle-particle ladder\nterm. Here, we derive a simple approximation to the BSIE of the\nparticle-particle ladder term that effectively corresponds to a rescaled\npair-specific MP2 BSIE, where the scaling factor depends on the spatially\naveraged correlation hole depth of the coupled-cluster and first-order pair\nwavefunctions. The evaluation of the derived expressions is simple to implement\nin any existing code. We demonstrate the effectiveness of the method for the\nuniform electron gas. Furthermore, we apply the method to coupled-cluster\ntheory calculations of atoms and molecules using FNOs. Employing the proposed\ncorrection and an increasing number of FNOs per occupied orbital, we\ndemonstrate for a test set that rapidly convergent closed and open-shell\nreaction energies, atomization energies, electron affinities, and ionization\npotentials can be obtained. Moreover, we show that a similarly excellent\ntrade-off between required virtual orbital basis set size and remaining BSIEs\ncan be achieved for the perturbative triples contribution to the CCSD(T) energy\nemploying FNOs and the (T*) approximation.\n"}
+{"abstract": "  Novel quantum technologies and devices place unprecedented demands on the\nperformance of experimental components, while their widespread deployment\nbeyond the laboratory necessitates increased robustness and fast, affordable\nproduction. We show how the use of additive manufacturing, together with\nmathematical optimization techniques and innovative designs, allows the\nproduction of compact, lightweight components with greatly enhanced\nperformance. We use such components to produce a magneto-optical trap that\ncaptures $\\sim 2 \\times 10^8$ rubidium atoms, employing for this purpose a\ncompact and highly stable device for spectroscopy and optical power\ndistribution, optimized neodymium magnet arrays for magnetic field generation,\nand a lightweight, additively manufactured ultra-high vacuum chamber. We show\nhow the use of additive manufacturing enables substantial weight reduction and\nstability enhancement, while also illustrating the transferability of our\napproach to experiments and devices across the quantum technology sector and\nbeyond.\n"}
+{"abstract": "  Twisted \\'etale groupoid algebras have been studied recently in the algebraic\nsetting by several authors in connection with an abstract theory of Cartan\npairs of rings. In this paper, we show that extensions of ample groupoids\ncorrespond in a precise manner to extensions of Boolean inverse semigroups. In\nparticular, discrete twists over ample groupoids correspond to certain abelian\nextensions of Boolean inverse semigroups and we show that they are classified\nby Lausch's second cohomology group of an inverse semigroup. The cohomology\ngroup structure corresponds to the Baer sum operation on twists.\n  We also define a novel notion of inverse semigroup crossed product,\ngeneralizing skew inverse semigroup rings, and prove that twisted Steinberg\nalgebras of Hausdorff ample groupoids are instances of inverse semigroup\ncrossed products. The cocycle defining the crossed product is the same cocycle\nthat classifies the twist in Lausch cohomology.\n"}
+{"abstract": "  Unconventional superconductivity and magnetism are intertwined on a\nmicroscopic level in a wide class of materials, including high-$T_c$ cuprates,\niron pnictides, and heavy-fermion compounds. A new approach to this most\nfundamental and hotly debated subject focuses on the role of interactions\nbetween superconducting electrons and bosonic fluctuations at the interface\nbetween adjacent layers in heterostructures. A recent state-of-the-art\nmolecular-beam-epitaxy technique has enabled us to fabricate superlattices\nconsisting of different heavy-fermion compounds with atomic thickness. These\nKondo superlattices provide a unique opportunity to study the mutual\ninteraction between unconventional superconductivity and magnetic order through\nthe atomic interface. Here, we design and fabricate hybrid Kondo superlattices\nconsisting of alternating layers of superconducting CeCoIn$_5$ with $d$-wave\npairing symmetry and nonmagnetic metal YbCoIn$_5$ or antiferromagnetic heavy\nfermion metals, such as CeRhIn$_5$ and CeIn$_3$. In these Kondo superlattices,\nsuperconducting heavy electrons are confined within the two-dimensional\nCeCoIn$_5$ block layers and interact with the neighboring nonmagnetic or\nmagnetic layers through the interface. In CeCoIn$_5$/YbCoIn$_5$ superlattices,\nthe superconductivity is strongly influenced by the local inversion symmetry\nbreaking at the interface. In CeCoIn$_5$/CeRhIn$_5$ and CeCoIn$_5$/CeIn$_3$\nsuperlattices, the superconducting and antiferromagnetic states coexist in\nspatially separated layers, but their mutual coupling via the interface\nsignificantly modifies the superconducting and magnetic properties. The\nfabrication of a wide variety of hybrid superlattices paves a new way to study\nthe relationship between unconventional superconductivity and magnetism in\nstrongly correlated materials.\n"}
+{"abstract": "  We calculate the plateau moduli of several single-chain slip-link and\nslip-spring models for entangled polymers. In these models, the entanglement\neffects are phenomenologically modeled by introducing topological constraints\nsuch as slip-links and slip-springs. The average number of segments between two\nneighboring slip-links or slip-springs, $N_{0}$, is an input parameter in these\nmodels. To analyze experimental data, the characteristic number of segments in\nentangled polymers $N_{e}$ estimated from the plateau modulus is used instead.\nBoth $N_{0}$ and $N_{e}$ characterize the topological constraints in entangled\npolymers, and naively $N_{0}$ is considered to be the same as $N_{e}$. However,\nearlier studies showed that $N_{0}$ and $N_{e}$ (or the plateau modulus) should\nbe considered as independent parameters. In this work, we show that due to the\nfluctuations at the short time scale, $N_{e}$ deviates from $N_{0}$. This means\nthat the relation between $N_{0}$ and the plateau modulus is not simple as\nnaively expected. The plateau modulus (or $N_{e}$) depends on the\nsubchain-scale details of the employed model, as well as the average number of\nsegments $N_{0}$. This is due to the fact that the subchain-scale fluctuation\nmechanisms depend on the model rather strongly. We theoretically calculate the\nplateau moduli for several single-chain slip-link and slip-spring models. Our\nresults explicitly show that the relation between $N_{0}$ and $N_{e}$ is\nmodel-dependent. We compare theoretical results with various simulation data in\nthe literature, and show that our theoretical expressions reasonably explain\nthe simulation results.\n"}
+{"abstract": "  We study, for the first time, the relations of two strong diffuse bands\n(DIBs) at 9633 and 9577~\\AA, commonly attributed to C$_{60}^+$, to other\ninterstellar features seen in optical and UV spectra including H{\\sc i}, Ca{\\sc\ni}, Fe{\\sc ii}, Na{\\sc i}, Ti{\\sc ii}, CN, CH, CH$^+$, and C$_2$ and DIBs 5780,\n5797, 6196, 6269, 6284, and 6614. We analyzed 62 lines of sight where the\nstellar contamination by Mg{\\sc ii} was corrected or found negligible for DIB\n9633. Equivalent widths of DIB 9577 were measured in 62 lines of sight. Poor\nmutual correlation between the strengths of the above features and the major\ndiffuse bands (5780 and 5797) as well as with other DIBs (with some exceptions)\nwere revealed. The considered DIBs are also poorly correlated with the features\nof neutral hydrogen, molecular carbon, and those of simple interstellar\nradicals. Perhaps this phenomenon can be explained if the diffuse band 9577 is\nan unresolved blend of two or more interstellar features. There are indications\nthat 9633 and 9577 diffuse bands are stronger in $\\sigma$-type clouds, i.e.\nthese features resemble the behavior of reasonably broad DIBs, which are strong\nin the lines of sight where the UV flux from the very hot nearby stars plays an\nimportant role.\n"}
+{"abstract": "  Users increasingly face multiple interface features on one hand, and\nconstraints on available resources (e.g., time, attention) on the other.\nUnderstanding the sensitivity of users' well-being to feature type and resource\nconstraints, is critical for informed design. Building on microeconomic theory,\nand focusing on social information features, users' interface choices were\nconceptualized as an exchange of resources (e.g., time), in return for access\nto goods (social information features). We studied how sensitive users'\nwell-being is to features' type, and to their cost level and type. We found\nthat (1) increased cost of feature use leads to decreased well-being, (2)\nusers' well-being is a function of features' cost type, and (3) users'\nwell-being is sensitive to differences in feature type. The approach used here\nto quantify user well-being derived from interface features offers a basis for\nasynchronous feature comparison.\n"}
+{"abstract": "  In this article, we study the propagation of scalar fields in the\ngravitational background of a spherically symmetric, electrically charged,\nregular-de Sitter black holes in higher dimensions. We carry out a thorough\ninvestigation of the grey body factors, by deriving analytical expressions for\nboth minimally and non-minimally coupled scalar fields. Then we study the\nprofile of grey body factors in terms of particle properties, namely the\nangular momentum quantum number and the non-minimal coupling constant, and the\nspacetime properties, namely the dimension, the cosmological constant and the\nnon-linear charge parameter. We then derive the Hawking radiation spectra for a\nhigher-dimensional electrically charged regular-de Sitter black hole. We find\nthat the non-minimal coupling may suppress the greybody factor and the\nnon-linear charge parameter may enhance it, but they both suppress the energy\nemission rate of Hawking radiation.\n"}
+{"abstract": "  Accurate ab initio calculations of 3d transition metal monoxide molecules\nhave attracted extensive attention because of its relevance in physical and\nchemical science, as well as theoretical challenges in treating strong electron\ncorrelation. Meanwhile, recent years have witnessed the rapid development of\nfull configuration interaction quantum Monte Carlo (FCIQMC) method to tackle\nelectron correlation. In this study, we carry out FCIQMC simulations to ScO,\nTiO and VO molecules and obtain accurate descriptions of 13 low-lying\nelectronic states (ScO $^2\\Sigma^+$, $^2\\Delta$, $^2\\Pi$; TiO $^3\\Delta$,\n$^1\\Delta$, $^1\\Sigma^+$, $^3\\Pi$, $^3\\Phi$; VO $^4\\Sigma^-$, $^4\\Phi$,\n$^4\\Pi$, $^2\\Gamma$, $^2\\Delta$), including states that have significant\nmulti-configurational character. The FCIQMC results are used to assess the\nperformance of several other wave function theory and density functional theory\nmethods. Our study highlights the challenging nature of electronic structure of\ntransition metal oxides and demonstrates FCIQMC as a promising technique going\nforward to treat more complex transition metal oxide molecules and materials.\n"}
+{"abstract": "  We investigate four-dimensional Heterotic solitons, defined as a particular\nclass of solutions of the equations of motion of Heterotic supergravity on a\nfour-manifold $M$. Heterotic solitons depend on a parameter $\\kappa$ and\nconsist of a Riemannian metric $g$, a metric connection with skew torsion $H$\non $TM$ and a closed one-form $\\varphi$ on $M$ satisfying a differential\nsystem. In the limit $\\kappa \\to 0$, Heterotic solitons reduce to a class of\ngeneralized Ricci solitons and can be considered as a higher-order curvature\nmodification of the latter. If the torsion $H$ is equal to the Hodge dual of\n$\\varphi$, Heterotic solitons consist of either flat tori or closed\nEinstein-Weyl structures on manifolds of type $S^1\\times S^3$ as introduced by\nP. Gauduchon. We prove that the moduli space of such closed Einstein-Weyl\nstructures is isomorphic to the product of $\\mathbb{R}$ with a certain finite\nquotient of the Cartan torus of the isometry group of the typical fiber of a\nnatural fibration $M\\to S^1$. We also consider the associated space of\nessential infinitesimal deformations, which we prove to be obstructed. More\ngenerally, we characterize several families of Heterotic solitons as\nsuspensions of certain three-manifolds with prescribed constant principal Ricci\ncurvatures, amongst which we find hyperbolic manifolds, manifolds covered by\n$\\widetilde{\\mathrm{Sl}}(2,\\mathbb{R})$ and E$(1,1)$ or certain Sasakian\nthree-manifolds. These solutions exhibit a topological dependence in the string\nslope parameter $\\kappa$ and yield, to the best of our knowledge, the first\nexamples of Heterotic compactification backgrounds not locally isomorphic to\nsupersymmetric compactification backgrounds.\n"}
+{"abstract": "  We analyze existing measurements of [Fe/H] and [$\\alpha$/Fe] for individual\nred giant branch (RGB) stars in the Giant Stellar Stream (GSS) of M31 to\ndetermine whether spatial abundance gradients are present. These measurements\nwere obtained from low- ($R \\sim 3000$) and moderate- ($R \\sim 6000$)\nresolution Keck/DEIMOS spectroscopy using spectral synthesis techniques as part\nof the Elemental Abundances in M31 survey. From a sample of 62 RGB stars\nspanning the GSS at 17, 22, and 33 projected kpc, we measure a [Fe/H] gradient\nof $-$0.018 $\\pm$ 0.003 dex kpc$^{-1}$ and negligible [$\\alpha$/Fe] gradient\nwith M31-centric radius. We investigate GSS abundance patterns in the outer\nhalo using additional [Fe/H] and [$\\alpha$/Fe] measurements for 6 RGB stars\nlocated along the stream at 45 and 58 projected kpc. These abundances provide\ntentative evidence that the trends in [Fe/H] and [$\\alpha$/Fe] beyond 40 kpc in\nthe GSS are consistent with those within 33 kpc. We also compare the GSS\nabundances to 65 RGB stars located along the possibly related Southeast (SE)\nshelf substructure at 12 and 18 projected kpc. The abundances of the GSS and SE\nshelf are consistent, supporting a common origin hypothesis, although this\ninterpretation may be complicated by the presence of [Fe/H] gradients in the\nGSS. We discuss the abundance patterns in the context of photometric studies\nfrom the literature and explore implications for the properties of the GSS\nprogenitor, suggesting that the high $\\langle$[$\\alpha$/Fe]$\\rangle$ of the GSS\n(+0.40 $\\pm$ 0.05 dex) favors a major merger scenario for its formation.\n"}
+{"abstract": "  In this paper, we present an approach to study the behavior of compliant\nplates in granular media and optimize the performance of a robot that utilizes\nthis technique for mobility. From previous work and fundamental tests on thin\nplate force generation inside granular media, we introduce an origami-inspired\nmechanism with non-linear compliance in the joints that can be used in granular\npropulsion. This concept utilizes one-sided joint limits to create an\nasymmetric gait cycle that avoids more complicated alternatives often found in\nother swimming/digging robots. To analyze its locomotion as well as its shape\nand propulsive force, we utilize granular Resistive Force Theory (RFT) as a\nstarting point. Adding compliance to this theory enables us to predict the\ntime-based evolution of compliant plates when they are dragged and rotated. It\nalso permits more rational design of swimming robots where fin design variables\nmay be optimized against the characteristics of the granular medium. This is\ndone using a Python-based dynamic simulation library to model the deformation\nof the plates and optimize aspects of the robot's gait. Finally, we prototype\nand test robot with a gait optimized using the modelling techniques mentioned\nabove.\n"}
+{"abstract": "  CsV$_3$Sb$_5$ is a newly discovered Kagome superconductor that attracts great\ninterest due to its topological nontrivial band structure and the coexistence\nof superconductivity and charge-density-wave (CDW) with many exotic properties.\nHere, we report the detailed characterization of the CDW gap in high-quality\nCsV$_3$Sb$_5$ single crystals using high-resolution angle-resolved\nphotoemission spectroscopy. We find that the CDW gap is strongly momentum\ndependent. While gapped around the $M$ point, the electronic states remain\ngapless around the $\\Gamma$ point and along the $\\Gamma$-$K$ direction. Such\nmomentum dependence indicates that the CDW is driven by the scattering of\nelectrons between neighboring $M$ points, where the band structure hosts\nmultiple saddle points and the density of state diverges near the Fermi level.\nOur observations of the partially gapped Fermi surface and strongly\nmomentum-dependent CDW gap not only provide a foundation for uncovering the\nmechanism of CDW in CsV$_3$Sb$_5$, but also shed light on the understanding of\nhow the CDW coexists with superconductivity in this topological Kagome\nsuperconductor.\n"}
+{"abstract": "  The double descent curve is one of the most intriguing properties of deep\nneural networks. It contrasts the classical bias-variance curve with the\nbehavior of modern neural networks, occurring where the number of samples nears\nthe number of parameters. In this work, we explore the connection between the\ndouble descent phenomena and the number of samples in the deep neural network\nsetting. In particular, we propose a construction which augments the existing\ndataset by artificially increasing the number of samples. This construction\nempirically mitigates the double descent curve in this setting. We reproduce\nexisting work on deep double descent, and observe a smooth descent into the\noverparameterized region for our construction. This occurs both with respect to\nthe model size, and with respect to the number epochs.\n"}
+{"abstract": "  To achieve high precision and accuracy for mass measurements of exotic nuclei\nby Time-of-flight (TOF) methods: high-resolution beam-line magnetic-rigidity\ntime-of-flight (B$\\rho$-TOF) and in-ring isochronous mass spectrometry (IMS), a\nlarge-area electrostatic detector which possesses high position resolution and\ngood timing resolution at the same time is developed at the Rare-RI Ring in\nRIBF, RIKEN Nishina Center, Japan. Besides TOF mass measurements, the detector\nsystem will also be used for heavy ion beam trajectory monitoring or momentum\nmeasurements for both beam-line and in-ring at the Rare-RI Ring. The position\nand timing measurements of heavy ions are performed by detecting the secondary\nelectrons (SEs) emitted from a conversion foil during the passage of the ion.\nThe SEs are accelerated and bent with an angle of $90^{\\circ}$ by electrostatic\nfields onto a micro-channel-plate (MCP) electron multiplier which is coupled\nwith a position-sensitive delay-line anode. The dependence of the timing and\nposition resolution on applied high voltages of the detector potential plates\nhas been studied systematically via simulation and experimentally. An\nisochronous condition of secondary electron transmission in the electrostatic\nfield of the detector is chosen to optimize the structure of the detector for\nhigh performance. The best achieved timing resolution is less than 50 ps (in\n$\\sigma$) and position resolution $\\sim$ 1 mm (in $\\sigma$) for 2 dimensions,\nrespectively. The overall efficiency is $\\sim$ 95$\\%$ for heavy ion beam and\n$\\sim$ 75$\\%$ for $\\alpha$ particle from $^{241}$Am source.\n"}
+{"abstract": "  A theorem of A. and C. R\\'enyi on periodic entire functions states that an\nentire function $f(z) $ must be periodic if $ P(f(z)) $ is periodic, where $\nP(z) $ is a non-constant polynomial. By extending this theorem, we can answer\nsome open questions related to the conjecture of C. C. Yang concerning\nperiodicity of entire functions. Moreover, we give more general forms for this\nconjecture and we prove, in particular, that $ f(z) $ is periodic if either $\nP(f(z)) f^{(k)}(z) $ or $ P(f(z))/f^{(k)}(z) $ is periodic, provided that $\nf(z) $ has a finite Picard exceptional value. We also investigate the\nperiodicity of $ f(z) $ when $ f(z)^{n}+a_{1} f^{\\prime}(z)+\\cdots+a_{k}\nf^{(k)}(z) $ is periodic. In all our results, the possibilities for the period\nof $ f(z) $ are determined precisely.\n"}
+{"abstract": "  We study the time evolution of an incompressible fluid with axial symmetry\nwithout swirl when the vorticity is sharply concentrated on $N$ annuli of radii\n$\\approx$ $r_0$ and thickness $\\epsilon$. We prove that when $r_0= |\\log\n\\epsilon|^\\alpha, \\,\\, \\alpha>2$, the vorticity field of the fluid converges as\n$\\epsilon \\to 0$ to the point vortex model, at least for a small but positive\ntime. This result generalizes a previous paper that assumed a power law for the\nrelation between $r_0$ and $\\epsilon$.\n"}
+{"abstract": "  We propose a method for detecting structural changes in a city using images\ncaptured from vehicular mounted cameras over traversals at two different times.\nWe first generate 3D point clouds for each traversal from the images and\napproximate GNSS/INS readings using Structure-from-Motion (SfM). A direct\ncomparison of the two point clouds for change detection is not ideal due to\ninaccurate geo-location information and possible drifts in the SfM. To\ncircumvent this problem, we propose a deep learning-based non-rigid\nregistration on the point clouds which allows us to compare the point clouds\nfor structural change detection in the scene. Furthermore, we introduce a dual\nthresholding check and post-processing step to enhance the robustness of our\nmethod. We collect two datasets for the evaluation of our approach. Experiments\nshow that our method is able to detect scene changes effectively, even in the\npresence of viewpoint and illumination differences.\n"}
+{"abstract": "  It can be argued that the ordinary description of the reversible quantum\nprocess between two one-to-one correlated measurement outcomes is incomplete\nbecause, by not specifying the direction of causality, it allows causal\nstructures that violate the time symmetry that is required of a reversible\nprocess. This also means that it can be completed simply by time-symmetrizing\nit, namely by requiring that the initial and final measurements evenly\ncontribute to the selection of their correlated pair of outcomes. This leaves\nthe description unaltered but shows that it is the quantum superposition of\nunobservable time-symmetrized instances whose causal structure is completely\ndefined. Each instance consists of a causal loop: the final measurement that\nchanges backwards in time the input state of the unitary transformation that\nleads to the state immediately before it. In former works, we have shown that\nsuch loops exactly explain the quantum computational speedup and quantum\nnonlocality. In this work we show that they lead to a completion of the\nanthropic principle that allows a universe evolution with quantum speedup.\n"}
+{"abstract": "  The nonlinear coefficient {\\gamma} of the step-index single-mode fiber (SMF)\nis inversely proportional to the effective area of that fiber. An analytical\nexpression for the effective area of the hybrid HE11-mode of the SMF is derived\nfor the first time, using its exact electromagnetic field expressions. The\neffective area is found in terms of generalized hypergeometric functions. A\nsimpler expression is also deduced under the weakly-guided fiber approximation,\nand is found to be in agreement with established theory.\n"}
+{"abstract": "  Much work has been done in the area of the cluster weighted model (CWM),\nwhich extends the finite mixture of regression model to include modelling of\nthe covariates. Although many types of distributions have been considered for\nboth the response and covariates, to our knowledge skewed distributions have\nnot yet been considered in this paradigm. Herein, a family of 24 novel CWMs are\nconsidered which allows both the covariates and response variables to be\nmodelled using one of four skewed distributions, or the normal distribution.\nParameter estimation is performed using the expectation-maximization algorithm\nand both simulated and real data are used for illustration.\n"}
+{"abstract": "  We applied the Koch snowflake fractal antenna in planning calibration of the\nFive-hundred-meter Aperture Spherical radio Telescope (FAST), hypothesizing\nsecond-order fractal primary reflectors can optimize the orientated sensitivity\nof the telescope. Meanwhile, on the grounds of NASA Science Working Group\nReport in 1984, we reexamine the strategy of Search for Extraterrestrial\nIntelligence (SETI). A mathematical analysis of the radar equation will be\nperformed in the first section, aiming to make it convenient to design a\nreceiver system that can detect activities of an extraterrestrial civilization,\naccording to the observable region of the narrowband. Taking advantage of the\ninherent potential of FAST, we simulate the theoretical detection of a\nKardashev Type I civilization by a snowflake-selected reflecting area.\n"}
+{"abstract": "  We consider the planar circular restricted three-body problem (PCRTBP), as a\nmodel for the motion of a spacecraft relative to the Earth-Moon system. We\nfocus on the Lagrange equilibrium points $L_1$ and $L_2$. There are families of\nLyapunov periodic orbits around either $L_1$ or $L_2$, forming Lyapunov\nmanifolds. There also exist homoclinic orbits to the Lyapunov manifolds around\neither $L_1$ or $L_2$, as well as heteroclinic orbits between the Lyapunov\nmanifold around $L_1$ and the one around $L_2$. The motion along the\nhomoclinic/heteroclinic orbits can be described via the scattering map, which\ngives the future asymptotic of a homoclinic orbit as a function of the past\nasymptotic. In contrast with the more customary Melnikov theory, we do not need\nto assume that the asymptotic orbits have a special nature (periodic,\nquasi-periodic, etc.).\n  We add a non-conservative, time-dependent perturbation, as a model for a\nthrust applied to the spacecraft for some duration of time, or for some other\neffect, such as solar radiation pressure. We compute the first order\napproximation of the perturbed scattering map, in terms of fast convergent\nintegrals of the perturbation along homoclinic/heteroclinic orbits of the\nunperturbed system. As a possible application, this result can be used to\ndetermine the trajectory of the spacecraft upon using the thrust.\n"}
+{"abstract": "  One of the serious threats related to climate change is an increase in the\nnumber and severity of extreme weather events. A prominent example are\nhurricanes, which result from rising coastal temperatures. Such extreme weather\nevents can cause extensive damages in infrastructure systems and, potentially,\ndestroy components in electricity transmission networks, which in turn can lead\nto major blackouts. In our recent work, we study the risk of hurricane-induced\ncascading failures in power systems of the U.S. East Coast using historical\nwind field data sets. For this purpose, we model the destruction of overhead\ntransmission lines during hurricanes, where each failing line causes a\nrerouting of power flow in the system. New power flows can overload additional\nlines, which are then automatically deactivated and thereby cause another\nrerouting of power flow and so on. Ultimately, a cascade of failures can unfold\nthat can black out large parts of the power system.\n"}
+{"abstract": "  Formal power series products appear in nonlinear control theory when systems\nmodeled by Chen-Fliess series are interconnected to form new systems. In fields\nlike adaptive control and learning systems, the coefficients of these formal\npower series are estimated sequentially with real-time data. The main goal of\nthe present article is to prove the continuity and analyticity of such products\nwith respect to several natural (locally convex) topologies on spaces of\nlocally convergent formal power series in order to establish foundational\nproperties behind these technologies. In addition, it is shown that a\ntransformation group central to describing the output feedback connection is in\nfact an analytic Lie group in this setting with certain regularity properties.\n"}
+{"abstract": "  Laser cooled ions trapped in a linear Paul trap are long-standing ideal\ncandidates for realizing quantum simulation, especially of many-body systems.\nThe properties that contribute to this also provide the opportunity to\ndemonstrate unexpected quantum phenomena in few-body systems. A pair of ions\ninteracting in such traps exchange vibrational quanta through the Coulomb\ninteraction. This linear interaction can be anharmonically modulated by an\nelementary coupling to the internal two-level structure of one of the ions.\nDriven by thermal energy in the passively coupled oscillators, which are\nthemselves coupled to the internal ground states of the ions, the nonlinear\ninteraction autonomously and unconditionally generates entanglement between the\nmechanical modes of the ions. We examine this counter-intuitive thermally\ninduced entanglement for several experimentally feasible model systems, and\npropose parameter regimes where state of the art trapped ion systems can\nproduce such phenomena. In addition, we demonstrate a multiqubit enhancement of\nsuch thermally induced entanglement.\n"}
+{"abstract": "  Span-extraction reading comprehension models have made tremendous advances\nenabled by the availability of large-scale, high-quality training datasets.\nDespite such rapid progress and widespread application, extractive reading\ncomprehension datasets in languages other than English remain scarce, and\ncreating such a sufficient amount of training data for each language is costly\nand even impossible. An alternative to creating large-scale high-quality\nmonolingual span-extraction training datasets is to develop multilingual\nmodeling approaches and systems which can transfer to the target language\nwithout requiring training data in that language. In this paper, in order to\nsolve the scarce availability of extractive reading comprehension training data\nin the target language, we propose a multilingual extractive reading\ncomprehension approach called XLRC by simultaneously modeling the existing\nextractive reading comprehension training data in a multilingual environment\nusing self-adaptive attention and multilingual attention. Specifically, we\nfirstly construct multilingual parallel corpora by translating the existing\nextractive reading comprehension datasets (i.e., CMRC 2018) from the target\nlanguage (i.e., Chinese) into different language families (i.e., English).\nSecondly, to enhance the final target representation, we adopt self-adaptive\nattention (SAA) to combine self-attention and inter-attention to extract the\nsemantic relations from each pair of the target and source languages.\nFurthermore, we propose multilingual attention (MLA) to learn the rich\nknowledge from various language families. Experimental results show that our\nmodel outperforms the state-of-the-art baseline (i.e., RoBERTa_Large) on the\nCMRC 2018 task, which demonstrate the effectiveness of our proposed\nmulti-lingual modeling approach and show the potentials in multilingual NLP\ntasks.\n"}
+{"abstract": "  The commoditization of Malware-as-a-Service (MaaS) allows criminals to obtain\nfinancial benefits at a low risk and with little technical background. One such\npopular product in the underground economy is ransomware. In ransomware\nattacks, data from infected systems is held hostage (encrypted) until a fee is\npaid to the criminals. This modus operandi disrupts legitimate businesses,\nwhich may become unavailable until the data is restored. A recent blackmailing\nstrategy adopted by criminals is to leak data online from the infected systems\nif the ransom is not paid. Besides reputational damage, data leakage might\nproduce further economical losses due to fines imposed by data protection laws.\nThus, research on prevention and recovery measures to mitigate the impact of\nsuch attacks is needed to adapt existing countermeasures to new strains.\n  In this work, we perform an in-depth analysis of Avaddon, a ransomware\noffered in the underground economy as an affiliate program business. This has\ninfected and leaked data from at least 23 organizations. Additionally, it runs\nDistributed Denial-of-Service (DDoS) attacks against victims that do not pay\nthe ransom. We first provide an analysis of the criminal business model from\nthe underground economy. Then, we identify and describe its technical\ncapabilities. We provide empirical evidence of links between this variant and a\nprevious family, suggesting that the same group was behind the development and,\npossibly, the operation of both campaigns.\n  Finally, we describe a method to decrypt files encrypted with Avaddon in real\ntime. We implement and test the decryptor in a tool that can recover the\nencrypted data from an infected system, thus mitigating the damage caused by\nthe ransomware. The tool is released open-source so it can be incorporated in\nexisting Antivirus engines.\n"}
+{"abstract": "  Whereas much of the success of the current generation of neural language\nmodels has been driven by increasingly large training corpora, relatively\nlittle research has been dedicated to analyzing these massive sources of\ntextual data. In this exploratory analysis, we delve deeper into the Common\nCrawl, a colossal web corpus that is extensively used for training language\nmodels. We find that it contains a significant amount of undesirable content,\nincluding hate speech and sexually explicit content, even after filtering\nprocedures. We discuss the potential impacts of this content on language models\nand conclude with future research directions and a more mindful approach to\ncorpus collection and analysis.\n"}
+{"abstract": "  In this work, we address the question of the adaptability of artificial\nneural networks (NNs) used for impairments mitigation in optical transmission\nsystems. We demonstrate that by using well-developed techniques based on the\nconcept of transfer learning, we can efficaciously retrain NN-based equalizers\nto adapt to the changes in the transmission system, using just a fraction (down\nto 1%) of the initial training data or epochs. We evaluate the capability of\ntransfer learning to adapt the NN to changes in the launch power, modulation\nformat, symbol rate, or even fiber plants (different fiber types and lengths).\nThe numerical examples utilize the recently introduced NN equalizer consisting\nof a convolutional layer coupled with bi-directional long-short term memory\n(biLSTM) recurrent NN element. Our analysis focuses on long-haul coherent\noptical transmission systems for two types of fibers: the standard single-mode\nfiber (SSMF) and the TrueWave Classic (TWC) fiber. We underline the specific\npeculiarities that occur when transferring the learning in coherent optical\ncommunication systems and draw the limits for the transfer learning efficiency.\nOur results demonstrate the effectiveness of transfer learning for the fast\nadaptation of NN architectures to different transmission regimes and scenarios,\npaving the way for engineering flexible and universal solutions for\nnonlinearity mitigation.\n"}
+{"abstract": "  Model-predictive-control (MPC) offers an optimal control technique to\nestablish and ensure that the total operation cost of multi-energy systems\nremains at a minimum while fulfilling all system constraints. However, this\nmethod presumes an adequate model of the underlying system dynamics, which is\nprone to modelling errors and is not necessarily adaptive. This has an\nassociated initial and ongoing project-specific engineering cost. In this\npaper, we present an on- and off-policy multi-objective reinforcement learning\n(RL) approach, that does not assume a model a priori, benchmarking this against\na linear MPC (LMPC - to reflect current practice, though non-linear MPC\nperforms better) - both derived from the general optimal control problem,\nhighlighting their differences and similarities. In a simple multi-energy\nsystem (MES) configuration case study, we show that a twin delayed deep\ndeterministic policy gradient (TD3) RL agent offers potential to match and\noutperform the perfect foresight LMPC benchmark (101.5%). This while the\nrealistic LMPC, i.e. imperfect predictions, only achieves 98%. While in a more\ncomplex MES system configuration, the RL agent's performance is generally lower\n(94.6%), yet still better than the realistic LMPC (88.9%). In both case\nstudies, the RL agents outperformed the realistic LMPC after a training period\nof 2 years using quarterly interactions with the environment. We conclude that\nreinforcement learning is a viable optimal control technique for multi-energy\nsystems given adequate constraint handling and pre-training, to avoid unsafe\ninteractions and long training periods, as is proposed in fundamental future\nwork.\n"}
+{"abstract": "  This study presents a large scale benchmarking on cloud based Speech-To-Text\nsystems: {Google Cloud Speech-To-Text}, {Microsoft Azure Cognitive Services},\n{Amazon Transcribe}, {IBM Watson Speech to Text}. For each systems, 40158 clean\nand noisy speech files about 101 hours are tested. Effect of background noise\non STT quality is also evaluated with 5 different Signal-to-noise ratios from\n40dB to 0dB. Results showed that {Microsoft Azure} provided lowest\ntranscription error rate $9.09\\%$ on clean speech, with high robustness to\nnoisy environment. {Google Cloud} and {Amazon Transcribe} gave similar\nperformance, but the latter is very limited for time-constraint usage. Though\n{IBM Watson} could work correctly in quiet conditions, it is highly sensible to\nnoisy speech which could strongly limit its application in real life\nsituations.\n"}
+{"abstract": "  Metal nano-aerogels combine a large surface area, a high structural\nstability, and a high catalytic activity towards a variety of chemical\nreactions. The performance of such nanostructures is underpinned by the\natomic-level distribution of their constituents. Yet monitoring their\nsub-nanoscale structure and composition to guide property optimization remains\nextremely challenging. Here, we synthesized Pd nano-aerogels from a K2PdCl4\nprecursor and two different NaBH4 reductant concentrations in distilled water.\nAtom probe tomography reveals that the aerogel is poly-crystalline and that\nimpurities (Na, K) are integrated from the solution into grain boundaries. Ab\ninitio calculations indicate that these impurities preferentially bound to the\nPd-metal surface and are ultimately found in grain boundaries forming as the\nparticles coalesce during synthesis, with Na atoms thermodynamically\nequilibrating with the surrounding solution and K atoms remaining between\ngrowing grains. If controlled, impurity integration, i.e. grain boundary\ndecoration, may offer opportunities for designing new nano-aerogels.\n"}
+{"abstract": "  To implement fair machine learning in a sustainable way, choosing the right\nfairness objective is key. Since fairness is a concept of justice which comes\nin various, sometimes conflicting definitions, this is not a trivial task\nthough. The most appropriate fairness definition for an artificial intelligence\n(AI) system is a matter of ethical standards and legal requirements, and the\nright choice depends on the particular use case and its context. In this\nposition paper, we propose to use a decision tree as means to explain and\njustify the implemented kind of fairness to the end users. Such a structure\nwould first of all support AI practitioners in mapping ethical principles to\nfairness definitions for a concrete application and therefore make the\nselection a straightforward and transparent process. However, this approach\nwould also help document the reasoning behind the decision making. Due to the\ngeneral complexity of the topic of fairness in AI, we argue that specifying\n\"fairness\" for a given use case is the best way forward to maintain confidence\nin AI systems. In this case, this could be achieved by sharing the reasons and\nprinciples expressed during the decision making process with the broader\naudience.\n"}
+{"abstract": "  This paper introduces a multi-scale speech style modeling method for\nend-to-end expressive speech synthesis. The proposed method employs a\nmulti-scale reference encoder to extract both the global-scale utterance-level\nand the local-scale quasi-phoneme-level style features of the target speech,\nwhich are then fed into the speech synthesis model as an extension to the input\nphoneme sequence. During training time, the multi-scale style model could be\njointly trained with the speech synthesis model in an end-to-end fashion. By\napplying the proposed method to style transfer task, experimental results\nindicate that the controllability of the multi-scale speech style model and the\nexpressiveness of the synthesized speech are greatly improved. Moreover, by\nassigning different reference speeches to extraction of style on each scale,\nthe flexibility of the proposed method is further revealed.\n"}
+{"abstract": "  We present a scenario where an axion-like field drives inflation until a\npotential barrier, which keeps a waterfall field at the origin, disappears and\na waterfall transition occurs. Such a barrier separates the scale of inflation\nfrom that of the waterfall transition. We find the observed spectrum of the\ncosmic microwave background indicates that the decay constant of the inflaton\nis well below the Planck scale, with the inflationary Hubble parameter spanning\na wide range. Further, our model involves dark matter candidates including the\ninflaton itself. Also, for a complex waterfall field, we can determine\ncosmologically the Peccei-Quinn scale associated with the strong CP problem.\n"}
+{"abstract": "  We study the role of average concurrence in entanglement swapping in quantum\nnetworks. We begin with qubit pure states, and there is a very simple rule\ngoverning the propagation of average concurrence in multiple swaps. We look at\nexamples of mixed qubit states, and find the relation for pure states gives an\nupper bound on what is possible with mixed states. We then move on to qudits,\nwhere we make use of the I-concurrence. Here the situation is not as simple as\nfor qubits, but in some cases relatively straightforward results can be\nobtained.\n"}
+{"abstract": "  The multiplayer promise set disjointness is one of the most widely used\nproblems from communication complexity in applications. In this problem there\nare $k$ players with subsets $S^1, \\ldots, S^k$, each drawn from $\\{1, 2,\n\\ldots, n\\}$, and we are promised that either the sets are (1) pairwise\ndisjoint, or (2) there is a unique element $j$ occurring in all the sets, which\nare otherwise pairwise disjoint. The total communication of solving this\nproblem with constant probability in the blackboard model is $\\Omega(n/k)$.\n  We observe for most applications, it instead suffices to look at what we call\nthe ``mostly'' set disjointness problem, which changes case (2) to say there is\na unique element $j$ occurring in at least half of the sets, and the sets are\notherwise disjoint. This change gives us a much simpler proof of an\n$\\Omega(n/k)$ randomized total communication lower bound, avoiding Hellinger\ndistance and Poincare inequalities. Using this we show several new results for\ndata streams:\n  \\begin{itemize} \\item for $\\ell_2$-Heavy Hitters, any $O(1)$-pass streaming\nalgorithm in the insertion-only model for detecting if an $\\eps$-$\\ell_2$-heavy\nhitter exists requires $\\min(\\frac{1}{\\eps^2}\\log \\frac{\\eps^2n}{\\delta},\n\\frac{1}{\\eps}n^{1/2})$ bits of memory, which is optimal up to a $\\log n$\nfactor. For deterministic algorithms and constant $\\eps$, this gives an\n$\\Omega(n^{1/2})$ lower bound, improving the prior $\\Omega(\\log n)$ lower\nbound. We also obtain lower bounds for Zipfian distributions. \\item for\n$\\ell_p$-Estimation, $p > 2$, we show an $O(1)$-pass $\\Omega(n^{1-2/p}\n\\log(1/\\delta))$ bit lower bound for outputting an $O(1)$-approximation with\nprobability $1-\\delta$, in the insertion-only model. This is optimal, and the\nbest previous lower bound was $\\Omega(n^{1-2/p} + \\log(1/\\delta))$.\n\\end{itemize}\n"}
+{"abstract": "  This article is a lecture note on the potential theory of (possibly\nnon-reversible) Markov processes and on the connection of this theory with\nquantitative analysis of the metastability of stochastic processes.\n"}
+{"abstract": "  Coupled oscillators are among the simplest composite quantum systems in which\nthe interplay of entanglement and interaction may be explored. We examine the\neffects of coupling on fluctuations of the coordinates and momenta of the\noscillators in a single-excitation entangled state. We discover that coupling\nacts as a mechanism for noise transfer between one pair of coordinate and\nmomentum and another. Through this noise transfer mechanism, the uncertainty\nproduct is lowered, on average, relatively to its non-coupled level for one\npair of coordinate and momentum and it is enhanced for the other pair. This\nnovel mechanism may be explored in precision measurements in\nentanglement-assisted sensing and metrology.\n"}
+{"abstract": "  The subject of this textbook is the analysis of Boolean functions. Roughly\nspeaking, this refers to studying Boolean functions $f : \\{0,1\\}^n \\to \\{0,1\\}$\nvia their Fourier expansion and other analytic means. Boolean functions are\nperhaps the most basic object of study in theoretical computer science, and\nFourier analysis has become an indispensable tool in the field. The topic has\nalso played a key role in several other areas of mathematics, from\ncombinatorics, random graph theory, and statistical physics, to Gaussian\ngeometry, metric/Banach spaces, and social choice theory.\n  The intent of this book is both to develop the foundations of the field and\nto give a wide (though far from exhaustive) overview of its applications. Each\nchapter ends with a \"highlight\" showing the power of analysis of Boolean\nfunctions in different subject areas: property testing, social choice,\ncryptography, circuit complexity, learning theory, pseudorandomness, hardness\nof approximation, concrete complexity, and random graph theory.\n  The book can be used as a reference for working researchers or as the basis\nof a one-semester graduate-level course. The author has twice taught such a\ncourse at Carnegie Mellon University, attended mainly by graduate students in\ncomputer science and mathematics but also by advanced undergraduates, postdocs,\nand researchers in adjacent fields. In both years most of Chapters 1-5 and 7\nwere covered, along with parts of Chapters 6, 8, 9, and 11, and some additional\nmaterial on additive combinatorics. Nearly 500 exercises are provided at the\nends of the book's chapters.\n"}
+{"abstract": "  In this work, we address multi-modal information needs that contain text\nquestions and images by focusing on passage retrieval for outside-knowledge\nvisual question answering. This task requires access to outside knowledge,\nwhich in our case we define to be a large unstructured passage collection. We\nfirst conduct sparse retrieval with BM25 and study expanding the question with\nobject names and image captions. We verify that visual clues play an important\nrole and captions tend to be more informative than object names in sparse\nretrieval. We then construct a dual-encoder dense retriever, with the query\nencoder being LXMERT, a multi-modal pre-trained transformer. We further show\nthat dense retrieval significantly outperforms sparse retrieval that uses\nobject expansion. Moreover, dense retrieval matches the performance of sparse\nretrieval that leverages human-generated captions.\n"}
+{"abstract": "  The advent of dispersion-engineered and highly nonlinear nanophotonics is\nexpected to open up an all-optical path towards the strong-interaction regime\nof quantum optics by combining high transverse field confinement with\nultra-short-pulse operation. Obtaining a full understanding of photon dynamics\nin such broadband devices, however, poses major challenges in the modeling and\nsimulation of multimode non-Gaussian quantum physics, highlighting the need for\nsophisticated reduced models that facilitate efficient numerical study while\nproviding useful physical insight. In this manuscript, we review our recent\nefforts in modeling broadband optical systems at varying levels of abstraction\nand generality, ranging from multimode extensions of quantum input-output\ntheory for sync-pumped oscillators to the development of numerical methods\nbased on a field-theoretic description of nonlinear waveguides. We expect our\nwork not only to guide ongoing theoretical and experimental efforts towards\nnext-generation quantum devices but also to uncover essential physics of\nbroadband quantum photonics.\n"}
+{"abstract": "  In this proceeding, we study time evolution of a complex scalar field, in\nsymmetry broken phase, in presence of oscillating spacetime metric background.\nWe show that spacetime oscillations lead to parametric resonance of the field.\nThis generates excitations in the field for a wide range of frequency of\nspacetime oscillations which ultimately lead to the formation of topological\nvortices. The lowest frequency cut-off to induce this phenomena is set by\nsystem size due to finite size effects.\n"}
+{"abstract": "  We study the general problem of Bayesian persuasion (optimal information\ndesign) with continuous actions and continuous state space in arbitrary\ndimensions. First, we show that with a finite signal space, the optimal\ninformation design is always given by a partition. Second, we take the limit of\nan infinite signal space and characterize the solution in terms of a\nMonge-Kantorovich optimal transport problem with an endogenous information\ntransport cost. We use our novel approach to: 1. Derive necessary and\nsufficient conditions for optimality based on Bregman divergences for\nnon-convex functions. 2. Compute exact bounds for the Hausdorff dimension of\nthe support of an optimal policy. 3. Derive a non-linear, second-order partial\ndifferential equation whose solutions correspond to regular optimal policies.\nWe illustrate the power of our approach by providing explicit solutions to\nseveral non-linear, multidimensional Bayesian persuasion problems.\n"}
+{"abstract": "  Light harvesting as the first step in photosynthesis is of prime importance\nfor life on earth. For a theoretical description of photochemical processes\nduring light harvesting, spectral densities are key quantities. They serve as\ninput functions for modeling the excitation energy transfer dynamics and\nspectroscopic properties. Herein, a recently developed procedure is applied to\ndetermine the spectral densities of the pigments in the minor antenna complex\nCP29 of photosystem II which has recently gained attention because of its\nactive role in non-photochemical quenching processes in higher plants. To this\nend, the density functional based tight binding (DFTB) method has been employed\nto enable simulation of the ground state dynamics in a\nquantum-mechanics/molecular mechanics (QM/MM) scheme for each chlorophyll\npigment. Subsequently, the time-dependent extension of the long-range corrected\nDFTB approach has been used to obtain the excitation energy fluctuations along\nthe ground-state trajectories also in a QM/MM setting. From these results the\nspectral densities have been determined and compared for different force fields\nand to spectral densities from other light-harvesting complexes. In addition,\nthe excitation energy transfer in the CP29 complex has been studied using\nensemble-average wave packet dynamics.\n"}
+{"abstract": "  With the advent of generative adversarial networks, synthesizing images from\ntextual descriptions has recently become an active research area. It is a\nflexible and intuitive way for conditional image generation with significant\nprogress in the last years regarding visual realism, diversity, and semantic\nalignment. However, the field still faces several challenges that require\nfurther research efforts such as enabling the generation of high-resolution\nimages with multiple objects, and developing suitable and reliable evaluation\nmetrics that correlate with human judgement. In this review, we contextualize\nthe state of the art of adversarial text-to-image synthesis models, their\ndevelopment since their inception five years ago, and propose a taxonomy based\non the level of supervision. We critically examine current strategies to\nevaluate text-to-image synthesis models, highlight shortcomings, and identify\nnew areas of research, ranging from the development of better datasets and\nevaluation metrics to possible improvements in architectural design and model\ntraining. This review complements previous surveys on generative adversarial\nnetworks with a focus on text-to-image synthesis which we believe will help\nresearchers to further advance the field.\n"}
+{"abstract": "  The Antarctic Impulse Transient Antenna (\\textsc{ANITA}) collaboration\n\\cite{Gorham:2016zah,Gorham:2018ydl,Gorham:2020zne} have reported observation\nof two anomalous events with noninverted polarity. These events are proven to\nbe hard to explain in terms of conventional cosmic rays (CR). We propose that\nthese anomalous events represent the direct manifestation of the dark matter\n(DM) annihilation events within the so-called axion quark nugget (AQN) DM\nmodel, which was originally invented for completely different purpose to\nexplain the observed similarity between the dark and the visible components in\nthe Universe, i.e. $\\Omega_{\\rm DM}\\sim \\Omega_{\\rm visible}$ without any\nfitting parameters. We support this proposal by demonstrating that the\nobservations \\cite{Gorham:2016zah,Gorham:2018ydl,Gorham:2020zne}, including the\nfrequency, intensity and time duration of the radio pulses nicely match the\nemission features of the upward going AQN events. We list a number of features\nof the AQN events which are very distinct from conventional CR air showers. The\nobservations (non-observation) of these features may substantiate (refute) our\nproposal.\n"}
+{"abstract": "  Predicting the chemical properties of a molecule is of great importance in\nmany applications, including drug discovery and material design. Machine\nlearning based molecular property prediction holds the promise of enabling\naccurate predictions at much less computationally complex cost when compared\nto, for example, Density Functional Theory (DFT) calculations. Various\nrepresentation learning methods in a supervised setting, including the features\nextracted using graph neural nets, have emerged for such tasks. However, the\nvast chemical space and the limited availability of labels make supervised\nlearning challenging, calling for learning a general-purpose molecular\nrepresentation. Recently, pre-trained transformer-based language models on\nlarge unlabeled corpus have produced state-of-the-art results in many\ndownstream natural language processing tasks. Inspired by this development, we\npresent molecular embeddings obtained by training an efficient transformer\nencoder model, MoLFormer. This model employs a linear attention mechanism\ncoupled with highly parallelized training on SMILES sequences of 1.1 billion\nunlabeled molecules from the PubChem and ZINC datasets. Experiments show that\nthe learned molecular representation outperforms supervised and unsupervised\ngraph neural net baselines on several regression and classification tasks from\n10 benchmark datasets, while performing competitively on others. Further\nanalyses, specifically through the lens of attention, demonstrate that\nMoLFormer indeed learns a molecule's local and global structural aspects. These\nresults provide encouraging evidence that large-scale molecular language models\ncan capture sufficient structural information to be able to predict diverse\nmolecular properties, including quantum-chemical properties\n"}
+{"abstract": "  In this paper, we study the fragmentation of a heavy quark into a jet near\nthreshold, meaning that final state jet carries most of the energy of the\nfragmenting heavy quark. Using the heavy quark fragmentation function, we\nsimultaneously resum large logarithms of the jet radius $R$ and $1-z$, where\n$z$ is the ratio of the jet energy to the initiating heavy quark energy. There\nare numerically significant corrections to the leading order rate due to this\nresummation. We also investigate the heavy quark fragmentation to a groomed\njet, using the soft drop grooming algorithm as an example. In order to do so,\nwe introduce a collinear-ultrasoft mode sensitive to the grooming region\ndetermined by the algorithm's $z_{\\mathrm{cut}}$ parameter. This allows us to\nresum large logarithms of $z_{\\mathrm{cut}}/(1-z)$, again leading to large\nnumerical corrections near the endpoint. A nice feature of the analysis of the\nheavy quark fragmenting to a groomed jet is the heavy quark mass $m$ renders\nthe algorithm infrared finite, allowing a perturbative calculation. We analyze\nthis for $E_JR \\sim m$ and $E_JR\\gg m$, where $E_J$ is the jet energy. To do\nthe latter case, we introduce an ultracollinear-soft mode, allowing us to resum\nlarge logarithms of $E_JR/m$. Finally, as an application we calculate the rate\nfor $e^+e^-$ collisions to produce a heavy quark jet in the endpoint region,\nwhere we show that grooming effects have a sizable contribution near the\nendpoint.\n"}
+{"abstract": "  Because of the fast advance rate and the improved personnel safety, tunnel\nboring machines (TBMs) have been widely used in a variety of tunnel\nconstruction projects. The dynamic modeling of TBM load parameters (including\ntorque, advance rate and thrust) plays an essential part in the design, safe\noperation and fault prognostics of this complex engineering system. In this\npaper, based on in-situ TBM operational data, we use the machine-learning (ML)\nmethods to build the real-time forecast models for TBM load parameters, which\ncan instantaneously provide the future values of the TBM load parameters as\nlong as the current data are collected. To decrease the model complexity and\nimprove the generalization, we also apply the least absolute shrinkage and\nselection (Lasso) method to extract the essential features of the forecast\ntask. The experimental results show that the forecast models based on\ndeep-learning methods, {\\it e.g.}, recurrent neural network and its variants,\noutperform the ones based on the shallow-learning methods, {\\it e.g.}, support\nvector regression and random forest. Moreover, the Lasso-based feature\nextraction significantly improves the performance of the resultant models.\n"}
+{"abstract": "  The scaled inverse of a nonzero element $a(x)\\in \\mathbb{Z}[x]/f(x)$, where\n$f(x)$ is an irreducible polynomial over $\\mathbb{Z}$, is the element $b(x)\\in\n\\mathbb{Z}[x]/f(x)$ such that $a(x)b(x)=c \\pmod{f(x)}$ for the smallest\npossible positive integer scale $c$. In this paper, we investigate the scaled\ninverse of $(x^i-x^j)$ modulo cyclotomic polynomial of the form $\\Phi_{p^s}(x)$\nor $\\Phi_{p^s q^t}(x)$, where $p, q$ are primes with $p<q$ and $s, t$ are\npositive integers. Our main results are that the coefficient size of the scaled\ninverse of $(x^i-x^j)$ is bounded by $p-1$ with the scale $p$ modulo\n$\\Phi_{p^s}(x)$, and is bounded by $q-1$ with the scale not greater than $q$\nmodulo $\\Phi_{p^s q^t}(x)$. Previously, the analogous result on cyclotomic\npolynomials of the form $\\Phi_{2^n}(x)$ gave rise to many lattice-based\ncryptosystems, especially, zero-knowledge proofs. Our result provides more\nflexible choice of cyclotomic polynomials in such cryptosystems. Along the way\nof proving the theorems, we also prove several properties of\n$\\{x^k\\}_{k\\in\\mathbb{Z}}$ in $\\mathbb{Z}[x]/\\Phi_{pq}(x)$ which might be of\nindependent interest.\n"}
+{"abstract": "  This work focuses on optimal controls of a class of stochastic SIS epidemic\nmodels under regime switching. By assuming that a decision maker can either\ninfluence the infectivity period or isolate infected individuals, our aim is to\nminimize the expected discounted cost due to illness, medical treatment, and\nthe adverse effect on the society. In addition, a model with the incorporation\nof vaccination is proposed. Numerical schemes are developed by approximating\nthe continuous-time dynamics using Markov chain approximation methods. It is\ndemonstrated that the approximation schemes converge to the optimal strategy as\nthe mesh size goes to zero. Numerical examples are provided to illustrate our\nresults.\n"}
+{"abstract": "  Non-reciprocal directional dichroism, also called the optical-diode effect,\nis an appealing functional property inherent to the large class of\nnon-centrosymmetric magnets. However, the in-situ electric control of this\nphenomenon is challenging as it requires a set of conditions to be fulfilled:\nSpecial symmetries of the magnetic ground state, spin-excitations with\ncomparable magnetic- and electric-dipole activity and switchable electric\npolarization. We demonstrate the isothermal electric switch between domains of\nBa$_2$CoGe$_2$O$_7$ possessing opposite magnetoelectric susceptibilities.\nCombining THz spectroscopy and multiboson spin-wave analysis, we show that\nunbalancing the population of antiferromagnetic domains generates the\nnon-reciprocal light absorption of spin excitations.\n"}
+{"abstract": "  To dynamically traverse challenging terrain, legged robots need to\ncontinually perceive and reason about upcoming features, adjust the locations\nand timings of future footfalls and leverage momentum strategically. We present\na pipeline that enables flexibly-parametrized trajectories for perceptive and\ndynamic quadruped locomotion to be optimized in an online, receding-horizon\nmanner. The initial guess passed to the optimizer affects the computation\nneeded to achieve convergence and the quality of the solution. We consider two\nmethods for generating good guesses. The first is a heuristic initializer which\nprovides a simple guess and requires significant optimization but is\nnonetheless suitable for adaptation to upcoming terrain. We demonstrate\nexperiments using the ANYmal C quadruped, with fully onboard sensing and\ncomputation, to cross obstacles at moderate speeds using this technique. Our\nsecond approach uses latent-mode trajectory regression (LMTR) to imitate expert\ndata - while avoiding invalid interpolations between distinct behaviors - such\nthat minimal optimization is needed. This enables high-speed motions that make\nmore expansive use of the robot's capabilities. We demonstrate it on flat\nground with the real robot and provide numerical trials that progress toward\ndeployment on terrain. These results illustrate a paradigm for advancing beyond\nshort-horizon dynamic reactions, toward the type of intuitive and adaptive\nlocomotion planning exhibited by animals and humans.\n"}
+{"abstract": "  This paper studies the application of the blended dynamics approach towards\ndistributed optimization problem where the global cost function is given by a\nsum of local cost functions. The benefits include (i) individual cost function\nneed not be convex as long as the global cost function is strongly convex and\n(ii) the convergence rate of the distributed algorithm is arbitrarily close to\nthe convergence rate of the centralized one. Two particular continuous-time\nalgorithms are presented using the proportional-integral-type couplings. One\nhas benefit of `initialization-free,' so that agents can join or leave the\nnetwork during the operation. The other one has the minimal amount of\ncommunication information. After presenting a general theorem that can be used\nfor designing distributed algorithms, we particularly present a distributed\nheavy-ball method and discuss its strength over other methods.\n"}
+{"abstract": "  Models in which the Higgs boson is a composite pseudo-Nambu-Goldstone boson\noffer attractive solutions to the Higgs mass naturalness problem. We consider\nthree such models based on the minimal $SO(5) \\rightarrow SO(4)$ symmetry\nbreaking pattern, and perform convergent global fits on the models under a\nBayesian framework in order to find the regions of their parameter spaces that\nbest fit a wide range of constraints, including recent Higgs measurements. We\nuse a novel technique to analyse the fine-tuning of the models, quantifying the\ntuning as the Kullback-Leibler divergence from the prior to the posterior\nprobability on the parameter space. Each model is found to be able to satisfy\nall constraints at the $3\\sigma$ level simultaneously. As a by-product of the\nfits, we analyse the collider phenomenology of our models in these viable\nregions. In two of the three models, we find that the $g g \\rightarrow H\n\\rightarrow \\gamma \\gamma$ cross section is less than ${\\sim}90$% that\npredicted by the SM, which is already in slight tension with experiment and\ncould potentially be ruled out in the future high-luminosity run of the LHC. In\naddition, the lightest fermions $F$ arising from the new strong dynamics in\nthese models are seen in general to lie above ${\\sim}1.1$ TeV, with the $F\n\\rightarrow tW^{+}$ and $F \\rightarrow \\bar{b}W^{+}$ decays offering\nparticularly promising channels for probing these models in future collider\nsearches.\n"}
+{"abstract": "  We present the ab initio results of the structural and magnetic properties of\nthe Pd host matrix implanted with Fe solute atoms at various concentrations. By\nmeans of density functional theory we confirm that iron impurities are able to\ninitialize significant magnetization of the Pd atoms, when the impurity\nconsentation exceeds 3 at.%. Besides, we demonstrate that the imposed\nmagnetization depends on impurity positions in the host matrix, in particular,\nthere is a maximum of magnetization for a uniform distribution of the iron\nimpurity.\n"}
+{"abstract": "  Discourse relations among arguments reveal logical structures of a debate\nconversation. However, no prior work has explicitly studied how the sequence of\ndiscourse relations influence a claim's impact. This paper empirically shows\nthat the discourse relations between two arguments along the context path are\nessential factors for identifying the persuasive power of an argument. We\nfurther propose DisCOC to inject and fuse the sentence-level structural\ndiscourse information with contextualized features derived from large-scale\nlanguage models. Experimental results and extensive analysis show that the\nattention and gate mechanisms that explicitly model contexts and texts can\nindeed help the argument impact classification task defined by Durmus et al.\n(2019), and discourse structures among the context path of the claim to be\nclassified can further boost the performance.\n"}
+{"abstract": "  Let $D=(V,A)$ be a digraphs without isolated vertices. A vertex-degree based\ninvariant $I(D)$ related to a real function $\\varphi$ of $D$ is defined as a\nsummation over all arcs, $I(D) = \\frac{1}{2}\\sum_{uv\\in\nA}{\\varphi(d_u^+,d_v^-)}$, where $d_u^+$ (resp. $d_u^-$) denotes the out-degree\n(resp. in-degree) of a vertex $u$. In this paper, we give the extremal values\nand extremal digraphs of $I(D)$ over all digraphs with $n$ non-isolated\nvertices. Applying these results, we obtain the extremal values of some\nvertex-degree based topological indices of digraphs, such as the Randi\\'{c}\nindex, the Zagreb index, the sum-connectivity index, the $GA$ index, the $ABC$\nindex and the harmonic index, and the corresponding extremal digraphs.\n"}
+{"abstract": "  Filamentary textures can take the form of braided, rope-like superstructures\nin nonlinear media such as plasmas and superfluids. The formation of similar\nsuperstructures in solids has been predicted, for example from flux lines in\nsuperconductors. However, their observation has proved challenging. Here, we\nuse electron microscopy and numerical methods to reveal braided superstructures\nof magnetic skyrmions in thin crystals of B20-type FeGe. Their discovery opens\nthe door to applications of rich topological landscapes of geometric braids in\nmagnetic solids.\n"}
+{"abstract": "  Radio frequency fingerprint identification (RFFI) is a promising device\nauthentication technique based on the transmitter hardware impairments. In this\npaper, we propose a scalable and robust RFFI framework achieved by deep\nlearning powered radio frequency fingerprint (RFF) extractor. Specifically, we\nleverage the deep metric learning to train an RFF extractor, which has\nexcellent generalization ability and can extract RFFs from previously unseen\ndevices. Any devices can be enrolled via the pre-trained RFF extractor and the\nRFF database can be maintained efficiently for allowing devices to join and\nleave. Wireless channel impacts the RFF extraction and is tackled by exploiting\nchannel independent feature and data augmentation. We carried out extensive\nexperimental evaluation involving 60 commercial off-the-shelf LoRa devices and\na USRP N210 software defined radio platform. The results have successfully\ndemonstrated that our framework can achieve excellent generalization abilities\nfor device classification and rogue device detection as well as effective\nchannel mitigation.\n"}
+{"abstract": "  A novel volume of fluid model (VoF) called explicit volume diffusion (EVD) is\ndeveloped for the simulation of interfacial flows, including those with\nturbulence and primary spray atomisation. The EVD model is derived by volume\naveraging the VoF equations over a physically-defined length scale. This\nintroduces unclosed sub-volume flux, sub-volume stress and volume averaged\nsurface tension force. Sub-volume fluctuations arise due to both turbulent\nmotions and other interface dynamics which can, in general, occur in both\nlaminar and turbulent flows. Both of these types of fluctuations are attenuated\nby the volume averaging process. The sub-volume flux is closed by a gradient\ndiffusion model and involves an explicit volume diffusion coefficient that is\nlinked to the physical length scale. The sub-volume stress closure introduces\nan explicit volume viscosity augmented by turbulent viscosity in turbulent\nflows. The volume averaged surface tension force closure is based on fractal\nproperties of wrinkled sub-volume interfaces. These closures are evaluated\nthrough a priori analysis of resolved flow simulations for a series of\ntwo-dimensional laminar and three-dimensional turbulent interfacial shear\nflows. Subsequently, full EVD simulations are validated for these shear flows\nand for a laboratory airblast spray jet. Numerical convergence is demonstrated\nby keeping the physical length scale constant while reducing the numerical grid\nsize so that numerical diffusion diminishes and becomes overwhelmed by the\nexplicit volume diffusion. A sensitivity analysis is also conducted for\nvariations in the physical length scale, which is compared to the boundary\nlayer thickness on the light fluid side of the interface.\n"}
+{"abstract": "  We present the system that we have developed for the identification and\nverification of pills using images that are taken by the VeriMedi device. The\nVeriMedi device is an Internet of Things device that takes pictures of a filled\npill vial from the bottom of the vial and uses the solution that is presented\nin this research to identify the pills in the vials. The solution has two\nserially connected deep learning solutions which do segmentation and\nidentification. The segmentation solution creates the masks for each pill in\nthe vial image by using the Mask R-CNN model, then segments and crops the pills\nand blurs the background. After that, the segmented pill images are sent to the\nidentification solution where a Deep Metric Learning model that is trained with\nProxy Anchor Loss (PAL) function generates embedding vectors for each pill\nimage. The generated embedding vectors are fed into a one-layer fully connected\nnetwork that is trained with the exact solution to predict each single pill\nimage. Then, the aggregation/verification function aggregates the multiple\npredictions coming from multiple single pill images and verifies the\ncorrectness of the final prediction with respect to predefined rules. Besides,\nwe enhanced the PAL with a better proxy initialization that increased the\nperformance of the models and let the model learn the new classes of images\ncontinually without retraining the model with the whole dataset. When the model\nthat is trained with initial classes is retrained only with new classes, the\naccuracy of the model increases for both old and new classes. The\nidentification solution that we have presented in this research can also be\nreused for other problem domains which require continual learning and/or\nFine-Grained Visual Categorization.\n"}
+{"abstract": "  The central molecular zone (CMZ) plays an essential role in regulating the\nnuclear ecosystem of our Galaxy. To get an insight into the magnetic fields of\nthe CMZ, we employ the Gradient Technique (GT), which is rooted in the\nanisotropy of magnetohydrodynamic turbulence. Our analysis is based on the data\nof multiple wavelengths, including molecular emission lines, radio 1.4 GHz\ncontinuum image, and Herschel 70 um image, as well as ionized [Ne II] and\nPaschen-alpha emissions. The results are compared with the observations of\nPlanck 353 GHz and High-resolution Airborne Wideband Camera Plus (HWAC+) 53 um\npolarized dust emissions. We map the wavelength-dependent magnetic field\norientation across the central molecular zone, including close-ups of the Radio\nArc and Sagittarius A West regions, on multi scales from ~0.1 pc to 10 pc. The\nmagnetic fields towards the central molecular zone traced by GT are globally\ncompatible with the polarization measurements, accounting for the contribution\nfrom the galactic foreground and background. This correspondence suggests that\nthe magnetic field and turbulence are dynamically crucial in the galactic\ncenter. We find that the magnetic fields associated with the Arched filaments\nand the thermal components of the Radio Arc are in good agreement with the\nHAWC+ polarization. Our measurement towards the non-thermal Radio Arc reveals\nthe poloidal magnetic field components in the galactic center. For Sagittarius\nA West region, we find a great agreement between the GT measurement using [Ne\nII] emission and HWAC+ 53 um observation. We use GT to predict the magnetic\nfields associated with ionized Paschen-alpha gas down to scales of 0.1 pc.\nThese results demonstrate the potential power of GT in the high-resolution\nmapping of magnetic fields and in decomposing contributions from different\nvelocity components and/or different gas phases.\n"}
+{"abstract": "  Despite recent advances in object detection using deep learning neural\nnetworks, these neural networks still struggle to identify objects in art\nimages such as paintings and drawings. This challenge is known as the cross\ndepiction problem and it stems in part from the tendency of neural networks to\nprioritize identification of an object's texture over its shape. In this paper\nwe propose and evaluate a process for training neural networks to localize\nobjects - specifically people - in art images. We generate a large dataset for\ntraining and validation by modifying the images in the COCO dataset using AdaIn\nstyle transfer. This dataset is used to fine-tune a Faster R-CNN object\ndetection network, which is then tested on the existing People-Art testing\ndataset. The result is a significant improvement on the state of the art and a\nnew way forward for creating datasets to train neural networks to process art\nimages.\n"}
+{"abstract": "  3D object detection is vital for many robotics applications. For tasks where\na 2D perspective range image exists, we propose to learn a 3D representation\ndirectly from this range image view. To this end, we designed a 2D\nconvolutional network architecture that carries the 3D spherical coordinates of\neach pixel throughout the network. Its layers can consume any arbitrary\nconvolution kernel in place of the default inner product kernel and exploit the\nunderlying local geometry around each pixel. We outline four such kernels: a\ndense kernel according to the bag-of-words paradigm, and three graph kernels\ninspired by recent graph neural network advances: the Transformer, the\nPointNet, and the Edge Convolution. We also explore cross-modality fusion with\nthe camera image, facilitated by operating in the perspective range image view.\nOur method performs competitively on the Waymo Open Dataset and improves the\nstate-of-the-art AP for pedestrian detection from 69.7% to 75.5%. It is also\nefficient in that our smallest model, which still outperforms the popular\nPointPillars in quality, requires 180 times fewer FLOPS and model parameters\n"}
+{"abstract": "  In a previous paper we generalized the definition of a multilinear map to\narbitrary groups and introduced two multiparty key-exchange protocols using\nnilpotent groups. In this paper we have a closer look at the protocols and will\naddress some incorrect cryptanalysis which have been proposed.\n"}
+{"abstract": "  In recent development of quantum technologies, a frequency conversion of\nquantum signals has been studied widely. We investigate the optic-microwave\nentanglement that is generated by applying an electro-optomechanical frequency\nconversion scheme to one mode in an optical two-mode squeezed vacuum state. We\nquantify entanglement of the converted two-mode Gaussian state, where surviving\nentanglement of the state is analyzed with respect to the parameters of the\nelectro-optomechanical system. Furthermore, we show that there exists an upper\nbound for the entanglement that survives after the conversion of highly\nentangled optical states. Our study provides a theoretical platform for a\npractical quantum illumination system.\n"}
+{"abstract": "  The 21 cm linear polarization due to Thomson scattering off free electrons\ncan probe the distribution of neutral hydrogen in the intergalactic medium\nduring the epoch of reionization, complementary to the 21 cm temperature\nfluctuations. Previous study (Babich & Loeb 2005) estimated the strength of\npolarization with a toy model and claimed that it can be detected with 1-month\nobservation of the Square Kilometre Array (SKA). Here we revisit this\ninvestigation with account of nonlinear terms due to inhomogeneous\nreionization, using seminumerical reionization simulations to provide the\nrealistic estimation of the 21 cm TE and EE angular power spectra ($C^{\\rm\nTE}_\\ell$ and $C^{\\rm EE}_\\ell$). We find that (1) both power spectra are\nenhanced on sub-bubble scales but suppressed on super-bubble scales, compared\nwith previous results; (2) $C^{\\rm TE}_\\ell$ displays a zero-crossing at\n$\\ell<100$, and its angular scale is sensitive to the scale-dependence of H I\nbias on large scales; (3) the ratios of the power spectrum to its maximum value\nduring reionization at a given $\\ell$, i.e. $C^{\\rm TE}_\\ell / C^{\\rm\nTE}_{\\ell,{\\rm max}} $ and $C^{\\rm EE}_{\\ell}/C^{\\rm EE}_{\\ell,{\\rm max}}$,\nshow robust correlations with the global ionized fraction. However, measurement\nof this signal will be very challenging not only because the overall strength\nis weaker than the sensitivity of SKA, but also because of the polarized\nforegrounds from diffuse synchrotron emission, and Faraday rotation which\nmodifies the observed polarization. Nevertheless, the 21 cm linear polarization\nsignal may still likely be detectable through other approaches, e.g. its\ncross-correlation with other probes.\n"}
+{"abstract": "  Cloud-based enterprise search services (e.g., AWS Kendra) have been\nentrancing big data owners by offering convenient and real-time search\nsolutions to them. However, the problem is that individuals and organizations\npossessing confidential big data are hesitant to embrace such services due to\nvalid data privacy concerns. In addition, to offer an intelligent search, these\nservices access the user search history that further jeopardizes his/her\nprivacy. To overcome the privacy problem, the main idea of this research is to\nseparate the intelligence aspect of the search from its pattern matching\naspect. According to this idea, the search intelligence is provided by an\non-premises edge tier and the shared cloud tier only serves as an exhaustive\npattern matching search utility. We propose Smartness At Edge (SAED mechanism\nthat offers intelligence in the form of semantic and personalized search at the\nedge tier while maintaining privacy of the search on the cloud tier. At the\nedge tier, SAED uses a knowledge-based lexical database to expand the query and\ncover its semantics. SAED personalizes the search via an RNN model that can\nlearn the user interest. A word embedding model is used to retrieve documents\nbased on their semantic relevance to the search query. SAED is generic and can\nbe plugged into existing enterprise search systems and enable them to offer\nintelligent and privacy-preserving search without enforcing any change on them.\nEvaluation results on two enterprise search systems under real settings and\nverified by human users demonstrate that SAED can improve the relevancy of the\nretrieved results by on average 24% for plain-text and 75% for encrypted\ngeneric datasets.\n"}
+{"abstract": "  This paper considers autonomous flight formation of fixed-wing air vehicles.\nDue to several issues such as limitations on communication bandwidth and low\nobservability requirements, an asynchronous control and communication approach\nis designed in order to reduce the number of updates between the vehicles. With\nthe implementation of an event-triggered controller, it is possible to obtain a\nmore efficient communication scheme while also achieving the convergence\nproperties of the desired formation.\n"}
+{"abstract": "  This paper develops a Multiset Rewriting language with explicit time for the\nspecification and analysis of Time-Sensitive Distributed Systems (TSDS). Goals\nare often specified using explicit time constraints. A good trace is an\ninfinite trace in which the goals are satisfied perpetually despite possible\ninterference from the environment. In our previous work (FORMATS 2016), we\ndiscussed two desirable properties of TSDSes, realizability (there exists a\ngood trace) and survivability (where, in addition, all admissible traces are\ngood). Here we consider two additional properties, recoverability (all\ncompliant traces do not reach points-of-no-return) and reliability (the system\ncan always continue functioning using a good trace). Following (FORMATS 2016),\nwe focus on a class of systems called Progressing Timed Systems (PTS), where\nintuitively only a finite number of actions can be carried out in a bounded\ntime period. We prove that for this class of systems the properties of\nrecoverability and reliability coincide and are PSPACE-complete. Moreover, if\nwe impose a bound on time (as in bounded model-checking), we show that for PTS\nthe reliability property is in the $\\Pi_2^p$ class of the polynomial hierarchy,\na subclass of PSPACE. We also show that the bounded survivability is both\nNP-hard and coNP-hard.\n"}
+{"abstract": "  The COnstrain Dark Energy with X-ray clusters (CODEX) sample contains the\nlargest flux limited sample of X-ray clusters at $0.35 < z < 0.65$. It was\nselected from ROSAT data in the 10,000 square degrees of overlap with BOSS,\nmapping a total number of 2770 high-z galaxy clusters. We present here the full\nresults of the CFHT CODEX program on cluster mass measurement, including a\nreanalysis of CFHTLS Wide data, with 25 individual lensing-constrained cluster\nmasses. We employ $lensfit$ shape measurement and perform a conservative\ncolour-space selection and weighting of background galaxies. Using the\ncombination of shape noise and an analytic covariance for intrinsic variations\nof cluster profiles at fixed mass due to large scale structure, miscentring,\nand variations in concentration and ellipticity, we determine the likelihood of\nthe observed shear signal as a function of true mass for each cluster. We\ncombine 25 individual cluster mass likelihoods in a Bayesian hierarchical\nscheme with the inclusion of optical and X-ray selection functions to derive\nconstraints on the slope $\\alpha$, normalization $\\beta$, and scatter\n$\\sigma_{\\ln \\lambda | \\mu}$ of our richness-mass scaling relation model in\nlog-space: $\\left<\\ln \\lambda | \\mu \\right> = \\alpha \\mu + \\beta$, with $\\mu =\n\\ln (M_{200c}/M_{\\mathrm{piv}})$, and $M_{\\mathrm{piv}} = 10^{14.81}\nM_{\\odot}$. We find a slope $\\alpha = 0.49^{+0.20}_{-0.15}$, normalization $\n\\exp(\\beta) = 84.0^{+9.2}_{-14.8}$ and $\\sigma_{\\ln \\lambda | \\mu} =\n0.17^{+0.13}_{-0.09}$ using CFHT richness estimates. In comparison to other\nweak lensing richness-mass relations, we find the normalization of the richness\nstatistically agreeing with the normalization of other scaling relations from a\nbroad redshift range ($0.0<z<0.65$) and with different cluster selection\n(X-ray, Sunyaev-Zeldovich, and optical).\n"}
+{"abstract": "  Sequential fundraising in two sided online platforms enable peer to peer\nlending by sequentially bringing potential contributors, each of whose\ndecisions impact other contributors in the market. However, understanding the\ndynamics of sequential contributions in online platforms for peer lending has\nbeen an open ended research question. The centralized investment mechanism in\nthese platforms makes it difficult to understand the implicit competition that\nborrowers face from a single lender at any point in time. Matching markets are\na model of pairing agents where the preferences of agents from both sides in\nterms of their preferred pairing for transactions can allow to decentralize the\nmarket. We study investment designs in two sided platforms using matching\nmarkets when the investors or lenders also face restrictions on the investments\nbased on borrower preferences. This situation creates an implicit competition\namong the lenders in addition to the existing borrower competition, especially\nwhen the lenders are uncertain about their standing in the market and thereby\nthe probability of their investments being accepted or the borrower loan\nrequests for projects reaching the reserve price. We devise a technique based\non sequential decision making that allows the lenders to adjust their choices\nbased on the dynamics of uncertainty from competition over time. We simulate\ntwo sided market matchings in a sequential decision framework and show the\ndynamics of the lender regret amassed compared to the optimal borrower-lender\nmatching and find that the lender regret depends on the initial preferences set\nby the lenders which could affect their learning over decision making steps.\n"}
+{"abstract": "  In this short communication, I gave a generalization of measurement postulate\nin quantum mechanics. It is regarding the case with partial measurement,\nnamely, measurement on only part of a wave function. Upon a partial\nmeasurement, the wavefunction will either collapse in one of the eigenstate\ncovered by the measured partial wavefunction; or collapses out of the measured\npart and shifts to the unmeasured part. An explanation in the WISE\n(Wavefunction Is System Entity) interpretation is given.\n"}
+{"abstract": "  Three-dimensional (3D) point clouds are important data representations in\nvisualization applications. The rapidly growing utility and popularity of point\ncloud processing strongly motivate a plethora of research activities on\nlarge-scale point cloud processing and feature extraction. In this work, we\ninvestigate point cloud resampling based on hypergraph signal processing\n(HGSP). We develop a novel method to extract sharp object features and reduce\nthe data size of point cloud representation. By directly estimating hypergraph\nspectrum based on hypergraph stationary processing, we design a spectral\nkernel-based filter to capture high-dimensional interactions among point signal\nnodes and to better preserve object surface outlines. Experimental results\nvalidate the effectiveness of hypergraph in representing point clouds, and\ndemonstrate the robustness of the proposed algorithm under noise.\n"}
+{"abstract": "  The large quantities of dust that have been found in a number of high\nredshift galaxies have led to suggestions that core-collapse supernovae (CCSNe)\nare the main sources of their dust and have motivated the measurement of the\ndust masses formed by local CCSNe. For Cassiopeia~A, an oxygen-rich remnant of\na Type~IIb CCSN, a dust mass of 0.6-1.1~M$_\\odot$ has already been determined\nby two different methods, namely (a) from its far-infrared spectral energy\ndistribution and (b) from analysis of the red-blue emission line asymmetries in\nits integrated optical spectrum. We present a third, independent, method for\ndetermining the mass of dust contained within Cas~A. This compares the relative\nfluxes measured in similar apertures from [O~{\\sc iii}] far-infrared and\nvisual-region emission lines, taking into account foreground dust extinction,\nin order to determine internal dust optical depths, from which corresponding\ndust masses can be obtained. Using this method we determine a dust mass within\nCas~A of at least 0.99$^{+0.10}_{-0.09}$~M$_\\odot$.\n"}
+{"abstract": "  For continuous self-maps of compact metric spaces, we consider the notions of\ngeneric and dense chaos introduced by Lasota and Snoha and their variations for\nthe distributional chaos, under the assumption of shadowing. We give some\nequivalent properties to generic uniform (distributional) chaos in terms of\nchains and prove their equivalence to generic (distributional) chaos under the\nshadowing. Several examples illustrating the main results are also given.\n"}
+{"abstract": "  Robust and persistent localisation is essential for ensuring the safe\noperation of autonomous vehicles. When operating in large and diverse urban\ndriving environments, autonomous vehicles are frequently exposed to situations\nthat violate the assumptions of algorithms, suffer from the failure of one or\nmore sensors, or other events that lead to a loss of localisation.\n  This paper proposes the use of a behavior tree framework that can monitor the\nperformance of localisation health metrics and triggers intelligent responses\nsuch as sensor switching and loss recovery. The algorithm presented selects the\nbest available sensor data at given time and location, and can perform a series\nof actions to react to adverse situations. The behavior tree encapsulates the\nsystem-level logic to give commands that make up the intelligent behaviors, so\nthat the localisation \"actuators\" (data association, optimisation, filters,\netc) can perform decoupled actions without needing context. Experimental\nresults to validate the algorithms are presented using the University of Sydney\nCampus dataset which was taken weekly over an 18 month period. A video showing\nthe online localisation process can be found here: https://youtu.be/353uKqXLV5g\n"}
+{"abstract": "  DPLL algorithm for solving the Boolean satisfiability problem (SAT) can be\nrepresented in the form of a procedure that, using heuristics $A$ and $B$,\nselect the variable $x$ from the input formula $\\varphi$ and the value $b$ and\nruns recursively on the formulas $\\varphi[x := b]$ and $\\varphi[x := 1 - b]$.\nExponential lower bounds on the running time of DPLL algorithms on\nunsatisfiable formulas follow from the lower bounds for tree-like resolution\nproofs. Lower bounds on satisfiable formulas are also known for some classes of\nDPLL algorithms such as \"myopic\" and \"drunken\" algorithms.\n  All lower bounds are made for the classes of DPLL algorithms that limit\nheuristics access to the formula. In this paper we consider DPLL algorithms\nwith heuristics that have unlimited access to the formula but use small memory.\nWe show that for any pair of heuristics with small memory there exists a family\nof satisfiable formulas $\\Phi_n$ such that a DPLL algorithm that uses these\nheuristics runs in exponential time on the formulas $\\Phi_n$.\n"}
+{"abstract": "  This report describes the speaker diarization system developed by the ABSP\nLaboratory team for the third DIHARD speech diarization challenge. Our primary\ncontribution is to develop acoustic domain identification (ADI) system for\nspeaker diarization. We investigate speaker embeddings based ADI system. We\napply a domain-dependent threshold for agglomerative hierarchical clustering.\nBesides, we optimize the parameters for PCA-based dimensionality reduction in a\ndomain-dependent way. Our method of integrating domain-based processing schemes\nin the baseline system of the challenge achieved a relative improvement of\n$9.63\\%$ and $10.64\\%$ in DER for core and full conditions, respectively, for\nTrack 1 of the DIHARD III evaluation set.\n"}
+{"abstract": "  Though deep learning has shown successful performance in classifying the\nlabel and severity stage of certain diseases, most of them give few\nexplanations on how to make predictions. Inspired by Koch's Postulates, the\nfoundation in evidence-based medicine (EBM) to identify the pathogen, we\npropose to exploit the interpretability of deep learning application in medical\ndiagnosis. By determining and isolating the neuron activation patterns on which\ndiabetic retinopathy (DR) detector relies to make decisions, we demonstrate the\ndirect relation between the isolated neuron activation and lesions for a\npathological explanation. To be specific, we first define novel pathological\ndescriptors using activated neurons of the DR detector to encode both spatial\nand appearance information of lesions. Then, to visualize the symptom encoded\nin the descriptor, we propose Patho-GAN, a new network to synthesize medically\nplausible retinal images. By manipulating these descriptors, we could even\narbitrarily control the position, quantity, and categories of generated\nlesions. We also show that our synthesized images carry the symptoms directly\nrelated to diabetic retinopathy diagnosis. Our generated images are both\nqualitatively and quantitatively superior to the ones by previous methods.\nBesides, compared to existing methods that take hours to generate an image, our\nsecond level speed endows the potential to be an effective solution for data\naugmentation.\n"}
+{"abstract": "  A new direct-detection-compatible signalling scheme is proposed for\nfiber-optic communication over short distances. Controlled inter-symbol\ninterference is exploited to extract phase information, thereby achieving\nspectral efficiencies about one bit less, per second per hertz, of those of a\ncoherent detector.\n"}
+{"abstract": "  Lung nodule malignancy prediction is an essential step in the early diagnosis\nof lung cancer. Besides the difficulties commonly discussed, the challenges of\nthis task also come from the ambiguous labels provided by annotators, since\ndeep learning models may learn, even amplify, the bias embedded in them. In\nthis paper, we propose a multi-view \"divide-and-rule\" (MV-DAR) model to learn\nfrom both reliable and ambiguous annotations for lung nodule malignancy\nprediction. According to the consistency and reliability of their annotations,\nwe divide nodules into three sets: a consistent and reliable set (CR-Set), an\ninconsistent set (IC-Set), and a low reliable set (LR-Set). The nodule in\nIC-Set is annotated by multiple radiologists inconsistently, and the nodule in\nLR-Set is annotated by only one radiologist. The proposed MV-DAR contains three\nDAR submodels to characterize a lung nodule from three orthographic views. Each\nDAR consists of a prediction network (Prd-Net), a counterfactual network\n(CF-Net), and a low reliable network (LR-Net), learning on CR-Set, IC-Set, and\nLR-Set, respectively. The image representation ability learned by CF-Net and\nLR-Net is then transferred to Prd-Net by negative-attention module (NA-Module)\nand consistent-attention module (CA-Module), aiming to boost the prediction\nability of Prd-Net. The MV-DAR model has been evaluated on the LIDC-IDRI\ndataset and LUNGx dataset. Our results indicate not only the effectiveness of\nthe proposed MV-DAR model in learning from ambiguous labels but also its\nsuperiority over present noisy label-learning models in lung nodule malignancy\nprediction.\n"}
+{"abstract": "  This paper is devoted to study the thermal aspects of a photon gas within the\ncontext of Planck-scale-modified dispersion relations. We study the spectrum of\nradiation and the correction to the \\textit{Stefan$-$Boltzmann} law in\ndifferent cases when the Lorentz symmetry is no longer preserved. Explicitly,\nwe examine two models within the context of CPT$-$even and CPT$-$odd sectors\nrespectively. To do so, three distinct scenarios of the Universe are\nconsidered: the Cosmic Microwave Background (CMB), the electroweak epoch, and\nthe inflationary era. Moreover, the equations of state in these cases turn out\nto display a dependence on Lorentz-breaking parameters. Finally, we also\nprovide for both theories the analyses of the Helmholtz free energy, the mean\nenergy, the entropy and the heat capacity.\n"}
+{"abstract": "  Developing machine learning models can be seen as a process similar to the\none established for traditional software development. A key difference between\nthe two lies in the strong dependency between the quality of a machine learning\nmodel and the quality of the data used to train or perform evaluations. In this\nwork, we demonstrate how different aspects of data quality propagate through\nvarious stages of machine learning development. By performing a joint analysis\nof the impact of well-known data quality dimensions and the downstream machine\nlearning process, we show that different components of a typical MLOps pipeline\ncan be efficiently designed, providing both a technical and theoretical\nperspective.\n"}
+{"abstract": "  In this paper, we treat an open problem related to the number of periodic\norbits of Hamiltonian diffeomorphisms on closed symplectic manifolds, so-called\ngeneric Conley conjecture. Generic Conley conjecture states that generically\nHamiltonian diffeomorphisms have infinitely many simple contractible periodic\norbits. We prove generic Conley conjecture for very wide classes of symplectic\nmanifolds.\n"}
+{"abstract": "  There is a rapidly growing literature on dark patterns, user interface\ndesigns -- typically related to shopping or privacy -- that researchers deem\nproblematic. Recent work has been predominantly descriptive, documenting and\ncategorizing objectionable user interfaces. These contributions have been\ninvaluable in highlighting specific designs for researchers and policymakers.\nBut the current literature lacks a conceptual foundation: What makes a user\ninterface a dark pattern? Why are certain designs problematic for users or\nsociety?\n  We review recent work on dark patterns and demonstrate that the literature\ndoes not reflect a singular concern or consistent definition, but rather, a set\nof thematically related considerations. Drawing from scholarship in psychology,\neconomics, ethics, philosophy, and law, we articulate a set of normative\nperspectives for analyzing dark patterns and their effects on individuals and\nsociety. We then show how future research on dark patterns can go beyond\nsubjective criticism of user interface designs and apply empirical methods\ngrounded in normative perspectives.\n"}
+{"abstract": "  An important class of structural models investigates the determinants of\nskill formation and the optimal timing of interventions. To achieve point\nidentification of the parameters, researcher typically normalize the scale and\nlocation of the unobserved skills. This paper shows that these seemingly\ninnocuous restrictions can severely impact the interpretation of the parameters\nand counterfactual predictions. For example, simply changing the units of\nmeasurements of observed variables might yield ineffective investment\nstrategies and misleading policy recommendations. To tackle these problems,\nthis paper provides a new identification analysis, which pools all restrictions\nof the model, characterizes the identified set of all parameters without\nnormalizations, illustrates which features depend on these normalizations, and\nintroduces a new set of important policy-relevant parameters that are\nidentified under weak assumptions and yield robust conclusions. As a byproduct,\nthis paper also presents a general and formal definition of when restrictions\nare truly normalizations.\n"}
+{"abstract": "  Exact solutions for quasineutral plasma acceleration of magnetized plasma in\nthe paraxial magnetic nozzle are obtained. It is shown that the non-monotonic\nmagnetic field with a local maximum of the magnetic field is a necessary\ncondition for the formation of the quasineutral accelerating potential\nstructure. A global nature of the accelerating potential that occurs as a\nresult of the constraint due to the regularity condition at the sonic point is\nemphasized and properties of such solutions are discussed for the case of\ngeneral polytropic equation of state for electrons.\n"}
+{"abstract": "  Effective information disclosure in the context of databases with a large\nconceptual schema is known to be a non-trivial problem. In particular the\nformulation of ad-hoc queries is a major problem in such contexts. Existing\napproaches for tackling this problem include graphical query interfaces, query\nby navigation, and query by construction. In this article we propose the point\nto point query mechanism that can be combined with the existing mechanism into\nan unprecedented computer supported query formulation mechanism. In a point to\npoint query a path through the information structure is build. This path can\nthen be used to formulate more complex queries. A point to point query is\ntypically useful when users know some object types which are relevant for their\ninformation need, but do not (yet) know how they are related in the conceptual\nschema. Part of the point to point query mechanism is therefore the selection\nof the most appropriate path between object types (points) in the conceptual\nschema. This article both discusses some of the pragmatic issues involved in\nthe point to point query mechanism, and the theoretical issues involved in\nfinding the relevant paths between selected object types.\n"}
+{"abstract": "  Automatically detecting/segmenting object(s) that blend in with their\nsurroundings is difficult for current models. A major challenge is that the\nintrinsic similarities between such foreground objects and background\nsurroundings make the features extracted by deep model indistinguishable. To\novercome this challenge, an ideal model should be able to seek valuable, extra\nclues from the given scene and incorporate them into a joint learning framework\nfor representation co-enhancement. With this inspiration, we design a novel\nMutual Graph Learning (MGL) model, which generalizes the idea of conventional\nmutual learning from regular grids to the graph domain. Specifically, MGL\ndecouples an image into two task-specific feature maps -- one for roughly\nlocating the target and the other for accurately capturing its boundary details\n-- and fully exploits the mutual benefits by recurrently reasoning their\nhigh-order relations through graphs. Importantly, in contrast to most mutual\nlearning approaches that use a shared function to model all between-task\ninteractions, MGL is equipped with typed functions for handling different\ncomplementary relations to maximize information interactions. Experiments on\nchallenging datasets, including CHAMELEON, CAMO and COD10K, demonstrate the\neffectiveness of our MGL with superior performance to existing state-of-the-art\nmethods.\n"}
+{"abstract": "  Let $\\mu_1 \\ge \\ldots \\ge \\mu_n$ denote the eigenvalues of a graph $G$ with\n$m$ edges and clique number $\\omega(G)$. Nikiforov proved a spectral version of\nTur\\'an's theorem that \\[ \\mu_1^2 \\le \\frac{2m(\\omega - 1)}{\\omega}, \\] and\nBollob\\'as and Nikiforov conjectured that for $G \\not = K_n$ \\[ \\mu_1^2 +\n\\mu_2^2 \\le \\frac{2m(\\omega - 1)}{\\omega}. \\] This paper proposes the\nconjecture that for all graphs $(\\mu_1^2 + \\mu_2^2)$ in this inequality can be\nreplaced by the sum of the squares of the $\\omega$ largest eigenvalues,\nprovided they are positive. We prove the conjecture for weakly perfect, Kneser,\nJohnson and classes of strongly regular graphs. We also provide experimental\nevidence and describe how the bound can be applied.\n"}
+{"abstract": "  A rotated odometer is an infinite interval exchange transformation (IET)\nobtained as a composition of the von Neumann-Kakutani map and a finite IET of\nintervals of equal length. In this paper, we consider rotated odometers for\nwhich the finite IET is of intervals of length $2^{-N}$, for some $N \\geq 1$.\nWe show that every such system is measurably isomorphic to a\n$\\mathbb{Z}$-action on a rooted tree, and that the unique minimal aperiodic\nsubsystem of this action is always measurably isomorphic to the action of the\nadding machine. We discuss the applications of this work to the study of group\nactions on binary trees.\n"}
+{"abstract": "  Near field acoustical signals from fireballs (ranges<200 km), when detected\nby dense ground networks, may be used to estimate the orientation of the\ntrajectory of a fireball (Pujol et al., 2005) as well as fragmentation\nlocations (Kalenda et al., 2014; Edwards and Hildebrand, 2004). Distinguishing\nballistic arrivals (from the cylindrical shock of the fireball)from\nfragmentation generated signals (quasi-spherical sources) remains a challenge,\nbut are obtainable through analysis of the acoustic path and the timing\nobserved at ground instruments. Here we describe an integrated computer code,\ntermed the Bolide Acoustic Modelling program or BAM, to estimate fireball\ntrajectories and energetics. We develop a new methodology for measuring energy\nrelease from bolide fragmentation episodes solely from acoustic measurements\nand incorporate this into BAM. We also explore the sensitivity of\nseismo-acoustic fireball solutions and energy estimates to uncertainty in the\nunderlying atmospheric model. Applying BAM to the Stubenberg meteorite\nproducing fireball, we find the total fireball energy from ballistic arrivals\nto be approximately $5 \\times 10^{10}$J which compares favorably to the optical\nestimate of $4.36 \\times 10^{10}$J. The combined fragmentation energy of the\nStubenberg event from acoustic data was found to be $1.47^{+0.28}_{-0.12}\n\\times 10^{10}$J, roughly one third of the ballistic or optical total energy.\nWe also show that measuring fireball velocities from acoustic data alone is\nvery challenging but may be possible for slow, deeply penetrating fireballs\nwith shallow entry angles occurring over dense seismic/infrasound networks.\n"}
+{"abstract": "  Despite the increased computational resources, the simulation-based design\noptimization (SBDO) procedure can be very expensive from a computational\nviewpoint, especially if high-fidelity solvers are required. Multi-fidelity\nmetamodels have been successfully applied to reduce the computational cost of\nthe SBDO process. In this context, the paper presents the performance\nassessment of an adaptive multi-fidelity metamodel based on a Gaussian process\nregression (MF-GPR) for noisy data. The MF-GPR is developed to: (i) manage an\narbitrary number of fidelity levels, (ii) deal with objective function\nevaluations affected by noise, and (iii) improve its fitting accuracy by\nadaptive sampling. Multi-fidelity is achieved by bridging a low-fidelity\nmetamodel with metamodels of the error between successive fidelity levels. The\nMF-GPR handles the numerical noise through regression. The adaptive sampling\nmethod is based on the maximum prediction uncertainty and includes rules to\nautomatically select the fidelity to sample. The MF-GPR performance are\nassessed on a set of five analytical benchmark problems affected by noisy\nobjective function evaluations. Since the noise introduces randomness in the\nevaluation of the objective function, a statistical analysis approach is\nadopted to assess the performance and the robustness of the MF-GPR. The paper\ndiscusses the efficiency and effectiveness of the MF-GPR in globally\napproximating the objective function and identifying the global minimum. One,\ntwo, and three fidelity levels are used. The results of the statistical\nanalysis show that the use of three fidelity levels achieves a more accurate\nglobal representation of the noise-free objective function compared to the use\nof one or two fidelities.\n"}
+{"abstract": "  We investigate four finiteness conditions related to residual finiteness:\ncomplete separability, strong subsemigroup separability, weak subsemigroup\nseparability and monogenic subsemigroup separability. For each of these\nproperties we examine under which conditions the property is preserved under\ndirect products. We also consider if any of the properties are inherited by the\nfactors in a direct product. We give necessary and sufficient conditions for\nfinite semigroups to preserve the properties of strong subsemigroup\nseparability and monogenic subsemigroup separability in a direct product.\n"}
+{"abstract": "  Pretraining has sparked groundswell of interest in deep learning workflows to\nlearn from limited data and improve generalization. While this is common for 2D\nimage classification tasks, its application to 3D medical imaging tasks like\nchest CT interpretation is limited. We explore the idea of whether pretraining\na model on realistic videos could improve performance rather than training the\nmodel from scratch, intended for tuberculosis type classification from chest CT\nscans. To incorporate both spatial and temporal features, we develop a hybrid\nconvolutional neural network (CNN) and recurrent neural network (RNN) model,\nwhere the features are extracted from each axial slice of the CT scan by a CNN,\nthese sequence of image features are input to a RNN for classification of the\nCT scan. Our model termed as ViPTT-Net, was trained on over 1300 video clips\nwith labels of human activities, and then fine-tuned on chest CT scans with\nlabels of tuberculosis type. We find that pretraining the model on videos lead\nto better representations and significantly improved model validation\nperformance from a kappa score of 0.17 to 0.35, especially for\nunder-represented class samples. Our best method achieved 2nd place in the\nImageCLEF 2021 Tuberculosis - TBT classification task with a kappa score of\n0.20 on the final test set with only image information (without using clinical\nmeta-data). All codes and models are made available.\n"}
+{"abstract": "  We give a quantitative characterization of the pairs of weights $(w,v)$ for\nwhich the dyadic version of the one-sided Hardy-Littlewood maximal operator\nsatisfies a restricted weak $(p,p)$ type inequality, for $1\\leq p<\\infty$. More\nprecisely, given any measurable set $E_0$ the estimate \\[w(\\{x\\in \\mathbb{R}^n:\nM^{+,d}(\\mathcal{X}_{E_0})(x)>t\\})\\leq\n\\frac{C[(w,v)]_{A_p^{+,d}(\\mathcal{R})}^p}{t^p}v(E_0)\\] holds if and only if\nthe pair $(w,v)$ belongs to $A_p^{+,d}(\\mathcal{R})$, that is\n\\[\\frac{|E|}{|Q|}\\leq\n[(w,v)]_{A_p^{+,d}(\\mathcal{R})}\\left(\\frac{v(E)}{w(Q)}\\right)^{1/p}\\] for\nevery dyadic cube $Q$ and every measurable set $E\\subset Q^+$. The proof\nfollows some ideas appearing in [Sheldy Ombrosi, \\emph{Weak weighted\ninequalities for a dyadic one-sided maximal function in {$\\Bbb R^n$}}, Proc.\nAmer. Math. Soc. \\textbf{133} (2005), no.~6, 1769--1775].\n  We also obtain a similar quantitative characterization for the non-dydadic\ncase in $\\mathbb{R}^2$ by following the main ideas in [L.~Forzani, F.~J.\nMart\\'{\\i}n-Reyes, and S.~Ombrosi, \\emph{Weighted inequalities for the\ntwo-dimensional one-sided {H}ardy-{L}ittlewood maximal function}, Trans. Amer.\nMath. Soc. \\textbf{363} (2011), no.~4, 1699--1719].\n"}
+{"abstract": "  This work regards the order polytopes arising from the class of generalized\nsnake posets and their posets of meet-irreducible elements. Among generalized\nsnake posets of the same rank, we characterize those whose order polytopes have\nminimal and maximal volume. We give a combinatorial characterization of the\ncircuits in these order polytopes and then conclude that every triangulation is\nunimodular. For a generalized snake word, we count the number of flips for the\ncanonical triangulation of these order polytopes. We determine that the flip\ngraph of the order polytope of the poset whose lattice of filters comes from a\nladder is the Cayley graph of a symmetric group. Lastly, we introduce an\noperation on triangulations called twists and prove that twists preserve\nregular triangulations.\n"}
+{"abstract": "  Our research presents the case-study of a mobile phone based, voice-driven\nplatform - Mobile Vaani, established with a goal to empower poor and\nmarginalized communities to create their own local media. In this paper, we\nderive a comprehensive theory of change for Mobile Vaani from data gathered\nusing the Most Significant Change technique. This paper contributes towards\nformulating a theory of change for technology-driven community media platforms\nwhich can be adapted to other ICTD interventions too.\n"}
+{"abstract": "  Off-policy evaluation (OPE) is the task of estimating the expected reward of\na given policy based on offline data previously collected under different\npolicies. Therefore, OPE is a key step in applying reinforcement learning to\nreal-world domains such as medical treatment, where interactive data collection\nis expensive or even unsafe. As the observed data tends to be noisy and\nlimited, it is essential to provide rigorous uncertainty quantification, not\njust a point estimation, when applying OPE to make high stakes decisions. This\nwork considers the problem of constructing non-asymptotic confidence intervals\nin infinite-horizon off-policy evaluation, which remains a challenging open\nquestion. We develop a practical algorithm through a primal-dual\noptimization-based approach, which leverages the kernel Bellman loss (KBL) of\nFeng et al.(2019) and a new martingale concentration inequality of KBL\napplicable to time-dependent data with unknown mixing conditions. Our algorithm\nmakes minimum assumptions on the data and the function class of the Q-function,\nand works for the behavior-agnostic settings where the data is collected under\na mix of arbitrary unknown behavior policies. We present empirical results that\nclearly demonstrate the advantages of our approach over existing methods.\n"}
+{"abstract": "  Solar activity in all its varied manifestations is driven by the magnetic\nfield. Particularly important for many purposes are two global quantities, the\nSun's total and open magnetic flux, which can be computed from sunspot number\nrecords using models. Such sunspot-driven models, however, do not take into\naccount the presence of magnetic flux during grand minima, such as the Maunder\nminimum. Here we present a major update of a widely used simple model, which\nnow takes into account the observation that the distribution of all magnetic\nfeatures on the Sun follows a single power law. The exponent of the power law\nchanges over the solar cycle. This allows for the emergence of small-scale\nmagnetic flux even when no sunspots are present for multiple decades and leads\nto non-zero total and open magnetic flux also in the deepest grand minima, such\nas the Maunder minimum, thus overcoming a major shortcoming of the earlier\nmodels. The results of the updated model compare well with the available\nobservations and reconstructions of the solar total and open magnetic flux.\nThis opens up the possibility of improved reconstructions of sunspot number\nfrom time series of cosmogenic isotope production rate.\n"}
+{"abstract": "  Generalization has been a long-standing challenge for reinforcement learning\n(RL). Visual RL, in particular, can be easily distracted by irrelevant factors\nin high-dimensional observation space. In this work, we consider robust policy\nlearning which targets zero-shot generalization to unseen visual environments\nwith large distributional shift. We propose SECANT, a novel self-expert cloning\ntechnique that leverages image augmentation in two stages to decouple robust\nrepresentation learning from policy optimization. Specifically, an expert\npolicy is first trained by RL from scratch with weak augmentations. A student\nnetwork then learns to mimic the expert policy by supervised learning with\nstrong augmentations, making its representation more robust against visual\nvariations compared to the expert. Extensive experiments demonstrate that\nSECANT significantly advances the state of the art in zero-shot generalization\nacross 4 challenging domains. Our average reward improvements over prior SOTAs\nare: DeepMind Control (+26.5%), robotic manipulation (+337.8%), vision-based\nautonomous driving (+47.7%), and indoor object navigation (+15.8%). Code\nrelease and video are available at https://linxifan.github.io/secant-site/.\n"}
+{"abstract": "  The federated learning (FL) framework trains a machine learning model using\ndecentralized data stored at edge client devices by periodically aggregating\nlocally trained models. Popular optimization algorithms of FL use vanilla\n(stochastic) gradient descent for both local updates at clients and global\nupdates at the aggregating server. Recently, adaptive optimization methods such\nas AdaGrad have been studied for server updates. However, the effect of using\nadaptive optimization methods for local updates at clients is not yet\nunderstood. We show in both theory and practice that while local adaptive\nmethods can accelerate convergence, they can cause a non-vanishing solution\nbias, where the final converged solution may be different from the stationary\npoint of the global objective function. We propose correction techniques to\novercome this inconsistency and complement the local adaptive methods for FL.\nExtensive experiments on realistic federated training tasks show that the\nproposed algorithms can achieve faster convergence and higher test accuracy\nthan the baselines without local adaptivity.\n"}
+{"abstract": "  This study investigates the effects of porosity on flying creatures such as\ndragonflies, moths, hummingbirds, etc. wing and shows that pores can affect\nwing performance. These studies were performed by 3D porous flapping wing flow\nanalyses on Comsol Multiphysics. In this study, we analyzed different numbers\nof the porous wing at different angles of inclination in order to see the\neffect of pores on lift and drag forces. To compare the results 9 different\nanalyses were performed. In these analyses, airflow velocity was taken as 5\nm/s, angle of attack as 5 degrees, frequency as 25 Hz, and flapping angle as 30\ndegrees. By keeping these values constant, the number of pores was changed to\n36, 48, and 60, and the pore angles of inclination to 60, 70, and 80 degrees.\nAnalyses were carried out by giving laminar flow to this wing designed in the\nComsol Multiphysics program. The importance of pores was investigated by\ncomparing the results of these analyses.\n"}
+{"abstract": "  We study the problem of distributed zero-order optimization for a class of\nstrongly convex functions. They are formed by the average of local objectives,\nassociated to different nodes in a prescribed network of connections. We\npropose a distributed zero-order projected gradient descent algorithm to solve\nthis problem. Exchange of information within the network is permitted only\nbetween neighbouring nodes. A key feature of the algorithm is that it can query\nonly function values, subject to a general noise model, that does not require\nzero mean or independent errors. We derive upper bounds for the average\ncumulative regret and optimization error of the algorithm which highlight the\nrole played by a network connectivity parameter, the number of variables, the\nnoise level, the strong convexity parameter of the global objective and certain\nsmoothness properties of the local objectives. When the bound is specified to\nthe standard undistributed setting, we obtain an improvement over the\nstate-of-the-art bounds, due to the novel gradient estimation procedure\nproposed here. We also comment on lower bounds and observe that the dependency\nover certain function parameters in the bound is nearly optimal.\n"}
+{"abstract": "  Calibrating a Reynolds-averaged Navier-Stokes (RANS) model against data leads\nto an improvement. Determining {\\it a priori} if such an improvement\ngeneralizes to flows outside the calibration data is an outstanding challenge.\nThis work attempts to address this challenge via global epistemic Uncertainty\nQuantification (UQ). Unlike the available epistemic UQ methods that are local\nand tell us a model's uncertainty at one specific flow condition, the global\nepistemic UQ method presented in this work tells us also whether a perturbation\nof the original model would generalize. Specifically, the global epistemic UQ\nmethod evaluates a potential improvement in terms of its \"effectiveness\" and\n\"inconsistency\". Any improvement can be put in one of the following four\nquadrants: first, high effectiveness, low inconsistency; second, high\neffectiveness, high inconsistency; third, low effectiveness, low inconsistency;\nand fourth, low effectiveness, high inconsistency. An improvement would\ngeneralize if and only if it is in the high effectiveness and low inconsistency\nquadrant. To demonstrate the concept, we apply the global epistemic UQ to full\nReynolds stress modeling of a stratified shear layer. The global epistemic UQ\nresults point to a model coefficient in the pressure-strain correlation closure\n(among others) as effective and consistent for predicting the quantity of\ninterest of shear layer's growth. We calibrate the model coefficient such that\nour RANS matches direct numerical simulation data at one flow condition. We\nshow that the calibrated model generalizes to several other test flow\nconditions. On the other hand, when calibrating a high inconsistency term, we\nget a model that works at only the calibrated condition.\n"}
+{"abstract": "  This paper describes the SPAPL system for the INTERSPEECH 2021 Challenge:\nShared Task on Automatic Speech Recognition for Non-Native Children's Speech in\nGerman. ~ 5 hours of transcribed data and ~ 60 hours of untranscribed data are\nprovided to develop a German ASR system for children. For the training of the\ntranscribed data, we propose a non-speech state discriminative loss (NSDL) to\nmitigate the influence of long-duration non-speech segments within speech\nutterances. In order to explore the use of the untranscribed data, various\napproaches are implemented and combined together to incrementally improve the\nsystem performance. First, bidirectional autoregressive predictive coding\n(Bi-APC) is used to learn initial parameters for acoustic modelling using the\nprovided untranscribed data. Second, incremental semi-supervised learning is\nfurther used to iteratively generate pseudo-transcribed data. Third, different\ndata augmentation schemes are used at different training stages to increase the\nvariability and size of the training data. Finally, a recurrent neural network\nlanguage model (RNNLM) is used for rescoring. Our system achieves a word error\nrate (WER) of 39.68% on the evaluation data, an approximately 12% relative\nimprovement over the official baseline (45.21%).\n"}
+{"abstract": "  In this article we describe, a numerical simulation has been performed for a\nchannel flow with sudden expansion. Different geometric expansion ratios are\nconsidered in the current study such as 1:2, 1:3, and 1:4. The bifurcation\nphenomenon has been investigated for the baseline case and the critical\nReynolds number is controlled using a passive modification in the channel. The\ngeometry of sudden expansion channel is modified by introducing a\ntwo-dimensional plate along the center line of the channel at the expansion\nplane. This is one of the passive control techniques, which has been explored\nin the present study. The computations indicate that the critical Reynolds\nnumber of the symmetry-breaking bifurcation increases as the length of the\nplate increases. An empirical relation has been developed amongst the critical\nReynolds number, length of control plates and expansion ratio (h/H). The\ncritical Reynolds number predicted by the empirical relation for different\nplate lengths are in good agreement with computational results.\n"}
+{"abstract": "  In previous work, we study the Gan-Gross-Prasad problem for unipotent\nrepresentations of finite classical groups. In this paper, we deduce the\nGan-Gross-Prasad problem for arbitrary representations from the unipotent\nrepresentations by Lusztig correspondence.\n"}
+{"abstract": "  Solar modules are currently characterized at standard test conditions (STC),\ndefined at 1000W/m2 and 25 {\\deg}C. However, solar modules in actual outdoor\noperating conditions typically operate at lower illumination and higher\ntemperature than STC, which significantly affects their performance ratio\n(average harvesting efficiency over efficiency in STC). Silicon heterojunction\n(SHJ) technology displays both good temperature coefficient and good\nlow-illumination performances, leading to outstanding performance ratios. We\ninvestigate here SHJ solar cells that use a-SiCx(n) layer as front doped layer\nwith different carbon contents under different climates conditions. Adding\ncarbon increases transparency but also resistive losses at room temperature\n(compared with carbon-free layers), leading to a significant decrease in\nefficiency at STC. We demonstrate that despite this difference at STC, the\ndifference in energy harvesting efficiency is much smaller in all investigated\nclimates. Furthermore, we show that a relative gain of 0.4 to 0.8 percent in\nharvesting efficiency is possible by adding a certain content of carbon in the\nfront (n) layer, compared with carbon-free cells optimized for STC.\n"}
+{"abstract": "  This article develops a new theoretical basis for decomposing signals that\nare formed by the linear superposition of a finite number of modes. Each mode\ndepends nonlinearly upon several parameters; we seek both these parameters and\nthe weights within the superposition. The particular focus of this article is\nupon solving the decomposition problem when the number of modes is not known in\nadvance. A sheaf-theoretic formalism is introduced that describes all modal\ndecomposition problems, and it is shown that minimizing the local consistency\nradius within this sheaf is guaranteed to solve them. Since the modes may or\nmay not be well-separated, a greedy algorithm that identifies the most distinct\nmodes first may not work reliably. This article introduces a novel mathematical\nformalism, aggregation sheaves, and shows how they characterize the behavior of\ngreedy algorithms that attempt to solve modal decomposition problems.\n"}
+{"abstract": "  Electronic Health Records (EHRs) are commonly used to investigate\nrelationships between patient health information and outcomes. Deep learning\nmethods are emerging as powerful tools to learn such relationships, given the\ncharacteristic high dimension and large sample size of EHR datasets. The\nPhysionet 2012 Challenge involves an EHR dataset pertaining to 12,000 ICU\npatients, where researchers investigated the relationships between clinical\nmeasurements, and in-hospital mortality. However, the prevalence and complexity\nof missing data in the Physionet data present significant challenges for the\napplication of deep learning methods, such as Variational Autoencoders (VAEs).\nAlthough a rich literature exists regarding the treatment of missing data in\ntraditional statistical models, it is unclear how this extends to deep learning\narchitectures. To address these issues, we propose a novel extension of VAEs\ncalled Importance-Weighted Autoencoders (IWAEs) to flexibly handle Missing Not\nAt Random (MNAR) patterns in the Physionet data. Our proposed method models the\nmissingness mechanism using an embedded neural network, eliminating the need to\nspecify the exact form of the missingness mechanism a priori. We show that the\nuse of our method leads to more realistic imputed values relative to the\nstate-of-the-art, as well as significant differences in fitted downstream\nmodels for mortality.\n"}
+{"abstract": "  A central goal of explainable artificial intelligence (XAI) is to improve the\ntrust relationship in human-AI interaction. One assumption underlying research\nin transparent AI systems is that explanations help to better assess\npredictions of machine learning (ML) models, for instance by enabling humans to\nidentify wrong predictions more efficiently. Recent empirical evidence however\nshows that explanations can have the opposite effect: When presenting\nexplanations of ML predictions humans often tend to trust ML predictions even\nwhen these are wrong. Experimental evidence suggests that this effect can be\nattributed to how intuitive, or human, an AI or explanation appears. This\neffect challenges the very goal of XAI and implies that responsible usage of\ntransparent AI methods has to consider the ability of humans to distinguish\nmachine generated from human explanations. Here we propose a quantitative\nmetric for XAI methods based on Turing's imitation game, a Turing Test for\nTransparency. A human interrogator is asked to judge whether an explanation was\ngenerated by a human or by an XAI method. Explanations of XAI methods that can\nnot be detected by humans above chance performance in this binary\nclassification task are passing the test. Detecting such explanations is a\nrequirement for assessing and calibrating the trust relationship in human-AI\ninteraction. We present experimental results on a crowd-sourced text\nclassification task demonstrating that even for basic ML models and XAI\napproaches most participants were not able to differentiate human from machine\ngenerated explanations. We discuss ethical and practical implications of our\nresults for applications of transparent ML.\n"}
+{"abstract": "  Deep neural networks (DNN) have achieved remarkable success in computer\nvision (CV). However, training and inference of DNN models are both memory and\ncomputation intensive, incurring significant overhead in terms of energy\nconsumption and silicon area. In particular, inference is much more\ncost-sensitive than training because training can be done offline with powerful\nplatforms, while inference may have to be done on battery powered devices with\nconstrained form factors, especially for mobile or edge vision applications. In\norder to accelerate DNN inference, model quantization was proposed. However\nprevious works only focus on the quantization rate without considering the\nefficiency of operations. In this paper, we propose Dendrite-Tree based Neural\nNetwork (DTNN) for energy-efficient inference with table lookup operations\nenabled by activation quantization. In DTNN both costly weight access and\narithmetic computations are eliminated for inference. We conducted experiments\non various kinds of DNN models such as LeNet-5, MobileNet, VGG, and ResNet with\ndifferent datasets, including MNIST, Cifar10/Cifar100, SVHN, and ImageNet. DTNN\nachieved significant energy saving (19.4X and 64.9X improvement on ResNet-18\nand VGG-11 with ImageNet, respectively) with negligible loss of accuracy. To\nfurther validate the effectiveness of DTNN and compare with state-of-the-art\nlow energy implementation for edge vision, we design and implement DTNN based\nMLP image classifiers using off-the-shelf FPGAs. The results show that DTNN on\nthe FPGA, with higher accuracy, could achieve orders of magnitude better energy\nconsumption and latency compared with the state-of-the-art low energy\napproaches reported that use ASIC chips.\n"}
+{"abstract": "  Domain walls in magnets, vortex lattices in superconductors, contact lines at\ndepinning, and many other systems can be modelled as an elastic system subject\nto quenched disorder. Its field theory possesses a well-controlled perturbative\nexpansion around its upper critical dimension. Contrary to standard field\ntheory, the renormalization group flow involves a function, the disorder\ncorrelator $\\Delta(w)$, therefore termed the functional renormalization group\n(FRG). $\\Delta(w)$ is a physical observable, the auto-correlation function of\nthe centre of mass of the elastic manifold. In this review, we give a\npedagogical introduction into its phenomenology and techniques. This allows us\nto treat both equilibrium (statics), and depinning (dynamics). Building on\nthese techniques, avalanche observables are accessible: distributions of size,\nduration, and velocity, as well as the spatial and temporal shape. Various\nequivalences between disordered elastic manifolds, and sandpile models exist:\nan elastic string driven at a point and the Oslo model; disordered elastic\nmanifolds and Manna sandpiles; charge density waves and Abelian sandpiles or\nloop-erased random walks. Each of these mappings requires specific techniques,\nwhich we develop, including modelling of discrete stochastic systems via\ncoarse-grained stochastic equations of motion, super-symmetry techniques, and\ncellular automata. Stronger than quadratic nearest-neighbour interactions lead\nto directed percolation, and non-linear surface growth with additional KPZ\nterms. On the other hand, KPZ without disorder can be mapped back to disordered\nelastic manifolds, either on the directed polymer for its steady state, or a\nsingle particle for its decay. Other topics covered are the relation between\nfunctional RG and replica symmetry breaking, and random field magnets. Emphasis\nis given to numerical and experimental tests of the theory.\n"}
+{"abstract": "  We conducted an online study with $n = 235$ Signal users on their\nunderstanding and usage of PINs in Signal. In our study, we observe a split in\nPIN management and composition strategies between users who can explain the\npurpose of the Signal PINs (56%; enthusiasts) and users who cannot (44%; casual\nusers). Encouraging adoption of PINs by Signal appears quite successful: only\n14% opted-out of setting a PIN entirely. Among those who did set a PIN, most\nenthusiasts had long, complex alphanumeric PINs generated by and saved in a\npassword manager. Meanwhile more casual Signal users mostly relied on short\nnumeric-only PINs. Our results suggest that better communication about the\npurpose of the Signal PIN could help more casual users understand the features\nPINs enable (such as that it is not simply a personal identification number).\nThis communication could encourage a stronger security posture.\n"}
+{"abstract": "  In this work we investigate novel spherical core-shell nanoparticles with\nband inversion. The core and the embedding medium are normal semiconductors\nwhile the shell material is assumed to be a topological insulator. The envelope\nfunctions are found to satisfy a Dirac-like equation that can be solved in a\nclosed form. The core-shell nanoparticle supports midgap bound states located\nat both interfaces due to band inversion. These states are robust since they\nare topologically protected. The energy spectrum presents mirror symmetry due\nto the chiral symmetry of the Dirac-like Hamiltonian. As a major result, we\nshow that the thickness of the shell acts as an additional parameter for the\nfine tuning of the energy levels, which paves the way for electronics and\noptoelectronics applications.\n"}
+{"abstract": "  The importance of parameter selection in supervised learning is well known.\nHowever, due to the many parameter combinations, an incomplete or an\ninsufficient procedure is often applied. This situation may cause misleading or\nconfusing conclusions. In this opinion paper, through an intriguing example we\npoint out that the seriousness goes beyond what is generally recognized. In the\ntopic of multi-label classification for medical code prediction, one\ninfluential paper conducted a proper parameter selection on a set, but when\nmoving to a subset of frequently occurring labels, the authors used the same\nparameters without a separate tuning. The set of frequent labels became a\npopular benchmark in subsequent studies, which kept pushing the state of the\nart. However, we discovered that most of the results in these studies cannot\nsurpass the approach in the original paper if a parameter tuning had been\nconducted at the time. Thus it is unclear how much progress the subsequent\ndevelopments have actually brought. The lesson clearly indicates that without\nenough attention on parameter selection, the research progress in our field can\nbe uncertain or even illusive.\n"}
+{"abstract": "  We study the aggregation of two risks when the marginal distributions are\nknown and the dependence structure is unknown, under the additional constraint\nthat one risk is smaller than or equal to the other. Risk aggregation problems\nwith the order constraint are closely related to the recently introduced notion\nof the directional lower (DL) coupling. The largest aggregate risk in concave\norder (thus, the smallest aggregate risk in convex order) is attained by the DL\ncoupling. These results are further generalized to calculate the best-case and\nworst-case values of tail risk measures. In particular, we obtain analytical\nformulas for bounds on Value-at-Risk. Our numerical results suggest that the\nnew bounds on risk measures with the extra order constraint can greatly improve\nthose with full dependence uncertainty.\n"}
+{"abstract": "  Bibliometric studies are a rare undertaking in the field of English language\nteaching, especially at a journal level. To celebrate the 30th anniversary of\nTEFLIN Journal, this study exhibits a bibliometric portrait of its publication,\nindexation, and citation from 1990 to 2019. Two pieces of free software were\nadopted to conduct the descriptive and network analyses of bibliographic data\nfrom Microsoft Academic. The publication and citation metrics have risen during\nits lifetime. The bibliographic network identifies the most productive authors,\ninstitutions, and countries along with the co-authorship pattern, type of\ntop-cited articles, and top-used keywords. The articles relatedness is also\nsighted in terms of the citation frequency and number of shared references.\nEven though the analyses were complicated by some missing articles and improper\nindexation, this study could still take a full-length bibliometric portrait of\nthe journal during its 30-year journey between the commitment to competence and\nthe quest for higher impact.\n"}
+{"abstract": "  Recent single-image super-resolution (SISR) networks, which can adapt their\nnetwork parameters to specific input images, have shown promising results by\nexploiting the information available within the input data as well as large\nexternal datasets. However, the extension of these self-supervised SISR\napproaches to video handling has yet to be studied. Thus, we present a new\nlearning algorithm that allows conventional video super-resolution (VSR)\nnetworks to adapt their parameters to test video frames without using the\nground-truth datasets. By utilizing many self-similar patches across space and\ntime, we improve the performance of fully pre-trained VSR networks and produce\ntemporally consistent video frames. Moreover, we present a test-time knowledge\ndistillation technique that accelerates the adaptation speed with less hardware\nresources. In our experiments, we demonstrate that our novel learning algorithm\ncan fine-tune state-of-the-art VSR networks and substantially elevate\nperformance on numerous benchmark datasets.\n"}
+{"abstract": "  The variety of services and functionality offered by various cloud service\nproviders (CSP) have exploded lately. Utilizing such services has created\nnumerous opportunities for enterprises infrastructure to become cloud-based\nand, in turn, assisted the enterprises to easily and flexibly offer services to\ntheir customers. The practice of renting out access to servers to clients for\ncomputing and storage purposes is known as Infrastructure as a Service (IaaS).\nThe popularity of IaaS has led to serious and critical concerns with respect to\nthe cyber security and privacy. In particular, malware is often leveraged by\nmalicious entities against cloud services to compromise sensitive data or to\nobstruct their functionality. In response to this growing menace, malware\ndetection for cloud environments has become a widely researched topic with\nnumerous methods being proposed and deployed. In this paper, we present online\nmalware detection based on process level performance metrics, and analyze the\neffectiveness of different baseline machine learning models including, Support\nVector Classifier (SVC), Random Forest Classifier (RFC), KNearest Neighbor\n(KNN), Gradient Boosted Classifier (GBC), Gaussian Naive Bayes (GNB) and\nConvolutional Neural Networks (CNN). Our analysis conclude that neural network\nmodels can most accurately detect the impact malware have on the process level\nfeatures of virtual machines in the cloud, and therefore are best suited to\ndetect them. Our models were trained, validated, and tested by using a dataset\nof 40,680 malicious and benign samples. The dataset was complied by running\ndifferent families of malware (collected from VirusTotal) in a live cloud\nenvironment and collecting the process level features.\n"}
+{"abstract": "  Protostellar jets have a fundamental role at the earliest evolution of\nprotostars of all masses. In the case of low-mass (<8 Msun) protostars, strong\nobservational evidence exists that the launching and collimation is due to the\nX- and/or disk-wind mechanisms. In these models, it is the protostar/disk\nsystem that creates all the necessary conditions to launch and collimate the\njets near the protostar via strong magnetic fields. The origin of jets from\nmore massive protostars has been investigated much less, in part because of the\ndifficulty of resolving the collimation zone in these more distant objects.\nHere we present the highest angular resolution observations of a jet powered by\na massive protostar, the Cep A HW2 radio jet. We imaged the radio emission at\nprojected distances of only ~20 au from the protostar, resolving the innermost\n100 au of a massive protostellar jet for the first time. The morphology of the\nradio jet emission in this massive object is very different than what is\nusually observed in jets from low-mass protostars. We found that the outflowing\nmaterial in HW2 has two components: a wide-angle wind launched from the\nprotostar/disk system, and a highly collimated jet starting at 20-30 au from\nthe protostar. We discuss two possible scenarios: an extension of the classical\ndisk-wind to a massive protostar, or external collimation of a wide-angle wind.\nThese results have important consequences for our understanding of how stars of\ndifferent masses are formed.\n"}
+{"abstract": "  We establish quantitative bounds on the $U^k[N]$ Gowers norms of the M\\\"obius\nfunction $\\mu$ and the von Mangoldt function $\\Lambda$ for all $k$, with error\nterms of shape $O((\\log\\log N)^{-c})$. As a consequence, we obtain quantitative\nbounds for the number of solutions to any linear system of equations of finite\ncomplexity in the primes, with the same shape of error terms. We also obtain\nthe first quantitative bounds on the size of sets containing no $k$-term\narithmetic progressions with shifted prime difference.\n"}
+{"abstract": "  Understanding the nature of charge carriers in doped Mott insulators holds\nthe key to unravelling puzzling properties of strongly correlated electron\nsystems, including cuprate superconductors. Several theoretical models\nsuggested that dopants can be understood as bound states of partons, the\nanalogues of quarks in high-energy physics. However, direct signatures of\nspinon-chargon bound states are lacking, both in experiment and theory. Here we\nnumerically identify long-lived rotational resonances at low doping, which\ndirectly reveal the microscopic structure of spinon-chargon bound states.\nSimilar to Regge trajectories reflecting the quark structure of mesons, we\nestablish a linear dependence of the rotational energy on the super-exchange\ncoupling. Rotational excitations are strongly suppressed in standard\nangle-resolved photo-emission (ARPES) spectra, but we propose a multi-photon\nrotational extension of ARPES where they have strong spectral weight. Our\nfindings suggest that multi-photon spectroscopy experiments should provide new\ninsights into emergent universal features of strongly correlated electron\nsystems.\n"}
+{"abstract": "  A manual for computations in direct Dark Matter detection phenomenology.\nFeaturing self-contained sections on non-relativistic expansion, elastic and\ninelastic scattering kinematics, Dark Matter velocity distribution, hadronic\nmatrix elements, nuclear form factors, cross sections, rate spectra and\nparameter-space constraints, as well as a handy two-page summary and Q&A\nsection for a quick reference. A pedagogical, yet general and model independent\nguide, with examples from standard and non-standard particle Dark Matter\nmodels.\n"}
+{"abstract": "  Identifying prostate cancer patients that are harboring aggressive forms of\nprostate cancer remains a significant clinical challenge. To shed light on this\nproblem, we develop an approach based on multispectral deep-ultraviolet (UV)\nmicroscopy that provides novel quantitative insight into the aggressiveness and\ngrade of this disease. First, we find that UV spectral signatures from\nendogenous molecules give rise to a phenotypical continuum that differentiates\ncritical structures of thin tissue sections with subcellular spatial\nresolution, including nuclei, cytoplasm, stroma, basal cells, nerves, and\ninflammation. Further, we show that this phenotypical continuum can be applied\nas a surrogate biomarker of prostate cancer malignancy, where patients with the\nmost aggressive tumors show a ubiquitous glandular phenotypical shift. Lastly,\nwe adapt a two-part Cycle-consistent Generative Adversarial Network to\ntranslate the label-free deep-UV images into virtual hematoxylin and eosin\n(H&E) stained images. Agreement between the virtual H&E images and the gold\nstandard H&E-stained tissue sections is evaluated by a panel of pathologists\nwho find that the two modalities are in excellent agreement. This work has\nsignificant implications towards improving our ability to objectively quantify\nprostate cancer grade and aggressiveness, thus improving the management and\nclinical outcomes of prostate cancer patients. This same approach can also be\napplied broadly in other tumor types to achieve low-cost, stain-free,\nquantitative histopathological analysis.\n"}
+{"abstract": "  The concept of quantum coherence and its possible use as a resource are\ncurrently the subject of active researches. Uncertainty and complementarity\nrelations for quantum coherence allow one to study its changes with respect to\nother characteristics of the process of interest. Protocols of quantum\ninformation processing often use measurements that have a special inner\nstructure. Quantum designs are considered as tools with a perspective of\nfruitful applications in quantum information processing. We obtain uncertainty\nand certainty relations for coherence averaged with respect to a set of\ndesign-structured POVMs of rank one. To characterize the degree of coherence,\nthe relative entropy of coherence is utilized. The derived relations are\nexemplified with quantum designs in the two-dimensional Hilbert space.\n"}
+{"abstract": "  We present a novel model for capturing the behavior of an agent exhibiting\nsunk-cost bias in a stochastic environment. Agents exhibiting sunk-cost bias\ntake into account the effort they have already spent on an endeavor when they\nevaluate whether to continue or abandon it. We model planning tasks in which an\nagent with this type of bias tries to reach a designated goal. Our model\nstructures this problem as a type of Markov decision process: loosely speaking,\nthe agent traverses a directed acyclic graph with probabilistic transitions,\npaying costs for its actions as it tries to reach a target node containing a\nspecified reward. The agent's sunk cost bias is modeled by a cost that it\nincurs for abandoning the traversal: if the agent decides to stop traversing\nthe graph, it incurs a cost of $\\lambda \\cdot C_{sunk}$, where ${\\lambda \\geq\n0}$ is a parameter that captures the extent of the bias and $C_{sunk}$ is the\nsum of costs already invested.\n  We analyze the behavior of two types of agents: naive agents that are unaware\nof their bias, and sophisticated agents that are aware of it. Since optimal\n(bias-free) behavior in this problem can involve abandoning the traversal\nbefore reaching the goal, the bias exhibited by these types of agents can\nresult in sub-optimal behavior by shifting their decisions about abandonment.\nWe show that in contrast to optimal agents, it is computationally hard to\ncompute the optimal policy for a sophisticated agent. Our main results quantify\nthe loss exhibited by these two types of agents with respect to an optimal\nagent. We present both general and topology-specific bounds.\n"}
+{"abstract": "  Optical quantum circuits can be optimized using gradient descent methods, as\nthe gates in a circuit can be parametrized by continuous parameters. However,\nthe parameter space as seen by the cost function is not Euclidean, which means\nthat the Euclidean gradient does not generally point in the direction of\nsteepest ascent. In order to retrieve the steepest ascent direction, in this\nwork we implement Natural Gradient descent in the optical quantum circuit\nsetting, which takes the local metric tensor into account. In particular, we\nadapt the Natural Gradient approach to a complex-valued parameter space. We\nthen compare the Natural Gradient approach to vanilla gradient descent and to\nAdam over two state preparation tasks: a single-photon source and a\nGottesman-Kitaev-Preskill state source. We observe that the NG approach has a\nfaster convergence (due in part to the possibility of using larger learning\nrates) and a significantly smoother decay of the cost function throughout the\noptimization.\n"}
+{"abstract": "  This paper deals with linear stochastic partial differential equations with\nvariable coefficients driven by L\\'{e}vy white noise. We first derive an\nexistence theorem for integral transforms of L\\'{e}vy white noise and prove the\nexistence of generalized and mild solutions of second order elliptic partial\ndifferential equations. Furthermore, we discuss the generalized electric\nSchr\\\"odinger operator for different potential functions $V$.\n"}
+{"abstract": "  We modularly build increasingly larger fragments of the ZX-calculus by\nmodularly adding new generators and relations, at each point, giving some\nconcrete semantics in terms of some category of spans. This is performed using\nLack's technique of composing props via distributive laws, as well as the\ntechnique of pushout cubes of Zanasi. We do this for the fragment of the\nZX-calculus with only the black $\\pi$-phase (and no Hadamard gate) as well as\nwell as the fragment which additionally has the and gate as a generator (which\nis equivalent to the natural number H-box fragment of the ZH-calculus). In the\nformer case, we show that this is equivalent to the full subcategory of spans\nof (possibly empty) free, finite dimensional affine $\\mathbb F_2$-vector\nspaces, where the objects are the non-empty affine vector spaces. In the latter\ncase, we show that this is equivalent to the full subcategory of spans of\nfinite sets where the objects are powers of the two element set. Because these\nfragments of the ZX-calculus have semantics in terms of full subcategories of\ncategories of spans, they can not be presented by distributive laws over\ngroupoids. Instead, we first construct their subcategories of partial\nisomorphisms via distributive laws over all isomorphims with subobjects\nadjoined. After which, the full subcategory of spans are obtained by freely\nadjoining units and counits the the semi-Frobenius structures given by the\ndiagonal and codiagonal maps.\n"}
+{"abstract": "  Measurements of the surface velocity and surface texture of a freely\npropagating shear jamming front in a dense suspension are compared. The\nvelocity fields are captured with particle image velocimetry (PIV), while the\nsurface texture is captured in a separated experiment by observing a direct\nreflection on the suspension surface with high-speed cameras. A method for\nquantifying the surface features and their orientation is presented based on\nthe fast Fourier transform of localised windows. The region that exhibits\nstrong surface features corresponds to the the solid-like jammed region\nidentified via the PIV measurements. Moreover, the surface features within the\njammed region are predominantly oriented in the same direction as the\neigenvectors of the strain tensor. Thus, from images of the free surface, our\nanalysis is able to show that the surface texture contains information on the\nprinciple strain directions and the propagation of the jamming front.\n"}
+{"abstract": "  Cell migration plays essential roles in development, wound healing, diseases,\nand in the maintenance of a complex body. Experiments in collective cell\nmigration generally measure quantities such as cell displacement and velocity.\nThe observed short-time diffusion regime for mean square displacement in\nsingle-cell migration experiments on flat surfaces calls into question the\ndefinition of cell velocity and the measurement protocol. Theoretical results\nin stochastic modeling for single-cell migration have shown that this fast\ndiffusive regime is explained by a white noise acting on displacement on the\ndirection perpendicular to the migrating cell polarization axis (not on\nvelocity). The prediction is that only the component of velocity parallel to\nthe polarization axis is a well-defined quantity, with a robust measurement\nprotocol. Here, we ask whether we can find a definition of a migrating-cell\npolarization that is able to predict the cell's subsequent displacement, based\non measurements of its shape. Supported by experimental evidence that cell\nnucleus lags behind the cell center of mass in a migrating cell, we propose a\nrobust parametrization for cell migration where the distance between cell\nnucleus and the cell's center of mass defines cell shape polarization. We\ntested the proposed methods by applying to a simulation model for\nthree-dimensional cells performed in the CompuCell3D environment, previously\nshown to reproduce biological cells kinematics migrating on a flat surface.\n"}
+{"abstract": "  Multi-armed bandits (MAB) provide a principled online learning approach to\nattain the balance between exploration and exploitation. Due to the superior\nperformance and low feedback learning without the learning to act in multiple\nsituations, Multi-armed Bandits drawing widespread attention in applications\nranging such as recommender systems. Likewise, within the recommender system,\ncollaborative filtering (CF) is arguably the earliest and most influential\nmethod in the recommender system. Crucially, new users and an ever-changing\npool of recommended items are the challenges that recommender systems need to\naddress. For collaborative filtering, the classical method is training the\nmodel offline, then perform the online testing, but this approach can no longer\nhandle the dynamic changes in user preferences which is the so-called cold\nstart. So how to effectively recommend items to users in the absence of\neffective information? To address the aforementioned problems, a multi-armed\nbandit based collaborative filtering recommender system has been proposed,\nnamed BanditMF. BanditMF is designed to address two challenges in the\nmulti-armed bandits algorithm and collaborative filtering: (1) how to solve the\ncold start problem for collaborative filtering under the condition of scarcity\nof valid information, (2) how to solve the sub-optimal problem of bandit\nalgorithms in strong social relations domains caused by independently\nestimating unknown parameters associated with each user and ignoring\ncorrelations between users.\n"}
+{"abstract": "  The Bernays-Sch\\\"onfinkel first-order logic fragment over simple linear real\narithmetic constraints BS(SLR) is known to be decidable. We prove that BS(SLR)\nclause sets with both universally and existentially quantified verification\nconditions (conjectures) can be translated into BS(SLR) clause sets over a\nfinite set of first-order constants. For the Horn case, we provide a Datalog\nhammer preserving validity and satisfiability. A toolchain from the BS(LRA)\nprover SPASS-SPL to the Datalog reasoner VLog establishes an effective way of\ndeciding verification conditions in the Horn fragment. This is exemplified by\nthe verification of supervisor code for a lane change assistant in a car and of\nan electronic control unit for a supercharged combustion engine.\n"}
+{"abstract": "  We study the existence and non-existence of classical solutions for\ninequalities of type $$ \\pm \\Delta^m u \\geq \\big(\\Psi(|x|)*u^p\\big)u^q\n\\quad\\mbox{ in } {\\mathbb R}^N (N\\geq 1). $$ Here, $\\Delta^m$ $(m\\geq 1)$ is\nthe polyharmonic operator, $p, q>0$ and $*$ denotes the convolution operator,\nwhere $\\Psi>0$ is a continuous non-increasing function. We devise new methods\nto deduce that solutions of the above inequalities satisfy the\npoly-superharmonic property. This further allows us to obtain various Liouville\ntype results. Our study is also extended to the case of systems of simultaneous\ninequalities.\n"}
+{"abstract": "  Over the last decades, the incidence of skin cancer, melanoma and\nnon-melanoma, has increased at a continuous rate. In particular for melanoma,\nthe deadliest type of skin cancer, early detection is important to increase\npatient prognosis. Recently, deep neural networks (DNNs) have become viable to\ndeal with skin cancer detection. In this work, we present a smartphone-based\napplication to assist on skin cancer detection. This application is based on a\nConvolutional Neural Network(CNN) trained on clinical images and patients\ndemographics, both collected from smartphones. Also, as skin cancer datasets\nare imbalanced, we present an approach, based on the mutation operator of\nDifferential Evolution (DE) algorithm, to balance data. In this sense, beyond\nprovides a flexible tool to assist doctors on skin cancer screening phase, the\nmethod obtains promising results with a balanced accuracy of 85% and a recall\nof 96%.\n"}
+{"abstract": "  The detection of gravitational waves emitted by binary black holes raises\nquestions regarding the origin of binaries. There are several models in the\nliterature involving binary evolution in both the field and clusters. We\ncompare the predictions of these models with observations and establish the\nreliability of this comparison. We use the likelihood calculation to compare\nthe models in the space spanned by the observed chirp mass and the luminosity\ndistance of the source. We rank the models by their ability to explain all\ncurrent gravitational wave detections. It is shown that the most probable\nmodels correspond to binary evolution with low metallicity. Several variants of\nsuch evolution have similar likelihoods. The globular cluster model, considered\nhere, is disfavoured. We present the usefulness of the method in distinguishing\nbetween models when new observations become available. We calculate the number\nof observations required to distinguish between each pair of models. We find\nthat the number varies from ten to several thousand for some pairs of models,\nyet almost two-thirds of pairs are distinguishable with at most 100\nobservations.\n"}
+{"abstract": "  Python is a popular programming language known for its flexibility,\nusability, readability, and focus on developer productivity. The quantum\nsoftware community has adopted Python on a number of large-scale efforts due to\nthese characteristics, as well as the remote nature of near-term quantum\nprocessors. The use of Python has enabled quick prototyping for quantum code\nthat directly benefits pertinent research and development efforts in quantum\nscientific computing. However, this rapid prototyping ability comes at the cost\nof future performant integration for tightly-coupled CPU-QPU architectures with\nfast-feedback. Here we present a language extension to Python that enables\nheterogeneous quantum-classical computing via a robust C++ infrastructure for\nquantum just-in-time (QJIT) compilation. Our work builds off the QCOR C++\nlanguage extension and compiler infrastructure to enable a single-source,\nquantum hardware-agnostic approach to quantum-classical computing that retains\nthe performance required for tightly coupled CPU-QPU compute models. We detail\nthis Pythonic extension, its programming model and underlying software\narchitecture, and provide a robust set of examples to demonstrate the utility\nof our approach.\n"}
+{"abstract": "  Control of molecular orientation is emerging as crucial for the\ncharacterization of the stereodynamics of kinetics processes beyond structural\nstereochemistry. The special role played in chiral discrimination phenomena has\nbeen particularly emphasized by the authors after their extensive probes of\nexperimental control of molecular alignment and orientation. In this work, the\nrole of the orientation has been demonstrated for the first time in\nfirst-principles molecular dynamics simulations: stationary points\ncharacterized on potential energy surfaces have been calculated for the study\nof chemical reactions occurring between the bisulfide anion HS- and oriented\nprototypical chiral molecules CHFXY (where X = CH3 or CN and Y = Cl or I). The\nimportant reaction channels are those corresponding to bimolecular nucleophilic\nsubstitution (SN2) and to bimolecular elimination (E2): their relative role has\nbeen assessed and alternative pathways due to the mirror forms of the oriented\nchiral molecule are revealed by the different reactivity of the two enantiomers\nof CHFCNI in SN2 reaction.\n"}
+{"abstract": "  A family of Lempel-Ziv factorizations is a well-studied string structure. The\nLZ-End factorization is a member of the family that achieved faster extraction\nof any substrings (Kreft & Navarro, TCS 2013). One of the interests for LZ-End\nfactorizations is the possible difference between the size of LZ-End and LZ77\nfactorizations. They also showed families of strings where the approximation\nratio of the number of LZ-End phrases to the number of LZ77 phrases\nasymptotically approaches 2. However, the alphabet size of these strings is\nunbounded. In this paper, we analyze the LZ-End factorization of the\nperiod-doubling sequence. We also show that the approximation ratio for the\nperiod-doubling sequence asymptotically approaches 2 for the binary alphabet.\n"}
+{"abstract": "  We establish an extension of the Hopf-Tsuji-Sullivan dichotomy to any Zariski\ndense discrete subgroup of a semisimple real algebraic group $G$. We then apply\nthis dichotomy to Anosov subgroups of $G$, which surprisingly presents a\ndifferent phenomenon depending on the rank of the ambient group $G$.\n"}
+{"abstract": "  The first stages of planet formation usually occur when the host star is\nstill in a (relatively) dense star-forming region, where the effects of the\nexternal environment may be important for understanding the outcome of the\nplanet formation process. In particular, star-forming regions that contain\nmassive stars have strong far ultraviolet (FUV) and extreme ultraviolet (EUV)\nradiation fields, which can induce mass-loss from protoplanetary discs due to\nphotoevaporation. In this paper we present a parameter-space study of the\nexpected FUV and EUV fields in N-body simulations of star-forming regions with\na range of initial conditions. We then use recently published models to\ndetermine the mass-loss due to photoevaporation from protoplanetary discs. In\nparticular, we focus on the effects of changing the initial degree of spatial\nstructure and initial virial ratio in the star-forming regions, as well as the\ninitial stellar density. We find that the FUV fields in star-forming regions\nare much higher than in the interstellar medium, even when the regions have\nstellar densities as low as in the Galactic field, due to the presence of\nintermediate-mass, and massive, stars (>5Msun). These strong radiation fields\nlead to the destruction of the gas component in protoplanetary discs within 1\nMyr, implying that gas giant planets must either form extremely rapidly (<1\nMyr), or that they exclusively form in star-forming regions like Taurus, which\ncontain no intermediate-mass or massive stars. The latter scenario is in direct\ntension with meteoritic evidence from the Solar system that suggests the Sun\nand its protoplanetary disc was born in close proximity to massive stars.\n"}
+{"abstract": "  Most of the existing single object trackers track the target in a unitary\nlocal search window, making them particularly vulnerable to challenging factors\nsuch as heavy occlusions and out-of-view movements. Despite the attempts to\nfurther incorporate global search, prevailing mechanisms that cooperate local\nand global search are relatively static, thus are still sub-optimal for\nimproving tracking performance. By further studying the local and global search\nresults, we raise a question: can we allow more dynamics for cooperating both\nresults? In this paper, we propose to introduce more dynamics by devising a\ndynamic attention-guided multi-trajectory tracking strategy. In particular, we\nconstruct dynamic appearance model that contains multiple target templates,\neach of which provides its own attention for locating the target in the new\nframe. Guided by different attention, we maintain diversified tracking results\nfor the target to build multi-trajectory tracking history, allowing more\ncandidates to represent the true target trajectory. After spanning the whole\nsequence, we introduce a multi-trajectory selection network to find the best\ntrajectory that delivers improved tracking performance. Extensive experimental\nresults show that our proposed tracking strategy achieves compelling\nperformance on various large-scale tracking benchmarks. The project page of\nthis paper can be found at https://sites.google.com/view/mt-track/.\n"}
+{"abstract": "  Young star clusters are dynamically active stellar systems and are a common\nbirthplace for massive stars. Low-mass star clusters ($\\sim{}300-10^3$\nM$_\\odot$) are more numerous than massive systems and are characterized by a\ntwo-body relaxation time scale of a few Myr: the most massive stars sink to the\ncluster core and dynamically interact with each other even before they give\nbirth to compact objects. Here, we explore the properties of black holes (BHs)\nand binary black holes (BBHs) formed in low-mass young star clusters, by means\nof a suite of $10^5$ direct $N$-body simulations with a high original binary\nfraction (100 % for stars with mass $>5$ M$_\\odot$). Most BHs are ejected in\nthe first $\\sim{}20$ Myr by dynamical interactions. Dynamical exchanges are the\nmain formation channel of BBHs, accounting for $\\sim{}40-80$ % of all the\nsystems. Most BBH mergers in low-mass young star clusters involve primary BHs\nwith mass $<40$ M$_\\odot$ and low mass ratios are extremely more common than in\nthe field. Comparing our data with those of more massive star clusters\n($10^3-3\\times{}10^4$ M$_\\odot$), we find a strong dependence of the percentage\nof exchanged BBHs on the mass of the host star cluster. In contrast, our\nresults show just a mild correlation between the mass of the host star cluster\nand the efficiency of BBH mergers.\n"}
+{"abstract": "  We report a discovery of a new long-period X-ray pulsar SRGA\nJ204318.2+443815/SRGe J204319.0+443820 in the Be binary system. The source was\nfound in the second all-sky survey by the Mikhail Pavlinsky telescope on board\nthe SRG mission. The follow-up observations with XMM-Newton, NICER and NuSTAR\nobservatories allowed us to discover a strong coherent signal in the source\nlight curve with the period of $\\sim742$ s. The pulsed fraction was found to\ndepend on the energy increasing from $\\sim20$% in soft X-rays to $>50$% at high\nenergies, as it is typical for X-ray pulsars. The source demonstrate a quite\nhard spectrum with an exponential cutoff at high energies and bolometric\nluminosity of $L_X \\simeq 4\\times10^{35}$ erg/s. Dedicated optical and infrared\nobservations with the RTT-150, NOT, Keck and Palomar telescopes revealed a\nnumber of emission lines (H$_{\\alpha}$, HeI, Pashen and Braket series) with the\nstrongly absorbed continuum. All of above suggests that SRGAJ204318.2+443815/\nSRGeJ204319.0+443820 is a new persistent low luminosity X-ray pulsar in a\ndistant binary system with a Be-star of the B0-B2e class. Thus the SRG\nobservatory allow us to unveil the hidden population of faint persistent\nobjects including the population of slowly rotating X-ray pulsars in Be\nsystems.\n"}
+{"abstract": "  The theme of the influence of Bourbaki's ideas and methodologies on\ndisciplines that transcend the mathematical field has been frequently addressed\nand, in particular, the reflection on the relationship between Bourbaki and\nOulipo has animated a passionate debate within literary criticism. The purpose\nof this contribution is to investigate the relations between Le Lionnais,\nQueneau and Roubaud - and more generally Oulipo - and Bourbaki. Through a\ncomparative examination between the two groups, the existence of an undeniable\ncharisma of the \"polycephalic mathematician\" on the artistic-literary movement\nwas highlighted.\n"}
+{"abstract": "  The ${\\it\\text{state-of-the-art}}$ theoretical description of magnetic\nmaterials relies on solving effective Heisenberg spin problems or their\ngeneralizations to relativistic or multi-spin-interaction cases that explicitly\nassume the presence of local magnetic moments in the system. We start with a\ngeneral interacting fermionic model that is often obtained in ${\\it\\text{ab\ninitio}}$ electronic structure calculations and show that the corresponding\nspin problem can be introduced even in the paramagnetic regime, which is\ncharacterized by a zero average value of the magnetization. Further, we derive\na physical criterion for the formation of the local magnetic moment and confirm\nthat the latter exists already at high temperatures well above the transition\nto the ordered magnetic state. The use of path-integral techniques allows us to\ndisentangle spin and electronic degrees of freedom and to carefully separate\nrotational dynamics of the local magnetic moment from Higgs fluctuations of its\nabsolute value. It also allows us to accurately derive the topological Berry\nphase and relate it to a physical bosonic variable that describes dynamics of\nthe spin degrees of freedom. As the result, we demonstrate that the equation of\nmotion for the derived spin problem takes a conventional Landau-Lifshitz form\nthat explicitly accounts for the Gilbert damping due to itinerant nature of the\noriginal electronic model.\n"}
+{"abstract": "  For many IoT domains, Machine Learning and more particularly Deep Learning\nbrings very efficient solutions to handle complex data and perform challenging\nand mostly critical tasks. However, the deployment of models in a large variety\nof devices faces several obstacles related to trust and security. The latest is\nparticularly critical since the demonstrations of severe flaws impacting the\nintegrity, confidentiality and accessibility of neural network models. However,\nthe attack surface of such embedded systems cannot be reduced to abstract flaws\nbut must encompass the physical threats related to the implementation of these\nmodels within hardware platforms (e.g., 32-bit microcontrollers). Among\nphysical attacks, Fault Injection Analysis (FIA) are known to be very powerful\nwith a large spectrum of attack vectors. Most importantly, highly focused FIA\ntechniques such as laser beam injection enable very accurate evaluation of the\nvulnerabilities as well as the robustness of embedded systems. Here, we propose\nto discuss how laser injection with state-of-the-art equipment, combined with\ntheoretical evidences from Adversarial Machine Learning, highlights worrying\nthreats against the integrity of deep learning inference and claims that join\nefforts from the theoretical AI and Physical Security communities are a urgent\nneed.\n"}
+{"abstract": "  In this paper, we present two new approaches for computing exact nonnegative\nmatrix factorizations (NMFs). Exact NMF can be defined as follows: given an\ninput nonnegative matrix $V \\in \\mathbb{R}_+^{F \\times N}$ and a factorization\nrank $K$, compute, if possible, two nonnegative matrices, $W \\in\n\\mathbb{R}_+^{F \\times K}$ and $H \\in \\mathbb{R}_+^{K \\times N}$, such that\n$V=WH$. The two proposed approaches to tackle exact NMF, which is NP-hard in\ngeneral, rely on the same two steps. First, we reformulate exact NMF as\nminimizing a concave function over a product of convex cones; one approach is\nbased on the exponential cone, and the other on the second-order cone. Second,\nwe solve these reformulations iteratively: at each step, we minimize exactly,\nover the feasible set, a majorization of the objective functions obtained via\nlinearization at the current iterate. Hence these subproblems are convex conic\nprograms and can be solved efficiently using dedicated algorithms. We show that\nour approaches, which we call successive conic convex approximations, are able\nto compute exact NMFs, and compete favorably with the state of the art when\napplied to several classes of nonnegative matrices; namely, randomly generated,\ninfinite rigid and slack matrices.\n"}
+{"abstract": "  Robots are moving towards applications in less structured environments, but\ntheir model-based controllers are challenged by the tasks' complexity and\nintrinsic environmental unpredictability. Studying biological motor control can\nprovide insights into overcoming these limitations due to the high dexterity\nand stability observable in humans and animals. This work presents a\ngeometrical solution to the postural optimisation of 7-DoF limbs-like\nmechanisms, which are robust to singularities and computationally efficient.\nThe theoretical formulation identified two separate decoupled optimisation\nstrategies. The shoulder and elbow strategy align the plane of motion with the\nexpected plane of motion and guarantee the reachability of the end-posture. The\nwrist strategy ensures the end-effector orientation, which is essential to\nretain manipulability when nearing a singular configuration. The numerical\nresults confirmed the theoretical observations and allowed us to identify the\neffect of different grasp strategies on system manipulability. The geometrical\nmethod was numerically tested in thousands of configurations proving to be both\nrobust and accurate. The tested scenarios include left and right arm postures,\nsingular configurations, and walking scenarios. The proposed geometrical\napproach can find application in developing efficient and robust interaction\ncontrollers that could be applied in computational neuroscience and robotics.\n"}
+{"abstract": "  In this research paper we modify a classical SIR model to better adapt to the\ndynamics of COVID-19, that is we propose the heterogeneous SQAIRD model where\nCOVID-19 spreads over a population of economic agents, namely: the elderly,\nadults and young people. We then compute and simulate an optimal control\nproblem faced by a Government, where its objective is to minimize the costs\ngenerated by the pandemics using as control a compulsory quarantine measure\n(that is, a lockdown). We first analyze the problem from a theoretical\nperspective, claiming that different lockdown policies (total lockdown, no\nlockdown or partial lockdown) may justified by different cost (concave or\nconvex) structures of the economies. We then focus on a particular cost\nstructure (convex costs) and we simulate a targeted optimal policy vs. a\nuniform optimal policy, by dividing the whole population in three demographic\ngroups (young, adults and old). We also simulate the dynamic of the pandemic\nwith no policy implemented. Simulations highlighted the fact that: a) a policy\nof lockdown is always better than the \\emph{laissez faire} policy, because it\nlimits the costs that the pandemic generates in an uncontrolled situation; b) a\ntargeted policy based on age of the individuals outperforms a uniform policy in\nterms of costs that it generates, being a targeted policy less costly and\nequally effective in the control of the pandemic.\n"}
+{"abstract": "  We study Hardy space $H^1_L(X)$ related to a self-adjoint operator $L$\ndefined on Euclidean domain $X \\subseteq \\mathbb{R}^d$. Under certain\nassumptions on the heat semigroup $\\exp(-tL)$ we prove characterization of\n$H^1_L(X)$ by the Riesz transforms related to $L$. As an application, we prove\nthe Riesz transform characterization for multidimensional Bessel and Laguerre\noperators.\n"}
+{"abstract": "  By using four layered graphene/gallium nitride (GaN) Schottky diodes with an\nundoped GaN spacer, we demonstrate highly effective gating of graphene at low\nbias rendering this type of structure very promising for potential\napplications. An observed Raman G band position shift larger than 8.5 cm-1\ncorresponds to an increase in carrier concentration of about 1.2x10^13 cm-2.\nThe presence of a distinct G band splitting together with a narrow symmetric 2D\nband indicates turbostratic layer stacking and suggests the presence of a high\npotential gradient near the Schottky junction even at zero bias. The subbands\ncharacterized by the highest Raman energies correspond to the largest\nconcentration of electrons. An analysis based on electroreflectance\nmeasurements and a modified Richardson equation confirmed that graphene on\nn-GaN separated by an undoped GaN spacer behaves like a capacitor at reverse\nbias. At least 60% of G subband position shifts occur at forward bias, which is\nrelated to a rapid reduction of electric field near the Schottky junction.\nRaman micromapping shows a high uniformity of gating efficiency on the surface.\nTherefore, our studies demonstrate the usefulness of few layer turbostratic\ngraphene deposited on GaN for tracing electron-phonon coupling in graphene.\nMultilayer graphene also provides uniform and stable electric contacts.\nMoreover, the observed bias sensitive G band splitting can be used as an\nindicator of charge transfer in sensor applications in the low bias regime.\n"}
+{"abstract": "  In many statistical problems, incorporating priors can significantly improve\nperformance. However, the use of prior knowledge in differentially private\nquery release has remained underexplored, despite such priors commonly being\navailable in the form of public datasets, such as previous US Census releases.\nWith the goal of releasing statistics about a private dataset, we present\nPMW^Pub, which -- unlike existing baselines -- leverages public data drawn from\na related distribution as prior information. We provide a theoretical analysis\nand an empirical evaluation on the American Community Survey (ACS) and ADULT\ndatasets, which shows that our method outperforms state-of-the-art methods.\nFurthermore, PMW^Pub scales well to high-dimensional data domains, where\nrunning many existing methods would be computationally infeasible.\n"}
+{"abstract": "  We report a self-consistent quasinormal mode theory for nanometer scale\nelectromagnetism where the possible nonlocal and quantum effects are treated\nthrough quantum surface responses. With Feibelman's frequency-dependent\n\\textit{d} parameters to describe the quantum surface responses, we formulate\nthe source-free Maxwell's equations into a generalized linear eigenvalue\nproblem to define the quasinormal modes. We then construct an orthonormal\nrelation for the modes and consequently unlock the powerful toolbox of modal\nanalysis. The orthonormal relation is validated by the reconstruction of the\nfull numerical results through modal contributions. Significant changes in the\nlandscape of the modes are observed due to the incorporation of the quantum\nsurface responses for a number of nanostructures. Our semi-analytical modal\nanalysis enables transparent physical interpretation of the spontaneous\nemission enhancement of a dipolar emitter as well as the near-field and\nfar-field responses of planewave excitations in the nanostructures.\n"}
+{"abstract": "  We present a novel class of high-order space-time finite element schemes for\nthe Poisson-Nernst-Planck (PNP) equations. We prove that our schemes are mass\nconservative, positivity preserving, and unconditionally energy stable for any\norder of approximation. To the best of our knowledge, this is the first class\nof (arbitrarily) high-order accurate schemes for the PNP equations that\nsimultaneously achieve all these three properties.\n  This is accomplished via (1) using finite elements to directly approximate\nthe so-called entropy variable instead of the density variable, and (2) using a\ndiscontinuous Galerkin (DG) discretization in time. The entropy variable\nformulation, which was originally developed by Metti et al. [17] under the name\nof a log-density formulation, guarantees both positivity of densities and a\ncontinuous-in-time energy stability result. The DG in time discretization\nfurther ensures an unconditional energy stability in the fully discrete level\nfor any approximation order, where the lowest order case is exactly the\nbackward Euler discretization and in this case we recover the method of Metti\net al. [17].\n"}
+{"abstract": "  This note is concerned with the presentation of new delay-dependent\ndissipativity-based convex conditions (expressed in the form of linear matrix\ninequalities) for the design of static output feedback (SOF) stabilizing gains\nfor open-loop unstable discrete-time systems with input time-varying delays. A\nmodified definition of QSR-dissipativity combined with the use of\nLyapunov-Krasovskii functionals as storage functions and the application of\nFinsler's Lemma lead to the gathering of non-interactive design conditions. We\nshow that, differently from most works dealing with controller design for\ntime-delayed systems, the developed conditions present very small conservatism\ncompared to stability analysis conditions derived with the same strategy. Due\nto being a particular case of SOF with an identity output matrix, static state\nfeedback (SSF) gains can also trivially be computed from the conditions.\n"}
+{"abstract": "  Molecule generation, which requires generating valid molecules with desired\nproperties, is a fundamental but challenging task. Recent years have witnessed\nthe rapid development of atom-level auto-regressive models, which usually\nconstruct graphs following sequential actions of adding atom-level nodes and\nedges. However, these atom-level models ignore high-frequency substructures,\nwhich not only capture the regularities of atomic combination in molecules but\nare also often related to desired chemical properties, and therefore may be\nsub-optimal for generating high-quality molecules. In this paper, we propose a\nmethod to automatically discover such common substructures, which we call graph\npieces, from given molecular graphs. We also present a graph piece variational\nautoencoder (GP-VAE) for generating molecular graphs based on graph pieces.\nExperiments show that our GP-VAE models not only achieve better performance\nthan the state-of-the-art baseline for distribution-learning, property\noptimization, and constrained property optimization tasks but are also\ncomputationally efficient.\n"}
+{"abstract": "  In this paper we present the new deep images from the VEGAS survey of three\nmassive ($M_{*} \\simeq 10^{12}$~M$_\\odot$) galaxies from the MUSE Most Massive\nGalaxies (M3G) project, with distances in the range $151\\leq D \\leq 183$ Mpc:\nPGC007748, PGC015524 and PGC049940. The long integration time and the wide\nfield of view of OmegaCam@VST allowed us to map the light and color\ndistributions down to $\\mu_g\\simeq30$~mag/arcsec$^2$ and out to $\\sim 2R_e$.\nThe deep data are crucial to estimate the contribution of the different\ngalaxy's components, in particular the accreted fraction in the stellar halo.\nThe available integral-field observations with MUSE cover a limited portion of\neach galaxy (out to $\\sim 1R_e$), but, from the imaging analysis we find that\nthey map the kinematics and stellar population beyond the first transition\nradius, where the contribution of the accreted component starts to dominate.\nThe main goal of this work is to correlate the scales of the different\ncomponents derived from the image analysis with the kinematics and stellar\npopulation profiles from the MUSE data. Results were used to address the\nassembly history of the three galaxies with the help of the theoretical\npredictions. Our results suggest that PGC049940 has the lowest accreted mass\nfraction of 77%. The higher accreted mass fraction estimated for PGC007748 and\nPGC015524 (86% and 89%, respectively), combined with the flat $\\lambda_R$\nprofiles suggest that a great majority of the mass has been acquired through\nmajor mergers, which have also shaped the shallower metallicity profiles\nobserved at larger radii.\n"}
+{"abstract": "  By using a set of scaling limits, the authors in \\cite{ADFL,SS} proposed a\nframework of deriving the Hall-MHD equations from the two-fluids Euler-Maxwell\nequations for electrons and ions. In this paper, we derive the Hall-MHD\nequations from the Navier-Stokes-Maxwell equations with generalized Ohm's law\nin a mathematically rigorous way via the spectral analysis and energy methods.\n"}
+{"abstract": "  Deepfakes pose growing challenges to the trust of information on the\nInternet. Thus, detecting deepfakes has attracted increasing attentions from\nboth academia and industry. State-of-the-art deepfake detection methods consist\nof two key components, i.e., face extractor and face classifier, which extract\nthe face region in an image and classify it to be real/fake, respectively.\nExisting studies mainly focused on improving the detection performance in\nnon-adversarial settings, leaving security of deepfake detection in adversarial\nsettings largely unexplored. In this work, we aim to bridge the gap. In\nparticular, we perform a systematic measurement study to understand the\nsecurity of the state-of-the-art deepfake detection methods in adversarial\nsettings. We use two large-scale public deepfakes data sources including\nFaceForensics++ and Facebook Deepfake Detection Challenge, where the deepfakes\nare fake face images; and we train state-of-the-art deepfake detection methods.\nThese detection methods can achieve 0.94--0.99 accuracies in non-adversarial\nsettings on these datasets. However, our measurement results uncover multiple\nsecurity limitations of the deepfake detection methods in adversarial settings.\nFirst, we find that an attacker can evade a face extractor, i.e., the face\nextractor fails to extract the correct face regions, via adding small Gaussian\nnoise to its deepfake images. Second, we find that a face classifier trained\nusing deepfakes generated by one method cannot detect deepfakes generated by\nanother method, i.e., an attacker can evade detection via generating deepfakes\nusing a new method. Third, we find that an attacker can leverage backdoor\nattacks developed by the adversarial machine learning community to evade a face\nclassifier. Our results highlight that deepfake detection should consider the\nadversarial nature of the problem.\n"}
+{"abstract": "  According to first principle Lattice QCD calculations, the transition from\nquark-gluon plasma to hadronic matter is a smooth crossover in the region\n$\\mu_{\\rm B}\\leq T_{c}$. In this range the ratio, $C_{6}/C_{2}$, of net-baryon\ndistributions are predicted to be negative. In this paper, we report the first\nmeasurement of the midrapidity net-proton $C_{6}/C_{2}$ from 27, 54.4 and 200\nGeV Au+Au collisions at RHIC. The dependence on collision centrality and\nkinematic acceptance in ($p_{T}$, $y$) are analyzed. While for 27 and 54.4 GeV\ncollisions the $C_{6}/C_{2}$ values are close to zero within uncertainties, it\nis observed that for 200 GeV collisions, the $C_{6}/C_{2}$ ratio becomes\nprogressively negative from peripheral to central collisions. Transport model\ncalculations without critical dynamics predict mostly positive values except\nfor the most central collisions within uncertainties. These observations seem\nto favor a smooth crossover in the high energy nuclear collisions at top RHIC\nenergy.\n"}
+{"abstract": "  Action recognition and detection in the context of long untrimmed video\nsequences has seen an increased attention from the research community. However,\nannotation of complex activities is usually time consuming and challenging in\npractice. Therefore, recent works started to tackle the problem of unsupervised\nlearning of sub-actions in complex activities. This paper proposes a novel\napproach for unsupervised sub-action learning in complex activities. The\nproposed method maps both visual and temporal representations to a latent space\nwhere the sub-actions are learnt discriminatively in an end-to-end fashion. To\nthis end, we propose to learn sub-actions as latent concepts and a novel\ndiscriminative latent concept learning (DLCL) module aids in learning\nsub-actions. The proposed DLCL module lends on the idea of latent concepts to\nlearn compact representations in the latent embedding space in an unsupervised\nway. The result is a set of latent vectors that can be interpreted as cluster\ncenters in the embedding space. The latent space itself is formed by a joint\nvisual and temporal embedding capturing the visual similarity and temporal\nordering of the data. Our joint learning with discriminative latent concept\nmodule is novel which eliminates the need for explicit clustering. We validate\nour approach on three benchmark datasets and show that the proposed combination\nof visual-temporal embedding and discriminative latent concepts allow to learn\nrobust action representations in an unsupervised setting.\n"}
+{"abstract": "  Accurately predicting the performance of different optimization algorithms\nfor previously unseen problem instances is crucial for high-performing\nalgorithm selection and configuration techniques. In the context of numerical\noptimization, supervised regression approaches built on top of exploratory\nlandscape analysis are becoming very popular. From the point of view of Machine\nLearning (ML), however, the approaches are often rather naive, using default\nregression or classification techniques without proper investigation of the\nsuitability of the ML tools. With this work, we bring to the attention of our\ncommunity the possibility to personalize regression models to specific types of\noptimization problems. Instead of aiming for a single model that works well\nacross a whole set of possibly diverse problems, our personalized regression\napproach acknowledges that different models may suite different types of\nproblems. Going one step further, we also investigate the impact of selecting\nnot a single regression model per problem, but personalized ensembles. We test\nour approach on predicting the performance of numerical optimization heuristics\non the BBOB benchmark collection.\n"}
+{"abstract": "  We introduce kernel thinning, a new procedure for compressing a distribution\n$\\mathbb{P}$ more effectively than i.i.d.\\ sampling or standard thinning. Given\na suitable reproducing kernel $\\mathbf{k}$ and $\\mathcal{O}(n^2)$ time, kernel\nthinning compresses an $n$-point approximation to $\\mathbb{P}$ into a\n$\\sqrt{n}$-point approximation with comparable worst-case integration error\nacross the associated reproducing kernel Hilbert space. With high probability,\nthe maximum discrepancy in integration error is\n$\\mathcal{O}_d(n^{-1/2}\\sqrt{\\log n})$ for compactly supported $\\mathbb{P}$ and\n$\\mathcal{O}_d(n^{-\\frac{1}{2}} (\\log n)^{(d+1)/2}\\sqrt{\\log\\log n})$ for\nsub-exponential $\\mathbb{P}$ on $\\mathbb{R}^d$. In contrast, an equal-sized\ni.i.d.\\ sample from $\\mathbb{P}$ suffers $\\Omega(n^{-1/4})$ integration error.\nOur sub-exponential guarantees resemble the classical quasi-Monte Carlo error\nrates for uniform $\\mathbb{P}$ on $[0,1]^d$ but apply to general distributions\non $\\mathbb{R}^d$ and a wide range of common kernels. We use our results to\nderive explicit non-asymptotic maximum mean discrepancy bounds for Gaussian,\nMat\\'ern, and B-spline kernels and present two vignettes illustrating the\npractical benefits of kernel thinning over i.i.d.\\ sampling and standard Markov\nchain Monte Carlo thinning, in dimensions $d=2$ through $100$.\n"}
+{"abstract": "  In heat exchangers, an incompressible fluid is heated initially and cooled at\nthe boundary. The goal is to transfer the heat to the boundary as efficiently\nas possible. In this paper we study a related steady version of this problem\nwhere a steadily stirred fluid is uniformly heated in the interior and cooled\non the boundary. For a given large P\\'eclet number, how should one stir to\nminimize some norm of the temperature? This version of the problem was\npreviously studied by Marcotte, Doering et\\ al.\\ (SIAM Appl.\\ Math '18) in a\ndisk, where the authors used matched asymptotics to show that when the P\\'eclet\nnumber, $\\pe$, is sufficiently large one can stir the fluid in a manner that\nensures the total heat is $O(1/\\pe)$. In this paper we confirm their results\nwith rigorous proofs, and also provide an almost matching lower bound. For\nsimplicity, we work on the infinite strip instead of the unit disk and the\nproof uses probabilistic techniques.\n"}
+{"abstract": "  Due to recent improvements in image resolution and acquisition speed,\nmaterials microscopy is experiencing an explosion of published imaging data.\nThe standard publication format, while sufficient for traditional data\ningestion scenarios where a select number of images can be critically examined\nand curated manually, is not conducive to large-scale data aggregation or\nanalysis, hindering data sharing and reuse. Most images in publications are\npresented as components of a larger figure with their explicit context buried\nin the main body or caption text, so even if aggregated, collections of images\nwith weak or no digitized contextual labels have limited value. To solve the\nproblem of curating labeled microscopy data from literature, this work\nintroduces the EXSCLAIM! Python toolkit for the automatic EXtraction,\nSeparation, and Caption-based natural Language Annotation of IMages from\nscientific literature. We highlight the methodology behind the construction of\nEXSCLAIM! and demonstrate its ability to extract and label open-source\nscientific images at high volume.\n"}
+{"abstract": "  By generalizing a geometric argument for frictionless spheres, a model is\nproposed for the jamming density $\\phi_J$ of mechanically stable packings of\nbidisperse, frictional spheres. The monodisperse, $\\mu_s$-dependent jamming\ndensity $\\phi_J^{\\mathrm{mono}}(\\mu_s)$ is the only input required in the\nmodel, where $\\mu_s$ is the coefficient of friction. The predictions of the\nmodel are validated by robust estimates of $\\phi_J$ obtained from computer\nsimulations of up to $10^7$ particles for a wide range of $\\mu_s$, and size\nratios up to 40:1. Although $\\phi_J$ varies nonmonotonically with the volume\nfraction of small spheres $f^s$ for all $\\mu_s$, its maximum value\n$\\phi_{J,\\mathrm{max}}$ at an optimal $f^{s}_{\\mathrm{max}}$ are both\n$\\mu_s$-dependent. The optimal $f^{s}_{\\mathrm{max}}$ is characterized by a\nsharp transition in the fraction of small rattler particles.\n"}
+{"abstract": "  We consider a heat conduction problem $S$ with mixed boundary conditions in a\n$n$-dimensional domain $\\Omega$ with regular boundary and a family of problems\n$S_{\\alpha}$ with also mixed boundary conditions in $\\Omega$, where $\\alpha>0$\nis the heat transfer coefficient on the portion of the boundary $\\Gamma_{1}$.\nIn relation to these state systems, we formulate Neumann boundary optimal\ncontrol problems on the heat flux $q$ which is definite on the complementary\nportion $\\Gamma_{2}$ of the boundary of $\\Omega$. We obtain existence and\nuniqueness of the optimal controls, the first order optimality conditions in\nterms of the adjoint state and the convergence of the optimal controls, the\nsystem state and the adjoint state when the heat transfer coefficient $\\alpha$\ngoes to infinity. Furthermore, we formulate particular boundary optimal control\nproblems on a real parameter $\\lambda$, in relation to the parabolic problems\n$S$ and $S_{\\alpha}$ and to mixed elliptic problems $P$ and $P_{\\alpha}$. We\nfind a explicit form for the optimal controls, we prove monotony properties and\nwe obtain convergence results when the parameter time goes to infinity.\n"}
+{"abstract": "  An antiproton plasma confined in a quasi-1D device is described in terms of a\nself-consistent fluid formulation using a variational approach. Unlike previous\ntreatments, the use of the time-dependent variational method allows to retain\nthe thermal and Coulomb effects. A certain Ansatz is proposed for the number\ndensity and fluid velocity fields, which reduces the problem essentially to\nordinary nonlinear differential equations. In adiabatic cooling, the frequency\nof the trap potential is slowly decreased. An adiabatic equation of state is\nassumed for closure. The numerical simulation of the nonlinear dynamics is\nperformed, for realistic parameters.\n"}
+{"abstract": "  In this article we have showed that a gradient $\\rho$-Einstein soliton with a\nvector field of bounded norm and satisfying some other conditions is isometric\nto the Euclidean sphere. Later, we have proved that a non-trivial complete\ngradient $\\rho$-Einstein soliton with finite weighted Dirichlet integral and\ncertain restriction on Ricci curvature must be of constant scalar curvature and\nsteady Ricci flat. Finally, we have proved that a non-shrinking or\nnon-expanding gradient traceless Ricci soliton possessing some conditions must\nbe steady.\n"}
+{"abstract": "  The two-stage methods for instance segmentation, e.g. Mask R-CNN, have\nachieved excellent performance recently. However, the segmented masks are still\nvery coarse due to the downsampling operations in both the feature pyramid and\nthe instance-wise pooling process, especially for large objects. In this work,\nwe propose a new method called RefineMask for high-quality instance\nsegmentation of objects and scenes, which incorporates fine-grained features\nduring the instance-wise segmenting process in a multi-stage manner. Through\nfusing more detailed information stage by stage, RefineMask is able to refine\nhigh-quality masks consistently. RefineMask succeeds in segmenting hard cases\nsuch as bent parts of objects that are over-smoothed by most previous methods\nand outputs accurate boundaries. Without bells and whistles, RefineMask yields\nsignificant gains of 2.6, 3.4, 3.8 AP over Mask R-CNN on COCO, LVIS, and\nCityscapes benchmarks respectively at a small amount of additional\ncomputational cost. Furthermore, our single-model result outperforms the winner\nof the LVIS Challenge 2020 by 1.3 points on the LVIS test-dev set and\nestablishes a new state-of-the-art. Code will be available at\nhttps://github.com/zhanggang001/RefineMask.\n"}
+{"abstract": "  Model-based deep reinforcement learning has achieved success in various\ndomains that require high sample efficiencies, such as Go and robotics.\nHowever, there are some remaining issues, such as planning efficient\nexplorations to learn more accurate dynamic models, evaluating the uncertainty\nof the learned models, and more rational utilization of models. To mitigate\nthese issues, we present MEEE, a model-ensemble method that consists of\noptimistic exploration and weighted exploitation. During exploration, unlike\nprior methods directly selecting the optimal action that maximizes the expected\naccumulative return, our agent first generates a set of action candidates and\nthen seeks out the optimal action that takes both expected return and future\nobservation novelty into account. During exploitation, different discounted\nweights are assigned to imagined transition tuples according to their model\nuncertainty respectively, which will prevent model predictive error propagation\nin agent training. Experiments on several challenging continuous control\nbenchmark tasks demonstrated that our approach outperforms other model-free and\nmodel-based state-of-the-art methods, especially in sample complexity.\n"}
+{"abstract": "  Considering a strong field limit, we investigate the retrolensing phenomenon\nin the vicinity of a Janis-Newman-Winicour (JNW) naked singularity embedded in\na scalar field. We assume that the light rays from a nearby source are\nreflected by the photon sphere of the naked singularity, acting as a lens, to\ncreate a pair of images. The analytic expressions of the lensing coefficients\n$\\bar{a}$ and $\\bar{b}$ are obtained, which are scalar dependent and discordant\nwith \\cite{Bozza:2002b}. Moreover, considering the powerful supermassive black\nhole candidates Sgr A* and M87*, we examined the influence of the scalar field\non the apparent brightness and angular positions of the parity images. Our\nresults are highlighted in correspondence with a non-scalar field gravity,\nspecifically the Schwarzschild gravity. We have found that the brightness\nincreases in the presence of the scalar field, while, the lensing coefficients\n$\\bar{a}$, $\\bar{b}$, the angular positions and the angular separation of the\nrelativistic images experience a reverse effect.\n"}
+{"abstract": "  Let $ 0\\le f\\in C^{0,1}(\\mathbb R^n)$. Given a domain $\\Omega\\subset \\mathbb\nR^n$, we prove that any stable solution to the equation $-\\Delta u=f(u)$ in\n$\\Omega$ satisfies a BMO interior regularity when $n=10$, and an Morrey\n$M^{p_n,4+2/(p_n-2)}$ interior regularity when $n\\ge 11$, where\n$$p_n=\\frac{2(n-2\\sqrt{n-1}-2)}{n-2\\sqrt{n-1}-4}. $$\n  This result is optimal as hinted by earlier results, and answers an open\nquestion raised by Cabr\\'e, Figalli, Ros-Oton and Serra. As an application, we\nshow a sharp Liouville property: Any stable solution $u \\in C^2(\\mathbb R^n)$\nto $-\\Delta u=f(u)$ in $\\mathbb R^n$ satisfying the growth condition, i.e.\\\n$|u(x)|= o\\left( \\log|x| \\right)$ as $|x|\\to+\\infty$ when $n=10$; or $|u(x)|=\no\\left( |x| ^{ -\\frac n2+\\sqrt{n-1}+2 }\\right)$ as $x|\\to+\\infty$ when $n\\ge\n11$, must be a constant. This extends the well-known Liouville property for\nradial stable solutions obtained by Villegas.\n"}
+{"abstract": "  The aim of quantum metrology is to estimate target parameters as precisely as\npossible. In this paper, we consider quantum metrology based on\nsymmetry-protected adiabatic transformation. We introduce a ferromagnetic Ising\nmodel with a transverse field as a probe and consider the estimation of a\nlongitudinal field. Without the transverse field, the ground state of the probe\nis given by the Greenberger-Horne-Zeilinger state, and thus the Heisenberg\nlimit estimation of the longitudinal field can be achieved through parity\nmeasurement. In our scheme, full information of the longitudinal field encoded\non parity is exactly mapped to global magnetization by symmetry-protected\nadiabatic transformation, and thus the parity measurement can be replaced with\nglobal magnetization measurement. Moreover, this scheme requires neither\naccurate control of individual qubits nor that of interaction strength. We\ndiscuss the effects of the finite transverse field and nonadiabatic transitions\nas imperfection of adiabatic transformation. By taking into account finite time\nduration for state preparation, sensing, and readout, we also compare\nperformance of the present scheme with a classical scheme in the absence and\npresence of dephasing.\n"}
+{"abstract": "  Postprocessing ensemble weather predictions to correct systematic errors has\nbecome a standard practice in research and operations. However, only few recent\nstudies have focused on ensemble postprocessing of wind gust forecasts, despite\nits importance for severe weather warnings. Here, we provide a comprehensive\nreview and systematic comparison of eight statistical and machine learning\nmethods for probabilistic wind gust forecasting via ensemble postprocessing,\nthat can be divided in three groups: State of the art postprocessing techniques\nfrom statistics (ensemble model output statistics (EMOS), member-by-member\npostprocessing, isotonic distributional regression), established machine\nlearning methods (gradient-boosting extended EMOS, quantile regression forests)\nand neural network-based approaches (distributional regression network,\nBernstein quantile network, histogram estimation network). The methods are\nsystematically compared using six years of data from a high-resolution,\nconvection-permitting ensemble prediction system that was run operationally at\nthe German weather service, and hourly observations at 175 surface weather\nstations in Germany. While all postprocessing methods yield calibrated\nforecasts and are able to correct the systematic errors of the raw ensemble\npredictions, incorporating information from additional meteorological predictor\nvariables beyond wind gusts leads to significant improvements in forecast\nskill. In particular, we propose a flexible framework of locally adaptive\nneural networks with different probabilistic forecast types as output, which\nnot only significantly outperform all benchmark postprocessing methods but also\nlearn physically consistent relations associated with the diurnal cycle,\nespecially the evening transition of the planetary boundary layer.\n"}
+{"abstract": "  The linearized Korteweg-De Vries equation can be written as a Hamilton-like\nsystem. However, the Hamilton energy depends on the time, and is a nonsymmetric\noperator on $L^2({\\bf R})$. By performing suitable unitary transforms on the\nHamilton energy, we can reduce this operator into one that is not independent\non the time but nonsymmetric. In this study, we consider the $L^2$-stability\nissues and smoothing estimates for this operator, and prove that it has no\neigenvalues.\n"}
+{"abstract": "  Consider an analytical function $f:V\\subset\\mathbb R^2\\rightarrow\\mathbb R$\nhaving $0$ as its regular value, a switching manifold $\\Sigma=f^{-1}(0)$ and a\npiecewise analytical vector field $X=(X^+,X^-)$, i.e. $X^\\pm$ are analytical\nvector fields defined on $\\Sigma^\\pm=\\{p\\in V: \\pm f(p)>0\\}$. We characterize\nwhen the vector field $X$ has a monodromic singular point in $\\Sigma$, called\n$\\Sigma$-monodromic singular point. Moreover, under certain conditions, we show\nthat a $\\Sigma$-monodromic singular point of $X$ has a neighborhood free of\nlimit cycles.\n"}
+{"abstract": "  We prove a generalization of the strong subadditivity of the von Neumann\nentropy for bosonic quantum Gaussian systems. Such generalization determines\nthe minimum values of linear combinations of the entropies of subsystems\nassociated to arbitrary linear functions of the quadratures, and holds for\narbitrary quantum states including the scenario where the entropies are\nconditioned on a memory quantum system. We apply our result to prove new\nentropic uncertainty relations with quantum memory, a generalization of the\nquantum Entropy Power Inequality, and the linear time scaling of the\nentanglement entropy produced by quadratic Hamiltonians.\n"}
+{"abstract": "  Advances in image-based dietary assessment methods have allowed nutrition\nprofessionals and researchers to improve the accuracy of dietary assessment,\nwhere images of food consumed are captured using smartphones or wearable\ndevices. These images are then analyzed using computer vision methods to\nestimate energy and nutrition content of the foods. Food image segmentation,\nwhich determines the regions in an image where foods are located, plays an\nimportant role in this process. Current methods are data dependent, thus cannot\ngeneralize well for different food types. To address this problem, we propose a\nclass-agnostic food image segmentation method. Our method uses a pair of eating\nscene images, one before start eating and one after eating is completed. Using\ninformation from both the before and after eating images, we can segment food\nimages by finding the salient missing objects without any prior information\nabout the food class. We model a paradigm of top down saliency which guides the\nattention of the human visual system (HVS) based on a task to find the salient\nmissing objects in a pair of images. Our method is validated on food images\ncollected from a dietary study which showed promising results.\n"}
+{"abstract": "  We prove that the polar degree of an arbitrarily singular projective\nhypersurface can be decomposed as a sum of non-negative numbers which represent\nlocal vanishing cycles of two different types. This yields lower bounds for the\npolar degree of any singular projective hypersurface.\n"}
+{"abstract": "  Predicting DNA-protein binding is an important and classic problem in\nbioinformatics. Convolutional neural networks have outperformed conventional\nmethods in modeling the sequence specificity of DNA-protein binding. However,\nnone of the studies has utilized graph convolutional networks for motif\ninference. In this work, we propose to use graph convolutional networks for\nmotif inference. We build a sequence k-mer graph for the whole dataset based on\nk-mer co-occurrence and k-mer sequence relationship and then learn DNA Graph\nConvolutional Network (DNA-GCN) for the whole dataset. Our DNA-GCN is\ninitialized with a one-hot representation for all nodes, and it then jointly\nlearns the embeddings for both k-mers and sequences, as supervised by the known\nlabels of sequences. We evaluate our model on 50 datasets from ENCODE. DNA-GCN\nshows its competitive performance compared with the baseline model. Besides, we\nanalyze our model and design several different architectures to help fit\ndifferent datasets.\n"}
+{"abstract": "  Recent studies show that neural natural language processing (NLP) models are\nvulnerable to backdoor attacks. Injected with backdoors, models perform\nnormally on benign examples but produce attacker-specified predictions when the\nbackdoor is activated, presenting serious security threats to real-world\napplications. Since existing textual backdoor attacks pay little attention to\nthe invisibility of backdoors, they can be easily detected and blocked. In this\nwork, we present invisible backdoors that are activated by a learnable\ncombination of word substitution. We show that NLP models can be injected with\nbackdoors that lead to a nearly 100% attack success rate, whereas being highly\ninvisible to existing defense strategies and even human inspections. The\nresults raise a serious alarm to the security of NLP models, which requires\nfurther research to be resolved. All the data and code of this paper are\nreleased at https://github.com/thunlp/BkdAtk-LWS.\n"}
+{"abstract": "  We develop new methods for computing the precise Dehn functions of coabelian\nsubgroups of direct products of groups, that is, subgroups which arise as\nkernels of homomorphisms from the direct product onto a free abelian group.\nThese improve and generalise previous results by Carter and Forester on Dehn\nfunctions of level sets in products of simply connected cube complexes, by\nBridson on Dehn functions of cocyclic groups and by Dison on Dehn functions of\ncoabelian groups. We then provide several applications of our methods to\nsubgroups of direct products of free groups, to groups with interesting\ngeometric finiteness properties and to subgroups of direct products of\nright-angled Artin groups.\n"}
+{"abstract": "  Quality estimation aims to measure the quality of translated content without\naccess to a reference translation. This is crucial for machine translation\nsystems in real-world scenarios where high-quality translation is needed. While\nmany approaches exist for quality estimation, they are based on supervised\nmachine learning requiring costly human labelled data. As an alternative, we\npropose a technique that does not rely on examples from human-annotators and\ninstead uses synthetic training data. We train off-the-shelf architectures for\nsupervised quality estimation on our synthetic data and show that the resulting\nmodels achieve comparable performance to models trained on human-annotated\ndata, both for sentence and word-level prediction.\n"}
+{"abstract": "  Resting state functional magnetic resonance images (fMRI) are commonly used\nfor classification of patients as having Alzheimer's disease (AD), mild\ncognitive impairment (MCI), or being cognitive normal (CN). Most methods use\ntime-series correlation of voxels signals during the observation period as a\nbasis for the classification. In this paper we show that Convolutional Neural\nNetwork (CNN) classification based on spatial correlation of time-averaged\nsignals yield a classification accuracy of up to 82% (sensitivity 86%,\nspecificity 80%)for a data set with 429 subjects (246 cognitive normal and 183\nAlzheimer patients). For the spatial correlation of time-averaged signal values\nwe use voxel subdomains around center points of the 90 regions AAL atlas. We\nform the subdomains as sets of voxels along a Hilbert curve of a bounding box\nin which the brain is embedded with the AAL regions center points serving as\nsubdomain seeds. The matrix resulting from the spatial correlation of the 90\narrays formed by the subdomain segments of the Hilbert curve yields a symmetric\n90x90 matrix that is used for the classification based on two different CNN\nnetworks, a 4-layer CNN network with 3x3 filters and with 4, 8, 16, and 32\noutput channels respectively, and a 2-layer CNN network with 3x3 filters and\nwith 4 and 8 output channels respectively. The results of the two networks are\nreported and compared.\n"}
+{"abstract": "  We consider the calculation of the thermal self-energy of a neutrino that\npropagates in a medium composed of fermions and scalars interacting via a\nYukawa-type coupling, in the case that the neutri no energy is much larger than\nthe fermion and scalar masses, as well as the temperature and chemical\npotentials of the background. In this kinematic regime the one-loop\ncontribution to the imaginary part of the self-energy is negligible. We\nconsider the two-loop contribution and we encounter the so-called pinch\nsingularities which are known to arise in higher loop self-energy calculations\nin Thermal Field Theory. With a judicious use of the properties and\nparametrizations of the thermal propagators the singularities are treated\neffectively and actually disappear. From the imaginary part of the self-energy,\nwe obtain a precise formula for the damping matrix expressed in terms of\nintegrals over the background particle distributions. The formulas predict a\nspecific dependence of the damping terms on the neutrino energy, depending on\nthe background conditions. For guidance to estimating the effects in specific\ncontexts, we compute the damping terms for several limiting cases of the\nmomentum distribution functions of the background particles. We discuss briefly\nthe connection between the results of our calculations for the damping matrix\nand the decoherence effects described in terms of the Lindblad equation.\n"}
+{"abstract": "  Name disambiguation is a complex but highly relevant challenge whenever\nanalysing real-world user data, such as data from version control systems. We\npropose gambit, a rule-based disambiguation tool that only relies on name and\nemail information. We evaluate its performance against two commonly used\nalgorithms with similar characteristics on manually disambiguated ground-truth\ndata from the Gnome GTK project. Our results show that gambit significantly\noutperforms both algorithms, achieving an F1 score of 0.985.\n"}
+{"abstract": "  Complicated assembly processes can be described as a sequence of two main\nactivities: grasping and insertion. While general grasping solutions are common\nin industry, insertion is still only applicable to small subsets of problems,\nmainly ones involving simple shapes in fixed locations and in which the\nvariations are not taken into consideration. Recently, RL approaches with prior\nknowledge (e.g., LfD or residual policy) have been adopted. However, these\napproaches might be problematic in contact-rich tasks since interaction might\nendanger the robot and its equipment. In this paper, we tackled this challenge\nby formulating the problem as a regression problem. By combining visual and\nforce inputs, we demonstrate that our method can scale to 16 different\ninsertion tasks in less than 10 minutes. The resulting policies are robust to\nchanges in the socket position, orientation or peg color, as well as to small\ndifferences in peg shape. Finally, we demonstrate an end-to-end solution for 2\ncomplex assembly tasks with multi-insertion objectives when the assembly board\nis randomly placed on a table.\n"}
+{"abstract": "  On the basis of density functional theory and semi-classical Boltzmann\ntheory, we have investigated the structural, elastic, electronic, optical and\nthermoelectric properties of 18--valence electron count rhodium based\nhalf-Heusler alloys focusing on RhTiP, RhTiAs, RhTiSb, and RhTiBi. The absence\nof imaginary frequencies in the phonon dispersion curve for these system\nverifies that they are structurally stable. RhTiP is ductile in nature, while\nothers are brittle. The alloys are found to be semiconducting with indirect\nband gaps ranging from 0.94 to 1.01 eV. Our calculations suggest these\nmaterials to have high absorption coefficient and optical conductivity in the\nultraviolet as well as visible region. While considering thermoelectricity, we\nfound that $p$--type doping is more favorable in improving the thermoelectric\nproperties. The calculated values of power factor with $p$-type doping are\ncomparable to some of the reported half-Heusler materials. The optimum figure\nof merit \\zt\\ is $\\sim1$ for RhTiBi suggesting it as a promising candidate for\nthermoelectric applications while RhTiP, RhTiAs, and RhTiSb with optimum \\zt \\\nvalues between 0.38 to 0.67 are possible candidates for use in thermoelectric\ndevices.\n"}
+{"abstract": "  Modeling fuzziness and imprecision in human rating data is a crucial problem\nin many research areas, including applied statistics, behavioral, social, and\nhealth sciences. Because of the interplay between cognitive, affective, and\ncontextual factors, the process of answering survey questions is a complex\ntask, which can barely be captured by standard (crisp) rating responses. Fuzzy\nrating scales have progressively been adopted to overcome some of the\nlimitations of standard rating scales, including their inability to disentangle\ndecision uncertainty from individual responses. The aim of this article is to\nprovide a novel fuzzy scaling procedure which uses Item Response Theory trees\n(IRTrees) as a psychometric model for the stage-wise latent response process.\nIn so doing, fuzziness of rating data is modeled using the overall rater's\npattern of responses instead of being computed using a single-item based\napproach. This offers a consistent system for interpreting fuzziness in terms\nof individual-based decision uncertainty. A simulation study and two empirical\napplications are adopted to assess the characteristics of the proposed model\nand provide converging results about its effectiveness in modeling fuzziness\nand imprecision in rating data.\n"}
+{"abstract": "  We consider the singularly perturbed problem $F_\\varepsilon\n(u,\\Omega):=\\int_\\Omega \\varepsilon |\\nabla^2u| + \\varepsilon^{-1}|1-|\\nabla\nu|^2|^2$ on bounded domains $\\Omega \\subset\\mathbb{R}^2$. Under appropriate\nboundary conditions, we prove that if $\\Omega$ is an ellipse then the\nminimizers of $F_\\varepsilon(\\cdot,\\Omega)$ converge to the viscosity solution\nof the eikonal equation $|\\nabla u|=1$ as $\\varepsilon \\to 0$.\n"}
+{"abstract": "  Assume that each species $l$ has its own jump rate $b_l$ in the multi-species\ntotally asymmetric simple exclusion process. We show that this model is\nintegrable in the sense that the Bethe Ansatz method is applicable to obtain\nthe transition probabilities for all possible $N$-particle systems with up to\n$N$ different species.\n"}
+{"abstract": "  In this work, we propose an approach for extrinsic sensor calibration from\nper-sensor ego-motion estimates. Our problem formulation is based on dual\nquaternions, enabling two different online capable solving approaches. We\nprovide a certifiable globally optimal and a fast local approach along with a\nmethod to verify the globality of the local approach. Additionally, means for\nintegrating previous knowledge, for example, a common ground plane for planar\nsensor motion, are described. Our algorithms are evaluated on simulated data\nand on a publicly available dataset containing RGB-D camera images. Further,\nour online calibration approach is tested on the KITTI odometry dataset, which\nprovides data of a lidar and two stereo camera systems mounted on a vehicle.\nOur evaluation confirms the short run time, state-of-the-art accuracy, as well\nas online capability of our approach while retaining the global optimality of\nthe solution at any time.\n"}
+{"abstract": "  We show that for every finite set of prime numbers $S$, there are at most\nfinitely many singular moduli that are $S$-units. The key new ingredient is\nthat for every prime number $p$, singular moduli are $p$-adically disperse. We\nprove analogous results for the Weber modular functions, the\n$\\lambda$-invariants and the McKay-Thompson series associated to the elements\nof the monster group. Finally, we also obtain that a modular function that\nspecializes to infinitely many algebraic units at quadratic imaginary numbers\nmust be a modular unit.\n"}
+{"abstract": "  Circuit quantum electrodynamics is one of the most promising platforms for\nefficient quantum simulation and computation. In recent groundbreaking\nexperiments, the immense flexibility of superconducting microwave resonators\nwas utilized to realize hyperbolic lattices that emulate quantum physics in\nnegatively curved space. Here we investigate experimentally feasible settings\nin which a few superconducting qubits are coupled to a bath of photons evolving\non the hyperbolic lattice. We compare our numerical results for finite lattices\nwith analytical results for continuous hyperbolic space on the Poincar\\'{e}\ndisk. We find good agreement between the two descriptions in the\nlong-wavelength regime. We show that photon-qubit bound states have a\ncurvature-limited size. We propose to use a qubit as a local probe of the\nhyperbolic bath, for example by measuring the relaxation dynamics of the qubit.\nWe find that, although the boundary effects strongly impact the photonic\ndensity of states, the spectral density is well described by the continuum\ntheory. We show that interactions between qubits are mediated by photons\npropagating along geodesics. We demonstrate that the photonic bath can give\nrise to geometrically-frustrated hyperbolic quantum spin models with\nfinite-range or exponentially-decaying interaction.\n"}
+{"abstract": "  A high-tech manufacturer often produces products that consist of many\nmodules. These modules are either sourced from one of its suppliers or produced\nin-house. In this paper we study an assembly system in which one module is\nsourced from a supplier with a fixed lead-time, while the other module is\nproduced by the manufacturer itself in a make-to-order production system. Since\nunavailability of one of the modules has costly consequences for the production\nof the end-product, it is important to coordinate between the ordering policy\nfor one module and the production of the other. We propose an order policy for\nthe lead-time module with base-stock levels depending on the number of\noutstanding orders in the production system of the in-house produced module. We\nprove monotonicity properties of this policy and show optimality. Furthermore,\nwe conduct a computational experiment to evaluate how the costs of this policy\ncompare to those of a policy with fixed base-stock levels and show that average\nsavings of 17% are attained.\n"}
+{"abstract": "  Given a sequence of Marcinkiewicz-Zygmund inequalities in $L_2$ on a usual\ncompact space $\\mathcal M$, Gr\\\"ochenig introduced the weighted least squares\npolynomials and the least squares quadrature from pointwise samples of a\nfunction, and obtained approximation theorems and quadrature errors. In this\npaper we obtain approximation theorems and quadrature errors on the sphere\nwhich are optimal. We also give upper bounds of the operator norms of the\nweighted least squares operators.\n"}
+{"abstract": "  We study finite-time horizon continuous-time linear-convex reinforcement\nlearning problems in an episodic setting. In this problem, the unknown linear\njump-diffusion process is controlled subject to nonsmooth convex costs. We show\nthat the associated linear-convex control problems admit Lipchitz continuous\noptimal feedback controls and further prove the Lipschitz stability of the\nfeedback controls, i.e., the performance gap between applying feedback controls\nfor an incorrect model and for the true model depends Lipschitz-continuously on\nthe magnitude of perturbations in the model coefficients; the proof relies on a\nstability analysis of the associated forward-backward stochastic differential\nequation. We then propose a novel least-squares algorithm which achieves a\nregret of the order $O(\\sqrt{N\\ln N})$ on linear-convex learning problems with\njumps, where $N$ is the number of learning episodes; the analysis leverages the\nLipschitz stability of feedback controls and concentration properties of\nsub-Weibull random variables.\n"}
+{"abstract": "  In the compact variable-gap undulator with Hydraulic-Assist Driver, miniature\nhydraulic cylinders compensate for ~90% of magnetic forces and mechanical\nactuators are used to accurately control the undulator gap. This method of\nhydraulic assist enables design simplicity and compactness of the undulator.\nProof-of-concept experiments and test results of the undulator prototype in\nstep-by-step motion mode indicated excellent accuracy and stability properties\nof the driver. In this note we present test results of hydraulic-assist driver\nin continuous motion mode. Results indicate that hydraulic-assist driver can\nprovide smooth continuous undulator gap change, keeping the undulator harmonic\nenergy variation within the required 1eV/sec, and speed and RMS normalized\nenergy errors at 1.8e-4 level (or less). That satisfies any application planned\nat CHESS.\n"}
+{"abstract": "  The rate and location of Binary Neutron Star (BNS) mergers are determined by\na combination of the star formation history and the Delay Time Distribution\n(DTD) function. In this paper, we couple the star formation rate histories\n(SFRHs) from the IllustrisTNG model to a series of varied assumptions for the\nBNS DTD to make predictions for the BNS merger host galaxy mass function. These\npredictions offer two outcomes: (i) in the near term: influence BNS merger\nevent follow-up strategy by scrutinizing where most BNS merger events are\nexpected to occur and (ii) in the long term: constrain the DTD for BNS merger\nevents once the host galaxy mass function is observationally well determined.\nFrom our fiducial model analysis, we predict that 50% of BNS mergers will occur\nin host galaxies with stellar mass between $10^{10} - 10^{11}$ $M_\\odot$, 68%\nbetween $4 \\times 10^{9} - 3\\times 10^{11}$ $M_\\odot$, and 95% between $4\n\\times 10^8 - 2 \\times 10^{12}$ $M_\\odot$. We find that the details of the DTD\nemployed does not have a strong effect on the peak of the host mass function.\nHowever, varying the DTD provides enough spread that the true DTD can be\ndetermined from enough electromagnetic observations of BNS mergers. Knowing the\ntrue DTD can help us determine the prevalence of BNS systems formed through\nhighly eccentric and short separation fast-merging channels and can constrain\nthe dominant source of r-process material.\n"}
+{"abstract": "  The HYDRO mini-application has been successfully used as a research vehicle\nin previous PRACE projects [6]. In this paper, we evaluate the benefits of the\ntasking model introduced in recent OpenMP standards [9]. We have developed a\nnew version of HYDRO using the concept of OpenMP tasks and this implementation\nis compared to already existing and optimized OpenMP versions of HYDRO.\n"}
+{"abstract": "  The non-collinear antiferromagnetic (AFM) structure makes Mn3Sn exhibit\nexotic properties. At present, it has been found that both the hydrostatic\npressure and the strain introduced by interstitial N atoms have a great\ninfluence on this magnetic structure. Here, the effect of the residual strain\n(RS) on it is investigated. AC and DC magnetic measurement results suggest that\nMn3Sn without RS has the non-collinear AFM structure only in the temperature\nrange of 285 K to 400 K; while Mn3Sn with RS has a non-coplanar AFM structure\nin the entire temperature range from 5 K to 400 K. Both anomalous Hall effect\nand topological Hall effect appears in Mn3Sn with RS, supporting the\nanticipated non-coplanar AFM structure. Our findings point out a method to\nrealize the chiral non-coplanar AFM structure through the engineering, thereby\nproviding a path for the construction of topological antiferromagnets.\n"}
+{"abstract": "  A flat solvmanifold is a compact quotient $\\Gamma\\backslash G$ where $G$ is a\nsimply-connected solvable Lie group endowed with a flat left invariant metric\nand $\\Gamma$ is a lattice of $G$. Any such Lie group can be written as\n$G=\\mathbb{R}^k\\ltimes_{\\phi} \\mathbb{R}^m$ with $\\mathbb{R}^m$ the nilradical.\nIn this article we focus on 6-dimensional splittable flat solvmanifolds, which\nare obtained quotienting $G$ by a lattice $\\Gamma$ that can be decomposed as\n$\\Gamma=\\Gamma_1\\ltimes_{\\phi}\\Gamma_2$, where $\\Gamma_1$ and $\\Gamma_2$ are\nlattices of $\\mathbb{R}^k$ and $\\mathbb{R}^m$, respectively. We obtain their\nclassification by analyzing the conjugacy classes of integer matrices of finite\norder in dimensions 4 and 5.\n"}
+{"abstract": "  Recently, language-guided global image editing draws increasing attention\nwith growing application potentials. However, previous GAN-based methods are\nnot only confined to domain-specific, low-resolution data but also lacking in\ninterpretability. To overcome the collective difficulties, we develop a\ntext-to-operation model to map the vague editing language request into a series\nof editing operations, e.g., change contrast, brightness, and saturation. Each\noperation is interpretable and differentiable. Furthermore, the only\nsupervision in the task is the target image, which is insufficient for a stable\ntraining of sequential decisions. Hence, we propose a novel operation planning\nalgorithm to generate possible editing sequences from the target image as\npseudo ground truth. Comparison experiments on the newly collected MA5k-Req\ndataset and GIER dataset show the advantages of our methods. Code is available\nat https://jshi31.github.io/T2ONet.\n"}
+{"abstract": "  The central (`bulge') region of the Milky Way is teeming with a significant\nfraction of mildly metal-deficient stars with atmospheres that are strongly\nenriched in cyanogen ($^{12}$C$^{14}$N). Some of these objects, which are also\nknown as nitrogen-enhanced stars, are hypothesised to be relics of the ancient\nassembly history of the Milky Way. Although the chemical similarity of\nnitrogen-enhanced stars to the unique chemical patterns observed in globular\nclusters has been observed, a direct connection between field stars and\nglobular clusters has not yet been proven. In this work, we report on\nhigh-resolution, near-infrared spectroscopic observations of the bulge globular\ncluster NGC 6723, and the serendipitous discovery of a star,\n2M18594405$-$3651518, located outside the cluster (near the tidal radius) but\nmoving on a similar orbit, providing the first clear piece of evidence of a\nstar that was very likely once a cluster member and has recently been ejected.\nIts nitrogen abundance ratio ([N/Fe]$\\gtrsim + 0.94$) is well above the typical\nGalactic field-star levels, and it exhibits noticeable enrichment in the heavy\n$s$-process elements (Ce, Nd, and Yb), along with moderate carbon enrichment;\nall characteristics are known examples in globular clusters. This result\nsuggests that some of the nitrogen-enhanced stars in the bulge likely\noriginated from the tidal disruption of globular clusters.\n"}
+{"abstract": "  Various methods for the recursive evaluation of scattering amplitudes in\nquantum field theory and string theory have been put forward during the last\ncouple of years. In these proceedings we describe a geometrical framework,\nwhich is believed to be capable of treating many of these recursions in a\nunified way. Our recursive framework is based on manipulating iterated\nintegrals on Riemann surfaces with boundaries. A geometric parameter appears as\nvariable of a differential equation of KZ or KZB type. The parameter\ninterpolates between two associated regularized boundary values, which contain\niterated integrals closely related to scattering amplitudes defined on two\ndifferent geometries.\n"}
+{"abstract": "  In Geometric function theory, occasionally attempts have been made to solve a\nparticular problem for the Ma-Minda classes, $\\mathcal{S}^*(\\psi)$ and\n$\\mathcal{C}(\\psi)$ of univalent starlike and convex functions, respectively.\nRecently, a popular radius problem generally known as Bohr's phenomenon has\nbeen studied in various settings, however little is known about Rogosinski\nradius. In this article, for a fixed $f\\in \\mathcal{S}^*(\\psi)$ or\n$\\mathcal{C}(\\psi),$ the class of analytic subordinants $S_{f}(\\psi):= \\{g :\ng\\prec f \\} $ is studied for the Bohr-Rogosinski phenomenon in a general\nsetting. It's applications to the classes $\\mathcal{S}^*(\\psi)$ and\n$\\mathcal{C}(\\psi)$ are also shown.\n"}
+{"abstract": "  A 2-uniform tiling is an edge-to-edge tiling by regular polygons having $2$\ndistinct transitivity classes of vertices. There are 20 distinct 2-uniform\ntilings (these are of $14$ different types) on the plane, and since the plane\nis the universal cover of the torus, it is natural to explore maps on the torus\nthat correspond to the 2-uniform tilings. In this article, we discuss that if a\nmap is the quotient of a plane's $2$-uniform lattice then what would be the\nbounds of the number of vertex orbits.\n  A 3-uniform tiling is an edge-to-edge tiling by regular polygons having $3$\ndistinct transitivity classes of vertices. There are $61$ distinct $3$-uniform\ntilings on the plane. In this article, we discuss that if a map is the quotient\nof a plane's $3$-uniform lattice then what would be the bounds of the number of\nvertex orbits.\n"}
+{"abstract": "  Turbulent plane Poiseuille and Couette flows share the same geometry, but\nproduce their flow rate owing to different external drivers, pressure gradient\nand shear respectively. By looking at integral energy fluxes, we pose and\nanswer the question of which flow performs better at creating flow rate. We\ndefine a flow {\\em efficiency}, that quantifies the fraction of power used to\nproduce flow rate instead of being wasted as a turbulent overhead; {\\em\neffectiveness}, instead, describes the amount of flow rate produced by a given\npower. The work by Gatti \\emph{et al.} (\\emph{J. Fluid Mech.} vol.857, 2018,\npp. 345--373), where the constant power input (CPI) concept was developed to\ncompare turbulent Poiseuille flows with drag reduction, is here extended to\ncompare different flows. By decomposing the mean velocity field into a laminar\ncontribution and a deviation, analytical expressions are derived which are the\nenergy-flux equivalents of the FIK identity. These concepts are applied to\nliterature data supplemented by a new set of direct numerical simulations, to\nfind that Couette flows are less efficient but more effective than Poiseuille\nones. The reason is traced to the more effective laminar component of Couette\nflows, which compensates for their higher turbulent activity. It is also\nobserved that, when the fluctuating fields of the two flows are fed with the\nsame total power fraction, Couette flows dissipate a smaller percentage of it\nvia turbulent dissipation. A decomposition of the fluctuating field into large\nand small scales explains this feature: Couette flows develop stronger\nlarge-scale structures, which alter the mean flow while contributing less\nsignificantly to dissipation.\n"}
+{"abstract": "  Propagation of ultrashort pulses at least a few tens of optical cycles in\nduration through a negative index material is investigated theoretically based\non the generalized nonlinear Schr\\\"{o}dinger equation with pseudo-quintic\nnonlinearity and self-steepening effect. Novel periodic waves of different\nforms are shown to exist in the system in the presence of higher-order effects.\nIt is found that such periodic structures exhibit an interesting chirping\nproperty which depends on the light field intensity. The nonlinearity in pulse\nchirp is found to be caused by the presence of self-steepening effect in the\nnegative index medium. The solutions also comprise dark, bright, and kink\nsolitary waves. Stability of the solitary wave solutions are proved\nanalytically using the theory of nonlinear dispersive waves. The stability of\nthe solutions is numerically studied under finite initial perturbations.\n"}
+{"abstract": "  Imagine, you enter a grocery store to buy food. How many peopledo you overlap\nwith in this store? How much time do you overlap witheach person in the store?\nIn this paper, we answer these questions bystudying the overlap times between\ncustomers in the infinite serverqueue. We compute in closed form the steady\nstate distribution ofthe overlap time between a pair of customers and the\ndistribution ofthe number of customers that an arriving customer will overlap\nwith.Finally, we define a residual process that counts the number of\nover-lapping customers that overlap in the queue for at least{\\delta}time\nunitsand compute its mean, variance, and distribution in the exponentialservice\nsetting\n"}
+{"abstract": "  In arXiv:1201.4067 and arXiv:1611.08030, Eyink and Shi and Chibbaro et al.,\nrespectively, formally derived an infinite, coupled hierarchy of equations for\nthe spectral correlation functions of a system of weakly interacting nonlinear\ndispersive waves with random phases in the standard kinetic limit. Analogously\nto the relationship between the Boltzmann hierarchy and Boltzmann equation,\nthis spectral hierarchy admits a special class of factorized solutions, where\neach factor is a solution to the wave kinetic equation (WKE). A question left\nopen by these works and highly relevant for the mathematical derivation of the\nWKE is whether solutions of the spectral hierarchy are unique, in particular\nwhether factorized initial data necessarily lead to factorized solutions. In\nthis article, we affirmatively answer this question in the case of 4-wave\ninteractions by showing, for the first time, that this spectral hierarchy is\nwell-posed in an appropriate function space. Our proof draws on work of Chen\nand Pavlovi\\'{c} for the Gross-Pitaevskii hierarchy in quantum many-body theory\nand of Germain et al. for the well-posedness of the WKE.\n"}
+{"abstract": "  Prostate cancer (PCa) is one of the most commonly diagnosed cancer and one of\nthe leading causes of death among men, with almost 1.41 million new cases and\naround 375,000 deaths in 2020. Artificial Intelligence algorithms have had a\nhuge impact in medical image analysis, including digital histopathology, where\nConvolutional Neural Networks (CNNs) are used to provide a fast and accurate\ndiagnosis, supporting experts in this task. To perform an automatic diagnosis,\nprostate tissue samples are first digitized into gigapixel-resolution\nwhole-slide images. Due to the size of these images, neural networks cannot use\nthem as input and, therefore, small subimages called patches are extracted and\npredicted, obtaining a patch-level classification. In this work, a novel patch\naggregation method based on a custom Wide & Deep neural network model is\npresented, which performs a slide-level classification using the patch-level\nclasses obtained from a CNN. The malignant tissue ratio, a 10-bin malignant\nprobability histogram, the least squares regression line of the histogram, and\nthe number of malignant connected components are used by the proposed model to\nperform the classification. An accuracy of 94.24% and a sensitivity of 98.87%\nwere achieved, proving that the proposed system could aid pathologists by\nspeeding up the screening process and, thus, contribute to the fight against\nPCa.\n"}
+{"abstract": "  In this paper, we explore and evaluate the use of ranking-based objective\nfunctions for learning simultaneously a word string and a word image encoder.\nWe consider retrieval frameworks in which the user expects a retrieval list\nranked according to a defined relevance score. In the context of a word\nspotting problem, the relevance score has been set according to the string edit\ndistance from the query string. We experimentally demonstrate the competitive\nperformance of the proposed model on query-by-string word spotting for both,\nhandwritten and real scene word images. We also provide the results for\nquery-by-example word spotting, although it is not the main focus of this work.\n"}
+{"abstract": "  We obtain an improvement of the John-Nirenberg inequality for the series of\nthe form $\\sum_{n=1}^{\\infty}n^{-1}e^{2\\pi i n^k x},\\;k>2,$ on intervals\nconsisting of points of a same convergent of their continued fractions. We also\nestablish a convergence criterion for these series.\n"}
+{"abstract": "  The amount of data collected from patients involved in clinical trials is\ncontinuously growing. All patient characteristics are potential covariates that\ncould be used to improve clinical trial analysis and power. However, the\nrestricted number of patients in phases I and II studies limits the possible\nnumber of covariates included in the analyses. In this paper, we investigate\nthe cost/benefit ratio of including covariates in the analysis of clinical\ntrials. Within this context, we address the long-running question \"What is the\noptimum number of covariates to include in a clinical trial?\" To further\nimprove the cost/benefit ratio of covariates, historical data can be leveraged\nto pre-specify the covariate weights, which can be viewed as the definition of\na new composite covariate. We analyze the use of a composite covariate while\nestimating the treatment effect in small clinical trials. A composite covariate\nlimits the loss of degrees of freedom and the risk of overfitting.\n"}
+{"abstract": "  The coupling between the angular momentum of a compact object and an external\ntidal field gives rise to the \"rotational\" tidal Love numbers, which affect the\ntidal deformability of a spinning self-gravitating body and enter the\ngravitational waveform of a binary inspiral at high post-Newtonian order. We\nprovide numerical evidence for a surprising \"hidden\" symmetry among the\nrotational tidal Love numbers with opposite parities, which are associated to\nperturbations belonging to separate sectors. This symmetry, whose existence had\nbeen suggested on the basis of a Lagrangian description of the tidal\ninteraction in a binary system, holds independently of the equation of state of\nthe star.\n"}
+{"abstract": "  Recent studies have highlighted the potential significance of intracluster\nmedium (ICM) clumping and its important implications for cluster cosmology and\nbaryon physics. Many of the ICM clumps can originate from infalling galaxies,\nas stripped interstellar medium (ISM) mixing into the hot ICM. However, a\ndirect connection between ICM clumping and stripped ISM has not been\nunambiguously established before. Here we present the discovery of the first\nand still the only known isolated cloud (or orphan cloud, OC) detected in both\nX-rays and H$\\alpha$ in the nearby cluster Abell 1367. With an effective radius\nof 30 kpc, this cloud has an average X-ray temperature of 1.6 keV, a bolometric\nX-ray luminosity of $\\sim 3.1\\times 10^{41}$ erg s$^{-1}$ and a hot gas mass of\n$\\sim 10^{10}\\ {\\rm M}_\\odot$. From the MUSE data, the OC shows an interesting\nvelocity gradient nearly along the east-west direction with a low level of\nvelocity dispersion of $\\sim 80$ km/s, which may suggest a low level of the ICM\nturbulence. The emission line diagnostics suggest little star formation in the\nmain H$\\alpha$ cloud and a LI(N)ER-like spectrum, but the excitation mechanism\nremain unclear. This example shows that the stripped ISM, even long time after\nthe initial removal from the galaxy, can still induce the ICM inhomogeneities.\nWe suggest that magnetic field can stabilize the OC by suppressing hydrodynamic\ninstabilities and thermal conduction. This example also suggests that at least\nsome ICM clumps are multi-phase in nature and implies that the ICM clumps can\nalso be traced in H$\\alpha$. Thus, future deep and wide-field H$\\alpha$ survey\ncan be used to probe the ICM clumping and turbulence.\n"}
+{"abstract": "  Following N.Kozyrev's idea about the influence of the gravitational fields of\nthe Sun and the Moon on the Earth's crust, we consider a low-frequency\nresonance of the Earth's crust blocks is happening before the occurrence of the\nearthquake. The resonance affects several geophysical parameters and is\nassociated with many short-term precursory phenomena; the most notable are the\nrelease of underground gases (radon), the air temperature rises, change of\nthermal radiation in the troposphere, and an increase in the electron density\nin the ionosphere. During the final stage of the earthquake genesis,\nlong-period gravity-seismic waves called the Kozyrev-Yagodin ( KaY-) wave are\nformed and moves from the periphery to the epicenter of the future earthquake.\nWe discuss that by integrating information from different stages of earthquake\ngenesis: the network observations of KaY waves and satellite thermal radiation,\nwe significantly could advance the accuracy and reliability of short-term\nearthquake forecasting, previously unachievable.\n"}
+{"abstract": "  Node clustering is a powerful tool in the analysis of networks. We introduce\na graph neural network framework to obtain node embeddings for directed\nnetworks in a self-supervised manner, including a novel probabilistic imbalance\nloss, which can be used for network clustering. Here, we propose directed flow\nimbalance measures, which are tightly related to directionality, to reveal\nclusters in the network even when there is no density difference between\nclusters. In contrast to standard approaches in the literature, in this paper,\ndirectionality is not treated as a nuisance, but rather contains the main\nsignal. DIGRAC optimizes directed flow imbalance for clustering without\nrequiring label supervision, unlike existing GNN methods, and can naturally\nincorporate node features, unlike existing spectral methods. Experimental\nresults on synthetic data, in the form of directed stochastic block models, and\nreal-world data at different scales, demonstrate that our method, based on flow\nimbalance, attains state-of-the-art results on directed graph clustering, for a\nwide range of noise and sparsity levels and graph structures and topologies.\n"}
+{"abstract": "  We consider the fractional mean curvature flow of entire Lipschitz graphs. We\nprovide regularity results, and we study the long time asymptotics of the flow.\nIn particular we show that in a suitable rescaled framework, if the initial\ngraph is a sublinear perturbation of a cone, the evolution asymptotically\napproaches an expanding self-similar solution. We also prove stability of\nhyperplanes and of convex cones in the unrescaled setting.\n"}
+{"abstract": "  In this paper, we quantitatively consider the enhance-dissipation effect of\nthe advection term to the $p$-Laplacian equations. More precisely, we show the\nmixing property of flow for the passive scalar enhances the dissipation process\nof the $p$-Laplacian in the sense of $L^2$ decay, that is, the $L^2$ decay can\nbe arbitrarily fast. The main ingredient of our argument is to understand the\nunderlying iteration structure inherited from the evolution $p$-Laplacian\nadvection equations. This extends the well-known results of the dissipation\nenhancement result of the linear Laplacian by Constantin, Kiselev, Ryzhik, and\nZlato\\v{s} into a non-linear setting.\n"}
+{"abstract": "  We settle the case of equality for the relative isoperimetric inequality\noutside any arbitrary convex set with not empty interior.\n"}
+{"abstract": "  Data scientists face a steep learning curve in understanding a new domain for\nwhich they want to build machine learning (ML) models. While input from domain\nexperts could offer valuable help, such input is often limited, expensive, and\ngenerally not in a form readily consumable by a model development pipeline. In\nthis paper, we propose Ziva, a framework to guide domain experts in sharing\nessential domain knowledge to data scientists for building NLP models. With\nZiva, experts are able to distill and share their domain knowledge using domain\nconcept extractors and five types of label justification over a representative\ndata sample. The design of Ziva is informed by preliminary interviews with data\nscientists, in order to understand current practices of domain knowledge\nacquisition process for ML development projects. To assess our design, we run a\nmix-method case-study to evaluate how Ziva can facilitate interaction of domain\nexperts and data scientists. Our results highlight that (1) domain experts are\nable to use Ziva to provide rich domain knowledge, while maintaining low mental\nload and stress levels; and (2) data scientists find Ziva's output helpful for\nlearning essential information about the domain, offering scalability of\ninformation, and lowering the burden on domain experts to share knowledge. We\nconclude this work by experimenting with building NLP models using the Ziva\noutput by our case study.\n"}
+{"abstract": "  This paper develops and evaluates the performance of an allocation agent to\nbe potentially integrated into the onboard Detect and Avoid (DAA) computer of\nan Unmanned Aircraft System (UAS). We consider a UAS that can be fully\ncontrolled by the onboard DAA system and by a remote human pilot. With a\ncommunication channel prone to latency, we consider a mixed initiative\ninteraction environment, where the control authority of the UAS is dynamically\nallocated by the allocation agent. In an encounter with a dynamic intruder, the\nprobability of collision may increase in the absence of pilot commands in the\npresence of latency. Moreover, a delayed pilot command may not result in safe\nresolution of the current scenario and need to be improvised. We design an\noptimization algorithm to reduce collision risk and refine delayed pilot\ncommands. Towards this end, a Markov Decision Process (MDP)and its solution are\nemployed to create a wait time map. The map consists of estimated times that\nthe UAS can wait for the remote pilot commands at each state. A command\nblending algorithm is designed to select an avoidance maneuver that prioritizes\nthe pilot intention extracted from the pilot commands. The wait time map and\nthe command blending algorithm are implemented and integrated into a\nclosed-loop simulator. We conduct ten thousands fast-time Monte Carlo\nsimulations and compare the performance of the integrated setup with a\nstandalone DAA setup. The simulation results show that the allocation agent\nenables the UAS to wait without inducing any near mid air collision (NMAC) and\nsevere loss of well clear (LoWC) while positively improve pilot involvement in\nthe encounter resolution.\n"}
+{"abstract": "  We prove that if ${(P_x)}_{x\\in \\mathscr X}$ is a family of probability\nmeasures which satisfy the log-Sobolev inequality and whose pairwise\nchi-squared divergences are uniformly bounded, and $\\mu$ is any mixing\ndistribution on $\\mathscr X$, then the mixture $\\int P_x \\, \\mathrm{d} \\mu(x)$\nsatisfies a log-Sobolev inequality. In various settings of interest, the\nresulting log-Sobolev constant is dimension-free. In particular, our result\nimplies a conjecture of Zimmermann and Bardet et al. that Gaussian convolutions\nof measures with bounded support enjoy dimension-free log-Sobolev inequalities.\n"}
+{"abstract": "  Science Information Network (SINET) is a Japanese academic backbone network\nfor more than 800 research institutions and universities. In this paper, we\npresent a multi-GPU-driven pipeline for handling huge session data of SINET.\nOur pipeline consists of ELK stack, multi-GPU server, and Splunk. A multi-GPU\nserver is responsible for two procedures: discrimination and histogramming.\nDiscrimination is dividing session data into ingoing/outgoing with subnet mask\ncalculation and network address matching. Histogramming is grouping\ningoing/outgoing session data into bins with map-reduce. In our architecture,\nwe use GPU for the acceleration of ingress/egress discrimination of session\ndata. Also, we use a tiling design pattern for building a two-stage map-reduce\nof CPU and GPU. Our multi-GPU-driven pipeline has succeeded in processing huge\nworkloads of about 1.2 to 1.6 billion session streams (500GB-650GB) within 24\nhours.\n"}
+{"abstract": "  For each degree p, we construct on any closed manifold a family of Riemannian\nmetrics, with fixed volume such that any positive eigenvalues of the rough and\nHodge Laplacians acting on differential p-forms converge to zero. In\nparticular, on the sphere, we can choose these Riemannian metrics as those of\nnon-negative sectional curvature. This is a generalization of the results by\nColbois and Maerten in 2010 to the case of higher degree forms.\n"}
+{"abstract": "  Stream fusion, also known as system combination, is a common technique in\nautomatic speech recognition for traditional hybrid hidden Markov model\napproaches, yet mostly unexplored for modern deep neural network end-to-end\nmodel architectures. Here, we investigate various fusion techniques for the\nall-attention-based encoder-decoder architecture known as the transformer,\nstriving to achieve optimal fusion by investigating different fusion levels in\nan example single-microphone setting with fusion of standard magnitude and\nphase features. We introduce a novel multi-encoder learning method that\nperforms a weighted combination of two encoder-decoder multi-head attention\noutputs only during training. Employing then only the magnitude feature encoder\nin inference, we are able to show consistent improvement on Wall Street Journal\n(WSJ) with language model and on Librispeech, without increase in runtime or\nparameters. Combining two such multi-encoder trained models by a simple late\nfusion in inference, we achieve state-of-the-art performance for\ntransformer-based models on WSJ with a significant WER reduction of 19%\nrelative compared to the current benchmark approach.\n"}
+{"abstract": "  The multicolor Ramsey number problem asks, for each pair of natural numbers\n$\\ell$ and $t$, for the largest $\\ell$-coloring of a complete graph with no\nmonochromatic clique of size $t$. Recent works of Conlon-Ferber and Wigderson\nhave improved the longstanding lower bound for this problem. We make a further\nimprovement by replacing an explicit graph appearing in their constructions by\na random graph. Graphs useful for this construction are exactly those relevant\nfor a problem of Erd\\H{o}s on graphs with no large cliques and few large\nindependent sets. We also make some basic observations about this problem.\n"}
+{"abstract": "  In this paper, a stage structured predator-prey model with general nonlinear\ntype of functional response is established and analyzed. The state-dependent\ntime delay (hereafter SDTD) is the time taken from predator's birth to its\nmaturity, formatted as a monotonical (ly) increasing, continuous(ly)\ndifferentiable and bounded function on the number of mature predator. The model\nis quite different from many previous models with SDTD, in the sense that the\nderivative of delay on the time is involved in the model. First, we have shown\nthat for a large class of commonly used types of functional responses,\nincluding Holling types I, II and III, Beddington-DeAngelis-type (hereafter\nBD-type), etc, the predator coexists with the prey permanently if and only if\nthe predator's net reproduction number is larger than one unit; Secondly, we\nhave discussed the local stability of the equilibria of the model; Finally, for\nthe special case of BD-type functional response, we claim that if the system is\npermanent, that is, the derivative of SDTD on the state is small enough and the\npredator interference is large enough, then the coexistence equilibrium is\nglobally asymptotically stable.\n"}
+{"abstract": "  Let $\\Lambda$ be an Artin algebra. In 2014, T. Adachi, O. Iyama and I. Reiten\nproved that the torsion funtorially finite classes in $\\mathrm{mod}\\,(\\Lambda)$\ncan be described by the $\\tau$-tilting theory. The aim of this paper is to\nintroduce the notion of $F$-torsion class in $\\mathrm{mod}\\,(\\Lambda)$, where\n$F$ is an additive subfunctor of $\\mathrm{Ext}^1_\\Lambda,$ and to characterize\nwhen these clases are preenveloping and $F$-preenveloping. In order to do that,\nwe introduce the notion of $F$-presilting $\\Lambda$-module. The latter is both\na generalization of $\\tau$-rigid and $F$-tilting in $\\mathrm{mod}(\\Lambda).$\n"}
+{"abstract": "  Time-dependent rotational electric polarizations have been proposed to\ngenerate temporally varying magnetic moments, for example, through a\ncombination of ferroelectric polarization and optical phonons. This phenomenon\nhas been called dynamical multiferroicity, but explicit experimental\ndemonstrations have been elusive to date. Here, we report the detection of a\ntemporal magnetic moment as high as 1.2 mu_B/atom in charge-doped thin film of\nsilicon under flexural strain. We demonstrate that the magnetic moment is\ngenerated by a combination of electric polarization arising from a\nflexoelectronic charge separation along the strain gradient and the deformation\npotential of phonons. The effect can be controlled by adjusting the external\nstrain gradient, doping concentration and dopant, and can be regarded as a\ndynamical multiferroic effect involving flexoelectronics polarization instead\nof ferroelectricity. The discovery of a large magnetic moment in silicon may\nenable the use of non-magnetic and non-ferroelectric semiconductors in various\nmultiferroic and spintronic applications.\n"}
+{"abstract": "  A mathematical model of COVID-19 with minimal compartments is developed. The\nmodel is simple enough to fit data on confirmed cases, estimate the hidden\ninfection figure and incorporate the effect of vaccination. With the effect of\nthe new variant being considered, the model fits well with Philippine data of\nconfirmed cases. With the fitted parameters, results show that a second peak,\nstrikingly similar in magnitude to the first, is to be expected if the current\ntransmission rate is increased by about 5\\% and already a complete disaster if\nit will go up to 20\\%. The model is then used to estimate time to achieve herd\nimmunity considering natural immunity from being infected and vaccination plan\nof the country. Results show that if the plan of 200,000 vaccinated people per\nday is achieved then herd immunity can be attained by March 2022. But if only\nhalf of that is achieved the time could go up to February 2023.\n"}
+{"abstract": "  A key challenge in wide adoption of sophisticated context-aware applications\nis the requirement of continuous sensing and context computing. This paper\npresents Panorama, a middleware that identifies collaboration opportunities to\noffload context computing tasks to nearby mobile devices as well as\ncloudlets/cloud. At the heart of Panorama is a multi-objective optimizer that\ntakes into account different constraints such as access cost, computation\ncapability, access latency, energy consumption and data privacy, and\nefficiently computes a collaboration plan optimized simultaneously for\ndifferent objectives such as minimizing cost, energy and/or execution time.\nPanorama provides support for discovering nearby devices and cloudlets/cloud,\ncomputing an optimal collaboration plan, distributing computation to\nparticipating devices, and getting the results back. The paper provides an\nextensive evaluation of Panorama via two representative context monitoring\napplications over a set of Android devices and a cloudlet/cloud under different\nconstraints.\n"}
+{"abstract": "  When developing reinforcement learning agents, the standard approach is to\ntrain an agent to converge to a fixed policy that is as close to optimal as\npossible for a single fixed reward function. If different agent behaviour is\nrequired in the future, an agent trained in this way must normally be either\nfully or partially retrained, wasting valuable time and resources. In this\nstudy, we leverage multi-objective reinforcement learning to create tunable\nagents, i.e. agents that can adopt a range of different behaviours according to\nthe designer's preferences, without the need for retraining. We apply this\ntechnique to sequential social dilemmas, settings where there is inherent\ntension between individual and collective rationality. Learning a single fixed\npolicy in such settings leaves one at a significant disadvantage if the\nopponents' strategies change after learning is complete. In our work, we\ndemonstrate empirically that the tunable agents framework allows easy adaption\nbetween cooperative and competitive behaviours in sequential social dilemmas\nwithout the need for retraining, allowing a single trained agent model to be\nadjusted to cater for a wide range of behaviours and opponent strategies.\n"}
+{"abstract": "  The Renormalization Group encodes three concepts that could be key to\naccelerate progress in quantum gravity. First, it provides a micro-macro\nconnection that could connect microscopic spacetime physics to phenomenology at\nobservationally accessible scales. Second, it enables a search for universality\nclasses that could link diverse quantum-gravity approaches and allow us to\ndiscover that distinct approaches could encode the same physics in\nmathematically distinct structures. Third, it enables the emergence of\nsymmetries at fixed points of the Renormalization Group flow, providing a way\nfor spacetime symmetries to emerge from settings in which these are broken at\nintermediate steps of the construction. These three concepts make the\nRenormalization Group an attractive method and conceptual underpinning of\nquantum gravity. Yet, in its traditional setup as a local coarse-graining, it\ncould appear at odds with concepts like background independence that are\nexpected of quantum gravity. Within the last years, several approaches to\nquantum gravity have found ways how these seeming contradictions could be\nreconciled and the power of the Renormalization Group approach unleashed in\nquantum gravity. This special issue brings together research papers and reviews\nfrom a broad range of quantum gravity approaches, providing a partial snapshot\nof this evolving field.\n"}
+{"abstract": "  Resonant metasurfaces are an attractive platform for enhancing the non-linear\noptical processes, such as second harmonic generation (SHG), since they can\ngenerate very large local electromagnetic fields while relaxing the\nphase-matching requirements. Here, we take this platform a step closer to the\npractical applications by demonstrating visible range, continuous wave (CW)\nSHG. We do so by combining the attractive material properties of gallium\nphosphide with engineered, high quality-factor photonic modes enabled by bound\nstates in the continuum. For the optimum case, we obtain efficiencies around\n5e-5 % W$^{-1}$ when the system is pumped at 1200 nm wavelength with CW\nintensities of 1 kW/cm$^2$. Moreover, we measure external efficiencies as high\nas 0.1 % W$^{-1}$ with pump intensities of only 10 MW/cm$^2$ for pulsed\nirradiation. This efficiency is higher than the values previously reported for\ndielectric metasurfaces, but achieved here with pump intensities that are two\norders of magnitude lower.\n"}
+{"abstract": "  We report results of large scale quantum Monte Carlo (QMC) simulations of\ngraphene. Using cutting-edge algorithmic improvements, we are able to consider\nspatial volumes, corresponding to 20808 electrons, that allow us to access\nenergy scales of direct relevance to experiments. Using constrained random\nphase approximation (cRPA) estimates of short ranged interactions combined with\na Coulomb tail akin to the vacuum, we are able to confront successfully\nnumerical and experimental estimates of the Fermi velocity renormalization.\nThese results and their comparison with perturbation theory not only show the\nnon-Fermi liquid character of graphene, but also prove the importance of\nlattice-scale physics for the quantitative description of the experimental data\nfor the Fermi velocity renormalization in suspended graphene.\n"}
+{"abstract": "  To make informed health policy decisions regarding a treatment, we must\nconsider both its cost and its clinical effectiveness. In past work, we\nintroduced the net benefit separation (NBS) as a novel measure of\ncost-effectiveness. The NBS is a probabilistic measure that characterizes the\nextent to which a treated patient will be more likely to experience benefit as\ncompared to an untreated patient. Due to variation in treatment response across\npatients, uncovering factors that influence cost-effectiveness can assist\npolicy makers in population-level decisions regarding resource allocation. In\nthis paper, we introduce a regression framework for NBS in order to estimate\ncovariate-specific NBS and find determinants of variation in NBS. Our approach\nis able to accommodate informative cost censoring through inverse probability\nweighting techniques, and addresses confounding through a semiparametric\nstandardization procedure. Through simulations, we show that NBS regression\nperforms well in a variety of common scenarios. We apply our proposed\nregression procedure to a realistic simulated data set as an illustration of\nhow our approach could be used to investigate the association between cancer\nstage, comorbidities and cost-effectiveness when comparing adjuvant radiation\ntherapy and chemotherapy in post-hysterectomy endometrial cancer patients.\n"}
+{"abstract": "  Reactor experiments are well suited to probe the possible loss of coherence\nof neutrino oscillations due to wave-packets separation. We combine data from\nthe short-baseline experiments Daya Bay and the Reactor Experiment for Neutrino\nOscillation (RENO) and from the long baseline reactor experiment KamLAND to\nobtain the best current limit on the reactor antineutrino wave-packet width,\n$\\sigma > 2.1 \\times 10^{-4}$ nm at 90% CL. We also find that the determination\nof standard oscillation parameters is robust, i.e., it is mostly insensitive to\nthe presence of hypothetical decoherence effects once one combines the results\nof the different reactor neutrino experiments.\n"}
+{"abstract": "  In type theory, we can express many practical ideas by attributing some\nadditional data to expressions we operate on during compilation. For instance,\nsome substructural type theories augment variables' typing judgments with the\ninformation of their usage. That is, they allow one to explicitly state how\nmany times - 0, 1, or many - a variable can be used. This solves the problem of\nresource usage control and allows us to treat variables as resources.\n  What's more, it often happens that this attributed information is interpreted\n(used) during the same compilation and erased before we run a program. A case\nin the point is that in the same substructural type theories, their type\ncheckers use these 0, 1, or many, to ensure that all variables are used as many\ntimes as these attributions say them to be.\n  Yet, there wasn't any programming language concept whose concern would be to\nallow a programmer to express these attributions in the language itself. That\nis, to let the programmer express which data the one wants to attribute to what\nexpressions and, most importantly, the meaning of the attributed data in their\nprogram.\n  As it turned out, the presence of such a concept allows us to express many\npractical ideas in the language itself. For instance, with appropriate means\nfor assigning the meaning of these attributions, this concept would allow one\nto express linear types as functionality in a separate program module, without\nthe need to refine the whole type system to add them.\n  In this paper, we present such a concept - we propose type properties. It\nallows a programmer to express these attributions while fulfilling the\nrequirement of being fully on the static level. That is, it allows one to\nexpress how to interpret these attributions during compilation and erases them\nbefore a program is passed to the runtime.\n"}
+{"abstract": "  With the growing interest in deep learning algorithms and computational\ndesign in the architectural field, the need for large, accessible and diverse\narchitectural datasets increases. We decided to tackle this problem by\nconstructing a field-specific synthetic data generation pipeline that generates\nan arbitrary amount of 3D data along with the associated 2D and 3D annotations.\nThe variety of annotations, the flexibility to customize the generated building\nand dataset parameters make this framework suitable for multiple deep learning\ntasks, including geometric deep learning that requires direct 3D supervision.\nCreating our building data generation pipeline we leveraged architectural\nknowledge from experts in order to construct a framework that would be modular,\nextendable and would provide a sufficient amount of class-balanced data\nsamples. Moreover, we purposefully involve the researcher in the dataset\ncustomization allowing the introduction of additional building components,\nmaterial textures, building classes, number and type of annotations as well as\nthe number of views per 3D model sample. In this way, the framework would\nsatisfy different research requirements and would be adaptable to a large\nvariety of tasks. All code and data are made publicly available.\n"}
+{"abstract": "  We report a scheme for generation of high-order vortex states using two-mode\nphoton-number squeezed states, generated via the non-linear process of\nSpontaneous Parametric Down Conversion. By applying a parametric rotation in\nquadrature space $(X,Y)$, using a $\\phi$ converter, the Gaussian quadrature\nprofile of the photon-number squeezed input state can be mapped into a\nsuperposition of Laguerre-Gauss modes with $N$ vortices or singularities, for\nan input state containing $N$ photons, thus mapping photon-number fluctuations\nto interference effects in quadrature space. Our scheme has the potential to\nimprove measurement sensitivity beyond the Standard Quantum Limit (SQL $\\propto\n\\sqrt{N}$), by exploiting the advantages of optical vortices, such as ease of\ncreation and detection, high dimensionality or topological properties, for\napplications requiring reduced uncertainty, such as quantum cryptography,\nquantum metrology and sensing.\n"}
+{"abstract": "  The interplay between timeliness and rate efficiency is investigated in\npacket erasure broadcast channels with feedback. A scheduling framework is\nproposed in which coding actions, as opposed to users, are scheduled to attain\ndesired tradeoffs between rate and age of information (AoI). This tradeoff is\nformalized by an upper bound on AoI as a function of the target rate\nconstraints and two lower bounds: one as a function of the communication rate\nand one as a function of the arrival rate. Simulation results show that (i)\ncoding can be beneficial in reducing AoI in the regime of moderate arrival\nrates even without rate constraints and the benefit increases with the number\nof users, and (ii) AoI increases with both the target rate constraint and the\narrival rate when either is kept fixed, but decreases with them when they are\nset to be equal.\n"}
+{"abstract": "  Recent acoustic and electrical-circuit experiments have reported the\nthird-order (or octupole) topological insulating phase, while its counterpart\nin classical magnetic systems is yet to be realized. Here we explore the\ncollective dynamics of magnetic vortices in three-dimensional breathing\ncuboids, and find that the vortex lattice can support zero-dimensional corner\nstates, one-dimensional hinge states, two-dimensional surface states, and\nthree-dimensional bulk states, when the ratio of alternating intralayer and\ninterlayer bond lengths goes beyond a critical value. We show that only the\ncorner states are stable against external frustrations because of the\ntopological protection. Full micromagnetic simulations verify our theoretical\npredictions with good agreement.\n"}
+{"abstract": "  Until recently, human behavioral data from reading has mainly been of\ninterest to researchers to understand human cognition. However, these human\nlanguage processing signals can also be beneficial in machine learning-based\nnatural language processing tasks. Using EEG brain activity to this purpose is\nlargely unexplored as of yet. In this paper, we present the first large-scale\nstudy of systematically analyzing the potential of EEG brain activity data for\nimproving natural language processing tasks, with a special focus on which\nfeatures of the signal are most beneficial. We present a multi-modal machine\nlearning architecture that learns jointly from textual input as well as from\nEEG features. We find that filtering the EEG signals into frequency bands is\nmore beneficial than using the broadband signal. Moreover, for a range of word\nembedding types, EEG data improves binary and ternary sentiment classification\nand outperforms multiple baselines. For more complex tasks such as relation\ndetection, further research is needed. Finally, EEG data shows to be\nparticularly promising when limited training data is available.\n"}
+{"abstract": "  Asaoka & Irie recently proved a $C^{\\infty}$ closing lemma of Hamiltonian\ndiffeomorphisms of closed surfaces. We reformulated their techniques into a\nmore general perturbation lemma for area-preserving diffeomorphism and proved a\n$C^{\\infty}$ closing lemma for area-preserving diffeomorphisms on a torus that\nis isotopic to identity. i.e., we show that the set of periodic orbits is dense\nfor a generic diffeomorphism isotopic to identity area-preserving\ndiffeomorphism on torus. The main tool is the flux vector of area-preserving\ndiffeomorphisms which is, different from Hamiltonian cases, non-zero in\ngeneral.\n"}
+{"abstract": "  The main feature of Hom-algebras is that the identities defining the\nstructures are twisted by linear maps. The purpose of this paper is to\nintroduce and study a Hom-type generalization of pre-Malcev algebras and\nM-dendriform algebras, called Hom-pre-Malcev algebras and Hom-M-dendriform\nalgebras. We also introduce the notion of $\\mathcal{O}$-operators of Hom-Malcev\nand Hom-pre-Malcev algebras and show the connections between Hom-Malcev,\nHom-pre-Malcev and Hom-M-dendriform algebras using $\\mathcal{O}$-operators.\nHom-pre-Malcev algebras and Hom-M-dendriform algebras generalize Hom-pre-Lie\nalgebras and Hom-L-dendriform algebras respectively to the alternative setting\nand fit into a bigger framework with a close relationship with\nHom-pre-alternative algebras and Hom-alternative quadri-algebras respectively.\n"}
+{"abstract": "  Treewidth is a well-known graph invariant with multiple interesting\napplications in combinatorics. On the practical side, many NP-complete problems\nare polynomial-time (sometimes even linear-time) solvable on graphs of bounded\ntreewidth. On the theoretical side, treewidth played an essential role in the\nproof of the celebrated Robertson-Seymour graph minor theorem. While defining\ntreewidth-like invariants on graphs and treewidth analogues on other sorts of\ncombinatorial objects (incl. hypergraphs, digraphs) has been a fruitful avenue\nof research, a direct, categorial description capturing multiple treewidth-like\ninvariants is yet to emerge. Here we report on our recent work on spined\ncategories (arXiv:2104.01841): categories equipped with extra structure that\npermits the definition of a functorial analogue of treewidth, the triangulation\nfunctor. The usual notion of treewidth is recovered as a special case, the\ntriangulation functor of a spined category with graphs as objects and graph\nmonomorphisms as arrows. The usual notion of treewidth for hypergraphs arises\nas the triangulation functor of a similar category of hypergraphs.\n"}
+{"abstract": "  Despite their appealing flexibility, deep neural networks (DNNs) are\nvulnerable against adversarial examples. Various adversarial defense strategies\nhave been proposed to resolve this problem, but they typically demonstrate\nrestricted practicability owing to unsurmountable compromise on universality,\neffectiveness, or efficiency. In this work, we propose a more practical\napproach, Lightweight Bayesian Refinement (LiBRe), in the spirit of leveraging\nBayesian neural networks (BNNs) for adversarial detection. Empowered by the\ntask and attack agnostic modeling under Bayes principle, LiBRe can endow a\nvariety of pre-trained task-dependent DNNs with the ability of defending\nheterogeneous adversarial attacks at a low cost. We develop and integrate\nadvanced learning techniques to make LiBRe appropriate for adversarial\ndetection. Concretely, we build the few-layer deep ensemble variational and\nadopt the pre-training & fine-tuning workflow to boost the effectiveness and\nefficiency of LiBRe. We further provide a novel insight to realise adversarial\ndetection-oriented uncertainty quantification without inefficiently crafting\nadversarial examples during training. Extensive empirical studies covering a\nwide range of scenarios verify the practicability of LiBRe. We also conduct\nthorough ablation studies to evidence the superiority of our modeling and\nlearning strategies.\n"}
+{"abstract": "  This article is not a proof of the Poincar\\'{e} conjecture but a discussion\nof the proof, its context, and some of the people who played a prominent role.\nIt is a personal, anecdotal account. There may be omission or transpositions as\nthese recollections are 40 years old and not supported by contemporaneous\nnotes, but memories feel surprisingly fresh. I have not looked up old papers to\ncheck details of statements; this article is merely a download from my current\nmental state.\n"}
+{"abstract": "  Time-evolving random graph models have appeared and have been studied in\nvarious fields of research over the past decades. However, the rigorous\nmathematical treatment of large graphs and their limits at the process-level is\nstill in its infancy. In this article, we adapt the approach of Athreya, den\nHollander and R\\\"ollin (2021+) to the setting of multigraphs and multigraphons,\nintroduced by Kolossv\\'ary and R\\'ath (2011). We then generalise the work of\nR\\'ath (2012) and R\\'ath and Szak\\'acs (2012), who analysed edge-flipping\ndynamics on the configuration model -- in contrast to their work, we establish\nweak convergence at the process-level, and by allowing removal and addition of\nedges, these limits are non-deterministic.\n"}
+{"abstract": "  We tackle the challenge of Visual Question Answering in multi-image setting\nfor the ISVQA dataset. Traditional VQA tasks have focused on a single-image\nsetting where the target answer is generated from a single image. Image set\nVQA, however, comprises of a set of images and requires finding connection\nbetween images, relate the objects across images based on these connections and\ngenerate a unified answer. In this report, we work with 4 approaches in a bid\nto improve the performance on the task. We analyse and compare our results with\nthree baseline models - LXMERT, HME-VideoQA and VisualBERT - and show that our\napproaches can provide a slight improvement over the baselines. In specific, we\ntry to improve on the spatial awareness of the model and help the model\nidentify color using enhanced pre-training, reduce language dependence using\nadversarial regularization, and improve counting using regression loss and\ngraph based deduplication. We further delve into an in-depth analysis on the\nlanguage bias in the ISVQA dataset and show how models trained on ISVQA\nimplicitly learn to associate language more strongly with the final answer.\n"}
+{"abstract": "  Despite their practical success, modern seq2seq architectures are unable to\ngeneralize systematically on several SCAN tasks. Hence, it is not clear if\nSCAN-style compositional generalization is useful in realistic NLP tasks. In\nthis work, we study the benefit that such compositionality brings about to\nseveral machine translation tasks. We present several focused modifications of\nTransformer that greatly improve generalization capabilities on SCAN and select\none that remains on par with a vanilla Transformer on a standard machine\ntranslation (MT) task. Next, we study its performance in low-resource settings\nand on a newly introduced distribution-shifted English-French translation task.\nOverall, we find that improvements of a SCAN-capable model do not directly\ntransfer to the resource-rich MT setup. In contrast, in the low-resource setup,\ngeneral modifications lead to an improvement of up to 13.1% BLEU score w.r.t. a\nvanilla Transformer. Similarly, an improvement of 14% in an accuracy-based\nmetric is achieved in the introduced compositional English-French translation\ntask. This provides experimental evidence that the compositional generalization\nassessed in SCAN is particularly useful in resource-starved and domain-shifted\nscenarios.\n"}
+{"abstract": "  A textile fabric consists of countless parallel vertical yarns (warps) and\nhorizontal yarns (wefts). While common looms can weave repetitive patterns,\nJacquard looms can weave the patterns without repetition restrictions. A\npattern in which the warps and wefts cross on a grid is defined in a binary\nmatrix. The binary matrix can define which warp and weft is on top at each grid\npoint of the Jacquard fabric. The process can be regarded as encoding from\npattern to textile. In this work, we propose a decoding method that generates a\nbinary pattern from a textile fabric that has been already woven. We could not\nuse a deep neural network to learn the process based solely on the training set\nof patterns and observed fabric images. The crossing points in the observed\nimage were not completely located on the grid points, so it was difficult to\ntake a direct correspondence between the fabric images and the pattern\nrepresented by the matrix in the framework of deep learning. Therefore, we\npropose a method that can apply the framework of deep learning via the\nintermediate representation of patterns and images. We show how to convert a\npattern into an intermediate representation and how to reconvert the output\ninto a pattern and confirm its effectiveness. In this experiment, we confirmed\nthat 93% of correct pattern was obtained by decoding the pattern from the\nactual fabric images and weaving them again.\n"}
+{"abstract": "  While it is often assumed that the orbital transport is short-ranged due to\nstrong crystal field potential and orbital quenching, we show that orbital\npropagation can be remarkably long-ranged in ferromagnets. In contrast to spin\ntransport, which exhibits an oscillatory decaying behavior by spin dephasing,\nthe injected orbital angular momentum does not oscillate and decays slowly.\nThis unusual feature is attributed to nearly degenerate states in\n$\\mathbf{k}$-space, which form hot-spots for the intrinsic orbital response. We\ndemonstrate this in a bilayer consisting of a nonmagnet and a ferromagnet,\nwhere the orbital Hall current is injected from a nonmagnet into a ferromagnet.\nInteraction of the orbital Hall current with the magnetization in the\nferromagnet results in an intrinsic response of the orbital angular momentum\nwhich propagates far beyond the spin dephasing length. This gives rise to a\ndistinct type of orbital torque on the magnetization, increasing with the\nthickness of the ferromagnet. Such behavior may serve as critical long-sought\nevidence of orbital transport to be directly tested in experiments. Our\nfindings open the possibility of using long-range orbital transport in\norbitronic device applications.\n"}
+{"abstract": "  In IPFS content identifiers are constructed based on the item's data\ntherefore the binding between an item's identifier and its data can be\ndeterministically verified. Nevertheless, once an item is modified, its\nidentifier also changes. Therefore when it comes to mutable content there is a\nneed for keeping track of the \"latest\" IPFS identifier. This is achieved using\nnaming protocols on top of IPFS, such as IPNS and DNSlink, that map a constant\nname to an IPFS identifier, allowing at the same time content owners to update\nthese mappings. Nevertheless, IPNS relies on a cryptographic key pair that\ncannot be rotated, and DNSlink does not provide content authenticity\nprotection. In this paper, we propose a naming protocol that combines DNSlink\nand decentralized identifiers to enable self-verifiable content items. Our\nprotocol provides content authenticity without imposing any security\nrequirement to DNSlink. Furthermore, our protocol prevent fake content even if\nattackers have access to the DNS server of the content owner or have access to\nthe content owner secret keys. Our proof of concept implementation shows that\nour protocol is feasible and can be used with existing IPFS tools.\n"}
+{"abstract": "  Automatic program repair (APR) is crucial to improve software reliability.\nRecently, neural machine translation (NMT) techniques have been used to fix\nsoftware bugs automatically. While promising, these approaches have two major\nlimitations. Their search space often does not contain the correct fix, and\ntheir search strategy ignores software knowledge such as strict code syntax.\nDue to these limitations, existing NMT-based techniques underperform the best\ntemplate-based approaches.\n  We propose CURE, a new NMT-based APR technique with three major novelties.\nFirst, CURE pre-trains a programming language (PL) model on a large software\ncodebase to learn developer-like source code before the APR task. Second, CURE\ndesigns a new code-aware search strategy that finds more correct fixes by\nfocusing on compilable patches and patches that are close in length to the\nbuggy code. Finally, CURE uses a subword tokenization technique to generate a\nsmaller search space that contains more correct fixes.\n  Our evaluation on two widely-used benchmarks shows that CURE correctly fixes\n57 Defects4J bugs and 26 QuixBugs bugs, outperforming all existing APR\ntechniques on both benchmarks.\n"}
+{"abstract": "  A very popular model in machine learning is the feedforward neural network\n(FFN). The FFN can approximate general functions and mitigate the curse of\ndimensionality. Here we introduce FFNs which represent sections of holomorphic\nline bundles on complex manifolds, and ask some questions about their\napproximating power. We also explain formal similarities between the standard\napproach to supervised learning and the problem of finding numerical Ricci flat\nK\\\"ahler metrics, which allow carrying some ideas between the two problems.\n"}
+{"abstract": "  With the rapid growth of renewable energy, lots of small photovoltaic (PV)\nprosumers emerge. Due to the uncertainty of solar power generation, there is a\nneed for aggregated prosumers to predict solar power generation and whether\nsolar power generation will be larger than load. This paper presents two\ninterpretable neural networks to solve the problem: one binary classification\nneural network and one regression neural network. The neural networks are built\nusing TensorFlow. The global feature importance and local feature contributions\nare examined by three gradient-based methods: Integrated Gradients, Expected\nGradients, and DeepLIFT. Moreover, we detect abnormal cases when predictions\nmight fail by estimating the prediction uncertainty using Bayesian neural\nnetworks. Neural networks, which are interpreted by gradient-based methods and\ncomplemented with uncertainty estimation, provide robust and explainable\nforecasting for decision-makers.\n"}
+{"abstract": "  In this work, we study the behavior of blow-up solutions to the\nmultidimensional restricted Euler--Poisson equations which are the localized\nversion of the full Euler--Poisson system. We provide necessary conditions for\nthe existence of finite-time blow-up solutions in terms of the initial data,\nand describe the asymptotic behavior of the solutions near blow up times. We\nalso identify a rich set of the initial data which yields global bounded\nsolutions.\n"}
+{"abstract": "  Typical fingerprint recognition systems are comprised of a spoof detection\nmodule and a subsequent recognition module, running one after the other. In\nthis paper, we reformulate the workings of a typical fingerprint recognition\nsystem. In particular, we posit that both spoof detection and fingerprint\nrecognition are correlated tasks. Therefore, rather than performing the two\ntasks separately, we propose a joint model for spoof detection and matching to\nsimultaneously perform both tasks without compromising the accuracy of either\ntask. We demonstrate the capability of our joint model to obtain an\nauthentication accuracy (1:1 matching) of TAR = 100% @ FAR = 0.1% on the FVC\n2006 DB2A dataset while achieving a spoof detection ACE of 1.44% on the LiveDet\n2015 dataset, both maintaining the performance of stand-alone methods. In\npractice, this reduces the time and memory requirements of the fingerprint\nrecognition system by 50% and 40%, respectively; a significant advantage for\nrecognition systems running on resource-constrained devices and communication\nchannels. The project page for our work is available at\nhttps://www.bit.ly/ijcb2021-unified .\n"}
+{"abstract": "  The COVID19 pandemic created a worldwide emergency as it is estimated that\nsuch a large number of infections are due to human-to-human transmission of the\nCOVID19. As a necessity, there is a need to track users who came in contact\nwith users having travel history, asymptomatic and not yet symptomatic, but\nthey can be in the future. To solve this problem, the present work proposes a\nsolution for contact tracing based on assisted GPS and cloud computing\ntechnologies. An application is developed to collect each user's assisted GPS\ncoordinates once all the users install this application. This application\nperiodically sends assisted GPS data to the cloud. To determine which devices\nare within the permissible limit of 5m, we perform clustering over assisted GPS\ncoordinates and track the clusters for about t mins to allow the measure of\nspread. We assume that it takes around 3 or 5 mins to get the virus from an\ninfected object. For clustering, the proposed M way like tree data structure\nstores the assisted GPS coordinates in degree, minute, and second format. Thus,\nevery user is mapped to a leaf node of the tree. We split the \"seconds\" part of\nthe assisted GPS location into m equal parts, which amount to d meter in\nlatitude(longitude). Hence, two users who are within d meter range will map to\nthe same leaf node. Thus, by mapping assisted GPS locations every t mins, we\ncan find out how many users came in contact with a particular user for at least\nt mins. Our work's salient feature is that it runs in linear time O(n) for n\nusers in the static case, i.e., when users are not moving. We also propose a\nvariant of our solution to handle the dynamic case, that is, when users are\nmoving. Besides, the proposed solution offers potential hotspot detection and\nsafe-route recommendation as an additional feature, and proof of concept is\npresented through experiments on simulated data of 10M users.\n"}
+{"abstract": "  We discuss three ways of obtaining the Born approximations for Coulomb\nscattering: The standard way, making use of a convergence factor (\"screening\"),\nOppenheimer's way using cylindrical (instead of spherical) coordinates, and\nfinally Landau and Lifshitz' way. The last one although it does require some\nbackground from the theory of generalized functions is nevertheless a very\ninstructive and important technique deserving more exposure to physicists.\n"}
+{"abstract": "  Online Travel Platforms are virtual two-sided marketplaces where guests\nsearch for accommodations and accommodation providers list their properties\nsuch as hotels and vacation rentals. The large majority of hotels are rated by\nofficial institutions with a number of stars indicating the quality of service\nthey provide. It is a simple and effective mechanism that contributes to match\nsupply with demand by helping guests to find options meeting their criteria and\naccommodation suppliers to market their product to the right segment directly\nimpacting the number of transactions on the platform. Unfortunately, no similar\nrating system exists for the large majority of vacation rentals, making it\ndifficult for guests to search and compare options and hard for vacation\nrentals suppliers to market their product effectively. In this work we describe\na machine learned quality rating system for vacation rentals. The problem is\nchallenging, mainly due to explainability requirements and the lack of ground\ntruth. We present techniques to address these challenges and empirical evidence\nof their efficacy. Our system was successfully deployed and validated through\nOnline Controlled Experiments performed in Booking. com, a large Online Travel\nPlatform, and running for more than one year, impacting more than a million\naccommodations and millions of guests.\n"}
+{"abstract": "  X-ray diffraction indicates that the structure of the recently discovered\nroom temperature carbonaceous sulfur hydride (C-S-H) superconductor is derived\nfrom previously established van der Waals compounds found in the H$_2$S-H$_2$\nand CH$_4$-H$_2$ systems. Crystals of the superconducting phase were produced\nby a photochemical synthesis technique leading to the superconducting critical\ntemperature $T_c$ of 288 K at 267 GPa. Single-crystal x-ray diffraction\npatterns measured from 124 to 178 GPa, within the pressure range of the\nsuperconducting phase, give an orthorhombic structure derived from the\nAl$_2$Cu-type determined for (H$_2$S)$_2$H$_2$ and (CH$_4$)$_2$H$_2$ that\ndiffers from those predicted and observed for the S-H system to these\npressures. The formation and stability of the C-S-H compound can be understood\nin terms of the close similarity in effective volumes of the H$_2$S and CH$_4$\ncomponents over a broad range of pressures. The relative amounts of carbon and\nsulfur in the structure is not determined, and denser carbon-bearing S-H\nstructures may form at higher pressures. The results are consistent with\nhole-doping enhancement of $T_c$ by carbon proposed for the room-temperature\nsuperconductivity in this system.\n"}
+{"abstract": "  The availability of social media simplifies the companies-customers\nrelationship. An effort to engage customers in conversation networks using\nsocial media is called Social Customer Relationship Management (SCRM). Social\nNetwork Analysis helps to understand network characteristics and how active the\nconversation network on social media. Calculating its network properties is\nbeneficial for measuring customer relationship performance. Financial\nTechnology, a new emerging industry that provides digital-based financial\nservices utilize social media to interact with its customers. Measuring SCRM\nperformance is needed in order to stay competitive among others. Therefore, we\naim to explore the SCRM performance of the Indonesia Fintech company. In terms\nof discovering the market majority thought in conversation networks, we perform\nsentiment analysis by classifying into positive and negative opinion. As case\nstudies, we investigate Twitter conversations about GoPay, OVO, Dana, and\nLinkAja during the observation period from 1st October until 1st November 2019.\nThe result of this research is beneficial for business intelligence purposes\nespecially in managing relationships with customers.\n"}
+{"abstract": "  The amount of video content and the number of applications based on\nmultimedia information increase each day. The development of new video coding\nstandards is a challenge to increase the compression rate and other important\nfeatures with a reasonable increase in the computational load. Video Experts\nTeam (JVET) of ITU-T and the JCT group within ISO/IEC have worked together to\nstandardize the Versatile Video Coding, approved finally in July 2020 as ITU-T\nH.266 | MPEG-I - Part 3 (ISO/IEC 23090-3) standard. This paper overviews some\ninteresting consumer electronic use cases, the compression tools described in\nthe standard, the current available real time implementations and the first\nindustrial trials done with this standard.\n"}
+{"abstract": "  Channel modeling is a critical issue when designing or evaluating the\nperformance of reconfigurable intelligent surface (RIS)-assisted\ncommunications. Inspired by the promising potential of learning-based methods\nfor characterizing the radio environment, we present a general approach to\nmodel the RIS end-to-end equivalent channel using the unsupervised\nexpectation-maximization (EM) learning algorithm. We show that an EM-based\napproximation through a simple mixture of two Nakagami-$m$ distributions\nsuffices to accurately approximating the equivalent channel, while allowing for\nthe incorporation of crucial aspects into RIS's channel modeling as spatial\nchannel correlation, phase-shift errors, arbitrary fading conditions, and\ncoexistence of direct and RIS channels. Based on the proposed analytical\nframework, we evaluate the outage probability under different settings of RIS's\nchannel features and confirm the superiority of this approach compared to\nrecent results in the literature.\n"}
+{"abstract": "  TANet is one of state-of-the-art 3D object detection method on KITTI and JRDB\nbenchmark, the network contains a Triple Attention module and Coarse-to-Fine\nRegression module to improve the robustness and accuracy of 3D Detection.\nHowever, since the original input data (point clouds) contains a lot of noise\nduring collecting the data, which will further affect the training of the\nmodel. For example, the object is far from the robot, the sensor is difficult\nto obtain enough pointcloud. If the objects only contains few point clouds, and\nthe samples are fed into model with the normal samples together during\ntraining, the detector will be difficult to distinguish the individual with few\npointcloud belong to object or background. In this paper, we propose TANet++ to\nimprove the performance on 3D Detection, which adopt a novel training strategy\non training the TANet. In order to reduce the negative impact by the weak\nsamples, the training strategy previously filtered the training data, and then\nthe TANet++ is trained by the rest of data. The experimental results shows that\nAP score of TANet++ is 8.98 higher than TANet on JRDB benchmark.\n"}
+{"abstract": "  We present a framework for the construction of portal effective theories\n(PETs) that couple effective field theories of the Standard Model (SM) to light\nhidden messenger fields. Using this framework we construct electroweak and\nstrong scale PETs that couple the SM to messengers carrying spin zero, one\nhalf, or one. The electroweak scale PETs encompass all portal operators up to\ndimension five, while the strong scale PETs additionally contain all portal\noperators of dimension six and seven that contribute at leading order to\nquark-flavour violating transitions. Using the strong scale PETs, we define a\nset of portal currents that couple hidden sectors to QCD, and construct portal\nchiral perturbation theories ($\\chi$PTs) that relate these currents to the\nlight pseudoscalar mesons. We estimate the coefficients of the portal $\\chi$PT\nLagrangian that are not fixed by SM observations using non-perturbative\nmatching techniques and give a complete list of the resulting one- and\ntwo-meson portal interactions. From those, we compute transition amplitudes for\nthree golden channels that are used in hidden sector searches at fixed target\nexperiments: i) charged kaon decay into a charged pion and a spin zero\nmessenger, ii) charged kaon decay into a charged lepton and a spin one half\nmessenger, and iii) neutral pion decay into a photon and a spin one messenger.\nFinally, we compare these amplitudes to specific expressions for models\nfeaturing light scalar particles, axion-like particles, heavy neutral leptons,\nand dark photons.\n"}
+{"abstract": "  A mesoporous bioactive glass (MBG) of molar composition 75SiO2-20CaO-5P2O5\n(MBG-75S) has been synthetized as a potential bioceramic for bone regeneration\npurposes. X-ray diffraction (XRD), Fourier transform infrared spectroscopy\n(FT-IR), nitrogen adsorption studies and transmission electron microscopy (TEM)\ndemonstrated that MBG-75Spossess a highly ordered mesoporous structure with\nhigh surface area and porosity, which would explain the high ionic exchange\nrate (mainly calcium and silicon soluble species) with the surrounded media.\nMBG-75S showed high biocompatibility in contact with Saos-2 osteoblast-like\ncells. Concentrations up to 1 mg/ml did not lead to significant alterations on\neither morphology or cell cycle. Regarding the effects on osteoclasts, MBG-75S\nallowed the differentiation of RAW-264.7 macrophages into osteoclast-like cells\nbut exhibiting a decreased resorptive activity. These results point out that\nMBG-75S does not inhibit osteoclastogenesis but reduces the osteoclast\nboneresorbing capability. Finally, in vitro studies focused on the innate\nimmune response, evidenced that MBG-75S allows the proliferation of macrophages\nwithout inducing their polarization towards the M1 pro-inflammatory phenotype.\nThis in vitro behavior is indicative that MBG-75S would just induce the\nrequired innate immune response without further inflammatory complications\nunder in vivo conditions. The overall behavior respect to osteoblasts,\nosteoclasts and macrophages, makes this MBG a very interesting candidate for\nbone grafting applications in osteoporotic patients.\n"}
+{"abstract": "  Recent work has proposed stochastic Plackett-Luce (PL) ranking models as a\nrobust choice for optimizing relevance and fairness metrics. Unlike their\ndeterministic counterparts that require heuristic optimization algorithms, PL\nmodels are fully differentiable. Theoretically, they can be used to optimize\nranking metrics via stochastic gradient descent. However, in practice, the\ncomputation of the gradient is infeasible because it requires one to iterate\nover all possible permutations of items. Consequently, actual applications rely\non approximating the gradient via sampling techniques. In this paper, we\nintroduce a novel algorithm: PL-Rank, that estimates the gradient of a PL\nranking model w.r.t. both relevance and fairness metrics. Unlike existing\napproaches that are based on policy gradients, PL-Rank makes use of the\nspecific structure of PL models and ranking metrics. Our experimental analysis\nshows that PL-Rank has a greater sample-efficiency and is computationally less\ncostly than existing policy gradients, resulting in faster convergence at\nhigher performance. PL-Rank further enables the industry to apply PL models for\nmore relevant and fairer real-world ranking systems.\n"}
+{"abstract": "  Cycling processes are important in many areas of physics ranging from lasers\nto topological insulators, often offering surprising insights into dynamical\nand structural aspects of the respective system. Here we report on a\nquantum-nonlinear wave-mixing experiment where resonant lasers and an optical\ncavity define a closed cycle between several ground and excited states of a\nsingle atom. We show that, for strong atom-cavity coupling and steady-state\ndriving, the entanglement between the atomic states and intracavity photon\nnumber suppresses the excited-state population via quantum interference,\neffectively reducing the cycle to the atomic ground states. The system dynamics\nthen result from transitions within a harmonic ladder of entangled dark states,\none for each cavity photon number, and a quantum Zeno blockade that generates\nantibunching in the photons emitted from the cavity. The reduced cycle\nsuppresses unwanted optical pumping into atomic states outside the cycle,\nthereby enhancing the number of emitted photons.\n"}
+{"abstract": "  We describe the spectrum of the Laplacian matrix of the commuting graph\nassociated to a finite group, using group theoretic information. Various graph\ninvariants (such as diameter, isoperimetric number, clique number, mean\ndistance) of the commuting graph associated to a finite group are determined.\nWe produce a number of examples to illustrate applications of our results.\n"}
+{"abstract": "  Electromagnetic crimping is a high-velocity joining method to join highly\nconductive workpieces where a pulsed magnetic field is applied without any\nworking medium or mechanical contact to deform the workpiece. This work\nexplores tube-to-tube joining of Copper outer tube and Stainless steel threaded\ninner tube using electromagnetic crimping. A non-coupled simulation model is\ndeveloped for the finite element analysis. ANSYS Maxwell package is used to\nobtain the magnetic field intensity, which is later converted to pressure using\nan analytical equation, and this pressure is applied to the two-tube working\ndomain in ANSYS Explicit Dynamics. Numerical simulations are done for different\ncombinations of discharge energies and pitches of the thread to analyse\ndeformation, stress and strain. The converged finite element results are\nvalidated using experimental data. The amount of deformation is found to be\nproportional to discharge energy and the pitch of the thread used. An empirical\nrelation is developed for the deformation as a function of discharge energy and\npitch. The relation is able to predict the deformation for other discharge\nenergies, which is later verified with ANSYS simulations.\n"}
+{"abstract": "  Benefiting from the powerful expressive capability of graphs, graph-based\napproaches have achieved impressive performance in various biomedical\napplications. Most existing methods tend to define the adjacency matrix among\nsamples manually based on meta-features, and then obtain the node embeddings\nfor downstream tasks by Graph Representation Learning (GRL). However, it is not\neasy for these approaches to generalize to unseen samples. Meanwhile, the\ncomplex correlation between modalities is also ignored. As a result, these\nfactors inevitably yield the inadequacy of providing valid information about\nthe patient's condition for a reliable diagnosis. In this paper, we propose an\nend-to-end Multimodal Graph Learning framework (MMGL) for disease prediction.\nTo effectively exploit the rich information across multi-modality associated\nwith diseases, amodal-attentional multi-modal fusion is proposed to integrate\nthe features of each modality by leveraging the correlation and complementarity\nbetween the modalities. Furthermore, instead of defining the adjacency matrix\nmanually as existing methods, the latent graph structure can be captured\nthrough a novel way of adaptive graph learning. It could be jointly optimized\nwith the prediction model, thus revealing the intrinsic connections among\nsamples. Unlike the previous transductive methods, our model is also applicable\nto the scenario of inductive learning for those unseen data. An extensive group\nof experiments on two disease prediction problems is then carefully designed\nand presented, demonstrating that MMGL obtains more favorable performances. In\naddition, we also visualize and analyze the learned graph structure to provide\nmore reliable decision support for doctors in real medical applications and\ninspiration for disease research.\n"}
+{"abstract": "  We calculate high-precision constraints on Natural Inflation relative to\ncurrent observational constraints from Planck 2018 + BICEP/Keck(BK15)\nPolarization + BAO on $r$ and $n_S$, including post-inflationary history of the\nuniverse. We find that, for conventional post-inflationary dynamics, Natural\nInflation with a cosine potential is disfavored at greater than 95\\% confidence\nout by current data. If we assume protracted reheating characterized by\n$\\overline{w}>1/3,$ Natural Inflation can be brought into agreement with\ncurrent observational constraints. However, bringing unmodified Natural\nInflation into the 68\\% confidence region requires values of $T_{\\mathrm{re}}$\nbelow the scale of electroweak symmetry breaking. The addition of a SHOES prior\non the Hubble Constant $H_0$ only worsens the fit.\n"}
+{"abstract": "  As cities become increasingly populated, urban planning plays a key role in\nensuring the equitable and inclusive development of metropolitan areas. MIT\nCity Science group created a data-driven tangible platform, CityScope, to help\ndifferent stakeholders, such as government representatives, urban planners,\ndevelopers, and citizens, collaboratively shape the urban scenario through the\nreal-time impact analysis of different urban interventions. This paper presents\nan agent-based model that characterizes citizens' behavioural patterns with\nrespect to housing and mobility choice that will constitute the first step in\nthe development of a dynamic incentive system for an open interactive\ngovernance process. The realistic identification and representation of the\ncriteria that affect this decision-making process will help understand and\nevaluate the impacts of potential housing incentives that aim to promote urban\ncharacteristics such as equality, diversity, walkability, and efficiency. The\ncalibration and validation of the model have been performed in a well-known\ngeographic area for the Group: Kendall Square in Cambridge, MA.\n"}
+{"abstract": "  Researchers have recently started to study how the emotional speech heard by\nyoung infants can affect their developmental outcomes. As a part of this\nresearch, hundreds of hours of daylong recordings from preterm infants' audio\nenvironments were collected from two hospitals in Finland and Estonia in the\ncontext of so-called APPLE study. In order to analyze the emotional content of\nspeech in such a massive dataset, an automatic speech emotion recognition (SER)\nsystem is required. However, there are no emotion labels or existing indomain\nSER systems to be used for this purpose. In this paper, we introduce this\ninitially unannotated large-scale real-world audio dataset and describe the\ndevelopment of a functional SER system for the Finnish subset of the data. We\nexplore the effectiveness of alternative state-of-the-art techniques to deploy\na SER system to a new domain, comparing cross-corpus generalization, WGAN-based\ndomain adaptation, and active learning in the task. As a result, we show that\nthe best-performing models are able to achieve a classification performance of\n73.4% unweighted average recall (UAR) and 73.2% UAR for a binary classification\nfor valence and arousal, respectively. The results also show that active\nlearning achieves the most consistent performance compared to the two\nalternatives.\n"}
+{"abstract": "  We give an overview of our recent theoretical studies of the thermodynamics\nof excitons, and other solid-state qubits, driven by time-dependent laser\nfields. We consider a single such emitter and describe how the formation of\nstrong-field dressed states allows the emitter to absorb or emit acoustic\nphonons in a controlled way. We present results for the heat absorption, and\nshow that the form of the driving field can be tailored to produce different\nthermodynamic processes, including both reversible and irreversible heat\nabsorption. We discuss these effects from the perspective of quantum\nthermodynamics and outline the possibility of using them for optical cooling of\nsolids to low temperatures.\n"}
+{"abstract": "  We describe a $p$-dimensional conformal defect of a free Dirac fermion on a\n$d$-dimensional flat space as boundary conditions on a conformally equivalent\nspace $\\mathbb{H}^{p+1} \\times \\mathbb{S}^{d-p-1}$. We classify allowed\nboundary conditions and find that the Dirichlet type of boundary conditions\nalways exists while the Neumann type of boundary condition exists only for a\ntwo-codimensional defect. For the two-codimensional defect, a double trace\ndeformation triggers a renormalization group flow from the Neumann boundary\ncondition to the Dirichlet boundary condition, and the free energy at UV fixed\npoint is always larger than that at IR fixed point. This provides us with\nfurther support of a conjectured $C$-theorem in DCFT.\n"}
+{"abstract": "  Consider the random Cayley graph of a finite group $G$ with respect to $k$\ngenerators chosen uniformly at random, with $1 \\ll \\log k \\ll \\log |G|$; denote\nit $G_k$. A conjecture of Aldous and Diaconis (1985) asserts, for $k \\gg \\log\n|G|$, that the random walk on this graph exhibits cutoff. Further, the cutoff\ntime should be a function only of $k$ and $|G|$, to sub-leading order. This was\nverified for all Abelian groups in the '90s. We extend the conjecture to $1 \\ll\nk \\lesssim \\log |G|$. We establish cutoff for all Abelian groups under the\ncondition $k - d(G) \\gg 1$, where $d(G)$ is the minimal size of a generating\nsubset of $G$, which is almost optimal. The cutoff time is described\n(abstractly) in terms of the entropy of random walk on $\\mathbb Z^k$. This\nabstract definition allows us to deduce that the cutoff time can be written as\na function only of $k$ and $|G|$ when $d(G) \\ll \\log |G|$ and $k - d(G) \\asymp\nk \\gg 1$; this is not the case when $d(G) \\asymp \\log |G| \\asymp k$. For\ncertain regimes of $k$, we find the limit profile of the convergence to\nequilibrium. Wilson (1997) conjectured that $\\mathbb Z_2^d$ gives rise to the\nslowest mixing time for $G_k$ amongst all groups of size at most $2^d$. We give\na partial answer, verifying the conjecture for nilpotent groups. This is\nobtained via a comparison result of independent interest between the mixing\ntimes of nilpotent $G$ and a corresponding Abelian group $\\overline G$, namely\nthe direct sum of the Abelian quotients in the lower central series of $G$. We\nuse this to refine a celebrated result of Alon and Roichman (1994): we show for\nnilpotent $G$ that $G_k$ is an expander provided $k - d(\\overline G) \\gtrsim\n\\log |G|$. As another consequence, we establish cutoff for nilpotent groups\nwith relatively small commutators, including high-dimensional special groups,\nsuch as Heisenberg groups.\n"}
+{"abstract": "  Underwater image restoration attracts significant attention due to its\nimportance in unveiling the underwater world. This paper elaborates on a novel\nmethod that achieves state-of-the-art results for underwater image restoration\nbased on the unsupervised image-to-image translation framework. We design our\nmethod by leveraging from contrastive learning and generative adversarial\nnetworks to maximize mutual information between raw and restored images.\nAdditionally, we release a large-scale real underwater image dataset to support\nboth paired and unpaired training modules. Extensive experiments with\ncomparisons to recent approaches further demonstrate the superiority of our\nproposed method.\n"}
+{"abstract": "  Distilling data into compact and interpretable analytic equations is one of\nthe goals of science. Instead, contemporary supervised machine learning methods\nmostly produce unstructured and dense maps from input to output. Particularly\nin deep learning, this property is owed to the generic nature of simple\nstandard link functions. To learn equations rather than maps, standard\nnon-linearities can be replaced with structured building blocks of atomic\nfunctions. However, without strong priors on sparsity and structure,\nrepresentational complexity and numerical conditioning limit this direct\napproach. To scale to realistic settings in science and engineering, we propose\nan informed equation learning system. It provides a way to incorporate expert\nknowledge about what are permitted or prohibited equation components, as well\nas a domain-dependent structured sparsity prior. Our system then utilizes a\nrobust method to learn equations with atomic functions exhibiting\nsingularities, as e.g. logarithm and division. We demonstrate several\nartificial and real-world experiments from the engineering domain, in which our\nsystem learns interpretable models of high predictive power.\n"}
+{"abstract": "  The random substitutional solid solution between the antiferromagnetic (AFM)\nfull-Heusler alloy Ru$_2$MnSn and the ferromagnetic (FM) full-Heusler alloy\nRu$_2$FeSn provides a rare opportunity to study FM-AFM phase competition in a\nnear-lattice-matched, cubic system, with full solubility. At intermediate $x$\nin Ru$_2$Mn$_{1-x}$Fe$_x$Sn this system displays suppressed magnetic ordering\ntemperatures, spatially coexisting FM and AFM order, and strong coercivity\nenhancement, despite rigorous chemical homogeneity. Here, we construct the most\ndetailed temperature- and $x$-dependent understanding of the magnetic phase\ncompetition and coexistence in this system to date, combining\nwide-temperature-range neutron diffraction and small-angle neutron scattering\nwith magnetometry and specific heat measurements on thoroughly characterized\npolycrystals. A complete magnetic phase diagram is generated, showing FM-AFM\ncoexistence between $x \\approx 0.30$ and $x \\approx 0.70$. Important new\ninsight is gained from the extracted length scales for magnetic phase\ncoexistence (25-100 nm), the relative magnetic volume fractions and ordering\ntemperatures, in addition to remarkable $x$-dependent trends in magnetic and\nelectronic contributions to specific heat. An unusual feature in the magnetic\nphase diagram (an intermediate FM phase) is also shown to arise from an\nextrinsic effect related to a minor Ru-rich secondary phase. The established\nmagnetic phase diagram is then discussed with the aid of phenomenological\nmodeling, clarifying the nature of the mesoscale phase coexistence with respect\nto the understanding of disordered Heisenberg models.\n"}
+{"abstract": "  Deep learning sequence models have been successfully applied to the task of\nmorphological inflection. The results of the SIGMORPHON shared tasks in the\npast several years indicate that such models can perform well, but only if the\ntraining data cover a good amount of different lemmata, or if the lemmata that\nare inflected at test time have also been seen in training, as has indeed been\nlargely the case in these tasks. Surprisingly, standard models such as the\nTransformer almost completely fail at generalizing inflection patterns when\nasked to inflect previously unseen lemmata -- i.e. under \"wug test\"-like\ncircumstances. While established data augmentation techniques can be employed\nto alleviate this shortcoming by introducing a copying bias through\nhallucinating synthetic new word forms using the alphabet in the language at\nhand, we show that, to be more effective, the hallucination process needs to\npay attention to substrings of syllable-like length rather than individual\ncharacters or stems. We report a significant performance improvement with our\nsubstring-based hallucination model over previous data hallucination methods\nwhen training and test data do not overlap in their lemmata.\n"}
+{"abstract": "  In this paper, we consider fractional Hartree (fHE) and fractional cubic\nnonlinear Schr\\\"odinger (fNLS) equations on $\\mathbb R^d$. We establish norm\ninflation (stronger phenomena than mere ill-posedness) for fHE and fNLS in\nFourier-Lebesgue and modulation spaces with negative regularity. (In\nparticular, these spaces include Sobolev spaces.) We further show that this can\nbe even worse by exhibiting norm inflation with infinite loss of regularity. To\nestablish later phenomena, we employ Fourier analytic approach and introduce\nnew resonant sets corresponding to the fractional dispersion\n$(-\\Delta)^{\\alpha/2}$.\n  In particular, we obtain norm inflation above scaling critical regularity in\nsome of these spaces when dispersion index $\\alpha$ is large enough. It turns\nout that our approach could treat both equations (fHE) and (fNLS) in a unified\nmanner. The method also should be applicable to wider classes of nonlinear\nequations with Hartree-type nonlinearity.\n"}
+{"abstract": "  Calibration of neural networks is a topical problem that is becoming\nincreasingly important for real-world use of neural networks. The problem is\nespecially noticeable when using modern neural networks, for which there is\nsignificant difference between the model confidence and the confidence it\nshould have. Various strategies have been successfully proposed, yet there is\nmore space for improvements. We propose a novel approach that introduces a\ndifferentiable metric for expected calibration error and successfully uses it\nas an objective for meta-learning, achieving competitive results with\nstate-of-the-art approaches. Our approach presents a new direction of using\nmeta-learning to directly optimize model calibration, which we believe will\ninspire further work in this promising and new direction.\n"}
+{"abstract": "  In the current control design of safety-critical autonomous systems, formal\nverification techniques are typically applied after the controller is designed\nto evaluate whether the required properties (e.g., safety) are satisfied.\nHowever, due to the increasing system complexity and the fundamental hardness\nof designing a controller with formal guarantees, such an open-loop process of\ndesign-then-verify often results in many iterations and fails to provide the\nnecessary guarantees. In this paper, we propose a correct-by-construction\ncontrol learning framework that integrates the verification into the control\ndesign process in a closed-loop manner, i.e., design-while-verify.\nSpecifically, we leverage the verification results (computed reachable set of\nthe system state) to construct feedback metrics for control learning, which\nmeasure how likely the current design of control parameters can meet the\nrequired reach-avoid property for safety and goal-reaching. We formulate an\noptimization problem based on such metrics for tuning the controller\nparameters, and develop an approximated gradient descent algorithm with a\ndifference method to solve the optimization problem and learn the controller.\nThe learned controller is formally guaranteed to meet the required reach-avoid\nproperty. By treating verifiability as a first-class objective and effectively\nleveraging the verification results during the control learning process, our\napproach can significantly improve the chance of finding a control design with\nformal property guarantees. This is demonstrated via a set of experiments on\nboth linear and non-linear systems that use model-based or neural network based\ncontrollers.\n"}
+{"abstract": "  In a topological description of elementary matter proposed by\nBilson-Thompson, the leptons and quarks of a single generation, together with\nthe electroweak gauge bosons, are represented as elements of the framed braid\ngroup of three ribbons. By identifying these braids with emergent topological\nexcitations of ribbon networks, it has been possible to encode this braid model\ninto the framework of quantum geometry provided by loop quantum gravity. In the\ncase of trivalent networks, it has not been possible to generate particle\ninteractions, because the braids correspond to noiseless subsystems, meaning\nthey commute with the evolution algebra generated by the local Pachner moves.\nIn the case of tetravalent networks, interactions are only possible when the\nmodel's original simplicity, in which interactions take place via the\ncomposition of braids, is sacrificed. We demonstrate that it possible to\npreserve both the original classification of fermions, as well as their\ninteraction via the braid product, if we embed the braid in a trivalent scheme,\nand supplement the local Pachner moves, with a non-local and graph changing\n1-handle attachment. Moreover, we use Kauffman-Lins recoupling theory to obtain\ninvariants of braided networks that distinguish topological configurations\nassociated to particles in the Bilson-Thompson model.\n"}
+{"abstract": "  Motivated by applications in reliable and secure communication, we address\nthe problem of tiling (or partitioning) a finite constellation in\n$\\mathbb{Z}_{2^L}^n$ by subsets, in the case that the constellation does not\npossess an abelian group structure. The property that we do require is that the\nconstellation is generated by a linear code through an injective mapping. The\nintrinsic relation between the code and the constellation provides a sufficient\ncondition for a tiling to exist. We also present a necessary condition.\nInspired by a result in group theory, we discuss results on tiling for the\nparticular case when the finer constellation is an abelian group as well.\n"}
+{"abstract": "  With an easy application of maximum principle, we establish a Schwarz-type\nlemma for squeezing function on finitely connected planar domains that directly\nyields the explicit formula for squeezing function on doubly connected domains\nobtained by Ng, Tang and Tsai.\n"}
+{"abstract": "  In this paper, we present deep learning frameworks for audio-visual scene\nclassification (SC) and indicate how individual visual and audio features as\nwell as their combination affect SC performance. Our extensive experiments,\nwhich are conducted on DCASE (IEEE AASP Challenge on Detection and\nClassification of Acoustic Scenes and Events) Task 1B development dataset,\nachieve the best classification accuracy of 82.2%, 91.1%, and 93.9% with audio\ninput only, visual input only, and both audio-visual input, respectively. The\nhighest classification accuracy of 93.9%, obtained from an ensemble of\naudio-based and visual-based frameworks, shows an improvement of 16.5% compared\nwith DCASE baseline.\n"}
+{"abstract": "  Opinion-evolution and spread processes on networks (e.g., infectious disease\nspread, opinion formation in social networks) are not only high dimensional but\nalso volatile and multiscale in nature. In this study, we explore whether\nsnapshot data from these processes can admit terse representations.\nSpecifically, using three case studies, we explore whether the data are\ncompressible in the Laplacian-eigenvector basis, in the sense that each\nsnapshot can be approximated well using a (possibly different) small set of\nbasis vectors. The first case study is concerned with a linear consensus model\nthat is subject to a stochastic input at an unknown location; both empirical\nand formal analyses are used to characterize compressibility. Second,\ncompressibility of state snapshots for a stochastic voter model is assessed via\nan empirical study. Finally, compressibility is studied for state-level daily\nCOVID-19 positivity-rate data. The three case studies indicate that state\nsnapshots from opinion-evolution and spread processes allow terse\nrepresentations, which nevertheless capture their rich propagative dynamics.\n"}
+{"abstract": "  In this paper, we explore an electromechanical metastructure consisting of a\nperiodic array of piezoelectric bimorphs with resistive-inductive loads for\nsimultaneous harvesting and attenuation of traveling wave energy. We develop\nfully coupled analytical models, i.e., an electroelastic transfer matrix\nmethod, and exploit both locally-resonant and Bragg band gaps to achieve a\nmultifunctional metastructure which is capable for maximum energy conversion\nand vibration mitigation in a broadband fashion. Our analytical and numerical\nresults show that the proposed metastructure can achieve energy harvesting\nefficiency up to 95% at the local resonance frequency of 3.18 kHz, while\nreaching about 51% at 5.8 kHz near the upper limit of the Bragg band gap. The\nbroadband vibration mitigation performance based on 50% power attenuation is\npredicted as 1.8 kHz and 1.1 kHz in the vicinity of the band gaps. The\ntheoretical frameworks and the applicability of the proposed metastructure are\nvalidated using a full-scale experimental setup.\n"}
+{"abstract": "  All hydrodynamical simulations of turbulent astrophysical phenomena require\nsub-grid scale models to properly treat energy dissipation and metal mixing. We\npresent the first implementation and application of an anisotropic eddy\nviscosity and metal mixing model in Lagrangian astrophysical simulations,\nincluding a dynamic procedure for the model parameter. We compare these two\nmodels directly to the common Smagorinsky and dynamic variant. Using the\nmesh-free finite mass method as an example, we show that the anisotropic model\nis best able to reproduce the proper Kolmogorov inertial range scaling in\nhomogeneous, isotropic turbulence. Additionally, we provide a method to\ncalibrate the metal mixing rate that ensures numerical convergence. In our\nfirst application to cosmological simulations, we find that all models strongly\nimpact the early evolution of galaxies leading to differences in enrichment and\nthermodynamic histories. The anisotropic model has the strongest impact, with\nlittle difference between the dynamic and the constant-coefficient variant. We\nalso find that the metal distribution functions in the circumgalactic gas are\nsignificantly tighter at all redshifts, with the anisotropic model providing\nthe tightest distributions. This is contrary to a recent study that found metal\nmixing to be relatively unimportant on cosmological scales. In all of our\nexperiments the constant-coefficient Smagorinsky and anisotropic models rivaled\ntheir dynamic counterparts, suggesting that the computationally inexpensive\nconstant-coefficient models are viable alternatives in cosmological contexts.\n"}
+{"abstract": "  In the context of forthcoming galaxy surveys, to ensure unbiased constraints\non cosmology and gravity when using non-linear structure information,\npercent-level accuracy is required when modelling the power spectrum. This\ncalls for frameworks that can accurately capture the relevant physical effects,\nwhile allowing for deviations from $\\Lambda$CDM. Massive neutrino and baryonic\nphysics are two of the most relevant such effects. We present an integration of\nthe halo model reaction frameworks for massive neutrinos and\nbeyond-$\\Lambda$CDM cosmologies. The integrated halo model reaction, combined\nwith a pseudo power spectrum modelled by HMCode2020 is then compared against\n$N$-body simulations that include both massive neutrinos and an $f(R)$\nmodification to gravity. We find that the framework is 4% accurate down to at\nleast $k\\approx 3 \\, h/{\\rm Mpc}$ for a modification to gravity of $|f_{\\rm\nR0}|\\leq 10^{-5}$ and for the total neutrino mass $M_\\nu \\equiv \\sum m_\\nu \\leq\n0.15$ eV. We also find that the framework is 4% consistent with EuclidEmulator2\nas well as the Bacco emulator for \\B{most of the considered} $\\nu w$CDM\ncosmologies down to at least $k \\approx 3 \\, h$/Mpc. Finally, we compare\nagainst hydrodynamical simulations employing HMCode2020's baryonic feedback\nmodelling on top of the halo model reaction. For $\\nu \\Lambda$CDM cosmologies\nwe find 2% accuracy for $M_\\nu \\leq 0.48$eV down to at least $k\\approx 5h$/Mpc.\nSimilar accuracy is found when comparing to $\\nu w$CDM hydrodynamical\nsimulations with $M_\\nu = 0.06$eV. This offers the first non-linear,\ntheoretically general means of accurately including massive neutrinos for\nbeyond-$\\Lambda$CDM cosmologies, and further suggests that baryonic, massive\nneutrino and dark energy physics can be reliably modelled independently.\n"}
+{"abstract": "  We address the problem of teaching a deep reinforcement learning (RL) agent\nto follow instructions in multi-task environments. Instructions are expressed\nin a well-known formal language -- linear temporal logic (LTL) -- and can\nspecify a diversity of complex, temporally extended behaviours, including\nconditionals and alternative realizations. Our proposed learning approach\nexploits the compositional syntax and the semantics of LTL, enabling our RL\nagent to learn task-conditioned policies that generalize to new instructions,\nnot observed during training. To reduce the overhead of learning LTL semantics,\nwe introduce an environment-agnostic LTL pretraining scheme which improves\nsample-efficiency in downstream environments. Experiments on discrete and\ncontinuous domains target combinatorial task sets of up to $\\sim10^{39}$ unique\ntasks and demonstrate the strength of our approach in learning to solve\n(unseen) tasks, given LTL instructions.\n"}
+{"abstract": "  We develop the formalism to analyze cluster synchronization for dynamical\nelements coupled via hypergraphs beyond pairwise interactions. We introduce the\nnotion of edge partitions and show how node and edge partitions allow us to\nidentify admissible cluster synchronization states and simplify their linear\nstability calculations. The analysis in terms of node and edge partitions\nprovides a principled way to track dynamics on hypergraphs, and the projected\nLaplacian matrices based on each edge cluster are essential to block\ndiagonalizing the Jacobian in order to reduce the dimensionality of the\nstability analysis. Our work enables detailed analysis of patterns of\nsynchronization beyond full synchronization and beyond dyadic interactions.\n"}
+{"abstract": "  Over the years, static taint analysis emerged as the analysis of choice to\ndetect some of the most common web application vulnerabilities, such as SQL\ninjection (SQLi) and cross-site scripting (XSS)~\\cite{OWASP}. Furthermore, from\nan implementation perspective, the IFDS dataflow framework stood out as one of\nthe most successful vehicles to implement static taint analysis for real-world\nJava applications. While existing approaches scale reasonably to medium-size\napplications (e.g. up to one hour analysis time for less than 100K lines of\ncode), our experience suggests that no existing solution can scale to very\nlarge industrial code bases (e.g. more than 1M lines of code). In this paper,\nwe present our novel IFDS-based solution to perform fast and precise static\ntaint analysis of very large industrial Java web applications. Similar to\nstate-of-the-art approaches to taint analysis, our IFDS-based taint analysis\nuses \\textit{access paths} to abstract objects and fields in a program.\nHowever, contrary to existing approaches, our analysis is demand-driven, which\nrestricts the amount of code to be analyzed, and does not rely on a\ncomputationally expensive alias analysis, thereby significantly improving\nscalability.\n"}
+{"abstract": "  We present a new practical framework based on deep reinforcement learning and\ndecision-time planning for real-world vehicle repositioning on ride-hailing (a\ntype of mobility-on-demand, MoD) platforms. Our approach learns the\nspatiotemporal state-value function using a batch training algorithm with deep\nvalue networks. The optimal repositioning action is generated on-demand through\nvalue-based policy search, which combines planning and bootstrapping with the\nvalue networks. For the large-fleet problems, we develop several algorithmic\nfeatures that we incorporate into our framework and that we demonstrate to\ninduce coordination among the algorithmically-guided vehicles. We benchmark our\nalgorithm with baselines in a ride-hailing simulation environment to\ndemonstrate its superiority in improving income efficiency meausred by\nincome-per-hour. We have also designed and run a real-world experiment program\nwith regular drivers on a major ride-hailing platform. We have observed\nsignificantly positive results on key metrics comparing our method with\nexperienced drivers who performed idle-time repositioning based on their own\nexpertise.\n"}
+{"abstract": "  Our present study involves the strange stars model in the framework of\n$f(R,T)$ theory of gravitation. We have taken a linear function of the Ricci\nscalar $R$ and the trace $T$ of the stress-energy tensor $T_{\\mu \\nu}$ for the\nexpression of $f(R,T)$, i.e., $f(R,T)=R+ 2 \\gamma T $ to obtain the proposed\nmodel, where $\\gamma$ is a coupling constant. Moreover, to solve the\nhydrostatic equilibrium equations, we consider a linear equation of state\nbetween the radial pressure $p_r$ and matter density $\\rho$ as $p_r=\\alpha\n\\rho-\\beta$, where $\\alpha$ and $\\beta$ are some positive constants, Both\n$\\alpha,\\,\\beta$ depend on coupling constant $\\gamma$ which have been also\ndepicted in this paper. By employing the Krori-Barua {\\em ansatz} already\nreported in the literature [J. Phys. A, Math. Gen. 8:508, 1975] we have found\nthe solutions of the field equations in $f (R, T )$ gravity. The effect of\ncoupling constant $\\gamma$ have been studied on the model parameters like\ndensity, pressures, anisotropic factor, compactness, surface redshift, etc.\nboth numerically and graphically. A suitable range for $\\gamma$ is also\nobtained. The physical acceptability and stability of the stellar system have\nbeen tested by different physical tests, e.g., the causality condition, Herrera\ncracking concept, relativistic adiabatic index, energy conditions, etc. One can\nregain the solutions in Einstein gravity when $\\gamma\\rightarrow 0$\n"}
+{"abstract": "  This short note describes a Bloom filter variant that takes advantage of\nmodern SIMD instructions to increase speed by 30%-450%. This filter, the split\nblock Bloom filter, is used by Apache Impala, Apache Kudu, Apache Parquet, and\nApache Arrow.\n"}
+{"abstract": "  Given a suitable solution $V(t,x)$ to the Korteweg--de Vries equation on the\nreal line, we prove global well-posedness for initial data $u(0,x) \\in V(0,x) +\nH^{-1}(\\mathbb{R})$.\n  Our conditions on $V$ do include regularity but do not impose any assumptions\non spatial asymptotics. We show that periodic profiles $V(0,x) \\in\nH^5(\\mathbb{R}/\\mathbb{Z})$ satisfy our hypotheses. In particular, we can treat\nlocalized perturbations of the much-studied periodic traveling wave solutions\n(cnoidal waves) of KdV. In our companion paper we show that smooth step-like\ninitial data also satisfy our hypotheses.\n  We employ the method of commuting flows introduced by Killip and Vi\\c{s}an;\nin the special case $V\\equiv 0$, we recover their sharp $H^{-1}(\\mathbb{R})$\nresult.\n"}
+{"abstract": "  We present measurements of differential cross sections and analyzing powers\nfor the elastic 2H(~d; d)d scattering process. The data were obtained using a\n130 MeV polarized deuteron beam. Cross sections and spin observables of the\nelastic scattering process were measured at the AGOR facility at KVI using two\nindependent setups, namely BINA and BBS. The data harvest at setups are in\nexcellent agreement with each other and allowed us to carry out a thorough\nsystematic analysis to provide the most accurate data in elastic\ndeuteron-deuteron scattering at intermediate energies. The results can be used\nto confront upcoming state-of-the-art calculations in the four-nucleon\nscattering domain, and will, thereby, provide further insights in the dynamics\nof three- and four-nucleon forces in few-nucleon systems.\n"}
+{"abstract": "  Bitcoin has emerged as a revolutionary payment system with its decentralized\nledger concept however it has significant problems such as high transaction\nfees and long confirmation times. Lightning Network (LN), which was introduced\nmuch later, solves most of these problems with an innovative concept called\noff-chain payments. With this advancement, Bitcoin has become an attractive\nvenue to perform micro-payments which can also be adopted in many IoT\napplications (e.g. toll payments). Nevertheless, it is not feasible to host LN\nand Bitcoin on IoT devices due to the storage, memory, and processing\nrequirements. Therefore, in this paper, we propose an efficient and secure\nprotocol that enables an IoT device to use LN through an untrusted gateway\nnode. The gateway hosts LN and Bitcoin nodes and can open & close LN channels,\nsend LN payments on behalf of the IoT device. This delegation approach is\npowered by a (2,2)-threshold scheme that requires the IoT device and the LN\ngateway to jointly perform all LN operations which in turn secures both\nparties' funds. Specifically, we propose to thresholdize LN's Bitcoin public\nand private keys as well as its commitment points. With these and several other\nprotocol level changes, IoT device is protected against revoked state\nbroadcast, collusion, and ransom attacks. We implemented the proposed protocol\nby changing LN's source code and thoroughly evaluated its performance using a\nRaspberry Pi. Our evaluation results show that computational and communication\ndelays associated with the protocol are negligible. To the best of our\nknowledge, this is the first work that implemented threshold cryptography in\nLN.\n"}
+{"abstract": "  Classic information extraction techniques consist in building questions and\nanswers about the facts. Indeed, it is still a challenge to subjective\ninformation extraction systems to identify opinions and feelings in context. In\nsentiment-based NLP tasks, there are few resources to information extraction,\nabove all offensive or hateful opinions in context. To fill this important gap,\nthis short paper provides a new cross-lingual and contextual offensive lexicon,\nwhich consists of explicit and implicit offensive and swearing expressions of\nopinion, which were annotated in two different classes: context dependent and\ncontext-independent offensive. In addition, we provide markers to identify hate\nspeech. Annotation approach was evaluated at the expression-level and achieves\nhigh human inter-annotator agreement. The provided offensive lexicon is\navailable in Portuguese and English languages.\n"}
+{"abstract": "  Reconfigurable intelligent surfaces (RISs) have attracted wide interest from\nindustry and academia since they can shape the wireless environment into a\ndesirable form with a low cost. In practice, RISs have three types of\nimplementations: 1) reflective, where signals can be reflected to the users on\nthe same side of the base station (BS), 2) transmissive, where signals can\npenetrate the RIS to serve the users on the opposite side of the BS, and 3)\nhybrid, where the RISs have a dual function of reflection and transmission.\nHowever, existing works focus on the reflective type RISs, and the other two\ntypes of RISs are not well investigated. In this letter, a downlink multi-user\nRIS-assisted communication network is considered, where the RIS can be one of\nthese types. We derive the system sum-rate, and discuss which type can yield\nthe best performance under a specific user distribution. Numerical results\nverify our analysis.\n"}
+{"abstract": "  A broadcast on a nontrivial connected graph G with vertex set V is a function\nf from V to {0,1,...,diam(G)} such that f(v) is at most the eccentricity of v\nfor all v in V. The weight of f is the sum of the function values taken over V.\nA vertex u hears f from v if f(v) is positive and d(u,v) is at most f(v). A\nbroadcast f is dominating if every vertex of G hears f. The upper broadcast\nnumber of G is {\\Gamma}_{b}(G), which is the maximum weight of a minimal\ndominating broadcast on G. A broadcast f is boundary independent if, for any\nvertex w that hears f from vertices v_{1},...,v_{k}, where k is at least 2, the\ndistance d(w,v_{i}) equals f(v_{i}) for each i. The maximum weight of a\nboundary independent broadcast is the boundary independence broadcast number\n{\\alpha}_{bn}(G). We compare {\\alpha}_{bn} to {\\Gamma}_{b}, showing that\nneither is an upper bound for the other. We show that the differences\n{\\Gamma}_{b}-{\\alpha}_{bn} and {\\alpha}_{bn}-{\\Gamma}_{b} are unbounded, the\nratio {\\alpha}_{bn}/{\\Gamma}_{b} is bounded for all graphs, and\n{\\Gamma}_{b}/{\\alpha}_{bn} is bounded for bipartite graphs but unbounded in\ngeneral.\n"}
+{"abstract": "  The spectrum efficiency (SE) and security of the secondary users (SUs) in the\ncognitive radio networks (CRNs) have become two main issues due to the\nlimitation interference to the primary users (PUs) and the shared spectrum with\nthe PUs. Intelligent reflecting surface (IRS) has been recently proposed as a\nrevolutionary technique which can help to enhance the SE and physical layer\nsecurity of wireless communications. This paper investigates the application of\nIRS in an underlay CRN, where a multi-antenna cognitive base station (CBS)\nutilizes spectrum assigned to the PU to communicate with a SU via IRS in the\npresence of multiple coordinated eavesdroppers (Eves). To achieve the trade-off\nbetween the secrecy rate (SR) and energy consumption, we investigate the\nsecrecy energy efficiency (SEE) maximization problem by jointly designing the\ntransmit beamforming at the CBS and the reflect beamforming at the IRS. To\nsolve the non-convex problem with coupled variables, we propose an iterative\nalternating optimization algorithm to solve the sub-problems alternately, by\nutilizing an iterative penalty function based algorithm for sub-problem 1 and\nthe difference of two-convex functions method for sub-problem 2. Furthermore,\nwe provide a second-order-cone-programming (SOCP) approximation approach to\nreduce the computational complexity. Finally, the simulation results\ndemonstrate that IRS can help significantly improve the SE and enhance the\nphysical layer security in the CRNs. Moreover, the effectiveness and\nsuperiority of our proposed algorithm in achieving the trade-off between the SR\nand energy consumption are verified.\n"}
+{"abstract": "  Most 3D shape completion approaches rely heavily on partial-complete shape\npairs and learn in a fully supervised manner. Despite their impressive\nperformances on in-domain data, when generalizing to partial shapes in other\nforms or real-world partial scans, they often obtain unsatisfactory results due\nto domain gaps. In contrast to previous fully supervised approaches, in this\npaper we present ShapeInversion, which introduces Generative Adversarial\nNetwork (GAN) inversion to shape completion for the first time. ShapeInversion\nuses a GAN pre-trained on complete shapes by searching for a latent code that\ngives a complete shape that best reconstructs the given partial input. In this\nway, ShapeInversion no longer needs paired training data, and is capable of\nincorporating the rich prior captured in a well-trained generative model. On\nthe ShapeNet benchmark, the proposed ShapeInversion outperforms the SOTA\nunsupervised method, and is comparable with supervised methods that are learned\nusing paired data. It also demonstrates remarkable generalization ability,\ngiving robust results for real-world scans and partial inputs of various forms\nand incompleteness levels. Importantly, ShapeInversion naturally enables a\nseries of additional abilities thanks to the involvement of a pre-trained GAN,\nsuch as producing multiple valid complete shapes for an ambiguous partial\ninput, as well as shape manipulation and interpolation.\n"}
+{"abstract": "  Self-supervised learning provides a promising path towards eliminating the\nneed for costly label information in representation learning on graphs.\nHowever, to achieve state-of-the-art performance, methods often need large\nnumbers of negative examples and rely on complex augmentations. This can be\nprohibitively expensive, especially for large graphs. To address these\nchallenges, we introduce Bootstrapped Graph Latents (BGRL) - a graph\nrepresentation learning method that learns by predicting alternative\naugmentations of the input. BGRL uses only simple augmentations and alleviates\nthe need for contrasting with negative examples, and is thus scalable by\ndesign. BGRL outperforms or matches prior methods on several established\nbenchmarks, while achieving a 2-10x reduction in memory costs. Furthermore, we\nshow that BGRL can be scaled up to extremely large graphs with hundreds of\nmillions of nodes in the semi-supervised regime - achieving state-of-the-art\nperformance and improving over supervised baselines where representations are\nshaped only through label information. In particular, our solution centered on\nBGRL constituted one of the winning entries to the Open Graph Benchmark - Large\nScale Challenge at KDD Cup 2021, on a graph orders of magnitudes larger than\nall previously available benchmarks, thus demonstrating the scalability and\neffectiveness of our approach.\n"}
+{"abstract": "  Majorana bosons, that is, tight bosonic analogs of the Majorana fermionic\nquasiparticles of condensed matter physics, are forbidden for gapped free\nbosonic matter within a standard Hamiltonian scenario. We show how the\ninterplay between dynamical metastability and nontrivial bulk topology makes\ntheir emergence possible in noninteracting bosonic chains undergoing Markovian\ndissipation. This leads to a distinctive form of topological metastability,\nwhereby a conserved Majorana boson localized on one edge is paired, in general,\nwith a symmetry generator localized on the opposite edge. We argue that\nMajorana bosons are robust against disorder and identifiable by signatures in\nthe zero-frequency steady-state power spectrum. Our results suggest that\nsymmetry-protected topological phases for free bosons may arise in transient\nmetastable regimes, which persist over practical timescales.\n"}
+{"abstract": "  Multivariate point processes are widely applied to model event-type data such\nas natural disasters, online message exchanges, financial transactions or\nneuronal spike trains. One very popular point process model in which the\nprobability of occurrences of new events depend on the past of the process is\nthe Hawkes process. In this work we consider the nonlinear Hawkes process,\nwhich notably models excitation and inhibition phenomena between dimensions of\nthe process. In a nonparametric Bayesian estimation framework, we obtain\nconcentration rates of the posterior distribution on the parameters, under mild\nassumptions on the prior distribution and the model. These results also lead to\nconvergence rates of Bayesian estimators. Another object of interest in\nevent-data modelling is to recover the graph of interaction - or Granger\nconnectivity graph - of the phenomenon. We provide consistency guarantees on\nBayesian methods for estimating this quantity; in particular, we prove that the\nposterior distribution is consistent on the graph adjacency matrix of the\nprocess, as well as a Bayesian estimator based on an adequate loss function.\n"}
+{"abstract": "  Causal structure discovery from observational data is fundamental to the\ncausal understanding of autonomous systems such as medical decision support\nsystems, advertising campaigns and self-driving cars. This is essential to\nsolve well-known causal decision making and prediction problems associated with\nthose real-world applications. Recently, recursive causal discovery algorithms\nhave gained particular attention among the research community due to their\nability to provide good results by using Conditional Independent (CI) tests in\nsmaller sub-problems. However, each of such algorithms needs a refinement\nfunction to remove undesired causal relations of the discovered graphs.\nNotably, with the increase of the problem size, the computation cost (i.e., the\nnumber of CI-tests) of the refinement function makes an algorithm expensive to\ndeploy in practice. This paper proposes a generic causal structure refinement\nstrategy that can locate the undesired relations with a small number of\nCI-tests, thus speeding up the algorithm for large and complex problems. We\ntheoretically prove the correctness of our algorithm. We then empirically\nevaluate its performance against the state-of-the-art algorithms in terms of\nsolution quality and completion time in synthetic and real datasets.\n"}
+{"abstract": "  Nonreciprocal radiation refers to electromagnetic wave radiation in which a\nstructure provides different responses under the change of the direction of the\nincident field. Modern wireless telecommunication systems demand versatile\napparatuses which are capable of full-duplex nonreciprocal wave processing and\namplification, especially in the reflective state. To realize such a unique,\nextraordinary and versatile functionality, we propose an architecture in which\na chain of series cascaded radiating patches are integrated with nonreciprocal\nphase shifters, providing an original and efficient apparatus for full-duplex\nreflective beamsteering. Such an ultrathin reflective metasurface can provide\ndirective and diverse radiation beams, large wave amplification, steerable\nbeams by simply changing the bias of the gradient active nonmagnetic\nnonreciprocal phase shifters, and is immune to undesired time harmonics. Having\naccomplished all these functionalities in the reflective state, the metasurface\nrepresents a conspicuous apparatus for efficient, controllable and programmable\nwave engineering.\n"}
+{"abstract": "  Electric-field noise due to surfaces disturbs the motion of nearby trapped\nions, compromising the fidelity of gate operations that are the basis for\nquantum computing algorithms. We present a method that predicts the effect of\ndielectric materials on the ion's motion. Such dielectrics are integral\ncomponents of ion traps. Quantitative agreement is found between a model with\nno free parameters and measurements of a trapped ion in proximity to dielectric\nmirrors. We expect that this approach can be used to optimize the design of\nion-trap-based quantum computers and network nodes.\n"}
+{"abstract": "  General Santos City, as the tuna capital of the Philippines, relies with the\npresence of tricycles in moving people and goods. Considered as a\nhighly-urbanized city, General Santos City serves as vital link of the entire\nSOCKSARGEN region's economic activities. With the current thrust of the city in\nproviding a sustainable transport service, several options were identified to\nadopt in the entire city, that includes cleaner and better transport mode.\nElectric tricycle is an after sought alternative that offers better choice in\nterms of identified factors of sustainable transport: reliability, safety,\ncomfort, environment, affordability, and facility. A literature review was\nconducted to provide a comparison of cost and emission between a motorized\ntricycle and an e-tricycle. The study identified the existing tricycle industry\nof the city and reviewed the modal share with the city's travel pattern. The\nsurvey revealed a number of hazards were with the current motorized tricycle\nthat needs to address for the welfare of the passengers and drivers. The study\nfavors the shift to adopting E-tricycle. The model derived from binary\nlogistics regression provided a 72.72% model accuracy. Based from the results\nand findings, electric tricycle can be an alternative mode of public transport\nin the city that highly support sustainable option that provides local populace\nto improve their quality of life through mobility and economic activity.\nFurther recommendation to local policy makers in the transport sector of the\ncity include the clustering of barangays for better traffic management and\nfranchise regulation, the inclusion of transport-related infrastructure related\nto tricycle service with their investment planning and programming, the roll\nout and implementation of tricycle code of the city, and the piloting activity\nof introducing e-tricycle in the city.\n"}
+{"abstract": "  The observation of superconductivity in infinite layer nickelate\n(Nd,Sr)NiO$_{2}$ thin films has led to rapid theoretical and experimental\ninvestigations of these copper-oxide-analogue systems [1-15]. Superconductivity\nhas also been found in (Pr,Sr)NiO$_{2}$ [16,17], but not previously in\n(La,Sr)NiO$_{2}$ [2], raising a fundamental question whether superconductivity\nis associated with the presence of rare-earth moments [18,19]. Here we show\nthat with significant materials optimization, substantial portions of the\nLa$_{1-x}$Sr$_{x}$NiO$_{2}$ phase diagram can enter the regime of coherent\nlow-temperature transport ($x$ = 0.14 - 0.20), with subsequent superconducting\ntransitions and a maximum onset of ~ 9 K at $x$ = 0.20. Additionally, we\nobserve the unexpected indication of a superconducting ground state in undoped\nLaNiO$_{2}$, which likely reflects the self-doped nature of the electronic\nstructure. Combining the results of (La/Pr/Nd)$_{1-x}$Sr$_{x}$NiO$_{2}$ reveals\na generalized superconducting dome, characterized by systematic shifts in the\nunit cell volume and in the relative electron-hole populations across the\nlanthanides.\n"}
+{"abstract": "  Stochastic local search (SLS) is a successful paradigm for solving the\nsatisfiability problem of propositional logic. A recent development in this\narea involves solving not the original instance, but a modified, yet logically\nequivalent one. Empirically, this technique was found to be promising as it\nimproves the performance of state-of-the-art SLS solvers.\n  Currently, there is only a shallow understanding of how this modification\ntechnique affects the runtimes of SLS solvers. Thus, we model this modification\nprocess and conduct an empirical analysis of the hardness of logically\nequivalent formulas. Our results are twofold. First, if the modification\nprocess is treated as a random process, a lognormal distribution perfectly\ncharacterizes the hardness; implying that the hardness is long-tailed. This\nmeans that the modification technique can be further improved by implementing\nan additional restart mechanism. Thus, as a second contribution, we\ntheoretically prove that all algorithms exhibiting this long-tail property can\nbe further improved by restarts. Consequently, all SAT solvers employing this\nmodification technique can be enhanced.\n"}
+{"abstract": "  The prediction of surrounding traffic participants behavior is a crucial and\nchallenging task for driver assistance and autonomous driving systems. Today's\napproaches mainly focus on modeling dynamic aspects of the traffic situation\nand try to predict traffic participants behavior based on this. In this article\nwe take a first step towards extending this common practice by calculating\nlocation-specific a-priori lane change probabilities. The idea behind this is\nstraight forward: The driving behavior of humans may vary in exactly the same\ntraffic situation depending on the respective location. E.g. drivers may ask\nthemselves: Should I pass the truck in front of me immediately or should I wait\nuntil reaching the less curvy part of my route lying only a few kilometers\nahead? Although, such information is far away from allowing behavior prediction\non its own, it is obvious that today's approaches will greatly benefit when\nincorporating such location-specific a-priori probabilities into their\npredictions. For example, our investigations show that highway interchanges\ntend to enhance driver's motivation to perform lane changes, whereas curves\nseem to have lane change-dampening effects. Nevertheless, the investigation of\nall considered local conditions shows that superposition of various effects can\nlead to unexpected probabilities at some locations. We thus suggest dynamically\nconstructing and maintaining a lane change probability map based on customer\nfleet data in order to support onboard prediction systems with additional\ninformation. For deriving reliable lane change probabilities a broad customer\nfleet is the key to success.\n"}
+{"abstract": "  A hybrid surface integral equation partial differential equation (SIE-PDE)\nformulation without the boundary condition requirement is proposed to solve the\ntransverse magnetic (TM) electromagnetic problems. In the proposed formulation,\nthe computational domain is decomposed into two overlapping domains: the SIE\nand PDE domains. In the SIE domain, complex structures with piecewise\nhomogeneous media, e.g., highly conductive media, are included. An equivalent\nmodel for those structures is constructed by replacing them with the background\nmedium and introducing a surface equivalent electric current density on an\nenclosed boundary to represent their electromagnetic effects. The remaining\ncomputational domain and homogeneous background medium replaced domain consist\nof the PDE domain, in which inhomogeneous or non-isotropic media are included.\nThrough combining the surface equivalent electric current density and the\ninhomogeneous Helmholtz equation, a hybrid SIE-PDE formulation is derived. It\nrequires no boundary conditions, and is mathematically equivalent to the\noriginal physical model. Through careful construction of basis functions to\nexpand electric fields and the equivalent current density, the discretized\nformulation is made compatible with the SIE and PDE domain interface. The\naccuracy and efficiency are validated through two numerical examples. Results\nshow that the proposed SIE-PDE formulation can obtain accurate results, and\nsignificant performance improvements in terms of CPU time and memory\nconsumption compared with the FEM are achieved.\n"}
+{"abstract": "  Time-delay interferometry (TDI) is a processing step essential for the\nscientific exploitation of LISA, as it reduces the otherwise overwhelming laser\nnoise in the interferometric measurements. The fundamental idea is to define\nnew laser-noise-free observables by combining appropriately time-shifted\nmeasurements. First- and second-generation TDI combinations cancel laser noise\nunder the assumption that the LISA armlengths are constant or evolve linearly\nwith time, respectively. We recently extended TDI by solving for the\nlaser-noise-free combinations implicitly, writing the likelihood of the data\ndirectly in terms of the basic measurements, and using a discretized\nrepresentation of the delays that can accommodate any time dependence of the\narmlengths. We named the resulting formalism \"TDI-infinity'' [PRD 103, 082001\n(2021)].\n  According to Tinto, Dhurandhar, and Joshi [arXiv/2105.02054], our\nmatrix-based approach is invalidated by the simplified start-up conditions\nassumed for the design matrix that connects the time series of laser-noise\nfluctuations to the time series of interferometric measurements along the LISA\narms. Here we respond that, if those boundary conditions are indeed\nunrealistic, they do not invalidate the algorithm, since one can simply\ntruncate the design matrix to exclude \"incomplete'' measurements, or set them\nto zero. Our formalism then proceeds unmodified, except that the length of the\nlaser-noise canceling time series is reduced by the number of excluded\nmeasurements. Tinto and colleagues further claim that the matrix formulation is\nmerely a finite representation of the polynomial ring of delay operators\nintroduced by Dhurandar, Nayak, and Vinet to formalize TDI. We show that this\nis only true if all interferometric delays are exact multiples of the sampling\ninterval, which will not be possible in practical contexts such as LISA.\n"}
+{"abstract": "  In this article, we investigate global norm of potential vector field in\nRicci soliton. In particular, we have deduced certain conditions so that the\npotential vector field has finite global norm in expanding Ricci soliton. We\nhave also showed that if the potential vector field has finite global norm in\ncomplete non-compact Ricci soliton having finite volume, then the scalar\ncurvature becomes constant.\n"}
+{"abstract": "  Transfer learning is critical for efficient information transfer across\nmultiple related learning problems. A simple, yet effective transfer learning\napproach utilizes deep neural networks trained on a large-scale task for\nfeature extraction. Such representations are then used to learn related\ndownstream tasks. In this paper, we investigate transfer learning capacity of\naudio representations obtained from neural networks trained on a large-scale\nsound event detection dataset. We build and evaluate these representations\nacross a wide range of other audio tasks, via a simple linear classifier\ntransfer mechanism. We show that such simple linear transfer is already\npowerful enough to achieve high performance on the downstream tasks. We also\nprovide insights into the attributes of sound event representations that enable\nsuch efficient information transfer.\n"}
+{"abstract": "  The prohibitive cost and low fidelity of experimental data in industry scale\nthermofluid systems limit the usefulness of pure data-driven machine learning\nmethods. Physics-informed neural networks (PINN) strive to overcome this by\nembedding the physics equations in the construction of the neural network loss\nfunction. In the present paper, the mixed-variable PINN methodology is applied\nto develop steady-state and transient surrogate models of incompressible\nlaminar flow with heat transfer through a 2D internal domain with obstructions.\nAutomatic spatial and temporal differentiation is applied to the partial\ndifferential equations for mass, momentum and energy conservation, and the\nresiduals are included in the loss function, together with the boundary and\ninitial values. Good agreement is obtained between the PINN and CFD results for\nboth the steady-state and transient cases, but normalization of the PDEs proves\nto be crucial. Although this proves the ability of the PINN approach to solve\nmultiple physics-based PDEs on a single domain, the PINN takes significantly\nlonger to solve than the traditional finite volume numerical methods utilized\nin commercial CFD software.\n"}
+{"abstract": "  The concept of synthetic dimensions in photonics has attracted rapidly\ngrowing interest in the past few years. Among a variety of photonic systems,\nthe ring resonator system under dynamic modulation has been investigated in\ndepth both in theory and experiment, and has proven to be a powerful way to\nbuild synthetic frequency dimensions. In this tutorial, we start with a\npedagogical introduction to the theoretical approaches in describing the\ndynamically modulated ring resonator system, and then review experimental\nmethods in building such a system. Moreover, we discuss important physical\nphenomena in synthetic dimensions, including nontrivial topological physics.\nOur tutorial provides a pathway towards studying the dynamically modulated ring\nresonator system, understanding synthetic dimensions in photonics, and\ndiscusses future prospects for both fundamental research and practical\napplications using synthetic dimensions.\n"}
+{"abstract": "  We compute the $\\Lambda$ and $\\overline{\\Lambda}$ global polarizations in\nsemi-central heavy-ion collisions using the core-corona model where the source\nof $\\Lambda$'s and $\\overline{\\Lambda}$'s is taken as consisting of a\nhigh-density core and a less dense corona. We show that the overall properties\nof the polarization excitation functions can be linked to the relative\nabundance of $\\Lambda$s coming from the core versus those coming from the\ncorona. For low collision energies, the former are more abundant whereas for\nhigher energies the latter become more abundant. The main consequence of this\nreversing of the relative abundance is that both polarizations peak at\ncollision energies $\\sqrt{s_{NN}}\\lesssim 10$ GeV. The exact positions and\nheights of these peaks depend not only on this reversal of relative abundances,\nbut also on the centrality class, which is directly related to the QGP volume\nand lifetime, as well as on the relative abundances of $\\Lambda$s and\n$\\overline{\\Lambda}$s in the core and corona regions. The intrinsic\npolarizations are computed from a field theoretical approach that links the\nalignment of the strange quark spin with the thermal vorticity and modeling the\nQGP volume and lifetime using a Bjorken expansion scenario. We predict that the\n$\\Lambda$ and $\\overline{\\Lambda}$ global polarizations should peak at the\nenergy range accessible to NICA and HADES.\n"}
+{"abstract": "  Machine learning applications to symbolic mathematics are becoming\nincreasingly popular, yet there lacks a centralized source of real-world\nsymbolic expressions to be used as training data. In contrast, the field of\nnatural language processing leverages resources like Wikipedia that provide\nenormous amounts of real-world textual data. Adopting the philosophy of\n\"mathematics as language,\" we bridge this gap by introducing a pipeline for\ndistilling mathematical expressions embedded in Wikipedia into symbolic\nencodings to be used in downstream machine learning tasks. We demonstrate that\na $\\textit{mathematical}$ $\\textit{language}$ $\\textit{model}$ trained on this\n\"corpus\" of expressions can be used as a prior to improve the performance of\nneural-guided search for the task of symbolic regression.\n"}
+{"abstract": "  In this paper, we extend the correspondence between Bayesian estimation and\noptimal smoothing in a Reproducing Kernel Hilbert Space (RKHS) adding a convexe\nconstraints on the solution. Through a sequence of approximating Hilbertian\nspaces and a discretized model, we prove that the Maximum A Posteriori (MAP) of\nthe posterior distribution is exactly the optimal constrained smoothing\nfunction in the RKHS. This paper can be read as a generalization of the paper\n[7] of Kimeldorf-Wahba where it is proved that the optimal smoothing solution\nis the mean of the posterior distribution.\n"}
+{"abstract": "  We present a determination of the perturbative QCD (pQCD) coupling using the\nV+A channel ALEPH $\\tau$-decay data. The determination involves the\ndouble-pinched Borel-Laplace Sum Rules and Finite Energy Sum Rules. The\ntheoretical basis is the Operator Product Expansion (OPE) of the V+A channel\nAdler function in which the higher order terms of the leading-twist part\noriginate from a model based on the known structure of the leading renormalons\nof this quantity. The applied evaluation methods are contour-improved\nperturbation theory (CIPT), fixed-order perturbation theory (FOPT), and\nPrincipal Value of the Borel resummation (PV). All the methods involve\ntruncations in the order of the coupling. In contrast to the truncated CIPT\nmethod, the truncated FOPT and PV methods account correctly for the suppression\nof various renormalon contributions of the Adler function in the mentioned sum\nrules. The extracted value of the ${\\overline {\\rm MS}}$ coupling is\n$\\alpha_s(m_{\\tau}^2) = 0.3116 \\pm 0.0073$ [$\\alpha_s(M_Z^2)=0.1176 \\pm\n0.0010$] for the average of the FOPT and PV methods, which we regard as our\nmain result. On the other hand, if we include in the average also the CIPT\nmethod, the resulting values are significantly higher, $\\alpha_s(m_{\\tau}^2) =\n0.3194 \\pm 0.0167$ [$\\alpha_s(M_Z^2)=0.1186 \\pm 0.0021$].\n"}
+{"abstract": "  In the past few years, there has been much work on incorporating fairness\nrequirements into algorithmic rankers, with contributions coming from the data\nmanagement, algorithms, information retrieval, and recommender systems\ncommunities. In this survey we give a systematic overview of this work,\noffering a broad perspective that connects formalizations and algorithmic\napproaches across subfields. An important contribution of our work is in\ndeveloping a common narrative around the value frameworks that motivate\nspecific fairness-enhancing interventions in ranking. This allows us to unify\nthe presentation of mitigation objectives and of algorithmic techniques to help\nmeet those objectives or identify trade-offs.\n"}
+{"abstract": "  In this work, we present bilayer flat-band Hamiltonians, in which all bulk\nstates are localized and specified by extensive local integrals of motion\n(LIOMs). The present systems are bilayer extension of Creutz ladder, which is\nstudied previously. In order to construct models, we employ building blocks,\ncube operators, which are linear combinations of fermions defined in each cube\nof the bilayer lattice. There are eight cubic operators, and the Hamiltonians\nare composed of the number operators of them, the LIOMs. A suitable arrangement\nof locations of the cube operators is needed to have exact projective\nHamiltonians. The projective Hamiltonians belong to a topological\nclassification class, BDI class. With the open boundary condition, the\nconstructed Hamiltonians have gapless edge modes, which commute with each other\nas well as the Hamiltonian. This result comes from a symmetry analogous to the\none-dimensional chiral symmetry of the BDI class. These results indicate that\nthe projective Hamiltonians describe a kind of symmetry protected topological\nphase matter. Careful investigation of topological indexes, such as Berry\nphase, string operator, is given. We also show that by using the gapless edge\nmodes, a generalized Sachdev-Ye-Kitaev (SYK) model is constructed.\n"}
+{"abstract": "  Traditional crowd counting approaches usually use Gaussian assumption to\ngenerate pseudo density ground truth, which suffers from problems like\ninaccurate estimation of the Gaussian kernel sizes. In this paper, we propose a\nnew measure-based counting approach to regress the predicted density maps to\nthe scattered point-annotated ground truth directly. First, crowd counting is\nformulated as a measure matching problem. Second, we derive a semi-balanced\nform of Sinkhorn divergence, based on which a Sinkhorn counting loss is\ndesigned for measure matching. Third, we propose a self-supervised mechanism by\ndevising a Sinkhorn scale consistency loss to resist scale changes. Finally, an\nefficient optimization method is provided to minimize the overall loss\nfunction. Extensive experiments on four challenging crowd counting datasets\nnamely ShanghaiTech, UCF-QNRF, JHU++, and NWPU have validated the proposed\nmethod.\n"}
+{"abstract": "  Group control of connected and autonomous vehicles on automated highways is\nchallenging for the advanced driver assistance systems (ADAS) and the automated\ndriving systems (ADS). This paper investigates the differential game-based\napproach to autonomous convoy control with the aim of deployment on automated\nhighways. Under the non-cooperative differential games, the coupled vehicles\nmake their decisions independently while their states are interdependent. The\nreceding horizon Nash equilibrium of the linear-quadratic differential game\nprovides the convoy a distributed state-feedback control strategy. This\napproach suffers a fundamental issue that neither a Nash equilibrium's\nexistence nor the uniqueness is guaranteed. We convert the individual\ndynamics-based differential game to a relative dynamics-based optimal control\nproblem that carries all the features of the differential game. The existence\nof a unique Nash control under the differential game corresponds to a unique\nsolution to the optimal control problem. The latter is shown, as well as the\nasymptotic stability of the closed-loop system. Simulations illustrate the\neffectiveness of the presented convey control scheme and how it well suits\nautomated highway driving scenarios.\n"}
+{"abstract": "  Autonomous driving is regarded as one of the most promising remedies to\nshield human beings from severe crashes. To this end, 3D object detection\nserves as the core basis of such perception system especially for the sake of\npath planning, motion prediction, collision avoidance, etc. Generally, stereo\nor monocular images with corresponding 3D point clouds are already standard\nlayout for 3D object detection, out of which point clouds are increasingly\nprevalent with accurate depth information being provided. Despite existing\nefforts, 3D object detection on point clouds is still in its infancy due to\nhigh sparseness and irregularity of point clouds by nature, misalignment view\nbetween camera view and LiDAR bird's eye of view for modality synergies,\nocclusions and scale variations at long distances, etc. Recently, profound\nprogress has been made in 3D object detection, with a large body of literature\nbeing investigated to address this vision task. As such, we present a\ncomprehensive review of the latest progress in this field covering all the main\ntopics including sensors, fundamentals, and the recent state-of-the-art\ndetection methods with their pros and cons. Furthermore, we introduce metrics\nand provide quantitative comparisons on popular public datasets. The avenues\nfor future work are going to be judiciously identified after an in-deep\nanalysis of the surveyed works. Finally, we conclude this paper.\n"}
+{"abstract": "  Wax is what you put on a surfboard to avoid slipping. It is an essential tool\nto go surfing... We introduce WAX-ML a research-oriented Python library\nproviding tools to design powerful machine learning algorithms and feedback\nloops working on streaming data. It strives to complement JAX with tools\ndedicated to time series. WAX-ML makes JAX-based programs easy to use for\nend-users working with pandas and xarray for data manipulation. It provides a\nsimple mechanism for implementing feedback loops, allows the implementation of\nonline learning and reinforcement learning algorithms with functions, and makes\nthem easy to integrate by end-users working with the object-oriented\nreinforcement learning framework from the Gym library. It is released with an\nApache open-source license on GitHub at https://github.com/eserie/wax-ml.\n"}
+{"abstract": "  We implement a model for the two-point statistics of biased tracers that\ncombines dark matter dynamics from $N$-body simulations with an analytic\nLagrangian bias expansion. Using Aemulus, a suite of $N$-body simulations built\nfor emulation of cosmological observables, we emulate the cosmology dependence\nof these nonlinear spectra from redshifts $z = 0$ to $z=2$. We quantify the\naccuracy of our emulation procedure, which is sub-per cent at $k=1\\, h {\\rm\nMpc}^{-1}$ for the redshifts probed by upcoming surveys and improves at higher\nredshifts. We demonstrate its ability to describe the statistics of complex\ntracer samples, including those with assembly bias and baryonic effects,\nreliably fitting the clustering and lensing statistics of such samples at\nredshift $z\\simeq 0.4$ to scales of $k_{\\rm max} \\approx 0.6\\,\nh\\mathrm{Mpc}^{-1}$. We show that the emulator can be used for unbiased\ncosmological parameter inference in simulated joint clustering and\ngalaxy--galaxy lensing analyses with data drawn from an independent $N$-body\nsimulation. These results indicate that our emulator is a promising tool that\ncan be readily applied to the analysis of current and upcoming datasets from\ngalaxy surveys.\n"}
+{"abstract": "  One class of competitive candidates for dark matter is ultralight bosons. If\nthey exist, these bosons may form long-lived bosonic clouds surrounding\nrotating black holes via superradiant instabilities, acting as sources of\ngravity and affecting the propagation of gravitational waves around the host\nblack hole. During extreme-mass-ratio inspirals, the bosonic clouds will\nsurvive the inspiral phase and can affect the quasinormal-mode frequencies of\nthe perturbed black-hole-bosonic-cloud system. In this work, we compute the\nshifts of gravitational quasinormal-mode frequencies of a rotating black hole\ndue to the presence of a surrounding bosonic cloud. We then perform a mock\nanalysis on simulated Laser Interferometer Space Antenna observational data\ncontaining injected ringdown signals from supermassive black holes with and\nwithout a bosonic cloud. We find that with less than an hour of observational\ndata of the ringdown phase of nearby supermassive black holes such as\nSagittarius A* and M32, we can rule out or confirm the existence of\ncloud-forming ultralight bosons of mass $ \\sim 10^{-17} \\rm eV$.\n"}
+{"abstract": "  Image Super Resolution (SR) finds applications in areas where images need to\nbe closely inspected by the observer to extract enhanced information. One such\nfocused application is an offline forensic analysis of surveillance feeds. Due\nto the limitations of camera hardware, camera pose, limited bandwidth, varying\nillumination conditions, and occlusions, the quality of the surveillance feed\nis significantly degraded at times, thereby compromising monitoring of\nbehavior, activities, and other sporadic information in the scene. For the\nproposed research work, we have inspected the effectiveness of four\nconventional yet effective SR algorithms and three deep learning-based SR\nalgorithms to seek the finest method that executes well in a surveillance\nenvironment with limited training data op-tions. These algorithms generate an\nenhanced resolution output image from a sin-gle low-resolution (LR) input\nimage. For performance analysis, a subset of 220 images from six surveillance\ndatasets has been used, consisting of individuals with varying distances from\nthe camera, changing illumination conditions, and complex backgrounds. The\nperformance of these algorithms has been evaluated and compared using both\nqualitative and quantitative metrics. These SR algo-rithms have also been\ncompared based on face detection accuracy. By analyzing and comparing the\nperformance of all the algorithms, a Convolutional Neural Network (CNN) based\nSR technique using an external dictionary proved to be best by achieving robust\nface detection accuracy and scoring optimal quantitative metric results under\ndifferent surveillance conditions. This is because the CNN layers progressively\nlearn more complex features using an external dictionary.\n"}
+{"abstract": "  Nas \\'ultimas d\\'ecadas houve forte mudan\\c{c}a no perfil das\npublica\\c{c}\\~oes em an\\'alise econ\\^omica do direito e nos m\\'etodos\nemp\\'iricos mais utilizados. Por\\'em, nos pr\\'oximos anos a mudan\\c{c}a pode\nser maior e mais r\\'apida, exigindo adapta\\c{c}\\~ao dos pesquisadores da\n\\'area. Neste cap\\'itulo analiso algumas tend\\^encias recentes de mudan\\c{c}a e\noportunidades futuras que se avizinham a partir avan\\c{c}os nas bases de dados,\nestat\\'istica, computa\\c{c}\\~ao e no arcabou\\c{c}o regulat\\'orio dos pa\\'ises.\nAvan\\c{c}o a hip\\'otese de que expans\\~ao de objetos e m\\'etodos favorecer\\'a\nequipes de pesquisa maiores e interdisciplinares e apresento evid\\^encias\ncircunstanciais a partir de dados bibliom\\'etricos de que isso j\\'a vem\nacontecendo no Journal of Law and Economics.\n"}
+{"abstract": "  Research in artificial intelligence (AI) for social good presupposes some\ndefinition of social good, but potential definitions have been seldom suggested\nand never agreed upon. The normative question of what AI for social good\nresearch should be \"for\" is not thoughtfully elaborated, or is frequently\naddressed with a utilitarian outlook that prioritizes the needs of the majority\nover those who have been historically marginalized, brushing aside realities of\ninjustice and inequity. We argue that AI for social good ought to be assessed\nby the communities that the AI system will impact, using as a guide the\ncapabilities approach, a framework to measure the ability of different policies\nto improve human welfare equity. Furthermore, we lay out how AI research has\nthe potential to catalyze social progress by expanding and equalizing\ncapabilities. We show how the capabilities approach aligns with a participatory\napproach for the design and implementation of AI for social good research in a\nframework we introduce called PACT, in which community members affected should\nbe brought in as partners and their input prioritized throughout the project.\nWe conclude by providing an incomplete set of guiding questions for carrying\nout such participatory AI research in a way that elicits and respects a\ncommunity's own definition of social good.\n"}
+{"abstract": "  In this paper, we focus on the relationship between the fifth Painlev\\'{e}\nequation and a Jacobi weight perturbed with random singularities,\n\\begin{equation*} w(z)=\\left(1-z^2\\right)^{\\alpha}{\\rm\ne}^{-\\frac{t}{z^2-k^2}},~~~z,k\\in[-1,1],~\\alpha,t>0. \\end{equation*} By using\nthe ladder operator approach, we obtain that an auxiliary quantity $R_n(t)$,\nwhich is closely related to the recurrence coefficients of monic polynomials\northogonal with $w(z)$, satisfies a particular Painlev\\'{e} V equation.\n"}
+{"abstract": "  Fluid simulations are often performed using the incompressible Navier-Stokes\nequations (INSE), leading to sparse linear systems which are difficult to solve\nefficiently in parallel. Recently, kinetic methods based on the\nadaptive-central-moment multiple-relaxation-time (ACM-MRT) model have\ndemonstrated impressive capabilities to simulate both laminar and turbulent\nflows, with quality matching or surpassing that of state-of-the-art INSE\nsolvers. Furthermore, due to its local formulation, this method presents the\nopportunity for highly scalable implementations on parallel systems such as\nGPUs. However, an efficient ACM-MRT-based kinetic solver needs to overcome a\nnumber of computational challenges, especially when dealing with complex solids\ninside the fluid domain. In this paper, we present multiple novel GPU\noptimization techniques to efficiently implement high-quality ACM-MRT-based\nkinetic fluid simulations in domains containing complex solids. Our techniques\ninclude a new communication-efficient data layout, a load-balanced\nimmersed-boundary method, a multi-kernel launch method using a simplified\nformulation of ACM-MRT calculations to enable greater parallelism, and the\nintegration of these techniques into a parametric cost model to enable\nautomated parameter search to achieve optimal execution performance. We also\nextended our method to multi-GPU systems to enable large-scale simulations. To\ndemonstrate the state-of-the-art performance and high visual quality of our\nsolver, we present extensive experimental results and comparisons to other\nsolvers.\n"}
+{"abstract": "  The information bottleneck (IB) principle has been suggested as a way to\nanalyze deep neural networks. The learning dynamics are studied by inspecting\nthe mutual information (MI) between the hidden layers and the input and output.\nNotably, separate fitting and compression phases during training have been\nreported. This led to some controversy including claims that the observations\nare not reproducible and strongly dependent on the type of activation function\nused as well as on the way the MI is estimated. Our study confirms that\ndifferent ways of binning when computing the MI lead to qualitatively different\nresults, either supporting or refusing IB conjectures. To resolve the\ncontroversy, we study the IB principle in settings where MI is non-trivial and\ncan be computed exactly. We monitor the dynamics of quantized neural networks,\nthat is, we discretize the whole deep learning system so that no approximation\nis required when computing the MI. This allows us to quantify the information\nflow without measurement errors. In this setting, we observed a fitting phase\nfor all layers and a compression phase for the output layer in all experiments;\nthe compression in the hidden layers was dependent on the type of activation\nfunction. Our study shows that the initial IB results were not artifacts of\nbinning when computing the MI. However, the critical claim that the compression\nphase may not be observed for some networks also holds true.\n"}
+{"abstract": "  Modelling the process that a listener actuates in deriving the words intended\nby a speaker requires setting a hypothesis on how lexical items are stored in\nmemory. This work aims at developing a system that imitates humans when\nidentifying words in running speech and, in this way, provide a framework to\nbetter understand human speech processing. We build a speech recognizer for\nItalian based on the principles of Stevens' model of Lexical Access in which\nwords are stored as hierarchical arrangements of distinctive features (Stevens,\nK. N. (2002). \"Toward a model for lexical access based on acoustic landmarks\nand distinctive features,\" J. Acoust. Soc. Am., 111(4):1872-1891). Over the\npast few decades, the Speech Communication Group at the Massachusetts Institute\nof Technology (MIT) developed a speech recognition system for English based on\nthis approach. Italian will be the first language beyond English to be\nexplored; the extension to another language provides the opportunity to test\nthe hypothesis that words are represented in memory as a set of\nhierarchically-arranged distinctive features, and reveal which of the\nunderlying mechanisms may have a language-independent nature. This paper also\nintroduces a new Lexical Access corpus, the LaMIT database, created and labeled\nspecifically for this work, that will be provided freely to the speech research\ncommunity. Future developments will test the hypothesis that specific acoustic\ndiscontinuities - called landmarks - that serve as cues to features, are\nlanguage independent, while other cues may be language-dependent, with powerful\nimplications for understanding how the human brain recognizes speech.\n"}
+{"abstract": "  The management of the energy consumption and thermal dissipation of\nmulti-core heterogeneous platforms is becoming increasingly important as it can\nhave direct impact on the platform performance. This paper discusses an\napproach that enables fast exploration and validation of heterogeneous system\non chips (SoCs) platform configurations with respect to their thermal\ndissipation. Such platforms can be configured to find the optimal trade-off\nbetween performance and power consumption. This directly reflects in the head\ndissipation of the platform, which when increases over a given threshold will\nactually decrease the performance of the platform. Therefore, it is important\nto be able to quickly probe and explore different configurations and identify\nthe most suitable one. However, this task is hindered by the large space of\npossible configurations of such platforms and by the time required to benchmark\neach configurations. As such, we propose an approach in which we construct a\nmodel of the thermal dissipation of a given platform using a system\nidentification methods and then we use this model to explore and validate\ndifferent configurations. The approach allows us to decrease the exploration\ntime with several orders of magnitude. We exemplify the approach on an\nOdroid-XU4 board featuring an Exynos 5422 SoC.\n"}
+{"abstract": "  Based on the Kharzeev-McLerran-Warringa (KMW) model that estimates strong\nelectromagnetic (EM) fields generated in relativistic heavy-ion collisions, we\ngeneralize the formulas of EM fields in the vacuum by incorporating the\nlongitudinal position dependence, the generalized charge distributions and\nretardation correction. We further generalize the formulas of EM fields in the\npure quark-gluon plasma (QGP) medium by incorporating a constant Ohm electric\nconductivity and also during the realistic early-time stages QGP evolution by\nusing a time-dependent electric conductivity. Using the extended KMW model, we\nobserve a slower time evolution and a more reasonable impact parameter $b$\ndependence of the magnetic field strength than those from the original KMW\nmodel in the vacuum. The inclusion of medium effects by using the lattice data\nhelps to further prolong the time evolution of magnetic field, such that the\nmagnetic field strength during the realistic QGP evolution at thermal\nfreeze-out time can meet the $1\\sigma$ bound constrained from experimentally\nmeasured difference in global polarizations of $\\Lambda$ and $\\bar{\\Lambda}$\nhyperons in Au+Au collisions at top RHIC energy. These generalized formulations\nin the extended KMW model will be potentially useful for many EM fields\nrelevant studies in heavy-ion collisions, especially at lower colliding\nenergies and for various species of colliding nuclei.\n"}
+{"abstract": "  Fetal motion is unpredictable and rapid on the scale of conventional MR scan\ntimes. Therefore, dynamic fetal MRI, which aims at capturing fetal motion and\ndynamics of fetal function, is limited to fast imaging techniques with\ncompromises in image quality and resolution. Super-resolution for dynamic fetal\nMRI is still a challenge, especially when multi-oriented stacks of image slices\nfor oversampling are not available and high temporal resolution for recording\nthe dynamics of the fetus or placenta is desired. Further, fetal motion makes\nit difficult to acquire high-resolution images for supervised learning methods.\nTo address this problem, in this work, we propose STRESS (Spatio-Temporal\nResolution Enhancement with Simulated Scans), a self-supervised\nsuper-resolution framework for dynamic fetal MRI with interleaved slice\nacquisitions. Our proposed method simulates an interleaved slice acquisition\nalong the high-resolution axis on the originally acquired data to generate\npairs of low- and high-resolution images. Then, it trains a super-resolution\nnetwork by exploiting both spatial and temporal correlations in the MR time\nseries, which is used to enhance the resolution of the original data.\nEvaluations on both simulated and in utero data show that our proposed method\noutperforms other self-supervised super-resolution methods and improves image\nquality, which is beneficial to other downstream tasks and evaluations.\n"}
+{"abstract": "  The detection of a wide range of substructures such as rings, cavities and\nspirals has become a common outcome of high spatial resolution imaging of\nprotoplanetary disks, both in the near-infrared scattered light and in the\nthermal millimetre continuum emission. The most frequent interpretation of\ntheir origin is the presence of planetary-mass companions perturbing the gas\nand dust distribution in the disk (perturbers), but so far the only bona-fide\ndetection has been the two giant planets around PDS 70. Here, we collect a\nsample of 15 protoplanetary disks showing substructures in SPHERE scattered\nlight images and present a homogeneous derivation of planet detection limits in\nthese systems. We also estimate the mass of these perturbers through a Hill\nradius prescription and a comparison to ALMA data. Assuming that one single\nplanet carves each substructure in scattered light, we find that more massive\nperturbers are needed to create gaps within cavities than rings, and that we\nmight be close to a detection in the cavities of RX J1604, RX J1615, Sz Cha, HD\n135344B and HD 34282. We reach typical mass limits in these cavities of 3-10\nMjup. For planets in the gaps between rings, we find that the detection limits\nof SPHERE are about an order of magnitude away in mass, and that the gaps of\nPDS 66 and HD 97048 seem to be the most promising structures for planet\nsearches. The proposed presence of massive planets causing spiral features in\nHD 135344B and HD 36112 are also within SPHERE's reach assuming hot-start\nmodels.These results suggest that current detection limits are able to detect\nhot-start planets in cavities, under the assumption that they are formed by a\nsingle perturber located at the centre of the cavity. More realistic planet\nmass constraints would help to clarify whether this is actually the case, which\nmight point to perturbers not being the only way of creating substructures.\n"}
+{"abstract": "  Causal graphs (CGs) are compact representations of the knowledge of the data\ngenerating processes behind the data distributions. When a CG is available,\ne.g., from the domain knowledge, we can infer the conditional independence (CI)\nrelations that should hold in the data distribution. However, it is not\nstraightforward how to incorporate this knowledge into predictive modeling. In\nthis work, we propose a model-agnostic data augmentation method that allows us\nto exploit the prior knowledge of the CI encoded in a CG for supervised machine\nlearning. We theoretically justify the proposed method by providing an excess\nrisk bound indicating that the proposed method suppresses overfitting by\nreducing the apparent complexity of the predictor hypothesis class. Using\nreal-world data with CGs provided by domain experts, we experimentally show\nthat the proposed method is effective in improving the prediction accuracy,\nespecially in the small-data regime.\n"}
+{"abstract": "  With the upcoming Run 3 of the LHC, the FASERv and SND@LHC detectors will\nstart a new era of neutrino physics using the far-forward high-energy neutrino\nbeam produced in collisions at ATLAS. This emerging LHC neutrino physics\nprogram requires reliable estimates of the LHC's forward neutrino fluxes and\ntheir uncertainties. In this paper we provide a new fast-neutrino flux\nsimulation, implemented as a RIVET module, to address this issue. We present\nthe expected energy distributions going through the FASERv and SND@LHC\ndetectors based on various commonly used event generators, analyze the origin\nof those neutrinos, and present the expected neutrino event rates.\n"}
+{"abstract": "  A large proportion of transiting planetary systems appear to possess only a\nsingle planet as opposed to multiple transiting planets. This excess of singles\nis indicative of significant mutual inclinations existing within a large number\nof planetary systems, but the origin of these misalignments is unclear.\nMoreover, recent observational characterization reveals that mutual\ninclinations tend to increase with proximity to the host star. These trends are\nboth consistent with the dynamical influence of a strong quadrupolar potential\narising from the host star during its early phase of rapid rotation, coupled\nwith a non-zero stellar obliquity. Here, we simulate a population of planetary\nsystems subject to the secular perturbation arising from a tilted, oblate host\nstar as it contracts and spins down subsequent to planet formation. We\ndemonstrate that this mechanism can reproduce the general increase in\nplanet-planet mutual inclinations with proximity to the host star, and\ndelineate a parameter space wherein the host star can drive dynamical\ninstabilities. We suggest that approximately 5-10\\% of low-mass Kepler systems\nare susceptible to this instability mechanism, suggesting that a significant\nnumber of single-transiting planets may truly be intrinsically single. We also\nreport a novel connection between instability and stellar obliquity reduction\nand make predictions that can be tested within upcoming TESS observations.\n"}
+{"abstract": "  We present general results on fluctuations and spatial correlations of the\nempirical density and current of Markovian diffusion in an equilibrium or\nnon-equilibrium steady state on all time scales. We unravel a deep connection\nbetween the kinematics of empirical current and \"dual-reversal\" symmetry. We\nhighlight the essential role of coarse-graining in space -- the fluctuations of\nempirical density and current defined in a point are insensitive to\nnon-conservative forces, and are proven to diverge on all time scales in\ndimensions higher than one, implying non-Gaussian statistics. A spatial\ncoarse-graining is required to uncover salient features of currents due to\nnon-conservative forces. We apply the results to minimal two-dimensional\nexamples of irreversible diffusion. Our findings provide a deeper understanding\nof time-averaged observables and may allow for a more reliable analysis of\nsingle-molecule and particle-tracking experiments.\n"}
+{"abstract": "  Feature selection (FS) is an important research topic in machine learning.\nUsually, FS is modelled as a+ bi-objective optimization problem whose\nobjectives are: 1) classification accuracy; 2) number of features. One of the\nmain issues in real-world applications is missing data. Databases with missing\ndata are likely to be unreliable. Thus, FS performed on a data set missing some\ndata is also unreliable. In order to directly control this issue plaguing the\nfield, we propose in this study a novel modelling of FS: we include reliability\nas the third objective of the problem. In order to address the modified\nproblem, we propose the application of the non-dominated sorting genetic\nalgorithm-III (NSGA-III). We selected six incomplete data sets from the\nUniversity of California Irvine (UCI) machine learning repository. We used the\nmean imputation method to deal with the missing data. In the experiments,\nk-nearest neighbors (K-NN) is used as the classifier to evaluate the feature\nsubsets. Experimental results show that the proposed three-objective model\ncoupled with NSGA-III efficiently addresses the FS problem for the six data\nsets included in this study.\n"}
+{"abstract": "  We propose a notion of minimal free resolutions for differential modules, and\nwe prove existence and uniqueness results for such resolutions. We also take\nthe first steps toward studying the structure of minimal free resolutions of\ndifferential modules. Our main result in this direction explains a sense in\nwhich the minimal free resolution of a differential module is a deformation of\nthe minimal free resolution of its homology; this leads to structural results\nthat mirror classical theorems about minimal free resolutions of modules.\n"}
+{"abstract": "  We consider a generalisation of the Basilica group to all odd primes: the\n$p$-Basilica groups acting on the $p$-adic tree. We show that the $p$-Basilica\ngroups have the $p$-congruence subgroup property but not the congruence\nsubgroup property nor the weak congruence subgroup property. This provides the\nfirst examples of weakly branch groups with such properties. In addition, the\n$p$-Basilica groups give the first examples of weakly branch, but not branch,\ngroups which are super strongly fractal. We compute the orders of the\ncongruence quotients of these groups, which enable us to determine the\nHausdorff dimensions of the $p$-Basilica groups. Lastly, we show that the\n$p$-Basilica groups do not possess maximal subgroups of infinite index and that\nthey have infinitely many non-normal maximal subgroups.\n"}
+{"abstract": "  U($N$) supersymmetric Yang-Mills theory naturally appears as the low-energy\neffective theory of a system of $N$ D-branes and open strings between them.\nTransverse spatial directions emerge from scalar fields, which are $N\\times N$\nmatrices with color indices; roughly speaking, the eigenvalues are the\nlocations of D-branes. In the past, it was argued that this simple 'emergent\nspace' picture cannot be used in the context of gauge/gravity duality, because\nthe ground-state wave function delocalizes at large $N$, leading to a conflict\nwith the locality in the bulk geometry. In this paper we show that this\nconventional wisdom is not correct: the ground-state wave function does not\ndelocalize, and there is no conflict with the locality of the bulk geometry.\nThis conclusion is obtained by clarifying the meaning of the 'diagonalization\nof a matrix' in Yang-Mills theory, which is not as obvious as one might think.\nThis observation opens up the prospect of characterizing the bulk geometry via\nthe color degrees of freedom in Yang-Mills theory, all the way down to the\ncenter of the bulk.\n"}
+{"abstract": "  We contribute to the theoretical understanding of randomized search\nheuristics for dynamic problems. We consider the classical vertex coloring\nproblem on graphs and investigate the dynamic setting where edges are added to\nthe current graph. We then analyze the expected time for randomized search\nheuristics to recompute high quality solutions. The (1+1)~Evolutionary\nAlgorithm and RLS operate in a setting where the number of colors is bounded\nand we are minimizing the number of conflicts. Iterated local search algorithms\nuse an unbounded color palette and aim to use the smallest colors and,\nconsequently, the smallest number of colors.\n  We identify classes of bipartite graphs where reoptimization is as hard as or\neven harder than optimization from scratch, i.e., starting with a random\ninitialization. Even adding a single edge can lead to hard symmetry problems.\nHowever, graph classes that are hard for one algorithm turn out to be easy for\nothers. In most cases our bounds show that reoptimization is faster than\noptimizing from scratch. We further show that tailoring mutation operators to\nparts of the graph where changes have occurred can significantly reduce the\nexpected reoptimization time. In most settings the expected reoptimization time\nfor such tailored algorithms is linear in the number of added edges. However,\ntailored algorithms cannot prevent exponential times in settings where the\noriginal algorithm is inefficient.\n"}
+{"abstract": "  Spoken language understanding (SLU) topic has seen a lot of progress these\nlast three years, with the emergence of end-to-end neural approaches. Spoken\nlanguage understanding refers to natural language processing tasks related to\nsemantic extraction from speech signal, like named entity recognition from\nspeech or slot filling task in a context of human-machine dialogue.\nClassically, SLU tasks were processed through a cascade approach that consists\nin applying, firstly, an automatic speech recognition process, followed by a\nnatural language processing module applied to the automatic transcriptions.\nThese three last years, end-to-end neural approaches, based on deep neural\nnetworks, have been proposed in order to directly extract the semantics from\nspeech signal, by using a single neural model. More recent works on\nself-supervised training with unlabeled data open new perspectives in term of\nperformance for automatic speech recognition and natural language processing.\nIn this paper, we present a brief overview of the recent advances on the French\nMEDIA benchmark dataset for SLU, with or without the use of additional data. We\nalso present our last results that significantly outperform the current\nstate-of-the-art with a Concept Error Rate (CER) of 11.2%, instead of 13.6% for\nthe last state-of-the-art system presented this year.\n"}
+{"abstract": "  Completely positive factorization (CPF) is a critical task with applications\nin many fields. This paper proposes a novel method for the CPF. Based on the\nidea of exterior point iteration, an optimization model is given, which aims to\northogonally transform a symmetric lower rank factor to be nonnegative. The\noptimization problem can be solved via a modified nonlinear conjugate gradient\nmethod iteratively. The iteration points locate on the exterior of the\northonormal manifold and the closed set whose transformed matrices are\nnonnegative before convergence generally. Convergence analysis is given for the\nlocal or global optimum of the objective function, together with the iteration\nalgorithm. Some potential issues that may affect the CPF are explored\nnumerically. The exterior point method performs much better than other\nalgorithms, not only in the efficiency of computational cost or accuracy, but\nalso in the ability to address the CPF in some hard cases.\n"}
+{"abstract": "  Dirac notation is the most common way to describe quantum states and\noperations on states. It is very convenient and allows for quick visual\ndistinction between vectors, scalars and operators. For quantum processes that\ninvolve interactions of multiple systems an even better visualisation has been\nproposed by Coecke and Kissinger, in the form of a diagrammatic formalism\n[CK2017]. Their notation expresses formulas in the form of diagrams, somewhat\nsimilar to Feynman diagrams, and is more general than the circuit notation for\nquantum computing.\n  This document consists of two parts. (1) We give a brief summary of the\ndiagrammatic notation of quantum processes, tailored to readers who already\nknow quantum physics and are not interested in general process theory. For this\naudience our summary is less daunting than the encyclopaedic book by Coecke and\nKissinger [CK2017], and on the other hand more accessible than the\nultra-compact introduction of [KTW2017]. We deviate a somewhat from\n[CK2017,KTW2017] in that we do not assume basis states to equal their own\ncomplex conjugate; this means that we do not use symmetric notation for basis\nstates, and it leads us to explicitly show arrows on wires where they are\nusually omitted.\n  (2) We extend the work of Kissinger, Tull and Westerbaan [KTW2017] which\ngives a diagrammatic security proof for BB84 and 6-state Quantum Key\nDistribution. Their proof is based on a sequence of diagrammatic manipulations\nthat works when the bases used in the protocol are mutually unbiased. We extend\nthis result to 8-state encoding, which has been proposed as a tool in quantum\nkey recycling protocols [SdV2017,LS2018], and which does not have mutually\nunbiased bases.\n"}
+{"abstract": "  Variations in the grain size distribution are to be expected in the\ninterstellar medium (ISM) due to grain growth and destruction. In this work, we\npresent a dust collision model to be implemented inside a magnetohydrodynamical\n(MHD) code that takes into account grain growth and shattering of charged dust\ngrains of a given composition (silicate or graphite). We integrate this model\nin the MHD code Athena, and builds on a previous implementation of the dynamics\nof charged dust grains in the same code. To demonstrate the performance of this\ncoagulation model, we study the variations in the grain size distribution of a\nsingle-sized population of dust with radius 0.05 $\\mu$m inside several dust\nfilaments formed during a 2D MHD simulation. We also consider a realistic dust\ndistribution with sizes ranging from 50 \\AA~to 0.25 $\\mu$m and analyze both the\nvariations in the size distribution for graphite and silicates, as well as of\nthe far ultraviolet extinction curve. From the obtained results, we conclude\nthat the methodology here presented, based on the MHD evolution of the equation\nof motion for a charged particle, is optimal for studying the coagulation of\ncharged dust grains in a diffuse regime such as a molecular cloud envelope.\nObservationally, these variations in the dust size distribution are translated\ninto variations in the far ultraviolet extinction curve, and they are mainly\ncaused by small graphite dust grains.\n"}
+{"abstract": "  Eigenstate thermalization in quantum many-body systems implies that\neigenstates at high energy are similar to random vectors. Identifying systems\nwhere at least some eigenstates are non-thermal is an outstanding question. In\nthis work we show that interacting quantum models that have a nullspace -- a\ndegenerate subspace of eigenstates at zero energy (zero modes), which\ncorresponds to infinite temperature, provide a route to non-thermal\neigenstates. We analytically show the existence of a zero mode which can be\nrepresented as a matrix product state for a certain class of local\nHamiltonians. In the more general case we use a subspace disentangling\nalgorithm to generate an orthogonal basis of zero modes characterized by\nincreasing entanglement entropy. We show evidence for an area-law entanglement\nscaling of the least entangled zero mode in the broad parameter regime, leading\nto a conjecture that all local Hamiltonians with the nullspace feature zero\nmodes with area-law entanglement scaling, and as such, break the strong\nthermalization hypothesis. Finally, we find zero-modes in constrained models\nand propose setup for observing their experimental signatures.\n"}
+{"abstract": "  Femtosecond laser pulse systems allows to modify in a precise and permanent\nway the optical properties of atransparent materials. This process enables the\ndirect writing of guiding structures in materials, commonlyknown as waveguides,\nwhich are the base for optical circuit fabrication.It is our interest to study\nthe main characteristics of the waveguides manufactured by the laser\nmicro-machining technique. Here, an analysis of the resulting refractive index\nprofile has been carried out. Thischaracteristic is essential for the design\nand simulation of integrated optical circuits. In particular we havedeveloped\nour research on the study of light coupling in a pair of type II waveguides\nmade in Lithium Niobate(LiNbO3).These experimental backgrounds provide us with\nelements to adjust and test the retrieved profile.Taking into account different\ndistance between tracks and writing energies, it is well known that the\ncouplinglength changes and the coupling ratio too. Then this study allows us to\nreconstruct the refractive index profileaccording to its manufacturing\nconditions. Modeling of the refractive index distribution profile is a key\nparameterto perform beam propagation mode simulations (BPM) to achieve more\nrealistic results. So, by means of thismethod it is possible to obtain a\ngeneral procedure to describe the characteristics of these kinds of\nwaveguides.As a model test, integrated waveguides were built to corroborate\ntheir light coupling. In a first stage it is designedthrough BPM simulations\nthen it is manufactured in an X-cut LiNbO3crystal in order to check its\noperationaccording to the simulations carried out\n"}
+{"abstract": "  In this chapter, we reflect on the use of Artificial Intelligence (AI) and\nits acceptance in clinical environments. We develop a general view of\nhindrances for clinical acceptance in the form of a pipeline model combining AI\nand clinical practise. We then link each challenge to the relevant stage in the\npipeline and discuss the necessary requirements in order to overcome each\nchallenge. We complement this discussion with an overview of opportunities for\nAI, which we currently see at the periphery of clinical workflows.\n"}
+{"abstract": "  We show that a non-Hermitian operator with a tridiagonal matrix\nrepresentation in a finite-dimensional vector space is similar to an Hermitian\noperator. The required condition is sufficient and simple examples show that it\nis not necessary. We derive quite general features of the eigenvalues and\neigenvectors for a somewhat particular case.\n"}
+{"abstract": "  The gas mass fraction in galaxy clusters has been widely used to determine\ncosmological parameters. This method assumes that the ratio of the cluster gas\nmass fraction to the cosmic baryon fraction ($\\gamma(z)$) is constant as a\nfunction of redshift. In this work, we look for a time evolution of $\\gamma(z)$\nat $R_{500}$ by using both the SPT-SZ and Planck Early SZ (ESZ) cluster data,\nin a model-independent fashion without any explicit dependence on the\nunderlying cosmology. For this calculation, we use a non-parametric functional\nform for the Hubble parameter obtained from Gaussian Process regression using\ncosmic chronometers. We parameterize $\\gamma(z)$ as: $\\gamma(z)=\n\\gamma_0(1+\\gamma_1 z)$ to constrain the redshift evolution. We find\ncontradictory results between both the samples. For SPT-SZ, $\\gamma (z)$\ndecreases as a function of redshift (at more than 5$\\sigma$), whereas a\npositive trend with redshift is found for Planck ESZ data (at more than\n4$\\sigma$). We however find that the $\\gamma_1$ values for a subset of SPT-SZ\nand Planck ESZ clusters between the same redshift interval agree to within\n$1\\sigma$. When we allow for a dependence on the halo mass in the evolution of\nthe gas depletion factor, the $4-5\\sigma$ discrepancy reduces to $2\\sigma$.\n"}
+{"abstract": "  Regulatory radio spectrum management is evolving from traditional static\nfrequency allocation and assignment schemes towards dynamic spectrum management\nand access schemes. This evolution is necessitated by a number of factors\nincluding underutilization of licensed spectrum bands, changing market and\ntechnological developments and increased demand for spectrum for emerging\napplications in multimedia communications, internet-of-things and fifth\ngeneration (5G) wireless networks. In simple terms dynamic spectrum management\ninvolves allowing unlicensed users known as secondary users (SUs) to access the\nlicensed spectrum of a licensed user also known as primary user (PU). This is\nprimarily achieved using spectrum sharing schemes that leverage spectrum\ndatabase and cognitive radio techniques. However, the use of spectrum database\nand cognitive radio techniques faces reliability, security and privacy concerns\nfor spectrum sharing. There is also a need to support other requirements of\ndynamic spectrum management such as secondary spectrum trading market and\ndynamic spectrum access coordination. In this work, we review the use of\nblockchains for enabling spectrum sharing and other aspects of dynamic spectrum\nmanagement. The review covers the use of blockchain to record spectrum\nmanagement information such as spectrum sensing results and spectrum auction\ntransactions in a secure manner. The article also covers the use of smart\ncontracts to support complex service-levelagreements (SLAs) between network\noperators which is key to supporting a self-organized secondary spectrum\nsharing market and enforcement of regulatory policies. A taxonomy of the\nintersection between blockchain and various concepts of dynamic spectrum\nmanagement is also provided\n"}
+{"abstract": "  Natural language video localization (NLVL), which aims to locate a target\nmoment from a video that semantically corresponds to a text query, is a novel\nand challenging task. Toward this end, in this paper, we present a\ncomprehensive survey of the NLVL algorithms, where we first propose the\npipeline of NLVL, and then categorize them into supervised and\nweakly-supervised methods, following by the analysis of the strengths and\nweaknesses of each kind of methods. Subsequently, we present the dataset,\nevaluation protocols and the general performance analysis. Finally, the\npossible perspectives are obtained by summarizing the existing methods.\n"}
+{"abstract": "  The discrete baroclinic modes of quasigeostrophic theory are incomplete and\nthe incompleteness manifests as a loss of information in the projection\nprocess. The incompleteness of the baroclinic modes is related to the presence\nof two previously unnoticed stationary step-wave solutions of the Rossby wave\nproblem with flat boundaries. These step-waves are the limit of surface\nquasigeostrophic waves as boundary buoyancy gradients vanish. A complete normal\nmode basis for quasigeostrophic theory is obtained by considering the\ntraditional Rossby wave problem with prescribed buoyancy gradients at the lower\nand upper boundaries. The presence of these boundary buoyancy gradients\nactivates the previously inert boundary degrees of freedom. These Rossby waves\nhave several novel properties such as the presence of multiple modes with no\ninternal zeros, a finite number of modes with negative norms, and their\nvertical structures form a basis capable of representing any quasigeostrophic\nstate with a differentiable series expansion. Using this complete basis, we are\nable to obtain a differentiable series expansion to the potential vorticity of\nBretherton (with Dirac delta contributions). We also examine the\nquasigeostrophic vertical velocity modes and derive a complete basis for such\nmodes as well. A natural application of these modes is the development of a\nweakly non-linear wave-interaction theory of geostrophic turbulence that takes\ntopography into account.\n"}
+{"abstract": "  Opioid and substance misuse is rampant in the United States today, with the\nphenomenon known as the opioid crisis. The relationship between substance use\nand mental health has been extensively studied, with one possible relationship\nbeing substance misuse causes poor mental health. However, the lack of evidence\non the relationship has resulted in opioids being largely inaccessible through\nlegal means. This study analyzes the substance misuse posts on social media\nwith the opioids being sold through crypto market listings. We use the Drug\nAbuse Ontology, state-of-the-art deep learning, and BERT-based models to\ngenerate sentiment and emotion for the social media posts to understand user\nperception on social media by investigating questions such as, which synthetic\nopioids people are optimistic, neutral, or negative about or what kind of drugs\ninduced fear and sorrow or what kind of drugs people love or thankful about or\nwhich drug people think negatively about or which opioids cause little to no\nsentimental reaction. We also perform topic analysis associated with the\ngenerated sentiments and emotions to understand which topics correlate with\npeople's responses to various drugs. Our findings can help shape policy to help\nisolate opioid use cases where timely intervention may be required to prevent\nadverse consequences, prevent overdose-related deaths, and worsen the epidemic.\n"}
+{"abstract": "  The advantages of evolutionary algorithms with respect to traditional methods\nhave been greatly discussed in the literature. While particle swarm optimizers\nshare such advantages, they outperform evolutionary algorithms in that they\nrequire lower computational cost and easier implementation, involving no\noperator design and few coefficients to be tuned. However, even marginal\nvariations in the settings of these coefficients greatly influence the dynamics\nof the swarm. Since this paper does not intend to study their tuning,\ngeneral-purpose settings are taken from previous studies, and virtually the\nsame algorithm is used to optimize a variety of notably different problems.\nThus, following a review of the paradigm, the algorithm is tested on a set of\nbenchmark functions and engineering problems taken from the literature. Later,\ncomplementary lines of code are incorporated to adapt the method to\ncombinatorial optimization as it occurs in scheduling problems, and a real case\nis solved using the same optimizer with the same settings. The aim is to show\nthe flexibility and robustness of the approach, which can handle a wide variety\nof problems.\n"}
+{"abstract": "  High Definition (HD) maps are maps with precise definitions of road lanes\nwith rich semantics of the traffic rules. They are critical for several key\nstages in an autonomous driving system, including motion forecasting and\nplanning. However, there are only a small amount of real-world road topologies\nand geometries, which significantly limits our ability to test out the\nself-driving stack to generalize onto new unseen scenarios. To address this\nissue, we introduce a new challenging task to generate HD maps. In this work,\nwe explore several autoregressive models using different data representations,\nincluding sequence, plain graph, and hierarchical graph. We propose HDMapGen, a\nhierarchical graph generation model capable of producing high-quality and\ndiverse HD maps through a coarse-to-fine approach. Experiments on the Argoverse\ndataset and an in-house dataset show that HDMapGen significantly outperforms\nbaseline methods. Additionally, we demonstrate that HDMapGen achieves high\nscalability and efficiency.\n"}
+{"abstract": "  We study the set $\\mathcal{S}$ of odd positive integers $n$ with the property\n${2n}/{\\sigma(n)} - 1 = 1/x$, for positive integer $x$, i.e., the set that\nrelates to odd perfect and odd \"spoof perfect\" numbers. As a consequence, we\nfind that if $D=pq$ denotes a spoof odd perfect number other than Descartes'\nexample, with pseudo-prime factor $p$, then $q>10^{12}$. Furthermore, we find\nirregularities in the ending digits of integers $n\\in\\mathcal{S}$ and study\naspects of its density, leading us to conjecture that the amount of numbers in\n$\\mathcal{S}$ below $k$ is $\\sim10 \\log(k)$.\n"}
+{"abstract": "  Because of the invisible human keypoints in images caused by illumination,\nocclusion and overlap, it is likely to produce unreasonable human pose\nprediction for most of the current human pose estimation methods. In this\npaper, we design a novel generative adversarial network (GAN) to improve the\nlocalization accuracy of visible joints when some joints are invisible. The\nnetwork consists of two simple but efficient modules, Cascade Feature Network\n(CFN) and Graph Structure Network (GSN). First, the CFN utilizes the prediction\nmaps from the previous stages to guide the prediction maps in the next stage to\nproduce accurate human pose. Second, the GSN is designed to contribute to the\nlocalization of invisible joints by passing message among different joints.\nAccording to GAN, if the prediction pose produced by the generator G cannot be\ndistinguished by the discriminator D, the generator network G has successfully\nobtained the underlying dependence of human joints. We conduct experiments on\nthree widely used human pose estimation benchmark datasets, LSP, MPII and COCO,\nwhose results show the effectiveness of our proposed framework.\n"}
+{"abstract": "  In the quests to uncovering the fundamental structures that underlie some of\nthe asymptotic Swampland conjectures we initiate the general study of\nasymptotic period vectors of Calabi- Yau manifolds. Our strategy is to exploit\nthe constraints imposed by completeness, symmetry, and positivity, which are\nformalized in asymptotic Hodge theory. We use these general principles to study\nthe periods near any boundary in complex structure moduli space and explain\nthat near most boundaries leading exponentially suppressed corrections must be\npresent for consistency. The only exception are period vectors near the\nwell-studied large complex structure point. Together with the classification of\npossible boundaries, our procedure makes it possible to construct general\nmodels for these asymptotic periods. The starting point for this construction\nis the sl(2)-data classifying the boundary, which we use to construct the\nasymptotic Hodge decomposition known as the nilpotent orbit. We then use the\nlatter to determine the asymptotic period vector. We explicitly carry out this\nprogram for all possible one- and two-moduli boundaries in Calabi-Yau\nthreefolds and write down general models for their asymptotic periods.\n"}
+{"abstract": "  Multi-connectivity facilitates higher throughput, shorter delay, and lower\noutage probability for a user in a wireless network. Considering these\npromises, a rationale policy for a network operator would be to implement\nmulti-connectivity for all of its users. In this paper, we investigate whether\nthe promises of multi-connectivity also hold in such a setting where all users\nof a network are connected through multiple links. In particular, we consider a\nnetwork where every user connects to its k closest base stations. Using a\nframework of stochastic geometry and probability theory, we obtain analytic\nexpressions for per-user throughput and outage probability of $k$-connectivity\nnetworks under several failure models. In contrast to the conclusions of\nprevious research, our analysis shows that per-user throughput decreases with\nincreasing k. However, multi-connected networks are more resilient against\nfailures than single connected networks as reflected with lower outage\nprobability and lead to higher fairness among the users. Consequently, we\nconclude that rather than implementing multi-connectivity for all users, a\nnetwork operator should consider it for its users who would benefit from\nadditional links the most, e.g., cell edge users.\n"}
+{"abstract": "  In this paper we introduce the concept of Wiener-Luxemburg amalgam spaces\nwhich are a modification of the more classical Wiener amalgam spaces intended\nto address some of the shortcomings the latter face in the context of\nrearrangement-invariant Banach function spaces.\n  We introduce the Wiener-Luxemburg amalgam spaces and study their properties,\nincluding (but nor limited to) their normability, embeddings between them and\ntheir associate spaces. We also study amalgams of quasi-Banach function spaces\nand introduce a necessary generalisation of the concept of associate spaces. We\nthen apply this general theory to resolve the question whether the\nHardy-Littlewood-P\\'{o}lya principle holds for all r.i. quasi-Banach function\nspaces. Finally, we illustrate the asserted shortcomings of Wiener amalgam\nspaces by providing counterexamples to certain properties of Banach function\nspaces as well as rearrangement invariance.\n"}
+{"abstract": "  The use of lattice tensor representations is explored to investigate the\nlattice Landau gauge gluon propagator for the pure SU(3) Yang-Mills gauge\ntheory in 4D. The analysis of several tensor bases allows to quantify the\ncompleteness of the various tensor bases considered, the deviations of the\nlattice results from the continuum theory due to the lattice artefacts and\nestimate the theoretical uncertainty in the propagator. Furthermore, our\nanalysis tests continuum based relations with the lattice data and show that\nthe lattice Landau gauge gluon propagator is described by a unique form factor,\nas in the continuum formulation.\n"}
+{"abstract": "  We study the problem of maximizing a monotone $k$-submodular function $f$\nunder a knapsack constraint, where a $k$-submodular function is a natural\ngeneralization of a submodular function to $k$ dimensions. We present a\ndeterministic $(\\frac12-\\frac{1}{2e})$-approximation algorithm that evaluates\n$f$ $O(n^5k^4)$ times.\n"}
+{"abstract": "  Given two sources of evidence about a latent variable, one can combine the\ninformation from both by multiplying the likelihoods of each piece of evidence.\nHowever, when one or both of the observation models are misspecified, the\ndistributions will conflict. We study this problem in the setting with two\nconflicting reward functions learned from different sources. In such a setting,\nwe would like to retreat to a broader distribution over reward functions, in\norder to mitigate the effects of misspecification. We assume that an agent will\nmaximize expected reward given this distribution over reward functions, and\nidentify four desiderata for this setting. We propose a novel algorithm,\nMultitask Inverse Reward Design (MIRD), and compare it to a range of simple\nbaselines. While all methods must trade off between conservatism and\ninformativeness, through a combination of theory and empirical results on a toy\nenvironment, we find that MIRD and its variant MIRD-IF strike a good balance\nbetween the two.\n"}
+{"abstract": "  Power networks, responsible for transporting electricity across large\ngeographical regions, are complex infrastructures on which modern life\ncritically depend. Variations in demand and production profiles, with\nincreasing renewable energy integration, as well as the high voltage network\ntechnology, constitute a real challenge for human operators when optimizing\nelectricity transportation while avoiding blackouts. Motivated to investigate\nthe potential of AI methods in enabling adaptability in power network\noperation, we have designed a L2RPN challenge to encourage the development of\nreinforcement learning solutions to key problems present in the next-generation\npower networks. The NeurIPS 2020 competition was well received by the\ninternational community attracting over 300 participants worldwide.\n  The main contribution of this challenge is our proposed comprehensive\n'Grid2Op' framework, and associated benchmark, which plays realistic sequential\nnetwork operations scenarios. The Grid2Op framework, which is open-source and\neasily re-usable, allows users to define new environments with its companion\nGridAlive ecosystem. Grid2Op relies on existing non-linear physical power\nnetwork simulators and let users create a series of perturbations and\nchallenges that are representative of two important problems: a) the\nuncertainty resulting from the increased use of unpredictable renewable energy\nsources, and b) the robustness required with contingent line disconnections. In\nthis paper, we give the competition highlights. We present the benchmark suite\nand analyse the winning solutions, including one super-human performance\ndemonstration. We propose our organizational insights for a successful\ncompetition and conclude on open research avenues. Given the challenge success,\nwe expect our work will foster research to create more sustainable solutions\nfor power network operations.\n"}
+{"abstract": "  In this paper, we investigate the relation between Bachelier and\nBlack-Scholes models driven by the infinitely divisible inverse subordinators.\nSuch models, in contrast to their classical equivalents, can be used in markets\nwhere periods of stagnation are observed. We introduce the subordinated\nCox-Ross-Rubinstein model and prove that the price of the underlying in that\nmodel converges in distribution to the price of underlying in the subordinated\nBlack-Scholes model defined in [25]. Motivated by this fact we price the\nselected option contracts using the binomial trees. The results are compared to\nother numerical methods.\n"}
+{"abstract": "  Majority-SAT is the problem of determining whether an input $n$-variable\nformula in conjunctive normal form (CNF) has at least $2^{n-1}$ satisfying\nassignments. Majority-SAT and related problems have been studied extensively in\nvarious AI communities interested in the complexity of probabilistic planning\nand inference. Although Majority-SAT has been known to be PP-complete for over\n40 years, the complexity of a natural variant has remained open:\nMajority-$k$SAT, where the input CNF formula is restricted to have clause width\nat most $k$.\n  We prove that for every $k$, Majority-$k$SAT is in P. In fact, for any\npositive integer $k$ and rational $\\rho \\in (0,1)$ with bounded denominator, we\ngive an algorithm that can determine whether a given $k$-CNF has at least $\\rho\n\\cdot 2^n$ satisfying assignments, in deterministic linear time (whereas the\nprevious best-known algorithm ran in exponential time). Our algorithms have\ninteresting positive implications for counting complexity and the complexity of\ninference, significantly reducing the known complexities of related problems\nsuch as E-MAJ-$k$SAT and MAJ-MAJ-$k$SAT. At the heart of our approach is an\nefficient method for solving threshold counting problems by extracting\nsunflowers found in the corresponding set system of a $k$-CNF.\n  We also show that the tractability of Majority-$k$SAT is somewhat fragile.\nFor the closely related GtMajority-SAT problem (where we ask whether a given\nformula has greater than $2^{n-1}$ satisfying assignments) which is known to be\nPP-complete, we show that GtMajority-$k$SAT is in P for $k\\le 3$, but becomes\nNP-complete for $k\\geq 4$. These results are counterintuitive, because the\n``natural'' classifications of these problems would have been PP-completeness,\nand because there is a stark difference in the complexity of GtMajority-$k$SAT\nand Majority-$k$SAT for all $k\\ge 4$.\n"}
+{"abstract": "  Knee arthroscopy is a minimally invasive surgical (MIS) procedure which is\nperformed to treat knee-joint ailment. Lack of visual information of the\nsurgical site obtained from miniaturized cameras make this surgical procedure\nmore complex. Knee cavity is a very confined space; therefore, surgical scenes\nare captured at close proximity. Insignificant context of knee atlas often\nmakes them unrecognizable as a consequence unintentional tissue damage often\noccurred and shows a long learning curve to train new surgeons. Automatic\ncontext awareness through labeling of the surgical site can be an alternative\nto mitigate these drawbacks. However, from the previous studies, it is\nconfirmed that the surgical site exhibits several limitations, among others,\nlack of discriminative contextual information such as texture and features\nwhich drastically limits this vision task. Additionally, poor imaging\nconditions and lack of accurate ground-truth labels are also limiting the\naccuracy. To mitigate these limitations of knee arthroscopy, in this work we\nproposed a scene segmentation method that successfully segments multi\nstructures.\n"}
+{"abstract": "  This paper investigates the problem of providing ultra-reliable and\nenergy-efficient virtual reality (VR) experiences for wireless mobile users. To\nensure reliable ultra-high-definition (UHD) video frame delivery to mobile\nusers and enhance their immersive visual experiences, a coordinated multipoint\n(CoMP) transmission technique and millimeter wave (mmWave) communications are\nexploited. Owing to user movement and time-varying wireless channels, the\nwireless VR experience enhancement problem is formulated as a\nsequence-dependent and mixed-integer problem with a goal of maximizing users'\nfeeling of presence (FoP) in the virtual world, subject to power consumption\nconstraints on access points (APs) and users' head-mounted displays (HMDs). The\nproblem, however, is hard to be directly solved due to the lack of users'\naccurate tracking information and the sequence-dependent and mixed-integer\ncharacteristics. To overcome this challenge, we develop a parallel echo state\nnetwork (ESN) learning method to predict users' tracking information by\ntraining fresh and historical tracking samples separately collected by APs.\nWith the learnt results, we propose a deep reinforcement learning (DRL) based\noptimization algorithm to solve the formulated problem. In this algorithm, we\nimplement deep neural networks (DNNs) as a scalable solution to produce integer\ndecision variables and solving a continuous power control problem to criticize\nthe integer decision variables. Finally, the performance of the proposed\nalgorithm is compared with various benchmark algorithms, and the impact of\ndifferent design parameters is also discussed. Simulation results demonstrate\nthat the proposed algorithm is more 4.14% energy-efficient than the benchmark\nalgorithms.\n"}
+{"abstract": "  We provide explicit generators and relations for the affine Kac-Moody groups,\nas well as a realization of them as (twisted) loop groups by means of Galois\ndescent considerations.\n"}
+{"abstract": "  We introduce the problem of robust subgroup discovery, i.e., finding a set of\ninterpretable descriptions of subsets that 1) stand out with respect to one or\nmore target attributes, 2) are statistically robust, and 3) non-redundant. Many\nattempts have been made to mine either locally robust subgroups or to tackle\nthe pattern explosion, but we are the first to address both challenges at the\nsame time from a global modelling perspective. First, we formulate the broad\nmodel class of subgroup lists, i.e., ordered sets of subgroups, for univariate\nand multivariate targets that can consist of nominal or numeric variables, and\nthat includes traditional top-1 subgroup discovery in its definition. This\nnovel model class allows us to formalise the problem of optimal robust subgroup\ndiscovery using the Minimum Description Length (MDL) principle, where we resort\nto optimal Normalised Maximum Likelihood and Bayesian encodings for nominal and\nnumeric targets, respectively. Second, as finding optimal subgroup lists is\nNP-hard, we propose SSD++, a greedy heuristic that finds good subgroup lists\nand guarantees that the most significant subgroup found according to the MDL\ncriterion is added in each iteration, which is shown to be equivalent to a\nBayesian one-sample proportions, multinomial, or t-test between the subgroup\nand dataset marginal target distributions plus a multiple hypothesis testing\npenalty. We empirically show on 54 datasets that SSD++ outperforms previous\nsubgroup set discovery methods in terms of quality and subgroup list size.\n"}
+{"abstract": "  We study the phase behaviour of a quasi-two dimensional cholesteric liquid\ncrystal shell. We characterise the topological phases arising close to the\nisotropic-cholesteric transition, and show that they differ in a fundamental\nway from those observed on a flat geometry. For spherical shells, we discover\ntwo types of quasi-two dimensional topological phases: finite quasicrystals and\namorphous structures, both made up by mixtures of polygonal tessellations of\nhalf-skyrmions. These structures generically emerge instead of regular double\ntwist lattices because of geometric frustration, which disallows a regular\nhexagonal tiling of curved space. For toroidal shells, the variations in the\nlocal curvature of the surface stabilises heterogeneous phases where\ncholesteric patterns coexist with hexagonal lattices of half-skyrmions.\nQuasicrystals, amorphous and heterogeneous structures could be sought\nexperimentally by self assembling cholesteric shells on the surface of emulsion\ndroplets.\n"}
+{"abstract": "  A systematic study of nonlocal and size effects on the energy transfer of a\ndipole (e.g. a molecule or a quantum dot) induced by the proximity of a metal\nslab is presented. Nonlocal effects are accounted for using the hydrodynamic\nmodel (HDM). We derive a general relation that connects the energy transfer\nrate to the linear charge density-density response function of the slab. This\nfunction is explicitly evaluated for the HDM and the local Drude model. We show\nthat a thin metal slab can support a series of higher-frequency surface plasma\nwave (SPW) modes in addition to the normal SPW modes thanks to the nonlocal\neffects. These modes markedly alter the response and the energy transfer\nprocess, as revealed in the structure of the energy transfer rate in the\nparameter space. Our findings are important for applications such as the\nrecently developed metal-induced energy transfer imaging, which relies on\naccurate modeling of the energy transfer rate.\n"}
+{"abstract": "  In April 2021, the European Commission proposed a Regulation on Artificial\nIntelligence, known as the AI Act. We present an overview of the Act and\nanalyse its implications, drawing on scholarship ranging from the study of\ncontemporary AI practices to the structure of EU product safety regimes over\nthe last four decades. Aspects of the AI Act, such as different rules for\ndifferent risk-levels of AI, make sense. But we also find that some provisions\nof the Draft AI Act have surprising legal implications, whilst others may be\nlargely ineffective at achieving their stated goals. Several overarching\naspects, including the enforcement regime and the risks of maximum\nharmonisation pre-empting legitimate national AI policy, engender significant\nconcern. These issues should be addressed as a priority in the legislative\nprocess.\n"}
+{"abstract": "  We introduce SelfExplain, a novel self-explaining model that explains a text\nclassifier's predictions using phrase-based concepts. SelfExplain augments\nexisting neural classifiers by adding (1) a globally interpretable layer that\nidentifies the most influential concepts in the training set for a given sample\nand (2) a locally interpretable layer that quantifies the contribution of each\nlocal input concept by computing a relevance score relative to the predicted\nlabel. Experiments across five text-classification datasets show that\nSelfExplain facilitates interpretability without sacrificing performance. Most\nimportantly, explanations from SelfExplain show sufficiency for model\npredictions and are perceived as adequate, trustworthy and understandable by\nhuman judges compared to existing widely-used baselines.\n"}
+{"abstract": "  Radhakrishnan \\emph{et.al} [Phys. Rev. Lett. \\textbf{124}, 110401 (2020)]\nproposed quantum discord to multipartite systems and derived explicit formulae\nfor any states. These results are significant in capturing quantum correlations\nfor multi-qubit systems. In this paper, we evaluate the geometric measure of\nmultipartite quantum discord and obtain results for a large family of\nmulti-qubit states. %Intriguingly, our results demonstrate that for the family\nof states, multipartite geometric discord decreases exponentially as the number\nof parties increases while multipartite quantum discord not,therefore, we\nconclude that multipartite geometric discord could not reflect multipartite\nquantum discord in all cases. Furthermore, we investigated the dynamic behavior\nof geometric discord for the family of two-, three- and four-qubit states under\nphase noise acting on the first qubit. And we discover that sudden change of\nmultipartite geometric discord can appear when phase noise act only on one part\nof the two-, three- and four-qubit states.\n"}
+{"abstract": "  We study an anisotropic, possibly non-homogeneous version of the evolution\n$p$-Laplacian equation when fast diffusion holds in all directions. We develop\nthe basic theory and prove symmetrization results from which we derive $L^1$ to\n$L^\\infty$ estimates. We prove the existence of a self-similar fundamental\nsolution of this equation in the appropriate exponent range, and uniqueness in\na smaller range. We also obtain the asymptotic behaviour of finite mass\nsolutions in terms of the self-similar solution. Positivity, decay rates as\nwell as other properties of the solutions are derived. The combination of\nself-similarity and anisotropy is not common in the related literature. It is\nhowever essential in our analysis and creates mathematical difficulties that\nare solved for fast diffusions.\n"}
+{"abstract": "  Multi-agent influence diagrams (MAIDs) are a popular form of graphical model\nthat, for certain classes of games, have been shown to offer key complexity and\nexplainability advantages over traditional extensive form game (EFG)\nrepresentations. In this paper, we extend previous work on MAIDs by introducing\nthe concept of a MAID subgame, as well as subgame perfect and trembling hand\nperfect equilibrium refinements. We then prove several equivalence results\nbetween MAIDs and EFGs. Finally, we describe an open source implementation for\nreasoning about MAIDs and computing their equilibria.\n"}
+{"abstract": "  In existing linear response theories for adiabatically driven cyclic heat\nengines, Onsager symmetry is identified only phenomenologically, and a relation\nbetween global and local Onsager coefficients, defined over one cycle and at\nany instant of a cycle, respectively, is not derived. To address this\nlimitation, we develop a linear response theory for the speed of adiabatically\nchanging parameters and temperature differences in generic Gaussian heat\nengines obeying Fokker--Planck dynamics. We establish a hierarchical\nrelationship between the global linear response relations, defined over one\ncycle of the heat engines, and the local ones, defined at any instant of the\ncycle. This yields a detailed expression for the global Onsager coefficients in\nterms of the local Onsager coefficients. Moreover, we derive an efficiency\nbound, which is tighter than the Carnot bound, for adiabatically driven linear\nirreversible heat engines based on the detailed global Onsager coefficients.\nFinally, we demonstrate the application of the theory using the simplest\nstochastic Brownian heat engine model.\n"}
+{"abstract": "  We observationally examine cosmological models based on primordial power\nspectra with quantized wavevectors. Introducing a linearly quantized power\nspectrum with $k_0=3.225\\times10^{-4}\\mathrm{Mpc}^{-1}$ and spacing $\\Delta k =\n2.257 \\times 10^{-4} \\mathrm{Mpc}^{-1}$ provides a better fit to the Planck\n2018 observations than the concordance baseline, with $\\Delta \\chi^2 = -8.55$.\nExtending the results of Lasenby et al [1], we show that the requirement for\nperturbations to remain finite beyond the future conformal boundary in a\nuniverse containing dark matter and a cosmological constant results in a\nlinearly quantized primordial power spectrum. It is found that the infrared\ncutoffs for this future conformal boundary quantized cosmology do not provide\ncosmic microwave background power spectra compatible with observations, but\nfuture theories may predict more observationally consistent quantized spectra.\n"}
+{"abstract": "  This paper presents a temporal classification method for all three subtasks\nof symbol segmentation, symbol recognition and relation classification in\nonline handwritten mathematical expressions (HMEs). The classification model is\ntrained by multiple paths of symbols and spatial relations derived from the\nSymbol Relation Tree (SRT) representation of HMEs. The method benefits from\nglobal context of a deep bidirectional Long Short-term Memory network, which\nlearns the temporal classification directly from online handwriting by the\nConnectionist Temporal Classification loss. To recognize an online HME, a\nsymbol-level parse tree with Context-Free Grammar is constructed, where symbols\nand spatial relations are obtained from the temporal classification results. We\nshow the effectiveness of the proposed method on the two latest CROHME\ndatasets.\n"}
+{"abstract": "  22. Shortening acquisition time and reducing the motion-artifact are two of\nthe most critical issues in MRI. As a promising solution, high-quality MRI\nimage restoration provides a new approach to achieve higher resolution without\ncosting additional acquisition time or modification on the pulse sequences.\nRecently, as to the rise of deep learning, convolutional neural networks have\nbeen proposed for super-resolution (SR) image generation and motion-artifact\nreduction (MAR) for MRI. Recent studies suggest that using perceptual feature\nspace loss and k space loss to capture the perceptual information and\nhigh-frequency information of images, respectively. However, the quality of\nreconstructed SR and MAR MR images is limited because the most important\ndetails of the informative area in the MR image, the edges and the structure,\ncannot be very well restored. Besides, lots of the SR approaches are trained by\nusing low-resolution images generated by applying bicubic or blur-downscale\ndegradation, which cannot represent the real process of MRI measurement. Such\ninconsistencies lead to performance degradation in the reconstruction of SR\nimages as well. This study reveals that using the L1 loss of SSIM and gradient\nmap edge quality loss could force the deep learning model to focus on studying\nthe features of edges and structure details of MR images, thus generating SR\nimages with more accurate, fruitful information and reduced motion-artifact. We\nemployed a state-of-the-art model, RCAN, as the network framework in both SR\nand MAR tasks, trained the model by using low-resolution images and\nmotion-artifact affected images which were generated by emulating how they are\nmeasured in the real MRI measurement to ensure the model can be easily applied\nin the practical clinic environment, and verified the trained model could work\nfairly well.\n"}
+{"abstract": "  Halide perovskites perform remarkably in optoelectronic devices including\ntandem photovoltaics. However, this exceptional performance is striking given\nthat perovskites exhibit deep charge carrier traps and spatial compositional\nand structural heterogeneity, all of which should be detrimental to\nperformance. Here, we resolve this long-standing paradox by providing a global\nvisualisation of the nanoscale chemical, structural and optoelectronic\nlandscape in halide perovskite devices, made possible through the development\nof a new suite of correlative, multimodal microscopy measurements combining\nquantitative optical spectroscopic techniques and synchrotron nanoprobe\nmeasurements. We show that compositional disorder dominates the optoelectronic\nresponse, while nanoscale strain variations even of large magnitude (~1 %) have\nonly a weak influence. Nanoscale compositional gradients drive carrier\nfunneling onto local regions associated with low electronic disorder, drawing\ncarrier recombination away from trap clusters associated with electronic\ndisorder and leading to high local photoluminescence quantum efficiency. These\nmeasurements reveal a global picture of the competitive nanoscale landscape,\nwhich endows enhanced defect tolerance in devices through spatial chemical\ndisorder that outcompetes both electronic and structural disorder.\n"}
+{"abstract": "  Image matting refers to the estimation of the opacity of foreground objects.\nIt requires correct contours and fine details of foreground objects for the\nmatting results. To better accomplish human image matting tasks, we propose the\nCascade Image Matting Network with Deformable Graph Refinement, which can\nautomatically predict precise alpha mattes from single human images without any\nadditional inputs. We adopt a network cascade architecture to perform matting\nfrom low-to-high resolution, which corresponds to coarse-to-fine optimization.\nWe also introduce the Deformable Graph Refinement (DGR) module based on graph\nneural networks (GNNs) to overcome the limitations of convolutional neural\nnetworks (CNNs). The DGR module can effectively capture long-range relations\nand obtain more global and local information to help produce finer alpha\nmattes. We also reduce the computation complexity of the DGR module by\ndynamically predicting the neighbors and apply DGR module to higher--resolution\nfeatures. Experimental results demonstrate the ability of our CasDGR to achieve\nstate-of-the-art performance on synthetic datasets and produce good results on\nreal human images.\n"}
+{"abstract": "  We consider the problem of dividing limited resources to individuals arriving\nover $T$ rounds. Each round has a random number of individuals arrive, and\nindividuals can be characterized by their type (i.e. preferences over the\ndifferent resources). A standard notion of `fairness' in this setting is that\nan allocation simultaneously satisfy envy-freeness and efficiency. The former\nis an individual guarantee, requiring that each agent prefers their own\nallocation over the allocation of any other; in contrast, efficiency is a\nglobal property, requiring that the allocations clear the available resources.\nFor divisible resources, when the number of individuals of each type are known\nupfront, the above desiderata are simultaneously achievable for a large class\nof utility functions. However, in an online setting when the number of\nindividuals of each type are only revealed round by round, no policy can\nguarantee these desiderata simultaneously, and hence the best one can do is to\ntry and allocate so as to approximately satisfy the two properties.\n  We show that in the online setting, the two desired properties (envy-freeness\nand efficiency) are in direct contention, in that any algorithm achieving\nadditive counterfactual envy-freeness up to a factor of $L_T$ necessarily\nsuffers a efficiency loss of at least $1 / L_T$. We complement this uncertainty\nprinciple with a simple algorithm, HopeGuardrail, which allocates resources\nbased on an adaptive threshold policy and is able to achieve any\nfairness-efficiency point on this frontier. In simulation results, our\nalgorithm provides allocations close to the optimal fair solution in hindsight,\nmotivating its use in practical applications as the algorithm is able to adapt\nto any desired fairness efficiency trade-off.\n"}
+{"abstract": "  Video instance segmentation (VIS) aims to segment and associate all instances\nof predefined classes for each frame in videos. Prior methods usually obtain\nsegmentation for a frame or clip first, and merge the incomplete results by\ntracking or matching. These methods may cause error accumulation in the merging\nstep. Contrarily, we propose a new paradigm -- Propose-Reduce, to generate\ncomplete sequences for input videos by a single step. We further build a\nsequence propagation head on the existing image-level instance segmentation\nnetwork for long-term propagation. To ensure robustness and high recall of our\nproposed framework, multiple sequences are proposed where redundant sequences\nof the same instance are reduced. We achieve state-of-the-art performance on\ntwo representative benchmark datasets -- we obtain 47.6% in terms of AP on\nYouTube-VIS validation set and 70.4% for J&F on DAVIS-UVOS validation set. Code\nis available at https://github.com/dvlab-research/ProposeReduce.\n"}
+{"abstract": "  To approximately compute the non-relativistic ground state of an electrically\nnon-neutral star, an exactly solvable model was recently introduced, and partly\nsolved, by Krivoruchenko, Nadyozhin, and Yudin. The model generalizes the\nwell-known Lane--Emden equation of a polytropic gas ball of index $n=1$ to a\ntwo-fluid setting. Here its complete solution is presented in terms of simple\nelementary functions; it is also generalized to a more-than-two-fluid setting\nwhere it remains exactly solvable. It is shown that, given the number of\nnuclei, a maximal negatively and a maximal positively charged solution exists,\nplus a continuous family of solutions which interpolates between these\nextremes. Numerical comparisons show that this exactly solvable model captures\nthe qualitative behavior of the more physical model it is supposed to\napproximate. Furthermore, it correctly answers the question: how non-neutral\ncan the star be? The answer is independent of the speed of light $c$ and the\nPlanck quantum $h$. It supports Penrose's weak cosmic censorship hypothesis, in\nthe sense that the bounds on the excess charge are compatible with the bound on\nthe charge of a Reissner--Weyl--Nordstr\\\"om black hole.\n"}
+{"abstract": "  We show that compositions of time-reversal and spatial symmetries, also known\nas the magnetic-space-group symmetries, protect topological invariants as well\nas surface states that are distinct from those of all preceding topological\nstates. We obtain, by explicit and exhaustive construction, the topological\nclassification of electronic band insulators that are magnetically ordered for\neach one of the 1421 magnetic space groups in three dimensions. We have also\ncomputed the symmetry-based indicators for each nontrivial class, and, by doing\nso, establish the complete mapping from symmetry representations to topological\ninvariants.\n"}
+{"abstract": "  Reinforcement learning agents have demonstrated remarkable achievements in\nsimulated environments. Data efficiency poses an impediment to carrying this\nsuccess over to real environments. The design of data-efficient agents calls\nfor a deeper understanding of information acquisition and representation. We\ndevelop concepts and establish a regret bound that together offer principled\nguidance. The bound sheds light on questions of what information to seek, how\nto seek that information, and it what information to retain. To illustrate\nconcepts, we design simple agents that build on them and present computational\nresults that demonstrate improvements in data efficiency.\n"}
+{"abstract": "  Inference is the task of drawing conclusions about unobserved variables given\nobservations of related variables. Applications range from identifying diseases\nfrom symptoms to classifying economic regimes from price movements.\nUnfortunately, performing exact inference is intractable in general. One\nalternative is variational inference, where a candidate probability\ndistribution is optimized to approximate the posterior distribution over\nunobserved variables. For good approximations, a flexible and highly expressive\ncandidate distribution is desirable. In this work, we use quantum Born machines\nas variational distributions over discrete variables. We apply the framework of\noperator variational inference to achieve this goal. In particular, we adopt\ntwo specific realizations: one with an adversarial objective and one based on\nthe kernelized Stein discrepancy. We demonstrate the approach numerically using\nexamples of Bayesian networks, and implement an experiment on an IBM quantum\ncomputer. Our techniques enable efficient variational inference with\ndistributions beyond those that are efficiently representable on a classical\ncomputer.\n"}
+{"abstract": "  Context. Studying gas chemistry in protoplanetary disks is key to\nunderstanding the process of planet formation. Sulfur chemistry in particular\nis poorly understood in interstellar environments, and the location of the main\nreservoirs remains unknown. Protoplanetary disks in Taurus are ideal targets\nfor studying the evolution of the composition of planet forming systems.\n  Aims. We aim to elucidate the chemical origin of sulfur-bearing molecular\nemission in protoplanetary disks, with a special focus on H$_2$S emission, and\nto identify candidate species that could become the main molecular sulfur\nreservoirs in protoplanetary systems.\n  Methods. We used IRAM 30m observations of nine gas-rich young stellar objects\n(YSOs) in Taurus to perform a survey of sulfur-bearing and oxygen-bearing\nmolecular species. In this paper we present our results for the CS 3-2 ($\\nu_0$\n= 146.969 GHz), H$_2$CO 2$_{11}$-1$_{10}$ ($\\nu_0$ = 150.498 GHz), and H$_2$S\n1$_{10}$-1$_{01}$ ($\\nu_0$ = 168,763 GHz) emission lines.\n  Results. We detected H$_2$S emission in four sources out of the nine\nobserved, significantly increasing the number of detections toward YSOs. We\nalso detected H$_2$CO and CS in six out of the nine. We identify a tentative\ncorrelation between H$_2$S 1$_{10}$-1$_{01}$ and H$_2$CO 2$_{11}$-1$_{10}$ as\nwell as a tentative correlation between H$_2$S 1$_{10}$-1$_{01}$ and H$_2$O\n8$_{18}$-7$_{07}$. By assuming local thermodynamical equilibrium, we computed\ncolumn densities for the sources in the sample, with N(o-H$_2$S) values ranging\nbetween $2.6\\times10^{12}$ cm$^{-2}$ and $1.5\\times10^{13}$ cm$^{-2}$.\n"}
+{"abstract": "  Titanium-based shape memory alloys, such as Ti2448, have attracted enormous\nattention owing to their unique thermomechanical properties and potential\nbiomedical applications. In this study, we develop a polycrystalline phase\nfield to investigate the grain size dependence of the martensitic\ntransformation and associated mechanical properties of nanograined Ti-Nb\nalloys. It is shown that a reduction of the average grain size strengthens the\nsuppression of the martensitic transformation (MT), leading to an increase of\nthe transformation stress, shrinkage of the stress hysteresis, and elimination\nof residual strain. The time-temperature-transformation curves of nano-grained\nTi-Nb alloys with different average grain sizes are obtained and the validity\nof Hall-Petch relation is also confirmed in all studied grain sizes.\nFurthermore, when the average grain size becomes ultrasmall, both the\ntemperature- and stress-induced MTs show the continuous second-order phase\ntransition behavior. These superior transformation characteristics are\nattributed to the high density of grain boundaries and the related dominant\nrole of the gradient energy at the nanoscale. Our results have profound\nimplications for the design and control of the properties in nano-grained shape\nmemory alloys.\n"}
+{"abstract": "  Well-designed molecular representations (fingerprints) are vital to combine\nmedical chemistry and deep learning. Whereas incorporating 3D geometry of\nmolecules (i.e. conformations) in their representations seems beneficial,\ncurrent 3D algorithms are still in infancy. In this paper, we propose a novel\nmolecular representation algorithm which preserves 3D conformations of\nmolecules with a Molecular Hamiltonian Network (HamNet). In HamNet, implicit\npositions and momentums of atoms in a molecule interact in the Hamiltonian\nEngine following the discretized Hamiltonian equations. These implicit\ncoordinations are supervised with real conformations with translation- &\nrotation-invariant losses, and further used as inputs to the Fingerprint\nGenerator, a message-passing neural network. Experiments show that the\nHamiltonian Engine can well preserve molecular conformations, and that the\nfingerprints generated by HamNet achieve state-of-the-art performances on\nMoleculeNet, a standard molecular machine learning benchmark.\n"}
+{"abstract": "  This paper introduces a model of a stylized organization that is comprised of\nseveral departments that autonomously allocate tasks. To do so, the departments\neither take short-sighted decisions that immediately maximize their utility or\ntake long-sighted decisions that aim at minimizing the interdependencies\nbetween tasks. The organization guides the departments' behavior by either an\nindividualistic, a balanced, or an altruistic linear incentive scheme. Even if\ntasks are perfectly decomposable, altruistic incentive schemes are preferred\nover individualistic incentive schemes since they substantially increase the\norganization's performance. Interestingly, if altruistic incentive schemes are\neffective, short-sighted decisions appear favorable since they do not only\nincrease performance in the short run but also result in significantly higher\nperformances in the long run.\n"}
+{"abstract": "  Machine learning techniques are gaining attention in the context of intrusion\ndetection due to the increasing amounts of data generated by monitoring tools,\nas well as the sophistication displayed by attackers in hiding their activity.\nHowever, existing methods often exhibit important limitations in terms of the\nquantity and relevance of the generated alerts. Recently, knowledge graphs are\nfinding application in the cybersecurity domain, showing the potential to\nalleviate some of these drawbacks thanks to their ability to seamlessly\nintegrate data from multiple domains using human-understandable vocabularies.\nWe discuss the application of machine learning on knowledge graphs for\nintrusion detection and experimentally evaluate a link-prediction method for\nscoring anomalous activity in industrial systems. After initial unsupervised\ntraining, the proposed method is shown to produce intuitively well-calibrated\nand interpretable alerts in a diverse range of scenarios, hinting at the\npotential benefits of relational machine learning on knowledge graphs for\nintrusion detection purposes.\n"}
+{"abstract": "  In this paper, we aim at improving the computational efficiency of graph\nconvolutional networks (GCNs) for learning on point clouds. The basic graph\nconvolution that is typically composed of a $K$-nearest neighbor (KNN) search\nand a multilayer perceptron (MLP) is examined. By mathematically analyzing the\noperations there, two findings to improve the efficiency of GCNs are obtained.\n(1) The local geometric structure information of 3D representations propagates\nsmoothly across the GCN that relies on KNN search to gather neighborhood\nfeatures. This motivates the simplification of multiple KNN searches in GCNs.\n(2) Shuffling the order of graph feature gathering and an MLP leads to\nequivalent or similar composite operations. Based on those findings, we\noptimize the computational procedure in GCNs. A series of experiments show that\nthe optimized networks have reduced computational complexity, decreased memory\nconsumption, and accelerated inference speed while maintaining comparable\naccuracy for learning on point clouds. Code will be available at\n\\url{https://github.com/ofsoundof/EfficientGCN.git}.\n"}
+{"abstract": "  In this paper, we present machine learning models based on random forest\nclassifiers, support vector machines, gradient boosted decision trees, and\nartificial neural networks to predict participation in cancer screening\nprograms in South Korea. The top performing model was based on gradient boosted\ndecision trees and achieved an area under the receiver operating characteristic\ncurve (AUC-ROC) of 0.8706 and average precision of 0.8776. The results of this\nstudy are encouraging and suggest that with further research, these models can\nbe directly applied to Korea's healthcare system, thus increasing participation\nin Korea's National Cancer Screening Program.\n"}
+{"abstract": "  While age of Information (AoI) has gained importance as a metric\ncharacterizing the fresh-ness of information in information-update systems and\ntime-critical applications, most previous studies on AoI have been theoretical.\nIn this chapter, we compile a set of recent works reporting API measurements in\nreal-life networks and experimental testbeds, and investigating practical\nissues such as synchronization, the role of various transport layer protocols,\ncongestion control mechanisms, application of machine learning for adaptation\nto network conditions, and device related bottlenecks such as limited\nprocessing power.\n"}
+{"abstract": "  We explore experimentally a quantum metamaterial based on a superconducting\nchip with 25 frequency-tunable transmon qubits coupled to a common coplanar\nresonator. The collective bright and dark modes are probed via the microwave\nresponse, i.e., by measuring the transmission amplitude of an external\nmicrowave signal. All qubits have individual control and readout lines. Their\nfrequency tunability allows to change the number N of resonantly coupled qubits\nand also to introduce a disorder in their excitation frequencies with\npreassigned distributions. While increasing N, we demonstrate the expected\n$N^{1/2}$ scaling law for the energy gap (Rabi splitting) between bright modes\naround the cavity frequency. By introducing a controllable disorder and\naveraging the transmission amplitude over a large number of realizations, we\ndemonstrate a decay of mesoscopic fluctuations which mimics an approach towards\nthe thermodynamic limit. The collective bright states survive in the presence\nof disorder when the strength of individual qubit coupling to the cavity\ndominates over the disorder strength.\n"}
+{"abstract": "  A series invariant for a certain class of closed 3-manifolds associated with\na type I Lie superalgebra sl(m|n) was introduced recently. We find a q-series\nfor the other Lie superalgebra of the same type of the minimum rank.\n"}
+{"abstract": "  Statistical techniques used in air pollution modelling usually lack the\npossibility to understand which predictors affect air pollution in which\nfunctional form; and are not able to regress on exceedances over certain\nthresholds imposed by authorities directly. The latter naturally induce\nconditional quantiles and reflect the seriousness of particular events. In the\npresent paper we focus on this important aspect by developing quantile\nregression models further. We propose a general Bayesian effect selection\napproach for additive quantile regression within a highly interpretable\nframework. We place separate normal beta prime spike and slab priors on the\nscalar importance parameters of effect parts and implement a fast Gibbs\nsampling scheme. Specifically, it enables to study quantile-specific covariate\neffects, allows these covariates to be of general functional form using\nadditive predictors, and facilitates the analysts' decision whether an effect\nshould be included linearly, non-linearly or not at all in the quantiles of\ninterest. In a detailed analysis on air pollution data in Madrid (Spain) we\nfind the added value of modelling extreme nitrogen dioxide (NO2) concentrations\nand how thresholds are driven differently by several climatological variables\nand traffic as a spatial proxy. Our results underpin the need of enhanced\nstatistical models to support short-term decisions and enable local authorities\nto mitigate or even prevent exceedances of NO2 concentration limits.\n"}
+{"abstract": "  Multicavity Klystron (MCK) is a high power microwave (HPM) vacuum electronic\ndevice used to amplify radio-frequency (RF) signals with numerous applications,\nincluding radar, radio navigation, space communication, television, radio\nrepeaters, and charged particle accelerators. The microwave-generating\ninteractions in klystrons take place in resonant cavities at discrete locations\nalong the beam. Importantly, there is no electromagnetic coupling between\ncavities, they are coupled only by the bunched electron beam, which drifts from\none cavity to the next. We advance here an analytic theory of MCKs operating in\nvoltage amplification mode associated with the maximal gain. This theory\nfeatures in particular exact formulas for the MCK instability frequencies, its\ndispersion relations and optimal values of the MCK parameters providing for\nmaximal gain.\n"}
+{"abstract": "  Indexing large-scale databases in main memory is still challenging today.\nLearned index structures -- in which the core components of classical indexes\nare replaced with machine learning models -- have recently been suggested to\nsignificantly improve performance for read-only range queries. However, a\nrecent benchmark study shows that learned indexes only achieve limited\nperformance improvements for real-world data on modern hardware. More\nspecifically, a learned model cannot learn the micro-level details and\nfluctuations of data distributions thus resulting in poor accuracy; or it can\nfit to the data distribution at the cost of training a big model whose\nparameters cannot fit into cache. As a consequence, querying a learned index on\nreal-world data takes a substantial number of memory lookups, thereby degrading\nperformance. In this paper, we adopt a different approach for modeling a data\ndistribution that complements the model fitting approach of learned indexes. We\npropose Shift-Table, an algorithmic layer that captures the micro-level data\ndistribution and resolves the local biases of a learned model at the cost of at\nmost one memory lookup. Our suggested model combines the low latency of lookup\ntables with learned indexes and enables low-latency processing of range\nqueries. Using Shift-Table, we achieve a speedup of 1.5X to 2X on real-world\ndatasets compared to trained and tuned learned indexes.\n"}
+{"abstract": "  The rapid development of artificial intelligence, especially deep learning\ntechnology, has advanced autonomous driving systems (ADSs) by providing precise\ncontrol decisions to counterpart almost any driving event, spanning from\nanti-fatigue safe driving to intelligent route planning. However, ADSs are\nstill plagued by increasing threats from different attacks, which could be\ncategorized into physical attacks, cyberattacks and learning-based adversarial\nattacks. Inevitably, the safety and security of deep learning-based autonomous\ndriving are severely challenged by these attacks, from which the\ncountermeasures should be analyzed and studied comprehensively to mitigate all\npotential risks. This survey provides a thorough analysis of different attacks\nthat may jeopardize ADSs, as well as the corresponding state-of-the-art defense\nmechanisms. The analysis is unrolled by taking an in-depth overview of each\nstep in the ADS workflow, covering adversarial attacks for various deep\nlearning models and attacks in both physical and cyber context. Furthermore,\nsome promising research directions are suggested in order to improve deep\nlearning-based autonomous driving safety, including model robustness training,\nmodel testing and verification, and anomaly detection based on cloud/edge\nservers.\n"}
+{"abstract": "  Cybercriminals are rapidly developing new malicious tools that leverage\nartificial intelligence (AI) to enable new classes of adaptive and stealthy\nattacks. New defensive methods need to be developed to counter these threats.\nSome cybersecurity professionals are speculating AI will enable corresponding\nnew classes of active cyber defence measures -- is this realistic, or currently\nmostly hype? The Alan Turing Institute, with expert guidance from the UK\nNational Cyber Security Centre and Defence Science Technology Laboratory,\npublished a research roadmap for AI for ACD last year. This position paper\nupdates the roadmap for two of the most promising AI approaches --\nreinforcement learning and causal inference - and describes why they could help\ntip the balance back towards defenders.\n"}
+{"abstract": "  Let $p$ be a prime and $G$ a finite group. A complex character of $G$ is\ncalled almost $p$-rational if its values belong to a cyclotomic field\n$\\mathbb{Q}(e^{2\\pi i/n})$ for some $n\\in \\mathbb{Z}^+$ prime to $p$ or\nprecisely divisible by $p$. We prove that, in contrast to usual $p$-rational\ncharacters, there are always \"many\" almost $p$-rational irreducible characters\nin finite groups. We obtain both explicit and asymptotic bounds for the number\nof almost $p$-rational irreducible characters of $G$ in terms of $p$. In fact,\nmotivated by the McKay-Navarro conjecture, we obtain the same bound for the\nnumber of such characters of $p'$-degree and prove that, in the minimal\nsituation, the number of almost $p$-rational irreducible $p'$-characters of $G$\ncoincides with that of $N_G(P)$ for $P\\in\\mathrm{Syl}_p(G)$. Lastly, we propose\na new way to detect the cyclicity of Sylow $p$-subgroups of a finite group $G$\nfrom its character table, using almost $p$-rational irreducible $p'$-characters\nand the blockwise refinement of the McKay-Navarro conjecture.\n"}
+{"abstract": "  Purpose: The purpose of this work is to develop a new approach for high\nspatial resolution dosimetry based on Raman micro-spectroscopy scanning of\nradiochromic film (RCF). We generate dose calibration curves over an extended\ndose range from 0-50 Gy and with improved sensitivity to low (<2 Gy) doses, in\naddition to evaluating uncertainties. Methods: Samples of RCF (EBT3) were\nirradiated at a broad dose-range of 0.03 Gy-50 Gy. Raman spectra were acquired\nwith a custom-built Raman micro-spectroscopy setup involving a 500 mW,\nmultimode 785 nm laser. The depth of focus of 34 um enabled the concurrent\ncollection of Raman spectra from the RCF active layer and the polyester\nlaminate. The pre-processed Raman spectra were normalized to the intensity of\nthe 1614 cm-1 Raman peak from the polyester laminate that was unaltered by\nradiation. The experimental, fitting and total dose uncertainty was determined\nacross the entire dose range for the dosimetry system of Raman\nmicro-spectroscopy and RCF. Results: High resolution in the dose response of\nthe RCF, even down to 0.03 Gy, was obtained in this study. The dynamic range of\nthe calibration curves based on all three Raman peaks in the RCF extended up to\n50 Gy with no saturation. At a spatial resolution of 30X30 um^2, the total\nuncertainty in estimating dose in the 0.5 Gy to 50 Gy dose range was [6 - 9]%\nfor all three Raman calibration curves. This consisted of the experimental\nuncertainty of [5 - 8]%, and the fitting uncertainty of [2.5 - 4.5]%.\nConclusions: The high spatial resolution experimental dosimetry technique based\non Raman micro-spectroscopy and RCF presented here, could become useful for\napplications in, as well as for applications based on small field dosimetry.\n"}
+{"abstract": "  In this work, optimized size distribution and optical properties in colloidal\nsynthesis of gold nanoparticles (GNPs) were obtained using a proposed\nultrasonic Tuerkevich-Frens method. The effect of three ultrasound (20 kHz)\nirradiation powers has been analyzed as size and shape control parameter. The\nGNPs colloidal solutions were obtained from chloroauric acid (HAuCl$_{4}$) and\ntrisodium citrate ($\\rm C_{6}H_{5}Na_{3}O_{7}\\cdot 2H_{2}O$) under continuous\nirradiation for 1 hour without any heat or stirring. The surface plasmon\nresonance (SPR) was monitored in the UV-Vis spectra every 10 minutes to found\nthe optimal time for localized SPR wavelength ($\\lambda_{\\rm LSPR}$) and the\n210 sample procedure reduces the $\\lambda_{\\rm LSPR}$ localization to 20\nminutes, while 150 and 60 samples show $\\lambda_{\\rm LSPR}$ in 60 minutes. The\nnucleation and growth of GNPs showed changes in shape and size distribution,\nwhich were associated with physical (cavitation, temperature) and chemical\n(radical generation, pH) conditions in the solution. The results showed\nquasispherical GNPs as pentakis dodecahedron ($\\lambda_{\\rm LSPR}$=560 nm),\ntriakis icosahedron ($\\lambda_{\\rm LSPR}$=535 nm), and tetrakis hexahedron\n($\\lambda_{\\rm LSPR}$= 525 nm) in a size range from 12-16 nm. US irradiation\ninduced a disproportionation process, electrons of AuCl$_{2}^-$ rapidly\nexchanged through the gold surface. After AuCl$_{4}^-$ and Cl$^-$ are desorbed\nand a complex tetrachloroaurate is recycled for the two-electron reduction by\ncitrate, aurophilic interaction between complexes AuCl$_{2}^-$, electrons\nexchange and gold seeds, the deposition of new gold atoms on the surface\npromoting the growth of GNPs. These mechanisms are enhanced by the cavitation\neffects and transmitted energy into the solution, showing that the plasmonic\nresponse from our nanoparticles can be tuned with this simple method and\nminimum intrumentation.\n"}
+{"abstract": "  The negatively charged silicon vacancy [V$_\\text{Si}(-)$] in silicon carbide\n(SiC) is a paramagnetic and optically active defect in hexagonal SiC.\nV$_\\text{Si}(-)$ defect possesses $S = 3/2$ spin with long spin coherence time\nand can be optically manipulated even at room temperature. Recently, electron\nspin resonance signals have been observed besides the signals associated with\nthe V$_\\text{Si}(-)$ defects in the 4H polytype of SiC. The corresponding\ncenters share akin properties to those of the V$_\\text{Si}(-)$ defects and thus\nthey may be promising candidates for quantum technology applications. However,\nthe exact origin of the new signals is unknown. In this paper we report\nV$_\\text{Si}(-)$-related pair defect models as possible candidates for the\nunknown centers. We determine the corresponding electronic structures and\nmagneto-optical properties as obtained by density functional theory (DFT)\ncalculations. We propose models for the recently observed electron paramagnetic\nresonance centers with predicting their optical signals for identification in\nfuture experiments.\n"}
+{"abstract": "  The UK has had a volatile political environment for some years now, with\nBrexit and leadership crises marking the past five years. With this work, we\nwanted to understand more about how the global health emergency, COVID-19,\ninfluences the amount, type or topics of abuse that UK politicians receive when\nengaging with the public. With this work, we wanted to understand more about\nhow the global health emergency, COVID-19, influences the amount, type or\ntopics of abuse that UK politicians receive when engaging with the public. This\nwork covers the period of June to December 2020 and analyses Twitter abuse in\nreplies to UK MPs. This work is a follow-up from our analysis of online abuse\nduring the first four months of the COVID-19 pandemic in the UK. The paper\nexamines overall abuse levels during this new seven month period, analyses\nreactions to members of different political parties and the UK government, and\nthe relationship between online abuse and topics such as Brexit, government's\nCOVID-19 response and policies, and social issues. In addition, we have also\nexamined the presence of conspiracy theories posted in abusive replies to MPs\nduring the period. We have found that abuse levels toward UK MPs were at an\nall-time high in December 2020 (5.4% of all reply tweets sent to MPs). This is\nalmost 1% higher that the two months preceding the General Election. In a\ndeparture from the trend seen in the first four months of the pandemic, MPs\nfrom the Tory party received the highest percentage of abusive replies from\nJuly 2020 onward, which stays above 5% starting from September 2020 onward, as\nthe COVID-19 crisis deepened and the Brexit negotiations with the EU started\nnearing completion.\n"}
+{"abstract": "  We introduce Combinatory Homomorphic Automatic Differentiation (CHAD), a\nprincipled, pure, provably correct define-then-run method for performing\nforward- and reverse-mode automatic differentiation (AD) on programming\nlanguages with expressive features. It implements AD as a compositional,\ntype-respecting source-code transformation that generates purely functional\ncode. This code transformation is principled in the sense that it is the unique\nhomomorphic (structure preserving) extension to expressive languages of the\nwell-known and unambiguous definitions of AD for a first-order functional\nlanguage. Correctness of the method follows by a (compositional) logical\nrelations argument that shows that the semantics of the syntactic derivative is\nthe usual calculus derivative of the semantics of the original program.\n  In their most elegant formulation, the transformations generate code with\nlinear types. However, the code transformations can be implemented in a\nstandard functional language lacking linear types: while the correctness proof\nrequires tracking of linearity, the actual transformations do not. In fact,\neven in a standard functional language, we can get all of the type-safety that\nlinear types give us: we can implement all linear types used to type the\ntransformations as abstract types, by using a basic module system.\n  In this paper, we detail the method when applied to a simple higher-order\nlanguage for manipulating statically sized arrays. However, we explain how the\nmethodology applies, more generally, to functional languages with other\nexpressive features. Finally, we discuss how the scope of CHAD extends beyond\napplications in AD to other dynamic program analyses that accumulate data in a\ncommutative monoid.\n"}
+{"abstract": "  The Massachusetts Attorney General issued an Enforcement Notice in 2016 to\nannounce a new interpretation of a key phrase in the state's assault weapons\nban. The Enforcement Notice increased sales of tagged assault rifles by 616% in\nthe first 5 days, followed by a 9% decrease over the next three weeks. Sales of\nHandguns and Shotguns did not change significantly. Tagged assault rifle sales\nfell 28-30% in 2017 compared to previous years, suggesting that the Enforcement\nNotice reduced assault weapon sales but also that many banned weapons continued\nto be sold. Tagged assault rifles sold most in 2017 in zip codes with higher\nhousehold incomes and proportions of white males. Overall, the results suggest\nthat the firearm market reacts rapidly to policy changes and partially complies\nwith firearm restrictions.\n"}
+{"abstract": "  A singularly perturbed system for doubly diffusive convection equations,\ncalled the artificial compressible system, is considered on a two-dimensional\ninfinite layer for a parameters range where the Hopf bifurcation occurs in the\ncorresponding incompressible system. The spectrum of the linearized operator in\na time periodic function space is investigated in detail near the bifurcation\npoint when the singular perturbation parameter is small. The results of this\npaper are the basis of the study of the nonlinear Hopf bifurcation problem and\nthe singular limit of the time periodic bifurcating solutions.\n"}
+{"abstract": "  We classify the elements of $W(\\tilde{A}_n)$ by giving a canonical reduced\nexpression for each, using basic tools among which affine length. We give some\ndirect consequences for such a canonical form: a description of left\nmultiplication by a simple reflection, a study of the right descent set, and a\nproof that the affine length is preserved along the tower of affine Coxeter\ngroups of type $\\tilde A$, which implies in particular that the corresponding\ntower of affine Hecke algebras is a faithful tower.\n"}
+{"abstract": "  Interpreters facilitate multi-lingual meetings but the affordable set of\nlanguages is often smaller than what is needed. Automatic simultaneous speech\ntranslation can extend the set of provided languages. We investigate if such an\nautomatic system should rather follow the original speaker, or an interpreter\nto achieve better translation quality at the cost of increased delay.\n  To answer the question, we release Europarl Simultaneous Interpreting Corpus\n(ESIC), 10 hours of recordings and transcripts of European Parliament speeches\nin English, with simultaneous interpreting into Czech and German. We evaluate\nquality and latency of speaker-based and interpreter-based spoken translation\nsystems from English to Czech. We study the differences in implicit\nsimplification and summarization of the human interpreter compared to a machine\ntranslation system trained to shorten the output to some extent. Finally, we\nperform human evaluation to measure information loss of each of these\napproaches.\n"}
+{"abstract": "  In recent years, the robotics community has made substantial progress in\nrobotic manipulation using deep reinforcement learning (RL). Effectively\nlearning of long-horizon tasks remains a challenging topic. Typical RL-based\nmethods approximate long-horizon tasks as Markov decision processes and only\nconsider current observation (images or other sensor information) as input\nstate. However, such approximation ignores the fact that skill-sequence also\nplays a crucial role in long-horizon tasks. In this paper, we take both the\nobservation and skill sequences into account and propose a\nskill-sequence-dependent hierarchical policy for solving a typical long-horizon\ntask. The proposed policy consists of a high-level skill policy (utilizing\nskill sequences) and a low-level parameter policy (responding to observation)\nwith corresponding training methods, which makes the learning much more\nsample-efficient. Experiments in simulation demonstrate that our approach\nsuccessfully solves a long-horizon task and is significantly faster than\nProximal Policy Optimization (PPO) and the task schema methods.\n"}
+{"abstract": "  We consider the lattice Higgs model on $\\mathbb{Z}^4$, with structure group\ngiven by $ \\mathbb{Z}_n $ for $ n \\geq 2 $. We compute the expected value of\nthe Wilson loop observable to leading order when the gauge coupling constant\nand hopping parameter are both sufficiently large. The leading order term is\nexpressed in terms of a quantity arising from the related but much simpler $\n\\mathbb{Z}_n $ model, which reduces to the Ising model when $n=2$. As part of\nthe proof, we construct a coupling between the lattice Higgs model and the $\n\\mathbb{Z}_n $ model.\n"}
+{"abstract": "  Spacetime theories obtained from perturbative string theory constructions are\nautomatically free of perturbative anomalies, but it is not settled whether\nthey are always free of global anomalies. Here we discuss a possible\n$\\mathbb{Z}_{24}$-valued pure gravitational anomaly of heterotic\ncompactifications down to two spacetime dimensions, and point out that it can\nbe shown to vanish using the theory of topological modular forms, assuming the\nvalidity of the Stolz-Teichner conjecture.\n"}
+{"abstract": "  Reinforcement learning in cooperative multi-agent settings has recently\nadvanced significantly in its scope, with applications in cooperative\nestimation for advertising, dynamic treatment regimes, distributed control, and\nfederated learning. In this paper, we discuss the problem of cooperative\nmulti-agent RL with function approximation, where a group of agents\ncommunicates with each other to jointly solve an episodic MDP. We demonstrate\nthat via careful message-passing and cooperative value iteration, it is\npossible to achieve near-optimal no-regret learning even with a fixed constant\ncommunication budget. Next, we demonstrate that even in heterogeneous\ncooperative settings, it is possible to achieve Pareto-optimal no-regret\nlearning with limited communication. Our work generalizes several ideas from\nthe multi-agent contextual and multi-armed bandit literature to MDPs and\nreinforcement learning.\n"}
+{"abstract": "  While traditional approaches to prevent the carbuncle phenomenon in gas\ndynamics simulations increase the viscosity on entropy and shear waves near\nshocks, it was quite recently suggested to instead decrease the viscosity on\nthe acoustic waves for low Mach numbers. The goal is to achieve what, in this\npaper, we call Mach number consistency: for all waves, the numerical viscosity\ndecreases with the same order of the Mach number when the Mach number tends to\nzero. We take the simple approach that was used for the proof of concept\ntogether with the simple model for the increased numerical viscosity on linear\nwaves and investigate the possibilities of combining both in an adaptive manner\nwhile locally maintaining Mach number consistency.\n"}
+{"abstract": "  Graph convolutional networks (GCNs) have achieved great success in dealing\nwith data of non-Euclidean structures. Their success directly attributes to\nfitting graph structures effectively to data such as in social media and\nknowledge databases. For image processing applications, the use of graph\nstructures and GCNs have not been fully explored. In this paper, we propose a\nnovel encoder-decoder network with added graph convolutions by converting\nfeature maps to vertexes of a pre-generated graph to synthetically construct\ngraph-structured data. By doing this, we inexplicitly apply graph Laplacian\nregularization to the feature maps, making them more structured. The\nexperiments show that it significantly boosts performance for image restoration\ntasks, including deblurring and super-resolution. We believe it opens up\nopportunities for GCN-based approaches in more applications.\n"}
+{"abstract": "  In this paper, we prove that the ratio of the modulus of the iterates of two\npoints in an escaping Fatou component may be bounded even if the orbit of the\ncomponent contains an infinite modulus annulus sequence and this case cannot\nhappen when the maximal modulus of the meromorphic function is large enough.\nTherefore, we extend the related results for entire functions to ones for\nmeromorphic functions with infinitely many poles. And we investigate the fast\nescaping Fatou components of meromorphic functions defined in [44] in terms of\nthe Nevanlinna characteristic instead of the maximal modulus in [12] and show\nthat the multiply-connected wandering domain is a part of the fast escaping set\nunder a growth condition of the maximal modulus. Finally we give examples of\nwandering domains escaping at arbitrary fast rate and slow rate.\n"}
+{"abstract": "  This paper studies the effect of the order of depth of mention on nested\nnamed entity recognition (NER) models. NER is an essential task in the\nextraction of biomedical information, and nested entities are common since\nmedical concepts can assemble to form larger entities. Conventional NER systems\nonly predict disjointed entities. Thus, iterative models for nested NER use\nmultiple predictions to enumerate all entities, imposing a predefined order\nfrom largest to smallest or smallest to largest. We design an order-agnostic\niterative model and a procedure to choose a custom order during training and\nprediction. To accommodate for this task, we propose a modification of the\nTransformer architecture to take into account the entities predicted in the\nprevious steps. We provide a set of experiments to study the model's\ncapabilities and the effects of the order on performance. Finally, we show that\nthe smallest to largest order gives the best results.\n"}
+{"abstract": "  In this work, we present a class of relativistic and well-behaved solution to\nEinstein's field equations for anisotropic matter distribution. We perform our\nanalysis by using the Buchdahl ansatz for the metric function grr. Three\ndifferent classes of new exact solution are found for anisotropy factor(delta).\nWe have analyzed our model with various physical aspects such as pressure\n(radial as well as transverse), energy density, anisotropy factor, mass,\ncompactness parameter, adiabatic index(gamma) and surface redshift. A graphical\nanalysis of energy conditions, TOV equation and causality condition indicates\nthe model are well behaved. The physical acceptability of the model has\nverified by considering compact objects with similar mass and radii, such as 4U\n1820-30,Vela X-1, PSR J1614-2230, LMC X-4, SMC X-1, 4U 1538-52, Her X-1, Cyg\nX-2, PSR B1913+16 and PSR J1903+327.\n"}
+{"abstract": "  Intelligent mobile robots are critical in several scenarios. However, as\ntheir computational resources are limited, mobile robots struggle to handle\nseveral tasks concurrently and yet guaranteeing real-timeliness. To address\nthis challenge and improve the real-timeliness of critical tasks under resource\nconstraints, we propose a fast context-aware task handling technique. To\neffectively handling tasks in real-time, our proposed context-aware technique\ncomprises of three main ingredients: (i) a dynamic time-sharing mechanism,\ncoupled with (ii) an event-driven task scheduling using reactive programming\nparadigm to mindfully use the limited resources; and, (iii) a lightweight\nvirtualized execution to easily integrate functionalities and their\ndependencies. We showcase our technique on a Raspberry-Pi-based robot with a\nvariety of tasks such as Simultaneous localization and mapping (SLAM), sign\ndetection, and speech recognition with a 42% speedup in total execution time\ncompared to the common Linux scheduler.\n"}
+{"abstract": "  Two-dimensional transition metal dichalcogenides (TMD) offer a unique\nplatform for creating van-der-Waals heterojunctions with fascinating physical\nproperties and promising applications in optoelectronics and valleytronics.\nBecause of their typical type-II band alignment, photoexcited electrons and\nholes can separate via interfacial charge transfer. To understand the nature\nand the dynamics of this charge transfer is of utmost importance for the design\nand efficiency of potential devices. However, systematic studies concerning the\ninfluence of the stacking angle on the charge transfer remain sparse. Here, we\napply time- and polarization resolved second-harmonic imaging microscopy to\ninvestigate the charge-transfer dynamics for three MoS$_2$/WSe$_2$\nheterostructures with different stacking angles at a previously unattainable\ntime-resolution of $\\approx$ 6 fs. For 1.70 eV excitation energy, electron\ntransfer from WSe$_2$ to MoS$_2$ is found to depend considerably on the\nstacking angle with the fastest transfer time observed to be as short as 12 fs.\nAt 1.85 eV excitation energy, ultrafast hole transfer from MoS$_2$ to\nhybridized states at the $\\Gamma$-point or to the K-points of WSe$_2$ has to be\nconsidered. Surprisingly, the corresponding decay dynamics show only a minor\nstacking-angle dependence indicating that radiative recombination of indirect\n$\\Gamma$-K excitons becomes the dominant decay route for all samples.\n"}
+{"abstract": "  Commercially available low-cost particulate matter (PM) sensors provide\noutput as total or size-specific particle counts and mass concentrations. These\nquantities are not measured directly but are estimated by the original\nequipment manufacturers' (OEM) proprietary algorithms and have inherent\nlimitations since particle scattering depends on their composition, size,\nshape, and complex index of refraction (CRI). Hence, there is a need to\ncharacterize and calibrate their performance under a controlled environment. We\npresent calibration algorithms for Plantower PMS A003 sensor as a function of\nparticle size and concentration. A standardized experimental protocol was used\nto control the PM level, environmental conditions and to evaluate\nsensor-to-sensor reproducibility. The calibration was based on tests when PMS\nA003 were exposed to different polydisperse standardized testing aerosols. The\nresults suggested particle size distribution from PMS A003 was shifted compared\nto reference instrument measures. For calibration of number concentration,\nlinear model without adjusting aerosol properties corrects the raw PMS A003\nmeasurement for specific size bins with normalized mean absolute error within\n4.0% of the reference instrument. Although the Bayesian Information Criterion\nsuggests that models adjusting for particle optical properties and relative\nhumidity are technically superior, they should be used with caution as the\nparticle properties used in fitting were within a narrow range for challenge\naerosols. The calibration models adjusted for particle CRI and density account\nfor non-linearity in the OEM's mass concentrations estimates and demonstrated\nlower error. These results have significant implications for using PMS A003 in\nhigh concentration environments, including indoor air quality and\noccupational/industrial exposure assessments, wildfire smoke, or near-source\nmonitoring scenarios.\n"}
+{"abstract": "  Stochastic averaging for a class of backward stochastic differential\nequations driven by both standard and fractional Brownian motions (SFrBSDEs in\nshort), is investigated. An averaged SFrBSDEs for the original SFrBSDEs is\nproposed, and their solutions are quantitatively compared. Under some\nappropriate assumptions, the solutions to original systems can be approximated\nby the solutions to averaged stochastic systems in the sense of mean square and\nalso in probability.\n"}
+{"abstract": "  This article discusses the problem of handwriting recognition in Kazakh and\nRussian languages. This area is poorly studied since in the literature there\nare almost no works in this direction. We have tried to describe various\napproaches and achievements of recent years in the development of handwritten\nrecognition models in relation to Cyrillic graphics. The first model uses deep\nconvolutional neural networks (CNNs) for feature extraction and a fully\nconnected multilayer perceptron neural network (MLP) for word classification.\nThe second model, called SimpleHTR, uses CNN and recurrent neural network (RNN)\nlayers to extract information from images. We also proposed the Bluechet and\nPuchserver models to compare the results. Due to the lack of available open\ndatasets in Russian and Kazakh languages, we carried out work to collect data\nthat included handwritten names of countries and cities from 42 different\nCyrillic words, written more than 500 times in different handwriting. We also\nused a handwritten database of Kazakh and Russian languages (HKR). This is a\nnew database of Cyrillic words (not only countries and cities) for the Russian\nand Kazakh languages, created by the authors of this work.\n"}
+{"abstract": "  Reconfigurable intelligent surface (RIS) provides a promising way to build\nthe programmable wireless transmission environments in the future. Owing to the\nlarge number of reflecting elements used at the RIS, joint optimization for the\nactive beamforming at the transmitter and the passive reflector at the RIS is\nusually complicated and time-consuming. To address this problem, this paper\nproposes a low-complexity joint beamforming and reflecting algorithm based on\nfractional programing (FP). Specifically, we first consider a RIS-aided\nmulti-user communication system with perfect channel state information (CSI)\nand formulate an optimization problem to maximize the sum rate of all users.\nSince the problem is nonconvex, we decompose the original problem into three\ndisjoint subproblems. By introducing favorable auxiliary variables, we derive\nthe closed-form expressions of the beamforming vectors and reflecting matrix in\neach subproblem, leading to a joint beamforming and reflecting algorithm with\nlow complexity. We then extend our approach to handle the case when\ntransmitter-RIS and RIS-receiver channels are not perfect and develop\ncorresponding low-complexity joint beamforming and reflecting algorithm with\npractical channel estimation. Simulation results have verified the\neffectiveness of the proposed algorithms as compared to various benchmark\nschemes.\n"}
+{"abstract": "  This paper studies intelligent reflecting surface (IRS) assisted active\ndevice detection. Since the locations of the devices are a priori unknown,\noptimal IRS beam alignment is not possible and a worst-case design for a given\ncoverage area is developed. To this end, we propose a generalized likelihood\nratio test (GLRT) detection scheme and an IRS phase-shift design that minimizes\nthe worst-case probability of misdetection. In addition to the proposed\noptimization-based phase-shift design, we consider two alternative suboptimal\ndesigns based on closed-form expressions for the IRS phase shifts. Our\nperformance analysis establishes the superiority of the optimization-based\ndesign, especially for large coverage areas. Furthermore, we investigate the\nimpact of scatterers on the proposed line-of-sight based design using\nsimulations.\n"}
+{"abstract": "  We explore the usage of meta-learning to derive the causal direction between\nvariables by optimizing over a measure of distribution simplicity. We\nincorporate a stochastic graph representation which includes latent variables\nand allows for more generalizability and graph structure expression. Our model\nis able to learn causal direction indicators for complex graph structures\ndespite effects of latent confounders. Further, we explore robustness of our\nmethod with respect to violations of our distributional assumptions and data\nscarcity. Our model is particularly robust to modest data scarcity, but is less\nrobust to distributional changes. By interpreting the model predictions as\nstochastic events, we propose a simple ensemble method classifier to reduce the\noutcome variability as an average of biased events. This methodology\ndemonstrates ability to infer the existence as well as the direction of a\ncausal relationship between data distributions.\n"}
+{"abstract": "  Intrusion Detection Systems (IDS) enhanced with Machine Learning (ML) have\ndemonstrated the capacity to efficiently build a prototype of \"normal\" cyber\nbehaviors in order to detect cyber threats' activity with greater accuracy than\ntraditional rule-based IDS. Because these are largely black boxes, their\nacceptance requires proof of robustness to stealthy adversaries. Since it is\nimpossible to build a baseline from activity completely clean of that of\nmalicious cyber actors (outside of controlled experiments), the training data\nfor deployed models will be poisoned with examples of activity that analysts\nwould want to be alerted about. We train an autoencoder-based anomaly detection\nsystem on network activity with various proportions of malicious activity mixed\nin and demonstrate that they are robust to this sort of poisoning.\n"}
+{"abstract": "  GW170817 has led to the first example of multi-messenger astronomy with\nobservations from gravitational wave interferometers and electromagnetic\ntelescopes combined to characterise the source. However, detections of the\nearly inspiral phase by the gravitational wave detectors would allow the\nobservation of the earlier stages of the merger in the electromagnetic band,\nimproving multi-messenger astronomy and giving access to new information. In\nthis paper, we introduce a new machine-learning-based approach to produce\nearly-warning alerts for an inspiraling binary neutron star system, based only\non the early inspiral part of the signal. We give a proof of concept to show\nthe possibility to use a combination of small convolutional neural networks\ntrained on the whitened detector strain in the time domain to detect and\nclassify early inspirals. Each of those is targeting a specific range of chirp\nmasses dividing the binary neutron star category into three sub-classes: light,\nintermediate and heavy. In this work, we focus on one LIGO detector at design\nsensitivity and generate noise from the design power spectral density. We show\nthat within this setup it is possible to produce an early alert up to 100\nseconds before the merger for the best-case scenario. We also present some\nfuture upgrades that will enhance the detection capabilities of our\nconvolutional neural networks. Finally, we also show that the current number of\ndetections for a realistic binary neutron star population is comparable to that\nof matched filtering and that there is a high probability to detect GW170817-\nand GW190425-like events at design sensitivity.\n"}
+{"abstract": "  The world ocean plays a key role in redistributing heat in the climate system\nand hence in regulating Earth's climate. Yet statistical analysis of ocean heat\ntransport suffers from partially incomplete large-scale data intertwined with\ncomplex spatio-temporal dynamics, as well as from potential model\nmisspecification. We present a comprehensive spatio-temporal statistical\nframework tailored to interpolating the global ocean heat transport using\nin-situ Argo profiling float measurements. We formalize the statistical\nchallenges using latent local Gaussian process regression accompanied by a\ntwo-stage fitting procedure. We introduce an approximate\nExpectation-Maximization algorithm to jointly estimate both the mean field and\nthe covariance parameters, and refine the potentially under-specified mean\nfield model with a debiasing procedure. This approach provides data-driven\nglobal ocean heat transport fields that vary in both space and time and can\nprovide insights into crucial dynamical phenomena, such as El Ni{\\~n}o \\& La\nNi{\\~n}a, as well as the global climatological mean heat transport field, which\nby itself is of scientific interest. The proposed framework and the Argo-based\nestimates are thoroughly validated with state-of-the-art multimission satellite\nproducts and shown to yield realistic subsurface ocean heat transport\nestimates.\n"}
+{"abstract": "  Existing generalization measures that aim to capture a model's simplicity\nbased on parameter counts or norms fail to explain generalization in\noverparameterized deep neural networks. In this paper, we introduce a new,\ntheoretically motivated measure of a network's simplicity which we call\nprunability: the smallest \\emph{fraction} of the network's parameters that can\nbe kept while pruning without adversely affecting its training loss. We show\nthat this measure is highly predictive of a model's generalization performance\nacross a large set of convolutional networks trained on CIFAR-10, does not grow\nwith network size unlike existing pruning-based measures, and exhibits high\ncorrelation with test set loss even in a particularly challenging double\ndescent setting. Lastly, we show that the success of prunability cannot be\nexplained by its relation to known complexity measures based on models' margin,\nflatness of minima and optimization speed, finding that our new measure is\nsimilar to -- but more predictive than -- existing flatness-based measures, and\nthat its predictions exhibit low mutual information with those of other\nbaselines.\n"}
+{"abstract": "  The timing performance of silicon sensors bump-bonded to Timepix3 ASICs is\ninvestigated, prior to and after different types of irradiation up to $8 \\times\n10^{15} 1 \\mathrm{\\,Me\\kern -0.1em V} \\mathrm{ \\,n_{eq}} {\\mathrm{\n\\,cm}}^{-2}$. The sensors have been tested with a beam of charged particles in\ntwo different configurations, perpendicular to and almost parallel to the\nincident beam. The second approach, known as the grazing angles method, is\nshown to be a powerful method to investigate not only the charge collection,\nbut also the time-to-threshold properties as a function of the depth at which\nthe charges are liberated.\n"}
+{"abstract": "  Reinforcement learning (RL) is a foundation of learning in biological systems\nand provides a framework to address numerous challenges with real-world\nartificial intelligence applications. Efficient implementations of RL\ntechniques could allow for agents deployed in edge-use cases to gain novel\nabilities, such as improved navigation, understanding complex situations and\ncritical decision making. Towards this goal, we describe a flexible\narchitecture to carry out reinforcement learning on neuromorphic platforms.\nThis architecture was implemented using an Intel neuromorphic processor and\ndemonstrated solving a variety of tasks using spiking dynamics. Our study\nproposes a usable energy efficient solution for real-world RL applications and\ndemonstrates applicability of the neuromorphic platforms for RL problems.\n"}
+{"abstract": "  Two novel nonlinear mode coupling processes for reversed shear Alfven\neigenmode (RSAE) nonlinear saturation are proposed and investigated. In the\nfirst process, RSAE nonlinearly couples to a co-propagating toroidal Alfven\neigenmode (TAE) with the same toroidal and poloidal mode numbers, and generates\na geodesic acoustic mode (GAM). In the second process, RSAE couples to a\ncounter-propagating TAE and generates an ion acoustic wave quasi-mode (IAW).\nThe condition for the two processes to occur is favored during current ramp.\nBoth processes contribute to effectively saturate the Alfvenic instabilities,\nas well as nonlinearly transfer of energy from energetic fusion alpha particles\nto fuel ions in burning plasmas.\n"}
+{"abstract": "  In recent works, we proposed a hypothesis that the turbulence in gases could\nbe produced by particles interacting via a potential, and examined the proposed\nmechanics of turbulence formation in a simple model of two particles for a\nvariety of different potentials. In this work, we use the same hypothesis to\ndevelop new fluid mechanics equations which model turbulent gas flow on a\nmacroscopic scale. The main difference between our approach and the\nconventional formalism is that we avoid replacing the potential interaction\nbetween particles with the Boltzmann collision integral. Due to this\ndifference, the velocity moment closure, which we implement for the shear\nstress and heat flux, relies upon the high Reynolds number condition, rather\nthan the Newton law of viscosity and the Fourier law of heat conduction. The\nresulting system of equations of fluid mechanics differs considerably from the\nstandard Euler and Navier-Stokes equations. A numerical simulation of our\nsystem shows that a laminar Bernoulli jet of an argon-like hard sphere gas in a\nstraight pipe rapidly becomes a turbulent flow. The time-averaged Fourier\nspectra of the kinetic energy of this flow exhibit Kolmogorov's negative\nfive-thirds power decay rate.\n"}
+{"abstract": "  Nested case-control (NCC) is a sampling method widely used for developing and\nevaluating risk models with expensive biomarkers on large prospective cohort\nstudies. The biomarker values are typically obtained on a sub-cohort,\nconsisting of all the events and a subset of non-events. However, when the\nnumber of events is not small, it might not be affordable to measure the\nbiomarkers on all of them. Due to the costs and limited availability of\nbio-specimens, only a subset of events is selected to the sub-cohort as cases.\nFor these \"untypical\" NCC studies, we propose a new weighting method for the\ninverse probability weighted (IPW) estimation. We also design a perturbation\nmethod to estimate the variance of the IPW estimator with our new weights. It\naccounts for between-subject correlations induced by the sampling processes for\nboth cases and controls through perturbing their sampling indicator variables,\nand thus, captures all the variations. Furthermore, we demonstrate,\nanalytically and numerically, that when cases consist of only a subset of\nevents, our new weight produces more efficient IPW estimators than the weight\nproposed in Samuelsen (1997) for a standard NCC design. We illustrate the\nestimating procedure with a study that aims to evaluate a biomarker-based risk\nprediction model using the Framingham cohort study.\n"}
+{"abstract": "  This paper introduces the notion of quantitative resilience of a control\nsystem. Following prior work, we study systems enduring a loss of control\nauthority over some of their actuators. Such a malfunction results in actuators\nproducing possibly undesirable inputs over which the controller has real-time\nreadings but no control. By definition, a system is resilient if it can still\nreach a target after a loss of control authority. However, after a malfunction\na resilient system might be significantly slower to reach a target compared to\nits initial capabilities. We quantify this loss of performance through the new\nconcept of quantitative resilience. We define this metric as the maximal ratio\nof the minimal times required to reach any target for the initial and\nmalfunctioning systems. Na\\\"ive computation of quantitative resilience directly\nfrom the definition is a time-consuming task as it requires solving four\nnested, possibly nonlinear, optimization problems. The main technical\ncontribution of this work is to provide an efficient method to compute\nquantitative resilience. Relying on control theory and on three novel geometric\nresults we reduce the computation of quantitative resilience to a single linear\noptimization problem. We illustrate our method on two numerical examples: an\nopinion dynamics scenario and a trajectory controller for low-thrust\nspacecrafts.\n"}
+{"abstract": "  We test the anisotropy in the Finslerian cosmological model with the X-ray\nand ultraviolet (UV) fluxes of 808 quasars. The dipole amplitude is\n$A_D=0.302_{ -0.124}^{ +0.185}$ and the dipole direction points towards $(l, b)\n= ( 288.92_{~ -28.80^{\\circ}}^{^{\\circ}+23.74^{\\circ}}, 6.10_{~\n-16.40^{\\circ}}^{^{\\circ} +16.55^{\\circ}} )$. We find that the dipole direction\nfrom the X-ray and UV fluxes of quasars is very close to the dipole direction\ngiven by the \"Joint Light-curve Analysis\" (JLA) compilation in the Finslerian\ncosmological model and the angular difference between the two dipole directions\nis only $10.44^{\\circ}$. We also find the angular difference between the dipole\ndirection from the 808 quasars in the Finslerian cosmological model and ones\nfrom the supernovae of type Ia (SNe Ia) samples in the dipole-modulated\n$\\Lambda$CDM model is around $30^{\\circ}$. Six gravitationally lensed quasars\nare considered to investigate the Hubble constant $H_0$ in the Finslerian\ncosmological model. We get a slightly smaller $H_0$ than the result given by\nthe six gravitationally lensed quasars. Finally, we forecast the future\nconstraints on the dipole parameters with the X-ray and UV fluxes of quasars.\nAs the number of simulations increases, the precisions of the parameters\nrelated to anisotropy in the Finslerian cosmological model improve\nsignificantly. The X-ray and UV fluxes of quasars have a promising future as a\nprobe of anisotropy in Finsler spacetime.\n"}
+{"abstract": "  During a geosteering operation the well path is intentionally adjusted in\nresponse to the new data acquired while drilling. To achieve consistent\nhigh-quality decisions, especially when drilling in complex environments,\ndecision support systems can help cope with high volumes of data and\ninterpretation complexities. They can assimilate the real-time measurements\ninto a probabilistic earth model and use the updated model for decision\nrecommendations.\n  Recently, machine learning (ML) techniques have enabled a wide range of\nmethods that redistribute computational cost from on-line to off-line\ncalculations. In this paper, we introduce two ML techniques into the\ngeosteering decision support framework. Firstly, a complex earth model\nrepresentation is generated using a Generative Adversarial Network (GAN).\nSecondly, a commercial extra-deep electromagnetic simulator is represented\nusing a Forward Deep Neural Network (FDNN).\n  The numerical experiments demonstrate that the combination of the GAN and the\nFDNN in an ensemble randomized maximum likelihood data assimilation scheme\nprovides real-time estimates of complex geological uncertainty. This yields\nreduction of geological uncertainty ahead of the drill-bit from the\nmeasurements gathered behind and around the well bore.\n"}
+{"abstract": "  Associative memory is a fundamental function in the brain. Here, we\ngeneralize the standard associative memory model to include long-range Hebbian\ninteractions at the learning stage, corresponding to a large synaptic\nintegration window. In our model, the Hebbian length can be arbitrarily large.\nThe spectral density of the coupling matrix is derived using the replica\nmethod, which is also shown to be consistent with the results obtained by\napplying the free probability method. The maximal eigenvalue is then obtained\nby an iterative equation, related to the paramagnetic to spin glass transition\nin the model. Altogether, this work establishes the connection between the\nassociative memory with arbitrary Hebbian length and the asymptotic\neigen-spectrum of the neural-coupling matrix.\n"}
+{"abstract": "  Let $U^+_q$ denote the positive part of the quantized enveloping algebra\n$U_q(\\widehat{\\mathfrak{sl}}_2)$. The algebra $U^+_q$ has a presentation\ninvolving two generators $W_0$, $W_1$ and two relations, called the $q$-Serre\nrelations. In 1993 I. Damiani obtained a PBW basis for $U^+_q$, consisting of\nsome elements $\\lbrace E_{n \\delta+ \\alpha_0} \\rbrace_{n=0}^\\infty$, $\\lbrace\nE_{n \\delta+ \\alpha_1} \\rbrace_{n=0}^\\infty$, $\\lbrace E_{n \\delta}\n\\rbrace_{n=1}^\\infty$. In 2019 we introduced the alternating central extension\n$\\mathcal U^+_q$ of $U^+_q$. We defined $\\mathcal U^+_q$ by generators and\nrelations. The generators, said to be alternating, are denoted $\\lbrace\n\\mathcal W_{-k}\\rbrace_{k=0}^\\infty$, $\\lbrace \\mathcal\nW_{k+1}\\rbrace_{k=0}^\\infty$, $ \\lbrace \\mathcal G_{k+1}\\rbrace_{k=0}^\\infty$,\n$\\lbrace \\mathcal {\\tilde G}_{k+1}\\rbrace_{k=0}^\\infty$. Let $\\langle \\mathcal\nW_0, \\mathcal W_1 \\rangle$ denote the subalgebra of $\\mathcal U^+_q$ generated\nby $\\mathcal W_0$, $\\mathcal W_1$. It is known that there exists an algebra\nisomorphism $U^+_q\\to \\langle \\mathcal W_0, \\mathcal W_1 \\rangle$ that sends\n$W_0 \\mapsto \\mathcal W_0$ and $W_1 \\mapsto \\mathcal W_1$. Via this isomorphism\nwe identify $U^+_q$ with $\\langle \\mathcal W_0, \\mathcal W_1 \\rangle$. In our\nmain result, we express the Damiani PBW basis elements in terms of the\nalternating generators. We give the answer in terms of generating functions.\n"}
+{"abstract": "  Explainable machine learning has become increasingly prevalent, especially in\nhealthcare where explainable models are vital for ethical and trusted automated\ndecision making. Work on the susceptibility of deep learning models to\nadversarial attacks has shown the ease of designing samples to mislead a model\ninto making incorrect predictions. In this work, we propose a model agnostic\nexplainability-based method for the accurate detection of adversarial samples\non two datasets with different complexity and properties: Electronic Health\nRecord (EHR) and chest X-ray (CXR) data. On the MIMIC-III and Henan-Renmin EHR\ndatasets, we report a detection accuracy of 77% against the Longitudinal\nAdversarial Attack. On the MIMIC-CXR dataset, we achieve an accuracy of 88%;\nsignificantly improving on the state of the art of adversarial detection in\nboth datasets by over 10% in all settings. We propose an anomaly detection\nbased method using explainability techniques to detect adversarial samples\nwhich is able to generalise to different attack methods without a need for\nretraining.\n"}
+{"abstract": "  We present a supersymmetric extension of the exotic Newtonian Chern-Simons\ngravity theory in three spacetime dimensions. The underlying new\nnon-relativistic superalgebra is obtained by expanding the $\\mathcal{N}=2$ AdS\nsuperalgebra and can be written as two copies of the enhanced Nappi-Witten\nalgebra, one of which is augmented by supersymmetry. We show that the exotic\nNewtonian superalgebra allows to introduce a cosmological constant to the\nextended Newtonian supergravity. Interestingly, the obtained supergravity\naction contains the extended Newton-Hooke supergravity as a sub-case.\n"}
+{"abstract": "  We investigate what the orbits of globular clusters (GCs) in the Fornax dwarf\nspheroidal (dSph) galaxy can teach us about dark matter (DM). This problem was\nrecently studied for ultralight dark matter (ULDM). We consider two additional\nmodels: (i) fermionic degenerate dark matter (DDM), where Pauli blocking should\nbe taken into account in the dynamical friction computation; and (ii)\nself-interacting dark matter (SIDM). We give a simple and direct Fokker-Planck\nderivation of dynamical friction, new in the case of DDM and reproducing\nprevious results in the literature for ULDM and cold DM. ULDM, DDM and SIDM\nwere considered in the past as leading to cores in dSphs, a feature that acts\nto suppress dynamical friction and prolong GC orbits. For DDM we derive a\nversion of the cosmological free streaming limit that is independent of the DM\nproduction mechanism, finding that DDM cannot produce an appreciable core in\nFornax without violating Ly-$\\alpha$ limits. If the Ly-$\\alpha$ limit is\ndiscounted for some reason, then stellar kinematics data does allow a DDM core\nwhich could prolong GC orbits. For SIDM we find that significant prolongation\nof GC orbits could be obtained for values of the self-interaction cross section\nconsidered in previous works. In addition to reassessing the inspiral time\nusing updated observational data, we give a new perspective on the so-called GC\ntiming problem, demonstrating that for a cuspy cold DM profile dynamical\nfriction predicts a $z=0$ radial distribution for the innermost GCs that is\nindependent of initial conditions. The observed orbits of Fornax GCs are\nconsistent with this expectation with a mild apparent fine-tuning at the level\nof $\\sim25\\%$.\n"}
+{"abstract": "  A classical result of Rothschild and van Lint asserts that if every non-zero\nFourier coefficient of a Boolean function $f$ over $\\mathbb{F}_2^{n}$ has the\nsame absolute value, namely $|\\hat{f}(\\alpha)|=1/2^k$ for every $\\alpha$ in the\nFourier support of $f$, then $f$ must be the indicator function of some affine\nsubspace of dimension $n-k$. In this paper we slightly generalize their result.\nOur main result shows that, roughly speaking, Boolean functions whose Fourier\ncoefficients take values in the set $\\{-2/2^k, -1/2^k, 0, 1/2^k, 2/2^k\\}$ are\nindicator functions of two disjoint affine subspaces of dimension $n-k$ or four\ndisjoint affine subspace of dimension $n-k-1$. Our main technical tools are\nresults from additive combinatorics which offer tight bounds on the affine span\nsize of a subset of $\\mathbb{F}_2^{n}$ when the doubling constant of the subset\nis small.\n"}
+{"abstract": "  We study the generalization performance of $\\text{full-batch}$ optimization\nalgorithms for stochastic convex optimization: these are first-order methods\nthat only access the exact gradient of the empirical risk (rather than\ngradients with respect to individual data points), that include a wide range of\nalgorithms such as gradient descent, mirror descent, and their regularized\nand/or accelerated variants. We provide a new separation result showing that,\nwhile algorithms such as stochastic gradient descent can generalize and\noptimize the population risk to within $\\epsilon$ after $O(1/\\epsilon^2)$\niterations, full-batch methods either need at least $\\Omega(1/\\epsilon^4)$\niterations or exhibit a dimension-dependent sample complexity.\n"}
+{"abstract": "  Several works have shown that the regularization mechanisms underlying deep\nneural networks' generalization performances are still poorly understood. In\nthis paper, we hypothesize that deep neural networks are regularized through\ntheir ability to extract meaningful clusters among the samples of a class. This\nconstitutes an implicit form of regularization, as no explicit training\nmechanisms or supervision target such behaviour. To support our hypothesis, we\ndesign four different measures of intraclass clustering, based on the neuron-\nand layer-level representations of the training data. We then show that these\nmeasures constitute accurate predictors of generalization performance across\nvariations of a large set of hyperparameters (learning rate, batch size,\noptimizer, weight decay, dropout rate, data augmentation, network depth and\nwidth).\n"}
+{"abstract": "  In this paper, we consider quasi-Newton methods and solve two open problems\nmentioned in Rodomanov and Nesterov's discussion section. First, we extent\nRodomanov and Nesterov's results to random general quasi-Newton methods, which\ninclude common DFP, BFGS, SR1 methods. Such random methods adopt a random\ndirection for updating the approximate Hessian matrix. Second, we focus on the\nspecific quasi-Newton methods: SR1 and BFGS methods. We provide improved\nversions of greedy and random methods with provable better explicit (local)\nsuperlinear convergence rates. Our analysis is closely related to the\napproximation of a given Hessian matrix, unconstrained quadratic objective, as\nwell as the general strongly convex, smooth, and strongly self-concordant\nfunctions.\n"}
+{"abstract": "  This study derives a method to consistently recover the second-order\nconvergence of the lattice Boltzmann method (LBM), which is frequently degraded\nby the improper discretisation of required source terms. The work focuses on\nadvection-diffusion models in which the source terms are dependent on the\nintensity of transported fields. The main findings are applicable to a wide\nrange of formulations within the LBM framework. All considered source terms are\ninterpreted as contributions to the zeroth-moment of the distribution function.\nThese account for sources in a scalar field, such as density, concentration or\ntemperature. In addition to this, certain immersed boundary methods can be\ninterpreted as a source term in their formulation, highlighting a further\napplication for this work.\n  This paper makes three primary contributions to the current state-of-the-art.\nFirstly, it identifies the differences observed between the ways source terms\nare included in the LBM schemes present in the literature. The algebraic\nmanipulations are explicitly presented in this paper to clarify the differences\nobserved, and identify their origin. Secondly, it derives in full detail, the\nimplicit relation between the value of the transported macroscopic field, and\nthe sum of the LBM densities. Moreover, this relation is valid for any source\nterm discretization scheme, and three equivalent forms of the second-order\naccurate collision operator are presented. Finally, closed-form solutions of\nthis implicit relation are shown for a variety of common models.\n  The second-order convergence of the proposed LBM schemes is verified on both\nlinear and non-linear source terms. Commonly used diffusive and acoustic\nscalings are discussed, and their pitfalls are identified. Moreover, for a\nsimplified case, the competing errors are shown visually with isolines of error\nin the space of spatial and temporal resolutions.\n"}
+{"abstract": "  The field of DNA nanotechnology has made it possible to assemble, with high\nyields, different structures that have actionable properties. For example,\nresearchers have created components that can be actuated. An exciting next step\nis to combine these components into multifunctional nanorobots that could,\npotentially, perform complex tasks like swimming to a target location in the\nhuman body, detect an adverse reaction and then release a drug load to stop it.\nHowever, as we start to assemble more complex nanorobots, the yield of the\ndesired nanorobot begins to decrease as the number of possible component\ncombinations increases. Therefore, the ultimate goal of this work is to develop\na predictive model to maximize yield. However, training predictive models\ntypically requires a large dataset. For the nanorobots we are interested in\nassembling, this will be difficult to collect. This is because high-fidelity\ndata, which allows us to characterize the shape and size of individual\nstructures, is very time-consuming to collect, whereas low-fidelity data is\nreadily available but only captures bulk statistics for different processes.\nTherefore, this work combines low- and high-fidelity data to train a generative\nmodel using a two-step process. We first use a relatively small, high-fidelity\ndataset to train a generative model. At run time, the model takes low-fidelity\ndata and uses it to approximate the high-fidelity content. We do this by\nbiasing the model towards samples with specific properties as measured by\nlow-fidelity data. In this work we bias our distribution towards a desired node\ndegree of a graphical model that we take as a surrogate representation of the\nnanorobots that this work will ultimately focus on. We have not yet accumulated\na high-fidelity dataset of nanorobots, so we leverage the MolGAN architecture\n[1] and the QM9 small molecule dataset [2-3] to demonstrate our approach.\n"}
+{"abstract": "  Groundstate magnetism of the one-band Hubbard model on the frustrated square\nlattice where both nearest-neighbour $t_1$ and next-nearest-neighbour $t_2$\nhoppings are considered at half-filling are revisited within mean field\napproximation. Two new magnetic phases are detected at intermediate strength of\nHubbard $U$ and relative strong frustration of $t_2/t_1$, named double-stripe\nand plaquette antiferromagnetic states, both of which are metallic and stable\neven at finite temperature and electron doping. The nature of the phase\ntransitions between different phases and the properties of the two new states\nare analyzed in detail. Our results of various magnetic states emerging from\ngeometric frustration in the minimal model suggests that distinct\nantiferromagnetism observed experimentally in the parent states of two\nhigh-T$_c$ superconducting families, i.e., cuprates and iron-based\nsuperconductors, may be understood from a unified microscopic origin,\nirrespective of orbital degrees of freedom, or hoppings further than\nnext-nearest neighbour, etc.\n"}
+{"abstract": "  A major trend in academia and data science is the rapid adoption of Bayesian\nstatistics for data analysis and modeling, leading to the development of\nprobabilistic programming languages (PPL). A PPL provides a framework that\nallows users to easily specify a probabilistic model and perform inference\nautomatically. PyAutoFit is a Python-based PPL which interfaces with all\naspects of the modeling (e.g., the model, data, fitting procedure,\nvisualization, results) and therefore provides complete management of every\naspect of modeling. This includes composing high-dimensionality models from\nindividual model components, customizing the fitting procedure and performing\ndata augmentation before a model-fit. Advanced features include database tools\nfor analysing large suites of modeling results and exploiting domain-specific\nknowledge of a problem via non-linear search chaining. Accompanying PyAutoFit\nis the autofit workspace (see https://github.com/Jammy2211/autofit_workspace),\nwhich includes example scripts and the HowToFit lecture series which introduces\nnon-experts to model-fitting and provides a guide on how to begin a project\nusing PyAutoFit. Readers can try PyAutoFit right now by going to the\nintroduction Jupyter notebook on Binder (see\nhttps://mybinder.org/v2/gh/Jammy2211/autofit_workspace/HEAD) or checkout our\nreadthedocs(see https://pyautofit.readthedocs.io/en/latest/) for a complete\noverview of PyAutoFit's features.\n"}
+{"abstract": "  Carton detection is an important technique in the automatic logistics system\nand can be applied to many applications such as the stacking and unstacking of\ncartons, the unloading of cartons in the containers. However, there is no\npublic large-scale carton dataset for the research community to train and\nevaluate the carton detection models up to now, which hinders the development\nof carton detection. In this paper, we present a large-scale carton dataset\nnamed Stacked Carton Dataset(SCD) with the goal of advancing the\nstate-of-the-art in carton detection. Images are collected from the internet\nand several warehourses, and objects are labeled using per-instance\nsegmentation for precise localization. There are totally 250,000 instance masks\nfrom 16,136 images. In addition, we design a carton detector based on RetinaNet\nby embedding Offset Prediction between Classification and Localization\nmodule(OPCL) and Boundary Guided Supervision module(BGS). OPCL alleviates the\nimbalance problem between classification and localization quality which boosts\nAP by 3.1% - 4.7% on SCD while BGS guides the detector to pay more attention to\nboundary information of cartons and decouple repeated carton textures. To\ndemonstrate the generalization of OPCL to other datasets, we conduct extensive\nexperiments on MS COCO and PASCAL VOC. The improvement of AP on MS COCO and\nPASCAL VOC is 1.8% - 2.2% and 3.4% - 4.3% respectively.\n"}
+{"abstract": "  Motion planning is an essential aspect of autonomous systems and robotics and\nis an active area of research. A recently-proposed sampling-based motion\nplanning algorithm, termed 'Generalized Shape Expansion' (GSE), has been shown\nto possess significant improvement in computational time over several existing\nwell-established algorithms. The GSE has also been shown to be\nprobabilistically complete. However, asymptotic optimality of the GSE is yet to\nbe studied. To this end, in this paper we show that the GSE algorithm is not\nasymptotically optimal by studying its behaviour for the promenade problem. In\norder to obtain a probabilistically complete and asymptotically optimal\ngeneralized shape-based algorithm, a modified version of the GSE, namely 'GSE*'\nalgorithm, is subsequently presented. The forementioned desired mathematical\nproperties of the GSE* algorithm are justified by its detailed analysis.\nNumerical simulations are found to be in line with the theoretical results on\nthe GSE* algorithm.\n"}
+{"abstract": "  The declaration of COVID-19 as a pandemic has largely amplified the spread of\nrelated information on social media, such as Twitter, Facebook, and\nWeChat.Unlike the previous studies which focused on how to detect the\nmisinformation or fake news related toCOVID-19, we investigate how the disease\nand information co-evolve in the population. We focus onCOVID-19and its\ninformation during the period when the disease was widely spread in China,\ni.e., from January 25th to March 24th, 2020. We first explore how the disease\nand information co-evolve via the spatial analysis of the two spreading\nprocesses. We visualize the geo-location of both disease and information at the\nprovince level and find that disease is more geo-localized compared to\ninformation. We find a high correlation between the disease and information\ndata, and also people care about the spread only when it comes to their\nneighborhood. Regard to the content of the information, we find that positive\nmessages are more negatively correlated with the disease compared to negative\nand neutral messages. Additionally, we introduce machine learning algorithms,\ni.e., linear regression and random forest, to further predict the number of\ninfected using different disease spatial related and information-related\ncharacteristics. We obtain that the disease spatial related characteristics of\nnearby cities can help to improve the prediction accuracy. Meanwhile,\ninformation-related characteristics can also help to improve the prediction\nperformance, but with a delay, i.e., the improvement comes from using, for\ninstance, the number of messages 10 days ago, for disease prediction. The\nmethodology proposed in this paper may shed light on new clues of emerging\ninfections\n"}
+{"abstract": "  We consider the effects of bound atomic electrons scattered by solar\nneutrinos due to the electromagnetic properties of neutrinos. This necessiate\nconsidering the recoil of atomic nucleus, which should be considered in the\nmomentum conservation, but that effect to the energy conservation is\nnegligible. This effect changes the kinematic behavior of the scattered\nelectron compared to that scattered on free electrons. We apply this effect to\nthe recent XENON1T data, but the bounds obtained from this is not very\nrestrictive. We obtained the bounds: the (transition) magnetic moment\n$|f_{\\alpha\\beta}|\\le 0.86\\times 10^{-7}$ (times the electron Bohr magneton)\nand the charge radius $|\\tilde{r}|< 4.30\\times 10^{-17\\,}{\\rm cm}$. For a\nnon-vanishing millicharge ($\\varepsilon$), the allowed bound is shown in the\n$\\tilde{r}^2-\\varepsilon$ plane.\n"}
+{"abstract": "  Being one of the most abundant atomic/molecular species observed in dense\nmolecular gas, atomic carbon ($\\mathrm{C}^0$) is a potential good tracer of\nmolecular gas mass in many chemical/physical environments, though the\n$\\mathrm{C^0}$ abundance variation outside the Galactic disk region is yet to\nbe fully known. This paper presents a wide-field 500 GHz [CI] map of the\nGalactic central molecular zone (CMZ) obtained with the ASTE 10-m telescope.\nPrincipal component analysis and non-LTE multi-transition analysis have shown\nthat the [CI] emission predominantly originates from the low-excitation gas\ncomponent with a 20-50 K temperature and $\\sim 10^3\\ \\mathrm{cm}^{-3}$ density,\nwhereas $\\mathrm{C^0}$ abundance is likely suppressed in the high-excitation\ngas component. The average $N(\\mathrm{C}^0)$/$N(\\mathrm{CO})$ abundance ratio\nin the CMZ is 0.3-0.4, which is 2-3 times that in the Galactic disk. The\n$N(\\mathrm{C}^0)$/$N(\\mathrm{CO})$ ratio increases to 0.7 in the innermost 10\npc region and to $\\sim2$ in the circumnuclear disk. We discovered\n$\\mathrm{C^0}$-rich regions distributed in a ring-shape encircling the\nsupernova remnant (SNR) Sgr A east, indicative that the\n$\\mathrm{C}^0$-enrichment in the central 10 pc region is a consequence of a\nmolecular cloud-SNR interaction. In the 15 atom/molecules included in principal\ncomponent analysis (PCA), CN is the only other species that increases in the\n[CI]-bright ring. The origin of the [CI]-bright ring is likely a cosmic-ray\ndominated region created by low-energy cosmic-ray particles accelerated by Sgr\nA east or primitive molecular gas collected by the SNR in which the\n$\\mathrm{C}^0$-to-CO conversion has not reached the equilibrium.\n"}
+{"abstract": "  We present a diffusion-based simulation and theoretical models for\nexplanation of photoluminescence (PL) emission intensity in semiconductor\nnanoplatelets. It is shown that the shape of PL intensity curves can be\nreproduced by the interplay of recombination, diffusion and trapping of\nexcitons. The emission intensity at short times is purely exponential and is\ndefined by recombination. At long times it is governed by the release of\nexcitons from surface traps and is characterized by a power-law tail. We show\nthat the crossover from one limit to another is controlled by diffusion\nproperties. This intermediate region exhibits a rich behaviour depending on the\nvalue of diffusivity. Proposed approach reproduces all the features of\nexperimental curves measured for different nanoplatelet systems.\n"}
+{"abstract": "  We study the cohomology of Lie superalgebras for the full complex of forms:\nsuperforms, pseudoforms and integral forms. We use the technique of spectral\nsequences to abstractly compute the Chevalley-Eilenberg cohomology. We first\nfocus on the superalgebra $\\mathfrak{osp}(2|2)$ and show that there exist\nnon-empty cohomology spaces among pseudoforms related to sub-superalgebras. We\nthen extend some classical theorems by Koszul, as to include pseudoforms and\nintegral forms. Further, we conjecture that the algebraic Poincar\\'e duality\nextends to Lie superalgebras, as long as all the complexes of forms are taken\ninto account and we prove that this holds true for $\\mathfrak{osp}(2|2)$. We\nfinally construct the cohomology representatives explicitly by using a\ndistributional realisation of pseudoforms and integral forms. On one hand,\nthese results show that the cohomology of Lie superalgebras is actually larger\nthan expected, whereas one restricts to superforms only; on the other hand, we\nshow the emergence of completely new cohomology classes represented by\npseudoforms. These classes realise as integral form classes of\nsub-superstructures.\n"}
+{"abstract": "  We analyze Andreev bound states (ABSs) that form in normal sections of a\nRashba nanowire that is only partially covered by a superconducting layer.\nThese ABSs are localized close to the ends of the superconducting section and\ncan be pinned to zero energy over a wide range of magnetic field strengths even\nif the nanowire is in the non-topological regime. For finite-size nanowires\n(typically $\\lesssim 1$ $\\mu$m in current experiments), the ABS localization\nlength is comparable to the length of the nanowire. The probability density of\nan ABS is therefore non-zero throughout the nanowire and\ndifferential-conductance calculations reveal a correlated zero-bias peak (ZBP)\nat both ends of the nanowire. When a second normal section hosts an additional\nABS at the opposite end of the superconducting section, the combination of the\ntwo ABSs can mimic the closing and reopening of the bulk gap in local and\nnon-local conductances accompanied by the appearance of the ZBP. These\nsignatures are reminiscent of those expected for Majorana bound states (MBSs)\nbut occur here in the non-topological regime. Our results demonstrate that\nconductance measurements of correlated ZBPs at the ends of a typical\nsuperconducting nanowire or an apparent closing and reopening of the bulk gap\nin the local and non-local conductance are not conclusive indicators for the\npresence of MBSs.\n"}
+{"abstract": "  At leading order, the $S$-matrices in QED and gravity are known to factorise,\nproviding unambiguous determinations of the parts divergent due to infrared\ncontributions. The soft $S$-matrices defined in this fashion are shown to be\ndefined entirely in terms of $2$ dimensional models on the celestial sphere,\ninvolving two real scalar fields, allowing us to express the soft $S$-matrices\nfor real as well as virtual divergences as two dimensional correlation\nfunctions. We discuss what this means for finding holographic representations\nof scattering amplitudes in QED and gravity and comment on simple double copy\nstructures that arise during the analysis.\n"}
+{"abstract": "  This paper reports a mass transfer model of a reactant flowing in a large\naspect ratio microfluidic chip made of a channel with electrodes on the side\nwalls. A semi-analytical solution to the two-dimensional Fickian diffusion of a\nreactant in a microchannel, including the electrochemical reaction at the\nelectrode interface, and the velocity profile obtained from the Navier-Stokes\nequations in a fully developed laminar regime is found . The solution is\nwritten in the Laplace domain in terms of transfer functions. The proposed\nsolution is an extension of the Leveque approximation describing the reactant\ndiffusion from the electrode to the middle of the microfluidic channel. The\nmain applications of this work are the use of the obtained transfer functions\nfor the measurement of the Faradic current density or the chemical\nconcentration at the electrode interface. The study can also be extended to the\nheat transfer in microfluidic electrochemical chips (temperature or heat flux\nmeasurements at the electrode interface).\n"}
+{"abstract": "  We study contract design for welfare maximization in the well known \"common\nagency\" model of [Bernheim and Whinston, 1986]. This model combines the\nchallenges of coordinating multiple principals with the fundamental challenge\nof contract design: that principals have incomplete information of the agent's\nchoice of action. Motivated by the significant social inefficiency of standard\ncontracts for such settings (which we formally quantify using a price of\nanarchy/stability analysis), we investigate whether and how a recent toolbox\ndeveloped for the first set of challenges under a complete-information\nassumption, VCG contracts [Lavi and Shamash, 2019], can be extended to\nincomplete information.\n  We define and characterize the class of \"incomplete information VCG contracts\n(IIVCG)\", and show it is the unique class guaranteeing truthfulness of the\nprincipals and welfare maximization by the agent. Our results reveal an\ninherent tradeoff between two important properties required to ensure\nparticipation in the contract: individual rationality (for the principals) and\nlimited liability (for the agent). We design a polynomial-time algorithm for\ndetermining whether a setting has an IIVCG contract with both properties. As\nour main result we design a polynomial-time \"algorithmic IIVCG\" contract: given\nvaluation reports from the principals it returns, if possible for the setting,\na payment scheme for the agent that constitutes an IIVCG contract with all\ndesired properties. We also give a sufficient graph-theoretic condition on the\npopulation of principals that ensures the existence of such an IIVCG contract.\n"}
+{"abstract": "  In these expository notes, we give a proof of regularity of Anosov splitting\nfor Anosov diffeomorphisms in a torus. We also generalize the idea to higher\ndimensions and to Anosov flows.\n"}
+{"abstract": "  Here is a direct problem for Sidon sets: Given a linear form $\\varphi = c_1\nx_1 + \\cdots + c_h x_h $, construct and describe sets $A$ that are Sidon sets\nfor $\\varphi$. This paper considers an inverse problem for Sidon sets: Given a\nset $A$, determine the linear forms $\\varphi$ such that $A$ is a Sidon set for\n$\\varphi$.\n"}
+{"abstract": "  There has been a rise in the use of Machine Learning as a Service (MLaaS)\nVision APIs as they offer multiple services including pre-built models and\nalgorithms, which otherwise take a huge amount of resources if built from\nscratch. As these APIs get deployed for high-stakes applications, it's very\nimportant that they are robust to different manipulations. Recent works have\nonly focused on typical adversarial attacks when evaluating the robustness of\nvision APIs. We propose two new aspects of adversarial image generation methods\nand evaluate them on the robustness of Google Cloud Vision API's optical\ncharacter recognition service and object detection APIs deployed in real-world\nsettings such as sightengine.com, picpurify.com, Google Cloud Vision API, and\nMicrosoft Azure's Computer Vision API. Specifically, we go beyond the\nconventional small-noise adversarial attacks and introduce secret embedding and\ntransparent adversarial examples as a simpler way to evaluate robustness. These\nmethods are so straightforward that even non-specialists can craft such\nattacks. As a result, they pose a serious threat where APIs are used for\nhigh-stakes applications. Our transparent adversarial examples successfully\nevade state-of-the art object detections APIs such as Azure Cloud Vision\n(attack success rate 52%) and Google Cloud Vision (attack success rate 36%).\n90% of the images have a secret embedded text that successfully fools the\nvision of time-limited humans but is detected by Google Cloud Vision API's\noptical character recognition. Complementing to current research, our results\nprovide simple but unconventional methods on robustness evaluation.\n"}
+{"abstract": "  Periodic water waves generate Stokes drift as manifest from the orbits of\nLagrangian particles not fully closing. Stokes drift can contribute to the\ntransport of floating marine litter, including plastic. Previously, marine\nlitter objects have been considered to be perfect Lagrangian tracers,\ntravelling with the Stokes drift of the waves. However, floating marine litter\nobjects have large ranges of sizes and densities, which potentially result in\ndifferent rates of transport by waves due to the non-Lagrangian behaviour of\nthe objects. Through a combination of theory and experiments for idealised\nspherical objects in deep-water waves, we show that different objects are\ntransported at different rates depending on their size and density, and that\nlarger buoyant objects can have increased drift compared with Lagrangian\ntracers. We show that the mechanism for the increased drift observed in our\nexperiments comprises the variable submergence and the corresponding dynamic\nbuoyancy force components in a direction perpendicular to the local water\nsurface. This leads to an amplification of the drift of these objects compared\nto the Stokes drift when averaged over the wave cycle. Using an expansion in\nwave steepness, we derive a closed-form approximation for this increased drift,\nwhich can be included in ocean-scale models of marine litter transport.\n"}
+{"abstract": "  Modern natural language understanding models depend on pretrained subword\nembeddings, but applications may need to reason about words that were never or\nrarely seen during pretraining. We show that examples that depend critically on\na rarer word are more challenging for natural language inference models. Then\nwe explore how a model could learn to use definitions, provided in natural\ntext, to overcome this handicap. Our model's understanding of a definition is\nusually weaker than a well-modeled word embedding, but it recovers most of the\nperformance gap from using a completely untrained word.\n"}
+{"abstract": "  The structure of the mixing matrix, in the electroweak quark sector with four\ngenerations of quarks is investigated. We conclude that the area of the\nunitarity quadrangle is not a good choice as a possible measure of the\nCP~violation. In search of new physics we analyze how the existence of the 4-th\nquark family may influence on the values of the Cabibbo-Kobayashi-Maskawa\nmatrix and we show that one can test for the existence of the 4-th generation\nusing the Jarlskog invariants of the known quarks only. The analysis based on\nthe measured unitary triangle exhibits some tension with the assumption of\nthree quark generations. The measurement of the unitarity triangle obtained\nfrom the scalar product of the second row/column of the CKM matrix by the\ncomplex conjugate of third row/column can provide information about the\nexistence of the fourth generation of quarks.\n"}
+{"abstract": "  The turn of the century has seen a tremendous rise in technological advances\nin the field of automobiles. With 5G technology on its way and the development\nin the IoT sector, cars will start interacting with each other using V2V\ncommunications and become much more autonomous. In this project, an effort is\nmade to move in the same direction by proposing a model for an automatic car\nwiper system that operates on sensing rain and snow on the windshield of a car.\nWe develop a prototype for our idea by integrating a servo motor and raindrop\nsensor with an AT89C51 Microcontroller.\n"}
+{"abstract": "  We propose that the non-linear Hall response with the preserved time-reversal\nsymmetry can be observed in the Bloch oscillation of the ultracold atoms in\noptical lattices. In the short-time limit of Bloch oscillations with a DC\ndriving field, the non-linear Hall current dominates with time reversal\nsymmetry and is a second-order response of the external field strength. The\nassociated Berry curvature dipole, which is the second order nonlinear\ncoefficient of the driving field, can be extracted from the dynamics. In the\npresence of an AC driving field, the nonlinear Hall response will double the\ndriving frequency in the case of time reversal symmetry.\n"}
+{"abstract": "  We survey aspects of locally nilpotent linear groups. Then we obtain a new\nclassification; namely, we classify the irreducible maximal locally nilpotent\nsubgroups of $\\mathrm{GL}(q, \\mathbb F)$ for prime $q$ and any field $\\mathbb\nF$.\n"}
+{"abstract": "  Meta-heuristic techniques are important as they are used to find solutions to\ncomputationally intractable problems. Simplistic methods such as exhaustive\nsearch become computationally expensive and unreliable as the solution space\nfor search algorithms increase. As no method is guaranteed to perform better\nthan all others in all classes of optimization search problems, there is a need\nto constantly find new and/or adapt old search algorithms. This research\nproposes an Infrasonic Search Algorithm, inspired from the Gravitational Search\nAlgorithm and the mating behaviour in peafowls. The Infrasonic Search Algorithm\nidentified competitive solutions to 23 benchmark unimodal and multimodal test\nfunctions compared to the Genetic Algorithm, Particle Swarm Optimization\nAlgorithm and the Gravitational Search Algorithm.\n"}
+{"abstract": "  An ultimate goal of the AI and ML fields is artificial general intelligence\n(AGI); although such systems remain science fiction, various models exhibit\naspects of AGI. In this work, we look at neuro-symbolic (NS)approaches to\nvisual question answering (VQA) from the perspective of AGI desiderata. We see\nhow well these systems meet these desiderata, and how the desiderata often pull\nthe scientist in opposing directions. It is my hope that through this work we\ncan temper model evaluation on benchmarks with a discussion of the properties\nof these systems and their potential for future extension.\n"}
+{"abstract": "  We study the $(m,b)$ Maker-Breaker percolation game on $\\mathbb{Z}^2$,\nintroduced by Day and Falgas-Ravry. As our first result, we show that Breaker\nhas a winning strategy for the $(m,b)$-game whenever $b \\geq (2-\\frac{1}{14} +\no(1))m$, breaking the ratio $2$ barrier proved by Day and Falgas-Ravry.\n  Addressing further questions of Day and Falgas-Ravry, we show that Breaker\ncan win the $(m,2m)$-game even if he allows Maker to claim $c$ edges before the\ngame starts, for any integer $c$, and that he can moreover win rather fast (as\na function of $c$).\n  Finally, we consider the game played on $\\mathbb{Z}^2$ after the usual bond\npercolation process with parameter $p$ was performed. We show that when $p$ is\nnot too much larger than $1/2$, Breaker almost surely has a winning strategy\nfor the $(1,1)$-game, even if Maker is allowed to choose the origin after the\nboard is determined.\n"}
+{"abstract": "  Automatic video summarization is still an unsolved problem due to several\nchallenges. The currently available datasets either have very short videos or\nhave few long videos of only a particular type. We introduce a new benchmarking\nvideo dataset called VISIOCITY (VIdeo SummarIzatiOn based on Continuity, Intent\nand DiversiTY) which comprises of longer videos across six different categories\nwith dense concept annotations capable of supporting different flavors of video\nsummarization and other vision problems. For long videos, human reference\nsummaries necessary for supervised video summarization techniques are difficult\nto obtain. We explore strategies to automatically generate multiple reference\nsummaries from indirect ground truth present in VISIOCITY. We show that these\nsummaries are at par with human summaries. We also present a study of different\ndesired characteristics of a good summary and demonstrate how it is normal to\nhave two good summaries with different characteristics. Thus we argue that\nevaluating a summary against one or more human summaries and using a single\nmeasure has its shortcomings. We propose an evaluation framework for better\nquantitative assessment of summary quality which is closer to human judgment.\nLastly, we present insights into how a model can be enhanced to yield better\nsummaries. Sepcifically, when multiple diverse ground truth summaries can\nexist, learning from them individually and using a combination of loss\nfunctions measuring different characteristics is better than learning from a\nsingle combined (oracle) ground truth summary using a single loss function. We\ndemonstrate the effectiveness of doing so as compared to some of the\nrepresentative state of the art techniques tested on VISIOCITY. We release\nVISIOCITY as a benchmarking dataset and invite researchers to test the\neffectiveness of their video summarization algorithms on VISIOCITY.\n"}
+{"abstract": "  In this paper, we study the geodesic deviation equation (GDE) within the\ncontext of the Brans-Dicke (BD) theory in $D$ dimensions. Then, we restrict our\nattention to the GDE for the fundamental observers and null vector field past\ndirected. Concerning the latter, in order to apply the retrieved GDE for the\ncosmological models as well as examining exact solutions, we study some\ncosmological models in general relativity (GR) and in the context of the BD\ntheory. For the BD framework, we provide two appropriate settings for dynamical\nsystem, which can be considered as the most extended case scenarios with\nrespect to those studied previously. Then, we investigate a few well-known BD\ncosmological models, for which we present the modified and corrected exact\nsolutions. Moreover, we show that the retrieved formalism of the GDE can also\nbe properly applicable for the modified BD theory (MBDT), whose matter and\npotential emerge from the geometry. For all of our herein cosmological models,\nwe investigate the energy conditions and depict the behavior of the deviation\nvector and observer area distance. We demonstrate that why the MBDT can be\nconsidered as a more appropriate candidate to describe the universe in\naccordance with the observational data as well as from theoretical viewpoint.\n"}
+{"abstract": "  Using sample surveys as a cost effective tool to provide estimates for\ncharacteristics of interest at population and sub-populations (area/domain)\nlevel has a long tradition in \"small area estimation\". However, the existence\nof outliers in the sample data can significantly affect the estimation for\nareas in which they occur, especially where the domain-sample size is small.\nBased on existing robust estimators for small area estimation we propose two\nnovel approaches for bias calibration. A series of simulations shows that our\nmethods lead to more efficient estimators in comparison with other existing\nbias-calibration methods. As a real data example we apply our estimators to\nobtain \\textit{Gini} coefficients in labour market areas of the Tuscany region\nof Italy, where our sources of information are the EU-SILC survey and the\nItalian census. This analysis shows that the new methods reveal a different\npicture than existing methods. We extend our ideas to predictions for\nnon-sampled areas.\n"}
+{"abstract": "  As an industry of primarily small and mid-size businesses, it is becoming\nincreasingly more difficult for British Columbia (BC)'s tree fruit growers to\ncompete with large, often vertically integrated producers from other regions.\nNew ways of managing information and resources collaboratively are needed to\ndevelop competitive strengths. This case study seeks to understand the\ninformation and knowledge management capabilities of the BC tree fruit cluster\nacross the value chain for six different data domains. A qualitative\nmethodology design of 21 in-depth interviews with cluster stakeholders provides\ninsights into the data quality, completeness and integration points, and then\napplies CMMI level criteria to assess the information management capabilities\nof the industry. Significant data and process gaps are identified. This paper\nexplores how the BC tree fruit industry can move forward from this position\nusing technology solutions to support the development of information and\nknowledge, and collective decision-making.\n"}
+{"abstract": "  Using the example of the harmonic oscillator, we illustrate the use of hybrid\ndynamical brackets in analyzing quantum-classical interaction. We only assume\nthat a hybrid dynamical bracket exists, is bilinear, and reduces to the pure\nquantum/classical bracket when acting on pure quantum/classical variables. Any\nhybrid bracket obeying these natural requirements will produce the same\ndynamics for pure classical or quantum variables, given a hybrid Hamiltonian.\nBackreaction is manifested in the evolution of a nonvanishing commutator\nbetween classical variables. The more massive the classical system is, the less\nit is affected by backreaction. Interestingly, we show that while pure\nvariables evolve to violate the pure canonical relations, they always obey the\nhybrid canonical relations. The dynamics of hybrid variables, on the other\nhand, is shown to require a fully specified and consistent hybrid bracket,\notherwise evolution cannot be defined.\n"}
+{"abstract": "  Anomalous users detection in social network is an imperative task for\nsecurity problems. Motivated by the great power of Graph Neural Networks(GNNs),\nmany current researches adopt GNN-based detectors to reveal the anomalous\nusers. However, the increasing scale of social activities, explosive growth of\nusers and manifold technical disguise render the user detection a difficult\ntask. In this paper, we propose an innovate Relevance-aware Anomalous Users\nDetection model (RAU-GNN) to obtain a fine-grained detection result. RAU-GNN\nfirst extracts multiple relations of all types of users in social network,\nincluding both benign and anomalous users, and accordingly constructs the\nmultiple user relation graph. Secondly, we employ relevance-aware GNN framework\nto learn the hidden features of users, and discriminate the anomalous users\nafter discriminating. Concretely, by integrating Graph Convolution Network(GCN)\nand Graph Attention Network(GAT), we design a GCN-based relation fusion layer\nto aggregate initial information from different relations, and a GAT-based\nembedding layer to obtain the high-level embeddings. Lastly, we feed the\nlearned representations to the following GNN layer in order to consolidate the\nnode embedding by aggregating the final users' embeddings. We conduct extensive\nexperiment on real-world datasets. The experimental results show that our\napproach can achieve high accuracy for anomalous users detection.\n"}
+{"abstract": "  Anytime motion planners are widely used in robotics. However, the\nrelationship between their solution quality and computation time is not well\nunderstood, and thus, determining when to quit planning and start execution is\nunclear. In this paper, we address the problem of deciding when to stop\ndeliberation under bounded computational capacity, so called meta-reasoning,\nfor anytime motion planning. We propose data-driven learning methods,\nmodel-based and model-free meta-reasoning, that are applicable to different\nenvironment distributions and agnostic to the choice of anytime motion\nplanners. As a part of the framework, we design a convolutional neural\nnetwork-based optimal solution predictor that predicts the optimal path length\nfrom a given 2D workspace image. We empirically evaluate the performance of the\nproposed methods in simulation in comparison with baselines.\n"}
+{"abstract": "  Let $V$ be a vector space endowed with a non-degenerate quadratic form $Q$.\nIf the base field $\\mathbb{F}$ is different from $\\mathbb{F}_2$, it is known\nthat every isometry can be written as a product of reflections. In this\narticle, we detail the structure of the poset of all minimal length reflection\nfactorizations of an isometry. If $\\mathbb{F}$ is an ordered field, we also\nstudy factorizations into positive reflections, i.e., reflections defined by\nvectors of positive norm. We characterize such factorizations, under the\nhypothesis that the squares of $\\mathbb{F}$ are dense in the positive elements\n(this includes Archimedean and Euclidean fields). In particular, we show that\nan isometry is a product of positive reflections if and only if its spinor norm\nis positive. As a final application, we explicitly describe the poset of all\nfactorizations of isometries of the hyperbolic space.\n"}
+{"abstract": "  We describe the construction of CMC surfaces with symmetries in $\\mathbb S^3$\nand $\\mathbb R^3$ using a CMC quadrilateral in a fundamental tetrahedron of a\ntessellation of the space. The fundamental piece is constructed by the\ngeneralized Weierstrass representation using a geometric flow on the space of\npotentials.\n"}
+{"abstract": "  Optimal zero-delay coding (quantization) of $\\mathbb{R}^d$-valued linearly\ngenerated Markov sources is studied under quadratic distortion. The structure\nand existence of deterministic and stationary coding policies that are optimal\nfor the infinite horizon average cost (distortion) problem are established.\nPrior results studying the optimality of zero-delay codes for Markov sources\nfor infinite horizons either considered finite alphabet sources or, for the\n$\\mathbb{R}^d$-valued case, only showed the existence of deterministic and\nnon-stationary Markov coding policies or those which are randomized. In\naddition to existence results, for finite blocklength (horizon) $T$ the\nperformance of an optimal coding policy is shown to approach the infinite time\nhorizon optimum at a rate $O(\\frac{1}{T})$. This gives an explicit rate of\nconvergence that quantifies the near-optimality of finite window\n(finite-memory) codes among all optimal zero-delay codes.\n"}
+{"abstract": "  This paper presents a framework for Convolutional Neural Network (CNN)-based\nquality enhancement task, by taking advantage of coding information in the\ncompressed video signal. The motivation is that normative decisions made by the\nencoder can significantly impact the type and strength of artifacts in the\ndecoded images. In this paper, the main focus has been put on decisions\ndefining the prediction signal in intra and inter frames. This information has\nbeen used in the training phase as well as input to help the process of\nlearning artifacts that are specific to each coding type. Furthermore, to\nretain a low memory requirement for the proposed method, one model is used for\nall Quantization Parameters (QPs) with a QP-map, which is also shared between\nluma and chroma components. In addition to the Post Processing (PP) approach,\nthe In-Loop Filtering (ILF) codec integration has also been considered, where\nthe characteristics of the Group of Pictures (GoP) are taken into account to\nboost the performance. The proposed CNN-based Quality Enhancement(QE) framework\nhas been implemented on top of the VVC Test Model (VTM-10). Experiments show\nthat the prediction-aware aspect of the proposed method improves the coding\nefficiency gain of the default CNN-based QE method by 1.52%, in terms of BD-BR,\nat the same network complexity compared to the default CNN-based QE filter.\n"}
+{"abstract": "  Cross-lingual speech adaptation aims to solve the problem of leveraging\nmultiple rich-resource languages to build models for a low-resource target\nlanguage. Since the low-resource language has limited training data, speech\nrecognition models can easily overfit. In this paper, we propose to use\nadapters to investigate the performance of multiple adapters for\nparameter-efficient cross-lingual speech adaptation. Based on our previous\nMetaAdapter that implicitly leverages adapters, we propose a novel algorithms\ncalled SimAdapter for explicitly learning knowledge from adapters. Our\nalgorithm leverages adapters which can be easily integrated into the\nTransformer structure.MetaAdapter leverages meta-learning to transfer the\ngeneral knowledge from training data to the test language. SimAdapter aims to\nlearn the similarities between the source and target languages during\nfine-tuning using the adapters. We conduct extensive experiments on\nfive-low-resource languages in Common Voice dataset. Results demonstrate that\nour MetaAdapter and SimAdapter methods can reduce WER by 2.98% and 2.55% with\nonly 2.5% and 15.5% of trainable parameters compared to the strong full-model\nfine-tuning baseline. Moreover, we also show that these two novel algorithms\ncan be integrated for better performance with up to 3.55% relative WER\nreduction.\n"}
+{"abstract": "  Magnetic confinement devices for nuclear fusion can be large and expensive.\nCompact stellarators are promising candidates for costreduction, but introduce\nnew difficulties: confinement in smaller volumes requires higher magnetic\nfield, which calls for higher coil-currents and ultimately causes higher\nLaplace forces on the coils-if everything else remains the same. This motivates\nthe inclusion of force reduction in stellarator coil optimization. In the\npresent paper we consider a coil winding surface, we prove that there is a\nnatural and rigorous way to define the Laplace force (despite the magnetic\nfield discontinuity across the current-sheet), we provide examples of cost\nassociated (peak force, surface-integral of the force squared) and discuss easy\ngeneralizations to parallel and normal force-components, as these will be\nsubject to different engineering constraints. Such costs can then be easily\nadded to the figure of merit in any multi-objective stellarator coil\noptimization code. We demonstrate this for a generalization of the REGCOIL code\n[1], which we rewrote in python, and provide numerical examples for the NCSX\n(now QUASAR) design. We present results for various definitions of the cost\nfunction, including peak force reductions by up to 40 %, and outline future\nwork for further reduction.\n"}
+{"abstract": "  Detecting, predicting, and alleviating traffic congestion are targeted at\nimproving the level of service of the transportation network. With increasing\naccess to larger datasets of higher resolution, the relevance of deep learning\nfor such tasks is increasing. Several comprehensive survey papers in recent\nyears have summarised the deep learning applications in the transportation\ndomain. However, the system dynamics of the transportation network vary greatly\nbetween the non-congested state and the congested state -- thereby\nnecessitating the need for a clear understanding of the challenges specific to\ncongestion prediction. In this survey, we present the current state of deep\nlearning applications in the tasks related to detection, prediction, and\nalleviation of congestion. Recurring and non-recurring congestion are discussed\nseparately. Our survey leads us to uncover inherent challenges and gaps in the\ncurrent state of research. Finally, we present some suggestions for future\nresearch directions as answers to the identified challenges.\n"}
+{"abstract": "  We present a simplified, task-agnostic multi-modal pre-training approach that\ncan accept either video or text input, or both for a variety of end tasks.\nExisting pre-training are task-specific by adopting either a single cross-modal\nencoder that requires both modalities, limiting their use for retrieval-style\nend tasks or more complex multitask learning with two unimodal encoders,\nlimiting early cross-modal fusion. We instead introduce new pretraining masking\nschemes that better mix across modalities (e.g. by forcing masks for text to\npredict the closest video embeddings) while also maintaining separability (e.g.\nunimodal predictions are sometimes required, without using all the input).\nExperimental results show strong performance across a wider range of tasks than\nany previous methods, often outperforming task-specific pre-training. Code is\nmade available at https://github.com/pytorch/fairseq/tree/main/examples/MMPT.\n"}
+{"abstract": "  In this paper, we propose an algorithmic framework dubbed inertial\nalternating direction methods of multipliers (iADMM), for solving a class of\nnonconvex nonsmooth multiblock composite optimization problems with linear\nconstraints. Our framework employs the general minimization-majorization (MM)\nprinciple to update each block of variables so as to not only unify the\nconvergence analysis of previous ADMM that use specific surrogate functions in\nthe MM step, but also lead to new efficient ADMM schemes. To the best of our\nknowledge, in the \\emph{nonconvex nonsmooth} setting, ADMM used in combination\nwith the MM principle to update each block of variables, and ADMM combined with\ninertial terms for the primal variables have not been studied in the\nliterature. Under standard assumptions, we prove the subsequential convergence\nand global convergence for the generated sequence of iterates. We illustrate\nthe effectiveness of iADMM on a class of nonconvex low-rank representation\nproblems.\n"}
+{"abstract": "  Recommender systems, which merely leverage user-item interactions for user\npreference prediction (such as the collaborative filtering-based ones), often\nface dramatic performance degradation when the interactions of users or items\nare insufficient. In recent years, various types of side information have been\nexplored to alleviate this problem. Among them, knowledge graph (KG) has\nattracted extensive research interests as it can encode users/items and their\nassociated attributes in the graph structure to preserve the relation\ninformation. In contrast, less attention has been paid to the item-item\nco-occurrence information (i.e., \\textit{co-view}), which contains rich\nitem-item similarity information. It provides information from a perspective\ndifferent from the user/item-attribute graph and is also valuable for the CF\nrecommendation models. In this work, we make an effort to study the potential\nof integrating both types of side information (i.e., KG and item-item\nco-occurrence data) for recommendation. To achieve the goal, we propose a\nunified graph-based recommendation model (UGRec), which integrates the\ntraditional directed relations in KG and the undirected item-item co-occurrence\nrelations simultaneously. In particular, for a directed relation, we transform\nthe head and tail entities into the corresponding relation space to model their\nrelation; and for an undirected co-occurrence relation, we project head and\ntail entities into a unique hyperplane in the entity space to minimize their\ndistance. In addition, a head-tail relation-aware attentive mechanism is\ndesigned for fine-grained relation modeling. Extensive experiments have been\nconducted on several publicly accessible datasets to evaluate the proposed\nmodel. Results show that our model outperforms several previous\nstate-of-the-art methods and demonstrate the effectiveness of our UGRec model.\n"}
+{"abstract": "  We introduce an inversion based method, denoted as IMAge-Guided model\nINvErsion (IMAGINE), to generate high-quality and diverse images from only a\nsingle training sample. We leverage the knowledge of image semantics from a\npre-trained classifier to achieve plausible generations via matching\nmulti-level feature representations in the classifier, associated with\nadversarial training with an external discriminator. IMAGINE enables the\nsynthesis procedure to simultaneously 1) enforce semantic specificity\nconstraints during the synthesis, 2) produce realistic images without generator\ntraining, and 3) give users intuitive control over the generation process. With\nextensive experimental results, we demonstrate qualitatively and quantitatively\nthat IMAGINE performs favorably against state-of-the-art GAN-based and\ninversion-based methods, across three different image domains (i.e., objects,\nscenes, and textures).\n"}
+{"abstract": "  Deep learning play a vital role in classifying different arrhythmias using\nthe electrocardiography (ECG) data. Nevertheless, training deep learning models\nnormally requires a large amount of data and it can lead to privacy concerns.\nUnfortunately, a large amount of healthcare data cannot be easily collected\nfrom a single silo. Additionally, deep learning models are like black-box, with\nno explainability of the predicted results, which is often required in clinical\nhealthcare. This limits the application of deep learning in real-world health\nsystems. In this paper, we design a new explainable artificial intelligence\n(XAI) based deep learning framework in a federated setting for ECG-based\nhealthcare applications. The federated setting is used to solve issues such as\ndata availability and privacy concerns. Furthermore, the proposed framework\nsetting effectively classifies arrhythmia's using an autoencoder and a\nclassifier, both based on a convolutional neural network (CNN). Additionally,\nwe propose an XAI-based module on top of the proposed classifier to explain the\nclassification results, which help clinical practitioners make quick and\nreliable decisions. The proposed framework was trained and tested using the\nMIT-BIH Arrhythmia database. The classifier achieved accuracy up to 94% and 98%\nfor arrhythmia detection using noisy and clean data, respectively, with\nfive-fold cross-validation.\n"}
+{"abstract": "  For all positive non-square integer multiplier k, there is an infinity of\nmultiples of triangular numbers which are also triangular numbers. With a\nsimple change of variables, these triangular numbers can be found using\nsolutions of Pell equations. With some conditions on parities of fundamental\nsolutions of the simple and generalized Pell equations, only odd solutions of\nthe generalized Pell equation are retained to provide many infinitely solutions\nfound on branches corresponding to each of the generalized fundamental\nsolutions. General algebraic expressions of fundamental solutions of the Pell\nequations are found for some values of the multiplier k in function of the\nclosest natural square. Further, among the expressions of Pell equation\nsolutions, a set of recurrent relations is identical to those found previously\nwithout the Pell equation solving method. It is found also that two constants\nof the problem of multiples of triangular numbers are directly related to the\nfundamental solutions of the simple Pell equation, which is an unexpected\nresult as it means that simple Pell equation fundamental solutions in all\ngenerality are related to constants in recurrent relations of the problem of\nfinding triangular numbers multiple of other triangular numbers.\n"}
+{"abstract": "  Executing computer vision models on streaming visual data, or streaming\nperception is an emerging problem, with applications in self-driving, embodied\nagents, and augmented/virtual reality. The development of such systems is\nlargely governed by the accuracy and latency of the processing pipeline. While\npast work has proposed numerous approximate execution frameworks, their\ndecision functions solely focus on optimizing latency, accuracy, or energy,\netc. This results in sub-optimum decisions, affecting the overall system\nperformance. We argue that the streaming perception systems should holistically\nmaximize the overall system performance (i.e., considering both accuracy and\nlatency simultaneously). To this end, we describe a new approach based on deep\nreinforcement learning to learn these tradeoffs at runtime for streaming\nperception. This tradeoff optimization is formulated as a novel deep contextual\nbandit problem and we design a new reward function that holistically integrates\nlatency and accuracy into a single metric. We show that our agent can learn a\ncompetitive policy across multiple decision dimensions, which outperforms\nstate-of-the-art policies on public datasets.\n"}
+{"abstract": "  We discuss the interconnections between AO*, adversarial game-searching\nalgorithms, e.g., proof number search and minimax search. The former was\ndeveloped in the context of a general AND/OR graph model, while the latter were\nmostly presented in game-trees which are sometimes modeled using AND/OR trees.\nIt is thus worth investigating to what extent these algorithms are related and\nhow they are connected. In this paper, we explicate the interconnections\nbetween these search paradigms. We argue that generalized proof number search\nmight be regarded as a more informed replacement of AO* for solving arbitrary\nAND/OR graphs, and the minimax principle might also extended to use dual\nheuristics.\n"}
+{"abstract": "  The classical Cauchy rigidity theorem for convex polytopes reads that if two\nconvex polytopes have isometric developments then they are congruent. In other\nwords, we can decide whether two polyhedra are isometric or not by using their\ndevelopments only. In this article, we study a similar problem about whether it\nis possible, using only the developments of two convex polyhedra of Euclidean\n3-space, to understand that these polyhedra are (or are not) affine-equivalent.\n"}
+{"abstract": "  An element $g$ in a group $G$ is called reversible (or real) if it is\nconjugate to $g^{-1}$ in $G$, i.e. there is a $h$ in $G$ such that $g^{-1}=\nhgh^{-1}$. The element $g$ in $G$ is called strongly reversible (or strongly\nreal) if $g$ is a product of two involutions (i.e. order one or two elements)\nin $G$. In this paper, we classify reversible and strongly reversible elements\nin the isometry groups of $\\mathbb F$-Hermitian spaces, where $\\mathbb F\n=\\mathbb C$ or $\\mathbb H$. More precisely, we classify reversible and strongly\nreversible elements in the groups $\\mathrm{Sp}(n) \\ltimes \\mathbb H^n$,\n$\\mathrm{U}(n) \\ltimes \\mathbb C^n$ and $\\mathrm{SU}n) \\ltimes \\mathbb C^n$. We\nalso give a new proof to the classification of strongly reversible elements in\n$\\mathrm{Sp}(n)$.\n"}
+{"abstract": "  Minimizing cross-entropy is a widely used method for training artificial\nneural networks. Many training procedures based on backpropagation use\ncross-entropy directly as their loss function. Instead, this theoretical essay\ninvestigates a dual process model with two processes, in which one process\nminimizes the Kullback-Leibler divergence while its dual counterpart minimizes\nthe Shannon entropy. Postulating that learning consists of two dual processes\ncomplementing each other, the model defines an equilibrium state for both\nprocesses in which the loss function assumes its minimum. An advantage of the\nproposed model is that it allows deriving the optimal learning rate and\nmomentum weight to update network weights for backpropagation. Furthermore, the\nmodel introduces the golden ratio and complex numbers as important new concepts\nin machine learning.\n"}
+{"abstract": "  The topology of a network associated with a reservoir computer is often taken\nso that the connectivity and the weights are chosen randomly. Optimization is\nhardly considered as the parameter space is typically too large. Here we\ninvestigate this problem for a class of reservoir computers for which we obtain\na decomposition of the reservoir dynamics into modes, which can be computed\nindependently of one another. Each mode depends on an eigenvalue of the network\nadjacency matrix. We then take a parametric approach in which the eigenvalues\nare parameters that can be appropriately designed and optimized. In addition,\nwe introduce the application of a time shift to each individual mode. We show\nthat manipulations of the individual modes, either in terms of the eigenvalues\nor the time shifts, can lead to dramatic reductions in the training error.\n"}
+{"abstract": "  Robust travel time predictions are of prime importance in managing any\ntransportation infrastructure, and particularly in rail networks where they\nhave major impacts both on traffic regulation and passenger satisfaction. We\naim at predicting the travel time of trains on rail sections at the scale of an\nentire rail network in real-time, by estimating trains' delays relative to a\ntheoretical circulation plan. Existing implementations within railway companies\ngenerally work using the approximation that a train's delay will stay constant\nfor the rest of its trip.\n  Predicting the evolution of a given train's delay is a uniquely hard problem,\ndistinct from mainstream road traffic forecasting problems, since it involves\nseveral hard-to-model phenomena: train spacing, station congestion and\nheterogeneous rolling stock among others. We first offer empirical evidence of\nthe previously unexplored phenomenon of delay propagation in the French\nNational Railway Network, leading to delays being amplified by interactions\nbetween trains. We then contribute a novel technique using the transformer\narchitecture and pre-trained embeddings to make real-time massively parallel\npredictions for train delays at the scale of the whole rail network (over 3k\ntrains at peak hours, making predictions at an average horizon of 70 minutes).\nOur approach yields very positive results on real-world data when compared to\ncurrently-used and experimental prediction techniques. Our work is in the early\nstages of implementation for industrial use at the French railway company SNCF\nfor passenger information systems, and a contender as a tool to aid traffic\nregulation decisions.\n"}
+{"abstract": "  We present a theoretical framework for characterizing incremental stability\nof nonlinear stochastic systems perturbed by compound Poisson shot noise and\nfinite-measure L\\'{e}vy noise. For each noise type, we compare trajectories of\nthe perturbed system with distinct noise sample paths against trajectories of\nthe nominal, unperturbed system. We show that for a finite number of jumps\narising from the noise process, the mean-squared error between the trajectories\nexponentially converge towards a bounded error ball across a finite interval of\ntime under practical boundedness assumptions. The convergence rate for shot\nnoise systems is the same as the exponentially-stable nominal system, but with\na tradeoff between the parameters of the shot noise process and the size of the\nerror ball. The convergence rate and the error ball for the L\\'{e}vy noise\nsystem are shown to be nearly direct sums of the respective quantities for the\nshot and white noise systems separately, a result which is analogous to the\nL\\'{e}vy-Khintchine theorem. We demonstrate our results using several simple\ncase studies: a 1D linear reference-tracking system, 2D linear time-varying\nsystems, a 2D nonlinear system, and a 2D nonlinear observer design problem.\n"}
+{"abstract": "  Thermodynamics tells us to expect underwater contact between two hydrophobic\nsurfaces to result in stronger adhesion compared to two hydrophilic surfaces.\nHowever, presence of water changes not only energetics, but also the dynamic\nprocess of reaching a final state, which couples solid deformation and liquid\nevacuation. These dynamics can create challenges for achieving strong\nunderwater adhesion/friction, which affects diverse fields including soft\nrobotics, bio-locomotion and tire traction. Closer investigation, requiring\nsufficiently precise resolution of film evacuation while simultaneously\ncontrolling surface wettability has been lacking. We perform high resolution\nin-situ frustrated total internal reflection imaging to track underwater\ncontact evolution between soft-elastic hemispheres of varying stiffness and\nsmooth-hard surfaces of varying wettability. Surprisingly, we find exponential\nrate of water evacuation from hydrophobic-hydrophobic (adhesive) contact is 3\norders of magnitude lower than that from hydrophobic-hydrophilic (non-adhesive)\ncontact. The trend of decreasing rate with decreasing wettability of glass\nsharply changes about a point where thermodynamic adhesion crosses zero,\nsuggesting a transition in mode of evacuation, which is illuminated by\n3-Dimensional spatiotemporal heightmaps. Adhesive contact is characterized by\nthe early localization of sealed puddles, whereas non-adhesive contact remains\nsmooth, with film-wise evacuation from one central puddle. Measurements with a\nhuman thumb and alternatively hydrophobic/hydrophilic glass surface demonstrate\npractical consequences of the same dynamics: adhesive interactions cause\ninstability in valleys and lead to a state of more trapped water and less\nintimate solid-solid contact.\n"}
+{"abstract": "  Bacteria live in environments that are continuously fluctuating and changing.\nExploiting any predictability of such fluctuations can lead to an increased\nfitness. On longer timescales bacteria can \"learn\" the structure of these\nfluctuations through evolution. However, on shorter timescales, inferring the\nstatistics of the environment and acting upon this information would need to be\naccomplished by physiological mechanisms. Here, we use a model of metabolism to\nshow that a simple generalization of a common regulatory motif (end-product\ninhibition) is sufficient both for learning continuous-valued features of the\nstatistical structure of the environment and for translating this information\ninto predictive behavior; moreover, it accomplishes these tasks near-optimally.\nWe discuss plausible genetic circuits that could instantiate the mechanism we\ndescribe, including one similar to the architecture of two-component signaling,\nand argue that the key ingredients required for such predictive behavior are\nreadily accessible to bacteria.\n"}
+{"abstract": "  Graph generative models are a highly active branch of machine learning. Given\nthe steady development of new models of ever-increasing complexity, it is\nnecessary to provide a principled way to evaluate and compare them. In this\npaper, we enumerate the desirable criteria for such a comparison metric and\nprovide an overview of the status quo of graph generative model comparison in\nuse today, which predominantly relies on maximum mean discrepancy (MMD). We\nperform a systematic evaluation of MMD in the context of graph generative model\ncomparison, highlighting some of the challenges and pitfalls researchers\ninadvertently may encounter. After conducting a thorough analysis of the\nbehaviour of MMD on synthetically-generated perturbed graphs as well as on\nrecently-proposed graph generative models, we are able to provide a suitable\nprocedure to mitigate these challenges and pitfalls. We aggregate our findings\ninto a list of practical recommendations for researchers to use when evaluating\ngraph generative models.\n"}
+{"abstract": "  The problem of achieving ultra-reliable and low-latency communication (URLLC)\nin multi-terminal networks has gained traction in the recent past owing to new\nwireless applications in vehicular networks. In the context of multi-hop\nnetworks, which is a classic example for multi-party communication, recent\nstudies have shown that automatic-repeat-request (ARQ) based decode-and-forward\n(DF) strategies are suitable for URLLC since the idea of distributing a given\nnumber of ARQs across the nodes provides fine control on the features of\nreliability and latency. Inspired by these developments, in this work, we\npropose a cooperative ARQ sharing strategy for URLLC in multi-hop networks. At\nthe heart of the proposed scheme lies the idea that every node is given the\nknowledge of the number of ARQs allotted to its preceding node in addition to\nthe ARQs allotted to itself. As a result, each node only needs to count the\nnumber of unsuccessful attempts of its preceding node, and then borrow the\nunused ARQs, thereby improving the reliability feature with no compromise in\nthe latency constraint. Using packet-drop-probability (PDP) as the reliability\nmetric for the proposed cooperative strategy, we formulate an optimization\nproblem of minimizing the PDP subject to a sum constraint on the total number\nof ARQs allotted across all the nodes. Supported by theoretical analysis on the\nbehavior of PDP, we present low-complexity algorithms to compute near-optimal\nARQ distributions for our strategy, and show that our strategy outperforms the\nexisting non-cooperative strategies.\n"}
+{"abstract": "  Although ultra-luminous X-ray sources (ULX) are important for astrophysics\ndue to their extreme apparent super-Eddington luminosities, their nature is\nstill poorly known. Theoretical and observational studies suggest that ULXs\ncould be a diversified group of objects composed of low-mass X-ray binaries,\nhigh-mass X-ray binaries and marginally also systems containing\nintermediate-mass black holes, which is supported by their presence in a\nvariety of environments. Observational data on the ULX donors could\nsignificantly boost our understanding of these systems, but only a few were\ndetected. There are several candidates, mostly red supergiants (RSGs), but\nsurveys are typically biased toward luminous near-infrared objects.\nNevertheless, it is worth exploring if RSGs can be members of ULX binaries. In\nsuch systems matter accreted onto the compact body would have to be provided by\nthe stellar wind of the companion, since a Roche-lobe overflow could be\nunstable for relevant mass-ratios. Here we present a comprehensive study of the\nevolution and population of wind-fed ULXs and provide a theoretical support for\nthe link between RSGs and ULXs. Our estimated upper limit on contribution of\nwind-fed ULX to the overall ULX population is $\\sim75$--$96\\%$ for young\n($<100$ Myr) star forming environments, $\\sim 49$--$87\\%$ for prolonged\nconstant star formation (e.g., disk of Milky Way), and $\\lesssim1\\%$ for\nenvironments in which star formation ceased long time ($>2$ Gyr) ago. We show\nalso that some wind-fed ULXs (up to $6\\%$) may evolve into merging double\ncompact objects (DCOs), but typical systems are not viable progenitors of such\nbinaries because of their large separations. We demonstrate that, the exclusion\nof wind-fed ULXs from population studies of ULXs, might have lead to\nsystematical errors in their conclusions.\n"}
+{"abstract": "  These lecture notes provide an informal introduction to the theory of\nnonnegative polynomials and sums of squares. We highlight the history and some\nrecent developments, especially the new connections with classical (complex)\nalgebraic geometry and commutative algebra. We briefly discuss the connection\nto semidefinite programming and applications. We expect the reader to be\nfamiliar with algebra and fundamentals of algebraic geometry, on the level of a\ngraduate course, and know basic facts about positive semidefinite matrices.\n"}
+{"abstract": "  A macroscopic effect can be induced by a local non-Hermitian term, when it\nmanifests simultaneously level coalescence of a full real degeneracy spectrum,\nleading to exceptional spectrum. In this paper, we propose a family of systems\nthat support such an intriguing property. It is generally consisted of two\narbitrary identical Hermitian sub-lattices in association with unidirectional\ncouplings between them. We show exactly that all single-particle eigenstates\ncoalesce in pairs, even only single unidirectional coupling appears. As an\napplication, we study the dynamic magnetization induced by complex fields in an\nitinerant electron system. It shows that an initial saturated ferromagnetic\nstate at half-filling can be driven into its opposite state according to the\ndynamics of high-order exceptional point. Numerical simulations for the\ndynamical processes of magnetization are performed for several representative\nsituations, including lattice dimensions, global random and local impurity\ndistributions. It shows that the dynamic magnetization processes exhibit\nuniversal behavior.\n"}
+{"abstract": "  The calculus of Dependent Object Types (DOT) has enabled a more principled\nand robust implementation of Scala, but its support for type-level computation\nhas proven insufficient. As a remedy, we propose $F^\\omega_{..}$, a rigorous\ntheoretical foundation for Scala's higher-kinded types. $F^\\omega_{..}$ extends\n$F^\\omega_{<:}$ with interval kinds, which afford a unified treatment of\nimportant type- and kind-level abstraction mechanisms found in Scala, such as\nbounded quantification, bounded operator abstractions, translucent type\ndefinitions and first-class subtyping constraints. The result is a flexible and\ngeneral theory of higher-order subtyping. We prove type and kind safety of\n$F^\\omega_{..}$, as well as weak normalization of types and undecidability of\nsubtyping. All our proofs are mechanized in Agda using a fully syntactic\napproach based on hereditary substitution.\n"}
+{"abstract": "  We report the detection of an atmosphere on a rocky exoplanet, GJ 1132 b,\nwhich is similar to Earth in terms of size and density. The atmospheric\ntransmission spectrum was detected using Hubble WFC3 measurements and shows\nspectral signatures of aerosol scattering, HCN, and CH$_{4}$ in a low mean\nmolecular weight atmosphere. We model the atmospheric loss process and conclude\nthat GJ 1132 b likely lost the original H/He envelope, suggesting that the\natmosphere that we detect has been reestablished. We explore the possibility of\nH$_{2}$ mantle degassing, previously identified as a possibility for this\nplanet by theoretical studies, and find that outgassing from ultrareduced magma\ncould produce the observed atmosphere. In this way we use the observed\nexoplanet transmission spectrum to gain insights into magma composition for a\nterrestrial planet. The detection of an atmosphere on this rocky planet raises\nthe possibility that the numerous powerfully irradiated Super-Earth planets,\nbelieved to be the evaporated cores of Sub-Neptunes, may, under favorable\ncircumstances, host detectable atmospheres.\n"}
+{"abstract": "  The article reviews the physics of Anderson localization on random regular\ngraphs (RRG) and its connections to many-body localization (MBL) in disordered\ninteracting systems. Properties of eigenstate and energy level correlations in\ndelocalized and localized phases, as well at criticality, are discussed. In the\nmany-body part, models with short-range and power-law interactions are\nconsidered, as well as the quantum-dot model representing the limit of the\n\"most long-range\" interaction. Central themes -- which are common to the RRG\nand MBL problems -- include ergodicity of the delocalized phase, localized\ncharacter of the critical point, strong finite-size effects, and fractal\nscaling of eigenstate correlations in the localized phase.\n"}
+{"abstract": "  In this letter, we consider an intelligent reflecting surface (IRS)-aided\nwireless relaying system, where a decode-and-forward relay (R) is employed to\nforward data from a source (S) to a destination (D), aided by M passive\nreflecting elements. We consider two practical IRS deployment strategies,\nnamely, single-IRS deployment where all reflecting elements are mounted on one\nsingle IRS that is deployed near S, R, or D, and multi-IRS deployment where the\nreflecting elements are allocated over three separate IRSs which are deployed\nnear S, R, and D, respectively. Under the line-of-sight (LoS) channel model, we\ncharacterize the capacity scaling orders with respect to an increasing M for\nthe IRS-aided relay system with different IRS deployment strategies. For\nsingle-IRS deployment, we show that deploying the IRS near R achieves the\nhighest capacity as compared to that near S or D. While for multi-IRS\ndeployment, we propose a practical cooperative IRS passive beamforming design\nwhich is analytically shown to achieve a larger capacity scaling order than the\nsingle-IRS deployment (i.e., near R or S/D) when M is sufficiently large.\nNumerical examples are provided, which validate our theoretical results.\n"}
+{"abstract": "  Continuous engineering of autonomous driving functions commonly requires\ndeploying vehicles in road testing to obtain inputs that cause problematic\ndecisions. Although the discovery leads to producing an improved system, it\nalso challenges the foundation of testing using equivalence classes and the\nassociated relative test coverage criterion. In this paper, we propose believed\nequivalence, where the establishment of an equivalence class is initially based\non expert belief and is subject to a set of available test cases having a\nconsistent valuation. Upon a newly encountered test case that breaks the\nconsistency, one may need to refine the established categorization in order to\nsplit the originally believed equivalence into two. Finally, we focus on\nmodules implemented using deep neural networks where every category partitions\nan input over the real domain. We present both analytical and lazy methods to\nsuggest the refinement. The concept is demonstrated in analyzing multiple\nautonomous driving modules, indicating the potential of our proposed approach.\n"}
+{"abstract": "  Non-alternating Hamiltonian Lie algebras in three variables over a perfect\nfield of characteristic 2 are considered. A classification of non-alternating\nHamiltonian forms over an algebra of divided powers in three variables and of\nthe corresponding simple Lie algebras is given. In particular, it is shown that\nthe non-alternating Hamiltonian form may not be equivalent to its linear part.\nIt is proved that the Lie algebras which correspond to the nonequivalent forms\nare not isomorphic.\n"}
+{"abstract": "  We propose a novel scenario of Dark Matter production naturally connected\nwith generation of gravitational waves. Dark Matter is modelled as a real\nscalar, which interacts with the hot primordial plasma through a portal\ncoupling to another scalar field. For a particular sign of the coupling, this\nsystem exhibits an inverse second order phase transition. The latter leads to\nan abundant Dark Matter production, even if the portal interaction is so weak\nthat the freeze-in mechanism is inefficient. The model predicts domain wall\nformation in the Universe, long time before the inverse phase transition. These\ndomain walls have a tension decreasing with time, and completely disappear at\nthe inverse phase transition, so that the problem of overclosing the Universe\nis avoided. The domain wall network emits gravitational waves with\ncharacteristics defined by those of Dark Matter. In particular, the peak\nfrequency of gravitational waves is determined by the portal coupling constant,\nand falls in the observable range for currently planned gravitational wave\ndetectors.\n"}
+{"abstract": "  Determining potential abilities of nanostructures to induce toxicity to\nbiological molecules is still a convoluted challenge in the realm of\nnanomedicine. Based on the unprecedented achievements of twodimensional\nnanomaterials in nearly all areas of applied sciences particularly medicine, we\ncarried out all-atom molecular dynamics simulations to assess the\nbiologically-important, yet-unmapped issue of the biocompatibility of 2D,\nhexagonal \\b{eta}-Si3N4 nanosheet via investigating its possible cross\ninteractions with both human serum albumin (HSA) and p53 tumor suppressor.\nExamining the conventional MD indicators in the presence and absence of the\nmonolayer revealed that hexagonal Si3N4 nanosheet weakly binds to these two\nproteins without inducing any important, dramatic change to their secondary\nstructures, revealing accordingly the biological compatibility of the monolayer\nin case it is released as therapeutics or carriers in vivo. This finding was\nalso broadly supported by the related, time-dependent behaviors of the\nprotein-monolayer as well as the protein-water interaction energies.\n"}
+{"abstract": "  Impressive progress has been made in audio-driven 3D facial animation\nrecently, but synthesizing 3D talking-head with rich emotion is still unsolved.\nThis is due to the lack of 3D generative models and available 3D emotional\ndataset with synchronized audios. To address this, we introduce 3D-TalkEmo, a\ndeep neural network that generates 3D talking head animation with various\nemotions. We also create a large 3D dataset with synchronized audios and\nvideos, rich corpus, as well as various emotion states of different persons\nwith the sophisticated 3D face reconstruction methods. In the emotion\ngeneration network, we propose a novel 3D face representation structure -\ngeometry map by classical multi-dimensional scaling analysis. It maps the\ncoordinates of vertices on a 3D face to a canonical image plane, while\npreserving the vertex-to-vertex geodesic distance metric in a least-square\nsense. This maintains the adjacency relationship of each vertex and holds the\neffective convolutional structure for the 3D facial surface. Taking a neutral\n3D mesh and a speech signal as inputs, the 3D-TalkEmo is able to generate vivid\nfacial animations. Moreover, it provides access to change the emotion state of\nthe animated speaker.\n  We present extensive quantitative and qualitative evaluation of our method,\nin addition to user studies, demonstrating the generated talking-heads of\nsignificantly higher quality compared to previous state-of-the-art methods.\n"}
+{"abstract": "  Satoh has defined a map from virtual knots to ribbon surfaces embedded in\n$S^4$. Herein, we generalize this map to virtual $m$-links, and use this to\nconstruct generalizations of welded and extended welded knots to higher\ndimensions. This also allows us to construct two new geometric pictures of\nvirtual $m$-links, including 1-links.\n"}
+{"abstract": "  Clustering is a fundamental task in the computer vision and machine learning\ncommunity. Although various methods have been proposed, the performance of\nexisting approaches drops dramatically when handling incomplete\nhigh-dimensional data (which is common in real world applications). To solve\nthe problem, we propose a novel deep incomplete clustering method, named Deep\nDistribution-preserving Incomplete Clustering with Optimal Transport (DDIC-OT).\nTo avoid insufficient sample utilization in existing methods limited by few\nfully-observed samples, we propose to measure distribution distance with the\noptimal transport for reconstruction evaluation instead of traditional\npixel-wise loss function. Moreover, the clustering loss of the latent feature\nis introduced to regularize the embedding with more discrimination capability.\nAs a consequence, the network becomes more robust against missing features and\nthe unified framework which combines clustering and sample imputation enables\nthe two procedures to negotiate to better serve for each other. Extensive\nexperiments demonstrate that the proposed network achieves superior and stable\nclustering performance improvement against existing state-of-the-art incomplete\nclustering methods over different missing ratios.\n"}
+{"abstract": "  For action recognition learning, 2D CNN-based methods are efficient but may\nyield redundant features due to applying the same 2D convolution kernel to each\nframe. Recent efforts attempt to capture motion information by establishing\ninter-frame connections while still suffering the limited temporal receptive\nfield or high latency. Moreover, the feature enhancement is often only\nperformed by channel or space dimension in action recognition. To address these\nissues, we first devise a Channel-wise Motion Enhancement (CME) module to\nadaptively emphasize the channels related to dynamic information with a\nchannel-wise gate vector. The channel gates generated by CME incorporate the\ninformation from all the other frames in the video. We further propose a\nSpatial-wise Motion Enhancement (SME) module to focus on the regions with the\ncritical target in motion, according to the point-to-point similarity between\nadjacent feature maps. The intuition is that the change of background is\ntypically slower than the motion area. Both CME and SME have clear physical\nmeaning in capturing action clues. By integrating the two modules into the\noff-the-shelf 2D network, we finally obtain a Comprehensive Motion\nRepresentation (CMR) learning method for action recognition, which achieves\ncompetitive performance on Something-Something V1 & V2 and Kinetics-400. On the\ntemporal reasoning datasets Something-Something V1 and V2, our method\noutperforms the current state-of-the-art by 2.3% and 1.9% when using 16 frames\nas input, respectively.\n"}
+{"abstract": "  For the most general Polya urn schemes, we establish the almost sure\nconvergence of its composition. The only requirement is that there are always\nenough balls of both colors, so that the extractions can be indefinitely\npursued according to the specifications of the model. We also consider the\nmethod for determining the probability of fulfilling this requirement, as a\nfunction of the initial number of balls of each color.\n"}
+{"abstract": "  We give a computational approach to theorem proving in homological algebra.\nThis approach is based on computations in the free abelian category of an\nadditive category $\\mathbf{A}$. We show that the free abelian category is\namenable to explicit computations whenever we can decide homotopy equations in\n$\\mathbf{A}$. As some consequences of our investigations, we recover Dowker's\nexplicit formula for the connecting homomorphism $\\partial$ in the snake lemma,\nwe find a universal sense in which $\\partial$ is unique, and we give a refined\nversion of the 5-lemma.\n"}
+{"abstract": "  Some observations such as those presented in Walker et al. show that the\nobserved entropy profiles of the intra-cluster medium (ICM) deviate from the\npower-law prediction of adiabatic simulations. This implies that\nnon-gravitational processes, which are absent in the simulations, may be\nimportant in the evolution of the ICM, and by quantifying the deviation, we may\nbe able to estimate the feedback energy in the ICM and use it as a probe of the\nnon-gravitational processes. To address this issue we calculate the ICM entropy\nprofiles in a sample of 47 galaxy clusters and groups, which have been observed\nout to at least $\\sim r_{500}$ with Chandra, XMM-Newton and/or Suzaku, by\nconstructing a physical model to incorporate the effects of both gravity and\nnon-gravitational processes to fit the observed gas temperature and surface\nbrightness profiles via Bayesian statistics. After carefully evaluating the\neffects of systematic errors, we find that the gas entropy profiles derived\nwith best-fit results of our model are consistent with the simulation-predicted\npower-law profile near the virial radius, while the flattened profiles reported\npreviously can be explained by introducing the gas clumping effect, the\nexistence of which is confirmed in 19 luminous targets in our sample. We\ncalculate the total feedback energy per particle and find that it decreases\nfrom $\\sim 10$ keV at the center to about zero at $\\sim 0.35$$r_{200}$ and is\nconsistent with zero outside $\\sim 0.35$$r_{200}$, implying the upper limit of\nthe feedback efficiency $\\sim 0.02$ for the super-massive black holes hosted in\nthe brightest cluster galaxies.\n"}
+{"abstract": "  Traditional learning approaches for classification implicitly assume that\neach mistake has the same cost. In many real-world problems though, the utility\nof a decision depends on the underlying context $x$ and decision $y$. However,\ndirectly incorporating these utilities into the learning objective is often\ninfeasible since these can be quite complex and difficult for humans to\nspecify.\n  We formally study this as agnostic learning with unknown utilities: given a\ndataset $S = \\{x_1, \\ldots, x_n\\}$ where each data point $x_i \\sim\n\\mathcal{D}$, the objective of the learner is to output a function $f$ in some\nclass of decision functions $\\mathcal{F}$ with small excess risk. This risk\nmeasures the performance of the output predictor $f$ with respect to the best\npredictor in the class $\\mathcal{F}$ on the unknown underlying utility $u^*$.\nThis utility $u^*$ is not assumed to have any specific structure. This raises\nan interesting question whether learning is even possible in our setup, given\nthat obtaining a generalizable estimate of utility $u^*$ might not be possible\nfrom finitely many samples. Surprisingly, we show that estimating the utilities\nof only the sampled points~$S$ suffices to learn a decision function which\ngeneralizes well.\n  We study mechanisms for eliciting information which allow a learner to\nestimate the utilities $u^*$ on the set $S$. We introduce a family of\nelicitation mechanisms by generalizing comparisons, called the $k$-comparison\noracle, which enables the learner to ask for comparisons across $k$ different\ninputs $x$ at once. We show that the excess risk in our agnostic learning\nframework decreases at a rate of $O\\left(\\frac{1}{k} \\right)$. This result\nbrings out an interesting accuracy-elicitation trade-off -- as the order $k$ of\nthe oracle increases, the comparative queries become harder to elicit from\nhumans but allow for more accurate learning.\n"}
+{"abstract": "  In this paper, we propose a fresh perspective on argumentation semantics, to\nview them as a relational database. It offers encapsulation of the underlying\nargumentation graph, and allows us to understand argumentation semantics under\na single, relational perspective, leading to the concept of relational\nargumentation semantics. This is a direction to understand argumentation\nsemantics through a common formal language. We show that many existing\nsemantics such as explanation semantics, multi-agent semantics, and more\ntypical semantics, that have been proposed for specific purposes, are\nunderstood in the relational perspective.\n"}
+{"abstract": "  Reputation is a powerful mechanism to enforce cooperation among unrelated\nindividuals through indirect reciprocity, but it suffers from disagreement\noriginating from private assessment, noise, and incomplete information. In this\nwork, we investigate stability of cooperation in the donation game by regarding\neach player's reputation and behaviour as continuous variables. Through\nperturbative calculation, we derive a condition that a social norm should\nsatisfy to give penalties to its close variants, provided that everyone\ninitially cooperates with a good reputation, and this result is supported by\nnumerical simulation. A crucial factor of the condition is whether a\nwell-reputed player's donation to an ill-reputed co-player is appreciated by\nother members of the society, and the condition can be reduced to a threshold\nfor the benefit-cost ratio of cooperation which depends on the reputational\nsensitivity to a donor's behaviour as well as on the behavioural sensitivity to\na recipient's reputation. Our continuum formulation suggests how indirect\nreciprocity can work beyond the dichotomy between good and bad even in the\npresence of inhomogeneity, noise, and incomplete information.\n"}
+{"abstract": "  The recently released spacecraft potential measured by the RPW instrument\non-board Solar Orbiter has been used to estimate the solar wind electron\ndensity in the inner heliosphere. Solar-wind electron density measured during\nJune 2020 has been analysed to obtain a thorough characterization of the\nturbulence and intermittency properties of the fluctuations. Magnetic field\ndata have been used to describe the presence of ion-scale waves. Selected\nintervals have been extracted to study and quantify the properties of\nturbulence. The Empirical Mode Decomposition has been used to obtain the\ngeneralized marginal Hilbert spectrum, equivalent to the structure functions\nanalysis, and additionally reducing issues typical of non-stationary, short\ntime series. The presence of waves was quantitatively determined introducing a\nparameter describing the time-dependent, frequency-filtered wave power. A well\ndefined inertial range with power-law scaling has been found almost everywhere.\nHowever, the Kolmogorov scaling and the typical intermittency effects are only\npresent in part of the samples. Other intervals have shallower spectra and more\nirregular intermittency, not described by models of turbulence. These are\nobserved predominantly during intervals of enhanced ion frequency wave\nactivity. Comparisons with compressible magnetic field intermittency (from the\nMAG instrument) and with an estimate of the solar wind velocity (using electric\nand magnetic field) are also provided to give general context and help\ndetermine the cause for the anomalous fluctuations.\n"}
+{"abstract": "  Next-generation neutrinoless double beta decay experiments aim for half-life\nsensitivities of ~$10^{27}$ yr, requiring suppressing backgrounds to <1\ncount/tonne/yr. For this, any extra background rejection handle, beyond\nexcellent energy resolution and the use of extremely radiopure materials, is of\nutmost importance. The NEXT experiment exploits differences in the spatial\nionization patterns of double beta decay and single-electron events to\ndiscriminate signal from background. While the former display two Bragg peak\ndense ionization regions at the opposite ends of the track, the latter\ntypically have only one such feature. Thus, comparing the energies at the track\nextremes provides an additional rejection tool. The unique combination of the\ntopology-based background discrimination and excellent energy resolution (1%\nFWHM at the Q-value of the decay) is the distinguishing feature of NEXT.\nPrevious studies demonstrated a topological background rejection factor of ~5\nwhen reconstructing electron-positron pairs in the $^{208}$Tl 1.6 MeV double\nescape peak (with Compton events as background), recorded in the NEXT-White\ndemonstrator at the Laboratorio Subterr\\'aneo de Canfranc, with 72% signal\nefficiency. This was recently improved through the use of a deep convolutional\nneural network to yield a background rejection factor of ~10 with 65% signal\nefficiency. Here, we present a new reconstruction method, based on the\nRichardson-Lucy deconvolution algorithm, which allows reversing the blurring\ninduced by electron diffusion and electroluminescence light production in the\nNEXT TPC. The new method yields highly refined 3D images of reconstructed\nevents, and, as a result, significantly improves the topological background\ndiscrimination. When applied to real-data 1.6 MeV $e^-e^+$ pairs, it leads to a\nbackground rejection factor of 27 at 57% signal efficiency.\n"}
+{"abstract": "  Histopathology image embedding is an active research area in computer vision.\nMost of the embedding models exclusively concentrate on a specific\nmagnification level. However, a useful task in histopathology embedding is to\ntrain an embedding space regardless of the magnification level. Two main\napproaches for tackling this goal are domain adaptation and domain\ngeneralization, where the target magnification levels may or may not be\nintroduced to the model in training, respectively. Although magnification\nadaptation is a well-studied topic in the literature, this paper, to the best\nof our knowledge, is the first work on magnification generalization for\nhistopathology image embedding. We use an episodic trainable domain\ngeneralization technique for magnification generalization, namely Model\nAgnostic Learning of Semantic Features (MASF), which works based on the Model\nAgnostic Meta-Learning (MAML) concept. Our experimental results on a breast\ncancer histopathology dataset with four different magnification levels show the\nproposed method's effectiveness for magnification generalization.\n"}
+{"abstract": "  Training coreference resolution models require comprehensively labeled data.\nA model trained on one dataset may not successfully transfer to new domains.\nThis paper investigates an approach to active learning for coreference\nresolution that feeds discrete annotations to an incremental clustering model.\nThe recent developments in incremental coreference resolution allow for a novel\napproach to active learning in this setting. Through this new framework, we\nanalyze important factors in data acquisition, like sources of model\nuncertainty and balancing reading and labeling costs. We explore different\nsettings through simulated labeling with gold data. By lowering the data\nbarrier for coreference, coreference resolvers can rapidly adapt to a series of\npreviously unconsidered domains.\n"}
+{"abstract": "  We model the Neupert effect that relates flare heating energy with the\nobserved SXR emission. The traditional form of the Neupert effect refers to the\ncorrelation between the time-integrated HXR or microwave light curve and the\nSXR light curve. In this paper, instead, we use as the proxy for heating energy\nthe ultraviolet (UV) emission at the foot-points of flare loops, and modify the\nmodel of the Neupert effect by taking into account the discrete nature of flare\nheating as well as cooling. In the modified empirical model, spatially resolved\nUV lightcurves from the transition region or upper chromosphere are each\nconvolved with a kernel function characterizing the decay of the flare loop\nemission. Contributions by all loops are summed to compare with the observed\ntotal SXR emission. The model has successfully reproduced the observed SXR\nemission from its rise to decay. To estimate heating energies in flare loops,\nwe also employ the UV Foot-point Calorimeter (UFC) method that infers heating\nrates in flare loops from these UV light curves and models evolution of flare\nloops with a zero-dimensional hydrodynamic code. The experiments show that a\nmultitude of impulsive heating events do not well reproduce the observed flare\nSXR light curve, but a two-phase heating model leads to better agreement with\nobservations. Comparison of the two models of the Neupert effect further allows\nus to calibrate the UFC method, and improve the estimate of heating rates in\nflare loops continuously formed by magnetic reconnection throughout the flare\nevolution.\n"}
+{"abstract": "  Motivated by cutting-edge applications like cryo-electron microscopy\n(cryo-EM), the Multi-Reference Alignment (MRA) model entails the learning of an\nunknown signal from repeated measurements of its images under the latent action\nof a group of isometries and additive noise of magnitude $\\sigma$. Despite\nsignificant interest, a clear picture for understanding rates of estimation in\nthis model has emerged only recently, particularly in the high-noise regime\n$\\sigma \\gg 1$ that is highly relevant in applications. Recent investigations\nhave revealed a remarkable asymptotic sample complexity of order $\\sigma^6$ for\ncertain signals whose Fourier transforms have full support, in stark contrast\nto the traditional $\\sigma^2$ that arise in regular models. Often prohibitively\nlarge in practice, these results have prompted the investigation of variations\naround the MRA model where better sample complexity may be achieved. In this\npaper, we show that \\emph{sparse} signals exhibit an intermediate $\\sigma^4$\nsample complexity even in the classical MRA model. Our results explore and\nexploit connections of the MRA estimation problem with two classical topics in\napplied mathematics: the \\textit{beltway problem} from combinatorial\noptimization, and \\textit{uniform uncertainty principles} from harmonic\nanalysis.\n"}
+{"abstract": "  Generalizing a result of \\cite{Z1991, CPZ} about elliptic modular forms, we\ngive a closed formula for the sum of all Hilbert Hecke eigenforms over a\ntotally real number field with strict class number $1$, multiplied by their\nperiod polynomials, as a single product of the Kronecker series.\n"}
+{"abstract": "  Inference on the extremal behaviour of spatial aggregates of precipitation is\nimportant for quantifying river flood risk. There are two classes of previous\napproach, with one failing to ensure self-consistency in inference across\ndifferent regions of aggregation and the other imposing highly restrictive\nassumptions. To overcome these issues, we propose a model for high-resolution\nprecipitation data, from which we can simulate realistic fields and explore the\nbehaviour of spatial aggregates. Recent developments have seen spatial\nextensions of the Heffernan and Tawn (2004) model for conditional multivariate\nextremes, which can handle a wide range of dependence structures. Our\ncontribution is twofold: extensions and improvements of this approach and its\nmodel inference for high-dimensional data; and a novel framework for deriving\naggregates addressing edge effects and sub-regions without rain. We apply our\nmodelling approach to gridded East-Anglia, UK precipitation data. Return-level\ncurves for spatial aggregates over different regions of various sizes are\nestimated and shown to fit very well to the data.\n"}
+{"abstract": "  Hateful memes pose a unique challenge for current machine learning systems\nbecause their message is derived from both text- and visual-modalities. To this\neffect, Facebook released the Hateful Memes Challenge, a dataset of memes with\npre-extracted text captions, but it is unclear whether these synthetic examples\ngeneralize to `memes in the wild'. In this paper, we collect hateful and\nnon-hateful memes from Pinterest to evaluate out-of-sample performance on\nmodels pre-trained on the Facebook dataset. We find that memes in the wild\ndiffer in two key aspects: 1) Captions must be extracted via OCR, injecting\nnoise and diminishing performance of multimodal models, and 2) Memes are more\ndiverse than `traditional memes', including screenshots of conversations or\ntext on a plain background. This paper thus serves as a reality check for the\ncurrent benchmark of hateful meme detection and its applicability for detecting\nreal world hate.\n"}
+{"abstract": "  Activists, journalists, and scholars have long raised critical questions\nabout the relationship between diversity, representation, and structural\nexclusions in data-intensive tools and services. We build on work mapping the\nemergent landscape of corporate AI ethics to center one outcome of these\nconversations: the incorporation of diversity and inclusion in corporate AI\nethics activities. Using interpretive document analysis and analytic tools from\nthe values in design field, we examine how diversity and inclusion work is\narticulated in public-facing AI ethics documentation produced by three\ncompanies that create application and services layer AI infrastructure: Google,\nMicrosoft, and Salesforce.\n  We find that as these documents make diversity and inclusion more tractable\nto engineers and technical clients, they reveal a drift away from civil rights\njustifications that resonates with the managerialization of diversity by\ncorporations in the mid-1980s. The focus on technical artifacts, such as\ndiverse and inclusive datasets, and the replacement of equity with fairness\nmake ethical work more actionable for everyday practitioners. Yet, they appear\ndivorced from broader DEI initiatives and other subject matter experts that\ncould provide needed context to nuanced decisions around how to operationalize\nthese values. Finally, diversity and inclusion, as configured by engineering\nlogic, positions firms not as ethics owners but as ethics allocators; while\nthese companies claim expertise on AI ethics, the responsibility of defining\nwho diversity and inclusion are meant to protect and where it is relevant is\npushed downstream to their customers.\n"}
+{"abstract": "  On page 10 of the 2018 National Academies Exoplanet Science Strategy document\n(NASEM 2018), 'Expect the unexpected' is described as a general principle of\nthe exoplanet field. But for the next 150 pages, this principle is apparently\nforgotten, as strategy decisions are repeatedly put forward based on our\nexpectations. This paper explores what exactly it might mean to 'expect the\nunexpected', and how this could possibly be achieved by the space science\ncommunity. An analogy with financial investment strategies is considered, where\na balanced portfolio of low/medium/high-risk investments is recommended. Whilst\nthis kind of strategy would certainly be advisable in many scientific contexts\n(past and present), in certain contexts, especially exploratory science, a\nsignificant disanalogy needs to be factored in: financial investors cannot\nchoose low-risk high-reward investments, but sometimes scientists can. The\nexistence of low-risk high-impact projects in cutting-edge space science\nsignificantly reduces the warrant for investing in high-risk projects, at least\nin the short term. However, high-risk proposals need to be fairly judged\nalongside medium- and low-risk proposals, factoring in both the degree of\npossible reward and the expected cost of the project. Attitudes towards\nhigh-risk high-impact projects within NASA since 2009 are critically analysed.\n"}
+{"abstract": "  Recently, the so-called relative transverse activity classifier, $R_{\\rm T}$,\nhas been proposed as a tool to disentangle the particle production originated\nfrom the soft and hard QCD processes in proton-proton (pp) collisions. $R_{\\rm\nT}$ is a useful quantity to study particle production in events with\nexceptionally large or small activity in the transverse region with respect to\nthe event-averaged mean. Contrary to the expectations, the preliminary results\nof the ALICE Collaboration indicate that, e.g., the proton-to-pion ratio does\nnot exhibit the characteristic enhancement at intermediate $p_{\\rm T}$ in\nevents with large $R_{\\rm T}$ with respect to minimum-bias pp collisions. In\nthis work, we investigate the origin of this effect using the PYTHIA 8 and\nHERWIG 7 Monte Carlo event generators. The effect is a consequence of a\nselection bias attributed to wide-angle gluon emissions which creates jets that\npopulate the transverse region. Therefore, we propose a modified version of\n$R_{\\rm T}$ in order to suppress its sensitivity to hard gluon Bremsstrahlung,\nand enhance the sensitivity to soft Multiparton Interactions (MPI). This\napproach could be useful in order to study the particle production in the\njet-like region as a function of MPI. The implementation of these ideas in data\nwill provide more insight into the production mechanisms of hadrons in\nhigh-multiplicity pp collisions, and its connection with heavy-ion phenomena.\n"}
+{"abstract": "  We show that axions interacting with abelian gauge fields obtain a potential\nfrom loops of magnetic monopoles. This is a consequence of the Witten effect:\nthe axion field causes the monopoles to acquire an electric charge and alters\ntheir energy spectrum. The axion potential can also be understood as a type of\ninstanton effect due to a Euclidean monopole worldline winding around its dyon\ncollective coordinate. We calculate this effect, which has features in common\nwith both nonabelian instantons and Euclidean brane instantons. To provide\nconsistency checks, we argue that this axion potential vanishes in the presence\nof a massless charged fermion and that it is robust against the presence of\nhigher-derivative corrections in the effective Lagrangian. Finally, as a first\nstep toward connecting with particle phenomenology and cosmology, we discuss\nthe regime in which this potential is important in determining the dark matter\nrelic abundance in a hidden sector containing an abelian gauge group,\nmonopoles, and axions.\n"}
+{"abstract": "  Permalloy films with one-dimensional (1D) profile modulation of submicron\nperiodicity are fabricated based on commercially available DVD-R discs and\nstudied using ferromagnetic resonance (FMR) method and micromagnetic numerical\nsimulations. The main resonance position shows in-plane angular dependence\nwhich is strongly reminiscent of that in ferromagnetic films with uniaxial\nmagnetic anisotropy. The main signal and additional low field lines are\nattributed to multiple standing spin wave resonances defined by the grating\nperiod. The results may present interest in magnetic metamaterials and\nmagnonics applications.\n"}
+{"abstract": "  The ability to communicate intention enables decentralized multi-agent robots\nto collaborate while performing physical tasks. In this work, we present\nspatial intention maps, a new intention representation for multi-agent\nvision-based deep reinforcement learning that improves coordination between\ndecentralized mobile manipulators. In this representation, each agent's\nintention is provided to other agents, and rendered into an overhead 2D map\naligned with visual observations. This synergizes with the recently proposed\nspatial action maps framework, in which state and action representations are\nspatially aligned, providing inductive biases that encourage emergent\ncooperative behaviors requiring spatial coordination, such as passing objects\nto each other or avoiding collisions. Experiments across a variety of\nmulti-agent environments, including heterogeneous robot teams with different\nabilities (lifting, pushing, or throwing), show that incorporating spatial\nintention maps improves performance for different mobile manipulation tasks\nwhile significantly enhancing cooperative behaviors.\n"}
+{"abstract": "  Intentional breaking of the lattice symmetry in solids is a key concept to\nalter the properties of materials by modifying their electronic band structure.\nHowever, the correlation of strain-induced effects and breaking of the lattice\nsymmetry is often indirect, resorting to vibrational spectroscopic techniques\nsuch as Raman scattering. Here, we demonstrate that reflectance anisotropy\nspectroscopy (RAS), which directly depends on the complex dielectric function,\nenables the direct observation of electronic band structure modulation.\nStudying the strain-induced symmetry breaking in copper, we show how uniaxial\nstrain lifts the degeneracy of states in the proximity of the both L and X\nsymmetry points, thus altering the matrix element for interband optical\ntransitions, directly observable in RAS. We corroborate our experimental\nresults by analysing the strain-induced changes in the electronic structure\nbased on ab-initio density functional theory calculations. The versatility to\nstudy breaking of the lattice symmetry by simple reflectance measurements opens\nup the possibility to gain a direct insight on the band-structure of other\nstrain-engineered materials, such as graphene and two-dimensional (2D)\ntransition metal dichalcogenides (TMDCs).\n"}
+{"abstract": "  This paper studies numerical methods for the approximation of elliptic PDEs\nwith lognormal coefficients of the form $-{\\rm div}(a\\nabla u)=f$ where\n$a=\\exp(b)$ and $b$ is a Gaussian random field. The approximant of the solution\n$u$ is an $n$-term polynomial expansion in the scalar Gaussian random variables\nthat parametrize $b$. We present a general convergence analysis of weighted\nleast-squares approximants for smooth and arbitrarily rough random field, using\na suitable random design, for which we prove optimality in the following sense:\ntheir convergence rate matches exactly or closely the rate that has been\nestablished in \\cite{BCDM} for best $n$-term approximation by Hermite\npolynomials, under the same minimial assumptions on the Gaussian random field.\nThis is in contrast with the current state of the art results for the\nstochastic Galerkin method that suffers the lack of coercivity due to the\nlognormal nature of the diffusion field. Numerical tests with $b$ as the\nBrownian bridge confirm our theoretical findings.\n"}
+{"abstract": "  It's long been accepted that continuous integration (CI) in software\nengineering increases the code quality of enterprise projects when adhered to\nby it's practitioners. But is any of that effort to increase code quality and\nvelocity directed towards improving software accessibility accommodations? What\nare the potential benefits quoted in literature? Does it fit with the modern\nagile way that teams operate in most enterprises? This paper attempts to map\nthe current scene of the software engineering effort spent on improving\naccessibility via continuous integration and it's hurdles to adoption as quoted\nby researchers. We also try to explore steps that agile teams may take to train\nmembers on how to implement accessibility testing and introduce key diagrams to\nvisualize processes to implement CI based accessibility testing procedures in\nthe software development lifecycle.\n"}
+{"abstract": "  Estimating position and orientation change of a mobile platform from two\nconsecutive point clouds provided by a high-resolution sensor is a key problem\nin autonomous navigation. In particular, scan matching algorithms aim to find\nthe translation and rotation of the platform such that the two point clouds\ncoincide. The association of measurements in point cloud one with measurements\nin point cloud two is a problem inherent to scan matching. Existing methods\nperform non-probabilistic data association, i.e., they assume a single\nassociation hypothesis. This leads to overconfident pose estimates and reduced\nestimation accuracy in ambiguous environments. Our probabilistic scan matching\napproach addresses this issue by considering all association hypotheses with\ntheir respective likelihoods. We formulate a holistic Bayesian estimation\nproblem for both data association and pose estimation and present the\ncorresponding joint factor graph. Near-optimum maximum a posteriori (MAP)\nestimates of the sensor pose are computed by performing iterative message\npassing on the factor graph. Our numerical study shows performance improvements\ncompared to non-probabilistic scan matching methods that are based on the\nnormal distributions transform (NDT) and implicit moving least squares (IMLS).\n"}
+{"abstract": "  For a compact Riemannian locally symmetric space $\\Gamma\\backslash G/K$ of\narbitrary rank we determine the location of certain Ruelle-Taylor resonances\nfor the Weyl chamber action. We provide a Weyl-lower bound on an appropriate\ncounting function for the Ruelle-Taylor resonances and establish a spectral gap\nwhich is uniform in $\\Gamma$ if $G/K$ is irreducible of higher rank. This is\nachieved by proving a quantum-classical correspondence, i.e. a\n1:1-correspondence between horocyclically invariant Ruelle-Taylor resonant\nstates and joint eigenfunctions of the algebra of invariant differential\noperators on $G/K$.\n"}
+{"abstract": "  3D porous scaffolds based on agarose and nanocrystalline apatite, two\nstructural components that act as a temporary mineralized extracellular matrix,\nwere prepared by the GELPOR3D method. This shaping technology allows the\nintroduction of thermally-labile molecules within the scaffolds during the\nfabrication procedure. An angiogenic protein, Vascular Endothelial Growth\nFactor, and an antibiotic, cephalexin, loaded in mesoporous silica\nnanoparticles, were included to design multifunctional scaffolds for bone\nreconstruction. The dual release of both molecules showed a pro-angiogenic\nbehaviour in chicken embryos grown ex ovo, while, at the same time providing an\nantibiotic local concentration capable of inhibiting Staphylococcus aureus\nbacterial growth. In this sense, different release patterns, monitored by\nUV-spectroscopy, could be tailored as a function of the cephalexin loading\nstrategy. The scaffold surface was characterized by a high hydrophilicity, as\ndetermined by contact angle measurements, that facilitated the adhesion and\nproliferation of preosteoblastic cells.\n"}
+{"abstract": "  In this article we study the problem of training intelligent agents using\nReinforcement Learning for the purpose of game development. Unlike systems\nbuilt to replace human players and to achieve super-human performance, our\nagents aim to produce meaningful interactions with the player, and at the same\ntime demonstrate behavioral traits as desired by game designers. We show how to\ncombine distinct behavioral policies to obtain a meaningful \"fusion\" policy\nwhich comprises all these behaviors. To this end, we propose four different\npolicy fusion methods for combining pre-trained policies. We further\ndemonstrate how these methods can be used in combination with Inverse\nReinforcement Learning in order to create intelligent agents with specific\nbehavioral styles as chosen by game designers, without having to define many\nand possibly poorly-designed reward functions. Experiments on two different\nenvironments indicate that entropy-weighted policy fusion significantly\noutperforms all others. We provide several practical examples and use-cases for\nhow these methods are indeed useful for video game production and designers.\n"}
+{"abstract": "  We construct relativistic quantum Markov semigroups from covariant completely\npositive maps. We proceed by generalizing a step in Stinespring's dilation to a\ngeneral system of imprimitivity and basing it on Poincar\\'e group. The\nresulting noise channels are relativistically consistent and the method is\napplicable to any fundamental particle, though we demonstrate it for the case\nof light-like particles. The Krauss decomposition of the relativistically\nconsistent completely positive identity preserving maps (our set up is in\nHeisenberg picture) enables us to construct the covariant quantum Markov\nsemigroups that are uniformly continuous. We induce representations from the\nlittle groups to ensure the quantum Markov semigroups that are ergodic due to\ntransitive systems imprimitivity.\n"}
+{"abstract": "  Business Model Canvas (BMC) is a tool widely used to describe startup\nbusiness models. Despite the various business aspects described, BMC pays a\nlittle emphasis on team-related factors. The importance of team-related factors\nin software development has been acknowledged widely in literature. While not\nas extensively studied, the importance of teams in software startups is also\nknown in both literature and among practitioners. In this paper, we propose\npotential changes to BMC to have the tool better reflect the importance of the\nteam, especially in a software startup environment. Based on a literature\nreview, we identify various components related to the team, which we then\nfurther support with empirical data. We do so by means of a qualitative case\nstudy of five startups.\n"}
+{"abstract": "  The 5$d$ based SrIrO$_3$ represents prototype example of nonmagnetic\ncorrelated metal which mainly originates from a combined effect of spin-orbit\ncoupling, lattice dimensionality and crystal structure. Therefore, tuning of\nthese parameters results in diverse physical properties in this material. Here,\nwe study the structural, magnetic and electrical transport behavior in\nepitaxial SrIrO$_3$ film ($\\sim$ 40 nm) grown on SrTiO$_3$ substrate. Opposed\nto bulk material, the SrIrO$_3$ film exhibits a ferromagnetic ordering at low\ntemperature below $\\sim$ 20 K. The electrical transport data indicate an\ninsulating behavior where the nature of charge transport follows Mott's\nvariable-range-hopping model. A positive magnetoresistance is recorded at 2 K\nwhich has correlation with magnetic moment. We further observe a nonlinear Hall\neffect at low temperature ($<$ 20 K) which arises due to an anomalous component\nof Hall effect. An anisotropic behavior of both magnetoresistance and Hall\neffect has been evidenced at low temperature which coupled with anomalous Hall\neffect indicate the development of ferromagnetic ordering. We believe that an\nenhanced (local) structural distortion caused by lattice strain at low\ntemperatures induces ferromagnetic ordering, thus showing structural\ninstability plays vital role to tune the physical properties in SrIrO$_3$.\n"}
+{"abstract": "  There has been a steady need to precisely extract structured knowledge from\nthe web (i.e. HTML documents). Given a web page, extracting a structured object\nalong with various attributes of interest (e.g. price, publisher, author, and\ngenre for a book) can facilitate a variety of downstream applications such as\nlarge-scale knowledge base construction, e-commerce product search, and\npersonalized recommendation. Considering each web page is rendered from an HTML\nDOM tree, existing approaches formulate the problem as a DOM tree node tagging\ntask. However, they either rely on computationally expensive visual feature\nengineering or are incapable of modeling the relationship among the tree nodes.\nIn this paper, we propose a novel transferable method, Simplified DOM Trees for\nAttribute Extraction (SimpDOM), to tackle the problem by efficiently retrieving\nuseful context for each node by leveraging the tree structure. We study two\nchallenging experimental settings: (i) intra-vertical few-shot extraction, and\n(ii) cross-vertical fewshot extraction with out-of-domain knowledge, to\nevaluate our approach. Extensive experiments on the SWDE public dataset show\nthat SimpDOM outperforms the state-of-the-art (SOTA) method by 1.44% on the F1\nscore. We also find that utilizing knowledge from a different vertical\n(cross-vertical extraction) is surprisingly useful and helps beat the SOTA by a\nfurther 1.37%.\n"}
+{"abstract": "  Quantum annealing in the transverse-field Ising model (TFIM) with open-system\ndynamics is known to use thermally-assisted tunneling to drive computation.\nHowever, it is still subject to debate whether quantum systems in the presence\nof decoherence are more useful than those using classical dynamics to drive\ncomputation. We contribute to this debate by introducing the perturbed\nferromagnetic chain (PFC), a chain of frustrated sub-systems where the degree\nof frustration scales inversely with the perturbation introduced by a tunable\nparameter. This gives us an easily embeddable gadget whereby problem hardness\ncan be tuned for systems of constant size. We outline the properties of the PFC\nand compare classical spin-vector Monte Carlo (SVMC) variants with the\nadiabatic quantum master equation. We demonstrate that SVMC methods get trapped\nin the exponentially large first-excited-state manifold when solving this\nfrustrated problem, whereas evolution using quantum dynamics remains in the\nlowest energy eigenstates. This results in significant differences in ground\nstate probability when using either classical or quantum annealing dynamics in\nthe TFIM.\n"}
+{"abstract": "  Gross--Joyce--Tanaka arXiv:2005.05637 proposed a wall-crossing conjecture for\nCalabi--Yau fourfolds. Assuming that it holds, we prove the conjecture of\nCao--Kool arXiv:1712.07347 for 0-dimensional DT4 invariants on projective\nCalabi--Yau 4-folds and then compute virtual fundamental classes of Hilbert\nschemes of points. As a results we are able to express generating series of\ninvariants in terms of universal power-series. On $\\CC^4$, Nekrasov proposed\ninvariants with a conjectured closed form arXiv:1712.08128. Assuming\nwall-crossing, we prove an analogue of his formula for compact CY 4-folds.\nFinally, we notice a relationship to corresponding generating series for\nQuot-schemes on elliptic surfaces which are also governed by a wall-crossing\nformula. This leads to Segre--Verlinde correspondence for Calabi--Yau\nfourfolds. We will study the relation further in the sequence to this paper.\n"}
+{"abstract": "  We theoretically study the orbital magnetic susceptibility produced by\nelectron states near a crossing point of two band-contact lines in a crystal.\nIt is shown that this susceptibility can have an unusual dependence on the\nFermi level and can change noticeably with the temperature when the Fermi level\nis in the vicinity of the crossing point. These features of the magnetic\nsusceptibility can be useful in detecting the crossing points in crystal. The\nobtained results explain the well-known temperature dependence of the magnetic\nsusceptibility of V$_3$Si.\n"}
+{"abstract": "  Recent formal classifications of crystalline topological insulators predict\nthat the combination of time-reversal and rotational symmetry gives rise to\ntopological invariants beyond the ones known for other lattice symmetries.\nAlthough the classification proves their existence, it does not indicate a way\nof calculating the values of those invariants. Here, we show that a specific\nset of concentric Wilson loops and line invariants yields the values of all\ntopological invariants in two-dimensional systems with pure rotation symmetry\nin class AII. Examples of this analysis are given for specific models with\ntwo-fold and three-fold rotational symmetry. We find new invariants that relate\nto the presence of higher-order topology and corner charges.\n"}
+{"abstract": "  Cosmological inference from cluster number counts is systematically limited\nby the accuracy of the mass calibration, i.e. the empirical determination of\nthe mapping between cluster selection observables and halo mass. In this work\nwe demonstrate a method to quantitatively determine the bias and uncertainties\nin weak-lensing mass calibration. To this end, we extract a library of\nprojected matter density profiles from hydrodynamical simulations. Accounting\nfor shear bias and noise, photometric redshift uncertainties, mis-centering,\ncluster member contamination, cluster morphological diversity, and\nline-of-sight projections, we produce a library of shear profiles. Fitting a\none-parameter model to these profiles, we extract the so-called \\emph{weak\nlensing mass} $M_\\text{WL}$. Relating the weak-lensing mass to the halo mass\nfrom gravity-only simulations with the same initial conditions as the\nhydrodynamical simulations allows us to estimate the impact of hydrodynamical\neffects on cluster number counts experiments. Creating new shear libraries for\n$\\sim$1000 different realizations of the systematics, provides a distribution\nof the parameters of the weak-lensing to halo mass relation, reflecting their\nsystematic uncertainty. This result can be used as a prior for cosmological\ninference. We also discuss the impact of the inner fitting radius on the\naccuracy, and determine the outer fitting radius necessary to exclude the\nsignal from neighboring structures. Our method is currently being applied to\ndifferent Stage~III lensing surveys, and can easily be extended to Stage~IV\nlensing surveys.\n"}
+{"abstract": "  Machine learning approaches in drug discovery, as well as in other areas of\nthe chemical sciences, benefit from curated datasets of physical molecular\nproperties. However, there is a lack of sufficiently large data collections\nthat include first-principle quantum chemical information on bioactive\nmolecules, such as single-point electronic properties, quantum mechanical wave\nfunctions and density-functional theory (DFT) matrices. The open-access QMugs\n(Quantum-Mechanical Properties of Drug-like Molecules) dataset fills this void.\nThe QMugs collection comprises quantum mechanical properties of more than 665k\nbiologically and pharmacologically relevant molecules extracted from the ChEMBL\ndatabase, totaling $\\sim$2M conformers. QMugs contains optimized molecular\ngeometries and thermodynamic data obtained via the semi-empirical method\nGFN2-xTB. Atomic and molecular properties (e.g., partial charges, energies, and\nrotational constants) are provided on both the GFN2-xTB and on the DFT\n($\\omega$B97X-D/def2-SVP) levels of theory. QMugs also comprises the respective\nquantum mechanical wave functions, including DFT density and orbital matrices,\ntotaling over 7 terabytes of uncompressed data. This dataset is intended to\nfacilitate the development of models that learn from molecular data on\ndifferent levels of theory while also providing insight into the corresponding\nrelationships between molecular structure and biological activity.\n"}
+{"abstract": "  For two unknown quantum states $\\rho$ and $\\sigma$ in an $N$-dimensional\nHilbert space, computing their fidelity $F(\\rho,\\sigma)$ is a basic problem\nwith many important applications in quantum computing and quantum information,\nfor example verification and characterization of the outputs of a quantum\ncomputer, and design and analysis of quantum algorithms. In this Letter, we\npropose a quantum algorithm that solves this problem in $\\text{poly}(\\log (N),\nr)$ time, where $r$ is the lower rank of $\\rho$ and $\\sigma$. This algorithm\nexhibits an exponential improvement over the best-known algorithm (based on\nquantum state tomography) in $\\text{poly}(N, r)$ time.\n"}
+{"abstract": "  Given a cusp form $f$ which is supersingular at a fixed prime $p$ away from\nthe level, and a Coleman family $F$ through one of its $p$-stabilisations, we\nconstruct a $2$-variable meromorphic $p$-adic $L$-function for the symmetric\nsquare of $F$, denoted $L^{\\mathrm{imp}}_p(\\mathrm{Sym}^2 F)$. We prove that\nthis new $p$-adic $L$-function interpolates values of complex imprimitive\nsymmetric square $L$-functions, for the various specialisations of the family\n$F$. It is in fact uniquely determined by its interpolation properties. We also\nprove that the function $L^{\\mathrm{imp}}_p(\\mathrm{Sym}^2 F)$ satisfies a\nfunctional equation. We use this $p$-adic $L$-function to prove a $p$-adic\nfactorisation formula, expressing the geometric $p$-adic $L$-function attached\nto the self-convolution of $F$, as the product of\n$L^{\\mathrm{imp}}_p(\\mathrm{Sym}^2 F)$ and a Kubota-Leopoldt $L$-function. This\nextends a result of Dasgupta in the ordinary case.\n  Using Beilinson-Flach classes constructed by Kings, Zerbes and the second\nauthor we construct motivic cohomology classes $b_f$, and prove that, under\nsome hypotheses, they differ by a scalar factor from the higher cyclotomic\nclasses constructed by Beilinson. Using this relation, we prove the\ninterpolation formulae for $L^{\\mathrm{imp}}_p(\\mathrm{Sym}^2 F)$ and the\nfactorisation formula.\n"}
+{"abstract": "  The BESIII Collaboration has observed a candidate for a $c \\bar c s \\bar u$\ntetraquark $Z_{cs}$ at $(3982.5^{+1.8}_{-2.6} \\pm 2.1)$ MeV and width\n$(12.8^{+5.3}_{-4.4} \\pm 3.0)$ MeV, while the LHCb Collaboration has observed a\n$Z_{cs}$ candidate in the $\\jpsi K^-$ channel with mass of $(4003 \\pm 6\n^{+4}_{-14})$ MeV and width $(131 \\pm 15 \\pm 26)$ MeV. In this note we examine\nthe possibility that these two states are distinct eigenstates of a mixing\nprocess similar to that which gives rise to two axial-vector mesons labeled by\nthe Particle Data Group $K_1(1270)$ and $K_1(1400)$. The main point is that on\ntop of a $\\bar c c$ pair, the $Z_{cs}$ states have the same light quark content\nas the $K_1$-s. In the compact tetraquark picture this implies several\nadditional states, analogous to members of the $K_1$ nonet. These states have\nnot yet been observed, nor are they required in the molecular approach. Thus\nexperimental discovery or exclusion of these extra states will be a critical\ntest for competing models of exotic mesons with hidden charm.\n"}
+{"abstract": "  Packet transmission scheduling on multi-hop wireless sensor networks with\n3-egress gateway linear topology is studied. Each node generates a data packet\nin every one cycle period and forwards it bounded for either of gateways at\nedges. We focus on centrally-managed Time Division Multiple Access (TDMA)-based\nslot allocations and provide the design of a packet transmission scheduling\nframework with static time-slot allocation and basic redundant transmission to\nreduce and recover packet losses. We proposed three general path models, which\nwould represent all possible variations of this topology, and derived a global\nstatic time-slot allocation on each of models to maximize the theoretical\nprobability that all packets are successfully delivered to one of the gateways\nwithin one cycle period.\n"}
+{"abstract": "  Machine learning algorithms with empirical risk minimization usually suffer\nfrom poor generalization performance due to the greedy exploitation of\ncorrelations among the training data, which are not stable under distributional\nshifts. Recently, some invariant learning methods for out-of-distribution (OOD)\ngeneralization have been proposed by leveraging multiple training environments\nto find invariant relationships. However, modern datasets are frequently\nassembled by merging data from multiple sources without explicit source labels.\nThe resultant unobserved heterogeneity renders many invariant learning methods\ninapplicable. In this paper, we propose Heterogeneous Risk Minimization (HRM)\nframework to achieve joint learning of latent heterogeneity among the data and\ninvariant relationship, which leads to stable prediction despite distributional\nshifts. We theoretically characterize the roles of the environment labels in\ninvariant learning and justify our newly proposed HRM framework. Extensive\nexperimental results validate the effectiveness of our HRM framework.\n"}
+{"abstract": "  We analyze Lindblad-Gorini-Kossakowski-Sudarshan-type generators for selected\nperiodically driven open quantum systems. All these generators can be obtained\nby temporal coarse-graining procedures, and we compare different\ncoarse-graining schemes. Similar as for undriven systems, we find that a\ndynamically adapted coarse-graining time, effectively yielding non-Markovian\ndynamics by interpolating through a series of different but invididually\nMarkovian solutions, gives the best results among the different coarse-graining\nschemes, albeit at highest computational cost.\n"}
+{"abstract": "  The objective of this study is to examine spatial patterns of impacts and\nrecovery of communities based on variances in credit card transactions. Such\nvariances could capture the collective effects of household impacts, disrupted\naccesses, and business closures, and thus provide an integrative measure for\nexamining disaster impacts and community recovery in disasters. Existing\nstudies depend mainly on survey and sociodemographic data for disaster impacts\nand recovery effort evaluations, although such data has limitations, including\nlarge data collection efforts and delayed timeliness results. In addition,\nthere are very few studies have concentrated on spatial patterns and\ndisparities of disaster impacts and short-term recovery of communities,\nalthough such investigation can enhance situational awareness during disasters\nand support the identification of disparate spatial patterns of disaster\nimpacts and recovery in the impacted regions. This study examines credit card\ntransaction data Harris County (Texas, USA) during Hurricane Harvey in 2017 to\nexplore spatial patterns of disaster impacts and recovery during from the\nperspective of community residents and businesses at ZIP code and county\nscales, respectively, and to further investigate their spatial disparities\nacross ZIP codes. The results indicate that individuals in ZIP codes with\npopulations of higher income experienced more severe disaster impact and\nrecovered more quickly than those located in lower-income ZIP codes for most\nbusiness sectors. Our findings not only enhance the understanding of spatial\npatterns and disparities in disaster impacts and recovery for better community\nresilience assessment, but also could benefit emergency managers, city\nplanners, and public officials in harnessing population activity data, using\ncredit card transactions as a proxy for activity, to improve situational\nawareness and resource allocation.\n"}
+{"abstract": "  This paper investigates problems associated with the valuation of callable\nAmerican volatility put options. Our approach involves modeling volatility\ndynamics as a mean-reverting 3/2 volatility process. We first propose a pricing\nformula for the perpetual American knock-out put. Under the given conditions,\nthe value of perpetual callable American volatility put options is discussed.\n"}
+{"abstract": "  Natural selection drives species to develop brains, with sizes that increase\nwith the complexity of the tasks to be tackled. Our goal is to investigate the\nbalance between the metabolic costs of larger brains compared to the advantage\nthey provide in solving general and combinatorial problems. Defining advantage\nas the performance relative to competitors, a two-player game based on the\nknapsack problem is used. Within this framework, two opponents compete over\nshared resources, with the goal of collecting more resources than the opponent.\nNeural nets of varying sizes are trained using a variant of the AlphaGo Zero\nalgorithm. A surprisingly simple relation, $N_A/(N_A+N_B)$, is found for the\nrelative win rate of a net with $N_A$ neurons against one with $N_B$. Success\nincreases linearly with investments in additional resources when the networks\nsizes are very different, i.e. when $N_A \\ll N_B$, with returns diminishing\nwhen both networks become comparable in size.\n"}
+{"abstract": "  Quantum phase transitions (QPTs) are usually associated with many-body\nsystems with large degrees of freedom approaching the thermodynamic limit. In\nsuch systems, the many-body ground state shows abrupt changes at zero\ntemperature when the control parameter of the Hamiltonian is scanned across a\nquantum critical point. Recently it has been realized that a QPT can also occur\nin a simple system composed of only a two-level atom and a single-mode bosonic\nfield, described by the quantum Rabi model (QRM). Here we report the first\nexperimental demonstration of a QPT in the QRM using a single trapped ion. We\nmeasure the average spin-up state population of the ion and the average phonon\nnumber in its spatial oscillation mode as two order parameters and observe the\nclear evidences of the phase transition via slow quench of the coupling between\nthe ion and its spatial motion. An experimental probe of the phase transitions\nin a fundamental quantum optics model without imposing the thermodynamic limit\nopens up a new window for the controlled study of QPTs and quantum critical\nphenomena.\n"}
+{"abstract": "  We investigate the fate of coherence in the dynamical evolution of a symmetry\nprotected quantum system. Under the formalism of system-plus-bath for open\nquantum system, the anti-unitary symmetry exhibits significant difference from\nthe unitary one in protecting initial coherence. Specifically, taking advantage\nof Lindblad master equation, we find that a pure state in the symmetry\nprotected degenerate subspace will decohere even though both the system\nHamiltonian and system-environment interaction respect the same anti-unitary\nsymmetry. In contrast, the coherence will persist when the protecting symmetry\nis unitary. We provide an elaborate classification table to illustrate what\nkinds of symmetry combinations are able to preserve the coherence of initial\nstate, which is confirmed by several concrete models in spin-$3/2$ system. Our\nresults could help to explore the possible experimental realization of stable\ntime-reversal symmetric topological states.\n"}
+{"abstract": "  Motility is a ubiquitous feature of microbial life on Earth, and is widely\nregarded as a promising biosignature candidate. In the search for motile\norganisms, it is therefore valuable to have rough estimates for the number of\nsuch microbes that one may expect to find in a given area or volume. In this\nwork, we explore this question by employing a simple theoretical model that\ntakes into account the amount of free energy available in a given environment\nand the energetic cost of motility. We present heuristic upper bounds for the\naverage biomass density and the number density of motile lifeforms for the\nMartian subsurface and the ocean of Enceladus by presuming that the motile\nmicrobes in question derive their energy from methanogenesis. We consequently\ndemonstrate that the resultant densities of motile organisms might be\npotentially comparable to, or much lower than, the total microbial densities\ndocumented in various extreme environments on Earth.\n"}
+{"abstract": "  The dynamics of polarized fluorescence in reduced nicotinamide adenine\ndinucleotide (NADH) at 436~nm under two-photon excitation at 720~nm by\nfemtosecond laser pulses in alcohol dehydrogenase (ADH)-containing buffer\nsolution has been studied experimentally and theoretically. A global fit\nprocedure was used for determination of the fluorescence parameters from\nexperimental data. The interpretation of the experimental results obtained was\nsupported by \\emph{ab initio} calculations of NADH structure in solutions. A\ntheoretical model was developed for description of the polarized fluorescence\ndecay in enzyme-NADH binary complexes that considered several possible\ninteraction scenarios. We suggest that the origin of a significant enhancement\nof the nanosecond decay time value in the ADH-bounded NADH compare with the\nfree NADH can be attributed to the significant decrease of non-radiative\nrelaxation probabilities due to decrease of charges separations in the\nnicotinamide ring in the conditions of an apolar ADH-NADH binding site\nenvironment. The existence of a single decay time in the ADH-NADH complex in\nthe nanosecond time-domain in comparison with two decay times observed in free\nNADH can be attributed to a single NADH unfolded \\emph{trans}-like conformation\nbounded within the ADH site. Comparison of the experimental data obtained and\nthe theory developed suggested the existence of an anisotropic relaxation time\nof about 1 ns related most likely with interactions between excited NADH and\nthe ADH binding site that resulted in the rearrangement of nuclear distribution\nand rotation of fluorescence transition dipole moment.\n"}
+{"abstract": "  Nitrogen-vacancy quantum defects in diamond offer a promising platform for\nmagnetometry because of their remarkable optical and spin properties. In this\nLetter, we present a high-sensitivity and wide-bandwidth fiber-based quantum\nmagnetometer for practical applications. By coating the diamond surface with\nsilver reflective film, both the fluorescence collection and excitation\nefficiency are enhanced. Additionally, tracking pulsed optically detected\nmagnetic resonance spectrum allowed a magnetic field sensitivity of $35$\npT$/\\sqrt{\\rm{Hz}}$ and a bandwidth of $4.1$ KHz. Finally, this magnetometer\nwas successfully applied to map the magnetic field induced by the\ncurrent-carrying copper-wire mesh. Such a stable and compact magnetometry can\nprovide a powerful tool in many areas of physical, chemical, and biological\nresearches.\n"}
+{"abstract": "  Recently, CycleGAN was shown to provide high-performance, ultra-fast\ndenoising for low-dose X-ray computed tomography (CT) without the need for a\npaired training dataset. Although this was possible thanks to cycle\nconsistency, CycleGAN requires two generators and two discriminators to enforce\ncycle consistency, demanding significant GPU resources and technical skills for\ntraining. A recent proposal of tunable CycleGAN with Adaptive Instance\nNormalization (AdaIN) alleviates the problem in part by using a single\ngenerator. However, two discriminators and an additional AdaIN code generator\nare still required for training. To solve this problem, here we present a novel\ncycle-free Cycle-GAN architecture, which consists of a single generator and a\ndiscriminator but still guarantees cycle consistency. The main innovation comes\nfrom the observation that the use of an invertible generator automatically\nfulfills the cycle consistency condition and eliminates the additional\ndiscriminator in the CycleGAN formulation. To make the invertible generator\nmore effective, our network is implemented in the wavelet residual domain.\nExtensive experiments using various levels of low-dose CT images confirm that\nour method can significantly improve denoising performance using only 10% of\nlearnable parameters and faster training time compared to the conventional\nCycleGAN.\n"}
+{"abstract": "  Let $(A,\\sigma)$ be an Azumaya algebra with involution over a regular ring\n$R$. We prove that the Gersten-Witt complex of $(A,\\sigma)$ defined by Gille is\nisomorphic to the Gersten-Witt complex of $(A,\\sigma)$ defined by\nBayer-Fluckiger, Parimala and the author. Advantages of both constructions are\nused to show that the Gersten-Witt complex is exact when $\\dim R\\leq 3$,\n$\\mathrm{ind}\\, A\\leq 2$ and $\\sigma$ is orthogonal or symplectic. This means\nthat the Grothendieck-Serre conjecture holds for the group $R$-scheme of\n$\\sigma$-unitary elements in $A$ under the same hypotheses; $R$ is not required\nto contain a field.\n"}
+{"abstract": "  The transfer matrix ${\\mathbf{M}}$ of a short-range potential may be\nexpressed in terms of the time-evolution operator for an effective two-level\nquantum system with a time-dependent non-Hermitian Hamiltonian. This leads to a\ndynamical formulation of stationary scattering. We explore the utility of this\nformulation in the study of the low-energy behavior of the scattering data. In\nparticular, for the exponentially decaying potentials, we devise a simple\niterative scheme for computing terms of arbitrary order in the series expansion\nof ${\\mathbf{M}}$ in powers of the wavenumber. The coefficients of this series\nare determined in terms of a pair of solutions of the zero-energy stationary\nSchr\\\"odinger equation. We introduce a transfer matrix for the latter equation,\nexpress it in terms of the time-evolution operator for an effective two-level\nquantum system, and use it to obtain a perturbative series expansion for the\nsolutions of the zero-energy stationary Schr\\\"odinger equation. Our approach\nallows for identifying the zero-energy resonances for scattering potentials in\nboth full line and half-line with zeros of the entries of the zero-energy\ntransfer matrix of the potential or its trivial extension to the full line.\n"}
+{"abstract": "  The dynamical system described herein uses a hybrid cellular automata (CA)\nmechanism to attain reversibility, and this approach is adapted to create a\nnovel block cipher algorithm called HCA. CA are widely used for modeling\ncomplex systems and employ an inherently parallel model. Therefore,\napplications derived from CA have a tendency to fit very well in the current\ncomputational paradigm where scalability and multi-threading potential are\nquite desirable characteristics. HCA model has recently received a patent by\nthe Brazilian agency INPI. Several evaluations and analyses performed on the\nmodel are presented here, such as theoretical discussions related to its\nreversibility and an analysis based on graph theory, which reduces HCA security\nto the well-known Hamiltonian cycle problem that belongs to the NP-complete\nclass. Finally, the cryptographic robustness of HCA is empirically evaluated\nthrough several tests, including avalanche property compliance and the NIST\nrandomness suite.\n"}
+{"abstract": "  We introduce a model of the diffusion of an epidemic with demographically\nheterogeneous agents interacting socially on a spatially structured network.\nContagion-risk averse agents respond behaviorally to the diffusion of the\ninfections by limiting their social interactions. Firms also respond by\nallowing employees to work remotely, depending on their productivity. The\nspatial structure induces local herd immunities along socio-demographic\ndimensions, which significantly affect the dynamics of infections. We study\nseveral non-pharmaceutical interventions; e.g., i) lockdown rules, which set\nthresholds on the spread of the infection for the closing and reopening of\neconomic activities; and ii) selective lockdowns, which restrict social\ninteractions by location (in the network) and by the demographic\ncharacteristics of the agents. Substantiating a \"Lucas critique\" argument, we\nassess the cost of naive discretionary policies ignoring agents and firms'\nbehavioral responses.\n"}
+{"abstract": "  Context: Securing microservice-based applications is crucial, as many IT\ncompanies are delivering their businesses through microservices. If security\nsmells affect microservice-based applications, they can possibly suffer from\nsecurity leaks and need to be refactored to mitigate the effects of security\nsmells therein. Objective: As the currently available knowledge on securing\nmicroservices is scattered across different pieces of white and grey\nliterature, our objective here is to distill well-known smells for securing\nmicroservices, together with the refactorings enabling to mitigate the effects\nof such smells. Method: To capture the state of the art and practice in\nsecuring microservices, we conducted a multivocal review of the existing white\nand grey literature on the topic. We systematically analyzed 58 studies\npublished from 2014 until the end of 2020. Results: Ten bad smells for securing\nmicroservices are identified, which we organized in a taxonomy, associating\neach smell with the security properties it may violate and the refactorings\nenabling to mitigate its effects. Conclusions: The security smells and the\ncorresponding refactorings have pragmatic value for practitioners, who can\nexploit them in their daily work on securing microservices. They also serve as\na starting point for researchers wishing to establish new research directions\non securing microservices.\n"}
+{"abstract": "  When quantum flavor Hall insulator phases of itinerant fermions are\ndisordered by strong quantum fluctuations, the condensation of skyrmion\ntextures of order parameter fields can lead to superconductivity. In this work,\nwe address the mechanism of skyrmion condensation by considering the scattering\nbetween (2+1)-dimensional, Weyl fermions and hedgehog type tunneling\nconfigurations of order parameters that violate the skyrmion-number\nconservation law. We show the quantized, flavor Hall conductivity\n($\\sigma^f_{xy}$) controls the degeneracy of topologically protected, fermion\nzero-modes, localized on hedgehogs, and the overlap between zero-mode\neigenfunctions or 't Hooft vertex determines the nature of pairing. We\ndemonstrate the quantum-disordered, flavor Hall insulators with $\\sigma^f_{xy}=\n2 N$ lead to different types of charge $2 N e^-$ superconductivity. Some\nimplications for the competition among flavor Hall insulators, the charge\n$2e^-$ paired states in BCS and pair-density-wave channels, and the composite,\ncharge $4e^-$ superconductors for twisted bilayer graphene are outlined.\n"}
+{"abstract": "  We present Turnstile+, a high-level, macros-based metaDSL for building\ndependently typed languages. With it, programmers may rapidly prototype and\niterate on the design of new dependently typed features and extensions. Or they\nmay create entirely new DSLs whose dependent type \"power\" is tailored to a\nspecific domain. Our framework's support of language-oriented programming also\nmakes it suitable for experimenting with systems of interacting components,\ne.g., a proof assistant and its companion DSLs. This paper explains the\nimplementation details of Turnstile+, as well as how it may be used to create a\nwide-variety of dependently typed languages, from a lightweight one with\nindexed types, to a full spectrum proof assistant, complete with a tactic\nsystem and extensions for features like sized types and SMT interaction.\n"}
+{"abstract": "  Deep equilibrium networks (DEQs) are a new class of models that eschews\ntraditional depth in favor of finding the fixed point of a single nonlinear\nlayer. These models have been shown to achieve performance competitive with the\nstate-of-the-art deep networks while using significantly less memory. Yet they\nare also slower, brittle to architectural choices, and introduce potential\ninstability to the model. In this paper, we propose a regularization scheme for\nDEQ models that explicitly regularizes the Jacobian of the fixed-point update\nequations to stabilize the learning of equilibrium models. We show that this\nregularization adds only minimal computational cost, significantly stabilizes\nthe fixed-point convergence in both forward and backward passes, and scales\nwell to high-dimensional, realistic domains (e.g., WikiText-103 language\nmodeling and ImageNet classification). Using this method, we demonstrate, for\nthe first time, an implicit-depth model that runs with approximately the same\nspeed and level of performance as popular conventional deep networks such as\nResNet-101, while still maintaining the constant memory footprint and\narchitectural simplicity of DEQs. Code is available at\nhttps://github.com/locuslab/deq .\n"}
+{"abstract": "  To describe a spin-$\\frac{1}{2}$ particle on the Bloch sphere with a radial\nmagnetic field and topological states of matter from the reciprocal space, we\nintroduce $C$ square ($C^2$) as a local formulation of the global topological\ninvariant. For the Haldane model on the honeycomb lattice, this $C^2$ can be\nmeasured from the Dirac points through circularly polarized light related to\nthe high-symmetry $M$ point(s). For the quantum spin Hall effect and the\nKane-Mele model, the $\\mathbb{Z}_2$ topological number robust to interactions\ncan be measured locally from a correspondence between the pfaffian and light.\nWe address a relation with a spin pump and the quantum spin Hall conductance.\nThe analogy between light and magnetic nuclear resonance may be applied for\nimaging, among other applications.\n"}
+{"abstract": "  Enabling high data-rate uplink cellular connectivity for drones is a\nchallenging problem, since a flying drone has a higher likelihood of having\nline-of-sight propagation to base stations that terrestrial UEs normally do not\nhave line-of-sight to. This may result in uplink inter-cell interference and\nuplink performance degradation for the neighboring ground UEs when drones\ntransmit at high data-rates (e.g., video streaming). We address this problem\nfrom a cellular operator's standpoint to support drone-sourced video streaming\nof a point of interest. We propose a low-complexity, closed-loop control system\nfor Open-RAN architectures that jointly optimizes the drone's location in space\nand its transmission directionality to support video streaming and minimize its\nuplink interference impact on the network. We prototype and experimentally\nevaluate the proposed control system on a dedicated outdoor multi-cell RAN\ntestbed, which is the first measurement campaign of its kind. Furthermore, we\nperform a large-scale simulation assessment of the proposed control system\nusing the actual cell deployment topologies and cell load profiles of a major\nUS cellular carrier. The proposed Open-RAN control scheme achieves an average\n19% network capacity gain over traditional BS-constrained control solutions and\nsatisfies the application data-rate requirements of the drone (e.g., to stream\nan HD video).\n"}
+{"abstract": "  To precisely measure and evaluate X-ray generation and evolution in a\nhohlraum during an implosion process, we present a two-dimensional (2D) time-\nand space-resolved diagnostic method by combining a compressed ultrafast\nphotography (CUP) system and a simplified version of space-resolving flux\ndetector (SSRFD). Numerical experiment results showed that the reconstruction\nquality of the conventional CUP significantly improved owing to the addition of\nthe external SSRFD, especially when a coded mask with a large pixel size was\nused in the CUP. Further, the performance of the CUP cooperation with the SSRFD\nwas better than that of adding an external charge-coupled device or streak\ncamera. Compared with existing ultrafast imaging techniques in laser fusion,\nthe proposed method has a prominent advantage of measuring the 2D evolution of\nimplosion by combining high temporal resolution of streak camera and high\nspatial resolution of SSRFD; moreover, it can provide guidance for designing\ndiagnostic experiments in laser fusion research.\n"}
+{"abstract": "  We consider a Lorentz violating scalar field cosmological model given by the\nmodified Einstein-aether theory defined in Weyl integrable geometry. The\nexistence of exact and analytic solutions is investigated for the case of a\nspatially flat Friedmann--Lema\\^{\\i}tre--Robertson--Walker background space. We\nshow that the theory admits cosmological solutions of special interests. In\naddition, we prove that the cosmological field equations admit the Lewis\ninvariant as a second conservation law, which indicates the integrability of\nthe field equations.\n"}
+{"abstract": "  Stroke poses an immense public health burden and remains among the primary\ncauses of death and disability worldwide. Emergent therapy is often precluded\nby late or indeterminate times of onset before initial clinical presentation.\nRapid, mobile, safe and low-cost stroke detection technology remains a deeply\nunmet clinical need. Past studies have explored the use of microwave and other\nsmall form-factor strategies for rapid stroke detection; however, widespread\nclinical adoption remains unrealized. Here, we investigated the use of\nmicrowave scattering perturbations from ultra-wide-band antenna arrays to learn\ndielectric signatures of disease. Two deep neural networks (DNNs) were used\nfor: 1) stroke detection (\"classification network\"), and 2) characterization of\nthe hemorrhage location and size (\"discrimination network\"). Dielectric\nsignatures were learned on a simulated cohort of 666 hemorrhagic stroke and\ncontrol subjects using 2D stochastic head models. The classification network\nyielded a stratified K-fold stroke detection accuracy of 94.60% with standard\ndeviation of 2.86% and an AUC of 0.996, while the discrimination network\nresulted in a mean squared error of <0.004 cm and <0.02 cm, for the stroke\nlocalization and size estimation, respectively. We report a novel approach to\nintelligent diagnostics using microwave wide-band scattering information thus\ncircumventing conventional image-formation.\n"}
+{"abstract": "  Theoretically, the helical edge states of two-dimensional topological\ninsulators are protected from coherent backscattering due to nonmagnetic\ndisorder provided electron interactions are not too strong. Experimentally, the\nedges typically do not demonstrate the systematic and robust quantization, at\nthe same time little is known about the sub-Kelvin temperature behavior. Here,\nwe report the surprising localization of the edge states in an 8 nm HgTe\nquantum well in zero magnetic field at millikelvin temperatures. Additionally,\nthe magnetoresistance data at 0.5 K for the edges few micrometers long suggests\nthe field-dependent localization length $l_B\\propto B^{-\\alpha}$, with $\\alpha$\nranging approximately from $1.6$ to $2.8$ at fields $B\\lesssim0.1\\,\\text{T}$\nand $\\alpha\\approx1.1$ at higher fields up to $0.5\\,\\text{T}$. In the frame of\ndisordered interacting edge, these values of $\\alpha$ correspond to the\nLuttinger liquid parameters $K\\approx 0.9-1.1$ and $K\\approx 0.6$,\nrespectively. We discuss possible scenarios which could result in the zero\nmagnetic field localization.\n"}
+{"abstract": "  We establish a Wold-type decomposition for isometric and isometric\nNica-covariant representations of the odometer semigroup. These generalize the\nWold-type decomposition for commuting pairs of isometries due to Popovici and\nfor pairs of doubly commuting isometries due to S{\\l}oci\\'nski.\n"}
+{"abstract": "  Generative diffusion processes are an emerging and effective tool for image\nand speech generation. In the existing methods, the underline noise\ndistribution of the diffusion process is Gaussian noise. However, fitting\ndistributions with more degrees of freedom, could help the performance of such\ngenerative models. In this work, we investigate other types of noise\ndistribution for the diffusion process. Specifically, we show that noise from\nGamma distribution provides improved results for image and speech generation.\nMoreover, we show that using a mixture of Gaussian noise variables in the\ndiffusion process improves the performance over a diffusion process that is\nbased on a single distribution. Our approach preserves the ability to\nefficiently sample state in the training diffusion process while using Gamma\nnoise and a mixture of noise.\n"}
+{"abstract": "  Coherent production of $J/\\psi$ mesons is studied in ultraperipheral\nlead-lead collisions at a nucleon-nucleon centre-of-mass energy of 5 TeV, using\na data sample collected by the LHCb experiment corresponding to an integrated\nluminosity of about $10 \\mu b^{-1}$. The $J/\\psi$ mesons are reconstructed in\nthe dimuon final state and are required to have transverse momentum below 1\nGeV. The cross-section within the rapidity range of $2.0 < y < 4.5$ is measured\nto be $4.45\\pm0.24\\pm0.18\\pm0.58$ mb, where the first uncertainty is\nstatistical, the second systematic and the third originates from the luminosity\ndetermination. The cross-section is also measured in $J/\\psi$ rapidity\nintervals. The results are compared to predictions from phenomenological\nmodels.\n"}
+{"abstract": "  Recently, chest X-ray report generation, which aims to automatically generate\ndescriptions of given chest X-ray images, has received growing research\ninterests. The key challenge of chest X-ray report generation is to accurately\ncapture and describe the abnormal regions. In most cases, the normal regions\ndominate the entire chest X-ray image, and the corresponding descriptions of\nthese normal regions dominate the final report. Due to such data bias,\nlearning-based models may fail to attend to abnormal regions. In this work, to\neffectively capture and describe abnormal regions, we propose the Contrastive\nAttention (CA) model. Instead of solely focusing on the current input image,\nthe CA model compares the current input image with normal images to distill the\ncontrastive information. The acquired contrastive information can better\nrepresent the visual features of abnormal regions. According to the experiments\non the public IU-X-ray and MIMIC-CXR datasets, incorporating our CA into\nseveral existing models can boost their performance across most metrics. In\naddition, according to the analysis, the CA model can help existing models\nbetter attend to the abnormal regions and provide more accurate descriptions\nwhich are crucial for an interpretable diagnosis. Specifically, we achieve the\nstate-of-the-art results on the two public datasets.\n"}
+{"abstract": "  We present a multi-wavelength photometric and spectroscopic analysis of\nthirteen \"Super-Chandrasekhar Mass\"/2003fg-like type Ia Supernova (SNe~Ia).\nNine of these objects were observed by the Carnegie Supernova Project.\n2003fg-like have slowly declining light curves ($\\Delta m_{15}$(B) $<$1.3 mag),\nand peak absolute $B$-band magnitudes between $-19<M_{B}<-21$~mag. Many\n2003fg-like are located in the same part of the luminosity width relation as\nnormal SNe~Ia. In the optical $B$ and $V$ bands, 2003fg-like look like normal\nSNe~Ia, but at redder wavelengths they diverge. Unlike other luminous SNe~Ia,\n2003fg-like generally have only one $i$-band maximum which peaks after the\nepoch of $B$-band maximum, while their NIR light curve rise times can be\n$\\gtrsim$40 days longer than those of normal SNe~Ia. They are also at least one\nmagnitude brighter in the NIR bands than normal SNe~Ia, peaking above $M_H <\n-19$~mag, and generally have negative Hubble residuals, which may be the cause\nof some systematics in dark energy experiments. Spectroscopically, 2003fg-like\nexhibit peculiarities such as unburnt carbon well past maximum light, a large\nspread (8000--12000~km/s) in SiII $\\lambda$6355 velocities at maximum light\nwith no rapid early velocity decline, and no clear $H$-band break at +10~d, e.\nWe find that SNe with a larger pseudo equivalent width of CII at maximum light\nhave lower SiII $\\lambda$6355 velocities and slower declining light curves.\nThere are also multiple factors that contribute to the peak luminosity of\n2003fg-like. The explosion of a C-O degenerate core inside a carbon-rich\nenvelope is consistent with these observations. Such a configuration may come\nfrom the core degenerate scenario.\n"}
+{"abstract": "  In most of neural machine translation distillation or stealing scenarios, the\ngoal is to preserve the performance of the target model (teacher). The\nhighest-scoring hypothesis of the teacher model is commonly used to train a new\nmodel (student). If reference translations are also available, then better\nhypotheses (with respect to the references) can be upsampled and poor\nhypotheses either removed or undersampled.\n  This paper explores the importance sampling method landscape (pruning,\nhypothesis upsampling and undersampling, deduplication and their combination)\nwith English to Czech and English to German MT models using standard MT\nevaluation metrics. We show that careful upsampling and combination with the\noriginal data leads to better performance when compared to training only on the\noriginal or synthesized data or their direct combination.\n"}
+{"abstract": "  We predict the emergence of novel collective electronic excitations in warm\ndense matter with an inhomogeneous electronic structure based on\nfirst-principles calculations. The emerging modes are controlled by the imposed\nperturbation amplitude. They include satellite signals around the standard\nplasmon feature, transformation of plasmons to optical modes, and\ndouble-plasmon modes. Most importantly, these modes exhibit a pronounced\ndependence on the temperature. This makes them potentially invaluable for the\ndiagnostics of plasma parameters in the warm dense matter regime. We\ndemonstrate that these modes can be probed with present experimental\ntechniques.\n"}
+{"abstract": "  We calculate steady-state models of radiation-driven super-Eddington winds\nand static expanded envelopes of neutron stars caused by high luminosities in\ntype I X-ray bursts. We use flux-limited diffusion to model the transition from\noptically thick to optically thin, and include effects of general relativity,\nallowing us to study the photospheric radius close to the star as the\nhydrostatic atmosphere evolves into a wind. We find that the photospheric\nradius evolves monotonically from static envelopes ($r_{\\rm ph}\\lesssim 50-70$\nkm) to winds ($r_{\\rm ph}\\approx 100-1000$ km). Photospheric radii of less than\n$100$ km, as observed in most photospheric radius expansion bursts, can be\nexplained by static envelopes, but only in a narrow range of luminosity. In\nmost bursts, we would expect the luminosity to increase further, leading to a\nwind with photospheric radius $\\gtrsim 100$ km. In the contraction phase, the\nexpanded envelope solutions show that the photosphere is still $\\approx 1$ km\nabove the surface when the effective temperature is only $3\\%$ away from its\nmaximum value. This is a possible systematic uncertainty when interpreting the\nmeasured Eddington fluxes from bursts at touchdown. We also discuss the\napplicability of steady-state models to describe the dynamics of bursts. In\nparticular, we show that the sub to super-Eddington transition during the burst\nrise is rapid enough that static models are not appropriate. Finally, we\nanalyze the strength of spectral shifts in our models. Expected shifts at the\nphotosphere are dominated by gravitational redshift, and are therefore\npredicted to be less than a few percent.\n"}
+{"abstract": "  We investigate a generalized form of the phenomenologically emergent dark\nenergy model, known as generalized emergent dark energy (GEDE), introduced by\nLi and Shafieloo [Astrophys. J. {\\bf 902}, 58 (2020)] in light of a series of\ncosmological probes and considering the evolution of the model at the level of\nlinear perturbations. This model introduces a free parameter $\\Delta$ that can\ndiscriminate between the $\\Lambda$CDM (corresponds to $\\Delta=0$) or the\nphenomenologically emergent dark energy (PEDE) (corresponds to $\\Delta=1$)\nmodels, allowing us to determine which model is preferred most by the fit of\nthe observational datasets. We find evidence in favor of the GEDE model for\nPlanck alone and in combination with R19, while the Bayesian model comparison\nis inconclusive when Supernovae Type Ia or BAO data are included. In\nparticular, we find that $\\Lambda$CDM model is disfavored at more than\n$2\\sigma$ CL for most of the observational datasets considered in this work and\nPEDE is in agreement with Planck 2018+BAO+R19 combination within $1\\sigma$ CL.\n"}
+{"abstract": "  In this work we obtain results related to the approximation of\n$h$-dimensional dominant subspaces and low rank approximations of matrices $\nA\\in\\mathbb K^{m\\times n}$ (where $\\mathbb K=\\mathbb R$ or $\\mathbb C)$ in case\nthere is no singular gap at the index $h$, i.e. if $\\sigma_h=\\sigma_{h+1}$\n(where $\\sigma_1\\geq \\ldots\\geq \\sigma_p\\geq 0$ denote the singular values of $\nA$, and $p=\\min\\{m,n\\}$). In order to do this, we develop a novel perspective\nfor the convergence analysis of the classical deterministic block Krylov\nmethods in this context. Indeed, starting with a matrix $ X\\in\\mathbb\nK^{n\\times r}$ with $r\\geq h$ satisfying a compatibility assumption with some\n$h$-dimensional right dominant subspace, we show that block Krylov methods\nproduce arbitrarily good approximations for both problems mentioned above. Our\napproach is based on recent work by Drineas, Ipsen, Kontopoulou and\nMagdon-Ismail on approximation of structural left dominant subspaces. The main\ndifference between our work and previous work on this topic is that instead of\nexploiting a singular gap at $h$ (which is zero in this case) we exploit the\nnearest existing singular gaps.\n"}
+{"abstract": "  We investigate adiabatic solutions to general relativity for a spacetime with\nspatial slices with boundary, by Manton approximation. This approximation tells\nus for a theory with a Lagrangian in the natural form, a motion that is\ndescribed as a slow motion on the space of vacua-static solutions that minimize\nthe energy -- is a good approximate solution. To apply this to the case of\ngeneral relativity we first bring it to the natural form by splitting space and\ntime and choosing Gaussian normal coordinates, where a spacetime is described\nby the metric on its spatial slices. Then following Manton we propose slow\nsolutions such that each slice is a slowly changing diffeomorphism of a\nreference slice, and thus each solution is described by a vector field on the\nspatial slice.\n  These solutions will have the property that the action will become a\nfunctional of the vector fields on the boundaries of the spatial slices.\nMoreover using the Hodge-Morrey-Friedrichs decomposition we will show that the\nconstraints of general relativity will identify a unique solution for a given\nboundary value. Then we comment on the structure of the space of vacua which we\nshow to be a (pseudo)-Riemannian homogeneous space. We illustrate our procedure\nfor a specific reference slice we choose: the 3d Euclidean round ball.\n"}
+{"abstract": "  The 19-dimensional TP06 cardiac muscle cell model is reduced to a\n17-dimensional version, which satisfies the required conditions for performing\nan analysis of its dynamics by means of bifurcation theory. The reformulated\nmodel is shown to be a good approximation of the original one. As a\nconsequence, one can extract fairly precise predictions of the behaviour of the\noriginal model from the bifurcation analysis of the modified model. Thus, the\nfindings of bifurcations linked to complex dynamics in the modified model -\nlike early afterdepolarisations (EADs), which can be precursors to cardiac\ndeath - predicts the occurrence of the same dynamics in the original model. It\nis shown that bifurcations linked to EADs in the modified model accurately\npredicts EADs in the original model at the single cell level. Finally, these\nbifurcations are linked to cardiac death at the tissue level by example.\n"}
+{"abstract": "  The dominant site of production of $r$-process elements remains unclear\ndespite recent observations of a neutron star merger. Observational constraints\non the properties of the sites can be obtained by comparing $r$-process\nabundances in different environments. The recent Gaia data releases and large\nsamples from high-resolution optical spectroscopic surveys are enabling us to\ncompare $r$-process element abundances between stars formed in an accreted\ndwarf galaxy, Gaia-Enceladus, and those formed in the Milky Way. We aim to\nunderstand the origin of $r$-process elements in Gaia-Enceladus. We first\nconstruct a sample of stars to study Eu abundances without being affected by\nthe detection limit. We then kinematically select 71 Gaia-Enceladus stars and\n93 in-situ stars from the Galactic Archaeology with HERMES (GALAH) DR3, of\nwhich 50 and 75 stars can be used to study Eu reliably. Gaia-Enceladus stars\nclearly show higher ratios of [{Eu}/{Mg}] than in-situ stars. High [{Eu}/{Mg}]\nalong with low [{Mg}/{Fe}] are also seen in relatively massive satellite\ngalaxies such as the LMC, Fornax, and Sagittarius dwarfs. On the other hand,\nunlike these galaxies, Gaia-Enceladus does not show enhanced [{Ba}/{Eu}] or\n[{La}/{Eu}] ratios suggesting a lack of significant $s$-process contribution.\nFrom comparisons with simple chemical evolution models, we show that the high\n[{Eu}/{Mg}] of Gaia-Enceladus can naturally be explained by considering\n$r$-process enrichment by neutron-star mergers with delay time distribution\nthat follows a similar power-law as type~Ia supernovae but with a shorter\nminimum delay time.\n"}
+{"abstract": "  We propose an intrinsic 3D Fabry-P\\'erot type interferometer, coined\n\"higher-order interferometer\", that utilizes the chiral hinge states of\nsecond-order topological insulators and cannot be equivalently mapped to 2D\nspace because of higher-order topology. Quantum interference patterns in the\ntwo-terminal conductance of this interferometer are controllable not only by\ntuning the strength but also, particularly, by rotating the direction of the\nmagnetic field applied perpendicularly to the transport direction. Remarkably,\nthe conductance exhibits a characteristic beating pattern with multiple\nfrequencies with respect to field strength or direction. Our novel\ninterferometer provides feasible and robust magneto-transport signatures to\nprobe the particular hinge states of higher-order topological insulators.\n"}
+{"abstract": "  In this article, we propose the approach to procedural optimization of a\nneural network, based on the combination of information theory and braid\ntheory. The network studied in the article implemented with the intersections\nbetween the braid strands, as well as simplified networks (a network with\nstrands without intersections and a simple convolutional deep neural network),\nare used to solve various problems of multiclass image classification that\nallow us to analyze the comparative effectiveness of the proposed architecture.\nThe simulation results showed BraidNet's comparative advantage in learning\nspeed and classification accuracy.\n"}
+{"abstract": "  This work oversees with the coupled Schr\\\"odinger-Boussinesq equations with\nconformable derivative, which have lots of applications in laser and plasma.\nThe said equations are reduced to a coupled stationary form using complex\ntravelling wave transformation. Next Painlev\\'e test applied to derived the\nintegrable cases of the reduced equation, after that using RCAM derived the\nsolution of reduced equations integrable and nonintegrable cases. Few theorems\nhave been presented and proved to ensure their boundedness. All presented\nboundedness cases have been checked and explained by plotting the solutions for\nparticulars values of parameters satisfying them. The obtained solutions of\nstationary form utilized to derive solutions of the coupled\nSchr\\\"odinger-Boussinesq equations with conformable derivative. The derived\nsolutions have been plotted and explained. From this, it appears that these\nsolutions propagate by maintaining their two-hump, W-shaped, M-shaped solutions\nshapes.\n"}
+{"abstract": "  Logistics has gained great attentions with the prosperous development of\ncommerce, which is often seen as the classic optimal vehicle routing problem.\nMeanwhile, electric vehicle (EV) has been widely used in logistic fleet to curb\nthe emission of green house gases in recent years. Solving the optimization\nproblem of joint routing and charging of multiple EVs is in a urgent need,\nwhose objective function includes charging time, charging cost, EVs travel\ntime, usage fees of EV and revenue from serving customers. This joint problem\nis formulated as a mixed integer programming (MIP) problem, which, however, is\nNP-hard due to integer restrictions and bilinear terms from the coupling\nbetween routing and charging decisions. The main contribution of this paper\nlies at proposing an efficient two stage algorithm that can decompose the\noriginal MIP problem into two linear programming (LP) problems, by exploiting\nthe exactness of LP relaxation and eliminating the coupled term. This algorithm\ncan achieve a nearoptimal solution in polynomial time. In addition, another\nvariant algorithm is proposed based on the two stage one, to further improve\nthe quality of solution.\n"}
+{"abstract": "  In this paper we present a rigorous derivation of the Boltzmann equation in a\ncompact domain with diffuse reflection boundary conditions. We consider a\nsystem of $N$ hard spheres of diameter $\\epsilon$ in a box $\\Lambda := [0, 1]\n\\times (R/Z)^2$. When a particle meets the boundary of the domain, it is\ninstantaneously reinjected into the box with a random direction, and conserving\nkinetic energy. We prove that the first marginal of the process converges in\nthe scaling $N\\epsilon^2 = 1$, $\\epsilon\\rightarrow 0$ to the solution of the\nBoltzmann equation, with the same short time restriction of Lanford's classical\ntheorem.\n"}
+{"abstract": "  It is widely believed that consistent theories of quantum gravity satisfy two\nbasic kinematic constraints: they are free from any global symmetry, and they\ncontain a complete spectrum of gauge charges. For compact, abelian gauge\ngroups, completeness follows from the absence of a 1-form global symmetry.\nHowever, this correspondence breaks down for more general gauge groups, where\nthe breaking of the 1-form symmetry is insufficient to guarantee a complete\nspectrum. We show that the correspondence may be restored by broadening our\nnotion of symmetry to include non-invertible topological operators, and prove\nthat their absence is sufficient to guarantee a complete spectrum for any\ncompact, possibly disconnected gauge group. In addition, we prove an analogous\nstatement regarding the completeness of twist vortices: codimension-2 objects\ndefined by a discrete holonomy around their worldvolume, such as cosmic strings\nin four dimensions. We discuss how this correspondence is modified in various,\nmore general contexts, including non-compact gauge groups, Higgsing of gauge\ntheories, and the addition of Chern-Simons terms. Finally, we discuss the\nimplications of our results for the Swampland program, as well as the\nphenomenological implications of the existence of twist strings.\n"}
+{"abstract": "  Neutrinos are particles that interact rarely, so identifying them requires\nlarge detectors which produce lots of data. Processing this data with the\ncomputing power available is becoming even more difficult as the detectors\nincrease in size to reach their physics goals. Liquid argon time projection\nchamber (LArTPC) neutrino experiments are expected to grow in the next decade\nto have 100 times more wires than in currently operating experiments, and\nmodernization of LArTPC reconstruction code, including parallelization both at\ndata- and instruction-level, will help to mitigate this challenge. The LArTPC\nhit finding algorithm is used across multiple experiments through a common\nsoftware framework. In this paper we discuss a parallel implementation of this\nalgorithm. Using a standalone setup we find speed up factors of two times from\nvectorization and 30--100 times from multi-threading on Intel architectures.\nThe new version has been incorporated back into the framework so that it can be\nused by experiments. On a serial execution, the integrated version is about 10\ntimes faster than the previous one and, once parallelization is enabled,\nfurther speedups comparable to the standalone program are achieved.\n"}
+{"abstract": "  In contrast with the 3-dimensional case (cf. \\cite{RaMo}), where rotationally\nsymmetric totally geodesic free boundary minimal surfaces have Morse index one;\nwe prove in this work that the Morse index of a free boundary rotationally\nsymmetric totally geodesic hypersurface of the $n$-dimensional Riemannnian\nSchwarzschild space with respect to variations that are tangential along the\nhorizon is zero, for $n\\geq4$.\n  Moreover, we show that there exist non-compact free boundary minimal\nhypersurfaces which are not totally geodesic, $n\\geq 8$, with Morse index equal\nto $0$. Also, it is shown that, for $n\\geq4$, there exist infinitely many\nnon-compact free boundary minimal hypersurfaces, which are not congruent to\neach other, with infinite Morse index.\n  We also study the density at infinity of a free boundary minimal hypersurface\nwith respect to a minimal cone constructed over a minimal hypersurface of the\nunit Euclidean sphere. We obtain a lower bound for the density in terms of the\narea of the boundary of the hypersurface and the area of the minimal\nhypersurface in the unit sphere. This lower bound is optimal in the sense that\nonly minimal cones achieve it.\n"}
+{"abstract": "  Android is one of the leading operating systems for smart phones in terms of\nmarket share and usage. Unfortunately, it is also an appealing target for\nattackers to compromise its security through malicious applications. To tackle\nthis issue, domain experts and researchers are trying different techniques to\nstop such attacks. All the attempts of securing Android platform are somewhat\nsuccessful. However, existing detection techniques have severe shortcomings,\nincluding the cumbersome process of feature engineering. Designing\nrepresentative features require expert domain knowledge. There is a need for\nminimizing human experts' intervention by circumventing handcrafted feature\nengineering. Deep learning could be exploited by extracting deep features\nautomatically. Previous work has shown that operational codes (opcodes) of\nexecutables provide key information to be used with deep learning models for\ndetection process of malicious applications. The only challenge is to feed\nopcodes information to deep learning models. Existing techniques use one-hot\nencoding to tackle the challenge. However, the one-hot encoding scheme has\nsevere limitations. In this paper, we introduce; (1) a novel technique for\nopcodes embedding, which we name Op2Vec, (2) based on the learned Op2Vec we\nhave developed a dataset for end-to-end detection of android malware.\nIntroducing the end-to-end Android malware detection technique avoids\nexpert-intensive handcrafted features extraction, and ensures automation. The\ncomparison shows that Op2Vec outperforms the existing one-hot encoding\ntechnique for opcode embedding and the developed dataset can provide\nsignificant insights for end-to-end detection of Android malware.\n"}
+{"abstract": "  We define Kakeya sets in the Heisenberg group and show that the Heisenberg\nHausdorff dimension of Kakeya sets in the first Heisenberg group is at least 3.\nThis lower bound is sharp since, under our definition, the $\\{xoy\\}$-plane is a\nKakeya set with Heisenberg Hausdorff dimension 3.\n"}
+{"abstract": "  In recent years there has been significant fundamental research into phonon\npolaritons, owing to their ability to compress light to extremely small\ndimensions, low-losses, and ability to support anisotropic propagation. In this\nperspective, after briefly reviewing the present state of mid-infrared\noptoelectronics, we will assess the potential of phonon-polariton based\nnanophotonics for infrared (3-100$\\mathrm{\\mu}$m) light sources, detectors and\nmodulators. These will operate in the Reststrahlen region where conventional\nsemiconductor light sources become ineffective. Drawing on the results from the\npast few years, we will sketch some promising paths to create such devices and\nwe will evaluate their practical advantages and disadvantages when compared to\nother approaches to infrared optoelectronics.\n"}
+{"abstract": "  Most fair regression algorithms mitigate bias towards sensitive sub\npopulations and therefore improve fairness at group level. In this paper, we\ninvestigate the impact of such implementation of fair regression on the\nindividual. More precisely, we assess the evolution of continuous predictions\nfrom an unconstrained to a fair algorithm by comparing results from baseline\nalgorithms with fair regression algorithms for the same data points. Based on\nour findings, we propose a set of post-processing algorithms to improve the\nutility of the existing fair regression approaches.\n"}
+{"abstract": "  Noise and low quality of ECG signals acquired from Holter or wearable devices\ndeteriorate the accuracy and robustness of R-peak detection algorithms. This\npaper presents a generic and robust system for R-peak detection in Holter ECG\nsignals. While many proposed algorithms have successfully addressed the problem\nof ECG R-peak detection, there is still a notable gap in the performance of\nthese detectors on such low-quality ECG records. Therefore, in this study, a\nnovel implementation of the 1D Convolutional Neural Network (CNN) is used\nintegrated with a verification model to reduce the number of false alarms. This\nCNN architecture consists of an encoder block and a corresponding decoder block\nfollowed by a sample-wise classification layer to construct the 1D segmentation\nmap of R- peaks from the input ECG signal. Once the proposed model has been\ntrained, it can solely be used to detect R-peaks possibly in a single channel\nECG data stream quickly and accurately, or alternatively, such a solution can\nbe conveniently employed for real-time monitoring on a lightweight portable\ndevice. The model is tested on two open-access ECG databases: The China\nPhysiological Signal Challenge (2020) database (CPSC-DB) with more than one\nmillion beats, and the commonly used MIT-BIH Arrhythmia Database (MIT-DB).\nExperimental results demonstrate that the proposed systematic approach achieves\n99.30% F1-score, 99.69% recall, and 98.91% precision in CPSC-DB, which is the\nbest R-peak detection performance ever achieved. Compared to all competing\nmethods, the proposed approach can reduce the false-positives and\nfalse-negatives in Holter ECG signals by more than 54% and 82%, respectively.\nResults also demonstrate similar or better performance than most competing\nalgorithms on MIT-DB with 99.83% F1-score, 99.85% recall, and 99.82% precision.\n"}
+{"abstract": "  Melanoma is an aggressive neoplasm responsible for the majority of deaths\nfrom skin cancer. Specifically, spitzoid melanocytic tumors are one of the most\nchallenging melanocytic lesions due to their ambiguous morphological features.\nThe gold standard for its diagnosis and prognosis is the analysis of skin\nbiopsies. In this process, dermatopathologists visualize skin histology slides\nunder a microscope, in a high time-consuming and subjective task. In the last\nyears, computer-aided diagnosis (CAD) systems have emerged as a promising tool\nthat could support pathologists in daily clinical practice. Nevertheless, no\nautomatic CAD systems have yet been proposed for the analysis of spitzoid\nlesions. Regarding common melanoma, no proposed system allows both the\nselection of the tumoral region and the prediction of the diagnosis as benign\nor malignant. Motivated by this, we propose a novel end-to-end\nweakly-supervised deep learning model, based on inductive transfer learning\nwith an improved convolutional neural network (CNN) to refine the embedding\nfeatures of the latent space. The framework is composed of a source model in\ncharge of finding the tumor patch-level patterns, and a target model focuses on\nthe specific diagnosis of a biopsy. The latter retrains the backbone of the\nsource model through a multiple instance learning workflow to obtain the\nbiopsy-level scoring. To evaluate the performance of the proposed methods, we\nperform extensive experiments on a private skin database with spitzoid lesions.\nTest results reach an accuracy of 0.9231 and 0.80 for the source and the target\nmodels, respectively. Besides, the heat map findings are directly in line with\nthe clinicians' medical decision and even highlight, in some cases, patterns of\ninterest that were overlooked by the pathologist due to the huge workload.\n"}
+{"abstract": "  We introduce a new pair of mutually dual bases of noncommutative symmetric\nfunctions and quasi-symmetric functions, and use it to derive generalizations\nof several results on the reduced incidence algebra of the lattice of\nnoncrossing partitions. As a consequence, we obtain a quasi-symmetric version\nof the Farahat-Higman algebra.\n"}
+{"abstract": "  We report Coulomb blockade transport studies of InAs nanowires grown with\nepitaxial superconducting Al and ferromagnetic insulator EuS on overlapping\nfacets. By comparing experimental results to a theoretical model, we associate\ncotunneling features in even-odd bias spectra with spin-polarized Andreev\nlevels, indicating that spin splitting exceeding the induced superconducting\ngap at zero applied magnetic field. Energies of the polarized subgap states can\nbe tuned on either side of zero by electrostatic gates.\n"}
+{"abstract": "  The empirical Archie's law, relating the measured electrical resistivity of\nan electrolyte-saturated rock to its connected porosity, is derived by\nconsideration of the fractal character of the pore space. The derivation thus\nimposes some conditions on the implementation and meaning of Archie's law.\n"}
+{"abstract": "  Quantum cryptography exploits principles of quantum physics for the secure\nprocessing of information. A prominent example is secure communication, i.e.,\nthe task of transmitting confidential messages from one location to another.\nThe cryptographic requirement here is that the transmitted messages remain\ninaccessible to anyone other than the designated recipients, even if the\ncommunication channel is untrusted. In classical cryptography, this can usually\nonly be guaranteed under computational hardness assumptions, e.g., that\nfactoring large integers is infeasible. In contrast, the security of quantum\ncryptography relies entirely on the laws of quantum mechanics. Here we review\nthis physical notion of security, focusing on quantum key distribution and\nsecure communication.\n"}
+{"abstract": "  Many real-world applications, such as those in medical domains,\nrecommendation systems, etc, can be formulated as large state space\nreinforcement learning problems with only a small budget of the number of\npolicy changes, i.e., low switching cost. This paper focuses on the linear\nMarkov Decision Process (MDP) recently studied in [Yang et al 2019, Jin et al\n2020] where the linear function approximation is used for generalization on the\nlarge state space. We present the first algorithm for linear MDP with a low\nswitching cost. Our algorithm achieves an\n$\\widetilde{O}\\left(\\sqrt{d^3H^4K}\\right)$ regret bound with a near-optimal\n$O\\left(d H\\log K\\right)$ global switching cost where $d$ is the feature\ndimension, $H$ is the planning horizon and $K$ is the number of episodes the\nagent plays. Our regret bound matches the best existing polynomial algorithm by\n[Jin et al 2020] and our switching cost is exponentially smaller than theirs.\nWhen specialized to tabular MDP, our switching cost bound improves those in\n[Bai et al 2019, Zhang et al 20020]. We complement our positive result with an\n$\\Omega\\left(dH/\\log d\\right)$ global switching cost lower bound for any\nno-regret algorithm.\n"}
+{"abstract": "  In the new era of Extremely Large Telescopes (ELTs) currently under\nconstruction, challenging requirements drive spectrograph designs towards\ntechniques that efficiently use a facility's light collection power. Operating\nin the single-mode (SM) regime, close to the diffraction limit, reduces the\nfootprint of the instrument compared to a conventional high-resolving power\nspectrograph. The custom built injection fiber system with 3D-printed\nmicro-lenses on top of it for the replicable high-resolution exoplanet and\nasteroseismology spectrograph at Subaru in combination with extreme adaptive\noptics of SCExAO, proved its high efficiency in a lab environment, manifesting\nup to ~77% of the theoretical predicted performance.\n"}
+{"abstract": "  In this paper we investigate equilibria of continuous differential equation\nmodels of network dynamics. The motivation comes from gene regulatory networks\nwhere each directed edge represents either down- or up-regulation, and is\nmodeled by a sigmoidal nonlinear function. We show that the existence and\nstability of equilibria of a sigmoidal system is determined by a combinatorial\nanalysis of the limiting switching system with piece-wise constant\nnon-linearities. In addition, we describe a local decomposition of a switching\nsystem into a product of simpler cyclic feedback systems, where the cycles in\neach decomposition correspond to a particular subset of network loops.\n"}
+{"abstract": "  It is conjectured that stationary black holes are characterized by the\ninverse hoop relation ${\\cal A}\\leq {\\cal C}^2/\\pi$, where ${\\cal A}$ and\n${\\cal C}$ are respectively the black-hole surface area and the circumference\nlength of the smallest ring that can engulf the black-hole horizon in every\ndirection. We explicitly prove that generic Kerr-Newman-(anti)-de Sitter black\nholes conform to this conjectured area-circumference relation.\n"}
+{"abstract": "  Given that there are a variety of stakeholders involved in, and affected by,\ndecisions from machine learning (ML) models, it is important to consider that\ndifferent stakeholders have different transparency needs. Previous work found\nthat the majority of deployed transparency mechanisms primarily serve technical\nstakeholders. In our work, we want to investigate how well transparency\nmechanisms might work in practice for a more diverse set of stakeholders by\nconducting a large-scale, mixed-methods user study across a range of\norganizations, within a particular industry such as health care, criminal\njustice, or content moderation. In this paper, we outline the setup for our\nstudy.\n"}
+{"abstract": "  We present a scalable tree search planning algorithm for large multi-agent\nsequential decision problems that require dynamic collaboration. Teams of\nagents need to coordinate decisions in many domains, but naive approaches fail\ndue to the exponential growth of the joint action space with the number of\nagents. We circumvent this complexity through an anytime approach that allows\nus to trade computation for approximation quality and also dynamically\ncoordinate actions. Our algorithm comprises three elements: online planning\nwith Monte Carlo Tree Search (MCTS), factored representations of local agent\ninteractions with coordination graphs, and the iterative Max-Plus method for\njoint action selection. We evaluate our approach on the benchmark SysAdmin\ndomain with static coordination graphs and achieve comparable performance with\nmuch lower computation cost than our MCTS baselines. We also introduce a\nmulti-drone delivery domain with dynamic, i.e., state-dependent coordination\ngraphs, and demonstrate how our approach scales to large problems on this\ndomain that are intractable for other MCTS methods. We provide an open-source\nimplementation of our algorithm at\nhttps://github.com/JuliaPOMDP/FactoredValueMCTS.jl.\n"}
+{"abstract": "  We classify finite-dimensional Nichols algebras over finite nilpotent groups\nof odd order in group-theoretical terms. The main step is to show that the\nconjugacy classes of such finite groups are either abelian or of type C; this\nproperty also holds for finite conjugacy classes of finitely generated\nnilpotent groups whose torsion has odd order. To extend our approach to the\nsetting of finite GK-dimension, we propose a new Conjecture on racks of type C.\nWe also prove that the bosonization a of Nichols algebra of a Yetter-Drinfeld\nmodule over a group whose support is an infinite conjugacy class has infinite\nGK-dimension. We apply this to the study of the finite GK-dimensional pointed\nHopf algebras over finitely generated torsion-free nilpotent groups.\n"}
+{"abstract": "  Collective orders and photo-induced phase transitions in quantum matter can\nevolve on timescales which are orders of magnitude slower than the femtosecond\nprocesses related to electronic motion in the solid. Quantum Boltzmann\nequations can potentially resolve this separation of timescales, but are often\nconstructed within a perturbative framework. Here we derive a quantum Boltzmann\nequation which only assumes a separation of timescales (taken into account\nthrough the gradient approximation for convolutions in time), but is based on a\nnon-perturbative scattering integral, and makes no assumption on the spectral\nfunction such as the quasiparticle approximation. In particular, a scattering\nintegral corresponding to non-equilibrium dynamical mean-field theory is\nevaluated in terms of an Anderson impurity model in a non-equilibrium steady\nstate with prescribed distribution functions. This opens the possibility to\ninvestigate dynamical processes in correlated solids with quantum impurity\nsolvers designed for the study of non-equilibrium steady states.\n"}
+{"abstract": "  Artificial intelligence has changed our day to day life in multitude ways. AI\ntechnology is rearing itself as a driving force to be reckoned with in the\nlargest industries in the world. AI has already engulfed our educational\nsystem, our businesses and our financial establishments. The future is definite\nthat machines with artificial intelligence will soon be captivating over\ntrained manual work that now is mostly cared by humans. Machines can carry out\nhuman-like tasks by new inputs as artificial intelligence makes it possible for\nmachines to learn from experience. AI data from web of science database from\n2008 to 2017 have been mapped to depict the average growth rate, relative\ngrowth rate, contribution made by authors in the view of research productivity,\nauthorship pattern and collaboration of AI literature. The Lotka's law on\nauthorship productivity of AI literature has been tested to confirm the\napplicability of the law to the present data set. A K-S test was applied to\nmeasure the degree of agreement between the distribution of the observed set of\ndata against the inverse general power relationship and the theoretical value\nof {\\alpha} =2. It is found that the inverse square law of Lotka follow as\nsuch.\n"}
+{"abstract": "  Video super-resolution (VSR), with the aim to restore a high-resolution video\nfrom its corresponding low-resolution version, is a spatial-temporal sequence\nprediction problem. Recently, Transformer has been gaining popularity due to\nits parallel computing ability for sequence-to-sequence modeling. Thus, it\nseems to be straightforward to apply the vision Transformer to solve VSR.\nHowever, the typical block design of Transformer with a fully connected\nself-attention layer and a token-wise feed-forward layer does not fit well for\nVSR due to the following two reasons. First, the fully connected self-attention\nlayer neglects to exploit the data locality because this layer relies on linear\nlayers to compute attention maps. Second, the token-wise feed-forward layer\nlacks the feature alignment which is important for VSR since this layer\nindependently processes each of the input token embeddings without any\ninteraction among them. In this paper, we make the first attempt to adapt\nTransformer for VSR. Specifically, to tackle the first issue, we present a\nspatial-temporal convolutional self-attention layer with a theoretical\nunderstanding to exploit the locality information. For the second issue, we\ndesign a bidirectional optical flow-based feed-forward layer to discover the\ncorrelations across different video frames and also align features. Extensive\nexperiments on several benchmark datasets demonstrate the effectiveness of our\nproposed method. The code will be available at\nhttps://github.com/caojiezhang/VSR-Transformer.\n"}
+{"abstract": "  This paper proposes a novel monitoring methodology for car-following control\nof automated vehicles that uses real-time measurements of spacing and velocity\nobtained through vehicle sensors. This study focuses on monitoring the time\ngap, a key parameter that dictates the desired following spacing of the\ncontrolled vehicle. The goal is to monitor deviations in actual time gap from a\ndesired setting and detect when it deviates beyond a control limit. A random\ncoefficient modeling is developed to systematically capture the stochastic\ndistribution of the time gap and derive a closed-form Bayesian updating scheme\nfor real-time inference. A control chart is then adopted to systematically set\nthe control limits and inform when the time gap setting should be changed.\nSimulation experiments are performed to demonstrate the effectiveness of the\nproposes method for monitoring the time gap and alerting when the parameter\nsetting needs to be changed.\n"}
+{"abstract": "  We report the formation of self-assembled monolayers of a molecular\nphotoswitch (azobenzene-bithiophene derivative, AzBT) on cobalt via a thiol\ncovalent bond. We study the electrical properties of the molecular junctions\nformed with the tip of a conductive atomic force microscope under ultra-high\nvacuum. The statistical analysis of the current-voltage curves shows two\ndistinct states of the molecule conductance, suggesting the coexistence of both\nthe trans and cis azobenzene isomers on the surface. The cis isomer population\n(trans isomer) increases (decreases) upon UV light irradiation. The situation\nis reversed under blue light irradiation. The experiments are confronted to\nfirst-principle calculations performed on the molecular junctions with the\nNon-Equilibrium Green's Function formalism combined with Density Functional\nTheory (NEGF/DFT). The theoretical results consider two different molecular\norientations for each isomer. Whereas the orientation does not affect the\nconductance of the trans isomer, it significantly modulates the conductance of\nthe cis isomer and the resulting conductance ON/OFF ratio of the molecular\njunction. This helps identifying the molecular orientation at the origin of the\nobserved current differences between the trans and cis forms. The ON state is\nassociated to the trans isomer irrespective of its orientation in the junction,\nwhile the OFF state is identified as a cis isomer with its azobenzene moiety\nfolded upward with respect to the bithiophene core. The experimental and\ncalculated ON/OFF conductance ratios have a similar order of magnitude. This\nconductance ratio seems reasonable to make these Co-AzBT molecular junctions a\ngood test-bed to further explore the relationship between the spin-polarized\ncharge transport, the molecule conformation and the molecule-Co spinterface.\n"}
+{"abstract": "  Metallic structures interacting with electromagnetic fields are known to\nexhibit properties similar to those found in atoms and molecules, such as\nmulti-photon and tunnel ionization. Developing this similarity beyond the\nelectron emission current, we generalize the wellknown Fowler-Nordheim model,\nand predict heretofore unrecognized source of nonlinear optical response from\nnano-structures exposed to illumination with intense optical pulses.\n"}
+{"abstract": "  We address the problem of form understanding: finding text entities and the\nrelationships/links between them in form images. The proposed FUDGE model\nformulates this problem on a graph of text elements (the vertices) and uses a\nGraph Convolutional Network to predict changes to the graph. The initial\nvertices are detected text lines and do not necessarily correspond to the final\ntext entities, which can span multiple lines. Also, initial edges contain many\nfalse-positive relationships. FUDGE edits the graph structure by combining text\nsegments (graph vertices) and pruning edges in an iterative fashion to obtain\nthe final text entities and relationships. While recent work in this area has\nfocused on leveraging large-scale pre-trained Language Models (LM), FUDGE\nachieves almost the same level of entity linking performance on the FUNSD\ndataset by learning only visual features from the (small) provided training\nset. FUDGE can be applied on forms where text recognition is difficult (e.g.\ndegraded or historical forms) and on forms in resource-poor languages where\npre-training such LMs is challenging. FUDGE is state-of-the-art on the\nhistorical NAF dataset.\n"}
+{"abstract": "  We make available some results about model theory cyclically ordered groups.\nWe start with a classification of complete theories of divisible abelian\ncyclically ordered groups. Then we look at the cyclically ordered groups where\nthe only parametrically definable subsets are finite unions of singletons and\nopen intervals, and those where the definable subsets are finite union of\nsingletons and c-convex subsets, where being c-convex is the analogue of being\nconvex in the linearly ordered case.\n"}
+{"abstract": "  Real-world data is often unbalanced and long-tailed, but deep models struggle\nto recognize rare classes in the presence of frequent classes. To address\nunbalanced data, most studies try balancing the data, the loss, or the\nclassifier to reduce classification bias towards head classes. Far less\nattention has been given to the latent representations learned with unbalanced\ndata. We show that the feature extractor part of deep networks suffers greatly\nfrom this bias. We propose a new loss based on robustness theory, which\nencourages the model to learn high-quality representations for both head and\ntail classes. While the general form of the robustness loss may be hard to\ncompute, we further derive an easy-to-compute upper bound that can be minimized\nefficiently. This procedure reduces representation bias towards head classes in\nthe feature space and achieves new SOTA results on CIFAR100-LT, ImageNet-LT,\nand iNaturalist long-tail benchmarks. We find that training with robustness\nincreases recognition accuracy of tail classes while largely maintaining the\naccuracy of head classes. The new robustness loss can be combined with various\nclassifier balancing techniques and can be applied to representations at\nseveral layers of the deep model.\n"}
+{"abstract": "  In this article, we study a class of semilinear scalar field equations on the\nreal line with variable coefficients in the linear terms. We establish\nasymptotic stability for the vacuum state with respect to perturbations in\n$H^1\\times L^2$, without placing any parity assumptions on the coefficients,\npotential, or initial data. Next, under a parity assumption, we show asymptotic\nstability for steady states that are \"almost constant\" in the spatial variable,\nwhich can be constructed because the of the non-translation-invariant linear\npart of our equation.\n"}
+{"abstract": "  We consider the pricing of VIX options in the rough Bergomi model [Bayer,\nFriz, and Gatheral, Pricing under rough volatility, Quantitative Finance 16(6),\n887-904, 2016]. In this setting, the VIX random variable is defined by the\none-dimensional integral of the exponential of a Gaussian process with\ncorrelated increments, hence approximate samples of the VIX can be constructed\nvia discretization of the integral and simulation of a correlated Gaussian\nvector. A Monte-Carlo estimator of VIX options based on a rectangle\ndiscretization scheme and exact Gaussian sampling via the Cholesky method has a\ncomputational complexity of order $\\mathcal O(\\varepsilon^{-4})$ when the\nmean-squared error is set to $\\varepsilon^2$. We demonstrate that this cost can\nbe reduced to $\\mathcal O(\\varepsilon^{-2} \\log^2(\\varepsilon))$ combining the\nscheme above with the multilevel method [Giles, Multilevel Monte Carlo path\nsimulation, Oper. Res. 56(3), 607-617, 2008], and further reduced to the\nasymptotically optimal cost $\\mathcal O(\\varepsilon^{-2})$ when using a\ntrapezoidal discretization. We provide numerical experiments highlighting the\nefficiency of the multilevel approach in the pricing of VIX options in such a\nrough forward variance setting.\n"}
+{"abstract": "  Given a collection of graphs $\\mathbf{G}=(G_1, \\ldots, G_m)$ with the same\nvertex set, an $m$-edge graph $H\\subset \\cup_{i\\in [m]}G_i$ is a transversal if\nthere is a bijection $\\phi:E(H)\\to [m]$ such that $e\\in E(G_{\\phi(e)})$ for\neach $e\\in E(H)$. We give asymptotically-tight minimum degree conditions for a\ngraph collection on an $n$-vertex set to have a transversal which is a copy of\na graph $H$, when $H$ is an $n$-vertex graph which is an $F$-factor or a tree\nwith maximum degree $o(n/\\log n)$.\n"}
+{"abstract": "  Many representative graph neural networks, e.g., GPR-GNN and ChebNet,\napproximate graph convolutions with graph spectral filters. However, existing\nwork either applies predefined filter weights or learns them without necessary\nconstraints, which may lead to oversimplified or ill-posed filters. To overcome\nthese issues, we propose BernNet, a novel graph neural network with theoretical\nsupport that provides a simple but effective scheme for designing and learning\narbitrary graph spectral filters. In particular, for any filter over the\nnormalized Laplacian spectrum of a graph, our BernNet estimates it by an\norder-$K$ Bernstein polynomial approximation and designs its spectral property\nby setting the coefficients of the Bernstein basis. Moreover, we can learn the\ncoefficients (and the corresponding filter weights) based on observed graphs\nand their associated signals and thus achieve the BernNet specialized for the\ndata. Our experiments demonstrate that BernNet can learn arbitrary spectral\nfilters, including complicated band-rejection and comb filters, and it achieves\nsuperior performance in real-world graph modeling tasks. Code is available at\nhttps://github.com/ivam-he/BernNet.\n"}
+{"abstract": "  We consider methods for transporting a prediction model and assessing its\nperformance for use in a new target population, when outcome and covariate\ninformation for model development is available from a simple random sample from\nthe source population, but only covariate information is available on a simple\nrandom sample from the target population. We discuss how to tailor the\nprediction model for use in the target population, how to assess model\nperformance in the target population (e.g., by estimating the target population\nmean squared error), and how to perform model and tuning parameter selection in\nthe context of the target population. We provide identifiability results for\nthe target population mean squared error of a potentially misspecified\nprediction model under a sampling design where the source study and the target\npopulation samples are obtained separately. We also introduce the concept of\nprediction error modifiers that can be used to reason about the need for\ntailoring measures of model performance to the target population and provide an\nillustration of the methods using simulated data.\n"}
+{"abstract": "  3D object detection is a key perception component in autonomous driving. Most\nrecent approaches are based on Lidar sensors only or fused with cameras. Maps\n(e.g., High Definition Maps), a basic infrastructure for intelligent vehicles,\nhowever, have not been well exploited for boosting object detection tasks. In\nthis paper, we propose a simple but effective framework - MapFusion to\nintegrate the map information into modern 3D object detector pipelines. In\nparticular, we design a FeatureAgg module for HD Map feature extraction and\nfusion, and a MapSeg module as an auxiliary segmentation head for the detection\nbackbone. Our proposed MapFusion is detector independent and can be easily\nintegrated into different detectors. The experimental results of three\ndifferent baselines on large public autonomous driving dataset demonstrate the\nsuperiority of the proposed framework. By fusing the map information, we can\nachieve 1.27 to 2.79 points improvements for mean Average Precision (mAP) on\nthree strong 3d object detection baselines.\n"}
+{"abstract": "  In order to study the circumgalactic medium (CGM) of galaxies we develop an\nautomated pipeline to estimate the optical continuum of quasars and detect\nintervening metal absorption line systems with a matched kernel convolution\ntechnique and adaptive S/N criteria. We process $\\sim$ one million quasars in\nthe latest Data Release 16 (DR16) of the Sloan Digital Sky Survey (SDSS) and\ncompile a large sample of $\\sim$ 160,000 MgII absorbers, together with $\\sim$\n70,000 FeII systems, in the redshift range $0.35<z_{abs}<2.3$. Combining these\nwith the SDSS DR16 spectroscopy of $\\sim1.1$ million luminous red galaxies\n(LRGs) and $\\sim 200,000$ emission line galaxies (ELGs), we investigate the\nnature of cold gas absorption at $0.5<z<1$. These large samples allow us to\ncharacterize the scale dependence of MgII with greater accuracy than in\nprevious work. We find that there is a strong enhancement of MgII absorption\nwithin $\\sim 50$ kpc of ELGs, and the covering fraction within $0.5r_{\\rm vir}$\nof ELGs is 2-5 times higher than for LRGs. Beyond 50 kpc, there is a sharp\ndecline in MgII for both kinds of galaxies, indicating a transition to the\nregime where the CGM is tightly linked with the dark matter halo. The MgII\ncovering fraction correlates strongly with stellar mass for LRGs, but weakly\nfor ELGs, where covering fractions increase with star formation rate. Our\nanalysis implies that cool circumgalactic gas has a different physical origin\nfor star forming versus quiescent galaxies.\n"}
+{"abstract": "  In this paper, we study the long-time behavior of the solution for the\nlinearized ideal MHD around sheared velocity and magnetic field under Stern\nstability condition. We prove that the velocity and magnetic field will\nconverge to sheared velocity and magnetic field as time approaches infinity.\nMoreover a new depletion phenomenon is proved: the horizontal velocity and\nmagnetic field at the critical points will decay to 0 as time approaches\ninfinity.\n"}
+{"abstract": "  The MR-Linac is a combination of an MR-scanner and radiotherapy linear\naccelerator (Linac) which holds the promise to increase the precision of\nradiotherapy treatments with MR-guided radiotherapy by monitoring motion during\nradiotherapy with MRI, and adjusting the radiotherapy plan accordingly. Optimal\nMR-guidance for respiratory motion during radiotherapy requires MR-based 3D\nmotion estimation with a latency of 200-500 ms. Currently this is still\nchallenging since typical methods rely on MR-images, and are therefore limited\nby the 3D MR-imaging latency. In this work, we present a method to perform\nnon-rigid 3D respiratory motion estimation with 170 ms latency, including both\nacquisition and reconstruction. The proposed method called real-time low-rank\nMR-MOTUS reconstructs motion-fields directly from k-space data, and leverages\nan explicit low-rank decomposition of motion-fields to split the large scale\n3D+t motion-field reconstruction problem posed in our previous work into two\nparts: (I) a medium-scale offline preparation phase and (II) a small-scale\nonline inference phase which exploits the results of the offline phase for\nreal-time computations. The method was validated on free-breathing data of five\nvolunteers, acquired with a 1.5T Elekta Unity MR-Linac. Results show that the\nreconstructed 3D motion-field are anatomically plausible, highly correlated\nwith a self-navigation motion surrogate (R = 0.975 +/- 0.0110), and can be\nreconstructed with a total latency of 170 ms that is sufficient for real-time\nMR-guided abdominal radiotherapy.\n"}
+{"abstract": "  Binary millisecond pulsars (MSPs) are detached binary systems consisting of a\nMSP and a He white dwarf. If the initial orbital periods of binary MSPs are\nless than 0.3 day, they would evolve toward ultra-compact binary pulsars due to\nthe rapid orbital shrinkage by the gravitational wave (GW) radiation. During\nthe orbital decay, the MSP with an ellipticity would spin down by the GW\nradiation and the magnetic dipole radiation. Our calculations indicate that the\nangular momentum loss is dominated by the GW radiation when the ellipticities\nof the neutron stars (NSs) are in the range of $(1-50)\\times 10^{-7}$, and the\nfrequencies of high-frequency GW signals from the rotating NSs are $10-100$ Hz\nwhen the binary pulsars can be visible as low-frequency GW sources. These\nhigh-frequency GW signals are possible to be detected by the aLIGO and the\nthird-generation GW detectors such as Einstein Telescope, depending on the\nfrequencies and the distances. Therefore, some ultra-compact binary pulsars\nhave an opportunity to become intriguing dual-line GW sources. By detecting the\nlow-frequency GW signals, the NS mass can be accurately derived. A dual-line\ndetection of two band GW signals could provide a constraint on the moment of\ninertia and the ellipticity of the NS. Thus the dual-line GW sources can be\npotentially applied to constrain the equation of state of the NS.\n"}
+{"abstract": "  We show that in the Dirac-Milne universe (a matter-antimatter symmetric\nuniverse where the two components repel each other), rotation curves are\ngenerically flat beyond the characteristic distance of about 3 virial radii,\nand that a Tully-Fisher relation with exponent $\\approx 3$ is satisfied. Using\n3D simulations with a modified version of the RAMSES code, we show that the\nDirac-Milne cosmology presents a Faber-Jackson relation with a very small\nscatter and an exponent equal to $\\approx 3$ between the mass and the velocity\ndispersion. We also show that the mass derived from the rotation curves\nassuming Newtonian gravity is systematically overestimated compared to the mass\nreally present. We also show that the Dirac-Milne universe, featuring a\npolarization between its matter and antimatter components, presents a behavior\nsimilar to that of MOND (Modified Newtonian Dynamics), characterized by an\nadditional surface gravity compared to the Newtonian case. We show that in the\nDirac-Milne universe, at the present epoch, the intensity of the additional\ngravitational field $g_{am}$ due to the presence of clouds of antimatter is of\nthe order of a few $10^{-11}$ m/s$^2$, similar to the characteristic\nacceleration of MOND. We study the evolution of this additional acceleration\n$g_{am}$ and show that it depends on the redshift, and is therefore not a\nfundamental constant. Combined with its known concordance properties on SNIa\nluminosity distance, age, nucleosynthesis and structure formation, the\nDirac-Milne cosmology may then represent an interesting alternative to the\n$\\Lambda$CDM, MOND, and other scenarios for explaining the Dark Matter and Dark\nEnergy conundrum.\n"}
+{"abstract": "  Cloth-Changing person re-identification (CC-ReID) aims at matching the same\nperson across different locations over a long-duration, e.g., over days, and\ntherefore inevitably meets challenge of changing clothing. In this paper, we\nfocus on handling well the CC-ReID problem under a more challenging setting,\ni.e., just from a single image, which enables high-efficiency and latency-free\npedestrian identify for real-time surveillance applications. Specifically, we\nintroduce Gait recognition as an auxiliary task to drive the Image ReID model\nto learn cloth-agnostic representations by leveraging personal unique and\ncloth-independent gait information, we name this framework as GI-ReID. GI-ReID\nadopts a two-stream architecture that consists of a image ReID-Stream and an\nauxiliary gait recognition stream (Gait-Stream). The Gait-Stream, that is\ndiscarded in the inference for high computational efficiency, acts as a\nregulator to encourage the ReID-Stream to capture cloth-invariant biometric\nmotion features during the training. To get temporal continuous motion cues\nfrom a single image, we design a Gait Sequence Prediction (GSP) module for\nGait-Stream to enrich gait information. Finally, a high-level semantics\nconsistency over two streams is enforced for effective knowledge\nregularization. Experiments on multiple image-based Cloth-Changing ReID\nbenchmarks, e.g., LTCC, PRCC, Real28, and VC-Clothes, demonstrate that GI-ReID\nperforms favorably against the state-of-the-arts. Codes are available at\nhttps://github.com/jinx-USTC/GI-ReID.\n"}
+{"abstract": "  Combinatorial Optimization (CO) has been a long-standing challenging research\ntopic featured by its NP-hard nature. Traditionally such problems are\napproximately solved with heuristic algorithms which are usually fast but may\nsacrifice the solution quality. Currently, machine learning for combinatorial\noptimization (MLCO) has become a trending research topic, but most existing\nMLCO methods treat CO as a single-level optimization by directly learning the\nend-to-end solutions, which are hard to scale up and mostly limited by the\ncapacity of ML models given the high complexity of CO. In this paper, we\npropose a hybrid approach to combine the best of the two worlds, in which a\nbi-level framework is developed with an upper-level learning method to optimize\nthe graph (e.g. add, delete or modify edges in a graph), fused with a\nlower-level heuristic algorithm solving on the optimized graph. Such a bi-level\napproach simplifies the learning on the original hard CO and can effectively\nmitigate the demand for model capacity. The experiments and results on several\npopular CO problems like Directed Acyclic Graph scheduling, Graph Edit Distance\nand Hamiltonian Cycle Problem show its effectiveness over manually designed\nheuristics and single-level learning methods.\n"}
+{"abstract": "  The map $TC(\\mathbb{S})^{\\wedge}_{p}\\to \\text{holim }\nTC(L_{n}\\mathbb{S})^{\\wedge}_{p}$ is a weak equivalence.\n"}
+{"abstract": "  This survey paper focuses on the estimation schemes in molecular\ncommunication (MC) systems. The existing studies in estimation schemes can be\ndivided into parameter estimation (e.g., distance, diffusion coefficient, and\nflow velocity) and channel estimation. In this paper, we present, for the first\ntime, a comprehensive survey on i) distance estimation, since distance is the\nmost widely estimated parameter in current studies, ii) estimation of other\nparameters (i.e. the parameters excluding distance), and iii) channel\nestimation that focuses on the channel impulse response (CIR). Moreover, we\nexamine the noise that may impact on the estimation performance and the metrics\napplied to evaluate the performance of different estimation schemes. Numerical\nresults are provided to compare the performance of different distance\nestimation schemes. In addition, future research directions in parameter\nestimation and channel estimation are identified and discussed.\n"}
+{"abstract": "  In this paper, we use a variety of machine learning methods to quantify the\nextent to which economic and technological factors are predictive of the\nprogression of Central Bank Digital Currencies (CBDC) within a country, using\nas our measure of this progression the CBDC project index (CBDCPI). We find\nthat a financial development index is the most important feature for our model,\nfollowed by the GDP per capita and an index of the voice and accountability of\nthe country's population. Our results are consistent with previous qualitative\nresearch which finds that countries with a high degree of financial development\nor digital infrastructure have more developed CBDC projects. Further, we obtain\nrobust results when predicting the CBDCPI at different points in time.\n"}
+{"abstract": "  In a Dyck path a peak which is (weakly) higher than all the preceding peaks\nis called a strict (weak) left to right maximum. We obtain explicit generating\nfunctions for both weak and strict left to right maxima in Dyck paths. The\nproofs of the associated asymptotics make use of analytic techniques such as\nMellin transforms, singularity analysis and formal residue calculus.\n"}
+{"abstract": "  In the first part of this work we have established an efficient method to\nobtain a topological classification of locally discrete, finitely generated,\nvirtually free subgroups of real-analytic circle diffeomorphisms. In this\nsecond part we describe several consequences, among which the solution (within\nthis setting) to an old conjecture by P. R. Dippolito [Ann. Math. 107 (1978),\n403-453] that actions with invariant Cantor sets must be semi-conjugate to\npiecewise linear actions. In addition, we exhibit examples of locally discrete,\nminimal actions which are not of Fuchsian type.\n"}
+{"abstract": "  Driven-dissipative many-body systems are difficult to analyze analytically\ndue to their non-equilibrium dynamics, dissipation and many-body interactions.\nIn this paper, we consider a driven-dissipative infinite-range Ising model with\nlocal spontaneous emission, which naturally emerges from the open Dicke model\nin the large-detuning limit. Utilizing an adaptation of the Suzuki-Trotter\nquantum-to-classical mapping, we develop an exact field-theoretical analysis\nand a diagrammatic representation of the spin model that can be understood from\na simple scattering picture. With this representation, we are able to analyze\ncritical behavior, finite-size scaling and the effective temperature near the\nrespective phase transition. Our formalism further allows a detailed study of\nthe ordered phase where we find a \"heating\" region within which the effective\ntemperature becomes negative, thereby exhibiting a truly non-equilibrium\nbehavior. At the phase transition, we find two distinct critical behaviors with\noverdamped and underdamped critical dynamics at generic and weakly-dissipative\ncritical points, respectively. We further show that the underdamped critical\nbehavior is robust against short-range perturbations and is not an artifact of\nthe mean-field nature of the model. To treat such perturbations, we extend our\ndiagrammatic representation to include the coupling to spin waves due to the\nshort-range interactions. The field-theoretical approach and the diagrammatics\ndeveloped in this work should prove useful in applications to generic\nshort-range driven-dissipative spin systems.\n"}
+{"abstract": "  The two Zero Degree Calorimeters (ZDCs) of the CMS experiment are located at\n$\\pm 140~$m from the collision point and detect neutral particles in the\n$|\\eta| > 8.3$ pseudorapidity region. This paper presents a study on the\nperformance of the ZDC in the 2016 pPb run. The response of the detectors to\nultrarelativistic neutrons is studied using in-depth Monte Carlo simulations. A\nmethod of signal extraction based on template fits is presented, along with a\ndedicated calibration procedure. A deconvolution technique for the correction\nof overlapping collision events is discussed.\n"}
+{"abstract": "  We prove explicit asymptotic formulae for some functions used in sieve\nmethods and show that there exists no odd multiperfect number of abundancy four\nwhose squared part is cubefree.\n"}
+{"abstract": "  In high-speed hyperconnected parcel logistics, intermediate hubs play a\ncritical role in reaching economies of scale through consolidating disperse\nflows of goods. However, resorting small-size parcels at every hub is\nresource-intensive and can increase hubs' workload and parcels' total travel\ntime. Such resorting can be reduced by smart containerized consolidation,\nencapsulating together parcels sharing service level and a subsequent\ndestination. In this study, we introduce an optimization model enabling to\nassess the impact of such consolidation in reducing the expected total\npickup-to-delivery times over a parcel logistic network and its consequences on\nurban logistic operations sustainability. We provide empirical results for a\nsynthetic urban environment, contrasting consolidation performance over\ndifferent operational and tactical capabilities, network configurations and\ndemand patterns.\n"}
+{"abstract": "  Numerical methods for stochastic differential equations with non-globally\nLipschitz coefficients are currently studied intensively. This article gives an\noverview of our work for the case that the drift coefficient is potentially\ndiscontinuous complemented by other important results in this area. To make the\ntopic accessible to a broad audience, we begin with a heuristic on SDEs and a\nmotivation.\n"}
+{"abstract": "  This paper bridges discrete and continuous optimization approaches for\ndecomposable submodular function minimization, in both the standard and\nparametric settings.\n  We provide improved running times for this problem by reducing it to a number\nof calls to a maximum flow oracle. When each function in the decomposition acts\non $O(1)$ elements of the ground set $V$ and is polynomially bounded, our\nrunning time is up to polylogarithmic factors equal to that of solving maximum\nflow in a sparse graph with $O(\\vert V \\vert)$ vertices and polynomial integral\ncapacities.\n  We achieve this by providing a simple iterative method which can optimize to\nhigh precision any convex function defined on the submodular base polytope,\nprovided we can efficiently minimize it on the base polytope corresponding to\nthe cut function of a certain graph that we construct. We solve this\nminimization problem by lifting the solutions of a parametric cut problem,\nwhich we obtain via a new efficient combinatorial reduction to maximum flow.\nThis reduction is of independent interest and implies some previously unknown\nbounds for the parametric minimum $s,t$-cut problem in multiple settings.\n"}
+{"abstract": "  State-of-the-art (SOTA) neural machine translation (NMT) systems translate\ntexts at sentence level, ignoring context: intra-textual information, like the\nprevious sentence, and extra-textual information, like the gender of the\nspeaker. Because of that, some sentences are translated incorrectly.\nPersonalised NMT (PersNMT) and document-level NMT (DocNMT) incorporate this\ninformation into the translation process. Both fields are relatively new and\nprevious work within them is limited. Moreover, there are no readily available\nrobust evaluation metrics for them, which makes it difficult to develop better\nsystems, as well as track global progress and compare different methods. This\nthesis proposal focuses on PersNMT and DocNMT for the domain of dialogue\nextracted from TV subtitles in five languages: English, Brazilian Portuguese,\nGerman, French and Polish. Three main challenges are addressed: (1)\nincorporating extra-textual information directly into NMT systems; (2)\nimproving the machine translation of cohesion devices; (3) reliable evaluation\nfor PersNMT and DocNMT.\n"}
+{"abstract": "  A uniform hypergraph $\\mathcal{H}$ is corresponding to an adjacency tensor\n$\\mathcal{A}_\\mathcal{H}$. We define an Estrada index of $\\mathcal{H}$ by using\nall the eigenvalues $\\lambda_1,\\dots,\\lambda_k$ of $\\mathcal{A}_\\mathcal{H}$ as\n$\\sum_{i=1}^k e^{\\lambda_i}$. The bounds for the Estrada indices of uniform\nhypergraphs are given. And we characterize the Estrada indices of $m$-uniform\nhypergraphs whose spectra of the adjacency tensors are $m$-symmetric.\nSpecially, we characterize the Estrada indices of uniform hyperstars.\n"}
+{"abstract": "  The interplay between topological electronic structure and superconductivity\nhas attracted tremendous research interests recently as they could induce\ntopological superconductivity (TSCs) which may be used to realize topological\nqubits for quantum computation. Among various TSC candidates, superconducting\nBaSn3 (Tc ~ 4.4 K) has been predicted to be a topological Dirac semimetal (TDS)\nhosting two pairs of Dirac points along the G - A direction. Here, by combining\nthe use of angle-resolved photoemission spectroscopy and ab initio\ncalculations, we identified the predicted topological Dirac fermions and\nconfirmed the TDS nature of the compound. In addition, we observed surface\nstates connecting the Dirac points. Our observations demonstrate BaSn3 as a\nsuperconductor with nontrivial topological electronic structures.\n"}
+{"abstract": "  We connect the Grover walk with sinks to the Grover walk with tails. The\nsurvival probability of the Grover walk with sinks in the long time limit is\ncharacterized by the centered generalized eigenspace of the Grover walk with\ntails. The centered eigenspace of the Grover walk is the attractor eigenspace\nof the Grover walk with sinks. It is described by the persistent eigenspace of\nthe underlying random walk whose support has no overlap to the boundaries of\nthe graph and combinatorial flow in the graph theory.\n"}
+{"abstract": "  Initiated in 2007, the INSPIRE Directive has set a legal framework to create\na European-wide Spatial Data Infrastructure (SDI) to support the European Union\n(EU) environmental policies. This chapter analyses the INSPIRE infrastructure\nfrom a Big Geospatial Data perspective, describing how data is shared in an\ninteroperable way by public sector organisations in the EU Member States and\nhow it is made available in and accessible within the infrastructure. The\nINSPIRE Geoportal, which is the entry point to the whole infrastructure, is\npresented in detail. To justify its nature of a Big Geospatial Data\ninfrastructure, the characteristics of INSPIRE data are mapped to those of Big\nData's six 'Vs'. Despite many good results achieved in terms of data sharing,\nsome challenges still remain related to data consumption from the user side.\nThe chapter concludes with a dedicated discussion on how INSPIRE, and\ntraditional SDIs in general, should evolve into modern data ecosystems to\naddress these challenges while also embracing the modern practices of data\nsharing through the web.\n"}
+{"abstract": "  The formulation of national research policies would benefit greatly from\nreliable strategic analysis of the scientific infrastructure, aimed at\nidentifying the relevant strengths and weaknesses at field level. Bibliometric\nmethodologies thus far proposed in the literature are not completely\nsatisfactory. This work proposes a novel \"output-to-input-oriented\" approach,\nwhich permits identification of research strengths and weaknesses on the basis\nof the ratios of top scientists and highly cited articles to research\nexpenditures in each field. The proposed approach is applied to the Italian\nacademic system. 2012-2016 scientific publications are analyzed, in the 218\nresearch fields where bibliometric assessment is appropriate.\n"}
+{"abstract": "  It is possible that bosonic dark matter forms halos around the Sun or the\nEarth. We discuss the possibility of probing such halos with atomic clocks.\nFocusing on either a Higgs portal or photon portal interaction between the dark\nmatter and the Standard Model, we search the possible parameter space for which\na clock on Earth and a clock in space would have a discernible frequency\ndifference. Bosonic dark matter halos surrounding the Earth can potentially be\nprobed with current optical atomic clocks.\n"}
+{"abstract": "  In this note, we propose a novel technique to reduce the algorithmic\ncomplexity of neural network training by using matrices of tropical real\nnumbers instead of matrices of real numbers. Since the tropical arithmetics\nreplaces multiplication with addition, and addition with max, we theoretically\nachieve several order of magnitude better constant factors in time complexities\nin the training phase. The fact that we replace the field of real numbers with\nthe tropical semiring of real numbers and yet achieve the same classification\nresults via neural networks come from deep results in topology and analysis,\nwhich we verify in our note. We then explore artificial neural networks in\nterms of tropical arithmetics and tropical algebraic geometry, and introduce\nthe multi-layered tropical neural networks as universal approximators. After\ngiving a tropical reformulation of the backpropagation algorithm, we verify the\nalgorithmic complexity is substantially lower than the usual backpropagation as\nthe tropical arithmetic is free of the complexity of usual multiplication.\n"}
+{"abstract": "  Tuberculosis (TB) is a top-10 cause of death worldwide. Though the WHO\nrecommends chest radiographs (CXRs) for TB screening, the limited availability\nof CXR interpretation is a barrier. We trained a deep learning system (DLS) to\ndetect active pulmonary TB using CXRs from 9 countries across Africa, Asia, and\nEurope, and utilized large-scale CXR pretraining, attention pooling, and noisy\nstudent semi-supervised learning. Evaluation was on (1) a combined test set\nspanning China, India, US, and Zambia, and (2) an independent mining population\nin South Africa. Given WHO targets of 90% sensitivity and 70% specificity, the\nDLS's operating point was prespecified to favor sensitivity over specificity.\nOn the combined test set, the DLS's ROC curve was above all 9 India-based\nradiologists, with an AUC of 0.90 (95%CI 0.87-0.92). The DLS's sensitivity\n(88%) was higher than the India-based radiologists (75% mean sensitivity),\np<0.001 for superiority; and its specificity (79%) was non-inferior to the\nradiologists (84% mean specificity), p=0.004. Similar trends were observed\nwithin HIV positive and sputum smear positive sub-groups, and in the South\nAfrica test set. We found that 5 US-based radiologists (where TB isn't endemic)\nwere more sensitive and less specific than the India-based radiologists (where\nTB is endemic). The DLS also remained non-inferior to the US-based\nradiologists. In simulations, using the DLS as a prioritization tool for\nconfirmatory testing reduced the cost per positive case detected by 40-80%\ncompared to using confirmatory testing alone. To conclude, our DLS generalized\nto 5 countries, and merits prospective evaluation to assist cost-effective\nscreening efforts in radiologist-limited settings. Operating point flexibility\nmay permit customization of the DLS to account for site-specific factors such\nas TB prevalence, demographics, clinical resources, and customary practice\npatterns.\n"}
+{"abstract": "  In this paper we introduce the notion of Modal Software Engineering:\nautomatically turning sequential, deterministic programs into semantically\nequivalent programs efficiently operating on inputs coming from multiple\noverlapping worlds. We are drawing an analogy between modal logics, and\nsoftware application domains where multiple sets of inputs (multiple worlds)\nneed to be processed efficiently. Typically those sets highly overlap, so\nprocessing them independently would involve a lot of redundancy, resulting in\nlower performance, and in many cases intractability. Three application domains\nare presented: reasoning about feature-based variability of Software Product\nLines (SPLs), probabilistic programming, and approximate programming.\n"}
+{"abstract": "  This work is meant to demonstrate new class of prime numbers -- cyclic prime\nnumbers, that can be derived from any prime number at certain numeric systems.\nCyclic prime numbers are also related to the cyclic numbers and full reptend\nprime numbers. Cyclic prime numbers, derived from the same prime number, could\nbe grouped. Conditions and properties of those groups between different numeric\nsystems are also the subjects of this paper.\n"}
+{"abstract": "  Gold-associated pathfinder minerals have been investigated by identifying\nhost minerals of Au for samples collected from an artisanal mining site near a\npotential gold mine (Kubi Gold Project) in Dunkwa-On-Offin in the central\nregion of Ghana. We find that for each composition of Au powder (impure) and\nthe residual black hematite/magnetite sand that remains after gold panning,\nthere is a unique set of associated diverse indicator minerals. These indicator\nminerals are identified as SiO2 (quartz), Fe3O4 (magnetite), and Fe2O3\n(hematite), while contributions from pyrite, arsenopyrites, iridosmine,\nscheelite, tetradymite, garnet, gypsum, and other sulfate materials are\ninsignificant. This constitutes a confirmative identification of Au pathfinding\nminerals in this particular mineralogical area. The findings suggest that X-ray\ndiffraction could also be applied in other mineralogical sites to aid in\nidentifying indicator minerals of Au and the location of ore bodies at reduced\nenvironmental and exploration costs.\n"}
+{"abstract": "  In product description generation (PDG), the user-cared aspect is critical\nfor the recommendation system, which can not only improve user's experiences\nbut also obtain more clicks. High-quality customer reviews can be considered as\nan ideal source to mine user-cared aspects. However, in reality, a large number\nof new products (known as long-tailed commodities) cannot gather sufficient\namount of customer reviews, which brings a big challenge in the product\ndescription generation task. Existing works tend to generate the product\ndescription solely based on item information, i.e., product attributes or title\nwords, which leads to tedious contents and cannot attract customers\neffectively. To tackle this problem, we propose an adaptive posterior network\nbased on Transformer architecture that can utilize user-cared information from\ncustomer reviews. Specifically, we first extend the self-attentive Transformer\nencoder to encode product titles and attributes. Then, we apply an adaptive\nposterior distillation module to utilize useful review information, which\nintegrates user-cared aspects to the generation process. Finally, we apply a\nTransformer-based decoding phase with copy mechanism to automatically generate\nthe product description. Besides, we also collect a large-scare Chinese product\ndescription dataset to support our work and further research in this field.\nExperimental results show that our model is superior to traditional generative\nmodels in both automatic indicators and human evaluation.\n"}
+{"abstract": "  Automated semantic segmentation and object detection are of great importance\nin geospatial data analysis. However, supervised machine learning systems such\nas convolutional neural networks require large corpora of annotated training\ndata. Especially in the geospatial domain, such datasets are quite scarce.\nWithin this paper, we aim to alleviate this issue by introducing a new\nannotated 3D dataset that is unique in three ways: i) The dataset consists of\nboth an Unmanned Aerial Vehicle (UAV) laser scanning point cloud and a 3D\ntextured mesh. ii) The point cloud features a mean point density of about 800\npts/sqm and the oblique imagery used for 3D mesh texturing realizes a ground\nsampling distance of about 2-3 cm. This enables the identification of\nfine-grained structures and represents the state of the art in UAV-based\nmapping. iii) Both data modalities will be published for a total of three\nepochs allowing applications such as change detection. The dataset depicts the\nvillage of Hessigheim (Germany), henceforth referred to as H3D. It is designed\nto promote research in the field of 3D data analysis on one hand and to\nevaluate and rank existing and emerging approaches for semantic segmentation of\nboth data modalities on the other hand. Ultimately, we hope that H3D will\nbecome a widely used benchmark dataset in company with the well-established\nISPRS Vaihingen 3D Semantic Labeling Challenge benchmark (V3D). The dataset can\nbe downloaded from\nhttps://ifpwww.ifp.uni-stuttgart.de/benchmark/hessigheim/default.aspx.\n"}
+{"abstract": "  Polar active particles constitute a wide class of synthetic colloids that are\nable to propel along a preferential direction, given by their polar axis. Here,\nwe demonstrate a generic self-phoretic mechanism that leads to their\nspontaneous chiralization through a symmetry breaking instability. We find that\nthe transition of an active particle from a polar to a chiral symmetry is\ncharacterized by the emergence of active rotation and of circular trajectories.\nWe show that the instability is driven by the advection of a solute that\ninteracts differently with the two portions of the particle surface and it\noccurs through a supercritical pitchfork bifurcation.\n"}
+{"abstract": "  The breaking of reciprocity is a topic of great interest in fundamental\nphysics and optical information processing applications. We demonstrate\nnon-reciprocal light transport in a quantum system of hot atoms by engineering\nthe dissipative atomic reservoir. Our scheme is based on the phase-sensitive\nlight transport in a multi-channel photon-atom interaction configuration, where\nthe phase of collective atomic excitations is tunable through external driving\nfields. Remarkably, we observe inter-channel quantum correlations which\noriginate from interactions with the judiciously engineered reservoir. The\nnon-reciprocal transport in a quantum optical atomic system constitutes a new\nparadigm for atom-based, non-reciprocal optics, and offers opportunities for\nquantum simulations with coupled optical channels.\n"}
+{"abstract": "  In this paper, we study a class of fully nonlinear contracting curvature\nflows of closed, uniformly convex hypersurfaces in the Euclidean space $\\mathbb\nR^{n+1}$ with the normal speed $\\Phi$ given by $r^\\alpha F^\\beta$ or $u^\\alpha\nF^\\beta$, where $F$ is a monotone, symmetric, inverse-concave, homogeneous of\ndegree one function of the principal curvatures, $r$ is the distance from the\nhypersurface to the origin and $u$ is the support function of hypersurface. If\n$\\alpha\\geq \\beta+1$ when $\\Phi=r^\\alpha F^\\beta$ or $\\alpha> \\beta+1$ when\n$\\Phi=u^\\alpha F^\\beta$, we prove that the flow exists for all times and\nconverges to the origin. After proper rescaling, we prove that the normalized\nflow converges exponentially in the $C^\\infty$ topology to a sphere centered at\nthe origin. Furthermore, for special inverse concave curvature function\n$F=K^{\\frac{s}{n}}F_1^{1-s}(s\\in(0, 1])$, where $K$ is Gauss curvature and\n$F_1$ is inverse-concave, we obtain the asymptotic convergence for the flow\nwith $\\Phi=u^\\alpha F^\\beta$ when $\\alpha=\\beta+1$. If $\\alpha<\\beta+1$, a\ncounterexample is given for the above convergence when speed equals to\n$r^\\alpha F^\\beta$.\n"}
+{"abstract": "  Dust is an essential ingredient of galaxies, determining the physical and\nchemical conditions in the interstellar medium. Several complementary\nobservational evidences indicate that the cosmic dust mass density\nsignificantly drops from redshift $z=1$ to $z=0$. Clearly, and for the first\ntime during cosmic evolution, dust must be destroyed more rapidly than it is\nformed. By considering the dust production/destruction processes acting in this\ncosmic time lapse, we find that the drop can be explained if dust is mainly\ndestroyed by astration (49\\% contribution in the fiducial case) and supernova\nshocks within galaxies (42\\%). Our results further imply that on average each\nsupernova destroys only $M_{d,sn} =0.45\\, M_\\odot$ of dust, i.e. $5-10$ times\nless than usually assumed, with a hard upper limit of $M_{d,sn} < {3.0}\nM_\\odot$ set by the available metal budget and maximal grain growth. The lower\nefficiency might be explained by effective shielding of dust against shock\nprocessing in pre-supernova wind shells.\n"}
+{"abstract": "  End-to-end Automatic Speech Recognition (ASR) models are commonly trained\nover spoken utterances using optimization methods like Stochastic Gradient\nDescent (SGD). In distributed settings like Federated Learning, model training\nrequires transmission of gradients over a network. In this work, we design the\nfirst method for revealing the identity of the speaker of a training utterance\nwith access only to a gradient. We propose Hessian-Free Gradients Matching, an\ninput reconstruction technique that operates without second derivatives of the\nloss function (required in prior works), which can be expensive to compute. We\nshow the effectiveness of our method using the DeepSpeech model architecture,\ndemonstrating that it is possible to reveal the speaker's identity with 34%\ntop-1 accuracy (51% top-5 accuracy) on the LibriSpeech dataset. Further, we\nstudy the effect of two well-known techniques, Differentially Private SGD and\nDropout, on the success of our method. We show that a dropout rate of 0.2 can\nreduce the speaker identity accuracy to 0% top-1 (0.5% top-5).\n"}
+{"abstract": "  We give a construction of a set $A \\subset \\mathbb N$ such that any subset\n$A' \\subset A$ with $|A'| \\gg |A|^{2/3}$ is neither an additive nor\nmultiplicative Sidon set. In doing so, we refute a conjecture of Klurman and\nPohoata.\n"}
+{"abstract": "  Continual learning is known for suffering from catastrophic forgetting, a\nphenomenon where earlier learned concepts are forgotten at the expense of more\nrecent samples. In this work, we challenge the assumption that continual\nlearning is inevitably associated with catastrophic forgetting by presenting a\nset of tasks that surprisingly do not suffer from catastrophic forgetting when\nlearned continually. We provide evidence that these reconstruction-type tasks\nexhibit positive forward transfer and that single-view 3D shape reconstruction\nimproves the performance on learned and novel categories over time. We provide\nthe novel analysis of knowledge transfer ability by looking at the output\ndistribution shift across sequential learning tasks. Finally, we show that the\nrobustness of these tasks leads to the potential of having a proxy\nrepresentation learning task for continual classification. The codebase,\ndataset, and pre-trained models released with this article can be found at\nhttps://github.com/rehg-lab/CLRec.\n"}
+{"abstract": "  Most ultraluminous X-ray sources (ULXs) are now thought to be powered by\nstellar-mass compact objects accreting at super-Eddington rates. While the\ndiscovery of evolutionary cycles have marked a breakthrough in our\nunderstanding of the accretion flow changes in the sub-Eddington regime in\nGalactic Black Hole Binaries, their evidence in the super-Eddington regime\nremained elusive. However, recent circumstantial evidence had hinted the\npresence of a recurrent evolutionary cycle in two archetypal ULXs, Holmberg II\nX-1 and NGC 5204 X-1. Here we build on our previous work and exploit the\nlong-term high-cadence monitoring of Swift-XRT in order to provide evidence of\nthe evolutionary cycle in these two sources and investigate the main physical\nparameters inducing their spectral transitions. We study the long-term\nevolution of both sources using hardness-intensity diagrams (HID) and by means\nof Lomb-Scargle periodograms and Gaussian processes modelling to look for\nperiodic variability. We show that both sources follow a recurrent evolutionary\npattern in the HID that can be characterized by the hard ultraluminous (HUL)\nand soft ultraluminous (SUL) spectral regimes, and a third state with\ncharacteristics similar to the supersoft ultraluminous (SSUL) state. The\ntransitions between the soft states seem aperiodic, as revealed by timing\nanalysis of the light curve of Holmberg II X-1, albeit further investigation is\nwarranted. The light curve of NGC 5204 X-1 shows a periodicity of $\\sim$ 200\ndays, possibly associated with the duration of the evolutionary cycle. We\nsupport a scenario in which the spectral changes from HUL to SUL are due to a\nperiodic increase of the mass-transfer rate and subsequent narrowing of the\nopening angle of the supercritical funnel. The narrower funnel, combined with\nstochastic variability imprinted by the wind, might explain the SUL--SSUL\nspectral changes.\n"}
+{"abstract": "  Landfall of a tropical cyclone is the event when it moves over the land after\ncrossing the coast of the ocean. It is important to know the characteristics of\nthe landfall in terms of location and time, well advance in time to take\npreventive measures timely. In this article, we develop a deep learning model\nbased on the combination of a Convolutional Neural network and a Long\nShort-Term memory network to predict the landfall's location and time of a\ntropical cyclone in six ocean basins of the world with high accuracy. We have\nused high-resolution spacial reanalysis data, ERA5, maintained by European\nCenter for Medium-Range Weather Forecasting (ECMWF). The model takes any 9\nhours, 15 hours, or 21 hours of data, during the progress of a tropical cyclone\nand predicts its landfall's location in terms of latitude and longitude and\ntime in hours. For 21 hours of data, we achieve mean absolute error for\nlandfall's location prediction in the range of 66.18 - 158.92 kilometers and\nfor landfall's time prediction in the range of 4.71 - 8.20 hours across all six\nocean basins. The model can be trained in just 30 to 45 minutes (based on ocean\nbasin) and can predict the landfall's location and time in a few seconds, which\nmakes it suitable for real time prediction.\n"}
+{"abstract": "  This paper gives the cohomology classification of finitistic spaces X\nequipped with free actions of the group G = S3 and the orbit space X/G is the\nintegral or mod 2 cohomology quaternion projective space HPn. We have proved\nthat X is the integral or mod 2 cohomology (4n+3)-sphere or the product of\n3-sphere and quaternion projective space HPn.\n  Similar results for G = S1 actions are also discussed.\n"}
+{"abstract": "  We present an example of the string correspondence: a relation between two\nclassical string solutions - one in Minkowski spacetime and another one in the\nAdS spacetime. The first solution describes a $2$d motion of the string with\nmassive ends and was derived in (https://doi.org/10.1103/PhysRevD.13.2364). The\nsecond solution is the accelerating string solution in the $\\textrm{AdS}_3$\nspacetime and describes a heavy quark-antiquark pair (arXiv:0804.1343).\n"}
+{"abstract": "  Advances in artificial intelligence are driven by technologies inspired by\nthe brain, but these technologies are orders of magnitude less powerful and\nenergy efficient than biological systems. Inspired by the nonlinear dynamics of\nneural networks, new unconventional computing hardware has emerged with the\npotential for extreme parallelism and ultra-low power consumption. Physical\nreservoir computing demonstrates this with a variety of unconventional systems\nfrom optical-based to spintronic. Reservoir computers provide a nonlinear\nprojection of the task input into a high-dimensional feature space by\nexploiting the system's internal dynamics. A trained readout layer then\ncombines features to perform tasks, such as pattern recognition and time-series\nanalysis. Despite progress, achieving state-of-the-art performance without\nexternal signal processing to the reservoir remains challenging. Here we show,\nthrough simulation, that magnetic materials in thin-film geometries can realise\nreservoir computers with greater than or similar accuracy to digital recurrent\nneural networks. Our results reveal that basic spin properties of magnetic\nfilms generate the required nonlinear dynamics and memory to solve machine\nlearning tasks. Furthermore, we show that neuromorphic hardware can be reduced\nin size by removing the need for discrete neural components and external\nprocessing. The natural dynamics and nanoscale size of magnetic thin-films\npresent a new path towards fast energy-efficient computing with the potential\nto innovate portable smart devices, self driving vehicles, and robotics.\n"}
+{"abstract": "  We introduce the quantum implementation of a binary classifier based on\ncosine similarity between data vectors. The proposed quantum algorithm\nevaluates the classifier on a set of data vectors with time complexity that is\nlogarithmic in the product of the set cardinality and the dimension of the\nvectors. It is based just on a suitable state preparation like the retrieval\nfrom a QRAM, a SWAP test circuit (two Hadamard gates and one Fredkin gate), and\na measurement process on a single qubit. Furthermore we present a simple\nimplementation of the considered classifier on the IBM quantum processor\nibmq_16_melbourne. Finally we describe the combination of the classifier with\nthe quantum version of a K-nearest neighbors algorithm within a hybrid\nquantum-classical structure.\n"}
+{"abstract": "  We study new classes of generic off-diagonal and diagonal cosmological\nsolutions for effective Einstein equations in modified gravity theories, MGTs,\nwith modified dispersion relations, MDRs, encoding possible violations of\n(local) Lorentz invariance, LIVs. Such MGTs are constructed for Lagrange\ndensities with two non-Riemannian volume forms (two-measure theories, TMTs) and\nassociated bimetric/ biconnection geometric structures. For conventional 2+2\nsplitting, we can describe such models in Finsler like variables, which is\nimportant for elaborating geometric methods of constructing exact and\nparametric solutions. Such formulations of general relativity, GR, and MGTs are\nconsidered for Lorentz manifolds and their (co) tangent bundles, in brief,\nFTMT. Off-diagonal metrics solving gravitational field equations in FTMTs are\ndetermined by generating functions, effective sources and integration constants\nand characterized by nonholonomic frame torsion effects. Restricting the class\nof integration functions, we can extract torsionless and diagonal\nconfigurations and model emergent cosmological theories with square scalar\ncurvature, $R^2$, when the global Weyl-scale symmetry is broken via nonlinear\ndynamical interactions with nonholonomic constraints. In the physical\nEinstein-Finsler frame, the constructions involve (i) nonlinear\nre-parametrization symmetries of the generating functions and effective\nsources; (ii) effective potentials for the scalar field with possible two flat\nregions which allows a unified description of locally anisotropic and/or\nisotropic early universe inflation related to acceleration cosmology and dark\nenergy; (iii) there are \"emergent universes\" described by (off-) diagonal\nsolutions for certain nonholonomic phases and parametric cosmological evolution\nresulting in various inflationary phases; (iv) we can reproduce in two-measure\ntheories massive gravity effects.\n"}
+{"abstract": "  This study aims to describe the response of two persistent chlorophyll-a\nmaxima to physical processes affecting the thermocline and nitracline position\nin the Eastern Tropical North Pacific (ETNP). We focused on Long Rossby Waves\ngiven their relevance to the ETNP circulation and its potential as a mechanism\nintroducing nutrients into the euphotic zone. We found the shallower\nchlorophyll-a maximum in oxygenated waters became more intense when denser\nwaters (more nutrients) moved toward the surface. It suggests that isopycnals\nand nitracline displacements modify the nutrient supply in the euphotic zone,\nwhich produces changes in phytoplankton growth. The suboxic and deeper\nchlorophyll a maximum showed a strong association with the 26 kg m^{-3}\nisopycnal being only mechanically displaced, and its chlorophyll-a content does\nnot seem to covary with irradiance or nutrients. The different responses of the\nchlorophyll-a maxima could be explained if different phytoplankton groups are\nassociated with them. Long Rossby Waves can affect the position of the\nthermocline, nitracline and isopycnals in an annual cycle, but it seems to be a\nbackground signal modulated by higher frequency processes such as mesoscale\neddies and other Rossby waves. The co-occurrence of processes can control the\nnitracline depth, and hence the input of nutrients into the euphotic zone that\ncan cause sporadic enhancements of the chlorophyll-a concentration of one\nmaximum.\n"}
+{"abstract": "  In this paper we establish the convergence case of Khintchine's theorem for\naffine hyperplanes in function field of positive characteristic. Along with\nthat, we also prove a quantitative version of the same. The main technique used\nin the proof is a dynamical result called `Quantitative nondivergence' due to\nD. Y. Kleinbock and G. A. Margulis [17].\n"}
+{"abstract": "  We study the structure of profinite polyadic groups and we prove that a\npolyadic topological group $(G, f)$ is profinite, if and only if, it is\ncompact, Hausdorff, totally disconnected. More generally, for a pseudo-variety\n(or a formation) of finite groups $\\mathfrak{X}$, we define the class of\n$\\mathfrak{X}$-polyadic groups, and we show that a polyadic group $(G, f)$ is\npro-$\\mathfrak{X}$, if and only if, it is compact, Hausdorff, totally\ndisconnected and for every open congruence $R$, the quotient $(G/R, f_R)$ is\n$\\mathfrak{X}$-polyadic.\n"}
+{"abstract": "  We study the problem of Safe Policy Improvement (SPI) under constraints in\nthe offline Reinforcement Learning (RL) setting. We consider the scenario\nwhere: (i) we have a dataset collected under a known baseline policy, (ii)\nmultiple reward signals are received from the environment inducing as many\nobjectives to optimize. We present an SPI formulation for this RL setting that\ntakes into account the preferences of the algorithm's user for handling the\ntrade-offs for different reward signals while ensuring that the new policy\nperforms at least as well as the baseline policy along each individual\nobjective. We build on traditional SPI algorithms and propose a novel method\nbased on Safe Policy Iteration with Baseline Bootstrapping (SPIBB, Laroche et\nal., 2019) that provides high probability guarantees on the performance of the\nagent in the true environment. We show the effectiveness of our method on a\nsynthetic grid-world safety task as well as in a real-world critical care\ncontext to learn a policy for the administration of IV fluids and vasopressors\nto treat sepsis.\n"}
+{"abstract": "  The time evolution of a two-component collisionless plasma is modeled by the\nVlasov-Poisson system. In this work, the setting is two and one-half\ndimensional, that is, the distribution functions of the particles species are\nindependent of the third space dimension. We consider the case that an external\nmagnetic field is present in order to confine the plasma in a given infinitely\nlong cylinder. After discussing global well-posedness of the corresponding\nCauchy problem, we construct stationary solutions which indeed have support\naway from their confinement device. Then, in the main part of this work we\ninvestigate the stability of such steady states, both with respect to\nperturbations in the initial data, where we employ the energy-Casimir method,\nand also with respect to perturbations in the external magnetic field.\n"}
+{"abstract": "  Hyperspectral image (HSI) contains both spatial pattern and spectral\ninformation which has been widely used in food safety, remote sensing, and\nmedical detection. However, the acquisition of hyperspectral images is usually\ncostly due to the complicated apparatus for the acquisition of optical\nspectrum. Recently, it has been reported that HSI can be reconstructed from\nsingle RGB image using convolution neural network (CNN) algorithms. Compared\nwith the traditional hyperspectral cameras, the method based on CNN algorithms\nis simple, portable and low cost. In this study, we focused on the influence of\nthe RGB camera spectral sensitivity (CSS) on the HSI. A Xenon lamp incorporated\nwith a monochromator were used as the standard light source to calibrate the\nCSS. And the experimental results show that the CSS plays a significant role in\nthe reconstruction accuracy of an HSI. In addition, we proposed a new HSI\nreconstruction network where the dimensional structure of the original\nhyperspectral datacube was modified by 3D matrix transpose to improve the\nreconstruction accuracy.\n"}
+{"abstract": "  Mid- to far-infrared (IR) lines are suited to study dust obscured regions in\ngalaxies, because IR spectroscopy allows us to explore the most hidden regions\nwhere heavily obscured star formation as well as accretion onto supermassive\nblack-holes occur. This is mostly important at redshifts of 1<z<3, when most of\nthe baryonic mass in galaxies has been assembled. We provide reliable\ncalibrations of the mid- to far-IR ionic fine structure lines, the brightest H2\npure rotational lines and the Polycyclic Aromatic Hydrocarbons (PAHs) features,\nthat will be used to analyse current and future observations in the mm/submm\nrange from the ground, as well as mid-IR spectroscopy from the upcoming James\nWebb Space Telescope. We use three samples of galaxies observed in the local\nUniverse: star forming galaxies, AGN and low-metallicity dwarf galaxies. For\neach population we derive different calibrations of the observed line\nluminosities versus the total IR luminosities. We derive spectroscopic\nmeasurements of SFR and BHAR using mid- and far-IR fine structure lines, H2\npure rotational lines and PAH features. We derive robust star-formation tracers\nbased on the [CII]158 $\\mu$m line; the sum of the [OI]63$\\mu$m and\n[OIII]88$\\mu$m lines; a combination of the neon and sulfur mid-IR lines; the\nbright PAH features at 6.2 and 11.3 $\\mu$m, and the H2 rotational lines at 9.7,\n12.3 and 17 $\\mu$m. We propose the [CII]158$\\mu$m line, the combination of two\nneon lines and, for solar-like metallicity galaxies that may harbor an AGN, the\nPAH11.3$\\mu$m feature as the best SFR tracers. A reliable measure of the BHAR\ncan be obtained using the [OIV]25.9 $\\mu$m and the [NeV]14.3 and 24.3 $\\mu$m\nlines. For the most commonly observed fine-structure lines in the far-IR we\ncompare our calibration with the existing ALMA observations of high redshift\ngalaxies finding overall a good agreement with local results.\n"}
+{"abstract": "  Partizan subtraction games are combinatorial games where two players, say\nLeft and Right, alternately remove a number n of tokens from a heap of tokens,\nwith $n \\in S_L$ (resp. $n \\in S_R$) when it is Left's (resp. Right's) turn.\nThe first player unable to move loses. These games were introduced by Fraenkel\nand Kotzig in 1987, where they introduced the notion of dominance, i.e. an\nasymptotic behavior of the outcome sequence where Left always wins if the heap\nis sufficiently large. In the current paper, we investigate the other kinds of\nbehaviors for the outcome sequence. In addition to dominance, three other\ndisjoint behaviors are defined, namely weak dominance, fairness and ultimate\nimpartiality. We consider the problem of computing this behavior with respect\nto $S_L$ and $S_R$, which is connected to the well-known Frobenius coin\nproblem. General results are given, together with arithmetic and geometric\ncharacterizations when the sets $S_L$ and $S_R$ have size at most 2.\n"}
+{"abstract": "  Quantum entanglement plays a crucial role in quantum information processing\ntasks and quantum mechanics, hence quantifying unknown entanglement is a\nfundamental task. However, this is also challenging, as entanglement cannot be\nmeasured by any observables directly. In this paper, we train neural networks\nto quantify unknown entanglement, where the input features of neural networks\nare the outcome statistics data produced by locally measuring target quantum\nstates, and the training labels are well-chosen quantities. For bipartite\nquantum states, this quantity is coherent information, which is a lower bound\nfor the entanglement of formation and the entanglement of distillation. For\nmultipartite quantum states, we choose this quantity as the geometric measure\nof entanglement. It turns out that the neural networks we train have very good\nperformance in quantifying unknown quantum states, and can beat previous\napproaches like semi-device-independent protocols for this problem easily in\nboth precision and application range. We also observe a surprising phenomenon\nthat on quantum states with stronger quantum nonlocality, the neural networks\ntend to have better performance, though we do not provide them any knowledge on\nquantum nonlocality.\n"}
+{"abstract": "  High-performing machine translation (MT) systems can help overcome language\nbarriers while making it possible for everyone to communicate and use language\ntechnologies in the language of their choice. However, such systems require\nlarge amounts of parallel sentences for training, and translators can be\ndifficult to find and expensive. Here, we present a data collection strategy\nfor MT which, in contrast, is cheap and simple, as it does not require\nbilingual speakers. Based on the insight that humans pay specific attention to\nmovements, we use graphics interchange formats (GIFs) as a pivot to collect\nparallel sentences from monolingual annotators. We use our strategy to collect\ndata in Hindi, Tamil and English. As a baseline, we also collect data using\nimages as a pivot. We perform an intrinsic evaluation by manually evaluating a\nsubset of the sentence pairs and an extrinsic evaluation by finetuning mBART on\nthe collected data. We find that sentences collected via GIFs are indeed of\nhigher quality.\n"}
+{"abstract": "  Tezos is a smart-contract blockchain. Tezos smart contracts are written in a\nlow-level stack-based language called Michelson. This article gives an overview\nof efforts using the Coq proof assistant to have stronger guarantees on\nMichelson smart contracts: the Mi-Cho-Coq framework, a Coq library defining\nformal semantics of Michelson, as well as an interpreter, a simple optimiser\nand a weakest-precondition calculus to reason about Michelson smart contracts;\nAlbert, an intermediate language that abstracts Michelson stacks with a\ncompiler written in Coq that targets Mi-Cho-Coq.\n"}
+{"abstract": "  False information and true information fact checking it, often co-exist in\nsocial networks, each competing to influence people in their spread paths. An\nefficient strategy here to contain false information is to proactively identify\nif nodes in the spread path are likely to endorse false information (i.e.\nfurther spread it) or refutation information (thereby help contain false\ninformation spreading). In this paper, we propose SCARLET (truSt and\nCredibility bAsed gRaph neuraL nEtwork model using aTtention) to predict likely\naction of nodes in the spread path. We aggregate trust and credibility features\nfrom a node's neighborhood using historical behavioral data and network\nstructure and explain how features of a spreader's neighborhood vary. Using\nreal world Twitter datasets, we show that the model is able to predict false\ninformation spreaders with an accuracy of over 87%.\n"}
+{"abstract": "  Multiple imputation is a well-established general technique for analyzing\ndata with missing values. A convenient way to implement multiple imputation is\nsequential regression multiple imputation (SRMI), also called chained equations\nmultiple imputation. In this approach, we impute missing values using\nregression models for each variable, conditional on the other variables in the\ndata. This approach, however, assumes that the missingness mechanism is missing\nat random, and it is not well-justified under not-at-random missingness without\nadditional modification. In this paper, we describe how we can generalize the\nSRMI imputation procedure to handle not-at-random missingness (MNAR) in the\nsetting where missingness may depend on other variables that are also missing.\nWe provide algebraic justification for several generalizations of standard SRMI\nusing Taylor series and other approximations of the target imputation\ndistribution under MNAR. Resulting regression model approximations include\nindicators for missingness, interactions, or other functions of the MNAR\nmissingness model and observed data. In a simulation study, we demonstrate that\nthe proposed SRMI modifications result in reduced bias in the final analysis\ncompared to standard SRMI, with an approximation strategy involving inclusion\nof an offset in the imputation model performing the best overall. The method is\nillustrated in a breast cancer study, where the goal is to estimate the\nprevalence of a specific genetic pathogenic variant.\n"}
+{"abstract": "  We present a caricature generation framework based on shape and style\nmanipulation using StyleGAN. Our framework, dubbed StyleCariGAN, automatically\ncreates a realistic and detailed caricature from an input photo with optional\ncontrols on shape exaggeration degree and color stylization type. The key\ncomponent of our method is shape exaggeration blocks that are used for\nmodulating coarse layer feature maps of StyleGAN to produce desirable\ncaricature shape exaggerations. We first build a layer-mixed StyleGAN for\nphoto-to-caricature style conversion by swapping fine layers of the StyleGAN\nfor photos to the corresponding layers of the StyleGAN trained to generate\ncaricatures. Given an input photo, the layer-mixed model produces detailed\ncolor stylization for a caricature but without shape exaggerations. We then\nappend shape exaggeration blocks to the coarse layers of the layer-mixed model\nand train the blocks to create shape exaggerations while preserving the\ncharacteristic appearances of the input. Experimental results show that our\nStyleCariGAN generates realistic and detailed caricatures compared to the\ncurrent state-of-the-art methods. We demonstrate StyleCariGAN also supports\nother StyleGAN-based image manipulations, such as facial expression control.\n"}
+{"abstract": "  We investigate the entanglement dynamics in a free-fermion chain initially\nprepared in a Fermi sea and subjected to localized losses (dissipative\nimpurity). We derive a formula describing the dynamics of the entanglement\nentropies in the hydrodynamic limit of long times and large intervals. The\nresult depends only on the absorption coefficient of the effective delta\npotential describing the impurity in the hydrodynamic limit. Genuine\ndissipation-induced entanglement is certified by the linear growth of the\nlogarithmic negativity. Finally, in the quantum Zeno regime at strong\ndissipation the entanglement growth is arrested (Zeno entanglement death).\n"}
+{"abstract": "  Neutrino oscillation physics has entered a new precision era, which poses\nmajor challenges to the level of control and diagnostics of the neutrino beams.\nIn this paper, we review the design of high-precision beams, their current\nlimitations, and the latest techniques envisaged to overcome such limits. We\nput emphasis on \"monitored neutrino beams\" and advanced diagnostics to\ndetermine the flux and flavor of the neutrinos produced at the source at the\nper-cent level. We also discuss ab-initio measurements of the neutrino energy\n-- i.e. measurements performed without relying on the event reconstruction at\nthe neutrino detector -- to remove any flux-induced bias in the determination\nof the cross sections.\n"}
+{"abstract": "  In this paper, a new Discontinuity Capturing Shallow Neural Network (DCSNN)\nfor approximating $d$-dimensional piecewise continuous functions and for\nsolving elliptic interface problems is developed. There are three novel\nfeatures in the present network; namely, (i) jump discontinuity is captured\nsharply, (ii) it is completely shallow consisting of only one hidden layer,\n(iii) it is completely mesh-free for solving partial differential equations\n(PDEs). We first continuously extend the $d$-dimensional piecewise continuous\nfunction in $(d+1)$-dimensional space by augmenting one coordinate variable to\nlabel the pieces of discontinuous function, and then construct a shallow neural\nnetwork to express this new augmented function. Since only one hidden layer is\nemployed, the number of training parameters (weights and biases) scales\nlinearly with the dimension and the neurons used in the hidden layer. For\nsolving elliptic interface equations, the network is trained by minimizing the\nmean squared error loss that consists of the residual of governing equation,\nboundary condition, and the interface jump conditions. We perform a series of\nnumerical tests to compare the accuracy and efficiency of the present network.\nOur DCSNN model is comparably efficient due to only moderate number of\nparameters needed to be trained (a few hundreds of parameters used throughout\nall numerical examples here), and the result shows better accuracy (and less\nparameters) than other method using piecewise deep neural network in\nliterature. We also compare the results obtained by the traditional grid-based\nimmersed interface method (IIM) which is designed particularly for elliptic\ninterface problems. Again, the present results show better accuracy than the\nones obtained by IIM. We conclude by solving a six-dimensional problem to show\nthe capability of the present network for high-dimensional applications.\n"}
+{"abstract": "  We give a coding based perspective, on a result of Erd\\'{o}s, on a lower\nbound for the diagonal ramsey numbers.\n"}
+{"abstract": "  Two-photon interference is widely used in quantum information processing,\ne.g., state engineering and designing quantum gates, as well as in quantum\nsensing and metrology. This includes the generation and characterization of\nN00N states, which provide a phase measurement sensitivity that is beyond the\nshot-noise limit. There have been numerous efforts in generating N00N states\nwith a photon number higher than 2, since their measurement sensitivity\nincreases with N. Both photon number state and modal (temporal, spectral, and\nspatial) properties of light offer advantages in sensing, where the latter is\nachieved through mode-multiplexed measurements. Here, we experimentally\ndemonstrate, with the aid of a recently developed photon-detection technology,\nmeasurement and characterization of up to tera-mode spatiotemporal correlations\nin two-photon interference and use it to generate high-dimensional two-photon\nN00N-states, which can be advantageous for multiphase estimation. We observe a\nhigh bi-photon interference and coalescence visibility of $\\sim64\\%$ and\n$\\sim88\\%$ for a tera ($10^{12}$) and 0.2 tera ($2\\times 10^{11}$)\nspatiotemporal modes, respectively. These results open up a route for practical\napplications of using the spatiotemporal degrees of freedom in two-photon\ninterference, and in particular, for quantum sensing and communication.\n"}
+{"abstract": "  MagnetoMyoGraphy (MMG) is a method of studying muscle function via weak\nmagnetic fields generated from human active organs and tissues. The\ncorrespondence between MMG and electromyography means directly derived from the\nMaxwell-Amp\\`ere law. Here, upon briefly describing the principles of voltage\ndistribution inside skeletal muscles due to the electrical stimulation, we\nprovide a protocol to determine the effects of the magnetic field generated\nfrom a time-changing action potential propagating in a group of skeletal muscle\ncells. The position-dependent and the magnetic field behaviour on account of\nthe different currents in muscle fibres are performed in temporal, spectral and\nspatial domains. The procedure covers identification of the fibre\nsubpopulations inside the fascicles of a given nerve section, characterization\nof soleus skeletal muscle currents, check of axial intracellular currents,\ncalculation of the generated magnetic field ultimately. We expect this protocol\nto take approximately 2-3 hours to complete for the whole finite-element\nanalysis.\n"}
+{"abstract": "  Current abstractive summarization systems outperform their extractive\ncounterparts, but their widespread adoption is inhibited by the inherent lack\nof interpretability. To achieve the best of both worlds, we propose EASE, an\nextractive-abstractive framework for evidence-based text generation and apply\nit to document summarization. We present an explainable summarization system\nbased on the Information Bottleneck principle that is jointly trained for\nextraction and abstraction in an end-to-end fashion. Inspired by previous\nresearch that humans use a two-stage framework to summarize long documents\n(Jing and McKeown, 2000), our framework first extracts a pre-defined amount of\nevidence spans as explanations and then generates a summary using only the\nevidence. Using automatic and human evaluations, we show that explanations from\nour framework are more relevant than simple baselines, without substantially\nsacrificing the quality of the generated summary.\n"}
+{"abstract": "  Training Reinforcement Learning (RL) agents in high-stakes applications might\nbe too prohibitive due to the risk associated to exploration. Thus, the agent\ncan only use data previously collected by safe policies. While previous work\nconsiders optimizing the average performance using offline data, we focus on\noptimizing a risk-averse criteria, namely the CVaR. In particular, we present\nthe Offline Risk-Averse Actor-Critic (O-RAAC), a model-free RL algorithm that\nis able to learn risk-averse policies in a fully offline setting. We show that\nO-RAAC learns policies with higher CVaR than risk-neutral approaches in\ndifferent robot control tasks. Furthermore, considering risk-averse criteria\nguarantees distributional robustness of the average performance with respect to\nparticular distribution shifts. We demonstrate empirically that in the presence\nof natural distribution-shifts, O-RAAC learns policies with good average\nperformance.\n"}
+{"abstract": "  The low water content of the terrestrial planets in the solar system suggests\nthat the protoplanets formed within the water snow line. Accurate prediction of\nthe snow line location moving with time provides a clue to constrain the\nformation process of the planets. In this paper, we investigate the migration\nof the snow line in protoplanetary disks whose accretion is controlled by\nlaminar magnetic fields, which have been proposed by various nonideal\nmagnetohydrodynamic (MHD) simulations. We propose an empirical model of the\ndisk temperature based on our nonideal MHD simulations, which show that the\naccretion heating is significantly less efficient than in turbulent disks, and\ncalculate the snow line location over time. We find that the snow line in the\nmagnetically accreting laminar disks moves inside the current Earth's orbit\nwithin 1 Myr after star formation, whereas the time for the conventional\nturbulent disk is much longer than 1 Myr. This result suggests that either the\nrocky protoplanets formed in such an early phase of the disk evolution, or the\nprotoplanets moved outward to the current orbits after they formed close to the\nprotosun.\n"}
+{"abstract": "  Spin glasses are notoriously difficult to study both analytically and\nnumerically due to the presence of frustration and metastability. Their highly\nnon-convex landscapes require collective updates to explore efficiently.\nCurrently, most state-of-the-art algorithms rely on stochastic spin clusters to\nperform non-local updates, but such \"cluster algorithms\" lack general\nefficiency. Here, we introduce a non-equilibrium approach for simulating spin\nglasses based on classical dynamics with memory. By simulating various classes\nof 3d spin glasses (Edwards-Anderson, partially-frustrated, and\nfully-frustrated models), we find that memory dynamically promotes critical\nspin clusters during time evolution, in a self-organizing manner. This\nfacilitates an efficient exploration of the low-temperature phases of spin\nglasses.\n"}
+{"abstract": "  This paper describes a system developed to help University students get more\nfrom their online lectures, tutorials, laboratory and other live sessions. We\ndo this by logging their attention levels on their laptops during live Zoom\nsessions and providing them with personalised video summaries of those live\nsessions. Using facial attention analysis software we create personalised video\nsummaries composed of just the parts where a student's attention was below some\nthreshold. We can also factor in other criteria into video summary generation\nsuch as parts where the student was not paying attention while others in the\nclass were, and parts of the video that other students have replayed\nextensively which a given student has not. Attention and usage based video\nsummaries of live classes are a form of personalised content, they are\neducational video segments recommended to highlight important parts of live\nsessions, useful in both topic understanding and in exam preparation. The\nsystem also allows a Professor to review the aggregated attention levels of\nthose in a class who attended a live session and logged their attention levels.\nThis allows her to see which parts of the live activity students were paying\nmost, and least, attention to. The Help-Me-Watch system is deployed and in use\nat our University in a way that protects student's personal data, operating in\na GDPR-compliant way.\n"}
+{"abstract": "  We consider the infinite-horizon, discrete-time full-information control\nproblem. Motivated by learning theory, as a criterion for controller design we\nfocus on regret, defined as the difference between the LQR cost of a causal\ncontroller (that has only access to past and current disturbances) and the LQR\ncost of a clairvoyant one (that has also access to future disturbances). In the\nfull-information setting, there is a unique optimal non-causal controller that\nin terms of LQR cost dominates all other controllers. Since the regret itself\nis a function of the disturbances, we consider the worst-case regret over all\npossible bounded energy disturbances, and propose to find a causal controller\nthat minimizes this worst-case regret. The resulting controller has the\ninterpretation of guaranteeing the smallest possible regret compared to the\nbest non-causal controller, no matter what the future disturbances are. We show\nthat the regret-optimal control problem can be reduced to a Nehari problem,\ni.e., to approximate an anticausal operator with a causal one in the operator\nnorm. In the state-space setting, explicit formulas for the optimal regret and\nfor the regret-optimal controller (in both the causal and the strictly causal\nsettings) are derived. The regret-optimal controller is the sum of the\nclassical $H_2$ state-feedback law and a finite-dimensional controller obtained\nfrom the Nehari problem. The controller construction simply requires the\nsolution to the standard LQR Riccati equation, in addition to two Lyapunov\nequations. Simulations over a range of plants demonstrates that the\nregret-optimal controller interpolates nicely between the $H_2$ and the\n$H_\\infty$ optimal controllers, and generally has $H_2$ and $H_\\infty$ costs\nthat are simultaneously close to their optimal values. The regret-optimal\ncontroller thus presents itself as a viable option for control system design.\n"}
+{"abstract": "  This paper provides a starting point for Software Engineering (SE)\nresearchers and practitioners faced with the problem of training machine\nlearning models on small datasets. Due to the high costs associated with\nlabeling data, in Software Engineering,there exist many small (< 1 000 samples)\nand medium-sized (< 100 000 samples) datasets. While deep learning has set the\nstate of the art in many machine learning tasks, it is only recently that it\nhas proven effective on small-sized datasets, primarily thanks to pre-training,\na semi-supervised learning technique that leverages abundant unlabelled data\nalongside scarce labelled data.In this work, we evaluate pre-trained\nTransformer models on a selection of 13 smaller datasets from the SE\nliterature, covering both,source code and natural language. Our results suggest\nthat pre-trained Transformers are competitive and in some cases superior to\nprevious models, especially for tasks involving natural language; whereas for\nsource code tasks, in particular for very small datasets,traditional machine\nlearning methods often has the edge.In addition, we experiment with several\ntechniques that ought to aid training on small datasets, including active\nlearning, data augmentation, soft labels, self-training and intermediate-task\nfine-tuning, and issue recommendations on when they are effective. We also\nrelease all the data, scripts, and most importantly pre-trained models for the\ncommunity to reuse on their own datasets.\n"}
+{"abstract": "  Let $p$ be a prime number and $E_{p}$ denote the elliptic curve $y^2=x^3+px$.\nIt is known that for $p$ which is congruent to $1, 9$ modulo $16$, the rank of\n$E_{p}$ over $\\mathbb{Q}$ is equal to $0, 2$. Under the condition that the\nBirch and Swinnerton-Dyer conjecture is true, we give a necessary and\nsufficient condition that the rank is $2$ in terms of the constant term of some\npolynomial that is defined by a recurrence formula.\n"}
+{"abstract": "  Peak estimation bounds extreme values of a function of state along\ntrajectories of a dynamical system. This paper focuses on extending peak\nestimation to continuous and discrete settings with time-independent and\ntime-dependent uncertainty. Techniques from optimal control are used to\nincorporate uncertainty into an existing occupation measure-based peak\nestimation framework, which includes special consideration for handling\nswitching uncertainties. The resulting infinite-dimensional linear programs can\nbe solved approximately with Linear Matrix Inequalities arising from the\nmoment-SOS hierarchy.\n"}
+{"abstract": "  The share-of-choice product design (SOCPD) problem is to find the product, as\ndefined by its attributes, that maximizes market share arising from a\ncollection of customer types or segments. When customers follow a logit model\nof choice, the market share is given by a weighted sum of logistic\nprobabilities, leading to the logit-based share-of-choice product design\nproblem. At first glance, this problem appears hopeless: one must optimize an\nobjective function that is neither convex nor concave, over an\nexponentially-sized discrete set of attribute combinations. In this paper, we\ndevelop an exact methodology for solving this problem based on modern integer,\nconvex and conic optimization. At the heart of our methodology is the\nsurprising result that the logit-based SOCPD problem can be exactly\nreformulated as a mixed-integer convex program. Using both synthetic problem\ninstances and instances derived from real conjoint data sets, we show that our\nmethodology can solve large instances to provable optimality or near optimality\nin operationally feasible time frames.\n"}
+{"abstract": "  Boom cranes are among the most used cranes to lift heavy loads. Although\nfairly simple mechanically, from the control viewpoint this kind of crane is a\nnonlinear underactuated system which presents several challenges, especially\nwhen con-trolled in the presence of constraints. To solve this problem, we\npropose an approach based on the Explicit Reference Governor (ERG), which does\nnot require any online optimization, thus making it computationally\ninexpensive. The proposed control scheme is able to steer the crane to a\ndesired position ensuring the respect of limited joint ranges, maximum\noscillation angle, and the avoidance of static obstacles.\n"}
+{"abstract": "  Recent works have proved the existence of symmetry-protected edge states in\ncertain one-dimensional topological band insulators and superconductors at the\ngap-closing points which define quantum phase transitions between two\ntopologically nontrivial phases. We show how this picture generalizes to\nmultiband critical models belonging to any of the chiral symmetry classes AIII,\nBDI, or CII of noninteracting fermions in one dimension.\n"}
+{"abstract": "  We report on a combined far- and near-field study of surface plasmon\npolaritons on freestanding perforated gold films. The samples are fabricated by\nfocused ion beam milling of {a periodic hole array into} a carbon membrane\nfollowed by thermal evaporation of gold and plasma ashing of the carbon film.\nOptical transmission spectra show a series of characteristic features, which\ncan be attributed to the excitation of surface plasmon modes via the periodic\nnanohole array. The corresponding near-field distributions are mapped by\nelectron energy-loss spectroscopy. Besides the optically bright surface plasmon\nmodes, we observe in the near-field an additional dark plasmon mode, which is\nabsent in the normal incidence far-field spectra. Our experimental results are\nin good agreement with numerical computations based on a discontinuous Galerkin\ntime-domain method.\n"}
+{"abstract": "  In steady state, the fuel cycle of a fusion plasma requires inward particle\nfluxes of fuel ions. These particle flows are also accompanied by heating. In\nthe case of classical transport in a rotating cylindrical plasma, this heating\ncan proceed through several distinct channels depending on the physical\nmechanisms involved. Some channels directly heat the fuel ions themselves,\nwhereas others heat electrons. Which channel dominates depends, in general, on\nthe details of the temperature, density, and rotation profiles of the plasma\nconstituents. However, remarkably, under relatively few assumptions concerning\nthese profiles, if the alpha particles, the byproducts of the fusion reaction,\ncan be removed directly by other means, a hot-ion mode tends to emerge\nnaturally.\n"}
+{"abstract": "  Orbiting a M dwarf 12 pc away, the transiting exoplanet GJ 1132b is a prime\ntarget for transmission spectroscopy. With a mass of 1.7 Earth masses and\nradius of 1.1 Earth radii, GJ 1132b's bulk density indicates that this planet\nis rocky. Yet with an equilibrium temperature of 580 K, GJ 1132b may still\nretain some semblance of an atmosphere. Understanding whether this atmosphere\nexists and its composition will be vital for understanding how the atmospheres\nof terrestrial planets orbiting M dwarfs evolve. We observe five transits of GJ\n1132b with the Wide Field Camera 3 (WFC3) on the Hubble Space Telescope (HST).\nWe find a featureless transmission spectrum from 1.1--1.7 microns, ruling out\ncloud-free atmospheres with metallicities <300x Solar with >4.8$\\sigma$\nconfidence. We combine our WFC3 results with transit depths from TESS and\narchival broadband and spectroscopic observations to find a featureless\nspectrum from 0.7--4.5 microns. GJ 1132b has a high mean molecular weight\natmosphere, possesses a high-altitude aerosol layer, or has effectively no\natmosphere. Higher precision observations are required to differentiate between\nthese possibilities. We explore the impact of hot and cold starspots on the\nobserved transmission spectrum GJ 1132b, quantifying the amplitude of\nspot-induced transit depth features. Using a simple Poisson model we estimate\nspot temperature contrasts, spot covering fractions, and spot sizes for GJ\n1132. These limits, and the modeling framework, may be useful for future\nobservations of GJ 1132b or other planets transiting similarly inactive M\ndwarfs.\n"}
+{"abstract": "  We report distance measurements for the Norma cluster based on the\nnear-infrared $J$- and $K_s$-band Fundamental Plane (FP) relations. Our\nsimultaneous $J$ and $K_s$-band photometry analyses were performed using 31\nearly-type galaxies in the nearby Norma cluster obtained using the 1.4 m\nInfraRed Survey Facility (IRSF) at the South African Astronomical Observatory.\nOur final $K_s$-band FP sample consists of 41 early-type galaxies from the\nNorma cluster observed using the IRSF and the New Technology Telescope (NTT) at\nthe European Southern Observatory. This is the largest cluster sample used for\npeculiar velocity studies in the Great Attractor region to date. From the\n$K_s$-band FP, we find a distance to the Norma cluster of $4915 \\pm 121$ km\ns$^{-1}$. The implied peculiar velocity for Norma is $44 \\pm 151$ km s$^{-1}$\nwhich further supports a small peculiar velocity for the Norma cluster.\n"}
+{"abstract": "  As deep neural networks require tremendous amount of computation and memory,\nanalog computing with emerging memory devices is a promising alternative to\ndigital computing for edge devices. However, because of the increasing\nsimulation time for analog computing system, it has not been explored. To\novercome this issue, analytically approximated simulators are developed, but\nthese models are inaccurate and narrow down the options for peripheral circuits\nfor multiply-accumulate operation (MAC). In this sense, we propose a\nmethodology, SEMULATOR (SiMULATOR by Emulating the analog computing block)\nwhich uses a deep neural network to emulate the behavior of crossbar-based\nanalog computing system. With the proposed neural architecture, we\nexperimentally and theoretically shows that it emulates a MAC unit for neural\ncomputation. In addition, the simulation time is incomparably reduced when it\ncompared to the circuit simulators such as SPICE.\n"}
+{"abstract": "  Compression is a standard procedure for making convolutional neural networks\n(CNNs) adhere to some specific computing resource constraints. However,\nsearching for a compressed architecture typically involves a series of\ntime-consuming training/validation experiments to determine a good compromise\nbetween network size and performance accuracy. To address this, we propose an\nimage complexity-guided network compression technique for biomedical image\nsegmentation. Given any resource constraints, our framework utilizes data\ncomplexity and network architecture to quickly estimate a compressed model\nwhich does not require network training. Specifically, we map the dataset\ncomplexity to the target network accuracy degradation caused by compression.\nSuch mapping enables us to predict the final accuracy for different network\nsizes, based on the computed dataset complexity. Thus, one may choose a\nsolution that meets both the network size and segmentation accuracy\nrequirements. Finally, the mapping is used to determine the convolutional\nlayer-wise multiplicative factor for generating a compressed network. We\nconduct experiments using 5 datasets, employing 3 commonly-used CNN\narchitectures for biomedical image segmentation as representative networks. Our\nproposed framework is shown to be effective for generating compressed\nsegmentation networks, retaining up to $\\approx 95\\%$ of the full-sized network\nsegmentation accuracy, and at the same time, utilizing $\\approx 32x$ fewer\nnetwork trainable weights (average reduction) of the full-sized networks.\n"}
+{"abstract": "  We prove an optimal version of Wigner's unitary-antiunitary theorem. The main\ntool in our proof is Gleason's theorem.\n"}
+{"abstract": "  We aim to address the problem of Natural Language Video Localization\n(NLVL)-localizing the video segment corresponding to a natural language\ndescription in a long and untrimmed video. State-of-the-art NLVL methods are\nalmost in one-stage fashion, which can be typically grouped into two\ncategories: 1) anchor-based approach: it first pre-defines a series of video\nsegment candidates (e.g., by sliding window), and then does classification for\neach candidate; 2) anchor-free approach: it directly predicts the probabilities\nfor each video frame as a boundary or intermediate frame inside the positive\nsegment. However, both kinds of one-stage approaches have inherent drawbacks:\nthe anchor-based approach is susceptible to the heuristic rules, further\nlimiting the capability of handling videos with variant length. While the\nanchor-free approach fails to exploit the segment-level interaction thus\nachieving inferior results. In this paper, we propose a novel Boundary Proposal\nNetwork (BPNet), a universal two-stage framework that gets rid of the issues\nmentioned above. Specifically, in the first stage, BPNet utilizes an\nanchor-free model to generate a group of high-quality candidate video segments\nwith their boundaries. In the second stage, a visual-language fusion layer is\nproposed to jointly model the multi-modal interaction between the candidate and\nthe language query, followed by a matching score rating layer that outputs the\nalignment score for each candidate. We evaluate our BPNet on three challenging\nNLVL benchmarks (i.e., Charades-STA, TACoS and ActivityNet-Captions). Extensive\nexperiments and ablative studies on these datasets demonstrate that the BPNet\noutperforms the state-of-the-art methods.\n"}
+{"abstract": "  In this paper, we consider the concentration property of solutions to the\ndispersive Ginzburg-Landau (or Gross-Pitaevskii) equation in three dimensions.\nOn a spatial domain, it has long been conjectured that such a solution\nconcentrates near some curve evolving according to the binormal curvature flow,\nand conversely, that a curve moving this way can be realized in a suitable\nsense by some solution to the dispersive Ginzburg-Landau equation. Some partial\nresults are known with rather strong symmetry assumptions.\n  Our main theorems here provide affirmative answer to both conjectures under\ncertain small curvature assumption. The results are valid for small but fixed\nmaterial parameter in the equation, in contrast to the general practice to take\nthis parameter to its zero limit. The advantage is that we can retain precise\ndescription of the vortex filament structure. The results hold on a long but\nfinite time interval, depending on the curvature assumption.\n"}
+{"abstract": "  The late evolutionary stages of low- and intermediate-mass stars are\ncharacterised by mass loss through a dust-driven stellar wind. Recent\nobservations reveal complex structures within these winds, that are believed to\nbe formed primarily via interaction with a companion. How these complexities\narise, and which structures are formed in which type of systems, is still\npoorly understood. Particularly, there is a lack of studies investigating the\nstructure formation in eccentric systems. We aim to improve our understanding\nof the wind morphology of eccentric AGB binary systems by investigating the\nmechanism responsible for the different small-scale structures and global\nmorphologies that arise in a polytropic wind with different velocities. Using\nthe smoothed particle hydrodynamics (SPH) code Phantom, we generate nine\ndifferent high-resolution, 3D simulations of an AGB star with a solar-mass\ncompanion with various wind velocity and eccentricity combinations. The models\nassume a polytropic gas, with no additional cooling. We conclude that for\nmodels with a high wind velocity, the short interaction with the companion\nresults in a regular spiral morphology, that is flattened. In the case of a\nlower wind velocity, the stronger interaction results in the formation of a\nhigh-energy region and bow-shock structure that can shape the wind into an\nirregular morphology if instabilities arise. High-eccentricity models show a\ncomplex, phase-dependent interaction leading to wind structures that are\nirregular in three dimensions. However, the significant interaction with the\ncompanion compresses matter into an equatorial density enhancement,\nirrespective of eccentricity.\n"}
+{"abstract": "  Considering the huge computational resources required by smoothed particle\nhydrodynamics (SPH) simulations and the overestimation of post-collision\nmaterials from perfect merging, we develop a statistical method to deal with\ncollisions during the formation of planetary systems by introducing random\nmaterial loss. In this method the mass and water content lost by the sole\noutcome from every merger vary randomly within a range dependent on the total\nmass and water content of colliding bodies. The application of the random loss\nmethod to the planet formation in the solar system shows a good consistency\nwith existing SPH results. We also apply this method to the extrasolar\nplanetary system 55 Cancri which hosts (at least) five planets and study the\nformation of terrestrial planets between the outermost two planets. A disk with\n500 Mars mass embryos in dynamically cold orbits before the late-stage\naccretion phase is assumed. Scenarios with different amounts of planetary\nembryos and different loss parameters are adopted in our simulations. The\nstatistical result from hundreds of simulations shows that an Earth-like planet\nwith water inventory of roughly 6 Earth ocean could form between 55 Cnc f and\nd. It may reside between 1.0 and 2.6 AU but the most likely region extends from\n1.5 to 2.1 AU. Thus the probability of this planet being in the potentially\nhabitable zone (0.59--1.43 AU) is relatively low, only around 10\\%. Planets 55\nCnc f and d could also be shaped and gain some water from giant impacts and\nconsequently the orbits of them may also change accordingly.\n"}
+{"abstract": "  We investigate the effect of strong interaction in the dressed energy levels\nof the two level emitters. Strong dipole-dipole interactions give rise to new\nsidebands in the fluorescence spectrum due to specific couplings among the\ncollective dressed levels which in turn depends on the spatial configuration of\natoms. These couplings are the main responsible for the frequencies and variety\nof sidebands. We explain the general method for finding the dressed energy\nlevels for a system of any number of strongly coupled atoms and we solve this\nproblem for two different spatial configurations of three coupled two-level\nemitters. We show that the coupling among dressed levels and consequently\nenergies and number of sidebands in the fluorescence spectrum are different for\neach configuration. Thus the fluorescence spectrum of strongly interacting\natoms contains information about the number and configuration of atoms.\n"}
+{"abstract": "  Multi-source domain adaptation aims at leveraging the knowledge from multiple\ntasks for predicting a related target domain. Hence, a crucial aspect is to\nproperly combine different sources based on their relations. In this paper, we\nanalyzed the problem for aggregating source domains with different label\ndistributions, where most recent source selection approaches fail. Our proposed\nalgorithm differs from previous approaches in two key ways: the model\naggregates multiple sources mainly through the similarity of semantic\nconditional distribution rather than marginal distribution; the model proposes\na \\emph{unified} framework to select relevant sources for three popular\nscenarios, i.e., domain adaptation with limited label on target domain,\nunsupervised domain adaptation and label partial unsupervised domain adaption.\nWe evaluate the proposed method through extensive experiments. The empirical\nresults significantly outperform the baselines.\n"}
+{"abstract": "  Research has shown assistance can provide many benefits to novices lacking\nthe mental models needed for problem solving in a new domain. However, varying\napproaches to assistance, such as subgoals and next-step hints, have been\nimplemented with mixed results. Next-Step hints are common in data-driven\ntutors due to their straightforward generation from historical student data, as\nwell as research showing positive impacts on student learning. However, there\nis a lack of research exploring the possibility of extending data-driven\nmethods to provide higher-level assistance. Therefore, we modified our\ndata-driven Next-Step hint generator to provide Waypoints, hints that are a few\nsteps ahead, representing problem-solving subgoals. We hypothesized that\nWaypoints would benefit students with high prior knowledge, and that Next-Step\nhints would most benefit students with lower prior knowledge. In this study, we\ninvestigated the influence of data-driven hint type, Waypoints versus Next-Step\nhints, on student learning in a logic proof tutoring system, Deep Thought, in a\ndiscrete mathematics course. We found that Next-Step hints were more beneficial\nfor the majority of students in terms of time, efficiency, and accuracy on the\nposttest. However, higher totals of successfully used Waypoints were correlated\nwith improvements in efficiency and time in the posttest. These results suggest\nthat Waypoint hints could be beneficial, but more scaffolding may be needed to\nhelp students follow them.\n"}
+{"abstract": "  Online social media has been a popular source for people to consume and share\nnews content. More recently, the spread of misinformation online has caused\nwidespread concerns. In this work, we focus on a critical task of detecting\nfauxtography on social media where the image and associated text together\nconvey misleading information. Many efforts have been made to mitigate\nmisinformation online, but we found that the fauxtography problem has not been\nfully addressed by existing work. Solutions focusing on detecting fake images\nor misinformed texts alone on social media often fail to identify the\nmisinformation delivered together by the image and the associated text of a\nfauxtography post. In this paper, we develop FauxWard, a novel graph\nconvolutional neural network framework that explicitly explores the complex\ninformation extracted from a user comment network of a social media post to\neffectively identify fauxtography. FauxWard is content-free in the sense that\nit does not analyze the visual or textual contents of the post itself, which\nmakes it robust against sophisticated fauxtography uploaders who intentionally\ncraft image-centric posts by editing either the text or image content. We\nevaluate FauxWard on two real-world datasets collected from mainstream social\nmedia platforms (i.e., Reddit and Twitter). The results show that FauxWard is\nboth effective and efficient in identifying fauxtography posts on social media.\n"}
+{"abstract": "  Deploying antenna arrays with an asymptotically large aperture will be\ncentral to achieving the theoretical gains of massive MIMO in beyond-5G\nsystems. Such extra-large MIMO (XL-MIMO) systems experience propagation\nconditions which are not typically observed in conventional massive MIMO\nsystems, such as spatial non-stationarities and near-field propagation.\nMoreover, standard precoding schemes, such as zero-forcing (ZF), may not apply\nto XL-MIMO transmissions due to the prohibitive complexity associated with such\na large-scale scenario. We propose two novel precoding schemes that aim at\nreducing the complexity without losing much performance. The proposed schemes\nleverage a plane-wave approximation and user grouping to obtain a\nlow-complexity approximation of the ZF precoder. Our simulation results show\nthat the proposed schemes offer a possibility for a performance and complexity\ntrade-off compared to the benchmark schemes.\n"}
+{"abstract": "  We consider a fermionic many body system in Zd with a short range interaction\nand quasi-periodic disorder. In the strong disorder regime and assuming a\nDiophantine condition on the frequencies and on the chemical potential, we\nprove at $T=0$ the exponential decay of the correlations and the vanishing of\nthe Drude weight, signaling Anderson localization in the ground state. The\nproof combines Ward Identities, Renormalization Group and KAM Lindstedt series\nmethods.\n"}
+{"abstract": "  This article presents a novel approach to incorporate visual cues from\nvideo-data from a wide-angle stereo camera system mounted at an urban\nintersection into the forecast of cyclist trajectories. We extract features\nfrom image and optical flow (OF) sequences using 3D convolutional neural\nnetworks (3D-ConvNet) and combine them with features extracted from the\ncyclist's past trajectory to forecast future cyclist positions. By the use of\nadditional information, we are able to improve positional accuracy by about 7.5\n% for our test dataset and by up to 22 % for specific motion types compared to\na method solely based on past trajectories. Furthermore, we compare the use of\nimage sequences to the use of OF sequences as additional information, showing\nthat OF alone leads to significant improvements in positional accuracy. By\ntraining and testing our methods using a real-world dataset recorded at a\nheavily frequented public intersection and evaluating the methods' runtimes, we\ndemonstrate the applicability in real traffic scenarios. Our code and parts of\nour dataset are made publicly available.\n"}
+{"abstract": "  We present a photonic reservoir computing, relying on a non-linear\nphase-to-amplitude mapping process, able to classify in real-time multi-Gbaud\ntime traces subject to transmission effects. This approach delivers an\nall-optical, low-power neuromorphic dispersion compensator.\n"}
+{"abstract": "  In this work we study $d$-dimensional majorant properties. We prove that a\nset of frequencies in ${\\mathbb Z}^d$ satisfies the strict majorant property on\n$L^p([0,1]^d)$ for all $p> 0$ if and only if the set is affinely independent.\nWe further construct three types of violations of the strict majorant property.\nAny set of at least $d+2$ frequencies in ${\\mathbb Z}^d$ violates the strict\nmajorant property on $L^p([0,1]^d)$ for an open interval of $p \\not\\in 2\n{\\mathbb N}$ of length 2. Any infinite set of frequencies in ${\\mathbb Z}^d$\nviolates the strict majorant property on $L^p([0,1]^d)$ for an infinite\nsequence of open intervals of $p \\not\\in 2 {\\mathbb N}$ of length $2$. Finally,\ngiven any $p>0$ with $p \\not\\in 2{\\mathbb N}$, we exhibit a set of $d+2$\nfrequencies on the moment curve in ${\\mathbb R}^d$ that violate the strict\nmajorant property on $L^p([0,1]^d).$\n"}
+{"abstract": "  Quantum computing promises significant improvements of computation\ncapabilities in various fields such as machine learning and complex\noptimization problems. Recent technological advancements suggest that the\nadiabatic quantum computing ansatz may soon see practical applications. In this\nwork, we adopt this computation paradigm to develop a quantum computation based\nsolver of the well-known weapon target assignment problem, an NP-hard nonlinear\ninteger programming optimization task. The feasibility of the presented model\nis demonstrated by numerical simulation of the adiabatic evolution of a system\nof quantum bits towards the optimal solution encoded in the model Hamiltonian.\nOver all, the described method is not limited to the context of weapon\nmanagement but is, with slight modifications to the model Hamiltonian,\napplicable to worker-task allocation optimization in general.\n"}
+{"abstract": "  Let $G$ be a connected graph. For an ordered set $S=\\{v_1,\\ldots,\nv_\\ell\\}\\subseteq V(G)$, the vector $r_G(v|S) = (d_G(v_1,v), \\ldots,\nd_G(v_\\ell,v))$ is called the metric $S$-representation of $v$. If for any pair\nof different vertices $u,v\\in V(G)$, the vectors $r(v|S)$ and $r(u|S)$ differ\nin at least $k$ positions, then $S$ is a $k$-metric generator for $G$. A\nsmallest $k$-metric generator for $G$ is a $k$-{\\em metric basis} for $G$, its\ncardinality being the $k$-metric dimension of $G$. A sharp upper bound and a\nclosed formulae for the $k$-metric dimension of the hierarchical product of\ngraphs is proved. Also, sharp lower bounds for the $k$-metric dimension of the\nsplice and link products of graphs are presented. An integer linear programming\nmodel for computing the $k$-metric dimension and a $k$-metric basis of a given\ngraph is proposed. These results are applied to bound or to compute the\n$k$-metric dimension of some classes of graphs that are of interest in\nmathematical chemistry.\n"}
+{"abstract": "  We present the low-resolution transmission spectra of the puffy hot Jupiter\nHAT-P-65b (0.53 M$_\\mathrm{Jup}$, 1.89 R$_\\mathrm{Jup}$, $T_\\mathrm{eq}=1930$\nK), based on two transits observed using the OSIRIS spectrograph on the 10.4 m\nGran Telescopio CANARIAS (GTC). The transmission spectra of the two nights are\nconsistent, covering the wavelength range 517--938 nm and consisting of mostly\n5 nm spectral bins. We perform equilibrium-chemistry spectral retrieval\nanalyses on the jointly fitted transmission spectrum and obtain an equilibrium\ntemperature of $1645^{+255}_{-244}$ K and a cloud coverage of\n$36^{+23}_{-17}$%, revealing a relatively clear planetary atmosphere. Based on\nfree-chemistry retrieval, we report strong evidence for TiO. Additional\nindividual analyses in each night reveal weak-to-moderate evidence for TiO in\nboth nights, but moderate evidence for Na or VO only in one of the nights.\nFuture high-resolution Doppler spectroscopy as well as emission observations\nwill help confirm the presence of TiO and constrain its role in shaping the\nvertical thermal structure of HAT-P-65b's atmosphere.\n"}
+{"abstract": "  We introduce a novel problem for diversity-aware clustering. We assume that\nthe potential cluster centers belong to a set of groups defined by protected\nattributes, such as ethnicity, gender, etc. We then ask to find a minimum-cost\nclustering of the data into $k$ clusters so that a specified minimum number of\ncluster centers are chosen from each group. We thus require that all groups are\nrepresented in the clustering solution as cluster centers, according to\nspecified requirements. More precisely, we are given a set of clients $C$, a\nset of facilities $\\pazocal{F}$, a collection $\\mathcal{F}=\\{F_1,\\dots,F_t\\}$\nof facility groups $F_i \\subseteq \\pazocal{F}$, budget $k$, and a set of\nlower-bound thresholds $R=\\{r_1,\\dots,r_t\\}$, one for each group in\n$\\mathcal{F}$. The \\emph{diversity-aware $k$-median problem} asks to find a set\n$S$ of $k$ facilities in $\\pazocal{F}$ such that $|S \\cap F_i| \\geq r_i$, that\nis, at least $r_i$ centers in $S$ are from group $F_i$, and the $k$-median cost\n$\\sum_{c \\in C} \\min_{s \\in S} d(c,s)$ is minimized. We show that in the\ngeneral case where the facility groups may overlap, the diversity-aware\n$k$-median problem is \\np-hard, fixed-parameter intractable, and inapproximable\nto any multiplicative factor. On the other hand, when the facility groups are\ndisjoint, approximation algorithms can be obtained by reduction to the\n\\emph{matroid median} and \\emph{red-blue median} problems. Experimentally, we\nevaluate our approximation methods for the tractable cases, and present a\nrelaxation-based heuristic for the theoretically intractable case, which can\nprovide high-quality and efficient solutions for real-world datasets.\n"}
+{"abstract": "  Vehicular ad hoc networks is a modern technology that holds an important\naspect in the transportation domain due to its abilities to increase traffic\nefficiency and safety. It is a another variant of Mobile ad-hoc networks that\nprovides Vehicles to Vehicles (V2V), Rode-side Unit to Road-side Unit(R2R) and\nVehicles to road-side Unit (V2R) communication.VANET is a multidimensional\nnetwork in which the vehicles ceaselessly alter their locations. Connected\nvehicles broadcast sensitive information which must be communicated with the\nneighbors in a safe and established environment. VANET may also contain\ndishonest nodes such as man in -the-middle attackers that aim to distribute and\nshare malicious content with the vehicles, thus contaminating the network with\nsecure information. In this situation implementing a trust among connected\nvehicles can raise security as every participating vehicle will create and\npropagate authentic, accurate,and trusted content within the network.In this\npaper we used a trust model to determine the trust level and eliminate the\nmalicious nodes.We created a simulation for calculating the trust level and\neliminating the malicious node in the wireless ad hoc networks using ns2\nsimulator and network animator(nam).The simulation results showed a better\nbandwidth in communication between the nodes after the trust level is updated\n"}
+{"abstract": "  In this paper, we consider the linear programming (LP) formulation for deep\nreinforcement learning. The number of the constraints depends on the size of\nstate and action spaces, which makes the problem intractable in large or\ncontinuous environments. The general augmented Lagrangian method suffers the\ndouble-sampling obstacle in solving the LP. Namely, the conditional\nexpectations originated from the constraint functions and the quadratic\npenalties in the augmented Lagrangian function impose difficulties in sampling\nand evaluation. Motivated from the updates of the multipliers, we overcome the\nobstacles in minimizing the augmented Lagrangian function by replacing the\nintractable conditional expectations with the multipliers. Therefore, a deep\nparameterized augment Lagrangian method is proposed. Furthermore, the\nreplacement provides a promising breakthrough to integrate the two steps in the\naugmented Lagrangian method into a single constrained problem. A general\ntheoretical analysis shows that the solutions generated from a sequence of the\nconstrained optimizations converge to the optimal solution of the LP if the\nerror is controlled properly. A theoretical analysis on the quadratic penalty\nalgorithm under neural tangent kernel setting shows the residual can be\narbitrarily small if the parameter in network and optimization algorithm is\nchosen suitably. Preliminary experiments illustrate that our method is\ncompetitive to other state-of-the-art algorithms.\n"}
+{"abstract": "  This paper explores Tadmor's minimum entropy principle for the relativistic\nhydrodynamics (RHD) equations and incorporates this principle into the design\nof robust high-order discontinuous Galerkin (DG) and finite volume schemes for\nRHD on general meshes. The schemes are proven to preserve numerical solutions\nin a global invariant region constituted by all the known intrinsic\nconstraints: minimum entropy principle, the subluminal constraint on fluid\nvelocity, and the positivity of pressure and rest-mass density. Relativistic\neffects lead to some essential difficulties in the present study, which are not\nencountered in the non-relativistic case. Most notably, in the RHD case the\nspecific entropy is a highly nonlinear implicit function of the conservative\nvariables, and, moreover, there is also no explicit formula of the flux in\nterms of the conservative variables. In order to overcome the resulting\nchallenges, we first propose a novel equivalent form of the invariant region,\nby skillfully introducing two auxiliary variables. As a notable feature, all\nthe constraints in the novel form are explicit and linear with respect to the\nconservative variables. This provides a highly effective approach to\ntheoretically analyze the invariant-region-preserving (IRP) property of schemes\nfor RHD, without any assumption on the IRP property of the exact Riemann\nsolver. Based on this, we prove the convexity of the invariant region and\nestablish the generalized Lax--Friedrichs splitting properties via technical\nestimates, lying the foundation for our IRP analysis. It is shown that the\nfirst-order Lax--Friedrichs scheme for RHD satisfies a local minimum entropy\nprinciple and is IRP under a CFL condition. Provably IRP high-order DG and\nfinite volume methods are developed for the RHD with the help of a simple\nscaling limiter. Several numerical examples demonstrate the effectiveness of\nthe proposed schemes.\n"}
+{"abstract": "  Antenna technology is at the basis of ubiquitous wireless communication\nsystems and sensors. Radiation is typically sustained by conduction currents\nflowing around resonant metallic objects that are optimized to enhance\nefficiency and bandwidth. However, resonant conductors are prone to large\nscattering of impinging waves, leading to challenges in crowded antenna\nenvironments due to blockage and distortion. Metasurface cloaks have been\nexplored in the quest of addressing this challenge by reducing antenna\nscattering, but with limited performance in terms of bandwidth, footprint and\noverall scattering reduction. Here we introduce a different route towards\nradio-transparent antennas, in which the cloak itself acts as the radiating\nelement, drastically reducing the overall footprint while enhancing scattering\nsuppression and bandwidth, without sacrificing other relevant radiation metrics\ncompared to conventional antennas. This technique offers a new application of\ncloaking technology, with promising features for crowded wireless communication\nplatforms and noninvasive sensing.\n"}
+{"abstract": "  The ongoing COVID-19 pandemic is being responded with various methods,\napplying vaccines, experimental treatment options, total lockdowns or partial\ncurfews. Weekend curfews is one of the methods to reduce the amount of infected\npersons and this method is practically applied in some countries such as\nTurkey. In this study, the effect of weekend curfews on reducing the spread of\na contagious disease, such as COVID-19, is modeled using a Monte Carlo\nalgorithm with a hybrid lattice model. In the simulation setup, a fictional\ncountry with three towns and 26,610 citizens were used as a model. Results\nindicate that applying a weekend curfew reduces the active cases significantly\nand is one of the efficient ways to fight the epidemic. The results also show\nthat applying personal precautions such as social distancing is important for\nreducing the number of cases and deaths.\n"}
+{"abstract": "  Daniely and Schacham recently showed that gradient descent finds adversarial\nexamples on random undercomplete two-layers ReLU neural networks. The term\n\"undercomplete\" refers to the fact that their proof only holds when the number\nof neurons is a vanishing fraction of the ambient dimension. We extend their\nresult to the overcomplete case, where the number of neurons is larger than the\ndimension (yet also subexponential in the dimension). In fact we prove that a\nsingle step of gradient descent suffices. We also show this result for any\nsubexponential width random neural network with smooth activation function.\n"}
+{"abstract": "  We present a short introduction to a non-standard cosmological scenario\nmotivated by the duality symmetries of string theory, in which the big bang\nsingularity is replaced with a \"big bounce\" at high but finite curvature. The\nbouncing epoch is prepared by a long (possibly infinitely extended) phase of\ncosmic evolution, starting from an initial state asymptotically approaching the\nstring perturbative vacuum.\n"}
+{"abstract": "  Agrivoltaic systems represent a key technology for reaching sustainable\ndevelopment goals reducing the completion of land for food versus land for\nenergy. Moreover, agrivoltaic systems are at the centre of the nexus between\nelectricity production, crop production, and irrigation water saving. In this\nstudy, an optimization model for vertically mounted agrivoltaic systems with\nbifacial photovoltaic models is developed. The model combines three main\nsubmodels: solar radiation and shadings, photovoltaic, and crop yield.\nValidation of the submodels is performed showing good agreement with measured\ndata and commercial software. The optimization model is set as multi objective\nto explore the trade-offs between competing agrivoltaic key performance\nindicators. The results shows that the row distance between bifacial\nphotovoltaic modules structure affects significantly the photosyntetically\nactive radiation distribution by reducing the crop yield of potato and oat of\nabout 50% by passing from 20 m to 5 m. The implementation of agrivoltaic system\nfor the investigated crops at the chosen location shows a land equivalent ratio\nabove 1.2 that justify the technology for reaching the country sustainability\ngoals.\n"}
+{"abstract": "  Linear optical multiports are widely used in photonic quantum information\nprocessing. Naturally, these devices are directionally-biased since photons\nalways propagate from the input ports toward the output ports. Recently, the\nconcept of directionally-unbiased linear optical multiports was proposed. These\ndirectionally-unbiased multiports allow photons to propagate along a reverse\ndirection, which can greatly reduce the number of required linear optical\nelements for complicated linear optical quantum networks. Here, we report an\nexperimental demonstration of a 3 x 3 directionally-unbiased linear optical\nfiber multiport using an optical tritter and mirrors. Compared to the previous\ndemonstration using bulk optical elements which works only with light sources\nwith a long coherence length, our experimental directionally-unbiased 3 x 3\noptical multiport does not require a long coherence length since it provides\nnegligible optical path length differences among all possible optical\ntrajectories. It can be a useful building block for implementing large-scale\nquantum walks on complex graph networks.\n"}
+{"abstract": "  Artificial intelligence (AI) will play an increasing role in cellular network\ndeployment, configuration and management. This paper examines the security\nimplications of AI-driven 6G radio access networks (RANs). While the expected\ntimeline for 6G standardization is still several years out, pre-standardization\nefforts related to 6G security are already ongoing and will benefit from\nfundamental and experimental research. The Open RAN (O-RAN) describes an\nindustry-driven open architecture and interfaces for building next generation\nRANs with AI control. Considering this architecture, we identify the critical\nthreats to data driven network and physical layer elements, the corresponding\ncountermeasures, and the research directions.\n"}
+{"abstract": "  This paper is concerned with the relativistic Boltzmann equation without\nangular cutoff. We establish the global-in-time existence, uniqueness and\nasymptotic stability for solutions nearby the relativistic Maxwellian to the\nspecial relativistic Boltzmann equation without any angular cutoff. We work in\nthe case of a spatially periodic box. We assume the generic hard-interaction\nand soft-interaction conditions on the collision kernel that were derived by\nDudy\\'nski and Ekiel-Je$\\dot{\\text{z}}$ewska (Comm. Math. Phys. 115(4):607-629,\n1985), and our assumptions include the case of Israel particles (J. Math. Phys.\n4:1163-1181, 1963). In this physical situation, the angular function in the\ncollision kernel is not locally integrable, and the collision operator behaves\nlike a fractional diffusion operator. We further derive the relativistic\nanalogue of the Carleman dual representation of Boltzmann collision operator.\nThis is the first global existence and stability result for relativistic\nBoltzmann equation without angular cutoff and this resolves the open question\nof perturbative global existence without the Grad's angular cut-off assumption.\n"}
+{"abstract": "  We study the problem of online peak-demand minimization under energy storage\nconstraints. It is motivated by an increasingly popular scenario where\nlarge-load customers utilize energy storage to reduce the peak procurement from\nthe grid, which accounts for up to $90\\%$ of their electric bills. The problem\nis uniquely challenging due to (i) the coupling of online decisions across time\nimposed by the inventory constraints and (ii) the noncumulative nature of the\npeak procurement. In this paper, we develop an optimal online algorithm for the\nproblem, attaining the best possible competitive ratio (CR) among all\ndeterministic and randomized algorithms. We show that the optimal CR can be\ncomputed in polynomial time, by solving a linear number of linear-fractional\nproblems. More importantly, we generalize our approach to develop an\n\\emph{anytime-optimal} online algorithm that achieves the best possible CR at\nany epoch, given the inputs and online decisions so far. The algorithm retains\nthe optimal worst-case performance and achieves adaptive average-case\nperformance. Simulation results based on real-world traces show that, under\ntypical settings, our algorithms improve peak reduction by over $19\\%$ as\ncompared to baseline alternatives.\n"}
+{"abstract": "  Titan's surface was revealed by Cassini's instruments, showing the presence\nof liquid hydrocarbons in lakes, and features like dry riverbed. In order to\nstudy the sediment transport in Titan's channels and to map distribution of the\nwater-ice signature in these terrains, we use a radiative transfer model to\nretrieve the surface albedo, after we estimated VIMS error via an original\nmethod. We also establish a criteria related to the intensity of the water ice\nsignature. The tuning of the radiative transfer model shows that the fractal\ndimension of Titan's aerosols is higher than previously thought, around 2.3 -\n2.4. We find spots of increasing signal of water ice downstream, at the margins\nof Tui Regio, that could correspond to alluvial fans, deltas or crater rims. We\nalso observe a very low water ice signal on Tui Regio, with a positive gradient\nbetween the central region and the boundary of the area, possibly due to the\nthickness variation of an evaporitic layer. The riverbeds show within the error\nbars a decreasing grain size from the top to the bottom of the channels.\n"}
+{"abstract": "  There has been a surge in remote sensing machine learning applications that\noperate on data from active or passive sensors as well as multi-sensor\ncombinations (Ma et al. (2019)). Despite this surge, however, there has been\nrelatively little study on the comparative value of 3D surface information for\nmachine learning classification tasks. Adding 3D surface information to RGB\nimagery can provide crucial geometric information for semantic classes such as\nbuildings, and can thus improve out-of-sample predictive performance. In this\npaper, we examine in-sample and out-of-sample classification performance of\nFully Convolutional Neural Networks (FCNNs) and Support Vector Machines (SVMs)\ntrained with and without 3D normalized digital surface model (nDSM)\ninformation. We assess classification performance using multispectral imagery\nfrom the International Society for Photogrammetry and Remote Sensing (ISPRS) 2D\nSemantic Labeling contest and the United States Special Operations Command\n(USSOCOM) Urban 3D Challenge. We find that providing RGB classifiers with\nadditional 3D nDSM information results in little increase in in-sample\nclassification performance, suggesting that spectral information alone may be\nsufficient for the given classification tasks. However, we observe that\nproviding these RGB classifiers with additional nDSM information leads to\nsignificant gains in out-of-sample predictive performance. Specifically, we\nobserve an average improvement in out-of-sample all-class accuracy of 14.4% on\nthe ISPRS dataset and an average improvement in out-of-sample F1 score of 8.6%\non the USSOCOM dataset. In addition, the experiments establish that nDSM\ninformation is critical in machine learning and classification settings that\nface training sample scarcity.\n"}
+{"abstract": "  In this paper, we present an attention-guided deformable convolutional\nnetwork for hand-held multi-frame high dynamic range (HDR) imaging, namely\nADNet. This problem comprises two intractable challenges of how to handle\nsaturation and noise properly and how to tackle misalignments caused by object\nmotion or camera jittering. To address the former, we adopt a spatial attention\nmodule to adaptively select the most appropriate regions of various exposure\nlow dynamic range (LDR) images for fusion. For the latter one, we propose to\nalign the gamma-corrected images in the feature-level with a Pyramid, Cascading\nand Deformable (PCD) alignment module. The proposed ADNet shows\nstate-of-the-art performance compared with previous methods, achieving a\nPSNR-$l$ of 39.4471 and a PSNR-$\\mu$ of 37.6359 in NTIRE 2021 Multi-Frame HDR\nChallenge.\n"}
+{"abstract": "  Semiconducting Transition Metal Dichalcogenides (TMDs) have significant\nnonlinear optical effects. In this work we have used second-harmonic generation\n(SHG) and the four-wave mixing (FWM) spectroscopy in resonance with the\nexcitons in MoS2, MoSe2, and WS2 monolayers to characterize the nonlinear\noptical properties of these materials. We show that trions and excitons are\nresponsible for enhancing the nonlinear optical response, and determine the\nexciton and trion energies by comparing with the photoluminescence spectra.\nMoreover, we extract the second and third order optical sheet susceptibility\nnear exciton energies and compare with values found in the literature. We also\ndemonstrate the ability to generate different nonlinear effects in a wide\nspectral range in the visible region for monolayer MoS2, opening the\npossibility of using two-dimensional materials for nonlinear optoelectronic and\nphotonic applications.\n"}
+{"abstract": "  Given a class $\\mathcal P$ of Banach spaces, a locally convex space (LCS) $E$\nis called {\\em multi-$\\mathcal P$} if $E$ can be isomorphically embedded into a\nproduct of spaces that belong to $\\mathcal P$. We investigate the question\nwhether the free locally convex space $L(X)$ is strongly nuclear, nuclear,\nSchwartz, multi-Hilbert or multi-reflexive.\n  If $X$ is a Tychonoff space containing an infinite compact subset then, as it\nfollows from the results of \\cite{Aus}, $L(X)$ is not nuclear. We prove that\nfor such $X$ the free LCS $L(X)$ has the stronger property of not being\nmulti-Hilbert. We deduce that if $X$ is a $k$-space, then the following\nproperties are equivalent: (1) $L(X)$ is strongly nuclear; (2) $L(X)$ is\nnuclear; (3) $L(X)$ is multi-Hilbert; (4) $X$ is countable and discrete. On the\nother hand, we show that $L(X)$ is strongly nuclear for every projectively\ncountable $P$-space (in particular, for every Lindel\\\"of $P$-space) $X$.\n  We observe that every Schwartz LCS is multi-reflexive. It is known that if\n$X$ is a $k_\\omega$-space, then $L(X)$ is a Schwartz LCS \\cite{Chasco}, hence\n$L(X)$ is multi-reflexive. We show that for any first-countable paracompact\n  (in particular, metrizable) space $X$ the converse is true, so $L(X)$ is\nmulti-reflexive if and only if $X$ is a $k_\\omega$-space, equivalently, if $X$\nis a locally compact and $\\sigma$-compact space.\n  Similarly, we show that for any first-countable paracompact space $X$ the\nfree abelian topological group $A(X)$ is a Schwartz group if and only if $X$ is\na locally compact space such that the set $X^{(1)}$ of all non-isolated points\nof $X$ is $\\sigma$-compact.\n"}
+{"abstract": "  By lowering the number of vertical connections in fully connected 3D\nnetworks-on-chip (NoCs), partially connected 3D NoCs (PC-3DNoCs) help alleviate\nreliability and fabrication issues. This paper proposes a novel, adaptive\ncongestion- and energy-aware elevator-selection scheme called AdEle to improve\nthe traffic distribution in PC-3DNoCs. AdEle employs an offline multi-objective\nsimulated-annealing-based algorithm to find good elevator subsets and an online\nelevator selection policy to enhance elevator selection during routing.\nCompared to the state-of- the-art techniques under different real-application\ntraffics and configuration scenarios, AdEle improves the network latency by\n10.9% on average (up to 14.6%) with less than 6.9% energy consumption overhead.\n"}
+{"abstract": "  In this paper, we demonstrate a domain inversion crystal structure to study\nthe cascaded three-wave mixing process in a two peak Stark-chirped rapid\nadiabatic passage. We have achieved efficient wavelength conversion, which can\nbe performed in intuitive order and counterintuitive order. The requirement of\ncrystal is reduced and the flexibility of structure design is improved. When\nthe conversion wavelength is fixed, increasing the coupling coefficient between\nthe two peaks can reduce the intensity of the intermediate wavelength while\nmaintaining high conversion efficiency. Compared with the cascaded wavelength\nconversion process based on stimulated Raman rapid adiabatic passage, the\ntwo-peak Stark-chirped rapid adiabatic passage has a larger convertible\nwavelength bandwidth. This scheme provides a theoretical basis for obtaining\nmid-infrared laser source via flexible crystal structure.\n"}
+{"abstract": "  Photonic quantum technologies$^1$, with applications in quantum\ncommunication, sensing as well as quantum simulation and computing, are on the\nverge of becoming commercially available. One crucial building block are\ntailored nanoscale integratable quantum light sources, matching the specific\nneeds of use-cases. Several different approaches to realize solid-state quantum\nemitters$^2$ with high performance$^3$ have been pursued. However, the\nproperties of the emitted single photons are always defined by the individual\nquantum light source and despite numerous quantum emitter tuning\ntechniques$^{4-7}$, scalability is still a major challenge. Here we show an\nemitter-independent method to tailor and control the properties of the single\nphoton emission. We demonstrate a laser-controlled down-conversion process from\nan excited state of a quantum three-level system$^8$. Starting from a biexciton\nstate, a tunable control laser field defines a virtual state in a stimulated\nprocess. From there, spontaneous emission to the ground state leads to\noptically controlled single photon emission. Based on this concept, we\ndemonstrate energy tuning of the single photon emission with a control laser\nfield. The nature of the involved quantum states furthermore provides a unique\nbasis for the future control of polarization and bandwidth, as predicted by\ntheory$^{9,10}$. Our demonstration marks an important step towards tailored\nsingle photon emission from a photonic quantum system based on quantum optical\nprinciples.\n"}
+{"abstract": "  In recent years, the existence of a hadronically stable $\\bar{b} \\bar{b} u d$\ntetraquark with quantum numbers $I(J^P) = 0(1^+)$ was confirmed by first\nprinciples lattice QCD computations. In this work we use lattice QCD to compare\ntwo frequently discussed competing structures for this tetraquark by\nconsidering meson-meson as well as diquark-antidiquark creation operators. We\nuse the static-light approximation, where the two $\\bar{b}$ quarks are assumed\nto be infinitely heavy with frozen positions, while the light $u$ and $d$\nquarks are fully relativistic. By minimizing effective energies and by solving\ngeneralized eigenvalue problems we determine the importance of the meson-meson\nand the diquark-antidiquark creation operators with respect to the ground\nstate. It turns out, that the diquark-antidiquark structure dominates for\n$\\bar{b} \\bar{b}$ separations $r < 0.25 \\, \\text{fm}$, whereas it becomes\nincreasingly more irrelevant for larger separations, where the $I(J^P) =\n0(1^+)$ tetraquark is mostly a meson-meson state. We also estimate the\nmeson-meson to diquark-antidiquark ratio of this tetraquark and find around\n$60\\% / 40\\%$.\n"}
+{"abstract": "  Gapless Luttinger liquid is conventionally viewed as topologically trivial,\nunless it hosts degenerate ground states and or entanglement spectrum, which\nnecessitates partial bulk degree of freedom to be gapped out. Here we predict\nan emergent gapless topological Luttinger liquid which is beyond the\nconventional scenarios and is characterized by the nontrivial many-body bulk\nspin texture, and propose feasible scheme for experimental observation. We\nconsider a one-dimensional spin-orbit coupled Fermi-Hubbard model with\nfractional filling, whose low-energy physics is effectively described by a\nspinless Luttinger liquid and is trivial in the conventional characterization.\nWe show that, as being tuned by the filling factor and interaction strength,\nthe many-body ground state may exhibit nontrivial winding in its bulk spin\ntexture in the projected momentum space, manifesting an emergent topological\nphase. A topological transition occurs when the projected spin-state at a high\nsymmetry momentum becomes fully mixed one, resulting from competing processes\nof particle scattering to the lower and higher subbands, for which the spin\ntexture at such momentum point is ill-defined, but the Luttinger liquid keeps\ngapless through the transition. Surprisingly, at relatively small filling the\nLuttinger liquid remains topologically nontrivial even at infinitely strong\ninteraction. The results can be generalized to finite temperature which\nfacilitates the real experimental detection. This work shows a novel gapless\ntopological Luttinger liquid whose characterization is beyond the low-energy\neffective theory, and can be verified based on current experiments.\n"}
+{"abstract": "  The traditional phase sensitivity function (PSF) has manifested its efficacy\nin investigating synchronization behaviors for limit-cycle oscillators.\nHowever, some subtle details may be ignored when the phase value is accumulated\nin space or the perturbation is space-dependent. In this paper, we compared\nspatial PSF with the traditional PSF for oscillators close to the saddle-node\nhomoclinic (SNH) bifurcation, also known as saddle-node on invariant circle\n(SNIC) bifurcation. It is found that the spatial phase sensitivity function\ncould reveal the phase accumulation feature on the limit cycle. Moreover, it is\nproved that for any two-dimensional smooth dynamical system, type II phase\nresponse curve is the only possible type. Finally, the synchronization\ndistributions of uncoupled SNH oscillator driven by common and independent\nnoises are studied, which shows the space-dependent coupling function of common\nnoise could have significant influences on the synchronization behavior.\n"}
+{"abstract": "  We theoretically consider spin pumping in a junction between a ferromagnetic\ninsulator (FI) and a two-dimensional electron gas (2DEG) in which the Rashba\nand Dresselhaus spin-orbit interactions coexist. Using second-order\nperturbation theory, we derive an increase in linewidth in the case of an\ninterfacial exchange coupling in a ferromagnetic resonance (FMR) experiment. We\nclarify how the enhancement of Gilbert damping depends on the resonant\nfrequency and spin orientation of the FI. We show that this setup of an FMR\nexperiment can provide information on the spin texture of 2DEG at the Fermi\nsurface.\n"}
+{"abstract": "  The Shapley value has become popular in the Explainable AI (XAI) literature,\nthanks, to a large extent, to a solid theoretical foundation, including four\n\"favourable and fair\" axioms for attribution in transferable utility games. The\nShapley value is provably the only solution concept satisfying these axioms. In\nthis paper, we introduce the Shapley value and draw attention to its recent\nuses as a feature selection tool. We call into question this use of the Shapley\nvalue, using simple, abstract \"toy\" counterexamples to illustrate that the\naxioms may work against the goals of feature selection. From this, we develop a\nnumber of insights that are then investigated in concrete simulation settings,\nwith a variety of Shapley value formulations, including SHapley Additive\nexPlanations (SHAP) and Shapley Additive Global importancE (SAGE).\n"}
+{"abstract": "  Concept learning approaches based on refinement operators explore partially\nordered solution spaces to compute concepts, which are used as binary\nclassification models for individuals. However, the number of concepts explored\nby these approaches can easily grow to the millions for complex learning\nproblems. This often leads to impractical runtimes. We propose to alleviate\nthis problem by predicting the length of target concepts before the exploration\nof the solution space. By these means, we can prune the search space during\nconcept learning. To achieve this goal, we compare four neural architectures\nand evaluate them on four benchmarks. Our evaluation results suggest that\nrecurrent neural network architectures perform best at concept length\nprediction with a macro F-measure ranging from 38% to 92%. We then extend the\nCELOE algorithm--which learns ALC concepts--with our concept length predictor.\nOur extension yields the algorithm CLIP. In our experiments, CLIP is at least\n7.5 times faster than other state-of-the-art concept learning algorithms for\nALC--including CELOE--and achieves significant improvements in the F-measure of\nthe concepts learned on 3 out of 4 datasets. For reproducibility, we provide\nour implementation in the public GitHub repository at\nhttps://github.com/ConceptLengthLearner/LearnLengths\n"}
+{"abstract": "  Large Transformer-based language models can aid human authors by suggesting\nplausible continuations of text written so far. However, current interactive\nwriting assistants do not allow authors to guide text generation in desired\ntopical directions. To address this limitation, we design a framework that\ndisplays multiple candidate upcoming topics, of which a user can select a\nsubset to guide the generation. Our framework consists of two components: (1) a\nmethod that produces a set of candidate topics by predicting the centers of\nword clusters in the possible continuations, and (2) a text generation model\nwhose output adheres to the chosen topics. The training of both components is\nself-supervised, using only unlabeled text. Our experiments demonstrate that\nour topic options are better than those of standard clustering approaches, and\nour framework often generates fluent sentences related to the chosen topics, as\njudged by automated metrics and crowdsourced workers.\n"}
+{"abstract": "  In continuous variable systems, it is often advantageous to study the\nsystem's covariance matrix rather than the more complex density operator.\nHowever, the time evolution of the covariance matrix is known to be\nself-contained only in the quadratic case, i.e. when the generator of the\nevolution is at most second-order in the position and momentum operators. Here,\nwe derive an explicit model of covariance matrix evolution of infinite order,\nwhich proves useful in scenarios as varied as random scattering and engineered\ndissipation. Our findings have broader implications for contemporary measures\nof non-Gaussianity.\n"}
+{"abstract": "  We consider the problem of the extraction of semantic attributes, supervised\nonly with classification labels. For example, when learning to classify images\nof birds into species, we would like to observe the emergence of features that\nzoologists use to classify birds. To tackle this problem, we propose training a\nneural network with discrete features in the last layer, which is followed by\ntwo heads: a multi-layered perceptron (MLP) and a decision tree. Since decision\ntrees utilize simple binary decision stumps we expect those discrete features\nto obtain semantic meaning. We present a theoretical analysis as well as a\npractical method for learning in the intersection of two hypothesis classes.\nOur results on multiple benchmarks show an improved ability to extract a set of\nfeatures that are highly correlated with the set of unseen attributes.\n"}
+{"abstract": "  Dislocations govern the properties of any crystals. Yet, how dislocation of\npentagonheptagon (5|7) in grain boundaries (GBs) affects the mechanical\nproperties of two-dimensional MoS2 crystals remains poorly known. Using\natomistic simulations and continuum disclination dipole model, we show that,\ndepending on the tilt angle and 5|7 dislocation arrangement, MoS2 GB strength\ncan be enhanced or reduced with tilt angle. For zigzag-tilt GBs primarily\ncomposed of Mo5|7+S5|7 dislocations, GB strength monotonically increases as the\nsquare of tilt angle. For armchair-tilt GBs with Mo5|7 or S5|7 dislocations,\nhowever, the trend of GB strength breaks down as 5|7 dislocations are\nnon-evenly spaced. Moreover, mechanical failure initiates at the bond shared by\n5|7 rings, in contrast to graphene where failure occurs at the bond shared by\n6|7 rings. This work provides new insights into mechanical design of synthetic\ntransition metal dichalcogenide crystals via dislocation engineering.\n"}
+{"abstract": "  Magnetic carbon nanostructures are currently under scrutiny for a wide\nspectrum of applications. Here, we theoretically investigate armchair graphene\nnanoribbons patterned with asymmetric edge extensions consisting of laterally\nfused naphtho groups, as recently fabricated via on-surface synthesis. We show\nthat an individual edge extension acts as a spin-$\\frac{1}{2}$ center and\ndevelops a sizable spin-polarization of the conductance around the band edges.\nThe Heisenberg exchange coupling between a pair of edge extensions is dictated\nby the position of the second naphtho group in the carbon backbone, thus\nenabling ferromagnetic, antiferromagnetic, or non-magnetic states. The periodic\narrangement of edge extensions yields full spin-polarization at the band\nextrema, and the accompanying ferromagnetic ground state can be driven into\nnon-magnetic or antiferromagnetic phases through external stimuli. Overall, our\nwork reveals precise tunability of the ${\\pi}$-magnetism in graphene\nnanoribbons induced by naphtho groups, thereby establishing these\none-dimensional architectures as suitable platforms for logic spintronics.\n"}
+{"abstract": "  Cloud applications are increasingly shifting from large monolithic services\nto complex graphs of loosely-coupled microservices. Despite the advantages of\nmodularity and elasticity microservices offer, they also complicate cluster\nmanagement and performance debugging, as dependencies between tiers introduce\nbackpressure and cascading QoS violations.\n  We present Sage, a machine learning-driven root cause analysis system for\ninteractive cloud microservices. Sage leverages unsupervised ML models to\ncircumvent the overhead of trace labeling, captures the impact of dependencies\nbetween microservices to determine the root cause of unpredictable performance\nonline, and applies corrective actions to recover a cloud service's QoS. In\nexperiments on both dedicated local clusters and large clusters on Google\nCompute Engine we show that Sage consistently achieves over 93% accuracy in\ncorrectly identifying the root cause of QoS violations, and improves\nperformance predictability.\n"}
+{"abstract": "  In this paper we deal with the heat equation with drift in $L_{d+1}$.\nBasically, we prove that, if the free term is in $L_{q}$ with high enough $q$,\nthen the equation is uniquely solvable in a rather unusual class of functions\nsuch that $\\partial_{t}u, D^{2}u\\in L_{p}$ with $p<d+1$ and $Du\\in L_{q}$.\n"}
+{"abstract": "  Tikhonov regularization with square-norm penalty for linear forward operators\nhas been studied extensively in the literature. However, the results on\nconvergence theory are based on technical proofs and difficult to interpret. It\nis also often not clear how those results translate into the discrete,\nnumerical setting. In this paper we present a new strategy to study the\nproperties of a regularization method on the example of Tikhonov\nregularization. The technique is based on the observation that Tikhonov\nregularization approximates the unknown exact solution in the range of the\nadjoint of the forward operator. This is closely related to the concept of\napproximate source conditions, which we generalize to describe not only the\napproximation of the unknown solution, but also noise-free and noisy data; all\nfrom the same source space. Combining these three approximation results we\nderive the well-known convergence results in a concise way and improve the\nunderstanding by tightening the relation between concepts such as convergence\nrates, parameter choice, and saturation. The new technique is not limited to\nTikhonov regularization, it can be applied also to iterative regularization,\nwhich we demonstrate by relating Tikhonov regularization and Landweber\niteration. Because the Tikhonov functional is no longer the centrepiece of the\nanalysis, we can show that Tikhonov regularization can be used for\noversmoothing regularization. All results are accompanied by numerical\nexamples.\n"}
+{"abstract": "  The question why the solar corona is much hotter than the visible solar\nsurface still puzzles solar researchers. Most theories of the coronal heating\ninvolve a tight coupling between the coronal magnetic field and the associated\nthermal structure. This coupling is based on two facts: (i) the magnetic field\nis the main source of the energy in the corona and (ii) the heat transfer\npreferentially happens along the magnetic field, while is suppressed across it.\nHowever, most of the information about the coronal heating is derived from\nanalysis of EUV or soft X-ray emissions, which are not explicitly sensitive to\nthe magnetic field. This paper employs another electromagnetic channel -- the\nsunspot-associated microwave gyroresonant emission, which is explicitly\nsensitive to both the magnetic field and thermal plasma. We use nonlinear\nforce-free field reconstructions of the magnetic skeleton dressed with a\nthermal structure as prescribed by a field-aligned hydrodynamics to constrain\nthe coronal heating model. We demonstrate that the microwave gyroresonant\nemission is extraordinarily sensitive to details of the coronal heating. We\ninfer heating model parameters consistent with observations.\n"}
+{"abstract": "  The exact critical Casimir amplitude is derived for anisotropic systems\nwithin the $d=2$ Ising universality class by combining conformal field theory\n(CFT) with anisotropic $\\varphi^4$ theory. Explicit results are presented for\nthe general anisotropic scalar $\\varphi^4$ model and for the fully anisotropic\ntriangular-lattice Ising model in finite rectangular and infinite strip\ngeometries with periodic boundary conditions (PBC). These results demonstrate\nthe validity of multiparameter universality for confined anisotropic systems\nand the nonuniversality of the critical Casimir amplitude. We find an\nunexpected complex form of self-similarity of the anisotropy effects near the\ninstability where weak anisotropy breaks down. This can be traced back to the\nproperty of modular invariance of isotropic CFT for $d=2$. More generally, for\n$d>2$ we predict the existence of self-similar structures of the finite-size\nscaling functions of $O(n)$-symmetric systems with planar anisotropies and PBC\nboth in the critical region for $ n \\geq 1$ as well as in the\nGoldstone-dominated low-temperature region for $ n \\geq 2$.\n"}
+{"abstract": "  Semantic segmentation is an extensively studied task in computer vision, with\nnumerous methods proposed every year. Thanks to the advent of deep learning in\nsemantic segmentation, the performance on existing benchmarks is close to\nsaturation. A natural question then arises: Does the superior performance on\nthe closed (and frequently re-used) test sets transfer to the open visual world\nwith unconstrained variations? In this paper, we take steps toward answering\nthe question by exposing failures of existing semantic segmentation methods in\nthe open visual world under the constraint of very limited human labeling\neffort. Inspired by previous research on model falsification, we start from an\narbitrarily large image set, and automatically sample a small image set by\nMAximizing the Discrepancy (MAD) between two segmentation methods. The selected\nimages have the greatest potential in falsifying either (or both) of the two\nmethods. We also explicitly enforce several conditions to diversify the exposed\nfailures, corresponding to different underlying root causes. A segmentation\nmethod, whose failures are more difficult to be exposed in the MAD competition,\nis considered better. We conduct a thorough MAD diagnosis of ten PASCAL VOC\nsemantic segmentation algorithms. With detailed analysis of experimental\nresults, we point out strengths and weaknesses of the competing algorithms, as\nwell as potential research directions for further advancement in semantic\nsegmentation. The codes are publicly available at\n\\url{https://github.com/QTJiebin/MAD_Segmentation}.\n"}
+{"abstract": "  Distance-based dynamic texture recognition is an important research field in\nmultimedia processing with applications ranging from retrieval to segmentation\nof video data. Based on the conjecture that the most distinctive characteristic\nof a dynamic texture is the appearance of its individual frames, this work\nproposes to describe dynamic textures as kernelized spaces of frame-wise\nfeature vectors computed using the Scattering transform. By combining these\nspaces with a basis-invariant metric, we get a framework that produces\ncompetitive results for nearest neighbor classification and state-of-the-art\nresults for nearest class center classification.\n"}
+{"abstract": "  The introduction of new tools in people's workflow has always been promotive\nof new creative paths. This paper discusses the impact of using computational\ntools in the performance of creative tasks, especially focusing on graphic\ndesign. The study was driven by a grounded theory methodology, applied to a set\nof semi-structured interviews, made to twelve people working in the areas of\ngraphic design, data science, computer art, music and data visualisation. Among\nother questions, the results suggest some scenarios in which it is or it is not\nworth investing in the development of new intelligent creativity-aiding tools.\n"}
+{"abstract": "  The construction of symmetry breaking differential operators, using invariant\npluri-harmonic polynomials, due to T. Ibukiyama in the context of the Siegel\nupper half space, is extended for scalar representations to general Hermitian\nsymmetric spaces of tube-type. The new context is described in terms of\nEuclidean Jordan algebras and their representations. As an example, new and\nexplicit differential operators are obtained for the restriction from the tube\ndomain over the light cone to the product of two upper half-planes.\n"}
+{"abstract": "  The structure of the electronic nonlinear optical conductivity is elucidated\nin a detailed study of the time-reversal symmetric two-band model. The\nnonlinear conductivity is decomposed as a sum of contributions related with\ndifferent regions of the First Brillouin Zone, defined by single or multiphoton\nresonances. All contributions are written in terms of the same integrals, which\ncontain all information specific to the particular model under study. In this\nway, ready-to-use formulas are provided that reduce the often tedious\ncalculations of the second and third order optical conductivity to the\nevaluation of a small set of similar integrals. In the scenario where charge\ncarriers are present prior to optical excitation, Fermi surface contributions\nmust also be considered and are shown to have an universal frequency\ndependence, tunable by doping. General characteristics are made evident in this\ntype of resonance-based analysis: the existence of step functions that\ndetermine the chemical potential dependence of electron-hole symmetric\ninsulators; the determination of the imaginary part by Hilbert transforms,\nsimpler than those of the nonlinear Kramers-Kr\\\"{o}nig relations; the absence\nof Drude peaks in the diagonal elements of the second order conductivity, among\nothers. As examples, analytical expressions are derived for the nonlinear\nconductivities of some simple systems: a very basic model of direct gap\nsemiconductors and the Dirac fermions of monolayer graphene.\n"}
+{"abstract": "  The Particle-Particle-Particle-Mesh algorithm elegantly extends the standard\nParticle-In-Cell scheme by direct summation of interaction that happens over\ndistances below or around mesh size. Generally, this allows for a more accurate\ndescription of Coulomb interactions and improves precision in the prediction of\nkey observables. Nevertheless, most implementations neglect electrostatic\nboundary conditions for the short-ranged interaction that are directly summed.\nIn this paper a variational description of the Particle-Particle-Particle-Mesh\nalgorithm will be developed for the first time and subsequently used to derive\ntemporally and spatially discrete equations of motion. We show that the error\ncommitted by neglecting boundary conditions on the short scale is directly tied\nto the discretization error induced by the computational grid.\n"}
+{"abstract": "  The objective speech quality assessment is usually conducted by comparing\nreceived speech signal with its clean reference, while human beings are capable\nof evaluating the speech quality without any reference, such as in the mean\nopinion score (MOS) tests. Non-intrusive speech quality assessment has\nattracted much attention recently due to the lack of access to clean reference\nsignals for objective evaluations in real scenarios. In this paper, we propose\na novel non-intrusive speech quality measurement model, MetricNet, which\nleverages label distribution learning and joint speech reconstruction learning\nto achieve significantly improved performance compared to the existing\nnon-intrusive speech quality measurement models. We demonstrate that the\nproposed approach yields promisingly high correlation to the intrusive\nobjective evaluation of speech quality on clean, noisy and processed speech\ndata.\n"}
+{"abstract": "  Automated detection of breast tumor in early stages using B-Mode Ultrasound\nimage is crucial for preventing widespread breast cancer specially among women.\nThis paper is primarily focusing on the classification of breast tumors through\nstatistical modelling such as Rician inverse Gaussian (RiIG) pdf of contourlet\ntransformed B-Mode image of breast tumors which is not reported yet in other\nearlier works. The suitability of RiIG distribution in modeling the contourlet\ncoefficients is illustrated and compared with that of Nakagami distribution.\nThe proposed method consists of pre-processing to remove the speckle noise,\nsegmentation of the lesion region, contourlet transform on the B-Mode\nUltrasound image and using the corresponding contourlet sub-band coefficients\nand the RiIG parameters, production of contourlet parametric (CP) images and\nweighted contourlet parametric (WCP) images. A number of geometrical,\nstatistical, and texture features are calculated from B-Mode and the contourlet\nparametric images. In order to classify the features, seven different\nclassifiers are employed. The proposed approach is applied to two different\ndatasets (Mendeley Data and Dataset B) those are available publicly. It is\nshown that with parametric images, accuracies in the range of 94-97% are\nachieved for different classifiers. Specifically, with the support vector\nmachine and k-nearest-neighbor classifier, very high accuracies of 97.2% and\n97.55% can be obtained for the Mendeley Data and Dataset B,respectively, using\nthe weighted contourlet parametric images.The reported classification\nperformance is also compared with that of other works using the datasets\nemployed in this paper. It is seen that the proposed approach using weighted\ncontourlet parametric images can provide a superior performance.\n"}
+{"abstract": "  We consider a Frenkel-Kontorova system of harmonic oscillators in a\ntwo-dimensional Euclidean lattice and we obtain a quantitative estimate on the\nangular function of the equilibria. The proof relies on a PDE method related to\na classical conjecture by E. De Giorgi, also in view of an elegant technique\nbased on complex variables that was introduced by A. Farina.\n  In the discrete setting, a careful analysis of the reminders is needed to\nexploit this type of methodologies inspired by continuum models.\n"}
+{"abstract": "  Methods for calculating lower bounds to the exact energy using the variance\nof the upper bound energy are discussed and explored. All the matrix elements\nof the Hamiltonian squared are collected and considered, and those for which no\nknown solutions could be found in the literature are derived for an explicitly\ncorrelated Gaussian (ECG) basis set. Analytical Solutions are determined for\ntwo-electron, mono-nuclear systems, in addition to a one-dimensional integral\nexpression which has use in polyatomic calculations. The newly derived integral\nexpressions have been implemented in the integral library of the QUANTEN\ncomputer program.\n"}
+{"abstract": "  Digital text is increasing day by day on the internet. It is very challenging\nto classify a large and heterogeneous collection of data, which require\nimproved information processing methods to organize text. To classify large\nsize of corpus, one common approach is to use hierarchical text classification,\nwhich aims to classify textual data in a hierarchical structure. Several\napproaches have been proposed to tackle classification of text but most of the\nresearch has been done on English language. This paper proposes a deep learning\nmodel for hierarchical text classification of news in Urdu language -\nconsisting of 51,325 sentences from 8 online news websites belonging to the\nfollowing genres: Sports; Technology; and Entertainment. The objectives of this\npaper are twofold: (1) to develop a large human-annotated dataset of news in\nUrdu language for hierarchical text classification; and (2) to classify Urdu\nnews hierarchically using our proposed model based on LSTM mechanism named as\nHierarchical Multi-layer LSTMs (HMLSTM). Our model consists of two modules:\nText Representing Layer, for obtaining text representation in which we use\nWord2vec embedding to transform the words to vector and Urdu Hierarchical LSTM\nLayer (UHLSTML) an end-to-end fully connected deep LSTMs network to perform\nautomatic feature learning, we train one LSTM layer for each level of the class\nhierarchy. We have performed extensive experiments on our self created dataset\nnamed as Urdu News Dataset for Hierarchical Text Classification (UNDHTC). The\nresult shows that our proposed method is very effective for hierarchical text\nclassification and it outperforms baseline methods significantly and also\nachieved good results as compare to deep neural model.\n"}
+{"abstract": "  In this paper, we consider stochastic optimal control of systems driven by\nstochastic differential equations with irregular drift coefficient. We\nestablish a necessary and sufficient stochastic maximum principle. To achieve\nthis, we first derive an explicit representation of the first variation process\n(in Sobolev sense ) of the controlled diffusion. Since the drift coefficient is\nnot smooth, the representation is given in terms of the local time of the state\nprocess. Then we construct a sequence of optimal control problems with smooth\ncoefficients by an approximation argument. Finally, we use Ekeland's\nvariational principle to obtain an approximating adjoint process from which we\nderive the maximum principle by passing to the limit.\n"}
+{"abstract": "  Archaeologists, like other scientists, are experiencing a data-flood in their\ndiscipline, fueled by a surge in computing power and devices that enable the\ncreation, collection, storage and transfer of an increasingly complex (and\nlarge) amount of data, such as remotely sensed imagery from a multitude of\nsources. In this paper, we pose the preliminary question if this increasing\navailability of information actually needs new computerized techniques, and\nArtificial Intelligence methods, to make new and deeper understanding into\narchaeological problems. Simply said, while it is a fact that Deep Learning\n(DL) has become prevalent as a type of machine learning design inspired by the\nway humans learn, and utilized to perform automatic actions people might\ndescribe as intelligent, we want to anticipate, here, a discussion around the\nsubject whether machines, trained following this procedure, can extrapolate,\nfrom archaeological data, concepts and meaning in the same way that humans\nwould do. Even prior to getting to technical results, we will start our\nreflection with a very basic concept: Is a collection of satellite images with\nnotable archaeological sites informative enough to instruct a DL machine to\ndiscover new archaeological sites, as well as other potential locations of\ninterest? Further, what if similar results could be reached with less\nintelligent machines that learn by having people manually program them with\nrules? Finally: If with barrier of meaning we refer to the extent to which\nhuman-like understanding can be achieved by a machine, where should be posed\nthat barrier in the archaeological data science?\n"}
+{"abstract": "  In this paper, we propose a new high order semi-implicit scheme for the all\nMach full Euler equations of gas dynamics. Material waves are treated\nexplicitly, while acoustic waves are treated implicitly, thus avoiding severe\nCFL restrictions for low Mach flows. High order accuracy in time is obtained by\nsemi-implicit temporal integrator based on the IMEX Runge-Kutta (IMEX-RK)\nframework. High order in space is achieved by finite difference WENO schemes\nwith characteristic-wise reconstructions adapted to the semi-implicit IMEX-RK\ntime discretization. Type A IMEX schemes are constructed to handle not\nwell-prepared initial conditions. Besides, these schemes are proven to be\nasymptotic preserving and asymptotically accurate as the Mach number vanishes\nfor well-prepared initial conditions. Divergence-free property of the\ntime-discrete schemes is proved. The proposed scheme can also well capture\ndiscontinuous solutions in the compressible regime, especially for two\ndimensional Riemann problems. Numerical tests in one and two space dimensions\nwill illustrate the effectiveness of the proposed schemes.\n"}
+{"abstract": "  We quantize the Hamilton equations instead of the Hamilton condition. The\nresulting equation has the simple form $-\\D u=0$ in a fiber bundle, where the\nLaplacian is the Laplacian of the Wheeler-DeWitt metric provided $n\\not=4$.\nUsing then separation of variables the solutions $u$ can be expressed as\nproducts of temporal and spatial eigenfunctions, where the spatial\neigenfunctions are eigenfunctions of the Laplacian in the symmetric space\n$SL(n,\\R[])/SO(n)$. Since one can define a Schwartz space and tempered\ndistributions in $SL(n,\\R[])/SO(n)$ as well as a Fourier transform, Fourier\nquantization can be applied such that the spatial eigenfunctions are\ntransformed to Dirac measures and the spatial Laplacian to a multiplication\noperator.\n"}
+{"abstract": "  Statistical preconditioning can be used to design fast methods for\ndistributed large-scale empirical risk minimization problems, for strongly\nconvex and smooth loss functions, allowing fewer communication rounds. Multiple\nworker nodes compute gradients in parallel, which are then used by the central\nnode to update the parameter by solving an auxiliary (preconditioned)\nsmaller-scale optimization problem. However, previous works require an exact\nsolution of an auxiliary optimization problem by the central node at every\niteration, which may be impractical. This paper proposes a method that allows\nthe inexact solution of the auxiliary problem, reducing the total computation\ntime. Moreover, for loss functions with high-order smoothness, we exploit the\nstructure of the auxiliary problem and propose a hyperfast second-order method\nwith complexity $\\tilde{O}(\\kappa^{1/5})$, where $\\kappa$ is the local\ncondition number. Combining these two building blocks (inexactness and\nhyperfast methods), we show complexity estimates for the proposed algorithm,\nwhich is provably better than classical variance reduction methods and has the\nsame convergence rate as statistical preconditioning with exact solutions.\nFinally, we illustrate the proposed method's practical efficiency by performing\nlarge-scale numerical experiments on logistic regression models.\n"}
+{"abstract": "  We study the problem of fair allocation of a set of indivisible items among\nagents with additive valuations, under cardinality constraints. In this setting\nthe items are partitioned into categories, each with its own limit on the\nnumber of items it may contribute to any bundle. One example of such a problem\nis allocating seats in a multitrack conference. We consider the fairness\nmeasure known as the maximin share (MMS) guarantee, and propose a novel\npolynomial-time algorithm for finding $1/2$-approximate MMS allocations. We\nextend the notions and algorithms related to ordered and reduced instances to\nwork with cardinality constraints, and combine these with a bag filling style\nprocedure. Our algorithm improves on that of Biswas and Barman (IJCAI-18), with\nits approximation ratio of $1/3$. We also present an optimizing algorithm,\nwhich for each instance, instead of fixing $\\alpha = 1/2$, uses bisection to\nfind the largest $\\alpha$ for which our algorithm obtains a valid\n$\\alpha$-approximate MMS allocation. Numerical tests show that our algorithm\nfinds strictly better approximations than the guarantee of $1/2$ for most\ninstances, in many cases surpassing $3/5$. The optimizing version of the\nalgorithm produces MMS allocations in a comparable number of instances to that\nof Biswas and Barman's algorithm, on average achieving a better approximation\nwhen MMS is not obtained.\n"}
+{"abstract": "  We are interested here in the program of reconstruction of quantum mechanics\nof the German physicist and philosopher Carl Friedrich von Weizs\\\"{a}cker,\nwhich still has some supporters today. In the major part of this article, we\nlimit ourselves to examining purely epistemological and philosophical\nquestions. The theory of the German physicist is often interpreted with\nreference to Kant, but the most of the time in a rather vague way (categories\nof understanding, theory of experience). We can afford to be more precise.\nFirst we situate the theory of fundamental alternatives (or Ur-alternatives) of\nvon Weizs\\\"{a}cker in the lineage of the Kantian theory of the transcendental\nIdeal. We then show that the physicist only substitutes for the classical\nlogic, on which Kant relied, a quantum logic allowing to generate, from the\nalternatives, via the local isomorphism between certain spinor groups and\ngroups linked to space-time, the whole of physical reality to which we have\naccess. After examining some problems, we finally show how this perspective\nleads to a quantum theory of information.\n"}
+{"abstract": "  In this paper we investigate the monotonicity properties related to the ratio\nof gamma functions, from which some related asymptotics and inequalities are\nestablished. Some special cases also confirm the conjectures of C.-P. Chen\n[Monotonicity properties, inequalities, and asymptotic expansions associated\nwith the gamma function. Appl. Math. Comput. 283 (2016), 385--396.].\n"}
+{"abstract": "  Quantum spin liquids hosting Majorana excitations have recently experienced\nrenewed interest for potential applications to topological quantum computation.\nPerforming logical operations with reduced poisoning requires to localize such\nquasiparticles at specific point of the system, with energies that are well\ndefined and inside the bulk energy gap. These are two defining features of\nsecond order topological insulators (SOTIs). Here, we show two spin models that\nsupport quantum spin liquid phases characterised by Majorana excitations and\nthat behave as SOTIs, one of which is analytically solvable thanks to a theorem\nby Lieb. We show that, depending on the values of spin couplings, it is\npossible to localize either fermions or Majorana particles at their corners.\n"}
+{"abstract": "  In this paper, we prove that many fractal sets generated by the associated\ndynamical systems only contain irrationals. As an application, we explicitly\nconstruct some overlapping self-similar sets which only consist of irrationals.\n"}
+{"abstract": "  We discuss resonance properties in three-body systems, with examples on\n$^{12}{\\rm C}$ and $^{24}{\\rm Mg}$. We use a microscopic cluster model, where\nthe generator coordinate is defined in the hyperspherical formalism. The\n$^{12}{\\rm C}$ nucleus is described by an $\\alpha+\\alpha+\\alpha$ structure,\nwhereas $^{24}{\\rm Mg}$ is considered as an $^{16}{\\rm O}+\\alpha+\\alpha$\nsystem. We essentially pay attention to resonances. We review various\ntechniques which may extend variational methods to resonances. We consider\n$0^+$ and $2^+$ states in $^{12}{\\rm C}$ and $^{24}{\\rm Mg}$. We show that the\nr.m.s. radius of a resonance is strongly sensitive to the variational basis.\nThis has consequences for the Hoyle state ($0^+_2$ state in $^{12}{\\rm C}$)\nwhose radius has been calculated or measured in several works. In $^{24}{\\rm\nMg}$, we identify two $0^+$ resonances slightly below the three-body threshold.\n"}
+{"abstract": "  Python has become the de-facto language for training deep neural networks,\ncoupling a large suite of scientific computing libraries with efficient\nlibraries for tensor computation such as PyTorch or TensorFlow. However, when\nmodels are used for inference they are typically extracted from Python as\nTensorFlow graphs or TorchScript programs in order to meet performance and\npackaging constraints. The extraction process can be time consuming, impeding\nfast prototyping. We show how it is possible to meet these performance and\npackaging constraints while performing inference in Python. In particular, we\npresent a way of using multiple Python interpreters within a single process to\nachieve scalable inference and describe a new container format for models that\ncontains both native Python code and data. This approach simplifies the model\ndeployment story by eliminating the model extraction step, and makes it easier\nto integrate existing performance-enhancing Python libraries. We evaluate our\ndesign on a suite of popular PyTorch models on Github, showing how they can be\npackaged in our inference format, and comparing their performance to\nTorchScript. For larger models, our packaged Python models perform the same as\nTorchScript, and for smaller models where there is some Python overhead, our\nmulti-interpreter approach ensures inference is still scalable.\n"}
+{"abstract": "  In many contexts Gaussian Mixtures (GM) are used to approximate probability\ndistributions, possibly time-varying. In some applications the number of GM\ncomponents exponentially increases over time, and reduction procedures are\nrequired to keep them reasonably limited. The GM reduction (GMR) problem can be\nformulated by choosing different measures of the dissimilarity of GMs before\nand after reduction, like the Kullback-Leibler Divergence (KLD) and the\nIntegral Squared Error (ISE). Since in no case the solution is obtained in\nclosed form, many approximate GMR algorithms have been proposed in the past\nthree decades, although none of them provides optimality guarantees. In this\nwork we discuss the importance of the choice of the dissimilarity measure and\nthe issue of consistency of all steps of a reduction algorithm with the chosen\nmeasure. Indeed, most of the existing GMR algorithms are composed by several\nsteps which are not consistent with a unique measure, and for this reason may\nproduce reduced GMs far from optimality. In particular, the use of the KLD, of\nthe ISE and normalized ISE is discussed and compared in this perspective.\n"}
+{"abstract": "  We present a robust control framework for time-critical systems in which\nsatisfying real-time constraints robustly is of utmost importance for the\nsafety of the system. Signal Temporal Logic (STL) provides a formal means to\nexpress a large variety of real-time constraints over signals and is suited for\nplanning and control purposes as it allows us to reason about the time\nrobustness of such constraints. The time robustness of STL particularly\nquantifies the extent to which timing uncertainties can be tolerated without\nviolating real-time specifications. In this paper, we first pose a control\nproblem in which we aim to find an optimal input sequence to a control system\nthat maximizes the time robustness of an STL constraint. We then propose a\nMixed Integer Linear Program (MILP) encoding and provide correctness guarantees\nalong with a complexity analysis of the encoding. We also show in two case\nstudies that maximizing STL time robustness allows to account for timing\nuncertainties of the underlying control system.\n"}
+{"abstract": "  RTNeural is a neural inferencing library written in C++. RTNeural is designed\nto be used in systems with hard real-time constraints, with additional emphasis\non speed, flexibility, size, and convenience. The motivation and design of the\nlibrary are described, as well as real-world use-cases, and performance\ncomparisons with other neural inferencing libraries.\n"}
+{"abstract": "  Mouse-tracking of computer system users represents a less expensive, but also\na far more applicable alternative to eye-tracking. The main disadvantage of\nmouse-tracking are errors manifested as discrepancies between the actual\neye-gaze position and the mouse cursor position. This paper presents a method\nfor automated correction of errors arising in mouse-tracking. Our approach\ndraws upon information theory and employs Shannon entropy. The method is based\non calculating the entropy of a visual representation of a Web page, i.e., we\nquantify information potential values of various positions. Information\nobtained, thereby, is paired with cumulative time intervals, spent by the mouse\ncursor in each position. In this way, we identify cursor positions that do not\nmatch eye-gaze positions. To verify the effectiveness of our method, we\ncompared the eye gaze and mouse cursor heat maps in the following ways: We\ncalculated the coefficient of correlation between the two; we computed\nEuclidean distance between their centers of gravity; and we performed visual\ncomparison.\n"}
+{"abstract": "  We study the minimal gap statistic for sequences of the form $\\left( \\alpha\nx_n \\right)_{n = 1}^{\\infty}$ where $\\left( x_n \\right)_{n = 1}^{\\infty}$ is a\nsequence of real numbers, and its connection to the additive energy of $\\left(\nx_n \\right)_{n = 1}^{\\infty}$. Inspired by a recent paper of Aistleitner,\nEl-Baz and Munsch we show conditionally on the Lindel\\\"{o}f Hypothesis that if\nthe additive energy is of lowest possible order then for almost all $\\alpha$,\nthe minimal gap $\\delta_{\\min}^{\\alpha} (N) = \\min \\left\\{ \\alpha x_m - \\alpha\nx_n \\bmod \\ 1 : 1 \\leq m \\neq n \\leq N \\right\\}$ is close to that of a random\nsequence, a result Rudnick showed for integer-valued sequences. We also show\nunconditional results in this direction, as well as some converse theorems\nabout sequences with large additive energy.\n"}
+{"abstract": "  Upper limb and hand functionality is critical to many activities of daily\nliving and the amputation of one can lead to significant functionality loss for\nindividuals. From this perspective, advanced prosthetic hands of the future are\nanticipated to benefit from improved shared control between a robotic hand and\nits human user, but more importantly from the improved capability to infer\nhuman intent from multimodal sensor data to provide the robotic hand perception\nabilities regarding the operational context. Such multimodal sensor data may\ninclude various environment sensors including vision, as well as human\nphysiology and behavior sensors including electromyography and inertial\nmeasurement units. A fusion methodology for environmental state and human\nintent estimation can combine these sources of evidence in order to help\nprosthetic hand motion planning and control.\n  In this paper, we present a dataset of this type that was gathered with the\nanticipation of cameras being built into prosthetic hands, and computer vision\nmethods will need to assess this hand-view visual evidence in order to estimate\nhuman intent. Specifically, paired images from human eye-view and hand-view of\nvarious objects placed at different orientations have been captured at the\ninitial state of grasping trials, followed by paired video, EMG and IMU from\nthe arm of the human during a grasp, lift, put-down, and retract style trial\nstructure. For each trial, based on eye-view images of the scene showing the\nhand and object on a table, multiple humans were asked to sort in decreasing\norder of preference, five grasp types appropriate for the object in its given\nconfiguration relative to the hand. The potential utility of paired eye-view\nand hand-view images was illustrated by training a convolutional neural network\nto process hand-view images in order to predict eye-view labels assigned by\nhumans.\n"}
+{"abstract": "  In both Newtonian gravity and Einstein gravity there is no force on a test\nparticle located inside a spherical cavity cut out of a static, spherically\nsymmetric mass distribution. Inside the cavity exterior matter is decoupled and\nthere is no external field effect that could act on the test particle. However,\nfor potentials other than the Newtonian potential or for geometries other than\nRicci flat ones this is no longer the case, and there then is an external field\neffect. We explore this possibility in various alternate gravity scenarios, and\nsuggest that such (Machian) external field effects can serve as a diagnostic\nfor gravitational theory.\n"}
+{"abstract": "  Deep-learning-based video processing has yielded transformative results in\nrecent years. However, the video analytics pipeline is energy-intensive due to\nhigh data rates and reliance on complex inference algorithms, which limits its\nadoption in energy-constrained applications. Motivated by the observation of\nhigh and variable spatial redundancy and temporal dynamics in video data\nstreams, we design and evaluate an adaptive-resolution optimization framework\nto minimize the energy use of multi-task video analytics pipelines. Instead of\nheuristically tuning the input data resolution of individual tasks, our\nframework utilizes deep reinforcement learning to dynamically govern the input\nresolution and computation of the entire video analytics pipeline. By\nmonitoring the impact of varying resolution on the quality of high-dimensional\nvideo analytics features, hence the accuracy of video analytics results, the\nproposed end-to-end optimization framework learns the best non-myopic policy\nfor dynamically controlling the resolution of input video streams to globally\noptimize energy efficiency. Governed by reinforcement learning, optical flow is\nincorporated into the framework to minimize unnecessary spatio-temporal\nredundancy that leads to re-computation, while preserving accuracy. The\nproposed framework is applied to video instance segmentation which is one of\nthe most challenging computer vision tasks, and achieves better energy\nefficiency than all baseline methods of similar accuracy on the YouTube-VIS\ndataset.\n"}
+{"abstract": "  Within the scientific goals of the THESEUS ESA/M5 candidate mission, a\ncritical item is a fast (within a few s) and accurate (<15 arcmin) Gamma-Ray\nBurst and high-energy transient location from a few keV up to hard X-ray energy\nband. For that purpose, the signal multiplexing based on coded masks is the\nselected option to achieve this goal. This contribution is implemented by the\nXGIS Imaging System, based on that technique. The XGIS Imaging System has the\nheritage of previous payload developments: LEGRI/Minisat-01, INTEGRAL,\nUFFO/Lomonosov and ASIM/ISS. In particular the XGIS Imaging System is an\nupgrade of the ASIM system in operation since 2018 on the International Space\nStation. The scientific goal is similar: to detect a gamma-ray transient. But\nwhile ASIM focuses on Terrestrial Gamma-ray Flashes, THESEUS aims for the GRBs.\nFor each of the two XGIS Cameras, the coded mask is located at 630 mm from the\ndetector layer. The coding pattern is implemented in a Tungsten plate (1 mm\nthickness) providing a good multiplexing capability up to 150 keV. In that way\nboth XGIS detector layers (based on Si and CsI detectors) have imaging\ncapabilities at the medium - hard X-ray domain. This is an improvement achieved\nduring the current THESEUS Phase-A. The mask is mounted on top of a collimator\nthat provides the mechanical assembly support, as well as good cosmic X-ray\nbackground shielding. The XGIS Imaging System preliminary structural and\nthermal design, and the corresponding analyses, are included in this\ncontribution, as it is a preliminary performance evaluation.\n"}
+{"abstract": "  Computing the convolution $A\\star B$ of two length-$n$ vectors $A,B$ is an\nubiquitous computational primitive. Applications range from string problems to\nKnapsack-type problems, and from 3SUM to All-Pairs Shortest Paths. These\napplications often come in the form of nonnegative convolution, where the\nentries of $A,B$ are nonnegative integers. The classical algorithm to compute\n$A\\star B$ uses the Fast Fourier Transform and runs in time $O(n\\log n)$.\n  However, often $A$ and $B$ satisfy sparsity conditions, and hence one could\nhope for significant improvements. The ideal goal is an $O(k\\log k)$-time\nalgorithm, where $k$ is the number of non-zero elements in the output, i.e.,\nthe size of the support of $A\\star B$. This problem is referred to as sparse\nnonnegative convolution, and has received considerable attention in the\nliterature; the fastest algorithms to date run in time $O(k\\log^2 n)$.\n  The main result of this paper is the first $O(k\\log k)$-time algorithm for\nsparse nonnegative convolution. Our algorithm is randomized and assumes that\nthe length $n$ and the largest entry of $A$ and $B$ are subexponential in $k$.\nSurprisingly, we can phrase our algorithm as a reduction from the sparse case\nto the dense case of nonnegative convolution, showing that, under some mild\nassumptions, sparse nonnegative convolution is equivalent to dense nonnegative\nconvolution for constant-error randomized algorithms. Specifically, if $D(n)$\nis the time to convolve two nonnegative length-$n$ vectors with success\nprobability $2/3$, and $S(k)$ is the time to convolve two nonnegative vectors\nwith output size $k$ with success probability $2/3$, then\n$S(k)=O(D(k)+k(\\log\\log k)^2)$.\n  Our approach uses a variety of new techniques in combination with some old\nmachinery from linear sketching and structured linear algebra, as well as new\ninsights on linear hashing, the most classical hash function.\n"}
+{"abstract": "  Complex decision making problems such as the privacy policy selection when\nsharing content in online social networks can significantly benefit from\nartificial intelligence systems. With the use of Computational Argumentation,\nit is possible to persuade human users to modify their initial decisions to\navoid potential privacy threats and violations. In this paper, we present a\nstudy performed over 186 teenage users aimed at analysing their behaviour when\nwe try to persuade them to modify their initial decisions in online social\nnetworks with different arguments. The results of the study revealed that the\npersonality traits and the social interaction data (e.g., number of comments,\nfriends, and likes) of our participants were significantly correlated with the\npersuasive power of the arguments. The findings from this analysis will help to\nmodel OSN users and define persuasive strategies for argumentation dialogues in\nthis domain.\n"}
+{"abstract": "  It follows from a theorem of Rosenthal that a compact space is $ccc$ if and\nonly if every Eberlein continuous image is metrizable. Motivated by this\nresult, for a class of compact spaces $\\mathcal{C}$ we define its orthogonal\n$\\mathcal{C}^\\perp$ as the class of all compact spaces for which every\ncontinuous image in $\\mathcal{C}$ is metrizable. We study how this operation\nrelates classes where centeredness is scarce with classes where it is abundant\n(like Eberlein and $ccc$ compacta), and also classes where independence is\nscarce (most notably weakly Radon-Nikod\\'ym compacta) with classes where it is\nabundant. We study these problems for zero-dimensional compact spaces with the\naid of Boolean algebras and show the main difficulties arising when passing to\nthe general setting. Our main results are the constructions of several relevant\nexamples.\n"}
+{"abstract": "  The interplay between electronic interactions and strong spin-orbit coupling\nis expected to create a plethora of fascinating correlated topological states\nof quantum matter. Of particular interest are magnetic Weyl semimetals\noriginally proposed in the pyrochlore iridates, which are only expected to\nreveal their topological nature in thin film form. To date, however, direct\nexperimental demonstrations of these exotic phases remain elusive, due to the\nlack of usable single crystals and the insufficient quality of available films.\nHere, we report on the discovery of the long-sought magnetic Weyl semi-metallic\nphase in (111)-oriented Eu$_2$Ir$_2$O$_7$ high-quality epitaxial thin films.\nThe topological magnetic state shows an intrinsic anomalous Hall effect with\ncolossal coercivity but vanishing net magnetization, which emerges below the\nonset of a peculiar magnetic phase with all-in-all-out antiferromagnetic\nordering. The observed anomalous Hall conductivity arises from the non-zero\nBerry curvature emanated by Weyl node pairs near the Fermi level that act as\nsources and sinks of Berry flux, activated by broken cubic crystal symmetry at\nthe top and bottom terminations of the thin film.\n"}
+{"abstract": "  Signal transduction is an important and basic mechanism to cell life\nactivities. The stochastic state transition of receptor induces the release of\nsignaling molecular, which triggers the state transition of other receptors. It\nconstructs a nonlinear sigaling network, and leads to robust switchlike\nproperties which are critical to biological function. Network architectures and\nstate transitions of receptor affect the performance of this biological\nnetwork. In this work, we perform a study of nonlinear signaling on biological\npolymorphic network by analyzing network dynamics of the Ca$^{2+}$ induced\nCa$^{2+}$ release mechanism, where fast and slow processes are involved and the\nreceptor has four conformational states. Three types of networks,\nErd\\\"os-R\\'enyi network, Watts-Strogatz network and BaraB\\'asi-Albert network,\nare considered with different parameters. The dynamics of the biological\nnetworks exhibit different patterns at different time scales. At short time\nscale, the second open state is essential to reproduce the quasi-bistable\nregime, which emerges at a critical strength of connection for all three states\ninvolved in the fast processes and disappears at another critical point. The\npattern at short time scale is not sensitive to the network architecture. At\nlong time scale, only monostable regime is observed, and difference of network\narchitectures affects the results more seriously. Our finding identifies\nfeatures of nonlinear signaling networks with multistate that may underlie\ntheir biological function.\n"}
+{"abstract": "  We consider a power transmission system monitored with Phasor Measurement\nUnits (PMUs) placed at significant, but not all, nodes of the system. Assuming\nthat a sufficient number of distinct single-line faults, specifically pre-fault\nstate and (not cleared) post-fault state, are recorded by the PMUs and are\navailable for training, we, first, design a comprehensive sequence of Neural\nNetworks (NNs) locating the faulty line. Performance of different NNs in the\nsequence, including Linear Regression, Feed-Forward NN, AlexNet, Graphical\nConvolutional NN, Neural Linear ODE and Neural Graph-based ODE, ordered\naccording to the type and amount of the power flow physics involved, are\ncompared for different levels of observability. Second, we build a sequence of\nadvanced Power-System-Dynamics-Informed and Neural-ODE based Machine Learning\nschemes trained, given pre-fault state, to predict the post-fault state and\nalso, in parallel, to estimate system parameters. Finally, third, and\ncontinuing to work with the first (fault localization) setting we design a\n(NN-based) algorithm which discovers optimal PMU placement.\n"}
+{"abstract": "  We investigate the electromagnetic transitions of the singly charmed baryons\nwith spin 3/2, based on a pion mean-field approach, also known as the chiral\nquark-soliton model, taking into account the rotational $1/N_c$ corrections and\nthe effects of flavor SU(3) symmetry breaking. We examine the valence- and\nsea-quark contributions to the electromagnetic transition form factors and find\nthat the quadrupole form factors of the sea-quark contributions dominate over\nthose of the valence-quark ones in the smaller $Q^2$ region, whereas the sea\nquarks only provide marginal contributions to the magnetic dipole transition\nform factors of the baryon sextet with spin 3/2. The effects of the flavor\nSU(3) symmetry breaking are in general very small except for the forbidden\ntransition $\\Xi_c^0\\gamma\\to \\Xi_c^{*0}$ by $U$-spin symmetry. We also discuss\nthe widths of the radiative decays for the baryon sextet with spin 3/2,\ncomparing the present results with those from other works.\n"}
+{"abstract": "  The recent realisations of hydrogen doped $Ln$FeAsO ($Ln$=Nd and Sm)\nsuperconducting epitaxial thin films call for further investigation of their\nstructural and electrical transport properties. Here, we report on the\nmicrostructure of a NdFeAs(O,H) epitaxial thin film and its temperature, field,\nand orientation dependencies of the resistivity and the critical current\ndensity $J_{\\rm c}$. The superconducting transition temperature $T_{\\rm c}$ is\ncomparable to NdFeAs(O,F). Transmission electron microscopy investigation\nsupported that hydrogen is homogenously substituted for oxygen. A high\nself-field $J_{\\rm c}$ of over 10 MA/cm$^2$ was recorded at 5 K, which is\nlikely to be caused by a short London penetration depth. The anisotropic\nGinzburg-Landau scaling for the angle dependence of $J_{\\rm c}$ yielded\ntemperature-dependent scaling parameters $\\gamma_{\\rm J}$ that decreased from\n1.6 at 30 K to 1.3 at 5 K. This is opposite to the behaviour of NdFeAs(O,F).\nAdditionally, $\\gamma_{\\rm J}$ of NdFeAs(O,H) is smaller than that of\nNdFeAs(O,F). Our results indicate that heavily electron doping by means of\nhydrogen substitution for oxygen in $Ln$FeAsO is highly beneficial for\nachieving high $J_{\\rm c}$ with low anisotropy without compromising $T_{\\rm\nc}$, which is favourable for high-field magnet applications.\n"}
+{"abstract": "  Triangle centrality is introduced for finding important vertices in a graph\nbased on the concentration of triangles surrounding each vertex. An important\nvertex in triangle centrality is at the center of many triangles, and therefore\nit may be in many triangles or none at all.\n  We give optimal algorithms that compute triangle centrality in $O(m\\sqrt{m})$\ntime and $O(m+n)$ space. Using fast matrix multiplication it takes\n$n^{\\omega+o(1)}$ time where $\\omega$ is the matrix product exponent.\n  On a Concurrent Read Exclusive Write (CREW) Parallel Random Access Memory\n(PRAM) machine, we give a near work-optimal algorithm that takes $O(\\log n)$\ntime using $O(m\\sqrt{m})$ CREW PRAM processors. In MapReduce, we show it takes\nfour rounds using $O(m\\sqrt{m})$ communication bits, and is therefore optimal.\n  We also give a deterministic algorithm to find the triangle neighborhood and\ntriangle count of each vertex in $O(m\\sqrt{m})$ time and $O(m+n)$ space.\n  Our empirical results demonstrate that triangle centrality uniquely\nidentified central vertices thirty-percent of the time in comparison to five\nother well-known centrality measures, while being asymptotically faster to\ncompute on sparse graphs than all but the most trivial of these other measures.\n"}
+{"abstract": "  We present a novel counterfactual framework for both Zero-Shot Learning (ZSL)\nand Open-Set Recognition (OSR), whose common challenge is generalizing to the\nunseen-classes by only training on the seen-classes. Our idea stems from the\nobservation that the generated samples for unseen-classes are often out of the\ntrue distribution, which causes severe recognition rate imbalance between the\nseen-class (high) and unseen-class (low). We show that the key reason is that\nthe generation is not Counterfactual Faithful, and thus we propose a faithful\none, whose generation is from the sample-specific counterfactual question: What\nwould the sample look like, if we set its class attribute to a certain class,\nwhile keeping its sample attribute unchanged? Thanks to the faithfulness, we\ncan apply the Consistency Rule to perform unseen/seen binary classification, by\nasking: Would its counterfactual still look like itself? If ``yes'', the sample\nis from a certain class, and ``no'' otherwise. Through extensive experiments on\nZSL and OSR, we demonstrate that our framework effectively mitigates the\nseen/unseen imbalance and hence significantly improves the overall performance.\nNote that this framework is orthogonal to existing methods, thus, it can serve\nas a new baseline to evaluate how ZSL/OSR models generalize. Codes are\navailable at https://github.com/yue-zhongqi/gcm-cf.\n"}
+{"abstract": "  The excitation ansatz for tensor networks is a powerful tool for simulating\nthe low-lying quasiparticle excitations above ground states of strongly\ncorrelated quantum many-body systems. Recently, the two-dimensional tensor\nnetwork class of infinite entangled pair states gained new ground state\noptimization methods based on automatic differentiation, which are at the same\ntime highly accurate and simple to implement. Naturally, the question arises\nwhether these new ideas can also be used to optimize the excitation ansatz,\nwhich has recently been implemented in two dimensions as well. In this paper,\nwe describe a straightforward way to reimplement the framework for excitations\nusing automatic differentiation, and demonstrate its performance for the\nHubbard model at half filling.\n"}
+{"abstract": "  Accurate prediction of machining cycle times is important in the\nmanufacturing industry. Usually, Computer Aided Manufacturing (CAM) software\nestimates the machining times using the commanded feedrate from the toolpath\nfile using basic kinematic settings. Typically, the methods do not account for\ntoolpath geometry or toolpath tolerance and therefore under estimate the\nmachining cycle times considerably. Removing the need for machine specific\nknowledge, this paper presents a data-driven feedrate and machining cycle time\nprediction method by building a neural network model for each machine tool\naxis. In this study, datasets composed of the commanded feedrate, nominal\nacceleration, toolpath geometry and the measured feedrate were used to train a\nneural network model. Validation trials using a representative industrial thin\nwall structure component on a commercial machining centre showed that this\nmethod estimated the machining time with more than 90% accuracy. This method\nshowed that neural network models have the capability to learn the behavior of\na complex machine tool system and predict cycle times. Further integration of\nthe methods will be critical in the implantation of digital twins in Industry\n4.0.\n"}
+{"abstract": "  Modern deep neural networks (DNNs) become frail when the datasets contain\nnoisy (incorrect) class labels. Robust techniques in the presence of noisy\nlabels can be categorized into two folds: developing noise-robust functions or\nusing noise-cleansing methods by detecting the noisy data. Recently,\nnoise-cleansing methods have been considered as the most competitive\nnoisy-label learning algorithms. Despite their success, their noisy label\ndetectors are often based on heuristics more than a theory, requiring a robust\nclassifier to predict the noisy data with loss values. In this paper, we\npropose a novel detector for filtering label noise. Unlike most existing\nmethods, we focus on each data's latent representation dynamics and measure the\nalignment between the latent distribution and each representation using the\neigendecomposition of the data gram matrix. Our framework, coined as filtering\nnoisy instances via their eigenvectors (FINE), provides a robust detector with\nderivative-free simple methods having theoretical guarantees. Under our\nframework, we propose three applications of the FINE: sample-selection\napproach, semi-supervised learning approach, and collaboration with\nnoise-robust loss functions. Experimental results show that the proposed\nmethods consistently outperform corresponding baselines for all three\napplications on various benchmark datasets.\n"}
+{"abstract": "  Climate change is a burning issue of our time, with the Sustainable\nDevelopment Goal (SDG) 13 of the United Nations demanding global climate\naction. Realizing the urgency, in 2015 in Paris, world leaders signed an\nagreement committing to taking voluntary action to reduce carbon emissions.\nHowever, the scale, magnitude, and climate action processes vary globally,\nespecially between developed and developing countries. Therefore, from\nparliament to social media, the debates and discussions on climate change\ngather data from wide-ranging sources essential to the policy design and\nimplementation. The downside is that we do not currently have the mechanisms to\npool the worldwide dispersed knowledge emerging from the structured and\nunstructured data sources.\n  The paper thematically discusses how NLP techniques could be employed in\nclimate policy research and contribute to society's good at large. In\nparticular, we exemplify symbiosis of NLP and Climate Policy Research via four\nmethodologies. The first one deals with the major topics related to climate\npolicy using automated content analysis. We investigate the opinions\n(sentiments) of major actors' narratives towards climate policy in the second\nmethodology. The third technique explores the climate actors' beliefs towards\npro or anti-climate orientation. Finally, we discuss developing a Climate\nKnowledge Graph.\n  The present theme paper further argues that creating a knowledge platform\nwould help in the formulation of a holistic climate policy and effective\nclimate action. Such a knowledge platform would integrate the policy actors'\nvaried opinions from different social sectors like government, business, civil\nsociety, and the scientific community. The research outcome will add value to\neffective climate action because policymakers can make informed decisions by\nlooking at the diverse public opinion on a comprehensive platform.\n"}
+{"abstract": "  We exploit a recent computational framework to model and detect financial\ncrises in stock markets, as well as shock events in cryptocurrency markets,\nwhich are characterized by a sudden or severe drop in prices. Our method\nmanages to detect all past crises in the French industrial stock market\nstarting with the crash of 1929, including financial crises after 1990 (e.g.\ndot-com bubble burst of 2000, stock market downturn of 2002), and all past\ncrashes in the cryptocurrency market, namely in 2018, and also in 2020 due to\ncovid-19. We leverage copulae clustering, based on the distance between\nprobability distributions, in order to validate the reliability of the\nframework; we show that clusters contain copulae from similar market states\nsuch as normal states, or crises. Moreover, we propose a novel regression model\nthat can detect successfully all past events using less than 10% of the\ninformation that the previous framework requires. We train our model by\nhistorical data on the industry assets, and we are able to detect all past\nshock events in the cryptocurrency market. Our tools provide the essential\ncomponents of our software framework that offers fast and reliable detection,\nor even prediction, of shock events in stock and cryptocurrency markets of\nhundreds of assets.\n"}
+{"abstract": "  A substantial fraction of Cataclysmic Variables (CVs) reveals non-solar\nabundances. A comprehensive list of CVs which includes those that have been\nexamined for these abundances is given. Three possible sources of these\nnon-solar abundances on the secondary are accretion during the red giant common\nenvelope phase, an Evolved Main Sequence secondary and nova-processed material.\nUse of the secondary's cross-section just on the escaping nova material to\nchange the abundances of its convective region has been the killing objection\nfor considering nova-processed material. The key element, ignored in other\nstudies, is that a thermonuclear runaway on a white dwarf causes a strong\npropagating shock wave which not only ejects material, but also produces a\nlarge amount of non-ejected material which forms a common envelope. This\nnova-produced common envelope contains a large amount of non-solar material. We\ndemonstrate that the secondary has the capacity and time to re-accrete enough\nof this material to acquire a significant non-solar convective region. This\nsame envelope interacting with the binary will produce a Frictional Angular\nMomentum Loss which can be the Consequential Angular Momentum Loss needed for\nthe average CV white dwarf mass, WD mass accretion rates, the period minimum,\nthe orbital period distribution, and the space density of CVs problems. This\ninteraction will decrease the orbital period which can cause the recently\nobserved sudden period decreases across nova eruptions. A simple, rapid\nevolutionary model of the secondary that includes the swept-up nova-produced\nmaterial and the increasing convective region is developed and applied to\nindividual CVs.\n"}
+{"abstract": "  As known, the degree of entanglement of biphoton states with respect to the\ntransverse components of photon wave vectors (momenta) or coordinates can be\ncharacterized either by the parameter K associated with the Schmidt\ndecompositions, or by the parameter R, defined as the ratio of the widths of\nthe unconditional and conditional single-particle distributions of biphoton\nstates. As entanglement is a fundamental characteristics of a state, it must be\nindependent of a choice of its different representations, i.e. its entanglement\nparameters must be identical in the coordinate and momentum representations.\nLikewise, entanglement itself and its characterization parameters must remain\nconstant along the photon propagation length. It's known, however, that only\nthe parameter K obeys these requirements, whereas the parameter R in the\ncoordinate representation deviates from K beyond the near zone and, thus,\nbecomes inapplicable as a measure of the degree of entablement. But, as shown\nbelow, there is a way of saving the parameter R by means of modifying its\ndefinition in such a way that the modified parameter R turns out to be as good\nas the parameter K both in the momentum and coordinate representations, as well\nas in the near and far zones and everywhere between them. We will discuss also\npossible ways of measuring this newly defined parameter R experimentally.\n"}
+{"abstract": "  When an expert operates a perilous dynamic system, ideal constraint\ninformation is tacitly contained in their demonstrated trajectories and\ncontrols. The likelihood of these demonstrations can be computed, given the\nsystem dynamics and task objective, and the maximum likelihood constraints can\nbe identified. Prior constraint inference work has focused mainly on\ndeterministic models. Stochastic models, however, can capture the uncertainty\nand risk tolerance that are often present in real systems of interest.\n  This paper extends maximum likelihood constraint inference to stochastic\napplications by using maximum causal entropy likelihoods. Furthermore, we\npropose an efficient algorithm that computes constraint likelihood and risk\ntolerance in a unified Bellman backup, allowing us to generalize to stochastic\nsystems without increasing computational complexity.\n"}
+{"abstract": "  Background: Measurement errors in terms of quantification or classification\nfrequently occur in epidemiologic data and can strongly impact inference.\nMeasurement errors may occur when ascertaining, recording or extracting data.\nAlthough the effects of measurement errors can be severe and are well\ndescribed, simple straight forward general analytic solutions are not readily\navailable for statistical analysis and measurement error is frequently not\nacknowledged or accounted for. Generally, to account for measurement error\nrequires some data where we can observe the variables once with and once\nwithout error, to establish the relationship between the two. Methods: Here we\ndescribe a general method accounting for measurement error in outcome and/or\npredictor variables for the parametric regression setting when there is a\nvalidation subsample where variables are measured once with and once without\nerror. The method does not describe and thus does not depend on the particular\nrelation between the variables measured with and without error, and is\ngenerally robust to the type of measurement error, for example nondifferential,\ndifferential or Berkson errors. Results: Simulation studies show how the method\nreduces bias compared to models based upon variables measured with error alone\nand reduces variances compared to models based upon the variables measured\nwithout error in the validation subsample alone. Conclusion: The proposed\nestimator has favorable properties in terms of bias and variance, is easily\nderived empirically, and is robust to different types of measurement error.\nThis method should be a valuable tool in the analysis of data with measurement\nerror.\n"}
+{"abstract": "  The estimation of functions with varying degrees of smoothness is a\nchallenging problem in the nonparametric function estimation. In this paper, we\npropose the LABS (L\\'{e}vy Adaptive B-Spline regression) model, an extension of\nthe LARK models, for the estimation of functions with varying degrees of\nsmoothness. LABS model is a LARK with B-spline bases as generating kernels. The\nB-spline basis consists of piecewise k degree polynomials with k-1 continuous\nderivatives and can express systematically functions with varying degrees of\nsmoothness. By changing the orders of the B-spline basis, LABS can\nsystematically adapt the smoothness of functions, i.e., jump discontinuities,\nsharp peaks, etc. Results of simulation studies and real data examples support\nthat this model catches not only smooth areas but also jumps and sharp peaks of\nfunctions. The proposed model also has the best performance in almost all\nexamples. Finally, we provide theoretical results that the mean function for\nthe LABS model belongs to the certain Besov spaces based on the orders of the\nB-spline basis and that the prior of the model has the full support on the\nBesov spaces.\n"}
+{"abstract": "  We present an estimation of lifetimes of massive galaxies with distinct UV\ncolors at $0.5 \\le z \\le 2.5$ in the COSMOS/UltraVISTA field. After dividing\nthe galaxy sample into subsamples of red sequence (RS), blue cloud (BC), and\ngreen valley (GV) galaxies in different redshift bins, according to their\nrest-frame extinction-corrected UV colors, we derive their lifetimes using\nclustering analyses. Several essentials that may influence the lifetime\nestimation have been explored, including the dark matter (DM) halo mass\nfunction (HMF), the width of redshift bin, the growth of DM halos within each\nredshift bin, and the stellar mass. We find that the HMF difference results in\nscatters of $\\sim0.2$ dex on lifetime estimation; adopting a redshift bin width\nof $\\Delta z = 0.5$ is good enough to estimate the lifetime; and no significant\neffect on lifetime estimation is found due to the growth of DM halos within\neach redshift bin. The galaxy subsamples with higher stellar masses generally\nhave shorter lifetimes; however, the lifetimes among different subsamples at z\n> 1:5 tend to be independent of stellar mass. Consistently, the\nclustering-based lifetime for each galaxy subsample agrees well with that\ninferred using the spectral energy distribution modeling. Moreover, the\nlifetimes of the RS and BC galaxies also coincide well with their typical gas\ndepletion timescales attributed to the consumption of star formation.\nInterestingly, the distinct lifetime behaviors of the GV galaxies at $z \\le\n1.5$ and $z>1.5$ can not be fully accounted for by their gas depletion\ntimescales. Instead, this discrepancy between the lifetimes and gas depletion\ntimescales of the GV galaxies suggests that there are additional physical\nprocesses, such as feedback of active galactic nuclei, accelerating the\nquenching of GV galaxies at high redshifts.\n"}
+{"abstract": "  The iterative and incremental nature of software development using models\ntypically makes a model of a system incomplete (i.e., partial) until a more\nadvanced and complete stage of development is reached. Existing model execution\napproaches (interpretation of models or code generation) do not support the\nexecution of partial models. Supporting the execution of partial models at the\nearly stages of software development allows early detection of defects, which\ncan be fixed more easily and at a lower cost. This paper proposes a conceptual\nframework for the execution of partial models, which consists of three steps:\nstatic analysis, automatic refinement, and input-driven execution. First, a\nstatic analysis that respects the execution semantics of models is applied to\ndetect problematic elements of models that cause problems for the execution.\nSecond, using model transformation techniques, the models are refined\nautomatically, mainly by adding decision points where missing information can\nbe supplied. Third, refined models are executed, and when the execution reaches\nthe decision points, it uses inputs obtained either interactively or by a\nscript that captures how to deal with partial elements. We created an execution\nengine called PMExec for the execution of partial models of UML-RT (i.e., a\nmodeling language for the development of soft real-time systems) that embodies\nour proposed framework. We evaluated PMExec based on several use-cases that\nshow that the static analysis, refinement, and application of user input can be\ncarried out with reasonable performance and that the overhead of approach,\nwhich is mostly due to the refinement and the increase in model complexity it\ncauses, is manageable. We also discuss the properties of the refinement\nformally and show how the refinement preserves the original behaviors of the\nmodel.\n"}
+{"abstract": "  The author makes use of infinite compositions and a limiting function to\nconstruct a $\\mathcal{C}^\\infty$ tetration function $\\mathcal{F}(t) = e \\tet\nt$. As a tetration function, $\\mathcal{F}$ satisfies $e^{\\mathcal{F}(t)} =\n\\mathcal{F}(t+1)$. Of it, $\\mathcal{F}$ takes $(-2,\\infty) \\to \\mathbb{R}$\nbijectively with strictly monotone growth, and is continuously differentiable\nhere. We then iterate this construction to derive arbitrary hyper-operations\n$e\\up^k t$. These hyper-operations are $\\mathcal{C}^\\infty$ strictly monotone\nbijections of $(\\alpha_k,\\infty) \\to \\mathbb{R}$ for $k$ even ($1-k > \\alpha_k\n\\ge -k$), and $\\mathcal{C}^\\infty$ strictly monotone bijections of $\\mathbb{R}\n\\to (\\alpha_k, \\infty)$ for $k$ odd. These hyper-operations satisfy the\nfunctional equation $e \\up^{k-1} (e \\up^k t) = e \\up^k (t+1)$ with the initial\nconditions $e \\up^1 t = e^t$ and $e \\up^k 0 = 1$.\n"}
+{"abstract": "  We use a sample of 809 photometrically classified type Ia supernovae (SNe Ia)\ndiscovered by the Dark Energy Survey (DES) along with 40415 field galaxies to\ncalculate the rate of SNe Ia per galaxy in the redshift range $0.2 < z <0.6$.\nWe recover the known correlation between SN Ia rate and galaxy stellar mass\nacross a broad range of scales $8.5 \\leq \\log(M_*/\\mathrm{M}_{\\odot}) \\leq\n11.25$. We find that the SN Ia rate increases with stellar mass as a power-law\nwith index $0.63 \\pm 0.02$, which is consistent with previous work. We use an\nempirical model of stellar mass assembly to estimate the average star-formation\nhistories (SFHs) of galaxies across the stellar mass range of our measurement.\nCombining the modelled SFHs with the SN Ia rates to estimate constraints on the\nSN Ia delay time distribution (DTD), we find the data are fit well by a\npower-law DTD with slope index $\\beta = -1.13 \\pm 0.05$ and normalisation $A =\n2.11 \\pm0.05 \\times\n10^{-13}~\\mathrm{SNe}~{\\mathrm{M}_{\\odot}}^{-1}~\\mathrm{yr}^{-1}$, which\ncorresponds to an overall SN Ia production efficiency $N_{\\mathrm{Ia}}/M_* =\n0.9~_{-0.7}^{+4.0} \\times 10^{-3}~\\mathrm{SNe}~\\mathrm{M}_{\\odot}^{-1}$. Upon\nsplitting the SN sample by properties of the light curves, we find a strong\ndependence on DTD slope with the SN decline rate, with slower-declining SNe\nexhibiting a steeper DTD slope. We interpret this as a result of a relationship\nbetween intrinsic luminosity and progenitor age, and explore the implications\nof the result in the context of SN Ia progenitors.\n"}
+{"abstract": "  The ring of q-character polynomials is a q-analog of the classical ring of\ncharacter polynomials for the symmetric groups. This ring consists of certain\nclass functions defined simultaneously on the groups $Gl_n(F_q)$ for all n,\nwhich we also interpret as statistics on matrices. Here we evaluate these\nstatistics on all matrices and work towards computing the structure constants\nof the product in this ring. We show that the statistics are periodically\npolynomial in q, and governed by universal polynomials $P_{\\lambda,\\mu}(q)$\nwhich we compute explicitly, indexed by pairs of integer partitions. The\nproduct structure is similarly polynomial in q in many cases, governed by\npolynomials $R_{\\lambda,\\mu}^{\\nu}(q)$ indexed by triples of partitions, which\nwe compute in some cases.\n  Our calculations seem to exhibit several unexpected patterns. Mainly, we\nconjecture that certain indecomposable statistics generate the whole ring, and\nindeed prove this for statistics associated with matrices consisting of up to 2\nJordan blocks. Furthermore, the coefficients we compute exhibit surprising\nstability phenomena, which in turn reflect stabilizations of joint moments as\nwell as multiplicities in the irreducible decomposition of tensor products of\nrepresentations of $Gl_n(F_q)$ for $n\\gg 1$. We use this stabilization to\ncompute the correlation of the number of unipotent Jordan blocks of two sizes.\n"}
+{"abstract": "  When modeling superconducting devices based on REBCO tapes andworking near or\nabove the critical current value (i.e. I> Ic), the power-law model is not\nalways accurate. In our previous works, we proposed the overcritical current\nmodel, based on a combination of fast pulsed current measurements and finite\nelementan alysis. The overcritical current model was provided in the form of\nlook-up tables and was validated experimentally. We showed that the\novercritical current model could better reproduce experimental measurements\nthan the power-law model, and that the power-law model predicts a faster quench\nthan the overcritical current model. In this contribution, we propose the\neta-beta model, a mathematical expression to model analytically the\novercritical current model, based on measurements performed between 77 K and 90\nK in self-field conditions. The proposed model is verified by comparing DC\nfault measurements with the results of numerical simulations using the eta-beta\nmodel to represent the electrical resistivity of the superconducting layer of\nREBCO tapes.\n"}
+{"abstract": "  We introduce an exact numerical technique to solve the nuclear pairing\nHamiltonian and to determine properties such as the even-odd mass differences\nor spectral functions for any element within the periodic table for any number\nof nuclear shells. In particular, we show that the nucleus is a system with\nsmall entanglement and can thus be described efficiently using a\none-dimensional tensor network (matrix-product state) despite the presence of\nlong-range interactions. Our approach is numerically cheap and accurate to\nessentially machine precision, even for large nuclei.\n  We apply this framework to compute the even-odd mass differences of all known\nlead isotopes from $^{178}$Pb to $^{220}$Pb in the very large configuration\nspace of 13 shells between the neutron magic numbers 82 and 184 (i.e., two\nmajor shells) and find good agreement with the experiment. To go beyond the\nground state, we calculate the two-neutron removal spectral function of\n$^{210}$Pb which relates to a two-neutron pickup experiment that probes\nneutron-pair excitations across the gap of $^{208}$Pb. Finally, we discuss the\ncapabilities of our method to treat pairing with non-zero angular momentum.\nThis is numerically more demanding, but one can still determine the lowest\nexcited states in the full configuration space of one major shell with modest\neffort, which we demonstrate for the $N=126$, $Z\\geq 82$ isotones.\n"}
+{"abstract": "  A treatment of many-electron polar and $s-d(f)$ exchange models is carried\nout in connection with the development of the theory of magnetism of transition\nand rare-earth metals, as well as their compounds. Particular emphasis is\nplaced on the derivation of the many-electron Heisenberg, Hubbard, Anderson and\n$t-J$ models, as well as the internal relationship between them. Among the\ntopics discussed are many-electron approaches in describing systems of $d$- and\n$f$-electrons, atomic representation of Hubbard's $X$-operators, slave-particle\nrepresentations, the problem of strong itinerant magnetism and the formation of\nlocal moments, the role of non-quasiparticle (incoherent states). The\napplication of these concepts to strongly correlated systems, in particular, to\nhalf-metallic ferromagnets and Kondo lattices, is discussed. A comparison is\nperformed with modern field-theoretical and topological approaches.\n"}
+{"abstract": "  In this paper, we derive the symmetry operators ($J$'s) in the asymmetric\ntwo-photon quantum Rabi models in terms of Bogoliubov operator approaches. $\nJ^2$ can be expressed as a polynomial in terms of the Hamiltonian, which\nuncovers the $\\mathbb{Z}_{2}$ nature of the hidden symmetry in this two-photon\nmodel rigorously. The previous symmetry operators in the asymmetric one-photon\nquantum Rabi models are reproduced readily in terms of Bogoliubov operator\napproaches, and the obtained operators are expressed much more concisely. It is\nfound that the polynomial degree of $J^2$ in the two-photon model is twice of\nthat in the one-photon model.\n"}
+{"abstract": "  Artificially created two-dimensional (2D) interfaces or structures are ideal\nfor seeking exotic phase transitions due to their highly tunable carrier\ndensity and interfacially enhanced many-body interactions. Here, we report the\ndiscovery of a metal-insulator transition (MIT) and an emergent gapped phase in\nthe metal-semiconductor interface that is created in 2H-MoTe$_2$ via\nalkali-metal deposition. Using angle-resolved photoemission spectroscopy, we\nfound that the electron-phonon coupling is strong at the interface as\ncharacterized by a clear observation of replica shake-off bands. Such strong\nelectron-phonon coupling interplays with disorder scattering, leading to an\nAnderson localization of polarons which could explain the MIT. The domelike\nemergent gapped phase could then be attributed to a polaron extended state or\nphonon-mediated superconductivity. Our results demonstrate the capability of\nalkali-metal deposition as an effective method to enhance the many-body\ninteractions in 2D semiconductors. The surface-doped 2H-MoTe$_2$ is a promising\ncandidate for realizing polaronic insulator and high-$T_c$ superconductivity.\n"}
+{"abstract": "  How to leverage dynamic contextual information in end-to-end speech\nrecognition has remained an active research area. Previous solutions to this\nproblem were either designed for specialized use cases that did not generalize\nwell to open-domain scenarios, did not scale to large biasing lists, or\nunderperformed on rare long-tail words. We address these limitations by\nproposing a novel solution that combines shallow fusion, trie-based deep\nbiasing, and neural network language model contextualization. These techniques\nresult in significant 19.5% relative Word Error Rate improvement over existing\ncontextual biasing approaches and 5.4%-9.3% improvement compared to a strong\nhybrid baseline on both open-domain and constrained contextualization tasks,\nwhere the targets consist of mostly rare long-tail words. Our final system\nremains lightweight and modular, allowing for quick modification without model\nre-training.\n"}
+{"abstract": "  Motivated by applications to congested traffic problems, we establish higher\nintegrability results for the gradient of local weak solutions to the strongly\ndegenerate or singular elliptic PDE $-\\mathrm{div}\\left((\\vert\\nabla\nu\\vert-1)_{+}^{q-1}\\frac{\\nabla u}{\\vert\\nabla u\\vert}\\right)=f$,\n$\\mathrm{in}\\,\\,\\Omega$, where $\\Omega$ is a bounded domain in $\\mathbb{R}^{n}$\nfor $n\\geq2$, $1<q<\\infty$ and $\\left(\\,\\cdot\\,\\right)_{+}$ stands for the\npositive part. We assume that the datum $f$ belongs to a suitable Sobolev or\nBesov space. The main novelty here is that we deal with the case of\nsubquadratic growth, i.e. $1<q<2$, which has so far been neglected. In the\nlatter case, we also prove the higher fractional differentiability of the\nsolution to a variational problem, which is characterized by the above\nequation. For the sake of completeness, we finally give a Besov regularity\nresult also in the case $q\\geq2$.\n"}
+{"abstract": "  Lumen formation plays an essential role in the morphogenesis of tissues\nduring development. Here we review the physical principles that play a role in\nthe growth and coarsening of lumens. Solute pumping by the cell, hydraulic\nflows driven by differences of osmotic and hydrostatic pressures, balance of\nforces between extracellular fluids and cell-generated cytoskeletal forces, and\nelectro-osmotic effects have been implicated in determining the dynamics and\nsteady-state of lumens. We use the framework of linear irreversible\nthermodynamics to discuss the relevant force, time and length scales involved\nin these processes. We focus on order of magnitude estimates of physical\nparameters controlling lumen formation and coarsening.\n"}
+{"abstract": "  Human-in-the-loop machine learning is widely used in artificial intelligence\n(AI) to elicit labels for data points from experts or to provide feedback on\nhow close the predicted results are to the target. This simplifies away all the\ndetails of the decision-making process of the expert. In this work, we allow\nthe experts to additionally produce decision rules describing their\ndecision-making; the rules are expected to be imperfect but to give additional\ninformation. In particular, the rules can extend to new distributions, and\nhence enable significantly improving performance for cases where the training\nand testing distributions differ, such as in domain adaptation. We apply the\nproposed method to lifelong learning and domain adaptation problems and discuss\napplications in other branches of AI, such as knowledge acquisition problems in\nexpert systems. In simulated and real-user studies, we show that decision rule\nelicitation improves domain adaptation of the algorithm and helps to propagate\nexpert's knowledge to the AI model.\n"}
+{"abstract": "  Modern drug discovery is often time-consuming, complex and cost-ineffective\ndue to the large volume of molecular data and complicated molecular properties.\nRecently, machine learning algorithms have shown promising results in virtual\nscreening of automated drug discovery by predicting molecular properties. While\nemerging learning methods such as graph neural networks and recurrent neural\nnetworks exhibit high accuracy, they are also notoriously computation-intensive\nand memory-intensive with operations such as feature embeddings or deep\nconvolutions. In this paper, we propose a viable alternative to existing\nlearning methods by presenting \\model, a method based on brain-inspired\nhyperdimensional computing (HDC) for molecular property prediction. We develop\nHDC encoders to project SMILES representation of a molecule into\nhigh-dimensional vectors that are used for HDC training and inference. We\nperform an extensive evaluation using 29 classification tasks from 3\nwidely-used molecule datasets (Clintox, BBBP, SIDER) under three splits methods\n(random, scaffold, and stratified). By an comprehensive comparison with 8\nexisting learning models including SOTA graph/recurrent neural networks, we\nshow that MoleHD is able to achieve highest ROC-AUC score on random and\nscaffold splits on average across 3 datasets and achieve second-highest on\nstratified split. Importantly, MoleHD achieves such performance with\nsignificantly reduced computing cost and training efforts. To the best of our\nknowledge, this is the first HDC-based method for drug discovery. The promising\nresults presented in this paper can potentially lead to a novel path in drug\ndiscovery research.\n"}
+{"abstract": "  One-shot learning is proposed to make a pretrained classifier workable on a\nnew dataset based on one labeled samples from each pattern. However, few of\nresearchers consider whether the dataset itself supports one-shot learning. In\nthis paper, we propose a set of definitions to explain what kind of datasets\ncan support one-shot learning and propose the concept \"absolute\ngeneralization\". Based on these definitions, we proposed a method to build an\nabsolutely generalizable classifier. The proposed method concatenates two\nsamples as a new single sample, and converts a classification problem to an\nidentity identification problem or a similarity metric problem. Experiments\ndemonstrate that the proposed method is superior to baseline on one-shot\nlearning datasets and artificial datasets.\n"}
+{"abstract": "  In this article, the authors introduce the spaces of Lipschitz type on spaces\nof homogeneous type in the sense of Coifman and Weiss, and discuss their\nrelations with Besov and Triebel-Lizorkin spaces. As an application, the\nauthors establish the difference characterization of Besov and Triebel-Lizorkin\nspaces on spaces of homogeneous type. A major novelty of this article is that\nall results presented in this article get rid of the dependence on the reverse\ndoubling assumption of the considered measure of the underlying space\n${\\mathcal X}$ via using the geometrical property of ${\\mathcal X}$ expressed\nby its dyadic reference points, dyadic cubes, and the (local) lower bound.\nMoreover, some results when $p\\le 1$ but near to $1$ are new even when\n${\\mathcal X}$ is an RD-space.\n"}
+{"abstract": "  We investigate the onset and evolution of zonal flows in a growing convective\nlayer when a stably-stratified fluid with a composition gradient is cooled from\nabove. This configuration allows the study of zonal flows for a wide range of\nvalues of the Rayleigh number, $Ra$, and aspect ratio of the convection zone\nwithin a given simulation. We perform a series of 2D simulations using the\nBoussinesq approximation, with aspect ratio of the computational domain between\n$1$ and $5$, and Prandtl number $Pr = 0.1$, 0.5, 1, and $7$. We find that for\nsquare domains zonal flows appear when the aspect ratio of the convective layer\nis smaller than two, and the evolution of the system depends on the Prandtl\nnumber. For $Pr\\leq 1$, the fluid experiences bursts of convective transport\nwith negligible convective transport between bursts. The magnitude and\nfrequency of the bursts are smaller at low $Pr$, which suggests that the\nbursting regime is stronger in a narrow range around $Pr=1$, as observed in\nprevious studies of thermal convection. For $Pr=7$, the structure of the flow\nconsists of tilted convective plumes, and the convective transport is sustained\nat all times. In wider domains, the aspect ratio of the convective zone is\nalways much larger than two and zonal flows do not appear. These results\nconfirm and extend to fluids with stable composition gradients previous\nfindings on thermal convection. The fact that zonal flows can be avoided by\nusing computational domains with large aspect ratios opens up the possibility\nof 2D studies of convective overshoot, layer formation and transport properties\nacross diffusive interfaces.\n"}
+{"abstract": "  The Cartesian-tree pattern matching is a recently introduced scheme of\npattern matching that detects fragments in a sequential data stream which have\na similar structure as a query pattern. Formally, Cartesian-tree pattern\nmatching seeks all substrings $S'$ of the text string $S$ such that the\nCartesian tree of $S'$ and that of a query pattern $P$ coincide. In this paper,\nwe present a new indexing structure for this problem called the Cartesian-tree\nPosition Heap (CPH). Let $n$ be the length of the input text string $S$, $m$\nthe length of a query pattern $P$, and $\\sigma$ the alphabet size. We show that\nthe CPH of $S$, denoted $\\mathsf{CPH}(S)$, supports pattern matching queries in\n$O(m (\\sigma + \\log (\\min\\{h, m\\})) + occ)$ time with $O(n)$ space, where $h$\nis the height of the CPH and $occ$ is the number of pattern occurrences. We\nshow how to build $\\mathsf{CPH}(S)$ in $O(n \\log \\sigma)$ time with $O(n)$\nworking space. Further, we extend the problem to the case where the text is a\nlabeled tree (i.e. a trie). Given a trie $T$ with $N$ nodes, we show that the\nCPH of $T$, denoted $\\mathsf{CPH}(T)$, supports pattern matching queries on the\ntrie in $O(m (\\sigma^2 + \\log (\\min\\{h, m\\})) + occ)$ time with $O(N \\sigma)$\nspace. We also show a construction algorithm for $\\mathsf{CPH}(T)$ running in\n$O(N \\sigma)$ time and $O(N \\sigma)$ working space.\n"}
+{"abstract": "  We present a splitting method for the one-dimensional Saint-Venant-Exner\nequations used for describing the bed evolution in shallow water systems. We\nadapt the flux vector splitting approach of Toro and Vazquez-Cend\\`on and\nidentify one subsystem of conservative equations (advection system) and one of\nnon-conservative equations (pressure system), both having a very simple\neigenstructure compared to the full system. The final numerical scheme is\nconstructed using a Godunov-type path-conservative scheme for the pressure\nsystem and a simple conservative Godunov method for the advection system and\nsolved following a coupled solution strategy. The resulting first-order\naccurate method is extended to second order of accuracy in space and time via\nthe ADER approach together with an AENO reconstruction technique. Accuracy,\nrobustness and well-balanced properties of the resulting scheme are assessed\nthrough a carefully selected suite of testcases. The scheme is exceedingly\nsimple, accurate and robust as the sophisticated Godunov methods. A distinctive\nfeature of the novel scheme is its flexibility in the choice of the sediment\ntransport closure formula, which makes it particularly attractive for\nscientific and engineering applications.\n"}
+{"abstract": "  We define an integer-valued virtual count of embedded pseudo-holomorphic\ncurves of two times a primitive homology class and arbitrary genus in\nsymplectic Calabi--Yau $3$-folds, which can be viewed as an extension of\nTaubes' Gromov invariant. The construction depends on a detailed study of\nbifurcations of moduli spaces of embedded pseudo-holomorphic curves which is\npartially motivated by Wendl's recent solution of Bryan--Pandharipande's\nsuper-rigidity conjecture.\n"}
+{"abstract": "  By treating black hole as the macroscopic stable state on the free energy\nlandscape, we propose that the stochastic dynamics of the black hole phase\ntransition can be effectively described by the Langevin equation or\nequivalently by the Fokker-Planck equation in phase space. We demonstrate the\nturnover of the kinetics for the charged anti-de Sitter black hole phase\ntransition, which shows that the mean first passage time is linear with the\nfriction in the high damping regime and inversely proportional to the friction\nin the low damping regime. The fluctuations in the kinetics are shown to be\nlarge/small in the high/low damping regime and the switching behavior from the\nsmall fluctuations to the large fluctuations takes place at the kinetic\nturnover point. Because the friction is a reflection of the microscopic degrees\nof freedom acting on the order parameter of the black hole, the turnover and\nthe corresponding fluctuations of the phase transition kinetics can be used to\nprobe the black hole microstructure.\n"}
+{"abstract": "  Lattice results on sigma terms and global analysis of parton momentum\nfractions are used to give the quark and glue fractions of the proton mass and\nrest energy. The mass decomposition in terms of the trace of the\nenergy-momentum tensor is renormalization group invariant. The decomposition of\nthe rest energy from the Hamiltonian and the gravitational form factors are\nscheme and scale dependent. The separation of the energy-momentum tensor into\nthe traceless part which is composed of the quark and glue parton momentum\nfractions and the trace part has the minimum scheme dependence.\n  We identify the glue part of the trace anomaly $\\langle H_{\\beta}\\rangle $ as\nthe vacuum energy from the glue condensate in the vacuum. From the metric term\nof the gravitational form factors, which is the stress part of the\nstress-energy-momentum tensor, we find that the trace part of the rest energy,\ndominated by $\\langle H_{\\beta}\\rangle$, gives a {\\it constant} restoring\npressure which balances that from the traceless part of the Hamiltonian to\nconfine the hadron, much like the cosmological constant Einstein introduced for\na static universe. From a lattice calculation of $\\langle H_{\\beta}\\rangle$ in\nthe charmonium, we deduce the associated string tension which turns out to be\nin good agreement with that from a Cornell potential which fits the charmonium\nspectrum.\n"}
+{"abstract": "  As an alternative to traditional fault injection-based methodologies and to\nexplore the applicability of modern machine learning algorithms in the field of\nreliability engineering, this paper proposes a systemic framework that explores\ngate-level netlist circuit abstractions to extract and exploit relevant feature\nrepresentations in a low-dimensional vector space. A scalable feature learning\nmethod on a graphical domain called node2vec algorithm had been utilized for\nefficiently extracting structural features of the netlist, providing a valuable\ndatabase to exercise a selection of machine learning (ML) or deep learning (DL)\nalgorithms aiming at predicting fault propagation metrics. The current work\nproposes to model the gate-level netlist as a Probabilistic Bayesian Graph\n(PGB) in the form of a Graph Modeling Language (GML) format. To accomplish this\ngoal, a Verilog Procedural Interface (VPI) library linked to standard\nsimulation tools has been built to map gate-level netlist into the graph model.\nThe extracted features have been used for predicting the Functional Derating\n(FDR) factors of individual flip-flops of a given circuit through Support\nVector Machine (SVM) and Deep Neural Network (DNN) algorithms. The results of\nthe approach have been compared against data obtained through first-principles\napproaches. The whole experiment was implemented on the features extracted from\nthe 10-Gigabit Ethernet MAC IEEE 802.3 standard circuit.\n"}
+{"abstract": "  Annotating a large-scale in-the-wild person re-identification dataset\nespecially of marathon runners is a challenging task. The variations in the\nscenarios such as camera viewpoints, resolution, occlusion, and illumination\nmake the problem non-trivial. Manually annotating bounding boxes in such\nlarge-scale datasets is cost-inefficient. Additionally, due to crowdedness and\nocclusion in the videos, aligning the identity of runners across multiple\ndisjoint cameras is a challenge. We collected a novel large-scale in-the-wild\nvideo dataset of marathon runners. The dataset consists of hours of recording\nof thousands of runners captured using 42 hand-held smartphone cameras and\ncovering real-world scenarios. Due to the presence of crowdedness and occlusion\nin the videos, the annotation of runners becomes a challenging task. We propose\na new scheme for tackling the challenges in the annotation of such large\ndataset. Our technique reduces the overall cost of annotation in terms of time\nas well as budget. We demonstrate performing fps analysis to reduce the effort\nand time of annotation. We investigate several annotation methods for\nefficiently generating tight bounding boxes. Our results prove that\ninterpolating bounding boxes between keyframes is the most efficient method of\nbounding box generation amongst several other methods and is 3x times faster\nthan the naive baseline method. We introduce a novel way of aligning the\nidentity of runners in disjoint cameras. Our inter-camera alignment tool\nintegrated with the state-of-the-art person re-id system proves to be\nsufficient and effective in the alignment of the runners across multiple\ncameras with non-overlapping views. Our proposed framework of annotation\nreduces the annotation cost of the dataset by a factor of 16x, also effectively\naligning 93.64% of the runners in the cross-camera setting.\n"}
+{"abstract": "  Ultrafunctions are a particular class of functions defined on a Non\nArchimedean field E. They have been introduced and studied in some previous\nworks. In this paper we develop the notion of fine ultrafunctions which\nimproves the older definitions in many crucial points. Some applications are\ngiven to show how ultrafunctions can be applied in studing Partial Differential\nEquations. In particular, it is possible to prove the existence of\nultrafunction solutions to ill posed evolution poblems.\n"}
+{"abstract": "  In scientific paper, we will show a proof of Legendre's conjecture based on a\nscheme for finding elements of \"active\" set ${\\rm H}_{m^{4}}$ and \"critical\"\nelement $C_{m^{4}}$ for number $m^{4}$ at each $m \\ge 3$.\n"}
+{"abstract": "  We consider the hadroproduction of a massive charged lepton plus the\ncorresponding neutrino through the Drell-Yan mechanism. We present a new\ncomputation of the mixed QCD-EW corrections to this process. The cancellation\nof soft and collinear singularities is achieved by using a formulation of the\n$q_T$ subtraction formalism derived from the next-to-next-to-leading order QCD\ncalculation for heavy-quark production. For the first time, all the real and\nvirtual contributions due to initial- and final-state radiation are\nconsistently included without any approximation, except for the finite part of\nthe two-loop virtual correction, which is computed in the pole approximation\nand suitably improved through a reweighting procedure. We demonstrate that our\ncalculation is reliable in both on-shell and off-shell regions, thereby\nproviding the first prediction of the mixed QCD-EW corrections in the entire\nregion of the lepton transverse momentum. The computed corrections are in\nqualitative agreement with what we obtain in a factorised approach of QCD and\nEW corrections. At large values of the lepton $p_T$, the mixed QCD-EW\ncorrections are negative and increase in size, to about $-20\\%$ with respect to\nthe next-to-leading order QCD result at $p_T=500\\,$GeV.\n"}
+{"abstract": "  The recursive model index (RMI) has recently been introduced as a\nmachine-learned replacement for traditional indexes over sorted data, achieving\nremarkably fast lookups. Follow-up work focused on explaining RMI's performance\nand automatically configuring RMIs through enumeration. Unfortunately,\nconfiguring RMIs involves setting several hyperparameters, the enumeration of\nwhich is often too time-consuming in practice. Therefore, in this work, we\nconduct the first inventor-independent broad analysis of RMIs with the goal of\nunderstanding the impact of each hyperparameter on performance. In particular,\nwe show that in addition to model types and layer size, error bounds and search\nalgorithms must be considered to achieve the best possible performance. Based\non our findings, we develop a simple-to-follow guideline for configuring RMIs.\nWe evaluate our guideline by comparing the resulting RMIs with a number of\nstate-of-the-art indexes, both learned and traditional. We show that our simple\nguideline is sufficient to achieve competitive performance with other learned\nindexes and RMIs whose configuration was determined using an expensive\nenumeration procedure. In addition, while carefully reimplementing RMIs, we are\nable to improve the build time by 2.5x to 6.3x.\n"}
+{"abstract": "  We improve on a result by Svetlana Jitomirskaya and Wencai Liu dealing with\ninhomogeneous Diophantine approximation in the coprime setting.\n"}
+{"abstract": "  Spark NLP is a Natural Language Processing (NLP) library built on top of\nApache Spark ML. It provides simple, performant and accurate NLP annotations\nfor machine learning pipelines that can scale easily in a distributed\nenvironment. Spark NLP comes with 1100 pre trained pipelines and models in more\nthan 192 languages. It supports nearly all the NLP tasks and modules that can\nbe used seamlessly in a cluster. Downloaded more than 2.7 million times and\nexperiencing nine times growth since January 2020, Spark NLP is used by 54% of\nhealthcare organizations as the worlds most widely used NLP library in the\nenterprise.\n"}
+{"abstract": "  Recent experimental developments in hydrogen-rich materials in high pressures\nhave put this class of materials above others in the race toward room\ntemperature superconductivity. As it is the basis of all the materials in this\nclass, the efforts to determine the properties of pure solid hydrogen at high\npressures remain intense. Most notably, a recent experimental study of the\nmetallization of hydrogen identified the crystal phase of the solid as the\nC2/c-24 molecular phase up to ~425 GPa. It is possible that the observed\nmetallization is caused by band structure effects and not a structural phase\ntransition, and the material remains in this crystal phase up to higher\npressures. Therefore it is of crucial importance to determine the\nsuperconducting properties of the C2/c-24 phase. Here, we employ a Wannier\nfunction-based dense k-point and q-point sampling to compute the\nelectron-phonon coupling and superconducting properties of molecular hydrogen\nin the C2/c-24 phase. We find that the material has a high superconducting\ntransition temperature of 242 K at 500 GPa. We also find that the transition\ntemperature rapidly increases with pressure in the 400 - 500 GPa range.\n"}
+{"abstract": "  Quantum key distribution (QKD), the distribution of quantum secured keys\nuseful for data encryption, is expected to have a crucial impact in the next\ndecades. However, although the notable achievements accomplished in the last\ntwenty years, many practical and serious challenges are limiting the full\ndeployment of this novel quantum technology in the current telecommunication\ninfrastructures. In particular, the co-propagation of quantum signals and\nhigh-speed data traffic within the same optical fiber, is not completely\nresolved, due to the intrinsic noise caused by the high intensity of the\nclassical signals. As a consequence, current co-propagation schemes limit the\namount of classical optical power in order to reduce the overall link noise.\nHowever, this ad-hoc solution restrains the overall range of possibilities for\na large-scale QKD deployment. Here, we propose and demonstrate a new method,\nbased on up-conversion assisted receiver, for co-propagating classical light\nand QKD signals. In addition, we compare the performances of this scheme with\nan off-the-shelf quantum receiver, equipped with a standard InGaAs detector,\nover different lengths of an installed fiber link. Our proposal exhibits higher\ntolerance for noise in comparison to the standard receiver, thus enabling the\ndistribution of secret keys in the condition of 4 dB-higher classical power.\n"}
+{"abstract": "  Aims: We aim to determine whether Jupiter's obliquity is bound to remain\nexceptionally small in the Solar System, or if it could grow in the future and\nreach values comparable to those of the other giant planets.\n  Methods: The spin axis of Jupiter is subject to the gravitational torques\nfrom its regular satellites and from the Sun. These torques evolve over time\ndue to the long-term variations of its orbit and to the migration of its\nsatellites. With numerical simulations, we explore the future evolution of\nJupiter's spin axis for different values of its moment of inertia and for\ndifferent migration rates of its satellites. Analytical formulas show the\nlocation and properties of all relevant resonances.\n  Results: Because of the migration of the Galilean satellites, Jupiter's\nobliquity is currently increasing, as it adiabatically follows the drift of a\nsecular spin-orbit resonance with the nodal precession mode of Uranus. Using\nthe current estimates of the migration rate of the satellites, the obliquity of\nJupiter can reach values ranging from 6{\\deg} to 37{\\deg} after 5 Gyrs from\nnow, according to the precise value of its polar moment of inertia. A faster\nmigration for the satellites would produce a larger increase in obliquity, as\nlong as the drift remains adiabatic.\n  Conclusions: Despite its peculiarly small current value, the obliquity of\nJupiter is no different from other obliquities in the Solar System: It is\nequally sensitive to secular spin-orbit resonances and it will probably reach\ncomparable values in the future.\n"}
+{"abstract": "  We revise the steady vortex surface theory following the recent finding of\nasymmetric vortex sheets (AM,2021). These surfaces avoid the Kelvin-Helmholtz\ninstability by adjusting their discontinuity and shape. The vorticity collapses\nto the sheet only in an exceptional case considered long ago by Burgers and\nTownsend, where it decays as a Gaussian on both sides of the sheet. In generic\nasymmetric vortex sheets (Shariff,2021), vorticity leaks to one side or\nanother, making such sheets inadequate for vortex sheet statistics and\nanomalous dissipation.\n  We conjecture that the vorticity in a turbulent flow collapses on a special\nkind of surface (confined vortex surface, or CVS), satisfying some equations\ninvolving the tangent components of the local strain tensor.\n  The most important qualitative observation is that the inequality needed for\nthis solution's stability breaks the Euler dynamics' time reversibility. We\ninterpret this as dynamic irreversibility. We have also represented the\nenstrophy as a surface integral, conserved in the Navier-Stokes equation in the\nturbulent limit, with vortex stretching and viscous diffusion terms exactly\ncanceling each other on the CVS surfaces.\n  We have studied the CVS equations for the cylindrical vortex surface for an\narbitrary constant background strain with two different eigenvalues. This\nequation reduces to a particular version of the stationary Birkhoff-Rott\nequation for the 2D flow with an extra nonanalytic term. We study some general\nproperties of this equation and reduce its solution to a fixed point of a map\non a sphere, guaranteed to exist by the Brouwer theorem.\n"}
+{"abstract": "  Let $p$ be a prime, and $\\mathbb{F}_p$ the field with $p$ elements. We prove\nthat if $G$ is a mild pro-$p$ group with quadratic $\\mathbb{F}_p$-cohomology\nalgebra $H^\\bullet(G,\\mathbb{F}_p)$, then the algebras\n$H^\\bullet(G,\\mathbb{F}_p)$ and $\\mathrm{gr}\\mathbb{F}_p[\\![G]\\!]$ - the latter\nbeing induced by the quotients of consecutive terms of the $p$-Zassenhaus\nfiltration of $G$ - are both Koszul, and they are quadratically dual to each\nother. Consequently, if the maximal pro-$p$ Galois group of a field is mild,\nthen Positselski's and Weigel's Koszulity conjectures hold true for such a\nfield.\n"}
+{"abstract": "  Approaches based on deep neural networks have achieved striking performance\nwhen testing data and training data share similar distribution, but can\nsignificantly fail otherwise. Therefore, eliminating the impact of distribution\nshifts between training and testing data is crucial for building\nperformance-promising deep models. Conventional methods assume either the known\nheterogeneity of training data (e.g. domain labels) or the approximately equal\ncapacities of different domains. In this paper, we consider a more challenging\ncase where neither of the above assumptions holds. We propose to address this\nproblem by removing the dependencies between features via learning weights for\ntraining samples, which helps deep models get rid of spurious correlations and,\nin turn, concentrate more on the true connection between discriminative\nfeatures and labels. Extensive experiments clearly demonstrate the\neffectiveness of our method on multiple distribution generalization benchmarks\ncompared with state-of-the-art counterparts. Through extensive experiments on\ndistribution generalization benchmarks including PACS, VLCS, MNIST-M, and NICO,\nwe show the effectiveness of our method compared with state-of-the-art\ncounterparts.\n"}
+{"abstract": "  Boosting is a popular machine learning algorithm in regression and\nclassification problems. Boosting can combine a sequence of regression trees to\nobtain accurate prediction. In the presence of outliers, traditional boosting,\nbased on optimizing convex loss functions, may show inferior results. In this\narticle, a unified robust boosting is proposed for more resistant estimation.\nThe method utilizes a recently developed concave-convex family for robust\nestimation, composite optimization by conjugation operator, and functional\ndecent boosting. As a result, an iteratively reweighted boosting algorithm can\nbe conveniently constructed with existing software. Applications in robust\nregression, classification and Poisson regression are demonstrated in the R\npackage ccboost.\n"}
+{"abstract": "  The number of social media sites have increased exponentially with new ones\ncashing in on the weaknesses of older ones and others going beyond community\nguidelines by offering uncensored content. The vendors of these platforms in\norder to have a wider reach do not place restrictions on viewing age, promises\nyoung people with fame, and other such attractive offers that make the youths\naddicted to the site. The possibility of hacking into accounts of users and\nusing same for fraud is another rave among Nigerian youths with desire for\nquick riches. The crash in prices of data, smart phones, and related digital\ndevices have increased availability and access thereby closing digital divide\nand widening its adverse effects on the youths morals and academic pursuits. It\nis important that the Nigerian government understand factors that contribute to\nthe dwindling performance level of students in government owned institutions to\nput in place policies and infrastructure that would help combat the challenges.\nThis study investigated the effects of social media on students academic\nactivities, mood and time management abilities. The result indicated that\nassociation between social media and academic activities is statistically\nsignificant. However, a negative association exists between them which implies\nthat the high the level of social media activity, the lower academic activities\nparticipation. Similar association was observed on the effects of social media\non students time management ability.\n"}
+{"abstract": "  A photonic-assisted multiple radio frequency (RF) measurement approach based\non stimulated Brillouin scattering (SBS) and frequency-to-time mapping with\nhigh accuracy and high-frequency resolution is reported. A two-tone signal is\nsingle-sideband (SSB) modulated on an optical carrier via a dual-parallel\nMach-Zehnder modulator to construct one SBS gain and two SBS losses for SBS\ngain bandwidth reduction. The unknown RF signal is also SSB modulated on a\ncarrier that has been modulated by a sweep signal, thus the unknown RF signal\nis converted to a sweep optical signal along with the sweep optical carrier.\nThe bandwidth-reduced SBS gain spectrum is detected by the sweep optical\nsignals at different specific time, mapping the RF frequencies to the time\ndomain. An experiment is performed. RF frequencies from 0.3 to 7.6 GHz are\nsimultaneously measured with a root mean square error of less than 1 MHz. In\naddition, the frequency resolution of the measurement can be much lower than 10\nMHz, which is now the best result in the RF frequency measurement methods\nemploying the SBS effect.\n"}
+{"abstract": "  We investigate a single field model in the context of the constant-roll\ninflation in which inflaton moves down to the minimum point of the potential\nwith a constant rate of rolling. We use a quintessential inflationary model\nobtained by a Lorentzian function which is dependent on the number of e-folds.\nWe present the inflationary analysis for the model and find the observational\nconstraints on the parameters space using the observations of CMB anisotropies\n\\textit{i.e.} the Planck and Keck/array datasets. We find the observationally\nacceptable values of the Width of the Lorentzian function as\n$0.3<\\Gamma\\leq0.5$ at the $68\\%$ CL and $\\Gamma\\leq0.3$ at the $95\\%$ CL when\n$\\xi=120$, $|\\beta|=0.02$ and $N=60$. Also, we acquire the observationally\nfavoured values of the amplitude of the Lorentzian function as $400<\\xi\\leq600$\nat the $68\\%$ CL and $\\xi\\leq400$ at the $95\\%$ CL when $\\Gamma=0.1$,\n$|\\beta|=0.02$ and $N=60$. Moreover, we study the model from the Weak Gravity\nConjecture approach using the swampland criteria.\n"}
+{"abstract": "  Engineering sketches form the 2D basis of parametric Computer-Aided Design\n(CAD), the foremost modeling paradigm for manufactured objects. In this paper\nwe tackle the problem of learning based engineering sketch generation as a\nfirst step towards synthesis and composition of parametric CAD models. We\npropose two generative models, CurveGen and TurtleGen, for engineering sketch\ngeneration. Both models generate curve primitives without the need for a sketch\nconstraint solver and explicitly consider topology for downstream use with\nconstraints and 3D CAD modeling operations. We find in our perceptual\nevaluation using human subjects that both CurveGen and TurtleGen produce more\nrealistic engineering sketches when compared with the current state-of-the-art\nfor engineering sketch generation.\n"}
+{"abstract": "  The Poisson-Boltzmann equation provides a mean-field theory of electrolyte\nsolutions at interfaces and in confinement, with numerous applications ranging\nfrom colloid science to nanofluidics. This formulary gathers important formulas\nfor the Poisson-Boltzmann description of a Z:Z electrolyte solution inside slit\nand cylindrical channels. Different approximated solutions and their range of\nvalidity are discussed, together with the full solution for the slit channel.\nThis formulary is meant to continuously evolve following feedback from the\nreaders.\n"}
+{"abstract": "  In recent years, significant effort has been invested verifying the\nreproducibility and robustness of research claims in social and behavioral\nsciences (SBS), much of which has involved resource-intensive replication\nprojects. In this paper, we investigate prediction of the reproducibility of\nSBS papers using machine learning methods based on a set of features. We\npropose a framework that extracts five types of features from scholarly work\nthat can be used to support assessments of reproducibility of published\nresearch claims. Bibliometric features, venue features, and author features are\ncollected from public APIs or extracted using open source machine learning\nlibraries with customized parsers. Statistical features, such as p-values, are\nextracted by recognizing patterns in the body text. Semantic features, such as\nfunding information, are obtained from public APIs or are extracted using\nnatural language processing models. We analyze pairwise correlations between\nindividual features and their importance for predicting a set of human-assessed\nground truth labels. In doing so, we identify a subset of 9 top features that\nplay relatively more important roles in predicting the reproducibility of SBS\npapers in our corpus. Results are verified by comparing performances of 10\nsupervised predictive classifiers trained on different sets of features.\n"}
+{"abstract": "  Pseudo-normality synthesis, which computationally generates a pseudo-normal\nimage from an abnormal one (e.g., with lesions), is critical in many\nperspectives, from lesion detection, data augmentation to clinical surgery\nsuggestion. However, it is challenging to generate high-quality pseudo-normal\nimages in the absence of the lesion information. Thus, expensive lesion\nsegmentation data have been introduced to provide lesion information for the\ngenerative models and improve the quality of the synthetic images. In this\npaper, we aim to alleviate the need of a large amount of lesion segmentation\ndata when generating pseudo-normal images. We propose a Semi-supervised Medical\nImage generative LEarning network (SMILE) which not only utilizes limited\nmedical images with segmentation masks, but also leverages massive medical\nimages without segmentation masks to generate realistic pseudo-normal images.\nExtensive experiments show that our model outperforms the best state-of-the-art\nmodel by up to 6% for data augmentation task and 3% in generating high-quality\nimages. Moreover, the proposed semi-supervised learning achieves comparable\nmedical image synthesis quality with supervised learning model, using only 50\nof segmentation data.\n"}
+{"abstract": "  We extend the Zee model by introducing a vector-like lepton doublet and a\nflavor dependent global $U(1)$ symmetry. Flavor changing neutral currents in\nthe quark sector can be naturally forbidden at tree level due to the $U(1)$\nsymmetry, while sufficient amount of lepton flavor violation is provided to\nexplain current neutrino oscillation data. In our model, additional sources of\nCP-violation appear in the lepton sector, but their contribution to electric\ndipole moments is much smaller than current experimental bounds due to the\nYukawa structure constrained by the $U(1)$ symmetry. We find that there is a\nparameter region where the strongly first order electroweak phase transition\ncan be realized, which is necessary for the successful scenario of the\nelectroweak baryogenesis in addition to new CP-violating phases. In the\nbenchmark points satisfying neutrino data, lepton flavor violation data and the\nstrongly first order phase transition, we show that an additional CP-even Higgs\nboson $H$ mainly decays into a lighter CP-odd Higgs boson $A$, i.e., $H \\to AZ$\nor $H \\to AA$ with a characteristic pattern of lepton flavor violating decays\nof $A$.\n"}
+{"abstract": "  A major challenge in flow through porous media is to better understand the\nlink between microstructure and macroscale flow and transport. For idealised\nmicrostructures, the mathematical framework of homogenisation theory can be\nused for this purpose. Here, we consider a two-dimensional microstructure\ncomprising an array of obstacles of smooth but arbitrary shape, the size and\nspacing of which can vary along the length of the porous medium. We use\nhomogenisation via the method of multiple scales to systematically upscale a\nnovel problem involving cells of varying area to obtain effective continuum\nequations for macroscale flow and transport. The equations are characterised by\nthe local porosity, a local anisotropic flow permeability, an effective local\nanisotropic solute diffusivity, and an effective local adsorption rate. These\nmacroscale properties depend nontrivially on the two degrees of microstructural\ngeometric freedom in our problem: obstacle size, and obstacle spacing. We\nexploit this dependence to construct and compare scenarios where the same\nporosity profile results from different combinations of obstacle size and\nspacing. We focus on a simple example geometry comprising circular obstacles on\na rectangular lattice, for which we numerically determine the macroscale\npermeability and effective diffusivity. We investigate scenarios where the\nporosity is spatially uniform but the permeability and diffusivity are not. Our\nresults may be useful in the design of filters, or for studying the impact of\ndeformation on transport in soft porous media.\n"}
+{"abstract": "  Recovering the 3D geometric structure of a face from a single input image is\na challenging active research area in computer vision. In this paper, we\npresent a novel method for reconstructing 3D heads from a single or multiple\nimage(s) using a hybrid approach based on deep learning and geometric\ntechniques. We propose an encoder-decoder network based on the U-net\narchitecture and trained on synthetic data only. It predicts both pixel-wise\nnormal vectors and landmarks maps from a single input photo. Landmarks are used\nfor the pose computation and the initialization of the optimization problem,\nwhich, in turn, reconstructs the 3D head geometry by using a parametric\nmorphable model and normal vector fields. State-of-the-art results are achieved\nthrough qualitative and quantitative evaluation tests on both single and\nmulti-view settings. Despite the fact that the model was trained only on\nsynthetic data, it successfully recovers 3D geometry and precise poses for\nreal-world images.\n"}
+{"abstract": "  We have investigated the Kondo physics of a single magnetic impurity embedded\nin multi-Dirac (Weyl) node fermionic systems. By using a generic effective\nmodel for the host material and employing a numerical renormalization group\napproach we access the low temperature behavior of the system, identifying the\nexistence of Kondo screening in single-, double-, and triple-Dirac (Weyl) node\nmodels. We find that in any multi-Dirac node systems the low-energy regime lies\nwithin one of the known classes of pseudogap Kondo problem, extensively studied\nin the literature. Kondo screening is also observed for time reversal symmetry\nbroken Weyl systems. This is, however, possible only in the particle-hole\nsymmetry broken regime obtained for finite chemical potential $\\mu$. Although\nweakly, breaking time-reversal symmetry suppresses the Kondo resonance,\nespecially in the single-node Weyl semimetals. More interesting Kondo screening\nregimes are obtained for inversion symmetry broken multi-Weyl fermions. In\nthese systems the Kondo regimes of double- and triple-Weyl node models are much\nricher than in the single-Weyl node model. While in the single-Weyl node model\nthe Kondo temperature increases monotonically with $|\\mu|$ regardless the value\nof the inversion symmetry breaking parameter $Q_0$, in double- and triple-Weyl\nnode models there are two distinct regimes: (i) For $Q_0< |\\mu|$ the Kondo\ntemperature depends strongly on $\\mu$, while (ii) for $Q_0 > |\\mu|$ the Kondo\ntemperature depends very weakly on $\\mu$, resembling the flat-band single\nimpurity Anderson model.\n"}
+{"abstract": "  Silent speech interfaces (SSI) aim to reconstruct the speech signal from a\nrecording of the articulatory movement, such as an ultrasound video of the\ntongue. Currently, deep neural networks are the most successful technology for\nthis task. The efficient solution requires methods that do not simply process\nsingle images, but are able to extract the tongue movement information from a\nsequence of video frames. One option for this is to apply recurrent neural\nstructures such as the long short-term memory network (LSTM) in combination\nwith 2D convolutional neural networks (CNNs). Here, we experiment with another\napproach that extends the CNN to perform 3D convolution, where the extra\ndimension corresponds to time. In particular, we apply the spatial and temporal\nconvolutions in a decomposed form, which proved very successful recently in\nvideo action recognition. We find experimentally that our 3D network\noutperforms the CNN+LSTM model, indicating that 3D CNNs may be a feasible\nalternative to CNN+LSTM networks in SSI systems.\n"}
+{"abstract": "  Model of a partial current-carrying torus loop anchored to the photosphere is\nanalyzed. Conditions of the catastrophic loss of equilibrium are considered and\ncorresponding value of the critical decay index of external magnetic field is\nfound. Taking into account line-tying conditions leads to non-monotonous\ndependence of the critical decay index on the height of the apex and length of\nthe flux rope (its endpoints separation). For relatively short flux ropes, the\ncritical decay index is significantly lower than unity, which is in contrast to\nwidespread models with the typical critical decay index above unity. The steep\ndecrease of the critical index with height at low heights is due to the sharp\nincrease of the curvature of the flux-rope axis that transforms from a nearly\nstraight line to a crescent.\n"}
+{"abstract": "  We consider in this paper the nonlinear elliptic equation with Neumann\nboundary condition \\begin{align*} \\begin{cases} \\Delta u=a|u|^{m-1}u\\,\\,\\mbox{\nin }\\,\\,\\rnp\\\\ \\dfrac{\\partial u}{\\partial t}=b|u|^{\\eta-1}u+f\\,\\,\\mbox{ on\n}\\,\\,\\partial\\rnp. \\end{cases} \\end{align*} For $a,b\\neq 0$,\n$m>\\frac{n+1}{n-1}$, $(n>1)$, $\\eta=\\frac{m+1}{2}$ and small data $f\\in\nL^{\\frac{nq}{n+1},\\infty}(\\partial\\rnp)$, $q=\\frac{(n+1)(m-1)}{m+1}$ we prove\nthat the problem is solvable. More precisely, we establish existence,\nuniqueness and continuous dependence of solutions on the boundary data $f$ in\nthe function space $\\X^{q}_{\\infty}$ where\n\\[\\|u\\|_{\\X^{q}_{\\infty}}=\\sup_{t>0}t^{\\frac{n+1}{q}-1}\\|u(\\cdot,t)\\|_{L^{\\infty}(\\partial\\rnp)}+\\|u\\|_{L^{\\frac{q(m+1)}{2},\\infty}(\\rnp)}+\\|\\nabla\nu\\|_{L^{q,\\infty}(\\rnp)}. \\] As a direct consequence, we obtain the local\nregularity property $C^{1,\\nu}_{loc}$, $\\nu\\in (0,1)$ of these solutions as\nwell as energy estimates for certain values of $m$. Boundary values decaying\nfaster than $|x|^{-(m+1)/(m-1)}$, $x\\in \\rn\\setminus\\{0\\}$ yield solvability\nand this decay property is shown to be sharp for positive nonlinearities.\n  Moreover, we are able to show that solutions inherit qualitative features of\nthe boundary data such as positivity, rotational symmetry with respect to the\n$(n+1)$-axis, radial monotonicity in the tangential variable and homogeneity.\nWhen $a,b>0$, the critical exponent $m_c$ for the existence of positive\nsolutions is identified, $m_c=(n+1)/(n-1)$.\n"}
+{"abstract": "  In this paper, an innovative threshold measurement equation is proposed to be\nemployed in a Realized-GARCH framework. The proposed framework employs a\nnonlinear threshold regression specification to consider the leverage effect\nand model the contemporaneous dependence between the observed realized measures\nand hidden volatility. A Bayesian Markov Chain Monte Carlo method is adapted\nand employed for the model estimation and forecasting, with its validity\nassessed via a simulation study. The usefulness of the proposed measurement\nequation in a Realized-GARCH model has been evaluated via a comprehensive\nempirical study, by forecasting the 1% and 2.5% Value-at-Risk and Expected\nShortfall on six market indices. The proposed framework is shown to be capable\nof producing competitive tail risk forecasting results, compared to the\noriginal Realized-GARCH. Especially, the proposed model is favoured during the\nhigh volatility 2008 Global Financial Crisis period.\n"}
+{"abstract": "  Intelligent reflecting surface (IRS) is envisioned to play a significant role\nin future wireless communication systems as an effective means of reconfiguring\nthe radio signal propagation environment. In this paper, we study a new\nmulti-IRS aided massive multiple-input multiple-output (MIMO) system, where a\nmulti-antenna BS transmits independent messages to a set of remote\nsingle-antenna users using orthogonal beams that are subsequently reflected by\ndifferent groups of IRSs via their respective multi-hop passive beamforming\nover pairwise line-of-sight (LoS) links. We aim to select optimal IRSs and\ntheir beam routing path for each of the users, along with the active/passive\nbeamforming at the BS/IRSs, such that the minimum received signal power among\nall users is maximized. This problem is particularly difficult to solve due to\na new type of path separation constraints for avoiding the IRS-reflected signal\ninduced interference among different users. To tackle this difficulty, we first\nderive the optimal BS/IRS active/passive beamforming solutions based on their\npractical codebooks given the reflection paths. Then we show that the resultant\nmulti-beam multi-hop routing problem can be recast as an equivalent\ngraph-optimization problem, which is however NP-complete. To solve this\nchallenging problem, we propose an efficient recursive algorithm to partially\nenumerate the feasible routing solutions, which is able to effectively balance\nthe performance-complexity trade-off. Numerical results demonstrate that the\nproposed algorithm achieves near-optimal performance with low complexity and\noutperforms other benchmark schemes. Useful insights into the optimal\nmulti-beam multi-hop routing design are also drawn under different setups of\nthe multi-IRS aided massive MIMO network.\n"}
+{"abstract": "  The wisdom of the crowd has long become the de facto approach for eliciting\ninformation from individuals or experts in order to predict the ground truth.\nHowever, classical democratic approaches for aggregating individual\n\\emph{votes} only work when the opinion of the majority of the crowd is\nrelatively accurate. A clever recent approach, \\emph{surprisingly popular\nvoting}, elicits additional information from the individuals, namely their\n\\emph{prediction} of other individuals' votes, and provably recovers the ground\ntruth even when experts are in minority. This approach works well when the goal\nis to pick the correct option from a small list, but when the goal is to\nrecover a true ranking of the alternatives, a direct application of the\napproach requires eliciting too much information. We explore practical\ntechniques for extending the surprisingly popular algorithm to ranked voting by\npartial votes and predictions and designing robust aggregation rules. We\nexperimentally demonstrate that even a little prediction information helps\nsurprisingly popular voting outperform classical approaches.\n"}
+{"abstract": "  We report the orbital X-ray variability of high mass X-ray binary (HMXB)\nGX301--2. GX301--2 undergone a spin up process in 2018--2020 with the period\nevolving from $\\sim$ 685 s to 670 s. The energy resolved pulse-profiles of the\npulsar in 1--60 keV varied from single peaked and sinusoidal shapes to\nmulti-peaked across different orbital phases. Pulse fractions evolving over\norbit had negative correlations with the X-ray flux. The broad-band X-ray\nenergy spectrum of the pulsar can be described with a partial covering negative\npositive cutoff power-law continuum model. Near the periastron passage of the\npulsar we found a strong variation in the additional column density ($NH_{2}$),\nwhich correlated with variation of the flux. Curves of growth for both Fe\nK$\\alpha$ and Fe K$\\beta$ lines were plotted to investigate the distribution of\nmatter around neutron star. We have also found the evidence of two cyclotron\nabsorption lines in the phase-averaged spectra in GX301--2, with one line of\n30--42 keV and the other line varying in 48--56 keV. Both two line's centroid\nenergies show the similar relationship with X-ray luminosity: positive\ncorrelation in lower luminosity range, and negative relation above a critical\nluminosity of $10^{37}$ erg s$^{-1}$. We estimated the surface magnetic field\nof the neutron star in GX301--2 at ~$(0.5-2)\\times 10^{13}$ G. Two cyclotron\nline energies have a nearly fixed ratio of ~1.63 while having a low strength\nratio (~0.05), suggesting that these two features may actually be one line.\n"}
+{"abstract": "  In this paper, we propose an $H(\\text{curl}^2)$-conforming quadrilateral\nspectral element method to solve quad-curl problems. Starting with generalized\nJacobi polynomials, we first introduce quasi-orthogonal polynomial systems for\nvector fields over rectangles. $H(\\text{curl}^2)$-conforming elements over\narbitrary convex quadrilaterals are then constructed explicitly in a\nhierarchical pattern using the contravariant transform together with the\nbilinear mapping from the reference square onto each quadrilateral. It is\nastonishing that both the simplest rectangular and quadrilateral spectral\nelements possess only 8 degrees of freedom on each physical element. In the\nsequel, we propose our $H(\\text{curl}^2)$-conforming quadrilateral spectral\nelement approximation based on the mixed weak formulation to solve the\nquad-curl equation and its eigenvalue problem. Numerical results show the\neffectiveness and efficiency of our method.\n"}
+{"abstract": "  We establish a congruence on sums of central $q$-binomial coefficients. From\nthis $q$-congruence, we derive the divisibility of the $q$-trinomial\ncoefficients introduced by Andrews and Baxter.\n"}
+{"abstract": "  Deep learning-based image reconstruction approaches have demonstrated\nimpressive empirical performance in many imaging modalities. These approaches\ngenerally require a large amount of high-quality training data, which is often\nnot available. To circumvent this issue, we develop a novel unsupervised\nknowledge-transfer paradigm for learned iterative reconstruction within a\nBayesian framework. The proposed approach learns an iterative reconstruction\nnetwork in two phases. The first phase trains a reconstruction network with a\nset of ordered pairs comprising of ground truth images and measurement data.\nThe second phase fine-tunes the pretrained network to the measurement data\nwithout supervision. Furthermore, the framework delivers uncertainty\ninformation over the reconstructed image. We present extensive experimental\nresults on low-dose and sparse-view computed tomography, showing that the\nproposed framework significantly improves reconstruction quality not only\nvisually, but also quantitatively in terms of PSNR and SSIM, and is competitive\nwith several state-of-the-art supervised and unsupervised reconstruction\ntechniques.\n"}
+{"abstract": "  A wide range of unconventional transport phenomena have recently been\nobserved in single-crystal delafossite metals. Here, we present a theoretical\nframework to elucidate electron transport using a combination of\nfirst-principles calculations and numerical modeling of the anisotropic\nBoltzmann transport equation. Using PdCoO$_2$ as a model system, we study\ndifferent microscopic electron and phonon scattering mechanisms and establish\nthe mean free path hierarchy of quasiparticles at different temperatures. We\ntreat the anisotropic Fermi surface explicitly to numerically obtain\nexperimentally-accessible transport observables, which bridge between the\n\"diffusive\", \"ballistic\", and \"hydrodynamic\" transport regime limits. We\nillustrate that distinction between the \"quasi-ballistic\", and\n\"quasi-hydrodynamic\" regimes is challenging and often needs to be quantitative\nin nature. From first-principles calculations, we populate the resulting\ntransport regime plots, and demonstrate how the Fermi surface orientation adds\ncomplexity to the observed transport signatures in micro-scale devices. Our\nwork provides key insights into microscopic interaction mechanisms on open\nhexagonal Fermi surfaces and establishes their connection to the macroscopic\nelectron transport in finite-size channels.\n"}
+{"abstract": "  In the present paper we study the nature of the trivariate generating series\nof weighted walks in the quarter plane. Combining the results of this paper to\nprevious ones, we complete the proof of the following theorem. The series\nsatisfies a nontrivial algebraic differential equation in one of its variable,\nif and only if it satisfies a nontrivial algebraic differential equation in\neach of its variables.\n"}
+{"abstract": "  We develop a kernel projected Wasserstein distance for the two-sample test,\nan essential building block in statistics and machine learning: given two sets\nof samples, to determine whether they are from the same distribution. This\nmethod operates by finding the nonlinear mapping in the data space which\nmaximizes the distance between projected distributions. In contrast to existing\nworks about projected Wasserstein distance, the proposed method circumvents the\ncurse of dimensionality more efficiently. We present practical algorithms for\ncomputing this distance function together with the non-asymptotic uncertainty\nquantification of empirical estimates. Numerical examples validate our\ntheoretical results and demonstrate good performance of the proposed method.\n"}
+{"abstract": "  From a given topological hypermap $H$, we define two related hypermaps\n$H^\\triangle$ and $H^\\nabla$ as complements of the ordinary dual hypermap $H^*$\nalong with the concepts of their edge hypermap quantum codes\n$\\mathcal{C}^\\triangle$ and $\\mathcal{C}^\\nabla$. We then show that, when the\nsets of special darts are naturally corresponded, the duality between the\nordinary hypermap quantum code $\\mathcal{C}$ from $H$ and the one\n$\\mathcal{C}^*$ from $H^*$ can be greatly simplified to the duality between\n$\\mathcal{C}^\\triangle$ and $\\mathcal{C}^\\nabla$.\n"}
+{"abstract": "  Deep learning models are susceptible to adversarial examples that have\nimperceptible perturbations in the original input, resulting in adversarial\nattacks against these models. Analysis of these attacks on the state of the art\ntransformers in NLP can help improve the robustness of these models against\nsuch adversarial inputs. In this paper, we present Adv-OLM, a black-box attack\nmethod that adapts the idea of Occlusion and Language Models (OLM) to the\ncurrent state of the art attack methods. OLM is used to rank words of a\nsentence, which are later substituted using word replacement strategies. We\nexperimentally show that our approach outperforms other attack methods for\nseveral text classification tasks.\n"}
+{"abstract": "  Event extraction (EE) has considerably benefited from pre-trained language\nmodels (PLMs) by fine-tuning. However, existing pre-training methods have not\ninvolved modeling event characteristics, resulting in the developed EE models\ncannot take full advantage of large-scale unsupervised data. To this end, we\npropose CLEVE, a contrastive pre-training framework for EE to better learn\nevent knowledge from large unsupervised data and their semantic structures\n(e.g. AMR) obtained with automatic parsers. CLEVE contains a text encoder to\nlearn event semantics and a graph encoder to learn event structures\nrespectively. Specifically, the text encoder learns event semantic\nrepresentations by self-supervised contrastive learning to represent the words\nof the same events closer than those unrelated words; the graph encoder learns\nevent structure representations by graph contrastive pre-training on parsed\nevent-related semantic structures. The two complementary representations then\nwork together to improve both the conventional supervised EE and the\nunsupervised \"liberal\" EE, which requires jointly extracting events and\ndiscovering event schemata without any annotated data. Experiments on ACE 2005\nand MAVEN datasets show that CLEVE achieves significant improvements,\nespecially in the challenging unsupervised setting. The source code and\npre-trained checkpoints can be obtained from https://github.com/THU-KEG/CLEVE.\n"}
+{"abstract": "  Deep learning is effective in graph analysis. It is widely applied in many\nrelated areas, such as link prediction, node classification, community\ndetection, and graph classification etc. Graph embedding, which learns\nlow-dimensional representations for vertices or edges in the graph, usually\nemploys deep models to derive the embedding vector. However, these models are\nvulnerable. We envision that graph embedding methods based on deep models can\nbe easily attacked using adversarial examples. Thus, in this paper, we propose\nGraphfool, a novel targeted label adversarial attack on graph embedding. It can\ngenerate adversarial graph to attack graph embedding methods via classifying\nboundary and gradient information in graph convolutional network (GCN).\nSpecifically, we perform the following steps: 1),We first estimate the\nclassification boundaries of different classes. 2), We calculate the minimal\nperturbation matrix to misclassify the attacked vertex according to the target\nclassification boundary. 3), We modify the adjacency matrix according to the\nmaximal absolute value of the disturbance matrix. This process is implemented\niteratively. To the best of our knowledge, this is the first targeted label\nattack technique. The experiments on real-world graph networks demonstrate that\nGraphfool can derive better performance than state-of-art techniques. Compared\nwith the second best algorithm, Graphfool can achieve an average improvement of\n11.44% in attack success rate.\n"}
+{"abstract": "  Pulsars can act as an excellent probe of the Milky Way magnetic field. The\naverage strength of the Galactic magnetic field component parallel to the line\nof sight can be estimated as $\\langle B_\\parallel \\rangle = 1.232 \\,\n\\text{RM}/\\text{DM}$, where $\\text{RM}$ and $\\text{DM}$ are the rotation and\ndispersion measure of the pulsar. However, this assumes that the thermal\nelectron density and magnetic field of the interstellar medium are\nuncorrelated. Using numerical simulations and observations, we test the\nvalidity of this assumption. Based on magnetohydrodynamical simulations of\ndriven turbulence, we show that the correlation between the thermal electron\ndensity and the small-scale magnetic field increases with increasing Mach\nnumber of the turbulence. We find that the assumption of uncorrelated thermal\nelectron density and magnetic fields is valid only for subsonic and transsonic\nflows, but for supersonic turbulence, the field strength can be severely\noverestimated by using $1.232 \\, \\text{RM}/\\text{DM}$. We then correlate\nexisting pulsar observations from the Australia Telescope National Facility\nwith regions of enhanced thermal electron density and magnetic fields probed by\n${^{12} \\mathrm {CO}}$ data of molecular clouds, magnetic fields from the\nZeeman splitting of the 21 cm line, neutral hydrogen column density, and\nH$\\alpha$ observations. Using these observational data, we show that the\nthermal electron density and magnetic fields are largely uncorrelated over kpc\nscales. Thus, we conclude that the relation $\\langle B_\\parallel \\rangle =\n1.232 \\, \\text{RM}/\\text{DM}$ provides a good estimate of the magnetic field on\nGalactic scales but might break down on sub - kpc scales.\n"}
+{"abstract": "  Several experimental studies have reported thermally activated behavior of\nthe mechanical response of solid Helium-4 which does not fit into the model of\nthermally activated Frenkel pairs in an ideal crystal. The purpose of the\npresent work is to investigate how structural topological defects modify the\nresponse. Using quantum Monte Carlo Worm Algorithm, we study temperature\ndependence of fluctuations of the total number of particles in samples of hcp\nsolid Helium-4 containing several types of dislocations. Such fluctuations can\nbe described by the thermal activation law with the activation energy dependent\nstrongly on the type of the dislocation and the crystal density. The extracted\nvalues cover a range from about 1K to 20K. It is also found that annihilation\nof a jog-antijog pair can produce a local region of the solid characterized by\nthe orthorhombic symmetry. This serendipitous observation suggests that hcp\nsolid Helium-4 can undergo a displacive phase transition into the orthorhombic\ncrystal under uniaxial stress of about 10-15%\n"}
+{"abstract": "  In this work we revisit the existence, stability and dynamics of unstable\ntraveling solitary waves in the context of lattice dynamical systems. We\nconsider a nonlinear lattice of an $\\alpha$-Fermi-Pasta-Ulam type with the\nadditional feature of all-to-all harmonic long-range interactions whose\nstrength decays exponentially with distance. The competition between the\nnonlinear nearest-neighbor terms and the longer-range linear ones yields two\nparameter regimes where the dependence of the energy $H$ of the traveling waves\non their velocity $c$ is non-monotonic and multivalued, respectively. We\nexamine both cases, and identify the exact (up to a prescribed numerical\ntolerance) traveling waves. To investigate the stability of the obtained\nsolutions, we compute their Floquet multipliers, thinking of the traveling wave\nproblem as a periodic one modulo shifts. We show that in the general case when\nthe relationship between $H$ and $c$ is not single-valued, the sufficient but\nnot necessary criterion for stability change is $H'(s)=0$, where $s$ is the\nparameter along the energy-velocity curve. Perturbing the unstable solutions\nalong the corresponding eigenvectors, we identify two different scenarios of\nthe dynamics of their transition to stable branches. In the first one, the\nperturbed wave slows down after expelling a dispersive wave. The second\nscenario involves an increase in the velocity of the perturbed wave accompanied\nby the formation of a slower small-amplitude traveling solitary wave.\n"}
+{"abstract": "  Let $\\mathcal{P}_r$ denote an almost-prime with at most $r$ prime factors,\ncounted according to multiplicity. In this paper, it is proved that for\n$\\alpha\\in\\mathbb{R}\\backslash\\mathbb{Q},\\,\\beta\\in\\mathbb{R}$ and\n$0<\\theta<10/1561$, there exist infinitely many primes $p$, such that $\\|\\alpha\np^2+\\beta\\|<p^{-\\theta}$ and $p+2=\\mathcal{P}_4$, which constitutes an\nimprovement upon the previous result.\n"}
+{"abstract": "  A given monoid usually admits many presentations by generators and relations\nand the notion of Tietze equivalence characterizes when two presentations\ndescribe the same monoid: it is the case when one can transform one\npresentation into the other using the two families of so-called Tietze\ntransformations. The goal of this article is to provide an abstract and\ngeometrical understanding of this well-known fact, by constructing a model\nstructuree on the category of presentations, in which two presentations are\nweakly equivalent when they present the same monoid. We show that Tietze\ntransformations form a pseudo-generating family of trivial cofibrations and\ngive a proof of the completeness of these transformations by an abstract\nargument in this setting.\n"}
+{"abstract": "  Deconvolution is the important problem of estimating the distribution of a\nquantity of interest from a sample with additive measurement error. Nearly all\nmethods in the literature are based on Fourier transformation because it is\nmathematically a very neat solution. However, in practice these methods are\nunstable, and produce bad estimates when signal-noise ratio or sample size are\nlow. In this paper, we develop a new deconvolution method based on maximum\nlikelihood with a smoothness penalty. We show that our new method has much\nbetter performance than existing methods, particularly for small sample size or\nsignal-noise ratio.\n"}
+{"abstract": "  We identify 225 filaments from an H$_2$ column density map constructed using\nsimultaneous $^{12}$CO, $^{13}$CO, and C$^{18}$O (J=1-0) observations carried\nout as a part of the MWISP project. We select 46 long filaments with lengths\nabove 1.2 pc to analyze the filament column density profiles. We divide the\nselected filaments into 397 segments and calculate the column density profiles\nfor each segment. The symmetries of the profiles are investigated. The\nproportion of intrinsically asymmetrical segments is 65.3$\\%$, and that of\nintrinsically symmetrical ones is 21.4$\\%$. The typical full width at half\nmaximum (FWHM) of the intrinsically symmetrical filament segments is $\\sim$\n0.67 pc with the Plummer-like fitting, and $\\sim$ 0.50 pc with the Gaussian\nfitting, respectively. The median FWHM widths derived from the second-moment\nmethod for intrinsically symmetrical and asymmetrical profiles are $\\sim$ 0.44\nand 0.46 pc, respectively. Close association exists between the filamentary\nstructures and the YSOs in the region.\n"}
+{"abstract": "  Several bacteria and bacteria strands form biofilms in different\nenvironmental conditions, e.g. pH, temperature, nutrients, etc. Biofilm growth,\ntherefore, is an extremely robust process. Because of this, while biofilm\ngrowth is a complex process affected by several variables, insights into\nbiofilm formation could be obtained studying simple schematic models. In this\nmanuscript, we describe a hybrid molecular dynamics and Monte Carlo model for\nthe simulation of the early stage formation of a biofilm, to explicitly\ndemonstrate that it is possible to account for most of the processes expected\nto be relevant. The simulations account for the growth and reproduction of the\nbacteria, for their interaction and motility, for the synthesis of\nextracellular polymeric substances and Psl trails. We describe the effect of\nthese processes on the early stage formation of biofilms, in two dimensions,\nand also discuss preliminary three-dimensional results.\n"}
+{"abstract": "  We discuss a pointwise numerical differentiation formula on multivariate\nscattered data, based on the coefficients of local polynomial interpolation at\nDiscrete Leja Points, written in Taylor's formula monomial basis. Error bounds\nfor the approximation of partial derivatives of any order compatible with the\nfunction regularity are provided, as well as sensitivity estimates to\nfunctional perturbations, in terms of the inverse Vandermonde coefficients that\nare active in the differentiation process. Several numerical tests are\npresented showing the accuracy of the approximation.\n"}
+{"abstract": "  Quantum computing holds the promise of improving the information processing\npower to levels unreachable by classical computation. Quantum walks are heading\nthe development of quantum algorithms for searching information on graphs more\nefficiently than their classical counterparts. A quantum-walk-based algorithm\nthat is standing out in the literature is the lackadaisical quantum walk. The\nlackadaisical quantum walk is an algorithm developed to search two-dimensional\ngrids whose vertices have a self-loop of weight $l$. In this paper, we address\nseveral issues related to the application of the lackadaisical quantum walk to\nsuccessfully search for multiple solutions on grids. Firstly, we show that only\none of the two stopping conditions found in the literature is suitable for\nsimulations. We also demonstrate that the final success probability depends on\nthe space density of solutions and the relative distance between solutions.\nFurthermore, this work generalizes the lackadaisical quantum walk to search for\nmultiple solutions on grids of arbitrary dimensions. In addition, we propose an\noptimal adjustment of the self-loop weight $l$ for such scenarios of arbitrary\ndimensions. It turns out the other fits of $l$ found in the literature are\nparticular cases. Finally, we observe a two-to-one relation between the steps\nof the lackadaisical quantum walk and the ones of Grover's algorithm, which\nrequires modifications in the stopping condition. In conclusion, this work\ndeals with practical issues one should consider when applying the lackadaisical\nquantum walk, besides expanding the technique to a wider range of search\nproblems.\n"}
+{"abstract": "  Practical applications of quantum computers require millions of physical\nqubits and it will be challenging for individual quantum processors to reach\nsuch qubit numbers. It is therefore timely to investigate the resource\nrequirements of quantum algorithms in a distributed setting, where multiple\nquantum processors are interconnected by a coherent network. We introduce an\nextension of the Message Passing Interface (MPI) to enable high-performance\nimplementations of distributed quantum algorithms. In turn, these\nimplementations can be used for testing, debugging, and resource estimation. In\naddition to a prototype implementation of quantum MPI, we present a performance\nmodel for distributed quantum computing, SENDQ. The model is inspired by the\nclassical LogP model, making it useful to inform algorithmic decisions when\nprogramming distributed quantum computers. Specifically, we consider several\noptimizations of two quantum algorithms for problems in physics and chemistry,\nand we detail their effects on performance in the SENDQ model.\n"}
+{"abstract": "  The results from the CMS search for supersymmetric particles based on Run-2\ndata recorded at a center-of-mass energy of 13 TeV are summarized. Strong and\nweak production of SUSY scenarios are considered. Results presented include the\nsearches for squarks and gluinos, direct production of charginos, neutralinos,\nand sleptons. These searches involve final state objects comprising jets,\nmissing transverse momentum, electrons or muons, taus or photons, as well as\nlong-lived particles. The data in these searches are found to be consistent\nwith standard model predictions and no significant excess is observed. Upper\nlimits have been set on the masses of supersymmetric particles from a variety\nof search channels.\n"}
+{"abstract": "  The development of deep neural networks and the emergence of pre-trained\nlanguage models such as BERT allow to increase performance on many NLP tasks.\nHowever, these models do not meet the same popularity for tweet summarization,\nwhich can probably be explained by the lack of existing collections for\ntraining and evaluation. Our contribution in this paper is twofold : (1) we\nintroduce a large dataset for Twitter event summarization, and (2) we propose a\nneural model to automatically summarize huge tweet streams. This extractive\nmodel combines in an original way pre-trained language models and vocabulary\nfrequency-based representations to predict tweet salience. An additional\nadvantage of the model is that it automatically adapts the size of the output\nsummary according to the input tweet stream. We conducted experiments using two\ndifferent Twitter collections, and promising results are observed in comparison\nwith state-of-the-art baselines.\n"}
+{"abstract": "  Topology and geometry of a sphere create constraints for particles that lie\non its surface which they otherwise do not experience in Euclidean space.\nNotably, the number of particles and the size of the system can be varied\nseparately, requiring a careful treatment of systems with one or several\ncharacteristic length scales. All this can make it difficult to precisely\ndetermine whether a particular system is in a disordered, fluid-like, or\ncrystal-like state. Here, we show how order transitions in systems of particles\ninteracting on the surface of a sphere can be detected by changes in two\nhyperuniformity parameters, derived from spherical structure factor and cap\nnumber variance. We demonstrate their use on two different systems -- solutions\nof the thermal Thomson problem and particles interacting via an ultra-soft\npotential of the generalized exponential model of order 4 -- each with a\ndistinct parameter regulating their degree of ordering. The hyperuniformity\nparameters are not only able to detect the order transitions in both systems,\nbut also point out the clear differences in the ordered distributions in each\ndue to the nature of the interaction leading to them. Our study shows that\nhyperuniformity analysis of particle distributions on the sphere provides a\npowerful insight into fluid- and crystal-like order on the sphere.\n"}
+{"abstract": "  A shape's Heesch number is the number of layers of copies of the shape that\ncan be placed around it without gaps or overlaps. Experimentation and\nexhaustive searching have turned up examples of shapes with finite Heesch\nnumbers up to six, but nothing higher. The computational problem of classifying\nsimple families of shapes by Heesch number can provide more experimental data\nto fuel our understanding of this topic. I present a technique for computing\nHeesch numbers of non-tiling polyforms using a SAT solver, and the results of\nexhaustive computation of Heesch numbers up to 19-ominoes, 17-hexes, and\n24-iamonds.\n"}
+{"abstract": "  The first epitaxial ferroelectric wurtzite film with clear\npolarization-electric field hysteresis behavior is presented. The coercive\nfield of this epitaxial Al0.7Sc0.3N film on W/c-sapphire substrate is 0.4 +-\n0.3 MV cm-1 (8 %) smaller than that of a conventional fiber textured film on a\nPt/TiOx/SiO2/Si substrate, attributed to the 0.01 +- 0.007 {\\AA} smaller c-axis\nlattice parameter in the epitaxial film. The strain and decrease of the\ncoercive field most likely originate from epitaxial strain rather than the\nmismatch in thermal coefficient of expansion. These results provide an insight\nfor further coercive field reduction of novel wurtzite ferroelectrics using\nepitaxial mismatch strain.\n"}
+{"abstract": "  Testing is the most direct and effective technique to ensure software\nquality. However, it is a burden for developers to understand the\npoorly-commented tests, which are common in industry environment projects.\nMobile applications (app) are GUI-intensive and event-driven, so test scripts\nfocusing on GUI interactions play a more important role in mobile app testing\nbesides the test cases for the source code. Therefore, more attention should be\npaid to the user interactions and the corresponding user event responses.\nHowever, test scripts are loosely linked to apps under test (AUT) based on\nwidget selectors, making it hard to map the operations to the functionality\ncode of AUT. In such a situation, code understanding algorithms may lose\nefficacy if directly applied to mobile app test scripts.\n  We present a novel approach, TestIntent, to infer the intent of mobile app\ntest scripts. TestIntent combines the GUI image understanding and code\nunderstanding technologies. The test script is transferred into an operation\nsequence model. For each operation, TestIntent extracts the operated widget\nselector and link the selector to the UI layout structure, which stores the\ndetailed information of the widgets, including coordinates, type, etc. With\ncode understanding technologies, TestIntent can locate response methods in the\nsource code. Afterwards, NLP algorithms are adopted to understand the code and\ngenerate descriptions. Also, TestIntent can locate widgets on the app GUI\nimages. Then, TestIntent can understand the widget intent with an\nencoder-decoder model. With the combination of the results from GUI and code\nunderstanding, TestIntent generates the test intents in natural language\nformat. We also conduct an empirical experiment, and the results prove the\noutstanding performance of TestIntent. A user study also declares that\nTestIntent can save developers' time to understand test scripts.\n"}
+{"abstract": "  We review recent interactions between mathematical theory of two-dimensional\ntopological order and operator algebras, particularly the Jones theory of\nsubfactors. The role of representation theory in terms of tensor categories is\nemphasized. Connections to 2-dimensional conformal field theory are also\npresented. In particular, we discuss anyon condensation, gapped domain walls\nand matrix product operators in terms of operator algebras.\n"}
+{"abstract": "  The integration of the equations of motion of N interacting particles,\nrepresents a classical problem in many branches of physics and chemistry. The\ndirect N-body problem is at the heart of simulations studying Coulomb Crystals.\nWe present an hand-optimized code for the latest AVX-512 set of instructions\nthat achieve a single core speed up of $\\approx 340\\%$ respect the version\noptimized by the compiler. The increase performance is due a optimization on\nthe organization of the memory access on the inner loop on the Coulomb and,\nspecially, on the usage of an intrinsic function to faster compute the\n$1/\\sqrt{x}$. Our parallelization, which is implemented in OpenMP, achieves an\nexcellent scalability with the number of cores. In total, we achieve $\\approx\n500GFLOPS$ using a just a standard WorkStation with one Intel Skylake CPU (10\ncores). It represents $\\approx 75\\%$ of the theoretical maximum number of\ndouble precision FLOPS corresponding to Fused Multiplication Addition (FMA)\noperations.\n"}
+{"abstract": "  Several methods for inversion of face recognition models were recently\npresented, attempting to reconstruct a face from deep templates. Although some\nof these approaches work in a black-box setup using only face embeddings,\nusually, on the end-user side, only similarity scores are provided. Therefore,\nthese algorithms are inapplicable in such scenarios. We propose a novel\napproach that allows reconstructing the face querying only similarity scores of\nthe black-box model. While our algorithm operates in a more general setup,\nexperiments show that it is query efficient and outperforms the existing\nmethods.\n"}
+{"abstract": "  Studies on online hate speech have mostly focused on the automated detection\nof harmful messages. Little attention has been devoted so far to the\ndevelopment of effective strategies to fight hate speech, in particular through\nthe creation of counter-messages. While existing manual scrutiny and\nintervention strategies are time-consuming and not scalable, advances in\nnatural language processing have the potential to provide a systematic approach\nto hatred management. In this paper, we introduce a novel ICT platform that NGO\noperators can use to monitor and analyze social media data, along with a\ncounter-narrative suggestion tool. Our platform aims at increasing the\nefficiency and effectiveness of operators' activities against islamophobia. We\ntest the platform with more than one hundred NGO operators in three countries\nthrough qualitative and quantitative evaluation. Results show that NGOs favor\nthe platform solution with the suggestion tool, and that the time required to\nproduce counter-narratives significantly decreases.\n"}
+{"abstract": "  We utilize the experimentally known difference of the $\\Lambda$ separation\nenergies of the mirror hypernuclei ${^4_\\Lambda \\rm He}$ and ${^4_\\Lambda \\rm\nH}$ to constrain the $\\Lambda$-neutron interaction. We include the leading\ncharge-symmetry breaking (CSB) interaction into our hyperon-nucleon interaction\nderived within chiral effective field theory at next-to-leading order. In\nparticular, we determine the strength of the two arising CSB contact terms by a\nfit to the differences of the separation energies of these hypernuclei in the\n$0^+$ and $1^+$ states, respectively. By construction, the resulting\ninteraction describes all low energy hyperon-nucleon scattering data, the\nhypertriton and the CSB in ${^4_\\Lambda \\rm He}$-${^4_\\Lambda \\rm H}$\naccurately. This allows us to provide first predictions for the $\\Lambda$n\nscattering lengths, based solely on available hypernuclear data.\n"}
+{"abstract": "  The dynamics of the spread of contagions such as viruses, infectious diseases\nor even rumors/opinions over contact networks (graphs) have effectively been\ncaptured by the well known \\textit{Susceptible-Infected-Susceptible} ($SIS$)\nepidemic model in recent years. When it comes to competition between two such\ncontagions spreading on overlaid graphs, their propagation is captured by\nso-called \\textit{bi-virus} epidemic models. Analysis of such dynamical systems\ninvolve the identification of equilibrium points and its convergence\nproperties, which determine whether either of the viruses dies out, or both\nsurvive together. We demonstrate how the existing works are unsuccessful in\ncharacterizing a large subset of the model parameter space, including all\nparameters for which the competitiveness of the bi-virus system is significant\nenough to attain coexistence of the epidemics. In this paper, we fill in this\nvoid and obtain convergence results for the entirety of the model parameter\nspace; giving precise conditions (necessary and sufficient) under which the\nsystem \\textit{globally converges} to a \\textit{trichotomy} of possible\noutcomes: a virus-free state, a single-virus state, and to a coexistence state\n-- the first such result.\n"}
+{"abstract": "  As more machine learning agents interact with humans, it is increasingly a\nprospect that an agent trained to perform a task optimally, using only a\nmeasure of task performance as feedback, can violate societal norms for\nacceptable behavior or cause harm. Value alignment is a property of intelligent\nagents wherein they solely pursue non-harmful behaviors or human-beneficial\ngoals. We introduce an approach to value-aligned reinforcement learning, in\nwhich we train an agent with two reward signals: a standard task performance\nreward, plus a normative behavior reward. The normative behavior reward is\nderived from a value-aligned prior model previously shown to classify text as\nnormative or non-normative. We show how variations on a policy shaping\ntechnique can balance these two sources of reward and produce policies that are\nboth effective and perceived as being more normative. We test our\nvalue-alignment technique on three interactive text-based worlds; each world is\ndesigned specifically to challenge agents with a task as well as provide\nopportunities to deviate from the task to engage in normative and/or altruistic\nbehavior.\n"}
+{"abstract": "  In many coordination problems, independently reasoning humans are able to\ndiscover mutually compatible policies. In contrast, independently trained\nself-play policies are often mutually incompatible. Zero-shot coordination\n(ZSC) has recently been proposed as a new frontier in multi-agent reinforcement\nlearning to address this fundamental issue. Prior work approaches the ZSC\nproblem by assuming players can agree on a shared learning algorithm but not on\nlabels for actions and observations, and proposes other-play as an optimal\nsolution. However, until now, this \"label-free\" problem has only been\ninformally defined. We formalize this setting as the label-free coordination\n(LFC) problem by defining the label-free coordination game. We show that\nother-play is not an optimal solution to the LFC problem as it fails to\nconsistently break ties between incompatible maximizers of the other-play\nobjective. We introduce an extension of the algorithm, other-play with\ntie-breaking, and prove that it is optimal in the LFC problem and an\nequilibrium in the LFC game. Since arbitrary tie-breaking is precisely what the\nZSC setting aims to prevent, we conclude that the LFC problem does not reflect\nthe aims of ZSC. To address this, we introduce an alternative informal\noperationalization of ZSC as a starting point for future work.\n"}
+{"abstract": "  Isomers with three- and five-nucleon-hole configurations have been\nestablished in $^{203}$Tl. These include newly identified levels with a\nthree-nucleon structure: {\\it I}$^{\\pi }$ = (15/2$^{-}$) with {\\it T}$_{1/2}$ =\n7.9(5) ns, and {\\it I}$^{\\pi }$ = (35/2$^{-}$) with {\\it T}$_{1/2}$ = 4.0(5)\nns. In addition, five-quasiparticle states: {\\it I}$^{\\pi }$ = (39/2$^{-}$)\nwith {\\it T}$_{1/2}$ = 1.9(2) ns, and {\\it I}$^{\\pi }$ = (49/2$^{+}$) with {\\it\nT}$_{1/2}$ = 3.4(4) ns have also been established. The previously determined\nlong-lived decay [{\\it T}$_{1/2}$ = 6.6(3) $\\mu $s from this work] is\nassociated with isomerism of the {\\it I}$^{\\pi }$ = (29/2$^{+}$) state. Levels\nabove this long-lived isomer have been identified through a delayed-prompt\ncoincidence measurement. Five-nucleon-hole states with excitation energies {\\it\nE}$_{x}$ $\\approx $ 7 MeV have been established as well as possible octupole\nexcitations of the $^{208}$Pb core built on these levels. The level scheme of\n$^{203}$Tl is extended up to {\\it E}$_{x}$ $\\approx $ 11 MeV with the inclusion\nof 25 new transitions. Empirical and shell-model calculations have been\nperformed to aid in the description of the observed states which are found to\nbe predominantly of intrinsic character.\n"}
+{"abstract": "  In present paper a well-behaved new model of anisotropic compact star in\n(3+1)-dimensional spacetime has been investigated in the background of\nEinstein's general theory of relativity. The model has been developed by\nchoosing $g_rr$ component as Krori-Barua (KB) ansatz [Krori and Barua in J.\nPhys. A, Math. Gen. 8:508, 1975]. The field equations have been solved by a\nproper choice of the anisotropy factor which is physically reasonable and well\nbehaved inside the stellar interior. Interior spacetime has been matched\nsmoothly to the exterior Schwarzschild vacuum solution and it has also been\ndepicted graphically. Model is free from all types of singularities and is in\nstatic equilibrium under different forces acting on the system. The stability\nof the model has been tested with the help of various conditions available in\nliterature. The solution is compatible with observed masses and radii of a few\ncompact stars.\n"}
+{"abstract": "  The theory of symmetry indicators has enabled database searches for\ntopological materials in normal conducting phases, which has led to several\nencyclopedic topological material databases. Here, based on recently developed\nsymmetry indicators for superconductors, we report our comprehensive search for\ntopological and nodal superconductors among nonmagnetic materials in Inorganic\nCrystal Structure Database. A myriad of topological superconductors with exotic\nboundary states are discovered. When materials are symmetry-enforced nodal\nsuperconductors, positions and shapes of the nodes are also identified. These\ndata are aggregated at Database of Topological and Nodal Supercoductors. We\nalso provide a subroutine Topological Supercon, which allows users to examine\nthe topological nature in the superconducting phase of any material themselves\nby uploading the result of first-principles calculations as an input. Our\ndatabase and subroutine, when combined with experiments, will help us\nunderstand the unconventional pairing mechanism and facilitate realizations of\nthe long-sought Majorana fermions promising for topological quantum\ncomputations.\n"}
+{"abstract": "  Open-source biodiversity databases contain a large amount of species\noccurrence records, but these are often spatially biased, which affects the\nreliability of species distribution models based on these records. Sample bias\ncorrection techniques include data filtering at the cost of record numbers or\nrequire considerable additional sampling effort. However, independent data are\nrarely available and assessment of the correction technique must rely on\nperformance metrics computed with subsets of the only available (biased) data,\nwhich may be misleading. Here we assess the extent to which an acknowledged\nsample bias correction technique is likely to improve models' ability to\npredict species distributions in the absence of independent data. We assessed\nthe variation in model predictions induced by the correction and model\nstochasticity. We present an index of the effect of correction relative to\nmodel stochasticity, the Relative Overlap Index (ROI). We tested whether the\nROI better represented the effect of correction than classic performance\nmetrics and absolute overlap metrics using 64 vertebrate species and 21 virtual\nspecies with a generated sample bias. When based on absolute overlaps and\ncross-validation performance metrics, we found no effect of correction, except\nfor cAUC. When considering its effect relative to model stochasticity, the\neffect of correction depended on the site and the species. Virtual species\nenabled us to verify that the correction actually improved distribution\npredictions and the biological relevance of the selected variables at the sites\nwith a clear gradient of sample bias, and when species distribution predictors\nare not correlated with sample bias patterns.\n"}
+{"abstract": "  We consider a model of baryogenesis that requires extending the Standard\nModel by two additional multi-TeV Higgs doublets that do not break electroweak\nsymmetry. Adopting the ``Spontaneous Flavor Violation\" framework, we can\narrange for the heavy Higgs states to have significant couplings to light\nquarks. This allows for the heavy scalars to be resonantly produced at a future\n100 TeV $pp$ collider and discovered in di-jet and top-pair final states up to\nmasses of $\\mathcal{O}$(10 TeV). The same mass range can also lead to signals\nin flavor experiments. Together, these measurements can play a complementary\nrole in probing the physics involved in this Higgs Troika baryogenesis\nscenario.\n"}
+{"abstract": "  Automatic segmentation methods are an important advancement in medical image\nanalysis. Machine learning techniques, and deep neural networks in particular,\nare the state-of-the-art for most medical image segmentation tasks. Issues with\nclass imbalance pose a significant challenge in medical datasets, with lesions\noften occupying a considerably smaller volume relative to the background. Loss\nfunctions used in the training of deep learning algorithms differ in their\nrobustness to class imbalance, with direct consequences for model convergence.\nThe most commonly used loss functions for segmentation are based on either the\ncross entropy loss, Dice loss or a combination of the two. We propose the\nUnified Focal loss, a new hierarchical framework that generalises Dice and\ncross entropy-based losses for handling class imbalance. We evaluate our\nproposed loss function on five publicly available, class imbalanced medical\nimaging datasets: CVC-ClinicDB, Digital Retinal Images for Vessel Extraction\n(DRIVE), Breast Ultrasound 2017 (BUS2017), Brain Tumour Segmentation 2020\n(BraTS20) and Kidney Tumour Segmentation 2019 (KiTS19). We compare our loss\nfunction performance against six Dice or cross entropy-based loss functions,\nacross 2D binary, 3D binary and 3D multiclass segmentation tasks, demonstrating\nthat our proposed loss function is robust to class imbalance and consistently\noutperforms the other loss functions. Source code is available at:\nhttps://github.com/mlyg/unified-focal-loss\n"}
+{"abstract": "  We connect the study of pseudodeterministic algorithms to two major open\nproblems about the structural complexity of $\\mathsf{BPTIME}$: proving\nhierarchy theorems and showing the existence of complete problems. Our main\ncontributions can be summarised as follows.\n  1. We build on techniques developed to prove hierarchy theorems for\nprobabilistic time with advice (Fortnow and Santhanam, FOCS 2004) to construct\nthe first unconditional pseudorandom generator of polynomial stretch computable\nin pseudodeterministic polynomial time (with one bit of advice) that is secure\ninfinitely often against polynomial-time computations. As an application of\nthis construction, we obtain new results about the complexity of generating and\nrepresenting prime numbers.\n  2. Oliveira and Santhanam (STOC 2017) established unconditionally that there\nis a pseudodeterministic algorithm for the Circuit Acceptance Probability\nProblem ($\\mathsf{CAPP}$) that runs in sub-exponential time and is correct with\nhigh probability over any samplable distribution on circuits on infinitely many\ninput lengths. We show that improving this running time or obtaining a result\nthat holds for every large input length would imply new time hierarchy theorems\nfor probabilistic time. In addition, we prove that a worst-case polynomial-time\npseudodeterministic algorithm for $\\mathsf{CAPP}$ would imply that\n$\\mathsf{BPP}$ has complete problems.\n  3. We establish an equivalence between pseudodeterministic construction of\nstrings of large $\\mathsf{rKt}$ complexity (Oliveira, ICALP 2019) and the\nexistence of strong hierarchy theorems for probabilistic time.\n  More generally, these results suggest new approaches for designing\npseudodeterministic algorithms for search problems and for unveiling the\nstructure of probabilistic time.\n"}
+{"abstract": "  We consider the question of sequential prediction under the log-loss in terms\nof cumulative regret. Namely, given a hypothesis class of distributions,\nlearner sequentially predicts the (distribution of the) next letter in sequence\nand its performance is compared to the baseline of the best constant predictor\nfrom the hypothesis class. The well-specified case corresponds to an additional\nassumption that the data-generating distribution belongs to the hypothesis\nclass as well. Here we present results in the more general misspecified case.\nDue to special properties of the log-loss, the same problem arises in the\ncontext of competitive-optimality in density estimation, and model selection.\nFor the $d$-dimensional Gaussian location hypothesis class, we show that\ncumulative regrets in the well-specified and misspecified cases asymptotically\ncoincide. In other words, we provide an $o(1)$ characterization of the\ndistribution-free (or PAC) regret in this case -- the first such result as far\nas we know. We recall that the worst-case (or individual-sequence) regret in\nthis case is larger by an additive constant ${d\\over 2} + o(1)$. Surprisingly,\nneither the traditional Bayesian estimators, nor the Shtarkov's normalized\nmaximum likelihood achieve the PAC regret and our estimator requires special\n\"robustification\" against heavy-tailed data. In addition, we show two general\nresults for misspecified regret: the existence and uniqueness of the optimal\nestimator, and the bound sandwiching the misspecified regret between\nwell-specified regrets with (asymptotically) close hypotheses classes.\n"}
+{"abstract": "  Here, we report the synthesis and physical properties of Co2FeGe (CFG)\nHeusler alloy (HA) nanoparticles (NPs). The NPs of size 23 nm are prepared\nusing the co-precipitation method. X-ray and selected area electron diffraction\npatterns have confirmed the cubic Heusler phase of the NPs with the\nA2-disorder. These NPs are soft ferromagnetic, and exhibit a high saturation\nmagnetization (Ms) along with a very high Curie temperature (Tc) of 1060 K. The\nobserved Tc value matches closely with the theoretically calculated one\nfollowing a model provided by Wurmehl et al. [1]. The high Ms and Tc make the\npresent system a potential candidate for magnetically activated nano-devices\nworking at high temperatures. The near-integral value 5.9 mehoB/f.u. of Ms at\nlow temperatures indicates that the half-metallic ferromagnetism is preserved\neven in the particles even on the 20 nm length scale. Additionally, we have\nfacilitated the existing HA-NP preparation method, which can be used in\nsynthesizing other HA-NPs. The first-principles density functional theory\ncomputations complement the experimental results.\n"}
+{"abstract": "  We report on a detailed study of the optical properties of CsV$_{3}$Sb$_{5}$\nat a large number of temperatures above and below the charge-density-wave (CDW)\ntransition. Above the CDW transition, the low-frequency optical conductivity\nreveals two Drude components with distinct widths. An examination of the band\nstructure allows us to ascribe the narrow Drude to multiple light and Dirac\nbands, and the broad Drude to the heavy bands near the $M$ points which form\nsaddle points near the Fermi level. Upon entering the CDW state, the opening of\nthe CDW gap is clearly observed. A large portion of the broad Drude is removed\nby the gap, whereas the narrow Drude is not affected. Meanwhile, an absorption\npeak associated with interband transitions near the saddle points shifts to\nhigher energy and grows in weight. These observations are consistent with the\nscenario that the CDW in CsV$_{3}$Sb$_{5}$ is driven by nesting of Fermi\nsurfaces near the saddle points at $M$.\n"}
+{"abstract": "  Silicon carbide silicon carbide (SiC SiC) composites are often used in\noxidizing environments at high temperatures. Measurements of the thermal\nconductance of the oxide layer provide a way to better understand the oxidation\nprocess with high spatial resolution. We use time domain thermoreflectance\n(TDTR) to map the thermal conductance of the oxide layer and the thermal\nconductivity of the SiC SiC composite with a spatial resolution of 3 {\\mu}m.\nHeterodyne detection using a 50 kHz modulated probe beam and a 10 MHz modulated\npump suppresses the coherent pick-up and enables faster data acquisition than\nwhat has previously been possible using sequential demodulation. By analyzing\nthe noise of the measured signals, we find that in the limit of small\nintegration time constants or low laser powers, the dominant source of noise is\nthe input noise of the preamplifier. The thermal conductance of the oxide that\nforms on the fiber region is lower than the oxide on the matrix due to small\ndifferences in thickness and thermal conductivity.\n"}
+{"abstract": "  Cold-start problems are enormous challenges in practical recommender systems.\nOne promising solution for this problem is cross-domain recommendation (CDR)\nwhich leverages rich information from an auxiliary (source) domain to improve\nthe performance of recommender system in the target domain. In these CDR\napproaches, the family of Embedding and Mapping methods for CDR (EMCDR) is very\neffective, which explicitly learn a mapping function from source embeddings to\ntarget embeddings with overlapping users. However, these approaches suffer from\none serious problem: the mapping function is only learned on limited\noverlapping users, and the function would be biased to the limited overlapping\nusers, which leads to unsatisfying generalization ability and degrades the\nperformance on cold-start users in the target domain. With the advantage of\nmeta learning which has good generalization ability to novel tasks, we propose\na transfer-meta framework for CDR (TMCDR) which has a transfer stage and a meta\nstage. In the transfer (pre-training) stage, a source model and a target model\nare trained on source and target domains, respectively. In the meta stage, a\ntask-oriented meta network is learned to implicitly transform the user\nembedding in the source domain to the target feature space. In addition, the\nTMCDR is a general framework that can be applied upon various base models,\ne.g., MF, BPR, CML. By utilizing data from Amazon and Douban, we conduct\nextensive experiments on 6 cross-domain tasks to demonstrate the superior\nperformance and compatibility of TMCDR.\n"}
+{"abstract": "  We investigate the iron-based superconductor Ba$_{1-x}$K$_x$Fe$_2$As$_2$\nusing intense terahertz (THz) light. In the superconducting state a THz Kerr\nsignal is observed and assigned to non-linear THz coupling to superconducting\ndegrees of freedom. The polarization dependence of the THz Kerr signal is\nremarkably sensitive to the coexistence of a nematic order. In the absence of\nnematic order the $C_4$ symmetric polarization dependence of the THz Kerr\nsignal is consistent with a coupling to the Higgs amplitude mode of the\nsuperconducting condensate. In the coexisting nematic and superconducting state\nthe signal becomes purely nematic with a vanishing $C_4$ symmetric component,\nsignaling the emergence of a new superconducting collective mode activated by\nnematicity.\n"}
+{"abstract": "  We present the characterization of a novel balanced homodyne detector\noperating in the mid-infrared. The challenging task of revealing\nnon-classicality in mid-infrared light, e.~g. in quantum cascade lasers\nemission, requires a high-performance detection system. Through the intensity\nnoise power spectral density analysis of the differential signal coming from\nthe incident radiation, we show that our setup is shot-noise limited. We\ndiscuss the experimental results with a view to possible applications to\nquantum technologies, such as free-space quantum communication.\n"}
+{"abstract": "  The universe is composed of galaxies that have diverse shapes. Once the\nstructure of a galaxy is determined, it is possible to obtain important\ninformation about its formation and evolution. Morphologically classifying\ngalaxies means cataloging them according to their visual appearance and the\nclassification is linked to the physical properties of the galaxy. A\nmorphological classification made through visual inspection is subject to\nbiases introduced by subjective observations made by human volunteers. For this\nreason, systematic, objective and easily reproducible classification of\ngalaxies has been gaining importance since the astronomer Edwin Hubble created\nhis famous classification method. In this work, we combine accurate visual\nclassifications of the Galaxy Zoo project with \\emph {Deep Learning} methods.\nThe goal is to find an efficient technique at human performance level\nclassification, but in a systematic and automatic way, for classification of\nelliptical and spiral galaxies. For this, a neural network model was created\nthrough an Ensemble of four other convolutional models, allowing a greater\naccuracy in the classification than what would be obtained with any one\nindividual. Details of the individual models and improvements made are also\ndescribed. The present work is entirely based on the analysis of images (not\nparameter tables) from DR1 (www.datalab.noao.edu) of the Southern Photometric\nLocal Universe Survey (S-PLUS). In terms of classification, we achieved, with\nthe Ensemble, an accuracy of $\\approx 99 \\%$ in the test sample (using\npre-trained networks).\n"}
+{"abstract": "  Properties of the ground state and the spectrum of elementary excitations are\ninvestigated for the low density ultracold spinor 3D Bose gas of particles with\narbitrary nonzero spin. Gross-Pitaevskii equations are derived. Within the\nframework of the considering interaction Hamiltonian it is shown that the\nground state spin structure and spin part of the chemical potential is\ndetermined by the renormalized interaction, being defined by the contribution\nof the virtual large momenta. The ferromagnetic structure of the ground state,\nand the equation of the phase, density, and spin dynamics are obtained from\nGross-Pitaevskii equations.\n"}
+{"abstract": "  We consider a singularly perturbed reaction diffusion problem as a first\norder two-by-two system. Using piecewise discontinuous polynomials for the\nfirst component and $H_{div}$-conforming elements for the second component we\nprovide a convergence analysis on layer adapted meshes and an optimal\nconvergence order in a balanced norm that is comparable with a balanced\n$H^2$-norm for the second order formulation.\n"}
+{"abstract": "  Given an input image from a source domain and a guidance image from a target\ndomain, unsupervised many-to-many image-to-image (UMMI2I) translation methods\nseek to generate a plausible example from the target domain that preserves\ndomain-invariant information of the input source image and inherits the\ndomain-specific information from the guidance image. For example, when\ntranslating female faces to male faces, the generated male face should have the\nsame expression, pose and hair color as the input female image, and the same\nfacial hairstyle and other male-specific attributes as the guidance male image.\nCurrent state-of-the art UMMI2I methods generate visually pleasing images, but,\nsince for most pairs of real datasets we do not know which attributes are\ndomain-specific and which are domain-invariant, the semantic correctness of\nexisting approaches has not been quantitatively evaluated yet. In this paper,\nwe propose a set of benchmarks and metrics for the evaluation of semantic\ncorrectness of these methods. We provide an extensive study of existing\nstate-of-the-art UMMI2I translation methods, showing that all methods, to\ndifferent degrees, fail to infer which attributes are domain-specific and which\nare domain-invariant from data, and mostly rely on inductive biases hard-coded\ninto their architectures.\n"}
+{"abstract": "  Inversion symmetry is a key symmetry in unconventional superconductors and\neven its local breaking can have profound implications. For inversion-symmetric\nsystems, there is a competition on a microscopic level between the spin-orbit\ncoupling associated with the local lack of inversion and hybridizing terms that\n`restore' inversion. Investigating a layered system with alternating\nmirror-symmetry breaking, we study this competition considering the spin\nresponse of different superconducting order parameters for the case of strong\nspin-orbit coupling. We find that signatures of the local non-centrosymmetry,\nsuch as an increased spin susceptibility in spin-singlet superconductors for\n$T\\rightarrow 0$, persist even into the quasi-three-dimensional regime. This\nleads to a direction dependent spin response which allows to distinguish\ndifferent superconducting order parameters. Furthermore, we identify several\nregimes with possible topological superconducting phases within a\nsymmetry-indicator analysis. Our results may have direct relevance for the\nrecently reported Ce-based superconductor CeRh$_2$As$_2$ and beyond.\n"}
+{"abstract": "  This paper's central theme is to prove the existence of an n-algebra whose\nmultiplication cannot be expressed employing any binary operation. Furthermore,\nto prove if two algebras are not isomorphic, this property does not hold for\n$3$-algebras corresponding to these two algebras. The proof drives applying\nsome results gotten early applying a new approach for the classification\nalgebras problem, introduced recently, which showed great success in solving\nmany classification algebras problems.\n"}
+{"abstract": "  Embedding quantum dots (QDs) on nanowire (NW) sidewalls allows the\nintegration of multi-layers of QDs into the active region of radial p-i-n\njunctions to greatly enhance light emission/absorption. However, the surface\ncurvature makes the growth much more challenging compared with growths on\nthin-films, particularly on NWs with small diameters ({\\O} <100 nm). Moreover,\nthe {110} sidewall facets of self-catalyzed NWs favor two-dimensional growth\n(2D), with the realization of three-dimensional (3D) Stranski-Krastanow growth\nbecoming extremely challenging. Here, we demonstrate thermally-driven formation\nof Ge dots on the {110} sidewalls facets of thin self-catalyzed NWs without\nusing any surfactant or surface treatment. The 2D-3D transition of the\npseudomorphic Ge layer grown on GaAs NWs is driven by energy minimization under\nhigh-temperature annealing. This method opens a new avenue to integrate QDs on\nNWs without any restriction on NW diameter or elastic strain, which can allow\nthe formation of QDs in a wider range of materials systems where the growth of\nislands by traditional mechanisms is not possible, with benefits for novel\nNWQD-based optoelectronic devices.\n"}
+{"abstract": "  Sulfur is a volatile chemical element that plays an important role in tracing\nthe chemical evolution of galaxies. However, its nucleosynthesis origin and\nabundance variations are still unclear. The goal of the present article is to\naccurately and precisely study the S-content of large number of stars located\nin the solar neighbourhood. We use the parametrisation of thousands of HR\nstellar spectra provided by the AMBRE Project, and combine it with the\nautomated abundance determination GAUGUIN to derive LTE sulfur abundances for\n1855 slow-rotating FGK-type stars. This is the largest and most precise\ncatalogue of S-abundances published to date. It covers a metallicity domain as\nhigh as ~2.5dex starting at [M/H]~-2.0dex. We find that the [S/M] abundances\nratio is compatible with a plateau-like distribution in the metal-poor regime,\nand then starts to decrease continuously at [M/H]~-1.0dex. This decrease\ncontinues towards negative values for supersolar metallicity stars as recently\nreported for Mg and as predicted by Galactic chemical evolution models.\nMoreover, sulfur-rich stars having [M/H] in the range [-1.0,-0.5] have very\ndifferent kinematical and orbital properties with respect to more metal-rich\nand sulfur-poor ones. Two disc components, associated with the thin and thick\ndiscs, are thus seen independently in kinematics and sulfur abundances. The\nsulfur radial gradients in the Galactic discs have also been estimated.\nFinally, the enrichment in sulfur with respect to iron is nicely correlated\nwith stellar ages: older metal-poor stars have higher [S/M] ratios than younger\nmetal-rich ones. This work has confirmed that sulfur is an alfa-element that\ncould be considered to explore the Galactic populations properties.\n"}
+{"abstract": "  The major paradigm of applying a pre-trained language model to downstream\ntasks is to fine-tune it on labeled task data, which often suffers instability\nand low performance when the labeled examples are scarce.~One way to alleviate\nthis problem is to apply post-training on unlabeled task data before\nfine-tuning, adapting the pre-trained model to target domains by contrastive\nlearning that considers either token-level or sequence-level similarity.\nInspired by the success of sequence masking, we argue that both token-level and\nsequence-level similarities can be captured with a pair of masked\nsequences.~Therefore, we propose complementary random masking (CRM) to generate\na pair of masked sequences from an input sequence for sequence-level\ncontrastive learning and then develop contrastive masked language modeling\n(CMLM) for post-training to integrate both token-level and sequence-level\ncontrastive learnings.~Empirical results show that CMLM surpasses several\nrecent post-training methods in few-shot settings without the need for data\naugmentation.\n"}
+{"abstract": "  The discovery of van der Waals (vdW) magnets opened up a new paradigm for\ncondensed matter physics and spintronic technologies. However, the operations\nof active spintronic devices with vdW magnets are so far limited to cryogenic\ntemperatures, inhibiting its broader practical applications. Here, for the\nfirst time, we demonstrate room temperature spin-valve devices using vdW\nitinerant ferromagnet Fe5GeTe2 in heterostructures with graphene. The tunnel\nspin polarization of the Fe5GeTe2/graphene vdW interface is detected to be\nsignificantly large ~ 45 % and negative at room temperature. Lateral spin-valve\ndevice design enables electrical control of spin signal and realization of\nbasic building blocks for device application such as efficient spin injection,\ntransport, precession, and detection functionalities. Furthermore, measurements\nwith different magnetic orientations provide unique insights into the magnetic\nanisotropy of Fe5GeTe2 and its relation with spin polarization and dynamics in\nthe heterostructure. These findings open opportunities for the applications of\nvdW magnet-based all-2D spintronic devices and integrated spin circuits at\nambient temperatures.\n"}
+{"abstract": "  The Landau-Lifshitz-Gilbert equation yields a mathematical model to describe\nthe evolution of the magnetization of a magnetic material, particularly in\nresponse to an external applied magnetic field. It allows one to take into\naccount various physical effects, such as the exchange within the magnetic\nmaterial itself. In particular, the Landau-Lifshitz-Gilbert equation encodes\nrelaxation effects, i.e., it describes the time-delayed alignment of the\nmagnetization field with an external magnetic field. These relaxation effects\nare an important aspect in magnetic particle imaging, particularly in the\ncalibration process. In this article, we address the data-driven modeling of\nthe system function in magnetic particle imaging, where the\nLandau-Lifshitz-Gilbert equation serves as the basic tool to include relaxation\neffects in the model. We formulate the respective parameter identification\nproblem both in the all-at-once and the reduced setting, present reconstruction\nalgorithms that yield a regularized solution and discuss numerical experiments.\nApart from that, we propose a practical numerical solver to the nonlinear\nLandau-Lifshitz-Gilbert equation, not via the classical finite element method,\nbut through solving only linear PDEs in an inverse problem framework.\n"}
+{"abstract": "  In this work, we investigate an inverse problem of recovering multiple orders\nin a time-fractional diffusion model from the data observed at one single point\non the boundary. We prove the unique recovery of the orders together with their\nweights, which does not require a full knowledge of the domain or medium\nproperties, e.g., diffusion and potential coefficients, initial condition and\nsource in the model. The proof is based on Laplace transform and asymptotic\nexpansion. Further, inspired by the analysis, we propose a numerical procedure\nfor recovering these parameters based on a nonlinear least-squares fitting with\neither fractional polynomials or rational approximations as the model function,\nand provide numerical experiments to illustrate the approach for small time\n$t$.\n"}
+{"abstract": "  Unmanned aerial vehicles (UAVs) have found widespread commercial, civilian,\nand military applications. Wireless communication has always been one of the\ncore technologies for UAV. However, the communication capacity is becoming a\nbottleneck for UAV to support more challenging application scenarios. The\nheavily-occupied sub-6 GHz frequency band is not sufficient to meet the ultra\nhigh-data-traffic requirements. The utilization of the millimeter-wave (mmWave)\nfrequency bands is a promising direction for UAV communications, where large\nantenna arrays can be packed in a small area on the UAV to perform\nthree-dimensional (3D) beamforming. On the other hand, UAVs serving as aerial\naccess points or relays can significantly enhance the coverage and quality of\nservice of the terrestrial mmWave cellular networks. In this paper, we provide\na comprehensive survey on mmWave beamforming enabled UAV communications and\nnetworking. The technical potential of and challenges for mmWave-UAV\ncommunications are presented first. Then, we provide an overview on relevant\nmmWave antenna structures and channel modeling. Subsequently, the technologies\nand solutions for UAV-connected mmWave cellular networks and mmWave-UAV ad hoc\nnetworks are reviewed, respectively. Finally, we present open issues and\npromising directions for future research in mmWave beamforming enabled UAV\ncommunications and networking.\n"}
+{"abstract": "  The majority of finite mixture models suffer from not allowing asymmetric\ntail dependencies within components and not capturing non-elliptical clusters\nin clustering applications. Since vine copulas are very flexible in capturing\nthese types of dependencies, we propose a novel vine copula mixture model for\ncontinuous data. We discuss the model selection and parameter estimation\nproblems and further formulate a new model-based clustering algorithm. The use\nof vine copulas in clustering allows for a range of shapes and dependency\nstructures for the clusters. Our simulation experiments illustrate a\nsignificant gain in clustering accuracy when notably asymmetric tail\ndependencies or/and non-Gaussian margins within the components exist. The\nanalysis of real data sets accompanies the proposed method. We show that the\nmodel-based clustering algorithm with vine copula mixture models outperforms\nthe other model-based clustering techniques, especially for the non-Gaussian\nmultivariate data.\n"}
+{"abstract": "  The paper treats an agent-based model with averaging dynamics to which we\nrefer as the K-averaging model. Broadly speaking, our model can be added to the\ngrowing list of dynamics exhibiting self-organization such as the well-known\nVicsek-type models [1, 2, 29]. In the $K$-averaging model, each of the $N$\nparticles updates their position by averaging over $K$ randomly selected\nparticles with additional noise. To make the $K$-averaging dynamics more\ntractable, we first establish a propagation of chaos type result in the limit\nof infinite particle number (i.e. $N \\to \\infty$) using a martingale technique.\nThen, we prove the convergence of the limit equation toward a suitable Gaussian\ndistribution in the sense of Wasserstein distance as well as relative entropy.\nWe provide additional numerical simulations to illustrate both results.\n"}
+{"abstract": "  Ant species such as Temnothorax albipennis select a new nest site in a\ndistributed fashion that, if modeled correctly, can serve as useful information\nfor site selection algorithms for robotic swarms and other applications.\nStudying and replicating the ants' house hunting behavior will also illuminate\nuseful distributed strategies that have evolved in nature. Many of the existing\nmodels of househunting behaviour for T. albipennis make the assumption that all\ncandidate nest sites are equally distant from the ants' home nest, or that an\nant has an equal probability of finding each candidate nest site. However,\nrealistically this is not the case, as nests that are further away from the\nhome nest and nests that are difficult to access are less likely to be found,\neven if they are of higher quality. We extend previous house-hunting models to\naccount for a pairwise distance metric between nests, compare our results to\nthose of real colonies, and use our results to examine the effects of house\nhunting in nests of different spatial orientations. Our incorporation of\ndistances in the ant model appear to match empirical data in situations where a\ndistance-quality tradeoff between nests is relevant. Furthermore, the model\ncontinues to be on par with previous house-hunting models in experiments where\nall candidate nests are equidistant from the home nest, as is typically\nassumed.\n"}
+{"abstract": "  We study a set of models of self-propelled particles that achieve collective\nmotion through similar alignment-based dynamics, considering versions with and\nwithout repulsive interactions that do not affect the heading directions. We\nexplore their phase space within a broad range of values of two nondimensional\nparameters (coupling strength and Peclet number), characterizing their\npolarization and degree of clustering. The resulting phase diagrams display\nequivalent, similarly distributed regions for all models with repulsion. The\ndiagrams without repulsion exhibit differences, in particular for high coupling\nstrengths. We compare the boundaries and representative states of all regions,\nidentifying various regimes that had not been previously characterized. We\nanalyze in detail three types of homogeneous polarized states, comparing them\nto existing theoretical and numerical results by computing their velocity and\ndensity correlations, giant number fluctuations, and local order-density\ncoupling. We find that they all deviate in one way or another from the\ntheoretical predictions, attributing these differences either to the remaining\ninhomogeneities or to finite-size effects. We discuss our results in terms of\nthe universal or specific features of each model, their thermodynamic limit,\nand the high mixing and low mixing regimes. Our study provides a broad,\noverarching perspective on the multiple phases and states found in\nalignment-based self-propelled particle models.\n"}
+{"abstract": "  We tackle the problem of visual search under resource constraints. Existing\nsystems use the same embedding model to compute representations (embeddings)\nfor the query and gallery images. Such systems inherently face a hard\naccuracy-efficiency trade-off: the embedding model needs to be large enough to\nensure high accuracy, yet small enough to enable query-embedding computation on\nresource-constrained platforms. This trade-off could be mitigated if gallery\nembeddings are generated from a large model and query embeddings are extracted\nusing a compact model. The key to building such a system is to ensure\nrepresentation compatibility between the query and gallery models. In this\npaper, we address two forms of compatibility: One enforced by modifying the\nparameters of each model that computes the embeddings. The other by modifying\nthe architectures that compute the embeddings, leading to compatibility-aware\nneural architecture search (CMP-NAS). We test CMP-NAS on challenging retrieval\ntasks for fashion images (DeepFashion2), and face images (IJB-C). Compared to\nordinary (homogeneous) visual search using the largest embedding model\n(paragon), CMP-NAS achieves 80-fold and 23-fold cost reduction while\nmaintaining accuracy within 0.3% and 1.6% of the paragon on DeepFashion2 and\nIJB-C respectively.\n"}
+{"abstract": "  The task of classifying emotions within a musical track has received\nwidespread attention within the Music Information Retrieval (MIR) community.\nMusic emotion recognition has traditionally relied on the use of acoustic\nfeatures, verbal features, and metadata-based filtering. The role of musical\nprosody remains under-explored despite several studies demonstrating a strong\nconnection between prosody and emotion. In this study, we restrict the input of\ntraditional machine learning algorithms to the features of musical prosody.\nFurthermore, our proposed approach builds upon the prior by classifying\nemotions under an expanded emotional taxonomy, using the Geneva Wheel of\nEmotion. We utilize a methodology for individual data collection from\nvocalists, and personal ground truth labeling by the artist themselves. We\nfound that traditional machine learning algorithms when limited to the features\nof musical prosody (1) achieve high accuracies for a single singer, (2)\nmaintain high accuracy when the dataset is expanded to multiple singers, and\n(3) achieve high accuracies when trained on a reduced subset of the total\nfeatures.\n"}
+{"abstract": "  We point out that the computation of true \\emph{proof} and \\emph{disproof}\nnumbers for proof number search in arbitrary directed acyclic graphs is\nNP-hard, an important theoretical result for proof number search. The proof\nrequires a reduction from SAT, which demonstrates that finding true\nproof/disproof number for arbitrary DAG is at least as hard as deciding if\narbitrary SAT instance is satisfiable, thus NP-hard.\n"}
+{"abstract": "  In this paper we provide a large family of rank-metric codes, which contains\nproperly the codes recently found by Longobardi and Zanella (2021) and by\nLongobardi, Marino, Trombetti and Zhou (2021). These codes are\n$\\mathbb{F}_{q^{2t}}$-linear of dimension $2$ in the space of linearized\npolynomials over $\\mathbb{F}_{q^{2t}}$, where $t$ is any integer greater than\n$2$, and we prove that they are maximum rank distance codes. For $t\\ge 5$, we\ndetermine their equivalence classes and these codes turn out to be inequivalent\nto any other construction known so far, and hence they are really new.\n"}
+{"abstract": "  In Transport Mode Detection, a great diversity of methodologies exist\naccording to the choice made on sensors, preprocessing, model used, etc. In\nthis domain, the comparisons between each option are not always complete.\nExperiments on a public, real-life dataset are led here to evaluate carefully\neach of the choices that were made, with a specific emphasis on data fusion\nmethods. Our most surprising finding is that none of the methods we implemented\nfrom the literature is better than a simple late fusion. Two important\ndecisions are the choice of a sensor and the choice of a representation for the\ndata: we found that using 2D convolutions on spectrograms with a logarithmic\naxis for the frequencies was better than 1-dimensional temporal\nrepresentations.\n"}
+{"abstract": "  In this paper, the supervisory control of a Discrete Event System (DES)\nanalyses states and events to construct an autonomous package delivery system.\nThe delivery system includes a legged robot in order to autonomously navigate\nuneven indoor terrain and a conveyor belt for transporting the package to the\nlegged robot.The aim of the paper is using the theory of supervisory control of\nDES to supervise and control machine's state and event and ensure robots\nautonomously collaborate. By applying the theory, we show the collaboration of\ntwo individual robots to deliver goods in a multi-floor environment. The\nobtained results from the theory of supervisory control are implemented and\nverified in a simulation environment.\n"}
+{"abstract": "  The purpose of this paper is to prove that if on a commutative hypergroup an\nexponential monomial has the property that the linear subspace of all sine\nfunctions in its variety is one dimensional, then this exponential monomial is\na linear combination of generalized moment functions.\n"}
+{"abstract": "  This paper presents a comprehensive study of algorithms for maintaining the\nnumber of all connected four-vertex subgraphs in a dynamic graph. Specifically,\nour algorithms maintain the number of any four-vertex subgraph, which is not a\nclique, in deterministic amortized update time $\\mathcal{O}(m^{1/2})$, resp.,\n$\\mathcal{O}(m^{2/3})$. Queries can be answered in constant time. For length-3\npaths, paws, 4-cycles, and diamonds these bounds match or are not far from\n(conditional) lower bounds: Based on the OMv conjecture we show that any\ndynamic algorithm that detects the existence of length-3 paths, 4-cycles,\ndiamonds, or 4-cliques takes amortized update time $\\Omega(m^{1/2-\\delta})$.\n  Additionally, for 4-cliques and all connected induced subgraphs, we show a\nlower bound of $\\Omega(m^{1-\\delta})$ for any small constant $\\delta > 0$ for\nthe amortized update time, assuming the static combinatorial 4-clique\nconjecture holds. This shows that the $\\mathcal{O}(m)$ algorithm by Eppstein et\nal. [9] for these subgraphs cannot be improved by a polynomial factor.\n"}
+{"abstract": "  Who joins extremist movements? Answering this question poses considerable\nmethodological challenges. Survey techniques are practically infeasible and\nselective samples provide no counterfactual. Assigning recruits to contextual\nunits provides one solution, but is vulnerable to problems of ecological\ninference. In this article, we take inspiration from epidemiology and the\nprotest literature and elaborate a technique to combine survey and ecological\napproaches. The rare events, multilevel Bayesian contaminated case-control\ndesign we propose accounts for individual-level and contextual factors, as well\nas spatial autocorrelation in the incidence of recruitment. We validate our\napproach by matching a sample of Islamic State (ISIS) fighters from nine\nMuslim-majority countries with representative population surveys enumerated\nshortly before recruits joined the movement. We find that high status\nindividuals in their early twenties who had university education were more\nlikely to join ISIS. We find more mixed evidence for relative deprivation.\n"}
+{"abstract": "  Range aggregate queries (RAQs) are an integral part of many real-world\napplications, where, often, fast and approximate answers for the queries are\ndesired. Recent work has studied answering RAQs using machine learning models,\nwhere a model of the data is learned to answer the queries. However, such\nmodelling choices fail to utilize any query specific information. To capture\nsuch information, we observe that RAQs can be represented by query functions,\nwhich are functions that take a query instance (i.e., a specific RAQ) as an\ninput and output its corresponding answer. Using this representation, we\nformulate the problem of learning to approximate the query function, and\npropose NeuroDB, a query specialized neural network framework, that answers\nRAQs efficiently. NeuroDB is query-type agnostic (i.e., it does not make any\nassumption about the underlying query type) and our observation that queries\ncan be represented by functions is not specific to RAQs. Thus, we investigate\nwhether NeuroDB can be used for other query types, by applying it to distance\nto nearest neighbour queries. We experimentally show that NeuroDB outperforms\nthe state-of-the-art for this query type, often by orders of magnitude.\nMoreover, the same neural network architecture as for RAQs is used, bringing to\nlight the possibility of using a generic framework to answer any query type\nefficiently.\n"}
+{"abstract": "  In dense stellar environments, nuclei may become unstable against electron\ncaptures and/or neutron emissions. These processes are of particular importance\nfor determining the internal constitution of white-dwarf cores and neutron-star\ncrusts. In this paper, the role of electron exchange and polarization effects\nis studied. In particular, the instability condition for the onset of electron\ncaptures and neutron emissions is extended so as to account for electron\nexchange and polarization. Moreover, general analytical expressions for the\ncorresponding density and pressure are derived. The corrections to the\nelectron-capture threshold in white-dwarf cores are found to be very small.\nLikewise, the neutron-drip density and pressure in the crusts of accreting and\nnonaccreting neutron stars are only slightly shifted. Depending on the nuclear\nmass model employed, electron polarization may change the composition of the\ncrust of nonaccreting neutron stars. On the other hand, the current\nuncertainties in the masses of neutron-rich Kr and Sr isotopes are found to be\nmore important than electron exchange and polarization effects.\n"}
+{"abstract": "  Motivated by the recent progresses in the formulation of geometric theories\nfor the fractional quantum Hall states, we propose a novel non-relativistic\ngeometric model for the Laughlin states based on an extension of the\nNappi-Witten geometry. We show that the U(1) gauge sector responsible for the\nfractional Hall conductance, the gravitational Chern-Simons action and Wen-Zee\nterm associated to the Hall viscosity can be derived from a single Chern-Simons\ntheory with a gauge connection that takes values in the extended Nappi-Witten\nalgebra. We then provide a new derivation of the chiral boson associated to the\ngapless edge states from the Wess-Zumino-Witten model that is induced by the\nChern-Simons theory on the boundary.\n"}
+{"abstract": "  We address the Mach limit problem for the Euler equations in the analytic\nspaces. We prove that, given analytic data, the solutions to the compressible\nEuler equations are uniformly bounded in a suitable analytic norm and then show\nthat the convergence toward the incompressible Euler solution holds in the\nanalytic norm. We also show that the same results hold more generally for\nGevrey data with the convergence in the Gevrey norms.\n"}
+{"abstract": "  We propose novel triangle relations, not the well-known triangle singularity,\nfor better understanding of the exotic XYZ states. Nine XYZ resonances,\nX(3872), Y(4230), Zc(3900), X(4012), Y(4360/4390), Zc(4020), X(4274), Y(4660),\nand Zcs(3985) have been classified into triples to construct three triangles\nbased on the assumption that they are all tetra-quark states. We also suggest\nsome channels deserving search for with high priority based on this hypothesis,\nas well as predictions of a few production/decay rates of these channels. We\nhope further experimental studies of the XYZ states will benefit from our\nresults.\n"}
+{"abstract": "  We calculate the Gromov--Hausdorff distance between a line segment and a\ncircle in the Euclidean plane. To do that, we introduced a few new notions like\nround spaces and nonlinearity degree of a metric space.\n"}
+{"abstract": "  In this paper we present a benchmark dataset generated as part of a project\nfor automatic identification of misogyny within online content, which focuses\nin particular on memes. The benchmark here described is composed of 800 memes\ncollected from the most popular social media platforms, such as Facebook,\nTwitter, Instagram and Reddit, and consulting websites dedicated to collection\nand creation of memes. To gather misogynistic memes, specific keywords that\nrefer to misogynistic content have been considered as search criterion,\nconsidering different manifestations of hatred against women, such as body\nshaming, stereotyping, objectification and violence. In parallel, memes with no\nmisogynist content have been manually downloaded from the same web sources.\nAmong all the collected memes, three domain experts have selected a dataset of\n800 memes equally balanced between misogynistic and non-misogynistic ones. This\ndataset has been validated through a crowdsourcing platform, involving 60\nsubjects for the labelling process, in order to collect three evaluations for\neach instance. Two further binary labels have been collected from both the\nexperts and the crowdsourcing platform, for memes evaluated as misogynistic,\nconcerning aggressiveness and irony. Finally for each meme, the text has been\nmanually transcribed. The dataset provided is thus composed of the 800 memes,\nthe labels given by the experts and those obtained by the crowdsourcing\nvalidation, and the transcribed texts. This data can be used to approach the\nproblem of automatic detection of misogynistic content on the Web relying on\nboth textual and visual cues, facing phenomenons that are growing every day\nsuch as cybersexism and technology-facilitated violence.\n"}
+{"abstract": "  We present a strangeness-neutral equation of state for QCD that exhibits\ncritical behavior and matches lattice QCD results for the Taylor-expanded\nthermodynamic variables up to fourth-order in $\\mu_B/T$. It is compatible with\nthe SMASH hadronic transport approach and has a range of temperatures and\nbaryonic chemical potentials relevant for phase II of the Beam Energy Scan at\nRHIC. We provide an updated version of the software BES-EoS, which produces an\nequation of state for QCD that includes a critical point in the 3D Ising model\nuniversality class. This new version also includes isentropic trejectories and\nthe critical contribution to the correlation length. Since heavy-ion collisions\nhave zero global net-strangeness density and a fixed ratio of electric charge\nto baryon number, the BES-EoS is more suitable to describe this system.\nComparison with the previous version of the EoS is thoroughly discussed.\n"}
+{"abstract": "  Purpose: The purpose of this work was to provide a flexible platform for\nFLASH research with protons by adapting a former clinical pencil beam scanning\ngantry to irradiations with ultrahigh dose rates.\n  Methods: PSI Gantry 1 treated patients until December 2018. We optimized the\nbeamline parameters to transport the 250 MeV beam extracted from the PSI COMET\naccelerator to the treatment room, maximizing the transmission of beam\nintensity to the sample. We characterized a dose monitor on the gantry to\nensure good control of the dose, delivered in spot-scanning mode. We\ncharacterized the beam for different dose rates and field sizes for\ntransmission irradiations. We explored scanning possibilities in order to\nenable conformal irradiations or transmission irradiations of large targets\n(with transverse scanning). Results: We achieved a transmission of 86 % from\nthe cyclotron to the treatment room. We reached a peak dose rate of 9000 Gy/s\nat 3 mm water equivalent depth, along the central axis of a single pencil beam.\nField sizes of up to 5x5 mm$^{2}$ were achieved for single spot FLASH\nirradiations. Fast transverse scanning allowed to cover a field of 16x1.2\ncm$^{2}$. With the use of a nozzle-mounted range shifter we are able to span\ndepths in water ranging from 19.6 to 37.9 cm. Various dose levels were\ndelivered with a precision within less than 1 %. Conclusions: We have realized\na proton FLASH irradiation setup able to investigate continuously a wide dose\nrate spectrum, from 1 to 9000 Gy/s in a single spot irradiation as well as in\nthe pencil beam scanning mode. As such, we have developed a versatile test\nbench for FLASH research.\n"}
+{"abstract": "  We establish the most general Szasz type estimates for homogeneous Besov and\nLizorkin-Triebel spaces, and their realizations.\n"}
+{"abstract": "  Context. The Vista Variables in the Via Lactea (VVV) near-infrared\nvariability survey explores some of the most complex regions of the Milky Way\nbulge and disk in terms of high extinction and high crowding. Aims. We add a\nnew wavelength dimension to the optical information available at the American\nAssociation of Variable Star Observers International Variable Star Index\n(VSX-AAVSO) catalogue to test the VVV survey near-infrared photometry to better\ncharacterise these objects. Methods. We cross-matched the VVV and the VSX-AAVSO\ncatalogues along with Gaia Data Release 2 photometry and parallax. Results. We\npresent a catalogue that includes accurate individual coordinates,\nnear-infrared magnitudes (ZY JHKs), extinctions Aks, and distances based on\nGaia parallaxes. We also show the near-infrared CMDs and spatial distributions\nfor the different VSX types of variable stars, including important distance\nindicators, such as RR Lyrae, Cepheids, and Miras. By analysing the photometric\nflags in our catalogue, we found that about 20% of the stars with measured and\nverified variability are flagged as non-stellar sources, even when they are\noutside of the saturation and/or noise regimes. Additionally, we pair-matched\nour sample with the VIVA catalogue and found that more than half of our sources\nare missing from the VVV variability list, mostly due to observations with low\nsignal-to-noise ratio or photometric problems with a low percentage due to\nfailures in the selection process. Conclusions. Our results suggest that the\ncurrent knowledge of the variability in the Galaxy is biased to nearby stars\nwith low extinction. The present catalogue also provides the groundwork for\ncharacterising the results of future large variability surveys such as the Vera\nC. Rubin Observatory Legacy Survey of Space and Time in the highly crowded and\nreddened regions of the Galactic plane, as well as follow-up campaigns for\n"}
+{"abstract": "  Virtualization enables information and communications technology industry to\nbetter manage computing resources. In this regard, improvements in\nvirtualization approaches together with the need for consistent runtime\nenvironment, lower overhead and smaller package size has led to the growing\nadoption of containers. This is a technology, which packages an application,\nits dependencies and Operating System (OS) to run as an isolated unit. However,\nthe pressing concern with the use of containers is its susceptibility to\nsecurity attacks. Consequently, a number of container scanning tools are\navailable for detecting container security vulnerabilities. Therefore, in this\nstudy, we investigate the quality of existing container scanning tools by\nproposing two metrics that reflects coverage and accuracy. We analyze 59\npopular public container images for Java applications hosted on DockerHub using\ndifferent container scanning tools (such as Clair, Anchore, and Microscanner).\nOur findings show that existing container scanning approach does not detect\napplication package vulnerabilities. Furthermore, existing tools do not have\nhigh accuracy, since 34% vulnerabilities are being missed by the best\nperforming tool. Finally, we also demonstrate quality of Docker images for Java\napplications hosted on DockerHub by assessing complete vulnerability landscape\ni.e., number of vulnerabilities detected in images.\n"}
+{"abstract": "  We compute symplectic cohomology for Milnor fibres of certain compound Du Val\nsingularities that admit small resolution by using homological mirror symmetry.\nOur computations suggest a new conjecture that the existence of a small\nresolution has strong implications for the symplectic cohomology and\nconversely. We also use our computations to give a contact invariant of the\nlink of the singularities and thereby distinguish many contact structures on\nconnected sums of $S^2 \\times S^3$.\n"}
+{"abstract": "  Neutrino transport and neutrino-matter interactions are known to play an\nimportant role in the evolution of neutron star mergers, and of their\npost-merger remnants. Neutrinos cool remnants, drive post-merger winds, and\ndeposit energy in the low-density polar regions where relativistic jets may\neventually form. Neutrinos also modify the composition of the ejected material,\nimpacting the outcome of nucleosynthesis in merger outflows and the properties\nof the optical/infrared transients that they power (kilonovae). So far, merger\nsimulations have largely relied on approximate treatments of the neutrinos\n(leakage, moments) that simplify the equations of radiation transport in a way\nthat makes simulations more affordable, but also introduces unquantifiable\nerrors in the results. To improve on these methods, we recently published a\nfirst simulation of neutron star mergers using a low-cost Monte-Carlo algorithm\nfor neutrino radiation transport. Our transport code limits costs in optically\nthick regions by placing a hard ceiling on the value of the absorption opacity\nof the fluid, yet all approximations made within the code are designed to\nvanish in the limit of infinite numerical resolution. We provide here an\nin-depth description of this algorithm, of its implementation in the SpEC\nmerger code, and of the expected impact of our approximations in optically\nthick regions. We argue that the latter is a subdominant source of error at the\naccuracy reached by current simulations, and for the interactions currently\nincluded in our code. We also provide tests of the most important features of\nthis code.\n"}
+{"abstract": "  This paper provides theoretical and practical arguments regarding the\npossibility of predicting strong and major earthquakes worldwide. Many strong\nand major earthquakes can be predicted at least two to five months in advance,\nbased on identifying stressed areas that begin to behave abnormally before\nstrong events, with the size of these areas corresponding to Dobrovolsky\nformula. We make predictions by combining knowledge from many different\ndisciplines: physics, geophysics, seismology, geology, and earth science, among\nothers. An integrated approach is used to identify anomalies and make\npredictions, including satellite remote sensing techniques and data from\nground-based instruments. Terabytes of information are currently processed\nevery day with many different multi-parametric prediction systems applied\nthereto. Alerts are issued if anomalies are confirmed by a few different\nsystems. It has been found that geophysical patterns of earthquake preparation\nand stress accumulation are similar for all key seismic regions. The same\nearthquake prediction methodologies and systems have been successfully applied\nin global practice since 2013, with the technology successfully used to\nretrospectively test against more than 700 strong and major earthquakes since\n1970.\n"}
+{"abstract": "  A high-rate yet low-cost air-to-ground (A2G) communication backbone is\nconceived for integrating the space and terrestrial network by harnessing the\nopportunistic assistance of the passenger planes or high altitude platforms\n(HAPs) as mobile base stations (BSs) and millimetre wave communication. The\nairliners act as the network-provider for the terrestrial users while relying\non satellite backhaul. Three different beamforming techniques relying on a\nlarge-scale planar array are used for transmission by the airliner/HAP for\nachieving a high directional gain, hence minimizing the interference among the\nusers. Furthermore, approximate spectral efficiency (SE) and area spectral\nefficiency (ASE) expressions are derived and quantified for diverse system\nparameters.\n"}
+{"abstract": "  We present a new high-resolution, non-oscillatory semi-discrete\ncentral-upwind scheme for one-dimensional two-layer shallow-water flows with\nfriction and entrainment along channels with arbitrary cross sections and\nbottom topography. These flows are described by a conditionally hyperbolic\nbalance law with non-conservative products. A detailed description of the\nproperties of the model is provided, including entropy inequalities and\nasymptotic approximations of the eigenvalues of the corresponding coefficient\nmatrix. The scheme extends existing central-upwind semi-discrete numerical\nmethods for hyperbolic conservation and balance laws and it satisfies two\nproperties crucial for the accurate simulation of shallow-water flows: it {\\it\npreserves the positivity} of the water depth for each layer, and it is {\\it\nwell balanced}, i.e., the source terms arising from the geometry of the channel\nare discretized so as to balance the non-linear hyperbolic flux gradients.\nAlong with the description of the scheme and proofs of these two properties, we\npresent several numerical experiments that demonstrate the robustness of the\nnumerical algorithm.\n"}
+{"abstract": "  Let ${H^p}\\left( \\mathbb{D} \\right)$ be the Hardy space of all holomorphic\nfunctions on the unit disk $\\mathbb{D}$ with exponent $p>0$. If $D\\ne\n\\mathbb{C}$ is a simply connected domain and $f$ is the Riemann mapping from\n$\\mathbb{D}$ onto $D$, then the Hardy number of $D$, introduced by Hansen, is\nthe supremum of all $p$ for which $f \\in {H^p}\\left( \\mathbb{D} \\right)$. Comb\ndomains are a well-studied class of simply connected domains that, in general,\nhave the form of the entire plane minus an infinite number of vertical rays. In\nthis paper we study the Hardy number of a class of comb domains with the aid of\nthe quasi-hyperbolic distance and we establish a necessary and sufficient\ncondition for the Hardy number of these domains to be equal to infinity.\nApplying this condition, we derive several results that show how the mutual\ndistances and the distribution of the rays affect the finiteness of the Hardy\nnumber. By a result of Burkholder our condition is also necessary and\nsufficient for all moments of the exit time of Brownian motion from comb\ndomains to be infinite.\n"}
+{"abstract": "  Modern machine learning increasingly requires training on a large collection\nof data from multiple sources, not all of which can be trusted. A particularly\nconcerning scenario is when a small fraction of poisoned data changes the\nbehavior of the trained model when triggered by an attacker-specified\nwatermark. Such a compromised model will be deployed unnoticed as the model is\naccurate otherwise. There have been promising attempts to use the intermediate\nrepresentations of such a model to separate corrupted examples from clean ones.\nHowever, these defenses work only when a certain spectral signature of the\npoisoned examples is large enough for detection. There is a wide range of\nattacks that cannot be protected against by the existing defenses. We propose a\nnovel defense algorithm using robust covariance estimation to amplify the\nspectral signature of corrupted data. This defense provides a clean model,\ncompletely removing the backdoor, even in regimes where previous methods have\nno hope of detecting the poisoned examples. Code and pre-trained models are\navailable at https://github.com/SewoongLab/spectre-defense .\n"}
+{"abstract": "  We compute the quasinormal frequencies of $d$-dimensional spherically\nsymmetric black holes with leading string $\\alpha'$ corrections in the eikonal\nlimit for tensorial gravitational perturbations and scalar test fields. We find\nthat, differently than in Einstein gravity, the real parts of the frequency are\nno longer equal for these two cases. The corresponding imaginary parts remain\nequal to the principal Lyapunov exponent corresponding to circular null\ngeodesics, to first order in $\\alpha'$. We also compute the radius of the\nshadow cast by these black holes.\n"}
+{"abstract": "  The set of all centralizers of elements in a finite group $G$ is denoted by\n$Cent(G)$ and $G$ is called $n-$centralizer if $|Cent(G)| = n$. In this paper,\nthe structure of centralizers in a non-abelian finite group $G$ with this\nproperty that $\\frac{G}{Z(G)} \\cong Z_{p^2} \\rtimes Z_{p^2}$ is obtained. As a\nconsequence, it is proved that such a group has exactly $[(p+1)^2+1]$ element\ncentralizers and the structure of the commuting conjugacy class graph of $G$ is\ncompletely determined.\n"}
+{"abstract": "  Dix formulated the inverse problem of recovering an elastic body from the\nmeasurements of wave fronts of point scatterers. We geometrize this problem in\nthe framework of linear elasticity, leading to the geometrical inverse problem\nof recovering a Finsler manifold from certain sphere data in a given open\nsubset of the manifold. We solve this problem locally along any geodesic\nthrough the measurement set.\n"}
+{"abstract": "  Limited measurement availability at the distribution grid presents challenges\nfor state estimation and situational awareness. This paper combines the\nadvantages of two sparsity-based state estimation approaches (matrix completion\nand compressive sensing) that have been proposed recently to address the\nchallenge of unobservability. The proposed approach exploits both the low rank\nstructure and a suitable transform domain representation to leverage the\ncorrelation structure of the spatio-temporal data matrix while incorporating\nthe power-flow constraints of the distribution grid. Simulations are carried\nout on three phase unbalanced IEEE 37 test system to verify the effectiveness\nof the proposed approach. The performance results reveal - (1) the superiority\nover traditional matrix completion and (2) very low state estimation errors for\nhigh compression ratios representing very low observability.\n"}
+{"abstract": "  In this article we present some new comparisons between the Heinz and Heron\noperator means, which improve some recent results known from the literature. We\nderive some refinements of these inequalities for unitarily invariant norms\nwith the help of the contractive maps.\n"}
+{"abstract": "  We determine the fundamental limit of the microresonator field uniformity. It\ncan be achieved in a specially designed microresonator, called a bat\nmicroresonator, fabricated at the optical fiber surface. We show that the\nrelative nonuniformity of an eigenmode amplitude along the axial length $L$ of\nan ideal bat microresonator cannot be smaller than ${{\\frac{1}{3}}}{\\pi\n}^2n^4_r{\\lambda }^{-4}Q^{-2}L^4$, where $n_r,\\ \\lambda $ and $Q$ are its\nrefractive index, the eigenmode wavelength and Q-factor. In the absence of\nlosses ($Q=\\infty $), this eigenmode has the amplitude independent of axial\ncoordinate and zero axial speed (i.e., is stopped) within the length $L$. For a\nsilica microresonator with $Q={10}^8$ this eigenmode has the axial speed\n$\\mathrm{\\sim}$ 10${}^{-4}$c, where c is the speed of light in vacuum, and its\nnonuniformity along the length 100 micron at wavelength $\\lambda =1.5$ micron\nis $\\mathrm{\\sim}$ 10${}^{-7}$. For a realistic fiber with diameter 100 micron\nand surface roughness 0.2 nm, the smallest eigenmode nonuniformity is\n$\\mathrm{\\sim}$ 0.0003. As an application, we consider a bat microresonator\nevanescently coupled to high Q-factor silica microspheres which serves as a\nreference supporting the angstrom-precise straight-line translation over the\ndistance $L$ exceeding a hundred microns.\n"}
+{"abstract": "  The increasing value of data held in enterprises makes it an attractive\ntarget to attackers. The increasing likelihood and impact of a cyber attack\nhave highlighted the importance of effective cyber risk estimation. We propose\ntwo methods for modelling Value-at-Risk (VaR) which can be used for any\ntime-series data. The first approach is based on Quantile Autoregression (QAR),\nwhich can estimate VaR for different quantiles, i.e. confidence levels. The\nsecond method, we term Competitive Quantile Autoregression (CQAR), dynamically\nre-estimates cyber risk as soon as new data becomes available. This method\nprovides a theoretical guarantee that it asymptotically performs as well as any\nQAR at any time point in the future. We show that these methods can predict the\nsize and inter-arrival time of cyber hacking breaches by running coverage\ntests. The proposed approaches allow to model a separate stochastic process for\neach significance level and therefore provide more flexibility compared to\npreviously proposed techniques. We provide a fully reproducible code used for\nconducting the experiments.\n"}
+{"abstract": "  The formulation of the hazy image is mainly dominated by the reflected lights\nand ambient airlight. Existing dehazing methods often ignore the depth cues and\nfail in distant areas where heavier haze disturbs the visibility. However, we\nnote that the guidance of the depth information for transmission estimation\ncould remedy the decreased visibility as distances increase. In turn, the good\ntransmission estimation could facilitate the depth estimation for hazy images.\nIn this paper, a deep end-to-end model that iteratively estimates image depths\nand transmission maps is proposed to perform an effective depth prediction for\nhazy images and improve the dehazing performance with the guidance of depth\ninformation. The image depth and transmission map are progressively refined to\nbetter restore the dehazed image. Our approach benefits from explicitly\nmodeling the inner relationship of image depth and transmission map, which is\nespecially effective for distant hazy areas. Extensive results on the\nbenchmarks demonstrate that our proposed network performs favorably against the\nstate-of-the-art dehazing methods in terms of depth estimation and haze\nremoval.\n"}
+{"abstract": "  One of the most commonly performed manipulation in a human's daily life is\npouring. Many factors have an effect on target accuracy, including pouring\nvelocity, rotation angle, geometric of the source, and the receiving\ncontainers. This paper presents an approach to increase the repeatability and\naccuracy of the robotic manipulator by estimating the change in the amount of\nwater of the pouring cup to a sequence of pouring actions using multiple layers\nof the deep recurrent neural network, especially gated recurrent units (GRU).\nThe proposed GRU model achieved a validation mean squared error as low as 1e-4\n(lbf) for the predicted value of weight f(t). This paper contains a\ncomprehensive evaluation and analysis of numerous experiments with various\ndesigns of recurrent neural networks and hyperparameters fine-tuning.\n"}
+{"abstract": "  S. Ramanujan introduced a technique in 1913 for providing analytic\nexpressions for certain Mellin-type integrals which is now known as Ramanujan's\nMaster Theorem. This technique was communicated through his \"Quarterly Reports\"\nand has a wide range of applicability in calculating the values of certain\ndefinite integrals. In this paper, we have presented some extensions of\nRamanujan's Master Theorem that arise from the k-gamma function and the p-k\ngamma function. Furthermore, we have established a Mellin type double integral\nalong with its corollaries. Some applications are established through the\nevaluation of a variety of definite integrals.\n"}
+{"abstract": "  Recent experimental discoveries show that hydrogen-rich compounds can reach\nroom temperature superconductivity, at least at high pressures. Also that there\nexist metallic hydrogen-abundant systems with critical temperatures of few\nKelvin, or even with no trace of superconductivity at all. By analyzing through\nfirst-principles calculations the structural and electronic properties of more\nthan one hundred compounds predicted to be superconductors in the literature,\nwe determine that the capacity of creating a bonding network of connected\nlocalized units is the key to enhance the critical temperature in\nhydrogen-based superconductors, explaining the large variety of critical\ntemperatures of superconducting hydrogen-rich materials. We define a magnitude\nnamed as the {\\it networking value}, which correlates well with the predicted\ncritical temperature, much better than any other descriptor analyzed thus far.\nThis magnitude can be easily calculated for any compound by analyzing\nisosurfaces of the electron localization function. By classifying the studied\ncompounds according to their bonding nature, we observe that the {\\it\nnetworking value} correlates with the critical temperature for all bonding\ntypes. Our analysis also highlights that systems with weakened covalent bonds\nare the most promising candidates for reaching high critical temperatures. The\ndiscovery of the positive correlation between superconductivity and the bonding\nnetwork offers the possibility of screening easily hydrogen-based compounds\nand, at the same time, sets clear paths for chemically engineering better\nsuperconductors.\n"}
+{"abstract": "  This paper studies high-dimensional regression with two-way structured data.\nTo estimate the high-dimensional coefficient vector, we propose the generalized\nmatrix decomposition regression (GMDR) to efficiently leverage any auxiliary\ninformation on row and column structures. The GMDR extends the principal\ncomponent regression (PCR) to two-way structured data, but unlike PCR, the GMDR\nselects the components that are most predictive of the outcome, leading to more\naccurate prediction. For inference on regression coefficients of individual\nvariables, we propose the generalized matrix decomposition inference (GMDI), a\ngeneral high-dimensional inferential framework for a large family of estimators\nthat include the proposed GMDR estimator. GMDI provides more flexibility for\nmodeling relevant auxiliary row and column structures. As a result, GMDI does\nnot require the true regression coefficients to be sparse; it also allows\ndependent and heteroscedastic observations. We study the theoretical properties\nof GMDI in terms of both the type-I error rate and power and demonstrate the\neffectiveness of GMDR and GMDI on simulation studies and an application to\nhuman microbiome data.\n"}
+{"abstract": "  COVID-19 infection caused by SARS-CoV-2 pathogen is a catastrophic pandemic\noutbreak all over the world with exponential increasing of confirmed cases and,\nunfortunately, deaths. In this work we propose an AI-powered pipeline, based on\nthe deep-learning paradigm, for automated COVID-19 detection and lesion\ncategorization from CT scans. We first propose a new segmentation module aimed\nat identifying automatically lung parenchyma and lobes. Next, we combined such\nsegmentation network with classification networks for COVID-19 identification\nand lesion categorization. We compare the obtained classification results with\nthose obtained by three expert radiologists on a dataset consisting of 162 CT\nscans. Results showed a sensitivity of 90\\% and a specificity of 93.5% for\nCOVID-19 detection, outperforming those yielded by the expert radiologists, and\nan average lesion categorization accuracy of over 84%. Results also show that a\nsignificant role is played by prior lung and lobe segmentation that allowed us\nto enhance performance by over 20 percent points. The interpretation of the\ntrained AI models, moreover, reveals that the most significant areas for\nsupporting the decision on COVID-19 identification are consistent with the\nlesions clinically associated to the virus, i.e., crazy paving, consolidation\nand ground glass. This means that the artificial models are able to\ndiscriminate a positive patient from a negative one (both controls and patients\nwith interstitial pneumonia tested negative to COVID) by evaluating the\npresence of those lesions into CT scans. Finally, the AI models are integrated\ninto a user-friendly GUI to support AI explainability for radiologists, which\nis publicly available at http://perceivelab.com/covid-ai.\n"}
+{"abstract": "  For spin systems, such as the $q$-colorings and independent-set models,\napproximating the partition function in the so-called non-uniqueness region,\nwhere the model exhibits long-range correlations, is typically computationally\nhard for bounded-degree graphs. We present new algorithmic results for\napproximating the partition function and sampling from the Gibbs distribution\nfor spin systems in the non-uniqueness region on random regular bipartite\ngraphs. We give an $\\mathsf{FPRAS}$ for counting $q$-colorings for even\n$q=O\\big(\\tfrac{\\Delta}{\\log{\\Delta}}\\big)$ on almost every $\\Delta$-regular\nbipartite graph. This is within a factor $O(\\log{\\Delta})$ of the sampling\nalgorithm for general graphs in the uniqueness region and improves\nsignificantly upon the previous best bound of\n$q=O\\big(\\tfrac{\\sqrt{\\Delta}}{(\\log\\Delta)^2}\\big)$ by Jenssen, Keevash, and\nPerkins (SODA'19). Analogously, for the hard-core model on independent sets\nweighted by $\\lambda>0$, we present an $\\mathsf{FPRAS}$ for estimating the\npartition function when $\\lambda=\\Omega\\big(\\tfrac{\\log{\\Delta}}{\\Delta}\\big)$,\nwhich improves upon previous results by an $\\Omega(\\log \\Delta)$ factor. Our\nresults for the colorings and hard-core models follow from a general result\nthat applies to arbitrary spin systems. Our main contribution is to show how to\nelevate probabilistic/analytic bounds on the marginal probabilities for the\ntypical structure of phases on random bipartite regular graphs into efficient\nalgorithms, using the polymer method. We further show evidence that our result\nfor colorings is within a constant factor of best possible using current\npolymer-method approaches.\n"}
+{"abstract": "  The COVID-19 pandemic forced almost all professional and amateur sports to be\nplayed without attending crowds. Thus, it induced a large-scale natural\nexperiment on the impact of social pressure on decision making and behavior in\nsports fields. Using a data set of 1027 rugby union matches from 11 tournaments\nin 10 countries, we find that home teams have won less matches and their points\ndifference decreased during the pandemics, shedding light on the impact of\ncrowd attendance on the {\\em home advantage} of sports teams.\n"}
+{"abstract": "  We study online convex optimization in the random order model, recently\nproposed by \\citet{garber2020online}, where the loss functions may be chosen by\nan adversary, but are then presented to the online algorithm in a uniformly\nrandom order. Focusing on the scenario where the cumulative loss function is\n(strongly) convex, yet individual loss functions are smooth but might be\nnon-convex, we give algorithms that achieve the optimal bounds and\nsignificantly outperform the results of \\citet{garber2020online}, completely\nremoving the dimension dependence and improving their scaling with respect to\nthe strong convexity parameter. Our analysis relies on novel connections\nbetween algorithmic stability and generalization for sampling\nwithout-replacement analogous to those studied in the with-replacement\ni.i.d.~setting, as well as on a refined average stability analysis of\nstochastic gradient descent.\n"}
+{"abstract": "  Using kinetic Monte Carlo simulations, we develop a framework to relate\nmorphological properties and microscopic dynamics during island growth,\ncoalescence, and initial formation of continuous heteroepitaxial films. The\naverage island width is controlled by adatom mobility on the substrate.\nSubsequent evolution strongly depends on the Ehrlich-Schw\\\"{o}ebel energy\nbarrier $E_{ES}$ of the deposited material. As $E_{ES}$ decreases, islands\nbecomes taller and their coalescence is delayed. For small islands and large\n$E_{ES}$, the global roughness increases as $W\\sim{\\text{thickness}}^{1/2}$ and\nthe local roughness increases at short scales (apparent anomalous scaling)\nbefore and after island coalescence. If the islands are wide, $W$ may have a\nplateau for large $E_{ES}$ and has a maximum for small $E_{ES}$ when the\nislands coalesce. This framework is applied to atomic-force microscopy data of\nthe initial stages of CdTe deposition on Kapton: a maximum of $W$ during island\ncoalescence indicates negligible ES barrier, consistently with scaling\nproperties of much thicker films, and the diffusion coefficient\n${10}^{-7}{\\text{--}}{10}^{-5}{\\text{cm}}^2/{\\text s}$ on the Kapton surface at\n$150\\,^{\\circ}\\mathrm{C}$ is estimated. Applications of the framework to other\nmaterials are suggested, in which the expected roles of ES barriers are\nhighlighted.\n"}
+{"abstract": "  At any epoch, particle physics must be open to completely unexpected\ndiscoveries, and that is reason enough to extend the reach of searches for\nultra-high energy (UHE) photons. The observation of a population of photons\nwith energies $E \\gtrsim 100$ EeV would for example imply the existence of\neither a completely new physical phenomena, or particle acceleration mechanisms\nheretofore never seen or imagined. But as we outline in this Letter of\nInterest, there are also good arguments for super-heavy dark matter (SHDM) in a\nparameter range such that it could be discovered via its decays to, in\nparticular, UHE photons. Only ultra-high energy cosmic ray observatories have\ncapabilities to detect UHE photons. We first investigate how current and future\nobservations can probe and constrain SHDM models in important directions, and\nthen outline some of the scenarios that motivate such searches. We also discuss\nconnections between constraints on SHDM and on the parameter values of\ncosmological models.\n"}
+{"abstract": "  The discovery of the moderate differential rotation between the core and the\nenvelope of evolved solar-like stars could be the signature of a strong\nmagnetic field trapped inside the radiative interior. The population of\nintermediate-mass red giants presenting a surprisingly low-amplitude of their\nmixed modes could also arise from the effect of an internal magnetic field.\nIndeed, stars more massive than about 1.1Ms are known to develop a convective\ncore during their main sequence, which could relax into a strong fossil\nmagnetic field trapped inside the core of the star for the rest of its\nevolution. The observations of mixed modes can constitute an excellent probe of\nthe deepest layers of evolved solar-like stars. The magnetic perturbation on\nmixed modes may thus be visible in asteroseismic data. To unravel which\nconstraints can be obtained from observations, we theoretically investigate the\neffects of a plausible mixed axisymmetric magnetic field with various\namplitudes on the mixed-mode frequencies of evolved solar-like stars. The\nfirst-order frequency perturbations are computed for dipolar and quadrupolar\nmixed modes. These computations are carried out for a range of stellar ages,\nmasses, and metallicities. We show that typical fossil-field strengths of 0.1-1\nMG, consistent with the presence of a dynamo in the convective core during the\nmain sequence, provoke significant asymmetries on mixed-mode frequency\nmultiplets during the red-giant branch. We show that these signatures may be\ndetectable in asteroseismic data for field amplitudes small enough for the\namplitude of the modes not to be affected by the conversion of gravity into\nAlfven waves inside the magnetised interior. Finally, we infer an upper limit\nfor the strength of the field, and the associated lower limit for the timescale\nof its action, to redistribute angular momentum in stellar interiors.\n"}
+{"abstract": "  In this paper, we establish some important results for the impulsive wave\nequation. We begin by proving the existence of a solution. Then, we study the\nimpulse approximate controllability where the control function acts on a\nsubdomain $\\omega$ and at one instant of time $\\tau \\in (0, T)$. Afterward, we\nstudy impulse observability\n"}
+{"abstract": "  We discuss the towers of finite \\'etale covers which were essentially\nintroduced by A.Tamagawa. The statement about correspondence between sections\nand cofinal towers is a folklore but perhaps not in a very explicit form. The\nlast section explains how the \"injectivity statement\" of Grothendieck section\nconjecture fails for abelian varieties, which is also known in some form.\n  The paper is based on an earlier article which was aimed to reinterpret\nanabelian setting in model theory terms.\n"}
+{"abstract": "  Cervical cancer is a malignant tumor that seriously threatens women's health,\nand is one of the most common that affects women worldwide. For its early\ndetection, colposcopic images of the cervix are used for searching for possible\ninjuries or abnormalities. An inherent characteristic of these images is the\npresence of specular reflections (brightness) that make it difficult to observe\nsome regions, which might imply misdiagnosis. In this paper, a new strategy\nbased on neural networks is introduced for eliminating specular reflections and\nestimating the unobserved anatomical cervix portion under the bright zones. For\novercoming the fact that the ground truth corresponding to the specular\nreflection regions is always unknown, the new strategy proposes the supervised\ntraining of a neural network to learn how to restore any hidden regions of\ncolposcopic images. Once the specular reflections are identified, they are\nremoved from the image, and the previously trained network is used to fulfill\nthese deleted areas. The quality of the processed images was evaluated\nquantitatively and qualitatively. In 21 of the 22 evaluated images, the\ndetected specular reflections were eliminated, whereas, in the remaining one,\nthese reflections were almost completely eliminated. The distribution of the\ncolors and the content of the restored images are similar to those of the\noriginals. The evaluation carried out by a specialist in Cervix Pathology\nconcluded that, after eliminating the specular reflections, the anatomical and\nphysiological elements of the cervix are observable in the restored images,\nwhich facilitates the medical diagnosis of cervical pathologies. Our method has\nthe potential to improve the early detection of cervical cancer.\n"}
+{"abstract": "  In spirit of Gan-Ichino's work on the Arthur's multiplicity formula for\nmetaplectic groups, we have established the Arthur's multiplicity formula for\neven orthogonal or unitary groups with Witt index less than or equal to one. In\nthat multiplicity formula, some local packets defined using the stable range\ntheta lifts are involved. In this paper, we prove that at non-Archimedean\nplaces, the definition of the local packets involved in that multiplicity\nformula is independent of the choice of the dual-pairs used in their\nconstruction. Moreover, at those places where the groups are quasi-split, we\nprove that the local packets involved are the same as the local A-packets\ndefined by Arthur/ Mok.\n"}
+{"abstract": "  We explore the relationships between two elliptic functions constructed by\nShen in the signature four Ramanujan theory.\n"}
+{"abstract": "  For classical discrete systems, we establish basic formulations that can\ntreat evolution of nonlinearity in canonical ensemble, in analogy with\nstochastic thermodynamics. We reveal that through the transition from one to\nanother state, information gain about the nonlinearity is identical to negative\nsum of entropy change for thermal bath and fluctuation of initial entropy\nchange for system from deterministic transition: Evolution of nonlinearity in\nthermodynamics on configuration space is bridged to nonlinearity on information\ngeometry. We derive lower bound of entropy production and conservation for\nentropy production in exponential form, respectively corresponding to the\nsecond law of thermodynamics and integral flutuation theorem for evolution of\nthe nonlinearity. We find that system average for nonlinearity information gain\nis bounded by entropy production for linear system.\n"}
+{"abstract": "  In this paper we describe the history of the LHCb experiment over the last\nthree decades, and its remarkable successes and achievements. LHCb was\nconceived primarily as a b-physics experiment, dedicated to CP violation\nstudies and measurements of very rare b decays, however the tremendous\npotential for c-physics was also clear. At first data taking, the versatility\nof the experiment as a general-purpose detector in the forward region also\nbecame evident, with measurements achievable such as electroweak physics, jets\nand new particle searches in open states. These were facilitated by the\nexcellent capability of the detector to identify muons and to reconstruct decay\nvertices close to the primary pp interaction region. By the end of the LHC Run\n2 in 2018, before the accelerator paused for its second long shut down, LHCb\nhad measured the CKM quark mixing matrix elements and CP violation parameters\nto world-leading precision in the heavy-quark systems. The experiment had also\nmeasured many rare decays of b and c quark mesons and baryons to below their\nStandard Model expectations, some down to branching ratios of order 10-9. In\naddition, world knowledge of b and c spectroscopy had improved significantly\nthrough discoveries of many new resonances already anticipated in the quark\nmodel, and also adding new exotic four and five quark states.\n"}
+{"abstract": "  We construct free resolutions for quotient rings $R/\\langle \\mathcal{I}',\n\\mathcal{I}\\mathcal{J}, \\mathcal{J}'\\rangle$, give conditions for the quotient\nto be realized as a fiber product, and give criteria for the construction to be\nminimal. We then specialize this result to fiber products over a field $k$ and\nrecover explicit formulas for Betti numbers, graded Betti numbers, and\nPoincar\\'{e} series.\n"}
+{"abstract": "  We present two methods for constructing a flat band (FB) system having a flat\nenergy dispersion over the entire Brillouin zone within tight-binding model,\nwhere the resulting Hamiltonian may not be easily obtained by existing methods\nbased on a bipartite graph and line graph techniques. In the first method, we\nderive a set of conditions equivalent to the appearance of FBs for a given\ngraph structure. This method allows parameter to be tuned so that systems with\na small number of sites per unit cell has a FB. In the second method, we show\nthat FB systems can be obtained by removing or adding sites to an existing FB\nsystem under specific rules. In particular, the site addition method enables us\nto construct multiple FB systems stemming from a single FB system. The FB\nsystem obtained by the second method has the characteristics that the component\nratios in the FB eigenstate are partially common to the original system. We\nillustrate how lattices having a FB can be constructed by applying the latter\nmethod starting from an existing lattice such as a kagome lattice,\ndemonstrating that a wide variety of lattices can possess a FB in the band\nstructure.\n"}
+{"abstract": "  This paper presents a mobile application for vote-casting and\nvote-verification based on the Selene e-voting protocol and explains how it was\ndeveloped and implemented using the User Experience Design process. The\nresulting interface was tested with 38 participants, and user experience data\nwas collected via questionnaires and semi-structured interviews on user\nexperience and perceived security. Results concerning the impact of displaying\nsecurity mechanisms on UX were presented in a complementary paper. Here we\nexpand on this analysis by studying the mental models revealed during the\ninterviews and compare them with theoretical security notions. Finally, we\npropose a list of improvements for designs of future voting protocols.\n"}
+{"abstract": "  Big progress has been achieved in domain adaptation in decades. Existing\nworks are always based on an ideal assumption that testing target domain are\ni.i.d. with training target domains. However, due to unpredictable corruptions\n(e.g., noise and blur) in real data like web images, domain adaptation methods\nare increasingly required to be corruption robust on target domains. In this\npaper, we investigate a new task, Corruption-agnostic Robust Domain Adaptation\n(CRDA): to be accurate on original data and robust against\nunavailable-for-training corruptions on target domains. This task is\nnon-trivial due to large domain discrepancy and unsupervised target domains. We\nobserve that simple combinations of popular methods of domain adaptation and\ncorruption robustness have sub-optimal CRDA results. We propose a new approach\nbased on two technical insights into CRDA: 1) an easy-to-plug module called\nDomain Discrepancy Generator (DDG) that generates samples that enlarge domain\ndiscrepancy to mimic unpredictable corruptions; 2) a simple but effective\nteacher-student scheme with contrastive loss to enhance the constraints on\ntarget domains. Experiments verify that DDG keeps or even improves performance\non original data and achieves better corruption robustness that baselines.\n"}
+{"abstract": "  Spin-orbit torques (SOTs) from transition metal dichalcogenides systems\n(TMDs) in conjunction with ferromagnetic materials are recently attractive in\nspintronics for their versatile features. However, most of the previously\nstudied crystalline TMDs are prepared by mechanical exfoliation, which limits\ntheir potentials for industrial applications. Here we show that amorphous WTe2\nheterostructures deposited by magnetron sputtering possess a sizable\ndamping-like SOT efficiency {\\xi}_DL^WTe2 ~ 0.20 and low damping constant\n{\\alpha} = 0.009/pm0.001. Only an extremely low critical switching current\ndensity J_c ~ 7.05\\times10^9 A/m^2 is required to achieve SOT-driven\nmagnetization switching. The SOT efficiency is further proved to depend on the\nW and Te relative compositions in the co-sputtered W_100-xTe_x samples, from\nwhich a sign change of {\\xi}_DL^WTe2 is observed. Besides, the electronic\ntransport in amorphous WTe2 is found to be semiconducting and is governed by a\nhopping mechanism. With the above advantages and rich tunability, amorphous and\nsemiconducting WTe2 serves as a unique SOT source for future spintronics\napplications.\n"}
+{"abstract": "  This paper introduces a dual-critic reinforcement learning (RL) framework to\naddress the problem of frame-level bit allocation in HEVC/H.265. The objective\nis to minimize the distortion of a group of pictures (GOP) under a rate\nconstraint. Previous RL-based methods tackle such a constrained optimization\nproblem by maximizing a single reward function that often combines a distortion\nand a rate reward. However, the way how these rewards are combined is usually\nad hoc and may not generalize well to various coding conditions and video\nsequences. To overcome this issue, we adapt the deep deterministic policy\ngradient (DDPG) reinforcement learning algorithm for use with two critics, with\none learning to predict the distortion reward and the other the rate reward. In\nparticular, the distortion critic works to update the agent when the rate\nconstraint is satisfied. By contrast, the rate critic makes the rate constraint\na priority when the agent goes over the bit budget. Experimental results on\ncommonly used datasets show that our method outperforms the bit allocation\nscheme in x265 and the single-critic baseline by a significant margin in terms\nof rate-distortion performance while offering fairly precise rate control.\n"}
+{"abstract": "  The phase field fracture method is attracting significant interest. Phase\nfield approaches have enabled predicting - on arbitrary geometries and\ndimensions - complex fracture phenomena such as crack branching, coalescence,\ndeflection and nucleation. In this work, we present a simple and robust\nimplementation of the phase field fracture method in the commercial finite\nelement package Abaqus. The implementation exploits the analogy between the\nphase field evolution law and the heat transfer equation, enabling the use of\nAbaqus' in-built features and circumventing the need for defining user\nelements. The framework is general, and is shown to accommodate different\nsolution schemes (staggered and monolithic), as well as various constitutive\nchoices for preventing damage under compression. The robustness and\napplicability of the numerical framework presented is demonstrated by\naddressing several 2D and 3D boundary value problems of particular interest.\nFocus is on the solution of paradigmatic case studies that are known to be\nparticularly demanding from a convergence perspective. The results reveal that\nour phase field fracture implementation can be readily combined with other\nadvanced computational features, such as contact, and deliver robust and\nprecise solutions. The code developed can be downloaded from\nwww.empaneda.com/codes.\n"}
+{"abstract": "  We experimentally investigate internal coastal Kelvin waves in a two-layer\nfluid system on a rotating table. Waves in our system propagate in the prograde\ndirection and are exponentially localized near the boundary. Our experiments\nverify the theoretical dispersion relation of the wave and show that the wave\namplitude decays exponentially along the propagation direction. We further\ndemonstrate that the waves can robustly propagate along boundaries of complex\ngeometries without being scattered and that adding obstacles to the wave\npropagation path does not cause additional attenuation.\n"}
+{"abstract": "  This work investigates the well-known problem of morphing attacks, which has\ndrawn considerable attention in the biometrics community. Morphed images have\nexposed face recognition systems' susceptibility to false acceptance, resulting\nin dire consequences, especially for national security applications. To detect\nmorphing attacks, we propose a method which is based on a discriminative 2D\nDiscrete Wavelet Transform (2D-DWT). A discriminative wavelet sub-band can\nhighlight inconsistencies between a real and a morphed image. We observe that\nthere is a salient discrepancy between the entropy of a given sub-band in a\nbona fide image, and the same sub-band's entropy in a morphed sample.\nConsidering this dissimilarity between these two entropy values, we find the\nKullback-Leibler divergence between the two distributions, namely the entropy\nof the bona fide and the corresponding morphed images. The most discriminative\nwavelet sub-bands are those with the highest corresponding KL-divergence\nvalues. Accordingly, 22 sub-bands are selected as the most discriminative ones\nin terms of morph detection. We show that a Deep Neural Network (DNN) trained\non the 22 discriminative sub-bands can detect morphed samples precisely. Most\nimportantly, the effectiveness of our algorithm is validated through\nexperiments on three datasets: VISAPP17, LMA, and MorGAN. We also performed an\nablation study on the sub-band selection.\n"}
+{"abstract": "  Hawkes Processes have undergone increasing popularity as default tools for\nmodeling self- and mutually exciting interactions of discrete events in\ncontinuous-time event streams. A Maximum Likelihood Estimation (MLE)\nunconstrained optimization procedure over parametrically assumed forms of the\ntriggering kernels of the corresponding intensity function are a widespread\ncost-effective modeling strategy, particularly suitable for data with few\nand/or short sequences. However, the MLE optimization lacks guarantees, except\nfor strong assumptions on the parameters of the triggering kernels, and may\nlead to instability of the resulting parameters .In the present work, we show\nhow a simple stabilization procedure improves the performance of the MLE\noptimization without these overly restrictive assumptions.This stabilized\nversion of the MLE is shown to outperform traditional methods over sequences of\nseveral different lengths.\n"}
+{"abstract": "  Measuring human capabilities to synchronize in time, adapt to perturbations\nto timing sequences or reproduce time intervals often require experimental\nsetups that allow recording response times with millisecond precision. Most\nsetups present auditory stimuli using either MIDI devices or specialized\nhardware such as Arduino and are often expensive or require calibration and\nadvanced programming skills. Here, we present in detail an experimental setup\nthat only requires an external sound card and minor electronic skills, works on\na conventional PC, is cheaper than alternatives and requires almost no\nprogramming skills. It is intended for presenting any auditory stimuli and\nrecording tapping response times with within 2 milliseconds precision (up to\n-2ms lag). This paper shows why desired accuracy in recording response times\nagainst auditory stimuli is difficult to achieve in conventional computer\nsetups, presents an experimental setup to overcome this and explains in detail\nhow to set it up and use the provided code. Finally, the code for analyzing the\nrecorded tapping responses was evaluated, showing that no spurious or missing\nevents were found in 94% of the analyzed recordings.\n"}
+{"abstract": "  We introduce the interactive tool pandemonium to cluster model predictions\nthat depend on a set of parameters. The model predictions are used to define\nthe coordinates in observable space which go into the clustering. The results\nof this partitioning are then visualized in both observable and parameter space\nto study correlations between them. The tool offers multiple choices for\ncoordinates, distance functions and linkage methods within hierarchical\nclustering. It provides a set of diagnostic statistics and visualization\nmethods to study the clustering results in order to interpret the outcome. The\nmethods are most useful in an interactive environment that enables exploration,\nand we have implemented them with a graphical user interface in R. We\ndemonstrate the concepts with an application to phenomenological studies in\nflavor physics in the context of the so-called B anomalies.\n"}
+{"abstract": "  Given a generic time-dependent many-body quantum state, we determine the\nassociated parent Hamiltonian. This procedure may require, in general,\ninteractions of any sort. Enforcing the requirement of a fixed set of\nengineerable Hamiltonians, we find the optimal Hamiltonian once a set of\nrealistic elementary interactions is defined. We provide three examples of this\napproach. We first apply the optimization protocol to the ground states of the\none-dimensional Ising model and a ferromagnetic $p$-spin model but with\ntime-dependent coefficients. We also consider a time-dependent state that\ninterpolates between a product state and the ground state of a $p$-spin model.\nWe determine the time-dependent optimal parent Hamiltonian for these states and\nanalyze the capability of this Hamiltonian of generating the state evolution.\nFinally, we discuss the connections of our approach to shortcuts to\nadiabaticity.\n"}
+{"abstract": "  Food resources face severe damages under extraordinary situations of\ncatastrophes such as earthquakes, cyclones, and tsunamis. Under such scenarios,\nspeedy assessment of food resources from agricultural land is critical as it\nsupports aid activity in the disaster hit areas. In this article, a deep\nlearning approach is presented for the detection and segmentation of coconut\ntress in aerial imagery provided through the AI competition organized by the\nWorld Bank in collaboration with OpenAerialMap and WeRobotics. Maked\nRegion-based Convolutional Neural Network approach was used identification and\nsegmentation of coconut trees. For the segmentation task, Mask R-CNN model with\nResNet50 and ResNet1010 based architectures was used. Several experiments with\ndifferent configuration parameters were performed and the best configuration\nfor the detection of coconut trees with more than 90% confidence factor was\nreported. For the purpose of evaluation, Microsoft COCO dataset evaluation\nmetric namely mean average precision (mAP) was used. An overall 91% mean\naverage precision for coconut trees detection was achieved.\n"}
+{"abstract": "  We present an epidemiological model for the effectiveness of CoronaMelder,\nthe Dutch digital contact tracing app developed on top of the Google/Apple\nExposure Notification framework. We compare the effectiveness of CoronaMelder\nwith manual contract tracing on a number of metrics. CoronaMelder turns out to\nhave a small but noticeable positive influence in slowing down the COVID-19\npandemic, an effect that will become more pronounced in an opened-up society\nwhere adoption of CoronaMelder is increased.\n"}
+{"abstract": "  Physical unclonable functions (PUFs) are widely considered in secret key\ngeneration for resource constrained devices. However, PUFs require additional\nhardware overhead. In this paper, we focus on developing a PUF-efficient,\nrobust, and secure key generation scheme. First, a novel method for extracting\nquaternary PUF responses is proposed to increase the entropy of a PUF response,\nin which a 2-bit response is extracted from evaluating a single PUF cell\nmultiple times. The probability masses of the responses can be adjusted by\nsetting parameters appropriately. Then, a chosen secret model based fuzzy\nextractor (FE) is designed to extract secret keys from the quaternary PUF\nresponses. To improve the security of this FE, it is modeled as a wiretap\nchannel system, and wiretap polar coding is adopted to reduce secrecy leakage.\nAn upper bound of secrecy leakage is also given in this paper, and it suggests\nthat an arbitrarily small (even zero) leakage can be achieved by properly\nchoosing parameters of the quaternary PUF responses generation. Comparison\nresults show that the required number of PUF cells to achieve the same level of\nsecrecy in our scheme is as low as half that of the state-of-the-art schemes.\n"}
+{"abstract": "  We derive sufficient conditions for strict dissipativity for optimal control\nof linear evolution equations on Hilbert spaces with a cost functional\nincluding linear and quadratic terms. We show that strict dissipativity with a\nparticular storage function is equivalent to ellipticity of a Lyapunov-like\noperator. Further we prove under a spectral decomposition assumption of the\nunderlying generator and an orthogonality condition of the resulting subspaces\nthat this ellipticity property holds under a detectability assumption. We\nillustrate our result by means of an example involving a heat equation on a\none-dimensional domain.\n"}
+{"abstract": "  Digital health applications that leverage multiple sources of patient data\nfor insights to patients' behaviours or disease symptoms as well as remote\npatient monitoring, nudging and treatments are becoming increasingly popular in\nvarious medical practices and research. One common issue among these\napplications is that they are generally based on project-specific solutions and\ndeveloped from scratch. Such application development fashion results in large\namounts of repetitive effort, for example, in building study specific websites\nand mobile frontends, deploying customised infrastructures, and collecting data\nthat may have already been collected in other studies and projects. What is\nworse, the data collected, and functions built cannot be easily reused by other\napplications. In this paper, we present an event-driven microservice platform,\nnamely DHLink, to address this issue. DHLink securely links existing digital\nhealth applications of different projects, facilitates real-time data sharing,\nand supports rapid application development by reusing data and functions of\nexisting digital health applications. In addition, comes with DHLink, a set of\nhighly generic and reusable microservices is provided, which allows developers\nto rapidly create a typical above-mentioned digital health application by only\ndeveloping the core algorithms. Two use cases outlined in this paper have shown\nthe use of DHLink and the set of microservices for application collaboration\nand new application development to be efficient and practical.\n"}
+{"abstract": "  Supercuspidal representations are usually infinite-dimensional, so the size\nof such a representation cannot be measured by its dimension; the formal degree\nis a better alternative. Hiraga, Ichino, and Ikeda conjectured a formula for\nthe formal degree of a supercuspidal in terms of its $L$-parameter only. Our\nfirst main result is to compute the formal degrees of the supercuspidal\nrepresentations constructed by Yu. Our second, using the first, is to verify\nthat Kaletha's regular supercuspidal $L$-packets satisfy the conjecture.\n"}
+{"abstract": "  We prove a general alternate circular summation formula of theta functions,\nwhich implies a great deal of theta-function identities. In particular, we\nrecover several identities in Ramanujan's Notebook from this identity. We also\nobtain two formulaes for $(q;q)_\\infty^{2n}$.\n"}
+{"abstract": "  Geometric predicates are a basic ingredient to implement a vast range of\nalgorithms in computational geometry. Modern implementations employ floating\npoint filtering techniques to combine efficiency and robustness, and\nstate-of-the-art predicates are guaranteed to be always exact while being only\nslightly slower than corresponding (inexact) floating point implementations.\nUnfortunately, if the input to these predicates is an intermediate construction\nof an algorithm, its floating point representation may be affected by an\napproximation error, and correctness is no longer guaranteed. This paper\nintroduces the concept of indirect geometric predicate: instead of taking the\nintermediate construction as an explicit input, an indirect predicate considers\nthe primitive geometric elements which are combined to produce such a\nconstruction. This makes it possible to keep track of the floating point\napproximation, and thus to exploit efficient filters and expansion arithmetic\nto exactly resolve the predicate with minimal overhead with respect to a naive\nfloating point implementation. As a representative example, we show how to\nextend standard predicates to the case of points of intersection of linear\nelements (i.e. lines and planes) and show that, on classical problems, this\napproach outperforms state-of-the-art solutions based on lazy exact\nintermediate representations.\n"}
+{"abstract": "  Much recent work in NLP has documented dataset artifacts, bias, and spurious\ncorrelations between input features and output labels. However, how to tell\nwhich features have \"spurious\" instead of legitimate correlations is typically\nleft unspecified. In this work we argue that for complex language understanding\ntasks, all simple feature correlations are spurious, and we formalize this\nnotion into a class of problems which we call competency problems. For example,\nthe word \"amazing\" on its own should not give information about a sentiment\nlabel independent of the context in which it appears, which could include\nnegation, metaphor, sarcasm, etc. We theoretically analyze the difficulty of\ncreating data for competency problems when human bias is taken into account,\nshowing that realistic datasets will increasingly deviate from competency\nproblems as dataset size increases. This analysis gives us a simple statistical\ntest for dataset artifacts, which we use to show more subtle biases than were\ndescribed in prior work, including demonstrating that models are\ninappropriately affected by these less extreme biases. Our theoretical\ntreatment of this problem also allows us to analyze proposed solutions, such as\nmaking local edits to dataset instances, and to give recommendations for future\ndata collection and model design efforts that target competency problems.\n"}
+{"abstract": "  Finite element approximation to a decoupled formulation for the quad--curl\nproblem is studied in this paper. The difficulty of constructing elements with\ncertain conformity to the quad--curl problems has been greatly reduced. For\nconvex domains, where the regularity assumption holds for Stokes equation, the\napproximation to the curl of the true solution has quadratic order of\nconvergence and first order for the energy norm. If the solution shows\nsingularity, an a posterior error estimator is developed and a separate marking\nadaptive finite element procedure is proposed, together with its convergence\nproved. Both the a priori and a posteriori error analysis are supported by the\nnumerical examples.\n"}
+{"abstract": "  We consider the problem of estimating the parameters a Gaussian Mixture Model\nwith K components of known weights, all with an identity covariance matrix. We\nmake two contributions. First, at the population level, we present a sharper\nanalysis of the local convergence of EM and gradient EM, compared to previous\nworks. Assuming a separation of $\\Omega(\\sqrt{\\log K})$, we prove convergence\nof both methods to the global optima from an initialization region larger than\nthose of previous works. Specifically, the initial guess of each component can\nbe as far as (almost) half its distance to the nearest Gaussian. This is\nessentially the largest possible contraction region. Our second contribution\nare improved sample size requirements for accurate estimation by EM and\ngradient EM. In previous works, the required number of samples had a quadratic\ndependence on the maximal separation between the K components, and the\nresulting error estimate increased linearly with this maximal separation. In\nthis manuscript we show that both quantities depend only logarithmically on the\nmaximal separation.\n"}
+{"abstract": "  Integral field unit (IFU) spectra of face-on star-forming galaxies from the\nMaNGA survey are stacked in radial bins so as to reach a S/N high enough to\nmeasure emission lines and Lick indices out to 2.5-3 R_e. Two thirds of\ngalaxies have stellar populations in the outer disks that are older, more metal\npoor and less dusty than in the inner disks. Recent bursts of star formation\nhave occurred more frequently in the outer disk, but extinction-corrected\nHalpha equivalent widths are significantly lower at fixed D_n(4000) in these\nregions. I examine the properties of a subset of galaxies with the the most\nH$\\alpha$ deficient outer disks. These regions contain young stellar\npopulations that must have formed within the last 0.5 Gyr, but\nextinction-corrected Halpha values well below the values predicted for a\nstandard Kroupa IMF. The Halpha deficient galaxies have flat D_n(4000) and\nHdelta_A profiles with little radial fluctuation, indicating that star\nformation has occurred extremely uniformly across the entire disk. The\nH$\\alpha$ line profiles indicate that the ionized gas kinematics is also very\nregular across the disk. The main clue to the origin of the Halpha deficiency\nis that it sets in at the same radius where the dust extinction abruptly\ndecreases, suggesting a mode of star formation deficient in massive stars in\nquiescent, HI-dominated gas. Finally, I have carried out a search for galaxies\nwith signatures of unusual Halpha kinematics and find that 15% of the sample\nexhibit evidence for significant ionized gas that is displaced from the\nsystemic velocity of the disk.\n"}
+{"abstract": "  Non-autoregressive translation (NAT) significantly accelerates the inference\nprocess via predicting the entire target sequence. However, recent studies show\nthat NAT is weak at learning high-mode of knowledge such as one-to-many\ntranslations. We argue that modes can be divided into various granularities\nwhich can be learned from easy to hard. In this study, we empirically show that\nNAT models are prone to learn fine-grained lower-mode knowledge, such as words\nand phrases, compared with sentences. Based on this observation, we propose\nprogressive multi-granularity training for NAT. More specifically, to make the\nmost of the training data, we break down the sentence-level examples into three\ntypes, i.e. words, phrases, sentences, and with the training goes, we\nprogressively increase the granularities. Experiments on Romanian-English,\nEnglish-German, Chinese-English, and Japanese-English demonstrate that our\napproach improves the phrase translation accuracy and model reordering ability,\ntherefore resulting in better translation quality against strong NAT baselines.\nAlso, we show that more deterministic fine-grained knowledge can further\nenhance performance.\n"}
+{"abstract": "  Recent developments in autonomous driving technology have proven that map\ndata may be used, not only for general routing purposes, but also for to\nenhance and complement common sensor data. This document reviews the most\ncommonly used interfaces and formats at each step of a selfhealing map data\nchain.\n"}
+{"abstract": "  The regular (Bardeen)-AdS (BAdS) black hole (BH) in the extended phase space\nis taken as an example for investigating the BH phase transition grade from\nboth macroscopic and microscopic points of view. The equation of state and\nthermodynamic quantities of this BH are obtained. It is found that the BAdS BH\nphase space in the extended phase space should be a second-order phase\ntransition near the critical point by verifying the Ehrenfest's equation, and\nthe possibility of its first-order phase transition can be ruled out by the\nentropy continuity and the heat capacity mutation. The critical exponents from\nthe microscopic structure are analytically and numerically presented with the\nLandau continuous phase transition theory by introducing a microscopic\norder-parameter.\n"}
+{"abstract": "  Quantum computers are becoming more mainstream. As more programmers are\nstarting to look at writing quantum programs, they need to test and debug their\ncode. In this paper, we discuss various use-cases for quantum computers, either\nstandalone or as part of a System of Systems. Based on these use-cases, we\ndiscuss some testing and debugging tactics that one can leverage to ensure the\nquality of the quantum software. We also highlight quantum-computer-specific\nissues and list novel techniques that are needed to address these issues. The\npractitioners can readily apply some of these tactics to their process of\nwriting quantum programs, while researchers can learn about opportunities for\nfuture work.\n"}
+{"abstract": "  SPIRAL 2 is a high intensity heavy ions beams accelerator project that has\nbeen going on for more than 10 years now. Countless efforts in different\ndisciplines made it what it is today. One of the most important steps after the\nset up of the different equipments has been the very first full cool down of\nthe superconducting cavities in an accelerator operation type configuration.\nWhile this has been a major achievement for the SPIRAL 2 teams, it also\nhi-lighted new challenges and constraints that would have to be addressed in\norder to have a high availability rate of the beam from the cryogenics side.\nThis paper retraces this particular episode.\n"}
+{"abstract": "  In project scheduling under processing times uncertainty, the Anchor-Robust\nProject Scheduling Problem is to find a baseline schedule of bounded makespan\nand a max-weight subset of jobs whose starting times are guaranteed. The\nproblem was proven NP-hard even for budgeted uncertainty. In the present work\nwe design mixed-integer programming (MIP) formulations that are valid for a\nvariety of uncertainty sets encompassing budgeted uncertainty. A new dominance\namong solutions is proposed, resulting into an MIP formulation. We further\nstudy the combinatorial structure of the problem. Non-trivial polynomial cases\nunder budgeted uncertainty are exhibited, where the dominance-based formulation\nyields a polyhedral characterization of integer solutions. In more general\ncases, the dominance-based formulation is shown to be tighter than all\npreviously known formulations. In numerical experiments we investigate how the\nformulation performs on instances around the polynomial cases, for both\nbudgeted uncertainty sets and more elaborate uncertainty sets involving several\nbudgets.\n"}
+{"abstract": "  Cognitive Diagnosis Models (CDMs) are a special family of discrete latent\nvariable models that are widely used in modern educational, psychological,\nsocial and biological sciences. A key component of CDMs is a binary $Q$-matrix\ncharacterizing the dependence structure between the items and the latent\nattributes. Additionally, researchers also assume in many applications certain\nhierarchical structures among the latent attributes to characterize their\ndependence. In most CDM applications, the attribute-attribute hierarchical\nstructures, the item-attribute $Q$-matrix, the item-level diagnostic model, as\nwell as the number of latent attributes, need to be fully or partially\npre-specified, which however may be subjective and misspecified as noted by\nmany recent studies. This paper considers the problem of jointly learning these\nlatent and hierarchical structures in CDMs from observed data with minimal\nmodel assumptions. Specifically, a penalized likelihood approach is proposed to\nselect the number of attributes and estimate the latent and hierarchical\nstructures simultaneously. An efficient expectation-maximization (EM) algorithm\nand a latent structure recovery algorithm are developed, and statistical\nconsistency theory is also established under mild conditions. The good\nperformance of the proposed method is illustrated by simulation studies and a\nreal data application in educational assessment.\n"}
+{"abstract": "  We give communication-based semantics and reasoning techniques for Polarized\nSILL, a rich session-typed programming language with general recursion. Its\nfeatures include channel and code transmission, synchronous and asynchronous\ncommunication, and functional programming. Our contributions are distinguished\nby their faithfulness to the process abstraction, i.e., to the premise that\ncommunication is the only observable phenomenon of processes. We give the first\nobserved communication semantics that supports general recursion and code\ntransmission. Observed communication semantics define the meaning of processes\nin terms of their observed communications. We use this observational semantics\nto define experiments on processes, and we give a communication-based testing\nequivalences framework for defining observational simulations and equivalences\non processes. This framework captures several natural equivalences, and we show\nthat one of these coincides with barbed congruence, the canonical notion of\nprocess equivalence.\n  Polarized SILL is defined using a substructural operational semantics based\non multiset rewriting. To ensure that our contributions are well-defined in the\npresence of non-termination, we introduce fairness for multiset rewriting\nsystems. We construct a fair scheduler, we give sufficient conditions for\ntraces to be fair, and we study the effects of permutation on fair traces.\n"}
+{"abstract": "  Recently, a variety of deformed $T^{1,1}$ manifolds, with which 2D non-linear\nsigma models (NLSMs) are classically integrable, have been presented by\nArutyunov, Bassi and Lacroix (ABL) [arXiv:2010.05573]. We refer to the NLSMs\nwith the integrable deformed $T^{1,1}$ as the ABL model for brevity. Motivated\nby this progress, we consider deriving the ABL model from a 4D Chern-Simons\n(CS) theory with a meromorphic one-form with four double poles and six simple\nzeros. We specify boundary conditions in the CS theory that give rise to the\nABL model and derive the sigma-model background with target-space metric and\nanti-symmetric two-form. Finally, we present two simple examples 1) an\nanisotropic $T^{1,1}$ model and 2) a $G/H$ $\\lambda$-model. The latter one can\nbe seen as a one-parameter deformation of the Guadagnini-Martellini-Mintchev\nmodel.\n"}
+{"abstract": "  With the establishment and maturation of the experimental programs searching\nfor new physics with sizeable couplings at the LHC, there is an increasing\ninterest in the broader particle and astrophysics community for exploring the\nphysics of light and feebly-interacting particles as a paradigm complementary\nto a New Physics sector at the TeV scale and beyond. FIPs 2020 has been the\nfirst workshop fully dedicated to the physics of feebly-interacting particles\nand was held virtually from 31 August to 4 September 2020. The workshop has\ngathered together experts from collider, beam dump, fixed target experiments,\nas well as from astrophysics, axions/ALPs searches, current/future neutrino\nexperiments, and dark matter direct detection communities to discuss progress\nin experimental searches and underlying theory models for FIPs physics, and to\nenhance the cross-fertilisation across different fields. FIPs 2020 has been\ncomplemented by the topical workshop \"Physics Beyond Colliders meets theory\",\nheld at CERN from 7 June to 9 June 2020. This document presents the summary of\nthe talks presented at the workshops and the outcome of the subsequent\ndiscussions held immediately after. It aims to provide a clear picture of this\nblooming field and proposes a few recommendations for the next round of\nexperimental results.\n"}
+{"abstract": "  In this study, we will discuss recent developments in risk management of the\nglobal financial and insurance business with respect to sustainable\ndevelopment. So far climate change aspects have been the dominant aspect in\nmanaging sustainability risks and opportunities, accompanied by the development\nof several legislative initiatives triggered by supervisory authorities.\nHowever, a sole concentration on these aspects misses out other important\neconomic and social facets of sustainable development goals formulated by the\nUN. Such aspects have very recently come into the focus of the European\nCommittee concerning the Solvency II project for the European insurance\nindustry. Clearly the new legislative expectations can be better handled by\nlarger insurance companies and holdings than by small- and medium-sized mutual\ninsurance companies which are numerous in central Europe, due to their historic\ndevelopment starting in the late medieval ages and early modern times. We\ntherefore also concentrate on strategies within the risk management of such\nsmall- and medium-sized enterprises that can be achieved without much effort,\nin particular those that are not directly related to climate change.\n"}
+{"abstract": "  Graph neural networks (GNNs) are a class of powerful machine learning tools\nthat model node relations for making predictions of nodes or links. GNN\ndevelopers rely on quantitative metrics of the predictions to evaluate a GNN,\nbut similar to many other neural networks, it is difficult for them to\nunderstand if the GNN truly learns characteristics of a graph as expected. We\npropose an approach to corresponding an input graph to its node embedding (aka\nlatent space), a common component of GNNs that is later used for prediction. We\nabstract the data and tasks, and develop an interactive multi-view interface\ncalled CorGIE to instantiate the abstraction. As the key function in CorGIE, we\npropose the K-hop graph layout to show topological neighbors in hops and their\nclustering structure. To evaluate the functionality and usability of CorGIE, we\npresent how to use CorGIE in two usage scenarios, and conduct a case study with\nfive GNN experts.\n"}
+{"abstract": "  When human cognition is modeled in Philosophy and Cognitive Science, there is\na pervasive idea that humans employ mental representations in order to navigate\nthe world and make predictions about outcomes of future actions. By\nunderstanding how these representational structures work, we not only\nunderstand more about human cognition but also gain a better understanding for\nhow humans rationalise and explain decisions. This has an influencing effect on\nexplainable AI, where the goal is to provide explanations of computer\ndecision-making for a human audience. We show that the Contextual Importance\nand Utility method for XAI share an overlap with the current new wave of\naction-oriented predictive representational structures, in ways that makes CIU\na reliable tool for creating explanations that humans can relate to and trust.\n"}
+{"abstract": "  We herein propose a variant of the projected inexact Levenberg--Marquardt\nmethod (ILMM) for solving constrained nonsmooth equations. Since the orthogonal\nprojection onto the feasible set may be computationally expensive, we propose a\nlocal ILMM with feasible inexact projections. By using assumption of\nsemi-smoothness and an error bound condition which is weaker than the standard\nfull rank assumption, we establish its convergence results, including results\non its rate. Finally, some computacional results are reported in order to\nillustrate the advantages of the new schemes.\n"}
+{"abstract": "  The coherent deflection of elliptic bunch with the Gaussian charge\ndistribution in the field of an opposite bunch with the Gaussian bunch\ndistribution is considered. The generalization of Bassetti-Erskine formulae is\nderived taking into account the angle between momenta of bunches.\n"}
+{"abstract": "  Atomic clauses are fundamental text units for understanding complex\nsentences. Identifying the atomic sentences within complex sentences is\nimportant for applications such as summarization, argument mining, discourse\nanalysis, discourse parsing, and question answering. Previous work mainly\nrelies on rule-based methods dependent on parsing. We propose a new task to\ndecompose each complex sentence into simple sentences derived from the tensed\nclauses in the source, and a novel problem formulation as a graph edit task.\nOur neural model learns to Accept, Break, Copy or Drop elements of a graph that\ncombines word adjacency and grammatical dependencies. The full processing\npipeline includes modules for graph construction, graph editing, and sentence\ngeneration from the output graph. We introduce DeSSE, a new dataset designed to\ntrain and evaluate complex sentence decomposition, and MinWiki, a subset of\nMinWikiSplit. ABCD achieves comparable performance as two parsing baselines on\nMinWiki. On DeSSE, which has a more even balance of complex sentence types, our\nmodel achieves higher accuracy on the number of atomic sentences than an\nencoder-decoder baseline. Results include a detailed error analysis.\n"}
+{"abstract": "  We propose a novel possibility to detect a very distinctive signal with more\nthan four muons originating from pair-produced vector-like leptons decaying to\na muon-philic $Z^\\prime$ boson. These new particles are good candidates to\nexplain the anomalies in the muon anomalous magnetic moment and the $b\\to\ns\\ell\\ell$ processes. The doublet (singlet) vector-like leptons lighter than\n1.3 (1.0) TeV are excluded by the latest data at the LHC if $\\mathrm{BR}(E\\to\nZ^\\prime \\mu) = 1$. We also show that the excess in the signal region with more\nthan five leptons can be explained by this scenario if the vector-like lepton\nis a weak singlet, with mass about 400 GeV and $\\mathrm{BR}(E\\to Z^\\prime \\mu)\n= 0.25$. The future prospects at the HL-LHC are discussed.\n"}
+{"abstract": "  Combining the intrinsic and extrinsic geometry, we generalize Einstein\nmanifolds to Integral-Einstein (IE) submanifolds. A Takahashi-type theorem is\nestablished to characterize minimal hypersurfaces with constant scalar\ncurvature (CSC) in unit spheres, which is the main object of the Chern\nconjecture: such hypersurfaces are isoparametric. For these hypersurfaces, we\nobtain some integral inequalities with the bounds characterizing exactly the\ntotally geodesic hypersphere, the non-IE minimal Clifford torus\n$S^{1}(\\sqrt{\\frac{1}{n}})\\times S^{n-1}(\\sqrt{\\frac{n-1}{n}})$ and the IE\nminimal CSC hypersurfaces. Moreover, if further the third mean curvature is\nconstant, then it is an IE hypersurface or an isoparametric hypersurface with\n$g\\leq2$ principal curvatures. In particular, all the minimal isoparametric\nhypersurfaces with $g\\geq3$ principal curvatures are IE hypersurfaces. As\napplications, we also obtain some spherical Bernstein theorems.\n"}
+{"abstract": "  Detecting security vulnerabilities in software before they are exploited has\nbeen a challenging problem for decades. Traditional code analysis methods have\nbeen proposed, but are often ineffective and inefficient. In this work, we\nmodel software vulnerability detection as a natural language processing (NLP)\nproblem with source code treated as texts, and address the automated software\nvenerability detection with recent advanced deep learning NLP models assisted\nby transfer learning on written English. For training and testing, we have\npreprocessed the NIST NVD/SARD databases and built a dataset of over 100,000\nfiles in $C$ programming language with 123 types of vulnerabilities. The\nextensive experiments generate the best performance of over 93\\% accuracy in\ndetecting security vulnerabilities.\n"}
+{"abstract": "  Author Name Disambiguation (AND) is the task of resolving which author\nmentions in a bibliographic database refer to the same real-world person, and\nis a critical ingredient of digital library applications such as search and\ncitation analysis. While many AND algorithms have been proposed, comparing them\nis difficult because they often employ distinct features and are evaluated on\ndifferent datasets. In response to this challenge, we present S2AND, a unified\nbenchmark dataset for AND on scholarly papers, as well as an open-source\nreference model implementation. Our dataset harmonizes eight disparate AND\ndatasets into a uniform format, with a single rich feature set drawn from the\nSemantic Scholar (S2) database. Our evaluation suite for S2AND reports\nperformance split by facets like publication year and number of papers,\nallowing researchers to track both global performance and measures of fairness\nacross facet values. Our experiments show that because previous datasets tend\nto cover idiosyncratic and biased slices of the literature, algorithms trained\nto perform well on one on them may generalize poorly to others. By contrast, we\nshow how training on a union of datasets in S2AND results in more robust models\nthat perform well even on datasets unseen in training. The resulting AND model\nalso substantially improves over the production algorithm in S2, reducing error\nby over 50% in terms of $B^3$ F1. We release our unified dataset, model code,\ntrained models, and evaluation suite to the research community.\nhttps://github.com/allenai/S2AND/\n"}
+{"abstract": "  At present emotion extraction from speech is a very important issue due to\nits diverse applications. Hence, it becomes absolutely necessary to obtain\nmodels that take into consideration the speaking styles of a person, vocal\ntract information, timbral qualities and other congenital information regarding\nhis voice. Our speech production system is a nonlinear system like most other\nreal world systems. Hence the need arises for modelling our speech information\nusing nonlinear techniques. In this work we have modelled our articulation\nsystem using nonlinear multifractal analysis. The multifractal spectral width\nand scaling exponents reveals essentially the complexity associated with the\nspeech signals taken. The multifractal spectrums are well distinguishable the\nin low fluctuation region in case of different emotions. The source\ncharacteristics have been quantified with the help of different non-linear\nmodels like Multi-Fractal Detrended Fluctuation Analysis, Wavelet Transform\nModulus Maxima. The Results obtained from this study gives a very good result\nin emotion clustering.\n"}
+{"abstract": "  We investigate the optical conductivity and far-infrared magneto-optical\nresponse of BaNiS$_2$, a simple square-lattice semimetal characterized by Dirac\nnodal lines that disperse exclusively along the out-of-plane direction. With\nthe magnetic field aligned along the nodal line the in-plane Landau level\nspectra show a nearly $\\sqrt{B}$ behavior, the hallmark of a conical-band\ndispersion with a small spin-orbit coupling gap. The optical conductivity\nexhibits an unusual temperature-independent isosbestic line, ending at a Van\nHove singularity. First-principles calculations unambiguously assign the\nisosbestic line to transitions across Dirac nodal states. Our work suggests a\nuniversal topology of the electronic structure of Dirac nodal lines.\n"}
+{"abstract": "  Channel capacity plays a crucial role in the development of modern\ncommunication systems as it represents the maximum rate at which information\ncan be reliably transmitted over a communication channel. Nevertheless, for the\nmajority of channels, finding a closed-form capacity expression remains an open\nchallenge. This is because it requires to carry out two formidable tasks a) the\ncomputation of the mutual information between the channel input and output, and\nb) its maximization with respect to the signal distribution at the channel\ninput. In this paper, we address both tasks. Inspired by implicit generative\nmodels, we propose a novel cooperative framework to automatically learn the\nchannel capacity, for any type of memory-less channel. In particular, we\nfirstly develop a new methodology to estimate the mutual information directly\nfrom a discriminator typically deployed to train adversarial networks, referred\nto as discriminative mutual information estimator (DIME). Secondly, we include\nthe discriminator in a cooperative channel capacity learning framework,\nreferred to as CORTICAL, where a discriminator learns to distinguish between\ndependent and independent channel input-output samples while a generator learns\nto produce the optimal channel input distribution for which the discriminator\nexhibits the best performance. Lastly, we prove that a particular choice of the\ncooperative value function solves the channel capacity estimation problem.\nSimulation results demonstrate that the proposed method offers high accuracy.\n"}
+{"abstract": "  While transition metal dichalcogenide (TMD) based moire materials have been\nshown to host various correlated electronic phenomena, topological states have\nnot been experimentally observed until now. In this work, using first\nprinciples calculations and continuum modeling, we reveal the displacement\nfield induced topological moire bands in AB-stacked TMD heterobilayer\nMoTe2/WSe2. Valley contrasting Chern bands with non-trivial spin texture are\nformed from interlayer hybridization between MoTe2 and WSe2 bands of nominally\nopposite spins. Our study establishes a recipe for creating topological bands\nin AB stacked TMD bilayers in general, which provides a highly tunable platform\nfor realizing quantum spin Hall and interaction induced quantum anomalous Hall\neffects.\n"}
+{"abstract": "  Formation control algorithms for multi-agent systems have gained much\nattention in the recent years due to the increasing amount of mobile and aerial\nrobotic swarms. The design of safe controllers for these vehicles is a\nsubstantial aspect for an increasing range of application domains. However,\nparts of the vehicle's dynamics and external disturbances are often unknown or\nvery time-consuming to model. To overcome this issue, we present a safe\nformation control law for multiagent systems based on double integrator\ndynamics by using Gaussian Processes for an online learning of the unknown\ndynamics. The presented approach guarantees a bounded error to desired\nformations with high probability, where the bound is explicitly given. A\nnumerical example highlights the effectiveness of the learning-based formation\ncontrol law.\n"}
+{"abstract": "  This paper develops a Bayesian computational platform at the interface\nbetween posterior sampling and optimization in models whose marginal\nlikelihoods are difficult to evaluate. Inspired by adversarial optimization,\nnamely Generative Adversarial Networks (GAN), we reframe the likelihood\nfunction estimation problem as a classification problem. Pitting a Generator,\nwho simulates fake data, against a Classifier, who tries to distinguish them\nfrom the real data, one obtains likelihood (ratio) estimators which can be\nplugged into the Metropolis-Hastings algorithm. The resulting Markov chains\ngenerate, at a steady state, samples from an approximate posterior whose\nasymptotic properties we characterize. Drawing upon connections with empirical\nBayes and Bayesian mis-specification, we quantify the convergence rate in terms\nof the contraction speed of the actual posterior and the convergence rate of\nthe Classifier. Asymptotic normality results are also provided which justify\ninferential potential of our approach. We illustrate the usefulness of our\napproach on examples which have posed a challenge for existing Bayesian\nlikelihood-free approaches.\n"}
+{"abstract": "  The practical application of sodium-ion hybrid capacitors is limited by their\nlow energy densities resulted from the kinetics mismatch between cathodes and\nanodes, and the fire safety related to the flammable electrolyte-separator\nsystem. Hence, we report a rational design of metal-organic frameworks (MOFs,\nUiO-66) modified PVDF-HFP separator. High tensile strength and dimensional\nthermal stability of the separator reduce the risk of electrode short circuit\ncaused by the separator deformation. MCC test demonstrates a reduction of 75%\nin peak heat release rate (pHRR), indicating an enhanced fire-resistant\nproperty of the separator. This is due to the transformation of UiO-66 into\nZrO2 accompanied by the consumption of oxygen and the formation of the barrier\nchar that suppresses further heat release. Quasi-solid-state electrolyte\nprepared based on this separator presents an enhanced ionic conductivity of\n2.44 mS*cm-1 and Na-ion transference number of 0.55, which are related to the\nhigh porosity ( >70%) and electrolyte uptake (~ 320%) of the separator.\nMoreover, the open metal sites of UiO-66 can capture PF6- and consequently\nliberate the Na+ for faster migration, thus reducing the kinetics mismatch\nbetween cathodes and anodes. Such multifunctional separator enables the\nquasi-solid-state Na-ion hybrid capacitor to achieve high energy density (182\nWh*kg-1 @31 W*kg-1) and power density (5280 W*kg-1 @22 Wh*kg-1), as well as\nexcellent cyclic stability (10000 cycles @1000 mA*g-1).\n  Keywords: Quasi-solid-state; PVDF-HFP; Metal-organic frameworks; Dimensional\nthermal stability; Fire safety; Selective charge transfer\n"}
+{"abstract": "  Magnitude-limited samples have shown that 20-25 per cent of cataclysmic\nvariables contain white dwarfs with magnetic fields of Mega Gauss strength, in\nstark contrast to the approximately 5 per cent of single white dwarfs with\nsimilar magnetic field strengths. Moreover, the lack of identifiable progenitor\nsystems for magnetic cataclysmic variables leads to considerable challenges\nwhen trying to understand how these systems form and evolve. Here we present a\nsample of six magnetic white dwarfs in detached binaries with low-mass stellar\ncompanions where we have constrained the stellar and binary parameters\nincluding, for the first time, reliable mass estimates for these magnetic white\ndwarfs. We find that they are systematically more massive than non-magnetic\nwhite dwarfs in detached binaries. These magnetic white dwarfs generally have\ncooling ages of more than 1 Gyr and reside in systems that are very close to\nRoche-lobe filling. Our findings are more consistent with these systems being\ntemporarily detached cataclysmic variables, rather than pre-cataclysmic\nbinaries, but we cannot rule out the latter possibility. We find that these\nsystems can display unusual asymmetric light curves that may offer a way to\nidentify them in larger numbers in future. Seven new candidate magnetic white\ndwarf systems are also presented, three of which have asymmetric light curves.\nFinally, we note that several newly identified magnetic systems have archival\nspectra where there is no clear evidence of magnetism, meaning that these\nbinaries have been previously missed. Nevertheless, there remains a clear lack\nof younger detached magnetic white dwarf systems.\n"}
+{"abstract": "  We study two polynomial counting questions in arithmetic statistics via a\ncombination of Fourier analytic and arithmetic methods. First, we obtain new\nquantitative forms of Hilbert's Irreducibility Theorem for degree $n$\npolynomials $f$ with $\\mathrm{Gal}(f) \\subseteq A_n$. We study this both for\nmonic polynomials and non-monic polynomials. Second, we study lower bounds on\nthe number of degree $n$ monic polynomials with almost prime discriminants, as\nwell as the closely related problem of lower bounds on the number of degree $n$\nnumber fields with almost prime discriminants.\n"}
+{"abstract": "  We propose a deterministic Kaczmarz algorithm for solving linear systems\n$A\\x=\\b$. Different from previous Kaczmarz algorithms, we use reflections in\neach step of the iteration. This generates a series of points distributed with\npatterns on a sphere centered at a solution. Firstly, we prove that taking the\naverage of $O(\\eta/\\epsilon)$ points leads to an effective approximation of the\nsolution up to relative error $\\epsilon$, where $\\eta$ is a parameter depending\non $A$ and can be bounded above by the square of the condition number. We also\nshow how to select these points efficiently. From the numerical tests, our\nKaczmarz algorithm usually converges more quickly than the (block) randomized\nKaczmarz algorithms. Secondly, when the linear system is consistent, the\nKaczmarz algorithm returns the solution that has the minimal distance to the\ninitial vector. This gives a method to solve the least-norm problem. Finally,\nwe prove that our Kaczmarz algorithm indeed solves the linear system\n$A^TW^{-1}A \\x = A^TW^{-1} \\b$, where $W$ is the low-triangular matrix such\nthat $W+W^T=2AA^T$. The relationship between this linear system and the\noriginal one is studied.\n"}
+{"abstract": "  As the data size in Machine Learning fields grows exponentially, it is\ninevitable to accelerate the computation by utilizing the ever-growing large\nnumber of available cores provided by high-performance computing hardware.\nHowever, existing parallel methods for clustering or regression often suffer\nfrom problems of low accuracy, slow convergence, and complex\nhyperparameter-tuning. Furthermore, the parallel efficiency is usually\ndifficult to improve while striking a balance between preserving model\nproperties and partitioning computing workloads on distributed systems. In this\npaper, we propose a novel and simple data structure capturing the most\nimportant information among data samples. It has several advantageous\nproperties supporting a hierarchical clustering strategy that is irrelevant to\nthe hardware parallelism, well-defined metrics for determining optimal\nclustering, balanced partition for maintaining the compactness property, and\nefficient parallelization for accelerating computation phases. Then we combine\nthe clustering with regression techniques as a parallel library and utilize a\nhybrid structure of data and model parallelism to make predictions. Experiments\nillustrate that our library obtains remarkable performance on convergence,\naccuracy, and scalability.\n"}
+{"abstract": "  MLOps is about taking experimental ML models to production, i.e., serving the\nmodels to actual users. Unfortunately, existing ML serving systems do not\nadequately handle the dynamic environments in which online data diverges from\noffline training data, resulting in tedious model updating and deployment\nworks. This paper implements a lightweight MLOps plugin, termed ModelCI-e\n(continuous integration and evolution), to address the issue. Specifically, it\nembraces continual learning (CL) and ML deployment techniques, providing\nend-to-end supports for model updating and validation without serving engine\ncustomization. ModelCI-e includes 1) a model factory that allows CL researchers\nto prototype and benchmark CL models with ease, 2) a CL backend to automate and\norchestrate the model updating efficiently, and 3) a web interface for an ML\nteam to manage CL service collaboratively. Our preliminary results demonstrate\nthe usability of ModelCI-e, and indicate that eliminating the interference\nbetween model updating and inference workloads is crucial for higher system\nefficiency.\n"}
+{"abstract": "  Implicit expressability was introduced by A.V. Kuznetsov in 1979 as\ngeneralization of functional expressability. Set of functions is called\nimplicitly complete if any function has an implicit representation over this\nset. The system of all implicitly maximal classes in three-valued logic is\ndescribed. The implicit completeness criterion is stated.\n"}
+{"abstract": "  Kinase-targeted drug design is challenging. It requires designing inhibitors\nthat can bind to specific kinases when all kinase catalytic domains share a\ncommon folding scaffold that binds ATP. Thus, obtaining the desired\nselectivity, given the whole human kinome, is a fundamental task during\nearly-stage drug discovery. This begins with deciphering the kinase-ligand\ncharacteristics, analyzing the structure-activity relationships, and\nprioritizing the desired drug molecules across the whole kinome. Currently,\nthere are more than 300 kinases with released PDB structures, which provides a\nsubstantial structural basis to gain these necessary insights. Here, we review\nin silico structure-based methods - notably, a function-site interaction\nfingerprint approach used in exploring the complete human kinome. In silico\nmethods can be explored synergistically with multiple cell-based or\nprotein-based assay platforms such as KINOMEscan. We conclude with new drug\ndiscovery opportunities associated with kinase signaling networks and using\nmachine/deep learning techniques broadly referred to as structural biomedical\ndata science.\n"}
+{"abstract": "  The $p$-Laplacian evolution equation in metric measure spaces has been\nstudied as the gradient flow in $L^2$ of the $p$-Cheeger energy (for $1 < p <\n\\infty$). In this paper, using the first-order differential structure on a\nmetric measure space introduced by Gigli, we characterize the subdifferential\nin $L^2$ of the $p$-Cheeger energy. This gives rise to a new definition of the\n$p$-Laplacian operator in metric measure spaces, which allows us to work with\nthis operator in more detail. In this way, we introduce a new notion of\nsolutions to the $p$-Laplacian evolution equation in metric measure spaces. For\n$p = 1$, we obtain a Green-Gauss formula similar to the one by Anzellotti for\nEuclidean spaces, and use it to characterise the $1$-Laplacian operator and\nstudy the total variation flow. We also study the asymptotic behaviour of the\nsolutions of the $p$-Laplacian evolution equation, showing that for $1 \\leq p <\n2$ we have finite extinction time.\n"}
+{"abstract": "  Airlines and other industries have been making use of sophisticated Revenue\nManagement Systems to maximize revenue for decades. While improving the\ndifferent components of these systems has been the focus of numerous studies,\nestimating the impact of such improvements on the revenue has been overlooked\nin the literature despite its practical importance. Indeed, quantifying the\nbenefit of a change in a system serves as support for investment decisions.\nThis is a challenging problem as it corresponds to the difference between the\ngenerated value and the value that would have been generated keeping the system\nas before. The latter is not observable. Moreover, the expected impact can be\nsmall in relative value. In this paper, we cast the problem as counterfactual\nprediction of unobserved revenue. The impact on revenue is then the difference\nbetween the observed and the estimated revenue. The originality of this work\nlies in the innovative application of econometric methods proposed for\nmacroeconomic applications to a new problem setting. Broadly applicable, the\napproach benefits from only requiring revenue data observed for\norigin-destination pairs in the network of the airline at each day, before and\nafter a change in the system is applied. We report results using real\nlarge-scale data from Air Canada. We compare a deep neural network\ncounterfactual predictions model with econometric models. They achieve\nrespectively 1% and 1.1% of error on the counterfactual revenue predictions,\nand allow to accurately estimate small impacts (in the order of 2%).\n"}
+{"abstract": "  We study a competitive facility location problem (CFLP), in which two firms\nsequentially select locations of new facilities, in order to maximize their\nmarket shares of customer demand that follows a probabilistic choice model.\nThis process is a Stackelberg game and admits a bilevel mixed-integer nonlinear\nprogram (MINLP) formulation. Through integer programming methods, we derive an\nequivalent, single-level MINLP reformulation. In addition, we exploit the\nproblem structures and derive two classes of valid inequalities, one based on\nsubmodularity and the other based on concave overestimation. We apply these\ninequalities in a branch-and-cut algorithm to find a globally optimal solution\nto CFLP. Furthermore, we propose an approximation algorithm for solving CFLP\nthat is computationally more effective. Notably, this algorithm admits a\nconstant approximation guarantee. Extensive numerical studies demonstrate that\nthe exact algorithm can significantly accelerate the solving of CFLP on problem\ninstances that have not been solved to optimality by existing methods. The\napproximation algorithm can find near-optimal solutions even more quickly.\n"}
+{"abstract": "  In this paper we research a model of multilayer circuits with a single\nlogical layer. We consider $\\lambda$-separable graphs as a support for\ncircuits. We establish the Shannon function lower bound $\\max\n\\bigl(\\frac{2^n}{n}, \\frac{2^n (1 - \\lambda)}{\\log k} \\bigr)$ for this type of\ncircuits where $k$ is the number of layers. For $d$-dimensional graphs, which\nare $\\lambda$-separable for $\\lambda = \\frac{d - 1}{d}$, this gives the Shannon\nfunction lower bound $\\frac{2^n}{\\min(n, d \\log k)}$. For multidimensional\nrectangular circuits the proved lower bound asymptotically matches to the upper\nbound.\n"}
+{"abstract": "  For the sake of recognizing and classifying textile defects, deep\nlearning-based methods have been proposed and achieved remarkable success in\nsingle-label textile images. However, detecting multi-label defects in a\ntextile image remains challenging due to the coexistence of multiple defects\nand small-size defects. To address these challenges, a multi-level,\nmulti-attentional deep learning network (MLMA-Net) is proposed and built to 1)\nincrease the feature representation ability to detect small-size defects; 2)\ngenerate a discriminative representation that maximizes the capability of\nattending the defect status, which leverages higher-resolution feature maps for\nmultiple defects. Moreover, a multi-label object detection dataset (DHU-ML1000)\nin textile defect images is built to verify the performance of the proposed\nmodel. The results demonstrate that the network extracts more distinctive\nfeatures and has better performance than the state-of-the-art approaches on the\nreal-world industrial dataset.\n"}
+{"abstract": "  Simulation-based Inference (SBI) is a widely used set of algorithms to learn\nthe parameters of complex scientific simulation models. While primarily run on\nCPUs in HPC clusters, these algorithms have been shown to scale in performance\nwhen developed to be run on massively parallel architectures such as GPUs.\nWhile parallelizing existing SBI algorithms provides us with performance gains,\nthis might not be the most efficient way to utilize the achieved parallelism.\nThis work proposes a new algorithm, that builds on an existing SBI method -\nApproximate Bayesian Computation with Sequential Monte Carlo(ABC-SMC). This new\nalgorithm is designed to utilize the parallelism not only for performance gain,\nbut also toward qualitative benefits in the learnt parameters. The key idea is\nto replace the notion of a single 'step-size' hyperparameter, which governs how\nthe state space of parameters is explored during learning, with step-sizes\nsampled from a tuned Beta distribution. This allows this new ABC-SMC algorithm\nto more efficiently explore the state-space of the parameters being learnt. We\ntest the effectiveness of the proposed algorithm to learn parameters for an\nepidemiology model running on a Tesla T4 GPU. Compared to the parallelized\nstate-of-the-art SBI algorithm, we get similar quality results in $\\sim 100$x\nfewer simulations and observe ~80x lower run-to-run variance across 10\nindependent trials.\n"}
+{"abstract": "  One-bit analog-to-digital converter (ADC), performing signal sampling as an\nextreme simple comparator, is an overwhelming technology for spectrum sensing\ndue to its low-cost, low-power consumptions and high sampling rate. In this\nletter, we propose a novel one-bit sensing approach based on the eigenvalue\nmoment ratio (EMR), which has been proved to be highly efficient for\nconventional multi-antenna spectrum sensing in $\\infty$-bit situation.\nParticularly, we determine the asymptotic distribution of one-bit EMR under\nnull hypothesis via the central limited theorem (CLT), allowing us to perform\nspectrum sensing with one-bit samples directly. Theoretical and simulation\nanalysis show the new approach can provide reasonably good sensing performance\nat a low hardware cost.\n"}
+{"abstract": "  Compositional Zero-Shot learning (CZSL) requires to recognize state-object\ncompositions unseen during training. In this work, instead of assuming prior\nknowledge about the unseen compositions, we operate in the open world setting,\nwhere the search space includes a large number of unseen compositions some of\nwhich might be unfeasible. In this setting, we start from the cosine similarity\nbetween visual features and compositional embeddings. After estimating the\nfeasibility score of each composition, we use these scores to either directly\nmask the output space or as a margin for the cosine similarity between visual\nfeatures and compositional embeddings during training. Our experiments on two\nstandard CZSL benchmarks show that all the methods suffer severe performance\ndegradation when applied in the open world setting. While our simple CZSL model\nachieves state-of-the-art performances in the closed world scenario, our\nfeasibility scores boost the performance of our approach in the open world\nsetting, clearly outperforming the previous state of the art.\n"}
+{"abstract": "  Ensuring the safety and efficacy of Cyber-Physical Systems (CPSs) is\nchallenging due to the large variability of their operating environment. Model\nchecking has been proposed for validation of CPSs, but the models of the\nenvironment are either too specific to capture the variability of the\nenvironment, or too abstract to provide counter-examples interpretable by\nexperts in the application domain. Domain-specific solutions to this problem\nrequire expertise in both formal methods and the application domain, which\nprevents effective application of model checking in CPSs validation. A\ndomain-independent framework based on timed-automata is proposed for\nabstraction and refinement of environment models during model checking. The\nframework maintains an abstraction tree of environment models, which provides\ninterpretable counter-examples while ensuring coverage of environment\nbehaviors. With the framework, experts in the application domain can\neffectively use model checking without expertise in formal methods.\n"}
+{"abstract": "  The hidden symmetry of the asymmetric quantum Rabi model (AQRM) with a\nhalf-integral bias (ibQRM$_{\\ell}$) was uncovered in recent studies by the\nexplicit construction of operators $J_\\ell$ commuting with the Hamiltonian. The\nexistence of such symmetry has been widely believed to cause the degeneration\nof the spectrum, that is, the crossings on the energy curves. In this paper we\npropose a conjectural relation between the symmetry and degeneracy for the\nibQRM$_{\\ell}$ given explicitly in terms of two polynomials appearing\nindependently in the respective investigations. Concretely, one of the\npolynomials appears as the quotient of the constraint polynomials that assure\nthe existence of degenerate solutions while the other determines a quadratic\nrelation (in general, it defines a curve of hyperelliptic type) between the\nibQRM$_{\\ell}$ Hamiltonian and its basic commuting operator $J_\\ell$. Following\nthis conjecture, we derive several interesting structural insights of the whole\nspectrum. For instance, the energy curves are naturally shown to lie on a\nsurface determined by the family of hyperelliptic curves by considering the\ncoupling constant as a variable. This geometric picture contains the\ngeneralization of the parity decomposition of the symmetric quantum Rabi model.\nMoreover, it allows us to describe a remarkable approximation of the first\n$\\ell$ energy curves by the zero-section of the corresponding hyperelliptic\ncurve. These investigations naturally lead to a geometric picture of the\n(hyper-)elliptic surfaces given by the Kodaira-N\\'eron type model for a family\nof curves over the projective line in connection with the energy curves, which\nmay be expected to provide a complex analytic proof of the conjecture.\n"}
+{"abstract": "  Scheduling the transmission of time-sensitive information from a source node\nto multiple users over error-prone communication channels is studied with the\ngoal of minimizing the long-term average age of information (AoI) at the users.\nA long-term average resource constraint is imposed on the source, which limits\nthe average number of transmissions. The source can transmit only to a single\nuser at each time slot, and after each transmission, it receives an\ninstantaneous ACK/NACK feedback from the intended receiver, and decides when\nand to which user to transmit the next update. Assuming the channel statistics\nare known, the optimal scheduling policy is studied for both the standard\nautomatic repeat request (ARQ) and hybrid ARQ (HARQ) protocols. Then, a\nreinforcement learning(RL) approach is introduced to find a near-optimal\npolicy, which does not assume any a priori information on the random processes\ngoverning the channel states. Different RL methods including average-cost\nSARSAwith linear function approximation (LFA), upper confidence reinforcement\nlearning (UCRL2), and deep Q-network (DQN) are applied and compared through\nnumerical simulations\n"}
+{"abstract": "  Several phenomena are known to attenuate waves in the marginal ice zone, such\nas wave scattering due to ice floes, viscous dissipation inside the ice and\nenergy dissipation caused by ice floe interactions. Our aim of this study is to\ninvestigate colliding ice floes and to quantify the Turbulent Kinetic Energy\n(TKE) dissipation in the surrounding water through direct observations. The\nfield work was carried out in Van Mijen Fjord on Svalbard, where an artificial\n3x4 m^2 ice floe was sawed out in the 1 m thick ice. Wave motion was simulated\nby pulling the ice floe back and forth in an oscillatory manner with two\nelectrical winches in a 4x6 m^2 pool. Ice floe motion was measured with a range\nmeter, and the water turbulence was measured with acoustic velocimeters. TKE\nfrequency spectra were found to contain an inertial subrange where energy was\ncascading at a rate proportional to f^(-5/3). From the spectra estimated at\nseveral vertical positions, the TKE dissipation rate was found to decrease\nexponentially with depth. The total TKE dissipation rate was estimated by\nassuming that turbulence was induced over an area spanning over the ice floe\nwidth and the pool length. The preliminary energy budget suggests that\napproximately 60% of the input power from the winches was dissipated in\nturbulence, which experimentally confirms that energy dissipation by induced\nturbulent water motion is an important mechanism for colliding ice floe fields.\n"}
+{"abstract": "  We consider the problem of bridging the gap between geometric tracking\ncontrol theory and implementation of model predictive control (MPC) for robotic\nsystems operating on manifolds. We propose a generic on-manifold MPC\nformulation based on a canonical representation of the system evolving on\nmanifolds. Then, we present a method that solves the on-manifold MPC\nformulation by linearizing the system along the trajectory under tracking.\nThere are two main advantages of the proposed scheme. The first is that the\nlinearized system leads to an equivalent error system represented by a set of\nminimal parameters without any singularity. Secondly, the process of system\nmodeling, error-system derivation, linearization and control has the manifold\nconstraints completely decoupled from the system descriptions, enabling the\ndevelopment of a symbolic MPC framework that naturally encapsulates the\nmanifold constraints. In this framework, users need only to supply\nsystem-specific descriptions without dealing with the manifold constraints. We\nimplement this framework and test it on a quadrotor unmanned aerial vehicle\n(UAV) operating on $SO(3) \\times \\mathbb{R}^n$ and an unmanned ground vehicle\n(UGV) moving on a curved surface. Real-world experiments show that the proposed\nframework and implementation achieve high tracking performance and\ncomputational efficiency even in highly aggressive aerobatic quadrotor\nmaneuvers.\n"}
+{"abstract": "  Communication of model updates between client nodes and the central\naggregating server is a major bottleneck in federated learning, especially in\nbandwidth-limited settings and high-dimensional models. Gradient quantization\nis an effective way of reducing the number of bits required to communicate each\nmodel update, albeit at the cost of having a higher error floor due to the\nhigher variance of the stochastic gradients. In this work, we propose an\nadaptive quantization strategy called AdaQuantFL that aims to achieve\ncommunication efficiency as well as a low error floor by changing the number of\nquantization levels during the course of training. Experiments on training deep\nneural networks show that our method can converge in much fewer communicated\nbits as compared to fixed quantization level setups, with little or no impact\non training and test accuracy.\n"}
+{"abstract": "  The LDN 1172/1174 cloud complex in the Cepheus Flare region presents a\nhub-filament structure with the reflection nebula, NGC 7023, illuminated by a\nHerbig Be star, HD 200775, which consists of the hub with a $\\sim$5 pc long\nnarrow filament attached to it. Formation of a sparse cluster of low- and\nintermediate-mass stars is presently taking place in the hub. The aim of this\nwork is to map the magnetic field geometry of LDN 1172/1174 to understand the\nrole played by the field lines in the formation of the molecular cloud. We made\nR-band polarization measurements of 249 stars projected on the entire LDN\n1172/1174 cloud complex to map the geometry of the magnetic field of this\nregion. The magnetic field geometry constructed from our R-band polarization\nmeasurements is found to be parallel to the elongated structure inferred from\nthe column density distribution of the cloud produced using the Herschel\nimages. Our R-band polarization measurements are found to be in good agreement\nwith those obtained from Planck. There is evidence of a possible distortion of\nthe magnetic fields toward the northwestern part of the cloud by HD 200775. The\nmagnetic field strength is estimated as $\\sim$30 $\\mu$G. The estimated star\nformation rate (SFR)/mass of 2.0$\\pm$1.3 \\%Myr$^{-1}$ and 0.4$\\pm$0.3\n\\%Myr$^{-1}$ for LDN 1172/1174 and the neighboring cloud complex, LDN\n1147/1158, respectively, are found to be consistent with the mean SFR/mass\nfound for the clouds with magnetic field orientations parallel and\nperpendicular to their elongated structures, respectively. These results\nsupport earlier findings that the clouds with magnetic field lines parallel to\ntheir long axes seem to have higher SFRs compared to those with the magnetic\nfield orientation perpendicular to the cloud elongation.\n"}
+{"abstract": "  In this paper, we present GEM as a General Evaluation benchmark for\nMultimodal tasks. Different from existing datasets such as GLUE, SuperGLUE,\nXGLUE and XTREME that mainly focus on natural language tasks, GEM is a\nlarge-scale vision-language benchmark, which consists of GEM-I for\nimage-language tasks and GEM-V for video-language tasks. Comparing with\nexisting multimodal datasets such as MSCOCO and Flicker30K for image-language\ntasks, YouCook2 and MSR-VTT for video-language tasks, GEM is not only the\nlargest vision-language dataset covering image-language tasks and\nvideo-language tasks at the same time, but also labeled in multiple languages.\nWe also provide two baseline models for this benchmark. We will release the\ndataset, code and baseline models, aiming to advance the development of\nmultilingual multimodal research.\n"}
+{"abstract": "  In this paper, we construct a class of non-weight modules over the\naffine-Virasoro algebra of type $A_1$ by taking tensor products of irreducibles\ndefined in [Q. Chen, J. Han, Non-weight modules over the affine-Virasoro\nalgebra of type $A_1$, J. Math. Phys. 60, 071707 (2019)] with irreducible\nhighest weight modules. The irreducibility and the isomorphism classes of these\nmodules are determined. Moreover, we show that these tensor product modules are\ndifferent from the known non-weight modules. Finally, we realize some tensor\nproduct modules as induced modules from modules over certain subalgebras of the\naffine-Virasoro algebra of type $A_1$, and give sufficient and necessary\nconditions for these induced modules to be reducible.\n"}
+{"abstract": "  In this paper, we expand the methodology presented in Mertens et. al (2020,\nBiometrical Journal) to the study of life-time (survival) outcome which is\nsubject to censoring and when imputation is used to account for missing values.\nWe consider the problem where missing values can occur in both the calibration\ndata as well as newly - to-be-predicted - observations (validation). We focus\non the Cox model. Methods are described to combine imputation with predictive\ncalibration in survival modeling subject to censoring. Application to\ncross-validation is discussed. We demonstrate how conclusions broadly confirm\nthe first paper which restricted to the study of binary outcomes only.\nSpecifically prediction-averaging appears to have superior statistical\nproperties, especially smaller predictive variation, as opposed to a direct\napplication of Rubin's rules. Distinct methods for dealing with the baseline\nhazards are discussed when using Rubin's rules-based approaches.\n"}
+{"abstract": "  In many practical applications, such as fraud detection, credit risk modeling\nor medical decision making, classification models for assigning instances to a\npredefined set of classes are required to be both precise as well as\ninterpretable. Linear modeling methods such as logistic regression are often\nadopted, since they offer an acceptable balance between precision and\ninterpretability. Linear methods, however, are not well equipped to handle\ncategorical predictors with high-cardinality or to exploit non-linear relations\nin the data. As a solution, data preprocessing methods such as\nweight-of-evidence are typically used for transforming the predictors. The\nbinning procedure that underlies the weight-of-evidence approach, however, has\nbeen little researched and typically relies on ad-hoc or expert driven\nprocedures. The objective in this paper, therefore, is to propose a formalized,\ndata-driven and powerful method.\n  To this end, we explore the discretization of continuous variables through\nthe binning of spline functions, which allows for capturing non-linear effects\nin the predictor variables and yields highly interpretable predictors taking\nonly a small number of discrete values. Moreover, we extend upon the\nweight-of-evidence approach and propose to estimate the proportions using\nshrinkage estimators. Together, this offers an improved ability to exploit both\nnon-linear and categorical predictors for achieving increased classification\nprecision, while maintaining interpretability of the resulting model and\ndecreasing the risk of overfitting.\n  We present the results of a series of experiments in a fraud detection\nsetting, which illustrate the effectiveness of the presented approach. We\nfacilitate reproduction of the presented results and adoption of the proposed\napproaches by providing both the dataset and the code for implementing the\nexperiments and the presented approach.\n"}
+{"abstract": "  Multichannel adaptive signal detection jointly uses the test and training\ndata to form an adaptive detector, and then make a decision on whether a target\nexists or not. Remarkably, the resulting adaptive detectors usually possess the\nconstant false alarm rate (CFAR) properties, and hence no additional CFAR\nprocessing is needed. Filtering is not needed as a processing procedure either,\nsince the function of filtering is embedded in the adaptive detector. Moreover,\nadaptive detection usually exhibits better detection performance than the\nfiltering-then-CFAR detection technique. Multichannel adaptive signal detection\nhas been more than 30 years since the first multichannel adaptive detector was\nproposed by Kelly in 1986. However, there are fewer overview articles on this\ntopic. In this paper we give a tutorial overview of multichannel adaptive\nsignal detection, with emphasis on Gaussian background. We present the main\ndeign criteria for adaptive detectors, investigate the relationship between\nadaptive detection and filtering-then-CFAR detection, relationship between\nadaptive detectors and adaptive filters, summarize typical adaptive detectors,\nshow numerical examples, give comprehensive literature review, and discuss some\npossible further research tracks.\n"}
+{"abstract": "  Multislice ptychography is a high-resolution microscopy technique used to\nimage multiple separate axial planes using a single illumination direction.\nHowever, multislice ptychography reconstructions are often degraded by\ncrosstalk, where some features on one plane erroneously contribute to the\nreconstructed image of another plane. Here, we demonstrate the use of a\nmodified \"double deep image prior\" (DDIP) architecture in mitigating crosstalk\nartifacts in multislice ptychography. Utilizing the tendency of generative\nneural networks to produce natural images, a modified DDIP method yielded good\nresults on experimental data. For one of the datasets, we show that using DDIP\ncould remove the need of using additional experimental data, such as from x-ray\nfluorescence, to suppress the crosstalk. Our method may help x-ray multislice\nptychography work for more general experimental scenarios.\n"}
+{"abstract": "  This demo paper presents the technical details and usage scenarios of\n$\\mu$SE: a mutation-based tool for evaluating security-focused static analysis\ntools for Android. Mutation testing is generally used by software practitioners\nto assess the robustness of a given test-suite. However, we leverage this\ntechnique to systematically evaluate static analysis tools and uncover and\ndocument soundness issues. $\\mu$SE's analysis has found 25 previously\nundocumented flaws in static data leak detection tools for Android. $\\mu$SE\noffers four mutation schemes, namely Reachability, Complex-reachability,\nTaintSink, and ScopeSink, which determine the locations of seeded mutants.\nFurthermore, the user can extend $\\mu$SE by customizing the API calls targeted\nby the mutation analysis. $\\mu$SE is also practical, as it makes use of\nfiltering techniques based on compilation and execution criteria that reduces\nthe number of ineffective mutations.\n"}
+{"abstract": "  Deep neural networks may perform poorly when training datasets are heavily\nclass-imbalanced. Recently, two-stage methods decouple representation learning\nand classifier learning to improve performance. But there is still the vital\nissue of miscalibration. To address it, we design two methods to improve\ncalibration and performance in such scenarios. Motivated by the fact that\npredicted probability distributions of classes are highly related to the\nnumbers of class instances, we propose label-aware smoothing to deal with\ndifferent degrees of over-confidence for classes and improve classifier\nlearning. For dataset bias between these two stages due to different samplers,\nwe further propose shifted batch normalization in the decoupling framework. Our\nproposed methods set new records on multiple popular long-tailed recognition\nbenchmark datasets, including CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT,\nPlaces-LT, and iNaturalist 2018. Code will be available at\nhttps://github.com/Jia-Research-Lab/MiSLAS.\n"}
+{"abstract": "  We provide a pure state formulation for hydrodynamic dynamics of isolated\nquantum many-body systems. A pure state describing quantum systems in local\nthermal equilibrium is constructed, which we call a local thermal pure quantum\n($\\ell$TPQ) state. We show that the thermodynamic functional and the\nexpectation values of local operators (including a real-time correlation\nfunction) calculated from the $\\ell$TPQ state converge to those from a local\nGibbs ensemble in the large fluid-cell limit. As a numerical demonstration, we\ninvestigate a one-dimensional spin chain and observe the hydrodynamic\nrelaxation obeying the Fourier's law. We further prove the second law of\nthermodynamics and the quantum fluctuation theorem, which are also validated\nnumerically. The $\\ell$TPQ formulation gives a useful theoretical basis to\ndescribe the emergent hydrodynamic behavior of quantum many-body systems\nfurnished with a numerical efficiency, being applicable to both the\nnon-relativistic and relativistic regimes.\n"}
+{"abstract": "  LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and\nMapping) serves as a basis for indoor cleaning, navigation, and many other\nuseful applications in both industry and household. From a series of LiDAR\nscans, it constructs an accurate, globally consistent model of the environment\nand estimates a robot position inside it. SLAM is inherently computationally\nintensive; it is a challenging problem to realize a fast and reliable SLAM\nsystem on mobile robots with a limited processing capability. To overcome such\nhurdles, in this paper, we propose a universal, low-power, and\nresource-efficient accelerator design for 2D LiDAR SLAM targeting\nresource-limited FPGAs. As scan matching is at the heart of SLAM, the proposed\naccelerator consists of dedicated scan matching cores on the programmable logic\npart, and provides software interfaces to facilitate the use. Our accelerator\ncan be integrated to various SLAM methods including the ROS (Robot Operating\nSystem)-based ones, and users can switch to a different method without\nmodifying and re-synthesizing the logic part. We integrate the accelerator into\nthree widely-used methods, i.e., scan matching, particle filter, and\ngraph-based SLAM. We evaluate the design in terms of resource utilization,\nspeed, and quality of output results using real-world datasets. Experiment\nresults on a Pynq-Z2 board demonstrate that our design accelerates scan\nmatching and loop-closure detection tasks by up to 14.84x and 18.92x, yielding\n4.67x, 4.00x, and 4.06x overall performance improvement in the above methods,\nrespectively. Our design enables the real-time performance while consuming only\n2.4W and maintaining accuracy, which is comparable to the software counterparts\nand even the state-of-the-art methods.\n"}
+{"abstract": "  In symmetry protected topological (SPT) phases, the combination of symmetries\nand a bulk gap stabilizes protected modes at surfaces or at topological\ndefects. Understanding the fate of these modes at a quantum critical point,\nwhen the protecting symmetries are on the verge of being broken, is an\noutstanding problem. This interplay of topology and criticality must\nincorporate both the bulk dynamics of critical points, often described by\nnontrivial conformal field theories, and SPT physics. Here, we study the\nsimplest nontrivial setting - that of a 0+1 dimensional topological mode - a\nquantum spin - coupled to a 2+1D critical bulk. Using the large-$N$ technique\nwe solve a series of models which, as a consequence of topology, demonstrate\nintermediate coupling fixed points. We compare our results to previous\nnumerical simulations and find good agreement. We also point out intriguing\nconnections to generalized Kondo problems and Sachdev-Ye-Kitaev (SYK) models.\nIn particular, we show that a Luttinger theorem derived for the complex SYK\nmodels, that relates the charge density to particle-hole asymmetry, also holds\nin our setting. These results should help stimulate further analytical study of\nthe interplay between SPT physics and quantum criticality.\n"}
+{"abstract": "  We study the problem of estimating a rank-$1$ signal in the presence of\nrotationally invariant noise-a class of perturbations more general than\nGaussian noise. Principal Component Analysis (PCA) provides a natural\nestimator, and sharp results on its performance have been obtained in the\nhigh-dimensional regime. Recently, an Approximate Message Passing (AMP)\nalgorithm has been proposed as an alternative estimator with the potential to\nimprove the accuracy of PCA. However, the existing analysis of AMP requires an\ninitialization that is both correlated with the signal and independent of the\nnoise, which is often unrealistic in practice. In this work, we combine the two\nmethods, and propose to initialize AMP with PCA. Our main result is a rigorous\nasymptotic characterization of the performance of this estimator. Both the AMP\nalgorithm and its analysis differ from those previously derived in the Gaussian\nsetting: at every iteration, our AMP algorithm requires a specific term to\naccount for PCA initialization, while in the Gaussian case, PCA initialization\naffects only the first iteration of AMP. The proof is based on a two-phase\nartificial AMP that first approximates the PCA estimator and then mimics the\ntrue AMP. Our numerical simulations show an excellent agreement between AMP\nresults and theoretical predictions, and suggest an interesting open direction\non achieving Bayes-optimal performance.\n"}
+{"abstract": "  We consider the problem of uncertainty quantification for an unknown low-rank\nmatrix $\\mathbf{X}$, given a partial and noisy observation of its entries. This\nquantification of uncertainty is essential for many real-world problems,\nincluding image processing, satellite imaging, and seismology, providing a\nprincipled framework for validating scientific conclusions and guiding\ndecision-making. However, existing literature has mainly focused on the\ncompletion (i.e., point estimation) of the matrix $\\mathbf{X}$, with little\nwork on investigating its uncertainty. To this end, we propose in this work a\nnew Bayesian modeling framework, called BayeSMG, which parametrizes the unknown\n$\\mathbf{X}$ via its underlying row and column subspaces. This Bayesian\nsubspace parametrization enables efficient posterior inference on matrix\nsubspaces, which represents interpretable phenomena in many applications. This\ncan then be leveraged for improved matrix recovery. We demonstrate the\neffectiveness of BayeSMG over existing Bayesian matrix recovery methods in\nnumerical experiments, image inpainting, and a seismic sensor network\napplication.\n"}
+{"abstract": "  We investigate the IR phases of non-supersymmetric (non-SUSY) $SO(N_c)$ gauge\ntheories with $N_F$ fermions in the vector representation obtained by\nperturbing the SUSY theory with anomaly mediated SUSY breaking (AMSB). We find\nthat of the wide variety of phases appearing in the SUSY theory only two\nsurvive: for $N_F<\\frac{3}{2} (N_c-2)$ the theory confines, breaking the\n$SU(N_F)$ global symmetry to $SO(N_F)$, while for $\\frac{3}{2}\n(N_c-2)<N_F<3(N_c-2)$ the theory flows to a (super)-conformal fixed point. The\nabelian Coulomb and free magnetic phases do not survive and collapse to the\nconfining phase. We also investigate the behavior of loop operators in order to\nprovide a clear distinction between the confining and screened phases. With the\nchoice of $Spin(N_c)$ for the global structure of the gauge group, we find that\nthe electric Wilson loop indeed obeys an area law, providing one of the first\ndemonstrations of true confinement with chiral symmetry breaking in a non-SUSY\ntheory. We identify monopole condensation as the dynamics underlying\nconfinement. These monopoles arise naturally for $N_F=N_c-2$. The case with\nsmaller number of flavors can be obtained by integrating out flavors, and we\nconfirm numerically that the monopole condensate persists in the presence of\nAMSB and mass perturbations.\n"}
+{"abstract": "  The software inevitably encounters the crash, which will take developers a\nlarge amount of effort to find the fault causing the crash (short for crashing\nfault). Developing automatic methods to identify the residence of the crashing\nfault is a crucial activity for software quality assurance. Researchers have\nproposed methods to predict whether the crashing fault resides in the stack\ntrace based on the features collected from the stack trace and faulty code,\naiming at saving the debugging effort for developers. However, previous work\nusually neglected the feature preprocessing operation towards the crash data\nand only used traditional classification models. In this paper, we propose a\nnovel crashing fault residence prediction framework, called ConDF, which\nconsists of a consistency based feature subset selection method and a\nstate-of-the-art deep forest model. More specifically, first, the feature\nselection method is used to obtain an optimal feature subset and reduce the\nfeature dimension by reserving the representative features. Then, a simplified\ndeep forest model is employed to build the classification model on the reduced\nfeature set. The experiments on seven open source software projects show that\nour ConDF method performs significantly better than 17 baseline methods on\nthree performance indicators.\n"}
+{"abstract": "  This is the first article in a series of two dealing with a matrix approach\n\\alex{for} aberration quantification and correction in ultrasound imaging.\nAdvanced synthetic beamforming relies on a double focusing operation at\ntransmission and reception on each point of the medium. Ultrasound matrix\nimaging (UMI) consists in decoupling the location of these transmitted and\nreceived focal spots. The response between those virtual transducers form the\nso-called focused reflection matrix that actually contains much more\ninformation than a raw ultrasound image. In this paper, a time-frequency\nanalysis of this matrix is performed, which highlights the single and multiple\nscattering contributions as well as the impact of aberrations in the\nmonochromatic and broadband regimes. Interestingly, this analysis enables the\nmeasurement of the incoherent input-output point spread function at any pixel\nof this image. A focusing criterion can then be built, and its evolution used\nto quantify the amount of aberration throughout the ultrasound image. In\ncontrast to the standard coherence factor used in the literature, this new\nindicator is robust to multiple scattering and electronic noise, thereby\nproviding a highly contrasted map of the focusing quality. As a\nproof-of-concept, UMI is applied here to the in-vivo study of a human calf, but\nit can be extended to any kind of ultrasound diagnosis or non-destructive\nevaluation.\n"}
+{"abstract": "  We prove sharp reverse H\\\"older inequalities for minima of multi-phase\nvariational integrals and apply them to Calder\\'on-Zygmund estimates for\nnonhomogeneous problems.\n"}
+{"abstract": "  We study a theory of reinforcement learning (RL) in which the learner\nreceives binary feedback only once at the end of an episode. While this is an\nextreme test case for theory, it is also arguably more representative of\nreal-world applications than the traditional requirement in RL practice that\nthe learner receive feedback at every time step. Indeed, in many real-world\napplications of reinforcement learning, such as self-driving cars and robotics,\nit is easier to evaluate whether a learner's complete trajectory was either\n\"good\" or \"bad,\" but harder to provide a reward signal at each step. To show\nthat learning is possible in this more challenging setting, we study the case\nwhere trajectory labels are generated by an unknown parametric model, and\nprovide a statistically and computationally efficient algorithm that achieves\nsub-linear regret.\n"}
+{"abstract": "  Generative Adversarial Networks (GANs) have significantly advanced image\nsynthesis, however, the synthesis quality drops significantly given a limited\namount of training data. To improve the data efficiency of GAN training, prior\nwork typically employs data augmentation to mitigate the overfitting of the\ndiscriminator yet still learn the discriminator with a bi-classification (i.e.,\nreal vs. fake) task. In this work, we propose a data-efficient Instance\nGeneration (InsGen) method based on instance discrimination. Concretely,\nbesides differentiating the real domain from the fake domain, the discriminator\nis required to distinguish every individual image, no matter it comes from the\ntraining set or from the generator. In this way, the discriminator can benefit\nfrom the infinite synthesized samples for training, alleviating the overfitting\nproblem caused by insufficient training data. A noise perturbation strategy is\nfurther introduced to improve its discriminative power. Meanwhile, the learned\ninstance discrimination capability from the discriminator is in turn exploited\nto encourage the generator for diverse generation. Extensive experiments\ndemonstrate the effectiveness of our method on a variety of datasets and\ntraining settings. Noticeably, on the setting of 2K training images from the\nFFHQ dataset, we outperform the state-of-the-art approach with 23.5% FID\nimprovement.\n"}
+{"abstract": "  The Covid-19 pandemic has radically changed our lives. Under different\ncircumstances, people react to it in various ways. One way is to work-from-home\nsince lockdown has been announced in many regions around the world. For some\nplaces, however, we don't know if people really work from home due to the lack\nof information. Since there are lots of uncertainties, it would be helpful for\nus to understand what really happen in these places if we can detect the\nreaction to the Covid-19 pandemic. Working from home indicates that people have\nchanged the way they interact with the Internet. People used to access the\nInternet in the company or at school during the day. Now it is more likely that\nthey access the Internet at home in the daytime. Therefore, the network usage\nchanges in one place can be used to indicate if people in this place actually\nwork from home. In this work, we reuse and analyze Trinocular outages data\n(around 5.1M responsive /24 blocks) over 6 months to find network usage changes\nby a new designed algorithm. We apply the algorithm to sets of /24 blocks in\nseveral cities and compare the detected network usage changes with real world\ncovid-19 events to verify if the algorithm can capture the changes reacting to\nthe Covid-19 pandemic. By applying the algorithm to all measurable /24 blocks\nto detect network usages changes, we conclude that network usage can be an\nindicator of the reaction to the Covid-19 pandemic.\n"}
+{"abstract": "  This paper considers the uplink of user-centric cell-free massive MIMO\n(multiple-input multiple-output) systems. We utilize the user-centric dynamic\ncooperation clustering (DCC) framework and derive the achievable spectral\nefficiency with two-layer decoding that is divided between the access points\nand the central processing unit (CPU). This decoding method is also known as\nlarge-scale fading decoding (LSFD). The fronthaul signaling load is analyzed\nand a nearly optimal second-layer decoding scheme at the CPU is proposed to\nreduce the fronthaul requirements compared to the optimal scheme. We also\nrevisit the joint optimization of LSFD weights and uplink power control and\nshow that the corresponding max-min fair optimization problem can be solved\noptimally via an efficient fixed-point algorithm. We provide simulations that\nbring new insights into the cell-free massive MIMO implementation.\n"}
+{"abstract": "  Most modern multiple object tracking (MOT) systems follow the\ntracking-by-detection paradigm, consisting of a detector followed by a method\nfor associating detections into tracks. There is a long history in tracking of\ncombining motion and appearance features to provide robustness to occlusions\nand other challenges, but typically this comes with the trade-off of a more\ncomplex and slower implementation. Recent successes on popular 2D tracking\nbenchmarks indicate that top-scores can be achieved using a state-of-the-art\ndetector and relatively simple associations relying on single-frame spatial\noffsets -- notably outperforming contemporary methods that leverage learned\nappearance features to help re-identify lost tracks. In this paper, we propose\nan efficient joint detection and tracking model named DEFT, or \"Detection\nEmbeddings for Tracking.\" Our approach relies on an appearance-based object\nmatching network jointly-learned with an underlying object detection network.\nAn LSTM is also added to capture motion constraints. DEFT has comparable\naccuracy and speed to the top methods on 2D online tracking leaderboards while\nhaving significant advantages in robustness when applied to more challenging\ntracking data. DEFT raises the bar on the nuScenes monocular 3D tracking\nchallenge, more than doubling the performance of the previous top method. Code\nis publicly available.\n"}
+{"abstract": "  Deep convolutional neural networks (DCNNs) have revolutionized computer\nvision and are often advocated as good models of the human visual system.\nHowever, there are currently many shortcomings of DCNNs, which preclude them as\na model of human vision. For example, in the case of adversarial attacks, where\nadding small amounts of noise to an image, including an object, can lead to\nstrong misclassification of that object. But for humans, the noise is often\ninvisible. If vulnerability to adversarial noise cannot be fixed, DCNNs cannot\nbe taken as serious models of human vision. Many studies have tried to add\nfeatures of the human visual system to DCNNs to make them robust against\nadversarial attacks. However, it is not fully clear whether human vision\ninspired components increase robustness because performance evaluations of\nthese novel components in DCNNs are often inconclusive. We propose a set of\ncriteria for proper evaluation and analyze different models according to these\ncriteria. We finally sketch future efforts to make DCCNs one step closer to the\nmodel of human vision.\n"}
+{"abstract": "  Euclids algorithm is widely used in calculating of GCD (Greatest Common\nDivisor) of two positive numbers. There are various fields where this division\nis used such as channel coding, cryptography, and error correction codes. This\nmakes the GCD a fundamental algorithm in number theory, so a number of methods\nhave been discovered to efficiently compute it. The main contribution of this\npaper is to investigate a method that computes the GCD of two 32-bit numbers\nbased on Euclidean algorithm which targets six different Xilinx chips. The\ncomplexity of this method that we call Optimized_GCDSAD is achieved by\nutilizing Sum of Absolute Difference (SAD) block which is based on a fast\ncarry-out generation function. The efficiency of the proposed architecture is\nevaluated based on criteria such as time (latency), area delay product (ADP)\nand space (slice number) complexity. The VHDL codes of these architectures have\nbeen implemented and synthesized through ISE 14.7. A detailed comparative\nanalysis indicates that the proposed Optimized_GCDSAD method based on SAD block\noutperforms previously known results.\n"}
+{"abstract": "  We present a formalization in Isabelle/HOL of quantum projective\nmeasurements, a class of measurements involving orthogonal projectors that is\nfrequently used in quantum computing. We also formalize the CHSH inequality, a\nresult that holds on arbitrary probability spaces, which can used to disprove\nthe existence of a local hidden-variable theory for quantum mechanics.\n"}
+{"abstract": "  Let $E$ be a CM number field and $F$ its maximal real subfield. We prove a\nlevel-raising result for regular algebraic conjugate self-dual automorphic\nrepresentations of $GL_n(\\mathbb{A}_E)$. This generalizes previously known\nresults of Thorne by removing certain hypotheses occurring in that work. In\nparticular, the level-raising prime $p$ is allowed to be unramified as opposed\nto inert in $F$, the field $E/F$ is not assumed to be everywhere unramified,\nand the field $E$ is allowed to be a general CM field.\n"}
+{"abstract": "  Stealthy false data injection attacks on cyber-physical systems (CPSs)\nintroduce erroneous measurement information to on-board sensors with the\npurpose to degrade system performance. An intelligent attacker is able to\nleverage knowledge of the system model and noise characteristics to alter\nsensor measurements while remaining undetected. To achieve this objective, the\nstealthy attack sequence is designed such that the detector performs similarly\nin the attacked and attack-free cases. Consequently, an attacker that wants to\nremain hidden will leave behind traces of inconsistent behavior, contradicting\nthe system model. To deal with this problem, we propose a runtime monitor to\nfind these inconsistencies in sensor measurements by monitoring for serial\ninconsistencies of the detection test measure. Specifically, we employ the\nchi-square fault detection procedure to monitor the magnitude and signed\nsequence of its chi-square test measure. We validate our approach with\nsimulations on an unmanned ground vehicle (UGV) under stealthy attacks and\ncompare the detection performance with various state-of-the-art anomaly\ndetectors.\n"}
+{"abstract": "  Field observations and laboratory experiments have shown that ash\nsedimentation can be significantly affected by collective settling mechanisms\nthat promote premature ash deposition, with important implications for\nassociated impacts. Among these mechanisms, settling-driven gravitational\ninstabilities result from the formation of a gravitationally-unstable particle\nboundary layer (PBL) that grows between volcanic ash clouds and the underlying\natmosphere. The PBL destabilises once it reaches a critical thickness,\ntriggering the formation of rapid, downward-moving ash fingers that remain\npoorly characterised. We simulate this process by coupling a Lattice Boltzmann\nmodel, which solves the Navier-Stokes equations for the fluid phase, with a\nWeighted Essentially Non Oscillatory (WENO) finite difference scheme which\nsolves the advection-diffusion-settling equation describing particle transport.\nSince the physical problem is advection dominated, the use of the WENO scheme\nreduces numerical diffusivity and ensures accurate tracking of the temporal\nevolution of the interface between the layers. We have validated the new model\nby showing that the simulated early-time growth rate of the instability is in\nvery good agreement with that predicted by linear stability analysis, whilst\nthe modelled late-stage behaviour also successfully reproduces quantitative\nresults from published laboratory experiments.\n"}
+{"abstract": "  We present a practical approach to treat static and dynamical correlation\naccurately in large multi-configurational systems. The static correlation is\naccounted for using the spin-flip approach which is well known for capturing\nstatic correlation accurately at low-computational expense. Unlike previous\napproaches to add dynamical correlation to spin-flip models which use\nperturbation theory or coupled-cluster theory, we explore the ability to use\nthe on-top pair-density functional theory approaches recently developed by\nGagliardi and co-workers (JCTC, 10, 3669, 2014). External relaxations are\ncarried out in the spin-flip calculations though a restricted active space\nframework for which a truncation scheme for the orbitals used in the external\nexcitation is presented. The performance of the approach is demonstrated by\ncomputing energy gaps between ground and excited states for diradicals,\ntriradicals and linear polyacene chains ranging from naphthalene to dodecacene.\nAccurate results are obtained using the new approach for these challenging\nopen-shell molecular systems.\n"}
+{"abstract": "  For a subgroup of $PGL(2,q)$ we show how some irreducible polynomials over\n$\\mathbb{F}_q$ arise from the field of invariant rational functions. The proofs\nrely on two actions of $PGL(2,F)$, one on the projective line over a field $F$\nand the other on the rational function field $F(x)$. The invariant functions in\n$F(x)$ are used to show that regular patterns exist in the factorization of\ncertain polynomials into irreducible polynomials. We use some results about\ngroup actions and the orbit polynomial, whose proofs are included. An unusual\nconnection to the conjugacy classes of $PGL(2,q)$ is shown. At the end of the\npaper we present an alternative approach, using Lang's theorem on algebraic\ngroups.\n"}
+{"abstract": "  We present algorithmic perturbative solution of $\\mathcal{N}=4$ SYM quantum\nspectral curve in the case of twist 2 operators, valid to in principle\narbitrary order in coupling constant. The latter treats operator spins as\narbitrary integer values and is written in terms of special class of functions\n-- products of rational functions in spectral parameter with sums of Baxter\npolynomials and Hurwitz functions. It is shown that this class of functions is\nclosed under elementary operations, such as shifts, partial fractions,\nmultiplication by spectral parameter and differentiation. Also, it is fully\nsufficient to solve arising non-homogeneous multiloop Baxter and first order\ndifference equations. As an application of the proposed method we present the\ncomputation of anomalous dimensions of twist 2 operators up to four loop order.\n"}
+{"abstract": "  The results of a search for gluino and squark pair production with the pairs\ndecaying via the lightest charginos into a final state consisting of two $W$\nbosons, the lightest neutralinos ($\\tilde\\chi^0_1$), and quarks, are presented.\nThe signal is characterised by the presence of a single charged lepton\n($e^{\\pm}$ or $\\mu^{\\pm}$) from a $W$ boson decay, jets, and missing transverse\nmomentum. The analysis is performed using 139 fb$^{-1}$ of proton-proton\ncollision data taken at a centre-of-mass energy $\\sqrt{s}=13$ TeV delivered by\nthe Large Hadron Collider and recorded by the ATLAS experiment. No\nstatistically significant excess of events above the Standard Model expectation\nis found. Limits are set on the direct production of squarks and gluinos in\nsimplified models. Masses of gluino (squark) up to 2.2 TeV (1.4 TeV) are\nexcluded at 95% confidence level for a light $\\tilde\\chi^0_1$.\n"}
+{"abstract": "  Infant motility assessment using intelligent wearables is a promising new\napproach for assessment of infant neurophysiological development, and where\nefficient signal analysis plays a central role. This study investigates the use\nof different end-to-end neural network architectures for processing infant\nmotility data from wearable sensors. We focus on the performance and\ncomputational burden of alternative sensor encoder and time-series modelling\nmodules and their combinations. In addition, we explore the benefits of data\naugmentation methods in ideal and non-ideal recording conditions. The\nexperiments are conducted using a data-set of multi-sensor movement recordings\nfrom 7-month-old infants, as captured by a recently proposed smart jumpsuit for\ninfant motility assessment. Our results indicate that the choice of the encoder\nmodule has a major impact on classifier performance. For sensor encoders, the\nbest performance was obtained with parallel 2-dimensional convolutions for\nintra-sensor channel fusion with shared weights for all sensors. The results\nalso indicate that a relatively compact feature representation is obtainable\nfor within-sensor feature extraction without a drastic loss to classifier\nperformance. Comparison of time-series models revealed that feed-forward\ndilated convolutions with residual and skip connections outperformed all\nRNN-based models in performance, training time, and training stability. The\nexperiments also indicate that data augmentation improves model robustness in\nsimulated packet loss or sensor dropout scenarios. In particular, signal- and\nsensor-dropout-based augmentation strategies provided considerable boosts to\nperformance without negatively affecting the baseline performance. Overall the\nresults provide tangible suggestions on how to optimize end-to-end neural\nnetwork training for multi-channel movement sensor data.\n"}
+{"abstract": "  The recent results from the Fermilab muon $g-2$ experiment, as well as the\npersisting hints of lepton flavor universality violation in $B$-meson decays,\npresent a very strong case for some flavor-nonuniversal beyond the Standard\nModel physics. We reinforce our previous claim that a minimal $R$-parity\nviolating supersymmetric framework with relatively light third-generation\nsfermions (dubbed as `RPV3') provides a natural, well-motivated framework for\nthe simultaneous explanation of all flavor anomalies, while being consistent\nwith current low- and high-energy experimental constraints. We further propose\ncomplementary tests and distinct signatures of this scenario in the high-$p_T$\nsearches at current and future colliders. In particular, we emphasize that the\ndominant resolution to muon $g-2$ in RPV3 comes from a sub-TeV scale sneutrino\nwith a relatively large coupling to muons, which leads to a spectacular\nfour-muon signal at the LHC.\n"}
+{"abstract": "  Deep learning based models are vulnerable to adversarial attacks. These\nattacks can be much more harmful in case of targeted attacks, where an attacker\ntries not only to fool the deep learning model, but also to misguide the model\nto predict a specific class. Such targeted and untargeted attacks are\nspecifically tailored for an individual sample and require addition of an\nimperceptible noise to the sample. In contrast, universal adversarial attack\ncalculates a special imperceptible noise which can be added to any sample of\nthe given dataset so that, the deep learning model is forced to predict a wrong\nclass. To the best of our knowledge these targeted and universal attacks on\ntime series data have not been studied in any of the previous works. In this\nwork, we have performed untargeted, targeted and universal adversarial attacks\non UCR time series datasets. Our results show that deep learning based time\nseries classification models are vulnerable to these attacks. We also show that\nuniversal adversarial attacks have good generalization property as it need only\na fraction of the training data. We have also performed adversarial training\nbased adversarial defense. Our results show that models trained adversarially\nusing Fast gradient sign method (FGSM), a single step attack, are able to\ndefend against FGSM as well as Basic iterative method (BIM), a popular\niterative attack.\n"}
+{"abstract": "  Accurate modeling of boundary conditions is crucial in computational physics.\nThe ever increasing use of neural networks as surrogates for physics-related\nproblems calls for an improved understanding of boundary condition treatment,\nand its influence on the network accuracy. In this paper, several strategies to\nimpose boundary conditions (namely padding, improved spatial context, and\nexplicit encoding of physical boundaries) are investigated in the context of\nfully convolutional networks applied to recurrent tasks. These strategies are\nevaluated on two spatio-temporal evolving problems modeled by partial\ndifferential equations: the 2D propagation of acoustic waves (hyperbolic PDE)\nand the heat equation (parabolic PDE). Results reveal a high sensitivity of\nboth accuracy and stability on the boundary implementation in such recurrent\ntasks. It is then demonstrated that the choice of the optimal padding strategy\nis directly linked to the data semantics. Furthermore, the inclusion of\nadditional input spatial context or explicit physics-based rules allows a\nbetter handling of boundaries in particular for large number of recurrences,\nresulting in more robust and stable neural networks, while facilitating the\ndesign and versatility of such networks.\n"}
+{"abstract": "  If gamma-ray bursts are at cosmological distances, they must be\ngravitationally lensed occasionally. The detection of lensed images with\nmillisecond-to-second time delays provides evidence for intermediate-mass black\nholes, a population that has been difficult to observe. Several studies have\nsearched for these delays in gamma-ray burst light curves, which would indicate\nan intervening gravitational lens. Among the $\\sim 10^4$ gamma-ray bursts\nobserved, there have been a handful of claimed lensing detections, but none\nhave been statistically robust. Here we present a Bayesian analysis identifying\ngravitational lensing in the light curve of GRB950830. The inferred lens mass\ndepends on the unknown lens redshift $z_l$, and is given by $(1+z_l)M_l =\n5.5^{+1.7}_{-0.9}\\times 10^4 $ M$_\\odot$ (90% credibility), which we interpret\nas evidence for an intermediate-mass black hole. The most probable\nconfiguration, with a lens redshift $z_l\\sim 1$ and a gamma-ray burst redshift\n$z_s\\sim 2$, yields a present day number density of $n_\\text{imbh}\\approx\n2.3^{+4.9}_{-1.6}\\times10^{3} \\text{ Mpc}^{-3}$ (90% credibility) with a\ndimensionless energy density $\\Omega_\\text{imbh} \\approx\n4.6^{+9.8}_{-3.3}\\times10^{-4}$. The false alarm probability for this detection\nis $\\sim0.6\\%$ with trial factors. While it is possible that GRB950830 was\nlensed by a globular cluster, it is unlikely since we infer a cosmic density\ninconsistent with predictions for globular clusters $\\Omega_\\text{gc} \\approx 8\n\\times 10^{-6}$ at 99.8% credibility. If a significant intermediate-mass black\nhole population exists, it could provide the seeds for the growth of\nsupermassive black holes in the early Universe.\n"}
+{"abstract": "  Recent studies have shown convolutional neural networks (CNNs) can be trained\nto perform modal decomposition using intensity images of optical fields. A\nfundamental limitation of these techniques is that the modal phases can not be\nuniquely calculated using a single intensity image. The knowledge of modal\nphases is crucial for wavefront sensing, alignment and mode matching\napplications. Heterodyne imaging techniques can provide images of the\ntransverse complex amplitude & phase profile of laser beams at high resolutions\nand frame rates. In this work we train a CNN to perform modal decomposition\nusing simulated heterodyne images, allowing the complete modal phases to be\npredicted. This is to our knowledge the first machine learning decomposition\nscheme to utilize complex phase information to perform modal decomposition. We\ncompare our network with a traditional overlap integral & center-of-mass\ncentering algorithm and show that it is both less sensitive to beam centering\nand on average more accurate.\n"}
+{"abstract": "  The problem of natural language processing over structured data has become a\ngrowing research field, both within the relational database and the Semantic\nWeb community, with significant efforts involved in question answering over\nknowledge graphs (KGQA). However, many of these approaches are either\nspecifically targeted at open-domain question answering using DBpedia, or\nrequire large training datasets to translate a natural language question to\nSPARQL in order to query the knowledge graph. Hence, these approaches often\ncannot be applied directly to complex scientific datasets where no prior\ntraining data is available.\n  In this paper, we focus on the challenges of natural language processing over\nknowledge graphs of scientific datasets. In particular, we introduce Bio-SODA,\na natural language processing engine that does not require training data in the\nform of question-answer pairs for generating SPARQL queries. Bio-SODA uses a\ngeneric graph-based approach for translating user questions to a ranked list of\nSPARQL candidate queries. Furthermore, Bio-SODA uses a novel ranking algorithm\nthat includes node centrality as a measure of relevance for selecting the best\nSPARQL candidate query. Our experiments with real-world datasets across several\nscientific domains, including the official bioinformatics Question Answering\nover Linked Data (QALD) challenge, show that Bio-SODA outperforms publicly\navailable KGQA systems by an F1-score of least 20% and by an even higher factor\non more complex bioinformatics datasets.\n"}
+{"abstract": "  We examine the problem of time delay estimation, or temporal calibration, in\nthe context of multisensor data fusion. Differences in processing intervals and\nother factors typically lead to a relative delay between measurement updates\nfrom disparate sensors. Correct (optimal) data fusion demands that the relative\ndelay must either be known in advance or identified online. There have been\nseveral recent proposals in the literature to determine the delay using\nrecursive, causal filters such as the extended Kalman filter (EKF). We\ncarefully review this formulation and show that there are fundamental issues\nwith the structure of the EKF (and related algorithms) when the delay is\nincluded in the filter state vector as a parameter to be estimated. These\nstructural issues, in turn, leave recursive filters prone to bias and\ninconsistency. Our theoretical analysis is supported by simulation studies that\ndemonstrate the implications in terms of filter performance; although tuning of\nthe filter noise variances may reduce the chance of inconsistency or\ndivergence, the underlying structural concerns remain. We offer brief\nsuggestions for ways to maintain the computational efficiency of recursive\nfiltering for temporal calibration while avoiding the drawbacks of the standard\nfiltering algorithms.\n"}
+{"abstract": "  Topological data analysis (TDA) studies the shape patterns of data.\nPersistent homology (PH) is a widely used method in TDA that summarizes\nhomological features of data at multiple scales and stores them in persistence\ndiagrams (PDs). In this paper, we propose a random persistence diagram\ngeneration (RPDG) method that generates a sequence of random PDs from the ones\nproduced by the data. RPDG is underpinned by (i) a model based on pairwise\ninteracting point processes for inference of persistence diagrams, and (ii) by\na reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithm for generating\nsamples of PDs. A first example, which is based on a synthetic dataset,\ndemonstrates the efficacy of RPDG and provides a detailed comparison with other\nexisting methods for sampling PDs. A second example demonstrates the utility of\nRPDG to solve a materials science problem given a real dataset of small sample\nsize.\n"}
+{"abstract": "  The motive of this paper is to discuss the local convergence of a two-step\nNewton type method of convergence rate three for solving nonlinear equations in\nBanach spaces. It is assumed that the first order derivative of nonlinear\noperator satisfies the generalized Lipschitz i.e. $L$-average condition. Also,\nsome results on convergence of the same method in Banach spaces are established\nunder the assumption that the derivative of the operators satisfies the radius\nor center Lipschitz condition with a weak $L$-average particularly it is\nassumed that $L$ is positive integrable function but not necessarily\nnon-decreasing.\n"}
+{"abstract": "  Although zero-shot learning (ZSL) has an inferential capability of\nrecognizing new classes that have never been seen before, it always faces two\nfundamental challenges of the cross modality and crossdomain challenges. In\norder to alleviate these problems, we develop a generative network-based ZSL\napproach equipped with the proposed Cross Knowledge Learning (CKL) scheme and\nTaxonomy Regularization (TR). In our approach, the semantic features are taken\nas inputs, and the output is the synthesized visual features generated from the\ncorresponding semantic features. CKL enables more relevant semantic features to\nbe trained for semantic-to-visual feature embedding in ZSL, while Taxonomy\nRegularization (TR) significantly improves the intersections with unseen images\nwith more generalized visual features generated from generative network.\nExtensive experiments on several benchmark datasets (i.e., AwA1, AwA2, CUB, NAB\nand aPY) show that our approach is superior to these state-of-the-art methods\nin terms of ZSL image classification and retrieval.\n"}
+{"abstract": "  We propose an algorithm inspired by optical coherent Ising machines to solve\nthe problem of polynomial unconstrained binary optimisation (PUBO). We\nbenchmark the proposed algorithm against existing PUBO algorithms on the\nextended Sherrington-Kirkpatrick model and random third-degree polynomial\npseudo-Boolean functions, and observe its superior performance. We also address\ninstances of practically relevant computational problems such as protein\nfolding and electronic structure calculations with problem sizes not accessible\nto existing quantum annealing devices. In particular, we successfully find the\nlowest-energy conformation of lattice protein molecules containing up to eleven\namino-acids. The application of our algorithm to quantum chemistry sheds light\non the shortcomings of approximating the electronic structure problem by a PUBO\nproblem, which, in turn, puts into question the applicability of quantum\nannealers in this context.\n"}
+{"abstract": "  The known three-component reaction-diffusion system modeling competition and\nco-existence of different language speakers is under study. A modification of\nthis system is proposed, which is examined by Lie symmetry method; furthermore\nexact solutions in the form of traveling fronts are constructed and their\nproperties are identified. Plots of the traveling fronts are presented and the\nrelevant interpretation describing the language shift occurred in Ukraine\nduring the Soviet times is suggested.\n"}
+{"abstract": "  Joint radar and communication (RadCom) systems have been proposed to achieve\nthe spectrum sharing between radar and communication in recent years. However,\nthe joint RadCom systems cause the clutter modulation and the performance\ndegradation of both radar and communication. As a consequence, it's very\ncritical to improve the performance of both radar and communication when\ndesigning joint RadCom systems. Firstly, this paper designs the constant\nmodulus dual function waveforms for spatial modulation based joint RadCom\nsystems. In order to solve the nonsmooth and nonconvex optimization problem, we\npropose a method based on alternating direction method of multipliers (ADMM) to\nobtain a suboptimal solution. Secondly, this paper analyses the effect of\nbeampattern variation on clutter modulation for spatial modulation based joint\nRadCom systems. Besides, this paper considers the design of robust doppler\nfilter for joint RadCom systems when the receive temporal steering vector is\nmismatched to the desired temporal steering vector. This paper aims to maximize\nthe worst signal-to-interference-plus-noise-ratio (SINR) of the doppler filter\nin the specific doppler interval under the similarity constraint and constant\nwhite noise gain constraint. In order to solve the nonconvex optimization\nproblem, we relax the original optimization problem to a semidefinite\nprogramming (SDP), which is convex and can be solved using off-the-shelf\noptimization solvers. What's more, the rank-one decomposition method is\nutilized to synthesize the weight vector of the proposed robust doppler filter.\nFinally, lots of simulation results are presented to show the robustness of the\ndesigned doppler filter.\n"}
+{"abstract": "  We study in this paper the possible existence of Roskilde-simple liquids and\ntheir isomorphs in a rough-wall nanoconfinement. Isomorphs are curves in the\nthermodynamic phase diagram along which structure and dynamics are invariant in\nsuitable nondimensionalized units. Two model liquids using molecular dynamics\ncomputer simulations are considered: the single-component Lennard-Jones (LJ)\nliquid and the Kob-Andersen binary LJ mixture, both of which in the bulk phases\nare known to have isomorphs. Nanoconfinement is implemented by adopting a\nslit-pore geometry with fcc crystalline walls; this implies inhomogenous\ndensity profiles both parallel and perpendicular to the confining walls.\nDespite this fact and consistent with an earlier study [Ingebrigtsen et. al,\nPhys. Rev. Lett. 111, 235901 (2013)] we find that these nanoconfined liquids\nhave isomorphs to a good approximation. More specifically, we show good scaling\nof inhomogenous density profiles, mean-square displacements, and higher-order\nstructures probed using the topological cluster classification algorithm along\nthe isomorphs. From this study, we conjecture that in experiments,\nRoskilde-simple liquids may exhibit isomorphs if confined in a suitable manner,\nfor example with carbon nanotubes. Our study thus provides an alternative\nframework for understanding nanoconfined liquids.\n"}
+{"abstract": "  Multi-speaker spoken datasets enable the creation of text-to-speech synthesis\n(TTS) systems which can output several voice identities. The multi-speaker\n(MSPK) scenario also enables the use of fewer training samples per speaker.\nHowever, in the resulting acoustic model, not all speakers exhibit the same\nsynthetic quality, and some of the voice identities cannot be used at all.\n  In this paper we evaluate the influence of the recording conditions, speaker\ngender, and speaker particularities over the quality of the synthesised output\nof a deep neural TTS architecture, namely Tacotron2. The evaluation is possible\ndue to the use of a large Romanian parallel spoken corpus containing over 81\nhours of data. Within this setup, we also evaluate the influence of different\ntypes of text representations: orthographic, phonetic, and phonetic extended\nwith syllable boundaries and lexical stress markings.\n  We evaluate the results of the MSPK system using the objective measures of\nequal error rate (EER) and word error rate (WER), and also look into the\ndistances between natural and synthesised t-SNE projections of the embeddings\ncomputed by an accurate speaker verification network. The results show that\nthere is indeed a large correlation between the recording conditions and the\nspeaker's synthetic voice quality. The speaker gender does not influence the\noutput, and that extending the input text representation with syllable\nboundaries and lexical stress information does not equally enhance the\ngenerated audio across all speaker identities. The visualisation of the t-SNE\nprojections of the natural and synthesised speaker embeddings show that the\nacoustic model shifts some of the speakers' neural representation, but not all\nof them. As a result, these speakers have lower performances of the output\nspeech.\n"}
+{"abstract": "  An increasing body of research focuses on using neural networks to model time\nseries. A common assumption in training neural networks via maximum likelihood\nestimation on time series is that the errors across time steps are\nuncorrelated. However, errors are actually autocorrelated in many cases due to\nthe temporality of the data, which makes such maximum likelihood estimations\ninaccurate. In this paper, in order to adjust for autocorrelated errors, we\npropose to learn the autocorrelation coefficient jointly with the model\nparameters. In our experiments, we verify the effectiveness of our approach on\ntime series forecasting. Results across a wide range of real-world datasets\nwith various state-of-the-art models show that our method enhances performance\nin almost all cases. Based on these results, we suggest empirical critical\nvalues to determine the severity of autocorrelated errors. We also analyze\nseveral aspects of our method to demonstrate its advantages. Finally, other\ntime series tasks are also considered to validate that our method is not\nrestricted to only forecasting.\n"}
+{"abstract": "  A prospective measurement of the differential cross section of $tWZ$\nproduction with respect to the transverse momentum of the $Z$ boson using a\ngeneral-purpose detector at the High-Luminosity Large Hadron Collider (HL-LHC)\nis described. Constraints on the Standard Model Effective Field Theory (SMEFT)\nenabled by the measurement are estimated. Uncertainties and inter-bin\ncorrelations are used to determine the measurement's expected covariance\nmatrix. A parametric model of the differential cross section in the SMEFT is\nconstructed and, together with the expected covariance matrix, is used to\ndetermine the expected posterior probability function of six SMEFT Wilson\ncoefficients. Expected 95% Bayesian credible intervals for each coefficient and\npair of coefficients are derived by marginalising this posterior function. The\nintervals suggest that the measurement will result in improved constraints on\nthe $c_{tZ}$ coefficient. For the other coefficients, the measurement will\nlikely provide weaker constraints than those derived from other HL-LHC\nmeasurements involving top quarks and $Z$ bosons. However, as the measurement\nis simultaneously sensitive to many coefficients, it will provide a useful\ninput to a SMEFT analysis that simultaneously utilises measurements of multiple\nprocesses.\n"}
+{"abstract": "  Scotogenic models are among the most popular possibilities to link dark\nmatter and neutrino masses. In this work we discuss a variant of the Scotogenic\nmodel that includes charged fermions and a doublet with hypercharge $3/2$.\nNeutrino masses are induced at the one-loop level thanks to the states\nbelonging to the dark sector. However, in contrast to the standard Scotogenic\nmodel, only the scalar dark matter candidate is viable in this version. After\npresenting the model and explaining some particularities about neutrino mass\ngeneration, we concentrate on its dark matter phenomenology. We show that the\nobserved dark matter relic density can be correctly reproduced in the usual\nparameter space regions found for the standard Scotogenic model or the Inert\nDoublet model. In addition, the presence of the charged fermions may open up\nnew regions, provided some tuning of the parameters is allowed.\n"}
+{"abstract": "  Twenty years have passed since Kechris' seminal survey paper [A. S. Kechris,\nNew directions in descriptive set theory. Bull. Symbolic Logic, 5(2):161-174,\n1999]. As a follow-up of that work, we review some ot the (anti-)classification\nresults that have been obtained in the last decade using Borel reducibility and\nits generalizations to uncountable cardinals.\n"}
+{"abstract": "  We theoretically study the antiferromagnetic domain wall motion actuated by\nan inhomogeneous external magnetic field. The Lagrangian and the equations of\nmotion of antiferromagnetic spins under an inhomogeneous magnetic field are\nderived, first in terms of the N\\'eel vector, and then using collective\ncoordinates of the domain wall. A solution is found that describes the\nactuation of a domain wall by an inhomogeneous field, in which the motion is\ninitiated by a paramagnetic response of wall magnetization, which is then\ndriven by a Stern-Gerlach like force. The effects of pinning potential are also\ninvestigated. These results are in good agreement with atomistic simulations.\nWhile the present formulation contains the so-called intrinsic magnetization\nassociated with N\\'eel texture, a supplementary discussion is given to\nreformulate the theory in terms of physical magnetization without the intrinsic\nmagnetization.\n"}
+{"abstract": "  The Raychaudhuri equations for the expansion, shear and vorticity are\ngeneralized in a spacetime with torsion for timelike as well as null\ncongruences. These equations are purely geometrical like the original\nRaychaudhuri equations and could be reduced to them when there is no torsion.\nUsing the Einstein-Cartan-Sciama-Kibble field equations the effective\nstress-energy tensor is derived. We also consider an Oppenheimer-Snyder model\nfor the gravitational collapse of dust. It is shown that the null energy\ncondition (NEC) is violated before the density of the collapsing dust reaches\nthe Planck density, hinting that the spacetime singularity may be avoided if\nthere is a non-zero torsion,i.e. if the collapsing dust particles possess\nintrinsic spin.\n"}
+{"abstract": "  We investigate the formation of the satellite galaxy population of a Milky\nWay-mass halo in a very highly resolved magneto-hydrodynamic cosmological\nzoom-in simulation (baryonic mass resolution $m_b =$ 800 $\\rm M_{\\odot}$). We\nshow that the properties of the central star-forming galaxy, such as the radial\nstellar surface density profile and star formation history, are: i) robust to\nstochastic variations associated with the so-called ``Butterfly Effect''; and\nii) well converged over 3.5 orders of magnitude in mass resolution. We find\nthat there are approximately five times as many satellite galaxies at this high\nresolution compared to a standard ($m_b\\sim 10^{4-5}\\, \\rm M_{\\odot}$)\nresolution simulation of the same system. This is primarily because 2/3rds of\nthe high resolution satellites do not form at standard resolution. A smaller\nfraction (1/6th) of the satellites present at high resolution form and disrupt\nat standard resolution; these objects are preferentially low-mass satellites on\nintermediate- to low-eccentricity orbits with impact parameters $\\lesssim 30$\nkpc. As a result, the radial distribution of satellites becomes substantially\nmore centrally concentrated at higher resolution, in better agreement with\nrecent observations of satellites around Milky Way-mass haloes. Finally, we\nshow that our galaxy formation model successfully forms ultra-faint galaxies\nand reproduces the stellar velocity dispersion, half-light radii, and $V$-band\nluminosities of observed Milky Way and Local Group dwarf galaxies across 6\norders of magnitude in luminosity ($10^3$-$10^{9}$ $\\rm L_{\\odot}$).\n"}
+{"abstract": "  A Hayward black hole and magnetically charged black hole based on rational\nnonlinear electrodynamics with the Lagrangian ${\\cal L} = -{\\cal\nF}/(1+2\\beta{\\cal F})$ (${\\cal F}$ is a field invariant) are considered. It was\nshown that the metric function in both models possesses a de Sitter core\nwithout singularities as $r\\rightarrow 0$. The behavior of the Hawking\ntemperature and the heat capacity in these models are similar. The phase\ntransitions take place when the Hawking temperature has a maximum, and black\nholes are thermodynamically stable at some event horizon radii when the heat\ncapacity is positive. We show that the source of gravity in the Hayward model\nis questionable.\n"}
+{"abstract": "  3D shape editing is widely used in a range of applications such as movie\nproduction, computer games and computer aided design. It is also a popular\nresearch topic in computer graphics and computer vision. In past decades,\nresearchers have developed a series of editing methods to make the editing\nprocess faster, more robust, and more reliable. Traditionally, the deformed\nshape is determined by the optimal transformation and weights for an energy\nterm. With increasing availability of 3D shapes on the Internet, data-driven\nmethods were proposed to improve the editing results. More recently as the deep\nneural networks became popular, many deep learning based editing methods have\nbeen developed in this field, which is naturally data-driven. We mainly survey\nrecent research works from the geometric viewpoint to those emerging neural\ndeformation techniques and categorize them into organic shape editing methods\nand man-made model editing methods. Both traditional methods and recent neural\nnetwork based methods are reviewed.\n"}
+{"abstract": "  With the rapid growth of traffic sensors deployed, a massive amount of\ntraffic flow data are collected, revealing the long-term evolution of traffic\nflows and the gradual expansion of traffic networks. How to accurately\nforecasting these traffic flow attracts the attention of researchers as it is\nof great significance for improving the efficiency of transportation systems.\nHowever, existing methods mainly focus on the spatial-temporal correlation of\nstatic networks, leaving the problem of efficiently learning models on networks\nwith expansion and evolving patterns less studied. To tackle this problem, we\npropose a Streaming Traffic Flow Forecasting Framework, TrafficStream, based on\nGraph Neural Networks (GNNs) and Continual Learning (CL), achieving accurate\npredictions and high efficiency. Firstly, we design a traffic pattern fusion\nmethod, cleverly integrating the new patterns that emerged during the long-term\nperiod into the model. A JS-divergence-based algorithm is proposed to mine new\ntraffic patterns. Secondly, we introduce CL to consolidate the knowledge\nlearned previously and transfer them to the current model. Specifically, we\nadopt two strategies: historical data replay and parameter smoothing. We\nconstruct a streaming traffic dataset to verify the efficiency and\neffectiveness of our model. Extensive experiments demonstrate its excellent\npotential to extract traffic patterns with high efficiency on long-term\nstreaming network scene. The source code is available at\nhttps://github.com/AprLie/TrafficStream.\n"}
+{"abstract": "  J. Stix proved that a curve of positive genus over $\\mathbb{Q}$ which maps to\na non-trivial Brauer-Severi variety satisfies the section conjecture. We prove\nthat, if $X$ is a curve of positive genus over a number field $k$ and the Weil\nrestriction $R_{k/\\mathbb{Q}}X$ admits a rational map to a non-trivial\nBrauer-Severi variety, then $X$ satisfies the section conjecture. As a\nconsequence, if $X$ maps to a Brauer-Severi variety $P$ such that the\ncorestriction\n$\\operatorname{cor}_{k/\\mathbb{Q}}([P])\\in\\operatorname{Br}(\\mathbb{Q})$ is\nnon-trivial, then $X$ satisfies the section conjecture.\n"}
+{"abstract": "  We present a 0.15$^{\\prime\\prime}$ resolution (21 au) ALMA 870 $\\mu$m\ncontinuum survey of 25 pointings containing 31 young stellar objects in the\nOphiuchus molecular clouds. Using the dust continuum as a proxy for dust mass\nand circumstellar disk radius in our sample, we report a mean mass of\n2.8$^{+2.1}_{-1.3}$ and 2.5$^{+9.2}_{-1.1}$ M$_{\\oplus}$ and a mean radii of\n23.5$^{+1.8}_{-1.2}$ and 16.5$^{+2.8}_{-0.9}$ au, for Class I and Flat spectrum\nprotostars, respectively. In addition, we calculate the multiplicity statistics\nof the dust surrounding young stellar objects in Ophiuchus. The multiplicity\nfraction (MF) and companion star fraction (CSF) of the combined Class I and\nFlats based solely on this work is 0.25 $\\pm$ 0.09 and 0.33 $\\pm$ 0.10,\nrespectively, which are consistent with the values for Perseus and Orion. While\nwe see clear differences in mass and radius between the Ophiuchus and\nPerseus/Orion protostellar surveys, we do not see any significant differences\nin the multiplicities of the various regions. We posit there are some\ndifferences in the conditions for star formation in Ophiuchus that strongly\naffects disk size (and consequently disk mass), but does not affect system\nmultiplicity, which could imply important variation in planet formation\nprocesses.\n"}
+{"abstract": "  Millisecond pulsars are very likely the main source of gamma-ray emission\nfrom globular clusters. However, the relative contributions of two separate\nemission processes--curvature radiation from millisecond pulsar magnetospheres\nvs. inverse Compton emission from relativistic pairs launched into the globular\ncluster environment by millisecond pulsars--have long been unclear. To address\nthis, we search for evidence of inverse Compton emission in 8-year\n$\\textit{Fermi}$-LAT data from the directions of 157 Milky Way globular\nclusters. We find a mildly statistically significant (3.8$\\sigma$) correlation\nbetween the measured globular cluster gamma-ray luminosities and their photon\nfield energy densities. However, this may also be explained by a hidden\ncorrelation between the photon field densities and the stellar encounter rates\nof globular clusters. Analysed $\\textit{in toto}$, we demonstrate that the\ngamma-ray emission of globular clusters can be resolved spectrally into two\ncomponents: i) an exponentially cut-off power law and ii) a pure power law. The\nlatter component--which we uncover at a significance of 8.2$\\sigma$--has a\npower index of 2.79 $\\pm$ 0.25. It is most naturally interpreted as inverse\nCompton emission by cosmic-ray electrons and positrons injected by millisecond\npulsars. We find the luminosity of this power-law component is comparable to,\nor slightly smaller than, the luminosity of the curved component, suggesting\nthe fraction of millisecond pulsar spin-down luminosity into relativistic\nleptons is similar to the fraction of the spin-down luminosity into prompt\nmagnetospheric radiation.\n"}
+{"abstract": "  The notion of retro Banach frame with the help of b-linear functional in\nn-Banach spaces is being presented. Some properties related to the construction\nof new retro Banach frame in n-Banach space have been studied. In n-Banach\nspaces, some perturbation results of retro Banach frame have been discussed.\nFinally, we give a condition in which finite sum of retro Banach frames is a\nretro Banach frame in n-Banach space.\n"}
+{"abstract": "  Conceptually, partial information decomposition (PID) is concerned with\nseparating the information contributions several sources hold about a certain\ntarget by decomposing the corresponding joint mutual information into\ncontributions such as synergistic, redundant, or unique information. Despite\nPID conceptually being defined for any type of random variables, so far, PID\ncould only be quantified for the joint mutual information of discrete systems.\nRecently, a quantification for PID in continuous settings for two or three\nsource variables was introduced. Nonetheless, no ansatz has managed to both\nquantify PID for more than three variables and cover general measure-theoretic\nrandom variables, such as mixed discrete-continuous, or continuous random\nvariables yet. In this work we will propose an information quantity, defining\nthe terms of a PID, which is well-defined for any number or type of source or\ntarget random variable. This proposed quantity is tightly related to a recently\ndeveloped local shared information quantity for discrete random variables based\non the idea of shared exclusions. Further, we prove that this newly proposed\ninformation-measure fulfills various desirable properties, such as satisfying a\nset of local PID axioms, invariance under invertible transformations,\ndifferentiability with respect to the underlying probability density, and\nadmitting a target chain rule.\n"}
+{"abstract": "  Modern modeling languages for general physical systems, such as Modelica,\nAmesim, or Simscape, rely on Differential Algebraic Equations (DAEs), i.e.,\nconstraints of the form f(\\dot{x},x,u)=0. This drastically facilitates modeling\nfrom first principles of the physics, as well as model reuse. In recent works\n[RR-9334], we presented the mathematical theory needed to establish the\ndevelopment of compilers and tools for DAE-based physical modeling languages on\nsolid mathematical grounds.At the core of this analysis sits the so-called\n*structural analysis*, whose purpose, at compile time, is to either identify\nunder- and over-specified subsystems (if any), or to rewrite the model in a\nform amenable of existing DAE solvers, including the handling of mode change\nevents. The notion of \"structure\" collects, for each mode and mode change\nevent, the variables and equations involved, as well as the *latent equations*\n(additional equations redundant with the system), needed to prepare the code\nsubmitted to the solver. The notion of DAE *index* (the minimal number of times\nany equation has to be possibly differentiated) is part of this structural\nanalysis. This report complements [RR-9334] by collecting all the needed\nbackground on structural analysis. The body of knowledge on structural analysis\nis large and scattered, which also motivated us to collect it in a single\nreport.We first explain the primary meaning of structural analysis of systems\nof equations, namely the study of their regularity or singularity in some\ngeneric sense. We then briefly review the body of graph theory used in this\ncontext. We develop some extensions, for which we are not aware of any\nreference, namely the structural analysis of systems of equations with\nexistential quantifiers. For the structural analysis of DAE systems, we focus\non John Pryce's Sigma-method, that we both summarize and extend to non-square\nsystems. The uses of these tools and methods in [RR9334] are highlighted in\nthis report.\n"}
+{"abstract": "  Imitation learning has been widely used to learn control policies for\nautonomous driving based on pre-recorded data. However, imitation learning\nbased policies have been shown to be susceptible to compounding errors when\nencountering states outside of the training distribution. Further, these agents\nhave been demonstrated to be easily exploitable by adversarial road users\naiming to create collisions. To overcome these shortcomings, we introduce\nAdversarial Mixture Density Networks (AMDN), which learns two distributions\nfrom separate datasets. The first is a distribution of safe actions learned\nfrom a dataset of naturalistic human driving. The second is a distribution\nrepresenting unsafe actions likely to lead to collision, learned from a dataset\nof collisions. During training, we leverage these two distributions to provide\nan additional loss based on the similarity of the two distributions. By\npenalising the safe action distribution based on its similarity to the unsafe\naction distribution when training on the collision dataset, a more robust and\nsafe control policy is obtained. We demonstrate the proposed AMDN approach in a\nvehicle following use-case, and evaluate under naturalistic and adversarial\ntesting environments. We show that despite its simplicity, AMDN provides\nsignificant benefits for the safety of the learned control policy, when\ncompared to pure imitation learning or standard mixture density network\napproaches.\n"}
+{"abstract": "  COVID-19 is having a dramatic impact on research and researchers. The\npandemic has underlined the severity of known challenges in research and\nsurfaced new ones, but also accelerated the adoption of innovations and\nmanifested new opportunities. This review considers early trends emerging from\nmeta-research on COVID-19. In particular, it focuses on the following topics:\ni) mapping COVID-19 research; ii) data and machine learning; iii) research\npractices including open access and open data, reviewing, publishing and\nfunding; iv) communicating research to the public; v) the impact of COVID-19 on\nresearchers, in particular with respect to gender and career trajectories. This\noverview finds that most early meta-research on COVID-19 has been reactive and\nfocused on short-term questions, while more recently a shift to consider the\nlong-term consequences of COVID-19 is taking place. Based on these findings,\nthe author speculates that some aspects of doing research during COVID-19 are\nmore likely to persist than others. These include: the shift to virtual for\nacademic events such as conferences; the use of openly accessible pre-prints;\nthe `datafication' of scholarly literature and consequent broader adoption of\nmachine learning in science communication; the public visibility of research\nand researchers on social and online media.\n"}
+{"abstract": "  We consider variational integrals of linear growth satisfying the condition\nof $\\mu$-ellipticity for some exponent $\\mu >1$ and prove that stationary\npoints $u$: $\\mathbb{R}^2 \\to \\mathbb{R}^N$ with the property \\[\n\\limsup_{|x|\\to \\infty} \\frac{|u(x)|}{|x|} < \\infty \\] must be affine\nfunctions. The latter condition can be dropped in the scalar case together with\nappropriate assumptions on the energy density providing an extension of\nBernstein's theorem.\n"}
+{"abstract": "  A distinct signature of compact extra dimensions would be a Kaluza-Klein\ntower of gravitational waves. Motivated by this prospect, we compute the\ncorresponding spectrum on a warped toroidal background. We evaluate in\nparticular the impact of the warp factor on the spectrum. To that end, we use\nthe complete warp factor H of standard string compactifications, generated by\nD-branes and orientifolds, thus connecting to recent works on stringy de Sitter\nconstructions. The problematic region close to an orientifold where H < 0 leads\nto unphysical tachyonic modes in the spectrum. We develop tools that overcome\nthis difficulty and lead to a tachyon-free spectrum. We show, in particular,\nthat the warp factor can lower the first Kaluza-Klein mass by at least 69%.\n"}
+{"abstract": "  Evolution algebras with one dimensional square are classified using the\ntheory of inner product spaces. More precisely, for $A$ an evolution algebra\nwith $\\dim(A^2) = 1$ and $a$ a generator of $A^2$, the product of $A$ is given\nby $xy = \\langle x,y\\rangle a$. Three broad classes of algebras are obtained:\n  (1) $a\\in\\hbox{Ann}(A)$;\n  (2) $a\\notin\\text{Ann}(A)$ and $a$ is isotropic relative to $\\langle\\cdot,\n\\cdot\\rangle$;\n  (3) $a\\notin\\text{Ann}(A)$ and $a$ is anisotropic relative to $\\langle\\cdot,\n\\cdot\\rangle$.\n"}
+{"abstract": "  Risk contributions of portfolios form an indispensable part of risk adjusted\nperformance measurement. The risk contribution of a portfolio, e.g., in the\nEuler or Aumann-Shapley framework, is given by the partial derivatives of a\nrisk measure applied to the portfolio return in direction of the asset weights.\nFor risk measures that are not positively homogeneous of degree 1, however,\nknown capital allocation principles do not apply. We study the class of lambda\nquantile risk measures, that includes the well-known Value-at-Risk as a special\ncase, but for which no known allocation rule is applicable. We prove\ndifferentiability and derive explicit formulae of the derivatives of lambda\nquantiles with respect to their portfolio composition, that is their risk\ncontribution. For this purpose, we define lambda quantiles on the space of\nportfolio compositions and consider generic (also non-linear) portfolio\noperators.\n  We further derive the Euler decomposition of lambda quantiles for generic\nportfolios and show that lambda quantiles are homogeneous in the space of\nportfolio compositions, with a homogeneity degree that depends on the portfolio\ncomposition and the lambda function. This result is in stark contrast to the\npositive homogeneity properties of risk measures defined on the space of random\nvariables which admit a constant homogeneity degree. We introduce a generalised\nversion of Euler contributions and Euler allocation rule, which are compatible\nwith risk measures of any homogeneity degree and non-linear portfolios. We\nfurther provide financial interpretations of the homogeneity degree of lambda\nquantiles and introduce the notion of event-specific homogeneity of portfolio\noperators.\n"}
+{"abstract": "  Given a finite set E and an operator sigma:2^{E}-->2^{E}, two subsets X,Y of\nthe ground set E are cospanning if sigma(X)=sigma(Y) (Korte, Lovasz, Schrader;\n1991). We investigate cospanning relations on violator spaces. A notion of a\nviolator space was introduced in (Gartner, Matousek, Rust, Skovrovn; 2008) as a\ncombinatorial framework that encompasses linear programming and other geometric\noptimization problems. Violator spaces are defined by violator operators. We\nintroduce co-violator spaces based on contracting operators known also as\nchoice functions. Let alpha,beta:2^{E}-->2^{E} be a violator operator and a\nco-violator operator, respectively. Cospanning characterizations of violator\nspaces allow us to obtain some new properties of violator operators,\nco-violator operators, and their interconnections. In particular, we show that\nuniquely generated violator spaces enjoy so-called Krein-Milman properties,\ni.e., alpha(beta(X))=alpha(X) and beta(alpha}(X))=beta(X) for every subset X of\nthe ground set E.\n"}
+{"abstract": "  We present a novel double-copy prescription for gauge fields at the\nLagrangian level and apply it both to the original double copy and the soft\ntheorem. The Yang-Mills Lagrangian in light-cone gauge is mapped directly to\nthe $\\mathcal{N}=0$ supergravity Lagrangian in light-cone gauge to trilinear\norder, and we show that the obtained result is manifestly equivalent to\nEinstein gravity at tree level up to this order. The application of the\ndouble-copy prescription to the soft-collinear effective QCD Lagrangian yields\nan effective description of an energetic Dirac fermion coupled to the graviton,\nKalb-Ramond, and dilaton fields, from which the fermionic gravitational soft\nand next-to-soft theorems follow.\n"}
+{"abstract": "  Within the framework of likelihood-based statistical tests for high energy\nphysics measurements, we derive generalized expressions for estimating the\nstatistical significance of discovery using the asymptotic approximations of\nWilks and Wald for a variety of measurement models. These models include\narbitrary numbers of signal regions, control regions, and Gaussian constraints.\nWe extend our expressions to use the representative or \"Asimov\" dataset\nproposed by Cowan et al. such that they are made data-free. While many of the\ngeneralized expressions are complicated and often involve solving systems of\ncoupled, multivariate equations, we show these expressions reduce to\nclosed-form results under simplifying assumptions. We also validate the\npredicted significance using toy-based data in select cases.\n"}
+{"abstract": "  Many robot applications call for autonomous exploration and mapping of\nunknown and unstructured environments. Information-based exploration\ntechniques, such as Cauchy-Schwarz quadratic mutual information (CSQMI) and\nfast Shannon mutual information (FSMI), have successfully achieved active\nbinary occupancy mapping with range measurements. However, as we envision\nrobots performing complex tasks specified with semantically meaningful objects,\nit is necessary to capture semantic categories in the measurements, map\nrepresentation, and exploration objective. This work develops a Bayesian\nmulti-class mapping algorithm utilizing range-category measurements. We derive\na closed-form efficiently computable lower bound for the Shannon mutual\ninformation between the multi-class map and the measurements. The bound allows\nrapid evaluation of many potential robot trajectories for autonomous\nexploration and mapping. We compare our method against frontier-based and FSMI\nexploration and apply it in a 3-D photo-realistic simulation environment.\n"}
+{"abstract": "  Topological structures of electromagnetic fields could give access to\nnontrivial light-matter interactions and additional degrees of freedom for\ninformation and energy transfer. A characteristic example of such\nelectromagnetic excitations are space-time non-separable single-cycle pulses,\nthe exact solutions of Maxwell equation of toroidal topology predicted by\nHellwarth and Nouchi in 1996 and recently observed experimentally. Here we\nintroduce a new family of electromagnetic excitation of toroidal topology with\nincreasing complexity in which the Hellwarth-Nouchi pulse is just the simplest\nmember. The electromagnetic excitations of the new family can be parametrised\nby a single real number and exhibit skyrmionic structures of various orders.\nThey feature multiple singularities in the electromagnetic and Poynting vector\nfields are accompanied by the fractal-like distributions of energy backflow.\nThe generalized family of toroidal electromagnetic excitation with salient\ntopologies are of interest for transient light-matter interactions, ultrafast\noptics, spectroscopy, and toroidal electrodynamics.\n"}
+{"abstract": "  GCIRS 7, the brightest star in the Galactic central parsec, formed $6\\pm2$\nMyr ago together with dozens of massive stars in a disk orbiting the central\nblack-hole. It has been argued that GCIRS 7 is a pulsating body, on the basis\nof photometric variability. We present the first medium-resolution ($R=500$),\nK-band spectro-interferometric observations of GCIRS 7, using the GRAVITY\ninstrument with the four auxiliary telescopes of the ESO VLTI. We looked for\nvariations using two epochs, namely 2017 and 2019. We find GCIRS 7 to be\nmoderately resolved with a uniform-disk photospheric diameter of\n$\\theta^*_\\text{UD}=1.55 \\pm 0.03$ mas ($R^*_\\text{UD}=1368 \\pm 26$ $R_\\odot$)\nin the K-band continuum. The narrow-band uniform-disk diameter increases above\n2.3 $\\mu$m, with a clear correlation with the CO band heads in the spectrum.\nThis correlation is aptly modeled by a hot ($T_\\text{L}=2368\\pm37$ K),\ngeometrically thin molecular shell with a diameter of\n$\\theta_\\text{L}=1.74\\pm0.03$ mas, as measured in 2017. The shell diameter\nincreased ($\\theta_\\text{L}=1.89\\pm0.03$ mas), while its temperature decreased\n($T_\\text{L}=2140\\pm42$ K) in 2019. In contrast, the photospheric diameter\n$\\theta^*_\\text{UD}$ and the extinction up to the photosphere of GCIRS 7\n($A_{\\mathrm{K}_\\mathrm{S}}=3.18 \\pm 0.16$) have the same value within\nuncertainties at the two epochs. In the context of previous interferometric and\nphoto-spectrometric measurements, the GRAVITY data allow for an interpretation\nin terms of photospheric pulsations. The photospheric diameter measured in 2017\nand 2019 is significantly larger than previously reported using the PIONIER\ninstrument ($\\theta_*=1.076 \\pm 0.093$ mas in 2013 in the H band). The\nparameters of the photosphere and molecular shell of GCIRS 7 are comparable to\nthose of other red supergiants that have previously been studied using\ninterferometry.\n"}
+{"abstract": "  In this article, we investigate the problem of entanglement characterization\nwith polarization measurements combined with maximum likelihood estimation\n(MLE). A realistic scenario is considered with measurement results distorted by\nrandom experimental errors. In particular, by imposing unitary rotations acting\non the measurement operators, we can test the performance of the tomographic\ntechnique versus the amount of noise. Then, dark counts are introduced to\nexplore the efficiency of the framework in a multi-dimensional noise scenario.\nThe concurrence is used as a figure of merit to quantify how well entanglement\nis preserved through noisy measurements. Quantum fidelity is computed to\nquantify the accuracy of state reconstruction. The results of numerical\nsimulations are depicted on graphs and discussed.\n"}
+{"abstract": "  Parareal is a well-studied algorithm for numerically integrating systems of\ntime-dependent differential equations by parallelising the temporal domain.\nGiven approximate initial values at each temporal sub-interval, the algorithm\nlocates a solution in a fixed number of iterations using a predictor-corrector,\nstopping once a tolerance is met. This iterative process combines solutions\nlocated by inexpensive (coarse resolution) and expensive (fine resolution)\nnumerical integrators. In this paper, we introduce a stochastic parareal\nalgorithm with the aim of accelerating the convergence of the deterministic\nparareal algorithm. Instead of providing the predictor-corrector with a\ndeterministically located set of initial values, the stochastic algorithm\nsamples initial values from dynamically varying probability distributions in\neach temporal sub-interval. All samples are then propagated by the numerical\nmethod in parallel. The initial values yielding the most continuous (smoothest)\ntrajectory across consecutive sub-intervals are chosen as the new, more\naccurate, set of initial values. These values are fed into the\npredictor-corrector, converging in fewer iterations than the deterministic\nalgorithm with a given probability. The performance of the stochastic\nalgorithm, implemented using various probability distributions, is illustrated\non systems of ordinary differential equations. When the number of sampled\ninitial values is large enough, we show that stochastic parareal converges\nalmost certainly in fewer iterations than the deterministic algorithm while\nmaintaining solution accuracy. Additionally, it is shown that the expected\nvalue of the convergence rate decreases with increasing numbers of samples.\n"}
+{"abstract": "  Optical flow is inherently a 2D search problem, and thus the computational\ncomplexity grows quadratically with respect to the search window, making large\ndisplacements matching infeasible for high-resolution images. In this paper, we\ntake inspiration from Transformers and propose a new method for high-resolution\noptical flow estimation with significantly less computation. Specifically, a 1D\nattention operation is first applied in the vertical direction of the target\nimage, and then a simple 1D correlation in the horizontal direction of the\nattended image is able to achieve 2D correspondence modeling effect. The\ndirections of attention and correlation can also be exchanged, resulting in two\n3D cost volumes that are concatenated for optical flow estimation. The novel 1D\nformulation empowers our method to scale to very high-resolution input images\nwhile maintaining competitive performance. Extensive experiments on Sintel,\nKITTI and real-world 4K ($2160 \\times 3840$) resolution images demonstrated the\neffectiveness and superiority of our proposed method. Code and models are\navailable at \\url{https://github.com/haofeixu/flow1d}.\n"}
+{"abstract": "  The multidisciplinary nature of response robotics has brought about a\ndiversified research community with extended expertise. Motivated by the recent\naccelerated rate of publications in the field, this paper analyzes the\ntechnical content, statistics, and implications of the literature from\nbibliometric standpoints. The aim is to study the global progress of response\nrobotics research and identify the contemporary trends. To that end, we\ninvestigated the collaboration mapping together with the citation network to\nformally recognize impactful and contributing authors, publications, sources,\ninstitutions, funding agencies, and countries. We found how natural and\nhuman-made disasters contributed to forming productive regional research\ncommunities, while there are communities that only view response robotics as an\napplication of their research. Furthermore, through an extensive discussion on\nthe bibliometric results, we elucidated the philosophy behind research priority\nshifts in response robotics and presented our deliberations on future research\ndirections.\n"}
+{"abstract": "  We solve the Dirichlet Problem of Monge-Amp\\`ere equation near an isolate Klt\nsingularity, which generalizes the result of Eyssidieux-Guedj-Zeriahi\n\\cite{EGZ}, where the Monge-Amp\\`ere equation is solved on singular varieties\nwithout boundary. As a corollary, we construct solutions to Monge-Amp\\`ere\nequation with isolated singularity on strongly pseudoconvex domain $\\Omega$\ncontained in $\\mathbb{C}^n$.\n"}
+{"abstract": "  Homomorphic secret sharing (HSS) allows multiple input clients to\nsecret-share their data among multiple servers such that each server is able to\nlocally compute a function on its shares to obtain a partial result and all\npartial results enable the reconstruction of the function's value on the\noutsourced data by an output client. The existing HSS schemes for {\\em\nhigh-degree} polynomials either {\\em require a large number of servers} or {\\em\nlack verifiability}, which is essential for ensuring the correctness of the\noutsourced computations. In this paper, we propose a two-server verifiable HSS\n(VHSS) model and construct a scheme that supports the computation of\nhigh-degree polynomials. The degree of the outsourced polynomials can be as\nhigh as a polynomial in the system's security parameter. Despite of using only\n2 servers, our VHSS ensures that each single server learns no information about\nthe outsourced data and no single server is able to persuade the client to\noutput a wrong function value. Our VHSS is significantly more efficient. When\ncomputing degree-7 polynomials, our scheme could be 3-10 times faster than the\npreviously best construction.\n"}
+{"abstract": "  One of the biggest problems in itemset mining is the requirement of\ndeveloping a data structure or algorithm, every time a user wants to extract a\ndifferent type of itemsets. To overcome this, we propose a method, called\nGeneric Itemset Mining based on Reinforcement Learning (GIM-RL), that offers a\nunified framework to train an agent for extracting any type of itemsets. In\nGIM-RL, the environment formulates iterative steps of extracting a target type\nof itemsets from a dataset. At each step, an agent performs an action to add or\nremove an item to or from the current itemset, and then obtains from the\nenvironment a reward that represents how relevant the itemset resulting from\nthe action is to the target type. Through numerous trial-and-error steps where\nvarious rewards are obtained by diverse actions, the agent is trained to\nmaximise cumulative rewards so that it acquires the optimal action policy for\nforming as many itemsets of the target type as possible. In this framework, an\nagent for extracting any type of itemsets can be trained as long as a reward\nsuitable for the type can be defined. The extensive experiments on mining high\nutility itemsets, frequent itemsets and association rules show the general\neffectiveness and one remarkable potential (agent transfer) of GIM-RL. We hope\nthat GIM-RL opens a new research direction towards learning-based itemset\nmining.\n"}
+{"abstract": "  An essential need for many model-based robot control algorithms is the\nability to quickly and accurately compute partial derivatives of the equations\nof motion. State of the art approaches to this problem often use analytical\nmethods based on the chain rule applied to existing dynamics algorithms.\nAlthough these methods are an improvement over finite differences in terms of\naccuracy, they are not always the most efficient. In this paper, we contribute\nnew closed-form expressions for the first-order partial derivatives of inverse\ndynamics, leading to a recursive algorithm. The algorithm is benchmarked\nagainst chain-rule approaches in Fortran and against an existing algorithm from\nthe Pinocchio library in C++. Tests consider computing the partial derivatives\nof inverse and forward dynamics for robots ranging from kinematic chains to\nhumanoids and quadrupeds. Compared to the previous open-source Pinocchio\nimplementation, our new analytical results uncover a key computational\nrestructuring that enables efficiency gains. Speedups of up to 1.4x are\nreported for calculating the partial derivatives of inverse dynamics for the\n50-dof Talos humanoid.\n"}
+{"abstract": "  Video summarization aims at generating a compact yet representative visual\nsummary that conveys the essence of the original video. The advantage of\nunsupervised approaches is that they do not require human annotations to learn\nthe summarization capability and generalize to a wider range of domains.\nPrevious work relies on the same type of deep features, typically based on a\nmodel pre-trained on ImageNet data. Therefore, we propose the incorporation of\nmultiple feature sources with chunk and stride fusion to provide more\ninformation about the visual content. For a comprehensive evaluation on the two\nbenchmarks TVSum and SumMe, we compare our method with four state-of-the-art\napproaches. Two of these approaches were implemented by ourselves to reproduce\nthe reported results. Our evaluation shows that we obtain state-of-the-art\nresults on both datasets, while also highlighting the shortcomings of previous\nwork with regard to the evaluation methodology. Finally, we perform error\nanalysis on videos for the two benchmark datasets to summarize and spot the\nfactors that lead to misclassifications.\n"}
+{"abstract": "  Many versions of Thermodynamic Uncertainty Relations (TUR) have recently been\ndiscovered, which impose lower bounds on relative fluctuations of integrated\ncurrents in irreversible dissipative processes, and suggest that there may be\nfundamental limitations on the precision of small scale machines and heat\nengines. In this work we rigorously demonstrate that TUR can be evaded by using\ndynamic protocols that are asymmetric under time-reversal. We illustrate our\nresults using a model heat engine using two-level systems, and also discuss\nheuristically the fundamental connections between TUR and time-reversal\nsymmetry.\n"}
+{"abstract": "  It is of extreme importance to monitor and manage the battery health to\nenhance the performance and decrease the maintenance cost of operating electric\nvehicles. This paper concerns the machine-learning-enabled state-of-health\n(SoH) prognosis for Li-ion batteries in electric trucks, where they are used as\nenergy sources. The paper proposes methods to calculate SoH and cycle life for\nthe battery packs. We propose autoregressive integrated modeling average\n(ARIMA) and supervised learning (bagging with decision tree as the base\nestimator; BAG) for forecasting the battery SoH in order to maximize the\nbattery availability for forklift operations. As the use of data-driven methods\nfor battery prognostics is increasing, we demonstrate the capabilities of ARIMA\nand under circumstances when there is little prior information available about\nthe batteries. For this work, we had a unique data set of 31 lithium-ion\nbattery packs from forklifts in commercial operations. On the one hand, results\nindicate that the developed ARIMA model provided relevant tools to analyze the\ndata from several batteries. On the other hand, BAG model results suggest that\nthe developed supervised learning model using decision trees as base estimator\nyields better forecast accuracy in the presence of large variation in data for\none battery.\n"}
+{"abstract": "  Neural Radiance Fields (NeRF) have recently gained a surge of interest within\nthe computer vision community for its power to synthesize photorealistic novel\nviews of real-world scenes. One limitation of NeRF, however, is its requirement\nof accurate camera poses to learn the scene representations. In this paper, we\npropose Bundle-Adjusting Neural Radiance Fields (BARF) for training NeRF from\nimperfect (or even unknown) camera poses -- the joint problem of learning\nneural 3D representations and registering camera frames. We establish a\ntheoretical connection to classical image alignment and show that\ncoarse-to-fine registration is also applicable to NeRF. Furthermore, we show\nthat na\\\"ively applying positional encoding in NeRF has a negative impact on\nregistration with a synthesis-based objective. Experiments on synthetic and\nreal-world data show that BARF can effectively optimize the neural scene\nrepresentations and resolve large camera pose misalignment at the same time.\nThis enables view synthesis and localization of video sequences from unknown\ncamera poses, opening up new avenues for visual localization systems (e.g.\nSLAM) and potential applications for dense 3D mapping and reconstruction.\n"}
+{"abstract": "  We show that in cubic crystals with anisotropic impurity centers the sum of\nsquares of the magnetic resonance (EPR) frequencies is invariant with respect\nto the magnetic field direction. The connection between such an invariant and\nthe g-tensor components of the impurity is derived for different types of\ncenters. The established regularity is confirmed experimentally for the\nspin-noise spectra of a CaF2-Nd3+ crystal. We show how this property of the EPR\nspectra can be efficiently used for the assignment of paramagnetic centers in\ncubic crystals.\n"}
+{"abstract": "  Graph transformer networks (GTN) are a variant of graph convolutional\nnetworks (GCN) that are targeted to heterogeneous graphs in which nodes and\nedges have associated type information that can be exploited to improve\ninference accuracy. GTNs learn important metapaths in the graph, create\nweighted edges for these metapaths, and use the resulting graph in a GCN.\nCurrently, the only available implementation of GTNs uses dense matrix\nmultiplication to find metapaths. Unfortunately, the space overhead of this\napproach can be large, so in practice it is used only for small graphs. In\naddition, the matrix-based implementation is not fine-grained enough to use\nrandom-walk based methods to optimize metapath finding. In this paper, we\npresent a graph-based formulation and implementation of the GTN metapath\nfinding problem. This graph-based formulation has two advantages over the\nmatrix-based approach. First, it is more space efficient than the original GTN\nimplementation and more compute-efficient for metapath sizes of practical\ninterest. Second, it permits us to implement a sampling method that reduces the\nnumber of metapaths that must be enumerated, allowing the implementation to be\nused for larger graphs and larger metapath sizes. Experimental results show\nthat our implementation is $6.5\\times$ faster than the original GTN\nimplementation on average for a metapath length of 4, and our sampling\nimplementation is $155\\times$ faster on average than this implementation\nwithout compromising on the accuracy of the GTN.\n"}
+{"abstract": "  We analyze the consistency of electroweak breaking, neutrino and dark matter\nphenomenology within the simplest scoto-seesaw model. By adding the minimal\ndark sector to the simplest \"missing partner\" type-I seesaw one has a physical\npicture for the neutrino oscillation lengths: the \"atmospheric\" mass scale\narises from the tree-level seesaw, while the \"solar\" scale is induced\nradiatively, mediated by the dark sector. We identify parameter regions\nconsistent with theoretical constraints, as well as dark matter relic abundance\nand direct detection searches. Using two-loop renormalization group equations\nwe explore the stability of the vacuum and the consistency of the underlying\ndark parity symmetry. One also has a lower bound for the neutrinoless double\nbeta decay amplitude.\n"}
+{"abstract": "  In this paper, we present AISHELL-4, a sizable real-recorded Mandarin speech\ndataset collected by 8-channel circular microphone array for speech processing\nin conference scenario. The dataset consists of 211 recorded meeting sessions,\neach containing 4 to 8 speakers, with a total length of 120 hours. This dataset\naims to bridge the advanced research on multi-speaker processing and the\npractical application scenario in three aspects. With real recorded meetings,\nAISHELL-4 provides realistic acoustics and rich natural speech characteristics\nin conversation such as short pause, speech overlap, quick speaker turn, noise,\netc. Meanwhile, accurate transcription and speaker voice activity are provided\nfor each meeting in AISHELL-4. This allows the researchers to explore different\naspects in meeting processing, ranging from individual tasks such as speech\nfront-end processing, speech recognition and speaker diarization, to\nmulti-modality modeling and joint optimization of relevant tasks. Given most\nopen source dataset for multi-speaker tasks are in English, AISHELL-4 is the\nonly Mandarin dataset for conversation speech, providing additional value for\ndata diversity in speech community. We also release a PyTorch-based training\nand evaluation framework as baseline system to promote reproducible research in\nthis field.\n"}
+{"abstract": "  We study the Fundamental Plane (FP) for a volume- and luminosity-limited\nsample of 560 early-type galaxies from the SAMI survey. Using r-band sizes and\nluminosities from new Multi-Gaussian Expansion (MGE) photometric measurements,\nand treating luminosity as the dependent variable, the FP has coefficients\na=1.294$\\pm$0.039, b= 0.912$\\pm$0.025, and zero-point c= 7.067$\\pm$0.078. We\nleverage the high signal-to-noise of SAMI integral field spectroscopy, to\ndetermine how structural and stellar-population observables affect the scatter\nabout the FP. The FP residuals correlate most strongly (8$\\sigma$ significance)\nwith luminosity-weighted simple-stellar-population (SSP) age. In contrast, the\nstructural observables surface mass density, rotation-to-dispersion ratio,\nS\\'ersic index and projected shape all show little or no significant\ncorrelation. We connect the FP residuals to the empirical relation between age\n(or stellar mass-to-light ratio $\\Upsilon_\\star$) and surface mass density, the\nbest predictor of SSP age amongst parameters based on FP observables. We show\nthat the FP residuals (anti-)correlate with the residuals of the relation\nbetween surface density and $\\Upsilon_\\star$. This correlation implies that\npart of the FP scatter is due to the broad age and $\\Upsilon_\\star$\ndistribution at any given surface mass density. Using virial mass and\n$\\Upsilon_\\star$ we construct a simulated FP and compare it to the observed FP.\nWe find that, while the empirical relations between observed stellar population\nrelations and FP observables are responsible for most (75%) of the FP scatter,\non their own they do not explain the observed tilt of the FP away from the\nvirial plane.\n"}
+{"abstract": "  We describe in details the interplay between binary symplectic geometry and\nquantum computation, with the ultimate goal of constructing highly structured\ncodebooks. The Binary Chirps (BCs) are Complex Grassmannian Lines in $N = 2^m$\ndimensions used in deterministic compressed sensing and random/unsourced\nmultiple access in wireless networks. Their entries are fourth roots of unity\nand can be described in terms of second order Reed-Muller codes. The Binary\nSubspace Chirps (BSSCs) are a unique collection of BCs of $\\textit{ranks}$\nranging from $r=0$ to $r = m$, embedded in $N$ dimensions according to an\non-off pattern determined by a rank $r$ binary subspace. This yields a codebook\nthat is asymptotically 2.38 times larger than the codebook of BCs, has the same\nminimum chordal distance as the codebook of BCs, and the alphabet is minimally\nextended from $\\{\\pm 1,\\pm i\\}$ to $\\{\\pm 1,\\pm i, 0\\}$. Equivalently, we show\nthat BSSCs are stabilizer states, and we characterize them as columns of a\nwell-controlled collection of Clifford matrices. By construction, the BSSCs\ninherit all the properties of BCs, which in turn makes them good candidates for\na variety of applications. For applications in wireless communication, we use\nthe rich algebraic structure of BSSCs to construct a low complexity decoding\nalgorithm that is reliable against Gaussian noise. In simulations, BSSCs\nexhibit an error probability comparable or slightly lower than BCs, both for\nsingle-user and multi-user transmissions.\n"}
+{"abstract": "  Line-graph (LG) lattices are known for having topological flat bands (FBs)\nfrom the destructive interference of Bloch wavefunctions encoded in lattice\nsymmetry. Here, we develop an atomic/molecular orbital design principle for the\nexistence of FBs in non-LG lattices. Using a generic tight-binding model, we\ndemonstrate that the underlying wavefunction symmetry of FBs in a LG lattice\ncan be transformed into the atomic/molecular orbital symmetry in a non-LG\nlattice. We show such orbital-designed topological FBs in three common 2D\nnon-LG, square, trigonal, and hexagonal lattices, where the chosen orbitals\nfaithfully reproduce the corresponding localized FB plaquette states of\ncheckerboard, Kagome, and diatomic-Kagome lattices, respectively. Fundamentally\nour theory enriches the FB physics; practically the proposed orbital design\nprinciple is expected to significantly expand the scope of FB materials, since\nmost materials have multiple atomic/molecular orbitals at each lattice site,\nrather than a single s orbital mandated in graph theory.\n"}
+{"abstract": "  3D video avatars can empower virtual communications by providing compression,\nprivacy, entertainment, and a sense of presence in AR/VR. Best 3D\nphoto-realistic AR/VR avatars driven by video, that can minimize uncanny\neffects, rely on person-specific models. However, existing person-specific\nphoto-realistic 3D models are not robust to lighting, hence their results\ntypically miss subtle facial behaviors and cause artifacts in the avatar. This\nis a major drawback for the scalability of these models in communication\nsystems (e.g., Messenger, Skype, FaceTime) and AR/VR. This paper addresses\nprevious limitations by learning a deep learning lighting model, that in\ncombination with a high-quality 3D face tracking algorithm, provides a method\nfor subtle and robust facial motion transfer from a regular video to a 3D\nphoto-realistic avatar. Extensive experimental validation and comparisons to\nother state-of-the-art methods demonstrate the effectiveness of the proposed\nframework in real-world scenarios with variability in pose, expression, and\nillumination. Please visit https://www.youtube.com/watch?v=dtz1LgZR8cc for more\nresults. Our project page can be found at\nhttps://www.cs.rochester.edu/u/lchen63.\n"}
+{"abstract": "  A deep learning-based monocular depth estimation (MDE) technique is proposed\nfor selection of most informative frames (key frames) of an endoscopic video.\nIn most of the cases, ground truth depth maps of polyps are not readily\navailable and that is why the transfer learning approach is adopted in our\nmethod. An endoscopic modalities generally capture thousands of frames. In this\nscenario, it is quite important to discard low-quality and clinically\nirrelevant frames of an endoscopic video while the most informative frames\nshould be retained for clinical diagnosis. In this view, a key-frame selection\nstrategy is proposed by utilizing the depth information of polyps. In our\nmethod, image moment, edge magnitude, and key-points are considered for\nadaptively selecting the key frames. One important application of our proposed\nmethod could be the 3D reconstruction of polyps with the help of extracted key\nframes. Also, polyps are localized with the help of extracted depth maps.\n"}
+{"abstract": "  All robots create consequential sound -- sound produced as a result of the\nrobot's mechanisms -- yet little work has explored how sound impacts\nhuman-robot interaction. Recent work shows that the sound of different robot\nmechanisms affects perceived competence, trust, human-likeness, and discomfort.\nHowever, the physical sound characteristics responsible for these perceptions\nhave not been clearly identified. In this paper, we aim to explore key\ncharacteristics of robot sound that might influence perceptions. A pilot study\nfrom our past work showed that quieter and higher-pitched robots may be\nperceived as more competent and less discomforting. To better understand how\nvariance in these attributes affects perception, we performed audio\nmanipulations on two sets of industrial robot arm videos within a series of\nfour new studies presented in this paper. Results confirmed that quieter robots\nwere perceived as less discomforting. In addition, higher-pitched robots were\nperceived as more energetic, happy, warm, and competent. Despite the robot's\nindustrial purpose and appearance, participants seemed to prefer more \"cute\"\n(or \"kawaii\") sound profiles, which could have implications for the design of\nmore acceptable and fulfilling sound profiles for human-robot interactions with\npractical collaborative robots.\n"}
+{"abstract": "  Course selection is a crucial activity for students as it directly impacts\ntheir workload and performance. It is also time-consuming, prone to\nsubjectivity, and often carried out based on incomplete information. This task\ncan, nevertheless, be assisted with computational tools, for instance, by\npredicting performance based on historical data. We investigate the effects of\nshowing grade predictions to students through an interactive visualization\ntool. A qualitative study suggests that in the presence of predictions,\nstudents may focus too much on maximizing their performance, to the detriment\nof other factors such as the workload. A follow-up quantitative study explored\nwhether these effects are mitigated by changing how predictions are conveyed.\nOur observations suggest the presence of a framing effect that induces students\nto put more effort into course selection when faced with more specific\npredictions. We discuss these and other findings and outline considerations for\ndesigning better data-driven course selection tools.\n"}
+{"abstract": "  Understanding the basic operational logics of the nervous system is essential\nto advancing neuroscientific research. However, theoretical efforts to tackle\nthis fundamental problem are lacking, despite the abundant empirical data about\nthe brain that has been collected in the past few decades. To address this\nshortcoming, this document introduces a hypothetical framework for the\nfunctional nature of primitive neural networks. It analyzes the idea that the\nactivity of neurons and synapses can symbolically reenact the dynamic changes\nin the world and thus enable an adaptive system of behavior. More\nsignificantly, the network achieves this without participating in an\nalgorithmic structure. When a neuron's activation represents some symbolic\nelement in the environment, each of its synapses can indicate a potential\nchange to the element and its future state. The efficacy of a synaptic\nconnection further specifies the element's particular probability for, or\ncontribution to, such a change. As it fires, a neuron's activation is\ntransformed to its postsynaptic targets, resulting in a chronological shift of\nthe represented elements. As the inherent function of summation in a neuron\nintegrates the various presynaptic contributions, the neural network mimics the\ncollective causal relationship of events in the observed environment.\n"}
+{"abstract": "  In recent years, the use of machine learning-based surrogate models for\ncomputational fluid dynamics (CFD) simulations has emerged as a promising\ntechnique for reducing the computational cost associated with engine design\noptimization. However, such methods still suffer from drawbacks. One main\ndisadvantage of is that the default machine learning (ML) hyperparameters are\noften severely suboptimal for a given problem. This has often been addressed by\nmanually trying out different hyperparameter settings, but this solution is\nineffective in a high-dimensional hyperparameter space. Besides this problem,\nthe amount of data needed for training is also not known a priori. In response\nto these issues that need to be addressed, the present work describes and\nvalidates an automated active learning approach, AutoML-GA, for surrogate-based\noptimization of internal combustion engines. In this approach, a Bayesian\noptimization technique is used to find the best machine learning\nhyperparameters based on an initial dataset obtained from a small number of CFD\nsimulations. Subsequently, a genetic algorithm is employed to locate the design\noptimum on the ML surrogate surface. In the vicinity of the design optimum, the\nsolution is refined by repeatedly running CFD simulations at the projected\noptimum and adding the newly obtained data to the training dataset. It is\ndemonstrated that AutoML-GA leads to a better optimum with a lower number of\nCFD simulations, compared to the use of default hyperparameters. The proposed\nframework offers the advantage of being a more hands-off approach that can be\nreadily utilized by researchers and engineers in industry who do not have\nextensive machine learning expertise.\n"}
+{"abstract": "  Open set domain recognition has got the attention in recent years. The task\naims to specifically classify each sample in the practical unlabeled target\ndomain, which consists of all known classes in the manually labeled source\ndomain and target-specific unknown categories. The absence of annotated\ntraining data or auxiliary attribute information for unknown categories makes\nthis task especially difficult. Moreover, exiting domain discrepancy in label\nspace and data distribution further distracts the knowledge transferred from\nknown classes to unknown classes. To address these issues, this work presents\nan end-to-end model based on attention-based GCN and semantic matching\noptimization, which first employs the attention mechanism to enable the central\nnode to learn more discriminating representations from its neighbors in the\nknowledge graph. Moreover, a coarse-to-fine semantic matching optimization\napproach is proposed to progressively bridge the domain gap. Experimental\nresults validate that the proposed model not only has superiority on\nrecognizing the images of known and unknown classes, but also can adapt to\nvarious openness of the target domain.\n"}
+{"abstract": "  We demonstrate, both analytically and experimentally, free-space pin-like\noptical vortex beams (POVBs).Such angular-momentum-carrying beams feature\ntunable peak intensity and undergo robust anti-diffracting propagation,\nrealized by judiciously modulating both the amplitude and the phase profile of\na standard laser beam. Specifically, they are generated by superimposing a\nradially-symmetric power-law phase on a helical phase structure, which allows\nthe inclusion of an orbital angular momentum term to the POVBs. During\npropagation in free-space, these POVBs initially exhibit autofocusing dynamics,\nand subsequently, their amplitude patterns morph into a high-order Bessel-like\nprofile characterized by a hollow-core and an annular main-lobe with a constant\nor tunable width during propagation. In contrast with numerous previous\nendeavors on Bessel beams, our work represents the first demonstration of\nlong-distance free-space generation of optical vortex 'pins', with their peak\nintensity evolution controlled by the impressed amplitude structure. Both the\nPoynting vectors and the optical radiation forces associated with these beams\nare also numerically analyzed, revealing novel properties that may be useful\nfor a wide range of applications.\n"}
+{"abstract": "  For an associative ring $R$ with identity, we study the absence of\n$k$-torsion in NK_1GQ(R); Bass nil-groups for the general quadratic or Bak's\nunitary groups. By using a graded version of Quillen--Suslin theory we deduce\nan analog for the graded rings.\n"}
+{"abstract": "  Generative Adversarial Networks (GANs) are powerful generative models that\nachieved strong results, mainly in the image domain. However, the training of\nGANs is not trivial, presenting some challenges tackled by different\nstrategies. Evolutionary algorithms, such as COEGAN, were recently proposed as\na solution to improve the GAN training, overcoming common problems that affect\nthe model, such as vanishing gradient and mode collapse. In this work, we\npropose an evaluation method based on t-distributed Stochastic Neighbour\nEmbedding (t-SNE) to assess the progress of GANs and visualize the distribution\nlearned by generators in training. We propose the use of the feature space\nextracted from trained discriminators to evaluate samples produced by\ngenerators and from the input dataset. A metric based on the resulting t-SNE\nmaps and the Jaccard index is proposed to represent the model quality.\nExperiments were conducted to assess the progress of GANs when trained using\nCOEGAN. The results show both by visual inspection and metrics that the\nEvolutionary Algorithm gradually improves discriminators and generators through\ngenerations, avoiding problems such as mode collapse.\n"}
+{"abstract": "  This is a companion paper to our previous work, where we proved the\nfiniteness of the Tate-Shafarevich group for an arbitrary torus $T$ over a\nfinitely generated field $K$ with respect to any divisorial set $V$ of places\nof $K$. Here, we extend this result to any $K$-group $D$ whose connected\ncomponent is a torus (for the same $V$), and as a consequence obtain a\nfiniteness result for the local-to-global conjugacy of maximal tori in\nreductive groups over finitely generated fields. Moreover, we prove the\nfiniteness of the Tate-Shafarevich group for tori over function fields $K$ of\nnormal varieties defined over base fields of characteristic zero and satisfying\nSerre's condition (F), in which case $V$ consists of the discrete valuations\nassociated with the prime divisors on the variety (geometric places). In this\nsituation, we also establish the finiteness of the number of $K$-isomorphism\nclasses of algebraic $K$-tori of a given dimension having good reduction at all\n$v \\in V$, and then discuss ways of extending this result to positive\ncharacteristic. Finally, we prove the finiteness of the number of isomorphism\nclasses of forms of an absolutely almost simple group defined over the function\nfield of a complex surface that have good reduction at all geometric places.\n"}
+{"abstract": "  The high penetration of volatile renewable energy sources such as solar make\nmethods for coping with the uncertainty associated with them of paramount\nimportance. Probabilistic forecasts are an example of these methods, as they\nassist energy planners in their decision-making process by providing them with\ninformation about the uncertainty of future power generation. Currently, there\nis a trend towards the use of deep learning probabilistic forecasting methods.\nHowever, the point at which the more complex deep learning methods should be\npreferred over more simple approaches is not yet clear. Therefore, the current\narticle presents a simple comparison between a long short-term memory neural\nnetwork and other more simple approaches. The comparison consists of training\nand comparing models able to provide one-day-ahead probabilistic forecasts for\na solar power system. Moreover, the current paper makes use of an open-source\ndataset provided during the Global Energy Forecasting Competition of 2014\n(GEFCom14).\n"}
+{"abstract": "  We generalize Zwanziger's pairwise little group to include a boost subgroup.\nWe do so by working in the celestial sphere representation of scattering\namplitudes. We propose that due to late time soft photon and graviton\nexchanges, matter particles in the asymptotic states in massless QED and\ngravity transform under the Poincare group with an additional pair of pairwise\ncelestial representations for each pair of matter particles. We demonstrate\nthat the massless abelian and gravitational exponentiation theorems are\nconsistent with the proposed pairwise Poincare transformation properties. For\nmassless QED we demonstrate that our results are consistent with the effects of\nthe Faddeev-Kulish dressing and the abelian exponentiation theorem for\ncelestial amplitudes found in [2012.04208]. We discuss electric and magnetic\ncharges simultaneously as it is especially natural to do so in this formalism.\n"}
+{"abstract": "  We present a spherical version of the theorem of Blaschke that every body of\nconstant width $w < \\frac{\\pi}{2}$ can be approximated as well as we wish in\nthe sense of the Hausdorff distance by a body of constant width $w$ whose\nboundary consists only of pieces of circles of radius $w$. This is a special\ncase of our theorem about approximation of spherical reduced bodies.\n"}
+{"abstract": "  We report on the status of a users' end-station, MAC: a Multipurpose station\nfor Atomic, molecular and optical sciences and Coherent diffractive imaging,\ndesigned for studies of structure and dynamics of matter in the femtosecond\ntime-domain. MAC is located in the E1 experimental hall on the high harmonic\ngeneration (HHG) beamline of the ELI Beamlines facility. The extreme\nultraviolet beam from the HHG beamline can be used at the MAC end-station\ntogether with a synchronized pump beam (which will cover the NIR/Vis/UV or THz\nrange) for time-resolved experiments on different samples. Sample delivery\nsystems at the MAC end-station include a molecular beam, a source for pure or\ndoped clusters, ultrathin cylindrical or flat liquid jets, and focused beams of\nsubstrate-free nanoparticles produced by an electrospray or a gas dynamic\nvirtual nozzle combined with an aerodynamic lens stack. We further present the\navailable detectors: electron/ion time-of-flight and velocity map imaging\nspectrometers and an X-ray camera, and discuss future upgrades: a magnetic\nbottle electron spectrometer, production of doped nanodroplets and the planned\ndevelopments of beam capabilities at the MAC end-station.\n"}
+{"abstract": "  It has recently been demonstrated that various topological states, including\nDirac, Weyl, nodal-line, and triple-point semimetal phases, can emerge in\nantiferromagnetic (AFM) half-Heusler compounds. However, how to determine the\nAFM structure and to distinguish different topological phases from transport\nbehaviors remains unknown. We show that, due to the presence of combined\ntime-reversal and fractional translation symmetry, the recently proposed\nsecond-order nonlinear Hall effect can be used to characterize different\ntopological phases with various AFM configurations. Guided by the symmetry\nanalysis, we obtain the expressions of the Berry curvature dipole for different\nAFM configurations. Based on the effective model, we explicitly calculate the\nBerry curvature dipole, which is found to be vanishingly small for the\ntriple-point semimetal phase, and large in the Weyl semimetal phase. Our\nresults not only put forward an effective method for the identification of\nmagnetic orders and topological phases in AFM half-Heusler materials, but also\nsuggest these materials as a versatile platform for engineering the non-linear\nHall effect.\n"}
+{"abstract": "  We consider the discretized Bachelier model where hedging is done on an\nequidistant set of times. Exponential utility indifference prices are studied\nfor path-dependent European options and we compute their non-trivial scaling\nlimit for a large number of trading times $n$ and when risk aversion is scaled\nlike $n\\ell$ for some constant $\\ell>0$. Our analysis is purely probabilistic.\nWe first use a duality argument to transform the problem into an optimal drift\ncontrol problem with a penalty term. We further use martingale techniques and\nstrong invariance principles and get that the limiting problem takes the form\nof a volatility control problem.\n"}
+{"abstract": "  This is an opinion paper. We hope to deliver a key message that current\nvisual recognition systems are far from complete, i.e., recognizing everything\nthat human can recognize, yet it is very unlikely that the gap can be bridged\nby continuously increasing human annotations. Based on the observation, we\nadvocate for a new type of pre-training task named learning-by-compression. The\ncomputational models (e.g., a deep network) are optimized to represent the\nvisual data using compact features, and the features preserve the ability to\nrecover the original data. Semantic annotations, when available, play the role\nof weak supervision. An important yet challenging issue is the evaluation of\nimage recovery, where we suggest some design principles and future research\ndirections. We hope our proposal can inspire the community to pursue the\ncompression-recovery tradeoff rather than the accuracy-complexity tradeoff.\n"}
+{"abstract": "  Video inpainting aims to fill the given spatiotemporal holes with realistic\nappearance but is still a challenging task even with prosperous deep learning\napproaches. Recent works introduce the promising Transformer architecture into\ndeep video inpainting and achieve better performance. However, it still suffers\nfrom synthesizing blurry texture as well as huge computational cost. Towards\nthis end, we propose a novel Decoupled Spatial-Temporal Transformer (DSTT) for\nimproving video inpainting with exceptional efficiency. Our proposed DSTT\ndisentangles the task of learning spatial-temporal attention into 2 sub-tasks:\none is for attending temporal object movements on different frames at same\nspatial locations, which is achieved by temporally-decoupled Transformer block,\nand the other is for attending similar background textures on same frame of all\nspatial positions, which is achieved by spatially-decoupled Transformer block.\nThe interweaving stack of such two blocks makes our proposed model attend\nbackground textures and moving objects more precisely, and thus the attended\nplausible and temporally-coherent appearance can be propagated to fill the\nholes. In addition, a hierarchical encoder is adopted before the stack of\nTransformer blocks, for learning robust and hierarchical features that maintain\nmulti-level local spatial structure, resulting in the more representative token\nvectors. Seamless combination of these two novel designs forms a better\nspatial-temporal attention scheme and our proposed model achieves better\nperformance than state-of-the-art video inpainting approaches with significant\nboosted efficiency.\n"}
+{"abstract": "  This paper resolves the problem of comparing the skein modules defined using\nthe skein relations discovered by R. Kirby and P. Melvin that underlie the\nReshetikhin-Turaev model for $SU(2)$ Chern-Simons theory to the Kauffman\nbracket skein modules. Several applications and examples are presented.\n"}
+{"abstract": "  In this work we study static black holes in the regularized 4D\nEinstein-Gauss-Bonnet theory of gravity; a shift-symmetric scalar-tensor theory\nthat belongs to the Horndeski class. This theory features a simple black hole\nsolution that can be written in closed form, and which we show is the unique\nstatic, spherically-symmetric and asymptotically-flat black hole vacuum\nsolution of the theory. We further show that no asymptotically-flat,\ntime-dependent, spherically-symmetric perturbations to this geometry are\nallowed, which suggests that it may be the only spherically-symmetric vacuum\nsolution that this theory admits (a result analogous to Birkhoff's theorem).\nFinally, we consider the thermodynamic properties of these black holes, and\nfind that their final state after evaporation is a remnant with a size\ndetermined by the coupling constant of the theory. We speculate that remnants\nof this kind from primordial black holes could act as dark matter, and we\nconstrain the parameter space for their formation mass, as well as the coupling\nconstant of the theory.\n"}
+{"abstract": "  We present a new approach for redirected walking in static and dynamic scenes\nthat uses techniques from robot motion planning to compute the redirection\ngains that steer the user on collision-free paths in the physical space. Our\nfirst contribution is a mathematical framework for redirected walking using\nconcepts from motion planning and configuration spaces. This framework\nhighlights various geometric and perceptual constraints that tend to make\ncollision-free redirected walking difficult. We use our framework to propose an\nefficient solution to the redirection problem that uses the notion of\nvisibility polygons to compute the free spaces in the physical environment and\nthe virtual environment. The visibility polygon provides a concise\nrepresentation of the entire space that is visible, and therefore walkable, to\nthe user from their position within an environment. Using this representation\nof walkable space, we apply redirected walking to steer the user to regions of\nthe visibility polygon in the physical environment that closely match the\nregion that the user occupies in the visibility polygon in the virtual\nenvironment. We show that our algorithm is able to steer the user along paths\nthat result in significantly fewer resets than existing state-of-the-art\nalgorithms in both static and dynamic scenes. Our project website is available\nat https://gamma.umd.edu/vis_poly/.\n"}
+{"abstract": "  We use the $SU(5)$ model to show the presence in grand unified theories of an\nelectroweak monopole and a magnetic dumbbell (\"meson\") made up of a\nmonopole-antimonopole pair connected by a $Z$-magnetic flux tube. The monopole\nis associated with the spontaneous breaking of the weak $SU(2)_L$ gauge\nsymmetry by the induced vacuum expectation value of a heavy scalar $SU(2)_L$\ntriplet with zero weak hypercharge contained in the adjoint Higgs 24-plet. This\nmonopole carries a Coulomb magnetic charge of $(3/4) (2\\pi/e)$ as well as\n$Z$-magnetic charge, where $2\\pi/e$ denotes the unit Dirac magnetic charge. Its\ntotal magnetic charge is $\\sqrt{3/8}(4\\pi/e)$, which is in agreement with the\nDirac quantization condition. The monopole weighs about 700 GeV, but because of\nthe attached $Z$-magnetic tube it exists, together with the antimonopole, in a\nmagnetic dumbbell configuration whose mass is expected to lie in the TeV range.\nThe presence of these topological structures in $SU(5)$ and $SO(10)$ and in\ntheir supersymmetric extensions provides an exciting new avenue for testing\nthese theories in high-energy colliders.\n"}
+{"abstract": "  We study a sequence of single server queues with customer abandonment\n(GI/GI/1+GI) under heavy traffic. The patience time distributions vary with the\nsequence, which allows for a wider scope of applications. It is known ([20,\n18]) that the sequence of scaled offered waiting time processes converges\nweakly to a reflecting diffusion process with non-linear drift, as the traffic\nintensity approaches one. In this paper, we further show that the sequence of\nstationary distributions and moments of the offered waiting times, with\ndiffusion scaling, converge to those of the limit diffusion process. This\njustifies the stationary performance of the diffusion limit as a valid\napproximation for the stationary performance of the GI/GI/1+GI queue.\nConsequently, we also derive the approximation for the abandonment probability\nfor the GI/GI/1+GI queue in the stationary state.\n"}
+{"abstract": "  We investigate the quantum cosmologies of the Bianchi IX and VIII models when\na cosmological constant, aligned electromagnetic field and free scalar field\nare present. The conserved quantity $p_{\\phi}$ associated with our free scalar\nfield results in $\\phi$ classically being a quantity which monotonically\nincreases with respect to time, thus allowing it to fulfill the role of an\n'emergent' internal clock for our constrained quantum systems. We embark on\nthis investigation to better understand how matter sources can affect general\nanisotropic quantum cosmologies. To aid us we use the Euclidean-signature semi\nclassical method to obtain our wave functions and analyze them. In addition we\nstudy briefly the quantum Taub models when an $e^{ \\phi}$ potential and aligned\nelectromagnetic field are present. One of the interesting things we found was\nthat our aligned electromagnetic field, depending on how strong it is, can\ncreate or destroy geometric states in our 'excited' state wave functions that\nquantum Bianchi IX universes can tunnel in and out of. This creation of a state\nis somewhat similar to how non-commutativity in the minisuperspace variables\ncan cause new quantum states to emerge in the quantum Kantowski-Sachs and\nBianchi I models. Our results further show the utility of the\nEuclidean-signature semi classical method towards tackling Lorentzian signature\nproblems without having to invoke a Wick rotation. This feature of not needing\nto apply a Wick rotation makes this method potentially very useful for tackling\na variety of problems in bosonic relativistic field theory and quantum gravity.\n"}
+{"abstract": "  A generalized Wigner-Moyal statistical theory of radiation is used to obtain\na general dispersion relation for Stimulated Brillouin Scattering (SBS) driven\nby a broadband radiation field with arbitrary statistics. The monochromatic\nlimit is recovered from our general result, reproducing the classic\nmonochromatic dispersion relation. The behavior of the growth rate of the\ninstability as a simultaneous function of the bandwidth of the pump wave, the\nintensity of the incident field and the wave number of the scattered wave is\nfurther explored by numerically solving the dispersion relation. Our results\nshow that the growth rate of SBS can be reduced by 1/3 for a bandwidth of 0.3\nnm, for typical experimental parameters.\n"}
+{"abstract": "  Despite the achievements of large-scale multimodal pre-training approaches,\ncross-modal retrieval, e.g., image-text retrieval, remains a challenging task.\nTo bridge the semantic gap between the two modalities, previous studies mainly\nfocus on word-region alignment at the object level, lacking the matching\nbetween the linguistic relation among the words and the visual relation among\nthe regions. The neglect of such relation consistency impairs the\ncontextualized representation of image-text pairs and hinders the model\nperformance and the interpretability. In this paper, we first propose a novel\nmetric, Intra-modal Self-attention Distance (ISD), to quantify the relation\nconsistency by measuring the semantic distance between linguistic and visual\nrelations. In response, we present Inter-modal Alignment on Intra-modal\nSelf-attentions (IAIS), a regularized training method to optimize the ISD and\ncalibrate intra-modal self-attentions from the two modalities mutually via\ninter-modal alignment. The IAIS regularizer boosts the performance of\nprevailing models on Flickr30k and MS COCO datasets by a considerable margin,\nwhich demonstrates the superiority of our approach.\n"}
+{"abstract": "  Enabling autonomous driving (AD) can be considered one of the biggest\nchallenges in today's technology. AD is a complex task accomplished by several\nfunctionalities, with environment perception being one of its core functions.\nEnvironment perception is usually performed by combining the semantic\ninformation captured by several sensors, i.e., lidar or camera. The semantic\ninformation from the respective sensor can be extracted by using convolutional\nneural networks (CNNs) for dense prediction. In the past, CNNs constantly\nshowed state-of-the-art performance on several vision-related tasks, such as\nsemantic segmentation of traffic scenes using nothing but the red-green-blue\n(RGB) images provided by a camera. Although CNNs obtain state-of-the-art\nperformance on clean images, almost imperceptible changes to the input,\nreferred to as adversarial perturbations, may lead to fatal deception. The goal\nof this article is to illuminate the vulnerability aspects of CNNs used for\nsemantic segmentation with respect to adversarial attacks, and share insights\ninto some of the existing known adversarial defense strategies. We aim to\nclarify the advantages and disadvantages associated with applying CNNs for\nenvironment perception in AD to serve as a motivation for future research in\nthis field.\n"}
+{"abstract": "  In this paper, we develop the theory of convolutional codes over finite\ncommutative chain rings. In particular, we focus on maximum distance profile\n(MDP) convolutional codes and we provide a characterization of these codes,\ngeneralizing the one known for fields. Moreover, we relate MDP convolutional\ncodes over a finite chain ring with MDP convolutional codes over its residue\nfield. Finally, we provide a construction of MDP convolutional codes over\nfinite chain rings generalizing the notion of superregular matrices.\n"}
+{"abstract": "  Hypergeometric functions over finite fields were introduced by Greene in the\n1980s as a finite field analogue of classical hypergeometric series. These\nfunctions, and their generalizations, naturally lend themselves to, and have\nbeen widely used in, character sum evaluations and counting points on algebraic\nvarieties. More interestingly, perhaps, are their links to Fourier coefficients\nof modular forms. In this paper, we outline the main results in this area and\nalso conjecture 13 new relations.\n"}
+{"abstract": "  We study a strongly Non-Markovian variant of random walk in which the\nprobability of visiting a given site $i$ is a function $f$ of number of\nprevious visits $v(i)$ to the site. If the probability is inversely\nproportional to number of visits to the site, say\n$f(i)=\\frac{1}{(v(i)+1)^{\\alpha}}$ the probability distribution of visited\nsites tends to be flat for ${\\alpha}>0$ compared to simple random walk. For\n$f(i)=e^{-v(i)}$, we observe a distribution with two peaks. The origin is no\nlonger the most probable site. The probability is maximum at site k(t) which\nincreases in time. For $f(i)=e^{-v(i)}$ and for ${\\alpha}>0$ the properties do\nnot change as the walk ages. However, for ${\\alpha}<0$, the properties are\nsimilar to simple random walk asymptotically. We study lattice covering time\nfor these functions. The lattice covering time scales as $N^{z}$, with $z=2$,\nfor ${\\alpha} \\le 0$, $z>2$ for ${\\alpha} >0$ and $z<2$ for $f(i)=e^{-v(i)}$.\n"}
+{"abstract": "  Expanded Blaum-Roth (EBR) codes consist of $n\\times n$ arrays such that lines\nof slopes $i$, $0\\leq i\\leq r-1$ for $2\\leq r<n$, as well as vertical lines,\nhave even parity. The codes are MDS with respect to columns, i.e., they can\nrecover any $r$ erased columns, if and only if $n$ is a prime number. Recently\na generalization of EBR codes, called generalized expanded Blaum-Roth (GEBR)\ncodes, was presented. GEBR codes consist of $p\\tau\\times (k+r)$ arrays, where\n$p$ is prime and $\\tau\\geq 1$, such that lines of slopes $i$, $0\\leq i\\leq\nr-1$, have even parity and every column in the array, when regarded as a\npolynomial, is a multiple of $1+x^{\\tau}$. In particular, it was shown that\nwhen $p$ is an odd prime number, 2 is primitive in $GF(p)$ and $\\tau = p^j$,\n$j\\geq 0$, the GEBR code consisting of $p\\tau\\times (p-1)\\tau$ arrays is MDS.\nWe extend this result further by proving that GEBR codes consisting of\n$p\\tau\\times p\\tau$ arrays are MDS if and only if $\\tau = p^j$, where $0\\leq j$\nand $p$ is any odd prime.\n"}
+{"abstract": "  Teaching industry staff on cybersecurity issues is a fundamental activity\nthat must be undertaken in order to guarantee the delivery of successful and\nrobust products to market. Much research attention has been devoted to this\ntopic over the last years. However, the research which has been done has not\nfocused on developing secure code in industrial environments. In this paper we\ntake a look at the constraints and requirements for delivering a training, by\nmeans of cybersecurity challenges, that covers secure coding topics from an\nindustry perspective. Using requirements engineering, we aim at understanding\nthe design requirements for such challenges. Along the way, we give details on\nour experience of delivering cybersecurity challenges in an industrial setting\nand show the outcome and lessons learned. The proposed requirements for\ncybersecurity challenges geared towards software developers in an industrial\nenvironment are based on systematic literature review, interviews with security\nexperts from the industry and semi-structured evaluation of participant\nfeedback.\n"}
+{"abstract": "  Ensembles of independently trained neural networks are a state-of-the-art\napproach to estimate predictive uncertainty in Deep Learning, and can be\ninterpreted as an approximation of the posterior distribution via a mixture of\ndelta functions. The training of ensembles relies on non-convexity of the loss\nlandscape and random initialization of their individual members, making the\nresulting posterior approximation uncontrolled. This paper proposes a novel and\nprincipled method to tackle this limitation, minimizing an $f$-divergence\nbetween the true posterior and a kernel density estimator in a function space.\nWe analyze this objective from a combinatorial point of view, and show that it\nis submodular with respect to mixture components for any $f$. Subsequently, we\nconsider the problem of ensemble construction, and from the marginal gain of\nthe total objective, we derive a novel diversity term for training ensembles\ngreedily. The performance of our approach is demonstrated on computer vision\nout-of-distribution detection benchmarks in a range of architectures trained on\nmultiple datasets. The source code of our method is publicly available at\nhttps://github.com/MIPT-Oulu/greedy_ensembles_training.\n"}
+{"abstract": "  In the last decade, autonomous vertical take-off and landing (VTOL) vehicles\nhave become increasingly important as they lower mission costs thanks to their\nre-usability. However, their development is complex, rendering even the basic\nexperimental validation of the required advanced guidance and control (G & C)\nalgorithms prohibitively time-consuming and costly. In this paper, we present\nthe design of an inexpensive small-scale VTOL platform that can be built from\noff-the-shelf components for less than 1000 USD. The vehicle design mimics the\nfirst stage of a reusable launcher, making it a perfect test-bed for G & C\nalgorithms. To control the vehicle during ascent and descent, we propose a\nreal-time optimization-based G & C algorithm. The key features are a real-time\nminimum fuel and free-final-time optimal guidance combined with an offset-free\ntracking model predictive position controller. The vehicle hardware design and\nthe G & C algorithm are experimentally validated both indoors and outdoor,\nshowing reliable operation in a fully autonomous fashion with all computations\ndone on-board and in real-time.\n"}
+{"abstract": "  We describe a simple and fast technique to perform ultrasound differential\nphase contrast (DPC) imaging in arbitrarily thick scattering media. Though\nconfigured in a reflection geometry, DPC is based on transmission imaging and\nis a direct analogue of optical differential interference contrast (DIC). DPC\nexploits the memory effect and works in combination with standard pulse-echo\nimaging, with no additional hardware or data requirements, enabling\ncomplementary phase contrast (in the transverse direction) without any need for\nintensive numerical computation. We experimentally demonstrate the principle of\nDPC using tissue phantoms with calibrated speed-of-sound inclusions.\n"}
+{"abstract": "  One-side head-tail (OHT) galaxies are radio galaxies with a peculiar shape.\nThey usually appear in galaxy clusters, but they have never been cataloged\nsystematically. We design an automatic procedure to search for them in the\nFaint Images of the Radio Sky at Twenty-Centimeters source catalog and compile\na sample with 115 HT candidates. After cross-checking with the Sloan Digital\nSky Survey photometric data and catalogs of galaxy clusters, we find that 69 of\nthem are possible OHT galaxies. Most of them are close to the center of galaxy\nclusters. The lengths of their tails do not correlate with the projection\ndistance to the center of the nearest galaxy clusters, but show weak\nanticorrelation with the cluster richness, and are inversely proportional to\nthe radial velocity differences between clusters and host galaxies. Our catalog\nprovides a unique sample to study this special type of radio galaxies.\n"}
+{"abstract": "  Offset Rademacher complexities have been shown to provide tight upper bounds\nfor the square loss in a broad class of problems including improper statistical\nlearning and online learning. We show that the offset complexity can be\ngeneralized to any loss that satisfies a certain general convexity condition.\nFurther, we show that this condition is closely related to both exponential\nconcavity and self-concordance, unifying apparently disparate results. By a\nnovel geometric argument, many of our bounds translate to improper learning in\na non-convex class with Audibert's star algorithm. Thus, the offset complexity\nprovides a versatile analytic tool that covers both convex empirical risk\nminimization and improper learning under entropy conditions. Applying the\nmethod, we recover the optimal rates for proper and improper learning with the\n$p$-loss for $1 < p < \\infty$, and show that improper variants of empirical\nrisk minimization can attain fast rates for logistic regression and other\ngeneralized linear models.\n"}
+{"abstract": "  Linear recurrent sequences are those whose elements are defined as linear\ncombinations of preceding elements, and finding recurrence relations is a\nfundamental problem in computer algebra. In this paper, we focus on sequences\nwhose elements are vectors over the ring $\\mathbb{A} = \\mathbb{K}[x]/(x^d)$ of\ntruncated polynomials. Finding the ideal of their recurrence relations has\napplications such as the computation of minimal polynomials and determinants of\nsparse matrices over $\\mathbb{A}$. We present three methods for finding this\nideal: a Berlekamp-Massey-like approach due to Kurakin, one which computes the\nkernel of some block-Hankel matrix over $\\mathbb{A}$ via a minimal approximant\nbasis, and one based on bivariate Pad\\'e approximation. We propose complexity\nimprovements for the first two methods, respectively by avoiding the\ncomputation of redundant relations and by exploiting the Hankel structure to\ncompress the approximation problem. Then we confirm these improvements\nempirically through a C++ implementation, and we discuss the above-mentioned\napplications.\n"}
+{"abstract": "  Simple dynamical models can produce intricate behaviors in large networks.\nThese behaviors can often be observed in a wide variety of physical systems\ncaptured by the network of interactions. Here we describe a phenomenon where\nthe increase of dimensions self-consistently generates a force field due to\ndynamical instabilities. This can be understood as an unstable (\"rumbling\")\ntunneling mechanism between minima in an effective potential. We dub this\ncollective and nonperturbative effect a \"Lyapunov force\" which steers the\nsystem towards the global minimum of the potential function, even if the full\nsystem has a constellation of equilibrium points growing exponentially with the\nsystem size. The system we study has a simple mapping to a flow network,\nequivalent to current-driven memristors. The mechanism is appealing for its\nphysical relevance in nanoscale physics, and to possible applications in\noptimization, novel Monte Carlo schemes and machine learning.\n"}
+{"abstract": "  We use the method of quadratic Chabauty on the quotients $X_0^+(N)$ of\nmodular curves $X_0(N)$ by their Fricke involutions to provably compute all the\nrational points of these curves for prime levels $N$ of genus four, five, and\nsix. We find that the only such curves with exceptional rational points are of\nlevels $137$ and $311$. In particular there are no exceptional rational points\non those curves of genus five and six. More precisely, we determine the\nrational points on the curves $X_0^+(N)$ for\n$N=137,173,199,251,311,157,181,227,263,163,197,211,223,269,271,359$.\n"}
+{"abstract": "  Recently the observation of a new pentaquark state, the hidden-charmed\nstrange $P_{cs}(4459)^0$, was reported by the LHCb Collaboration. The\nspin-parity quantum numbers of this state were not determined as a result of\ninsufficient statistics. To shed light on its quantum numbers, we investigate\nits decay, $P_{cs}(4459)^0 \\rightarrow J/\\psi \\Lambda $, the mode that this\nstate has been observed, within the QCD sum rule framework. We obtain the width\nof this decay assigning the spin-parity quantum numbers of $P_{cs}(4459)^0$\nstate as $J^P=\\frac{1}{2}^-$ and its substructure as diquark-diquark-antiquark.\nTo this end, we first calculate the strong coupling constants defining the\nconsidered decay and then use them in the width calculations. The obtained\nwidth is consistent with the experimental observation, confirming the quantum\nnumbers $J^P=\\frac{1}{2}^-$ and compact pentaquark nature for $P_{cs}(4459)^0$\nstate.\n"}
+{"abstract": "  Domain adaptation deals with training models using large scale labeled data\nfrom a specific source domain and then adapting the knowledge to certain target\ndomains that have few or no labels. Many prior works learn domain agnostic\nfeature representations for this purpose using a global distribution alignment\nobjective which does not take into account the finer class specific structure\nin the source and target domains. We address this issue in our work and propose\nan instance affinity based criterion for source to target transfer during\nadaptation, called ILA-DA. We first propose a reliable and efficient method to\nextract similar and dissimilar samples across source and target, and utilize a\nmulti-sample contrastive loss to drive the domain alignment process. ILA-DA\nsimultaneously accounts for intra-class clustering as well as inter-class\nseparation among the categories, resulting in less noisy classifier boundaries,\nimproved transferability and increased accuracy. We verify the effectiveness of\nILA-DA by observing consistent improvements in accuracy over popular domain\nadaptation approaches on a variety of benchmark datasets and provide insights\ninto the proposed alignment approach. Code will be made publicly available at\nhttps://github.com/astuti/ILA-DA.\n"}
+{"abstract": "  Quantum process tomography might be the most important paradigm shift which\nhas yet to be translated fully into theoretical chemistry. Its fundamental\nstrength, long established in quantum information science, offers a wealth of\ninformation about quantum dynamic processes which lie at the heart of many (if\nnot all) chemical processes. However, due to its complexity its application to\nreal chemical systems is currently beyond experimental reach. Furthermore, it\nis susceptible to errors due to experimental and theoretical inaccuracies and\ndisorder has long been thought to be an obstacle in its applicability. Here, I\npresent the first results of a study into the use of quantum light for quantum\nprocess tomography. By using a toy model and comparing numerical simulations to\ntheoretical predictions the possible enhancement of using non-conventional\nlight is studied. It is found, however, that disorder is necessary make the use\nof quantum light suitable for process tomography and that, in contrast to\nconventional wisdom, disorder can make the results more accurate than in an\nordered system.\n"}
+{"abstract": "  Neural Machine Translation models are sensitive to noise in the input texts,\nsuch as misspelled words and ungrammatical constructions. Existing robustness\ntechniques generally fail when faced with unseen types of noise and their\nperformance degrades on clean texts. In this paper, we focus on three types of\nrealistic noise that are commonly generated by humans and introduce the idea of\nvisual context to improve translation robustness for noisy texts. In addition,\nwe describe a novel error correction training regime that can be used as an\nauxiliary task to further improve translation robustness. Experiments on\nEnglish-French and English-German translation show that both multimodal and\nerror correction components improve model robustness to noisy texts, while\nstill retaining translation quality on clean texts.\n"}
+{"abstract": "  We study Weyl fermions in a linear class of topologically trivial\nG\\\"odel-type geometries in Einstein's general relativity. We solve the Weyl\nequation and evaluate in detail the energy eigenvalues and the corresponding\nwave functions.\n"}
+{"abstract": "  A saturated fusion system over a finite $p$-group $S$ is a category whose\nobjects are the subgroups of $S$ and whose morphisms are injective\nhomomorphisms between the subgroups satisfying certain axioms. A fusion system\nover $S$ is realized by a finite group $G$ if $S$ is a Sylow $p$-subgroup of\n$G$ and morphisms in the category are those induced by conjugation in $G$. One\nrecurrent question in this subject is to find criteria as to whether a given\nsaturated fusion system is realizable or not.\n  One main result in this paper is that a saturated fusion system is realizable\nif all of its components (in the sense of Aschbacher) are realizable. Another\nresult is that all realizable fusion systems are tame: a finer condition on\nrealizable fusion systems that involves describing automorphisms of a fusion\nsystem in terms of those of some group that realizes it. Stated in this way,\nthese results depend on the classification of finite simple groups, but we also\ngive more precise formulations whose proof is independent of the\nclassification.\n"}
+{"abstract": "  We investigate imbibition of a monodisperse emulsion into a low-aspect ratio\nmicrofluidic channel with the height h comparable to the droplet diameter d.\nFor confinement ratio d/h = 1.2, the tightly confined disk-like droplets in the\nchannel move more slowly compared to the average suspension velocity. Behind\nthe meniscus that drives the imbibition, there is a droplet-free region,\nseparated from the suspension region by a sharp concentration front. The\nsuspension exhibits strong droplet density and velocity fluctuations, but on\naverage, the suspension domain remains uniform. For weaker confinement, d/h =\n0.65, the spherical droplets move faster than the average suspension flow,\nresulting in the formation of a dynamically unstable high-concentration region\nnear the meniscus. We describe the macroscopic suspension dynamics using linear\ntransport equations for the particle-phase flux and suspension flux that are\ndriven by the local pressure gradient. A dipolar particle interaction model\nexplains the observed large density and velocity fluctuations in terms of the\ndynamics of elongated particle clusters with different orientations.\n"}
+{"abstract": "  Laplace's equation appears frequently in physical applications involving\nconservable quantities. Among these applications, miniaturized devices have\nbeen of interest, in particular those using interdigitated arrays. Therefore,\nwe solved the two-dimensional Laplace's equation for a shallow or finite domain\nconsisting of interdigitated boundaries. We achieved this by using Jacobian\nelliptic functions to conformally transform the interdigitated domain into a\nparallel plates domain. The obtained expressions for potential distribution,\nflux density and flux allow for arbitrary domain height, different band widths\nand asymmetric potentials at the interdigitated array, besides considering\nfringing effects at both ends of the array. All these expressions depend only\non relative dimensions, instead of absolute ones. With these results we showed\nthat the behavior in shallow or finite domains approaches that of a\nsemi-infinite domain, when its height is greater than the separation between\nthe centers of consecutive bands. We also found that, for any desired but fixed\nflux, bands of equal width minimize the total surface of the interdigitated\narray. Finally, we present approximate expressions for the flux, based on\nelementary functions, which can be applied to ease the calculation of currents\n(faradaic or non-faradaic), capacitances and resistances among other possible\napplications.\n"}
+{"abstract": "  Cross-modal retrieval is an important functionality in modern search engines,\nas it increases the user experience by allowing queries and retrieved objects\nto pertain to different modalities. In this paper, we focus on the\nimage-sentence retrieval task, where the objective is to efficiently find\nrelevant images for a given sentence (image-retrieval) or the relevant\nsentences for a given image (sentence-retrieval). Computer vision literature\nreports the best results on the image-sentence matching task using deep neural\nnetworks equipped with attention and self-attention mechanisms. They evaluate\nthe matching performance on the retrieval task by performing sequential scans\nof the whole dataset. This method does not scale well with an increasing amount\nof images or captions. In this work, we explore different preprocessing\ntechniques to produce sparsified deep multi-modal features extracting them from\nstate-of-the-art deep-learning architectures for image-text matching. Our main\nobjective is to lay down the paths for efficient indexing of complex\nmulti-modal descriptions. We use the recently introduced TERN architecture as\nan image-sentence features extractor. It is designed for producing fixed-size\n1024-d vectors describing whole images and sentences, as well as\nvariable-length sets of 1024-d vectors describing the various building\ncomponents of the two modalities (image regions and sentence words\nrespectively). All these vectors are enforced by the TERN design to lie into\nthe same common space. Our experiments show interesting preliminary results on\nthe explored methods and suggest further experimentation in this important\nresearch direction.\n"}
+{"abstract": "  In $\\rm\\Lambda$CDM cosmology, Dark Matter (DM) and neutrinos are assumed to\nbe non-interacting. However, it is possible to have scenarios, where\nDM-neutrino interaction may be present, leading to scattering of DM with\nneutrinos and annihilation of DM into neutrinos. We investigate the viability\nof such scenarios in the light of cosmological data by making use of the Planck\n2018 dataset (high-l TT+TE+EE, low-l TT, low-l EE) and constrain these\nprocesses in the light of the same. We also discuss a viable particle DM model\nwhere DM-neutrino interaction is present, and map the constraints obtained to\nthe parameter space of the model.\n"}
+{"abstract": "  In this paper, we propose a deep evolutionary learning (DEL) process that\nintegrates fragment-based deep generative model and multi-objective\nevolutionary computation for molecular design. Our approach enables (1)\nevolutionary operations in the latent space of the generative model, rather\nthan the structural space, to generate novel promising molecular structures for\nthe next evolutionary generation, and (2) generative model fine-tuning using\nnewly generated high-quality samples. Thus, DEL implements a data-model\nco-evolution concept which improves both sample population and generative model\nlearning. Experiments on two public datasets indicate that sample population\nobtained by DEL exhibits improved property distributions, and dominates samples\ngenerated by multi-objective Bayesian optimization algorithms.\n"}
+{"abstract": "  Antibubbles, which consist of a shell of a low-density fluid inside a\nhigh-density fluid, have several promising applications. We show, via extensive\ndirect numerical simulations (DNSs), in both two and three dimensions (2D and\n3D), that the spatiotemporal evolution of antibubbles can be described\nnaturally by the coupled Cahn-Hilliard-Navier-Stokes (CHNS) equations for a\nbinary fluid. Our DNSs capture elegantly the gravity-induced thinning and\nbreakup of an antibubble via the time evolution of the Cahn-Hilliard scalar\norder parameter field $\\phi$, which varies continuously across interfaces, so\nwe do not have to enforce complicated boundary conditions at the moving\nantibubble interfaces. To ensure that our results are robust, we supplement our\nCHNS simulations with sharp-interface Volume-of-Fluid (VoF) DNSs. We track the\nthickness of the antibubble and calculate the dependence of the lifetime of an\nantibubble on several parameters; we show that our DNS results agree with\nvarious experimental results; in particular, the velocity with which the arms\nof the antibubble retract after breakup scales as $\\sigma^{1/2}$, where\n$\\sigma$ is the surface tension.\n"}
+{"abstract": "  Transfer learning can boost the performance on the targettask by leveraging\nthe knowledge of the source domain. Recent worksin neural architecture search\n(NAS), especially one-shot NAS, can aidtransfer learning by establishing\nsufficient network search space. How-ever, existing NAS methods tend to\napproximate huge search spaces byexplicitly building giant super-networks with\nmultiple sub-paths, anddiscard super-network weights after a child structure is\nfound. Both thecharacteristics of existing approaches causes repetitive network\ntrainingon source tasks in transfer learning. To remedy the above issues, we\nre-duce the super-network size by randomly dropping connection betweennetwork\nblocks while embedding a larger search space. Moreover, wereuse super-network\nweights to avoid redundant training by proposinga novel framework consisting of\ntwo modules, the neural architecturesearch module for architecture transfer and\nthe neural weight searchmodule for weight transfer. These two modules conduct\nsearch on thetarget task based on a reduced super-networks, so we only need to\ntrainonce on the source task. We experiment our framework on both MS-COCO and\nCUB-200 for the object detection and fine-grained imageclassification tasks,\nand show promising improvements with onlyO(CN)super-network complexity.\n"}
+{"abstract": "  In the paper, we introduce and address the problem of structural averaged\ncontrollability for linear ensemble systems. We provide examples highlighting\nthe differences between this problem and others. In particular, we show that\nstructural averaged controllability is strictly weaker than structural\ncontrollability for single (or ensembles of) linear systems. We establish a set\nof necessary or sufficient conditions for sparsity patterns to be structurally\naveraged controllable.\n"}
+{"abstract": "  The critical quantum metrology, which exploits the quantum phase transition\nfor high precision measurement, has gained increasing attention recently. The\ncritical quantum metrology with the continuous quantum phase transition,\nhowever, is experimentally very challenging since the continuous quantum phase\ntransition only exists at the thermal dynamical limit. Here, we propose an\nadiabatic scheme on a perturbed Ising spin model with the first order quantum\nphase transition. By employing the Landau-Zener anticrossing, we can not only\nencode the unknown parameter in the ground state but also tune the energy gap\nto control the evolution time of the adiabatic passage. We experimentally\nimplement the adiabatic scheme on the nuclear magnetic resonance and show that\nthe achieved precision attains the Heisenberg scaling. The advantages of the\nscheme-easy implementation, robust against the decay, tunable energy gap-are\ncritical for practical applications of quantum metrology.\n"}
+{"abstract": "  We propose a new iterative optimization method for the {\\bf Data-Fitting}\n(DF) problem in Machine Learning, e.g. Neural Network (NN) training. The\napproach relies on {\\bf Graphical Model} (GM) representation of the DF problem,\nwhere variables are fitting parameters and factors are associated with the\nInput-Output (IO) data. The GM results in the {\\bf Belief Propagation}\nEquations considered in the {\\bf Large Deviation Limit} corresponding to the\npractically important case when the number of the IO samples is much larger\nthan the number of the fitting parameters. We suggest the {\\bf Message Passage\nDescent} algorithm which relies on the piece-wise-polynomial representation of\nthe model DF function. In contrast with the popular gradient descent and\nrelated algorithms our MPD algorithm rely on analytic (not automatic)\ndifferentiation, while also (and most importantly) it descents through the\nrugged DF landscape by \\emph{making non local updates of the parameters} at\neach iteration. The non-locality guarantees that the MPD is not trapped in the\nlocal-minima, therefore resulting in better performance than locally-updated\nalgorithms of the gradient-descent type. We illustrate superior performance of\nthe algorithm on a Feed-Forward NN with a single hidden layer and a\npiece-wise-linear activation function.\n"}
+{"abstract": "  Medical image segmentation is routinely performed to isolate regions of\ninterest, such as organs and lesions. Currently, deep learning is the state of\nthe art for automatic segmentation, but is usually limited by the need for\nsupervised training with large datasets that have been manually segmented by\ntrained clinicians. The goal of semi-superised and unsupervised image\nsegmentation is to greatly reduce, or even eliminate, the need for training\ndata and therefore to minimze the burden on clinicians when training\nsegmentation models. To this end we introduce a novel network architecture for\ncapable of unsupervised and semi-supervised image segmentation called\nTricycleGAN. This approach uses three generative models to learn translations\nbetween medical images and segmentation maps using edge maps as an intermediate\nstep. Distinct from other approaches based on generative networks, TricycleGAN\nrelies on shape priors rather than colour and texture priors. As such, it is\nparticularly well-suited for several domains of medical imaging, such as\nultrasound imaging, where commonly used visual cues may be absent. We present\nexperiments with TricycleGAN on a clinical dataset of kidney ultrasound images\nand the benchmark ISIC 2018 skin lesion dataset.\n"}
+{"abstract": "  Measurements of response inhibition components of reactive inhibition and\nproactive inhibition within the stop signal paradigm have been of special\ninterest for researchers since the 1980s. While frequentist nonparametric and\nBayesian parametric methods have been proposed to precisely estimate the entire\ndistribution of reactive inhibition, quantified by stop signal reaction\ntimes(SSRT), there is no method yet in the stop-signal task literature to\nprecisely estimate the entire distribution of proactive inhibition. We\nintroduce an Asymmetric Laplace Gaussian (ALG) model to describe the\ndistribution of proactive inhibition. The proposed method is based on two\nassumptions of independent trial type(go/stop) reaction times, and Ex-Gaussian\n(ExG) models for them. Results indicated that the four parametric, ALG model\nuniquely describes the proactive inhibition distribution and its key shape\nfeatures; and, its hazard function is monotonically increasing as are its three\nparametric ExG components. In conclusion, both response inhibition components\ncan be uniquely modeled via variations of the four parametric ALG model\ndescribed with their associated similar distributional features.\n"}
+{"abstract": "  The purpose of this paper is to introduce and investigate the notion of\nderivation for quandle algebras. More precisely, we describe the symmetries on\nstructure constants providing a characterization for a linear map to be a\nderivation. We obtain a complete characterization of derivations in the case of\nquandle algebras of \\emph{dihedral quandles} over fields of characteristic\nzero, and provide the dimensionality of the Lie algebra of derivations. Many\nexplicit examples and computations are given over both zero and positive\ncharacteristic.\n  Furthermore, we investigate inner derivations, in the sense of Schafer for\nnon-associative structures. We obtain necessary conditions for the Lie\ntransformation algebra of quandle algebras of Alexander quandles, with explicit\ncomputations in low dimensions.\n"}
+{"abstract": "  The directional mean shift (DMS) algorithm is a nonparametric method for\npursuing local modes of densities defined by kernel density estimators on the\nunit hypersphere. In this paper, we show that any DMS iteration can be viewed\nas a generalized Expectation-Maximization (EM) algorithm; in particular, when\nthe von Mises kernel is applied, it becomes an exact EM algorithm. Under the\n(generalized) EM framework, we provide a new proof for the ascending property\nof density estimates and demonstrate the global convergence of directional mean\nshift sequences. Finally, we give a new insight into the linear convergence of\nthe DMS algorithm.\n"}
+{"abstract": "  Streaming processing of speech audio is required for many contemporary\npractical speech recognition tasks. Even with the large corpora of manually\ntranscribed speech data available today, it is impossible for such corpora to\ncover adequately the long tail of linguistic content that's important for tasks\nsuch as open-ended dictation and voice search. We seek to address both the\nstreaming and the tail recognition challenges by using a language model (LM)\ntrained on unpaired text data to enhance the end-to-end (E2E) model. We extend\nshallow fusion and cold fusion approaches to streaming Recurrent Neural Network\nTransducer (RNNT), and also propose two new competitive fusion approaches that\nfurther enhance the RNNT architecture. Our results on multiple languages with\nvarying training set sizes show that these fusion methods improve streaming\nRNNT performance through introducing extra linguistic features. Cold fusion\nworks consistently better on streaming RNNT with up to a 8.5% WER improvement.\n"}
+{"abstract": "  Eddy current stimulated thermography is an emerging technique for\nnon-destructive testing and evaluation of conductive materials. However,\nquantitative estimation of the depth of subsurface defects in metallic\nmaterials by thermography techniques remains challenging due to significant\nlateral thermal diffusion. This work presents the application of eddy current\npulse compression thermography to detect surface and subsurface defects with\nvarious depths in an aluminum sample. Kernel Principal Component analysis and\nLow Rank Sparse modelling were used to enhance the defective area, and cross\npoint feature was exploited to quantitatively evaluate the defects depth. Based\non experimental results, it is shown that the crossing point feature has a\nmonotonic relationship with surface and subsurface defects depth, and it can\nalso indicate whether the defect is within or beyond the eddy current skin\ndepth. In addition, the comparison study between aluminum and composites in\nterms of impulse response and proposed features are also presented.\n"}
+{"abstract": "  Cooperative ad hoc unmanned aerial vehicle (UAV) networks need essential\nsecurity services to ensure their communication security. Cryptography, as the\ninseparable tool for providing security services, requires a robust key\nmanagement system. Alas, the absence of infrastructure in cooperative networks\nleads to the infeasibility of providing conventional key management systems.\nKey pre-distribution schemes have shown promising performance in different\ncooperative networks due to their lightweight nature. However, intermediate\ndecryption-encryption (DE) steps and the lack of key updates are the most\nconcerning issues they suffer from. In this paper, we propose a simple and\neffective key management algorithm inspired by the idea of key\npre-distribution, where it utilizes the highly dynamic UAV node movement in 3D\nspace to provide the key update feature and optimizes the number of\nintermediate DE steps. Although it is a general model for any mobile ad hoc\nnetwork, we have selected UAV network as an example domain to show the\nefficiency of the model given the high mobility. We define the communication\ndensity parameter to analytically show that using any highly dynamic random\nmovement pattern leads our algorithm to work effectively. To show the proposed\nalgorithm's effectiveness, we exhaustively analyze its security and performance\nin the UAV network using the ns-3 network simulator. Results validate our\nanalytical findings and show how the highly dynamic UAV network movement helps\nour algorithm to provide the key update feature and to optimize the number of\nDE steps.\n"}
+{"abstract": "  There has been emerging interest to use transductive learning for adversarial\nrobustness (Goldwasser et al., NeurIPS 2020; Wu et al., ICML 2020). Compared to\ntraditional \"test-time\" defenses, these defense mechanisms \"dynamically\nretrain\" the model based on test time input via transductive learning; and\ntheoretically, attacking these defenses boils down to bilevel optimization,\nwhich seems to raise the difficulty for adaptive attacks. In this paper, we\nfirst formalize and analyze modeling aspects of transductive robustness. Then,\nwe propose the principle of attacking model space for solving bilevel attack\nobjectives, and present an instantiation of the principle which breaks previous\ntransductive defenses. These attacks thus point to significant difficulties in\nthe use of transductive learning to improve adversarial robustness. To this\nend, we present new theoretical and empirical evidence in support of the\nutility of transductive learning.\n"}
+{"abstract": "  We treat the lattice sine-Gordon equation and two of its generalised\nsymmetries as a compatible system. Elimination of shifts from the two\nsymmetries of the lattice sine-Gordon equation yields an integrable NLS-type\nsystem. An auto-B\\\"acklund transformation and a superposition formula for the\nNLS-type system is obtained by elimination of shifts from the lattice\nsine-Gordon equation and its down-shifted version. We use the obtained formulae\nto calculate a superposition of two and three elementary solutions.\n"}
+{"abstract": "  We present a possible correlation between the properties of scattered and\nthermal radiation from dust and the principal dust characteristics responsible\nfor this relationship. To this end, we use the NASA/PDS archival polarimetric\ndata on cometary dust in the Red (0.62--0.73 $\\mu$m) and K (2.00--2.39 $\\mu$m)\ndomains to leverage the relative excess of the polarisation degree of a comet\nto the average trend at the given phase angle ($P_{\\rm excess}$) as a metric of\nthe dust's scattered light characteristics. The flux excess of silicate\nemissions to the continuum around 10 $\\mu$m ($F_{\\rm Si}/F_{\\rm cont}$) is\nadopted from previous studies as a metric of the dust's MIR feature. The two\nmetrics show a positive correlation when $P_{\\rm excess}$ is measured in the K\ndomain. No significant correlation was identified in the Red domain. The\ngas-rich comets have systematically weaker $F_{\\rm Si}/F_{\\rm cont}$ than the\ndust-rich ones, yet both groups retain the same overall tendency with different\nslope values. The observed positive correlation between the two metrics\nindicates that composition is a peripheral factor in characterising the dust's\npolarimetric and silicate emission properties. The systematic difference in\n$F_{\\rm Si}/F_{\\rm cont}$ for gas-rich versus dust-rich comets would rather\ncorrespond with the difference in their dust size distribution. Hence, our\nresults suggest that the current MIR spectral models of cometary dust should\nprioritise the dust size and porosity over the composition. With light\nscattering being sensitive to different size scales in two wavebands, we expect\nthe K-domain polarimetry to be sensitive to the properties of dust aggregates,\nsuch as size and porosity, which might have been influenced by evolutionary\nprocesses. On the other hand, the Red-domain polarimetry reflects the\ncharacteristics of sub-$\\mu$m constituents in the aggregate.\n"}
+{"abstract": "  It has been shown in a recent work by Yakir-Zeitouni that the minimum modulus\nof random trigonometric polynomials with Gaussian coefficients has a limiting\nexponential distribution. We show this is a universal phenomenon. Our approach\nrelates the joint distribution of small values of the polynomial at a fixed\nnumber $m$ of points on the circle to the distribution of a certain random walk\nin a $4m$-dimensional phase space. Under Diophantine approximation conditions\non the angles, we obtain strong small ball estimates and a local central limit\ntheorem for the distribution of the walk.\n"}
+{"abstract": "  We reformulate the twistor construction for hyper- and quaternion-K\\\"ahler\nmanifolds, introducing new sigma models that compute scalar potentials for the\ngeometry. These sigma models have the twistor space of the quaternionic\nmanifold as their target and encode finite non-linear perturbations of the flat\nstructures. In the hyperk\\\"ahler case our twistor sigma models compute both\nPlebanski fundamental forms (including the K\\\"ahler potential), while in the\nquaternion-K\\\"ahler setting the twistor sigma model computes the K\\\"ahler\npotential for the hyperk\\\"ahler structure on non-projective twistor space. In\nfour-dimensions, one of the models provides the generating functional of\ntree-level MHV graviton scattering amplitudes; perturbations of the\nhyperk\\\"ahler structure corresponding to positive helicity gravitons. The sigma\nmodel's perturbation theory gives rise to a sum of tree diagrams observed\npreviously in the literature, and their summation via a matrix tree theorem\ngives a first-principles derivation of Hodges' formula for MHV graviton\namplitudes directly from general relativity. We generalise the twistor sigma\nmodel to higher-degree (defined in the first case with a cosmological\nconstant), giving a new generating principle for the full tree-level graviton\nS-matrix.\n"}
+{"abstract": "  A challenge in designing self-assembling building blocks is to ensure the\ntarget state is both thermodynamically stable and kinetically accessible. These\ntwo objectives are known to be typically in competition, but it is not known\nhow to simultaneously optimize them. We consider this problem through the lens\nof multi-objective optimization theory: we develop a genetic algorithm to\ncompute the Pareto fronts characterizing the tradeoff between equilibrium\nprobability and folding rate, for a model system of small polymers of colloids\nwith tunable short-ranged interaction energies. We use a coarse-grained model\nfor the particles' dynamics that allows us to efficiently search over\nparameters, for systems small enough to be enumerated. For most target states\nthere is a tradeoff when the number of types of particles is small, with\nmedium-weak bonds favouring fast folding, and strong bonds favouring high\nequilibrium probability. The tradeoff disappears when the number of particle\ntypes reaches a value $m_*$, that is usually much less than the total number of\nparticles. This general approach of computing Pareto fronts allows one to\nidentify the minimum number of design parameters to avoid a\nthermodynamic-kinetic tradeoff. However, we argue, by contrasting our\ncoarse-grained model's predictions with those of Brownian dynamics simulations,\nthat particles with short-ranged isotropic interactions should generically have\na tradeoff, and avoiding it in larger systems will require\norientation-dependent interactions.\n"}
+{"abstract": "  We propose a two-population lattice Boltzmann model on standard lattices for\nthe simulation of compressible flows. The model is fully on-lattice and uses\nthe single relaxation time Bhatnagar-Gross-Krook kinetic equations along with\nappropriate correction terms to recover the Navier-Stokes-Fourier equations.\nThe accuracy and performance of the model are analyzed through simulations of\ncompressible benchmark cases including Sod shock tube, sound generation in\nshock-vortex interaction and compressible decaying turbulence in a box with\neddy shocklets. It is demonstrated that the present model provides an accurate\nrepresentation of compressible flows, even in the presence of turbulence and\nshock waves.\n"}
+{"abstract": "  We present the first Fermi - Large Area Telescope (LAT) solar flare catalog\ncovering the 24 th solar cycle. This catalog contains 45 Fermi -LAT solar\nflares (FLSFs) with emission in the gamma-ray energy band (30 MeV - 10 GeV)\ndetected with a significance greater than 5 sigma over the years 2010-2018. A\nsubsample containing 37 of these flares exhibit delayed emission beyond the\nprompt-impulsive hard X-ray phase with 21 flares showing delayed emission\nlasting more than two hours. No prompt-impulsive emission is detected in four\nof these flares. We also present in this catalog the observations of GeV\nemission from 3 flares originating from Active Regions located behind the limb\n(BTL) of the visible solar disk. We report the light curves, spectra, best\nproton index and localization (when possible) for all the FLSFs. The gamma-ray\nspectra is consistent with the decay of pions produced by >300 MeV protons.\nThis work contains the largest sample of high-energy gamma-ray flares ever\nreported and provides the unique opportunity to perform population studies on\nthe different phases of the flare and thus allowing to open a new window in\nsolar physics.\n"}
+{"abstract": "  Molecular dynamics simulations are used to show that strong magnetization\nsignificantly increases the space and time scales associated with interparticle\ncorrelations. The physical mechanism responsible is a channeling effect whereby\nparticles are confined to move along narrow cylinders with a width\ncharacterized by the gyroradius and a length characterized by the collision\nmean free path. The predominant interaction is $180^\\circ$ collisions at the\nends of the collision cylinders, resulting in a long-range correlation parallel\nto the magnetic field. Its influence is demonstrated via the dependence of the\nvelocity autocorrelation functions and self-diffusion coefficients on the\ndomain size and run time in simulations of the one-component plasma. A very\nlarge number of particles, and therefore domain size, must be used to resolve\nthe long-range correlations, suggesting that the number of charged particles in\nthe collection must increase in order to constitute a plasma. Correspondingly,\nthis effect significantly delays the time it takes to reach a diffusive regime,\nin which the mean square displacement of particles increases linearly in time.\nThis result presents challenges for connecting measurements in non-neutral and\nultracold neutral plasma experiments, as well as molecular dynamics\nsimulations, with fluid transport properties due to their finite size.\n"}
+{"abstract": "  Although the share of platform work is very small compared to conventional\nand traditional employment system, research shows that the use of platform work\nis increasingly growing all over the world. Trade unions have also paid special\nattention to the platform work because they know that the transfer of a\npercentage of human resources to the platforms is undeniable. To this end, the\ntrade unions prepare themselves for the challenges and dynamics of this\nemerging phenomenon in the field of human resources. Using a qualitative\nresearch method and a case study of Hungary, the present study aimed to\nidentify the strategies adopted by Trade Unions to manage the transition to\nplatform works and provide suggestions for both practitioners and future\nresearch.\n"}
+{"abstract": "  Differential buoyancy surface sources in the ocean may induce a\ndensity-driven flow that joins faster flow components to create a multi-scale,\n3D flow. Potential temperature and salinity are active tracers that determine\nthe ocean's potential density: their distribution strongly affects the\ndensity-driven component while the overall flow affects their distribution. We\npresent a robust framework that allows one to study the effects of a general 3D\nflow on a density-driven velocity component, by constructing a modular\nobservation-based 3D model of intermediate complexity. The model contains an\nincompressible velocity that couples two advection-diffusion equations, for\ntemperature and salinity. Instead of solving the Navier-Stokes equations for\nthe velocity, we consider a flow composed of several temporally separated,\nspatially predetermined modes. One of these modes models the density-driven\nflow: its spatial form describes the density-driven flow structure and its\nstrength is determined dynamically by average density differences. The other\nmodes are completely predetermined, consisting of any incompressible, possibly\nunsteady, 3D flow, e.g. as determined by kinematic models, observations, or\nsimulations. The model is a non-linear, weakly coupled system of two non-local\nPDEs. We prove its well-posedness in the sense of Hadamard, and obtain rigorous\nbounds regarding analytical solutions. The model's relevance to oceanic systems\nis demonstrated by tuning the model to mimic the North Atlantic ocean's\ndynamics. In one limit the model recovers a simplified oceanic box model and in\nanother limit a kinematic model of oceanic chaotic advection, suggesting it can\nbe utilized to study spatially dependent feedback processes in the ocean.\n"}
+{"abstract": "  Memory encoding by cyclic shear is a reliable process to store information in\njammed solids, yet its underlying mechanism and its connection to the amorphous\nstructure are not fully understood. When a jammed sphere packing is repeatedly\nsheared with cycles of the same strain amplitude, it optimizes its mechanical\nresponse to the cyclic driving and stores a memory of it. We study memory by\ncyclic shear training as a function of the underlying stability of the\namorphous structure in marginally stable and highly stable packings, the latter\nproduced by minimizing the potential energy using both positional and radial\ndegrees of freedom. We find that jammed solids need to be marginally stable in\norder to store a memory by cyclic shear. In particular, highly stable packings\nstore memories only after overcoming brittle yielding and the cyclic shear\ntraining takes place in the shear band, a region which we show to be marginally\nstable.\n"}
+{"abstract": "  In this paper, an autonomous aerial manipulation task of pulling a plug out\nof an electric socket is conducted, where maintaining the stability and\nrobustness is challenging due to sudden disappearance of a large interaction\nforce. The abrupt change in the dynamical model before and after the separation\nof the plug can cause destabilization or mission failure. To accomplish aerial\nplug-pulling, we employ the concept of hybrid automata to divide the task into\nthree operative modes, i.e, wire-pulling, stabilizing, and free-flight. Also, a\nstrategy for trajectory generation and a design of disturbance-observer-based\ncontrollers for each operative mode are presented. Furthermore, the theory of\nhybrid automata is used to prove the stability and robustness during the mode\ntransition. We validate the proposed trajectory generation and control method\nby an actual wire-pulling experiment with a multirotor-based aerial\nmanipulator.\n"}
+{"abstract": "  Few-shot semantic segmentation aims at learning to segment a target object\nfrom a query image using only a few annotated support images of the target\nclass. This challenging task requires to understand diverse levels of visual\ncues and analyze fine-grained correspondence relations between the query and\nthe support images. To address the problem, we propose Hypercorrelation Squeeze\nNetworks (HSNet) that leverages multi-level feature correlation and efficient\n4D convolutions. It extracts diverse features from different levels of\nintermediate convolutional layers and constructs a collection of 4D correlation\ntensors, i.e., hypercorrelations. Using efficient center-pivot 4D convolutions\nin a pyramidal architecture, the method gradually squeezes high-level semantic\nand low-level geometric cues of the hypercorrelation into precise segmentation\nmasks in coarse-to-fine manner. The significant performance improvements on\nstandard few-shot segmentation benchmarks of PASCAL-5i, COCO-20i, and FSS-1000\nverify the efficacy of the proposed method.\n"}
+{"abstract": "  Although the real shapes and trajectories of erupting solar prominences in\nthree dimensions have been intensively studied, the three-dimensional (3D)\nshapes of stable prominences before eruptions have not been measured accurately\nso far. We intend to make such a measurement to constrain 3D prominence models\nand to extend our knowledge of prominences. Using multiperspective observations\nfrom the Atmospheric Imaging Assembly on board SDO and the Extreme Ultraviolet\nImager on board STEREO, we reconstructed 3D coordinates of three stable\nprominences: a quiescent, an intermediate, and a mixed type. Based on the 3D\ncoordinates, we measured the height, length, and inclination angle of the legs\nof these prominences. To study the spatial relationship between the footpoints\nof prominences and photospheric magnetic structures, we also used the Global\nOscillation Network Group H alpha images and magnetograms from the HMI on board\nthe SDO. In three stable prominences, we find that the axes of the prominence\nlegs are inclined by 68 degrees on average to the solar surface. Legs at\ndifferent locations along a prominence axis have different heights with a two-\nto threefold difference. Our investigation suggests that over 96% of prominence\nfootpoints in a sample of 70 footpoints are located at supergranular\nboundaries. The widths of two legs have similar values measured in two\northogonal lines of sight. We also find that a prominence leg above the solar\nlimb showed horizontal oscillations with larger amplitudes at higher locations.\nWith a limited image resolution and number of cases, our measurement suggests\nthat the legs of prominences may have various orientations and do not always\nstand vertically on the surface of the sun. Moreover, the locations of\nprominence legs are closely related to supergranules.\n"}
+{"abstract": "  Respiratory rate (RR) is a clinical sign representing ventilation. An\nabnormal change in RR is often the first sign of health deterioration as the\nbody attempts to maintain oxygen delivery to its tissues. There has been a\ngrowing interest in remotely monitoring of RR in everyday settings which has\nmade photoplethysmography (PPG) monitoring wearable devices an attractive\nchoice. PPG signals are useful sources for RR extraction due to the presence of\nrespiration-induced modulations in them. The existing PPG-based RR estimation\nmethods mainly rely on hand-crafted rules and manual parameters tuning. An\nend-to-end deep learning approach was recently proposed, however, despite its\nautomatic nature, the performance of this method is not ideal using the real\nworld data. In this paper, we present an end-to-end and accurate pipeline for\nRR estimation using Cycle Generative Adversarial Networks (CycleGAN) to\nreconstruct respiratory signals from raw PPG signals. Our results demonstrate a\nhigher RR estimation accuracy of up to 2$\\times$ (mean absolute error of\n1.9$\\pm$0.3 using five fold cross validation) compared to the state-of-th-art\nusing a identical publicly available dataset. Our results suggest that CycleGAN\ncan be a valuable method for RR estimation from raw PPG signals.\n"}
+{"abstract": "  Remaining useful life (RUL) estimation is a crucial component in the\nimplementation of intelligent predictive maintenance and health management.\nDeep neural network (DNN) approaches have been proven effective in RUL\nestimation due to their capacity in handling high-dimensional non-linear\ndegradation features. However, the applications of DNN in practice face two\nchallenges: (a) online update of lifetime information is often unavailable, and\n(b) uncertainties in predicted values may not be analytically quantified. This\npaper addresses these issues by developing a hybrid DNN-based prognostic\napproach, where a Wiener-based-degradation model is enhanced with adaptive\ndrift to characterize the system degradation. An LSTM-CNN encoder-decoder is\ndeveloped to predict future degradation trajectories by jointly learning noise\ncoefficients as well as drift coefficients, and adaptive drift is updated via\nBayesian inference. A computationally efficient algorithm is proposed for the\ncalculation of RUL distributions. Numerical experiments are presented using\nturbofan engines degradation data to demonstrate the superior accuracy of RUL\nprediction of our proposed approach.\n"}
+{"abstract": "  Though stellar-mass black holes (BHs) are likely abundant in the Milky Way\n(N=10^8-10^9), only ~20 have been detected to date, all in accreting binary\nsystems (Casares 2006). Gravitational microlensing is a proposed technique to\nsearch for isolated BHs, which to date have not been detected. Two microlensing\nevents, MACHO-1996-BLG-5 (M96-B5) and MACHO-1998-BLG-6 (M98-B6), initially\nobserved near the lens-source minimum angular separation in 1996 and 1998,\nrespectively, have long Einstein crossing times (>300 days), identifying the\nlenses as candidate black holes. Twenty years have elapsed since the time of\nlens-source closest approach for each of these events, indicating that if the\nlens and source are both luminous, and if their relative proper motion is\nsufficiently large, the two components should be spatially resolvable. We\nattempt to eliminate the possibility of a stellar lens for these events by: (1)\nusing Keck near-infrared adaptive optics images to search for a potentially\nnow-resolved, luminous lens; and (2) examining multi-band photometry of the\nsource to search for flux contributions from a potentially unresolved, luminous\nlens. We combine detection limits from NIRC2 images with light curve data to\neliminate all non-BH lenses for relative lens-source proper motions above 0.81\nmas/yr for M96-B5 and 2.48 mas/yr for M98-B6. Further, we use WFPC2 broadband\nimages to eliminate the possibility of stellar lenses at any proper motion. We\npresent the narrow range of non-BH possibilities allowed by our varied\nanalyses. Finally, we suggest future observations that would constrain the\nremaining parameter space with the methods developed in this work.\n"}
+{"abstract": "  Several recent studies have indicated that artificial subhalo disruption (the\nspontaneous, non-physical disintegration of a subhalo) remains prevalent in\nstate-of-the-art dark matter-only cosmological simulations. In order to\nquantify the impact of disruption on the inferred subhalo demographics, we\naugment the semi-analytical SatGen dynamical subhalo evolution model with an\nimproved treatment of tidal stripping that is calibrated using the DASH\ndatabase of idealized high-resolution simulations of subhalo evolution, which\nare free from artificial disruption. We also develop a model of artificial\ndisruption that reproduces the statistical properties of disruption in the\nBolshoi simulation. Using this framework, we predict subhalo mass functions\n(SHMFs), number density profiles, and substructure mass fractions and study how\nthese quantities are impacted by artificial disruption and mass resolution\nlimits. We find that artificial disruption affects these quantities at the\n$10-20\\%$ level, ameliorating previous concerns that it may suppress the SHMF\nby as much as a factor of two. We demonstrate that semi-analytical substructure\nmodeling must include orbit integration in order to properly account for\nsplashback haloes, which make up roughly half of the subhalo population. We\nshow that the resolution limit of $N$-body simulations, rather than artificial\ndisruption, is the primary cause of the radial bias in subhalo number density\nfound in dark matter-only simulations. Hence, we conclude that the mass\nresolution remains the primary limitation of using such simulations to study\nsubhaloes. Our model provides a fast, flexible, and accurate alternative to\nstudying substructure statistics in the absence of both numerical resolution\nlimits and artificial disruption.\n"}
+{"abstract": "  We present an approach for automated in-situ monitoring of phytoplankton and\nzooplankton communities based on a dual magnification dark-field imaging\nmicroscope/camera. We describe the Dual Scripps Plankton Camera (DSPC) system\nand associated image processing, and assess its capabilities in detecting and\ncharacterizing plankton species of different size and taxonomic categories, and\nin measuring their abundances in both laboratory and field applications. In the\nlaboratory, body size and abundance estimates by the DSPC significantly and\nrobustly scale with the same measurements derived by traditional microscopy. In\nthe field, a DSPC installed permanently at 3 m depth in Lake Greifensee\n(Switzerland), delivered images of plankton individuals, colonies, and\nheterospecific aggregates without disrupting natural arrangements of\ninteracting organisms, their microenvironment or their behavior at hourly\ntimescales. The DSPC was able to track the dynamics of taxa in the size range\nbetween ~10 $\\mu$m to ~ 1 cm, covering virtually all the components of the\nplanktonic food web (including parasites and potentially toxic cyanobacteria).\nComparing data from the field-deployed DSPC to traditional sampling and\nmicroscopy revealed a general overall agreement in estimates of plankton\ndiversity and abundances, despite imaging limitations in detecting small\nphytoplankton species and rare and large zooplankton taxa (e.g. carnivorous\nzooplankton). The most significant disagreements between traditional methods\nand the DSPC resided in the measurements of community properties of\nzooplankton, organisms that are heterogeneously distributed spatially and\ntemporally, and whose demography appeared to be better captured by automated\nimaging. Time series collected by the DSPC depicted ecological succession\npatterns, algal bloom dynamics and circadian fluctuations with a temporal\nfrequency and morphological [continues...]\n"}
+{"abstract": "  The recent COVID-19 pandemic has increased the focus on hygienic and\ncontactless identity verification methods. However, the pandemic led to the\nwide use of face masks, essential to keep the pandemic under control. The\neffect of wearing a mask on face recognition in a collaborative environment is\ncurrently sensitive yet understudied issue. Recent reports have tackled this by\nevaluating the masked probe effect on the performance of automatic face\nrecognition solutions. However, such solutions can fail in certain processes,\nleading to performing the verification task by a human expert. This work\nprovides a joint evaluation and in-depth analyses of the face verification\nperformance of human experts in comparison to state-of-the-art automatic face\nrecognition solutions. This involves an extensive evaluation with 12 human\nexperts and 4 automatic recognition solutions. The study concludes with a set\nof take-home messages on different aspects of the correlation between the\nverification behavior of human and machine.\n"}
+{"abstract": "  We reveal limitations of several standard coupled-cluster (CC) methods with\nperturbation-theorybased noniterative or approximate iterative treatments of\ntriple excitations when applied to thedetermination of highly accurate\npotential energy curves (PECs) of ionic dimers, such as the X $^2\\Sigma^+_g$\nelectronic ground state of Rb$_2^+$. Such computations are of current interest\nfor the understanding ofion-atom interactions in the ultracold regime. We\ndemonstrate that these coupled-cluster methodslead to an unphysical long-range\nbarrier for the Rb$_2^+$ system. The barrier is small but clearly spoilsthe\nentire long-range behavior of the PEC. The effect is also found for other\nX$_2^+$ systems, like X = Li, Na, and K. Calculations using a new flexible\nframework for obtaining leading perturbativetriples corrections derived using\nan analytic CC singles and doubles (CCSD) energy derivativeformulation\ndemonstrate that the origin of this problem lies in the use of $T_3$ amplitudes\nobtained from approximate CC singles, doubles and triples (CCSDT) amplitude\nequations. It is shown thatthe unphysical barrier is related to a symmetry\ninstability of the underlying Hartree-Fock mean-fieldsolution. Physically\nmeaningful perturbative corrections in the long-range tail of the PEC\nmayinstead be obtained using symmetry-broken reference determinants.\n"}
+{"abstract": "  Word meaning is notoriously difficult to capture, both synchronically and\ndiachronically. In this paper, we describe the creation of the largest resource\nof graded contextualized, diachronic word meaning annotation in four different\nlanguages, based on 100,000 human semantic proximity judgments. We thoroughly\ndescribe the multi-round incremental annotation process, the choice for a\nclustering algorithm to group usages into senses, and possible - diachronic and\nsynchronic - uses for this dataset.\n"}
+{"abstract": "  The Astrometric Gravitation Probe mission is a modern version of the 1919\nDyson-Eddington-Davidson experiment, based on a space-borne telescope with a\npermanent built-in eclipse, provided by a coronagraphic system. The expected\nimprovement on experimental bounds to General Relativity and competing\ngravitation theories is by at least two orders of magnitude. The measurement\nprinciple is reviewed, in particular the principle of Fizeau-like combination\nof a set of individual inverted coronagraphs simultaneously feeding a common\nhigh resolution telescope. Also, the payload has a dual field of view property,\nin order to support simultaneous observations of stellar fields either very\nclose, or far away, from the Sun, i.e. fields affected by either high or low\nlight bending. We discuss a set of solutions introduced in the optical design\nto improve on technical feasibility and robustness of the optical performance\nagainst perturbations, in particular induced by manufacturing and alignment\ntolerances, and launch stresses.\n"}
+{"abstract": "  In this essay I argue that the Hilbert space of states dual to a traversable\nwormhole is smaller than the tensor product of the independent Hilbert spaces\nof the boundary field theories. From the point of view of semiclassical\nphysics, the decrease in the number of states is perceived as an emergent,\nnon-local interaction stabilizing the wormhole. This presents new possibilities\nfor models of radiating black holes and raises questions about results\nestablished under the spell of the tensor product.\n"}
+{"abstract": "  We study a generalization of the two-dimensional transverse-field Ising\nmodel, combining both ferromagnetic and antiferromagnetic two-body\ninteractions, that hosts exact global and local Z2 gauge symmetries. Using\nexact diagonalization and stochastic series expansion quantum Monte Carlo\nmethods, we confirm the existence of the topological phase in line with\nprevious theoretical predictions. Our simulation results show that the\ntransition between the confined topological phase and the deconfined\nparamagnetic phase is of first-order, in contrast to the conventional Z2\nlattice gauge model in which the transition maps onto that of the standard\nIsing model and is continuous. We further generalize the model by replacing the\ntransverse field on the gauge spins with a ferromagnetic XX interaction while\nkeeping the local gauge symmetry intact. We find that the Z2 topological phase\nremains stable, while the paramagnetic phase is replaced by a ferromagnetic\nphase. The topological-ferromagnetic quantum phase transition is also of\nfirst-order. For both models, we discuss the low-energy spinon and vison\nexcitations of the topological phase and their avoided level crossings\nassociated with the first-order quantum phase transitions.\n"}
+{"abstract": "  The sensibility and sensitivity of the environment play a decisive role in\nthe safe and secure operation of autonomous vehicles. This perception of the\nsurrounding is way similar to human visual representation. The human's brain\nperceives the environment by utilizing different sensory channels and develop a\nview-invariant representation model. Keeping in this context, different\nexteroceptive sensors are deployed on the autonomous vehicle for perceiving the\nenvironment. The most common exteroceptive sensors are camera, Lidar and radar\nfor autonomous vehicle's perception. Despite being these sensors have\nillustrated their benefit in the visible spectrum domain yet in the adverse\nweather conditions, for instance, at night, they have limited operation\ncapability, which may lead to fatal accidents. In this work, we explore thermal\nobject detection to model a view-invariant model representation by employing\nthe self-supervised contrastive learning approach. For this purpose, we have\nproposed a deep neural network Self Supervised Thermal Network (SSTN) for\nlearning the feature embedding to maximize the information between visible and\ninfrared spectrum domain by contrastive learning, and later employing these\nlearned feature representation for the thermal object detection using\nmulti-scale encoder-decoder transformer network. The proposed method is\nextensively evaluated on the two publicly available datasets: the FLIR-ADAS\ndataset and the KAIST Multi-Spectral dataset. The experimental results\nillustrate the efficacy of the proposed method.\n"}
+{"abstract": "  How can minorities of individuals overturn social conventions? The theory of\ncritical mass states that when a committed minority reaches a critical size a\ncascade of behavioural changes can occur, overturning apparently stable social\nnorms. Evidence comes from several theoretical and empirical studies in which\nminorities of very different sizes, including extremely small ones, manage to\nbring a system to its tipping point. Here, we explore this diversity of\nscenarios by introducing a crucial element of realism into a standard model for\nsocial convention, namely, the possibility of group interactions. Indeed, usual\nmodels adopt all-to-all or pairwise descriptions of the social structure, thus\nneglecting the effects of groups. Instead, we investigate their role by\nexplicitly including many-body interactions with variable internal social\ninfluence into the framework and leveraging real-world interaction data\nincluding groups of different sizes. We find that the critical mass necessary\nto trigger behaviour change can indeed be very small (in some cases down to\n0.3%) if individuals are less prone to change their views. Higher-order (group)\ninteractions turn out to have a complex role: the ability of the committed\nminority to overturn existing norms is non-monotonic in the group size. The\nresults, analytically confirmed within a simplified mean-field approach, hold\nin a broad region of the parameter space. Our findings reconcile the different\nsizes of critical mass found in previous empirical and theoretical\ninvestigations by unveiling the critical role played by groups in modulating\nthe minority takeover. This further highlights the importance of the emerging\nfield of higher-order networks, beyond pairwise interactions.\n"}
+{"abstract": "  The success of Deep Artificial Neural Networks (DNNs) in many domains created\na rich body of research concerned with hardware accelerators for\ncompute-intensive DNN operators. However, implementing such operators\nefficiently with complex hardware intrinsics such as matrix multiply is a task\nnot yet automated gracefully. Solving this task often requires joint program\nand data layout transformations. First solutions to this problem have been\nproposed, such as TVM, UNIT or ISAMIR, which work on a loop-level\nrepresentation of operators and specify data layout and possible program\ntransformations before the embedding into the operator is performed. This\ntop-down approach creates a tension between exploration range and search space\ncomplexity, especially when also exploring data layout transformations such as\nim2col, channel packing or padding.\n  In this work, we propose a new approach to this problem. We created a\nbottom-up method that allows the joint transformation of both compuation and\ndata layout based on the found embedding. By formulating the embedding as a\nconstraint satisfaction problem over the scalar dataflow, every possible\nembedding solution is contained in the search space. Adding additional\nconstraints and optmization targets to the solver generates the subset of\npreferable solutions.\n  An evaluation using the VTA hardware accelerator with the Baidu DeepBench\ninference benchmark shows that our approach can automatically generate code\ncompetitive to reference implementations. Further, we show that dynamically\ndetermining the data layout based on intrinsic and workload is beneficial for\nhardware utilization and performance. In cases where the reference\nimplementation has low hardware utilization due to its fixed deployment\nstrategy, we achieve a geomean speedup of up to x2.813, while individual\noperators can improve as much as x170.\n"}
+{"abstract": "  Future linear $e^+e^-$ colliders aim for extremely high precision\nmeasurements. To achieve this, not only excellent detectors and well controlled\nmachine conditions are needed, but also the best possible estimate of\nbackgrounds. To avoid that lacking channels and too low statistics becomes a\nmajor source of systematic errors in data-MC comparisons, all SM channels with\nthe potential to yield at least a few events under the full lifetime of the\nprojects need to be generated, with statistics largely exceeding that of the\nreal data. Also machine conditions need to be accurately taken into account.\nThis includes beam-polarisation, interactions due to the photons inevitably\npresent in the highly focused beams, and coherent interactions of whole\nbunches. This endeavour has already been partly achieved in preparing design\ndocuments for both the ILC and CLIC: Comprehensive samples of fully simulated\nand reconstructed events are available for use. In this contribution, we\npresent how the generation of physics events at linear colliders is categorised\nand organised, and the tools used. Also covered is how different aspects of\nmachine conditions, different sources of spurious interactions (such as\nbeam-induced backgrounds) are treated and the tools involved for these aspects.\n"}
+{"abstract": "  In this paper, we study the singularities of spacelike constant mean\ncurvature one (CMC 1) surfaces in the de Sitter 3-space. We prove the duality\nbetween generalized conelike singular points and 5/2-cuspidal edges on\nspacelike CMC 1 surfaces. To describe the duality between $A_{k+3}$\nsingularities and cuspidal $S_k$ singularities, we introduce two invariants,\ncalled the $\\alpha$-invariant and $\\sigma$-invariant, of spacelike CMC 1\nsurfaces at their singular points. Moreover, we give a classification of\nnon-degenerate singular points on spacelike CMC 1 surfaces.\n"}
+{"abstract": "  Membership of stars in open clusters is one of the most crucial parameters in\nstudies of star clusters. Gaia opened a new window in the estimation of\nmembership because of its unprecedented 6-D data. In the present study, we used\npublished membership data of nine open star clusters as a training set to find\nnew members from Gaia DR2 data using a supervised random forest model with a\nprecision of around 90\\%. The number of new members found is often double the\npublished number. Membership probability of a larger sample of stars in\nclusters is a major benefit in determination of cluster parameters like\ndistance, extinction and mass functions. We also found members in the outer\nregions of the cluster and found sub-structures in the clusters studied. The\ncolor magnitude diagrams are more populated and enriched by the addition of new\nmembers making their study more promising.\n"}
+{"abstract": "  Ultra-reliable low-latency Vehicle-to-Everything (V2X) communications are\nneeded to meet the extreme requirements of enhanced driving applications.\nMillimeter-Wave (24.25-52.6 GHz) or sub-THz (>100 GHz) V2X communications are a\nviable solution, provided that the highly collimated beams are kept aligned\nduring vehicles' maneuverings. In this work, we propose a sensor-assisted\ndynamic Beamwidth and Power Control (BPC) system to counteract the detrimental\neffect of vehicle dynamics, exploiting data collected by on-board inertial and\npositioning sensors, mutually exchanged among vehicles over a parallel low-rate\nlink, e.g., 5G New Radio (NR) Frequency Range 1 (FR1). The proposed BPC\nsolution works on top of a sensor-aided Beam Alignment and Tracking (BAT)\nsystem, overcoming the limitations of fixed-beamwidth systems and optimizing\nthe performance in challenging Vehicle-to-Vehicle (V2V) scenarios, even if\nextensions to Vehicle-to-Infrastructure (V2I) use-cases are feasible. We\nvalidate the sensor-assisted dynamic BPC on real trajectories and sensors' data\ncollected by a dedicated experimental campaign. The goal is to show the\nadvantages of the proposed BPC strategy in a high data-rate Line-Of-Sight (LOS)\nV2V context, and to outline the requirements in terms of sensors' sampling time\nand accuracy, along with the end-to-end latency on the control channel.\n"}
+{"abstract": "  In this paper, we propose an energy-efficient federated meta-learning\nframework. The objective is to enable learning a meta-model that can be\nfine-tuned to a new task with a few number of samples in a distributed setting\nand at low computation and communication energy consumption. We assume that\neach task is owned by a separate agent, so a limited number of tasks is used to\ntrain a meta-model. Assuming each task was trained offline on the agent's local\ndata, we propose a lightweight algorithm that starts from the local models of\nall agents, and in a backward manner using projected stochastic gradient ascent\n(P-SGA) finds a meta-model. The proposed method avoids complex computations\nsuch as computing hessian, double looping, and matrix inversion, while\nachieving high performance at significantly less energy consumption compared to\nthe state-of-the-art methods such as MAML and iMAML on conducted experiments\nfor sinusoid regression and image classification tasks.\n"}
+{"abstract": "  The concept of gyrogroups is a generalization of groups which do not\nexplicitly have associativity. Recently, Atiponrat extended the idea of\ntopological (paratopological) groups to topological (paratopological)\ngyrogroups. In this paper, we prove that every regular (Hausdorff) locally\ngyroscopic invariant paratopological gyrogroup $G$ is completely regular\n(function Hausdorff). These results improve theorems of Banakh and Ravsky for\nparatopological groups. Also, we extend the Pontrjagin conditions of\n(para)topological groups to (para)topological gyrogroups.\n"}
+{"abstract": "  In this work we present for the first time the general solution of the\ntemporal evolution equation arising from the matching of a conformally flat\ninterior to the Vaidya solution. This problem was first articulated by Banerjee\net al. (A. Banerjee, S. B. Dutta Choudhury, and Bidyut K. Bhui, Phys. Rev. D,\n40 (670) 1989) in which they provided a particular solution of the temporal\nequation. This simple, exact solution has been widely utilised in modeling\ndissipative collapse with the most notable result being prediction of the\navoidance of the horizon as the collapse proceeds. We study the dynamics of\ndissipative collapse arising from the general solution obtained via the method\nof symmetries and of the singularity analysis. We show that the end-state of\ncollapse for our model is significantly different from the widely used linear\nsolution.\n"}
+{"abstract": "  We propose a learning framework based on stochastic Bregman iterations to\ntrain sparse neural networks with an inverse scale space approach. We derive a\nbaseline algorithm called LinBreg, an accelerated version using momentum, and\nAdaBreg, which is a Bregmanized generalization of the Adam algorithm. In\ncontrast to established methods for sparse training the proposed family of\nalgorithms constitutes a regrowth strategy for neural networks that is solely\noptimization-based without additional heuristics. Our Bregman learning\nframework starts the training with very few initial parameters, successively\nadding only significant ones to obtain a sparse and expressive network. The\nproposed approach is extremely easy and efficient, yet supported by the rich\nmathematical theory of inverse scale space methods. We derive a statistically\nprofound sparse parameter initialization strategy and provide a rigorous\nstochastic convergence analysis of the loss decay and additional convergence\nproofs in the convex regime. Using only 3.4% of the parameters of ResNet-18 we\nachieve 90.2% test accuracy on CIFAR-10, compared to 93.6% using the dense\nnetwork. Our algorithm also unveils an autoencoder architecture for a denoising\ntask. The proposed framework also has a huge potential for integrating sparse\nbackpropagation and resource-friendly training.\n"}
+{"abstract": "  We theoretically implement some hyperparallel optical elements, including\nquantum single photon transistor, router, and dynamic random access memory\n(DRAM). The inevitable side leakage and the imperfect birefringence of the\nquantum dot (QD)-cavity mediates are taken into account, and unity fidelities\nof our optical elements can be achieved. The hyperparallel constructions are\nbased on polarization and spatial degrees of freedom (DOFs) of the photon to\nincrease the parallel efficiency, improve the capacity of channel, save the\nquantum resources, reduce the operation time, and decrease the environment\nnoises. Moreover, the practical schemes are robust against the side leakage and\nthe coupling strength limitation in the microcavities.\n"}
+{"abstract": "  In domains where computational resources and labeled data are limited, such\nas in robotics, deep networks with millions of weights might not be the optimal\nsolution. In this paper, we introduce a connectivity scheme for pyramidal\narchitectures to increase their capacity for learning features. Experiments on\nfacial expression recognition of unseen people demonstrate that our approach is\na potential candidate for applications with restricted resources, due to good\ngeneralization performance and low computational cost. We show that our\napproach generalizes as well as convolutional architectures in this task but\nuses fewer trainable parameters and is more robust for low-resolution faces.\n"}
+{"abstract": "  In this paper, we propose a new signal organization method to work in the\nstructure of the multi level coding (MLC). The transmit bits are divided into\nopportunistic bit (OB) and conventional bit (CB), which are mapped to the lower\nlevel- and higher level signal in parallel to the MLC, respectively. Because\nthe OB's mapping does not require signal power explicitly, the energy of the CB\nmodulated symbol can be doubled. As the result, the overall mutual information\nof the proposed method is found higher than that of the conventional BPSK in\none dimensional case. Moreover, the extension of the method to the\ntwo-complex-dimension shows the better performance over the QPSK. The numerical\nresults confirm this approach.\n"}
+{"abstract": "  We address the novel task of jointly reconstructing the 3D shape, texture,\nand motion of an object from a single motion-blurred image. While previous\napproaches address the deblurring problem only in the 2D image domain, our\nproposed rigorous modeling of all object properties in the 3D domain enables\nthe correct description of arbitrary object motion. This leads to significantly\nbetter image decomposition and sharper deblurring results. We model the\nobserved appearance of a motion-blurred object as a combination of the\nbackground and a 3D object with constant translation and rotation. Our method\nminimizes a loss on reconstructing the input image via differentiable rendering\nwith suitable regularizers. This enables estimating the textured 3D mesh of the\nblurred object with high fidelity. Our method substantially outperforms\ncompeting approaches on several benchmarks for fast moving objects deblurring.\nQualitative results show that the reconstructed 3D mesh generates high-quality\ntemporal super-resolution and novel views of the deblurred object.\n"}
+{"abstract": "  HERMES (High Energy Rapid Modular Ensemble of Satellites) is an innovative\nmission aiming to observe transient high-energy events such as gamma-ray bursts\n(GRBs) through a constellation of CubeSats hosting a broadband X and gamma-ray\ndetector. The detector is based on a solid-state Silicon Drift Detector (SDD)\ncoupled to a scintillator crystal, and is sensitive in the 2 keV to 2 MeV band.\nAn accurate evaluation of the foreseen in-orbit instrumental background is\nessential to assess the scientific performance of the experiment. An outline of\nthe Monte Carlo simulations of the HERMES payload will be provided, describing\nthe various contributions on the total background and the optimization\nstrategies followed in the instrument design. Moreover, the simulations were\nused in order to derive the effective area and response matrices of the\ninstrument, also as a function of the source location with respect to the\ndetector frame of reference.\n"}
+{"abstract": "  Understanding and manipulation of the laser processing quality during the\nablation of solids have crucial importance from fundamental and industrial\nperspectives. Here we have studied the effect of external magnetic field on the\nmicro-material processing of silicon by ultrashort laser pulses. It was found\nexperimentally that such a field directed along the laser beam improves the\nquality and efficiency of the material removal. Additionally, we observe that\nthe formation of laser-induced periodic surface structures (LIPSS) in a\nmulti-pulse regime is affected by the external magnetic field. Our results open\na route towards efficient and controllable ultrafast laser micromachining.\n"}
+{"abstract": "  Distributed Nash equilibrium (NE) seeking problem for multi-coalition games\nhas attracted increasing attention in recent years, but the research mainly\nfocuses on the case without agreement demand within coalitions. This paper\nconsiders a class of networked games among multiple coalitions where each\ncoalition contains multiple agents that cooperate to minimize the sum of their\ncosts, subject to the demand of reaching an agreement on their state values.\nFurthermore, the underlying network topology among the agents does not need to\nbe balanced. To achieve the goal of NE seeking within such a context, two\nestimates are constructed for each agent, namely, an estimate of partial\nderivatives of the cost function and an estimate of global state values, based\non which, an iterative state updating law is elaborately designed. Linear\nconvergence of the proposed algorithm is demonstrated. It is shown that the\nconsistency-constrained multi-coalition games investigated in this paper put\nthe well-studied networked games among individual players and distributed\noptimization in a unified framework, and the proposed algorithm can easily\ndegenerate into solutions to these problems.\n"}
+{"abstract": "  Mobile application security has been a major area of focus for security\nresearch over the course of the last decade. Numerous application analysis\ntools have been proposed in response to malicious, curious, or vulnerable apps.\nHowever, existing tools, and specifically, static analysis tools, trade\nsoundness of the analysis for precision and performance and are hence soundy.\nUnfortunately, the specific unsound choices or flaws in the design of these\ntools is often not known or well-documented, leading to misplaced confidence\namong researchers, developers, and users. This paper describes the\nMutation-based Soundness Evaluation ($\\mu$SE) framework, which systematically\nevaluates Android static analysis tools to discover, document, and fix flaws,\nby leveraging the well-founded practice of mutation analysis. We implemented\n$\\mu$SE and applied it to a set of prominent Android static analysis tools that\ndetect private data leaks in apps. In a study conducted previously, we used\n$\\mu$SE to discover $13$ previously undocumented flaws in FlowDroid, one of the\nmost prominent data leak detectors for Android apps. Moreover, we discovered\nthat flaws also propagated to other tools that build upon the design or\nimplementation of FlowDroid or its components. This paper substantially extends\nour $\\mu$SE framework and offers an new in-depth analysis of two more major\ntools in our 2020 study, we find $12$ new, undocumented flaws and demonstrate\nthat all $25$ flaws are found in more than one tool, regardless of any\ninheritance-relation among the tools. Our results motivate the need for\nsystematic discovery and documentation of unsound choices in soundy tools and\ndemonstrate the opportunities in leveraging mutation testing in achieving this\ngoal.\n"}
+{"abstract": "  We describe a framework for reformulating and solving optimization problems\nthat generalizes the well-known framework originally introduced by Benders. We\ndiscuss details of the application of the procedures to several classes of\noptimization problems that fall under the umbrella of multilevel/multistage\nmixed integer linear optimization problems. The application of this abstract\nframework to this broad class of problems provides new insights and a broader\ninterpretation of the core ideas, especially as they relate to duality and the\nvalue functions of optimization problems that arise in this context.\n"}
+{"abstract": "  We propose protocols for determining the distances in Hilbert space between\npure and mixed quantum states prepared on a quantum computer. In the case of\npure quantum states, the protocol is based on measuring the square of modulus\nof scalar product between certain states. Determination of the distance between\nmixed quantum states is reduced to measuring the squares of modules of scalar\nproducts between all pure states included in the mixed states. In addition, we\ndevelop a protocol that allows one to determine the speed of evolution of the\nspin system simulated by a quantum computer. These protocols we apply to\nmeasure distances and speeds of evolution of different quantum systems\nimplemented on the ibmq-santiago quantum computer.\n"}
+{"abstract": "  We study hydrodynamic limits of three kinds of one-dimensional stochastic\nlog-gases known as Dyson's Brownian motion model, its chiral version, and the\nBru--Wishart process studied in dynamical random matrix theory. We define the\nmeasure-valued processes so that their Cauchy transforms solve the complex\nBurgers-type equations. We show that applications of the method of\ncharacteristic curves to these partial differential equations provide\nfunctional equations relating the Cauchy transforms of measures at an arbitrary\ntime and those at the initial time. We rewrite the complex Burgers-like\nequations and functional equations for the Cauchy transforms of the log-gas\nmeasures to those for the $R$-transforms and the $S$-transforms of the measures\nwhich play central roles in free probability theory. Some of the results are\ndiscussed using the notion of free convolutions.\n"}
+{"abstract": "  Whilst lattice-based cryptosystems are believed to be resistant to quantum\nattack, they are often forced to pay for that security with inefficiencies in\nimplementation. This problem is overcome by ring- and module-based schemes such\nas Ring-LWE or Module-LWE, whose keysize can be reduced by exploiting its\nalgebraic structure, allowing for neater and faster computations. Many rings\nmay be chosen to define such cryptoschemes, but cyclotomic rings, due to their\ncyclic nature allowing for easy multiplication, are the community standard.\nHowever, there is still much uncertainty as to whether this structure may be\nexploited to an adversary's benefit. In this paper, we show that the\ndecomposition group of a cyclotomic ring of arbitrary conductor may be utilised\nin order to significantly decrease the dimension of the ideal (or module)\nlattice required to solve a given instance of SVP. Moreover, we show that there\nexist a large number of rational primes for which, if the prime ideal factors\nof an ideal lie over primes of this form, give rise to an \"easy\" instance of\nSVP. However, it is important to note that this work does not break Ring-LWE or\nModule-LWE, since the security reduction is from worst case ideal or module SVP\nto average case Ring-LWE or Module-LWE respectively, and is one way.\n"}
+{"abstract": "  We maximize the production of biogas in a gradostat at steady state. The\nphysical decision variables are the water, substrate, and biomass entering each\ntank and the flows through the interconnecting pipes. Our main technical focus\nis the nonconvex constraint describing microbial growth. We formulate a\nrelaxation and prove that it is exact when the gradostat is outflow connected,\nits system matrix is irreducible, and the growth rate satisfies a simple\ncondition. The relaxation has second-order cone representations for the Monod\nand Contois growth rates. We extend the steady state models to the case of\nmultiple time periods by replacing the derivatives with numerical\napproximations instead of setting them to zero. The resulting optimizations are\nsecond-order cone programs, which can be solved at large scales using standard\nindustrial software.\n"}
+{"abstract": "  Black-box optimization (BBO) algorithms are concerned with finding the best\nsolutions for problems with missing analytical details. Most classical methods\nfor such problems are based on strong and fixed a priori assumptions, such as\nGaussianity. However, the complex real-world problems, especially when the\nglobal optimum is desired, could be very far from the a priori assumptions\nbecause of their diversities, causing unexpected obstacles to these methods. In\nthis study, we propose a generative adversarial net-based broad-spectrum global\noptimizer (OPT-GAN) which estimates the distribution of optimum gradually, with\nstrategies to balance exploration-exploitation trade-off. It has potential to\nbetter adapt to the regularity and structure of diversified landscapes than\nother methods with fixed prior, e.g. Gaussian assumption or separability.\nExperiments conducted on BBO benchmarking problems and several other benchmarks\nwith diversified landscapes exhibit that OPT-GAN outperforms other traditional\nand neural net-based BBO algorithms.\n"}
+{"abstract": "  In computational material design, ionic radius is one of the most important\nphysical parameters used to predict material properties. Motivated by the\nprogress in computational materials science and material informatics, we extend\nthe renowned Shannon's table from 475 ions to 987 ions. Accordingly, a rigorous\nMachine Learning (ML) approach is employed to extend the ionic radii table\nusing all possible combinations of Oxidation States (OS) and Coordination\nNumbers (CN) available in crystallographic repositories. An ionic-radius\nregression model for Shannon's database is developed as a function of the\nperiod number, the valence orbital configuration, OS, CN, and Ionization\nPotential. In the Gaussian Process Regression (GPR) model, the reached R-square\n$R^2$ accuracy is 99\\% while the root mean square error of radii is 0.0332 \\AA.\nThe optimized GPR model is then employed for predicting a new set of ionic\nradii for uncommon combinations of OS and CN extracted by harnessing crystal\nstructures from materials project databases. The generated data is consolidated\nwith the reputable Shannon's data and is made available online in a database\nrepository \\url{https://cmd-ml.github.io/}.\n"}
+{"abstract": "  We show that any $m\\times m$ matrix $M$ with integer entries and $\\det M\n=\\Delta \\neq 0$ can be equipped by a finite digit set\n$\\mathcal{D}\\subset\\mathbb{Z}^m$ such that any integer $m$-dimensional vector\nbelongs to the set $$ {\\rm Fin}_{\\mathcal{D}}(M)= \\Bigl\\{\\sum_{k\\in I}M^k {d}_k\n: \\emptyset\\neq I \\text{ finite subset of } \\mathbb{Z} \\text{ and } {d}_k \\in\n\\mathcal{D} \\text{ for each } k \\in I\\Bigr\\} \\subset \\bigcup\\limits_{k\\in\n\\mathbb{N}} \\frac{1}{\\Delta^k}\\mathbb{Z}^{m} \\,. $$ We also characterize the\nmatrices $M$ for which the sets $ {\\rm Fin}_{\\mathcal{D}}(M)$ and $\n\\bigcup\\limits_{k\\in \\mathbb{N}} \\frac{1}{\\Delta^k}\\mathbb{Z}^{m}$ coincide.\n"}
+{"abstract": "  Feynman-Vernon influence functional (IF) was originally introduced to\ndescribe the effect of a quantum environment on the dynamics of an open quantum\nsystem. We apply the IF approach to describe quantum many-body dynamics in\nisolated spin systems, viewing the system as an environment for its local\nsubsystems. While the IF can be computed exactly only in certain many-body\nmodels, it generally satisfies a self-consistency equation, provided the\nsystem, or an ensemble of systems, are translationally invariant. We view the\nIF as a fictitious wavefunction in the temporal domain, and approximate it\nusing matrix-product states (MPS). This approach is efficient provided the\ntemporal entanglement of the IF is sufficiently low. We illustrate the\nversatility of the IF approach by analyzing several models that exhibit a range\nof dynamical behaviors, from thermalizing to many-body localized. In\nparticular, we study the non-equilibrium dynamics in the quantum Ising model in\nboth Floquet and Hamiltonian settings. We find that temporal entanglement\nentropy may be significantly lower compared to the spatial entanglement and\nanalyze the IF in the continuous-time limit. We simulate the\nthermodynamic-limit evolution of local observables in various regimes,\nincluding the relaxation of impurities embedded in an infinite-temperature\nchain, and the long-lived oscillatory dynamics of the magnetization associated\nwith the confinement of excitations. By incorporating disorder-averaging into\nthe formalism, we analyze discrete time-crystalline response using the IF\nmethod. In this case, we find that the temporal entanglement entropy scales\nlogarithmically with evolution time. The IF approach offers a new lens on\nmany-body non-equilibrium phenomena, both in ergodic and non-ergodic regimes,\nconnecting the theory of open quantum systems theory to quantum statistical\nphysics.\n"}
+{"abstract": "  Recent advances in automatic music transcription (AMT) have achieved highly\naccurate polyphonic piano transcription results by incorporating onset and\noffset detection. The existing literature, however, focuses mainly on the\nleverage of deep and complex models to achieve state-of-the-art (SOTA)\naccuracy, without understanding model behaviour. In this paper, we conduct a\ncomprehensive examination of the Onsets-and-Frames AMT model, and pinpoint the\nessential components contributing to a strong AMT performance. This is achieved\nthrough exploitation of a modified additive attention mechanism. The\nexperimental results suggest that the attention mechanism beyond a moderate\ntemporal context does not benefit the model, and that rule-based\npost-processing is largely responsible for the SOTA performance. We also\ndemonstrate that the onsets are the most significant attentive feature\nregardless of model complexity. The findings encourage AMT research to weigh\nmore on both a robust onset detector and an effective post-processor.\n"}
+{"abstract": "  The Neural Tangent Kernel (NTK) is the wide-network limit of a kernel defined\nusing neural networks at initialization, whose embedding is the gradient of the\noutput of the network with respect to its parameters. We study the \"after\nkernel\", which is defined using the same embedding, except after training, for\nneural networks with standard architectures, on binary classification problems\nextracted from MNIST and CIFAR-10, trained using SGD in a standard way. For\nsome dataset-architecture pairs, after a few epochs of neural network training,\na hard-margin SVM using the network's after kernel is much more accurate than\nwhen the network's initial kernel is used. For networks with an architecture\nsimilar to VGG, the after kernel is more \"global\", in the sense that it is less\ninvariant to transformations of input images that disrupt the global structure\nof the image while leaving the local statistics largely intact. For fully\nconnected networks, the after kernel is less global in this sense. The after\nkernel tends to be more invariant to small shifts, rotations and zooms; data\naugmentation does not improve these invariances. The (finite approximation to\nthe) conjugate kernel, obtained using the last layer of hidden nodes,\nsometimes, but not always, provides a good approximation to the NTK and the\nafter kernel.\n  Training a network with a larger learning rate (while holding the training\nerror constant) produces a better kernel, as measured by the test error of a\nhard-margin SVM. The after kernels of networks trained with larger learning\nrates tend to be more global, and more invariant to small shifts, rotations and\nzooms.\n"}
+{"abstract": "  Solar twins are key in different areas of astrophysics, however only just\nover a hundred were identified and well-studied in the last two decades. In\nthis work, we take advantage of the very precise \\textit{Gaia} (DR2/EDR3),\nTycho and 2MASS photometric systems to create the Inti survey of new solar\ntwins in the Northern Hemisphere. The spectra of our targets were initially\nobtained with spectrographs of moderate resolution (ARCES and Goodman\nspectrographs with $R$ = 31500 and 11930, respectively) to find the best solar\ntwin candidates and then observed at McDonald Observatory with higher resolving\npower (TS23, $R$ = 60000) and signal-to-noise ratio (SNR $\\sim$ 300-500). The\nstellar parameters were estimated through the differential spectroscopic\nequilibrium relative to the Sun, which allow us to achieve a high internal\nprecision ($\\sigma(T_{\\rm{eff}})$ = 15 K, $\\sigma(\\log g)$ = 0.03 dex,\n$\\sigma$([Fe/H]) = 0.01 dex, and $\\sigma(v_{t})$ = 0.03 km s$^{-1}$). We\npropose a new class of stars with evolution similar to the Sun: \\textit{solar\nproxy}, which is useful to perform studies related to the evolution of the Sun,\nsuch as its rotational and magnetic evolution. Its definition is based on\nmetallicity ($-$0.15 dex $\\leq$ [Fe/H] $\\leq$ +0.15 dex) and mass (0.95\nM$_{\\odot}$ $\\leq$ M $\\leq$ 1.05 M$_{\\odot}$) constraints, thus assuring that\nthe star follows a similar evolutionary path as the Sun along the main\nsequence. Based on this new definition, we report 70 newly identified solar\nproxies, 46 solar analogs and 13 solar-type stars. In addition, we identified 9\n\\textit{close solar twins} whose stellar parameters are the most similar to\nthose of the Sun.\n"}
+{"abstract": "  Python has become a popular programming language because of its excellent\nprogrammability. Many modern software packages utilize Python for high-level\nalgorithm design and depend on native libraries written in C/C++/Fortran for\nefficient computation kernels. Interaction between Python code and native\nlibraries introduces performance losses because of the abstraction lying on the\nboundary of Python and native libraries. On the one side, Python code,\ntypically run with interpretation, is disjoint from its execution behavior. On\nthe other side, native libraries do not include program semantics to understand\nalgorithm defects.\n  To understand the interaction inefficiencies, we extensively study a large\ncollection of Python software packages and categorize them according to the\nroot causes of inefficiencies. We extract two inefficiency patterns that are\ncommon in interaction inefficiencies. Based on these patterns, we develop\nPieProf, a lightweight profiler, to pinpoint interaction inefficiencies in\nPython applications. The principle of PieProf is to measure the inefficiencies\nin the native execution and associate inefficiencies with high-level Python\ncode to provide a holistic view. Guided by PieProf, we optimize 17 real-world\napplications, yielding speedups up to 6.3$\\times$ on application level.\n"}
+{"abstract": "  Neighborhood search is a cornerstone of state-of-the-art traveling salesman\nand vehicle routing metaheuristics. While neighborhood exploration procedures\nare well developed for problems with individual services, their counterparts\nfor one-to-one pickup-and-delivery problems have been more scarcely studied. A\ndirect extension of classic neighborhoods is often inefficient or complex due\nto the necessity of jointly considering service pairs. To circumvent these\nissues, we introduce major improvements to existing neighborhood searches for\nthe pickup-and-delivery traveling salesman problem and new large neighborhoods.\nWe show that the classical Relocate-Pair neighborhood can be fully explored in\n$O(n^2)$ instead of $O(n^3)$ time. We adapt the 4-Opt and Balas-Simonetti\nneighborhoods to consider precedence constraints. Moreover, we introduce an\nexponential-size neighborhood called 2k-Opt, which includes all solutions\ngenerated by multiple nested 2-Opts and can be searched in $O(n^2)$ time using\ndynamic programming. We conduct extensive computational experiments,\nhighlighting the significant contribution of these new neighborhoods and\nspeed-up strategies within two classical metaheuristics. Notably, our approach\npermits to repeatedly solve small pickup-and-delivery problem instances to\noptimality or near-optimality within milliseconds, and therefore it represents\na valuable tool for time-critical applications such as meal delivery or\nmobility of demand.\n"}
+{"abstract": "  Coupled map lattices (CMLs) are prototypical dynamical systems on\nnetworks/graphs. They exhibit complex patterns generated via the interplay of\ndiffusive/Laplacian coupling and nonlinear reactions modelled by a single\niterated map at each node; the maps are often taken as unimodal, e.g., logistic\nor tent maps. In this letter, we propose a class of higher-order coupled\ndynamical systems involving the hypergraph Laplacian, which we call coupled\nhypergraph maps (CHMs). By combining linearized (in-)stability analysis of\nsynchronized states, hypergraph spectral theory, and numerical methods, we\ndetect robust regions of chaotic cluster synchronization occurring in parameter\nspace upon varying coupling strength and the main bifurcation parameter of the\nunimodal map. Furthermore, we find key differences between Laplacian and\nhypergraph Laplacian coupling and detect various other classes of periodic and\nquasi-periodic patterns. The results show the high complexity of coupled graph\nmaps and indicate that they might be an excellent universal model class to\nunderstand the similarities and differences between dynamics on classical\ngraphs and dynamics on hypergraphs.\n"}
+{"abstract": "  After recalling some basic facts about F-wound commutative unipotent\nalgebraic groups over an imperfect field F we study their regular integral\nmodels over Dedekind schemes of positive characteristic and compute the group\nof isomorphisms classes of torsors of one-dimensional groups.\n"}
+{"abstract": "  Multilingual pretrained representations generally rely on subword\nsegmentation algorithms to create a shared multilingual vocabulary. However,\nstandard heuristic algorithms often lead to sub-optimal segmentation,\nespecially for languages with limited amounts of data. In this paper, we take\ntwo major steps towards alleviating this problem. First, we demonstrate\nempirically that applying existing subword regularization methods(Kudo, 2018;\nProvilkov et al., 2020) during fine-tuning of pre-trained multilingual\nrepresentations improves the effectiveness of cross-lingual transfer. Second,\nto take full advantage of different possible input segmentations, we propose\nMulti-view Subword Regularization (MVR), a method that enforces the consistency\nbetween predictions of using inputs tokenized by the standard and probabilistic\nsegmentations. Results on the XTREME multilingual benchmark(Hu et al., 2020)\nshow that MVR brings consistent improvements of up to 2.5 points over using\nstandard segmentation algorithms.\n"}
+{"abstract": "  Mixed Integer Linear Programs (MILP) are well known to be NP-hard problems in\ngeneral. Even though pure optimization-based methods, such as constraint\ngeneration, are guaranteed to provide an optimal solution if enough time is\ngiven, their use in online applications is still a great challenge due to their\nusual excessive time requirements. To alleviate their computational burden,\nsome machine learning techniques have been proposed in the literature, using\nthe information provided by previously solved MILP instances. Unfortunately,\nthese techniques report a non-negligible percentage of infeasible or suboptimal\ninstances.\n  By linking mathematical optimization and machine learning, this paper\nproposes a novel approach that speeds up the traditional constraint generation\nmethod, preserving feasibility and optimality guarantees. In particular, we\nfirst identify offline the so-called invariant constraint set of past MILP\ninstances. We then train (also offline) a machine learning method to learn an\ninvariant constraint set as a function of the problem parameters of each\ninstance. Next, we predict online an invariant constraint set of the new unseen\nMILP application and use it to initialize the constraint generation method.\nThis warm-started strategy significantly reduces the number of iterations to\nreach optimality, and therefore, the computational burden to solve online each\nMILP problem is significantly reduced. Very importantly, the proposed\nmethodology inherits the feasibility and optimality guarantees of the\ntraditional constraint generation method. The computational performance of the\nproposed approach is quantified through synthetic and real-life MILP\napplications.\n"}
+{"abstract": "  We continue the study dilation of linear maps on vector spaces introduced by\nBhat, De, and Rakshit. This notion is a variant of vector space dilation\nintroduced by Han, Larson, Liu, and Liu. We derive vector space versions of\nWold decomposition, Halmos dilation, N-dilation, inter-twining lifting theorem\nand a variant of Ando dilation. It is noted further that unlike a kind of\nuniqueness of Halmos dilation of strict contractions on Hilbert spaces, vector\nspace version of Halmos dilation can not be characterized.\n"}
+{"abstract": "  We introduce a novel amortised resource analysis couched in a type-and-effect\nsystem. Our analysis is formulated in terms of the physicist's method of\namortised analysis, and is potential-based. The type system makes use of\nlogarithmic potential functions and is the first such system to exhibit\n*logarithmic amortised complexity*. With our approach we target the automated\nanalysis of self-adjusting data structures, like splay trees, which so far have\nonly manually been analysed in the literature. In particular, we have\nimplemented a semi-automated prototype, which successfully analyses the zig-zig\ncase of *splaying*, once the type annotations are fixed.\n"}
+{"abstract": "  We investigate the relation of black hole mass versus host stellar mass and\nthat of mass accretion rate versus star formation rate (SFR) in moderately\nluminous ($\\log L_{\\rm bol} \\sim 44.5-46.5\\ {\\rm erg\\ s^{-1}}$), X-ray selected\nbroad-line active galactic nuclei (AGNs) at $z=1.18-1.68$ in the\nSubaru/XMM-Newton Deep Field. The far-infrared to far-ultraviolet spectral\nenergy distributions of 85 AGNs are reproduced with the latest version of Code\nInvestigating GALaxy Emission ({\\tt CIGALE}), where the AGN clumpy torus model\n{\\tt SKIRTOR} is implemented. Most of their hosts are confirmed to be\nmain-sequence star-forming galaxies. We find that the mean ratio of the black\nhole mass ($M_{\\rm BH}$) to the total stellar mass ($M_{\\rm stellar}$) is $\\log\nM_{\\rm BH}/M_{\\rm stellar} = -2.2$, which is similar to the local black\nhole-to-bulge mass ratio. This suggests that if the host galaxies of these\nmoderately luminous AGNs at $z\\sim1.4$ are dominated by bulges, they already\nestablished the local black hole mass-bulge mass relation; if they are disk\ndominant, their black holes are overmassive relative to the bulges. AGN\nbolometric luminosities and SFR show a good correlation with ratios higher than\nthat expected from the local black hole-to-bulge mass relation, suggesting that\nthese AGNs are in a SMBH-growth dominant phase.\n"}
+{"abstract": "  We present high angular resolution imaging observations of 517 host stars of\nTESS exoplanet candidates using the `Alopeke and Zorro speckle cameras at\nGemini North and South. The sample consists mainly of bright F, G, K stars at\ndistances of less than 500 pc. Our speckle observations span angular\nresolutions of ~20 mas out to 1.2 arcsec, yielding spatial resolutions of <10\nto 500 AU for most stars, and our contrast limits can detect companion stars\n5-9 magnitudes fainter than the primary at optical wavelengths. We detect 102\nclose stellar companions and determine the separation, magnitude difference,\nmass ratio, and estimated orbital period for each system. Our observations of\nexoplanet host star binaries reveal that they have wider separations than field\nbinaries, with a mean orbital semi-major axis near 100 AU. Other imaging\nstudies have suggested this dearth of very closely separated binaries in\nsystems which host exoplanets, but incompleteness at small separations makes it\ndifficult to disentangle unobserved companions from a true lack of companions.\nWith our improved angular resolution and sensitivity, we confirm that this lack\nof close exoplanet host binaries is indeed real. We also search for a\ncorrelation between planetary orbital radii vs. binary star separation, but\ngiven the very short orbital periods of the TESS planets, we do not find any\nclear trend. We do note that in exoplanet systems containing binary host stars,\nthere is an observational bias against detecting Earth-size planet transits due\nto transit depth dilution caused by the companion star.\n"}
+{"abstract": "  Recent development in AI has enabled the expansion of its application to\nmultiple domains. From medical treatment, gaming, manufacturing to daily\nbusiness processes. A huge amount of money has been poured into AI research due\nto its exciting discoveries. Technology giants like Google, Facebook, Amazon,\nand Baidu are the driving forces in the field today. But the rapid growth and\nexcitement that the technology offers obscure us from looking at the impact it\nbrings on our society. This short paper gives a brief history of AI and\nsummarizes various social, economic and ethical issues that are impacting our\nsociety today. We hope that this work will provide a useful starting point and\nperhaps reference for newcomers and stakeholders of the field.\n"}
+{"abstract": "  In this work, we provide robust bounds on the tail probabilities and the tail\nindex of heavy-tailed distributions in the context of model misspecification.\nThey are defined as the optimal value when computing the worst-case tail\nbehavior over all models within some neighborhood of the reference model. The\nchoice of the discrepancy between the models used to build this neighborhood\nplays a crucial role in assessing the size of the asymptotic bounds. We\nevaluate the robust tail behavior in ambiguity sets based on the Wasserstein\ndistance and Csisz\\'ar $f$-divergence and obtain explicit expressions for the\ncorresponding asymptotic bounds. In an application to Danish fire insurance\nclaims we compare the difference between these bounds and show the importance\nof the choice of discrepancy measure.\n"}
+{"abstract": "  When aggregating logically interconnected judgments from $n$ agents, the\nresult might be inconsistent with the logical connection. This inconsistency is\nknown as the doctrinal paradox, which plays a central role in the field of\njudgment aggregation. Despite a large body of literature on the worst-case\nanalysis of the doctrinal paradox, little is known about its likelihood under\nnatural statistical models, except for a few i.i.d. distributions [List, 2005].\n  In this paper, we characterize the likelihood of the doctrinal paradox under\na much more general and realistic model called the smoothed social choice\nframework [Xia, 2020b], where agents' ground truth judgments are arbitrarily\ncorrelated while the noises are independent. Our main theorem states that under\nmild conditions, the smoothed likelihood of the doctrinal paradox is either\n$0$, $\\exp(-\\Theta(n))$, $\\Theta(n^{-1/2})$ or $\\Theta(1)$. This not only\nanswers open questions by List [2005] for i.i.d. distributions but also draws\nclear lines between situations with frequent and with vanishing paradoxes.\n"}
+{"abstract": "  Multi-object tracking (MOT) is an essential task in the computer vision\nfield. With the fast development of deep learning technology in recent years,\nMOT has achieved great improvement. However, some challenges still remain, such\nas sensitiveness to occlusion, instability under different lighting conditions,\nnon-robustness to deformable objects, etc. To address such common challenges in\nmost of the existing trackers, in this paper, a tracklet booster algorithm is\nproposed, which can be built upon any other tracker. The motivation is simple\nand straightforward: split tracklets on potential ID-switch positions and then\nconnect multiple tracklets into one if they are from the same object. In other\nwords, the tracklet booster consists of two parts, i.e., Splitter and\nConnector. First, an architecture with stacked temporal dilated convolution\nblocks is employed for the splitting position prediction via label smoothing\nstrategy with adaptive Gaussian kernels. Then, a multi-head self-attention\nbased encoder is exploited for the tracklet embedding, which is further used to\nconnect tracklets into larger groups. We conduct sufficient experiments on\nMOT17 and MOT20 benchmark datasets, which demonstrates promising results.\nCombined with the proposed tracklet booster, existing trackers usually can\nachieve large improvements on the IDF1 score, which shows the effectiveness of\nthe proposed method.\n"}
+{"abstract": "  This paper presents a new framework for human body part segmentation based on\nDeep Convolutional Neural Networks trained using only synthetic data. The\nproposed approach achieves cutting-edge results without the need of training\nthe models with real annotated data of human body parts. Our contributions\ninclude a data generation pipeline, that exploits a game engine for the\ncreation of the synthetic data used for training the network, and a novel\npre-processing module, that combines edge response maps and adaptive histogram\nequalization to guide the network to learn the shape of the human body parts\nensuring robustness to changes in the illumination conditions. For selecting\nthe best candidate architecture, we perform exhaustive tests on manually\nannotated images of real human body limbs. We further compare our method\nagainst several high-end commercial segmentation tools on the body parts\nsegmentation task. The results show that our method outperforms the other\nmodels by a significant margin. Finally, we present an ablation study to\nvalidate our pre-processing module. With this paper, we release an\nimplementation of the proposed approach along with the acquired datasets.\n"}
+{"abstract": "  Research in action detection has grown in the recentyears, as it plays a key\nrole in video understanding. Modelling the interactions (either spatial or\ntemporal) between actors and their context has proven to be essential for this\ntask. While recent works use spatial features with aggregated temporal\ninformation, this work proposes to use non-aggregated temporal information.\nThis is done by adding an attention based method that leverages spatio-temporal\ninteractions between elements in the scene along the clip.The main contribution\nof this work is the introduction of two cross attention blocks to effectively\nmodel the spatial relations and capture short range temporal\ninteractions.Experiments on the AVA dataset show the advantages of the proposed\napproach that models spatio-temporal relations between relevant elements in the\nscene, outperforming other methods that model actor interactions with their\ncontext by +0.31 mAP.\n"}
+{"abstract": "  In lattice quantum field theory studies, parameters defining the lattice\ntheory must be tuned toward criticality to access continuum physics. Commonly\nused Markov chain Monte Carlo (MCMC) methods suffer from critical slowing down\nin this limit, restricting the precision of continuum extrapolations. Further\ndifficulties arise when measuring correlation functions of operators widely\nseparated in spacetime: for most correlation functions, an exponentially severe\nsignal-to-noise problem is encountered as the operators are taken to be widely\nseparated. This dissertation details two new techniques to address these\nissues. First, we define a novel MCMC algorithm based on generative flow-based\nmodels. Such models utilize machine learning methods to describe efficient\napproximate samplers for distributions of interest. Independently drawn\nflow-based samples are then used as proposals in an asymptotically exact\nMetropolis-Hastings Markov chain. We address incorporating symmetries of\ninterest, including translational and gauge symmetries. We secondly introduce\nan approach to \"deform\" Monte Carlo estimators based on contour deformations\napplied to the domain of the path integral. The deformed estimators associated\nwith an observable give equivalent unbiased measurements of that observable,\nbut generically have different variances. We define families of deformed\nmanifolds for lattice gauge theories and introduce methods to efficiently\noptimize the choice of manifold (the \"observifold\"), minimizing the deformed\nobservable variance. Finally, we demonstrate that flow-based MCMC can mitigate\ncritical slowing down and observifolds can exponentially reduce variance in\nproof-of-principle applications to scalar $\\phi^4$ theory and $\\mathrm{U}(1)$\nand $\\mathrm{SU}(N)$ lattice gauge theories.\n"}
+{"abstract": "  Gradient methods have applications in multiple fields, including signal\nprocessing, image processing, and dynamic systems. In this paper, we present a\nnonlinear gradient method for solving convex supra-quadratic functions by\ndeveloping the search direction, that done by hybridizing between the two\nconjugate coefficients HRM [2] and NHS [1]. The numerical results proved the\neffectiveness of the presented method by applying it to solve standard problems\nand reaching the exact solution if the objective function is quadratic convex.\nAlso presented in this article, an application to the problem of named entities\nin the Arabic medical language, as it proved the stability of the proposed\nmethod and its efficiency in terms of execution time.\n"}
+{"abstract": "  Animal pose estimation is an important field that has received increasing\nattention in the recent years. The main challenge for this task is the lack of\nlabeled data. Existing works circumvent this problem with pseudo labels\ngenerated from data of other easily accessible domains such as synthetic data.\nHowever, these pseudo labels are noisy even with consistency check or\nconfidence-based filtering due to the domain shift in the data. To solve this\nproblem, we design a multi-scale domain adaptation module (MDAM) to reduce the\ndomain gap between the synthetic and real data. We further introduce an online\ncoarse-to-fine pseudo label updating strategy. Specifically, we propose a\nself-distillation module in an inner coarse-update loop and a mean-teacher in\nan outer fine-update loop to generate new pseudo labels that gradually replace\nthe old ones. Consequently, our model is able to learn from the old pseudo\nlabels at the early stage, and gradually switch to the new pseudo labels to\nprevent overfitting in the later stage. We evaluate our approach on the TigDog\nand VisDA 2019 datasets, where we outperform existing approaches by a large\nmargin. We also demonstrate the generalization ability of our model by testing\nextensively on both unseen domains and unseen animal categories. Our code is\navailable at the project website.\n"}
+{"abstract": "  Machine Learning compilers like TVM allow a fast and flexible deployment on\nembedded CPUs. This enables the use of non-standard operators, which are common\nin ML compression techniques. However, it is necessary to understand the\nlimitations of typical compute-intense operators in ML workloads to design a\nproper solution. This is the first in-detail analysis of dense and convolution\noperators, generated with TVM, that compares to the fundamental hardware limits\nof embedded ARM processors. Thereby it explains the gap between computational\npeak performance, theoretical and measured, and real-world state-of-the-art\nresults, created with TVM and openBLAS. Instead, one can see that\nsingle-precision general matrix multiply (GEMM) and convolutions are bound by\nL1-cache-read bandwidth. Explorations of 8-bit and bit-serial quantized\noperators show that quantization can be used to achieve relevant speedups\ncompared to cache-bound floating-point operators. However, the performance of\nquantized operators highly depends on the interaction between data layout and\nbit packing.\n"}
+{"abstract": "  Interpreting how does deep neural networks (DNNs) make predictions is a vital\nfield in artificial intelligence, which hinders wide applications of DNNs.\nVisualization of learned representations helps we humans understand the vision\nof DNNs. In this work, visualized images that can activate the neural network\nto the target classes are generated by back-propagation method. Here, rotation\nand scaling operations are applied to introduce the transformation invariance\nin the image generating process, which we find a significant improvement on\nvisualization effect. Finally, we show some cases that such method can help us\nto gain insight into neural networks.\n"}
+{"abstract": "  Adaptive systems react to changes in their environment by changing their\nbehavior. Identifying these needed adaptations is very difficult, but central\nto requirements elicitation for adaptive systems. As the necessary or potential\nadaptations are typically not obvious to the stakeholders, the problem is how\nto effectively elicit adaptation-relevant information. One approach is to use\ncreativity techniques to support the systematic identification and elicitation\nof adaptation requirements. In particular, here, we analyze a set of creativity\ntriggers defined for systematic exploration of potential adaptation\nrequirements. We compare these triggers with brainstorming as a baseline in a\ncontrolled experiment with 85 master students. The results indicate that the\nproposed triggers are suitable for the efficient elicitation of adaptive\nrequirements and that the 15 trigger questions produce significantly more\nrequirements fragments than solo brainstorming.\n"}
+{"abstract": "  In order to solve fractional variational problems, there exist two theorems\nof necessary conditions: an Euler-Lagrange equation which involves Caputo and\nRiemann-Liouville fractional derivatives, and other Euler-Lagrange equation\nthat involves only Caputo derivatives. In this article, we make a comparison\nsolving a particular fractional variational problem with both methods to obtain\nsome conclusions about which method gives the optimal solution.\n"}
+{"abstract": "  A fundamental ability of humans is to utilize commonsense knowledge in\nlanguage understanding and question answering. In recent years, many\nknowledge-enhanced Commonsense Question Answering (CQA) approaches have been\nproposed. However, it remains unclear: (1) How far can we get by exploiting\nexternal knowledge for CQA? (2) How much potential of knowledge has been\nexploited in current CQA models? (3) Which are the most promising directions\nfor future CQA? To answer these questions, we benchmark knowledge-enhanced CQA\nby conducting extensive experiments on multiple standard CQA datasets using a\nsimple and effective knowledge-to-text transformation framework. Experiments\nshow that: (1) Our knowledge-to-text framework is effective and achieves\nstate-of-the-art performance on CommonsenseQA dataset, providing a simple and\nstrong knowledge-enhanced baseline for CQA; (2) The potential of knowledge is\nstill far from being fully exploited in CQA -- there is a significant\nperformance gap from current models to our models with golden knowledge; and\n(3) Context-sensitive knowledge selection, heterogeneous knowledge\nexploitation, and commonsense-rich language models are promising CQA\ndirections.\n"}
+{"abstract": "  Refining low-resolution (LR) spatial fields with high-resolution (HR)\ninformation, often known as statistical downscaling, is challenging as the\ndiversity of spatial datasets often prevents direct matching of observations.\nYet, when LR samples are modeled as aggregate conditional means of HR samples\nwith respect to a mediating variable that is globally observed, the recovery of\nthe underlying fine-grained field can be framed as taking an \"inverse\" of the\nconditional expectation, namely a deconditioning problem. In this work, we\npropose a Bayesian formulation of deconditioning which naturally recovers the\ninitial reproducing kernel Hilbert space formulation from Hsu and Ramos (2019).\nWe extend deconditioning to a downscaling setup and devise efficient\nconditional mean embedding estimator for multiresolution data. By treating\nconditional expectations as inter-domain features of the underlying field, a\nposterior for the latent field can be established as a solution to the\ndeconditioning problem. Furthermore, we show that this solution can be viewed\nas a two-staged vector-valued kernel ridge regressor and show that it has a\nminimax optimal convergence rate under mild assumptions. Lastly, we demonstrate\nits proficiency in a synthetic and a real-world atmospheric field downscaling\nproblem, showing substantial improvements over existing methods.\n"}
+{"abstract": "  Transition systems (TS) and Petri nets (PN) are important models of\ncomputation ubiquitous in formal methods for modeling systems. An important\nproblem is how to extract from a given TS a PN whose reachability graph is\nequivalent (with a suitable notion of equivalence) to the original TS.\n  This paper addresses the decomposition of transition systems into\nsynchronizing state machines (SMs), which are a class of Petri nets where each\ntransition has one incoming and one outgoing arc and all markings have exactly\none token. This is an important case of the general problem of extracting a PN\nfrom a TS. The decomposition is based on the theory of regions, and it is shown\nthat a property of regions called excitation-closure is a sufficient condition\nto guarantee the equivalence between the original TS and a decomposition into\nSMs.\n  An efficient algorithm is provided which solves the problem by reducing its\ncritical steps to the maximal independent set problem (to compute a minimal set\nof irredundant SMs) or to satisfiability (to merge the SMs). We report\nexperimental results that show a good trade-off between quality of results vs.\ncomputation time.\n"}
+{"abstract": "  In recent years, Graph Neural Networks (GNNs) appear to be state-of-the-art\nalgorithms for analyzing non-euclidean graph data. By applying deep-learning to\nextract high-level representations from graph structures, GNNs achieve\nextraordinary accuracy and great generalization ability in various tasks.\nHowever, with the ever-increasing graph sizes, more and more complicated GNN\nlayers, and higher feature dimensions, the computational complexity of GNNs\ngrows exponentially. How to inference GNNs in real time has become a\nchallenging problem, especially for some resource-limited edge-computing\nplatforms.\n  To tackle this challenge, we propose BlockGNN, a software-hardware co-design\napproach to realize efficient GNN acceleration. At the algorithm level, we\npropose to leverage block-circulant weight matrices to greatly reduce the\ncomplexity of various GNN models. At the hardware design level, we propose a\npipelined CirCore architecture, which supports efficient block-circulant\nmatrices computation. Basing on CirCore, we present a novel BlockGNN\naccelerator to compute various GNNs with low latency. Moreover, to determine\nthe optimal configurations for diverse deployed tasks, we also introduce a\nperformance and resource model that helps choose the optimal hardware\nparameters automatically. Comprehensive experiments on the ZC706 FPGA platform\ndemonstrate that on various GNN tasks, BlockGNN achieves up to $8.3\\times$\nspeedup compared to the baseline HyGCN architecture and $111.9\\times$ energy\nreduction compared to the Intel Xeon CPU platform.\n"}
+{"abstract": "  Molecular phenotyping by gene expression profiling is common in contemporary\ncancer research and in molecular diagnostics. However, molecular profiling\nremains costly and resource intense to implement, and is just starting to be\nintroduced into clinical diagnostics. Molecular changes, including genetic\nalterations and gene expression changes, occuring in tumors cause morphological\nchanges in tissue, which can be observed on the microscopic level. The\nrelationship between morphological patterns and some of the molecular\nphenotypes can be exploited to predict molecular phenotypes directly from\nroutine haematoxylin and eosin (H&E) stained whole slide images (WSIs) using\ndeep convolutional neural networks (CNNs). In this study, we propose a new,\ncomputationally efficient approach for disease specific modelling of\nrelationships between morphology and gene expression, and we conducted the\nfirst transcriptome-wide analysis in prostate cancer, using CNNs to predict\nbulk RNA-sequencing estimates from WSIs of H&E stained tissue. The work is\nbased on the TCGA PRAD study and includes both WSIs and RNA-seq data for 370\npatients. Out of 15586 protein coding and sufficiently frequently expressed\ntranscripts, 6618 had predicted expression significantly associated with\nRNA-seq estimates (FDR-adjusted p-value < 1*10-4) in a cross-validation. 5419\n(81.9%) of these were subsequently validated in a held-out test set. We also\ndemonstrate the ability to predict a prostate cancer specific cell cycle\nprogression score directly from WSIs. These findings suggest that contemporary\ncomputer vision models offer an inexpensive and scalable solution for\nprediction of gene expression phenotypes directly from WSIs, providing\nopportunity for cost-effective large-scale research studies and molecular\ndiagnostics.\n"}
+{"abstract": "  From superconductors to atomic nuclei, strongly-interacting many-body systems\nare ubiquitous in nature. Measuring the microscopic structure of such systems\nis a formidable challenge, often met by particle knockout scattering\nexperiments. While such measurements are fundamental for mapping the structure\nof atomic nuclei, their interpretation is often challenged by quantum\nmechanical initial- and final-state interactions (ISI/FSI) of the incoming and\nscattered particles. Here we overcome this fundamental limitation by measuring\nthe quasi-free scattering of 48 GeV/c 12C ions from hydrogen. The distribution\nof single protons is studied by detecting two protons at large angles in\ncoincidence with an intact 11B nucleus. The 11B detection is shown to select\nthe transparent part of the reaction and exclude the otherwise large ISI/FSI\nthat would break the 11B apart. By further detecting residual 10B and 10Be\nnuclei, we also identified short-range correlated (SRC) nucleon-nucleon pairs,\nand provide direct experimental evidence for the separation of the pair\nwave-function from that of the residual many-body nuclear system. All measured\nreactions are well described by theoretical calculations that do not contain\nISI/FSI distortions. Our results thus showcase a new ability to study the\nshort-distance structure of short-lived radioactive atomic nuclei at the\nforthcoming FAIR and FRIB facilities. These studies will be pivotal for\ndeveloping a ground-breaking microscopic understanding of the structure and\nproperties of nuclei far from stability and the formation of visible matter in\nthe universe.\n"}
+{"abstract": "  In this paper aspects related to handling of intraday imbalances for hydro\nand wind power are addressed. The definition of imbalance cost is established\nand used to describe the potential benefits of shifting from plant-specific\nschedules to a common load requirement for wind and hydropower units in the\nsame price area. The Nordpool intraday pay-as-bid market has been the basis for\nevaluation of imbalances, and some main characteristics for this market has\nbeen described. We consider how internal handling of complementary imbalances\nwithin the same river system with high inflow uncertainty and constrained\nreservoirs can reduce volatility in short-term marginal cost and risk compared\nto trading in the intraday market. We have also shown the the imbalance cost\nfor a power producer with both wind and hydropower assets can be reduced by\ninternal balancing in combination with sales and purchase in a pay-as-bid\nintraday market\n"}
+{"abstract": "  Elastic Riemannian metrics have been used successfully in the past for\nstatistical treatments of functional and curve shape data. However, this usage\nhas suffered from an important restriction: the function boundaries are assumed\nfixed and matched. Functional data exhibiting unmatched boundaries typically\narise from dynamical systems with variable evolution rates such as COVID-19\ninfection rate curves associated with different geographical regions. In this\ncase, it is more natural to model such data with sliding boundaries and use\npartial matching, i.e., only a part of a function is matched to another\nfunction. Here, we develop a comprehensive Riemannian framework that allows for\npartial matching, comparing, and clustering of functions under both phase\nvariability and uncertain boundaries. We extend past work by: (1) Forming a\njoint action of the time-warping and time-scaling groups; (2) Introducing a\nmetric that is invariant to this joint action, allowing for a gradient-based\napproach to elastic partial matching; and (3) Presenting a modification that,\nwhile losing the metric property, allows one to control relative influence of\nthe two groups. This framework is illustrated for registering and clustering\nshapes of COVID-19 rate curves, identifying essential patterns, minimizing\nmismatch errors, and reducing variability within clusters compared to previous\nmethods.\n"}
+{"abstract": "  One way to assess a certain aspect of the value of scientific research is to\nmeasure the attention it receives on social media. While previous research has\nmostly focused on the \"number of mentions\" of scientific research on social\nmedia, the current study applies \"topic networks\" to measure public attention\nto scientific research on Twitter. Topic networks are the networks of\nco-occurring author keywords in scholarly publications and networks of\nco-occurring hashtags in the tweets mentioning those scholarly publications.\nThis study investigates which topics in opioid scholarly publications have\nreceived public attention on Twitter. Additionally, it investigates whether the\ntopic networks generated from the publications tweeted by all accounts (bot and\nnon-bot accounts) differ from those generated by non-bot accounts. Our analysis\nis based on a set of opioid scholarly publications from 2011 to 2019 and the\ntweets associated with them. We use co-occurrence network analysis to generate\ntopic networks. Results indicated that Twitter users have mostly used generic\nterms to discuss opioid publications, such as \"Opioid,\" \"Pain,\" \"Addiction,\"\n\"Treatment,\" \"Analgesics,\" \"Abuse,\" \"Overdose,\" and \"Disorders.\" Results\nconfirm that topic networks provide a legitimate method to visualize public\ndiscussions of health-related scholarly publications and how Twitter users\ndiscuss health-related scientific research differently from the scientific\ncommunity. There was a substantial overlap between the topic networks based on\nthe tweets by all accounts and non-bot accounts. This result indicates that it\nmight not be necessary to exclude bot accounts for generating topic networks as\nthey have a negligible impact on the results.\n"}
+{"abstract": "  Viewing a quantum thermal machine as a non-Hermitian quantum system, we\ncharacterize in full generality its analytical time-dependent dynamics by\nderiving the spectrum of its non-Hermitian Liouvillian for an arbitrary initial\nstate. We show that the thermal machine features a number of Liouvillian\nexceptional points (EPs) for experimentally realistic parameters, in particular\na third-dorder exceptional point that leaves signatures both in short and\nlong-time regimes. Remarkably, we demonstrate that this EP corresponds to a\nregime of critical decay for the quantum thermal machine towards its steady\nstate, bearing a striking resemblance with a critically damped harmonic\noscillator. These results open up exciting possibilities for the precise\ndynamical control of quantum thermal machines exploiting exceptional points\nfrom non-Hermitian physics and are amenable to state-of-the-art solid-state\nplatforms such as semiconducting and superconducting devices.\n"}
+{"abstract": "  We study the asymptotic behavior of solutions to the defocusing\nmass-subcritical Hartree NLS $iu_t + \\Delta u = F(u) = (|x|^{-\\gamma}*|u|^2)u$\non $\\mathbb{R}^d$, $d\\geq 2$, $\\frac{4}{3} < \\gamma < 2$. We show that the\nscattering problem associated to this equation is analytically well-posed in\nthe weighted spaces $\\Sigma = H^1\\cap\\mathcal{F}H^1$ and $\\mathcal{F}H^1$.\nFurthermore, we show that the same problem fails to be analytically well-posed\nfor data in $L^2$. This constitutes an infinite loss of regularity between the\nscattering problems in weighted spaces and in $L^2$. This further develops an\nearlier investigation initiated by the author in which a finite breakdown of\nregularity was proved for the $L^2$ scattering problem for the mass-subcritical\nNLS with power nonlinearity $F(u) = |u|^pu$.\n"}
+{"abstract": "  We present the study of dynamical status of the globular cluster NGC 1851. A\ncombination of multiwavelength space and ground-based data sets are used for\nthe present analysis. In order to select the genuine cluster members, we used\nthe astro-photometric data available from HST and GAIA-DR2 catalogs. The BSS\nradial distribution of the cluster is plotted from the center of the cluster to\nthe outskirts. The radial distribution of BSS shows a central peak, followed by\na dip at the intermediate radii (rmin ~ 90'') and a rising trend in the\noutskirts. We also estimated A+rh parameter as 0.391 +/- 0.006 to validate the\nfindings of the radial distribution study. On the basis of the minima in the\nBSS radial distribution and the value of A+rh parameter, we conclude that NGC\n1851 belongs to Family II classification and is an intermediate dynamical state\ncluster.\n"}
+{"abstract": "  In this work we study the formation of $N^*$'s as a consequence of the\ndynamics involved in the $ND\\bar D^*-N\\bar D D^*$ system when the $D\\bar\nD^*-\\bar D D^*$ subsystem generates $X(3872)$ in isospin 0 and $Z_c(3900)$ in\nisospin 1. States with isospin $I=1/2$ and mass in the energy region\n$4400-4600$ MeV are obtained with spin-parity $J^P=1/2^+$ and $3/2^+$,\npredicting in this way the existence of $N^*$ resonances with hidden charm and\na three-body nature.\n"}
+{"abstract": "  Given any (not necessarily connected) finite graph in the combinatorial\nsense, we construct calibrated 3-dimensional homologically area minimizing\noriented surface on a $7$-dimensional closed compact Riemannian manifold, so\nthat the singular set of the surface consists of precisely this finite graph.\nThe constructions are based on some unpublished ideas of Professor Camillo De\nLellis and Professor Robert Bryant.\n"}
+{"abstract": "  In the scientific literature, the term emergent phenomena is invoked in the\ncontext of a collective behavior observed in a complex adaptive system that\nexhibits no correspondence with the behavior of the system constituents.\nAlthough this description is too generic to be termed a definition, it alludes\nto the fact that emergent phenomena are closely related to esoteric\nobservations and neoteric developments in science. With these impressions, we\naim to investigate a variety of observed behavior from the perspective of an\nemergentist. Starting with a few familiar portrayals of emergence, we devote a\nlarge body of this narrative review to explain instances of emergence in\ncondensed matter systems, namely the phase transition phenomena, spontaneous\nsymmetry breaking, and macroscopic quantum phenomena. At the same time, to\npresent a broader perspective, we cross the domain boundaries to also provide a\nsuccinct description of emergent phenomena prevailing in social sciences,\neconomics, computing environments, and in biological systems, as well.\n"}
+{"abstract": "  When designing plans in engineering, it is often necessary to consider\nattributes associated to objects, e.g. the location of a robot. Our aim in this\npaper is to incorporate attributes into existing categorical formalisms for\nplanning, namely those based on symmetric monoidal categories and string\ndiagrams. To accomplish this, we define a notion of a \"symmetric monoidal\ncategory with attributes.\" This is a symmetric monoidal category in which\nobjects are equipped with retrievable information and where the interactions\nbetween objects and information are governed by an \"attribute structure.\" We\ndiscuss examples and semantics of such categories in the context of robotics to\nillustrate our definition.\n"}
+{"abstract": "  We study the shapes of all Newton-Okounkov bodies $\\Delta_{v}(D)$ of a given\nbig divisor $D$ on a surface $S$ with respect to all rank 2 valuations $v$ of\n$K(S)$. We obtain upper bounds for, and in many cases we determine exactly, the\npossible numbers of vertices of the bodies $\\Delta_{v}(D)$. The upper bounds\nare expressed in terms of Picard numbers and they are birationally invariant,\nas they do not depend on the model $\\tilde{S}$ where the valuation $v$ becomes\na flag valuation. We also conjecture that the set of all Newton-Okounkov bodies\nof a single ample divisor $D$ determines the Picard number of $S$, and prove\nthat this is the case for Picard number 1, by an explicit characterization of\nsurfaces of Picard number 1 in terms of Newton-Okounkov bodies.\n"}
+{"abstract": "  Embedded systems acquire information about the real world from sensors and\nprocess it to make decisions and/or for transmission. In some situations, the\nrelationship between the data and the decision is complex and/or the amount of\ndata to transmit is large (e.g. in biologgers). Artificial Neural Networks\n(ANNs) can efficiently detect patterns in the input data which makes them\nsuitable for decision making or compression of information for data\ntransmission. However, ANNs require a substantial amount of energy which\nreduces the lifetime of battery-powered devices. Therefore, the use of Spiking\nNeural Networks can improve such systems by providing a way to efficiently\nprocess sensory data without being too energy-consuming. In this work, we\nintroduce a low-powered neuron model called Integrate-and-Fire which exploits\nthe charge and discharge properties of the capacitor. Using parallel and series\nRC circuits, we developed a trainable neuron model that can be expressed in a\nrecurrent form. Finally, we trained its simulation with an artificially\ngenerated dataset of dog postures and implemented it as hardware that showed\npromising energetic properties. This paper is the full text of the research,\npresented at the 20th International Conference on Artificial Intelligence and\nSoft Computing Web System (ICAISC 2021)\n"}
+{"abstract": "  3D morphable models are widely used for the shape representation of an object\nclass in computer vision and graphics applications. In this work, we focus on\ndeep 3D morphable models that directly apply deep learning on 3D mesh data with\na hierarchical structure to capture information at multiple scales. While great\nefforts have been made to design the convolution operator, how to best\naggregate vertex features across hierarchical levels deserves further\nattention. In contrast to resorting to mesh decimation, we propose an attention\nbased module to learn mapping matrices for better feature aggregation across\nhierarchical levels. Specifically, the mapping matrices are generated by a\ncompatibility function of the keys and queries. The keys and queries are\ntrainable variables, learned by optimizing the target objective, and shared by\nall data samples of the same object class. Our proposed module can be used as a\ntrain-only drop-in replacement for the feature aggregation in existing\narchitectures for both downsampling and upsampling. Our experiments show that\nthrough the end-to-end training of the mapping matrices, we achieve\nstate-of-the-art results on a variety of 3D shape datasets in comparison to\nexisting morphable models.\n"}
+{"abstract": "  We define a class of transversal slices in spaces which are quasi-Poisson for\nthe action of a complex semisimple group G. This is a multiplicative analogue\nof Whittaker reduction. One example is the multiplicative universal centralizer\nof G, which is equipped with the usual symplectic structure in this way. We\nconstruct a smooth relative compactification by taking the closure of each\ncentralizer fiber in the wonderful compactification of G. By realizing this\npartial compactification as a transversal in a larger quasi-Poisson variety, we\nshow that it is smooth and log-symplectic.\n"}
+{"abstract": "  Storage and retrieval of data in a computer memory plays a major role in\nsystem performance. Traditionally, computer memory organization is static -\ni.e., they do not change based on the application-specific characteristics in\nmemory access behaviour during system operation. Specifically, the association\nof a data block with a search pattern (or cues) as well as the granularity of a\nstored data do not evolve. Such a static nature of computer memory, we observe,\nnot only limits the amount of data we can store in a given physical storage,\nbut it also misses the opportunity for dramatic performance improvement in\nvarious applications. On the contrary, human memory is characterized by\nseemingly infinite plasticity in storing and retrieving data - as well as\ndynamically creating/updating the associations between data and corresponding\ncues. In this paper, we introduce Neural Storage (NS), a brain-inspired\nlearning memory paradigm that organizes the memory as a flexible neural memory\nnetwork. In NS, the network structure, strength of associations, and\ngranularity of the data adjust continuously during system operation, providing\nunprecedented plasticity and performance benefits. We present the associated\nstorage/retrieval/retention algorithms in NS, which integrate a formalized\nlearning process. Using a full-blown operational model, we demonstrate that NS\nachieves an order of magnitude improvement in memory access performance for two\nrepresentative applications when compared to traditional content-based memory.\n"}
+{"abstract": "  Application Specific Integrated Circuits (ASICs) are commonly used to\nefficiently process the signals from sensors and detectors in space. Wire\nbonding is a space qualified technique of making interconnections between ASICs\nand their substrate packaging board for power, control and readout of the\nASICs. Wire bonding is nearly ubiquitous in modern space programs, but their\nexposed wires can be prone to damage during assembly and subject to electric\ninterference during operations. Additional space around the ASICs needed for\nwire bonding also impedes efficient packaging of large arrays of detectors.\nHere we introduce the Through Silicon Vias (TSV) technology that replaces wire\nbonds and eliminates their shortcomings. We have successfully demonstrated the\nfeasibility of implementing TSVs to existing ASIC wafers (a.k.a. a via-last\nprocess) developed for processing the X-ray signals from the X-ray imaging\nCdZnTe detectors on the Nuclear Spectroscopic Telescope Array (NuSTAR) Small\nExplorer mission that was launched in 2012. While TSVs are common in the\nsemiconductor industry, this is the first (to our knowledge) successful\napplication for Astrophysics imaging instrumentation. We expect that the TSV\ntechnology will simplify the detector assembly, and thus will enable\nsignificant cost and schedule savings in assembly of large area CdZnTe\ndetectors.\n"}
+{"abstract": "  In recent years, we have witnessed a surge of interests in learning a\nsuitable distance metric from weakly supervised data. Most existing methods aim\nto pull all the similar samples closer while push the dissimilar ones as far as\npossible. However, when some classes of the dataset exhibit multimodal\ndistribution, these goals conflict and thus can hardly be concurrently\nsatisfied. Additionally, to ensure a valid metric, many methods require a\nrepeated eigenvalue decomposition process, which is expensive and numerically\nunstable. Therefore, how to learn an appropriate distance metric from weakly\nsupervised data remains an open but challenging problem. To address this issue,\nin this paper, we propose a novel weakly supervised metric learning algorithm,\nnamed MultimoDal Aware weakly supervised Metric Learning (MDaML). MDaML\npartitions the data space into several clusters and allocates the local cluster\ncenters and weight for each sample. Then, combining it with the weighted\ntriplet loss can further enhance the local separability, which encourages the\nlocal dissimilar samples to keep a large distance from the local similar\nsamples. Meanwhile, MDaML casts the metric learning problem into an\nunconstrained optimization on the SPD manifold, which can be efficiently solved\nby Riemannian Conjugate Gradient Descent (RCGD). Extensive experiments\nconducted on 13 datasets validate the superiority of the proposed MDaML.\n"}
+{"abstract": "  The unprecedented growth in the easy availability of photo-editing tools has\nendangered the power of digital images.An image was supposed to be worth more\nthan a thousand words,but now this can be said only if it can be authenticated\northe integrity of the image can be proved to be intact. In thispaper, we\npropose a digital image forensic technique for JPEG images. It can detect any\nforgery in the image if the forged portion called a ghost image is having a\ncompression quality different from that of the cover image. It is based on\nresaving the JPEG image at different JPEG qualities, and the detection of the\nforged portion is maximum when it is saved at the same JPEG quality as the\ncover image. Also, we can precisely predictthe JPEG quality of the cover image\nby analyzing the similarity using Structural Similarity Index Measure (SSIM) or\nthe energyof the images. The first maxima in SSIM or the first minima inenergy\ncorrespond to the cover image JPEG quality. We created adataset for varying\nJPEG compression qualities of the ghost and the cover images and validated the\nscalability of the experimental results.We also, experimented with varied\nattack scenarios, e.g. high-quality ghost image embedded in low quality of\ncover image,low-quality ghost image embedded in high-quality of cover image,and\nghost image and cover image both at the same quality.The proposed method is\nable to localize the tampered portions accurately even for forgeries as small\nas 10x10 sized pixel blocks.Our technique is also robust against other attack\nscenarios like copy-move forgery, inserting text into image, rescaling\n(zoom-out/zoom-in) ghost image and then pasting on cover image.\n"}
+{"abstract": "  Dr. Ron Knott constructed a graph of all Primitive Pythagorean Triples (PPTs)\nwith legs up to length 10,000, using Mathematica. The patterns are very\ninteresting, suggesting conic sections. We show that they indeed are parabolic\ncurves which follow in a natural way from the mathematics of the subject\nmatter.\n"}
+{"abstract": "  With recent advances in sequencing technology it has become affordable and\npractical to sequence genomes to very high depth-of-coverage, allowing\nresearchers to discover low-frequency variants in the genome. However, due to\nthe errors in sequencing it is an active area of research to develop algorithms\nthat can separate noise from the true variants. LoFreq is a state of the art\nalgorithm for low-frequency variant detection but has a relatively long runtime\ncompared to other tools. In addition to this, the interface for running in\nparallel could be simplified, allowing for multithreading as well as\ndistributing jobs to a cluster. In this work we describe some specific\ncontributions to LoFreq that remedy these issues.\n"}
+{"abstract": "  Question answering (QA) in English has been widely explored, but multilingual\ndatasets are relatively new, with several methods attempting to bridge the gap\nbetween high- and low-resourced languages using data augmentation through\ntranslation and cross-lingual transfer. In this project, we take a step back\nand study which approaches allow us to take the most advantage of existing\nresources in order to produce QA systems in many languages. Specifically, we\nperform extensive analysis to measure the efficacy of few-shot approaches\naugmented with automatic translations and permutations of\ncontext-question-answer pairs. In addition, we make suggestions for future\ndataset development efforts that make better use of a fixed annotation budget,\nwith a goal of increasing the language coverage of QA datasets and systems.\nCode and data for reproducing our experiments are available here:\nhttps://github.com/NavidRajabi/EMQA.\n"}
+{"abstract": "  We present a quantum synthesis algorithm designed to produce short circuits\nand to scale well in practice. The main contribution is a novel representation\nof circuits able to encode placement and topology using generic \"gates\", which\nallows the QFAST algorithm to replace expensive searches over circuit\nstructures with few steps of numerical optimization. When compared against\noptimal depth, search based state-of-the-art techniques, QFAST produces\ncomparable results: 1.19x longer circuits up to four qubits, with an increase\nin compilation speed of 3.6x. In addition, QFAST scales up to seven qubits.\nWhen compared with the state-of-the-art \"rule\" based decomposition techniques\nin Qiskit, QFAST produces circuits shorter by up to two orders of magnitude\n(331x), albeit 5.6x slower. We also demonstrate the composability with other\ntechniques and the tunability of our formulation in terms of circuit depth and\nrunning time.\n"}
+{"abstract": "  We study non-Hermitian Aubry-Andr\\'{e}-Harper models with p-wave pairing,\nwhere the non-Hermiticity is introduced by on-site complex quasiperiodic\npotentials. By analysing the $\\mathcal{PT}$ symmetry breaking, winding numbers\nof energy spectra, localization and fractal dimensions of states, and fate of\nMajorana fermions, a complete phase diagram on Anderson localization and\ntopological phase transitions is obtained. In particular, the non-Hermitian\ntopological nature of Anderson localization phase transitions from extended to\ncritical and then to localized phases is identified, using both analytical and\nnumerical methods. In the critical phase the complex spectrum is topological\nnontrivial with a fractional winding number. In the localized phase the\nanalytical localization length of states can apply to the Hermitian case, which\nis absent so far. Both the non-Hermiticity and disorder are detrimental to\nMajorana fermions.\n"}
+{"abstract": "  King 2, one of the oldest clusters in the Milky Way, with an age of $\\sim$ 6\nGyr and distance of $\\sim 5700$ pc, has been observed with UVIT payload on the\n\\textit{ASTROSAT}. With membership information derived from {\\it Gaia} EDR3,\nthe cluster is found to have 39 blue straggler stars (BSSs). We created\nmulti-wavelength spectra-energy distributions (SED) of all the BSSs. Out of 10\nUV detected BSSs, 6 bright ones fitted with double component SEDs and were\nfound to have hotter companions with properties similar to extreme horizontal\nbranch (EHB)/subdwarf B (sdB) stars, with a range in luminosity and\ntemperature, suggesting a diversity among the hot companions. We suggest that\nat least 15\\% of BSSs in this cluster are formed via mass-transfer pathway.\nWhen we compared their properties to EHBs and hotter companions to BSS in open\nand globular clusters, we suggest that EHB/sdBs like companions can form in\nbinaries of open clusters as young as 6 Gyr.\n"}
+{"abstract": "  The ability to quickly learn from a small quantity oftraining data widens the\nrange of machine learning applications. In this paper, we propose a\ndata-efficient image captioning model, VisualGPT, which leverages the\nlinguistic knowledge from a large pretrained language model(LM). A crucial\nchallenge is to balance between the use of visual information in the image and\nprior linguistic knowledge acquired from pretraining. We designed a novel\nself-resurrecting encoder-decoder attention mechanism to quickly adapt the\npretrained LM as the language decoder ona small amount of in-domain training\ndata. The proposed self-resurrecting activation unit produces sparse\nactivations but has reduced susceptibility to zero gradients. We train the\nproposed model, VisualGPT, on 0.1%, 0.5% and 1% of MSCOCO and Conceptual\nCaptions training data. Under these conditions, we outperform the best baseline\nmodel by up to 10.8% CIDEr on MS COCO and upto 5.4% CIDEr on Conceptual\nCaptions. Further, Visual-GPT achieves the state-of-the-art result on IU X-ray,\na medical report generation dataset. To the best of our knowledge, this is the\nfirst work that improves data efficiency of image captioning by utilizing LM\npretrained on unimodal data. Our code is available at:\nhttps://github.com/Vision-CAIR/VisualGPT.\n"}
+{"abstract": "  This paper proposes a novel integrated dynamic method based on Behavior Trees\nfor planning and allocating tasks in mixed human robot teams, suitable for\nmanufacturing environments. The Behavior Tree formulation allows encoding a\nsingle job as a compound of different tasks with temporal and logic\nconstraints. In this way, instead of the well-studied offline centralized\noptimization problem, the role allocation problem is solved with multiple\nsimplified online optimization sub-problem, without complex and cross-schedule\ntask dependencies. These sub-problems are defined as Mixed-Integer Linear\nPrograms, that, according to the worker-actions related costs and the workers'\navailability, allocate the yet-to-execute tasks among the available workers. To\ncharacterize the behavior of the developed method, we opted to perform\ndifferent simulation experiments in which the results of the action-worker\nallocation and computational complexity are evaluated. The obtained results,\ndue to the nature of the algorithm and to the possibility of simulating the\nagents' behavior, should describe well also how the algorithm performs in real\nexperiments.\n"}
+{"abstract": "  Previous works on neural text-to-speech (TTS) have been addressed on limited\nspeed in training and inference time, robustness for difficult synthesis\nconditions, expressiveness, and controllability. Although several approaches\nresolve some limitations, there has been no attempt to solve all weaknesses at\nonce. In this paper, we propose STYLER, an expressive and controllable TTS\nframework with high-speed and robust synthesis. Our novel audio-text aligning\nmethod called Mel Calibrator and excluding autoregressive decoding enable rapid\ntraining and inference and robust synthesis on unseen data. Also, disentangled\nstyle factor modeling under supervision enlarges the controllability in\nsynthesizing process leading to expressive TTS. On top of it, a novel noise\nmodeling pipeline using domain adversarial training and Residual Decoding\nempowers noise-robust style transfer, decomposing the noise without any\nadditional label. Various experiments demonstrate that STYLER is more effective\nin speed and robustness than expressive TTS with autoregressive decoding and\nmore expressive and controllable than reading style non-autoregressive TTS.\nSynthesis samples and experiment results are provided via our demo page, and\ncode is available publicly.\n"}
+{"abstract": "  Faces play a central role in the combinatorial and computational aspects of\npolyhedra. In this paper, we present the first formalization of faces of\npolyhedra in the proof assistant Coq. This builds on the formalization of a\nlibrary providing the basic constructions and operations over polyhedra,\nincluding projections, convex hulls and images under linear maps. Moreover, we\ndesign a special mechanism which automatically introduces an appropriate\nrepresentation of a polyhedron or a face, depending on the context of the\nproof. We demonstrate the usability of this approach by establishing some of\nthe most important combinatorial properties of faces, namely that they\nconstitute a family of graded atomistic and coatomistic lattices closed under\ninterval sublattices. We also prove a theorem due to Balinski on the\n$d$-connectedness of the adjacency graph of polytopes of dimension $d$.\n"}
+{"abstract": "  The success of Neural Machine Translation (NMT) largely depends on the\navailability of large bitext training corpora. Due to the lack of such large\ncorpora in low-resource language pairs, NMT systems often exhibit poor\nperformance. Extra relevant monolingual data often helps, but acquiring it\ncould be quite expensive, especially for low-resource languages. Moreover,\ndomain mismatch between bitext (train/test) and monolingual data might degrade\nthe performance. To alleviate such issues, we propose AUGVIC, a novel data\naugmentation framework for low-resource NMT which exploits the vicinal samples\nof the given bitext without using any extra monolingual data explicitly. It can\ndiversify the in-domain bitext data with finer level control. Through extensive\nexperiments on four low-resource language pairs comprising data from different\ndomains, we have shown that our method is comparable to the traditional\nback-translation that uses extra in-domain monolingual data. When we combine\nthe synthetic parallel data generated from AUGVIC with the ones from the extra\nmonolingual data, we achieve further improvements. We show that AUGVIC helps to\nattenuate the discrepancies between relevant and distant-domain monolingual\ndata in traditional back-translation. To understand the contributions of\ndifferent components of AUGVIC, we perform an in-depth framework analysis.\n"}
+{"abstract": "  A recent development in the theory of fractional differential equations with\nvariable coefficients has been a method for obtaining an exact solution in the\nform of an infinite series involving nested fractional integral operators. This\nsolution representation is constructive but difficult to calculate in practice.\nHere we show a new representation of the solution function, as a convergent\nseries of single fractional integrals, which will be easier to use for\ncomputational work and applications. In the particular case of constant\ncoefficients, the solution is given in terms of the Mittag-Leffler function. We\nalso show some applications in Cauchy problems for partial differential\nequations involving both time-fractional and space-fractional operators and\nwith time-dependent coefficients.\n"}
+{"abstract": "  The purpose of this paper is to study the incompressible non-resistive MHD\nequations in $\\mathbb{R}^3$. We establish the global well-posedness of\nclassical solutions if the initial data is axially symmetric and the swirl\ncomponents of the velocity and magnetic vorticity vanish. In particular, the\nspecial axially symmetric initial data can be arbitrarily large and satisfy low\nregularity assumptions.\n"}
+{"abstract": "  In the presence of interactions the frequency of a simple harmonic oscillator\ndeviates from the noninteracting one. Various methods can be used to compute\nthe changes to the frequency perturbatively. Some of them resemble the methods\nused in quantum field theory where the bare values of the parameters of the\ntheory run when an interaction is added. In this paper, we review some of these\ntechniques and introduce some new ones in line with quantum field theory\nmethods. Moreover, we investigate the case of more than one oscillator to see\nhow the frequencies of small oscillations change when non-linear terms are\nadded to a linear system and observe an interesting beat phenomenon in\ndegenerate coupled oscillatory systems.\n"}
+{"abstract": "  Scene understanding requires the extraction and representation of scene\ncomponents together with their properties and inter-relations. We describe a\nmodel in which meaningful scene structures are extracted from the image by an\niterative process, combining bottom-up (BU) and top-down (TD) networks,\ninteracting through a symmetric bi-directional communication between them\n(counter-streams structure). The model constructs a scene representation by the\niterative use of three components. The first model component is a BU stream\nthat extracts selected scene elements, properties and relations. The second\ncomponent (cognitive augmentation) augments the extracted visual representation\nbased on relevant non-visual stored representations. It also provides input to\nthe third component, the TD stream, in the form of a TD instruction,\ninstructing the model what task to perform next. The TD stream then guides the\nBU visual stream to perform the selected task in the next cycle. During this\nprocess, the visual representations extracted from the image can be combined\nwith relevant non-visual representations, so that the final scene\nrepresentation is based on both visual information extracted from the scene and\nrelevant stored knowledge of the world. We describe how a sequence of\nTD-instructions is used to extract from the scene structures of interest,\nincluding an algorithm to automatically select the next TD-instruction in the\nsequence. The extraction process is shown to have favorable properties in terms\nof combinatorial generalization, generalizing well to novel scene structures\nand new combinations of objects, properties and relations not seen during\ntraining. Finally, we compare the model with relevant aspects of the human\nvision, and suggest directions for using the BU-TD scheme for integrating\nvisual and cognitive components in the process of scene understanding.\n"}
+{"abstract": "  High dynamic range (HDR) video reconstruction from sequences captured with\nalternating exposures is a very challenging problem. Existing methods often\nalign low dynamic range (LDR) input sequence in the image space using optical\nflow, and then merge the aligned images to produce HDR output. However,\naccurate alignment and fusion in the image space are difficult due to the\nmissing details in the over-exposed regions and noise in the under-exposed\nregions, resulting in unpleasing ghosting artifacts. To enable more accurate\nalignment and HDR fusion, we introduce a coarse-to-fine deep learning framework\nfor HDR video reconstruction. Firstly, we perform coarse alignment and pixel\nblending in the image space to estimate the coarse HDR video. Secondly, we\nconduct more sophisticated alignment and temporal fusion in the feature space\nof the coarse HDR video to produce better reconstruction. Considering the fact\nthat there is no publicly available dataset for quantitative and comprehensive\nevaluation of HDR video reconstruction methods, we collect such a benchmark\ndataset, which contains $97$ sequences of static scenes and 184 testing pairs\nof dynamic scenes. Extensive experiments show that our method outperforms\nprevious state-of-the-art methods. Our dataset, code and model will be made\npublicly available.\n"}
+{"abstract": "  Motivated by sequential budgeted allocation problems, we investigate online\nmatching problems where connections between vertices are not i.i.d., but they\nhave fixed degree distributions -- the so-called configuration model. We\nestimate the competitive ratio of the simplest algorithm, GREEDY, by\napproximating some relevant stochastic discrete processes by their continuous\ncounterparts, that are solutions of an explicit system of partial differential\nequations. This technique gives precise bounds on the estimation errors, with\narbitrarily high probability as the problem size increases. In particular, it\nallows the formal comparison between different configuration models. We also\nprove that, quite surprisingly, GREEDY can have better performance guarantees\nthan RANKING, another celebrated algorithm for online matching that usually\noutperforms the former.\n"}
+{"abstract": "  For a compact Hausdorff space $K$, we give descriptions of the dual of\n$C(K)^\\delta$, the Dedekind completion of the Banach lattice $C(K)$ of\ncontinuous, real-valued functions on $K$. We characterize those functionals\nwhich are $\\sigma$-order continuous and order continuous, respectively, in\nterms of Oxtoby's category measures. This leads to a purely topological\ncharacterization of hyper-Stonean spaces.\n"}
+{"abstract": "  In gravitational-wave data analysis, we regularly work with a host of\nnon-trivial prior probabilities on compact binary masses, redshifts, and spins.\nWe must regularly manipulate these priors, computing the implied priors on a\ntransformed basis of parameters or reweighting posterior samples from one prior\nto another. Here, I detail some common manipulations, presenting a table of\nJacobians with which to transform priors between mass parametrizations,\ndescribing the conversion between source- and detector-frame priors, and\nderiving analytic expressions for priors on the \"effective spin\" parameters\nregularly invoked in gravitational-wave astronomy.\n"}
+{"abstract": "  In recent years, the need for neutral benchmark studies that focus on the\ncomparison of methods from computational sciences has been increasingly\nrecognised by the scientific community. While general advice on the design and\nanalysis of neutral benchmark studies can be found in recent literature,\ncertain amounts of flexibility always exist. This includes the choice of data\nsets and performance measures, the handling of missing performance values and\nthe way the performance values are aggregated over the data sets. As a\nconsequence of this flexibility, researchers may be concerned about how their\nchoices affect the results or, in the worst case, may be tempted to engage in\nquestionable research practices (e.g. the selective reporting of results or the\npost-hoc modification of design or analysis components) to fit their\nexpectations or hopes. To raise awareness for this issue, we use an example\nbenchmark study to illustrate how variable benchmark results can be when all\npossible combinations of a range of design and analysis options are considered.\nWe then demonstrate how the impact of each choice on the results can be\nassessed using multidimensional unfolding. In conclusion, based on previous\nliterature and on our illustrative example, we claim that the multiplicity of\ndesign and analysis options combined with questionable research practices lead\nto biased interpretations of benchmark results and to over-optimistic\nconclusions. This issue should be considered by computational researchers when\ndesigning and analysing their benchmark studies and by the scientific community\nin general in an effort towards more reliable benchmark results.\n"}
+{"abstract": "  Knowledge about entities and their interrelations is a crucial factor of\nsuccess for tasks like question answering or text summarization. Publicly\navailable knowledge graphs like Wikidata or DBpedia are, however, far from\nbeing complete. In this paper, we explore how information extracted from\nsimilar entities that co-occur in structures like tables or lists can help to\nincrease the coverage of such knowledge graphs. In contrast to existing\napproaches, we do not focus on relationships within a listing (e.g., between\ntwo entities in a table row) but on the relationship between a listing's\nsubject entities and the context of the listing. To that end, we propose a\ndescriptive rule mining approach that uses distant supervision to derive rules\nfor these relationships based on a listing's context. Extracted from a suitable\ndata corpus, the rules can be used to extend a knowledge graph with novel\nentities and assertions. In our experiments we demonstrate that the approach is\nable to extract up to 3M novel entities and 30M additional assertions from\nlistings in Wikipedia. We find that the extracted information is of high\nquality and thus suitable to extend Wikipedia-based knowledge graphs like\nDBpedia, YAGO, and CaLiGraph. For the case of DBpedia, this would result in an\nincrease of covered entities by roughly 50%.\n"}
+{"abstract": "  In this paper, we study how to fairly allocate m indivisible chores to n\n(asymmetric) agents. We consider (weighted) proportionality up to any item\n(PROPX) and show that a (weighted) PROPX allocation always exists and can be\ncomputed efficiently. For chores, we argue that PROPX might be a more reliable\nrelaxation for proportionality by the facts that any PROPX allocation ensures\n2-approximation of maximin share (MMS) fairness [Budish, 2011] for symmetric\nagents and of anyprice share (APS) fairness [Babaioff et al, 2021] for\nasymmetric agents. APS allocations for chores have not been studied before the\ncurrent work, and our result implies a 2-approximation algorithm. Another\nby-product result is that an EFX and a weighted EF1 allocation for indivisible\nchores exist if all agents have the same ordinal preference, which might be of\nindependent interest. We then consider the partial information setting and\ndesign algorithms that only use agents' ordinal preferences to compute\napproximately PROPX allocations. Our algorithm achieves a 2-approximation for\nboth symmetric and asymmetric agents, and the approximation ratio is optimal.\nFinally, we study the price of fairness (PoF), i.e., the loss in social welfare\nby enforcing allocations to be (weighted) PROPX. We prove that the tight ratio\nfor PoF is Theta(n) for symmetric agents and unbounded for asymmetric agents.\n"}
+{"abstract": "  In order to define graph transformations by the simultaneous application of\nconcurrent rules, we have adopted in previous work a structure of attributed\ngraphs stable by unions. We analyze the consequences on parallel independence,\na property that characterizes the possibility to resort to sequential\nrewriting. This property turns out to depend not only on the left-hand side of\nrules, as in algebraic approaches to graph rewriting, but also on their\nright-hand side. It is then shown that, of three possible definitions of\nparallel rewriting, only one is convenient in the light of parallel\nindependence.\n"}
+{"abstract": "  We present a linearly arrayed, 16-element, superconducting nanowire\nsingle-photon detector with 83.4$\\%$ system detection efficiency at 1550 nm and\na mean per-element dead-time of 9.6-ns, enabling counting at 1 giga-count per\nsecond with $>50\\%$ System Detection Efficiency. This device was designed and\nfabricated in an existing scalable commercial process.\n"}
+{"abstract": "  Widespread uptake of COVID-19 vaccines is necessary to achieve herd immunity.\nHowever, surveys have found concerning numbers of U.S. adults hesitant or\nunwilling to be vaccinated. Online misinformation may play an important role in\nvaccine hesitancy, but we lack a clear picture of the extent to which it will\nimpact vaccination uptake. Here, we study how vaccination rates and vaccine\nhesitancy are associated with levels of online misinformation about vaccines\nshared by 1.6 million Twitter users geolocated at the U.S. state and county\nlevels. We find a negative relationship between misinformation and vaccination\nuptake rates. Online misinformation is also correlated with vaccine hesitancy\nrates taken from survey data. Associations between vaccine outcomes and\nmisinformation remain significant when accounting for political as well as\ndemographic and socioeconomic factors. While vaccine hesitancy is strongly\nassociated with Republican vote share, we observe that the effect of online\nmisinformation on hesitancy is strongest across Democratic rather than\nRepublican counties. These results suggest that addressing online\nmisinformation must be a key component of interventions aimed to maximize the\neffectiveness of vaccination campaigns.\n"}
+{"abstract": "  For a graph $G$ the imbalance of an edge $uv$ of $G$ is\n$|deg_G(u)-deg_G(v)|$. Irregularity of a graph $G$ is defined as the sum of\nimbalances over all edges of $G$. In this paper we consider expansions and Pell\ngraphs. If $H$ is an expansion of $G$ with respect to the sets $V_1$ and $V_2$,\nwe express the irregularity of $H$ using $G, V_1$ and $V_2$. For Pell graphs\nthe imbalance of their edges is studied. The number of edges of a Pell graph\nwith a fixed imbalance $k$ is expressed. Using these results the irregularity\nand the $\\sigma$-index of Pell graphs are given.\n"}
+{"abstract": "  Gravitational waves from the distant sources are gravitationally lensed\nduring their propagation through the intervening matter inhomogeneities before\narriving at detectors. It has been proposed in the literature that the variance\nof the lensed waveform can be used to extract information of the matter power\nspectrum at very small scales and of low-mass dark halos. In this paper, we\nshow that the variance of the amplitude fluctuation and that of the phase\nfluctuation of the lensed waveform obey a simple relation irrespective of the\nshape of the matter power spectrum. We study conditions under which this\nrelation can be violated and discuss some potential applications of the\nrelation. This relation may be used to confirm the robustness of claimed\nobservations of gravitational lensing of gravitational waves and the subsequent\nreconstruction of the matter power spectrum.\n"}
+{"abstract": "  Strain induced by a magnetic field is a common phenomenon for ferromagnets,\nbut few antiferromagnets show large strain induced by a magnetic field. On the\nbasis of linear strain measurements of sintered samples of triangular\nantiferromagnet ACrS2 (A = Cu, Ag, and Au) in magnetic fields up to 9 T, the\nAgCrS2 sample was found to show a large strain, yielding a large volume change\nover 700 ppm, which is one of the largest volume changes measured to date for\nan antiferromagnet. This large strain appeared only at the N\\'eel temperature\nof 42 K and was not restored to its initial state when the applied magnetic\nfield was decreased to zero; however, it was initialized by cooling the sample\nto far below the N\\'eel temperature. These results suggest that the coexistence\nof magnetically ordered and paramagnetic phases at the first-order phase\ntransition plays an important role. AuCrS2 showed a magnetic-field-induced\nstrain with similar features, although it was smaller than that in AgCrS2.\n"}
+{"abstract": "  Given a theory containing both heavy and light fields (the UV theory), a\nstandard procedure is to integrate out the heavy field to obtain an effective\nfield theory (EFT) for the light fields. Typically the EFT equations of motion\nconsist of an expansion involving higher and higher derivatives of the fields,\nwhose truncation at any finite order may not be well-posed. In this paper we\naddress the question of how to make sense of the EFT equations of motion, and\nwhether they provide a good approximation to the classical UV theory. We\npropose an approach to solving EFTs which leads to a well-posedness statement.\nFor a particular choice of UV theory we rigorously derive the corresponding EFT\nand show that a large class of classical solutions to the UV theory are well\napproximated by EFT solutions. We also consider solutions of the UV theory\nwhich are not well approximated by EFT solutions and demonstrate that these are\nclose, in an averaged sense, to solutions of a modified EFT.\n"}
+{"abstract": "  Computing the gradient of a function provides fundamental information about\nits behavior. This information is essential for several applications and\nalgorithms across various fields. One common application that require gradients\nare optimization techniques such as stochastic gradient descent, Newton's\nmethod and trust region methods. However, these methods usually requires a\nnumerical computation of the gradient at every iteration of the method which is\nprone to numerical errors. We propose a simple limited-memory technique for\nimproving the accuracy of a numerically computed gradient in this\ngradient-based optimization framework by exploiting (1) a coordinate\ntransformation of the gradient and (2) the history of previously taken descent\ndirections. The method is verified empirically by extensive experimentation on\nboth test functions and on real data applications. The proposed method is\nimplemented in the R package smartGrad and in C++.\n"}
+{"abstract": "  We analyze the appearance of the trilinear squark-Higgs couplings $x_q$ in\nGreen functions in the Higgs sector of the MSSM and in threshold corrections to\nthe SM. The results are constraints on maximal powers of $x_q$ in QCD-related\nloop corrections. In practice these often imply all-order resummations of\nleading or subleading $x_q$ contributions in SM-parametrized expressions. We\npresent a variety of all-order resummation relations for $\\Delta \\lambda$ which\ninclude such $x_q$-enhanced terms and different orders in Yukawa and gauge\ncouplings. We contrast which terms cannot be resummed.\n"}
+{"abstract": "  The emergence of glassy dynamics and the glass transition in dense disordered\nsystems is still not fully understood theoretically. Mode-coupling theory (MCT)\nhas shown to be effective in describing some of the non-trivial features of\nglass formation, but it cannot explain the full glassy phenomenology due to the\nstrong approximations on which it is based. Generalized mode-coupling theory\n(GMCT) is a hierarchical extension of the theory, which is able to outclass MCT\nby carefully describing the dynamics of higher order correlations in its\ngeneralized framework. Unfortunately, the theory has so far only been developed\nfor single component systems and as a result works poorly for highly\npolydisperse materials. In this paper, we solve this problem by developing GMCT\nfor multi-component systems. We use it to predict the glassy dynamics of the\nbinary Kob-Andersen Lennard-Jones mixture, as well as its purely repulsive\nWeeks-Chandler-Andersen analogue. Our results show that each additional level\nof the GMCT hierarchy gradually improves the predictive power of GMCT beyond\nits previous limit. This implies that our theory is able to harvest more\ninformation from the static correlations, thus being able to better understand\nthe role of attraction in supercooled liquids from a first-principles\nperspective.\n"}
+{"abstract": "  Vast amount of data generated from networks of sensors, wearables, and the\nInternet of Things (IoT) devices underscores the need for advanced modeling\ntechniques that leverage the spatio-temporal structure of decentralized data\ndue to the need for edge computation and licensing (data access) issues. While\nfederated learning (FL) has emerged as a framework for model training without\nrequiring direct data sharing and exchange, effectively modeling the complex\nspatio-temporal dependencies to improve forecasting capabilities still remains\nan open problem. On the other hand, state-of-the-art spatio-temporal\nforecasting models assume unfettered access to the data, neglecting constraints\non data sharing. To bridge this gap, we propose a federated spatio-temporal\nmodel -- Cross-Node Federated Graph Neural Network (CNFGNN) -- which explicitly\nencodes the underlying graph structure using graph neural network (GNN)-based\narchitecture under the constraint of cross-node federated learning, which\nrequires that data in a network of nodes is generated locally on each node and\nremains decentralized. CNFGNN operates by disentangling the temporal dynamics\nmodeling on devices and spatial dynamics on the server, utilizing alternating\noptimization to reduce the communication cost, facilitating computations on the\nedge devices. Experiments on the traffic flow forecasting task show that CNFGNN\nachieves the best forecasting performance in both transductive and inductive\nlearning settings with no extra computation cost on edge devices, while\nincurring modest communication cost.\n"}
+{"abstract": "  The use of ultrasonic guided waves to probe the materials/structures for\ndamage continues to increase in popularity for non-destructive evaluation (NDE)\nand structural health monitoring (SHM). The use of high-frequency waves such as\nthese offers an advantage over low-frequency methods from their ability to\ndetect damage on a smaller scale. However, in order to assess damage in a\nstructure, and implement any NDE or SHM tool, knowledge of the behaviour of a\nguided wave throughout the material/structure is important (especially when\ndesigning sensor placement for SHM systems). Determining this behaviour is\nextremely diffcult in complex materials, such as fibre-matrix composites, where\nunique phenomena such as continuous mode conversion takes place. This paper\nintroduces a novel method for modelling the feature-space of guided waves in a\ncomposite material. This technique is based on a data-driven model, where prior\nphysical knowledge can be used to create structured machine learning tools;\nwhere constraints are applied to provide said structure. The method shown makes\nuse of Gaussian processes, a full Bayesian analysis tool, and in this paper it\nis shown how physical knowledge of the guided waves can be utilised in\nmodelling using an ML tool. This paper shows that through careful consideration\nwhen applying machine learning techniques, more robust models can be generated\nwhich offer advantages such as extrapolation ability and physical\ninterpretation.\n"}
+{"abstract": "  Fock-Goncharov's moduli spaces $\\mathscr{X}_{{\\rm PGL}_3,\\frak{S}}$ of framed\n${\\rm PGL}_3$-local systems on punctured surfaces $\\frak{S}$ provide prominent\nexamples of cluster $\\mathscr{X}$-varieties and higher Teichm\\\"uller spaces. In\na previous paper of the author (arXiv:2011.14765), the so-called ${\\rm SL}_3$\nquantum trace map is constructed for each triangulable punctured surface\n$\\frak{S}$ and its ideal triangulation $\\Delta$, as a homomorphism from the\nstated ${\\rm SL}_3$-skein algebra of the surface to a quantum torus algebra\nthat deforms the ring of Laurent polynomials in the cube-roots of the cluster\ncoordinate variables for the cluster $\\mathscr{X}$-chart for $\\mathscr{X}_{{\\rm\nPGL}_3,\\frak{S}}$ associated to $\\Delta$. We develop quantum mutation maps\nbetween special subalgebras of the cube-root quantum torus algebras for\ndifferent triangulations, and show that the ${\\rm SL}_3$ quantum trace maps are\ncompatible under these quantum mutation maps. As a result, the quantum ${\\rm\nSL}_3$-${\\rm PGL}_3$ duality map constructed in the previous paper is shown to\nbe independent of the choice of an ideal triangulation.\n"}
+{"abstract": "  A typical crowdsourcing software development(CSD) marketplace consists of a\nlist of software tasks as service demands and a pool of freelancer developers\nas service suppliers. Highly dynamic and competitive CSD market places may\nresult in task failure due to unforeseen risks, such as increased competition\nover shared worker supply, or uncertainty associated with workers' experience\nand skills, and so on. To improve CSD effectiveness, it is essential to better\nunderstand and plan with respect to dynamic worker characteristics and risks\nassociated with CSD processes. In this paper, we present a hybrid simulation\nmodel, CrowdSim, to forecast crowdsourcing task failure risk in competitive CSD\nplatforms. CrowdSim is composed of three layered components: the macro-level\nreflects the overall crowdsourcing platform based on system dynamics,the\nmeso-level represents the task life cycle based on discrete event simulation,\nand the micro-level models the crowd workers' decision-making processes based\non agent-based simulation. CrowdSim is evaluated through three CSD decision\nscenarios to demonstrate its effectiveness, using a real-world historical\ndataset and the results demonstrate CrowdSim's potential in empowering\ncrowdsourcing managers to explore crowdsourcing outcomes with respect to\ndifferent task scheduling options.\n"}
+{"abstract": "  Federated Learning marks a turning point in the implementation of\ndecentralized machine learning (especially deep learning) for wireless devices\nby protecting users' privacy and safeguarding raw data from third-party access.\nIt assigns the learning process independently to each client. First, clients\nlocally train a machine learning model based on local data. Next, clients\ntransfer local updates of model weights and biases (training data) to a server.\nThen, the server aggregates updates (received from clients) to create a global\nlearning model. However, the continuous transfer between clients and the server\nincreases communication costs and is inefficient from a resource utilization\nperspective due to the large number of parameters (weights and biases) used by\ndeep learning models. The cost of communication becomes a greater concern when\nthe number of contributing clients and communication rounds increases. In this\nwork, we propose a novel framework, FedZip, that significantly decreases the\nsize of updates while transferring weights from the deep learning model between\nclients and their servers. FedZip implements Top-z sparsification, uses\nquantization with clustering, and implements compression with three different\nencoding methods. FedZip outperforms state-of-the-art compression frameworks\nand reaches compression rates up to 1085x, and preserves up to 99% of bandwidth\nand 99% of energy for clients during communication.\n"}
+{"abstract": "  We study differential rotation in late-stage shell convection in a 3D\nhydrodynamic simulation of a rapidly rotating $16M_\\odot$ helium star with a\nparticular focus on the convective oxygen shell. We find that the oxygen shell\ndevelops a quasi-stationary pattern of differential rotation that is neither\ndescribed by uniform angular velocity as assumed in current stellar evolution\nmodels of supernova progenitors, nor by uniform specific angular momentum.\nInstead, the oxygen shell develops a positive angular velocity gradient with\nfaster rotation at the equator than at the pole by tens of percent. We show\nthat the angular momentum transport inside the convection zone is not\nadequately captured by a diffusive mixing-length flux proportional to the\nangular velocity or angular momentum gradient. Zonal flow averages reveal\nstable large-scale meridional flow and an entropy deficit near the equator that\nmirrors the patterns in the angular velocity. The structure of the flow is\nreminiscent of simulations of stellar surface convection zones and the\ndifferential rotation of the Sun, suggesting that similar effects are involved;\nfuture simulations will need to address in more detail how the interplay of\nbuoyancy, inertial forces, and turbulent stresses shapes differential rotation\nduring late-stage convection in massive stars. Our findings may have\nimplications for neutron star birth spin periods and supernova explosion\nscenarios that involve rapid core rotation. If convective regions develop\npositive angular velocity gradients, angular momentum could be shuffled out of\nthe core region more efficiently, potentially making the formation of\nmillisecond magnetars more difficult.\n"}
+{"abstract": "  In the decremental single-source shortest paths problem, the goal is to\nmaintain distances from a fixed source $s$ to every vertex $v$ in an $m$-edge\ngraph undergoing edge deletions. In this paper, we conclude a long line of\nresearch on this problem by showing a near-optimal deterministic data structure\nthat maintains $(1+\\epsilon)$-approximate distance estimates and runs in\n$m^{1+o(1)}$ total update time.\n  Our result, in particular, removes the oblivious adversary assumption\nrequired by the previous breakthrough result by Henzinger et al. [FOCS'14],\nwhich leads to our second result: the first almost-linear time algorithm for\n$(1-\\epsilon)$-approximate min-cost flow in undirected graphs where capacities\nand costs can be taken over edges and vertices. Previously, algorithms for max\nflow with vertex capacities, or min-cost flow with any capacities required\nsuper-linear time. Our result essentially completes the picture for approximate\nflow in undirected graphs.\n  The key technique of the first result is a novel framework that allows us to\ntreat low-diameter graphs like expanders. This allows us to harness expander\nproperties while bypassing shortcomings of expander decomposition, which almost\nall previous expander-based algorithms needed to deal with. For the second\nresult, we break the notorious flow-decomposition barrier from the\nmultiplicative-weight-update framework using randomization.\n"}
+{"abstract": "  We present algorithms for the Max-Cover and Max-Unique-Cover problems in the\ndata stream model. The input to both problems are $m$ subsets of a universe of\nsize $n$ and a value $k\\in [m]$. In Max-Cover, the problem is to find a\ncollection of at most $k$ sets such that the number of elements covered by at\nleast one set is maximized. In Max-Unique-Cover, the problem is to find a\ncollection of at most $k$ sets such that the number of elements covered by\nexactly one set is maximized. Our goal is to design single-pass algorithms that\nuse space that is sublinear in the input size. Our main algorithmic results\nare:\n  If the sets have size at most $d$, there exist single-pass algorithms using\n$\\tilde{O}(d^{d+1} k^d)$ space that solve both problems exactly. This is\noptimal up to polylogarithmic factors for constant $d$.\n  If each element appears in at most $r$ sets, we present single pass\nalgorithms using $\\tilde{O}(k^2 r/\\epsilon^3)$ space that return a $1+\\epsilon$\napproximation in the case of Max-Cover. We also present a single-pass algorithm\nusing slightly more memory, i.e., $\\tilde{O}(k^3 r/\\epsilon^{4})$ space, that\n$1+\\epsilon$ approximates Max-Unique-Cover.\n  In contrast to the above results, when $d$ and $r$ are arbitrary, any\nconstant pass $1+\\epsilon$ approximation algorithm for either problem requires\n$\\Omega(\\epsilon^{-2}m)$ space but a single pass $O(\\epsilon^{-2}mk)$ space\nalgorithm exists. In fact any constant-pass algorithm with an approximation\nbetter than $e/(e-1)$ and $e^{1-1/k}$ for Max-Cover and Max-Unique-Cover\nrespectively requires $\\Omega(m/k^2)$ space when $d$ and $r$ are unrestricted.\nEn route, we also obtain an algorithm for a parameterized version of the\nstreaming Set-Cover problem.\n"}
+{"abstract": "  We derive in the closed and unimprovable form the bilateral non-asymptotic\nrelations between growth of entire functions and decay rate at infinity of its\nTaylor coefficients. We investigate the functions of one as well as of several\ncomplex variables.\n  We will apply the convex analysis: Young-Fenchel (Legendre) transform, Young\ninequality, saddle-point method etc.\n"}
+{"abstract": "  The production of K$^{*}(892)^{0}$ and $\\phi(1020)$ mesons in proton-proton\n(pp) and lead-lead (Pb-Pb) collisions at $\\sqrt{s_\\mathrm{NN}} = 5.02$ TeV has\nbeen measured using the ALICE detector at the Large Hadron Collider (LHC). The\ntransverse momentum ($p_{\\mathrm{T}}$) distributions of K$^{*}(892)^{0}$ and\n$\\phi(1020)$ mesons have been measured at midrapidity $(|y|<0.5)$ up to\n$p_{\\mathrm{T}} = 20$ GeV$/c$ in inelastic pp collisions and for several Pb-Pb\ncollision centralities. The collision centrality and collision energy\ndependence of the average transverse momenta agree with the radial flow\nscenario observed with stable hadrons, showing that the effect is stronger for\nmore central collisions and higher collision energies. The $\\mathrm{K^{*0}/K}$\nratio is found to be suppressed in Pb-Pb collisions relative to pp collisions:\nthis indicates a loss of the measured K$^{*}(892)^{0}$ signal due to\nrescattering of its decay products in the hadronic phase. In contrast, for the\nlonger-lived $\\phi(1020)$ mesons, no such suppression is observed. The nuclear\nmodification factors ($R_{\\rm AA}$) of K$^{*}(892)^{0}$ and $\\phi(1020)$ mesons\nare calculated using pp reference spectra at the same collision energy. In\ncentral Pb-Pb collisions for $p_{\\rm T} > 8$ GeV$/c$, the $R_{\\rm AA}$ values\nof K$^{*}(892)^{0}$ and $\\phi(1020)$ are below unity and observed to be similar\nto those of pions, kaons, and (anti)protons. The $R_{\\rm AA}$ values at high\n$p_{\\mathrm T}$ for K$^{*}(892)^{0}$ and $\\phi(1020)$ mesons are in agreement\nwithin uncertainties for $\\sqrt{s_\\mathrm{NN}} = 5.02$ and 2.76 TeV.\n"}
+{"abstract": "  We analyse Sector 20 TESS photometry of the ultra-hot Jupiter WASP-12b, and\nextract its phase curve to study the planet's atmospheric properties. We\nsuccessfully recover the phase curve with an amplitude of 549 $\\pm$ 62 ppm, and\na secondary eclipse depth of 609$^{+74}_{-73}$ ppm. The peak of the phase curve\nis shifted by 0.049 $\\pm$ 0.015 in phase, implying that the brightest spot in\nthe atmosphere is shifted from the substellar point towards the planet's\nevening terminator. Assuming zero albedo, the eclipse depth infers a day-side\nbrightness temperature of 3128$^{+64}_{-68}$ K. No significant detection of\nflux from the night-side is found at 60 $\\pm$ 97 ppm, implying a night-side\nbrightness temperature of $<$2529 K (1-$\\sigma$). We do not detect any\nsignificant variability in the light from the planet over the $\\sim$27 days of\nthe TESS observations. Finally, we note that an ephemeris model taking orbital\ndecay into account provides a significantly better fit than a constant-period\nmodel.\n"}
+{"abstract": "  In this paper, we will study the large scale structure formation using the\ngravitational partition function. We will assertively argue that the system of\ngravitating galaxies can be analyzed using the Tsallis statistical mechanics.\nThe divergences in the Tsallis gravitational partition function can be removed\nusing the mathematical riches of the generalization of the dimensional\nregularization (GDR). The finite gravitational partition function thus obtained\nwill be used to evaluate the thermodynamics of the system of galaxies and thus,\nto understand the clustering of galaxies in the universe. The correlation\nfunction which is believed to contain the information of clustering of galaxies\nwill also be discussed in this formalism.\n"}
+{"abstract": "  For second-order elliptic or parabolic equations with subcritical or critical\ndrifts, it is well-known that the Harnack inequality holds and their bounded\nweak solutions are H\\\"older continuous. We construct time-independent\nsupercritical drifts in $L^{n-\\lambda}(\\mathbb{R}^n)$ with arbitrarily small\n$\\lambda>0$ such that the Harnack inequality and the H\\\"older continuity fail\nin both the elliptic and the parabolic cases, thus confirming a conjecture by\nSeregin, Silvestre, Sverak and Zlatos. These results are sharp, and they also\napply to a toy model of the axi-symmetric Navier-Stokes equations in space\ndimension $3$.\n"}
+{"abstract": "  Active particles often swim in confined environments. The transport\nmechanisms, especially the global one as reflected by the Taylor dispersion\nmodel, are of great practical interest to various applications. For active\ndispersion process in confined flows, previous analytical studies focused on\nthe long-time asymptotic values of dispersion characteristics. Only several\nnumerical studies preliminarily investigated the temporal evolution. Extending\nrecent studies of Jiang & Chen (J. Fluid Mech., vol. 877, 2019, pp. 1--34; J.\nFluid Mech., vol. 899, 2020, A18), this work makes the first analytical attempt\nto investigate the transient process. The temporal evolution of the local\ndistribution in the confined-section--orientation space, drift, dispersivity\nand skewness, is explored based on moments of distributions. We introduce the\nbiorthogonal expansion method for solutions because the classic integral\ntransform method for passive transport problems is not applicable due to the\nself-propulsion effect. Two types of boundary condition, the reflective\ncondition and the Robin condition for wall accumulation, are imposed\nrespectively. A detailed study on spherical and ellipsoidal swimmers dispersing\nin a plane Poiseuille flow demonstrates the influences of the swimming, shear\nflow, wall accumulation and particle shape on the transient dispersion process\nafter a point-source release. The swimming-induced diffusion makes the local\ndistribution reach its equilibrium state faster than that of passive particles.\nThough the wall accumulation significantly affects the evolution of the local\ndistribution and the drift, the time scale to reach the Taylor regime is not\nobviously changed. The shear-induced alignment of ellipsoidal particles can\nenlarge the dispersivity but has less influence on the drift and the skewness.\n"}
+{"abstract": "  The main result of the present paper is the proof of the Strange Duality for\nelliptic surfaces -- a duality between global sections of determinantal line\nbundles on moduli spaces of stable sheaves on a fixed elliptic surface. For\nthis, we employ the \"Marian-Oprea trick\": using Bridgeland's birational\nisomorphisms, we reduce the problem from a pair of general moduli spaces to a\npair of Hilbert schemes. The latter case is a theorem by Marian-Oprea.\n"}
+{"abstract": "  Arising from modulation instability, Turing rolls in optical Kerr\nmicroresonators have been used in the generation of optical frequency combs and\nthe synthesis of microwave and terahertz frequencies. In this work, by applying\nelectro-optic modulation on terahertz-frequency Turing rolls, we implement\nelectro-optic frequency division with a microcomb to synthesize variable\nlow-noise microwave signals. We also actively stabilize the terahertz\noscillations to a microwave reference via intracavity power modulation,\nobtaining fractional frequency instabilities that are better than those of the\nfree-running situation by up to six orders of magnitude. This study not only\nhighlights the extraordinary spectral purity of Turing roll oscillations but\nalso opens the way for bidirectional terahertz-to-microwave links with hybrid\noptical frequency comb techniques.\n"}
+{"abstract": "  Axion-like particles (ALPs) are pseudo-scalar particles that are candidates\nfor ultralight dark matter. ALPs interact with photons slightly and cause the\nrotational oscillation of linear polarization. DANCE searches for ALP dark\nmatter by enhancing the rotational oscillation in a bow-tie ring cavity. The\nsignal to noise ratio of DANCE can be improved by long-term observation, and we\nare planning a year-long observation for the final DANCE. In this document, I\nwill report on the control systems of the ring cavity we developed for the\nfuture long-term observation.\n"}
+{"abstract": "  Kink-like flapping motions of current sheets are commonly observed in the\nmagnetotail. Such oscillations have periods of a few minutes down to a few\nseconds and they propagate toward the flanks of the plasma sheet. Here, we\nreport a short-period ($T\\approx25$ s) flapping event of a thin current sheet\nobserved by the Magnetospheric Multiscale (MMS) spacecraft in the dusk-side\nplasma sheet following a fast Earthward plasma flow. We characterize the\nflapping structure using the multi-spacecraft spatiotemporal derivative and\ntiming methods, and we find that the wave-like structure is propagating along\nthe average current direction with a phase velocity comparable to the ion\nvelocity. We show that the wavelength of the oscillating current sheet scales\nwith its thickness as expected for a drift-kink mode. The decoupling of the ion\nbulk motion from the electron bulk motion suggests that the current sheet is\nthin. We discuss the presence of the lower hybrid waves associated with\ngradients of density as a broadening process of the thin current sheet.\n"}
+{"abstract": "  This paper proposes a novel multiple-input multiple-output (MIMO) symbol\ndetector that incorporates a deep reinforcement learning (DRL) agent into the\nMonte Carlo tree search (MCTS) detection algorithm. We first describe how the\nMCTS algorithm, used in many decision-making problems, is applied to the MIMO\ndetection problem. Then, we introduce a self-designed deep reinforcement\nlearning agent, consisting of a policy value network and a state value network,\nwhich is trained to detect MIMO symbols. The outputs of the trained networks\nare adopted into a modified MCTS detection algorithm to provide useful node\nstatistics and facilitate enhanced tree search process. The resulted scheme,\ntermed the DRL-MCTS detector, demonstrates significant improvements over the\noriginal MCTS detection algorithm and exhibits favorable performance compared\nto other existing linear and DNN-based detection methods under varying channel\nconditions.\n"}
+{"abstract": "  In this short note we develop a constitutive relation that is linear in both\nthe Cauchy stress and the linearized strain, by linearizing implicit\nconstitutive relations between the stress and the deformation gradient that\nhave been put into place to describe the response of elastic bodies (see\n\\cite{rajagopal2003implicit}), by assuming that the displacement gradient is\nsmall. These implicit equations include the classical linearized elastic\nconstitutive approximation as well as constitutive relations that imply\nlimiting strain, as special subclasses.\n"}
+{"abstract": "  In this paper, a decomposition theorem for (covariant) unitary group\nrepresentations on Kaplansky-Hilbert modules over Stone algebras is\nestablished, which generalizes the well-known Hilbert space case (where it\ncoincides with the decomposition of Jacobs, de Leeuw and Glicksberg). The proof\nrests heavily on the operator theory on Kaplansky-Hilbert modules, in\nparticular the spectral theorem for Hilbert-Schmidt homomorphisms on such\nmodules. As an application, a generalization of the celebrated\nFurstenberg-Zimmer structure theorem to the case of measure-preserving actions\nof arbitrary groups on arbitrary probability spaces is established.\n"}
+{"abstract": "  How the brain processes information from external stimuli in order to\nperceive the world and act on it is one of the greatest questions in\nneuroscience. To address this question different time series analyzes\ntechniques have been employed to characterize the statistical properties of\nbrain signals during cognitive tasks. Typically response-specific processes are\naddressed by comparing the time course of average event-related potentials in\ndifferent trials type. Here we analyze monkey Local Field Potentials data\nduring visual pattern discrimination called Go/No-Go task in the light of\ninformation theory quantifiers. We show that the Bandt-Pompe symbolization\nmethodology to calculate entropy and complexity of data is a useful tool to\ndistinguish response-related differences between Go and No-Go trials. We\npropose to use an asymmetry index to statistically validate trial type\ndifferences. Moreover, by using the multi-scale approach and embedding time\ndelays to downsample the data we can estimate the important time scales in\nwhich the relevant information is been processed.\n"}
+{"abstract": "  Developments in computer science in recent years are moving into hospitals.\nSurgeons are faced with ever new technical challenges. Visual perception plays\na key role in most of these. Diagnostic and training models are needed to\noptimize the training of young surgeons. In this study, we present a model for\nclassifying experts, 4th-year residents and 3rd-year residents, using only eye\nmovements. We show a model that uses a minimal set of features and still\nachieve a robust accuracy of 76.46 % to classify eye movements into the correct\nclass. Likewise, in this study, we address the evolutionary steps of visual\nperception between three expertise classes, forming a first step towards a\ndiagnostic model for expertise.\n"}
+{"abstract": "  We discuss the integer sequence transform $a \\mapsto b$ where $b_n$ is the\nnumber of real roots of the polynomial $a_0 + a_1x + a_2x^2 + \\cdots + a_nx^n$.\nIt is shown that several sequences $a$ give the trivial sequence $b = (0,1,0,1,\n0,1,\\ldots)$, i.e., ${b_n = n \\bmod 2}$, among them the Catalan numbers,\ncentral binomial coefficients, $n!$ and $\\binom{n+k}{n}$ for a fixed $k$. We\nalso look at some sequences $a$ for which $b$ is more interesting such as $a_n\n= (n+1)^k$ for $k \\geq 3$. Further, general procedures are given for\nconstructing real sequences $a_n$ for which $b_n$ is either always maximal or\nminimal.\n"}
+{"abstract": "  We study the stability and instability of ALE Ricci-flat metrics around which\na Lojasiewicz inequality is satisfied for a variation of Perelman's\n$\\lambda$-functional adapted to the ALE situation and denoted\n$\\lambda_{\\operatorname{ALE}}$. This functional was introduced by the authors\nin a recent work and it has been proven that it satisfies a good enough\nLojasiewicz inequality at least in neighborhoods of integrable ALE Ricci-flat\nmetrics in dimension larger than or equal to 5.\n"}
+{"abstract": "  When trained on large, unfiltered crawls from the internet, language models\npick up and reproduce all kinds of undesirable biases that can be found in the\ndata: they often generate racist, sexist, violent or otherwise toxic language.\nAs large models require millions of training examples to achieve good\nperformance, it is difficult to completely prevent them from being exposed to\nsuch content. In this paper, we first demonstrate a surprising finding:\npretrained language models recognize, to a considerable degree, their\nundesirable biases and the toxicity of the content they produce. We refer to\nthis capability as self-diagnosis. Based on this finding, we then propose a\ndecoding algorithm that, given only a textual description of the undesired\nbehavior, reduces the probability of a language model producing problematic\ntext. We refer to this approach as self-debiasing. Self-debiasing does not rely\non manually curated word lists, nor does it require any training data or\nchanges to the model's parameters. While we by no means eliminate the issue of\nlanguage models generating biased text, we believe our approach to be an\nimportant step in this direction.\n"}
+{"abstract": "  A joint determination of the reactor antineutrino spectra resulting from the\nfission of $^{235}$U and $^{239}$Pu has been carried out by the Daya Bay and\nPROSPECT collaborations. This Letter reports the level of consistency of\n$^{235}$U spectrum measurements from the two experiments and presents new\nresults from a joint analysis of both data sets. The measurements are found to\nbe consistent. The combined analysis reduces the degeneracy between the\ndominant $^{235}$U and $^{239}$Pu isotopes and improves the uncertainty of the\n$^{235}$U spectral shape to about 3\\%. The ${}^{235}$U and $^{239}$Pu\nantineutrino energy spectra are unfolded from the jointly deconvolved reactor\nspectra using the Wiener-SVD unfolding method, providing a data-based reference\nfor other reactor antineutrino experiments and other applications. This is the\nfirst measurement of the $^{235}$U and $^{239}$Pu spectra based on the\ncombination of experiments at low- and highly enriched uranium reactors.\n"}
+{"abstract": "  It has been a century since the species-area relationship (SAR) was first\nproposed as a power law to explain how species richness scales with area. There\nhave been many attempts to explain the origin of this predominant form. Apart\nfrom the power law, numerous empirical studies also report a semi-log form of\nthe SAR, but very few have addressed its incidence. In this work, we test\nwhether these relationships could emerge from the assembly of large random\ncommunities on island-like systems. We reformulate the generalized\nLotka-Volterra model by introducing an area parameter that determines the\nspecies richness of the assembled communities. Our analysis demonstrates that\nthe two most widely reported relationship forms can emerge due to differences\nin immigration rates and skewness towards weak interactions. We particularly\nhighlight the incidence of the semi-log SAR for low immigration rates from a\nsource pool, which is consistent with several previous empirical studies. The\ntwo SAR forms might show good fits to data over a large span of areas but a\npower-law overestimates species richness on smaller islands in remote\narchipelagoes.\n"}
+{"abstract": "  We propose the first general PAC-Bayesian generalization bounds for\nadversarial robustness, that estimate, at test time, how much a model will be\ninvariant to imperceptible perturbations in the input. Instead of deriving a\nworst-case analysis of the risk of a hypothesis over all the possible\nperturbations, we leverage the PAC-Bayesian framework to bound the averaged\nrisk on the perturbations for majority votes (over the whole class of\nhypotheses). Our theoretically founded analysis has the advantage to provide\ngeneral bounds (i) that are valid for any kind of attacks (i.e., the\nadversarial attacks), (ii) that are tight thanks to the PAC-Bayesian framework,\n(iii) that can be directly minimized during the learning phase to obtain a\nrobust model on different attacks at test time.\n"}
+{"abstract": "  This review describes an emerging field of waveguide quantum electrodynamics\n(WQED) studying interaction of photons propagating in a waveguide with\nlocalized quantum emitters. In such systems, atoms and guided photons are\nhybridized with each other and form polaritons that can propagate along the\nwaveguide, contrary to the cavity quantum optics setup. Emerging in such a\nsystem collective light-atom interactions result in super- and sub-radiant\nquantum states, that are promising for quantum information processing, and give\nrise to peculiar quantum correlations between photons. The review is aimed at\nboth experimentalists and theoreticians from various fields of physics\ninterested in the rapidly developing subject of WQED. We highlight recent\ngroundbreaking experiments performed for different quantum platforms, including\ncold atoms, superconducting qubits, semiconductor quantum dots, quantum\nsolid-state defects and at the same time provide a comprehensive introduction\ninto various theoretical techniques to study atom-photon interactions in the\nwaveguide.\n"}
+{"abstract": "  Mapping the thermal transport properties of materials at the nanoscale is of\ncritical importance for optimizing heat conduction in nanoscale devices.\nSeveral methods to determine the thermal conductivity of materials have been\ndeveloped, most of them yielding an average value across the sample, thereby\ndisregarding the role of local variations. Here, we present a method for the\nspatially-resolved assessment of the thermal conductivity of suspended graphene\nby using a combination of confocal Raman thermometry and a finite-element\ncalculations-based fitting procedure. We demonstrate the working principle of\nour method by extracting the two-dimensional thermal conductivity map of one\npristine suspended single-layer graphene sheet and one irradiated using helium\nions. Our method paves the way for spatially resolving the thermal conductivity\nof other types of layered materials. This is particularly relevant for the\ndesign and engineering of nanoscale thermal circuits (e.g. thermal diodes).\n"}
+{"abstract": "  One of the most popular methods of the machine learning prediction\nexplanation is the SHapley Additive exPlanations method (SHAP). An imprecise\nSHAP as a modification of the original SHAP is proposed for cases when the\nclass probability distributions are imprecise and represented by sets of\ndistributions. The first idea behind the imprecise SHAP is a new approach for\ncomputing the marginal contribution of a feature, which fulfils the important\nefficiency property of Shapley values. The second idea is an attempt to\nconsider a general approach to calculating and reducing interval-valued Shapley\nvalues, which is similar to the idea of reachable probability intervals in the\nimprecise probability theory. A simple special implementation of the general\napproach in the form of linear optimization problems is proposed, which is\nbased on using the Kolmogorov-Smirnov distance and imprecise contamination\nmodels. Numerical examples with synthetic and real data illustrate the\nimprecise SHAP.\n"}
+{"abstract": "  We consider a family of tensor network states defined on regular lattices\nthat can be efficiently prepared with a quantum computer by adiabatic\nevolution. This family comprises relevant classes of states, such as injective\nMatrix Product and Projected Entangled-Pair States, and some corresponding to\nclassical spin models. We show how uniform lower bounds to the gap of the\nparent Hamiltonian along the adiabatic trajectory can be efficiently computed\nusing semi-definite programming. We also derive a set of observables whose\nexpectation values can be easily determined and that form a complete set, in\nthe sense that they uniquely characterize the state. We identify a subset of\nthose observables which can be efficiently computed if one has access to the\nquantum state and local measurements, and analyze how they can be used in\nverification procedures.\n"}
+{"abstract": "  In this paper we give an example of a closed, strongly one-sided dense set\nwhich is not of uniform density type. We also show that there is a set of\nuniform density type which is not of strong uniform density type.\n"}
+{"abstract": "  We calculate thermodynamic potentials and their derivatives for the\nthree-dimensional $O(2)$ model using tensor-network methods to investigate the\nwell-known second-order phase transition. We also consider the model at\nnon-zero chemical potential to study the Silver Blaze phenomenon, which is\nrelated to the particle number density at zero temperature. Furthermore, the\ntemperature dependence of the number density is explored using asymmetric\nlattices. Our results for both zero and non-zero magnetic field, temperature,\nand chemical potential are consistent with those obtained using other methods.\n"}
+{"abstract": "  We show that viscoelastic plane Poiseuille flow becomes linearly unstable in\nthe absence of inertia, in the limit of high elasticities, for ultra-dilute\npolymer solutions. While inertialess elastic instabilities have been predicted\nfor curvilinear shear flows, this is the first ever report of a purely elastic\nlinear instability in a rectilinear shear flow. The novel instability continues\nupto a Reynolds number ($Re$) of $O(1000)$, corresponding to the recently\nidentified elasto-inertial turbulent state believed to underlie the\nmaximum-drag-reduced regime. Thus, for highly elastic ultra-dilute polymer\nsolutions, a single linearly unstable modal branch may underlie transition to\nelastic turbulence at zero $Re$, and to elasto-inertial turbulence at moderate\n$Re$, implying the existence of continuous pathways connecting the turbulent\nstates to each other, and to the laminar base state.\n"}
+{"abstract": "  Null shells are a useful geometric construction to study the propagation of\ninfinitesimally thin concentrations of massless particles or impulsive waves.\nIn this paper, we determine and study the necessary and sufficient conditions\nfor the matching of two spacetimes with respective null embedded hypersurfaces\nas boundaries. Whenever the matching is possible, it is shown to depend on a\ndiffeomorphism between the set of null generators in each boundary and a scalar\nfunction, called step function, that determines a shift of points along the\nnull generators. Generically there exists at most one possible matching but in\nsome circumstances this is not so. When the null boundaries are totally\ngeodesic, the point-to-point identification between them introduces a freedom\nwhose nature and consequences are analyzed in detail. The expression for the\nenergy-momentum tensor of a general null shell is also derived. Finally, we\nfind the most general shell (with non-zero energy, energy flux and pressure)\nthat can be generated by matching two Minkowski regions across a null\nhyperplane. This allows us to show how the original Penrose's cut-and-paste\nconstruction fits and connects with the standard matching formalism.\n"}
+{"abstract": "  Anytime a robot manipulator is controlled via visual feedback, the\ntransformation between the robot and camera frame must be known. However, in\nthe case where cameras can only capture a portion of the robot manipulator in\norder to better perceive the environment being interacted with, there is\ngreater sensitivity to errors in calibration of the base-to-camera transform. A\nsecondary source of uncertainty during robotic control are inaccuracies in\njoint angle measurements which can be caused by biases in positioning and\ncomplex transmission effects such as backlash and cable stretch. In this work,\nwe bring together these two sets of unknown parameters into a unified problem\nformulation when the kinematic chain is partially visible in the camera view.\nWe prove that these parameters are non-identifiable implying that explicit\nestimation of them is infeasible. To overcome this, we derive a smaller set of\nparameters we call Lumped Error since it lumps together the errors of\ncalibration and joint angle measurements. A particle filter method is presented\nand tested in simulation and on two real world robots to estimate the Lumped\nError and show the efficiency of this parameter reduction.\n"}
+{"abstract": "  Integrating external knowledge into commonsense reasoning tasks has shown\nprogress in resolving some, but not all, knowledge gaps in these tasks. For\nknowledge integration to yield peak performance, it is critical to select a\nknowledge graph (KG) that is well-aligned with the given task's objective. We\npresent an approach to assess how well a candidate KG can correctly identify\nand accurately fill in gaps of reasoning for a task, which we call KG-to-task\nmatch. We show this KG-to-task match in 3 phases: knowledge-task\nidentification, knowledge-task alignment, and knowledge-task integration. We\nalso analyze our transformer-based KG-to-task models via commonsense probes to\nmeasure how much knowledge is captured in these models before and after KG\nintegration. Empirically, we investigate KG matches for the SocialIQA (SIQA)\n(Sap et al., 2019b), Physical IQA (PIQA) (Bisk et al., 2020), and MCScript2.0\n(Ostermann et al., 2019) datasets with 3 diverse KGs: ATOMIC (Sap et al.,\n2019a), ConceptNet (Speer et al., 2017), and an automatically constructed\ninstructional KG based on WikiHow (Koupaee and Wang, 2018). With our methods we\nare able to demonstrate that ATOMIC, an event-inference focused KG, is the best\nmatch for SIQA and MCScript2.0, and that the taxonomic ConceptNet and\nWikiHow-based KGs are the best matches for PIQA across all 3 analysis phases.\nWe verify our methods and findings with human evaluation.\n"}
+{"abstract": "  Over the years, with the advancement of technology, Web technology has many\nimprovements. In the early days, the web was one-way communication, and only\nthe customer was able to see the content of the site and could not enter\ninformation. However, day by day, the web made significant progress, and\ntechnologies such as HTTP, ajax, WebSocket introduced that make pages dynamic\nand Give us both sides. In short, it is a new type of communications protocol,\nwhich was faster and more efficient than previous communication protocols.\nAfter the web socket's unveiling, like any other technology, Its security has\nbeen discussed, and technology's security has always been a challenge for us.\nTherefore, in this article, we examine the structure and security problems that\ncan occur in a web socket to choose an excellent alternative to HTTP and use\nit.\n"}
+{"abstract": "  The Winograd Schema (WS) has been proposed as a test for measuring\ncommonsense capabilities of models. Recently, pre-trained language model-based\napproaches have boosted performance on some WS benchmarks but the source of\nimprovement is still not clear. This paper suggests that the apparent progress\non WS may not necessarily reflect progress in commonsense reasoning. To support\nthis claim, we first show that the current evaluation method of WS is\nsub-optimal and propose a modification that uses twin sentences for evaluation.\nWe also propose two new baselines that indicate the existence of artifacts in\nWS benchmarks. We then develop a method for evaluating WS-like sentences in a\nzero-shot setting to account for the commonsense reasoning abilities acquired\nduring the pretraining and observe that popular language models perform\nrandomly in this setting when using our more strict evaluation. We conclude\nthat the observed progress is mostly due to the use of supervision in training\nWS models, which is not likely to successfully support all the required\ncommonsense reasoning skills and knowledge.\n"}
+{"abstract": "  We develop a workflow to use current quantum computing hardware for solving\nquantum many-body problems, using the example of the fermionic Hubbard model.\nConcretely, we study a four-site Hubbard ring that exhibits a transition from a\nproduct state to an intrinsically interacting ground state as hopping\namplitudes are changed. We locate this transition and solve for the ground\nstate energy with high quantitative accuracy using a variational quantum\nalgorithm executed on an IBM quantum computer. Our results are enabled by a\nvariational ansatz that takes full advantage of the maximal set of commuting\n$\\mathbb{Z}_2$ symmetries of the problem and a Lanczos-inspired error\nmitigation algorithm. They are a benchmark on the way to exploiting near term\nquantum simulators for quantum many-body problems.\n"}
+{"abstract": "  An important issue in heterogeneous wireless networks is how to optimally\nutilize various radio resources. While many methods have been proposed for\nmanaging radio resources in each network, these methods are not suitable for\nheterogeneous wireless networks. In this study, a new management method is\nproposed which provides acceptable service quality and roaming rate, reduces\nthe cost of the service, and utilizes big data technology for its operation. In\nour proposed scheme, by considering various parameters such as the type of the\nservice, the information related to the location of the user, the movement\ndirection of the user, the cost of the service, and a number of other\nstatistical measures, the most suitable technology for radio access will be\nselected. It is expected that besides improving the decision making accuracy in\nselecting the radio access technology and the balanced distribution of network\nresources, the proposed method provides lower roaming and lower probability of\nstopping roaming requests entering the network. By considering the various\nservice classes and various quality of service requirements regarding delay,\nvibration and so on, this can be useful in optimal implementation of\nheterogeneous wireless networks.\n"}
+{"abstract": "  Deep learning has received extensive research interest in developing new\nmedical image processing algorithms, and deep learning based models have been\nremarkably successful in a variety of medical imaging tasks to support disease\ndetection and diagnosis. Despite the success, the further improvement of deep\nlearning models in medical image analysis is majorly bottlenecked by the lack\nof large-sized and well-annotated datasets. In the past five years, many\nstudies have focused on addressing this challenge. In this paper, we reviewed\nand summarized these recent studies to provide a comprehensive overview of\napplying deep learning methods in various medical image analysis tasks.\nEspecially, we emphasize the latest progress and contributions of\nstate-of-the-art unsupervised and semi-supervised deep learning in medical\nimage analysis, which are summarized based on different application scenarios,\nincluding classification, segmentation, detection, and image registration. We\nalso discuss the major technical challenges and suggest the possible solutions\nin future research efforts.\n"}
+{"abstract": "  We study the inflationary consequences of Degenerate Higher Order Scalar\nTensor (DHOST) theories in a de Sitter background. We perturb the de Sitter\nbackground by operators breaking either the degeneracy condition, i.e\nscordatura DHOST, or the shift symmetry in the scalar field. We first consider\nderivative scordatura and find that in all cases the power spectra of curvature\nperturbations are scale-invariant. We then investigate small perturbations by\nan axion-like potential, and show that in this scenario the power spectrum\nbecomes scale-dependent. The modifications to the spectral index and its first\ntwo derivatives are compatible with the latest inflationary constraints.\nMoreover the tensor to scalar ratio and the non-Gaussianities of these models\ncould be within reach of future experiments.\n"}
+{"abstract": "  The way developers implement their algorithms and how these implementations\nbehave on modern CPUs are governed by the design and organization of these. The\nvectorization units (SIMD) are among the few CPUs' parts that can and must be\nexplicitly controlled. In the HPC community, the x86 CPUs and their\nvectorization instruction sets were de-facto the standard for decades. Each new\nrelease of an instruction set was usually a doubling of the vector length\ncoupled with new operations. Each generation was pushing for adapting and\nimproving previous implementations. The release of the ARM scalable vector\nextension (SVE) changed things radically for several reasons. First, we expect\nARM processors to equip many supercomputers in the next years. Second, SVE's\ninterface is different in several aspects from the x86 extensions as it\nprovides different instructions, uses a predicate to control most operations,\nand has a vector size that is only known at execution time. Therefore, using\nSVE opens new challenges on how to adapt algorithms including the ones that are\nalready well-optimized on x86. In this paper, we port a hybrid sort based on\nthe well-known Quicksort and Bitonic-sort algorithms. We use a Bitonic sort to\nprocess small partitions/arrays and a vectorized partitioning implementation to\ndivide the partitions. We explain how we use the predicates and how we manage\nthe non-static vector size. We explain how we efficiently implement the sorting\nkernels. Our approach only needs an array of O(log N) for the recursive calls\nin the partitioning phase, both in the sequential and in the parallel case. We\ntest the performance of our approach on a modern ARMv8.2 and assess the\ndifferent layers of our implementation by sorting/partitioning integers, double\nfloating-point numbers, and key/value pairs of integers. Our approach is faster\nthan the GNU C++ sort algorithm by a speedup factor of 4 on average.\n"}
+{"abstract": "  Road traffic accidents, especially vehicle pedestrian collisions in\ncrosswalk, globally pose a severe threat to human lives and have become a\nleading cause of premature deaths. In order to protect such vulnerable road\nusers from collisions, it is necessary to recognize possible conflict in\nadvance and warn to road users, not post facto. A breakthrough for proactively\npreventing pedestrian collisions is to recognize pedestrian's potential risks\nbased on vision sensors such as CCTVs. In this study, we propose a predictive\ncollision risk area estimation system at unsignalized crosswalks. The proposed\nsystem applied trajectories of vehicles and pedestrians from video footage\nafter preprocessing, and then predicted their trajectories by using deep LSTM\nnetworks. With use of predicted trajectories, this system can infer collision\nrisk areas statistically, further severity of levels is divided as danger,\nwarning, and relative safe. In order to validate the feasibility and\napplicability of the proposed system, we applied it and assess the severity of\npotential risks in two unsignalized spots in Osan city, Korea.\n"}
+{"abstract": "  Order diagrams allow human analysts to understand and analyze structural\nproperties of ordered data. While an experienced expert can create easily\nreadable order diagrams, the automatic generation of those remains a hard task.\nIn this work, we adapt force-directed approaches, which are known to generate\naesthetically-pleasing drawings of graphs, to the realm of order diagrams. Our\nalgorithm ReDraw thereby embeds the order in a high dimension and then\niteratively reduces the dimension until a two-dimensional drawing is achieved.\nTo improve aesthetics, this reduction is equipped with two force-directed steps\nwhere one optimizes on distances of nodes and the other on distances of lines\nin order to satisfy a set of a priori fixed conditions. By respecting an\ninvariant about the vertical position of the elements in each step of our\nalgorithm we ensure that the resulting drawings satisfy all necessary\nproperties of order diagrams. Finally, we present the results of a user study\nto demonstrate that our algorithm outperforms comparable approaches on drawings\nof lattices with a high degree of distributivity.\n"}
+{"abstract": "  It is required to find an optimal order of constructing the edges of a\nnetwork so as to minimize the sum of the weighted connection times of relevant\npairs of vertices. Construction can be performed anytime anywhere in the\nnetwork, with a fixed overall construction speed. The problem is strongly\nNP-hard even on stars. We present polynomial algorithms for the problem on\ntrees with a fixed number of leaves, and on general networks with a fixed\nnumber of relevant pairs.\n"}
+{"abstract": "  A multiple server setting is considered, where each server has tunable speed,\nand increasing the speed incurs an energy cost. Jobs arrive to a single queue,\nand each job has two types of sub-tasks, map and reduce, and a {\\bf precedence}\nconstraint among them: any reduce task of a job can only be processed once all\nthe map tasks of the job have been completed. In addition to the scheduling\nproblem, i.e., which task to execute on which server, with tunable speed, an\nadditional decision variable is the choice of speed for each server, so as to\nminimize a linear combination of the sum of the flow times of jobs/tasks and\nthe total energy cost. The precedence constraints present new challenges for\nthe speed scaling problem with multiple servers, namely that the number of\ntasks that can be executed at any time may be small but the total number of\noutstanding tasks might be quite large. We present simple speed scaling\nalgorithms that are shown to have competitive ratios, that depend on the power\ncost function, and/or the ratio of the size of the largest task and the\nshortest reduce task, but not on the number of jobs, or the number of servers.\n"}
+{"abstract": "  One of the key challenges when developing a predictive model is the\ncapability to describe the domain knowledge and the cause-effect relationships\nin a simple way. Decision rules are a useful and important methodology in this\ncontext, justifying their application in several areas, particularly in\nclinical practice. Several machine-learning classifiers have exploited the\nadvantageous properties of decision rules to build intelligent prediction\nmodels, namely decision trees and ensembles of trees (ETs). However, such\nmethodologies usually suffer from a trade-off between interpretability and\npredictive performance. Some procedures consider a simplification of ETs, using\nheuristic approaches to select an optimal reduced set of decision rules. In\nthis paper, we introduce a novel step to those methodologies. We create a new\ncomponent to predict if a given rule will be correct or not for a particular\npatient, which introduces personalization into the procedure. Furthermore, the\nvalidation results using three public clinical datasets suggest that it also\nallows to increase the predictive performance of the selected set of rules,\nimproving the mentioned trade-off.\n"}
+{"abstract": "  Denoising and demosaicking are essential yet correlated steps to reconstruct\na full color image from the raw color filter array (CFA) data. By learning a\ndeep convolutional neural network (CNN), significant progress has been achieved\nto perform denoising and demosaicking jointly. However, most existing CNN-based\njoint denoising and demosaicking (JDD) methods work on a single image while\nassuming additive white Gaussian noise, which limits their performance on\nreal-world applications. In this work, we study the JDD problem for real-world\nburst images, namely JDD-B. Considering the fact that the green channel has\ntwice the sampling rate and better quality than the red and blue channels in\nCFA raw data, we propose to use this green channel prior (GCP) to build a\nGCP-Net for the JDD-B task. In GCP-Net, the GCP features extracted from green\nchannels are utilized to guide the feature extraction and feature upsampling of\nthe whole image. To compensate for the shift between frames, the offset is also\nestimated from GCP features to reduce the impact of noise. Our GCP-Net can\npreserve more image structures and details than other JDD methods while\nremoving noise. Experiments on synthetic and real-world noisy images\ndemonstrate the effectiveness of GCP-Net quantitatively and qualitatively.\n"}
+{"abstract": "  Network functions virtualization (NFV) is a new concept that has received the\nattention of both researchers and network providers. NFV decouples network\nfunctions from specialized hardware devices and virtualizes these network\nfunctions as software instances called virtualized network functions (VNFs).\nNFV leads to various benefits, including more flexibility, high resource\nutilization, and easy upgrades and maintenances. Despite recent works in this\nfield, placement and chaining of VNFs need more attention. More specifically,\nsome of the existing works have considered only the placement of VNFs and\nignored the chaining part. So, they have not provided an integrated view of\nhost or bandwidth resources and propagation delay of paths. In this paper, we\nsolve the VNF placement and chaining problem as an optimization problem based\non the particle swarm optimization (PSO) algorithm. Our goal is to minimize the\nrequired number of used servers, the average propagation delay of paths, and\nthe average utilization of links while meeting network demands and constraints.\nBased on the obtained results, the algorithm proposed in this study can find\nfeasible and high-quality solutions.\n"}
+{"abstract": "  We show that the (3+1)-dimensional gauged non-linear sigma model minimally\ncoupled to a U(1) gauge field possesses analytic solutions representing gauged\nsolitons at finite Baryon density whose electromagnetic field is a Force Free\nPlasma. These gauged solitons manifest a crystalline structure and generate in\na very natural way persistent currents able to support Force Free Plasma\nelectromagnetic fields. The trajectories of charged test particles moving\nwithin these configurations can be characterized. Quite surprisingly, despite\nthe non-integrable nature of the theory, some of the perturbations of these\ngauged solitons allow to identify a proper resurgent parameter. In particular,\nthe perturbations of the solitons profile are related to the Lam\\'e operator.\nOn the other hand, the electromagnetic perturbations on the configurations\nsatisfy a two-dimensional effective Schrodinger equation, where the soliton\nbackground interacts with the electromagnetic perturbations through an\neffective two-dimensional periodic potential. We studied numerically the band\nenergy spectrum for different values of the free parameters of the theory and\nwe found that bands-gaps are modulated by the potential strength. Finally we\ncompare our crystal solutions with those of the (1+1)- dimesional Gross-Neveu\nmodel.\n"}
+{"abstract": "  Magnetic skyrmions are stable topological solitons with complex non-coplanar\nspin structures. Their nanoscopic size and the low electric currents required\nto initiate and control their motion has opened a new field of research,\nskyrmionics, that aims at using skyrmions as information carriers for data\nstorage and manipulation. Recent advances in skyrmionics prompt for a thorough\nunderstanding of the detailed three-dimensional (3D) spin texture of a skyrmion\nincluding skyrmion-skyrmion interactions and their coupling to surfaces and\ninterfaces. These properties crucially affect application-related aspects such\nas the stability and mobility of skyrmions in confined structures. To date,\nhowever, experimental techniques to measure the three-dimensional spin texture\nwith nanometer resolution are largely missing. We therefore adapt holographic\nvector field electron tomography to the problem and report on the first\nquantitative reconstruction of the 3D spin texture of skyrmions with sub-10\nnanometer resolution. The reconstructed textures reveal a variety of previously\nunseen local deviations from a homogeneous Bloch character within the skyrmion\ntubes (SkTs), details of the collapse of the skyrmion texture at surfaces, and\na correlated modulation of the SkT in FeGe along their tube axes. The\nquantitative 3D data of the magnetic induction also allow to experimentally\nconfirm some principles of skyrmion formation by deriving spatially resolved\nmaps of the magnetic energy density across these magnetic solitons.\n"}
+{"abstract": "  We report on experimental results obtained from collisions of slow highly\ncharged Ar9+ ions with a carbon monoxide dimer (CO)2 target. A COLTRIMS setup\nand a Coulomb explosion imaging approach are used to reconstruct the structure\nof the CO dimers. The three dimensional structure is deduced from the 2-body\nand 3-body dissociation channels from which both the intermolecular bond length\nand the relative orientation of the two molecules are determined. For the\n3-body channels, the experimental data are interpreted with the help of a\nclassical model in which the trajectories of the three emitted fragments are\nnumerically integrated. We measured the equilibrium intermolecular distance to\nbe Re = 4.2 A. The orientation of both CO molecules with respect to the dimer\naxis is found to be quasi-isotropic due to the large vibrational temperature of\nthe gas jet.\n"}
+{"abstract": "  A standard approach to quantum computing is based on the idea of promoting a\nclassically simulable and fault-tolerant set of operations to a universal set\nby the addition of magic quantum states. In this context, we develop a general\nframework to discuss the value of the available, non-ideal magic resources,\nrelative to those ideally required. We single out a quantity, the\nQuantum-assisted Robustness of Magic (QRoM), which measures the overhead of\nsimulating the ideal resource with the non-ideal ones through\nquasiprobability-based methods. This extends error mitigation techniques,\noriginally developed for Noisy Intermediate Scale Quantum (NISQ) devices, to\nthe case where qubits are logically encoded. The QRoM shows how the addition of\nnoisy magic resources allows to boost classical quasiprobability simulations of\na quantum circuit and allows the construction of explicit protocols,\ninterpolating between classical simulation and an ideal quantum computer.\n"}
+{"abstract": "  We have conducted a systematic survey for z $<$ 0.04 active Galactic nuclei\n(AGNs) that may have changed spectral class over the past decade. We use\nSkyMapper, Pan-STARRS and the V\\'eron-Cetty & V\\'eron (2010) catalogue to\nsearch the entire sky for these ``changing-look'' AGNs using a variety of\nselection methods, where Pan-STARRS has a coverage of 3$\\pi$ steradians (sky\nnorth of Declination $-30^\\circ$) and SkyMapper has coverage of $\\sim$\n21,000$~\\rm{deg^2}$ (sky south of Declination $0^\\circ$). We use small aperture\nphotometry to measure how colour and flux have changed over time, where a\nchange may indicate a change in spectral type. Optical colour and flux are used\nas a proxy for changing H$\\alpha$ equivalent width, while WISE 3.4 $\\mu$m flux\nis used to look for changes in the hot dust component. We have identified four\nAGNs with varying spectra selected using our optical colour selection method.\nThree AGNs were confirmed from recent observations with WiFeS on the 2.3 m\ntelescope at Siding Spring and the other was identified from archival spectra\nalone. From this, we identify two new changing look AGNs; NGC 1346 and 2MASX\nJ20075129-1108346. We also recover Mrk 915 and Mrk 609, which are known to have\nvarying spectra in the literature, but they do not meet our specific criteria\nfor changing look AGNs.\n"}
+{"abstract": "  Over the last two years, The Alan Turing Institute and the Information\nCommissioner's Office (ICO) have been working together to discover ways to\ntackle the difficult issues surrounding explainable AI. The ultimate product of\nthis joint endeavour, Explaining decisions made with AI, published in May 2020,\nis the most comprehensive practical guidance on AI explanation produced\nanywhere to date. We have put together this workbook to help support the uptake\nof that guidance. The goal of the workbook is to summarise some of main themes\nfrom Explaining decisions made with AI and then to provide the materials for a\nworkshop exercise that has been built around a use case created to help you\ngain a flavour of how to put the guidance into practice. In the first three\nsections, we run through the basics of Explaining decisions made with AI. We\nprovide a precis of the four principles of AI explainability, the typology of\nAI explanations, and the tasks involved in the explanation-aware design,\ndevelopment, and use of AI/ML systems. We then provide some reflection\nquestions, which are intended to be a launching pad for group discussion, and a\nstarting point for the case-study-based exercise that we have included as\nAppendix B. In Appendix A, we go into more detailed suggestions about how to\norganise the workshop. These recommendations are based on two workshops we had\nthe privilege of co-hosting with our colleagues from the ICO and Manchester\nMetropolitan University in January 2021. The participants of these workshops\ncame from both the private and public sectors, and we are extremely grateful to\nthem for their energy, enthusiasm, and tremendous insight. This workbook would\nsimply not exist without the commitment and keenness of all our collaborators\nand workshop participants.\n"}
+{"abstract": "  Over the last few years of the heyday of hybrid halide perovskites, so many\nmetal cations additives have been tested to improve their optoelectronic\nproperties that it is already difficult to find an element that has not yet\nbeen tried. In general, the variety of these approaches is united under the\nname \"doping\", however, there is currently no clear understanding of the\nmechanisms of the influence of the metal ion additives on the properties of the\nlead halide perovskite materials. For many ions there is even no consensus on\nthe most fundamental questions: what lattice position does a given ion occupy\nand is it incorporated in the structure at all? Here, we derived a system of\neffective radii of different metal ions in the iodine environment for the set\nof iodide compounds and reveal their crystal chemical role in the APbI3\nperovskites. We analysed the possible lattice positions for 40 most common\nmonovalent, divalent, and trivalent metals to reveal whether they could\nsuccessfully enter into the perovskite structures. We show that, at most, three\nparameters - effective size, electronegativity and the softness of metal ions\nare the main ones for crystal chemical analysis of the possibility of metal\ndoping of hybrid halide perovskites. Our results provide a useful theoretical\nguidance to rationalize and improve current doping strategies of hybrid halide\nperovskites with metal ions.\n"}
+{"abstract": "  In this paper, we examine the Hyers-Ulam and Hyers-Ulam-Rassias stability of\nsolutions of a general class of nonlinear Volterra integral equations. By using\na fixed point alternative and improving a technique commonly used in similar\nproblems, we extend and improve some well-known results on this problem. We\nalso provide some examples visualizing the improvement of the results\nmentioned.\n"}
+{"abstract": "  We show that there is an equivalence of $\\infty$-categories between Lie\nalgebroids and certain kinds of curved Lie algebras. For this we develop a\nmethod to study the $\\infty$-category of curved Lie algebras using the homotopy\ntheory of algebras over a complete operad.\n"}
+{"abstract": "  Robots and autonomous systems need to know where they are within a map to\nnavigate effectively. Thus, simultaneous localization and mapping or SLAM is a\ncommon building block of robot navigation systems. When building a map via a\nSLAM system, robots need to re-recognize places to find loop closure and reduce\nthe odometry drift. Image-based place recognition received a lot of attention\nin computer vision, and in this work, we investigate how such approaches can be\nused for 3D LiDAR data. Recent LiDAR sensors produce high-resolution 3D scans\nin combination with comparably stable intensity measurements. Through a\ncylindrical projection, we can turn this information into a panoramic image. As\na result, we can apply techniques from visual place recognition to LiDAR\nintensity data. The question of how well this approach works in practice has\nnot been answered so far. This paper provides an analysis of how such visual\ntechniques can be with LiDAR data, and we provide an evaluation on different\ndatasets. Our results suggest that this form of place recognition is possible\nand an effective means for determining loop closures.\n"}
+{"abstract": "  In this paper we explore whatever combining two chaotic dynamical systems\nusing the fuzzy logic operator XOR can maintain or not the chaotic properties\nof the resulting dynamical system. This study is motivated by techniques used\nin applications to secure communications ,images encryption and cryptography.\n"}
+{"abstract": "  Multi-person pose estimation and tracking serve as crucial steps for video\nunderstanding. Most state-of-the-art approaches rely on first estimating poses\nin each frame and only then implementing data association and refinement.\nDespite the promising results achieved, such a strategy is inevitably prone to\nmissed detections especially in heavily-cluttered scenes, since this\ntracking-by-detection paradigm is, by nature, largely dependent on visual\nevidences that are absent in the case of occlusion. In this paper, we propose a\nnovel online approach to learning the pose dynamics, which are independent of\npose detections in current fame, and hence may serve as a robust estimation\neven in challenging scenarios including occlusion. Specifically, we derive this\nprediction of dynamics through a graph neural network~(GNN) that explicitly\naccounts for both spatial-temporal and visual information. It takes as input\nthe historical pose tracklets and directly predicts the corresponding poses in\nthe following frame for each tracklet. The predicted poses will then be\naggregated with the detected poses, if any, at the same frame so as to produce\nthe final pose, potentially recovering the occluded joints missed by the\nestimator. Experiments on PoseTrack 2017 and PoseTrack 2018 datasets\ndemonstrate that the proposed method achieves results superior to the state of\nthe art on both human pose estimation and tracking tasks.\n"}
+{"abstract": "  This article proves a weak Harnack inequality with a tail term for sign\nchanging supersolutions of a mixed local and nonlocal parabolic equation. Our\nargument is purely analytic. It is based on energy estimates and the Moser\niteration technique. Instead of the parabolic John-Nirenberg lemma, we adopt a\nlemma of Bombieri to the mixed local and nonlocal parabolic case. To this end,\nwe prove an appropriate reverse H\\\"older inequality and a logarithmic estimate\nfor weak supersolutions.\n"}
+{"abstract": "  Transportation services play a crucial part in the development of modern\nsmart cities. In particular, on-demand ridesharing services, which group\ntogether passengers with similar itineraries, are already operating in several\nmetropolitan areas. These services can be of significant social and\nenvironmental benefit, by reducing travel costs, road congestion and CO2\nemissions.\n  Unfortunately, despite their advantages, not many people opt to use these\nridesharing services. We believe that increasing the user satisfaction from the\nservice will cause more people to utilize it, which, in turn, will improve the\nquality of the service, such as the waiting time, cost, travel time, and\nservice availability. One possible way for increasing user satisfaction is by\nproviding appropriate explanations comparing the alternative modes of\ntransportation, such as a private taxi ride and public transportation. For\nexample, a passenger may be more satisfied from a shared-ride if she is told\nthat a private taxi ride would have cost her 50% more. Therefore, the problem\nis to develop an agent that provides explanations that will increase the user\nsatisfaction.\n  We model our environment as a signaling game and show that a rational agent,\nwhich follows the perfect Bayesian equilibrium, must reveal all of the\ninformation regarding the possible alternatives to the passenger. In addition,\nwe develop a machine learning based agent that, when given a shared-ride along\nwith its possible alternatives, selects the explanations that are most likely\nto increase user satisfaction. Using feedback from humans we show that our\nmachine learning based agent outperforms the rational agent and an agent that\nrandomly chooses explanations, in terms of user satisfaction.\n"}
+{"abstract": "  We present a versatile automated theorem proving framework which is capable\nof automated proofs of outer bounds in network information theory, discovery of\ninner bounds in network information theory (in conjunction with the method by\nLee and Chung), simplification of capacity regions involving auxiliary random\nvariables, deduction of properties of information-theoretic quantities (e.g.\nWyner and G\\'acs-K\\\"orner common information), and discovery of\nnon-Shannon-type inequalities, under a unified framework. Our method is based\non the linear programming approach for proving Shannon-type information\ninequalities studied by Yeung and Zhang, together with a novel pruning method\nfor searching auxiliary random variables. We introduce the concept of\nexistential information inequalities, which provides an axiomatic framework for\na wide range of problems in information theory. To demonstrate the use of the\nframework, we present a new outer bound for the broadcast channel discovered by\nthe framework.\n"}
+{"abstract": "  In this study, we investigate the cyclotron resonance effect in the\nfirst-order AC current, magnetic ratchet effect, and second harmonic generation\nin phosphorene in the presence of a steady tilted magnetic field and under THz\nlaser radiation. We establish that various cyclotron resonances exist in the\ndeduced currents based on the angular frequency of the incoming light. These\nresonances are dependent on the $\\omega_{c_{+}}$ value, a function of the\ncarrier charge, the perpendicular magnetic field, and the effective masses\nalong the armchair or zigzag edges. We discuss the direction and the magnitude\nof the deduced currents for various radiation polarizations. We compare the\nresults with a zero perpendicular magnetic field. Cyclotron resonance for the\nfirst order AC current occurs at $\\omega= \\pm \\omega_{c_{+}}$. The deduced\ncurrent declines if the perpendicular magnetic field is zero. Meanwhile, for\nthe ratchet current, cyclotron resonances occur at $\\omega= \\pm\n\\omega_{c_{+}}$, $\\omega= \\pm 2\\omega_{c_{+}}$, and radiation helicity affects\nthe deduced current for circularly polarized light. Cyclotron resonance for the\nsecond harmonic generation current occurs at $\\omega= \\pm \\omega_{c_{+}}$,\n$\\omega= \\pm 2 \\omega_{c_{+}}$ and $\\omega= \\pm \\omega_{c_{+}}/2 $ and the\ncurrent is stronger compared to the case with no perpendicular magnetic field.\nAs the magnetic field rotates in the plane of anisotropic phosphorene, separate\ndirections are predicted for the second harmonic generation--related current.\nIt is noteworthy that the magnitude of the ratchet and second harmonic\ngeneration current are within the same range and comparable to the magnetic\nratchet current in monolayer graphene, $\\mu A / cm$.\n"}
+{"abstract": "  We consider the strongly connected components (SCCs) of a uniform directed\ngraph on $n$ vertices with i.i.d. in- and out-degree pairs distributed as\n$(D^-,D^+)$, with $\\mathbb E[D^+]=\\mathbb E[D^-]=\\mu$. We condition on equal\ntotal in- and out-degree. A phase transition for the emergence of a giant SCC\nis known to occur at the critical value $\\mathbb E[D^-D^+] = \\mu$. We study the\nmodel at this critical value and, additionally, require that $\\mathbb\nE[(D^-)^i(D^+)^j]<\\infty$ for all $i+j\\leq 3$, and for $(i,j)=(1,3)$ and\n$(i,j)=(3,1)$. We show that, under these conditions, the SCCs ranked by\ndecreasing number of edges with distances rescaled by $n^{-1/3}$ converge in\ndistribution to a sequence of finite strongly connected directed multigraphs\nwith edge lengths, and that these are either $3$-regular or loops. The limit\nobjects lie in a $3$-parameter family, which contains the scaling limit of the\nSCCs in the directed Erd\\H{o}s-R\\'enyi model at criticality as found by\nGoldschmidt and Stephenson (2019). This is the first universality result for\nthe scaling limit of a critical directed graph model and the first quantitative\nresult on the directed configuration model at criticality. As a trivial\nconsequence, the largest SCCs at criticality contain $\\Theta(n^{1/3})$ vertices\nand edges in probability, and the diameter of the directed graph at criticality\nis $\\Omega(n^{1/3})$ in probability. We use a metric on the space of weighted\nmultigraphs in which two multigraphs are close if there are compatible\nisomorphisms between their vertex and edge sets which roughly preserve the edge\nlengths. We use the product topology on the sequence of multigraphs. Our method\nof proof involves a depth-first exploration of the directed graph, resulting in\na spanning forest with additional identifications, of which we study the limit\nunder rescaling.\n"}
+{"abstract": "  Multidimensional scaling in networks allows for the discovery of latent\ninformation about their structure by embedding nodes in some feature space.\nIdeological scaling for users in social networks such as Twitter is an example,\nbut similar settings can include diverse applications in other networks and\neven media platforms or e-commerce. A growing literature of ideology scaling\nmethods in social networks restricts the scaling procedure to nodes that\nprovide interpretability of the feature space: on Twitter, it is common to\nconsider the sub-network of parliamentarians and their followers. This allows\nto interpret inferred latent features as indices for ideology-related concepts\ninspecting the position of members of parliament. While effective in inferring\nmeaningful features, this is generally restrained to these sub-networks,\nlimiting interesting applications such as country-wide measurement of\npolarization and its evolution. We propose two methods to propagate ideological\nfeatures beyond these sub-networks: one based on homophily (linked users have\nsimilar ideology), and the other on structural similarity (nodes with similar\nneighborhoods have similar ideologies). In our methods, we leverage the concept\nof neighborhood ideological coherence as a parameter for propagation. Using\nTwitter data, we produce an ideological scaling for 370K users, and analyze the\ntwo families of propagation methods on a population of 6.5M users. We find\nthat, when coherence is considered, the ideology of a user is better estimated\nfrom those with similar neighborhoods, than from their immediate neighbors.\n"}
+{"abstract": "  Positivity and Perron-Frobenius theory provide an elegant framework for the\nconvergence analysis of linear consensus algorithms. Here we consider a\ngeneralization of these ideas to the analysis of nonlinear consensus algorithms\non the circle and establish tools for the design of consensus protocols that\nmonotonically converge to target formations on the circle.\n"}
+{"abstract": "  One of the advantages of kinetic inductance detectors is their intrinsic\nfrequency domain multiplexing capability. However, fabrication imperfections\nusually give rise to resonance frequency deviations, which create frequency\ncollision and limit the array yield. Here we study the resonance frequency\ndeviation of a 4-inch kilo-pixel lumped-element kinetic inductance detector\n(LEKID) array using optical mapping. Using the measured resonator dimensions\nand film thickness, the fractional deviation can be explained within $\\pm\n25\\times 10^{-3}$, whereas the residual deviation is due to variation of\nelectric film properties. Using the capacitor trimming technique, the\nfractional deviation is decreased by a factor of 14. The yield of the trimming\nprocess is found to be 97%. The mapping yield, measured under a 110~K\nbackground, is improved from 69% to 76%, which can be further improved to 81%\nafter updating our readout system. With the improvement in yield, the capacitor\ntrimming technique may benefit future large-format LEKID arrays.\n"}
+{"abstract": "  For a gambler with side information, Kelly betting gives the optimal log\ngrowth rate of the gambler's fortune, which is closely related to the mutual\ninformation between the correct winner and the noisy side information. We show\nconditions under which optimal Kelly betting can be implemented using\nsingle-letter codes. We show that single-letter coding is optimal for a wide\nvariety of systems; for example, all systems with diagonal reward matrices\nadmit optimal single-letter codes. We also show that important classes of\nsystems do not admit optimal single-letter codes for Kelly betting, such as\nwhen the side information is passed through a Z channel. Our results are\nimportant to situations where the computational complexity of the gambler is\nconstrained, and may lead to new insights into the fitness value of information\nfor biological systems.\n"}
+{"abstract": "  Electro-optical control of on-chip photonic devices is an essential tool for\nefficient integrated photonics. Lithium niobate on insulator (LNOI) is an\nemerging platform for on-chip photonics due to its large electro-optic\ncoefficient and high nonlinearity [1]. Integrating quantum emitters into LNOI\nwould extend their versatile use in classic photonics to quantum computing and\ncommunication [2, 3]. Here, we incorporate single rare-earth ions (REI) quantum\nemitters in electro-optical tunable lithium niobite (LN) thin films and\ndemonstrate control of LN microcavities coupled to REI over a frequency range\nof 160 GHz with 5 \\mus switching speed. Dynamical control of the cavities\nenables the modulation of the Purcell enhancement of the REIs with short time\nconstants. Using the Purcell enhancement, we show evidence of detecting single\nYb3+ ions in LN cavities. Coupling quantum emitters in fast tunable photonic\ndevices is an efficient method to shape the waveform of the emitter [4]. It\nalso offers a platform to encode quantum information in the integration of a\nspectral-temporal-spatial domain to achieve high levels of channel\nmultiplexing, as well as an approach to generate deterministic single-photon\nsources [5, 6].\n"}
+{"abstract": "  The performances of Low Gain Avalanche diode (LGAD) sensors from a neutron\nirradiation campaign with fluences of 0.8 x 10^15, 15 x 10^15 and 2.5 x 10^15\nneq/cm2 are reported in this article. These LGAD sensors are developed by the\nInstitute of High Energy Physics, Chinese Academy of Sciences and the Novel\nDevice Laboratory for the High Granularity Timing Detector of the High\nLuminosity Large Hadron Collider. The timing resolution and collected charge of\nthe LGAD sensors were measured with electrons from a beta source. After\nirradiation with a fluence of 2.5 x 10^15 neq/cm2, the collected charge\ndecreases from 40 fC to 7 fC, the signal-to-noise ratio deteriorates from 48 to\n12, and the timing resolution increases from 29 ps to 39 ps.\n"}
+{"abstract": "  Attention mechanisms have shown promising results in sequence modeling tasks\nthat require long-term memory. Recent work investigated mechanisms to reduce\nthe computational cost of preserving and storing memories. However, not all\ncontent in the past is equally important to remember. We propose Expire-Span, a\nmethod that learns to retain the most important information and expire the\nirrelevant information. This forgetting of memories enables Transformers to\nscale to attend over tens of thousands of previous timesteps efficiently, as\nnot all states from previous timesteps are preserved. We demonstrate that\nExpire-Span can help models identify and retain critical information and show\nit can achieve strong performance on reinforcement learning tasks specifically\ndesigned to challenge this functionality. Next, we show that Expire-Span can\nscale to memories that are tens of thousands in size, setting a new state of\nthe art on incredibly long context tasks such as character-level language\nmodeling and a frame-by-frame moving objects task. Finally, we analyze the\nefficiency of Expire-Span compared to existing approaches and demonstrate that\nit trains faster and uses less memory.\n"}
+{"abstract": "  Respiratory ailments such as asthma, chronic obstructive pulmonary disease\n(COPD), pneumonia, and lung cancer are life-threatening. Respiration rate (RR)\nis a vital indicator of the wellness of a patient. Continuous monitoring of RR\ncan provide early indication and thereby save lives. However, a real-time\ncontinuous RR monitoring facility is only available at the intensive care unit\n(ICU) due to the size and cost of the equipment. Recent researches have\nproposed Photoplethysmogram (PPG) and/ Electrocardiogram (ECG) signals for RR\nestimation however, the usage of ECG is limited due to the unavailability of it\nin wearable devices. Due to the advent of wearable smartwatches with built-in\nPPG sensors, it is now being considered for continuous monitoring of RR. This\npaper describes a novel approach to RR estimation using machine learning (ML)\nmodels with the PPG signal features. Feature selection algorithms were used to\nreduce computational complexity and the chance of overfitting. The best ML\nmodel and the best feature selection algorithm combination was fine-tuned to\noptimize its performance using hyperparameter optimization. Gaussian Process\nRegression (GPR) with fitrgp feature selection algorithm outperformed all other\ncombinations and exhibits a root mean squared error (RMSE), mean absolute error\n(MAE), and two-standard deviation (2SD) of 2.57, 1.91, and 5.13 breaths per\nminute, respectively. This ML model based RR estimation can be embedded in\nwearable devices for real-time continuous monitoring of the patient.\n"}
+{"abstract": "  Single particle tracking has found broad applications in the life and\nphysical sciences, enabling the observation and characterisation of nano- and\nmicroscopic motion. Fluorescence-based approaches are ideally suited for\nhigh-background environments, such as tracking lipids or proteins in or on\ncells, due to superior background rejection. Scattering-based detection is\npreferable when localisation precision and imaging speed are paramount due to\nthe in principle infinite photon budget. Here, we show that micromirror-based\ntotal internal reflection dark field microscopy enables background suppression\npreviously only reported for interferometric scattering microscopy, resulting\nin nm localisation precision at 6 $\\mu$s exposure time for 20 nm gold\nnanoparticles with a 25 x 25 $\\mu$m$^{2}$ field of view. We demonstrate the\ncapabilities of our implementation by characterizing sub-nm deterministic flows\nof 20 nm gold nanoparticles at liquid-liquid interfaces. Our results approach\nthe optimal combination of background suppression, localisation precision and\ntemporal resolution achievable with pure scattering-based imaging and tracking\nof nanoparticles at regular interfaces.\n"}
+{"abstract": "  We study the chaotic behavior of multidimensional Hamiltonian systems in the\npresence of nonlinearity and disorder. It is known that any localized initial\nexcitation in a large enough linear disordered system spreads for a finite\namount of time and then halts forever. This phenomenon is called Anderson\nlocalization (AL). What happens to AL when nonlinearity is introduced is an\ninteresting question which has been considered in several studies over the past\ndecades. However, the characteristics and the asymptotic fate of such\nevolutions still remain an issue of intense debate due to their computational\ndifficulty, especially in systems of more than one spatial dimension. As the\nspreading of initially localized wave packets is a non-equilibrium\nthermalization process related to the ergodic and chaotic properties of the\nsystem, in our work we investigate the properties of chaos studying the\nbehavior of observables related to the system's tangent dynamics. In\nparticular, we consider the disordered discrete nonlinear Schr\\\"odinger (DDNLS)\nequation of one (1D) and two (2D) spatial dimensions. We present detailed\ncomputations of the time evolution of the system's maximum Lyapunov exponent\n(MLE--$\\Lambda$), and the related deviation vector distribution (DVD). We find\nthat although the systems' MLE decreases in time following a power law\n$t^{\\alpha_\\Lambda}$ with $\\alpha_\\Lambda <0$ for both the weak and strong\nchaos regimes, no crossover to the behavior $\\Lambda \\propto t^{-1}$ (which is\nindicative of regular motion) is observed. In addition, the analysis of the\nDVDs reveals the existence of random fluctuations of chaotic hotspots with\nincreasing amplitudes inside the excited part of the wave packet, which assist\nin homogenizing chaos and contribute to the thermalization of more lattice\nsites.\n"}
+{"abstract": "  This study investigates the origin and sustenance of self induced\noscillations of shock structures in a hypersonic flow over a double wedge\nconfiguration. Previously, various researchers have considered the double wedge\nflow configuration for inviscid flow with variations of different inflows as\nwell as geometric parameters such as inflow Mach number , wedge angles, and\nwedge lengths. Few recently published articles reveal an unsteady flow physics\ninvolved with the hypersonic viscous flow for double wedge configuration with\nlarge second wedge angles. However, the reason for such self sustained flow\noscillations is not completely clear. The present work seeks out to investigate\nthe origin of such oscillations in a low enthalpy hypersonic flow with\ndifferent aft wedge angles and wedge length ratios.In the current study,\nviscous flow over a double wedge at Mach 7 and fore wedge angle of 30{\\deg} is\nconsidered. An improved version of rhoCentralFoam solver in OpenFOAM is used to\ninvestigate the double wedge flow over different grid resolutions in the\nseparation region and shock shock interaction region. This study corroborates\nthe observation from the previous literature with an improvement in the range\nof parameters which results in a self sustained periodic oscillation. The\npresent study also suggests that the unsteadiness becomes possible when the\nincidence shock is in the proximity of the aft wedge expansion corner as a\nconsequence of different wedge length ratios(L1/L2) or aft wedge angles\n({\\theta}2). Flow can still be steady at a large aft wedge angle if the\nincidence shock is far ahead of the aft wedge expansion corner.\n"}
+{"abstract": "  Is a logicist bound to the claim that as a matter of analytic truth there is\nan actual infinity of objects? If Hume's Principle is analytic then in the\nstandard setting the answer appears to be yes. Hodes's work pointed to a way\nout by offering a modal picture in which only a potential infinity was posited.\nHowever, this project was abandoned due to apparent failures of cross-world\npredication. We re-explore this idea and discover that in the setting of the\npotential infinite one can interpret first-order Peano arithmetic, but not\nsecond-order Peano arithmetic. We conclude that in order for the logicist to\nweaken the metaphysically loaded claim of necessary actual infinities, they\nmust also weaken the mathematics they recover.\n"}
+{"abstract": "  The general method of graph coarsening or graph reduction has been a\nremarkably useful and ubiquitous tool in scientific computing and it is now\njust starting to have a similar impact in machine learning. The goal of this\npaper is to take a broad look into coarsening techniques that have been\nsuccessfully deployed in scientific computing and see how similar principles\nare finding their way in more recent applications related to machine learning.\nIn scientific computing, coarsening plays a central role in algebraic multigrid\nmethods as well as the related class of multilevel incomplete LU\nfactorizations. In machine learning, graph coarsening goes under various names,\ne.g., graph downsampling or graph reduction. Its goal in most cases is to\nreplace some original graph by one which has fewer nodes, but whose structure\nand characteristics are similar to those of the original graph. As will be\nseen, a common strategy in these methods is to rely on spectral properties to\ndefine the coarse graph.\n"}
+{"abstract": "  Standard meta-learning for representation learning aims to find a common\nrepresentation to be shared across multiple tasks. The effectiveness of these\nmethods is often limited when the nuances of the tasks' distribution cannot be\ncaptured by a single representation. In this work we overcome this issue by\ninferring a conditioning function, mapping the tasks' side information (such as\nthe tasks' training dataset itself) into a representation tailored to the task\nat hand. We study environments in which our conditional strategy outperforms\nstandard meta-learning, such as those in which tasks can be organized in\nseparate clusters according to the representation they share. We then propose a\nmeta-algorithm capable of leveraging this advantage in practice. In the\nunconditional setting, our method yields a new estimator enjoying faster\nlearning rates and requiring less hyper-parameters to tune than current\nstate-of-the-art methods. Our results are supported by preliminary experiments.\n"}
+{"abstract": "  In their recent paper titled \"Large Associative Memory Problem in\nNeurobiology and Machine Learning\" [arXiv:2008.06996] the authors gave a\nbiologically plausible microscopic theory from which one can recover many dense\nassociative memory models discussed in the literature. We show that the layers\nof the recent \"MLP-mixer\" [arXiv:2105.01601] as well as the essentially\nequivalent model in [arXiv:2105.02723] are amongst them.\n"}
+{"abstract": "  This paper presents a generic feature-based navigation framework for\nautonomous vehicles using a soft constrained Particle Filter. Selected map\nfeatures, such as road and landmark locations, and vehicle states are used for\ndesigning soft constraints. After obtaining features of mapped landmarks in\ninstance-based segmented images acquired from a monocular camera,\nvehicle-to-landmark distances are predicted using Gaussian Process Regression\n(GPR) models in a mixture of experts approach. Both mean and variance outputs\nof GPR models are used for implementing adaptive constraints. Experimental\nresults confirm that the use of image segmentation features improves the\nvehicle-to-landmark distance prediction notably, and that the proposed soft\nconstrained approach reliably localizes the vehicle even with reduced number of\nlandmarks and noisy observations.\n"}
+{"abstract": "  We present a comprehensive study of a model system of repulsive\nself-propelled disks in two dimensions with ferromagnetic and nematic velocity\nalignment interactions. We characterize the phase behavior of the system as a\nfunction of the alignment and self-propulsion strength, featuring orientational\norder for strong alignment and Motility-Induced Phase Separation (MIPS) at\nmoderate alignment but high enough self-propulsion. We derive a microscopic\ntheory for these systems yielding a close set of hydrodynamic equations from\nwhich we perform a linear stability analysis of the homogenous disordered\nstate. This analysis predicts MIPS in the presence of aligning torques. The\nnature of the continuum theory allows for an explicit quantitative comparison\nwith particle-based simulations, which consistently shows that ferromagnetic\nalignment fosters phase separation, while nematic alignment does not alter\neither the nature or the location of the instability responsible for it. In the\nferromagnetic case, such behavior is due to an increase of the imbalance of the\nnumber of particle collisions along different orientations, giving rise to the\nself-trapping of particles along their self-propulsion direction. On the\ncontrary, the anisotropy of the pair correlation function, which encodes this\nself-trapping effect, is not significantly affected by nematic torques. Our\nwork shows the predictive power of such microscopic theories to describe\ncomplex active matter systems with different interaction symmetries and sheds\nlight on the impact of velocity-alignment interactions in Motility-Induced\nPhase Separation.\n"}
+{"abstract": "  Recently a new kind of quintessential inflation coming from the Lorentzian\ndistribution has been introduced in [1,2]. The model leads to a very simple\npotential, which basically depends on two parameters, belonging to the class of\n$\\alpha$-attractors and depicting correctly the early and late time\naccelerations of our universe. The potential emphasizes a {\\it cosmological\nseesaw mechanism} (CSSM) that produces a large inflationary vacuum energy in\none side of the potential and a very small value of dark energy on the right\nhand side of the potential. {Here we show that the model agrees with the recent\nobservations and with the reheating constraints. Therefore the model gives a\nreasonable scenario beyond the standard $\\Lambda$CDM that includes the\ninflationary epoch.}\n"}
+{"abstract": "  Exclusive electro-weak processes have peculiar features which make them\ncomplementary to usually discussed deeply virtual electroproduction processes\nsuch as deep virtual Compton scattering or meson production (and the\ncorresponding crossed reactions). They allow in particular single charmed meson\nproduction, which we study in two different contexts : electroproduction at an\nelectron ion collider and neutrino-production at a medium energy neutrino\nfacility. We rely on the QCD collinear factorization framework where\ngeneralized parton distributions allow physicists to perform a nucleon\ntomography.\n"}
+{"abstract": "  The rules governing the essentially algebraic notion of a category with\nfamilies have been observed (independently) by Steve Awodey and Marcelo Fiore\nto precisely match those of a representable natural transformation between\npresheaves. This provides us with a natural, functorial description of\nessentially algebraic objects which are used to model dependent type theory;\nfollowing Steve Awodey, we call them 'natural models'.\n  We can view natural models from several different viewpoints, of which we\nfocus on three in this thesis. First, natural models are essentially algebraic,\nmeaning that they can be described by specifying operations between sorts,\nsubject to equational axioms; this allows us to assemble natural models into a\ncategory with certain beneficial properties. Second, since natural models are\nnatural transformations between presheaves, they are morphisms in a locally\ncartesian closed category, meaning that they can be regarded as polynomials.\nThird, since natural models admit interpretations of dependent type theory, we\ncan use them to provide a functorial semantics.\n"}
+{"abstract": "  System behavior is often expressed by causal relations in requirements (e.g.,\nIf event 1, then event 2). Automatically extracting this embedded causal\nknowledge supports not only reasoning about requirements dependencies, but also\nvarious automated engineering tasks such as seamless derivation of test cases.\nHowever, causality extraction from natural language is still an open research\nchallenge as existing approaches fail to extract causality with reasonable\nperformance. We understand causality extraction from requirements as a two-step\nproblem: First, we need to detect if requirements have causal properties or\nnot. Second, we need to understand and extract their causal relations. At\npresent, though, we lack knowledge about the form and complexity of causality\nin requirements, which is necessary to develop a suitable approach addressing\nthese two problems. We conduct an exploratory case study with 14,983 sentences\nfrom 53 requirements documents originating from 18 different domains and shed\nlight on the form and complexity of causality in requirements. Based on our\nfindings, we develop a tool-supported approach for causality detection (CiRA).\nThis constitutes a first step towards causality extraction from NL\nrequirements. We report on a case study and the resulting tool-supported\napproach for causality detection in requirements. Our case study corroborates,\namong other things, that causality is, in fact, a widely used linguistic\npattern to describe system behavior, as about a third of the analyzed sentences\nare causal. We further demonstrate that our tool CiRA achieves a macro-F1 score\nof 82 % on real word data and that it outperforms related approaches with an\naverage gain of 11.06 % in macro-Recall and 11.43 % in macro-Precision.\nFinally, we disclose our open data sets as well as our tool to foster the\ndiscourse on the automatic detection of causality in the RE community.\n"}
+{"abstract": "  The catch-up effect and the Matthew effect offer opposing characterizations\nof globalization: the former predicts an eventual convergence as the poor can\ngrow faster than the rich due to free exchanges of complementary resources,\nwhile the latter, a deepening inequality between the rich and the poor. To\nunderstand these effects on the globalization of research, we conduct an\nin-depth study based on scholarly and patent publications covering STEM\nresearch from 218 countries/regions over the past four decades, covering more\nthan 55 million scholarly articles and 1.7 billion citations. Unique to this\ninvestigation is the simultaneous examination of both the research output and\nits impact in the same data set, using a novel machine learning based measure,\ncalled saliency, to mitigate the intrinsic biases in quantifying the research\nimpact. The results show that the two effects are in fact co-occurring: there\nare clear indications of convergence among the high income and upper middle\nincome countries across the STEM fields, but a widening gap is developing that\nsegregates the lower middle and low income regions from the higher income\nregions. Furthermore, the rate of convergence varies notably among the STEM\nsub-fields, with the highly strategic area of Artificial Intelligence (AI)\nsandwiched between fields such as Medicine and Materials Science that occupy\nthe opposite ends of the spectrum. The data support the argument that a leading\nexplanation of the Matthew effect, namely, the preferential attachment theory,\ncan actually foster the catch-up effect when organizations from lower income\ncountries forge substantial research collaborations with those already\ndominant. The data resoundingly show such collaborations benefit all parties\ninvolved, and a case of role reversal can be seen in the Materials Science\nfield where the most advanced signs of convergence are observed.\n"}
+{"abstract": "  Employing the ultrafast control of electronic states of a semiconductor\nquantum dot in a cavity, we introduce a novel approach to achieve on-demand\nemission of single photons with almost perfect indistinguishability and photon\npairs with near ideal entanglement. Our scheme is based on optical excitation\noff-resonant to a cavity mode followed by ultrafast control of the electronic\nstates using the time-dependent quantum-confined Stark effect, which then\nallows for cavity-resonant emission. Our theoretical analysis takes into\naccount cavity-loss mechanisms, the Stark effect, and phonon-induced dephasing\nallowing realistic predictions for finite temperatures.\n"}
+{"abstract": "  We show here how, using Euler's integration method and an associated function\nbounding the error in function of time, one can generate structures closely\nsurrounding the invariant tori of dynamical systems. Such structures are\nconstructed from a finite number of balls of $\\mathbb{R}^n$ and encompass the\ndeformations of the tori when small perturbations of the flow of the system\noccur.\n"}
+{"abstract": "  We simulate neutrino-antineutrino oscillations caused by strong magnetic\nfields in dense matter. With the strong magnetic fields and large neutrino\nmagnetic moments, Majorana neutrinos can reach flavor equilibrium. We find that\nthe flavor equilibration of neutrino-antineutrino oscillations is sensitive to\nthe values of the baryon density and the electron fraction inside the matter.\nThe neutrino-antineutrino oscillations are suppressed in the case of the large\nbaryon density in neutron (proton)-rich matter. On the other hand, the flavor\nequilibration occurs when the electron fraction is close to $0.5$ even in the\nlarge baryon density. From the simulations, we propose a necessary condition\nfor the equilibration of neutrino-antineutrino oscillations in dense matter. We\nalso study whether such necessary condition is satisfied near the proto-neutron\nstar by using results of neutrino hydrodynamic simulations of core-collapse\nsupernovae. In our explosion model, the flavor equilibration would be possible\nif the magnetic field on the surface of the proto-neutron star is larger than\n$10^{14}$ G which is the typical value of the magnetic fields of magnetars.\n"}
+{"abstract": "  The spiking neural network (SNN) has been attracting considerable attention\nnot only as a mathematical model for the brain, but also as an energy-efficient\ninformation processing model for real-world applications. In particular, SNNs\nbased on temporal coding are expected to be much more efficient than those\nbased on rate coding, because the former requires substantially fewer spikes to\ncarry out tasks. As SNNs are continuous-state and continuous-time models, it is\nfavorable to implement them with analog VLSI circuits. However, the\nconstruction of the entire system with continuous-time analog circuits would be\ninfeasible when the system size is very large. Therefore, mixed-signal circuits\nmust be employed, and the time discretization and quantization of the synaptic\nweights are necessary. Moreover, the analog VLSI implementation of SNNs\nexhibits non-idealities, such as the effects of noise and device mismatches, as\nwell as other constraints arising from the analog circuit operation. In this\nstudy, we investigated the effects of the time discretization and/or weight\nquantization on the performance of SNNs. Furthermore, we elucidated the effects\nthe lower bound of the membrane potentials and the temporal fluctuation of the\nfiring threshold. Finally, we propose an optimal approach for the mapping of\nmathematical SNN models to analog circuits with discretized time.\n"}
+{"abstract": "  The small GTPases Rac and Rho are known to regulate eukaryotic cell shape,\npromoting front protrusion (Rac) or rear retraction (Rho) of the cell edge.\nSuch cell deformation changes the contact and adhesion of cell to the\nextracellular matrix (ECM), while ECM signaling through integrin receptors also\naffects GTPase activity. We develop and investigate a model for this three-way\nfeedback loop in 1D and 2D spatial domains, as well as in a fully deforming 2D\ncell shape. The model consists of reaction-diffusion equations solved\nnumerically with open-source software, Morpheus, and with custom-built cellular\nPotts model simulations. We find a variety of patterns and cell behaviors,\nincluding persistent polarity, flipped front-back cell polarity oscillations,\nand random protrusion-retraction. We show that the observed spatial patterns\ndepend on the cell shape, and vice versa. The cell stiffness and biophysical\nproperties also affect patterning, overall cell migration phenotypes.\n"}
+{"abstract": "  We study the propagation of a massless scalar wave in de Sitter spacetime\nperturbed by an arbitrary central mass. By focusing on the late time limit,\nthis probes the portion of the scalar signal traveling inside the null cone.\nUnlike in asymptotically flat spacetimes, the amplitude of the scalar field\ndetected by an observer at timelike infinity does not decay back to zero but\ndevelops a spacetime constant shift - both at zeroth and first order in the\ncentral mass M. This indicates that massless scalar field propagation in\nasymptotically de Sitter spacetimes exhibits both linear and nonlinear\ntail-induced memories. On the other hand, for sufficiently late retarded times,\nthe monopole portion of the scalar signal measured at null infinity is found to\nbe amplified relative to its timelike infinity counterpart, by its nonlinear\ninteractions with the gravitation field at first order in M.\n"}
+{"abstract": "  The helicity of a magnetic field is a fundamental property that is conserved\nin ideal MHD. It can be explored in the stellar context by mapping large-scale\nmagnetic fields across stellar surfaces using Zeeman-Doppler imaging. A recent\nstudy of 51 stars in the mass range 0.1-1.34 M$_\\odot$ showed that the\nphotospheric magnetic helicity density follows a single power law when plotted\nagainst the toroidal field energy, but splits into two branches when plotted\nagainst the poloidal field energy. These two branches divide stars above and\nbelow $\\sim$ 0.5 M$_\\odot$. We present here a novel method of visualising the\nhelicity density in terms of the linkage of the toroidal and poloidal fields\nthat are mapped across the stellar surface. This approach allows us to classify\nthe field linkages that provide the helicity density for stars of different\nmasses and rotation rates. We find that stars on the lower-mass branch tend to\nhave toroidal fields that are non-axisymmetric and so link through regions of\npositive and negative poloidal field. A lower-mass star may have the same\nhelicity density as a higher-mass star, despite having a stronger poloidal\nfield. Lower-mass stars are therefore less efficient at generating large-scale\nhelicity.\n"}
+{"abstract": "  Fuzzy Dark Matter (FDM), consisting of ultralight bosons ($m_{\\rm b} \\sim\n10^{-22}\\ \\rm eV$), is an intriguing alternative to Cold Dark Matter. Numerical\nsimulations that solve the Schr\\\"odinger-Poisson (SP) equation show that FDM\nhalos consist of a central solitonic core, which is the ground state of the SP\nequation, surrounded by an envelope of interfering excited states. These\nexcited states also interfere with the soliton, causing it to oscillate and\nexecute a confined random walk with respect to the halo center of mass. Using\nhigh-resolution numerical simulations of a $6.6 \\times 10^9 M_{\\odot}$ FDM halo\nwith $m_{\\rm b} = 8 \\times 10^{-23}\\ \\rm eV$ in isolation, we demonstrate that\nthe wobbling, oscillating soliton gravitationally perturbs nuclear objects,\nsuch as supermassive black holes or dense star clusters, causing them to\ndiffuse outwards. In particular, we show that, on average, objects with mass\n$\\lesssim 0.3 \\%$ of the soliton mass ($M_{\\rm sol}$) are expelled from the\nsoliton in $\\sim 3\\ \\rm Gyr$, after which they continue their outward diffusion\ndue to gravitational interactions with the soliton and the halo granules. More\nmassive objects ($\\gtrsim 1 \\% M_{\\rm sol}$), while executing a random walk,\nremain largely confined to the soliton due to dynamical friction. We also\npresent an effective treatment of the diffusion, based on kinetic theory, that\naccurately reproduces the outward motion of low mass objects and briefly\ndiscuss how the observed displacements of star clusters and active galactic\nnuclei from the centers of their host galaxies can be used to constrain FDM.\n"}
+{"abstract": "  Speaker recognition deals with recognizing speakers by their speech. Most\nspeaker recognition systems are built upon two stages, the first stage extracts\nlow dimensional correlation embeddings from speech, and the second performs the\nclassification task. The robustness of a speaker recognition system mainly\ndepends on the extraction process of speech embeddings, which are primarily\npre-trained on a large-scale dataset. As the embedding systems are pre-trained,\nthe performance of speaker recognition models greatly depends on domain\nadaptation policy, which may reduce if trained using inadequate data. This\npaper introduces a speaker recognition strategy dealing with unlabeled data,\nwhich generates clusterable embedding vectors from small fixed-size speech\nframes. The unsupervised training strategy involves an assumption that a small\nspeech segment should include a single speaker. Depending on such a belief, a\npairwise constraint is constructed with noise augmentation policies, used to\ntrain AutoEmbedder architecture that generates speaker embeddings. Without\nrelying on domain adaption policy, the process unsupervisely produces\nclusterable speaker embeddings, termed unsupervised vectors (u-vectors). The\nevaluation is concluded in two popular speaker recognition datasets for English\nlanguage, TIMIT, and LibriSpeech. Also, a Bengali dataset is included to\nillustrate the diversity of the domain shifts for speaker recognition systems.\nFinally, we conclude that the proposed approach achieves satisfactory\nperformance using pairwise architectures.\n"}
+{"abstract": "  We show that lenders face more uncertainty when assessing default risk of\nhistorically under-served groups in US credit markets and that this information\ndisparity is a quantitatively important driver of inefficient and unequal\ncredit market outcomes. We first document that widely used credit scores are\nstatistically noisier indicators of default risk for historically under-served\ngroups. This noise emerges primarily through the explanatory power of the\nunderlying credit report data (e.g., thin credit files), not through issues\nwith model fit (e.g., the inability to include protected class in the scoring\nmodel). Estimating a structural model of lending with heterogeneity in\ninformation, we quantify the gains from addressing these information\ndisparities for the US mortgage market. We find that equalizing the precision\nof credit scores can reduce disparities in approval rates and in credit\nmisallocation for disadvantaged groups by approximately half.\n"}
+{"abstract": "  Let $K = R$ or $C$. We study basic invariants of submanifolds of solutions\n$\\mathcal{M} = \\{ y = Q(x,a,b)\\} = \\{b = P(a,x,y)\\}$ in coordinates $x \\in\nK^{n\\geqslant 1}$, $y \\in K$, $a \\in K^{m\\geqslant 1}$, $b \\in K$ under\nsplit-diffeomorphisms $(x,y,a,b) \\,\\longmapsto\\, \\big(\nf(x,y),\\,g(x,y),\\,\\varphi(a,b),\\,\\psi(a,b) \\big)$. Two Levi forms exist, and\nhave the same rank $r \\leqslant \\min (n,m)$. If $\\mathcal{M}$ is\n$k$-nondegenerate with respect to parameters and $l$-nondegenerate with respect\nto variables, $\\mbox{Aut}(\\mathcal{M})$ is a local Lie group of dimension: \\[\n\\dim\\, \\mbox{Aut} (\\mathcal{M}) \\,\\,\\leqslant\\,\\,\n{\\textstyle{\\binom{n+1+2k+2l}{2k+2l}}}\\,\\, \\min\\, \\big\\{ (n+1),\\, (m+1) \\big\\}.\n\\] Mainly, our goal is to set up foundational material addressed to CR\ngeometers. We focus on $n = m = 2$, assuming $r = 1$. In coordinates $(x,y,z,\na,b,c)$, a local equation is: \\[ z \\,=\\, c + xa + \\beta\\,xxb +\n\\underline{\\beta}\\,yaa + c\\,{\\rm O}_{x,y,a,b}(2) + {\\rm O}_{x,y,a,b,c}(4), \\]\nwith $\\beta$ and $\\underline{\\beta}$ representing the two $2$-nondegeneracy\ninvariants at $0$. The associated para-CR PDE system: \\[ z_y \\,=\\,\n\\big(x,y,z,z_x,z_{xx}\\big) \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\& \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\\n\\ z_{xxx} \\,=\\, H\\big(x,y,z,z_x,z_{xx}\\big), \\] satisfies $F_{z_{xx}} \\equiv 0$\nfrom Levi degeneracy. We show in details that the hypothesis of\n$2$-nondegeneracy with respect to variables is equivalent to $F_{z_x z_x} \\neq\n0$. This gives CR-geometric meaning to the first two para-CR relative\ndifferential invariants encountered independently in arXiv:2003.08166 .\n"}
+{"abstract": "  We develop a statistical model for the testing of disease prevalence in a\npopulation. The model assumes a binary test result, positive or negative, but\nallows for biases in sample selection and both type I (false positive) and type\nII (false negative) testing errors. Our model also incorporates multiple test\ntypes and is able to distinguish between retesting and exclusion after testing.\nOur quantitative framework allows us to directly interpret testing results as a\nfunction of errors and biases. By applying our testing model to COVID-19\ntesting data and actual case data from specific jurisdictions, we are able to\nestimate and provide uncertainty quantification of indices that are crucial in\na pandemic, such as disease prevalence and fatality ratios.\n"}
+{"abstract": "  In this paper, the problem on formation and construction of a\nmultidimensional shock wave is studied for the first order conservation law\n$\\partial_t u+\\partial_x F(u)+\\partial_y G(u)=0$ with smooth initial data\n$u_0(x,y)$. It is well-known that the smooth solution $u$ will blow up on the\ntime $T^*=-\\frac{1}{\\min{H(\\xi,\\eta)}}$ when $\\min{H(\\xi,\\eta})<0$ holds for\n$H(\\xi,\\eta)=\\partial_{\\xi}(F'(u_0(\\xi,\\eta)))+\\partial_{\\eta}(G'(u_0(\\xi,\\eta)))$,\nmore precisely, only the first order derivatives $\\nabla_{t,x,y}u$ blow up on\n$t=T^*$ meanwhile $u$ itself is still continuous until $t=T^*$. Under the\ngeneric nondegenerate condition of $H(\\xi,\\eta)$, we construct a local weak\nentropy solution $u$ for $t\\ge T^*$ which is not uniformly Lipschitz continuous\non two sides of a shock surface $\\Sigma$. The strength of the constructed shock\nis zero on the initial blowup curve $\\Gamma$ and then gradually increases for\n$t>T^*$. Additionally, in the neighbourhood of $\\Gamma$, some detailed and\nprecise descriptions on the singularities of solution $u$ are given.\n"}
+{"abstract": "  We study the task of clustering in directed networks. We show that using the\neigenvalue/eigenvector decomposition of the adjacency matrix is simpler than\nall common methods which are based on a combination of data regularization and\nSVD truncation, and works well down to the very sparse regime where the edge\ndensity has constant order. Our analysis is based on a Master Theorem\ndescribing sharp asymptotics for isolated eigenvalues/eigenvectors of sparse,\nnon-symmetric matrices with independent entries. We also describe the limiting\ndistribution of the entries of these eigenvectors; in the task of digraph\nclustering with spectral embeddings, we provide numerical evidence for the\nsuperiority of Gaussian Mixture clustering over the widely used k-means\nalgorithm.\n"}
+{"abstract": "  We present ground-based optical transmission spectroscopy of the low-density\nhot Jupiter WASP-88b covering the wavelength range 4413-8333 {\\AA} with the\nFORS2 spectrograph on the Very Large Telescope. The FORS2 white light curves\nexhibit a significant time-correlated noise which we model using a Gaussian\nProcess and remove as a wavelength-independent component from the spectroscopic\nlight curves. We analyse complementary photometric observations from the\nTransiting Exoplanet Survey Satellite and refine the system properties and\nephemeris. We find a featureless transmission spectrum with increased\nabsorption towards shorter wavelengths. We perform an atmospheric retrieval\nanalysis with the AURA code, finding tentative evidence for haze in the upper\natmospheric layers and a lower likelihood for a dense cloud deck. Whilst our\nretrieval analysis results point toward clouds and hazes, further evidence is\nneeded to definitively reject a clear-sky scenario.\n"}
+{"abstract": "  Behavior of quantum liquids is a fascinating topic in physics. Even in a\nstrongly correlated case, the linear response of a given system to an external\nfield is described by the fluctuation-dissipation relations based on the\ntwo-body correlations in the equilibrium. However, to explore nonlinear\nnon-equilibrium behaviors of the system beyond this well-established regime,\nthe role of higher order correlations starting from the three-body correlations\nmust be revealed. In this work, we experimentally investigate a controllable\nquantum liquid realized in a Kondo-correlated quantum dot and prove the\nrelevance of the three-body correlations in the nonlinear conductance at finite\nmagnetic field, which validates the recent Fermi liquid theory extended to the\nnon-equilibrium regime.\n"}
+{"abstract": "  Healthy cartilage is a remarkable tissue, able to withstand tens of millions\nof loading cycles with minimal damage. While much is known about the structural\norigins of its compressive mechanics, how composition determines its mechanical\nbehavior in shear, a major mode of failure, is poorly understood. Through a\ncombination of microscale structure-function experiments and modeling, we\ndevelop a rigidity percolation framework to explain the structural origins of\ncartilage shear mechanics. This framework provides a new quantitative\nunderstanding for how the known degradative events in osteoarthritis determine\nthe mechanical changes that are a hallmark of this disease. As such, this work\nprovides a road map for understanding disease progression and in combination\nwith non-invasive techniques such as MRI will enable more effective diagnosis\nand treatment.\n"}
+{"abstract": "  We consider an age-size structured cell population model based on the cell\ncycle length. The model is described by a first order partial differential\nequation with initial-boundary conditions. Using the theory of semigroups of\npositive operators we establish new criteria for an asynchronous exponential\ngrowth of solutions to such equations. We discuss the question of exponential\nsize growth of cells. We study in detail a constant size growth model and a\nmodel with target size division. We also present versions of the model when the\npopulation is heterogeneous.\n"}
+{"abstract": "  Violation of the Wiedemann Franz law through decoupling the charge and heat\ntransports in a material not only leads to new fundamental physics, but also is\na boon for many of the advanced materials including thermoelectric materials\nfor achieving their novel or superior properties. Here, we demonstrate this in\nnickel nanoparticles with incredible enhancement in Lorentz number to several\norders of magnitude as size drops. This is in stark contrast to Fermi liquid\nbehavior of conventional metals, revealing the compelling confirmation for\nunconventional quasiparticle dynamics where charge and heat transport\nindependent of each other.\n"}
+{"abstract": "  In this preliminary (and unpolished) version of the paper, we study an\nasynchronous online learning setting with a network of agents. At each time\nstep, some of the agents are activated, requested to make a prediction, and pay\nthe corresponding loss. Some feedback is then revealed to these agents and is\nlater propagated through the network. We consider the case of full, bandit, and\nsemi-bandit feedback. In particular, we construct a reduction to delayed\nsingle-agent learning that applies to both the full and the bandit feedback\ncase and allows to obtain regret guarantees for both settings. We complement\nthese results with a near-matching lower bound.\n"}
+{"abstract": "  We have recently proposed two pile loading controllers that learn from human\ndemonstrations: a neural network (NNet) [1] and a random forest (RF) controller\n[2]. In the field experiments the RF controller obtained clearly better success\nrates. In this work, the previous findings are drastically revised by\nexperimenting summer time trained controllers in winter conditions. The winter\nexperiments revealed a need for additional sensors, more training data, and a\ncontroller that can take advantage of these. Therefore, we propose a revised\nneural controller (NNetV2) which has a more expressive structure and uses a\nneural attention mechanism to focus on important parts of the sensor and\ncontrol signals. Using the same data and sensors to train and test the three\ncontrollers, NNetV2 achieves better robustness against drastically changing\nconditions and superior success rate. To the best of our knowledge, this is the\nfirst work testing a learning-based controller for a heavy-duty machine in\ndrastically varying outdoor conditions and delivering high success rate in\nwinter, being trained in summer.\n"}
+{"abstract": "  The high cost of density functional theory has hitherto limited the ab initio\nprediction of equation of state (EOS). In this article, we employ a combination\nof large scale computing, advanced simulation techniques, and smart data\nscience strategies to provide an unprecedented, ab initio performance analysis\nof the high explosive pentaerythritol tetranitrate (PETN). Comparison to both\nexperiment and thermochemical predictions reveals important quantitative\nlimitations of DFT for EOS prediction, and thus the assessment of high\nexplosives. In particular, we find DFT predicts the energy of PETN detonation\nproducts to be systematically too high relative to the unreacted neat\ncrystalline material, resulting in an underprediction of the detonation\nvelocity, pressure, and temperature at the Chapman-Jouguet (CJ) state. The\nenergetic bias can be partially accounted for by high-level electronic\nstructure calculations of the product molecules. We also demonstrate a modeling\nstrategy for mapping chemical composition across a wide parameter space with\nlimited numerical data, the results of which suggest additional molecular\nspecies to consider in thermochemical modeling.\n"}
+{"abstract": "  In many industrial applications, rubber-based materials are routinely used in\nconjunction with various penetrants or diluents in gaseous or liquid form. It\nis of interest to estimate theoretically the penetration depth as well as the\namount of diffusants stored inside the material. In this framework, we prove\nthe global solvability and explore the large time-behavior of solutions to a\none-phase free boundary problem with nonlinear kinetic condition that is able\nto describe the migration of diffusants into rubber. The key idea in the proof\nof the large time behavior is to benefit of a contradiction argument, since it\nis difficult to obtain uniform estimates for the growth rate of the free\nboundary due to the use of a Robin boundary condition posed at the fixed\nboundary.\n"}
+{"abstract": "  Student evaluations of teaching (SET) are commonly used in universities for\nassessing teaching quality. However, previous literature shows that in software\nengineering students tend to rate certain topics higher than others: In\nparticular students tend to value programming and software construction over\nsoftware design, software engineering models and methods, or soft skills. We\nhypothesize that these biases also play a role in SET responses collected from\nstudents. The objective of this study is to investigate how the topic of a\nsoftware engineering course affects the SET metrics. We accomplish this by\nperforming multilevel regression analysis on SET data collected in a software\nengineering programme. We analyzed a total of 1295 student evaluations from 46\nuniversity courses in a Finnish university. The results of the analysis\nverifies that the student course evaluations exhibit similar biases as\ndistinguished by previous software engineering education research. The type of\nthe course can predict a higher SET rating. In our dataset, software\nconstruction and programming courses received higher SET ratings compared to\ncourses on software engineering processes, models, and methods.\n"}
+{"abstract": "  We consider the problem of learning structures and parameters of\nContinuous-time Bayesian Networks (CTBNs) from time-course data under minimal\nexperimental resources. In practice, the cost of generating experimental data\nposes a bottleneck, especially in the natural and social sciences. A popular\napproach to overcome this is Bayesian optimal experimental design (BOED).\nHowever, BOED becomes infeasible in high-dimensional settings, as it involves\nintegration over all possible experimental outcomes. We propose a novel\ncriterion for experimental design based on a variational approximation of the\nexpected information gain. We show that for CTBNs, a semi-analytical expression\nfor this criterion can be calculated for structure and parameter learning. By\ndoing so, we can replace sampling over experimental outcomes by solving the\nCTBNs master-equation, for which scalable approximations exist. This alleviates\nthe computational burden of sampling possible experimental outcomes in\nhigh-dimensions. We employ this framework in order to recommend interventional\nsequences. In this context, we extend the CTBN model to conditional CTBNs in\norder to incorporate interventions. We demonstrate the performance of our\ncriterion on synthetic and real-world data.\n"}
+{"abstract": "  Over the complex numbers, there are 92 plane conics meeting 8 general lines\nin projective 3-space. Using the Euler class and local degree from motivic\nhomotopy theory, we give an enriched version of this result over any perfect\nfield. This provides a weighted count of the number of plane conics meeting 8\ngeneral lines, where the weight of each conic is determined the geometry of its\nintersections with the 8 given lines. As a corollary, real conics meeting 8\ngeneral lines come in two families of equal size.\n"}
+{"abstract": "  This paper studies the optimal state estimation problem for interconnected\nsystems. Each subsystem can obtain its own measurement in real time, while, the\nmeasurements transmitted between the subsystems suffer from random delay. The\noptimal estimator is analytically designed for minimizing the conditional error\ncovariance. Due to the random delay, the error covariance of the estimation is\nrandom. The boundedness of the expected error covariance (EEC) is analyzed. In\nparticular, a new condition that is easy to verify is established for the\nboundedness of EEC. Further, the properties about EEC with respect to the delay\nprobability is studied. We found that there exists a critical probability such\nthat the EEC is bounded if the delay probability is below the critical\nprobability. Also, a lower and upper bound of the critical probability is\neffectively computed. Finally, the proposed results are applied to a power\nsystem, and the effectiveness of the designed methods is illustrated by\nsimulations.\n"}
+{"abstract": "  Chromates are widely used for their anticorrosive properties. Unfortunately,\nthey are highly hazardous with environmental agencies regulating their levels\nto below 10 ppb in drinking water. As anion exchange resins are typically used\nfor removal, predictive dynamic models are necessary to make quick decisions\nrather than relying on experimental data that could take several days to\nimplement. With various dynamic models currently applied to simulate the ion\nexchange process, the Thomas model was picked for its simplicity and better\naccuracy when compared to other models. The Thomas model contains two\nparameters, the constant (KT) and the maximum resin capacity (qm), which are\nempirically calculated. Unfortunately, the model demonstrated large parameter\nfluctuations with no correlation to varying contact times or inlet chromate\nconcentrations. Therefore, fixing both parameters will lead to failed model\npredictive behavior. By fixing the value of qm and proposing a linear\nrelationship of KT with resin contact time and inlet chromate concentration,\nthe accuracy of the model was improved five-fold, demonstrating its potential\nfor better process controls.\n"}
+{"abstract": "  A new method of reaction analysis for proton elastic scattering is proposed,\ncombining systematic analyses of nuclear structure and reactions in a series of\nisotopes. This method is applied to $^{204}$Pb$(p,p)$, $^{206}$Pb$(p,p)$, and\n$^{208}$Pb$(p,p)$ at $E_p=295$ MeV to obtain improved neutron densities and\nradii from experimental cross section data. The reaction calculation is\nperformed according to the relativistic impulse approximation using the\nmodified Murdock and Horowitz model with density-dependent effective $NN$\ninteraction. Analysis of Pb isotopes is performed using theoretical densities\ngiven by the relativistic Hartree-Bogoliubov and nonrelativistic Skyrme\nHartree-Fock-Bogoliubov calculations and the experimental density extracted\nfrom the $(p,p)$ data at 295 MeV. The isotopic ratios ($R(\\sigma)$) of the\n$^{A}$Pb$(p,p)$-to-$^{206}$Pb$(p,p)$ cross sections are analyzed in connection\nwith the isotopic differences ($D(\\rho_n)$ and $D(r_n)$) in neutron densities\n$(\\rho_n(r))$ and radii $(r_n)$ between $^A$Pb and $^{208}$Pb. A hole-model\nanalysis is performed, assuming $^{204}$Pb and $^{206}$Pb as a $^{208}$Pb core\nand neutron holes to clarify the one-to-one correspondence between isotopic\nstructure properties such as $D(\\rho_n)$ and $D(r_n)$ and the isotopic\ncross-section ratio. By fitting $R(\\sigma)$, the values of $D(\\rho_n)$ and\n$D(r_n)$ are extracted from the experimental $(p,p)$ cross section data with\nless uncertainty of the structure and reaction models. The isotopic neutron\nradius difference between $^{206}$Pb and $^{208}$Pb was obtained as\n$D(r_n)=-0.012$ fm with an acceptable range of $D(r_n)=-0.03 \\sim -0.006$ fm.\n"}
+{"abstract": "  We present a comprehensive analysis of the loop induced top quark FCNC\nsignals at the LHC within one class of the simplified model. The loop level\nFCNC interactions are well motivated to avoid the hierarchy of the top quark\ncouplings from the new physics and standard model. Such a theory will posit a\nMajorana dark matter candidate and could be tested through dark matter relic\ndensity, direct detection experiments (the scattering between dark matter and\nheavy nuclei), and the collider signals at the LHC. We find that the\nspin-independent (SI) scattering between Majorana dark matter and nuclei will\nvanish at the leading order, while the next-to-leading order correction to the\nSI scattering becomes significance to constrain the parameter space of the\nmodel. A detailed comparison from direct detection experiments and LHC searches\nis also discussed and both of them are very important to full constrain the\nmodel.\n"}
+{"abstract": "  We show that for any $d=d(n)$ with $d_0(\\epsilon) \\le d =o(n)$, with high\nprobability, the size of a largest induced cycle in the random graph $G(n,d/n)$\nis $(2\\pm \\epsilon)\\frac{n}{d}\\log d$. This settles a long-standing open\nproblem in random graph theory.\n"}
+{"abstract": "  Conformation-dependent design of polymer sequences can be considered as a\ntool to control macromolecular self-assembly. We consider the monomer unit\nsequences created via the modification of polymers in a homogeneous melt in\naccordance with the spatial positions of the monomer units. The geometrical\npatterns of lamellae, hexagonally packed cylinders, and balls arranged in a\nbody-centered cubic lattice are considered as typical microphase-separated\nmorphologies of block copolymers. Random trajectories of polymer chains are\ndescribed by the diffusion-type equations and, in parallel, simulated in the\ncomputer modeling. The probability distributions of block length $k$, which are\nanalogous to the first-passage probabilities, are calculated analytically and\ndetermined from the computer simulations. In any domain, the probability\ndistribution can be described by the asymptote $~k^{-3/2}$ at moderate values\nof $k$ if the spatial size of the block is less than the smallest\ncharacteristic size of the domain. For large blocks, the exponential asymptote\n$exp(-const \\, k a^2/d_{as}^2)$ is valid, $d_{as}$ being the asymptotic domain\nlength (a is the monomer unit size). The number average block lengths and their\ndispersities change linearly with the block length for lamellae, cylinders, and\nballs, when the domain is characterized by a single characteristic size. If the\ndomain is described by more than one size, the number average block length can\ngrow nonlinearly with the domain sizes and the length das can depend on all of\nthem.\n"}
+{"abstract": "  Convolutional neural networks (CNNs) have shown outstanding performance on\nimage denoising with the help of large-scale datasets. Earlier methods naively\ntrained a single CNN with many pairs of clean-noisy images. However, the\nconditional distribution of the clean image given a noisy one is too\ncomplicated and diverse, so that a single CNN cannot well learn such\ndistributions. Therefore, there have also been some methods that exploit\nadditional noise level parameters or train a separate CNN for a specific noise\nlevel parameter. These methods separate the original problem into easier\nsub-problems and thus have shown improved performance than the naively trained\nCNN. In this step, we raise two questions. The first one is whether it is an\noptimal approach to relate the conditional distribution only to noise level\nparameters. The second is what if we do not have noise level information, such\nas in a real-world scenario. To answer the questions and provide a better\nsolution, we propose a novel Bayesian framework based on the variational\napproximation of objective functions. This enables us to separate the\ncomplicated target distribution into simpler sub-distributions. Eventually, the\ndenoising CNN can conquer noise from each sub-distribution, which is generally\nan easier problem than the original. Experiments show that the proposed method\nprovides remarkable performance on additive white Gaussian noise (AWGN) and\nreal-noise denoising while requiring fewer parameters than recent\nstate-of-the-art denoisers.\n"}
+{"abstract": "  Human brain performs remarkably well in segregating a particular speaker from\ninterfering ones in a multi-speaker scenario. It has been recently shown that\nwe can quantitatively evaluate the segregation capability by modelling the\nrelationship between the speech signals present in an auditory scene and the\ncortical signals of the listener measured using electroencephalography (EEG).\nThis has opened up avenues to integrate neuro-feedback into hearing aids\nwhereby the device can infer user's attention and enhance the attended speaker.\nCommonly used algorithms to infer the auditory attention are based on linear\nsystems theory where the speech cues such as envelopes are mapped on to the EEG\nsignals. Here, we present a joint convolutional neural network (CNN) - long\nshort-term memory (LSTM) model to infer the auditory attention. Our joint\nCNN-LSTM model takes the EEG signals and the spectrogram of the multiple\nspeakers as inputs and classifies the attention to one of the speakers. We\nevaluated the reliability of our neural network using three different datasets\ncomprising of 61 subjects where, each subject undertook a dual-speaker\nexperiment. The three datasets analysed corresponded to speech stimuli\npresented in three different languages namely German, Danish and Dutch. Using\nthe proposed joint CNN-LSTM model, we obtained a median decoding accuracy of\n77.2% at a trial duration of three seconds. Furthermore, we evaluated the\namount of sparsity that our model can tolerate by means of magnitude pruning\nand found that the model can tolerate up to 50% sparsity without substantial\nloss of decoding accuracy.\n"}
+{"abstract": "  Combining the ideas of Riesz $s$-energy and $\\log$-energy, we introduce the\nso-called $s,\\log^t$-energy. In this paper, we investigate the asymptotic\nbehaviors for $N,t$ fixed and $s$ varying of minimal $N$-point\n$s,\\log^t$-energy constants and configurations of an infinite compact metric\nspace of diameter less than $1$. In particular, we study certain continuity and\ndifferentiability properties of minimal $N$-point $s,\\log^t$-energy constants\nin the variable $s$ and we show that in the limits as $s\\rightarrow \\infty$ and\nas $s\\rightarrow s_0>0,$ minimal $N$-point $s,\\log^t$-energy configurations\ntend to an $N$-point best-packing configuration and a minimal $N$-point\n$s_0,\\log^t$-energy configuration, respectively. Furthermore, the optimality of\n$N$ distinct equally spaced points on circles in $\\mathbb{R}^2$ for some\ncertain $s,\\log^t$ energy problems was proved.\n"}
+{"abstract": "  Let $R$ be a commutative ring with identity and $S$ a multiplicatively closed\nsubset of $R$. This paper aims to introduce the concept of $S$-$n$-ideals as a\ngeneralization of $n$-ideals. An ideal $I$ of $R$ disjoint with $S$ is called\nan $S$-$n$-ideal if there exists $s\\in S$ such that whenever $ab\\in I$ for\n$a,b\\in R$, then $sa\\in\\sqrt{0}$ or $sb\\in I$. The relationship among $S$%\n-$n$-ideals, $n$-ideals, $S$-prime and $S$-primary ideals are clarified.\nSeveral properties, characterizations and examples are presented such as\ninvestigating $S$-$n$-ideals under various contexts of constructions including\ndirect products, localizations and homomorphic images. Furthermore, for $m\\in%\n%TCIMACRO{\\U{2115} }% %BeginExpansion \\mathbb{N} %EndExpansion $ and some\nparticular $S$, all $S$-$n$-ideals of the ring $% %TCIMACRO{\\U{2124} }%\n%BeginExpansion \\mathbb{Z} %EndExpansion _{m}$ are completely determined. The\nlast section is devoted for studying the $S$-$n$-ideals of the idealization\nring and amalgamated algebra.\n"}
+{"abstract": "  In the recent decade, high-throughput plant phenotyping techniques, which\ncombine non-invasive image analysis and machine learning, have been\nsuccessfully applied to identify and quantify plant health and diseases.\nHowever, these techniques usually do not consider the progressive nature of\nplant stress and often require images showing severe signs of stress to ensure\nhigh confidence detection, thereby reducing the feasibility for early detection\nand recovery of plants under stress. To overcome the problem mentioned above,\nwe propose a deep learning pipeline for the temporal analysis of the visual\nchanges induced in the plant due to stress and apply it to the specific water\nstress identification case in Chickpea plant shoot images. For this, we have\nconsidered an image dataset of two chickpea varieties JG-62 and Pusa-372, under\nthree water stress conditions; control, young seedling, and before flowering,\ncaptured over five months. We have employed a variant of Convolutional Neural\nNetwork - Long Short Term Memory (CNN-LSTM) network to learn spatio-temporal\npatterns from the chickpea plant dataset and use them for water stress\nclassification. Our model has achieved ceiling level classification performance\nof 98.52% on JG-62 and 97.78% on Pusa-372 chickpea plant data and has\noutperformed the best reported time-invariant technique by at least 14% for\nboth JG-62 and Pusa-372 species, to the best of our knowledge. Furthermore, our\nCNN-LSTM model has demonstrated robustness to noisy input, with a less than\n2.5% dip in average model accuracy and a small standard deviation about the\nmean for both species. Lastly, we have performed an ablation study to analyze\nthe performance of the CNN-LSTM model by decreasing the number of temporal\nsession data used for training.\n"}
+{"abstract": "  The starting point of securing a network is having a concise overview of it.\nAs networks are becoming more and more complex both in general and with the\nintroduction of IoT technology and their topological peculiarities in\nparticular, this is increasingly difficult to achieve. Especially in\ncyber-physical environments, such as smart factories, gaining a reliable\npicture of the network can be, due to intertwining of a vast amount of devices\nand different protocols, a tedious task. Nevertheless, this work is necessary\nto conduct security audits, compare documentation with actual conditions or\nfound vulnerabilities using an attacker's view, for all of which a reliable\ntopology overview is pivotal. For security auditors, however, there might not\nmuch information, such as asset management access, be available beforehand,\nwhich is why this paper assumes network to audit as a complete black box. The\ngoal is therefore to set security auditors in a condition of, without having\nany a priori knowledge at all, automatically gaining a topology oversight. This\npaper describes, in the context of a bigger system that uses active scanning to\ndetermine the network topology, an approach to automate the first steps of this\nprocedure: passively scanning the network and determining the network's scope,\nas well as gaining a valid address to perform the active scanning. This allows\nfor bootstrapping an automatic network discovery process without prior\nknowledge.\n"}
+{"abstract": "  The aim of this paper is to study the logarithmic submajorisations\ninequalities for operators in a finite von Neumann algebra. Firstly, some\nlogarithmic submajorisations inequalities due to Garg and Aulja are extended to\nthe case of operators in a finite von Neumann algebra. As an application, we\nget some new Fuglede-Kadison determinant inequalities of operators in that\ncircumstance. Secondly, we improve and generalize to the setting of finite von\nNeumann algebras, a generalized H\\\"{o}lder type generalized singular numbers\ninequality.\n"}
+{"abstract": "  The performance of deep reinforcement learning methods prone to degenerate\nwhen applied to environments with non-stationary dynamics. In this paper, we\nutilize the latent context recurrent encoders motivated by recent Meta-RL\nmaterials, and propose the Latent Context-based Soft Actor Critic (LC-SAC)\nmethod to address aforementioned issues. By minimizing the contrastive\nprediction loss function, the learned context variables capture the information\nof the environment dynamics and the recent behavior of the agent. Then combined\nwith the soft policy iteration paradigm, the LC-SAC method alternates between\nsoft policy evaluation and soft policy improvement until it converges to the\noptimal policy. Experimental results show that the performance of LC-SAC is\nsignificantly better than the SAC algorithm on the MetaWorld ML1 tasks whose\ndynamics changes drasticly among different episodes, and is comparable to SAC\non the continuous control benchmark task MuJoCo whose dynamics changes slowly\nor doesn't change between different episodes. In addition, we also conduct\nrelevant experiments to determine the impact of different hyperparameter\nsettings on the performance of the LC-SAC algorithm and give the reasonable\nsuggestions of hyperparameter setting.\n"}
+{"abstract": "  We consider the problem of under and over-approximating the image of general\nvector-valued functions over bounded sets, and apply the proposed solution to\nthe estimation of reachable sets of uncertain non-linear discrete-time\ndynamical systems. Such a combination of under and over-approximations is very\nvaluable for the verification of properties of embedded and cyber-physical\ncontrolled systems. Over-approximations prove properties correct, while\nunder-approximations can be used for falsification. Coupled, they provide a\nmeasure of the conservatism of the analysis. This work introduces a general\nframework relying on computations of robust ranges of vector-valued functions.\nThis framework allows us to extend for under-approximation many precision\nrefinements that are classically used for over-approximations, such as affine\napproximations, Taylor models, quadrature formulae and preconditioning methods.\nWe end by evaluating the efficiency and precision of our approach, focusing on\nthe application to the analysis of discrete-time dynamical systems with inputs\nand disturbances, on different examples from the literature.\n"}
+{"abstract": "  A comparison was made between $Gaia$ magnitudes and magnitudes obtained from\nground-based observations for astrometric radio sources . The comparison showed\nthat these magnitudes often not agree well. There may be several reasons for\nthis disagreement. Nevertheless, such an analysis can serve as an additional\nfilter for verification of the object cross-identification. On the other hand,\nit can help to detect possible errors in optical magnitudes of astrometric\nradio sources coming from unreliable or inconsistent data sources.\n"}
+{"abstract": "  We generate high-order harmonics in gaseous medium with tunable photon energy\nusing time domain interferometry of double pulses in a non-collinear generation\ngeometry. The method is based on the fact that the generated harmonics inherit\ncertain spectral properties of the driving laser. The two temporally delayed\nultrashort laser pulses, identical in all parameters, are produced by a\ncustom-made split-and-delay unit utilizing wave front splitting without a\nsignificant energy loss. The arrangement is easy to implement in any attosecond\npulse generation beamline, and is suitable for the production of an extreme\nultraviolet source with simply and quickly variable central photon energy,\nuseful for a broad range of applications.\n"}
+{"abstract": "  Let $G$ be a connected $n$-vertex graph in a proper minor-closed class\n$\\mathcal G$. We prove that the extension complexity of the spanning tree\npolytope of $G$ is $O(n^{3/2})$. This improves on the $O(n^2)$ bounds following\nfrom the work of Wong (1980) and Martin (1991). It also extends a result of\nFiorini, Huynh, Joret, and Pashkovich (2017), who obtained a $O(n^{3/2})$ bound\nfor graphs embedded in a fixed surface. Our proof works more generally for all\ngraph classes admitting strongly sublinear balanced separators: We prove that\nfor every constant $\\beta$ with $0<\\beta<1$, if $\\mathcal G$ is a graph class\nclosed under induced subgraphs such that all $n$-vertex graphs in $\\mathcal G$\nhave balanced separators of size $O(n^\\beta)$, then the extension complexity of\nthe spanning tree polytope of every connected $n$-vertex graph in $\\mathcal{G}$\nis $O(n^{1+\\beta})$. We in fact give two proofs of this result, one is a direct\nconstruction of the extended formulation, the other is via communication\nprotocols. Using the latter approach we also give a short proof of the $O(n)$\nbound for planar graphs due to Williams (2002).\n"}
+{"abstract": "  In this article, we prove new geometric integral inequalities for free\nboundary hypersurfaces in the unit ball of a space form. We also give some\napplications of these formula in order to obtain some rigidity results. For\ninstance, we are able to show, in the three dimensional case, a relationship\nbetween the geometry of a hypersurface and the topology of its boundary.\nMoreover, we also reobtain the classical Nitsche Theorem as well as its\ngeneralisation for constant mean curvature surfaces in space forms balls, and\nwe provide a new point of view to the Catenoid Conjecture.\n"}
+{"abstract": "  Metric learning algorithms aim to learn a distance function that brings the\nsemantically similar data items together and keeps dissimilar ones at a\ndistance. The traditional Mahalanobis distance learning is equivalent to find a\nlinear projection. In contrast, Deep Metric Learning (DML) methods are proposed\nthat automatically extract features from data and learn a non-linear\ntransformation from input space to a semantically embedding space. Recently,\nmany DML methods are proposed focused to enhance the discrimination power of\nthe learned metric by providing novel sampling strategies or loss functions.\nThis approach is very helpful when both the training and test examples are\ncoming from the same set of categories. However, it is less effective in many\napplications of DML such as image retrieval and person-reidentification. Here,\nthe DML should learn general semantic concepts from observed classes and employ\nthem to rank or identify objects from unseen categories. Neglecting the\ngeneralization ability of the learned representation and just emphasizing to\nlearn a more discriminative embedding on the observed classes may lead to the\noverfitting problem. To address this limitation, we propose a framework to\nenhance the generalization power of existing DML methods in a Zero-Shot\nLearning (ZSL) setting by general yet discriminative representation learning\nand employing a class adversarial neural network. To learn a more general\nrepresentation, we propose to employ feature maps of intermediate layers in a\ndeep neural network and enhance their discrimination power through an attention\nmechanism. Besides, a class adversarial network is utilized to enforce the deep\nmodel to seek class invariant features for the DML task. We evaluate our work\non widely used machine vision datasets in a ZSL setting.\n"}
+{"abstract": "  Sheet cavitation appears in many hydraulic applicationss and can lead to\ntechnical issues. Some fundamental outcomes such as the complex topology of\n3-Dimensional cavitation pockets and their associated dynamics need to be\ncarefully visited.In the present paper, the dynamics of partial cavitation\ndeveloping in a 3-D Venturi geometry and the interaction with sidewalls are\nnumerically investigated. The simulations are performed usinf a one-fluid\ncompressible Reynolds-Averaged Navier-Stokes (RANS solver associated to a\nnon-linear turbulence model and a void transport-equation model. A detailed\nanalysis of this cavitating flow is carried out using innovative tools such as\nSpectral Proper Orthogonal Decompositions. Particular attenttion is paid in the\nstudy of 3-D effects by comparing numerical results obtained with sidewalls and\nperiodic conditions. A three-dimensional dynamics of the sheet cavitation,\nunrelated to the presence of sidewalls, is identified and discussed.\n"}
+{"abstract": "  This paper addresses the dynamics of locally-resonant sandwich beams, where\nmulti-degree-of-freedom viscously-damped resonators are periodically\ndistributed within the core matrix. Using an equivalent single-layer Timoshenko\nbeam model coupled with mass-spring-dashpot subsystems representing the\nresonators, two solution methods are presented. The first is a direct\nintegration method providing the exact frequency response under arbitrary\nloads. The second is a complex modal analysis approach obtaining exact modal\nimpulse and frequency response functions, upon deriving appropriate\northogonality conditions for the complex modes. The challenging issue of\ncalculating all eigenvalues, without missing anyone, is solved applying a\nrecently-introduced contour-integral algorithm to a characteristic equation\nbuilt as determinant of an exact frequency-response matrix, whose size is $4\n\\times 4$ regardless of the number of resonators. Numerical applications prove\nexactness and robustness of the proposed solutions.\n"}
+{"abstract": "  Communication is a critical part of enabling multi-agent systems to\ncooperate. This means that applying formal methods to protocols governing\ncommunication within multi-agent systems provides useful confidence in its\nreliability. In this paper, we describe the formal verification of a complex\ncommunication protocol that coordinates agents merging maps of their\nenvironment. The protocol was used by the LFC team in the 2019 edition of the\nMulti-Agent Programming Contest (MAPC). Our specification of the protocol is\nwritten in Communicating Sequential Processes (CSP), which is a well-suited\napproach to specifying agent communication protocols due to its focus on\nconcurrent communicating systems. We validate the specification's behaviour\nusing scenarios where the correct behaviour is known, and verify that\neventually all the maps have merged.\n"}
+{"abstract": "  We argue that novel (highly nonclassical) quantum extremal surfaces play a\ncrucial role in reconstructing the black hole interior even for isolated,\nsingle-sided, non-evaporating black holes (i.e. with no auxiliary reservoir).\nSpecifically, any code subspace where interior outgoing modes can be excited\nwill have a quantum extremal surface in its maximally mixed state. We argue\nthat as a result, reconstruction of interior outgoing modes is always\nexponentially complex. Our construction provides evidence in favor of a strong\nPython's lunch proposal: that nonminimal quantum extremal surfaces are the\nexclusive source of exponential complexity in the holographic dictionary. We\nalso comment on the relevance of these quantum extremal surfaces to the\ngeometrization of state dependence in the typicality arguments for firewalls.\n"}
+{"abstract": "  Self-driving cars must detect other vehicles and pedestrians in 3D to plan\nsafe routes and avoid collisions. State-of-the-art 3D object detectors, based\non deep learning, have shown promising accuracy but are prone to over-fit to\ndomain idiosyncrasies, making them fail in new environments -- a serious\nproblem if autonomous vehicles are meant to operate freely. In this paper, we\npropose a novel learning approach that drastically reduces this gap by\nfine-tuning the detector on pseudo-labels in the target domain, which our\nmethod generates while the vehicle is parked, based on replays of previously\nrecorded driving sequences. In these replays, objects are tracked over time,\nand detections are interpolated and extrapolated -- crucially, leveraging\nfuture information to catch hard cases. We show, on five autonomous driving\ndatasets, that fine-tuning the object detector on these pseudo-labels\nsubstantially reduces the domain gap to new driving environments, yielding\ndrastic improvements in accuracy and detection reliability.\n"}
+{"abstract": "  Covariate-specific treatment effects (CSTEs) represent heterogeneous\ntreatment effects across subpopulations defined by certain selected covariates.\nIn this article, we consider marginal structural models where CSTEs are\nlinearly represented using a set of basis functions of the selected covariates.\nWe develop a new approach in high-dimensional settings to obtain not only\ndoubly robust point estimators of CSTEs, but also model-assisted confidence\nintervals, which are valid when a propensity score model is correctly specified\nbut an outcome regression model may be misspecified. With a linear outcome\nmodel and subpopulations defined by discrete covariates, both point estimators\nand confidence intervals are doubly robust for CSTEs. In contrast, confidence\nintervals from existing high-dimensional methods are valid only when both the\npropensity score and outcome models are correctly specified. We establish\nasymptotic properties of the proposed point estimators and the associated\nconfidence intervals. We present simulation studies and empirical applications\nwhich demonstrate the advantages of the proposed method compared with competing\nones.\n"}
+{"abstract": "  In a recent paper (Hajdukovic 2020a) quantum vacuum was considered as a\nsource of gravity and, the simplest phenomenon, the gravitational polarization\nof the quantum vacuum by an immersed point-like body, was studied. In the\npresent paper, we have derived the effective gravitational charge density of\nthe quantum vacuum, caused by two point-like bodies. Among others, the obtained\nresult proves that quantum vacuum can have regions with a negative effective\ngravitational charge density. Hence, quantum vacuum, the \"ocean\" in which all\nmatter of the Universe is immersed, acts as a complex fluid with a very\nvariable gravitational charge density that might include both positive and\nnegative densities; a crucial prediction that can be tested within the Solar\nSystem. In general case of N>2 point-like bodies, immersed in the quantum\nvacuum, the analytical solutions are not possible, and the use of numerical\nmethods is inevitable. The key point is that an appropriate numerical method,\nfor the calculation of the effective gravitational charge of the quantum vacuum\ninduced by N immersed bodies, might be crucial in description of galaxies,\nwithout the involvement of dark matter or a modification of gravity. The\ndevelopment of such a valuable numerical method, is not possible, without a\nprevious (and in this study achieved) understanding of the impact of a two-body\nsystem.\n"}
+{"abstract": "  We derive semiclassical equations of motion for an accelerated wavepacket in\na two-band system. We show that these equations can be formulated in terms of\nthe static band geometry described by the quantum metric. We consider the\nspecific cases of the Rashba Hamiltonian with and without a Zeeman term. The\nsemiclassical trajectories are in full agreement with the ones found by solving\nthe Schr\\\"odinger equation. This formalism successfully describes the adiabatic\nlimit and the anomalous Hall effect traditionally attributed to Berry\ncurvature. It also describes the opposite limit of coherent band superposition\ngiving rise to a spatially oscillating Zitterbewegung motion. At $k=0$, such\nwavepacket exhibits a circular trajectory in real space, with its radius given\nby the square root of the quantum metric. This quantity appears as a universal\nlength scale, providing a geometrical origin of the Compton wavelength.\n"}
+{"abstract": "  The bulk polarization is a $\\mathbb{Z}_2$ topological invariant\ncharacterizing non-interacting systems in one dimension with chiral or\nparticle-hole symmetries. We show that the bulk polarization can always be\ndetermined from the single-particle entanglement spectrum, even in the absence\nof symmetries that quantize it. In the symmetric case, the known relation\nbetween the bulk polarization and the number of virtual topological edge modes\nis recovered. We use the bulk polarization to compute Chern numbers in 1D and\n2D, which illuminates their known relation to the entanglement spectrum.\nFurthermore we discuss an alternative bulk polarization that can carry more\ninformation about the surface spectrum than the conventional one and can\nsimplify the calculation of Chern numbers.\n"}
+{"abstract": "  Due to agile developments in the field of robotics and human-robot\ninteraction, prospective robotic agents are intended to play the role of\nteammates and partner with humans to perform operations, rather than tools that\nare replacing humans helping humans in a specific task. this notion of\npartnering with robots raises new challenges for human-robot interaction (HRI),\nwhich gives rise to a new field of research in HRI, namely human-robot trust.\nWhere humans and robots are working as partners, the performance of the work\ncan be diminished if humans do not trust robots appropriately. Considering the\nimpact of human-robot trust observed in different HRI fields, many researchers\nhave investigated the field of human-robot trust and examined various concerns\nrelated to human-robot trust. In this work, we review the past works on\nhuman-robot trust based on the research topics and discuss selected trends in\nthis field. Based on these reviews, we finally propose some ideas and areas of\npotential future research at the end of this paper.\n"}
+{"abstract": "  Differential wavefront sensing is an essential technique for optimising the\nperformance of many precision interferometric experiments. Perhaps the most\nextensive application of this is for alignment sensing using radio-frequency\nbeats measured with quadrant photodiodes. Here we present a new technique that\nuses optical demodulation to measure such optical beats at significantly higher\nresolutions using commercial laboratory equipment. We experimentally\ndemonstrate that the images captured can be digitally processed to generate\nwavefront error signals and use these in a closed loop control system for\ncorrect wavefront errors for alignment and mode-matching a beam into an optical\ncavity to 99.9\\%. This experiment paves the way for the correction of even\nhigher order errors when paired with higher order wavefront actuators. Such a\nsensing scheme could find use in optimizing complex interferometers consisting\nof coupled cavities, such as those found in gravitational wave detectors, or\nsimply just for sensing higher order wavefront errors in heterodyne\ninterferometric table-top experiments.\n"}
+{"abstract": "  We describe how simple machine learning methods successfully predict\ngeometric properties from Hilbert series (HS). Regressors predict embedding\nweights in projective space to ${\\sim}1$ mean absolute error, whilst\nclassifiers predict dimension and Gorenstein index to $>90\\%$ accuracy with\n${\\sim}0.5\\%$ standard error. Binary random forest classifiers managed to\ndistinguish whether the underlying HS describes a complete intersection with\nhigh accuracies exceeding $95\\%$. Neural networks (NNs) exhibited success\nidentifying HS from a Gorenstein ring to the same order of accuracy, whilst\ngeneration of 'fake' HS proved trivial for NNs to distinguish from those\nassociated to the three-dimensional Fano varieties considered.\n"}
+{"abstract": "  In this work we introduce a novel stochastic algorithm dubbed SNIPS, which\ndraws samples from the posterior distribution of any linear inverse problem,\nwhere the observation is assumed to be contaminated by additive white Gaussian\nnoise. Our solution incorporates ideas from Langevin dynamics and Newton's\nmethod, and exploits a pre-trained minimum mean squared error (MMSE) Gaussian\ndenoiser. The proposed approach relies on an intricate derivation of the\nposterior score function that includes a singular value decomposition (SVD) of\nthe degradation operator, in order to obtain a tractable iterative algorithm\nfor the desired sampling. Due to its stochasticity, the algorithm can produce\nmultiple high perceptual quality samples for the same noisy observation. We\ndemonstrate the abilities of the proposed paradigm for image deblurring,\nsuper-resolution, and compressive sensing. We show that the samples produced\nare sharp, detailed and consistent with the given measurements, and their\ndiversity exposes the inherent uncertainty in the inverse problem being solved.\n"}
+{"abstract": "  These lecture notes have been prepared for a series of lectures given at the\nSummer School \"From kinetic equations to statistical mechanics\", (see\nhttps://www.lebesgue.fr/content/sem2021-equat_cynet ) organised by the Henri\nLebesgue Center in Saint Jean de Monts, from June 28th to July 2nd 2021.\n"}
+{"abstract": "  In this paper, we study the problem of blood glucose forecasting and provide\na deep personalized solution. Predicting blood glucose level in people with\ndiabetes has significant value because health complications of abnormal glucose\nlevel are serious, sometimes even leading to death. Therefore, having a model\nthat can accurately and quickly warn patients of potential problems is\nessential. To develop a better deep model for blood glucose forecasting, we\nanalyze the data and detect important patterns. These observations helped us to\npropose a method that has several key advantages over existing methods: 1- it\nlearns a personalized model for each patient as well as a global model; 2- it\nuses an attention mechanism and extracted time features to better learn\nlong-term dependencies in the data; 3- it introduces a new, robust training\nprocedure for time series data. We empirically show the efficacy of our model\non a real dataset.\n"}
+{"abstract": "  The Burkhardt-Cottingham (BC) sum rule connects the twist-3 light-cone parton\ndistribution function (PDF) $g_{T}(x)$ to the twist-2 helicity PDF $g_{1}(x)$.\nThe chiral-odd counterpart of the BC sum rule relates the twist-3 light-cone\nPDF $h_{L}(x)$ to the twist-2 transversity PDF $h_{1}(x)$. These BC-type sum\nrules can also be derived for the corresponding quasi-PDFs. We perform a\nperturbative check of the BC-type sum rules in the quark target model and the\nYukawa model, by going beyond the ultra-violet (UV) divergent terms. We employ\ndimensional regularization (DR) and cut-off schemes to regulate UV divergences,\nand show that the BC-type sum rules hold for DR, while they are generally\nviolated when using a cut-off. This violation can be traced back to the\nbreaking of rotational invariance. We find corresponding results for the sum\nrule relating the mass of the target to the twist-3 PDF $e(x)$. Moreover, we\nsupplement our analytical results with numerical calculations.\n"}
+{"abstract": "  We examine the degree structure $\\mathbf{ER}$ of equivalence relations on\n$\\omega$ under computable reducibility. We examine when pairs of degrees have a\njoin. In particular, we show that sufficiently incomparable pairs of degrees do\nnot have a join but that some incomparable degrees do, and we characterize the\ndegrees which have a join with every finite equivalence relation. We show that\nthe natural classes of finite, light, and dark degrees are definable in\n$\\mathbf{ER}$. We show that every equivalence relation has continuum many\nself-full strong minimal covers, and that $\\mathbf{d}\\oplus \\mathbf{Id_1}$\nneedn't be a strong minimal cover of a self-full degree $\\mathbf{d}$. Finally,\nwe show that the theory of the degree structure $\\mathbf{ER}$ as well as the\ntheories of the substructures of light degrees and of dark degrees are each\ncomputably isomorphic with second order arithmetic.\n"}
+{"abstract": "  In this work, we consider a system of multidimensional wave equations coupled\nby velocities with one localized fractional boundary damping. First, using a\ngeneral criteria of Arendt- Batty, by assuming that the boundary control region\nsatisfy some geometric conditions, under the equality speed propagation and the\ncoupling parameter of the two equations is small enough, we show the strong\nstability of our system in the absence of the compactness of the resolvent. Our\nsystem is not uniformly stable in general since it is the case of the interval.\nHence, we look for a polynomial decay rate for smooth initial data for our\nsystem by applying a frequency domain approach combining with a multiplier\nmethod. Indeed, by assuming that the boundary control region satisfy some\ngeometric conditions and the waves propagate with equal speed and the coupling\nparameter term is small enough, we establish a polynomial energy decay rate for\nsmooth solutions, which depends on the order of the fractional derivative.\n"}
+{"abstract": "  Optimal transport is a framework that facilitates the most efficient\nallocation of a limited amount of resources. However, the most efficient\nallocation scheme does not necessarily preserve the most fairness. In this\npaper, we establish a framework which explicitly considers the fairness of\ndynamic resource allocation over a network with heterogeneous participants. As\ncomputing the transport strategy in a centralized fashion requires significant\ncomputational resources, it is imperative to develop computationally light\nalgorithm that can be applied to large scale problems. To this end, we develop\na fully distributed algorithm for fair and dynamic optimal transport with\nprovable convergence using alternating method of multipliers. In the designed\nalgorithm, each corresponding pair of resource supplier and receiver compute\ntheir own solutions and update the transport schemes through negotiation\niteratively which do not require a central planner. The distributed algorithm\ncan yield a fair and efficient resource allocation mechanism over a network. We\ncorroborate the obtained results through case studies.\n"}
+{"abstract": "  An impressive effort is being placed in order to develop new strategies that\nallow an efficient computation of multi-loop multi-leg Feynman integrals and\nscattering amplitudes, with a particular emphasis on removing spurious\nsingularities and numerical instabilities. In this article, we describe an\ninnovative geometric approach based on graph theory to unveil the causal\nstructure of any multi-loop multi-leg amplitude in Quantum Field Theory. Our\npurely geometric construction reproduces faithfully the manifestly causal\nintegrand-level behaviour of the Loop-Tree Duality representation. We found\nthat the causal structure is fully determined by the vertex matrix, through a\nsuitable definition of connected partitions of the underlying diagrams. Causal\nrepresentations for a given topological family are obtained by summing over\nsubsets of all the possible causal entangled thresholds that originate\nconnected and oriented partitions of the underlying topology. These results are\ncompatible with Cutkosky rules. Moreover, we found that diagrams with the same\nnumber of vertices and multi-edges exhibit similar causal structures,\nregardless of the number of loops.\n"}
+{"abstract": "  For given Boolean algebras $\\mathbb{A}$ and $\\mathbb{B}$ we endow the space\n$\\mathcal{H}(\\mathbb{A},\\mathbb{B})$ of all Boolean homomorphisms from\n$\\mathbb{A}$ to $\\mathbb{B}$ with various topologies and study convergence\nproperties of sequences in $\\mathcal{H}(\\mathbb{A},\\mathbb{B})$. We are in\nparticular interested in the situation when $\\mathbb{B}$ is a measure algebra\nas in this case we obtain a natural tool for studying topological convergence\nproperties of sequences of ultrafilters on $\\mathbb{A}$ in random extensions of\nthe set-theoretical universe. This appears to have strong connections with Dow\nand Fremlin's result stating that there are Efimov spaces in the random model.\nWe also investigate relations between topologies on\n$\\mathcal{H}(\\mathbb{A},\\mathbb{B})$ for a Boolean algebra $\\mathbb{B}$\ncarrying a strictly positive measure and convergence properties of sequences of\nmeasures on $\\mathbb{A}$.\n"}
+{"abstract": "  Person re-identification (re-ID) in first-person (egocentric) vision is a\nfairly new and unexplored problem. With the increase of wearable video\nrecording devices, egocentric data becomes readily available, and person\nre-identification has the potential to benefit greatly from this. However,\nthere is a significant lack of large scale structured egocentric datasets for\nperson re-identification, due to the poor video quality and lack of individuals\nin most of the recorded content. Although a lot of research has been done in\nperson re-identification based on fixed surveillance cameras, these do not\ndirectly benefit egocentric re-ID. Machine learning models trained on the\npublicly available large scale re-ID datasets cannot be applied to egocentric\nre-ID due to the dataset bias problem. The proposed algorithm makes use of\nneural style transfer (NST) that incorporates a variant of Convolutional Neural\nNetwork (CNN) to utilize the benefits of both fixed camera vision and\nfirst-person vision. NST generates images having features from both egocentric\ndatasets and fixed camera datasets, that are fed through a VGG-16 network\ntrained on a fixed-camera dataset for feature extraction. These extracted\nfeatures are then used to re-identify individuals. The fixed camera dataset\nMarket-1501 and the first-person dataset EGO Re-ID are applied for this work\nand the results are on par with the present re-identification models in the\negocentric domain.\n"}
+{"abstract": "  During the early stages of developing Case-Based Reasoning (CBR) systems the\ndefinition of similarity measures is challenging since this task requires\ntransferring implicit knowledge of domain experts into knowledge\nrepresentations. While an entire CBR system is very explanatory, the similarity\nmeasure determines the ranking but do not necessarily show which features\ncontribute to high (or low) rankings. In this paper we present our work on\nopening the knowledge engineering process for similarity modelling. This work\npresent is a result of an interdisciplinary research collaboration between AI\nand public health researchers developing e-Health applications. During this\nwork explainability and transparency of the development process is crucial to\nallow in-depth quality assurance of the by the domain experts.\n"}
+{"abstract": "  Offline Chinese handwriting text recognition is a long-standing research\ntopic in the field of pattern recognition. In previous studies, text detection\nand recognition are separated, which leads to the fact that text recognition is\nhighly dependent on the detection results. In this paper, we propose a robust\nend-to-end Chinese text page spotter framework. It unifies text detection and\ntext recognition with text kernel that integrates global text feature\ninformation to optimize the recognition from multiple scales, which reduces the\ndependence of detection and improves the robustness of the system. Our method\nachieves state-of-the-art results on the CASIA-HWDB2.0-2.2 dataset and\nICDAR-2013 competition dataset. Without any language model, the correct rates\nare 99.12% and 94.27% for line-level recognition, and 99.03% and 94.20% for\npage-level recognition, respectively.\n"}
+{"abstract": "  Graph neural networks (GNNs) have been widely used in deep learning on\ngraphs. They can learn effective node representations that achieve superior\nperformances in graph analysis tasks such as node classification and node\nclustering. However, most methods ignore the heterogeneity in real-world\ngraphs. Methods designed for heterogeneous graphs, on the other hand, fail to\nlearn complex semantic representations because they only use meta-paths instead\nof meta-graphs. Furthermore, they cannot fully capture the content-based\ncorrelations between nodes, as they either do not use the self-attention\nmechanism or only use it to consider the immediate neighbors of each node,\nignoring the higher-order neighbors. We propose a novel Higher-order\nAttribute-Enhancing (HAE) framework that enhances node embedding in a\nlayer-by-layer manner. Under the HAE framework, we propose a Higher-order\nAttribute-Enhancing Graph Neural Network (HAEGNN) for heterogeneous network\nrepresentation learning. HAEGNN simultaneously incorporates meta-paths and\nmeta-graphs for rich, heterogeneous semantics, and leverages the self-attention\nmechanism to explore content-based nodes interactions. The unique higher-order\narchitecture of HAEGNN allows examining the first-order as well as higher-order\nneighborhoods. Moreover, HAEGNN shows good explainability as it learns the\nimportances of different meta-paths and meta-graphs. HAEGNN is also\nmemory-efficient, for it avoids per meta-path based matrix calculation.\nExperimental results not only show HAEGNN superior performance against the\nstate-of-the-art methods in node classification, node clustering, and\nvisualization, but also demonstrate its superiorities in terms of memory\nefficiency and explainability.\n"}
+{"abstract": "  Recently a number of studies demonstrated impressive performance on diverse\nvision-language multi-modal tasks such as image captioning and visual question\nanswering by extending the BERT architecture with multi-modal pre-training\nobjectives. In this work we explore a broad set of multi-modal representation\nlearning tasks in the medical domain, specifically using radiology images and\nthe unstructured report. We propose Medical Vision Language Learner (MedViLL)\nwhich adopts a Transformer-based architecture combined with a novel multimodal\nattention masking scheme to maximize generalization performance for both\nvision-language understanding tasks (image-report retrieval, disease\nclassification, medical visual question answering) and vision-language\ngeneration task (report generation). By rigorously evaluating the proposed\nmodel on four downstream tasks with two chest X-ray image datasets (MIMIC-CXR\nand Open-I), we empirically demonstrate the superior downstream task\nperformance of MedViLL against various baselines including task-specific\narchitectures.\n"}
+{"abstract": "  The cross section for W or Z boson production in association with two photons\nis measured in proton-proton collisions at a centre-of-mass energy of 13 TeV.\nThe data set corresponds to an integrated luminosity of 137 fb$^{-1}$ collected\nby the CMS experiment at the LHC. The W $\\to$ $\\ell\\nu$ and Z $\\to$ $\\ell\\ell$\ndecay modes (where $\\ell =$ e, $\\mu$) are used to extract the W$\\gamma\\gamma$\nand Z$\\gamma\\gamma$ cross sections in a phase space defined by electron (muon)\nwith transverse momentum larger than 30 GeV and photon transverse momentum\nlarger than 20 GeV. All leptons and photons are required to have absolute\npseudorapidity smaller than 2.5. The measured cross sections in this phase\nspace are $\\sigma$(W$\\gamma\\gamma$) = 13.6 $^{+1.9}_{-1.9}$ (stat)\n${}^{+4.0}_{-4.0}$ (syst) $\\pm$ 0.08 (PDF + scale) fb and\n$\\sigma$(Z$\\gamma\\gamma$) = 5.41 $^{+0.58}_{-0.55}$ (stat) ${}^{+0.64}_{-0.70}$\n(syst) $\\pm$ 0.06 (PDF + scale) fb. Limits on anomalous quartic gauge couplings\nare set in the framework of an effective field theory with dimension-8\noperators.\n"}
+{"abstract": "  Automatic hate speech detection in online social networks is an important\nopen problem in Natural Language Processing (NLP). Hate speech is a\nmultidimensional issue, strongly dependant on language and cultural factors.\nDespite its relevance, research on this topic has been almost exclusively\ndevoted to English. Most supervised learning resources, such as labeled\ndatasets and NLP tools, have been created for this same language. Considering\nthat a large portion of users worldwide speak in languages other than English,\nthere is an important need for creating efficient approaches for multilingual\nhate speech detection. In this work we propose to address the problem of\nmultilingual hate speech detection from the perspective of transfer learning.\nOur goal is to determine if knowledge from one particular language can be used\nto classify other language, and to determine effective ways to achieve this. We\npropose a hate specific data representation and evaluate its effectiveness\nagainst general-purpose universal representations most of which, unlike our\nproposed model, have been trained on massive amounts of data. We focus on a\ncross-lingual setting, in which one needs to classify hate speech in one\nlanguage without having access to any labeled data for that language. We show\nthat the use of our simple yet specific multilingual hate representations\nimproves classification results. We explain this with a qualitative analysis\nshowing that our specific representation is able to capture some common\npatterns in how hate speech presents itself in different languages.\n  Our proposal constitutes, to the best of our knowledge, the first attempt for\nconstructing multilingual specific-task representations. Despite its\nsimplicity, our model outperformed the previous approaches for most of the\nexperimental setups. Our findings can orient future solutions toward the use of\ndomain-specific representations.\n"}
+{"abstract": "  Spin-orbit coupling of planetary systems plays an important role in the\ndynamics and habitability of planets. However, symplectic integrators that can\naccurately simulate not only how orbit affects spin but also how spin affects\norbit have not been constructed for general systems. Thus, we develop\nsymplectic Lie-group integrators to simulate systems consisting gravitationally\ninteracting rigid bodies. A user friendly package (GRIT\n\\url{https://github.com/GRIT-RBSim/GRIT}) is provided and external forcings\nsuch as tidal interactions are also included. As a demonstration, this package\nis applied to Trappist-I. It shows that the differences in transit timing\nvariations due to spin-orbit coupling could reach a few min in ten year\nmeasurements, and strong planetary perturbations can push Trappist-I f, g and h\nout of the synchronized states.\n"}
+{"abstract": "  This paper studies a multi-armed bandit (MAB) version of the range-searching\nproblem. In its basic form, range searching considers as input a set of points\n(on the real line) and a collection of (real) intervals. Here, with each\nspecified point, we have an associated weight, and the problem objective is to\nfind a maximum-weight point within every given interval.\n  The current work addresses range searching with stochastic weights: each\npoint corresponds to an arm (that admits sample access) and the point's weight\nis the (unknown) mean of the underlying distribution. In this MAB setup, we\ndevelop sample-efficient algorithms that find, with high probability,\nnear-optimal arms within the given intervals, i.e., we obtain PAC (probably\napproximately correct) guarantees. We also provide an algorithm for a\ngeneralization wherein the weight of each point is a multi-dimensional vector.\nThe sample complexities of our algorithms depend, in particular, on the size of\nthe optimal hitting set of the given intervals.\n  Finally, we establish lower bounds proving that the obtained sample\ncomplexities are essentially tight. Our results highlight the significance of\ngeometric constructs -- specifically, hitting sets -- in our MAB setting.\n"}
+{"abstract": "  Biomedical networks are universal descriptors of systems of interacting\nelements, from protein interactions to disease networks, all the way to\nhealthcare systems and scientific knowledge. With the remarkable success of\nrepresentation learning in providing powerful predictions and insights, we have\nwitnessed a rapid expansion of representation learning techniques into\nmodeling, analyzing, and learning with such networks. In this review, we put\nforward an observation that long-standing principles of networks in biology and\nmedicine -- while often unspoken in machine learning research -- can provide\nthe conceptual grounding for representation learning, explain its current\nsuccesses and limitations, and inform future advances. We synthesize a spectrum\nof algorithmic approaches that, at their core, leverage graph topology to embed\nnetworks into compact vector spaces, and capture the breadth of ways in which\nrepresentation learning is proving useful. Areas of profound impact include\nidentifying variants underlying complex traits, disentangling behaviors of\nsingle cells and their effects on health, assisting in diagnosis and treatment\nof patients, and developing safe and effective medicines.\n"}
+{"abstract": "  We study a model of two-dimensional classical dimers on the square lattice\nwith strong geometric constraints (there is exactly one bond with the nearest\npoint for every point in the lattice). This model corresponds to the quantum\ndimer model suggested by D.S. Rokhsar and S.A. Kivelson (1988). We use the\ndirected-loop algorithm to show the system undergoes a Berezinskii-Kostelitz\nThousless transition (BKT transition) in finite temperatures. After that, if we\ndestroy the geometric constraint of dimers, the topological transition will\ntransfer to a quasi one-order transition. For the dimer updates, we also\nintroduce a new cluster updating algorithm called the edged cluster algorithm.\nBy this method, we succeed in rapidly traversing the winding (topological)\nsections uniformly and widening the effective matrical ensemble to include more\ntopological sections.\n"}
+{"abstract": "  We investigate inverse proximity effects in a spin-triplet superconductor\n(TSC) interfaced with a ferromagnet (FM), assuming different types of magnetic\nprofiles and chiral or helical pairings. The region of the coexistence of\nspin-triplet superconductivity and magnetism is significantly influenced by the\norientation and spatial extension of the magnetization with respect to the spin\nconfiguration of the Cooper pairs, resulting into clearcut anisotropy\nsignatures. A characteristic mark of the inverse proximity effect arises in the\ninduced spin-polarization at the TSC interface. This is unexpectedly stronger\nwhen the magnetic proximity is weaker, thus unveiling immediate detection\nsignatures for spin-triplet pairs. We show that an anomalous magnetic proximity\ncan occur at the interface between the itinerant ferromagnet, SrRuO$_3$, and\nthe unconventional superconductor Sr$_2$RuO$_4$. Such scenario indicates the\npotential to design characteristic inverse proximity effects in experimentally\navailable SrRuO$_3$-Sr$_2$RuO$_4$ heterostructures and to assess the occurrence\nof spin-triplet pairs in the highly debated superconducting phase of\nSr$_2$RuO$_4$.\n"}
+{"abstract": "  Neural Radiance Fields (NeRF) are able to reconstruct scenes with\nunprecedented fidelity, and various recent works have extended NeRF to handle\ndynamic scenes. A common approach to reconstruct such non-rigid scenes is\nthrough the use of a learned deformation field mapping from coordinates in each\ninput image into a canonical template coordinate space. However, these\ndeformation-based approaches struggle to model changes in topology, as\ntopological changes require a discontinuity in the deformation field, but these\ndeformation fields are necessarily continuous. We address this limitation by\nlifting NeRFs into a higher dimensional space, and by representing the 5D\nradiance field corresponding to each individual input image as a slice through\nthis \"hyper-space\". Our method is inspired by level set methods, which model\nthe evolution of surfaces as slices through a higher dimensional surface. We\nevaluate our method on two tasks: (i) interpolating smoothly between \"moments\",\ni.e., configurations of the scene, seen in the input images while maintaining\nvisual plausibility, and (ii) novel-view synthesis at fixed moments. We show\nthat our method, which we dub HyperNeRF, outperforms existing methods on both\ntasks. Compared to Nerfies, HyperNeRF reduces average error rates by 4.1% for\ninterpolation and 8.6% for novel-view synthesis, as measured by LPIPS.\nAdditional videos, results, and visualizations are available at\nhttps://hypernerf.github.io.\n"}
+{"abstract": "  Reconfigurable intelligent surface (RIS) emerges as a promising technology\nfor the next generation networks. In this paper, we utilize the tools from\nstochastic geometry to study the performance of a RIS-assisted millimeter wave\n(mmWave) cellular network. Specifically, the locations of the base stations\n(BSs) and the midpoints of the blockages are modeled as two independent Poisson\npoint processes (PPP) where the blockages are modeled by line boolean model and\na fraction of the blockages are coated with RISs. The distinguish\ncharacteristics of mmWave communications, i.e., the directional beamforming and\ndifferent path loss laws for line-of-sight (LOS) and non-line-of-sight (NLOS),\nare incorporated into the analysis. We derive the expressions of the coverage\nprobability and the area spectral efficiency. The coverage probability under\nthe special case where the blockage parameter is sufficiently small is also\nderived. Numerical results demonstrate that better coverage performance and\nhigher energy efficiency can be achieved by the large-scale deployment of RISs.\nIn addition, the tradeoff between the BS and RIS densities is investigated and\nthe results show that the RISs are excellent supplementary for the traditional\nnetworks to improve the coverage probability with limited power consumption.\n"}
+{"abstract": "  Method: In this study, a new method is introduced for distinguishing\nnoise-free segments of ECG from noisy segments that use sample amplitude\ndispersion with an adoptive threshold for variance of samples amplitude and a\nmethod which uses compatibility of detected beats in ECG and some of the other\nsignals which are related to the heart activity such as BP, arterial pressure\n(ART) and pulmonary artery pressure (PAP). A prioritization is applied in other\npulsatile signals based on the amplitude and clarity of the peaks on them, and\na fusion strategy is employed for segments on which ECG is noisy and other\navailable signals in the data, which contain peaks corresponding to R peak of\nthe ECG, are scored in three steps scoring function.\n"}
+{"abstract": "  With the proliferation of data movement across the Internet, global data\ntraffic per year has already exceeded the Zettabyte scale. The network\ninfrastructure and end-systems facilitating the vast data movement consume an\nextensive amount of electricity, measured in terawatt-hours per year. This\nmassive energy footprint costs the world economy billions of dollars partially\ndue to energy consumed at the network end-systems. Although extensive research\nhas been done on managing power consumption within the core networking\ninfrastructure, there is little research on reducing the power consumption at\nthe end-systems during active data transfers. This paper presents a novel\ncross-layer optimization framework, called Cross-LayerHLA, to minimize energy\nconsumption at the end-systems by applying machine learning techniques to\nhistorical transfer logs and extracting the hidden relationships between\ndifferent parameters affecting both the performance and resource utilization.\nIt utilizes offline analysis to improve online learning and dynamic tuning of\napplication-level and kernel-level parameters with minimal overhead. This\napproach minimizes end-system energy consumption and maximizes data transfer\nthroughput. Our experimental results show that Cross-LayerHLA outperforms other\nstate-of-the-art solutions in this area.\n"}
+{"abstract": "  Motivated by numerical schemes for large scale geophysical flow, we consider\nthe rotating shallow water and Boussinesq equations on the whole space with\nhorizontal kinetic energy backscatter source terms built from negative\nviscosity and stabilising hyperviscosity with constant parameters. We study the\nimpact of this energy input through various explicit flows, which are\nsimultaneously solving the nonlinear equations and the linear equations that\narise upon dropping the transport nonlinearity, i.e. the linearisation in the\nzero state. These include barotropic, parallel and Kolmogorov flows as well as\nmonochromatic inertia gravity waves. With focus on stable stratification we\nfind that the backscatter generates numerous solutions of this type that grow\nexponentially and unboundedly, also with vertical structure. This signifies the\npossibility of undesired energy concentration into specific modes due to the\nbackscatter. Families of steady state flows of this type arise as well and\nsuperposition principles in the nonlinear equations provide explicit sufficient\nconditions for instability of some of these. For certain steady barotropic\nflows of this type we provide numerical evidence of eigenmodes whose growth\nrates are proportional to the amplitude factor of the flow. For all other\narising steady solutions we prove this is not possible.\n"}
+{"abstract": "  In single particle analysis (SPA), the task is to recover the scattering\npotential of a macromolecular structure from cryo-electron microscope images of\nmany copies of the structure in unknown orientations. The idealized, noise-free\nSPA inverse problem has been shown to be uniquely solvable - up to hand - when\nthe forward model is based on the ray transform. More accurate forward models\ntake the non-zero curvature of the Ewald sphere into account. We analyze an\nEwald sphere corrected forward model for SPA and use the diffraction slice\ntheorem to prove that the corresponding inverse problem is uniquely solvable,\nincluding the hand of the structure.\n"}
+{"abstract": "  Polycrystalline solids can exhibit material properties that differ\nsignificantly from those of equivalent single-crystal samples, in part, because\nof a spontaneous redistribution of mobile point defects into so-called\nspace-charge regions adjacent to grain boundaries. The general analytical form\nof these space-charge regions is known only in the dilute limit, where\ndefect-defect correlations can be neglected. Using kinetic Monte Carlo\nsimulations of a three-dimensional Coulomb lattice gas, we show that\ngrain-boundary space-charge regions in non-dilute solid electrolytes exhibit\noverscreening -- damped oscillatory space-charge profiles -- and underscreening\n-- decay lengths that are longer than the corresponding Debye length and that\nincrease with increasing defect-defect interaction strength. Overscreening and\nunderscreening are known phenomena in concentrated liquid electrolytes, and the\nobservation of functionally analogous behaviour in solid electrolyte\nspace-charge regions suggests that the same underlying physics drives behaviour\nin both classes of systems. We therefore expect theoretical approaches\ndeveloped to study non-dilute liquid electrolytes to be equally applicable to\nfuture studies of solid electrolytes.\n"}
+{"abstract": "  We introduce the notion of $q$-analogs of strongly regular graphs and give\nsome first examples of such structures. We prove a necessary condition on the\nparameters and show the connection to designs over finite fields.\n"}
+{"abstract": "  Partial control is a technique used in systems with transient chaos. The aim\nof this control method is to avoid the escape of the orbits from a region Q of\nthe phase space where the transient chaotic dynamics takes place. This\ntechnique is based on finding a special subset of Q called the safe set. The\nchaotic orbit can be sustained in the safe set with a minimum amount of\ncontrol. In this work we develop a control strategy to gradually lead any\nchaotic orbit in Q to the safe set by using the safety function. With the\ntechnique proposed here, the safe set can be converted into a global attractor\nof Q.\n"}
+{"abstract": "  After affecting the world in unexpected ways, COVID-19 has started mutating\nwhich is evident with the insurgence of its new variants. The governments,\nhospitals, schools, industries, and humans, in general, are looking for a\npotential solution in the vaccine which will eventually be available but its\ntimeline for eradicating the virus is yet unknown. Several researchers have\nencouraged and recommended the use of good practices such as physical\nhealthcare monitoring, immunity-boosting, personal hygiene, mental healthcare,\nand contact tracing for slowing down the spread of the virus. In this article,\nwe propose the use of wearable/mobile sensors integrated with the Internet of\nEverything to cover the spectrum of good practices in an automated manner. We\npresent hypothetical frameworks for each of the good practice modules and\npropose the COvid-19 Resistance Framework using the Internet of Everything\n(CORFIE) to tie all the individual modules in a unified architecture. We\nenvision that CORFIE would be influential in assisting people with the new\nnormal for current and future pandemics as well as instrumental in halting the\neconomic losses, respectively. We also provide potential challenges and their\nprobable solutions in compliance with the proposed CORFIE.\n"}
+{"abstract": "  We study the homological algebra in the category $\\mathcal{P}_p$ of strict\npolynomial functors of degree $p$ over a field of positive characteristic $p$.\nWe determine the decomposition matrix of our category and we calculate the\nExt-groups between functors important from the point of view of representation\ntheory. Our results include computations of the Ext-algebras for simple\nfunctors and Schur functors and the Ext-groups between Schur and Weyl functors.\nWe observe that the category $\\mathcal{P}_p$ has a Kazhdan-Lusztig theory and\nwe show that the dg algebras computing the Ext-algebras for simple functors and\nSchur functors are formal. These last results allow one to describe the bounded\nderived category of $\\mathcal{P}_p$ as derived categories of certain explicitly\ndescribed graded algebras. We also generalize our results to all blocks of\n$p$-weight 1 in $\\mathcal{P}_e$ for $e > p$.\n"}
+{"abstract": "  Path planning for multiple robots (MRPP) represents a task of finding\nnon-colliding paths for robots through which they can navigate from their\ninitial positions to specified goal positions. The problem is usually modeled\nusing undirected graphs where robots move between vertices across edges.\nContemporary optimal solving algorithms include dedicated search-based methods,\nthat solve the problem directly, and compilation-based algorithms that reduce\nMRPP to a different formalism for which an efficient solver exists, such as\nconstraint programming (CP), mixed integer programming (MIP), or Boolean\nsatisfiability (SAT). In this paper, we enhance existing SAT-based algorithm\nfor MRPP via spartification of the set of candidate paths for each robot from\nwhich target Boolean encoding is derived. Suggested sparsification of the set\nof paths led to smaller target Boolean formulae that can be constructed and\nsolved faster while optimality guarantees of the approach have been kept.\n"}
+{"abstract": "  We define a stratification of the moduli stack of coherent sheaves on an\nelliptic curve which allows us (1) to give an explicit description of the\nirreducible components of the global nilpotent cone of elliptic curves, (2) to\nestablish an explicit bijection between the simple objects of the category of\nperverse sheaves defined by Schiffmann to categorify the elliptic Hall algebra\n(the so-called spherical Eisenstein sheaves) and the irreducible components of\nthe global nilpotent cone and (3) to give an explicit description and\nparametrization of the perverse sheaves on the moduli stack of coherent sheaves\non an elliptic curve having nilpotent singular support. Along the way, we find\na combinatorial parametrization of the irreducible components of the semistable\nlocus of the elliptic global nilpotent cone.\n"}
+{"abstract": "  Traditionally, it was accepted that a relational database can be normalized\nstep-by-step, from a set of un-normalized tables to tables in $1NF$, then to\n$2NF$, then to $3NF$, then (possibly) to $BCNF$. The rule applied to a table in\n$1NF$ in order to transform it to a set of tables in $2NF$ seems to be too\nstraightforward to pose any difficulty.\n  However, we show that, depending on the set of functional dependencies, it is\nimpossible to reach $2NF$ and stop there; one must, in these cases, perform the\nnormalization from $1NF$ to $3NF$ as an indecomposable move. The minimal setup\nto exhibit the phenomena requires a single composite key, and two partially\noverlapping chains of transitive dependencies.\n"}
+{"abstract": "  BL Lacertae (BL Lac) is categorized as TeV blazar and considered as a\npossible source of astrophysical neutrinos. In 2020, the brightest X-ray flare\never detected from it. A detailed study can answer many puzzling questions\nrelated to multiband emissions and fast-flux variability often seen in this\nkind of source. We have performed the temporal and spectral analysis of the\nbrightest flare. The variability is characterized by the fractional variability\namplitude and the variability time. We found that the source has crossed all\nits previous limits of flux and reached the maximum ever seen from it in\noptical and X-rays. It is highly variable in X-rays with fractional variability\nabove 100$\\%$ (1.8397$\\pm$0.0181) and the fastest variability time of 11.28\nhours within a day. The broadband light curves correlation with X-ray suggest a\ntime lag of one day. A broadband SED modeling is pursued to understand the\npossible physical mechanisms responsible for broadband emission. Modeling\nrequires two emission regions located at two different sites to explain the low\nand high flux states. A significant spectral change is observed in the\noptical-UV and X-ray spectrum during the high state, which eventually leads to\nshifts in the location of the synchrotron peak towards higher energy,\nsuggesting an emergence of a new HBL component.\n"}
+{"abstract": "  A reliable, remote, and continuous real-time respiratory sound monitor with\nautomated respiratory sound analysis ability is urgently required in many\nclinical scenarios-such as in monitoring disease progression of coronavirus\ndisease 2019-to replace conventional auscultation with a handheld stethoscope.\nHowever, a robust computerized respiratory sound analysis algorithm has not yet\nbeen validated in practical applications. In this study, we developed a lung\nsound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds\n(duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels,\n13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze\nlabels, 686 stridor labels, and 4,740 rhonchi labels), and 15,606 discontinuous\nadventitious sound labels (all crackles). We conducted benchmark tests for long\nshort-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM\n(BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM,\nCNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and\nadventitious sound detection. We also conducted a performance comparison\nbetween the LSTM-based and GRU-based models, between unidirectional and\nbidirectional models, and between models with and without a CNN. The results\nrevealed that these models exhibited adequate performance in lung sound\nanalysis. The GRU-based models outperformed, in terms of F1 scores and areas\nunder the receiver operating characteristic curves, the LSTM-based models in\nmost of the defined tasks. Furthermore, all bidirectional models outperformed\ntheir unidirectional counterparts. Finally, the addition of a CNN improved the\naccuracy of lung sound analysis, especially in the CAS detection tasks.\n"}
+{"abstract": "  In a wireless network, gathering information at the base station about mobile\nusers based only on uplink channel measurements is an interesting challenge.\nIndeed, accessing the users locations and predicting their downlink channels\nwould be particularly useful in order to optimize the network efficiency. In\nthis paper, a supervised machine learning approach addressing these tasks in an\nunified way is proposed. It relies on a labeled database that can be acquired\nin a simple way by the base station while operating. The proposed regression\nmethod can be seen as a computationally efficient two layers neural network\ninitialized with a non-parametric estimator. It is illustrated on realistic\nchannel data, both for the positioning and channel mapping tasks, achieving\nbetter results than previously proposed approaches, at a lower cost.\n"}
+{"abstract": "  Reconfigurable intelligent surfaces (RISs) have emerged as a cost- and\nenergy-efficient technology that can customize and program the physical\npropagation environment by reflecting radio waves in preferred directions.\nHowever, the purely passive reflection of RISs not only limits the end-to-end\nchannel beamforming gains, but also hinders the acquisition of accurate channel\nstate information for the phase control at RISs. In this paper, we provide an\noverview of a hybrid relay-reflecting intelligent surface (HR-RIS)\narchitecture, in which only a few elements are active and connected to power\namplifiers and radio frequency chains. The introduction of a small number of\nactive elements enables a remarkable system performance improvement which can\nalso compensate for losses due to hardware impairments such as the deployment\nof limited-resolution phase shifters. Particularly, the active processing\nfacilitates efficient channel estimation and localization at HR-RISs. We\npresent two practical architectures for HR-RISs, namely, fixed and dynamic\nHR-RISs, and discuss their applications to beamforming, channel estimation, and\nlocalization. The benefits, key challenges, and future research directions for\nHR-RIS-aided communications are also highlighted. Numerical results for an\nexemplary deployment scenario show that HR-RISs with only four active elements\ncan attain up to 42.8 percent and 41.8 percent improvement in spectral\nefficiency and energy efficiency, respectively, compared with conventional\nRISs.\n"}
+{"abstract": "  The standard way to demonstrate the relevance of chiral symmetry for the NN\ninteraction is to consider higher partial waves of NN scattering which are\ncontrolled entirely by chiral pion-exchanges (since contacts vanish). However,\nin applications of NN-potentials to nuclear structure and reactions, the lower\npartial waves are the important ones, making the largest contributions. Lower\npartial waves are sensitive to the short-range potential, and so, when the\nshort-range contacts were to dominate over the chiral pion-contributions in\nlower partial waves, then the predictions from \"chiral potentials\" would have\nlittle to do with chiral symmetry. To address this issue, we investigate\nsystematically the role of the (chiral) one- and two-pion exchanges, on the one\nhand, and the effect of the contacts, on the other hand, in the lower partial\nwaves of NN scattering. We are able to clearly identify the signature of chiral\nsymmetry in lower partial waves. Our study has also a pedagogical spin-off as\nit demonstrates in detail how the reproduction of the lower partial-wave phase\nshifts comes about from the various ingredients of the theory.\n"}
+{"abstract": "  Multilayer position-sensitive 10B-RPC thermal neutron detectors offer an\nattractive combination of sub-millimeter spatial resolution and high (>50%)\ndetection efficiency. Here we describe a new position reconstruction method\nbased on a statistical approach. Using experimental data, we compare the\nperformance of this method with that of the centroid reconstruction. Both\nmethods result in a similar image linearity/uniformity and spatial resolution.\nHowever, the statistical method allows to improve the image quality at the\ndetector periphery, offers more flexible event filtering and allows to develop\nautomatic quality monitoring procedures for early detection of situations when\na change in the detector operation conditions starts to affect reconstruction\nquality.\n"}
+{"abstract": "  The advent of increasingly large and complex datasets has fundamentally\naltered the way that scientists conduct astronomy research. The need to work\nclosely to the data has motivated the creation of online science platforms,\nwhich include a suite of software tools and services, therefore going beyond\ndata storage and data access. We present two example applications of Jupyter as\na part of astrophysical science platforms for professional researchers and\nstudents. First, the Astro Data Lab is developed and operated by NOIRLab with a\nmission to serve the astronomy community with now over 1500 registered users.\nSecond, the Dark Energy Spectroscopic Instrument science platform serves its\ngeographically distributed team comprising about 900 collaborators from over 90\ninstitutions. We describe the main uses of Jupyter and the interfaces that\nneeded to be created to embed it within science platform ecosystems. We use\nthese examples to illustrate the broader concept of empowering researchers and\nproviding them with access to not only large datasets but also cutting-edge\nsoftware, tools, and data services without requiring any local installation,\nwhich can be relevant for a wide range of disciplines. Future advances may\ninvolve science platform networks, and tools for simultaneously developing\nJupyter notebooks to facilitate collaborations.\n"}
+{"abstract": "  Generally, people behave in social dilemmas such as proself and prosocial.\nHowever, inside social groups, people have a tendency to choose prosocial\nalternatives due to in-group favoritism. The bioelectrical activity of the\nhuman brain shows the differences between proself and prosocial exist even out\nof a socialized group. Moreover, a group socialization strengthens these\ndifferences. We used EEG System, \"Neuron-Spectrum-4/EPM\" (16 channels), to\ntrack the brain bioelectrical activity during decision making in laboratory\nexperiments with the Prisoner's dilemma game and the short-term socialization\nstage. We compared the spatial distribution of the spectral density during the\ndifferent experimental parts. The noncooperative decision was characterized by\nthe increased values of spectral the beta rhythm in the orbital regions of\nprefrontal cortex. The cooperative choice, on the contrary, was accompanied by\nthe theta-rhythm activation in the central cortex regions in both hemispheres\nand the high-frequency alpha rhythm in the medial regions of the prefrontal\ncortex. People who increased the cooperation level after the socialization\nstage was initially different from the ones who decreased the cooperation in\nterms of the bioelectrical activity. Well-socialized participants differed by\nincreased values of spectral density of theta-diapason and decreased values of\nspectral density of beta-diapason in the middle part of frontal lobe. People\nwho decreased the cooperation level after the socialization stage was\ncharacterized by decreased values of spectral density of alpha rhythm in the\nmiddle and posterior convex regions of both hemispheres.\n"}
+{"abstract": "  Let G be a reductive group and L a Levi subgroup. Parabolic induction and\nrestriction are a pair of adjoint functors between Ad-equivariant derived\ncategories of either constructible sheaves or (not necessarily holonomic)\nD-modules on G and L, respectively. Bezrukavnikov and Yom Din proved,\ngeneralizing a classic result of Lusztig, that these functors are exact. In\nthis paper, we consider a special case where L=T is a maximal torus. We give\nexplicit formulas for parabolic induction and restriction in terms of the\nHarish-Chandra D-module on G x T. We show that this module is flat over D(T),\nwhich easily implies that parabolic induction and restriction are exact\nfunctors between the corresponding abelian categories of D-modules.\n"}
+{"abstract": "  In this paper we investigate the $(g-2)_\\mu$ discrepancy in the context of\nthe R-parity conserving next-to-minimal supersymmetric Standard Model plus\nright-handed neutrinos superfields. The model has the ability to reproduce\nneutrino physics data and includes the interesting possibility to have the\nright-handed sneutrino as the lightest supersymmetric particle and a viable\ndark matter candidate. Since right-handed sneutrinos are singlets, no new\ncontributions for $\\delta a_{\\mu}$ with respect to the MSSM and NMSSM are\npresent. However, the possibility to have the right-handed sneutrino as the\nlightest supersymmetric particle opens new ways to escape Large Hadron Collider\nand direct detection constraints. In particular, we find that dark matter\nmasses within $10 \\lesssim m_{\\tilde{\\nu}_{R}} \\lesssim 600$ GeV are fully\ncompatible with current experimental constraints. Remarkably, not only spectra\nwith light sleptons are needed, but we obtain solutions with $m_{\\tilde{\\mu}}\n\\gtrsim 600$ GeV in the entire dark matter mass range that could be probed by\nnew $(g-2)_\\mu$ data in the near future. In addition, dark matter direct\ndetection experiments will be able to explore a sizable portion of the allowed\nparameter space with $m_{\\tilde{\\nu}_{R}} \\lesssim 300$ GeV, while indirect\ndetection experiments will be able to probe a much smaller fraction within $200\n\\lesssim m_{\\tilde{\\nu}_{R}} \\lesssim 350$ GeV.\n"}
+{"abstract": "  We analyze the early stage of evolution of a universe with two scale factors\nproposed in [1] when matter is present. The scale factors describe two causally\ndisconnected patches of the universe interacting trough a non-trivial Poisson\nbracket structure in the momentum sector characterized by one parameter \\kappa.\nWe studied two scenarios in which one of the patches is always filled with\nrelativistic matter while the other contains relativistic matter in one case,\nand non-relativistic matter in the second case. By solving numerically the set\nof equations governing the dynamics, we found that the energy content of one\nsector drains to the other and from here it is possible to constraint the\ndeformation parameter \\kappa by imposing that the decay of the energy density\nhappens, at most, at the Big Bang Nucleosynthesis temperature in order to\nreturn to the usual behavior of radiation. The relation with Non Standard\nCosmologies is also addressed.\n"}
+{"abstract": "  Activity and self-generated motion are fundamental features observed in many\nliving and non-living systems. Given that inter-particle adhesive forces are\nknown to regulate particle dynamics, we investigate how adhesion strength\ncontrols the boundary growth and roughness in an active particle aggregate.\nUsing particle based simulations incorporating both activity (birth, death and\ngrowth) and systematic physical interactions (elasticity and adhesion), we\nestablish that inter-particle adhesion strength ($f^{ad}$) controls the surface\nroughness of a densely packed three-dimensional(3D) active particle aggregate\nexpanding into a highly viscous medium. We discover that the surface roughness\nof a 3D active particle aggregate increases in proportion to the inter-particle\nadhesion strength, $f^{ad}$. We show that asymmetry in the radial and\ntangential active particle mean squared displacement (MSD) suppresses 3D\nsurface roughness at lower adhesion strengths. By analyzing the statistical\nproperties of particle displacements at the aggregate periphery, we determine\nthat the 3D surface roughness is driven by the movement of active particle\ntowards the core at high inter-particle adhesion strengths. Our results\nelucidate the physics controlling the expansion of adhesive 3D active particle\ncollectives into a highly viscous medium, with implications into understanding\nstochastic interface growth in active matter systems characterized by self\ngenerated particle flux.\n"}
+{"abstract": "  Group IV and V monolayers are the promising state-of-the-art 2D materials\nowing to their high carrier mobility, tunable bandgaps, and optical linear\ndichroism along with outstanding electronic and thermoelectric properties.\nFurthermore, recent studies reveal the stability of free-standing 2D monolayers\nby hydrogenation. Inspired by this, we systematically predict and investigate\nthe structure and properties of various hydrogen saturated silicon phosphide\n(H-Si-P) monolayers, based on first-principles calculations. According to the\nresults, H-Si-P monolayers belong to indirect bandgap semiconductors with a\nhighly stable structure. Their bandgaps and band edge positions assessed using\naccurate hybrid functional are shown to be effectively adjusted by applying a\nbiaxial strain. Furthermore, the absorption spectra of these monolayers,\nsimulated in the context of time-dependent density functional theory, exhibit\ntheir excellent potential for solar energy conversion and visible-light-driven\nphotocatalytic water splitting. In this respect, this work provides valuable\nguidance for finding more 2D semiconductors and nanostructures for\nnanoelectronic and optoelectronic applications, as well as for photocatalytic\nwater splitting.\n"}
+{"abstract": "  Antiferromagnetism (AF) such as Neel ordering is often closely related to\nCoulomb interactions such as Hubbard repulsion in two-dimensional (2D) systems.\nWhether Neel AF ordering in 2D can be dominantly induced by electron-phonon\ncouplings (EPC) has not been completely understood. Here, by employing\nnumerically-exact sign-problem-free quantum Monte Carlo (QMC) simulations, we\nshow that optical Su-Schrieffer-Heeger (SSH) phonons with frequency $\\omega$\nand EPC constant $\\lambda$ can induce AF ordering for a wide range of phonon\nfrequency $\\omega>\\omega_c$. For $\\omega<\\omega_c$, a valence-bond-solid (VBS)\norder appears and there is a direct quantum phase transition between VBS and AF\nphases at $\\omega_c$. The phonon mechanism of the AF ordering is related to the\nfact that SSH phonons directly couple to electron hopping whose second-order\nprocess can induce an effective AF spin exchange. Our results shall shed new\nlights to understanding AF ordering in correlated quantum materials.\n"}
+{"abstract": "  This study aimed to investigate the antitumor effect of ultrasmall gold\nnanoparticles (AuNPs), around 3 nm, stabilized by the anionic polysaccharide\ngum arabic (GA-AuNPs). The focus was downregulation of cancer hallmarks of\naggressive tumors, using a highly metastatic model of melanoma. We first\ndemonstrated that GA-AuNPs showed excellent stability under biological\nenvironment. Non-cytotoxic concentrations to seven different cell lines,\nincluding tumorigenic and non-tumorigenic cells, were determined by standard 2D\nin vitro assays. Gold concentrations below 2.4 mg L-1 were non-cytotoxic and\ntherefore chosen for further analyses. Cells exposed to GA-AuNPs were uptaken\nby melanoma cells through endocytic processes. Next, we described remarkable\nbiological properties using non-cytotoxic concentrations of this nanomaterial.\nInvasion through an extracellular matrix barrier as well as 3D growth capacity\n(anchorage-independent colony formation and spheroids growth) were negatively\naffected by 2.4 mg L-1 GA-AuNPs. Additionally, exposed spheroids showed\nmorphological changes, suggesting that GA-AuNPs could penetrate into the\npreformed tumor and affect its integrity. All together these results\ndemonstrate that side effects, such as cytotoxicity, can be avoided by choosing\nthe right concentration, nevertheless, preserving desirable effects such as\nmodulation of key tumor cell malignancy features.\n"}
+{"abstract": "  In this paper we present results of determination of the statistical\nprecision of Higgs branching fraction measurement, for Higgs decay to ZZ$^\\ast$\npairs at 3 TeV and 350 GeV CLIC. Measurements are simulated with the CLIC_ILD\ndetector model, taking into consideration all relevant physics and beam-induced\nbackground processes. It is shown that the product of the branching fraction\nBR(${H\\rightarrow\\thinspace ZZ^\\ast}$) and the Higgs production cross-section\ncan be measured with a relative statistical uncertainty of 3% (18%) at 3 TeV\n(350 GeV) center-of-mass energy, using semileptonic final states and assuming\nintegrated luminosity of 5 (1) ab$^{-1}$.\n"}
+{"abstract": "  The interaction of strong pulsed femtosecond laser field with atoms having\nthree equivalent electrons in the outer shell ($p^3$ configuration, e.g.\nnitrogen) is studied via numerical integration of a time-dependent\nSchr\\\"{o}dinger equation on a grid approach. Single, double and triple\nionization yields originating from a completely antisymmetric wave function are\ncalculated and extracted using a restricted-geometry model with the soft-core\npotential and three active electrons. The direct triple ionization channel is\nfound to produce a larger yield than the channel connected with single and then\ndirect double ionization. Compared against earlier results investigating the $n\ns^ 2 n p^1$ configuration, we propose that the differences found here might in\nfact be accessible through electron's momentum distribution.\n"}
+{"abstract": "  We propose nonvolatile binary stable states based on microelectromechanical\nsystems (MEMS). They may be used as MEMS memory, where the bistable memory is\nthe buckling direction of a plate of a capacitor. The binary memory is switched\nby applying a voltage to the MEMS. Furthermore, we propose an Ising model by\nmaking a series of buckled MEMS. It is a great merit of this mechanism that the\nannealing process is automatically carried out due to the damping effect of the\nspring. We find a duality relation between the parallel-plate MEMS under\nconstant-voltage condition and the comb-teeth MEMS under constant-charge\ncondition.\n"}
+{"abstract": "  New carbon forms exhibiting extraordinary physico-chemical properties can be\ngenerated from nanostructured precursors under extreme pressure. Nevertheless,\nsynthesis of such fascinating materials is often not well understood that\nresults, as is the case of C60 precursor, in irreproducibility of the results\nand impeding further progress in the materials design. Here the semiconducting\namorphous carbon having bandgaps of 0.1-0.3 eV and the advantages of isotropic\nsuperhardness and superior toughness over single-crystal diamond and inorganic\nglasses are produced from transformation of fullerene at high pressure and\nmoderate temperatures. A systematic investigation of the structure and bonding\nevolution was carried out by using rich arsenal of complimentary\ncharacterization methods, which helps to build a model of the transformation\nthat can be used in further high p,T synthesis of novel nanocarbon systems for\nadvanced applications. The produced amorphous carbon materials have the\npotential of demanding optoelectronic applications that diamond and graphene\ncannot achieve\n"}
+{"abstract": "  Abolition is the process of destroying and then rebuilding the structures\nthat impede liberation. This paper addresses the particular case of Black folk\nin the United States, but is relevant to the global decolonization movement.\nUsing notions of abolition and infrastructures of feeling developed by Ruth\nWilson Gilmore, I view Historically Black Colleges and Universities ( HBCUs )\nas a particular kind of abolitionist project, created for the explicit purpose\nof nurturing and sustaining Black excellence particularly within the sciences.\nI then examine how artificial intelligence (AI) in particular and computing in\ngeneral have contributed to racial oppression and the further confinement and\ndiminishing of Black existence. I conclude by examining how the space held by\nHBCUs in computing might contribute to a re-imagining of AI as a technology\nthat enhances the possibility and actualization of Black life.\n"}
+{"abstract": "  Face recognition (FR) systems are fast becoming ubiquitous. However,\ndifferential performance among certain demographics was identified in several\nwidely used FR models. The skin tone of the subject is an important factor in\naddressing the differential performance. Previous work has used modeling\nmethods to propose skin tone measures of subjects across different\nilluminations or utilized subjective labels of skin color and demographic\ninformation. However, such models heavily rely on consistent background and\nlighting for calibration, or utilize labeled datasets, which are time-consuming\nto generate or are unavailable. In this work, we have developed a novel and\ndata-driven skin color measure capable of accurately representing subjects'\nskin tone from a single image, without requiring a consistent background or\nillumination. Our measure leverages the dichromatic reflection model in RGB\nspace to decompose skin patches into diffuse and specular bases.\n"}
+{"abstract": "  The interplay between structural and electronic degrees of freedom in complex\nmaterials is the subject of extensive debate in physics and materials science.\nParticularly interesting questions pertain to the nature and extent of\npre-transitional short-range order in diverse systems ranging from shape-memory\nalloys to unconventional superconductors, and how this microstructure affects\nmacroscopic properties. Here we use neutron and X-ray scattering to uncover\nuniversal structural fluctuations in La$_{2-x}$Sr$_x$CuO$_4$ and\nTl$_2$Ba$_2$CuO$_{6+{\\delta}}$, two cuprate superconductors with distinct point\ndisorder effects and optimal superconducting transition temperatures. The\nfluctuations are present in wide doping and temperature ranges, including\ncompositions that maintain high average structural symmetry, and they exhibit\nunusual, yet simple scaling laws. We relate this behavior to pre-transitional\nphenomena in a broad class of systems with martensitic transitions, and argue\nthat it can be understood as a rare-region effect caused by intrinsic, doping-\nand compound-independent nanoscale inhomogeneity. We also uncover remarkable\nparallels with superconducting fluctuations, which indicates that the\nunderlying inhomogeneity plays a pivotal role in cuprate physics.\n"}
+{"abstract": "  In this paper, we investigate when system identification is statistically\neasy or hard, in the finite sample regime. Statistically easy to learn linear\nsystem classes have sample complexity that is polynomial with the system\ndimension. Most prior research in the finite sample regime falls in this\ncategory, focusing on systems that are directly excited by process noise.\nStatistically hard to learn linear system classes have worst-case sample\ncomplexity that is at least exponential with the system dimension, regardless\nof the identification algorithm. Using tools from minimax theory, we show that\nclasses of linear systems can be hard to learn. Such classes include, for\nexample, under-actuated or under-excited systems with weak coupling among the\nstates. Having classified some systems as easy or hard to learn, a natural\nquestion arises as to what system properties fundamentally affect the hardness\nof system identifiability. Towards this direction, we characterize how the\ncontrollability index of linear systems affects the sample complexity of\nidentification. More specifically, we show that the sample complexity of\nrobustly controllable linear systems is upper bounded by an exponential\nfunction of the controllability index. This implies that identification is easy\nfor classes of linear systems with small controllability index and potentially\nhard if the controllability index is large. Our analysis is based on recent\nstatistical tools for finite sample analysis of system identification as well\nas a novel lower bound that relates controllability index with the least\nsingular value of the controllability Gramian.\n"}
+{"abstract": "  Mathematical models are becoming increasingly important in magnetic resonance\nimaging (MRI), as they provide a mechanistic approach for making a link between\ntissue microstructure and signals acquired using the medical imaging\ninstrument. The Bloch equations, which describes spin and relaxation in a\nmagnetic field, is a set of integer order differential equations with a\nsolution exhibiting mono-exponential behaviour in time. Parameters of the model\nmay be estimated using a non-linear solver, or by creating a dictionary of\nmodel parameters from which MRI signals are simulated and then matched with\nexperiment. We have previously shown the potential efficacy of a magnetic\nresonance fingerprinting (MRF) approach, i.e. dictionary matching based on the\nclassical Bloch equations, for parcellating the human cerebral cortex. However,\nthis classical model is unable to describe in full the mm-scale MRI signal\ngenerated based on an heterogenous and complex tissue micro-environment. The\ntime-fractional order Bloch equations has been shown to provide, as a function\nof time, a good fit of brain MRI signals. We replaced the integer order Bloch\nequations with the previously reported time-fractional counterpart within the\nMRF framework and performed experiments to parcellate human gray matter, which\nis cortical brain tissue with different cyto-architecture at different spatial\nlocations. Our findings suggest that the time-fractional order parameters,\n{\\alpha} and {\\beta}, potentially associate with the effect of interareal\narchitectonic variability, hypothetically leading to more accurate cortical\nparcellation.\n"}
+{"abstract": "  The iron-chalcogenide high temperature superconductor Fe(Se,Te) (FST) has\nbeen reported to exhibit complex magnetic ordering and nontrivial band topology\nwhich may lead to novel superconducting phenomena. However, the recent studies\nhave so far been largely concentrated on its band and spin structures while its\nmesoscopic electronic and magnetic response, crucial for future device\napplications, has not been explored experimentally. Here, we used scanning\nsuperconducting quantum interference device microscopy for its sensitivity to\nboth local diamagnetic susceptibility and current distribution in order to\nimage the superfluid density and supercurrent in FST. We found that in FST with\n10% interstitial Fe, whose magnetic structure was heavily disrupted, bulk\nsuperconductivity was significantly suppressed whereas edge still preserved\nstrong superconducting diamagnetism. The edge dominantly carried supercurrent\ndespite of a very long magnetic penetration depth. The temperature dependence\nof the superfluid density and supercurrent distribution were distinctively\ndifferent between the edge and the bulk. Our Heisenberg modeling showed that\nmagnetic dopants stabilize anti-ferromagnetic spin correlation along the edge,\nwhich may contribute towards its robust superconductivity. Our observations\nhold implication for FST as potential platforms for topological quantum\ncomputation and superconducting spintronics.\n"}
+{"abstract": "  This is the second lecture note on the error analysis of interpolation on\nsimplicial elements without the shape regularity assumption (the previous one\nis arXiv:1908.03894). In this manuscript, we explain the error analysis of\nLagrange interpolation on (possibly anisotropic) tetrahedrons. The manuscript\nis not intended to be a research paper. We hope that, in the future, it will be\nmerged into a textbook on the mathematical theory of the finite element\nmethods.\n"}
+{"abstract": "  Inertial-sensor-based attitude estimation is a crucial technology in various\napplications, from human motion tracking to autonomous aerial and ground\nvehicles. Application scenarios differ in characteristics of the performed\nmotion, presence of disturbances, and environmental conditions. Since\nstate-of-the-art attitude estimators do not generalize well over these\ncharacteristics, their parameters must be tuned for the individual motion\ncharacteristics and circumstances. We propose RIANN, a ready-to-use, neural\nnetwork-based, parameter-free, real-time-capable inertial attitude estimator,\nwhich generalizes well across different motion dynamics, environments, and\nsampling rates, without the need for application-specific adaptations. We\ngather six publicly available datasets of which we exploit two datasets for the\nmethod development and the training, and we use four datasets for evaluation of\nthe trained estimator in three different test scenarios with varying practical\nrelevance. Results show that RIANN outperforms state-of-the-art attitude\nestimation filters in the sense that it generalizes much better across a\nvariety of motions and conditions in different applications, with different\nsensor hardware and different sampling frequencies. This is true even if the\nfilters are tuned on each individual test dataset, whereas RIANN was trained on\ncompletely separate data and has never seen any of these test datasets. RIANN\ncan be applied directly without adaptations or training and is therefore\nexpected to enable plug-and-play solutions in numerous applications, especially\nwhen accuracy is crucial but no ground-truth data is available for tuning or\nwhen motion and disturbance characteristics are uncertain. We made RIANN\npublicly available.\n"}
+{"abstract": "  In this paper, we introduce the Kaizen framework that uses a continuously\nimproving teacher to generate pseudo-labels for semi-supervised speech\nrecognition (ASR). The proposed approach uses a teacher model which is updated\nas the exponential moving average (EMA) of the student model parameters. We\ndemonstrate that it is critical for EMA to be accumulated with full-precision\nfloating point. The Kaizen framework can be seen as a continuous version of the\niterative pseudo-labeling approach for semi-supervised training. It is\napplicable for different training criteria, and in this paper we demonstrate\nits effectiveness for frame-level hybrid hidden Markov model-deep neural\nnetwork (HMM-DNN) systems as well as sequence-level Connectionist Temporal\nClassification (CTC) based models.\n  For large scale real-world unsupervised public videos in UK English and\nItalian languages the proposed approach i) shows more than 10% relative word\nerror rate (WER) reduction over standard teacher-student training; ii) using\njust 10 hours of supervised data and a large amount of unsupervised data closes\nthe gap to the upper-bound supervised ASR system that uses 650h or 2700h\nrespectively.\n"}
+{"abstract": "  In logic-based knowledge representation, query answering has essentially\nreplaced mere satisfiability checking as the inferencing problem of primary\ninterest. For knowledge bases in the basic description logic ALC, the\ncomputational complexity of conjunctive query (CQ) answering is well known to\nbe ExpTime-complete and hence not harder than satisfiability. This does not\nchange when the logic is extended by certain features (such as counting or role\nhierarchies), whereas adding others (inverses, nominals or transitivity\ntogether with role-hierarchies) turns CQ answering exponentially harder. We\ncontribute to this line of results by showing the surprising fact that even\nextending ALC by just the Self operator - which proved innocuous in many other\ncontexts - increases the complexity of CQ entailment to 2ExpTime. As common for\nthis type of problem, our proof establishes a reduction from alternating Turing\nmachines running in exponential space, but several novel ideas and encoding\ntricks are required to make the approach work in that specific, restricted\nsetting.\n"}
+{"abstract": "  The Aharonov-Bohm (A-B) effect showed that the phase of electron wave pattern\ncould be changed by the excluded electromagnetic field, the region where\nelectromagnetic field is zero. This apparent non-local effect has been\nexplained by mainly two salient interpretations called \"the interpretation of\nelectromagnetic potentials\" and \"the interpretation of interaction energy\". In\nthis paper the author reviews and investigates both the interpretations and\nanalysis their differences. Then the author clarifies in details and extend the\napproach of interaction energy interpretation and argue that as quantum\nmechanics involved energy of quantum systems and electromagnetic energy\ncontained in electromagnetic field, the natural and more physically acceptable\ninterpretation of the effect should be \"the interpretation of interaction\nenergy\".\n"}
+{"abstract": "  Stellar tidal streams are sensitive tracers of the properties of the\ngravitational potential in which they orbit and detailed observations of their\ndensity structure can be used to place stringent constraints on fluctuations in\nthe potential caused by, e.g., the expected populations of dark matter subhalos\nin the standard cold dark matter paradigm (CDM). Simulations of the evolution\nof stellar streams in live $N$-body halos without low-mass dark-matter\nsubhalos, however, indicate that streams exhibit significant perturbations on\nsmall scales even in the absence of substructure. Here we demonstrate, using\nhigh-resolution $N$-body simulations combined with sophisticated semi-analytic\nand simple analytic models, that the mass resolutions of\n$10^4$--$10^5\\,\\rm{M}_{\\odot}$ commonly used to perform such simulations cause\nspurious stream density variations with a similar magnitude on large scales as\nthose expected from a CDM-like subhalo population and an order of magnitude\nlarger on small, yet observable, scales. We estimate that mass resolutions of\n$\\approx100\\,\\rm{M}_{\\odot}$ ($\\approx1\\,\\rm{M}_{\\odot}$) are necessary for\nspurious, numerical density variations to be well below the CDM subhalo\nexpectation on large (small) scales. That streams are sensitive to a\nsimulation's particle mass down to such small masses indicates that streams are\nsensitive to dark matter clustering down to these low masses if a significant\nfraction of the dark matter is clustered or concentrated in this way, for\nexample, in MACHO models with masses of $10$--$100\\,\\rm{M}_{\\odot}$.\n"}
+{"abstract": "  The relic gravitational wave background due to tensor linear perturbations\ngenerated during Higgs inflation is computed. Both the Standard Model and a\nwell-motivated phenomenological completion (that accounts for all the\nexperimentally confirmed evidence of new physics) are considered. We focus on\ncritical Higgs inflation, which improves on the non-critical version and\nfeatures an amplification of the tensor fluctuations. The latter property\nallows us to establish that future space-borne interferometers, such as DECIGO,\nBBO and ALIA, may detect the corresponding primordial gravitational waves.\n"}
+{"abstract": "  Building footprint segmentations for high resolution images are increasingly\ndemanded for many remote sensing applications. By the emerging deep learning\napproaches, segmentation networks have made significant advances in the\nsemantic segmentation of objects. However, these advances and the increased\naccess to satellite images require the generation of accurate object boundaries\nin satellite images. In the current paper, we propose a novel network-based on\nPix2Pix methodology to solve the problem of inaccurate boundaries obtained by\nconverting satellite images into maps using segmentation networks in order to\nsegment building footprints. To define the new network named G2G, our framework\nincludes two generators where the first generator extracts localization\nfeatures in order to merge them with the boundary features extracted from the\nsecond generator to segment all detailed building edges. Moreover, different\nstrategies are implemented to enhance the quality of the proposed networks'\nresults, implying that the proposed network outperforms state-of-the-art\nnetworks in segmentation accuracy with a large margin for all evaluation\nmetrics. The implementation is available at\nhttps://github.com/A2Amir/A-Novel-Adaptive-Deep-Network-for-Building-Footprint-Segmentation.\n"}
+{"abstract": "  We present a constrained density functional perturbation theory scheme for\nthe calculation of structural and harmonic vibrational properties of insulators\nin the presence of an excited and thermalized electron-hole plasma. The method\nis ideal to tame ultrafast light induced structural transitions in the regime\nwhere the photocarriers thermalize faster than the lattice, the electron-hole\nrecombination time is longer than the phonon period and the photocarrier\nconcentration is large enough to be approximated by an electron-hole plasma.\nThe complete derivation presented here includes total energy, forces and stress\ntensor, variable cell structural optimization, harmonic vibrational properties\nand the electron-phonon interaction. We discuss in detail the case of zone\ncenter optical phonons not conserving the number of electrons and inducing a\nFermi shift in the photo-electron and hole distributions. We validate our\nimplementation by comparing with finite differences in Te and VSe2. By\ncalculating the evolution of the phonon spectrum of Te, Si and GaAs as a\nfunction of the fluence of the incoming laser light, we demonstrate that even\nat low fluences, corresponding to approximately 0.1 photocarriers per cell, the\nphonon spectrum is substantially modified with respect to the ground state one\nwith new Kohn anomalies appearing and a substantial softening of zone center\noptical phonons. Our implementation can be efficiently used to detect\nreversible transient phases and irreversible structural transition induced by\nultrafast light absorption.\n"}
+{"abstract": "  The absolute/relative debate on the nature of space and time is ongoing for\nthousands of years. Here we attempt to investigate space and time from the\ninformation theoretic point of view to understand spatial and temporal\ncorrelations under the relative assumption. Correlations, as a measure of\nrelationship between two quantities, do not distinguish space and time in\nclassical probability theory; quantum correlations in space are well-studied\nbut temporal correlations are not well understood. The thesis investigates\nquantum correlations in space-time, by treating temporal correlations equally\nin form as spatial correlations and unifying quantum correlations in space and\ntime. In particular, we follow the pseudo-density matrix formalism in which\nquantum states in spacetime are properly defined by correlations from\nmeasurements. We first review classical correlations, quantum correlations in\nspace and time, to motivate the pseudo-density matrix formalism in finite\ndimensions. Next we generalise the pseudo-density matrix formulation to the\nGaussian case, general continuous variables via Wigner representations, and\ngeneral measurement processes like weak measurements. Then we compare the\npseudo-density matrix formalism with other spacetime formulations: indefinite\ncausal structures, consistent histories, generalised non-local games,\nout-of-time-order correlation functions, and path integrals. We argue that in\nnon-relativistic quantum mechanics, different spacetime formulations are\nclosely related via quantum correlations, except path integrals. Finally, we\napply the pseudo-density matrix formulation to time crystals. By defining time\ncrystals as long-range order in time, we analyse continuous and discrete time\ntranslation symmetry as well as discuss the existence of time crystals from an\nalgebraic point of view. Finally, we summarise our work and provide the outlook\nfor future directions.\n"}
+{"abstract": "  Building on the embedding of an $n$-abelian category $\\mathscr{M}$ into an\nabelian category $\\mathcal{A}$ as an $n$-cluster-tilting subcategory of\n$\\mathcal{A}$, in this paper we relate the $n$-torsion classes of $\\mathscr{M}$\nwith the torsion classes of $\\mathcal{A}$. Indeed, we show that every\n$n$-torsion class in $\\mathscr{M}$ is given by the intersection of a torsion\nclass in $\\mathcal{A}$ with $\\mathscr{M}$. Moreover, we show that every chain\nof $n$-torsion classes in the $n$-abelian category $\\mathscr{M}$ induces a\nHarder-Narasimhan filtration for every object of $\\mathscr{M}$. We use the\nrelation between $\\mathscr{M}$ and $\\mathcal{A}$ to show that every\nHarder-Narasimhan filtration induced by a chain of $n$-torsion classes in\n$\\mathscr{M}$ can be induced by a chain of torsion classes in $\\mathcal{A}$.\nFurthermore, we show that $n$-torsion classes are preserved by Galois covering\nfunctors, thus we provide a way to systematically construct new (chains of)\n$n$-torsion classes.\n"}
+{"abstract": "  We introduce LAM, a subsystem of IMALL2 with restricted additive rules able\nto manage duplication linearly, called linear additive rules. LAM is presented\nas the type assignment system for a calculus endowed with copy constructors,\nwhich deal with substitution in a linear fashion. As opposed to the standard\nadditive rules, the linear additive rules do not affect the complexity of term\nreduction: typable terms of LAM enjoy linear strong normalization. Moreover, a\nmildly weakened version of cut-elimination for this system is proven which\ntakes a cubic number of steps. Finally, we define a sound translation from\nproofs of LAM into linear lambda terms of IMLL2, and we study its complexity.\n"}
+{"abstract": "  In this paper we present results from a radio-optical study of the galaxy\npopulations of the galaxy clusters Abell 1300 and MACS J1931.8$-$2634, a merger\nand a relaxed system respectively both located at $z \\sim 0.3$, aimed at\nfinding evidence of merger-induced radio emission. Radio observations are taken\nat 1.28 GHz with the MeerKAT interferometer during its early-stage\ncommissioning phase, and combined with archive optical data. We generated\ncatalogues containing 107 and 162 radio sources in the A$~$1300 and MACS\nJ1931.8--2634 cluster fields respectively, above a 0.2 mJy threshold and within\na 30~arcmin radius from the cluster centre (corresponding to 8.1 and 8.8 Mpc\nrespectively). By cross-correlating the radio and optical catalogues, and\nincluding spectroscopic information, 9 and 6 sources were found to be cluster\nmembers and used to construct the radio luminosity functions respectively for\nboth clusters. The comparison of the radio source catalogues between the two\ncluster fields leads to a marginal difference, with a $2\\sigma$ statistical\nsignificance. We derived the radio luminosity function at 1.28 GHz in both\nclusters, in the power range $22.81 < \\rm {log~P_{1.28~GHz}~(W/Hz)} < 25.95$,\nand obtained that in A 1300 the radio luminosity function averaged over the\nfull radio power interval is only $3.3 \\pm 1.9$ times higher than the MACS\nJ1931.8--2634 one, suggesting no statistical difference in their probability to\nhost nuclear radio emission. We conclude that, at least for the two clusters\nstudied here, the role of cluster mergers in affecting the statistical\nproperties of the radio galaxy population is negligible.\n"}
+{"abstract": "  We introduce the notion of Mixed Symmetry Quantum Phase Transition (MSQPT) as\nsingularities in the transformation of the lowest-energy state properties of a\nsystem of identical particles inside each permutation symmetry sector $\\mu$,\nwhen some Hamiltonian control parameters $\\lambda$ are varied. We use a\nthree-level Lipkin-Meshkov-Glick (LMG) model, with $U(3)$ dynamical symmetry,\nto exemplify our construction. After reviewing the construction of $U(3)$\nunirreps using Young tableaux and Gelfand basis, we firstly study the case of a\nfinite number $N$ of three-level atoms, showing that some precursors\n(fidelity-susceptibility, level population, etc.) of MSQPTs appear in all\npermutation symmetry sectors. Using coherent (quasi-classical) states of $U(3)$\nas variational states, we compute the lowest-energy density for each sector\n$\\mu$ in the thermodynamic $N\\to\\infty$ limit. Extending the control parameter\nspace by $\\mu$, the phase diagram exhibits four distinct quantum phases in the\n$\\lambda$-$\\mu$ plane that coexist at a quadruple point. The ground state of\nthe whole system belongs to the fully symmetric sector $\\mu=1$ and shows a\nfour-fold degeneracy, due to the spontaneous breakdown of the parity symmetry\nof the Hamiltonian. The restoration of this discrete symmetry leads to the\nformation of four-component Schr\\\"odinger cat states.\n"}
+{"abstract": "  Pan-omics, pan-cancer analysis has advanced our understanding of the\nmolecular heterogeneity of cancer, expanding what was known from single-cancer\nor single-omics studies. However, pan-cancer, pan-omics analyses have been\nlimited in their ability to use information from multiple sources of data\n(e.g., omics platforms) and multiple sample sets (e.g., cancer types) to\npredict important clinical outcomes, like overall survival. We address the\nissue of prediction across multiple high-dimensional sources of data and\nmultiple sample sets by using exploratory results from BIDIFAC+, a method for\nintegrative dimension reduction of bidimensionally-linked matrices, in a\npredictive model. We apply a Bayesian hierarchical model that performs variable\nselection using spike-and-slab priors which are modified to allow for the\nborrowing of information across clustered data. This method is used to predict\noverall patient survival from the Cancer Genome Atlas (TCGA) using data from 29\ncancer types and 4 omics sources. Our model selected patterns of variation\nidentified by BIDIFAC+ that differentiate clinical tumor subtypes with markedly\ndifferent survival outcomes. We also use simulations to evaluate the\nperformance of the modified spike-and-slab prior in terms of its variable\nselection accuracy and prediction accuracy under different underlying\ndata-generating frameworks. Software and code used for our analysis can be\nfound at https://github.com/sarahsamorodnitsky/HierarchicalSS_PanCanPanOmics/ .\n"}
+{"abstract": "  While convolutional neural networks (CNNs) trained by back-propagation have\nseen unprecedented success at semantic segmentation tasks, they are known to\nstruggle on out-of-distribution data. Markov random fields (MRFs) on the other\nhand, encode simpler distributions over labels that, although less flexible\nthan UNets, are less prone to over-fitting. In this paper, we propose to fuse\nboth strategies by computing the product of distributions of a UNet and an MRF.\nAs this product is intractable, we solve for an approximate distribution using\nan iterative mean-field approach. The resulting MRF-UNet is trained jointly by\nback-propagation. Compared to other works using conditional random fields\n(CRFs), the MRF has no dependency on the imaging data, which should allow for\nless over-fitting. We show on 3D neuroimaging data that this novel network\nimproves generalisation to out-of-distribution samples. Furthermore, it allows\nthe overall number of parameters to be reduced while preserving high accuracy.\nThese results suggest that a classic MRF smoothness prior can allow for less\nover-fitting when principally integrated into a CNN model. Our implementation\nis available at https://github.com/balbasty/nitorch.\n"}
+{"abstract": "  By definition, the $p$-terminal dc-Josephson current is sensitive to the\nsuperconducting phase variables of $p$ terminals. In the paper, we establish\nprotocol for direct detection of the $p$-terminal dc-Josephson effect with\n$p\\ge 3$ in a device containing $N$ superconducting leads\n$S_1,\\,S_2,\\,...,\\,S_N$ having the phase variables\n$\\varphi_1,\\,\\varphi_2,\\,...,\\,\\varphi_N$. The calculated signal ${\\chi}^{(N)}$\ncan be probed in microwave experiments, and it corresponds to the higher-order\nnonlocal inverse inductance obtained from differentiating the current $I_1$\nthrough $S_1$ with respect to the remaining $N-2$ independent phase differences\n$\\varphi_2-\\varphi_N,\\,\\varphi_3-\\varphi_N,\\, ...,\\varphi_{N-1}-\\varphi_N$. We\nfind that the values $p\\le N-2$ do not contribute to ${\\chi}^{(N)}$, and that\n${\\chi}^{(N)}\\ne 0$ implies evidence for the $p=N-1$ or the $p=N$-terminal\ndc-Josephson currents. For $N=4$ superconducting leads, we demonstrate that\n${\\chi}^{(4)}\\ne 0$ implies evidence for the $p=3$ or $p=4$ dc-Josephson\neffect, irrespective of the $p=2$-terminal dc-Josephson current. Thus, we\nprovide a way to demonstrate the dc-Josephson effect with more than three\nterminals (i.e. with $p\\ge 3$) in a device containing more than four\nsuperconducting leads (i.e. with $N\\ge 4$). The predicted ${\\chi}^{(4)}$ is\n\"yes or no\" answer to the $p\\ge 3$ dc-Josephson effect, i.e. for $N=4$,\nnonvanishingly small $\\chi^{(4)}\\ne 0$ implies the $p=3$ or $p=4$-terminal\ndc-Josephson effect and vanishingly small $\\chi^{(4)}=0$ implies absence of the\n$p=3$ and $p=4$-terminal dc-Josephson effect. The paper can be viewed as\ngeneralizing the recently considered $\\varphi$-junctions in Andreev molecules\nto arbitrary number $N$ of the superconducting leads, and it relies on basic\nproperties of the dc-Josephson effect that are not directly related to\nnontrivial topology and Weyl point singularities.\n"}
+{"abstract": "  We provide a dynamical proof of the van der Corput inequality for sequences\nin Hilbert spaces that is based on the Furstenberg correspondence principle.\nThis is done by reducing the inequality to the mean ergodic theorem for\ncontractions on Hilbert spaces. The key difficulty therein is that the\nFurstenberg correspondence principle is, a priori, limited to scalar-valued\nsequences. We therefore discuss how interpreting the Furstenberg correspondence\nprinciple via the Gelfand--Naimark--Segal construction for C*-algebras allows\nto study not just scalar but general Hilbert space-valued sequences in terms of\nunitary operators. This yields a proof of the van der Corput inequality in the\nspirit of the Furstenberg correspondence principle and the flexibility of this\nmethod is discussed via new proofs for different variants of the inequality.\n"}
+{"abstract": "  The elliptic interface problems with discontinuous and high-contrast\ncoefficients appear in many applications and often lead to huge condition\nnumbers of the corresponding linear systems. Thus, it is highly desired to\nconstruct high order schemes to solve the elliptic interface problems with\ndiscontinuous and high-contrast coefficients. Let $\\Gamma$ be a smooth curve\ninside a rectangular region $\\Omega$. In this paper, we consider the elliptic\ninterface problem $-\\nabla\\cdot (a \\nabla u)=f$ in $\\Omega\\setminus \\Gamma$\nwith Dirichlet boundary conditions, where the coefficient $a$ and the source\nterm $f$ are smooth in $\\Omega\\setminus \\Gamma$ and the two nonzero jump\ncondition functions $[u]$ and $[a\\nabla u\\cdot \\vec{n}]$ across $\\Gamma$ are\nsmooth along $\\Gamma$. To solve such elliptic interface problems, we propose a\nhigh order compact finite difference scheme for numerically computing both the\nsolution $u$ and the gradient $\\nabla u$ on uniform Cartesian grids without\nchanging coordinates into local coordinates. Our numerical experiments confirm\nthe fourth order accuracy for computing the solution $u$, the gradient $\\nabla\nu$ and the velocity $a \\nabla u$ of the proposed compact finite difference\nscheme on uniform meshes for the elliptic interface problems with discontinuous\nand high-contrast coefficients.\n"}
+{"abstract": "  Organ-at-risk contouring is still a bottleneck in radiotherapy, with many\ndeep learning methods falling short of promised results when evaluated on\nclinical data. We investigate the accuracy and time-savings resulting from the\nuse of an interactive-machine-learning method for an organ-at-risk contouring\ntask. We compare the method to the Eclipse contouring software and find strong\nagreement with manual delineations, with a dice score of 0.95. The annotations\ncreated using corrective-annotation also take less time to create as more\nimages are annotated, resulting in substantial time savings compared to manual\nmethods, with hearts that take 2 minutes and 2 seconds to delineate on average,\nafter 923 images have been delineated, compared to 7 minutes and 1 seconds when\ndelineating manually. Our experiment demonstrates that\ninteractive-machine-learning with corrective-annotation provides a fast and\naccessible way for non computer-scientists to train deep-learning models to\nsegment their own structures of interest as part of routine clinical workflows.\n  Source code is available at\n\\href{https://github.com/Abe404/RootPainter3D}{this HTTPS URL}.\n"}
+{"abstract": "  We revisit the Kitaev model for fault tolerant quantum computing on a square\nlattice with underlying quantum double $D(G)$ symmetry, where $G$ is a finite\ngroup. We provide projection operators for its quasiparticles content as\nirreducible representations of $D(G)$ and combine this with $D(G)$-bimodule\nproperties of open ribbon excitation spaces $L(s_0,s_1)$ to show how open\nribbons can be used to teleport information between their endpoints $s_0,s_1$.\nWe give a self-contained account that builds on earlier work but emphasises\napplications to quantum computing as surface code theory, including gates on\n$D(S_3)$. We show how the theory reduces to a simpler theory for toric codes in\nthe case of $D( \\Bbb Z_n)\\cong \\Bbb C\\Bbb Z_n^2$, including toric ribbon\noperators and their braiding. In the other direction, we show how our\nconstructions generalise to $D(H)$ models based on a finite-dimensional Hopf\nalgebra $H$, including site actions of $D(H)$ and partial results on ribbon\nequivariance even when the Hopf algebra is not semisimple.\n"}
+{"abstract": "  In 2010, Hrushovski--Loeser showed that the Berkovich analytification of a\nquasi-projective variety over a non-Archimedean valued field admits a\ndeformation retraction onto a finite simplicial complex. In this article, we\nadapt the tools and methods developed by Hrushovski--Loeser to study if such\ndeformation retractions can be obtained to be compatible with respect to a\ngiven morphism. Amongst other results, we show that compatible deformation\nretractions exist over a constructible partition of the base and prove the\ngeneral statement in the case of a morphism of relative dimension 1 where the\ntarget is a smooth connected curve.\n"}
+{"abstract": "  We present LaLaLoc to localise in environments without the need for prior\nvisitation, and in a manner that is robust to large changes in scene\nappearance, such as a full rearrangement of furniture. Specifically, LaLaLoc\nperforms localisation through latent representations of room layout. LaLaLoc\nlearns a rich embedding space shared between RGB panoramas and layouts inferred\nfrom a known floor plan that encodes the structural similarity between\nlocations. Further, LaLaLoc introduces direct, cross-modal pose optimisation in\nits latent space. Thus, LaLaLoc enables fine-grained pose estimation in a scene\nwithout the need for prior visitation, as well as being robust to dynamics,\nsuch as a change in furniture configuration. We show that in a domestic\nenvironment LaLaLoc is able to accurately localise a single RGB panorama image\nto within 8.3cm, given only a floor plan as a prior.\n"}
+{"abstract": "  While the Heisenberg model for magnetic Mott insulators on planar lattice\nstructures is comparatively well understood in the case of transition metal\nions, the intrinsic spin-orbit entanglement of 4f magnetic ions on such\nlattices shows fascinating new physics largely due to corresponding strong\nanisotropies both in their single-ion and their exchange properties. We show\nhere that the Yb delafossites, containing perfect magnetic Yb$^{3+}$ triangular\nlattice planes with pseudospin $s=1/2$ at low temperatures, are an ideal\nplatform to study these new phenomena. Competing frustrated interactions may\nlead to an absence of magnetic order associated to a gapless spin liquid ground\nstate with a huge linear specific heat exceeding that of many heavy fermions,\nwhereas the application of a magnetic field induces anisotropic magnetic order\nwith successive transitions into different long ranged ordered structures. In\nthis comparative study, we discuss our experimental findings in terms of a\nunified crystal-field and exchange model. We combine electron paramagnetic\nresonance (EPR) experiments and results from neutron scattering with\nmeasurements of the magnetic susceptibility, isothermal magnetization up to\nfull polarization, and specific heat to determine the relevant model\nparameters. The impact of the crystal field is discussed as well as the\nsymmetry-compatible form of the exchange tensor, and we give explicit\nexpressions for the anisotropic g factor, the temperature dependence of the\nsusceptibility, the exchange-narrowed EPR linewidth and the saturation field.\n"}
+{"abstract": "  As a fire erupts, the first few minutes can be critical, and first\nrespondents must race to the scene to analyze the situation and act fast before\nit gets out of hand. Factors such as road traffic condition and distance may\nnot allow quick rescue operation using traditional means and methods, leading\nto unmanageable spreading of fire, injuries or even deaths that can be avoided.\nFireFly drone-based rescue consists of a squad of highly equipped drones that\nwill be the first responders to the fire site. Their intervention will make the\ntask of the fire rescue team much more effective and will contribute to reduce\nthe overall damage. As soon as the fire is detected by in-building implanted\nsensors, the fire department would deploy a set of FireFly drones that would\nfly to the site, scan the building, and send live fire status information to\nthe Fire fighter team. The drones would have the ability to identify trapped\nhumans using AI based pattern recognition tools (using sensors and thermal\ncameras) and then drop them rescue kits as appropriate. The drones will also be\nequipped with fire detection and recognition capabilities and be able to drop\nfire extinguishing balls as first attempts to put off seeds of fires before\nthey evolve. The integration of drones with firefighting will allow for ease of\naccess and control of fire outbreaks. Drones will also result in increased\nresponse time, prevention of further damage, and allow relaying of vital\ninformation to out of reach places regarding the characteristics of the fire\nscene.\n"}
+{"abstract": "  The signal resulting from magnetic resonance spectroscopy is occupied by\nnoises and irregularities so in the further analysis preprocessing techniques\nhave to be introduced. The main idea of the paper is to develop a model of a\nsignal as a sum of harmonics and to find its parameters. Such an approach is\nbased on singular value decomposition applied to the data arranged in the\nHankel matrix (HSVD) and can be used in each step of preprocessing techniques.\nFor that purpose a method has was tested on real phantom data.\n"}
+{"abstract": "  This paper revisits feature pyramids networks (FPN) for one-stage detectors\nand points out that the success of FPN is due to its divide-and-conquer\nsolution to the optimization problem in object detection rather than\nmulti-scale feature fusion. From the perspective of optimization, we introduce\nan alternative way to address the problem instead of adopting the complex\nfeature pyramids - {\\em utilizing only one-level feature for detection}. Based\non the simple and efficient solution, we present You Only Look One-level\nFeature (YOLOF). In our method, two key components, Dilated Encoder and Uniform\nMatching, are proposed and bring considerable improvements. Extensive\nexperiments on the COCO benchmark prove the effectiveness of the proposed\nmodel. Our YOLOF achieves comparable results with its feature pyramids\ncounterpart RetinaNet while being $2.5\\times$ faster. Without transformer\nlayers, YOLOF can match the performance of DETR in a single-level feature\nmanner with $7\\times$ less training epochs. With an image size of\n$608\\times608$, YOLOF achieves 44.3 mAP running at 60 fps on 2080Ti, which is\n$13\\%$ faster than YOLOv4. Code is available at\n\\url{https://github.com/megvii-model/YOLOF}.\n"}
+{"abstract": "  We study the transient phenomena appearing in a subgap region of the double\nquantum dot coupled in series between the superconducting and normal metallic\nleads, focusing on the development of the superconducting proximity effect. For\nthe uncorrelated nanostructure we derive explicit expressions of the\ntime-dependent occupancies in both quantum dots, charge currents, and electron\npairing induced on individual dots and between them. We show that the initial\nconfigurations substantially affect the dynamical processes, in which the\nin-gap bound states emerge upon coupling the double quantum dot to\nsuperconducting reservoir. In particular, the superconducting proximity effect\nwould be temporarily blocked whenever the quantum dots are initially singly\noccupied. Such {\\it triplet}/{\\it Andreev blockade} has been recently reported\nexperimentally for double quantum dots embedded in the Josephson [D. Bouman et\nal., Phys. Rev. B 102, 220505 (2020)] and Andreev [P. Zhang et al.,\narXiv:2102.03283 (2021)] junctions. We also address the role of correlation\neffects within the lowest-order decoupling scheme and by the time-dependent\nnumerical renormalization group calculations. Competition of the repulsive\nCoulomb interactions with the superconducting proximity effect leads to\nrenormalization of the in-gap quasiparticles, speeding up the quantum\noscillations and narrowing a region of transient phenomena, whereas the\ndynamical Andreev blockade is well pronounced in the weak inter-dot coupling\nlimit. We propose feasible methods for detecting the characteristic time-scales\nthat could be observable by the Andreev spectroscopy.\n"}
+{"abstract": "  The abundance of interstellar ice constituents is usually expressed with\nrespect to the water ice because, in denser regions, a significant portion of\nthe interstellar grain surface would be covered by water ice. The binding\nenergy (BE), or adsorption energy of the interstellar species regulates the\nchemical complexity of the interstellar grain mantle. Due to the high abundance\nof water ice, the BE of surface species with the water is usually provided and\nwidely used in astrochemical modeling. However, the hydrogen molecules would\ncover some part of the grain mantle in the denser and colder part of the\ninterstellar medium. Even at around ~ 10K, few atoms and simple molecules with\nlower adsorption energies can migrate through the surface. The BE of the\nsurface species with H2 substrate would be very different from that of a water\nsubstrate. However, adequate information regarding these differences is\nlacking. Here, we employ the quantum chemical calculation to provide the BE of\n95 interstellar species with H2 substrate. These are representative of the BEs\nof species to a H2 overlayer on a grain surface. On average, we notice that the\nBE with the H2 monomer substrate is almost ten times lower than the BE of these\nspecies reported earlier with the H2 O c-tetramer configuration. The encounter\ndesorption of H and H2 was introduced (with ED (H, H2 ) =45 K and ED (H2 , H2 )\n=23 K) to have a realistic estimation of the abundances of the surface species\nin the colder and denser region. Our quantum chemical calculations yield higher\nadsorption energy of H2 than that of H (ED (H, H2 ) = 23 - 25 K and ED (H2, H2\n) =67 - 79 K). We further implement an astrochemical model to study the effect\nof encounter desorption with the resent realistic estimation. The encounter\ndesorption of the N atom (calculations yield ED (N, H2 ) =83 K) is introduced\nto study the differences with its inclusion.\n"}
+{"abstract": "  The effective incorporation of cross-utterance information has the potential\nto improve language models (LMs) for automatic speech recognition (ASR). To\nextract more powerful and robust cross-utterance representations for the\nTransformer LM (TLM), this paper proposes the R-TLM which uses hidden states in\na long short-term memory (LSTM) LM. To encode the cross-utterance information,\nthe R-TLM incorporates an LSTM module together with a segment-wise recurrence\nin some of the Transformer blocks. In addition to the LSTM module output, a\nshortcut connection using a fusion layer that bypasses the LSTM module is also\ninvestigated. The proposed system was evaluated on the AMI meeting corpus, the\nEval2000 and the RT03 telephone conversation evaluation sets. The best R-TLM\nachieved 0.9%, 0.6%, and 0.8% absolute WER reductions over the single-utterance\nTLM baseline, and 0.5%, 0.3%, 0.2% absolute WER reductions over a strong\ncross-utterance TLM baseline on the AMI evaluation set, Eval2000 and RT03\nrespectively. Improvements on Eval2000 and RT03 were further supported by\nsignificance tests. R-TLMs were found to have better LM scores on words where\nrecognition errors are more likely to occur. The R-TLM WER can be further\nreduced by interpolation with an LSTM-LM.\n"}
+{"abstract": "  The Roadmap is performed in the context of a Ph.D. research in collaboration\nbetween the Experimental Software Engineering Group, from the Systems\nEngineering and Computing Program of the Federal University of Rio de Janeiro\n(COPPE/UFRJ) and the Laboratory of Industrial and Human Automation Control,\nMechanical engineering and Computer Science (LAMIH UMR CNRS 8201) in the\nUniversit\\`e Polytechnique Hauts-de-France (UPHF).\n  The Roadmap resulted from an investigation on the particularities of IoT\napplications. It is the concrete organization of the concepts and evidence\ngathered from different experimental studies. It comes to support the\ndefinition of IoT software systems, with specific items for the project team to\ndiscuss and define the essential aspects related to the specifying, designing,\nand implementing an IoT application.\n"}
+{"abstract": "  Magnetic beads attract each other forming chains. We pushed such chains into\nan inclined Hele-Shaw cell and discovered that they spontaneously form\nself-similar patterns. Depending on the angle of inclination of the cell, two\ncompletely different situations emerge, namely, above the static friction angle\nthe patterns resemble the stacking of a rope and below they look similar to a\nfortress from above. Moreover, locally the first pattern forms a square\nlattice, while the second pattern exhibits triangular symmetry. For both\npatterns, the size distributions of enclosed areas follow power laws. We\ncharacterize the morphological transition between the two patterns\nexperimentally and numerically and explain the change in polarization as a\ncompetition between friction-induced buckling and gravity.\n"}
+{"abstract": "  We consider the $0$-Extension problem, where we are given an undirected graph\n$\\mathcal{G}=(V,E)$ equipped with non-negative edge weights $w:E\\rightarrow\n\\mathbb{R}^+$, a collection $ T=\\{ t_1,\\ldots,t_k\\}\\subseteq V$ of $k$ special\nvertices called terminals, and a semi-metric $D$ over $T$. The goal is to\nassign every non-terminal vertex to a terminal while minimizing the sum over\nall edges of the weight of the edge multiplied by the distance in $D$ between\nthe terminals to which the endpoints of the edge are assigned. $0$-Extension\nadmits two known algorithms, achieving approximations of $O(\\log{k})$\n[C{\\u{a}}linescu-Karloff-Rabani SICOMP '05] and $O(\\log{k}/\\log{\\log{k}})$\n[Fakcharoenphol-Harrelson-Rao-Talwar SODA '03]. Both known algorithms are based\non rounding a natural linear programming relaxation called the metric\nrelaxation, in which $D$ is extended from $T$ to the entire of $V$. The current\nbest known integrality gap for the metric relaxation is $\\Omega\n(\\sqrt{\\log{k}})$. In this work we present an improved integrality gap of\n$\\Omega(\\log^{\\frac{2}{3}}k)$ for the metric relaxation. Our construction is\nbased on the randomized extension of one graph by another, a notion that\ncaptures lifts of graphs as a special case and might be of independent\ninterest. Inspired by algebraic topology, our analysis of the gap instance is\nbased on proving no continuous section (in the topological sense) exists in the\nrandomized extension.\n"}
+{"abstract": "  The Anti-Extradition Law Amendment Bill protests in Hong Kong present a rich\ncontext for exploring information security practices among protesters due to\ntheir large-scale urban setting and highly digitalised nature. We conducted\nin-depth, semi-structured interviews with 11 participants of these protests.\nResearch findings reveal how protesters favoured Telegram and relied on its\nsecurity for internal communication and organisation of on-the-ground\ncollective action; were organised in small private groups and large public\ngroups to enable collective action; adopted tactics and technologies that\nenable pseudonymity; and developed a variety of strategies to detect\ncompromises and to achieve forms of forward secrecy and post-compromise\nsecurity when group members were (presumed) arrested. We further show how group\nadministrators had assumed the roles of leaders in these 'leaderless' protests\nand were critical to collective protest efforts.\n"}
+{"abstract": "  The finding of a new formulation of the magnetization vector of a quantum\nsystem interacting with a static uniform magnetic field\\cite{Selenu1} is\nreported. There a gauge invariant form of its divergence is shown being\nexpressed as a function of the electronic current per state coupled with the\nBerry curvature of the quantum system. A Fourier analysis of the magnetization\nvector and magnetization density is reported as an application of the presented\nformula it could be applied in the context of computational\nmodelling\\cite{Martin} of quantum matter.\n"}
+{"abstract": "  This paper introduces a martingale that characterizes two properties of\nevolving forecast distributions. Ideal forecasts of a future event behave as\nmartingales, sequen- tially updating the forecast to leverage the available\ninformation as the future event approaches. The threshold martingale introduced\nhere measures the proportion of the forecast distribution lying below a\nthreshold. In addition to being calibrated, a threshold martingale has\nquadratic variation that accumulates to a total determined by a quantile of the\ninitial forecast distribution. Deviations from calibration or to- tal\nvolatility signal problems in the underlying model. Calibration adjustments are\nwell-known, and we augment these by introducing a martingale filter that\nimproves volatility while guaranteeing smaller mean squared error. Thus,\npost-processing can rectify problems with calibration and volatility without\nrevisiting the original forecast- ing model. We apply threshold martingales\nfirst to forecasts from simulated models and then to models that predict the\nwinner in professional basketball games.\n"}
+{"abstract": "  Floquet-Bloch lattices are systems in which wave packets are subjet to\nperiodic modulations both in time and space, showing rich dynamics. While this\ntype of lattices is difficult to implement in solid-state physics, optical\nsystems have provided excellent platforms to probe their physics: among other\neffects, they have revealed genuine phenomena such as the anomalous Floquet\ntopological insulator and the funnelling of light into localised interface\nmodes. Despite the crucial importance of the band dispersion in the photon\ndynamics and the topological properties of the lattice, the direct experimental\nmeasurement of the Floquet-Bloch bands has remained elusive. Here we report the\ndirect measurement of the Floquet-Bloch bands of a photonic lattice with a\nsingle shot method. We use a system of two coupled fibre rings that implements\na time-multiplexed Floquet-Bloch lattice. By Fourier transforming the impulse\nresponse of the lattice we obtain the band structure together with an accurate\ncharacterization of the lattice eigenmodes, i. e. the amplitudes and the phases\nof the Floquet-Bloch eigenvectors over the entire Brillouin zone. Our results\nopen promising perspectives for the observation of topological effects in the\nlinear and nonlinear regime in Floquet systems.\n"}
+{"abstract": "  Many neuroscientific applications require robust and accurate localization of\nneurons. It is still an unsolved problem because of the enormous variation in\nintensity, texture, spatial overlap, morphology and background artifacts. In\naddition, curation of a large dataset containing complete manual annotation of\nneurons from high-resolution images to train a classifier requires significant\ntime and effort. We present Cellcounter, a deep learning-based model trained on\nimages containing incompletely-annotated neurons with highly-varied morphology\nand control images containing artifacts and background structures. Leveraging\nthe striking self-learning ability, Cellcounter gradually labels neurons,\nobviating the need for time-intensive complete annotation. Cellcounter shows\nits efficacy over the state of the arts in the accurate localization of neurons\nwhile significantly reducing false-positive detection in several protocols.\n"}
+{"abstract": "  Indoor positioning is currently recognized as one of the important features\nin emergency, commercial and industrial applications. The 5G network enhances\nmobility, flexibility, reliability, and security to new higher levels which\ngreatly benefit the IoT and industrial applications. Industrial IoT (IIoT)\nuse-cases are characterized by ambitious system requirements for positioning\naccuracy in many verticals. For example, on the factory floor, it is important\nto locate assets and moving objects such as forklifts. The deployment design\nfor different IIoT environments has a significant impact on the positioning\nper-performance in terms of both accuracy and availability of the service.\nIndoor factory (InF) and indoor open office (IOO) are two available and\nstandardized Third Generation Partnership Project (3GPP) scenarios for\nevaluation of indoor channel models and positioning performance in IIoT use\ncases. This paper aims to evaluate the positioning performance in terms of\naccuracy and availability while considering different deployment strategies.\nOur simulation-based evaluation shows that deployment plays a vital role when\nit comes to achieving high accuracy positioning performance. It is for example\nfavorable to deploy the 5G Transmission and Reception Points (TRPs) on the\nwalls of the factory halls than deploying them attached to the ceiling.\n"}
+{"abstract": "  Deployment, operation and maintenance of large IT systems becomes\nincreasingly complex and puts human experts under extreme stress when problems\noccur. Therefore, utilization of machine learning (ML) and artificial\nintelligence (AI) is applied on IT system operation and maintenance -\nsummarized in the term AIOps. One specific direction aims at the recognition of\nre-occurring anomaly types to enable remediation automation. However, due to IT\nsystem specific properties, especially their frequent changes (e.g. software\nupdates, reconfiguration or hardware modernization), recognition of reoccurring\nanomaly types is challenging. Current methods mainly assume a static\ndimensionality of provided data. We propose a method that is invariant to\ndimensionality changes of given data. Resource metric data such as CPU\nutilization, allocated memory and others are modelled as multivariate time\nseries. The extraction of temporal and spatial features together with the\nsubsequent anomaly classification is realized by utilizing TELESTO, our novel\ngraph convolutional neural network (GCNN) architecture. The experimental\nevaluation is conducted in a real-world cloud testbed deployment that is\nhosting two applications. Classification results of injected anomalies on a\ncassandra database node show that TELESTO outperforms the alternative GCNNs and\nachieves an overall classification accuracy of 85.1%. Classification results\nfor the other nodes show accuracy values between 85% and 60%.\n"}
+{"abstract": "  In this paper, a novel Byzantine consensus protocol among $n$ players is\nproposed for the partially synchronous model. In particular, by assuming that\nstandard cryptography is unbreakable, and that\n$n>\\max\\bigl(\\frac{3}{2}k+3t,2(k+t)\\bigr)$, this protocol is an equilibrium\nwhere no coalition of $k$ rational players can coordinate to increase their\nexpected utility regardless of the arbitrary behavior of up to $t$ Byzantine\nplayers. We show that a baiting strategy is necessary and sufficient to solve\nthis, so-called rational agreement problem. First, we show that it is\nimpossible to solve this rational agreement problem without implementing a\nbaiting strategy, a strategy that rewards rational players for betraying its\ncoalition, by exposing undeniable proofs of fraud. Second, we propose the\nHuntsman protocol that solves the rational agreement problem by building recent\nadvances in the context of accountable Byzantine agreement in partial\nsynchrony. This protocol finds applications in distributed ledgers where\nplayers are incentivized to steal assets by leading other players to a\ndisagreement on two distinct decisions where they ``double spend''.\n"}
+{"abstract": "  The weight distribution of the cosets of maximum distance separable (MDS)\ncodes is considered. In 1990, P.G. Bonneau proposed a relation to obtain the\nfull weight distribution of a coset of an MDS code with minimum distance $d$\nusing the known numbers of vectors of weights $\\le d-2$ in this coset. In this\npaper, the Bonneau formula is transformed into a more structured and convenient\nform. The new version of the formula allows to consider effectively cosets of\ndistinct weights $W$. (The weight $W$ of a coset is the smallest Hamming weight\nof any vector in the coset.) For each of the considered $W$ or regions of $W$,\nspecial relations more simple than the general ones are obtained. For the MDS\ncode cosets of weight $W=1$ and weight $W=d-1$ we obtain formulas of the weight\ndistributions depending only on the code parameters. This proves that all the\ncosets of weight $W=1$ (as well as $W=d-1$) have the same weight distribution.\nThe cosets of weight $W=2$ or $W=d-2$ may have different weight distributions;\nin this case, we proved that the distributions are symmetrical in some sense.\nThe weight distributions of the cosets of MDS codes corresponding to arcs in\nthe projective plane $\\mathrm{PG}(2,q)$ are also considered. For MDS codes of\ncovering radius $R=d-1$ we obtain the number of the weight $W=d-1$ cosets and\ntheir weight distribution that gives rise to a certain classification of the\nso-called deep holes. We show that any MDS code of covering radius $R=d-1$ is\nan almost perfect multiple covering of the farthest-off points (deep holes);\nmoreover, it corresponds to an optimal multiple saturating set in the\nprojective space $\\mathrm{PG}(N,q)$.\n"}
+{"abstract": "  LiteBIRD is a JAXA-led strategic Large-Class satellite mission designed to\nmeasure the polarization of the cosmic microwave background and cosmic\nforegrounds from 34 to 448 GHz across the entire sky from L2 in the late\n2020's. The primary focus of the mission is to measure primordially generated\nB-mode polarization at large angular scales. Beyond its primary scientific\nobjective LiteBIRD will generate a data-set capable of probing a number of\nscientific inquiries including the sum of neutrino masses. The primary\nresponsibility of United States will be to fabricate the three flight model\nfocal plane units for the mission. The design and fabrication of these focal\nplane units is driven by heritage from ground based experiments and will\ninclude both lenslet-coupled sinuous antenna pixels and horn-coupled orthomode\ntransducer pixels. The experiment will have three optical telescopes called the\nlow frequency telescope, mid frequency telescope, and high frequency telescope\neach of which covers a portion of the mission's frequency range. JAXA is\nresponsible for the construction of the low frequency telescope and the\nEuropean Consortium is responsible for the mid- and high- frequency telescopes.\nThe broad frequency coverage and low optical loading conditions, made possible\nby the space environment, require development and adaptation of detector\ntechnology recently deployed by other cosmic microwave background experiments.\nThis design, fabrication, and characterization will take place at UC Berkeley,\nNIST, Stanford, and Colorado University, Boulder. We present the current status\nof the US deliverables to the LiteBIRD mission.\n"}
+{"abstract": "  We study the influence of anisotropy, treated as a dimensional crossover\nbetween 1D and 3D system, on the topological instability induced by a\n(self-consistent) uniaxial periodic potential. The mechanism on which the\ninstability is based involves the topological reconstruction of the Fermi\nsurface, from initially closed pockets to the surface with open Fermi sheets,\ncreating two peculiar points in the band dispersion - the saddle point and\nelliptical point, between which the pseudogap in electron density of states\ndevelops. The self-consistent periodic potential appears as a result of\ninteractions, either electron-phonon, or electron electron, which, linked with\nthe topological instability of the system, results in formation of a new ground\nstate of the system - the density wave provided that the relevant coupling\nconstant is larger than critical. Our analysis shows that the phase transition\ntakes place along the whole continuous interval of a dimensional crossover\nbetween 1D and 3D, but that the critical coupling strength significantly\nincreases with the dimensionality of the system. It is our intention to give an\ninitial framework for understanding the nature of charge density waves\nexperimentally observed in a number of materials, like high$-T_c$ cuprates or\ngraphite intercalates, both being materials with a closed, rather isotropic\nFermi surface far from the nesting regime.\n"}
+{"abstract": "  We describe and make available the game-based material developed for a\nlaboratory run at several Italian science festivals to popularize NLP among\nyoung students.\n"}
+{"abstract": "  We present variability analyses of twenty pointed XMM-Newton observations of\nthe high energy peaked TeV blazar PG 1553+113 taken during 2010 to 2018. We\nfound intraday variability in the total X-ray energy range (0.3 -- 10 keV) in\n16 out of 19 light curves or a duty cycle of ~84%. A discrete correlation\nfunction analysis of the intraday light curves in the soft and hard X-ray bands\npeaks on zero lag, showing that the emission in hard and soft bands are\nco-spatial and emitted from the same population of leptons. Red-noise dominates\nthe power spectral density (PSD) of all the LCs although the PSDs have a range\nof spectral slopes from -2.36 to -0.14. On longer timescales, the optical and\nUV variability patterns look almost identical and well correlated, as are the\nsoft and hard X-ray bands, but the optical/UV variations are not correlated to\nthose in the X-ray band, indicating that the optical/UV and X-ray emissions are\nemitted by two different populations of leptons. We briefly discuss physical\nmechanisms which may be capable of explaining the observed flux and spectral\nvariability of PG 1553+113 on these diverse timescales.\n"}
+{"abstract": "  We demonstrate a method of micromotion minimization of a trapped ion in a\nlinear Paul trap based on the precision measurement of the ion trapping\nposition displacement due to a stray electric field in the radial plane by ion\nfluorescence imaging. The amount of displacement in the radial plane is\nproportional to the strength of a stray electric field. Therefore, we evaluated\nthe micromotion compensation condition by measuring the ion displacements from\nthe ion equilibrium position using two different radial trap frequencies with\nvarious combinations of the compensation voltage. The residual electric field\nuncertainty of this technique reached a few volts per meter. This compensation\ntechnique does not depend on the orientation of the incident cooling laser or\nthe detuning and imaging direction. Therefore, this method is suitable for a\nplanar ion trap, a stylus ion trap, which limits the propagation angle of\nlasers, or miniaturized ion trap systems for sensing and metrological\napplications.\n"}
+{"abstract": "  This paper concerns the local study of analytic constrained differential\nsystems (or impasse systems) of the form $A(x)\\dot{x} = F(x)$,\n$x\\in\\mathbb{R}^{2}$, where $F$ is a vector field and $A$ is a matrix valued\nfunction. Using techniques of resolution of singularities with weighted blow\nups, we extend well-known results in the literature on topological\nclassification of phase portraits of planar vector fields to the context of\nsuch class of systems. We also study and classify phase portraits of\nconstrained systems near singular points of the so called impasse set\n$\\Delta=\\{x:\\det A (x) = 0\\}$.\n"}
+{"abstract": "  A cubic polynomial $P$ with a non-repelling fixed point $b$ is said to be\n\\emph{immediately renormalizable} if there exists a (connected) quadratic-like\ninvariant filled Julia set $K^*$ such that $b\\in K^*$. In that case exactly one\ncritical point of $P$ does not belong to $K^*$. We show that if, in addition,\nthe Julia set of $P$ has no (pre)periodic cutpoints then this critical point is\nrecurrent.\n"}
+{"abstract": "  Vanadium disulfide (VS_{2}) attracts elevated interests for its\ncharge-density wave (CDW) phase transition, ferromagnetism, and catalytic\nreactivity, but the electronic structure of monolayer has not been well\nunderstood yet. Here we report synthesis of epitaxial 1T VS_{2} monolayer on\nbilayer graphene grown by molecular-beam epitaxy (MBE). Angle-resolved\nphotoemission spectroscopy (ARPES) measurements reveal that Fermi surface with\nsix elliptical pockets centered at the M points shows gap opening at low\ntemperature. Temperature-dependence of the gap size suggests existence of CDW\nphase transition above room temperature. Our observations provide important\nevidence to understand the strongly correlated electron physics and the related\nsurface catalytic properties in two-dimensional transition-metal\ndichalcogenides (TMDCs).\n"}
+{"abstract": "  Due to the wide use of highly-valuable and large-scale deep neural networks\n(DNNs), it becomes crucial to protect the intellectual property of DNNs so that\nthe ownership of disputed or stolen DNNs can be verified. Most existing\nsolutions embed backdoors in DNN model training such that DNN ownership can be\nverified by triggering distinguishable model behaviors with a set of secret\ninputs. However, such solutions are vulnerable to model fine-tuning and\npruning. They also suffer from fraudulent ownership claim as attackers can\ndiscover adversarial samples and use them as secret inputs to trigger\ndistinguishable behaviors from stolen models. To address these problems, we\npropose a novel DNN watermarking solution, named HufuNet, for protecting the\nownership of DNN models. We evaluate HufuNet rigorously on four benchmark\ndatasets with five popular DNN models, including convolutional neural network\n(CNN) and recurrent neural network (RNN). The experiments demonstrate HufuNet\nis highly robust against model fine-tuning/pruning, kernels cutoff/supplement,\nfunctionality-equivalent attack, and fraudulent ownership claims, thus highly\npromising to protect large-scale DNN models in the real-world.\n"}
+{"abstract": "  A discussion about conditions on the profiles of voters in order to avoid\ncontradictory decisions that have been designed by Sen and others.\n"}
+{"abstract": "  We explore thermodynamic contributions to the three-dimensional de Sitter\nhorizon originating from metric and Chern-Simons gauge field fluctuations. In\nEuclidean signature these are computed by the partition function of gravity\ncoupled to matter semi-classically expanded about the round three-sphere\nsaddle. We investigate a corresponding Lorentzian picture - drawing inspiration\nfrom the topological entanglement entropy literature - in the form of an\nedge-mode theory residing at the de Sitter horizon. We extend the discussion to\nthree-dimensional gravity with positive cosmological constant, viewed\n(semi-classically) as a complexified Chern-Simons theory. The putative\ngravitational edge-mode theory is a complexified version of the chiral\nWess-Zumino-Witten model associated to the edge-modes of ordinary Chern-Simons\ntheory. We introduce and solve a family of complexified Abelian Chern-Simons\ntheories as a way to elucidate some of the more salient features of the\ngravitational edge-mode theories. We comment on the relation to the\nAdS$_4$/CFT$_3$ correspondence.\n"}
+{"abstract": "  The emergence of spatial and temporal coherence of light emitted from\nsolid-state systems is a fundamental phenomenon, rooting in a plethora of\nmicroscopic processes. It is intrinsically aligned with the control of\nlight-matter coupling, and canonical for laser oscillation. However, it also\nemerges in the superradiance of multiple, phase-locked emitters, and more\nrecently, coherence and long-range order have been investigated in bosonic\ncondensates of thermalized light, as well as in exciton-polaritons driven to a\nground state via stimulated scattering. Here, we experimentally show that the\ninteraction between photons in a Fabry-Perot microcavity and excitons in an\natomically thin WSe$_2$ layer is sufficient such that the system enters the\nhybridized regime of strong light-matter coupling at ambient conditions. Via\nMichelson interferometry, we capture clear evidence of increased spatial and\ntemporal coherence of the emitted light from the spatially confined system\nground-state. The coherence build-up is accompanied by a threshold-like\nbehaviour of the emitted light intensity, which is a fingerprint of a polariton\nlaser effect. Valley-physics is manifested in the presence of an external\nmagnetic field, which allows us to manipulate K and K' polaritons via the\nValley-Zeeman-effect. Our findings are of high application relevance, as they\nconfirm the possibility to use atomically thin crystals as simple and versatile\ncomponents of coherent light-sources, and in valleytronic applications at room\ntemperature.\n"}
+{"abstract": "  Intelligent reflecting surface (IRS) has emerged as a promising paradigm to\nimprove the capacity and reliability of a wireless communication system by\nsmartly reconfiguring the wireless propagation environment. To achieve the\npromising gains of IRS, the acquisition of the channel state information (CSI)\nis essential, which however is practically difficult since the IRS does not\nemploy any transmit/receive radio frequency (RF) chains in general and it has\nlimited signal processing capability. In this paper, we study the uplink\nchannel estimation problem for an IRS-aided multiuser single-input multi-output\n(SIMO) system, and propose a novel two-phase channel estimation (2PCE) strategy\nwhich can alleviate the negative effects caused by error propagation in the\nexisting three-phase channel estimation approach, i.e., the channel estimation\nerrors in previous phases will deteriorate the estimation performance in later\nphases, and enhance the channel estimation performance with the same amount of\nchannel training overhead as in the existing approach. Moreover, the asymptotic\nmean squared error (MSE) of the 2PCE strategy is analyzed when the least-square\n(LS) channel estimation method is employed, and we show that the 2PCE strategy\ncan outperform the existing approach. Finally, extensive simulation results are\npresented to validate the effectiveness of the 2PCE strategy.\n"}
+{"abstract": "  Artificial Intelligence (AI), and in particular, the explainability thereof,\nhas gained phenomenal attention over the last few years. Whilst we usually do\nnot question the decision-making process of these systems in situations where\nonly the outcome is of interest, we do however pay close attention when these\nsystems are applied in areas where the decisions directly influence the lives\nof humans. It is especially noisy and uncertain observations close to the\ndecision boundary which results in predictions which cannot necessarily be\nexplained that may foster mistrust among end-users. This drew attention to AI\nmethods for which the outcomes can be explained. Bayesian networks are\nprobabilistic graphical models that can be used as a tool to manage\nuncertainty. The probabilistic framework of a Bayesian network allows for\nexplainability in the model, reasoning and evidence. The use of these methods\nis mostly ad hoc and not as well organised as explainability methods in the\nwider AI research field. As such, we introduce a taxonomy of explainability in\nBayesian networks. We extend the existing categorisation of explainability in\nthe model, reasoning or evidence to include explanation of decisions. The\nexplanations obtained from the explainability methods are illustrated by means\nof a simple medical diagnostic scenario. The taxonomy introduced in this paper\nhas the potential not only to encourage end-users to efficiently communicate\noutcomes obtained, but also support their understanding of how and, more\nimportantly, why certain predictions were made.\n"}
+{"abstract": "  We present a calculation of the pion and kaon Mellin moment $\\langle x^3\n\\rangle$ extracted directly in lattice QCD using a three-derivative local\noperator. We use one ensemble of gauge configurations with two degenerate\nlight, a strange and a charm quark ($N_f=2+1+1$) of maximally twisted mass\nfermions with clover improvement. The ensemble reproduces a pion mass $\\sim260$\nMeV, and a kaon mass $\\sim530$ MeV. Excited-states contamination is evaluated\nusing four values of the source-sink time separation within the range of\n$1.12-1.67$ fm. We use an operator that is free of mixing, and apply a\nmultiplicative renormalization function calculated non-perturbatively. Our\nresults are converted to the $\\overline{\\rm MS}$ scheme and evolved at a scale\nof 2 GeV, using three-loop expressions in perturbation theory. The final values\nare $\\langle x^3 \\rangle_\\pi^{u^+}=0.024(18)_{\\rm stat}(2)_{\\rm syst}$,\n$\\langle x^3 \\rangle_K^{u^+}=0.035(6)_{\\rm stat}(3)_{\\rm syst}$, and $\\langle\nx^3 \\rangle_K^{s^+}=0.075(5)_{\\rm stat}(1)_{\\rm syst}$, where the systematic\nerror is the uncertainty due to excited state contamination.\n  We combine $\\langle x^3 \\rangle$ with the two lower moments to obtain the\nratios $\\langle x^3 \\rangle/\\langle x \\rangle$ and $\\langle x^3 \\rangle/\\langle\nx^2 \\rangle$, as well as ratios between the pion and kaon moments. In addition,\nwe reconstruct the $x$-dependence of the pion and kaon PDFs via 2- and\n3-parameter fits to our results. We find that the reconstruction is feasible\nand that our lattice data favor a large $x$-dependence that falls as $(1-x)^2$\nfor both the pion and kaon PDFs. We integrate the reconstructed PDFs to extract\nthe higher moments with $4\\leq n\\leq 6$. Finally, we compare the pion and kaon\nPDFs, as well as the ratios of their moments, to address the effect of SU(3)\nflavor symmetry breaking.\n"}
+{"abstract": "  With the advent of 2-dimensional Convolution Neural Networks (2D CNNs), the\nface recognition accuracy has reached above 99%. However, face recognition is\nstill a challenge in real world conditions. A video, instead of an image, as an\ninput can be more useful to solve the challenges of face recognition in real\nworld conditions. This is because a video provides more features than an image.\nHowever, 2D CNNs cannot take advantage of the temporal features present in the\nvideo. We therefore, propose a framework called $Sf_{3}CNN$ for face\nrecognition in videos. The $Sf_{3}CNN$ framework uses 3-dimensional Residual\nNetwork (3D Resnet) and A-Softmax loss for face recognition in videos. The use\nof 3D ResNet helps to capture both spatial and temporal features into one\ncompact feature map. However, the 3D CNN features must be highly discriminative\nfor efficient face recognition. The use of A-Softmax loss helps to extract\nhighly discriminative features from the video for face recognition. $Sf_{3}CNN$\nframework gives an increased accuracy of 99.10% on CVBL video database in\ncomparison to the previous 97% on the same database using 3D ResNets.\n"}
+{"abstract": "  In this work, we propose an economical model to address some open\ncosmological problems such as the absence of the initial cosmological\nsingularity, an early acceleration of the Universe and the generation of\nmatter-antimatter asymmetry. The model is based on a scenario in which the\nearly Universe consists of a non-linear electrodynamics fields. It is found\nthat the non-linear electrodynamics model has an equation of state\n$p=\\frac{1}{3} \\rho - \\frac{4}{3} \\beta \\rho^{1+\\alpha}$ which shows that the\nUniverse undergoes an early epoch acceleration to a radiation era given by $p\n=\\frac{1}{3} \\rho$. We show that the singularities in the energy density,\npressure and curvature are absent at early stages. In our scenario, the baryon\nasymmetry is generated by the non-linearity parameter $\\beta$. Additionally, we\ncalculate the resulting baryon asymmetry and discuss how a successful\ngravitational baryogenesis is obtained for different values of the model's\nparameter space.\n"}
+{"abstract": "  This paper considers the massive connectivity problem in an asynchronous\ngrant-free random access system, where a huge number of devices sporadically\ntransmit data to a base station (BS) with imperfect synchronization. The goal\nis to design algorithms for joint user activity detection, delay detection, and\nchannel estimation. By exploiting the sparsity on both user activity and\ndelays, we formulate a hierarchical sparse signal recovery problem in both the\nsingle-antenna and the multiple-antenna scenarios. While traditional compressed\nsensing algorithms can be applied to these problems, they suffer high\ncomputational complexity and often require the perfect statistical information\nof channel and devices. This paper solves these problems by designing the\nLearned Approximate Message Passing (LAMP) network, which belongs to\nmodel-driven deep learning approaches and ensures efficient performance without\ntremendous training data. Particularly, in the multiple-antenna scenario, we\ndesign three different LAMP structures, namely, distributed, centralized and\nhybrid ones, to balance the performance and complexity. Simulation results\ndemonstrate that the proposed LAMP networks can significantly outperform the\nconventional AMP method thanks to their ability of parameter learning. It is\nalso shown that LAMP has robust performance to the maximal delay spread of the\nasynchronous users.\n"}
+{"abstract": "  We investigate the Yukawa coupling unification for the third generation in a\nclass of $\\mathsf{SO(10)}$ unified models which are consistent with the 4.2\n$\\sigma$ deviation from the standard model of the muon $g-2$ seen by the\nFermilab experiment E989. A recent analysis in supergravity grand unified\nmodels shows that such an effect can arise from supersymmetric loops\ncorrection. Using a neural network, we further analyze regions of the parameter\nspace where Yukawa coupling unification consistent with the Fermilab result can\nappear. In the analysis we take into account the contributions to Yukawas from\nthe cubic and the quartic interactions. We test the model at the high\nluminosity and high energy LHC and estimate the integrated luminosities needed\nto discover sparticles predicted by the model.\n"}
+{"abstract": "  For general relativistic, inviscid, axisymmetric flow around Kerr black hole\none may choose different flow thickness. The stationary flow equations can be\nsolved using methods of dynamical system to get transonic accretion flows ,\ni.e, flow infalling in the blackhole that turns supersonic from subsonic with\ndecreasing radial distance, or vice versa. This transonic flows are obtained by\nchoosing the particular flow passing through critical points of phase portrait.\nFor certain flow thickness like the one maintaining conical shape, the sonic\npoint coincide with the critical point. But there are certain flows maintaining\nhydrostatic equilibrium, such as the one described by Novikov-Thorne, where the\nsonic point is not same as the critical point. We perturb the flow for both\nkind of flow and study the behaviour of linear perturbation which behaves like\nmassless scalar field in some curved spacetime, known as, analogue space time.\nWe draw the compactified causal structure, i.e, Penrose Carter diagram for both\nkind of analogue metric and prove that for both cases critical points are the\nacoustic horizons, whereas in the case where sonic points do not coincide with\ncritical points, the sonic points are not the acoustic horizon, as one may\nexpect from the definition of sound speed.\n"}
+{"abstract": "  The relativistic theory of the time- and position-dependent energy and\nmomentum densities of light in glasses and other low-loss dispersive media,\nwhere different wavelengths of light propagate at different phase velocities,\nhas remained a largely unsolved challenge until now. This is astonishing in\nview of the excellent theoretical understanding of Maxwell's equations and the\nabundant experimental measurements of optical phenomena in dispersive media.\nThe challenge is related to the complexity of the interference patterns of\npartial waves and to the coupling of the field and medium dynamics by the\noptical force. In this work, we use the mass-polariton theory of light [Phys.\nRev. A 96, 063834 (2017)] to derive the stress-energy-momentum (SEM) tensors of\nthe field and the dispersive medium. Our starting point, the fundamental local\nconservation laws of energy and momentum, is close to that of a recent\ntheoretical work on light in dispersive media by Philbin [Phys. Rev. A 83,\n013823 (2011)], which however, excludes the power-conversion and force density\nsource terms describing the coupling between the field and the medium. In the\ngeneral inertial frame, we present the SEM tensors in terms of Lorentz scalars,\nfour-vectors, and field tensors that reflect in a transparent way the Lorentz\ncovariance of the theory. The SEM tensors of the field and the medium are\nsymmetric, form-invariant, and in full accordance with the covariance principle\nof the special theory of relativity. The SEM tensor of the coupled field-medium\nstate of light also has zero four-divergence. Therefore, light in a dispersive\nmedium has well-defined four-momentum and rest frame. The volume integrals of\nthe total energy and momentum densities of light agree with the model of\nmass-polariton quasiparticles having a nonzero rest mass. The predictions of\nour work are directly accessible to experiments.\n"}
+{"abstract": "  We present the Time Lag/Delay Reconstructor (TLDR), an algorithm for\nreconstructing velocity delay maps in the Maximum A Posteriori framework for\nreverberation mapping. Reverberation mapping is a tomographical method for\nstudying the kinematics and geometry of the broad-line region of active\ngalactic nuclei at high spatial resolution. Leveraging modern image\nreconstruction techniques, including Total Variation and Compressed Sensing,\nTLDR applies multiple regularization schemes to re-construct velocity delay\nmaps using the Alternating Direction Method of Multipliers. Along with the\ndetailed description of the TLDR algorithm we present test reconstructions from\nTLDR applied to synthetic reverberation mapping spectra as well as a\npreliminary reconstruction of the H\\b{eta}feature of Arp 151 from the 2008 Lick\nActive Galactic Nuclei Monitoring Project.\n"}
+{"abstract": "  Does the proof-of-work protocol serve its intended purpose of supporting\ndecentralized cryptocurrency mining? To address this question, we develop a\ngame-theoretical model where miners first invest in hardware to improve the\nefficiency of their operations, and then compete for mining rewards in a\nrent-seeking game. We argue that because of capacity constraints faced by\nminers, centralization in mining is lower than indicated by both public\ndiscourse and recent academic work. We show that advancements in hardware\nefficiency do not necessarily lead to larger miners increasing their advantage,\nbut rather allow smaller miners to expand and new miners to enter the\ncompetition. Our calibrated model illustrates that hardware efficiency has a\nsmall impact on the cost of attacking a network, while the mining reward has a\nsignificant impact. This highlights the vulnerability of smaller and emerging\ncryptocurrencies, as well as of established cryptocurrencies transitioning to a\nfee-based mining reward scheme.\n"}
+{"abstract": "  2D image representations are in regular grids and can be processed\nefficiently, whereas 3D point clouds are unordered and scattered in 3D space.\nThe information inside these two visual domains is well complementary, e.g., 2D\nimages have fine-grained texture while 3D point clouds contain plentiful\ngeometry information. However, most current visual recognition systems process\nthem individually. In this paper, we present a \\emph{bidirectional projection\nnetwork (BPNet)} for joint 2D and 3D reasoning in an end-to-end manner. It\ncontains 2D and 3D sub-networks with symmetric architectures, that are\nconnected by our proposed \\emph{bidirectional projection module (BPM)}. Via the\n\\emph{BPM}, complementary 2D and 3D information can interact with each other in\nmultiple architectural levels, such that advantages in these two visual domains\ncan be combined for better scene recognition. Extensive quantitative and\nqualitative experimental evaluations show that joint reasoning over 2D and 3D\nvisual domains can benefit both 2D and 3D scene understanding simultaneously.\nOur \\emph{BPNet} achieves top performance on the ScanNetV2 benchmark for both\n2D and 3D semantic segmentation. Code is available at\n\\url{https://github.com/wbhu/BPNet}.\n"}
+{"abstract": "  We focus on the secular changes of the orbital elements of a planet in the\nsolar system, determined by the magnetic-like part of a gravitational wave\nfield. Using Fermi coordinates we show that the total force acting on a test\nparticle is made of two contributions: a gravito-electric one and a\ngravito-magnetic one. While the electric-like force has been thoroughly\ndiscussed in the past, the effect of the gravito-magnetic force, which depends\non the velocity of the test particle, has not been considered yet. We obtain\napproximated results to some orders in the orbital eccentricity and show that\nthese effects are much smaller than the corresponding gravito-electric ones.\n"}
+{"abstract": "  Measurement-device-independent quantum key distribution (MDI-QKD) is a\ntechnique for quantum-secured communication that eliminates all detector\nside-channels, although is currently limited by implementation complexity and\nlow secure key rates. Here, we introduce a simple and compact MDI-QKD system\ndesign at gigahertz clock rates with enhanced resilience to laser fluctuations\n- thus enabling free-running semiconductor laser sources to be employed without\nspectral or phase feedback. This is achieved using direct laser modulation,\ncarefully exploiting gain-switching and injection-locking laser dynamics to\nencode phase-modulated time-bin bits. Our design enables secure key rates that\nimprove upon the state of the art by an order of magnitude, up to 8 bps at 54\ndB channel loss and 2 kbps in the finite-size regime for 30 dB channel loss.\nThis greatly simplified MDI-QKD system design and proof-of-principle\ndemonstration shows that MDI-QKD is a practical, high-performance solution for\nfuture quantum communication networks.\n"}
+{"abstract": "  Amorphous solids appear to react elastically to small external strains, but\nin contrast to ideal elastic media, plastic responses abound immediately, at\nany value of the strain. Such plastic responses are quasi-localized in nature,\nwith the ``cheapest\" one being a quadrupolar source. The existence of such\nplastic responses results in {\\em screened elasticity} in which strains and\nstresses can either quantitatively or qualitatively differ from the un-screened\ntheory, depending on the specific screening mechanism. Here we offer a theory\nof such screening effects by plastic quadrupoles, dipoles and monopoles,\nexplain their natural appearance, and point out the analogy to electrostatic\nscreening by electric charges and dipoles. For low density of quadrupoles the\neffect is to normalize the elastic moduli without a qualitative change compared\nto pure elasticity theory; for higher density of quadrupoles the screening\neffects result in qualitative changes. Predictions for the spatial dependence\nof displacement fields caused by local sources of strains are provided and\ncompared to numerical simulations. We find that anomalous elasticity is richer\nthan electrostatics in having a screening mode that does not appear in the\nelectrostatic analog\n"}
+{"abstract": "  We analyze the extracellular stimulation current and the charge injection\ncapacitity (CIC) of microelectrodes coated with an insulating layer to prevent\ntoxic electrochemical effects in bioelectronic applications. We show for a\nmicroelectrode coated with an insulating ferroelectric layer, that the\nferroelectric polarization current contributes to the extracellular stimulation\ncurrent. Depending on the remanent polarization $P_{\\rm r}$ of the\nferroelectric, the polarization current in the switching regime can increase\nthe CIC by up to two orders of magnitude as compared to the commonly used\nextracellular capacitive stimulation with microelectrodes that are coated with\na dielectric layer.\n"}
+{"abstract": "  We investigated the suitability of the astronomical 15 GHz VLBA observing\nprogram MOJAVE-5 for estimation of geodetic parameters, such as station\ncoordinates and Earth orientation parameters. We processed contemporary\ngeodetic dual-band RV and CN experiments observed at 2.3 GHz and 8.6 GHz\nstarting on September 2016 through July 2020 as reference dataset. We showed\nthat the baseline length repeatability from MOJAVE-5 experiments is only a\nfactor of 1.5 greater than from the dedicated geodetic dataset and still below\n1~ppb. The wrms of the difference of estimated EOP with respect to the\nreference IERS C04 time series are a factor of 1.3 to 1.8 worse. We isolated\nthree major differences between the datasets in terms their possible impact on\nthe geodetic results, i.e. the scheduling approach, treatment of the\nionospheric delay, and selection of target radio sources. We showed that the\nmajor factor causing discrepancies in the estimated geodetic parameters is the\ndifferent scheduling approach of the datasets. We conclude that systematic\nerrors in MOJAVE-5 dataset are low enough for these data to be used as an\nexcellent testbed for further investigations on the radio source structure\neffects in geodesy and astrometry.\n"}
+{"abstract": "  Supervised deep learning has swiftly become a workhorse for accelerated MRI\nin recent years, offering state-of-the-art performance in image reconstruction\nfrom undersampled acquisitions. Training deep supervised models requires large\ndatasets of undersampled and fully-sampled acquisitions typically from a\nmatching set of subjects. Given scarce access to large medical datasets, this\nlimitation has sparked interest in unsupervised methods that reduce reliance on\nfully-sampled ground-truth data. A common framework is based on the deep image\nprior, where network-driven regularization is enforced directly during\ninference on undersampled acquisitions. Yet, canonical convolutional\narchitectures are suboptimal in capturing long-range relationships, and\nrandomly initialized networks may hamper convergence. To address these\nlimitations, here we introduce a novel unsupervised MRI reconstruction method\nbased on zero-Shot Learned Adversarial TransformERs (SLATER). SLATER embodies a\ndeep adversarial network with cross-attention transformer blocks to map noise\nand latent variables onto MR images. This unconditional network learns a\nhigh-quality MRI prior in a self-supervised encoding task. A zero-shot\nreconstruction is performed on undersampled test data, where inference is\nperformed by optimizing network parameters, latent and noise variables to\nensure maximal consistency to multi-coil MRI data. Comprehensive experiments on\nbrain MRI datasets clearly demonstrate the superior performance of SLATER\nagainst several state-of-the-art unsupervised methods.\n"}
+{"abstract": "  Quantitative susceptibility mapping (QSM) has demonstrated great potential in\nquantifying tissue susceptibility in various brain diseases. However, the\nintrinsic ill-posed inverse problem relating the tissue phase to the underlying\nsusceptibility distribution affects the accuracy for quantifying tissue\nsusceptibility. Recently, deep learning has shown promising results to improve\naccuracy by reducing the streaking artifacts. However, there exists a mismatch\nbetween the observed phase and the theoretical forward phase estimated by the\nsusceptibility label. In this study, we proposed a model-based deep learning\narchitecture that followed the STI (susceptibility tensor imaging) physical\nmodel, referred to as MoDL-QSM. Specifically, MoDL-QSM accounts for the\nrelationship between STI-derived phase contrast induced by the susceptibility\ntensor terms (ki13,ki23,ki33) and the acquired single-orientation phase. The\nconvolution neural networks are embedded into the physical model to learn a\nregularization term containing prior information. ki33 and phase induced by\nki13 and ki23 terms were used as the labels for network training. Quantitative\nevaluation metrics (RSME, SSIM, and HFEN) were compared with recently developed\ndeep learning QSM methods. The results showed that MoDL-QSM achieved superior\nperformance, demonstrating its potential for future applications.\n"}
+{"abstract": "  Automatic verification of array manipulating programs is a challenging\nproblem because it often amounts to the inference of in ductive quantified loop\ninvariants which, in some cases, may not even be firstorder expressible. In\nthis paper, we suggest a novel verification tech nique that is based on\ninduction on userdefined rank of program states as an alternative to\nloopinvariants. Our technique, dubbed inductive rank reduction, works in two\nsteps. Firstly, we simplify the verification problem and prove that the program\nis correct when the input state con tains an input array of length B or less,\nusing the length of the array as the rank of the state. Secondly, we employ a\nsqueezing function g which converts a program state sigma with an array of\nlength > B to a state g(sigma) containing an array of length minus 1 or less.\nWe prove that when g satisfies certain natural conditions then if the program\nviolates its specification on sigma then it does so also on g(sigma). The\ncorrectness of the program on inputs with arrays of arbitrary lengths follows\nby induction. We make our technique automatic for array programs whose length\nof execution is proportional to the length of the input arrays by (i) perform\ning the first step using symbolic execution, (ii) verifying the conditions\nrequired of g using Z3, and (iii) providing a heuristic procedure for syn\nthesizing g. We implemented our technique and applied it successfully to\nseveral interesting arraymanipulating programs, including a bidirec tional\nsummation program whose loop invariant cannot be expressed in firstorder logic\nwhile its specification is quantifier free.\n"}
+{"abstract": "  In current seismic acquisition practice, there is an increasing drive for\nsparsely (in space) acquired data, often in irregular geometry. These surveys\ncan trade off subsurface information for efficiency/cost - creating a problem\nof \"missing seismic data\" that can greatly hinder subsequent processing and\ninterpretation. Reconstruction of regularly sampled dense data from highly\nsparse, irregular data can therefore aid in processing and interpretation of\nthese far sparser, more efficient seismic surveys. Here, two methods are\ncompared to solve the reconstruction problem in both space-time and\nwavenumber-frequency domain. This requires an operator that maps sparse to\ndense data: the operator is generally unknown, being the inverse of a known\ndata sampling operator. As such, here the deterministic inversion is\nefficiently solved by least squares optimisation using a numerically efficient\nPython-based linear operator representation. An alternative approach is\nprobabilistic and uses deep learning. Here, two deep learning architectures are\nbenchmarked against each other and the deterministic approach; a Recurrent\nInference Machine (RIM), which is designed specifically to solve inverse\nproblems given known forward operators, and the well-known U-Net. The trained\ndeep learning networks are capable of successfully mapping sparse to dense\nseismic data for a range of different datasets and decimation percentages,\nthereby significantly reducing spatial aliasing in the wavenumber-frequency\ndomain. The deterministic inversion on the contrary, could not reconstruct the\nmissing data and thus did not reduce the undesired spatial aliasing. Results\nshow that the application of Deep Learning for seismic reconstruction is\npromising, but the treatment of large-volume, multi-component seismic datasets\nwill require dedicated learning architectures not yet realisable with existing\ntools.\n"}
+{"abstract": "  A capacitive displacement system was constructed to measure the\nelectric-field-induced piezoelectric strain in the simple form of either a bulk\nor thin film. The system can determine an AC displacement of 2 pm precisely by\nusing a lock-in detection, and can measure the large displacement within a\nrange of $\\pm$ 25 $\\mu$m with a sub-nanometer resolution. The system can also\nbe used to measure the variation in strain within a temperature range of 210 -\n450 K, allowing the evaluation of the temperature coefficient of a\npiezoelectric constant and the studies on the effects of a phase transition on\nthe piezoelectric response. Experimental results on quartz, PZT ceramics and\nthin films, and BaTiO$_3$ confirm the capabilities of the developed system.\n"}
+{"abstract": "  Intensity modulators are an essential component in optics for controlling\nfree-space beams. Many applications require the intensity of a free-space beam\nto be modulated at a single frequency, including wide-field lock-in detection\nfor sensitive measurements, mode-locking in lasers, and phase-shift\ntime-of-flight imaging (LiDAR). Here, we report a new type of single frequency\nintensity modulator that we refer to as a longitudinal piezoelectric resonant\nphotoelastic modulator. The modulator consists of a thin lithium niobate wafer\ncoated with transparent surface electrodes. One of the fundamental acoustic\nmodes of the modulator is excited through the surface electrodes, confining an\nacoustic standing wave to the electrode region. The modulator is placed between\noptical polarizers; light propagating through the modulator and polarizers is\nintensity modulated with a wide acceptance angle and record breaking modulation\nefficiency in the megahertz frequency regime. As an illustration of the\npotential of our approach, we show that the proposed modulator can be\nintegrated with a standard image sensor to effectively convert it into a\ntime-of-flight imaging system.\n"}
+{"abstract": "  An $n$-vertex graph is said to to be $(p,\\beta)$-bijumbled if for any vertex\nsets $A,B\\subseteq V(G)$, we have \\[e(A,B)=p|A||B|\\pm \\beta \\sqrt{|A||B|}.\\]\n  We prove that for any $3\\leq r\\in \\mathbb{N}$ and $c>0$ there exists an\n$\\varepsilon>0$ such that any $n$-vertex $(p,\\beta)$-bijumbled graph with $n\\in\nr \\mathbb{N}$, $\\delta(G)\\geq cpn$ and $\\beta \\leq \\varepsilon p^{r-1}n$,\ncontains a $K_r$-factor. This implies a corresponding result for the stronger\npseudorandom notion of $(n,d,\\lambda)$-graphs.\n  For the case of triangle factors, that is when $r=3$, this result resolves a\nconjecture of Krivelevich, Sudakov and Szab\\'o from 2004 and it is tight due to\na pseudorandom triangle-free construction of Alon. In fact, in this case even\nmore is true: as a corollary to this result and a result of Han, Kohayakawa,\nPerson and the author, we can conclude that the same condition of\n$\\beta=o(p^2n)$ actually guarantees that a $(p,\\beta)$-bijumbled graph $G$\ncontains every graph on $n$ vertices with maximum degree at most 2.\n"}
+{"abstract": "  This paper is devoted to providing quantitative bounds on the location of\neigenvalues, both discrete and embedded, of non self-adjoint Lam\\'e operators\nof elasticity $-\\Delta^\\ast + V$ in terms of suitable norms of the potential\n$V$. In particular, this allows to get sufficient conditions on the size of the\npotential such that the point spectrum of the perturbed operator remains empty.\nIn three dimensions we show full spectral stability under suitable\nform-subordinated perturbations: we prove that the spectrum is purely\ncontinuous and coincides with the non negative semi-axis as in the free case.\n"}
+{"abstract": "  Reasonable setting of traffic signals can be very helpful in alleviating\ncongestion in urban traffic networks. Meta-heuristic optimization algorithms\nhave proved themselves to be able to find high-quality signal timing plans.\nHowever, they generally suffer from performance deterioration when solving\nlarge-scale traffic signal optimization problems due to the huge search space\nand limited computational budget. Directing against this issue, this study\nproposes a surrogate-assisted cooperative signal optimization (SCSO) method.\nDifferent from existing methods that directly deal with the entire traffic\nnetwork, SCSO first decomposes it into a set of tractable sub-networks, and\nthen achieves signal setting by cooperatively optimizing these sub-networks\nwith a surrogate-assisted optimizer. The decomposition operation significantly\nnarrows the search space of the whole traffic network, and the\nsurrogate-assisted optimizer greatly lowers the computational burden by\nreducing the number of expensive traffic simulations. By taking Newman fast\nalgorithm, radial basis function and a modified estimation of distribution\nalgorithm as decomposer, surrogate model and optimizer, respectively, this\nstudy develops a concrete SCSO algorithm. To evaluate its effectiveness and\nefficiency, a large-scale traffic network involving crossroads and T-junctions\nis generated based on a real traffic network. Comparison with several existing\nmeta-heuristic algorithms specially designed for traffic signal optimization\ndemonstrates the superiority of SCSO in reducing the average delay time of\nvehicles.\n"}
+{"abstract": "  We prove that a probability solution of the stationary Kolmogorov equation\ngenerated by a first order perturbation $v$ of the Ornstein--Uhlenbeck operator\n$L$ possesses a highly integrable density with respect to the Gaussian measure\nsatisfying the non-perturbed equation provided that $v$ is sufficiently\nintegrable. More generally, a similar estimate is proved for solutions to\ninequalities connected with Markov semigroup generators under the curvature\ncondition $CD(\\theta,\\infty)$. For perturbations from $L^p$ an analog of the\nLog-Sobolev inequality is obtained. It is also proved in the Gaussian case that\nthe gradient of the density is integrable to all powers. We obtain\ndimension-free bounds on the density and its gradient, which also covers the\ninfinite-dimensional case.\n"}
+{"abstract": "  A repeating source of fast radio bursts (FRBs) is recently discovered from a\nglobular cluster of M81. Association to a globular cluster (or other old\nstellar systems) suggests that strongly magnetized neutron stars, which are the\nmost likely objects responsible for FRBs, are born not only when young massive\nstars undergo core-collapse, but also by mergers of old white dwarfs. We find\nthat the fractional contribution to the total FRB rate by old stellar\npopulations is at least a few percent, and the precise fraction can be\nconstrained by FRB searches in the directions of nearby galaxies, both\nstar-forming and elliptical ones. Using very general arguments, we show that\nthe activity time of the M81-FRB object is between 10^4 and 10^6 years, and\nmore likely of order 10^5 years. The energetics of radio outbursts puts a lower\nlimit on the magnetic field strength of 10^{13} G, and the spin period > 0.2\nsec, thereby ruling out the source being a milli-second pulsar. The upper limit\non the persistent X-ray luminosity (provided by Chandra), together with the\nhigh FRB luminosity and frequent repetitions, severely constrains (or rules\nout) the possibility that the M81-FRB is a scaled-up version of giant pulses\nfrom Galactic pulsars. Finally, the 50 ns variability time of the FRB\nlightcurve suggests that the emission is produced in a compact region inside\nthe neutron star magnetosphere, as it cannot be accounted for when the emission\nis at distances > 10^{10} cm.\n"}
+{"abstract": "  We consider the task of retrieving audio using free-form natural language\nqueries. To study this problem, which has received limited attention in the\nexisting literature, we introduce challenging new benchmarks for text-based\naudio retrieval using text annotations sourced from the Audiocaps and Clotho\ndatasets. We then employ these benchmarks to establish baselines for\ncross-modal audio retrieval, where we demonstrate the benefits of pre-training\non diverse audio tasks. We hope that our benchmarks will inspire further\nresearch into cross-modal text-based audio retrieval with free-form text\nqueries.\n"}
+{"abstract": "  Direct numerical simulation has been carried out on one attached and two\nseparated boundary layer flows (involving small and large separation) under the\ninfluence of an adverse pressure gradient. A unified picture of the\npre-transitional boundary layer for the three cases has been provided that\nreveals a \"mixed-mode\" instability, involving contribution from instability\nwaves and streamwise streaks. A time-frequency analysis of the transitional\nvelocity signals has been performed which shows that as the Reynolds number\ndecreases, the character of the time traces evolves continuously from a\n\"spotty\" behaviour (exhibiting distinct turbulent spots) for the attached case\nto a \"non-spotty\" behaviour (involving more \"uniform\" distribution of turbulent\nfluctuations in time) for the large separation case, encompassing the entire\nspectrum of transition scenarios. The variation of the intermittency factor\nwithin the transition zone is seen to compare well with the Narasimha universal\nintermittency distribution. We find that although the time variation of\nvelocity for large separation is non-spotty (or more \"uniform\"), the spanwise\nvariation of velocity is spotty, showing a clear clustering of high-wavenumber\nfluctuations separated by quasi-laminar regions. Thus, all the cases exhibit\nspottiness in the transition zone with different manifestations. We present a\nphysical cartoon for the transition scenarios for the attached and separated\ncases, using the ideas of vortex-wall interaction and instability of spanwise\nvortical structures. We find that concentrated breakdown is exhibited by all\nthe three cases near the transition onset and the spot breakdown processes are\nbroadly consistent with the postulates underlying the universal intermittency\ndistribution.\n"}
+{"abstract": "  This paper brings together the concepts of navigation transformation and\nharmonic functions to form navigation functions that are\ncorrect-by-construction in the sense that no tuning is required. The form of\nthe navigation function is explicitly related to the number of obstacles in the\nenvironment. This enables application of navigation functions for autonomous\nrobot navigation in partially or fully unknown environments, with the\ncapability of on-the-fly adjustment of the navigation function when new\nobstacles are discovered by the robot. Appropriate navigation controllers,\napplicable to robots with local, sector bounded sensing, are presented and\nanalyzed for a~kinematic point-mass robot and then for the dynamic point-mass\nrobot system. The closed form nature of the proposed navigation scheme provides\nfor online, fast-feedback based navigation. In addition to the analytic\nguarantees, simulation studies are presented to verify the effectiveness of the\nmethodology.\n"}
+{"abstract": "  Observations of hot-Jupiter atmospheres show large variations in the location\nof the hot spot and the amplitude of spectral features. Atmospheric flow\nsimulations using the commonly-employed forcing and initialization have\ngenerally produced a large, monolithic patch of stationary hot area located\neastward of the substellar point at $\\sim 3\\!\\times\\! 10^{-3}$ MPa pressure\nlevel. Here we perform high-resolution (up to T682) pseudospectral simulations\nthat accurately capture small-scale eddies and waves, inherent in hot-Jupiter\natmospheres due to ageostrophy. The atmospheres contain a large number of\nintense storms over a wide range of scales, including the planetary-scale. The\nlatter sized storms dictate the large-scale spatial distribution and temporal\nvariability of hot, as well as cold, regions over the planet. In addition, the\nlarge storms exhibit quasi-periodic life cycles within multiple equilibrium\nstates -- all identifiable in the disk-integrated time series of the\ntemperature flux.\n"}
+{"abstract": "  Collisionless shocks are ubiquitous in the Universe and are held responsible\nfor the production of non-thermal particles and high-energy radiation. In the\nabsence of particle collisions in the system, theoretical works show that the\ninteraction of an expanding plasma with a pre-existing electromagnetic\nstructure (as in our case) is able to induce energy dissipation and allow for\nshock formation. Shock formation can alternatively take place when two plasmas\ninteract, through microscopic instabilities inducing electromagnetic fields\nwhich are able in turn to mediate energy dissipation and shock formation. Using\nour platform where we couple a fast-expanding plasma induced by high-power\nlasers (JLF/Titan at LLNL and LULI2000) with high-strength magnetic fields, we\nhave investigated the generation of magnetized collisionless shock and the\nassociated particle energization. We have characterized the shock to be\ncollisionless and super-critical. We report here on measurements of the plasma\ndensity, temperature, the electromagnetic field structures, and particle\nenergization in the experiments, under various conditions of ambient plasma and\nB-field. We have also modeled the formation of the shocks using macroscopic\nhydrodynamic simulations and the associated particle acceleration using kinetic\nparticle-in-cell simulations. As a companion paper of\n\\citet{yao2020laboratory}, here we show additional results of the experiments\nand simulations, providing more information to reproduce them and demonstrating\nthe robustness of our interpreted proton energization mechanism to be shock\nsurfing acceleration.\n"}
+{"abstract": "  Nonholonomic mechanical systems encompass a large class of practically\ninteresting robotic structures, such as wheeled mobile robots, space\nmanipulators, and multi-fingered robot hands. However, few results exist on the\ncooperative control of such systems in a generic, distributed approach. In this\nwork we extend a recently developed distributed Interconnection and Damping\nAssignment Passivity-Based Control (IDA-PBC) method to such systems. More\nspecifically, relying on port-Hamiltonian system modelling for networks of\nmechanical systems, we propose a full-state stabilization control law for a\nclass of nonholonomic systems within the framework of distributed IDA-PBC. This\nenables the cooperative control of heterogeneous, underactuated and\nnonholonomic systems with a unified control law. This control law primarily\nrelies on the notion of Passive Configuration Decomposition (PCD) and a novel,\nnon-smooth desired potential energy function proposed here. A low-level\ncollision avoidance protocol is also implemented in order to achieve dynamic\ninter-agent collision avoidance, enhancing the practical relevance of this\nwork. Theoretical results are tested in different simulation scenarios in order\nto highlight the applicability of the derived method.\n"}
+{"abstract": "  Online continual learning for image classification studies the problem of\nlearning to classify images from an online stream of data and tasks, where\ntasks may include new classes (class incremental) or data nonstationarity\n(domain incremental). One of the key challenges of continual learning is to\navoid catastrophic forgetting (CF), i.e., forgetting old tasks in the presence\nof more recent tasks. Over the past few years, many methods and tricks have\nbeen introduced to address this problem, but many have not been fairly and\nsystematically compared under a variety of realistic and practical settings. To\nbetter understand the relative advantages of various approaches and the\nsettings where they work best, this survey aims to (1) compare state-of-the-art\nmethods such as MIR, iCARL, and GDumb and determine which works best at\ndifferent experimental settings; (2) determine if the best class incremental\nmethods are also competitive in domain incremental setting; (3) evaluate the\nperformance of 7 simple but effective trick such as \"review\" trick and nearest\nclass mean (NCM) classifier to assess their relative impact. Regarding (1), we\nobserve iCaRL remains competitive when the memory buffer is small; GDumb\noutperforms many recently proposed methods in medium-size datasets and MIR\nperforms the best in larger-scale datasets. For (2), we note that GDumb\nperforms quite poorly while MIR -- already competitive for (1) -- is also\nstrongly competitive in this very different but important setting. Overall,\nthis allows us to conclude that MIR is overall a strong and versatile method\nacross a wide variety of settings. For (3), we find that all 7 tricks are\nbeneficial, and when augmented with the \"review\" trick and NCM classifier, MIR\nproduces performance levels that bring online continual learning much closer to\nits ultimate goal of matching offline training.\n"}
+{"abstract": "  The record-breaking achievements of deep neural networks (DNNs) in image\nclassification and detection tasks resulted in a surge of new computer vision\napplications during the past years. However, their computational complexity is\nrestricting their deployment to powerful stationary or complex dedicated\nprocessing hardware, limiting their use in smart edge processing applications.\nWe propose an automated deployment framework for DNN acceleration at the edge\non field-programmable gate array (FPGA)-based cameras. The framework\nautomatically converts an arbitrary-sized and quantized trained network into an\nefficient streaming-processing IP block that is instantiated within a generic\nadapter block in the FPGA. In contrast to prior work, the accelerator is purely\nlogic and thus supports end-to-end processing on FPGAs without on-chip\nmicroprocessors. Our mapping tool features automatic translation from a trained\nCaffe network, arbitrary layer-wise fixed-point precision for both weights and\nactivations, an efficient XNOR implementation for fully binary layers as well\nas a balancing mechanism for effective allocation of computational resources in\nthe streaming dataflow. To present the performance of the system we employ this\ntool to implement two CNN edge processing networks on an FPGA-based high-speed\ncamera with various precision settings showing computational throughputs of up\nto 337GOPS in low-latency streaming mode (no batching), running entirely on the\ncamera.\n"}
+{"abstract": "  In spite that Federated Learning (FL) is well known for its privacy\nprotection when training machine learning models among distributed clients\ncollaboratively, recent studies have pointed out that the naive FL is\nsusceptible to gradient leakage attacks. In the meanwhile, Differential Privacy\n(DP) emerges as a promising countermeasure to defend against gradient leakage\nattacks. However, the adoption of DP by clients in FL may significantly\njeopardize the model accuracy. It is still an open problem to understand the\npracticality of DP from a theoretic perspective. In this paper, we make the\nfirst attempt to understand the practicality of DP in FL through tuning the\nnumber of conducted iterations. Based on the FedAvg algorithm, we formally\nderive the convergence rate with DP noises in FL. Then, we theoretically\nderive: 1) the conditions for the DP based FedAvg to converge as the number of\nglobal iterations (GI) approaches infinity; 2) the method to set the number of\nlocal iterations (LI) to minimize the negative influence of DP noises. By\nfurther substituting the Laplace and Gaussian mechanisms into the derived\nconvergence rate respectively, we show that: 3) The DP based FedAvg with the\nLaplace mechanism cannot converge, but the divergence rate can be effectively\nprohibited by setting the number of LIs with our method; 4) The learning error\nof the DP based FedAvg with the Gaussian mechanism can converge to a constant\nnumber finally if we use a fixed number of LIs per GI. To verify our\ntheoretical findings, we conduct extensive experiments using two real-world\ndatasets. The results not only validate our analysis results, but also provide\nuseful guidelines on how to optimize model accuracy when incorporating DP into\nFL\n"}
+{"abstract": "  High-power tunable lasers with good longitudinal and transverse modes are\nfundamental tools for exploring quantum physics. Here we report a high-power\ncontinuous-wave injection-locked titanium:sapphire laser with a low-loss cavity\nconfiguration, where only a laser crystal was installed in the laser cavity.\nAlthough the transverse mode was affected by a thermal lens formed in the laser\ncrystal, the focal length of the thermal lens could be shifted via the\ntemperature of the laser crystal holder or the pump power. As a result, we\nfound a condition that 10 W single-frequency oscillation with a good transverse\nmode and a slope efficiency of 51% were achieved.\n"}
+{"abstract": "  In Online Social Networks (OSN) numerous are the cases in which users create\nmultiple accounts that publicly seem to belong to different people but are\nactually fake identities of the same person. These fictitious characters can be\nexploited to carry out abusive behaviors such as manipulating opinions,\nspreading fake news and disturbing other users. In literature this problem is\nknown as the Sockpuppet problem. In our work we focus on Telegram, a\nwide-spread instant messaging application, often known for its exploitation by\nmembers of organized crime and terrorism, and more in general for its high\npresence of people who have offensive behaviors.\n"}
+{"abstract": "  In this paper, we consider a class of optimal control problems governed by\nstate-equations of Kobayashi--Warren--Carter type. The control is given by\nphysical temperature. The focus is on problems in dimensions less than equal to\n4. The results are divided in four Main Theorems, concerned with: solvability\nand parameter-dependence of state-equations and optimal control problems; the\nfirst order necessary optimality conditions for these regularized optimal\ncontrol problems. Subsequently, we derive the limiting systems and optimality\nconditions and study their well-posedness.\n"}
+{"abstract": "  Quantum error correction and symmetries play central roles in quantum\ninformation science and physics. It is known that quantum error-correcting\ncodes covariant with respect to continuous symmetries cannot correct erasure\nerrors perfectly (an important case being the Eastin-Knill theorem), in\ncontrast to the case without symmetry constraints. Furthermore, there are\nfundamental limits on the accuracy of such covariant codes for approximate\nquantum error correction. Here, we consider the quantum error correction\ncapability of random covariant codes. In particular, we show that\n$U(1)$-covariant codes generated by Haar random $U(1)$-symmetric unitaries,\ni.e. unitaries that commute with the charge operator (or conserve the charge),\ntypically saturate the fundamental limits to leading order in terms of both the\naverage- and worst-case purified distances against erasure noise. We note that\nthe results hold for symmetric variants of unitary 2-designs, and comment on\nthe convergence problem of charge-conserving random circuits. Our results not\nonly indicate (potentially efficient) randomized constructions of optimal\n$U(1)$-covariant codes, but also reveal fundamental properties of random\ncharge-conserving unitaries, which may underlie important models of complex\nquantum systems in wide-ranging physical scenarios where conservation laws are\npresent, such as black holes and many-body spin systems.\n"}
+{"abstract": "  While relation extraction is an essential task in knowledge acquisition and\nrepresentation, and new-generated relations are common in the real world, less\neffort is made to predict unseen relations that cannot be observed at the\ntraining stage. In this paper, we formulate the zero-shot relation extraction\nproblem by incorporating the text description of seen and unseen relations. We\npropose a novel multi-task learning model, zero-shot BERT (ZS-BERT), to\ndirectly predict unseen relations without hand-crafted attribute labeling and\nmultiple pairwise classifications. Given training instances consisting of input\nsentences and the descriptions of their relations, ZS-BERT learns two functions\nthat project sentences and relation descriptions into an embedding space by\njointly minimizing the distances between them and classifying seen relations.\nBy generating the embeddings of unseen relations and new-coming sentences based\non such two functions, we use nearest neighbor search to obtain the prediction\nof unseen relations. Experiments conducted on two well-known datasets exhibit\nthat ZS-BERT can outperform existing methods by at least 13.54\\% improvement on\nF1 score.\n"}
+{"abstract": "  The performance of Non-orthogonal Multiple Access (NOMA) system dramatically\ndecreases in the presence of inter-cell interference. This condition gets more\nchallenging if a smart jammer is interacting in a network. In this paper, the\nNOMA power allocation of two independent Base Stations (BSs) against a smart\njammer is, modeled as a sequential game. In this game, at first, each BS as a\nleader independently chooses its power allocation strategy. Then, the smart\njammer as the follower selects its optimal strategy based on the strategies of\nthe BSs. The solutions of this game are, derived under different conditions.\nBased on the game-theoretical analysis, three new schemes are proposed for\nanti-jamming NOMA power allocation in a two-cell scenario called a) Q-Learning\nbased Unselfish (QLU) NOMA power allocation scheme, b) Deep Q-Learning based\nUnselfish (DQLU) NOMA power allocation scheme, and c) Hot Booting Deep\nQ-Learning based Unselfish (HBDQLU) NOMA power allocation scheme. In these\nmethods the BSs do not coordinate with each other. But our analysis\ntheoretically predicts that with high probability, the proposed methods will\nconverge to the optimal strategy from the total network point of view.\nSimulation results show the convergence of the proposed schemes and also their\noutperformance with respect to the Q-Learning-based Selfish (QLS) NOMA power\nallocation method.\n"}
+{"abstract": "  Square-root topological phases have been discussed mainly for systems with\nchiral symmetry. In this paper, we analyze the topology of the squared\nHamiltonian for systems preserving the time-reversal and particle-hole\nsymmetry. Our analysis elucidates that two-dimensional systems of class CII\nhost helical edge states due to the nontrivial topology of the squared\nHamiltonian in contrast to the absence of ordinary topological phases. The\nemergence of the helical edge modes is demonstrated by analyzing a toy model.\nWe also show the emergence of surface states induced by the non-trivial\ntopology of the squared Hamiltonian in three dimensions.\n"}
+{"abstract": "  Many scientific datasets are compositional in nature. Important examples\ninclude species abundances in ecology, rock compositions in geology, topic\ncompositions in large-scale text corpora, and sequencing count data in\nmolecular biology. Here, we provide a causal view on compositional data in an\ninstrumental variable setting where the composition acts as the cause.\nThroughout, we pay particular attention to the interpretation of compositional\ncauses from the viewpoint of interventions and crisply articulate potential\npitfalls for practitioners. Focusing on modern high-dimensional microbiome\nsequencing data as a timely illustrative use case, our analysis first reveals\nthat popular one-dimensional information-theoretic summary statistics, such as\ndiversity and richness, may be insufficient for drawing causal conclusions from\necological data. Instead, we advocate for multivariate alternatives using\nstatistical data transformations and regression techniques that take the\nspecial structure of the compositional sample space into account. In a\ncomparative analysis on synthetic and semi-synthetic data we show the\nadvantages and limitations of our proposal. We posit that our framework may\nprovide a useful starting point for cause-effect estimation in the context of\ncompositional data.\n"}
+{"abstract": "  This paper present a construction and the analysis of a class of non-Gaussian\npositive-definite matrix-valued homogeneous random fields with uncertain\nspectral measure for stochastic elliptic operators. Then the stochastic\nelliptic boundary value problem in a bounded domain of the 3D-space is\nintroduced and analyzed for stochastic homogenization.\n"}
+{"abstract": "  We study an equation governed by a discontinuous fully nonlinear operator.\nSuch discontinuities are solution-dependent, which introduces a free boundary.\nWorking under natural assumptions, we prove the existence of $L^p$-viscosity\nand strong solutions to the problem. The operator does not satisfy the usual\nstructure conditions and to obtain the existence of solutions we resort to\nsolving approximate problems, combined with an iterative scheme. We believe our\nstrategy can be applied to other classes of non-variational free boundary\nproblems.\n"}
+{"abstract": "  The in vitro clonogenic assay is a technique to study the ability of a cell\nto form a colony in a culture dish. By optical imaging, dishes with stained\ncolonies can be scanned and assessed digitally. Identification, segmentation\nand counting of stained colonies play a vital part in high-throughput screening\nand quantitative assessment of biological assays. Image processing of such\npictured/scanned assays can be affected by image/scan acquisition artifacts\nlike background noise and spatially varying illumination, and contaminants in\nthe suspension medium. Although existing approaches tackle these issues, the\nsegmentation quality requires further improvement, particularly on noisy and\nlow contrast images. In this work, we present an objective and versatile\nmachine learning procedure to amend these issues by characterizing, extracting\nand segmenting inquired colonies using principal component analysis, k-means\nclustering and a modified watershed segmentation algorithm. The intention is to\nautomatically identify visible colonies through spatial texture assessment and\naccordingly discriminate them from background in preparation for successive\nsegmentation. The proposed segmentation algorithm yielded a similar quality as\nmanual counting by human observers. High F1 scores (>0.9) and low\nroot-mean-square errors (around 14%) underlined good agreement with ground\ntruth data. Moreover, it outperformed a recent state-of-the-art method. The\nmethodology will be an important tool in future cancer research applications.\n"}
+{"abstract": "  Strong Coulomb correlations together with multi-valley electronic bands in\nthe presence of spin-orbit interaction and possible new optoelectronic\napplications are at the heart of studies of the rich physics of excitons in\nsemiconductor structures made of monolayers of transition metal dichalcogenides\n(TMD). In intrinsic TMD monolayers the basic, intravalley excitons are formed\nby a hole from the top of the valence band and an electron either from the\nlower or upper spin-orbit-split conduction band subbands: one of these excitons\nis optically active, the second one is \"dark\", although possibly observed under\nspecial conditions. Here we demonstrate the s-series of Rydberg dark exciton\nstates in monolayer WSe$_2$, which appears in addition to a conventional bright\nexciton series in photoluminescence spectra measured in high in-plane magnetic\nfields. The comparison of energy ladders of bright and dark Rydberg excitons is\nshown to be a method to experimentally evaluate one of the missing band\nparameters in TMD monolayers: the amplitude of the spin-orbit splitting of the\nconduction band.\n"}
+{"abstract": "  Multi-task learning (MTL) and attention mechanism have been proven to\neffectively extract robust acoustic features for various speech-related tasks\nin noisy environments. In this study, we propose an attention-based MTL (ATM)\napproach that integrates MTL and the attention-weighting mechanism to\nsimultaneously realize a multi-model learning structure that performs speech\nenhancement (SE) and speaker identification (SI). The proposed ATM system\nconsists of three parts: SE, SI, and attention-Net (AttNet). The SE part is\ncomposed of a long-short-term memory (LSTM) model, and a deep neural network\n(DNN) model is used to develop the SI and AttNet parts. The overall ATM system\nfirst extracts the representative features and then enhances the speech signals\nin LSTM-SE and specifies speaker identity in DNN-SI. The AttNet computes\nweights based on DNN-SI to prepare better representative features for LSTM-SE.\nWe tested the proposed ATM system on Taiwan Mandarin hearing in noise test\nsentences. The evaluation results confirmed that the proposed system can\neffectively enhance speech quality and intelligibility of a given noisy input.\nMoreover, the accuracy of the SI can also be notably improved by using the\nproposed ATM system.\n"}
+{"abstract": "  Given the algebra of observables of a quantum system subject to selection\nrules, a state can be represented by different density matrices. As a result,\ndifferent von Neumann entropies can be associated with the same state.\nMotivated by a minimality property of the von Neumann entropy of a density\nmatrix with respect to its possible decompositions into pure states, we give a\npurely algebraic definition of entropy for states of an algebra of observables,\nthus solving the above ambiguity. The entropy so defined satisfies all the\ndesirable thermodynamic properties, and reduces to the von Neumann entropy in\nthe quantum mechanical case. Moreover, it can be shown to be equal to the von\nNeumann entropy of the unique representative density matrix belonging to the\noperator algebra of a multiplicity-free Hilbert-space representation.\n"}
+{"abstract": "  In prescriptive analytics, the decision-maker observes historical samples of\n$(X, Y)$, where $Y$ is the uncertain problem parameter and $X$ is the\nconcurrent covariate, without knowing the joint distribution. Given an\nadditional covariate observation $x$, the goal is to choose a decision $z$\nconditional on this observation to minimize the cost $\\mathbb{E}[c(z,Y)|X=x]$.\nThis paper proposes a new distributionally robust approach under Wasserstein\nambiguity sets, in which the nominal distribution of $Y|X=x$ is constructed\nbased on the Nadaraya-Watson kernel estimator concerning the historical data.\nWe show that the nominal distribution converges to the actual conditional\ndistribution under the Wasserstein distance. We establish the out-of-sample\nguarantees and the computational tractability of the framework. Through\nsynthetic and empirical experiments about the newsvendor problem and portfolio\noptimization, we demonstrate the strong performance and practical value of the\nproposed framework.\n"}
+{"abstract": "  In this paper, a practical fractional-order variable-gain super-twisting\nalgorithm (PFVSTA) is proposed to improve the tracking performance of wafer\nstages for semiconductor manufacturing. Based on the sliding mode control\n(SMC), the proposed PFVSTA enhances the tracking performance from three\naspects: 1) alleviating the chattering phenomenon via super-twisting algorithm\nand a novel fractional-order sliding surface~(FSS) design, 2) improving the\ndynamics of states on the sliding surface with fast response and small\novershoots via the designed novel FSS and 3) compensating for disturbances via\nvariable-gain control law. Based on practical conditions, this paper analyzes\nthe stability of the controller and illustrates the theoretical principle to\ncompensate for the uncertainties caused by accelerations. Moreover, numerical\nsimulations prove the effectiveness of the proposed sliding surface and control\nscheme, and they are in agreement with the theoretical analysis. Finally,\npractice-based comparative experiments are conducted. The results show that the\nproposed PFVSTA can achieve much better tracking performance than the\nconventional methods from various perspectives.\n"}
+{"abstract": "  The input-output behaviour of the Wiener neuronal model subject to\nalternating input is studied under the assumption that the effect of such an\ninput is to make the drift itself of an alternating type. Firing densities and\nrelated statistics are obtained via simulations of the sample-paths of the\nprocess in the following three cases: the drift changes occur during random\nperiods characterized by (i) exponential distribution, (ii) Erlang distribution\nwith a preassigned shape parameter, and (iii) deterministic distribution. The\nobtained results are compared with those holding for the Wiener neuronal model\nsubject to sinusoidal input\n"}
+{"abstract": "  This paper introduces SuSketch, a design tool for first person shooter\nlevels. SuSketch provides the designer with gameplay predictions for two\ncompeting players of specific character classes. The interface allows the\ndesigner to work side-by-side with an artificially intelligent creator and to\nreceive varied types of feedback such as path information, predicted balance\nbetween players in a complete playthrough, or a predicted heatmap of the\nlocations of player deaths. The system also proactively designs alternatives to\nthe level and class pairing, and presents them to the designer as suggestions\nthat improve the predicted balance of the game. SuSketch offers a new way of\nintegrating machine learning into mixed-initiative co-creation tools, as a\nsurrogate of human play trained on a large corpus of artificial playtraces. A\nuser study with 16 game developers indicated that the tool was easy to use, but\nalso highlighted a need to make SuSketch more accessible and more explainable.\n"}
+{"abstract": "  We consider a setting in which agents are allocated land plots and they have\nadditive preferences over which plot they get and who their neighbor is.\nStrategyproofness, Pareto optimality, and individual rationality are three\nfundamental properties in economic design. We present two impossibility results\nshowing that the three properties are incompatible in this context.\n"}
+{"abstract": "  Modifying gravity at large distances by means of a massive graviton may\nexplain the observed acceleration of the Universe without Dark Energy. The\nstandard paradigm for Massive Gravity is the Fierz-Pauli theory, which,\nnonetheless, displays well known flaws in its massless limit. The most serious\none is represented by the vDVZ discontinuity, which consists in a disagreement\nbetween the massless limit of the Fierz-Pauli theory and General Relativity.\nOur approach is based on a field theoretical treatment of Massive Gravity:\nGeneral Relativity, in the weak field approximation, is treated as a gauge\ntheory of a symmetric rank-2 tensor field. This leads us to propose an\nalternative theory of linearized Massive Gravity, describing five degrees of\nfreedom of the graviton, with a good massless limit, without vDVZ\ndiscontinuity, and depending on one mass parameter only, in agreement with the\nFierz-Pauli theory.\n"}
+{"abstract": "  One of the most appealing aspects of photonic crystal structures is the\nphotonic bandgap created in structures with sufficiently high dielectric\ncontrasts between constituent materials. Understanding how specific photonic\ncrystal structures and their associated stopband positions can selectively\ninterfere with incoming light is vital for implementing these structured\ndielectrics in a range of optical applications. Metallo-dielectric photonic\ncrystals act to incorporate metal particles into the ordered arrangement of\nthese structures. We examined copper, nickel and gold metal infiltration into\npolystyrene opals and TiO2 inverse opals. We report a consistent optical\nphenomena directly associated with the creation of metallo-dielectric photonic\ncrystal structures. More pronounced and numerous diffraction resonances emerge\nin opal photonic crystals with a metal deposited across the top layer. Common\nto both opal and inverse opal structures, was a blue-shift in the position of\nthe (111) photonic stopband which increased in magnitude with greater metal\ncontent in the structure. We investigate the origin of the photonic stopband\nblue-shift by variation of the metal content and the placement of metal in the\nphotonic crystal structure. Metal introduced to structured dielectric media\ntunes the photonic stopband by altering the effective dielectric constant of\nthe photonic crystal.\n"}
+{"abstract": "  The ability to deterministically generate genuine multi-partite entanglement\nis fundamental for the advancement of quantum information science. We show that\nthe interaction between entangled twin beams of light and an atomic ensemble\nunder conditions for electromagnetically induced transparency leads to the\ngeneration of genuine hybrid tri-partite entanglement between the two input\nfields and the atomic ensemble. In such a configuration, the system is driven\nthrough dissipation to a steady state given by the hybrid entangled state. To\nshow the presence of the genuine hybrid entanglement, we introduce a new\napproach to treat the atomic operators that makes it possible to show a\nviolation of a tri-partite entanglement criterion based on the properties of\nthe two optical fields and collective properties of the atomic ensemble.\nAdditionally, we show that while each of the input optical fields does not\nexhibit single beam quadrature squeezing, as the fields propagate through the\natomic medium their individual quadratures can become squeezed and in some\ncases oscillate between the presence and absence of squeezing. Finally, we\npropose a technique to characterize the tri-partite entanglement through joint\nmeasurements of the fields leaving the atomic medium, making such an approach\nexperimentally accessible.\n"}
+{"abstract": "  We present a conceptually simple framework for object instance segmentation\ncalled Contour Proposal Network (CPN), which detects possibly overlapping\nobjects in an image while simultaneously fitting closed object contours using\nan interpretable, fixed-sized representation based on Fourier Descriptors. The\nCPN can incorporate state of the art object detection architectures as backbone\nnetworks into a single-stage instance segmentation model that can be trained\nend-to-end. We construct CPN models with different backbone networks, and apply\nthem to instance segmentation of cells in datasets from different modalities.\nIn our experiments, we show CPNs that outperform U-Nets and Mask R-CNNs in\ninstance segmentation accuracy, and present variants with execution times\nsuitable for real-time applications. The trained models generalize well across\ndifferent domains of cell types. Since the main assumption of the framework are\nclosed object contours, it is applicable to a wide range of detection problems\nalso outside the biomedical domain. An implementation of the model architecture\nin PyTorch is freely available.\n"}
+{"abstract": "  In this paper, we propose a phase attention residual network (PA-ResSeg) to\nmodel multi-phase features for accurate liver tumor segmentation, in which a\nphase attention (PA) is newly proposed to additionally exploit the images of\narterial (ART) phase to facilitate the segmentation of portal venous (PV)\nphase. The PA block consists of an intra-phase attention (Intra-PA) module and\nan inter-phase attention (Inter-PA) module to capture channel-wise\nself-dependencies and cross-phase interdependencies, respectively. Thus it\nenables the network to learn more representative multi-phase features by\nrefining the PV features according to the channel dependencies and\nrecalibrating the ART features based on the learned interdependencies between\nphases. We propose a PA-based multi-scale fusion (MSF) architecture to embed\nthe PA blocks in the network at multiple levels along the encoding path to fuse\nmulti-scale features from multi-phase images. Moreover, a 3D boundary-enhanced\nloss (BE-loss) is proposed for training to make the network more sensitive to\nboundaries. To evaluate the performance of our proposed PA-ResSeg, we conducted\nexperiments on a multi-phase CT dataset of focal liver lesions (MPCT-FLLs).\nExperimental results show the effectiveness of the proposed method by achieving\na dice per case (DPC) of 0.77.87, a dice global (DG) of 0.8682, a volumetric\noverlap error (VOE) of 0.3328 and a relative volume difference (RVD) of 0.0443\non the MPCT-FLLs. Furthermore, to validate the effectiveness and robustness of\nPA-ResSeg, we conducted extra experiments on another multi-phase liver tumor\ndataset and obtained a DPC of 0.8290, a DG of 0.9132, a VOE of 0.2637 and a RVD\nof 0.0163. The proposed method shows its robustness and generalization\ncapability in different datasets and different backbones.\n"}
+{"abstract": "  In unmanned aerial vehicle (UAV)-assisted data collection system, UAVs can be\ndeployed to charge ground terminals (GTs) via wireless power transfer (WPT) and\ncollect data from them via wireless information transmission (WIT). In this\npaper, we aim to minimize the time required by a UAV via jointly optimizing the\ntrajectory of the UAV and the transmission scheduling for all the GTs. This\nproblem is formulated as a mixed integer nonlinear programming (MINLP) which\nare difficult to address in general. To this end, we develop an iterative\nalgorithm based on binary search and successive convex optimization (SCO) to\nsolve it. The simulation shows that our proposed solution outperforms the\nbenchmark algorithms.\n"}
+{"abstract": "  We construct Barrow holographic dark energy in the case of non-flat universe.\nIn particular, considering closed and open spatial geometry we extract the\ndifferential equations that determine the evolution of the dark-energy density\nparameter, and we provide the analytical expression for the corresponding dark\nenergy equation-of-state parameter. We show that the scenario can describe the\nthermal history of the universe, with the sequence of matter and dark energy\nepochs. Comparing to the flat case, where the phantom regime is obtained for\nrelative large Barrow exponents, the incorporation of positive curvature leads\nthe universe into the phantom regime for significantly smaller values.\nAdditionally, in the case of negative curvature we find a reversed behavior,\nnamely for increased Barrow exponent we acquire algebraically higher\ndark-energy equation-of-state parameters. Furthermore, we confront the scenario\nwith Hubble parameter measurements and supernova type Ia data. Hence, the\nincorporation of slightly non-flat spatial geometry to Barrow holographic dark\nenergy improves the phenomenology while keeping the new Barrow exponent to\nsmaller values.\n"}
+{"abstract": "  The Convolutional Neural Network has amazed us with its usage on several\napplications. Age range estimation using CNN is emerging due to its application\nin myriad of areas which makes it a state-of-the-art area for research and\nimprove the estimation accuracy. A deep CNN model is used for identification of\npeople's age range in our proposed work. At first, we extracted only face\nimages from image dataset using MTCNN to remove unnecessary features other than\nface from the image. Secondly, we used random crop technique for data\naugmentation to improve the model performance. We have used the concept of\ntransfer learning in our research. A pretrained face recognition model i.e\nVGG-Face is used to build our model for identification of age range whose\nperformance is evaluated on Adience Benchmark for confirming the efficacy of\nour work. The performance in test set outperformed existing state-of-the-art by\nsubstantial margins.\n"}
+{"abstract": "  Accurate layout estimation is crucial for planning and navigation in robotics\napplications, such as self-driving. In this paper, we introduce the Stereo\nBird's Eye ViewNetwork (SBEVNet), a novel supervised end-to-end framework for\nestimation of bird's eye view layout from a pair of stereo images. Although our\nnetwork reuses some of the building blocks from the state-of-the-art deep\nlearning networks for disparity estimation, we show that explicit depth\nestimation is neither sufficient nor necessary. Instead, the learning of a good\ninternal bird's eye view feature representation is effective for layout\nestimation. Specifically, we first generate a disparity feature volume using\nthe features of the stereo images and then project it to the bird's eye view\ncoordinates. This gives us coarse-grained information about the scene\nstructure. We also apply inverse perspective mapping (IPM) to map the input\nimages and their features to the bird's eye view. This gives us fine-grained\ntexture information. Concatenating IPM features with the projected feature\nvolume creates a rich bird's eye view representation which is useful for\nspatial reasoning. We use this representation to estimate the BEV semantic map.\nAdditionally, we show that using the IPM features as a supervisory signal for\nstereo features can give an improvement in performance. We demonstrate our\napproach on two datasets:the KITTI dataset and a synthetically generated\ndataset from the CARLA simulator. For both of these datasets, we establish\nstate-of-the-art performance compared to baseline techniques.\n"}
+{"abstract": "  Fractured metal fragments with rough and irregular surfaces are often found\nat crime scenes. Current forensic practice visually inspects the complex jagged\ntrajectory of fractured surfaces to recognize a ``match'' using comparative\nmicroscopy and physical pattern analysis. We developed a novel computational\nframework, utilizing the basic concepts of fracture mechanics and statistical\nanalysis to provide quantitative match analysis for match probability and error\nrates. The framework employs the statistics of fracture surfaces to become\nnon-self-affine with unique roughness characteristics at relevant microscopic\nlength scale, dictated by the intrinsic material resistance to fracture and its\nmicrostructure. At such a scale, which was found to be greater than two\ngrain-size or micro-feature-size, we establish that the material intrinsic\nproperties, microstructure, and exposure history to external forces on an\nevidence fragment have the premise of uniqueness, which quantitatively\ndescribes the microscopic features on the fracture surface for forensic\ncomparisons. The methodology utilizes 3D spectral analysis of overlapping\ntopological images of the fracture surface and classifies specimens with very\nhigh accuracy using statistical learning. Cross correlations of image-pairs in\ntwo frequency ranges are used to develop matrix variate statistical models for\nthe distributions among matching and non-matching pairs of images, and provides\na decision rule for identifying matches and determining error rates. A set of\nthirty eight different fracture surfaces of steel articles were correctly\nclassified. The framework lays the foundations for forensic applications with\nquantitative statistical comparison across a broad range of fractured materials\nwith diverse textures and mechanical properties.\n"}
+{"abstract": "  Entangled two-photon spectroscopy is expected to provide advantages compared\nwith classical protocols. It is achieved by coherently controlling the spectral\nproperties of energy-entangled photons. We present here an experimental setup\nthat allows the spectral shaping of entangled photons with high resolution. We\nevaluate its performances by detecting sum frequency generation in a non-linear\ncrystal. The efficiency of the process is compared when performed with\nclassical or entangled light.\n"}
+{"abstract": "  Understanding interactions between objects in an image is an important\nelement for generating captions. In this paper, we propose a relationship-based\nneural baby talk (R-NBT) model to comprehensively investigate several types of\npairwise object interactions by encoding each image via three different\nrelationship-based graph attention networks (GATs). We study three main\nrelationships: \\textit{spatial relationships} to explore geometric\ninteractions, \\textit{semantic relationships} to extract semantic interactions,\nand \\textit{implicit relationships} to capture hidden information that could\nnot be modelled explicitly as above. We construct three relationship graphs\nwith the objects in an image as nodes, and the mutual relationships of pairwise\nobjects as edges. By exploring features of neighbouring regions individually\nvia GATs, we integrate different types of relationships into visual features of\neach node. Experiments on COCO dataset show that our proposed R-NBT model\noutperforms state-of-the-art models trained on COCO dataset in three image\ncaption generation tasks.\n"}
+{"abstract": "  The widespread dissemination of forged images generated by Deepfake\ntechniques has posed a serious threat to the trustworthiness of digital\ninformation. This demands effective approaches that can detect perceptually\nconvincing Deepfakes generated by advanced manipulation techniques. Most\nexisting approaches combat Deepfakes with deep neural networks by mapping the\ninput image to a binary prediction without capturing the consistency among\ndifferent pixels. In this paper, we aim to capture the subtle manipulation\nartifacts at different scales for Deepfake detection. We achieve this with\ntransformer models, which have recently demonstrated superior performance in\nmodeling dependencies between pixels for a variety of recognition tasks in\ncomputer vision. In particular, we introduce a Multi-modal Multi-scale\nTRansformer (M2TR), which uses a multi-scale transformer that operates on\npatches of different sizes to detect the local inconsistency at different\nspatial levels. To improve the detection results and enhance the robustness of\nour method to image compression, M2TR also takes frequency information, which\nis further combined with RGB features using a cross modality fusion module.\nDeveloping and evaluating Deepfake detection methods requires large-scale\ndatasets. However, we observe that samples in existing benchmarks contain\nsevere artifacts and lack diversity. This motivates us to introduce a\nhigh-quality Deepfake dataset, SR-DF, which consists of 4,000 DeepFake videos\ngenerated by state-of-the-art face swapping and facial reenactment methods. On\nthree Deepfake datasets, we conduct extensive experiments to verify the\neffectiveness of the proposed method, which outperforms state-of-the-art\nDeepfake detection methods.\n"}
+{"abstract": "  JUNO is a 20-kton liquid scintillator detector aiming to determine the\nneutrino mass ordering, precisely measure the oscillation parameters, detect\nthe astrophysical neutrinos and search for exotic physics. It is designed to\nreach an energy resolution of 3% at 1 MeV with the highest ever PMT coverage,\nusing two types of 20 inch phototubes: MCP-PMT from NNVT and dynode-PMT from\nHamamatsu. In this article, the gain and charge response of the MCP and dynode\nPMTs are investigated with the study of JUNO Central Detector prototype. The\nlinearity of the MCP-PMT charge output is measured too to check the effect of a\nlong tail on its charge spectrum.\n"}
+{"abstract": "  The high dynamics and heterogeneous interactions in the complicated urban\nsystems have raised the issue of uncertainty quantification in spatiotemporal\nhuman mobility, to support critical decision-makings in risk-aware web\napplications such as urban event prediction where fluctuations are of\nsignificant interests. Given the fact that uncertainty quantifies the potential\nvariations around prediction results, traditional learning schemes always lack\nuncertainty labels, and conventional uncertainty quantification approaches\nmostly rely upon statistical estimations with Bayesian Neural Networks or\nensemble methods. However, they have never involved any spatiotemporal\nevolution of uncertainties under various contexts, and also have kept suffering\nfrom the poor efficiency of statistical uncertainty estimation while training\nmodels with multiple times. To provide high-quality uncertainty quantification\nfor spatiotemporal forecasting, we propose an uncertainty learning mechanism to\nsimultaneously estimate internal data quality and quantify external uncertainty\nregarding various contextual interactions. To address the issue of lacking\nlabels of uncertainty, we propose a hierarchical data turbulence scheme where\nwe can actively inject controllable uncertainty for guidance, and hence provide\ninsights to both uncertainty quantification and weak supervised learning.\nFinally, we re-calibrate and boost the prediction performance by devising a\ngated-based bridge to adaptively leverage the learned uncertainty into\npredictions. Extensive experiments on three real-world spatiotemporal mobility\nsets have corroborated the superiority of our proposed model in terms of both\nforecasting and uncertainty quantification.\n"}
+{"abstract": "  Recent advances in the areas of multimodal machine learning and artificial\nintelligence (AI) have led to the development of challenging tasks at the\nintersection of Computer Vision, Natural Language Processing, and Embodied AI.\nWhereas many approaches and previous survey pursuits have characterised one or\ntwo of these dimensions, there has not been a holistic analysis at the center\nof all three. Moreover, even when combinations of these topics are considered,\nmore focus is placed on describing, e.g., current architectural methods, as\nopposed to also illustrating high-level challenges and opportunities for the\nfield. In this survey paper, we discuss Embodied Vision-Language Planning\n(EVLP) tasks, a family of prominent embodied navigation and manipulation\nproblems that jointly use computer vision and natural language. We propose a\ntaxonomy to unify these tasks and provide an in-depth analysis and comparison\nof the new and current algorithmic approaches, metrics, simulated environments,\nas well as the datasets used for EVLP tasks. Finally, we present the core\nchallenges that we believe new EVLP works should seek to address, and we\nadvocate for task construction that enables model generalizability and furthers\nreal-world deployment.\n"}
+{"abstract": "  We discuss a model of a $n$-person, non-cooperative stochastic game, inspired\nby the discrete version of the red-and-black gambling problem introduced by\nDubins and Savage in 1965. Our main theorem generalizes a result of Pontiggia\nfrom 2007 which provides conditions upon which bold strategies for all players\nform a Nash equilibrium. Our tool is a functional inequality, which is\nintroduced and discussed in the present paper. It allows us to avoid\nrestrictive assumptions of super-multiplicativity and super-additivity, which\nappear in Pontiggia' and other author works. We terminate the paper with some\nexamples which in particular show that our approach leads to a essentially\nlarger class of probability functions than existed in the literature so far.\n"}
+{"abstract": "  With increasing distributed energy resoures (DERs) integration, the strategic\nbehavior of a DER aggregator in electricity markets will significantly affect\nthe secure operation of the distribution system. In this paper, the\ninteractions among the DER aggregator, energy and reserve markets, and\ndistribution system are investigated through a single-leader-multi-follower\nStackelberg game model with the DER aggregator as the leader and the\nindependent system operator and distribution system operator as the followers.\nTo guarantee the operation security of the distribution system, security check\nproblems under three different scenarios are involved in the follower level,\nwhich is linearized using a mixed-integer linearized power flow model. Then,\nusing the strong duality theorem, the proposed model is converted into a\nbi-level mixed-integer linear (BMILP) programming model with only mixed-integer\nlinear follower-level problems. Next, an accelerated relaxation-based bi-level\nreformulation and decomposition algorithm is proposed to solve the BMILP\nproblem. Finally, case studies are carried out on a constructed integrated\ntransmission and distribution (T&D) system and a practical integrated T&D\nsystem to verify the effectiveness of the proposed model and algorithm. The\nsimulation results indicate that the available downward reserve of the DER\naggregator will decrease with the security limitation of the distribution\nsystem.\n"}
+{"abstract": "  We report lowest-order series expansions for primary matrix functions of\nquantum states based on a perturbation theory for functions of linear\noperators. Our theory enables efficient computation of functions of perturbed\nquantum states that assume only knowledge of the eigenspectrum of the zeroth\norder state and the density matrix elements of a zero-trace, Hermitian\nperturbation operator, not requiring analysis of the full state or the\nperturbation term. We develop theories for two classes of quantum state\nperturbations, perturbations that preserve the vector support of the original\nstate and perturbations that extend the support beyond the support of the\noriginal state. We highlight relevant features of the two situations, in\nparticular the fact that functions and measures of perturbed quantum states\nwith preserved support can be elegantly and efficiently represented using\nFr\\'echet derivatives. We apply our perturbation theories to find simple\nexpressions for four of the most important quantities in quantum information\ntheory that are commonly computed from density matrices: the Von Neumann\nentropy, the quantum relative entropy, the quantum Chernoff bound, and the\nquantum fidelity.\n"}
+{"abstract": "  The baryon acoustic oscillation feature can be used as a standard\ncosmological ruler. In practice, for sub-percent level accuracy on the distance\nscale, it must be standardized. The physical reason why is understood, so we\nuse this to develop an algorithm which improves the estimated scale. The\nalgorithm exploits the fact that, over the range of scales where the initial\ncorrelation function is well-fit by a polynomial, the leading order effects\nwhich distort the length of the ruler can be accounted for analytically. Tests\nof the method in numerical simulations show that it provides simple and fast\nreconstruction of the full shape of the BAO feature, as well as sub-percent\ndetermination of the linear point in the correlation function of biased tracers\nwith minimal assumptions about the underlying cosmological model or the nature\nof the observed tracers. Our results also suggest that, for least squares\nestimators of the correlation function, half-integer generalized Laguerre\nfunctions are a particularly useful choice.\n"}
+{"abstract": "  In this paper, we introduce a challenging global large-scale ship database\n(called GLSD), designed specifically for ship detection tasks. The designed\nGLSD database includes a total of 140,616 annotated instances from 100,729\nimages. Based on the collected images, we propose 13 categories that widely\nexists in international routes. These categories include sailing boat, fishing\nboat, passenger ship, war ship, general cargo ship, container ship, bulk cargo\ncarrier, barge, ore carrier, speed boat, canoe, oil carrier, and tug. The\nmotivations of developing GLSD include the following: 1) providing a refined\nship detection database; 2) providing the worldwide researchers of ship\ndetection and exhaustive label information (bounding box and ship class label)\nin one uniform global database; and 3) providing a large-scale ship database\nwith geographic information (port and country information) that benefits\nmulti-modal analysis. In addition, we discuss the evaluation protocols given\nimage characteristics in GLSD and analyze the performance of selected\nstate-of-the-art object detection algorithms on GSLD, providing baselines for\nfuture studies. More information regarding the designed GLSD can be found at\nhttps://github.com/jiaming-wang/GLSD.\n"}
+{"abstract": "  We study stability of metric approximations of countable groups with respect\nto groups endowed with ultrametrics, the main case study being a $p$-adic\nanalogue of Ulam stability, where we take $\\mathrm{GL}_n(\\mathbb{Z}_p)$ as\napproximating groups instead of $\\mathrm{U}(n)$. For finitely presented groups,\nthe ultrametric nature implies equivalence of the pointwise and uniform\nstability problems, and the profinite one implies that the corresponding\napproximation property is equivalent to residual finiteness. Moreover, a group\nis uniformly stable if and only if its largest residually finite quotient is.\nWe provide several examples of uniformly stable groups: these include finite\ngroups, virtually free groups, some groups acting on rooted trees, and certain\nlamplighter and (Generalized) Baumslag--Solitar groups.\n"}
+{"abstract": "  Relying on the local orientation of nanostructures, Pancharatnam-Berry\nmetasurfaces are currently enabling a new generation of polarization-sensitive\noptical devices. A systematical mesoscopic description of topological\nmetasurfaces is developed, providing a deeper understanding of the physical\nmechanisms leading to the polarization-dependent breaking of translational\nsymmetry in contrast with propagation phase effects. These theoretical results,\nalong with interferometric experiments, contribute to the development of a\nsolid theoretical framework for arbitrary polarization-dependent metasurfaces.\n"}
+{"abstract": "  We generalize to the ${\\rm RCD}(0,N)$ setting a family of monotonicity\nformulas by Colding and Minicozzi for positive harmonic functions in Riemannian\nmanifolds with non-negative Ricci curvature. Rigidity and almost rigidity\nstatements are also proven, the second appearing to be new even in the smooth\nsetting. Motivated by the recent work in [AFM] we also introduce the notion of\nelectrostatic potential in ${\\rm RCD}$ spaces, which also satisfies our\nmonotonicity formulas. Our arguments are mainly based on new estimates for\nharmonic functions in ${\\rm RCD}(K,N)$ spaces and on a new functional version\nof the `(almost) outer volume cone implies (almost) outer metric cone' theorem.\n"}
+{"abstract": "  The epidemic threshold of a social system is the ratio of infection and\nrecovery rate above which a disease spreading in it becomes an epidemic. In the\nabsence of pharmaceutical interventions (i.e. vaccines), the only way to\ncontrol a given disease is to move this threshold by non-pharmaceutical\ninterventions like social distancing, past the epidemic threshold corresponding\nto the disease, thereby tipping the system from epidemic into a non-epidemic\nregime. Modeling the disease as a spreading process on a social graph, social\ndistancing can be modeled by removing some of the graphs links. It has been\nconjectured that the largest eigenvalue of the adjacency matrix of the\nresulting graph corresponds to the systems epidemic threshold. Here we use a\nMarkov chain Monte Carlo (MCMC) method to study those link removals that do\nwell at reducing the largest eigenvalue of the adjacency matrix. The MCMC\nmethod generates samples from the relative canonical network ensemble with a\ndefined expectation value of $\\lambda_{max}$. We call this the\n\"well-controlling network ensemble\" (WCNE) and compare its structure to\nrandomly thinned networks with the same link density. We observe that networks\nin the WCNE tend to be more homogeneous in the degree distribution and use this\ninsight to define two ad-hoc removal strategies, which also substantially\nreduce the largest eigenvalue. A targeted removal of 80\\% of links can be as\neffective as a random removal of 90\\%, leaving individuals with twice as many\ncontacts.\n"}
+{"abstract": "  We prove new Fourier restriction estimates to the unit sphere $S^{d-1}$ on\nthe class of $O(d-k)\\times O(k)$-symmetric functions, for every $d\\geq 4$ and\n$2\\leq k\\leq d-2$. As an application, we establish the existence of maximizers\nfor the endpoint Tomas--Stein inequality within that class. Moreover, we\nconstruct examples showing that the range of Lebesgue exponents in our\nestimates is sharp in the Tomas--Stein regime.\n"}
+{"abstract": "  This paper studies the formation stabilization problem of asynchronous\nnonlinear multi-agent systems (MAS) subject to parametric uncertainties,\nexternal disturbances and bounded time-varying communication delays. A\nself-triggered min-max distributed model predictive control (DMPC) approach is\nproposed to handle these practical issues. At triggering instants, each agent\nsolves a local min-max optimization problem based on local system states and\npredicted system states of neighbors, determines its next triggering instant\nand broadcasts its predicted state trajectory to its neighbors. As a result,\nthe communication load is greatly alleviated while retaining robustness and\ncomparable control performance compared to periodic algorithms. In order to\nhandle time-varying delays, a novel consistency constraint is incorporated into\neach local optimization problem to restrict the deviation between the newest\npredicted states and previously broadcast predicted states. Consequently, each\nagent can utilize previously predicted states of its neighbors to achieve\ncooperation in the presence of time-varying delays and asynchronous\ncommunication induced by the distributed triggered scheduler. The recursive\nfeasibility of the proposed algorithm and the closed-loop stability of MAS at\ntriggering time instants are proven. Finally, numerical simulations are\nconducted to verify the efficiency of the proposed control method.\n"}
+{"abstract": "  Causal induction, i.e., identifying unobservable mechanisms that lead to the\nobservable relations among variables, has played a pivotal role in modern\nscientific discovery, especially in scenarios with only sparse and limited\ndata. Humans, even young toddlers, can induce causal relationships surprisingly\nwell in various settings despite its notorious difficulty. However, in contrast\nto the commonplace trait of human cognition is the lack of a diagnostic\nbenchmark to measure causal induction for modern Artificial Intelligence (AI)\nsystems. Therefore, in this work, we introduce the Abstract Causal REasoning\n(ACRE) dataset for systematic evaluation of current vision systems in causal\ninduction. Motivated by the stream of research on causal discovery in Blicket\nexperiments, we query a visual reasoning system with the following four types\nof questions in either an independent scenario or an interventional scenario:\ndirect, indirect, screening-off, and backward-blocking, intentionally going\nbeyond the simple strategy of inducing causal relationships by covariation. By\nanalyzing visual reasoning architectures on this testbed, we notice that pure\nneural models tend towards an associative strategy under their chance-level\nperformance, whereas neuro-symbolic combinations struggle in backward-blocking\nreasoning. These deficiencies call for future research in models with a more\ncomprehensive capability of causal induction.\n"}
+{"abstract": "  The benzene radical anion, well-known in organic chemistry as the first\nintermediate in the Birch reduction of benzene in liquid ammonia, exhibits\nintriguing properties from the point of view of quantum chemistry. Notably, it\nhas the character of a metastable shape resonance in the gas phase, while\nmeasurements in solution find it to be experimentally detectable and stable. In\nthis light, our previous calculations performed in bulk liquid ammonia\nexplicitly reveal that solvation leads to stabilization. Here, we focus on the\ntransition of the benzene radical anion from an unstable gas-phase ion to a\nfully solvated bound species by explicit ionization calculations of the radical\nanion solvated in molecular clusters of increasing size. The computational cost\nof the largest systems is mitigated by combining density functional theory with\nauxiliary methods including effective fragment potentials or approximating the\nbulk by polarizable continuum models. Using this methodology, we obtain the\ncluster size dependence of the vertical binding energy of the benzene radical\nanion converging to the value of $-$2.3 eV at a modest computational cost.\n"}
+{"abstract": "  We study the numerical range of the Weighted Composition Operator over the\nMittag-Leffler space of entire functions.\n"}
+{"abstract": "  We address the worst-user bottleneck of wireless coded caching, which is\nknown to severely diminish cache-aided multicasting gains due to the\nfundamental worst-channel limitation of multicasting transmission. We consider\nthe quasi-static Rayleigh fading Broadcast Channel, for which we first show\nthat the effective coded caching gain of the XOR-based standard coded-caching\nscheme completely vanishes in the low-SNR regime. Then, we reveal that this\ncollapse is not intrinsic to coded caching. We do so by presenting a novel\nscheme that can fully recover the coded caching gains by capitalizing on one\naspect that has to date remained unexploited: the shared side information\nbrought about by the effectively unavoidable file-size constraint. As a\nconsequence, the worst-user effect is dramatically ameliorated, as it is\nsubstituted by a much more subtle worst-group-of-users effect, where the\nsuggested grouping is fixed, and it is decided before the channel or the\ndemands are known. In some cases, the theoretical gains are completely\nrecovered, and this is done without any user selection technique. We analyze\nthe achievable rate performance of the proposed scheme and derive insightful\nperformance approximations which prove to be very precise.\n"}
+{"abstract": "  Node copying is an important mechanism for network formation, yet most models\nassume uniform copying rules. Motivated by observations of heterogeneous\ntriadic closure in real networks, we introduce the concept of a hidden network\nmodel - a generative two-layer model in which an observed network evolves\naccording to the structure of an underlying hidden layer - and apply the\nframework to a model of heterogeneous copying. Framed in a social context,\nthese two layers represent a node's inner social circle, and wider social\ncircle, such that the model can bias copying probabilities towards, or against,\na node's inner circle of friends. Comparing the case of extreme inner circle\nbias to an equivalent model with uniform copying, we find that heterogeneous\ncopying suppresses the power-law degree distributions commonly seen in copying\nmodels, and results in networks with much higher clustering than even the most\noptimum scenario for uniform copying. Similarly large clustering values are\nfound in real collaboration networks, lending empirical support to the\nmechanism.\n"}
+{"abstract": "  Synthesis of new elements at the upper border of the charts of nuclei and\ninvestigation of their decay properties and nuclear structure has been one of\nthe main research topics in low energy nuclear physics since more than five\ndecades. Main items are the quest for the heaviest nuclei that can exist and\nthe verification of the theoretical predicted spherical proton and neutron\nshells at Z = 114, 120 or 126 and N = 172 or 184. The scope of the present\npaper is to illustrate some technical and physical aspects in investigation of\nthe heaviest nuclei ('superheavy nuclei') and to critical discuss some selected\nresults, which from a strict scientific point of view are not completely clear\nso far, making partly also suggestions for alternatively interpretations. A\ncomplete review of the whole field of superheavy element research, however, is\nout of the scope of this paper.\n"}
+{"abstract": "  We present DistillFlow, a knowledge distillation approach to learning optical\nflow. DistillFlow trains multiple teacher models and a student model, where\nchallenging transformations are applied to the input of the student model to\ngenerate hallucinated occlusions as well as less confident predictions. Then, a\nself-supervised learning framework is constructed: confident predictions from\nteacher models are served as annotations to guide the student model to learn\noptical flow for those less confident predictions. The self-supervised learning\nframework enables us to effectively learn optical flow from unlabeled data, not\nonly for non-occluded pixels, but also for occluded pixels. DistillFlow\nachieves state-of-the-art unsupervised learning performance on both KITTI and\nSintel datasets. Our self-supervised pre-trained model also provides an\nexcellent initialization for supervised fine-tuning, suggesting an alternate\ntraining paradigm in contrast to current supervised learning methods that\nhighly rely on pre-training on synthetic data. At the time of writing, our\nfine-tuned models ranked 1st among all monocular methods on the KITTI 2015\nbenchmark, and outperform all published methods on the Sintel Final benchmark.\nMore importantly, we demonstrate the generalization capability of DistillFlow\nin three aspects: framework generalization, correspondence generalization and\ncross-dataset generalization.\n"}
+{"abstract": "  The Central Molecular Zone (CMZ) is a ring-like structure sitting at the\ncenter of the Milky Way. Using the 870 $\\mu$m continuum map from the APEX\nTelescope Large Area Survey of the Galaxy (ATLASGAL), we study the anisotropy\nof the density structure of the gas in the Central Molecular Zone (CMZ) using\nthe 2D correlation function. To quantify the spatial anisotropy, we define the\ncritical angle $\\theta_{\\rm half}$, as well as the anisotropy parameter\n$A\\equiv \\frac{\\pi}{4\\theta_{\\rm half}}-1$. We find that the density structure\nis strongly anisotropic at the large scale (~ 100 pc), and the degree of\nspatial anisotropy decreases with the decreasing scale. At the scale of ~ 10\npc, the structure is still mildly anisotropic. In our analyses, we provide a\nquantitative description of the anisotropic density structure of gas in the\nCMZ, and the formalism can be applied to different regions to study their\ndifferences.\n"}
+{"abstract": "  The lower the distortion of an estimator, the more the distribution of its\noutputs generally deviates from the distribution of the signals it attempts to\nestimate. This phenomenon, known as the perception-distortion tradeoff, has\ncaptured significant attention in image restoration, where it implies that\nfidelity to ground truth images comes at the expense of perceptual quality\n(deviation from statistics of natural images). However, despite the increasing\npopularity of performing comparisons on the perception-distortion plane, there\nremains an important open question: what is the minimal distortion that can be\nachieved under a given perception constraint? In this paper, we derive a closed\nform expression for this distortion-perception (DP) function for the mean\nsquared-error (MSE) distortion and the Wasserstein-2 perception index. We prove\nthat the DP function is always quadratic, regardless of the underlying\ndistribution. This stems from the fact that estimators on the DP curve form a\ngeodesic in Wasserstein space. In the Gaussian setting, we further provide a\nclosed form expression for such estimators. For general distributions, we show\nhow these estimators can be constructed from the estimators at the two extremes\nof the tradeoff: The global MSE minimizer, and a minimizer of the MSE under a\nperfect perceptual quality constraint. The latter can be obtained as a\nstochastic transformation of the former.\n"}
+{"abstract": "  Recently, coordinated attack campaigns started to become more widespread on\nthe Internet. In May 2017, WannaCry infected more than 300,000 machines in 150\ncountries in a few days and had a large impact on critical infrastructure.\nExisting threat sharing platforms cannot easily adapt to emerging attack\npatterns. At the same time, enterprises started to adopt machine learning-based\nthreat detection tools in their local networks. In this paper, we pose the\nquestion: \\emph{What information can defenders share across multiple networks\nto help machine learning-based threat detection adapt to new coordinated\nattacks?} We propose three information sharing methods across two networks, and\nshow how the shared information can be used in a machine-learning\nnetwork-traffic model to significantly improve its ability of detecting evasive\nself-propagating malware.\n"}
+{"abstract": "  We describe a technique for self consistently characterizing both the quantum\nstate of a single qubit system, and the positive-operator-valued measure (POVM)\nthat describes measurements on the system. The method works with only ten\nmeasurements. We assume that a series of unitary transformations performed on\nthe quantum state are fully known, while making minimal assumptions about both\nthe density operator of the state and the POVM. The technique returns\nmaximum-likely estimates of both the density operator and the POVM. To\nexperimentally demonstrate the method, we perform reconstructions of over 300\nstate-measurement pairs and compare them to their expected density operators\nand POVMs. We find that 95% of the reconstructed POVMs have fidelities of 0.98\nor greater, and 92% of the density operators have fidelities that are 0.98 or\ngreater.\n"}
+{"abstract": "  The fine structure of the exciton spectrum, containing optically allowed\n(bright) and forbidden (dark) exciton states, determines the radiation\nefficiency in nanostructures. We study time-resolved micro-photoluminescence in\nMoS$_2$ monolayers and bilayers, both unstrained and compressively strained, in\na wide temperature range (10-300 K) to distinguish between exciton states\noptically allowed and forbidden, both in spin and momentum, as well as to\nestimate their characteristic decay times and contributions to the total\nradiation intensity. The decay times were found to either increase or decrease\nwith increasing temperature, indicating the lowest bright or lowest dark state,\nrespectively. Our results unambiguously show that, in an unstrained monolayer,\nthe spin-allowed state is the lowest for a series of A excitons (1.9 eV) with\nthe dark state being < 2 meV higher, and that the splitting energy can increase\nseveral times at compression. In contrast, in the indirect exciton series in\nbilayers (1.5 eV), the spin-forbidden state is the lowest, being about 3 meV\nbelow the bright one. The strong effect of strain on the exciton spectrum can\nexplain the large scatter among the published data and must be taken into\naccount to realize the desired optical properties of 2D MoS$_2$.\n"}
+{"abstract": "  Since reward functions are hard to specify, recent work has focused on\nlearning policies from human feedback. However, such approaches are impeded by\nthe expense of acquiring such feedback. Recent work proposed that agents have\naccess to a source of information that is effectively free: in any environment\nthat humans have acted in, the state will already be optimized for human\npreferences, and thus an agent can extract information about what humans want\nfrom the state. Such learning is possible in principle, but requires simulating\nall possible past trajectories that could have led to the observed state. This\nis feasible in gridworlds, but how do we scale it to complex tasks? In this\nwork, we show that by combining a learned feature encoder with learned inverse\nmodels, we can enable agents to simulate human actions backwards in time to\ninfer what they must have done. The resulting algorithm is able to reproduce a\nspecific skill in MuJoCo environments given a single state sampled from the\noptimal policy for that skill.\n"}
+{"abstract": "  Direct observations of gravitational waves at frequencies around deci-Hertz\nwill play a crucial role in fully exploiting the potential of multi-messenger\nastronomy. In this chapter, we discuss the detection landscape for the next\nseveral decades of the deci-Hertz gravitational-wave spectrum. We provide an\noverview of the experimental frontiers being considered to probe this\nchallenging regime and the astrophysics and fundamental goals accessible\ntowards them. This includes interferometric observatories in space with\nheliocentric and geocentric satellites, cubesats, lunar-based experiments and\natom intereferometry. A major focus of this chapter is towards the technology\nbehind DECi-hertz Interferometer Gravitational wave Observatory (DECIGO) and\nits scientific pathfinder mission concept B-DECIGO.\n"}
+{"abstract": "  We introduce the GANformer, a novel and efficient type of transformer, and\nexplore it for the task of visual generative modeling. The network employs a\nbipartite structure that enables long-range interactions across the image,\nwhile maintaining computation of linear efficiency, that can readily scale to\nhigh-resolution synthesis. It iteratively propagates information from a set of\nlatent variables to the evolving visual features and vice versa, to support the\nrefinement of each in light of the other and encourage the emergence of\ncompositional representations of objects and scenes. In contrast to the classic\ntransformer architecture, it utilizes multiplicative integration that allows\nflexible region-based modulation, and can thus be seen as a generalization of\nthe successful StyleGAN network. We demonstrate the model's strength and\nrobustness through a careful evaluation over a range of datasets, from\nsimulated multi-object environments to rich real-world indoor and outdoor\nscenes, showing it achieves state-of-the-art results in terms of image quality\nand diversity, while enjoying fast learning and better data-efficiency. Further\nqualitative and quantitative experiments offer us an insight into the model's\ninner workings, revealing improved interpretability and stronger\ndisentanglement, and illustrating the benefits and efficacy of our approach. An\nimplementation of the model is available at\nhttps://github.com/dorarad/gansformer.\n"}
+{"abstract": "  We prove that the $f$-divergences between univariate Cauchy distributions are\nall symmetric, and can be expressed as strictly increasing scalar functions of\nthe symmetric chi-squared divergence. We report the corresponding scalar\nfunctions for the total variation distance, the Kullback-Leibler divergence,\nthe squared Hellinger divergence, and the Jensen-Shannon divergence among\nothers. Next, we give conditions to expand the $f$-divergences as converging\ninfinite series of higher-order power chi divergences, and illustrate the\ncriterion for converging Taylor series expressing the $f$-divergences between\nCauchy distributions. We then show that the symmetric property of\n$f$-divergences holds for multivariate location-scale families with prescribed\nmatrix scales provided that the standard density is even which includes the\ncases of the multivariate normal and Cauchy families. However, the\n$f$-divergences between multivariate Cauchy densities with different scale\nmatrices are shown asymmetric. Finally, we present several metrizations of\n$f$-divergences between univariate Cauchy distributions and further report\ngeometric embedding properties of the Kullback-Leibler divergence.\n"}
+{"abstract": "  Quantum cascade lasers (QCLs) represent a fascinating accomplishment of\nquantum engineering and enable the direct generation of terahertz (THz)\nfrequency radiation from an electrically-biased semiconductor heterostructure.\nTheir large spectral bandwidth, high output powers and quantum-limited\nlinewidths have facilitated the realization of THz pulses by active\nmode-locking and passive generation of optical frequency combs (FCs) through\nintracavity four-wave-mixing, albeit over a restricted operational regime.\nHere, we conceive an integrated architecture for the generation of high power\n(10 mW) THz FCs comprising an ultrafast THz polaritonic reflector, exploiting\nintersubband cavity polaritons, and a broad bandwidth (2.3-3.8 THz)\nheterogeneous THz QCL. Quantum cascade lasers (QCLs) represent a fascinating\naccomplishment of quantum engineering and enable the direct generation of\nterahertz (THz) frequency radiation from an electrically-biased semiconductor\nheterostructure. By tuning the group delay dispersion in an integrated\ngeometry, through the exploitation of light induced bleaching of the\nintersubband-based THz polaritons, we demonstrate spectral reshaping of the QCL\nemission and stable FC operation over an operational dynamic range of up to\n38%, characterized by a single and narrow (down to 700 Hz) intermode beatnote.\nOur concept provides design guidelines for a new generation of compact,\ncost-effective, electrically driven chip-scale FC sources based on ultrafast\npolariton dynamics, paving the way towards the generation of mode locked THz\nmicro-lasers that will strongly impact a broad range of applications in\nultrafast sciences, data storage, high-speed communication and spectroscopy.\n"}
+{"abstract": "  Recently there has been a renaissance in identifiability results in deep\ngenerative models, not least for non-linear ICA. For i.i.d. data, prior works\nhave assumed access to a sufficiently-informative auxiliary set of\nobservations, denoted $\\mathbf{u}$. We show here how identifiability can be\nobtained in the absence of this side-information. Previous methods have had to\nmake strong assumptions in order to obtain identifiable models. Here we obtain\nempirically identifiable models under a much looser set of constraints. In\nparticular, we focus on generative models which perform clustering in their\nlatent space -- a model structure which matches previous identifiable models,\nbut with the learnt clustering providing a synthetic form of auxiliary\ninformation. We evaluate our proposals, including via statistical tests, and\nfind that the learned clusterings function effectively: deep generative models\nwith latent clusterings are empirically identifiable, to the same degree as\nmodels which rely on side information.\n"}
+{"abstract": "  In search of the ferromagnetic insulators for spintronic device applications,\nwe have studied the electronic, optical, and magnetic properties\nofLa2CrNiO6(LCNO) using the first principle density functional theory (DFT)and\nmonte carlo simulation technique. Firstly, we have adopted the sol-gel method\nfor preparation of LCNO and refined the X-ray diffracted value using the\nfullprof suite under the Rietveld mechanism. The obtained orthorhombic Pbnm and\ncubic Fm-3m space groups are used as the input of the DFT calculations. Both\nthe structures have shown the half metallicity and insult-ing bandgap towards\nthe majority and minority spin direction respectively. From the magnetic moment\ncalculations, we have seen the low spin state ofNi3+and the double exchange\n(DE) interaction is responsible for the ferro-magneticity of LCNO. The optical\ndielectric constant is 10 and the refractive index is 3.2 for orthorhombic\nstructure. The transition temperature (TC) is determined to 110 K from MCS\nstudy, which has well agreed with experimental value (111 K). The RCP value is\ndetermined to 12 J/Kg with a 5 tesla applied field, which is good for\nmagnetocaloric applications.\n"}
+{"abstract": "  We consider singular quasilinear stochastic partial differential equations\n(SPDEs) studied in \\cite{FHSX}, which are defined in paracontrolled sense. The\nmain aim of the present article is to establish the global-in-time solvability\nfor a particular class of SPDEs with origin in particle systems and, under a\ncertain additional condition on the noise, prove the convergence of the\nsolutions to stationary solutions as $t\\to\\infty$. We apply the method of\nenergy inequality and Poincar\\'e inequality. It is essential that the\nPoincar\\'e constant can be taken uniformly in an approximating sequence of the\nnoise. We also use the continuity of the solutions in the enhanced noise,\ninitial values and coefficients of the equation, which we prove in this article\nfor general SPDEs discussed in \\cite{FHSX} except that in the enhanced noise.\nMoreover, we apply the initial layer property of improving regularity of the\nsolutions in a short time.\n"}
+{"abstract": "  Ultraluminous X-ray sources (ULXs) were identified as a separate class of\nobjects in 2000 based on data from the Chandra X-Ray Observatory. These are\nunique objects: their X-ray luminosities exceed the Eddington limit for a\ntypical stellar-mass black hole. For a long time, the nature of ULXs remained\nunclear. However, the gradual accumulation of data, new results of X-ray and\noptical spectroscopy, and the study of the structure and energy of nebulae\nsurrounding ULXs led to the understanding that most of the ultraluminous X-ray\nsources must be supercritical accretion disks like SS 433. The discovery of\nneutron stars in a number of objects only increased the confidence of the\nscientific community in the conclusions obtained, since the presence of neutron\nstars in such systems clearly indicates a supercritical accretion regime. In\nthis review, we systematize the main facts about the observational\nmanifestations of ULXs and SS 433 in the X-ray and optical ranges and discuss\ntheir explanation from the point of view of the supercritical accretion theory.\n"}
+{"abstract": "  We investigate the impact of geometric constriction on the viscous flow of\nelectron liquid through quantum point contacts. We provide analysis on the\nelectric potential distribution given the setup of a slit configuration and use\nthe method of conformal mapping to obtain analytical results. The potential\nprofile can be tested and contrasted experimentally with the scanning tunneling\npotentiometry technique. We discuss intricate physics that underlies the Gurzhi\neffect, i.e., the enhancement of conductivity in the viscous flow, and compare\nresults for different boundary conditions. In addition, we calculate the\ntemperature dependence of the momentum relaxation time as a result of impurity\nassisted quasiballistic interference effects and discuss various correlational\ncorrections that lead to the violation of Matthiessen's rule in the\nhydrodynamic regime. We caution that spatially inhomogeneous profiles of\ncurrent in the Gurzhi crossover between Ohmic and Stokes flows might also\nappear in the nonhydrodynamic limit where nonlocality plays an important role.\nThis conclusion is corroborated by calculation of dispersive conductivity in\nthe weakly impure limit.\n"}
+{"abstract": "  The MultiplicitySequence package for Macaulay2 computes the multiplicity\nsequence of a graded ideal in a standard graded ring over a field, as well as\nseveral invariants of monomial ideals related to integral dependence. We\ndiscuss two strategies implemented for computing multiplicity sequences: one\nvia the bivariate Hilbert polynomial, and the other via the technique of\ngeneral elements.\n"}
+{"abstract": "  Within the framework of the $s$-$d(f)$ exchange model in the mean-field\napproximation for square, simple cubic, body-centered and face-centered cubic\nlattices, the formation of a ferromagnetic, spiral, and commensurate\nantiferromagnetic order is investigated. The possibility of the formation of\ninhomogeneous states (magnetic phase separation), which necessarily arises\nduring first-order phase transitions in the electron filling parameter, is\ntaken into account. The saturation of the antiferromagnetic and spiral states\nis studied depending on the parameters of the model. The results obtained\ninclude a rich variety of magnetic structures and phase transitions, allowing\nthe interpretation of magnetic properties of semiconducting and metallic\nsystems containing magnetic atoms.\n"}
+{"abstract": "  The paper follows an operadic approach to provide a bialgebraic description\nof substitution for Lie-Butcher series. We first show how the well-known\nbialgebraic description for substitution in Butcher's $B$-series can be\nobtained from the pre-Lie operad. We then apply the same construction to the\npost-Lie operad to arrive at a bialgebra $\\mathcal{Q}$. By considering a module\nover the post-Lie operad, we get a cointeraction between $\\mathcal{Q}$ and the\nHopf algebra $\\mathcal{H}_N$ that describes composition for Lie-Butcher series.\nWe use this coaction to describe substitution for Lie-Butcher series.\n"}
+{"abstract": "  We study the dynamics of proppants carried by fluid driven into an evolving\npenny-shaped fracture. The behaviour of the slurry flow is investigated in two\nphases: pressurised injection and elastic closure. During injection the slurry\nis modelled using a frictional rheology that takes into account the\nshear-induced migration and jamming of the proppants. Making pragmatic\nassumptions of negligible toughness and cross-fracture fluid slip, we find\nself-similar solutions supporting a range of proppant concentration profiles.\nIn particular, we define an effective viscosity, which equates the fracture\nevolution of a slurry flow with a given proppant volume fraction, to a\nNewtonian flow with a particular viscosity. Using this framework, we are able\nto make predictions about the geometry of the growing fracture and the\nsignificance of tip screen-out. In the closure phase, proppants are modelled as\nincompressible and radially immobile within the narrowing fracture. The effects\nof proppant concentration on the geometry of the residual propped fracture are\nexplored in full. The results have important applications to industrial\nfracking and geological dike formation by hot, intruding magma.\n"}
+{"abstract": "  The structure of an RNA molecule plays a significant role in its biological\nfunction. Predicting structure given a one dimensional sequence of RNA\nnucleotide bases is a difficult and important problem. Many computer programs\n(known as in silico) are available for predicting 2-dimensional (secondary)\nstructures however 3-dimensional (tertiary) structure prediction is much more\ndifficult mainly due to the far greater number of feasible solutions and fewer\nexperimental data on the thermodynamic energies of 3D structures. It is also\nchallenging to verify the most likely three dimensional structure even with the\navailability of sophisticated x-ray crystallography and nuclear magnetic\nresonance imaging technologies. In this paper we develop three dimensional RNA\nfolding predictions by adding penalty and reward parameters to a previous two\ndimensional approach based on Quadratic Unconstrained Binary Optimization\n(QUBO) models. These parameters provide flexibility in the amount of three\ndimensional folding allowed. We address the problem of multiple near-optimal\nstructures via a new weighted similarity structure measure and illustrate\nfolding pathways via progressively improving local optimal solutions. The\nproblems are solved via a new commercial QUBO solver AlphaQUBO (Meta-Analytics,\n2020) that solves problems having hundreds of thousands of binary variables.\n"}
+{"abstract": "  We propose a novel method to detect cosmic magnification signals by\ncross-correlating foreground convergence fields constructed from galaxy shear\nmeasurements with background galaxy positional distributions, namely\nshear-number density correlation. We apply it to the Hyper Suprime-Cam Subaru\nStrategic Program (HSC-SSP) survey data. With 27 non-independent data points\nand their full covariance, $\\chi_0^2\\approx 34.1$ and $\\chi_T^2\\approx 24.0$\nwith respect to the null and the cosmological model with the parameters from\nHSC shear correlation analyses in Hamana et al. 2020 (arXiv:1906.06041),\nrespectively. The Bayes factor of the two is $\\log_{10}B_{T0}\\approx 2.2$\nassuming equal model probabilities of null and HSC cosmology, showing a clear\ndetection of the magnification signals. Theoretically, the ratio of the\nshear-number density and shear-shear correlations can provide a constraint on\nthe effective multiplicative shear bias $\\bar m$ using internal data\nthemselves. We demonstrate the idea with the signals from our HSC-SSP mock\nsimulations and rescaling the statistical uncertainties to a survey of\n$15000\\deg^2$. For two-bin analyses with background galaxies brighter than\n$m_{lim}=23$, the combined analyses lead to a forecasted constraint of\n$\\sigma(\\bar m) \\sim 0.032$, $2.3$ times tighter than that of using the\nshear-shear correlation alone. Correspondingly, $\\sigma(S_8)$ with\n$S_8=\\sigma_8(\\Omega_\\mathrm{m}/0.3)^{0.5}$ is tightened by $\\sim 2.1$ times.\nImportantly, the joint constraint on $\\bar m$ is nearly independent of\ncosmological parameters. Our studies therefore point to the importance of\nincluding the shear-number density correlation in weak lensing analyses, which\ncan provide valuable consistency tests of observational data, and thus to\nsolidify the derived cosmological constraints.\n"}
+{"abstract": "  A {\\it $k$-uniform hypergraph} $\\mathcal{H}=(V, E)$ consists of a set $V$ of\nvertices and a set $E$ of hyperedges ($k$-hyperedges), which is a family of\n$k$-subsets of $V$. A {\\it forbidden $k$-homogeneous (or forbidden\n$k$-hypergraph)} access structure $\\mathcal{A}$ is represented by a $k$-uniform\nhypergraph $\\mathcal{H}=(V, E)$ and has the following property: a set of\nvertices (participants) can reconstruct the secret value from their shares in\nthe secret sharing scheme if they are connected by a $k$-hyperedge or their\nsize is at least $k+1$. A forbidden $k$-homogeneous access structure has been\nstudied by many authors under the terminology of $k$-uniform access structures.\nIn this paper, we provide efficient constructions on the total share size of\nlinear secret sharing schemes for sparse and dense $k$-uniform access\nstructures for a constant $k$ using the hypergraph decomposition technique and\nthe monotone span programs.\n"}
+{"abstract": "  The goal of entity matching in knowledge graphs is to identify entities that\nrefer to the same real-world objects using some similarity metric. The result\nof entity matching can be seen as a set of entity pairs interpreted as the\nsame-as relation. However, the identified set of pairs may fail to satisfy some\nstructural properties, in particular transitivity, that are expected from the\nsame-as relation. In this work, we show that an ad-hoc enforcement of\ntransitivity, i.e. taking the transitive closure, on the identified set of\nentity pairs may decrease precision dramatically. We therefore propose a\nmethodology that starts with a given similarity measure, generates a set of\nentity pairs that are identified as referring to the same real-world objects,\nand applies the cluster editing algorithm to enforce transitivity without\nadding many spurious links, leading to overall improved performance.\n"}
+{"abstract": "  We propose a method to establish time reversal symmetry violation at future\nneutrino oscillation experiments in a largely model-independent way. We\nintroduce a general parametrization of flavour transition probabilities which\nholds under weak assumptions and covers a large class of new-physics scenarios.\nThis can be used to search for the presence of T-odd components in the\ntransition probabilities by comparing data at different baselines but at the\nsame neutrino energies. We show that this test can be performed already with\nexperiments at three different baselines and might be feasible with experiments\nunder preparation/consideration.\n"}
+{"abstract": "  We present the $360^\\circ$ catalogue of physical properties of Hi-GAL compact\nsources, detected between 70 and 500 $\\mu$m. This release not only completes\nthe analogous catalogue previously produced by the Hi-GAL collaboration for\n$-71^\\circ \\lesssim \\ell \\lesssim 67^\\circ$, but also meaningfully improves it\nthanks to a new set of heliocentric distances, 120808 in total. About a third\nof the 150223 entries are located in the newly added portion of the Galactic\nplane. A first classification based on detection at 70 $\\mu$m as a signature of\nongoing star-forming activity distinguishes between protostellar sources\n(23~per cent of the total) and starless sources, with the latter further\nclassified as gravitationally bound (pre-stellar) or unbound. The integral of\nthe spectral energy distribution, including ancillary photometry from\n$\\lambda=21$ to 1100 $\\mu$m, gives the source luminosity and other bolometric\nquantities, while a modified black body fitted to data for $\\lambda \\geq 160\\,\n\\mu$m yields mass and temperature. All tabulated clump properties are then\nderived using photometry and heliocentric distance, where possible. Statistics\nof these quantities are discussed with respect to both source Galactic location\nand evolutionary stage. No strong differences in the distributions of\nevolutionary indicators are found between the inner and outer Galaxy. However,\nmasses and densities in the inner Galaxy are on average significantly larger,\nresulting in a higher number of clumps that are candidates to host massive star\nformation. Median behaviour of distance-independent parameters tracing source\nevolutionary status is examined as a function of the Galactocentric radius,\nshowing no clear evidence of correlation with spiral arm positions.\n"}
+{"abstract": "  In this third paper of a series on the precision of obtaining ages of stellar\npopulations using the full spectrum fitting technique, we examine the precision\nof this technique in deriving possible age spreads within a star cluster. We\ntest how well an internal age spread can be resolved as a function of cluster\nage, population mass fraction, and signal-to-noise (S/N) ratio. For this test,\nthe two ages (Age (SSP1) and Age (SSP2)) are free parameters along with the\nmass fraction of SSP1. We perform the analysis on 118,800 mock star clusters\ncovering all ages in the range 6.8 < log (age/yr) < 10.2, with mass fractions\nfrom 10% to 90% for two age gaps (0.2 dex and 0.5 dex). Random noise is added\nto the model spectra to achieve S/N ratios between 50 to 100 per wavelength\npixel. We find that the mean of the derived Age (SSP1) generally matches the\nreal Age (SSP1) to within 0.1 dex up to ages around log (age/yr) = 9.5. The\nprecision decreases for log (age/yr) > 9.6 for any mass fraction or S/N, due to\nthe similarity of SED shapes for those ages. In terms of the recovery of age\nspreads, we find that the derived age spreads are often larger than the real\nones, especially for log(age/yr) < 8.0 and high mass fractions of SSP1.\nIncreasing the age gap in the mock clusters improves the derived parameters,\nbut Age (SSP2) is still overestimated for the younger ages.\n"}
+{"abstract": "  The basic idea here is that observation (or one's experience) is fundamental\nand the `atomic world' is postulated as the source of such observation. Once\nthis source has been inferred to exist one may attempt to explicitly derive its\nstructure in such a way that the observation itself can be reproduced. And so\nhere is a purely quantum mechanical model of observation coupled to its\nsupposed source, and the observation itself is realised as a projection of this\nquantum system.\n"}
+{"abstract": "  It goes back to Ahlfors that a real algebraic curve admits a real-fibered\nmorphism to the projective line if and only if the real part of the curve\ndisconnects its complex part. Inspired by this result, we are interested in\ncharacterising real algebraic varieties of dimension $n$ admitting real-fibered\nmorphisms to the $n$-dimensional projective space. We present a criterion to\nclassify real-fibered morphisms that arise as finite surjective linear\nprojections from an embedded variety which relies on topological linking\nnumbers. We address special attention to real algebraic surfaces. We classify\nall real-fibered morphisms from real del Pezzo surfaces to the projective plane\nand determine which such morphisms arise as the composition of a projective\nembedding with a linear projection. Furthermore, we give some insights in the\ncase of real conic bundles.\n"}
+{"abstract": "  A low loss propagating electromagnetic wave is shown to exist at a gradual\ninterface between two lossy conductive media. The electromagnetic frequency\nrange of this phenomenon may span from UV optics to RF range. In particular, it\nis demonstrated that such a surface wave may be guided by a seafloor-seawater\ninterface and it may be used in radio communication and imaging underwater.\nSimilar surface waves may also be guided by various tissue boundaries inside a\nhuman body. For example, such surface wave solutions may exist at planar\ninterfaces between skull bones and grey matter inside a human head at 6 GHz.\n"}
+{"abstract": "  Dual-phase noble-gas time projection chambers (TPCs) have improved the\nsensitivities for dark matter direct search in past decades. The capability of\nTPCs to reconstruct 3-D vertexes of keV scale recoilings is one of the most\nadvantageous features. In this work, we develop two horizontal position\nreconstruction algorithms for the PandaX-II dark matter search experiment using\nthe dual-phase liquid xenon TPC. Both algorithms are optimized by the\n$^{83m}$Kr calibration events and use photon distribution of ionization signals\namong photomultiplier tubes to infer the positions. According to the events\ncoming from the gate electrode, the uncertainties in the horizontal positions\nare 3.4 mm (3.9 mm) in the analytical (simulation-based) algorithm for an\nionization signal with several thousand photon electrons in the center of the\nTPC\n"}
+{"abstract": "  The present paper deals with the control problems governed by fractional\nnon-instantaneous impulsive functional evolution equations with state-dependent\ndelay involving Caputo fractional derivatives in Banach spaces. The main\nobjective of this work is to formulate sufficient conditions for the\napproximate controllability of the considered system in separable reflexive\nBanach spaces. We have exploited the resolvent operator technique and\nSchauder's fixed point theorem in the proofs to achieve this goal. The\napproximate controllability of linear system is discussed in detail, which\nlacks in the existing literature. We also provide an example to illustrate the\nefficiency of the developed results. Moreover, we point out some shortcomings\nof the existing works in the context of characterization of mild solution and\nphase space, and approximate controllability of fractional order impulsive\nsystems in Banach spaces.\n"}
+{"abstract": "  Belief propagation (BP) decoding of low-density parity-check (LDPC) codes\nwith various dynamic decoding schedules have been proposed to improve the\nefficiency of the conventional flooding schedule. As the ultimate goal of an\nideal LDPC code decoder is to have correct bit decisions, a dynamic decoding\nschedule should be variable node (VN)-centric and be able to find the VNs with\nprobable incorrect decisions and having a good chance to be corrected if chosen\nfor update. We propose a novel and effective metric called conditional\ninnovation (CI) which serves this design goal well. To make the most of dynamic\nscheduling which produces high-reliability bit decisions, we limit our search\nfor the candidate VNs to those related to the latest updated nodes only.\n  Based on the CI metric and the new search guideline separately or in\ncombination, we develop several highly efficient decoding schedules. To reduce\ndecoding latency, we introduce multi-edge updating versions which offer extra\nlatency-performance tradeoffs. Numerical results show that both single-edge and\nmulti-edge algorithms provide better decoding performance against most dynamic\nschedules and the CI-based algorithms are particularly impressive at the first\nfew decoding iterations.\n"}
+{"abstract": "  Complex systems like human mobility exhibit a wide range of collective\ndynamics. Ride-sharing, the combination of multiple similar trips into one, is\na particularly prominent example due to the complex interplay between the\nrequests and the vehicle fleet. Several contributions have analyzed the\ndynamics of the routing and the service efficiency of ride-sharing fleets.\nHere, we adopt a complementary perspective and study the adoption of\nride-sharing from the perspective of its users. Collective interactions of the\nusers to avoid detours in shared rides give rise to spontaneous symmetry\nbreaking, pattern formation dynamics, and bistability, potentially limiting the\nadoption of ride-sharing. Our results may provide a framework to understand\nreal-world adoption patterns of ride-sharing in complex urban settings and\n(re)design ride-sharing services and incentives to promote sustainable shared\nmobility.\n"}
+{"abstract": "  We investigate the breakdown of magneto-hydrodynamics at low temperature\n($T$) with black holes whose extremal geometry is AdS$_2\\times$R$^2$. The\nbreakdown is identified by the equilibration scales ($\\omega_{\\text{eq}},\nk_{\\text{eq}}$) defined as the collision point between the diffusive\nhydrodynamic mode and the longest-lived non-hydrodynamic mode. We show\n($\\omega_{\\text{eq}}, k_{\\text{eq}}$) at low $T$ is determined by the diffusion\nconstant $D$ and the scaling dimension $\\Delta(0)$ of an infra-red operator:\n$\\omega_{\\text{eq}} = 2\\pi T \\Delta(0), \\, k_{\\text{eq}}^2 =\n\\omega_{\\text{eq}}/D$, where $\\Delta(0)=1$ in the presence of magnetic fields.\nFor the purpose of comparison, we have analytically shown $\\Delta(0)=2$ for the\naxion model independent of the translational symmetry breaking pattern\n(explicit or spontaneous), which is complementary to previous numerical\nresults. Our results support the conjectured universal upper bound of the\nenergy diffusion $D \\,\\le\\, \\omega_{\\text{eq}}/k_{\\text{eq}}^2 \\,:=\\,\nv_{\\text{eq}}^2 \\, \\tau_{\\text{eq}}$ where $v_{\\text{eq}}:=\n\\omega_{\\text{eq}}/k_{\\text{eq}}$ and\n$\\tau_{\\text{eq}}:=\\omega_{\\text{eq}}^{-1}$ are the velocity and the timescale\nassociated to equilibration, implying that the breakdown of hydrodynamics sets\nthe upper bound of the diffusion constant $D$ at low $T$.\n"}
+{"abstract": "  In this paper, we study a new generalization of the Lorentz cone, called the\nMonotone Extended Second Order Cone (MESOC). We investigate basic properties of\nMESOC including computation of its Lyapunov rank and proving its reducibility.\nMoreover, we show that in an ambient space, a cylinder is an isotonic\nprojection set with respect to MESOC. We also examine a Nonlinear\nComplementarity Problem on a cylinder, which is equivalent to a suitable Mixed\nComplementarity Problem and provide a computational example illustrating\napplicability of MESOC.\n"}
+{"abstract": "  The exceptional region of the parameter space (ERPS) of the two Higgs doublet\nmodel (2HDM) is defined to be the parameter regime where the scalar potential\ntakes on a very special form. In the standard parameterization of the 2HDM\nscalar potential with squared mass parameters $m_{11}^2$, $m_{22}^2$,\n$m_{12}^2$, and dimensionless couplings, $\\lambda_1$, $\\lambda_2$,\n$\\ldots,\\lambda_7$, the ERPS corresponds to $\\lambda_1=\\lambda_2$,\n$\\lambda_7=-\\lambda_6$, $m_{11}^2=m_{22}^2$ and $m_{12}^2=0$, corresponding to\na scalar potential with an enhanced generalized CP symmetry called GCP2. Many\nspecial features persist if $\\lambda_1=\\lambda_2$ and $\\lambda_7=-\\lambda_6$\nare retained while allowing for $m_{11}^2\\neq m_{22}^2$ and/or $m_{12}^2\\neq\n0$, corresponding to a scalar potential with a softly-broken GCP2 symmetry,\nwhich we designate as the ERPS4. In this paper, we examine many of the special\nfeatures of the ERPS4, as well as even more specialized cases within the ERPS4\nframework in which additional constraints on the scalar potential parameters\nare imposed. By surveying the landscape of the ERPS4, we complete the\nclassification of 2HDM scalar potentials that exhibit an exact Higgs alignment\n(where the tree-level couplings of one neutral scalar coincide with those of\nthe Standard Model Higgs boson), due to a residual symmetry that is unbroken in\nthe vacuum. One surprising aspect of the ERPS4 is the possibility that the\nscalar sector is CP-conserving despite the presence of a complex parameter of\nthe scalar potential whose complex phase cannot be removed by separate\nrephasings of the two scalar doublet fields. The significance of the ERPS4\nregime for custodial symmetry is also discussed, and the cases where a\ncustodial symmetric 2HDM scalar potential preserves an exact Higgs alignment\nare elucidated.\n"}
+{"abstract": "  Network slicing (NS) and multi-access edge computing (MEC) are new paradigms\nwhich play key roles in 5G and beyond networks. NS allows network operators\n(NOs) to divide the available network resources into multiple logical NSs for\nproviding dedicated virtual networks tailored to the specific service/business\nrequirements. MEC enables NOs to provide diverse ultra-low latency services for\nsupporting the needs of different industry verticals by moving computing\nfacilities to the network edge. NS can be constructed by instantiating a set of\nvirtual network functions (VNFs) on top of MEC cloud servers for provisioning\ndiverse latency-sensitive communication services (e.g., autonomous driving and\naugmented reality) on demand at a lesser cost and time. However, VNFs, MEC\ncloud servers, and communication links are subject to failures due to software\nbugs, misconfiguration, overloading, hardware faults, cyber attacks, power\noutage, and natural/man-made disaster. Failure of a critical network component\ndisrupts services abruptly and leads to users' dissatisfaction, which may\nresult in revenue loss for the NOs. In this paper, we present a novel approach\nbased on multi-connectivity in 5G networks to tackle this problem and our\nproposed approach is resilient against i) failure of VNFs, ii) failure of local\nservers within MEC, iii) failure of communication links, and iv) failure of an\nentire MEC cloud facility in regional level. To this end, we formulate the\nproblem as a binary integer programming (BIP) model in order to optimally\ndeploy NSs with the minimum cost, and prove it is NP-hard. To overcome time\ncomplexity, we propose an efficient genetic algorithm based heuristic to obtain\nnear-optimal solution in polynomial time. By extensive simulations, we show\nthat our proposed approach not only reduces resource wastage, but also improves\nthroughput while providing high resiliency against failures.\n"}
+{"abstract": "  The lack of guided exercises and practical opportunities to learn about\ncybersecurity in a practical way makes it difficult for security experts to\nimprove their proficiency. Capture the Flag events and Cyber Ranges are ideal\nfor cybersecurity training. Thereby, the participants usually compete in teams\nagainst each other, or have to defend themselves in a specific scenario. As\norganizers of yearly events, we present a taxonomy for interactive cyber\ntraining and education. The proposed taxonomy includes different factors of the\ntechnical setup, audience, training environment, and training setup. By the\ncomprehensive taxonomy, different aspects of interactive training are\nconsidered. This can help trainings to improve and to be established\nsuccessfully. The provided taxonomy is extendable and can be used in further\napplication areas as research on new security technologies.\n"}
+{"abstract": "  We consider a class of constrained multi-agent optimization problems where\nthe goal is to cooperatively minimize a sum of agent-specific nondifferentiable\nmerely convex functions. The constraint set is characterized as a variational\ninequality (VI) problem where each agent is associated with a local monotone\nmapping. We first discuss how the proposed formulation can be employed in\naddressing several important classes of distributed optimization problems,\nincluding those subject to complementarity constraints, nonlinear equality\nconstraints, and equilibrium constraints. Our main contributions are as\nfollows: (i) We develop an iteratively regularized incremental gradient method\nwhere at each iteration, agents communicate over a cycle graph to update their\nsolution iterates using their local information about the objective and the\nmapping. The proposed method is single-timescale in the sense that it does not\ninvolve any excessive hard-to-project computation per iteration. (ii) We derive\nnon-asymptotic agent-wise convergence rates for the suboptimality of the global\nobjective function and infeasibility of the VI constraints measured by a\nsuitably defined dual gap function. (iii) To analyze the convergence rate in\nthe solution space, assuming the objective function is strongly convex and\nsmooth, we derive non-asymptotic agent-wise rates on an error metric that\nrelates the generated iterates with the Tikhonov trajectory. The proposed\nmethod appears to be the first fully iterative scheme equipped with iteration\ncomplexity that can address distributed optimization problems with VI\nconstraints. We provide preliminary numerical experiments for computing the\nbest equilibrium in a transportation network problem. We also compare the\nperformance of the proposed scheme with that of the existing incremental\ngradient methods in addressing constrained finite-sum problems.\n"}
+{"abstract": "  Four different finite element level-set (FE-LS) formulations are compared for\nthe modeling of grain growth in the context of polycrystalline structures and,\nmoreover, two of them are presented for the first time using anisotropic grain\nboundary (GB) energy and mobility. Mean values and distributions are compared\nusing the four formulations. First, we present the strong and weak formulations\nfor the different models and the crystallographic parameters used at the\nmesoscopic scale. Second, some Grim Reaper analytical cases are presented and\ncompared with the simulation results, here the evolutions of individual\nmultiple junctions are followed. Additionally, large scale simulations are\npresented. Anisotropic GB energy and mobility are respectively defined as\nfunctions of the misorientation/inclination and disorientation. The evolution\nof the disorientation distribution function (DDF) is computed and its evolution\nis in accordance with prior works. We found that the formulation called\n\"Anisotropic\" is the more physical one but it could be replaced at the\nmesoscopic scale by an Isotropic formulation for simple microstructures\npresenting an initial Mackenzie-type DDF.\n"}
+{"abstract": "  The effect of scattering of conduction electrons by dynamical spin\nfluctuations on the thermopower in metals near a thermal phase transition into\nan antiferromagnetic phase is considered. We are interested in a transition at\nroom temperature, as has been studied in a heterostructure involving layers of\nIrMn. We show that the electrical resistivity exhibits a narrow but low peak at\nthe transition, which may be difficult to detect on top of the main\ncontributions induced by phonons and impurities. By contrast, the thermopower\nis found to exhibit a prominent peak both as a function of temperature T for\nfixed layer thickness tAFM and as a function of tAFM for fixed T: We conjecture\nthat the transition temperature Tc is a function of both tAFM and the Fermi\nenergy EF . Both dependencies give rise to a sharp peak of the thermopower as a\nfunction of T or tAFM near the transition. The estimated magnitude of the peak\nfor the case of three-dimensional longitudinal spin fluctuations is in good\nagreement with experiment.\n"}
+{"abstract": "  Issue trackers, such as Jira, have become the prevalent collaborative tools\nin software engineering for managing issues, such as requirements, development\ntasks, and software bugs. However, issue trackers inherently focus on the\nlife-cycle of single issues although issues have and express dependencies on\nother issues that constitute an issue dependency network in a large complex\ncollaborative projects. The objective of this study is to develop supportive\nsolutions for the improved management of dependent issues in an issue tracker.\nThis study follows Design Science methodology, consisting of elicitation of\ndrawbacks, and construction and evaluation of a solution and system. The study\nwas carried out in the context of The Qt Company's Jira, which exemplifies an\nactively used, almost two decade old issue tracker with over 100,000 issues.\nThe drawbacks capture how users operate with issue trackers to handle issue\ninformation in large, collaborative and long-lived projects. The basis of the\nsolution is to keep issues and dependencies as separate objects and\nautomatically construct an issue graph. Dependency detection complements the\nissue graph by proposing missing dependencies, and consistency check and\ndiagnosis identify incompatible issue priorities and release assignments.\nJira's plugin and service-based system architecture realizes the functional and\nquality concerns of the system implementation. We show how to adopt the\nsupporting intelligent techniques of an issue tracker in a complex use context\nand a large data-set. The solution takes into account integrated and holistic\nsystem-view, practical applicability and utility, and the practical\ncharacteristics, such as inherent incompleteness, of issue data.\n"}
+{"abstract": "  In a close look at the replica wormhole derivation of the island conjecture,\nwe note a discrepancy in the derived matrix eigenvalues in the limit where the\nHawking radiation entropy goes to zero. The small corrections to the density\nmatrix for the radiation infer that ${\\rm tr}(\\rho_\\textsf{R}^2)$, equally a\nsum over the eigenvalues squared, must be small ($\\ll1$) at all times. This is\nin contradiction with an ensemble dominated by a density matrix with an\neigenvalue close to unity. Also, the radiation entropy has been approximated by\nthe ensemble averaged entropy, as in a final state analysis. For\n$\\rho_\\textsf{R}=\\rho_\\textsf{R}(\\sigma)$ with $\\sigma$ the set of variables\nused in the average, the ensemble average is only an accurate estimate of the\nentropy after $\\sigma$ has been measured. At the formation of each Hawking\npair, there is a distribution over $\\sigma$ that affects the entropy. Without a\nreal time determination of $\\sigma$, the radiation entropy follows Hawking's\nresult during the evaporation. A question remains of how quickly such\ndeterminations can be assumed to occur. A complete argument for that the\nradiation entropy follows the Page curve should address this.\n"}
+{"abstract": "  We study the notion of a causal time-evolution of a conserved nonlocal\nphysical quantity in a globally hyperbolic spacetime $\\mathcal{M}$. The role of\nthe `global time' is played by a chosen Cauchy temporal function $\\mathcal{T}$,\nwhereas the instantaneous configurations of the nonlocal quantity are modeled\nby probability measures $\\mu_t$ supported on the corresponding time slices\n$\\mathcal{T}^{-1}(t)$. We show that the causality of such an evolution can be\nexpressed in three equivalent ways: (i) via the causal precedence relation\n$\\preceq$ extended to probability measures, (ii) with the help of a probability\nmeasure $\\sigma$ on the space of future-directed continuous causal curves\nendowed with the compact-open topology and (iii) through a causal vector field\n$X$ of $L^2_{\\textrm{loc}}$-regularity, with which the map $t \\mapsto \\mu_t$\nsatisfies the continuity equation in the distributional sense. In the course of\nthe proof we find that the compact-open topology is sensitive to the\ndifferential properties of the causal curves, being equal to a topology induced\nfrom a suitable $H^1_{\\textrm{loc}}$-Sobolev space. This enables us to\nconstruct $X$ as a vector field in a sense `tangent' to $\\sigma$. In addition,\nwe discuss the general covariance of descriptions (i)-(iii), unraveling the\ngeometrical, observer-independent notions behind them.\n"}
+{"abstract": "  In this paper, we generalize existing frameworks for\n$\\mathcal{H}_2\\otimes\\mathcal{L}_2$-optimal model order reduction to a broad\nclass of parametric linear time-invariant systems. To this end, we derive\nfirst-order necessary ptimality conditions for a class of structured\nreduced-order models, and then building on those, propose a\nstability-preserving optimization-based method for computing locally\n$\\mathcal{H}_2\\otimes\\mathcal{L}_2$-optimal reduced-order models. We also make\na theoretical comparison to existing approaches in the literature, and in\nnumerical experiments, show how our new method, with reasonable computational\neffort, produces stable optimized reduced-order models with significantly lower\napproximation errors.\n"}
+{"abstract": "  Robots will be expected to manipulate a wide variety of objects in complex\nand arbitrary ways as they become more widely used in human environments. As\nsuch, the rearrangement of objects has been noted to be an important benchmark\nfor AI capabilities in recent years. We propose NeRP (Neural Rearrangement\nPlanning), a deep learning based approach for multi-step neural object\nrearrangement planning which works with never-before-seen objects, that is\ntrained on simulation data, and generalizes to the real world. We compare NeRP\nto several naive and model-based baselines, demonstrating that our approach is\nmeasurably better and can efficiently arrange unseen objects in fewer steps and\nwith less planning time. Finally, we demonstrate it on several challenging\nrearrangement problems in the real world.\n"}
+{"abstract": "  It is essential to explore the stability and activity of real-system\nnanoparticles theoretically. While applications of theoretical methods for this\npurpose can be found in literature, the expensive computational costs of\nconventional theoretical methods hinder their massive applications to practical\nmaterials design. With the recent development of neural network algorithms\nalong with the advancement of computer systems, neural network potentials have\nemerged as a promising candidate for the description of a wide range of\nmaterials, including metals and molecules, with a reasonable computational\ntime. In this study, we successfully validate a universal neural network\npotential, PFP, for the description of monometallic Ru nanoparticles, PdRuCu\nternary alloy nanoparticles, and the NO adsorption on Rh nanoparticles against\nfirst-principles calculations. We further conduct molecular dynamics\nsimulations on the NO-Rh system and challenge the PFP to describe a large,\nsupported Pt nanoparticle system.\n"}
+{"abstract": "  Very recently, the Tibet-AS$\\gamma$ collaboration reported the detection of\n$\\gamma$ rays from the galactic disk in the energy range of 100 TeV -- 1 PeV.\nRemarkably, many of these $\\gamma$ rays were observed apart from known very\nhigh energy (E$>$ 100 GeV) $\\gamma$-ray sources. These results are best\nunderstood if these diffuse $\\gamma$ rays: 1) were produced by a conventional\nrather than an exotic (i.e. dark matter decay or annihilation) process, 2) have\na hadronic rather than a leptonic origin, 3) were produced in impulsive rather\nthan stable sources or, alternatively, in optically thick sources. In addition\nto that, the detection of the sub-PeV diffuse $\\gamma$ rays implies a limit on\nthe flux of neutrinos from the Galactic disk and a lower limit on the rigidity\nof the cutoff in the Galactic cosmic ray spectrum.\n"}
+{"abstract": "  We generalize to arbitrary categories of algebras the notion of an\nNS-algebra. We do this by using a bimodule property, as we did for defining the\ngeneral notions of a dendriform and tridendriform algebra. We show that several\ntypes of operators lead to NS-algebras: Nijenhuis operators, twisted\nRota-Baxter operators and relative Rota-Baxter operators of arbitrary weight.\n"}
+{"abstract": "  The concepts of blameworthiness and wrongness are of fundamental importance\nin human moral life. But to what extent are humans disposed to blame\nartificially intelligent agents, and to what extent will they judge their\nactions to be morally wrong? To make progress on these questions, we adopted\ntwo novel strategies. First, we break down attributions of blame and wrongness\ninto more basic judgments about the epistemic and conative state of the agent,\nand the consequences of the agent's actions. In this way, we are able to\nexamine any differences between the way participants treat artificial agents in\nterms of differences in these more basic judgments. our second strategy is to\ncompare attributions of blame and wrongness across human, artificial, and group\nagents (corporations). Others have compared attributions of blame and wrongness\nbetween human and artificial agents, but the addition of group agents is\nsignificant because these agents seem to provide a clear middle-ground between\nhuman agents (for whom the notions of blame and wrongness were created) and\nartificial agents (for whom the question remains open).\n"}
+{"abstract": "  The discrimination of coherent states is a key task in optical communication\nand quantum key distribution protocols. In this work, we use a\nphoton-number-resolving detector, the transition-edge sensor, to discriminate\nbinary-phase-shifted coherent states at a telecom wavelength. Owing to its\ndynamic range and high efficiency, we achieve a bit error probability that\nunconditionally exceeds the standard quantum limit (SQL) by up to 7.7 dB. The\nimprovement to the SQL persists for signals containing up to approximately\nseven photons on average and is achieved in a single shot (i.e. without\nmeasurement feedback), thus making our approach compatible with larger\nbandwidths.\n"}
+{"abstract": "  This paper investigates the exotic phenomena exhibited by links of\ndisconnected surfaces with boundary that are properly embedded in the 4-ball.\nOur main results provide two different constructions of exotic pairs of surface\nlinks that are Brunnian, meaning that all proper sublinks of the surface are\ntrivial. We then modify these core constructions to vary the number of\ncomponents in the exotic links, the genera of the components, and the number of\ncomponents that must be removed before the surfaces become unlinked. Our\narguments extend two tools from 3-dimensional knot theory into the\n4-dimensional setting: satellite operations, especially Bing doubling, and\ncovering links in branched covers.\n"}
+{"abstract": "  A linear layout of a graph typically consists of a total vertex order, and a\npartition of the edges into sets of either non-crossing edges, called stacks,\nor non-nested edges, called queues. The stack (queue) number of a graph is the\nminimum number of required stacks (queues) in a linear layout. Mixed linear\nlayouts combine these layouts by allowing each set of edges to form either a\nstack or a queue. In this work we initiate the study of the mixed page number\nof a graph which corresponds to the minimum number of such sets.\n  First, we study the edge density of graphs with bounded mixed page number.\nThen, we focus on complete and complete bipartite graphs, for which we derive\nlower and upper bounds on their mixed page number. Our findings indicate that\ncombining stacks and queues is more powerful in various ways compared to the\ntwo traditional layouts.\n"}
+{"abstract": "  The continuous version of a fundamental result of Khinchin says that a\nhalf-infinite torus line in the unit square $[0,1]^2$ exhibits superdensity,\nwhich is a best form of time-quantitative density, if and only if the slope of\nthe geodesic is a badly approximable number. In this paper, we give a proof of\nthe extension of this result of Khinchin to the case when the unit torus\n$[0,1]^2$ is replaced by a finite polysquare surface, or square tiled surface.\nThe argument is based on diophantine approximation and continued fractions,\ntraditional tools in number theory. In particular, we use the famous\n$3$-distance theorem in diophantine approximation combined with an iterative\nprocess. In short, this is a very number-theoretic study of a very\nnumber-theoretic problem.\n  This paper improves on an earlier result of the authors and Yang where it is\nshown that badly approximable numbers that satisfy a quite severe technical\nrestriction on the digits of their continued fractions lead to superdense\ngeodesics. Here we overcome this technical impediment.\n  This paper is self-contained, and the reader does not need any knowledge of\ndynamical systems.\n"}
+{"abstract": "  Quantum computation represents an emerging framework to solve lattice gauge\ntheories (LGT) with arbitrary gauge groups, a general and long-standing problem\nin computational physics. While quantum computers may encode LGT using only\npolynomially increasing resources, a major openissue concerns the violation of\ngauge-invariance during the dynamics and the search for groundstates. Here, we\npropose a new class of parametrized quantum circuits that can represent states\nbelonging only to the physical sector of the total Hilbert space. This class of\ncircuits is compact yet flexible enough to be used as a variational ansatz to\nstudy ground state properties, as well as representing states originating from\na real-time dynamics. Concerning the first application, the structure of the\nwavefunction ansatz guarantees the preservation of physical constraints such as\nthe Gauss law along the entire optimization process, enabling reliable\nvariational calculations. As for the second application, this class of quantum\ncircuits can be used in combination with timedependent variational quantum\nalgorithms, thus drastically reducing the resource requirements to access\ndynamical properties.\n"}
+{"abstract": "  We investigate the complexity of computing the Zariski closure of a finitely\ngenerated group of matrices. The Zariski closure was previously shown to be\ncomputable by Derksen, Jeandel, and Koiran, but the termination argument for\ntheir algorithm appears not to yield any complexity bound. In this paper we\nfollow a different approach and obtain a bound on the degree of the polynomials\nthat define the closure. Our bound shows that the closure can be computed in\nelementary time. We also obtain upper bounds on the length of chains of linear\nalgebraic groups, where all the groups are generated over a fixed number field.\n"}
+{"abstract": "  Echo State Networks (ESNs) are a special type of recurrent neural networks\n(RNNs), in which the input and recurrent connections are traditionally\ngenerated randomly, and only the output weights are trained. Despite the recent\nsuccess of ESNs in various tasks of audio, image and radar recognition, we\npostulate that a purely random initialization is not the ideal way of\ninitializing ESNs. The aim of this work is to propose an unsupervised\ninitialization of the input connections using the K-Means algorithm on the\ntraining data. We show that for a large variety of datasets this initialization\nperforms equivalently or superior than a randomly initialized ESN whilst\nneeding significantly less reservoir neurons. Furthermore, we discuss that this\napproach provides the opportunity to estimate a suitable size of the reservoir\nbased on prior knowledge about the data.\n"}
+{"abstract": "  In this paper, we develop safe reinforcement-learning-based controllers for\nsystems tasked with accomplishing complex missions that can be expressed as\nlinear temporal logic specifications, similar to those required by\nsearch-and-rescue missions. We decompose the original mission into a sequence\nof tracking sub-problems under safety constraints. We impose the safety\nconditions by utilizing barrier functions to map the constrained optimal\ntracking problem in the physical space to an unconstrained one in the\ntransformed space. Furthermore, we develop policies that intermittently update\nthe control signal to solve the tracking sub-problems with reduced burden in\nthe communication and computation resources. Subsequently, an actor-critic\nalgorithm is utilized to solve the underlying Hamilton-Jacobi-Bellman\nequations. Finally, we support our proposed framework with stability proofs and\nshowcase its efficacy via simulation results.\n"}
+{"abstract": "  Purpose\n  A Magnetic Resonance Imaging (MRI) exam typically consists of several\nsequences that yield different image contrasts. Each sequence is parameterized\nthrough multiple acquisition parameters that influence image contrast,\nsignal-to-noise ratio, acquisition time, and/or resolution. Depending on the\nclinical indication, different contrasts are required by the radiologist to\nmake a diagnosis. As MR sequence acquisition is time consuming and acquired\nimages may be corrupted due to motion, a method to synthesize MR images with\nadjustable contrast properties is required.\n  Methods\n  Therefore, we trained an image-to-image generative adversarial network\nconditioned on the MR acquisition parameters repetition time and echo time. Our\napproach is motivated by style transfer networks, whereas the \"style\" for an\nimage is explicitly given in our case, as it is determined by the MR\nacquisition parameters our network is conditioned on.\n  Results\n  This enables us to synthesize MR images with adjustable image contrast. We\nevaluated our approach on the fastMRI dataset, a large set of publicly\navailable MR knee images, and show that our method outperforms a benchmark\npix2pix approach in the translation of non-fat-saturated MR images to\nfat-saturated images. Our approach yields a peak signal-to-noise ratio and\nstructural similarity of 24.48 and 0.66, surpassing the pix2pix benchmark model\nsignificantly.\n  Conclusion\n  Our model is the first that enables fine-tuned contrast synthesis, which can\nbe used to synthesize missing MR contrasts or as a data augmentation technique\nfor AI training in MRI.\n"}
+{"abstract": "  In problem solving, understanding the problem that one seeks to solve is an\nessential initial step. In this paper, we propose computational methods for\nfacilitating problem understanding through the task of recognizing the unknown\nin specifications of long Math problems. We focus on the topic of Probability.\nOur experimental results show that learning models yield strong results on the\ntask, a promising first step towards human interpretable, modular approaches to\nunderstanding long Math problems.\n"}
+{"abstract": "  Cyber-Physical Systems (CPSs) play an increasingly significant role in many\ncritical applications. These valuable applications attract various\nsophisticated attacks. This paper considers a stealthy estimation attack, which\naims to modify the state estimation of the CPSs. The intelligent attackers can\nlearn defense strategies and use clandestine attack strategies to avoid\ndetection. To address the issue, we design a Chi-square detector in a Digital\nTwin (DT), which is an online digital model of the physical system. We use a\nSignaling Game with Evidence (SGE) to find the optimal attack and defense\nstrategies. Our analytical results show that the proposed defense strategies\ncan mitigate the impact of the attack on the physical estimation and guarantee\nthe stability of the CPSs. Finally, we use an illustrative application to\nevaluate the performance of the proposed framework.\n"}
+{"abstract": "  We study mixing times for the totally asymmetric simple exclusion process\n(TASEP) on a segment of size $N$ with open boundaries. We focus on the maximal\ncurrent phase, and prove that the mixing time is of order $N^{3/2}$, up to\nlogarithmic corrections. In the triple point, where the TASEP with open\nboundaries approaches the Uniform distribution on the state space, we show that\nthe mixing time is precisely of order $N^{3/2}$. This is conjectured to be the\ncorrect order of the mixing time for a wide range of particle systems with\nmaximal current. Our arguments rely on a connection to last-passage\npercolation, and recent results on moderate deviations of last-passage times.\n"}
+{"abstract": "  Although training data is essential for machine learning, railway companies\nare facing difficulties in gathering adequate images of defective equipment due\nto their proactive replacement of would be defective equipment. Nevertheless,\nproactive replacement is indispensable for safe and undisturbed operation of\npublic transport. In this research, we have developed a model using CycleGAN to\ngenerate artificial images of defective equipment instead of real images. By\nadopting these generated images as training data, we verified that these images\nare indistinguishable from real images and they play a vital role in enhancing\nthe accuracy of the defect detection models.\n"}
+{"abstract": "  Novel multimodal imaging methods are capable of generating extensive, super\nhigh resolution datasets for preclinical research. Yet, a massive lack of\nannotations prevents the broad use of deep learning to analyze such data. So\nfar, existing generative models fail to mitigate this problem because of\nfrequent labeling errors. In this paper, we introduce a novel generative method\nwhich leverages real anatomical information to generate realistic image-label\npairs of tumours. We construct a dual-pathway generator, for the anatomical\nimage and label, trained in a cycle-consistent setup, constrained by an\nindependent, pretrained segmentor. The generated images yield significant\nquantitative improvement compared to existing methods. To validate the quality\nof synthesis, we train segmentation networks on a dataset augmented with the\nsynthetic data, substantially improving the segmentation over baseline.\n"}
+{"abstract": "  We present a benchmark database of public social media postings from the\nUnited Kingdom related to the Covid-19 pandemic for academic research purposes,\nalong with some initial analysis, including a taxonomy of key themes organised\nby keyword. This release supports the findings of a research study funded by\nthe Scottish Government Chief Scientist Office that aims to investigate social\nsentiment in order to understand the response to public health measures\nimplemented during the pandemic.\n"}
+{"abstract": "  Federated learning is an appealing framework for analyzing sensitive data\nfrom distributed health data networks. Under this framework, data partners at\nlocal sites collaboratively build an analytical model under the orchestration\nof a coordinating site, while keeping the data decentralized. While integrating\ninformation from multiple sources may boost statistical efficiency, existing\nfederated learning methods mainly assume data across sites are homogeneous\nsamples of the global population, failing to properly account for the extra\nvariability across sites in estimation and inference. Drawing on a\nmulti-hospital electronic health records network, we develop an efficient and\ninterpretable tree-based ensemble of personalized treatment effect estimators\nto join results across hospital sites, while actively modeling for the\nheterogeneity in data sources through site partitioning. The efficiency of this\napproach is demonstrated by a study of causal effects of oxygen saturation on\nhospital mortality and backed up by comprehensive numerical results.\n"}
+{"abstract": "  Algorithmic recourse aims to provide actionable recommendations to\nindividuals to obtain a more favourable outcome from an automated\ndecision-making system. As it involves reasoning about interventions performed\nin the physical world, recourse is fundamentally a causal problem. Existing\nmethods compute the effect of recourse actions using a causal model learnt from\ndata under the assumption of no hidden confounding and modelling assumptions\nsuch as additive noise. Building on the seminal work of Balke and Pearl (1994),\nwe propose an alternative approach for discrete random variables which relaxes\nthese assumptions and allows for unobserved confounding and arbitrary\nstructural equations. The proposed approach only requires specification of the\ncausal graph and confounding structure and bounds the expected counterfactual\neffect of recourse actions. If the lower bound is above a certain threshold,\ni.e., on the other side of the decision boundary, recourse is guaranteed in\nexpectation.\n"}
+{"abstract": "  We consider the effect of a partial transpose on the limit $*$-distribution\nof a Haar distributed random unitary matrix. If we fix, $b$, the number of\nblocks, we show that the partial transpose can be decomposed into a sum of $b$\nmatrices which are asymptotically free and identically distributed. We then\nconsider the joint effect of different block decompositions and show that under\nsome mild assumptions we also get asymptotic freeness.\n"}
+{"abstract": "  Social media has become an essential part of the daily routines of children\nand adolescents. Moreover, enormous efforts have been made to ensure the\npsychological and emotional well-being of young users as well as their safety\nwhen interacting with various social media platforms. In this paper, we\ninvestigate the exposure of those users to inappropriate comments posted on\nYouTube videos targeting this demographic. We collected a large-scale dataset\nof approximately four million records and studied the presence of five\nage-inappropriate categories and the amount of exposure to each category. Using\nnatural language processing and machine learning techniques, we constructed\nensemble classifiers that achieved high accuracy in detecting inappropriate\ncomments. Our results show a large percentage of worrisome comments with\ninappropriate content: we found 11% of the comments on children's videos to be\ntoxic, highlighting the importance of monitoring comments, particularly on\nchildren's platforms.\n"}
+{"abstract": "  We introduce environment predictive coding, a self-supervised approach to\nlearn environment-level representations for embodied agents. In contrast to\nprior work on self-supervised learning for images, we aim to jointly encode a\nseries of images gathered by an agent as it moves about in 3D environments. We\nlearn these representations via a zone prediction task, where we intelligently\nmask out portions of an agent's trajectory and predict them from the unmasked\nportions, conditioned on the agent's camera poses. By learning such\nrepresentations on a collection of videos, we demonstrate successful transfer\nto multiple downstream navigation-oriented tasks. Our experiments on the\nphotorealistic 3D environments of Gibson and Matterport3D show that our method\noutperforms the state-of-the-art on challenging tasks with only a limited\nbudget of experience.\n"}
+{"abstract": "  The Besicovitch projection theorem states that if a subset $E$ of the plane\nhas finite length in the sense of Hausdorff measure and is purely unrectifiable\n(so its intersection with any Lipschitz graph has zero length), then almost\nevery orthogonal projection of $E$ to a line will have zero measure. In other\nwords, the Favard length of a purely unrectifiable $1$-set vanishes. In this\narticle, we show that when linear projections are replaced by certain nonlinear\nprojections called curve projections, this result remains true. In fact, we go\nfurther and use multiscale analysis to prove a quantitative version of this\nBesicovitch nonlinear projection theorem. Roughly speaking, we show that if a\nsubset of the plane has finite length in the sense of Hausdorff and is nearly\npurely unrectifiable, then its Favard curve length is very small. Our\ntechniques build on those of Tao, who in [Tao09] proves a quantification of the\noriginal Besicovitch projection theorem.\n"}
+{"abstract": "  In this paper, we present a data-driven secondary controller for regulating\nto some desired values several variables of interest in a power system, namely,\nelectrical frequency, voltage magnitudes at critical buses, and active power\nflows through critical lines. The power generation system is based on\ndistributed energy resources (DERs) interfaced with either grid-forming (GFM)\nor grid-following (GFL) inverters. The secondary controller is based on online\nfeedback optimization leveraging the learned sensitivities of the changes in\nthe system frequency, voltage magnitudes at critical buses, and active power\nflows through critical lines to the changes in inverter active and reactive\npower setpoints. To learn the sensitivities accurately from data, the feedback\noptimization has a built-in mechanism for keeping the secondary control inputs\npersistently exciting without degrading its performance. The feedback\noptimization also utilizes the learned power-voltage characteristics of\nphotovoltaic (PV) arrays to compute DC-link voltage setpoints so as to allow\nthe PV arrays to track the power setpoints. To learn the power-voltage\ncharacteristics, we separately execute a data-driven approach that fits a\nconcave polynomial to the collected power-voltage measurements by solving a\nsum-of-squares (SoS) optimization. We showcase the secondary controller using\nthe modified IEEE-14 bus test system, in which conventional energy sources are\nreplaced with inverter-interfaced DERs.\n"}
+{"abstract": "  Nematic polymer networks (NPNs) are nematic elastomers within which the\nnematic director is enslaved to the elastic deformation. The elastic free\nenergy of a NPN sheet of thickness $h$ has both stretching and bending\ncomponents (the former scaling like h, the latter scaling like $h^3$). NPN\nsheets bear a director field $\\mathbf{m}$ imprinted in them (usually, uniformly\nthroughout their thickness); they can be activated by changing the nematic\norder (e.g. by illumination or heating). This paper illustrates an attempt to\ncompute the bending energy of a NPN sheet and to show which role it can play in\ndetermining the activated shape. Our approach is approximate: the activated\nsurface consists of flat sectors connected by ridges, where the unit normal\njumps and the bending energy is concentrated. By increasing the number of\nridges, we should get closer to the real situation, where the activated surface\nis smooth and the bending energy is distributed on it. The method is applied to\na disk with imprinted a spiraling hedgehog. It is shown that upon activation\nthe disk, like a tiny hand, is able to grab a rigid lamina.\n"}
+{"abstract": "  We consider a general class of integro-differential evolution equations which\nincludes the governing equation of the generalized grey Brownian motion and the\ntime- and space-fractional heat equation. We present a general relation between\nthe parameters of the equation and the distribution of the underlying\nstochastic processes, as well as discuss different classes of processes\nproviding stochastic solutions of these equations. For a subclass of evolution\nequations, containing Saigo-Maeda generalized time-fractional operators, we\ndetermine the parameters of the corresponding processes explicitly. Moreover,\nwe explain how self-similar stochastic solutions with stationary increments can\nbe obtained via linear fractional L\\'evy motion for suitable\npseudo-differential operators in space.\n"}
+{"abstract": "  We classify the indecomposable objects in the monoidal center of Deligne's\ninterpolation category $Rep(S_t)$ by viewing $Rep(S_t)$ as a model-theoretic\nlimit in rank and characteristic. We further prove that the center of\n$Rep(S_t)$ is semisimple if and only if $t$ is not a non-negative integer. In\naddition, we identify the associated graded Grothendieck ring of this monoidal\ncenter with that of the graded sum of the centers of representation categories\nof finite symmetric groups with an induction product.\n"}
+{"abstract": "  Despite recent advances in its theoretical understanding, there still remains\na significant gap in the ability of existing PAC-Bayesian theories on\nmeta-learning to explain performance improvements in the few-shot learning\nsetting, where the number of training examples in the target tasks is severely\nlimited. This gap originates from an assumption in the existing theories which\nsupposes that the number of training examples in the observed tasks and the\nnumber of training examples in the target tasks follow the same distribution,\nan assumption that rarely holds in practice. By relaxing this assumption, we\ndevelop two PAC-Bayesian bounds tailored for the few-shot learning setting and\nshow that two existing meta-learning algorithms (MAML and Reptile) can be\nderived from our bounds, thereby bridging the gap between practice and\nPAC-Bayesian theories. Furthermore, we derive a new computationally-efficient\nPACMAML algorithm, and show it outperforms existing meta-learning algorithms on\nseveral few-shot benchmark datasets.\n"}
+{"abstract": "  For any positive integer $t$, a \\emph{$t$-broom} is a graph obtained from\n$K_{1,t+1}$ by subdividing an edge once. In this paper, we show that, for\ngraphs $G$ without induced $t$-brooms, we have $\\chi(G) = o(\\omega(G)^{t+1})$,\nwhere $\\chi(G)$ and $\\omega(G)$ are the chromatic number and clique number of\n$G$, respectively. When $t=2$, this answers a question of Schiermeyer and\nRanderath. Moreover, for $t=2$, we strengthen the bound on $\\chi(G)$ to\n$7.5\\omega(G)^2$, confirming a conjecture of Sivaraman. For $t\\geq 3$ and\n\\{$t$-broom, $K_{t,t}$\\}-free graphs, we improve the bound to\n$o(\\omega^{t-1+\\frac{2}{t+1}})$.\n"}
+{"abstract": "  The range mode problem is a fundamental problem and there is a lot of work\nabout it. There is also some work for the dynamic version of it and the\nenumerating version of it, but there is no previous research about the dynamic\nand enumerating version of it. We found an efficient algorithm for it.\n"}
+{"abstract": "  This paper studies the problem of decentralized continuum deformation\ncoordination of multi-agent systems aided by cooperative localization. We treat\nagents as particles inside a triangular continuum (deformable body) in a2-D\nmotion space and let the continuum deformation coordination be defined by three\nleaders located at vertices of a triangle, called the leading triangle. The\nleaders desired trajectories are assigned as the solution of a constrained\noptimal control problem such that safety requirements are satisfied in the\npresence of disturbance and measurement noise. Followers distributed inside the\nleading tri-angle acquire continuum deformation in a decentralized fashion by\nintegrating cooperative localization and local communication. Specifically,\ncooperative localization estimates the global positions of all agents using\nrelative position measurements based primarily on proximity of agents.\nSimulation results are presented for a network of ten agents.\n"}
+{"abstract": "  In robotic domains, learning and planning are complicated by continuous state\nspaces, continuous action spaces, and long task horizons. In this work, we\naddress these challenges with Neuro-Symbolic Relational Transition Models\n(NSRTs), a novel class of models that are data-efficient to learn, compatible\nwith powerful robotic planning methods, and generalizable over objects. NSRTs\nhave both symbolic and neural components, enabling a bilevel planning scheme\nwhere symbolic AI planning in an outer loop guides continuous planning with\nneural models in an inner loop. Experiments in four robotic planning domains\nshow that NSRTs can be learned after only tens or hundreds of training\nepisodes, and then used for fast planning in new tasks that require up to 60\nactions and involve many more objects than were seen during training. Video:\nhttps://tinyurl.com/chitnis-nsrts\n"}
+{"abstract": "  One knows that, the connected graded ring $P^{\\otimes h}= \\mathbb Z/2[t_1,\n\\ldots, t_h]= \\{P_n^{\\otimes h}\\}_{n\\geq 0},$ which is graded by the degree of\nthe homogeneous terms $P^{\\otimes h}_n$ of degree $n$ in $h$ generators with\nthe degree of each $t_i$ being one, admits a left action of $\\mathcal A$ as\nwell as a right action of the general linear group $GL_h.$ A central problem of\nhomotopy theory is to determine the structure of the space of\n$GL_h$-coinvariants, $\\mathbb Z/2\\otimes_{GL_h}{\\rm Ann}_{\\overline{\\mathcal\nA}}[P^{\\otimes h}_n]^{*}.$ Solving this problem is very difficult and still\nopen for $h\\geq 4.$ In this Note, our intent is of studying the dimension of\n$\\mathbb Z/2\\otimes_{GL_h}{\\rm Ann}_{\\overline{\\mathcal A}}[P^{\\otimes\nh}_n]^{*}$ for the case $h = 4$ and the \"generic\" degrees $n$ of the form\n$n_{k, r, s} = k(2^{s} - 1) + r.2^{s},$ where $k,\\, r,\\, s$ are positive\nintegers. Applying the results, we investigate the behaviour of the Singer\ncohomological \"transfer\" of rank $4.$ Singer's transfer is a homomorphism from\na certain subquotient of the divided power algebra $\\Gamma(a_1^{(1)}, \\ldots,\na_h^{(1)})$ to mod-2 cohomology groups ${\\rm Ext}_{\\mathcal A}^{h, h+*}(\\mathbb\nZ/2, \\mathbb Z/2)$ of the algebra $\\mathcal A.$ This homomorphism is useful for\ndepicting the Ext groups. Additionally, in higher ranks, by using the results\non $\\mathcal A$-generators for $P^{\\otimes 5}$ and $P^{\\otimes 6},$ we show in\nAppendix that the transfer of rank 5 is an isomorphism in som certain degrees\nof the form $n_{k, r, s}$, and that the transfer of rank 6 does not detect the\nnon-zero elements $h_2^{2}g_1 = h_4Ph_2\\in {\\rm Ext}_{\\mathcal A}^{6, 6+n_{6,\n10, 1}}(\\mathbb Z/2, \\mathbb Z/2)$, and $D_2\\in {\\rm Ext}_{\\mathcal A}^{6,\n6+n_{6, 10, 2}}(\\mathbb Z/2, \\mathbb Z/2).$ Besides, we also probe the behavior\nof the Singer transfer of ranks 7 and 8 in internal degrees $\\leq 15.$\n"}
+{"abstract": "  First examples of symmetric Hadamard matrices of orders 508 and 764 are\nconstructed. The method used is known as the propus construction. A conjecture\nregarding this method is formally proposed but it appears implicitly in three\nprevious joint papers with N. A. Balonin et alias. The conjecture has been\nverified for Hadamard matrices of order 4v with odd v<54. In this note the\nconjecture is verified for v=55,57,59,61 and partially verified for v=63.\n"}
+{"abstract": "  Everyday conversations require understanding everyday events, which in turn,\nrequires understanding temporal commonsense concepts interwoven with those\nevents. Despite recent progress with massive pre-trained language models (LMs)\nsuch as T5 and GPT-3, their capability of temporal reasoning in dialogs remains\nlargely under-explored. In this paper, we present the first study to\ninvestigate pre-trained LMs for their temporal reasoning capabilities in\ndialogs by introducing a new task and a crowd-sourced English challenge set,\nTIMEDIAL. We formulate TIME-DIAL as a multiple-choice cloze task with over 1.1K\ncarefully curated dialogs. Empirical results demonstrate that even the best\nperforming models struggle on this task compared to humans, with 23 absolute\npoints of gap in accuracy. Furthermore, our analysis reveals that the models\nfail to reason about dialog context correctly; instead, they rely on shallow\ncues based on existing temporal patterns in context, motivating future research\nfor modeling temporal concepts in text and robust contextual reasoning about\nthem. The dataset is publicly available at:\nhttps://github.com/google-research-datasets/timedial.\n"}
+{"abstract": "  We demonstrate that a separation of the velocity field in large and small\nscales according to a streamwise Fourier decomposition identifies subspaces\nwith stable Lyapunov exponents and allows the dynamics to exhibit properties of\nan inertial manifold, such as the synchronization of the small scales in\nsimulations sharing the same large scales or equivalently the decay of all\nsmall scale flow states to the state uniquely determined from the large scale\nflow. This behaviour occurs for deviations with streamwise wavelength smaller\nthan 130 wall units which was shown in earlier studies to correspond to the\nstreamwise spectral peak of the cross-flow velocity components of the top\nLyapunov vector of the turbulent flow.\n"}
+{"abstract": "  We probe the accuracy of linear ridge regression employing a 3-body local\ndensity representation deriving from the atomic cluster expansion. We benchmark\nthe accuracy of this framework in the prediction of formation energies in\nmolecules, and forces-energies in solids. We find that such a simple regression\nframework performs on par with state-of-the-art machine learning methods, which\nare, in most cases, more complex and more computationally demanding.\nSubsequently, we look for ways to sparsify the descriptor and further improve\nthe computational efficiency of the method. To this aim, we use both PCA and\nLASSO regression for energy fitting on six single-elements datasets. Both\nmethods highlight the possibility to construct a descriptor that is 4 times\nsmaller than the original, with an equal or even improved accuracy.\nFurthermore, we find that the reduced descriptors share a sizable fraction of\ntheir features across the six independent datasets, hinting at the possibility\nto design material-agnostic, optimally compressed, and accurate, descriptors.\n"}
+{"abstract": "  We present new stellar mass functions at $z\\sim6$, $z\\sim7$, $z\\sim8$,\n$z\\sim9$ and, for the first time, $z\\sim10$, constructed from $\\sim800$\nLyman-Break galaxies previously identified over the XDF/UDF, parallels and the\nfive CANDELS fields. Our study is distinctive due to (1) the much deeper\n($\\sim200$ hour) wide-area Spitzer/IRAC imaging at $3.6\\mu$m and $4.5\\mu$m from\nthe GOODS Re-ionization Era wide Area Treasury from Spitzer (GREATS) program\nand (2) consideration of $z\\sim6-10$ sources over a $3\\times$ larger area than\nprevious HST+Spitzer studies. The Spitzer/IRAC data enable $\\ge2\\sigma$\nrest-frame optical detections for an unprecedented $50\\%$ of galaxies down to a\nstellar mass limit of $\\sim10^{8}\\mathcal{M}_\\odot$ across all redshifts.\nSchechter fits to our volume densities suggest a combined evolution in\ncharacteristic mass $\\mathcal{M}^*$ and normalization factor $\\phi^*$ between\n$z\\sim6$ and $z\\sim8$. The stellar mass density (SMD) increases by\n$\\sim1000\\times$ in the $\\sim500$ Myr between $z\\sim10$ and $z\\sim6$, with\nindications of a steeper evolution between $z\\sim10$ and $z\\sim8$, similar to\nthe previously-reported trend of the star-formation rate density. Strikingly,\nabundance matching to the Bolshoi-Planck simulation indicates halo mass\ndensities evolving at approximately the same rate as the SMD between $z\\sim10$\nand $z\\sim4$. Our results show that the stellar-to-halo mass ratios, a proxy\nfor the star-formation efficiency, do not change significantly over the huge\nstellar mass build-up occurred from $z\\sim10$ to $z\\sim6$, indicating that the\nassembly of stellar mass closely mirrors the build-up in halo mass in the first\n$\\sim1$ Gyr of cosmic history. JWST is poised to extend these results into the\n\"first galaxy\" epoch at $z\\gtrsim10$.\n"}
+{"abstract": "  Co-prime sampling is a strategy for acquiring the signal below the Nyquist\nrate. The prototype and extended co-prime samplers require two low rate\nsub-samplers. One of the sub-samplers in the extended co-prime scheme is not\nutilized for every alternate co-prime period. Therefore, this paper proposes a\ntime multiplexing strategy to utilize the vacant slots. It describes the\ndeviation from the existing theory. Closed-form expressions for weight\nfunctions are provided. Acquisition of two signals is described with three\nsamplers as well as two samplers. A generalized structure is also proposed with\nan extremely sparse co-prime sampling strategy.\n"}
+{"abstract": "  During plastic deformation of crystalline materials, point defects such as\nvacancies and interstitials are generated by jogs on moving dislocations. A\ndetailed model for jog formation and transport during plastic deformation was\ndeveloped within the vector density-based continuum dislocation dynamics\nframework (Lin and El-Azab, 2020; Xia and El-Azab, 2015). As a part of this\nmodel, point defect generation associated with jog transport was formulated in\nterms of the volume change due to the non-conservative motion of jogs. Balance\nequations for the vacancies and interstitials including their rate of\ngeneration due to jog transport were also formulated. A two-way coupling\nbetween point defects and dislocation dynamics was then completed by including\nthe stress contributed by the eigen-strain of point defects. A jog drag stress\nwas further introduced into the mobility law of dislocations to account for the\nenergy dissipation during point defects generation. A number of test problems\nand a fully coupled simulation of dislocation dynamics and point defect\ngeneration and diffusion were performed. The results show that there is an\nasymmetry of vacancy and interstitial generation due to the different formation\nenergies of the two types of defects. The results also show that a higher\nhardening rate and a higher dislocation density are obtained when the point\ndefect generation mechanism is coupled to dislocation dynamics.\n"}
+{"abstract": "  As a first step towards a realistic phenomenological description of vector\nand axial-vector mesons in nuclear matter, we calculate the spectral functions\nof the $\\rho$ and the $a_1$ meson in a chiral baryon-meson model as a\nlow-energy effective realization of QCD, taking into account the effects of\nfluctuations from scalar mesons, nucleons, and vector mesons within the\nFunctional Renormalization Group (FRG) approach. The phase diagram of the\neffective hadronic theory exhibits a nuclear liquid-gas phase transition as\nwell as a chiral phase transition at a higher baryon-chemical potential. The\nin-medium $\\rho$ and $a_1$ spectral functions are calculated by using the\npreviously introduced analytically-continued FRG (aFRG) method. Our results\nshow strong modifications of the spectral functions in particular near the\ncritical endpoints of both phase transitions which may well be of relevance for\nelectromagnetic rates in heavy-ion collisions or neutrino emissivities in\nneutron-star merger events.\n"}
+{"abstract": "  Tomographic reconstruction, despite its revolutionary impact on a wide range\nof applications, suffers from its ill-posed nature in that there is no unique\nsolution because of limited and noisy measurements. Traditional\noptimization-based reconstruction relies on regularization to address this\nissue; however, it faces its own challenge because the type of regularizer and\nchoice of regularization parameter are a critical but difficult decision.\nMoreover, traditional reconstruction yields point estimates for the\nreconstruction with no further indication of the quality of the solution. In\nthis work we address these challenges by exploring Gaussian processes (GPs).\nOur proposed GP approach yields not only the reconstructed object through the\nposterior mean but also a quantification of the solution uncertainties through\nthe posterior covariance. Furthermore, we explore the flexibility of the GP\nframework to provide a robust model of the information across various length\nscales in the object, as well as the complex noise in the measurements. We\nillustrate the proposed approach on both synthetic and real tomography images\nand show its unique capability of uncertainty quantification in the presence of\nvarious types of noise, as well as reconstruction comparison with existing\nmethods.\n"}
+{"abstract": "  We investigated and demonstrated a 1.3 $\\mu$m-band laser grown by\nmetalorganic vapour phase epitaxy (MOVPE) on a specially engineered metamorphic\nparabolic graded In$_x$Ga$_{1-x}$As buffer and epitaxial structure on a GaAs\nsubstrate. Bottom and upper cladding layers were built as a combination of\nAlInGaAs and InGaP alloys in a superlattice sequence. This was implemented to\novercome (previously unreported) detrimental surface epitaxial dynamics and\ninstabilities: when single alloys are utilised to achieve thick layers on\nmetamorphic structures, surface instabilities induce defect generation. This\nhas represented a historically limiting factor for metamorphic lasers by MOVPE.\nWe describe a number of alternative strategies to achieve smooth surface\nmorphology to obtain efficient compressively strained In$_{0.4}$Ga$_{0.6}$As\nquantum wells in the active layer. The resulting lasers exhibited low lasing\nthreshold with total slope efficiency of 0.34 W/A for a 500 $\\mu$m long ridge\nwaveguide device. The emission wavelength is extended as far as 1360 nm.\n"}
+{"abstract": "  We initiate a study of the composition properties of interactive\ndifferentially private mechanisms. An interactive differentially private\nmechanism is an algorithm that allows an analyst to adaptively ask queries\nabout a sensitive dataset, with the property that an adversarial analyst's view\nof the interaction is approximately the same regardless of whether or not any\nindividual's data is in the dataset. Previous studies of composition of\ndifferential privacy have focused on non-interactive algorithms, but\ninteractive mechanisms are needed to capture many of the intended applications\nof differential privacy and a number of the important differentially private\nprimitives.\n  We focus on concurrent composition, where an adversary can arbitrarily\ninterleave its queries to several differentially private mechanisms, which may\nbe feasible when differentially private query systems are deployed in practice.\nWe prove that when the interactive mechanisms being composed are pure\ndifferentially private, their concurrent composition achieves privacy\nparameters (with respect to pure or approximate differential privacy) that\nmatch the (optimal) composition theorem for noninteractive differential\nprivacy. We also prove a composition theorem for interactive mechanisms that\nsatisfy approximate differential privacy. That bound is weaker than even the\nbasic (suboptimal) composition theorem for noninteractive differential privacy,\nand we leave closing the gap as a direction for future research, along with\nunderstanding concurrent composition for other variants of differential\nprivacy.\n"}
+{"abstract": "  I begin this review by first defining what is meant by exoplanet\ndemographics, and then motivating why we would like as broad a picture of\nexoplanet demographics as possible. I then outline the methodology and pitfalls\nto measuring exoplanet demographics in practice. I next review the methods of\ndetecting exoplanets, focusing on the ability of these methods to detect wide\nseparation planets. For the purposes of this review, I define wide separation\nas separations beyond the 'snow line' of the protoplanetary disk, which is at\nroughly $\\sim3$ au for a sunlike star. I note that this definition is somewhat\narbitrary, and the practical boundary depends on the host star mass, planet\nmass and radius, and detection method. I review the approximate scaling\nrelations for the signal-to-noise ratio for the detectability of exoplanets as\na function of the relevant physical parameters, including the host star\nproperties. I provide abroad overview of what has already been learned from the\ntransit, radial velocity, direct imaging, and microlensing methods. I outline\nthe challenges to synthesizing the demographics using different methods and\ndiscuss some preliminary first steps in this direction. Finally, I describe\nfuture prospects for providing a nearly complete statistical census of\nexoplanets.\n"}
+{"abstract": "  The narrow s-wave Feshbach resonance of a $^6$Li Fermi gas shows strong\nthree-body loss, which is proposed to be used to measure the minute change of a\nmagnetic field around the resonance. However, the eddy current will cause\nultracold atom experiencing a magnetic field delayed to the desired magnetic\nfield from the current of the magnetic coils. The elimination of the eddy\ncurrent effect will play a key role in any experiments that motivated to\nmeasure the magnetic field to the precision of a part per million stability.\nHere, we apply a method to correct the eddy current effect for precision\nmeasurement of the magnetic field. We first record the three-body loss\ninfluenced by the effect of induced eddy current, then use a certain model to\nobtain the time constant of the actual magnetic field by fitting the atom loss.\nThis precisely determines the actual magnetic field according to the time\nresponse of the three-body loss. After that, we implement the desired magnetic\nfield to the atoms so that we can analyze the three-body loss across the whole\nnarrow Feshbach resonance. The results show that the three-body recombination\nis the dominated loss mechanism near the resonance. We expect this practical\nmethod of correcting the eddy current error of the magnetic field can be\nfurther applied to the future studies of quantum few- and many-body physics\nnear a narrow Feshbach resonance.\n"}
+{"abstract": "  This paper presents a novel approach for the automatic offline grasp pose\nsynthesis on known rigid objects for parallel jaw grippers. We use several\ncriteria such as gripper stroke, surface friction, and a collision check to\ndetermine suitable 6D grasp poses on an object. In contrast to most available\napproaches, we neither aim for the best grasp pose nor for as many grasp poses\nas possible, but for a highly diverse set of grasps distributed all along the\nobject. In order to accomplish this objective, we employ a clustering algorithm\nto the sampled set of grasps. This allows to simultaneously reduce the set of\ngrasp pose candidates and maintain a high variance in terms of position and\norientation between the individual grasps. We demonstrate that the grasps\ngenerated by our method can be successfully used in real-world robotic grasping\napplications.\n"}
+{"abstract": "  Integrating extracted knowledge from the Web to knowledge graphs (KGs) can\nfacilitate tasks like question answering. We study relation integration that\naims to align free-text relations in subject-relation-object extractions to\nrelations in a target KG. To address the challenge that free-text relations are\nambiguous, previous methods exploit neighbor entities and relations for\nadditional context. However, the predictions are made independently, which can\nbe mutually inconsistent. We propose a two-stage Collective Relation\nIntegration (CoRI) model, where the first stage independently makes candidate\npredictions, and the second stage employs a collective model that accesses all\ncandidate predictions to make globally coherent predictions. We further improve\nthe collective model with augmented data from the portion of the target KG that\nis otherwise unused. Experiment results on two datasets show that CoRI can\nsignificantly outperform the baselines, improving AUC from .677 to .748 and\nfrom .716 to .780, respectively.\n"}
+{"abstract": "  Researches have demonstrated that low bit-width (e.g., INT8) quantization can\nbe employed to accelerate the inference process. It makes the gradient\nquantization very promising since the backward propagation requires\napproximately twice more computation than forward one. Due to the variability\nand uncertainty of gradient distribution, a lot of methods have been proposed\nto attain training stability. However, most of them ignore the channel-wise\ngradient distributions and the impact of gradients with different magnitudes,\nresulting in the degradation of final accuracy. In this paper, we propose a\nnovel INT8 quantization training framework for convolutional neural network to\naddress the above issues. Specifically, we adopt Gradient Vectorized\nQuantization to quantize the gradient, based on the observation that layer-wise\ngradients contain multiple distributions along the channel dimension. Then,\nMagnitude-aware Clipping Strategy is introduced by taking the magnitudes of\ngradients into consideration when minimizing the quantization error, and we\npresent a theoretical derivation to solve the quantization parameters of\ndifferent distributions. Experimental results on broad range of computer vision\ntasks, such as image classification, object detection and video classification,\ndemonstrate that the proposed Distribution Adaptive INT8 Quantization training\nmethod has achieved almost lossless training accuracy for different backbones,\nincluding ResNet, MobileNetV2, InceptionV3, VGG and AlexNet, which is superior\nto the state-of-the-art techniques. Moreover, we further implement the INT8\nkernel that can accelerate the training iteration more than 200% under the\nlatest Turing architecture, i.e., our method excels on both training accuracy\nand speed.\n"}
+{"abstract": "  We show scalable and complete suppression of the recently reported\nterahertz-pulse-induced switching between different resistance states of\nantiferromagnetic CuMnAs thin films by ultrafast gating. The gating\nfunctionality is achieved by an optically generated transiently conductive\nparallel channel in the semiconducting substrate underneath the metallic layer.\nThe photocarrier lifetime determines the time scale of the suppression. As we\ndo not observe a direct impact of the optical pulse on the state of CuMnAs, all\nobserved effects are primarily mediated by the substrate. The sample region of\nsuppressed resistance switching is given by the optical spot size, thereby\nmaking our scheme potentially applicable for transient low-power masking of\nstructured areas with feature sizes of ~100 nm and even smaller.\n"}
+{"abstract": "  This report presents research conducted on amplified self-healing\nelectrostatic (HASEL) actuators. HASEL actuators are comprised of a dielectric\nfluid sealed between two inextensible layers with bonded, flexible electrodes\non its outer surface. When charge is applied to the electrodes, Coulomb force\ncompresses the fluid and causes the actuator to contract. In this work a\nfaster, more customizable and convenient way of creating a HASEL actuator is\npresented, using a laser engraver to heat-seal and cut polypropylene sheets.\nUsing this technique, a hydraulically actuated soft lens is fabricated and\ndemonstrated.\n"}
+{"abstract": "  Deep Learning models have become potential candidates for auditory\nneuroscience research, thanks to their recent successes on a variety of\nauditory tasks. Yet, these models often lack interpretability to fully\nunderstand the exact computations that have been performed. Here, we proposed a\nparametrized neural network layer, that computes specific spectro-temporal\nmodulations based on Gabor kernels (Learnable STRFs) and that is fully\ninterpretable. We evaluated predictive capabilities of this layer on Speech\nActivity Detection, Speaker Verification, Urban Sound Classification and Zebra\nFinch Call Type Classification. We found out that models based on Learnable\nSTRFs are on par for all tasks with different toplines, and obtain the best\nperformance for Speech Activity Detection. As this layer is fully\ninterpretable, we used quantitative measures to describe the distribution of\nthe learned spectro-temporal modulations. The filters adapted to each task and\nfocused mostly on low temporal and spectral modulations. The analyses show that\nthe filters learned on human speech have similar spectro-temporal parameters as\nthe ones measured directly in the human auditory cortex. Finally, we observed\nthat the tasks organized in a meaningful way: the human vocalizations tasks\ncloser to each other and bird vocalizations far away from human vocalizations\nand urban sounds tasks.\n"}
+{"abstract": "  We present detailed timing and spectral studies of the black hole candidate\nMAXI J0637$-$430 during its 2019-2020 outburst using observations with the {\\em\nNeutron Star Interior Composition Explorer (NICER)} and the {\\em Neil Gehrels\nSwift Observatory}. We find that the source evolves through the\nsoft-intermediate, high-soft, hard-intermediate and low-hard states during the\noutburst. No evidence of quasi-periodic oscillations is found in the power\ndensity spectra of the source. Weak variability with fractional rms amplitude\n$<5\\%$ is found in the softer spectral states. In the hard-intermediate and\nhard states, high variability with the fractional rms amplitude of $>20\\%$ is\nobserved. The $0.7-10$ keV spectra with {\\em NICER} are studied with a combined\ndisk-blackbody and nthcomp model along with the interstellar absorption. The\ntemperature of the disc is estimated to be $0.6$ keV in the rising phase and\ndecreased slowly to $0.1$ keV in the declining phase. The disc component was\nnot detectable or absent during the low hard state. From the state-transition\nluminosity and the inner edge of the accretion flow, we estimate the mass of\nthe black hole to be in the range of 5$-$12 $M_{\\odot}$, assuming the source\ndistance of $d<10$ kpc.\n"}
+{"abstract": "  Localization approach to $\\mathcal N=2$ superconformal $SU(N) \\times SU(N)$\nquiver theory leads to a non-Gaussian two-matrix model representation for the\nexpectation value of BPS circular $SU(N)$ Wilson loop $\\langle\\mathcal\nW\\rangle$. We study the subleading $1/N^2$ term in the large $N$ expansion of\n$\\langle\\mathcal W\\rangle$ at weak and strong coupling. We concentrate on the\ncase of the symmetric quiver with equal gauge couplings which is equivalent to\nthe $\\mathbb Z_{2}$ orbifold of the $SU(2N)$ $\\mathcal N=4$ SYM theory. This\norbifold gauge theory should be dual to type IIB superstring in ${\\rm\nAdS}_5\\times (S^{5}/\\mathbb Z_{2})$. We present a string theory argument\nsuggesting that the $1/N^2$ term in $\\langle\\mathcal W\\rangle$ in the orbifold\ntheory should have the same strong-coupling asymptotics $ \\lambda^{3/2}$ as in\nthe $\\mathcal N=4$ SYM case. We support this prediction by a numerical study of\nthe localization matrix model on the gauge theory side. We also find a relation\nbetween the $1/N^2$ term in the Wilson loop expectation value and the\nderivative of the free energy of the orbifold gauge theory on 4-sphere.\n"}
+{"abstract": "  As communication systems that employ millimeter wave (mmWave) frequency bands\nmust use large antenna arrays to overcome the severe propagation loss of mmWave\nsignals, hybrid beamforming has been considered as an integral component of\nmmWave communications. Recently, intelligent reflecting surface (IRS) has been\nproposed as an innovative technology that can significantly improve the\nperformance of mmWave communication systems through the use of low-cost passive\nreflecting elements. In this paper, we study IRS-aided mmWave multiple-input\nmultiple-output (MIMO) systems with hybrid beamforming architectures. We first\nexploit the sparse-scattering structure and large dimension of mmWave channels\nto develop the joint design of IRS reflection matrix and hybrid beamformer for\nnarrowband MIMO systems. Then, we generalize the proposed joint design to\nbroadband MIMO systems with orthogonal frequency division multiplexing (OFDM)\nmodulation by leveraging the angular sparsity of frequency-selective mmWave\nchannels. Simulation results demonstrate that the proposed joint designs can\nsignificantly enhance the spectral efficiency of the systems of interest and\nachieve superior performance over the existing designs.\n"}
+{"abstract": "  We study a class of deterministic finite-horizon two-player nonzero-sum\ndifferential games where players are endowed with different kinds of controls.\nWe assume that Player 1 uses piecewise-continuous controls, while Player 2 uses\nimpulse controls. For this class of games, we seek to derive conditions for the\nexistence of feedback Nash equilibrium strategies for the players. More\nspecifically, we provide a verification theorem for identifying such\nequilibrium strategies, using the Hamilton-Jacobi-Bellman (HJB) equations for\nPlayer 1 and the quasi-variational inequalities (QVIs) for Player 2. Further,\nwe show that the equilibrium number of interventions by Player 2 is upper\nbounded. Furthermore, we specialize the obtained results to a scalar two-player\nlinear-quadratic differential game. In this game, Player 1's objective is to\ndrive the state variable towards a specific target value, and Player 2 has a\nsimilar objective with a different target value. We provide, for the first\ntime, an analytical characterization of the feedback Nash equilibrium in a\nlinear-quadratic differential game with impulse control. We illustrate our\nresults using numerical experiments.\n"}
+{"abstract": "  Silicon (Si) donors in GaAs have been the topic of extensive studies since Si\nis the most common and well understood n-type dopant in III-V semiconductor\ndevices and substrates. The indirect bandgap of AlAs compared to the direct one\nof GaAs leads to interesting effects when introducing Si dopants. Here we\npresent a study of cross-sectional scanning tunneling microscopy (X-STM) and\ndensity functional theory (DFT) calculations to study Si donors in AlAs at the\natomic scale. Based on their crystal symmetry and contrast strengths, we\nidentify Si donors up to four layers below the (110) surface of AlAs.\nInterestingly, their short-range local density of states (LDOS) is very similar\nto Si atoms in the (110) surface of GaAs. Additionally we show high-resolution\nimages of Si donors in all these layers. For empty state imaging, the\nexperimental and simulated STM images based on DFT show excellent agreement for\nSi donor up to two layers below the surface.\n"}
+{"abstract": "  We derive several existence results concerning cycle types and, more\ngenerally, the \"mapping behavior\" of complete mappings. Our focus is on\nso-called first-order cyclotomic mappings, which are functions on a finite\nfield $\\mathbb{F}_q$ that fix $0$ and restrict to the multiplication $x\\mapsto\na_ix$ by a fixed element $a_i\\in\\mathbb{F}_q$ on each coset $C_i$ of a given\nsubgroup $C$ of $\\mathbb{F}_q^{\\ast}$. The gist of two of our main results is\nthat as long as $q$ is large enough relative to the index\n$|\\mathbb{F}_q^{\\ast}:C|$, all cycle types of first-order cyclotomic\npermutations with only long cycles on $\\mathbb{F}_q^{\\ast}$ can be achieved\nthrough a complete mapping, as can all permutations of the cosets of $C$. Our\nthird main result provides new examples of complete mappings $f$ such that both\n$f$ and its associated orthomorphism $f+\\operatorname{id}$ permute the nonzero\nfield elements in one cycle.\n"}
+{"abstract": "  Motivated by recent transport experiments, we theoretically study the quantum\nHall effect in topological semimetal films. Owing to the confinement effect,\nthe bulk subbands originating from the chiral Landau levels establish energy\ngaps that have quantized Hall conductance and can be observed in relatively\nthick films. We find that the quantum Hall state is strongly anisotropic for\ndifferent confinement directions not only due to the presence of the surface\nstates but also because of the bulk chiral Landau levels. As a result, we\nre-examine the quantum Hall effect from the surface Fermi arcs and chiral modes\nin Weyl semimetals and give a more general view into this problem. Besides, we\nalso find that when a topological Dirac semimetal is confined in its rotational\nsymmetry axis, it hosts both quantum Hall and quantum spin Hall states, in\nwhich the helical edge states are protected by the conservation of the spin-$z$\ncomponent.\n"}
+{"abstract": "  We investigate the phenomenology of a scalar top-philic dark matter candidate\nwhen adding a dimension-five contact interaction term, as motivated by possible\nunderlying extensions of the Standard Model such as composite Higgs models. We\nshow that the presence of contact interactions can have a major impact on the\ndark matter relic density as well as on its direct and indirect detection\nprospects, while the collider phenomenology of the model is unaffected. This\nunderlines the complementarity of collider and cosmological constraints on dark\nmatter models.\n"}
+{"abstract": "  User interface design is a complex task that involves designers examining a\nwide range of options. We present Spacewalker, a tool that allows designers to\nrapidly search a large design space for an optimal web UI with integrated\nsupport. Designers first annotate each attribute they want to explore in a\ntypical HTML page, using a simple markup extension we designed. Spacewalker\nthen parses the annotated HTML specification, and intelligently generates and\ndistributes various configurations of the web UI to crowd workers for\nevaluation. We enhanced a genetic algorithm to accommodate crowd worker\nresponses from pairwise comparison of UI designs, which is crucial for\nobtaining reliable feedback. Based on our experiments, Spacewalker allows\ndesigners to effectively search a large design space of a UI, using the\nlanguage they are familiar with, and improve their design rapidly at a minimal\ncost.\n"}
+{"abstract": "  Ultra-diffuse galaxies (UDGs) are very low-surface brightness galaxies with\nlarge effective radii. Spectroscopic measurements of a few UDGs have revealed a\nlow dark matter content, based on the internal motion of stars or globular\nclusters (GCs). This is in contrast to the large number of GCs found for these\nsystems, from which it would be expected to correspond to a large dark matter\nhalo mass. Here we present HST+ACS observations for the UDG MATLAS-2019 in the\nNGC5846 group. Using the F606W and F814W filters, we trace the GC population\ntwo magnitudes below the peak of the GC luminosity function (GCLF). Employing\nBayesian considerations, we identify 26+-6 GCs associated with the dwarf,\nyielding a large specific frequency of S_N=58+-14. We use the turnover of the\nGCLF to derive a distance of 21+-2 Mpc, which is consistent with the NGC5846\ngroup of galaxies. Due to the superior image quality of the HST, we are able to\nresolve the GCs and measure their sizes, which are consistent with the sizes of\nGCs around Local Group galaxies. Using the linear relation between the total\nmass of galaxies and of GCs, we derive a halo mass of 0.9(+-0.2) *10^11 M_solar\n(M_solar/L_solar>1000). The high abundance of GCs, together with the small\nuncertainties, make MATLAS-2019 one of the most extreme UDGs, which likely sets\nan upper limit of the number of GCs for UDGs.\n"}
+{"abstract": "  Structured covariance matrix estimation in the presence of missing data is\naddressed in this paper with emphasis on radar signal processing applications.\nAfter a motivation of the study, the array model is specified and the problem\nof computing the maximum likelihood estimate of a structured covariance matrix\nis formulated. A general procedure to optimize the observed-data likelihood\nfunction is developed resorting to the expectation-maximization algorithm. The\ncorresponding convergence properties are thoroughly established and the rate of\nconvergence is analyzed. The estimation technique is contextualized for two\npractically relevant radar problems: beamforming and detection of the number of\nsources. In the former case an adaptive beamformer leveraging the EM-based\nestimator is presented; in the latter, detection techniques generalizing the\nclassic Akaike information criterion, minimum description length, and\nHannan-Quinn information criterion, are introduced. Numerical results are\nfinally presented to corroborate the theoretical study.\n"}
+{"abstract": "  When the characteristic of the underlying field is at least 5, we prove that\nthe adjustment matrix for blocks of $q$-Schur algebras of weights 3 and 4 is\nthe identity matrix. Moreover, we show that the decomposition numbers for\nweight 3 blocks of $q$-Schur algebras are bounded above by one.\n"}
+{"abstract": "  Fairness is an important trait of open, free markets. Ethereum is a platform\nmeant to enable digital, decentralized markets. Though many researchers debate\nthe market's fairness, there are few discussions around the fairness of\nautomated markets, such as those hosted on Ethereum. In this paper, using pilot\nstudies, we consider unfair factors caused by adding the program. Because\nCryptoKitties is one of the major blockchain-based games and has been in\noperation for an extended period of time, we focus on its market to examine\nfairness. As a result, we concluded that a gene determination algorithm in this\ngame has little randomness, and a significant advantage to gain profit is given\nto players who know its bias over those who do not. We state incompleteness and\nimpact of the algorithm and other factors. Besides, we suppose countermeasures\nto reduce CryptoKitties' unfairness as a market.\n"}
+{"abstract": "  We give a quasipolynomial-time algorithm for learning stochastic decision\ntrees that is optimally resilient to adversarial noise. Given an\n$\\eta$-corrupted set of uniform random samples labeled by a size-$s$ stochastic\ndecision tree, our algorithm runs in time\n$n^{O(\\log(s/\\varepsilon)/\\varepsilon^2)}$ and returns a hypothesis with error\nwithin an additive $2\\eta + \\varepsilon$ of the Bayes optimal. An additive\n$2\\eta$ is the information-theoretic minimum.\n  Previously no non-trivial algorithm with a guarantee of $O(\\eta) +\n\\varepsilon$ was known, even for weaker noise models. Our algorithm is\nfurthermore proper, returning a hypothesis that is itself a decision tree;\npreviously no such algorithm was known even in the noiseless setting.\n"}
+{"abstract": "  Latent variable models have been successfully applied in lossless compression\nwith the bits-back coding algorithm. However, bits-back suffers from an\nincrease in the bitrate equal to the KL divergence between the approximate\nposterior and the true posterior. In this paper, we show how to remove this gap\nasymptotically by deriving bits-back coding algorithms from tighter variational\nbounds. The key idea is to exploit extended space representations of Monte\nCarlo estimators of the marginal likelihood. Naively applied, our schemes would\nrequire more initial bits than the standard bits-back coder, but we show how to\ndrastically reduce this additional cost with couplings in the latent space.\nWhen parallel architectures can be exploited, our coders can achieve better\nrates than bits-back with little additional cost. We demonstrate improved\nlossless compression rates in a variety of settings, especially in\nout-of-distribution or sequential data compression.\n"}
+{"abstract": "  This paper develops a model based reinforcement learning (MBRL) framework for\nlearning online the value function of an infinite-horizon optimal control\nproblem while obeying safety constraints expressed as control barrier functions\n(CBFs). Our approach is facilitated by the development of a novel class of\nCBFs, termed Lyapunov-like CBFs (LCBFs), that retain the beneficial properties\nof CBFs for developing minimally-invasive safe control policies while also\npossessing desirable Lyapunov-like qualities such as positive\nsemi-definiteness. We show how these LCBFs can be used to augment a\nlearning-based control policy to guarantee safety and then leverage this\napproach to develop a safe exploration framework in a MBRL setting. We\ndemonstrate that our developed approach can handle more general safety\nconstraints than comparative methods through a variety of numerical examples.\n"}
+{"abstract": "  Significant advances in video compression system have been made in the past\nseveral decades to satisfy the nearly exponential growth of Internet-scale\nvideo traffic. From the application perspective, we have identified three major\nfunctional blocks including pre-processing, coding, and post-processing, that\nhave been continuously investigated to maximize the end-user quality of\nexperience (QoE) under a limited bit rate budget. Recently, artificial\nintelligence (AI) powered techniques have shown great potential to further\nincrease the efficiency of the aforementioned functional blocks, both\nindividually and jointly. In this article, we review extensively recent\ntechnical advances in video compression system, with an emphasis on deep neural\nnetwork (DNN)-based approaches; and then present three comprehensive case\nstudies. On pre-processing, we show a switchable texture-based video coding\nexample that leverages DNN-based scene understanding to extract semantic areas\nfor the improvement of subsequent video coder. On coding, we present an\nend-to-end neural video coding framework that takes advantage of the stacked\nDNNs to efficiently and compactly code input raw videos via fully data-driven\nlearning. On post-processing, we demonstrate two neural adaptive filters to\nrespectively facilitate the in-loop and post filtering for the enhancement of\ncompressed frames. Finally, a companion website hosting the contents developed\nin this work can be accessed publicly at\nhttps://purdueviper.github.io/dnn-coding/.\n"}
+{"abstract": "  We study the allocation of indivisible goods among groups of agents using\nwell-known fairness notions such as envy-freeness and proportionality. While\nthese notions cannot always be satisfied, we provide several bounds on the\noptimal relaxations that can be guaranteed. For instance, our bounds imply that\nwhen the number of groups is constant and the $n$ agents are divided into\ngroups arbitrarily, there exists an allocation that is envy-free up to\n$\\Theta(\\sqrt{n})$ goods, and this bound is tight. Moreover, we show that while\nsuch an allocation can be found efficiently, it is NP-hard to compute an\nallocation that is envy-free up to $o(\\sqrt{n})$ goods even when a fully\nenvy-free allocation exists. Our proofs make extensive use of tools from\ndiscrepancy theory.\n"}
+{"abstract": "  Two-dimensional (2D) layered magnetic materials are generating a great amount\nof interest for the next generation of electronic devices thanks to their\nremarkable properties associated to spin dynamics. The recently discovered\nlayered VI$_3$ ferromagnetic phase belongs to this family, although a full\nunderstanding of its properties is limited by an ill-defined crystallographic\nstructure. This is not any longer true. Here, we investigate the VI$_3$ crystal\nstructure upon cooling using both synchrotron X-ray and neutron powder\ndiffraction and provide structural models for the two structural transitions\noccurring at 76 K and 32 K. Moreover, we confirm by magnetic measurements that\nVI$_3$ becomes ferromagnetic at 50 K and discuss the difficulty of solving its\nfull magnetic structure by neutrons. We equally determined the magnetic\nproperties of our recently reported LiVI$_3$ phase, which is alike the\nwell-known CrI$_3$ ferromagnetic phase in terms of electronic and\ncrystallographic structures and found to our surprise an antiferromagnetic\nbehavior with a N\\'eel temperature of 12 K. Such a finding provides extra clues\nfor a better understanding of magnetism in these low dimension compounds.\nFinally, the easiness of preparing novel Li-based 2D magnetic materials by\nchemical/electrochemical means opens wide the opportunity to design materials\nwith exotic properties.\n"}
+{"abstract": "  We prove new slope inequalities for relatively minimal fibred surfaces,\nshowing an influence of the relative irregularity, of the unitary rank and of\nthe Clifford index on the slope. The argument uses Xiao's method and a new\nClifford-type inequality for subcanonical systems on non-hyperelliptic curves.\n"}
+{"abstract": "  SU UMa stars are characterized by \"superoutbursts\" which are brighter at\nmaximum light and which last much longer than the more frequent \"ordinary\"\noutbursts of these dwarf novae. Although there are now more than 1180 SU UMa\ntype dwarf novae catalogued, our knowledge on their superoutburst cycle length\nCso was hitherto limited to about 6$\\%$ of the entire sample of known SU UMa\nstars. Using public data bases we have determined new Cso values for a total of\n206 additional SU UMa stars in the range 17 d $<$ Cso $<$ 4590 d (including\nsome ER UMa and WZ Sge type representants) within total time intervals between\n2 and 57 years, and with an estimated uncertainty of $\\pm$11$\\%$. This way, we\nare increasing our present knowledge of Cso values by a factor $\\sim$3.8. Its\ndistribution is characterized by a broad maximum around Cso $\\approx$ 270 days,\nand slowly decreasing numbers till Cso $\\approx$ 800 d. The domain Cso $>$ 450\nd was unexplored until now; we add here 106 cases ($\\sim$51$\\%$ of our total\nsample) in this range of long cycles, implying a better statistical basis for\nfuture studies of their distribution. Our sample contains 16 known WZ Sge\nstars, and we propose WZ Sge membership for 5 others hitherto classified as\nordinary SU UMa stars. Individual superoutburst timings deviate in average\nabout $\\pm$7$\\%$ of the cycle length from their overall linear ephemeris,\nconrming a pronounced quasi-periodic repeatability of superoutbursts. All\nrelevant parameters are listed with their errors, and a table with individual\nsuperoutburst epochs of our targets is given, enabling future researchers to\ncombine our results with other (past or future) observations.\n"}
+{"abstract": "  Upcoming measurements of the 21-cm line of neutral hydrogen will open a new\nobservational window into the early stages of structure growth, providing a\nunique opportunity for probing large-scale cosmological signatures using the\nsmall-scale signals from the first stars. In this paper we evaluate the\ndetection significance of compensated isocurvature perturbations (CIPs) from\nobservations of the 21-cm hydrogen-line during the cosmic-dawn era. CIPs are\nmodulations of the relative baryon and dark-matter density that leave the total\nmatter density unchanged. We find that, under different assumptions for\nfeedback and foregrounds, the ongoing HERA and upcoming SKA1-low experiments\nwill provide constraints on uncorrelated CIPs at the level of $\\sigma(A_{\\rm\nCIP})= 10^{-3}-10^{-4}$, comparable to the sensitivity of upcoming CMB\nexperiments, and potentially exceeding the constraints from cosmic-variance\nlimited BAO surveys.\n"}
+{"abstract": "  We analyze the computational power of discrete-time recurrent neural networks\n(NNs) with the saturated-linear activation function within the Chomsky\nhierarchy. This model restricted to integer weights coincides with binary-state\nNNs with the Heaviside activation function, which are equivalent to finite\nautomata (Chomsky level 3) recognizing regular languages (REG), while rational\nweights make this model Turing-complete even for three analog-state units\n(Chomsky level 0). For the intermediate model $\\alpha$ANN of a binary-state NN\nthat is extended with $\\alpha\\geq 0$ extra analog-state neurons with rational\nweights, we have established the analog neuron hierarchy 0ANNs $\\subset$ 1ANNs\n$\\subset$ 2ANNs $\\subseteq$ 3ANNs. The separation 1ANNs $\\subsetneqq$ 2ANNs has\nbeen witnessed by the non-regular deterministic context-free language (DCFL)\n$L_\\#=\\{0^n1^n\\mid n\\geq 1\\}$ which cannot be recognized by any 1ANN even with\nreal weights, while any DCFL (Chomsky level 2) is accepted by a 2ANN with\nrational weights. In this paper, we strengthen this separation by showing that\nany non-regular DCFL cannot be recognized by 1ANNs with real weights, which\nmeans (DCFLs $\\setminus$ REG) $\\subset$ (2ANNs $\\setminus$ 1ANNs), implying\n1ANNs $\\cap$ DCFLs = 0ANNs. For this purpose, we have shown that $L_\\#$ is the\nsimplest non-regular DCFL by reducing $L_\\#$ to any language in this class,\nwhich is by itself an interesting achievement in computability theory.\n"}
+{"abstract": "  Low-cardinality activations permit an algorithm based on fetching the\ninference values from pre-calculated lookup tables instead of calculating them\nevery time. This algorithm can have extensions, some of which offer abilities\nbeyond those of the currently used algorithms. It also allows for a simpler and\nmore effective CNN-specialized hardware.\n"}
+{"abstract": "  Before the launch of the Kepler Space Telescope, most studies of the rapidly\noscillating Ap (roAp) stars were conducted with ground-based photometric $B$\nobservations, supplemented with high-resolution time-resolved spectroscopy and\nsome space observations with the WIRE, MOST and BRITE satellites. These modes\nof observation often only provided information on a single star at a time,\nhowever, Kepler provided the opportunity to observe hundreds of thousands of\nstars simultaneously. Over the duration of the primary 4-yr Kepler mission, and\nits 4-yr reconfigured K2 mission, the telescope observed at least 14 new and\nknown roAp stars. This paper provides a summary the results of these\nobservations, including a first look at the entire data sets, and provides a\nlook forward to NASA's TESS mission.\n"}
+{"abstract": "  open Master Hearing Aid (openMHA) was developed and provided to the hearing\naid research community as an open-source software platform with the aim to\nsupport sustainable and reproducible research towards improvement and new types\nof assistive hearing systems not limited by proprietary software. The software\noffers a flexible framework that allows the users to conduct hearing aid\nresearch using tools and a number of signal processing plugins provided with\nthe software as well as the implementation of own methods. The openMHA software\nis independent of a specific hardware and supports Linux, MacOS and Windows\noperating systems as well as 32-bit and 64-bit ARM-based architectures such as\nused in small portable integrated systems. www.openmha.org\n"}
+{"abstract": "  A method for recognizing the types of linear acoustic gravity waves (AGWs) in\nthe atmosphere from satellite measurements is proposed. It is shown that the\npolarization relations between fluctuations of wave parameters (velocity,\ndensity, temperature, and pressure) for freely propagating waves, as well as\nevanescent wave modes, differ significantly, which makes it possible to\nidentify different types of atmospheric waves in experimental data. A\ndiagnostic diagram is proposed, with the help of which, from the phase shifts\nof the observed parameters, it is possible to determine the type of wave, as\nwell as the direction of its movement relative to the vertical. Using phase\nshifts between fluctuations of the velocity and thermodynamic parameters of the\natmosphere, not only the type of the wave, but also its spectral\ncharacteristics can be determined. The verification of the proposed method for\nidentification of polar wave disturbances in measurements from the low-orbit\nsatellite Dynamics Explorer 2 was carried out. The verification showed that the\npolarization relations of AGW in the thermosphere mainly correspond to the\ngravity branch of acoustic-gravity waves freely propagating in the direction\nfrom bottom to top. This conclusion is consistent with other results of AGW\nobservations in the atmosphere and ionosphere by ground-based and satellite\nmethods. No evanescent waves were observed on the considered orbits.\n"}
+{"abstract": "  Circumcenters play an important role in the design and analysis of\naccelerating various iterative methods in optimization. In this work, we\npropose Bregman (pseudo-)circumcenters associated with finite sets. We show the\nexistence and give explicit formulae for the unique backward and forward\nBregman pseudo-circumcenters of finite sets. Moreover, we use duality to\nestablish connections between backward and forward Bregman\n(pseudo-)circumcenters. Various examples are presented to illustrate the\nbackward and forward Bregman (pseudo-)circumcenters of finite sets. Our general\nframework for circumcenters paves the way for the development of accelerating\niterative methods by Bregman circumcenters.\n"}
+{"abstract": "  We present a slightly modified version of the well known \"geometric Lorenz\nattractor\". It consists in a C1 open set O of vector fields in R3 having an\nattracting region U containing: (1) a unique singular saddle point sigma; (2) a\nunique attractor Lambda containing the singular point; (3) the maximal\ninvariant in U contains at most 2 chain recurrence classes, which are Lambda\nand (at most) one hyperbolic horseshoe. The horseshoe and the singular\nattractor have a collision along the union of 2 co-dimension 1 sub-manifolds\nwhich divide O in 3 regions. By crossing this collision locus, the attractor\nand the horseshoe may merge in a two-sided Lorenz attractor, or they may\nexchange their nature: the Lorenz attractor expel the singular point sigma and\nbecomes a horseshoe and the horseshoe absorbs sigma becoming a Lorenz\nattractor.\n  By crossing this collision locus, the attractor and the horseshoe may merge\nin a two-sided Lorenz attractor, or they may exchange their nature: the Lorenz\nattractor expel the singular point sigma and becomes a horseshoe and the\nhorseshoe absorbs sigma becoming a Lorenz attractor.\n"}
+{"abstract": "  Monte Carlo Tree Search (MCTS) is a powerful approach to designing\ngame-playing bots or solving sequential decision problems. The method relies on\nintelligent tree search that balances exploration and exploitation. MCTS\nperforms random sampling in the form of simulations and stores statistics of\nactions to make more educated choices in each subsequent iteration. The method\nhas become a state-of-the-art technique for combinatorial games, however, in\nmore complex games (e.g. those with high branching factor or real-time ones),\nas well as in various practical domains (e.g. transportation, scheduling or\nsecurity) an efficient MCTS application often requires its problem-dependent\nmodification or integration with other techniques. Such domain-specific\nmodifications and hybrid approaches are the main focus of this survey. The last\nmajor MCTS survey has been published in 2012. Contributions that appeared since\nits release are of particular interest for this review.\n"}
+{"abstract": "  This paper introduces an algorithm to perform optimal reorientation of a\nspacecraft during a high speed flyby mission that maximizes the time a certain\ntarget is kept within the field of view of scientific instruments. The method\ndirectly handles the nonlinear dynamics of the spacecraft, sun exclusion\nconstraint, torque and momentum limits on the reaction wheels as well as\npotential faults in these actuators. A sequential convex programming approach\nwas used to reformulate non-convex pointing objectives and other constraints in\nterms of a series of novel convex cardinality minimization problems. These\nsubproblems were then efficiently solved even on limited hardware resources\nusing convex programming solvers implementing second-order conic constraints.\nThe proposed method was applied to a scenario that involved maximizing the\nscience time for the upcoming Comet Interceptor flyby mission developed by the\nEuropean Space Agency. Extensive simulation results demonstrate the capability\nof the approach to generate viable trajectories even in the presence of\nreaction wheel failures or prior dust particle impacts.\n"}
+{"abstract": "  We construct non-unique Leray-Hopf solutions for some dyadic models for\nmagnetohydrodynamics when the intermittency dimension $\\delta$ is less than 1.\nIn contrast, uniqueness of Leray-Hopf solution is established in the case of\n$\\delta\\geq 1$. Analogous results on uniqueness and non-uniqueness of\nLeray-Hopf solution are also obtained for dyadic models of MHD with fractional\ndiffusion.\n"}
+{"abstract": "  In the past years there has been significant progress in Intelligent\nTransport Systems in the domains of traffic management, driver assistance and\ndriver information. Within the German research project COmmunication Network\nVEhicle Road Global Extension (CONVERGE), a system architecture for the\nflexible interaction between different service providers and communication\nnetwork operators will be designed and implemented, called Car2X Systems\nNetwork architecture. The Car2X Systems Network will establish a completely new\nopen communication-, services-, and organization architecture that reflects\nstate of the art communication technologies and IT security. Through\nwell-defined access methods, service providers like traffic control centers or\nvehicle manufacturers can be integrated into the open and secure system\nnetwork. The ultimate goal is the decentralized and dynamic coupling of all\nsystems and actors across national and organizational borders. This paper is\nfocusing on the requirements and the concept of a service-oriented architecture\nfor publishing and finding services inside the system with intelligent\ninformation distribution based on geographical and organizational conditions.\n"}
+{"abstract": "  We present a general method of constructing \\textit{in-situ} pseodopotentials\nfrom first principles, all-electron, full-potential electronic structure\ncalculations of a solid. The method is applied to bcc Na, at equilibrium\nvolume. The essential steps of the method involve (i) calculating an\nall-electron Kohn-Sham eigenstate. (ii) Replacing the oscillating part of the\nwavefunction (inside the muffin-tin spheres) of this state, with a smooth\nfunction. (iii) Representing the smooth wavefunction in a Fourier series, and\n(iv) inverting the Kohn-Sham equation, to extract the pseudopotential that\nproduces the state generated in steps (i)-(iii). It is shown that an\n\\textit{in-situ} pseudopotential can reproduce an all-electron, full-potential\neigenvalue up to the sixth significant digit. A comparison of the all-electron\ntheory, \\textit{in-situ} pseudopotential theory and the standard nonlocal\npseudopotential theory demonstrates good agreement, e.g., in the energy\ndispersion of the 3$s$ band state of bcc Na.\n"}
+{"abstract": "  Elaborating on works by Abouzaid and Mescher, we prove that for a Morse\nfunction on a smooth compact manifold, its Morse cochain complex can be endowed\nwith an $\\Omega B As$-algebra structure by counting moduli spaces of perturbed\nMorse gradient trees. This rich structure descends to its already known\n$A_\\infty$-algebra structure. We then introduce the notion of $\\Omega B\nAs$-morphism between two $\\Omega B As$-algebras and prove that given two Morse\nfunctions, one can construct an $\\Omega B As$-morphism between their associated\n$\\Omega B As$-algebras by counting moduli spaces of two-colored perturbed Morse\ngradient trees. This morphism induces a standard $A_\\infty$-morphism between\nthe induced $A_\\infty$-algebras. We work with integer coefficients, and provide\nto this extent a detailed account on the sign conventions for $A_\\infty$ (resp.\n$\\Omega B As$)-algebras and $A_\\infty$ (resp. $\\Omega B As$)-morphisms, using\npolytopes (resp. moduli spaces) which explicitly realize the dg-operadic\nobjects encoding them. Our proofs also involve showing at the level of\npolytopes that an $\\Omega B As$-morphism between $\\Omega B As$-algebras\nnaturally induces an $A_\\infty$-morphism between $A_\\infty$-algebras. This\npaper comes in particular with a short survey on operads, $A_\\infty$-algebras\nand $A_\\infty$-morphisms, the associahedra and the multiplihedra. All the\ndetails on transversality, gluing maps, signs and orientations for the moduli\nspaces defining the algebraic structures on the Morse cochains are thorougly\ncarried out. It moreover lays the basis for a second article in which we solve\nthe problem of finding a satisfactory homotopic notion of higher morphisms\nbetween $A_\\infty$-algebras and between $\\Omega B As$-algebras, and show how\nthis higher algebra of $A_\\infty$ and $\\Omega B As$-algebras naturally arises\nin the context of Morse theory.\n"}
+{"abstract": "  Quantum gravity is a challenge in physics, and the existence of graviton is\nthe prime question at present. We study the detectability of the quantum noise\ninduced by gravitons in this letter. The correlation of the quantum noise in\nthe squeezed state is calculated, but we are surprised to find that the result\nis the same as those from Grishchuk et.al. and Parikh et.al. This implies that\nthey are essentially about one thing: the quantum noise from the gravitons. The\nfurther discussion shows the quantum noise should be completed from the leading\norder of the interaction between gravitation and the detector. The squeezed\nfactor is estimated, and with it, the spectrum of the quantum noise is found to\nbe of the form $\\Omega_0 \\sim \\nu^{4+a_0 \\beta+b_0}$. After comparing it to the\nsensitivities of several gravitational wave detectors, we conclude that the\nquantum noise is detectable in the future.\n"}
+{"abstract": "  We study the change of quantization for a class of global pseudodifferential\noperators of infinite order in the setting of ultradifferentiable functions of\nBeurling type. The composition of different quantizations as well as the\ntranspose of a quantization are also analysed, with applications to the Weyl\ncalculus. We also compare global $\\omega$-hypoellipticity and global\n$\\omega$-regularity of these classes of pseudodifferential operators.\n"}
+{"abstract": "  We calculate the leading corrections to jet momentum broadening and\nmedium-induced branching that arise from the velocity of the moving medium at\nfirst order in opacity. These results advance our knowledge of jet quenching\nand demonstrate how it couples to collective flow of the quark-gluon plasma in\nheavy-ion collisions and to the orbital motion of partons in cold nuclear\nmatter in deep inelastic scattering at the electron-ion collider. We also\ncompute the leading corrections to jet momentum broadening due to transverse\ngradients of temperature and density. We find that these effects lead to both\nanisotropic transverse momentum diffusion proportional to the medium velocity\nand anisotropic medium-induced radiation emitted preferentially in the\ndirection of the flow. We isolate the relevant sub-eikonal corrections by\nworking with jets composed of scalar particles with arbitrary color factors\ninteracting with the medium by scalar QCD. Appropriate substitution of the\ncolor factors and light-front wave functions allow us to immediately apply the\nresults to a range of processes including $q \\rightarrow q g$ branching in real\nQCD. The resulting general expressions can be directly coupled to hydrodynamic\nsimulations on an event-by-event basis to study the correlations between jet\nquenching and the dynamics of various forms of nuclear matter.\n"}
+{"abstract": "  HERMES (High Energy Rapid Modular Ensemble of Satellites) Technological and\nScientific pathfinder is a space borne mission based on a LEO constellation of\nnano-satellites. The 3U CubeSat buses host new miniaturized detectors to probe\nthe temporal emission of bright high-energy transients such as Gamma-Ray Bursts\n(GRBs). Fast transient localization, in a field of view of several steradians\nand with arcmin-level accuracy, is gained by comparing time delays among the\nsame event detection epochs occurred on at least 3 nano-satellites. With a\nlaunch date in 2022, HERMES transient monitoring represents a keystone\ncapability to complement the next generation of gravitational wave experiments.\nIn this paper we will illustrate the HERMES payload design, highlighting the\ntechnical solutions adopted to allow a wide-energy-band and sensitive X-ray and\ngamma-ray detector to be accommodated in a CubeSat 1U volume together with its\ncomplete control electronics and data handling system.\n"}
+{"abstract": "  We consider a time-like geodesics in the background of rotating\nEinstein-Born-Infeld (EBI) black hole to examine the horizon and ergosphere\nstructure. The effective potential that governs the particle's motion in the\nspacetime and the innermost stable circular orbits (ISCO) is also studied. A\nqualitative analysis is conducted to find the redshifted ultrahigh\ncentre-of-mass (CM) energy as a result of a two-particle collision specifically\nnear the horizon. The recent Event Horizon Telescope (EHT) triggered a surge of\ninterest in strong gravitational lensing by black holes, which provide a new\ntool comparing the black hole lensing in general relativity and alternate\ngravity theories. Motivated by this, we also discussed both strong and\nweak-field gravitational lensing in the space-time discretely for a uniform\nplasma and a singular isothermal sphere. We calculated the light deflection\ncoefficients $\\bar{a}$ and $\\bar{b}$ in the strong field limits, and their\nvariance with the rotational parameter $a$ for different plasma frequency as\nwell as in vacuum. For EBI black holes, we found that plasma's presence\nincreases the photon sphere radius, the deflection angle, the deflection\ncoefficients $\\bar{a}$, $\\bar{b}$, the angular positions and the angular\nseparation between the relativistic images. It is also shown that with\nincreasing spin the impact of plasma on a strong gravitational lensing becomes\nsmaller as the spin parameter grows in the prograde orbit ($a>0$). For extreme\nblack holes, the strong gravitational effects in the homogenous plasma are\nsimilar to those of in a vacuum. We investigate strong gravitational lensing\neffects by supermassive black holes Sgr A* and M87*...\n"}
+{"abstract": "  Defeasible reasoning is the mode of reasoning where conclusions can be\noverturned by taking into account new evidence. A commonly used method in\ncognitive science and logic literature is to handcraft argumentation supporting\ninference graphs. While humans find inference graphs very useful for reasoning,\nconstructing them at scale is difficult. In this paper, we automatically\ngenerate such inference graphs through transfer learning from another NLP task\nthat shares the kind of reasoning that inference graphs support. Through\nautomated metrics and human evaluation, we find that our method generates\nmeaningful graphs for the defeasible inference task. Human accuracy on this\ntask improves by 20% by consulting the generated graphs. Our findings open up\nexciting new research avenues for cases where machine reasoning can help human\nreasoning. (A dataset of 230,000 influence graphs for each defeasible query is\nlocated at: https://tinyurl.com/defeasiblegraphs.)\n"}
+{"abstract": "  An enumeration kernel as defined by Creignou et al. [Theory Comput. Syst.\n2017] for a parameterized enumeration problem consists of an algorithm that\ntransforms each instance into one whose size is bounded by the parameter plus a\nsolution-lifting algorithm that efficiently enumerates all solutions from the\nset of the solutions of the kernel. We propose to consider two new versions of\nenumeration kernels by asking that the solutions of the original instance can\nbe enumerated in polynomial time or with polynomial delay from the kernel\nsolutions. Using the NP-hard Matching Cut problem parameterized by structural\nparameters such as the vertex cover number or the cyclomatic number of the\ninput graph, we show that the new enumeration kernels present a useful notion\nof data reduction for enumeration problems which allows to compactly represent\nthe set of feasible solutions.\n"}
+{"abstract": "  We establish generalizations of the Feldman-Moore theorem, the Glimm-Effros\ndichotomy, and the Lusin-Novikov uniformization theorem from Polish spaces to\ntheir quotients by Borel orbit equivalence relations.\n"}
+{"abstract": "  We analyse the performance of several iterative algorithms for the\nquantisation of a probability measure $\\mu$, based on the minimisation of a\nMaximum Mean Discrepancy (MMD). Our analysis includes kernel herding, greedy\nMMD minimisation and Sequential Bayesian Quadrature (SBQ). We show that the\nfinite-sample-size approximation error, measured by the MMD, decreases as $1/n$\nfor SBQ and also for kernel herding and greedy MMD minimisation when using a\nsuitable step-size sequence. The upper bound on the approximation error is\nslightly better for SBQ, but the other methods are significantly faster, with a\ncomputational cost that increases only linearly with the number of points\nselected. This is illustrated by two numerical examples, with the target\nmeasure $\\mu$ being uniform (a space-filling design application) and with $\\mu$\na Gaussian mixture.\n"}
+{"abstract": "  We curated WikiPII, an automatically labeled dataset composed of Wikipedia\nbiography pages, annotated for personal information extraction. Although\nautomatic annotation can lead to a high degree of label noise, it is an\ninexpensive process and can generate large volumes of annotated documents. We\ntrained a BERT-based NER model with WikiPII and showed that with an adequately\nlarge training dataset, the model can significantly decrease the cost of manual\ninformation extraction, despite the high level of label noise. In a similar\napproach, organizations can leverage text mining techniques to create\ncustomized annotated datasets from their historical data without sharing the\nraw data for human annotation. Also, we explore collaborative training of NER\nmodels through federated learning when the annotation is noisy. Our results\nsuggest that depending on the level of trust to the ML operator and the volume\nof the available data, distributed training can be an effective way of training\na personal information identifier in a privacy-preserved manner. Research\nmaterial is available at https://github.com/ratmcu/wikipiifed.\n"}
+{"abstract": "  Transport calculations for neutronic design require accurate nuclear data and\nvalidated computational tools. In the Spallation Physics Group, at the European\nSpallation Source, we perform shielding and neutron beam calculations to help\nthe deployment of the instrument suite for the current high brilliance (top)\nmoderator, as well for the design of the high intensity bottom moderator,\ncurrently under study for the facility. This work includes providing the best\navailable nuclear data in addition to improving models and tools when\nnecessary. In this paper we present the status of these activities, which\ninclude a set of thermal scattering kernels for moderator, reflector, and\nstructural materials, the development of new kernels for beryllium considering\ncrystallite size effects, nanodiamonds, liquid hydrogen and deuterium based on\npath integral molecular dynamics, and the use of the software package NCrystal\nto assist the development of nuclear data in the framework of the new HighNESS\nproject.\n"}
+{"abstract": "  Several Open Source Software (OSS) projects depend on the continuity of their\ndevelopment communities to remain sustainable. Understanding how developers\nbecome inactive or why they take breaks can help communities prevent\nabandonment and incentivize developers to come back. In this paper, we propose\na novel method to identify developers' inactive periods by analyzing the\nindividual rhythm of contributions to the projects. Using this method, we\nquantitatively analyze the inactivity of core developers in 18 OSS\norganizations hosted on GitHub. We also survey core developers to receive their\nfeedback about the identified breaks and transitions. Our results show that our\nmethod was effective for identifying developers' breaks. About 94% of the\nsurveyed core developers agreed with our state model of inactivity; 71% and 79%\nof them acknowledged their breaks and state transition, respectively. We also\nshow that all core developers take breaks (at least once) and about a half of\nthem (~45%}) have completely disengaged from a project for at least one year.\nWe also analyzed the probability of transitions to/from inactivity and found\nthat developers who pause their activity have a ~35-55\\% chance to return to an\nactive state; yet, if the break lasts for a year or longer, then the\nprobability of resuming activities drops to ~21-26%, with a ~54% chance of\ncomplete disengagement. These results may support the creation of policies and\nmechanisms to make OSS community managers aware of breaks and potential project\nabandonment.\n"}
+{"abstract": "  In this paper, we investigate the use of a mass lumped fully explicit time\nstepping scheme for the discretisation of the wave equation with underlying\nmaterial parameters that vary at arbitrarily fine scales. We combine the\nleapfrog scheme for the temporal discretisation with the multiscale technique\nknown as Localized Orthogonal Decomposition for the spatial discretisation. To\nspeed up the method and to make it fully explicit, a special mass lumping\napproach is introduced that relies on an appropriate interpolation operator.\nThis operator is also employed in the construction of the Localized Orthogonal\nDecomposition and is a key feature of the approach. We prove that the method\nconverges with second order in the energy norm, with a leading constant that\ndoes not depend on the scales at which the material parameters vary. We also\nillustrate the performance of the mass lumped method in a set of numerical\nexperiments.\n"}
+{"abstract": "  The transformation between the metallic ($\\beta$) and semi-conducting\n($\\alpha$) allotropes of tin is still not well understood. The phase transition\ntemperature stated in the literature, 286.2 K, seems to be inconsistent with\nrecent calorimetric measurements. In this paper, this intriguing aspect has\nbeen explored in Sn and Sn-Cu (alloyed 0.5% Cu by weight) using temperature\nresolved synchrotron x-ray diffraction measurements performed at the Indus-2\nfacility. Additionally, the $\\alpha \\rightleftharpoons \\beta$ Sn transition has\nbeen recorded using in-situ heating/cooling experiments in a scanning electron\nmicroscope. Based on these measurements, a protocol has been suggested to\nreduce the formation of $\\alpha$-Sn in potentially susceptible systems. This\nwill be useful in experiments like TIN.TIN (The INdia-based TIN detector),\nwhich proposes to employ ~100 - 1000 kg of superconducting tin-based detectors\nto search for neutrinoless double beta decay in the isotope $^{124}$Sn.\n"}
+{"abstract": "  In this paper, we present a non-parametric structured latent variable model\nfor image generation, called NP-DRAW, which sequentially draws on a latent\ncanvas in a part-by-part fashion and then decodes the image from the canvas.\nOur key contributions are as follows. 1) We propose a non-parametric prior\ndistribution over the appearance of image parts so that the latent variable\n``what-to-draw'' per step becomes a categorical random variable. This improves\nthe expressiveness and greatly eases the learning compared to Gaussians used in\nthe literature. 2) We model the sequential dependency structure of parts via a\nTransformer, which is more powerful and easier to train compared to RNNs used\nin the literature. 3) We propose an effective heuristic parsing algorithm to\npre-train the prior. Experiments on MNIST, Omniglot, CIFAR-10, and CelebA show\nthat our method significantly outperforms previous structured image models like\nDRAW and AIR and is competitive to other generic generative models. Moreover,\nwe show that our model's inherent compositionality and interpretability bring\nsignificant benefits in the low-data learning regime and latent space editing.\nCode is available at https://github.com/ZENGXH/NPDRAW.\n"}
+{"abstract": "  We study continuous finite element dicretizations for one dimensional\nhyperbolic partial differential equations. The main contribution of the paper\nis to provide a fully discrete spectral analysis, which is used to suggest\noptimal values of the CFL number and of the stabilization parameters involved\nin different types of stabilization operators. In particular, we analyze the\nstreamline-upwind Petrov-Galerkin (SUPG) stabilization technique, the\ncontinuous interior penalty (CIP) stabilization method and the local projection\nstabilization (LPS). Three different choices for the continuous finite element\nspace are compared: Bernstein polynomials, Lagrangian polynomials on equispaced\nnodes, and Lagrangian polynomials on Gauss-Lobatto cubature nodes. For the last\nchoice, we only consider inexact quadrature based on the formulas corresponding\nto the degrees of freedom of the element, which allows to obtain a fully\ndiagonal mass matrix. We also compare different time stepping strategies,\nnamely Runge-Kutta (RK), strong stability preserving RK (SSPRK) and deferred\ncorrection time integration methods. The latter allows to alleviate the\ncomputational cost as the mass matrix inversion is replaced by the high order\ncorrection iterations. To understand the effects of these choices, both\ntime-continuous and fully discrete Fourier analysis are performed. These allow\nto compare all the different combinations in terms of accuracy and stability,\nas well as to provide suggestions for optimal values discretization parameters\ninvolved. The results are thoroughly verified numerically both on linear and\nnon-linear problems, and error-CPU time curves are provided. Our final\nconclusions suggest that cubature elements combined with SSPRK and CIP or LPS\nstabilization are the most promising combinations.\n"}
+{"abstract": "  We consider the problem of computing the integral $$\n  \\int_{\\mathcal{U}(d)} u_{i_1j_1}\\cdots u_{i_nj_n} \\bar{u}_{i'_1j'_1} \\cdots\n\\bar{u}_{i'_{n'}j'_{n'}} dU, $$ where the integration takes place with respect\nto the probability Haar measure on the unitary group $\\mathcal{U}(d)$, and the\n$u_{ij}$ denotes the $ij$-th entry of a unitary matrix $U$. We present a\nunified approach connecting classical results, the explicit formula for the\nintegral given by B. Collins and P. Sniady and subsequent works of various\nauthors providing different points of view. Finally we are able to provide an\nexplicit formula for the $2n$-th moment of the trace of a unitary Haar random\nmatrix, generalizing a result of P. Diaconis.\n"}
+{"abstract": "  Advances in deep neural networks (DNNs) have shown tremendous promise in the\nmedical domain. However, the deep learning tools that are helping the domain,\ncan also be used against it. Given the prevalence of fraud in the healthcare\ndomain, it is important to consider the adversarial use of DNNs in manipulating\nsensitive data that is crucial to patient healthcare. In this work, we present\nthe design and implementation of a DNN-based image translation attack on\nbiomedical imagery. More specifically, we propose Jekyll, a neural style\ntransfer framework that takes as input a biomedical image of a patient and\ntranslates it to a new image that indicates an attacker-chosen disease\ncondition. The potential for fraudulent claims based on such generated 'fake'\nmedical images is significant, and we demonstrate successful attacks on both\nX-rays and retinal fundus image modalities. We show that these attacks manage\nto mislead both medical professionals and algorithmic detection schemes.\nLastly, we also investigate defensive measures based on machine learning to\ndetect images generated by Jekyll.\n"}
+{"abstract": "  In response to the ongoing COVID-19 pandemic, we present a robust deep\nlearning pipeline that is capable of identifying correct and incorrect\nmask-wearing from real-time video streams. To accomplish this goal, we devised\ntwo separate approaches and evaluated their performance and run-time\nefficiency. The first approach leverages a pre-trained face detector in\ncombination with a mask-wearing image classifier trained on a large-scale\nsynthetic dataset. The second approach utilizes a state-of-the-art object\ndetection network to perform localization and classification of faces in one\nshot, fine-tuned on a small set of labeled real-world images. The first\npipeline achieved a test accuracy of 99.97% on the synthetic dataset and\nmaintained 6 FPS running on video data. The second pipeline achieved a mAP(0.5)\nof 89% on real-world images while sustaining 52 FPS on video data. We have\nconcluded that if a larger dataset with bounding-box labels can be curated,\nthis task is best suited using object detection architectures such as YOLO and\nSSD due to their superior inference speed and satisfactory performance on key\nevaluation metrics.\n"}
+{"abstract": "  Quantum cryptography is known for enabling functionalities that are\nunattainable using classical information alone. Recently, Secure Software\nLeasing (SSL) has emerged as one of these areas of interest. Given a target\ncircuit $C$ from a circuit class, SSL produces an encoding of $C$ that enables\na recipient to evaluate $C$, and also enables the originator of the software to\nverify that the software has been returned -- meaning that the recipient has\nrelinquished the possibility of any further use of the software. Clearly, such\na functionality is unachievable using classical information alone, since it is\nimpossible to prevent a user from keeping a copy of the software. Recent\nresults have shown the achievability of SSL using quantum information for a\nclass of functions called compute-and-compare (these are a generalization of\nthe well-known point functions). These prior works, however all make use of\nsetup or computational assumptions. Here, we show that SSL is achievable for\ncompute-and-compare circuits without any assumptions.\n  Our technique involves the study of quantum copy-protection, which is a\nnotion related to SSL, but where the encoding procedure inherently prevents a\nwould-be quantum software pirate from splitting a single copy of an encoding\nfor $C$ into two parts, each of which enables a user to evaluate $C$. We show\nthat point functions can be copy-protected without any assumptions, for a novel\nsecurity definition involving one honest and one malicious evaluator; this is\nachieved by showing that from any quantum message authentication code, we can\nderive such an honest-malicious copy-protection scheme. We then show that a\ngeneric honest-malicious copy-protection scheme implies SSL; by prior work,\nthis yields SSL for compute-and-compare functions.\n"}
+{"abstract": "  Energy eigenstates in the random transverse field Edwards-Anderson (EA) model\nand the random bond quantum Heisenberg XYZ model in a $d$-dimensional finite\ncubic lattice are obtained for sufficiently weak interactions. The\nDatta-Kennedy-Kirkwood-Thomas convergent perturbative expansion using the\ncontraction mapping theorem is developed for quantum spin systems with site-\nand bond-dependent interactions. This expansion enables us to obtain energy\neigenstates in the random transverse field free spin model perturbed by\nsufficiently weak longitudinal exchange interactions. This expansion is useful\nalso for the EA model perturbed by sufficiently weak transverse fields and\nbond-dependent XY exchange interactions. In these models, their perturbations\nsplit the two fold degenerate energy eigenvalues because of the ${\\mathbb Z}_2$\nsymmetry in the unperturbed EA model. It is shown that the energy gap between\nsplit energy eigenvalues is exponentially small in the system size. We provide\na sufficient condition on the perturbation for absence of level crossing\nbetween arbitrary energy eigenstates.\n"}
+{"abstract": "  Background: Software is an important windows to offer a variety of complex\ninstrument control and data processing for nuclear magnetic resonance (NMR)\nspectrometer. NMR software should allow researchers to flexibly implement\nvarious functionality according to the requirement of applications. Scripting\nsystem can offer an open environment for NMR users to write custom programs\nwith basic libraries. Emerging technologies, especially multivariate\nstatistical analysis and artificial intelligence, have been successfully\napplied to NMR applications such as metabolomics and biomacromolecules.\nScripting system should support more complex NMR libraries, which will enable\nthe emerging technologies to be easily implemented in the scripting\nenvironment. Result: Here, a novel NMR scripting system named \"NMRPy\" is\nintroduced. In the scripting system, both Java based NMR methods and original\nCPython based libraries are supported. A module was built as a bridge to\nintegrate the runtime environment of Java and CPython. It works as an extension\nin CPython environment, as well as interacts with Java part by Java Native\nInterface. Leveraging the bridge, Java based instrument control and data\nprocessing methods can be called as a CPython style. Compared with traditional\nscripting system, NMRPy is easier for NMR researchers to develop complex\nfunctionality with fast numerical computation, multivariate statistical\nanalysis, deep learning etc. Non-uniform sampling and protein structure\nprediction methods based on deep learning can be conveniently integrated into\nNMRPy. Conclusion: NMRPy offers a user-friendly environment to implement custom\nfunctionality leveraging its powerful basic NMR and rich CPython libraries. NMR\napplications with emerging technologies can be easily integrated. The scripting\nsystem is free of charge and can be downloaded by visiting\nhttp://www.spinstudioj.net/nmrpy.\n"}
+{"abstract": "  This paper is devoted to numerical simulations of the short-term behavior of\nthe spatial temperature distribution in a geothermal energy storage. Such\nsimulations are needed for the optimal control and management of residential\nheating systems equipped with an underground thermal storage. We apply\nnumerical methods derived in our companion paper [8]. There we studied the\ngoverning initial boundary value problem for a linear heat equation with\nconvection. Further, we perform extensive numerical experiments in which we\ninvestigate properties of the spatio-temporal temperature distribution and of\nits aggregated characteristics.\n"}
+{"abstract": "  In this paper we study the spectral gap of the path graph and illustrate an\ninteresting effect which has been described recently in the continuous setting.\nMore explicitly, in the large-volume limit and in the presence of a certain\nexternal potential, it is shown that the spectral gap converges to zero\nstrictly faster than it does for the free Laplacian. The underlying mechanism\nis a combination of the increase in volume and an effective degeneracy of the\nground state in the limiting regime.\n"}
+{"abstract": "  In this paper, we study the performance and generalizability of three\napproaches for AD detection from speech on the recent ADReSSo challenge\ndataset: 1) using conventional acoustic features 2) using novel pre-trained\nacoustic embeddings 3) combining acoustic features and embeddings. We find that\nwhile feature-based approaches have a higher precision, classification\napproaches relying on the combination of embeddings and features prove to have\na higher, and more balanced performance across multiple metrics of performance.\nOur best model, using such a combined approach, outperforms the acoustic\nbaseline in the challenge by 2.8\\%.\n"}
+{"abstract": "  Recent works recognized lidars as an inherently streaming data source and\nshowed that the end-to-end latency of lidar perception models can be reduced\nsignificantly by operating on wedge-shaped point cloud sectors rather then the\nfull point cloud. However, due to use of cartesian coordinate systems these\nmethods represent the sectors as rectangular regions, wasting memory and\ncompute. In this work we propose using a polar coordinate system and make two\nkey improvements on this design. First, we increase the spatial context by\nusing multi-scale padding from neighboring sectors: preceding sector from the\ncurrent scan and/or the following sector from the past scan. Second, we improve\nthe core polar convolutional architecture by introducing feature undistortion\nand range stratified convolutions. Experimental results on the nuScenes dataset\nshow significant improvements over other streaming based methods. We also\nachieve comparable results to existing non-streaming methods but with lower\nlatencies.\n"}
+{"abstract": "  Let $\\mu_2(\\Omega)$ be the first positive eigenvalue of the Neumann Laplacian\nin a bounded domain $\\Omega\\subset\\mathbb{R}^N$. It was proved by Szeg\\H{o} for\n$N=2$ and by Weinberger for $N \\geq 2$ that among all equimeasurable domains\n$\\mu_2(\\Omega)$ attains its global maximum if $\\Omega$ is a ball. In the\npresent work, we develop the approach of Weinberger in two directions. Firstly,\nwe refine the Szeg\\H{o}-Weinberger result for a class of domains of the form\n$\\Omega_{\\text{out}}\\setminus\\overline{\\Omega}_{\\text{in}}$ which are either\ncentrally symmetric or symmetric of order $2$ (with respect to every coordinate\nplane $(x_i,x_j)$) by showing that\n$\\mu_{2}(\\Omega_{\\text{out}}\\setminus\\overline{\\Omega}_{\\text{in}})\\leq\\mu_2(B_\\beta\\setminus\\overline{B}_\\alpha)$,\nwhere $B_\\alpha, B_\\beta$ are balls centered at the origin such that\n$B_\\alpha\\subset\\Omega_{\\text{in}}$ and\n$|\\Omega_{\\text{out}}\\setminus\\overline{\\Omega}_{\\text{in}}|=|B_\\beta\\setminus\\overline{B}_\\alpha|$.\nSecondly, we provide Szeg\\H{o}-Weinberger type inequalities for higher\neigenvalues by imposing additional symmetry assumptions on the domain. Namely,\nif $\\Omega_{\\text{out}}\\setminus\\overline{\\Omega}_{\\text{in}}$ is symmetric of\norder $4$, then we prove\n$\\mu_{i}(\\Omega_{\\text{out}}\\setminus\\overline{\\Omega}_{\\text{in}})\\leq\\mu_i(B_\\beta\\setminus\\overline{B}_\\alpha)$\nfor $i=3,\\dots,N+2$, where we also allow $\\Omega_{\\text{in}}$ and $B_\\alpha$ to\nbe empty. If $N=2$ and the domain is symmetric of order $8$, then the latter\ninequality persists for $i=5$. Counterexamples to the obtained inequalities for\ndomains outside of the considered symmetry classes are given. The existence and\nproperties of nonradial domains with required symmetries in higher dimensions\nare discussed. As an auxiliary result, we obtain the non-radiality of the\neigenfunctions associated to $\\mu_{N+2}(B_\\beta\\setminus\\overline{B}_\\alpha)$.\n"}
+{"abstract": "  Over the past year, remote speech intelligibility testing has become a\npopular and necessary alternative to traditional in-person experiments due to\nthe need for physical distancing during the COVID-19 pandemic. A remote\nframework was developed for conducting speech intelligibility tests with normal\nhearing listeners. In this study, subjects used their personal computers to\ncomplete sentence recognition tasks in anechoic and reverberant listening\nenvironments. The results obtained using this remote framework were compared\nwith previously collected in-lab results, and showed higher levels of speech\nintelligibility among remote study participants than subjects who completed the\ntest in the laboratory.\n"}
+{"abstract": "  A problem of identification of piecewise-constant unknown parameters of a\nlinear regression equation (LRE) is considered. Such parameters change their\nvalues over the interval of the regressor finite (rather than persistent)\nexcitation. To solve it, the previously proposed I-DREM procedure and the\nintegral-based filtering method with the exponential forgetting and resetting\nare improved: the integral of the filter equations is taken over the finite\ntime intervals, which belong to the finite excitation time range. This allows\none to obtain an exponentially bounded identification error over the excitation\ninterval, and, when the LRE parameters are constant outside such interval, to\nprovide exponential convergence of the identification error to zero. In\naddition, the applied method of the regressor excitation normalization makes it\npossible to obtain the same rate of convergence of the parameter error for the\nregressors of various amplitudes. The stability and all the above-mentioned\nproperties are proved for the derived identification method. The results of\nnumerical experiments (including the case of the noise-contaminated regressor\nand output measurements) fully support the obtained theoretical results.\n"}
+{"abstract": "  Image co-segmentation is an active computer vision task that aims to segment\nthe common objects from a set of images. Recently, researchers design various\nlearning-based algorithms to undertake the co-segmentation task. The main\ndifficulty in this task is how to effectively transfer information between\nimages to make conditional predictions. In this paper, we present CycleSegNet,\na novel framework for the co-segmentation task. Our network design has two key\ncomponents: a region correspondence module which is the basic operation for\nexchanging information between local image regions, and a cycle refinement\nmodule, which utilizes ConvLSTMs to progressively update image representations\nand exchange information in a cycle and iterative manner. Extensive experiments\ndemonstrate that our proposed method significantly outperforms the\nstate-of-the-art methods on four popular benchmark datasets -- PASCAL VOC\ndataset, MSRC dataset, Internet dataset, and iCoseg dataset, by 2.6%, 7.7%,\n2.2%, and 2.9%, respectively.\n"}
+{"abstract": "  Here we derive some results on so called quantitative Runge approximation in\nthe case of the time-harmonic Maxwell equations. This provides a Runge\napproximation having more explicit quantitative information. We additionally\nderive some results on the conditional stability of the Cauchy problem for the\ntime-harmonic Maxwell equations.\n"}
+{"abstract": "  Classical spectrum analyzers are widely used but lack micro- or nanoscale\nspatial resolution. On the other hand, quantum sensors, capable of working with\nnanoscale precision, do not provide precise frequency resolution over a wide\nrange of frequencies. Using a single spin in diamond, we present a measurement\nprotocol for quantum probes which enables full signal reconstruction on a\nnanoscale spatial resolution up to potentially 100 GHz. We achieve\n$58\\,\\mathrm{nT/\\sqrt{Hz}}$ amplitude and $0.095\\,\\mathrm{rad/\\sqrt{Hz}}$ phase\nsensitivity and a relative uncertainty of $10^{-12}$ for a 1.51 GHz signal\nwithin 10 s of integration. This technique opens the way to quantum spectrum\nanalysis methods and yields many interesting potential applications.\n"}
+{"abstract": "  Carbon Nanotubes (CNTs)-polymer composites are promising candidates for a\nmyriad of applications. Ad-hoc CNTs-polymer composite fabrication techniques\ninherently pose roadblock to optimized processing resulting in microstructural\ndefects i.e., void formation, poor interfacial adhesion, wettability, and\nagglomeration of CNTs inside the polymer matrix. Although improvement in the\nmicrostructures can be achieved via additional processing steps such\nas-mechanical methods and/or chemical functionalization, the resulting\ncomposites are somewhat limited in structural and functional performances.\nHere, we demonstrate that 3D printing technique like-direct ink writing offers\nimproved processing of CNTs-polymer composites. The shear-induced flow of an\nengineered nanocomposite ink through the micronozzle offers some benefits\nincluding reducing the number of voids within the epoxy, improving CNTs\ndispersion and adhesion with epoxy, and partially aligns the CNTs. Such\nmicrostructural changes result in superior mechanical performance and heat\ntransfer in the composites compared to their mold-casted counterparts. This\nwork demonstrates the advantages of 3D printing over traditional fabrication\nmethods, beyond the ability to rapidly fabricate complex architectures, to\nachieve improved processing dynamics for fabricating CNT-polymer nanocomposites\nwith better structural and functional properties.\n"}
+{"abstract": "  We construct mixing solutions to the incompressible porous media equation\nstarting from Muskat type data in the partially unstable regime. In particular,\nwe consider bubble and turned type interfaces with Sobolev regularity. As a\nby-product, we prove the continuation of the evolution of IPM after the\nRayleigh-Taylor and smoothness breakdown exhibited in [18,17]. At each time\nslice the space is split into three evolving domains: two non-mixing zones and\na mixing zone which is localized in a neighborhood of the unstable region. In\nthis way, we show the compatibility between the classical Muskat problem and\nthe convex integration method.\n"}
+{"abstract": "  Tree-child networks, one of the prominent network classes in phylogenetics,\nhave been introduced for the purpose of modeling reticulate evolution.\nRecently, the first author together with Gittenberger and Mansouri (2019)\nshowed that the number ${\\rm TC}_{\\ell,k}$ of tree-child networks with $\\ell$\nleaves and $k$ reticulation vertices has the first-order asymptotics \\[ {\\rm\nTC}_{\\ell,k}\\sim c_k\\left(\\frac{2}{e}\\right)^{\\ell}\\ell^{\\ell+2k-1},\\qquad\n(\\ell\\rightarrow\\infty). \\] Moreover, they also computed $c_k$ for $k=1,2,$ and\n$3$. In this short note, we give a second approach to the above result which is\nbased on a recent (algorithmic) approach for the counting of tree-child\nnetworks due to Cardona and Zhang (2020). This second approach is also capable\nof giving a simple, closed-form expression for $c_k$, namely,\n$c_k=2^{k-1}\\sqrt{2}/k!$ for all $k\\geq 0$.\n"}
+{"abstract": "  It is well known that value at a non-zero algebraic number of each of the\nfunctions $e^{x}, \\ln x, \\sin x, \\cos x, \\tan x, \\csc x, \\sec x, \\cot x, \\sinh\nx,$ $ \\cosh x,$ $ \\tanh x,$ and $\\coth x$ is transcendental number (see Theorem\n9.11 of \\cite{N}). In the work, we show that for any one of the above mentioned\nfunctions, $f(x)$, and for a polynomial $g(x)$ with rational coefficients the\nzero, if any, of the equation $f(x)=g(x)$ is a transcendental number. We also\nshow that if $f(x)$ and $g(x)$ are polynomials with rational coefficients, then\na zero of the equation $e^{f(x)}=g(x)$ is a transcendental number. Finally we\nshow that the existence of an abelian group whose non-zero elements are\ntranscendental numbers.\n"}
+{"abstract": "  Gravitational Wave interferometers achieve their profound sensitivity by\ncombining a Michelson interferometer with optical cavities, suspended masses,\nand now, squeezed quantum states of light. These states modify the measurement\nprocess of the LIGO, VIRGO and GEO600 interferometers to reduce the quantum\nnoise that masks astrophysical signals; thus, improvements to squeezing are\nessential to further expand our gravitational view of the universe. Further\nreducing quantum noise will require both lowering decoherence from losses as\nwell more sophisticated manipulations to counter the quantum back-action from\nradiation pressure. Both tasks require fully understanding the physical\ninteractions between squeezed light and the many components of km-scale\ninterferometers. To this end, data from both LIGO observatories in observing\nrun three are expressed using frequency-dependent metrics to analyze each\ndetector's quantum response to squeezed states. The response metrics are\nderived and used to concisely describe physical mechanisms behind squeezing's\nsimultaneous interaction with transverse-mode selective optical cavities and\nthe quantum radiation pressure noise of suspended mirrors. These metrics and\nrelated analysis are broadly applicable for cavity-enhanced optomechanics\nexperiments that incorporate external squeezing, and -- for the first time --\ngive physical descriptions of every feature so far observed in the quantum\nnoise of the LIGO detectors.\n"}
+{"abstract": "  Data augmentation has been widely used to improve deep nerual networks\nperformance. Numerous approaches are suggested, for example, dropout,\nregularization and image augmentation, to avoid over-ftting and enhancing\ngeneralization of neural networks. One of the sub-area within data augmentation\nis image mixing and deleting. This specific type of augmentation either mixes\ntwo images or delete image regions to hide or make certain characteristics of\nimages confusing for the network to force it to emphasize on overall structure\nof object in image. The model trained with this approach has shown to perform\nand generalize well as compared to one trained without imgage mixing or\ndeleting. Additional benefit achieved with this method of training is\nrobustness against image corruptions. Due to its low compute cost and success\nin recent past, many techniques of image mixing and deleting are proposed. This\npaper provides detailed review on these devised approaches, dividing\naugmentation strategies in three main categories cut and delete, cut and mix\nand mixup. The second part of paper emprically evaluates these approaches for\nimage classification, finegrained image recognition and object detection where\nit is shown that this category of data augmentation improves the overall\nperformance for deep neural networks.\n"}
+{"abstract": "  Despite the success of machine learning applications in science, industry,\nand society in general, many approaches are known to be non-robust, often\nrelying on spurious correlations to make predictions. Spuriousness occurs when\nsome features correlate with labels but are not causal; relying on such\nfeatures prevents models from generalizing to unseen environments where such\ncorrelations break. In this work, we focus on image classification and propose\ntwo data generation processes to reduce spuriousness. Given human annotations\nof the subset of the features responsible (causal) for the labels (e.g.\nbounding boxes), we modify this causal set to generate a surrogate image that\nno longer has the same label (i.e. a counterfactual image). We also alter\nnon-causal features to generate images still recognized as the original labels,\nwhich helps to learn a model invariant to these features. In several\nchallenging datasets, our data generations outperform state-of-the-art methods\nin accuracy when spurious correlations break, and increase the saliency focus\non causal features providing better explanations.\n"}
+{"abstract": "  Rough path analysis is developed in the full Besov scale. This extends, and\nessentially concludes, an investigation started by [Pr\\\"omel--Trabs, Rough\ndifferential equations driven by signals in {B}esov spaces. J. Diff. Equ.\n2016], further studied in a series of papers by Liu, Pr\\\"omel and Teichmann. A\nnew Besov sewing lemma, a real-analysis result of interest in its own right,\nplays a key role, and the flexibility in the choice of Besov parameters allows\nfor the treatment of equations not available in the H\\\"older or variation\nsettings. Important classes of stochastic processes fit in the present\nframework.\n"}
+{"abstract": "  With the mass deployment of computing-intensive applications and\ndelay-sensitive applications on end devices, only adequate computing resources\ncan meet differentiated services' delay requirements. By offloading tasks to\ncloud servers or edge servers, computation offloading can alleviate computing\nand storage limitations and reduce delay and energy consumption. However, few\nof the existing offloading schemes take into consideration the cloud-edge\ncollaboration and the constraint of energy consumption and task dependency.\nThis paper builds a collaborative computation offloading model in cloud and\nedge computing and formulates a multi-objective optimization problem.\nConstructed by fusing optimal transport and Policy-Based RL, we propose an\nOptimal-Transport-Based RL approach to resolve the offloading problem and make\nthe optimal offloading decision for minimizing the overall cost of delay and\nenergy consumption. Simulation results show that the proposed approach can\neffectively reduce the cost and significantly outperforms existing optimization\nsolutions.\n"}
+{"abstract": "  We study a model of $N$ fermions in a quantum dot, coupled to $M$ bosons by a\ndisorder-induced complex Yukawa coupling (Yukawa-SYK model), in order to\nexplore the interplay between non-Fermi liquid and superconductivity in a\nstrongly coupled, (quantum-)critical environment. We analyze the phase diagram\nof the model for an arbitrary complex interaction and arbitrary ratio of $N/M$,\nwith special focus on the two regimes of non-Fermi-liquid behavior: an SYK-like\nbehavior with a power-law frequency dependence of the fermionic self-energy and\nan impurity-like behavior with frequency independent self-energy. We show that\nthe crossover between the two. can be reached by varying either the strength of\nthe fermion-boson coupling or the ratio $M/N$. We next argue that in both\nregimes the system is unstable to superconductivity if the strength of\ntime-reversal-symmetry-breaking disorder is below a certain threshold. We show\nhow the corresponding onset temperatures vary between the two regimes. We argue\nthat the superconducting state is highly unconventional with an infinite set of\nminima of the condensation energy at $T=0$, corresponding to topologically\ndifferent gap functions. We discuss in detail similarities and differences\nbetween this model and the model of dispersion-full fermions tuned to a\nmetallic quantum-critical point, with an effective singular dynamical\ninteraction $V(\\Omega) \\propto 1/|\\Omega|^\\gamma$ (the $\\gamma-$model).\n"}
+{"abstract": "  We prove that there are at least seeds many exact embedded Lagrangian\nfillings for Legendrian links of type $\\mathsf{ADE}$. We also provide seeds\nmany Lagrangian fillings with certain symmetries for type $\\mathsf{BCFG}$. Our\nmain tools are $N$-graphs and the combinatorics of seed patterns of finite\ntype.\n"}
+{"abstract": "  Credit scoring is a major application of machine learning for financial\ninstitutions to decide whether to approve or reject a credit loan. For sake of\nreliability, it is necessary for credit scoring models to be both accurate and\nglobally interpretable. Simple classifiers, e.g., Logistic Regression (LR), are\nwhite-box models, but not powerful enough to model complex nonlinear\ninteractions among features. Fortunately, automatic feature crossing is a\npromising way to find cross features to make simple classifiers to be more\naccurate without heavy handcrafted feature engineering. However, credit scoring\nis usually based on different aspects of users, and the data usually contains\nhundreds of feature fields. This makes existing automatic feature crossing\nmethods not efficient for credit scoring. In this work, we find local\npiece-wise interpretations in Deep Neural Networks (DNNs) of a specific feature\nare usually inconsistent in different samples, which is caused by feature\ninteractions in the hidden layers. Accordingly, we can design an automatic\nfeature crossing method to find feature interactions in DNN, and use them as\ncross features in LR. We give definition of the interpretation inconsistency in\nDNN, based on which a novel feature crossing method for credit scoring\nprediction called DNN2LR is proposed. Apparently, the final model, i.e., a LR\nmodel empowered with cross features, generated by DNN2LR is a white-box model.\nExtensive experiments have been conducted on both public and business datasets\nfrom real-world credit scoring applications. Experimental shows that, DNN2LR\ncan outperform the DNN model, as well as several feature crossing methods.\nMoreover, comparing with the state-of-the-art feature crossing methods, i.e.,\nAutoCross, DNN2LR can accelerate the speed for feature crossing by about 10 to\n40 times on datasets with large numbers of feature fields.\n"}
+{"abstract": "  We study the elastic response of rigid, wire frame particles in concentrated,\nglassy suspensions to a step strain by applying the simple, geometric methods\ndeveloped in part I. The wire frame particles are comprised of thin, rigid rods\nof length $L$ and their number density, $\\rho$, is such that $\\rho L^3 \\gg 1$.\nWe specifically compare rigid rods to L-shapes made of two equal length rods\njoined at right angles. The behaviour of wire frames is found to be strikingly\ndifferent from that of rods. The linear elasticity scales like $\\rho^3 L^6$ for\nL-shaped particles, whereas it scales proportional to $\\rho$ for rods and the\nnon-linear response shows a transition from shear hardening to shear softening\nat a critical density $\\rho_c \\sim \\sqrt{K / k_B T L^6}$, where $K$ is the\nbending modulus of the particles. For realistic particles made of double\nstranded DNA, this transition occurs at densities of about $\\rho L^3 \\sim 10$.\nThe reason for these differences is that wire frames can be forced to bend by\nthe entanglements with their surroundings, whereas rods always remain straight.\nThis is found to be very important even for small strains, with most particles\nbeing bent above a critical strain $\\gamma_c \\sim (\\rho L^3)^{-1}$.\n"}
+{"abstract": "  We generalize the first law of black hole mechanics to the rotating, charged\nC-metric and to the Ernst metric, both of which have the charged C-metric as a\nspecial case. All of these metrics are (3+1)-dimensional, have vanishing\ncosmological constant, and physically describe a pair of black holes pulled\napart to null infinity by some external force. Our first laws are global in the\nsense of applying to an entire patch of spacetime, as opposed to a neighborhood\nof the black hole. They are formulated with respect to \"boost time\", whose\nprimacy is motivated by the celestial holographic approach to scattering\namplitudes.\n"}
+{"abstract": "  We applied a hybrid-dimensional flow model to pressure transients recorded\nduring pumping experiments conducted at the Reiche Zeche underground research\nlaboratory to study the normal opening behavior of fractures due to fluid\ninjection. Two distinct types of pressure responses to flow-rate steps were\nidentified and numerically modelled using a radial-symmetric flow formulation\nfor a fracture that comprises a non-linear constitutive relation for the\ncontact mechanics governing reversible fracture surface interaction. These two\ngroups represent radial-symmetric and plane-axisymmetric flow regimes from a\nconventional pressure-diffusion perspective. A comprehensive parameter study\ninto the sensitivity of the applied hydro-mechanical model to changes in\ncharacteristic fracture parameters revealed an interrelation between fracture\nlength and normal fracture stiffness that yield a match between field\nobservations and numerical results. Fracture stiffness values increase with\ncorresponding fracture length. Decomposition of the acting normal stresses into\na stresses associated with the deformation state of the global fracture\ngeometry and the contact stresses indicates that geometrically induced stresses\ncontribute the more the lower the total effective normal stress and the shorter\nthe fracture. Separating the contributions of the local contact mechanics and\nthe overall fracture geometry to fracture normal stiffness indicates that the\nlatter, the geometrical stiffness, constitutes a lower bound for total\nstiffness; its relevance increases with decreasing fracture length, too. Our\nstudy demonstrates that non-linear hydro-mechanical coupling can lead to vastly\ndifferent hydraulic responses and thus provides an alternative to conventional\npressure-diffusion analysis that requires changes in flow regime to cover the\nfull range of observations.\n"}
+{"abstract": "  As a promising avenue to obtain new extreme ultraviolet light source and\ndetect electronic properties, high-harmonic generation (HHG) has been actively\ndeveloped in both theory and experiment. In solids lacking inversion symmetry,\nwhen electrons undergo a nonadiabatic transition, a directional charge shift\noccurs and is characterized by shift vector, which measures the real-space\nshift of the photoexcited electron and hole. For the first time, we have\nrevealed that shift vector plays prominent roles in the real-space tunneling\nmechanism of three-step model for electrons under strong laser fields. Since\nshift vector is determined by the topological properties of related wave\nfunctions, we expect HHG with its contribution can provide direct knowledge on\nthe band topology in noncentrosymmetric topological insulators (TIs). In both\nKane-Mele model and realistic material BiTeI, we have found that the shift\nvector reverses when band inversion happens during the topological phase\ntransition between normal and topological insulators. Under oscillating strong\nlaser fields, the reversal of shift vector leads to completely opposite\nradiation time of high-order harmonics. This makes HHG a feasible all-optical\nstrong-field method to directly identify the band inversion in\nnon-centrosymmetric TIs.\n"}
+{"abstract": "  The Gierer-Meinhardt system occurs in morphogenesis, where the development of\nan organism from a single cell is modelled. One of the steps in the\ndevelopment, is the formation of spatial patterns of the cell structure,\nstarting from an almost homogeneous cell distribution. Turing proposed in his\npioneering work different activator-inhibitor systems with different diffusion\nrates, which could trigger the emergence of such cell structures.\nMathematically, one describes these activator-inhibitor systems as a coupled\nsystems of reaction-diffusion equations with hugely different diffusion\ncoefficients and highly nonlinear interaction. One famous example of these\nsystems is the Gierer-Meinhardt system. These systems usually are not of\nmonotone type, such that one has to apply other techniques. The purpose of this\narticle is to study the stochastic reaction-diffusion Gierer-Meinhardt system\nwith homogeneous Neumann boundary condition on a one or two-dimensional bounded\nspatial domain. To be more precise, we perturb the original Gierer-Meinhardt\nsystem by an infinite-dimensional Wiener process and show under which\nconditions on the Wiener process and the system, a solution exists. In\ndimension one, we even show the pathwise uniqueness. In dimension two,\nuniqueness is still an open question.\n"}
+{"abstract": "  We report the detection of an Al II line at 2669.155 Angstroms in 11\nmetal-poor stars, using ultraviolet spectra obtained with the Space Telescope\nImaging Spectrograph on board the Hubble Space Telescope. We derive Al\nabundances from this line using a standard abundance analysis, assuming local\nthermodynamic equilibrium (LTE). The mean [Al/Fe] ratio is -0.06 +/- 0.04\n(sigma = 0.22) for these 11 stars spanning -3.9 < [Fe/H] < -1.3, or [Al/Fe] =\n-0.10 +/- 0.04 (sigma = 0.18) for 9 stars spanning -3.0 < [Fe/H] < -1.3 if two\ncarbon-enhanced stars are excluded. We use these abundances to perform an\nempirical test of non-LTE (NLTE) abundance corrections predicted for resonance\nlines of Al I, including the commonly-used optical Al I line at 3961 Angstroms.\nThe Al II line is formed in LTE, and the abundance derived from this line\nmatches that derived from high-excitation Al I lines predicted to have minimal\nNLTE corrections. The differences between the abundance derived from the Al II\nline and the LTE abundance derived from Al I resonance lines are +0.4 to +0.9\ndex, which match the predicted NLTE corrections for the Al I resonance lines.\nWe conclude that the NLTE abundance calculations are approximately correct and\nshould be applied to LTE abundances derived from Al I lines.\n"}
+{"abstract": "  This paper introduces a neural network approach for fitting the Lee-Carter\nand the Poisson Lee-Carter model on multiple populations. We develop some\nneural networks that replicate the structure of the individual LC models and\nallow their joint fitting by analysing the mortality data of all the considered\npopulations simultaneously. The neural network architecture is specifically\ndesigned to calibrate each individual model using all available information\ninstead of using a population-specific subset of data as in the traditional\nestimation schemes. A large set of numerical experiments performed on all the\ncountries of the Human Mortality Database (HMD) shows the effectiveness of our\napproach. In particular, the resulting parameter estimates appear smooth and\nless sensitive to the random fluctuations often present in the mortality rates'\ndata, especially for low-population countries. In addition, the forecasting\nperformance results significantly improved as well.\n"}
+{"abstract": "  We investigate the dynamics of spheroids immersed in the journal bearing flow\nsubject to a contractible non-reciprocal loop. We show how geometric phases\nappear not only in the position, but also in the orientation of such particles.\nWe show how control and targeting can be carried out on spheroidal particles in\nthe flow.\n"}
+{"abstract": "  Most adversarial attack methods on text classification can change the\nclassifier's prediction by synonym substitution. We propose the adversarial\nsentence rewriting sampler (ASRS), which rewrites the whole sentence to\ngenerate more similar and higher-quality adversarial examples. Our method\nachieves a better attack success rate on 4 out of 7 datasets, as well as\nsignificantly better sentence quality on all 7 datasets. ASRS is an\nindispensable supplement to the existing attack methods, because classifiers\ncannot resist the attack from ASRS unless they are trained on adversarial\nexamples found by ASRS.\n"}
+{"abstract": "  As recent advancements in physics and astronomy rapidly rewrite textbooks,\nthere is a growing need in keeping abreast of the latest knowledge in these\nfields. Reading preprints is one of the effective ways to do this. By having\njournal clubs where people can read and discuss journals together, the benefits\nof reading journals become more prevalent. We present an investigative study of\nunderstanding the factors that affect the success of preprint journal clubs in\nastronomy, more commonly known as Astro-ph/Astro-Coffee (hereafter called AC).\nA survey was disseminated to understand how institutions from different\ncountries implement AC. We interviewed 9 survey respondents and from their\nresponses we identified four important factors that make AC successful:\ncommitment (how the organizer and attendees participate in AC), environment\n(how conducive and comfortable AC is conducted), content (the discussed topics\nin AC and how they are presented), and objective (the main goal/s of conducting\nAC). We also present the format of our AC, an elective class which was\nevaluated during the Spring Semester 2020 (March 2020 - June 2020). Our\nevaluation with the attendees showed that enrollees (those who are enrolled and\nare required to present papers regularly) tend to be more committed in\nattending compared to audiences (those who are not enrolled and are not\nrequired to present papers regularly). In addition, participants tend to find\npapers outside their research field harder to read. Finally, we showed an\nimprovement in the weekly number of papers read after attending AC of those who\npresent papers regularly, and a high satisfaction rating of our AC. We\nsummarize the areas of improvement in our AC implementation, and we encourage\nother institutions to evaluate their own AC in accordance with the four\naforementioned factors to assess the effectiveness of their AC in reaching\ntheir goals.\n"}
+{"abstract": "  We develop the Lifshitz theory of van der Waals forces in a wedge of a\ndielectric material. The non-planar geometry of the problem requires\ndetermining point-wise distribution of stresses. The findings are relevant to a\nwide range of phenomena from crack propagation to contact line motion. First,\nthe stresses prove to be anisotropic as opposed to the classical fluid\nmechanics treatment of the contact line problem. Second, the wedge\nconfiguration is always unstable with its angle tending either to collapse or\nunfold. The presented theory unequivocally demonstrates quantum nature of the\nforces dictating the wedge behavior, which cannot be accounted for with the\nclassical methods.\n"}
+{"abstract": "  We determine the dimension of the moduli space of non-Abelian vortices in\nYang-Mills-Chern-Simons-Higgs theory in 2+1 dimensions for gauge groups\n$G=({\\rm U}(1)\\times G')/\\mathbb{Z}_{n_0}$ with $G'$ being an arbitrary\nsemi-simple group and $n_0$ the greatest common divisor of the Abelian charges\nof the $G'$ invariants. The calculation is carried out using a Callias-type\nindex theorem, the moduli matrix approach and a D-brane setup in Type IIB\nstring theory. We prove that the index theorem gives the number of zeromodes or\nmoduli of the non-Abelian vortices, extend the moduli matrix approach to the\nYang-Mills-Chern-Simons-Higgs theory and finally derive the effective\nLagrangian of Collie and Tong using string theory.\n"}
+{"abstract": "  Despite rapid progress in the recent past, current speech recognition systems\nstill require labeled training data which limits this technology to a small\nfraction of the languages spoken around the globe. This paper describes\nwav2vec-U, short for wav2vec Unsupervised, a method to train speech recognition\nmodels without any labeled data. We leverage self-supervised speech\nrepresentations to segment unlabeled audio and learn a mapping from these\nrepresentations to phonemes via adversarial training. The right representations\nare key to the success of our method. Compared to the best previous\nunsupervised work, wav2vec-U reduces the phoneme error rate on the TIMIT\nbenchmark from 26.1 to 11.3. On the larger English Librispeech benchmark,\nwav2vec-U achieves a word error rate of 5.9 on test-other, rivaling some of the\nbest published systems trained on 960 hours of labeled data from only two years\nago. We also experiment on nine other languages, including low-resource\nlanguages such as Kyrgyz, Swahili and Tatar.\n"}
+{"abstract": "  With near-term quantum devices available and the race for fault-tolerant\nquantum computers in full swing, researchers became interested in the question\nof what happens if we replace a supervised machine learning model with a\nquantum circuit. While such \"quantum models\" are sometimes called \"quantum\nneural networks\", it has been repeatedly noted that their mathematical\nstructure is actually much more closely related to kernel methods: they analyse\ndata in high-dimensional Hilbert spaces to which we only have access through\ninner products revealed by measurements. This technical manuscript summarises\nand extends the idea of systematically rephrasing supervised quantum models as\na kernel method. With this, a lot of near-term and fault-tolerant quantum\nmodels can be replaced by a general support vector machine whose kernel\ncomputes distances between data-encoding quantum states. Kernel-based training\nis then guaranteed to find better or equally good quantum models than\nvariational circuit training. Overall, the kernel perspective of quantum\nmachine learning tells us that the way that data is encoded into quantum states\nis the main ingredient that can potentially set quantum models apart from\nclassical machine learning models.\n"}
+{"abstract": "  Software sandboxing or software-based fault isolation (SFI) is a lightweight\napproach to building secure systems out of untrusted components. Mozilla, for\nexample, uses SFI to harden the Firefox browser by sandboxing third-party\nlibraries, and companies like Fastly and Cloudflare use SFI to safely co-locate\nuntrusted tenants on their edge clouds. While there have been significant\nefforts to optimize and verify SFI enforcement, context switching in SFI\nsystems remains largely unexplored: almost all SFI systems use\n\\emph{heavyweight transitions} that are not only error-prone but incur\nsignificant performance overhead from saving, clearing, and restoring registers\nwhen context switching. We identify a set of \\emph{zero-cost conditions} that\ncharacterize when sandboxed code has sufficient structured to guarantee\nsecurity via lightweight \\emph{zero-cost} transitions (simple function calls).\nWe modify the Lucet Wasm compiler and its runtime to use zero-cost transitions,\neliminating the undue performance tax on systems that rely on Lucet for\nsandboxing (e.g., we speed up image and font rendering in Firefox by up to\n29.7\\% and 10\\% respectively). To remove the Lucet compiler and its correct\nimplementation of the Wasm specification from the trusted computing base, we\n(1) develop a \\emph{static binary verifier}, VeriZero, which (in seconds)\nchecks that binaries produced by Lucet satisfy our zero-cost conditions, and\n(2) prove the soundness of VeriZero by developing a logical relation that\ncaptures when a compiled Wasm function is semantically well-behaved with\nrespect to our zero-cost conditions. Finally, we show that our model is useful\nbeyond Wasm by describing a new, purpose-built SFI system, SegmentZero32, that\nuses x86 segmentation and LLVM with mostly off-the-shelf passes to enforce our\nzero-cost conditions; our prototype performs on-par with the state-of-the-art\nNative Client SFI system.\n"}
+{"abstract": "  We study the properties of eigenvalues and corresponding eigenfunctions\ngenerated by a defect in the gaps of the spectrum of a high-contrast random\noperator. We consider a family of elliptic operators $\\mathcal{A}^\\varepsilon$\nin divergence form whose coefficients possess double porosity type scaling and\nare perturbed on a fixed-size compact domain. The coefficients of\n$\\mathcal{A}^\\varepsilon$ are random variables generated, in an appropriate\nsense, by an ergodic dynamical system. Working in the gaps of the limiting\nspectrum of the unperturbed operator $\\widehat{\\mathcal{A}}^\\varepsilon$, we\nshow that the point spectrum of $\\mathcal{A}^\\epsilon$ converges in the sense\nof Hausdorff to the point spectrum of the homogenised operator\n$\\mathcal{A}^\\mathrm{hom}$ as $\\varepsilon \\to 0$. Furthermore, we prove that\nthe eigenfunctions of $\\mathcal{A}^\\varepsilon$ decay exponentially at infinity\nuniformly for sufficiently small $\\varepsilon$. This, in turn, yields strong\nstochastic two-scale convergence of such eigenfunctions to eigenfunctions of\n$\\mathcal{A}^\\mathrm{hom}$.\n"}
+{"abstract": "  Given a $p$-adic group $G$ equipped with an action of a finite group\n$\\Gamma\\subset\\mathrm{Aut}_F(\\mathbf{G})$, and a reductive fixed-point subgroup\n$G^\\Gamma$, we establish a relationship between constructions of types for\nthese two groups due to Yu, Kim--Yu and Fintzen, and generalizes the\nrelationship between general linear and classical groups observed by Stevens.\nAs an application, given a parabolically induced or supercuspidal\nrepresentation $\\pi$ of $G$, we explicitly identify a number of the inertial\nequivalence classes occurring in the representation $\\pi|_{G^{[\\Gamma]}}$.\n"}
+{"abstract": "  Phytochemical composition of ethanol leaf extract of Diodella sarmentosa was\nprofiled with GC-MS and the inhibitory property of the extract against total\nmicrobial dehydrogenases were assessed. The major constituents of the extract\nwere squalene (29.50%), Phytol (24.68%), phenol, 3-pentadecyl- (18.58%),\n1-Butanol, 3-methyl- (9.09%) and n-Hexadecanoic acid (7.78%). The minimum\ninhibitory concentration (MIC) of the extract against broad spectrum of\nmicrobial population was assessed. Bacillus subtilis, Candidas spp, and\nPenicillium spp were more sensitive to the treatment and thus; were further\ninvestigated using Dehydrogenase activity assay method. Total dehydrogenase\nactivities of Bacillus subtilis, Candidas spp, and Penicillium spp at the\nextract concentration range of 0 to 2000mg/ml were progressively inhibited at\nincreasing extract concentrations. The threshold inhibitory concentrations\n(IC50) of the extracts against Candidas spp, Penicillium spp and Bacillus\nsubtilis were 275micro. g/ml, 322 micro. g/ml and 411 micro. g/ml respectively.\nOur findings suggested the extract as a useful source of antimicrobial\nphytochemicals for pharmaceutical use.\n"}
+{"abstract": "  A novel first-order moving-average model for analyzing time series observed\nat irregularly spaced intervals is introduced. Two definitions are presented,\nwhich are equivalent under Gaussianity. The first one relies on normally\ndistributed data and the specification of second-order moments. The second\ndefinition provided is more flexible in the sense that it allows for\nconsidering other distributional assumptions. The statistical properties are\ninvestigated along with the one-step linear predictors and their mean squared\nerrors. It is established that the process is strictly stationary under\nnormality and weakly stationary in the general case. Maximum likelihood and\nbootstrap estimation procedures are discussed and the finite-sample behavior of\nthese estimates is assessed through Monte Carlo experiments. In these\nsimulations, both methods perform well in terms of estimation bias and standard\nerrors, even with relatively small sample sizes. Moreover, we show that for\nnon-Gaussian data, for t-Student and Generalized errors distributions, the\nparameters of the model can be estimated precisely by maximum likelihood. The\nproposed IMA model is compared to the continuous autoregressive moving average\n(CARMA) models, exhibiting good performance. Finally, the practical application\nand usefulness of the proposed model are illustrated with two real-life data\nexamples.\n"}
+{"abstract": "  The warm absorbers observed in more than half of all nearby active galactic\nnuclei (AGN) are tracers of ionized outflows located at parsec scale distances\nfrom the central engine. If the smallest inferred ionization parameters\ncorrespond to plasma at a few $10^4$~K, then the gas undergoes a transition\nfrom being bound to unbound provided it is further heated to $\\sim 10^6$~K at\nlarger radii. Dannen et al. recently discovered that under these circumstances,\nthermally driven wind solutions are unsteady and even show very dense clumps\ndue to thermal instability. To explore the observational consequences of these\nnew wind solutions, we compute line profiles based on the one-dimensional\nsimulations of Dannen et al. We show how the line profiles from even a simple\nsteady state wind solution depend on the ionization energy (IE) of absorbing\nions, which is a reflection of the wind ionization stratification. To organize\nthe diversity of the line shapes, we group them into four categories: weak\nGaussians, saturated boxy profiles with and without an extended blue wing, and\nbroad weak profiles. The lines with profiles in the last two categories are\nproduced by ions with the highest IE that probe the fastest regions. Their\nmaximum blueshifts agree with the highest flow velocities in thermally unstable\nmodels, both steady state and clumpy versions. In contrast, the maximum\nblueshifts of the most high IE lines in thermally stable models can be less\nthan half of the actual solution velocities. Clumpy solutions can additionally\nimprint distinguishable absorption troughs at widely separated velocities.\n"}
+{"abstract": "  Matching module plays a critical role in display advertising systems. Without\nquery from user, it is challenging for system to match user traffic and ads\nsuitably. System packs up a group of users with common properties such as the\nsame gender or similar shopping interests into a crowd. Here term crowd can be\nviewed as a tag over users. Then advertisers bid for different crowds and\ndeliver their ads to those targeted users. Matching module in most industrial\ndisplay advertising systems follows a two-stage paradigm. When receiving a user\nrequest, matching system (i) finds the crowds that the user belongs to; (ii)\nretrieves all ads that have targeted those crowds. However, in applications\nsuch as display advertising at Alibaba, with very large volumes of crowds and\nads, both stages of matching have to truncate the long-tailed parts for online\nserving, under limited latency. That's to say, not all ads have the chance to\nparticipate in online matching. This results in sub-optimal result for both\nadvertising performance and platform revenue. In this paper, we study the\ntruncation problem and propose a Truncation Free Matching System (TFMS). The\nbasic idea is to decouple the matching computation from the online pipeline.\nInstead of executing the two-stage matching when user visits, TFMS utilizes a\nnear-line truncation-free matching to pre-calculate and store those top\nvaluable ads for each user. Then the online pipeline just needs to fetch the\npre-stored ads as matching results. In this way, we can jump out of online\nsystem's latency and computation cost limitations, and leverage flexible\ncomputation resource to finish the user-ad matching. TFMS has been deployed in\nour productive system since 2019, bringing (i) more than 50% improvement of\nimpressions for advertisers who encountered truncation before, (ii) 9.4%\nRevenue Per Mile gain, which is significant enough for the business.\n"}
+{"abstract": "  Systems with absorbing configurations usually lead to a unique stationary\nprobability measure called quasi stationary state (QSS) defined with respect to\nthe survived samples. We show that the birth death diffusion (BBD) processes\nexhibit universal phases and phase transitions when the birth and death rates\ndepend on the instantaneous particle density and their time scales are\nexponentially separated from the diffusion rates. In absence of birth, these\nmodels exhibit non-unique QSSs and lead to an absorbing phase transition (APT)\nat some critical nonzero death rate; the usual notion of universality is broken\nas the critical exponents of APT here depend on the initial density\ndistribution.\n"}
+{"abstract": "  Here we prove that the Hilbert-Kunz mulitiplicity of a quadric hypersurface\nof dimension $d$ and odd characteristic $p\\geq 2d-4$ is bounded below by\n$1+m_d$, where $m_d$ is the $d^{th}$ coefficient in the expansion of\n$\\mbox{sec}+\\mbox{tan}$. This proves a part of the long standing conjecture of\nWatanabe-Yoshida. We also give an upper bound on the HK multiplicity of such a\nhypersurface.\n  We approach the question using the HK density function and the classification\nof ACM bundles on the smooth quadrics via matrix factorizations.\n"}
+{"abstract": "  The self-adjoint, positive Markov operator defined by the P\\'olya-Gamma Gibbs\nsampler (under a proper normal prior) is shown to be trace-class, which implies\nthat all non-zero elements of its spectrum are eigenvalues. Consequently, the\nspectral gap is $1-\\lambda_*$, where $\\lambda_* \\in [0,1)$ is the second\nlargest eigenvalue. A method of constructing an asymptotically valid confidence\ninterval for an upper bound on $\\lambda_*$ is developed by adapting the\nclassical Monte Carlo technique of Qin et al. (2019) to the P\\'olya-Gamma Gibbs\nsampler. The results are illustrated using the German credit data. It is also\nshown that, in general, uniform ergodicity does not imply the trace-class\nproperty, nor does the trace-class property imply uniform ergodicity.\n"}
+{"abstract": "  We prove a version of the strong Taylor's conjecture for stable graphs: if\n$G$ is a stable graph whose chromatic number is strictly greater than\n$\\beth_2(\\aleph_0)$ then $G$ contains all finite subgraphs of Sh$_n(\\omega)$\nand thus has elementary extensions of unbounded chromatic number. This\ncompletes the picture from our previous work. The main new model theoretic\ningredient is a generalization of the classical construction of\nEhrenfeucht-Mostowski models to an infinitary setting, giving a new\ncharacterization of stability.\n"}
+{"abstract": "  The excitation of soliton states in optical layers exhibiting Kerr\nnonlinearities is theoretically investigated. The optical transmission\ncoefficient is obtained as a function of the nonlinearity power of an incident\nmonochromatic electromagnetic wave polarized in the transverse electric\nconfiguration. In materials exhibiting a defocusing nonlinearity, soliton\nexcitations are not observed if the nonlinearity power associated with the\nincident electromagnetic wave is below a well-defined value. At such a limiting\nvalue of the nonlinearity power, a soliton excitation with an electric-field\namplitude independent of the position is found within the nonlinear layer,\nregardless of the incident-wave frequency value.\n"}
+{"abstract": "  In order to further overcome the difficulties of the existing models in\ndealing with the non-stationary and nonlinear characteristics of high-frequency\nfinancial time series data, especially its weak generalization ability, this\npaper proposes an ensemble method based on data denoising methods, including\nthe wavelet transform (WT) and singular spectrum analysis (SSA), and long-term\nshort-term memory neural network (LSTM) to build a data prediction model, The\nfinancial time series is decomposed and reconstructed by WT and SSA to denoise.\nUnder the condition of denoising, the smooth sequence with effective\ninformation is reconstructed. The smoothing sequence is introduced into LSTM\nand the predicted value is obtained. With the Dow Jones industrial average\nindex (DJIA) as the research object, the closing price of the DJIA every five\nminutes is divided into short-term (1 hour), medium-term (3 hours) and\nlong-term (6 hours) respectively. . Based on root mean square error (RMSE),\nmean absolute error (MAE), mean absolute percentage error (MAPE) and absolute\npercentage error standard deviation (SDAPE), the experimental results show that\nin the short-term, medium-term and long-term, data denoising can greatly\nimprove the accuracy and stability of the prediction, and can effectively\nimprove the generalization ability of LSTM prediction model. As WT and SSA can\nextract useful information from the original sequence and avoid overfitting,\nthe hybrid model can better grasp the sequence pattern of the closing price of\nthe DJIA. And the WT-LSTM model is better than the benchmark LSTM model and\nSSA-LSTM model.\n"}
+{"abstract": "  This paper describes an application, called Medici, designed to produce\nsynthetic data for social network graphs, which can be used for analysis,\nhypothesis testing and application development by researchers and practitioners\nin the field. It builds on previous work by providing an integrated system, and\na user friendly screen interface. It can be run with default values to produce\ngraph data and statistics, which can then be used for further processing. The\nsystem is made publicly available in a Github Java project. The annex provides\na user manual with a screen by screen guide.\n"}
+{"abstract": "  Leibniz's Monadology mentions perceptional and sentimental variations of the\nindividual in the city. It is the interaction of people with people and events.\nFilm festivals are highly sentimental events of multicultural cities. Each\nmovie has a different sentimental effect and the interactions with the movies\nhave reflections that can be observed on social media. This analysis aims to\napply distant reading on Berlinale tweets collected during the festival. On\ncontrary to close reading, distant reading let authors to observe patterns in\nlarge collection of data. The analysis is temporal and sentimental in\nmultilingual domain and strongly positive and negative time intervals are\nanalysed. For this purpose, we trained a deep sentiment network with\nmultilingual embeddings. These multilingual embeddings are aligned in latent\nspace. We trained the network with a multilingual dataset in three languages\nEnglish, German and Spanish. The trained algorithm has a 0.78 test score and\napplied on Tweets with Berlinale hashtag during the festival. Although the\nsentimental analysis does not reflect the award-winning films, we observe\nweekly routine on the relationship between sentimentality, which can mislead a\nclose reading analysis. We have also remarks on popularity of the director or\nactors.\n"}
+{"abstract": "  The field of Artificial Intelligence (AI) has undoubtedly received\nsignificant attention in recent years. AI is being adopted to provide solutions\nto problems in fields such as medicine, engineering, education, government and\nseveral other domains. In order to analyze the state of the art of research in\nthe field of AI, we present a systematic literature review focusing on the\nEvolution of AI programming languages. We followed the systematic literature\nreview method by searching relevant databases like SCOPUS, IEEE Xplore and\nGoogle Scholar. EndNote reference manager was used to catalog the relevant\nextracted papers. Our search returned a total of 6565 documents, whereof 69\nstudies were retained. Of the 69 retained studies, 15 documents discussed LISP\nprogramming language, another 34 discussed PROLOG programming language, the\nremaining 20 documents were spread between Logic and Object Oriented\nProgramming (LOOP), ARCHLOG, Epistemic Ontology Language with Constraints\n(EOLC), Python, C++, ADA and JAVA programming languages. This review provides\ninformation on the year of implementation, development team, capabilities,\nlimitations and applications of each of the AI programming languages discussed.\nThe information in this review could guide practitioners and researchers in AI\nto make the right choice of languages to implement their novel AI methods.\n"}
+{"abstract": "  Explainable AI has attracted much research attention in recent years with\nfeature attribution algorithms, which compute \"feature importance\" in\npredictions, becoming increasingly popular. However, there is little analysis\nof the validity of these algorithms as there is no \"ground truth\" in the\nexisting datasets to validate their correctness. In this work, we develop a\nmethod to quantitatively evaluate the correctness of XAI algorithms by creating\ndatasets with known explanation ground truth. To this end, we focus on the\nbinary classification problems. String datasets are constructed using formal\nlanguage derived from a grammar. A string is positive if and only if a certain\nproperty is fulfilled. Symbols serving as explanation ground truth in a\npositive string are part of an explanation if and only if they contributes to\nfulfilling the property. Two popular feature attribution explainers, Local\nInterpretable Model-agnostic Explanations (LIME) and SHapley Additive\nexPlanations (SHAP), are used in our experiments.We show that: (1)\nclassification accuracy is positively correlated with explanation accuracy; (2)\nSHAP provides more accurate explanations than LIME; (3) explanation accuracy is\nnegatively correlated with dataset complexity.\n"}
+{"abstract": "  We study a sub monolayer He-4 adsorbed on fluorographene (GF) and on\nhexagonal boron nitride (hBN) at low coverage. The adsorption potentials have\nbeen computed ab-initio with a suitable density functional theory including\ndispersion forces. The properties of the adsorbed He-4 atoms have been computed\nat finite temperature with path integral Monte Carlo and at T=0 K with\nvariational path integral. From both methods we find that the lowest energy\nstate of He-4 on GF is a superfluid. Due to the very large corrugation of the\nadsorption potential this superfluid has a very strong spatial anisotropy, the\nratio between the largest and smallest areal density being about 6, the\nsuperfluid fraction at the lowest T is about 55%, and the temperature of the\ntransition to the normal state is in the range 0.5-1 K. Thus, GF offers a\nplatform for studying the properties of a strongly interacting highly\nanisotropic bosonic superfluid. At a larger coverage He-4 has a transition to\nan ordered commensurate state with occupation of 1/6 of the adsorption sites.\nThis phase is stable up to a transition temperature located between 0.5 and\n1~K. The system has a triangular order similar to that of He-4 on graphite. The\nlowest energy state of He-4 on hBN is an ordered commensurate state with\noccupation of 1/3 of the adsorption sites and triangular symmetry. A disordered\nstate is present at lower coverage as a metastable state. In the presence of an\nelectric field the corrugation of the adsorption potential is slightly\nincreased but up to a magnitude of 1 V/Ang. the effect is small and does not\nchange the stability of the phases of He-4 on GF and hBN. We have verified that\nalso in the case of graphene such electric field does not modify the stability\nof the commensurate sqrt{3}*sqrt{3}R30 phase.\n"}
+{"abstract": "  Deceptive attacks exploiting the innate and the acquired vulnerabilities of\nhuman users have posed severe threats to information and infrastructure\nsecurity. This work proposes INADVERT, a systematic solution that generates\ninteractive visual aids in real-time to prevent users from inadvertence and\ncounter visual-deception attacks. Based on the eye-tracking outcomes and proper\ndata compression, the INADVERT platform automatically adapts the visual aids to\nthe user's varying attention status captured by the gaze location and duration.\nWe extract system-level metrics to evaluate the user's average attention level\nand characterize the magnitude and frequency of the user's mind-wandering\nbehaviors. These metrics contribute to an adaptive enhancement of the user's\nattention through reinforcement learning. To determine the optimal\nhyper-parameters in the attention enhancement mechanism, we develop an\nalgorithm based on Bayesian optimization to efficiently update the design of\nthe INADVERT platform and maximize the accuracy of the users' phishing\nrecognition.\n"}
+{"abstract": "  Photon is the fundamental quantum of electromagnetic fields, whose mass,\n$m_{\\gamma}$, should be strictly zero in Maxwell's theory. But not all theories\nadopt this hypothesis. If the rest mass of the photon is not zero, there will\nbe an additional time delay between photons of different frequencies after they\ntravel through a fixed distance. By analyzing the time delay, we can measure or\nconstrain the photon mass. Fast radio bursts (FRBs) -- transient radio bursts\ncharacterized by millisecond duration and cosmological propagation -- are\nexcellent astrophysical laboratories to constrain $m_{\\gamma}$. In this work we\nuse a catalog of 129 FRBs in a Bayesian framework to constrain $m_{\\gamma}$. As\na result, we obtain a new bound on the photon mass, $m_{\\gamma} \\leq 3.1\\times\n10^{-51}\\rm\\,kg\\simeq 1.7 \\times 10^{-15}\\,eV/c^2$ ($m_{\\gamma} \\leq 3.9\\times\n10^{-51}\\rm\\,kg \\simeq 2.2 \\times 10^{-15}\\,eV/c^2$) at the $68\\%$ $(95\\%$)\nconfidence level. The result represents the best limit purely from kinematic\nanalysis of light propagation. The bound on the photon mass will be tighter in\nthe near future with increment in the number of FRBs, more accurate measurement\nof the redshift for FRBs, and refinement in the knowledge about the origin of\ndispersion measures (DMs).\n"}
+{"abstract": "  Event-driven sensors such as LiDAR and dynamic vision sensor (DVS) have found\nincreased attention in high-resolution and high-speed applications. A lot of\nwork has been conducted to enhance recognition accuracy. However, the essential\ntopic of recognition delay or time efficiency is largely under-explored. In\nthis paper, we present a spiking learning system that uses the spiking neural\nnetwork (SNN) with a novel temporal coding for accurate and fast object\nrecognition. The proposed temporal coding scheme maps each event's arrival time\nand data into SNN spike time so that asynchronously-arrived events are\nprocessed immediately without delay. The scheme is integrated nicely with the\nSNN's asynchronous processing capability to enhance time efficiency. A key\nadvantage over existing systems is that the event accumulation time for each\nrecognition task is determined automatically by the system rather than pre-set\nby the user. The system can finish recognition early without waiting for all\nthe input events. Extensive experiments were conducted over a list of 7 LiDAR\nand DVS datasets. The results demonstrated that the proposed system had\nstate-of-the-art recognition accuracy while achieving remarkable time\nefficiency. Recognition delay was shown to reduce by 56.3% to 91.7% in various\nexperiment settings over the popular KITTI dataset.\n"}
+{"abstract": "  Real-time, guaranteed safe trajectory planning is vital for navigation in\nunknown environments. However, real-time navigation algorithms typically\nsacrifice robustness for computation speed. Alternatively, provably safe\ntrajectory planning tends to be too computationally intensive for real-time\nreplanning. We propose FaSTrack, Fast and Safe Tracking, a framework that\nachieves both real-time replanning and guaranteed safety. In this framework,\nreal-time computation is achieved by allowing any trajectory planner to use a\nsimplified \\textit{planning model} of the system. The plan is tracked by the\nsystem, represented by a more realistic, higher-dimensional \\textit{tracking\nmodel}. We precompute the tracking error bound (TEB) due to mismatch between\nthe two models and due to external disturbances. We also obtain the\ncorresponding tracking controller used to stay within the TEB. The\nprecomputation does not require prior knowledge of the environment. We\ndemonstrate FaSTrack using Hamilton-Jacobi reachability for precomputation and\nthree different real-time trajectory planners with three different\ntracking-planning model pairs.\n"}
+{"abstract": "  We revisit constraints on annihilating dark matter based on the global 21cm\nsignature observed by EDGES. For this purpose, we used the numerical data of\nthe latest N-body simulation performed by state-of-the-art standard in order to\nestimate the boost factor at high redshifts ($z$ = 10 - 100), which enhances\nthe annihilation of dark matter in course of structure formation. By taking\ninto account to what fraction injected energy from dark matter annihilation\ncontributes to ionization, excitation and heating of intergalactic medium\nduring dark ages, we estimated how large the global 21cm absorption can be. By\nassuming the thermal freezeout scenario, we find that $m_{\\rm DM} < 15$ GeV and\n$m_{\\rm DM} < 3$ GeV have been excluded at 95$\\%$ C.L. for the annihilation\nmodes into $b\\bar{b}$ and $e^+ e^-$, respectively.\n"}
+{"abstract": "  Advances in Reinforcement Learning (RL) span a wide variety of applications\nwhich motivate development in this area. While application tasks serve as\nsuitable benchmarks for real world problems, RL is seldomly used in practical\nscenarios consisting of abrupt dynamics. This allows one to rethink the problem\nsetup in light of practical challenges. We present Trade Execution using\nReinforcement Learning (TradeR) which aims to address two such practical\nchallenges of catastrophy and surprise minimization by formulating trading as a\nreal-world hierarchical RL problem. Through this lens, TradeR makes use of\nhierarchical RL to execute trade bids on high frequency real market experiences\ncomprising of abrupt price variations during the 2019 fiscal year COVID19 stock\nmarket crash. The framework utilizes an energy-based scheme in conjunction with\nsurprise value function for estimating and minimizing surprise. In a\nlarge-scale study of 35 stock symbols from the S&P500 index, TradeR\ndemonstrates robustness to abrupt price changes and catastrophic losses while\nmaintaining profitable outcomes. We hope that our work serves as a motivating\nexample for application of RL to practical problems.\n"}
+{"abstract": "  A generic qubit unitary operator affected by depolarizing noise is duplicated\nand inserted in a quantum switch process realizing a superposition of causal\norders. The characterization of the resulting switched quantum channel is\nworked out for its action on the joint state of the probe-control qubit pair.\nThe switched channel is then specifically investigated for the important\nmetrological task of phase estimation on the noisy unitary operator, with the\nperformance assessed by the Fisher information, classical or quantum. A\ncomparison is made with conventional techniques of estimation where the noisy\nunitary is directly probed in a one-stage or two-stage cascade with definite\norder, or several uses of them with two or more qubits. In the switched channel\nwith indefinite order, specific properties are reported, meaningful for\nestimation and not present with conventional techniques. It is shown that the\ncontrol qubit, although it never directly interacts with the unitary, can\nnevertheless be measured alone for effective estimation, while discarding the\nprobe qubit that interacts with the unitary. Also, measurement of the control\nqubit maintains the possibility of efficient estimation in difficult conditions\nwhere conventional estimation becomes less efficient, as with ill-configured\ninput probes, or in blind situations when the axis of the unitary is unknown.\nEffective estimation by measuring the control qubit remains possible even when\nthe input probe tends to align with the axis of the unitary, or with a fully\ndepolarized input probe, while in these conditions conventional estimation gets\ninoperative. Measurement of the probe qubit of the switched channel is also\nshown to add useful capabilities for phase estimation. The results contribute\nto the analysis of switched quantum channels with indefinite order for\ninformation processing, and uncover new possibilities for qubit metrology.\n"}
+{"abstract": "  The recent launch of Solar Orbiter and BepiColombo opened a brief window in\nwhich these two spacecraft were positioned in a constellation that allows for\nthe detailed sampling of any Earth-directed CMEs. Fortunately, two such events\noccurred with in situ detections of an ICME by Solar Orbiter on the 19th of\nApril and the 28th of May 2020. These two events were subsequently also\nobserved in situ by BepiColombo and Wind around a day later. We attempt to\nreconstruct the observed in situ magnetic field measurements for all three\nspacecraft simultaneously using an empirical magnetic flux rope model. This\nallows us to test the validity of our flux rope model on a larger and more\nglobal scale and allows for cross-validation of the analysis with different\nspacecraft combinations. Finally, we can also compare the results from the in\nsitu modeling to remote observations obtained from the STEREO-A heliospheric\nimagers. We make use of the 3D coronal rope ejection model in order to simulate\nthe ICME evolution. We adapt a previously developed ABC-SMC fitting algorithm\nfor the application to multi point scenarios. We show that we are able to\ngenerally reconstruct the flux ropes signatures at three different spacecraft\npositions simultaneously using our model in combination with the flux rope\nfitting algorithm. For the well-behaved 19th of April ICME our approach works\nvery well. The 28th of May ICME, on the other hand, shows the limitations of\nour approach. Unfortunately, the usage of multi-point observations for these\nevents does not appear to solve inherent issues, such as the estimation of the\nmagnetic field twist or flux rope aspect-ratios due to the specific\nconstellation of the spacecraft positions. As our general approach can be used\nfor any fast forward simulation-based model we give a blueprint for future\nstudies using more advanced ICME models.\n"}
+{"abstract": "  Disordered hyperuniformity (DHU) is a recently discovered novel state of\nmany-body systems that is characterized by vanishing normalized\ninfinite-wavelength density fluctuations similar to a perfect crystal, yet\npossesses an amorphous structure like a liquid or glass. Here we investigate\nequilibrium DHU states of Brownian particles induced by external potentials. In\nparticular, we analytically derive sufficient conditions on the external\npotentials in order to achieve distinct classes of DHU density distributions of\nBrownian particles in thermal equilibrium, based on the stationary-state\nsolutions of the corresponding Smoluchowski equation. We show for a wide\nspectrum of tight-binding potentials, the desirable DHU states of Brownian\nparticles can be controlled and achieved by imposing proper hyperuniformity\nconditions on the potentials. Moreover, we find that thermal motions in these\nsystems tend to enhance hyperuniformity. We also analyze the evolution dynamics\nof an initial density distribution (hyperuniform or non-hyperuniform) to the\ndesirable equilibrium DHU state determined by the prescribed external\npotentials, which is shown to be coupled with the full spectra of the force\nfields associated with the imposed potentials. We find that although the\ntransient density distribution can rapidly develop local patterns reminiscent\nof those in the equilibrium distribution, which is governed by the fast\ndynamics induced by the external potential, the overall distribution is still\nmodulated by the initial density fluctuations which are relaxed through slow\ndiffusive dynamics. Our study has implications for the fabrication of designer\nDHU materials.\n"}
+{"abstract": "  While even perfect numbers are completely characterized, the existence or\notherwise of odd perfect numbers is an open problem. We address that problem\nand prove that if a natural number is odd, then it's not perfect.\n"}
+{"abstract": "  The increasing availability of structured datasets, from Web tables and\nopen-data portals to enterprise data, opens up opportunities~to enrich\nanalytics and improve machine learning models through relational data\naugmentation. In this paper, we introduce a new class of data augmentation\nqueries: join-correlation queries. Given a column $Q$ and a join column $K_Q$\nfrom a query table $\\mathcal{T}_Q$, retrieve tables $\\mathcal{T}_X$ in a\ndataset collection such that $\\mathcal{T}_X$ is joinable with $\\mathcal{T}_Q$\non $K_Q$ and there is a column $C \\in \\mathcal{T}_X$ such that $Q$ is\ncorrelated with $C$. A na\\\"ive approach to evaluate these queries, which first\nfinds joinable tables and then explicitly joins and computes correlations\nbetween $Q$ and all columns of the discovered tables, is prohibitively\nexpensive. To efficiently support correlated column discovery, we 1) propose a\nsketching method that enables the construction of an index for a large number\nof tables and that provides accurate estimates for join-correlation queries,\nand 2) explore different scoring strategies that effectively rank the query\nresults based on how well the columns are correlated with the query. We carry\nout a detailed experimental evaluation, using both synthetic and real data,\nwhich shows that our sketches attain high accuracy and the scoring strategies\nlead to high-quality rankings.\n"}
+{"abstract": "  User authorization queries in the context of role-based access control have\nattracted considerable interest in the last 15 years. Such queries are used to\ndetermine whether it is possible to allocate a set of roles to a user that\nenables the user to complete a task, in the sense that all the permissions\nrequired to complete the task are assigned to the roles in that set. Answering\nsuch a query, in general, must take into account a number of factors,\nincluding, but not limited to, the roles to which the user is assigned and\nconstraints on the sets of roles that can be activated. Answering such a query\nis known to be NP-hard. The presence of multiple parameters and the need to\nfind efficient and exact solutions to the problem suggest that a multi-variate\napproach will enable us to better understand the complexity of the user\nauthorization query problem (UAQ). In this paper, we establish a number of\ncomplexity results for UAQ. Specifically, we show the problem remains hard even\nwhen quite restrictive conditions are imposed on the structure of the problem.\nOur FPT results show that we have to use either a parameter with potentially\nquite large values or quite a restricted version of UAQ. Moreover, our second\nFPT algorithm is complex and requires sophisticated, state-of-the-art\ntechniques. In short, our results show that it is unlikely that all variants of\nUAQ that arise in practice can be solved reasonably quickly in general.\n"}
+{"abstract": "  The Zakharov system in dimension $d=2,3$ is shown to have a local unique\nsolution for any initial values in the energy space $H^{s} \\times H^{l} \\times\nH^{l-1}$, where the range of regularity $(s, l)$ is extended, especially at\n$s=l-1$. The result is obtained mainly by the normal form reduction and the\nStrichartz estimates.\n"}
+{"abstract": "  Electron-positron pairs, produced in intense laser-solid interactions, are\ndiagnosed using magnetic spectrometers with image plates, such as the National\nIgnition Facility (NIF) Electron Positron Proton Spectrometers (EPPS). Although\nmodeling can help infer the quantitative value, the accuracy of the models\nneeds to be verified to ensure measurement quality. The dispersion of\nlow-energy electrons and positrons may be affected by fringe magnetic fields\nnear the entrance of the EPPS. We have calibrated the EPPS with six electron\nbeams from a Siemens Oncor linear accelerator (linac) ranging in energy from\n$2.7$--$15.2$ $\\mathrm{MeV}$ as they enter the spectrometer. A Geant4 TOPAS\nMonte-Carlo simulation was set up to match depth dose curves and lateral\nprofiles measured in water at $100$ $\\mathrm{cm}$ source-surface distance. An\naccurate relationship was established between the bending magnet current\nsetting and the energy of the electron beam at the exit window. The simulations\nand measurements were used to determine the energy distributions of the six\nelectron beams at the EPPS slit. Analysis of the scanned image plates together\nwith the determined energy distribution arriving in the spectrometer provide\nimproved dispersion curves for the EPPS.\n"}
+{"abstract": "  Using cryogenic transmission electron microscopy, we revealed three\ndimensional (3D) structural details of the electrochemically plated lithium\n(Li) flakes and their solid electrolyte interphase (SEI), including the\ncomposite SEI skin-layer and SEI fossil pieces buried inside the Li matrix. As\nthe SEI skin-layer is largely comprised of nanocrystalline LiF and Li2O in\namorphous polymeric matrix, when complete Li stripping occurs, the compromised\nSEI three-dimensional framework buckles, forming nanoscale bends and wrinkles.\nWe showed that the flexibility and resilience of the SEI skin-layer plays a\nvital role in preserving an intact SEI 3D framework after Li stripping. The\nintact SEI network enables the nucleation and growth of the newly plated Li\ninside the previously formed SEI network in the subsequent cycles, preventing\nadditional large amount of SEI formation between newly plated Li metal and the\nelectrolyte. In addition, cells cycled under the accurately controlled uniaxial\npressure can further enhance the repeated utilization of the SEI framework and\nimprove the coulombic efficiency (CE) by up to 97%, demonstrating an effective\nstrategy of reducing the formation of additional SEI and inactive dead Li. The\nidentification of such flexible and porous 3D SEI framework clarifies the\nworking mechanism of SEI in lithium metal anode for batteries. The insights\nprovided in this work will inspire researchers to design more functional\nartificial 3D SEI on other metal anodes to improve rechargeable metal battery\nwith long cycle life.\n"}
+{"abstract": "  In this dissertation, several components of large-scale solar flows are\nstudied observationally: solar equatorial Rossby waves (waves of radial\nvorticity), large-scale convection, and surface flows around active regions.\nMaps of horizontal flows are derived from photospheric observations by the\nHelioseismic and Magnetic Imager (HMI) aboard the Solar Dynamics Observatory\n(SDO) using two different techniques: granulation tracking and local\nhelioseismology. First, the eigenfunctions of solar Rossby waves are measured\nfrom helioseismic ring-diagram flow maps with a correlation method and a\nspectral analysis. Down to $9$ Mm below the surface, the dependence of the\nradial vorticity with radius $r$ is consistent with $r^{m-1}$, for a given\nlongitudinal wavenumber $m$. At the surface, the eigenfunctions are\ncomplex-valued. The real part decreases away from the equator and switches sign\naround $\\pm 20-30^\\circ$. The imaginary part is small, but nonzero, and may be\ndue to wave attenuation. This may have implications for the transport of\nangular momentum in the latitudinal direction. Second, we revisit previous\nmeasurements of power spectra of longitudinal velocities near the solar\nsurface, obtained from time-distance and ring-diagram helioseismology. Several\nissues in these past helioseismic analyses are identified and corrected. The\ncorrections are not sufficient to remove the discrepancy between the\nmeasurements. I thus present new velocity power spectra from granulation\ntracking and ring-diagram helioseismology. The two new measurements are close\nto each other near the solar surface, and the corresponding kinetic energy\ndecreases with increasing spatial scale.\n"}
+{"abstract": "  Iconology is a branch of art history that investigates the meaning of\nartworks in relation to their social and cultural background. Nowadays, several\ninterdisciplinary research fields leverage theoretical frameworks close to\niconology to pursue quantitative Art History with data science methods and\nSemantic Web technologies. However, while Iconographic studies have been\nrecently addressed in ontologies, a complete description of aspects relevant to\niconological studies is still missing. In this article, we present a\npreliminary study on eleven case studies selected from the literature and we\nenvision new terms for extending existing ontologies. We validate new terms\naccording to a common evaluation method and we discuss our results in the light\nof the opportunities that such an extended ontology would arise in the\ncommunity of Digital Art History.\n"}
+{"abstract": "  We retrace Davenport's solution to Wahba's classic problem of aligning two\npointclouds using the formalism of Geometric Algebra (GA). GA proves to be a\nnatural backdrop for this problem involving three-dimensional rotations due to\nthe isomorphism between unit-length quaternions and rotors. While the solution\nto this problem is not a new result, it is hoped that its treatment in GA will\nhave tutorial value as well as open the door to addressing more complex\nproblems in a similar way.\n"}
+{"abstract": "  A family of quantum fields is said to be strongly local if it generates a\nlocal net of von Neumann algebras. There are few methods of showing directly\nstrong locality of a quantum field. Among them, linear energy bounds are the\nmost widely used, yet a chiral conformal field of conformal weight $d>2$ cannot\nadmit linear energy bounds. In this paper we give a new direct method to prove\nstrong locality in two-dimensional conformal field theory. We prove that if a\nchiral conformal field satisfies an energy bound of degree $d-1$, then it also\nsatisfies a certain local version of the energy bound, and this in turn implies\nstrong locality. A central role in our proof is played by diffeomorphism\nsymmetry. As a concrete application, we show that the vertex operator algebra\ngiven by a unitary vacuum representation of the $\\mathcal{W}_3$-algebra is\nstrongly local. For central charge $c > 2$, this yields a new conformal net. We\nfurther prove that these nets do not satisfy strong additivity, and hence are\nnot completely rational.\n"}
+{"abstract": "  In this work, we explore different approaches to combine modalities for the\nproblem of automated age-suitability rating of movie trailers. First, we\nintroduce a new dataset containing videos of movie trailers in English\ndownloaded from IMDB and YouTube, along with their corresponding\nage-suitability rating labels. Secondly, we propose a multi-modal deep learning\npipeline addressing the movie trailer age suitability rating problem. This is\nthe first attempt to combine video, audio, and speech information for this\nproblem, and our experimental results show that multi-modal approaches\nsignificantly outperform the best mono and bimodal models in this task.\n"}
+{"abstract": "  This paper discusses the results obtained for different techniques applied\nfor performing the sentiment analysis of social media (Twitter) code-mixed text\nwritten in Hinglish. The various stages involved in performing the sentiment\nanalysis were data consolidation, data cleaning, data transformation and\nmodelling. Various data cleaning techniques were applied, data was cleaned in\nfive iterations and the results of experiments conducted were noted after each\niteration. Data was transformed using count vectorizer, one hot vectorizer,\ntf-idf vectorizer, doc2vec, word2vec and fasttext embeddings. The models were\ncreated using various machine learning algorithms such as SVM, KNN, Decision\nTrees, Random Forests, Naive Bayes, Logistic Regression, and ensemble voting\nclassifiers. The data was obtained from a task on Codalab competition website\nwhich was listed as Task:9 on the Semeval-2020 competition website. The models\ncreated were evaluated using the F1-score (macro). The best F1-score of 69.07\nwas achieved using ensemble voting classifier.\n"}
+{"abstract": "  We observed 51 sources in the Q-U-I JOint TEnerife (QUIJOTE) cosmological\nfields which were brighter than 1 Jy at 30 GHz in the Planck Point Source\nCatalogue (version 1), with the Very Large Array at 28 -- 40 GHz, in order to\ncharacterise their high-radio-frequency variability and polarization\nproperties. We find a roughly log-normal distribution of polarization fractions\nwith a median of 2%, in agreement with previous studies, and a median rotation\nmeasure (RM) of $\\approx$ 1110 rad m$^{-2}$ with one outlier up to $\\approx$\n64000 rad m$^{-2}$ which is among the highest RMs measured in quasar cores. We\nfind hints of a correlation between the total intensity flux density and median\npolarization fraction. We find 59% of sources are variable in total intensity,\nand 100% in polarization at $3\\sigma$ level, with no apparent correlation\nbetween total intensity variability and polarization variability. This\nindicates that it will be difficult to model these sources without simultaneous\npolarimetric monitoring observations and they will need to be masked for\ncosmological analysis.\n"}
+{"abstract": "  We report about observations of the solar U+III bursts on 5 June of 2020 by\nmeans of a new active antenna designed to receive radiation in 4-70 MHz. This\ninstrument can serve as a prototype of the ultra-long-wavelength radiotelescope\nfor observations on the farside of the Moon. Our analysis of experimental data\nis based on simultaneous records obtained with the antenna arrays GURT and NDA\nin high frequency and time resolution, e-Callisto network as well as by using\nthe space-based observatories STEREO and WIND. The results from this\nobservational study confirm the model of Reid and Kontar (2017).\n"}
+{"abstract": "  Continuously Variable Series Reactor (CVSR) can regulate its reactance on the\nac side using the nonlinear ferromagnetic core, shared by an ac and a dc\nwinding for applications like power flow control, oscillation damping, load\nbalancing and others. In order to use a CVSR in the power grid, it is essential\nto know its operating characteristics. The Gyrator-Capacitor approach has been\napplied to model the magnetic circuit coupled with the electrical circuit. In\nthe paper, we investigate the CVSR behavior in terms of induced voltages across\nthe dc winding, flux densities (B) through the core legs, and power exchange\nbetween the ac controlled and the dc control circuit.\n"}
+{"abstract": "  As the COVID19 spreads across the world, prevention measures are becoming the\nessential weapons to combat the pandemic in the period of crisis. The lockdown\nmeasure is the most controversial one as it imposes an overwhelming impact on\nour economy and society. Especially when and how to enforce the lockdown\nmeasures are the most challenging questions considering both economic and\nepidemiological costs. In this paper, we extend the classic SIR model to find\noptimal decision making to balance between economy and people's health during\nthe outbreak of COVID-19. In our model, we intend to solve a two phases\noptimization problem: policymakers control the lockdown rate to maximize the\noverall welfare of the society; people in different health statuses take\ndifferent decisions on their working hours and consumption to maximize their\nutility. We develop a novel method to estimate parameters for the model through\nvarious additional sources of data. We use the Cournot equilibrium to model\npeople's behavior and also consider the cost of death in order to leverage\nbetween economic and epidemic costs. The analysis of simulation results\nprovides scientific suggestions for policymakers to make critical decisions on\nwhen to start the lockdown and how strong it should be during the whole period\nof the outbreak. Although the model is originally proposed for the COVID19\npandemic, it can be generalized to address similar problems to control the\noutbreak of other infectious diseases with lockdown measures.\n"}
+{"abstract": "  We report an inverse-designed, high numerical aperture (~0.44), extended\ndepth of focus (EDOF) meta-optic, which exhibit a lens-like point spread\nfunction (PSF). The EDOF meta-optic maintains a focusing efficiency comparable\nto that of a hyperboloid metalens throughout its depth of focus. Exploiting the\nextended depth of focus and computational post-processing, we demonstrate\nbroadband imaging across the full visible spectrum using a 1 mm, f/1\nmeta-optic. Unlike other canonical EDOF meta-optics, characterized by phase\nmasks such as a log-asphere or cubic function, our design exhibits a highly\ninvariant PSF across ~290nm optical bandwidth, which leads to significantly\nimproved image quality, as quantified by structural similarity metrics.\n"}
+{"abstract": "  Over the last several decades, quantum entanglement has been intensively\nstudied for potential applications in quantum information science. The\nHong-Ou-Mandel (HOM) dip is the most important test tool for direct proof of\nentanglement between paired photons, whose coincidence detection results in\nanticorrelation due to photon bunching on a beam splitter. Although\nanticorrelation is due to destructive quantum interference between paired\nphotons, a wavelength-sensitive interference fringe has never been observed in\nany HOM-type experiments. Here, a typical HOM dip is investigated for entangled\nphoton pairs generated by parametric down conversion processes (SPDC) to\nunderstand fundamental physics of anticorrelation. In addition, a pure\ncoherence optics-based Hong-Ou-Mandel scheme is proposed and analyzed for\ngeneral understanding of anticorrelation in an interferometric system. This\nstudy sheds light on deterministic quantum correlation control and opens the\ndoor to potential applications of on-demand quantum information science.\n"}
+{"abstract": "  Imitation learning seeks to circumvent the difficulty in designing proper\nreward functions for training agents by utilizing expert behavior. With\nenvironments modeled as Markov Decision Processes (MDP), most of the existing\nimitation algorithms are contingent on the availability of expert\ndemonstrations in the same MDP as the one in which a new imitation policy is to\nbe learned. In this paper, we study the problem of how to imitate tasks when\nthere exist discrepancies between the expert and agent MDP. These discrepancies\nacross domains could include differing dynamics, viewpoint, or morphology; we\npresent a novel framework to learn correspondences across such domains.\nImportantly, in contrast to prior works, we use unpaired and unaligned\ntrajectories containing only states in the expert domain, to learn this\ncorrespondence. We utilize a cycle-consistency constraint on both the state\nspace and a domain agnostic latent space to do this. In addition, we enforce\nconsistency on the temporal position of states via a normalized position\nestimator function, to align the trajectories across the two domains. Once this\ncorrespondence is found, we can directly transfer the demonstrations on one\ndomain to the other and use it for imitation. Experiments across a wide variety\nof challenging domains demonstrate the efficacy of our approach.\n"}
+{"abstract": "  Context: Pull-based development model is widely used in open source, leading\nthe trends in distributed software development. One aspect which has garnered\nsignificant attention is studies on pull request decision - identifying factors\nfor explanation. Objective: This study builds on a decade long research on pull\nrequest decision to explain it. We empirically investigate how factors\ninfluence pull request decision and scenarios that change the influence of\nfactors. Method: We identify factors influencing pull request decision on\nGitHub through a systematic literature review and infer it by mining archival\ndata. We collect a total of 3,347,937 pull requests with 95 features from\n11,230 diverse projects on GitHub. Using this data, we explore the relations of\nthe factors to each other and build mixed-effect logistic regression models to\nempirically explain pull request decision. Results: Our study shows that a\nsmall number of factors explain pull request decision with the integrator same\nor different from the submitter as the most important factor. We also noted\nthat some factors are important only in special cases e.g., the percentage of\nfailed builds is important for pull request decision when continuous\nintegration is used.\n"}
+{"abstract": "  High-energy irradiation is a driver for atmospheric evaporation and mass loss\nin exoplanets. This work is based on data from eROSITA, the soft X-ray\ninstrument aboard SRG (Spectrum Roentgen Gamma) mission, as well as archival\ndata from other missions, we aim to characterise the high-energy environment of\nknown exoplanets and estimate their mass loss rates. We use X-ray source\ncatalogues from eROSITA, XMM-Newton, Chandra and ROSAT to derive X-ray\nluminosities of exoplanet host stars in the 0.2-2 keV energy band with an\nunderlying coronal, i.e. optically thin thermal spectrum. We present a\ncatalogue of stellar X-ray and EUV luminosities, exoplanetary X-ray and EUV\nirradiation fluxes and estimated mass loss rates for a total of 287 exoplanets,\n96 among them being characterised for the first time from new eROSITA\ndetections. We identify 14 first time X-ray detections of transiting exoplanets\nthat are subject to irradiation levels known to cause observable evaporation\nsignatures in other exoplanets, which makes them suitable targets for follow-up\nobservations.\n"}
+{"abstract": "  The noteworthy four-planet HR 8799 system teeters on the brink of\ngravitational instability and contains an A-type host star which is\ncharacteristic of the progenitors of the majority of known white dwarf\nplanetary system hosts. Gozdziewski and Migaszewski (2020) have demonstrated\nthat the system can retain all four planets for at least 1 Gyr along the main\nsequence if the planets evolve within an externally unperturbed 8:4:2:1 mean\nmotion resonance configuration. Here we propagate forward their most stable fit\nbeyond the main sequence, and incorporate external effects from Galactic tides\nand stellar flybys. We find that (i) giant branch mass loss always breaks the\nresonance, and usually triggers the ejection of two of the planets, (ii)\nstellar flybys and Galactic tides rarely break the resonance during the\nmain-sequence and giant branch phases, but play a crucial role in determining\nthe final planetary configurations around the eventual white dwarf host star,\nand (iii) the meanderings of the surviving planets vary significantly,\noccupying regions from under 1 au to thousands of au. The ubiquitous survival\nof at least one planet and the presence of the debris discs in the system\nshould allow for dynamical pathways for the white dwarf to be metal-polluted.\n"}
+{"abstract": "  We prove that every metric graph which is a tree has an orthonormal sequence\nof Laplace-eigenfunctions of full support. This implies that the number of\nnodal domains $\\nu_n$ of the $n$-th eigenfunction of the Laplacian with\nstandard conditions satisfies $\\nu_n/n \\to 1$ along a subsequence and has\npreviously only been known in special cases such as mutually rationally\ndependent or rationally independent side lengths. It shows in particular that\nthe Pleijel nodal domain asymptotics from two- or higher dimensional domains\ncannot occur on these graphs: Despite their more complicated topology, they\nstill behave as in the one-dimensional case. We prove an analogous result on\ngeneral metric graphs under the condition that they have at least one Dirichlet\nvertex. Furthermore, we generalize our results to Delta vertex conditions and\nto edgewise constant potentials. The main technical contribution is a new\nexpression for a secular function in which modifications to the graph, to\nvertex conditions, and to the potential are particularly easy to understand.\n"}
+{"abstract": "  In this work, we present a family of time and space high order finite volume\nschemes for the solution of the full Boltzmann equation. The velocity space is\napproximated by using a discrete ordinate approach while the collisional\nintegral is solved by spectral methods. The space reconstruction is realized by\nintegrating the distribution function, describing the state of the system, over\narbitrary shaped and closed control volumes using a Central Weighted ENO\n(CWENO) technique. Compared to other reconstruction methods, this approach\npermits to keep compact stencil sizes which is a remarkable property in the\ncontext of kinetic equations due to the considerable demand of computational\nresources. The full discretization is then obtained by combining the previous\nphase-space approximation with high order Implicit-Explicit (IMEX) Runge Kutta\nschemes. These methods guarantee stability, accuracy and preservation of the\nasymptotic state. Comparisons of the Boltzmann model with simpler relaxation\ntype kinetic models (like BGK) is proposed showing the capability of the\nBoltzmann equation to capture different physical solutions. The theoretical\norder of convergence is numerically measured in different regimes and the\nmethods are tested on several standard two-dimensional benchmark problems in\ncomparison with Direct Simulation Monte Carlo results. The article ends with a\nprototype engineering problem consisting of a subsonic and a supersonic flow\naround a NACA 0012 airfoil. All test cases are run with MPI parallelization on\nseveral threads, thus making the proposed methods suitable for parallel\ndistributed memory supercomputers.\n"}
+{"abstract": "  Deep learning is a rapidly developing approach in the field of infrared and\nvisible image fusion. In this context, the use of dense blocks in deep networks\nsignificantly improves the utilization of shallow information, and the\ncombination of the Generative Adversarial Network (GAN) also improves the\nfusion performance of two source images. We propose a new method based on dense\nblocks and GANs , and we directly insert the input image-visible light image in\neach layer of the entire network. We use SSIM and gradient loss functions that\nare more consistent with perception instead of mean square error loss. After\nthe adversarial training between the generator and the discriminator, we show\nthat a trained end-to-end fusion network -- the generator network -- is finally\nobtained. Our experiments show that the fused images obtained by our approach\nachieve good score based on multiple evaluation indicators. Further, our fused\nimages have better visual effects in multiple sets of contrasts, which are more\nsatisfying to human visual perception.\n"}
+{"abstract": "  For a map (function) $F(x):\\ftwo^n\\rightarrow\\ftwo^n$ and a given $y$ in the\nimage of $F$ the problem of \\emph{local inversion} of $F$ is to find all\ninverse images $x$ in $\\ftwo^n$ such that $y=F(x)$. In Cryptology, such a\nproblem arises in Cryptanalysis of One way Functions (OWFs). The well known\nTMTO attack in Cryptanalysis is a probabilistic algorithm for computing one\nsolution of local inversion using $O(\\sqrt N)$ order computation in offline as\nwell as online for $N=2^n$. This paper proposes a complete algorithm for\nsolving the local inversion problem which uses linear complexity for a unique\nsolution in a periodic orbit. The algorithm is shown to require an offline\ncomputation to solve a hard problem (possibly requiring exponential\ncomputation) and an online computation dependent on $y$ that of repeated\nforward evaluation $F(x)$ on points $x$ in $\\ff_{2^n}$ which is polynomial time\nat each evaluation. However the forward evaluation is repeated at most as many\nnumber of times as the Linear Complexity of the sequence $\\{y,F(y),\\ldots\\}$ to\nget one possible solution when this sequence is periodic. All other solutions\nare obtained in chains $\\{e,F(e),\\ldots\\}$ for all points $e$ in the Garden of\nEden (GOE) of the map $F$. Hence a solution $x$ exists iff either the former\nsequence is periodic or a solution occurs in a chain starting from a point in\nGOE. The online computation then turns out to be polynomial time $O(L^k)$ in\nthe linear complexity $L$ of the sequence to compute one possible solution in a\nperiodic orbit or $O(l)$ the chain length for a fixed $n$. Hence this is a\ncomplete algorithm for solving the problem of finding all rational solutions\n$x$ of the equation $F(x)=y$ for a given $y$ and a map $F$ in $\\ff_{2^n}$.\n"}
+{"abstract": "  We give a bijective proof of a result by R.~Mantaci and F.~Rakotondrajao from\n2003, regarding even and odd derangement with a fixed number of excedances. We\nrefine this result by also considering the set of right-to-left minima.\n"}
+{"abstract": "  Multi-modal clustering, which explores complementary information from\nmultiple modalities or views, has attracted people's increasing attentions.\nHowever, existing works rarely focus on extracting high-level semantic\ninformation of multiple modalities for clustering. In this paper, we propose\nContrastive Multi-Modal Clustering (CMMC) which can mine high-level semantic\ninformation via contrastive learning. Concretely, our framework consists of\nthree parts. (1) Multiple autoencoders are optimized to maintain each\nmodality's diversity to learn complementary information. (2) A feature\ncontrastive module is proposed to learn common high-level semantic features\nfrom different modalities. (3) A label contrastive module aims to learn\nconsistent cluster assignments for all modalities. By the proposed multi-modal\ncontrastive learning, the mutual information of high-level features is\nmaximized, while the diversity of the low-level latent features is maintained.\nIn addition, to utilize the learned high-level semantic features, we further\ngenerate pseudo labels by solving a maximum matching problem to fine-tune the\ncluster assignments. Extensive experiments demonstrate that CMMC has good\nscalability and outperforms state-of-the-art multi-modal clustering methods.\n"}
+{"abstract": "  In this paper, incremental decode-and-forward (IDF) and incremental selective\ndecode-and-forward (ISDF) relaying are proposed to improve the spectral\nefficiency of power line communication. Contrary to the traditional\ndecode-and-forward (DF) relaying, IDF and ISDF strategies utilize the relay\nonly if the direct link ceases to attain a certain information rate, thereby\nimproving the spectral efficiency. The path gain through the power line is\nassumed to be log-normally distributed with high distance-dependent attenuation\nand the additive noise is from a Bernoulli-Gaussian process. Closed-form\nexpressions for the outage probability, and approximate closed-form expressions\nfor the end-to-end average channel capacity and the average bit error rate for\nbinary phase-shift keying are derived. Furthermore, a closed-form expression\nfor the fraction of times the relay is in use is derived as a measure of the\nspectral efficiency. Comparative analysis of IDF and ISDF with traditional DF\nrelaying is presented. It is shown that IDF is a specific case of ISDF and can\nobtain optimal spectral efficiency without compromising the outage performance.\nBy employing power allocation to minimize the outage probability, it is\nrealized that the power should be allocated in accordance with the inter-node\ndistances and channel parameters.\n"}
+{"abstract": "  Location information is proven to benefit the deep learning models on\ncapturing the manifold structure of target objects, and accordingly boosts the\naccuracy of medical image segmentation. However, most existing methods encode\nthe location information in an implicit way, e.g. the distance transform maps,\nwhich describe the relative distance from each pixel to the contour boundary,\nfor the network to learn. These implicit approaches do not fully exploit the\nposition information (i.e. absolute location) of targets. In this paper, we\npropose a novel loss function, namely residual moment (RM) loss, to explicitly\nembed the location information of segmentation targets during the training of\ndeep learning networks. Particularly, motivated by image moments, the\nsegmentation prediction map and ground-truth map are weighted by coordinate\ninformation. Then our RM loss encourages the networks to maintain the\nconsistency between the two weighted maps, which promotes the segmentation\nnetworks to easily locate the targets and extract manifold-structure-related\nfeatures. We validate the proposed RM loss by conducting extensive experiments\non two publicly available datasets, i.e., 2D optic cup and disk segmentation\nand 3D left atrial segmentation. The experimental results demonstrate the\neffectiveness of our RM loss, which significantly boosts the accuracy of\nsegmentation networks.\n"}
+{"abstract": "  In monolayer transition metal dichalcogenide semiconductors, valley coherence\ndegrades rapidly due to a combination of fast scattering and inter-valley\nexchange interaction. This leads to a sub-picosecond valley coherence time,\nmaking coherent manipulation of exciton a highly formidable task. Using\nmonolayer MoS$_2$ sandwiched between top and bottom graphene, here we\ndemonstrate perfect valley coherence by observing 100% degree of linear\npolarization of excitons in steady state photoluminescence. This is achieved in\nthis unique design through a combined effect of (a) suppression in exchange\ninteraction due to enhanced dielectric screening, (b) reduction in exciton\nlifetime due to a fast inter-layer transfer to graphene, and (c) operating in\nthe motional narrowing regime. We disentangle the role of the key parameters\naffecting valley coherence by using a combination of calculation (solutions of\nBethe-Salpeter and steady-state Maialle-Silva-Sham equations) and choice of\nsystematic design of experiments using four different stacks with varying\nscreening and exciton lifetime. To the best of our knowledge, this is the first\nreport in which the valley coherence timescale has been significantly enhanced\nbeyond the exciton radiative lifetime in monolayer semiconductors.\n"}
+{"abstract": "  The dominant physical formation mechanism(s) for ultra-diffuse galaxies\n(UDGs) is still poorly understood. Here, we combine new, deep imaging from the\nJeanne Rich Telescope with deep integral field spectroscopy from the Keck II\ntelescope to investigate the formation of UDG1137+16 (dw1137+16). Our new\nanalyses confirm both its environmental association with the low density UGC\n6594 group, along with its large size of 3.3 kpc and status as a UDG. The new\nimaging reveals two distinct stellar components for UDG1137+16, indicating that\na central stellar body is surrounded by an outer stellar envelope undergoing\ntidal interaction. Both the components have approximately similar stellar\nmasses. From our integral field spectroscopy we measure a stellar velocity\ndispersion within the half-light radius (15 $\\pm$ 4 $\\mathrm{km\\ s^{-1}}$) and\nfind that UDG1137+16 is similar to some other UDGs in that it is likely dark\nmatter dominated. Incorporating literature measurements, we also examine the\ncurrent state of UDG observational kinematics. Placing these data on the\ncentral stellar velocity dispersion -- stellar mass relation, we suggest there\nis little evidence for UDG1137+16 being created through a strong tidal\ninteraction. Finally, we investigate the constraining power current dynamical\nmass estimates (from stellar and globular cluster velocity dispersions) have on\nthe total halo mass of UDGs. As most are measured within the half-light radius,\nthey are unable to accurately constrain UDG total halo masses.\n"}
+{"abstract": "  We will use analytic function theory and Fourier analysis to establish a\ncharacterization for some classical umbral calculus, which will focus on the\ngeneralization of the evaluation function. Although we cannot cover all the\numbral calculus people care about, the part about Bernoulli numbers can still\nanswer an open question about fractional sums raised by M\\\"uller and Schleicher\nin 2005 and synthesize some common analysis results. We will only develop the\nresults which are sufficient to serve the purpose of this article, but at the\nsame time, we will briefly mention some possible extensions.\n"}
+{"abstract": "  Single-turn coil (STC) technique is a convenient way to generate ultrahigh\nmagnetic fields of more than 100 T. During the field generation, the STC\nexplosively destructs outward due to the Maxwell stress and Joule heating.\nUnfortunately, the STC does not work at its full potential because it has\nalready expanded when the maximum magnetic field is reached. Here, we propose\nan easy way to delay the expansion and increase the maximum field by using a\nmass-loaded STC. By loading clay on the STC, the field profile drastically\nchanges, and the maximum field increases by 4 %. This method offers an access\nto higher magnetic fields for physical property measurements.\n"}
+{"abstract": "  In Machine Learning, feature selection entails selecting a subset of the\navailable features in a dataset to use for model development. There are many\nmotivations for feature selection, it may result in better models, it may\nprovide insight into the data and it may deliver economies in data gathering or\ndata processing. For these reasons feature selection has received a lot of\nattention in data analytics research. In this paper we provide an overview of\nthe main methods and present practical examples with Python implementations.\nWhile the main focus is on supervised feature selection techniques, we also\ncover some feature transformation methods.\n"}
+{"abstract": "  We present a theoretical method for deriving the stress tensor and elastic\nresponse of ordered systems within a Ginzburg-Landau type density field theory\nin the linear regime. This is based on spatially coarse graining the\nmicroscopic stress which is determined by the variation of a free energy with\nrespect to mass displacements. We find simple expressions for the stress tensor\nfor phase field crystal (PFC) models for different crystal symmetries in two\nand three dimensions. Using tetradic product sums of reciprocal lattice\nvectors, we calculate elastic constants and show that they are directly related\nto the symmetries of the reciprocal lattices. We also show that except for bcc\nlattices, there are regions of model parameters for which the elastic response\nis isotropic. The predicted elastic stress-strain curves are verified by\nnumerical strain-controlled bulk and shear deformations. Since the method is\nindependent of a reference state, it extends also to defected crystals. We\nexemplify this by considering an edge and screw dislocation in the simple cubic\nlattice.\n"}
+{"abstract": "  Recent advances in statistics introduced versions of the central limit\ntheorem for high-dimensional vectors, allowing for the construction of\nconfidence regions for high-dimensional parameters. In this note, $s$-sparsely\nconvex high-dimensional confidence regions are compared with respect to their\nvolume. Specific confidence regions which are based on $\\ell_p$-balls are found\nto have exponentially smaller volume than the corresponding hypercube. The\ntheoretical results are validated by a comprehensive simulation study.\n"}
+{"abstract": "  A quasiconformal tree is a doubling metric tree in which the diameter of each\narc is bounded above by a fixed multiple of the distance between its endpoints.\nIn this paper we show that every quasiconformal tree bi-Lipschitz embeds in\nsome Euclidean space, with the ambient dimension and the bi-Lipschitz constant\ndepending only on the doubling and bounded turning constants of the tree. This\nanswers Question 1.6 in \\cite{DV} (arXiv:2007.12297).\n"}
+{"abstract": "  Three-loop counterterms for the Standard Model (SM) revealed that the matrix\nof anomalous dimensions ($\\gamma$) of quarks is divergent in the $d \\to 4$\nlimit unless a carefully chosen non-Hermitian square-root of $Z$ matrix is used\nin the textbook formula for $\\gamma$. Here, an alternative prescription is\ngiven, which expresses $\\gamma$ and $\\beta$ functions directly in terms of\ncounterterms (instead of $\\sqrt{Z}$ and conventional `bare couplings') and\nproduces finite results. In the SM, this prescription $automatically$\nreproduces results obtained previously by adjusting $\\sqrt{Z}$.\n"}
+{"abstract": "  The PrandtlPlane aircraft has been recently considered as a possible\ncandidate to foster the ambition of a greener aviation. Despite the relevant\namount of research carried out in the last years, several aspects of this novel\nconfiguration still need further insight to pave the way to future\napplications. Among them, the coupled flight-dynamic and aeroelastic response\nis addressed in this work by means of a dedicated in-house framework. For the\nevaluation of the aerodynamic forces, an enhanced Doublet Lattice Method, able\nto take into account terms typically neglected by classic formulations, is\nemployed. First, flight-dynamic aspects are considered, showing how effects of\ninteraction between Short Period or Dutch Roll with elastic modes remarkably\ndeteriorate the flying qualities. Then, focus is on the aeroelastic stability\nof the aircraft. As observed also in previous literature efforts on this\nconfiguration, flutter onset is considerably different when considering the\naircraft being free in the air or fixed in space. Thanks to the adopted\nformulation it is shown how, for this PrandtlPlane, the aerodynamic coupling of\nelastic and rigid modes has a beneficial effect on flutter onset. However, the\ndifferent modal properties, consequence of the diverse boundary conditions,\nwhen switching from fixed-in-space to free-flying aircraft, also play a\nrelevant role in determining the occurrence of flutter. Whereas for the\nlongitudinal case both effects contribute increasing flutter speed, for the\nlateral-directional case the variation in modal properties has a detrimental\nand dominating effect, leading to a flutter speedwell within the flight\nenvelope.Finally,the work discusses the contributions of the additional terms\nmodeled by the enhanced Doublet Lattice Method, showing how they induce a\nconsiderable effect when modeling the flight dynamics of the flexible aircraft.\n"}
+{"abstract": "  In paper by I.T. Habibullin and our joint paper the algorithm for\nclassification of integrable equations with three independent variables was\nproposed. This method is based on the requirement of the existence of an\ninfinite set of Darboux integrable reductions and on the notion of the\ncharacteristic Lie-Rinehart algebras. The method was applied for the\nclassification of integrable cases of different subclasses of equations\n$u_{n,xy} = f(u_{n+1},u_n,u_{n-1}, u_{n,x},u_{n,y})$ of special forms. Under\nthis approach the novel integrable chain was obtained. In present paper we\nconstruct Lax pair for the novel chain. To construct the Lax pair, we use the\nscheme suggested in papers by E.V. Ferapontov. We also study the periodic\nreduction of the chain.\n"}
+{"abstract": "  Attitude estimation is the process of computing the orientation angles of an\nobject with respect to a fixed frame of reference. Gyroscope, accelerometer,\nand magnetometer are some of the fundamental sensors used in attitude\nestimation. The orientation angles computed from these sensors are combined\nusing the sensor fusion methodologies to obtain accurate estimates. The\ncomplementary filter is one of the widely adopted techniques whose performance\nis highly dependent on the appropriate selection of its gain parameters. This\npaper presents a novel cascaded architecture of the complementary filter that\nemploys a nonlinear and linear version of the complementary filter within one\nframework. The nonlinear version is used to correct the gyroscope bias, while\nthe linear version estimates the attitude angle. The significant advantage of\nthe proposed architecture is its independence of the filter parameters, thereby\navoiding tuning the filters gain parameters. The proposed architecture does not\nrequire any mathematical modeling of the system and is computationally\ninexpensive. The proposed methodology is applied to the real-world datasets,\nand the estimation results were found to be promising compared to the other\nstate-of-the-art algorithms.\n"}
+{"abstract": "  We conducted a survey of 67 graduate students enrolled in the Privacy and\nSecurity in Healthcare course at Indiana University Purdue University\nIndianapolis. This was done to measure user preference and their understanding\nof usability and security of three different Electronic Health Records\nauthentication methods: single authentication method (username and password),\nSingle sign-on with Central Authentication Service (CAS) authentication method,\nand a bio-capsule facial authentication method. This research aims to explore\nthe relationship between security and usability, and measure the effect of\nperceived security on usability in these three aforementioned authentication\nmethods. We developed a formative-formative Partial Least Square Structural\nEquation Modeling (PLS-SEM) model to measure the relationship between the\nlatent variables of Usability, and Security. The measurement model was\ndeveloped using five observed variables (measures). - Efficiency and\nEffectiveness, Satisfaction, Preference, Concerns, and Confidence. The results\nobtained highlight the importance and impact of these measures on the latent\nvariables and the relationship among the latent variables. From the PLS-SEM\nanalysis, it was found that security has a positive impact on usability for\nSingle sign-on and bio-capsule facial authentication methods. We conclude that\nthe facial authentication method was the most secure and usable among the three\nauthentication methods. Further, descriptive analysis was done to draw out the\ninteresting findings from the survey regarding the observed variables.\n"}
+{"abstract": "  Scientific CMOS image sensors are a modern alternative for a typical CCD\ndetectors, as they offer both low read-out noise, large sensitive area, and\nhigh frame rates. All these makes them promising devices for a modern\nwide-field sky surveys. However, the peculiarities of CMOS technology have to\nbe properly taken into account when analyzing the data. In order to\ncharacterize these, we performed an extensive laboratory testing of two Andor\ncameras based on sCMOS chips -- Andor Neo and Andor Marana. Here we report its\nresults, especially on the temporal stability, linearity and image persistence.\nWe also present the results of an on-sky testing of these sensors connected to\na wide-field lenses, and discuss its applications for an astronomical sky\nsurveys.\n"}
+{"abstract": "  In this paper, we study the existence of minimizers of the Sobolev quotient\nfor a class of nonlocal operators with an orthotropic structure having\ndifferent exponents of integrability and different orders of differentiability.\nOur method is based on the concentration-compactness principle which we extend\nto this class of operators. One consequence of our main result is the existence\nof a nontrivial nonnegative solution to the corresponding critical problem.\n"}
+{"abstract": "  With the successes of $f(R)$ theory as a neutral modification of Einstein's\ngeneral relativity (GR), we continue our study in this field and attempt to\nfind general %natural { neutral} and charged black hole (BH) solutions. In the\nprevious papers \\cite{Nashed:2020mnp,Nashed:2020tbp}, we applied the field\nequation of the $f(R)$ gravity to a spherically symmetric space-time\n$ds^2=-U(r)dt^2+\\frac{dr^2}{V(r)}+r^2 \\left( d\\theta^2+\\sin^2\\theta d\\phi^2\n\\right)$ with unequal metric potentials $U(r)$ and $V(r)$ and with/without\nelectric charge. {Then we have obtained equations which include all the\npossible static solutions with spherical symmetry.} To ensure the closed form\nof system of the resulting differential equations in order to obtain specific\nsolutions, we assumed the derivative of the $f(R)$ with respect to the scalar\ncurvature $R$ to have a form %$F_1(r)=\\frac{df(R(r))}{dR(r)} \\propto\n%{\\color{red} 1 +} %\\frac{c}{r^n}$ but in case $n>2$, the resulting black hole\nsolutions with/without charge do not %generate asymptotically GR BH solutions\nin the limit $c\\rightarrow 0$ which means that the only case that can generate\nGR BHs is $n=2$. %In this paper, we assume another form, i.e., $F_1(r)\n{=\\frac{df(R(r))}{dR(r)} } = 1-\\frac{F_0-\\left(n-3\\right)}{r^n}$ with a\nconstant $F_0$ and show that we can generate asymptotically GR BH solutions for\n$n>2$ but we show that the $n=2$ case is not allowed. This form of $F_1(r)$\ncould be the most acceptable physical form that we can generate from it\nphysical metric potentials that can have a well-known asymptotic form and we\nobtain the metric of the Einstein general relativity in the limit of $F_0\\to\nn-3$. We show that the form of the electric charge depends on $n$ and that\n$n\\neq 2$.}\n"}
+{"abstract": "  Online algorithms for detecting changepoints, or abrupt shifts in the\nbehavior of a time series, are often deployed with limited resources, e.g., to\nedge computing settings such as mobile phones or industrial sensors. In these\nscenarios it may be beneficial to trade the cost of collecting an environmental\nmeasurement against the quality or \"fidelity\" of this measurement and how the\nmeasurement affects changepoint estimation. For instance, one might decide\nbetween inertial measurements or GPS to determine changepoints for motion. A\nBayesian approach to changepoint detection is particularly appealing because we\ncan represent our posterior uncertainty about changepoints and make active,\ncost-sensitive decisions about data fidelity to reduce this posterior\nuncertainty. Moreover, the total cost could be dramatically lowered through\nactive fidelity switching, while remaining robust to changes in data\ndistribution. We propose a multi-fidelity approach that makes cost-sensitive\ndecisions about which data fidelity to collect based on maximizing information\ngain with respect to changepoints. We evaluate this framework on synthetic,\nvideo, and audio data and show that this information-based approach results in\naccurate predictions while reducing total cost.\n"}
+{"abstract": "  A Q-system in a C* 2-category is a unitary version of a separable Frobenius\nalgebra object and can be viewed as a unitary version of a higher idempotent.\nWe define a higher unitary idempotent completion for C* 2-categories called\nQ-system completion and study its properties. We show that the C* 2-category of\nright correspondences of unital C*-algebras is Q-system complete by\nconstructing an inverse realization $\\dag$ 2-functor. We use this result to\nconstruct induced actions of group theoretical unitary fusion categories on\ncontinuous trace C*-algebras with connected spectra.\n"}
+{"abstract": "  Network science is the field dedicated to the investigation and analysis of\ncomplex systems via their representations as networks. We normally model such\nnetworks as graphs: sets of nodes connected by sets of edges and a number of\nnode and edge attributes. This deceptively simple object is the starting point\nof never-ending complexity, due to its ability to represent almost every facet\nof reality: chemical interactions, protein pathways inside cells, neural\nconnections inside the brain, scientific collaborations, financial relations,\ncitations in art history, just to name a few examples. If we hope to make sense\nof complex networks, we need to master a large analytic toolbox: graph and\nprobability theory, linear algebra, statistical physics, machine learning,\ncombinatorics, and more.\n  This book aims at providing the first access to all these tools. It is\nintended as an \"Atlas\", because its interest is not in making you a specialist\nin using any of these techniques. Rather, after reading this book, you will\nhave a general understanding about the existence and the mechanics of all these\napproaches. You can use such an understanding as the starting point of your own\ncareer in the field of network science. This has been, so far, an\ninterdisciplinary endeavor. The founding fathers of this field come from many\ndifferent backgrounds: mathematics, sociology, computer science, physics,\nhistory, digital humanities, and more. This Atlas is charting your path to be\nsomething different from all of that: a pure network scientist.\n"}
+{"abstract": "  We investigate the noise sensitivity of the top eigenvector of a sparse\nrandom symmetric matrix. Let $v$ be the top eigenvector of an $N\\times N$\nsparse random symmetric matrix with an average of $d$ non-zero centered entries\nper row. We resample $k$ randomly chosen entries of the matrix and obtain\nanother realization of the random matrix with top eigenvector $v^{[k]}$.\nBuilding on recent results on sparse random matrices and a noise sensitivity\nanalysis previously developed for Wigner matrices, we prove that, if $d\\geq\nN^{2/9}$, with high probability, when $k \\ll N^{5/3}$, the vectors $v$ and\n$v^{[k]}$ are almost collinear and, on the contrary, when $k\\gg N^{5/3}$, the\nvectors $v$ and $v^{[k]}$ are almost orthogonal. A similar result holds for the\neigenvector associated to the second largest eigenvalue of the adjacency matrix\nof an Erd\\H{o}s-R\\'enyi random graph with average degree $d \\geq N^{2/9}$.\n"}
+{"abstract": "  Faraday complexity describes whether a spectropolarimetric observation has\nsimple or complex magnetic structure. Quickly determining the Faraday\ncomplexity of a spectropolarimetric observation is important for processing\nlarge, polarised radio surveys. Finding simple sources lets us build rotation\nmeasure grids, and finding complex sources lets us follow these sources up with\nslower analysis techniques or further observations. We introduce five features\nthat can be used to train simple, interpretable machine learning classifiers\nfor estimating Faraday complexity. We train logistic regression and extreme\ngradient boosted tree classifiers on simulated polarised spectra using our\nfeatures, analyse their behaviour, and demonstrate our features are effective\nfor both simulated and real data. This is the first application of machine\nlearning methods to real spectropolarimetry data. With 95 per cent accuracy on\nsimulated ASKAP data and 90 per cent accuracy on simulated ATCA data, our\nmethod performs comparably to state-of-the-art convolutional neural networks\nwhile being simpler and easier to interpret. Logistic regression trained with\nour features behaves sensibly on real data and its outputs are useful for\nsorting polarised sources by apparent Faraday complexity.\n"}
+{"abstract": "  In this paper, the one-dimensional compressible Navier-Stokes system with\nouter pressure boundary conditions is investigated. Under some suitable\nassumptions, we prove that the specific volume and the temperature are bounded\nfrom below and above independently of time, and then give the local and global\nexistence of strong solutions. Furthermore, we also obtain the convergence of\nthe global strong solution to a stationary state and the nonlinearly stability\nof its convergence. It is worth noticing that all the assumptions imposed on\nthe initial data are the same as Takeyuki Nagasawa [Japan.J.Appl.Math.(1988)].\nTherefore, our work can be regarded as an improvement of the results of\nTakeyuki.\n"}
+{"abstract": "  Linear-time algorithms that are traditionally used to shuffle data on CPUs,\nsuch as the method of Fisher-Yates, are not well suited to implementation on\nGPUs due to inherent sequential dependencies. Moreover, existing parallel\nshuffling algorithms show unsatisfactory performance on GPU architectures\nbecause they incur a large number of read/write operations to high latency\nglobal memory. To address this, we provide a method of generating pseudo-random\npermutations in parallel by fusing suitable pseudo-random bijective functions\nwith stream compaction operations. Our algorithm, termed `bijective shuffle'\ntrades increased per-thread arithmetic operations for reduced global memory\ntransactions. It is work-efficient, deterministic, and only requires a single\nglobal memory read and write per shuffle input, thus maximising use of global\nmemory bandwidth. To empirically demonstrate the correctness of the algorithm,\nwe develop a consistent, linear time, statistical test for the quality of\npseudo-random permutations based on kernel space embeddings. Empirical results\nshow that the bijective shuffle algorithm outperforms competing algorithms on\nmulticore CPUs and GPUs, showing improvements of between one and two orders of\nmagnitude and approaching peak device bandwidth.\n"}
+{"abstract": "  OBJECTIVE: Leverage existing biomedical NLP tools and DS domain terminology\nto produce a novel and comprehensive knowledge graph containing dietary\nsupplement (DS) information for discovering interactions between DS and drugs,\nor Drug-Supplement Interactions (DSI). MATERIALS AND METHODS: We created\nSemRepDS (an extension of SemRep), capable of extracting semantic relations\nfrom abstracts by leveraging a DS-specific terminology (iDISK) containing\n28,884 DS terms not found in the UMLS. PubMed abstracts were processed using\nSemRepDS to generate semantic relations, which were then filtered using a\nPubMedBERT-based model to remove incorrect relations before generating our\nknowledge graph (SuppKG). Two pathways are used to identify potential DS-Drug\ninteractions which are then evaluated by medical professionals for mechanistic\nplausibility. RESULTS: Comparison analysis found that SemRepDS returned 206.9%\nmore DS relations and 158.5% more DS entities than SemRep. The fine-tuned BERT\nmodel obtained an F1 score of 0.8605 and removed 43.86% of the relations,\nimproving the precision of the relations by 26.4% compared to pre-filtering.\nSuppKG consists of 2,928 DS-specific nodes. Manual review of findings\nidentified 44 (88%) proposed DS-Gene-Drug and 32 (64%) proposed\nDS-Gene1-Function-Gene2-Drug pathways to be mechanistically plausible.\nDISCUSSION: The additional relations extracted using SemRepDS generated SuppKG\nthat was used to find plausible DSI not found in the current literature. By the\nnature of the SuppKG, these interactions are unlikely to have been found using\nSemRep without the expanded DS terminology. CONCLUSION: We successfully extend\nSemRep to include DS information and produce SuppKG which can be used to find\npotential DS-Drug interactions.\n"}
+{"abstract": "  Data scientists often collaborate with clients to analyze data to meet a\nclient's needs. What does the end-to-end workflow of a data scientist's\ncollaboration with clients look like throughout the lifetime of a project? To\ninvestigate this question, we interviewed ten data scientists (5 female, 4\nmale, 1 non-binary) in diverse roles across industry and academia. We\ndiscovered that they work with clients in a six-stage outer-loop workflow,\nwhich involves 1) laying groundwork by building trust before a project begins,\n2) orienting to the constraints of the client's environment, 3) collaboratively\nframing the problem, 4) bridging the gap between data science and domain\nexpertise, 5) the inner loop of technical data analysis work, 6) counseling to\nhelp clients emotionally cope with analysis results. This novel outer-loop\nworkflow contributes to CSCW by expanding the notion of what collaboration\nmeans in data science beyond the widely-known inner-loop technical workflow\nstages of acquiring, cleaning, analyzing, modeling, and visualizing data. We\nconclude by discussing the implications of our findings for data science\neducation, parallels to design work, and unmet needs for tool development.\n"}
+{"abstract": "  It is demonstrated theoretically that the circularly polarized irradiation of\ntwo-dimensional conducting systems can produce the composite bosons consisting\nof two electrons with different effective masses (different charge carriers),\nwhich are stable due to the Fermi sea of conduction electrons. As a result, the\noptically induced mixture of paired electrons and normal conduction electrons\n(the hybrid Bose-Fermi system) appears. Elementary excitations in such a hybrid\nsystem are analyzed and possible manifestations of the light-induced electron\npairing are discussed for semiconductor quantum wells.\n"}
+{"abstract": "  This paper focuses on the modulation instability, conservation laws and\nlocalized wave solutions of the generalized coupled Fokas-Lenells equation.\nBased on the theory of linear stability analysis, distribution pattern of\nmodulation instability gain G in the (K,k) frequency plane is depicted, and the\nconstraints for the existence of rogue waves are derived. Subsequently, we\nconstruct the infinitely many conservation laws for the generalized coupled\nFokas-Lenells equation from the Riccati-type formulas of the Lax pair. In\naddition, the compact determinant expressions of the N-order localized wave\nsolutions are given via generalized Darboux transformation, including\nhigher-order rogue waves and interaction solutions among rogue waves with\nbright-dark solitons or breathers. These solutions are parameter controllable:\n(m_i,n_i) and (\\alpha,\\beta) control the structure and ridge deflection of\nsolution respectively, while the value of |d| controls the strength of\ninteraction to realize energy exchange. Especially, when d=0, the interaction\nsolutions degenerate into the corresponding order of rogue waves.\n"}
+{"abstract": "  Tensor algebra finds applications in various domains, and these applications,\nespecially when accelerated on spatial hardware accelerators, can deliver high\nperformance and low power. Spatial hardware accelerator exhibits complex design\nspace. Prior approaches based on manual implementation lead to low programming\nproductivity, rendering thorough design space exploration impossible. In this\npaper, we propose TensorLib, a framework for generating spatial hardware\naccelerator for tensor algebra applications. TensorLib is motivated by the\nobservation that, different dataflows share common hardware modules, which can\nbe reused across different designs. To build such a framework, TensorLib first\nuses Space-Time Transformation to explore different dataflows, which can\ncompactly represent the hardware dataflow using a simple transformation matrix.\nNext, we identify the common structures of different dataflows and build\nparameterized hardware module templates with Chisel. Our generation framework\ncan select the needed hardware modules for each dataflow, connect the modules\nusing a specified interconnection pattern, and automatically generate the\ncomplete hardware accelerator design. TensorLib remarkably improves the\nproductivity for the development and optimization of spatial hardware\narchitecture, providing a rich design space with trade-offs in performance,\narea, and power. Experiments show that TensorLib can automatically generate\nhardware designs with different dataflows and achieve 21\\% performance\nimprovement on FPGA compared to the state-of-the-arts.\n"}
+{"abstract": "  In this paper we demonstrate that valley-ridge inflection (VRI) points of a\npotential energy surface (PES) have a dynamical influence on the fate of\ntrajectories of the underlying Hamiltonian system. These points have attracted\nthe attention of chemists in the past decades when studying selectivity\nproblems in organic chemical reactions whose energy landscape exhibits a\npost-transition-state bifurcation in the region between two sequential saddles\nwithout an intervening energy minimum. To address the dynamical significance of\nvalley-ridge inflection points, we construct a symmetric potential energy\nfunction that allows us to move the location of the VRI point while keeping the\nlocations and energies of the critical points fixed. In this setup, we carry\nout a parametric study of the dynamical behavior of ensembles of trajectories\nin terms of the energy of the chemical system and the position of the VRI\npoint. Our analysis reveals that the location of the VRI point controls the\nfraction of trajectories that recross the high energy saddle region of the PES\nwithout entering either of the potential wells that are separated by the low\nenergy saddle.\n"}
+{"abstract": "  Measuring biases of vision systems with respect to protected attributes like\ngender and age is critical as these systems gain widespread use in society.\nHowever, significant correlations between attributes in benchmark datasets make\nit difficult to separate algorithmic bias from dataset bias. To mitigate such\nattribute confounding during bias analysis, we propose a matching approach that\nselects a subset of images from the full dataset with balanced attribute\ndistributions across protected attributes. Our matching approach first projects\nreal images onto a generative adversarial network (GAN)'s latent space in a\nmanner that preserves semantic attributes. It then finds image matches in this\nlatent space across a chosen protected attribute, yielding a dataset where\nsemantic and perceptual attributes are balanced across the protected attribute.\nWe validate projection and matching strategies with qualitative, quantitative,\nand human annotation experiments. We demonstrate our work in the context of\ngender bias in multiple open-source facial-recognition classifiers and find\nthat bias persists after removing key confounders via matching. Code and\ndocumentation to reproduce the results here and apply the methods to new data\nis available at https://github.com/csinva/matching-with-gans .\n"}
+{"abstract": "  We revisit the corrections to black holes due to the $R^4$ terms in the\naction. We discuss corrections to the metric and possible scalar fields, as\nwell as corrections to thermodynamic quantities. We also comment on the large\n$D$ limit of the solutions.\n"}
+{"abstract": "  In this article, we prove a toroidalization principle for finite actions on\nklt singularities. As an application, we prove that the Jordan property for the\nregional fundamental group of klt singularities can be realized geometrically:\nby extracting a toric singularity over the klt germ. In the course of the\nproof, we will prove statements about finite actions on dual complexes and\nalmost fixed points in the fibers of equivariant Fano type morphisms.\nFurthermore, we will prove that the rank of a fundamental group of the klt\nsingularity is bounded above by its regularity.\n"}
+{"abstract": "  Semi-supervised (SS) inference has received much attention in recent years.\nApart from a moderate-sized labeled data, L, the SS setting is characterized by\nan additional, much larger sized, unlabeled data, U. The setting of |U| >> |L|,\nmakes SS inference unique and different from the standard missing data\nproblems, owing to natural violation of the so-called 'positivity' or 'overlap'\nassumption. However, most of the SS literature implicitly assumes L and U to be\nequally distributed, i.e., no selection bias in the labeling. Inferential\nchallenges in missing at random (MAR) type labeling allowing for selection\nbias, are inevitably exacerbated by the decaying nature of the propensity score\n(PS). We address this gap for a prototype problem, the estimation of the\nresponse's mean. We propose a double robust SS (DRSS) mean estimator and give a\ncomplete characterization of its asymptotic properties. The proposed estimator\nis consistent as long as either the outcome or the PS model is correctly\nspecified. When both models are correctly specified, we provide inference\nresults with a non-standard consistency rate that depends on the smaller size\n|L|. The results are also extended to causal inference with imbalanced\ntreatment groups. Further, we provide several novel choices of models and\nestimators of the decaying PS, including a novel offset logistic model and a\nstratified labeling model. We present their properties under both high and low\ndimensional settings. These may be of independent interest. Lastly, we present\nextensive simulations and also a real data application.\n"}
+{"abstract": "  Many theoretical studies of the voter model (or variations thereupon) involve\norder parameters that are population-averaged. While enlightening, such\nquantities may obscure important statistical features that are only apparent on\nthe level of the individual. In this work, we ask which factors contribute to a\nsingle voter maintaining a long-term statistical bias for one opinion over the\nother in the face of social influence. To this end, a modified version of the\nnetwork voter model is proposed, which also incorporates quenched disorder in\nthe interaction strengths between individuals and the possibility of\nantagonistic relationships. We find that a sparse interaction network and\nheterogeneity in interaction strengths give rise to the possibility of\narbitrarily long-lived individual biases, even when there is no\npopulation-averaged bias for one opinion over the other. This is demonstrated\nby calculating the eigenvalue spectrum of the weighted network Laplacian using\nthe theory of sparse random matrices.\n"}
+{"abstract": "  We establish transportation cost inequalities (TCI) with respect to the\nquantum Wasserstein distance by introducing quantum extensions of well-known\nclassical methods: first, using a non-commutative version of Ollivier's coarse\nRicci curvature, we prove that high temperature Gibbs states of commuting\nHamiltonians on arbitrary hypergraphs $H=(V,E)$ satisfy a TCI with constant\nscaling as $O(|V|)$. Second, we argue that the temperature range for which the\nTCI holds can be enlarged by relating it to recently established modified\nlogarithmic Sobolev inequalities. Third, we prove that the inequality still\nholds for fixed points of arbitrary reversible local quantum Markov semigroups\non regular lattices, albeit with slightly worsened constants, under a seemingly\nweaker condition of local indistinguishability of the fixed points. Finally, we\nuse our framework to prove Gaussian concentration bounds for the distribution\nof eigenvalues of quasi-local observables and argue the usefulness of the TCI\nin proving the equivalence of the canonical and microcanonical ensembles and an\nexponential improvement over the weak Eigenstate Thermalization Hypothesis.\n"}
+{"abstract": "  Phenotypic noise underpins homeostasis and fitness of individual cells. Yet,\nthe extent to which noise shapes cell-to-population properties in microbial\nactive matter remains poorly understood. By quantifying variability in\nconfluent \\textit{E.coli} strains, we catalogue noise across different\nphenotypic traits. The noise, measured over different temperatures serving as\nproxy for cellular activity, spanned more than two orders of magnitude. The\nmaximum noise was associated with the cell geometry and the critical colony\narea at the onset of mono-to-multilayer transition (MTMT), while the lower\nbound was set by the critical time of the MTMT. Our results, supported by a\nhydrodynamic model, suggest that a trade-off between the noise in the cell\ngeometry and the growth rate can lead to the self-regulation of the MTMT\ntiming. The MTMT cascades synchronous emergence of hydrodynamic fields,\nactively enhancing the micro-environmental transport. Our results highlight how\ninterplay of phenotypic noise triggers emergent deterministic properties, and\nreveal the role of multifield topology--of the colony structure and\nhydrodynamics--to insulate confluent systems from the inherent noise associated\nwith natural cell-environment settings.\n"}
+{"abstract": "  Within the field of instance segmentation, most of the state-of-the-art deep\nlearning networks rely nowadays on cascade architectures, where multiple object\ndetectors are trained sequentially, re-sampling the ground truth at each step.\nThis offers a solution to the problem of exponentially vanishing positive\nsamples. However, it also translates into an increase in network complexity in\nterms of the number of parameters. To address this issue, we propose\nRecursively Refined R-CNN (R^3-CNN) which avoids duplicates by introducing a\nloop mechanism instead. At the same time, it achieves a quality boost using a\nrecursive re-sampling technique, where a specific IoU quality is utilized in\neach recursion to eventually equally cover the positive spectrum. Our\nexperiments highlight the specific encoding of the loop mechanism in the\nweights, requiring its usage at inference time. The R^3-CNN architecture is\nable to surpass the recently proposed HTC model, while reducing the number of\nparameters significantly. Experiments on COCO minival 2017 dataset show\nperformance boost independently from the utilized baseline model. The code is\navailable online at https://github.com/IMPLabUniPr/mmdetection/tree/r3_cnn.\n"}
+{"abstract": "  Using a phase-field model which incorporates enhanced diffusion at the\nnanowire surfaces, we study the effect of different parameters on the stability\nof intersecting nanowires. Our study shows that at the intersection of\nnanowires, sintering (curvature driven material flow) leads to the formation of\njunctions. These junctions act the initiators of nanowire break-up. The\nsubsequent break-ups take place due to Rayleigh instability at the arms away\nfrom these junctions. Finally, at long time scales, the fragments coarsen due\nto the differences in sizes. The radii of the nanowires that form the junction,\nthe difference in size of the intersecting nanowires and the angle of\nintersection play a dominant role in determining the kinetics of break-up while\nthe density of intersections has little or no effect on the kinetics. We\nrationalise our results using maps of (i) mean curvatures (and, hence, chemical\npotentials), and, (ii) Interfacial Shape Distributions (ISDs) (which are based\non probability densities associated with different combinations of the two\nprincipal curvatures). Finally, we use the moment of inertia tensor to\ncharacterise the (non-spherical) shapes and morphologies of (central) nanowire\nfragments at the junctions.\n"}
+{"abstract": "  Learning meaningful representations of data is an important aspect of machine\nlearning and has recently been successfully applied to many domains like\nlanguage understanding or computer vision. Instead of training a model for one\nspecific task, representation learning is about training a model to capture all\nuseful information in the underlying data and make it accessible for a\npredictor. For predictive process analytics, it is essential to have all\nexplanatory characteristics of a process instance available when making\npredictions about the future, as well as for clustering and anomaly detection.\nDue to the large variety of perspectives and types within business process\ndata, generating a good representation is a challenging task. In this paper, we\npropose a novel approach for representation learning of business process\ninstances which can process and combine most perspectives in an event log. In\nconjunction with a self-supervised pre-training method, we show the\ncapabilities of the approach through a visualization of the representation\nspace and case retrieval. Furthermore, the pre-trained model is fine-tuned to\nmultiple process prediction tasks and demonstrates its effectiveness in\ncomparison with existing approaches.\n"}
+{"abstract": "  This is a tutorial and survey paper on kernels, kernel methods, and related\nfields. We start with reviewing the history of kernels in functional analysis\nand machine learning. Then, Mercer kernel, Hilbert and Banach spaces,\nReproducing Kernel Hilbert Space (RKHS), Mercer's theorem and its proof,\nfrequently used kernels, kernel construction from distance metric, important\nclasses of kernels (including bounded, integrally positive definite, universal,\nstationary, and characteristic kernels), kernel centering and normalization,\nand eigenfunctions are explained in detail. Then, we introduce types of use of\nkernels in machine learning including kernel methods (such as kernel support\nvector machines), kernel learning by semi-definite programming, Hilbert-Schmidt\nindependence criterion, maximum mean discrepancy, kernel mean embedding, and\nkernel dimensionality reduction. We also cover rank and factorization of kernel\nmatrix as well as the approximation of eigenfunctions and kernels using the\nNystr{\\\"o}m method. This paper can be useful for various fields of science\nincluding machine learning, dimensionality reduction, functional analysis in\nmathematics, and mathematical physics in quantum mechanics.\n"}
+{"abstract": "  Smartphones have enabled effortless capturing and sharing of documents in\ndigital form. The documents, however, often undergo various types of\ndegradation due to aging, stains, or shortcoming of capturing environment such\nas shadow, non-uniform lighting, etc., which reduces the comprehensibility of\nthe document images. In this work, we consider the problem of document image\ncleanup on embedded applications such as smartphone apps, which usually have\nmemory, energy, and latency limitations due to the device and/or for best human\nuser experience. We propose a light-weight encoder decoder based convolutional\nneural network architecture for removing the noisy elements from document\nimages. To compensate for generalization performance with a low network\ncapacity, we incorporate the perceptual loss for knowledge transfer from\npre-trained deep CNN network in our loss function. In terms of the number of\nparameters and product-sum operations, our models are 65-1030 and 3-27 times,\nrespectively, smaller than existing state-of-the-art document enhancement\nmodels. Overall, the proposed models offer a favorable resource versus accuracy\ntrade-off and we empirically illustrate the efficacy of our approach on several\nreal-world benchmark datasets.\n"}
+{"abstract": "  In grammar-based compression a string is represented by a context-free\ngrammar, also called a straight-line program (SLP), that generates only that\nstring. We refine a recent balancing result stating that one can transform an\nSLP of size $g$ in linear time into an equivalent SLP of size $O(g)$ so that\nthe height of the unique derivation tree is $O(\\log N)$ where $N$ is the length\nof the represented string (FOCS 2019). We introduce a new class of balanced\nSLPs, called contracting SLPs, where for every rule $A \\to \\beta_1 \\dots\n\\beta_k$ the string length of every variable $\\beta_i$ on the right-hand side\nis smaller by a constant factor than the string length of $A$. In particular,\nthe derivation tree of a contracting SLP has the property that every subtree\nhas logarithmic height in its leaf size. We show that a given SLP of size $g$\ncan be transformed in linear time into an equivalent contracting SLP of size\n$O(g)$ with rules of constant length.\n  We present an application to the navigation problem in compressed unranked\ntrees, represented by forest straight-line programs (FSLPs). We extend a linear\nspace data structure by Reh and Sieber (2020) by the operation of moving to the\n$i$-th child in time $O(\\log d)$ where $d$ is the degree of the current node.\nContracting SLPs are also applied to the finger search problem over\nSLP-compressed strings where one wants to access positions near to a\npre-specified finger position, ideally in $O(\\log d)$ time where $d$ is the\ndistance between the accessed position and the finger. We give a linear space\nsolution where one can access symbols or move the finger in time $O(\\log d +\n\\log^{(t)} N)$ for any constant $t$ where $\\log^{(t)} N$ is the $t$-fold\nlogarithm of $N$. This improves a previous solution by Bille, Christiansen,\nCording, and G{\\o}rtz (2018) with access/move time $O(\\log d + \\log \\log N)$.\n"}
+{"abstract": "  For graphs $G$ and $H$, we say that $G$ is $H$-free if it does not contain\n$H$ as an induced subgraph. Already in the early 1980s Alekseev observed that\nif $H$ is connected, then the \\textsc{Max Weight Independent Set} problem\n(MWIS) remains \\textsc{NP}-hard in $H$-free graphs, unless $H$ is a path or a\nsubdivided claw, i.e., a graph obtained from the three-leaf star by subdividing\neach edge some number of times (possibly zero). Since then determining the\ncomplexity of MWIS in these remaining cases is one of the most important\nproblems in algorithmic graph theory.\n  A general belief is that the problem is polynomial-time solvable, which is\nwitnessed by algorithmic results for graphs excluding some small paths or\nsubdivided claws. A more conclusive evidence was given by the recent\nbreakthrough result by Gartland and Lokshtanov [FOCS 2020]: They proved that\nMWIS can be solved in quasipolynomial time in $H$-free graphs, where $H$ is any\nfixed path. If $H$ is an arbitrary subdivided claw, we know much less: The\nproblem admits a QPTAS and a subexponential-time algorithm [Chudnovsky et al.,\nSODA 2019].\n  In this paper we make an important step towards solving the problem by\nshowing that for any subdivided claw $H$, MWIS is polynomial-time solvable in\n$H$-free graphs of bounded degree.\n"}
+{"abstract": "  Adapter-based tuning has recently arisen as an alternative to fine-tuning. It\nworks by adding light-weight adapter modules to a pretrained language model\n(PrLM) and only updating the parameters of adapter modules when learning on a\ndownstream task. As such, it adds only a few trainable parameters per new task,\nallowing a high degree of parameter sharing. Prior studies have shown that\nadapter-based tuning often achieves comparable results to fine-tuning. However,\nexisting work only focuses on the parameter-efficient aspect of adapter-based\ntuning while lacking further investigation on its effectiveness. In this paper,\nwe study the latter. We first show that adapter-based tuning better mitigates\nforgetting issues than fine-tuning since it yields representations with less\ndeviation from those generated by the initial PrLM. We then empirically compare\nthe two tuning methods on several downstream NLP tasks and settings. We\ndemonstrate that 1) adapter-based tuning outperforms fine-tuning on\nlow-resource and cross-lingual tasks; 2) it is more robust to overfitting and\nless sensitive to changes in learning rates.\n"}
+{"abstract": "  Functional autonomous systems often realize complex tasks by utilizing state\nmachines comprised of discrete primitive behaviors and transitions between\nthese behaviors. This architecture has been widely studied in the context of\nquasi-static and dynamics-independent systems. However, applications of this\nconcept to dynamical systems are relatively sparse, despite extensive research\non individual dynamic primitive behaviors, which we refer to as \"motion\nprimitives.\" This paper formalizes a process to determine dynamic-state aware\nconditions for transitions between motion primitives in the context of safety.\nThe result is framed as a \"motion primitive graph\" that can be traversed by\nstandard graph search and planning algorithms to realize functional autonomy.\nTo demonstrate this framework, dynamic motion primitives -- including standing\nup, walking, and jumping -- and the transitions between these behaviors are\nexperimentally realized on a quadrupedal robot.\n"}
+{"abstract": "  We consider the problem of explainable $k$-medians and $k$-means introduced\nby Dasgupta, Frost, Moshkovitz, and Rashtchian~(ICML 2020). In this problem,\nour goal is to find a threshold decision tree that partitions data into $k$\nclusters and minimizes the $k$-medians or $k$-means objective. The obtained\nclustering is easy to interpret because every decision node of a threshold tree\nsplits data based on a single feature into two groups. We propose a new\nalgorithm for this problem which is $\\tilde O(\\log k)$ competitive with\n$k$-medians with $\\ell_1$ norm and $\\tilde O(k)$ competitive with $k$-means.\nThis is an improvement over the previous guarantees of $O(k)$ and $O(k^2)$ by\nDasgupta et al (2020). We also provide a new algorithm which is $O(\\log^{3/2}\nk)$ competitive for $k$-medians with $\\ell_2$ norm. Our first algorithm is\nnear-optimal: Dasgupta et al (2020) showed a lower bound of $\\Omega(\\log k)$\nfor $k$-medians; in this work, we prove a lower bound of $\\tilde\\Omega(k)$ for\n$k$-means. We also provide a lower bound of $\\Omega(\\log k)$ for $k$-medians\nwith $\\ell_2$ norm.\n"}
+{"abstract": "  We study the problem of building entity tagging systems by using a few rules\nas weak supervision. Previous methods mostly focus on disambiguation entity\ntypes based on contexts and expert-provided rules, while assuming entity spans\nare given. In this work, we propose a novel method TALLOR that bootstraps\nhigh-quality logical rules to train a neural tagger in a fully automated\nmanner. Specifically, we introduce compound rules that are composed from simple\nrules to increase the precision of boundary detection and generate more diverse\npseudo labels. We further design a dynamic label selection strategy to ensure\npseudo label quality and therefore avoid overfitting the neural tagger.\nExperiments on three datasets demonstrate that our method outperforms other\nweakly supervised methods and even rivals a state-of-the-art distantly\nsupervised tagger with a lexicon of over 2,000 terms when starting from only 20\nsimple rules. Our method can serve as a tool for rapidly building taggers in\nemerging domains and tasks. Case studies show that learned rules can\npotentially explain the predicted entities.\n"}
+{"abstract": "  User authentication through gait analysis is a promising application of\ndiscriminative neural networks -- particularly due to the ubiquity of the\nprimary sources of gait accelerometry, in-pocket cellphones. However,\nconventional machine learning models are often too large and computationally\nexpensive to enable inference on low-resource mobile devices. We propose that\nbinarized neural networks can act as robust discriminators, maintaining both an\nacceptable level of accuracy while also dramatically decreasing memory\nrequirements, thereby enabling on-device inference. To this end, we propose\nBiPedalNet, a compact CNN that nearly matches the state-of-the-art on the\nPadova gait dataset, with only 1/32 of the memory overhead.\n"}
+{"abstract": "  We study nodal sets of Steklov eigenfunctions in a bounded domain with\n$\\mathcal{C}^2$ boundary. Our first result is a lower bound for the Hausdorff\nmeasure of the nodal set: we show that for $u_{\\lambda}$ a Steklov\neigenfunction, with eigenvalue $\\lambda\\neq 0$,\n$\\mathcal{H}^{d-1}(\\{u_{\\lambda}=0\\})\\geq c_{\\Omega}$, where $c_{\\Omega}$ is\nindependent of $\\lambda$. We also prove an almost sharp upper bound, namely\n$\\mathcal{H}^{d-1}(\\{u_{\\lambda}=0\\})\\leq C_{\\Omega}\\lambda\\log(\\lambda+e)$.\n"}
+{"abstract": "  Document-level relation extraction aims to extract relations among multiple\nentity pairs from a document. Previously proposed graph-based or\ntransformer-based models utilize the entities independently, regardless of\nglobal information among relational triples. This paper approaches the problem\nby predicting an entity-level relation matrix to capture local and global\ninformation, parallel to the semantic segmentation task in computer vision.\nHerein, we propose a Document U-shaped Network for document-level relation\nextraction. Specifically, we leverage an encoder module to capture the context\ninformation of entities and a U-shaped segmentation module over the image-style\nfeature map to capture global interdependency among triples. Experimental\nresults show that our approach can obtain state-of-the-art performance on three\nbenchmark datasets DocRED, CDR, and GDA.\n"}
+{"abstract": "  We show that the threshold condition for two pump photons to convert into a\npair of the sideband ones in Kerr microresonators with high-quality factors\nbreaks the pump laser parameter space into a sequence of narrow in frequency\nand broad in power Arnold tongues. Instability tongues become a dominant\nfeature in resonators with the finesse dispersion parameter close to and above\none. As pump power is increased, the tongues expand and cross by forming a line\nof cusps, i.e., the threshold of complexity, where more sideband pairs become\nunstable. We elaborate theory for the tongues and threshold of complexity, and\nreport the synchronisation and frequency-domain symmetry breaking effects\ninside the tongues.\n"}
+{"abstract": "  Many existing mortality models follow the framework of classical factor\nmodels, such as the Lee-Carter model and its variants. Latent common factors in\nfactor models are defined as time-related mortality indices (such as $\\kappa_t$\nin the Lee-Carter model). Factor loadings, which capture the linear\nrelationship between age variables and latent common factors (such as $\\beta_x$\nin the Lee-Carter model), are assumed to be time-invariant in the classical\nframework. This assumption is usually too restrictive in reality as mortality\ndatasets typically span a long period of time. Driving forces such as medical\nimprovement of certain diseases, environmental changes and technological\nprogress may significantly influence the relationship of different variables.\nIn this paper, we first develop a factor model with time-varying factor\nloadings (time-varying factor model) as an extension of the classical factor\nmodel for mortality modelling. Two forecasting methods to extrapolate the\nfactor loadings, the local regression method and the naive method, are proposed\nfor the time-varying factor model. From the empirical data analysis, we find\nthat the new model can capture the empirical feature of time-varying factor\nloadings and improve mortality forecasting over different horizons and\ncountries. Further, we propose a novel approach based on change point analysis\nto estimate the optimal `boundary' between short-term and long-term\nforecasting, which is favoured by the local linear regression and naive method,\nrespectively. Additionally, simulation studies are provided to show the\nperformance of the time-varying factor model under various scenarios.\n"}
+{"abstract": "  As the roadside perception plays an increasingly significant role in the\nConnected Automated Vehicle Highway(CAVH) technologies, there are immediate\nneeds of challenging real-world roadside datasets for bench marking and\ntraining various computer vision tasks such as 2D/3D object detection and\nmulti-sensor fusion. In this paper, we firstly introduce a challenging\nBAAI-VANJEE roadside dataset which consist of LiDAR data and RGB images\ncollected by VANJEE smart base station placed on the roadside about 4.5m high.\nThis dataset contains 2500 frames of LiDAR data, 5000 frames of RGB images,\nincluding 20% collected at the same time. It also contains 12 classes of\nobjects, 74K 3D object annotations and 105K 2D object annotations. By providing\na real complex urban intersections and highway scenes, we expect the\nBAAI-VANJEE roadside dataset will actively assist the academic and industrial\ncircles to accelerate the innovation research and achievement transformation in\nthe field of intelligent transportation in big data era.\n"}
+{"abstract": "  Recent works on voice conversion (VC) focus on preserving the rhythm and the\nintonation as well as the linguistic content. To preserve these features from\nthe source, we decompose current non-parallel VC systems into two encoders and\none decoder. We analyze each module with several experiments and reassemble the\nbest components to propose Assem-VC, a new state-of-the-art any-to-many\nnon-parallel VC system. We also examine that PPG and Cotatron features are\nspeaker-dependent, and attempt to remove speaker identity with adversarial\ntraining. Code and audio samples are available at\nhttps://github.com/mindslab-ai/assem-vc.\n"}
+{"abstract": "  We calculate the resummed perturbative free energy of ${\\cal N}=4$\nsupersymmetric Yang-Mills in four spacetime dimensions ($\\text{SYM}_{4,4}$)\nthrough second order in the 't Hooft coupling $\\lambda$ at finite temperature\nand zero chemical potential. Our final result is ultraviolet finite and all\ninfrared divergences generated at three-loop level are canceled by summing over\n$\\text{SYM}_{4,4}$ ring diagrams. Non-analytic terms at ${\\cal\nO}({\\lambda}^{3/2}) $ and $ {\\cal O}({\\lambda}^2 \\log\\lambda )$ are generated\nby dressing the $A_0$ and scalar propagators. The gauge-field Debye mass $m_D$\nand the scalar thermal mass $M$ are determined from their corresponding\nfinite-temperature self-energies. Based on this, we obtain the three-loop\nthermodynamic functions of $\\text{SYM}_{4,4}$ to ${\\cal O}(\\lambda^2)$. We\ncompare our final result with prior results obtained in the weak- and\nstrong-coupling limits and construct a generalized Pad\\'{e} approximant that\ninterpolates between the weak-coupling result and the large-$N_c$\nstrong-coupling result. Our results suggest that the ${\\cal O}(\\lambda^2)$\nweak-coupling result for the scaled entropy density is a quantitatively\nreliable approximation to the scaled entropy density for $0 \\leq \\lambda\n\\lesssim 2$.\n"}
+{"abstract": "  In this paper, we study the shifting zeros with its heights and an analogue\nto difference radical. We focus on the Stothers-Mason theorem by using falling\nfactorials. As applications, we discuss the difference version of the Fermat\ntype functional equations. Some examples are given.\n"}
+{"abstract": "  We study a system of Skorokhod stochastic differential equations (SDEs)\nmodeling the pairwise dispersion (in spatial dimension $d=2$) of heavy\nparticles transported by a rough self-similar, turbulent flow with H\\\"{o}lder\nexponent $h\\in (0,1)$. Under the assumption that $h>0$ is sufficiently small,\nwe use Lyapunov methods and control theory to show that the Markovian system is\nnonexplosive and has a unique, exponentially attractive invariant probability\nmeasure. Furthermore, our Lyapunov construction is radially sharp and gives\npartial confirmation on a predicted asymptotic behavior with respect to the\nH\\\"{o}lder exponent $h$ of the invariant probability measure. A physical\ninterpretation of the asymptotics is that intermittent clustering is weakened\nwhen the carrier flow is sufficiently rough.\n"}
+{"abstract": "  We study the entanglement production for Dirac and Klein-Gordon fields in an\nexpanding spacetime characterized by the presence of torsion. Torsion is here\nconsidered according to the Einstein-Cartan theory with a conformally flat\nFriedmann-Robertson-Walker spacetime. In this framework, torsion is seen as an\nexternal field, fulfilling precise constraints directly got from the\ncosmological constant principle. For Dirac field, we find that torsion\nincreases the amount of entanglement. This turns out to be particularly evident\nfor small values of particle momentum. We discuss the roles of Pauli exclusion\nprinciple in view of our results, and, in particular, we propose an\ninterpretation of the two maxima that occur for the entanglement entropy in\npresence of torsion. For Klein-Gordon field, and differently from the Dirac\ncase, the model can be exactly solved by adopting the same scale factor as in\nthe Dirac case. Again, we show how torsion affects the amount of entanglement,\nproviding a robust physical motivation behind the increase or decrease of\nentanglement entropy. A direct comparison of our findings is also discussed in\nview of previous results derived in absence of torsion. To this end, we give\nprominence on how our expectations would change in terms of the coupling\nbetween torsion and the scale factor for both Dirac and Klein-Gordon fields.\n"}
+{"abstract": "  Recent development of Deep Reinforcement Learning (DRL) has demonstrated\nsuperior performance of neural networks in solving challenging problems with\nlarge or even continuous state spaces. One specific approach is to deploy\nneural networks to approximate value functions by minimising the Mean Squared\nBellman Error (MSBE) function. Despite great successes of DRL, development of\nreliable and efficient numerical algorithms to minimise the MSBE is still of\ngreat scientific interest and practical demand. Such a challenge is partially\ndue to the underlying optimisation problem being highly non-convex or using\nincomplete gradient information as done in Semi-Gradient algorithms. In this\nwork, we analyse the MSBE from a smooth optimisation perspective and develop an\nefficient Approximate Newton's algorithm.\n  First, we conduct a critical point analysis of the error function and provide\ntechnical insights on optimisation and design choices for neural networks. When\nthe existence of global minima is assumed and the objective fulfils certain\nconditions, suboptimal local minima can be avoided when using over-parametrised\nneural networks. We construct a Gauss Newton Residual Gradient algorithm based\non the analysis in two variations. The first variation applies to discrete\nstate spaces and exact learning. We confirm theoretical properties of this\nalgorithm such as being locally quadratically convergent to a global minimum\nnumerically. The second employs sampling and can be used in the continuous\nsetting. We demonstrate feasibility and generalisation capabilities of the\nproposed algorithm empirically using continuous control problems and provide a\nnumerical verification of our critical point analysis. We outline the\ndifficulties of combining Semi-Gradient approaches with Hessian information. To\nbenefit from second-order information complete derivatives of the MSBE must be\nconsidered during training.\n"}
+{"abstract": "  The influence of zinc ions on the thermochromic properties of polycrystalline\nCu$_{1-x}$Zn$_x$MoO$_4$ ($x$=0.10, 0.50, 0.90) solid solutions was studied by\nX-ray absorption spectroscopy at the Cu, Zn and Mo K-edges. Detailed structural\ninformation on the local environment of metal ions was obtained from the\nsimultaneous analysis of EXAFS spectra measured at three metal absorption edges\nusing the reverse Monte Carlo method. Thermochromic phase transition with the\nhysteretic behaviour between $\\alpha$ and $\\gamma$ phases was observed in\nCu$_{0.90}$Zn$_{0.10}$MoO$_4$ solid solution. It was found that the local\nenvironment of molybdenum ions is most susceptible to the substitution of\ncopper for zinc and, upon cooling, transforms from tetrahedral MoO$_4$ to\ndistorted octahedral MoO$_6$.\n"}
+{"abstract": "  Data movement between the CPU and main memory is a first-order obstacle\nagainst improving performance, scalability, and energy efficiency in modern\nsystems. Computer systems employ a range of techniques to reduce overheads tied\nto data movement, spanning from traditional mechanisms (e.g., deep multi-level\ncache hierarchies, aggressive hardware prefetchers) to emerging techniques such\nas Near-Data Processing (NDP), where some computation is moved close to memory.\nOur goal is to methodically identify potential sources of data movement over a\nbroad set of applications and to comprehensively compare traditional\ncompute-centric data movement mitigation techniques to more memory-centric\ntechniques, thereby developing a rigorous understanding of the best techniques\nto mitigate each source of data movement.\n  With this goal in mind, we perform the first large-scale characterization of\na wide variety of applications, across a wide range of application domains, to\nidentify fundamental program properties that lead to data movement to/from main\nmemory. We develop the first systematic methodology to classify applications\nbased on the sources contributing to data movement bottlenecks. From our\nlarge-scale characterization of 77K functions across 345 applications, we\nselect 144 functions to form the first open-source benchmark suite (DAMOV) for\nmain memory data movement studies. We select a diverse range of functions that\n(1) represent different types of data movement bottlenecks, and (2) come from a\nwide range of application domains. Using NDP as a case study, we identify new\ninsights about the different data movement bottlenecks and use these insights\nto determine the most suitable data movement mitigation mechanism for a\nparticular application. We open-source DAMOV and the complete source code for\nour new characterization methodology at https://github.com/CMU-SAFARI/DAMOV.\n"}
+{"abstract": "  Mobile health (mHealth) apps such as menstrual trackers provide a rich source\nof self-tracked health observations that can be leveraged for health-relevant\nresearch. However, such data streams have questionable reliability since they\nhinge on user adherence to the app. Therefore, it is crucial for researchers to\nseparate true behavior from self-tracking artifacts. By taking a machine\nlearning approach to modeling self-tracked cycle lengths, we can both make more\ninformed predictions and learn the underlying structure of the observed data.\nIn this work, we propose and evaluate a hierarchical, generative model for\npredicting next cycle length based on previously-tracked cycle lengths that\naccounts explicitly for the possibility of users skipping tracking their\nperiod. Our model offers several advantages: 1) accounting explicitly for\nself-tracking artifacts yields better prediction accuracy as likelihood of\nskipping increases; 2) because it is a generative model, predictions can be\nupdated online as a given cycle evolves, and we can gain interpretable insight\ninto how these predictions change over time; and 3) its hierarchical nature\nenables modeling of an individual's cycle length history while incorporating\npopulation-level information. Our experiments using mHealth cycle length data\nencompassing over 186,000 menstruators with over 2 million natural menstrual\ncycles show that our method yields state-of-the-art performance against neural\nnetwork-based and summary statistic-based baselines, while providing insights\non disentangling menstrual patterns from self-tracking artifacts. This work can\nbenefit users, mHealth app developers, and researchers in better understanding\ncycle patterns and user adherence.\n"}
+{"abstract": "  Bellwether effect refers to the existence of exemplary projects (called the\nBellwether) within a historical dataset to be used for improved prediction\nperformance. Recent studies have shown an implicit assumption of using recently\ncompleted projects (referred to as moving window) for improved prediction\naccuracy. In this paper, we investigate the Bellwether effect on software\neffort estimation accuracy using moving windows. The existence of the\nBellwether was empirically proven based on six postulations. We apply\nstatistical stratification and Markov chain methodology to select the\nBellwether moving window. The resulting Bellwether moving window is used to\npredict the software effort of a new project. Empirical results show that\nBellwether effect exist in chronological datasets with a set of exemplary and\nrecently completed projects representing the Bellwether moving window. Result\nfrom this study has shown that the use of Bellwether moving window with the\nGaussian weighting function significantly improve the prediction accuracy.\n"}
+{"abstract": "  Pre-trained Transformer language models (LM) have become go-to text\nrepresentation encoders. Prior research fine-tunes deep LMs to encode text\nsequences such as sentences and passages into single dense vector\nrepresentations for efficient text comparison and retrieval. However, dense\nencoders require a lot of data and sophisticated techniques to effectively\ntrain and suffer in low data situations. This paper finds a key reason is that\nstandard LMs' internal attention structure is not ready-to-use for dense\nencoders, which needs to aggregate text information into the dense\nrepresentation. We propose to pre-train towards dense encoder with a novel\nTransformer architecture, Condenser, where LM prediction CONditions on DENSE\nRepresentation. Our experiments show Condenser improves over standard LM by\nlarge margins on various text retrieval and similarity tasks.\n"}
+{"abstract": "  We develop a quenched thermodynamic formalism for open random dynamical\nsystems generated by finitely branched, piecewise-monotone mappings of the\ninterval. The openness refers to the presence of holes in the interval, which\nterminate trajectories once they enter; the holes may also be random. Our\nrandom driving is generated by an invertible, ergodic, measure-preserving\ntransformation $\\sigma$ on a probability space $(\\Omega,\\mathscr{F},m)$. For\neach $\\omega\\in\\Omega$ we associate a piecewise-monotone, surjective map\n$T_\\omega:I\\to I$, and a hole $H_\\omega\\subset I$; the map $T_\\omega$, the\nrandom potential $\\varphi_\\omega$, and the hole $H_\\omega$ generate the\ncorresponding open transfer operator $\\mathcal{L}_\\omega$. For a contracting\npotential, under a condition on the open random dynamics in the spirit of\nLiverani--Maume-Deschamps, we prove there exists a unique random probability\nmeasure $\\nu_\\omega$ supported on the survivor set ${X}_{\\omega,\\infty}$\nsatisfying $\\nu_{\\sigma(\\omega)}(\\mathcal{L}_\\omega\nf)=\\lambda_\\omega\\nu_\\omega(f)$. We also prove the existence of a unique random\nfamily of functions $q_\\omega$ that satisfy $\\mathcal{L}_\\omega\nq_\\omega=\\lambda_\\omega q_{\\sigma(\\omega)}$. These yield an ergodic random\ninvariant measure $\\mu=\\nu q$ supported on the global survivor set, while $q$\ncombined with the random closed conformal measure yields a unique random\nabsolutely continuous conditional invariant measure (RACCIM) $\\eta$ supported\non $I$. We prove quasi-compactness of the transfer operator cocycle and\nexponential decay of correlations for $\\mu$. Finally, the escape rates of the\nrandom closed conformal measure and the RACCIM $\\eta$ coincide, and are given\nin terms of the expected pressure, as is the Hausdorff dimension of the\nsurviving set $X_{\\omega,\\infty}$. We provide examples of our general theory,\nincluding random $\\beta$-transformations and random Lasota-Yorke maps.\n"}
+{"abstract": "  We present here a version of the Artin-Schreier polynomial that works in any\ncharacteristic. Let $C_p$ be the cyclic group of prime order $p$. Equivalently,\nwe prove one can lift degree $p$ cyclic $C_p$ extensions over local rings $R,M$\nwhere $R/M$ has characteristic $p$ and $R$ has arbitrary characterstic.\n  Let $\\rho \\in \\Bbb C$ be a primitive $p$ root of one. We first consider the\ncase $R$ has a primitive $p$ root of one, by which we mean that there is a\ngiven homomorphism $f: \\Bbb Z[\\rho] \\to R$. In this context we can write a\nspecific polynomial which generalizes the Artin-Schreier polynomial. We next\nconsider arbtitrary $R$ and construct a mixed characteristic \"generic\" Galois\nextension that proves the lifting result, but here we do not supply a\npolynomial.\n  It is useful to view these results in terms of Galois actions. If $C_p$ is\ngenerated by $\\sigma$, then Artin-Schreier polynomials describe $C_p$ Galois\nextensions generated by elements $\\theta$ where $\\sigma(\\theta) = \\theta + 1$.\nIf $\\rho' = f(\\rho)$, then our lifted Galois action is $\\sigma(\\theta) =\n\\rho'\\theta + 1$. In section two we descend this extension to rings without\nroot of one. In section three we use this new description of cyclic extensions,\ngive the obvious description of the corresponding cyclic algebras, and then\ndefine a new algebra which specializes to general differential crossed products\nin characteristic $p$ but which are cyclic in characteristic not $p$. The\nalgebra in question is generated by $x,y$ subject to the relation $xy -\n\\rho{yx} = 1$.\n"}
+{"abstract": "  Although the adoption rate of deep neural networks (DNNs) has tremendously\nincreased in recent years, a solution for their vulnerability against\nadversarial examples has not yet been found. As a result, substantial research\nefforts are dedicated to fix this weakness, with many studies typically using a\nsubset of source images to generate adversarial examples, treating every image\nin this subset as equal. We demonstrate that, in fact, not every source image\nis equally suited for this kind of assessment. To do so, we devise a\nlarge-scale model-to-model transferability scenario for which we meticulously\nanalyze the properties of adversarial examples, generated from every suitable\nsource image in ImageNet by making use of three of the most frequently deployed\nattacks. In this transferability scenario, which involves seven distinct DNN\nmodels, including the recently proposed vision transformers, we reveal that it\nis possible to have a difference of up to $12.5\\%$ in model-to-model\ntransferability success, $1.01$ in average $L_2$ perturbation, and $0.03$\n($8/225$) in average $L_{\\infty}$ perturbation when $1,000$ source images are\nsampled randomly among all suitable candidates. We then take one of the first\nsteps in evaluating the robustness of images used to create adversarial\nexamples, proposing a number of simple but effective methods to identify\nunsuitable source images, thus making it possible to mitigate extreme cases in\nexperimentation and support high-quality benchmarking.\n"}
+{"abstract": "  The event sequence of many diverse systems is represented as a sequence of\ndiscrete events in a continuous space. Examples of such an event sequence are\nearthquake aftershock events, financial transactions, e-commerce transactions,\nsocial network activity of a user, and the user's web search pattern. Finding\nsuch an intricate pattern helps discover which event will occur in the future\nand when it will occur. A Hawkes process is a mathematical tool used for\nmodeling such time series discrete events. Traditionally, the Hawkes process\nuses a critical component for modeling data as an intensity function with a\nparameterized kernel function. The Hawkes process's intensity function involves\ntwo components: the background intensity and the effect of events' history.\nHowever, such parameterized assumption can not capture future event\ncharacteristics using past events data precisely due to bias in modeling kernel\nfunction. This paper explores the recent advancement using novel deep\nlearning-based methods to model kernel function to remove such parametrized\nkernel function. In the end, we will give potential future research directions\nto improve modeling using the Hawkes process.\n"}
+{"abstract": "  In this work, we build upon the Active Inference (AIF) and Predictive Coding\n(PC) frameworks to propose a neural architecture comprising a generative model\nfor sensory prediction, and a distinct generative model for motor trajectories.\nWe highlight how sequences of sensory predictions can act as rails guiding\nlearning, control and online adaptation of motor trajectories. We furthermore\ninquire the effects of bidirectional interactions between the motor and the\nvisual modules. The architecture is tested on the control of a simulated\nrobotic arm learning to reproduce handwritten letters.\n"}
+{"abstract": "  The capacity of line networks with buffer size constraints is an open, but\npractically important problem. In this paper, we consider line networks formed\nby discrete memoryless channels, and study the achievable rate of batched\ncodes, which include several existing coding schemes studied in literature for\nline networks as special cases. A batched code has an outer code and an inner\ncode. The outer code encodes the information messages into batches, each of\nwhich is a group of coded symbols, while the inner code performs recoding on\nthe symbols belonging to the same batch at all nodes that forwarding a batch.\nBatched codes enable us to impose various block-length and buffer size\nconstraints by tuning coding parameters. Using a technique that captures the\ncommunication bottleneck of line networks, we derive upper bounds on the\nachievable rates of batched codes as functions of line network length for\nseveral coding parameter sets. We also discuss specific recoding schemes that\ncan achieve rates of the same order of the line network length for these coding\nparameter sets. Our results shed light on how to design large multi-hop network\ncommunications in practical scenarios using a unified coding framework instead\nof the traditional layered approach.\n"}
+{"abstract": "  Isospin-1/2 $D\\pi$ scattering amplitudes are computed using lattice QCD,\nworking in a single volume of approximately $(3.6\\; \\mathrm{fm})^3$ and with a\nlight quark mass corresponding to $m_\\pi\\approx239$ MeV. The spectrum of the\nelastic $D\\pi$ energy region is computed yielding 20 energy levels. Using the\nL\\\"uscher finite-volume quantisation condition, these energies are translated\ninto constraints on the infinite-volume scattering amplitudes and hence enable\nus to map out the energy dependence of elastic $D\\pi$ scattering. By\nanalytically continuing a range of scattering amplitudes, a $D_0^\\ast$\nresonance pole is consistently found strongly coupled to the $S$-wave $D\\pi$\nchannel, with a mass $m\\approx 2200$ MeV and a width $\\Gamma\\approx400$ MeV.\nCombined with earlier work investigating the $D_{s0}^\\ast$, and $D_0^\\ast$ with\nheavier light quarks, similar couplings between each of these scalar states and\ntheir relevant meson-meson scattering channels are determined. The mass of the\n$D_0^\\ast$ is consistently found well below that of the $D_{s0}^\\ast$, in\ncontrast to the currently reported experimental result.\n"}
+{"abstract": "  We prove that a quasi-finite endomorphism of an algebraic variety over an\nalgebraically closed field of characteristic zero, that is injective on the\ncomplement of a closed subvariety, is an automorphism. We also prove that an\nendomorphism of complex algebraic variety that is injective on the complement\nof a closed subvariety of codimension at least $2$, is an automorphism.\n"}
+{"abstract": "  Despite their simple formulation, short range classical antiferromagnetic\nIsing models on frustrated lattices give rise to exotic phases of matter, in\nparticular due to their macroscopic ground state degeneracy. Recent experiments\non artificial spin systems comprising arrays of chirally coupled nanomagnets\nprovide a significant strengthening of the nearest neighbour couplings compared\nto systems with dipolar-coupled nanomagnets. This opens the way to design\nartificial spin systems emulating Ising models with nearest neighbour\ncouplings. In this paper, we compare the results of an extensive investigation\nwith tensor network and Monte Carlo simulations of the nearest- and\nfurther-neighbour ($J_1-J_2-J_{3||}$) kagome Ising antiferromagnet with the\nexperimental spin-spin correlations of a kagome lattice of chirally coupled\nnanomagnets. Even though the ratios between the further neighbour couplings and\nthe nearest neighbour coupling estimated from micromagnetic simulations are\nmuch smaller than for dipolar-coupled nanomagnets, we show that they still play\nan essential role in the selection of the correlations.\n"}
+{"abstract": "  The Internet of Things (IoT) is affecting national innovation ecosystems and\nthe approach of organizations to innovation and how they create and capture\nvalue in everyday business activities. The Internet of Things (IoT), is\ndisruptive, and it will change the manner in which human resources are\ndeveloped and managed, calling for a new and adaptive human resource\ndevelopment approach. The Classical Internet communication form is human-human.\nThe prospect of IoT is that every object will have a unique way of\nidentification and can be addressed so that every object can be connected. The\ncommunication forms will expand from human-human to human-human, human-thing,\nand thing-thing. This will bring a new challenge to how Human Resource\nDevelopment (HRD) is practiced. This paper provides an overview of the Internet\nof Things and conceptualizes the role of HRD in the age of the Internet of\nThings. Keywords:\n"}
+{"abstract": "  It has been demonstrated many times that the behavior of the human visual\nsystem is connected to the statistics of natural images. Since machine learning\nrelies on the statistics of training data as well, the above connection has\ninteresting implications when using perceptual distances (which mimic the\nbehavior of the human visual system) as a loss function. In this paper, we aim\nto unravel the non-trivial relationships between the probability distribution\nof the data, perceptual distances, and unsupervised machine learning. To this\nend, we show that perceptual sensitivity is correlated with the probability of\nan image in its close neighborhood. We also explore the relation between\ndistances induced by autoencoders and the probability distribution of the\ntraining data, as well as how these induced distances are correlated with human\nperception. Finally, we find perceptual distances do not always lead to\nnoticeable gains in performance over Euclidean distance in common image\nprocessing tasks, except when data is scarce and the perceptual distance\nprovides regularization. We propose this may be due to a \\emph{double-counting}\neffect of the image statistics, once in the perceptual distance and once in the\ntraining procedure.\n"}
+{"abstract": "  The nodal line semimetals have attracted much attention due to their unique\ntopological electronic structure and exotic physical properties. A genuine\nnodal line semimetal is qualified by the presence of Dirac nodes along a line\nin the momentum space that are protected against the spin-orbit coupling. In\naddition, it requires that the Dirac points lie close to the Fermi level\nallowing to dictate the macroscopic physical properties. Although the material\nrealization of nodal line semimetals have been theoretically predicted in\nnumerous compounds, only a few of them have been experimentally verified and\nthe realization of a genuine nodal line semimetal is particularly rare. Here we\nreport the realization of a genuine nodal line semimetal in LaSbTe. We\ninvestigated the electronic structure of LaSbTe by band structure calculations\nand angle-resolved photoemission (ARPES) measurements. Taking spin-orbit\ncoupling into account, our band structure calculations predict that a nodal\nline is formed in the boundary surface of the Brillouin zone which is robust\nand lies close to the Fermi level. The Dirac nodes along the X-R line in\nmomentum space are directly observed in our ARPES measurements and the energies\nof these Dirac nodes are all close to the Fermi level. These results constitute\nclear evidence that LaSbTe is a genuine nodal line semimetal,providing a new\nplatform to explore for novel phenomena and possible applications associated\nwith the nodal line semimetals.\n"}
+{"abstract": "  When we experience an event, it feels like our previous experiences, our\ninterpretations of that event (e.g., aesthetics, emotions), and our current\nstate will determine how we will remember it. However, recent work has revealed\na strong sway of the visual world itself in influencing what we remember and\nforget. Certain items -- including certain faces, words, images, and movements\n-- are intrinsically memorable or forgettable across observers, regardless of\nindividual differences. Further, neuroimaging research has revealed that the\nbrain is sensitive to memorability both rapidly and automatically during late\nperception. These strong consistencies in memory across people may reflect the\nbroad organizational principles of our sensory environment, and may reveal how\nthe brain prioritizes information before encoding items into memory. In this\nchapter, I will discuss our current state-of-the-art understanding of\nmemorability for visual information, and what these findings imply about how we\nperceive and remember visual events.\n"}
+{"abstract": "  In this work, we perform a systematical investigation about the possible\nhidden and doubly heavy molecular states with open and hidden strangeness from\ninteractions of $D^{(*)}{\\bar{D}}^{(*)}_{s}$/$B^{(*)}{\\bar{B}}^{(*)}_{s}$,\n${D}^{(*)}_{s}{\\bar{D}}^{(*)}_{s}$/${{B}}^{(*)}_{s}{\\bar{B}}^{(*)}_{s}$,\n${D}^{(*)}D_{s}^{(*)}$/${B}^{(*)}B_{s}^{(*)}$, and\n$D_{s}^{(*)}D_{s}^{(*)}$/$B_{s}^{(*)}B_{s}^{(*)}$ in a quasipotential\nBethe-Salpeter equation approach. The interactions of the systems considered\nare described within the one-boson-exchange model, which includes exchanges of\nlight mesons and $J/\\psi/\\Upsilon$ meson. Possible molecular states are\nsearched for as poles of scattering amplitudes of the interactions considered.\nThe results suggest that recently observed $Z_{cs}(3985)$ can be assigned as a\nmolecular state of $D^*\\bar{D}_s+D\\bar{D}^*_s$, which is a partner of\n$Z_c(3900)$ state as a $D\\bar{D}^*$ molecular state. The calculation also\nfavors the existence of hidden heavy states $D_s\\bar{D}_s/B_s\\bar{B}_s$ with\nspin parity $J^P=0^+$, $D_s\\bar{D}^*_s/B_s\\bar{B}^*_s$ with $1^{+}$, and\n$D^*_s\\bar{D}^*_s/B^*_s\\bar{B}^*_s$ with $0^+$, $1^+$, and $2^+$. In the doubly\nheavy sector, the bound states can be found from the interactions\n$(D^*D_s+DD^*_s)/(B^*B_s+BB^*_s)$ with $1^+$, $D_s\\bar{D}_s^*/B_s\\bar{B}_s^*$\nwith $1^+$, $D^*D^*_s/B^*B^*_s$ with $1^+$ and $2^+$, and\n$D^*_sD^*_s/B^*_sB^*_s$ with $1^+$ and $2^+$. Some other interactions are also\nfound attractive, but may be not strong enough to produce a bound state. The\nresults in this work are helpful for understanding the $Z_{cs}(3985)$, and\nfuture experimental search for the new molecular states.\n"}
+{"abstract": "  We evaluate the masses and decay constants of the $0^+$ and $1^-$ open-charm\n$(\\bar{c}\\bar{d})(us)$ tetraquarks and molecular states from QCD spectral sum\nrules (QSSR) by using QCD Laplace sum rule (LSR). This method takes into\naccount the stability criteria where the factorized perturbative NLO\ncorrections and the contributions of quark and gluon condensates up to\ndimension-6 in the OPE are included. We confront our results with the $D^- K^+$\ninvariant mass recently reported by LHCb from $B^+ \\to D^+(D^- K^+)$ decays. We\nexpect that the resonance near the $D^- K^+$ threshold can be originated from\nthe $0^{+}(D^-K^+)$ molecule and/or $D^- K^+$ scattering. The $X_0(2900)$\nscalar state and the resonance $X_J(3150)$ (if $J = 0$) can emerge from a\nminimal mixing model, with a tiny mixing angle $\\theta_0 \\simeq (5.2 \\pm\n1.9)^0$, between a scalar Tetramole $({\\cal T}_{\\!\\!{\\cal M}0})$ (superposition\nof nearly degenerated hypothetical molecules and compact tetraquarks states\nwith the same quantum numbers), having a mass $M_{{\\cal T}_{\\!\\!{\\cal M}0}} =\n2743(18)$ MeV, and the first radial excitation of the $D^- K^+$ molecule with\nmass $M_{(DK)_1} = 3678(310)$ MeV. In an analogous way, the $X_1(2900)$ and the\n$X_J(3350)$ (if $J = 1$) could be a mixture between the vector Tetramole\n$({\\cal T}_{\\!\\!{\\cal M}1})$, with a mass $M_{{\\cal T}_{\\!\\!{\\cal M}1}} =\n2656(20)$ MeV, and its first radial excitation having a mass $M_{{\\cal\nT}_{\\!\\!{\\cal M}1}} = 4592(141)$ MeV with an angle $\\theta_0 \\simeq (9.1 \\pm\n0.6)^0$. A (non)-confirmation of these statements requires experimental\nfindings of the quantum numbers of the resonances at $3150$ and $3350$ MeV.\n"}
+{"abstract": "  From new detailed experimental data, we found that the Radial Distribution\nFunction (RDF) of inertial particles in turbulence grows explosively with\n$r^{-6}$ scaling as the collision radius is approached. We corrected a theory\nby Yavuz et al. (Phys. Rev. Lett. 120, 244504 (2018)) based on hydrodynamic\ninteractions between pairs of weakly inertial particles, and demonstrate that\neven this corrected theory cannot explain the observed RDF behavior. We explore\nseveral alternative mechanisms for the discrepancy that were not included in\nthe theory and show that none of them are likely the explanation, suggesting\nnew, yet to be identified physical mechanisms are at play.\n"}
+{"abstract": "  Reflection is an often addressed design goal in Human-Computer Interaction\n(HCI) research. An increasing number of artefacts for reflection have been\ndeveloped in recent years. However, evaluating if and how an interactive\ntechnology helps a user reflect is still complex. This makes it difficult to\ncompare artefacts (or prototypes) for reflection, impeding future design\nefforts. To address this issue, we developed the \\emph{Technology-Supported\nReflection Inventory} (TSRI), which is a scale that evaluates how effectively a\nsystem supports reflection. We first created a list of possible scale items\nbased on past work in defining reflection. The items were then reviewed by\nexperts. Next, we performed exploratory factor analysis to reduce the scale to\nits final length of nine items. Subsequently, we confirmed test-retest validity\nof our instrument, as well as its construct validity. The TSRI enables\nresearchers and practitioners to compare prototypes designed to support\nreflection.\n"}
+{"abstract": "  We demonstrate a 13.3 MHz repetition rate, broadband THz source with\nmilliwatt-average power, obtained by collinear optical rectification of a\nhigh-power Yb-doped thin-disk laser in the organic crystal BNA\n(N-benzyl-2-methyl-4-nitroaniline). Our source reaches a maximum THz average\npower of 0.95 mW with an optical-to-THz efficiency of 4e-4 and a spectral\nbandwidth spanning up to 6 THz at -50 dB, driven by 2.4 W average power (after\nan optical chopper with duty cycle of 10%), 85 fs-pulses. This high average\npower excitation was possible without damaging the crystal by using a\ndiamond-heatsinked crystal with significantly improved thermal properties. To\nthe best of our knowledge, this result represents the highest THz average power\nreported so far using the commercially available organic crystal BNA, showing\nthe potential of these crystals for high average power, high repetition rate\nfemtosecond excitation. The combination of high power, high dynamic range, high\nrepetition rate and broadband spectrum makes the demonstrated THz source highly\nattractive to improve various time-domain spectroscopy applications.\nFurthermore, we present a first exploration of the thermal behavior of BNA in\nthis excitation regime, showing that thermal effects are the main limitation in\naverage power scaling in these crystals.\n"}
+{"abstract": "  The problem of beam alignment (BA) in a cell-free massive multiple-input\nmultiple-output (CF-mMIMO) system operating at millimeter wave (mmWaves)\ncarrier frequencies is considered in this paper. Two estimation algorithms are\nproposed, in association with a protocol that permits simultaneous estimation,\non a shared set of frequencies, for each user equipment (UE), of the direction\nof arrival and departure of the radio waves associated to the strongest\npropagation paths from each of the surrounding access points (APs), so that\nUE-AP association can take place. The proposed procedure relies on the\nexistence of a reliable control channel at sub-6 GHz frequency, so as to enable\nexchange of estimated values between the UEs and the network, and assumes that\nAPs can be identifies based on the prior knowledge of the orthogonal channels\nand transmit beamforming codebook. A strategy for assigning codebook entries to\nthe several APs is also proposed, with the aim of minimizing the mutual\ninterference between APs that are assigned the same entry. Numerical results\nshow the effectiveness of the proposed detection strategy, thus enabling one\nshot fast BA for CF-mMIMO systems.\n"}
+{"abstract": "  A search with minimal model dependence for physics beyond the Standard Model\nin events featuring three or four leptons ($3\\ell$ and $4\\ell$, $\\ell = e,\\mu$)\nis presented. The analysis aims to be sensitive to a wide range of potential\nnew-physics theories simultaneously. This analysis uses data from $pp$\ncollisions delivered by the Large Hadron Collider at a centre-of-mass energy of\n$\\sqrt{s} = 13$ TeV and recorded with the ATLAS detector, corresponding to the\nfull Run 2 dataset of 139 fb$^{-1}$. The $3\\ell$ and $4\\ell$ phase space is\ndivided into 22 event categories according to the number of leptons in the\nevent, the missing transverse momentum, the invariant mass of the leptons, and\nthe presence of leptons originating from a $Z$-boson candidate. These event\ncategories are analysed independently for the presence of deviations from the\nStandard Model. No statistically significant deviations from the Standard Model\npredictions are observed. Upper limits for all signal regions are reported in\nterms of the visible cross-section.\n"}
+{"abstract": "  The polarization-adjusted convolutional (PAC) codes concatenate the polar\ntransform and the convolutional transform to improve the decoding performance\nof the finite-length polar codes, where the rate-profile is used to construct\nthe PAC codes by setting the positions of frozen bits. However, the optimal\nrateprofile method of PAC codes is still unknown. In this paper, an optimized\nrate-profile algorithm of PAC codes is proposed. First, we propose the\nnormalized compression factor (NCF) to quantify the transmission efficiency of\nuseful information, showing that the distribution of useful information that\nneeds to be transmitted after the convolutional transform should be adaptive to\nthe capacity profile after finite-length polar transform. This phenomenon\nindicates that the PAC code improves the transmission efficiency of useful\ninformation, which leads to a better decoding performance than the polar codes\nwith the same length. Then, we propose a novel rate-profile method of PAC\ncodes, where a quadratic optimization model is established and the Euclidean\nnorm of the NCF spectrum is adopted to construct the objective function.\nFinally, a heuristic bit-swapping strategy is designed to search for the frozen\nset with high objective function values, where the search space is limited by\nconsidering the only bits with medium Hamming weight of the row index.\nSimulation results show that the PAC codes with the proposed optimized\nrate-profile construction have better decoding performance than the PAC codes\nwith the originally proposed Reed-Muller design construction.\n"}
+{"abstract": "  Data visualizations summarize high-dimensional distributions in two or three\ndimensions. Dimensionality reduction entails a loss of information, and what is\npreserved differs between methods. Existing methods preserve the local or the\nglobal geometry of the points, and most techniques do not consider labels. Here\nwe introduce \"hypersphere2sphere\" (H2S), a new method that aims to visualize\nnot the points, but the relationships between the labeled distributions. H2S\nfits a hypersphere to each labeled set of points in a high-dimensional space\nand visualizes each hypersphere as a sphere in 3D (or circle in 2D). H2S\nperfectly captures the geometry of up to 4 hyperspheres in 3D (or 3 in 2D), and\napproximates the geometry for larger numbers of distributions, matching the\nsizes (radii), and the pairwise separations (between-center distances) and\noverlaps (along the center-connection line). The resulting visualizations are\nrobust to sampling imbalances. Leveraging labels and the sphere as the simplest\ngeometrical primitive, H2S provides an important addition to the toolbox of\nvisualization techniques.\n"}
+{"abstract": "  It is well-documented that word embeddings trained on large public corpora\nconsistently exhibit known human social biases. Although many methods for\ndebiasing exist, almost all fixate on completely eliminating biased information\nfrom the embeddings and often diminish training set size in the process. In\nthis paper, we present a simple yet effective method for debiasing GloVe word\nembeddings (Pennington et al., 2014) which works by incorporating explicit\ninformation about training set bias rather than removing biased data outright.\nOur method runs quickly and efficiently with the help of a fast bias gradient\napproximation method from Brunet et al. (2019). As our approach is akin to the\nnotion of 'source criticism' in the humanities, we term our method\nSource-Critical GloVe (SC-GloVe). We show that SC-GloVe reduces the effect size\non Word Embedding Association Test (WEAT) sets without sacrificing training\ndata or TOP-1 performance.\n"}
+{"abstract": "  This is a concise introduction to the topic of nonextensive Tsallis\nstatistics meant especially for those interested in its relation to high energy\nproton-proton, proton-nucleus, and nucleus-nucleus collisions. The three types\nof Tsallis statistics are reviewed. Only one of them is consistent with the\nfundamental hypothesis of equilibrium statistical mechanics. The single\nparticle distributions associated with it, namely Boltzmann, Fermi-Dirac, and\nBose-Einstein, are derived. These are not equilibrium solutions to the\nconventional Boltzmann transport equation which must be modified in a rather\nnonintuitive manner for them to be so. Nevertheless the Boltzmann limit of the\nTsallis distribution is extremely efficient in representing a wide variety of\nsingle particle distributions in high energy proton-proton, proton-nucleus, and\nnucleus-nucleus collisions with only three parameters, one of them being the\nso-called nonextensitivity parameter $q$. This distribution interpolates\nbetween an exponential at low transverse energy, reflecting thermal\nequilibrium, to a power law at high transverse energy, reflecting the\nasymptotic freedom of QCD. It should not be viewed as a fundamental new\nparameter representing nonextensive behavior in these collisions.\n"}
+{"abstract": "  Advances in isolating, controlling and entangling quantum systems are\ntransforming what was once a curious feature of quantum mechanics into a\nvehicle for disruptive scientific and technological progress. Pursuing the\nvision articulated by Feynman, a concerted effort across many areas of research\nand development is introducing prototypical digital quantum devices into the\ncomputing ecosystem available to domain scientists. Through interactions with\nthese early quantum devices, the abstract vision of exploring\nclassically-intractable quantum systems is evolving toward becoming a tangible\nreality. Beyond catalyzing these technological advances, entanglement is\nenabling parallel progress as a diagnostic for quantum correlations and as an\norganizational tool, both guiding improved understanding of quantum many-body\nsystems and quantum field theories defining and emerging from the Standard\nModel. From the perspective of three domain science theorists, this article\ncompiles thoughts about the interface on entanglement, complexity, and quantum\nsimulation in an effort to contextualize recent NISQ-era progress with the\nscientific objectives of nuclear and high-energy physics.\n"}
+{"abstract": "  Many works in the recent literature introduce semantic mapping methods that\nuse CNNs (Convolutional Neural Networks) to recognize semantic properties in\nimages. The types of properties (eg.: room size, place category, and objects)\nand their classes (eg.: kitchen and bathroom, for place category) are usually\npredefined and restricted to a specific task. Thus, all the visual data\nacquired and processed during the construction of the maps are lost and only\nthe recognized semantic properties remain on the maps. In contrast, this work\nintroduces a topological semantic mapping method that uses deep visual features\nextracted by a CNN (GoogLeNet), from 2D images captured in multiple views of\nthe environment as the robot operates, to create, through averages,\nconsolidated representations of the visual features acquired in the regions\ncovered by each topological node. These representations allow flexible\nrecognition of semantic properties of the regions and use in other visual\ntasks. Experiments with a real-world indoor dataset showed that the method is\nable to consolidate the visual features of regions and use them to recognize\nobjects and place categories as semantic properties, and to indicate the\ntopological location of images, with very promising results.\n"}
+{"abstract": "  We present a new definition of non-ambiguous trees (NATs) as labelled binary\ntrees. We thus get a differential equation whose solution can be described\ncombinatorially. This yields a new formula for the number of NATs. We also\nobtain q-versions of our formula. We finally generalise NATs to higher\ndimension.\n"}
+{"abstract": "  Hexagonal chains are a special class of catacondensed benzenoid system and\nphenylene chains are a class of polycyclic aromatic compounds. Recently, A\nfamily of Sombor indices was introduced by Gutman in the chemical graph theory.\nIt had been examined that these indices may be successfully applied on modeling\nthermodynamic properties of compounds. In this paper, we study the expected\nvalues of the Sombor indices in random hexagonal chains, phenylene chains, and\nconsider the Sombor indices of some chemical graphs such as graphene, coronoid\nsystems and carbon nanocones.\n"}
+{"abstract": "  Explanations are used in recommender systems for various reasons. Users have\nto be supported in making (high-quality) decisions more quickly. Developers of\nrecommender systems want to convince users to purchase specific items. Users\nshould better understand how the recommender system works and why a specific\nitem has been recommended. Users should also develop a more in-depth\nunderstanding of the item domain. Consequently, explanations are designed in\norder to achieve specific \\emph{goals} such as increasing the transparency of a\nrecommendation or increasing a user's trust in the recommender system. In this\npaper, we provide an overview of existing research related to explanations in\nrecommender systems, and specifically discuss aspects relevant to group\nrecommendation scenarios. In this context, we present different ways of\nexplaining and visualizing recommendations determined on the basis of\npreference aggregation strategies.\n"}
+{"abstract": "  Multi-access edge computing (MEC) emerges as an essential part of the\nupcoming Fifth Generation (5G) and future beyond-5G mobile communication\nsystems. It brings computation power to the edge of cellular networks, which is\nclose to the energy-constrained user devices, and therewith allows the users to\noffload tasks to the edge computing nodes for a low-latency computation with\nlow battery consumption. However, due to the high dynamics of user demand and\nserver load, task congestion may occur at the edge nodes, leading to long\nqueuing delay. Such delays can significantly degrade the quality of experience\n(QoE) of some latency-sensitive applications, raise the risk of service outage,\nand cannot be efficiently resolved by conventional queue management solutions.\n  In this article, we study an latency-outage critical scenario, where the\nusers intend to reduce the risk of latency outage. We propose an\nimpatience-based queuing strategy for such users to intelligently choose\nbetween MEC offloading and local computation, allowing them to rationally\nrenege from the task queue. The proposed approach is demonstrated by numerical\nsimulations as efficient for generic service model, when a perfect queue\ninformation is available. For the practical case where the users obtain no\nperfect queue information, we design a optimal online learning strategy to\nenable its application in Poisson service scenarios.\n"}
+{"abstract": "  Reinforcement learning competitions have formed the basis for standard\nresearch benchmarks, galvanized advances in the state-of-the-art, and shaped\nthe direction of the field. Despite this, a majority of challenges suffer from\nthe same fundamental problems: participant solutions to the posed challenge are\nusually domain-specific, biased to maximally exploit compute resources, and not\nguaranteed to be reproducible. In this paper, we present a new framework of\ncompetition design that promotes the development of algorithms that overcome\nthese barriers. We propose four central mechanisms for achieving this end:\nsubmission retraining, domain randomization, desemantization through domain\nobfuscation, and the limitation of competition compute and environment-sample\nbudget. To demonstrate the efficacy of this design, we proposed, organized, and\nran the MineRL 2020 Competition on Sample-Efficient Reinforcement Learning. In\nthis work, we describe the organizational outcomes of the competition and show\nthat the resulting participant submissions are reproducible, non-specific to\nthe competition environment, and sample/resource efficient, despite the\ndifficult competition task.\n"}
+{"abstract": "  We use a cosmological counterpart of the cylindrical Halilsoy solution to\nillustrate properties of cross-polarized standing gravitational waves.\n"}
+{"abstract": "  Dynamic Algorithm Configuration (DAC) aims to dynamically control a target\nalgorithm's hyperparameters in order to improve its performance. Several\ntheoretical and empirical results have demonstrated the benefits of dynamically\ncontrolling hyperparameters in domains like evolutionary computation, AI\nPlanning or deep learning. Replicating these results, as well as studying new\nmethods for DAC, however, is difficult since existing benchmarks are often\nspecialized and incompatible with the same interfaces. To facilitate\nbenchmarking and thus research on DAC, we propose DACBench, a benchmark library\nthat seeks to collect and standardize existing DAC benchmarks from different AI\ndomains, as well as provide a template for new ones. For the design of\nDACBench, we focused on important desiderata, such as (i) flexibility, (ii)\nreproducibility, (iii) extensibility and (iv) automatic documentation and\nvisualization. To show the potential, broad applicability and challenges of\nDAC, we explore how a set of six initial benchmarks compare in several\ndimensions of difficulty.\n"}
+{"abstract": "  The observational structure function of the scintillations of the radio\npulsar PSR B0950+08, was fitted, a decade ago, with a power law with index $1\n\\pm 0.01$. This was interpreted as an {\\em appreciable deviation} from the,\ncommonly observed index of $5/3$, expected for Kolmogorov turbulence. In this\npaper it is suggested that the observations are consistent with a Kolmogorov\nturbulence and that the {\\em apparent} deviation is due to a turbulent region\nwith an effective depth which is {\\em comparable} to the observed lateral\nscales on the plane of the sky, spanned by the pulsar beam. Alternatively, the\nfitted index of $1$ is consistent with an underlying compressive turbulence and\nan even {\\it smaller} depth. In the first interpretation the depth is $(5.5 \\pm\n1.8) \\times 10^8 cm$. In the second one, the depth is $\\lesssim 4\\times 10^7 cm\n$. These estimates lend support for the existence of extremely thin, ionized\nscattering screens in the local interstellar cloud, that have been proposed a\ndecade ago.\n"}
+{"abstract": "  Deep networks are now ubiquitous in large-scale multi-center imaging studies.\nHowever, the direct aggregation of images across sites is contraindicated for\ndownstream statistical and deep learning-based image analysis due to\ninconsistent contrast, resolution, and noise. To this end, in the absence of\npaired data, variations of Cycle-consistent Generative Adversarial Networks\nhave been used to harmonize image sets between a source and target domain.\nImportantly, these methods are prone to instability, contrast inversion,\nintractable manipulation of pathology, and steganographic mappings which limit\ntheir reliable adoption in real-world medical imaging. In this work, based on\nan underlying assumption that morphological shape is consistent across imaging\nsites, we propose a segmentation-renormalized image translation framework to\nreduce inter-scanner heterogeneity while preserving anatomical layout. We\nreplace the affine transformations used in the normalization layers within\ngenerative networks with trainable scale and shift parameters conditioned on\njointly learned anatomical segmentation embeddings to modulate features at\nevery level of translation. We evaluate our methodologies against recent\nbaselines across several imaging modalities (T1w MRI, FLAIR MRI, and OCT) on\ndatasets with and without lesions. Segmentation-renormalization for translation\nGANs yields superior image harmonization as quantified by Inception distances,\ndemonstrates improved downstream utility via post-hoc segmentation accuracy,\nand improved robustness to translation perturbation and self-adversarial\nattacks.\n"}
+{"abstract": "  We propose a new type of locally interacting quantum circuits which are\ngenerated by unitary interactions round-a-face (IRF). Specifically, we discuss\na set (or manifold) of dual-unitary IRFs with local Hilbert space dimension $d$\n(DUIRF$(d)$) which generate unitary evolutions both in space and time\ndirections of an extended 1+1 dimensional lattice. We show how arbitrary\ndynamical correlation functions of local observables can be evaluated in terms\nof finite dimensional completely positive trace preserving unital maps, in\ncomplete analogy to recently studied circuits made of dual unitary brick gates\n(DUBG). In fact, we show that the simplest non-trivial (non-vanishing) local\ncorrelation functions in dual-unitary IRF circuits involve observables\nnon-trivially supported on at least two sites. We completely characterise the\n10-dimensional manifold of DUIRF$(2)$ for qubits ($d=2$) and provide, for\n$d=3,4,5,6,7$, empirical estimates of its dimensionality based on numerically\ndetermined dimensions of tangent spaces at an ensemble of random instances of\ndual-unitary IRF gates. In parallel, we apply the same algorithm to determine\n${\\rm dim}\\,{\\rm DUBG}(d)$ and show that they are of similar order though\nsystematically larger than ${\\rm dim}\\,{\\rm DUIRF}(d)$ for $d=2,3,4,5,6,7$. It\nis remarkable that both sets have rather complex topology for $d\\ge 3$ in the\nsense that the dimension of the tangent space varies among different randomly\ngenerated points of the set. Finally, we provide additional data on\ndimensionality of the chiral extension of DUBG circuits with distinct local\nHilbert spaces of dimensions $d\\neq d'$ residing at even/odd lattice sites.\n"}
+{"abstract": "  The dynamic state index (DSI) is a scalar field that combines variational\ninformation on the total energy and enstrophy of a flow field with the second\nlaw of thermodynamics. Its magnitude is a combined local measure for\nnon-stationarity, diabaticity, and dissipation in the flow, and it has been\nshown to provide good qualitative indications for the onset and presence of\nprecipitation and the organization of storms. The index has been derived thus\nfar for ideal fluid models only, however, so that one may expect more detailed\ninsights from a revised definition of the quantity that includes more complex\naerothermodynamics. The present paper suggests definitions of DSI-like\nindicators for flows of moist air with phase changes and precipitation. In this\nway, the DSI is generalized to signal deviations from a variety of different\ntypes of balanced states. A comparison of these indices evaluated with respect\nto one and the same flow field enables the user to test whether the flow\ninternally balances any combination of the physical processes encoded in the\ngeneralized DSI-indices.\n"}
+{"abstract": "  In this paper we introduce a technique to determine the sumset $A+A$, where\n$A$ is the indicator function of the 0's occurring in a fixed point $x$ of a\nsubstitution on the alphabet $\\{0,1\\}$.\n"}
+{"abstract": "  We investigate the phenomenology of an extension of the Standard Model (SM)\nby a non-abelian gauge group $SU(2)_{HS}$ where all SM particles are singlets\nunder this gauge group, and a new scalar representation $\\phi$ that is singlet\nunder SM gauge group and doublet under $SU(2)_{HS}$. In this model, the dark\nmatter (DM) candidates are the three mass degenerate dark photons $A_{i}$\n$(i=1,2,3)$ of $SU(2)_{HS}$; and the hidden sector interacts with the (SM)\nparticles through the Higgs portal interactions. Consequently, there will be a\nnew CP-even scalar $\\eta$ that could be either heavier or lighter than the\nSM-like Higgs. By taking into account all theoretical and experimental\nconstraints such as perturbativity, unitarity, vacuum stability, non-SM Higgs\ndecays, DM direct detection, DM relic density, we found viable DM is possible\nin the range from GeV to TeV. Within the viable parameters space, the both of\nthe triple Higgs coupling and the di-Higgs production at LHC14 could be\nenhanced or reduced depending on the scalar mixing and the mass of the scalar\nparticle $\\eta$.\n"}
+{"abstract": "  In this paper we study dispersive wave equation using the method of multiple\nscales (MMS) and perform several numerical tests to investigate its accuracy.\nThe key feature of our MMS solution is the linearity of the amplitude equation\nand the complex nature of the time-frequency. The MMS is tested as an initial\nvalue problem using three choices of the dispersion model, one toy and two\nLorentz models. Depending on the parameters of the problem, the amplitude\nequation can be both well- or ill-posed. Despite the ill-posedness, the MMS\nsolution remains a valid approximation of the solution to the original\nnonlinear model.\n"}
+{"abstract": "  The Schr\\\"odinger bridge problem (SBP) finds the most likely stochastic\nevolution between two probability distributions given a prior stochastic\nevolution. As well as applications in the natural sciences, problems of this\nkind have important applications in machine learning such as dataset alignment\nand hypothesis testing. Whilst the theory behind this problem is relatively\nmature, scalable numerical recipes to estimate the Schr\\\"odinger bridge remain\nan active area of research. We prove an equivalence between the SBP and maximum\nlikelihood estimation enabling direct application of successful machine\nlearning techniques. We propose a numerical procedure to estimate SBPs using\nGaussian process and demonstrate the practical usage of our approach in\nnumerical simulations and experiments.\n"}
+{"abstract": "  The peculiarities of the inverse square law of Newtonian gravity in standard\nBig Bang Cosmology are discussed. It is shown that the incorporation of an\nadditive term to Newtonian gravitation, as the inverse Yukawa-like field,\nallows remove the incompatibility between the flatness of the Universe and the\ndensity of matter in the Friedmann equation, provides a new approach for dark\nenergy, and enable theoretical deduce the Hubble-Lemaitre's law. The source of\nthis inverse Yukawa-like field is the ordinary baryonic matter and represents\nthe large-scale contribution of gravity in accordance with the Mach's\nprinciple. It's heuristically build from a specular reflection of the Yukawa\npotential, in agreement with astronomical and laboratory observables, result\nnull in the inner solar system, weakly attractive in ranges of interstellar\ndistances, very attractive in distance ranges comparable to the clusters of\ngalaxies and repulsive in cosmic scales. Its implications in the missing mass\nof Zwicky, Virial Theorem, Kepler's Third Law in Globular Clusters, rotations\ncurves of galaxies, gravitational redshift and the Jean's mass are discussed.\nThe inclusion of the inverse Yukawa-like field in Newtonian gravitation\npredicts a graviton mass of at least 10-64 kg and could be an alternative to\nthe paradigm of non-baryonic dark matter concomitant with the observables of\nthe Big Bang.\n"}
+{"abstract": "  It is proved that a generic simple, closed, piecewise regular curve in space\ncan be the boundary of only finitely many developable surfaces with\nnonvanishing mean curvature. The relevance of this result in the context of the\ndynamics of developable surfaces is discussed.\n"}
+{"abstract": "  The tower number $\\mathfrak t$ and the ultrafilter number $\\mathfrak u$ are\ncardinal characteristics from set theory. They are based on combinatorial\nproperties of classes of subsets of~$\\omega$ and the almost inclusion relation\n$\\subseteq^*$ between such subsets. We consider analogs of these cardinal\ncharacteristics in computability theory.\n  We show that the mass problem of ultrafilter bases is equivalent to the mass\nproblem of computing a function that dominates all computable functions, and\nhence, by Martin's characterization, it captures highness. On the other hand,\nthe mass problem for maximal towers is below the mass problem of computing a\nnon-low set. We also show that some, but not all, noncomputable low sets\ncompute maximal towers: Every noncomputable (low) c.e.\\ set computes a maximal\ntower but no 1-generic $\\Delta^0_2$-set does so.\n  We finally consider the mass problems of maximal almost disjoint, and of\nmaximal independent families. We show that they are Medvedev equivalent to\nmaximal towers, and to ultrafilter bases, respectively.\n"}
+{"abstract": "  ML-enabled software systems have been incorporated in many public\ndemonstrations for automated driving (AD) systems. Such solutions have also\nbeen considered as a crucial approach to aim at SAE Level 5 systems, where the\npassengers in such vehicles do not have to interact with the system at all\nanymore. Already in 2016, Nvidia demonstrated a complete end-to-end approach\nfor training the complete software stack covering perception, planning and\ndecision making, and the actual vehicle control. While such approaches show the\ngreat potential of such ML-enabled systems, there have also been demonstrations\nwhere already changes to single pixels in a video frame can potentially lead to\ncompletely different decisions with dangerous consequences. In this paper, a\nstructured analysis has been conducted to explore video degradation effects on\nthe performance of an ML-enabled pedestrian detector. Firstly, a baseline of\napplying YOLO to 1,026 frames with pedestrian annotations in the KITTI Vision\nBenchmark Suite has been established. Next, video degradation candidates for\neach of these frames were generated using the leading video codecs libx264,\nlibx265, Nvidia HEVC, and AV1: 52 frames for the various compression presets\nfor color and gray-scale frames resulting in 104 degradation candidates per\noriginal KITTI frame and 426,816 images in total. YOLO was applied to each\nimage to compute the intersection-over-union (IoU) metric to compare the\nperformance with the original baseline. While aggressively lossy compression\nsettings result in significant performance drops as expected, it was also\nobserved that some configurations actually result in slightly better IoU\nresults compared to the baseline. The findings show that carefully chosen lossy\nvideo configurations preserve a decent performance of particular ML-enabled\nsystems while allowing for substantial savings when storing or transmitting\ndata.\n"}
+{"abstract": "  We study the behaviour of the solutions to the quasilinear heat equation with\na reaction restricted to a half-line $$ u_t=(u^m)_{xx}+a(x) u^p, $$ $m, p>0$\nand $a(x)=1$ for $x>0$, $a(x)=0$ for $x<0$. We first characterize the global\nexistence exponent $p_0=1$ and the Fujita exponent $p_c=m+2$. Then we pass to\nstudy the grow-up rate in the case $p\\le1$ and the blow-up rate for $p>1$. In\nparticular we show that the grow-up rate is different as for global reaction if\n$p>m$ or $p=1\\neq m$.\n"}
+{"abstract": "  Understanding the electrodes' surface morphology influence on the ions'\ndistribution is essential for designing the supercapacitors with enhanced\nenergy density characteristics. We develop a model for the structure of\nelectrolytes near the rough surface of electrodes. The model describes an\neffective electrostatic field's increase and associated intensification of\nions' spatial separation at the electrode-electrolyte interface. These\nadsorption-induced local electric and structure properties result in notably\nincreased values and sharpened form of the DC dependence on the applied\npotential. Such capacitance behavior is observed in many published simulations,\nand its description is beyond the capabilities of the established\nflat-electrodes theories. The proposed approach could extend the quantitatively\nverified models providing a new instrument of the electrodes surface-parameters\noptimization for specific electrolytes.\n"}
+{"abstract": "  This paper introduces a plug-and-play descriptor that can be effectively\nadopted for image retrieval tasks without prior initialization or preparation.\nThe description method utilizes the recently proposed Vision Transformer\nnetwork while it does not require any training data to adjust parameters. In\nimage retrieval tasks, the use of Handcrafted global and local descriptors has\nbeen very successfully replaced, over the last years, by the Convolutional\nNeural Networks (CNN)-based methods. However, the experimental evaluation\nconducted in this paper on several benchmarking datasets against 36\nstate-of-the-art descriptors from the literature demonstrates that a neural\nnetwork that contains no convolutional layer, such as Vision Transformer, can\nshape a global descriptor and achieve competitive results. As fine-tuning is\nnot required, the presented methodology's low complexity encourages adoption of\nthe architecture as an image retrieval baseline model, replacing the\ntraditional and well adopted CNN-based approaches and inaugurating a new era in\nimage retrieval approaches.\n"}
+{"abstract": "  Automated predictions require explanations to be interpretable by humans. One\ntype of explanation is a rationale, i.e., a selection of input features such as\nrelevant text snippets from which the model computes the outcome. However, a\nsingle overall selection does not provide a complete explanation, e.g.,\nweighing several aspects for decisions. To this end, we present a novel\nself-interpretable model called ConRAT. Inspired by how human explanations for\nhigh-level decisions are often based on key concepts, ConRAT extracts a set of\ntext snippets as concepts and infers which ones are described in the document.\nThen, it explains the outcome with a linear aggregation of concepts. Two\nregularizers drive ConRAT to build interpretable concepts. In addition, we\npropose two techniques to boost the rationale and predictive performance\nfurther. Experiments on both single- and multi-aspect sentiment classification\ntasks show that ConRAT is the first to generate concepts that align with human\nrationalization while using only the overall label. Further, it outperforms\nstate-of-the-art methods trained on each aspect label independently.\n"}
+{"abstract": "  As one of the most popular methods in the field of reinforcement learning,\nQ-learning has received increasing attention. Recently, there have been more\ntheoretical works on the regret bound of algorithms that belong to the\nQ-learning class in different settings. In this paper, we analyze the\ncumulative regret when conducting Nash Q-learning algorithm on 2-player\nturn-based stochastic Markov games (2-TBSG), and propose the very first gap\ndependent logarithmic upper bounds in the episodic tabular setting. This bound\nmatches the theoretical lower bound only up to a logarithmic term. Furthermore,\nwe extend the conclusion to the discounted game setting with infinite horizon\nand propose a similar gap dependent logarithmic regret bound. Also, under the\nlinear MDP assumption, we obtain another logarithmic regret for 2-TBSG, in both\ncentralized and independent settings.\n"}
+{"abstract": "  We say that a subgroup $H$ is isolated in a group $G$ if for every $x\\in G$\nwe have either $x\\in H$ or $\\langle x\\rangle\\cap H=1$. In this short note, we\ndescribe the set of isolated subgroups of a finite abelian group. The technique\nused is based on an interesting connection between isolated subgroups and the\nfunction sum of element orders of a finite group.\n"}
+{"abstract": "  While radar and video data can be readily fused at the detection level,\nfusing them at the pixel level is potentially more beneficial. This is also\nmore challenging in part due to the sparsity of radar, but also because\nautomotive radar beams are much wider than a typical pixel combined with a\nlarge baseline between camera and radar, which results in poor association\nbetween radar pixels and color pixel. A consequence is that depth completion\nmethods designed for LiDAR and video fare poorly for radar and video. Here we\npropose a radar-to-pixel association stage which learns a mapping from radar\nreturns to pixels. This mapping also serves to densify radar returns. Using\nthis as a first stage, followed by a more traditional depth completion method,\nwe are able to achieve image-guided depth completion with radar and video. We\ndemonstrate performance superior to camera and radar alone on the nuScenes\ndataset. Our source code is available at https://github.com/longyunf/rc-pda.\n"}
+{"abstract": "  This paper analytically and numerically investigates misalignment and\nmode-mismatch induced power coupling coefficients and losses as a function of\nHermite Gauss (HG) mode order. We show that higher-order HG modes are more\nsusceptible to beam perturbations when, for example, coupling into optical\ncavities: the misalignment and mode-mismatch-induced power coupling losses\nscale linearly and quadratically with respect to the mode indices respectively.\nAs a result, the mode-mismatch tolerance for the $\\mathrm{HG}_{3,3}$ mode is\nreduced to a factor of 0.28 relative to the currently-used $\\mathrm{HG}_{0,0}$\nmode. This is a potential hurdle to using higher-order modes to reduce thermal\nnoise in future gravitational-wave detectors.\n"}
+{"abstract": "  We study a nonlinear evolutionary partial differential equation that can be\nviewed as a generalization of the heat equation where the temperature gradient\nis a~priori bounded but the heat flux provides merely \\mbox{$L^1$-coercivity}.\nApplying higher differentiability techniques in space and time, choosing a\nspecial weighted norm (equivalent to the Euclidean norm in $\\mathbb{R}^d$),\nincorporating finer properties of integrable functions and using the concept of\nrenormalized solution, we prove long-time and large-data existence and\nuniqueness of weak solution, with an $L^1$-integrable flux, to an initial\nspatially-periodic problem for all values of a positive model parameter. If\nthis parameter is smaller than $2/(d+1)$, where $d$ denotes the spatial\ndimension, we obtain higher integrability of the flux. As the developed\napproach is not restricted to a scalar equation, we also present an analogous\nresult for nonlinear parabolic systems in which the nonlinearity, being the\ngradient of a strictly convex function, gives an a-priori $L^\\infty$-bound on\nthe gradient of the unknown solution.\n"}
+{"abstract": "  Normal-mode oscillation frequencies computed from stellar models differ from\nthose which would be measured from stars with identical interior structures,\nbecause of modelling errors in the near-surface layers. These frequency\ndifferences are referred to as the asteroseismic \"surface term\". The vast\nmajority of solar-like oscillators which have been observed, and which are\nexpected to be observed in the near future, are evolved stars which exhibit\nmixed modes. For these evolved stars, the inference of stellar properties from\nthese mode frequencies has been shown to depend on how this surface term is\ncorrected for. We show that existing parametrisations of the surface term\naccount for mode mixing only to first order in perturbation theory, if at all,\nand therefore may not be adequate for evolved stars. Moreover, existing\nnonparametric treatments of the surface term do not account for mode mixing. We\nderive both a first-order construction, and a more general approach, for one\nparticular class of nonparametric methods. We illustrate the limits of\nfirst-order approximations from both analytic considerations and using\nnumerical injection-recovery tests on stellar models. First-order corrections\nfor the surface term are strictly only applicable where the size of the surface\nterm is much smaller than both the coupling strength between the mixed p- and\ng-modes, as well as the local g-mode spacing. Our more general matrix\nconstruction may be applied to evolved stars, where perturbation theory cannot\nbe relied upon.\n"}
+{"abstract": "  Detecting anomalies has become increasingly critical to the financial service\nindustry. Anomalous events are often indicative of illegal activities such as\nfraud, identity theft, network intrusion, account takeover, and money\nlaundering. Financial anomaly detection use cases face serious challenges due\nto the dynamic nature of the underlying patterns especially in adversarial\nenvironments such as constantly changing fraud tactics. While retraining the\nmodels with the new patterns is absolutely essential; keeping up with the rapid\nchanges introduces other challenges as it moves the model away from older\npatterns or continuously grows the size of the training data. The resulting\ndata growth is hard to manage and it reduces the agility of the models'\nresponse to the latest attacks. Due to the data size limitations and the need\nto track the latest patterns, older time periods are often dropped in practice,\nwhich in turn, causes vulnerabilities. In this study, we propose a label\naugmentation approach to utilize the learning from older models to boost the\nlatest. Experimental results show that the proposed approach provides a\nsignificant reduction in training time, while providing potential performance\nimprovement.\n"}
+{"abstract": "  In this paper, we present a ship detection pipeline for low-cost medium\nresolution satellite optical imagery obtained from ESA Sentinel-2 and Planet\nLabs Dove constellations. This optical satellite imagery is readily available\nfor any place on Earth and underutilized in the maritime domain, compared to\nexisting solutions based on synthetic-aperture radar (SAR) imagery. We\ndeveloped a ship detection method based on a state-of-the-art\ndeep-learning-based object detection method which was developed and evaluated\non a large-scale dataset that was collected and automatically annotated with\nthe help of Automatic Identification System (AIS) data.\n"}
+{"abstract": "  We introduce Explearn, an online algorithm that learns to jointly output\npredictions and explanations for those predictions. Explearn leverages Gaussian\nProcesses (GP)-based contextual bandits. This brings two key benefits. First,\nGPs naturally capture different kinds of explanations and enable the system\ndesigner to control how explanations generalize across the space by virtue of\nchoosing a suitable kernel. Second, Explearn builds on recent results in\ncontextual bandits which guarantee convergence with high probability. Our\ninitial experiments hint at the promise of the approach.\n"}
+{"abstract": "  In this paper we study the relationship between Lyapunov exponents and the\ninduced map on cohomology for $C^{1}-$diffeomorphisms on compact manifolds. We\nshow that if the induced map on cohomology has spectral radius strictly larger\nthan 1, then the diffeomorphism has an invariant ergodic measure with at least\none positive Lyapunov exponent. Furthermore, if the diffeomorphism also\npreserves a continuous volume form then it has an invariant ergodic measure\nwith at least one positive and one negative Lyapunov exponent, in agreement\nwith Shub's entropy conjecture. We also consider diffeomorphisms preserving a\nmeasure equivalent to volume. In this case we show that if the Lyapunov metric\nsatisfies an integrability condition and the induced map on cohomology has\nspectral radius strictly larger than 1, then the diffeomorphism has non-zero\nLyapunov exponents on a set of positive volume.\n"}
+{"abstract": "  Precise control of charged particles in radio-frequency (Paul) traps requires\nminimising excess micromotion induced by stray electric fields. We present a\nmethod to detect and compensate such fields through amplitude modulation of the\nradio-frequency trapping field. Modulation at frequencies close to the motional\nmodes of the trapped particle excites coherent motion whose amplitude linearly\ndepends on the stray field. In trapped-ion experiments, this motion can be\ndetected by recording the arrival times of photons scattered during laser\ncooling. Only a single laser beam is required to resolve fields in multiple\ndirections. In a demonstration using a $^{88}\\mathrm{Sr}^{+}$ ion in a surface\nelectrode trap, we achieve a sensitivity of $0.1\\, \\mathrm{V}\\,\n\\mathrm{m}^{-1}\\, /\\, \\sqrt{\\mathrm{Hz}}$ and a minimal uncertainty of $0.015\\,\n\\mathrm{V}\\, \\mathrm{m}^{-1}$.\n"}
+{"abstract": "  Cerebrovascular accident, or commonly known as stroke, is an acute disease\nwith extreme impact on patients and healthcare systems and is the second\nlargest cause of death worldwide. Fast and precise stroke lesion detection and\nlocation is an extreme important process with regards to stroke diagnosis,\ntreatment, and prognosis. Except from the manual segmentation approach, machine\nlearning based segmentation methods are the most promising ones when\nconsidering efficiency and accuracy, and convolutional neural network based\nmodels are the first of its kind. However, most of these neural network models\ndo not really align with the brain anatomical structures. Intuitively, this\nwork presents a more brain alike model which mimics the anatomical structure of\nthe human visual cortex. Through the preliminary experiments on the stroke\nlesion segmentation task, the proposed model is found to be able to perform\nequally well or better to the de-facto standard U-Net. Part of the\nimplementation will be made available at https://github.com/DarkoBomer/VCA-Net.\n"}
+{"abstract": "  During the expansion of a heavy ion collision, the system passes close to the\n$O(4)$ critical point of QCD, and thus the fluctuations of the order parameter\n$(\\sigma, \\vec{\\pi})$ are expected to be enhanced. Our goal is to compute how\nthese enhanced fluctuations modify the transport coefficients of QCD near the\npseudo-critical point. We also make a phenomenological estimate for how chiral\nfluctuations could effect the momentum spectrum of soft pions.\n  We first formulate the appropriate stochastic hydrodynamic equations close to\nthe $O(4)$ critical point. Then, working in mean field, we determine the\ncorrelation functions of the stress tensor and the currents which result from\nthis stochastic real time theory, and use these correlation functions to\ndetermine the scaling behavior of the transport coefficients. The hydrodynamic\ntheory also describes the propagation of pion waves, fixing the scaling\nbehavior of the dispersion curve of soft pions. We present scaling functions\nfor the shear viscosity and the charge conductivities near the pseudo-critical\npoint, and estimate the absolute magnitude of the critical fluctuations to\nthese parameters and the bulk viscosity.\n  Using the calculated pion dispersion curve, we estimate the expected critical\nenhancement of soft pion yields, and this estimate provides a plausible\nexplanation for the excess seen in experiment relative to ordinary hydrodynamic\ncomputations. Our results motivate further phenomenological and numerical work\non the implications of chiral symmetry on real time properties of thermal QCD\nnear the pseudo-critical point.\n"}
+{"abstract": "  In this article, we present an elementary proof of a special case of\nRamanujan's formula for $ \\zeta(2m +1)$ in terms of hyperbolic cotangent sums\nusing Mittag-Leffler expansion of hyperbolic cotangent and Euler's formula for\n$ \\zeta(2m) $. We also show that our approach produces an elementary proof of\nthe more involved Ramanujan's formula for $ \\zeta(2m+1)$.\n"}
+{"abstract": "  Multimode fibers (MMFs) show great promise as miniature probes for sensing,\nimaging and spectroscopy applications. Different parameters of the fibers, such\nas numerical aperture, refractive index profile and length, have been already\noptimized for better performance. Here we investigate the role of the core\nshape, in particular for wavefront shaping applications where a focus is formed\nat the output of the MMF. We demonstrate that in contrast to a conventional\nround-core MMF, a square-core design doesn't suffer from focus aberrations.\nMoreover, we find that how the interference pattern behind a square-core fiber\ndecorrelates with the input frequency is largely independent of the input light\ncoupling. Finally, we demonstrate that a square core shape provides an\non-average uniform distribution of the output intensity, free from the\ninput-output correlations seen in round fibers, showing great promise for\nimaging and spectroscopy applications.\n"}
+{"abstract": "  We apply the automated AnomalyFinder algorithm of Paper I (Zang et al. 2021b)\nto 2018-2019 light curves from the $\\simeq 13\\,{\\rm deg}^2$ covered by the six\nKMTNet prime fields, with cadences $\\Gamma \\geq 2\\,{\\rm hr}^{-1}$. We find a\ntotal of 11 planets with mass ratios $q<2\\times 10^{-4}$, including six newly\ndiscovered planets, one planet that was reported in Paper I, and recovery of\nfour previously discovered planets. One of the new planets,\nOGLE-2018-BLG-0977Lb, is in a planetary-caustic event, while the other five\n(OGLE-2018-BLG-0506Lb, OGLE-2018-BLG-0516Lb, OGLE-2019-BLG-1492Lb,\nKMT-2019-BLG-0253, and KMT-2019-BLG-0953) are revealed by a \"dip\" in the light\ncurve as the source crosses the host-planet axis on the opposite side of the\nplanet. These subtle signals were missed in previous by-eye searches. The\nplanet-host separations (scaled to the Einstein radius), $s$, and planet-host\nmass ratios, $q$, are, respectively, $(s,q\\times 10^5) = (0.88, 4.1)$,\n$(0.96\\pm 0.10, 8.3)$, $(0.94\\pm 0.07, 13)$, $(0.97\\pm 0.07, 18)$,\n$(0.97\\pm0.04,4.1)$, and $(0.74,18)$, where the \"$\\pm$\" indicates a discrete\ndegeneracy. The 11 planets are spread out over the range $-5<\\log q < -3.7$.\nTogether with the two planets previously reported with $q\\sim 10^{-5}$ from the\n2018-2019 non-prime KMT fields, this result suggests that planets toward the\nbottom of this mass-ratio range may be more common than previously believed.\n"}
+{"abstract": "  Analog meters equipped with one or multiple pointers are wildly utilized to\nmonitor vital devices' status in industrial sites for safety concerns. Reading\nthese legacy meters {\\bi autonomously} remains an open problem since estimating\npointer origin and direction under imaging damping factors imposed in the wild\ncould be challenging. Nevertheless, high accuracy, flexibility, and real-time\nperformance are demanded. In this work, we propose the Vector Detection Network\n(VDN) to detect analog meters' pointers given their images, eliminating the\nbarriers for autonomously reading such meters using intelligent agents like\nrobots. We tackled the pointer as a two-dimensional vector, whose initial point\ncoincides with the tip, and the direction is along tail-to-tip. The network\nestimates a confidence map, wherein the peak pixels are treated as vectors'\ninitial points, along with a two-layer scalar map, whose pixel values at each\npeak form the scalar components in the directions of the coordinate axes. We\nestablished the Pointer-10K dataset composing of real-world analog meter images\nto evaluate our approach due to no similar dataset is available for now.\nExperiments on the dataset demonstrated that our methods generalize well to\nvarious meters, robust to harsh imaging factors, and run in real-time.\n"}
+{"abstract": "  We establish a method for calculating the Poincar\\'e series of moduli spaces\nconstructed as quotients of smooth varieties by suitable non-reductive group\nactions; examples of such moduli spaces include moduli spaces of unstable\nvector or Higgs bundles on a smooth projective curve, with a Harder-Narasimhan\ntype of length two. To do so, we first prove a result concerning the smoothness\nof fixed point sets for suitable non-reductive group actions on smooth\nvarieties. This enables us to prove that quotients of smooth varieties by such\nnon-reductive group actions, which can be constructed using Non-Reductive GIT\nvia a sequence of blow-ups, have at worst finite quotient singularities. We\nconclude the paper by providing explicit formulae for the Poincar\\'e series of\nthese non-reductive GIT quotients.\n"}
+{"abstract": "  The dipole blockade phenomenon is a direct consequence of strong\ndipole-dipole interaction, where only single atom can be excited because the\ndoubly excited state is shifted out of resonance. The corresponding two-body\nentanglement with non-zero concurrence induced by the dipole blockade effect is\nan important resource for quantum information processing. Here, we propose a\nnovel physical mechanism for realizing dipole blockade without the\ndipole-dipole interaction, where two qubits coupled to a cavity, are driven by\na coherent field. By suitably chosen placements of the qubits in the cavity and\nby adjusting the relative decay strengths of the qubits and cavity field, we\nkill many unwanted excitation pathways. This leads to dipole blockade. In\naddition, we show that these two qubits are strongly entangled over a broad\nregime of the system parameters. We show that a strong signature of this dipole\nblockade is the bunching property of the cavity photons which thus provides a\npossible measurement of the dipole blockade. We present dynamical features of\nthe dipole blockade without dipole-dipole interaction. The proposal presented\nin this work can be realized not only in traditional cavity QED, but also in\nnon-cavity topological photonics involving edge modes.\n"}
+{"abstract": "  The Euler tour technique is a classical tool for designing parallel graph\nalgorithms, originally proposed for the PRAM model. We ask whether it can be\nadapted to run efficiently on GPU. We focus on two established applications of\nthe technique: (1) the problem of finding lowest common ancestors (LCA) of\npairs of nodes in trees, and (2) the problem of finding bridges in undirected\ngraphs. In our experiments, we compare theoretically optimal algorithms using\nthe Euler tour technique against simpler heuristics supposed to perform\nparticularly well on typical instances. We show that the Euler tour-based\nalgorithms not only fulfill their theoretical promises and outperform practical\nheuristics on hard instances, but also perform on par with them on easy\ninstances.\n"}
+{"abstract": "  Fast radio bursts (FRBs) are mysterious radio transients with millisecond\ndurations. Recently, a periodic activity of $\\sim$16 day and a possible\nperiodicity of $\\sim$159 day were detected to arise from FRB 180916.J0158+65\nand FRB 121102, respectively, and the spin period of a slow-rotation magnetar\nwas further considered to be one of possibilities to explain the periodic\nactivities of repeating FRBs. For isolated neutron stars, the spin evolution\nsuggests that it's difficult to reach several hours. In this work, we mainly\nfocus on the possible maximum spin period of isolated NSs / magnetars dominated\nby an interaction between star's magnetic field and the disk. We find that the\ndisk wind plays an important role in spin evolution, whose influence varies the\npower law index in the evolution equation of mass flow rate. For a magnetar\nwithout disk wind, the longest spin period is tens of hours. When the disk wind\nwith a classical parameter is involved, the maximum spin period can reach\nhundreds of hours. But for a much extremely large index of mass flow rate due\nto disk wind or other angular momentum extraction processes, a spin period of\n$\\sim$(16-160) days is still possible.\n"}
+{"abstract": "  We study the extent to which wide neural networks may be approximated by\nGaussian processes when initialized with random weights. It is a\nwell-established fact that as the width of a network goes to infinity, its law\nconverges to that of a Gaussian process. We make this quantitative by\nestablishing explicit convergence rates for the central limit theorem in an\ninfinite-dimensional functional space, metrized with a natural transportation\ndistance. We identify two regimes of interest; when the activation function is\npolynomial, its degree determines the rate of convergence, while for\nnon-polynomial activations, the rate is governed by the smoothness of the\nfunction.\n"}
+{"abstract": "  Increasing deployment of photovoltaics (PV) plants demands for cheap and fast\ninspection. A viable tool for this task is thermographic imaging by unmanned\naerial vehicles (UAV). In this work, we develop a computer vision tool for the\nsemi-automatic extraction of PV modules from thermographic UAV videos. We use\nit to curate a dataset containing 4.3 million IR images of 107842 PV modules\nfrom thermographic videos of seven different PV plants. To demonstrate its use\nfor automated PV plant inspection, we train a ResNet-50 to classify ten common\nmodule anomalies with more than 90 % test accuracy. Experiments show that our\ntool generalizes well to different PV plants. It successfully extracts PV\nmodules from 512 out of 561 plant rows. Failures are mostly due to an\ninappropriate UAV trajectory and erroneous module segmentation. Including all\nmanual steps our tool enables inspection of 3.5 MW p to 9 MW p of PV\ninstallations per day, potentially scaling to multi-gigawatt plants due to its\nparallel nature. While we present an effective method for automated PV plant\ninspection, we are also confident that our approach helps to meet the growing\ndemand for large thermographic datasets for machine learning tasks, such as\npower prediction or unsupervised defect identification.\n"}
+{"abstract": "  In this paper we present grammatical interpretations of the alternating\nEulerian polynomials of types A and B. As applications, we derive several\nproperties of the type B alternating Eulerian polynomials, including\ncombinatorial expansions, recurrence relations and generating functions. We\nestablish an interesting connection between alternating Eulerian polynomials of\ntype B and left peak polynomials of permutations in the symmetric group, which\nimplies that the type B alternating Eulerian polynomials have gamma-vectors\nalternate in sign.\n"}
+{"abstract": "  Classical approaches in cluster analysis are typically based on a feature\nspace analysis. However, many applications lead to datasets with additional\nspatial information and a ground truth with spatially coherent classes, which\nwill not necessarily be reconstructed well by standard clustering methods.\nMotivated by applications in hyperspectral imaging, we introduce in this work\nclustering models based on orthogonal nonnegative matrix factorization, which\ninclude an additional total variation (TV) regularization procedure on the\ncluster membership matrix to enforce the needed spatial coherence in the\nclusters. We propose several approaches with different optimization techniques,\nwhere the TV regularization is either performed as a subsequent postprocessing\nstep or included into the clustering algorithm. Finally, we provide a numerical\nevaluation of all proposed methods on a hyperspectral dataset obtained from a\nmatrix-assisted laser desorption/ionisation imaging measurement, which leads to\nsignificantly better clustering results compared to classical clustering\nmodels.\n"}
+{"abstract": "  The aim of this paper is to introduce the notion of intuitionistic fuzzy Lie\nsubalgebras and intutionistic fuzzy Lie ideals of n-Lie algebras. It is a\ngeneralization of intuitionistic fuzzy Lie algebras. Then, we investigate some\nof characteristics of intutionistic fuzzy Lie ideals (resp. subalgebras) of\nn-Lie algeras. Finally, we define the image and the inverse image of\nintuitionistic fuzzy Lie subalgebra under n-Lie algebra homomorphism. The\nproperties of intuitionistic fuzzy n-Lie subalgebras and intuitionistic fuzzy\nLie ideals under homomorphisms of n-Lie algebras are studied. Finally, we\ndefine the intuitionistic fuzzy quotient n-Lie algebra by an intuitionistic\nfuzzy ideal of n-Lie algebra and prove that it is a n-Lie algebra.\n"}
+{"abstract": "  We give an algorithm that generates a uniformly random contingency table with\nspecified marginals, i.e. a matrix with non-negative integer values and\nspecified row and column sums. Such algorithms are useful in statistics and\ncombinatorics. When $\\Delta^4< M/5$, where $\\Delta$ is the maximum of the row\nand column sums and $M$ is the sum of all entries of the matrix, our algorithm\nruns in time linear in $M$ in expectation. Most previously published algorithms\nfor this problem are approximate samplers based on Markov chain Monte Carlo,\nwhose provable bounds on the mixing time are typically polynomials with rather\nlarge degrees.\n"}
+{"abstract": "  One of the most serious public health problems in Peru and worldwide is\nTuberculosis (TB), which is produced by a bacterium known as Mycobacterium\ntuberculosis. The purpose of this work is to facilitate and automate the\ndiagnosis of tuberculosis using the MODS method and using lens-free microscopy,\nas it is easier to calibrate and easier to use by untrained personnel compared\nto lens microscopy. Therefore, we employed a U-Net network on our collected\ndata set to perform automatic segmentation of cord shape bacterial accumulation\nand then predict tuberculosis. Our results show promising evidence for\nautomatic segmentation of TB cords, and thus good accuracy for TB prediction.\n"}
+{"abstract": "  We consider the problem of simultaneous approximation of real numbers\n$\\theta_1, \\ldots,\\theta_n$ with rationals and the dual problem of\napproximating zero with the values of the linear form\n$x_0+\\theta_1x_1+\\ldots+\\theta_nx_n$ at integer points. In this setting we\nanalyse two transference inequalities obtained by Schmidt and Summerer. We\npresent a rather simple geometric observation, which proves their result. We\nalso derive several corollaries previously unknown. Particularly, we show that,\ntogether with the transference inequalities for uniform exponents, Schmidt and\nSummerer's inequalities imply the inequalities by Bugeaud and Laurent and \"one\nhalf\" of the inequalities by Marnat and Moshchevitin. Besides that, we show\nthat our main construction provides a rather simple proof of Nesterenko's\nlinear independence criterion.\n"}
+{"abstract": "  Quantum sensing is one of the key areas which exemplifies the superiority of\nquantum technologies. Nonetheless, most quantum sensing protocols operate\nefficiently only when the unknown parameters vary within a very narrow region,\ni.e., local sensing. Here, we provide a systematic formulation for quantifying\nthe precision of a probe for multi-parameter global sensing when there is no\nprior information about the parameters. In many-body probes, in which extra\ntunable parameters exist, our protocol can tune the performance for harnessing\nthe quantum criticality over arbitrarily large sensing intervals. For the\nsingle-parameter sensing, our protocol optimizes a control field such that an\nIsing probe is tuned to always operate around its criticality. This\nsignificantly enhances the performance of the probe even when the interval of\ninterest is so large that the precision is bounded by the standard limit. For\nthe multi-parameter case, our protocol optimizes the control fields such that\nthe probe operates at the most efficient point along its critical line.\nInterestingly, for an Ising probe, it is predominantly determined by the\nlongitudinal field. Finally, we show that even a simple magnetization\nmeasurement significantly benefits from our optimization and moderately\ndelivers the theoretical precision.\n"}
+{"abstract": "  Attempts to find black hole microstates using the Hamiltonian phase space\napproach have been made on the Schwarzschild spacetime. Since the Schwarzschild\nspacetime is also in the larger family of the Kerr spacetimes, and both are\nasymptotically flat, the Kerr black hole is a good option for the method\ndevelopment. The Kerr black hole is a spinning one. We perform this analysis on\nthe Kerr spacetime and we obtain promising results using the covariant phase\nspace analysis. Although we have forced ourselves to use the Bondi fall-off\nconditions, we find the gauge degrees of freedom that could be good candidates\nfor the black hole microstates. The charge algebra on the boundary could be a\nVirasoro algebra that has a different central term than the Schwarzschild black\nhole. The two dimensional theory on the black hole boundary is conjectured to\nbe conformally invariant.\n"}
+{"abstract": "  How do you incentivize self-interested agents to $\\textit{explore}$ when they\nprefer to $\\textit{exploit}$? We consider complex exploration problems, where\neach agent faces the same (but unknown) MDP. In contrast with traditional\nformulations of reinforcement learning, agents control the choice of policies,\nwhereas an algorithm can only issue recommendations. However, the algorithm\ncontrols the flow of information, and can incentivize the agents to explore via\ninformation asymmetry. We design an algorithm which explores all reachable\nstates in the MDP. We achieve provable guarantees similar to those for\nincentivizing exploration in static, stateless exploration problems studied\npreviously. To the best of our knowledge, this is the first work to consider\nmechanism design in a stateful, reinforcement learning setting.\n"}
+{"abstract": "  Multiport interferometers based on integrated beamsplitter meshes are widely\nused in photonic technologies. While the rectangular mesh is favored for its\ncompactness and uniformity, its geometry resists conventional\nself-configuration approaches, which are essential to programming large meshes\nin the presence of fabrication error. Here, we present a new configuration\nalgorithm, related to the $2\\times 2$ block decomposition of a unitary matrix,\nthat overcomes this limitation. Our proposed algorithm is robust to errors,\nrequires no prior knowledge of the process variations, and relies only on\nexternal sources and detectors. We show that self-configuration using this\ntechnique reduces the effect of fabrication errors by the same quadratic factor\nobserved in triangular meshes. This relaxes a significant limit to the size of\nmultiport interferometers, removing a major roadblock to the scaling of optical\nquantum and machine-learning hardware.\n"}
+{"abstract": "  Ion channels are important proteins for physiological information transfer\nand functional control. To predict the microscopic origins of their\nvoltage-conductance characteristics, we here applied dissipation-corrected\ntargeted Molecular Dynamics in combination with Langevin equation simulations\nto potassium diffusion through the Gramicidin A channel as a test system.\nPerforming a non-equilibrium principal component analysis on backbone dihedral\nangles, we find coupled protein-ion dynamics to occur during ion transfer. The\ndissipation-corrected free energy profiles correspond well to predictions from\nother biased simulation methods. The incorporation of an external electric\nfield in Langevin simulations enables the prediction of macroscopic observables\nin the form of I-V characteristics.\n"}
+{"abstract": "  This paper is the second part of a two part paper which introduces the study\nof the Whitney Equisingularity of families of Symmetric determinantal\nsingularities. This study reveals how to use the multiplicity of polar curves\nassociated to a generic deformation of a singularity to control the Whitney\nequisingularity type of these curves.\n"}
+{"abstract": "  We construct a class of lattice Hamiltonians that exhibit fractional Hall\nconductivity. These Hamiltonians, while not being exactly solvable, can be\ncontrollably solved in their low energy sectors, through a combination of\nperturbative and exact techniques. Our construction demonstrates a systematic\nway to circumvent the Kapustin-Fidkowski no-go theorem and is generalizable.\n"}
+{"abstract": "  The age-metallicity relation is a fundamental tool for constraining the\nchemical evolution of the Galactic disc. In this work we analyse the\nobservational properties of this relation using binary stars that have not\ninteracted consisting of a white dwarf - from which we can derive the total age\nof the system - and a main sequence star - from which we can derive the\nmetallicity as traced by the [Fe/H] abundances. Our sample consists of 46\nwidely separated, but unresolved spectroscopic binaries identified within the\nSloan Digital Sky Survey, and 189 white dwarf plus main sequence common proper\nmotion pairs identified within the second data release of Gaia. This is\ncurrently the largest white dwarf sample for which the metallicity of their\nprogenitors have been determined. We find a flat age-metallicity relation\ndisplaying a scatter of [Fe/H] abundances of approximately 0.5 dex around the\nsolar metallicity at all ages. This independently confirms the lack of\ncorrelation between age and metallicity in the solar neighbourhood that is\nfound in previous studies focused on analysing single main sequence stars and\nopen clusters.\n"}
+{"abstract": "  Handwritten Text Recognition (HTR) remains a challenging problem to date,\nlargely due to the varying writing styles that exist amongst us. Prior works\nhowever generally operate with the assumption that there is a limited number of\nstyles, most of which have already been captured by existing datasets. In this\npaper, we take a completely different perspective -- we work on the assumption\nthat there is always a new style that is drastically different, and that we\nwill only have very limited data during testing to perform adaptation. This\nresults in a commercially viable solution -- the model has the best shot at\nadaptation being exposed to the new style, and the few samples nature makes it\npractical to implement. We achieve this via a novel meta-learning framework\nwhich exploits additional new-writer data through a support set, and outputs a\nwriter-adapted model via single gradient step update, all during inference. We\ndiscover and leverage on the important insight that there exists few key\ncharacters per writer that exhibit relatively larger style discrepancies. For\nthat, we additionally propose to meta-learn instance specific weights for a\ncharacter-wise cross-entropy loss, which is specifically designed to work with\nthe sequential nature of text data. Our writer-adaptive MetaHTR framework can\nbe easily implemented on the top of most state-of-the-art HTR models.\nExperiments show an average performance gain of 5-7% can be obtained by\nobserving very few new style data. We further demonstrate via a set of ablative\nstudies the advantage of our meta design when compared with alternative\nadaption mechanisms.\n"}
+{"abstract": "  Throughout cosmological simulations, the properties of the matter density\nfield in the initial conditions have a decisive impact on the features of the\nstructures formed today. In this paper we use a random-forest classification\nalgorithm to infer whether or not dark matter particles, traced back to the\ninitial conditions, would end up in dark matter halos whose masses are above\nsome threshold. This problem might be posed as a binary classification task,\nwhere the initial conditions of the matter density field are mapped into\nclassification labels provided by a halo finder program. Our results show that\nrandom forests are effective tools to predict the output of cosmological\nsimulations without running the full process. These techniques might be used in\nthe future to decrease the computational time and to explore more efficiently\nthe effect of different dark matter/dark energy candidates on the formation of\ncosmological structures.\n"}
+{"abstract": "  Continuous-time quantum walks (CTQW) have shown the capability to perform\nefficiently the spatial search of a marked site on many kinds of graphs.\nHowever, most of such graphs are hard to realize in an experimental setting.\nHere we study CTQW spatial search on a planar triangular lattice by means of\nboth numerical simulations and experiments. The experiments are performed using\nthree-dimensional waveguide arrays fabricated by femtosecond laser pulses,\nilluminated by coherent light. We show that the retrieval of the marked site by\nthe quantum walker is accomplished with higher probability than the classical\ncounterpart, in a convenient time window placed early in the evolution.\n"}
+{"abstract": "  Many synthetic quantum systems allow particles to have dispersion relations\nthat are neither linear nor quadratic functions. Here, we explore\nsingle-particle scattering in general spatial dimension $D\\geq 1$ when the\ndensity of states diverges at a specific energy. To illustrate the underlying\nprinciples in an experimentally relevant setting, we focus on waveguide quantum\nelectrodynamics (QED) problems (i.e. $D=1$) with dispersion relation\n$\\epsilon(k)=\\pm |d|k^m$, where $m\\geq 2$ is an integer. For a large class of\nthese problems for any positive integer $m$, we rigorously prove that when\nthere are no bright zero-energy eigenstates, the $S$-matrix evaluated at an\nenergy $E\\to 0$ converges to a universal limit that is only dependent on $m$.\nWe also give a generalization of a key index theorem in quantum scattering\ntheory known as Levinson's theorem -- which relates the scattering phases to\nthe number of bound states -- to waveguide QED scattering for these more\ngeneral dispersion relations. We then extend these results to general integer\ndimensions $D \\geq 1$, dispersion relations $\\epsilon(\\boldsymbol{k}) =\n|\\boldsymbol{k}|^a$ for a $D$-dimensional momentum vector $\\boldsymbol{k}$ with\nany real positive $a$, and separable potential scattering.\n"}
+{"abstract": "  APR techniques can be extremely time consuming since (1) a large number of\npatches can be generated for a given bug, and (2) each patch needs to be\nexecuted on the original tests to ensure its correctness. In the literature,\nvarious techniques (e.g., based on learning, mining, and constraint solving)\nhave been proposed/studied to reduce the number of patches. However, there is\nlimited study on the impact of test selection for each patch (e.g., only the\ntests affected by the patch need to be executed as the other tests would keep\nthe same outcomes and can be skipped), and few APR systems actually apply test\nselection. Therefore, this paper conducts the first extensive study to\ninvestigate the impact of Regression Test Selection (RTS) on APR. More\nspecifically, we implemented widely-used RTS techniques at different levels for\n12 state-of-the-art APR systems with over 2M patches. Our study reveals various\npractical guidelines for future APR, including: (1) the number of patches\nwidely used for measuring APR efficiency can incur skewed conclusions, and the\nuse of inconsistent RTS configurations can further skew the conclusion; (2) all\nstudied RTS techniques can substantially improve APR efficiency and should be\nconsidered in future APR work; (3) method- and statement-level RTS outperform\nclass-level RTS substantially, and should be preferred; (4) RTS techniques can\nsubstantially outperform state-of-the-art test prioritization techniques for\nAPR, and combining them can further improve APR efficiency; and (5) traditional\nregression test prioritization widely studied in regression testing performs\neven better than APR-specific test prioritization when combined with most RTS\ntechniques. Furthermore, we also present the detailed impact of different patch\ncategories and patch validation strategies on our findings.\n"}
+{"abstract": "  Objective: The spinous process angle (SPA) is one of the essential parameters\nto denote three-dimensional (3-D) deformity of spine. We propose an automatic\nsegmentation method based on Stacked Hourglass Network (SHN) to detect the\nspinous processes (SP) on ultrasound (US) spine images and to measure the SPAs\nof clinical scoliotic subjects. Methods: The network was trained to detect\nvertebral SP and laminae as five landmarks on 1200 ultrasound transverse images\nand validated on 100 images. All the processed transverse images with\nhighlighted SP and laminae were reconstructed into a 3D image volume, and the\nSPAs were measured on the projected coronal images. The trained network was\ntested on 400 images by calculating the percentage of correct keypoints (PCK);\nand the SPA measurements were evaluated on 50 scoliotic subjects by comparing\nthe results from US images and radiographs. Results: The trained network\nachieved a high average PCK (86.8%) on the test datasets, particularly the PCK\nof SP detection was 90.3%. The SPAs measured from US and radiographic methods\nshowed good correlation (r>0.85), and the mean absolute differences (MAD)\nbetween two modalities were 3.3{\\deg}, which was less than the clinical\nacceptance error (5{\\deg}). Conclusion: The vertebral features can be\naccurately segmented on US spine images using SHN, and the measurement results\nof SPA from US data was comparable to the gold standard from radiography.\n"}
+{"abstract": "  Screening for the diagnosis of glaucoma through a fundus image can be\ndetermined by the optic cup to disc diameter ratio (CDR), which requires the\nsegmentation of the cup and disc regions. In this paper, we propose two novel\napproaches, namely Parameter-Shared Branched Network (PSBN) andWeak Region of\nInterest Model-based segmentation (WRoIM) to identify disc and cup boundaries.\nUnlike the previous approaches, the proposed methods are trained end-to-end\nthrough a single neural network architecture and use dynamic cropping instead\nof manual or traditional computer vision-based cropping. We are able to achieve\nsimilar performance as that of state-of-the-art approaches with less number of\nnetwork parameters. Our experiments include comparison with different best\nknown methods on publicly available Drishti-GS1 and RIM-ONE v3 datasets. With\n$7.8 \\times 10^6$ parameters our approach achieves a Dice score of 0.96/0.89\nfor disc/cup segmentation on Drishti-GS1 data whereas the existing\nstate-of-the-art approach uses $19.8\\times 10^6$ parameters to achieve a dice\nscore of 0.97/0.89.\n"}
+{"abstract": "  We study disorder operator, defined as a symmetry transformation applied to a\nfinite region, across a continuous quantum phase transition in $(2+1)d$. We\nshow analytically that at a conformally-invariant critical point with U(1)\nsymmetry, the disorder operator with a small U(1) rotation angle defined on a\nrectangle region exhibits power-law scaling with the perimeter of the\nrectangle. The exponent is proportional to the current central charge of the\ncritical theory. Such a universal scaling behavior is due to the sharp corners\nof the region and we further obtain a general formula for the exponent when the\ncorner is nearly smooth. To probe the full parameter regime, we carry out\nsystematic computation of the U(1) disorder parameter in the square lattice\nBose-Hubbard model across the superfluid-insulator transition with large-scale\nquantum Monte Carlo simulations, and confirm the presence of the universal\ncorner correction. The exponent of the corner term determined from numerical\nsimulations agrees well with the analytical predictions.\n"}
+{"abstract": "  The Multi-slit Solar Explorer (MUSE) is a proposed NASA MIDEX mission,\ncurrently in Phase A, composed of a multi-slit EUV spectrograph (in three\nnarrow spectral bands centered around 171A, 284A, and 108A) and an EUV context\nimager (in two narrow passbands around 195A and 304A). MUSE will provide\nunprecedented spectral and imaging diagnostics of the solar corona at high\nspatial (<0.5 arcsec), and temporal resolution (down to ~0.5s) thanks to its\ninnovative multi-slit design. By obtaining spectra in 4 bright EUV lines (Fe IX\n171A , Fe XV 284A, Fe XIX-Fe XXI 108A) covering a wide range of transition\nregion and coronal temperatures along 37 slits simultaneously, MUSE will for\nthe first time be able to \"freeze\" (at a cadence as short as 10 seconds) with a\nspectroscopic raster the evolution of the dynamic coronal plasma over a wide\nrange of scales: from the spatial scales on which energy is released (~0.5\narcsec) to the large-scale often active-region size (170 arcsec x 170 arcsec)\natmospheric response. We use advanced numerical modeling to showcase how MUSE\nwill constrain the properties of the solar atmosphere on the spatio-temporal\nscales (~0.5 arcsec, ~20 seconds) and large field-of-view on which various\nstate-of-the-art models of the physical processes that drive coronal heating,\nsolar flares and coronal mass ejections (CMEs) make distinguishing and testable\npredictions. We describe how the synergy between MUSE, the single-slit,\nhigh-resolution Solar-C EUVST spectrograph, and ground-based observatories\n(DKIST and others) can address how the solar atmosphere is energized, and the\ncritical role MUSE plays because of the multi-scale nature of the physical\nprocesses involved. In this first paper, we focus on how comparisons between\nMUSE observations and theoretical models will significantly further our\nunderstanding of coronal heating mechanisms.\n"}
+{"abstract": "  In developed countries like the USA, Germany, and the UK, the security forces\nused highly sophisticated equipment, fast vehicles, drones, and helicopters to\ncatch offenders' vehicles. Whereas, in developing countries with limited\nresources such schemes cannot be utilized due to management cost and other\nconstraints. In this paper, we proposed a framework called CVEH that enables\ndeveloping countries to profile the offender vehicle movements through\ncrowdsourcing technique and act as an early warning system to the law forcing\nagencies. It also engages citizens to play their role in improving security\nconditions. The proposed CVEH framework allows Vehicle-to-Infrastructure (V2I)\ncommunication to monitor the movement of the offender's vehicle and shared its\ninformation with the Command and Control (CC) centre. The CC centre projects\nthe path and engages nearly located law enforcement agencies. CVEH is developed\nand evaluated on android smartphones. Simulations conducted for this study\nexhibit the effectiveness of our framework.\n"}
+{"abstract": "  In a simple extension of the standard model (SM), a pair of vector like\nlepton doublets ($L_1$ and $L_2$) and a $SU(2)_L$ scalar doublet ($\\eta$) have\nbeen introduced to help in accommodating the discrepancy in determination of\nthe anomalous magnetic moments of the light leptons, namely, $e$ and $\\mu$.\nMoreover, to make our scenario friendly to a Dirac like neutrino and also for a\nconsistent dark matter phenomenology, we specifically add a singlet scalar\n($S$) and a singlet fermion ($\\psi$) in the set-up. A discrete symmetry\n$\\mathcal{Z}_2\\times\\mathcal{Z}_2^\\prime$ has been imposed under which all the\nSM particles are even while the new particles may be assumed to have odd\ncharges. In a bottom-up approach, with a minimal particle content, we\nsystematically explore the available parameter space in terms of couplings and\nmasses of the new particles. Here a number of observables associated with the\nSM leptons have been considered, e.g., masses and mixings of neutrinos, $(g-2)$\nanomalies of $e$, $\\mu$, charged lepton flavor violating (cLFV) observables and\nthe dark matter (DM) phenomenology of a singlet-doublet dark matter. Neutrinos,\npromoted as the Dirac type states, acquire mass at one loop level after the\ndiscrete $\\mathcal{Z}_2^\\prime$ symmetry gets softly broken, while the unbroken\n$\\mathcal{Z}_2$ keeps the dark matter stable. The mixing between the singlet\n$\\psi$ and the doublet vector lepton can be constrained to satisfy the\nelectroweak precision observables and the spin independent (SI) direct\ndetection (DD) cross section of the dark matter. In this analysis, potentially\nimportant LHC bounds have also been discussed.\n"}
+{"abstract": "  Inspired by the formulation of quantum-electrodynamical time-dependent\ndensity functional theory (QED-TDDFT) by Rubio and coworkers, we propose an\nimplementation that uses dimensionless amplitudes for describing the photonic\ncontributions to QED-TDDFT electron-photon eigenstates. The leads to a\nsymmetric QED-TDDFT coupling matrix, which is expected to facilitate the future\ndevelopment of analytic derivatives. Through a Gaussian atomic basis\nimplementation of the QED-TDDFT method, we examined the effect of dipole\nself-energy, rotating wave approximation, and the Tamm-Dancoff approximation on\nthe QED-TDDFT eigenstates of model compounds (ethene, formaldehyde, and\nbenzaldehyde) in an optical cavity. We highlight, in the strong coupling\nregime, the role of higher-energy and off-resonance excited states with large\ntransition dipole moments in the direction of the photonic field, which are\nautomatically accounted for in our QED-TDDFT calculations and might\nsubstantially affect the energy and composition of polaritons associated with\nlower-energy electronic states.\n"}
+{"abstract": "  Many ML models are opaque to humans, producing decisions too complex for\nhumans to easily understand. In response, explainable artificial intelligence\n(XAI) tools that analyze the inner workings of a model have been created.\nDespite these tools' strength in translating model behavior, critiques have\nraised concerns about the impact of XAI tools as a tool for `fairwashing` by\nmisleading users into trusting biased or incorrect models. In this paper, we\ncreated a framework for evaluating explainable AI tools with respect to their\ncapabilities for detecting and addressing issues of bias and fairness as well\nas their capacity to communicate these results to their users clearly. We found\nthat despite their capabilities in simplifying and explaining model behavior,\nmany prominent XAI tools lack features that could be critical in detecting\nbias. Developers can use our framework to suggest modifications needed in their\ntoolkits to reduce issues likes fairwashing.\n"}
+{"abstract": "  In the past few years, we have witnessed an increased interest in the use of\n2D materials for the realization of hybrid photonic nonlinear waveguides.\nAlthough graphene has attracted most of the attention, other families of 2D\nmaterials such as transition metal dichalcogenides have also shown promising\nnonlinear performances. In this work, we propose a strategy for designing\nsilicon nitride waveguide structures embedded with molybdenum disulfide for\nnonlinear applications. The transverse geometry of the hybrid waveguides\nstructure is optimized for high third order nonlinear effects using\noptogeometrical engineering and multiple layers of molybdenum disulfide.\nStacking multiple monolayers, results in an improvement of 2 orders of\nmagnitude in comparison with standard silicon nitride waveguides. The\nperformance of the hybrid waveguides is then investigated in terms of four wave\nmixing enhancement in micro ring resonator configurations. A 6,3 dB signal\nidler conversion efficiency is reached around 1550 nm wavelength for a 5 mW\npumping level.\n"}
+{"abstract": "  This note discusses the Wigner function representation from the standpoint of\nestablishing a holography-like correspondence between the descriptions of a\ngeneric quantum system in the phase space ('bulk') picture versus its spacetime\n('boundary') counterpart. Under certain circumstances the former might reduce\nto the classical dynamics of a local metric-like variable while the latter\ntakes on the form of some bosonized collective field hydrodynamics. This\ngeneric pseudo-holographic duality neither relies on any particular symmetry of\nthe system in question, nor does it require any connection to an underlying\n'string theory', as in the various 'ad hoc' scenarios of applied holography.\n"}
+{"abstract": "  There is great need for high intensity proton beams from compact particle\naccelerators in particle physics, medical isotope production, and materials-\nand energy-research. To address this need, we present, for the first time, a\ndesign for a compact isochronous cyclotron that will be able to deliver 10 mA\nof 60 MeV protons - an order of magnitude higher than on-market compact\ncyclotrons and a factor four higher than research machines. A key breakthrough\nis that vortex motion is incorporated in the design of a cyclotron, leading to\nclean extraction. Beam losses on the septa of the electrostatic extraction\nchannels stay below 50 W (a factor four below the required safety limit), while\nmaintaining good beam quality. We present a set of highly accurate\nparticle-in-cell simulations, and an uncertainty quantification of select beam\ninput parameters using machine learning, showing the robustness of the design.\nThis design can be utilized for beams for experiments in particle and nuclear\nphysics, materials science and medical physics as well as for industrial\napplications.\n"}
+{"abstract": "  The macroscopic behavior of many materials is complex and the end result of\nmechanisms that operate across a broad range of disparate scales. An imperfect\nknowledge of material behavior across scales is a source of epistemic\nuncertainty of the overall material behavior. However, assessing this\nuncertainty is difficult due to the complex nature of material response and the\nprohibitive computational cost of integral calculations. In this paper, we\nexploit the multiscale and hierarchical nature of material response to develop\nan approach to quantify the overall uncertainty of material response without\nthe need for integral calculations. Specifically, we bound the uncertainty at\neach scale and then combine the partial uncertainties in a way that provides a\nbound on the overall or integral uncertainty. The bound provides a conservative\nestimate on the uncertainty. Importantly, this approach does not require\nintegral calculations that are prohibitively expensive. We demonstrate the\nframework on the problem of ballistic impact of a polycrystalline magnesium\nplate. Magnesium and its alloys are of current interest as promising\nlight-weight structural and protective materials. Finally, we remark that the\napproach can also be used to study the sensitivity of the overall response to\nparticular mechanisms at lower scales in a materials-by-design approach.\n"}
+{"abstract": "  We propose in this paper to consider the stock market as a physical system\nassimilate to a fluid evolving in a macroscopic space subject to a Force that\ninfluences its movement over time where this last is arising from the collision\nbetween the supply and the demand of Financial agents. In fluid mechanics, this\nForce also results from the collisions of fluid molecules led by its physical\nproperty such as density, viscosity, and surface tension. The purpose of this\narticle is to show that the dynamism of the stock market behavior can be\nexplained qualitatively and quantitatively by considering the supply & demand\ncollision as the result of Financial agents physical properties defined by\nStokes Law. The first objective of this article is to show theoretically that\nfluid mechanics equations can be used to describe stock market physical\nproperties. The second objective based on the knowledge of stock market\nphysical properties is to propose an Econophysics analog of the stock market\nviscosity and Reynolds number to measure stock market conditions, whether\nlaminar, transitory, or turbulent. The Reynolds Number defined in this way can\nbe applied in research into the study and classification of stock market\ndynamics phases through for instance the creation of Econophysics analog of\nModdy diagram, this last could be seen as a physical way to quantify asset and\nstock index idiosyncratic risk. The last objective is to present evidence from\na computer simulation that the stock market behavior can be a priori, and\nposteriori explained by physical properties (viscosity & density) quantifiable\nby fluid mechanics law (Stokes law) and measurable with the stock market\nReynolds Number.\n"}
+{"abstract": "  We present a simplified model of a vehicle driving on a nonplanar road. A\nparametric surface is used to describe the nonplanar road which can describe\nany combination of curvature, bank and slope. We show that the proposed\nmodeling approach generalizes planar vehicle models that reference a\ncenterline, such as the Frenet model.\n  We use the proposed approach for vehicle path planning and following using\nmodel predictive control. We also model and control vehicle contact with the\nroad surface. We demonstrate that the proposed controller improves speed and\nlane following on complex roads compared to planar vehicle controllers, and\nmitigates loss of control on complex road surfaces including off-camber turns.\n"}
+{"abstract": "  Countries, companies, and universities are increasingly competing over\ntop-tier artificial intelligence (AI) researchers. Where are these researchers\nlikely to immigrate and what affects their immigration decisions? We conducted\na survey $(n = 524)$ of the immigration preferences and motivations of\nresearchers that had papers accepted at one of two prestigious AI conferences:\nthe Conference on Neural Information Processing Systems (NeurIPS) and the\nInternational Conference on Machine Learning (ICML). We find that the U.S. is\nthe most popular destination for AI researchers, followed by the U.K., Canada,\nSwitzerland, and France. A country's professional opportunities stood out as\nthe most common factor that influences immigration decisions of AI researchers,\nfollowed by lifestyle and culture, the political climate, and personal\nrelations. The destination country's immigration policies were important to\njust under half of the researchers surveyed, while around a quarter noted\ncurrent immigration difficulties to be a deciding factor. Visa and immigration\ndifficulties were perceived to be a particular impediment to conducting AI\nresearch in the U.S., the U.K., and Canada. Implications of the findings for\nthe future of AI talent policies and governance are discussed.\n"}
+{"abstract": "  In this paper, the sharp interface limit for the compressible non-isentropic\nNavier-Stokes/Allen-Cahn system is derived by the method of matched asymptotic\nexpansion. We show that the leading order problem satisfies the compressible\nNavier-Stokes equations with the interface being a free boundary. We discuss\ntwo cases in terms of different phase field diffusion coefficients. One is\n$M_{\\epsilon}=O(1)$ and $M_{\\epsilon}=O(\\frac{1}{\\epsilon})$, where $\\epsilon$\nis the interface thickness. We have observed that the velocity and the\ntemperature of the compressible immiscible two-phase fluids continuously\nthrough the interface. There is a jump for the tension tensor at the interface,\nthis jump depends on the surface tension and the mean curvature of the\ninterface. In particular, for the first case $M_{\\epsilon}=O(1)$, no matter how\nthe density changes through the interface, the velocity of the interface in the\nnormal direction is the same as the normal velocity of the fluid along the\ninterface. But for the second case $M_{\\epsilon}=O(\\frac{1}{\\epsilon})$, This\nphenomenon cann't occur where the density passes continuously through the\ninterface.In fact, on this part of the interface, the normal velocity of the\ninterface is determined by the mean curvature of the interface, the velocity\nand the density of the compressible immiscible two-phase fluids. That's where\nthe phase transition happens.\n"}
+{"abstract": "  We prove the relative Grauert-Riemenschneider vanishing, Kawamata-Viehweg\nvanishing, and Koll\\'ar injectivity theorems for $\\mathbf{Q}$-schemes, solving\nconjectures of Boutot and Kawakita. Our proof uses Grothendieck's limit theorem\nfor sheaf cohomology and Zariski-Riemann spaces. As an application, we extend\nBoutot's theorem to the case of locally quasi-excellent $\\mathbf{Q}$-algebras\nby showing that if $R \\to R'$ is a cyclically pure homomorphism of locally\nquasi-excellent $\\mathbf{Q}$-algebras, and $R'$ has rational singularities,\nthen $R$ has rational singularities. This solves a conjecture of Boutot and\nanswers a question of Schoutens in the locally quasi-excellent case.\n"}
+{"abstract": "  We construct Markov processes for modeling the rupture of edges in a\ntwo-dimensional foam. We first describe a network model for tracking\ntopological information of foam networks with a state space of combinatorial\nembeddings. Through a mean-field rule for randomly selecting neighboring cells\nof a rupturing edge, we consider a simplified version of the network model in\nthe sequence space $\\ell_1(\\mathbb N)$ which counts total numbers of cells with\n$n\\ge 3$ sides ($n$-gons). Under a large cell limit, we show that number\ndensities of $n$-gons in the mean field model are solutions of an infinite\nsystem of nonlinear kinetic equations. This system is comparable to the\nSmoluchowski coagulation equation for coalescing particles under a\nmultiplicative collision kernel, suggesting gelation behavior. Numerical\nsimulations reveal gelation in the mean-field model, and also comparable\nstatistical behavior between the network and mean-field models.\n"}
+{"abstract": "  Photonic cluster states are a powerful resource for measurement-based quantum\ncomputing and loss-tolerant quantum communication. Proposals to generate\nmulti-dimensional lattice cluster states have identified coupled spin-photon\ninterfaces, spin-ancilla systems, and optical feedback mechanisms as potential\nschemes. Following these, we propose the generation of multi-dimensional\nlattice cluster states using a single, efficient spin-photon interface coupled\nstrongly to a nuclear register. Our scheme makes use of the contact hyperfine\ninteraction to enable universal quantum gates between the interface spin and a\nlocal nuclear register and funnels the resulting entanglement to photons via\nthe spin-photon interface. Among several quantum emitters, we identify the\nsilicon-29 vacancy centre in diamond, coupled to a nanophotonic structure, as\npossessing the right combination of optical quality and spin coherence for this\nscheme. We show numerically that using this system a 2x5-sized cluster state\nwith a lower-bound fidelity of 0.5 and repetition rate of 65 kHz is achievable\nunder currently realised experimental performances and with feasible technical\noverhead. Realistic gate improvements put 100-photon cluster states within\nexperimental reach.\n"}
+{"abstract": "  This paper presents an approach for constructing distributed storage system\nbased on micro-service architecture. By building storage functionalities using\nmicro services, we can provide new capabilities to a distributed storage system\nin a flexible way. We take erasure coding and compression as two case studies\nto show how to build a micro-service based distributed storage system. We also\nshow that by building erasure coding and compression as micro-services, the\ndistributed storage system still achieves reasonable performance compared to\nthe monolithic one.\n"}
+{"abstract": "  The focus of our paper is the identification and correction of non-word\nerrors in OCR text. Such errors may be the result of incorrect insertion,\ndeletion, or substitution of a character, or the transposition of two adjacent\ncharacters within a single word. Or, it can be the result of word boundary\nproblems that lead to run-on errors and incorrect-split errors. The traditional\nN-gram correction methods can handle single-word errors effectively. However,\nthey show limitations when dealing with split and merge errors. In this paper,\nwe develop an unsupervised method that can handle both errors. The method we\ndevelop leads to a sizable improvement in the correction rates. This tutorial\npaper addresses very difficult word correction problems - namely incorrect\nrun-on and split errors - and illustrates what needs to be considered when\naddressing such problems. We outline a possible approach and assess its success\non a limited study.\n"}
+{"abstract": "  One of the advantages of the phase-field method (PFM) is its ability to\nincorporate elastic interactions that dominate solid-state processes including\nphase transformations and plastic deformation. As mechanical equilibrium is\nattained much faster than chemical equilibrium, the former should be used as a\nconstraint explicitly in deriving the governing equations of time-evolution of\nPFM order parameters. Current models for elastically anisotropic and\ninhomogeneous media in the literature do not impose such a constraint in their\ngoverning equations. In particular, they ignore the dependence of the total\nstrain on the order parameters while evaluating the variational derivative of\nthe elastic energy (VDEE). There is no mathematical proof to support this\ntreatment and the fundamental thermodynamic consistency of such a models could\nbe challenged. In this work, we present a rigorous and physically transparent\nformulation of PFM governing equations for elastically anisotropic and\ninhomogeneous media in which the mechanical equilibrium is explicitly used as a\nconstraint. From our formulation, we show that VDEE computed in the\nLeo-Lowengrub-Jou (1998) (LLJ) model is thermodynamically consistent. We also\nshow that the Wang-Jin-Khachaturyan (2003) (WJK) model has significant error in\nVDEE calculation. We proposed a first-order correction term to the WJK model\nwhich nullifies the error in VDEE and makes the model thermodynamically\nconsistent. Also, we computed the VDEE by a numerical method with-out using any\nassumptions and compared the numerical VDEE with the LLJ, WJK and modified WJK\nmodels.\n"}
+{"abstract": "  Let $(X,p)$ be a Ricci limit space. We show that for any $\\epsilon > 0$ and\n$x \\in X$, there exists $r< \\epsilon$, depending on $\\epsilon$ and $x$, so that\nany loop in $B_{r}(x)$ is contractible in $B_{\\epsilon}(x)$. In particular, $X$\nis semi-locally simply connected. Then we show that the generalized Margulis\nlemma holds for Ricci limit spaces of $n$-manifolds.\n"}
+{"abstract": "  Intelligent resident surveillance is one of the most essential smart\ncommunity services. The increasing demand for security needs surveillance\nsystems to be able to detect anomalies in surveillance scenes. Employing\nhigh-capacity computational devices for intelligent surveillance in residential\nsocieties is costly and not feasible. Therefore, we propose anomaly detection\nfor intelligent surveillance using CPU-only edge devices. A modular framework\nto capture object-level inferences and tracking is developed. To cope with\npartial occlusions, posture deformations, and complex scenes, we employed\nfeature encoding and trajectory association governed by two metrices\ncomplementing to each other. The elements of an anomaly detection framework are\noptimized to run on CPU-only edge devices with sufficient frames per second\n(FPS). The experimental results indicate the proposed method is feasible and\nachieves satisfactory results in real-life scenarios.\n"}
+{"abstract": "  Model information can be used to predict future trajectories, so it has huge\npotential to avoid dangerous region when implementing reinforcement learning\n(RL) on real-world tasks, like autonomous driving. However, existing studies\nmostly use model-free constrained RL, which causes inevitable constraint\nviolations. This paper proposes a model-based feasibility enhancement technique\nof constrained RL, which enhances the feasibility of policy using generalized\ncontrol barrier function (GCBF) defined on the distance to constraint boundary.\nBy using the model information, the policy can be optimized safely without\nviolating actual safety constraints, and the sample efficiency is increased.\nThe major difficulty of infeasibility in solving the constrained policy\ngradient is handled by an adaptive coefficient mechanism. We evaluate the\nproposed method in both simulations and real vehicle experiments in a complex\nautonomous driving collision avoidance task. The proposed method achieves up to\nfour times fewer constraint violations and converges 3.36 times faster than\nbaseline constrained RL approaches.\n"}
+{"abstract": "  Although quasi-Keplerian discs are among the most common astrophysical\nstructures, computation of secular angular momentum transport within them\nroutinely presents a considerable practical challenge. In this work, we\ninvestigate the secular small-inclination dynamics of a razor-thin particle\ndisc as the continuum limit of a discrete Lagrange-Laplace secular perturbative\ntheory and explore the analogy between the ensuing secular evolution --\nincluding non-local couplings of self-gravitating discs -- and quantum\nmechanics. We find the 'quantum' Hamiltonian that describes the time evolution\nof the system and demonstrate the existence of a conserved inner product. The\nlowest-frequency normal modes are numerically approximated by performing a Wick\nrotation on the equations of motion. These modes are used to quantify the\naccuracy of a much simpler local-coupling model, revealing that it predicts the\nshape of the normal modes to a high degree of accuracy, especially in narrow\nannuli, even though it fails to predict their eigenfrequencies.\n"}
+{"abstract": "  In this work, we study global existence, eventual smoothness and asymptotical\nbehavior of positive solutions for a two-species chemotaxis consumption model\nin a bounded smooth but not necessarily convex domain $\\Omega\\subset\n\\mathbb{R}^n (n=2,3,4,5)$ with nonnegative initial data and homogeneous Neumann\nboundary data Under a smallness condition, boundedness of classical solutions\nand stabilization to constant equilibrium is known. Here, without any smallness\ncondition, we show global existence and uniform-in-time boundedness of\nclassical solutions in 2D and global existence, eventual smoothness and\nasymptotical behavior (in convex domains) of weak solutions in nD (n=3,4,5).\nOur findings also extend and improve the one-species chemotaxis-consumption\nmodel studied in relevant literature.\n"}
+{"abstract": "  Due to 5G millimeter wave (mmWave), spatial channel parameters are becoming\nhighly resolvable, enabling accurate vehicle localization and mapping. We\npropose a novel method of radio simultaneous localization and mapping (SLAM)\nwith the Dirichlet process (DP). The DP, which can estimate the number of\nclusters as well as clustering, is capable of identifying the locations of\nreflectors by classifying signals when such 5G signals are reflected and\nreceived from various objects. We generate birth points using the measurements\nfrom 5G mmWave signals received by the vehicle and classify objects by\nclustering birth points generated over time. Each time we use the DP clustering\nmethod, we can map landmarks in the environment in challenging situations where\nfalse alarms exist in the measurements and change the cardinality of received\nsignals. Simulation results demonstrate the performance of the proposed scheme.\nBy comparing the results with the SLAM based on the Rao-Blackwellized\nprobability hypothesis density filter, we confirm a slight drop in SLAM\nperformance, but as a result, we validate that it has a significant gain in\ncomputational complexity.\n"}
+{"abstract": "  We analyze the possible effect of rings on orbital velocities in galaxies.\nThe superposition of the central force with the gravitational forces induced by\nthe rings opens up various possibilities of the course of orbital velocities.\nThe orbital velocity depends on the position of the star in the ring. We\nillustrate this dependence on several models, where we show the course of\npotential curves and the curves of field strength.\n"}
+{"abstract": "  Blur and noise corrupting Computed Tomography (CT) images can hide or distort\nsmall but important details, negatively affecting the diagnosis. In this paper,\nwe present a novel gradient-based Plug-and-Play algorithm, constructed on the\nHalf-Quadratic Splitting scheme, and we apply it to restore CT images. In\nparticular, we consider different schemes encompassing external and internal\ndenoisers as priors, defined on the image gradient domain. The internal prior\nis based on the Total Variation functional. The external denoiser is\nimplemented by a deep Convolutional Neural Network (CNN) trained on the\ngradient domain (and not on the image one, as in state-of-the-art works). We\nalso prove a general fixed-point convergence theorem under weak assumptions on\nboth internal and external denoisers. The experiments confirm the effectiveness\nof the proposed framework in restoring blurred noisy CT images, both in\nsimulated and real medical settings. The achieved enhancements in the restored\nimages are really remarkable, if compared to the results of many\nstate-of-the-art methods.\n"}
+{"abstract": "  The Pacific Northwest Smart Grid Demonstration was an electricity grid\nmodernization project conducted in the Northwest U.S. This paper presents the\nanalysis of renewable generation at the Renewable Energy Park located in the\nCity of Ellensburg, WA. The community energy park concept is an intriguing\nmodel for community investment in renewable resources,but the lessons in this\npaper should be considered.\n"}
+{"abstract": "  The Institutional Analysis and Development (IAD) framework is a conceptual\ntoolbox put forward by Elinor Ostrom and colleagues in an effort to identify\nand delineate the universal common variables that structure the immense variety\nof human interactions. The framework identifies rules as one of the core\nconcepts to determine the structure of interactions, and acknowledges their\npotential to steer a community towards more beneficial and socially desirable\noutcomes. This work presents the first attempt to turn the IAD framework into a\ncomputational model to allow communities of agents to formally perform what-if\nanalysis on a given rule configuration. To do so, we define the Action\nSituation Language -- or ASL -- whose syntax is hgighly tailored to the\ncomponents of the IAD framework and that we use to write descriptions of social\ninteractions. ASL is complemented by a game engine that generates its semantics\nas an extensive-form game. These models, then, can be analyzed with the\nstandard tools of game theory to predict which outcomes are being most\nincentivized, and evaluated according to their socially relevant properties.\n"}
+{"abstract": "  Two dimensional crystalline membranes in isotropic embedding space exhibit a\nflat phase with anomalous elasticity, relevant e.g., for graphene. Here we\nstudy their thermal fluctuations in the absence of exact rotational invariance\nin the embedding space. An example is provided by a membrane in an\norientational field, tuned to a critical buckling point by application of\nin-plane stresses. Through a detailed analysis, we show that the transition is\nin a new universality class. The self-consistent screening method predicts a\nsecond order transition, with modified anomalous elasticity exponents at\ncriticality, while the RG suggests a weakly first order transition.\n"}
+{"abstract": "  We address the problem of determining optimal sensor precisions for\nestimating the states of linear time-varying discrete-time stochastic dynamical\nsystems, with guaranteed bounds on the estimation errors. This is performed in\nthe Kalman filtering framework, where the sensor precisions are treated as\nvariables. They are determined by solving a constrained convex optimization\nproblem, which guarantees the specified upper bound on the posterior error\nvariance. Optimal sensor precisions are determined by minimizing the l1 norm,\nwhich promotes sparseness in the solution and indirectly addresses the sensor\nselection problem. The theory is applied to realistic flight mechanics and\nastrodynamics problems to highlight its engineering value. These examples\ndemonstrate the application of the presented theory to a) determine redundant\nsensing architectures for linear time invariant systems, b) accurately estimate\nstates with low-cost sensors, and c) optimally schedule sensors for linear\ntime-varying systems.\n"}
+{"abstract": "  Due to the significant delay and cost associated with experimental tests, a\nmodel based evaluation of concrete compressive strength is of high value, both\nfor the purpose of strength prediction as well as the mixture optimization. In\nthis regard, several recent studies have employed state-of-the-art regression\nmodels in order to achieve a good prediction model, employing available\nexperimental data sets. Nevertheless, while each of the employed models can\nbetter adapt to a specific nature of the input data, the accuracy of each\nindividual model is limited due to the sensitivity to the choice of\nhyperparameters and the learning strategy. In the present work, we take a\nfurther step towards improving the accuracy of the prediction model via the\nweighted combination of multiple regression methods. Moreover, a (GA)-based\nmulti-objective mixture optimization is proposed, building on the obtained\nmulti-regression model. In particular, we present a data aided framework where\nthe regression methods based on artificial neural network, random forest\nregression, and polynomial regression are jointly implemented to predict the\ncompressive strength of concrete. The outcome of the individual regression\nmodels are then combined via a linear weighting strategy and optimized over the\ntraining data set as a quadratic convex optimization problem. It is worth\nmentioning that due to the convexity of the formulated problem, the globally\noptimum weighting strategy is obtained via standard numerical solvers.\nEmploying the proposed GA-based optimization, a Pareto front of the cost-CS\ntrade-of has been obtained employing the available data set. Moreover, the\nresulting accuracy of the proposed multi-model prediction method is shown to\noutperform the available single-model regression methods in the literature by a\nvaluable margin, via numerical simulations.\n"}
+{"abstract": "  We develop a stable finite difference method for the elastic wave equations\nin bounded media, where the material properties can be discontinuous at curved\ninterfaces. The governing equations are discretized in second order form by a\nfourth or sixth order accurate summation-by-parts operator. The mesh size is\ndetermined by the velocity structure of the material, resulting in\nnonconforming grid interfaces with hanging nodes. We use order-preserving\ninterpolation and the ghost point technique to couple adjacent mesh blocks in\nan energy-conserving manner, which is supported by a fully discrete stability\nanalysis. In numerical experiments, we demonstrate that the convergence rate is\noptimal, and is the same as when a globally uniform mesh is used in a single\ndomain. In addition, with a predictor-corrector time integration method, we\nobtain time stepping stability with stepsize almost the same as given by the\nusual Courant Friedrichs Lewy condition.\n"}
+{"abstract": "  Flexible manufacturing in the process industry requires control systems to\nachieve time-varying setpoints (e.g., product specifications) based on market\ndemand. Contraction theory provides a useful framework for\nreference-independent system analysis and tracking control for nonlinear\nsystems. However, determination of the control contraction metrics and control\nlaws can be very difficult for general nonlinear systems. This work develops an\napproach to discrete-time contraction analysis and control using neural\nnetworks. The methodology involves training a neural network to learn a\ncontraction metric and feedback gain. The resulting contraction-based\ncontroller embeds the trained neural network and is capable of achieving\nefficient tracking of time-varying references, with a full range of model\nuncertainty, without the need for controller structure redesign. This is a\nrobust approach that can deal with bounded parametric uncertainties in the\nprocess model, which are commonly encountered in industrial (chemical)\nprocesses. Simulation examples are provided to illustrate the above approach.\n"}
+{"abstract": "  We study a novel type of non-Fermi liquid where there exist an infinite\nnumber of critical bosonic modes instead of finite bosonic modes for the\nconventional ones. We consider itinerant magnets with both conduction electrons\nand fluctuating magnetic moments in three dimensions. With\nDzyaloshinskii-Moriya interaction, the moments fluctuate near a boson surface\nin the reciprocal space at low energies when the system approaches an ordering\ntransition. The infinite number of critical modes on the boson surface strongly\nscatter the infinite gapless electrons on the Fermi surface and convert the\nmetallic sector into a non-Fermi liquid. We explain the novel physical\nproperties of this non-Fermi liquid. On the ordered side, a conventional\nnon-Fermi liquid emerges due to the scattering by the gapless Goldstone mode\nfrom the spontaneous breaking of the global rotational symmetry. We discuss the\ngeneral structure of the phase diagram in the vicinity of the quantum phase\ntransition and clarify various crossover behaviors.\n"}
+{"abstract": "  The Clebsh-Gordan coefficients for the Lie algebra\n  $\\mathfrak{gl}_3$ in the Gelfand-Tsetlin base are calculated. In contrast to\nprevious papers the result is given as an explicit formula. To obtain the\nresult a realization of a representation in the space of functions on the group\n$GL_3$ is used. The keystone fact that allows to carry the calculation of\nClebsh-Gordan coefficients is the theorem that says that functions\ncorresponding to Gelfand-Tsetlin base vectors can be expressed through\ngeneralized hypergeometric functions.\n"}
+{"abstract": "  Understanding the generation mechanism of the heating flux is essential for\nthe design of hypersonic vehicles. We proposed a novel formula to decompose the\nheat flux coefficient into the contributions of different terms by integrating\nthe conservative equation of the total energy. The reliability of the formula\nis well demonstrated by the direct numerical simulation results of a hypersonic\ntransitional boundary layer. Through this formula, the exact process of the\nenergy transport in the boundary layer can be explained and the dominant\ncontributors to the heat flux can be explored, which are beneficial for the\nprediction of the heat and design of the thermal protection devices\n"}
+{"abstract": "  We define Gaussian assignment process, determine the asymptotic behavior of\nits maximum's expectation and suggest an explicit strategy that attains the\ncorresponding asymptotics.\n"}
+{"abstract": "  We give asymptotic estimates for the mean number of divisors of integers\nwithout small prime factors, integers with bounded ratios of consecutive\ndivisors, and for practical numbers. In the last case, this confirms a\nconjecture of Margenstern.\n"}
+{"abstract": "  The wide adoption of smart meters makes residential load data available and\nthus improves the understanding of the energy consumption behavior. Many\nexisting studies have focused on smart-meter data analysis, but the drivers of\nenergy consumption behaviors are not well understood. This paper aims to\ncharacterize and estimate users' load patterns based on their demographic and\nsocioeconomic information. We adopt the symbolic aggregate approximation (SAX)\nmethod to process the load data and use the K-Means method to extract key load\npatterns. We develop a deep neural network (DNN) to analyze the relationship\nbetween users' load patterns and their demographic and socioeconomic features.\nUsing real-world load data, we validate our framework and demonstrate the\nconnections between load patterns and household demographic and socioeconomic\nfeatures. We also take two regression models as benchmarks for comparisons.\n"}
+{"abstract": "  Clockwork models can explain the flavor hierarchies in the Standard Model\nquark and lepton spectrum. We construct supersymmetric versions of such flavor\nclockwork models. The zero modes of the clockwork are identified with the\nfermions and sfermions of the Minimal Supersymmetric Standard Model. In\naddition to generating a hierarchical fermion spectrum, the clockwork also\npredicts a specific flavor structure for the soft SUSY breaking sfermion\nmasses. We find sizeable flavor mixing among first and second generation\nsquarks. Constraints from Kaon oscillations require the masses of either\nsquarks or gluinos to be above a scale of $\\sim 3$ PeV.\n"}
+{"abstract": "  We study a discrete-time dynamical system of wild mosquito population with\nparameters: $\\beta$ - the birth rate of adults; $\\alpha$ - maximum emergence\nrete; $\\mu>0$ - the death rate of adults; $\\gamma$ - Allee effects. We prove\nthat if $\\gamma\\geq\\frac{\\alpha(\\beta-\\mu)}{\\mu^2}$ then the mosquito\npopulation dies and if $\\gamma<\\frac{\\alpha(\\beta-\\mu)}{\\mu^2}$ holds then\nextinction or survival of the mosquito population depends on their initial\nstate.\n"}
+{"abstract": "  We aim to gain insight into the effect of network and faculae on solar\nirradiance from their apparent intensity. Taking full-disc observations from\nthe Solar Dynamics Observatory, we examined the intensity contrast of network\nand faculae in the continuum and core of the Fe I 6173 {\\AA} line and 1700\n{\\AA}, including the variation with magnetic flux density, distance from disc\ncentre, nearby magnetic fields, and time. The brightness of network and faculae\nis believed to be suppressed by nearby magnetic fields from its effect on\nconvection. The difference in intensity contrast between the quiet-Sun network\nand active region faculae, noted by various studies, arises because active\nregions are more magnetically crowded and is not due to any fundamental\nphysical differences between network and faculae. These results highlight that\nsolar irradiance models need to include the effect of nearby magnetic fields on\nnetwork and faculae brightness. We found evidence that suggests that departures\nfrom local thermal equilibrium (LTE) might have limited effect on intensity\ncontrast. This could explain why solar irradiance models that are based on the\nintensity contrast of solar surface magnetic features calculated assuming LTE\nreproduce the observed spectral variability even where the LTE assumption\nbreaks down. Certain models of solar irradiance employ chromospheric indices as\ndirect indications of the effect of network and faculae on solar irradiance.\nBased on past studies of the Ca II K line and on the intensity contrast\nmeasurements derived here, we show that the fluctuations in chromospheric\nemission from network and faculae are a reasonable estimate of the emission\nfluctuations in the middle photosphere, but not of those in the lower\nphotosphere. The data set, which extends from 2010 to 2018, indicates that\nintensity contrast was stable to about 3% in this period.\n"}
+{"abstract": "  Similar to Earth, Saturn's largest moon, Titan, possesses a system of\nhigh-altitude zonal winds (or jets) that encircle the globe. Using the Atacama\nLarge Millimeter/submillimeter Array (ALMA) in August 2016, Lellouch et al.\n(2019) discovered an equatorial jet at much higher altitudes than previously\nknown, with a surprisingly fast speed of up to ~340 m/s, but the origin of such\nhigh velocities is not yet understood. We obtained spectrally and spatially\nresolved ALMA observations in May 2017 to map Titan's 3D global wind field and\ncompare our results with a reanalysis of the August 2016 data. Doppler wind\nvelocity maps were derived in the altitude range ~300-1000 km (from the upper\nstratosphere to the thermosphere). At the highest, thermospheric altitudes, a\n47% reduction in the equatorial zonal wind speed was measured over the 9-month\nperiod (corresponding to L_s = 82-90 degrees on Titan). This is interpreted as\ndue to a dramatic slowing and loss of confinement (broadening) of the\nrecently-discovered thermospheric equatorial jet, as a result of dynamical\ninstability. These unexpectedly-rapid changes in the upper-atmospheric dynamics\nare consistent with strong variability of the jet's primary driving mechanism.\n"}
+{"abstract": "  Image registration aims to establish spatial correspondence across pairs, or\ngroups of images, and is a cornerstone of medical image computing and\ncomputer-assisted-interventions. Currently, most deep learning-based\nregistration methods assume that the desired deformation fields are globally\nsmooth and continuous, which is not always valid for real-world scenarios,\nespecially in medical image registration (e.g. cardiac imaging and abdominal\nimaging). Such a global constraint can lead to artefacts and increased errors\nat discontinuous tissue interfaces. To tackle this issue, we propose a\nweakly-supervised Deep Discontinuity-preserving Image Registration network\n(DDIR), to obtain better registration performance and realistic deformation\nfields. We demonstrate that our method achieves significant improvements in\nregistration accuracy and predicts more realistic deformations, in registration\nexperiments on cardiac magnetic resonance (MR) images from UK Biobank Imaging\nStudy (UKBB), than state-of-the-art approaches.\n"}
+{"abstract": "  Our goal is to find combinations of facts that optimally summarize data sets.\nWe consider this problem in the context of voice query interfaces for simple,\nexploratory data analysis. Here, the system answers voice queries with a short\nsummary of relevant data. Finding optimal voice data summaries is\ncomputationally expensive. Prior work in this domain has exploited sampling and\nincremental processing. Instead, we rely on a pre-processing stage generating\nsummaries of data subsets in a batch operation. This step reduces run time\noverheads by orders of magnitude.\n  We present multiple algorithms for the pre-processing stage, realizing\ndifferent tradeoffs between optimality and data processing overheads. We\nanalyze our algorithms formally and compare them experimentally with prior\nmethods for generating voice data summaries. We report on multiple user studies\nwith a prototype system implementing our approach. Furthermore, we report on\ninsights gained from a public deployment of our system on the Google Assistant\nPlatform.\n"}
+{"abstract": "  Despite significant progress, we show that state of the art 3D human pose and\nshape estimation methods remain sensitive to partial occlusion and can produce\ndramatically wrong predictions although much of the body is observable. To\naddress this, we introduce a soft attention mechanism, called the Part\nAttention REgressor (PARE), that learns to predict body-part-guided attention\nmasks. We observe that state-of-the-art methods rely on global feature\nrepresentations, making them sensitive to even small occlusions. In contrast,\nPARE's part-guided attention mechanism overcomes these issues by exploiting\ninformation about the visibility of individual body parts while leveraging\ninformation from neighboring body-parts to predict occluded parts. We show\nqualitatively that PARE learns sensible attention masks, and quantitative\nevaluation confirms that PARE achieves more accurate and robust reconstruction\nresults than existing approaches on both occlusion-specific and standard\nbenchmarks. The code and data are available for research purposes at {\\small\n\\url{https://pare.is.tue.mpg.de/}}\n"}
+{"abstract": "  This paper presents a summary of the Masked Face Recognition Competitions\n(MFR) held within the 2021 International Joint Conference on Biometrics (IJCB\n2021). The competition attracted a total of 10 participating teams with valid\nsubmissions. The affiliations of these teams are diverse and associated with\nacademia and industry in nine different countries. These teams successfully\nsubmitted 18 valid solutions. The competition is designed to motivate solutions\naiming at enhancing the face recognition accuracy of masked faces. Moreover,\nthe competition considered the deployability of the proposed solutions by\ntaking the compactness of the face recognition models into account. A private\ndataset representing a collaborative, multi-session, real masked, capture\nscenario is used to evaluate the submitted solutions. In comparison to one of\nthe top-performing academic face recognition solutions, 10 out of the 18\nsubmitted solutions did score higher masked face verification accuracy.\n"}
+{"abstract": "  We prove the cosmetic crossing conjecture for genus one knots with\nnon-trivial Alexander polynomial. We also prove the conjecture for genus one\nknots with trivial Alexander polynomial, under some additional assumptions.\n"}
+{"abstract": "  Continuum robotic manipulators are increasingly adopted in minimal invasive\nsurgery. However, their nonlinear behavior is challenging to model accurately,\nespecially when subject to external interaction, potentially leading to poor\ncontrol performance. In this letter, we investigate the feasibility of adopting\na model-free multiagent reinforcement learning (RL), namely multiagent deep Q\nnetwork (MADQN), to control a 2-degree of freedom (DoF) cable-driven continuum\nsurgical manipulator. The control of the robot is formulated as a one-DoF, one\nagent problem in the MADQN framework to improve the learning efficiency.\nCombined with a shielding scheme that enables dynamic variation of the action\nset boundary, MADQN leads to efficient and importantly safer control of the\nrobot. Shielded MADQN enabled the robot to perform point and trajectory\ntracking with submillimeter root mean square errors under external loads, soft\nobstacles, and rigid collision, which are common interaction scenarios\nencountered by surgical manipulators. The controller was further proven to be\neffective in a miniature continuum robot with high structural nonlinearitiy,\nachieving trajectory tracking with submillimeter accuracy under external\npayload.\n"}
+{"abstract": "  The generalized Benjamin-Bona-Mahony-Burgers equation (gBBMB) describes the\nflow of blood through a long, viscoelastic artery. In this article we introduce\na formulation of gBBMB valid on networks with semi-infinite edges joined at a\nsingle junction, with the network's edges corresponding to a segment of the\narterial tree. To reflect sudden changes in the material properties of blood\nvessels, the coefficients of gBBMB are allowed to take different values on each\nedge of the network. Critically, our formulation ensures that the total mass of\nthe solution to gBBMB is constant in time, even in the presence of dissipation.\nWe also establish local-in-time well-posedness of this new formulation for\nsufficiently regular initial data. Then, we show how energy methods can be used\nto extend the local solution to a solution valid for all positive times,\nprovided certain constraints are imposed on the parameters of the model PDE and\nthe network. To build intuition for how waves scatter off the central junction\nof a network with two edges, we demonstrate the results of some numerical\nsimulations.\n"}
+{"abstract": "  In the past three decades, a large number of metaheuristics have been\nproposed and shown high performance in solving complex optimization problems.\nWhile most variation operators in existing metaheuristics are empirically\ndesigned, this paper aims to design new operators automatically, which are\nexpected to be search space independent and thus exhibit robust performance on\ndifferent problems. For this purpose, this work first investigates the\ninfluence of translation invariance, scale invariance, and rotation invariance\non the search behavior and performance of some representative operators. Then,\nwe deduce the generic form of translation, scale, and rotation invariant\noperators. Afterwards, a principled approach is proposed for the automated\ndesign of operators, which searches for high-performance operators based on the\ndeduced generic form. The experimental results demonstrate that the operators\ngenerated by the proposed approach outperform state-of-the-art ones on a\nvariety of problems with complex landscapes and up to 1000 decision variables.\n"}
+{"abstract": "  Head-mounted device-based human-computer interaction often requires\negocentric recognition of hand gestures and fingertips detection. In this\npaper, a unified approach of egocentric hand gesture recognition and fingertip\ndetection is introduced. The proposed algorithm uses a single convolutional\nneural network to predict the probabilities of finger class and positions of\nfingertips in one forward propagation. Instead of directly regressing the\npositions of fingertips from the fully connected layer, the ensemble of the\nposition of fingertips is regressed from the fully convolutional network.\nSubsequently, the ensemble average is taken to regress the final position of\nfingertips. Since the whole pipeline uses a single network, it is significantly\nfast in computation. Experimental results show that the proposed method\noutperforms the existing fingertip detection approaches including the Direct\nRegression and the Heatmap-based framework. The effectiveness of the proposed\nmethod is also shown in-the-wild scenario as well as in a use-case of virtual\nreality.\n"}
+{"abstract": "  $\\textit{Thailand-UK Python+Astronomy Summer School}$ (ThaiPASS), a\ncollaborative project comprising UK and Thai institutions and assess its impact\nand possible application to schools in the United Kingdom. Since its inception\nin 2018, the annual ThaiPASS have trained around 60 Thai high-school students\nin basic data handling skills using Python in the context of various astronomy\ntopics, using current research from the teaching team. Our impact assessment of\nthe 5 day summer schools show an overwhelmingly positive response from students\nin both years, with over $80\\%$ of students scoring the activities above\naverage in all activities but one. We use this data to suggest possible future\nimprovements. We also discuss how ThaiPASS may inspire further outreach and\nengagement activities within the UK and beyond.\n"}
+{"abstract": "  Digital Twin is an emerging technology at the forefront of Industry 4.0, with\nthe ultimate goal of combining the physical space and the virtual space. To\ndate, the Digital Twin concept has been applied in many engineering fields,\nproviding useful insights in the areas of engineering design, manufacturing,\nautomation, and construction industry. While the nexus of various technologies\nopens up new opportunities with Digital Twin, the technology requires a\nframework to integrate the different technologies, such as the Building\nInformation Model used in the Building and Construction industry. In this work,\nan Information Fusion framework is proposed to seamlessly fuse heterogeneous\ncomponents in a Digital Twin framework from the variety of technologies\ninvolved. This study aims to augment Digital Twin in buildings with the use of\nAI and 3D reconstruction empowered by unmanned aviation vehicles. We proposed a\ndrone-based Digital Twin augmentation framework with reusable and customisable\ncomponents. A proof of concept is also developed, and extensive evaluation is\nconducted for 3D reconstruction and applications of AI for defect detection.\n"}
+{"abstract": "  Estimating the spatially varying microstructures of heterogeneous and locally\nanisotropic media non-destructively is necessary for the accurate detection of\nflaws and reliable monitoring of manufacturing processes. Conventional\nalgorithms used for solving this inverse problem come with significant\ncomputational cost, particularly in the case of high dimensional non-linear\ntomographic problems. In this paper, we propose a framework which uses deep\nneural networks (DNNs) with full aperture, pitch-catch and pulse-echo\ntransducer configurations to reconstruct material maps of crystallographic\norientation. We also present the first ever application of generative\nadversarial networks (GANs) to achieve super resolution of ultrasonic\ntomographic images, providing a factor-four increase in image resolution and up\nto a 50% increase in structural similarity. The importance of including\nappropriate prior knowledge in the GAN training dataset to increase inversion\naccuracy is highlighted; known information about the material's structure\nshould be present in the training data. We show that after a computationally\nexpensive training process, the DNNs and GANs can be used in less that one\nsecond (0.9 seconds on a standard desktop computer) to provide a high\nresolution map of the material's grain orientations.\n"}
+{"abstract": "  Richard P. Feynman's work on gravitation, as can be inferred from several\npublished and unpublished sources, is reviewed. Feynman was involved with this\nsubject at least from late 1954 to the late 1960s, giving several pivotal\ncontributions to it. Even though he published only three papers, much more\nmaterial is available, beginning with the records of his many interventions at\nthe Chapel Hill conference in 1957, which are here analyzed in detail, and show\nthat he had already considerably developed his ideas on gravity. In addition he\nexpressed deep thoughts about fundamental issues in quantum mechanics which\nwere suggested by the problem of quantum gravity, such as superpositions of the\nwave functions of macroscopic objects and the role of the observer. Feynman\nalso lectured on gravity several times. Besides the famous lectures given at\nCaltech in 1962-63, he extensively discussed this subject in a series of\nlectures delivered at the Hughes Aircraft Company in 1966-67, whose focus was\non astronomy and astrophysics. All this material allows to reconstruct a\ndetailed picture of Feynman's ideas on gravity and of their evolution until the\nlate sixties. According to him, gravity, like electromagnetism, has quantum\nfoundations, therefore general relativity has to be regarded as the classical\nlimit of an underlying quantum theory; this quantum theory should be\ninvestigated by computing physical processes, as if they were experimentally\naccessible. The same attitude is shown with respect to gravitational waves, as\nis evident also from an unpublished letter addressed to Victor F. Weisskopf. In\naddition, an original approach to gravity, which closely mimics (and probably\nwas inspired by) the derivation of the Maxwell equations given by Feynman in\nthat period, is sketched in the unpublished Hughes lectures.\n"}
+{"abstract": "  In addition to the existing strong indications for lepton flavour university\nviolation (LFUV) in low energy precision experiments, CMS recently released an\nanalysis of non-resonant di-lepton pairs which could constitute the first sign\nof LFUV in high-energy LHC searches. In this article we show that the Cabibbo\nangle anomaly, an (apparent) violation of first row and column CKM unitarity\nwith $\\approx3\\,\\sigma$ significance, and the CMS result can be correlated and\ncommonly explained in a model independent way by the operator $[Q_{\\ell\nq}^{(3)}]_{1111} =\n(\\bar{\\ell}_1\\gamma^{\\mu}\\sigma^I\\ell_1)(\\bar{q}_1\\gamma_{\\mu}\\sigma^Iq_1)$.\nThis is possible without violating the bounds from the non-resonant di-lepton\nsearch of ATLAS (which interestingly also observed slightly more events than\nexpected in the electron channel) nor from $R(\\pi)=\\pi \\to\\mu\\nu/\\pi \\to e\n\\nu$. We find a combined preference for the new physics hypothesis of\n$4.5\\,\\sigma$ and predict $1.0004<R(\\pi)<1.0009$ (95\\%~CL) which can be tested\nin the near future with the forthcoming results of the PEN experiment.\n"}
+{"abstract": "  We use a Monte Carlo radiative transfer model (MCRTM) to simulate the UBVRI\nlight curves, images and linear polarization of a light echo from supernova\nSN$~$1987A in the Large Magellanic Cloud (LMC) using various dust cloud shapes,\nsizes, and optical properties. We compare the theoretical simulations to the\nobservations of AT2019xis, a light echo detected at a large angular distance\n(4.05$^{'}$) from SN$~$1987A. We estimate the size and optical thickness of the\ndust cloud based on the simulation results and the observations of Optical\nGravitational Lensing Experiment (OGLE-IV) Transient Detection System (OTDS)\nI-band light curve. The mass of the dust cloud is calculated using the\nestimated size, optical thickness and extinction coefficient. If the dust cloud\nis assumed to correspond to a gas-to-dust ratio of 300, the total mass of the\ndust cloud is approximately 7.8-9.3 $M_{\\odot}$. Based on these theoretical\nmodels, we show that the morphological shapes of the light echoes in the\nwavelength range in or shorter than the U-band to be very different from those\nin the longer wavelength bands, and the difference carries important\ninformation on the early UV radiation of SN$~$1987A.\n"}
+{"abstract": "  For real-world deployments, it is critical to allow robots to navigate in\ncomplex environments autonomously. Traditional methods usually maintain an\ninternal map of the environment, and then design several simple rules, in\nconjunction with a localization and planning approach, to navigate through the\ninternal map. These approaches often involve a variety of assumptions and prior\nknowledge. In contrast, recent reinforcement learning (RL) methods can provide\na model-free, self-learning mechanism as the robot interacts with an initially\nunknown environment, but are expensive to deploy in real-world scenarios due to\ninefficient exploration. In this paper, we focus on efficient navigation with\nthe RL technique and combine the advantages of these two kinds of methods into\na rule-based RL (RuRL) algorithm for reducing the sample complexity and cost of\ntime. First, we use the rule of wall-following to generate a closed-loop\ntrajectory. Second, we employ a reduction rule to shrink the trajectory, which\nin turn effectively reduces the redundant exploration space. Besides, we give\nthe detailed theoretical guarantee that the optimal navigation path is still in\nthe reduced space. Third, in the reduced space, we utilize the Pledge rule to\nguide the exploration strategy for accelerating the RL process at the early\nstage. Experiments conducted on real robot navigation problems in hex-grid\nenvironments demonstrate that RuRL can achieve improved navigation performance.\n"}
+{"abstract": "  Quest for new states of matter near an ordered phase is a promising route for\nmaking modern physics forward. By probing thermal properties of a ferroelectric\n(FE) crystal Ba1-xSrxAl2O4, we have clarified that low-energy excitation of\nacoustic phonons is remarkably enhanced with critical behavior at the border of\nthe FE phase. The phonon spectrum is significantly damped toward the FE phase\nboundary and transforms into glasslike phonon excitation which is reminiscent\nof a boson peak. This system thus links long-standing issues of amorphous\nsolids and structural instability in crystals to pave the way to controlling\nlattice fluctuation as a new tuning parameter.\n"}
+{"abstract": "  This study is about public-private research collaboration. In particular, we\nwant to measure how the propensity of academics to collaborate with their\ncolleagues from private firms varies over time and whether the typical profile\nof such academics change. Furthermore, we investigate the change of the weights\nof main drivers underlying the academics' propensity to collaborate with\nindustry. In order to achieve such goals, we apply an inferential model on a\ndataset of professors working in Italian universities in two subsequent\nperiods, 2010-2013 and 2014-2017. Results can be useful for supporting the\ndefinition of policies aimed at fostering public-private research\ncollaborations, and should be taken into account when assessing their\neffectiveness afterwards.\n"}
+{"abstract": "  We propose a framework for Bayesian Likelihood-Free Inference (LFI) based on\nGeneralized Bayesian Inference. To define the generalized posterior, we use\nScoring Rules (SRs), which evaluate probabilistic models given an observation.\nAs in LFI we can sample from the model (but not evaluate the likelihood), we\nemploy SRs with easy empirical estimators. Our framework includes novel\napproaches and popular LFI techniques (such as Bayesian Synthetic Likelihood),\nwhich benefit from the generalized Bayesian interpretation. Our method enjoys\nposterior consistency in a well-specified setting when a strictly-proper SR is\nused (i.e., one whose expectation is uniquely minimized when the model\ncorresponds to the data generating process). Further, we prove a finite sample\ngeneralization bound and outlier robustness for the Kernel and Energy Score\nposteriors, and propose a strategy suitable for the LFI setup for tuning the\nlearning rate in the generalized posterior. We run simulations studies with\npseudo-marginal Markov Chain Monte Carlo (MCMC) and compare with related\napproaches, which we show do not enjoy robustness and consistency.\n"}
+{"abstract": "  The emergence of order in collective dynamics is a fascinating phenomenon\nthat characterizes many natural systems consisting of coupled entities.\nSynchronization is such an example where individuals, usually represented by\neither linear or nonlinear oscillators, can spontaneously act coherently with\neach other when the interactions' configuration fulfills certain conditions.\nHowever, synchronization is not always perfect, and the coexistence of coherent\nand incoherent oscillators, broadly known in the literature as chimera states,\nis also possible. Although several attempts have been made to explain how\nchimera states are created, their emergence, stability, and robustness remain a\nlong-debated question. We propose an approach that aims to establish a robust\nmechanism through which chimeras originate. We first introduce a\nstability-breaking method where clusters of synchronized oscillators can\nemerge. Similarly, one or more clusters of oscillators may remain incoherent\nwithin yielding a particular class of patterns that we here name cluster\nchimera states.\n"}
+{"abstract": "  The Cosserat continuum is used in this paper to regularize the ill-posed\ngoverning equations of the Cauchy/Maxwell continuum. Most available\nconstitutive models adopt yield and plastic potential surfaces with a circular\ndeviatoric section. This is a too crude an approximation which hinders the\napplication of the Cosserat continuum into practice, particularly in the\ngeotechnical domain. An elasto-plastic constitutive model for the linear\nformulation of the Cosserat continuum is here presented, which features\nnon-associated flow and hardening/softening behaviour, whilst linear\nhyper-elasticity is adopted to reproduce the recoverable response. For the\nformulation of the yield and plastic potential functions, a definition of the\n\\textit{equivalent von Mises stress} is used which is based on Hencky's\ninterpretation of the von Mises criterion and also on the theory of\nrepresentations. The dependency on the Lode's angle of both the yield and\nplastic potential functions is introduced through the adoption of a recently\nproposed \\textit{Generalized classical} criterion, which rigorously defines\nmost of the classical yield and failure criteria.\n"}
+{"abstract": "  Despite thousands of spectroscopic detections, only four isolated white\ndwarfs exhibit Balmer emission lines. The temperature inversion mechanism is a\npuzzle over 30 years old that has defied conventional explanations. One\nhypothesis is a unipolar inductor that achieves surface heating via ohmic\ndissipation of a current loop between a conducting planet and a magnetic white\ndwarf. To investigate this model, new time-resolved spectroscopy,\nspectropolarimetry, and photometry of the prototype GD 356 are studied. The\nemission features vary in strength on the rotational period, but in anti-phase\nwith the light curve, consistent with a cool surface spot beneath an optically\nthin chromosphere. Possible changes in the line profiles are observed at the\nsame photometric phase, potentially suggesting modest evolution of the emission\nregion, while the magnetic field varies by 10 per cent over a full rotation.\nThese comprehensive data reveal neither changes to the photometric period, nor\nadditional signals such as might be expected from an orbiting body. A closer\nexamination of the unipolar inductor model finds points of potential failure:\nthe observed rapid stellar rotation will inhibit current carriers due to the\ncentrifugal force, there may be no supply of magnetospheric ions, and no\nanti-phase flux changes are expected from ohmic surface heating. Together with\nthe highly similar properties of the four cool, emission-line white dwarfs,\nthese facts indicate that the chromospheric emission is intrinsic. A\ntantalizing possibility is that intrinsic chromospheres may manifest in\n(magnetic) white dwarfs, and in distinct parts of the HR diagram based on\nstructure and composition.\n"}
+{"abstract": "  We discuss reduced-scaling strategies employing recently introduced\nsub-system embedding sub-algebras coupled-cluster formalism (SES-CC) to\ndescribe many-body systems. These strategies utilize properties of the SES-CC\nformulations where the equations describing certain classes of sub-systems can\nbe integrated into a computational flows composed coupled eigenvalue problems\nof reduced dimensionality. Additionally, these flows can be determined at the\nlevel of the CC Ansatz by the inclusion of selected classes of cluster\namplitudes, which define the wave function \"memory\" of possible partitionings\nof the many-body system into constituent sub-systems. One of the possible ways\nof solving these coupled problems is through implementing procedures, where the\ninformation is passed between the sub-systems in a self-consistent manner. As a\nspecial case, we consider local flow formulations where the so-called local\ncharacter of correlation effects can be closely related to properties of\nsub-system embedding sub-algebras employing localized molecular basis. We also\ngeneralize flow equations to the time domain and to downfolding methods\nutilizing double exponential unitary CC Ansatz (DUCC), where reduced\ndimensionality of constituent sub-problems offer a possibility of efficient\nutilization of limited quantum resources in modeling realistic systems.\n"}
+{"abstract": "  Thermodynamics of quantum systems and quantum thermal machines are rapidly\ndeveloping fields, which have already delivered several promising results, as\nwell as raised many intriguing questions. Many-body quantum machines present\nnew opportunities stemming from many-body effects. At the same time, they pose\nnew challenges related to many-body physics. In this short review we discuss\nsome of the recent developments on technologies based on many-body quantum\nsystems. We mainly focus on many-body effects in quantum thermal machines. We\nalso briefly address the role played by many-body systems in the development of\nquantum batteries and quantum probes.\n"}
+{"abstract": "  We state and prove that a certain class of smooth functions said to be\nBH-separable is a meagre subset for the Fr\\'echet topology. Because these\nfunctions are the only admissible Hamiltonians for Arnold-Liouville systems\nadmitting a bi-Hamiltonian structure, we get that, generically,\nArnold-Liouville systems cannot be bi-Hamiltonian. At the end of the paper, we\ndetermine, both as a concrete representation of our general result and as an\nillustrative list, which polynomial Hamiltonians $H$ of the form\n$H(x,y)=xy+ax^3+bx^2y+cxy^2+dy^3$ are BH-separable.\n"}
+{"abstract": "  We investigate the incompressible and compressible heat conducting boundary\nlayer with applying the two-dimensional self-similar Ansatz. Analytic solutions\ncan be found for the incompressible case which can be expressed with special\nfunctions. The parameter dependencies are studied and discussed in details. In\nthe last part of our study we present the ordinary differential equation (ODE)\nsystem which is obtained for compressible boundary layers.\n"}
+{"abstract": "  The fluctuation-dissipation theorem is a fundamental result in statistical\nphysics that establishes a connection between the response of a system subject\nto a perturbation and the fluctuations associated with observables in\nequilibrium. Here we derive its version within a resource-theoretic framework,\nwhere one investigates optimal quantum state transitions under thermodynamic\nconstraints. More precisely, we first characterise optimal thermodynamic\ndistillation processes, and then prove a relation between the amount of free\nenergy dissipated in such processes and the free energy fluctuations of the\ninitial state of the system. Our results apply to initial states given by\neither asymptotically many identical pure systems or arbitrary number of\nindependent energy-incoherent systems, and allow not only for a state\ntransformation, but also for the change of Hamiltonian. The\nfluctuation-dissipation relations we derive enable us to find the optimal\nperformance of thermodynamic protocols such as work extraction, information\nerasure and thermodynamically-free communication, up to second-order\nasymptotics in the number $N$ of processed systems. We thus provide a first\nrigorous analysis of these thermodynamic protocols for quantum states with\ncoherence between different energy eigenstates in the intermediate regime of\nlarge but finite $N$.\n"}
+{"abstract": "  In addition to high accuracy, robustness is becoming increasingly important\nfor machine learning models in various applications. Recently, much research\nhas been devoted to improving the model robustness by training with noise\nperturbations. Most existing studies assume a fixed perturbation level for all\ntraining examples, which however hardly holds in real tasks. In fact, excessive\nperturbations may destroy the discriminative content of an example, while\ndeficient perturbations may fail to provide helpful information for improving\nthe robustness. Motivated by this observation, we propose to adaptively adjust\nthe perturbation levels for each example in the training process. Specifically,\na novel active learning framework is proposed to allow the model to\ninteractively query the correct perturbation level from human experts. By\ndesigning a cost-effective sampling strategy along with a new query type, the\nrobustness can be significantly improved with a few queries. Both theoretical\nanalysis and experimental studies validate the effectiveness of the proposed\napproach.\n"}
+{"abstract": "  Recently, Kostochka and Yancey proved that a conjecture of Ore is\nasymptotically true by showing that every $k$-critical graph satisfies\n$|E(G)|\\geq\\left\\lceil\\left(\\frac{k}{2}-\\frac{1}{k-1}\\right)|V(G)|-\\frac{k(k-3)}{2(k-1)}\\right\\rceil.$\nThey also characterized the class of graphs that attain this bound and showed\nthat it is equivalent to the set of $k$-Ore graphs. We show that for any\n$k\\geq33$ there exists an $\\varepsilon>0$ so that if $G$ is a $k$-critical\ngraph, then\n$|E(G)|\\geq\\left(\\frac{k}{2}-\\frac{1}{k-1}+\\varepsilon_k\\right)|V(G)|-\\frac{k(k-3)}{2(k-1)}-(k-1)\\varepsilon\nT(G)$, where $T(G)$ is a measure of the number of disjoint $K_{k-1}$ and\n$K_{k-2}$ subgraphs in $G$. This also proves for $k\\geq33$ the following\nconjecture of Postle regarding the asymptotic density: For every $k\\geq4$ there\nexists an $\\varepsilon_k>0$ such that if $G$ is a $k$-critical $K_{k-2}$-free\ngraph, then $|E(G)|\\geq\n\\left(\\frac{k}{2}-\\frac{1}{k-1}+\\varepsilon_k\\right)|V(G)|-\\frac{k(k-3)}{2(k-1)}$.\nAs a corollary, our result shows that the number of disjoint $K_{k-2}$\nsubgraphs in a $k$-Ore graph scales linearly with the number of vertices and,\nfurther, that the same is true for graphs whose number of edges is close to\nKostochka and Yancey's bound.\n"}
+{"abstract": "  SrTiO$_3$-based two-dimensional electron gases (2DEGs) can be formed through\nthe deposition of epitaxial oxides like LaAlO$_3$ or of reactive metals such as\nAl. Such 2DEGs possess a finite Rashba spin-orbit coupling that has recently\nbeen harnessed to interconvert charge and spin currents through the direct and\ninverse Edelstein and spin Hall effects. Here we compare the formation and\nproperties of 2DEGs generated in SrTiO$_3$ by the growth of Al, Ta and Y\nultrathin films by magnetron sputtering. By combining in situ and ex situ X-ray\nphotoelectron spectroscopy (XPS) we gain insight into the reduction of the\nSrTiO$_3$ and the appearance of Ti$^{3+}$ states associated with 2DEG\nformation, its reoxidation by exposure to the air, and the transformation of\nthe metal into its binary oxides. We extract the carrier densities through\nmagnetotransport and compare them with the XPS data. Finally, working with\nsamples covered by an extra layer of NiFe, we perform spin-pumping\nferromagnetic resonance experiments and investigate spin-charge conversion as a\nfunction of gate voltage. We identify trends in the data across the different\nsample systems and discuss them as a function of the carrier density and the\ntransparency of the metal oxide tunnel barrier.\n"}
+{"abstract": "  The impact of user satisfaction in policy learning task-oriented dialogue\nsystems has long been a subject of research interest. Most current models for\nestimating the user satisfaction either (i) treat out-of-context short-texts,\nsuch as product reviews, or (ii) rely on turn features instead of on\ndistributed semantic representations. In this work we adopt deep neural\nnetworks that use distributed semantic representation learning for estimating\nthe user satisfaction in conversations. We evaluate the impact of modelling\ncontext length in these networks. Moreover, we show that the proposed\nhierarchical network outperforms state-of-the-art quality estimators.\nFurthermore, we show that applying these networks to infer the reward function\nin a Partial Observable Markov Decision Process (POMDP) yields to a great\nimprovement in the task success rate.\n"}
+{"abstract": "  In this paper, we introduce an R software package for simulating plays and\ndrives using play-by-play data from the National Football League. The\nsimulations are generated by sampling play-by-play data from previous football\nseasons.The sampling procedure adds statistical rigor to any decisions or\ninferences arising from examining the simulations. We highlight that the\npackage is particularly useful as a data-driven tool for evaluating potential\nin-game strategies or rule changes within the league. We demonstrate its\nutility by evaluating the oft-debated strategy of $\\textit{going for it}$ on\nfourth down and investigating whether or not teams should pass more than the\ncurrent standard.\n"}
+{"abstract": "  Having the ability to acquire inherent skills from environments without any\nexternal rewards or supervision like humans is an important problem. We propose\na novel unsupervised skill discovery method named Information Bottleneck Option\nLearning (IBOL). On top of the linearization of environments that promotes more\nvarious and distant state transitions, IBOL enables the discovery of diverse\nskills. It provides the abstraction of the skills learned with the information\nbottleneck framework for the options with improved stability and encouraged\ndisentanglement. We empirically demonstrate that IBOL outperforms multiple\nstate-of-the-art unsupervised skill discovery methods on the\ninformation-theoretic evaluations and downstream tasks in MuJoCo environments,\nincluding Ant, HalfCheetah, Hopper and D'Kitty.\n"}
+{"abstract": "  We present a deep reinforcement learning-based artificial intelligence agent\nthat could provide optimized development plans given a basic description of the\nreservoir and rock/fluid properties with minimal computational cost. This\nartificial intelligence agent, comprising of a convolutional neural network,\nprovides a mapping from a given state of the reservoir model, constraints, and\neconomic condition to the optimal decision (drill/do not drill and well\nlocation) to be taken in the next stage of the defined sequential field\ndevelopment planning process. The state of the reservoir model is defined using\nparameters that appear in the governing equations of the two-phase flow. A\nfeedback loop training process referred to as deep reinforcement learning is\nused to train an artificial intelligence agent with such a capability. The\ntraining entails millions of flow simulations with varying reservoir model\ndescriptions (structural, rock and fluid properties), operational constraints,\nand economic conditions. The parameters that define the reservoir model,\noperational constraints, and economic conditions are randomly sampled from a\ndefined range of applicability. Several algorithmic treatments are introduced\nto enhance the training of the artificial intelligence agent. After appropriate\ntraining, the artificial intelligence agent provides an optimized field\ndevelopment plan instantly for new scenarios within the defined range of\napplicability. This approach has advantages over traditional optimization\nalgorithms (e.g., particle swarm optimization, genetic algorithm) that are\ngenerally used to find a solution for a specific field development scenario and\ntypically not generalizable to different scenarios.\n"}
+{"abstract": "  While the prevalence of large pre-trained language models has led to\nsignificant improvements in the performance of NLP systems, recent research has\ndemonstrated that these models inherit societal biases extant in natural\nlanguage. In this paper, we explore a simple method to probe pre-trained\nlanguage models for gender bias, which we use to effect a multi-lingual study\nof gender bias towards politicians. We construct a dataset of 250k politicians\nfrom most countries in the world and quantify adjective and verb usage around\nthose politicians' names as a function of their gender. We conduct our study in\n7 languages across 6 different language modeling architectures. Our results\ndemonstrate that stance towards politicians in pre-trained language models is\nhighly dependent on the language used. Finally, contrary to previous findings,\nour study suggests that larger language models do not tend to be significantly\nmore gender-biased than smaller ones.\n"}
+{"abstract": "  In the early 1960s, P. Malliavin and L. A. Rubel gave a complete description\nof pairs of distributions of positive points $Z$ and $W$ such that for each\nentire function of exponential type $g\\neq 0$ that vanishes on $W$, there is an\nentire function of exponential type $f\\neq 0$ such that $f$ vanishes on $Z$ and\nsatisfies the inequality $|f|\\leq |g|$ everywhere on the imaginary axis. We\nextend this result to much more general distributions of complex points $Z$ and\n$W$ lying outside of some pair of vertical angles containing the imaginary axis\nas the points approach $\\infty$.\n"}
+{"abstract": "  Bigraphs are a universal computational modelling formalism for the spatial\nand temporal evolution of a system in which entities can be added and removed.\nWe extend bigraphs to probablistic bigraphs, and then again to action bigraphs,\nwhich include non-determinism and rewards. The extensions are implemented in\nthe BigraphER toolkit and illustrated through examples of virus spread in\ncomputer networks and data harvesting in wireless sensor systems. BigraphER\nalso supports the existing stochastic bigraphs extension of Krivine et al., and\nusing BigraphER we give, for the first time, a direct implementation of the\nmembrane budding model used to motivate stochastic bigraphs.\n"}
+{"abstract": "  We make a systematic study of the isospin symmetry of fragmentation functions\nby taking decay contributions into account. We assume the isospin symmetry in\nstrong interactions and show that in the unpolarized case the isospin symmetry\nis held for fragmentation functions of $\\Lambda$ and only tiny violations are\nallowed for other hadrons such as nucleon and pions due to the contributions\nfrom weak decays. We present a rough estimate of the magnitudes of such\nviolations. In the polarized case, we show that the isospin symmetry violation\nfor $\\Lambda$ production should be tiny and the recent Belle data on the\ntransverse polarization of $\\Lambda$ can be reproduced if the isospin symmetry\nis kept in the corresponding polarized fragmentation functions.\n"}
+{"abstract": "  There is growing interest in data-driven weather prediction (DDWP), for\nexample using convolutional neural networks such as U-NETs that are trained on\ndata from models or reanalysis. Here, we propose 3 components to integrate with\ncommonly used DDWP models in order to improve their physical consistency and\nforecast accuracy. These components are 1) a deep spatial transformer added to\nthe latent space of the U-NETs to preserve a property called equivariance,\nwhich is related to correctly capturing rotations and scalings of features in\nspatio-temporal data, 2) a data-assimilation (DA) algorithm to ingest noisy\nobservations and improve the initial conditions for next forecasts, and 3) a\nmulti-time-step algorithm, which combines forecasts from DDWP models with\ndifferent time steps through DA, improving the accuracy of forecasts at short\nintervals. To show the benefit/feasibility of each component, we use\ngeopotential height at 500~hPa (Z500) from ERA5 reanalysis and examine the\nshort-term forecast accuracy of specific setups of the DDWP framework. Results\nshow that the equivariance-preserving networks (U-STNs) clearly outperform the\nU-NETs, for example improving the forecast skill by $45\\%$. Using a sigma-point\nensemble Kalman (SPEnKF) algorithm for DA and U-STN as the forward model, we\nshow that stable, accurate DA cycles are achieved even with high observation\nnoise. The DDWP+DA framework substantially benefits from large ($O(1000)$)\nensembles that are inexpensively generated with the data-driven forward model\nin each DA cycle. The multi-time-step DDWP+DA framework also shows promises,\ne.g., it reduces the average error by factors of 2-3.\n"}
+{"abstract": "  This work presents ReBeatICG, a real-time, low-complexity beat-to-beat\nimpedance cardiography (ICG) delineation algorithm that allows hemodynamic\nparameters monitoring. The proposed procedure relies only on the ICG signal\ncompared to most algorithms found in the literature that rely on synchronous\nelectrocardiogram signal (ECG) recordings. ReBeatICG was designed with\nimplementation on an ultra-low-power microcontroller (MCU) in mind. The\ndetection accuracy of the developed algorithm is tested against points manually\nlabeled by cardiologists. It achieves a detection Gmean accuracy of 94.9%,\n98.6%, 90.3%, and 84.3% for the B, C, X, and O points, respectively.\nFurthermore, several hemodynamic parameters were calculated based on annotated\ncharacteristic points and compared with values generated from the\ncardiologists' annotations. ReBeatICG achieved mean error rates of 0.11ms,\n9.72ms, 8.32ms, and 3.97% for HR, LVET, IVRT, and relative C-point amplitude,\nrespectively.\n"}
+{"abstract": "  We present a detailed convergence analysis for an operator splitting scheme\nproposed in [C. Liu et al.,J. Comput. Phys., 436, 110253, 2021] for a\nreaction-diffusion system with detailed balance. The numerical scheme has been\nconstructed based on a recently developed energetic variational formulation, in\nwhich the reaction part is reformulated in terms of the reaction trajectory,\nand both the reaction and diffusion parts dissipate the same free energy. The\nscheme is energy stable and positivity-preserving. In this paper, the detailed\nconvergence analysis and error estimate are performed for the operator\nsplitting scheme. The nonlinearity in the reaction trajectory equation, as well\nas the implicit treatment of nonlinear and singular logarithmic terms, impose\nchallenges in numerical analysis. To overcome these difficulties, we make use\nof the convex nature of the logarithmic nonlinear terms, which are treated\nimplicitly in the chemical reaction stage. In addition, a combination of a\nrough error estimate and a refined error estimate leads to a desired bound of\nthe numerical error in the reaction stage, in the discrete maximum norm.\nFurthermore, a discrete maximum principle yields the evolution bound of the\nnumerical error function at the diffusion stage. As a direct consequence, a\ncombination of the numerical error analysis at different stages and the\nconsistency estimate for the operator splitting results in the convergence\nestimate of the numerical scheme for the full reaction-diffusion system.\n"}
+{"abstract": "  Rapid and large scanning of a dissipative Kerr-microresonator soliton comb\nwith the characterization of all comb modes along with the separation of the\ncomb modes is imperative for the emerging applications of the frequency-scanned\nsoliton combs. However, the scan speed is limited by the gain of feedback\nsystems and the measurement of the frequency shift of all comb modes has not\nbeen demonstrated. To overcome the limitation of the feedback, we incorporate\nthe feedback with the feedforward. With the additional gain of > 40 dB by a\nfeedforward signal, a dissipative Kerr-microresonator soliton comb is scanned\nby 70 GHz in 500 $\\mu$s, 50 GHz in 125 $\\mu$s, and 25 GHz in 50 $\\mu$s (= 500\nTHz/s). Furthermore, we propose and demonstrate a method to measure the\nfrequency shift of all comb modes, in which an imbalanced Mach-Zehnder\ninterferometer with two outputs with different wavelengths is used. Because of\nthe two degrees of freedom of optical frequency combs, the measurement at the\ntwo different wavelengths enables the estimation of the frequency shift of all\ncomb modes.\n"}
+{"abstract": "  In this work we investigate the existence and uniqueness of Struwe-like\nsolutions for a system of partial differential equations modeling the dynamics\nof magnetoviscoelastic fluids. The considered system couples a Navier-Stokes\ntype equation with a dissipative equation for the deformation tensor and a\nLandau-Lifshitz-Gilbert type equation for the magnetization field. The main\npurpose is to establish a well-posedness theory in a two-dimensional periodic\ndomain under standard assumption of critical regularity for the (possibly\nlarge) initial data. We prove that the considered weak solutions are everywhere\nsmooth, except for a discrete set of time values. The proof of the uniqueness\nis based on suitable energy estimates for the solutions within a functional\nframework which is less regular than the one of the Struwe energy level. These\nestimates rely on several techniques of harmonic analysis and paradifferential\ncalculus.\n"}
+{"abstract": "  We study reinforcement learning for the optimal control of Branching Markov\nDecision Processes (BMDPs), a natural extension of (multitype) Branching Markov\nChains (BMCs). The state of a (discrete-time) BMCs is a collection of entities\nof various types that, while spawning other entities, generate a payoff. In\ncomparison with BMCs, where the evolution of a each entity of the same type\nfollows the same probabilistic pattern, BMDPs allow an external controller to\npick from a range of options. This permits us to study the best/worst behaviour\nof the system. We generalise model-free reinforcement learning techniques to\ncompute an optimal control strategy of an unknown BMDP in the limit. We present\nresults of an implementation that demonstrate the practicality of the approach.\n"}
+{"abstract": "  Multi-intent SLU can handle multiple intents in an utterance, which has\nattracted increasing attention. However, the state-of-the-art joint models\nheavily rely on autoregressive approaches, resulting in two issues: slow\ninference speed and information leakage. In this paper, we explore a\nnon-autoregressive model for joint multiple intent detection and slot filling,\nachieving more fast and accurate. Specifically, we propose a Global-Locally\nGraph Interaction Network (GL-GIN) where a local slot-aware graph interaction\nlayer is proposed to model slot dependency for alleviating uncoordinated slots\nproblem while a global intent-slot graph interaction layer is introduced to\nmodel the interaction between multiple intents and all slots in the utterance.\nExperimental results on two public datasets show that our framework achieves\nstate-of-the-art performance while being 11.5 times faster.\n"}
+{"abstract": "  The Bell's inequality is a strong criterion to distinguish classical and\nquantum mechanical aspects of reality. Its violation is the net effect of the\nexistence of non-locality in systems, an advantage for quantum mechanics (QM)\nover classical physics. The quantum mechanical world is under the control of\nthe Heisenberg uncertainty principle (HUP) that is generalized by quantum\ngravity (QG) scenarios, called generalized uncertainty principle (GUP). Here,\nthe effects of GUP on the square of Bell operators of qubits and qutrits are\nstudied. The achievements claim that the violation quality of the square of\nBell inequalities may be a tool to get a better understanding of the quantum\nfeatures of gravity. In this regard, it is obtained that the current accuracy\nof the Stern-Gerlach experiments implies upper bounds on the values of the GUP\nparameters.\n"}
+{"abstract": "  Clinical notes are unstructured text generated by clinicians during patient\nencounters. Clinical notes are usually accompanied by a set of metadata codes\nfrom the International Classification of Diseases(ICD). ICD code is an\nimportant code used in various operations, including insurance, reimbursement,\nmedical diagnosis, etc. Therefore, it is important to classify ICD codes\nquickly and accurately. However, annotating these codes is costly and\ntime-consuming. So we propose a model based on bidirectional encoder\nrepresentations from transformers (BERT) using the sequence attention method\nfor automatic ICD code assignment. We evaluate our approach on the medical\ninformation mart for intensive care III (MIMIC-III) benchmark dataset. Our\nmodel achieved performance of macro-averaged F1: 0.62898 and micro-averaged F1:\n0.68555 and is performing better than a performance of the state-of-the-art\nmodel using the MIMIC-III dataset. The contribution of this study proposes a\nmethod of using BERT that can be applied to documents and a sequence attention\nmethod that can capture important sequence in-formation appearing in documents.\n"}
+{"abstract": "  We investigate the role of gentle algebras in higher homological algebra. In\nthe first part of the paper, we show that if the module category of a gentle\nalgebra $\\Lambda$ contains a $d$-cluster tilting subcategory for some $d \\geq\n2$, then $\\Lambda$ is a radical square zero Nakayama algebra. This gives a\ncomplete classification of weakly $d$-representation finite gentle algebras. In\nthe second part, we use a geometric model of the derived category to prove a\nsimilar result in the triangulated setup. More precisely, we show that if\n$\\mathcal{D}^b(\\Lambda)$ contains a $d$-cluster tilting subcategory that is\nclosed under $[d]$, then $\\Lambda$ is derived equivalent to an algebra of\nDynkin type $A$. Furthermore, our approach gives a geometric characterization\nof all $d$-cluster tilting subcategories of $\\mathcal{D}^b(\\Lambda)$ that are\nclosed under $[d]$.\n"}
+{"abstract": "  Model-based paradigms for decision-making and control are becoming ubiquitous\nin robotics. They rely on the ability to efficiently learn a model of the\nsystem from data. Structured Mechanical Models (SMMs) are a data-efficient\nblack-box parameterization of mechanical systems, typically fit to data by\nminimizing the error between predicted and observed accelerations or next\nstates. In this work, we propose a methodology for fitting SMMs to data by\nminimizing the discrete Euler-Lagrange residual. To study our methodology, we\nfit models to joint-angle time-series from undamped and damped\ndouble-pendulums, studying the quality of learned models fit to data with and\nwithout observation noise. Experiments show that our methodology learns models\nthat are better in accuracy to those of the conventional schemes for fitting\nSMMs. We identify use cases in which our method is a more appropriate\nmethodology. Source code for reproducing the experiments is available at\nhttps://github.com/sisl/delsmm.\n"}
+{"abstract": "  Consider the focusing inhomogeneous nonlinear Schr\\\"odinger equation in\n$H^1(\\mathbb{R}^N)$, $$iu_t + \\Delta u + |x|^{-b}|u|^{p-1}u=0,$$ when $b > 0$\nand $N \\geq 3$ in the intercritical case $0 < s_c <1$. In previous works, the\nsecond author, as well as Farah, Guzm\\'an and Murphy, applied the\nconcentration-compactness approach to prove scattering below the mass-energy\nthreshold for radial and non-radial data. Recently, the first author adapted\nthe Dodson-Murphy approach for radial data, followed by Murphy, who proved\nscattering for non-radial solutions in the 3d cubic case, for $b<1/2$. This\nwork generalizes the recent result of Murphy, allowing a broader range of\nvalues for the parameters $p$ and $b$, as well as allowing any dimension $N\n\\geq 3$. It also gives a simpler proof for scattering nonradial, avoiding the\nKenig-Merle road map. We exploit the decay of the nonlinearity, which, together\nwith Virial-Morawetz-type estimates, allows us to drop the radial assumption.\n"}
+{"abstract": "  Hyperspectral image fusion (HIF) is critical to a wide range of applications\nin remote sensing and many computer vision applications. Most traditional HIF\nmethods assume that the observation model is predefined or known. However, in\nreal applications, the observation model involved are often complicated and\nunknown, which leads to the serious performance drop of many advanced HIF\nmethods. Also, deep learning methods can achieve outstanding performance, but\nthey generally require a large number of image pairs for model training, which\nare difficult to obtain in realistic scenarios. Towards these issues, we\nproposed a self-regression learning method that alternatively reconstructs\nhyperspectral image (HSI) and estimate the observation model. In particular, we\nadopt an invertible neural network (INN) for restoring the HSI, and two\nfully-connected network (FCN) for estimating the observation model. Moreover,\n\\emph{SoftMax} nonlinearity is applied to the FCN for satisfying the\nnon-negative, sparsity and equality constraints. Besides, we proposed a local\nconsistency loss function to constrain the observation model by exploring\ndomain specific knowledge. Finally, we proposed an angular loss function to\nimprove spectral reconstruction accuracy. Extensive experiments on both\nsynthetic and real-world dataset show that our model can outperform the\nstate-of-the-art methods\n"}
+{"abstract": "  We propose a dense indirect visual odometry method taking as input externally\nestimated optical flow fields instead of hand-crafted feature correspondences.\nWe define our problem as a probabilistic model and develop a generalized-EM\nformulation for the joint inference of camera motion, pixel depth, and\nmotion-track confidence. Contrary to traditional methods assuming\nGaussian-distributed observation errors, we supervise our inference framework\nunder an (empirically validated) adaptive log-logistic distribution model.\nMoreover, the log-logistic residual model generalizes well to different\nstate-of-the-art optical flow methods, making our approach modular and agnostic\nto the choice of optical flow estimators. Our method achieved top-ranking\nresults on both TUM RGB-D and KITTI odometry benchmarks. Our open-sourced\nimplementation is inherently GPU-friendly with only linear computational and\nstorage growth.\n"}
+{"abstract": "  Session-based recommendation aims at predicting the next item given a\nsequence of previous items consumed in the session, e.g., on e-commerce or\nmultimedia streaming services. Specifically, session data exhibits some unique\ncharacteristics, i.e., session consistency and sequential dependency over items\nwithin the session, repeated item consumption, and session timeliness. In this\npaper, we propose simple-yet-effective linear models for considering the\nholistic aspects of the sessions. The comprehensive nature of our models helps\nimprove the quality of session-based recommendation. More importantly, it\nprovides a generalized framework for reflecting different perspectives of\nsession data. Furthermore, since our models can be solved by closed-form\nsolutions, they are highly scalable. Experimental results demonstrate that the\nproposed linear models show competitive or state-of-the-art performance in\nvarious metrics on several real-world datasets.\n"}
+{"abstract": "  We probe type IIA geometries with \\emph{folded} semiclassical strings those\nencode the strong coupling dynamics associated with long operators in a class\nof SCFTs pertaining to spacetimes whose dimension vary from $6d$ down to $2d$.\nWe particularly focus on folded string configurations those are extended\nthrough stack of flavor $ Dp $ ($ p=6,8 $) branes localized along the internal\nmanifold. Considering some specific examples within the realm of $\n\\mathcal{N}=2 $ linear quivers, we show that the associated string spectrum\nexhibits a pole as the string approaches flavor D6 branes. We identify this as\na \\emph{fragmentation} of long operators in the dual $ \\mathcal{N}=2 $ SCFTs.\nOn the other hand, a similar analysis for $ \\mathcal{N}=(1,0) $ SCFTs in $6d$\nas well as $ \\mathcal{N}=(0,4) $ SCFTs in $ 4d $ reveals a set of new\ndispersion relations at strong coupling. Based on semiclassical calculations,\nwe set an argument that explains either of these cases.\n"}
+{"abstract": "  We investigate different methods for regularizing quantile regression when\npredicting either a subset of quantiles or the full inverse CDF. We show that\nminimizing an expected pinball loss over a continuous distribution of quantiles\nis a good regularizer even when only predicting a specific quantile. For\npredicting multiple quantiles, we propose achieving the classic goal of\nnon-crossing quantiles by using deep lattice networks that treat the quantile\nas a monotonic input feature, and we discuss why monotonicity on other features\nis an apt regularizer for quantile regression. We show that lattice models\nenable regularizing the predicted distribution to a location-scale family.\nLastly, we propose applying rate constraints to improve the calibration of the\nquantile predictions on specific subsets of interest and improve fairness\nmetrics. We demonstrate our contributions on simulations, benchmark datasets,\nand real quantile regression problems.\n"}
+{"abstract": "  To a simple graph we associate a so-called graph series, which can be viewed\nas the Hilbert--Poincar\\'e series of a certain infinite jet scheme. We study\nnew $q$-representations and examine modular properties of several examples\nincluding Dynkin diagrams of finite and affine type. Notably, we obtain new\nformulas for graph series of type $A_7$ and $A_8$ in terms of \"sum of tails\"\nseries, and of type $D_4$ and $D_5$ in the form of indefinite theta functions\nof signature $(1,1)$. We also study examples related to sums of powers of\ndivisors corresponding to $5$-cycles. For several examples of graphs, we prove\nthat graph series are so-called mixed quantum modular forms.\n"}
+{"abstract": "  We consider a game in which the action set of each player is uncountable, and\nshow that, from weak assumptions on the common prior, any mixed strategy has an\napproximately equivalent pure strategy. The assumption of this result can be\nfurther weakened if we consider the purification of a Nash equilibrium.\nCombined with the existence theorem for a Nash equilibrium, we derive an\nexistence theorem for a pure strategy approximated Nash equilibrium under\nsufficiently weak assumptions. All of the pure strategies we derive in this\npaper can take a finite number of possible actions.\n"}
+{"abstract": "  We study the conormal sheaves and singular schemes of 1-dimensional\nfoliations on smooth projective varieties $X$ of dimension 3 and Picard rank 1.\nWe prove that if the singular scheme has dimension 0, then the conormal sheaf\nis $\\mu$-stable whenever the tangent bundle $TX$ is stable, and apply this fact\nto the characterization of certain irreducible components of the moduli space\nof rank 2 reflexive sheaves on $\\mathbb{P}^3$ and on a smooth quadric\nhypersurface $Q_3\\subset\\mathbb{P}^4$. Finally, we give a classification of\nlocal complete intersection foliations, that is, foliations with locally free\nconormal sheaves, of degree 0 and 1 on $Q_3$.\n"}
+{"abstract": "  Fabrication process variations can significantly influence the performance\nand yield of nano-scale electronic and photonic circuits. Stochastic spectral\nmethods have achieved great success in quantifying the impact of process\nvariations, but they suffer from the curse of dimensionality. Recently,\nlow-rank tensor methods have been developed to mitigate this issue, but two\nfundamental challenges remain open: how to automatically determine the tensor\nrank and how to adaptively pick the informative simulation samples. This paper\nproposes a novel tensor regression method to address these two challenges. We\nuse a $\\ell_{q}/ \\ell_{2}$ group-sparsity regularization to determine the\ntensor rank. The resulting optimization problem can be efficiently solved via\nan alternating minimization solver. We also propose a two-stage adaptive\nsampling method to reduce the simulation cost. Our method considers both\nexploration and exploitation via the estimated Voronoi cell volume and\nnonlinearity measurement respectively. The proposed model is verified with\nsynthetic and some realistic circuit benchmarks, on which our method can well\ncapture the uncertainty caused by 19 to 100 random variables with only 100 to\n600 simulation samples.\n"}
+{"abstract": "  Let $a\\in \\mathbb{Z}$ and $\\rho$ be a root of $f_a(x)=x^3-ax^2-(a+3)x-1$,\nthen the number field $K_a=\\mathbb{Q}(\\rho)$ is called a simplest cubic field.\nIn this paper we consider the family of unit equations $u_1+u_2=n$ where\n$u_1,u_2\\in \\mathbb{Z}[\\rho]^*$ and $n\\in \\mathbb{Z}$. We completely solve the\nunit equations under the restriction $|n|\\leq \\max\\{1,|a|^{1/3}\\}$.\n"}
+{"abstract": "  This paper reports the use of a qualitative methodology for conducting\nlongitudinal case study research on software development. We provide a detailed\ndescription and explanation of appropriate methods of qualitative data\ncollection and analysis that can be utilized by other researchers in the\nsoftware engineering field. Our aim is to illustrate the utility of\nlongitudinal case study research, as a complement to existing methodologies for\nstudying software development, so as to enable the community to develop a\nfuller and richer understanding of this complex, multi-dimensional phenomenon.\nWe discuss the insights gained and lessons learned from applying a longitudinal\nqualitative approach to an empirical case study of a software development\nproject in a large multi-national organization. We evaluate the methodology\nused to emphasize its strengths and to address the criticisms traditionally\nmade of qualitative research.\n"}
+{"abstract": "  We determine the 1-form symmetry group for any 4d N = 2 class S theory\nconstructed by compactifying a 6d N=(2,0) SCFT on a Riemann surface with\narbitrary regular untwisted and twisted punctures. The 6d theory has a group of\nmutually non-local dimension-2 surface operators, modulo screening.\nCompactifying these surface operators leads to a group of mutually non-local\nline operators in 4d, modulo screening and flavor charges. Complete\nspecification of a 4d theory arising from such a compactification requires a\nchoice of a maximal subgroup of mutually local line operators, and the 1-form\nsymmetry group of the chosen 4d theory is identified as the Pontryagin dual of\nthis maximal subgroup. We also comment on how to generalize our results to\ncompactifications involving irregular punctures. Finally, to complement the\nanalysis from 6d, we derive the 1-form symmetry from a Type IIB realization of\nclass S theories.\n"}
+{"abstract": "  We develop a novel algorithm for large-scale holographic reconstruction of 3D\nparticle fields. Our method is based on a multiple-scattering beam propagation\nmethod (BPM) combined with sparse regularization that enables recovering dense\n3D particles of high refractive index contrast from a single hologram. We show\nthat the BPM-computed hologram generates intensity statistics closely matching\nwith the experimental measurements and provides up to 9$\\times$ higher accuracy\nthan the single-scattering model. To solve the inverse problem, we devise a\ncomputationally efficient algorithm, which reduces the computation time by two\norders of magnitude as compared to the state-of-the-art\nmultiple-scattering-based technique. We demonstrate superior reconstruction\naccuracy in both simulations and experiments under different scattering\nstrengths. We show that the BPM reconstruction significantly outperforms the\nsingle-scattering method in particular for deep imaging depths and high\nparticle densities.\n"}
+{"abstract": "  We study Landau-Ginzburg orbifolds $(f,G)$ with $f=x_1^n+\\ldots+x_N^n$ and\n$G=S\\ltimes G^d$, where $S\\subseteq S_N$ and $G^d$ is either the maximal group\nof scalar symmetries of $f$ or the intersection of the maximal diagonal\nsymmetries of $f$ with $\\mathrm{SL}_N(\\mathbb{C})$. We construct a mirror map\nbetween the corresponding phase spaces and prove that it is an isomorphism\nrestricted to a certain subspace of the phase space when $n=N$ is a prime\nnumber. When $S$ satisfies the condition PC of Ebeling and Gusein-Zade this\nsubspace coincides with the full space. We also show that two phase spaces are\nisomorphic for $n=N=5$.\n"}
+{"abstract": "  Balancing is a fundamental need for legged robots due to their unstable\nfloating-base nature. Balance control has been thoroughly studied for simple\nmodels such as the linear inverted pendulum thanks to the concept of the\ninstantaneous capture point (ICP), yet the constant center of mass height\nassumption limits the application. This paper explores balancing of the\nvariable-height inverted pendulum (VHIP) model by introducing the\n\\emph{instantaneous capture input} (ICI), an extension of the ICP based on its\nkey properties. Namely, the ICI can be computed as a function of the state, and\nwhen this function is used as the control policy, the ICI is rendered\nstationary and the system will eventually come to a stop. This characterization\ninduces an analytical region of capturable states for the VHIP, which can be\nused to conceptually guide where to step. To further address state and control\nconstraints during recovery, we present and theoretically analyze an explicit\nICI-based controller with online optimal feedback gains. Simulations\ndemonstrate the validity of our controller for capturability maintenance\ncompared to an approach based on the divergent component of motion.\n"}
+{"abstract": "  Variational quantum algorithms aim at harnessing the power of noisy\nintermediate-scale quantum computers, by using a classical optimizer to train a\nparameterized quantum circuit to solve tractable quantum problems. The\nvariational quantum eigensolver is one of the aforementioned algorithms\ndesigned to determine the ground-state of many-body Hamiltonians. Here, we\napply the variational quantum eigensolver to study the ground-state properties\nof $N$-component fermions. With such knowledge, we study the persistent current\nof interacting SU($N$) fermions, which is employed to reliably map out the\ndifferent quantum phases of the system. Our approach lays out the basis for a\ncurrent-based quantum simulator of many-body systems that can be implemented on\nnoisy intermediate-scale quantum computers.\n"}
+{"abstract": "  Can we complete pre-training of Vision Transformers (ViT) without natural\nimages and human-annotated labels? Although a pre-trained ViT seems to heavily\nrely on a large-scale dataset and human-annotated labels, recent large-scale\ndatasets contain several problems in terms of privacy violations, inadequate\nfairness protection, and labor-intensive annotation. In the present paper, we\npre-train ViT without any image collections and annotation labor. We\nexperimentally verify that our proposed framework partially outperforms\nsophisticated Self-Supervised Learning (SSL) methods like SimCLRv2 and MoCov2\nwithout using any natural images in the pre-training phase. Moreover, although\nthe ViT pre-trained without natural images produces some different\nvisualizations from ImageNet pre-trained ViT, it can interpret natural image\ndatasets to a large extent. For example, the performance rates on the CIFAR-10\ndataset are as follows: our proposal 97.6 vs. SimCLRv2 97.4 vs. ImageNet 98.0.\n"}
+{"abstract": "  Animal brains evolved to optimize behavior in dynamically changing\nenvironments, selecting actions that maximize future rewards. A large body of\nexperimental work indicates that such optimization changes the wiring of neural\ncircuits, appropriately mapping environmental input onto behavioral outputs. A\nmajor unsolved scientific question is how optimal wiring adjustments, which\nmust target the connections responsible for rewards, can be accomplished when\nthe relation between sensory inputs, action taken, environmental context with\nrewards is ambiguous. The computational problem of properly targeting cues,\ncontexts and actions that lead to reward is known as structural, contextual and\ntemporal credit assignment respectively. In this review, we survey prior\napproaches to these three types of problems and advance the notion that the\nbrain's specialized neural architectures provide efficient solutions. Within\nthis framework, the thalamus with its cortical and basal ganglia interactions\nserve as a systems-level solution to credit assignment. Specifically, we\npropose that thalamocortical interaction is the locus of meta-learning where\nthe thalamus provides cortical control functions that parametrize the cortical\nactivity association space. By selecting among these control functions, the\nbasal ganglia hierarchically guide thalamocortical plasticity across two\ntimescales to enable meta-learning. The faster timescale establishes contextual\nassociations to enable rapid behavioral flexibility while the slower one\nenables generalization to new contexts. Incorporating different thalamic\ncontrol functions under this framework clarifies how thalamocortical-basal\nganglia interactions may simultaneously solve the three credit assignment\nproblems.\n"}
+{"abstract": "  Media bias can lead to increased political polarization, and thus, the need\nfor automatic mitigation methods is growing. Existing mitigation work displays\narticles from multiple news outlets to provide diverse news coverage, but\nwithout neutralizing the bias inherent in each of the displayed articles.\nTherefore, we propose a new task, a single neutralized article generation out\nof multiple biased articles, to facilitate more efficient access to balanced\nand unbiased information. In this paper, we compile a new dataset NeuWS, define\nan automatic evaluation metric, and provide baselines and multiple analyses to\nserve as a solid starting point for the proposed task. Lastly, we obtain a\nhuman evaluation to demonstrate the alignment between our metric and human\njudgment.\n"}
+{"abstract": "  In phylogenetics, it is important for the phylogenetic network model\nparameters to be identifiable so that the evolutionary histories of a group of\nspecies can be consistently inferred. However, as the complexity of the\nphylogenetic network models grows, the identifiability of network models\nbecomes increasingly difficult to analyze. As an attempt to analyze the\nidentifiability of network models, we check whether two networks are\ndistinguishable. In this paper, we specifically study the distinguishability of\nphylogenetic network models associated with level-2 networks. Using an\nalgebraic approach, namely using discrete Fourier transformation, we present\nsome results on the distinguishability of some level-2 networks, which\ngeneralize earlier work on the distinguishability of level-1 networks. In\nparticular, we study simple and semisimple level-2 networks. Simple and\nsemisimple level-2 networks can be thought as generalizations of level-1 sunlet\nand cycle networks, respectively. Moreover, we also compare the varieties\nassociated with semisimple level-2 and cycle networks.\n"}
+{"abstract": "  We demonstrate the roles of $D_{(s)}^\\ast$ and their contributions in the\nquasi-two-body decays $B_{(s)} \\to D_{(s)} h h^\\prime$ ($h, h^\\prime = \\{\\pi,\nK\\}$) in the perturbative QCD approach, stemming from the quark flavour\nchanging $\\bar{b} \\to \\bar{c} \\, q_2 \\, \\bar{q}_1$ and $\\bar{b} \\to c \\,\n\\bar{q_1} \\, \\bar{q_2}$ with $q_1, q_2 = \\{s/d, u\\}$. The main motivation of\nthis study is the measurements of significant derivations from the simple\nphase-space model in the channels $B_{(s)} \\to D_{(s)} h h^\\prime$ at $B$\nfactories and LHC, which is now clarified as the Breit-Wigner-tail effects from\nthe corresponding intermediate resonant states $D_{(s)}^\\ast$. We confirm that\nthese effect from $D^\\ast$ is small ($\\sim 5\\%$) in the quasi-two-body $B_{(s)}\n\\to D \\pi \\pi (K)$ decaying channels, and predict the tiny ($< 1 \\%$)\ncontributions from $D^\\ast$ in the $B_{(s)} \\to D_s K \\pi (K)$ decaying\nchannels, our result for the $B_s \\to D K \\pi (K)$ decaying channels\ncontributed only from the Breit-Wigner-tail effect of $D_s^\\ast$ is in\nagreement with the current LHCb measurement. We recommend the Belle-II and the\nLHCb collaborations to restudy the processes $B^+\\to \\bar D^{\\ast 0}\\pi^+(K^+)\n\\to D^-\\pi^+\\pi^+(K^+)$ to reveal the structure of $D^{\\ast 0}$ and the strong\ndecay $D^{\\ast 0} \\to D^+ \\pi^-$.\n"}
+{"abstract": "  The paper proposes a new method to determine a binary control signal for an\nanalog linear system such that the state, or some output, of the system follows\na given target trajectory. The method can also be used for digital-to-analog\nconversion. The heart of the proposed method is a new binary-enforcing NUV\nprior (normal with unknown variance). The resulting computations, for each\nplanning period, amount to iterating forward-backward Gaussian message passing\nrecursions (similar to Kalman smoothing), with a complexity (per iteration)\nthat is linear in the planning horizon. In consequence, the proposed method is\nnot limited to a short planning horizon.\n"}
+{"abstract": "  In this paper we prove a higher differentiability result for the solutions to\na class of obstacle problems in the form\n  \\begin{equation*} \\label{obst-def0} \\min\\left\\{\\int_\\Omega F(x,Dw) dx : w\\in\n\\mathcal{K}_{\\psi}(\\Omega)\\right\\} \\end{equation*} where $\\psi\\in\nW^{1,p(x)}(\\Omega)$ is a fixed function called obstacle and\n$\\mathcal{K}_{\\psi}=\\{w \\in W^{1,p(x)}_{0}(\\Omega)+u_0: w \\ge \\psi \\,\\,\n\\textnormal{a.e. in $\\Omega$}\\}$ is the class of the admissible functions, for\na suitable boundary value $ u_0 $. We deal with a convex integrand $F$ which\nsatisfies the $p(x)$-growth conditions\n\\begin{equation*}\\label{growth}|\\xi|^{p(x)}\\le F(x,\\xi)\\le\nC(1+|\\xi|^{p(x)}),\\quad p(x)>1 \\end{equation*}\n"}
+{"abstract": "  The {\\em override} operation $\\sqcup$ is a natural one in computer science,\nand has connections with other areas of mathematics such as hyperplane\narrangements. For arbitrary functions $f$ and $g$, $f\\sqcup g$ is the function\nwith domain $dom(f)\\cup dom(g)$ that agrees with $f$ on $dom(f)$ and with $g$\non $dom(g) \\backslash dom(f)$. Jackson and the author have shown that there is\nno finite axiomatisation of algebras of functions of signature $(\\sqcup)$. But\nadding operations (such as {\\em update}) to this minimal signature can lead to\nfinite axiomatisations. For the functional signature $(\\sqcup,\\backslash)$\nwhere $\\backslash$ is set-theoretic difference, Cirulis has given a finite\nequational axiomatisation as subtraction o-semilattices. Define $f\\curlyvee\ng=(f\\sqcup g)\\cap (g\\sqcup f)$ for all functions $f$ and $g$; this is the\nlargest domain restriction of the binary relation $f\\cup g$ that gives a\npartial function. Now $f\\cap g=f\\backslash(f\\backslash g)$ and $f\\sqcup\ng=f\\curlyvee(f\\curlyvee g)$ for all functions $f,g$, so the signatures\n$(\\curlyvee)$ and $(\\sqcup,\\cap)$ are both intermediate between $(\\sqcup)$ and\n$(\\sqcup,\\backslash)$ in expressive power. We show that each is finitely\naxiomatised, with the former giving a proper quasivariety and the latter the\nvariety of associative distributive o-semilattices in the sense of Cirulis.\n"}
+{"abstract": "  ARM TrustZone is the de-facto hardware TEE implementation on mobile devices\nlike smartphones. As a vendor-centric TEE, TrustZone greatly overlooks the\nstrong protection demands and requirements from the App developers. Several\nsecurity solutions have been proposed to enable the TEE-assisted isolation in\nthe Normal World of ARM, attempting to balance the security and usability.\nHowever, they are still not full-fledged in serving Apps' needs. In this paper,\nwe introduce LEAP, which is a lightweight App developer Centric TEE solution in\nthe Normal World. LEAP offers the auto DevOps tool to help developers to\nprepare the codes running on it, enables isolated codes to execute in parallel\nand access peripheral (e.g. mobile GPUs) with ease, and dynamically manage\nsystem resources upon Apps' requests. We implement the LEAP prototype on the\noff-the-shelf ARM platform without any hardware change. We perform the\ncomprehensive analyses and experiments to demonstrate that LEAP is efficient in\ndesign, comprehensive in support, and convenient in adoption.\n"}
+{"abstract": "  Blindly decoding a signal requires estimating its unknown transmit\nparameters, compensating for the wireless channel impairments, and identifying\nthe modulation type. While deep learning can solve complex problems, digital\nsignal processing (DSP) is interpretable and can be more computationally\nefficient. To combine both, we propose the dual path network (DPN). It consists\nof a signal path of DSP operations that recover the signal, and a feature path\nof neural networks that estimate the unknown transmit parameters. By\ninterconnecting the paths over several recovery stages, later stages benefit\nfrom the recovered signals and reuse all the previously extracted features. The\nproposed design is demonstrated to provide 5% improvement in modulation\nclassification compared to alternative designs lacking either feature sharing\nor access to recovered signals. The estimation results of DPN along with its\nblind decoding performance are shown to outperform a blind signal processing\nalgorithm for BPSK and QPSK on a simulated dataset. An over-the-air\nsoftware-defined-radio capture was used to verify DPN results at high SNRs. DPN\ndesign can process variable length inputs and is shown to outperform relying on\nfixed length inputs with prediction averaging on longer signals by up to 15% in\nmodulation classification.\n"}
+{"abstract": "  There has been a significant recent progress in the field of Embodied AI with\nresearchers developing models and algorithms enabling embodied agents to\nnavigate and interact within completely unseen environments. In this paper, we\npropose a new dataset and baseline models for the task of Rearrangement. We\nparticularly focus on the task of Room Rearrangement: an agent begins by\nexploring a room and recording objects' initial configurations. We then remove\nthe agent and change the poses and states (e.g., open/closed) of some objects\nin the room. The agent must restore the initial configurations of all objects\nin the room. Our dataset, named RoomR, includes 6,000 distinct rearrangement\nsettings involving 72 different object types in 120 scenes. Our experiments\nshow that solving this challenging interactive task that involves navigation\nand object interaction is beyond the capabilities of the current\nstate-of-the-art techniques for embodied tasks and we are still very far from\nachieving perfect performance on these types of tasks. The code and the dataset\nare available at: https://ai2thor.allenai.org/rearrangement\n"}
+{"abstract": "  Self-supervised speech representation learning (speech SSL) has demonstrated\nthe benefit of scale in learning rich representations for Automatic Speech\nRecognition (ASR) with limited paired data, such as wav2vec 2.0. We investigate\nthe existence of sparse subnetworks in pre-trained speech SSL models that\nachieve even better low-resource ASR results. However, directly applying widely\nadopted pruning methods such as the Lottery Ticket Hypothesis (LTH) is\nsuboptimal in the computational cost needed. Moreover, we show that the\ndiscovered subnetworks yield minimal performance gain compared to the original\ndense network. We present Prune-Adjust-Re-Prune (PARP), which discovers and\nfinetunes subnetworks for much better performance, while only requiring a\nsingle downstream ASR finetuning run. PARP is inspired by our surprising\nobservation that subnetworks pruned for pre-training tasks need merely a slight\nadjustment to achieve a sizeable performance boost in downstream ASR tasks.\nExtensive experiments on low-resource ASR verify (1) sparse subnetworks exist\nin mono-lingual/multi-lingual pre-trained speech SSL, and (2) the computational\nadvantage and performance gain of PARP over baseline pruning methods. In\nparticular, on the 10min Librispeech split without LM decoding, PARP discovers\nsubnetworks from wav2vec 2.0 with an absolute 10.9%/12.6% WER decrease compared\nto the full model. We further demonstrate the effectiveness of PARP via:\ncross-lingual pruning without any phone recognition degradation, the discovery\nof a multi-lingual subnetwork for 10 spoken languages in 1 finetuning run, and\nits applicability to pre-trained BERT/XLNet for natural language tasks.\n"}
+{"abstract": "  We report on time-resolved measurements of electron number density by\ncontinuous-wave laser absorption in a low-energy nanosecond-scale\nlaser-produced spark in atmospheric pressure air. Laser absorption is a result\nof free-free and bound-free electron excitation, with the absorption\ncoefficient modeled and evaluated using estimates of the time-variation in\nelectron temperature and probe laser absorption path length. Plasma electron\nnumber densities are determined to be as high as $n_\\text{e}=7\\times10^{19}$\ncm$^{-3}$, and decay to $1/e$ of their peak values over a period of about 50 ns\nfollowing plasma formation using a 20 mJ, 10 ns pulse width frequency-doubled\nNd:YAG laser. The measured plasma densities at later times are shown to be in\nreasonable agreement with Stark broadening measurements of the\n3s[$^5S{^o}$]-3p[$^5P$] electronic transition in atomic oxygen at 777 nm. This\nstudy provides support for the use of such continuous wave laser absorption for\ntime resolved electron density measurements in low energy spark discharges in\nair, provided that an estimate of the electron temperature and laser path\nlength can be made by accompanying diagnostics.\n"}
+{"abstract": "  Mader conjectured that for any tree $T$ of order $m$, every $k$-connected\ngraph $G$ with minimum degree at least $\\lfloor\\frac{3k}{2}\\rfloor +m-1$\ncontains a subtree $T'\\cong T$ such that $G-V(T')$ is $k$-connected. In this\npaper, we give a characterization for a subgraph to contain an embedding of a\nspecified tree avoiding some vertex. As a corollary, we confirm Mader's\nconjecture for $k\\leq3$.\n"}
+{"abstract": "  We study the triply heavy baryons $\\Omega_{QQQ}$ $(Q=c, b)$ in the QCD sum\nrules by performing the first calculation of the next-to-leading order (NLO)\ncontribution to the perturbative QCD part of the correlation functions.\nCompared with the leading order (LO) result, the NLO contribution is found to\nbe very important to the $\\Omega_{QQQ}$. This is because the NLO not only\nresults in a large correction, but also reduces the parameter dependence,\nmaking the Borel platform more distinct, especially for the $\\Omega_{bbb}$ in\nthe $\\overline{\\rm{MS}}$ scheme, where the platform appears only at NLO but not\nat LO. Particularly, owing to the inclusion of the NLO contribution, the\nrenormalization schemes ($\\bar {MS}$ and On-Shell) dependence and the scale\ndependence are significantly reduced. Consequently, after including the NLO\ncontribution to the perturbative part in the QCD sum rules, the masses are\nestimated to be $4.53^{+0.26}_{-0.11}$ GeV for $\\Omega_{ccc}$ and\n$14.27^{+0.33}_{-0.32}$ GeV for $\\Omega_{bbb}$, where the results are obtained\nat $\\mu=M_B$ with errors including those from the variation of the\nrenormalization scale $\\mu$ in the range $(0.8-1.2) M_B$. A careful study of\nthe $\\mu$ dependence in a wide range is further performed, which shows that the\nLO results are very sensitive to the choice of $\\mu$ whereas the NLO results\nare considerably better. In addition to the $\\mu=M_B$ result, a more stable\nvalue, (4.75-4.80) GeV, for the $\\Omega_{ccc}$ mass is found in the range of\n$\\mu=(1.2-2.0) M_B$, which should be viewed as a more relevant prediction in\nour NLO approach because of $\\mu$ dependence.\n"}
+{"abstract": "  Affordances are the possibilities of actions the environment offers to the\nindividual. Ordinary objects (hammer, knife) usually have many affordances\n(grasping, pounding, cutting), and detecting these allow artificial agents to\nunderstand what are their possibilities in the environment, with obvious\napplication in Robotics. Proposed benchmarks and state-of-the-art prediction\nmodels for supervised affordance segmentation are usually modifications of\npopular object segmentation models such as Mask R-CNN. We observe that\ntheoretically, these popular object segmentation methods should be sufficient\nfor detecting affordances masks. So we ask the question: is it necessary to\ntailor new architectures to the problem of learning affordances? We show that\napplying the out-of-the-box Mask R-CNN to the problem of affordances\nsegmentation outperforms the current state-of-the-art. We conclude that the\nproblem of supervised affordance segmentation is included in the problem of\nobject segmentation and argue that better benchmarks for affordance learning\nshould include action capacities.\n"}
+{"abstract": "  Supersymmetric circular Wilson loops in $\\mathcal{N}=4$ Super-Yang-Mills\ntheory are discussed starting from their Gaussian matrix model representations.\nPrevious results on the generating functions of Wilson loops are reviewed and\nextended to the more general case of two different loop contours, which is\nnecessary to discuss coincident loops with opposite orientations. A\ncombinatorial formula representing the connected correlators of multiply wound\nWilson loops in terms of the matrix model solution is derived. Two new results\nare obtained on the expectation value of the circular Wilson loop, the\nexpansion of which into a series in $1/N$ and to all orders in the 't~Hooft\ncoupling $\\lambda$ was derived by Drukker and Gross about twenty years ago. The\nconnected correlators of two multiply wound Wilson loops with arbitrary winding\nnumbers are calculated as a series in $1/N$. The coefficient functions are\nderived not only as power series in $\\lambda$, but also to all orders in\n$\\lambda$ by expressing them in terms of the coefficients of the Drukker and\nGross series. This provides an efficient way to calculate the $1/N$ series,\nwhich can probably be generalized to higher-point correlators.\n"}
+{"abstract": "  We construct a Connes spectral triple or `Dirac operator' on the non-reduced\nfuzzy sphere $C_\\lambda[S^2]$ as realised using quantum Riemannian geometry\nwith a central quantum metric $g$ of Euclidean signature and its associated\nquantum Levi-Civita connection. The Dirac operator is characterised uniquely up\nto unitary equivalence within our quantum Riemannian geometric setting and an\nassumption that the spinor bundle is trivial and rank 2 with a central basis.\nThe spectral triple has KO dimension 3 and in the case of the round metric,\nessentially recovers a previous proposal motivated by rotational symmetry.\n"}
+{"abstract": "  We investigate modifications of a stochastic polymer picture through a shift\nin the boundary between the system and an external environment. A conventional\nbead-and-spring model serving as the coarse-graining model is given by the\nLangevin equation for all the monomers subject to white noise. However,\nstochastic motion for only a tagged monomer is observed to occur in the\npresence of colored noise. The qualitative change in the observations arises\nfrom the boundary shift decided by the observer. The Langevin dynamics analyses\ninterpret the colored noise as the emergence of the polymeric elastic force,\nresulting in additional heat in the tagged monomer observation. Being\ndistinguished from coarse-graining based on scale separation, the projection of\ncomparable internal degrees of freedom is also discussed in light of the\nfluctuation theorem and the stochastic polymer thermodynamics.\n"}
+{"abstract": "  We propose a model of the substructural logic of Bunched Implications (BI)\nthat is suitable for reasoning about quantum states. In our model, the\nseparating conjunction of BI describes separable quantum states. We develop a\nprogram logic where pre- and post-conditions are BI formulas describing quantum\nstates -- the program logic can be seen as a counterpart of separation logic\nfor imperative quantum programs. We exercise the logic for proving the security\nof quantum one-time pad and secret sharing, and we show how the program logic\ncan be used to discover a flaw in Google Cirq's tutorial on the Variational\nQuantum Algorithm (VQA).\n"}
+{"abstract": "  The main purpose of this paper is to study generalized (self-) reciprocal\nAppell polynomials, which play a certain role in connection with Faulhaber-type\npolynomials. More precisely, we show for any Appell sequence when satisfying a\nreflection relation that the Appell polynomials can be described by\nFaulhaber-type polynomials, which arise from a quadratic variable substitution.\nFurthermore, the coefficients of the latter polynomials are given by values of\nderivatives of generalized reciprocal Appell polynomials. Subsequently, we show\nsome applications to the Bernoulli and Euler polynomials. In the context of\npower sums the results transfer to the classical Faulhaber polynomials.\n"}
+{"abstract": "  Relativistic p+A collisions may produce droplets of quark gluon plasma (QGP)\nthat quickly develop a toroidal vortex structure similar to that of an\nexpanding smoke ring. We present viscous relativistic hydrodynamic calculations\nof ultra-central p+A collisions and develop an experimental observable to probe\nthe structure, correlating the polarization and momentum of hyperons emitted\nfrom the collision. The effect is robust against changes in the definition of\nvorticity used to calculate the polarization. Experiments at RHIC and LHC may\ntest the existence and strength of the vortex toroids, bringing new evidence to\nbear on the question of collectivity in the smallest QGP droplets.\n"}
+{"abstract": "  We show that unconventional boundary phenomena occur in the $3Q$ charge bond\norders on the hexagonal lattices. At the Van Hove singularity with three\nnesting momenta, $3Q$ orders can trigger a $\\text{C}_6$-symmetric insulator\nunder bond modulations. On the kagome lattice, in-gap corner states appear in\nthe energy spectrum and carry fractional corner charge $-2e/3$. Such corner\nphenomena originate from the corner filling anomaly and indicate a higher-order\ntopological insulator. The in-gap corner states are also observed on the\ntriangular lattice. The honeycomb lattice does not support fractional corner\ncharges, while in-gap edge states are observed. We discuss possible indications\nto the experimentally uncovered charge bond orders in the kagome metals\n$\\text{AV}_3\\text{Sb}_5$ with $\\text{A}=\\text{K},\\text{Rb},\\text{Cs}$. With\nlayer stacking along the out-of-plane direction, the corner states can\nconstitute the hinge states with fractional charge densities.\n"}
+{"abstract": "  We investigate the quantum thermodynamic cycle of a quantum heat engine\ncarrying out an Otto thermodynamic cycle. We use the thermal properties of a\nmoving heat bath with relativistic velocity with respect to the cold bath. As a\nworking medium, we use a two-level system and a harmonic oscillator that\ninteract with a moving heat bath and a static cold bath. In the current work,\nthe quantum heat engine is studied in the high and low temperatures regime.\nUsing quantum thermodynamics and the theory of open quantum system we obtain\nthe total produced work, the efficiency and the efficiency at maximum power.\nThe maximum efficiency of the Otto quantum heat engine depends only on the\nratio of the minimum and maximum energy gaps. On the contrary, the efficiency\nat maximum power and the extracted work decreases with the velocity since the\nmotion of the heat bath has an energy cost for the quantum heat engine.\nFurthermore, the efficiency at maximum power depends on the nature of the\nworking medium.\n"}
+{"abstract": "  In this paper, we introduce a Lorentzian Anosov family (LAfamily) up to a\nsequence of distributions of null vectors. We prove for each p Mi, where Mi is\na Lorentzian manifold for i Z the tangent space Mi at p has a unique splitting\nand this splitting varies continuously on a sequence via the distance function\ncreated by a unique torsion-free semi-Riemannian connection. We present three\nexamples of LA-families. Also, we define Lorentzian shadowing property of type\nI and II and prove some results related to this property.\n"}
+{"abstract": "  We present a quasi-static elasticity model that accounts for damage evolution\nbased on the ideas of Kachanov 1958 and and Rabotnov 1968. We analyze the\nresulting strongly nonlinear system of differential equations in view of\nwell-posedness. The specific feature is that the displacements are connected to\nthe damage evolution equation via a Nemytskii- or superposition- operator. The\nnovelty in this work is that we present an inverse problem in a parameter\nidentification setting, in which we are able to identify the shape of this\nNemytskii-operator. From the material modelling point of view, the relation of\nmaterial damage and displacements is modelled by the shape of this operator. We\nestablish the Fr\\'echet-derivative of the forward operator as well as the\nadjoint of the derivative and characterize both via systems of linear\ndifferential equations. We prove ill-posedness of the inverse problem and\nprovide a sufficient condition for the classical nonlinear Landweber method to\nconverge.\n"}
+{"abstract": "  This paper presents a physical interface for collaborative mobile\nmanipulators in industrial manufacturing and logistics applications. The\nproposed work builds on our earlier MOCA-MAN interface, through which an\noperator could be physically coupled to a mobile manipulator to be assisted in\nperforming daily activities. The previous interface was based on a magnetic\nclamp attached to one arm of the user for the coupling stage, and a bracelet\nbased on EMG sensors on the other arm for human-robot communication via\ngestures. The new interface instead presents the following additions: i) An\nindustrial-like design that allows the worker to couple/decouple easily and to\noperate mobile manipulators locally; ii) A simplistic communication channel via\na simple buttons board that allows controlling the robot with one hand only;\niii) The interface offers enhanced loco-manipulation capabilities that do not\ncompromise the worker mobility. Besides, an experimental evaluation with six\nhuman subjects is carried out to analyze the enhanced locomotion and\nflexibility of the proposed interface in terms of mobility constraint,\nusability, and physical load reduction.\n"}
+{"abstract": "  Intelligent systems are increasingly part of our everyday lives and have been\nintegrated seamlessly to the point where it is difficult to imagine a world\nwithout them. Physical manifestations of those systems on the other hand, in\nthe form of embodied agents or robots, have so far been used only for specific\napplications and are often limited to functional roles (e.g. in the industry,\nentertainment and military fields). Given the current growth and innovation in\nthe research communities concerned with the topics of robot navigation,\nhuman-robot-interaction and human activity recognition, it seems like this\nmight soon change. Robots are increasingly easy to obtain and use and the\nacceptance of them in general is growing. However, the design of a socially\ncompliant robot that can function as a companion needs to take various areas of\nresearch into account. This paper is concerned with the navigation aspect of a\nsocially-compliant robot and provides a survey of existing solutions for the\nrelevant areas of research as well as an outlook on possible future directions.\n"}
+{"abstract": "  We describe the non-minimal Standard Model, consisting of minimalistic\nextensions of the Standard Model, which for all we know is the theory of the\nuniverse, able to describe all of the universe from the beginning of time.\nExtensions discussed are an extra neutrino and a new Higgs model. We introduce\nthe principle of global relativity and discuss how the theory can be largely\nderived from this principle. One is led to the unification of forces into SU(5)\nand a form of dark matter. We discuss the limitations of the theory, showing\nthat it is not the theory of everything. However we argue that it is the only\npart, that is within conceivable reach of physical experiment or astronomical\nobservations. It is argued, that at the Planck scale the universe is\neffectively three-dimensional.\n"}
+{"abstract": "  NLP has a significant role in advancing healthcare and has been found to be\nkey in extracting structured information from radiology reports. Understanding\nrecent developments in NLP application to radiology is of significance but\nrecent reviews on this are limited. This study systematically assesses recent\nliterature in NLP applied to radiology reports. Our automated literature search\nyields 4,799 results using automated filtering, metadata enriching steps and\ncitation search combined with manual review. Our analysis is based on 21\nvariables including radiology characteristics, NLP methodology, performance,\nstudy, and clinical application characteristics. We present a comprehensive\nanalysis of the 164 publications retrieved with each categorised into one of 6\nclinical application categories. Deep learning use increases but conventional\nmachine learning approaches are still prevalent. Deep learning remains\nchallenged when data is scarce and there is little evidence of adoption into\nclinical practice. Despite 17% of studies reporting greater than 0.85 F1\nscores, it is hard to comparatively evaluate these approaches given that most\nof them use different datasets. Only 14 studies made their data and 15 their\ncode available with 10 externally validating results. Automated understanding\nof clinical narratives of the radiology reports has the potential to enhance\nthe healthcare process but reproducibility and explainability of models are\nimportant if the domain is to move applications into clinical use. More could\nbe done to share code enabling validation of methods on different institutional\ndata and to reduce heterogeneity in reporting of study properties allowing\ninter-study comparisons. Our results have significance for researchers\nproviding a systematic synthesis of existing work to build on, identify gaps,\nopportunities for collaboration and avoid duplication.\n"}
+{"abstract": "  Two boron-rich chalcogenides B12S and B12Se isostructural to\n{\\alpha}-rhombohedral boron were synthesized by chemical reaction of the\nelements at high-pressure - high-temperature conditions. The crystal structures\nand stoichiometries of both compounds were confirmed by Rietveld refinement and\nelemental analysis. The experimental Raman spectra of B12S and B12Se were\ninvestigated for the first time. All observed Raman bands have been attributed\nto the theoretically calculated phonon modes, and the mode assignment has been\nperformed.\n"}
+{"abstract": "  We show that causality is violated in the early stages of state-of-the-art\nheavy-ion hydrodynamic simulations. Up to $30\\%$ of the initial fluid cells\nviolate nonlinear causality constraints while the rest are uncertain. Only\nafter 2-3 fm$/c$ of evolution do we find that $\\sim 50\\%$ of the fluid cells\nare definitely causal. Inclusion of pre-equilibrium evolution significantly\nreduces the number of acausal cells, but it does not eliminate them. Our\nfindings suggest that relativistic causality places stringent constraints on\nthe available model parameter space of heavy-ion collision simulations.\n"}
+{"abstract": "  Deep learning algorithms have made many breakthroughs and have various\napplications in real life. Computational resources become a bottleneck as the\ndata and complexity of the deep learning pipeline increases. In this paper, we\npropose optimized deep learning pipelines in multiple aspects of training\nincluding time and memory. OpTorch is a machine learning library designed to\novercome weaknesses in existing implementations of neural network training.\nOpTorch provides features to train complex neural networks with limited\ncomputational resources. OpTorch achieved the same accuracy as existing\nlibraries on Cifar-10 and Cifar-100 datasets while reducing memory usage to\napproximately 50%. We also explore the effect of weights on total memory usage\nin deep learning pipelines. In our experiments, parallel encoding-decoding\nalong with sequential checkpoints results in much improved memory and time\nusage while keeping the accuracy similar to existing pipelines. OpTorch python\npackage is available at available at https://github.com/cbrl-nuces/optorch\n"}
+{"abstract": "  We propose a Hamiltonian approach to reproduce the relevant elements of the\ncenturies-old Subak irrigation system in Bali, showing a cluster-size power-law\ndistribution. The resulting system presents two equilibria. The first balance\nis between energy and entropy contributions. The second lies in the energy\ncontribution: a local Potts-type interaction and a long-range\nanti-ferromagnetic one without attenuation. Finite-size scaling analysis shows\nthat as a result of the second balance, the transition balancing energy and\nentropy contributions for low values of the antiferromagnetic contribution is\nabsorbed by the transition for high values of that contribution as the system\nsize increases. The system thus presents only a weak-first order phase\ntransition at the thermodynamic limit. This extends the Hamiltonian framework\nto a new domain of coupled human-environmental interactions.\n"}
+{"abstract": "  The remarkable advancements in Deep Learning (DL) algorithms have fueled\nenthusiasm for using Artificial Intelligence (AI) technologies in almost every\ndomain; however, the opaqueness of these algorithms put a question mark on\ntheir applications in safety-critical systems. In this regard, the\n`explainability' dimension is not only essential to both explain the inner\nworkings of black-box algorithms, but it also adds accountability and\ntransparency dimensions that are of prime importance for regulators, consumers,\nand service providers. eXplainable Artificial Intelligence (XAI) is the set of\ntechniques and methods to convert the so-called black-box AI algorithms to\nwhite-box algorithms, where the results achieved by these algorithms and the\nvariables, parameters, and steps taken by the algorithm to reach the obtained\nresults, are transparent and explainable. To complement the existing literature\non XAI, in this paper, we take an `engineering' approach to illustrate the\nconcepts of XAI. We discuss the stakeholders in XAI and describe the\nmathematical contours of XAI from engineering perspective. Then we take the\nautonomous car as a use-case and discuss the applications of XAI for its\ndifferent components such as object detection, perception, control, action\ndecision, and so on. This work is an exploratory study to identify new avenues\nof research in the field of XAI.\n"}
+{"abstract": "  We propose a Dirac neutrino portal dark matter scenario by minimally\nextending the particle content of the Standard Model (SM) with three right\nhanded neutrinos ($\\nu_R$), a Dirac fermion dark matter candidate ($\\psi$) and\na complex scalar ($\\phi$), all of which are singlets under the SM gauge group.\nAn additional $\\mathbb{Z}_4$ symmetry has been introduced for the stability of\ndark matter candidate $\\psi$ and also ensuring the Dirac nature of light\nneutrinos at the same time. Both the right handed neutrinos and the dark matter\nthermalise with the SM plasma due to a new Yukawa interaction involving\n$\\nu_R$, $\\psi$ and $\\phi$ while the latter maintains thermal contact via the\nHiggs portal interaction. The decoupling of $\\nu_R$ occurs when $\\phi$ loses\nits kinetic equilibrium with the SM plasma and thereafter all three\n$\\mathbb{Z}_4$ charged particles form an equilibrium among themselves with a\ntemperature $T_{\\nu_R}$. The dark matter candidate $\\psi$ finally freezes out\nwithin the dark sector and preserves its relic abundance. We have found that in\nthe present scenario, some portion of low mass dark matter\n($M_{\\psi}\\lesssim10$ GeV) is already excluded by the Planck 2018 data for\nkeeping $\\nu_R$s in the thermal bath below a temperature of 600 MeV and thereby\nproducing an excess contribution to $N_{\\rm eff}$. The next generation\nexperiments like CMB-S4, SPT-3G etc. will have the required sensitivities to\nprobe the entire model parameter space of this minimal scenario, especially the\nlow mass range of $\\psi$ where direct detection experiments are still not\ncapable enough for detection.\n"}
+{"abstract": "  We study Hibridizable Discontinuous Galerkin (HDG) discretizations for a\nclass of non-linear interior elliptic boundary value problems posed in curved\ndomains where both the source term and the diffusion coefficient are\nnon-linear. We consider the cases where the non-linear diffusion coefficient\ndepends on the solution and on the gradient of the solution. To sidestep the\nneed for curved elements, the discrete solution is computed on a polygonal\nsubdomain that is not assumed to interpolate the true boundary, giving rise to\nan unfitted computational mesh. We show that, under mild assumptions on the\nsource term and the computational domain, the discrete systems are well posed.\nFurthermore, we provide a priori error estimates showing that the discrete\nsolution will have optimal order of convergence as long as the distance between\nthe curved boundary and the computational boundary remains of the same order of\nmagnitude as the mesh parameter.\n"}
+{"abstract": "  Let $\\mu$ be an $n$-dimensional finite positive measure on $\\mathbb{R}^m$. We\nobtain a $T1$ condition sufficient for the boundedness of Calder\\'{o}n-Zygmund\noperators on $\\textrm{RBMO}(\\mu)$, the regular BMO space of Tolsa.\n"}
+{"abstract": "  Transverse (Hall-effect) and Goos--H\\\"{a}nchen shifts of light beams\nreflected/refracted at planar interfaces are important wave phenomena, which\ncan be significantly modified and enhanced by the presence of intrinsic orbital\nangular momentum (OAM) in the beam. Recently, optical spatiotemporal vortex\npulses (STVPs) carrying a purely transverse intrinsic OAM were predicted\ntheoretically and generated experimentally. Here we consider the reflection and\nrefraction of such pulses at a planar isotropic interface. We find\ntheoretically and confirm numerically novel types of the OAM-dependent\ntransverse and longitudinal pulse shifts. Remarkably, the longitudinal shifts\ncan be regarded as time delays, which appear, in contrast to the well-known\nWigner time delay, without temporal dispersion of the reflection/refraction\ncoefficients. Such time delays allow one to realize OAM-controlled slow\n(subluminal) and fast (superluminal) pulse propagation without medium\ndispersion. These results can have important implications in various problems\ninvolving scattering of localized vortex states carrying transverse OAM.\n"}
+{"abstract": "  Segmentation of enhancing tumours or lesions from MRI is important for\ndetecting new disease activity in many clinical contexts. However, accurate\nsegmentation requires the inclusion of medical images (e.g., T1 post contrast\nMRI) acquired after injecting patients with a contrast agent (e.g.,\nGadolinium), a process no longer thought to be safe. Although a number of\nmodality-agnostic segmentation networks have been developed over the past few\nyears, they have been met with limited success in the context of enhancing\npathology segmentation. In this work, we present HAD-Net, a novel offline\nadversarial knowledge distillation (KD) technique, whereby a pre-trained\nteacher segmentation network, with access to all MRI sequences, teaches a\nstudent network, via hierarchical adversarial training, to better overcome the\nlarge domain shift presented when crucial images are absent during inference.\nIn particular, we apply HAD-Net to the challenging task of enhancing tumour\nsegmentation when access to post-contrast imaging is not available. The\nproposed network is trained and tested on the BraTS 2019 brain tumour\nsegmentation challenge dataset, where it achieves performance improvements in\nthe ranges of 16% - 26% over (a) recent modality-agnostic segmentation methods\n(U-HeMIS, U-HVED), (b) KD-Net adapted to this problem, (c) the pre-trained\nstudent network and (d) a non-hierarchical version of the network (AD-Net), in\nterms of Dice scores for enhancing tumour (ET). The network also shows\nimprovements in tumour core (TC) Dice scores. Finally, the network outperforms\nboth the baseline student network and AD-Net in terms of uncertainty\nquantification for enhancing tumour segmentation based on the BraTs 2019\nuncertainty challenge metrics. Our code is publicly available at:\nhttps://github.com/SaverioVad/HAD_Net\n"}
+{"abstract": "  With the goal of designing novel inhibitors for SARS-CoV-1 and SARS-CoV-2, we\npropose the general molecule optimization framework, Molecular Neural Assay\nSearch (MONAS), consisting of three components: a property predictor which\nidentifies molecules with specific desirable properties, an energy model which\napproximates the statistical similarity of a given molecule to known training\nmolecules, and a molecule search method. In this work, these components are\ninstantiated with graph neural networks (GNNs), Deep Energy Estimator Networks\n(DEEN) and Monte Carlo tree search (MCTS), respectively. This implementation is\nused to identify 120K molecules (out of 40-million explored) which the GNN\ndetermined to be likely SARS-CoV-1 inhibitors, and, at the same time, are\nstatistically close to the dataset used to train the GNN.\n"}
+{"abstract": "  We revisit some ideas of K.-M.~Perfekt who has provided an elegant framework\nto detect the biduality between function or sequence spaces defined in terms of\nsome $o$- resp.\\ $O$-condition. We present new proofs under somewhat weaker\nassumptions than before and apply the result to Lipschitz spaces.\n"}
+{"abstract": "  Automatic multiple-choice question generation (MCQG) is a useful yet\nchallenging task in Natural Language Processing (NLP). It is the task of\nautomatic generation of correct and relevant questions from textual data.\nDespite its usefulness, manually creating sizeable, meaningful and relevant\nquestions is a time-consuming and challenging task for teachers. In this paper,\nwe present an NLP-based system for automatic MCQG for Computer-Based Testing\nExamination (CBTE).We used NLP technique to extract keywords that are important\nwords in a given lesson material. To validate that the system is not perverse,\nfive lesson materials were used to check the effectiveness and efficiency of\nthe system. The manually extracted keywords by the teacher were compared to the\nauto-generated keywords and the result shows that the system was capable of\nextracting keywords from lesson materials in setting examinable questions. This\noutcome is presented in a user-friendly interface for easy accessibility.\n"}
+{"abstract": "  We discuss a particular non-relativistic limit of NS-NS gravity that can be\ntaken at the level of the action and equations of motion, without imposing any\ngeometric constraints by hand. This relies on the fact that terms that diverge\nin the limit and that come from the Vielbein in the Einstein-Hilbert term and\nfrom the kinetic term of the Kalb-Ramond two-form field cancel against each\nother. This cancelling of divergences is the target space analogue of a similar\ncancellation that takes place at the level of the string sigma model between\nthe Vielbein in the kinetic term and the Kalb-Ramond field in the Wess-Zumino\nterm. The limit of the equations of motion leads to one equation more than the\nlimit of the action, due to the emergence of a local target space scale\ninvariance in the limit. Some of the equations of motion can be solved by scale\ninvariant geometric constraints. These constraints define a so-called\nDilatation invariant String Newton-Cartan geometry.\n"}
+{"abstract": "  We show existence of a natural rational structure on periodic cyclic\nhomology, conjectured by L. Katzarkov, M. Kontsevich, T. Pantev, for several\nclasses of dg-categories, including proper connective $\\mathbb{C}$-dg-algebras\nand dg-categories of local systems. The main ingredient is derived nilpotent\ninvariance of A. Blanc's semi-topological K-theory, which we establish along\nthe way.\n"}
+{"abstract": "  We obtain the complete and independent bases of effective operators at mass\ndimension 5, 6, 7, 8, 9 in both standard model effective field theory with\nlight sterile right-handed neutrinos ($\\nu$SMEFT) and low energy effective\nfield theory with light sterile neutrinos ($\\nu$LEFT). These theories provide\nsystematical parametrizations on all possible Lorentz-invariant physical\neffects involving in the Majorana/Dirac neutrinos, with/without the lepton\nnumber violations. In the $\\nu$SMEFT, we find that there are 2 (18), 29 (1614),\n80 (4206), 323 (20400), 1358 (243944) independent operators with sterile\nneutrinos included at the dimension 5, 6, 7, 8, 9 for one (three) generation of\nfermions, while 24, 5223, 3966, 25425, 789426 independent operators in the\n$\\nu$LEFT.\n"}
+{"abstract": "  Unsupervised health monitoring has gained much attention in the last decade\nas the most practical real-time structural health monitoring (SHM) approach.\nAmong the proposed unsupervised techniques in the literature, there are still\nobstacles to robust and real-time health monitoring. These barriers include\nloss of information from dimensionality reduction in feature extraction steps,\ncase-dependency of those steps, lack of a dynamic clustering, and detection\nresults' sensitivity to user-defined parameters. This study introduces an\nunsupervised real-time SHM method with a mixture of low- and high-dimensional\nfeatures without a case-dependent extraction scheme. Both features are used to\ntrain multi-ensembles of Generative Adversarial Networks (GAN) and one-class\njoint Gaussian distribution models (1-CG). A novelty detection system of\nlimit-state functions based on GAN and 1-CG models' detection scores is\nconstructed. The Resistance of those limit-state functions (detection\nthresholds) is tuned to user-defined parameters with the GAN-generated data\nobjects by employing the Monte Carlo histogram sampling through a\nreliability-based analysis. The tuning makes the method robust to user-defined\nparameters, which is crucial as there is no rule for selecting those parameters\nin a real-time SHM. The proposed novelty detection framework is applied to two\nstandard SHM datasets to illustrate its generalizability: Yellow Frame (twenty\ndamage classes) and Z24 Bridge (fifteen damage classes). All different damage\ncategories are identified with low sensitivity to the initial choice of\nuser-defined parameters with both introduced dynamic and static baseline\napproaches with few or no false alarms.\n"}
+{"abstract": "  Existing public person Re-Identification~(ReID) datasets are small in modern\nterms because of labeling difficulty. Although unlabeled surveillance video is\nabundant and relatively easy to obtain, it is unclear how to leverage these\nfootage to learn meaningful ReID representations. In particular, most existing\nunsupervised and domain adaptation ReID methods utilize only the public\ndatasets in their experiments, with labels removed. In addition, due to small\ndata sizes, these methods usually rely on fine tuning by the unlabeled training\ndata in the testing domain to achieve good performance. Inspired by the recent\nprogress of large-scale self-supervised image classification using contrastive\nlearning, we propose to learn ReID representation from large-scale unlabeled\nsurveillance video alone. Assisted by off-the-shelf pedestrian detection tools,\nwe apply the contrastive loss at both the image and the tracklet levels.\nTogether with a principal component analysis step using camera labels freely\navailable, our evaluation using a large-scale unlabeled dataset shows far\nsuperior performance among unsupervised methods that do not use any training\ndata in the testing domain. Furthermore, the accuracy improves with the data\nsize and therefore our method has great potential with even larger and more\ndiversified datasets.\n"}
+{"abstract": "  We study H\\'{e}non maps with biholomorphic escaping sets. We show that if $H$\nand $F$ are two H\\'{e}non maps of degree $d$ with biholomorphic escaping sets,\nthen there exist complex numbers $\\alpha,\\beta$ and $\\gamma$ with\n$\\alpha^{d+1}=\\beta$ and $\\beta^{d-1}=\\gamma^{d-1}=1$ such that with the\nfollowing linear automorphisms $$ B(x,y)=(\\gamma \\alpha \\beta^{-1}x,\n\\alpha^{-1}y) \\quad\\text{and}\\quad L(x,y)=(\\gamma^{-1}\\beta x,\\beta y ) $$one\nhas $$ F\\equiv L \\circ B \\circ H \\circ B. $$\n"}
+{"abstract": "  Deep Neural Networks (DNNs) are capable of solving complex problems in\ndomains related to embedded systems, such as image and natural language\nprocessing. To efficiently implement DNNs on a specific FPGA platform for a\ngiven cost criterion, e.g. energy efficiency, an enormous amount of design\nparameters has to be considered from the topology down to the final hardware\nimplementation. Interdependencies between the different design layers have to\nbe taken into account and explored efficiently, making it hardly possible to\nfind optimized solutions manually. An automatic, holistic design approach can\nimprove the quality of DNN implementations on FPGA significantly. To this end,\nwe present a cross-layer design space exploration methodology. It comprises\noptimizations starting from a hardware-aware topology search for DNNs down to\nthe final optimized implementation for a given FPGA platform. The methodology\nis implemented in our Holistic Auto machine Learning for FPGAs (HALF)\nframework, which combines an evolutionary search algorithm, various\noptimization steps and a library of parametrizable hardware DNN modules. HALF\nautomates both the exploration process and the implementation of optimized\nsolutions on a target FPGA platform for various applications. We demonstrate\nthe performance of HALF on a medical use case for arrhythmia detection for\nthree different design goals, i.e. low-energy, low-power and high-throughput\nrespectively. Our FPGA implementation outperforms a TensorRT optimized model on\nan Nvidia Jetson platform in both throughput and energy consumption.\n"}
+{"abstract": "  COVID-19 has resulted in a public health global crisis. The pandemic control\nnecessitates epidemic models that capture the trends and impacts on infectious\nindividuals. Many exciting models can implement this but they lack practical\ninterpretability. This study combines the epidemiological and network theories\nand proposes a framework with causal interpretability in response to this\nissue. This framework consists of an extended epidemic model in interconnected\nnetworks and a dynamic structure that has major human mobility. The networked\ncausal analysis focuses on the stochastic processing mechanism. It highlights\nthe social infectivity as the intervention estimator between the observable\neffect (the number of daily new cases) and unobservable causes (the number of\ninfectious persons). According to an experiment on the dataset for Tokyo\nmetropolitan areas, the computational results indicate the propagation features\nof the symptomatic and asymptomatic infectious persons. These new\nspatiotemporal findings can be beneficial for policy decision making.\n"}
+{"abstract": "  In low-power wide-area networks (LPWANs), various trade-offs among the\nbandwidth, data rates, and energy per bit have different effects on the quality\nof service under different propagation conditions (e.g. fading and multipath),\ninterference scenarios, multi-user requirements, and design constraints. Such\ncompromises, and the manner in which they are implemented, further affect other\ntechnical aspects, such as system's computational complexity and power\nefficiency. At the same time, this difference in trade-offs also adds to the\ntechnical flexibility in addressing a broader range of IoT applications. This\npaper addresses a physical layer LPWAN approach based on the Aggregate Spread\nPulse Modulation (ASPM) and provides a brief assessment of its properties in\nadditive white Gaussian noise (AWGN) channel. In the binary ASPM the control of\nthe quality of service is performed through the change in the spectral\nefficiency, i.e., the data rate at a given bandwidth. Implementing M-ary\nencoding in ASPM further enables controlling service quality through changing\nthe energy per bit (in about an order of magnitude range) as an additional\ntrade-off parameter. Such encoding is especially useful for improving the\nASPM's energy per bit performance, thus increasing its range and overall energy\nefficiency, and making it more attractive for use in LPWANs for IoT\napplications.\n"}
+{"abstract": "  Sampling from a complex distribution $\\pi$ and approximating its intractable\nnormalizing constant Z are challenging problems. In this paper, a novel family\nof importance samplers (IS) and Markov chain Monte Carlo (MCMC) samplers is\nderived. Given an invertible map T, these schemes combine (with weights)\nelements from the forward and backward Orbits through points sampled from a\nproposal distribution $\\rho$. The map T does not leave the target $\\pi$\ninvariant, hence the name NEO, standing for Non-Equilibrium Orbits. NEO-IS\nprovides unbiased estimators of the normalizing constant and self-normalized IS\nestimators of expectations under $\\pi$ while NEO-MCMC combines multiple NEO-IS\nestimates of the normalizing constant and an iterated sampling-importance\nresampling mechanism to sample from $\\pi$. For T chosen as a discrete-time\nintegrator of a conformal Hamiltonian system, NEO-IS achieves state-of-the art\nperformance on difficult benchmarks and NEO-MCMC is able to explore highly\nmultimodal targets. Additionally, we provide detailed theoretical results for\nboth methods. In particular, we show that NEO-MCMC is uniformly geometrically\nergodic and establish explicit mixing time estimates under mild conditions.\n"}
+{"abstract": "  As software projects are very diverse, each software development process must\nbe adjusted to the needs of the project and the corresponding development team.\nFrequently, we find different methods and practices combined in a so-called\nhybrid development method. Research has shown that these hybrid methods evolve\nover time and are devised based on experience. However, when devising a hybrid\nmethod, the methods and practices used should cover the whole software project\nwith its different phases including, among others, project management,\nrequirements analysis, quality management, risk management, and implementation.\nIn this paper, we analyze which methods and practices are used in which phase\nof a software project. Based on an initial survey with 27 practitioners, we\nprovide a mapping of methods and practices to different project phases and vice\nversa. Despite the preliminary nature of our study and the small sample size,\nwe observe three remarkable aspects: (1) there are discrepancies between the\nintended use of methods and practices according to literature and the real use\nin practice, (2) practices are used more consistently than methods, and (3)\nparts of the software lifecycle such as maintenance and evolution are hardly\ncovered by widely distributed methods and practices. Consequently, when\ndevising a development process, it is worth a thought whether all phases of the\nsoftware lifecycle are addressed or not.\n"}
+{"abstract": "  Contextual Bandits find important use cases in various real-life scenarios\nsuch as online advertising, recommendation systems, healthcare, etc. However,\nmost of the algorithms use flat feature vectors to represent context whereas,\nin the real world, there is a varying number of objects and relations among\nthem to model in the context. For example, in a music recommendation system,\nthe user context contains what music they listen to, which artists create this\nmusic, the artist albums, etc. Adding richer relational context representations\nalso introduces a much larger context space making exploration-exploitation\nharder. To improve the efficiency of exploration-exploitation knowledge about\nthe context can be infused to guide the exploration-exploitation strategy.\nRelational context representations allow a natural way for humans to specify\nknowledge owing to their descriptive nature. We propose an adaptation of\nKnowledge Infused Policy Gradients to the Contextual Bandit setting and a novel\nKnowledge Infused Policy Gradients Upper Confidence Bound algorithm and perform\nan experimental analysis of a simulated music recommendation dataset and\nvarious real-life datasets where expert knowledge can drastically reduce the\ntotal regret and where it cannot.\n"}
+{"abstract": "  Transforming a causal system from a given initial state to a desired target\nstate is an important task permeating multiple fields including control theory,\nbiology, and materials science. In causal models, such transformations can be\nachieved by performing a set of interventions. In this paper, we consider the\nproblem of identifying a shift intervention that matches the desired mean of a\nsystem through active learning. We define the Markov equivalence class that is\nidentifiable from shift interventions and propose two active learning\nstrategies that are guaranteed to exactly match a desired mean. We then derive\na worst-case lower bound for the number of interventions required and show that\nthese strategies are optimal for certain classes of graphs. In particular, we\nshow that our strategies may require exponentially fewer interventions than the\npreviously considered approaches, which optimize for structure learning in the\nunderlying causal graph. In line with our theoretical results, we also\ndemonstrate experimentally that our proposed active learning strategies require\nfewer interventions compared to several baselines.\n"}
+{"abstract": "  We review some important results by Gross, Hacking, Keel, and Kontsevich on\ncluster algebra theory, namely, the column sign-coherence of $C$-matrices and\nthe Laurent positivity, both of which were conjectured by Fomin and Zelevinsky.\nWe digest and reconstruct the proofs of these conjectures by Gross et al. still\nbased on their scattering diagram method, however, without relying on toric\ngeometry. At the same time, we also give a detailed account of the\ncorrespondence between the notions of cluster patterns and scattering diagrams.\nMost of the results in this text are found in or translated from the known\nresults in the literature. However, the approach, the construction of logic and\nproofs, and the overall presentation are new. Also, as an application of the\nresults and the techniques in the text, we show that there is a one-to-one\ncorrespondence between $g$-vectors and cluster variables in cluster patterns\nwith arbitrary coefficients.\n"}
+{"abstract": "  We show that for any sequence $(f_n)_{n\\ge n_0}$ of positive integers\nsatisfying $\\sum_{n=n_0}^\\infty f_n/2^n \\le 1/4$ and $f_n\\le f_{n+1}\\le 2f_n$,\nthere exists an infinite antichain $\\mathcal{F}$ of finite subsets of\n$\\mathbb{N}$ such that $|\\mathcal{F}\\cap 2^{[n]}| \\geq f_n$ for all $n\\ge n_0$.\nIt follows that for any $\\varepsilon>0$ there exists an antichain\n$\\mathcal{F}\\subseteq 2^{\\mathbb{N}}$ such that $$\\liminf_{n \\to \\infty}\n|\\mathcal{F}\\cap 2^{[n]}| \\cdot \\left(\\frac{2^n}{n\\log^{1+\\varepsilon}\nn}\\right)^{-1} > 0.$$ This resolves a problem of Sudakov, Tomon and Wagner in a\nstrong form, and is essentially tight.\n"}
+{"abstract": "  Low-carbon electricity is a key enabler in combating climate change.\nDecarbonising the power sector is now at the centre of global and European\npolicies. As the IPCC highlights, pathways where the power sector rapidly\ndecarbonises by 2030 have higher chances of keeping global warming below\n1.5$^\\circ$C. The electricity sector should be fully decarbonised by 2050 to\nmeet either the 1.5$^\\circ$C or 2$^\\circ$C targets. This means that EU policy\nefforts should focus on supporting a maximum reduction of emissions per unit of\nelectricity by 2030 and net-zero emissions by 2050. Reaching these targets is\none of the most pressing questions EU policymakers face today. In light of the\nCOVID-19 crisis, EU policies should guide a cost-effective, reliable and\nenvironmentally sound transition of the power sector, benefiting EU research\nand innovation and its citizens. This meta-analysis provides a novel view on\nhistorical data and compares data from modelling scenarios identified in the\nliterature. It assesses the current and future role of nuclear energy in\ndecarbonizing the EU power sector, while reviewing socio-economic implications\nthat could arise if limited public support nearly excludes nuclear fission\nelectricity from the future EU power mix. This work highlights relevant\nsocio-economic policy implications and actionable policy recommendations.\n"}
+{"abstract": "  We investigate the strongly lensed (\\mu x10-100) Lyman continuum (LyC)\ngalaxy, dubbed Sunburst, at z=2.37, taking advantage of a new accurate model of\nthe lens. A characterization of the intrinsic (delensed) properties of the\ngalaxy yields a size of ~3 sq.kpc, a luminosity Muv=-20.3,and a stellar mass\nM~10^9 Msun;16% of the ultraviolet light is located in a 3 Myr old\ngravitationally-bound young massive star cluster (YMC) with an effective radius\nof Re~8 pc and a dynamical mass of ~10^7 Msun (similar to the stellar mass),\nfrom which LyC radiation is detected (\\lambda < 912A). The inferred outflowing\ngas velocity (>300 km/s) exceeds the escape velocity of the star cluster. The\nresulting escape fraction of the ionizing radiation emerging from the Sunburst\ngalaxy is >6-12%, whilst it is >46-93% if inferred from the YMC. 12 additional\nlikely star clusters with 3<Re<20 pc are identified in the galaxy from which we\nderive a cluster formation efficiency \\Gamma>~30%, which is consistent with the\nhigh \\Gamma derived in local galaxies experiencing extreme gas physical\nconditions. The presence of the YMC influences the morphology (nucleation),\nphotometry (photometric jumps) and spectroscopic output (nebular emission) of\nthe entire galaxy. The de-lensed LyC and UV (1600A) magnitudes of the YMC are\n~30.6 and ~26.9, whilst the galaxy has m1600~24.8. A relatively large\nrest-frame equivalent width of EWrest(Hb+[OIII]4959-5007)~450A emerges from the\ngalaxy with the YMC contributing to ~30%. If O-type stars are mainly forged in\nstar clusters, then such engines were the key ionizing agents during\nreionization and the increasing occurrence of high EW lines (Hb+[OIII])\nobserved at z>6.5 might be an indirect signature of a high \\Gamma at\nreionization.Future facilities (like VLT/MAVIS or ELT), will probe bound\nclusters on moderately magnified (\\mu<5-10) galaxies across cosmic epochs up to\nreionization[ABRIDGED]\n"}
+{"abstract": "  The reduction and control of particulate matter generated by fossil fuel\ncombustion are among the main issues for actual and future combustion devices\ndue to the increasingly stringent emission regulations. Recently, various fuels\nhave been investigated as a potential substitute or additive for diesel and\ngasoline. This work focuses on how oxymethylene ether-3 (OME3), the smallest\npromising OME compound, affects carbon particulate formation when blended with\nethylene in burner-stabilized premixed flames at different equivalence ratios.\nParticle size distribution (PSD) and Laser Induced Fluorescence (LIF) and\nIncandescence (LII) along with numerical (Conditional Quadrature Method of\nMoments - CQMOM, based on D'Anna physico-chemical soot model) investigations\nwere conducted to study particle formation and growth in pure ethylene and\nethylene/OME3 flames. The soot volume fraction and PSD indicate a reduction in\nthe total number and the size of the soot particles at all equivalence ratios,\nwhile the number of small nanoparticles remains almost unchanged. The CQMOM\nmodel is able to predict similar trends for the soot volume fraction and, using\nthe entropy maximization concept, the general shape of the PSD for both pure\nethylene and OME3-blended flames, compared to the experimental measurements.\nFurther, carbon particulate matter was thermophoretically sampled in the\nhighest equivalence ratio conditions and spectroscopically analyzed. The soot\nstructure was investigated using UV-Visible and Raman spectroscopy, finding a\nslightly higher aromaticity for the pure ethylene soot. FTIR analysis showed\nthat carbon particulate matter produced from an OME3-doped flame contained\nlarger amounts of oxygen, mainly in the form of C=O.\n"}
+{"abstract": "  Recent studies on lattice QCD have shown the emergence of large symmetries at\nhigh temperature. This includes not only the restoration $SU(n_F)_L \\times\nSU(n_F)_R$, but also the effective emergence of an unexpected symmetry group,\nnamely $SU(2)_{CS}$, which contains $U(1)_A$ as subgroup. At the same time, at\nhigh $T$, a gap in Dirac spectrum appears. As it is argued in several works of\n\\textit{L. Glozman et al.}, there should be a connection between a gap in the\nDirac spectrum and the presence of $SU(2)_{CS}$.In this paper, we analyze\nwhether the quark propagator can be invariant under $SU(n_F)_L \\times\nSU(n_F)_R$ and $SU(2)_{CS}$ transformations, in case of a gap in the Dirac\nspectrum, and consequently the invariance of hadron correlators, giving the\ncondition for a quark propagator to be invariant under $SU(2)_{CS}$.\n"}
+{"abstract": "  Although deep learning (DL) has received much attention in accelerated MRI,\nrecent studies suggest small perturbations may lead to instabilities in\nDL-based reconstructions, leading to concern for their clinical application.\nHowever, these works focus on single-coil acquisitions, which is not practical.\nWe investigate instabilities caused by small adversarial attacks for multi-coil\nacquisitions. Our results suggest that, parallel imaging and multi-coil CS\nexhibit considerable instabilities against small adversarial perturbations.\n"}
+{"abstract": "  The paper presents an innovative method for tourist route planning inside a\ndestination. The necessity of reorganizing the tourist routes within a\ndestination comes as an immediate response to the Covid-19 crisis. The\nimplementation of the method inside tourist destinations can be an important\nadvantage in transforming a destination into a safer destination in times of\nCovid-19 and post-Covid-19. The existing trend of shortening the tourist stay\nlength has been accelerated while the epidemic became a pandemic. Moreover, the\nwariness for future pandemics has brought to the spotlight the issue of\novercrowded attractions inside a destination at certain moments. The method\nproposed in this paper proposes a backtracking algorithm, more precisely an\nadaptation of the travelling salesman problem. The method presented aims to\nfacilitate the navigation inside a destination and to revive certain\nless-visited sightseeing spots inside a destination while facilitating the\nsocial distancing measures imposed by Covid-19.\n"}
+{"abstract": "  Knowledge distillation is a widely used paradigm for inheriting information\nfrom a complicated teacher network to a compact student network and maintaining\nthe strong performance. Different from image classification, object detectors\nare much more sophisticated with multiple loss functions in which features that\nsemantic information rely on are tangled. In this paper, we point out that the\ninformation of features derived from regions excluding objects are also\nessential for distilling the student detector, which is usually ignored in\nexisting approaches. In addition, we elucidate that features from different\nregions should be assigned with different importance during distillation. To\nthis end, we present a novel distillation algorithm via decoupled features\n(DeFeat) for learning a better student detector. Specifically, two levels of\ndecoupled features will be processed for embedding useful information into the\nstudent, i.e., decoupled features from neck and decoupled proposals from\nclassification head. Extensive experiments on various detectors with different\nbackbones show that the proposed DeFeat is able to surpass the state-of-the-art\ndistillation methods for object detection. For example, DeFeat improves\nResNet50 based Faster R-CNN from 37.4% to 40.9% mAP, and improves ResNet50\nbased RetinaNet from 36.5% to 39.7% mAP on COCO benchmark. Our implementation\nis available at https://github.com/ggjy/DeFeat.pytorch.\n"}
+{"abstract": "  It is well known that the mathematical structure underlying renormalization\nin perturbative quantum field theory is based on a Hopf algebra of Feynman\ndiagrams. A precondition for this is locality of the field theory.\nConsequently, one might suspect that non-local field theories such as matrix or\ntensor field theories cannot benefit from a similar algebraic understanding.\nHere I show that, on the contrary, the renormalization and perturbative\ndiagramatics of a broad class of such field theories is based in the same way\non a Hopf algebra. These theories are characterized by interaction vertices\nwith graphs as external structure leading to Feynman diagrams which can be\nsummed up under the concept of \"2-graphs\". From the renormalization\nperspective, such graph-like interactions are as much local as point-like\ninteractions. They differ in combinatorial details as I exemplify with the\ncentral identity for the perturbative series of combinatorial correlation\nfunctions. This sets the stage for a systematic study of perturbative\nrenormalization as well as non-perturbative aspects, e.g. Dyson-Schwinger\nequations, for a number of combinatorially non-local field theories with\npossible applications to quantum gravity, statistical models and more.\n"}
+{"abstract": "  Previous studies of the stellar mean metallicity and [Mg/Fe] values of\nmassive elliptical (E)~galaxies suggest that their stars were formed in a very\nshort timescale which cannot be reconciled with estimates from stellar\npopulation synthesis (SPS) studies and with hierarchical-assembly. Applying the\npreviously developed chemical evolution code, GalIMF, which allows an\nenvironment-dependent stellar initial mass function (IMF) to be applied in the\nintegrated galaxy initial mass function (IGIMF) theory instead of an invariant\ncanonical IMF, the star formation timescales (SFT) of E galaxies are\nre-evaluated. The code's uniqueness lies in it allowing the galaxy-wide IMF and\nassociated chemical enrichment to evolve as the physical conditions in the\ngalaxy change. The calculated SFTs become consistent with the independent SPS\nresults if the number of type Ia supernovae (SNIa) per unit stellar mass\nincreases for more massive E~galaxies. This is a natural outcome of galaxies\nwith higher star-formation rates producing more massive star clusters, spawning\na larger number of SNIa progenitors per star. The calculations show E~galaxies\nwith a stellar mass $\\approx 10^{9.5} M_\\odot$ to have had the longest mean\nSFTs of $\\approx2\\,$Gyr. The bulk of more massive E~galaxies were formed faster\n(SFT$\\,\\approx 1\\,$Gyr) leading to domination by M~dwarf stars and larger\ndynamical mass-to-light ratios as observed, while lower-mass galaxies tend to\nlose their gas supply more easily due to their shallower potential and\ntherefore also have similarly-short mean SFTs. This work achieves, for the\nfirst time, consistency of the SFTs for early-type galaxies between\nchemical-enrichment and SPS modelling and leads to an improved understanding of\nhow the star formation environment may affect the total number of SNIa per unit\nstellar mass formed.\n"}
+{"abstract": "  Tree-based machine learning techniques, such as Decision Trees and Random\nForests, are top performers in several domains as they do well with limited\ntraining datasets and offer improved interpretability compared to Deep Neural\nNetworks (DNN). However, while easier to train, they are difficult to optimize\nfor fast inference without accuracy loss in von Neumann architectures due to\nnon-uniform memory access patterns. Recently, we proposed a novel analog, or\nmulti-bit, content addressable memory(CAM) for fast look-up table operations.\nHere, we propose a design utilizing this as a computational primitive for rapid\ntree-based inference. Large random forest models are mapped to arrays of analog\nCAMs coupled to traditional analog random access memory (RAM), and the unique\nfeatures of the analog CAM enable compression and high performance. An\noptimized architecture is compared with previously proposed tree-based model\naccelerators, showing improvements in energy to decision by orders of magnitude\nfor common image classification tasks. The results demonstrate the potential\nfor non-volatile analog CAM hardware in accelerating large tree-based machine\nlearning models.\n"}
+{"abstract": "  The principal aim of this paper is to employ Bessel-type operators in proving\nthe inequality \\begin{align*} \\int_0^\\pi dx \\, |f'(x)|^2 \\geq\n\\dfrac{1}{4}\\int_0^\\pi dx \\, \\dfrac{|f(x)|^2}{\\sin^2\n(x)}+\\dfrac{1}{4}\\int_0^\\pi dx \\, |f(x)|^2,\\quad f\\in H_0^1 ((0,\\pi)),\n\\end{align*} where both constants $1/4$ appearing in the above inequality are\noptimal. In addition, this inequality is strict in the sense that equality\nholds if and only if $f \\equiv 0$. This inequality is derived with the help of\nthe exactly solvable, strongly singular, Dirichlet-type Schr\\\"{o}dinger\noperator associated with the differential expression \\begin{align*}\n\\tau_s=-\\dfrac{d^2}{dx^2}+\\dfrac{s^2-(1/4)}{\\sin^2 (x)}, \\quad s \\in\n[0,\\infty), \\; x \\in (0,\\pi). \\end{align*}\n  The new inequality represents a refinement of Hardy's classical inequality\n\\begin{align*} \\int_0^\\pi dx \\, |f'(x)|^2 \\geq \\dfrac{1}{4}\\int_0^\\pi dx \\,\n\\dfrac{|f(x)|^2}{x^2}, \\quad f\\in H_0^1 ((0,\\pi)), \\end{align*} it also\nimproves upon one of its well-known extensions in the form \\begin{align*}\n\\int_0^\\pi dx \\, |f'(x)|^2 \\geq \\dfrac{1}{4}\\int_0^\\pi dx \\,\n\\dfrac{|f(x)|^2}{d_{(0,\\pi)}(x)^2}, \\quad f\\in H_0^1 ((0,\\pi)), \\end{align*}\nwhere $d_{(0,\\pi)}(x)$ represents the distance from $x \\in (0,\\pi)$ to the\nboundary $\\{0,\\pi\\}$ of $(0,\\pi)$.\n"}
+{"abstract": "  We address how to robustly interpret natural language refinements (or\ncritiques) in recommender systems. In particular, in human-human recommendation\nsettings people frequently use soft attributes to express preferences about\nitems, including concepts like the originality of a movie plot, the noisiness\nof a venue, or the complexity of a recipe. While binary tagging is extensively\nstudied in the context of recommender systems, soft attributes often involve\nsubjective and contextual aspects, which cannot be captured reliably in this\nway, nor be represented as objective binary truth in a knowledge base. This\nalso adds important considerations when measuring soft attribute ranking. We\npropose a more natural representation as personalized relative statements,\nrather than as absolute item properties. We present novel data collection\ntechniques and evaluation approaches, and a new public dataset. We also propose\na set of scoring approaches, from unsupervised to weakly supervised to fully\nsupervised, as a step towards interpreting and acting upon soft attribute based\ncritiques.\n"}
+{"abstract": "  We establish the conjecture of Reiner and Yong for an explicit combinatorial\nformula for the expansion of a Grothendieck polynomial into the basis of\nLascoux polynomials. This expansion is a subtle refinement of its symmetric\nfunction version due to Buch, Kresch, Shimozono, Tamvakis, and Yong, which\ngives the expansion of stable Grothendieck polynomials indexed by permutations\ninto Grassmannian stable Grothendieck polynomials. Our expansion is the\nK-theoretic analogue of that of a Schubert polynomial into Demazure characters,\nwhose symmetric analogue is the expansion of a Stanley symmetric function into\nSchur functions. Our expansions extend to flagged Grothendieck polynomials.\n"}
+{"abstract": "  In the style of Lindstr\\\"om's theorem for classical first-order logic, this\narticle characterizes propositional bi-intuitionistic logic as the maximal\n(with respect to expressive power) abstract logic satisfying a certain form of\ncompactness, the Tarski union property and preservation under bi-asimulations.\nSince bi-intuitionistic logic introduces new complexities in the intuitionistic\nsetting by adding the analogue of a backwards looking modality, the present\npaper constitutes a non-trivial modification of previous work done by the\nauthors for intuitionistic logic in: G. Badia and G. Olkhovikov. A Lindstr\\\"om\ntheorem for intuitionistic propositional logic. Notre Dame Journal of Formal\nLogic, 61 (1): 11-30 (2020).\n"}
+{"abstract": "  We propose a new architecture to approximately learn incentive compatible,\nrevenue-maximizing auctions from sampled valuations. Our architecture uses the\nSinkhorn algorithm to perform a differentiable bipartite matching which allows\nthe network to learn strategyproof revenue-maximizing mechanisms in settings\nnot learnable by the previous RegretNet architecture. In particular, our\narchitecture is able to learn mechanisms in settings without free disposal\nwhere each bidder must be allocated exactly some number of items. In\nexperiments, we show our approach successfully recovers multiple known optimal\nmechanisms and high-revenue, low-regret mechanisms in larger settings where the\noptimal mechanism is unknown.\n"}
+{"abstract": "  Background. Many mutation reduction strategies, which aim to reduce the\nnumber of mutants, have been proposed. Problem. It is important to measure the\nability of a mutation reduction strategy to maintain test suite effectiveness\nevaluation. However, existing evaluation indicators are unable to measure the\n\"order-preserving ability\". Objective. We aim to propose evaluation indicators\nto measure the \"order-preserving ability\" of a mutation reduction strategy,\nwhich is important but missing in our community. Method. Given a test suite on\na Software Under Test (SUT) with a set of original mutants, we leverage the\ntest suite to generate a group of test suites that have a partial order\nrelationship in fault detecting potential. When evaluating a reduction\nstrategy, we first construct two partial order relationships among the\ngenerated test suites in terms of mutation score, one with the original mutants\nand another with the reduced mutants. Then, we measure the extent to which the\ntwo partial order relationships are consistent. The more consistent the two\npartial order relationships are, the stronger the Order Preservation (OP) of\nthe mutation reduction strategy is, and the more effective the reduction\nstrategy is. Furthermore, we propose Effort-aware Relative Order Preservation\n(EROP) to measure how much gain a mutation reduction strategy can provide\ncompared with a random reduction strategy. Result. The experimental results\nshow that OP and EROP are able to efficiently measure the \"order-preserving\nability\" of a mutation reduction strategy. As a result, they have a better\nability to distinguish various mutation reduction strategies compared with the\nexisting evaluation indicators. Conclusion. We suggest, for the researchers,\nthat OP and EROP should be used to measure the effectiveness of a mutant\nreduction strategy.\n"}
+{"abstract": "  Released pollutants and poisonous chemicals in the highly-populated urban\nareas can spread via wind flow and affect the health, safety, and wellbeing of\nthose close to the source and others downstream. Being able to predict the\ntransport of airborne contaminants in such urban areas could help stakeholders\nto plan emergency responses. Airflow due to the wind in urban areas with\nclosely packed buildings results in complicated aerodynamics around buildings\nthat, in turn, can lead to intricate contaminant transport phenomena. We\nconduct high-resolution large-eddy simulation (LES) of airflow in Southern\nManhattan, a highly-populated area in New York City, due to a dominant wind\nblowing from south to north. The LES results show complex flow dynamics around\nthe buildings of various heights and, consequently, complicated contaminant\ntransport processes from a source-point which is located in front of the New\nYork Stock Exchange building at 30 m above ground. The flow tends to align with\nthe streets that are fairly parallel to the wind direction creating\nhigh-velocity core regions, while the airflow loses its momentum markedly\nfarther downwind in the urban area. For as long as the source of contamination\nexists, the pollution plume rapidly travels through the streets reaching\ndownwind areas. When the source is removed, contaminants that are trapped in\nthe low-momentum urban area linger to exit the flow domain.\n"}
+{"abstract": "  The recent boom in computational chemistry has enabled several projects aimed\nat discovering useful materials or catalysts. We acknowledge and address two\nrecurring issues in the field of computational catalyst discovery. First,\ncalculating macro-scale catalyst properties is not straight-forward when using\nensembles of atomic-scale calculations (e.g., density functional theory). We\nattempt to address this issue by creating a multiscale model that estimates\nbulk catalyst activity using adsorption energy predictions from both density\nfunctional theory and machine learning models. The second issue is that many\ncatalyst discovery efforts seek to optimize catalyst properties, but\noptimization is an inherently exploitative objective that is in tension with\nthe explorative nature of early-stage discovery projects. In other words: why\ninvest so much time finding a \"best\" catalyst when it is likely to fail for\nsome other, unforeseen problem? We address this issue by relaxing the catalyst\ndiscovery goal into a classification problem: \"What is the set of catalysts\nthat is worth testing experimentally?\" Here we present a catalyst discovery\nmethod called myopic multiscale sampling, which combines multiscale modeling\nwith automated selection of density functional theory calculations. It is an\nactive classification strategy that seeks to classify catalysts as \"worth\ninvestigating\" or \"not worth investigating\" experimentally. Our results show a\n~7-16 times speedup in catalyst classification relative to random sampling.\nThese results were based on offline simulations of our algorithm on two\ndifferent datasets: a larger, synthesized dataset and a smaller, real dataset.\n"}
+{"abstract": "  Young neutron stars (NSs) have magnetic fields $B$ in the range\n$10^{12}-10^{15}$ G, believed to be generated by dynamo action at birth. We\nargue that such a dynamo is actually too inefficient to explain the strongest\nof these fields. Dynamo action in the mature star is also unlikely. Instead we\npropose a promising new precession-driven dynamo and examine its basic\nproperties, as well as arguing for a revised mean-field approach to NS dynamos.\nThe precession-driven dynamo could also play a role in field generation in\nmain-sequence stars.\n"}
+{"abstract": "  This paper develops a method of biologically guided deep learning for\npost-radiation FDG-PET image outcome prediction based on pre-radiation images\nand radiotherapy dose information. Based on the classic reaction-diffusion\nmechanism, a novel biological model was proposed using a partial differential\nequation that incorporates spatial radiation dose distribution as a\npatient-specific treatment information variable. A 7-layer\nencoder-decoder-based convolutional neural network (CNN) was designed and\ntrained to learn the proposed biological model. As such, the model could\ngenerate post-radiation FDG-PET image outcome predictions with possible\ntime-series transition from pre-radiotherapy image states to post-radiotherapy\nstates. The proposed method was developed using 64 oropharyngeal patients with\npaired FDG-PET studies before and after 20Gy delivery (2Gy/daily fraction) by\nIMRT. In a two-branch deep learning execution, the proposed CNN learns specific\nterms in the biological model from paired FDG-PET images and spatial dose\ndistribution as in one branch, and the biological model generates post-20Gy\nFDG-PET image prediction in the other branch. The proposed method successfully\ngenerated post-20Gy FDG-PET image outcome prediction with breakdown\nillustrations of biological model components. Time-series FDG-PET image\npredictions were generated to demonstrate the feasibility of disease response\nrendering. The developed biologically guided deep learning method achieved\npost-20Gy FDG-PET image outcome predictions in good agreement with ground-truth\nresults. With break-down biological modeling components, the outcome image\npredictions could be used in adaptive radiotherapy decision-making to optimize\npersonalized plans for the best outcome in the future.\n"}
+{"abstract": "  We develop a thermal description for photon modes within the context of\nbouncing universe. Within this study, we start with a Lorentz-breaking\ndispersion relation which accounts for modified Friedmann equations with a\nbounce solution. We calculate the spectral radiance, the entropy, the Helmholtz\nfree energy, the heat capacity, and the mean energy. Nevertheless, the latter\ntwo ones turn out to contribute only in a trivial manner. Furthermore, one\nintriguing aspect gives rise to: for some configurations of $\\eta$, $l_{P}$,\nand $T$, the thermodynamical behavior of the system seems to indicate\ninstability. However, despite of showing this particularity, the solutions of\nthe thermodynamic functions in general turn out to agree with the literature,\ne.g., the second law of thermodynamics is maintained. More so, all the results\nare derived \\textit{analytically} and three different regimes of temperature of\nthe universe are also considered, i.e., the inflationary era, the electroweak\nepoch and the cosmic microwave background.\n"}
+{"abstract": "  The success of state-of-the-art machine learning is essentially all based on\ndifferent variations of gradient descent algorithms that minimize some version\nof a cost or loss function. A fundamental limitation, however, is the need to\ntrain these systems in either supervised or unsupervised ways by exposing them\nto typically large numbers of training examples. Here, we introduce a\nfundamentally novel conceptual approach to machine learning that takes\nadvantage of a neurobiologically derived model of dynamic signaling,\nconstrained by the geometric structure of a network. We show that MNIST images\ncan be uniquely encoded and classified by the dynamics of geometric networks\nwith nearly state-of-the-art accuracy in an unsupervised way, and without the\nneed for any training.\n"}
+{"abstract": "  We study the convergence rate of Bregman gradient methods for convex\noptimization in the space of measures on a d-dimensional manifold. Under basic\nregularity assumptions, we show that the suboptimality gap at iteration k is in\nO(log(k)k^{--1}) for multiplicative updates, while it is in O(k^{--q/(d+q)})\nfor additive updates for some q = {1, 2, 4} determined by the structure of the\nobjective function. Our flexible proof strategy, based on approximation\narguments, allows to painlessly cover all Bregman Proximal Gradient Methods\n(PGM) and their acceleration (APGM) under various geometries such as the\nhyperbolic entropy and L^p divergences. We also prove the tightness of our\nanalysis with matching lower bounds and confirm the theoretical results with\nnumerical experiments on low dimensional problems. Note that all these\noptimization methods must additionally pay the computational cost of\ndiscretization, which can be exponential in d.\n"}
+{"abstract": "  We present a Mathematica program package MagneticTB, which can generate the\ntight-binding model for arbitrary magnetic space group. The only input\nparameters in MagneticTB are the (magnetic) space group number and the orbital\ninformation in each Wyckoff positions. Some useful functions including getting\nthe matrix expression for symmetry operators, manipulating the energy band\nstructure by parameters and interfacing with other software are also developed.\nMagneticTB can help to investigate the physical properties in both magnetic and\nnon-magnetic system, especially for topological properties.\n"}
+{"abstract": "  Abuse on the Internet is an important societal problem of our time. Millions\nof Internet users face harassment, racism, personal attacks, and other types of\nabuse across various platforms. The psychological effects of abuse on\nindividuals can be profound and lasting. Consequently, over the past few years,\nthere has been a substantial research effort towards automated abusive language\ndetection in the field of NLP. In this position paper, we discuss the role that\nmodeling of users and online communities plays in abuse detection.\nSpecifically, we review and analyze the state of the art methods that leverage\nuser or community information to enhance the understanding and detection of\nabusive language. We then explore the ethical challenges of incorporating user\nand community information, laying out considerations to guide future research.\nFinally, we address the topic of explainability in abusive language detection,\nproposing properties that an explainable method should aim to exhibit. We\ndescribe how user and community information can facilitate the realization of\nthese properties and discuss the effective operationalization of explainability\nin view of the properties.\n"}
+{"abstract": "  We propose easily verifiable necessary and sufficient conditions for the\nlinearizability of two-input systems by an endogenous dynamic feedback with a\ndimension of at most two.\n"}
+{"abstract": "  We analyze the $L^1$-mixing of a generalization of the Averaging process\nintroduced by Aldous. The process takes place on a growing sequence of graphs\nwhich we assume to be finite-dimensional, in the sense that the random walk on\nthose geometries satisfies a family of Nash inequalities. As a byproduct of our\nanalysis, we provide a complete picture of the total variation mixing of a\ndiscrete dual of the Averaging process, which we call Binomial Splitting\nprocess. A single particle of this process is essentially the random walk on\nthe underlying graph. When several particles evolve together, they interact by\nsynchronizing their jumps when placed on neighboring sites. We show that, given\n$k$ the number of particles and $n$ the (growing) size of the underlying graph,\nthe system exhibits cutoff in total variation if $k\\to\\infty$ and $k=O(n^2)$.\nFinally, we exploit the duality between the two processes to show that the\nBinomial Splitting satisfies a version of Aldous' spectral gap identity,\nnamely, the relaxation time of the process is independent of the number of\nparticles.\n"}
+{"abstract": "  Nous montrons la propri\\'et\\'e du trou spectral pour la famille des graphes\nde Cayley obtenus par r\\'eduction modulo $q$ d'un sous-groupe de\n$\\mathrm{SL}_d(\\mathbb{Z})$ dont l'adh\\'erence de Zariski est un\n$\\mathbb{Q}$-groupe simple.\n  --\n  We show a spectral gap property for the family of Cayley graphs obtained by\nreduction modulo $q$ of a subgroup of $\\mathrm{SL}_d(\\mathbb{Z})$ whose Zariski\nclosure is a simple $\\mathbb{Q}$-group.\n"}
+{"abstract": "  Diffusion is a fundamental graph procedure and has been a basic building\nblock in a wide range of theoretical and empirical applications such as graph\npartitioning and semi-supervised learning on graphs. In this paper, we study\ncomputationally efficient diffusion primitives beyond random walk.\n  We design an $\\widetilde{O}(m)$-time randomized algorithm for the\n$\\ell_2$-norm flow diffusion problem, a recently proposed diffusion model based\non network flow with demonstrated graph clustering related applications both in\ntheory and in practice. Examples include finding locally-biased low conductance\ncuts. Using a known connection between the optimal dual solution of the flow\ndiffusion problem and the local cut structure, our algorithm gives an\nalternative approach for finding such cuts in nearly linear time.\n  From a technical point of view, our algorithm contributes a novel way of\ndealing with inequality constraints in graph optimization problems. It adapts\nthe high-level algorithmic framework of nearly linear time Laplacian system\nsolvers, but requires several new tools: vertex elimination under constraints,\na new family of graph ultra-sparsifiers, and accelerated proximal gradient\nmethods with inexact proximal mapping computation.\n"}
+{"abstract": "  Information verification is quite a challenging task, this is because many\ntimes verifying a claim can require picking pieces of information from multiple\npieces of evidence which can have a hierarchy of complex semantic relations.\nPreviously a lot of researchers have mainly focused on simply concatenating\nmultiple evidence sentences to accept or reject claims. These approaches are\nlimited as evidence can contain hierarchical information and dependencies. In\nthis research, we aim to verify facts based on evidence selected from a list of\narticles taken from Wikipedia. Pretrained language models such as XLNET are\nused to generate meaningful representations and graph-based attention and\nconvolutions are used in such a way that the system requires little additional\ntraining to learn to verify facts.\n"}
+{"abstract": "  We have performed a systematical analysis of lepton and quark masses models\nbased on $\\Gamma_4\\cong S_4$ modular symmetry with gCP symmetry. We have\nconsidered both cases that neutrinos are Majorana particles and Dirac\nparticles. All possible nontrivial representation assignments of matter fields\nare considered, and the most general form of fermion mass matrices are given.\nThe phenomenologically viable models with the lowest number of free parameters\ntogether with the results of fit are presented. We find out nine lepton models\nwith seven real free parameters including the real and imaginary parts of\nmodulus for Majorana neutrinos, which can accommodate the lepton masses and\nneutrino oscillation data. The prediction for leptogenesis is studied in an\nexample lepton model. The observed baryon asymmetry as well as lepton masses\nand mixing angles can be explained. For Dirac neutrinos, four lepton models\nwith five real free couplings are compatible with experimental data. Ten quark\nmodels containing seven couplings are found to be able to accommodate the\nhierarchical quark masses and mixing angles and CP violation phase.\nFurthermore, the $S_4$ modular symmetry can provide a unified description of\nlepton and quark flavor structure, and a benchmark model is presented.\n"}
+{"abstract": "  MicroRNAs (miRNAs) are endogenous small non-coding RNAs that play an\nimportant role in post-transcriptional gene regulation. However, the\nexperimental determination of miRNA sequence and structure is both expensive\nand time-consuming. Therefore, computational and machine learning-based\napproaches have been adopted to predict novel microRNAs. With the involvement\nof data science and machine learning in biology, multiple research studies have\nbeen conducted to find microRNAs with different computational methods and\ndifferent miRNA features. Multiple approaches are discussed in detail\nconsidering the learning algorithm/s used, features considered, dataset/s used\nand the criteria used in evaluations. This systematic review focuses on the\nmachine learning methods developed for miRNA identification in plants. This\nwill help researchers to gain a detailed idea about past studies and identify\nnovel paths that solve drawbacks occurred in past studies. Our findings\nhighlight the need for plant-specific computational methods for miRNA\nidentification.\n"}
+{"abstract": "  Various measures have been taken in different countries to mitigate the\nCovid-19 epidemic. But, throughout the world, many citizens don't understand\nwell how these measures are taken and even question the decisions taken by\ntheir government. Should the measures be more (or less) restrictive? Are they\ntaken for a too long (or too short) period of time? To provide some\nquantitative elements of response to these questions, we consider the\nwell-known SEIR model for the Covid-19 epidemic propagation and propose a\npragmatic model of the government decision-making operation. Although simple\nand obviously improvable, the proposed model allows us to study the tradeoff\nbetween health and economic aspects in a pragmatic and insightful way. Assuming\na given number of phases for the epidemic and a desired tradeoff between health\nand economic aspects, it is then possible to determine the optimal duration of\neach phase and the optimal severity level for each of them. The numerical\nanalysis is performed for the case of France but the adopted approach can be\napplied to any country. One of the takeaway messages of this analysis is that\nbeing able to implement the optimal 4-phase epidemic management strategy in\nFrance would have led to 1.05 million infected people and a GDP loss of 231\nbillion euro instead of 6.88 million of infected and a loss of 241 billion\neuro. This indicates that, seen from the proposed model perspective, the\neffectively implemented epidemic management strategy is good economically,\nwhereas substantial improvements might have been obtained in terms of health\nimpact. Our analysis indicates that the lockdown/severe phase should have been\nmore severe but shorter, and the adjustment phase occurred earlier. Due to the\nnatural tendency of people to deviate from the official rules, updating\nmeasures every month over the whole epidemic episode seems to be more\nappropriate.\n"}
+{"abstract": "  A key challenge on the path to developing agents that learn complex\nhuman-like behavior is the need to quickly and accurately quantify\nhuman-likeness. While human assessments of such behavior can be highly\naccurate, speed and scalability are limited. We address these limitations\nthrough a novel automated Navigation Turing Test (ANTT) that learns to predict\nhuman judgments of human-likeness. We demonstrate the effectiveness of our\nautomated NTT on a navigation task in a complex 3D environment. We investigate\nsix classification models to shed light on the types of architectures best\nsuited to this task, and validate them against data collected through a human\nNTT. Our best models achieve high accuracy when distinguishing true human and\nagent behavior. At the same time, we show that predicting finer-grained human\nassessment of agents' progress towards human-like behavior remains unsolved.\nOur work takes an important step towards agents that more effectively learn\ncomplex human-like behavior.\n"}
+{"abstract": "  3D point-cloud-based perception is a challenging but crucial computer vision\ntask. A point-cloud consists of a sparse, unstructured, and unordered set of\npoints. To understand a point-cloud, previous point-based methods, such as\nPointNet++, extract visual features through hierarchically aggregation of local\nfeatures. However, such methods have several critical limitations: 1) Such\nmethods require several sampling and grouping operations, which slow down the\ninference speed. 2) Such methods spend an equal amount of computation on each\npoints in a point-cloud, though many of points are redundant. 3) Such methods\naggregate local features together through downsampling, which leads to\ninformation loss and hurts the perception performance. To overcome these\nchallenges, we propose a novel, simple, and elegant deep learning model called\nYOGO (You Only Group Once). Compared with previous methods, YOGO only needs to\nsample and group a point-cloud once, so it is very efficient. Instead of\noperating on points, YOGO operates on a small number of tokens, each of which\nsummarizes the point features in a sub-region. This allows us to avoid\ncomputing on the redundant points and thus boosts efficiency.Moreover, YOGO\npreserves point-wise features by projecting token features to point features\nalthough the computation is performed on tokens. This avoids information loss\nand can improve point-wise perception performance. We conduct thorough\nexperiments to demonstrate that YOGO achieves at least 3.0x speedup over\npoint-based baselines while delivering competitive classification and\nsegmentation performance on the ModelNet, ShapeNetParts and S3DIS datasets.\n"}
+{"abstract": "  Collaborative inference has recently emerged as an intriguing framework for\napplying deep learning to Internet of Things (IoT) applications, which works by\nsplitting a DNN model into two subpart models respectively on\nresource-constrained IoT devices and the cloud. Even though IoT applications'\nraw input data is not directly exposed to the cloud in such framework,\nrevealing the local-part model's intermediate output still entails privacy\nrisks. For mitigation of privacy risks, differential privacy could be adopted\nin principle. However, the practicality of differential privacy for\ncollaborative inference under various conditions remains unclear. For example,\nit is unclear how the calibration of the privacy budget epsilon will affect the\nprotection strength and model accuracy in presence of the state-of-the-art\nreconstruction attack targeting collaborative inference, and whether a good\nprivacy-utility balance exists. In this paper, we provide the first systematic\nstudy to assess the effectiveness of differential privacy for protecting\ncollaborative inference in presence of the reconstruction attack, through\nextensive empirical evaluations on various datasets. Our results show\ndifferential privacy can be used for collaborative inference when confronted\nwith the reconstruction attack, with insights provided about privacyutility\ntrade-offs. Specifically, across the evaluated datasets, we observe there\nexists a suitable privacy budget range (particularly 100<=epsilon<=200 in our\nevaluation) providing a good tradeoff between utility and privacy protection.\nOur key observation drawn from our study is that differential privacy tends to\nperform better in collaborative inference for datasets with smaller intraclass\nvariations, which, to our knowledge, is the first easy-toadopt practical\nguideline.\n"}
+{"abstract": "  White dwarfs which exhibit transit signatures of planetary debris and\naccreted planetary material provide exceptional opportunities to probe the\nmaterial composition and dynamical structure of planetary systems. Although\nprevious theoretical work investigating the role of minor body disruption\naround white dwarfs has focussed on spherical bodies, Solar System asteroids\ncan be more accurately modelled as triaxial ellipsoids. Here we present an\nanalytical framework to identify the type of disruption (tidal fragmentation,\ntotal sublimation or direct impact) experienced by triaxial asteroids\napproaching white dwarfs on extremely eccentric (e \\sim 1) orbits. This\nframework is then used to identify the outcomes for simplified Main belt\nanalogues of 100 bodies across five different white dwarf temperatures. We also\npresent an empirical relationship between cooling age and effective temperature\nfor both DA and DB white dwarfs to identify the age of the white dwarfs\nconsidered here. We find that using a purely spherical shape model can\nunderestimate the physical size and radial distance at which an asteroid is\nsubjected to complete sublimation, and these differences increase with greater\nelongation of the body. Contrastingly, fragmentation always occurs in the\nlargest semi-axis of a body and so can be modelled by a sphere of that radius.\nBoth fragmentation and sublimation are greatly affected by the body's material\ncomposition, and hence by the composition of their progenitor asteroid belts.\nThe white dwarf temperature, and hence cooling age, can affect the expected\ndebris distribution: higher temperatures sublimate large elongated asteroids,\nand cooler temperatures accommodate more direct impacts.\n"}
+{"abstract": "  Multilayer perceptrons (MLPs) have been successfully used to represent 3D\nshapes implicitly and compactly, by mapping 3D coordinates to the corresponding\nsigned distance values or occupancy values. In this paper, we propose a novel\npositional encoding scheme, called Spline Positional Encoding, to map the input\ncoordinates to a high dimensional space before passing them to MLPs, for\nhelping to recover 3D signed distance fields with fine-scale geometric details\nfrom unorganized 3D point clouds. We verified the superiority of our approach\nover other positional encoding schemes on tasks of 3D shape reconstruction from\ninput point clouds and shape space learning. The efficacy of our approach\nextended to image reconstruction is also demonstrated and evaluated.\n"}
+{"abstract": "  Seeking the equivalent entities among multi-source Knowledge Graphs (KGs) is\nthe pivotal step to KGs integration, also known as \\emph{entity alignment}\n(EA). However, most existing EA methods are inefficient and poor in\nscalability. A recent summary points out that some of them even require several\ndays to deal with a dataset containing 200,000 nodes (DWY100K). We believe\nover-complex graph encoder and inefficient negative sampling strategy are the\ntwo main reasons. In this paper, we propose a novel KG encoder -- Dual\nAttention Matching Network (Dual-AMN), which not only models both intra-graph\nand cross-graph information smartly, but also greatly reduces computational\ncomplexity. Furthermore, we propose the Normalized Hard Sample Mining Loss to\nsmoothly select hard negative samples with reduced loss shift. The experimental\nresults on widely used public datasets indicate that our method achieves both\nhigh accuracy and high efficiency. On DWY100K, the whole running process of our\nmethod could be finished in 1,100 seconds, at least 10* faster than previous\nwork. The performances of our method also outperform previous works across all\ndatasets, where Hits@1 and MRR have been improved from 6% to 13%.\n"}
+{"abstract": "  We consider the problem of quantifying uncertainty for the estimation error\nof the leading eigenvector from Oja's algorithm for streaming principal\ncomponent analysis, where the data are generated IID from some unknown\ndistribution. By combining classical tools from the U-statistics literature\nwith recent results on high-dimensional central limit theorems for quadratic\nforms of random vectors and concentration of matrix products, we establish a\n$\\chi^2$ approximation result for the $\\sin^2$ error between the population\neigenvector and the output of Oja's algorithm. Since estimating the covariance\nmatrix associated with the approximating distribution requires knowledge of\nunknown model parameters, we propose a multiplier bootstrap algorithm that may\nbe updated in an online manner. We establish conditions under which the\nbootstrap distribution is close to the corresponding sampling distribution with\nhigh probability, thereby establishing the bootstrap as a consistent\ninferential method in an appropriate asymptotic regime.\n"}
+{"abstract": "  We reconsider the issue of the search for a nonzero electric dipole form\nfactor (EDM) $d_\\tau(s)$ using optimal observables in $\\tau^+\\tau^-$ production\nby $e^+ e^-$ collisions in the center-of-mass energy range from the $\\tau$-pair\nthreshold to about $\\sqrt{s} \\sim 15$ GeV. We discuss the general formalism of\noptimal observables and apply it to two $CP$-odd observables that are sensitive\nto the real and imaginary part of $d_\\tau(s)$, respectively. We compute the\nexpectation values and covariances of these optimal $CP$ observables for\n  $\\tau$-pair production at $\\sqrt{s}=10.58$ GeV with subsequent decays of\n$\\tau^\\pm$ into major leptonic or semihadronic modes. For the $\\tau$ decays to\ntwo pions and three charged pions we take the full kinematic information of the\nhadronic system into account. Assuming that the Belle II experiment at the KEKB\naccelerator will eventually\n  analyze data corresponding to an integrated luminosity of 50 ab$^{-1}$ and\napplying acceptance cuts on the final-state pions we find that 1~s.d.\nsensitivities $\\delta {\\rm Re} d_\\tau = 6.8 \\times 10^{-20}$ e cm and $\\delta\n{\\rm} Im d_\\tau = 4.0 \\times 10^{-20}$ e cm can be obtained with events where\nboth $\\tau$'s decay semihadronically.\n  In the ideal case where no cuts on the final-state particles are applied we\nfind with 50 ab$^{-1}$ at $\\sqrt{s}=10.58$ GeV corresponding to $4.5 \\times\n10^{10}$ $\\tau^+ \\tau^-$ events the 1 s.d. sensitivities $\\delta {\\rm Re}\nd_\\tau = 5.8 \\times 10^{-20}$ e cm and $\\delta {\\rm} Im d_\\tau = 3.2 \\times\n10^{-20}$ e cm, again for events where both $\\tau$ leptons decay\nsemihadronically. Furthermore, we analyze the potential magnitude of the $\\tau$\nEDM form factor in the type-II two-Higgs doublet extension and in two scalar\nleptoquark extensions of the Standard Model.\n"}
+{"abstract": "  Motivated by the success of Transformers in natural language processing (NLP)\ntasks, there emerge some attempts (e.g., ViT and DeiT) to apply Transformers to\nthe vision domain. However, pure Transformer architectures often require a\nlarge amount of training data or extra supervision to obtain comparable\nperformance with convolutional neural networks (CNNs). To overcome these\nlimitations, we analyze the potential drawbacks when directly borrowing\nTransformer architectures from NLP. Then we propose a new\n\\textbf{Convolution-enhanced image Transformer (CeiT)} which combines the\nadvantages of CNNs in extracting low-level features, strengthening locality,\nand the advantages of Transformers in establishing long-range dependencies.\nThree modifications are made to the original Transformer: \\textbf{1)} instead\nof the straightforward tokenization from raw input images, we design an\n\\textbf{Image-to-Tokens (I2T)} module that extracts patches from generated\nlow-level features; \\textbf{2)} the feed-froward network in each encoder block\nis replaced with a \\textbf{Locally-enhanced Feed-Forward (LeFF)} layer that\npromotes the correlation among neighboring tokens in the spatial dimension;\n\\textbf{3)} a \\textbf{Layer-wise Class token Attention (LCA)} is attached at\nthe top of the Transformer that utilizes the multi-level representations.\n  Experimental results on ImageNet and seven downstream tasks show the\neffectiveness and generalization ability of CeiT compared with previous\nTransformers and state-of-the-art CNNs, without requiring a large amount of\ntraining data and extra CNN teachers. Besides, CeiT models also demonstrate\nbetter convergence with $3\\times$ fewer training iterations, which can reduce\nthe training cost significantly\\footnote{Code and models will be released upon\nacceptance.}.\n"}
+{"abstract": "  Simulating quantum many-body systems is a highly demanding task since the\nrequired resources grow exponentially with the dimension of the system. In the\ncase of fermionic systems, this is even harder since nonlocal interactions\nemerge due to the antisymmetric character of the fermionic wave function. Here,\nwe introduce a digital-analog quantum algorithm to simulate a wide class of\nfermionic Hamiltonians including the paradigmatic Fermi-Hubbard model. These\ndigital-analog methods allow quantum algorithms to run beyond digital versions\nvia an efficient use of coherence time. Furthermore, we exemplify our\ntechniques with a low-connected architecture for realistic digital-analog\nimplementations of specific fermionic models.\n"}
+{"abstract": "  An important characteristic of the Dilaton cosmological model is the\nGasperini-Veneziano duality transformation which follows from the existence of\nthe $O\\left( d,d\\right) $ symmetry. In this study, we consider the equivalent\nDilaton theory in teleparallel dark energy with the $O\\left( d,d\\right) $\nsymmetry, while the equivalent teleparallel-duality transformation is\npresented. The classical solution of the field equations is derived. Finally\nthe Wheeler-DeWitt equation of quantum cosmology is discussed.\n"}
+{"abstract": "  Gender disparity in science is one of the most focused debating points among\nauthorities and the scientific community. Over the last few decades, numerous\ninitiatives have endeavored to accelerate gender equity in academia and\nresearch society. However, despite the ongoing efforts, gaps persist across the\nworld, and more measures need to be taken. Using social network analysis,\nnatural language processing, and machine learning, in this study, we\ncomprehensively analyzed gender-specific patterns in the highly\ninterdisciplinary and evolving field of artificial intelligence for the period\nof 2000-2019. Our findings suggest an overall increasing rate of mixed-gender\ncollaborations. From the observed gender-specific collaborative patterns, the\nexistence of disciplinary homophily at both dyadic and team levels is\nconfirmed. However, a higher preference was observed for female researchers to\nform homophilous collaborative links. Our core-periphery analysis indicated a\nsignificant positive association between having diverse collaboration and\nscientific performance and experience. We found evidence in support of\nexpecting the rise of new female superstar researchers in the artificial\nintelligence field.\n"}
+{"abstract": "  Machine Learning (ML) has been embraced as a powerful tool by the financial\nindustry, with notable applications spreading in various domains including\ninvestment management. In this work, we propose a full-cycle data-driven\ninvestment robo-advising framework, consisting of two ML agents. The first\nagent, an inverse portfolio optimization agent, infers an investor's risk\npreference and expected return directly from historical allocation data using\nonline inverse optimization. The second agent, a deep reinforcement learning\n(RL) agent, aggregates the inferred sequence of expected returns to formulate a\nnew multi-period mean-variance portfolio optimization problem that can be\nsolved using deep RL approaches. The proposed investment pipeline is applied on\nreal market data from April 1, 2016 to February 1, 2021 and has shown to\nconsistently outperform the S&P 500 benchmark portfolio that represents the\naggregate market optimal allocation. The outperformance may be attributed to\nthe the multi-period planning (versus single-period planning) and the\ndata-driven RL approach (versus classical estimation approach).\n"}
+{"abstract": "  Recently, we presented a new Two-Bar Charts Packing Problem (2-BCPP), in\nwhich it is necessary to pack two-bar charts (2-BCs) in a unit-height strip of\nminimum length. The problem is a generalization of the Bin Packing Problem and\n2-D Vector Packing Problem. Earlier, we have proposed several polynomial\napproximation algorithms. In particular, when each 2-BC has at least one bar of\nheight more than 1/2, we have proposed a 3/2--approximation polynomial\nalgorithm. This paper proposes an $O(n^3)$--time 16/11--approximation algorithm\nfor packing 2-BCs when at least one bar of each BC has a height not less than\n1/2 and an $O(n^{2.5})$--time 5/4--approximation algorithm for packing\nnon-increasing or non-decreasing 2-BCs when each 2-BC has at least one bar\nwhich height is more than 1/2, where $n$ is the number of 2-BCs.\n"}
+{"abstract": "  A new regime of proton-driven plasma wakefield acceleration is discovered, in\nwhich the plasma nonlinearity increases the phase velocity of the excited wave\ncompared to that of the protons. If the beam charge is much larger than\nminimally necessary to excite a nonlinear wave, there is sufficient freedom in\nchoosing the longitudinal plasma density profile to make the wave speed close\nto the speed of light. This allows electrons or positrons to be accelerated to\nabout 200 GeV with a 400 GeV proton driver.\n"}
+{"abstract": "  We consider the chiral model of twisted bilayer graphene introduced by\nTarnopolsky-Kruchkov-Vishwanath (TKV). TKV have proved that for inverse twist\nangles $\\alpha$ such that the effective Fermi velocity at the moir\\'e $K$ point\nvanishes, the chiral model has a perfectly flat band at zero energy over the\nwhole Brillouin zone. By a formal expansion, TKV found that the Fermi velocity\nvanishes at $\\alpha \\approx .586$. In this work, we give a proof that the Fermi\nvelocity vanishes for at least one $\\alpha$ between $.57$ and $.61$ by\nrigorously justifying TKV's formal expansion of the Fermi velocity over a\nsufficiently large interval of $\\alpha$ values. The idea of the proof is to\nproject the TKV Hamiltonian onto a finite dimensional subspace, and then expand\nthe Fermi velocity in terms of explicitly computable linear combinations of\nmodes in the subspace, while controlling the error. The proof relies on two\npropositions whose proofs are computer-assisted, i.e., numerical computation\ntogether with worst-case estimates on the accumulation of round-off error which\nshow that round-off error cannot possibly change the conclusion of the\ncomputation. The propositions give a bound below on the spectral gap of the\nprojected Hamiltonian, an Hermitian $80 \\times 80$ matrix whose spectrum is\nsymmetric about $0,$ and verify that two real 18th order polynomials, which\napproximate the numerator of the Fermi velocity, take values with definite sign\nwhen evaluated at specific values of $\\alpha$. Together with TKV's work our\nresult proves existence of at least one perfectly flat band of the chiral\nmodel.\n"}
+{"abstract": "  Knowledge distillation (KD), transferring knowledge from a cumbersome teacher\nmodel to a lightweight student model, has been investigated to design efficient\nneural architectures. Generally, the objective function of KD is the\nKullback-Leibler (KL) divergence loss between the softened probability\ndistributions of the teacher model and the student model with the temperature\nscaling hyperparameter tau. Despite its widespread use, few studies have\ndiscussed the influence of such softening on generalization. Here, we\ntheoretically show that the KL divergence loss focuses on the logit matching\nwhen tau increases and the label matching when tau goes to 0 and empirically\nshow that the logit matching is positively correlated to performance\nimprovement in general. From this observation, we consider an intuitive KD loss\nfunction, the mean squared error (MSE) between the logit vectors, so that the\nstudent model can directly learn the logit of the teacher model. The MSE loss\noutperforms the KL divergence loss, explained by the difference in the\npenultimate layer representations between the two losses. Furthermore, we show\nthat sequential distillation can improve performance and that KD, particularly\nwhen using the KL divergence loss with small tau, mitigates the label noise.\nThe code to reproduce the experiments is publicly available online at\nhttps://github.com/jhoon-oh/kd_data/.\n"}
+{"abstract": "  We study the stochastic viscous nonlinear wave equations (SvNLW) on $\\mathbb\nT^2$, forced by a fractional derivative of the space-time white noise $\\xi$. In\nparticular, we consider SvNLW with the singular additive forcing\n$D^\\frac{1}{2}\\xi$ such that solutions are expected to be merely distributions.\nBy introducing an appropriate renormalization, we prove local well-posedness of\nSvNLW. By establishing an energy bound via a Yudovich-type argument, we also\nprove global well-posedness of the defocusing cubic SvNLW. Lastly, in the\ndefocusing case, we prove almost sure global well-posedness of SvNLW with\nrespect to certain Gaussian random initial data.\n"}
+{"abstract": "  Tomography is a widely used tool for analyzing microstructures in three\ndimensions (3D). The analysis, however, faces difficulty because the\nconstituent materials produce similar grey-scale values. Sometimes, this\nprompts the image segmentation process to assign a pixel/voxel to the wrong\nphase (active material or pore). Consequently, errors are introduced in the\nmicrostructure characteristics calculation. In this work, we develop a\nfiltering algorithm called PerSplat based on topological persistence (a\ntechnique used in topological data analysis) to improve segmentation quality.\nOne problem faced when evaluating filtering algorithms is that real image data\nin general are not equipped with the `ground truth' for the microstructure\ncharacteristics. For this study, we construct synthetic images for which the\nground-truth values are known. On the synthetic images, we compare the pore\ntortuosity and Minkowski functionals (volume and surface area) computed with\nour PerSplat filter and other methods such as total variation (TV) and\nnon-local means (NL-means). Moreover, on a real 3D image, we visually compare\nthe segmentation results provided by our filter against TV and NL-means. The\nexperimental results indicate that PerSplat provides a significant improvement\nin segmentation quality.\n"}
+{"abstract": "  Personalized image aesthetic assessment (PIAA) has recently become a hot\ntopic due to its usefulness in a wide variety of applications such as\nphotography, film and television, e-commerce, fashion design and so on. This\ntask is more seriously affected by subjective factors and samples provided by\nusers. In order to acquire precise personalized aesthetic distribution by small\namount of samples, we propose a novel user-guided personalized image aesthetic\nassessment framework. This framework leverages user interactions to retouch and\nrank images for aesthetic assessment based on deep reinforcement learning\n(DRL), and generates personalized aesthetic distribution that is more in line\nwith the aesthetic preferences of different users. It mainly consists of two\nstages. In the first stage, personalized aesthetic ranking is generated by\ninteractive image enhancement and manual ranking, meanwhile two policy networks\nwill be trained. The images will be pushed to the user for manual retouching\nand simultaneously to the enhancement policy network. The enhancement network\nutilizes the manual retouching results as the optimization goals of DRL. After\nthat, the ranking process performs the similar operations like the retouching\nmentioned before. These two networks will be trained iteratively and\nalternatively to help to complete the final personalized aesthetic assessment\nautomatically. In the second stage, these modified images are labeled with\naesthetic attributes by one style-specific classifier, and then the\npersonalized aesthetic distribution is generated based on the multiple\naesthetic attributes of these images, which conforms to the aesthetic\npreference of users better.\n"}
+{"abstract": "  In this work, we propose a method to simultaneously perform (i) biometric\nrecognition (i.e., identify the individual), and (ii) device recognition,\n(i.e., identify the device) from a single biometric image, say, a face image,\nusing a one-shot schema. Such a joint recognition scheme can be useful in\ndevices such as smartphones for enhancing security as well as privacy. We\npropose to automatically learn a joint representation that encapsulates both\nbiometric-specific and sensor-specific features. We evaluate the proposed\napproach using iris, face and periocular images acquired using near-infrared\niris sensors and smartphone cameras. Experiments conducted using 14,451 images\nfrom 15 sensors resulted in a rank-1 identification accuracy of upto 99.81% and\na verification accuracy of upto 100% at a false match rate of 1%.\n"}
+{"abstract": "  In this paper, we establish a high-dimensional CLT for the sample mean of\n$p$-dimensional spatial data observed over irregularly spaced sampling sites in\n$\\mathbb{R}^d$, allowing the dimension $p$ to be much larger than the sample\nsize $n$. We adopt a stochastic sampling scheme that can generate irregularly\nspaced sampling sites in a flexible manner and include both pure increasing\ndomain and mixed increasing domain frameworks. To facilitate statistical\ninference, we develop the spatially dependent wild bootstrap (SDWB) and justify\nits asymptotic validity in high dimensions by deriving error bounds that hold\nalmost surely conditionally on the stochastic sampling sites. Our dependence\nconditions on the underlying random field cover a wide class of random fields\nsuch as Gaussian random fields and continuous autoregressive moving average\nrandom fields. Through numerical simulations and a real data analysis, we\ndemonstrate the usefulness of our bootstrap-based inference in several\napplications, including joint confidence interval construction for\nhigh-dimensional spatial data and change-point detection for spatio-temporal\ndata.\n"}
+{"abstract": "  Recently cycloarene has been experimentally obtained in a self-assembled\nstructure, forming graphene-like monoatomic layered systems. Here, we establish\nthe bandgap engineering/prediction in cycloarene assemblies within a\ncombination of density functional theory and tight-binding Hamiltonians. Our\nresults show a weak dependence of the gap with the assembly geometry,\ncontrasting a strong dependence with the inter-molecule bond density. We\nderived a effective model that allows the interpretation of the arising energy\ngap for general particle-hole symmetric molecular arranges based on\ninter-molecular bond strength.\n"}
+{"abstract": "  We present a paradigm for integrated photonic devices based upon broadband\nslab-confined collimated beams that are launched with half-Maxwell fisheye\nlenses. Although it is challenging to match to the low-index focus of the lens\nwhilst maintaining adequate field confinement for a well-defined point source,\nintegrated dielectric slot waveguides prove highly suitable, yielding\ncollimators of 90% efficiency and over one octave bandwidth. Terahertz\ntechnology will benefit from such broadband slab-confined beams to replace\nfree-space optics, toward compact, mass-producible systems that do not require\nmanual optical alignment. We present two prototype systems to demonstrate the\nversatility of this concept, namely a diagonally-set distributed Bragg\nreflector as frequency-division diplexer for terahertz communications, and an\nattenuated total reflection-based liquid sensor. Both are enabled by oblique\nin-slab reflections that are collected at a location other than the originating\nlens, which is not attainable using ordinary single-mode channel waveguides.\n"}
+{"abstract": "  The BaBar collaboration has measured the ratio\n$R_{\\tau\\mu}^{\\Upsilon(3S)}=\\mathcal{B}(\\Upsilon(3S)\\rightarrow\\tau^+\\tau^-)/\\mathcal{B}(\\Upsilon(3S)\\rightarrow\\mu^+\\mu^-)$\nwith a high level of precision. This measurement utilized a 28 fb$^{-1}$\ndataset collected at a center-of-mass energy of 10.355 GeV. The measured ratio,\n$R_{\\tau\\mu}^{\\Upsilon(3S)}$, is measured to be\n$R_{\\tau\\mu}^{\\Upsilon(3S)}=0.966\\pm0.008_{\\textrm{stat}}\\pm0.014_{\\textrm{syst}}$.\nThis value is within 2 standard deviations of the standard model prediction\n$R_{\\tau\\mu}^{\\Upsilon(3S)}$=0.9948. The new measurement is approximately a\nfactor of 6$\\times$ more precise than the only prior measurement. This\nincreased precision is in part due to a more complete analysis of the radiative\ntail in the $\\Upsilon(3S)$ decay, in addition to a significant increase in\nstatistics.\n"}
+{"abstract": "  The Milky Way's metal-poor stars are nearby ancient objects that are used to\nstudy early chemical evolution and the assembly and structure of the Milky Way.\nHere we present reliable metallicities of $\\sim280,000$ stars with $-3.75\n\\lesssim$ [Fe/H] $\\lesssim -0.75$ down to $g=17$ derived using\nmetallicity-sensitive photometry from the second data release (DR2) of the\nSkyMapper Southern Survey. We use the dependency of the flux through the\nSkyMapper $v$ filter on the strength of the Ca II K absorption features, in\ntandem with SkyMapper $u,g,i$ photometry, to derive photometric metallicities\nfor these stars. We find that metallicities derived in this way compare well to\nmetallicities derived in large-scale spectroscopic surveys, and use such\ncomparisons to calibrate and quantify systematics as a function of location,\nreddening, and color. We find good agreement with metallicities from the\nAPOGEE, LAMOST, and GALAH surveys, based on a standard deviation of\n$\\sigma\\sim0.25$dex of the residuals of our photometric metallicities with\nrespect to metallicities from those surveys. We also compare our derived\nphotometric metallicities to metallicities presented in a number of\nhigh-resolution spectroscopic studies to validate the low metallicity end\n([Fe/H] $< -2.5$) of our photometric metallicity determinations. In such\ncomparisons, we find the metallicities of stars with photometric [Fe/H] $<\n-2.5$ in our catalog show no significant offset and a scatter of\n$\\sigma\\sim$0.31dex level relative to those in high-resolution work when\nconsidering the cooler stars ($g-i > 0.65$) in our sample. We also present an\nexpanded catalog containing photometric metallicities of $\\sim720,000$ stars as\na data table for further exploration of the metal-poor Milky Way.\n"}
+{"abstract": "  This paper considers the problem of differentially private semi-supervised\ntransfer and multi-task learning. The notion of \\emph{membership-mapping} has\nbeen developed using measure theory basis to learn data representation via a\nfuzzy membership function. An alternative conception of deep autoencoder,\nreferred to as \\emph{Conditionally Deep Membership-Mapping Autoencoder\n(CDMMA)}, is considered for transferrable deep learning. Under\npractice-oriented settings, an analytical solution for the learning of CDMMA\ncan be derived by means of variational optimization. The paper proposes a\ntransfer and multi-task learning approach that combines CDMMA with a tailored\nnoise adding mechanism to achieve a given level of privacy-loss bound with the\nminimum perturbation of the data. Numerous experiments were carried out using\nMNIST, USPS, Office, and Caltech256 datasets to verify the competitive robust\nperformance of the proposed methodology.\n"}
+{"abstract": "  Positivity bounds - constraints on any low-energy effective field theory\nimposed by the fundamental axioms of unitarity, causality and locality in the\nUV - have recently been used to constrain scalar-tensor theories of dark\nenergy. However, the coupling to matter fields has so far played a limited\nrole. We show that demanding positivity when including interactions with\nstandard matter fields leads to further constraints on the dark energy\nparameter space. We demonstrate how implementing these bounds as theoretical\npriors affects cosmological parameter constraints and explicitly illustrate the\nimpact on a specific Effective Field Theory for dark energy. We also show in\nthis model that the existence of a standard UV completion requires that\ngravitational waves must travel superluminally on cosmological backgrounds.\n"}
+{"abstract": "  This paper theoretically characterizes the degrees-of-freedom (DoF) region of\norder-$(K-1)$ messages for the $K$-user multiple-input multiple-output (MIMO)\nbroadcast channel with delayed channel state information at the transmitter\n(CSIT) and arbitrary antenna configurations, where the transmitter has $M$\nantennas and the receiver $i=1,2,\\cdots,K$ has $N_i$ antennas. For the\nconverse, we first derive the DoF region of order-$(K-1)$ messages for the\n$K$-user MIMO broadcast channel with no CSIT and arbitrary antenna\nconfigurations with the aid of the proposed Genie-bound, and then establish the\nDoF outer region. For the achievability, we first design a 2-phase transmission\nscheme, and then propose a backward/forward cancellation algorithm for\ndecoding. Specifically, we efficiently derive the achievable DoF region from\nthe designed transmission scheme by transformation approach. The main\nimplication of this paper is that for the order-$(K-1)$ messages of $K$-user\nMIMO broadcast channel, the DoF region with delayed CSIT is larger than the DoF\nregion with no CSIT when $N_2<M$, where $N_1 \\le N_2 \\le \\cdots \\le N_K$.\n"}
+{"abstract": "  Ordinal pattern dependence is a multivariate dependence measure based on the\nco-movement of two time series. In strong connection to ordinal time series\nanalysis, the ordinal information is taken into account to derive robust\nresults on the dependence between the two processes. This article deals with\nordinal pattern dependence for long-range dependent time series including mixed\ncases of short- and long-range dependence. We investigate the limit\ndistributions for estimators of ordinal pattern dependence. In doing so we\npoint out the differences that arise for the underlying time series having\ndifferent dependence structures. Depending on these assumptions, central and\nnon-central limit theorems are proven. The limit distributions for the latter\nones can be included in the class of multivariate Rosenblatt processes.\nFinally, a simulation study is provided to illustrate our theoretical findings.\n"}
+{"abstract": "  Two-sided marketplaces are standard business models of many online platforms\n(e.g., Amazon, Facebook, LinkedIn), wherein the platforms have consumers,\nbuyers or content viewers on one side and producers, sellers or\ncontent-creators on the other. Consumer side measurement of the impact of a\ntreatment variant can be done via simple online A/B testing. Producer side\nmeasurement is more challenging because the producer experience depends on the\ntreatment assignment of the consumers. Existing approaches for producer side\nmeasurement are either based on graph cluster-based randomization or on certain\ntreatment propagation assumptions. The former approach results in low-powered\nexperiments as the producer-consumer network density increases and the latter\napproach lacks a strict notion of error control. In this paper, we propose (i)\na quantification of the quality of a producer side experiment design, and (ii)\na new experiment design mechanism that generates high-quality experiments based\non this quantification. Our approach, called UniCoRn (Unifying Counterfactual\nRankings), provides explicit control over the quality of the experiment and its\ncomputation cost. Further, we prove that our experiment design is optimal to\nthe proposed design quality measure. Our approach is agnostic to the density of\nthe producer-consumer network and does not rely on any treatment propagation\nassumption. Moreover, unlike the existing approaches, we do not need to know\nthe underlying network in advance, making this widely applicable to the\nindustrial setting where the underlying network is unknown and challenging to\npredict a priori due to its dynamic nature. We use simulations to validate our\napproach and compare it against existing methods. We also deployed UniCoRn in\nan edge recommendation application that serves tens of millions of members and\nbillions of edge recommendations daily.\n"}
+{"abstract": "  The recent low-rank prior based models solve the tensor completion problem\nefficiently. However, these models fail to exploit the local patterns of\ntensors, which compromises the performance of tensor completion. In this paper,\nwe propose a novel hierarchical prior regularized matrix factorization model\nfor tensor completion. This model hierarchically incorporates the low-rank\nprior, total variation prior, and sparse coding prior into a matrix\nfactorization, simultaneously characterizing both the global low-rank property\nand the local smoothness of tensors. For solving the proposed model, we use the\nalternating direction method of multipliers to establish our algorithm.\nBesides, the complexity and convergence are investigated to further validate\nthe algorithm effectiveness. The proposed scheme is then evaluated through\nvarious data sets. Experiment results verify that, the proposed method\noutperforms several state-of-the-art approaches.\n"}
+{"abstract": "  Protecting against multi-step attacks of uncertain duration and timing forces\ndefenders into an indefinite, always ongoing, resource-intensive response. To\neffectively allocate resources, a defender must be able to analyze multi-step\nattacks under assumption of constantly allocating resources against an\nuncertain stream of potentially undetected attacks. To achieve this goal, we\npresent a novel methodology that applies a game-theoretic approach to the\nattack, attacker, and defender data derived from MITRE's ATT&CK Framework. Time\nto complete attack steps is drawn from a probability distribution determined by\nattacker and defender strategies and capabilities. This constraints attack\nsuccess parameters and enables comparing different defender resource allocation\nstrategies. By approximating attacker-defender games as Markov processes, we\nrepresent the attacker-defender interaction, estimate the attack success\nparameters, determine the effects of attacker and defender strategies, and\nmaximize opportunities for defender strategy improvements against an uncertain\nstream of attacks. This novel representation and analysis of multi-step attacks\nenables defender policy optimization and resource allocation, which we\nillustrate using the data from MITRE's APT3 ATT&CK Evaluations.\n"}
+{"abstract": "  In this work we analyze strategies for convolutional neural network scaling;\nthat is, the process of scaling a base convolutional network to endow it with\ngreater computational complexity and consequently representational power.\nExample scaling strategies may include increasing model width, depth,\nresolution, etc. While various scaling strategies exist, their tradeoffs are\nnot fully understood. Existing analysis typically focuses on the interplay of\naccuracy and flops (floating point operations). Yet, as we demonstrate, various\nscaling strategies affect model parameters, activations, and consequently\nactual runtime quite differently. In our experiments we show the surprising\nresult that numerous scaling strategies yield networks with similar accuracy\nbut with widely varying properties. This leads us to propose a simple fast\ncompound scaling strategy that encourages primarily scaling model width, while\nscaling depth and resolution to a lesser extent. Unlike currently popular\nscaling strategies, which result in about $O(s)$ increase in model activation\nw.r.t. scaling flops by a factor of $s$, the proposed fast compound scaling\nresults in close to $O(\\sqrt{s})$ increase in activations, while achieving\nexcellent accuracy. This leads to comparable speedups on modern memory-limited\nhardware (e.g., GPU, TPU). More generally, we hope this work provides a\nframework for analyzing and selecting scaling strategies under various\ncomputational constraints.\n"}
+{"abstract": "  This paper formulates a framework for the analysis and distributed control of\ninterconnected systems from the behavioural perspective. The discussions are\ncarried out from the viewpoint of set theory and the results are completely\nrepresentation-free. The core of a dynamical system can be represented as the\nset of all trajectories admissible through the system and interconnections are\ninterpreted as constraints on the choice of trajectories. We develop a\nstructure in which the interconnected behaviour can be directly built from the\nbehaviours of the subsystems in an explicit way without any presumed forms of\nrepresentations. We show that the interconnected behaviour can also be fully\nobtained from local observations of the subsystem. Furthermore, we develop the\nnecessary and sufficient conditions for the existence of distributed controller\nbehaviours and their explicit construction. Due to the entirely\nrepresentation-free nature of this framework, it unites various representations\nand descriptions of features of dynamical systems (e.g. models, dissipativity,\ndata, etc.) as behaviours, allowing for the formation of a unified platform for\nthe analysis and distributed control for interconnected systems.\n"}
+{"abstract": "  Dark photons and mirror matter are well-motivated dark matter candidates. It\nis possible that both of them arose during the compactification and symmetry\nbreaking scenario of the heterotic $E_8\\times E_8$ string theory and are\nrelated to each other. In this case, dark photons can become a natural portal\ninto the mirror world. Unfortunately, the expected magnitude of the induced\ninteractions of ordinary matter with mirror matter is too small to be of\nphenomenological interest.\n"}
+{"abstract": "  In any conformally invariant gravitational theory, the space of exact\nsolutions is greatly enlarged. The Weyl's conformal invariance can then be\nspontaneously broken to spherically symmetric vacuum solutions that exclude the\nspacetime region inside the black hole's event horizon from our Universe. We\nbaptize these solutions ''conformalons''. It turns out that for all such\nspacetimes nothing can reach the Schwarzschild event horizon in a finite amount\nof proper time for conformally coupled ``massive'' particles, or finite values\nof the affine parameter for massless particles. Therefore, for such vacuum\nsolutions the event horizon is an asymptotic region of the Universe. As a\ngeneral feature, all conformalons show a gravitational blueshift instead of a\ngravitational redshift at the unattainable event horizon, hence avoiding the\nTrans-Planckian problem in the Hawking evaporation process. The latter happens\nas usual near the event horizon, but now the annihilation process between the\nmatter and Hawking's negative energy particles takes place outside of the event\nhorizon. Indeed, in these solutions the gravitational collapse consists of\nmatter that falls down forever towards the horizon without ever reaching it.\nAccording to a previous work that has been faithfully adapted to the\nconformalons' scenario, the information is not lost in the whole process of\nsingularity-free collapse and evaporation, as evident from the Penrose diagram.\n"}
+{"abstract": "  Dimensionality reduction (DR) of data is a crucial issue for many machine\nlearning tasks, such as pattern recognition and data classification. In this\npaper, we present a quantum algorithm and a quantum circuit to efficiently\nperform linear discriminant analysis (LDA) for dimensionality reduction.\nFirstly, the presented algorithm improves the existing quantum LDA algorithm to\navoid the error caused by the irreversibility of the between-class scatter\nmatrix $S_B$ in the original algorithm. Secondly, a quantum algorithm and\nquantum circuits are proposed to obtain the target state corresponding to the\nlow-dimensional data. Compared with the best-known classical algorithm, the\nquantum linear discriminant analysis dimensionality reduction (QLDADR)\nalgorithm has exponential acceleration on the number $M$ of vectors and a\nquadratic speedup on the dimensionality $D$ of the original data space, when\nthe original dataset is projected onto a polylogarithmic low-dimensional space.\nMoreover, the target state obtained by our algorithm can be used as a submodule\nof other quantum machine learning tasks. It has practical application value of\nmake that free from the disaster of dimensionality.\n"}
+{"abstract": "  We study the reciprocal variety to the linear space of symmetric matrices\n(LSSM) of catalecticant matrices associated with ternary quartics. With\nnumerical tools, we obtain 85 to be its degree and 36 to be the ML-degree of\nthe LSSM. We provide a geometric explanation to why equality between these two\ninvariants is not reached, as opposed to the case of binary forms, by\ndescribing the intersection of the reciprocal variety and the orthogonal of the\nLSSM in the rank loci. Moreover, we prove that only the rank-$1$ locus, namely\nthe Veronese surface $\\nu_4(\\mathbb{P}^2)$, contributes to the degree of the\nreciprocal variety.\n"}
+{"abstract": "  Gravitational lensing of gravitational waves (GWs) is a powerful probe of the\nmatter distribution in the universe. Here we study the lensing effect induced\nby dark matter (DM) halos on the GW signals from merging massive black holes,\nand we revisit the possibility of detection using the Laser Interferometer\nSpace Antenna (LISA). In particular, we include the halos in the low-mass range\nof $10^5-10^9\\, M_\\odot$ since they are the most numerous according to the cold\nDM model. In addition, we employ the matched-filtering technique to search for\nweak diffraction signatures when the MBHBs have large impact parameters\n($y\\sim10^2$). We find that about $(20-40)\\%$ of the MBHB in the mass range of\n$10^5-10^6M_\\odot$ and the redshift range of $4-10$ should show detectable\nwave-optics effects. The uncertainty comes mainly from the mass function of DM\nhalos. Not detecting any signal during the LISA mission would imply that DM\nhalos are significantly more massive than $10^8\\,M_\\odot$.\n"}
+{"abstract": "  Sliding is one of the fundamental mechanical movements in machinery. In\nmacroscopic systems, double-rack pinion machines employ gears to slide two\nlinear tracks along opposite directions. In microscopic systems, kinesin-5\nproteins crosslink and slide apart antiparallel microtubules, promoting spindle\nbipolarity and elongation during mitosis. Here we demonstrate an artificial\nnanoscopic analog, in which gold nanocrystals can mediate coordinated sliding\nof two antiparallel DNA origami filaments powered by DNA fuels. Stepwise and\nreversible sliding along opposite directions is in situ monitored and confirmed\nusing fluorescence spectroscopy. A theoretical model including different energy\ntransfer mechanisms is developed to understand the observed fluorescence\ndynamics. We further show that such sliding can also take place in the presence\nof multiple DNA sidelocks that are introduced to inhibit the relative\nmovements. Our work enriches the toolbox of DNA-based nanomachinery, taking one\nstep further toward the vision of molecular nanofactories.\n"}
+{"abstract": "  The advent of cost effective cloud computing over the past decade and\never-growing accumulation of high-fidelity clinical data in a modern hospital\nsetting is leading to new opportunities for translational medicine. Machine\nlearning is driving the appetite of the research community for various types of\nsignal data such as patient vitals. Health care systems, however, are ill\nsuited for massive processing of large volumes of data. In addition, due to the\nsheer magnitude of the data being collected, it is not feasible to retain all\nof the data in health care systems in perpetuity. This gold mine of information\ngets purged periodically thereby losing invaluable future research\nopportunities. We have developed a highly scalable solution that: a) siphons\noff patient vital data on a nightly basis from on-premises bio-medical systems\nto a cloud storage location as a permanent archive, b) reconstructs the\ndatabase in the cloud, c) generates waveforms, alarms and numeric data in a\nresearch-ready format, and d) uploads the processed data to a storage location\nin the cloud ready for research.\n  The data is de-identified and catalogued such that it can be joined with\nElectronic Medical Records (EMR) and other ancillary data types such as\nelectroencephalogram (EEG), radiology, video monitoring etc. This technique\neliminates the research burden from health care systems. This highly scalable\nsolution is used to process high density patient monitoring data aggregated by\nthe Philips Patient Information Center iX (PIC iX) hospital surveillance system\nfor archival storage in the Philips Data Warehouse Connect enterprise-level\ndatabase. The solution is part of a broader platform that supports a secure\nhigh performance clinical data science platform.\n"}
+{"abstract": "  Recently a new class of quantum algorithms that are based on the quantum\ncomputation of the connected moment expansion has been reported to find the\nground and excited state energies. In particular, the Peeters-Devreese-Soldatov\n(PDS) formulation is found variational and bearing the potential for further\ncombining with the existing variational quantum infrastructure. Here we find\nthat the PDS formulation can be considered as a new energy functional of which\nthe PDS energy gradient can be employed in a conventional variational quantum\nsolver. In comparison with the usual variational quantum eigensolver (VQE) and\nthe original static PDS approach, this new variational quantum solver offers an\neffective approach to navigate the dynamics to be free from getting trapped in\nthe local minima that refer to different states, and achieve high accuracy at\nfinding the ground state and its energy through the rotation of the trial wave\nfunction of modest quality, thus improves the accuracy and efficiency of the\nquantum simulation. We demonstrate the performance of the proposed variational\nquantum solver for toy models, H$_2$ molecule, and strongly correlated planar\nH$_4$ system in some challenging situations. In all the case studies, the\nproposed variational quantum approach outperforms the usual VQE and static PDS\ncalculations even at the lowest order. We also discuss the limitations of the\nproposed approach and its preliminary execution for model Hamiltonian on the\nNISQ device.\n"}
+{"abstract": "  We report on some findings concerning Connes' noncommutative distance $d$ on\na weighted undirected graph $G$. Our main result is the lower bound\n$\\ell/\\Delta(G)\\le d$ where $\\ell$ is the geodesic distance and $\\Delta(G)$ the\ndegree of $G$. It is obtained thanks to an auxiliary spectral triple on the\ncollection of the edges of $G$.\n"}
+{"abstract": "  The directions of an infinite graph $G$ are a tangle-like description of its\nends: they are choice functions that choose compatibly for all finite vertex\nsets $X\\subseteq V(G)$ a component of $G-X$. Although every direction is\ninduced by a ray, there exist directions of graphs that are not uniquely\ndetermined by any countable subset of their choices. We characterise these\ndirections and their countably determined counterparts in terms of star-like\nsubstructures or rays of the graph. Curiously, there exist graphs whose\ndirections are all countably determined but which cannot be distinguished all\nat once by countably many choices. We structurally characterise the graphs\nwhose directions can be distinguished all at once by countably many choices,\nand we structurally characterise the graphs which admit no such countably many\nchoices. Our characterisations are phrased in terms of normal trees and\ntree-decompositions. Our four (sub)structural characterisations imply\ncombinatorial characterisations of the four classes of infinite graphs that are\ndefined by the first and second axiom of countability applied to their end\nspaces: the two classes of graphs whose end spaces are first countable or\nsecond countable, respectively, and the complements of these two classes.\n"}
+{"abstract": "  A Bayesian functorial characterization of the classical relative entropy (KL\ndivergence) of finite probabilities was recently obtained by Baez and Fritz.\nThis was then generalized to standard Borel spaces by Gagn\\'e and Panangaden.\nHere, we provide preliminary calculations suggesting that the\nfinite-dimensional quantum (Umegaki) relative entropy might be characterized in\na similar way. Namely, we explicitly prove that it defines an affine functor in\nthe special case where the relative entropy is finite. A recent non-commutative\ndisintegration theorem provides a key ingredient in this proof.\n"}
+{"abstract": "  In this paper, we present a Neural Network (NN) model based on Neural\nArchitecture Search (NAS) and self-learning for received signal strength (RSS)\nmap reconstruction out of sparse single-snapshot input measurements, in the\ncase where data-augmentation by side deterministic simulations cannot be\nperformed. The approach first finds an optimal NN architecture and\nsimultaneously train the deduced model over some ground-truth measurements of a\ngiven (RSS) map. These ground-truth measurements along with the predictions of\nthe model over a set of randomly chosen points are then used to train a second\nNN model having the same architecture. Experimental results show that signal\npredictions of this second model outperforms non-learning based interpolation\nstate-of-the-art techniques and NN models with no architecture search on five\nlarge-scale maps of RSS measurements.\n"}
+{"abstract": "  Recent developments in neural networks have shown the potential of estimating\ndrag on irregular rough surfaces. Nevertheless, the difficulty of obtaining a\nlarge high-fidelity dataset to train neural networks is deterring their use in\npractical applications. In this study, we propose a transfer learning framework\nto model the drag on irregular rough surfaces even with a limited amount of\ndirect numerical simulations. We show that transfer learning of empirical\ncorrelations, reported in the literature, can significantly improve the\nperformance of neural networks for drag prediction. This is because empirical\ncorrelations include `approximate knowledge' of the drag dependency in\nhigh-fidelity physics. The `approximate knowledge' allows neural networks to\nlearn the surface statistics known to affect drag more efficiently. The\ndeveloped framework can be applied to applications where acquiring a large\ndataset is difficult, but empirical correlations have been reported.\n"}
+{"abstract": "  Importance of monsoon rainfall cannot be ignored as it affects round the year\nactivities ranging from agriculture to industrial. Accurate rainfall estimation\nand prediction is very helpful in decision making in the sectors of water\nresource management and agriculture. Due to dynamic nature of monsoon rainfall,\nit's accurate prediction becomes very challenging task. In this paper, we\nanalyze and evaluate various deep learning approaches such as one dimensional\nConvolutional Neutral Network, Multi-layer Perceptron and Wide Deep Neural\nNetworks for the prediction of summer monsoon rainfall in Indian state of\nRajasthan.For our analysis purpose we have used two different types of datasets\nfor our experiments. From IMD grided dataset, rainfall data of 484 coordinates\nare selected which lies within the geographical boundaries of Rajasthan. We\nhave also collected rainfall data of 158 rain gauge station from water\nresources department. The comparison of various algorithms on both these data\nsets is presented in this paper and it is found that Deep Wide Neural Network\nbased model outperforms the other two approaches.\n"}
+{"abstract": "  We study the influence of strong spin-orbit interaction on the formation of\nflat bands in relaxed twisted bilayer WSe$_2$. Flat bands, well separated in\nenergy, emerge at the band edges for twist angles ($\\theta$) close to 0$^o$ and\n60$^o$. For $\\theta$ close to 0$^o$, the interlayer hybridization together with\na moir\\'{e} potential determines the electronic structure. The bands near the\nvalence band edge have non-trivial topology, with Chern numbers equal to +1 or\n$-$1. We propose that this can be probed experimentally for twist angles less\nthan a critical angle of 3.5$^o$. For $\\theta$ near 60$^o$, the flattening of\nthe bands arising from the K point of the unit cell Brillouin zone is a result\nof atomic rearrangements in the individual layers. Our findings on the flat\nbands and the localization of their wavefunctions for both ranges of $\\theta$\nmatch well with recent experimental observations [1,2].\n"}
+{"abstract": "  We construct a nonseparable Banach space $\\mathcal X$ (actually, of density\ncontinuum) such that any uncountable subset $\\mathcal Y$ of the unit sphere of\n$\\mathcal X$ contains uncountably many points distant by less than $1$ (in\nfact, by less then $1-\\varepsilon$ for some $\\varepsilon>0$). This solves in\nthe negative the central problem of the search for a nonseparable version of\nKottman's theorem which so far has produced many deep positive results for\nspecial classes of Banach spaces and has related the global properties of the\nspaces to the distances between points of uncountable subsets of the unit\nsphere.\n  The property of our space is strong enough to imply that it contains neither\nan uncountable Auerbach system nor an uncountable equilateral set. The space is\na strictly convex renorming of the Johnson-Lindenstrauss space induced by an\n$\\mathbb R$-embeddable almost disjoint family of subsets of $\\mathbb N$. We\nalso show that this special feature of the almost disjoint family is essential\nto obtain the above properties.\n"}
+{"abstract": "  The existence of stationary distributions to distribution dependent\nstochastic differential equations are investigated by using the ergodicity of\nthe associated decoupled equation and the Schauder fixed point theorem. By\nusing Zvonkin's transformation, we also establish the existence result for\nequations with singular coefficients. Instead of the uniqueness, the\nnon-uniqueness of stationary distributions are considered for equations with\nregular coefficients. Concrete examples including McKean-Vlasov stochastic\nequations with the quadratic interaction and the non-quadratic interaction, and\nequations with a bounded and discontinuous drift are presented to illustrate\nour non-uniqueness results.\n"}
+{"abstract": "  We present methods and preliminary observations of two pulse Direct Laser\nAcceleration in a Laser-Driven Plasma Accelerator. This acceleration mechanism\nuses a second co-propagating laser pulse to overlap and further accelerate\nelectrons in a wakefield bubble, increasing energy at the cost of emittance\nwhen compared to traditional laser wakefield acceleration (LWFA). To this end,\nwe introduce a method of femtosecond scale control of time delay between two\nco-propagating pulses. We show energy enhancement when the separation between\nthe two pulses approaches the bubble radius.\n"}
+{"abstract": "  The ability to extend the field of view of laparoscopy images can help the\nsurgeons to obtain a better understanding of the anatomical context. However,\ndue to tissue deformation, complex camera motion and significant\nthree-dimensional (3D) anatomical surface, image pixels may have non-rigid\ndeformation and traditional mosaicking methods cannot work robustly for\nlaparoscopy images in real-time. To solve this problem, a novel two-dimensional\n(2D) non-rigid simultaneous localization and mapping (SLAM) system is proposed\nin this paper, which is able to compensate for the deformation of pixels and\nperform image mosaicking in real-time. The key algorithm of this 2D non-rigid\nSLAM system is the expectation maximization and dual quaternion (EMDQ)\nalgorithm, which can generate smooth and dense deformation field from sparse\nand noisy image feature matches in real-time. An uncertainty-based loop closing\nmethod has been proposed to reduce the accumulative errors. To achieve\nreal-time performance, both CPU and GPU parallel computation technologies are\nused for dense mosaicking of all pixels. Experimental results on \\textit{in\nvivo} and synthetic data demonstrate the feasibility and accuracy of our\nnon-rigid mosaicking method.\n"}
+{"abstract": "  Robustness is a key concern for Rust library development because Rust\npromises no risks of undefined behaviors if developers use safe APIs only.\nFuzzing is a practical approach for examining the robustness of programs.\nHowever, existing fuzzing tools are not directly applicable to library APIs due\nto the absence of fuzz targets. It mainly relies on human efforts to design\nfuzz targets case by case which is labor-intensive. To address this problem,\nthis paper proposes a novel automated fuzz target generation approach for\nfuzzing Rust libraries via API dependency graph traversal. We identify several\nessential requirements for library fuzzing, including validity and\neffectiveness of fuzz targets, high API coverage, and efficiency. To meet these\nrequirements, we first employ breadth-first search with pruning to find API\nsequences under a length threshold, then we backward search longer sequences\nfor uncovered APIs, and finally we optimize the sequence set as a set covering\nproblem. We implement our fuzz target generator and conduct fuzzing experiments\nwith AFL++ on several real-world popular Rust projects. Our tool finally\ngenerates 7 to 118 fuzz targets for each library with API coverage up to 0.92.\nWe exercise each target with a threshold of 24 hours and find 30\npreviously-unknown bugs from seven libraries.\n"}
+{"abstract": "  Eigenmode coalescence imparts remarkable properties to non-hermitian time\nevolution, culminating in a purely non-hermitian spectral degeneracy known as\nan exceptional point (EP). Here, we revisit time evolution at the EP and\nclassify two-level non-hermitian Hamiltonians in terms of the M\\\"obius group.\nWe then leverage that classification to study dynamical EP encircling, by\napplying it to periodically-modulated (Floquet) Hamiltonians. This reveals that\nFloquet non-hermitian systems exhibit rich physics whose complexity is not\ncaptured by an EP-encircling rule. For example, Floquet EPs can occur without\nencircling and vice-versa. Instead, we show that the elaborate interplay\nbetween non-hermitian and modulation instabilities is better understood through\nthe lens of parametric resonance.\n"}
+{"abstract": "  Black hole superradiance is a powerful tool in the search for ultra-light\nbosons. Constraints on the existence of such particles have been derived from\nthe observation of highly spinning black holes, absence of continuous\ngravitational-wave signals, and of the associated stochastic background.\nHowever, these constraints are only strictly speaking valid in the limit where\nthe boson's interactions can be neglected. In this work we investigate the\nextent to which the superradiant growth of an ultra-light dark photon can be\nquenched via scattering processes with ambient electrons. For dark photon\nmasses $m_{\\gamma^\\prime} \\gtrsim 10^{-17}\\,{\\rm eV}$, and for reasonable\nvalues of the ambient electron number density, we find superradiance can be\nquenched prior to extracting a significant fraction of the black-hole spin. For\nsufficiently large $m_{\\gamma^\\prime}$ and small electron number densities, the\nin-medium suppression of the kinetic mixing can be efficiently removed, and\nquenching occurs for mixings $\\chi_0 \\gtrsim \\mathcal{O}(10^{-8})$; at low\nmasses, however, in-medium effects strongly inhibit otherwise efficient\nscattering processes from dissipating energy. Intriguingly, this quenching\nleads to a time- and energy-oscillating electromagnetic signature, with\nluminosities potentially extending up to $\\sim 10^{57}\\,{\\rm erg / s}$,\nsuggesting that such events should be detectable with existing telescopes. As a\nbyproduct we also show that superradiance cannot be used to constrain a small\nmass for the Standard Model photon.\n"}
+{"abstract": "  The modular structure of brain networks supports specialized information\nprocessing, complex dynamics, and cost-efficient spatial embedding.\nInter-individual variation in modular structure has been linked to differences\nin performance, disease, and development. There exist many data-driven methods\nfor detecting and comparing modular structure, the most popular of which is\nmodularity maximization. Although modularity maximization is a general\nframework that can be modified and reparamaterized to address domain-specific\nresearch questions, its application to neuroscientific datasets has, thus far,\nbeen narrow. Here, we highlight several strategies in which the\n``out-of-the-box'' version of modularity maximization can be extended to\naddress questions specific to neuroscience. First, we present approaches for\ndetecting ``space-independent'' modules and for applying modularity\nmaximization to signed matrices. Next, we show that the modularity maximization\nframe is well-suited for detecting task- and condition-specific modules.\nFinally, we highlight the role of multi-layer models in detecting and tracking\nmodules across time, tasks, subjects, and modalities. In summary, modularity\nmaximization is a flexible and general framework that can be adapted to detect\nmodular structure resulting from a wide range of hypotheses. This article\nhighlights opens multiple frontiers for future research and applications.\n"}
+{"abstract": "  We select 37 most common and realistic dense matter equation of states to\nintegrate the general relativistic stellar structure equations for static\nspherically symmetric matter configurations. For all these models, we check the\ncompliance of the acceptability conditions that every stellar model should\nsatisfy. It was found that some of the non-relativistic equation of states\nviolate the causality and/or the dominant energy condition and that adiabatic\ninstabilities appear in the inner crust for all equation of state considered.\n"}
+{"abstract": "  Let $G$ be a graph on $n$ vertices, its adjacency matrix and degree diagonal\nmatrix are denoted by $A(G)$ and $D(G)$, respectively. In 2017, Nikiforov\n\\cite{0007} introduced the matrix $A_{\\alpha}(G)=\\alpha D(G)+(1-\\alpha)A(G)$\nfor $\\alpha\\in [0, 1].$ The $A_\\alpha$-spectrum of a graph $G$ consists of all\nthe eigenvalues (including the multiplicities) of $A_\\alpha(G).$ A graph $G$ is\nsaid to be determined by the generalized $A_{\\alpha}$-spectrum (or,\nDGA$_\\alpha$S for short) if whenever $H$ is a graph such that $H$ and $G$ share\nthe same $A_{\\alpha}$-spectrum and so do their complements, then $H$ is\nisomorphic to $G$. In this paper, when $\\alpha$ is rational, we present a\nsimple arithmetic condition for a graph being DGA$_\\alpha$S. More precisely,\nput $A_{c_\\alpha}:={c_\\alpha}A_\\alpha(G),$ here ${c_\\alpha}$ is the smallest\npositive integer such that $A_{c_\\alpha}$ is an integral matrix. Let\n$\\tilde{W}_{{\\alpha}}(G)=\\left[{\\bf 1},\\frac{A_{c_\\alpha}{\\bf\n1}}{c_\\alpha},\\ldots, \\frac{A_{c_\\alpha}^{n-1}{\\bf 1}}{c_\\alpha}\\right]$, where\n${\\bf 1}$ denotes the all-ones vector. We prove that if $\\frac{\\det\n\\tilde{W}_{{\\alpha}}(G)}{2^{\\lfloor\\frac{n}{2}\\rfloor}}$ is an odd and\nsquare-free integer and the rank of $\\tilde{W}_{{\\alpha}}(G)$ is full over\n$\\mathbb{F}_p$ for each odd prime divisor $p$ of $c_\\alpha$, then $G$ is\nDGA$_\\alpha$S except for even $n$ and odd $c_\\alpha\\,(\\geqslant 3)$. By our\nobtained results in this paper we may deduce the main results in \\cite{0005}\nand \\cite{0002}.\n"}
+{"abstract": "  Advanced manufacturing (AM) technologies, such as nanoscale additive\nmanufacturing process, enable the fabrication of nanoscale architected\nmaterials which has received great attention due to their prominent properties.\nHowever, few studies delve into the functional gradient cellular architecture\non nanoscale. This work studied the gradient nano-Gyroid architected material\nmade of copper (Cu) by molecular dynamic (MD) simulations. The result reveals\nthat, unlike homogeneous architecture, gradient Gyroid not only shows novel\nlayer-by-layer deformation behavior, but also processes significantly better\nenergy absorption ability. Moreover, this deformation behavior and energy\nabsorption are predictable and designable, which demonstrates its highly\nprogrammable potential.\n"}
+{"abstract": "  Quantum computing offers a powerful new paradigm of information processing\nthat has the potential to transform a wide range of industries. In the pursuit\nof the tantalizing promises of a universal quantum computer, a multitude of new\nknowledge and expertise has been developed, enabling the construction of novel\nquantum algorithms as well as increasingly robust quantum hardware. In\nparticular, we have witnessed rapid progress in the circuit quantum\nelectrodynamics (cQED) technology, which has emerged as one of the most\npromising physical systems that is capable of addressing the key challenges in\nrealizing full-stack quantum computing on a large scale. In this article, we\npresent some of the most crucial building blocks developed by the cQED\ncommunity in recent years and a pr\\'{e}cis of the latest achievements towards\nrobust universal quantum computation. More importantly, we aim to provide a\nsynoptic outline of the core techniques that underlie most cQED experiments and\noffer a practical guide for a novice experimentalist to design, construct, and\ncharacterize their first quantum device\n"}
+{"abstract": "  The advantages of operating selected transmission lines at frequencies other\nthan the standard 50 or 60 Hz are numerous, encompassing increased power\ntransfer capacity and better utilization of existing infrastructure. While high\nvoltage DC (HVDC) is by far the most well-established example, there has been\nan emerging interest low frequency AC (LFAC) transmission in applications\nranging from offshore wind to railway systems and mining. In this paper, we\ninvestigate the use of LFAC as a transmission upgrade and propose models and\nanalysis methods to determine the optimal choice of frequency. The paper first\npresents an optimal power flow model with frequency as a variable, assuming\nmodular multilevel converters for frequency conversion. Using this model, we\nanalyze LFAC as an embedded upgrade in a transmission system using existing\nlines. We quantify the system-wide advantages from improved power flow control\nand frequency reduction and find that an LFAC upgrade achieves similar and\nsometimes better results compared with HVDC upgrades. Finally, we analyze the\nfactors which determine the optimal frequency for these upgraded transmission\nlines, and we demonstrate the benefits of changing the frequency in response to\ndifferent system topologies and operating conditions.\n"}
+{"abstract": "  Understanding and explaining the mistakes made by trained models is critical\nto many machine learning objectives, such as improving robustness, addressing\nconcept drift, and mitigating biases. However, this is often an ad hoc process\nthat involves manually looking at the model's mistakes on many test samples and\nguessing at the underlying reasons for those incorrect predictions. In this\npaper, we propose a systematic approach, conceptual counterfactual\nexplanations(CCE), that explains why a classifier makes a mistake on a\nparticular test sample(s) in terms of human-understandable concepts (e.g. this\nzebra is misclassified as a dog because of faint stripes). We base CCE on two\nprior ideas: counterfactual explanations and concept activation vectors, and\nvalidate our approach on well-known pretrained models, showing that it explains\nthe models' mistakes meaningfully. In addition, for new models trained on data\nwith spurious correlations, CCE accurately identifies the spurious correlation\nas the cause of model mistakes from a single misclassified test sample. On two\nchallenging medical applications, CCE generated useful insights, confirmed by\nclinicians, into biases and mistakes the model makes in real-world settings.\nThe code for CCE is publicly available and can easily be applied to explain\nmistakes in new models.\n"}
+{"abstract": "  The spin-1/2 antiferromagnetic Heisenberg systems are studied on three\ntypical diamond-type hierarchical lattices (systems A, B and C) with fractal\ndimensions 1.63, 2 and 2.58, respectively, and the phase diagrams, critical\nphenomena and quantum correlations are calculated by a combination of the\nequivalent transformation and real-space renormalization group methods. We find\nthat there exist a reentrant behavior for system A and a finite temperature\ntransition in the isotropic Heisenberg limit for system C (not for system B).\nUnlike the ferromagnetic case, the Neel temperatures of antiferromagnetic\nsystems A and B are inversely proportional to ln(Delta_c-Delta) (when\nDelta->Delta_c) and ln Delta (when Delta->0), respectively. And we also find\nthat there is a turning point of quantum correlation in the isotropic\nHeisenberg limit Delta=0 where there is a peak of the contour and no matter how\nlarge the size of system is, quantum correlation will change to zero in the\nIsing limit for the three systems. The quantum correlation decreases with the\nincrease of lattice size L and it is almost zero when L>=30 for system A, and\nfor systems B and C, they still exist when L is larger than that of system A.\nMoreover, as an example, we discuss the error of result in system A, which is\ninduced by the noncommutativity.\n"}
+{"abstract": "  The effectiveness of resource allocation under emergencies especially\nhurricane disasters is crucial. However, most researchers focus on emergency\nresource allocation in a ground transportation system. In this paper, we\npropose Learning-to-Dispatch (L2D), a reinforcement learning (RL) based air\nroute dispatching system, that aims to add additional flights for hurricane\nevacuation while minimizing the airspace's complexity and air traffic\ncontroller's workload. Given a bipartite graph with weights that are learned\nfrom the historical flight data using RL in consideration of short- and\nlong-term gains, we formulate the flight dispatch as an online maximum weight\nmatching problem. Different from the conventional order dispatch problem, there\nis no actual or estimated index that can evaluate how the additional evacuation\nflights influence the air traffic complexity. Then we propose a multivariate\nreward function in the learning phase and compare it with other univariate\nreward designs to show its superior performance. The experiments using the\nreal-world dataset for Hurricane Irma demonstrate the efficacy and efficiency\nof our proposed schema.\n"}
+{"abstract": "  We construct the general permutation invariant Gaussian 2-matrix model for\nmatrices of arbitrary size $D$. The parameters of the model are given in terms\nof variables defined using the representation theory of the symmetric group\n$S_D$. A correspondence is established between the permutation invariant\npolynomial functions of the matrix variables (the observables of the model) and\ndirected colored graphs, which sheds light on stability properties in the large\n$D$ counting of these invariants. The refined counting of the graphs is given\nin terms of double cosets involving permutation groups defined by the local\nstructure of the graphs. Linear and quadratic observables are transformed to an\n$S_D$ representation theoretic basis and are used to define the convergent\nGaussian measure. The perturbative rules for the computation of expectation\nvalues of graph-basis observables of any degree are given in terms of the\nrepresentation theoretic parameters. Explicit results for a number of\nobservables of degree up to four are given along with a Sage programme that\ncomputes general expectation values.\n"}
+{"abstract": "  Continued follow-up of WISEA J153429.75-104303.3, announced in Meisner et al\n(2020), has proven it to have an unusual set of properties. New imaging data\nfrom Keck/MOSFIRE and HST/WFC3 show that this object is one of the few faint\nproper motion sources known with J-ch2 > 8 mag, indicating a very cold\ntemperature consistent with the latest known Y dwarfs. Despite this, it has\nW1-W2 and ch1-ch2 colors ~1.6 mag bluer than a typical Y dwarf. A new\ntrigonometric parallax measurement from a combination of WISE, Spitzer, and HST\nastrometry confirms a nearby distance of $16.3^{+1.4}_{-1.2}$ pc and a large\ntransverse velocity of $207.4{\\pm}15.9$ km/s. The absolute J, W2, and ch2\nmagnitudes are in line with the coldest known Y dwarfs, despite the highly\ndiscrepant W1-W2 and ch1-ch2 colors. We explore possible reasons for the unique\ntraits of this object and conclude that it is most likely an old, metal-poor\nbrown dwarf and possibly the first Y subdwarf. Given that the object has an HST\nF110W magnitude of 24.7 mag, broad-band spectroscopy and photometry from JWST\nare the best options for testing this hypothesis.\n"}
+{"abstract": "  Knowledge Tracing (KT), tracking a human's knowledge acquisition, is a\ncentral component in online learning and AI in Education. In this paper, we\npresent a simple, yet effective strategy to improve the generalization ability\nof KT models: we propose three types of novel data augmentation, coined\nreplacement, insertion, and deletion, along with corresponding regularization\nlosses that impose certain consistency or monotonicity biases on the model's\npredictions for the original and augmented sequence. Extensive experiments on\nvarious KT benchmarks show that our regularization scheme consistently improves\nthe model performances, under 3 widely-used neural networks and 4 public\nbenchmarks, e.g., it yields 6.3% improvement in AUC under the DKT model and the\nASSISTmentsChall dataset.\n"}
+{"abstract": "  We devise a condition strictly between the existence of an $n$-ary and an\n$n{+}1$-ary near-unanimity term. We evaluate exactly the distributivity and\nmodularity levels implied by such a condition.\n"}
+{"abstract": "  Administrative data, or non-probability sample data, are increasingly being\nused to obtain official statistics due to their many benefits over survey\nmethods. In particular, they are less costly, provide a larger sample size, and\nare not reliant on the response rate. However, it is difficult to obtain an\nunbiased estimate of the population mean from such data due to the absence of\ndesign weights. Several estimation approaches have been proposed recently using\nan auxiliary probability sample which provides representative covariate\ninformation of the target population. However, when this covariate information\nis high-dimensional, variable selection is not a straight-forward task even for\na subject matter expert. In the context of efficient and doubly robust\nestimation approaches for estimating a population mean, we develop two data\nadaptive methods for variable selection using the outcome adaptive LASSO and a\ncollaborative propensity score, respectively. Simulation studies are performed\nin order to verify the performance of the proposed methods versus competing\nmethods. Finally, we presented an anayisis of the impact of Covid-19 on\nCanadians.\n"}
+{"abstract": "  Breaking of time-reversal and point-group spatial symmetries can have a\nprofound impact on superconductivity. One of the most extraordinary effects,\ndue to the application of a magnetic field, is represented by the Abrikosov\nvortices with charged supercurrents circulating around their cores. Whether a\nsimilar phenomenon can be obtained by exploiting spatial symmetry breaking,\ne.g. through electric fields or mechanical strain, is a fundamentally relevant\nbut not yet fully settled problem. Here, we show that in two-dimensional\nspin-singlet superconductors with unusually low degree of spatial symmetry\ncontent, vortices with supercurrents carrying angular momentum around the core\ncan form and be energetically stable. The vortex has zero net magnetic flux\nsince it is made up of counter-propagating Cooper pairs with opposite orbital\nmoments. By solving self-consistently the Bogoliubov - de Gennes equations in\nreal space, we demonstrate that the orbital vortex is stable and we unveil the\nspatial distribution of the superconducting order parameter around its core.\nThe resulting amplitude has a characteristic pattern with a pronounced angular\nanisotropy that deviates from the profile of conventional magnetic vortices.\nThese hallmarks guide predictions and proposals for the experimental detection.\n"}
+{"abstract": "  We study the performance of the gradient play algorithm for stochastic games\n(SGs), where each agent tries to maximize its own total discounted reward by\nmaking decisions independently based on current state information which is\nshared between agents. Policies are directly parameterized by the probability\nof choosing a certain action at a given state. We show that Nash equilibria\n(NEs) and first-order stationary policies are equivalent in this setting, and\ngive a local convergence rate around strict NEs. Further, for a subclass of SGs\ncalled Markov potential games (which includes the cooperative setting with\nidentical rewards among agents as an important special case), we design a\nsample-based reinforcement learning algorithm and give a non-asymptotic global\nconvergence rate analysis for both exact gradient play and our sample-based\nlearning algorithm. Our result shows that the number of iterations to reach an\n$\\epsilon$-NE scales linearly, instead of exponentially, with the number of\nagents. Local geometry and local stability are also considered, where we prove\nthat strict NEs are local maxima of the total potential function and\nfully-mixed NEs are saddle points.\n"}
+{"abstract": "  The quest for practical waveguides operating in the terahertz range faces two\nmajor hurdles: large losses and high rigidity. While recent years have been\nmarked by remarkable progress in lowering the impact of material losses using\nhollow-core guidance, such waveguides are typically not flexible. Here we\nexperimentally and numerically investigate antiresonant dielectric waveguides\nmade of polyurethane, a commonly used dielectric with a low Young's modulus.\nThe hollow-core nature of antiresonant fibers leads to low transmission losses\nusing simple structures, whereas the low Young's modulus of polyurethane makes\nthem extremely flexible. The structures presented enable millimeter-wave\nmanipulation in centimeter-thick waveguides in the same spirit as conventional\n(visible- and near-IR-) optical fibers, i.e. conveniently and reconfigurably.\nWe investigate two canonical antiresonant geometries formed by one- and\nsix-tubes, experimentally comparing their transmission, bend losses and mode\nprofiles. The waveguides under investigation have loss below 1 dB/cm in their\nsub-THz transmission bands, increasing by 1 dB/cm for a bend radius of about 10\ncm, which is analogous to bending standard $125 \\mu{\\rm m}$ diameter fiber to a\n1.2 mm radius.\n"}
+{"abstract": "  A better understanding of dispersion in natural streams requires knowledge of\nlongitudinal dispersion coefficient(LDC). Various methods have been proposed\nfor predictions of LDC. Those studies can be grouped into three types:\nanalytical, statistical and ML-driven researches(Implicit and explicit).\nHowever, a comprehensive evaluation of them is still lacking. In this paper, we\nfirst present an in-depth analysis of those methods and find out their defects.\nThis is carried out on an extensive database composed of 660 samples of\nhydraulic and channel properties worldwide. The reliability and\nrepresentativeness of utilized data are enhanced through the deployment of the\nSubset Selection of Maximum Dissimilarity(SSMD) for testing set selection and\nthe Inter Quartile Range(IQR) for removal of the outlier. The evaluation\nreveals the rank of those methods as: ML-driven method > the statistical method\n> the analytical method. Whereas implicit ML-driven methods are black-boxes in\nnature, explicit ML-driven methods have more potential in prediction of LDC.\nBesides, overfitting is a universal problem in existing models. Those models\nalso suffer from a fixed parameter combination. To establish an interpretable\nmodel for LDC prediction with higher performance, we then design a novel\nsymbolic regression method called evolutionary symbolic regression\nnetwork(ESRN). It is a combination of genetic algorithms and neural networks.\nStrategies are introduced to avoid overfitting and explore more parameter\ncombinations. Results show that the ESRN model has superiorities over other\nexisting symbolic models in performance. The proposed model is suitable for\npractical engineering problems due to its advantage in low requirement of\nparameters (only w and U* are required). It can provide convincing solutions\nfor situations where the field test cannot be carried out or limited field\ninformation can be obtained.\n"}
+{"abstract": "  The building of a stellar structure requires knowing the Rosseland mean\nopacity at each layer of the model. This mean opacity is very often\ninterpolated in pre-computed tables due to the overwhelming time to compute it\nfrom monochromatic cross sections. The main drawback to using tables is that\nthe opacities can be inconsistent with the actual local chemical composition,\nfor instance in the regions of the star where nucleosynthesis occurs. We\ndeveloped a strategy that allows very fast calculations of Rosseland opacities\nfrom monochromatic cross sections. This method has been implemented in the\nToulouse-Geneva evolution code, which we used to compute evolutionary tracks\nwith models whose Rosseland opacities are fully consistent with the chemical\nmix everywhere in the star. Our self-consistent models show very small\nstructural differences compared to models where the Rosseland opacity is\ncomputed with a fixed metal mixture. The main-sequence evolutionary tracks are\nalmost the same for models of mass ranging from 2 to 8 solar masses. At a given\nsurface gravity the relative difference in age is lower than 2% and generally\nbelow 1% between the two kinds of calculations, the self-consistent model being\nyounger most of the time. Unless such a precision in age is sought out, the use\nof tabulated Rosseland opacities with a metal content defined globally is still\nacceptable, at least in main-sequence stars where the chemical mix changes only\nthrough nucleosynthesis.\n"}
+{"abstract": "  We investigate how the production of gravitational waves (GWs) is affected by\nthe GW velocity $(c_T)$ during preheating after inflation. For instance, we\nsimulate the production of GWs after $\\lambda\\phi^4$ chaotic inflation, and\nfind that GW spectrum is enhanced for $c_T<1$, but distorted (suppressed at low\nfrequency, but enhanced at high frequency) for $c_T>1$.\n"}
+{"abstract": "  Electroactive polymer thin films undergo repeated reversible structural\nchange during operation in electrochemical applications. While synchrotron\nX-ray scattering is powerful for the characterization of stand-alone and\nex-situ organic thin films, in situ structural characterization has been\nunderutilized--in large part due to complications arising from supporting\nelectrolyte scattering. This has greatly hampered the development of\napplication relevant structure property relationships. Therefore, we have\ndeveloped a new methodology for in situ and operando X-ray characterization\nthat separates the incident and scattered X-ray beam path from the electrolyte.\nAs a proof of concept, we demonstrate the in situ structural changes of\nweakly-scattering, organic mixed ionic-electronic conductor thin films in an\naqueous electrolyte environment, enabling access to previously unexplored\nchanges in the pi-pi peak and diffuse scatter in situ, while capturing the\nsolvent swollen thin film structure which was inaccessible in previous ex situ\nstudies. These in situ measurements improve the sensitivity to structural\nchanges, capturing minute changes not possible ex situ, and have multimodal\npotential such as combined Raman measurements that also serve to validate the\ntrue in situ/operando conditions of the cell. Finally, we examine new\ndirections enabled by this operando cell design and compare state of the art\nmeasurements.\n"}
+{"abstract": "  Small-Signal Stability (SSS) is crucial for the control of power grids.\nHowever, False Data Injection (FDI) attacks against SSS can impact the grid\nstability, hence, the security of SSS needs to be studied. This paper proposes\na formal method of synthesizing FDI attack vectors (i.e., a set of measurements\nto be altered) that can destabilize power systems. We formulate an FDI attack\nas an optimization problem using AC power flow, SSS model, and stability\nconstraints. The attacker capability is modeled as the accessibility to a\nlimited set of measurements. The solution of the proposed FDI attack model\nprovides a destabilizing attack vector if exists. We implement the proposed\nmechanism and evaluate its performance by conducting several case studies using\nthe WSCC 3-machine 9-bus system. The case study results showed that the\npossibility of random FDIs (i.e., with no knowledge of the power system) in\nlaunching a destabilizing attack is too low to be successful. However, an\nintelligent attacker can leverage the grid knowledge to make the system\nunstable, even with limited access to the measurements.\n"}
+{"abstract": "  A fundamental problem in numerical analysis and approximation theory is\napproximating smooth functions by polynomials. A much harder version under\nrecent consideration is to enforce bounds constraints on the approximating\npolynomial. In this paper, we consider the problem of approximating functions\nby polynomials whose Bernstein coefficients with respect to a given degree\nsatisfy such bounds, which implies such bounds on the approximant. We frame the\nproblem as an inequality-constrained optimization problem and give an algorithm\nfor finding the Bernstein coefficients of the exact solution. Additionally, our\nmethod can be modified slightly to include equality constraints such as mass\npreservation. It also extends naturally to multivariate polynomials over a\nsimplex.\n"}
+{"abstract": "  An unconfined strongly swirled flow is investigated for different Reynolds\nnumbers using particle image velocimetry (PIV) and Large Eddy Simulation (LES)\nwith a Thickened Flame (TF) model. Both reacting and non-reacting flow results\nare presented. In the LES-TF approach, the flame front is resolved on the\ncomputational grid through artificial thickening and the individual species\ntransport equations are directly solved with the reaction rates specified using\nArrhenius chemistry. Good agreement is found when comparing predictions with\nthe experimental data. Also the predicted RMS fluctuations exhibit a\ndouble-peak profile with one peak in the burnt and the other in the un-burnt\nregion. The measured and predicted heat release distributions are in\nqualitative agreement with each other and exhibit the highest values along the\ninner edge of the shear layer. The precessing vortex core (PVC) is clearly\nobserved in both the non-reacting and reacting cases. However, it appears more\naxially-elongated for the reacting cases and the oscillations in the PVC are\ndamped with reactions.\n"}
+{"abstract": "  Tukey order are used to compare the cofinal complexity of partially order\nsets (posets). We prove that there is a $2^\\mathfrak{c}$-sized collection of\nsub-posets in $2^\\omega$ which forms an antichain in the sense of Tukey\nordering. Using the fact that any boundedly-complete sub-poset of\n$\\omega^\\omega$ is a Tukey quotient of $\\omega^\\omega$, we answer two open\nquestions published in \\cite{FKL16}.\n  The relation between $P$-base and strong Pytkeev$^\\ast$ property is\ninvestigated. Let $P$ be a poset equipped with a second-countable topology in\nwhich every convergent sequence is bounded. Then we prove that any topological\nspace with a $P$-base has the strong Pytkeev$^\\ast$ property. Furthermore, we\nprove that every uncountably-dimensional locally convex space (lcs) with a\n$P$-base contains an infinite-dimensional metrizable compact subspace. Examples\nin function spaces are given.\n"}
+{"abstract": "  We study the phase transitions of a fluid confined in a capillary slit made\nfrom two adjacent walls each of which are a periodic composite of stripes of\ntwo different materials. For wide slits the capillary condensation occurs at a\npressure which is described accurately by a combination of the Kelvin equation\nand the Cassie law for an averaged contact angle. However, for narrow slits the\ncondensation occurs in two steps involving an intermediate bridging phase, with\nthe corresponding pressures described by two new Kelvin equations. These are\ncharacterised by different contact angles due to interfacial pinning, with one\nlarger and one smaller than the Cassie angle. We determine the triple point and\npredict two types of dispersion force induced Derjaguin-like corrections due to\nmesoscopic volume reduction and the singular free-energy contribution from\nnano-droplets and bubbles. We test these predictions using a fully microscopic\ndensity functional model which confirms their validity even for molecularly\nnarrow slits. Analogous mesoscopic corrections are also predicted for two\ndimensional systems arising from thermally induced interfacial wandering.\n"}
+{"abstract": "  In this work, we propose a novel convolutional autoencoder based architecture\nto generate subspace specific feature representations that are best suited for\nclassification task. The class-specific data is assumed to lie in low\ndimensional linear subspaces, which could be noisy and not well separated,\ni.e., subspace distance (principal angle) between two classes is very low. The\nproposed network uses a novel class-specific self expressiveness (CSSE) layer\nsandwiched between encoder and decoder networks to generate class-wise subspace\nrepresentations which are well separated. The CSSE layer along with encoder/\ndecoder are trained in such a way that data still lies in subspaces in the\nfeature space with minimum principal angle much higher than that of the input\nspace. To demonstrate the effectiveness of the proposed approach, several\nexperiments have been carried out on state-of-the-art machine learning datasets\nand a significant improvement in classification performance is observed over\nexisting subspace based transformation learning methods.\n"}
+{"abstract": "  Recent advances in multi-agent reinforcement learning have been largely\nlimited in training one model from scratch for every new task. The limitation\nis due to the restricted model architecture related to fixed input and output\ndimensions. This hinders the experience accumulation and transfer of the\nlearned agent over tasks with diverse levels of difficulty (e.g. 3 vs 3 or 5 vs\n6 multi-agent games). In this paper, we make the first attempt to explore a\nuniversal multi-agent reinforcement learning pipeline, designing one single\narchitecture to fit tasks with the requirement of different observation and\naction configurations. Unlike previous RNN-based models, we utilize a\ntransformer-based model to generate a flexible policy by decoupling the policy\ndistribution from the intertwined input observation with an importance weight\nmeasured by the merits of the self-attention mechanism. Compared to a standard\ntransformer block, the proposed model, named as Universal Policy Decoupling\nTransformer (UPDeT), further relaxes the action restriction and makes the\nmulti-agent task's decision process more explainable. UPDeT is general enough\nto be plugged into any multi-agent reinforcement learning pipeline and equip\nthem with strong generalization abilities that enables the handling of multiple\ntasks at a time. Extensive experiments on large-scale SMAC multi-agent\ncompetitive games demonstrate that the proposed UPDeT-based multi-agent\nreinforcement learning achieves significant results relative to\nstate-of-the-art approaches, demonstrating advantageous transfer capability in\nterms of both performance and training speed (10 times faster).\n"}
+{"abstract": "  Physical representations of data offer physical and spatial ways of looking\nat, navigating, and interacting with data. While digital fabrication has\nfacilitated the creation of objects with data-driven geometry, rendering data\nas a physically fabricated object is still a daunting leap for many\nphysicalization designers. Rendering in the scope of this research refers to\nthe back-and-forth process from digital design to digital fabrication and its\nspecific challenges. We developed a corpus of example data physicalizations\nfrom research literature and physicalization practice. This survey then unpacks\nthe \"rendering\" phase of the extended InfoVis pipeline in greater detail\nthrough these examples, with the aim of identifying ways that researchers,\nartists, and industry practitioners \"render\" physicalizations using digital\ndesign and fabrication tools.\n"}
+{"abstract": "  To model is to represent. The threshold of decidability defines two\nepistemological choices: one model (or a finite number of models) suffices for\nrepresenting the dynamics below the undecidable; above this threshold (defined\nas G-complexity), every model is partial, no complete modeling is possible.\n"}
+{"abstract": "  It is known that the estimating equations for quantile regression (QR) can be\nsolved using an EM algorithm in which the M-step is computed via weighted least\nsquares, with weights computed at the E-step as the expectation of independent\ngeneralized inverse-Gaussian variables. This fact is exploited here to extend\nQR to allow for random effects in the linear predictor. Convergence of the\nalgorithm in this setting is established by showing that it is a generalized\nalternating minimization (GAM) procedure. Another modification of the EM\nalgorithm also allows us to adapt a recently proposed method for variable\nselection in mean regression models to the QR setting. Simulations show the\nresulting method significantly outperforms variable selection in QR models\nusing the lasso penalty. Applications to real data include a frailty QR\nanalysis of hospital stays, and variable selection for age at onset of lung\ncancer and for riboflavin production rate using high-dimensional gene\nexpression arrays for prediction.\n"}
+{"abstract": "  The Zwicky Transient Facility recently announced the detection of an optical\ntransient AT2020blt at redshift $z=2.9$, consistent with the afterglow of a\ngamma-ray burst. No prompt emission was observed. We analyse AT2020blt with\ndetailed models, showing the data are best explained as the afterglow of an\non-axis long gamma-ray burst, ruling out other hypotheses such as a cocoon and\na low-Lorentz factor jet. We search \\textit{Fermi} data for prompt emission,\nsetting deeper upper limits on the prompt emission than in the original\ndetection paper. Together with \\konus{} observations, we show that the\ngamma-ray efficiency of AT2020blt is $\\lesssim 2.8\\%$, lower than $98.4\\%$ of\nobserved gamma-ray bursts. We speculate that AT2020blt and AT2021any belong to\nthe low-efficiency tail of long gamma-ray burst distributions that are\nbeginning to be readily observed due to the capabilities of new observatories\nlike the Zwicky Transient Facility.\n"}
+{"abstract": "  Conditioning is crucial in applied science when inference involving time\nseries is involved. Belief calculus is an effective way of handling such\ninference in the presence of epistemic uncertainty -- unfortunately, different\napproaches to conditioning in the belief function framework have been proposed\nin the past, leaving the matter somewhat unsettled. Inspired by the geometric\napproach to uncertainty, in this paper we propose an approach to the\nconditioning of belief functions based on geometrically projecting them onto\nthe simplex associated with the conditioning event in the space of all belief\nfunctions. We show here that such a geometric approach to conditioning often\nproduces simple results with straightforward interpretations in terms of\ndegrees of belief. This raises the question of whether classical approaches,\nsuch as for instance Dempster's conditioning, can also be reduced to some form\nof distance minimisation in a suitable space. The study of families of\ncombination rules generated by (geometric) conditioning rules appears to be the\nnatural prosecution of the presented research.\n"}
+{"abstract": "  This paper studies linear convergence of the subspace constrained mean shift\n(SCMS) algorithm, a well-known algorithm for identifying a density ridge\ndefined by a kernel density estimator. By arguing that the SCMS algorithm is a\nspecial variant of a subspace constrained gradient ascent (SCGA) algorithm with\nan adaptive step size, we derive linear convergence of such SCGA algorithm.\nWhile the existing research focuses mainly on density ridges in the Euclidean\nspace, we generalize density ridges and the SCMS algorithm to directional data.\nIn particular, we establish the stability theorem of density ridges with\ndirectional data and prove the linear convergence of our proposed directional\nSCMS algorithm.\n"}
+{"abstract": "  We describe a general approach to modeling rational decision-making agents\nwho adopt either quantum or classical mechanics based on the Quantum Bayesian\n(QBist) approach to quantum theory. With the additional ingredient of a scheme\nby which the properties of one agent may influence another, we arrive at a\nflexible framework for treating multiple interacting quantum and classical\nBayesian agents. We present simulations in several settings to illustrate our\nconstruction: quantum and classical agents receiving signals from an exogenous\nsource, two interacting classical agents, two interacting quantum agents, and\ninteractions between classical and quantum agents. A consistent treatment of\nmultiple interacting users of quantum theory may allow us to properly interpret\nexisting multi-agent protocols and could suggest new approaches in other areas\nsuch as quantum algorithm design.\n"}
+{"abstract": "  An important issue when using Machine Learning algorithms in recent research\nis the lack of interpretability. Although these algorithms provide accurate\npoint predictions for various learning problems, uncertainty estimates\nconnected with point predictions are rather sparse. A contribution to this gap\nfor the Random Forest Regression Learner is presented here. Based on its\nOut-of-Bag procedure, several parametric and non-parametric prediction\nintervals are provided for Random Forest point predictions and theoretical\nguarantees for its correct coverage probability is delivered. In a second part,\na thorough investigation through Monte-Carlo simulation is conducted evaluating\nthe performance of the proposed methods from three aspects: (i) Analyzing the\ncorrect coverage rate of the proposed prediction intervals, (ii) Inspecting\ninterval width and (iii) Verifying the competitiveness of the proposed\nintervals with existing methods. The simulation yields that the proposed\nprediction intervals are robust towards non-normal residual distributions and\nare competitive by providing correct coverage rates and comparably narrow\ninterval lengths, even for comparably small samples.\n"}
+{"abstract": "  Speech evaluation is an essential component in computer-assisted language\nlearning (CALL). While speech evaluation on English has been popular, automatic\nspeech scoring on low resource languages remains challenging. Work in this area\nhas focused on monolingual specific designs and handcrafted features stemming\nfrom resource-rich languages like English. Such approaches are often difficult\nto generalize to other languages, especially if we also want to consider\nsuprasegmental qualities such as rhythm. In this work, we examine three\ndifferent languages that possess distinct rhythm patterns: English\n(stress-timed), Malay (syllable-timed), and Tamil (mora-timed). We exploit\nrobust feature representations inspired by music processing and vector\nrepresentation learning. Empirical validations show consistent gains for all\nthree languages when predicting pronunciation, rhythm and intonation\nperformance.\n"}
+{"abstract": "  The $\\textit{planar slope number}$ $psn(G)$ of a planar graph $G$ is the\nminimum number of edge slopes in a planar straight-line drawing of $G$. It is\nknown that $psn(G) \\in O(c^\\Delta)$ for every planar graph $G$ of degree\n$\\Delta$. This upper bound has been improved to $O(\\Delta^5)$ if $G$ has\ntreewidth three, and to $O(\\Delta)$ if $G$ has treewidth two. In this paper we\nprove $psn(G) \\in \\Theta(\\Delta)$ when $G$ is a Halin graph, and thus has\ntreewidth three. Furthermore, we present the first polynomial upper bound on\nthe planar slope number for a family of graphs having treewidth four. Namely we\nshow that $O(\\Delta^2)$ slopes suffice for nested pseudotrees.\n"}
+{"abstract": "  We survey Natural Language Processing (NLP) approaches to summarizing,\nsimplifying, and generating patents' text. While solving these tasks has\nimportant practical applications - given patents' centrality in the R&D process\n- patents' idiosyncrasies open peculiar challenges to the current NLP state of\nthe art. This survey aims at a) describing patents' characteristics and the\nquestions they raise to the current NLP systems, b) critically presenting\nprevious work and its evolution, and c) drawing attention to directions of\nresearch in which further work is needed. To the best of our knowledge, this is\nthe first survey of generative approaches in the patent domain.\n"}
+{"abstract": "  A splitting of the fundamental optical modes in micro/nano-cavities\ncomprising semiconductor heterostructures is commonly observed. Given that this\nsplitting plays an important role for the light-matter interaction and hence\nquantum technology applications, a method for controlling the mode-splitting is\nimportant. In this work we use an open microcavity composed of a \"bottom\"\nsemiconductor distributed Bragg reflector (DBR) incorporating an n-i-p\nheterostructure, paired with a \"top\" curved dielectric DBR. We measure the\nmode-splitting as a function of wavelength across the stopband. We demonstrate\na reversible in-situ technique to tune the mode-splitting by applying uniaxial\nstress to the semiconductor DBR. The method exploits the photoelastic effect of\nthe semiconductor materials. We achieve a maximum tuning of $\\sim$11 GHz. The\nstress applied to the heterostructure is determined by observing the\nphotoluminescence of quantum dots embedded in the sample, converting a spectral\nshift to a stress via deformation potentials. A thorough study of the\nmode-splitting and its tuning across the stop-band leads to a quantitative\nunderstanding of the mechanism behind the results.\n"}
+{"abstract": "  {We present a study of neutron star models that contain dark matter (DM) in\nthe core. The DM is assumed to have a particle nature and to be\nself-interacting. Using constraints on the mass and radius of neutron stars, we\ninvestigate the allowed properties of either bosonic or fermionic DM particles.\nWe consider cases where it constitutes up to 15\\% of the mass of the star, even\nthough conventional mechanisms cannot generate such large fractions. For this\npurpose three different models of neutron stars are considered, the first\ninvolving nucleons only, the second including hyperons, and the last involving\nstrange matter in the core. Different EoSs are constructed for the various\ncases of fermionic and bosonic DM. These EoSs are solved for selected\nproperties of the DM particles and the results are tested against mass, radius\nand tidal deformability constraints for neutron stars. The distribution of\nenergy density of DM and ordinary matter inside the neutron stars is also\npresented. It is found that if the DM is fermionic in nature it does not just\nsit in the core but it is present everywhere in the star, from the centre to\noutside the surface and may even envelop it.\n"}
+{"abstract": "  Evaluating robustness of machine-learning models to adversarial examples is a\nchallenging problem. Many defenses have been shown to provide a false sense of\nsecurity by causing gradient-based attacks to fail, and they have been broken\nunder more rigorous evaluations. Although guidelines and best practices have\nbeen suggested to improve current adversarial robustness evaluations, the lack\nof automatic testing and debugging tools makes it difficult to apply these\nrecommendations in a systematic manner. In this work, we overcome these\nlimitations by (i) defining a set of quantitative indicators which unveil\ncommon failures in the optimization of gradient-based attacks, and (ii)\nproposing specific mitigation strategies within a systematic evaluation\nprotocol. Our extensive experimental analysis shows that the proposed\nindicators of failure can be used to visualize, debug and improve current\nadversarial robustness evaluations, providing a first concrete step towards\nautomatizing and systematizing current adversarial robustness evaluations. Our\nopen-source code is available at:\nhttps://github.com/pralab/IndicatorsOfAttackFailure.\n"}
+{"abstract": "  CO2+H2 greenhouse warming has recently emerged as a promising scenario to\nsufficiently warm the early martian surface to allow the formation of valley\nnetworks and lakes. Here we present numerical 3-D global climate simulations of\nthe early martian climate that we have performed assuming dense CO2+H2\natmospheres. Our climate model, derived from earlier works by Forget et al.\n(2013) and Wordsworth et al. (2013), is coupled to an asynchronous model of the\nlong-term evolution of martian glaciers and lakes. Simulations were carried out\nat 40{\\deg} obliquity to investigate how (i) water content and (ii) H2 content\n(added to 1 or 2 bars of CO2) can shape the climate and hydrologic cycle of\nearly Mars. We show that the adiabatic cooling mechanism (Wordsworth et al.\n2013) that leads to the accumulation of ice deposits in the southern highlands\nin cold climate (the so called 'icy highland scenario') also works in warm\nclimates, with impact crater lakes acting as the main water reservoirs. This\nproduces rainfall mainly localized in the southern highlands of Mars. If one\nadjust (i) the amount of CO2 and H2, (ii) the size and location of the water\nreservoirs, and (iii) the ancient topography (i.e. by removing Tharsis), the\nspatial patterns of surface runoff (from rainfall or snowmelt) in the\nsimulations can match -- with a few exceptions -- the observed distribution of\nvalley networks and impact crater lakes. Although our results are obtained for\nCO2-dominated atmospheres enriched with H2, they should also apply to assess\nthe impact of any combination of powerful long-lived greenhouse gases on early\nMars.\n"}
+{"abstract": "  The one-dimensional ($1D$) Galerkin-truncated Burgers equation, with both\ndissipation and noise terms included, is studied using spectral methods. When\nthe truncation-scale Reynolds number $R_{\\rm min}$ is varied, from very small\nvalues to order $1$ values, the scale-dependent correlation time $\\tau(k)$ is\nshown to follow the expected crossover from the short-distance $\\tau(k) \\sim\nk^{-2}$ Edwards-Wilkinson scaling to the universal long-distance\nKardar-Parisi-Zhang scaling $\\tau(k) \\sim k^{-3/2}$. In the inviscid limit:\n$R_{\\rm min}\\to \\infty$, we show that the system displays {\\it another}\ncrossover to the Galerkin-truncated inviscid-Burgers regime that admits\nthermalised solutions with $\\tau(k) \\sim k^{-1}$. The scaling form of the\ntime-correlation functions are shown to follow the known analytical laws and\nthe skewness and excess kurtosis of the interface increments distributions are\ncharacterised.\n"}
+{"abstract": "  In the break of COVID-19 pandemic, mass testing has become essential to\nreduce the spread of the virus. Several recent studies suggest that a\nsignificant number of COVID-19 patients display no physical symptoms\nwhatsoever. Therefore, it is unlikely that these patients will undergo COVID-19\ntest, which increases their chances of unintentionally spreading the virus.\nCurrently, the primary diagnostic tool to detect COVID-19 is RT-PCR test on\ncollected respiratory specimens from the suspected case. This requires patients\nto travel to a laboratory facility to be tested, thereby potentially infecting\nothers along the way.It is evident from recent researches that asymptomatic\nCOVID-19 patients cough and breath in a different way than the healthy people.\nSeveral research groups have created mobile and web-platform for crowdsourcing\nthe symptoms, cough and breathing sounds from healthy, COVID-19 and Non-COVID\npatients. Some of these data repositories were made public. We have received\nsuch a repository from Cambridge University team under data-sharing agreement,\nwhere we have cough and breathing sound samples for 582 and 141 healthy and\nCOVID-19 patients, respectively. 87 COVID-19 patients were asymptomatic, while\nrest of them have cough. We have developed an Android application to\nautomatically screen COVID-19 from the comfort of people homes. Test subjects\ncan simply download a mobile application, enter their symptoms, record an audio\nclip of their cough and breath, and upload the data anonymously to our servers.\nOur backend server converts the audio clip to spectrogram and then apply our\nstate-of-the-art machine learning model to classify between cough sounds\nproduced by COVID-19 patients, as opposed to healthy subjects or those with\nother respiratory conditions. The system can detect asymptomatic COVID-19\npatients with a sensitivity more than 91%.\n"}
+{"abstract": "  Recently, a new cosmological framework, dubbed Ricci Cosmology, has been\nproposed. Such a framework has emerged from the study of relativistic dynamics\nof fluids out of equilibrium in a curved background and is characterised by the\npresence of deviations from the equilibrium pressure in the energy-momentum\ntensor which are due to linear terms in the Ricci scalar and the Ricci tensor.\nThe coefficients in front of such terms are called the second order transport\ncoefficients and they parametrise the fluid response to the pressure terms\narising from the spacetime curvature.\n  Under the preliminary assumption that the second order transport coefficients\nare constant, we find the simplest solution of Ricci cosmology in which the\npresence of pressure terms causes a departure from the perfect fluid redshift\nscaling for matter components filling the Universe. In order to test the\nviability of this solution, we make four different ans\\\"{a}tze on the transport\ncoefficients, giving rise to four different cases of our model. On the physical\nground of the second law of thermodynamics for fluids with non-equilibrium\npressure, we find some theoretical bounds (priors) on the parameters of the\nmodels. Our main concern is then the check of each of the case against the\nstandard set of cosmological data in order to obtain the observational bounds\non the second order transport coefficients. We find those bounds also realising\nthat Ricci cosmology model is compatible with $\\Lambda$CDM cosmology for all\nthe ans\\\"{a}tze.\n"}
+{"abstract": "  Due to a high heterogeneity in pose and size and to a limited number of\navailable data, segmentation of pediatric images is challenging for deep\nlearning methods. In this work, we propose a new CNN architecture that is pose\nand scale invariant thanks to the use of Spatial Transformer Network (STN). Our\narchitecture is composed of three sequential modules that are estimated\ntogether during training: (i) a regression module to estimate a similarity\nmatrix to normalize the input image to a reference one; (ii) a differentiable\nmodule to find the region of interest to segment; (iii) a segmentation module,\nbased on the popular UNet architecture, to delineate the object. Unlike the\noriginal UNet, which strives to learn a complex mapping, including pose and\nscale variations, from a finite training dataset, our segmentation module\nlearns a simpler mapping focusing on images with normalized pose and size.\nFurthermore, the use of an automatic bounding box detection through STN allows\nsaving time and especially memory, while keeping similar performance. We test\nthe proposed method in kidney and renal tumor segmentation on abdominal\npediatric CT scanners. Results indicate that the estimated STN homogenization\nof size and pose accelerates the segmentation (25h), compared to standard\ndata-augmentation (33h), while obtaining a similar quality for the kidney\n(88.01\\% of Dice score) and improving the renal tumor delineation (from 85.52\\%\nto 87.12\\%).\n"}
+{"abstract": "  The ultrasound (US) screening of the infant hip is vital for the early\ndiagnosis of developmental dysplasia of the hip (DDH). The US diagnosis of DDH\nrefers to measuring alpha and beta angles that quantify hip joint development.\nThese two angles are calculated from key anatomical landmarks and structures of\nthe hip. However, this measurement process is not trivial for sonographers and\nusually requires a thorough understanding of complex anatomical structures. In\nthis study, we propose a multi-task framework to learn the relationships among\nlandmarks and structures jointly and automatically evaluate DDH. Our multi-task\nnetworks are equipped with three novel modules. Firstly, we adopt Mask R-CNN as\nthe basic framework to detect and segment key anatomical structures and add one\nlandmark detection branch to form a new multi-task framework. Secondly, we\npropose a novel shape similarity loss to refine the incomplete anatomical\nstructure prediction robustly and accurately. Thirdly, we further incorporate\nthe landmark-structure consistent prior to ensure the consistency of the bony\nrim estimated from the segmented structure and the detected landmark. In our\nexperiments, 1,231 US images of the infant hip from 632 patients are collected,\nof which 247 images from 126 patients are tested. The average errors in alpha\nand beta angles are 2.221 degrees and 2.899 degrees. About 93% and 85%\nestimates of alpha and beta angles have errors less than 5 degrees,\nrespectively. Experimental results demonstrate that the proposed method can\naccurately and robustly realize the automatic evaluation of DDH, showing great\npotential for clinical application.\n"}
+{"abstract": "  We investigate the interaction of weak light fields with two-dimensional\nlattices of atoms, in which two-photon coupling establishes conditions of\nelectromagnetically induced transparency and excites high lying atomic Rydberg\nstates. This system features different interactions that act on disparate\nlength scales, from zero-range defect scattering of atomic excitations and\nfinite-range dipole exchange interactions to long-range Rydberg-state\ninteractions that span the entire array. Analyzing their interplay, we identify\nconditions that yield a nonlinear quantum mirror which coherently splits\nincident fields into correlated photon-pairs in a single transverse mode, while\ntransmitting single photons unaffected. Such strong photon-photon interactions\nin the absence of otherwise detrimental photon losses in Rydberg-EIT arrays\nopens up a promising approach for the generation and manipulation of quantum\nlight, and the exploration of many-body phenomena with interacting photons.\n"}
+{"abstract": "  Dark matter may self-interact through a continuum of low-mass states. This\nhappens if dark matter couples to a strongly-coupled nearly-conformal hidden\nsector. This type of theory is holographically described by brane-localized\ndark matter interacting with bulk fields in a slice of 5D anti-de Sitter space.\nThe long-range potential in this scenario depends on a non-integer power of the\nspatial separation, in contrast to the Yukawa potential generated by the\nexchange of a single 4D mediator. The resulting self-interaction cross section\nscales like a non-integer power of velocity. We identify the Born, classical\nand resonant regimes and investigate them using state-of-the-art numerical\nmethods. We demonstrate the viability of our continuum-mediated framework to\naddress the astrophysical small-scale structure anomalies. Investigating the\ncontinuum-mediated Sommerfeld enhancement, we demonstrate that a pattern of\nresonances can occur depending on the non-integer power. We conclude that\ncontinuum mediators introduce novel power-law scalings which open new\npossibilities for dark matter self-interaction phenomenology.\n"}
+{"abstract": "  To traverse complex three-dimensional terrainwith large obstacles, animals\nand robots must transition across different modes. However, the most\nmechanistic understanding of terrestrial locomotion concerns how to generate\nand stabilize near-steady-state, single-mode locomotion (e.g. walk, run). We\nknow little about how to use physical interaction to make robust locomotor\ntransitions. Here, we review our progress towards filling this gap by\ndiscovering terradynamic principles of multi-legged locomotor transitions,\nusing simplified model systems representing distinct challenges in complex\nthree-dimensional terrain. Remarkably, general physical principles emerge\nacross diverse model systems, by modelling locomotor-terrain interaction using\na potential energy landscape approach. The animal and robots' stereotyped\nlocomotor modes are constrained by physical interaction. Locomotor transitions\nare stochastic, destabilizing, barrier-crossing transitions on the landscape.\nThey can be induced by feed-forward self-propulsion and are facilitated by\nfeedbackcontrolled active adjustment. General physical principles and\nstrategies from our systematic studies already advanced robot performance in\nsimple model systems. Efforts remain to better understand the intelligence\naspect of locomotor transitions and how to compose larger-scale potential\nenergy landscapes of complex three-dimensional terrains from simple landscapes\nof abstracted challenges. This will elucidate how the neuromechanical control\nsystem mediates physical interaction to generate multi-pathway locomotor\ntransitions and lead to advancements in biology, physics, robotics and\ndynamical systems theory.\n"}
+{"abstract": "  The leading explanation of the $\\textit{Fermi}$ Galactic center $\\gamma$-ray\nexcess is the extended emission from a unresolved population of millisecond\npulsars (MSPs) in the Galactic bulge. Such a population would, along with the\nprompt $\\gamma$ rays, also inject large quantities of electrons/positrons\n($e^\\pm$) into the interstellar medium. These $e^\\pm$ could potentially\ninverse-Compton (IC) scatter ambient photons into $\\gamma$ rays that fall\nwithin the sensitivity range of the upcoming Cherenkov Telescope Array (CTA).\nIn this article, we examine the detection potential of CTA to this signature by\nmaking a realistic estimation of the systematic uncertainties on the Galactic\ndiffuse emission model at TeV-scale $\\gamma$-ray energies. We forecast that, in\nthe event that $e^\\pm$ injection spectra are harder than $E^{-2}$, CTA has the\npotential to robustly discover the IC signature of a putative Galactic bulge\nMSP population sufficient to explain the GCE for $e^\\pm$ injection efficiencies\nin the range $\\approx 2.9-74.1\\%$, or higher, depending on the level of\nmismodeling of the Galactic diffuse emission components. On the other hand, for\nspectra softer than $E^{-2.5}$, a reliable CTA detection would require an\nunphysically large $e^\\pm$ injection efficiency of $\\gtrsim 158\\%$. However,\neven this pessimistic conclusion may be avoided in the plausible event that MSP\nobservational and/or modeling uncertainties can be reduced. We further find\nthat, in the event that an IC signal were detected, CTA can successfully\ndiscriminate between an MSP and a dark matter origin for the radiating $e^\\pm$.\n"}
+{"abstract": "  A useful application of event sensing is visual odometry, especially in\nsettings that require high-temporal resolution. The state-of-the-art method of\ncontrast maximisation recovers the motion from a batch of events by maximising\nthe contrast of the image of warped events. However, the cost scales with image\nresolution and the temporal resolution can be limited by the need for large\nbatch sizes to yield sufficient structure in the contrast image. In this work,\nwe propose spatiotemporal registration as a compelling technique for\nevent-based rotational motion estimation. We theoretcally justify the approach\nand establish its fundamental and practical advantages over contrast\nmaximisation. In particular, spatiotemporal registration also produces feature\ntracks as a by-product, which directly supports an efficient visual odometry\npipeline with graph-based optimisation for motion averaging. The simplicity of\nour visual odometry pipeline allows it to process more than 1 M events/second.\nWe also contribute a new event dataset for visual odometry, where motion\nsequences with large velocity variations were acquired using a high-precision\nrobot arm.\n"}
+{"abstract": "  The mean of 430 visual magnitudes of VisorSats adjusted to a distance of\n550-km (the operational altitude) is 5.92 +/-0.04. This is the characteristic\nbrightness of these satellites when observed at zenith. VisorSats average 1.29\nmagnitudes fainter than the original-design Starlink satellites and, thus, they\nare 31% as bright.\n"}
+{"abstract": "  Lossy Image compression is necessary for efficient storage and transfer of\ndata. Typically the trade-off between bit-rate and quality determines the\noptimal compression level. This makes the image quality metric an integral part\nof any imaging system. While the existing full-reference metrics such as PSNR\nand SSIM may be less sensitive to perceptual quality, the recently introduced\nlearning methods may fail to generalize to unseen data. In this paper we\npropose the largest image compression quality dataset to date with human\nperceptual preferences, enabling the use of deep learning, and we develop a\nfull reference perceptual quality assessment metric for lossy image compression\nthat outperforms the existing state-of-the-art methods. We show that the\nproposed model can effectively learn from thousands of examples available in\nthe new dataset, and consequently it generalizes better to other unseen\ndatasets of human perceptual preference.\n"}
+{"abstract": "  This paper introduces WaveGrad 2, a non-autoregressive generative model for\ntext-to-speech synthesis. WaveGrad 2 is trained to estimate the gradient of the\nlog conditional density of the waveform given a phoneme sequence. The model\ntakes an input phoneme sequence, and through an iterative refinement process,\ngenerates an audio waveform. This contrasts to the original WaveGrad vocoder\nwhich conditions on mel-spectrogram features, generated by a separate model.\nThe iterative refinement process starts from Gaussian noise, and through a\nseries of refinement steps (e.g., 50 steps), progressively recovers the audio\nsequence. WaveGrad 2 offers a natural way to trade-off between inference speed\nand sample quality, through adjusting the number of refinement steps.\nExperiments show that the model can generate high fidelity audio, approaching\nthe performance of a state-of-the-art neural TTS system. We also report various\nablation studies over different model configurations. Audio samples are\navailable at https://wavegrad.github.io/v2.\n"}
+{"abstract": "  The accuracy of a discrete-time channel model based on regular perturbation\nis numerically studied for unamplified links. We analyse the distance between\ndiscrete nonlinear interference points and show that such distance can be used\nto estimate the effective channel memory.\n"}
+{"abstract": "  Text-to-speech (TTS) synthesis is the process of producing synthesized speech\nfrom text or phoneme input. Traditional TTS models contain multiple processing\nsteps and require external aligners, which provide attention alignments of\nphoneme-to-frame sequences. As the complexity increases and efficiency\ndecreases with every additional step, there is expanding demand in modern\nsynthesis pipelines for end-to-end TTS with efficient internal aligners. In\nthis work, we propose an end-to-end text-to-waveform network with a novel\nreinforcement learning based duration search method. Our proposed generator is\nfeed-forward and the aligner trains the agent to make optimal duration\npredictions by receiving active feedback from actions taken to maximize\ncumulative reward. We demonstrate accurate alignments of phoneme-to-frame\nsequence generated from trained agents enhance fidelity and naturalness of\nsynthesized audio. Experimental results also show the superiority of our\nproposed model compared to other state-of-the-art TTS models with internal and\nexternal aligners.\n"}
+{"abstract": "  We exhibit families of non trivial (i.e. not Kaehler-Einstein) radial\nKaehler-Ricci solitons (KRS), both complete and not complete, which can be\nKaehler immersed into infinite dimensional complex space forms. This result\nshows that the triviality of a KRS induced by a finite dimensional complex\nspace form proved in [12] does not hold when the ambient space is allowed to be\ninfinite dimensional. Moreover, we show that the radial potential of a radial\nKRS induced by a non-elliptic complex space form is necessarily defined at the\norigin.\n"}
+{"abstract": "  The ability to tailor laser light on a chip using integrated photonics has\nallowed for extensive control over fundamental light-matter interactions in\nmanifold quantum systems including atoms, trapped ions, quantum dots, and\ndefect centers. Free electrons, enabling high-resolution microscopy for\ndecades, are increasingly becoming the subject of laser-based quantum\nmanipulation. Using free-space optical excitation and intense laser pulses,\nthis has led to the observation of free-electron quantum walks, attosecond\nelectron pulses, and imaging of electromagnetic fields. Enhancing the\ninteraction with electron beams through chip-based photonics promises unique\napplications in nanoscale quantum control and sensing, but has yet to enter\nelectron microscopy. Here, we merge integrated photonics with electron\nmicroscopy, demonstrating coherent phase modulation of an electron beam using a\nsilicon nitride microresonator driven by a continuous-wave laser. The high-Q\nfactor (~$10^6$) cavity enhancement and a waveguide designed for phase matching\nlead to efficient electron-light scattering at unprecedentedly low,\nfew-microwatt optical powers. Specifically, we fully deplete the initial\nelectron state at a cavity-coupled power of 6 $\\mu$W and create >500 photon\nsidebands for only 38 mW in the bus waveguide. Moreover, we demonstrate $\\mu$eV\nelectron energy gain spectroscopy (EEGS). Providing simultaneous optical and\nelectronic spectroscopy of the resonant cavity, the fiber-coupled photonic\nstructures feature single-mode electron-light interaction with full control\nover the input and output channels. This approach establishes a versatile\nframework for exploring free-electron quantum optics, with future developments\nin strong coupling, local quantum probing, and electron-photon entanglement.\nOur results highlight the potential of integrated photonics to efficiently\ninterface free electrons and light.\n"}
+{"abstract": "  Consumers only discover at the first seller which product best fits their\nneeds, then check its price online, then decide on buying. Switching sellers is\ncostly. Equilibrium prices fall in the switching cost, eventually to the\nmonopoly level, despite the exit of lower-value consumers when changing sellers\nbecomes costlier. More expensive switching makes some buyers exit the market,\nleaving fewer inframarginal buyers to the sellers. Marginal buyers may change\nin either direction, so for a range of parameters, all firms cut prices.\n"}
+{"abstract": "  The large-time behavior of a nonlinearly coupled pair of measure-valued\ntransport equations with discontinuous boundary conditions, parameterized by a\npositive real-valued parameter $\\lambda$, is considered. These equations\ndescribe the hydrodynamic or fluid limit of many-server queues with reneging\n(with traffic intensity $\\lambda$), which model phenomena in diverse\ndisciplines, including biology and operations research. For a broad class of\nreneging distributions with finite mean and service distributions with finite\nmean and hazard rate function that is either decreasing or bounded away from\nzero and infinity, it is shown that if the fluid equations have a unique\ninvariant state, then the Dirac measure at this state is the unique random\nfixed point of the fluid equations, which implies that the stationary\ndistributions of scaled $N$-server systems converge to the unique invariant\nstate of the corresponding fluid equations. Moreover, when $\\lambda\\ne 1$, it\nis shown that the solution to the fluid equation starting from any initial\ncondition converges to this unique invariant state in the large time limit. The\nproof techniques are different under the two sets of assumptions on the service\ndistribution. When the hazard rate function is decreasing, a reformulation of\nthe dynamics in terms of a certain renewal equation is used, in conjunction\nwith recursive asymptotic estimates. When the hazard rate function is bounded\naway from zero and infinity, the proof uses an extended relative entropy\nfunctional as a Lyapunov function. Analogous large-time convergence results are\nalso established for a system of coupled measure-valued equations modeling a\nmulticlass queue.\n"}
+{"abstract": "  Reservoir simulation and adaptation (also known as history matching) are\ntypically considered as separate problems. While a set of models are aimed at\nthe solution of the forward simulation problem assuming all initial geological\nparameters are known, the other set of models adjust geological parameters\nunder the fixed forward simulation model to fit production data. This results\nin many difficulties for both reservoir engineers and developers of new\nefficient computation schemes. We present a unified approach to reservoir\nsimulation and adaptation problems. A single neural network model allows a\nforward pass from initial geological parameters of the 3D reservoir model\nthrough dynamic state variables to well's production rates and backward\ngradient propagation to any model inputs and variables. The model fitting and\ngeological parameters adaptation both become the optimization problem over\nspecific parts of the same neural network model. Standard gradient-based\noptimization schemes can be used to find the optimal solution. Using real-world\noilfield model and historical production rates we demonstrate that the\nsuggested approach allows reservoir simulation and history matching with a\nbenefit of several orders of magnitude simulation speed-up. Finally, to\npropagate this research we open-source a Python-based framework DeepField that\nallows standard processing of reservoir models and reproducing the approach\npresented in this paper.\n"}
+{"abstract": "  Domain knowledge is useful to improve the generalization performance of\nlearning machines. Sign constraints are a handy representation to combine\ndomain knowledge with learning machine. In this paper, we consider constraining\nthe signs of the weight coefficients in learning the linear support vector\nmachine, and develop two optimization algorithms for minimizing the empirical\nrisk under the sign constraints. One of the two algorithms is based on the\nprojected gradient method, in which each iteration of the projected gradient\nmethod takes $O(nd)$ computational cost and the sublinear convergence of the\nobjective error is guaranteed. The second algorithm is based on the Frank-Wolfe\nmethod that also converges sublinearly and possesses a clear termination\ncriterion. We show that each iteration of the Frank-Wolfe also requires $O(nd)$\ncost. Furthermore, we derive the explicit expression for the minimal iteration\nnumber to ensure an $\\epsilon$-accurate solution by analyzing the curvature of\nthe objective function. Finally, we empirically demonstrate that the sign\nconstraints are a promising technique when similarities to the training\nexamples compose the feature vector.\n"}
+{"abstract": "  We consider tests of short-distance modifications of gravity based on neutron\ninterferometry in the scenario of large extra dimensions. Avoiding the\nnon-computability problem in the calculation of the internal gravitational\npotential of extended sources, typical of models with zero-width brane, we\ndetermine the neutron optical potential associated with the higher-dimension\ngravitational interaction between the incident neutron and a material medium in\nthe context of thick brane theories. Proceeding this way, we identify the\nphysical quantity of the extra dimension model that the neutron interferometry\nis capable of constraining. We also consider interferometric experiments in\nwhich the phase shifter is an electric field, as in the test of the\nAharanov-Casher effect. We argue that this experiment, with this non-baryonic\nsource, can be viewed as a test of the short-range behavior of Post-Newtonian\nparameters that measure the capacity of the pressure and the internal energy\nfor producing gravity.\n"}
+{"abstract": "  Topological signals defined on nodes, links and higher dimensional simplices\ndefine the dynamical state of a network or of a simplicial complex. As such,\ntopological signals are attracting increasing attention in network theory,\ndynamical systems, signal processing and machine learning. Topological signals\ndefined on the nodes are typically studied in network dynamics, while\ntopological signals defined on links are much less explored. Here we\ninvestigate topological synchronization describing locally coupled topological\nsignals defined on the nodes and on the links of a network. The dynamics of\nsignals defined on the nodes is affected by a phase lag depending on the\ndynamical state of nearby links and vice versa, the dynamics of topological\nsignals defined on the links is affected by a phase lag depending on the\ndynamical state of nearby nodes. We show that topological synchronization on a\nfully connected network is explosive and leads to a discontinuous forward\ntransition and a continuous backward transition. The analytical investigation\nof the phase diagram provides an analytical expression for the critical\nthreshold of the discontinuous explosive synchronization. The model also\ndisplays an exotic coherent synchronized phase, also called rhythmic phase,\ncharacterized by having non-stationary order parameters which can shed light on\ntopological mechanisms for the emergence of brain rhythms.\n"}
+{"abstract": "  This paper investigates regularized estimation of Kronecker-structured\ncovariance matrices (CM) for complex elliptically symmetric (CES) data. To\nobtain a well-conditioned estimate of the CM, we add penalty terms of\nKullback-Leibler divergence to the negative log-likelihood function of the\nassociated complex angular Gaussian (CAG) distribution. This is shown to be\nequivalent to regularizing Tyler's fixed-point equations by shrinkage. A\nsufficient condition that the solution exists is discussed. An iterative\nalgorithm is applied to solve the resulting fixed-point iterations and its\nconvergence is proved. In order to solve the critical problem of tuning the\nshrinkage factors, we then introduce three methods by exploiting oracle\napproximating shrinkage (OAS) and cross-validation (CV). When the training\nsamples are limited, the proposed estimator, referred to as the robust\nshrinkage Kronecker estimator (RSKE), has better performance compared with\nseveral existing methods. Simulations are conducted for validating the proposed\nestimator and demonstrating its high performance.\n"}
+{"abstract": "  In this paper we introduce a method for stacking data cubelets extracted from\ninterferometric surveys of galaxies in the redshifted 21-cm H\\,\\textsc{i} line.\nUnlike the traditional spectral stacking technique, which stacks\none-dimensional spectra extracted from data cubes, we examine a method based on\nimage domain stacks which makes deconvolution possible. To test the validity of\nthis assumption, we mock a sample of 3622 equatorial galaxies extracted from\nthe GAMA survey, recently imaged as part of a DINGO-VLA project. We first\nexamine the accuracy of the method using a noise-free simulation and note that\nthe stacked image and flux estimation are dramatically improved compared to\ntraditional stacking. The extracted H\\,\\textsc{i} mass from the deconvolved\nimage agrees with the average input mass to within 3\\%. However, with\ntraditional spectral stacking, the derived H\\,\\textsc{i} is incorrect by\ngreater than a factor of 2. For a more realistic case of a stack with finite\nS/N, we also produced 20 different noise realisations to closely mimic the\nproperties of the DINGO-VLA interferometric survey. We recovered the predicted\naverage H\\,\\textsc{i} mass to within $\\sim$4\\%. Compared with traditional\nspectral stacking, this technique extends the range of science applications\nwhere stacking can be used, and is especially useful for characterizing the\nemission from extended sources with interferometers.\n"}
+{"abstract": "  Connected and autonomous vehicles (CAVs) are an innovative form of\ntraditional vehicles. Automotive Ethernet replaces the controller area network\nand FlexRay to support the large throughput required by high-definition\napplications. As CAVs have numerous functions, they exhibit a large attack\nsurface and an increased vulnerability to attacks. However, no previous studies\nhave focused on intrusion detection in automotive Ethernet-based networks. In\nthis paper, we present an intrusion detection method for detecting audio-video\ntransport protocol (AVTP) stream injection attacks in automotive Ethernet-based\nnetworks. To the best of our knowledge, this is the first such method developed\nfor automotive Ethernet. The proposed intrusion detection model is based on\nfeature generation and a convolutional neural network (CNN). To evaluate our\nintrusion detection system, we built a physical BroadR-Reach-based testbed and\ncaptured real AVTP packets. The experimental results show that the model\nexhibits outstanding performance: the F1-score and recall are greater than\n0.9704 and 0.9949, respectively. In terms of the inference time per input and\nthe generation intervals of AVTP traffic, our CNN model can readily be employed\nfor real-time detection.\n"}
+{"abstract": "  The robust principles of treating interference as noise (TIN) when it is\nsufficiently weak, and avoiding it when it is not, form the background of this\nwork. Combining TIN with the topological interference management (TIM)\nframework that identifies optimal interference avoidance schemes, we formulate\na TIM-TIN problem for multilevel topological interference management, wherein\nonly a coarse knowledge of channel strengths and no knowledge of channel phases\nis available to transmitters. To address the TIM-TIN problem, we first propose\nan analytical baseline approach, which decomposes a network into TIN and TIM\ncomponents, allocates the signal power levels to each user in the TIN\ncomponent, allocates signal vector space dimensions to each user in the TIM\ncomponent, and guarantees that the product of the two is an achievable number\nof signal dimensions available to each user in the original network. Next, a\ndistributed numerical algorithm called ZEST is developed. The convergence of\nthe algorithm is demonstrated, leading to the duality of the TIM-TIN problem\n(in terms of GDoF). Numerical results are also provided to demonstrate the\nsuperior sum-rate performance and fast convergence of ZEST.\n"}
+{"abstract": "  Federated learning is a new learning paradigm that decouples data collection\nand model training via multi-party computation and model aggregation. As a\nflexible learning setting, federated learning has the potential to integrate\nwith other learning frameworks. We conduct a focused survey of federated\nlearning in conjunction with other learning algorithms. Specifically, we\nexplore various learning algorithms to improve the vanilla federated averaging\nalgorithm and review model fusion methods such as adaptive aggregation,\nregularization, clustered methods, and Bayesian methods. Following the emerging\ntrends, we also discuss federated learning in the intersection with other\nlearning paradigms, termed as federated x learning, where x includes multitask\nlearning, meta-learning, transfer learning, unsupervised learning, and\nreinforcement learning. This survey reviews the state of the art, challenges,\nand future directions.\n"}
+{"abstract": "  Observations have revealed that disturbances in the cold neutral atomic\nhydrogen (HI) in galaxies are ubiquitous, but the reasons for these\ndisturbances remain unclear. While some studies suggest that asymmetries in\nintegrated HI spectra (global HI asymmetry) are higher in HI-rich systems,\nothers claim that they are preferentially found in HI-poor galaxies. In this\nwork, we utilise the ALFALFA and xGASS surveys, plus a sample of post-merger\ngalaxies, to clarify the link between global HI asymmetry and the gas\nproperties of galaxies. Focusing on star-forming galaxies in ALFALFA, we find\nthat elevated global HI asymmetry is not associated with a change in the HI\ncontent of a galaxy, and that only the galaxies with the highest global HI\nasymmetry show a small increase in specific star-formation rate (sSFR).\nHowever, we show that the lack of a trend with HI content is because ALFALFA\nmisses the gas-poor tail of the star-forming main-sequence. Using xGASS to\nobtain a sample of star-forming galaxies that is representative in both sSFR\nand HI content, we find that global HI asymmetric galaxies are typically more\ngas-poor than symmetric ones at fixed stellar mass, with no change in sSFR. Our\nresults highlight the complexity of the connection between galaxy properties\nand global HI asymmetry. This is further confirmed by the fact that even\npost-merger galaxies show both symmetric and asymmetric HI spectra,\ndemonstrating that merger activity does not always lead to an asymmetric global\nHI spectrum.\n"}
+{"abstract": "  We introduce DBS, a new technique for user selection in downlink multi-user\ncommunications with extra-large (XL) antenna arrays. DBS categorizes users\naccording to their equivalent distance to the antenna array. Such\ncategorization effectively accounts for inter-user interference while largely\nreducing the computational burden. Results show that (i) DBS achieves the same\nperformance as the reference zero-forcing beamforming scheme with a lower\ncomplexity; (ii) a simplified version of DBS achieves a similar performance\nwhen realistic spherical-wavefront (SW) propagation features are considered;\n(iii) SW propagation brings additional degrees of freedom, which allows for\nincreasing the number of served users.\n"}
+{"abstract": "  The emerging possibilities to steer and control electronic motion on subcycle\ntime scales with strong electric fields enable studying the nonperturbative\noptical response and Bloch bands' topological properties, originated from\nBerry's trilogy: connection, curvature, and phase. This letter introduces a\ntheoretical framework for the nonperturbative electron dynamics in\ntwo-dimensional (2D) crystalline solids induced by the few-cycle and\nstrong-field optical lasers. In the presented model, the expression associated\nwith the Bloch band topology and broken crystal symmetry merges\nself-consistently in the system observables such as High Harmonic Generation\n(HHG). This singles out our work from recent HHG calculations from the\nstrongly-driven systems. Concisely, in our theoretical experiment on 2D\nmaterials in the strong-field optical regime, we show that Bloch band topology\nand broken symmetry manifest themselves in several ways: the momentum-resolved\nattosecond interferometry of electron wave packets, anomalous and chiral\nvelocity in both intraband and interband dynamics, anomalous Hall current and\nrespective HHG highly sensitive to the laser waveform, multiple plateau-cutoff\nstructures in both longitudinal and transverse HHG, the formation of even\nharmonics in the perpendicular polarization with respect to the driving laser,\nsingular jumps across the phase diagram of the HHG, attosecond chirp, and\nultrafast valley polarization induced by the chiral gauge field that is robust\nto lattice imperfections and scattering. The link between HHG and solid-state\nband geometry offers an all-optical reconstruction of electron band structure\nby optical means, and accelerates studies on the non-equilibrium Floquet\nengineering, topologically-protected nonlinear spin and edge currents,\nvalleytronics, quantum computing and high-temperature superconductivity on\nsub-femtosecond time scales.\n"}
+{"abstract": "  We discuss a semi-discrete analogue of the Unified Transform Method,\nintroduced by A. S. Fokas, to solve initial-boundary-value problems for linear\nevolution partial differential equations of constant coefficients. The\nsemi-discrete method is applied to various spacial discretizations of several\nfirst and second-order linear equations on the half-line $x \\geq 0$, producing\nthe exact solution for the semi-discrete problem, given appropriate initial and\nboundary data. We additionally show how the Unified Transform Method treats\nderivative boundary conditions and ghost points introduced by the choice of\ndiscretization stencil. We consider the continuum limit of the semi-discrete\nsolutions and provide several numerical examples.\n"}
+{"abstract": "  Scalar bosonic stars (BSs) stand out as a multi-purpose model of exotic\ncompact objects. We enlarge the landscape of such (asymptotically flat,\nstationary, everywhere regular) objects by considering multiple fields\n(possibly) with different frequencies. This allows for new morphologies ${\\it\nand}$ a stabilization mechanism for different sorts of unstable BSs. First, any\nodd number of complex fields, yields a continuous family of BSs departing from\nthe spherical, equal frequency, $\\ell-$BSs. As the simplest illustration, we\nconstruct the $\\ell$ = ${\\it 1}$ ${\\it BSs}$ ${\\it family}$, that includes\nseveral single frequency solutions, including even parity (such as spinning BSs\nand a toroidal, static BS) and odd parity (a dipole BS) limits. Second, these\nlimiting solutions are dynamically unstable, but can be stabilized by a ${\\it\nhybrid}$-$\\ell$ construction: adding a sufficiently large fundamental $\\ell=0$\nBS of another field, with a different frequency. Evidence for this dynamical\nrobustness is obtained by non-linear numerical simulations of the corresponding\nEinstein-(complex, massive) Klein-Gordon system, both in formation and\nevolution scenarios, and a suggestive correlation between stability and energy\ndistribution is observed. Similarities and differences with vector BSs are\nanticipated.\n"}
+{"abstract": "  We present Query2Prod2Vec, a model that grounds lexical representations for\nproduct search in product embeddings: in our model, meaning is a mapping\nbetween words and a latent space of products in a digital shop. We leverage\nshopping sessions to learn the underlying space and use merchandising\nannotations to build lexical analogies for evaluation: our experiments show\nthat our model is more accurate than known techniques from the NLP and IR\nliterature. Finally, we stress the importance of data efficiency for product\nsearch outside of retail giants, and highlight how Query2Prod2Vec fits with\npractical constraints faced by most practitioners.\n"}
+{"abstract": "  The thermal Sunyaev-Zeldovich effect contains information about the thermal\nhistory of the universe, observable in maps of the Compton $y$ parameter;\nhowever, it does not contain information about the redshift of the sources.\nRecent papers have utilized a tomographic approach, cross-correlating the\nCompton $y$ map with the locations of galaxies with known redshift, in order to\ndeproject the signal along the line of sight. In this paper, we test the\nvalidity and accuracy of this tomographic approach to probe the thermal history\nof the universe. We use the state-of-the-art cosmological hydrodynamical\nsimulation, Magneticum, for which the thermal history of the universe is a\nknown quantity. The key ingredient is the Compton-$y$-weighted halo bias,\n$b_y$, computed from the halo model. We find that, at redshifts currently\navailable, the method reproduces the correct mean thermal pressure (or the\ndensity-weighted mean temperature) to high accuracy, validating and confirming\nthe results of previous papers. At higher redshifts ($z\\gtrsim 2.5$), there is\nsignificant disagreement between $b_y$ from the halo model and the simulation.\n"}
+{"abstract": "  We consider a prototypical nonlinear parabolic equation whose flux has three\ndistinguished features: it is nonlinear with respect to both the unknown and\nits gradient, it is homogeneous, and it depends only on the direction of the\ngradient. For such equation, we obtain existence and uniqueness of entropy\nsolutions to the Dirichlet problem, the homogeneous Neumann problem, and the\nCauchy problem. Qualitative properties of solutions, such as finite speed of\npropagation and the occurrence of waiting-time phenomena, with sharp bounds,\nare shown. We also discuss the formation of jump discontinuities both at the\nboundary of the solutions' support and in the bulk.\n"}
+{"abstract": "  We investigate the effect of the thermal imbalance on the structural\nstability of the magnetohydrodynamic model of the preflare current layer\n(Ledentsov, Sol. Phys. 296, 74, 2021). The piecewise homogeneous model of the\ncurrent layer is supplemented by a magnetic field longitudinal with respect to\nthe direction of the current. It is shown that the presence of a weak\nlongitudinal field does not change the previously calculated spatial period of\nthe thermal instability in the most expected range of the parameters of the\npreflare current layer and, moreover, contributes to the formation of the\ninstability. On the other hand, a strong longitudinal magnetic field\ncontributes to the spatial stabilization of the current layer.\n"}
+{"abstract": "  This paper describes a KGD-type model of a hydraulic fracture created by\ninjecting fluid in weak, poorly consolidated rocks. The model is based on the\nkey assumption that the hydraulic fracture is propagating within a domain where\nthe hydraulic fields are quasi-stationary. By further assuming a negligible\ntoughness, the fracture is shown to grow as a square root of time. The\nasymptotic fracture propagation regimes at small and large time are constructed\nand the transient solution is computed by solving a nonlinear system of\nalgebraic equations formulated in terms of the fracture aperture. At early time\nthe fracture is hydraulically invisible and the injection pressure increases\nwith time $t$ as $\\log t$, while at late time leak-off from the borehole is\nnegligible and the injection pressure decreases as $t^{-1/4}$. According to\nthis model, the peak injection pressure observed when injecting fluid in weak,\npoorly consolidated rocks should not be interpreted as indicating a breakdown\nof the formation, but rather as marking the transition between two asymptotic\nflow regimes. The timescale that legislates the transition between the small\nand large time asymptotic regimes is shown to be a strongly nonlinear function\nof a dimensionless injection rate.\n"}
+{"abstract": "  With the heightened volatility in stock prices during the Covid-19 pandemic,\nthe need for price forecasting has become more critical. We investigated the\nforecast performance of four models including Long-Short Term Memory, XGBoost,\nAutoregression, and Last Value on stock prices of Facebook, Amazon, Tesla,\nGoogle, and Apple in COVID-19 pandemic time to understand the accuracy and\npredictability of the models in this highly volatile time region. To train the\nmodels, the data of all stocks are split into train and test datasets. The test\ndataset starts from January 2020 to April 2021 which covers the COVID-19\npandemic period. The results show that the Autoregression and Last value models\nhave higher accuracy in predicting the stock prices because of the strong\ncorrelation between the previous day and the next day's price value.\nAdditionally, the results suggest that the machine learning models (Long-Short\nTerm Memory and XGBoost) are not performing as well as Autoregression models\nwhen the market experiences high volatility.\n"}
+{"abstract": "  Large Transformer-based language models are pre-trained on corpora of varying\nsizes, for a different number of steps and with different batch sizes. At the\nsame time, more fundamental components, such as the pre-training objective or\narchitectural hyperparameters, are modified. In total, it is therefore\ndifficult to ascribe changes in performance to specific factors. Since\nsearching the hyperparameter space over the full systems is too costly, we\npre-train down-scaled versions of several popular Transformer-based\narchitectures on a common pre-training corpus and benchmark them on a subset of\nthe GLUE tasks (Wang et al., 2018). Specifically, we systematically compare\nthree pre-training objectives for different shape parameters and model sizes,\nwhile also varying the number of pre-training steps and the batch size. In our\nexperiments MLM + NSP (BERT-style) consistently outperforms MLM (RoBERTa-style)\nas well as the standard LM objective. Furthermore, we find that additional\ncompute should be mainly allocated to an increased model size, while training\nfor more steps is inefficient. Based on these observations, as a final step we\nattempt to scale up several systems using compound scaling (Tan and Le, 2019)\nadapted to Transformer-based language models.\n"}
+{"abstract": "  Following Brown[1], we construct composite operators for the scalar $\\phi^3$\ntheory in six dimensions using renormalisation group methods with dimensional\nregularisation. We express bare scalar operators in terms of renormalised\ncomposite operators of low dimension, then do this with traceless tensor\noperators. We then express the bare energy momentum tensor in terms of the\nrenormalised composite operators, with some terms having divergent\ncoefficients. We subtract these away and obtain a manifestly finite energy\ntensor. The subtracted terms are transverse, so this does not affect the\nconservation of the energy momentum tensor. The trace of this finite improved\nenergy momentum tensor vanishes at the fixed point indicating conformal\ninvariance. Interestingly it is not RG-invariant except at the fixed point, but\ncan be made RG invariant everywhere by further addition of transverse terms,\nwhose coefficients vanish at the fixed point.\n"}
+{"abstract": "  The distributional reinforcement learning (RL) approach advocates for\nrepresenting the complete probability distribution of the random return instead\nof only modelling its expectation. A distributional RL algorithm may be\ncharacterised by two main components, namely the representation and\nparameterisation of the distribution and the probability metric defining the\nloss. This research considers the unconstrained monotonic neural network (UMNN)\narchitecture, a universal approximator of continuous monotonic functions which\nis particularly well suited for modelling different representations of a\ndistribution (PDF, CDF, quantile function). This property enables the\ndecoupling of the effect of the function approximator class from that of the\nprobability metric. The paper firstly introduces a methodology for learning\ndifferent representations of the random return distribution. Secondly, a novel\ndistributional RL algorithm named unconstrained monotonic deep Q-network\n(UMDQN) is presented. Lastly, in light of this new algorithm, an empirical\ncomparison is performed between three probability quasimetrics, namely the\nKullback-Leibler divergence, Cramer distance and Wasserstein distance. The\nresults call for a reconsideration of all probability metrics in distributional\nRL, which contrasts with the dominance of the Wasserstein distance in recent\npublications.\n"}
+{"abstract": "  We propose a self-consistent theoretical framework of chorus wave excitation,\nwhich describes the evolution of the whistler fluctuation spectrum as well as\nthe supra-thermal electron distribution function. The renormalized hot electron\nresponse is cast in the form of a Dyson-like equation, which then leads to\nevolution equations for nonlinear fluctuation growth and frequency shift. This\napproach allows us to analytically derive for the first time exactly the same\nexpression for the chorus chirping rate originally proposed by Vomvoridis et\nal.,1982. Chorus chirping is shown to correspond to maximization of wave\nparticle power exchange, where each individual wave belonging to the whistler\nwave packet is characterized by small nonlinear frequency shift. We also show\nthat different interpretations of chorus chirping proposed in published\nliterature have a consistent reconciliation within the present theoretical\nframework, which further illuminates the analogy with similar phenomena in\nfusion plasmas and free electron laser physics.\n"}
+{"abstract": "  Recent methods for long-tailed instance segmentation still struggle on rare\nobject classes with few training data. We propose a simple yet effective\nmethod, Feature Augmentation and Sampling Adaptation (FASA), that addresses the\ndata scarcity issue by augmenting the feature space especially for rare\nclasses. Both the Feature Augmentation (FA) and feature sampling components are\nadaptive to the actual training status -- FA is informed by the feature mean\nand variance of observed real samples from past iterations, and we sample the\ngenerated virtual features in a loss-adapted manner to avoid over-fitting. FASA\ndoes not require any elaborate loss design, and removes the need for\ninter-class transfer learning that often involves large cost and\nmanually-defined head/tail class groups. We show FASA is a fast, generic method\nthat can be easily plugged into standard or long-tailed segmentation\nframeworks, with consistent performance gains and little added cost. FASA is\nalso applicable to other tasks like long-tailed classification with\nstate-of-the-art performance.\n"}
+{"abstract": "  ELECTRA pretrains a discriminator to detect replaced tokens, where the\nreplacements are sampled from a generator trained with masked language\nmodeling. Despite the compelling performance, ELECTRA suffers from the\nfollowing two issues. First, there is no direct feedback loop from\ndiscriminator to generator, which renders replacement sampling inefficient.\nSecond, the generator's prediction tends to be over-confident along with\ntraining, making replacements biased to correct tokens. In this paper, we\npropose two methods to improve replacement sampling for ELECTRA pre-training.\nSpecifically, we augment sampling with a hardness prediction mechanism, so that\nthe generator can encourage the discriminator to learn what it has not\nacquired. We also prove that efficient sampling reduces the training variance\nof the discriminator. Moreover, we propose to use a focal loss for the\ngenerator in order to relieve oversampling of correct tokens as replacements.\nExperimental results show that our method improves ELECTRA pre-training on\nvarious downstream tasks.\n"}
+{"abstract": "  For unramified reductive groups, we determine the connected components of\naffine Deligne-Lusztig varieties in the partial affine flag varieties. Based on\nthe work of Hamacher-Kim and Zhou, this result allows us to verify, in the\nunramified group case, the He-Rapoport axioms, the ``almost product structure\"\nof Newton strata, and the precise description of mod $p$ isogeny classes\npredicted by the Langlands-Rapoport conjecture, for the Kisin-Pappas integral\nmodels of Shimura varieties of Hodge type with parahoric level structure.\n"}
+{"abstract": "  Generative Adversarial Networks (GANs) are able to generate high-quality\nimages, but it remains difficult to explicitly specify the semantics of\nsynthesized images. In this work, we aim to better understand the semantic\nrepresentation of GANs, and thereby enable semantic control in GAN's generation\nprocess. Interestingly, we find that a well-trained GAN encodes image semantics\nin its internal feature maps in a surprisingly simple way: a linear\ntransformation of feature maps suffices to extract the generated image\nsemantics. To verify this simplicity, we conduct extensive experiments on\nvarious GANs and datasets; and thanks to this simplicity, we are able to learn\na semantic segmentation model for a trained GAN from a small number (e.g., 8)\nof labeled images. Last but not least, leveraging our findings, we propose two\nfew-shot image editing approaches, namely Semantic-Conditional Sampling and\nSemantic Image Editing. Given a trained GAN and as few as eight semantic\nannotations, the user is able to generate diverse images subject to a\nuser-provided semantic layout, and control the synthesized image semantics. We\nhave made the code publicly available.\n"}
+{"abstract": "  The citizenship identities of a nation's occupants enable the state to\nidentify and authenticate them unquestionably. These documents help individuals\nin recognizing themselves and to profit from the rights and advantages given to\nthem by the legislature or the constitution of the land. There are problems in\nthe traditional way of issuance f these identities and many hurdles that impede\npeople from getting their benefits or exercising their rights. These\npaper-based identities can be forged easily and are hard to authenticate at\nvarious civil end points. There are reports of identity thefts. In this paper,\nwe are discussing how Blockchain can be employed to overcome these problems and\nmakes these identities confidential, immutable, and secured. Blockchain\ntechnology can help the governing bodies in maintaining and verifying these\nidentities in a quick manner with less chance for human errors, meaning more\nreach for government plans and aid\n"}
+{"abstract": "  Let K be a cubic number field. In this paper, we study the Ramanujan sums\nc_{J}(I), where I and J are integral ideals in O_{K}. The asymptotic behaviour\nof sums of c_{J}(I) over both I and J is investigated.\n"}
+{"abstract": "  We study magnetohydrodynamic stability of neutron star core matter composed\nof neutrons, protons and leptons threaded by a magnetar-strength magnetic field\n$10^{14}$--$10^{17}$ G, where quantum electrodynamical effects and Landau\nquantization of fermions are important. Stability is determined using the\nFriedman--Schutz formalism for the canonical energy of fluid perturbations,\nwhich we calculate for a magnetizable fluid with $H\\neq B$. Using this and the\nEuler--Heisenberg--Fermi--Dirac Lagrangian for a strongly magnetized fluid of\nLandau-quantized charged fermions, we calculate the local stability criteria\nfor a fluid slab as a stand-in for a segment of a neutron star core, accounting\nfor magnetic and composition gradient buoyancy. The slab is threaded by a field\northogonal to the gravitational field, the Cartesian analogy to a toroidal\nfield. We find that, for sufficiently strong fields $B\\gtrsim10^{15}$ G, the\nmagnetized fluid is unstable to a magnetosonic-type instability with growth\ntimes of order $10^{-3}$ s. The instability is triggered by sharp changes in\nthe second-order field derivative of the Euler--Heisenberg--Fermi--Dirac\nLagrangian which occur where additional Landau levels start being populated.\nThese sharp changes are divergent at zero temperature, but are finite for\nnonzero temperature, so realistic neutron star core temperatures $5\\times10^7$\nK$<T<5\\times10^8$ K are used. We conjecture that this mechanism could promote\nthe formation of magnetic domains as predicted by Blandford and Hernquist\n(1982) and Suh and Mathews (2010).\n"}
+{"abstract": "  Several Ising-type magnetic van der Waals (vdW) materials exhibit stable\nmagnetic ground states. Despite these clear experimental demonstrations, a\ncomplete theoretical and microscopic understanding of their magnetic anisotropy\nis still lacking. In particular, the validity limit of identifying their\none-dimensional (1-D) Ising nature has remained uninvestigated in a\nquantitative way. Here we performed the complete mapping of magnetic anisotropy\nfor a prototypical Ising vdW magnet FePS$_3$ for the first time. Combining\ntorque magnetometry measurements with their magnetostatic model analysis and\nthe relativistic density functional total energy calculations, we successfully\nconstructed the three-dimensional (3-D) mappings of the magnetic anisotropy in\nterms of magnetic torque and energy. The results not only quantitatively\nconfirm that the easy axis is perpendicular to the $ab$ plane, but also reveal\nthe anisotropies within the $ab$, $ac$, and $bc$ planes. Our approach can be\napplied to the detailed quantitative study of magnetism in vdW materials.\n"}
+{"abstract": "  Swarming behavior, where coherent motion emerges from the interactions of\nmany mobile agents, is ubiquitous in physics and biology. Moreover, there are\nmany efforts to replicate swarming dynamics in mobile robotic systems which\ntake inspiration from natural swarms. In particular, understanding how swarms\ncome apart, change their behavior, and interact with other swarms is a research\ndirection of special interest to the robotics and defense communities. Here we\ndevelop a theoretical approach that can be used to predict the parameters under\nwhich colliding swarms form a stable milling state. Our analytical methods rely\non the assumption that, upon collision, two swarms oscillate near a\nlimit-cycle, where each swarm rotates around the other while maintaining an\napproximately constant density. Using our methods, we are able to predict the\ncritical swarm-swarm interaction coupling (below which two colliding swarms\nmerely scatter) for nearly aligned collisions as a function of physical swarm\nparameters. We show that the critical coupling corresponds to a saddle-node\nbifurcation of a limit-cycle in the constant-density approximation. Finally, we\nshow preliminary results from experiments in which two swarms of micro UAVs\ncollide and form a milling state, which is in general agreement with our\ntheory.\n"}
+{"abstract": "  We give an exposition of the hidden subgroup problem for dihedral groups from\nthe point of view of the standard hidden subgroup quantum algorithm for finite\ngroups. In particular, we recall the obstructions for strong Fourier sampling\nto succeed, but at the same time, show how the standard algorithm can be\nmodified to establish polynomial quantum query complexity. Finally, we explain\na new connection between the dihedral coset problem and cloning of quantum\nstates.\n"}
+{"abstract": "  In this paper we examine the puzzle of neutron formation in atmospheric\ndischarge. While conventional explanation relies on photonuclear reactions,\nvoltages used to recreate such discharges in the lab (1 MV) are thought to be\nnot high enough to accelerate electrons beyond the threshold of photonuclear\nreactions (10.5 MeV). We have proposed a solution to the neutron formation\npuzzle and point out that rapidly changing magnetic vector potential creates\nstrong electrokinetic field that can accelerate free electrons to energies that\nare much higher than what is possible with the voltage of the discharge. We\nreport on the development of highly sophisticated and extremely sensitive\nneutron detector system that can reliably detect neutron fluxes smaller than\n0.1 CPS above background and is completely impervious to EM noise. Finally, we\npresent measurements of weak yet statistically significant neutron fluxes\noriginating from sustained 20-30 kV / 20-30 mA X-ray producing discharge in\ndilute atmosphere.\n"}
+{"abstract": "  We formulate a substantial improvement on Buchberger's algorithm for\nGr\\\"obner bases of zero-dimensional ideals. The improvement scales down the\nphenomenon of intermediate expression swell as well as the complexity of\nBuchberger's algorithm to a significant degree. The idea is to compute a new\ntype of bases over principal ideal rings instead of over fields like Gr\\\"obner\nbases. The generalizations of Buchberger's algorithm from over fields to over\nrings are abundant in the literature. However they are limited to either\ncomputations of strong Gr\\\"obner bases or modular computations of the numeral\ncoefficients of ideal bases with no essential improvement on the algorithmic\ncomplexity. In this paper we make pseudo-divisions with multipliers to enhance\ncomputational efficiency. In particular, we develop a new methodology in\ndetermining the authenticity of the factors of the pseudo-eliminant, i.e., we\ncompare the factors with the multipliers of the pseudo-divisions instead of the\nleading coefficients of the basis elements. In order to find out the exact form\nof the eliminant, we contrive a modular algorithm of proper divisions over\nprincipal quotient rings with zero divisors. The pseudo-eliminant and proper\neliminants and their corresponding bases constitute a decomposition of the\noriginal ideal. In order to address the ideal membership problem, we elaborate\non various characterizations of the new type of bases. In the complexity\nanalysis we devise a scenario linking the rampant intermediate coefficient\nswell to B\\'ezout coefficients, partially unveiling the mystery of hight-level\ncomplexity associated with the computation of Gr\\\"obner bases. Finally we make\nexemplary computations to demonstrate the conspicuous difference between\nGr\\\"obner bases and the new type of bases.\n"}
+{"abstract": "  We consider a peer-to-peer electricity market, where agents hold private\ninformation that they might not want to share. The problem is modeled as a\nnoncooperative communication game, which takes the form of a Generalized Nash\nEquilibrium Problem, where the agents determine their randomized reports to\nshare with the other market players, while anticipating the form of the\npeer-to-peer market equilibrium. In the noncooperative game, each agent decides\non the deterministic and random parts of the report, such that (a) the distance\nbetween the deterministic part of the report and the truthful private\ninformation is bounded and (b) the expectation of the privacy loss random\nvariable is bounded. This allows each agent to change her privacy level. We\ncharacterize the equilibrium of the game, prove the uniqueness of the\nVariational Equilibria and provide a closed form expression of the privacy\nprice. In addition, we provide a closed form expression to measure the impact\nof the privacy preservation caused by inclusion of random noise and\ndeterministic deviation from agents' true values. Numerical illustrations are\npresented on the 14-bus IEEE network.\n"}
+{"abstract": "  It is well known that in the Noetherian local ring with infinite residue\nfield the reduction of $\\mm$-primary ideal may be given in the form of a\nsufficiently general linear combination of its generators. In the paper we give\na condition for the existence of such reduction in terms of the sum of degrees\nof the ideal fiber cone prime divisors in the case of any Noetherian local\nring.\n"}
+{"abstract": "  Traditional bulk load flexibility options, such as load shifting and load\ncurtailment, for managing uncertainty in power markets limit the diversity of\noptions and ignore the preferences of the individual loads, thus reducing\nefficiency and welfare. This paper proposes an alternative to bulk load\nflexibility options for managing uncertainty in power markets: a reinforcement\nlearning based dynamic matching market. We propose a novel hybrid\nlearning-based model for maximizing social welfare in the dynamic matching\nmarket. The key features of our model is a fixed rule-based function and a\nlearnable component that can be trained by data gathered online with no prior\nknowledge or expert supervision. The output of the learnable component is a\nprobability distribution over the matching decisions for the individual\ncustomers. The proposed hybrid model enables the learning algorithm to find an\neffective matching policy that simultaneously satisfies the customers'\nservicing preferences. The simulations show that the learning algorithm learns\nan effective matching policy for different generation-consumption profiles and\nexhibits better performance compared to standard online matching heuristics\nsuch as Match on Arrival, Match to the Highest, and Match to the Earliest\nDeadline policies.\n"}
+{"abstract": "  The availability of large spatial data geocoded at accurate locations has\nfueled a growing interest in spatial modeling and analysis of point processes.\nThe proposed research is motivated by the intensity estimation problem for\nlarge spatial point patterns on complex domains in $\\mathbb{R}^2$ (e.g.,\ndomains with irregular boundaries, sharp concavities, and/or interior holes due\nto geographic constraints) and linear networks, where many existing spatial\npoint process models suffer from the problems of \"leakage\" and computation. We\npropose an efficient intensity estimation algorithm to estimate the spatially\nvarying intensity function and to study the varying relationship between\nintensity and explanatory variables on complex domains. The method is built\nupon a graph regularization technique and hence can be flexibly applied to\npoint patterns on complex domains such as regions with irregular boundaries and\nholes, or linear networks. An efficient proximal gradient optimization\nalgorithm is proposed to handle large spatial point patterns. We also derive\nthe asymptotic error bound for the proposed estimator. Numerical studies are\nconducted to illustrate the performance of the method. Finally, we apply the\nmethod to study and visualize the intensity patterns of the accidents on the\nWestern Australia road network, and the spatial variations in the effects of\nincome, lights condition, and population density on the Toronto homicides\noccurrences.\n"}
+{"abstract": "  The principles of density-functional theory are studied for finite lattice\nsystems represented by graphs. Surprisingly, the fundamental Hohenberg-Kohn\ntheorem is found void in general, while many insights into the topological\nstructure of the density-potential mapping can be won. We give precise\nconditions for a ground state to be uniquely v-representable and are able to\nprove that this property holds for almost all densities. A set of examples\nillustrates the theory and demonstrates the non-convexity of the pure-state\nconstrained-search functional.\n"}
+{"abstract": "  Coarse structural nested mean models are used to estimate treatment effects\nfrom longitudinal observational data. Coarse structural nested mean models lead\nto a large class of estimators. It turns out that estimates and standard errors\nmay differ considerably within this class. We prove that, under additional\nassumptions, there exists an explicit solution for the optimal estimator within\nthe class of coarse structural nested mean models. Moreover, we show that even\nif the additional assumptions do not hold, this optimal estimator is\ndoubly-robust: it is consistent and asymptotically normal not only if the model\nfor treatment initiation is correct, but also if a certain outcome-regression\nmodel is correct.\n  We compare the optimal estimator to some naive choices within the class of\ncoarse structural nested mean models in a simulation study. Furthermore, we\napply the optimal and naive estimators to study how the CD4 count increase due\nto one year of antiretroviral treatment (ART) depends on the time between HIV\ninfection and ART initiation in recently infected HIV infected patients. Both\nin the simulation study and in the application, the use of optimal estimators\nleads to substantial increases in precision.\n"}
+{"abstract": "  Existing deep learning methods for solving mean-field games (MFGs) with\ncommon noise fix the sampling common noise paths and then solve the\ncorresponding MFGs. This leads to a nested-loop structure with millions of\nsimulations of common noise paths in order to produce accurate solutions, which\nresults in prohibitive computational cost and limits the applications to a\nlarge extent. In this paper, based on the rough path theory, we propose a novel\nsingle-loop algorithm, named signatured deep fictitious play, by which we can\nwork with the unfixed common noise setup to avoid the nested-loop structure and\nreduce the computational complexity significantly. The proposed algorithm can\naccurately capture the effect of common uncertainty changes on mean-field\nequilibria without further training of neural networks, as previously needed in\nthe existing machine learning algorithms. The efficiency is supported by three\napplications, including linear-quadratic MFGs, mean-field portfolio game, and\nmean-field game of optimal consumption and investment. Overall, we provide a\nnew point of view from the rough path theory to solve MFGs with common noise\nwith significantly improved efficiency and an extensive range of applications.\nIn addition, we report the first deep learning work to deal with extended MFGs\n(a mean-field interaction via both the states and controls) with common noise.\n"}
+{"abstract": "  A group topology is said to be linear if open subgroups form a base of\nneighborhoods of the identity element. It is proved that the existence of a\nnondiscrete extremally disconnected group of Ulam nonmeasurable cardinality\nwith linear topology implies that of a nondiscrete extremally disconnected\ngroup of cardinality at most $2^\\omega$ with linear topology.\n"}
+{"abstract": "  The task of identifying emotions from a given music track has been an active\npursuit in the Music Information Retrieval (MIR) community for years. Music\nemotion recognition has typically relied on acoustic features, social tags, and\nother metadata to identify and classify music emotions. The role of lyrics in\nmusic emotion recognition remains under-appreciated in spite of several studies\nreporting superior performance of music emotion classifiers based on features\nextracted from lyrics. In this study, we use the transformer-based approach\nmodel using XLNet as the base architecture which, till date, has not been used\nto identify emotional connotations of music based on lyrics. Our proposed\napproach outperforms existing methods for multiple datasets. We used a robust\nmethodology to enhance web-crawlers' accuracy for extracting lyrics. This study\nhas important implications in improving applications involved in playlist\ngeneration of music based on emotions in addition to improving music\nrecommendation systems.\n"}
+{"abstract": "  In this paper, we investigate some qualitative properties of solutions to the\nMoore-Gibson-Thompson (MGT) equation and the Jordan-MGT (JMGT) equation with\nmemory of type I in the whole space $\\mathbb{R}^n$, which are the fundamental\nmodels for acoustic waves in relaxing hereditary fluids. First of all, we study\nsharp estimates of solutions to the viscous and inviscid MGT equation with\nmemory, where regularity-loss decay properties appear in the inviscid case.\nThen, not only formal expansion of solution with respect to the small sound\ndiffusivity but also rigorous justification of inviscid limit in\n$L^{\\infty}([0,\\infty)\\times\\mathbb{R}^n)$ are derived by WKB analysis.\nConcerning the viscous JMGT equation of Kuznetsov-type and Westervelt-type in\nhereditary fluids, we prove global (in time) solvability and obtain some\nestimates of them. This paper studies acoustic waves in hereditary fluids from\nsome different viewpoints comparing with the previous researches.\n"}
+{"abstract": "  We propose a postselecting parity-swap amplifier for Schr\\\"odinger cat states\nthat does not require the amplified state to be known a priori. The device is\nbased on a previously-implemented state comparison amplifier for coherent\nstates. It consumes only Gaussian resource states, which provides an advantage\nover some cat state amplifiers. It requires simple Geiger-mode photodetectors\nand works with high fidelity and approximately twofold gain.\n"}
+{"abstract": "  Visual Place Recognition (VPR) is often characterized as being able to\nrecognize the same place despite significant changes in appearance and\nviewpoint. VPR is a key component of Spatial Artificial Intelligence, enabling\nrobotic platforms and intelligent augmentation platforms such as augmented\nreality devices to perceive and understand the physical world. In this paper,\nwe observe that there are three \"drivers\" that impose requirements on spatially\nintelligent agents and thus VPR systems: 1) the particular agent including its\nsensors and computational resources, 2) the operating environment of this\nagent, and 3) the specific task that the artificial agent carries out. In this\npaper, we characterize and survey key works in the VPR area considering those\ndrivers, including their place representation and place matching choices. We\nalso provide a new definition of VPR based on the visual overlap -- akin to\nspatial view cells in the brain -- that enables us to find similarities and\ndifferences to other research areas in the robotics and computer vision fields.\nWe identify numerous open challenges and suggest areas that require more\nin-depth attention in future works.\n"}
+{"abstract": "  Rate-Splitting Multiple Access (RSMA) has recently appeared as a powerful and\nrobust multiple access and interference management strategy for downlink\nMulti-user (MU) multi-antenna communications. In this work, we study the\nprecoder design problem for RSMA scheme in downlink MU systems with both\nperfect and imperfect Channel State Information at the Transmitter (CSIT) and\nassess the role and benefits of transmitting multiple common streams. Unlike\nexisting works which have considered single-antenna receivers (Multiple-Input\nSingle-Output--MISO), we propose and extend the RSMA framework for\nmulti-antenna receivers (Multiple-Input Multiple-Output--MIMO) and formulate\nthe precoder optimization problem with the aim of maximizing the Weighted\nErgodic Sum-Rate (WESR). Precoder optimization is solved using Sample Average\nApproximation (SAA) together with the proposed vectorization and Weighted\nMinimum Mean Square Error (WMMSE) based approach. Achievable sum-Degree of\nFreedom (DoF) of RSMA is derived for the proposed framework as an increasing\nfunction of the number of transmitted common and private streams, which is\nfurther validated by the Ergodic Sum Rate (ESR) performance using Monte Carlo\nsimulations. Conventional MU-MIMO based on linear precoders and Non-Orthogonal\nMultiple Access (NOMA) schemes are considered as baselines. Numerical results\nshow that with imperfect CSIT, the sum-DoF and ESR performance of RSMA is\nsuperior than that of the two baselines, and is increasing with the number of\ntransmitted common streams. Moreover, by better managing the interference, RSMA\nnot only has significant ESR gains over baseline schemes but is more robust to\nCSIT inaccuracies, network loads and user deployments.\n"}
+{"abstract": "  Suppose that $X$ is a smooth projective variety and that $C$ is a general\nmember of a family of free rational curves on $X$. We prove several statements\nshowing that the Harder-Narasimhan filtration of $T_{X}|_{C}$ is approximately\nthe same as the restriction of the Harder-Narasimhan filtration of $T_{X}$ with\nrespect to the class of $C$. When $X$ is a Fano variety, we prove that the set\nof all restricted tangent bundles for general free rational curves is\ncontrolled by a finite set of data. We then apply our work to analyze Peyre's\n\"freeness\" formulation of Manin's Conjecture in the setting of rational curves.\n"}
+{"abstract": "  This paper presents a cooperative dynamic task assignment framework for a\ncertain class of Autonomous Underwater Vehicles (AUVs) employed to control\noutbreak of Crown-Of-Thorns Starfish (COTS) in Australia's Great Barrier Reef.\nThe problem of monitoring and controlling the COTS is transcribed into a\nconstrained task assignment problem in which eradicating clusters of COTS, by\nthe injection system of COTSbot AUVs, is considered as a task. A probabilistic\nmap of the operating environment including seabed terrain, clusters of COTS,\nand coastlines is constructed. Then, a novel heuristic algorithm called\nHeuristic Fleet Cooperation (HFC) is developed to provide a cooperative\ninjection of the COTSbot AUVs to the maximum possible COTS in an assigned\nmission time. Extensive simulation studies together with quantitative\nperformance analysis are conducted to demonstrate the effectiveness and\nrobustness of the proposed cooperative task assignment algorithm in eradicating\nthe COTS in the Great Barrier Reef.\n"}
+{"abstract": "  Neural coupled oscillators are a useful building block in numerous models and\napplications. They were analyzed extensively in theoretical studies and more\nrecently, in biologically realistic simulations of spiking neural networks. The\nadvent of mixed-signal analog/digital neuromorphic electronic circuits provides\nnew means for implementing neural coupled oscillators on compact low-power\nspiking neural network hardware platforms. However, their implementation on\nthis noisy, low-precision and inhomogeneous computing substrate raises new\nchallenges with regards to stability and controllability. In this work, we\npresent a robust, spiking neural network model of neural coupled oscillators\nand validate it with an implementation on a mixed-signal neuromorphic\nprocessor. We demonstrate its robustness showing how to reliably control and\nmodulate the oscillator's frequency and phase shift, despite the variability of\nthe silicon synapse and neuron properties. We show how this ultra-low power\nneural processing system can be used to build an adaptive cardiac pacemaker\nmodulating the heart rate with respect to the respiration phases and compare it\nwith surface ECG and respiratory signal recordings of dogs at rest. The\nimplementation of our model in neuromorphic electronic hardware shows its\nrobustness on a highly variable substrate and extends the toolbox for\napplications requiring rhythmic outputs such as pacemakers.\n"}
+{"abstract": "  A basic requirement for a mathematical model is often that its solution\n(output) shouldn't change much if the model's parameters (input) are perturbed.\nThis is important because the exact values of parameters may not be known and\none would like to avoid being mislead by an output obtained using incorrect\nvalues. Thus, it's rarely enough to address an application by formulating a\nmodel, solving the resulting optimization problem and presenting the solution\nas the answer. One would need to confirm that the model is suitable, i.e.,\n\"good,\" and this can, at least in part, be achieved by considering a family of\noptimization problems constructed by perturbing parameters of concern. The\nresulting sensitivity analysis uncovers troubling situations with unstable\nsolutions, which we referred to as \"bad\" models, and indicates better model\nformulations. Embedding an actual problem of interest within a family of\nproblems is also a primary path to optimality conditions as well as\ncomputationally attractive, alternative problems, which under ideal\ncircumstances, and when properly tuned, may even furnish the minimum value of\nthe actual problem. The tuning of these alternative problems turns out to be\nintimately tied to finding multipliers in optimality conditions and thus\nemerges as a main component of several optimization algorithms. In fact, the\ntuning amounts to solving certain dual optimization problems. In this tutorial,\nwe'll discuss the opportunities and insights afforded by this broad\nperspective.\n"}
+{"abstract": "  By means of a truncation condition on the parameters, the elliptic\nRuijsenaars difference operators are restricted onto a finite lattice of points\nencoded by bounded partitions. A corresponding orthogonal basis of joint\neigenfunctions is constructed in terms of polynomials on the joint spectrum. In\nthe trigonometric limit, this recovers the diagonalization of the truncated\nMacdonald difference operators by a finite-dimensional basis of Macdonald\npolynomials.\n"}
+{"abstract": "  Machine learning has enabled the prediction of quantum chemical properties\nwith high accuracy and efficiency, allowing to bypass computationally costly ab\ninitio calculations. Instead of training on a fixed set of properties, more\nrecent approaches attempt to learn the electronic wavefunction (or density) as\na central quantity of atomistic systems, from which all other observables can\nbe derived. This is complicated by the fact that wavefunctions transform\nnon-trivially under molecular rotations, which makes them a challenging\nprediction target. To solve this issue, we introduce general SE(3)-equivariant\noperations and building blocks for constructing deep learning architectures for\ngeometric point cloud data and apply them to reconstruct wavefunctions of\natomistic systems with unprecedented accuracy. Our model achieves speedups of\nover three orders of magnitude compared to ab initio methods and reduces\nprediction errors by up to two orders of magnitude compared to the previous\nstate-of-the-art. This accuracy makes it possible to derive properties such as\nenergies and forces directly from the wavefunction in an end-to-end manner. We\ndemonstrate the potential of our approach in a transfer learning application,\nwhere a model trained on low accuracy reference wavefunctions implicitly learns\nto correct for electronic many-body interactions from observables computed at a\nhigher level of theory. Such machine-learned wavefunction surrogates pave the\nway towards novel semi-empirical methods, offering resolution at an electronic\nlevel while drastically decreasing computational cost. Additionally, the\npredicted wavefunctions can serve as initial guess in conventional ab initio\nmethods, decreasing the number of iterations required to arrive at a converged\nsolution, thus leading to significant speedups without any loss of accuracy or\nrobustness.\n"}
+{"abstract": "  Heterostructures between topological insulators (TI) and magnetic insulators\nrepresent a pathway to realize the quantum anomalous Hall effect (QAHE). Using\ndensity functional theory based systematic screening and investigation of\nthermodynamic, magnetic and topological properties of heterostructures, we\ndemonstrate that forming a type-I heterostructure between a wide gap\nantiferromagnetic insulator Cr$_2$O$_3$ and a TI-film, such as Sb$_2$Te$_3$,\ncan lead to pinning of the Fermi-level at the center of the gap, even when\nmagnetically doped. Cr-doping in the heterostructure increases the gap to\n$\\sim$ 64.5 meV, with a large Zeeman energy from the interfacial Cr dopants,\nthus overcoming potential metallicity due to band bending effects. By fitting\nthe band-structure around the Fermi-level to a 4-band k.p model Hamiltonian, we\nshow that Cr doped Sb$_2$Te$_3$/Cr$_2$O$_3$ is a Chern insulator with a Chern\nnumber C = -1. Transport calculations further show chiral edge-modes localized\nat the top/bottom of the TI-film to be the dominant current carriers in the\nmaterial. Our predictions of a large interfacial magnetism due to Cr-dopants,\nthat coupled antiferromagnetically to the AFM substrate is confirmed by our\npolarised neutron reflectometry measurements on MBE grown Cr doped\nSb$_2$Te$_3$/Cr$_2$O$_3$ heterostructures, and is consistent with a positive\nexchange bias measured in such systems recently. Consequently, Cr doped\nSb$_2$Te$_3$/Cr$_2$O$_3$ heterostructure represents a promising platform for\nthe development of functional topological magnetic devices, with high\ntunability.\n"}
+{"abstract": "  We present a novel approach to robustly detect and perceive vehicles in\ndifferent camera views as part of a cooperative vehicle-infrastructure system\n(CVIS). Our formulation is designed for arbitrary camera views and makes no\nassumptions about intrinsic or extrinsic parameters. First, to deal with\nmulti-view data scarcity, we propose a part-assisted novel view synthesis\nalgorithm for data augmentation. We train a part-based texture inpainting\nnetwork in a self-supervised manner. Then we render the textured model into the\nbackground image with the target 6-DoF pose. Second, to handle various camera\nparameters, we present a new method that produces dense mappings between image\npixels and 3D points to perform robust 2D/3D vehicle parsing. Third, we build\nthe first CVIS dataset for benchmarking, which annotates more than 1540 images\n(14017 instances) from real-world traffic scenarios. We combine these novel\nalgorithms and datasets to develop a robust approach for 2D/3D vehicle parsing\nfor CVIS. In practice, our approach outperforms SOTA methods on 2D detection,\ninstance segmentation, and 6-DoF pose estimation, by 4.5%, 4.3%, and 2.9%,\nrespectively. More details and results are included in the supplement. To\nfacilitate future research, we will release the source code and the dataset on\nGitHub.\n"}
+{"abstract": "  Markov chain Monte Carlo (MCMC) algorithms have long been the main workhorses\nof Bayesian inference. Among them, Hamiltonian Monte Carlo (HMC) has recently\nbecome very popular due to its efficiency resulting from effective use of the\ngradients of the target distribution. In privacy-preserving machine learning,\ndifferential privacy (DP) has become the gold standard in ensuring that the\nprivacy of data subjects is not violated. Existing DP MCMC algorithms either\nuse random-walk proposals, or do not use the Metropolis--Hastings (MH)\nacceptance test to ensure convergence without decreasing their step size to\nzero. We present a DP variant of HMC using the MH acceptance test that builds\non a recently proposed DP MCMC algorithm called the penalty algorithm, and adds\nnoise to the gradient evaluations of HMC. We prove that the resulting algorithm\nconverges to the correct distribution, and is ergodic. We compare DP-HMC with\nthe existing penalty, DP-SGLD and DP-SGNHT algorithms, and find that DP-HMC has\nbetter or equal performance than the penalty algorithm, and performs more\nconsistently than DP-SGLD or DP-SGNHT.\n"}
+{"abstract": "  Electromyography (EMG) is a way of measuring the bioelectric activities that\ntake place inside the muscles. EMG is usually performed to detect abnormalities\nwithin the nerves or muscles of a target area. The recent developments in the\nfield of Machine Learning allow us to use EMG signals to teach machines the\ncomplex properties of human movements. Modern machines are capable of detecting\nnumerous human activities and distinguishing among them solely based on the EMG\nsignals produced by those activities. However, success in accomplishing this\ntask mostly depends on the learning technique used by the machine to analyze\nEMG signals; and even the latest algorithms do not result in flawless\nclassification. In this study, a novel classification method has been described\nemploying a multichannel Convolutional Neural Network (CNN) that interprets\nsurface EMG signals by the properties they exhibit in the power domain. The\nproposed method was tested on a well-established EMG dataset, and the result\nyields very high classification accuracy. This learning model will help\nresearchers to develop prosthetic arms capable of detecting various hand\ngestures to mimic them afterwards.\n"}
+{"abstract": "  Internet of Things (IoT) is considered as a key enabler of health\ninformatics. IoT-enabled devices are used for in-hospital and in-home patient\nmonitoring to collect and transfer biomedical data pertaining to blood\npressure, electrocardiography (ECG), blood sugar levels, body temperature, etc.\nAmong these devices, wearables have found their presence in a wide range of\nhealthcare applications. These devices generate data in real-time and transmit\nthem to nearby gateways and remote servers for processing and visualization.\nThe data transmitted by these devices are vulnerable to a range of adversarial\nthreats, and as such, privacy and integrity need to be preserved. In this\npaper, we present LightIoT, a lightweight and secure communication approach for\ndata exchanged among the devices of a healthcare infrastructure. LightIoT\noperates in three phases: initialization, pairing, and authentication. These\nphases ensure the reliable transmission of data by establishing secure sessions\namong the communicating entities (wearables, gateways and a remote server).\nStatistical results exhibit that our scheme is lightweight, robust, and\nresilient against a wide range of adversarial attacks and incurs much lower\ncomputational and communication overhead for the transmitted data in the\npresence of existing approaches.\n"}
+{"abstract": "  While complex numbers are essential in mathematics, they are not needed to\ndescribe physical experiments, expressed in terms of probabilities, hence real\nnumbers. Physics however aims to explain, rather than describe, experiments\nthrough theories. While most theories of physics are based on real numbers,\nquantum theory was the first to be formulated in terms of operators acting on\ncomplex Hilbert spaces. This has puzzled countless physicists, including the\nfathers of the theory, for whom a real version of quantum theory, in terms of\nreal operators, seemed much more natural. In fact, previous works showed that\nsuch \"real quantum theory\" can reproduce the outcomes of any multipartite\nexperiment, as long as the parts share arbitrary real quantum states. Thus, are\ncomplex numbers really needed in the quantum formalism? Here, we show this to\nbe case by proving that real and complex quantum theory make different\npredictions in network scenarios comprising independent states and\nmeasurements. This allows us to devise a Bell-like experiment whose successful\nrealization would disprove real quantum theory, in the same way as standard\nBell experiments disproved local physics.\n"}
+{"abstract": "  With the increasing scale of search engine marketing, designing an efficient\nbidding system is becoming paramount for the success of e-commerce companies.\nThe critical challenges faced by a modern industrial-level bidding system\ninclude: 1. the catalog is enormous, and the relevant bidding features are of\nhigh sparsity; 2. the large volume of bidding requests induces significant\ncomputation burden to both the offline and online serving. Leveraging\nextraneous user-item information proves essential to mitigate the sparsity\nissue, for which we exploit the natural language signals from the users' query\nand the contextual knowledge from the products. In particular, we extract the\nvector representations of ads via the Transformer model and leverage their\ngeometric relation to building collaborative bidding predictions via\nclustering. The two-step procedure also significantly reduces the computation\nstress of bid evaluation and optimization. In this paper, we introduce the\nend-to-end structure of the bidding system for search engine marketing for\nWalmart e-commerce, which successfully handles tens of millions of bids each\nday. We analyze the online and offline performances of our approach and discuss\nhow we find it as a production-efficient solution.\n"}
+{"abstract": "  COVID-19 pandemic has spread globally for months. Due to its long incubation\nperiod and high testing cost, there is no clue showing its spread speed is\nslowing down, and hence a faster testing method is in dire need. This paper\nproposes an efficient Evolutionary Multi-objective neural ARchitecture Search\n(EMARS) framework, which can automatically search for 3D neural architectures\nbased on a well-designed search space for COVID-19 chest CT scan\nclassification. Within the framework, we use weight sharing strategy to\nsignificantly improve the search efficiency and finish the search process in 8\nhours. We also propose a new objective, namely potential, which is of benefit\nto improve the search process's robustness. With the objectives of accuracy,\npotential, and model size, we find a lightweight model (3.39 MB), which\noutperforms three baseline human-designed models, i.e., ResNet3D101 (325.21\nMB), DenseNet3D121 (43.06 MB), and MC3\\_18 (43.84 MB). Besides, our\nwell-designed search space enables the class activation mapping algorithm to be\neasily embedded into all searched models, which can provide the\ninterpretability for medical diagnosis by visualizing the judgment based on the\nmodels to locate the lesion areas.\n"}
+{"abstract": "  The idea of using lexical translations to define sense inventories has a long\nhistory in lexical semantics. We propose a theoretical framework which allows\nus to answer the question of why this apparently reasonable idea failed to\nproduce useful results. We formally prove several propositions on how the\ntranslations of a word relate to its senses, as well as on the relationship\nbetween synonymy and polysemy. We empirically validate our theoretical findings\non BabelNet, and demonstrate how they could be used to perform unsupervised\nword sense disambiguation of a substantial fraction of the lexicon.\n"}
+{"abstract": "  We formalize Hall's Marriage Theorem in the Lean theorem prover for inclusion\nin mathlib, which is a community-driven effort to build a unified mathematics\nlibrary for Lean. One goal of the mathlib project is to contain all of the\ntopics of a complete undergraduate mathematics education.\n  We provide three presentations of the main theorem statement: in terms of\nindexed families of finite sets, of relations on types, and of matchings in\nbipartite graphs. We also formalize a version of K\\H{o}nig's lemma (in terms of\ninverse limits) to boost the theorem to the case of countably infinite index\nsets. We give a description of the design of the recent mathlib library for\nsimple graphs, and we also give a necessary and sufficient condition for a\nsimple graph to carry a function.\n"}
+{"abstract": "  In this Letter, we describe how a spectrum of entropic perturbations\ngenerated during a period of slow contraction can source a nearly\nscale-invariant spectrum of curvature perturbations on length scales larger\nthan the Hubble radius during the transition from slow contraction to a\nclassical non-singular bounce (the `graceful exit' phase). The sourcing occurs\nnaturally through higher-order scalar field kinetic terms common to classical\n(non-singular) bounce mechanisms. We present a concrete example in which, by\nthe end of the graceful exit phase, the initial entropic fluctuations have\nbecome negligible and the curvature fluctuations have a nearly scale-invariant\nspectrum with an amplitude consistent with observations.\n"}
+{"abstract": "  We generalize a terminating summation formula to a unilateral nonterminating,\nand further, a bilateral summation formula by a property of analytic functions.\nThe unilateral one is proved to be a $q$-analogue of a $_4F_3$-summation\nformula. And, an identity unifying Jacobi's triple product identity and the\nquintuple product identity is obtained as a special case of the bilateral one.\n"}
+{"abstract": "  \"Minimum Falling Path Sum\" (MFPS) is classic question in programming - \"Given\na grid of size $N{\\times}N$ with integers in cells, return the minimum sum of a\nfalling path through grid. A falling path starts at any cell in the first row\nand ends in last row, with the rule of motion - the next element after the cell\n$(i,j)$ is one of the cells $(i+1,j-1),(i+1,j)$ and $(i+1,j+1)$\". This problem\nhas linear solution (LS) (i.e. O($N^2$)) using dynamic programming method\n(DPM). There is an Multi-Agent version of MFPS called \"Cherry Pickup II\" (CP2).\nCP2 is a search for the maximum sum of 2 falling paths started from top\ncorners, where each covered cell summed up one time. All known fast solutions\nof CP2 uses DPM, but have O($N^3$) time complexity on grid $N{\\times}N$. Here\nwe offer a LS of CP2 (also using DPM) as finding maximum total weight of 2\nvertex-disjoint paths. Also, we extend this LS for some extended version of CP2\nwith wider motion rules.\n"}
+{"abstract": "  Explainable Artificial Intelligence (XAI) has in recent years become a\nwell-suited framework to generate human understandable explanations of black\nbox models. In this paper, we present a novel XAI visual explanation algorithm\ndenoted SIDU that can effectively localize entire object regions responsible\nfor prediction in a full extend. We analyze its robustness and effectiveness\nthrough various computational and human subject experiments. In particular, we\nassess the SIDU algorithm using three different types of evaluations\n(Application, Human and Functionally-Grounded) to demonstrate its superior\nperformance. The robustness of SIDU is further studied in presence of\nadversarial attack on black box models to better understand its performance.\n"}
+{"abstract": "  Let H be a complex Lie group acting holomorphically on a complex analytic\nspace X such that the restriction to X_{\\mathrm{red}} of every H-invariant\nregular function on X is constant. We prove that an H-equivariant holomorphic\nvector bundle E over X is $H$-finite, meaning f_1(E)= f_2(E) as H-equivariant\nbundles for two distinct polynomials f_1 and f_2 whose coefficients are\nnonnegative integers, if and only if the pullback of E along some H-equivariant\nfinite \\'etale covering of X is trivial as an H-equivariant bundle.\n"}
+{"abstract": "  This work is devoted to review the modern geometric description of the\nLagrangian and Hamiltonian formalisms of the Hamilton--Jacobi theory. The\nrelation with the \"classical\" Hamiltonian approach using canonical\ntransformations is also analyzed. Furthermore, a more general framework for the\ntheory is also briefly explained. It is also shown how, from this generic\nframework, the Lagrangian and Hamiltonian cases of the theory for dynamical\nsystems are recovered, and how the model can be extended to other types of\nphysical systems, such as higher-order dynamical systems and (first-order)\nclassical field theories in their multisymplectic formulation.\n"}
+{"abstract": "  In this paper, we prove the convergence from the atomistic model to the\nPeierls--Nabarro (PN) model of two-dimensional bilayer system with complex\nlattice. We show that the displacement field of the dislocation solution of the\nPN model converges to the dislocation solution of the atomistic model with\nsecond-order accuracy. The consistency of PN model and the stability of\natomistic model are essential in our proof. The main idea of our approach is to\nuse several low-degree polynomials to approximate the energy due to atomistic\ninteractions of different groups of atoms of the complex lattice.\n"}
+{"abstract": "  We study asymptotic topological regularity of CAT(0) spaces. We prove that if\na purely n-dimensional, proper, geodesically complete CAT(0) space has small\nvolume growth, then it is homeomorphic to the n-dimensional Euclidean space. We\nalso discuss asymptotic geometry of proper, geodesically complete CAT(0) spaces\nof small volume growth.\n"}
+{"abstract": "  In this paper, constructing a class of ideals of $B_1(X)$ from proper ideals\nof $C(X)$ a formula for real maximal ideals of $C(X)$ is established and\nutilizing these ideals of $B_1(X)$, closed maximal ideals in $B_1(X)$ are\nintroduced. It is observed that the real maximal ideals of $B_1(X)$ is that\nsubfamily of closed maximal ideals whose contraction in $C(X)$ is contained in\na real maximal ideal of $C(X)$. A one-one correspondence between the class of\nreal maximal ideals of $C(X)$ and those of $B_1(X)$ is proved. Also, it is\nobserved that an injective map from the class of hyper-real maximal ideals of\n$C(X)$ into the class of hyper-real maximal ideals of $B_1(X)$ exists. These\nobservations finally lead to the conclusion that within the class of\nnon-discrete perfectly normal $T_1$ spaces, cardinality of the collection\n$\\MMM(C(X))$, of all maximal ideals of $C(X)$, is strictly less than the\ncardinality of the collection $\\MMM(B_1(X))$, of all maximal ideals of\n$B_1(X)$. Finally, it is proved that Tychonoff space is real compact if and\nonly if every real maximal ideal of $B_1(X)$ is fixed. As a consequence, within\nthe class of perfectly normal $T_1$ real compact spaces $B_1(X) = {B_1}^*(X)$\nif and only if $X$ is finite.\n"}
+{"abstract": "  Road infrastructure maintenance inspection is typically a labour-intensive\nand critical task to ensure the safety of all the road users. In this work, we\npropose a detailed methodology to use state-of-the-art techniques in artificial\nintelligence and computer vision to automate a sizeable portion of the\nmaintenance inspection subtasks and reduce the labour costs. The proposed\nmethodology uses state-of-the-art computer vision techniques such as object\ndetection and semantic segmentation to automate inspections on primary road\nstructures such as the road surface, markings, barriers (guardrails) and\ntraffic signs. The models are mostly trained on commercially viable datasets\nand augmented with proprietary data. We demonstrate that our AI models can not\nonly automate and scale maintenance inspections on primary road structures but\nalso result in higher recall compared to traditional manual inspections.\n"}
+{"abstract": "  Multi-Layer Perceptrons (MLPs) make powerful functional representations for\nsampling and reconstruction problems involving low-dimensional signals like\nimages,shapes and light fields. Recent works have significantly improved their\nability to represent high-frequency content by using periodic activations or\npositional encodings. This often came at the expense of generalization: modern\nmethods are typically optimized for a single signal. We present a new\nrepresentation that generalizes to multiple instances and achieves\nstate-of-the-art fidelity. We use a dual-MLP architecture to encode the\nsignals. A synthesis network creates a functional mapping from a\nlow-dimensional input (e.g. pixel-position) to the output domain (e.g. RGB\ncolor). A modulation network maps a latent code corresponding to the target\nsignal to parameters that modulate the periodic activations of the synthesis\nnetwork. We also propose a local-functional representation which enables\ngeneralization. The signal's domain is partitioned into a regular grid,with\neach tile represented by a latent code. At test time, the signal is encoded\nwith high-fidelity by inferring (or directly optimizing) the latent code-book.\nOur approach produces generalizable functional representations of images,\nvideos and shapes, and achieves higher reconstruction quality than prior works\nthat are optimized for a single signal.\n"}
+{"abstract": "  Verifying specifications for large-scale modern engineering systems can be a\ntime-consuming task, as most formal verification methods are limited to systems\nof modest size. Recently, contract-based design and verification has been\nproposed as a modular framework for specifications, and\nlinear-programming-based techniques have been presented for verifying that a\ngiven system satisfies a given contract. In this paper, we extend this\nassume/guarantee framework by presenting necessary and sufficient conditions\nfor a collection of contracts on individual components to refine a contract on\nthe composed system. These conditions can be verified by solving linear\nprograms, whose number grows linearly with the number of specifications defined\nby the contracts. We exemplify the tools developed using a case study\nconsidering safety in a car-following scenario, where noise and time-varying\ndelay are considered.\n"}
+{"abstract": "  We propose the use of physics-informed neural networks for solving the\nshallow-water equations on the sphere. Physics-informed neural networks are\ntrained to satisfy the differential equations along with the prescribed initial\nand boundary data, and thus can be seen as an alternative approach to solving\ndifferential equations compared to traditional numerical approaches such as\nfinite difference, finite volume or spectral methods. We discuss the training\ndifficulties of physics-informed neural networks for the shallow-water\nequations on the sphere and propose a simple multi-model approach to tackle\ntest cases of comparatively long time intervals. We illustrate the abilities of\nthe method by solving the most prominent test cases proposed by Williamson et\nal. [J. Comput. Phys. 102, 211-224, 1992].\n"}
+{"abstract": "  Modern change detection (CD) has achieved remarkable success by the powerful\ndiscriminative ability of deep convolutions. However, high-resolution remote\nsensing CD remains challenging due to the complexity of objects in the scene.\nObjects with the same semantic concept may show distinct spectral\ncharacteristics at different times and spatial locations. Most recent CD\npipelines using pure convolutions are still struggling to relate long-range\nconcepts in space-time. Non-local self-attention approaches show promising\nperformance via modeling dense relations among pixels, yet are computationally\ninefficient. Here, we propose a bitemporal image transformer (BIT) to\nefficiently and effectively model contexts within the spatial-temporal domain.\nOur intuition is that the high-level concepts of the change of interest can be\nrepresented by a few visual words, i.e., semantic tokens. To achieve this, we\nexpress the bitemporal image into a few tokens, and use a transformer encoder\nto model contexts in the compact token-based space-time. The learned\ncontext-rich tokens are then feedback to the pixel-space for refining the\noriginal features via a transformer decoder. We incorporate BIT in a deep\nfeature differencing-based CD framework. Extensive experiments on three CD\ndatasets demonstrate the effectiveness and efficiency of the proposed method.\nNotably, our BIT-based model significantly outperforms the purely convolutional\nbaseline using only 3 times lower computational costs and model parameters.\nBased on a naive backbone (ResNet18) without sophisticated structures (e.g.,\nFPN, UNet), our model surpasses several state-of-the-art CD methods, including\nbetter than four recent attention-based methods in terms of efficiency and\naccuracy. Our code is available at https://github.com/justchenhao/BIT\\_CD.\n"}
+{"abstract": "  Let $G$ be a reductive linear algebraic group over an algebraically closed\nfield $\\mathbb{K}$ of characteristic $2$. Fix a parabolic subgroup $P$ such\nthat the corresponding parabolic subgroup over $\\mathbb{C}$ has abelian\nunipotent radical and fix a Levi subgroup $L\\subseteq P$. We parametrize the\norbits of a Borel $B\\subseteq P$ over the Hermitian symmetric variety $G/L$\nsupposing the root system $\\Phi$ is irreducible. For $\\Phi$ simply laced we\nprove a combinatorial characterization of the Bruhat order over these orbits.\nWe also prove a formula to compute the dimension of the orbits from\ncombinatorial characteristics of their representatives.\n"}
+{"abstract": "  Given a location-based social network, how to find the communities that are\nhighly relevant to query users and have top overall scores in multiple\nattributes according to user preferences? Typically, in the face of such a\nproblem setting, we can model the network as a multi-attributed road-social\nnetwork, in which each user is linked with location information and $d$\n($\\geq\\! 1$) numerical attributes. In practice, user preferences (i.e.,\nweights) are usually inherently uncertain and can only be estimated with\nbounded accuracy, because a human user is not able to designate exact values\nwith absolute precision. Inspired by this, we introduce a normative community\nmodel suitable for multi-criteria decision making, called multi-attributed\ncommunity (MAC), based on the concepts of $k$-core and a novel dominance\nrelationship specific to preferences. Given uncertain user preferences, namely,\nan approximate representation of weights, the MAC search reports the exact\ncommunities for each of the possible weight settings. We devise an elegant\nindex structure to maintain the dominance relationships, based on which two\nalgorithms are developed to efficiently compute the top-$j$ MACs. The\nefficiency and scalability of our algorithms and the effectiveness of MAC model\nare demonstrated by extensive experiments on both real-world and synthetic\nroad-social networks.\n"}
+{"abstract": "  The Super-Scaling Approach (SuSA) model, based on the analogies between\nelectron and neutrino interactions with nuclei, is reviewed and its application\nto the description of neutrino-nucleus scattering is presented. The\ncontribution of both one- and two-body relativistic currents is considered. A\nselection of results is presented where theoretical predictions are compared\nwith cross section measurements from the main ongoing neutrino oscillation\nexperiments.\n"}
+{"abstract": "  Curated scientific databases play an important role in the scientific\nendeavour and support is needed for the significant effort that goes into their\ncreation and maintenance. This demonstration and case study illustrate how\ncuration support has been developed in the Links cross-tier programming\nlanguage, a functional, strongly typed language with language-integrated query\nand support for temporal databases. The chosen case study uses weekly released\nCovid-19 fatality figures from the Scottish government which exhibit updates to\npreviously released data. This data allows the capture and query of update\nprovenance in our prototype. This demonstration will highlight the potential\nfor language-integrated support for curation to simplify and streamline\nprototyping of web-applications in support of scientific databases\n"}
+{"abstract": "  The \"particle in a box\" problem is investigated for a relativistic particle\nobeying the Klein-Gordon equation. To find the bound states, the standard\nmethods known from elementary non-relativistic quantum mechanics can only be\nemployed for \"shallow\" wells. For deeper wells, when the confining potentials\nbecome supercritical, we show that a method based on a scattering expansion\naccounts for Klein tunneling (undamped propagation outside the well) and the\nKlein paradox (charge density increase inside the well). We will see that in\nthe infinite well limit, the wavefunction outside the well vanishes and Klein\ntunneling is suppressed: quantization is thus recovered, similarly to the\nnon-relativistic particle in a box. In addition, we show how wavepackets can be\nconstructed semi-analytically from the scattering expansion, accounting for the\ndynamics of Klein tunneling in a physically intuitive way\n"}
+{"abstract": "  Nowadays the semi-tensor product (STP) approach to finite games has become a\npromising new direction. This paper provides a comprehensive survey on this\nprosperous field. After a brief introduction for STP and finite (networked)\ngames, a description for the principle and fundamental technique of STP\napproach to finite games is presented. Then several problems and recent results\nabout theory and applications of finite games via STP are presented. A brief\ncomment about the potential use of STP to artificial intelligence is also\nproposed.\n"}
+{"abstract": "  Conventional representation learning algorithms for knowledge graphs (KG) map\neach entity to a unique embedding vector. Such a shallow lookup results in a\nlinear growth of memory consumption for storing the embedding matrix and incurs\nhigh computational costs when working with real-world KGs. Drawing parallels\nwith subword tokenization commonly used in NLP, we explore the landscape of\nmore parameter-efficient node embedding strategies with possibly sublinear\nmemory requirements. To this end, we propose NodePiece, an anchor-based\napproach to learn a fixed-size entity vocabulary. In NodePiece, a vocabulary of\nsubword/sub-entity units is constructed from anchor nodes in a graph with known\nrelation types. Given such a fixed-size vocabulary, it is possible to bootstrap\nan encoding and embedding for any entity, including those unseen during\ntraining. Experiments show that NodePiece performs competitively in node\nclassification, link prediction, and relation prediction tasks while retaining\nless than 10% of explicit nodes in a graph as anchors and often having 10x\nfewer parameters.\n"}
+{"abstract": "  Massive black holes (BHs) in dwarf galaxies can provide strong constraints on\nBH seeds, however reliably detecting them is notoriously difficult. High\nresolution radio observations were recently used to identify accreting massive\nBHs in nearby dwarf galaxies, with a significant fraction found to be\nnon-nuclear. Here we present the first results of our optical follow-up of\nthese radio-selected active galactic nuclei (AGNs) in dwarf galaxies using\nintegral field unit (IFU) data from Gemini-North. We focus on the dwarf galaxy\nJ1220+3020, which shows no clear optical AGN signatures in its nuclear SDSS\nspectrum covering the radio source. With our new IFU data, we confirm the\npresence of an active BH via the AGN coronal line [Fe X] and enhanced [O I]\nemission coincident with the radio source. Furthermore, we detect broad\nH$\\alpha$ emission and estimate a BH mass of $M_{\\rm BH}=10^{4.9}M_\\odot$. We\ncompare the narrow emission line ratios to standard BPT diagnostics and shock\nmodels. Spatially-resolved BPT diagrams show some AGN signatures, particularly\nin [O I]/H$\\alpha$, but overall do not unambiguously identify the AGN. A\ncomparison of our data to shock models clearly indicates shocked emission\nsurrounding the AGN. The physical model most consistent with the data is an\nactive BH with a radiatively inefficient accretion flow (RIAF) that both\nphotoionizes and shock-excites the surrounding gas. We conclude that feedback\nis important in radio-selected BHs in dwarf galaxies, and that radio surveys\nmay probe a population of low accretion-rate BHs in dwarf galaxies that cannot\nbe detected through optical surveys alone.\n"}
+{"abstract": "  This paper introduces a novel dataset to help researchers evaluate their\ncomputer vision and audio models for accuracy across a diverse set of age,\ngenders, apparent skin tones and ambient lighting conditions. Our dataset is\ncomposed of 3,011 subjects and contains over 45,000 videos, with an average of\n15 videos per person. The videos were recorded in multiple U.S. states with a\ndiverse set of adults in various age, gender and apparent skin tone groups. A\nkey feature is that each subject agreed to participate for their likenesses to\nbe used. Additionally, our age and gender annotations are provided by the\nsubjects themselves. A group of trained annotators labeled the subjects'\napparent skin tone using the Fitzpatrick skin type scale. Moreover, annotations\nfor videos recorded in low ambient lighting are also provided. As an\napplication to measure robustness of predictions across certain attributes, we\nprovide a comprehensive study on the top five winners of the DeepFake Detection\nChallenge (DFDC). Experimental evaluation shows that the winning models are\nless performant on some specific groups of people, such as subjects with darker\nskin tones and thus may not generalize to all people. In addition, we also\nevaluate the state-of-the-art apparent age and gender classification methods.\nOur experiments provides a thorough analysis on these models in terms of fair\ntreatment of people from various backgrounds.\n"}
+{"abstract": "  In this paper, we consider an imperfect finite beam lying on a nonlinear\nfoundation, whose dimensionless stiffness is reduced from $1$ to $k$ as the\nbeam deflection increases. Periodic equilibrium solutions are found\nanalytically and are in good agreement with a numerical resolution, suggesting\nthat localized buckling does not appear for a finite beam. The equilibrium\npaths may exhibit a limit point whose existence is related to the imperfection\nsize and the stiffness parameter $k$ through an explicit condition. The limit\npoint decreases with the imperfection size while it increases with the\nstiffness parameter. We show that the decay/growth rate is sensitive to the\nrestoring force model. The analytical results on the limit load may be of\nparticular interest for engineers in structural mechanics\n"}
+{"abstract": "  We describe a torsion pendulum with a large mass-quadrupole moment and a\nresonant frequency of 2.8 mHz, whose angle is measured using a modified\nMichelson interferometer. The system achieved noise levels of $\\sim200\\\n\\text{prad}/\\sqrt{\\text{Hz}}$ between 0.2-30 Hz and $\\sim10\\\n\\text{prad}/\\sqrt{\\text{Hz}}$ above 100 Hz. Such a system can be applied to a\nbroad range of fields from the study of rotational seismic motion and\nelastogravity signals to gravitational wave observation and tests of gravity.\n"}
+{"abstract": "  We investigate the solutions of the conjugate equations aya^(-1)=y^2 and\naya^(-1)=y^(-2) in the symmetric group S_{n}. Here a is a fixed (constant) and\ny is a single unknown permutation (in S_{n}). It turns out that the existence\nof a non-trivial solution y heavily depends on the type of a.\n"}
+{"abstract": "  The Blanchet link homology theory is an oriented model of Khovanov homology,\nfunctorial over the integers with respect to link cobordisms. We formulate a\nstable homotopy refinement of the Blanchet theory, based on a comparison of the\nBlanchet and Khovanov chain complexes associated to link diagrams. The\nconstruction of the stable homotopy type relies on the signed Burnside category\napproach of Sarkar-Scaduto-Stoffregen.\n"}
+{"abstract": "  GRETA, the Gamma-Ray Energy Tracking Array, is an array of highly-segmented\nHPGe detectors designed to track gamma-rays emitted in beam-physics\nexperiments. Its high detection efficiency and state-of-the-art position\nresolution enable it to reject Compton background and also sequence detected\ninteractions via Compton kinematics. In this paper, we use simulated photon\ntracks to estimate how well interactions can be sequenced in the GRETA\ndetector. This lays the groundwork for subsequent gamma-ray imaging\napplications such as nuclear lifetime measurements.\n"}
+{"abstract": "  In this article, we present an $O(N \\log N)$ rapidly convergent algorithm for\nthe numerical approximation of the convolution integral with radially symmetric\nweakly singular kernels and compactly supported densities. To achieve the\nreduced computational complexity, we utilize the Fast Fourier Transform (FFT)\non a uniform grid of size $N$ for approximating the convolution. To facilitate\nthis and maintain the accuracy, we primarily rely on a periodic Fourier\nextension of the density with a suitably large period depending on the support\nof the density. The rate of convergence of the method increases with increasing\nsmoothness of the periodic extension and, in fact, approximations exhibit\nsuper-algebraic convergence when the extension is infinitely differentiable.\nFurthermore, when the density has jump discontinuities, we utilize a certain\nFourier smoothing technique to accelerate the convergence to achieve the\nquadratic rate in the overall approximation. Finally, we apply the integration\nscheme for numerical solution of certain partial differential equations.\nMoreover, we apply the quadrature to obtain a fast and high-order Nyst\\\"om\nsolver for the solution of the Lippmann-Schwinger integral equation. We\nvalidate the performance of the proposed scheme in terms of accuracy as well as\ncomputational efficiency through a variety of numerical experiments.\n"}
+{"abstract": "  Partial observations of continuous time-series dynamics at arbitrary time\nstamps exist in many disciplines. Fitting this type of data using statistical\nmodels with continuous dynamics is not only promising at an intuitive level but\nalso has practical benefits, including the ability to generate continuous\ntrajectories and to perform inference on previously unseen time stamps. Despite\nexciting progress in this area, the existing models still face challenges in\nterms of their representational power and the quality of their variational\napproximations. We tackle these challenges with continuous latent process flows\n(CLPF), a principled architecture decoding continuous latent processes into\ncontinuous observable processes using a time-dependent normalizing flow driven\nby a stochastic differential equation. To optimize our model using maximum\nlikelihood, we propose a novel piecewise construction of a variational\nposterior process and derive the corresponding variational lower bound using\ntrajectory re-weighting. Our ablation studies demonstrate the effectiveness of\nour contributions in various inference tasks on irregular time grids.\nComparisons to state-of-the-art baselines show our model's favourable\nperformance on both synthetic and real-world time-series data.\n"}
+{"abstract": "  In this paper we discuss classical and quantum aspects of cosmological models\nin Brans-Dicke theory. First, we review cosmological bounce solution in\nBrans-Dicke theory that obeys energy conditions (without ghost) for a universe\nfilled with radiative fluid. Then we quantize this classical model in a\ncanonical way, establishing the corresponding Wheeler-DeWitt equation in the\nminisuperspace, and analyze the quantum solutions. When the energy conditions\nare violated, corresponding to the case $\\omega<-3/2$, the energy is bounded\nfrom below and singularity-free solutions are found. However, in the case\n$\\omega> -3/2$ we cannot compute the evolution of the scale factor by\nevaluating the expectation values because the wave function is not finite\n(energy spectrum is not bounded from below). But we can analyze this case using\nBohmian mechanics and the de Broglie-Bohm interpretation of quantum mechanics.\nUsing this approach, the classical and quantum results can be compared for any\nvalue of $\\omega$.\n"}
+{"abstract": "  We investigate wide-angle photo- and electroproduction of pions within the\nhandbag mechanism in which the $\\gamma^{(*)} N\\to \\pi N'$ amplitudes factorize\ninto hard partonic subprocess amplitudes, $\\gamma^{(*)} q\\to \\pi q'$, and form\nfactors representing $1/x$-moments of generalized parton distributions (GPDs).\nThe subprocess is calculated to twist-3 accuracy taking into account the 2- and\n3-body Fock components of the pion. Both components are required for achieving\ngauge invariance in QED and QCD. The twist-2 and twist-3 distribution\namplitudes (DAs) of the pion as well as the form factors are taken from our\nstudy of $\\pi^0$ photoproduction. Extensive results on photoproduction of\ncharged pions are presented and compared to experiment. Predictions on\nelectroproduction of pions as well as on spin effects are also given. As a\nbyproduct of our analysis we also obtain the complete twist-3 subprocess\namplitudes contributing to deeply-virtual electroproduction of pions.\n"}
+{"abstract": "  A charge space $(X,\\mathcal{A},\\mu)$ is a generalisation of a measure space,\nconsisting of a sample space $X$, a field of subsets $\\mathcal{A}$ and a\nfinitely additive measure $\\mu$, also known as a charge. Key properties a\nreal-valued function on $X$ may possess include $T_1$-measurability and\nintegrability. These properties are generalisations of corresponding properties\nof real-valued functions on a (countably additive) measure space. However,\nthese properties are less well studied than their measure-theoretic\ncounterparts.\n  This paper describes new characterisations of $T_1$-measurability and\nintegrability in the case that the charge space is bounded, that is, $\\mu(X) <\n\\infty$. These characterisations are convenient for analytic purposes; for\nexample, they facilitate simple proofs that $T_1$-measurability is equivalent\nto conventional measurability and integrability is equivalent to Lebesgue\nintegrability, if $(X,\\mathcal{A},\\mu)$ is a complete measure space.\n  Several additional contributions to the theory of bounded charges are also\npresented. New characterisations of equality almost everywhere of two\nreal-valued functions on a bounded charge space are provided. Necessary and\nsufficient conditions for the function space $L_1(X,\\mathcal{A},\\mu)$ to be a\nBanach space are determined. Lastly, the concept of completion of a measure\nspace is generalised for charge spaces, and it is shown that under certain\nconditions, completion of a charge space adds no new equivalence classes to the\nquotient space $\\mathcal{L}_p(X,\\mathcal{A},\\mu)$.\n"}
+{"abstract": "  We consider a dilute suspension of active (self-propelling) particles in a\nvisco-elastic fluid. Particles are charged and constrained to move in a two\ndimensional harmonic trap. Their dynamics is coupled to a constant magnetic\nfield applied perpendicular to their motion via Lorentz force. Due to the\nfinite activity, the generalised fluctuation-dissipation relation (GFDR) breaks\ndown, driving the system away from equilibrium. While breaking GFDR, we have\nshown that the system can have finite classical orbital magnetism only when the\ndynamics of the system contains finite inertia. The orbital magnetic moment has\nbeen calculated exactly. Remarkably, we find that when the elastic dissipation\ntime scale of the medium is larger (smaller) than the persistence time scale of\nthe self-propelling particles, the system is diamagnetic (paramagnetic).\nTherefore, for a given strength of the magnetic field, the system undergoes a\nnovel transition from diamagnetic to paramagnetic state (and vice-versa) simply\nby tuning the time scales of underlying physical processes, such as, active\nfluctuations and visco-elastic dissipation. Interestingly, we also find that\nthe magnetic moment, which vanishes at equilibrium, behaves non-monotonically\nwith respect to increasing persistence of self-propulsion, that drives the\nsystem out of equilibrium\n"}
+{"abstract": "  We reassess the impact of short-distance constraints for the longitudinal\ncomponent of the hadronic light-by-light amplitude on the anomalous magnetic\nmoment of the muon, $a_\\mu=(g-2)_\\mu/2$, by comparing different solutions that\nhave recently appeared in the literature. In particular, we analyze the\nrelevance of the exact axial anomaly and its impact on $a_\\mu$ and conclude\nthat it remains rather limited. We show that all recently proposed solutions\nagree well within uncertainties on the numerical estimate of the impact of\nshort-distance constraints on $a_\\mu$, despite differences in the concrete\nimplementation. We also take into account the recently calculated perturbative\ncorrections to the massless quark loop to update our estimate and outline the\npath towards future improvements.\n"}
+{"abstract": "  We calculate exclusive production of a longitudinally polarized heavy vector\nmeson at next-to-leading order in the dipole picture. The large quark mass\nallows us to separately include both the first QCD correction proportional to\nthe coupling constant $\\alpha_s$, and the first relativistic correction\nsuppressed by the quark velocity $v^2$. Both of these corrections are found to\nbe numerically important in $\\mathrm{J}/\\psi$ production. The results obtained\nare directly suitable for phenomenological calculations. We also demonstrate\nhow vector meson production provides complementary information to structure\nfunction analyses when one extracts the initial condition for the energy\nevolution of the proton small-$x$ structure.\n"}
+{"abstract": "  Nanopapers based on graphene and related materials were recently proposed for\napplication in heat spreader applications. To overcome typical limitations in\nbrittleness of such materials, this work addressed the combination of graphite\nnanoplatelets (GNP) with a soft, tough and crystalline polymer, acting as an\nefficient binder between nanoplates. With this aim, polycaprolactone (PCL) was\nselected and exploited in this paper. The crystalline organization of PCL\nwithin the nanopaper was studied to investigate the effect of polymer\nconfinement between GNP. Thermomechanical properties were studied by dynamic\nmechanical analyses at variable temperature and creep measurements at high\ntemperature, demonstrating superior resistance at temperatures well above PCL\nmelting. Finally, the heat conduction properties on the nanopapers were\nevaluated, resulting in outstanding values above 150 Wm-1K-1.\n"}
+{"abstract": "  The standard paradigm of cosmology assumes General Relativity (GR) is a valid\ntheory for gravity at scales in which it has not been properly tested.\nDeveloping novel tests of GR and its alternatives is crucial if we want to give\nstrength to the model or find departures from GR in the data. Since\nalternatives to GR are usually defined through nonlinear equations, designing\nnew tests for these theories implies a jump in complexity and thus, a need for\nrefining the simulation techniques. We summarize existing techniques for\ndealing with modified gravity (MG) in the context of cosmological simulations.\n$N$-body codes for MG are usually based on standard gravity codes. We describe\nthe required extensions, classifying the models not according to their original\nmotivation, but by the numerical challenges that must be faced by numericists.\nMG models usually give rise to elliptic equations, for which multigrid\ntechniques are well suited. Thus, we devote a large fraction of this review to\ndescribing this particular technique. Contrary to other reviews on multigrid\nmethods, we focus on the specific techniques that are required to solve MG\nequations and describe useful tricks. Finally, we describe extensions for going\nbeyond the static approximation and dealing with baryons.\n"}
+{"abstract": "  There is much confusion in the literature over Hurst exponent (H). The\npurpose of this paper is to illustrate the difference between fractional\nBrownian motion (fBm) on the one hand and Gaussian Markov processes where H is\ndifferent to 1/2 on the other. The difference lies in the increments, which are\nstationary and correlated in one case and nonstationary and uncorrelated in the\nother. The two- and one-point densities of fBm are constructed explicitly. The\ntwo-point density does not scale. The one-point density for a semi-infinite\ntime interval is identical to that for a scaling Gaussian Markov process with H\ndifferent to 1/2 over a finite time interval. We conclude that both Hurst\nexponents and one-point densities are inadequate for deducing the underlying\ndynamics from empirical data. We apply these conclusions in the end to make a\nfocused statement about nonlinear diffusion.\n"}
+{"abstract": "  Both observations and recent numerical simulations of the circumgalactic\nmedium (CGM) support the hypothesis that a self-regulating feedback loop\nsuspends the gas density of the ambient CGM close to the galaxy in a state with\na ratio of cooling time to freefall time >10. This limiting ratio is thought to\narise because circumgalactic gas becomes increasingly susceptible to multiphase\ncondensation as the ratio declines. If the timescale ratio gets too small, then\ncold clouds precipitate out of the CGM, rain into the galaxy, and fuel\nenergetic feedback that raises the ambient cooling time. The astrophysical\norigin of this so-called precipitation limit is not simple but is critical to\nunderstanding the CGM and its role in galaxy evolution. This paper therefore\nattempts to interpret its origin as simply as possible, relying mainly on\nconceptual reasoning and schematic diagrams. It illustrates how the\nprecipitation limit can depend on both the global configuration of a galactic\natmosphere and the degree to which dynamical disturbances drive CGM\nperturbations. It also frames some tests of the precipitation hypothesis that\ncan be applied to both CGM observations and numerical simulations of galaxy\nevolution.\n"}
+{"abstract": "  Optimizing parameterized quantum circuits (PQCs) is the leading approach to\nmake use of near-term quantum computers. However, very little is known about\nthe cost function landscape for PQCs, which hinders progress towards\nquantum-aware optimizers. In this work, we investigate the connection between\nthree different landscape features that have been observed for PQCs: (1)\nexponentially vanishing gradients (called barren plateaus), (2) exponential\ncost concentration about the mean, and (3) the exponential narrowness of minina\n(called narrow gorges). We analytically prove that these three phenomena occur\ntogether, i.e., when one occurs then so do the other two. A key implication of\nthis result is that one can numerically diagnose barren plateaus via cost\ndifferences rather than via the computationally more expensive gradients. More\nbroadly, our work shows that quantum mechanics rules out certain cost\nlandscapes (which otherwise would be mathematically possible), and hence our\nresults are interesting from a quantum foundations perspective.\n"}
+{"abstract": "  The Rosetta mission provided us with detailed data of the surface of the\nnucleus of comet 67P/Churyumov-Gerasimenko.In order to better understand the\nphysical processes associated with the comet activity and the surface evolution\nof its nucleus, we performed a detailed comparative morphometrical analysis of\ntwo depressions located in the Ash region. To detect morphological temporal\nchanges, we compared pre- and post-perihelion high-resolution (pixel scale of\n0.07-1.75 m) OSIRIS images of the two depressions. We quantified the changes\nusing the dynamic heights and the gravitational slopes calculated from the\nDigital Terrain Model (DTM) of the studied area using the ArcGIS software\nbefore and after perihelion. Our comparative morphometrical analysis allowed us\nto detect and quantify the temporal changes that occurred in two depressions of\nthe Ash region during the last perihelion passage. We find that the two\ndepressions grew by several meters. The area of the smallest depression\n(structure I) increased by 90+/-20%, with two preferential growths: one close\nto the cliff associated with the apparition of new boulders at its foot, and a\nsecond one on the opposite side of the cliff. The largest depression (structure\nII) grew in all directions, increasing in area by 20+/-5%, and no new deposits\nhave been detected. We interpreted these two depression changes as being driven\nby the sublimation of ices, which explains their global growth and which can\nalso trigger landslides. The deposits associated with depression II reveal a\nstair-like topography, indicating that they have accumulated during several\nsuccessive landslides from different perihelion passages. Overall, these\nobservations bring additional evidence of complex active processes and\nreshaping events occurring on short timescales, such as depression growth and\nlandslides, and on longer timescales, such as cliff retreat.\n"}
+{"abstract": "  Frame reconstruction (current or future frame) based on Auto-Encoder (AE) is\na popular method for video anomaly detection. With models trained on the normal\ndata, the reconstruction errors of anomalous scenes are usually much larger\nthan those of normal ones. Previous methods introduced the memory bank into AE,\nfor encoding diverse normal patterns across the training videos. However, they\nare memory-consuming and cannot cope with unseen new scenarios in the testing\ndata. In this work, we propose a dynamic prototype unit (DPU) to encode the\nnormal dynamics as prototypes in real time, free from extra memory cost. In\naddition, we introduce meta-learning to our DPU to form a novel few-shot\nnormalcy learner, namely Meta-Prototype Unit (MPU). It enables the fast\nadaption capability on new scenes by only consuming a few iterations of update.\nExtensive experiments are conducted on various benchmarks. The superior\nperformance over the state-of-the-art demonstrates the effectiveness of our\nmethod.\n"}
+{"abstract": "  We prove that (strong) fully-concurrent bisimilarity and causal-net\nbisimilarity are decidable for finite bounded Petri nets. The proofs are based\non a generalization of the ordered marking proof technique that Vogler used to\ndemonstrate that (strong) fully-concurrent bisimilarity (or, equivalently,\nhistorypreserving bisimilarity) is decidable on finite safe nets.\n"}
+{"abstract": "  Based on equivalent-dynamic-linearization model (EDLM), we propose a kind of\nmodel predictive control (MPC) for single-input and single-output (SISO)\nnonlinear or linear systems. After compensating the EDLM with disturbance for\nmultiple-input and multiple-output nonlinear or linear systems, the MPC\ncompensated with disturbance is proposed to address the disturbance rejection\nproblem. The system performance analysis results are much clear compared with\nthe system stability analyses on MPC in current works. And this may help the\nengineers understand how to design, analyze and apply the controller in\npractical.\n"}
+{"abstract": "  Point Cloud Sampling and Recovery (PCSR) is critical for massive real-time\npoint cloud collection and processing since raw data usually requires large\nstorage and computation. In this paper, we address a fundamental problem in\nPCSR: How to downsample the dense point cloud with arbitrary scales while\npreserving the local topology of discarding points in a case-agnostic manner\n(i.e. without additional storage for point relationship)? We propose a novel\nLocally Invertible Embedding for point cloud adaptive sampling and recovery\n(PointLIE). Instead of learning to predict the underlying geometry details in a\nseemingly plausible manner, PointLIE unifies point cloud sampling and\nupsampling to one single framework through bi-directional learning.\nSpecifically, PointLIE recursively samples and adjusts neighboring points on\neach scale. Then it encodes the neighboring offsets of sampled points to a\nlatent space and thus decouples the sampled points and the corresponding local\ngeometric relationship. Once the latent space is determined and that the deep\nmodel is optimized, the recovery process could be conducted by passing the\nrecover-pleasing sampled points and a randomly-drawn embedding to the same\nnetwork through an invertible operation. Such a scheme could guarantee the\nfidelity of dense point recovery from sampled points. Extensive experiments\ndemonstrate that the proposed PointLIE outperforms state-of-the-arts both\nquantitatively and qualitatively. Our code is released through\nhttps://github.com/zwb0/PointLIE.\n"}
+{"abstract": "  Mixed linear regression (MLR) model is among the most exemplary statistical\ntools for modeling non-linear distributions using a mixture of linear models.\nWhen the additive noise in MLR model is Gaussian, Expectation-Maximization (EM)\nalgorithm is a widely-used algorithm for maximum likelihood estimation of MLR\nparameters. However, when noise is non-Gaussian, the steps of EM algorithm may\nnot have closed-form update rules, which makes EM algorithm impractical. In\nthis work, we study the maximum likelihood estimation of the parameters of MLR\nmodel when the additive noise has non-Gaussian distribution. In particular, we\nconsider the case that noise has Laplacian distribution and we first show that\nunlike the the Gaussian case, the resulting sub-problems of EM algorithm in\nthis case does not have closed-form update rule, thus preventing us from using\nEM in this case. To overcome this issue, we propose a new algorithm based on\ncombining the alternating direction method of multipliers (ADMM) with EM\nalgorithm idea. Our numerical experiments show that our method outperforms the\nEM algorithm in statistical accuracy and computational time in non-Gaussian\nnoise case.\n"}
+{"abstract": "  We study automorphism and birational automorphism groups of varieties over\nfields of positive characteristic from the point of view of Jordan and\n$p$-Jordan property. In particular, we show that the Cremona group of rank $2$\nover a field of characteristic $p>0$ is $p$-Jordan, and the birational\nautomorphism group of an arbitrary geometrically irreducible algebraic surface\nis nilpotently $p$-Jordan of class at most $2$. Also, we show that the\nautomorphism group of a smooth geometrically irreducible projective variety of\nnon-negative Kodaira dimension is Jordan in the usual sense.\n"}
+{"abstract": "  One of the most important early results from the Parker Solar Probe (PSP) is\nthe ubiquitous presence of magnetic switchbacks, whose origin is under debate.\nUsing a three-dimensional direct numerical simulation of the equations of\ncompressible magnetohydrodynamics from the corona to 40 solar radii, we\ninvestigate whether magnetic switchbacks emerge from granulation-driven\nAlfv\\'en waves and turbulence in the solar wind. The simulated solar wind is an\nAlfv\\'enic slow-solar-wind stream with a radial profile consistent with various\nobservations, including observations from PSP. As a natural consequence of\nAlfv\\'en-wave turbulence, the simulation reproduced magnetic switchbacks with\nmany of the same properties as observed switchbacks, including Alfv\\'enic v-b\ncorrelation, spherical polarization (low magnetic compressibility), and a\nvolume filling fraction that increases with radial distance. The analysis of\npropagation speed and scale length shows that the magnetic switchbacks are\nlarge-amplitude (nonlinear) Alfv\\'en waves with discontinuities in the magnetic\nfield direction. We directly compare our simulation with observations using a\nvirtual flyby of PSP in our simulation domain. We conclude that at least some\nof the switchbacks observed by PSP are a natural consequence of the growth in\namplitude of spherically polarized Alfv\\'en waves as they propagate away from\nthe Sun.\n"}
+{"abstract": "  All the news that's (un)fit to publish.\n"}
+{"abstract": "  Bifurcation diagram is a powerful tool that visually gives information about\nthe behavior of the equilibrium points of a dynamical system respect to the\nvarying parameter. This paper proposes an educational algorithm by which the\nlocal bifurcation diagram could be plotted manually and fast in an easy and\nstraightforward way. To the students, this algorithmic method seems to be\nsimpler and more straightforward than mathematical plotting methods in\neducational and ordinary problems during the learning and studying of courses\nrelated to dynamical systems and bifurcation diagrams. For validation, the\nalgorithm has been applied to several educational examples in the course of\ndynamical systems.\n"}
+{"abstract": "  SpeechBrain is an open-source and all-in-one speech toolkit. It is designed\nto facilitate the research and development of neural speech processing\ntechnologies by being simple, flexible, user-friendly, and well-documented.\nThis paper describes the core architecture designed to support several tasks of\ncommon interest, allowing users to naturally conceive, compare and share novel\nspeech processing pipelines. SpeechBrain achieves competitive or\nstate-of-the-art performance in a wide range of speech benchmarks. It also\nprovides training recipes, pretrained models, and inference scripts for popular\nspeech datasets, as well as tutorials which allow anyone with basic Python\nproficiency to familiarize themselves with speech technologies.\n"}
+{"abstract": "  We propose Citrinet - a new end-to-end convolutional Connectionist Temporal\nClassification (CTC) based automatic speech recognition (ASR) model. Citrinet\nis deep residual neural model which uses 1D time-channel separable convolutions\ncombined with sub-word encoding and squeeze-and-excitation. The resulting\narchitecture significantly reduces the gap between non-autoregressive and\nsequence-to-sequence and transducer models. We evaluate Citrinet on\nLibriSpeech, TED-LIUM2, AISHELL-1 and Multilingual LibriSpeech (MLS) English\nspeech datasets. Citrinet accuracy on these datasets is close to the best\nautoregressive Transducer models.\n"}
+{"abstract": "  This paper proposes the trajectory tracking problem between an autonomous\nunderwater vehicle (AUV) and a mobile surface ship, both equipped with optical\ncommunication transceivers. The challenging issue is to maintain stable\nconnectivity between the two autonomous vehicles within an optical\ncommunication range. We define a directed optical line-of-sight (LoS) link\nbetween the two-vehicle systems. The transmitter is mounted on the AUV while\nthe surface ship is equipped with an optical receiver. However, this optical\ncommunication channel needs to preserve a stable transmitter-receiver position\nto reinforce service quality, which typically includes a bit rate and bit error\nrates. A cone-shaped beam region of the optical receiver is approximated based\non the channel model; then, a minimum bit rate is ensured if the AUV\ntransmitter remains inside of this region. Additionally, we design two control\nalgorithms for the transmitter to drive the AUV and maintain it in the\ncone-shaped beam region under an uncertain oceanic environment. Lyapunov\nfunction-based analysis that ensures asymptotic stability of the resulting\nclosed-loop tracking error is used to design the proposed NLPD controller.\nNumerical simulations are performed using MATLAB/Simulink to show the\ncontrollers' ability to achieve favorable tracking in the presence of the solar\nbackground noise within competitive times. Finally, results demonstrate the\nproposed NLPD controller improves the tracking error performance more than\n$70\\%$ under nominal conditions and $35\\%$ with model uncertainties and\ndisturbances compared to the original PD strategy.\n"}
+{"abstract": "  The detection of contextual anomalies is a challenging task for surveillance\nsince an observation can be considered anomalous or normal in a specific\nenvironmental context. An unmanned aerial vehicle (UAV) can utilize its aerial\nmonitoring capability and employ multiple sensors to gather contextual\ninformation about the environment and perform contextual anomaly detection. In\nthis work, we introduce a deep neural network-based method (CADNet) to find\npoint anomalies (i.e., single instance anomalous data) and contextual anomalies\n(i.e., context-specific abnormality) in an environment using a UAV. The method\nis based on a variational autoencoder (VAE) with a context sub-network. The\ncontext sub-network extracts contextual information regarding the environment\nusing GPS and time data, then feeds it to the VAE to predict anomalies\nconditioned on the context. To the best of our knowledge, our method is the\nfirst contextual anomaly detection method for UAV-assisted aerial surveillance.\nWe evaluate our method on the AU-AIR dataset in a traffic surveillance\nscenario. Quantitative comparisons against several baselines demonstrate the\nsuperiority of our approach in the anomaly detection tasks. The codes and data\nwill be available at https://bozcani.github.io/cadnet.\n"}
+{"abstract": "  We report results of our study of a newly synthesized honeycomb iridate\nNaxIrO3 (0.60 < x < 0.80). Single-crystal NaxIrO3 adopts a honeycomb lattice\nnoticeably without distortions and stacking disorder inherently existent in its\nsister compound Na2IrO3. The oxidation state of the Ir ion is a mixed valence\nstate resulting from a majority Ir5+(5d4) ion and a minority Ir6+(5d3) ion.\nNaxIrO3 is a Mott insulator likely with a predominant pseudospin = 1 state. It\nexhibits an effective moment of 1.1 Bohr Magneton/Ir and a Curie-Weiss\ntemperature of -19 K but with no discernable long-range order above 1 K. The\nphysical behavior below 1 K features two prominent anomalies at Th = 0.9 K and\nTl = 0.12 K in both the heat capacity and AC magnetic susceptibility.\nIntermediate between Th and Tl lies a pronounced temperature linearity of the\nheat capacity with a large slope of 77 mJ/mole K2, a feature expected for\nhighly correlated metals but not at all for insulators. These results along\nwith comparison drawn with the honeycomb lattices Na2IrO3 and (Na0.2Li0.8)2IrO3\npoint to an exotic ground state in a proximity to a possible Kitaev spin\nliquid.\n"}
+{"abstract": "  Symbolic control techniques aim to satisfy complex logic specifications. A\ncritical step in these techniques is the construction of a symbolic (discrete)\nabstraction, a finite-state system whose behaviour mimics that of a given\ncontinuous-state system. The methods used to compute symbolic abstractions,\nhowever, require knowledge of an accurate closed-form model. To generalize them\nto systems with unknown dynamics, we present a new data-driven approach that\ndoes not require closed-form dynamics, instead relying only the ability to\nevaluate successors of each state under given inputs. To provide guarantees for\nthe learned abstraction, we use the Probably Approximately Correct (PAC)\nstatistical framework. We first introduce a PAC-style behavioural relationship\nand an appropriate refinement procedure. We then show how the symbolic\nabstraction can be constructed to satisfy this new behavioural relationship.\nMoreover, we provide PAC bounds that dictate the number of data required to\nguarantee a prescribed level of accuracy and confidence. Finally, we present an\nillustrative example.\n"}
+{"abstract": "  Remote sample recovery is a rapidly evolving application of Small Unmanned\nAircraft Systems (sUAS) for planetary sciences and space exploration.\nDevelopment of cyber-physical systems (CPS) for autonomous deployment and\nrecovery of sensor probes for sample caching is already in progress with NASA's\nMARS 2020 mission. To challenge student teams to develop autonomy for sample\nrecovery settings, the 2020 NSF CPS Challenge was positioned around the launch\nof the MARS 2020 rover and sUAS duo. This paper discusses perception and\ntrajectory planning for sample recovery by sUAS in a simulation environment.\nOut of a total of five teams that participated, the results of the top two\nteams have been discussed. The OpenUAV cloud simulation framework deployed on\nthe Cyber-Physical Systems Virtual Organization (CPS-VO) allowed the teams to\nwork remotely over a month during the COVID-19 pandemic to develop and simulate\nautonomous exploration algorithms. Remote simulation enabled teams across the\nglobe to collaborate in experiments. The two teams approached the task of probe\nsearch, probe recovery, and landing on a moving target differently. This paper\nis a summary of teams' insights and lessons learned, as they chose from a wide\nrange of perception sensors and algorithms.\n"}
+{"abstract": "  With the development of blockchain technologies, the number of smart\ncontracts deployed on blockchain platforms is growing exponentially, which\nmakes it difficult for users to find desired services by manual screening. The\nautomatic classification of smart contracts can provide blockchain users with\nkeyword-based contract searching and helps to manage smart contracts\neffectively. Current research on smart contract classification focuses on\nNatural Language Processing (NLP) solutions which are based on contract source\ncode. However, more than 94% of smart contracts are not open-source, so the\napplication scenarios of NLP methods are very limited. Meanwhile, NLP models\nare vulnerable to adversarial attacks. This paper proposes a classification\nmodel based on features from contract bytecode instead of source code to solve\nthese problems. We also use feature selection and ensemble learning to optimize\nthe model. Our experimental studies on over 3,300 real-world Ethereum smart\ncontracts show that our model can classify smart contracts without source code\nand has better performance than baseline models. Our model also has good\nresistance to adversarial attacks compared with NLP-based models. In addition,\nour analysis reveals that account features used in many smart contract\nclassification models have little effect on classification and can be excluded.\n"}
+{"abstract": "  Infrastructure sharing is a widely discussed and implemented approach and is\nsuccessfully adopted in telecommunications networks today. In practice, it is\nimplemented through prior negotiated Service Level Agreements (SLAs) between\nthe parties involved. However, it is recognised that these agreements are\ndifficult to negotiate, monitor and enforce. For future 6G networks, resource\nand infrastructure sharing is expected to play an even greater role. It will be\na crucial technique for reducing overall infrastructure costs and increasing\noperational efficiencies for operators. More efficient SLA mechanisms are thus\ncrucial to the success of future networks. In this work, we present \"BEAT\", an\nautomated, transparent and accountable end-to-end architecture for network\nsharing based on blockchain and smart contracts. This work focuses on a\nparticular type of blockchain, Permissioned Distributed Ledger (PDL), due to\nits permissioned nature allowing for industry-compliant SLAs with stringent\ngovernance. Our architecture can be implemented with minimal hardware changes\nand with minimal overheads.\n"}
+{"abstract": "  Before Brexit, one of the greatest causes of arguments amongst British\nfamilies was the question of the nature of Jaffa Cakes. Some argue that their\nsize and host environment (the biscuit aisle) should make them a biscuit in\ntheir own right. Others consider that their physical properties (e.g. they\nharden rather than soften on becoming stale) suggest that they are in fact\ncake. In order to finally put this debate to rest, we re-purpose technologies\nused to classify transient events. We train two classifiers (a Random Forest\nand a Support Vector Machine) on 100 recipes of traditional cakes and biscuits.\nOur classifiers have 95 percent and 91 percent accuracy respectively. Finally\nwe feed two Jaffa Cake recipes to the algorithms and find that Jaffa Cakes are,\nwithout a doubt, cakes. Finally, we suggest a new theory as to why some believe\nJaffa Cakes are biscuits.\n"}
+{"abstract": "  The pixels in an image, and the objects, scenes, and actions that they\ncompose, determine whether an image will be memorable or forgettable. While\nmemorability varies by image, it is largely independent of an individual\nobserver. Observer independence is what makes memorability an image-computable\nmeasure of information, and eligible for automatic prediction. In this chapter,\nwe zoom into memorability with a computational lens, detailing the\nstate-of-the-art algorithms that accurately predict image memorability relative\nto human behavioral data, using image features at different scales from raw\npixels to semantic labels. We discuss the design of algorithms and\nvisualizations for face, object, and scene memorability, as well as algorithms\nthat generalize beyond static scenes to actions and videos. We cover the\nstate-of-the-art deep learning approaches that are the current front runners in\nthe memorability prediction space. Beyond prediction, we show how recent A.I.\napproaches can be used to create and modify visual memorability. Finally, we\npreview the computational applications that memorability can power, from\nfiltering visual streams to enhancing augmented reality interfaces.\n"}
+{"abstract": "  We consider linear preferential attachment random trees with additive\nfitness, where fitness is defined as the random initial vertex attractiveness.\nWe show that when the fitness distribution has positive bounded support, the\nweak local limit of this family can be constructed using a sequence of mixed\nPoisson point processes. We also provide a rate of convergence of the total\nvariation distance between the r- neighbourhood of the uniformly chosen vertex\nin the preferential attachment tree and that of the root vertex of its weak\nlocal limit. We apply the theorem to obtain the limiting degree distributions\nof the uniformly chosen vertex and its ancestors, that is, the vertices that\nare on the path between the uniformly chosen vertex and the initial vertex.\nRates of convergence in the total variation distance are established for these\nresults.\n"}
+{"abstract": "  In this manuscript, we investigate the oscillatory behaviour of the\nanisotropy in the diagonal Bianchi-I spacetimes. Our starting point is a\nsimplification of Einstein's equations using only observable or physical\nvariables. As a consequence, we are able to: (a) Prove general results\nconcerning the existence of oscillations of the anisotropy in the primordial\nand the late-time universe. For instance, in the expanding scenario, we show\nthat a past weakly mixmaster behaviour (oscillations as we approach the Kasner\nsolutions) might appear even with no violation of the usual energy conditions,\nwhile in the future, the pulsation (oscillations around isotropic solutions)\nseems to be most favored; (b) Determine a large scheme for deriving classes of\nphysically motivated exact solutions, and we give some (including the general\nbarotropic perfect fluid and the magnetic one); (c) Understand the physical\nconditions for the occurrence of the isotropization or anisotropization during\nthe cosmological evolution; (d) Understand how anisotropy and energy density\nare converted one into another. In particular, we call attention to the\npresence of a residue in the energy density in a late-time isotropic universe\ncoming from its past anisotropic behaviour.\n"}
+{"abstract": "  A fundamental challenge faced by existing Fine-Grained Sketch-Based Image\nRetrieval (FG-SBIR) models is the data scarcity -- model performances are\nlargely bottlenecked by the lack of sketch-photo pairs. Whilst the number of\nphotos can be easily scaled, each corresponding sketch still needs to be\nindividually produced. In this paper, we aim to mitigate such an upper-bound on\nsketch data, and study whether unlabelled photos alone (of which they are many)\ncan be cultivated for performances gain. In particular, we introduce a novel\nsemi-supervised framework for cross-modal retrieval that can additionally\nleverage large-scale unlabelled photos to account for data scarcity. At the\ncentre of our semi-supervision design is a sequential photo-to-sketch\ngeneration model that aims to generate paired sketches for unlabelled photos.\nImportantly, we further introduce a discriminator guided mechanism to guide\nagainst unfaithful generation, together with a distillation loss based\nregularizer to provide tolerance against noisy training samples. Last but not\nleast, we treat generation and retrieval as two conjugate problems, where a\njoint learning procedure is devised for each module to mutually benefit from\neach other. Extensive experiments show that our semi-supervised model yields\nsignificant performance boost over the state-of-the-art supervised\nalternatives, as well as existing methods that can exploit unlabelled photos\nfor FG-SBIR.\n"}
+{"abstract": "  Bayesian Knowledge Tracing, a model used for cognitive mastery estimation,\nhas been a hallmark of adaptive learning research and an integral component of\ndeployed intelligent tutoring systems (ITS). In this paper, we provide a brief\nhistory of knowledge tracing model research and introduce pyBKT, an accessible\nand computationally efficient library of model extensions from the literature.\nThe library provides data generation, fitting, prediction, and cross-validation\nroutines, as well as a simple to use data helper interface to ingest typical\ntutor log dataset formats. We evaluate the runtime with various dataset sizes\nand compare to past implementations. Additionally, we conduct sanity checks of\nthe model using experiments with simulated data to evaluate the accuracy of its\nEM parameter learning and use real-world data to validate its predictions,\ncomparing pyBKT's supported model variants with results from the papers in\nwhich they were originally introduced. The library is open source and open\nlicense for the purpose of making knowledge tracing more accessible to\ncommunities of research and practice and to facilitate progress in the field\nthrough easier replication of past approaches.\n"}
+{"abstract": "  A spatially inhomogeneous, trapped two-component Bose-Einstein condensate of\ncold atoms in the phase separation mode has been numerically simulated. It has\nbeen demonstrated for the first time that the surface tension between the\ncomponents makes possible the existence of drops of a denser phase floating on\nthe surface of a less dense phase. Depending on the harmonic trap anisotropy\nand other system parameters, a stable equilibrium of the drop is achieved\neither at the poles or at the equator. The drop flotation sometimes persists\neven in the presence of an attached quantized vortex.\n"}
+{"abstract": "  We can compress a rectifier network while exactly preserving its underlying\nfunctionality with respect to a given input domain if some of its neurons are\nstable. However, current approaches to determine the stability of neurons with\nRectified Linear Unit (ReLU) activations require solving or finding a good\napproximation to multiple discrete optimization problems. In this work, we\nintroduce an algorithm based on solving a single optimization problem to\nidentify all stable neurons. Our approach is on median 183 times faster than\nthe state-of-art method on CIFAR-10, which allows us to explore exact\ncompression on deeper (5 x 100) and wider (2 x 800) networks within minutes.\nFor classifiers trained under an amount of L1 regularization that does not\nworsen accuracy, we can remove up to 56% of the connections on the CIFAR-10\ndataset. The code is available at the following link,\nhttps://github.com/yuxwind/ExactCompression.\n"}
+{"abstract": "  A central goal in experimental high energy physics is to detect new physics\nsignals that are not explained by known physics. In this paper, we aim to\nsearch for new signals that appear as deviations from known Standard Model\nphysics in high-dimensional particle physics data. To do this, we determine\nwhether there is any statistically significant difference between the\ndistribution of Standard Model background samples and the distribution of the\nexperimental observations, which are a mixture of the background and a\npotential new signal. Traditionally, one also assumes access to a sample from a\nmodel for the hypothesized signal distribution. Here we instead investigate a\nmodel-independent method that does not make any assumptions about the signal\nand uses a semi-supervised classifier to detect the presence of the signal in\nthe experimental data. We construct three test statistics using the classifier:\nan estimated likelihood ratio test (LRT) statistic, a test based on the area\nunder the ROC curve (AUC), and a test based on the misclassification error\n(MCE). Additionally, we propose a method for estimating the signal strength\nparameter and explore active subspace methods to interpret the proposed\nsemi-supervised classifier in order to understand the properties of the\ndetected signal. We investigate the performance of the methods on a data set\nrelated to the search for the Higgs boson at the Large Hadron Collider at CERN.\nWe demonstrate that the semi-supervised tests have power competitive with the\nclassical supervised methods for a well-specified signal, but much higher power\nfor an unexpected signal which might be entirely missed by the supervised\ntests.\n"}
+{"abstract": "  Real world data is mostly unlabeled or only few instances are labeled.\nManually labeling data is a very expensive and daunting task. This calls for\nunsupervised learning techniques that are powerful enough to achieve comparable\nresults as semi-supervised/supervised techniques. Contrastive self-supervised\nlearning has emerged as a powerful direction, in some cases outperforming\nsupervised techniques. In this study, we propose, SelfGNN, a novel contrastive\nself-supervised graph neural network (GNN) without relying on explicit\ncontrastive terms. We leverage Batch Normalization, which introduces implicit\ncontrastive terms, without sacrificing performance. Furthermore, as data\naugmentation is key in contrastive learning, we introduce four feature\naugmentation (FA) techniques for graphs. Though graph topological augmentation\n(TA) is commonly used, our empirical findings show that FA perform as good as\nTA. Moreover, FA incurs no computational overhead, unlike TA, which often has\nO(N^3) time complexity, N-number of nodes. Our empirical evaluation on seven\npublicly available real-world data shows that, SelfGNN is powerful and leads to\na performance comparable with SOTA supervised GNNs and always better than SOTA\nsemi-supervised and unsupervised GNNs. The source code is available at\nhttps://github.com/zekarias-tilahun/SelfGNN.\n"}
+{"abstract": "  Stochastic gradient descent (SGD) is the main approach for training deep\nnetworks: it moves towards the optimum of the cost function by iteratively\nupdating the parameters of a model in the direction of the gradient of the loss\nevaluated on a minibatch. Several variants of SGD have been proposed to make\nadaptive step sizes for each parameter (adaptive gradient) and take into\naccount the previous updates (momentum). Among several alternative of SGD the\nmost popular are AdaGrad, AdaDelta, RMSProp and Adam which scale coordinates of\nthe gradient by square roots of some form of averaging of the squared\ncoordinates in the past gradients and automatically adjust the learning rate on\na parameter basis. In this work, we compare Adam based variants based on the\ndifference between the present and the past gradients, the step size is\nadjusted for each parameter. We run several tests benchmarking proposed methods\nusing medical image data. The experiments are performed using ResNet50\narchitecture neural network. Moreover, we have tested ensemble of networks and\nthe fusion with ResNet50 trained with stochastic gradient descent. To combine\nthe set of ResNet50 the simple sum rule has been applied. Proposed ensemble\nobtains very high performance, it obtains accuracy comparable or better than\nactual state of the art. To improve reproducibility and research efficiency the\nMATLAB source code used for this research is available at GitHub:\nhttps://github.com/LorisNanni.\n"}
+{"abstract": "  Using a mechanism which allows naturally small Dirac neutrino masses and its\nlinkage to a dark gauge $U(1)_D$ symmetry, a realistic Dirac neutrino mass\nmatrix is derived from $S_3$. The dark sector naturally contains a fermion\nsinglet having a small seesaw mass. It is thus a good candidate for freeze-in\ndark matter from the decay of the $U(1)_D$ Higgs boson.\n"}
+{"abstract": "  For nitride-based InGaN and AlGaN quantum well (QW) LEDs, the potential\nfluctuations caused by natural alloy disorders limit the lateral intra-QW\ncarrier diffusion length and current spreading. The diffusion length mainly\nimpacts the overall LED efficiency through sidewall nonradiative recombination,\nespecially for $\\mu$LEDs. In this paper, we study the carrier lateral diffusion\nlength for nitride-based green, blue, and ultraviolet C (UVC) QWs in three\ndimensions. We solve the Poisson and drift-diffusion equations in the framework\nof localization landscape theory. The full three-dimensional model includes the\neffects of random alloy composition fluctuations and electric fields in the\nQWs. The dependence of the minority carrier diffusion length on the majority\ncarrier density is studied with a full three-dimensional model. The results\nshow that the diffusion length is limited by the potential fluctuations and the\nrecombination rate, the latter being controlled by the polarization-induced\nelectric field in the QWs and by the screening of the internal electric fields\nby carriers.\n"}
+{"abstract": "  Model predictive control is an advanced control approach for multivariable\nsystems with constraints, which is reliant on an accurate dynamic model. Most\nreal dynamic models are however affected by uncertainties, which can lead to\nclosed-loop performance deterioration and constraint violations. In this paper\nwe introduce a new algorithm to explicitly consider time-invariant stochastic\nuncertainties in optimal control problems. The difficulty of propagating\nstochastic variables through nonlinear functions is dealt with by combining\nGaussian processes with polynomial chaos expansions. The main novelty in this\npaper is to use this combination in an efficient fashion to obtain mean and\nvariance estimates of nonlinear transformations. Using this algorithm, it is\nshown how to formulate both chance-constraints and a probabilistic objective\nfor the optimal control problem. On a batch reactor case study we firstly\nverify the ability of the new approach to accurately approximate the\nprobability distributions required. Secondly, a tractable stochastic nonlinear\nmodel predictive control approach is formulated with an economic objective to\ndemonstrate the closed-loop performance of the method via Monte Carlo\nsimulations.\n"}
+{"abstract": "  Controller design for nonlinear systems with Control Lyapunov Function (CLF)\nbased quadratic programs has recently been successfully applied to a diverse\nset of difficult control tasks. These existing formulations do not address the\ngap between design with continuous time models and the discrete time sampled\nimplementation of the resulting controllers, often leading to poor performance\non hardware platforms. We propose an approach to close this gap by synthesizing\nsampled-data counterparts to these CLF-based controllers, specified as\nquadratically constrained quadratic programs (QCQPs). Assuming feedback\nlinearizability and stable zero-dynamics of a system's continuous time model,\nwe derive practical stability guarantees for the resulting sampled-data system.\nWe demonstrate improved performance of the proposed approach over continuous\ntime counterparts in simulation.\n"}
+{"abstract": "  Both FCM and PCM clustering methods have been widely applied to pattern\nrecognition and data clustering. Nevertheless, FCM is sensitive to noise and\nPCM occasionally generates coincident clusters. PFCM is an extension of the PCM\nmodel by combining FCM and PCM, but this method still suffers from the\nweaknesses of PCM and FCM. In the current paper, the weaknesses of the PFCM\nalgorithm are corrected and the enhanced possibilistic fuzzy c-means (EPFCM)\nclustering algorithm is presented. EPFCM can still be sensitive to noise.\nTherefore, we propose an interval type-2 enhanced possibilistic fuzzy c-means\n(IT2EPFCM) clustering method by utilizing two fuzzifiers $(m_1, m_2)$ for fuzzy\nmemberships and two fuzzifiers $({\\theta}_1, {\\theta}_2)$ for possibilistic\ntypicalities. Our computational results show the superiority of the proposed\napproaches compared with several state-of-the-art techniques in the literature.\nFinally, the proposed methods are implemented for analyzing microarray gene\nexpression data.\n"}
+{"abstract": "  Societal biases resonate in the retrieved contents of information retrieval\n(IR) systems, resulting in reinforcing existing stereotypes. Approaching this\nissue requires established measures of fairness in respect to the\nrepresentation of various social groups in retrieval results, as well as\nmethods to mitigate such biases, particularly in the light of the advances in\ndeep ranking models. In this work, we first provide a novel framework to\nmeasure the fairness in the retrieved text contents of ranking models.\nIntroducing a ranker-agnostic measurement, the framework also enables the\ndisentanglement of the effect on fairness of collection from that of rankers.\nTo mitigate these biases, we propose AdvBert, a ranking model achieved by\nadapting adversarial bias mitigation for IR, which jointly learns to predict\nrelevance and remove protected attributes. We conduct experiments on two\npassage retrieval collections (MSMARCO Passage Re-ranking and TREC Deep\nLearning 2019 Passage Re-ranking), which we extend by fairness annotations of a\nselected subset of queries regarding gender attributes. Our results on the\nMSMARCO benchmark show that, (1) all ranking models are less fair in comparison\nwith ranker-agnostic baselines, and (2) the fairness of Bert rankers\nsignificantly improves when using the proposed AdvBert models. Lastly, we\ninvestigate the trade-off between fairness and utility, showing that we can\nmaintain the significant improvements in fairness without any significant loss\nin utility.\n"}
+{"abstract": "  Boundary based blackbox attack has been recognized as practical and\neffective, given that an attacker only needs to access the final model\nprediction. However, the query efficiency of it is in general high especially\nfor high dimensional image data. In this paper, we show that such efficiency\nhighly depends on the scale at which the attack is applied, and attacking at\nthe optimal scale significantly improves the efficiency. In particular, we\npropose a theoretical framework to analyze and show three key characteristics\nto improve the query efficiency. We prove that there exists an optimal scale\nfor projective gradient estimation. Our framework also explains the\nsatisfactory performance achieved by existing boundary black-box attacks. Based\non our theoretical framework, we propose Progressive-Scale enabled projective\nBoundary Attack (PSBA) to improve the query efficiency via progressive scaling\ntechniques. In particular, we employ Progressive-GAN to optimize the scale of\nprojections, which we call PSBA-PGAN. We evaluate our approach on both spatial\nand frequency scales. Extensive experiments on MNIST, CIFAR-10, CelebA, and\nImageNet against different models including a real-world face recognition API\nshow that PSBA-PGAN significantly outperforms existing baseline attacks in\nterms of query efficiency and attack success rate. We also observe relatively\nstable optimal scales for different models and datasets. The code is publicly\navailable at https://github.com/AI-secure/PSBA.\n"}
+{"abstract": "  Shared-account Cross-domain Sequential recommendation (SCSR) is the task of\nrecommending the next item based on a sequence of recorded user behaviors,\nwhere multiple users share a single account, and their behaviours are available\nin multiple domains. Existing work on solving SCSR mainly relies on mining\nsequential patterns via RNN-based models, which are not expressive enough to\ncapture the relationships among multiple entities. Moreover, all existing\nalgorithms try to bridge two domains via knowledge transfer in the latent\nspace, and the explicit cross-domain graph structure is unexploited. In this\nwork, we propose a novel graph-based solution, namely DA-GCN, to address the\nabove challenges. Specifically, we first link users and items in each domain as\na graph. Then, we devise a domain-aware graph convolution network to learn\nuser-specific node representations. To fully account for users' domain-specific\npreferences on items, two novel attention mechanisms are further developed to\nselectively guide the message passing process. Extensive experiments on two\nreal-world datasets are conducted to demonstrate the superiority of our DA-GCN\nmethod.\n"}
+{"abstract": "  Since neural networks are data-hungry, incorporating data augmentation in\ntraining is a widely adopted technique that enlarges datasets and improves\ngeneralization. On the other hand, aggregating predictions of multiple\naugmented samples (i.e., test-time augmentation) could boost performance even\nfurther. In the context of person re-identification models, it is common\npractice to extract embeddings for both the original images and their\nhorizontally flipped variants. The final representation is the mean of the\naforementioned feature vectors. However, such scheme results in a gap between\ntraining and inference, i.e., the mean feature vectors calculated in inference\nare not part of the training pipeline. In this study, we devise the FlipReID\nstructure with the flipping loss to address this issue. More specifically,\nmodels using the FlipReID structure are trained on the original images and the\nflipped images simultaneously, and incorporating the flipping loss minimizes\nthe mean squared error between feature vectors of corresponding image pairs.\nExtensive experiments show that our method brings consistent improvements. In\nparticular, we set a new record for MSMT17 which is the largest person\nre-identification dataset. The source code is available at\nhttps://github.com/nixingyang/FlipReID.\n"}
+{"abstract": "  We show that color-breaking vacua may develop at high temperature in the\nMini-Split Supersymmetry (SUSY) scenario. This can lead to a nontrivial\ncosmological history of the Universe, including strong first order phase\ntransitions and domain wall production. Given the typical PeV energy scale\nassociated with Mini-Split SUSY models, a stochastic gravitational wave\nbackground at frequencies around 1 kHz is expected. We study the potential for\ndetection of such a signal in future gravitational wave experiments.\n"}
+{"abstract": "  We report on the observation and coherent excitation of atoms on the narrow\ninner-shell orbital transition, connecting the erbium ground state\n$[\\mathrm{Xe}] 4f^{12} (^3\\text{H}_6)6s^{2}$ to the excited state\n$[\\mathrm{Xe}] 4f^{11}(^4\\text{I}_{15/2})^05d (^5\\text{D}_{3/2}) 6s^{2}\n(15/2,3/2)^0_7$. This transition corresponds to a wavelength of 1299 nm and is\noptically closed. We perform high-resolution spectroscopy to extract the\n$g_J$-factor of the $1299$-nm state and to determine the frequency shift for\nfour bosonic isotopes. We further demonstrate coherent control of the atomic\nstate and extract a lifetime of 178(19) ms which corresponds to a linewidth of\n0.9(1) Hz. The experimental findings are in good agreement with our\nsemi-empirical model. In addition, we present theoretical calculations of the\natomic polarizability, revealing several different magic-wavelength conditions.\nFinally, we make use of the vectorial polarizability and confirm a possible\nmagic wavelength at 532 nm.\n"}
+{"abstract": "  Viscosity overshoot of entangled polymer melts has been observed under shear\nflow and uniaxial elongational flow, but has never been observed under biaxial\nelongational flow. We confirmed the presence of viscosity overshoot under\nbiaxial elongational flows observed in a mixed system of ring and linear\npolymers expressed by coarse-grained molecular dynamics simulations. The\novershoot was found to be more pronounced in weakly entangled melts.\nFurthermore, the threshold strain rate $\\dot{\\varepsilon}_{\\rm th}$\ndistinguishing linear and nonlinear behaviors was found to be dependent on the\nlinear chain length as $\\dot{\\varepsilon}_{\\rm th}(N)\\sim N^{-1/2}$, which\ndiffers from the conventional relationship, $\\dot{\\varepsilon}_{\\rm th}(N) \\sim\nN^{-2}$, expected from the inverse of the Rouse relaxation time. We have\nconcluded that the cooperative interactions between rings and linear chains\nwere enhanced under biaxial elongational flow.\n"}
+{"abstract": "  Gassert's paper \"A NOTE ON THE MONOGENEITY OF POWER MAPS\" is cited at least\nby $17$ papers in the context of monogeneity of pure number fields despite some\nerrors that it contains and remarks on it. In this note, we point out some of\nthese errors, and make some improvements on it.\n"}
+{"abstract": "  Regular arrays of two-level emitters at distances smaller that the transition\nwavelength collectively scatter, absorb and emit photons. The strong\ninter-particle dipole coupling creates large energy shifts of the collective\ndelocalized excitations, which generates a highly nonlinear response at the\nsingle and few photon level. This should allow to implement nanoscale\nnon-classical light sources via weak coherent illumination. At the generic\ntailored examples of regular chains or polygons we show that the fields emitted\nperpendicular to the illumination direction exhibit a strong directional\nconfinement with genuine quantum properties as antibunching. For short\ninterparticle distances superradiant directional emission can enhance the\nradiated intensity by an order of magnitude compared to a single atom focused\nto a strongly confined solid angle but still keeping the anti-bunching\nparameter at the level of $g^{(2)}(0) \\approx 10^{-2}$.\n"}
+{"abstract": "  We study perturbations of the self-adjoint periodic Sturm--Liouville operator\n\\[\n  A_0 = \\frac{1}{r_0}\\left(-\\frac{\\mathrm d}{\\mathrm dx} p_0 \\frac{\\mathrm\nd}{\\mathrm dx} + q_0\\right) \\] and conclude under $L^1$-assumptions on the\ndifferences of the coefficients that the essential spectrum and absolutely\ncontinuous spectrum remain the same. If a finite first moment condition holds\nfor the differences of the coefficients, then at most finitely many eigenvalues\nappear in the spectral gaps. This observation extends a seminal result by\nRofe-Beketov from the 1960s. Finally, imposing a second moment condition we\nshow that the band edges are no eigenvalues of the perturbed operator.\n"}
+{"abstract": "  A magnetic skyrmion crystal (SkX) with a swirling spin configuration, which\nis one of topological spin crystals as a consequence of an interference between\nmultiple spin density waves, shows a variety of noncoplanar spin patterns\ndepending on a way of superposing the waves. By focusing on a phase degree of\nfreedom among the constituent waves in the SkX, we theoretically investigate a\nposition of the skyrmion core on a discrete lattice, which is relevant with the\nsymmetry of the SkX. The results are obtained for the double exchange\n(classical Kondo lattice) model on a discrete triangular lattice by the\nvariational calculations. We find that the skyrmion cores in both two SkXs with\nthe skyrmion number of one and two are locked at the interstitial site on the\ntriangular lattice, while it is located at the onsite by introducing a\nrelatively large easy-axis single-ion anisotropy. The variational parameters\nand the resultant Fermi surfaces in each SkX spin texture are also discussed.\nThe different symmetry of the Fermi surfaces depending on the core position is\nobtained when the skyrmion crystal is commensurate with the lattice. The\ndifferent Fermi-surface topology is directly distinguished by an electric probe\nof angle-resolved photoemission spectroscopy. Furthermore, we show that the\nSkXs obtained by the variational calculations are also confirmed by numerical\nsimulations on the basis of the kernel polynomial method and the Langevin\ndynamics for the double exchange model and the simulated annealing for an\neffective spin model.\n"}
+{"abstract": "  It is proved that for any $0<\\beta<\\alpha$, any bounded Ahlfors\n$\\alpha$-regular space contains a $\\beta$-regular compact subset that embeds\nbiLipschitzly in an ultrametric with distortion at most\n$O(\\alpha/(\\alpha-\\beta))$. The bound on the distortion is asymptotically tight\nwhen $\\beta\\to \\alpha$. The main tool used in the proof is a regular form of\nthe ultrametric skeleton theorem.\n"}
+{"abstract": "  In recent years online shopping has gained momentum and became an important\nvenue for customers wishing to save time and simplify their shopping process. A\nkey advantage of shopping online is the ability to read what other customers\nare saying about products of interest. In this work, we aim to maintain this\nadvantage in situations where extreme brevity is needed, for example, when\nshopping by voice. We suggest a novel task of extracting a single\nrepresentative helpful sentence from a set of reviews for a given product. The\nselected sentence should meet two conditions: first, it should be helpful for a\npurchase decision and second, the opinion it expresses should be supported by\nmultiple reviewers. This task is closely related to the task of Multi Document\nSummarization in the product reviews domain but differs in its objective and\nits level of conciseness. We collect a dataset in English of sentence\nhelpfulness scores via crowd-sourcing and demonstrate its reliability despite\nthe inherent subjectivity involved. Next, we describe a complete model that\nextracts representative helpful sentences with positive and negative sentiment\ntowards the product and demonstrate that it outperforms several baselines.\n"}
+{"abstract": "  Quantum computing is a promising paradigm to solve computationally\nintractable problems. Various companies such as, IBM, Rigetti and D-Wave offer\nquantum computers using a cloud-based platform that possess several interesting\nfeatures. These factors motivate a new threat model. To mitigate this threat,\nwe propose two flavors of QuPUF: one based on superposition, and another based\non decoherence. Experiments on real IBM quantum hardware show that the proposed\nQuPUF can achieve inter-die Hamming Distance(HD) of 55% and intra-HD as low as\n4%, as compared to ideal cases of 50% and 0% respectively. The proposed QuPUFs\ncan also be used as a standalone solution for any other application.\n"}
+{"abstract": "  FASER$\\nu$ at the CERN Large Hadron Collider (LHC) is designed to directly\ndetect collider neutrinos for the first time and study their cross sections at\nTeV energies, where no such measurements currently exist. In 2018, a pilot\ndetector employing emulsion films was installed in the far-forward region of\nATLAS, 480 m from the interaction point, and collected 12.2 fb$^{-1}$ of\nproton-proton collision data at a center-of-mass energy of 13 TeV. We describe\nthe analysis of this pilot run data and the observation of the first neutrino\ninteraction candidates at the LHC. This milestone paves the way for high-energy\nneutrino measurements at current and future colliders.\n"}
+{"abstract": "  This work extends the framework of the partially-averaged Navier-Stokes\n(PANS) equations to variable-density flow, \\text{i.e.}, multi-material and/or\ncompressible mixing problems with density variations and production of\nturbulence kinetic energy by both shear and buoyancy mechanisms. The proposed\nmethodology is utilized to derive the PANS BHR-LEVM closure. This includes\n\\textit{a-priori} testing to analyze and develop guidelines toward the\nefficient selection of the parameters controlling the physical resolution and,\nconsequently, the range of resolved scales of PANS. Two archetypal test-cases\ninvolving transient turbulence, hydrodynamic instabilities, and coherent\nstructures are used to illustrate the accuracy and potential of the method: the\nTaylor-Green vortex (TGV) at Reynolds number $\\mathrm{Re}=3000$, and the\nRayleigh-Taylor (RT) flow at Atwood number $0.5$ and\n$(\\mathrm{Re})_{\\max}\\approx 500$. These representative problems, for which\nturbulence is generated by shear and buoyancy processes, constitute the initial\nvalidation space of the new model, and their results are comprehensively\ndiscussed in two subsequent studies. The computations indicate that PANS can\naccurately predict the selected flow problems, resolving only a fraction of the\nscales of large eddy simulation and direct numerical simulation strategies. The\nresults also reiterate that the physical resolution of the PANS model must\nguarantee that the key instabilities and coherent structures of the flow are\nresolved. The remaining scales can be modeled through an adequate turbulence\nscale-dependent closure.\n"}
+{"abstract": "  Working in two space dimensions, we show that the orientational order\nemerging from self-propelled polar particles aligning nematically is\nquasi-long-ranged beyond $\\ell_{\\rm r}$, the scale associated to induced\nvelocity reversals, which is typically extremely large and often cannot even be\nmeasured. Below $\\ell_{\\rm r}$, nematic order is long-range. We construct and\nstudy a hydrodynamic theory for this de facto phase and show that its structure\nand symmetries differ from conventional descriptions of active nematics. We\ncheck numerically our theoretical predictions, in particular the presence of\n$\\pi$-symmetric propagative sound modes, and provide estimates of all scaling\nexponents governing long-range space-time correlations.\n"}
+{"abstract": "  In chirped pulse experiments, magnitude Fourier transform is used to generate\nfrequency domain spectra. The application of window function as a tool for\nlineshape correction and signal-to-noise ratio (SnR) enhancement is rarely\ndiscussed in chirped spectroscopy, with the only exception of using\nKaiser-Bessel window and trivial rectangular window. We present a specific\nwindow function, called \"Voigt-1D\" window, designed for chirped pulse\nspectroscopy. The window function corrects the magnitude Fourier-transform\nspectra to Voigt lineshape, and offers wide tunability to control the SnR and\nlineshape of the final spectral lines. We derived the mathematical properties\nof the window function, and evaluated the performance of the window function in\ncomparison to the Kaiser-Bessel window on experimental and simulated data sets.\nOur result shows that, compared with un-windowed spectra, the Voigt-1D window\nis able to produce 100 % SnR enhancement on average.\n"}
+{"abstract": "  The aim of this paper is to provide the geometrical structure of a\ngravitational field that includes the addition of dark matter in the framework\nof a Riemannian and a Riemann--Sasaki spacetime. By means of the classical\nRiemannian geometric methods we arrive at modified geodesic equations, tidal\nforces, and Einstein and Raychaudhuri equations to account for extra dark\ngravity. We further examine an application of this approach in cosmology.\nMoreover, a possible extension of this model on the tangent bundle is studied\nin order to examine the behavior of dark matter in a unified geometric model of\ngravity with more degrees of freedom. Particular emphasis shall be laid on the\nproblem of the geodesic motion under the influence of dark matter.\n"}
+{"abstract": "  Being able to predict stock prices might be the unspoken wish of stock\ninvestors. Although stock prices are complicated to predict, there are many\ntheories about what affects their movements, including interest rates, news and\nsocial media. With the help of Machine Learning, complex patterns in data can\nbe identified beyond the human intellect. In this thesis, a Machine Learning\nmodel for time series forecasting is created and tested to predict stock\nprices. The model is based on a neural network with several layers of LSTM and\nfully connected layers. It is trained with historical stock values, technical\nindicators and Twitter attribute information retrieved, extracted and\ncalculated from posts on the social media platform Twitter. These attributes\nare sentiment score, favourites, followers, retweets and if an account is\nverified. To collect data from Twitter, Twitter's API is used. Sentiment\nanalysis is conducted with VADER. The results show that by adding more Twitter\nattributes, the MSE between the predicted prices and the actual prices improved\nby 3%. With technical analysis taken into account, MSE decreases from 0.1617 to\n0.1437, which is an improvement of around 11%. The restrictions of this study\ninclude that the selected stock has to be publicly listed on the stock market\nand popular on Twitter and among individual investors. Besides, the stock\nmarkets' opening hours differ from Twitter, which constantly available. It may\ntherefore introduce noises in the model.\n"}
+{"abstract": "  In low-dimensional systems, indistinguishable particles can display\nstatistics that interpolate between bosons and fermions. Signatures of these\n\"anyons\" have been detected in two-dimensional quasiparticle excitations of the\nfractional quantum Hall effect, however experimental access to these\nquasiparticles remains limited. As an alternative to these \"topological\nanyons,\" we propose \"statistical anyons\" realized through a statistical mixture\nof particles with bosonic and fermionic symmetry. We show that the framework of\nstatistical anyons is equivalent to the generalized exclusion statistics (GES)\npioneered by Haldane, significantly broadening the range of systems to which\nGES apply. We develop the full thermodynamic characterizations of these\nstatistical anyons, including both equilibrium and nonequilibrium behavior. To\ndevelop a complete picture, we compare the performance of quantum heat engines\nwith working mediums of statistical anyons and traditional topological anyons,\ndemonstrating the effects of the anyonic phase in both local equilibrium and\nfully nonequilibrium regimes. In addition, methods of optimizing engine\nperformance through shortcuts to adiabaticity are investigated, using both\nlinear response and fast forward techniques.\n"}
+{"abstract": "  The eukaryotic cell's cytoskeleton is a prototypical example of an active\nmaterial: objects embedded within it are driven by molecular motors acting on\nthe cytoskeleton, leading to anomalous diffusive behavior. Experiments tracking\nthe behavior of cell-attached objects have observed anomalous diffusion with a\ndistribution of displacements that is non-Gaussian, with heavy tails. This has\nbeen attributed to \"cytoquakes\" or other spatially extended collective effects.\nWe show, using simulations and analytical theory, that a simple continuum\nactive gel model driven by fluctuating force dipoles naturally creates heavy\npower-law tails in cytoskeletal displacements. We predict that this power law\nexponent should depend on the geometry and dimensionality of where force\ndipoles are distributed through the cell; we find qualitatively different\nresults for force dipoles in a 3D cytoskeleton and a quasi-two-dimensional\ncortex. We then discuss potential applications of this model both in cells and\nin synthetic active gels.\n"}
+{"abstract": "  The conversion and interaction between quantum signals at a single-photon\nlevel are essential for scalable quantum photonic information technology. Using\na fully-optimized, periodically-poled lithium niobate microring, we demonstrate\nultra-efficient sum-frequency generation on chip. The external quantum\nefficiency reaches $(65\\pm3)\\%$ with only $(104\\pm4)$ $\\mu$W pump power,\nimproving the state-of-the-art by over one order of magnitude. At the peak\nconversion, $3\\times10^{-5}$ noise photon is created during the cavity\nlifetime, which meets the requirement of quantum applications using\nsingle-photon pulses. Using pump and signal in single-photon coherent states,\nwe directly measure the conversion probability produced by a single pump photon\nto be $10^{-5}$ -- breaking the record by 100 times -- and the photon-photon\ncoupling strength to be 9.1 MHz. Our results mark a new milestone toward\nquantum nonlinear optics at the ultimate single photon limit, creating new\nbackground in highly integrated photonics and quantum optical computing.\n"}
+{"abstract": "  Developing sustainable scientific software for the needs of the scientific\ncommunity requires expertise in both software engineering and domain science.\nThis can be challenging due to the unique needs of scientific software, the\ninsufficient resources for modern software engineering practices in the\nscientific community, and the complexity of evolving scientific contexts for\ndevelopers. These difficulties can be reduced if scientists and developers\ncollaborate. We present a case study wherein scientists from the SuperNova\nEarly Warning System collaborated with software developers from the Scalable\nCyberinfrastructure for Multi-Messenger Astrophysics project. The collaboration\naddressed the difficulties of scientific software development, but presented\nadditional risks to each team. For the scientists, there was a concern of\nrelying on external systems and lacking control in the development process. For\nthe developers, there was a risk in supporting the needs of an user-group while\nmaintaining core development. We mitigated these issues by utilizing an Agile\nScrum framework to orchestrate the collaboration. This promoted communication\nand cooperation, ensuring that the scientists had an active role in development\nwhile allowing the developers to quickly evaluate and implement the scientists'\nsoftware requirements. While each system was still in an early stage, the\ncollaboration provided benefits for each group: the scientists kick-started\ntheir development by using an existing platform, and the developers utilized\nthe scientists' use-case to improve their systems. This case study suggests\nthat scientists and software developers can avoid some difficulties of\nscientific computing by collaborating and can address emergent concerns using\nAgile Scrum methods.\n"}
+{"abstract": "  Horava gravity is a proposal for completing general relativity in the\nultraviolet by interactions that violate Lorentz invariance at very high\nenergies. We focus on (2+1)-dimensional projectable Horava gravity, a theory\nwhich is renormalizable and perturbatively ultraviolet-complete, enjoying an\nasymptotically free ultraviolet fixed point. Adding a small cosmological\nconstant to regulate the long distance behavior of the metric, we search for\nall circularly symmetric stationary vacuum solutions with vanishing angular\nmomentum and approaching the de Sitter metric with a possible angle deficit at\ninfinity. We find a two-parameter family of such geometries. Apart from the\ncosmological de Sitter horizon, these solutions generally contain another\nKilling horizon and should therefore be interpreted as black holes from the\nviewpoint of the low-energy theory. Contrary to naive expectations, their\ncentral singularity is not resolved by the higher derivative terms present in\nthe action. It is unknown at present if these solutions form as a result of\ngravitational collapse. The only solution regular everywhere is just the de\nSitter metric devoid of any black hole horizon.\n"}
+{"abstract": "  We analyze the popular kernel polynomial method (KPM) for approximating the\nspectral density (eigenvalue distribution) of an $n\\times n$ Hermitian matrix\n$A$. We prove that a simple and practical variant of the KPM algorithm can\napproximate the spectral density to $\\epsilon$ accuracy in the Wasserstein-1\ndistance with roughly $O({1}/{\\epsilon})$ matrix-vector multiplications with\n$A$. This yields a provable linear time result for the problem with better\n$\\epsilon$ dependence than prior work.\n  The KPM variant we study is based on damped Chebyshev polynomial expansions.\nWe show that it is stable, meaning that it can be combined with any approximate\nmatrix-vector multiplication algorithm for $A$. As an application, we develop\nan $O(n\\cdot \\text{poly}(1/\\epsilon))$ time algorithm for computing the\nspectral density of any $n\\times n$ normalized graph adjacency or Laplacian\nmatrix. This runtime is sublinear in the size of the matrix, and assumes sample\naccess to the graph.\n  Our approach leverages several tools from approximation theory, including\nJackson's seminal work on approximation with positive kernels [Jackson, 1912],\nand stability properties of three-term recurrence relations for orthogonal\npolynomials.\n"}
+{"abstract": "  We propose a new method of generating gamma rays with orbital angular\nmomentum (OAM). Accelerated partially-stripped ions are used as an energy\nup-converter. Irradiating an optical laser beam with OAM on ultrarelativistic\nions, they are excited to a state of large angular momentum. Gamma rays with\nOAM are emitted in their deexcitation process. We examine the excitation cross\nsection and deexcitation rate.\n"}
+{"abstract": "  While the event horizon of a black hole could cast a shadow that was observed\nrecently, a central singularity without horizon could also give rise to such a\nfeature. This leaves us with a question on the nature of the supermassive black\nholes at the galactic centers, and if they admit an event horizon necessarily.\nWe point out that observations of motion of stars around the galactic center\nshould give a clear idea of the nature of this central supermassive object. We\nexamine and discuss here recent developments that indicate intriguing behavior\nof the star motions that could possibly distinguish the existence or otherwise\nof an event horizon at the galactic center. We compare the motion of the S2\nstar with these theoretical results, fitting the observational data with\ntheory, and it is seen that the star motions and precession of their orbits\naround the galactic center provide important clues on the nature of this\ncentral compact object.\n"}
+{"abstract": "  Influence maximization (IM) is the problem of finding a seed vertex set that\nmaximizes the expected number of vertices influenced under a given diffusion\nmodel. Due to the NP-Hardness of finding an optimal seed set, approximation\nalgorithms are frequently used for IM. In this work, we describe a fast,\nerror-adaptive approach that leverages Count-Distinct sketches and hash-based\nfused sampling. To estimate the number of influenced vertices throughout a\ndiffusion, we use per-vertex Flajolet-Martin sketches where each sketch\ncorresponds to a sampled subgraph. To efficiently simulate the diffusions, the\nreach-set cardinalities of a single vertex are stored in memory in a\nconsecutive fashion. This allows the proposed algorithm to estimate the number\nof influenced vertices in a single step for simulations at once. For a faster\nIM kernel, we rebuild the sketches in parallel only after observing estimation\nerrors above a given threshold. Our experimental results show that the proposed\nalgorithm yields high-quality seed sets while being up to 119x faster than a\nstate-of-the-art approximation algorithm. In addition, it is up to 62x faster\nthan a sketch-based approach while producing seed sets with 3%-12% better\ninfluence scores\n"}
+{"abstract": "  Purpose: Segmentation of surgical instruments in endoscopic videos is\nessential for automated surgical scene understanding and process modeling.\nHowever, relying on fully supervised deep learning for this task is challenging\nbecause manual annotation occupies valuable time of the clinical experts.\n  Methods: We introduce a teacher-student learning approach that learns jointly\nfrom annotated simulation data and unlabeled real data to tackle the erroneous\nlearning problem of the current consistency-based unsupervised domain\nadaptation framework.\n  Results: Empirical results on three datasets highlight the effectiveness of\nthe proposed framework over current approaches for the endoscopic instrument\nsegmentation task. Additionally, we provide analysis of major factors affecting\nthe performance on all datasets to highlight the strengths and failure modes of\nour approach.\n  Conclusion: We show that our proposed approach can successfully exploit the\nunlabeled real endoscopic video frames and improve generalization performance\nover pure simulation-based training and the previous state-of-the-art. This\ntakes us one step closer to effective segmentation of surgical tools in the\nannotation scarce setting.\n"}
+{"abstract": "  Kagome metals AV3Sb5 (A = K, Rb, and Cs) exhibit superconductivity at 0.9-2.5\nK and charge-density wave (CDW) at 78-103 K. Key electronic states associated\nwith the CDW and superconductivity remain elusive. Here, we investigate\nlow-energy excitations of CsV3Sb5 by angle-resolved photoemission spectroscopy.\nWe found an energy gap of 70-100 meV at the Dirac-crossing points of linearly\ndispersive bands, pointing to an importance of spin-orbit coupling. We also\nfound a signature of strongly Fermi-surface and momentum-dependent CDW gap\ncharacterized by the larger energy gap of maximally 70 meV for a band forming a\nsaddle point around the M point, the smaller (0-18 meV) gap for a band forming\nmassive Dirac cones, and a zero gap at the Gamma-centered electron pocket. The\nobserved highly anisotropic CDW gap which is enhanced around the M point\nsignifies an importance of scattering channel connecting the saddle points,\nlaying foundation for understanding the nature of CDW and superconductivity in\nAV3Sb5.\n"}
+{"abstract": "  Let G be a permutation group, acting on a set \\Omega of size n. A subset B of\n\\Omega is a base for G if the pointwise stabilizer G_(B) is trivial. Let b(G)\nbe the minimal size of a base for G. A subgroup G of Sym(n) is large base if\nthere exist integers m and r \\geq 1 such that Alt(m)^r \\unlhd G \\leq Sym(m) \\wr\nSym(r), where the action of Sym(m) is on k-element subsets of {1,...,m} and the\nwreath product acts with product action. In this paper we prove that if G is\nprimitive and not large base, then either G is the Mathieu group M24 in its\nnatural action on 24 points, or b(G) \\leq \\lceil \\log n\\rceil+1. Furthermore,\nwe show that there are infinitely many primitive groups G that are not large\nbase for which b(G) > log n + 1, so our bound is optimal.\n"}
+{"abstract": "  Nebular HeII emission implies the presence of energetic photons (E$\\ge$54\neV). Despite the great deal of effort dedicated to understanding HeII\nionization, its origin has remained mysterious, particularly in metal-deficient\nstar-forming (SF) galaxies. Unfolding HeII-emitting, metal-poor starbursts at z\n~ 0 can yield insight into the powerful ionization processes occurring in the\nprimordial universe. Here we present a new study on the effects that X-ray\nsources have on the HeII ionization in the extremely metal-poor galaxy IZw18 (Z\n~ 3 % Zsolar), whose X-ray emission is dominated by a single high-mass X-ray\nbinary (HMXB). This study uses optical integral field spectroscopy, archival\nHubble Space Telescope observations, and all of the X-ray data sets publicly\navailable for IZw18. We investigate the time-variability of the IZw18 HMXB for\nthe first time; its emission shows small variations on timescales from days to\ndecades. The best-fit models for the HMXB X-ray spectra cannot reproduce the\nobserved HeII ionization budget of IZw18, nor can recent photoionization models\nthat combine the spectra of both very low metallicity massive stars and the\nemission from HMXB. We also find that the IZw18 HMXB and the HeII-emission peak\nare spatially displaced at a projected distance of $\\simeq$ 200 pc. These\nresults reduce the relevance of X-ray photons as the dominant HeII ionizing\nmode in IZw18, which leaves uncertain what process is responsible for the bulk\nof its HeII ionization. This is in line with recent work discarding X-ray\nbinaries as the main source responsible for HeII ionization in SF galaxies.\n"}
+{"abstract": "  We present the discovery and characterization of five hot and warm Jupiters\n-- TOI-628 b (TIC 281408474; HD 288842), TOI-640 b (TIC 147977348), TOI-1333 b\n(TIC 395171208, BD+47 3521A), TOI-1478 b (TIC 409794137), and TOI-1601 b (TIC\n139375960) -- based on data from NASA's Transiting Exoplanet Survey Satellite\n(TESS). The five planets were identified from the full frame images and were\nconfirmed through a series of photometric and spectroscopic follow-up\nobservations by the $TESS$ Follow-up Observing Program (TFOP) Working Group.\nThe planets are all Jovian size (R$_{\\rm P}$ = 1.01-1.77 R$_{\\rm J}$) and have\nmasses that range from 0.85 to 6.33 M$_{\\rm J}$. The host stars of these\nsystems have F and G spectral types (5595 $\\le$ T$_{\\rm eff}$ $\\le$ 6460 K) and\nare all relatively bright (9 $<V<$ 10.8, 8.2 $<K<$ 9.3) making them well-suited\nfor future detailed characterization efforts. Three of the systems in our\nsample (TOI-640 b, TOI-1333 b, and TOI-1601 b) orbit subgiant host stars (log\ng$_*$ $<$4.1). TOI-640 b is one of only three known hot Jupiters to have a\nhighly inflated radius (R$_{\\rm P}$ > 1.7R$_{\\rm J}$, possibly a result of its\nhost star's evolution) and resides on an orbit with a period longer than 5\ndays. TOI-628 b is the most massive hot Jupiter discovered to date by $TESS$\nwith a measured mass of $6.31^{+0.28}_{-0.30}$ M$_{\\rm J}$ and a statistically\nsignificant, non-zero orbital eccentricity of e = $0.074^{+0.021}_{-0.022}$.\nThis planet would not have had enough time to circularize through tidal forces\nfrom our analysis, suggesting that it might be remnant eccentricity from its\nmigration. The longest period planet in this sample, TOI-1478 b (P = 10.18\ndays), is a warm Jupiter in a circular orbit around a near-Solar analogue.\nNASA's $TESS$ mission is continuing to increase the sample of\nwell-characterized hot and warm Jupiters, complementing its primary mission\ngoals.\n"}
+{"abstract": "  We try to understand which morphisms of complex analytic spaces come from\nalgebraic geometry. We start with a series of conjectures, and then give some\npartial solutions.\n"}
+{"abstract": "  Quantum computers can provide solutions to classically intractable problems\nunder specific and adequate conditions. However, current devices have only\nlimited computational resources, and an effort is made to develop useful\nquantum algorithms under these circumstances. This work experimentally\ndemonstrates that a single-qubit device can host a universal classifier. The\nquantum processor used in this work is based on ion traps, providing highly\naccurate control on small systems. The algorithm chosen is the re-uploading\nscheme, which can address general learning tasks. Ion traps suit the needs of\naccurate control required by re-uploading. In the experiment here presented, a\nset of non-trivial classification tasks are successfully carried. The training\nprocedure is performed in two steps combining simulation and experiment. Final\nresults are benchmarked against exact simulations of the same method and also\nclassical algorithms, showing a competitive performance of the ion-trap quantum\nclassifier. This work constitutes the first experimental implementation of a\nclassification algorithm based on the re-uploading scheme.\n"}
+{"abstract": "  This review paper discusses the science of astrometric catalogs, their\ncurrent applications and future prospects for making progress in fundamental\nastronomy, astrophysics and gravitational physics. We discuss the concept of\nfundamental catalogs, their practical realizations, and future prospects.\nParticular attention is paid to the astrophysical implementations of the\ncatalogs such as the measurement of the Oort constants, the secular aberration\nand parallax, and asteroseismology. We also consider the use of the fundamental\ncatalogs in gravitational physics for testing general theory of relativity and\ndetection of ultra-long gravitational waves of cosmological origin.\n"}
+{"abstract": "  We propose a scheme to implement general quantum measurements, also known as\nPositive Operator Valued Measures (POVMs) in dimension $d$ using only classical\nresources and a single ancillary qubit. Our method is based on the\nprobabilistic implementation of $d$-outcome measurements which is followed by\npostselection of some of the received outcomes. We conjecture that the success\nprobability of our scheme is larger than a constant independent of $d$ for all\nPOVMs in dimension $d$. Crucially, this conjecture implies the possibility of\nrealizing arbitrary nonadaptive quantum measurement protocol on a\n$d$-dimensional system using a single auxiliary qubit with only a\n\\emph{constant} overhead in sampling complexity. We show that the conjecture\nholds for typical rank-one Haar-random POVMs in arbitrary dimensions.\nFurthermore, we carry out extensive numerical computations showing success\nprobability above a constant for a variety of extremal POVMs, including\nSIC-POVMs in dimension up to 1299. Finally, we argue that our scheme can be\nfavourable for the experimental realization of POVMs, as noise compounding in\ncircuits required by our scheme is typically substantially lower than in the\nstandard scheme that directly uses Naimark's dilation theorem.\n"}
+{"abstract": "  Inspired by our previous work on the boundedness of Toeplitz operators, we\nintroduce weak BMO and VMO type conditions, denoted by BWMO and VWMO,\nrespectively, for functions on the open unit disc of the complex plane. We show\nthat the average function of a function $f$ in BWMO is boundedly oscillating,\nand the analogous result holds for $f$ in VWMO. The result is applied for\ngeneralizations of known results on the essential spectra and norms of Toeplitz\noperators. Finally, we provide examples of functions satisfying the VWMO\ncondition which are not in the classical VMO or even in BMO.\n"}
+{"abstract": "  Understanding the low-temperature pure state structure of spin glasses\nremains an open problem in the field of statistical mechanics of disordered\nsystems. Here we study Monte Carlo dynamics, performing simulations of the\ngrowth of correlations following a quench from infinite temperature to a\ntemperature well below the spin-glass transition temperature $T_c$ for a\none-dimensional Ising spin glass model with diluted long-range interactions. In\nthis model, the probability $P_{ij}$ that an edge $\\{i,j\\}$ has nonvanishing\ninteraction falls as a power-law with chord distance,\n$P_{ij}\\propto1/R_{ij}^{2\\sigma}$, and we study a range of values of $\\sigma$\nwith $1/2<\\sigma<1$. We consider a correlation function $C_{4}(r,t)$. A dynamic\ncorrelation length that shows power-law growth with time $\\xi(t)\\propto\nt^{1/z}$ can be identified in the data and, for large time $t$, $C_{4}(r,t)$\ndecays as a power law $r^{-\\alpha_d}$ with distance $r$ when $r\\ll \\xi(t)$. The\ncalculation can be interpreted in terms of the maturation metastate averaged\nGibbs state, or MMAS, and the decay exponent $\\alpha_d$ differentiates between\na trivial MMAS ($\\alpha_d=0$), as expected in the droplet picture of spin\nglasses, and a nontrivial MMAS ($\\alpha_d\\ne 0$), as in the\nreplica-symmetry-breaking (RSB) or chaotic pairs pictures. We find nonzero\n$\\alpha_d$ even in the regime $\\sigma >2/3$ which corresponds to short-range\nsystems below six dimensions. For $\\sigma < 2/3$, the decay exponent $\\alpha_d$\nfollows the RSB prediction for the decay exponent $\\alpha_s = 3 - 4 \\sigma$ of\nthe static metastate, consistent with a conjectured statics-dynamics relation,\nwhile it approaches $\\alpha_d=1-\\sigma$ in the regime $2/3<\\sigma<1$; however,\nit deviates from both lines in the vicinity of $\\sigma=2/3$.\n"}
+{"abstract": "  Different theories of gravity can admit the same black hole solution, but the\nparameters usually have different physical interpretations. In this work we\nstudy in depth the linear term $\\beta r$ in the redshift function of black\nholes, which arises in conformal gravity, de Rham-Gabadadze-Tolley (dRGT)\nmassive gravity, $f(R)$ gravity (as approximate solution) and general\nrelativity. Geometrically we quantify the parameter $\\beta$ in terms of the\ncurvature invariants. Astrophysically we found that $\\beta$ can be expressed in\nterms of the cosmological constant, the photon orbit radius and the innermost\nstable circular orbit (ISCO) radius. The metric degeneracy can be broken once\nblack hole thermodynamics is taken into account. Notably, we show that under\nHawking evaporation, different physical theories with the same black hole\nsolution (at the level of the metric) can lead to black hole remnants with\ndifferent values of their physical masses with direct consequences on their\nviability as dark matter candidates. In particular, the mass of the graviton in\nmassive gravity can be expressed in terms of the cosmological constant and of\nthe formation epoch of the remnant. Furthermore the upper bound of remnant mass\ncan be estimated to be around $0.5 \\times 10^{27}$ kg.\n"}
+{"abstract": "  This study reports the magnetization switching induced by spin-orbit torque\n(SOT) from the spin current generated in Co2MnGa magnetic Weyl semimetal (WSM)\nthin films. We deposited epitaxial Co2MnGa thin films with highly B2-ordered\nstructure on MgO(001) substrates. The SOT was characterized by harmonic Hall\nmeasurements in a Co2MnGa/Ti/CoFeB heterostructure and a relatively large spin\nHall efficiency of -7.8% was obtained.The SOT-induced magnetization switching\nof the perpendicularly magnetized CoFeB layer was further demonstrated using\nthe structure. The symmetry of second harmonic signals, thickness dependence of\nspin Hall efficiency, and shift of anomalous Hall loops under applied currents\nwere also investigated. This study not only contributes to the understanding of\nthe mechanisms of spin-current generation from magnetic-WSM-based\nheterostructures, but also paves a way for the applications of magnetic WSMs in\nspintronic devices.\n"}
+{"abstract": "  In this article, we derive analytically the complex optical spectrum of a\npulsed laser source obtained when a frequency comb generated by phase\nmodulation is input into a synchronized intensity modulator. We then show how\nthis knowledge of the spectrum may help to achieve unprecedented accuracy\nduring the experimental spectrum correction step usually carried out with an\noptical spectrum processor. In numerical examples, for a given average power we\npresent up to a 75 % increase in peak power and an enhancement of the\nextinction ratio by at least three orders of magnitude. This method also\nenables large-factor rate-multiplications of these versatile coherent sources\nusing the Talbot effect with negligible degradation of the signal.\n"}
+{"abstract": "  The analysis of 20 years of spectrophotometric data of the double shell\nplanetary nebula PM\\,1-188 is presented, aiming to determine the time evolution\nof the emission lines and the physical conditions of the nebula, as a\nconsequence of the systematic fading of its [WC\\,10] central star whose\nbrightness has declined by about 10 mag in the past 40 years. Our main results\ninclude that the [\\ion{O}{iii}], [\\ion{O}{ii}], [\\ion{N}{ii}] line intensities\nare increasing with time in the inner nebula as a consequence of an increase in\nelectron temperature from 11,000 K in 2005 to more than 14,000 K in 2018, due\nto shocks. The intensity of the same lines are decreasing in the outer nebula,\ndue to a decrease in temperature, from 13,000 K to 7,000 K, in the same period.\nThe chemical composition of the inner and outer shells was derived and they are\nsimilar. Both nebulae present subsolar O, S and Ar abundances, while they are\nHe, N and Ne rich. For the outer nebula the values are 12+log He/H=\n11.13$\\pm$0.05, 12+log O/H = 8.04$\\pm$0.04, 12+log N/H= 7.87$\\pm$0.06, 12+log\nS/H = 7.18$\\pm$0.10 and 12+log Ar = 5.33$\\pm$0.16. The O, S and Ar abundances\nare several times lower than the average values found in disc non-Type I PNe,\nand are reminiscent of some halo PNe. From high resolution spectra, an outflow\nin the N-S direction was found in the inner zone. Position-velocity diagrams\nshow that the outflow expands at velocities in the $-$150 to 100 km s$^{-1}$\nrange, and both shells have expansion velocities of about 40 km s$^{-1}$.\n"}
+{"abstract": "  We investigate long-lived particles (LLPs) produced in pair from neutral\ncurrents and decaying into a displaced electron plus two jets at the LHC,\nutilizing the proposed minimum ionizing particle timing detector at CMS. We\nstudy two benchmark models: the R-parity-violating supersymmetry with the\nlightest neutralinos being the lightest supersymmetric particle and two\ndifferent $U(1)$ extensions of the standard model with heavy neutral leptons\n(HNLs). The light neutralinos are produced from the standard model $Z$-boson\ndecays via small Higgsino components, and the HNLs arise from decays of a heavy\ngauge boson, $Z'$. By simulating the signal processes at the HL-LHC with the\ncenter-of-mass energy $\\sqrt{s}=$ 14 TeV and integrated luminosity of 3\nab$^{-1}$, our analyses indicate that the search strategy based on a timing\ntrigger and the final state kinematics has the potential to probe the parameter\nspace that is complementary to other traditional LLP search strategies such as\nthose based on the displaced vertex.\n"}
+{"abstract": "  Algorithms produce a growing portion of decisions and recommendations both in\npolicy and business. Such algorithmic decisions are natural experiments\n(conditionally quasi-randomly assigned instruments) since the algorithms make\ndecisions based only on observable input variables. We use this observation to\ndevelop a treatment-effect estimator for a class of stochastic and\ndeterministic decision-making algorithms. Our estimator is shown to be\nconsistent and asymptotically normal for well-defined causal effects. A key\nspecial case of our estimator is a multidimensional regression discontinuity\ndesign. We apply our estimator to evaluate the effect of the Coronavirus Aid,\nRelief, and Economic Security (CARES) Act, where hundreds of billions of\ndollars worth of relief funding is allocated to hospitals via an algorithmic\nrule. Our estimates suggest that the relief funding has little effect on\nCOVID-19-related hospital activity levels. Naive OLS and IV estimates exhibit\nsubstantial selection bias.\n"}
+{"abstract": "  Concurrent accesses to databases are typically encapsulated in transactions\nin order to enable isolation from other concurrent computations and resilience\nto failures. Modern databases provide transactions with various semantics\ncorresponding to different trade-offs between consistency and availability.\nSince a weaker consistency model provides better performance, an important\nissue is investigating the weakest level of consistency needed by a given\nprogram (to satisfy its specification). As a way of dealing with this issue, we\ninvestigate the problem of checking whether a given program has the same set of\nbehaviors when replacing a consistency model with a weaker one. This property\nknown as robustness generally implies that any specification of the program is\npreserved when weakening the consistency. We focus on the robustness problem\nfor consistency models which are weaker than standard serializability, namely,\ncausal consistency, prefix consistency, and snapshot isolation. We show that\nchecking robustness between these models is polynomial time reducible to a\nstate reachability problem under serializability. We use this reduction to also\nderive a pragmatic proof technique based on Lipton's reduction theory that\nallows to prove programs robust. We have applied our techniques to several\nchallenging applications drawn from the literature of distributed systems and\ndatabases.\n"}
+{"abstract": "  Given the prevalence of pre-trained contextualized representations in today's\nNLP, there have been several efforts to understand what information such\nrepresentations contain. A common strategy to use such representations is to\nfine-tune them for an end task. However, how fine-tuning for a task changes the\nunderlying space is less studied. In this work, we study the English BERT\nfamily and use two probing techniques to analyze how fine-tuning changes the\nspace. Our experiments reveal that fine-tuning improves performance because it\npushes points associated with a label away from other labels. By comparing the\nrepresentations before and after fine-tuning, we also discover that fine-tuning\ndoes not change the representations arbitrarily; instead, it adjusts the\nrepresentations to downstream tasks while preserving the original structure.\nFinally, using carefully constructed experiments, we show that fine-tuning can\nencode training sets in a representation, suggesting an overfitting problem of\na new kind.\n"}
+{"abstract": "  In this article, we discuss how to solve information-gathering problems\nexpressed as rho-POMDPs, an extension of Partially Observable Markov Decision\nProcesses (POMDPs) whose reward rho depends on the belief state. Point-based\napproaches used for solving POMDPs have been extended to solving rho-POMDPs as\nbelief MDPs when its reward rho is convex in B or when it is\nLipschitz-continuous. In the present paper, we build on the POMCP algorithm to\npropose a Monte Carlo Tree Search for rho-POMDPs, aiming for an efficient\non-line planner which can be used for any rho function. Adaptations are\nrequired due to the belief-dependent rewards to (i) propagate more than one\nstate at a time, and (ii) prevent biases in value estimates. An asymptotic\nconvergence proof to epsilon-optimal values is given when rho is continuous.\nExperiments are conducted to analyze the algorithms at hand and show that they\noutperform myopic approaches.\n"}
+{"abstract": "  Modern vehicles equipped with on-board units (OBU) are playing an essential\nrole in the smart city revolution. The vehicular processing resources, however,\nare not used to their fullest potential. The concept of vehicular clouds is\nproposed to exploit the underutilized vehicular resources to supplement cloud\ncomputing services to relieve the burden on cloud data centers and improve\nquality of service. In this paper we introduce a vehicular cloud architecture\nsupported by fixed edge computing nodes and the central cloud. A mixed integer\nlinear programming (MLP) model is developed to optimize the allocation of the\ncomputing demands in the distributed architecture while minimizing power\nconsumption. The results show power savings as high as 84% over processing in\nthe conventional cloud. A heuristic with performance approaching that of the\nMILP model is developed to allocate computing demands in real time.\n"}
+{"abstract": "  Nambu dynamics is a generalized Hamiltonian dynamics of more than two\nvariables, whose time evolutions are given by the Nambu bracket, a\ngeneralization of the canonical Poisson bracket. Nambu dynamics can always be\nrepresented in the form of noncanonical Hamiltonian dynamics by defining the\nnoncanonical Poisson bracket by means of the Nambu bracket. For the time\nevolution to be consistent, the Nambu bracket must satisfy the fundamental\nidentity, while the noncanonical Poisson bracket must satisfy the Jacobi\nidentity. However, in many degrees of freedom systems, it is well known that\nthe fundamental identity does not hold. In the present paper we show that, even\nif the fundamental identity is violated, the Jacobi identity for the\ncorresponding noncanonical Hamiltonian dynamics could hold. As an example, we\nevaluate these identities for a semiclassical system of two coupled\noscillators.\n"}
+{"abstract": "  How to measure the incremental Return On Ad Spend (iROAS) is a fundamental\nproblem for the online advertising industry. A standard modern tool is to run\nrandomized geo experiments, where experimental units are non-overlapping\nad-targetable geographical areas (Vaver & Koehler 2011). However, how to design\na reliable and cost-effective geo experiment can be complicated, for example:\n1) the number of geos is often small, 2) the response metric (e.g. revenue)\nacross geos can be very heavy-tailed due to geo heterogeneity, and furthermore\n3) the response metric can vary dramatically over time. To address these\nissues, we propose a robust nonparametric method for the design, called Trimmed\nMatch Design (TMD), which extends the idea of Trimmed Match (Chen & Au 2019)\nand furthermore integrates the techniques of optimal subset pairing and sample\nsplitting in a novel and systematic manner. Some simulation and real case\nstudies are presented. We also point out a few open problems for future\nresearch.\n"}
+{"abstract": "  Visually realistic GAN-generated images have recently emerged as an important\nmisinformation threat. Research has shown that these synthetic images contain\nforensic traces that are readily identifiable by forensic detectors.\nUnfortunately, these detectors are built upon neural networks, which are\nvulnerable to recently developed adversarial attacks. In this paper, we propose\na new anti-forensic attack capable of fooling GAN-generated image detectors.\nOur attack uses an adversarially trained generator to synthesize traces that\nthese detectors associate with real images. Furthermore, we propose a technique\nto train our attack so that it can achieve transferability, i.e. it can fool\nunknown CNNs that it was not explicitly trained against. We demonstrate the\nperformance of our attack through an extensive set of experiments, where we\nshow that our attack can fool eight state-of-the-art detection CNNs with\nsynthetic images created using seven different GANs.\n"}
+{"abstract": "  We study a generalized Blume-Capel model on the simple cubic lattice. In\naddition to the nearest neighbor coupling there is a next to next to nearest\nneighbor coupling. In order to quantify spatial anisotropy, we determine the\ncorrelation length in the high temperature phase of the model for three\ndifferent directions. It turns out that the spatial anisotropy depends very\nlittle on the dilution parameter $D$ of the model and is essentially determined\nby the ratio of the nearest neighbor and the next to next to nearest neighbor\ncoupling. This ratio is tuned such that the leading contribution to the spatial\nanisotropy is eliminated. Next we perform a finite size scaling (FSS) study to\ntune $D$ such that also the leading correction to scaling is eliminated. Based\non this FSS study, we determine the critical exponents $\\nu=0.62998(5)$ and\n$\\eta=0.036284(40)$, which are in nice agreement with the more accurate results\nobtained by using the conformal bootstrap method. Furthermore we provide\naccurate results for fixed point values of dimensionless quantities such as the\nBinder cumulant and for the critical couplings. These results provide the\ngroundwork for broader studies of universal properties of the three-dimensional\nIsing universality class.\n"}
+{"abstract": "  Sign language translation (SLT) is often decomposed into video-to-gloss\nrecognition and gloss-to-text translation, where a gloss is a sequence of\ntranscribed spoken-language words in the order in which they are signed. We\nfocus here on gloss-to-text translation, which we treat as a low-resource\nneural machine translation (NMT) problem. However, unlike traditional\nlow-resource NMT, gloss-to-text translation differs because gloss-text pairs\noften have a higher lexical overlap and lower syntactic overlap than pairs of\nspoken languages. We exploit this lexical overlap and handle syntactic\ndivergence by proposing two rule-based heuristics that generate pseudo-parallel\ngloss-text pairs from monolingual spoken language text. By pre-training on the\nthus obtained synthetic data, we improve translation from American Sign\nLanguage (ASL) to English and German Sign Language (DGS) to German by up to\n3.14 and 2.20 BLEU, respectively.\n"}
+{"abstract": "  A type of polar self-propelled particle generates a torque that makes it\nnaturally drawn to higher-density areas. The collective behaviour this induces\nin assemblies of particles constitutes a new form of phase separation in active\nfluids.\n"}
+{"abstract": "  When fonts are used on documents, they are intentionally selected by\ndesigners. For example, when designing a book cover, the typography of the text\nis an important factor in the overall feel of the book. In addition, it needs\nto be an appropriate font for the rest of the book cover. Thus, we propose a\nmethod of generating a book title image based on its context within a book\ncover. We propose an end-to-end neural network that inputs the book cover, a\ntarget location mask, and a desired book title and outputs stylized text\nsuitable for the cover. The proposed network uses a combination of a\nmulti-input encoder-decoder, a text skeleton prediction network, a perception\nnetwork, and an adversarial discriminator. We demonstrate that the proposed\nmethod can effectively produce desirable and appropriate book cover text\nthrough quantitative and qualitative results.\n"}
+{"abstract": "  The music of Northern Myanmar Kachin ethnic group is compared to the music of\nwestern China, Xijiang based Uyghur music, using timbre and pitch feature\nextraction and machine learning. Although separated by Tibet, the muqam\ntradition of Xinjiang might be found in Kachin music due to myths of Kachin\norigin, as well as linguistic similarities, e.g., the Kachin term 'makan' for a\nmusical piece. Extractions were performed using the apollon and COMSAR\n(Computational Music and Sound Archiving) frameworks, on which the Ethnographic\nSound Recordings Archive (ESRA) is based, using ethnographic recordings from\nESRA next to additional pieces. In terms of pitch, tonal systems were compared\nusing Kohonen self-organizing map (SOM), which clearly clusters Kachin and\nUyghur musical pieces. This is mainly caused by the Xinjiang muqam music\nshowing just fifth and fourth, while Kachin pieces tend to have a higher fifth\nand fourth, next to other dissimilarities. Also, the timbre features of\nspectral centroid and spectral sharpness standard deviation clearly tells\nUyghur from Kachin pieces, where Uyghur music shows much larger deviations.\nAlthough more features will be compared in the future, like rhythm or melody,\nthese already strong findings might introduce an alternative comparison\nmethodology of ethnic groups beyond traditional linguistic definitions.\n"}
+{"abstract": "  Enabling out-of-distribution (OOD) detection for DNNs is critical for their\nsafe and reliable operation in the open world. Despite recent progress, current\nworks often consider a coarse level of granularity in the OOD problem, which\nfail to approximate many real-world fine-grained tasks where high granularity\nmay be expected between the in-distribution (ID) data and the OOD data (e.g.,\nidentifying novel bird species for a bird classification system in the wild).\nIn this work, we start by carefully constructing four large-scale fine-grained\ntest environments in which existing methods are shown to have difficulties. We\nfind that current methods, including ones that include a large/diverse set of\noutliers during DNN training, have poor coverage over the broad region where\nfine-grained OOD samples locate. We then propose Mixture Outlier Exposure\n(MixOE), which effectively expands the covered OOD region by mixing ID data and\ntraining outliers, and regularizes the model behaviour by linearly decaying the\nprediction confidence as the input transitions from ID to OOD. Extensive\nexperiments and analyses demonstrate the effectiveness of MixOE for improving\nOOD detection in fine-grained settings.\n"}
+{"abstract": "  Embedding static graphs in low-dimensional vector spaces plays a key role in\nnetwork analytics and inference, supporting applications like node\nclassification, link prediction, and graph visualization. However, many\nreal-world networks present dynamic behavior, including topological evolution,\nfeature evolution, and diffusion. Therefore, several methods for embedding\ndynamic graphs have been proposed to learn network representations over time,\nfacing novel challenges, such as time-domain modeling, temporal features to be\ncaptured, and the temporal granularity to be embedded. In this survey, we\noverview dynamic graph embedding, discussing its fundamentals and the recent\nadvances developed so far. We introduce the formal definition of dynamic graph\nembedding, focusing on the problem setting and introducing a novel taxonomy for\ndynamic graph embedding input and output. We further explore different dynamic\nbehaviors that may be encompassed by embeddings, classifying by topological\nevolution, feature evolution, and processes on networks. Afterward, we describe\nexisting techniques and propose a taxonomy for dynamic graph embedding\ntechniques based on algorithmic approaches, from matrix and tensor\nfactorization to deep learning, random walks, and temporal point processes. We\nalso elucidate main applications, including dynamic link prediction, anomaly\ndetection, and diffusion prediction, and we further state some promising\nresearch directions in the area.\n"}
+{"abstract": "  Two types of non-holonomic constraints (imposing a prescription on velocity)\nare analyzed, connected to an end of a (visco)elastic rod, straight in its\nundeformed configuration. The equations governing the nonlinear dynamics are\nobtained and then linearized near the trivial equilibrium configuration. The\ntwo constraints are shown to lead to the same equations governing the\nlinearized dynamics of the Beck (or Pfluger) column in one case and of the Reut\ncolumn in the other. Therefore, although the structural systems are fully\nconservative (when viscosity is set to zero), they exhibit flutter and\ndivergence instability. In addition, the Ziegler's destabilization paradox is\nfound when dissipation sources are introduced. It follows that these features\nare proven to be not only a consequence of 'unrealistic non-conservative loads'\n(as often stated in the literature), rather, the models proposed by Beck, Reut,\nand Ziegler can exactly describe the linearized dynamics of structures subject\nto non-holonomic constraints, which are made now fully accessible to\nexperiments.\n"}
+{"abstract": "  We consider an improper reinforcement learning setting where a learner is\ngiven $M$ base controllers for an unknown Markov decision process, and wishes\nto combine them optimally to produce a potentially new controller that can\noutperform each of the base ones. This can be useful in tuning across\ncontrollers, learnt possibly in mismatched or simulated environments, to obtain\na good controller for a given target environment with relatively few trials.\n  \\par We propose a gradient-based approach that operates over a class of\nimproper mixtures of the controllers. We derive convergence rate guarantees for\nthe approach assuming access to a gradient oracle. The value function of the\nmixture and its gradient may not be available in closed-form; however, we show\nthat we can employ rollouts and simultaneous perturbation stochastic\napproximation (SPSA) for explicit gradient descent optimization. Numerical\nresults on (i) the standard control theoretic benchmark of stabilizing an\ninverted pendulum and (ii) a constrained queueing task show that our improper\npolicy optimization algorithm can stabilize the system even when the base\npolicies at its disposal are unstable\\footnote{Under review. Please do not\ndistribute.}.\n"}
+{"abstract": "  In this article, we introduce a novel variant of the Tsetlin machine (TM)\nthat randomly drops clauses, the key learning elements of a TM. In effect, TM\nwith drop clause ignores a random selection of the clauses in each epoch,\nselected according to a predefined probability. In this way, additional\nstochasticity is introduced in the learning phase of TM. Along with producing\nmore distinct and well-structured patterns that improve the performance, we\nalso show that dropping clauses increases learning robustness. To explore the\neffects clause dropping has on accuracy, training time, and interpretability,\nwe conduct extensive experiments on various benchmark datasets in natural\nlanguage processing (NLP) (IMDb and SST2) as well as computer vision (MNIST and\nCIFAR10). In brief, we observe from +2% to +4% increase in accuracy and 2x to\n4x faster learning. We further employ the Convolutional TM to document\ninterpretable results on the CIFAR10 dataset. To the best of our knowledge,\nthis is the first time an interpretable machine learning algorithm has been\nused to produce pixel-level human-interpretable results on CIFAR10. Also,\nunlike previous interpretable methods that focus on attention visualisation or\ngradient interpretability, we show that the TM is a more general interpretable\nmethod. That is, by producing rule-based propositional logic expressions that\nare \\emph{human}-interpretable, the TM can explain how it classifies a\nparticular instance at the pixel level for computer vision and at the word\nlevel for NLP.\n"}
+{"abstract": "  An important problem in quantum information is to construct multiqubit\nunextendible product bases (UPBs). By using the unextendible orthogonal\nmatrices, we construct a 7-qubit UPB of size 11. It solves an open problem in\n[Quantum Information Processing 19:185 (2020)]. Next, we graph-theoretically\nshow that the UPB is locally indistinguishable in the bipartite systems of two\nqubits and five qubits, respectively. It turns out that the UPB corresponds to\na complete graph with 11 vertices constructed by three sorts of nonisomorphic\ngraphs. Taking the graphs as product vectors, we show that they are in three\ndifferent orbits up to local unitary equivalence. Moreover, we also present the\nnumber of sorts of nonisomorphic graphs of complete graphs of some known UPBs\nand their orbits.\n"}
+{"abstract": "  We study the effect of a small fermion mass in the formulation of the\non-shell effective field theory (OSEFT). This is our starting point to derive\nsmall mass corrections to the chiral kinetic theory. In the massless case, only\nfour Wigner functions are needed to describe positive and negative energy\nfermions of left and right chirality, corresponding to the vectorial components\nof a fermionic two-point Green's function. As soon as mass correction are\nintroduced, tensorial components are also needed, while the scalar components\nstrictly vanish in the OSEFT. The tensorial components are conveniently\nparametrized in the so-called spin coherence function, which describe quantum\ncoherent mixtures of left-right and right-left chiral fermions, of either\npositive or negative energy. We show that, up to second order in the energy\nexpansion, vectorial and tensorial components are decoupled, and obey the same\ndispersion law and transport equation, depending on their respective chirality.\nWe study the mass modifications of the reparametrization invariance of the\nOSEFT, and check that vector and tensorial components are related by the\nassociated symmetry transformations. We study how the macroscopic properties of\nthe system are described in terms of the whole set of Wigner functions, and\ncheck that our framework allows to account for the mass modifications to the\nchiral anomaly equation.\n"}
+{"abstract": "  The Molecular Ridge in the LMC extends several kiloparsecs south from 30\nDoradus, and it contains ~30% of the molecular gas in the entire galaxy.\nHowever, the southern end of the Molecular Ridge is quiescent - it contains\nalmost no massive star formation, which is a dramatic decrease from the very\nactive massive star-forming regions 30 Doradus, N159, and N160. We present new\nALMA and APEX observations of the Molecular Ridge at a resolution as high as\n~16'' (~3.9 pc) with molecular lines 12CO(1-0), 13CO(1-0), 12CO(2-1),\n13CO(2-1), and CS(2-1). We analyze these emission lines with our new multi-line\nnon-LTE fitting tool to produce maps of T_kin, n_H2, and N_CO across the region\nbased on models from RADEX. Using simulated data for a range of parameter space\nfor each of these variables, we evaluate how well our fitting method can\nrecover these physical parameters for the given set of molecular lines. We then\ncompare the results of this fitting with LTE and X_CO methods of obtaining mass\nestimates and how line ratios correspond with physical conditions. We find that\nthis fitting tool allows us to more directly probe the physical conditions of\nthe gas and estimate values of T_kin, n_H2, and N_CO that are less subject to\nthe effects of optical depth and line-of-sight projection than previous\nmethods. The fitted n_H2 values show a strong correlation with the presence of\nYSOs, and with the total and average mass of the associated YSOs. Typical star\nformation diagnostics, such as mean density, dense gas fraction, and virial\nparameter do not show a strong correlation with YSO properties.\n"}
+{"abstract": "  In this paper, we propose an offline-online strategy based on the Localized\nOrthogonal Decomposition (LOD) method for elliptic multiscale problems with\nrandomly perturbed diffusion coefficient. We consider a periodic deterministic\ncoefficient with local defects that occur with probability $p$. The offline\nphase pre-computes entries to global LOD stiffness matrices on a single\nreference element (exploiting the periodicity) for a selection of defect\nconfigurations. Given a sample of the perturbed diffusion the corresponding LOD\nstiffness matrix is then computed by taking linear combinations of the\npre-computed entries, in the online phase. Our computable error estimates show\nthat this yields a good coarse-scale approximation of the solution for small\n$p$, which is illustrated by extensive numerical experiments. This makes the\nproposed technique attractive already for moderate sample sizes in a Monte\nCarlo simulation.\n"}
+{"abstract": "  A novel approach for solving the general absolute value equation $Ax+B|x| =\nc$ where $A,B\\in \\mathbb{R}^{m\\times n}$ and $c\\in \\mathbb{R}^m$ is presented.\nWe reformulate the equation as a feasibility problem which we solve via the\nmethod of alternating projections (MAP). The fixed points set of the\nalternating projections map is characterized under nondegeneracy conditions on\n$A$ and $B$. Furthermore, we prove linear convergence of the algorithm. Unlike\nmost of the existing approaches in the literature, the algorithm presented here\nis capable of handling problems with $m\\neq n$, both theoretically and\nnumerically.\n"}
+{"abstract": "  We prove a quantitative version of the classical Tits' alternative for\ndiscrete groups acting on packed Gromov-hyperbolic spaces supporting a convex\ngeodesic bicombing. Some geometric consequences, as uniform estimates on\nsystole, diastole, algebraic entropy and critical exponent of the groups, will\nbe presented. Finally we will study the behaviour of these group actions under\nlimits, providing new examples of compact classes of metric spaces.\n"}
+{"abstract": "  Recent work has explored how complementary strengths of humans and artificial\nintelligence (AI) systems might be productively combined. However, successful\nforms of human-AI partnership have rarely been demonstrated in real-world\nsettings. We present the iterative design and evaluation of Lumilo, smart\nglasses that help teachers help their students in AI-supported classrooms by\npresenting real-time analytics about students' learning, metacognition, and\nbehavior. Results from a field study conducted in K-12 classrooms indicate that\nstudents learn more when teachers and AI tutors work together during class. We\ndiscuss implications of this research for the design of human-AI partnerships.\nWe argue for more participatory approaches to research and design in this area,\nin which practitioners and other stakeholders are deeply, meaningfully involved\nthroughout the process. Furthermore, we advocate for theory-building and for\nprincipled approaches to the study of human-AI decision-making in real-world\ncontexts.\n"}
+{"abstract": "  We investigate the problem of nonparametric estimation of the trend for\nstochastic differential equations with delay and driven by a fractional\nBrownian motion through the method of kernel-type estimation for the estimation\nof a probability density function.\n"}
+{"abstract": "  High fidelity segmentation of both macro and microvascular structure of the\nretina plays a pivotal role in determining degenerative retinal diseases, yet\nit is a difficult problem. Due to successive resolution loss in the encoding\nphase combined with the inability to recover this lost information in the\ndecoding phase, autoencoding based segmentation approaches are limited in their\nability to extract retinal microvascular structure. We propose RV-GAN, a new\nmulti-scale generative architecture for accurate retinal vessel segmentation to\nalleviate this. The proposed architecture uses two generators and two\nmulti-scale autoencoding discriminators for better microvessel localization and\nsegmentation. In order to avoid the loss of fidelity suffered by traditional\nGAN-based segmentation systems, we introduce a novel weighted feature matching\nloss. This new loss incorporates and prioritizes features from the\ndiscriminator's decoder over the encoder. Doing so combined with the fact that\nthe discriminator's decoder attempts to determine real or fake images at the\npixel level better preserves macro and microvascular structure. By combining\nreconstruction and weighted feature matching loss, the proposed architecture\nachieves an area under the curve (AUC) of 0.9887, 0.9914, and 0.9887 in\npixel-wise segmentation of retinal vasculature from three publicly available\ndatasets, namely DRIVE, CHASE-DB1, and STARE, respectively. Additionally,\nRV-GAN outperforms other architectures in two additional relevant metrics, mean\nintersection-over-union (Mean-IOU) and structural similarity measure (SSIM).\n"}
+{"abstract": "  We present a phase-space study of two stellar groups located at the core of\nthe Orion complex: Brice\\~no-1 and Orion Belt Population-near (OBP-near). We\nidentify the groups with the unsupervised clustering algorithm, Shared Nearest\nNeighbor (SNN), which previously identified twelve new stellar substructures in\nthe Orion complex. For each of the two groups, we derive the 3D space motions\nof individual stars using Gaia EDR3 proper motions supplemented by radial\nvelocities from Gaia DR2, APOGEE-2, and GALAH DR3. We present evidence for\nradial expansion of the two groups from a common center. Unlike previous work,\nour study suggests that evidence of stellar group expansion is confined only to\nOBP-near and Brice\\~no-1 whereas the rest of the groups in the complex show\nmore complicated motions. Interestingly, the stars in the two groups lie at the\ncenter of a dust shell, as revealed via an extant 3D dust map. The exact\nmechanism that produces such coherent motions remains unclear, while the\nobserved radial expansion and dust shell suggest that massive stellar feedback\ncould have influenced the star formation history of these groups.\n"}
+{"abstract": "  Renormalization-Group (RG) improvement has been frequently applied to capture\nthe effect of quantum corrections on cosmological and black-hole spacetimes.\nThis work utilizes an algebraically complete set of curvature invariants to\nestablish that: On the one hand, RG improvement at the level of the metric is\ncoordinate-dependent. On the other hand, a newly proposed RG improvement at the\nlevel of curvature invariants is coordinate-independent. Spherically-symmetric\nand axially-symmetric black-hole spacetimes serve as physically relevant\nexamples.\n"}
+{"abstract": "  In this paper, we propose a unified pre-training approach called UniSpeech to\nlearn speech representations with both unlabeled and labeled data, in which\nsupervised phonetic CTC learning and phonetically-aware contrastive\nself-supervised learning are conducted in a multi-task learning manner. The\nresultant representations can capture information more correlated with phonetic\nstructures and improve the generalization across languages and domains. We\nevaluate the effectiveness of UniSpeech for cross-lingual representation\nlearning on public CommonVoice corpus. The results show that UniSpeech\noutperforms self-supervised pretraining and supervised transfer learning for\nspeech recognition by a maximum of 13.4% and 17.8% relative phone error rate\nreductions respectively (averaged over all testing languages). The\ntransferability of UniSpeech is also demonstrated on a domain-shift speech\nrecognition task, i.e., a relative word error rate reduction of 6% against the\nprevious approach.\n"}
+{"abstract": "  CdTe is a key thin-film photovoltaic technology. Non-radiative electron-hole\nrecombination reduces the solar conversion efficiency from an ideal value of\n32% to a current champion performance of 22%. The cadmium vacancy (V_Cd) is a\nprominent acceptor species in p-type CdTe; however, debate continues regarding\nits structural and electronic behavior. Using ab initio defect techniques, we\ncalculate a negative-U double-acceptor level for V_Cd, while reproducing the\nV_Cd^-1 hole-polaron, reconciling theoretical predictions with experimental\nobservations. We find the cadmium vacancy facilitates rapid charge-carrier\nrecombination, reducing maximum power-conversion efficiency by over 5% for\nuntreated CdTe -- a consequence of tellurium dimerization, metastable\nstructural arrangements, and anharmonic potential energy surfaces for carrier\ncapture.\n"}
+{"abstract": "  Quasi-geostrophic (QG) theory describes the dynamics of synoptic scale flows\nin the trophosphere that are balanced with respect to both acoustic and\ninternal gravity waves. Within this framework, effects of (turbulent) friction\nnear the ground are usually represented by Ekman Layer theory. The troposphere\ncovers roughly the lowest ten kilometers of the atmosphere while Ekman layer\nheights are typically just a few hundred meters. However, this two-layer\nasymptotic theory does not explicitly account for substantial changes of the\npotential temperature stratification due to diabatic heating associated with\ncloud formation or with radiative and turbulent heat fluxes, which, in the\nmiddle latitudes, can be particularly important in about the lowest three\nkilometers. To address this deficiency, this paper extends the classical\nQG-Ekman layer model by introducing an intermediate, dynamically and\nthermodynamically active layer, called the \"diabatic layer\" (DL) from here on.\nThe flow in this layer is also in acoustic, hydrostatic, and geostrophic\nbalance but, in contrast to QG flow, variations of potential temperature are\nnot restricted to small deviations from a stable and time independent\nbackground stratification. Instead, within the diabatic layer, diabatic\nprocesses are allowed to affect the leading-order stratification. As a\nconsequence, the diabatic layer modifies the pressure field at the top of the\nEkman layer, and with it the intensity of Ekman pumping seen by the\nquasi-geostrophic bulk flow. The result is the proposed extended\nquasi-geostrophic three-layer QG-DL-Ekman model for mid-latitude (dry and\nmoist) dynamics.\n"}
+{"abstract": "  Quantization has become a popular technique to compress neural networks and\nreduce compute cost, but most prior work focuses on studying quantization\nwithout changing the network size. Many real-world applications of neural\nnetworks have compute cost and memory budgets, which can be traded off with\nmodel quality by changing the number of parameters. In this work, we use ResNet\nas a case study to systematically investigate the effects of quantization on\ninference compute cost-quality tradeoff curves. Our results suggest that for\neach bfloat16 ResNet model, there are quantized models with lower cost and\nhigher accuracy; in other words, the bfloat16 compute cost-quality tradeoff\ncurve is Pareto-dominated by the 4-bit and 8-bit curves, with models primarily\nquantized to 4-bit yielding the best Pareto curve. Furthermore, we achieve\nstate-of-the-art results on ImageNet for 4-bit ResNet-50 with\nquantization-aware training, obtaining a top-1 eval accuracy of 77.09%. We\ndemonstrate the regularizing effect of quantization by measuring the\ngeneralization gap. The quantization method we used is optimized for\npracticality: It requires little tuning and is designed with hardware\ncapabilities in mind. Our work motivates further research into optimal numeric\nformats for quantization, as well as the development of machine learning\naccelerators supporting these formats. As part of this work, we contribute a\nquantization library written in JAX, which is open-sourced at\nhttps://github.com/google-research/google-research/tree/master/aqt.\n"}
+{"abstract": "  Learning quickly and continually is still an ambitious task for neural\nnetworks. Indeed, many real-world applications do not reflect the learning\nsetting where neural networks shine, as data are usually few, mostly unlabelled\nand come as a stream. To narrow this gap, we introduce FUSION - Few-shot\nUnSupervIsed cONtinual learning - a novel strategy which aims to deal with\nneural networks that \"learn in the wild\", simulating a real distribution and\nflow of unbalanced tasks. We equip FUSION with MEML - Meta-Example\nMeta-Learning - a new module that simultaneously alleviates catastrophic\nforgetting and favours the generalisation and future learning of new tasks. To\nencourage features reuse during the meta-optimisation, our model exploits a\nsingle inner loop per task, taking advantage of an aggregated representation\nachieved through the use of a self-attention mechanism. To further enhance the\ngeneralisation capability of MEML, we extend it by adopting a technique that\ncreates various augmented tasks and optimises over the hardest. Experimental\nresults on few-shot learning benchmarks show that our model exceeds the other\nbaselines in both FUSION and fully supervised case. We also explore how it\nbehaves in standard continual learning consistently outperforming\nstate-of-the-art approaches.\n"}
+{"abstract": "  The origin of boson peak -- an excess of density of states over Debye's model\nin glassy solids -- is still under intense debate, among which some theories\nand experiments suggest that boson peak is related to van-Hove singularity.\nHere we show that boson peak and van-Hove singularity are well separated\nidentities, by measuring the vibrational density of states of a two-dimensional\ngranular system, where packings are tuned gradually from a crystalline, to\npolycrystals, and to an amorphous material. We observe a coexistence of well\nseparated boson peak and van-Hove singularities in polycrystals, in which the\nvan-Hove singularities gradually shift to higher frequency values while\nbroadening their shapes and eventually disappear completely when the structural\ndisorder $\\eta$ becomes sufficiently high. By analyzing firstly the strongly\ndisordered system ($\\eta=1$) and the disordered granular crystals ($\\eta=0$),\nand then systems of intermediate disorder with $\\eta$ in between, we find that\nboson peak is associated with spatially uncorrelated random flucutations of\nshear modulus $\\delta G/\\langle G \\rangle$ whereas the smearing of van-Hove\nsingularities is associated with spatially correlated fluctuations of shear\nmodulus $\\delta G/\\langle G \\rangle$.\n"}
+{"abstract": "  We present the discovery of NGTS-19b, a high mass transiting brown dwarf\ndiscovered by the Next Generation Transit Survey (NGTS). We investigate the\nsystem using follow up photometry from the South African Astronomical\nObservatory, as well as sector 11 TESS data, in combination with radial\nvelocity measurements from the CORALIE spectrograph to precisely characterise\nthe system. We find that NGTS-19b is a brown dwarf companion to a K-star, with\na mass of $69.5 ^{+5.7}_{-5.4}$ M$_{Jup}$ and radius of $1.034\n^{+0.055}_{-0.053}$ R$_{Jup}$. The system has a reasonably long period of 17.84\ndays, and a high degree of eccentricity of $0.3767 ^{+0.0061}_{-0.0061}$. The\nmass and radius of the brown dwarf imply an age of $0.46 ^{+0.26}_{-0.15}$ Gyr,\nhowever this is inconsistent with the age determined from the host star SED,\nsuggesting that the brown dwarf may be inflated. This is unusual given that its\nlarge mass and relatively low levels of irradiation would make it much harder\nto inflate. NGTS-19b adds to the small, but growing number of brown dwarfs\ntransiting main sequence stars, and is a valuable addition as we begin to\npopulate the so called brown dwarf desert.\n"}
+{"abstract": "  A $3$-Prismatoid $P$ is the convex hull of two convex polygons $A$ and $B$\nwhich lie in parallel planes $H_A, H_B\\subset\\mathbb{R}^3$. Let $A'$ be the\northogonal projection of $A$ onto $H_B$. A prismatoid is called nested if $A'$\nproperly contained in $B$, or vice versa. We show that nested prismatoids can\nbe edge-unfolded.\n"}
+{"abstract": "  We introduce a simple entropy-based formalism to characterize the role of\nmixing in pressure-balanced multiphase clouds, and demonstrate example\napplications using Enzo-E (magneto)hydrodynamic simulations. Under this\nformalism, the high-dimensional description of the system's state at a given\ntime is simplified to the joint distribution of mass over pressure ($P$) and\nentropy ($K=P/\\rho^\\gamma$). As a result, this approach provides a way for\n(empirically and analytically) quantifying the impact of different initial\nconditions and sets of physics on the system evolution. We find that mixing\npredominantly alters the distribution along the $K$ direction and illustrate\nhow the formalism can be used to model mixing and cooling for fluid elements\noriginating in the cloud. We further confirm and generalize a previously\nsuggested criterion for cloud growth in the presence of radiative cooling, and\ndemonstrate that the shape of the cooling curve, particularly at the low\ntemperature end, can play an important role in controlling condensation.\nMoreover, we discuss the capacity of our approach to generalize such a\ncriterion to apply to additional sets of physics, and to build intuition for\nthe impact of subtle higher order effects not directly addressed by the\ncriterion.\n"}
+{"abstract": "  Recently, a first-order differentiator based on time-varying gains was\nintroduced in the literature, in its non recursive form, for a class of\ndifferentiable signals $y(t)$, satisfying $|\\ddot{y}(t)|\\leq L(t-t_0)$, for a\nknown function $L(t-t_0)$, such that $\\frac{1}{L(t-t_0)}\\left|\\frac{d\n{L}(t-t_0)}{dt}\\right|\\leq M$ with a known constant $M$. It has been shown that\nsuch differentiator is globally finite-time convergent. In this paper, we\nredesign such an algorithm, using time base generators (a class of time-varying\ngains), to obtain a differentiator algorithm for the same class of signals,\nwith guaranteed convergence before a desired time, i.e., with fixed-time\nconvergence with an a priori user-defined upper bound for the settling time.\nThus, our approach can be applied for scenarios under time-constraints.\n  We present numerical examples exposing the contribution with respect to\nstate-of-the-art algorithms.\n"}
+{"abstract": "  The application of digital technologies in agriculture can improve\ntraditional practices to adapt to climate change, reduce Greenhouse Gases (GHG)\nemissions, and promote a sustainable intensification for food security. Some\nauthors argued that we are experiencing a Digital Agricultural Revolution (DAR)\nthat will boost sustainable farming. This study aims to find evidence of the\nongoing DAR process and clarify its roots, what it means, and where it is\nheading. We investigated the scientific literature with bibliometric analysis\ntools to produce an objective and reproducible literature review. We retrieved\n4995 articles by querying the Web of Science database in the timespan\n2012-2019, and we analyzed the obtained dataset to answer three specific\nresearch questions: i) what is the spectrum of the DAR-related terminology?;\nii) what are the key articles and the most influential journals, institutions,\nand countries?; iii) what are the main research streams and the emerging\ntopics? By grouping the authors' keywords reported on publications, we\nidentified five main research streams: Climate-Smart Agriculture (CSA),\nSite-Specific Management (SSM), Remote Sensing (RS), Internet of Things (IoT),\nand Artificial Intelligence (AI). To provide a broad overview of each of these\ntopics, we analyzed relevant review articles, and we present here the main\nachievements and the ongoing challenges. Finally, we showed the trending topics\nof the last three years (2017, 2018, 2019).\n"}
+{"abstract": "  In this thesis, we provide new insights into the theory of cascade feedback\nlinearization of control systems. In particular, we present a new explicit\nclass of cascade feedback linearizable control systems, as well as a new\nobstruction to the existence of a cascade feedback linearization for a given\ninvariant control system. These theorems are presented in Chapter 4, where\ntruncated versions of operators from the calculus of variations are introduced\nand explored to prove these new results. This connection reveals new geometry\nbehind cascade feedback linearization and establishes a foundation for future\nexciting work on the subject with important consequences for dynamic feedback\nlinearization.\n"}
+{"abstract": "  In this work, we prove a novel one-shot multi-sender decoupling theorem\ngeneralising Dupuis result. We start off with a multipartite quantum state, say\non A1 A2 R, where A1, A2 are treated as the two sender systems and R is the\nreference system. We apply independent Haar random unitaries in tensor product\non A1 and A2 and then send the resulting systems through a quantum channel. We\nwant the channel output B to be almost in tensor with the untouched reference\nR. Our main result shows that this is indeed the case if suitable entropic\nconditions are met. An immediate application of our main result is to obtain a\none-shot simultaneous decoder for sending quantum information over a k-sender\nentanglement unassisted quantum multiple access channel (QMAC). The rate region\nachieved by this decoder is the natural one-shot quantum analogue of the\npentagonal classical rate region. Assuming a simultaneous smoothing conjecture,\nthis one-shot rate region approaches the optimal rate region of Yard, Dein the\nasymptotic iid limit. Our work is the first one to obtain a non-trivial\nsimultaneous decoder for the QMAC with limited entanglement assistance in both\none-shot and asymptotic iid settings; previous works used unlimited\nentanglement assistance.\n"}
+{"abstract": "  The capability of reinforcement learning (RL) agent directly depends on the\ndiversity of learning scenarios the environment generates and how closely it\ncaptures real-world situations. However, existing environments/simulators lack\nthe support to systematically model distributions over initial states and\ntransition dynamics. Furthermore, in complex domains such as soccer, the space\nof possible scenarios is infinite, which makes it impossible for one research\ngroup to provide a comprehensive set of scenarios to train, test, and benchmark\nRL algorithms. To address this issue, for the first time, we adopt an existing\nformal scenario specification language, SCENIC, to intuitively model and\ngenerate interactive scenarios. We interfaced SCENIC to Google Research Soccer\nenvironment to create a platform called SCENIC4RL. Using this platform, we\nprovide a dataset consisting of 36 scenario programs encoded in SCENIC and\ndemonstration data generated from a subset of them. We share our experimental\nresults to show the effectiveness of our dataset and the platform to train,\ntest, and benchmark RL algorithms. More importantly, we open-source our\nplatform to enable RL community to collectively contribute to constructing a\ncomprehensive set of scenarios.\n"}
+{"abstract": "  In this paper, we describe novel components for extracting clinically\nrelevant information from medical conversations which will be available as\nGoogle APIs. We describe a transformer-based Recurrent Neural Network\nTransducer (RNN-T) model tailored for long-form audio, which can produce rich\ntranscriptions including speaker segmentation, speaker role labeling,\npunctuation and capitalization. On a representative test set, we compare\nperformance of RNN-T models with different encoders, units and streaming\nconstraints. Our transformer-based streaming model performs at about 20% WER on\nthe ASR task, 6% WDER on the diarization task, 43% SER on periods, 52% SER on\ncommas, 43% SER on question marks and 30% SER on capitalization. Our recognizer\nis paired with a confidence model that utilizes both acoustic and lexical\nfeatures from the recognizer. The model performs at about 0.37 NCE. Finally, we\ndescribe a RNN-T based tagging model. The performance of the model depends on\nthe ontologies, with F-scores of 0.90 for medications, 0.76 for symptoms, 0.75\nfor conditions, 0.76 for diagnosis, and 0.61 for treatments. While there is\nstill room for improvement, our results suggest that these models are\nsufficiently accurate for practical applications.\n"}
+{"abstract": "  Visual sensors serve as a critical component of the Internet of Things (IoT).\nThere is an ever-increasing demand for broad applications and higher\nresolutions of videos and cameras in smart homes and smart cities, such as in\nsecurity cameras. To utilize this large volume of video data generated from\nnetworks of visual sensors for various machine vision applications, it needs to\nbe compressed and securely transmitted over the Internet. H.266/VVC, as the new\ncompression standard, brings the highest compression for visual data. To\nprovide security along with high compression, a selective encryption method for\nhiding information of videos is presented for this new compression standard.\nSelective encryption methods can lower the computation overhead of the\nencryption while keeping the video bitstream format which is useful when the\nvideo goes into untrusted blocks such as transcoding or watermarking. Syntax\nelements that represent considerable information are selected for the\nencryption, i.e., luma Intra Prediction Modes (IPMs), Motion Vector Difference\n(MVD), and residual signs., then the results of the proposed method are\ninvestigated in terms of visual security and bit rate change. Our experiments\nshow that the encrypted videos provide higher visual security compared to other\nsimilar works in previous standards, and integration of the presented\nencryption scheme into the VVC encoder has little impact on the bit rate\nefficiency (results in 2% to 3% bit rate increase).\n"}
+{"abstract": "  Deep learning based molecular graph generation and optimization has recently\nbeen attracting attention due to its great potential for de novo drug design.\nOn the one hand, recent models are able to efficiently learn a given graph\ndistribution, and many approaches have proven very effective to produce a\nmolecule that maximizes a given score. On the other hand, it was shown by\nprevious studies that generated optimized molecules are often unrealistic, even\nwith the inclusion of mechanics to enforce similarity to a dataset of real drug\nmolecules. In this work we use a hybrid approach, where the dataset\ndistribution is learned using an autoregressive model while the score\noptimization is done using the Metropolis algorithm, biased toward the learned\ndistribution. We show that the resulting method, that we call learned realism\nsampling (LRS), produces empirically more realistic molecules and outperforms\nall recent baselines in the task of molecule optimization with similarity\nconstraints.\n"}
+{"abstract": "  In single-reference coupled-cluster (CC) methods, one has to solve a set of\nnon-linear polynomial equations in order to determine the so-called amplitudes\nwhich are then used to compute the energy and other properties. Although it is\nof common practice to converge to the (lowest-energy) ground-state solution, it\nis also possible, thanks to tailored algorithms, to access higher-energy roots\nof these equations which may or may not correspond to genuine excited states.\nHere, we explore the structure of the energy landscape of variational CC (VCC)\nand we compare it with its (projected) traditional version (TCC) in the case\nwhere the excitation operator is restricted to paired double excitations\n(pCCD). By investigating two model systems (the symmetric stretching of the\nlinear \\ce{H4} molecule and the continuous deformation of the square \\ce{H4}\nmolecule into a rectangular arrangement) in the presence of weak and strong\ncorrelations, the performance of VpCCD and TpCCD are gauged against their\nconfiguration interaction (CI) equivalent, known as doubly-occupied CI (DOCI),\nfor reference Slater determinants made of ground- or excited-state Hartree-Fock\norbitals or state-specific orbitals optimized directly at the VpCCD level. The\ninfluence of spatial symmetry breaking is also investigated.\n"}
+{"abstract": "  Multi-agent value-based approaches recently make great progress, especially\nvalue decomposition methods. However, there are still a lot of limitations in\nvalue function factorization. In VDN, the joint action-value function is the\nsum of per-agent action-value function while the joint action-value function of\nQMIX is the monotonic mixing of per-agent action-value function. To some\nextent, QTRAN reduces the limitation of joint action-value functions that can\nbe represented, but it has unsatisfied performance in complex tasks. In this\npaper, in order to extend the class of joint value functions that can be\nrepresented, we propose a novel actor-critic method called NQMIX. NQMIX\nintroduces an off-policy policy gradient on QMIX and modify its network\narchitecture, which can remove the monotonicity constraint of QMIX and\nimplement a non-monotonic value function factorization for the joint\naction-value function. In addition, NQMIX takes the state-value as the learning\ntarget, which overcomes the problem in QMIX that the learning target is\noverestimated. Furthermore, NQMIX can be extended to continuous action space\nsettings by introducing deterministic policy gradient on itself. Finally, we\nevaluate our actor-critic methods on SMAC domain, and show that it has a\nstronger performance than COMA and QMIX on complex maps with heterogeneous\nagent types. In addition, our ablation results show that our modification of\nmixer is effective.\n"}
+{"abstract": "  We present a chronology of the formation and early evolution of the Oort\ncloud by simulations. These simulations start with the Solar System being born\nwith planets and asteroids in a stellar cluster orbiting the Galactic center.\nUpon ejection from its birth environment, we continue to follow the evolution\nof the Solar System while it navigates the Galaxy as an isolated planetary\nsystem. We conclude that the range in semi-major axis between 100au and several\n10$^3$\\,au still bears the signatures of the Sun being born in a\n1000MSun/pc$^3$ star cluster, and that most of the outer Oort cloud formed\nafter the Solar System was ejected. The ejection of the Solar System, we argue,\nhappened between 20Myr and 50Myr after its birth. Trailing and leading trails\nof asteroids and comets along the Sun's orbit in the Galactic potential are the\nby-product of the formation of the Oort cloud. These arms are composed of\nmaterial that became unbound from the Solar System when the Oort cloud formed.\nToday, the bulk of the material in the Oort cloud ($\\sim 70$\\%) originates from\nthe region in the circumstellar disk that was located between $\\sim 15$\\,au and\n$\\sim 35$\\,au, near the current location of the ice giants and the Centaur\nfamily of asteroids. According to our simulations, this population is\neradicated if the ice-giant planets are born in orbital resonance. Planet\nmigration or chaotic orbital reorganization occurring while the Solar System is\nstill a cluster member is, according to our model, inconsistent with the\npresence of the Oort cloud. About half the inner Oort cloud, between 100 and\n$10^4$\\,au, and a quarter of the material in the outer Oort cloud, $\\apgt\n10^4$\\,au, could be non-native to the Solar System but was captured from\nfree-floating debris in the cluster or from the circumstellar disk of other\nstars in the birth cluster.\n"}
+{"abstract": "  We study outer Lipschitz geometry of real semialgebraic or, more general,\ndefinable in a polynomially bounded o-minimal structure over the reals, surface\ngerms. In particular, any definable H\\\"older triangle is either Lipschitz\nnormally embedded or contains some \"abnormal\" arcs. We show that abnormal arcs\nconstitute finitely many \"abnormal zones\" in the space of all arcs, and\ninvestigate geometric and combinatorial properties of abnormal surface germs.\nWe establish a strong relation between geometry and combinatorics of abnormal\nH\\\"older triangles.\n"}
+{"abstract": "  We propose a lattice spin model on a cubic lattice that shares many of the\nproperties of the 3D toric code and the X-cube fracton model. The model, made\nof Z_3 degrees of freedom at the links, has the vertex, the cube, and the\nplaquette terms. Being a stabilizer code the ground states are exactly solved.\nWith only the vertex and the cube terms present, we show that the ground state\ndegeneracy (GSD) is 3^(L3+3L-1) where L is the linear dimension of the cubic\nlattice. In addition to fractons, there are free vertex excitations we call the\nfreeons. With the addition of the plaquette terms, GSD is vastly reduced to\n3^3, with fracton, fluxon, and freeon excitations, among which only the freeons\nare deconfined. The model is called the AB model if only the vertex (A_v) and\nthe cube (B_c) terms are present, and the ABC model if in addition the\nplaquette terms (C_p) are included. The AC model consisting of vertex and\nplaquette terms is the Z_3 3D toric code. The extensive GSD of the AB model\nderives from the existence of both local and non-local logical operators that\nconnect different ground states. The latter operators are identical to the\nlogical operators of the Z_3 X-cube model. Fracton excitations are immobile and\naccompanied by the creation of fluxons - plaquettes having nonzero flux. In the\nABC model, such fluxon creation costs energy and ends up confining the\nfractons. Unlike past models of fractons, vertex excitations are free to move\nin any direction and pick up a non-trivial statistical phase when passing\nthrough a fluxon or a fracton cluster.\n"}
+{"abstract": "  Results of analysis of 60010 data photometric observations from the AAVSO\ninternational database are presented, which span 120 years of monitoring. The\nperiodogram analysis shows the best fit period of 70.74d, a half of typically\npublished periods for smaller intervals. Contrary to expectation for\ndeep/shallow minima, the changes between them are not so regular. There may be\nseries of deep (or shallow) minima without alternations. There may be two\nacting periods of 138.5 days and 70.74, so the beat modulation may be expected.\nThe dependence of the phases of deep minima argue for two alternating periods\nwith a characteristic life-time of a mode of 30years. These phenomenological\nresults better explain the variability than the model of chaos.\n"}
+{"abstract": "  In this worldwide spread of SARS-CoV-2 (COVID-19) infection, it is of utmost\nimportance to detect the disease at an early stage especially in the hot spots\nof this epidemic. There are more than 110 Million infected cases on the globe,\nsofar. Due to its promptness and effective results computed tomography\n(CT)-scan image is preferred to the reverse-transcription polymerase chain\nreaction (RT-PCR). Early detection and isolation of the patient is the only\npossible way of controlling the spread of the disease. Automated analysis of\nCT-Scans can provide enormous support in this process. In this article, We\npropose a novel approach to detect SARS-CoV-2 using CT-scan images. Our method\nis based on a very intuitive and natural idea of analyzing shapes, an attempt\nto mimic a professional medic. We mainly trace SARS-CoV-2 features by\nquantifying their topological properties. We primarily use a tool called\npersistent homology, from Topological Data Analysis (TDA), to compute these\ntopological properties. We train and test our model on the \"SARS-CoV-2 CT-scan\ndataset\" \\citep{soares2020sars}, an open-source dataset, containing 2,481\nCT-scans of normal and COVID-19 patients. Our model yielded an overall\nbenchmark F1 score of $99.42\\% $, accuracy $99.416\\%$, precision $99.41\\%$, and\nrecall $99.42\\%$. The TDA techniques have great potential that can be utilized\nfor efficient and prompt detection of COVID-19. The immense potential of TDA\nmay be exploited in clinics for rapid and safe detection of COVID-19 globally,\nin particular in the low and middle-income countries where RT-PCR labs and/or\nkits are in a serious crisis.\n"}
+{"abstract": "  Observations of positional offsets between the location of X-ray and radio\nfeatures in many resolved, extragalactic jets indicates that the emitting\nregions are not co-spatial, an important piece of evidence in the debate over\nthe origin of the X-ray emission on kpc scales. The existing literature is\nnearly exclusively focused on jets with sufficiently deep Chandra observations\nto yield accurate positions for X-ray features, but most of the known X-ray\njets are detected with tens of counts or fewer, making detailed morphological\ncomparisons difficult. Here we report the detection of X-ray-to-radio\npositional offsets in 15 extragalactic jets from an analysis of 22 sources with\nlow-count Chandra observations, where we utilized the Low-count Image\nReconstruction Algorithm (LIRA). This algorithm has allowed us to account for\neffects such as Poisson background fluctuations and nearby point sources which\nhave previously made the detection of offsets difficult in shallow\nobservations. Using this method, we find that in 55 % of knots with detectable\noffsets, the X-rays peak upstream of the radio, questioning the applicability\nof one-zone models, including the IC/CMB model for explaining the X-ray\nemission. We also report the non-detection of two previously claimed X-ray\njets. Many, but not all, of our sources, follow a loose trend of increasing\noffset between the X-ray and radio emission, as well as a decreasing X-ray to\nradio flux ratio along the jet.\n"}
+{"abstract": "  In this paper, we deal with an elliptic problem with the Dirichlet boundary\ncondition. We operate in Sobolev spaces and the main analytic tool we use is\nthe Lax-Milgram lemma. First, we present the variational approach of the\nproblem which allows us to apply different functional analysis techniques. Then\nwe study thoroughly the well-posedness of the problem. We conclude our work\nwith a solution of the problem using numerical analysis techniques and the free\nsoftware freefem++.\n"}
+{"abstract": "  The aim of this article is to study semigroups of composition operators on\nthe BMOA-type spaces $BMOA_p$, and on their \"little oh\" analogues $VMOA_p$. The\nspaces $BMOA_p$ were introduced by R. Zhao as part of the large family of\nF(p,q,s) spaces, and are the M\\\"{o}bius invariant subspaces of the Dirichlet\nspaces $D^p_{p-1}$. We study the maximal subspace of strong continuity,\nproviding a sufficient condition on the infinitesimal generator of ${\\phi}$,\nunder which $[{\\phi}_t,BMOA_p]=VMOA_p$, and a related necessary condition in\nthe case where the Denjoy - Wolff point of the semigroup is in $\\mathbb{D}$.\nFurther, we characterize those semigroups, for which $[{\\phi}_t,\nBMOA_p]=VMOA_p$, in terms of the resolvent operator of the infinitesimal\ngenerator of $T_t$. In addition we provide a connection between the maximal\nsubspace of strong continuity and the Volterra-type operators $T_g$. We\ncharacterize the symbols g for which $T_g$ acting from $BMOA$ to $BMOA_1$ is\nbounded or compact, thus extending a related result to the case $p=1$. We also\nprove that for $1<p<2$ compactness of $T_g$ on $BMOA_p$ is equivalent to weak\ncompactness.\n"}
+{"abstract": "  Titanium nitride (TiN) is a paradigm of refractory transition metal nitrides\nwith great potential in vast applications. Generally, the plasmonic performance\nof TiN can be tuned by oxidation, which was thought to be only temperature-,\noxygen partial pressure-, and time-dependent. Regarding the role of\ncrystallographic orientation in the oxidation and resultant optical properties\nof TiN films, little is known thus far. Here we reveal that both the oxidation\nresistance behavior and the plasmonic performance of epitaxial TiN films follow\nthe order of (001) < (110) < (111). The effects of crystallographic orientation\non the lattice constants, optical properties, and oxidation levels of epitaxial\nTiN films have been systematically studied by combined high-resolution X-ray\ndiffraction, spectroscopic ellipsometry, X-ray absorption spectroscopy, and\nX-ray photoemission spectroscopy. To further understand the role of\ncrystallographic orientation in the initial oxidation process of TiN films,\ndensity-functional-theory calculations are carried out, indicating the energy\ncost of oxidation is (001) < (110) < (111), consistent with the experiments.\nThe superior endurance of the (111) orientation against mild oxidation can\nlargely alleviate the previously stringent technical requirements for the\ngrowth of TiN films with high plasmonic performance. The crystallographic\norientation can also offer an effective controlling parameter to design\nTiN-based plasmonic devices with desired peculiarity, e.g., superior chemical\nstability against mild oxidation or large optical tunability upon oxidation.\n"}
+{"abstract": "  Recently, the MiniBooNE experiment at Fermilab has updated the results with\nincreased data and reported an excess of $560.6 \\pm 119.6$ electronlike events\n($4.7\\sigma$) in the neutrino operation mode. In this paper, we propose a\nscenario to account for the excess where a Dirac-type sterile neutrino,\nproduced by a charged kaon decay through the neutrino mixing, decays into a\nleptophilic axionlike particle ($\\ell$ALP) and a muon neutrino. The\nelectron-positron pairs produced from the $\\ell$ALP decays can be interpreted\nas electronlike events provided that their opening angle is sufficiently small.\nIn our framework, we consider the $\\ell$ALP with a mass $m^{}_a =\n20\\,\\text{MeV}$ and an inverse decay constant $c^{}_e/f^{}_a =\n10^{-2}\\,\\text{GeV}^{-1}$, allowed by the astrophysical and experimental\nconstraints. Then, after integrating the predicted angular or visible energy\nspectra of the $\\ell$ALP to obtain the total excess event number, we find that\nour scenario with sterile neutrino masses within $150\\,\\text{MeV}\\lesssim\nm^{}_N \\lesssim 380 \\,\\text{MeV}$ ($150\\,\\text{MeV}\\lesssim m^{}_N \\lesssim 180\n\\,\\text{MeV}$) and neutrino mixing parameters between $10^{-10} \\lesssim\n|U_{\\mu 4}|^2 \\lesssim 10^{-8}$ ($3\\times 10^{-7} \\lesssim |U_{\\mu 4}|^2\n\\lesssim 8 \\times10^{-7}$) can explain the MiniBooNE data.\n"}
+{"abstract": "  Artificial stock market simulation based on agent is an important means to\nstudy financial market. Based on the assumption that the investors are composed\nof a main fund, small trend and contrarian investors characterized by four\nparameters, we simulate and research a kind of financial phenomenon with the\ncharacteristics of pyramid schemes. Our simulation results and theoretical\nanalysis reveal the relationships between the rate of return of the main fund\nand the proportion of the trend investors in all small investors, the small\ninvestors' parameters of taking profit and stopping loss, the order size of the\nmain fund and the strategies adopted by the main fund. Our work are helpful to\nexplain the financial phenomenon with the characteristics of pyramid schemes in\nfinancial markets, design trading rules for regulators and develop trading\nstrategies for investors.\n"}
+{"abstract": "  We consider estimating the effect of a treatment on the progress of subjects\ntested both before and after treatment assignment. A vast literature compares\nthe competing approaches of modeling the post-test score conditionally on the\npre-test score versus modeling the difference, namely the gain score. Our\ncontribution resides in analyzing the merits and drawbacks of the two\napproaches in a multilevel setting. This is relevant in many fields, for\nexample education with students nested into schools. The multilevel structure\nraises peculiar issues related to the contextual effects and the distinction\nbetween individual-level and cluster-level treatment. We derive approximate\nanalytical results and compare the two approaches by a simulation study. For an\nindividual-level treatment our findings are in line with the literature,\nwhereas for a cluster-level treatment we point out the key role of the cluster\nmean of the pre-test score, which favors the conditioning approach in settings\nwith large clusters.\n"}
+{"abstract": "  We report a systematic measurement of cumulants, $C_{n}$, for net-proton,\nproton and antiproton multiplicity distributions, and correlation functions,\n$\\kappa_n$, for proton and antiproton multiplicity distributions up to the\nfourth order in Au+Au collisions at $\\sqrt{s_{\\mathrm {NN}}}$ = 7.7, 11.5,\n14.5, 19.6, 27, 39, 54.4, 62.4 and 200 GeV. The $C_{n}$ and $\\kappa_n$ are\npresented as a function of collision energy, centrality and kinematic\nacceptance in rapidity, $y$, and transverse momentum, $p_{T}$. The data were\ntaken during the first phase of the Beam Energy Scan (BES) program (2010 --\n2017) at the BNL Relativistic Heavy Ion Collider (RHIC) facility. The\nmeasurements are carried out at midrapidity ($|y| <$ 0.5) and transverse\nmomentum 0.4 $<$ $p_{\\rm T}$ $<$ 2.0 GeV/$c$, using the STAR detector at RHIC.\nWe observe a non-monotonic energy dependence ($\\sqrt{s_{\\mathrm {NN}}}$ = 7.7\n-- 62.4 GeV) of the net-proton $C_{4}$/$C_{2}$ with the significance of\n3.1$\\sigma$ for the 0-5\\% central Au+Au collisions. This is consistent with the\nexpectations of critical fluctuations in a QCD-inspired model. Thermal and\ntransport model calculations show a monotonic variation with $\\sqrt{s_{\\mathrm\n{NN}}}$. For the multiparticle correlation functions, we observe significant\nnegative values for a two-particle correlation function, $\\kappa_2$, of protons\nand antiprotons, which are mainly due to the effects of baryon number\nconservation. Furthermore, it is found that the four-particle correlation\nfunction, $\\kappa_4$, of protons plays a role in determining the energy\ndependence of proton $C_4/C_1$ below 19.6 GeV, which cannot be understood by\nthe effect of baryon number conservation.\n"}
+{"abstract": "  We address the challenge of policy evaluation in real-world applications of\nreinforcement learning systems where the available historical data is limited\ndue to ethical, practical, or security considerations. This constrained\ndistribution of data samples often leads to biased policy evaluation estimates.\nTo remedy this, we propose that instead of policy evaluation, one should\nperform policy comparison, i.e. to rank the policies of interest in terms of\ntheir value based on available historical data. In addition we present the\nLimited Data Estimator (LDE) as a simple method for evaluating and comparing\npolicies from a small number of interactions with the environment. According to\nour theoretical analysis, the LDE is shown to be statistically reliable on\npolicy comparison tasks under mild assumptions on the distribution of the\nhistorical data. Additionally, our numerical experiments compare the LDE to\nother policy evaluation methods on the task of policy ranking and demonstrate\nits advantage in various settings.\n"}
+{"abstract": "  Significant clustering around the rarest luminous quasars is a feature\npredicted by dark matter theory combined with number density matching\narguments. However, this expectation is not reflected by observations of\nquasars residing in a diverse range of environments. Here, we assess the\ntension in the diverse clustering of visible $i$-band dropout galaxies around\nluminous $z\\sim6$ quasars. Our approach uses a simple empirical method to\nderive the median luminosity to halo mass relation, $L_{c}(M_{h})$ for both\nquasars and galaxies under the assumption of log-normal luminosity scatter,\n$\\Sigma_{Q}$ and $\\Sigma_{G}$. We show that higher $\\Sigma_{Q}$ reduces the\naverage halo mass hosting a quasar of a given luminosity, thus introducing at\nleast a partial reversion to the mean in the number count distribution of\nnearby Lyman-Break galaxies. We generate a large sample of mock Hubble Space\nTelescope fields-of-view centred across rare $z\\sim6$ quasars by resampling\npencil beams traced through the dark matter component of the BlueTides\ncosmological simulation. We find that diverse quasar environments are expected\nfor $\\Sigma_{Q}>0.4$, consistent with numerous observations and theoretical\nstudies. However, we note that the average number of galaxies around the\ncentral quasar is primarily driven by galaxy evolutionary processes in\nneighbouring halos, as embodied by our parameter $\\Sigma_{G}$, instead of a\ndifference in the large scale structure around the central quasar host,\nembodied by $\\Sigma_{Q}$. We conclude that models with $\\Sigma_{G}>0.3$ are\nconsistent with current observational constraints on high-z quasars, and that\nsuch a value is comparable to the scatter estimated from hydrodynamical\nsimulations of galaxy formation.\n"}
+{"abstract": "  Here we develop a method for investigating global strong solutions of\npartially dissipative hyperbolic systems in the critical regularity setting.\nCompared to the recent works by Kawashima and Xu, we use hybrid Besov spaces\nwith different regularity exponent in low and high frequency. This allows to\nconsider more general data and to track the exact dependency on the dissipation\nparameter for the solution. Our approach enables us to go beyond the L^2\nframework in the treatment of the low frequencies of the solution, which is\ntotally new, to the best of our knowledge. Focus is on the one-dimensional\nsetting (the multi-dimensional case will be considered in a forthcoming paper)\nand, for expository purpose, the first part of the paper is devoted to a toy\nmodel that may be seen as a simplification of the compressible Euler system\nwith damping. More elaborated systems (including the compressible Euler system\nwith general increasing pressure law) are considered at the end of the paper.\n"}
+{"abstract": "  The input space of a neural network with ReLU-like activations is partitioned\ninto multiple linear regions, each corresponding to a specific activation\npattern of the included ReLU-like activations. We demonstrate that this\npartition exhibits the following encoding properties across a variety of deep\nlearning models: (1) {\\it determinism}: almost every linear region contains at\nmost one training example. We can therefore represent almost every training\nexample by a unique activation pattern, which is parameterized by a {\\it neural\ncode}; and (2) {\\it categorization}: according to the neural code, simple\nalgorithms, such as $K$-Means, $K$-NN, and logistic regression, can achieve\nfairly good performance on both training and test data. These encoding\nproperties surprisingly suggest that {\\it normal neural networks well-trained\nfor classification behave as hash encoders without any extra efforts.} In\naddition, the encoding properties exhibit variability in different scenarios.\n{Further experiments demonstrate that {\\it model size}, {\\it training time},\n{\\it training sample size}, {\\it regularization}, and {\\it label noise}\ncontribute in shaping the encoding properties, while the impacts of the first\nthree are dominant.} We then define an {\\it activation hash phase chart} to\nrepresent the space expanded by {model size}, training time, training sample\nsize, and the encoding properties, which is divided into three canonical\nregions: {\\it under-expressive regime}, {\\it critically-expressive regime}, and\n{\\it sufficiently-expressive regime}. The source code package is available at\n\\url{https://github.com/LeavesLei/activation-code}.\n"}
+{"abstract": "  Light binary convolutional neural networks (LB-CNN) are particularly useful\nwhen implemented in low-energy computing platforms as required in many\nindustrial applications. Herein, a framework for optimizing compact LB-CNN is\nintroduced and its effectiveness is evaluated. The framework is freely\navailable and may run on free-access cloud platforms, thus requiring no major\ninvestments. The optimized model is saved in the standardized .h5 format and\ncan be used as input to specialized tools for further deployment into specific\ntechnologies, thus enabling the rapid development of various intelligent image\nsensors. The main ingredient in accelerating the optimization of our model,\nparticularly the selection of binary convolution kernels, is the Chainer/Cupy\nmachine learning library offering significant speed-ups for training the output\nlayer as an extreme-learning machine. Additional training of the output layer\nusing Keras/Tensorflow is included, as it allows an increase in accuracy.\nResults for widely used datasets including MNIST, GTSRB, ORL, VGG show very\ngood compromise between accuracy and complexity. Particularly, for face\nrecognition problems a carefully optimized LB-CNN model provides up to 100%\naccuracies. Such TinyML solutions are well suited for industrial applications\nrequiring image recognition with low energy consumption.\n"}
+{"abstract": "  Given a Lie group $G$, we elaborate the dynamics on $T^*T^*G$ and $T^*TG$,\nwhich is given by a Hamiltonian, as well as the dynamics on the Tulczyjew\nsymplectic space $TT^*G$, which may be defined by a Lagrangian or a Hamiltonian\nfunction. As the trivializations we adapted respect the group structures of the\niterated bundles, we exploit all possible subgroup reductions (Poisson,\nsymplectic or both) of higher order dynamics.\n"}
+{"abstract": "  X-ray polarimetry promises us an unprecedented look at the structure of\nmagnetic fields and on the processes at the base of acceleration of particles\nup to ultrarelativistic energies in relativistic jets. Crucial pieces of\ninformation are expected from observations of blazars (that are characterized\nby the presence of a jet pointing close to the Earth), in particular of the\nsubclass defined by a synchrotron emission extending to the X-ray band\n(so-called high synchrotron peak blazars, HSP). In this review, I give an\naccount of some of the models and numerical simulations developed to predict\nthe polarimetric properties of HSP at high energy, contrasting the predictions\nof scenarios assuming particle acceleration at shock fronts with those that are\nbased on magnetic reconnection, and I discuss the prospects for the\nobservations of the upcoming Imaging X-ray Polarimetry Explorer (IXPE)\nsatellite.\n"}
+{"abstract": "  In robotic bin-picking applications, the perception of texture-less, highly\nreflective parts is a valuable but challenging task. The high glossiness can\nintroduce fake edges in RGB images and inaccurate depth measurements especially\nin heavily cluttered bin scenario. In this paper, we present the ROBI\n(Reflective Objects in BIns) dataset, a public dataset for 6D object pose\nestimation and multi-view depth fusion in robotic bin-picking scenarios. The\nROBI dataset includes a total of 63 bin-picking scenes captured with two active\nstereo camera: a high-cost Ensenso sensor and a low-cost RealSense sensor. For\neach scene, the monochrome/RGB images and depth maps are captured from sampled\nview spheres around the scene, and are annotated with accurate 6D poses of\nvisible objects and an associated visibility score. For evaluating the\nperformance of depth fusion, we captured the ground truth depth maps by\nhigh-cost Ensenso camera with objects coated in anti-reflective scanning spray.\nTo show the utility of the dataset, we evaluated the representative algorithms\nof 6D object pose estimation and multi-view depth fusion on the full dataset.\nEvaluation results demonstrate the difficulty of highly reflective objects,\nespecially in difficult cases due to the degradation of depth data quality,\nsevere occlusions and cluttered scene. The ROBI dataset is available online at\nhttps://www.trailab.utias.utoronto.ca/robi.\n"}
+{"abstract": "  We give an explicit formula for the zeroth $\\mathbb{A}^1$-homology sheaf of a\nsmooth proper variety. We also provide a simple proof of a theorem of\nKahn-Sujatha which describes hom sets in the birational localization of the\ncategory of smooth varieties.\n"}
+{"abstract": "  The diffusion of innovations theory has been studied for years. Previous\nresearch efforts mainly focus on key elements, adopter categories, and the\nprocess of innovation diffusion. However, most of them only consider single\ninnovations. With the development of modern technology, recurrent innovations\ngradually come into vogue. In order to reveal the characteristics of recurrent\ninnovations, we present the first large-scale analysis of the adoption of\nrecurrent innovations in the context of mobile app updates. Our analysis\nreveals the adoption behavior and new adopter categories of recurrent\ninnovations as well as the features that have impact on the process of\nadoption.\n"}
+{"abstract": "  Important advances have recently been achieved in developing procedures\nyielding uniformly valid inference for a low dimensional causal parameter when\nhigh-dimensional nuisance models must be estimated. In this paper, we review\nthe literature on uniformly valid causal inference and discuss the costs and\nbenefits of using uniformly valid inference procedures. Naive estimation\nstrategies based on regularisation, machine learning, or a preliminary model\nselection stage for the nuisance models have finite sample distributions which\nare badly approximated by their asymptotic distributions. To solve this serious\nproblem, estimators which converge uniformly in distribution over a class of\ndata generating mechanisms have been proposed in the literature. In order to\nobtain uniformly valid results in high-dimensional situations, sparsity\nconditions for the nuisance models need typically to be made, although a double\nrobustness property holds, whereby if one of the nuisance model is more sparse,\nthe other nuisance model is allowed to be less sparse. While uniformly valid\ninference is a highly desirable property, uniformly valid procedures pay a high\nprice in terms of inflated variability. Our discussion of this dilemma is\nillustrated by the study of a double-selection outcome regression estimator,\nwhich we show is uniformly asymptotically unbiased, but is less variable than\nuniformly valid estimators in the numerical experiments conducted.\n"}
+{"abstract": "  The planar Hall effect (PHE), wherein a rotating magnetic field in the plane\nof a sample induces oscillating transverse voltage, has recently garnered\nattention in a wide range of topological metals and insulators. The observed\ntwofold oscillations in $\\rho_{yx}$ as the magnetic field completes one\nrotation are the result of chiral, orbital and/or spin effects. The\nantiperovskites $A_3B$O ($A$ = Ca, Sr, Ba; $B$ = Sn, Pb) are topological\ncrystalline insulators whose low-energy excitations are described by a\ngeneralized Dirac equation for fermions with total angular momentum $J = 3/2$.\nWe report unusual sixfold oscillations in the PHE of Sr$_3$SnO, which persisted\nnearly up to room temperature. Multiple harmonics (twofold, fourfold and\nsixfold), which exhibited distinct field and temperature dependencies, were\ndetected in $\\rho_{xx}$ and $\\rho_{yx}$. These observations are more diverse\nthan those in other Dirac and Weyl semimetals and point to a richer interplay\nof microscopic processes underlying the PHE in the antiperovskites.\n"}
+{"abstract": "  In this paper, we derive a stability result for $L_1$ and $L_{\\infty}$\nperturbations of diffusions under weak regularity conditions on the\ncoefficients. In particular, the drift terms we consider can be unbounded with\nat most linear growth, and we do not require uniform convergence of perturbed\ndiffusions. Instead, we require a weaker convergence condition in a special\nmetric introduced in this paper, related to the Holder norm of the diffusion\nmatrix differences. Our approach is based on a special version of the\nMcKean-Singer parametrix expansion.\n"}
+{"abstract": "  Given a generically finite local extension of valuation rings $V \\subset W$,\nthe question of whether $W$ is the localization of a finitely generated\n$V$-algebra is significant for approaches to the problem of local\nuniformization of valuations using ramification theory. Hagen Knaf proposed a\ncharacterization of when $W$ is essentially of finite type over $V$ in terms of\nclassical invariants of the extension of associated valuations. Knaf's\nconjecture has been verified in important special cases by Cutkosky and\nNovacoski using local uniformization of Abhyankar valuations and resolution of\nsingularities of excellent surfaces in arbitrary characteristic, and by\nCutkosky for valuation rings of function fields of characteristic $0$ using\nembedded resolution of singularities. In this paper we prove Knaf's conjecture\nin full generality.\n"}
+{"abstract": "  The relationship between words in a sentence often tells us more about the\nunderlying semantic content of a document than its actual words, individually.\nIn this work, we propose two novel algorithms, called Flexible Lexical Chain II\nand Fixed Lexical Chain II. These algorithms combine the semantic relations\nderived from lexical chains, prior knowledge from lexical databases, and the\nrobustness of the distributional hypothesis in word embeddings as building\nblocks forming a single system. In short, our approach has three main\ncontributions: (i) a set of techniques that fully integrate word embeddings and\nlexical chains; (ii) a more robust semantic representation that considers the\nlatent relation between words in a document; and (iii) lightweight word\nembeddings models that can be extended to any natural language task. We intend\nto assess the knowledge of pre-trained models to evaluate their robustness in\nthe document classification task. The proposed techniques are tested against\nseven word embeddings algorithms using five different machine learning\nclassifiers over six scenarios in the document classification task. Our results\nshow the integration between lexical chains and word embeddings representations\nsustain state-of-the-art results, even against more complex systems.\n"}
+{"abstract": "  In this paper, we consider a reconfigurable intelligent surface\n(RIS)-assisted two-way relay network, in which two users exchange information\nthrough the base station (BS) with the help of an RIS. By jointly designing the\nphase shifts at the RIS and beamforming matrix at the BS, our objective is to\nmaximize the minimum signal-to-noise ratio (SNR) of the two users, under the\ntransmit power constraint at the BS. We first consider the single-antenna BS\ncase, and propose two algorithms to design the RIS phase shifts and the BS\npower amplification parameter, namely the SNR-upper-bound-maximization (SUM)\nmethod, and genetic-SNR-maximization (GSM) method. When there are multiple\nantennas at the BS, the optimization problem can be approximately addressed by\nsuccessively solving two decoupled subproblems, one to optimize the RIS phase\nshifts, the other to optimize the BS beamforming matrix. The first subproblem\ncan be solved by using SUM or GSM method, while the second subproblem can be\nsolved by using optimized beamforming or maximum-ratio-beamforming method. The\nproposed algorithms have been verified through numerical results with\ncomputational complexity analysis.\n"}
+{"abstract": "  We give a double copy construction for the symmetries of the self-dual\nsectors of Yang-Mills (YM) and gravity, in the light-cone formulation. We find\nan infinite set of double copy constructible symmetries. We focus on two\nfamilies which correspond to the residual diffeomorphisms on the gravitational\nside. For the first one, we find novel non-perturbative double copy rules in\nthe bulk. The second family has a more striking structure, as a\nnon-perturbative gravitational symmetry is obtained from a perturbatively\ndefined symmetry on the YM side.\n  At null infinity, we find the YM origin of the subset of extended\nBondi-Metzner-Sachs (BMS) symmetries that preserve the self-duality condition.\nIn particular, holomorphic large gauge YM symmetries are double copied to\nholomorphic supertranslations. We also identify the single copy of\nsuperrotations with certain non-gauge YM transformations that to our knowledge\nhave not been previously presented in the literature.\n"}
+{"abstract": "  We use a Hamiltonian (transition matrix) description of height-restricted\nDyck paths on the plane in which generating functions for the paths arise as\nmatrix elements of the propagator to evaluate the length and area generating\nfunction for paths with arbitrary starting and ending points, expressing it as\na rational combination of determinants. Exploiting a connection between random\nwalks and quantum exclusion statistics that we previously established, we\nexpress this generating function in terms of grand partition functions for\nexclusion particles in a finite harmonic spectrum and present an alternative,\nsimpler form for its logarithm that makes its polynomial structure explicit.\n"}
+{"abstract": "  We prove that for a domain $\\Omega \\subset \\mathbb{R}^n$, being\n$(\\epsilon,\\delta)$ in the sense of Jones is equivalent to being an extension\ndomain for bmo$(\\Omega)$, the nonhonomogeneous version of the space of function\nof bounded mean oscillation on $\\Omega$. In the process we demonstrate that\nthese conditions are equivalent to local versions of two other conditions\ncharacterizing uniform domains, one involving the presence of length cigars\nbetween nearby points and the other a local version of the quasi-hyperbolic\nuniform condition. Our results show that the definition of bmo$(\\Omega)$ is\nclosely connected to the geometry of the domain.\n"}
+{"abstract": "  There is accelerating interest in developing memory devices using\nantiferromagnetic (AFM) materials, motivated by the possibility for\nelectrically controlling AFM order via spin-orbit torques, and its read-out via\nmagnetoresistive effects. Recent studies have shown, however, that high current\ndensities create non-magnetic contributions to resistive switching signals in\nAFM/heavy metal (AFM/HM) bilayers, complicating their interpretation. Here we\nintroduce an experimental protocol to unambiguously distinguish current-induced\nmagnetic and nonmagnetic switching signals in AFM/HM structures, and\ndemonstrate it in IrMn$_3$/Pt devices. A six-terminal double-cross device is\nconstructed, with an IrMn$_3$ pillar placed on one cross. The differential\nvoltage is measured between the two crosses with and without IrMn$_3$ after\neach switching attempt. For a wide range of current densities, reversible\nswitching is observed only when write currents pass through the cross with the\nIrMn$_3$ pillar, eliminating any possibility of non-magnetic switching\nartifacts. Micromagnetic simulations support our findings, indicating a complex\ndomain-mediated switching process.\n"}
+{"abstract": "  Graph Convolutional Networks (GCNs) are increasingly adopted in large-scale\ngraph-based recommender systems. Training GCN requires the minibatch generator\ntraversing graphs and sampling the sparsely located neighboring nodes to obtain\ntheir features. Since real-world graphs often exceed the capacity of GPU\nmemory, current GCN training systems keep the feature table in host memory and\nrely on the CPU to collect sparse features before sending them to the GPUs.\nThis approach, however, puts tremendous pressure on host memory bandwidth and\nthe CPU. This is because the CPU needs to (1) read sparse features from memory,\n(2) write features into memory as a dense format, and (3) transfer the features\nfrom memory to the GPUs. In this work, we propose a novel GPU-oriented data\ncommunication approach for GCN training, where GPU threads directly access\nsparse features in host memory through zero-copy accesses without much CPU\nhelp. By removing the CPU gathering stage, our method significantly reduces the\nconsumption of the host resources and data access latency. We further present\ntwo important techniques to achieve high host memory access efficiency by the\nGPU: (1) automatic data access address alignment to maximize PCIe packet\nefficiency, and (2) asynchronous zero-copy access and kernel execution to fully\noverlap data transfer with training. We incorporate our method into PyTorch and\nevaluate its effectiveness using several graphs with sizes up to 111 million\nnodes and 1.6 billion edges. In a multi-GPU training setup, our method is\n65-92% faster than the conventional data transfer method, and can even match\nthe performance of all-in-GPU-memory training for some graphs that fit in GPU\nmemory.\n"}
+{"abstract": "  In this paper, we study the phenomenon of quantum interference in the\npresence of external gravitational fields described by alternative theories of\ngravity. We analyze both non-relativistic and relativistic effects induced by\nthe underlying curved background on a superposed quantum system. In the\nnon-relativistic regime, it is possible to come across a gravitational\ncounterpart of the Bohm-Aharonov effect, which results in a phase shift\nproportional to the derivative of the modified Newtonian potential. On the\nother hand, beyond the Newtonian approximation, the relativistic nature of\ngravity plays a crucial r\\^ole. Indeed, the existence of a gravitational time\ndilation between the two arms of the interferometer causes a loss of coherence\nthat is in principle observable in quantum interference patterns. We work in\nthe context of generalized quadratic theories of gravity to compare their\nphysical predictions with the analogous outcomes in general relativity. In so\ndoing, we show that the decoherence rate strongly depends on the gravitational\nmodel under investigation, which means that this approach turns out to be a\npromising test bench to probe and discriminate among all the extensions of\nEinstein's theory in future experiments.\n"}
+{"abstract": "  The analysis of the double-diffusion model and\n$\\mathbf{H}(\\mathrm{div})$-conforming method introduced in [B\\\"urger, M\\'endez,\nRuiz-Baier, SINUM (2019), 57:1318--1343] is extended to the time-dependent\ncase. In addition, the efficiency and reliability analysis of residual-based\n{\\it a posteriori} error estimators for the steady, semi-discrete, and fully\ndiscrete problems is established. The resulting methods are applied to simulate\nthe sedimentation of small particles in salinity-driven flows. The method\nconsists of Brezzi-Douglas-Marini approximations for velocity and compatible\npiecewise discontinuous pressures, whereas Lagrangian elements are used for\nconcentration and salinity distribution. Numerical tests confirm the properties\nof the proposed family of schemes and of the adaptive strategy guided by the\n{\\it a posteriori} error indicators.\n"}
+{"abstract": "  As robots are becoming more and more ubiquitous in human environments, it\nwill be necessary for robotic systems to better understand and predict human\nactions. However, this is not an easy task, at times not even for us humans,\nbut based on a relatively structured set of possible actions, appropriate cues,\nand the right model, this problem can be computationally tackled. In this\npaper, we propose to use an ensemble of long-short term memory (LSTM) networks\nfor human action prediction. To train and evaluate models, we used the MoGaze\ndataset - currently the most comprehensive dataset capturing poses of human\njoints and the human gaze. We have thoroughly analyzed the MoGaze dataset and\nselected a reduced set of cues for this task. Our model can predict (i) which\nof the labeled objects the human is going to grasp, and (ii) which of the macro\nlocations the human is going to visit (such as table or shelf). We have\nexhaustively evaluated the proposed method and compared it to individual cue\nbaselines. The results suggest that our LSTM model slightly outperforms the\ngaze baseline in single object picking accuracy, but achieves better accuracy\nin macro object prediction. Furthermore, we have also analyzed the prediction\naccuracy when the gaze is not used, and in this case, the LSTM model\nconsiderably outperformed the best single cue baseline\n"}
+{"abstract": "  Pretraining on large labeled datasets is a prerequisite to achieve good\nperformance in many computer vision tasks like 2D object recognition, video\nclassification etc. However, pretraining is not widely used for 3D recognition\ntasks where state-of-the-art methods train models from scratch. A primary\nreason is the lack of large annotated datasets because 3D data is both\ndifficult to acquire and time consuming to label. We present a simple\nself-supervised pertaining method that can work with any 3D data - single or\nmultiview, indoor or outdoor, acquired by varied sensors, without 3D\nregistration. We pretrain standard point cloud and voxel based model\narchitectures, and show that joint pretraining further improves performance. We\nevaluate our models on 9 benchmarks for object detection, semantic\nsegmentation, and object classification, where they achieve state-of-the-art\nresults and can outperform supervised pretraining. We set a new\nstate-of-the-art for object detection on ScanNet (69.0% mAP) and SUNRGBD (63.5%\nmAP). Our pretrained models are label efficient and improve performance for\nclasses with few examples.\n"}
+{"abstract": "  Open quantum systems can be systematically controlled by making changes to\ntheir environment. A well-known example is the spontaneous radiative decay of\nan electronically excited emitter, such as an atom or a molecule, which is\nsignificantly influenced by the feedback from the emitter's environment, for\nexample, by the presence of reflecting surfaces. A prerequisite for a\ndeliberate control of an open quantum system is to reveal the physical\nmechanisms that determine the state of the system. Here, we investigate the\nBose-Einstein condensation of a photonic Bose gas in an environment with\ncontrolled dissipation and feedback realised by a potential landscape that\neffectively acts as a Mach-Zehnder interferometer. Our measurements offer a\nhighly systematic picture of Bose-Einstein condensation under non-equilibrium\nconditions. We show that the condensation process is an interplay between\nminimising the energy of the condensate, minimising particle losses and\nmaximising constructive feedback from the environment. In this way our\nexperiments reveal physical mechanisms involved in the formation of a\nBose-Einstein condensate, which typically remain hidden when the system is\nclose to thermal equilibrium. Beyond a deeper understanding of Bose-Einstein\ncondensation, our results open new pathways in quantum simulation with optical\nBose-Einstein condensates.\n"}
+{"abstract": "  Non-abelian anyons are highly desired for topological quantum computation\npurposes, with Majorana fermions providing a promising route, particularly zero\nmodes with non-trivial mutual statistics. Yet realizing Majorana zero modes in\nmatter is a challenge, with various proposals in chiral superconductors,\nnanowires, and spin liquids, but no clear experimental examples. Heavy fermion\nmaterials have long been known to host Majorana fermions at two-channel Kondo\nimpurity sites, however, these impurities cannot be moved adiabatically and\ngenerically occur in metals, where the absence of a gap removes the topological\nprotection. Here, we consider an ordered lattice of these two-channel Kondo\nimpurities, which at quarter-filling form a Kondo insulator. We show that\ntopological defects in this state will host Majorana zero modes, or possibly\nmore complicated parafermions. These states are protected by the insulating gap\nand may be adiabatically braided, providing the novel possibility of realizing\ntopological quantum computation in heavy fermion materials.\n"}
+{"abstract": "  The immersion and the interaction are the important features of the driving\nsimulator. To improve these characteristics, this paper proposes a low-cost and\nmark-less driver head tracking framework based on the head pose estimation\nmodel, which makes the view of the simulator can automatically align with the\ndriver's head pose. The proposed method only uses the RGB camera without the\nother hardware or marker. To handle the error of the head pose estimation\nmodel, this paper proposes an adaptive Kalman Filter. By analyzing the error\ndistribution of the estimation model and user experience, the proposed Kalman\nFilter includes the adaptive observation noise coefficient and loop closure\nmodule, which can adaptive moderate the smoothness of the curve and keep the\ncurve stable near the initial position. The experiments show that the proposed\nmethod is feasible, and it can be used with different head pose estimation\nmodels.\n"}
+{"abstract": "  In this article we solve this ancient problem of perfect tuning in all keys\nand present a system were all harmonies are conserved at once. It will become\nclear, when we expose our solution, why this solution could not be found in the\nway in which earlier on musicians and scientist have been approaching the\nproblem. We follow indeed a different approach. We first construct a\nmathematical representation of the complete harmony by means of a vector space,\nwhere the different tones are represented in complete harmonic way for all keys\nat once. One of the essential differences with earlier systems is that tones\nwill no longer be ordered within an octave, and we find the octave-like\nordering back as a projection of our system. But it is exactly by this\nprojection procedure that the possibility to create a harmonic system for all\nkeys at once is lost. So we see why the old way of ordering tones within an\noctave could not lead to a solution of the problem. We indicate in which way a\nreal musical instrument could be built that realizes our harmonic scheme.\nBecause tones are no longer ordered within an octave such a musical instrument\nwill be rather unconventional. It is however a physically realizable musical\ninstrument, at least for the Pythagorean harmony. We also indicate how perfect\nharmonies of every dimension could be realized by computers.\n"}
+{"abstract": "  We use idealized N-body simulations of equilibrium stellar disks embedded\nwithin course-grained dark matter haloes to study the effects of spurious\ncollisional heating on disk structure and kinematics. Collisional heating\nartificially increases the vertical and radial velocity dispersions of disk\nstars, as well as the thickness and size of disks; the effects are felt at all\ngalacto-centric radii. The integrated effects of collisional heating are\ndetermined by the mass of dark matter halo particles (or equivalently, by the\nnumber of particles at fixed halo mass), their local density and characteristic\nvelocity dispersion, but are largely insensitive to the stellar particle mass.\nThe effects can therefore be reduced by increasing the mass resolution of dark\nmatter in cosmological simulations, with limited benefits from increasing the\nbaryonic (or stellar) mass resolution. We provide a simple empirical model that\naccurately captures the effects of spurious collisional heating on the\nstructure and kinematics of simulated disks, and use it to assess the\nimportance of disk heating for simulations of galaxy formation. We find that\nthe majority of state-of-the-art zoom simulations, and a few of the\nhighest-resolution, smallest-volume cosmological runs, are in principle able to\nresolve thin stellar disks in Milky Way-mass haloes, but most large-volume\ncosmological simulations cannot. For example, dark matter haloes resolved with\nfewer than $\\approx 10^6$ particles will collisionally heat stars near the\nstellar half-mass radius such that their vertical velocity dispersion increases\nby $\\gtrsim 10$ per cent of the halo's virial velocity in approximately one\nHubble time.\n"}
+{"abstract": "  Scattering polarization tends to dominate the linear polarization signals of\nthe Ca II 8542 A line in weakly magnetized areas ($B \\lesssim 100$ G),\nespecially when the observing geometry is close to the limb. In this paper we\nevaluate the degree of applicability of existing non-LTE spectral line\ninversion codes (which assume that the spectral line polarization is due to the\nZeeman effect only) at inferring the magnetic field vector and, particularly,\nits transverse component. To this end, we use the inversion code STiC to\nextract the strength and orientation of the magnetic field from synthetic\nspectropolarimetric data generated with the Hanle-RT code. The latter accounts\nfor the generation of polarization through scattering processes as well as the\njoint actions of the Hanle and the Zeeman effects. We find that, when the\ntransverse component of the field is stronger than $\\sim$80 G, the inversion\ncode is able to retrieve accurate estimates of the transverse field strength as\nwell as its azimuth in the plane of the sky. Below this threshold, the\nscattering polarization signatures become the major contributors to the linear\npolarization signals and often mislead the inversion code into severely over-\nor under-estimating the field strength. Since the line-of-sight component of\nthe field is derived from the circular polarization signal, which is not\naffected by atomic alignment, the corresponding inferences are always good.\n"}
+{"abstract": "  We present lattice results for the non-perturbative Collins-Soper (CS)\nkernel, which describes the energy-dependence of transverse momentum-dependent\nparton distributions (TMDs). The CS kernel is extracted from the ratios of\nfirst Mellin moments of quasi-TMDs evaluated at different nucleon momenta.The\nanalysis is done with dynamical $N_f=2+1$ clover fermions for the CLS ensemble\nH101 ($a=0.0854\\,\\mathrm{fm}$, $m_{\\pi}=m_K=422\\,\\mathrm{MeV}$). The computed\nCS kernel is in good agreement with experimental extractions and previous\nlattice studies.\n"}
+{"abstract": "  One crucial objective of multi-task learning is to align distributions across\ntasks so that the information between them can be transferred and shared.\nHowever, existing approaches only focused on matching the marginal feature\ndistribution while ignoring the semantic information, which may hinder the\nlearning performance. To address this issue, we propose to leverage the label\ninformation in multi-task learning by exploring the semantic conditional\nrelations among tasks. We first theoretically analyze the generalization bound\nof multi-task learning based on the notion of Jensen-Shannon divergence, which\nprovides new insights into the value of label information in multi-task\nlearning. Our analysis also leads to a concrete algorithm that jointly matches\nthe semantic distribution and controls label distribution divergence. To\nconfirm the effectiveness of the proposed method, we first compare the\nalgorithm with several baselines on some benchmarks and then test the\nalgorithms under label space shift conditions. Empirical results demonstrate\nthat the proposed method could outperform most baselines and achieve\nstate-of-the-art performance, particularly showing the benefits under the label\nshift conditions.\n"}
+{"abstract": "  We study the giant component problem slightly above the critical regime for\npercolation on Poissonian random graphs in the scale-free regime, where the\nvertex weights and degrees have a diverging second moment. Critical percolation\non scale-free random graphs have been observed to have incredibly subtle\nfeatures that are markedly different compared to those in random graphs with\nconverging second moment. In particular, the critical window for percolation\ndepends sensitively on whether we consider single- or multi-edge versions of\nthe Poissonian random graph.\n  In this paper, and together with our companion paper with Bhamidi, we build a\nbridge between these two cases. Our results characterize the part of the barely\nsupercritical regime where the size of the giant components are approximately\nsame for the single- and multi-edge settings. The methods for establishing\nconcentration of giant for the single- and multi-edge versions are quite\ndifferent. While the analysis in the multi-edge case is based on scaling limits\nof exploration processes, the single-edge setting requires identification of a\ncore structure inside certain high-degree vertices that forms the giant\ncomponent.\n"}
+{"abstract": "  We present two generalized hybrid kinetic-Hall magnetohydrodynamics (MHD)\nmodels describing the interaction of a two-fluid bulk plasma, which consists of\nthermal ions and electrons, with energetic, suprathermal ion populations\ndescribed by Vlasov dynamics. The dynamics of the thermal components are\ngoverned by standard fluid equations in the Hall MHD limit with the electron\nmomentum equation providing an Ohm's law with Hall and electron pressure terms\ninvolving a gyrotropic electron pressure tensor. The coupling of the bulk,\nlow-energy plasma with the energetic particle dynamics is accomplished through\nthe current density (current coupling scheme; CCS) and the ion pressure tensor\nappearing in the momentum equation (pressure coupling scheme; PCS) in the first\nand the second model, respectively. The CCS is a generalization of two\nwell-known models, because in the limit of vanishing energetic and thermal ion\ndensities we recover the standard Hall MHD and the hybrid\nkinetic-ions/fluid-electron model, respectively. This provides us with the\ncapability to study in a continuous manner the global impact of the energetic\nparticles in a regime extending from vanishing to dominant energetic particle\ndensities. The noncanonical Hamiltonian structures of the CCS and PCS, which\ncan be exploited to study equilibrium and stability properties through the\nenergy-Casimir variational principle, are identified. As a first application\nhere, we derive a generalized Hall MHD Grad--Shafranov--Bernoulli system for\ntranslationally symmetric equilibria with anisotropic electron pressure and\nkinetic effects owing to the presence of energetic particles using the PCS.\n"}
+{"abstract": "  Given a set $B\\subset \\mathbb{N}$, we investigate the existence of a set\n$A\\subset \\mathbb{N}$ such that the sumset $A+B = \\{a + b\\,:\\, a\\in A, b\\in\nB\\}$ has a prescribed asymptotic density. A set $B = \\{b_1, b_2, \\ldots\\}$ is\nsaid to be highly sparse if $B$ is either finite or infinite with\n$\\lim_{j\\rightarrow\\infty} b_{j+1}/b_j = \\infty$. In this note, we prove that\nif $B$ is highly sparse, such a set $A$ exists. This generalizes a recent\nresult by Faisant et al.\n"}
+{"abstract": "  We present the novel Efficient Line Segment Detector and Descriptor (ELSD) to\nsimultaneously detect line segments and extract their descriptors in an image.\nUnlike the traditional pipelines that conduct detection and description\nseparately, ELSD utilizes a shared feature extractor for both detection and\ndescription, to provide the essential line features to the higher-level tasks\nlike SLAM and image matching in real time. First, we design the one-stage\ncompact model, and propose to use the mid-point, angle and length as the\nminimal representation of line segment, which also guarantees the\ncenter-symmetry. The non-centerness suppression is proposed to filter out the\nfragmented line segments caused by lines' intersections. The fine offset\nprediction is designed to refine the mid-point localization. Second, the line\ndescriptor branch is integrated with the detector branch, and the two branches\nare jointly trained in an end-to-end manner. In the experiments, the proposed\nELSD achieves the state-of-the-art performance on the Wireframe dataset and\nYorkUrban dataset, in both accuracy and efficiency. The line description\nability of ELSD also outperforms the previous works on the line matching task.\n"}
+{"abstract": "  Quantum graphs are defined by having a Laplacian defined on the edges a\nmetric graph with boundary conditions on each vertex such that the resulting\noperator, L, is self-adjoint. We use Neumann boundary conditions. The spectrum\nof L does not determine the graph uniquely, that is, there exist non-isomorphic\ngraphs with the same spectra. There are few known examples of pairs of\nnon-isomorphic but isospectral quantum graphs. We have found all pairs of\nisospectral but non-isomorphic equilateral connected quantum graphs with at\nmost seven vertices. We find three isospectral triplets including one involving\na loop. We also present a combinatorial method to generate arbitrarily large\nsets of isospectral graphs and give an example of an isospectral set of four.\nThis has been done this using computer algebra. We discuss the possibilities\nthat our program is incorrect, present our tests and open source it for\ninspection at github.com/meapistol/Spectra-of-graphs.\n"}
+{"abstract": "  Spin ensembles coupled to optical cavities provide a powerful platform for\nengineering synthetic quantum matter. Recently, we demonstrated that cavity\nmediated infinite range interactions can induce fast scrambling in a Heisenberg\n$XXZ$ spin chain (Phys. Rev. Research {\\bf 2}, 043399 (2020)). In this work, we\nanalyze the kaleidoscope of quantum phases that emerge in this system from the\ninterplay of these interactions. Employing both analytical spin-wave theory as\nwell as numerical DMRG calculations, we find that there is a large parameter\nregime where the continuous $U(1)$ symmetry of this model is spontaneously\nbroken and the ground state of the system exhibits $XY$ order. This kind of\nsymmetry breaking and the consequent long range order is forbidden for short\nrange interacting systems by the Mermin-Wagner theorem. Intriguingly, we find\nthat the $XY$ order can be induced by even an infinitesimally weak infinite\nrange interaction. Furthermore, we demonstrate that in the $U(1)$ symmetry\nbroken phase, the half chain entanglement entropy violates the area law\nlogarithmically. Finally, we discuss a proposal to verify our predictions in\nstate-of-the-art quantum emulators.\n"}
+{"abstract": "  The detection and quantification of quantum coherence play significant roles\nin quantum information processing. We present an efficient way of tomographic\nwitnessing for both theoretical and experimental detection of coherence. We\nprove that a coherence witness is optimal if and only if all of its diagonal\nelements are zero. Naturally, we obtain a bona fide homographic measure of\ncoherence given by the sum of the absolute values of the real and the imaginary\nparts of the non-diagonal entries of a density matrix, together with its\ninteresting relations with other coherence measures like $l_1$ norm coherence\nand robust of coherence.\n"}
+{"abstract": "  The growing size and complexity of software in embedded systems poses new\nchallenges to the safety assessment of embedded control systems. In industrial\npractice, the control software is mostly treated as a black box during the\nsystem's safety analysis. The appropriate representation of the failure\npropagation of the software is a pressing need in order to increase the\naccuracy of safety analyses. However, it also increase the effort for creating\nand maintaining the safety analysis models (such as fault trees) significantly.\nIn this work, we present a method to automatically generate Component Fault\nTrees from Continuous Function Charts. This method aims at generating the\nfailure propagation model of the detailed software specification. Hence,\ncontrol software can be included into safety analyses without additional manual\neffort required to construct the safety analysis models of the software.\nMoreover, safety analyses created during early system specification phases can\nbe verified by comparing it with the automatically generated one in the\ndetailed specification phased.\n"}
+{"abstract": "  We initiate the study of computational complexity of graph coverings, aka\nlocally bijective graph homomorphisms, for {\\em graphs with semi-edges}. The\nnotion of graph covering is a discretization of coverings between surfaces or\ntopological spaces, a notion well known and deeply studied in classical\ntopology. Graph covers have found applications in discrete mathematics for\nconstructing highly symmetric graphs, and in computer science in the theory of\nlocal computations. In 1991, Abello et al. asked for a classification of the\ncomputational complexity of deciding if an input graph covers a fixed target\ngraph, in the ordinary setting (of graphs with only edges). Although many\ngeneral results are known, the full classification is still open. In spite of\nthat, we propose to study the more general case of covering graphs composed of\nnormal edges (including multiedges and loops) and so-called semi-edges.\nSemi-edges are becoming increasingly popular in modern topological graph\ntheory, as well as in mathematical physics. They also naturally occur in the\nlocal computation setting, since they are lifted to matchings in the covering\ngraph. We show that the presence of semi-edges makes the covering problem\nconsiderably harder; e.g., it is no longer sufficient to specify the vertex\nmapping induced by the covering, but one necessarily has to deal with the edge\nmapping as well. We show some solvable cases, and completely characterize the\ncomplexity of the already very nontrivial problem of covering one- and\ntwo-vertex (multi)graphs with semi-edges. Our NP-hardness results are proven\nfor simple input graphs, and in the case of regular two-vertex target graphs,\neven for bipartite ones. This provides a strengthening of previously known\nresults for covering graphs without semi-edges, and may contribute to better\nunderstanding of this notion and its complexity.\n"}
+{"abstract": "  In this paper we introduce the stack of polarized twisted conics and we use\nit to give a new point of view on $\\overline{\\mathcal{M}}_2$. In particular, we\npresent a new and independent approach to the computation of the integral Chow\nring of $\\overline{\\mathcal{M}}_2$, previously determined by Eric Larson.\n"}
+{"abstract": "  The proton electric and magnetic form factors, $G_E$ and $G_M$, are\nintrinsically connected to the spatial distribution of charge and magnetization\nin the proton. For decades, Rosenbluth separation measurements of the angular\ndependence of elastic e$^-$-p scattering were used to extract $G_E$ and $G_M$.\nMore recently, polarized electron scattering measurements, aiming to improve\nthe precision of $G_E$ extractions, showed significant disagreement with\nRosenbluth measurements at large momentum transfers ($Q^2$). This discrepancy\nis generally attributed to neglected two-photon exchange (TPE) corrections.\n  At larger $Q^2$ values, a new `Super-Rosenbluth' technique was used to\nimprove the precision of the Rosenbluth extraction, allowing for a better\nquantification of the discrepancy, while comparisons of e$^+$-p and e$^-$-p\nscattering indicated the presence of TPE corrections, but at $Q^2$ values below\nwhere a clear discrepancy is observed. In this work, we demonstrate the\nsignificant benefits to combining the Super-Rosenbluth technique with positron\nbeam measurements. This approach provides a greater kinematic reach and is\ninsensitive to some of the key systematic uncertainties in previous positron\nmeasurements.\n"}
+{"abstract": "  A matroid is uniform if and only if it has no minor isomorphic to\n$U_{1,1}\\oplus U_{0,1}$ and is paving if and only if it has no minor isomorphic\nto $U_{2,2}\\oplus U_{0,1}$. This paper considers, more generally, when a\nmatroid $M$ has no $U_{k,k}\\oplus U_{0,\\ell}$-minor for a fixed pair of\npositive integers $(k,\\ell)$. Calling such a matroid $(k,\\ell)$-uniform, it is\nshown that this is equivalent to the condition that every rank-$(r(M)-k)$ flat\nof $M$ has nullity less than $\\ell$. Generalising a result of Rajpal, we prove\nthat for any pair $(k,\\ell)$ of positive integers and prime power $q$, only\nfinitely many simple cosimple $GF(q)$-representable matroids are \\kl-uniform.\nConsequently, if Rota's Conjecture holds, then for every prime power $q$, there\nexists a pair $(k_q,\\ell_q)$ of positive integers such that every excluded\nminor of $GF(q)$-representability is $(k_q,\\ell_q)$-uniform. We also determine\nall binary $(2,2)$-uniform matroids and show the maximally $3$-connected\nmembers to be $Z_5\\backslash t, AG(4,2), AG(4,2)^*$ and a particular self-dual\nmatroid $P_{10}$. Combined with results of Acketa and Rajpal, this completes\nthe list of binary $(k,\\ell)$-uniform matroids for which $k+\\ell\\leq 4$.\n"}
+{"abstract": "  Tracking non-rigidly deforming scenes using range sensors has numerous\napplications including computer vision, AR/VR, and robotics. However, due to\nocclusions and physical limitations of range sensors, existing methods only\nhandle the visible surface, thus causing discontinuities and incompleteness in\nthe motion field. To this end, we introduce 4DComplete, a novel data-driven\napproach that estimates the non-rigid motion for the unobserved geometry.\n4DComplete takes as input a partial shape and motion observation, extracts 4D\ntime-space embedding, and jointly infers the missing geometry and motion field\nusing a sparse fully-convolutional network. For network training, we\nconstructed a large-scale synthetic dataset called DeformingThings4D, which\nconsists of 1972 animation sequences spanning 31 different animals or humanoid\ncategories with dense 4D annotation. Experiments show that 4DComplete 1)\nreconstructs high-resolution volumetric shape and motion field from a partial\nobservation, 2) learns an entangled 4D feature representation that benefits\nboth shape and motion estimation, 3) yields more accurate and natural\ndeformation than classic non-rigid priors such as As-Rigid-As-Possible (ARAP)\ndeformation, and 4) generalizes well to unseen objects in real-world sequences.\n"}
+{"abstract": "  The automatic analysis of fine art paintings presents a number of novel\ntechnical challenges to artificial intelligence, computer vision, machine\nlearning, and knowledge representation quite distinct from those arising in the\nanalysis of traditional photographs. The most important difference is that many\nrealist paintings depict stories or episodes in order to convey a lesson,\nmoral, or meaning. One early step in automatic interpretation and extraction of\nmeaning in artworks is the identifications of figures (actors). In Christian\nart, specifically, one must identify the actors in order to identify the\nBiblical episode or story depicted, an important step in understanding the\nartwork. We designed an automatic system based on deep convolutional neural\nnetworks and simple knowledge database to identify saints throughout six\ncenturies of Christian art based in large part upon saints symbols or\nattributes. Our work represents initial steps in the broad task of automatic\nsemantic interpretation of messages and meaning in fine art.\n"}
+{"abstract": "  The scaling of the turbulent spectra provides a key measurement that allows\nto discriminate between different theoretical predictions of turbulence. In the\nsolar wind, this has driven a large number of studies dedicated to this issue\nusing in-situ data from various orbiting spacecraft. While a semblance of\nconsensus exists regarding the scaling in the MHD and dispersive ranges, the\nprecise scaling in the transition range and the actual physical mechanisms that\ncontrol it remain open questions. Using the high-resolution data in the inner\nheliosphere from Parker Solar Probe (PSP) mission, we find that the sub-ion\nscales (i.e., at the frequency f ~ [2, 9] Hz) follow a power-law spectrum f^a\nwith a spectral index a varying between -3 and -5.7. Our results also show that\nthere is a trend toward and anti-correlation between the spectral slopes and\nthe power amplitudes at the MHD scales, in agreement with previous studies: the\nhigher the power amplitude the steeper the spectrum at sub-ion scales. A\nsimilar trend toward an anti-correlation between steep spectra and increasing\nnormalized cross helicity is found, in agreement with previous theoretical\npredictions about the imbalanced solar wind. We discuss the ubiquitous nature\nof the ion transition range in solar wind turbulence in the inner heliosphere.\n"}
+{"abstract": "  The theory of spectral filtering is a remarkable tool to understand the\nstatistical properties of learning with kernels. For least squares, it allows\nto derive various regularization schemes that yield faster convergence rates of\nthe excess risk than with Tikhonov regularization. This is typically achieved\nby leveraging classical assumptions called source and capacity conditions,\nwhich characterize the difficulty of the learning task. In order to understand\nestimators derived from other loss functions, Marteau-Ferey et al. have\nextended the theory of Tikhonov regularization to generalized self concordant\nloss functions (GSC), which contain, e.g., the logistic loss. In this paper, we\ngo a step further and show that fast and optimal rates can be achieved for GSC\nby using the iterated Tikhonov regularization scheme, which is intrinsically\nrelated to the proximal point method in optimization, and overcomes the\nlimitation of the classical Tikhonov regularization.\n"}
+{"abstract": "  It is well known that there are asymmetric dependence structures between\nfinancial returns. In this paper we use a new nonparametric measure of local\ndependence, the local Gaussian correlation, to improve portfolio allocation. We\nextend the classical mean-variance framework, and show that the portfolio\noptimization is straightforward using our new approach, only relying on a\ntuning parameter (the bandwidth). The new method is shown to outperform the\nequally weighted (1/N) portfolio and the classical Markowitz portfolio for\nmonthly asset returns data.\n"}
+{"abstract": "  We report on the fabrication of fractal dendrites by laser induced melting of\naluminum alloys. We target boron carbide (B4C) that is one of the most\neffective radiation-absorbing materials which is characterised by a low\ncoefficient of thermal expansion. Due to the high fragility of B4C crystals we\nwere able to introduce its nanoparticles into a stabilization aluminum matrix\nof AA385.0. The high intensity laser field action led to the formation of\ncomposite dendrite structures under the effect of local surface melting. The\nmodelling of the dendrite cluster growth confirms its fractal nature and sheds\nlight on the pattern behavior of the resulting quasicrystal structure.\n"}
+{"abstract": "  Let $H$ be a simple undirected graph. The family of all matchings of $H$\nforms a simplicial complex called the matching complex of $H$. Here , we give a\nclassification of all graphs with a Gorenstein matching complex. Also we study\nwhen the matching complex of $H$ is Cohen-Macaulay and, in certain classes of\ngraphs, we fully characterize those graphs which have a Cohen-Macaulay matching\ncomplex. In particular, we characterize when the matching complex of a graph\nwith girth at least 5 or a complete graph is Cohen-Macaulay.\n"}
+{"abstract": "  Ultra-relativistic heavy-ion collisions are expected to produce the strongest\nelectromagnetic fields in the known Universe. These highly-Lorentz contracted\nfields can manifest themselves as linearly polarized quasi-real photons that\ncan interact via the Breit-Wheeler process to produce lepton anti-lepton pairs.\nThe energy and momentum distribution of the produced dileptons carry\ninformation about the strength and spatial distribution of the colliding\nfields. Recently it has been demonstrated that photons from these fields can\ninteract even in heavy-ion collisions with hadronic overlap, providing a purely\nelectromagnetic probe of the produced medium. In this review we discuss the\nrecent theoretical progress and experimental advances for mapping the\nultra-strong electromagnetic fields produced in heavy-ion collisions via\nmeasurement of the Breit-Wheeler process.\n"}
+{"abstract": "  In this paper we are dealing with the issue of finding possibly short\nsynchronizing words in automata with weight assigned to each letter in the\nalphabet $\\Sigma$. First we discuss some complexity problems, and then we\npresent new approximation algorithm in four variations.\n"}
+{"abstract": "  In this paper, we consider the discrete Laguerre polynomials $P_{n, N}(z)$\northogonal with respect to the weight function $w(x) = x^{\\alpha} e^{-N cx}$\nsupported on the infinite nodes $L_N = \\{ x_{k,N} = \\frac{k^2}{N^2}, k \\in\n\\mathbb{N} \\}$. We focus on the \"band-saturated region\" situation when the\nparameter $c > \\frac{\\pi^2}{4}$. As $n \\to \\infty$, uniform expansions for\n$P_{n, n}(z)$ are achieved for $z$ in different regions in the complex plane.\nTypically, the Airy-function expansions and Gamma-function expansions are\nderived for $z$ near the endpoints of the band and the origin, respectively.\nThe asymptotics for the normalizing coefficient $h_{n, N}$, recurrence\ncoefficients $\\mathscr{B}_{n, N}$ and $\\mathscr{A}_{n, N}^2$, are also\nobtained. Our method is based on the Deift-Zhou steepest descent method for\nRiemann-Hilbert problems.\n"}
+{"abstract": "  Debugging is an essential process with a large share of the development\neffort, being a relentless quest for offensive code through tracing, inspection\nand iterative running sessions. Probably every developer has been in a\nsituation with a clear wish to rewind time just for a while, only to retry some\nactions alternatively, instead of restarting the entire session. Well, the\ngenie to fulfill such a wish is known as a reverse debugger. Their inherent\ntechnical complexity makes them very hard to implement, while the imposed\nexecution overhead turns them to less preferable for adoption. There are only a\nfew available, most being off-line tools, working on recorded, previously run,\nsessions. We consider live reverse debuggers both challenging and promising,\nsince they can fit into existing forward debuggers, and we developed the first\nlive reverse debugger on top of LLDB, discussing in detail our implementation\napproach.\n"}
+{"abstract": "  Graph structures are powerful tools for modeling the relationships between\ntextual elements. Graph-of-Words (GoW) has been adopted in many Natural\nLanguage tasks to encode the association between terms. However, GoW provides\nfew document-level relationships in cases when the connections between\ndocuments are also essential. For identifying sub-events on social media like\nTwitter, features from both word- and document-level can be useful as they\nsupply different information of the event. We propose a hybrid Graph-of-Tweets\n(GoT) model which combines the word- and document-level structures for modeling\nTweets. To compress large amount of raw data, we propose a graph merging method\nwhich utilizes FastText word embeddings to reduce the GoW. Furthermore, we\npresent a novel method to construct GoT with the reduced GoW and a Mutual\nInformation (MI) measure. Finally, we identify maximal cliques to extract\npopular sub-events. Our model showed promising results on condensing\nlexical-level information and capturing keywords of sub-events.\n"}
+{"abstract": "  Environmental epidemiologists are increasingly interested in establishing\ncausality between exposures and health outcomes. A popular model for causal\ninference is the Rubin Causal Model (RCM), which typically seeks to estimate\nthe average difference in study units' potential outcomes. An important\nassumption under RCM is no interference; that is, the potential outcomes of one\nunit are not affected by the exposure status of other units. The no\ninterference assumption is violated if we expect spillover or diffusion of\nexposure effects based on units' proximity to other units and several other\ncausal estimands arise. Air pollution epidemiology typically violates this\nassumption when we expect upwind events to affect downwind or nearby locations.\nThis paper adapts causal assumptions from social network research to address\ninterference and allow estimation of both direct and spillover causal effects.\nWe use propensity score-based methods to estimate these effects when\nconsidering the effects of the Environmental Protection Agency's 2005\nnonattainment designations for particulate matter with aerodynamic diameter\nless than 2.5 micrograms per cubic meter (PM2.5) on lung cancer incidence using\ncounty-level data obtained from the Surveillance, Epidemiology, and End Results\n(SEER) Program. We compare these methods in a rigorous simulation study that\nconsiders both spatially autocorrelated variables, interference, and missing\nconfounders. We find that pruning and matching based on the propensity score\nproduces the highest probability coverage of the true causal effects and lower\nmean squared error. When applied to the research question, we found protective\ndirect and spillover causal effects.\n"}
+{"abstract": "  With the widespread use of Deep Neural Networks (DNNs), machine learning\nalgorithms have evolved in two diverse directions -- one with ever-increasing\nconnection density for better accuracy and the other with more compact sizing\nfor energy efficiency. The increase in connection density increases on-chip\ndata movement, which makes efficient on-chip communication a critical function\nof the DNN accelerator. The contribution of this work is threefold. First, we\nillustrate that the point-to-point (P2P)-based interconnect is incapable of\nhandling a high volume of on-chip data movement for DNNs. Second, we evaluate\nP2P and network-on-chip (NoC) interconnect (with a regular topology such as a\nmesh) for SRAM- and ReRAM-based in-memory computing (IMC) architectures for a\nrange of DNNs. This analysis shows the necessity for the optimal interconnect\nchoice for an IMC DNN accelerator. Finally, we perform an experimental\nevaluation for different DNNs to empirically obtain the performance of the IMC\narchitecture with both NoC-tree and NoC-mesh. We conclude that, at the tile\nlevel, NoC-tree is appropriate for compact DNNs employed at the edge, and\nNoC-mesh is necessary to accelerate DNNs with high connection density.\nFurthermore, we propose a technique to determine the optimal choice of\ninterconnect for any given DNN. In this technique, we use analytical models of\nNoC to evaluate end-to-end communication latency of any given DNN. We\ndemonstrate that the interconnect optimization in the IMC architecture results\nin up to 6$\\times$ improvement in energy-delay-area product for VGG-19\ninference compared to the state-of-the-art ReRAM-based IMC architectures.\n"}
+{"abstract": "  Currently, urban autonomous driving remains challenging because of the\ncomplexity of the driving environment. Learning-based approaches, such as\nreinforcement learning (RL) and imitation learning (IL), have indicated\nsuperiority over rule-based approaches, showing great potential to make\ndecisions intelligently, but they still do not work well in urban driving\nsituations. To better tackle this problem, this paper proposes a novel\nlearning-based method that combines deep reinforcement learning with expert\ndemonstrations, focusing on longitudinal motion control in autonomous driving.\nOur proposed method employs the soft actor-critic structure and modifies the\nlearning process of the policy network to incorporate both the goals of\nmaximizing reward and imitating the expert. Moreover, an adaptive prioritized\nexperience replay is designed to sample experience from both the agent's\nself-exploration and expert demonstration, in order to improve the sample\nefficiency. The proposed method is validated in a simulated urban roundabout\nscenario and compared with various prevailing RL and IL baseline approaches.\nThe results manifest that the proposed method has a faster training speed, as\nwell as better performance in navigating safely and time-efficiently. The\nablation study reveals that the prioritized replay and expert demonstration\nfilter play important roles in our proposed method.\n"}
+{"abstract": "  Video quality assessment (VQA) is now a fast-growing subject, maturing in the\nfull reference (FR) case, yet challenging in the exploding no reference (NR)\ncase. We investigate variants of the popular VMAF video quality assessment\nalgorithm for the FR case, using both support vector regression and feedforward\nneural networks. We extend it to the NR case, using some different features but\nsimilar learning, to develop a partially unified framework for VQA. When fully\ntrained, FR algorithms such as VMAF perform very well on test datasets,\nreaching 90%+ match in PCC and SRCC; but for predicting performance in the\nwild, we train/test from scratch for each database. With an 80/20 train/test\nsplit, we still achieve about 90% performance on average in both PCC and SRCC,\nwith up to 7-9% gains over VMAF, using an improved motion feature and better\nregression. Moreover, we even get decent performance (about 75%) if we ignore\nthe reference, treating FR as NR, partly justifying our attempts at\nunification. In the true NR case, we reduce complexity vs. leading recent\nalgorithms VIDEVAL, RAPIQUE, yet achieve performance within 3-5%. Moreover, we\ndevelop a method to analyze the saliency of features, and conclude that for\nboth VIDEVAL and RAPIQUE, a small subset of their features are providing the\nbulk of the performance. In short, we find encouraging improvements in\ntrainability in FR, while constraining training complexity against leading\nmethods in NR, elucidating the saliency of features for feature selection.\n"}
+{"abstract": "  Primordial black holes (PBHs), formed out of large overdensities in the early\nUniverse, are a viable dark matter (DM) candidate over a broad range of masses.\nUltra-light, asteroid-mass PBHs with masses around $10^{17}$ g are particularly\ninteresting as current observations allow them to constitute the entire DM\ndensity. PBHs in this mass range emit $\\sim$ MeV photons via Hawking radiation\nwhich can directly be detected by the gamma ray telescopes, such as the\nupcoming AMEGO. In this work we forecast how well an instrument with the\nsensitivity of AMEGO will be able to detect, or rule out, PBHs as a DM\ncandidate, by searching for their evaporating signature when marginalizing over\nthe Galactic and extra-Galactic gamma-ray backgrounds. We find that an\ninstrument with the sensitivity of AMEGO could exclude non-rotating PBHs as the\nonly DM component for masses up to $7 \\times 10^{17}$ g at 95% confidence level\n(C.L.) for a monochromatic mass distribution, improving upon current bounds by\nnearly an order of magnitude. The forecasted constraints are more stringent for\nPBHs that have rotation, or which follow extended mass distributions.\n"}
+{"abstract": "  The diurnal cycle CO$_2$ emissions from fossil fuel combustion and cement\nproduction reflect seasonality, weather conditions, working days, and more\nrecently the impact of the COVID-19 pandemic. Here, for the first time we\nprovide a daily CO$_2$ emission dataset for the whole year of 2020 calculated\nfrom inventory and near-real-time activity data (called Carbon Monitor project:\nhttps://carbonmonitor.org). It was previously suggested from preliminary\nestimates that did not cover the entire year of 2020 that the pandemics may\nhave caused more than 8% annual decline of global CO$_2$ emissions. Here we\nshow from detailed estimates of the full year data that the global reduction\nwas only 5.4% (-1,901 MtCO$_2$, ). This decrease is 5 times larger than the\nannual emission drop at the peak of the 2008 Global Financial Crisis. However,\nglobal CO$_2$ emissions gradually recovered towards 2019 levels from late April\nwith global partial re-opening. More importantly, global CO$_2$ emissions even\nincreased slightly by +0.9% in December 2020 compared with 2019, indicating the\ntrends of rebound of global emissions. Later waves of COVID-19 infections in\nlate 2020 and corresponding lockdowns have caused further CO$_2$ emissions\nreductions particularly in western countries, but to a much smaller extent than\nthe declines in the first wave. That even substantial world-wide lockdowns of\nactivity led to a one-time decline in global CO$_2$ emissions of only 5.4% in\none year highlights the significant challenges for climate change mitigation\nthat we face in the post-COVID era. These declines are significant, but will be\nquickly overtaken with new emissions unless the COVID-19 crisis is utilized as\na break-point with our fossil-fuel trajectory, notably through policies that\nmake the COVID-19 recovery an opportunity to green national energy and\ndevelopment plans.\n"}
+{"abstract": "  A laser multicharged ion source was used to perform interfacial treatment of\nthe 4H-SiC/ SiO2 interface using B and Ba ions. A Q-switched Nd:YAG laser\n(wavelength {\\lambda} = 1064 nm, pulse width {\\tau} = 7 ns, and fluence F = 135\nJ/cm2) was used to ablate B and Ba targets to generate multicharged ions. The\nions were deflected by an electrostatic field to separate them from the\nneutrals. The multicharged ions were used for nanometer layer growth and\nshallow ion implantation in 4H-SiC. Several metal-oxide-semiconductor\ncapacitors (MOSCAP) were fabricated with a combination of B and Ba at the\nSiC/SiO2 interface. High-low C-V measurements were used to characterize the\nMOSCAPs. The B interfacial layer reduced the MOSCAP flatband voltage from 4.5\nto 0.04 V, while the Ba layer had a negligible effect.\n"}
+{"abstract": "  Nanoconfinement has been shown to drastically affect the physical properties\nof water. Its effects on reactivity and dissociation, however, remain\ncontroversial, despite their importance to understand aqueous chemistry at\ninterfaces, pores, aerosolos or protein cavities, and the growing evidence\nindicating the acceleration of chemical kinetics in confined environments. The\ndissociation constant $K_w$ in nanospaces has been assessed from experiments\nand simulations in a few specific cases, leading to dissimilar conclusions.\nHere, using carefully designed ab-initio simulations and data-science tools, we\ndemonstrate, challenging current misconceptions, that the energetics of bulk\nwater dissociation remains unchanged to surprisingly small length-scales\nincluding clusters of only a dozen water molecules. This is rooted in the fact\nthat most of the free-energy involved in water autoionization comes from\nbreaking the O-H covalent bond, which has a comparable barrier in the bulk\nliquid or in droplets of nanometer size. The subsequent separation of the\nhydroxide and hydronium species contributes a marginal fraction of the total\nfree-energy, for which reason it turns out that confinement exerts little\ncontrol on this process. The present work provides a definitive and fundamental\ndescription of the mechanism and thermodynamics of water dissociation at\ndifferent scales with broader implications on water's self-ionization at the\nair-liquid interface and on chemical reactivity under nanoconfinement.\n"}
+{"abstract": "  Recent experiments have demonstrated strong light-matter coupling between\nelectromagnetic nanoresonators and pristine sheets of two-dimensional\nsemiconductors, and it has been speculated whether these systems can enter the\nquantum regime operating at the few-polariton level. To address this question,\nwe present a first-principles microscopic quantum theory for the interaction\nbetween excitons in an infinite sheet of two-dimensional material and a\nlocalised electromagnetic resonator. We find that the light-matter interaction\nbreaks the symmetry of the otherwise translation-invariant system and thereby\neffectively generates a localised exciton mode, which is coupled to an\nenvironment of residual exciton modes. This dissipative coupling increases with\ntighter lateral confinement, and our analysis reveals this to be a potential\nchallenge in realising nonlinear exciton-exciton interaction. Nonetheless, we\npredict that polariton blockade due to nonlinear exciton-exciton interactions\nis well within reach for nanoresonators coupled to transition-metal\ndichalcogenides, provided that the lateral resonator mode confinement can be\nsufficiently small that the nonlinearity overcomes the polariton dephasing\ncaused by phonon interactions.\n"}
+{"abstract": "  The sample efficiency of Bayesian optimization(BO) is often boosted by\nGaussian Process(GP) surrogate models. However, on mixed variable spaces,\nsurrogate models other than GPs are prevalent, mainly due to the lack of\nkernels which can model complex dependencies across different types of\nvariables. In this paper, we propose the frequency modulated (FM) kernel\nflexibly modeling dependencies among different types of variables, so that BO\ncan enjoy the further improved sample efficiency. The FM kernel uses distances\non continuous variables to modulate the graph Fourier spectrum derived from\ndiscrete variables. However, the frequency modulation does not always define a\nkernel with the similarity measure behavior which returns higher values for\npairs of more similar points. Therefore, we specify and prove conditions for FM\nkernels to be positive definite and to exhibit the similarity measure behavior.\nIn experiments, we demonstrate the improved sample efficiency of GP BO using FM\nkernels (BO-FM).On synthetic problems and hyperparameter optimization problems,\nBO-FM outperforms competitors consistently. Also, the importance of the\nfrequency modulation principle is empirically demonstrated on the same\nproblems. On joint optimization of neural architectures and SGD\nhyperparameters, BO-FM outperforms competitors including Regularized\nevolution(RE) and BOHB. Remarkably, BO-FM performs better even than RE and BOHB\nusing three times as many evaluations.\n"}
+{"abstract": "  The state-of-the-art on basic, single-antecedent anaphora has greatly\nimproved in recent years. Researchers have therefore started to pay more\nattention to more complex cases of anaphora such as split-antecedent anaphora,\nas in Time-Warner is considering a legal challenge to Telecommunications Inc's\nplan to buy half of Showtime Networks Inc-a move that could lead to all-out war\nbetween the two powerful companies. Split-antecedent anaphora is rarer and more\ncomplex to resolve than single-antecedent anaphora; as a result, it is not\nannotated in many datasets designed to test coreference, and previous work on\nresolving this type of anaphora was carried out in unrealistic conditions that\nassume gold mentions and/or gold split-antecedent anaphors are available. These\nsystems also focus on split-antecedent anaphors only. In this work, we\nintroduce a system that resolves both single and split-antecedent anaphors, and\nevaluate it in a more realistic setting that uses predicted mentions. We also\nstart addressing the question of how to evaluate single and split-antecedent\nanaphors together using standard coreference evaluation metrics.\n"}
+{"abstract": "  Starting with twisted bilayer graphene, graphene-based moir\\'e materials have\nrecently been established as a new platform for studying strong electron\ncorrelations. In this paper, we study twisted graphene monolayers on trilayer\ngraphene and demonstrate that this system can host flat bands when the twist\nangle is close to the magic-angle of 1.16$^\\circ$. When monolayer graphene is\ntwisted on ABA trilayer graphene, the flat bands are not isolated, but are\nintersected by a Dirac cone with a large Fermi velocity. In contrast, graphene\ntwisted on ABC trilayer graphene (denoted AtABC) exhibits a gap between flat\nand remote bands. Since ABC trilayer graphene and twisted bilayer graphene are\nknown to host broken-symmetry phases, we further investigate the ostensibly\nsimilar magic angle AtABC system. We study the effect of electron-electron\ninteractions in AtABC using both Hartree theory and an atomic Hubbard theory to\ncalculate the magnetic phase diagram as a function of doping, twist angle, and\nperpendicular electric field. Our analysis reveals a rich variety of magnetic\norderings, including ferromagnetism and ferrimagnetism, and demonstrates that a\nperpendicular electric field makes AtABC more susceptible to magnetic ordering.\n"}
+{"abstract": "  Accurately describing and detecting 2D and 3D keypoints is crucial to\nestablishing correspondences across images and point clouds. Despite a plethora\nof learning-based 2D or 3D local feature descriptors and detectors having been\nproposed, the derivation of a shared descriptor and joint keypoint detector\nthat directly matches pixels and points remains under-explored by the\ncommunity. This work takes the initiative to establish fine-grained\ncorrespondences between 2D images and 3D point clouds. In order to directly\nmatch pixels and points, a dual fully convolutional framework is presented that\nmaps 2D and 3D inputs into a shared latent representation space to\nsimultaneously describe and detect keypoints. Furthermore, an ultra-wide\nreception mechanism in combination with a novel loss function are designed to\nmitigate the intrinsic information variations between pixel and point local\nregions. Extensive experimental results demonstrate that our framework shows\ncompetitive performance in fine-grained matching between images and point\nclouds and achieves state-of-the-art results for the task of indoor visual\nlocalization. Our source code will be available at [no-name-for-blind-review].\n"}
+{"abstract": "  Using the two-level approximation of the energy barrier, we perform extensive\nkinetic Monte Carlo simulations to probe the relaxation characteristics in a\ntwo-dimensional ($L^{}_x\\times L^{}_y$) array of magnetic nanoparticle as a\nfunction of dipolar interaction strength $h^{}_d$, aspect ratio\n$A^{}_r=L^{}_y/L^{}_x$, and temperature $T$. In the case of weak dipolar\ninteraction ($h^{}_d\\approx0$) and substantial temperature, the magnetic\nrelaxation follows the N\\'eel Brown model as expected. Interestingly, the\ndipolar interaction of enough strength is found to induce antiferromagnetic\ncoupling in the square arrangement of MNPs ($A^{}_r=1.0$), resulting in the\nfastening of magnetic relaxation with $h^{}_d$. There is also a rapid increase\nin relaxation even with $A^{}_r<100$ above a particular dipolar interaction\nstrength $h^{\\star}_d$, which gets enhanced with $A^{}_r$. Remarkably, there is\na slowing down of magnetic relaxation with $h^{}_d$ for the highly anisotropic\nsystem such as linear chain of MNPs. It is because the dipolar interaction\ninduces ferromagnetic interaction in such a case. The thermal fluctuations also\naffect the relaxation properties drastically. In the case of weak dipolar\nlimit, magnetization relaxes rapidly with $T$ because of enhancement in thermal\nfluctuations. The effect of dipolar interaction and aspect ratio on the\nmagnetic relaxation is also clearly indicated in the variation of N\\'eel\nrelaxation time $\\tau^{}_N$. In the presence of strong dipolar interaction\n($h^{}_d>0.3$) and $A^{}_r=1.0$, $\\tau^{}_N$ decreases with $h^{}_d$ for a\ngiven temperature. On the other hand, there is an increase in $\\tau^{}_N$ with\n$h^{}_d$ for huge $A^{}_r$ $(>100)$. We believe that the concepts presented in\nthis work are beneficial for the efficient use of self-assembled MNPs array in\ndata storage and other related applications.\n"}
+{"abstract": "  A permutation $\\pi$ contains a pattern $\\sigma$ if and only if there is a\nsubsequence in $\\pi$ with its letters are in the same relative order as those\nin $\\sigma$. Partially ordered patterns (POPs) provide a convenient way to\ndenote patterns in which the relative order of some of the letters does not\nmatter. This paper elucidates connections between the avoidance sets of a few\nPOPs with other combinatorial objects, directly answering five open questions\nposed by Gao and Kitaev \\cite{gao-kitaev-2019}. This was done by thoroughly\nanalysing the avoidance sets and developing recursive algorithms to derive\nthese sets and their corresponding combinatorial objects in parallel, which\nyielded a natural bijection. We also analysed an avoidance set whose simple\npermutations are enumerated by the Fibonacci numbers and derived an algorithm\nto obtain them recursively.\n"}
+{"abstract": "  We study the implication of $J/\\psi$ decay into invisible particles for light\nsterile neutrino and sub-GeV dark matter (DM). The low-energy effective field\ntheories (EFTs) are used for the description of general neutrino interactions\nand the Dirac fermion DM coupled to charm quark. For $J/\\psi\\to \\gamma+{\\rm\ninvisible}$, we perform the likelihood fits for the individual neutrino and DM\noperators with distinct Lorentz structures and photon spectra. The limits on\nthe decay branching fractions are obtained for different neutrino or DM\nscenarios and then converted to the lower bounds on the new energy scales. The\nmost stringent bounds on the energy scale in neutrino and DM EFTs are 12.8 GeV\nand 11.6 GeV, respectively. The purely invisible decay $J/\\psi\\to {\\rm\ninvisible}$ provides complementary constraints on the effective operators. The\nrelevant bound on the energy scale is above 100 GeV for the dipole operators.\nWe also evaluate the limit on the DM-nucleon scattering cross section converted\nfrom $J/\\psi$ data. The data of $J/\\psi$ invisible decays are sensitive to the\nlight DM mass range where the DM direct detection experiments cannot probe yet.\nThe future Super Tau Charm Factory after one year run can push the limits down\nby two orders of magnitude.\n"}
+{"abstract": "  Superpixels are higher-order perceptual groups of pixels in an image, often\ncarrying much more information than raw pixels. There is an inherent relational\nstructure to the relationship among different superpixels of an image. This\nrelational information can convey some form of domain information about the\nimage, e.g. relationship between superpixels representing two eyes in a cat\nimage. Our interest in this paper is to construct computer vision models,\nspecifically those based on Deep Neural Networks (DNNs) to incorporate these\nsuperpixels information. We propose a methodology to construct a hybrid model\nthat leverages (a) Convolutional Neural Network (CNN) to deal with spatial\ninformation in an image, and (b) Graph Neural Network (GNN) to deal with\nrelational superpixel information in the image. The proposed deep model is\nlearned using a generic hybrid loss function that we call a `hybrid' loss. We\nevaluate the predictive performance of our proposed hybrid vision model on four\npopular image classification datasets: MNIST, FMNIST, CIFAR-10 and CIFAR-100.\nMoreover, we evaluate our method on three real-world classification tasks:\nCOVID-19 X-Ray Detection, LFW Face Recognition, and SOCOFing Fingerprint\nIdentification. The results demonstrate that the relational superpixel\ninformation provided via a GNN could improve the performance of standard\nCNN-based vision systems.\n"}
+{"abstract": "  We consider the Cauchy problem for the Hardy parabolic equation $\\partial_t\nu-\\Delta u=|x|^{-\\gamma}u^p$ with initial data $u_0$ singular at some point\n$z$. Our main results show that, if $z\\neq 0$, then the optimal strength of the\nsingularity of $u_0$ at $z$ for the solvability of the equation is the same as\nthat of the Fujita equation $\\partial_t u-\\Delta u=u^p$. Moreover, if $z=0$,\nthen the optimal singularity for the Hardy parabolic equation is weaker than\nthat of the Fujita equation. We also obtain analogous results for a fractional\ncase $\\partial_t u+(-\\Delta)^{\\theta/2} u=|x|^{-\\gamma}u^p$ with $0<\\theta<2$.\n"}
+{"abstract": "  We extend the notion of quasi-transitive orientations of graphs to\n2-edge-coloured graphs. By relating quasi-transitive $2$-edge-colourings to an\nequivalence relation on the edge set of a graph, we classify those graphs that\nadmit a quasi-transitive $2$-edge-colouring. As a contrast to\nGhouil\\'{a}-Houri's classification of quasi-transitively orientable graphs as\ncomparability graphs, we find quasi-transitively $2$-edge-colourable graphs do\nnot admit a forbiddden subgraph characterization. Restricting the problem to\ncomparability graphs, we show that the family of uniquely quasi-transitively\norientable comparability graphs is exactly the family of comparabilty graphs\nthat admit no quasi-transitive $2$-edge-colouring.\n"}
+{"abstract": "  The properties of exotic stars are investigated. In particular, we study\nobjects made entirely of dark matter and we take into account intrinsic\nanisotropies which have been ignored so far. We obtain exact analytical\nsolutions to the structure equations and we we show that those solutions i) are\nwell behaved within General Relativity, and ii) are capable of describing\nrealistic astrophysical configurations.\n"}
+{"abstract": "  Spatial confinement of matter in functional nanostructures has propelled\nthese systems to the forefront of nanoscience, both as a playground for exotic\nphysics and quantum phenomena and in multiple applications including\nplasmonics, optoelectronics, and sensing. In parallel, the emergence of\nmonochromated electron energy loss spectroscopy (EELS) has enabled exploration\nof local nanoplasmonic functionalities within single nanoparticles and the\ncollective response of nanoparticle assemblies, providing deep insight into the\nassociated mechanisms. However, modern synthesis processes for plasmonic\nnanostructures are often limited in the types of accessible geometry and\nmaterials, and even then, limited to spatial precisions on the order of tens of\nnm, precluding the direct exploration of critical aspects of the\nstructure-property relationships. Here, we use the atomic-sized probe of the\nscanning transmission electron microscope (STEM) to perform precise sculpting\nand design of nanoparticle configurations. Furthermore, using low-loss (EELS),\nwe provide dynamic analyses of evolution of the plasmonic response during the\nsculpting process. We show that within self-assembled systems of nanoparticles,\nindividual nanoparticles can be selectively removed, reshaped, or arbitrarily\npatterned with nanometer-level resolution, effectively modifying the plasmonic\nresponse in both space and energy domains. This process significantly increases\nthe scope for design possibilities and presents opportunities for arbitrary\nstructure development, which are ultimately key for nanophotonic design.\nNanosculpting introduces yet another capability to the electron microscope.\n"}
+{"abstract": "  We show that a one-dimensional ordered fermionic lattice system with\npower-law-decaying hopping, when connected to two baths at its two ends with\ndifferent chemical potentials at zero temperature, features two phases showing\nsub-diffusive scaling of conductance with system size. These phases have no\nanalogues in the isolated system (i.e, in absence of the baths) where the\ntransport is perfectly ballistic. In the open system scenario, interestingly,\nthere occurs two chemical-potential-driven sub-diffusive to ballistic phase\ntransitions at zero temperature. We discuss how these phase transitions, to our\nknowledge, are different from all the known non-equilibrium quantum phase\ntransitions. We provide a clear understanding of the microscopic origin of\nthese phases and argue that the sub-diffusive phases are robust against the\npresence of arbitrary number-conserving many-body interactions in the system.\nThese phases showing sub-diffusive scaling of conductance with system size in a\ntwo-terminal set-up are therefore universal properties of all ordered\none-dimensional number-conserving fermionic systems with power-law-decaying\nhopping at zero temperature.\n"}
+{"abstract": "  Session types denote message protocols between concurrent processes, allowing\na type-safe expression of inter-process communication. Although previous work\ndemonstrate a well-defined notion of subtyping where processes have different\nperceptions of the protocol, these formulations were limited to linear session\ntypes where each channel of communication has a unique provider and client. In\nthis paper, we extend subtyping to shared session types where channels can now\nhave multiple clients instead of a single client. We demonstrate that this\ngeneralization can statically capture protocol requirements that span multiple\nphases of interactions of a client with a shared service provider, something\nnot possible in prior proposals. Moreover, the phases are manifest in the type\nof the client.\n"}
+{"abstract": "  Zero Trust security model permits to secure cloud native applications while\nencrypting all network communication, authenticating, and authorizing every\nrequest. The service mesh can enable Zero Trust using a side-car proxy without\nchanges to the application code. To the best of our knowledge, no previous work\nhas provided a performance analysis of Zero Trust in a multi-cloud environment.\nThis paper proposes a multi-cloud framework and a testing workflow to analyze\nperformance of the data plane under load and the impact on the control plane,\nwhen Zero Trust is enabled. The results of preliminary tests show that Istio\nhas reduced latency variability in responding to sequential HTTP requests.\nResults also reveal that the overall CPU and memory usage can increase based on\nservice mesh configuration and the cloud environment.\n"}
+{"abstract": "  Modern Automatic Speech Recognition (ASR) systems can achieve high\nperformance in terms of recognition accuracy. However, a perfectly accurate\ntranscript still can be challenging to read due to disfluency, filter words,\nand other errata common in spoken communication. Many downstream tasks and\nhuman readers rely on the output of the ASR system; therefore, errors\nintroduced by the speaker and ASR system alike will be propagated to the next\ntask in the pipeline. In this work, we propose an ASR post-processing model\nthat aims to transform the incorrect and noisy ASR output into a readable text\nfor humans and downstream tasks. We leverage the Metadata Extraction (MDE)\ncorpus to construct a task-specific dataset for our study. Since the dataset is\nsmall, we propose a novel data augmentation method and use a two-stage training\nstrategy to fine-tune the RoBERTa pre-trained model. On the constructed test\nset, our model outperforms a production two-step pipeline-based post-processing\nmethod by a large margin of 13.26 on readability-aware WER (RA-WER) and 17.53\non BLEU metrics. Human evaluation also demonstrates that our method can\ngenerate more human-readable transcripts than the baseline method.\n"}
+{"abstract": "  Identifying an entanglement island requires exquisite control over the\nentropy of quantum fields, which is available only in toy models. Here we\npresent a set of sufficient conditions that guarantee the existence of an\nisland and place an upper bound on the entropy computed by the island rule.\nThis is enough to derive the main features of the Page curve for an evaporating\nblack hole in any spacetime dimension. Our argument makes use of Wall's maximin\nformulation and the Quantum Focusing Conjecture. As a corollary, we derive a\nnovel entropy bound.\n"}
+{"abstract": "  The present work reports on the numerical investigation of NOx in three\nturbulent piloted diffusion flames of different levels of extinction. The study\ninvolves two-dimensional axisymmetric modeling of combustion in these flames\nwith fairly detailed chemistry, i.e. GRI 3.0 mechanism. The main focus of the\nstudy is to analyze the effects of the two different combustion model\napproaches, such as infinitely fast chemistry based unsteady flamelet and\nfinite rate chemistry based EDC, in predicting the NOx formation in three\npiloted methane jet flames (Sandia D, E, and F). The EDC approach is able to\npredict the passive scalar quantities but shows over-prediction in the reactive\nscalar quantities and NO prediction, while the unsteady flamelet modeling is\nfound to be essential in predicting the accurate formation of slow kinetic\nspecies like NOx. The inability of flamelet and EDC approach in capturing\nlocalized flame extinction is observed, which lead to an over-prediction of NOx\nat larger downstream locations. Further, the dominance of NOx formation\npathways is investigated in all three flames.\n"}
+{"abstract": "  We prove that the fundamental group of a finite graph of convergence groups\nwith parabolic edge groups is a convergence group. Using this result, under\nsome mild assumptions, we prove combination theorems for a graph of convergence\ngroups with dynamically quasi-convex edge groups (Theorems 1.3, 1.5). In the\nproofs of these results, we are generalizing Dahmani's technique. Finally, we\nprove that the fundamental group of a graph of relatively hyperbolic groups\nwith edge groups either parabolic or infinite cyclic is relatively hyperbolic\nand construct the Bowditch boundary.\n"}
+{"abstract": "  Energy efficiency and energy conservation are one of the most crucial\nconstraints for meeting the 20MW power envelope desired for exascale systems.\nTowards this, most of the research in this area has been focused on the\nutilization of user-controllable hardware switches such as per-core dynamic\nvoltage frequency scaling (DVFS) and software controlled clock modulation at\nthe application level. In this paper, we present a tuning plugin for the\nPeriscope Tuning Framework which integrates fine-grained autotuning at the\nregion level with DVFS and uncore frequency scaling (UFS). The tuning is based\non a feed-forward neural network which is formulated using Performance\nMonitoring Counters (PMC) supported by x86 systems and trained using\nstandardized benchmarks. Experiments on five standardized hybrid benchmarks\nshow an energy improvement of 16.1% on average when the applications are tuned\naccording to our methodology as compared to 7.8% for static tuning.\n"}
+{"abstract": "  We investigate the sensitivity of the FASER$\\nu$ detector to new physics in\nthe form of non-standard neutrino interactions. FASER$\\nu$, which has recently\nbeen installed 480 m downstream of the ATLAS interaction point, will for the\nfirst time study interactions of multi-TeV neutrinos from a controlled source.\nOur formalism -- which is applicable to any current and future neutrino\nexperiment -- is based on the Standard Model Effective Theory~(SMEFT) and its\ncounterpart, Weak Effective Field Theory~(WEFT), below the electroweak scale.\nStarting from the WEFT Lagrangian, we compute the coefficients that modify\nneutrino production in meson decays and detection via deep-inelastic\nscattering, and we express the new physics effects in terms of modified flavor\ntransition probabilities. For some coupling structures, we find that FASER$\\nu$\nwill be able to constrain interactions that are two to three orders of\nmagnitude weaker than Standard Model weak interactions, implying that the\nexperiment will be indirectly probing new physics at the multi-TeV scale. In\nsome cases, FASER$\\nu$ constraints will become comparable to existing limits -\nsome of them derived for the first time in this paper - already with\n$150~$fb${}^{-1}$ of data.\n"}
+{"abstract": "  The BKK theorem states that the mixed volume of the Newton polytopes of a\nsystem of polynomial equations upper bounds the number of isolated torus\nsolutions of the system. Homotopy continuation solvers make use of this fact to\npick efficient start systems. For systems where the mixed volume bound is not\nattained, such methods are still tracking more paths than necessary. We propose\na strategy of improvement by lifting a system to an equivalent system with a\nstrictly lower mixed volume at the expense of more variables. We illustrate\nthis idea providing lifting constructions for arbitrary bivariate systems and\ncertain dense-enough systems.\n"}
+{"abstract": "  In this paper, we are introducing a novel model of artificial intelligence,\nthe functional neural network for modeling of human decision-making processes.\nThis neural network is composed of multiple artificial neurons racing in the\nnetwork. Each of these neurons has a similar structure programmed independently\nby the users and composed of an intention wheel, a motor core and a sensory\ncore representing the user itself and racing at a specific velocity. The\nmathematics of the neuron's formulation and the racing mechanism of multiple\nnodes in the network will be discussed, and the group decision process with\nfuzzy logic and the transformation of these conceptual methods into practical\nmethods of simulation and in operations will be developed. Eventually, we will\ndescribe some possible future research directions in the fields of finance,\neducation and medicine including the opportunity to design an intelligent\nlearning agent with application in business operations supervision. We believe\nthat this functional neural network has a promising potential to transform the\nway we can compute decision-making and lead to a new generation of neuromorphic\nchips for seamless human-machine interactions.\n"}
+{"abstract": "  An approach to reduce motion artifacts in Quantitative Susceptibility Mapping\nusing deep learning is proposed. We use an affine motion model with randomly\ncreated motion profiles to simulate motion-corrupted QSM images. The simulated\nQSM image is paired with its motion-free reference to train a neural network\nusing supervised learning. The trained network is tested on unseen simulated\nmotion-corrupted QSM images, in healthy volunteers and in Parkinson's disease\npatients. The results show that motion artifacts, such as ringing and ghosting,\nwere successfully suppressed.\n"}
+{"abstract": "  In this paper, we determine the harvested power region of a two-user\nmultiple-input single-output (MISO) wireless power transfer (WPT) system for a\nnon-linear model of the rectennas at the energy harvester (EH) nodes. To this\nend, we characterize the distributions of the transmit symbol vector that\nachieve individual points on the boundary of this region. Each distribution is\nobtained as solution of an optimization problem where we maximize a weighted\nsum of the average harvested powers at the EH nodes under a constraint on the\npower budget of the transmitter. We prove that the optimal transmit strategy\nemploys two beamforming vectors and scalar unit norm transmit symbols with\narbitrary phase. To determine the beamforming vectors, we propose an iterative\nalgorithm based on a two-dimensional grid search, semi-definite relaxation, and\nsuccessive convex approximation. Our numerical results reveal that the proposed\ndesign outperforms two baseline schemes based on a linear EH model and a single\nbeamforming vector, respectively. Finally, we observe that the harvested power\nregion is convex and the power harvested at one EH node can be traded for a\nhigher harvested power at the other node.\n"}
+{"abstract": "  In graph theory and network analysis, node degree is defined as a simple but\npowerful centrality to measure the local influence of node in a complex\nnetwork. Preferential attachment based on node degree has been widely adopted\nfor modeling network growth. However, many evidences exist which show deviation\nof real network growth from what a pure degree-based model suggests. It seems\nthat node degree is not a reliable measure for predicting the preference of\nnewcomers in attaching to the network, or at least, it does not tell the whole\nstory. In this paper, we argue that there is another dimension to network\ngrowth, one that we call node \"coreness\". The new dimension gives insights on\nthe global influence of nodes, in comparison to the local view the degree\nmetric provides. We found that the probability of existing nodes attracting new\nnodes generally follows an exponential dependence on node coreness, while at\nthe same time, follows a power-law dependence on node degree. That is to say,\nhigh-coreness nodes are more powerful than high-degree nodes in attracting\nnewcomers. The new dimension further discloses some hidden phenomena which\nhappen in the process of network growth. The power of node degree in attracting\nnewcomers increases over time while the influence of coreness decreases, and\nfinally, they reach a state of equilibrium in the growth. All these theories\nhave been tested on real-world networks.\n"}
+{"abstract": "  We have performed density functional calculations in conjunction with the\nlinearized Migdal-Eliashberg equations and the functional derivative approach,\nwhich takes into account the energy-range dependence of the density of states\nat the Fermi level ($N(\\varepsilon)$ variable on the scale of phonon energy),\nto determine the evolution of the critical temperature ($T_{c}$) and the\nisotope effect coefficient ($\\alpha$) of H$_{3}$S as a function of pressure on\nits $lm\\bar{3}m$ crystal-structure range (from 162 to 250 GPa). Such approach,\nin comparison with $N(\\varepsilon)$=$N(0)$=const., improves the agreement of\n$T_{c}$ with available experiments on the whole range of studied pressure.\nConsidering for $\\alpha$ two main contributions: one positive coming from the\nelectron-phonon (el-ph) and the other negative from the electron-electron\ninteraction (el-el), we obtained a monotonic decrement as a function of\npressure, independent of applied scheme ($N(\\varepsilon)$ or $N(0)$). However,\nwhen $N(\\varepsilon)$ is taken into account, an important renormalization\noccurs on both contributions, el-ph and el-el, improving the agreement with\nexperimental data, specially for the high-pressure regime. The observed\nevolution of $T_{c}$ and $\\alpha$ as a function of pressure indicates, thus,\nthe crucial role of the energy-dependence on $N(\\varepsilon)$ for the proper\nanalysis and description of the superconducting state on high-$T_{c}$ metal\nhydrides as H$_{3}$S, by considering the role of its van Hove singularities.\n"}
+{"abstract": "  We propose a novel deep Gaussian process (DGP) inference method for computer\nmodel emulation using stochastic imputation. By stochastically imputing the\nlatent layers, the approach transforms the DGP into the linked GP, a\nstate-of-the-art surrogate model formed by linking a system of feed-forward\ncoupled GPs. This transformation renders a simple while efficient DGP training\nprocedure that only involves optimizations of conventional stationary GPs. In\naddition, the analytically tractable mean and variance of the linked GP allows\none to implement predictions from DGP emulators in a fast and accurate manner.\nWe demonstrate the method in a series of synthetic examples and real-world\napplications, and show that it is a competitive candidate for efficient DGP\nsurrogate modeling in comparison to the variational inference and the\nfully-Bayesian approach. A $\\texttt{Python}$ package $\\texttt{dgpsi}$\nimplementing the method is also produced and available at\nhttps://github.com/mingdeyu/DGP.\n"}
+{"abstract": "  We consider the two-dimensional ideal Fermi gas subject to a magnetic field\nwhich is perpendicular to the Euclidean plane $\\mathbb R^2$ and whose strength\n$B(x)$ at $x\\in\\mathbb R^2$ converges to some $B_0>0$ as $\\|x\\|\\to\\infty$.\nFurthermore, we allow for an electric potential $V_\\varepsilon$ which vanishes\nat infinity. They define the single-particle Landau Hamiltonian of our Fermi\ngas (up to gauge fixing). Starting from the ground state of this Fermi gas with\nchemical potential $\\mu\\ge B_0$ we study the asymptotic growth of its bipartite\nentanglement entropy associated to $L\\Lambda$ as $L\\to\\infty$ for some fixed\nbounded region $\\Lambda\\subset\\mathbb R^2$. We show that its leading order in\n$L$ does not depend on the perturbations $B_\\varepsilon := B_0 - B$ and\n$V_\\varepsilon$ if they satisfy some mild decay assumptions. Our result holds\nfor all $\\alpha$-R\\' enyi entropies $\\alpha>1/3$; for $\\alpha\\le 1/3$, we have\nto assume in addition some differentiability of the perturbations\n$B_\\varepsilon$ and $V_\\varepsilon$. The case of a constant magnetic field\n$B_\\varepsilon = 0$ and with $V_\\varepsilon= 0$ was treated recently for\ngeneral $\\mu$ by Leschke, Sobolev and Spitzer. Our result thus proves the\nstability of that area law under the same regularity assumptions on the\nboundary $\\partial \\Lambda$.\n"}
+{"abstract": "  We theoretically address minimal search strategies of active, self-propelled\nparticles towards hidden targets in three-dimensional space. The particles can\nsense if a target is close, e.g., by detecting signaling molecules released by\na target, but they cannot deduce any directional cues. We focus on composite\nsearch strategies, where particles switch between extensive outer search and\nintensive inner search; inner search is started when the proximity of a target\nis detected and ends again when a certain inner search time has elapsed. In the\nsimplest strategy, active particles move ballistically during outer search, and\ntransiently reduce their directional persistence during inner search. In a\nsecond, adaptive strategy, particles exploit a dynamic scattering effect by\nreducing directional persistence only outside a well-defined target zone. These\ntwo search strategies require only minimal information processing capabilities\nand a single binary or tertiary internal state, respectively, yet increases the\nrate of target encounter substantially. The optimal inner search time scales as\na power-law with exponent -2/3 with target density, reflecting a trade-off\nbetween exploration and exploitation.\n"}
+{"abstract": "  Symmetric nonnegative matrix factorization (SNMF) has demonstrated to be a\npowerful method for data clustering. However, SNMF is mathematically formulated\nas a non-convex optimization problem, making it sensitive to the initialization\nof variables. Inspired by ensemble clustering that aims to seek a better\nclustering result from a set of clustering results, we propose self-supervised\nSNMF (S$^3$NMF), which is capable of boosting clustering performance\nprogressively by taking advantage of the sensitivity to initialization\ncharacteristic of SNMF, without relying on any additional information.\nSpecifically, we first perform SNMF repeatedly with a random nonnegative matrix\nfor initialization each time, leading to multiple decomposed matrices. Then, we\nrank the quality of the resulting matrices with adaptively learned weights,\nfrom which a new similarity matrix that is expected to be more discriminative\nis reconstructed for SNMF again. These two steps are iterated until the\nstopping criterion/maximum number of iterations is achieved. We mathematically\nformulate S$^3$NMF as a constraint optimization problem, and provide an\nalternative optimization algorithm to solve it with the theoretical convergence\nguaranteed. Extensive experimental results on $10$ commonly used benchmark\ndatasets demonstrate the significant advantage of our S$^3$NMF over $12$\nstate-of-the-art methods in terms of $5$ quantitative metrics. The source code\nis publicly available at https://github.com/jyh-learning/SSSNMF.\n"}
+{"abstract": "  We present a background model for dark matter searches using an array of\nNaI(Tl) crystals in the COSINE-100 experiment that is located in the Yangyang\nunderground laboratory. The model includes background contributions from both\ninternal and external sources, including cosmogenic radionuclides and surface\n$^{210}$Pb contamination. To improve the model in the low energy region, with\nthe threshold lowered to 1 keV, we used a depth profile of $^{210}$Pb\ncontamination in the surface of the NaI(Tl) crystals determined in a comparison\nbetween measured and simulated spectra. We also considered the effect of the\nenergy scale errors propagated from the statistical uncertainties and the\nnonlinear detector response at low energies. The 1.7 years COSINE-100 data\ntaken between October 21, 2016 and July 18, 2018 were used for this analysis.\nThe Geant4 toolkit version 10.4.2 was utilized throughout the Monte Carlo\nsimulations for the possible internal and external origins. In particular, the\nversion provides a non-Gaussian peak around 50 keV originating from beta decays\nof $^{210}$Pb in a good agreement with the measured background. This improved\nmodel estimates that the activities of $^{210}$Pb and $^{3}$H are the dominant\nsources of the backgrounds with an average level of 2.73$\\pm$0.14\ncounts/day/keV/kg in the energy region of 1-6 keV, using COSINE-100 data with a\ntotal exposure of 97.7 kg$\\cdot$years.\n"}
+{"abstract": "  In this paper we prove the existence of a complete cap of ${\\rm PG}(4n+1, q)$\nof size $2(q^{2n+1}-1)/(q-1)$, for each prime power $q>2$. It is obtained by\nprojecting two disjoint Veronese varieties of ${\\rm PG}(2n^2+3n, q)$ from a\nsuitable $(2n^2-n-2)$-dimensional projective space. This shows that the trivial\nlower bound for the size of the smallest complete cap of ${\\rm PG}(4n+1, q)$ is\nessentially sharp.\n"}
+{"abstract": "  We study a classical model for the atom that considers the movement of $n$\ncharged particles of charge $-1$ (electrons) interacting with a fixed nucleus\nof charge $\\mu >0$. We show that two global branches of spatial relative\nequilibria bifurcate from the $n$-polygonal relative equilibrium for each\ncritical values $\\mu =s_{k}$ for $k\\in \\lbrack 2,...,n/2]$. In these solutions,\nthe $n$ charges form $n/h$-groups of regular $h$-polygons in space, where $h$\nis the greatest common divisor of $k$ and $n$. Furthermore, each spatial\nrelative equilibrium has a global branch of relative periodic solutions for\neach normal frequency satisfying some nonresonant condition. We obtain\ncomputer-assisted proofs of the existence of several spatial relative\nequilibria on global branches away from the $n$-polygonal relative equilibrium.\nMoreover, the nonresonant condition of the normal frequencies for some spatial\nrelative equilibria is verified rigorously using computer-assisted proofs.\n"}
+{"abstract": "  For every given real value of the ratio $\\mu:=A_X/G_X>1$ of the arithmetic\nand geometric means of a positive random variable $X$ and every real $v>0$,\nexact upper bounds on the right- and left-tail probabilities\n$\\mathsf{P}(X/G_X\\ge v)$ and $\\mathsf{P}(X/G_X\\le v)$ are obtained, in terms of\n$\\mu$ and $v$. In particular, these bounds imply that $X/G_X\\to1$ in\nprobability as $A_X/G_X\\downarrow1$. Such a result may be viewed as a converse\nto a reverse Jensen inequality for the strictly concave function $f=\\ln$,\nwhereas the well-known Cantelli and Chebyshev inequalities may be viewed as\nconverses to a reverse Jensen inequality for the strictly concave quadratic\nfunction $f(x) \\equiv -x^2$. As applications of the mentioned new results,\nimprovements of the Markov, Bernstein--Chernoff, sub-Gaussian, and\nBennett--Hoeffding probability inequalities are given.\n"}
+{"abstract": "  Recently regular decision processes have been proposed as a well-behaved form\nof non-Markov decision process. Regular decision processes are characterised by\na transition function and a reward function that depend on the whole history,\nthough regularly (as in regular languages). In practice both the transition and\nthe reward functions can be seen as finite transducers. We study reinforcement\nlearning in regular decision processes. Our main contribution is to show that a\nnear-optimal policy can be PAC-learned in polynomial time in a set of\nparameters that describe the underlying decision process. We argue that the\nidentified set of parameters is minimal and it reasonably captures the\ndifficulty of a regular decision process.\n"}
+{"abstract": "  Recently, a new form of online shopping becomes more and more popular, which\ncombines live streaming with E-Commerce activity. The streamers introduce\nproducts and interact with their audiences, and hence greatly improve the\nperformance of selling products. Despite of the successful applications in\nindustries, the live stream E-commerce has not been well studied in the data\nscience community. To fill this gap, we investigate this brand-new scenario and\ncollect a real-world Live Stream E-Commerce (LSEC) dataset. Different from\nconventional E-commerce activities, the streamers play a pivotal role in the\nLSEC events. Hence, the key is to make full use of rich interaction information\namong streamers, users, and products. We first conduct data analysis on the\ntripartite interaction data and quantify the streamer's influence on users'\npurchase behavior. Based on the analysis results, we model the tripartite\ninformation as a heterogeneous graph, which can be decomposed to multiple\nbipartite graphs in order to better capture the influence. We propose a novel\nLive Stream E-Commerce Graph Neural Network framework (LSEC-GNN) to learn the\nnode representations of each bipartite graph, and further design a multi-task\nlearning approach to improve product recommendation. Extensive experiments on\ntwo real-world datasets with different scales show that our method can\nsignificantly outperform various baseline approaches.\n"}
+{"abstract": "  Motivated by twisted graphene multilayers, we study interaction of a Chern\ninsulator with circularly polarized light. The interaction energy contains an\nantisymmetric term that couples to the helicity of incident light. For a\ntwo-band Chern insulator, this term is expressed as an integral involving the\nBerry curvature of the system. Taking advantage of this interaction, we propose\nan experimental protocol for switching topological memory based on orbital\nmagnetization by circularly polarized light and discuss its feasibility for the\nChern insulators found in twisted graphene multilayers.\n"}
+{"abstract": "  We describe a modular optically-pumped magnetometer (OPM) system which\nenables fast prototyping and testing of new measurement schemes. Quick\nreconfiguration of self-contained laser and sensor modules allow easy\nconstruction of various array layouts. The modularity of this system enables\nscaling of shared light-source arrays, and development of methods for high\ndensity array management for magnetic imaging and sensing in both medical and\nindustrial fields. We demonstrate the OPM system in a first-order axial\ngradiometer configuration with a magnetic field gradient sensitivity of 10.4\n$fT/cm/\\sqrt{Hz}$. To illustrate the capabilities of this system, we measured\nalpha-rhythms from the brain of a human participant, and assessed the\nmagnetometer sensitivity both with single sensor channels and in a differential\ngradiometer configuration.\n"}
+{"abstract": "  The debate on gravity theories to extend or modify General Relativity is very\nactive today because of the issues related to ultra-violet and infra-red\nbehavior of Einstein's theory. In the first case, we have to address the\nQuantum Gravity problem. In the latter, dark matter and dark energy, governing\nthe large scale structure and the cosmological evolution, seem to escape from\nany final fundamental theory and detection. The state of art is that, up to\nnow, no final theory, capable of explaining gravitational interaction at any\nscale, has been formulated. In this perspective, many research efforts are\ndevoted to test theories of gravity by space-based experiments. Here we propose\nstraightforward tests by the GINGER experiment, which, being Earth based,\nrequires little modeling of external perturbation, allowing a thorough analysis\nof the systematics, crucial for experiments where sensitivity breakthrough is\nrequired. Specifically, we want to show that it is possible to constrain\nparameters of gravity theories, like scalar-tensor or Horava-Lifshitz gravity,\nby considering their post-Newtonian limits matched with experimental data. In\nparticular, we use the Lense-Thirring measurements provided by GINGER to find\nout relations among the parameters of theories and finally compare the results\nwith those provided by LARES and Gravity Probe-B satellites.\n"}
+{"abstract": "  We generalize and unify the $f(R,T)$ and $f(R,L_m)$ type gravity models by\nassuming that the gravitational Lagrangian is given by an arbitrary function of\nthe Ricci scalar $R$, of the trace of the energy-momentum tensor $T$, and of\nthe matter Lagrangian $L_m$, so that $L_{grav}=f(R,L_m,T)$. We obtain the\ngravitational field equations in the metric formalism, the equations of motion\nfor test particles, and the energy and momentum balance equations, which follow\nfrom the covariant divergence of the energy-momentum tensor. Generally, the\nmotion is non-geodesic, and takes place in the presence of an extra force\northogonal to the four-velocity. The Newtonian limit of the equations of motion\nis also investigated, and the expression of the extra acceleration is obtained\nfor small velocities and weak gravitational fields. The generalized Poisson\nequation is also obtained in the Newtonian limit, and the Dolgov-Kawasaki\ninstability is also investigated. The cosmological implications of the theory\nare investigated for a homogeneous, isotropic and flat Universe for two\nparticular choices of the Lagrangian density $f(R,L_m,T)$ of the gravitational\nfield, with a multiplicative and additive algebraic structure in the matter\ncouplings, respectively, and for two choices of the matter Lagrangian, by using\nboth analytical and numerical methods.\n"}
+{"abstract": "  We present a spectroscopic and imaging study of an abnormal active galactic\nnucleus (AGN), 2MASX J00423991+3017515. This AGN is newly identified in the\nhard X-rays by the Swift BAT All-Sky survey and found in an edge-on disk galaxy\ninteracting with a nearby companion. Here, we analyze the first optical spectra\nobtained for this system (taken in 2011 and 2016), high-resolution imaging\ntaken with the Hubble Space Telescope and Chandra X-ray Observatory, and 1\"\nimaging with the Very Large Array. Two unique properties are revealed: the\npeaks of the broad Balmer emission lines (associated with gas orbiting very\nnear the supermassive black hole) are blue shifted from the corresponding\nnarrow line emission and host galaxy absorption by 1540 km/s, and the AGN is\nspatially displaced from the apparent center of its host galaxy by 3.8 kpc. We\nexplore several scenarios to explain these features, along with other\nanomalies, and propose that 2MASX J00423991+3017515 may be an AGN with an\nunusually strong wind residing in a uniquely configured major merger, or that\nit is an AGN recoiling from either a gravitational \"slingshot\" in a three-body\ninteraction or from a kick due to the asymmetric emission of gravitational\nwaves following the coalescence of two progenitor supermassive black holes.\n"}
+{"abstract": "  Recent advances in meta-learning has led to remarkable performances on\nseveral few-shot learning benchmarks. However, such success often ignores the\nsimilarity between training and testing tasks, resulting in a potential bias\nevaluation. We, therefore, propose a generative approach based on a variant of\nLatent Dirichlet Allocation to analyse task similarity to optimise and better\nunderstand the performance of meta-learning. We demonstrate that the proposed\nmethod can provide an insightful evaluation for meta-learning algorithms on two\nfew-shot classification benchmarks that matches common intuition: the more\nsimilar the higher performance. Based on this similarity measure, we propose a\ntask-selection strategy for meta-learning and show that it can produce more\naccurate classification results than methods that randomly select training\ntasks.\n"}
+{"abstract": "  We come back on the dynamical properties of k-essential cosmological models\nand show how the interesting phenomenological features of those models are\nrelated to the existence of boundaries in the phase surface. We focus our\nattention to the branching points where the energy density has an extremum and\nthe effective speed of sound diverges. We discuss the behaviour of solutions of\na general class of cosmological models exhibiting such curves and give two\npossible interpretations; the most interesting possibility regards the arrow of\ntime that is reversed in trespassing the branching curve. This study teaches to\nus something new about general FLRW cosmologies where the fluids driving the\ncosmic evolution have equations of state that are multivalued functions of the\nenergy density and other thermodynamical quantities.\n"}
+{"abstract": "  Results from higher order mean field calculations of light interacting with\natom arrays are presented for calculations of one- and two-time expectation\nvalues. The atoms are approximated as two-levels and are fixed in space.\nCalculations were performed for mean field approximations that include the\nexpectation value of one operator (mean field), two operators (mean field-2),\nand three operators (mean field-3). For the one-time expectation values, we\nexamined three different situations to understand the convergence with\nincreasing order of mean field and some limitations of higher order mean field\napproximations. As a representation of a two-time expectation value, we\ncalculated the $g^{(2)}(\\tau )$ for a line of atoms illuminated by a\nperpendicular plane wave at several emission angles and two different\nintensities. For many cases, the mean field-2 will be sufficiently accurate to\nquantitatively predict the response of the atoms as measured by one-time\nexpectation values. However, the mean field-3 approximation will often be\nneeded for two-time expectation values.\n"}
+{"abstract": "  Deep neural networks have been shown to be vulnerable to adversarial examples\ndeliberately constructed to misclassify victim models. As most adversarial\nexamples have restricted their perturbations to $L_{p}$-norm, existing defense\nmethods have focused on these types of perturbations and less attention has\nbeen paid to unrestricted adversarial examples; which can create more realistic\nattacks, able to deceive models without affecting human predictions. To address\nthis problem, the proposed adversarial attack generates an unrestricted\nadversarial example with a limited number of parameters. The attack selects\nthree points on the input image and based on their locations transforms the\nimage into an adversarial example. By limiting the range of movement and\nlocation of these three points and using a discriminatory network, the proposed\nunrestricted adversarial example preserves the image appearance. Experimental\nresults show that the proposed adversarial examples obtain an average success\nrate of 93.5% in terms of human evaluation on the MNIST and SVHN datasets. It\nalso reduces the model accuracy by an average of 73% on six datasets MNIST,\nFMNIST, SVHN, CIFAR10, CIFAR100, and ImageNet. It should be noted that, in the\ncase of attacks, lower accuracy in the victim model denotes a more successful\nattack. The adversarial train of the attack also improves model robustness\nagainst a randomly transformed image.\n"}
+{"abstract": "  Specifying reward functions for robots that operate in environments without a\nnatural reward signal can be challenging, and incorrectly specified rewards can\nincentivise degenerate or dangerous behavior. A promising alternative to\nmanually specifying reward functions is to enable robots to infer them from\nhuman feedback, like demonstrations or corrections. To interpret this feedback,\nrobots treat as approximately optimal a choice the person makes from a choice\nset, like the set of possible trajectories they could have demonstrated or\npossible corrections they could have made. In this work, we introduce the idea\nthat the choice set itself might be difficult to specify, and analyze choice\nset misspecification: what happens as the robot makes incorrect assumptions\nabout the set of choices from which the human selects their feedback. We\npropose a classification of different kinds of choice set misspecification, and\nshow that these different classes lead to meaningful differences in the\ninferred reward and resulting performance. While we would normally expect\nmisspecification to hurt, we find that certain kinds of misspecification are\nneither helpful nor harmful (in expectation). However, in other situations,\nmisspecification can be extremely harmful, leading the robot to believe the\nopposite of what it should believe. We hope our results will allow for better\nprediction and response to the effects of misspecification in real-world reward\ninference.\n"}
+{"abstract": "  Designing provably efficient algorithms with general function approximation\nis an important open problem in reinforcement learning. Recently, Wang et\nal.~[2020c] establish a value-based algorithm with general function\napproximation that enjoys\n$\\widetilde{O}(\\mathrm{poly}(dH)\\sqrt{K})$\\footnote{Throughout the paper, we\nuse $\\widetilde{O}(\\cdot)$ to suppress logarithm factors. } regret bound, where\n$d$ depends on the complexity of the function class, $H$ is the planning\nhorizon, and $K$ is the total number of episodes. However, their algorithm\nrequires $\\Omega(K)$ computation time per round, rendering the algorithm\ninefficient for practical use. In this paper, by applying online sub-sampling\ntechniques, we develop an algorithm that takes\n$\\widetilde{O}(\\mathrm{poly}(dH))$ computation time per round on average, and\nenjoys nearly the same regret bound. Furthermore, the algorithm achieves low\nswitching cost, i.e., it changes the policy only\n$\\widetilde{O}(\\mathrm{poly}(dH))$ times during its execution, making it\nappealing to be implemented in real-life scenarios. Moreover, by using an\nupper-confidence based exploration-driven reward function, the algorithm\nprovably explores the environment in the reward-free setting. In particular,\nafter $\\widetilde{O}(\\mathrm{poly}(dH))/\\epsilon^2$ rounds of exploration, the\nalgorithm outputs an $\\epsilon$-optimal policy for any given reward function.\n"}
+{"abstract": "  We report new branching fraction measurements for 199 UV and optical\ntransitions of Hf II. These transitions range in wavelength (wavenumber) from\n2068- 6584 A (48322-15183 cm-1) and originate in 17 odd-parity upper levels\nranging in energy from 38578-53227 cm-1. The branching fractions are combined\nwith radiative lifetimes reported in an earlier study to produce a set of\ntransition probabilities and log(gf) values with accuracy ranging from 5-25%.\nComparison is made to transition probabilities from the literature where such\ndata exist. We use these new transition probabilities to derive improved Hf\nabundances in two metal-poor stars. HD 196944 is enhanced in s-process\nelements, and we derive log epsilon (Hf) = -0.72 +/- 0.03 (sigma = 0.09) from\n12 Hf II lines. HD 222925 is enhanced in r-process elements, and we derive log\nepsilon (Hf) = 0.32 +/- 0.03 (sigma = 0.11) from 20 Hf II lines. These\nmeasurements greatly expand the number of potentially useful Hf II lines for\nanalysis in UV and optical spectra.\n"}
+{"abstract": "  Neural cryptography is the application of artificial neural networks in the\nsubject of cryptography. The functionality of this solution is based on a tree\nparity machine. It uses artificial neural networks to perform secure key\nexchange between network entities. This article proposes improvements to the\nsynchronization of two tree parity machines. The improvement is based on\nlearning artificial neural network using input vectors which have a wider range\nof values than binary ones. As a result, the duration of the synchronization\nprocess is reduced. Therefore, tree parity machines achieve common weights in a\nshorter time due to the reduction of necessary bit exchanges. This approach\nimproves the security of neural cryptography\n"}
+{"abstract": "  This paper provides a set of cycling problems in linear programming. These\nproblems should be useful for researchers to develop and test new simplex\nalgorithms. As matter of the fact, this set of problems is used to test a\nrecently proposed double pivot simplex algorithm for linear programming.\n"}
+{"abstract": "  We consider a nonlinearly coupled electromechanical system, and develop a\nquantitative theory for two-phonon cooling. In the presence of two-phonon\ncooling, the mechanical Hilbert space is effectively reduced to its ground and\nfirst excited states, thus forming a mechanical qubit. This allows for\nperforming quantum operations at the level of individual mechanical phonons and\npreparing nonclassical mechanical states with negative Wigner functions. We\npropose a scheme for performing arbitrary Bloch sphere rotations, and derive\nthe fidelity in the specific case of a $\\pi$-pulse. We characterise detrimental\nprocesses that reduce the coherence in the system, and demonstrate that our\nscheme can be implemented in state-of-the-art electromechanical devices.\n"}
+{"abstract": "  Model-free reinforcement learning (RL) for legged locomotion commonly relies\non a physics simulator that can accurately predict the behaviors of every\ndegree of freedom of the robot. In contrast, approximate reduced-order models\nare often sufficient for many model-based control strategies. In this work we\nexplore how RL can be effectively used with a centroidal model to generate\nrobust control policies for quadrupedal locomotion. Advantages over RL with a\nfull-order model include a simple reward structure, reduced computational\ncosts, and robust sim-to-real transfer. We further show the potential of the\nmethod by demonstrating stepping-stone locomotion, two-legged in-place balance,\nbalance beam locomotion, and sim-to-real transfer without further adaptations.\nAdditional Results: https://www.pair.toronto.edu/glide-quadruped/.\n"}
+{"abstract": "  We find a sharp condition on the density-dependent coefficient of damping of\na one-dimensional repulsive Euler-Poisson system, which makes it possible to\nsuppress the formation of singularities in the solution of the Cauchy problem\nwith arbitrary smooth data. In the context of plasma physics, this means the\npossibility of suppressing the breakdown of arbitrary oscillations of cold\nplasma.\n"}
+{"abstract": "  Understanding of sedimentation dynamics of particles in bounded fluids is of\ncrucial importance for a wide variety of processes. While there is profound\nknowledge base regarding the sedimentation of rigid solid particles, the\nfundamental principles of sedimentation dynamics of elastic, nonheavy spheres\nin bounded fluids are not well understood. Therefore, we performed\nsedimentation of deformable, elastic solid spheres with particle Reynolds\nnumbers much smaller than 1 in a model experiment. The spheres started from\nrest in a rectangular duct with a width of about 23 times the radius R of the\nsphere. The particle dynamics of elastic spheres differed fundamentally from\nthat of rigid spheres. Elastic effects were found to take place on\ncomparatively large time scales, such that elastic spheres underwent four\nphases of sedimentation. Phases I and II, including transient acceleration and\na short steady velocity plateau, are comparable with sedimentation of rigid\nspheres. From a characteristic onset position of about 10R, deformability\neffects kick in and a second acceleration appears (phase III). In the fourth\nphase, the deformable spheres reach the terminal sedimentation velocity. The\nsofter the spheres are, the higher the terminal velocity is. In the present\nsetup, a terminal velocity up to 9 percent higher than the velocity for\ncomparable rigid spheres was reached. By means of the obtained data, insights\ninto the dynamics are given that could serve as basic approaches for modelling\nthe dynamics of elastic spheres in bounded fluids.\n"}
+{"abstract": "  Uncertainty quantification plays an important role in applications that\ninvolve simulating ensembles of trajectories of dynamical systems. Conrad et\nal. (Stat. Comput., 2017) proposed randomisation of deterministic time\nintegration methods as a strategy for quantifying uncertainty due to time\ndiscretisation. We consider this strategy for systems that are described by\ndeterministic, possibly non-autonomous operator differential equations defined\non a Banach space or a Gelfand triple. We prove pathwise and expected error\nbounds on the random trajectories, given an assumption on the local truncation\nerror of the underlying deterministic time integration and an assumption that\nthe absolute moments of the random variables decay with the time step. Our\nanalysis shows that the error analysis for differential equations in\nfinite-dimensional Euclidean space carries over to infinite-dimensional\nsettings.\n"}
+{"abstract": "  We introduce a new paradigm for scaling simulations with projected\nentangled-pair states (PEPS) for critical strongly-correlated systems, allowing\nfor reliable extrapolations of PEPS data with relatively small bond dimensions\n$D$. The key ingredient consists of using the effective correlation length\n$\\chi$ for inducing a collapse of data points, $f(D,\\chi)=f(\\xi(D,\\chi))$, for\narbitrary values of $D$ and the environment bond dimension $\\chi$. As such we\ncircumvent the need for extrapolations in $\\chi$ and can use many distinct data\npoints for a fixed value of $D$. Here, we need that the PEPS has been optimized\nusing a fixed-$\\chi$ gradient method, which can be achieved using a novel\ntensor-network algorithm for finding fixed points of 2-D transfer matrices, or\nby using the formalism of backwards differentiation. We test our hypothesis on\nthe critical 3-D dimer model, the 3-D classical Ising model, and the 2-D\nquantum Heisenberg model.\n"}
+{"abstract": "  The Andreev spectrum of a quantum dot embedded in a hybrid\nsemiconductor-superconductor interferometer can be modulated by electrostatic\ngating, magnetic flux through the interferometer, and Zeeman splitting from\nin-plane magnetic field. We demonstrate parity transitions in the embedded\nquantum dot system, and show that the Zeeman-driven transition is accompanied\nby a 0-{\\pi} transition in the superconducting phase across the dot. We further\ndemonstrate that flux through the interferometer modulates both dot parity and\n0-{\\pi} transitions.\n"}
+{"abstract": "  Capacitated lot-sizing problems (CLSPs) are important and challenging\noptimization problems in production planning. Amongst the many approaches\ndeveloped for CLSPs, constructive heuristics are known to be the most intuitive\nand fastest method for finding good feasible solutions for the CLSPs, and\ntherefore are often used as a subroutine in building more sophisticated exact\nand metaheuristic approaches. Classical constructive heuristics, such as the\nperiod-by-period heuristics and lot elimination heuristics, are first\nintroduced in the 1990s, and thereafter widely used in solving the CLSPs. This\npaper evaluates the performance of period-by-period and lot elimination\nheuristics, and improves the heuristics using perturbation techniques and\nself-adaptive methods. We have also proposed a procedure for automatically\nadjusting the parameters of the proposed heuristics so that the values of the\nparameters can be chosen based on features of individual instances.\nExperimental results show that the proposed self-adaptive randomized\nperiod-by-period constructive heuristics are efficient and can find better\nsolutions with less computational time than the tabu search and lot elimination\nheuristics. When the proposed constructive heuristic is used in a basic tabu\nsearch framework, high-quality solutions with 0.88% average optimality gap can\nbe obtained on benchmark instances of 12 periods and 12 items, and optimality\ngap within 1.2% for the instances with 24 periods and 24 items.\n"}
+{"abstract": "  We consider a cell-free hybrid massive multiple-input multiple-output (MIMO)\nsystem with $K$ users and $M$ access points (APs), each with $N_a$ antennas and\n$N_r< N_a$ radio frequency (RF) chains. When $K\\ll M{N_a}$, efficient uplink\nchannel estimation and data detection with reduced number of pilots can be\nperformed based on low-rank matrix completion. However, such a scheme requires\nthe central processing unit (CPU) to collect received signals from all APs,\nwhich may enable the CPU to infer the private information of user locations. We\ntherefore develop and analyze privacy-preserving channel estimation schemes\nunder the framework of differential privacy (DP). As the key ingredient of the\nchannel estimator, two joint differentially private noisy matrix completion\nalgorithms based respectively on Frank-Wolfe iteration and singular value\ndecomposition are presented. We provide an analysis on the tradeoff between the\nprivacy and the channel estimation error. In particular, we show that the\nestimation error can be mitigated while maintaining the same privacy level by\nincreasing the payload size with fixed pilot size; and the scaling laws of both\nthe privacy-induced and privacy-independent error components in terms of\npayload size are characterized. Simulation results are provided to further\ndemonstrate the tradeoff between privacy and channel estimation performance.\n"}
+{"abstract": "  A multi-decade exploration into the theoretical foundations of artificial and\nnatural general intelligence, which has been expressed in a series of books and\npapers and used to guide a series of practical and research-prototype software\nsystems, is reviewed at a moderate level of detail. The review covers\nunderlying philosophies (patternist philosophy of mind, foundational\nphenomenological and logical ontology), formalizations of the concept of\nintelligence, and a proposed high level architecture for AGI systems partly\ndriven by these formalizations and philosophies. The implementation of specific\ncognitive processes such as logical reasoning, program learning, clustering and\nattention allocation in the context and language of this high level\narchitecture is considered, as is the importance of a common (e.g. typed\nmetagraph based) knowledge representation for enabling \"cognitive synergy\"\nbetween the various processes. The specifics of human-like cognitive\narchitecture are presented as manifestations of these general principles, and\nkey aspects of machine consciousness and machine ethics are also treated in\nthis context. Lessons for practical implementation of advanced AGI in\nframeworks such as OpenCog Hyperon are briefly considered.\n"}
+{"abstract": "  Survival analysis is a technique to predict the times of specific outcomes,\nand is widely used in predicting the outcomes for intensive care unit (ICU)\ntrauma patients. Recently, deep learning models have drawn increasing attention\nin healthcare. However, there is a lack of deep learning methods that can model\nthe relationship between measurements, clinical notes and mortality outcomes.\nIn this paper we introduce BERTSurv, a deep learning survival framework which\napplies Bidirectional Encoder Representations from Transformers (BERT) as a\nlanguage representation model on unstructured clinical notes, for mortality\nprediction and survival analysis. We also incorporate clinical measurements in\nBERTSurv. With binary cross-entropy (BCE) loss, BERTSurv can predict mortality\nas a binary outcome (mortality prediction). With partial log-likelihood (PLL)\nloss, BERTSurv predicts the probability of mortality as a time-to-event outcome\n(survival analysis). We apply BERTSurv on Medical Information Mart for\nIntensive Care III (MIMIC III) trauma patient data. For mortality prediction,\nBERTSurv obtained an area under the curve of receiver operating characteristic\ncurve (AUC-ROC) of 0.86, which is an improvement of 3.6% over baseline of\nmultilayer perceptron (MLP) without notes. For survival analysis, BERTSurv\nachieved a concordance index (C-index) of 0.7. In addition, visualizations of\nBERT's attention heads help to extract patterns in clinical notes and improve\nmodel interpretability by showing how the model assigns weights to different\ninputs.\n"}
+{"abstract": "  We propose Partially Interpretable Estimators (PIE) which attribute a\nprediction to individual features via an interpretable model, while a\n(possibly) small part of the PIE prediction is attributed to the interaction of\nfeatures via a black-box model, with the goal to boost the predictive\nperformance while maintaining interpretability. As such, the interpretable\nmodel captures the main contributions of features, and the black-box model\nattempts to complement the interpretable piece by capturing the \"nuances\" of\nfeature interactions as a refinement. We design an iterative training algorithm\nto jointly train the two types of models. Experimental results show that PIE is\nhighly competitive to black-box models while outperforming interpretable\nbaselines. In addition, the understandability of PIE is comparable to simple\nlinear models as validated via a human evaluation.\n"}
+{"abstract": "  Most of the current supervised automatic music transcription (AMT) models\nlack the ability to generalize. This means that they have trouble transcribing\nreal-world music recordings from diverse musical genres that are not presented\nin the labelled training data. In this paper, we propose a semi-supervised\nframework, ReconVAT, which solves this issue by leveraging the huge amount of\navailable unlabelled music recordings. The proposed ReconVAT uses\nreconstruction loss and virtual adversarial training. When combined with\nexisting U-net models for AMT, ReconVAT achieves competitive results on common\nbenchmark datasets such as MAPS and MusicNet. For example, in the few-shot\nsetting for the string part version of MusicNet, ReconVAT achieves F1-scores of\n61.0% and 41.6% for the note-wise and note-with-offset-wise metrics\nrespectively, which translates into an improvement of 22.2% and 62.5% compared\nto the supervised baseline model. Our proposed framework also demonstrates the\npotential of continual learning on new data, which could be useful in\nreal-world applications whereby new data is constantly available.\n"}
+{"abstract": "  How do we formalize the challenge of credit assignment in reinforcement\nlearning? Common intuition would draw attention to reward sparsity as a key\ncontributor to difficult credit assignment and traditional heuristics would\nlook to temporal recency for the solution, calling upon the classic eligibility\ntrace. We posit that it is not the sparsity of the reward itself that causes\ndifficulty in credit assignment, but rather the \\emph{information sparsity}. We\npropose to use information theory to define this notion, which we then use to\ncharacterize when credit assignment is an obstacle to efficient learning. With\nthis perspective, we outline several information-theoretic mechanisms for\nmeasuring credit under a fixed behavior policy, highlighting the potential of\ninformation theory as a key tool towards provably-efficient credit assignment.\n"}
+{"abstract": "  In traditional software programs, it is easy to trace program logic from\nvariables back to input, apply assertion statements to block erroneous\nbehavior, and compose programs together. Although deep learning programs have\ndemonstrated strong performance on novel applications, they sacrifice many of\nthe functionalities of traditional software programs. With this as motivation,\nwe take a modest first step towards improving deep learning programs by jointly\ntraining a generative model to constrain neural network activations to \"decode\"\nback to inputs. We call this design a Decodable Neural Network, or DecNN. Doing\nso enables a form of compositionality in neural networks, where one can\nrecursively compose DecNN with itself to create an ensemble-like model with\nuncertainty. In our experiments, we demonstrate applications of this\nuncertainty to out-of-distribution detection, adversarial example detection,\nand calibration -- while matching standard neural networks in accuracy. We\nfurther explore this compositionality by combining DecNN with pretrained\nmodels, where we show promising results that neural networks can be regularized\nfrom using protected features.\n"}
+{"abstract": "  The Onsager Lie algebra $O$ is often used to study integrable lattice models.\nThe universal enveloping algebra of $O$ admits a $q$-deformation $O_q$ called\nthe $q$-Onsager algebra. Recently, an algebra $\\mathcal O_q$ was introduced\ncalled the alternating central extension of $O_q$. In this paper we introduce a\nLie algebra $\\mathcal O$ that is roughly described by the following two\nanalogies: (i) $\\mathcal O$ is to $O$ as $\\mathcal O_q$ is to $O_q$; (ii) $O_q$\nis to $O$ as $\\mathcal O_q$ is to $\\mathcal O$. We call $\\mathcal O$ the\nalternating central extension of $O$. This paper contains a comprehensive\ndescription of $\\mathcal O$.\n"}
+{"abstract": "  In this paper we give local and global parametric classifications of a class\nof Einstein submanifolds of Euclidean space. The highlight is for submanifolds\nof codimension two since in this case our assumptions are only of intrinsic\nnature.\n"}
+{"abstract": "  Sparse level-set formulations allow practitioners to find the minimum 1-norm\nsolution subject to likelihood constraints. Prior art requires this constraint\nto be convex. In this letter, we develop an efficient approach for nonconvex\nlikelihoods, using Regula Falsi root-finding techniques to solve the level-set\nformulation. Regula Falsi methods are simple, derivative-free, and efficient,\nand the approach provably extends level-set methods to the broader class of\nnonconvex inverse problems. Practical performance is illustrated using\nl1-regularized Student's t inversion, which is a nonconvex approach used to\ndevelop outlier-robust formulations.\n"}
+{"abstract": "  In 2020, Cameron et al. introduced the restricted numerical range of a\ndigraph (directed graph) as a tool for characterizing digraphs and studying\ntheir algebraic connectivity. In particular, digraphs with a restricted\nnumerical range of a single point, a horizontal line segment, and a vertical\nline segment were characterized as $k$-imploding stars, directed joins of\nbidirectional digraphs, and regular tournaments, respectively. In this article,\nwe extend these results by investigating digraphs whose restricted numerical\nrange is a convex polygon in the complex plane. We provide computational\nmethods for identifying these polygonal digraphs and show that these digraphs\ncan be broken into three disjoint classes: normal, restricted-normal, and\npseudo-normal digraphs, all of which are closed under the digraph complement.\nWe prove sufficient conditions for normal digraphs and show that the directed\njoin of two normal digraphs results in a restricted-normal digraph. Also, we\nprove that directed joins are the only restricted-normal digraphs when the\norder is square-free or twice a square-free number. Finally, we provide methods\nto construct restricted-normal digraphs that are not directed joins for all\norders that are neither square-free nor twice a square-free number.\n"}
+{"abstract": "  The contact process is a particular case of birth-and-death processes on\ninfinite particle configurations. We consider the contact models on locally\ncompact separable metric spaces. We prove the existence of a one-parameter set\nof invariant measures in the critical regime under the condition imposed on the\nassociated Markov jump process. This condition, roughly speaking, requires the\nseparation of any pair of trajectories of this jump process. The general scheme\ncan be applied to the contact process on the lattice in a heterogeneous and\nrandom environments as well as to the contact process on graphs and on\nmanifolds.\n"}
+{"abstract": "  Recent astronomical data have provided the primordial deuterium abundance\nwith percent precision. As a result, Big Bang nucleosynthesis may provide a\nconstraint on the universal baryon to photon ratio that is as precise as, but\nindependent from, analyses of the cosmic microwave background. However, such a\nconstraint requires that the nuclear reaction rates governing the production\nand destruction of primordial deuterium are sufficiently well known. Here, a\nnew measurement of the $^2$H($p,\\gamma$)$^3$He cross section is reported. This\nnuclear reaction dominates the error on the predicted Big Bang deuterium\nabundance. A proton beam of 400-1650keV beam energy was incident on solid\ntitanium deuteride targets, and the emitted $\\gamma$-rays were detected in two\nhigh-purity germanium detectors at angles of 55$^\\circ$ and 90$^\\circ$,\nrespectively. The deuterium content of the targets has been obtained in situ by\nthe $^2$H($^3$He,$p$)$^4$He reaction and offline using the Elastic Recoil\nDetection method. The astrophysical S-factor has been determined at center of\nmass energies between 265 and 1094 keV, addressing the uppermost part of the\nrelevant energy range for Big Bang nucleosynthesis and complementary to ongoing\nwork at lower energies. The new data support a higher S-factor at Big Bang\ntemperatures than previously assumed, reducing the predicted deuterium\nabundance.\n"}
+{"abstract": "  We present a novel adaptive host-chip modular architecture for video\nacquisition to optimize an overall objective task constrained under a given bit\nrate. The chip is a high resolution imaging sensor such as gigapixel focal\nplane array (FPA) with low computational power deployed on the field remotely,\nwhile the host is a server with high computational power. The communication\nchannel data bandwidth between the chip and host is constrained to accommodate\ntransfer of all captured data from the chip. The host performs objective task\nspecific computations and also intelligently guides the chip to optimize\n(compress) the data sent to host. This proposed system is modular and highly\nversatile in terms of flexibility in re-orienting the objective task. In this\nwork, object tracking is the objective task. While our architecture supports\nany form of compression/distortion, in this paper we use quadtree\n(QT)-segmented video frames. We use Viterbi (Dynamic Programming) algorithm to\nminimize the area normalized weighted rate-distortion allocation of resources.\nThe host receives only these degraded frames for analysis. An object detector\nis used to detect objects, and a Kalman Filter based tracker is used to track\nthose objects. Evaluation of system performance is done in terms of Multiple\nObject Tracking Accuracy (MOTA) metric. In this proposed novel architecture,\nperformance gains in MOTA is obtained by twice training the object detector\nwith different system generated distortions as a novel 2-step process.\nAdditionally, object detector is assisted by tracker to upscore the region\nproposals in the detector to further improve the performance.\n"}
+{"abstract": "  One of the most important lessons from the success of deep learning is that\nlearned representations tend to perform much better at any task compared to\nrepresentations we design by hand. Yet evolution of evolvability algorithms,\nwhich aim to automatically learn good genetic representations, have received\nrelatively little attention, perhaps because of the large amount of\ncomputational power they require. The recent method Evolvability ES allows\ndirect selection for evolvability with little computation. However, it can only\nbe used to solve problems where evolvability and task performance are aligned.\nWe propose Quality Evolvability ES, a method that simultaneously optimizes for\ntask performance and evolvability and without this restriction. Our proposed\napproach Quality Evolvability has similar motivation to Quality Diversity\nalgorithms, but with some important differences. While Quality Diversity aims\nto find an archive of diverse and well-performing, but potentially genetically\ndistant individuals, Quality Evolvability aims to find a single individual with\na diverse and well-performing distribution of offspring. By doing so Quality\nEvolvability is forced to discover more evolvable representations. We\ndemonstrate on robotic locomotion control tasks that Quality Evolvability ES,\nsimilarly to Quality Diversity methods, can learn faster than objective-based\nmethods and can handle deceptive problems.\n"}
+{"abstract": "  Owing to a reduced solar background and low propagation losses in the\natmosphere, the 2- to 2.5-$\\mu$m waveband is a promising candidate for daylight\nquantum communication. This spectral region also offers low losses and low\ndispersion in hollow-core fibers and in silicon waveguides. We demonstrate for\nthe first time near-maximally entangled photon pairs at 2.1 $\\mu$m that could\nsupport device independent quantum key distribution (DIQKD) assuming\nsufficiently high channel efficiencies. The state corresponds to a positive\nsecure-key rate (0.254 bits/pair, with a quantum bit error rate of 3.8%) based\non measurements in a laboratory setting with minimal channel loss and\ntransmission distance. This is promising for the future implementation of DIQKD\nat 2.1 $\\mu$m.\n"}
+{"abstract": "  Collaboration skills are important for future software engineers. In computer\nscience education, these skills are often practiced through group assignments,\nwhere students develop software collaboratively. The approach that students\ntake in these assignments varies widely, but often involves a division of\nlabour. It can then be argued whether collaboration still takes place. The\ndiscipline of computing education is especially interesting in this context,\nbecause some of its specific features (such as the variation in entry skill\nlevel and the use of source code repositories as collaboration platforms) are\nlikely to influence the approach taken within groupwork. The aim of this\nresearch is to gain insight into the work division and allocation strategies\napplied by computer science students during group assignments. To this end, we\ninterviewed twenty students of four universities. The thematic analysis shows\nthat students tend to divide up the workload to enable working independently,\nwith pair programming and code reviews being often employed. Motivated\nprimarily by grade and efficiency factors, students choose and allocate tasks\nprimarily based on their prior expertise and preferences. Based on our\nfindings, we argue that the setup of group assignments can limit student\nmotivation for practicing new software engineering skills, and that\ninterventions are needed towards encouraging experimentation and learning.\n"}
+{"abstract": "  Event sourced systems are increasing in popularity because they are reliable,\nflexible, and scalable. In this article, we point a microscope at a software\narchitecture pattern that is rapidly gaining popularity in industry, but has\nnot received as much attention from the scientific community. We do so through\nconstructivist grounded theory, which proves a suitable qualitative method for\nextracting architectural knowledge from practitioners. Based on the discussion\nof 19 event sourced systems we explore the rationale for and the context of the\nevent sourcing pattern. A description of the pattern itself and its relation to\nother patterns as discussed with practitioners is given. The description itself\nis grounded in the experience of 25 engineers, making it a reliable source for\nboth new practitioners and scientists. We identify five challenges that\npractitioners experience: event system evolution, the steep learning curve,\nlack of available technology, rebuilding projections, and data privacy. For the\nfirst challenge of event system evolution, we uncover five tactics and\nsolutions that support practitioners in their design choices when developing\nevolving event sourced systems: versioned events, weak schema, upcasting,\nin-place transformation, and copy-and-transform.\n"}
+{"abstract": "  Since its launch, the Alpha Magnetic Spectrometer-02 (AMS-02) has delivered\noutstanding quality measurements of the spectra of cosmic-ray (CR) species,\n$\\bar{p}$, $e^{\\pm}$, and nuclei (H--O, Ne, Mg, Si, Fe), which resulted in a\nnumber of breakthroughs. The most recent AMS-02 result is the measurement of\nthe spectrum of CR fluorine up to $\\sim$2 TV. Given its very low solar system\nabundance, fluorine in CRs is thought to be mostly secondary, produced in\nfragmentations of heavier species, predominantly Ne, Mg, and Si. Similar to the\nbest-measured secondary-to-primary boron to carbon nuclei ratio that is widely\nused to study the origin and propagation of CR species, the precise fluorine\ndata would allow the origin of Si-group nuclei to be studied independently.\nMeanwhile, the secondary origin of CR fluorine has never been tested in a wide\nenergy range due to the lack of accurate CR data. In this paper, we use the\nfirst ever precise measurements of the fluorine spectrum by AMS-02 together\nwith ACE-CRIS and Voyager 1 data to actually test this paradigm. Our detailed\nmodeling shows an excess below 10 GV in the fluorine spectrum that may hint at\na primary fluorine component. We also provide an updated local interstellar\nspectrum (LIS) of fluorine in the rigidity range from few MV to $\\sim$2 TV. Our\ncalculations employ the self-consistent GalProp-HelMod framework that has\nproved to be a reliable tool in deriving the LIS of CR $\\bar{p}$, $e^{-}$, and\nnuclei $Z\\le28$.\n"}
+{"abstract": "  A big, diverse and balanced training data is the key to the success of deep\nneural network training. However, existing publicly available datasets used in\nfacial landmark localization are usually much smaller than those for other\ncomputer vision tasks. A small dataset without diverse and balanced training\nsamples cannot support the training of a deep network effectively. To address\nthe above issues, this paper presents a novel Separable Batch Normalization\n(SepBN) module with a Cross-protocol Network Training (CNT) strategy for robust\nfacial landmark localization. Different from the standard BN layer that uses\nall the training data to calculate a single set of parameters, SepBN considers\nthat the samples of a training dataset may belong to different sub-domains.\nAccordingly, the proposed SepBN module uses multiple sets of parameters, each\ncorresponding to a specific sub-domain. However, the selection of an\nappropriate branch in the inference stage remains a challenging task because\nthe sub-domain of a test sample is unknown. To mitigate this difficulty, we\npropose a novel attention mechanism that assigns different weights to each\nbranch for automatic selection in an effective style. As a further innovation,\nthe proposed CNT strategy trains a network using multiple datasets having\ndifferent facial landmark annotation systems, boosting the performance and\nenhancing the generalization capacity of the trained network. The experimental\nresults obtained on several well-known datasets demonstrate the effectiveness\nof the proposed method.\n"}
+{"abstract": "  This paper concerns the design of a Fourier based pseudospectral numerical\nmethod for the model of European Option Pricing with transaction costs under\nExponential Utility derived by Davis, Panas and Zariphopoulou. Computing the\noption price involves solving two stochastic optimal control problems. With a\nExponential Utility function, the dimension of the problem can be reduced, but\none has to deal with high absolute values in the objective function. In this\npaper, we propose two changes of variables that reduce the impact of the\nexponential growth. We propose a Fourier pseudospectral method to solve the\nresulting non linear equation. Numerical analysis of the stability,\nconsistency, convergence and localization error of the method are included.\nNumerical experiments support the theoretical results. The effect of\nincorporating transaction costs is also studied.\n"}
+{"abstract": "  Bipartite networks are a natural representation of the interactions between\nentities from two different types. The organization (or topology) of such\nnetworks gives insight to understand the systems they describe as a whole.\nHere, we rely on motifs which provide a meso-scale description of the topology.\nMoreover, we consider the bipartite expected degree distribution (B-EDD) model\nwhich accounts for both the density of the network and possible imbalances\nbetween the degrees of the nodes. Under the B-EDD model, we prove the\nasymptotic normality of the count of any given motif, considering sparsity\nconditions. We also provide close-form expressions for the mean and the\nvariance of this count. This allows to avoid computationally prohibitive\nresampling procedures. Based on these results, we define a goodness-of-fit test\nfor the B-EDD model and propose a family of tests for network comparisons. We\nassess the asymptotic normality of the test statistics and the power of the\nproposed tests on synthetic experiments and illustrate their use on ecological\ndata sets.\n"}
+{"abstract": "  In \\cite{butman1976} the linear coding scheme is applied, $X_t\n=g_t\\Big(\\Theta - {\\bf E}\\Big\\{\\Theta\\Big|Y^{t-1}, V_0=v_0\\Big\\}\\Big)$,\n$t=2,\\ldots,n$, $X_1=g_1\\Theta$, with $\\Theta: \\Omega \\to {\\mathbb R}$, a\nGaussian random variable, to derive a lower bound on the feedback rate, for\nadditive Gaussian noise (AGN) channels, $Y_t=X_t+V_t, t=1, \\ldots, n$, where\n$V_t$ is a Gaussian autoregressive (AR) noise, and $\\kappa \\in [0,\\infty)$ is\nthe total transmitter power. For the unit memory AR noise, with parameters $(c,\nK_W)$, where $c\\in [-1,1]$ is the pole and $K_W$ is the variance of the\nGaussian noise, the lower bound is $C^{L,B} =\\frac{1}{2} \\log \\chi^2$, where\n$\\chi =\\lim_{n\\longrightarrow \\infty} \\chi_n$ is the positive root of\n$\\chi^2=1+\\Big(1+ \\frac{|c|}{\\chi}\\Big)^2 \\frac{\\kappa}{K_W}$, and the sequence\n$\\chi_n \\triangleq \\Big|\\frac{g_n}{g_{n-1}}\\Big|, n=2, 3, \\ldots,$ satisfies a\ncertain recursion, and conjectured that $C^{L,B}$ is the feedback capacity.\n  In this correspondence, it is observed that the nontrivial lower bound\n$C^{L,B}=\\frac{1}{2} \\log \\chi^2$ such that $\\chi >1$, necessarily implies the\nscaling coefficients of the feedback code, $g_n$, $n=1,2, \\ldots$, grow\nunbounded, in the sense that, $\\lim_{n\\longrightarrow\\infty}|g_n| =+\\infty$.\nThe unbounded behaviour of $g_n$ follows from the ratio limit theorem of a\nsequence of real numbers, and it is verified by simulations. It is then\nconcluded that such linear codes are not practical, and fragile with respect to\na mismatch between the statistics of the mathematical model of the channel and\nthe real statistics of the channel. In particular, if the error is perturbed by\n$\\epsilon_n>0$ no matter how small, then $X_n =g_t\\Big(\\Theta - {\\bf\nE}\\Big\\{\\Theta\\Big|Y^{t-1}, V_0=v_0\\Big\\}\\Big)+g_n \\epsilon_n$, and\n$|g_n|\\epsilon_n \\longrightarrow \\infty$, as $n \\longrightarrow \\infty$.\n"}
+{"abstract": "  We study a model for frustrated tunneling ionization using ultrashort laser\npulses. The model is based on the strong field approximation and it employs the\nsaddle point approximation to predict quasiclassical trajectories that are\ncaptured on Rydberg states. We present a classification of the saddle-point\nsolutions and explore their behavior as functions of angular momentum of the\nfinal state, as well as the carrier--envelope phase (CEP) of the laser pulse.\nWe compare the final state population computed by the model to results obtained\nby numerical propagation of the time-dependent Schr\\\"odinger equation (TDSE)\nfor the hydrogen atom. While we find qualitative agreement in the CEP\ndependence of the populations in principal quantum numbers, $n$, the\npopulations to individual angular momentum channels, $\\ell$, are found to be\ninconsistent between model and TDSE. Thus, our results show that improvements\nof the quasiclassical trajectories are in order for a quantitative model of\nfrustrated tunneling ionizaiton.\n"}
+{"abstract": "  Tomonaga-Luttinger liquids (TLLs) can be used to effectively describe\none-dimensional quantum many-body systems such as ultracold atoms, charges in\nnanowires, superconducting circuits, and gapless spin chains. Their properties\nare given by two parameters, the propagation velocity and the Luttinger\nparameter. Here we study inhomogeneous TLLs where these are promoted to\nfunctions of position and demonstrate that they profoundly affect the dynamics:\nIn general, besides curving the light cone, we show that propagation is no\nlonger ballistically localized to the light-cone trajectories, different from\nstandard homogeneous TLLs. Specifically, if the Luttinger parameter depends on\nposition, the dynamics features pronounced spreading into the light cone, which\ncannot be understood via a simple superposition of waves as in the\nHuygens-Fresnel principle. This is the case for ultracold atoms in a parabolic\ntrap, which serves as our main motivation, and we discuss possible experimental\nobservations in such systems.\n"}
+{"abstract": "  Correlation acts as a critical role in the tracking field, especially in\nrecent popular Siamese-based trackers. The correlation operation is a simple\nfusion manner to consider the similarity between the template and the search\nregion. However, the correlation operation itself is a local linear matching\nprocess, leading to lose semantic information and fall into local optimum\neasily, which may be the bottleneck of designing high-accuracy tracking\nalgorithms. Is there any better feature fusion method than correlation? To\naddress this issue, inspired by Transformer, this work presents a novel\nattention-based feature fusion network, which effectively combines the template\nand search region features solely using attention. Specifically, the proposed\nmethod includes an ego-context augment module based on self-attention and a\ncross-feature augment module based on cross-attention. Finally, we present a\nTransformer tracking (named TransT) method based on the Siamese-like feature\nextraction backbone, the designed attention-based fusion mechanism, and the\nclassification and regression head. Experiments show that our TransT achieves\nvery promising results on six challenging datasets, especially on large-scale\nLaSOT, TrackingNet, and GOT-10k benchmarks. Our tracker runs at approximatively\n50 fps on GPU. Code and models are available at\nhttps://github.com/chenxin-dlut/TransT.\n"}
+{"abstract": "  The regular monitoring of flat-spectrum radio quasars (FSRQs) in\n$\\gamma$-rays by Fermi-LAT since past 12 years indicated six sources who\nexhibited extreme $\\gamma$-ray outbursts crossing daily flux of $10^{-5}$\nphotons/cm$^{2}$/s. We obtained nearly-simultaneous multi-wavelength data of\nthese sources in radio to $\\gamma$-ray waveband from OVRO, Steward Observatory,\nSMARTS, Swift-UVOT, Swift-XRT, and Fermi-LAT. The time-averaged broadband\nSpectral Energy Distributions (SEDs) of these sources in quiescent states were\nstudied to get an idea about the underlying baseline radiation processes. We\nmodeled the SEDs using one-zone leptonic synchrotron and inverse-Compton\nemission scenario from broken power-law electron energy distribution inside a\nspherical plasma blob, relativistically moving down a conical jet. The model\ntakes into account inverse-Compton scattering of externally and locally\noriginated seed photons in the jet. The big blue bumps visible in quiescent\nstate SEDs helped to estimate the accretion disk luminosities and central black\nhole masses. We found a correlation between the magnetic field inside the\nemission region and the ratio of emission region distance to disk luminosity,\nwhich implies that the magnetic field decreases with an increase in emission\nregion distance and decrease in disk luminosity, suggesting a disk-jet\nconnection. The high-energy index of the electron distribution was also found\nto be correlated with observed $\\gamma$-ray luminosity as $\\gamma$-rays are\nproduced by high-energy particles. In most cases, kinetic power carried by\nelectrons can account for jet radiation power as jets become radiatively\ninefficient during quiescent states.\n"}
+{"abstract": "  Examining a 20th-century Scandinavian legal theoretical tradition, we can\nextract an ontological naturalistic, a logical empiristic, and a modern\nidealistic rationale. We introduce the mathematical syntactic figure present in\nthe `logical empiricism' in a contemporary mathematical logic. A new formal\nframework for describing explicit purchase statutes (Sweden) is gradually\ndeveloped and subsequently proposed. This new framework is based on a\nmany-sorted first-order logic (MFOL) approach, where the semantics are grounded\nin concrete `physical' objects and situations with a legal relevance.\nSpecifically, we present a concrete formal syntactic translation of one of the\ncentral statutes of Swedish legislation for the purchase of immovable property.\nAdditionally, we discuss the potential implications that a subsequent\ndevelopment of such formalisations would have for constructing artificial\nagents (e.g., software) that can be used as `co-creative' legal assistance for\nsolving highly complex legal issues concerning the transfer of property, among\nothers.\n"}
+{"abstract": "  Virtual try-on methods aim to generate images of fashion models wearing\narbitrary combinations of garments. This is a challenging task because the\ngenerated image must appear realistic and accurately display the interaction\nbetween garments. Prior works produce images that are filled with artifacts and\nfail to capture important visual details necessary for commercial applications.\nWe propose Outfit Visualization Net (OVNet) to capture these important details\n(e.g. buttons, shading, textures, realistic hemlines, and interactions between\ngarments) and produce high quality multiple-garment virtual try-on images.\nOVNet consists of 1) a semantic layout generator and 2) an image generation\npipeline using multiple coordinated warps. We train the warper to output\nmultiple warps using a cascade loss, which refines each successive warp to\nfocus on poorly generated regions of a previous warp and yields consistent\nimprovements in detail. In addition, we introduce a method for matching outfits\nwith the most suitable model and produce significant improvements for both our\nand other previous try-on methods. Through quantitative and qualitative\nanalysis, we demonstrate our method generates substantially higher-quality\nstudio images compared to prior works for multi-garment outfits. An interactive\ninterface powered by this method has been deployed on fashion e-commerce\nwebsites and received overwhelmingly positive feedback.\n"}
+{"abstract": "  Within the framework of Einstein-Gauss-Bonnet theory in five-dimensional\nspacetime ($5D$ EGB), we derive the hydrostatic equilibrium equations and solve\nthem numerically to obtain the neutron stars for both isotropic and anisotropic\ndistribution of matter. The mass-radius relations are obtained for SLy equation\nof state, which describes both the solid crust and the liquid core of neutron\nstars, and for a wide range of the Gauss-Bonnet coupling parameter $\\alpha$.\nMore specifically, we find that the contribution of the Gauss-Bonnet term leads\nto substantial deviations from the Einstein gravity. We also discuss that after\na certain value of $\\alpha$, the theory admits higher maximum masses compared\nwith general relativity, however, the causality condition is violated in the\nhigh-mass region. Finally, our results are compared with the recent\nobservations data on mass-radius diagram.\n"}
+{"abstract": "  Deep models have improved state-of-the-art for both supervised and\nunsupervised learning. For example, deep embedded clustering (DEC) has greatly\nimproved the unsupervised clustering performance, by using stacked autoencoders\nfor representation learning. However, one weakness of deep modeling is that the\nlocal neighborhood structure in the original space is not necessarily preserved\nin the latent space. To preserve local geometry, various methods have been\nproposed in the supervised and semi-supervised learning literature (e.g.,\nspectral clustering and label propagation) using graph Laplacian\nregularization. In this paper, we combine the strength of deep representation\nlearning with measure propagation (MP), a KL-divergence based graph\nregularization method originally used in the semi-supervised scenario. The main\nassumption of MP is that if two data points are close in the original space,\nthey are likely to belong to the same class, measured by KL-divergence of class\nmembership distribution. By taking the same assumption in the unsupervised\nlearning scenario, we propose our Deep Embedded Clustering Aided by Measure\nPropagation (DECAMP) model. We evaluate DECAMP on short text clustering tasks.\nOn three public datasets, DECAMP performs competitively with other\nstate-of-the-art baselines, including baselines using additional data to\ngenerate word embeddings used in the clustering process. As an example, on the\nStackoverflow dataset, DECAMP achieved a clustering accuracy of 79%, which is\nabout 5% higher than all existing baselines. These empirical results suggest\nthat DECAMP is a very effective method for unsupervised learning.\n"}
+{"abstract": "  Many physical, biological and neural systems behave as coupled oscillators,\nwith characteristic phase coupling across different frequencies. Methods such\nas $n:m$ phase locking value and bi-phase locking value have previously been\nproposed to quantify phase coupling between two resonant frequencies (e.g. $f$,\n$2f/3$) and across three frequencies (e.g. $f_1$, $f_2$, $f_1+f_2$),\nrespectively. However, the existing phase coupling metrics have their\nlimitations and limited applications. They cannot be used to detect or quantify\nphase coupling across multiple frequencies (e.g. $f_1$, $f_2$, $f_3$, $f_4$,\n$f_1+f_2+f_3-f_4$), or coupling that involves non-integer multiples of the\nfrequencies (e.g. $f_1$, $f_2$, $2f_1/3+f_2/3$). To address the gap, this paper\nproposes a generalized approach, named multi-phase locking value (M-PLV), for\nthe quantification of various types of instantaneous multi-frequency phase\ncoupling. Different from most instantaneous phase coupling metrics that measure\nthe simultaneous phase coupling, the proposed M-PLV method also allows the\ndetection of delayed phase coupling and the associated time lag between coupled\noscillators. The M-PLV has been tested on cases where synthetic coupled signals\nare generated using white Gaussian signals, and a system comprised of multiple\ncoupled R\\\"ossler oscillators. Results indicate that the M-PLV can provide a\nreliable estimation of the time window and frequency combination where the\nphase coupling is significant, as well as a precise determination of time lag\nin the case of delayed coupling. This method has the potential to become a\npowerful new tool for exploring phase coupling in complex nonlinear dynamic\nsystems.\n"}
+{"abstract": "  Transcriptomic analysis are characterized by being not directly quantitative\nand only providing relative measurements of expression levels up to an unknown\nindividual scaling factor. This difficulty is enhanced for differential\nexpression analysis. Several methods have been proposed to circumvent this lack\nof knowledge by estimating the unknown individual scaling factors however, even\nthe most used one, are suffering from being built on hardly justifiable\nbiological hypotheses or from having weak statistical background. Only two\nmethods withstand this analysis: one based on largest connected graph component\nhardly usable for large amount of expressions like in NGS, the second based on\n$\\log$-linear fits which unfortunately require a first step which uses one of\nthe methods described before.\n  We introduce a new procedure for differential analysis in the context of\ntranscriptomic data. It is the result of pooling together several differential\nanalyses each based on randomly picked genes used as reference genes. It\nprovides a differential analysis free from the estimation of the individual\nscaling factors or any other knowledge. Theoretical properties are investigated\nboth in term of FWER and power. Moreover in the context of Poisson or negative\nbinomial modelization of the transcriptomic expressions, we derived a test with\nnon asymptotic control of its bounds. We complete our study by some empirical\nsimulations and apply our procedure to a real data set of hepatic miRNA\nexpressions from a mouse model of non-alcoholic steatohepatitis (NASH), the\nCDAHFD model. This study on real data provides new hits with good biological\nexplanations.\n"}
+{"abstract": "  The world today is experiencing an abundance of music like no other time, and\nattempts to group music into clusters have become increasingly prevalent.\nCommon standards for grouping music were songs, artists, and genres, with\nartists or songs exploring similar genres of music seen as related. These\nclustering attempts serve critical purposes for various stakeholders involved\nin the music industry. For end users of music services, they may want to group\ntheir music so that they can easily navigate inside their music library; for\nmusic streaming platforms like Spotify, companies may want to establish a solid\ndataset of related songs in order to successfully provide personalized music\nrecommendations and coherent playlists to their users. Due to increased\ncompetition in the streaming market, platforms are trying their best to find\nnovel ways of learning similarities between audio to gain competitive\nadvantage. Our team, comprised of music lovers with different tastes, was\ninterested in the same issue, and created Music-Circles, an interactive\nvisualization of music from the Billboard. Music-Circles links audio feature\ndata offered by Spotify to popular songs to create unique vectors for each\nsong, and calculate similarities between these vectors to cluster them. Through\ninteracting with Music-Circles, users can gain understandings of audio\nfeatures, view characteristic trends in popular music, and find out which music\ncluster they belong to.\n"}
+{"abstract": "  We propose a manifold matching approach to generative models which includes a\ndistribution generator (or data generator) and a metric generator. In our\nframework, we view the real data set as some manifold embedded in a\nhigh-dimensional Euclidean space. The distribution generator aims at generating\nsamples that follow some distribution condensed around the real data manifold.\nIt is achieved by matching two sets of points using their geometric shape\ndescriptors, such as centroid and $p$-diameter, with learned distance metric;\nthe metric generator utilizes both real data and generated samples to learn a\ndistance metric which is close to some intrinsic geodesic distance on the real\ndata manifold. The produced distance metric is further used for manifold\nmatching. The two networks are learned simultaneously during the training\nprocess. We apply the approach on both unsupervised and supervised learning\ntasks: in unconditional image generation task, the proposed method obtains\ncompetitive results compared with existing generative models; in\nsuper-resolution task, we incorporate the framework in perception-based models\nand improve visual qualities by producing samples with more natural textures.\nExperiments and analysis demonstrate the feasibility and effectiveness of the\nproposed framework.\n"}
+{"abstract": "  We introduce a two-dimensional Hele-Shaw type free boundary model for\nmotility of eukaryotic cells on substrates. The key ingredients of this model\nare the Darcy law for overdamped motion of the cytoskeleton gel (active gel)\ncoupled with advection-diffusion equation for myosin density leading to\nelliptic-parabolic Keller-Segel system. This system is supplemented with\nHele-Shaw type boundary conditions: Young-Laplace equation for pressure and\ncontinuity of velocities. We first show that radially symmetric stationary\nsolutions become unstable and bifurcate to traveling wave solutions at a\ncritical value of the total myosin mass. Next we perform linear stability\nanalysis of these traveling wave solutions and identify the type of bifurcation\n(sub- or supercritical). Our study sheds light on the mathematics underlying\ninstability/stability transitions in this model. Specifically, we show that\nthese transitions occur via generalized eigenvectors of the linearized\noperator.\n"}
+{"abstract": "  The algebraic monoid structure of an incidence algebra is investigated. We\nshow that the multiplicative structure alone determines the algebra\nautomorphisms of the incidence algebra. We present a formula that expresses the\ncomplexity of the incidence monoid with respect to the two sided action of its\nmaximal torus in terms of the zeta polynomial of the poset. In addition, we\ncharacterize the finite (connected) posets whose incidence monoids have\ncomplexity $\\leq 1$. Finally, we determine the covering relations of the\nadherence order on the incidence monoid of a star poset.\n"}
+{"abstract": "  In this paper, we consider a frequency-based portfolio optimization problem\nwith $m \\geq 2$ assets when the expected logarithmic growth (ELG) rate of\nwealth is used as the performance metric. With the aid of the notion called\ndominant asset, it is known that the optimal ELG level is achieved by investing\nall available funds on that asset. However, such an \"all-in\" strategy is\narguably too risky to implement in practice. Motivated by this issue, we study\nthe case where the portfolio weights are chosen in a rather ad-hoc manner and a\nbuy-and-hold strategy is subsequently used. Then we show that, if the\nunderlying portfolio contains a dominant asset, buy and hold on that specific\nasset is asymptotically log-optimal with a sublinear rate of convergence. This\nresult also extends to the scenario where a trader either does not have a\nprobabilistic model for the returns or does not trust a model obtained from\nhistorical data. To be more specific, we show that if a market contains a\ndominant asset, buy and hold a market portfolio involving nonzero weights for\neach asset is asymptotically log-optimal. Additionally, this paper also\nincludes a conjecture regarding the property called high-frequency maximality.\nThat is, in the absence of transaction costs, high-frequency rebalancing is\nunbeatable in the ELG sense. Support for the conjecture, involving a lemma for\na weak version of the conjecture, is provided. This conjecture, if true,\nenables us to improve the log-optimality result obtained previously. Finally, a\nresult that indicates a way regarding an issue about when should one to\nrebalance their portfolio if needed, is also provided. Examples, some involving\nsimulations with historical data, are also provided along the way to illustrate\nthe~theory.\n"}
+{"abstract": "  In this paper, we develop a new classification method for manifold-valued\ndata in the framework of probabilistic learning vector quantization. In many\nclassification scenarios, the data can be naturally represented by symmetric\npositive definite matrices, which are inherently points that live on a curved\nRiemannian manifold. Due to the non-Euclidean geometry of Riemannian manifolds,\ntraditional Euclidean machine learning algorithms yield poor results on such\ndata. In this paper, we generalize the probabilistic learning vector\nquantization algorithm for data points living on the manifold of symmetric\npositive definite matrices equipped with Riemannian natural metric\n(affine-invariant metric). By exploiting the induced Riemannian distance, we\nderive the probabilistic learning Riemannian space quantization algorithm,\nobtaining the learning rule through Riemannian gradient descent. Empirical\ninvestigations on synthetic data, image data , and motor imagery EEG data\ndemonstrate the superior performance of the proposed method.\n"}
+{"abstract": "  The extreme loads experienced by the wind turbine in the extreme wind events\nare critical for the evaluation of structural reliability. Hence, the load\nalleviation control methods need to be designed and deployed to reduce the\nadverse effects of extreme wind events. This work demonstrates that the extreme\nloads are highly correlated to wind conditions such as turbulence-induced wind\nshears. Based on this insight, this work proposes a turbulence-based load\nalleviation control strategy for adapting the controller to changes in wind\ncondition. The estimation of the rotor averaged wind shear based on the rotor\nloads is illustrated, and is herein used to statistically characterize the\nextreme wind events for control purpose. To demonstrates the benefits,\nsimulations are carried out using high-fidelity aero-elastic tool and the DTU\n10 MW reference turbine in normal and extreme turbulence wind conditions. The\nresults indicate that the proposed method can effectively decrease the\nexceedance probability of the extreme loads. Meanwhile, the method can minimize\nthe loss of annual energy production in normal operating condition.\n"}
+{"abstract": "  The asymptotics of the ground state $u(r)$ of the Schr\\\"odinger--Newton\nequation in $\\mathbb{R}^3$ was determined by V. Moroz and J. van Schaftingen to\nbe $u(r) \\sim A e^{-r}/ r^{1 - \\|u\\|_2^2/8\\pi}$ for some $A>0$, in units that\nfix the exponential rate to unity. They left open the value of $\\|u\\|_2^2$, the\nsquared $L^2$ norm of $u$. Here it is rigorously shown that $2^{1/3}3\\pi^2\\leq\n\\|u\\|_2^2\\leq 2^{3}\\pi^{3/2}$. It is reported that numerically\n$\\|u\\|_2^2\\approx 14.03\\pi$, revealing that the monomial prefactor of $e^{-r}$\nincreases with $r$ in a concave manner. Asymptotic results are proposed for the\nSchr\\\"odinger--Newton equation with external $\\sim - K/r$ potential, and for\nthe related Hartree equation of a bosonic atom or ion.\n"}
+{"abstract": "  In this work, we extended a stochastic model for football leagues based on\nthe team's potential [R. da Silva et al. Comput. Phys. Commun. \\textbf{184}\n661--670 (2013)] for making predictions instead of only performing a successful\ncharacterization of the statistics on the punctuation of the real leagues. Our\nadaptation considers the advantage of playing at home when considering the\npotential of the home and away teams. The algorithm predicts the tournament's\noutcome by using the market value or/and the ongoing team's performance as\ninitial conditions in the context of Monte Carlo simulations. We present and\ncompare our results to the worldwide known SPI predictions performed by the\n\"FiveThirtyEight\" project. The results show that the algorithm can deliver good\npredictions even with a few ingredients and in more complicated seasons like\nthe 2020 editions where the matches were played without fans in the stadiums.\n"}
+{"abstract": "  In this paper we investigate how the bootstrap can be applied to time series\nregressions when the volatility of the innovations is random and\nnon-stationary. The volatility of many economic and financial time series\ndisplays persistent changes and possible non-stationarity. However, the theory\nof the bootstrap for such models has focused on deterministic changes of the\nunconditional variance and little is known about the performance and the\nvalidity of the bootstrap when the volatility is driven by a non-stationary\nstochastic process. This includes near-integrated volatility processes as well\nas near-integrated GARCH processes. This paper develops conditions for\nbootstrap validity in time series regressions with non-stationary, stochastic\nvolatility. We show that in such cases the distribution of bootstrap statistics\n(conditional on the data) is random in the limit. Consequently, the\nconventional approaches to proving bootstrap validity, involving weak\nconvergence in probability of the bootstrap statistic, fail to deliver the\nrequired results. Instead, we use the concept of `weak convergence in\ndistribution' to develop and establish novel conditions for validity of the\nwild bootstrap, conditional on the volatility process. We apply our results to\nseveral testing problems in the presence of non-stationary stochastic\nvolatility, including testing in a location model, testing for structural\nchange and testing for an autoregressive unit root. Sufficient conditions for\nbootstrap validity include the absence of statistical leverage effects, i.e.,\ncorrelation between the error process and its future conditional variance. The\nresults are illustrated using Monte Carlo simulations, which indicate that the\nwild bootstrap leads to size control even in small samples.\n"}
+{"abstract": "  In automotive domain, operation of secondary tasks like accessing\ninfotainment system, adjusting air conditioning vents, and side mirrors\ndistract drivers from driving. Though existing modalities like gesture and\nspeech recognition systems facilitate undertaking secondary tasks by reducing\nduration of eyes off the road, those often require remembering a set of\ngestures or screen sequences. In this paper, we have proposed two different\nmodalities for drivers to virtually touch the dashboard display using a laser\ntracker with a mechanical switch and an eye gaze switch. We compared\nperformances of our proposed modalities against conventional touch modality in\nautomotive environment by comparing pointing and selection times of\nrepresentative secondary task and also analysed effect on driving performance\nin terms of deviation from lane, average speed, variation in perceived workload\nand system usability. We did not find significant difference in driving and\npointing performance between laser tracking system and existing touchscreen\nsystem. Our result also showed that the driving and pointing performance of the\nvirtual touch system with eye gaze switch was significantly better than the\nsame with mechanical switch. We evaluated the efficacy of the proposed virtual\ntouch system with eye gaze switch inside a real car and investigated acceptance\nof the system by professional drivers using qualitative research. The\nquantitative and qualitative studies indicated importance of using multimodal\nsystem inside car and highlighted several criteria for acceptance of new\nautomotive user interface.\n"}
+{"abstract": "  This paper aims to provide an overview of the ethical concerns in artificial\nintelligence (AI) and the framework that is needed to mitigate those risks, and\nto suggest a practical path to ensure the development and use of AI at the\nUnited Nations (UN) aligns with our ethical values. The overview discusses how\nAI is an increasingly powerful tool with potential for good, albeit one with a\nhigh risk of negative side-effects that go against fundamental human rights and\nUN values. It explains the need for ethical principles for AI aligned with\nprinciples for data governance, as data and AI are tightly interwoven. It\nexplores different ethical frameworks that exist and tools such as assessment\nlists. It recommends that the UN develop a framework consisting of ethical\nprinciples, architectural standards, assessment methods, tools and\nmethodologies, and a policy to govern the implementation and adherence to this\nframework, accompanied by an education program for staff.\n"}
+{"abstract": "  In the formalism of generalized holographic dark energy (HDE), the\nholographic cut-off is generalized to depend upon $L_\\mathrm{IR} =\nL_\\mathrm{IR} \\left( L_\\mathrm{p}, \\dot L_\\mathrm{p}, \\ddot L_\\mathrm{p},\n\\cdots, L_\\mathrm{f}, \\dot L_\\mathrm{f}, \\cdots, a\\right)$ with $L_\\mathrm{p}$\nand $L_\\mathrm{f}$ are the particle horizon and the future horizon,\nrespectively (moreover $a$ is the scale factor of the universe). Based on such\nformalism, in the present paper, we show that a wide class of dark energy (DE)\nmodels can be regarded as different candidates of the generalized HDE family,\nwith respective cut-offs. This can be thought as a symmetry between the\ngeneralized HDE and different DE models. In this regard, we consider several\nentropic dark energy models - like Tsallis entropic DE, the R\\'{e}nyi entropic\nDE, and the Sharma-Mittal entropic DE - and showed that they are indeed\nequivalent with the generalized HDE. Such equivalence between the entropic DE\nand the generalized HDE is extended to the scenario where the respective\nexponents of the entropy functions are allowed to vary with the expansion of\nthe universe. Besides the entropic DE models, the correspondence with the\ngeneralized HDE is also established for the Quintessence and for the Ricci DE\nmodels. In all the above cases, the effective equation of state (EoS) parameter\ncorresponds to the holographic energy density are determined, by which the\nequivalence of various DE models with the respective generalized HDE models are\nfurther confirmed. The equivalent holographic cut-offs are determined by two\nways: (1) in terms of the particle horizon and its derivatives, (2) in terms of\nthe future horizon horizon and its derivatives.\n"}
+{"abstract": "  Automated program verification is a difficult problem. It is undecidable even\nfor transition systems over Linear Integer Arithmetic (LIA). Extending the\ntransition system with theory of Arrays, further complicates the problem by\nrequiring inference and reasoning with universally quantified formulas. In this\npaper, we present a new algorithm, Quic3, that extends IC3 to infer universally\nquantified invariants over the combined theory of LIA and Arrays. Unlike other\napproaches that use either IC3 or an SMT solver as a black box, Quic3 carefully\nmanages quantified generalization (to construct quantified invariants) and\nquantifier instantiation (to detect convergence in the presence of\nquantifiers). While Quic3 is not guaranteed to converge, it is guaranteed to\nmake progress by exploring longer and longer executions. We have implemented\nQuic3 within the Constrained Horn Clause solver engine of Z3 and experimented\nwith it by applying Quic3 to verifying a variety of public benchmarks of array\nmanipulating C programs.\n"}
+{"abstract": "  Fission properties of the actinide nuclei are deduced from theoretical\nanalysis. We investigate potential energy surfaces and fission barriers and\npredict the fission fragment mass-yields of actinide isotopes. The results are\ncompared with experimental data where available. The calculations were\nperformed in the macroscopic-microscopic approximation with the\nLublin-Strasbourg Drop (LSD) for the macroscopic part and the microscopic\nenergy corrections were evaluated in the Yukawa-folded potential. The Fourier\nnuclear shape parametrization is used to describe the nuclear shape, including\nthe non-axial degree of freedom. The fission fragment mass-yields of considered\nnuclei are evaluated within a 3D collective model using the Born-Oppenheimer\napproximation.\n"}
+{"abstract": "  Light beams carrying orbital-angular-momentum (OAM) play an important role in\noptical manipulation and communication owing to their unbounded state space.\nHowever, it is still challenging to efficiently discriminate OAM modes with\nlarge topological charges and thus only a small part of the OAM states have\nbeen usually used. Here we demonstrate that neural networks can be trained to\nsort OAM modes with large topological charges and unknown superpositions. Using\nintensity images of OAM modes generalized in simulations and experiments as the\ninput data, we illustrate that our neural network has great generalization\npower to recognize OAM modes of large topological charges beyond training areas\nwith high accuracy. Moreover, the trained neural network can correctly classify\nand predict arbitrary superpositions of two OAM modes with random topological\ncharges. Our machine learning approach only requires a small portion of\nexperimental samples and significantly reduces the cost in experiments, which\npaves the way to study the OAM physics and increase the state space of OAM\nbeams in practical applications.\n"}
+{"abstract": "  Product quantization (PQ) is a widely used technique for ad-hoc retrieval.\nRecent studies propose supervised PQ, where the embedding and quantization\nmodels can be jointly trained with supervised learning. However, there is a\nlack of appropriate formulation of the joint training objective; thus, the\nimprovements over previous non-supervised baselines are limited in reality. In\nthis work, we propose the Matching-oriented Product Quantization (MoPQ), where\na novel objective Multinoulli Contrastive Loss (MCL) is formulated. With the\nminimization of MCL, we are able to maximize the matching probability of query\nand ground-truth key, which contributes to the optimal retrieval accuracy.\nGiven that the exact computation of MCL is intractable due to the demand of\nvast contrastive samples, we further propose the Differentiable Cross-device\nSampling (DCS), which significantly augments the contrastive samples for\nprecise approximation of MCL. We conduct extensive experimental studies on four\nreal-world datasets, whose results verify the effectiveness of MoPQ. The code\nis available at https://github.com/microsoft/MoPQ.\n"}
+{"abstract": "  We show that a conjecture of Putman-Wieland, which posits the nonexistence of\nfinite orbits for higher Prym representations of the mapping class group, is\nequivalent to the existence of surface-by-surface and surface-by-free groups\nwhich do not virtually algebraically fiber.\n"}
+{"abstract": "  Children in a large number of international and cross-cultural families in\nand outside of the US learn and speak more than one language. However, parents\noften struggle to acquaint their young children with their local language if\nthe child spends majority of time at home and with their spoken language if\nthey go to daycare or school. By reviewing relevant literature about the role\nof screen media content in young children's language learning, and interviewing\na subset of parents raising multilingual children, we explore the potential of\ndesigning conversational user interfaces which can double as an assistive\nlanguage aid.We present a preliminary list of objectives to guide the the\ndesign of conversational user interfaces dialogue for young children's\nbilingual language acquisition.\n"}
+{"abstract": "  Inelastic scattering experiments are key methods for mapping the full\ndispersion of fundamental excitations of solids in the ground as well as\nnon-equilibrium states. A quantitative analysis of inelastic scattering in\nterms of phonon excitations requires identifying the role of multi-phonon\nprocesses. Here, we develop an efficient first-principles methodology for\ncalculating the all-phonon quantum mechanical structure factor of solids. We\ndemonstrate our method by obtaining excellent agreement between measurements\nand calculations of the diffuse scattering patterns of black phosphorus,\nshowing that multi-phonon processes play a substantial role. The present\napproach constitutes a step towards the interpretation of static and\ntime-resolved electron, X-ray, and neutron inelastic scattering data.\n"}
+{"abstract": "  We introduce a family of Generalized Continuous Maxwell Demons (GCMDs)\noperating on idealized single-bit equilibrium devices that combine the\nsingle-measurement Szilard and the Continuous Maxwell Demon protocols. We\nderive the cycle-distributions for extracted work, information-content and\ntime, and compute the power and information-to-work efficiency fluctuations for\nthe different models. We show that the efficiency at maximum power is maximal\nfor an opportunistic protocol of continuous-type in the dynamical regime\ndominated by rare events. We also extend the analysis to finite-time work\nextracting protocols by mapping them to a three-state GCMD. We show that\ndynamical finite-time correlations in this model increase the\ninformation-to-work conversion efficiency, underlining the role of temporal\ncorrelations in optimizing information-to-energy conversion.\n"}
+{"abstract": "  The UN-Habitat estimates that over one billion people live in slums around\nthe world. However, state-of-the-art techniques to detect the location of slum\nareas employ high-resolution satellite imagery, which is costly to obtain and\nprocess. As a result, researchers have started to look at utilising free and\nopen-access medium resolution satellite imagery. Yet, there is no clear\nconsensus on which data preparation and machine learning approaches are the\nmost appropriate to use with such imagery data. In this paper, we evaluate two\ntechniques (multi-spectral data and grey-level co-occurrence matrix feature\nextraction) on an open-access dataset consisting of labelled Sentinel-2 images\nwith a spatial resolution of 10 meters. Both techniques were paired with a\ncanonical correlation forests classifier. The results show that the grey-level\nco-occurrence matrix performed better than multi-spectral data for all four\ncities. It had an average accuracy for the slum class of 97% and a mean\nintersection over union of 94%, while multi-spectral data had 75% and 64% for\nthe respective metrics. These results indicate that open-access satellite\nimagery with a resolution of at least 10 meters may be suitable for keeping\ntrack of development goals such as the detection of slums in cities.\n"}
+{"abstract": "  Legal English is a sublanguage that is important for everyone but not for\neveryone to understand. Pretrained models have become best practices among\ncurrent deep learning approaches for different problems. It would be a waste or\neven a danger if these models were applied in practice without knowledge of the\nsublanguage of the law. In this paper, we raise the issue and propose a trivial\nsolution by introducing BERTLaw a legal sublanguage pretrained model. The\npaper's experiments demonstrate the superior effectiveness of the method\ncompared to the baseline pretrained model\n"}
+{"abstract": "  In this article, we first introduced the inflated unit Lindley distribution\nconsidering zero or/and one inflation scenario and studied its basic\ndistributional and structural properties. Both the distributions are shown to\nbe members of exponential family with full rank. Different parameter estimation\nmethods are discussed and supporting simulation studies to check their efficacy\nare also presented. Proportion of students passing the high school leaving\nexamination for the schools across the state of Manipur in India for the year\n2020 are then modeled using the proposed distributions and compared with the\ninflated beta distribution to justify its benefits.\n"}
+{"abstract": "  Readability assessment is the task of evaluating the reading difficulty of a\ngiven piece of text. Although research on computational approaches to\nreadability assessment is now two decades old, there is not much work on\nsynthesizing this research. This article is a brief survey of contemporary\nresearch on developing computational models for readability assessment. We\nidentify the common approaches, discuss their shortcomings, and identify some\nchallenges for the future. Where possible, we also connect computational\nresearch with insights from related work in other disciplines such as education\nand psychology.\n"}
+{"abstract": "  Event-based sensors have the potential to optimize energy consumption at\nevery stage in the signal processing pipeline, including data acquisition,\ntransmission, processing and storage. However, almost all state-of-the-art\nsystems are still built upon the classical Nyquist-based periodic signal\nacquisition. In this work, we design and validate the Polygonal Approximation\nSampler (PAS), a novel circuit to implement a general-purpose event-based\nsampler using a polygonal approximation algorithm as the underlying sampling\ntrigger. The circuit can be dynamically reconfigured to produce a coarse or a\ndetailed reconstruction of the analog input, by adjusting the error threshold\nof the approximation. The proposed circuit is designed at the Register Transfer\nLevel and processes each input sample received from the ADC in a single clock\ncycle. The PAS has been tested with three different types of archetypal signals\ncaptured by wearable devices (electrocardiogram, accelerometer and respiration\ndata) and compared with a standard periodic ADC. These tests show that\nsingle-channel signals, with slow variations and constant segments (like the\nused single-lead ECG and the respiration signals) take great advantage from the\nused sampling technique, reducing the amount of data used up to 99% without\nsignificant performance degradation. At the same time, multi-channel signals\n(like the six-dimensional accelerometer signal) can still benefit from the\ndesigned circuit, achieving a reduction factor up to 80% with minor performance\ndegradation. These results open the door to new types of wearable sensors with\nreduced size and higher battery lifetime.\n"}
+{"abstract": "  Space information networks (SIN) are facing an ever-increasing thirst for\nhigh-speed and high-capacity seamless data transmission due to the integration\nof ground, air, and space communications. However, this imposes a new paradigm\non the architecture design of the integrated SIN. Recently, reconfigurable\nintelligent surfaces (RISs) and mobile edge computing (MEC) are the most\npromising techniques, conceived to improve communication and computation\ncapability by reconfiguring the wireless propagation environment and\noffloading. Hence, converging RISs and MEC in SIN is becoming an effort to reap\nthe double benefits of computation and communication. In this article, we\npropose an RIS-assisted collaborative MEC architecture for SIN and discuss its\nimplementation. Then we present its potential benefits, major challenges, and\nfeasible applications. Subsequently, we study different cases to evaluate the\nsystem data rate and latency. Finally, we conclude with a list of open issues\nin this research area.\n"}
+{"abstract": "  The presence of stable, compact circumbinary discs of gas and dust around\npost-asymptotic giant branch (post-AGB) binary systems has been well\nestablished. We focus on one such system: IRAS 08544-4431. We present an\ninterferometric multi-wavelength analysis of the circumstellar environment of\nIRAS 08544-4431. The aim is to constrain different contributions to the total\nflux in the H, K, L, and N-bands in the radial direction. The data from\nVLTI/PIONIER, VLTI/GRAVITY, and VLTI/MATISSE range from the near-infrared,\nwhere the post-AGB star dominates, to the mid-infrared, where the disc\ndominates. We fitted two geometric models to the visibility data to reproduce\nthe circumbinary disc: a ring with a Gaussian width and a flat disc model with\na temperature gradient. The flux contributions from the disc, the primary star\n(modelled as a point-source), and an over-resolved component are recovered\nalong with the radial size of the emission, the temperature of the disc as a\nfunction of radius, and the spectral dependencies of the different components.\nThe trends of all visibility data were well reproduced with the geometric\nmodels. The near-infrared data were best fitted with a Gaussian ring model\nwhile the mid-infrared data favoured a temperature gradient model. This implies\nthat a vertical structure is present at the disc inner rim, which we attribute\nto a rounded puffed-up inner rim. The N-to-K size ratio is 2.8, referring to a\ncontinuous flat source, analogues to young stellar objects. By combining\noptical interferometric instruments operating at different wavelengths we can\nresolve the complex structure of circumstellar discs and study the\nwavelength-dependent opacity profile. A detailed radial, vertical, and\nazimuthal structural analysis awaits a radiative transfer treatment in 3D to\ncapture all non-radial complexity.\n"}
+{"abstract": "  We report on the development and extensive characterization of co-sputtered\ntantala-zirconia thin films, with the goal to decrease coating Brownian noise\nin present and future gravitational-wave detectors. We tested a variety of\nsputtering processes of different energies and deposition rates, and we\nconsidered the effect of different values of cation ratio $\\eta =$ Zr/(Zr+Ta)\nand of post-deposition heat treatment temperature $T_a$ on the optical and\nmechanical properties of the films. Co-sputtered zirconia proved to be an\nefficient way to frustrate crystallization in tantala thin films, allowing for\na substantial increase of the maximum annealing temperature and hence for a\ndecrease of coating mechanical loss. The lowest average coating loss was\nobserved for an ion-beam sputtered sample with $\\eta = 0.485 \\pm 0.004$\nannealed at 800 $^{\\circ}$C, yielding $\\overline{\\varphi} = 1.8 \\times\n10^{-4}$. All coating samples showed cracks after annealing. Although in\nprinciple our measurements are sensitive to such defects, we found no evidence\nthat our results were affected. The issue could be solved, at least for\nion-beam sputtered coatings, by decreasing heating and cooling rates down to 7\n$^{\\circ}$C/h. While we observed as little optical absorption as in the\ncoatings of current gravitational-wave interferometers (0.5 parts per million),\nfurther development will be needed to decrease light scattering and avoid the\nformation of defects upon annealing.\n"}
+{"abstract": "  Beginning with the Everett-DeWitt many-worlds interpretation of quantum\nmechanics, there have been a series of proposals for how the state vector of a\nquantum system might split at any instant into orthogonal branches, each of\nwhich exhibits approximately classical behavior. Here we propose a\ndecomposition of a state vector into branches by finding the minimum of a\nmeasure of the mean squared quantum complexity of the branches in the branch\ndecomposition. In a non-relativistic formulation of this proposal, branching\noccurs repeatedly over time, with each branch splitting successively into\nfurther sub-branches among which the branch followed by the real world is\nchosen randomly according to the Born rule. In a Lorentz covariant version, the\nreal world is a single random draw from the set of branches at asymptotically\nlate time, restored to finite time by sequentially retracing the set of\nbranching events implied by the late time choice. The complexity measure\ndepends on a parameter $b$ with units of volume which sets the boundary between\nquantum and classical behavior. The value of $b$ is, in principle, accessible\nto experiment.\n"}
+{"abstract": "  We prove existence, uniqueness and non-negativity of solutions of certain\nintegral equations describing the density of states $u(z)$ in the spectral\ntheory of soliton gases for the one dimensional integrable focusing Nonlinear\nSchr\\\"{o}dinger Equation (fNLS) and for the Korteweg de Vries (KdV) equation.\nOur proofs are based on ideas and methods of potential theory. In particular,\nwe show that the minimizing (positive) measure for certain energy functional is\nabsolutely continuous and its density $u(z)\\geq 0$ solves the required integral\nequation. In a similar fashion we show that $v(z)$, the temporal analog of\n$u(z)$, is the difference of densities of two absolutely continuous measures.\nTogether, integral equations for $u,v$ represent nonlinear dispersion relation\nfor the fNLS soliton gas. We also discuss smoothness and other properties of\nthe obtained solutions. Finally, we obtain exact solutions of the above\nintegral equations in the case of a KdV condensate and a bound state fNLS\ncondensate. Our results is a first step towards a mathematical foundation for\nthe spectral theory of soliton and breather gases, which appeared in work of El\nand Tovbis, Phys. Rev. E, 2020. It is expected that the presented ideas and\nmethods will be useful for studying similar classes of integral equation\ndescribing, for example, breather gases for the fNLS, as well as soliton gases\nof various integrable systems.\n"}
+{"abstract": "  The second law of thermodynamics is asymmetric with respect to time as it\nsays that the entropy of the universe must have been lower in the past and will\nbe higher in the future. How this time-asymmetric law arises from the\ntime-symmetric equations of motion has been the subject of extensive discussion\nin the scientific literature. The currently accepted resolution of the problem\nis to assume that the universe began in a low entropy state for an unknown\nreason. But the probability of this happening by chance is exceedingly small,\nif all microstates are assigned equal a-priori probabilities. In this paper, I\nexplore another possible explanation, which is that our observations of the\ntime-asymmetric increase of entropy could simply be the result of the way we\nassign a-priori probabilities differently to past and future events.\n"}
+{"abstract": "  Recently, there have been several papers that discuss the extension of the\nPinball loss Support Vector Machine (Pin-SVM) model, originally proposed by\nHuang et al.,[1][2]. Pin-SVM classifier deals with the pinball loss function,\nwhich has been defined in terms of the parameter $\\tau$. The parameter $\\tau$\ncan take values in $[ -1,1]$. The existing Pin-SVM model requires to solve the\nsame optimization problem for all values of $\\tau$ in $[ -1,1]$. In this paper,\nwe improve the existing Pin-SVM model for the binary classification task. At\nfirst, we note that there is major difficulty in Pin-SVM model (Huang et al.\n[1]) for $ -1 \\leq \\tau < 0$. Specifically, we show that the Pin-SVM model\nrequires the solution of different optimization problem for $ -1 \\leq \\tau <\n0$. We further propose a unified model termed as Unified Pin-SVM which results\nin a QPP valid for all $-1\\leq \\tau \\leq 1$ and hence more convenient to use.\nThe proposed Unified Pin-SVM model can obtain a significant improvement in\naccuracy over the existing Pin-SVM model which has also been empirically\njustified by extensive numerical experiments with real-world datasets.\n"}
+{"abstract": "  The presence of non-zero helicity in intergalactic magnetic fields is a\nsmoking gun for their primordial origin since they have to be generated by\nprocesses that break CP invariance. As an experimental signature for the\npresence of helical magnetic fields, an estimator $Q$ based on the triple\nscalar product of the wave-vectors of photons generated in electromagnetic\ncascades from, e.g., TeV blazars, has been suggested previously. We propose to\napply deep learning to helicity classification employing Convolutional Neural\nNetworks and show that this method outperforms the $Q$ estimator.\n"}
+{"abstract": "  Being able to segment unseen classes not observed during training is an\nimportant technical challenge in deep learning, because of its potential to\nreduce the expensive annotation required for semantic segmentation. Prior\nzero-label semantic segmentation works approach this task by learning\nvisual-semantic embeddings or generative models. However, they are prone to\noverfitting on the seen classes because there is no training signal for them.\nIn this paper, we study the challenging generalized zero-label semantic\nsegmentation task where the model has to segment both seen and unseen classes\nat test time. We assume that pixels of unseen classes could be present in the\ntraining images but without being annotated. Our idea is to capture the latent\ninformation on unseen classes by supervising the model with self-produced\npseudo-labels for unlabeled pixels. We propose a consistency regularizer to\nfilter out noisy pseudo-labels by taking the intersections of the pseudo-labels\ngenerated from different augmentations of the same image. Our framework\ngenerates pseudo-labels and then retrain the model with human-annotated and\npseudo-labelled data. This procedure is repeated for several iterations. As a\nresult, our approach achieves the new state-of-the-art on PascalVOC12 and\nCOCO-stuff datasets in the challenging generalized zero-label semantic\nsegmentation setting, surpassing other existing methods addressing this task\nwith more complex strategies.\n"}
+{"abstract": "  We consider a diffusion given by a small noise perturbation of a dynamical\nsystem driven by a potential function with a finite number of local minima. The\nclassical results of Freidlin and Wentzell show that the time this diffusion\nspends in the domain of attraction of one of these local minima is\napproximately exponentially distributed and hence the diffusion should behave\napproximately like a Markov chain on the local minima. By the work of Bovier\nand collaborators, the local minima can be associated with the small\neigenvalues of the diffusion generator. In Part I of this work, by applying a\nMarkov mapping theorem, we used the eigenfunctions of the generator to couple\nthis diffusion to a Markov chain whose generator has eigenvalues equal to the\neigenvalues of the diffusion generator that are associated with the local\nminima and established explicit formulas for conditional probabilities\nassociated with this coupling. The fundamental question now becomes to relate\nthe coupled Markov chain to the approximate Markov chain suggested by the\nresults of Freidlin and Wentzel. In this paper, we take up this question and\nprovide a complete analysis of this relationship in the special case of a\ndouble-well potential in one dimension.\n"}
+{"abstract": "  To reconcile the two experimental findings on La_{2-x}Sr_{x}CuO_4, namely,\nFermi surface (FS) observed by angle-resolved photoemission spectroscopy and\nsharp incommensurate magnetic peaks by neutron scattering, we propose a picture\nthat a quasi-one-dimensional FS (q-1dFS) is realized in each CuO_{2} plane\nwhose q-1d direction alternates along the c-axis.\n"}
+{"abstract": "  We consider the problem of finding the subset of order statistics that\ncontains the most information about a sample of random variables drawn\nindependently from some known parametric distribution. We leverage\ninformation-theoretic quantities, such as entropy and mutual information, to\nquantify the level of informativeness and rigorously characterize the amount of\ninformation contained in any subset of the complete collection of order\nstatistics. As an example, we show how these informativeness metrics can be\nevaluated for a sample of discrete Bernoulli and continuous Uniform random\nvariables. Finally, we unveil how our most informative order statistics\nframework can be applied to image processing applications. Specifically, we\ninvestigate how the proposed measures can be used to choose the coefficients of\nthe L-estimator filter to denoise an image corrupted by random noise. We show\nthat both for discrete (e.g., salt-pepper noise) and continuous (e.g., mixed\nGaussian noise) noise distributions, the proposed method is competitive with\noff-the-shelf filters, such as the median and the total variation filters, as\nwell as with wavelet-based denoising methods.\n"}
+{"abstract": "  In [6] the authors gave a generalization of the concept of Igusa-Todorov\nalgebra and proved that those algebras, named Lat-Igusa-Todorov (or LIT for\nshort), satisfy the finitistic dimension conjecture. In this paper we explore\nthe scope of that generalization and give conditions for a triangular matrix\nalgebra to be LIT in terms of the algebras and the module used in its\ndefinition.\n"}
+{"abstract": "  Classical machine learning (ML) provides a potentially powerful approach to\nsolving challenging quantum many-body problems in physics and chemistry.\nHowever, the advantages of ML over more traditional methods have not been\nfirmly established. In this work, we prove that classical ML algorithms can\nefficiently predict ground state properties of gapped Hamiltonians in finite\nspatial dimensions, after learning from data obtained by measuring other\nHamiltonians in the same quantum phase of matter. In contrast, under widely\naccepted complexity theory assumptions, classical algorithms that do not learn\nfrom data cannot achieve the same guarantee. We also prove that classical ML\nalgorithms can efficiently classify a wide range of quantum phases of matter.\nOur arguments are based on the concept of a classical shadow, a succinct\nclassical description of a many-body quantum state that can be constructed in\nfeasible quantum experiments and be used to predict many properties of the\nstate. Extensive numerical experiments corroborate our theoretical results in a\nvariety of scenarios, including Rydberg atom systems, 2D random Heisenberg\nmodels, symmetry-protected topological phases, and topologically ordered\nphases.\n"}
+{"abstract": "  We investigate a mechanism for a super-massive black hole at the center of a\ngalaxy to wander in the nucleus region. A situation is supposed in which the\ncentral black hole tends to move by the gravitational attractions from the\nnearby molecular clouds in a nuclear bulge but is braked via the dynamical\nfrictions by the ambient stars there. We estimate the approximate kinetic\nenergy of the black hole in an equilibrium between the energy gain rate through\nthe gravitational attractions and the energy loss rate through the dynamical\nfrictions, in a nuclear bulge composed of a nuclear stellar disk and a nuclear\nstellar cluster as observed from our Galaxy. The wandering distance of the\nblack hole in the gravitational potential of the nuclear bulge is evaluated to\nget as large as several 10 pc, when the black hole mass is relatively small.\nThe distance, however, shrinks as the black hole mass increases and the\nequilibrium solution between the energy gain and loss disappears when the black\nhole mass exceeds an upper limit. As a result, we can expect the following\nscenario for the evolution of the black hole mass: When the black hole mass is\nsmaller than the upper limit, mass accretion of the interstellar matter in the\ncircum-nuclear region, causing the AGN activities, makes the black hole mass\nlarger. However, when the mass gets to the upper limit, the black hole loses\nthe balancing force against the dynamical friction and starts spiraling\ndownward to the gravity center. From simple parameter scaling, the upper mass\nlimit of the black hole is found to be proportional to the bulge mass and this\ncould explain the observed correlation of the black hole mass with the bulge\nmass.\n"}
+{"abstract": "  We study the estimation of the linear discriminant with projection pursuit, a\nmethod that is blind in the sense that it does not use the class labels in the\nestimation. Our viewpoint is asymptotic and, as our main contribution, we\nderive central limit theorems for estimators based on three different\nprojection indices, skewness, kurtosis and their convex combination. The\nresults show that in each case the limiting covariance matrix is proportional\nto that of linear discriminant analysis (LDA), an unblind estimator of the\ndiscriminant. An extensive comparative study between the asymptotic variances\nreveals that projection pursuit is able to achieve efficiency equal to LDA when\nthe groups are arbitrarily well-separated and their sizes are reasonably\nbalanced. We conclude with a real data example and a simulation study\ninvestigating the validity of the obtained asymptotic formulas for finite\nsamples.\n"}
+{"abstract": "  Different from traditional knowledge graphs (KGs) where facts are represented\nas entity-relation-entity triplets, hyper-relational KGs (HKGs) allow triplets\nto be associated with additional relation-entity pairs (a.k.a qualifiers) to\nconvey more complex information. How to effectively and efficiently model the\ntriplet-qualifier relationship for prediction tasks such as HKG completion is\nan open challenge for research. This paper proposes to improve the\nbest-performing method in HKG completion, namely STARE, by introducing two\nnovel revisions: (1) Replacing the computation-heavy graph neural network\nmodule with light-weight entity/relation embedding processing techniques for\nefficiency improvement without sacrificing effectiveness; (2) Adding a\nqualifier-oriented auxiliary training task for boosting the prediction power of\nour approach on HKG completion. The proposed approach consistently outperforms\nSTARE in our experiments on three benchmark datasets, with significantly\nimproved computational efficiency.\n"}
+{"abstract": "  Although there are a small number of work to conduct patent research by\nbuilding knowledge graph, but without constructing patent knowledge graph using\npatent documents and combining latest natural language processing methods to\nmine hidden rich semantic relationships in existing patents and predict new\npossible patents. In this paper, we propose a new patent vacancy prediction\napproach named PatentMiner to mine rich semantic knowledge and predict new\npotential patents based on knowledge graph (KG) and graph attention mechanism.\nFirstly, patent knowledge graph over time (e.g. year) is constructed by\ncarrying out named entity recognition and relation extrac-tion from patent\ndocuments. Secondly, Common Neighbor Method (CNM), Graph Attention Networks\n(GAT) and Context-enhanced Graph Attention Networks (CGAT) are proposed to\nperform link prediction in the constructed knowledge graph to dig out the\npotential triples. Finally, patents are defined on the knowledge graph by means\nof co-occurrence relationship, that is, each patent is represented as a fully\nconnected subgraph containing all its entities and co-occurrence relationships\nof the patent in the knowledge graph; Furthermore, we propose a new patent\nprediction task which predicts a fully connected subgraph with newly added\nprediction links as a new pa-tent. The experimental results demonstrate that\nour proposed patent predic-tion approach can correctly predict new patents and\nContext-enhanced Graph Attention Networks is much better than the baseline.\nMeanwhile, our proposed patent vacancy prediction task still has significant\nroom to im-prove.\n"}
+{"abstract": "  The fast-growing Emerging Market (EM) economies and their improved\ntransparency and liquidity have attracted international investors. However, the\nexternal price shocks can result in a higher level of volatility as well as\ndomestic policy instability. Therefore, an efficient risk measure and hedging\nstrategies are needed to help investors protect their investments against this\nrisk. In this paper, a daily systemic risk measure, called FRM (Financial Risk\nMeter) is proposed. The FRM-EM is applied to capture systemic risk behavior\nembedded in the returns of the 25 largest EMs FIs, covering the BRIMST (Brazil,\nRussia, India, Mexico, South Africa, and Turkey), and thereby reflects the\nfinancial linkages between these economies. Concerning the Macro factors, in\naddition to the Adrian and Brunnermeier (2016) Macro, we include the EM\nsovereign yield spread over respective US Treasuries and the above-mentioned\ncountries currencies. The results indicated that the FRM of EMs FIs reached its\nmaximum during the US financial crisis following by COVID 19 crisis and the\nMacro factors explain the BRIMST FIs with various degrees of sensibility. We\nthen study the relationship between those factors and the tail event network\nbehavior to build our policy recommendations to help the investors to choose\nthe suitable market for in-vestment and tail-event optimized portfolios. For\nthat purpose, an overlapping region between portfolio optimization strategies\nand FRM network centrality is developed. We propose a robust and\nwell-diversified tail-event and cluster risk-sensitive portfolio allocation\nmodel and compare it to more classical approaches\n"}
+{"abstract": "  Deep neural networks offer numerous potential applications across geoscience,\nfor example, one could argue that they are the state-of-the-art method for\npredicting faults in seismic datasets. In quantitative reservoir\ncharacterization workflows, it is common to incorporate the uncertainty of\npredictions thus such subsurface models should provide calibrated probabilities\nand the associated uncertainties in their predictions. It has been shown that\npopular Deep Learning-based models are often miscalibrated, and due to their\ndeterministic nature, provide no means to interpret the uncertainty of their\npredictions. We compare three different approaches to obtaining probabilistic\nmodels based on convolutional neural networks in a Bayesian formalism, namely\nDeep Ensembles, Concrete Dropout, and Stochastic Weight Averaging-Gaussian\n(SWAG). These methods are consistently applied to fault detection case studies\nwhere Deep Ensembles use independently trained models to provide fault\nprobabilities, Concrete Dropout represents an extension to the popular Dropout\ntechnique to approximate Bayesian neural networks, and finally, we apply SWAG,\na recent method that is based on the Bayesian inference equivalence of\nmini-batch Stochastic Gradient Descent. We provide quantitative results in\nterms of model calibration and uncertainty representation, as well as\nqualitative results on synthetic and real seismic datasets. Our results show\nthat the approximate Bayesian methods, Concrete Dropout and SWAG, both provide\nwell-calibrated predictions and uncertainty attributes at a lower computational\ncost when compared to the baseline Deep Ensemble approach. The resulting\nuncertainties also offer a possibility to further improve the model performance\nas well as enhancing the interpretability of the models.\n"}
+{"abstract": "  Forecasting competitions are the equivalent of laboratory experimentation\nwidely used in physical and life sciences. They provide useful, objective\ninformation to improve the theory and practice of forecasting, advancing the\nfield, expanding its usage and enhancing its value to decision and\npolicymakers. We describe ten design attributes to be considered when\norganizing forecasting competitions, taking into account trade-offs between\noptimal choices and practical concerns like costs, as well as the time and\neffort required to participate in them. Consequently, we map all major past\ncompetitions in respect to their design attributes, identifying similarities\nand differences between them, as well as design gaps, and making suggestions\nabout the principles to be included in future competitions, putting a\nparticular emphasis on learning as much as possible from their implementation\nin order to help improve forecasting accuracy and uncertainty. We discuss that\nthe task of forecasting often presents a multitude of challenges that can be\ndifficult to be captured in a single forecasting contest. To assess the caliber\nof a forecaster, we, therefore, propose that organizers of future competitions\nconsider a multi-contest approach. We suggest the idea of a forecasting\n\"athlon\", where different challenges of varying characteristics take place.\n"}
+{"abstract": "  We consider the problem of online reinforcement learning for the Stochastic\nShortest Path (SSP) problem modeled as an unknown MDP with an absorbing state.\nWe propose PSRL-SSP, a simple posterior sampling-based reinforcement learning\nalgorithm for the SSP problem. The algorithm operates in epochs. At the\nbeginning of each epoch, a sample is drawn from the posterior distribution on\nthe unknown model dynamics, and the optimal policy with respect to the drawn\nsample is followed during that epoch. An epoch completes if either the number\nof visits to the goal state in the current epoch exceeds that of the previous\nepoch, or the number of visits to any of the state-action pairs is doubled. We\nestablish a Bayesian regret bound of $O(B_\\star S\\sqrt{AK})$, where $B_\\star$\nis an upper bound on the expected cost of the optimal policy, $S$ is the size\nof the state space, $A$ is the size of the action space, and $K$ is the number\nof episodes. The algorithm only requires the knowledge of the prior\ndistribution, and has no hyper-parameters to tune. It is the first such\nposterior sampling algorithm and outperforms numerically previously proposed\noptimism-based algorithms.\n"}
+{"abstract": "  In our research, we focus on the response to the non-consensual distribution\nof intimate or sexually explicit digital images of adults, also referred as\nrevenge porn, from the point of view of the victims. In this paper, we present\na preliminary expert analysis of the process for reporting revenge porn abuses\nin selected content sharing platforms. Among these, we included social\nnetworks, image hosting websites, video hosting platforms, forums, and\npornographic sites. We looked at the way to report abuse, concerning both the\nnon-consensual online distribution of private sexual image or video (revenge\npornography), as well as the use of deepfake techniques, where the face of a\nperson can be replaced on original visual content with the aim of portraying\nthe victim in the context of sexual behaviours. This preliminary analysis is\ndirected to understand the current practices and potential issues in the\nprocedures designed by the providers for reporting these abuses.\n"}
+{"abstract": "  In January 2019, the UK Government published its Maritime 2050 on Navigating\nthe Future strategy. In the strategy, the government highlighted the importance\nof digitalization (with well-designed regulatory support) to achieve its goal\nof ensuring that the UK plays a global leadership role in the maritime sector.\nPorts, the gateways for 95% of UK trade movements, were identified as key sites\nfor investment in technological innovation. The government identified the\npotential of the Internet of Things (IoT), in conjunction with other\ninformation-sharing technologies, such as shared data platforms, and Artificial\nIntelligence applications (AI), to synchronize processes within the port\necosystem leading to improved efficiency, safety, and environmental benefits,\nincluding improved air quality and lower greenhouse gas emissions.\n"}
+{"abstract": "  Consider a nonuniformly hyperbolic map $ T $ modelled by a Young tower with\ntails of the form $ O(n^{-\\beta}) $, $ \\beta>2 $. We prove optimal moment\nbounds for Birkhoff sums $ \\sum_{i=0}^{n-1}v\\circ T^i $ and iterated sums $\n\\sum_{0\\le i<j<n}v\\circ T^i\\, w\\circ T^j $, where $ v,w:M\\to \\Bbb{R}$ are\n(dynamically) H\\\"older observables. Previously iterated moment bounds were only\nknown for $ \\beta>5$. Our method of proof is as follows; (i) prove that $ T $\nsatisfies an abstract functional correlation bound, (ii) use a weak dependence\nargument to show that the functional correlation bound implies moment\nestimates.\n  Such iterated moment bounds arise when using rough path theory to prove\ndeterministic homogenisation results. Indeed, by a recent result of Chevyrev,\nFriz, Korepanov, Melbourne & Zhang we have convergence an It\\^o diffusion for\nfast-slow systems of the form \\[\nx^{(n)}_{k+1}=x_k^{(n)}+n^{-1}a(x_k^{(n)},y_k)+n^{-1/2}b(x_k^{(n)},y_k) , \\quad\ny_{k+1}=T y_k \\] in the optimal range $ \\beta>2. $\n"}
+{"abstract": "  Recently, vision transformers and MLP-based models have been developed in\norder to address some of the prevalent weaknesses in convolutional neural\nnetworks. Due to the novelty of transformers being used in this domain along\nwith the self-attention mechanism, it remains unclear to what degree these\narchitectures are robust to corruptions. Despite some works proposing that data\naugmentation remains essential for a model to be robust against corruptions, we\npropose to explore the impact that the architecture has on corruption\nrobustness. We find that vision transformer architectures are inherently more\nrobust to corruptions than the ResNet-50 and MLP-Mixers. We also find that\nvision transformers with 5 times fewer parameters than a ResNet-50 have more\nshape bias. Our code is available to reproduce.\n"}
+{"abstract": "  Recent trend towards increasing large machine learning models require both\ntraining and inference tasks to be distributed. Considering the huge cost of\ntraining these models, it is imperative to unlock optimizations in computation\nand communication to obtain best performance. However, current logical\nseparation between computation and communication kernels in deep learning\nframeworks misses the optimization opportunities across such barrier. Breaking\nthis abstraction with a holistic consideration can provide many optimizations\nto provide performance improvements in distributed workloads. Manually applying\nthese optimizations needs modifications in underlying computation and\ncommunication libraries for each scenario, which is time consuming and\nerror-prone.\n  Therefore, we present CoCoNeT, with a DSL to express a program with both\ncomputation and communication. CoCoNeT contains several machine learning aware\ntransformations to optimize a program and a compiler to generate high\nperformance kernels. Providing both computation and communication as first\nclass constructs allows users to work on a high-level abstraction and apply\npowerful optimizations, such as fusion or overlapping of communication and\ncomputation. CoCoNeT enables us to optimize data-, model-and pipeline-parallel\nworkloads in large language models with only a few lines of code. Experiments\nshow CoCoNeT significantly outperforms state-of-the-art distributed machine\nlearning implementations.\n"}
+{"abstract": "  Rapid progress in additive manufacturing methods has created a new class of\nultralight and strong architected metamaterials that resemble periodic truss\nstructures. The mechanical performance of these metamaterials with a very large\nnumber of unit cells is ultimately limited by their tolerance to damage and\ndefects, but an understanding of this sensitivity has remained elusive. Using a\nstretching-dominated micro-architecture and metamaterial specimens comprising\nmillions of unit-cells we show that not only is the stress intensity factor, as\nused in conventional elastic fracture mechanics, insufficient to characterize\nfracture but also that conventional fracture testing protocols are inadequate.\nVia a combination of numerical calculations and asymptotic analyses, we extend\nthe ideas of fracture mechanics and develop a general test and design protocol\nfor the failure of metamaterials.\n"}
+{"abstract": "  A fundamental advantage of Petri net models is the possibility to\nautomatically compute useful system invariants from the syntax of the net.\nClassical techniques used for this are place invariants, P-components, siphons\nor traps. Recently, Bozga et al. have presented a novel technique for the\n\\emph{parameterized} verification of safety properties of systems with a ring\nor array architecture. They show that the statement \\enquote{for every instance\nof the parameterized Petri net, all markings satisfying the linear invariants\nassociated to all the P-components, siphons and traps of the instance are safe}\ncan be encoded in \\acs{WS1S} and checked using tools like MONA. However, while\nthe technique certifies that this infinite set of linear invariants extracted\nfrom P-components, siphons or traps are strong enough to prove safety, it does\nnot return an explanation of this fact understandable by humans. We present a\nCEGAR loop that constructs a \\emph{finite} set of \\emph{parameterized}\nP-components, siphons or traps, whose infinitely many instances are strong\nenough to prove safety. For this we design parameterization procedures for\ndifferent architectures.\n"}
+{"abstract": "  I discuss possible consequences of A. D. Sakharov's hypothesis of\ncosmological transitions with changes in the signature of the metric, based on\nthe path integral approach. This hypothesis raises a number of mathematical and\nphilosophical questions. Mathematical questions concern the definition of the\npath integral to include integration over spacetime regions with different\nsignatures of the metric. One possible way to describe the changes in the\nsignature is to admit time and space coordinates to be purely imaginary. It may\nlook like a generalization of what we have in the case of pseudo-Riemannian\nmanifolds with a non-trivial topology. The signature in these regions can be\nfixed by special gauge conditions on components of the metric tensor. The\nproblem is what boundary conditions should be imposed on the boundaries of\nthese regions and how they should be taken into account in the definition of\nthe path integral. The philosophical question is what distinguishes the time\ncoordinate among other coordinates but the sign of the corresponding principal\nvalue of the metric tensor. In particular, I try to speculate how the existence\nof the regions with different signature can affect the evolution of the\nUniverse.\n"}
+{"abstract": "  Decomposing taxes by source (labor, capital, sales), we analyze the impact of\nautomation (1) on tax revenues, (2) the structure of taxation, and (3) identify\nchannels of impact in 19 EU countries during 1995-2016. Robots and Information\nand Communication Technologies (ICT) are different technologies designed to\nautomate manual (robots) or cognitive tasks (ICT).\n  Until 2007, robot diffusion led to decreasing factor and tax income, and a\nshift from taxes on capital to goods. ICTs changed the structure of taxation\nfrom capital to labor. We find decreasing employment, but increasing wages and\nlabor income. After 2008, robots have no effect but we find an ICT-induced\nincrease in capital income, a rise of services, but no effect on taxation.\nAutomation goes through different phases with different economic impacts which\naffect the amount and structure of taxes. Whether automation erodes taxation\ndepends (a) on the technology type, (b) the stage of diffusion and (c) local\nconditions.\n"}
+{"abstract": "  In this paper, we propose the novel problem of Subteam Replacement: given a\nteam of people embedded in a social network to complete a certain task, and a\nsubset of members - subteam - in this team which have become unavailable, find\nanother set of people who can perform the subteam's role in the larger team.\nThe ability to simultaneously replace multiple team members is highly\nappreciated in settings such as corporate management where team structure is\nhighly volatile and large-scale changes are commonplace. We conjecture that a\ngood candidate subteam should have high skill and structural similarity with\nthe replaced subteam while sharing a similar connection with the larger team as\na whole. Based on this conjecture, we propose a novel graph kernel which\nevaluates the goodness of candidate subteams in this holistic way freely\nadjustable to the need of the situation. To tackle the significant\ncomputational difficulties, we combine our kernel with a fast approximate\nalgorithm which (a) employs effective pruning strategies, (b) exploits the\nsimilarity between candidate team structures to reduce kernel computations, and\n(c) features a solid theoretical bound obtained from mathematical properties of\nthe problem. We extensively test our solution on both synthetic and real\ndatasets to demonstrate its consistency and efficiency. Our proposed graph\nkernel results in more suitable replacements being proposed compared to graph\nkernels used in previous work, and our algorithm consistently outperforms\nalternative choices by finding near-optimal solutions while scaling linearly\nwith the size of the replaced subteam.\n"}
+{"abstract": "  We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social\nMedia Mining for Health Applications (SMM4H) shared tasks. Our system is based\non fine-tuning Distill- BERT on each task, as well as first fine-tuning the\nmodel on the other task. We explore how much fine-tuning is necessary for\naccurately classifying tweets as containing self-reported COVID-19 symptoms\n(Task 5) or whether a tweet related to COVID-19 is self-reporting, non-personal\nreporting, or a literature/news mention of the virus (Task 6).\n"}
+{"abstract": "  The multi-point Taylor polynomial, which is the general, unique and of\nminimum degree ($mk+m-1$) polynomial $P_{k,m}(x)$ which interpolates a\nfunction's derivatives in multiple points is presented in its explicit form. A\nproof that this expression satisfies the multi-point Taylor polynomial's\ndefining property is given. Namely, it is proven that for a k-differentiable\nfunction $f$ and a set of different m-points $\\{a_1,...,a_m\\}$, this polynomial\nsatisfies $P^{(n)}_{k,m}(a_i) = f^{(n)}(a_i)\\quad \\forall \\, i = 1,...,m\\quad\n\\&\\quad \\forall \\, n = 0,...,k$. A discussion regarding previous expressions\npresented in the literature, which mostly consisted in recursion formulas and\nnot explicit formulas, is made.\n"}
+{"abstract": "  We propose a new sensing method based on the measurement of the second-order\nautocorrelation of the output of micro- and nanolasers with intensity feedback.\nThe sensing function is implemented through the feedback-induced threshold\nshift, whose photon statistics is controlled by the feedback level in a\ncharacteristic way for different laser sizes. The specific response offers\nperformances which can be adapted to different kinds of sensors. We propose the\nimplementation of two schemes capable of providing a quantitative sensing\nsignal and covering a broad range of feedback levels: one is utilizing the\nevolution of g$^{(2)}$(0), the other one is the ratio between central and side\npeaks in g$^{(2)}(\\tau)$. Laser-threshold-based sensing could, thanks to its\npotential sensitivity, gain relevance in biomolecular diagnostics and security\nmonitoring.\n"}
+{"abstract": "  Technical debt has become a well-known metaphor among software professionals,\nvisualizing how shortcuts taken during development can accumulate and become a\nburden for software projects. In the traditional notion of technical debt,\nsoftware developers borrow from the maintainability and extensibility of a\nsoftware system, thus they are the ones paying the interest. User experience\n(UX) debt, on the other hand, focuses on shortcuts taken to speed up\ndevelopment at the expense of subpar usability, thus mainly borrowing from\nusers' efficiency. With this article, we want to build awareness for this\noften-overlooked form of technical debt by outlining classes of UX debts that\nwe observed in practice and by pointing to the lack of research and tool\nsupport targeting UX debt in general.\n"}
+{"abstract": "  High spectral resolution observations toward the low mass-loss rate C-rich,\nJ-type AGB star Y CVn have been carried out at 7.5, 13.1 and 14.0 um with\nSOFIA/EXES and IRTF/TEXES. Around 130 HCN and H13CN lines of bands v2, 2v2,\n2v2-v2, 3v2-2v2, 3v2-v2, and 4v2-2v2 have been identified involving lower\nlevels with energies up to ~3900 K. These lines have been complemented with the\npure rotational lines J=1-0 and 3-2 of the vibrational states up to 2v2\nacquired with the IRAM 30 m telescope, and with the continuum taken with ISO.\nWe have analyzed the data with a ro-vibrational diagram and a code which models\nthe absorption and emission of the circumstellar envelope of an AGB star. The\ncontinuum is produced by the star with a small contribution from dust grains\ncomprising warm to hot SiC and cold amorphous carbon. The HCN abundance\ndistribution seems to be anisotropic. The ejected gas is accelerated up to the\nterminal velocity (~8 km/s) from the photosphere to ~3R* but there is evidence\nof higher velocities (>9-10 km/s) beyond this region. In the vicinity of Y CVn,\nthe line widths are as high as ~10 km/s, which implies a maximum turbulent\nvelocity of 6 km/s or the existence of other physical mechanisms probably\nrelated to matter ejection that involve higher gas expansion velocities than\nexpected. HCN is rotationally and vibrationally out of LTE throughout the whole\nenvelope. A difference of about 1500 K in the rotational temperature at the\nphotosphere is needed to explain the observations at 7.5 and 13-14 um. Our\nanalysis finds a total HCN column density that ranges from ~2.1E+18 to 3.5E+18\ncm^{-2}, an abundance with respect to H2 of 3.5E-05 to 1.3E-04, and a 12C/13C\nisotopic ratio of ~2.5 throughout the whole envelope.\n"}
+{"abstract": "  We consider a randomised version of Kleene's realisability interpretation of\nintuitionistic arithmetic in which computability is replaced with randomised\ncomputability with positive probability. In particular, we show that (i) the\nset of randomly realisable statements is closed under intuitionistic\nfirst-order logic, but (ii) different from the set of realisable statements,\nthat (iii) \"realisability with probability 1\" is the same as realisability and\n(iv) that the axioms of bounded Heyting's arithmetic are randomly realisable,\nbut some instances of the full induction scheme fail to be randomly realisable.\n"}
+{"abstract": "  Photoluminescence (PL) is a light-matter quantum interaction associated with\nthe chemical potential of light formulated by the Generalized Planck's law.\nWithout knowing the inherent temperature dependence of chemical potential, the\nGeneralized Planck's law is insufficient to characterize PL(T). Recent\nexperiments showed that PL at low temperatures conserves the emitted photon\nrate, accompanied by a blue-shift and transition to thermal emission at a\nhigher temperature. Here, we theoretically study temperature-dependent PL by\nincluding phononic interactions in a detailed balance analysis. Our solution\nvalidates recent experiments and predicts important relations, including i) An\ninherent relation between emissivity and the quantum efficiency of a system,\nii) A universal point defined by the pump and the temperature where the\nemission rate is fixed to any material, iii) A new phonon-induced quenching\nmechanism, and iv) Thermalization of the photon spectrum. These findings are\nrelevant to and important for all photonic fields where the temperature is\ndominant.\n"}
+{"abstract": "  An important step in the task of neural network design, such as\nhyper-parameter optimization (HPO) or neural architecture search (NAS), is the\nevaluation of a candidate model's performance. Given fixed computational\nresources, one can either invest more time training each model to obtain more\naccurate estimates of final performance, or spend more time exploring a greater\nvariety of models in the configuration space. In this work, we aim to optimize\nthis exploration-exploitation trade-off in the context of HPO and NAS for image\nclassification by accurately approximating a model's maximal performance early\nin the training process. In contrast to recent accelerated NAS methods\ncustomized for certain search spaces, e.g., requiring the search space to be\ndifferentiable, our method is flexible and imposes almost no constraints on the\nsearch space. Our method uses the evolution history of features of a network\nduring the early stages of training to build a proxy classifier that matches\nthe peak performance of the network under consideration. We show that our\nmethod can be combined with multiple search algorithms to find better solutions\nto a wide range of tasks in HPO and NAS. Using a sampling-based search\nalgorithm and parallel computing, our method can find an architecture which is\nbetter than DARTS and with an 80% reduction in wall-clock search time.\n"}
+{"abstract": "  We present an analysis of the kinematics of 14 satellites of the Milky Way\n(MW). We use proper motions (PMs) from the $Gaia$ Early Data Release 3 (EDR3)\nand line-of-sight velocities ($v_{\\mathrm{los}}$) available in the literature\nto derive the systemic 3D motion of these systems. For six of them, namely the\nCarina, Draco, Fornax, Sculptor, Sextans, and Ursa Minor dwarf spheroidal\ngalaxies (dSph), we study the internal kinematics projecting the stellar PMs\ninto radial, $V_R$ (expansion/contraction), and tangential, $V_T$ (rotation),\nvelocity components with respect to the centre of mass. We find significant\nrotation in the Carina ($|V_T| = 9.6 \\pm 4.5 \\ {\\rm{km \\ s^{-1}}}\\>$), Fornax\n($|V_T| = 2.8 \\pm 1.3 \\ {\\rm{km \\ s^{-1}}}\\>$), and Sculptor ($|V_T| = 3.0 \\pm\n1.0 \\ {\\rm{km \\ s^{-1}}}\\>$) dSphs. Besides the Sagittarius dSph, these are the\nfirst measurements of internal rotation in the plane of the sky in the MW's\nclassical dSphs. All galaxies except Carina show $|V_T| / \\sigma_v < 1$. We\nfind that slower rotators tend to show, on average, larger sky-projected\nellipticity (as expected for a sample with random viewing angles) and are\nlocated at smaller Galactocentric distances (as expected for tidal stirring\nscenarios in which rotation is transformed into random motions as satellites\nsink into the parent halo). However, these trends are small and not\nstatistically significant, indicating that rotation has not played a dominant\nrole in shaping the 3D structure of these galaxies. Either tidal stirring had a\nweak impact on the evolution of these systems or it perturbed them with similar\nefficiency regardless of their current Galactocentric distance.\n"}
+{"abstract": "  In this paper, a numerical study is conducted to investigate boiling of a\ncryogen on a solid surface as well as on a liquid surface. Both single mode and\nmulti-mode boiling is reported for boiling on to a solid surface. In case of\nboiling on a liquid surface, liquid nitrogen is selected as the cryogen\n(boiling fluid) and water is chosen as the base fluid (heating fluid).\nDifferent flow instabilities and their underlying consequences during boiling\nof a cryogen are also discussed. For the boiling on a solid surface, in the\nsingle mode, bubble growth, its departure, and area weighted average heat flux\nare reported, where they increase linearly with increase in the wall superheat.\nAsymmetry in the bubble growth and departure of 2nd batch of the vapor bubbles\nhave been observed due to local fluctuations and turbulence created just after\nthe pinch off of the 1st batch of vapor bubbles in case of multi-mode boiling\non the solid surface. Boiling of LN2 on a liquid surface is reported for a base\nfluid (Water) temperature of 300 K. Vapor film thickness decreases with time\nand the minimum film thickness just before rupture is 7.62 micrometer,\ndominance of thermocapillary over vapor thrust causes breaking of the vapor\nfilm at 0.0325s. The difference in evaporation rate and vapor generation,\nbefore and after vapor film collapse is significant.\n"}
+{"abstract": "  Generating videos with content and motion variations is a challenging task in\ncomputer vision. While the recent development of GAN allows video generation\nfrom latent representations, it is not easy to produce videos with particular\ncontent of motion patterns of interest. In this paper, we propose Dual Motion\nTransfer GAN (Dual-MTGAN), which takes image and video data as inputs while\nlearning disentangled content and motion representations. Our Dual-MTGAN is\nable to perform deterministic motion transfer and stochastic motion generation.\nBased on a given image, the former preserves the input content and transfers\nmotion patterns observed from another video sequence, and the latter directly\nproduces videos with plausible yet diverse motion patterns based on the input\nimage. The proposed model is trained in an end-to-end manner, without the need\nto utilize pre-defined motion features like pose or facial landmarks. Our\nquantitative and qualitative results would confirm the effectiveness and\nrobustness of our model in addressing such conditioned image-to-video tasks.\n"}
+{"abstract": "  The infinite many symmetries of DS(Davey-Stewartson) system are closely\nconnected to the integrable deformations of surfaces in $\\mathbb{R}^{4}$. In\nthis paper, we give a direct algorithm to construct the expression of the\nDS(Davey-Stewartson) hierarchy by two scalar pseudo-differential operators\ninvolved with $\\partial$ and $\\hat{\\partial}$.\n"}
+{"abstract": "  Young giant planets and brown dwarf companions emit near-infrared radiation\nthat can be linearly polarized up to several percent. This polarization can\nreveal the presence of a circumsubstellar accretion disk, rotation-induced\noblateness of the atmosphere, or an inhomogeneous distribution of atmospheric\ndust clouds. We measured the near-infrared linear polarization of 20 known\ndirectly imaged exoplanets and brown dwarf companions with the high-contrast\nimager SPHERE-IRDIS at the VLT. We reduced the data using the IRDAP pipeline to\ncorrect for the instrumental polarization and crosstalk with an absolute\npolarimetric accuracy <0.1% in the degree of polarization. We report the first\ndetection of polarization originating from substellar companions, with a\npolarization of several tenths of a percent for DH Tau B and GSC 6214-210 B in\nH-band. By comparing the measured polarization with that of nearby stars, we\nfind that the polarization is unlikely to be caused by interstellar dust.\nBecause the companions have previously measured hydrogen emission lines and red\ncolors, the polarization most likely originates from circumsubstellar disks.\nThrough radiative transfer modeling, we constrain the position angles of the\ndisks and find that the disks must have high inclinations. The presence of\nthese disks as well as the misalignment of the disk of DH Tau B with the disk\naround its primary star suggest in situ formation of the companions. For the 18\nother companions, we do not detect significant polarization and place\nsubpercent upper limits on their degree of polarization. These non-detections\nmay indicate the absence of circumsubstellar disks, a slow rotation rate of\nyoung companions, the upper atmospheres containing primarily submicron-sized\ndust grains, and/or limited cloud inhomogeneity. Finally, we present images of\nthe circumstellar disks of DH Tau, GQ Lup, PDS 70, Beta Pic, and HD 106906.\n"}
+{"abstract": "  In superconducting quantum circuits (SQCs), chiral routing quantum\ninformation is often realized with the ferrite circulators, which are usually\nbuck, lossy and require strong magnetic fields. To overcome these problems, we\npropose a novel method to realize chiral quantum networks by exploiting the\ngiant atom effects in SQC platforms. By assuming each coupling point modulated\nwith time, the interaction becomes momentum-dependent, and the giant atoms will\nchirally emit photons due to interference effects. The chiral factor can\napproach 1, and both the emission direction and rate can be freely tuned by the\nmodulating signals. We demonstrate that the high-fidelity state transfer\nbetween remote giant atoms can be realized. Our proposal can be integrated on\nthe superconducting chip easily, and has the potential to work as a tunable\ntoolbox for quantum information processing in future chiral quantum networks.\n"}
+{"abstract": "  We extend HPQCD's earlier $n_f=4$ lattice-QCD analysis of the ratio of\n$\\overline{\\mathrm{MSB}}$ masses of the $b$ and $c$ quark to include results\nfrom finer lattices (down to 0.03fm) and a new calculation of QED contributions\nto the mass ratio. We find that\n$\\overline{m}_b(\\mu)/\\overline{m}_c(\\mu)=4.586(12)$ at renormalization scale\n$\\mu=3$\\,GeV. This result is nonperturbative. Combining it with HPQCD's recent\nlattice QCD$+$QED determination of $\\overline{m}_c(3\\mathrm{GeV})$ gives a new\nvalue for the $b$-quark mass: $\\overline{m}_b(3\\mathrm{GeV}) = 4.513(26)$GeV.\nThe $b$-mass corresponds to $\\overline{m}_b(\\overline{m}_b, n_f=5) =\n4.202(21)$GeV. These results are the first based on simulations that include\nQED.\n"}
+{"abstract": "  In the fractional nonrelativistic potential model, the decomposition of heavy\nquarkonium in a hot magnetized medium is investigated. The analytical solution\nof the fractional radial Schrodinger equation for the hot-magnetized\ninteraction potential is displayed by using the conformable fractional\nNikiforov-Uvarov method. Analytical expressions for the energy eigenvalues and\nthe radial wave function are obtained for arbitrary quantum numbers. Next, we\nstudy the charmonium and bottmonium binding energies for different magnetic\nfield values in the thermal medium. The effect of the fractional parameter on\nthe decomposition temperature is also analyzed for charmonium and bottomonium\nin the presence of hot magnetized media. We conclude that the dissociation of\nheavy quarkonium in the fractional nonrelativistic potential model is more\npractical than the classical nonrelativistic potential model.\n"}
+{"abstract": "  The study of advanced quantum devices for energy storage has attracted the\nattention of the scientific community in the past few years. Although several\ntheoretical progresses have been achieved recently, experimental proposals of\nplatforms operating as quantum batteries under ambient conditions are still\nlacking. In this context, this work presents a feasible realization of a\nquantum battery in a carboxylate-based metal complex, which can store a finite\namount of extractable work under the form of quantum discord at room\ntemperature, and recharge by thermalization with a reservoir. Moreover, the\nstored work can be evaluated through non-destructive measurements of the\ncompound's magnetic susceptibility. These results pave the way for the\ndevelopment of enhanced energy storage platforms through material engineering.\n"}
+{"abstract": "  We define the doubling zeta integral for smooth families of representations\nof classical groups. Following this we prove a rationality result for these\nzeta integrals and show that they satisfy a functional equation. Moreover, we\nshow that there exists an apropriate normalizing factor which allows us to\nconstruct $\\gamma$-factors for smooth families out of the functional equation.\nWe prove that under certain hypothesis, specializing this $\\gamma$-factor at a\npoint of the family yields the $\\gamma$-factor defined by Piateski-Shapiro and\nRallis.\n"}
+{"abstract": "  Future generation wireless networks are designed with extremely low delay\nrequirements which makes even small contributed delays important. On the other\nhand, software defined networking (SDN) has been introduced as a key enabler of\nfuture wireless and cellular networks in order to make them more flexible. In\nSDN, a central controller manages all network equipments by setting the\nmatch-action pairs in flow tables of the devices. However, these flow tables\nhave limited capacity and thus are not capable of storing the rules of all the\nusers. In this paper, we consider an SDN-enabled base station (SD-BS) in a cell\nequipped with a limited capacity flow table. We analyze the expected delay\nincurred in processing of the incoming packets to the SD-BS and present a\nmathematical expression for it in terms of density of the users and cell area.\n"}
+{"abstract": "  We train deep generative models on datasets of reflexive polytopes. This\nenables us to compare how well the models have picked up on various global\nproperties of generated samples. Our datasets are complete in the sense that\nevery single example, up to changes of coordinate, is included in the dataset.\nUsing this property we also perform tests checking to what extent the models\nare merely memorizing the data. We also train models on the same dataset\nrepresented in two different ways, enabling us to measure which form is easiest\nto learn from. We use these experiments to show that deep generative models can\nlearn to generate geometric objects with non-trivial global properties, and\nthat the models learn some underlying properties of the objects rather than\nsimply memorizing the data.\n"}
+{"abstract": "  Nano-membrane tri-gate beta-gallium oxide (\\b{eta}-Ga2O3) field-effect\ntransistors (FETs) on SiO2/Si substrate fabricated via exfoliation have been\ndemonstrated for the first time. By employing electron beam lithography, the\nminimum-sized features can be defined with a 50 nm fin structure. For\nhigh-quality interface between \\b{eta}-Ga2O3 and gate dielectric, atomic\nlayer-deposited 15-nm-thick aluminum oxide (Al2O3) was utilized with\nTri-methyl-aluminum (TMA) self-cleaning surface treatment. The fabricated\ndevices demonstrate extremely low subthreshold slope (SS) of 61 mV/dec, high\ndrain current (IDS) ON/OFF ratio of 1.5 X 109, and negligible transfer\ncharacteristic hysteresis. We also experimentally demonstrated robustness of\nthese devices with current-voltage (I-V) characteristics measured at\ntemperatures up to 400 {\\deg}C.\n"}
+{"abstract": "  This technical report outlines the fundamental workings of the game logic\nbehind Ludii, a general game system, that can be used to play a wide variety of\ngames. Ludii is a program developed for the ERC-funded Digital Ludeme Project,\nin which mathematical and computational approaches are used to study how games\nwere played, and spread, throughout history. This report explains how general\ngame states and equipment are represented in Ludii, and how the rule ludemes\ndictating play are implemented behind the scenes, giving some insight into the\ncore game logic behind the Ludii general game player. This guide is intended to\nhelp game designers using the Ludii game description language to understand it\nmore completely and make fuller use of its features when describing their\ngames.\n"}
+{"abstract": "  In the classical partial vertex cover problem, we are given a graph $G$ and\ntwo positive integers $R$ and $L$. The goal is to check whether there is a\nsubset $V'$ of $V$ of size at most $R$, such that $V'$ covers at least $L$\nedges of $G$. The problem is NP-hard as it includes the Vertex Cover problem.\nPrevious research has addressed the extension of this problem where one has\nweight-functions defined on sets of vertices and edges of $G$. In this paper,\nwe consider the following version of the problem where on the input we are\ngiven an edge-weighted bipartite graph $G$, and three positive integers $R$,\n$S$ and $T$. The goal is to check whether $G$ has a subset $V'$ of vertices of\n$G$ of size at most $R$, such that the edges of $G$ covered by $V'$ have weight\nat least $S$ and they include a matching of weight at least $T$. In the paper,\nwe address this problem from the perspective of fixed-parameter tractability.\nOne of our hardness results is obtained via a reduction from the bi-objective\nknapsack problem, which we show to be W[1]-hard with respect to one of\nparameters. We believe that this problem might be useful in obtaining similar\nresults in other situations.\n"}
+{"abstract": "  Cytoskeletal networks are the main actuators of cellular mechanics, and a\nfoundational example for active matter physics. In cytoskeletal networks,\nmotion is generated on small scales by filaments that push and pull on each\nother via molecular-scale motors. These local actuations give rise to large\nscale stresses and motion. To understand how microscopic processes can give\nrise to self-organized behavior on larger scales it is important to consider\nwhat mechanisms mediate long-ranged mechanical interactions in the systems. Two\nscenarios have been considered in the recent literature. The first are systems\nwhich are relatively sparse, in which most of the large scale momentum transfer\nis mediated by the solvent in which cytoskeletal filaments are suspended. The\nsecond, are systems in which filaments are coupled via crosslink molecules\nthroughout. Here, we review the differences and commonalities between the\nphysics of these two regimes. We also survey the literature for the numbers\nthat allow us to place a material within either of these two classes.\n"}
+{"abstract": "  We are interested in the effect of Dirichlet boundary conditions on the nodal\nlength of Laplace eigenfunctions. We study random Gaussian Laplace\neigenfunctions on the two dimensional square and find a two terms asymptotic\nexpansion for the expectation of the nodal length in any square of side larger\nthan the Planck scale, along a denisty one sequence of energy levels. The proof\nrelies on a new study of lattice points in small arcs, and shows that the said\nexpectation is independent of the position of the square, giving the same\nasymptotic expansion both near and far from the boundaries.\n"}
+{"abstract": "  In agreement with the gravitational-wave events which are constantly\nincreasing, new aspects of the internal structure of compact stars have come to\nlight. A scenario in which a first order transition takes place inside these\nstars is of particular interest as it can lead, under conditions, to a third\ngravitationally stable branch (besides white dwarfs and neutron stars). This is\nknown as the twin star scenario. The new branch yields stars with the same mass\nas normal compact stars but quite different radii. In the current work, we\nfocus on hybrid stars undergone a hadron to quark phase transition near their\ncore and how this new stable configuration arises. Emphasis is to be given\nespecially in the aspects of the phase transition and its parameterization in\ntwo different ways, namely with Maxwell construction and with Gibbs\nconstruction. Qualitative findings of mass-radius relations of these stars will\nalso be presented.\n"}
+{"abstract": "  Current densities are induced in the electronic structure of molecules when\nthey are exposed to external magnetic fields. Aromatic molecular rings sustain\nnet diatropic ring currents, whereas the net ring current in antiaromatic\nmolecular rings is paratropic and flows in the opposite, non-classical\ndirection. We present computational methods and protocols to calculate, analyse\nand visualise magnetically induced current densities in molecules. Calculated\nring-current strengths are used for quantifying the degree of aromaticity. The\nmethods have been demonstrated by investigating ring-current strengths and the\ndegree of aromaticity of aromatic, antiaromatic and non-aromatic six-membered\nhydrocarbon rings. Current-density pathways and ring-current strengths of\naromatic and antiaromatic porphyrinoids and other polycyclic molecules have\nbeen studied. The aromaticity and current density of M\\\"obius-twisted molecules\nhas been investigated to find the dependence on the twist and the spatial\ndeformation of the molecular ring. Current densities of fullerene, gaudiene and\ntoroidal carbon nanotubes have also been studied.\n"}
+{"abstract": "  Photometric observations of accreting, low-mass, pre-main-sequence stars\n(i.e., Classical T Tauri stars; CTTS) have revealed different categories of\nvariability. Several of these classifications have been linked to changes in\n$\\dot{M}$. To test how accretion variability conditions lead to different\nlight-curve morphologies, we used 1D hydrodynamic simulations of accretion\nalong a magnetic field line coupled with radiative transfer models and a simple\ntreatment of rotation to generate synthetic light curves. We adopted previously\ndeveloped metrics in order to classify observations to facilitate comparisons\nbetween observations and our models. We found that stellar mass, magnetic field\ngeometry, corotation radius, inclination, and turbulence all play roles in\nproducing the observed light curves and that no single parameter is entirely\ndominant in controlling the observed variability. While the periodic behavior\nof the light curve is most strongly affected by the inclination, it is also a\nfunction of the magnetic field geometry and inner disk turbulence. Objects with\neither pure dipole fields, strong aligned octupole components, or high\nturbulence in the inner disk all tend to display accretion bursts. Objects with\nanti-aligned octupole components or aligned, weaker octupole components tend to\nshow light curves with slightly fewer bursts. We did not find clear monotonic\ntrends between the stellar mass and empirical classification. This work\nestablishes the groundwork for more detailed characterization of well-studied\ntargets as more light curves of CTTS become available through missions such as\nthe Transiting Exoplanet Survey Satellite (TESS).\n"}
+{"abstract": "  Much recent literature has formulated structure-from-motion (SfM) as a\nself-supervised learning problem where the goal is to jointly learn neural\nnetwork models of depth and egomotion through view synthesis. Herein, we\naddress the open problem of how to optimally couple the depth and egomotion\nnetwork components. Toward this end, we introduce several notions of coupling,\ncategorize existing approaches, and present a novel tightly-coupled approach\nthat leverages the interdependence of depth and egomotion at training and at\ninference time. Our approach uses iterative view synthesis to recursively\nupdate the egomotion network input, permitting contextual information to be\npassed between the components without explicit weight sharing. Through\nsubstantial experiments, we demonstrate that our approach promotes consistency\nbetween the depth and egomotion predictions at test time, improves\ngeneralization on new data, and leads to state-of-the-art accuracy on indoor\nand outdoor depth and egomotion evaluation benchmarks.\n"}
+{"abstract": "  Instances-reweighted adversarial training (IRAT) can significantly boost the\nrobustness of trained models, where data being less/more vulnerable to the\ngiven attack are assigned smaller/larger weights during training. However, when\ntested on attacks different from the given attack simulated in training, the\nrobustness may drop significantly (e.g., even worse than no reweighting). In\nthis paper, we study this problem and propose our solution--locally reweighted\nadversarial training (LRAT). The rationale behind IRAT is that we do not need\nto pay much attention to an instance that is already safe under the attack. We\nargue that the safeness should be attack-dependent, so that for the same\ninstance, its weight can change given different attacks based on the same\nmodel. Thus, if the attack simulated in training is mis-specified, the weights\nof IRAT are misleading. To this end, LRAT pairs each instance with its\nadversarial variants and performs local reweighting inside each pair, while\nperforming no global reweighting--the rationale is to fit the instance itself\nif it is immune to the attack, but not to skip the pair, in order to passively\ndefend different attacks in future. Experiments show that LRAT works better\nthan both IRAT (i.e., global reweighting) and the standard AT (i.e., no\nreweighting) when trained with an attack and tested on different attacks.\n"}
+{"abstract": "  A Cayley (di)graph $Cay(G,S)$ of a group $G$ with respect to a subset $S$ of\n$G$ is called normal if the right regular representation of $G$ is a normal\nsubgroup in the full automorphism group of $Cay(G,S)$, and is called a\nCI-(di)graph if for every $T\\subseteq G$, $Cay(G,S)\\cong Cay(G,T)$ implies that\nthere is $\\sigma\\in Aut(G)$ such that $S^\\sigma=T$. We call a group $G$ a\nNDCI-group if all normal Cayley digraphs of $G$ are CI-digraphs, and a\nNCI-group if all normal Cayley graphs of $G$ are CI-graphs, respectively. In\nthis paper, we prove that a cyclic group of order $n$ is a NDCI-group if and\nonly if $8\\nmid n$, and is a NCI-group if and only if either $n=8$ or $8\\nmid\nn$.\n"}
+{"abstract": "  Recently the first example of a family of pro-$p$ groups, for $p$ a prime,\nwith full normal Hausdorff spectrum was constructed. In this paper we further\ninvestigate this family by computing their finitely generated Hausdorff\nspectrum with respect to each of the five standard filtration series: the\n$p$-power series, the iterated $p$-power series, the lower $p$-series, the\nFrattini series and the dimension subgroup series. Here the finitely generated\nHausdorff spectra of these groups consist of infinitely many rational numbers,\nand their computation requires a rather technical approach. This result also\ngives further evidence to the non-existence of a finitely generated pro-$p$\ngroup with uncountable finitely generated Hausdorff spectrum.\n"}
+{"abstract": "  While Semi-supervised learning has gained much attention in computer vision\non image data, yet limited research exists on its applicability in the time\nseries domain. In this work, we investigate the transferability of\nstate-of-the-art deep semi-supervised models from image to time series\nclassification. We discuss the necessary model adaptations, in particular an\nappropriate model backbone architecture and the use of tailored data\naugmentation strategies. Based on these adaptations, we explore the potential\nof deep semi-supervised learning in the context of time series classification\nby evaluating our methods on large public time series classification problems\nwith varying amounts of labelled samples. We perform extensive comparisons\nunder a decidedly realistic and appropriate evaluation scheme with a unified\nreimplementation of all algorithms considered, which is yet lacking in the\nfield. We find that these transferred semi-supervised models show significant\nperformance gains over strong supervised, semi-supervised and self-supervised\nalternatives, especially for scenarios with very few labelled samples.\n"}
+{"abstract": "  In this article, we give a proof of multiplicativity for $\\gamma$-factors, an\nequality of parabolically induced and inducing factors, in the context of the\nBraverman-Kazhdan/Ngo program, under the assumption of commutativity of the\ncorresponding Fourier transforms and a certain generalized Harish-Chandra\ntransform. We also discuss the resolution of singularities and their\nrationality for reductive monoids, which are among the basic objects in the\nprogram.\n"}
+{"abstract": "  Stationary waves in the condensate of electron-hole pairs in the $n-p$\nbilayer system are studied. The system demonstrates the transition from a\nuniform (superfluid) to a nonuniform (supersolid) state. The precursor of this\ntransition is the appearance of the roton-type minimum in the collective mode\nspectrum. Stationary waves occur in the flow of the condensate past an\nobstacle. It is shown that the roton-type minimum manifests itself in a rather\ncomplicated stationary wave pattern with several families of crests which cross\none another. It is found that the stationary wave pattern is essentially\nmodified under variation in the density of the condensate and under variation\nin the flow velocity. It is shown that the pattern is formed in the main part\nby shortwave modes in the case of a point obstacle. The contribution of\nlongwave modes is clearly visible in the case of a weak extended obstacle,\nwhere the stationary wave pattern resembles the ship wave pattern.\n"}
+{"abstract": "  Let $q$ be an odd prime power. Denote by $r(q)$ the value $q$ modulo 4. In\nthis paper we establish a correspondence between two types of maximal cliques\nof order $\\frac{q+r(q)}{2}$ in the Paley graph of order $q^2$.\n"}
+{"abstract": "  Pool boiling is one of the efficient ways to remove heat from high-power\nelectronics, heat exchangers, and nuclear reactors. Nowadays, the pool boiling\nmethod is tried to exercise frequently due to its ability to remove high heat\nflux compared with natural/forced convection, while maintaining at low wall\nsuperheat. But this pool boiling heat transfer capacity is also limited by an\nimportant parameter, called critical heat flux (CHF). At the point of CHF, the\nheat transfer coefficient (HTC) drastically decreases due to the change of the\nheat transfer regime from nucleate boiling to film boiling. This is why, the\nenhancement of CHF, while maintaining low wall superheat is of great interest\nto engineers and researchers.\n"}
+{"abstract": "  In a previous paper, second- and fourth-order explicit symplectic integrators\nwere designed for a Hamiltonian of the Schwarzschild black hole. Following this\nwork, we continue to trace the possibility of the construction of explicit\nsymplectic integrators for a Hamiltonian of charged particles moving around a\nReissner-Nordstrom black hole with an external magnetic field. Such explicit\nsymplectic methods are still available when the Hamiltonian is separated into\nfive independently integrable parts with analytical solutions as explicit\nfunctions of proper time. Numerical tests show that the proposed algorithms\nshare the desirable properties in their long-term stability, precision and\nefficiency for appropriate choices of step sizes. For the applicability of one\nof the new algorithms, the effects of the black hole's charge, the Coulomb part\nof the electromagnetic potential and the magnetic parameter on the dynamical\nbehavior are surveyed. Under some circumstances, the extent of chaos gets\nstrong with an increase of the magnetic parameter from a global phase-space\nstructure. No the variation of the black hole's charge but the variation of the\nCoulomb part is considerably sensitive to affect the regular and chaotic\ndynamics of particles' orbits. A positive Coulomb part is easier to induce\nchaos than a negative one.\n"}
+{"abstract": "  Heterogeneous teams of robots, leveraging a balance between autonomy and\nhuman interaction, bring powerful capabilities to the problem of exploring\ndangerous, unstructured subterranean environments. Here we describe the\nsolution developed by Team CSIRO Data61, consisting of CSIRO, Emesent and\nGeorgia Tech, during the DARPA Subterranean Challenge. These presented systems\nwere fielded in the Tunnel Circuit in August 2019, the Urban Circuit in\nFebruary 2020, and in our own Cave event, conducted in September 2020. A unique\ncapability of the fielded team is the homogeneous sensing of the platforms\nutilised, which is leveraged to obtain a decentralised multi-agent SLAM\nsolution on each platform (both ground agents and UAVs) using peer-to-peer\ncommunications. This enabled a shift in focus from constructing a pervasive\ncommunications network to relying on multi-agent autonomy, motivated by\nexperiences in early circuit events. These experiences also showed the\nsurprising capability of rugged tracked platforms for challenging terrain,\nwhich in turn led to the heterogeneous team structure based on a BIA5 OzBot\nTitan ground robot and an Emesent Hovermap UAV, supplemented by smaller tracked\nor legged ground robots. The ground agents use a common CatPack perception\nmodule, which allowed reuse of the perception and autonomy stack across all\nground agents with minimal adaptation.\n"}
+{"abstract": "  Black phosphorus (BP) analogous tin(II) sulfide (SnS) has recently emerged as\nan attractive building block for electronic devices due to its highly\nanisotropic response. Two-dimensional (2D) SnS has shown to exhibit in-plane\nanisotropy in optical and electrical properties. However, the limitations in\ngrowing ultrasmall structures of SnS hinder the experimental exploration of\nanisotropic behavior in low dimension. Here, we present an elegant approach of\nsynthesizing highly crystalline nanometer-sized SnS sheets. Ultrasmall SnS\nexhibits two distinct valleys along armchair and zig-zag directions due to\nin-plane structural anisotropy like bulk SnS. We show that in such SnS\nnanosheet dots, the band gaps corresponding to two valleys are increased due to\nquantum confinement effect. We particularly observe that SnS quantum dots (QDs)\nshow excitation energy dependent photoluminescence (PL), which originates from\nthe two nondegenerate valleys. Our work may open up an avenue to show the\npotential of SnS QDs for new functionalities in electronics and\noptoelectronics.\n"}
+{"abstract": "  We consider the typical behaviour of random dynamical systems of\norder-preserving interval homeomorphisms with a positive Lyapunov exponent\ncondition at the endpoints. Our study removes any requirement for continuous\ndifferentiability save the existence of finite derivatives of the\nhomeomorphisms at the endpoints of the interval. We construct a suitable Baire\nspace structure for this class of systems. Generically within our Baire space,\nwe show that the stationary measure is singular with respect to the Lebesgue\nmeasure, but has full support on $[0,1]$. This provides an answer to a question\nraised by Alsed\\`a and Misiurewicz.\n"}
+{"abstract": "  Consumer applications are becoming increasingly smarter and most of them have\nto run on device ecosystems. Potential benefits are for example enabling\ncross-device interaction and seamless user experiences. Essential for today's\nsmart solutions with high performance are machine learning models. However,\nthese models are often developed separately by AI engineers for one specific\ndevice and do not consider the challenges and potentials associated with a\ndevice ecosystem in which their models have to run. We believe that there is a\nneed for tool-support for AI engineers to address the challenges of\nimplementing, testing, and deploying machine learning models for a next\ngeneration of smart interactive consumer applications. This paper presents\npreliminary results of a series of inquiries, including interviews with AI\nengineers and experiments for an interactive machine learning use case with a\nSmartwatch and Smartphone. We identified the themes through interviews and\nhands-on experience working on our use case and proposed features, such as data\ncollection from sensors and easy testing of the resources consumption of\nrunning pre-processing code on the target device, which will serve as\ntool-support for AI engineers.\n"}
+{"abstract": "  The aim of this article is to show the global existence of both martingale\nand pathwise solutions of stochastic equations with a monotone operator, of the\nLadyzenskaya-Smagorinsky type, driven by a general Levy noise. The classical\napproach based on using directly the Galerkin approximation is not valid.\nInstead, our approach is based on using appropriate approximations for the\nmonotone operator, Galerkin approximations and on the theory of martingale\nsolutions.\n"}
+{"abstract": "  Machine learning has been proven to be effective in various application\nareas, such as object and speech recognition on mobile systems. Since a\ncritical key to machine learning success is the availability of large training\ndata, many datasets are being disclosed and published online. From a data\nconsumer or manager point of view, measuring data quality is an important first\nstep in the learning process. We need to determine which datasets to use,\nupdate, and maintain. However, not many practical ways to measure data quality\nare available today, especially when it comes to large-scale high-dimensional\ndata, such as images and videos. This paper proposes two data quality measures\nthat can compute class separability and in-class variability, the two important\naspects of data quality, for a given dataset. Classical data quality measures\ntend to focus only on class separability; however, we suggest that in-class\nvariability is another important data quality factor. We provide efficient\nalgorithms to compute our quality measures based on random projections and\nbootstrapping with statistical benefits on large-scale high-dimensional data.\nIn experiments, we show that our measures are compatible with classical\nmeasures on small-scale data and can be computed much more efficiently on\nlarge-scale high-dimensional datasets.\n"}
+{"abstract": "  The population of the elderly people has kept increasing rapidly over the\nworld in the past decades. Solutions that are able to effectively support the\nelderly people to live independently at their home are thus urgently needed.\nAmbient assisted living (AAL) aims to provide products and services with\nambient intelligence to build a safe environment around people in need. With\nthe high prevalence of multiple chronic diseases, the elderly people often need\ndifferent levels of care management to prolong independent living at home. An\neffective AAL system should provide the required clinical support as an\nextension to the services provided in hospitals. Following the rapid growth of\navailable data, together with the wide application of machine learning\ntechnologies, we are now able to build intelligent ambient assisted systems to\nfulfil such a request. This paper discusses different levels of intelligence in\nAAL. We also introduce our solution for building an intelligent AAL system with\nthe discussed technologies. Taking semantic web technology as its backbone,\nsuch an AAL system is able to aggregate information from different sources,\nsolve the semantic gap between different data sources, and perform adaptive and\npersonalized carepath management based on the ambient environment.\n"}
+{"abstract": "  Following the theory of information measures based on the cumulative\ndistribution function, we propose the fractional generalized cumulative\nentropy, and its dynamic version. These entropies are particularly suitable to\ndeal with distributions satisfying the proportional reversed hazard model. We\nstudy the connection with fractional integrals, and some bounds and comparisons\nbased on stochastic orderings, that allow to show that the proposed measure is\nactually a variability measure. The investigation also involves various notions\nof reliability theory, since the considered dynamic measure is a suitable\nextension of the mean inactivity time. We also introduce the empirical\ngeneralized fractional cumulative entropy as a non-parametric estimator of the\nnew measure. It is shown that the empirical measure converges to the proposed\nnotion almost surely. Then, we address the stability of the empirical measure\nand provide an example of application to real data. Finally, a central limit\ntheorem is established under the exponential distribution.\n"}
+{"abstract": "  The most commonly used interface between a video game and the human user is a\nhandheld \"game controller\", \"game pad\", or in some occasions an \"arcade stick.\"\nDirectional pads, analog sticks and buttons - both digital and analog - are\nlinked to in-game actions. One or multiple simultaneous inputs may be necessary\nto communicate the intentions of the user. Activating controls may be more or\nless convenient depending on their position and size. In order to enable the\nuser to perform all inputs which are necessary during gameplay, it is thus\nimperative to find a mapping between in-game actions and buttons, analog\nsticks, and so on. We present simple formats for such mappings as well as for\nthe constraints on possible inputs which are either determined by a physical\ngame controller or required to be met for a game software, along with methods\nto transform said constraints via a button-action mapping and to check one\nconstraint set against another, i.e., to check whether a button-action mapping\nallows a controller to be used in conjunction with a game software, while\npreserving all desired properties.\n"}
+{"abstract": "  Safety constraints and optimality are important, but sometimes conflicting\ncriteria for controllers. Although these criteria are often solved separately\nwith different tools to maintain formal guarantees, it is also common practice\nin reinforcement learning to simply modify reward functions by penalizing\nfailures, with the penalty treated as a mere heuristic. We rigorously examine\nthe relationship of both safety and optimality to penalties, and formalize\nsufficient conditions for safe value functions: value functions that are both\noptimal for a given task, and enforce safety constraints. We reveal the\nstructure of this relationship through a proof of strong duality, showing that\nthere always exists a finite penalty that induces a safe value function. This\npenalty is not unique, but upper-unbounded: larger penalties do not harm\noptimality. Although it is often not possible to compute the minimum required\npenalty, we reveal clear structure of how the penalty, rewards, discount\nfactor, and dynamics interact. This insight suggests practical, theory-guided\nheuristics to design reward functions for control problems where safety is\nimportant.\n"}
+{"abstract": "  This paper critically examines arguments against independence, a measure of\ngroup fairness also known as statistical parity and as demographic parity. In\nrecent discussions of fairness in computer science, some have maintained that\nindependence is not a suitable measure of group fairness. This position is at\nleast partially based on two influential papers (Dwork et al., 2012, Hardt et\nal., 2016) that provide arguments against independence. We revisit these\narguments, and we find that the case against independence is rather weak. We\nalso give arguments in favor of independence, showing that it plays a\ndistinctive role in considerations of fairness. Finally, we discuss how to\nbalance different fairness considerations.\n"}
+{"abstract": "  The International Olympiad in Cryptography NSUCRYPTO is the unique Olympiad\ncontaining scientific mathematical problems for professionals, school and\nuniversity students from any country. Its aim is to involve young researchers\nin solving curious and tough scientific problems of modern cryptography. In\n2020, it was held for the seventh time. Prizes and diplomas were awarded to 84\nparticipants in the first round and 49 teams in the second round from 32\ncountries. In this paper, problems and their solutions of NSUCRYPTO'2020 are\npresented. We consider problems related to attacks on ciphers and hash\nfunctions, protocols, permutations, primality tests, etc. We discuss several\nopen problems on JPEG encoding, Miller -- Rabin primality test, special bases\nin the vector space, AES-GCM. The problem of a modified Miller -- Rabin\nprimality test was solved during the Olympiad. The problem for finding special\nbases was partially solved.\n"}
+{"abstract": "  We give two examples which show that rational nef and anti-nef polytopes are\nnot uniform even for klt surface pairs, answering a question of Chen-Han. We\nalso show that rational nef polytopes are uniform when the Cartier indices are\nuniformly bounded.\n"}
+{"abstract": "  Let $G=(V,E)$ be a simple graph. A dominating set of $G$ is a subset\n$D\\subseteq V$ such that every vertex not in $D$ is adjacent to at least one\nvertex in $D$. The cardinality of a smallest dominating set of $G$, denoted by\n$\\gamma(G)$, is the domination number of $G$. A dominating set $D$ is an\naccurate dominating set of $G$, if no $|D|$-element subset of $V\\setminus D$ is\na dominating set of $G$. The accurate domination number, $\\gamma_a(G)$, is the\ncardinality of a smallest accurate dominating set $D$. In this paper, after\npresenting preliminaries, we count the number of accurate dominating sets of\nsome specific graphs.\n"}
+{"abstract": "  We construct conforming finite elements for the spaces\n$H(\\text{sym}\\,\\text{Curl})$ and $H(\\text{dev}\\,\\text{sym}\\,\\text{Curl})$.\nThose are spaces of matrix-valued functions with symmetric or\ndeviatoric-symmetric $\\text{Curl}$ in a Lebesgue space, and they appear in\nvarious models of nonstandard solid mechanics. The finite elements are not\n$H(\\text{Curl})$-conforming. We show the construction, prove conformity and\nunisolvence, and point out optimal approximation error bounds.\n"}
+{"abstract": "  Assessment of replicability is critical to ensure the quality and rigor of\nscientific research. In this paper, we discuss inference and modeling\nprinciples for replicability assessment. Targeting distinct application\nscenarios, we propose two types of Bayesian model criticism approaches to\nidentify potentially irreproducible results in scientific experiments. They are\nmotivated by established Bayesian prior and posterior predictive model-checking\nprocedures and generalize many existing replicability assessment methods.\nFinally, we discuss the statistical properties of the proposed replicability\nassessment approaches and illustrate their usages by simulations and examples\nof real data analysis, including the data from the Reproducibility Project:\nPsychology and a systematic review of impacts of pre-existing cardiovascular\ndisease on COVID-19 outcomes.\n"}
+{"abstract": "  This paper proposes a macroscopic model to describe the equilibrium\ndistribution of passenger arrivals for the morning commute problem in a\ncongested urban rail transit system. We use a macroscopic train operation\nsub-model developed by Seo et al (2017a,b) to express the interaction between\nthe dynamics of passengers and trains in a simplified manner while maintaining\ntheir essential physical relations. The equilibrium conditions of the proposed\nmodel are derived and a solution method is provided. The characteristics of the\nequilibrium are then examined through analytical discussion and numerical\nexamples. As an application of the proposed model, we analyze a simple\ntime-dependent timetable optimization problem with equilibrium constraints and\nreveal that a \"capacity increasing paradox\" exists such that a higher dispatch\nfrequency can increase the equilibrium cost. Furthermore, insights into the\ndesign of the timetable are obtained and the timetable influence on passengers'\nequilibrium travel costs are evaluated.\n"}
+{"abstract": "  5G D2D Communication promises improvements in energy and spectral efficiency,\noverall system capacity, and higher data rates. However, to achieve optimum\nresults it is important to select wisely the Transmission mode of the D2D\nDevice to form clusters in the most fruitful positions in terms of Sum Rate and\nPower Consumption. Towards this end, this paper investigates the use of\nDistributed Artificial Intelligence (DAI) and innovative to D2D, Machine\nLearning (ML) approaches to achieve satisfactory results in terms of Spectral\nEfficiency (SE), Power Consumption (PC) and execution time, with the creation\nof clusters and backhauling D2D network under existing Base Station/Small Cell.\nAdditionally, one of the major factors that affect the creation of high-quality\nclusters under a D2D network is the number of the Devices. Therefore, this\npaper focuses on a small (<=200) number of Devices, with the purpose to\nidentify the limits of each approach in terms of number of devices.\nSpecifically, to identify where it is beneficial to form a cluster, investigate\nthe critical point that gains increases rapidly and at the end examine the\napplicability of 5G requirements. Additionally, prior work presented a\nDistributed Artificial Intelligence (DAI) Solution/Framework in D2D and a DAIS\nTransmission Mode Selection (TMS) plan was proposed. In this paper DAIS is\nfurther examined, improved in terms of thresholds evaluation, evaluated, and\ncompared with other approaches (AI/ML). The results obtained demonstrate the\nexceptional performance of DAIS, compared to all other related approaches in\nterms of SE, PC, execution time and cluster formation efficiency. Also, results\nshow that the investigated AI/ML approaches are also beneficial for\nTransmission Mode Selection (TMS) in 5G D2D communication, even with a smaller\n(i.e., >=5 D2D Relay,>=50 D2D Multi Hop Relay) numbers of devices as a lower\nlimits.\n"}
+{"abstract": "  For any nonconstant f,g in C(x) such that the numerator H(x,y) of f(x)-g(y)\nis irreducible, we compute the genus of the normalization of the curve\nH(x,y)=0. We also prove an analogous formula in arbitrary characteristic when f\nand g have no common wildly ramified branch points, and generalize to (possibly\nreducible) fiber products of nonconstant morphisms of curves f:A-->D and\ng:B-->D.\n"}
+{"abstract": "  This paper introduces a feedback-based temperature controller design for\nintelligent regulation of food internal temperature inside of standard\nconvection ovens. Typical convection ovens employ an open-loop control system\nthat requires a person to estimate the amount of time needed to cook foods in\norder to achieve desired internal temperatures. This approach, however, can\nresult in undesired results with the final food temperatures being too high or\ntoo low due to the inherent difficulty in accurately predicting required\ncooking times without continuously measuring internal states and accounting for\nnoise in the system. By implementing and introducing a feedback controller with\na full-order Luenberger observer to create a closed-loop system, an oven can be\ninstrumented to measure and regulate the internal temperatures of food in order\nto automatically control oven heat output and confidently achieve desired\nresults.\n"}
+{"abstract": "  Stabilized cat codes can provide a biased noise channel with a set of\nbias-preserving (BP) gates, which can significantly reduce the resource\noverhead for fault-tolerant quantum computing. All existing schemes of BP\ngates, however, require adiabatic quantum evolution, with performance limited\nby excitation loss and non-adiabatic errors during the adiabatic gates. In this\nwork, we apply a derivative-based leakage suppression technique to overcome\nnon-adiabatic errors, so that we can implement fast BP gates on Kerr-cat qubits\nwith improved gate fidelity while maintaining high noise bias. When applied to\nconcatenated quantum error correction, the fast BP gates can not only improve\nthe logical error rate but also reduce resource overhead, which enables more\nefficient implementation of fault-tolerant quantum computing.\n"}
+{"abstract": "  We study some asymptotic properties of cylinder processes in the plane\ndefined as union sets of dilated straight lines (appearing as mutually\noverlapping infinitely long strips) derived from a stationary independently\nmarked point process on the real line, where the marks describe thickness and\norientation of individual cylinders. Such cylinder processes form an important\nclass of (in general non-stationary) planar random sets. We observe the\ncylinder process in an unboundedly growing domain $\\rho K$ when $\\rho \\to\n\\infty\\,$, where the set $K$ is compact and star-shaped w.r.t. the origin ${\\bf\no}$ being an inner point of $K$. Provided the unmarked point process satisfies\na Brillinger-type mixing condition and the thickness of the typical cylinder\nhas a finite second moment we prove a (weak) law of large numbers as well as a\nformula of the asymptotic variance for the area of the cylinder process in\n$\\rho K$. Due to the long-range dependencies of the cylinder process, this\nvariance increases proportionally to $\\rho^3$.\n"}
+{"abstract": "  Depth information matters in RGB-D semantic segmentation task for providing\nadditional geometric information to color images. Most existing methods exploit\na multi-stage fusion strategy to propagate depth feature to the RGB branch.\nHowever, at the very deep stage, the propagation in a simple element-wise\naddition manner can not fully utilize the depth information. We propose\nGlobal-Local propagation network (GLPNet) to solve this problem. Specifically,\na local context fusion module(L-CFM) is introduced to dynamically align both\nmodalities before element-wise fusion, and a global context fusion\nmodule(G-CFM) is introduced to propagate the depth information to the RGB\nbranch by jointly modeling the multi-modal global context features. Extensive\nexperiments demonstrate the effectiveness and complementarity of the proposed\nfusion modules. Embedding two fusion modules into a two-stream encoder-decoder\nstructure, our GLPNet achieves new state-of-the-art performance on two\nchallenging indoor scene segmentation datasets, i.e., NYU-Depth v2 and SUN-RGBD\ndataset.\n"}
+{"abstract": "  We prove the Second Vanishing Theorem for local cohomology modules of an\nunramified regular local ring in its full generality and provide a new proof of\nthe Second Vanishing Theorem in prime characteristic $p$. As an application of\nour vanishing theorem for unramified regular local rings, we extend our\ntopological characterization of the highest Lyubeznik number of an\nequi-characteristic local ring to the setting of mixed characteristic. An upper\nbound of local cohomological dimension in mixed characteristic is also obtained\nby partially extending Lyubeznik's vanishing theorem in prime characteristic\n$p$ to mixed characteristic.\n"}
+{"abstract": "  Recent X-ray observations by Jiang et al. have identified an active galactic\nnucleus (AGN) in the bulgeless spiral galaxy NGC 3319, located just\n$14.3\\pm1.1\\,$Mpc away, and suggest the presence of an intermediate-mass black\nhole (IMBH; $10^2\\leq M_\\bullet/\\mathrm{M_{\\odot}}\\leq10^5$) if the Eddington\nratios are as high as 3 to $3\\times10^{-3}$. In an effort to refine the black\nhole mass for this (currently) rare class of object, we have explored multiple\nblack hole mass scaling relations, such as those involving the (not previously\nused) velocity dispersion, logarithmic spiral-arm pitch angle, total galaxy\nstellar mass, nuclear star cluster mass, rotational velocity, and colour of NGC\n3319, to obtain ten mass estimates, of differing accuracy. We have calculated a\nmass of $3.14_{-2.20}^{+7.02}\\times10^4\\,\\mathrm{M_\\odot}$, with a confidence\nof 84% that it is $\\leq$$10^5\\,\\mathrm{M_\\odot}$, based on the combined\nprobability density function from seven of these individual estimates. Our\nconservative approach excluded two black hole mass estimates (via the nuclear\nstar cluster mass, and the fundamental plane of black hole activity\n$\\unicode{x2014}$ which only applies to black holes with low accretion rates)\nthat were upper limits of $\\sim$$10^5\\,{\\rm M}_{\\odot}$, and it did not use the\n$M_\\bullet\\unicode{x2013}L_{\\rm 2-10\\,keV}$ relation's prediction of\n$\\sim$$10^5\\,{\\rm M}_{\\odot}$. This target provides an exceptional opportunity\nto study an IMBH in AGN mode and advance our demographic knowledge of black\nholes. Furthermore, we introduce our novel method of meta-analysis as a\nbeneficial technique for identifying new IMBH candidates by quantifying the\nprobability that a galaxy possesses an IMBH.\n"}
+{"abstract": "  We propose a novel method to extract global and local features of functional\ntime series. The global features concerning the dominant modes of variation\nover the entire function domain, and local features of function variations over\nparticular short intervals within function domain, are both important in\nfunctional data analysis. Functional principal component analysis (FPCA),\nthough a key feature extraction tool, only focus on capturing the dominant\nglobal features, neglecting highly localized features. We introduce a FPCA-BTW\nmethod that initially extracts global features of functional data via FPCA, and\nthen extracts local features by block thresholding of wavelet (BTW)\ncoefficients. Using Monte Carlo simulations, along with an empirical\napplication on near-infrared spectroscopy data of wood panels, we illustrate\nthat the proposed method outperforms competing methods including FPCA and\nsparse FPCA in the estimation functional processes. Moreover, extracted local\nfeatures inheriting serial dependence of the original functional time series\ncontribute to more accurate forecasts. Finally, we develop asymptotic\nproperties of FPCA-BTW estimators, discovering the interaction between\nconvergence rates of global and local features.\n"}
+{"abstract": "  A volatility surface is an important tool for pricing and hedging\nderivatives. The surface shows the volatility that is implied by the market\nprice of an option on an asset as a function of the option's strike price and\nmaturity. Often, market data is incomplete and it is necessary to estimate\nmissing points on partially observed surfaces. In this paper, we show how\nvariational autoencoders can be used for this task. The first step is to derive\nlatent variables that can be used to construct synthetic volatility surfaces\nthat are indistinguishable from those observed historically. The second step is\nto determine the synthetic surface generated by our latent variables that fits\navailable data as closely as possible. As a dividend of our first step, the\nsynthetic surfaces produced can also be used in stress testing, in market\nsimulators for developing quantitative investment strategies, and for the\nvaluation of exotic options. We illustrate our procedure and demonstrate its\npower using foreign exchange market data.\n"}
+{"abstract": "  Most methods for publishing data with privacy guarantees introduce randomness\ninto datasets which reduces the utility of the published data. In this paper,\nwe study the privacy-utility tradeoff by taking maximal leakage as the privacy\nmeasure and the expected Hamming distortion as the utility measure. We study\nthree different but related problems. First, we assume that the data-generating\ndistribution (i.e., the prior) is known, and we find the optimal privacy\nmechanism that achieves the smallest distortion subject to a constraint on\nmaximal leakage. Then, we assume that the prior belongs to some set of\ndistributions, and we formulate a min-max problem for finding the smallest\ndistortion achievable for the worst-case prior in the set, subject to a maximal\nleakage constraint. Lastly, we define a partial order on privacy mechanisms\nbased on the largest distortion they generate. Our results show that when the\nprior distribution is known, the optimal privacy mechanism fully discloses\nsymbols with the largest prior probabilities, and suppresses symbols with the\nsmallest prior probabilities. Furthermore, we show that sets of priors that\ncontain more uniform distributions lead to larger distortion, while privacy\nmechanisms that distribute the privacy budget more uniformly over the symbols\ncreate smaller worst-case distortion.\n"}
+{"abstract": "  We propose a novel high-fidelity expressive speech synthesis model, UniTTS,\nthat learns and controls overlapping style attributes avoiding interference.\nUniTTS represents multiple style attributes in a single unified embedding space\nby the residuals between the phoneme embeddings before and after applying the\nattributes. The proposed method is especially effective in controlling multiple\nattributes that are difficult to separate cleanly, such as speaker ID and\nemotion, because it minimizes redundancy when adding variance in speaker ID and\nemotion, and additionally, predicts duration, pitch, and energy based on the\nspeaker ID and emotion. In experiments, the visualization results exhibit that\nthe proposed methods learned multiple attributes harmoniously in a manner that\ncan be easily separated again. As well, UniTTS synthesized high-fidelity speech\nsignals controlling multiple style attributes. The synthesized speech samples\nare presented at https://jackson-kang.github.io/paper_works/UniTTS/demos.\n"}
+{"abstract": "  To construct the rotation curve of the Galaxy, classical Cepheids with proper\nmotions, parallaxes and line-of-sight velocities from the Gaia DR2 Catalog are\nused in large part. The working sample formed from literature data contains\nabout 800 Cepheids with estimates of their age. We determined that the linear\nrotation velocity of the Galaxy at a solar distance is $V_0=240\\pm3$~km\ns$^{-1}$. In this case, the distance from the Sun to the axis of rotation of\nthe Galaxy is found to be $R_0=8.27\\pm0.10$~kpc. A spectral analysis of radial\nand residual tangential velocities of Cepheids younger than 120 Myr showed\nclose estimates of the parameters of the spiral density wave obtained from data\nboth at present time and in the past. So, the value of the wavelength\n$\\lambda_{R,\\theta}$ is in the range of [2.4--3.0] kpc, the pitch angle\n$i_{R,\\theta}$ is in the range of [$-13^\\circ$,$-10^\\circ$] for a four-arm\npattern model, the amplitudes of the radial and tangential perturbations are\n$f_R\\sim12$~km s$^{-1}$ and $f_\\theta\\sim9$~km s$^{-1}$, respectively.\nVelocities of Cepheids older than 120 Myr are currently giving a wavelength\n$\\lambda_{R,\\theta}\\sim5$~kpc. This value differs significantly from one that\nwe obtained from the samples of young Cepheids. An analysis of positions and\nvelocities of old Cepheids, calculated by integrating their orbits backward in\ntime, made it possible to determine significantly more reliable values of the\nparameters of the spiral density wave: wavelength $\\lambda_{R,\\theta}=2.7$~kpc,\namplitudes of radial and tangential perturbations are $f_R=7.9$~km s$^{-1}$ and\n$f_\\theta=5$~km s$^{-1}$, respectively.\n"}
+{"abstract": "  Intraoperative Gamma Probe (IPG) remains the current gold standard modality\nfor sentinel lymph node identification and tumor removal in cancer patients.\nHowever, even alongside the optical dyes they do not meet with <5% false\nnegative rates (FNR) requirement, a key metric suggested by the American\nSociety of Clinical Oncology (ASCO). We are aiming to reduce FNR by using time\nof flight (TOF) PET detector technology in the limited angle geometry system by\nusing only two detector buckets in coincidence, where one small-area detector\nis placed above the patient and the other with larger detection-area, placed\njust under the patient bed. For proof of concept, we used two Hamamatsu TOF PET\ndetector modules (C13500-4075YC-12) featuring 12x12 array of 4.2x4.2x20 mm3 LFS\ncrystal pixels, one-one coupled to silicon photomultiplier (SiPM) pixels.\nDetector coincidence timing resolution (CTR) measured 271 ps FWHM for the whole\ndetector. We 3D printed lesion phantom containing spheres with 2-10 mm in\ndiameter, representing lymph nodes, and placed it inside a 10-liter warm\nbackground water phantom. Experimental results show that with sub-minute data\nacquisition, 6 mm diameter spheres can be identified in the image when a lesion\nphantom with 10:1 activity ratio to background is used. Simulation results are\nin good agreement with the experimental data, by resolving 6 mm diameters\nlesions with 60 seconds acquisition time, in 25 cm deep background water\nphantom with 10:1 activity ratio. The image quality improves as the CTR\nimproves in the simulation, and with decreasing background water phantom depth\nor lesion to background activity ratio, in the experiment. With the results\npresented here we conclude that limited angle TOF PET detector is a major step\nforward for intraoperative applications in that, improved lesion detectability\nis beyond what the conventional Gamma- and NIR-based probes could achieve.\n"}
+{"abstract": "  To facilitate effective human-robot interaction (HRI), trust-aware HRI has\nbeen proposed, wherein the robotic agent explicitly considers the human's trust\nduring its planning and decision making. The success of trust-aware HRI depends\non the specification of a trust dynamics model and a trust-behavior model. In\nthis study, we proposed one novel trust-behavior model, namely the reverse\npsychology model, and compared it against the commonly used disuse model. We\nexamined how the two models affect the robot's optimal policy and the\nhuman-robot team performance. Results indicate that the robot will deliberately\n\"manipulate\" the human's trust under the reverse psychology model. To correct\nthis \"manipulative\" behavior, we proposed a trust-seeking reward function that\nfacilitates trust establishment without significantly sacrificing the team\nperformance.\n"}
+{"abstract": "  We explore variations of the dust extinction law of the Milky Way by\nselecting stars from the Swift/UVOT Serendipitous Source Catalogue,\ncross-matched with Gaia DR2 and 2MASS to produce a sample of 10,452 stars out\nto ~4kpc with photometry covering a wide spectral window. The near ultraviolet\npassbands optimally encompass the 2175A bump, so that we can simultaneously fit\nthe net extinction, quoted in the V band (A$_V$), the steepness of the\nwavelength dependence ($\\delta$) and the bump strength (E$_b$). The methodology\ncompares the observed magnitudes with theoretical stellar atmospheres from the\nmodels of Coelho. Significant correlations are found between these parameters,\nrelated to variations in dust composition, that are complementary to similar\nscaling relations found in the more complex dust attenuation law of galaxies -\nthat also depend on the distribution of dust among the stellar populations\nwithin the galaxy. We recover the strong anticorrelation between A$_V$ and\nGalactic latitude, as well as a weaker bump strength at higher extinction.\n$\\delta$ is also found to correlate with latitude, with steeper laws towards\nthe Galactic plane. Our results suggest that variations in the attenuation law\nof galaxies cannot be fully explained by dust geometry.\n"}
+{"abstract": "  We present evidence that many common convolutional neural networks (CNNs)\ntrained for face verification learn functions that are nearly equivalent under\nrotation. More specifically, we demonstrate that one face verification model's\nembeddings (i.e. last-layer activations) can be compared directly to another\nmodel's embeddings after only a rotation or linear transformation, with little\nperformance penalty. This finding is demonstrated using IJB-C 1:1 verification\nacross the combinations of ten modern off-the-shelf CNN-based face verification\nmodels which vary in training dataset, CNN architecture, method of angular loss\ncalculation, or some combination of the 3. These networks achieve a mean true\naccept rate of 0.96 at a false accept rate of 0.01. When instead evaluating\nembeddings generated from two CNNs, where one CNN's embeddings are mapped with\na linear transformation, the mean true accept rate drops to 0.95 using the same\nverification paradigm. Restricting these linear maps to only perform rotation\nproduces a mean true accept rate of 0.91. These mappings' existence suggests\nthat a common representation is learned by models despite variation in training\nor structure. We discuss the broad implications a result like this has,\nincluding an example regarding face template security.\n"}
+{"abstract": "  This paper presents an advance on image interpolation based on ant colony\nalgorithm (AACA) for high-resolution image scaling. The difference between the\nproposed algorithm and the previously proposed optimization of bilinear\ninterpolation based on ant colony algorithm (OBACA) is that AACA uses global\nweighting, whereas OBACA uses a local weighting scheme. The strength of the\nproposed global weighting of the AACA algorithm depends on employing solely the\npheromone matrix information present on any group of four adjacent pixels to\ndecide which case deserves a maximum global weight value or not. Experimental\nresults are further provided to show the higher performance of the proposed\nAACA algorithm with reference to the algorithms mentioned in this paper.\n"}
+{"abstract": "  Portrait matting is an important research problem with a wide range of\napplications, such as video conference app, image/video editing, and\npost-production. The goal is to predict an alpha matte that identifies the\neffect of each pixel on the foreground subject. Traditional approaches and most\nof the existing works utilized an additional input, e.g., trimap, background\nimage, to predict alpha matte. However, providing additional input is not\nalways practical. Besides, models are too sensitive to these additional inputs.\nIn this paper, we introduce an additional input-free approach to perform\nportrait matting using Generative Adversarial Nets (GANs). We divide the main\ntask into two subtasks. For this, we propose a segmentation network for the\nperson segmentation and the alpha generation network for alpha matte\nprediction. While the segmentation network takes an input image and produces a\ncoarse segmentation map, the alpha generation network utilizes the same input\nimage as well as a coarse segmentation map that is produced by the segmentation\nnetwork to predict the alpha matte. Besides, we present a segmentation encoding\nblock to downsample the coarse segmentation map and provide feature\nrepresentation to the residual block. Furthermore, we propose border loss to\npenalize only the borders of the subject separately which is more likely to be\nchallenging and we also adapt perceptual loss for portrait matting. To train\nthe proposed system, we combine two different popular training datasets to\nimprove the amount of data as well as diversity to address domain shift\nproblems in the inference time. We tested our model on three different\nbenchmark datasets, namely Adobe Image Matting dataset, Portrait Matting\ndataset, and Distinctions dataset. The proposed method outperformed the MODNet\nmethod that also takes a single input.\n"}
+{"abstract": "  The academic, socioemotional, and health impacts of school policies\nthroughout the COVID-19 pandemic have been a source of many important questions\nthat require accurate information about the extent of onsite schooling that has\nbeen occurring throughout the pandemic. This paper investigates school\noperational status data sources during the COVID-19 pandemic, comparing\nself-report data collected nationally on the household level through a\nFacebook-based survey with data collected at district and county levels\nthroughout the country. The percentage of households reporting in-person\ninstruction within each county is compared to the district and county data at\nthe state and county levels. The results show high levels of consistency\nbetween the sources at the state level and for large counties. The consistency\nlevels across sources support the usage of the Facebook-based COVID-19 Symptom\nSurvey as a source to answer questions about the educational experiences,\nfactors, and impacts related to K-12 education across the nation during the\npandemic.\n"}
+{"abstract": "  We review the theoretical aspects relevant in the description of high energy\nheavy ion collisions, with an emphasis on the learnings about the underlying\nQCD phenomena that have emerged from these collisions.\n"}
+{"abstract": "  Bounded rationality is an important consideration stemming from the fact that\nagents often have limits on their processing abilities, making the assumption\nof perfect rationality inapplicable to many real tasks. We propose an\ninformation-theoretic approach to the inference of agent decisions under\nSmithian competition. The model explicitly captures the boundedness of agents\n(limited in their information-processing capacity) as the cost of information\nacquisition for expanding their prior beliefs. The expansion is measured as the\nKullblack-Leibler divergence between posterior decisions and prior beliefs.\nWhen information acquisition is free, the homo economicus agent is recovered,\nwhile in cases when information acquisition becomes costly, agents instead\nrevert to their prior beliefs. The maximum entropy principle is used to infer\nleast-biased decisions based upon the notion of Smithian competition formalised\nwithin the Quantal Response Statistical Equilibrium framework. The\nincorporation of prior beliefs into such a framework allowed us to\nsystematically explore the effects of prior beliefs on decision-making in the\npresence of market feedback, as well as importantly adding a temporal\ninterpretation to the framework. We verified the proposed model using\nAustralian housing market data, showing how the incorporation of prior\nknowledge alters the resulting agent decisions. Specifically, it allowed for\nthe separation of past beliefs and utility maximisation behaviour of the agent\nas well as the analysis into the evolution of agent beliefs.\n"}
+{"abstract": "  Multi-agent Markov Decision Processes (MMDPs) arise in a variety of\napplications including target tracking, control of multi-robot swarms, and\nmultiplayer games. A key challenge in MMDPs occurs when the state and action\nspaces grow exponentially in the number of agents, making computation of an\noptimal policy computationally intractable for medium- to large-scale problems.\nOne property that has been exploited to mitigate this complexity is transition\nindependence, in which each agent's transition probabilities are independent of\nthe states and actions of other agents. Transition independence enables\nfactorization of the MMDP and computation of local agent policies but does not\nhold for arbitrary MMDPs. In this paper, we propose an approximate transition\ndependence property, called $\\delta$-transition dependence and develop a metric\nfor quantifying how far an MMDP deviates from transition independence. Our\ndefinition of $\\delta$-transition dependence recovers transition independence\nas a special case when $\\delta$ is zero. We develop a polynomial time algorithm\nin the number of agents that achieves a provable bound on the global optimum\nwhen the reward functions are monotone increasing and submodular in the agent\nactions. We evaluate our approach on two case studies, namely, multi-robot\ncontrol and multi-agent patrolling example.\n"}
+{"abstract": "  We report on the follow-up $XMM-Newton$ observation of the persistent X-ray\npulsar CXOU J225355.1+624336, discovered with the CATS@BAR project on archival\n$Chandra$ data. The source was detected at $f_{\\rm X}$(0.5-10 keV) = 3.4$\\times\n10^{-12}$ erg cm$^{-2}$ s$^{-1}$, a flux level which is fully consistent with\nthe previous observations performed with $ROSAT$, $Swift$, and $Chandra$. The\nmeasured pulse period $P$ = 46.753(3) s, compared with the previous\nmeasurements, implies a constant spin down at an average rate $\\dot P =\n5.3\\times 10^{-10}$ s s$^{-1}$. The pulse profile is energy dependent, showing\nthree peaks at low energy and a less structured profile above about 3.5 keV.\nThe pulsed fraction slightly increases with energy. We described the\ntime-averaged EPIC spectrum with four different emission models: a partially\ncovered power law, a cut-off power law, and a power law with an additional\nthermal component (either a black body or a collisionally ionized gas). In all\ncases we obtained equally good fits, so it was not possible to prefer or reject\nany emission model on the statistical basis. However, we disfavour the presence\nof the thermal components, since their modeled X-ray flux, resulting from a\nregion larger than the neutron star surface, would largely dominate the X-ray\nemission from the pulsar. The phase-resolved spectral analysis showed that a\nsimple flux variation cannot explain the source variability and proved that it\nis characterized by a spectral variability along the pulse phase. The results\nof the $XMM-Newton$ observation confirmed that CXOU J225355.1+624336 is a BeXB\nwith a low-luminosity ($L_{\\rm X} \\sim 10^{34-35}$ erg s$^{-1}$), a limited\nvariability, and a constant spin down. Therefore, they reinforce the source\nclassification as a persistent BeXB.\n"}
+{"abstract": "  Let us consider a Gaussian probability on a Banach space. We prove the\nexistence of an intermediate Banach space between the space where the Gaussian\nmeasure lives and its RKHS. Such a space has full probability and a compact\nembedding. This extends what happens with Wiener measure, where the\nintermediate space can be chosen as a space of H\\\"older paths. From this result\nit is very simple to deduce a result of exponential tightness for Gaussian\nprobabilities.\n"}
+{"abstract": "  Laser cooling of matter through anti-Stokes photoluminescence, where the\nemitted frequency of light exceeds that of the impinging laser by virtue of\nabsorption of thermal vibrational energy, has been successfully realized in\ncondensed media, and in particular with rare earth doped systems achieving\nsub-100K solid state optical refrigeration. Studies suggest that laser cooling\nin semiconductors has the potential of achieving temperatures down to ~10K and\nthat its direct integration can usher unique high-performance nanostructured\nsemiconductor devices. While laser cooling of nanostructured II-VI\nsemiconductors has been reported recently, laser cooling of indirect bandgap\nsemiconductors such as group IV silicon and germanium remains a major\nchallenge. Here we report on the anomalous observation of dominant anti-Stokes\nphotoluminescence in germanium nanocrystals. We attribute this result to the\nconfluence of ultra-high purity nanocrystal germanium, generation of high\ndensity of electron-hole plasma, the inherent degeneracy of longitudinal and\ntransverse optical phonons in non-polar indirect bandgap semiconductors, and\ncommensurate spatial confinement effects. At high laser intensities, laser\ncooling with lattice temperature as low as ~50K is inferred.\n"}
+{"abstract": "  Major scandals in corporate history have urged the need for regulatory\ncompliance, where organizations need to ensure that their controls (processes)\ncomply with relevant laws, regulations, and policies. However, keeping track of\nthe constantly changing legislation is difficult, thus organizations are\nincreasingly adopting Regulatory Technology (RegTech) to facilitate the\nprocess. To this end, we introduce regulatory information retrieval (REG-IR),\nan application of document-to-document information retrieval (DOC2DOC IR),\nwhere the query is an entire document making the task more challenging than\ntraditional IR where the queries are short. Furthermore, we compile and release\ntwo datasets based on the relationships between EU directives and UK\nlegislation. We experiment on these datasets using a typical two-step pipeline\napproach comprising a pre-fetcher and a neural re-ranker. Experimenting with\nvarious pre-fetchers from BM25 to k nearest neighbors over representations from\nseveral BERT models, we show that fine-tuning a BERT model on an in-domain\nclassification task produces the best representations for IR. We also show that\nneural re-rankers under-perform due to contradicting supervision, i.e., similar\nquery-document pairs with opposite labels. Thus, they are biased towards the\npre-fetcher's score. Interestingly, applying a date filter further improves the\nperformance, showcasing the importance of the time dimension.\n"}
+{"abstract": "  First person action recognition is an increasingly researched topic because\nof the growing popularity of wearable cameras. This is bringing to light\ncross-domain issues that are yet to be addressed in this context. Indeed, the\ninformation extracted from learned representations suffers from an intrinsic\nenvironmental bias. This strongly affects the ability to generalize to unseen\nscenarios, limiting the application of current methods in real settings where\ntrimmed labeled data are not available during training. In this work, we\npropose to leverage over the intrinsic complementary nature of audio-visual\nsignals to learn a representation that works well on data seen during training,\nwhile being able to generalize across different domains. To this end, we\nintroduce an audio-visual loss that aligns the contributions from the two\nmodalities by acting on the magnitude of their feature norm representations.\nThis new loss, plugged into a minimal multi-modal action recognition\narchitecture, leads to strong results in cross-domain first person action\nrecognition, as demonstrated by extensive experiments on the popular\nEPIC-Kitchens dataset.\n"}
+{"abstract": "  Accurate prediction of pedestrian crossing behaviors by autonomous vehicles\ncan significantly improve traffic safety. Existing approaches often model\npedestrian behaviors using trajectories or poses but do not offer a deeper\nsemantic interpretation of a person's actions or how actions influence a\npedestrian's intention to cross in the future. In this work, we follow the\nneuroscience and psychological literature to define pedestrian crossing\nbehavior as a combination of an unobserved inner will (a probabilistic\nrepresentation of binary intent of crossing vs. not crossing) and a set of\nmulti-class actions (e.g., walking, standing, etc.). Intent generates actions,\nand the future actions in turn reflect the intent. We present a novel\nmulti-task network that predicts future pedestrian actions and uses predicted\nfuture action as a prior to detect the present intent and action of the\npedestrian. We also designed an attention relation network to incorporate\nexternal environmental contexts thus further improve intent and action\ndetection performance. We evaluated our approach on two naturalistic driving\ndatasets, PIE and JAAD, and extensive experiments show significantly improved\nand more explainable results for both intent detection and action prediction\nover state-of-the-art approaches. Our code is available at:\nhttps://github.com/umautobots/pedestrian_intent_action_detection.\n"}
+{"abstract": "  Let $\\Delta$ denote a non-degenerate $k$-simplex in $\\mathbb{R}^k$. The set\n$\\text{Sim}(\\Delta)$ of simplices in $\\mathbb{R}^k$ similar to $\\Delta$ is\ndiffeomorphic to $O(k)\\times [0,\\infty)\\times \\mathbb{R}^k$, where the factor\nin $O(k)$ is a matrix called the {\\em pose}. Among $(k-1)$-spheres smoothly\nembedded in $\\mathbb{R}^k$ and isotopic to the identity, there is a dense\nfamily of spheres, for which the subset of $\\text{Sim}(\\Delta)$ of simplices\ninscribed in each embedded sphere contains a similar simplex of every pose\n$U\\in O(k)$. Further, the intersection of $\\text{Sim}(\\Delta)$ with the\nconfiguration space of $k+1$ distinct points on an embedded sphere is a\nmanifold whose top homology class maps to the top class in $O(k)$ via the pose\nmap. This gives a high dimensional generalization of classical results on\ninscribing families of triangles in plane curves. We use techniques established\nin our previous paper on the square-peg problem where we viewed inscribed\nsimplices in spheres as transverse intersections of submanifolds of\ncompactified configuration spaces.\n"}
+{"abstract": "  We propose an approach that links density functional theory (DFT) and\nmolecular dynamics (MD) simulation to study fluid behavior in nanopores in\ncontact with bulk (macropores). It consists of two principal steps. First, the\ntheoretical calculation of fluid composition and density distribution in\nnanopore under specified thermodynamic conditions using DFT. Second, MD\nsimulation of the confined system with obtained characteristics. Thus, we\ninvestigate an open system in a grand canonical ensemble. This method allows us\nto investigate both structural and dynamic properties of confined fluid at\ngiven bulk conditions and do not require computationally expensive simulation\nof bulk reservoir. In this work, we obtain equilibrium density profiles of pure\nmethane, ethane and carbon dioxide and their binary mixtures in slitlike\nnanopores with carbon walls. Good agreement of structures obtained by theory\nand simulation confirms the applicability of the proposed method.\n"}
+{"abstract": "  Brown dwarfs with well-determined ages, luminosities, and masses provide rare\nbut valuable tests of low-temperature atmospheric and evolutionary models. We\npresent the discovery and dynamical mass measurement of a substellar companion\nto HD 47127, an old ($\\approx$7-10 Gyr) G5 main sequence star with a mass\nsimilar to the Sun. Radial velocities of the host star with the Harlan J. Smith\nTelescope uncovered a low-amplitude acceleration of 1.93 $\\pm$ 0.08 m s$^{-1}$\nyr$^{-1}$ based on 20 years of monitoring. We subsequently recovered a faint\n($\\Delta H$=13.14 $\\pm$ 0.15 mag) co-moving companion at 1.95$''$ (52 AU) with\nfollow-up Keck/NIRC2 adaptive optics imaging. The radial acceleration of HD\n47127 together with its tangential acceleration from Hipparcos and Gaia EDR3\nastrometry provide a direct measurement of the three-dimensional acceleration\nvector of the host star, enabling a dynamical mass constraint for HD 47127 B\n(67.5-177 $M_\\mathrm{Jup}$ at 95% confidence) despite the small fractional\norbital coverage of the observations. The absolute $H$-band magnitude of HD\n47127 B is fainter than the benchmark T dwarfs HD 19467 B and Gl 229 B but\nbrighter than Gl 758 B and HD 4113 C, suggesting a late-T spectral type.\nAltogether the mass limits for HD 47127 B from its dynamical mass and the\nsubstellar boundary imply a range of 67-78 $M_\\mathrm{Jup}$ assuming it is\nsingle, although a preference for high masses of $\\approx$100 $M_\\mathrm{Jup}$\nfrom dynamical constraints hints at the possibility that HD 47127 B could\nitself be a binary pair of brown dwarfs or that another massive companion\nresides closer in. Regardless, HD 47127 B will be an excellent target for more\nrefined orbital and atmospheric characterization in the future.\n"}
+{"abstract": "  In this chapter, we derive and analyse models for consensus dynamics on\nhypergraphs. As we discuss, unless there are nonlinear node interaction\nfunctions, it is always possible to rewrite the system in terms of a new\nnetwork of effective pairwise node interactions, regardless of the initially\nunderlying multi-way interaction structure. We thus focus on dynamics based on\na certain class of non-linear interaction functions, which can model different\nsociological phenomena such as peer pressure and stubbornness. Unlike for\nlinear consensus dynamics on networks, we show how our nonlinear model dynamics\ncan cause shifts away from the average system state. We examine how these\nshifts are influenced by the distribution of the initial states, the underlying\nhypergraph structure and different forms of non-linear scaling of the node\ninteraction function.\n"}
+{"abstract": "  Transformers are state-of-the-art models for a variety of sequence modeling\ntasks. At their core is an attention function which models pairwise\ninteractions between the inputs at every timestep. While attention is powerful,\nit does not scale efficiently to long sequences due to its quadratic time and\nspace complexity in the sequence length. We propose RFA, a linear time and\nspace attention that uses random feature methods to approximate the softmax\nfunction, and explore its application in transformers. RFA can be used as a\ndrop-in replacement for conventional softmax attention and offers a\nstraightforward way of learning with recency bias through an optional gating\nmechanism. Experiments on language modeling and machine translation demonstrate\nthat RFA achieves similar or better performance compared to strong transformer\nbaselines. In the machine translation experiment, RFA decodes twice as fast as\na vanilla transformer. Compared to existing efficient transformer variants, RFA\nis competitive in terms of both accuracy and efficiency on three long text\nclassification datasets. Our analysis shows that RFA's efficiency gains are\nespecially notable on long sequences, suggesting that RFA will be particularly\nuseful in tasks that require working with large inputs, fast decoding speed, or\nlow memory footprints.\n"}
+{"abstract": "  We analyze the connections between the non-Markovianity degree of the most\ngeneral phase-damping qubit maps and their legitimate mixtures. Using the\nresults for image non-increasing dynamical maps, we formulate the necessary and\nsufficient conditions for the Pauli maps to satisfy specific divisibility\ncriteria. Next, we examine how the non-Markovianity properties for (in general\nnoninvertible) Pauli dynamical maps influence the properties of their convex\ncombinations. Our results are illustrated with instructive examples. For\nP-divisible maps, we propose a legitimate time-local generator whose all\ndecoherence rates are temporarily infinite.\n"}
+{"abstract": "  In this paper we attempt to examine the possibility of construction of a\ntraversable wormhole on the Randall-Sundrum braneworld with ordinary matter\nemploying the Kuchowicz potential as one of the metric potentials. In this\nscenario, the wormhole shape function is obtained and studied, along with\nvalidity of Null Energy Condition (NEC) and the junction conditions at the\nsurface of the wormhole are used to obtain a few of the model parameters. The\ninvestigation, besides giving an estimate for the bulk equation of state\nparameter, draws important constraints on the brane tension which is a novel\nattempt in this aspect and very interestingly the constraints imposed by a\nphysically plausible traversable wormhole is in high confirmity with those\ndrawn from more general space-times or space-time independent situations\ninvolved in fundamental physics. Also, we go on to claim that the possible\nexistence of a wormhole may very well indicate that we live on a three-brane\nuniverse.\n"}
+{"abstract": "  We consider a numerical scheme for the approximation of a system that couples\nthe evolution of a two--dimensional hypersurface to a reaction--diffusion\nequation on the surface. The surfaces are assumed to be graphs and evolve\naccording to forced mean curvature flow. The method uses continuous, piecewise\nlinear finite elements in space and a backward Euler scheme in time. Assuming\nthe existence of a smooth solution we prove optimal error bounds both in\n$L^\\infty(L^2)$ and in $L^2(H^1)$. We present several numerical experiments\nthat confirm our theoretical findings and apply the method in order to simulate\ndiffusion induced grain boundary motion.\n"}
+{"abstract": "  This paper presents a new prescribed performance control scheme for the\nattitude tracking of the three degree-of-freedom (3-DOF) helicopter system with\nlumped disturbances under mechanical constraints. First, a novel prescribed\nperformance function is defined to guarantee that the tracking error\nperformance has a small overshoot in the transient process and converges to an\narbitrary small region within a predetermined time in the steady-state process\nwithout knowing the initial tracking error in advance. Then, based on the novel\nprescribed performance function, an error transformation combined with the\nsmooth finite-time control method we proposed before is employed to drive the\nelevation and pitch angles to track given desired trajectories with guaranteed\ntracking performance. The theoretical analysis of finite-time Lyapunov\nstability indicates that the closed-loop system is fast finite-time uniformly\nultimately boundedness. Finally, comparative experiment results illustrate the\neffectiveness and superiority of the proposed control scheme.\n"}
+{"abstract": "  One of the challenges in many-body physics is determining the effects of\nphonons on strongly correlated electrons. The difficulty arises from strong\ncorrelations at differing energy scales -- for band metals, Migdal-Eliashberg\ntheory accurately determines electron-phonon coupling effects due to the\nabsence of vertex corrections -- but strongly correlated electrons require a\nmore complex description and the standard Migdal-Eliashberg approach does not\nnecessarily apply. In this work, we solve for the atomic limit Green's function\nof the Holstein-Hubbard model with both time-dependent electron-electron and\nelectron-phonon couplings. We then examine the photoemission spectra (PES) of\nthis model in and out of equilibrium. Next we use similar methods to exactly\nsolve an extended version of the Hatsugai-Komoto model, and examine its\nbehavior in and out of equilibrium. These calculations lead us to propose using\nthe first moment of the photoemission spectra to signal non-equilibrium changes\nin electron-electron and electron-phonon couplings.\n"}
+{"abstract": "  The paper treats pseudodifferential operators $P=Op(p(\\xi ))$ with\nhomogeneous complex symbol $p(\\xi )$ of order $2a>0$, generalizing the\nfractional Laplacian $(-\\Delta )^a$ but lacking its symmetries, and taken to\nact on the halfspace $R^n_+$. The operators are seen to satisfy a principal\n$\\mu $-transmission condition relative to $R^n_+$, but generally not the full\n$\\mu $-transmission condition satisfied by $(-\\Delta )^a$ and related operators\n(with $\\mu =a$). However, $P$ acts well on the so-called $\\mu $-transmission\nspaces over $R^n_+$ (defined in earlier works), and when $P$ moreover is\nstrongly elliptic, these spaces are the solution spaces for the homogeneous\nDirichlet problem for $P$, leading to regularity results with a factor $x_n^\\mu\n$ (in a limited range of Sobolev spaces). The information is then shown to be\nsufficient to establish an integration by parts formula over $R^n_+$ for $P$\nacting on such functions. The formulation in Sobolev spaces, and the results on\nstrongly elliptic operators going beyond operators with real kernels, are new.\n  Furthermore, large solutions with nonzero Dirichlet traces are described, and\na halfways Green's formula is established, for this new class of operators.\n  Since the principal $\\mu $-transmission condition has weaker requirements\nthan the full $\\mu $-transmission condition assumed in earlier papers, new\narguments were needed, relying on work of Vishik and Eskin instead of the\nBoutet de Monvel theory. The results cover the case of nonsymmetric operators\nwith real kernel that were only partially treated in a preceding paper.\n"}
+{"abstract": "  Representing complex 3D objects as simple geometric primitives, known as\nshape abstraction, is important for geometric modeling, structural analysis,\nand shape synthesis. In this paper, we propose an unsupervised shape\nabstraction method to map a point cloud into a compact cuboid representation.\nWe jointly predict cuboid allocation as part segmentation and cuboid shapes and\nenforce the consistency between the segmentation and shape abstraction for\nself-learning. For the cuboid abstraction task, we transform the input point\ncloud into a set of parametric cuboids using a variational auto-encoder\nnetwork. The segmentation network allocates each point into a cuboid\nconsidering the point-cuboid affinity. Without manual annotations of parts in\npoint clouds, we design four novel losses to jointly supervise the two branches\nin terms of geometric similarity and cuboid compactness. We evaluate our method\non multiple shape collections and demonstrate its superiority over existing\nshape abstraction methods. Moreover, based on our network architecture and\nlearned representations, our approach supports various applications including\nstructured shape generation, shape interpolation, and structural shape\nclustering.\n"}
+{"abstract": "  Grammatical Evolution (GE) is one of the most popular Genetic Programming\n(GP) variants, and it has been used with success in several problem domains.\nSince the original proposal, many enhancements have been proposed to GE in\norder to address some of its main issues and improve its performance.\n  In this paper we propose Probabilistic Grammatical Evolution (PGE), which\nintroduces a new genotypic representation and new mapping mechanism for GE.\nSpecifically, we resort to a Probabilistic Context-Free Grammar (PCFG) where\nits probabilities are adapted during the evolutionary process, taking into\naccount the productions chosen to construct the fittest individual. The\ngenotype is a list of real values, where each value represents the likelihood\nof selecting a derivation rule. We evaluate the performance of PGE in two\nregression problems and compare it with GE and Structured Grammatical Evolution\n(SGE).\n  The results show that PGE has a a better performance than GE, with\nstatistically significant differences, and achieved similar performance when\ncomparing with SGE.\n"}
+{"abstract": "  We analyze internal device physics, performance limitations, and optimization\noptions for a unique laser design with multiple active regions separated by\ntunnel junctions, featuring surprisingly wide quantum wells. Contrary to common\nassumptions, these quantum wells are revealed to allow for perfect screening of\nthe strong built-in polarization field, while optical gain is provided by\nhigher quantum levels. However, internal absorption, low p-cladding\nconductivity, and self-heating are shown to strongly limit the laser\nperformance.\n"}
+{"abstract": "  Adversarial robustness has become a topic of growing interest in machine\nlearning since it was observed that neural networks tend to be brittle. We\npropose an information-geometric formulation of adversarial defense and\nintroduce FIRE, a new Fisher-Rao regularization for the categorical\ncross-entropy loss, which is based on the geodesic distance between natural and\nperturbed input features. Based on the information-geometric properties of the\nclass of softmax distributions, we derive an explicit characterization of the\nFisher-Rao Distance (FRD) for the binary and multiclass cases, and draw some\ninteresting properties as well as connections with standard regularization\nmetrics. Furthermore, for a simple linear and Gaussian model, we show that all\nPareto-optimal points in the accuracy-robustness region can be reached by FIRE\nwhile other state-of-the-art methods fail. Empirically, we evaluate the\nperformance of various classifiers trained with the proposed loss on standard\ndatasets, showing up to 2\\% of improvements in terms of robustness while\nreducing the training time by 20\\% over the best-performing methods.\n"}
+{"abstract": "  Deep learning (DL) relies on massive amounts of labeled data, and improving\nits labeled sample-efficiency remains one of the most important problems since\nits advent. Semi-supervised learning (SSL) leverages unlabeled data that are\nmore accessible than their labeled counterparts. Active learning (AL) selects\nunlabeled instances to be annotated by a human-in-the-loop in hopes of better\nperformance with less labeled data. Given the accessible pool of unlabeled data\nin pool-based AL, it seems natural to use SSL when training and AL to update\nthe labeled set; however, algorithms designed for their combination remain\nlimited. In this work, we first prove that convergence of gradient descent on\nsufficiently wide ReLU networks can be expressed in terms of their Gram matrix'\neigen-spectrum. Equipped with a few theoretical insights, we propose\nconvergence rate control (CRC), an AL algorithm that selects unlabeled data to\nimprove the problem conditioning upon inclusion to the labeled set, by\nformulating an acquisition step in terms of improving training dynamics.\nExtensive experiments show that SSL algorithms coupled with CRC can achieve\nhigh performance using very few labeled data.\n"}
+{"abstract": "  An $\\ell$-facial edge-coloring of a plane graph is a coloring of its edges\nsuch that any two edges at distance at most $\\ell$ on a boundary walk of any\nface receive distinct colors. It is the edge-coloring variant of the\n$\\ell$-facial vertex coloring, which arose as a generalization of the\nwell-known cyclic coloring. It is conjectured that at most $3\\ell + 1$ colors\nsuffice for an $\\ell$-facial edge-coloring of any plane graph. The conjecture\nhas only been confirmed for $\\ell \\le 2$, and in this paper, we prove its\nvalidity for $\\ell = 3$.\n"}
+{"abstract": "  Despite the increasing interest, the research field which studies the\nconcepts of work and heat at quantum level has suffered from two main\ndrawbacks: first, the difficulty to properly define and measure the work, heat\nand internal energy variation in a quantum system and, second, the lack of\nexperiments. Here, we report a full characterization of the dissipated heat,\nwork and internal energy variation in a two-level quantum system interacting\nwith an engineered environment. We use the IBMQ quantum computer to implement\nthe driven system's dynamics in a dissipative environment. The experimental\ndata allow us to construct quasi-probability distribution functions from which\nwe recover the correct averages of work, heat and internal energy variation in\nthe dissipative processes. Interestingly, by increasing the environment\ncoupling strength, we observe a reduction of the pure quantum features of the\nenergy exchange processes that we interpret as the emergence of the classical\nlimit. This makes the present approach a privileged tool to study, understand\nand exploit quantum effects in energy exchanges.\n"}
+{"abstract": "  Many machine learning tasks involve subpopulation shift where the testing\ndata distribution is a subpopulation of the training distribution. For such\nsettings, a line of recent work has proposed the use of a variant of empirical\nrisk minimization(ERM) known as distributionally robust optimization (DRO). In\nthis work, we apply DRO to real, large-scale tasks with subpopulation shift,\nand observe that DRO performs relatively poorly, and moreover has severe\ninstability. We identify one direct cause of this phenomenon: sensitivity of\nDRO to outliers in the datasets. To resolve this issue, we propose the\nframework of DORO, for Distributional and Outlier Robust Optimization. At the\ncore of this approach is a refined risk function which prevents DRO from\noverfitting to potential outliers. We instantiate DORO for the Cressie-Read\nfamily of R\\'enyi divergence, and delve into two specific instances of this\nfamily: CVaR and $\\chi^2$-DRO. We theoretically prove the effectiveness of the\nproposed method, and empirically show that DORO improves the performance and\nstability of DRO with experiments on large modern datasets, thereby positively\naddressing the open question raised by Hashimoto et al., 2018.\n"}
+{"abstract": "  We study two-sided reputational bargaining with opportunities to issue an\nultimatum -- threats to force dispute resolution. Each player is either a\njustified type, who never concedes and issues an ultimatum whenever an\nopportunity arrives, or an unjustified type, who can concede, wait, or bluff\nwith an ultimatum. In equilibrium, the presence of ultimatum opportunities can\nharm or benefit a player by decelerating or accelerating reputation building.\nWhen only one player can issue an ultimatum, equilibrium play is unique. The\nhazard rate of dispute resolution is discontinuous and piecewise monotonic in\ntime. As the probabilities of being justified vanish, agreement is immediate\nand efficient, and if the set of justifiable demands is rich, payoffs modify\nAbreu and Gul (2000), with the discount rate replaced by the ultimatum\nopportunity arrival rate if the former is smaller. When both players' ultimatum\nopportunities arrive sufficiently fast, there may exist multiple equilibria in\nwhich their reputations do not build up and negotiation lasts forever.\n"}
+{"abstract": "  Overparametrized interpolating models have drawn increasing attention from\nmachine learning. Some recent studies suggest that regularized interpolating\nmodels can generalize well. This phenomenon seemingly contradicts the\nconventional wisdom that interpolation tends to overfit the data and performs\npoorly on test data. Further, it appears to defy the bias-variance trade-off.\nAs one of the shortcomings of the existing theory, the classical notion of\nmodel degrees of freedom fails to explain the intrinsic difference among the\ninterpolating models since it focuses on estimation of in-sample prediction\nerror. This motivates an alternative measure of model complexity which can\ndifferentiate those interpolating models and take different test points into\naccount. In particular, we propose a measure with a proper adjustment based on\nthe squared covariance between the predictions and observations. Our analysis\nwith least squares method reveals some interesting properties of the measure,\nwhich can reconcile the \"double descent\" phenomenon with the classical theory.\nThis opens doors to an extended definition of model degrees of freedom in\nmodern predictive settings.\n"}
+{"abstract": "  Solving for detailed chemical kinetics remains one of the major bottlenecks\nfor computational fluid dynamics simulations of reacting flows using a\nfinite-rate-chemistry approach. This has motivated the use of fully connected\nartificial neural networks to predict stiff chemical source terms as functions\nof the thermochemical state of the combustion system. However, due to the\nnonlinearities and multi-scale nature of combustion, the predicted solution\noften diverges from the true solution when these deep learning models are\ncoupled with a computational fluid dynamics solver. This is because these\napproaches minimize the error during training without guaranteeing successful\nintegration with ordinary differential equation solvers. In the present work, a\nnovel neural ordinary differential equations approach to modeling chemical\nkinetics, termed as ChemNODE, is developed. In this deep learning framework,\nthe chemical source terms predicted by the neural networks are integrated\nduring training, and by computing the required derivatives, the neural network\nweights are adjusted accordingly to minimize the difference between the\npredicted and ground-truth solution. A proof-of-concept study is performed with\nChemNODE for homogeneous autoignition of hydrogen-air mixture over a range of\ncomposition and thermodynamic conditions. It is shown that ChemNODE accurately\ncaptures the correct physical behavior and reproduces the results obtained\nusing the full chemical kinetic mechanism at a fraction of the computational\ncost.\n"}
+{"abstract": "  Graphics Processing Units (GPUs) have been widely used to accelerate\nartificial intelligence, physics simulation, medical imaging, and information\nvisualization applications. To improve GPU performance, GPU hardware designers\nneed to identify performance issues by inspecting a huge amount of\nsimulator-generated traces. Visualizing the execution traces can reduce the\ncognitive burden of users and facilitate making sense of behaviors of GPU\nhardware components. In this paper, we first formalize the process of GPU\nperformance analysis and characterize the design requirements of visualizing\nexecution traces based on a survey study and interviews with GPU hardware\ndesigners. We contribute data and task abstraction for GPU performance\nanalysis. Based on our task analysis, we propose Daisen, a framework that\nsupports data collection from GPU simulators and provides visualization of the\nsimulator-generated GPU execution traces. Daisen features a data abstraction\nand trace format that can record simulator-generated GPU execution traces.\nDaisen also includes a web-based visualization tool that helps GPU hardware\ndesigners examine GPU execution traces, identify performance bottlenecks, and\nverify performance improvement. Our qualitative evaluation with GPU hardware\ndesigners demonstrates that the design of Daisen reflects the typical workflow\nof GPU hardware designers. Using Daisen, participants were able to effectively\nidentify potential performance bottlenecks and opportunities for performance\nimprovement. The open-sourced implementation of Daisen can be found at\ngitlab.com/akita/vis. Supplemental materials including a demo video, survey\nquestions, evaluation study guide, and post-study evaluation survey are\navailable at osf.io/j5ghq.\n"}
+{"abstract": "  A set of boundary conditions called the Transpiration-Resistance Model (TRM)\nare investigated in altering near-wall turbulence. The TRM has been previously\nproposed by \\citet{Lacis2020} as a means of representing the net effect of\nsurface micro-textures on their overlying bulk flows. It encompasses\nconventional Navier-slip boundary conditions relating the streamwise and\nspanwise velocities to their respective shears through the slip lengths\n$\\ell_x$ and $\\ell_z$. In addition, it features a transpiration condition\naccounting for the changes induced in the wall-normal velocity by expressing it\nin terms of variations of the wall-parallel velocity shears through the\ntranspiration lengths $m_x$ and $m_z$. Greater levels of drag increase occur\nwhen more transpiration takes place at the boundary plane, with turbulent\ntranspiration being predominately coupled to the spanwise shear component for\ncanonical near-wall turbulence. The TRM can reproduce the effect of a\nhomogeneous and structured roughness up to ${k^+}\\,{\\approx18}$. In this\ntransitionally rough flow regime, the transpiration lengths of the TRM must be\nempirically determined. The \\emph{transpiration factor} is defined as the\nproduct between the slip and transpiration lengths, i.e. $(m\\ell)_{x,z}$. This\nfactor contains the compound effect of the wall-parallel velocity occurring at\nthe boundary plane and increased permeability, both of which lead to the\ntransport of momentum in the wall-normal direction. A linear relation between\nthe transpiration factor and the roughness function is observed for regularly\ntextured surfaces in the transitionally rough regime of turbulence. The\nrelations obtained between the transpiration factor and the roughness function\nshow that such effective flow quantities can be suitable measures for\ncharacterizing rough surfaces in this flow regime.\n"}
+{"abstract": "  This paper addresses the trajectory tracking problem of an autonomous\ntractor-trailer system by using a fast distributed nonlinear model predictive\ncontrol algorithm in combination with nonlinear moving horizon estimation for\nthe state and parameter estimation in which constraints on the inputs and the\nstates can be incorporated. The proposed control algorithm is capable of\ndriving the tractor-trailer system to any desired trajectory ensuring high\ncontrol accuracy and robustness against environmental disturbances.\n"}
+{"abstract": "  We introduce FaDIV-Syn, a fast depth-independent method for novel view\nsynthesis. Related methods are often limited by their depth estimation stage,\nwhere incorrect depth predictions can lead to large projection errors. To avoid\nthis issue, we efficiently warp input images into the target frame for a range\nof assumed depth planes. The resulting plane sweep volume (PSV) is directly fed\ninto our network, which first estimates soft PSV masks in a self-supervised\nmanner, and then directly produces the novel output view. We therefore\nside-step explicit depth estimation. This improves efficiency and performance\non transparent, reflective, thin, and feature-less scene parts. FaDIV-Syn can\nperform both interpolation and extrapolation tasks and outperforms\nstate-of-the-art extrapolation methods on the large-scale RealEstate10k\ndataset. In contrast to comparable methods, it achieves real-time performance\ndue to its lightweight architecture. We thoroughly evaluate ablations, such as\nremoving the Soft-Masking network, training from fewer examples as well as\ngeneralization to higher resolutions and stronger depth discretization.\n"}
+{"abstract": "  The unique properties of blockchain enable central requirements of\ndistributed secure logging: Immutability, integrity, and availability.\nEspecially when providing transparency about data usages, a blockchain-based\nsecure log can be beneficial, as no trusted third party is required. Yet, with\ndata governed by privacy legislation such as the GDPR or CCPA, the core\nadvantage of immutability becomes a liability. After a rightful request, an\nindividual's personal data need to be rectified or deleted, which is impossible\nin an immutable blockchain. To solve this issue, we exploit a legal property of\npseudonymized data: They are only regarded personal data if they can be\nassociated with an individual's identity. We make use of this fact by\npresenting P3, a pseudonym provisioning system for secure usage logs including\na protocol for recording new usages. For each new block, a one-time transaction\npseudonym is generated. The pseudonym generation algorithm guarantees\nunlinkability and enables proof of ownership. These properties enable\nGDPR-compliant use of blockchain, as data subjects can exercise their legal\nrights with regards to their personal data. The new-usage protocol ensures\nnon-repudiation, and therefore accountability and liability. Most importantly,\nour approach does not require a trusted third party and is independent of the\nutilized blockchain software.\n"}
+{"abstract": "  The spectrum of laser-plasma generated X-rays is very important, it\ncharacterizes electron dynamics in plasma and is basic for applications.\nHowever, the accuracies and efficiencies of existing methods to diagnose the\nspectrum of laser-plasma based X-ray pulse are not very high, especially in the\nrange of several hundred keV. In this study, a new method based on electron\ntracks detection to measure the spectrum of laser-plasma produced X-ray pulses\nis proposed and demonstrated. Laser-plasma generated X-rays are scattered in a\nmulti-pixel silicon tracker. Energies and scattering directions of Compton\nelectrons can be extracted from the response of the detector, and then the\nspectrum of X-rays can be reconstructed. Simulations indicate that the energy\nresolution of this method is approximately 20% for X-rays from 200 to 550 keV\nfor a silicon-on-insulator pixel detector with 12 $\\rm \\mu$m pixel pitch and\n500 $\\rm \\mu$m depletion region thickness. The results of a proof-of-principle\nexperiment based on a Timepix3 detector are also shown.\n"}
+{"abstract": "  We present spectroscopy of individual stars in 26 Magellanic Cloud (MC) star\nclusters with the aim of estimating dynamical masses and $V$-band mass-to-light\n($M/L_V$) ratios over a wide range in age and metallicity. We obtained 3137\nhigh-resolution stellar spectra with M2FS on the \\textit{Magellan}/Clay\nTelescope. Combined with 239 published spectroscopic results of comparable\nquality, we produced a final sample of 2787 stars with good quality spectra for\nkinematic analysis in the target clusters. Line-of-sight velocities measured\nfrom these spectra and stellar positions within each cluster were used in a\ncustomized expectation-maximization (EM) technique to estimate cluster\nmembership probabilities. Using appropriate cluster structural parameters and\ncorresponding single-mass dynamical models, this technique ultimately provides\nself-consistent total mass and $M/L_V$ estimates for each cluster. Mean\nmetallicities for the clusters were also obtained and tied to a scale based on\ncalcium IR triplet metallicites. We present trends of the cluster $M/L_V$\nvalues with cluster age, mass and metallicity, and find that our results run\nabout 40 per cent on average lower than the predictions of a set of simple\nstellar population (SSP) models. Modified SSP models that account for internal\nand external dynamical effects greatly improve agreement with our results, as\ncan models that adopt a strongly bottom-light IMF. To the extent that dynamical\nevolution must occur, a modified IMF is not required to match data and models.\nIn contrast, a bottom-heavy IMF is ruled out for our cluster sample as this\nwould lead to higher predicted $M/L_V$ values, significantly increasing the\ndiscrepancy with our observations.\n"}
+{"abstract": "  Current vision systems are trained on huge datasets, and these datasets come\nwith costs: curation is expensive, they inherit human biases, and there are\nconcerns over privacy and usage rights. To counter these costs, interest has\nsurged in learning from cheaper data sources, such as unlabeled images. In this\npaper we go a step further and ask if we can do away with real image datasets\nentirely, instead learning from noise processes. We investigate a suite of\nimage generation models that produce images from simple random processes. These\nare then used as training data for a visual representation learner with a\ncontrastive loss. We study two types of noise processes, statistical image\nmodels and deep generative models under different random initializations. Our\nfindings show that it is important for the noise to capture certain structural\nproperties of real data but that good performance can be achieved even with\nprocesses that are far from realistic. We also find that diversity is a key\nproperty to learn good representations. Datasets, models, and code are\navailable at https://mbaradad.github.io/learning_with_noise.\n"}
+{"abstract": "  The detection of polylines is usually either bound to branchless polylines or\nformulated in a recurrent way, prohibiting their use in real-time systems.\n  We propose an approach that builds upon the idea of single shot object\ndetection. Reformulating the problem of polyline detection as a bottom-up\ncomposition of small line segments allows to detect bounded, dashed and\ncontinuous polylines with a single head. This has several major advantages over\nprevious methods. Not only is the method at 187 fps more than suited for\nreal-time applications with virtually any restriction on the shapes of the\ndetected polylines. By predicting multiple line segments for each cell, even\nbranching or crossing polylines can be detected.\n  We evaluate our approach on three different applications for road marking,\nlane border and center line detection. Hereby, we demonstrate the ability to\ngeneralize to different domains as well as both implicit and explicit polyline\ndetection tasks.\n"}
+{"abstract": "  We develop the covariant phase space formalism allowing for non-vanishing\nflux, anomalies and field dependence in the vector field generators. We\nconstruct a charge bracket that generalizes the one introduced by Barnich and\nTroessaert and includes contributions from the Lagrangian and its anomaly. This\nbracket is uniquely determined by the choice of Lagrangian representative of\nthe theory. We then extend the notion of corner symmetry algebra to include the\nsurface translation symmetries and prove that the charge bracket provides a\ncanonical representation of the extended corner symmetry algebra. This\nrepresentation property is shown to be equivalent to the projection of the\ngravitational equations of motion on the corner, providing us with an encoding\nof the bulk dynamics in a locally holographic manner.\n"}
+{"abstract": "  We introduce Virasoro operators for any Landau-Ginzburg pair (W, G) where W\nis a non-degenerate quasi-homogeneous polynomial and G is a certain group of\ndiagonal symmetries. We propose a conjecture that the total ancestor potential\nof the FJRW theory of the pair (W, G) is annihilated by these Virasoro\noperators. We prove the conjecture in various cases, including: (1) invertible\npolynomials with the maximal group, (2) two-variable homogeneous Fermat\npolynomials with the minimal group, (3) certain Calabi-Yau polynomials with\ngroups. We also discuss the connections among Virasoro constraints, mirror\nsymmetry of Landau-Ginzburg models, and Landau-Ginzburg/Calabi-Yau\ncorrespondence.\n"}
+{"abstract": "  Although the unscented Kalman filter (UKF) is applicable to nonlinear\nsystems, it turns out that, for linear systems, UKF does not specialize to the\nclassical Kalman filter. This situation suggests that it may be advantageous to\nmodify UKF in such a way that, for linear systems, the Kalman filter is\nrecovered. The ultimate goal is thus to develop modifications of UKF that\nspecialize to the Kalman filter for linear systems and have improved accuracy\nfor nonlinear systems. With this motivation, this paper presents two\nmodifications of UKF that specialize to the Kalman filter for linear systems.\nThe first modification (EUKF-A) requires the Jacobian of the dynamics map,\nwhereas the second modification (EUKF-C) requires the Jacobian of the\nmeasurement map. For various nonlinear examples, the accuracy of EUKF-A and\nEUKF-C is compared to the accuracy of UKF.\n"}
+{"abstract": "  In this article we show why flying and rotating beer mats, CDs, or other flat\ndisks will eventually flip in the air and end up flying with backspin, thus,\nmaking them unusable as frisbees. The crucial effect responsible for the\nflipping is found to be the lift attacking not in the center of mass but\nslightly offset to the forward edge. This induces a torque leading to a\nprecession towards backspin orientation. An effective theory is developed\nproviding an approximate solution for the disk's trajectory with a minimal set\nof parameters. Our theoretical results are confronted with experimental results\nobtained using a beer mat shooting apparatus and a high speed camera. Very good\nagreement is found.\n"}
+{"abstract": "  Line intensity mapping (LIM) proposes to efficiently observe distant faint\ngalaxies and map the matter density field at high redshift. Building upon the\nformalism in the companion paper, we first highlight the degeneracies between\ncosmology and astrophysics in LIM. We discuss what can be constrained from\nmeasurements of the mean intensity and redshift-space power spectra. With a\nsufficient spectral resolution, the large-scale redshift-space distortions of\nthe 2-halo term can be measured, helping to break the degeneracy between bias\nand mean intensity. With a higher spectral resolution, measuring the\nsmall-scale redshift-space distortions disentangles the 1-halo and shot noise\nterms. Cross-correlations with external galaxy catalogs or lensing surveys\nfurther break degeneracies. We derive requirements for experiments similar to\nSPHEREx, HETDEX, CDIM, COMAP and CONCERTO. We then revisit the question of the\noptimality of the LIM observables, compared to galaxy detection, for\nastrophysics and cosmology. We use a matched filter to compute the luminosity\ndetection threshold for individual sources. We show that LIM contains\ninformation about galaxies too faint to detect, in the high-noise or\nhigh-confusion regimes. We quantify the sparsity and clustering bias of the\ndetected sources and compare them to LIM, showing in which cases LIM is a\nbetter tracer of the matter density. We extend previous work by answering these\nquestions as a function of Fourier scale, including for the first time the\neffect of cosmic variance, pixel-to-pixel correlations, luminosity-dependent\nclustering bias and redshift-space distortions.\n"}
+{"abstract": "  This paper aims at using the functional renormalization group formalism to\nstudy the equilibrium states of a stochastic process described by a\nquench--disordered multilinear Langevin equation. Such an equation\ncharacterizes the evolution of a time-dependent $N$-vector\n$q(t)=\\{q_1(t),\\cdots q_N(t)\\}$ and is traditionally encountered in the\ndynamical description of glassy systems at and out of equilibrium through the\nso-called Glauber model. From the connection between Langevin dynamics and\nquantum mechanics in imaginary time, we are able to coarse-grain the path\nintegral of the problem in the Fourier modes, and to construct a\nrenormalization group flow for effective euclidean action. In the large\n$N$-limit we are able to solve the flow equations for both matrix and tensor\ndisorder. The numerical solutions of the resulting exact flow equations are\nthen investigated using standard local potential approximation, taking into\naccount the quench disorder. In the case where the interaction is taken to be\nmatricial, for finite $N$ the flow equations are also solved. However, the case\nof finite $N$ and taking into account the non-equilibrum process will be\nconsidered in a companion investigation.\n"}
+{"abstract": "  Semi-discrete and fully discrete mixed finite element methods are considered\nfor Maxwell-model-based problems of wave propagation in linear viscoelastic\nsolid. This mixed finite element framework allows the use of a large class of\nexisting mixed conforming finite elements for elasticity in the spatial\ndiscretization. In the fully discrete scheme, a Crank-Nicolson scheme is\nadopted for the approximation of the temporal derivatives of stress and\nvelocity variables. Error estimates of the semi-discrete and fully discrete\nschemes, as well as an unconditional stability result for the fully discrete\nscheme, are derived. Numerical experiments are provided to verify the\ntheoretical results.\n"}
+{"abstract": "  A novel approach for electrochemical tuning of alcohol oxidase (AOx) and\nalcohol dehydrogenase (ADH) biocatalysis towards butanol-1 oxidation by\nincorporating enzymes in various designs of amperometric biosensors is\npresented. The biosensors were developed by using commercial graphene\noxide-based screen-printed electrodes and varying enzyme producing strains,\nencapsulation approaches (layer-by-layer (LbL) or one-step electrodeposition\n(EcD)), layers composition and structure, operating conditions (applied\npotential values) and introducing mediators (Meldola Blue and Prussian Blue) or\nPd-nanoparticles (Pd-NPs). Simultaneous analysis/screening of multiple crucial\nsystem parameters during the enzyme engineering process allowed to identify\nwithin a period of one month that four out of twelve proposed designs\ndemonstrated a good signal reproducibility and linear response (up to 14.6 mM\nof butanol) under very low applied potentials (from -0.02 to -0.32 V). Their\nmechanical stability was thoroughly investigated by multi-analytical techniques\nprior to butanol determination in cell-free samples from an anaerobic butanol\nfermentation. The EcD-based biosensor that incorporates ADH, NAD+, Pd-NPs and\nNafion showed no loss of enzyme activity after preparation and demonstrated\ncapabilities towards low potential (-0.12 V) detection of butanol-1 in\nfermentation medium (4 mM) containing multiple electroactive species with\nalmost 15 times enhanced sensitivity (0.2282 $\\mu$A/mM $\\pm$ 0.05) when\ncompared to the LbL design. Furthermore, the ADH-Nafion bonding for the S.\ncerevisiae strain was confirmed to be 3 times higher than for E. coli.\n"}
+{"abstract": "  Electronic states of a correlated material can be effectively modified by\nstructural variations delivered from a single-crystal substrate. In this\nletter, we show that the CrN films grown on MgO (001) substrates have a (001)\norientation, whereas the CrN films on {\\alpha}-Al2O3 (0001) substrates are\noriented along (111) direction parallel to the surface normal. Transport\nproperties of CrN films are remarkably different depending on crystallographic\norientations. The critical thickness for the metal-insulator transition (MIT)\nin CrN 111 films is significantly larger than that of CrN 001 films. In\ncontrast to CrN 001 films without apparent defects, scanning transmission\nelectron microscopy results reveal that CrN 111 films exhibit strain-induced\nstructural defects, e. g. the periodic horizontal twinning domains, resulting\nin an increased electron scattering facilitating an insulating state.\nUnderstanding the key parameters that determine the electronic properties of\nultrathin conductive layers is highly desirable for future technological\napplications.\n"}
+{"abstract": "  Statistical Hypothesis Testing (SHT) is a class of inference methods whereby\none makes use of empirical data to test a hypothesis and often emit a judgment\nabout whether to reject it or not. In this paper we focus on the logical aspect\nof this strategy, which is largely independent of the adopted school of\nthought, at least within the various frequentist approaches. We identify SHT as\ntaking the form of an unsound argument from Modus Tollens in classical logic,\nand, in order to rescue SHT from this difficulty, we propose that it can\ninstead be grounded in t-norm based fuzzy logics. We reformulate the\nfrequentists' SHT logic by making use of a fuzzy extension of modus Tollens to\ndevelop a model of truth valuation for its premises. Importantly, we show that\nit is possible to preserve the soundness of Modus Tollens by exploring the\nvarious conventions involved with constructing fuzzy negations and fuzzy\nimplications (namely, the S and R conventions). We find that under the S\nconvention, it is possible to conduct the Modus Tollens inference argument\nusing Zadeh's compositional extension and any possible t-norm. Under the R\nconvention we find that this is not necessarily the case, but that by mixing\nR-implication with S-negation we can salvage the product t-norm, for example.\nIn conclusion, we have shown that fuzzy logic is a legitimate framework to\ndiscuss and address the difficulties plaguing frequentist interpretations of\nSHT.\n"}
+{"abstract": "  We present the first version of a system for interactive generation of\ntheatre play scripts. The system is based on a vanilla GPT-2 model with several\nadjustments, targeting specific issues we encountered in practice. We also list\nother issues we encountered but plan to only solve in a future version of the\nsystem. The presented system was used to generate a theatre play script planned\nfor premiere in February 2021.\n"}
+{"abstract": "  We consider the revenue maximization problem in social advertising, where a\nsocial network platform owner needs to select seed users for a group of\nadvertisers, each with a payment budget, such that the total expected revenue\nthat the owner gains from the advertisers by propagating their ads in the\nnetwork is maximized. Previous studies on this problem show that it is\nintractable and present approximation algorithms. We revisit this problem from\na fresh perspective and develop novel efficient approximation algorithms, both\nunder the setting where an exact influence oracle is assumed and under one\nwhere this assumption is relaxed. Our approximation ratios significantly\nimprove upon the previous ones. Furthermore, we empirically show, using\nextensive experiments on four datasets, that our algorithms considerably\noutperform the existing methods on both the solution quality and computation\nefficiency.\n"}
+{"abstract": "  A spectral-energy distribution (SED) model for Type Ia supernovae (SNe Ia) is\na critical tool for measuring precise and accurate distances across a large\nredshift range and constraining cosmological parameters. We present an improved\nmodel framework, SALT3, which has several advantages over current models\nincluding the leading SALT2 model (SALT2.4). While SALT3 has a similar\nphilosophy, it differs from SALT2 by having improved estimation of\nuncertainties, better separation of color and light-curve stretch, and a\npublicly available training code. We present the application of our training\nmethod on a cross-calibrated compilation of 1083 SNe with 1207 spectra. Our\ncompilation is $2.5\\times$ larger than the SALT2 training sample and has\ngreatly reduced calibration uncertainties. The resulting trained SALT3.K21\nmodel has an extended wavelength range $2000$-$11000$ angstroms (1800 angstroms\nredder) and reduced uncertainties compared to SALT2, enabling accurate use of\nlow-$z$ $I$ and $iz$ photometric bands. Including these previously discarded\nbands, SALT3.K21 reduces the Hubble scatter of the low-z Foundation and CfA3\nsamples by 15% and 10%, respectively. To check for potential systematic\nuncertainties we compare distances of low ($0.01<z<0.2$) and high ($0.4<z<0.6$)\nredshift SNe in the training compilation, finding an insignificant $2\\pm14$\nmmag shift between SALT2.4 and SALT3.K21. While the SALT3.K21 model was trained\non optical data, our method can be used to build a model for rest-frame NIR\nsamples from the Roman Space Telescope. Our open-source training code, public\ntraining data, model, and documentation are available at\nhttps://saltshaker.readthedocs.io/en/latest/, and the model is integrated into\nthe sncosmo and SNANA software packages.\n"}
+{"abstract": "  We all know that in the dense anisotropic interior of the star,\nneutrino-neutrino forward-scattering can lead to fast collective neutrino\noscillations, which has striking consequences on flavor dependent neutrino\nemission and can be crucial for the evolution of a supernova and its neutrino\nsignal. The flavor evolution of such dense neutrino system is governed by a\nlarge number of coupled nonlinear partial differential equations which are\nalmost always very difficult to solve. Although the triggering, initial linear\ngrowth and the condition for fast oscillations to occur are understood by\n\"Linear stability analysis\" but this fails to answer an important question:\n\"what is the impact of fast flavor conversion on observable neutrino fluxes or\nthe supernova explosion mechanism?\". This is a significantly harder problem\nthat requires understanding the nature of the final state solution in the\nnonlinear regime. Moving towards this direction we present one of the first\nnumerical as well as an analytical study of the coupled flavor evolution of a\nnon-stationary and inhomogeneous dense neutrino system in the nonlinear regime\nconsidering one spatial dimension and a spectrum of velocity modes. This work\ngives a clear picture of the final state flavor dynamics of such systems\nspecifying its dependence on space-time coordinates, phase space variables as\nwell as the lepton asymmetry and thus can have significant implications for the\nsupernova astrophysics as well as its associated neutrino phenomenology even\nfor the most realistic scenario.\n"}
+{"abstract": "  Coherent quantum phase slips are expected to lead to a blockade of dc\nconduction in sufficiently narrow superconducting nanowires below a certain\ncritical voltage. We present measurements of NbN nanowires in which not only is\na critical voltage observed, but also in which this critical voltage may be\ntuned using a side-gate electrode. The critical voltage varies periodically as\nthe applied gate voltage is varied. While the observations are qualitatively as\nexpected for quantum interference between coherent quantum phase slip elements,\nthe period of the tuning is orders of magnitude larger than expected on the\nbasis of simple capacitance considerations. Furthermore, two significant abrupt\nchanges in the period of the variations during measurements of one nanowire are\nobserved, an observation which constrains detailed explanations for the\nbehaviour. The plausibility of an explanation assuming that the behaviour\narises from granular Josephson junctions in the nanowire is also considered.\n"}
+{"abstract": "  We construct the complementary short-range correlation relativistic\nlocal-density-approximation functional to be used in relativistic\nrange-separated density-functional theory based on a Dirac-Coulomb Hamiltonian\nin the no-pair approximation. For this, we perform relativistic\nrandom-phase-approximation calculations of the correlation energy of the\nrelativistic homogeneous electron gas with a modified electron-electron\ninteraction, we study the high-density behavior, and fit the results to a\nparametrized expression. The obtained functional should eventually be useful\nfor electronic-structure calculations of strongly correlated systems containing\nheavy elements.\n"}
+{"abstract": "  We show that if the complement of a Donaldson hypersurface in a closed,\nintegral symplectic manifold has the homology of a subcritical Stein manifold,\nthen the hypersurface is of degree one. In particular, this demonstrates a\nconjecture by Biran and Cieliebak on subcritical polarisations of symplectic\nmanifolds. Our proof is based on a simple homological argument using ideas of\nKulkarni-Wood.\n"}
+{"abstract": "  Density functional theory based computational study has been conducted in\norder to investigate the effect of substitution of Cr and Co components by Si\non the structure, mechanical, electronic, and magnetic properties of the high\nentropy alloy CrCoNiFe. It is found that the presence of a moderate\nconcentration of Si substitutes (up to 12.5 %) does not significantly reduce\nthe structural and mechanical stability of CrCoNiFe while it may modify its\nelectronic and magnetic properties. Based on that, Si is proposed as a cheap\nand functional material for partial substitution of Cr or Co in CrCoNiFe.\n"}
+{"abstract": "  Argument mining systems often consider contextual information, i.e.\ninformation outside of an argumentative discourse unit, when trained to\naccomplish tasks such as argument component identification, classification, and\nrelation extraction. However, prior work has not carefully analyzed the utility\nof different contextual properties in context-aware models. In this work, we\nshow how two different types of contextual information, local discourse context\nand speaker context, can be incorporated into a computational model for\nclassifying argument components in multi-party classroom discussions. We find\nthat both context types can improve performance, although the improvements are\ndependent on context size and position.\n"}
+{"abstract": "  The effect of a spatially uniform magnetic field on the shear rheology of a\ndilute emulsion of monodispersed ferrofluid droplets, immersed in a\nnon-magnetizable immiscible fluid, is investigated using direct numerical\nsimulations. The direction of the applied magnetic field is normal to the shear\nflow direction. The droplets extra stress tensor arising from the presence of\ninterfacial forces of magnetic nature is modeled on the basis of the seminal\nwork of G. K. Batchelor, J. Fluid Mech., 41.3 (1970) under the assumptions of a\nlinearly magnetizable ferrofluid phase and negligible inertia. The results show\nthat even relatively small magnetic fields can have significant consequences on\nthe rheological properties of the emulsion due to the magnetic forces that\ncontribute to deform and orient the droplets towards the direction of the\napplied magnetic vector. In particular, we have observed an increase of the\neffective (bulk) viscosity and a reversal of the sign of the two normal stress\ndifferences with respect to the case without magnetic field for those\nconditions where the magnetic force prevails over the shearing force.\nComparisons between the results of our model with a direct integration of the\nviscous stress have provided an indication of its reliability to predict the\neffective viscosity of the suspension. Moreover, this latter quantity has been\nfound to behave as a monotonic increasing function of the applied magnetic\nfield for constant shearing flows (\"magneto-thickening\" behaviour), which\nallowed us to infer a simple constitutive equation describing the emulsion\nviscosity.\n"}
+{"abstract": "  A good understanding of the confinement of energetic ions in non-axisymmetric\nmagnetic fields is key for the design of reactors based on the stellarator\nconcept. In this work, we develop a model that, based on the radially-local\nbounce-averaged drift-kinetic equation, classifies orbits and succeeds in\npredicting configuration-dependent aspects of the prompt losses of energetic\nions in stellarators. Such a model could in turn be employed in the\noptimization stage of the design of new devices.\n"}
+{"abstract": "  State-of-the-art visual analytics techniques in application domains are often\ndesigned by VA professionals over qualitative requirement collected from end\nusers. These VA techniques may not leverage users' domain knowledge about how\nto achieve their analytical goals. In this position paper, we propose a\nuser-driven design process of VA applications centered around a new concept\ncalled analytical representation (AR). AR features a formal abstraction of user\nrequirement and their desired analytical trails for certain VA application, and\nis independent of the actual visualization design. A conceptual graph schema is\nintroduced to define the AR abstraction, which can be created manually or\nconstructed by semi-automated tools. Designing VA applications with AR provides\na shared opportunity for both optimal analysis blueprint from the perspective\nof end users and optimal visualization/algorithm from the perspective of VA\ndesigners. We demonstrate the usage of the design process in two case studies.\n"}
+{"abstract": "  The field of two-dimensional materials has been developing at an impressive\npace, with atomically thin crystals of an increasing number of different\ncompounds that have become available, together with techniques enabling their\nassembly into functional heterostructures. The strategy to detect these\natomically thin crystals has however remained unchanged since the discovery of\ngraphene. Such an absence of evolution is starting to pose problems because for\nmany of the 2D materials of current interest the optical contrast provided by\nthe commonly used detection procedure is insufficient to identify the presence\nof individual monolayers or to determine unambiguously the thickness of\natomically thin multilayers. Here we explore an alternative detection strategy,\nin which the enhancement of optical contrast originates from the use of\noptically inhomogeneous substrates, leading to diffusively reflected light.\nOwing to its peculiar polarization properties and to its angular distribution,\ndiffusively reflected light allows a strong contrast enhancement to be achieved\nthrough the implementation of suitable illumination-detection schemes. We\nvalidate this conclusion by carrying out a detailed quantitative analysis of\noptical contrast, which fully reproduces our experimental observations on over\n60 WSe$_2$ mono-, bi-, and trilayers. We further validate the proposed strategy\nby extending our analysis to atomically thin phosphorene, InSe, and graphene\ncrystals. Our conclusion is that the use of diffusively reflected light to\ndetect and identify atomically thin layers is an interesting alternative to the\ncommon detection scheme based on Fabry-Perot interference, because it enables\natomically thin layers to be detected on substrates others than the commonly\nused Si/SiO$_2$, and it may offer higher sensitivity depending on the specific\n2D material considered.\n"}
+{"abstract": "  Panoptic segmentation presents a new challenge in exploiting the merits of\nboth detection and segmentation, with the aim of unifying instance segmentation\nand semantic segmentation in a single framework. However, an efficient solution\nfor panoptic segmentation in the emerging domain of LiDAR point cloud is still\nan open research problem and is very much under-explored. In this paper, we\npresent a fast and robust LiDAR point cloud panoptic segmentation framework,\nreferred to as Panoptic-PolarNet. We learn both semantic segmentation and\nclass-agnostic instance clustering in a single inference network using a polar\nBird's Eye View (BEV) representation, enabling us to circumvent the issue of\nocclusion among instances in urban street scenes. To improve our network's\nlearnability, we also propose an adapted instance augmentation technique and a\nnovel adversarial point cloud pruning method. Our experiments show that\nPanoptic-PolarNet outperforms the baseline methods on SemanticKITTI and\nnuScenes datasets with an almost real-time inference speed. Panoptic-PolarNet\nachieved 54.1% PQ in the public SemanticKITTI panoptic segmentation leaderboard\nand leading performance for the validation set of nuScenes.\n"}
+{"abstract": "  Classical machine learning algorithms often assume that the data are drawn\ni.i.d. from a stationary probability distribution. Recently, continual learning\nemerged as a rapidly growing area of machine learning where this assumption is\nrelaxed, namely, where the data distribution is non-stationary, i.e., changes\nover time. However, data distribution drifts may interfere with the learning\nprocess and erase previously learned knowledge; thus, continual learning\nalgorithms must include specialized mechanisms to deal with such distribution\ndrifts. A distribution drift may change the class labels distribution, the\ninput distribution, or both. Moreover, distribution drifts might be abrupt or\ngradual. In this paper, we aim to identify and categorize different types of\ndata distribution drifts and potential assumptions about them, to better\ncharacterize various continual-learning scenarios. Moreover, we propose to use\nthe distribution drift framework to provide more precise definitions of several\nterms commonly used in the continual learning field.\n"}
+{"abstract": "  Using elementary techniques from analytic perturbation theory of Hermitian\nmatrices, we devise a simple strategy to detect positive quantum capacities of\nquantum channels and their complements. Several noteworthy examples, such as\nthe depolarizing and transpose-depolarizing channels (including the\nWerner-Holevo channel), dephasing channels, generalized Pauli channels,\nmulti-level amplitude damping channels, and (conjugate) diagonal unitary\ncovariant channels, serve to aptly exhibit the utility of our method. Our main\nresult leads to simplified proofs of certain existing structure theorems for\nthe class of degradable quantum channels, and an extension of their\napplicability to the larger class of more capable quantum channels.\n"}
+{"abstract": "  Convergence is a fundamental topic in analysis that is most commonly modelled\nusing topology. However, there are many natural convergences that are not given\nby any topology; e.g., convergence almost everywhere of a sequence of\nmeasurable functions and order convergence of nets in vector lattices. The\ntheory of convergence structures provides a framework for studying more general\nmodes of convergence. It also has one particularly striking feature: it is\nformalized using the language of filters. This paper develops a general theory\nof convergence in terms of nets. We show that it is equivalent to the\nfilter-based theory and present some translations between the two areas. In\nparticular, we provide a characterization of pretopological convergence\nstructures in terms of nets. We also use our results to unify certain topics in\nvector lattices with general convergence theory.\n"}
+{"abstract": "  In this paper, we establish a general weighted Hardy type inequality for the\n$% p-$Laplace operator with Robin boundary condition. We provide various\nconcrete examples to illustrate our results for different weights. Furthermore,\nwe present some Heisenberg-Pauli-Weyl type inequalities with boundary terms on\nballs with radius $R$ at the origin in $\\mathbb{R}^n$.\n"}
+{"abstract": "  Probabilistic model checking aims to prove whether a Markov decision process\n(MDP) satisfies a temporal logic specification. The underlying methods rely on\nan often unrealistic assumption that the MDP is precisely known. Consequently,\nparametric MDPs (pMDPs) extend MDPs with transition probabilities that are\nfunctions over unspecified parameters. The parameter synthesis problem is to\ncompute an instantiation of these unspecified parameters such that the\nresulting MDP satisfies the temporal logic specification. We formulate the\nparameter synthesis problem as a quadratically constrained quadratic program\n(QCQP), which is nonconvex and is NP-hard to solve in general. We develop two\napproaches that iteratively obtain locally optimal solutions. The first\napproach exploits the so-called convex-concave procedure (CCP), and the second\napproach utilizes a sequential convex programming (SCP) method. The techniques\nimprove the runtime and scalability by multiple orders of magnitude compared to\nblack-box CCP and SCP by merging ideas from convex optimization and\nprobabilistic model checking. We demonstrate the approaches on a satellite\ncollision avoidance problem with hundreds of thousands of states and tens of\nthousands of parameters and their scalability on a wide range of commonly used\nbenchmarks.\n"}
+{"abstract": "  To enhance adversarial robustness, adversarial training learns deep neural\nnetworks on the adversarial variants generated by their natural data. However,\nas the training progresses, the training data becomes less and less attackable,\nundermining the robustness enhancement. A straightforward remedy is to\nincorporate more training data, but sometimes incurring an unaffordable cost.\nIn this paper, to mitigate this issue, we propose the guided interpolation\nframework (GIF): in each epoch, the GIF employs the previous epoch's meta\ninformation to guide the data's interpolation. Compared with the vanilla mixup,\nthe GIF can provide a higher ratio of attackable data, which is beneficial to\nthe robustness enhancement; it meanwhile mitigates the model's linear behavior\nbetween classes, where the linear behavior is favorable to generalization but\nnot to the robustness. As a result, the GIF encourages the model to predict\ninvariantly in the cluster of each class. Experiments demonstrate that the GIF\ncan indeed enhance adversarial robustness on various adversarial training\nmethods and various datasets.\n"}
+{"abstract": "  Purpose: to assess the efficacy of THEIA, an artificial intelligence for\nscreening diabetic retinopathy in a multi-center prospective study. To validate\nthe potential application of THEIA as clinical decision making assistant in a\nnational screening program. Methods: 902 patients were recruited from either an\nurban large eye hospital, or a semi-rural optometrist led screening provider,\nas they were attending their appointment as part of New Zealand Diabetic\nScreening programme. These clinics used a variety of retinal cameras and a\nrange of operators. The de-identified images were then graded independently by\nthree senior retinal specialists, and final results were aggregated using New\nZealand grading scheme, which is then converted to referable\\non-referable and\nHealthy\\mild\\more than mild\\vision threatening categories. Results: compared to\nground truth, THEIA achieved 100% sensitivity and [95.35%-97.44%] specificity,\nand negative predictive value of 100%. THEIA also did not miss any patients\nwith more than mild or vision threatening disease. The level of agreement\nbetween the clinicians and the aggregated results was (k value: 0.9881, 0.9557,\nand 0.9175), and the level of agreement between THEIA and the aggregated labels\nwas (k value: 0.9515). Conclusion: Our multi-centre prospective trial showed\nthat THEIA does not miss referable disease when screening for diabetic\nretinopathy and maculopathy. It also has a very high level of granularity in\nreporting the disease level. Since THEIA is being tested on a variety of\ncameras, operating in a range of clinics (rural\\urban,\nophthalmologist-led\\optometrist-led), we believe that it will be a suitable\naddition to a public diabetic screening program.\n"}
+{"abstract": "  We present a map from the travelling salesman problem (TSP), a prototypical\nNP-complete combinatorial optimisation task, to the ground state associated\nwith a system of many-qudits. Conventionally, the TSP is cast into a quadratic\nunconstrained binary optimisation (QUBO) problem, that can be solved on an\nIsing machine. The size of the corresponding physical system's Hilbert space is\n$2^{N^2}$, where $N$ is the number of cities considered in the TSP. Our\nproposal provides a many-qudit system with a Hilbert space of dimension\n$2^{N\\log_2N}$, which is considerably smaller than the dimension of the Hilbert\nspace of the system resulting from the usual QUBO map. This reduction can yield\na significant speedup in quantum and classical computers. We simulate and\nvalidate our proposal using variational Monte Carlo with a neural quantum\nstate, solving the TSP in a linear layout for up to almost 100 cities.\n"}
+{"abstract": "  It is known in Hilbert space frame theory that a Bessel sequence can be\nexpanded to a frame. Contrary to Hilbert space situation, using a result of\nCasazza and Christensen, we show that there are Banach spaces and approximate\nBessel sequences which can not be expanded to approximate Schauder frames. We\ncharacterize Banach spaces in which one can expand approximate Bessel sequences\nto approximate Schauder frames.\n"}
+{"abstract": "  This analysis presents the possibility for the search for Dark Matter (DM)\nparticles using events with a Z$^{\\prime}$ heavy gauge boson and a large\nmissing transverse momentum at the Large Hadron Collider (LHC). We consider the\nmuonic decay of Z$^{\\prime}$. The analyzed Monte Carlo samples were the Open\nsimulated files produced by the CMS collaboration for proton-proton collisions\ncorrespond to an integrated luminosity of the LHC run-I with 19.7 fb$^{-1}$ at\n$\\sqrt{s} = $ 8 TeV. Two scenarios, one simplified benchmark scenario so called\nDark Higgs and the effective field theory (EFT) formalism, were used for\ninterpretations. Limits are set on both Z$^{\\prime}$, dark matter masses and\nthe cutoff scale of the EFT.\n"}
+{"abstract": "  This paper proposes an accelerated method for approximately solving partially\nobservable Markov decision process (POMDP) problems offline. Our method\ncarefully combines two existing tools: Anderson acceleration (AA) and the fast\ninformed bound (FIB) method. Adopting AA, our method rapidly solves an\napproximate Bellman equation with an efficient combination of previous solution\nestimates. Furthermore, the use of FIB alleviates the scalability issue\ninherent in POMDPs. We show the convergence of the overall algorithm to the\nsuboptimal solution obtained by FIB. We further consider a simulation-based\nmethod and prove that the approximation error is bounded explicitly. The\nperformance of our algorithm is evaluated on several benchmark problems. The\nresults of our experiments demonstrate that the proposed algorithm converges\nsignificantly faster without degrading the quality of the solution compared to\nits standard counterpart.\n"}
+{"abstract": "  Today's mobile devices contain densely packaged system-on-chips (SoCs) with\nmulti-core, high-frequency CPUs and complex pipelines. In parallel,\nsophisticated SoC-assisted security mechanisms have become commonplace for\nprotecting device data, such as trusted execution environments, full-disk and\nfile-based encryption. Both advancements have dramatically complicated the use\nof conventional physical attacks, requiring the development of specialised\nattacks. In this survey, we consolidate recent developments in physical fault\ninjections and side-channel attacks on modern mobile devices. In total, we\ncomprehensively survey over 50 fault injection and side-channel attack papers\npublished between 2009-2021. We evaluate the prevailing methods, compare\nexisting attacks using a common set of criteria, identify several challenges\nand shortcomings, and suggest future directions of research.\n"}
+{"abstract": "  The neutronic properties of Molten Salt Reactor are different from that of\ntraditional solid-fuel reactors due to its nuclear fuel particularity. Based\nupon MCNP code, the influence of the size and shape of fuel salt channel on\nneutron physics of MSR cell was studied systematically in this work. The\nresults show that the infinite multiplication factors increases first and then\ndecreases with the change of graphite cell size under the condition of given\nfuel volume fraction. In the case of the same FVF and average chord length,\nwhen the average chord length is relatively small, the k values with different\nfuel salt channel shapes are in good agreement; when the average chord length\nis relatively large, the k values with different fuel salt channel shapes are\ngreatly different. In addition, some examples of practical application of this\nwork are illustrated in the end, including cell selection for the core and\nthermal expansion displacement analysis of the cell.\n"}
+{"abstract": "  In this paper a variation of the classic vector quantization problem is\nconsidered. In the standard formulation, a quantizer is designed to minimize\nthe distortion between input and output when the number of reconstruction\npoints is fixed. We consider, instead, the scenario in which the number of\ncomparators used in quantization is fixed. More precisely, we study the case in\nwhich a vector quantizer of dimension d is comprised of k comparators, each\nreceiving a linear combination of the inputs and producing the output value\none/zero if this linear combination is above/below a certain threshold. In\nreconstruction, the comparators' output is mapped to a reconstruction point,\nchosen so as to minimize a chosen distortion measure between the quantizer\ninput and its reconstruction. The Comparison-Limited Vector Quantization (CLVQ)\nproblem is then defined as the problem of optimally designing the configuration\nof the compactors and the choice of reconstruction points so as to minimize the\ngiven distortion. In this paper, we design a numerical optimization algorithm\nfor the CLVQ problem. This algorithm leverages combinatorial geometrical\nnotions to describe the hyperplane arrangement induced by the configuration of\nthe comparators. It also relies on a genetic genetic meta heuristic to improve\nthe selection of the quantizer initialization and avoid local minima\nencountered during optimization. We numerically evaluate the performance of our\nalgorithm in the case of input distributions following uniform and Gaussian\ni.i.d. sources to be compressed under quadratic distortion and compare it to\nthe classic Linde-Buzo-Gray (LBG) algorithm.\n"}
+{"abstract": "  We study the general properties of the moduli spaces of SO(3) vortices over\norbifold Riemann surfaces and use these to characterize the solutions of the\nSO(3) monopole equations on Seifert manifolds following in the footsteps of\nMrowka, Ozsv\\'ath and Yu. We also study the solutions to the SO(3) monopole\nequations on S1 x (Riemann Surface) in order to motivate the construction of a\nversion of monopole Floer homology, which we call framed monopole Floer\nhomology, in analogy with the construction given by Kronheimer and Mrowka for\nthe case of instanton Floer homology. Finally, the SO(3) vortex moduli spaces\nprovide a nice toy model for recent work due to Feehan and Leness regarding the\nstudy of a natural Morse-Bott function on the moduli space of SO(3) monopoles\nover Kahler manifolds. In particular, we compute the Morse-Bott indices of this\nfunction.\n"}
+{"abstract": "  In medical and biological research, longitudinal data and survival data types\nare commonly seen. Traditional statistical models mostly consider to deal with\neither of the data types, such as linear mixed models for longitudinal data,\nand the Cox models for survival data, while they do not adjust the association\nbetween these two different data types. It is desirable to have a joint\nmodeling approach which accomadates both data types and the dependency between\nthem. In this paper, we extend traditional single-index models to a new joint\nmodeling approach, by replacing the single-index component to a varying\ncoefficient component to deal with longitudinal outcomes, and accomadate the\nrandom censoring problem in survival analysis by nonparametric synthetic data\nregression for the link function. Numerical experiments are conducted to\nevaluate the finite sample performance.\n"}
+{"abstract": "  For a finitely generated group $G$ and collection of subgroups $\\mathcal{P}$\nwe prove that the relative Dehn function of a pair $(G,\\mathcal{P})$ is\ninvariant under quasi-isometry of pairs. Along the way we show quasi-isometries\nof pairs preserve almost malnormality of the collection and fineness of the\nassociated coned off Cayley graphs. We also prove that for a cocompact simply\nconnected combinatorial $G$-$2$-complex $X$ with finite edge stabilisers, the\ncombinatorial Dehn function is well-defined if and only if the $1$-skeleton of\n$X$ is fine.\n  We also show that if $H$ is a hyperbolically embedded subgroup of a finitely\npresented group $G$, then the relative Dehn function of the pair $(G, H)$ is\nwell-defined. In the appendix, it is shown that show that the Baumslag-Solitar\ngroup $\\mathrm{BS}(k,l)$ has a well-defined Dehn function with respect to the\ncyclic subgroup generated by the stable letter if and only if neither $k$\ndivides $l$ nor $l$ divides $k$.\n"}
+{"abstract": "  Magnetic Resonance Imaging (MRI) is a vital component of medical imaging.\nWhen compared to other image modalities, it has advantages such as the absence\nof radiation, superior soft tissue contrast, and complementary multiple\nsequence information. However, one drawback of MRI is its comparatively slow\nscanning and reconstruction compared to other image modalities, limiting its\nusage in some clinical applications when imaging time is critical. Traditional\ncompressive sensing based MRI (CS-MRI) reconstruction can speed up MRI\nacquisition, but suffers from a long iterative process and noise-induced\nartefacts. Recently, Deep Neural Networks (DNNs) have been used in sparse MRI\nreconstruction models to recreate relatively high-quality images from heavily\nundersampled k-space data, allowing for much faster MRI scanning. However,\nthere are still some hurdles to tackle. For example, directly training DNNs\nbased on L1/L2 distance to the target fully sampled images could result in\nblurry reconstruction because L1/L2 loss can only enforce overall image or\npatch similarity and does not take into account local information such as\nanatomical sharpness. It is also hard to preserve fine image details while\nmaintaining a natural appearance. More recently, Generative Adversarial\nNetworks (GAN) based methods are proposed to solve fast MRI with enhanced image\nperceptual quality. The encoder obtains a latent space for the undersampling\nimage, and the image is reconstructed by the decoder using the GAN loss. In\nthis chapter, we review the GAN powered fast MRI methods with a comparative\nstudy on various anatomical datasets to demonstrate the generalisability and\nrobustness of this kind of fast MRI while providing future perspectives.\n"}
+{"abstract": "  Diamond quantum thermometry exploits the optical and electrical spin\nproperties of colour defect centres in diamonds and, acts as a quantum sensing\nmethod exhibiting ultrahigh precision and robustness. Compared to the existing\nluminescent nanothermometry techniques, a diamond quantum thermometer can be\noperated over a wide temperature range and a sensor spatial scale ranging from\nnanometres to micrometres. Further, diamond quantum thermometry is employed in\nseveral application, including electronics and biology, to explore these fields\nwith nanoscale temperature measurements. This review covers the operational\nprinciples of diamond quantum thermometry for spin-based and all-optical\nmethods, material development of diamonds with a focus on thermometry, and\nexamples of applications in electrical and biological systems with demand-based\ntechnological requirements.\n"}
+{"abstract": "  The SARS-CoV-2 virus and COVID-19 disease have posed unprecedented and\noverwhelming demand, challenges and opportunities to domain, model and data\ndriven modeling. This paper provides a comprehensive review of the challenges,\ntasks, methods, progress, gaps and opportunities in relation to modeling\nCOVID-19 problems, data and objectives. It constructs a research landscape of\nCOVID-19 modeling tasks and methods, and further categorizes, summarizes,\ncompares and discusses the related methods and progress of modeling COVID-19\nepidemic transmission processes and dynamics, case identification and tracing,\ninfection diagnosis and medical treatments, non-pharmaceutical interventions\nand their effects, drug and vaccine development, psychological, economic and\nsocial influence and impact, and misinformation, etc. The modeling methods\ninvolve mathematical and statistical models, domain-driven modeling by\nepidemiological compartmental models, medical and biomedical analysis, AI and\ndata science in particular shallow and deep machine learning, simulation\nmodeling, social science methods, and hybrid modeling.\n"}
+{"abstract": "  In this paper, we investigate if we could make the self-training -- a simple\nbut popular framework -- work better for semi-supervised segmentation. Since\nthe core issue in semi-supervised setting lies in effective and efficient\nutilization of unlabeled data, we notice that increasing the diversity and\nhardness of unlabeled data is crucial to performance improvement. Being aware\nof this fact, we propose to adopt the most plain self-training scheme coupled\nwith appropriate strong data augmentations on unlabeled data (namely ST) for\nthis task, which surprisingly outperforms previous methods under various\nsettings without any bells and whistles. Moreover, to alleviate the negative\nimpact of the wrongly pseudo labeled images, we further propose an advanced\nself-training framework (namely ST++), that performs selective re-training via\nselecting and prioritizing the more reliable unlabeled images. As a result, the\nproposed ST++ boosts the performance of semi-supervised model significantly and\nsurpasses existing methods by a large margin on the Pascal VOC 2012 and\nCityscapes benchmark. Overall, we hope this straightforward and simple\nframework will serve as a strong baseline or competitor for future works. Code\nis available at https://github.com/LiheYoung/ST-PlusPlus.\n"}
+{"abstract": "  The terahertz current generated by a photoconductive device (PCD) saturates\nas the power of the input optical pump is increased. This behavior is induced\nby various screening effects that stem from the interactions between\nelectromagnetic (EM) fields and semiconductor carriers. In this work, these\nscreening effects are numerically analyzed for the first time using a\nfully-coupled multiphysics approach. Unlike the previously developed simulation\nframeworks, this approach rigorously models the nonlinear coupling between the\nEM fields and the carriers and therefore is capable of accounting for the\nscreening effects. It is demonstrated that the results obtained using this\nmultiphysics approach and actual experiments are in excellent agreement. The\noptical- and radiation-field screening effects are identified in the simulation\nresults and the optical-field screening is found to play a more dominant role\nin the saturation of the PCD output under high optical pump power levels.\n"}
+{"abstract": "  Convolutional neural networks are very popular nowadays. Training neural\nnetworks is not an easy task. Each convolution corresponds to a structured\ntransformation matrix. In order to help avoid the exploding/vanishing gradient\nproblem, it is desirable that the singular values of each transformation matrix\nare not large/small in the training process. We propose three new\nregularization terms for a convolutional kernel tensor to constrain the\nsingular values of each transformation matrix. We show how to carry out the\ngradient type methods, which provides new insight about the training of\nconvolutional neural networks.\n"}
+{"abstract": "  We propose a data-driven model order reduction (MOR) technique for\nparametrized partial differential equations that exhibit parameter-dependent\njump-discontinuities. Such problems have poor-approximability in a linear space\nand therefore, are challenging for standard MOR techniques. We build upon the\nmethodology of approximating the map between the parameter domain and the\nexpansion coefficients of the reduced basis via regression. The online stage\nqueries the regression model for the expansion coefficients and recovers a\nreduced approximation for the solution. We propose to apply this technique to a\ntransformed solution that results from composing the solution with a spatial\ntransform. Unlike the (untransformed) solution, it is sufficiently regular\nalong the parameter domain and thus, is well-approximable in a low-dimensional\nlinear space. To recover an approximation for the (untransformed) solution, we\npropose an online efficient regression-based technique that approximates the\ninverse of the spatial transform. Our method features a decoupled online and\noffline stage, and benchmark problems involving hyperbolic and parabolic\nequations demonstrate its effectiveness.\n"}
+{"abstract": "  We determine the irreducible components of Igusa varieties for Shimura\nvarieties of Hodge type and use that to determine the irreducible components of\ncentral leaves. In particular, we show that the discrete Hecke-orbit conjecture\nis false in general. Our method combines recent work of D'Addezio on monodromy\nof compatible local systems with a generalisation of a method of Hida, using\nthe Honda-Tate theory for Shimura varieties of Hodge type developed by\nKisin-Madapusi Pera-Shin. We also determine the irreducible components of\nNewton strata in Shimura varieties of Hodge type by combining our results with\nrecent work of Zhou-Zhu.\n"}
+{"abstract": "  Over-the-air computation (AirComp) has been recognized as a low-latency\nsolution for wireless sensor data fusion, where multiple sensors send their\nmeasurement signals to a receiver simultaneously for computation. Most existing\nwork only considered performing AirComp over a single frequency channel.\nHowever, for a sensor network with a massive number of nodes, a single\nfrequency channel may not be sufficient to accommodate the large number of\nsensors, and the AirComp performance will be very limited. So it is highly\ndesirable to have more frequency channels for large-scale AirComp systems to\nbenefit from multi-channel diversity. In this letter, we propose an\n$M$-frequency AirComp system, where each sensor selects a subset of the $M$\nfrequencies and broadcasts its signal over these channels under a certain power\nconstraint. We derive the optimal sensors' transmission and receiver's signal\nprocessing methods separately, and develop an algorithm for joint design to\nachieve the best AirComp performance. Numerical results show that increasing\none frequency channel can improve the AirComp performance by threefold compared\nto the single-frequency case.\n"}
+{"abstract": "  Exceptional points are a ubiquitous concept widely present in\ndriven-dissipative coupled systems described by a non-Hermitian Hamiltonian. It\nis characterized by the degeneracy of the Hamiltonian's eigenvalues and\ncoalescence of corresponding eigenvectors. Recent developments demonstrated\nthat exceptional points can play an important role in photonics. However, to\ndate, exceptional points have been extensively examined in the systems\nsupporting only a few optical modes, thereby leaving the observation of\ncollective (multimode) effects outside of the scope of study. In the present\npaper, we analyze the role of exceptional points in nonlinear multimode\nphotonics. Specifically, we provide insights into complex nonlinear dynamics\narising in a continuous wave-driven pair of strongly coupled nonlinear\nmicro-resonators (i.e. a nonlinear photonic dimer) operating in the multimode\nregime. Investigating this system, which is known to possess exceptional\npoints, we find two fundamentally different nonlinear regimes of operation\ncorresponding to effective parity-time symmetric and broken parity-time\nsymmetry states. We demonstrate that the photonic dimer can be critically\ncoupled to a bus waveguide, thereby, providing an efficient generation of the\ndissipative Kerr solitons on both sides of the exceptional point. The\nparity-time symmetric case, which corresponds to a pair of symmetrically split\nresonances, has been recently shown to exhibit a variety of emergent phenomena\nincluding gear soliton generation, symmetry breaking, and soliton hopping.\nDissipative solitons generation in the parity-time symmetry broken case -\nleading to the dissipation splitting - up to now remains unexplored.\n"}
+{"abstract": "  Recent experiments suggest the possibility to tune superconductivity in\nmetallic nanowires by application of modest gate voltages. It is largely\ndebated whether the effect is due to an electric field at the superconductor\nsurface or small currents of high-energy electrons. We shed light on this\nmatter by studying the suppression of superconductivity in sample geometries\nwhere the roles of electric field and electron-current flow can be clearly\nseparated. Our results show that suppression of superconductivity does not\ndepend on the presence or absence of an electric field at the surface of the\nnanowire, but requires a current of high-energy electrons. The suppression is\nmost efficient when electrons are injected into the nanowire, but similar\nresults are obtained also when electrons are passed between two remote\nelectrodes at a distance $d$ to the nanowire (with $d$ in excess of\n$1~\\mathrm{\\mu m}$). In the latter case, high-energy electrons decay into\nphonons which propagate through the substrate and affect superconductivity in\nthe nanowire by generating quasiparticles. We show that this process involves a\nnon-thermal phonon distribution, with marked differences from the loss of\nsuperconductivity due to Joule heating near the nanowire or an increase in the\nbath temperature.\n"}
+{"abstract": "  Automatic breast lesion segmentation in ultrasound helps to diagnose breast\ncancer, which is one of the dreadful diseases that affect women globally.\nSegmenting breast regions accurately from ultrasound image is a challenging\ntask due to the inherent speckle artifacts, blurry breast lesion boundaries,\nand inhomogeneous intensity distributions inside the breast lesion regions.\nRecently, convolutional neural networks (CNNs) have demonstrated remarkable\nresults in medical image segmentation tasks. However, the convolutional\noperations in a CNN often focus on local regions, which suffer from limited\ncapabilities in capturing long-range dependencies of the input ultrasound\nimage, resulting in degraded breast lesion segmentation accuracy. In this\npaper, we develop a deep convolutional neural network equipped with a global\nguidance block (GGB) and breast lesion boundary detection (BD) modules for\nboosting the breast ultrasound lesion segmentation. The GGB utilizes the\nmulti-layer integrated feature map as a guidance information to learn the\nlong-range non-local dependencies from both spatial and channel domains. The BD\nmodules learn additional breast lesion boundary map to enhance the boundary\nquality of a segmentation result refinement. Experimental results on a public\ndataset and a collected dataset show that our network outperforms other medical\nimage segmentation methods and the recent semantic segmentation methods on\nbreast ultrasound lesion segmentation. Moreover, we also show the application\nof our network on the ultrasound prostate segmentation, in which our method\nbetter identifies prostate regions than state-of-the-art networks.\n"}
+{"abstract": "  Because of their capacity-approaching performance, graph-based codes have a\nwide range of applications, including communications and storage. In these\ncodes, unequal error protection (UEP) can offer performance gains with limited\nrate loss. Recent empirical results in magnetic recording (MR) systems show\nthat extra protection for the parity bits of a low-density parity-check (LDPC)\ncode via constrained coding results in significant density gains. In\nparticular, when UEP is applied via more reliable parity bits, higher fidelity\nmessages of parity bits are spread to all bits by message passing algorithm,\nenabling performance gains. Threshold analysis is a tool to measure the\neffectiveness of a graph-based code or coding scheme. In this paper, we provide\na theoretical analysis of this UEP idea using extrinsic information transfer\n(EXIT) charts in the binary erasure channel (BEC) and the binary symmetric\nchannel (BSC). We use EXIT functions to investigate the effect of change in\nmutual information of parity bits on the overall coding scheme. We propose a\nsetup in which parity bits of a repeat-accumulate (RA) LDPC code have lower\nerasure or crossover probabilities than input information bits. We derive the\na-priori and extrinsic mutual information functions for check nodes and\nvariable nodes of the code. After applying our UEP setup to the information\nfunctions, we formulate a linear programming problem to find the optimal degree\ndistribution that maximizes the code rate under the decoding convergence\nconstraint. Results show that UEP via higher fidelity parity bits achieves up\nto about $17\\%$ and $28\\%$ threshold gains on BEC and BSC, respectively.\n"}
+{"abstract": "  Underground physics experiments such as dark matter direct detection need to\nkeep control of the background contribution. Hosting these experiments in\nunderground facilities helps to minimize certain background sources such as the\ncosmic rays. One of the largest remaining background sources is the radon\nemanated from the rocks enclosing the research facility. The radon particles\ncould be deposited inside the detectors when they are opened to perform the\nmaintenance operations. Therefore, forecasting the radon levels is a crucial\ntask in an attempt to schedule the maintenance operations when radon level is\nminimum. In the past, deep learning models have been implemented to forecast\nthe radon time series at the Canfranc Underground Laboratory (LSC), in Spain,\nwith satisfactory results. When forecasting time series, the past values of the\ntime series are taken as input variables. The present work focuses on\nunderstanding the relative contribution of these input variables to the\npredictions generated by neural networks. The results allow us to understand\nhow the predictions of the time series depend on the input variables. These\nresults may be used to build better predictors in the future.\n"}
+{"abstract": "  Developing specialized cloud-based and open-source testbeds is a practical\napproach to investigate network slicing functionalities in the fifth-generation\n(5G) mobile networks. This paper provides a comprehensive review of most of the\nexisting cost-efficient and small-scale testbeds that partially or fully deploy\nnetwork slicing. First, we present relevant software packages for the three\nmain functional blocks of the ETSI NFV MANO framework and for emulating the\naccess and core network domains. Second, we define primary and secondary design\ncriteria for deploying network slicing testbeds. These design criteria are\nlater used for comparison between the testbeds. Third, we present the\nstate-of-the-art testbeds, including their design objectives, key technologies,\nnetwork slicing deployment, and experiments. Next, we evaluate the testbeds\naccording to the defined design criteria and present an in-depth summary table.\nThis assessment concludes with the superiority of some of them over the rest\nand the most dominant software packages satisfying the ETSI NFV MANO framework.\nFinally, challenges, potential solutions, and future works of network slicing\ntestbeds are discussed.\n"}
+{"abstract": "  This paper is devoted to the study of the expected-integral multifunctions\ngiven in the form \\begin{equation*}\n\\operatorname{E}_\\Phi(x):=\\int_T\\Phi_t(x)d\\mu, \\end{equation*} where\n$\\Phi\\colon T\\times\\mathbb{R}^n \\rightrightarrows \\mathbb{R}^m$ is a set-valued\nmapping on a measure space $(T,\\mathcal{A},\\mu)$. Such multifunctions appear in\napplications to stochastic programming, which require developing efficient\ncalculus rules of generalized differentiation. Major calculus rules are\ndeveloped in this paper for coderivatives of multifunctions\n$\\operatorname{E}_\\Phi$ and second-order subdifferentials of the corresponding\nexpected-integral functionals with applications to constraint systems arising\nin stochastic programming. The paper is self-contained with presenting in the\npreliminaries some needed results on sequential first-order subdifferential\ncalculus of expected-integral functionals taken from the first paper of this\nseries.\n"}
+{"abstract": "  Recurrent transducer models have emerged as a promising solution for speech\nrecognition on the current and next generation smart devices. The transducer\nmodels provide competitive accuracy within a reasonable memory footprint\nalleviating the memory capacity constraints in these devices. However, these\nmodels access parameters from off-chip memory for every input time step which\nadversely effects device battery life and limits their usability on low-power\ndevices.\n  We address transducer model's memory access concerns by optimizing their\nmodel architecture and designing novel recurrent cell designs. We demonstrate\nthat i) model's energy cost is dominated by accessing model weights from\noff-chip memory, ii) transducer model architecture is pivotal in determining\nthe number of accesses to off-chip memory and just model size is not a good\nproxy, iii) our transducer model optimizations and novel recurrent cell reduces\noff-chip memory accesses by 4.5x and model size by 2x with minimal accuracy\nimpact.\n"}
+{"abstract": "  Demonstrated for a digital image sensor based camera is a calibration target\noptimized method for finding the Camera Response Function (CRF). The proposed\nmethod uses localized known target zone pixel outputs spatial averaging and\nhistogram analysis for saturated pixel detection. Using the proposed CRF\ngeneration method with a 87 dB High Dynamic Range (HDR) silicon CMOS image\nsensor camera viewing a 90 dB HDR calibration target, experimentally produced\nis a non-linear CRF with a limited 40 dB linear CRF zone. Next, a 78 dB test\ntarget is deployed to test the camera with this measured CRF and its restricted\n40 dB zone. By engaging the proposed minimal exposures, weighting free,\nmulti-exposure imaging method with 2 images, demonstrated is a highly robust\nrecovery of the test target. In addition, the 78 dB test target recovery with\n16 individual DR value patches stays robust over a factor of 20 change in test\ntarget illumination lighting. In comparison, a non-robust test target image\nrecovery is produced by 5 leading prior-art multi-exposure HDR recovery\nalgorithms using 16 images having 16 different exposure times, with each\nconsecutive image having a sensor dwell time increasing by a factor of 2.\nFurther validation of the proposed HDR image recovery method is provided using\ntwo additional experiments, the first using a 78 dB calibrated target combined\nwith a natural indoor scene to form a hybrid design target and a second\nexperiment using an uncalibrated indoor natural scene. The proposed technique\napplies to all digital image sensor based cameras having exposure time and\nillumination controls. In addition, the proposed methods apply to various\nsensor technologies, spectral bands, and imaging applications.\n"}
+{"abstract": "  Motivated by recent work involving the analysis of biomedical imaging data,\nwe present a novel procedure for constructing simultaneous confidence corridors\nfor the mean of imaging data. We propose to use flexible bivariate splines over\ntriangulations to handle irregular domain of the images that is common in brain\nimaging studies and in other biomedical imaging applications. The proposed\nspline estimators of the mean functions are shown to be consistent and\nasymptotically normal under some regularity conditions. We also provide a\ncomputationally efficient estimator of the covariance function and derive its\nuniform consistency. The procedure is also extended to the two-sample case in\nwhich we focus on comparing the mean functions from two populations of imaging\ndata. Through Monte Carlo simulation studies we examine the finite-sample\nperformance of the proposed method. Finally, the proposed method is applied to\nanalyze brain Positron Emission Tomography (PET) data in two different studies.\nOne dataset used in preparation of this article was obtained from the\nAlzheimer's Disease Neuroimaging Initiative (ADNI) database.\n"}
+{"abstract": "  The paper deals with group dualities. A group duality is simply a pair $(G,\nH)$ where $G$ is an abstract abelian group and $H$ a subgroup of characters\ndefined on $G$. A group topology $\\tau$ defined on $G$ is {\\it compatible} with\nthe group duality (also called dual pair) $(G, H)$ if $G$ equipped with $\\tau$\nhas dual group $H$.\n  A topological group $(G, \\tau)$ gives rise to the natural duality $(G,\nG^\\wedge)$, where $G^\\wedge$ stands for the group of continuous characters on\n$G$. We prove that the existence of a $g$-barrelled topology on $G$ compatible\nwith the dual pair $(G, G^\\wedge)$ is equivalent to the semireflexivity in\nPontryagin's sense of the group $G^\\wedge$ endowed with the pointwise\nconvergence topology $\\sigma(G^\\wedge, G)$. We also deal with $k$-group\ntopologies. We prove that the existence of $k$-group topologies on $G$\ncompatible with the duality $(G, G^\\wedge)$ is determined by a sort of\ncompleteness property of its Bohr topology $\\sigma (G, G^\\wedge)$ (Theorem\n3.3).\n"}
+{"abstract": "  Enhancing the spatio-temporal observability of distributed energy resources\n(DERs) is crucial for achieving secure and efficient operations in distribution\ngrids. This paper puts forth a joint recovery framework for residential loads\nby leveraging the complimentary strengths of heterogeneous measurements in real\ntime. The proposed framework integrates low-resolution smart meter data\ncollected at every load node with fast-sampled feeder-level measurements from\nlimited number of distribution phasor measurement units. To address the lack of\ndata, we exploit two key characteristics for the loads and DERs, namely the\nsparse changes due to infrequent activities of appliances and electric vehicles\n(EVs) and the locational dependence of solar photovoltaic (PV) generation.\nAccordingly, meaningful regularization terms are introduced to cast a convex\nload recovery problem, which will be further simplified to reduce the\ncomputational complexity. The load recovery solutions can be utilized to\nidentify the EV charging events at each load node and to infer the total\nbehind-the-meter PV output. Numerical tests using real-world data have\ndemonstrated the effectiveness of the proposed approaches in enhancing the\nvisibility of grid-edge DERs.\n"}
+{"abstract": "  Magnetic flux rope, formed by the helical magnetic field lines, can sometimes\nremain its shape while carrying significant plasma flow that is aligned with\nthe local magnetic field. We report the existence of such structures and static\nflux ropes by applying the Grad-Shafranov-based algorithm to the Parker Solar\nProbe (PSP) in-situ measurements in the first five encounters. These structures\nare detected at heliocentric distances, ranging from 0.13 to 0.66 au, in a\ntotal of 4-month time period. We find that flux ropes with field-aligned flows,\nalthough occur more frequently, have certain properties similar to those of\nstatic flux ropes, such as the decaying relations of the magnetic fields within\nstructures with respect to heliocentric distances. Moreover, these events are\nmore likely with magnetic pressure dominating over the thermal pressure. About\none-third of events are detected in the relatively fast-speed solar wind.\nTaking into account the high Alfvenicity, we also compare with switchback\nspikes identified during three encounters and interpret their inter-relations.\nWe find that some switchbacks can be detected when the spacecraft traverses\nflux rope-like structures. The cross-section maps for selected events are\npresented via the new Grad-Shafranov type reconstruction. Finally, the possible\nevolution of the magnetic flux rope structures in the inner heliosphere is\ndiscussed.\n"}
+{"abstract": "  In the last 50 years, equilibrium density functional theory (DFT) has been\nproven to be a powerful, versatile and predictive approach for the statics and\nstructure of classical particles. This theory can be extended to the\nnonequilibrium dynamics of completely overdamped Brownian colloidal particles\ntowards so-called dynamical density functional theory (DDFT). The success of\nDDFT makes it a promising candidate for a first-principle description of active\nmatter. In this lecture, we shall first recapitulate classical DDFT for passive\ncolloidal particles typically described by Smoluchowski equation. After a basic\nderivation of DDFT from the Smoluchowski equation, we discuss orientational\ndegrees of freedom and the effect of hydrodynamic interactions for passive\nparticles. This brings us into an ideal position to generalize DDFT towards\nactive matter. In particular we distinguish between \"dry active matter\" which\nis composed of self-propelled particles that contain no hydrodynamic flow\neffects of a surrounding solvent and \"wet active matter\" where the hydrodynamic\nflow fields generated by the microswimmers are taken into account. For the\nlatter, DDFT is a tool which unifies thermal fluctuations, direct particle\ninteractions, external driving fields and hydrodynamic effects arising from\ninternal self-propulsion discriminating between \"pushers\" and \"pullers\". A\nnumber of recent applications is discussed including transient clustering of\nself-propelled rods and the spontaneous formation of a hydrodynamic pump in\nconfined microswimmers.\n"}
+{"abstract": "  In this paper, a new key-agreement scheme is proposed and analyzed. In\naddition to being provably secure in shared secret key indistinguishability\nmodel, the scheme has an interesting feature: while using exponentiation over a\ncyclic subgroup to establish the key-agreement, the generator of that subgroup\nis hidden to secure the scheme against adversaries that are capable of solving\nthe Discrete Logarithm Problem, which means that the scheme might be candidate\nas a post-quantum key exchange scheme.\n"}
+{"abstract": "  Fano resonances and Rabi splittings are routinely reported in the scientific\nliterature. Asymmetric resonance lineshapes are usually associated with Fano\nresonances, and two split peaks in the spectrum are often attributed to a Rabi\nsplitting. True Fano resonances and Rabi splittings are unequivocal signatures\nof coherent coupling between subsystems. However, can the same spectral\nlineshapes characterizing Fano resonances and Rabi splittings arise from a\npurely incoherent sum of intensities? Here we answer this question through\nexperiments with a tunable Fabry-P\\'erot cavity containing a\nCsPbBr\\textsubscript{3} perovskite crystal. By measuring the transmission and\nphotoluminescence of this system using microscope objectives with different\nnumerical aperture ($NA$), we find that even a modest $NA=0.4$ can artificially\ngenerate Fano resonances and Rabi splittings. We furthermore show that this\nmodest $NA$ can obscure the anti-crossing of a bona fide strongly coupled\nlight-matter system. Through transfer matrix calculations we confirm that these\nspectral artefacts are due to the incoherent sum of transmitted intensities at\ndifferent angles captured by the $NA$. Our results are relevant to the wide\nnanophotonics community, characterizing dispersive optical systems with high\nnumerical aperture microscope objectives. We conclude with general guidelines\nto avoid pitfalls in the characterization of such optical systems.\n"}
+{"abstract": "  The integrated nested Laplace approximation (INLA) is a well-known and\npopular technique for spatial modeling with a user-friendly interface in the\nR-INLA package. Unfortunately, only a certain class of latent Gaussian models\nare amenable to fitting with INLA. In this paper we describe Template Model\nBuilder (TMB), an existing technique which is well-suited to fitting complex\nspatio-temporal models. TMB is relatively unknown to the spatial statistics\ncommunity, but is a highly flexible random effects modeling tool which allows\nusers to define complex random effects models through simple C++ templates.\nAfter contrasting the methodology behind TMB with INLA, we provide a\nlarge-scale simulation study assessing and comparing R-INLA and TMB for\ncontinuous spatial models, fitted via the Stochastic Partial Differential\nEquations (SPDE) approximation. The results show that the predictive fields\nfrom both methods are comparable in most situations even though TMB estimates\nfor fixed or random effects may have slightly larger bias than R-INLA. We also\npresent a smaller discrete spatial simulation study, in which both approaches\nperform well. We conclude with an analysis of breast cancer incidence and\nmortality data using a joint model which cannot be fit with INLA.\n"}
+{"abstract": "  We perform a detailed analysis of the asymptotic behavior of a multifield\ncosmological model with phantom terms. Specifically, we consider the\nChiral-Phantom model consisting of two scalar fields with a mixed kinetic term,\nwhile one scalar field has negative kinetic energy, that is, it has phantom\nproperties. We show that the Hubble function can change sign, and we study the\nglobal evolution of the field equation in the finite and infinity regions with\nthe use of Poincar\\'{e} variables. We find that in the Chiral-Phantom model the\nlimit of the quintessence scalar field is recovered while the cosmological\nevolution differs from the standard hyperbolic theory. Finally, the linear\ncosmological perturbations are studied.\n"}
+{"abstract": "  A model of the Galaxy with the outer ring R1R2 can explain the observed\ndistribution of the radial, VR, and azimuthal, VT, velocity components along\nthe Galactocentric distance, R, derived from the Gaia EDR3 data. We selected\nstars from the Gaia EDR3 catalogue with reliable parallaxes, proper motions and\nline-of-sight velocities lying near the Galactic plane, |z|<200 pc, and in the\nsector of the Galactocentic angles |theta|<15 degrees and calculated the median\nvelocities VR and VT in small bins along the distance R. The distribution of\nobserved velocities appears to have some specific features: the radial velocity\nVR demonstrates a smooth fall from +5 km s-1 at the distance of R=R0-1.5 kpc to\n-3 km s-1 at R=R0+1.0 kpc while the azimuthal velocity VT shows a sharp drop by\n7 km s-1 in the distance interval R0<R<R0+1.0 kpc, where R0 is the solar\nGalactocentric distance. We build a model of the Galaxy including bulge, bar,\ndisc and halo components, which reproduces the observed specific features of\nthe velocity distribution in the Galactocentric distance interval |R-R0|< 1.5\nkpc. The best agreement corresponds to the time 1.8+/-0.5 Gyr after the start\nof the simulation. A model of the Galaxy with the bar rotating at the angular\nvelocity of Omega_b=55+/-3 km s-1 kpc-1, which sets the OLR of the bar at the\ndistance of R0-0.5+/-0.4 kpc, provides the best agreement between the model and\nobserved velocities. The position angle of the bar, theta_b, corresponding to\nthe best agreement between the model and observed velocities is theta_b=45+/-15\ndegrees.\n"}
+{"abstract": "  In recent years, Evolutionary Algorithms (EAs) have frequently been adopted\nto evolve instances for optimization problems that pose difficulties for one\nalgorithm while being rather easy for a competitor and vice versa. Typically,\nthis is achieved by either minimizing or maximizing the performance difference\nor ratio which serves as the fitness function. Repeating this process is useful\nto gain insights into strengths/weaknesses of certain algorithms or to build a\nset of instances with strong performance differences as a foundation for\nautomatic per-instance algorithm selection or configuration. We contribute to\nthis branch of research by proposing fitness-functions to evolve instances that\nshow large performance differences for more than just two algorithms\nsimultaneously. As a proof-of-principle, we evolve instances of the\nmulti-component Traveling Thief Problem~(TTP) for three incomplete TTP-solvers.\nOur results point out that our strategies are promising, but unsurprisingly\ntheir success strongly relies on the algorithms' performance complementarity.\n"}
+{"abstract": "  Grasp detection in clutter requires the robot to reason about the 3D scene\nfrom incomplete and noisy perception. In this work, we draw insight that 3D\nreconstruction and grasp learning are two intimately connected tasks, both of\nwhich require a fine-grained understanding of local geometry details. We thus\npropose to utilize the synergies between grasp affordance and 3D reconstruction\nthrough multi-task learning of a shared representation. Our model takes\nadvantage of deep implicit functions, a continuous and memory-efficient\nrepresentation, to enable differentiable training of both tasks. We train the\nmodel on self-supervised grasp trials data in simulation. Evaluation is\nconducted on a clutter removal task, where the robot clears cluttered objects\nby grasping them one at a time. The experimental results in simulation and on\nthe real robot have demonstrated that the use of implicit neural\nrepresentations and joint learning of grasp affordance and 3D reconstruction\nhave led to state-of-the-art grasping results. Our method outperforms baselines\nby over 10% in terms of grasp success rate. Additional results and videos can\nbe found at https://sites.google.com/view/rpl-giga2021\n"}
+{"abstract": "  After major hurricanes, electric production is significantly reduced owing to\nnot only electric infrastructure destruction, but also the economic crisis\nassociated with damage to private and public activities. The full restoration\nof the electric infrastructure is not always simultaneously performed with the\nfull restoration of electric production. Here, we describe the electric\nproduction curves for the islands of Saint-Martin, Saint-Barthelemy, and Puerto\nRico in the Caribbean, where two major hurricanes occurred in 2017. After the\nmajor hurricanes, the electric energy production was characterised by a slow\nrecovery followed by a stable phase during several months, corresponding to\napproximately $75\\%$ of the initial electric production. A resilience time of\nseveral months (1 month $< t_s <$ 5 months) is necessary to attain the new\nelectric energy production equilibrium $E_{is}$, which is lower than the\ninitial electric energy production $E_i$. The reduction in electric energy\nconsumption per capita is of $20-25\\%$. In Saint-Martin, during the\npost-hurricane stable phase, the electric production was only approximately\n$60\\%$ of the initial electric energy production instead of $75\\%$. The\nreduction of 15 percent in the electric production of Saint-Martin after\nHurricane Irma could be attributed to the migration of approximately 8 000\ninhabitants (approximately $23\\%$) outside the island. This approach makes it\npossible to anticipate the production of electricity during several months\nafter a major hurricane in the Caribbean islands.\n"}
+{"abstract": "  A heuristic description of the spin-rotation-gravity coupling is presented\nand the implications of the corresponding gravitomagnetic Stern--Gerlach force\nare briefly mentioned. It is shown, within the framework of linearized general\nrelativity, that the gravitomagnetic Stern--Gerlach force reduces in the\nappropriate correspondence limit to the classical Mathisson spin-curvature\nforce.\n"}
+{"abstract": "  The spectral density of bound pairs in ideal 1D, 2D and Bethe lattices is\ncomputed for weak and strong interactions. The computations are performed with\nGreen's functions by an efficient recursion method in real space. For the range\nof interaction strengths within which bound states are predominantly single\npairs, the spectral profiles guide to the energy bandwidths where the bound\npairs can be maximized.\n"}
+{"abstract": "  We theoretically study electric quadrupole orders in f-electron systems on\nthe fcc lattice. Qudrupole degrees of freedom $O_{20}$ and $O_{22}$ originate\nin the non-Kramers doublet ground state $\\Gamma_3$ of ion with $f^2$ electron\nconfiguration. For discussing quadrupole orders, we use a minimal model with\nisotropic ($J$) and anisotropic ($K$) nearest-neighbor interactions, and\ndetermine the phase diagram using a four-site mean-field approximation at zero\nand finite temperatures. Quadrupoles couple the $\\Gamma_3$ doublet to the\nsinglet excited state $\\Gamma_1$, and its effects on canting antiferro orders\nare examined in detail. We found that this coupling leads to a rich phase\ndiagram including two- and four-sublattice antiferro phases, and that two\nphases show a partial order of quadrupoles at finite temperatures.\n"}
+{"abstract": "  Van der Waals (vdW) heterostructures consisting of bilayer graphene (BLG)\nencapsulated within monolayers of strong spin-orbit semiconductor WS$_2$ or\nferromagnetic semiconductor Cr$_2$Ge$_2$Te$_6$ (CGT), are investigated. By\nperforming realistic first-principles calculations we capture the essential BLG\nband structure features, including layer- and sublattice-resolved proximity\nspin-orbit or exchange couplings. For different relative twist angles (0 or\n60$^{\\circ}$) of the WS$_2$ layers, and the magnetizations (parallel or\nantiparallel) of the CGT layers, with respect to BLG, the low energy bands are\nfound and characterized by a series of fit parameters of model Hamiltonians.\nThese effective models are then employed to investigate the tunability of the\nrelevant energy dispersions by a gate field. For WS$_2$/BLG/WS$_2$\nencapsulation we find that twisting allows to turn off the spin splittings away\nfrom the $K$ points, due to opposite proximity-induced valley-Zeeman couplings\nin the two sheets of BLG. Close to the $K$ points the electron spins are\npolarized out of the plane. This polarization can be flipped by applying a gate\nfield. As for magnetic CGT/BLG/CGT structures, we realize the recently proposed\nspin-valve effect, whereby a gap opens for antiparallel magnetizations of the\nCGT layers. Furthermore, we find that for the antiferromagnetic orientation the\nelectron states away from $K$ have vanishingly weak proximity exchange, while\nthe states close to $K$ remain spin polarized in the presence of an electric\nfield. The induced magnetization can be flipped by changing the gate field.\nThese findings should be useful for spin transport, spin filtering, and spin\nrelaxation anisotropy studies of BLG-based vdW heterostructures.\n"}
+{"abstract": "  In this work, we study the computational complexity of reducing the squared\ngradient magnitude for smooth minimax optimization problems. First, we present\nalgorithms with accelerated $\\mathcal{O}(1/k^2)$ last-iterate rates, faster\nthan the existing $\\mathcal{O}(1/k)$ or slower rates for extragradient, Popov,\nand gradient descent with anchoring. The acceleration mechanism combines\nextragradient steps with anchoring and is distinct from Nesterov's\nacceleration. We then establish optimality of the $ \\mathcal{O}(1/k^2)$ rate\nthrough a matching lower bound.\n"}
+{"abstract": "  Terahertz (THz) communications are regarded as a pillar technology for the\nsixth generation (6G) wireless systems, by offering multi-ten-GHz bandwidth. To\novercome the short transmission distance and huge propagation loss,\nultra-massive (UM) MIMO systems that employ sub-millimeter wavelength antennas\narray are proposed to enable an enticingly high array gain. In the UM-MIMO\nsystems, hybrid beamforming stands out for its great potential in promisingly\nhigh data rate and reduced power consumption. In this paper, challenges and\nfeatures of the THz hybrid beamforming design are investigated, in light of the\ndistinctive THz peculiarities. Specifically, we demonstrate that the spatial\ndegree-of-freedom (SDoF) is less than 5, which is caused by the extreme\nsparsity of the THz channel. The blockage problem caused by the huge reflection\nand scattering losses, as high as 15 dB or over, is studied. Moreover, we\nanalyze the challenges led by the array containing 1024 or more antennas,\nincluding the requirement for intelligent subarray architecture, strict energy\nefficiency, and propagation characterization based on spherical-wave\npropagation mechanisms. Owning up to hundreds of GHz bandwidth, beam squint\neffect could cause over 5~dB array gain loss, when the fractional bandwidth\nexceeds 10%. Inspired by these facts, three novel THz-specific hybrid\nbeamforming architectures are presented, including widely-spaced\nmulti-subarray, dynamic array-of-subarrays, and true-time-delay-based\narchitectures. We also demonstrate the potential data rate, power consumption,\nand array gain capabilities for THz communications. As a roadmap of THz hybrid\nbeamforming design, multiple open problems and potential research directions\nare elaborated.\n"}
+{"abstract": "  In this paper, we introduce a reproducible cleaning process for the text\nextracted from PDFs using n-gram models. Our approach compares the originally\nextracted text with the text generated from, or expected by, these models using\nearlier text as stimulus. To guide this process, we introduce the notion of a\nconsistency score, which refers to the proportion of text that is expected by\nthe model. This is used to monitor changes during the cleaning process, and\nacross different corpuses. We illustrate our process on text from the book Jane\nEyre and introduce both a Shiny application and an R package to make our\nprocess easier for others to adopt.\n"}
+{"abstract": "  Discovering the physical requirements for meeting the indefinite permittivity\nin natural material as well as proposing a new natural hyperbolic media offer a\npossible route to significantly improve our knowledge and ability to confine\nand controlling light in optoelectronic devices. We demonstrate the\nhyperbolicity in a class of materials with hexagonal P6/mmm and P6$_{3}$/mmc\nlayered crystal structures and its physical origin is thoroughly investigated.\nBy utilizing density functional theory and solving the Bethe-Salpeter equation\n(BSE), we find that the layered crystal structure and symmetry imposed\nconstraints in Li$_{3}$N gives rise to an exceedingly strong anisotropy in\noptical responses along in- and out-of-plane directions of the crystals making\nit a natural hyperbolic in a broad spectral range from the visible spectrum to\nthe ultraviolet. More excitingly, the hyperbolicity relation to anisotropic\ninterband absorption in addition to the impressive dependency of the conduction\nband to the lattice constant along the out-of-plane direction provide the\nhyperbolicity tunability in these hexagonal structures under strain, doping,\nand alloying. Our findings not only suggest a large family of real hexagonal\ncompounds as a unique class of materials for realization of the highly tunable\nbroad band hyperbolicity but also offers an approach to search for new\nhyperbolic\n"}
+{"abstract": "  The production of heavy-mass elements due to the rapid neutron-capture\nmechanism (r-process) is associated with astrophysical scenarios, such as\nsupernovae and neutron-star mergers. In the r-process the capture of neutrons\nis followed by $\\beta$-decays until nuclear stability is reached. A key element\nin the chain of nuclear weak-decays leading to the production of isotopes may\nbe the change of the parameters controlling the neutrino sector, due to the\nmixing of active and sterile species. In this work we have addressed this\nquestion and calculated $\\beta$-decay rates for the nuclei involved in the\nr-process chains as a function of the neutrino mixing parameters. These rates\nwere then used in the calculation of the abundance of the heavy elements\nproduced in core-collapse supernova and in neutron-star mergers, starting from\ndifferent initial mass-fraction distributions. The analysis shows that the\ncore-collapse supernova environment contributes with approximately $30\\%$ of\nthe total heavy nuclei abundance while the neutron-star merger contributes with\nabout $70\\%$ of it. Using available experimental data we have performed a\nstatistical analysis to set limits on the active-sterile neutrino mixing angle\nand found a best-fit value $\\sin^2 2\\theta_{14}=0.22$, a value comparable with\nthose found in other studies reported in the literature.\n"}
+{"abstract": "  Change point detection becomes more and more important as datasets increase\nin size, where unsupervised detection algorithms can help users process data.\nTo detect change points, a number of unsupervised algorithms have been\ndeveloped which are based on different principles. One approach is to define an\noptimisation problem and minimise a cost function along with a penalty\nfunction. In the optimisation approach, the choice of the cost function affects\nthe predictions made by the algorithm. In extension to the existing studies, a\nnew type of cost function using Tikhonov regularisation is introduced. Another\napproach uses Bayesian statistics to calculate the posterior probability\ndistribution of a specific point being a change point. It uses a priori\nknowledge on the distance between consecutive change points and a likelihood\nfunction with information about the segments. The optimisation and Bayesian\napproaches for offline change point detection are studied and applied to\nsimulated datasets as well as a real world multi-phase dataset. The approaches\nhave previously been studied separately and a novelty lies in comparing the\npredictions made by the two approaches in a specific setting, consisting of\nsimulated datasets and a real world example. The study has found that the\nperformance of the change point detection algorithms are affected by the\nfeatures in the data.\n"}
+{"abstract": "  Hexagonal boron nitride (hBN)-long-known as a thermally stable ceramic-is now\navailable as atomically smooth, single-crystalline flakes, revolutionizing its\nuse in optoelectronics. For nanophotonics, these flakes offer strong\nnonlinearities, hyperbolic dispersion, and single-photon emission, providing\nunique properties for optical and quantum-optical applications. For\nnanoelectronics, their pristine surfaces, chemical stability, and wide bandgap\nhave made them the key substrate, encapsulant, and gate dielectric for\ntwo-dimensional electronic devices. However, while exploring these advantages,\nresearchers have been restricted to flat flakes or those patterned with basic\nslits and holes, severely limiting advanced architectures. If freely varying\nflake profiles were possible, the hBN structure would present a powerful design\nparameter to further manipulate the flow of photons, electrons, and excitons in\nnext-generation devices. Here, we demonstrate freeform nanostructuring of hBN\nby combining thermal scanning-probe lithography and reactive-ion etching to\nshape flakes with surprising fidelity. We leverage sub-nanometer height control\nand high spatial resolution to produce previously unattainable flake structures\nfor a broad range of optoelectronic applications. For photonics, we fabricate\nmicroelements and show the straightforward transfer and integration of such\nelements by placing a spherical hBN microlens between two planar mirrors to\nobtain a stable, high-quality optical microcavity. We then decrease the\npatterning length scale to introduce Fourier surfaces for electrons, creating\nsophisticated, high-resolution landscapes in hBN, offering new possibilities\nfor strain and band-structure engineering. These capabilities can advance the\ndiscovery and exploitation of emerging phenomena in hyperbolic metamaterials,\npolaritonics, twistronics, quantum materials, and 2D optoelectronic devices.\n"}
+{"abstract": "  In this article, we study algorithms for nonnegative matrix factorization\n(NMF) in various applications involving streaming data. Utilizing the continual\nnature of the data, we develop a fast two-stage algorithm for highly efficient\nand accurate NMF. In the first stage, an alternating non-negative least squares\n(ANLS) framework is used, in combination with active set method with warm-start\nstrategy for the solution of subproblems. In the second stage, an interior\npoint method is adopted to accelerate the local convergence. The convergence of\nthe proposed algorithm is proved. The new algorithm is compared with some\nexisting algorithms in benchmark tests using both real-world data and synthetic\ndata. The results demonstrate the advantage of our algorithm in finding\nhigh-precision solutions.\n"}
+{"abstract": "  Counterfactual quantum communication is unique in its own way that allows\nremote parties to transfer information without sending any message carrier in\nthe channel. Although no message carrier travels in the channel at the time of\nsuccessful information transmission, it is impossible to transmit information\nfaster than the speed of light, thus without an information carrier. In this\npaper, we address an important question What carries the information in\ncounterfactual quantum communication? and optimize the resource efficiency of\nthe counterfactual quantum communication in terms of the number of channels\nused, time consumed to transmit 1-bit classical information between two remote\nparties, and the number of qubits required to accomplish the counterfactual\nquantum communication.\n"}
+{"abstract": "  GJ9827 is a bright star hosting a planetary system with three transiting\nplanets. As a multi-planet system with planets that sprawl within the\nboundaries of the radius gap between terrestrial and gaseous planets, GJ9827 is\nan optimal target to study the evolution of the atmospheres of close-in planets\nwith a common evolutionary history and their dependence from stellar\nirradiation. Here, we report on the Hubble Space Telescope (HST) and CARMENES\ntransit observations of GJ9827 planets b and d. We performed a stellar and\ninterstellar medium characterization from the ultraviolet HST spectra,\nobtaining fluxes for Ly-alpha and MgII of F(Ly-alpha) = (5.42+0.96-0.75) X\n10^{-13} erg cm^{-2} s^{-1} and F(MgII) = (5.64 +- 0.24) X 10^{-14} erg cm^{-2}\ns^{-1}. We also investigated a possible absorption signature in Ly-alpha in the\natmosphere of GJ9827b during a transit event from HST spectra, as well as\nH-alpha and HeI signature for the atmosphere of GJ9827b and d from CARMENES\nspectra. We found no evidence of an extended atmosphere in either of the\nplanets. This result is also supported by our analytical estimations of\nmass-loss based on the measured radiation fields for all the three planets of\nthis system, which led to a mass-loss rate of 0.4, 0.3, and 0.1 planetary\nmasses per Gyr, for GJ9827b, c, and d respectively. These values indicate that\nthe planets could have lost their volatiles quickly in their evolution and\nprobably do not retain an atmosphere at the current stage.\n"}
+{"abstract": "  We consider an initial-boundary value problem for the $n$-dimensional wave\nequation with the variable sound speed, $n\\geq 1$. We construct three-level\nimplicit in time and compact in space (three-point in each space direction) 4th\norder finite-difference schemes on the uniform rectangular meshes including\ntheir one-parameter (for $n=2$) and three-parameter (for $n=3$) families. We\nalso show that some already known methods can be converted into such schemes.\nIn a unified manner, we prove the conditional stability of schemes in the\nstrong and weak energy norms together with the 4th order error estimate under\nnatural conditions on the time step. We also transform an unconditionally\nstable 4th order two-level scheme suggested for $n=2$ to the three-level form,\nextend it for any $n\\geq 1$ and prove its stability. We also give an example of\na compact scheme for non-uniform in space and time rectangular meshes. We\nsuggest simple fast iterative methods based on FFT to implement the schemes. A\nnew effective initial guess to start iterations is given too. We also present\npromising results of numerical experiments.\n"}
+{"abstract": "  This is the second article in a series about Hurwitz spaces. We associate,\nwith each nice couple $(\\mathcal{X},\\mathcal{Y})$ of subspaces of $\\mathbb{C}$\nand with each PMQ-group pair $(\\mathcal{Q},G)$, a coordinate-free Hurwitz space\n$\\mathrm{Hur}(\\mathcal{X},\\mathcal{Y};\\mathcal{Q},G)$. We define maps of\nHurwitz spaces induced functorially by morphisms of nice couples and of\nPMQ-group pairs. For a locally finite PMQ $\\mathcal{Q}$ we prove a\nhomeomorphism between $\\mathrm{Hur}((0,1)^2;\\mathcal{Q}_+)$ and the simplicial\nHurwitz space $\\mathrm{Hur}^{\\Delta}(\\mathcal{Q})$.\n"}
+{"abstract": "  Laser based ranging (LiDAR) - already ubiquitously used in industrial\nmonitoring, atmospheric dynamics, or geodesy - is key sensor technology.\nCoherent laser ranging, in contrast to time-of-flight approaches, is immune to\nambient light, operates continuous wave allowing higher average powers, and\nyields simultaneous velocity and distance information. State-of-the-art\ncoherent single laser-detector architectures reach hundreds of kilopixel per\nsecond rates. While emerging applications such as autonomous driving, robotics,\nand augmented reality mandate megapixel per second point sampling to support\nreal-time video-rate imaging. Yet, such rates of coherent LiDAR have not been\ndemonstrated. Here we report a swept dual-soliton microcomb technique enabling\ncoherent ranging and velocimetry at megapixel per second line scan measurement\nrates with up to 64 spectrally dispersed optical channels. It is based on\nrecent advances in photonic chip-based microcombs that offer a solution to\nreduce complexity both on the transmitter and receiver sides.\nMulti-heterodyning two synchronously frequency-modulated microcombs yields\ndistance and velocity information of all individual ranging channels on a\nsingle receiver alleviating the need for individual separation, detection, and\ndigitization. The reported LiDAR implementation is hardware-efficient,\ncompatible with photonic integration, and demonstrates the significant\nadvantages of acquisition speed afforded by the convergence of optical\ntelecommunication and metrology technologies. We anticipate our research will\nmotivate further investigation of frequency swept microresonator dual-comb\napproach in the neighboring fields of linear and nonlinear spectroscopy,\noptical coherence tomography.\n"}
+{"abstract": "  We report a non-blocking high-resolution digital delay line based on an\nasynchronous circuit design. Field programmable gate array logic primitives\nwere used as a source of delay and optimally arranged using combinatorial\noptimization. This approach allows for an efficient trade-off of the resolution\nand a delay range together with minimized dead time operation. We demonstrate\nthe method by implementing the delay line adjustable from 23 ns up to 1635 ns\nwith a resolution of 10 ps. We present a detailed experimental characterization\nof the device focusing on thermal instability, timing jitter, and pulse\nspreading, which represent three main issues of the asynchronous design. We\nfound a linear dependence of the delay on the temperature with the slope of 0.2\nps/K per a logic primitive. We measured the timing jitter of the delay to be in\nthe range of 7 ps - 165 ps, linearly increasing over the dynamic range of the\ndelay. We reduced the effect of pulse spreading by introducing pulse shrinking\ncircuits, and reached the overall dead time of 4 ns - 22.5 ns within the\ndynamic range of the delay. The presented non-blocking delay line finds usage\nin applications where the dead time minimization is crucial, and tens of\npicoseconds excess jitter is acceptable, such as in many advanced photonic\nnetworks.\n"}
+{"abstract": "  Recent developments in deep learning have significantly improved the quality\nof synthesized singing voice audio. However, prominent neural singing voice\nsynthesis systems suffer from slow inference speed due to their autoregressive\ndesign. Inspired by MLP-Mixer, a novel architecture introduced in the vision\nliterature for attention-free image classification, we propose MLP Singer, a\nparallel Korean singing voice synthesis system. To the best of our knowledge,\nthis is the first work that uses an entirely MLP-based architecture for voice\nsynthesis. Listening tests demonstrate that MLP Singer outperforms a larger\nautoregressive GAN-based system, both in terms of audio quality and synthesis\nspeed. In particular, MLP Singer achieves a real-time factor of up to 200 and\n3400 on CPUs and GPUs respectively, enabling order of magnitude faster\ngeneration on both environments.\n"}
+{"abstract": "  We present results on the nature of extreme ejective feedback episodes and\nthe physical conditions of a population of massive ($\\rm M_* \\sim 10^{11}\nM_{\\odot}$), compact starburst galaxies at z = 0.4-0.7. We use data from\nKeck/NIRSPEC, SDSS, Gemini/GMOS, MMT, and Magellan/MagE to measure rest-frame\noptical and near-IR spectra of 14 starburst galaxies with extremely high star\nformation rate surface densities (mean $\\rm \\Sigma_{SFR} \\sim 3000 \\,M_{\\odot}\nyr^{-1} kpc^{-2}$) and powerful galactic outflows (maximum speeds v$_{98} \\sim$\n1000-3000 km s$^{-1}$). Our unique data set includes an ensemble of both\nemission [OII]$\\lambda\\lambda$3726,3729, H$\\beta$,\n[OIII]$\\lambda\\lambda$4959,5007, H$\\alpha$, [NII]$\\lambda\\lambda$6548,6583, and\n[SII]$\\lambda\\lambda$6716,6731) and absorption MgII$\\lambda\\lambda$2796,2803,\nand FeII$\\lambda$2586) lines that allow us to investigate the kinematics of the\ncool gas phase (T$\\sim$10$^4$ K) in the outflows. Employing a suite of line\nratio diagnostic diagrams, we find that the central starbursts are\ncharacterized by high electron densities (median n$_e \\sim$ 530 cm$^{-3}$), and\nhigh metallicity (solar or super-solar). We show that the outflows are most\nlikely driven by stellar feedback emerging from the extreme central starburst,\nrather than by an AGN. We also present multiple intriguing observational\nsignatures suggesting that these galaxies may have substantial Lyman continuum\n(LyC) photon leakage, including weak [SII] nebular emission lines. Our results\nimply that these galaxies may be captured in a short-lived phase of extreme\nstar formation and feedback where much of their gas is violently blown out by\npowerful outflows that open up channels for LyC photons to escape.\n"}
+{"abstract": "  By incorporating aspects of coordination and collaboration, workflow\nimplementations of information systems require a sound conceptualisation of\n\\EM{business processing} semantics. Traditionally, the success of conceptual\nmodelling techniques has depended largely on the adequacy of conceptualisation,\nexpressive power, comprehensibility and formal foundation. An equally important\nrequirement, particularly with the increased conceptualisation of business\naspects, is \\EM{business suitability}. In this paper, the focus is on the\nbusiness suitability of workflow modelling for a commonly encountered class of\n(operational) business processing, e.g. those of insurance claims, bank loans\nand land conveyancing. A general assessment is first conducted on some\n\\EM{integrated} techniques characterising well-known paradigms - structured\nprocess modelling, object-oriented modelling, behavioural process modelling and\nbusiness-oriented modelling. Through this, an insight into business suitability\nwithin the broader perspective of technique adequacy, is gained. A specific\nbusiness suitability diagnosis then follows using a particular characterisation\nof business processing, i.e.\\ one where the intuitive semantics and\ninter-relationship of business services and business processes are nuanced. As\na result, five business suitability principles are elicited. These are proposed\nfor a more detailed understanding and (synthetic) development of workflow\nmodelling techniques. Accordingly, further insight into workflow specification\nlanguages and workflow globalisation in open distributed architectures may also\nbe gained.\n"}
+{"abstract": "  The recent development of high-performance computing enables us to generate\nspatio-temporal high-resolution data of nonlinear dynamical systems and to\nanalyze them for deeper understanding of their complex nature. This trend can\nbe found in a wide range of science and engineering communities, which suggests\nthat detailed investigations on efficient data handling in physical science\nmust be required in future. To this end, we introduce the use of convolutional\nneural networks (CNNs) to achieve an efficient data storage and estimation of\nscientific big data derived from nonlinear dynamical systems. The CNN is\nutilized to reconstruct three-dimensional data from a few numbers of\ntwo-dimensional sections in a computationally friendly manner. The present\nmodel is a combination of two- and three-dimensional CNNs, which allows users\nto save only some of the two-dimensional sections to reconstruct the volumetric\ndata. As an example of three-dimensional data, we consider a fluid flow around\na square cylinder at the diameter-based Reynolds number $Re_D$ of 300, and show\nthat volumetric fluid flow data can successfully be reconstructed with the\npresent method from as few as five sections. Furthermore, we also propose a\ncombination of the present CNN-based reconstruction with an adaptive\nsampling-based super-resolution analysis to augment the data compression\ncapability of the present methods. Our report can be a significant bridge\ntoward practical data handling for not only the fluid mechanics field but also\na vast range of physical sciences.\n"}
+{"abstract": "  Primordial black holes (PBHs) from the early Universe have been connected\nwith the nature of dark matter and can significantly affect cosmological\nhistory. We show that coincidence dark radiation and density fluctuation\ngravitational wave signatures associated with evaporation of $\\lesssim10^9$ g\nPBHs can be used to explore and obtain important hints about the formation\nmechanisms of spinning and non-spinning PBHs spanning orders of magnitude in\nmass-range, which is challenging to do otherwise.\n"}
+{"abstract": "  Many reinforcement learning (RL) agents require a large amount of experience\nto solve tasks. We propose Contrastive BERT for RL (CoBERL), an agent that\ncombines a new contrastive loss and a hybrid LSTM-transformer architecture to\ntackle the challenge of improving data efficiency. CoBERL enables efficient,\nrobust learning from pixels across a wide range of domains. We use\nbidirectional masked prediction in combination with a generalization of recent\ncontrastive methods to learn better representations for transformers in RL,\nwithout the need of hand engineered data augmentations. We find that CoBERL\nconsistently improves performance across the full Atari suite, a set of control\ntasks and a challenging 3D environment.\n"}
+{"abstract": "  The coexistence of metallicity and ferroelectricity has been an intriguing\nand controversial phenomenon as these two material properties are considered\nincompatible in bulk. We clarify the concept of ferroelectric metal by\nrevisiting the original definitions for ferroelectric and metal.\nTwo-dimensional (2D) ferroelectrics with out-of-plane polarization can be\nengineered via layer stacking to a genuine ferroelectric metal characterized by\nswitchable polarization and non-zero density of states at the Fermi level. We\ndemonstrate that 2D ferroelectric metals can serve as electrically-tunable,\nhigh-quality electrocatalysts.\n"}
+{"abstract": "  An in-depth understanding of the particular environment is crucial in\nreinforcement learning (RL). To address this challenge, the decision-making\nprocess of a mobile collaborative robotic assistant modeled by the Markov\ndecision process (MDP) framework is studied in this paper. The optimal\nstate-action combinations of the MDP are calculated with the non-linear Bellman\noptimality equations. This system of equations can be solved with relative ease\nby the computational power of Wolfram Mathematica, where the obtained optimal\naction-values point to the optimal policy. Unlike other RL algorithms, this\nmethodology does not approximate the optimal behavior, it gives the exact,\nexplicit solution, which provides a strong foundation for our study. With this,\nwe offer new insights into understanding the action selection mechanisms in RL\nby presenting various small modifications on the very same schema that lead to\ndifferent optimal policies.\n"}
+{"abstract": "  There is an escalating need for methods to identify latent patterns in text\ndata from many domains. We introduce a new method to identify topics in a\ncorpus and represent documents as topic sequences. Discourse Atom Topic\nModeling draws on advances in theoretical machine learning to integrate topic\nmodeling and word embedding, capitalizing on the distinct capabilities of each.\nWe first identify a set of vectors (\"discourse atoms\") that provide a sparse\nrepresentation of an embedding space. Atom vectors can be interpreted as latent\ntopics: Through a generative model, atoms map onto distributions over words;\none can also infer the topic that generated a sequence of words. We illustrate\nour method with a prominent example of underutilized text: the U.S. National\nViolent Death Reporting System (NVDRS). The NVDRS summarizes violent death\nincidents with structured variables and unstructured narratives. We identify\n225 latent topics in the narratives (e.g., preparation for death and physical\naggression); many of these topics are not captured by existing structured\nvariables. Motivated by known patterns in suicide and homicide by gender, and\nrecent research on gender biases in semantic space, we identify the gender bias\nof our topics (e.g., a topic about pain medication is feminine). We then\ncompare the gender bias of topics to their prevalence in narratives of female\nversus male victims. Results provide a detailed quantitative picture of\nreporting about lethal violence and its gendered nature. Our method offers a\nflexible and broadly applicable approach to model topics in text data.\n"}
+{"abstract": "  In graphene, where the electron-electron scattering is dominant, electrons\ncollectively act as a fluid. This hydrodynamic behaviour of charge carriers\nleads to exciting nonlinear phenomena such as solitary waves and shocks, among\nothers. In the future, such waves might be exploited on plasmonic devices,\neither for modulation or signal propagation along graphene waveguides. We study\nthe nature of nonlinear perturbations by performing the reductive perturbation\nmethod on the hydrodynamic description of graphene electrons, taking into\nconsideration the effect of Bohm quantum potential and odd viscosity. Thus,\nderiving a dissipative Kadomtsev-Petviashvili equation for the bidimensional\nflow as well as its unidimensional limit in the form of Korteweg-de\nVries-Burgers. The stability analysis of these equations unveils the existence\nof unstable modes that can be excited and launched through graphene plasmonic\ndevices.\n"}
+{"abstract": "  An infinitesimal deformation of $C^{\\infty}(\\mathbb R^d)$ has a vanishing\nprimary obstruction if and only if its skew form is Poisson, and therefore is\nintegrable to a full deformation of $C^{\\infty}(\\mathbb R^d)$ by the work of\nKontsevich. This is further equivalent to having its coefficients satisfy a set\nof $\\binom{d}{3}$ nonlinear partial differential equations. The single such\nequation in the case $d=3$ suggests that a big bang might be localized to an\ninstant in time and point in space. This note also reexamines the Basic\nUniversal Deformation Formula (UDF) which asserts that if $D_1, D_2$ are\ncommuting derivations of an associative algebra $\\mathcal{A}$ over the\nrationals then exponentiation of $D_1\\smile D_2$ provides a full deformation of\n$\\mathcal{A}$. It gives further applications to quantization and to UDFs with\nnon-commuting derivations.\n"}
+{"abstract": "  Global existence is established for classical solutions to a chemotaxis model\nwith signal-dependent motility for a general class of motility functions\n$\\gamma$ which may in particular decay in an arbitrary way at infinity.\nAssuming further that $\\gamma$ is non-increasing and decays sufficiently slowly\nat infinity, in the sense that $\\gamma(s)\\sim s^{-k}$ as $s\\to\\infty$ for some\n$k\\in (0,N/(N-2)_+)$, it is also shown that global solutions are uniformly\nbounded with respect to time. The admissible decay of $\\gamma$ at infinity here\nis higher than in previous works.\n"}
+{"abstract": "  As the proportion of variable inverter-based renewable energy generation in\nelectricity systems increases from a minority to the majority of total supply,\nthe complexity and cost of providing ancillary system services increases in\nparallel. Australia is experiencing this shift now - from having the third most\ncarbon-intensive electricity sector in the world in 2010, to now having\npenetrations of variable renewable energy (VRE) regularly reaching 100 percent\nin some regions, with world-leading uptake of distributed energy resources\n(DER). This paper presents pioneering work exploring new technical, economic\nand regulatory frameworks for the provision of Essential System Services (ESS),\nalso known as ancillary services, in power systems dominated by variable\ninverter-based renewable energy resources. We explore the recent application of\nthe concept of demand curves and nomograms to the procurement of ESS, and\nemerging design principles for frameworks in facilitating the evolution from\ndefault provision of system services by synchronous generation, to\nco-optimisation of services through unit commitment, to independent provision\nof services through inverter based resources. The paper also analyses the\nrecent technical and financial success of the world's largest battery, the\nHornsdale Power Reserve in South Australia, and its ability to inform how\nfuture electricity market frameworks may incentivize and accommodate new\ntechnological capability, both first and Nth of a kind systems. Finally, the\npaper reviews emerging energy system technological capability, including the\nprovision of synthetic inertia and RoCoF control, grid-forming inverters, and\nadvanced DER aggregation in providing ESS and system restart capability for\nsecure, resilient and island-able grids.\n"}
+{"abstract": "  In this paper, we contribute a new million-scale face benchmark containing\nnoisy 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M\nfaces (WebFace42M) training data, as well as an elaborately designed\ntime-constrained evaluation protocol. Firstly, we collect 4M name list and\ndownload 260M faces from the Internet. Then, a Cleaning Automatically utilizing\nSelf-Training (CAST) pipeline is devised to purify the tremendous WebFace260M,\nwhich is efficient and scalable. To the best of our knowledge, the cleaned\nWebFace42M is the largest public face recognition training set and we expect to\nclose the data gap between academia and industry. Referring to practical\nscenarios, Face Recognition Under Inference Time conStraint (FRUITS) protocol\nand a test set are constructed to comprehensively evaluate face matchers.\n  Equipped with this benchmark, we delve into million-scale face recognition\nproblems. A distributed framework is developed to train face recognition models\nefficiently without tampering with the performance. Empowered by WebFace42M, we\nreduce relative 40% failure rate on the challenging IJB-C set, and ranks the\n3rd among 430 entries on NIST-FRVT. Even 10% data (WebFace4M) shows superior\nperformance compared with public training set. Furthermore, comprehensive\nbaselines are established on our rich-attribute test set under\nFRUITS-100ms/500ms/1000ms protocol, including MobileNet, EfficientNet,\nAttentionNet, ResNet, SENet, ResNeXt and RegNet families. Benchmark website is\nhttps://www.face-benchmark.org.\n"}
+{"abstract": "  Pulsed nuclear magnetic resonance (NMR) is widely used in high-precision\nmagnetic field measurements. The absolute value of the magnetic field is\ndetermined from the precession frequency of nuclear magnetic moments. The\nHilbert transform is widely used to extract the phase function from the\nobserved free induction decay (FID) signal and then its frequency. In this\npaper, a detailed implementation of a Hilbert-transform based FID frequency\nextraction method is described. How artifacts and noise level in the FID signal\naffect the extracted phase function are derived analytically. A method of\nmitigating the artifacts in the extracted phase function of an FID is\ndiscussed. Correlations between noises of the phase function samples are\nstudied for different noise spectra. We discovered that the error covariance\nmatrix for the extracted phase function is nearly singular and improper for\nconstructing the $\\chi^2$ used in the fitting routine. A down-sampling method\nfor fixing the singular covariance matrix has been developed, so that the\nminimum $\\chi^2$-fit yields properly the statistical uncertainty of the\nextracted frequency. Other practical methods of obtaining the statistical\nuncertainty are also discussed.\n"}
+{"abstract": "  In this paper, we investigate the problem of reasoning over natural language\nstatements. Prior neural based approaches do not explicitly consider the\ninter-dependency among answers and their proofs. In this paper, we propose\nPRobr, a novel approach for joint answer prediction and proof generation. PRobr\ndefines a joint probabilistic distribution over all possible proof graphs and\nanswers via an induced graphical model. We then optimize the model using\nvariational approximation on top of neural textual representation. Experiments\non multiple datasets under diverse settings (fully supervised, few-shot and\nzero-shot evaluation) verify the effectiveness of PRobr, e.g., achieving\n10%-30% improvement on QA accuracy in few/zero-shot evaluation. Our codes and\nmodels can be found at https://github.com/changzhisun/PRobr/.\n"}
+{"abstract": "  Instance segmentation models today are very accurate when trained on large\nannotated datasets, but collecting mask annotations at scale is prohibitively\nexpensive. We address the partially supervised instance segmentation problem in\nwhich one can train on (significantly cheaper) bounding boxes for all\ncategories but use masks only for a subset of categories. In this work, we\nfocus on a popular family of models which apply differentiable cropping to a\nfeature map and predict a mask based on the resulting crop. Under this family,\nwe study Mask R-CNN and discover that instead of its default strategy of\ntraining the mask-head with a combination of proposals and groundtruth boxes,\ntraining the mask-head with only groundtruth boxes dramatically improves its\nperformance on novel classes. This training strategy also allows us to take\nadvantage of alternative mask-head architectures, which we exploit by replacing\nthe typical mask-head of 2-4 layers with significantly deeper off-the-shelf\narchitectures (e.g. ResNet, Hourglass models). While many of these\narchitectures perform similarly when trained in fully supervised mode, our main\nfinding is that they can generalize to novel classes in dramatically different\nways. We call this ability of mask-heads to generalize to unseen classes the\nstrong mask generalization effect and show that without any specialty modules\nor losses, we can achieve state-of-the-art results in the partially supervised\nCOCO instance segmentation benchmark. Finally, we demonstrate that our effect\nis general, holding across underlying detection methodologies (including\nanchor-based, anchor-free or no detector at all) and across different backbone\nnetworks. Code and pre-trained models are available at https://git.io/deepmac.\n"}
+{"abstract": "  We prove that the bi-invariant Einstein metric on $SU_{2n+1}$ is isolated in\nthe moduli space of Einstein metrics, even though it admits infinitesimal\ndeformations. This gives a non-K\\\"ahler, non-product example of this phenomenon\nadding to the famous example of $\\mathbb{CP}^{2n}\\times\\mathbb{CP}^{1}$ found\nby Koiso. We apply our methods to derive similar solitonic rigidity results for\nthe K\\\"ahler--Einstein metrics on `odd' Grassmannians. We also make explicit a\nconnection between non-integrable deformations and the dynamical instability of\nmetrics under Ricci flow.\n"}
+{"abstract": "  We introduce an algorithm to solve linear inverse problems regularized with\nthe total (gradient) variation in a gridless manner. Contrary to most existing\nmethods, that produce an approximate solution which is piecewise constant on a\nfixed mesh, our approach exploits the structure of the solutions and consists\nin iteratively constructing a linear combination of indicator functions of\nsimple polygons.\n"}
+{"abstract": "  Since the introduction of electronic cigarettes to the United States market\nin 2007, vaping prevalence has surged in both adult and adolescent populations.\nE-cigarettes are advertised as a safer alternative to traditional cigarettes\nand as a method of smoking cessation, but the U.S. government and health\nprofessionals are concerned that e-cigarettes attract young non-smokers. Here,\nwe develop and analyze a dynamical systems model of competition between\ntraditional and electronic cigarettes for users. With this model, we predict\nthe change in smoking prevalence due to the introduction of vaping, and we\ndetermine the conditions under which e-cigarettes present a net public health\nbenefit or harm to society.\n"}
+{"abstract": "  A \"scheduled\" arrival process is one in which the n th arrival is scheduled\nfor time n, but instead occurs at a different time. The difference between the\nscheduled time and the arrival time is called the perturbation. The sequence of\nperturbations is assumed to be iid. We describe here the behavior of a single\nserver queue fed by such traffic in which the processing times are\ndeterministic. A particular focus is on perturbation with Pareto-like tails but\nwith finite mean. We obtain tail approximations for the steady-state workload\nin both cases where the queue is critically loaded and under a heavy-traffic\nregime. A key to our approach is our analysis of the tail behavior of a sum of\nindependent Bernoulli random variables with success probability following a\npower law with parameter strictly larger than 1.\n"}
+{"abstract": "  Outlier ensemble methods have shown outstanding performance on the discovery\nof instances that are significantly different from the majority of the data.\nHowever, without the awareness of fairness, their applicability in the ethical\nscenarios, such as fraud detection and judiciary judgement system, could be\ndegraded. In this paper, we propose to reduce the bias of the outlier ensemble\nresults through a fairness-aware ensemble framework. Due to the lack of ground\ntruth in the outlier detection task, the key challenge is how to mitigate the\ndegradation in the detection performance with the improvement of fairness. To\naddress this challenge, we define a distance measure based on the output of\nconventional outlier ensemble techniques to estimate the possible cost\nassociated with detection performance degradation. Meanwhile, we propose a\npost-processing framework to tune the original ensemble results through a\nstacking process so that we can achieve a trade off between fairness and\ndetection performance. Detection performance is measured by the area under ROC\ncurve (AUC) while fairness is measured at both group and individual level.\nExperiments on eight public datasets are conducted. Results demonstrate the\neffectiveness of the proposed framework in improving fairness of outlier\nensemble results. We also analyze the trade-off between AUC and fairness.\n"}
+{"abstract": "  Double holography plays a crucial role in recent studies of Hawking radiation\nand information paradox by relating an intermediate picture, in which a\ndynamical gravity living on an end-of-the-world brane is coupled to a\nnon-gravitational heat bath, to a much better-understood BCFT picture as well\nas a bulk picture. In this paper, causal structures in generic double\nholographic setups are studied. We find that the causal structure in the bulk\npicture is compatible with causality in the BCFT picture, thanks to a\ngeneralization of the Gao-Wald theorem. On the other hand, consistency with the\nbulk causal structure requires the effective theory in the intermediate picture\nto contain a special type of superluminal and nonlocal effect which is\nsignificant at long range or IR. These are confirmed by both geometrical\nanalysis and commutators of microscopic fields. Subregion correspondences in\ndouble holography are discussed with the knowledge of this nonlocality.\nPossible fundamental origins of this nonlocality and its difference with other\ntypes of nonlocality will also be discussed.\n"}
+{"abstract": "  This article reviews deep learning applications in biomedical optics with a\nparticular emphasis on image formation. The review is organized by imaging\ndomains within biomedical optics and includes microscopy, fluorescence lifetime\nimaging, in vivo microscopy, widefield endoscopy, optical coherence tomography,\nphotoacoustic imaging, diffuse tomography, and functional optical brain\nimaging. For each of these domains, we summarize how deep learning has been\napplied and highlight methods by which deep learning can enable new\ncapabilities for optics in medicine. Challenges and opportunities to improve\ntranslation and adoption of deep learning in biomedical optics are also\nsummarized.\n"}
+{"abstract": "  The level of experimental precision that will be achieved with LHC Run-III\ndata and in the forthcoming High Luminosity stage calls for equally accurate\ntheoretical predictions to compare with. Here we present updated cross section\ncalculations for the electroweak production of SUSY particles at the LHC with\naNNLO+NNLL (approximate next-to-next-to-leading-order plus\nnext-to-next-to-leading-logarithmic) accuracy. Results are shown for slepton\npair production and for electroweakinos pair production in their mostly\nhiggsino or gaugino configuration, finding a further significant reduction of\nthe factorisation and renormalisation scale dependence that stabilises the\npredictions to the permil level.\n"}
+{"abstract": "  The study of topology protected electronic properties is a fascinating topic\nin present day condensed matter physics research. New topological materials are\nfrequently being proposed and explored through various experimental techniques.\nTa$_{3}$SiTe$_{6}$ is a newly predicted topological semimetal with fourfold\ndegenerate nodal-line crossing in absence of spin-orbit coupling (SOC) and an\nhourglass Dirac loop, when SOC is included. Recent angle-resolved photoemission\nspectroscopy study in this material, has also confirmed Dirac like dispersions\nand two nodal-lines near the Fermi energy, protected by nonsymmorphic glide\nmirror symmetry. In this work, we present the detailed magnetotransport\nproperties of single crystalline Ta$_{3}$SiTe$_{6}$. A nonsaturating\nmagnetoresistance has been observed. Hall measurements reveal hole type charge\ncarriers with high carrier density and a moderate value of carrier mobility.\nFurthermore, we report a robust planar Hall effect, which persists up to high\ntemperatures. These results validate the nontrivial nature of the electronic\nband structure.\n"}
+{"abstract": "  Quantification of uncertainty in point cloud matching is critical in many\ntasks such as pose estimation, sensor fusion, and grasping. Iterative closest\npoint (ICP) is a commonly used pose estimation algorithm which provides a point\nestimate of the transformation between two point clouds. There are many sources\nof uncertainty in this process that may arise due to sensor noise, ambiguous\nenvironment, and occlusion. However, for safety critical problems such as\nautonomous driving, a point estimate of the pose transformation is not\nsufficient as it does not provide information about the multiple solutions.\nCurrent probabilistic ICP methods usually do not capture all sources of\nuncertainty and may provide unreliable transformation estimates which can have\na detrimental effect in state estimation or decision making tasks that use this\ninformation. In this work we propose a new algorithm to align two point clouds\nthat can precisely estimate the uncertainty of ICP's transformation parameters.\nWe develop a Stein variational inference framework with gradient based\noptimization of ICP's cost function. The method provides a non-parametric\nestimate of the transformation, can model complex multi-modal distributions,\nand can be effectively parallelized on a GPU. Experiments using 3D kinect data\nas well as sparse indoor/outdoor LiDAR data show that our method is capable of\nefficiently producing accurate pose uncertainty estimates.\n"}
+{"abstract": "  Scalable quantum computing can become a reality with error correction,\nprovided coherent qubits can be constructed in large arrays. The key premise is\nthat physical errors can remain both small and sufficiently uncorrelated as\ndevices scale, so that logical error rates can be exponentially suppressed.\nHowever, energetic impacts from cosmic rays and latent radioactivity violate\nboth of these assumptions. An impinging particle ionizes the substrate,\nradiating high energy phonons that induce a burst of quasiparticles, destroying\nqubit coherence throughout the device. High-energy radiation has been\nidentified as a source of error in pilot superconducting quantum devices, but\nlacking a measurement technique able to resolve a single event in detail, the\neffect on large scale algorithms and error correction in particular remains an\nopen question. Elucidating the physics involved requires operating large\nnumbers of qubits at the same rapid timescales as in error correction, exposing\nthe event's evolution in time and spread in space. Here, we directly observe\nhigh-energy rays impacting a large-scale quantum processor. We introduce a\nrapid space and time-multiplexed measurement method and identify large bursts\nof quasiparticles that simultaneously and severely limit the energy coherence\nof all qubits, causing chip-wide failure. We track the events from their\ninitial localised impact to high error rates across the chip. Our results\nprovide direct insights into the scale and dynamics of these damaging error\nbursts in large-scale devices, and highlight the necessity of mitigation to\nenable quantum computing to scale.\n"}
+{"abstract": "  Longitudinal magnetoresistance (LMR) refers to the change in resistance due\nto a magnetic field when the current and the magnetic field are parallel to\neach other. For this to be nonzero in the semiclassical regime, kinetic theory\nstipulates that the electronic dispersion must satisfy certain conditions: it\nshould either be sufficiently anisotropic or have topological features. The\nformer results in a positive LMR while the latter results in a negative LMR.\nHere, I propose a different mechanism that leads to LMR in any dispersion\nwithout a need to satisfy the above requirements. The mechanism is quantum in\norigin but is applicable in the semiclassical regime. It arises due to the\nchange in the density of states with the magnetic field and is not kinetic in\norigin. Remarkably, LMR is found to be negative even if the dispersion is\nnontopological, provided it is nonparabolic. An analytical expression is\nderived for this novel contribution to LMR. It is found to depend on the\norbital magnetic susceptibility. The analytical findings are confirmed by\nnumerical calculations.\n"}
+{"abstract": "  Demand forecasting is extremely important in revenue management. After all,\nit is one of the inputs to an optimisation method which aim is to maximize\nrevenue. Most, if not all, forecasting methods use historical data to forecast\nthe future, disregarding the \"why\". In this paper, we combine data from\nmultiple sources, including competitor data, pricing, social media, safety and\nairline reviews. Next, we study five competitor pricing movements that, we\nhypothesize, affect customer behavior when presented a set of itineraries.\nUsing real airline data for ten different OD-pairs and by means of Extreme\nGradient Boosting, we show that customer behavior can be categorized into\nprice-sensitive, schedule-sensitive and comfort ODs. Through a simulation\nstudy, we show that this model produces forecasts that result in higher revenue\nthan traditional, time series forecasts.\n"}
+{"abstract": "  Nasal adhesions are a known postoperative complication following surgical\nprocedures for nasal airway obstruction (NAO); and are a common cause of\nsurgical failure, with patients often reporting significant NAO, despite\nrelatively minor adhesion size. Division of such nasal adhesions often provides\nmuch greater relief than anticipated, based on the minimal reduction in\ncross-sectional area associated with the adhesion. The available literature\nregarding nasal adhesions provides little evidence examining their quantitative\nand qualitative effects on nasal airflow using objective measures. This study\nexamined the impact of nasal adhesions at various anatomical sites on nasal\nairflow and mucosal cooling using computational fluid dynamics (CFD). A\nhigh-resolution CT scan of the paranasal sinuses of a 25-year-old, healthy\nfemale patient was segmented to create a three-dimensional nasal airway model.\nVirtual nasal adhesions of 2.5~mm diameter were added to various locations\nwithin the nasal cavity, representing common sites seen following NAO surgery.\nA series of models with single adhesions were created. CFD analysis was\nperformed on each model and compared with a baseline no-adhesion model,\ncomparing airflow and heat and mass transfer. The nasal adhesions resulted in\nno significant change in bulk airflow patterns through the nasal cavity.\nHowever, significant changes were observed in local airflow and mucosal cooling\naround and immediately downstream to the nasal adhesions. These were most\nevident with anterior nasal adhesions at the internal valve and anterior\ninferior turbinate.\n"}
+{"abstract": "  The axion, a hypothetical elementary pseudoscalar, is expected to solve the\nstrong CP problem of QCD and is also a promising candidate for dark matter. The\nmost sensitive axion search experiments operate at millikelvin temperatures and\nhence rely on instrumentation that carries signals from a system at cryogenic\ntemperatures to room temperature instrumentation. One of the biggest limiting\nfactors affecting the parameter scanning speed of these detectors is the noise\nadded by the components in the signal detection chain. Since the first\namplifier in the chain limits the minimum noise, low-noise amplification is of\nparamount importance. This paper reports on the operation of a flux-driven\nJosephson parametric amplifier (JPA) operating at around 2.3 GHz with added\nnoise approaching the quantum limit. The JPA was employed as a first stage\namplifier in an experimental setting similar to the ones used in haloscope\naxion detectors. By operating the JPA at a gain of 19 dB and cascading it with\ntwo cryogenic amplifiers operating at 4 K, noise temperatures as low as 120 mK\nwere achieved for the whole signal detection chain.\n"}
+{"abstract": "  Thanks to the great progress of machine learning in the last years, several\nArtificial Intelligence (AI) techniques have been increasingly moving from the\ncontrolled research laboratory settings to our everyday life. AI is clearly\nsupportive in many decision-making scenarios, but when it comes to sensitive\nareas such as health care, hiring policies, education, banking or justice, with\nmajor impact on individuals and society, it becomes crucial to establish\nguidelines on how to design, develop, deploy and monitor this technology.\nIndeed the decision rules elaborated by machine learning models are data-driven\nand there are multiple ways in which discriminatory biases can seep into data.\nAlgorithms trained on those data incur the risk of amplifying prejudices and\nsocietal stereotypes by over associating protected attributes such as gender,\nethnicity or disabilities with the prediction task. Starting from the extensive\nexperience of the National Metrology Institute on measurement standards and\ncertification roadmaps, and of Politecnico di Torino on machine learning as\nwell as methods for domain bias evaluation and mastering, we propose a first\njoint effort to define the operational steps needed for AI fairness\ncertification. Specifically we will overview the criteria that should be met by\nan AI system before coming into official service and the conformity assessment\nprocedures useful to monitor its functioning for fair decisions.\n"}
+{"abstract": "  This paper studies the canonical symmetric connection $\\nabla$ associated to\nany Lie group $G$. The salient properties of $\\nabla$ are stated and proved.\nThe Lie symmetries of the geodesic system of a general linear connection are\nformulated. The results are then applied to $\\nabla$ in the special case where\nthe Lie algebra $\\g$ of $G$, has a codimension one abelian nilradical. The\nconditions that determine a Lie symmetry in such a case are completely\nintegrated. Finally the results obtained are compared with some\nfour-dimensional Lie groups whose Lie algebras have three-dimensional abelian\nnilradicals, for which the calculations were performed by MAPLE.\n"}
+{"abstract": "  Presolar silicon carbide (SiC) grains in meteoritic samples can help\nconstrain circumstellar condensation processes and conditions in C-rich stars\nand core-collapse supernovae. This study presents our findings on eight\npresolar SiC grains from AGB stars (four mainstream and one Y grain) and\ncore-collapse supernovae (three X grains), chosen on the basis of {\\mu}-Raman\nspectral features that were indicative of their having unusual non-3C polytypes\nand/or high degrees of crystal disorder. Analytical transmission electron\nmicroscopy (TEM), which provides elemental compositional and structural\ninformation, shows evidence for complex histories for the grains. Our TEM\nresults confirm the presence of non-3C,2H crystal domains. Minor element\nheterogeneities and/or subgrains were observed in all grains analyzed for their\ncompositions. The C/O ratios inferred for the parent stars varied from 0.98 to\ngreater than or equal to 1.03. Our data show that SiC condensation can occur\nunder a wide range of conditions, in which environmental factors other than\ntemperature (e.g., pressure, gas composition, heterogeneous nucleation on\npre-condensed phases) play a significant role. Based on previous {\\mu}-Raman\nstudies, about 10% of SiC grains may have infrared (IR) spectral features that\nare influenced by crystal defects, porosity, and/or subgrains. Future\nsub-diffraction limited IR measurements of complex SiC grains might shed\nfurther light on the relative contributions of each of these features to the\nshape and position of the characteristic IR 11-{\\mu}m SiC feature and thus\nimprove the interpretation of IR spectra of AGB stars like those that produced\nthe presolar SiC grains.\n"}
+{"abstract": "  The search for an ideal single-photon source has generated significant\ninterest in discovering novel emitters in materials as well as developing new\nmanipulation techniques to gain better control over the emitters' properties.\nQuantum emitters in atomically thin two-dimensional (2D) materials have proven\nvery attractive with high brightness, operation under ambient conditions, and\nthe ability to be integrated with a wide range of electronic and photonic\nplatforms. This perspective highlights some of the recent advances in quantum\nlight generation from 2D materials, focusing on hexagonal boron nitride and\ntransition metal dichalcogenides (TMDs). Efforts in engineering and\ndeterministically creating arrays of quantum emitters in 2D materials, their\nelectrical excitation, and their integration with photonic devices are\ndiscussed. Lastly, we address some of the challenges the field is facing and\nthe near-term efforts to tackle them. We provide an outlook towards efficient\nand scalable quantum light generation from 2D materials towards controllable\nand addressable on-chip quantum sources.\n"}
+{"abstract": "  $J/\\psi$ production in p-p ultra-peripheral collisions through the elastic\nand inelastic photoproduction processes, where the virtual photons emitted from\nthe projectile interact with the target, are studied. The comparisions between\nthe exact treatment results and the ones of equivalent photon approximation are\nexpressed as $Q^{2}$ (virtuality of photon), $z$ and $p_{T}$ distributions, and\nthe total cross sections are also estimated. The method developed by Martin and\nRyskin is employed to avoid double counting when the different production\nmechanism are considered simultaneously. The numerical results indicate that,\nthe equivalent photon approximation can be only applied to the coherent or\nelastic electromagnetic process, the improper choice of $Q^{2}_{\\mathrm{max}}$\nand $y_{\\mathrm{max}}$ will cause obvious errors. And the exact treatment is\nneeded to deal accurately with the $J/\\psi$ photoproduction.\n"}
+{"abstract": "  When a finite order vector autoregressive model is fitted to VAR($\\infty$)\ndata the asymptotic distribution of statistics obtained via smooth functions of\nleast-squares estimates requires care. L\\\"utkepohl and Poskitt (1991) provide a\nclosed-form expression for the limiting distribution of (structural) impulse\nresponses for sieve VAR models based on the Delta method. Yet, numerical\nsimulations have shown that confidence intervals built in such way appear\noverly conservative. In this note I argue that these results stem naturally\nfrom the limit arguments used in L\\\"utkepohl and Poskitt (1991), that they\nmanifest when sieve inference is improperly applied, and that they can be\n\"remedied\" by either using bootstrap resampling or, simply, by using standard\n(non-sieve) asymptotics.\n"}
+{"abstract": "  Prior research on self-supervised learning has led to considerable progress\non image classification, but often with degraded transfer performance on object\ndetection. The objective of this paper is to advance self-supervised pretrained\nmodels specifically for object detection. Based on the inherent difference\nbetween classification and detection, we propose a new self-supervised pretext\ntask, called instance localization. Image instances are pasted at various\nlocations and scales onto background images. The pretext task is to predict the\ninstance category given the composited images as well as the foreground\nbounding boxes. We show that integration of bounding boxes into pretraining\npromotes better task alignment and architecture alignment for transfer\nlearning. In addition, we propose an augmentation method on the bounding boxes\nto further enhance the feature alignment. As a result, our model becomes weaker\nat Imagenet semantic classification but stronger at image patch localization,\nwith an overall stronger pretrained model for object detection. Experimental\nresults demonstrate that our approach yields state-of-the-art transfer learning\nresults for object detection on PASCAL VOC and MSCOCO.\n"}
+{"abstract": "  Powered lower-limb exoskeletons provide assistive torques to coordinate limb\nmotion during walking in individuals with movement disorders. Advances in\nsensing and actuation have improved the wearability and portability of\nstate-of-the-art exoskeletons for walking. Cable-driven exoskeletons offload\nthe actuators away from the user, thus rendering light-weight devices to\nfacilitate locomotion training. However, cable-driven mechanisms experience a\nslacking behavior if tension is not accurately controlled. Moreover,\ncounteracting forces can arise between the agonist and antagonist motors\nyielding undesired joint motion. In this paper, the strategy is to develop two\ncontrol layers to improve the performance of a cable-driven exoskeleton. First,\na joint tracking controller is designed using a high-gain robust approach to\ntrack desired knee and hip trajectories. Second, a motor synchronization\nobjective is developed to mitigate the effects of cable slacking for a pair of\nelectric motors that actuate each joint. A sliding-mode robust controller is\ndesigned for the motor synchronization objective. A Lyapunov-based stability\nanalysis is developed to guarantee a uniformly ultimately bounded result for\njoint tracking and exponential tracking for the motor synchronization\nobjective. Moreover, an average dwell time analysis provides a bound on the\nnumber of motor switches when allocating the control between motors that\nactuate each joint. An experimental result with an able-bodied individual\nillustrates the feasibility of the developed control methods.\n"}
+{"abstract": "  Hot Jupiters are predicted to have hot, clear daysides and cooler, cloudy\nnightsides. Recently, an asymmetric signature of iron absorption has been\nresolved in the transmission spectrum of WASP-76b using ESPRESSO on ESO's Very\nlarge Telescope. This feature is interpreted as being due to condensation of\niron on the nightside, resulting in a different absorption signature from the\nevening than from the morning limb of the planet. It represents the first time\nthat a chemical gradient has been observed across the surface of a single\nexoplanet. In this work, we confirm the presence of the asymmetric iron feature\nusing archival HARPS data of four transits. The detection shows that such\nfeatures can also be resolved by observing multiple transits on smaller\ntelescopes. By increasing the number of planets where these condensation\nfeatures are detected, we can make chemical comparisons between exoplanets and\nmap condensation across a range of parameters for the first time.\n"}
+{"abstract": "  We propose a primal-dual interior-point method (IPM) with convergence to\nsecond-order stationary points (SOSPs) of nonlinear semidefinite optimization\nproblems, abbreviated as NSDPs. As far as we know, the current algorithms for\nNSDPs only ensure convergence to first-order stationary points such as\nKarush-Kuhn-Tucker points. The proposed method generates a sequence\napproximating SOSPs while minimizing a primal-dual merit function for NSDPs by\nusing scaled gradient directions and directions of negative curvature. Under\nsome assumptions, the generated sequence accumulates at an SOSP with a\nworst-case iteration complexity. This result is also obtained for a primal IPM\nwith slight modification. Finally, our numerical experiments show the benefits\nof using directions of negative curvature in the proposed method.\n"}
+{"abstract": "  We present a control design for semilinear and quasilinear 2x2 hyperbolic\npartial differential equations with the control input at one boundary and a\nnonlinear ordinary differential equation coupled to the other. The controller\ncan be designed to asymptotically stabilize the system at an equilibrium or\nrelative to a reference signal. Two related but different controllers for\nsemilinear and general quasilinear systems are presented and the additional\nchallenges in quasilinear systems are discussed. Moreover, we present an\nobserver that estimates the distributed PDE state and the unmeasured ODE state\nfrom measurements at the actuated boundary only, which can be used to also\nsolve the output feedback control problem.\n"}
+{"abstract": "  Sentiment analysis is an important task in natural language processing (NLP).\nMost of existing state-of-the-art methods are under the supervised learning\nparadigm. However, human annotations can be scarce. Thus, we should leverage\nmore weak supervision for sentiment analysis. In this paper, we propose a\nposterior regularization framework for the variational approach to the weakly\nsupervised sentiment analysis to better control the posterior distribution of\nthe label assignment. The intuition behind the posterior regularization is that\nif extracted opinion words from two documents are semantically similar, the\nposterior distributions of two documents should be similar. Our experimental\nresults show that the posterior regularization can improve the original\nvariational approach to the weakly supervised sentiment analysis and the\nperformance is more stable with smaller prediction variance.\n"}
+{"abstract": "  Locality Sensitive Hashing (LSH) is an effective method of indexing a set of\nitems to support efficient nearest neighbors queries in high-dimensional\nspaces. The basic idea of LSH is that similar items should produce hash\ncollisions with higher probability than dissimilar items.\n  We study LSH for (not necessarily convex) polygons, and use it to give\nefficient data structures for similar shape retrieval. Arkin et al. represent\npolygons by their \"turning function\" - a function which follows the angle\nbetween the polygon's tangent and the $ x $-axis while traversing the perimeter\nof the polygon. They define the distance between polygons to be variations of\nthe $ L_p $ (for $p=1,2$) distance between their turning functions. This metric\nis invariant under translation, rotation and scaling (and the selection of the\ninitial point on the perimeter) and therefore models well the intuitive notion\nof shape resemblance.\n  We develop and analyze LSH near neighbor data structures for several\nvariations of the $ L_p $ distance for functions (for $p=1,2$). By applying our\nschemes to the turning functions of a collection of polygons we obtain\nefficient near neighbor LSH-based structures for polygons. To tune our\nstructures to turning functions of polygons, we prove some new properties of\nthese turning functions that may be of independent interest.\n  As part of our analysis, we address the following problem which is of\nindependent interest. Find the vertical translation of a function $ f $ that is\nclosest in $ L_1 $ distance to a function $ g $. We prove tight bounds on the\napproximation guarantee obtained by the translation which is equal to the\ndifference between the averages of $ g $ and $ f $.\n"}
+{"abstract": "  The weak-field Schwarzschild and NUT solutions of general relativity are\ngravitoelectromagnetically dual to each other, except on the positive $z$-axis.\nThe presence of non-locality weakens this duality and violates it within a\nsmeared region around the positive $z$-axis, whose typical transverse size is\ngiven by the scale of non-locality. We restore an exact non-local\ngravitoelectromagnetic duality everywhere via a manifestly dual modification of\nthe linearized non-local field equations. In the limit of vanishing\nnon-locality we recover the well-known results from weak-field general\nrelativity.\n"}
+{"abstract": "  In this paper, we determine the 4-adic complexity of the balanced quaternary\nsequences of period $2p$ and $2(2^n-1)$ with ideal autocorrelation defined by\nKim et al. (ISIT, pp. 282-285, 2009) and Jang et al. (ISIT, pp. 278-281, 2009),\nrespectively. Our results show that the 4-adic complexity of the quaternary\nsequences defined in these two papers is large enough to resist the attack of\nthe rational approximation algorithm.\n"}
+{"abstract": "  GPUs are now used for a wide range of problems within HPC. However, making\nefficient use of the computational power available with multiple GPUs is\nchallenging. The main challenges in achieving good performance are memory\nlayout, affecting memory bandwidth, effective use of the memory spaces with a\nGPU, inter-GPU communication, and synchronization. We address these problems\nwith the Ripple library, which provides a unified view of the computational\nspace across multiple dimensions and multiple GPUs, allows polymorphic data\nlayout, and provides a simple graph interface to describe an algorithm from\nwhich inter-GPU data transfers can be optimally scheduled. We describe the\nabstractions provided by Ripple to allow complex computations to be described\nsimply, and to execute efficiently across many GPUs with minimal overhead. We\nshow performance results for a number of examples, from particle motion to\nfinite-volume methods and the eikonal equation, as well as showing good strong\nand weak scaling results across multiple GPUs.\n"}
+{"abstract": "  We construct a structure-preserving finite element method and time-stepping\nscheme for compressible barotropic magnetohydrodynamics (MHD) both in the ideal\nand resistive cases, and in the presence of viscosity. The method is deduced\nfrom the geometric variational formulation of the equations. It preserves the\nbalance laws governing the evolution of total energy and magnetic helicity, and\npreserves mass and the constraint $ \\operatorname{div}B = 0$ to machine\nprecision, both at the spatially and temporally discrete levels. In particular,\nconservation of energy and magnetic helicity hold at the discrete levels in the\nideal case. It is observed that cross helicity is well conserved in our\nsimulation in the ideal case.\n"}
+{"abstract": "  The main result of this paper is to study the local deformations of\nCalabi-Yau $\\partial\\bar{\\partial}$-manifold that are co-polarised by the\nGauduchon metric by considering the subfamily of co-polarised fibres by the\nclass of Aeppli/De Rham-Gauduchon cohomology of Gauduchon metric given at the\nbeginning on the central fibre. In the latter part, we prove that the $p$-SKT\n$h$-$\\partial\\bar{\\partial}$-property is deformation open by constructing and\nstudying a new notion called $hp$-Hermitian symplectic ($hp$-HS) form.\n"}
+{"abstract": "  Disentangled representations can be useful in many downstream tasks, help to\nmake deep learning models more interpretable, and allow for control over\nfeatures of synthetically generated images that can be useful in training other\nmodels that require a large number of labelled or unlabelled data. Recently,\nflow-based generative models have been proposed to generate realistic images by\ndirectly modeling the data distribution with invertible functions. In this\nwork, we propose a new flow-based generative model framework, named GLOWin,\nthat is end-to-end invertible and able to learn disentangled representations.\nFeature disentanglement is achieved by factorizing the latent space into\ncomponents such that each component learns the representation for one\ngenerative factor. Comprehensive experiments have been conducted to evaluate\nthe proposed method on a public brain tumor MR dataset. Quantitative and\nqualitative results suggest that the proposed method is effective in\ndisentangling the features from complex medical images.\n"}
+{"abstract": "  This paper considers the problem of decentralized monitoring of a class of\nnon-functional properties (NFPs) with quantitative operators, namely cumulative\ncost properties. The decentralized monitoring of NFPs can be a non-trivial task\nfor several reasons: (i) they are typically expressed at a high abstraction\nlevel where inter-event dependencies are hidden, (ii) NFPs are difficult to be\nmonitored in a decentralized way, and (iii) lack of effective decomposition\ntechniques. We address these issues by providing a formal framework for\ndecentralised monitoring of LTL formulas with quantitative operators. The\npresented framework employs the tableau construction and a formula unwinding\ntechnique (i.e., a transformation technique that preserves the semantics of the\noriginal formula) to split and distribute the input LTL formula and the\ncorresponding quantitative constraint in a way such that monitoring can be\nperformed in a decentralised manner. The employment of these techniques allows\nprocesses to detect early violations of monitored properties and perform some\ncorrective or recovery actions. We demonstrate the effectiveness of the\npresented framework using a case study based on a Fischertechnik training\nmodel,a sorting line which sorts tokens based on their color into storage bins.\nThe analysis of the case study shows the effectiveness of the presented\nframework not only in early detection of violations, but also in developing\nfailure recovery plans that can help to avoid serious impact of failures on the\nperformance of the system.\n"}
+{"abstract": "  A card guessing game is played between two players, Guesser and Dealer. At\nthe beginning of the game, the Dealer holds a deck of $n$ cards (labeled $1,\n..., n$). For $n$ turns, the Dealer draws a card from the deck, the Guesser\nguesses which card was drawn, and then the card is discarded from the deck. The\nGuesser receives a point for each correctly guessed card. With perfect memory,\na Guesser can keep track of all cards that were played so far and pick at\nrandom a card that has not appeared so far, yielding in expectation $\\ln n$\ncorrect guesses. With no memory, the best a Guesser can do will result in a\nsingle guess in expectation. We consider the case of a memory bounded Guesser\nthat has $m < n$ memory bits. We show that the performance of such a memory\nbounded Guesser depends much on the behavior of the Dealer. In more detail, we\nshow that there is a gap between the static case, where the Dealer draws cards\nfrom a properly shuffled deck or a prearranged one, and the adaptive case,\nwhere the Dealer draws cards thoughtfully, in an adversarial manner.\nSpecifically:\n  1. We show a Guesser with $O(\\log^2 n)$ memory bits that scores a near\noptimal result against any static Dealer.\n  2. We show that no Guesser with $m$ bits of memory can score better than\n$O(\\sqrt{m})$ correct guesses, thus, no Guesser can score better than $\\min\n\\{\\sqrt{m}, \\ln n\\}$, i.e., the above Guesser is optimal.\n  3. We show an efficient adaptive Dealer against which no Guesser with $m$\nmemory bits can make more than $\\ln m + 2 \\ln \\log n + O(1)$ correct guesses in\nexpectation.\n  These results are (almost) tight, and we prove them using compression\narguments that harness the guessing strategy for encoding.\n"}
+{"abstract": "  Chromium iodide monolayers, which have different magnetic properties in\ncomparison to the bulk chromium iodide, have been shown to form skyrmionic\nstates in applied electromagnetic fields or in Janus-layer devices. In this\nwork, we demonstrate that spin-canted solutions can be induced into monolayer\nchromium iodide by select substitution of iodide atoms with isovalent\nimpurities. Several concentrations and spatial configurations of halide\nsubstitutional defects are selected to probe the coupling between the local\ndefect-induced geometric distortions and orientation of chromium magnetic\nmoments. This work provides atomic-level insight into how atomically precise\nstrain-engineering can be used to create and control complex magnetic patterns\nin chromium iodide layers and lays out the foundation for investigating the\nfield- and geometric-dependent magnetic properties in similar two-dimensional\nmaterials.\n"}
+{"abstract": "  We formalize the concept of the modular energy operator within the Page and\nWooters timeless framework. As a result, this operator is elevated to the same\nstatus as the more studied modular operators of position and momentum. In\nanalogy with dynamical nonlocality in space associated with the modular\nmomentum, we introduce and analyze the nonlocality in time associated with the\nmodular energy operator. Some applications of our formalization are provided\nthrough illustrative examples.\n"}
+{"abstract": "  Models that top leaderboards often perform unsatisfactorily when deployed in\nreal world applications; this has necessitated rigorous and expensive\npre-deployment model testing. A hitherto unexplored facet of model performance\nis: Are our leaderboards doing equitable evaluation? In this paper, we\nintroduce a task-agnostic method to probe leaderboards by weighting samples\nbased on their `difficulty' level. We find that leaderboards can be\nadversarially attacked and top performing models may not always be the best\nmodels. We subsequently propose alternate evaluation metrics. Our experiments\non 10 models show changes in model ranking and an overall reduction in\npreviously reported performance -- thus rectifying the overestimation of AI\nsystems' capabilities. Inspired by behavioral testing principles, we further\ndevelop a prototype of a visual analytics tool that enables leaderboard\nrevamping through customization, based on an end user's focus area. This helps\nusers analyze models' strengths and weaknesses, and guides them in the\nselection of a model best suited for their application scenario. In a user\nstudy, members of various commercial product development teams, covering 5\nfocus areas, find that our prototype reduces pre-deployment development and\ntesting effort by 41% on average.\n"}
+{"abstract": "  The nuclides inhaled during nuclear accidents usually cause internal\ncontamination of the lungs with low activity. Although a parallel-hole imaging\nsystem, which is widely used in medical gamma cameras, has a high resolution\nand good image quality, owing to its extremely low detection efficiency, it\nremains difficult to obtain images of inhaled lung contamination. In this\nstudy, the Monte Carlo method was used to study the internal lung contamination\nimaging using the MPA-MURA coded-aperture collimator. The imaging system\nconsisted of an adult male lung model, with a mosaicked, pattern-centered, and\nanti-symmetric MURA coded-aperture collimator model and a CsI(Tl) detector\nmodel. The MLEM decoding algorithm was used to reconstruct the internal\ncontamination image, and the complementary imaging method was used to reduce\nthe number of artifacts. The full width at half maximum of the I-131 point\nsource image reconstructed by the mosaicked, pattern-centered, and\nanti-symmetric Modified uniformly redundant array (MPA-MURA) coded-aperture\nimaging reached 2.51 mm, and the signal-to-noise ratio of the simplified\nrespiratory tract source (I-131) image reconstructed through MPA-MURA\ncoded-aperture imaging was 3.98 dB. Although the spatial resolution of MPA-MURA\ncoded aperture imaging is not as good as that of parallel-hole imaging, the\ndetection efficiency of PMA-MURA coded-aperture imaging is two orders of\nmagnitude higher than that of parallel hole collimator imaging. Considering the\nlow activity level of internal lung contamination caused by nuclear accidents,\nPMA-MURA coded-aperture imaging has significant potential for the development\nof lung contamination imaging.\n"}
+{"abstract": "  In 2018, Renes [IEEE Trans. Inf. Theory, vol. 64, no. 1, pp. 577-592 (2018)]\n(arXiv:1701.05583) developed a general theory of channel duality for\nclassical-input quantum-output (CQ) channels. That result showed that a number\nof well-known duality results for linear codes on the binary erasure channel\ncould be extended to general classical channels at the expense of using dual\nproblems which are intrinsically quantum mechanical. One special case of this\nduality is a connection between coding for error correction (resp. wire-tap\nsecrecy) on the quantum pure-state channel (PSC) and coding for wire-tap\nsecrecy (resp. error correction) on the classical binary symmetric channel\n(BSC). While this result has important implications for classical coding, the\nmachinery behind the general duality result is rather challenging for\nresearchers without a strong background in quantum information theory. In this\nwork, we leverage prior results for linear codes on PSCs to give an alternate\nderivation of the aforementioned special case by computing closed-form\nexpressions for the performance metrics. The noted prior results include\noptimality of the square-root measurement (SRM) for linear codes on the PSC and\nthe Fourier duality of linear codes. We also show that the SRM forms a\nsuboptimal measurement for channel coding on the BSC (when interpreted as a CQ\nproblem) and secret communications on the PSC. Our proofs only require linear\nalgebra and basic group theory, though we use the quantum Dirac notation for\nconvenience.\n"}
+{"abstract": "  When writing source code, programmers have varying levels of freedom when it\ncomes to the creation and use of identifiers. Do they habitually use the same\nidentifiers, names that are different to those used by others? Is it then\npossible to tell who the author of a piece of code is by examining these\nidentifiers? If so, can we use the presence or absence of identifiers to assist\nin correctly classifying programs to authors? Is it possible to hide the\nprovenance of programs by identifier renaming? In this study, we assess the\nimportance of three types of identifiers in source code author classification\nfor two different Java program data sets. We do this through a sequence of\nexperiments in which we disguise one type of identifier at a time. These\nexperiments are performed using as a tool the Source Code Author Profiles\n(SCAP) method. The results show that, although identifiers when examined as a\nwhole do not seem to reflect program authorship for these data sets, when\nexamined separately there is evidence that class names do signal the author of\nthe program. In contrast, simple variables and method names used in Java\nprograms do not appear to reflect program authorship. On the contrary, our\nanalysis suggests that such identifiers are so common as to mask authorship. We\nbelieve that these results have applicability in relation to the robustness of\ncode plagiarism analysis and that the underlying methods could be valuable in\ncases of litigation arising from disputes over program authorship.\n"}
+{"abstract": "  We propose a hybrid architecture composed of a fully convolutional network\n(FCN) and a Dempster-Shafer layer for image semantic segmentation. In the\nso-called evidential FCN (E-FCN), an encoder-decoder architecture first\nextracts pixel-wise feature maps from an input image. A Dempster-Shafer layer\nthen computes mass functions at each pixel location based on distances to\nprototypes. Finally, a utility layer performs semantic segmentation from mass\nfunctions and allows for imprecise classification of ambiguous pixels and\noutliers. We propose an end-to-end learning strategy for jointly updating the\nnetwork parameters, which can make use of soft (imprecise) labels. Experiments\nusing three databases (Pascal VOC 2011, MIT-scene Parsing and SIFT Flow) show\nthat the proposed combination improves the accuracy and calibration of semantic\nsegmentation by assigning confusing pixels to multi-class sets.\n"}
+{"abstract": "  In this paper, we propose panoramic annular simultaneous localization and\nmapping (PA-SLAM), a visual SLAM system based on panoramic annular lens. A\nhybrid point selection strategy is put forward in the tracking front-end, which\nensures repeatability of keypoints and enables loop closure detection based on\nthe bag-of-words approach. Every detected loop candidate is verified\ngeometrically and the $Sim(3)$ relative pose constraint is estimated to perform\npose graph optimization and global bundle adjustment in the back-end. A\ncomprehensive set of experiments on real-world datasets demonstrates that the\nhybrid point selection strategy allows reliable loop closure detection, and the\naccumulated error and scale drift have been significantly reduced via global\noptimization, enabling PA-SLAM to reach state-of-the-art accuracy while\nmaintaining high robustness and efficiency.\n"}
+{"abstract": "  Parker Solar Probe (PSP) is providing an unprecedented view of the Sun's\ncorona as it progressively dips closer into the solar atmosphere with each\nsolar encounter. Each set of observations provides a unique opportunity to test\nand constrain global models of the solar corona and inner heliosphere and, in\nturn, use the model results to provide a global context for interpreting such\nobservations. In this study, we develop a set of global magnetohydrodynamic\n(MHD) model solutions of varying degrees of sophistication for PSP's first four\nencounters and compare the results with in situ measurements from PSP,\nStereo-A, and Earth-based spacecraft, with the objective of assessing which\nmodels perform better or worse. All models were primarily driven by the\nobserved photospheric magnetic field using data from Solar Dynamics\nObservatory's Helioseismic and Magnetic Imager (HMI) instrument. Overall, we\nfind that there are substantial differences between the model results, both in\nterms of the large-scale structure of the inner heliosphere during these time\nperiods, as well as in the inferred time-series at various spacecraft. The\n\"thermodynamic\" model, which represents the \"middle ground\", in terms of model\ncomplexity, appears to reproduce the observations most closely for all four\nencounters. Our results also contradict an earlier study that had hinted that\nthe open flux problem may disappear nearer the Sun. Instead, our results\nsuggest that this \"missing\" solar flux is still missing even at 26.9 Rs, and\nthus it cannot be explained by interplanetary processes. Finally, the model\nresults were also used to provide a global context for interpreting the\nlocalized in situ measurements.\n"}
+{"abstract": "  Peer code review is a widely adopted software engineering practice to ensure\ncode quality and ensure software reliability in both the commercial and\nopen-source software projects. Due to the large effort overhead associated with\npracticing code reviews, project managers often wonder, if their code reviews\nare effective and if there are improvement opportunities in that respect. Since\nproject managers at Samsung Research Bangladesh (SRBD) were also intrigued by\nthese questions, this research developed, deployed, and evaluated a\nproduction-ready solution using the Balanced SCorecard (BSC) strategy that SRBD\nmanagers can use in their day-to-day management to monitor individual\ndeveloper's, a particular project's or the entire organization's code review\neffectiveness. Following the four-step framework of the BSC strategy, we-- 1)\ndefined the operation goals of this research, 2) defined a set of metrics to\nmeasure the effectiveness of code reviews, 3) developed an automated mechanism\nto measure those metrics, and 4) developed and evaluated a monitoring\napplication to inform the key stakeholders. Our automated model to identify\nuseful code reviews achieves 7.88% and 14.39% improvement in terms of accuracy\nand minority class F1 score respectively over the models proposed in prior\nstudies. It also outperforms human evaluators from SRBD, that the model\nreplaces, by a margin of 25.32% and 23.84% respectively in terms of accuracy\nand minority class F1 score. In our post-deployment survey, SRBD developers and\nmanagers indicated that they found our solution as useful and it provided them\nwith important insights to help their decision makings.\n"}
+{"abstract": "  The community detection problem requires to cluster the nodes of a network\ninto a small number of well-connected \"communities\". There has been substantial\nrecent progress in characterizing the fundamental statistical limits of\ncommunity detection under simple stochastic block models. However, in\nreal-world applications, the network structure is typically dynamic, with nodes\nthat join over time. In this setting, we would like a detection algorithm to\nperform only a limited number of updates at each node arrival. While standard\nvoting approaches satisfy this constraint, it is unclear whether they exploit\nthe network information optimally. We introduce a simple model for networks\ngrowing over time which we refer to as streaming stochastic block model\n(StSBM). Within this model, we prove that voting algorithms have fundamental\nlimitations. We also develop a streaming belief-propagation (StreamBP)\napproach, for which we prove optimality in certain regimes. We validate our\ntheoretical findings on synthetic and real data.\n"}
+{"abstract": "  We address the problems of identifying malware in network telemetry logs and\nproviding \\emph{indicators of compromise} -- comprehensible explanations of\nbehavioral patterns that identify the threat. In our system, an array of\nspecialized detectors abstracts network-flow data into comprehensible\n\\emph{network events} in a first step. We develop a neural network that\nprocesses this sequence of events and identifies specific threats, malware\nfamilies and broad categories of malware. We then use the\n\\emph{integrated-gradients} method to highlight events that jointly constitute\nthe characteristic behavioral pattern of the threat. We compare network\narchitectures based on CNNs, LSTMs, and transformers, and explore the efficacy\nof unsupervised pre-training experimentally on large-scale telemetry data. We\ndemonstrate how this system detects njRAT and other malware based on behavioral\npatterns.\n"}
+{"abstract": "  Nine point sources appeared within half an hour on a region within $\\sim$ 10\narcmin of a red-sensitive photographic plate taken in April 1950 as part of the\nhistoric Palomar Sky Survey. All nine sources are absent on both previous and\nlater photographic images, and absent in modern surveys with CCD detectors\nwhich go several magnitudes deeper. We present deep CCD images with the\n10.4-meter Gran Telescopio Canarias (GTC), reaching brightness $r \\sim 26$ mag,\nthat reveal possible optical counterparts, although these counterparts could\nequally well be just chance projections. The incidence of transients in the\ninvestigated photographic plate is far higher than expected from known\ndetection rates of optical counterparts to e.g.\\ flaring dwarf stars, Fast\nRadio Bursts (FRBs), Gamma Ray Bursts (GRBs) or microlensing events. One\npossible explanation is that the plates have been subjected to an unknown type\nof contamination producing mainly point sources with of varying intensities\nalong with some mechanism of concentration within a radius of $\\sim$ 10 arcmin\non the plate. If contamination as an explanation can be fully excluded, another\npossibility is fast (t $<0.5$ s) solar reflections from objects near\ngeosynchronous orbits. An alternative route to confirm the latter scenario is\nby looking for images from the First Palomar Sky Survey where multiple\ntransients follow a line.\n"}
+{"abstract": "  Inverse problems constrained by partial differential equations (PDEs) play a\ncritical role in model development and calibration. In many applications, there\nare multiple uncertain parameters in a model which must be estimated. Although\nthe Bayesian formulation is attractive for such problems, computational cost\nand high dimensionality frequently prohibit a thorough exploration of the\nparametric uncertainty. A common approach is to reduce the dimension by fixing\nsome parameters (which we will call auxiliary parameters) to a best estimate\nand using techniques from PDE-constrained optimization to approximate\nproperties of the Bayesian posterior distribution. For instance, the maximum a\nposteriori probability (MAP) and the Laplace approximation of the posterior\ncovariance can be computed. In this article, we propose using\nhyper-differential sensitivity analysis (HDSA) to assess the sensitivity of the\nMAP point to changes in the auxiliary parameters. We establish an\ninterpretation of HDSA as correlations in the posterior distribution.\nFoundational assumptions for HDSA require satisfaction of the optimality\nconditions which are not always feasible or appropriate as a result of\nill-posedness in the inverse problem. We introduce novel theoretical and\ncomputational approaches to justify and enable HDSA for ill-posed inverse\nproblems by projecting the sensitivities on likelihood informed subspaces and\ndefining a posteriori updates. Our proposed framework is demonstrated on a\nnonlinear multi-physics inverse problem motivated by estimation of spatially\nheterogenous material properties in the presence of spatially distributed\nparametric modeling uncertainties.\n"}
+{"abstract": "  In this paper, we consider the complex flows when all three regimes\npre-Darcy, Darcy and post-Darcy may be present in different portions of a same\ndomain. We unify all three flow regimes under mathematics formulation. We\ndescribe the flow of a single-phase fluid in $\\R^d, d\\ge 2$ by a nonlinear\ndegenerate system of density and momentum. A mixed finite element method is\nproposed for the approximation of the solution of the above system. The\nstability of the approximations are proved; the error estimates are derived for\nthe numerical approximations for both continuous and discrete time procedures.\nThe continuous dependence of numerical solutions on physical parameters are\ndemonstrated. Experimental studies are presented regarding convergence rates\nand showing the dependence of the solution on the physical parameters.\n"}
+{"abstract": "  To classify images based on their content is one of the most studied topics\nin the field of computer vision. Nowadays, this problem can be addressed using\nmodern techniques such as Convolutional Neural Networks (CNN), but over the\nyears different classical methods have been developed. In this report, we\nimplement an image classifier using both classic computer vision and deep\nlearning techniques. Specifically, we study the performance of a Bag of Visual\nWords classifier using Support Vector Machines, a Multilayer Perceptron, an\nexisting architecture named InceptionV3 and our own CNN, TinyNet, designed from\nscratch. We evaluate each of the cases in terms of accuracy and loss, and we\nobtain results that vary between 0.6 and 0.96 depending on the model and\nconfiguration used.\n"}
+{"abstract": "  We present the first linear polarization measurements from the 2015\nlong-duration balloon flight of SPIDER, an experiment designed to map the\npolarization of the cosmic microwave background (CMB) on degree angular scales.\nResults from these measurements include maps and angular power spectra from\nobservations of 4.8% of the sky at 95 and 150 GHz, along with the results of\ninternal consistency tests on these data. While the polarized CMB anisotropy\nfrom primordial density perturbations is the dominant signal in this region of\nsky, Galactic dust emission is also detected with high significance; Galactic\nsynchrotron emission is found to be negligible in the SPIDER bands. We employ\ntwo independent foreground-removal techniques in order to explore the\nsensitivity of the cosmological result to the assumptions made by each. The\nprimary method uses a dust template derived from Planck data to subtract the\nGalactic dust signal. A second approach, employing a joint analysis of SPIDER\nand Planck data in the harmonic domain, assumes a modified-blackbody model for\nthe spectral energy distribution of the dust with no constraint on its spatial\nmorphology. Using a likelihood that jointly samples the template amplitude and\n$r$ parameter space, we derive 95% upper limits on the primordial\ntensor-to-scalar ratio from Feldman-Cousins and Bayesian constructions, finding\n$r<0.11$ and $r<0.19$, respectively. Roughly half the uncertainty in $r$\nderives from noise associated with the template subtraction. New data at 280\nGHz from SPIDER's second flight will complement the Planck polarization maps,\nproviding powerful measurements of the polarized Galactic dust emission.\n"}
+{"abstract": "  For the High Luminosity upgrade of the Large Hadron Collider the current\nATLAS Inner Detector will be replaced by an all-silicon Inner Tracker. The\npixel detector will consist of five barrel layers and a number of rings,\nresulting in about 13 m^2 of instrumented area. Due to the huge non-ionising\nfluence (1e16 neq/cm^2) and ionising dose (5 MGy), the two innermost layers,\ninstrumented with 3D pixel sensors and 100 um thin planar sensors, will be\nreplaced after about five years of operation. Each pixel layer comprises hybrid\ndetector modules that will be read out by novel ASICs, implemented in 65 nm\nCMOS technology, with a bandwidth of up to 5 Gbit/s. Data will be transmitted\noptically to the off-detector readout system. To save material in the servicing\ncables, serial powering is employed for the supply voltage of the readout\nASICs. Large scale prototyping programmes are being carried out by all\nsubsystems.\n  This paper will give an overview of the layout and current status of the\ndevelopment of the ITk Pixel Detector.\n"}
+{"abstract": "  We construct the first smooth bubbling geometries using the Weyl formalism.\nThe solutions are obtained from Einstein theory coupled to a two-form gauge\nfield in six dimensions with two compact directions. We classify the charged\nWeyl solutions in this framework. Smooth solutions consist of a chain of\nKaluza-Klein bubbles that can be neutral or wrapped by electromagnetic fluxes,\nand are free of curvature and conical singularities. We discuss how such\ntopological structures are prevented from gravitational collapse without\nstruts. When embedded in type IIB, the class of solutions describes D1-D5-KKm\nsolutions in the non-BPS regime, and the smooth bubbling solutions have the\nsame conserved charges as a static four-dimensional non-extremal Cvetic-Youm\nblack hole.\n"}
+{"abstract": "  Metaphorical expressions are difficult linguistic phenomena, challenging\ndiverse Natural Language Processing tasks. Previous works showed that\nparaphrasing a metaphor as its literal counterpart can help machines better\nprocess metaphors on downstream tasks. In this paper, we interpret metaphors\nwith BERT and WordNet hypernyms and synonyms in an unsupervised manner, showing\nthat our method significantly outperforms the state-of-the-art baseline. We\nalso demonstrate that our method can help a machine translation system improve\nits accuracy in translating English metaphors to 8 target languages.\n"}
+{"abstract": "  The transfer of tasks with sometimes far-reaching moral implications to\nautonomous systems raises a number of ethical questions. In addition to\nfundamental questions about the moral agency of these systems, behavioral\nissues arise. This article focuses on the responsibility of agents who decide\non our behalf. We investigate the empirically accessible question of whether\nthe production of moral outcomes by an agent is systematically judged\ndifferently when the agent is artificial and not human. The results of a\nlaboratory experiment suggest that decision-makers can actually rid themselves\nof guilt more easily by delegating to machines than by delegating to other\npeople. Our results imply that the availability of artificial agents could\nprovide stronger incentives for decision makers to delegate morally sensitive\ndecisions.\n"}
+{"abstract": "  Background: Prokaryotic viruses, which infect bacteria and archaea, are the\nmost abundant and diverse biological entities in the biosphere. To understand\ntheir regulatory roles in various ecosystems and to harness the potential of\nbacteriophages for use in therapy, more knowledge of viral-host relationships\nis required. High-throughput sequencing and its application to the microbiome\nhave offered new opportunities for computational approaches for predicting\nwhich hosts particular viruses can infect. However, there are two main\nchallenges for computational host prediction. First, the empirically known\nvirus-host relationships are very limited. Second, although sequence similarity\nbetween viruses and their prokaryote hosts have been used as a major feature\nfor host prediction, the alignment is either missing or ambiguous in many\ncases. Thus, there is still a need to improve the accuracy of host prediction.\nResults: In this work, we present a semi-supervised learning model, named\nHostG, to conduct host prediction for novel viruses. We construct a knowledge\ngraph by utilizing both virus-virus protein similarity and virus-host DNA\nsequence similarity. Then graph convolutional network (GCN) is adopted to\nexploit viruses with or without known hosts in training to enhance the learning\nability. During the GCN training, we minimize the expected calibrated error\n(ECE) to ensure the confidence of the predictions. We tested HostG on both\nsimulated and real sequencing data and compared its performance with other\nstate-of-the-art methods specifcally designed for virus host classification\n(VHM-net, WIsH, PHP, HoPhage, RaFAH, vHULK, and VPF-Class). Conclusion: HostG\noutperforms other popular methods, demonstrating the efficacy of using a\nGCN-based semi-supervised learning approach. A particular advantage of HostG is\nits ability to predict hosts from new taxa.\n"}
+{"abstract": "  We propose a method to generate cutting-planes from multiple covers of\nknapsack constraints. The covers may come from different knapsack inequalities\nif the weights in the inequalities form a totally-ordered set. Thus, we\nintroduce and study the structure of a totally-ordered multiple knapsack set.\nThe valid multi-cover inequalities we derive for its convex hull have a number\nof interesting properties. First, they generalize the well-known (1,\nk)-configuration inequalities. Second, they are not aggregation cuts. Third,\nthey cannot be generated as a rank-1 Chvatal-Gomory cut from the inequality\nsystem consisting of the knapsack constraints and all their minimal cover\ninequalities. We also provide conditions under which the inequalities are\nfacets for the convex hull of the totally-ordered knapsack set, as well as\nconditions for those inequalities to fully characterize its convex hull. We\ngive an integer program to solve the separation and provide numerical\nexperiments that showcase the strength of these new inequalities.\n"}
+{"abstract": "  The latest Industrial revolution has helped industries in achieving very high\nrates of productivity and efficiency. It has introduced data aggregation and\ncyber-physical systems to optimize planning and scheduling. Although,\nuncertainty in the environment and the imprecise nature of human operators are\nnot accurately considered for into the decision making process. This leads to\ndelays in consignments and imprecise budget estimations. This widespread\npractice in the industrial models is flawed and requires rectification. Various\nother articles have approached to solve this problem through stochastic or\nfuzzy set model methods. This paper presents a comprehensive method to\nlogically and realistically quantify the non-deterministic uncertainty through\nprobabilistic uncertainty modelling. This method is applicable on virtually all\nIndustrial data sets, as the model is self adjusting and uses\nepsilon-contamination to cater to limited or incomplete data sets. The results\nare numerically validated through an Industrial data set in Flanders, Belgium.\nThe data driven results achieved through this robust scheduling method\nillustrate the improvement in performance.\n"}
+{"abstract": "  We consider the problem of learning to simplify medical texts. This is\nimportant because most reliable, up-to-date information in biomedicine is dense\nwith jargon and thus practically inaccessible to the lay audience. Furthermore,\nmanual simplification does not scale to the rapidly growing body of biomedical\nliterature, motivating the need for automated approaches. Unfortunately, there\nare no large-scale resources available for this task. In this work we introduce\na new corpus of parallel texts in English comprising technical and lay\nsummaries of all published evidence pertaining to different clinical topics. We\nthen propose a new metric based on likelihood scores from a masked language\nmodel pretrained on scientific texts. We show that this automated measure\nbetter differentiates between technical and lay summaries than existing\nheuristics. We introduce and evaluate baseline encoder-decoder Transformer\nmodels for simplification and propose a novel augmentation to these in which we\nexplicitly penalize the decoder for producing \"jargon\" terms; we find that this\nyields improvements over baselines in terms of readability.\n"}
+{"abstract": "  We say that $M$ and $S$ form a \\textsl{splitting} of $G$ if every nonzero\nelement $g$ of $G$ has a unique representation of the form $g=ms$ with $m\\in M$\nand $s\\in S$, while $0$ has no such representation. The splitting is called\n{\\it nonsingular} if $\\gcd(|G|, a) = 1$ for any $a\\in M$.\n  In this paper, we focus our study on nonsingular splittings of cyclic groups.\nWe introduce a new notation --direct KM logarithm and we prove that if there is\na prime $q$ such that $M$ splits $\\mathbb{Z}_q$, then there are infinitely many\nprimes $p$ such that $M$ splits $\\mathbb{Z}_p$.\n"}
+{"abstract": "  This paper presents a simple technique of multifractal traffic modeling. It\nproposes a method of fitting model to a given traffic trace. A comparison of\nsimulation results obtained for an exemplary trace, multifractal model and\nMarkov Modulated Poisson Process models has been performed.\n"}
+{"abstract": "  This paper explores a novel connection between two areas: shape analysis of\nsurfaces and unbalanced optimal transport. Specifically, we characterize the\nsquare root normal field (SRNF) shape distance as the pullback of the\nWasserstein-Fisher-Rao (WFR) unbalanced optimal transport distance. In\naddition, we propose a new algorithm for computing the WFR distance and present\nnumerical results that highlight the effectiveness of this algorithm. As a\nconsequence of our results we obtain a precise method for computing the SRNF\nshape distance directly on piecewise linear surfaces and gain new insights\nabout the degeneracy of this distance.\n"}
+{"abstract": "  In this paper, we build up a scaled homology theory, $lc$-homology, for\nmetric spaces such that every metric space can be visually regarded as \"locally\ncontractible\" with this newly-built homology. We check that $lc$-homology\nsatisfies all Eilenberg-Steenrod axioms except exactness axiom whereas its\ncorresponding $lc$-cohomology satisfies all axioms for cohomology. This\nhomology can relax the smooth manifold restrictions on the compact metric space\nsuch that the entropy conjecture will hold for the first $lc$-homology group.\n"}
+{"abstract": "  We present multispectral analysis (radio, H$\\alpha$, UV/EUV, and hard X-ray)\nof a confined flare from 2015 March 12. This flare started within the active\nregion NOAA 12 297 and then it expanded into a large preexisting magnetic rope\nembedded with a cold filament. The expansion started with several brightenings\nlocated along the rope. This process was accompanied by a group of slowly\npositively drifting bursts in the 0.8--2 GHz range. The frequency drift of\nthese bursts was 45 -- 100 MHz s$^{-1}$. One of the bursts had an S-like form.\nDuring the brightening of the rope we observed an unique bright EUV structure\ntransverse to the rope axis. The structure was observed in a broad range of\ntemperatures and it moved along the rope with the velocity of about 240 km\ns$^{-1}$. When the structure dissipated, we saw a plasma further following\ntwisted threads in the rope. The observed slowly positively drifting bursts\nwere interpreted considering particle beams and we show that one with the\nS-like form could be explained by the beam propagating through the helical\nstructure of the magnetic rope. The bright structure transverse to the rope\naxis was interpreted considering line-of-sight effects and the\ndissipation-spreading process, which we found to be more likely.\n"}
+{"abstract": "  Coupled-cluster theory with single and double excitations (CCSD) is a\npromising ab initio method for the electronic structure of three-dimensional\nmetals, for which second-order perturbation theory (MP2) diverges in the\nthermodynamic limit. However, due to the high cost and poor convergence of CCSD\nwith respect to basis size, applying CCSD to periodic systems often leads to\nlarge basis set errors. In a common \"composite\" method, MP2 is used to recover\nthe missing dynamical correlation energy through a focal-point correction, but\nthe inadequacy of MP2 for metals raises questions about this approach. Here we\ndescribe how high-energy excitations treated by MP2 can be \"downfolded\" into a\nlow-energy active space to be treated by CCSD. Comparing how the composite and\ndownfolding approaches perform for the uniform electron gas, we find that the\nlatter converges more quickly with respect to the basis set size. Nonetheless,\nthe composite approach is surprisingly accurate because it removes the\nproblematic MP2 treatment of double excitations near the Fermi surface. Using\nthe method to estimate the CCSD correlation energy in the combined complete\nbasis set and thermodynamic limits, we find CCSD recovers over 90% of the exact\ncorrelation energy at $r_s=4$. We also test the composite and downfolding\napproaches with the random-phase approximation used in place of MP2, yielding a\nmethod that is more effective but more expensive.\n"}
+{"abstract": "  Recently, low-dimensional vector space representations of knowledge graphs\n(KGs) have been applied to find answers to conjunctive queries (CQs) over\nincomplete KGs. However, the current methods only focus on inductive reasoning,\ni.e. answering CQs by predicting facts based on patterns learned from the data,\nand lack the ability of deductive reasoning by applying external domain\nknowledge. Such (expert or commonsense) domain knowledge is an invaluable\nresource which can be used to advance machine intelligence. To address this\nshortcoming, we introduce a neural-symbolic method for ontology-mediated CQ\nanswering over incomplete KGs that operates in the embedding space. More\nspecifically, we propose various data augmentation strategies to generate\ntraining queries using query-rewriting based methods and then exploit a novel\nloss function for training the model. The experimental results demonstrate the\neffectiveness of our training strategies and the new loss function, i.e., our\nmethod significantly outperforms the baseline in the settings that require both\ninductive and deductive reasoning.\n"}
+{"abstract": "  In digital signal processing time-frequency transforms are used to analyze\ntime-varying signals with respect to their spectral contents over time. Apart\nfrom the commonly used short-time Fourier transform, other methods exist in\nliterature, such as the Wavelet, Stockwell or Wigner-Ville transform.\nConsequently, engineers working on digital signal processing tasks are often\nfaced with the question which transform is appropriate for a specific\napplication. To address this question, this paper first briefly introduces the\ndifferent transforms. Then it compares them with respect to the achievable\nresolution in time and frequency and possible artifacts. Finally, the paper\ncontains a gallery of time-frequency representations of numerous signals from\ndifferent fields of applications to allow for visual comparison.\n"}
+{"abstract": "  Partial differential equations (PDEs) play a fundamental role in modeling and\nsimulating problems across a wide range of disciplines. Recent advances in deep\nlearning have shown the great potential of physics-informed neural networks\n(PINNs) to solve PDEs as a basis for data-driven modeling and inverse analysis.\nHowever, the majority of existing PINN methods, based on fully-connected NNs,\npose intrinsic limitations to low-dimensional spatiotemporal parameterizations.\nMoreover, since the initial/boundary conditions (I/BCs) are softly imposed via\npenalty, the solution quality heavily relies on hyperparameter tuning. To this\nend, we propose the novel physics-informed convolutional-recurrent learning\narchitectures (PhyCRNet and PhyCRNet-s) for solving PDEs without any labeled\ndata. Specifically, an encoder-decoder convolutional long short-term memory\nnetwork is proposed for low-dimensional spatial feature extraction and temporal\nevolution learning. The loss function is defined as the aggregated discretized\nPDE residuals, while the I/BCs are hard-encoded in the network to ensure\nforcible satisfaction (e.g., periodic boundary padding). The networks are\nfurther enhanced by autoregressive and residual connections that explicitly\nsimulate time marching. The performance of our proposed methods has been\nassessed by solving three nonlinear PDEs (e.g., 2D Burgers' equations, the\n$\\lambda$-$\\omega$ and FitzHugh Nagumo reaction-diffusion equations), and\ncompared against the start-of-the-art baseline algorithms. The numerical\nresults demonstrate the superiority of our proposed methodology in the context\nof solution accuracy, extrapolability and generalizability.\n"}
+{"abstract": "  A limiting factor towards the wide routine use of wearables devices for\ncontinuous healthcare monitoring is their cumbersome and obtrusive nature. This\nis particularly true for electroencephalography (EEG) recordings, which require\nthe placement of multiple electrodes in contact with the scalp. In this work,\nwe propose to identify the optimal wearable EEG electrode set-up, in terms of\nminimal number of electrodes, comfortable location and performance, for\nEEG-based event detection and monitoring. By relying on the demonstrated power\nof autoencoder (AE) networks to learn latent representations from\nhigh-dimensional data, our proposed strategy trains an AE architecture in a\none-class classification setup with different electrode set-ups as input data.\nThe resulting models are assessed using the F-score and the best set-up is\nchosen according to the established optimal criteria. Using alpha wave\ndetection as use case, we demonstrate that the proposed method allows to detect\nan alpha state from an optimal set-up consisting of electrodes in the forehead\nand behind the ear, with an average F-score of 0.78. Our results suggest that a\nlearning-based approach can be used to enable the design and implementation of\noptimized wearable devices for real-life healthcare monitoring.\n"}
+{"abstract": "  The hybrid electric system has good potential for unmanned tracked vehicles\ndue to its excellent power and economy. Due to unmanned tracked vehicles have\nno traditional driving devices, and the driving cycle is uncertain, it brings\nnew challenges to conventional energy management strategies. This paper\nproposes a novel energy management strategy for unmanned tracked vehicles based\non local speed planning. The contributions are threefold. Firstly, a local\nspeed planning algorithm is adopted for the input of driving cycle prediction\nto avoid the dependence of traditional vehicles on driver's operation.\nSecondly, a prediction model based on Convolutional Neural Networks and Long\nShort-Term Memory (CNN-LSTM) is proposed, which is used to process both the\nplanned and the historical velocity series to improve the prediction accuracy.\nFinally, based on the prediction results, the model predictive control\nalgorithm is used to realize the real-time optimization of energy management.\nThe validity of the method is verified by simulation using collected data from\nactual field experiments of our unmanned tracked vehicle. Compared with\nmulti-step neural networks, the prediction model based on CNN-LSTM improves the\nprediction accuracy by 20%. Compared with the traditional regular energy\nmanagement strategy, the energy management strategy based on model predictive\ncontrol reduces fuel consumption by 7%.\n"}
+{"abstract": "  Suppose $\\phi$ is a $\\mathbb{Z}/4$-cover of a curve over an algebraically\nclosed field $k$ of characteristic $2$, and $\\phi_1$ is its\n$\\mathbb{Z}/2$-sub-cover. Suppose, moreover, that $\\Phi_1$ is a lift of\n$\\phi_1$ to a complete discrete valuation ring $R$ that is a finite extension\nof the ring of Witt vectors $W(k)$ (hence in characteristic zero). We show that\nthere exists a finite extension $R'$ of $R$, and a lift $\\Phi$ of $\\phi$ to\n$R'$ with a sub-cover isomorphic to $\\Phi_1 \\otimes_k R'$. This gives the first\nnon-trivial family of cyclic covers where Sa\\\"idi's refined lifting conjecture\nholds.\n"}
+{"abstract": "  Given a sequence $(\\xi_n)$ of standard i.i.d complex Gaussian random\nvariables, Peres and Vir\\'ag (in the paper ``Zeros of the i.i.d. Gaussian power\nseries: a conformally invariant determinantal process'' {\\it Acta Math.} (2005)\n194, 1-35) discovered the striking fact that the zeros of the random power\nseries $f(z) = \\sum_{n=1}^\\infty \\xi_n z^{n-1}$ in the complex unit disc\n$\\mathbb{D}$ constitute a determinantal point process. The study of the zeros\nof the general random series\n  $f(z)$ where the restriction of independence is relaxed upon the random\nvariables $(\\xi_n)$ is an important open problem. This paper proves that if\n$(\\xi_n)$ is an infinite sequence of complex Gaussian random variables such\nthat their covariance matrix is invertible and its inverse is a Toeplitz\nmatrix, then the zero set of $f(z)$ constitutes a determinantal point process\nwith the same distribution as the case of i.i.d variables studied by Peres and\nVir\\'ag. The arguments are based on some interplays between Hardy spaces and\nreproducing kernels. Illustrative examples are constructed from classical\nToeplitz matrices and the classical fractional Gaussian noise.\n"}
+{"abstract": "  Referring Expression Comprehension (REC) has become one of the most important\ntasks in visual reasoning, since it is an essential step for many\nvision-and-language tasks such as visual question answering. However, it has\nnot been widely used in many downstream tasks because it suffers 1) two-stage\nmethods exist heavy computation cost and inevitable error accumulation, and 2)\none-stage methods have to depend on lots of hyper-parameters (such as anchors)\nto generate bounding box. In this paper, we present a proposal-free one-stage\n(PFOS) model that is able to regress the region-of-interest from the image,\nbased on a textual query, in an end-to-end manner. Instead of using the\ndominant anchor proposal fashion, we directly take the dense-grid of an image\nas input for a cross-attention transformer that learns grid-word\ncorrespondences. The final bounding box is predicted directly from the image\nwithout the time-consuming anchor selection process that previous methods\nsuffer. Our model achieves the state-of-the-art performance on four referring\nexpression datasets with higher efficiency, comparing to previous best\none-stage and two-stage methods.\n"}
+{"abstract": "  In this paper a martingale problem for super-Brownian motion with interactive\nbranching is derived. The uniqueness of the solution to the martingale problem\nis obtained by using the pathwise uniqueness of the solution to a corresponding\nsystem of SPDEs with proper boundary conditions. The existence of the solution\nto the martingale problem and the Holder continuity of the density process are\nalso studied.\n"}
+{"abstract": "  Diffusion pore imaging is an extension of diffusion-weighted nuclear magnetic\nresonance imaging enabling the direct measurement of the shape of arbitrarily\nformed, closed pores by probing diffusion restrictions using the motion of\nspin-bearing particles. Examples of such pores comprise cells in biological\ntissue or oil containing cavities in porous rocks. All pores contained in the\nmeasurement volume contribute to one reconstructed image, which reduces the\nproblem of vanishing signal at increasing resolution present in conventional\nmagnetic resonance imaging. It has been previously experimentally demonstrated\nthat pore imaging using a combination of a long and a narrow magnetic field\ngradient pulse is feasible. In this work, an experimental verification is\npresented showing that pores can be imaged using short gradient pulses only.\nExperiments were carried out using hyperpolarized xenon gas in well-defined\npores. The phase required for pore image reconstruction was retrieved from\ndouble diffusion encoded (DDE) measurements, while the magnitude could either\nbe obtained from DDE signals or classical diffusion measurements with single\nencoding. The occurring image artifacts caused by restrictions of the gradient\nsystem, insufficient diffusion time, and by the phase reconstruction approach\nwere investigated. Employing short gradient pulses only is advantageous\ncompared to the initial long-narrow approach due to a more flexible sequence\ndesign when omitting the long gradient and due to faster convergence to the\ndiffusion long-time limit, which may enable application to larger pores.\n"}
+{"abstract": "  This work presents a simulation framework to generate human micro-Dopplers in\nWiFi based passive radar scenarios, wherein we simulate IEEE 802.11g complaint\nWiFi transmissions using MATLAB's WLAN toolbox and human animation models\nderived from a marker-based motion capture system. We integrate WiFi\ntransmission signals with the human animation data to generate the\nmicro-Doppler features that incorporate the diversity of human motion\ncharacteristics, and the sensor parameters. In this paper, we consider five\nhuman activities. We uniformly benchmark the classification performance of\nmultiple machine learning and deep learning models against a common dataset.\nFurther, we validate the classification performance using the real radar data\ncaptured simultaneously with the motion capture system. We present experimental\nresults using simulations and measurements demonstrating good classification\naccuracy of $\\geq$ 95\\% and $\\approx$ 90\\%, respectively.\n"}
+{"abstract": "  The behavior of colloidal particles with a hard core and a soft shell has\nattracted the attention for researchers in the physical-chemistry interface not\nonly due the large number of applications, but due the unique properties of\nthese systems in bulk and at interfaces. The adsorption at the boundary of two\nphases can provide information about the molecular arrangement. In this way, we\nperform Langevin Dynamics simulations of polymer-grafted nanoparticles. We\nemployed a recently obtained core-softened potential to analyze the relation\nbetween adsorption, structure and dynamic properties of the nanoparticles near\na solid repulsive surface. Two cases were considered: flat or structured walls.\nAt low temperatures, a maxima is observed in the adsorption. It is related to a\nfluid to clusters transition and with a minima in the contact layer diffusion -\nand is explained by the competition between the scales in the core-softened\ninteraction. Due the long range repulsion, the particles stay at the distance\ncorrespondent to this length scale at low densities, and overcome the repulsive\nbarrier as the packing increases, However, increasing the temperature, the gain\nin kinetic energy allows the colloids to overcome the long range repulsion\nbarrier even at low densities. As consequence, there is no competition and no\nmaxima was observed in the adsorption.\n"}
+{"abstract": "  We examine stability of summation by parts (SBP) numerical schemes that use\nhyperboloidal slices to include future null infinity in the computational\ndomain. This inclusion serves to mitigate outer boundary effects and, in the\nfuture, will help reduce systematic errors in gravitational waveform\nextraction. We also study a setup with truncation error matching. Our\nSBP-Stable scheme guarantees energy-balance for a class of linear wave\nequations at the semidiscrete level. We develop also specialized dissipation\noperators. The whole construction is made at second order accuracy in spherical\nsymmetry, but could be straightforwardly generalized to higher order or\nspectral accuracy without symmetry. In a practical implementation we evolve\nfirst a scalar field obeying the linear wave equation and observe, as expected,\nlong term stability and norm convergence. We obtain similar results with a\npotential term. To examine the limitations of the approach we consider a\nmassive field, whose equations of motion do not regularize, and whose dynamics\nnear null infinity, which involve excited incoming pulses that can not be\nresolved by the code, is very different to that in the massless setting. We\nstill observe excellent energy conservation, but convergence is not\nsatisfactory. Overall our results suggest that compactified hyperboloidal\nslices are likely to be provably effective whenever the asymptotic solution\nspace is close to that of the wave equation.\n"}
+{"abstract": "  In this paper we count the number $N_n^{\\text{tor}}(X)$ of $n$-dimensional\nalgebraic tori over $\\mathbb{Q}$ whose Artin conductor of the associated\ncharacter is bounded by $X$. This can be understood as a generalization of\ncounting number fields of given degree by discriminant. We suggest a conjecture\non the asymptotics of $N_n^{\\text{tor}}(X)$ and prove that this conjecture\nfollows from Malle's conjecture for tori over $\\mathbb{Q}$. We also prove that\n$N_2^{\\text{tor}}(X) \\ll_{\\varepsilon} X^{1 + \\varepsilon}$, and this upper\nbound can be improved to $N_2^{\\text{tor}}(X) \\ll_{\\varepsilon} X (\\log X)^{1 +\n\\varepsilon}$ under the assumption of the Cohen-Lenstra heuristics for $p=3$.\n"}
+{"abstract": "  We present herein an introduction to implementing 2-color cellular automata\non quantum annealing systems, such as the D-Wave quantum computer. We show that\nimplementing nearest-neighbor cellular automata is possible. We present an\nimplementation of Wolfram's cellular automata Rule 110, which has previously\nbeen shown to be a universal Turing machine, as a QUBO suitable for use on\nquantum annealing systems. We demonstrate back-propagation of cellular automata\nrule sets to determine initial cell states for a desired later system state. We\nshow 2-D 2-color cellular automata, such as Conway's Game of Life, can be\nexpressed for quantum annealing systems.\n"}
+{"abstract": "  Low-dose computed tomography (CT) allows the reduction of radiation risk in\nclinical applications at the expense of image quality, which deteriorates the\ndiagnosis accuracy of radiologists. In this work, we present a High-Quality\nImaging network (HQINet) for the CT image reconstruction from Low-dose computed\ntomography (CT) acquisitions. HQINet was a convolutional encoder-decoder\narchitecture, where the encoder was used to extract spatial and temporal\ninformation from three contiguous slices while the decoder was used to recover\nthe spacial information of the middle slice. We provide experimental results on\nthe real projection data from low-dose CT Image and Projection Data\n(LDCT-and-Projection-data), demonstrating that the proposed approach yielded a\nnotable improvement of the performance in terms of image quality, with a rise\nof 5.5dB in terms of peak signal-to-noise ratio (PSNR) and 0.29 in terms of\nmutual information (MI).\n"}
+{"abstract": "  Automatic network management driven by Artificial Intelligent technologies\nhas been heatedly discussed over decades. However, current reports mainly focus\non theoretic proposals and architecture designs, works on practical\nimplementations on real-life networks are yet to appear. This paper proposes\nour effort toward the implementation of knowledge graph driven approach for\nautonomic network management in software defined networks (SDNs), termed as\nSeaNet. Driven by the ToCo ontology, SeaNet is reprogrammed based on Mininet (a\nSDN emulator). It consists three core components, a knowledge graph generator,\na SPARQL engine, and a network management API. The knowledge graph generator\nrepresents the knowledge in the telecommunication network management tasks into\nformally represented ontology driven model. Expert experience and network\nmanagement rules can be formalized into knowledge graph and by automatically\ninferenced by SPARQL engine, Network management API is able to packet\ntechnology-specific details and expose technology-independent interfaces to\nusers. The Experiments are carried out to evaluate proposed work by comparing\nwith a commercial SDN controller Ryu implemented by the same language Python.\nThe evaluation results show that SeaNet is considerably faster in most\ncircumstances than Ryu and the SeaNet code is significantly more compact.\nBenefit from RDF reasoning, SeaNet is able to achieve O(1) time complexity on\ndifferent scales of the knowledge graph while the traditional database can\nachieve O(nlogn) at its best. With the developed network management API, SeaNet\nenables researchers to develop semantic-intelligent applications on their own\nSDNs.\n"}
+{"abstract": "  This paper introduces a deep learning method for solving an elliptic\nhemivariational inequality (HVI). In this method, an expectation minimization\nproblem is first formulated based on the variational principle of underlying\nHVI, which is solved by stochastic optimization algorithms using three\ndifferent training strategies for updating network parameters. The method is\napplied to solve two practical problems in contact mechanics, one of which is a\nfrictional bilateral contact problem and the other of which is a frictionless\nnormal compliance contact problem. Numerical results show that the deep\nlearning method is efficient in solving HVIs and the adaptive mesh-free\nmultigrid algorithm can provide the most accurate solution among the three\nlearning methods.\n"}
+{"abstract": "  Autonomous cars can reduce road traffic accidents and provide a safer mode of\ntransport. However, key technical challenges, such as safe navigation in\ncomplex urban environments, need to be addressed before deploying these\nvehicles on the market. Teleoperation can help smooth the transition from human\noperated to fully autonomous vehicles since it still has human in the loop\nproviding the scope of fallback on driver. This paper presents an Active Safety\nSystem (ASS) approach for teleoperated driving. The proposed approach helps the\noperator ensure the safety of the vehicle in complex environments, that is,\navoid collisions with static or dynamic obstacles. Our ASS relies on a model\npredictive control (MPC) formulation to control both the lateral and\nlongitudinal dynamics of the vehicle. By exploiting the ability of the MPC\nframework to deal with constraints, our ASS restricts the controller's\nauthority to intervene for lateral correction of the human operator's commands,\navoiding counter-intuitive driving experience for the human operator. Further,\nwe design a visual feedback to enhance the operator's trust over the ASS. In\naddition, we propose an MPC's prediction horizon data based novel predictive\ndisplay to mitigate the effects of large latency in the teleoperation system.\nWe tested the performance of the proposed approach on a high-fidelity vehicle\nsimulator in the presence of dynamic obstacles and latency.\n"}
+{"abstract": "  Recent enhancements in neuroscience, like the development of new and powerful\nrecording techniques of the brain activity combined with the increasing\nanatomical knowledge provided by atlases and the growing understanding of\nneuromodulation principles, allow studying the brain at a whole new level,\npaving the way to the creation of extremely detailed effective network models\ndirectly from observed data. Leveraging the advantages of this integrated\napproach, we propose a method to infer models capable of reproducing the\ncomplex spatio-temporal dynamics of the slow waves observed in the experimental\nrecordings of the cortical hemisphere of a mouse under anesthesia. To reliably\nclaim the good match between data and simulations, we implemented a versatile\nensemble of analysis tools, applicable to both experimental and simulated data\nand capable to identify and quantify the spatio-temporal propagation of waves\nacross the cortex. In order to reproduce the observed slow wave dynamics, we\nintroduced an inference procedure composed of two steps: the inner and the\nouter loop. In the inner loop, the parameters of a mean-field model are\noptimized by likelihood maximization, exploiting the anatomical knowledge to\ndefine connectivity priors. The outer loop explores \"external\" parameters,\nseeking for an optimal match between the simulation outcome and the data,\nrelying on observables (speed, directions, and frequency of the waves) apt for\nthe characterization of cortical slow waves; the outer loop includes a periodic\nneuro-modulation for better reproduction of the experimental recordings. We\nshow that our model is capable to reproduce most of the features of the\nnon-stationary and non-linear dynamics displayed by the biological network.\nAlso, the proposed method allows to infer which are the relevant modifications\nof parameters when the brain state changes, e.g. according to anesthesia\nlevels.\n"}
+{"abstract": "  To detect and segment objects in images based on their content is one of the\nmost active topics in the field of computer vision. Nowadays, this problem can\nbe addressed using Deep Learning architectures such as Faster R-CNN or YOLO,\namong others. In this paper, we study the behaviour of different configurations\nof RetinaNet, Faster R-CNN and Mask R-CNN presented in Detectron2. First, we\nevaluate qualitatively and quantitatively (AP) the performance of the\npre-trained models on KITTI-MOTS and MOTSChallenge datasets. We observe a\nsignificant improvement in performance after fine-tuning these models on the\ndatasets of interest and optimizing hyperparameters. Finally, we run inference\nin unusual situations using out of context datasets, and present interesting\nresults that help us understanding better the networks.\n"}
+{"abstract": "  Discovering the partial differential equations underlying spatio-temporal\ndatasets from very limited and highly noisy observations is of paramount\ninterest in many scientific fields. However, it remains an open question to\nknow when model discovery algorithms based on sparse regression can actually\nrecover the underlying physical processes. In this work, we show the design\nmatrices used to infer the equations by sparse regression can violate the\nirrepresentability condition (IRC) of the Lasso, even when derived from\nanalytical PDE solutions (i.e. without additional noise). Sparse regression\ntechniques which can recover the true underlying model under violated IRC\nconditions are therefore required, leading to the introduction of the\nrandomised adaptive Lasso. We show once the latter is integrated within the\ndeep learning model discovery framework DeepMod, a wide variety of nonlinear\nand chaotic canonical PDEs can be recovered: (1) up to $\\mathcal{O}(2)$ higher\nnoise-to-sample ratios than state-of-the-art algorithms, (2) with a single set\nof hyperparameters, which paves the road towards truly automated model\ndiscovery.\n"}
+{"abstract": "  Stars originate from the dense interstellar medium, which exhibits\nfilamentary structure to scales of $\\sim 1$ kpc in galaxies like our Milky Way.\nWe explore quantitatively how much resulting large-scale correlation there is\namong different stellar clusters and associations in $\\textit{orbit phase\nspace}$, characterized here by actions and angles. As a starting point, we\nidentified 55 prominent stellar overdensities in the 6D space of orbit\n(actions) and orbital phase (angles), among the $\\sim$ 2.8 million stars with\nradial velocities from Gaia EDR3 and $d \\leq 800$ pc. We then explored the\norbital $\\textit{phase}$ distribution of all sample stars in the same\n$\\textit{orbit}$ patch as any one of these 55 overdensities. We find that very\ncommonly numerous other distinct orbital phase overdensities exist along these\nsame orbits, like pearls on a string. These `pearls' range from known stellar\nclusters to loose, unrecognized associations. Among orbit patches defined by\none initial orbit-phase overdensity 50% contain at least 8 additional\norbital-phase pearls of 10 cataloged members; 20% of them contain 20 additional\npearls. This is in contrast to matching orbit patches sampled from a smooth\nmock catalog, or random nearby orbit patches, where there are only 2 (or 5,\nrespectively) comparable pearls. Our findings quantify for the first time how\ncommon it is for star clusters and associations to form at distinct orbital\nphases of nearly the same orbit. This may eventually offer a new way to probe\nthe 6D orbit structure of the filamentary interstellar medium.\n"}
+{"abstract": "  Statistical models are inherently uncertain. Quantifying or at least\nupper-bounding their uncertainties is vital for safety-critical systems such as\nautonomous vehicles. While standard neural networks do not report this\ninformation, several approaches exist to integrate uncertainty estimates into\nthem. Assessing the quality of these uncertainty estimates is not\nstraightforward, as no direct ground truth labels are available. Instead,\nimplicit statistical assessments are required. For regression, we propose to\nevaluate uncertainty realism -- a strict quality criterion -- with a\nMahalanobis distance-based statistical test. An empirical evaluation reveals\nthe need for uncertainty measures that are appropriate to upper-bound\nheavy-tailed empirical errors. Alongside, we transfer the variational U-Net\nclassification architecture to standard supervised image-to-image tasks. We\nadopt it to the automotive domain and show that it significantly improves\nuncertainty realism compared to a plain encoder-decoder model.\n"}
+{"abstract": "  Since thin-film silicon solar cells have limited optical absorption, we\nexplore the effect of a nanostructured back reflector to recycle the unabsorbed\nlight. As a back reflector we investigate a 3D photonic band gap crystal made\nfrom silicon that is readily integrated with the thin films. We numerically\nobtain the optical properties by solving the 3D time-harmonic Maxwell equations\nusing the finite-element method, and model silicon with experimentally\ndetermined optical constants. The absorption enhancement relevant for\nphotovoltaics is obtained by weighting the absorption spectra with the AM 1.5\nstandard solar spectrum. We study thin films either thicker ($L_{Si} = 2400$\nnm) or much thinner ($L_{Si} = 80$ nm) than the wavelength of light. At $L_{Si}\n= 2400$ nm, the 3D photonic band gap crystal enhances the spectrally averaged\n($\\lambda = 680$ nm to $880$ nm) silicon absorption by $2.22$x (s-pol.) to\n$2.45$x (p-pol.), which exceeds the enhancement of a perfect metal back\nreflector ($1.47$ to $1.56$x). The absorption is enhanced by the (i) broadband\nangle and polarization-independent reflectivity in the 3D photonic band gap,\nand (ii) the excitation of many guided modes in the film by the crystal's\nsurface diffraction leading to enhanced path lengths. At $L_{Si} = 80$ nm, the\nphotonic crystal back reflector yields a striking average absorption\nenhancement of $9.15$x, much more than $0.83$x for a perfect metal, which is\ndue to a remarkable guided mode confined within the combined thickness of the\nthin film and the photonic crystal's Bragg attenuation length. The broad\nbandwidth of the 3D photonic band gap leads to the back reflector's Bragg\nattenuation length being much shorter than the silicon absorption length.\nConsequently, light is confined inside the thin film and the absorption\nenhancements are not due to the additional thickness of the photonic crystal\nback reflector.\n"}
+{"abstract": "  Technologies that augment face-to-face interactions with a digital sense of\nself have been used to support conversations. That work has employed one\nhomogenous technology, either 'off-the-shelf' or with a bespoke prototype,\nacross all participants. Beyond speculative instances, it is unclear what\ntechnology individuals themselves would choose, if any, to augment their social\ninteractions; what influence it may exert; or how use of heterogeneous devices\nmay affect the value of this augmentation. This is important, as the devices\nthat we use directly affect our behaviour, influencing affordances and how we\nengage in social interactions. Through a study of 28 participants, we compared\nhead-mounted display, smartphones, and smartwatches to support digital\naugmentation of self during face-to-face interactions within a group. We\nidentified a preference among participants for head-mounted displays to support\nprivacy, while smartwatches and smartphones better supported conversational\nevents (such as grounding and repair), along with group use through\nscreen-sharing. Accordingly, we present software and hardware design\nrecommendations and user interface guidelines for integrating a digital form of\nself into face-to-face conversations.\n"}
+{"abstract": "  We briefly report the modern status of heavy quark sum rules (HQSR) based on\nstability criteria by emphasizing the recent progresses for determining the QCD\nparameters (alpha_s, m_{c,b} and gluon condensates)where their correlations\nhave been taken into account. The results: alpha_s(M_Z)=0.1181(16)(3),\nm_c(m_c)=1286(16) MeV, m_b(m_b)=4202(7) MeV,<alpha_s G^2> = (6.49+-0.35)10^-2\nGeV^4, < g^3 G^3 >= (8.2+-1.0) GeV^2 <alpha_s G^2> and the ones from recent\nlight quark sum rules are summarized in Table 2. One can notice that the SVZ\nvalue of <alpha_s G^2> has been underestimated by a factor 1.6, <g^3 G^3> is\nmuch bigger than the instanton model estimate, while the four-quark condensate\nwhich mixes under renormalization is incompatible with the vacuum saturation\nwhich is phenomenologically violated by a factor (2~4). The uses of HQSR for\nmolecules and tetraquarks states are commented.\n"}
+{"abstract": "  Many developers and organizations implement apps for Android, the most widely\nused operating system for mobile devices. Common problems developers face are\nthe various hardware devices, customized Android variants, and frequent\nupdates, forcing them to implement workarounds for the different versions and\nvariants of Android APIs used in practice. In this paper, we contribute the\nAndroid Compatibility checkS dataSet (AndroidCompass) that comprises changes to\ncompatibility checks developers use to enforce workarounds for specific Android\nversions in their apps. We extracted 80,324 changes to compatibility checks\nfrom 1,394 apps by analyzing the version histories of 2,399 projects from the\nF-Droid catalog. With AndroidCompass, we aim to provide data on when and how\ndevelopers introduced or evolved workarounds to handle Android\nincompatibilities. We hope that AndroidCompass fosters research to deal with\nversion incompatibilities, address potential design flaws, identify security\nconcerns, and help derive solutions for other developers, among others-helping\nresearchers to develop and evaluate novel techniques, and Android app as well\nas operating-system developers in engineering their software.\n"}
+{"abstract": "  With recent advances in distantly supervised (DS) relation extraction (RE),\nconsiderable attention is attracted to leverage multi-instance learning (MIL)\nto distill high-quality supervision from the noisy DS. Here, we go beyond label\nnoise and identify the key bottleneck of DS-MIL to be its low data utilization:\nas high-quality supervision being refined by MIL, MIL abandons a large amount\nof training instances, which leads to a low data utilization and hinders model\ntraining from having abundant supervision. In this paper, we propose\ncollaborative adversarial training to improve the data utilization, which\ncoordinates virtual adversarial training (VAT) and adversarial training (AT) at\ndifferent levels. Specifically, since VAT is label-free, we employ the\ninstance-level VAT to recycle instances abandoned by MIL. Besides, we deploy AT\nat the bag-level to unleash the full potential of the high-quality supervision\ngot by MIL. Our proposed method brings consistent improvements (~ 5 absolute\nAUC score) to the previous state of the art, which verifies the importance of\nthe data utilization issue and the effectiveness of our method.\n"}
+{"abstract": "  In this paper, we study the Ricci flow on a closed manifold of dimension $n\n\\ge 4$ and finite time interval $[0,T)~(T < \\infty)$ on which the scalar\ncurvature are uniformly bounded. We prove that if such flow of dimension $4 \\le\nn \\le 7$ has finite time singularities, then every blow-up sequence of a\nlocally Type I singularity has certain property. Here, locally Type I\nsingularity is what Buzano and Di-Matteo defined.\n"}
+{"abstract": "  Abstract Contextuality is a property of systems of random variables. The\nidentity of a random variable in a system is determined by its joint\ndistribution with all other random variables in the same context. When context\nchanges, a variable measuring some property is instantly replaced by another\nrandom variable measuring the same property, or instantly disappears if this\nproperty is not measured in the new context. This replacement/disappearance\nrequires no action, signaling, or disturbance, although it does not exclude\nthem. The difference between two random variables measuring the same property\nin different contexts is measured by their maximal coupling, and the system is\nnoncontextual if one of its overall couplings has these maximal couplings as\nits marginals.\n"}
+{"abstract": "  Cued Speech (CS) is a visual communication system for the deaf or hearing\nimpaired people. It combines lip movements with hand cues to obtain a complete\nphonetic repertoire. Current deep learning based methods on automatic CS\nrecognition suffer from a common problem, which is the data scarcity. Until\nnow, there are only two public single speaker datasets for French (238\nsentences) and British English (97 sentences). In this work, we propose a\ncross-modal knowledge distillation method with teacher-student structure, which\ntransfers audio speech information to CS to overcome the limited data problem.\nFirstly, we pretrain a teacher model for CS recognition with a large amount of\nopen source audio speech data, and simultaneously pretrain the feature\nextractors for lips and hands using CS data. Then, we distill the knowledge\nfrom teacher model to the student model with frame-level and sequence-level\ndistillation strategies. Importantly, for frame-level, we exploit multi-task\nlearning to weigh losses automatically, to obtain the balance coefficient.\nBesides, we establish a five-speaker British English CS dataset for the first\ntime. The proposed method is evaluated on French and British English CS\ndatasets, showing superior CS recognition performance to the state-of-the-art\n(SOTA) by a large margin.\n"}
+{"abstract": "  Researchers and practitioners increasingly consider a human-centered\nperspective in the design of machine learning-based applications, especially in\nthe context of Explainable Artificial Intelligence (XAI). However, clear\nmethodological guidance in this context is still missing because each new\nsituation seems to require a new setup, which also creates different\nmethodological challenges. Existing case study collections in XAI inspired us;\ntherefore, we propose a similar collection of case studies for human-centered\nXAI that can provide methodological guidance or inspiration for others. We want\nto showcase our idea in this workshop by describing three case studies from our\nresearch. These case studies are selected to highlight how apparently small\ndifferences require a different set of methods and considerations. With this\nworkshop contribution, we would like to engage in a discussion on how such a\ncollection of case studies can provide a methodological guidance and critical\nreflection.\n"}
+{"abstract": "  Quantum annealing solves combinatorial optimization problems by finding the\nenergetic ground states of an embedded Hamiltonian. However, quantum annealing\ndynamics under the embedded Hamiltonian may violate the principles of adiabatic\nevolution and generate excitations that correspond to errors in the computed\nsolution. Here we empirically benchmark the probability of chain breaks and\nidentify sweet spots for solving a suite of embedded Hamiltonians. We further\ncorrelate the physical location of chain breaks in the quantum annealing\nhardware with the underlying embedding technique and use these localized rates\nin a tailored post-processing strategies. Our results demonstrate how to use\ncharacterization of the quantum annealing hardware to tune the embedded\nHamiltonian and remove computational errors.\n"}
+{"abstract": "  The dynamics of an open quantum system with balanced gain and loss is not\ndescribed by a PT-symmetric Hamiltonian but rather by Lindblad operators.\nNevertheless the phenomenon of PT-symmetry breaking and the impact of\nexceptional points can be observed in the Lindbladean dynamics. Here we briefly\nreview the development of PT symmetry in quantum mechanics, and the\ncharacterisation of PT-symmetry breaking in open quantum systems in terms of\nthe behaviour of the speed of evolution of the state.\n"}
+{"abstract": "  Let X and Y be oriented topological manifolds of dimension n + 2, and let K\nand J be connected, locally-flat, oriented, n-dimensional submanifolds of X and\nY. We show that up to orientation preserving homeomorphism there is a\nwell-defined connected sum K # J in X # Y. For n = 1, the proof is classical,\nrelying on results of Rado and Moise. For dimensions n = 3 and n > 5, results\nof Edwards-Kirby, Kirby, and Kirby-Siebenmann concerning higher dimensional\ntopological manifolds are required. For n = 2, 4, and 5, Freedman and Quinn's\nwork on topological four-manifolds is needed. The truth of the corresponding\nstatement for higher codimension seems to be unknown.\n"}
+{"abstract": "  Chiral superconductors are expected to carry a spontaneous, chiral and\nperpetual current along the sample edge. However, despite the availability of\nseveral candidate materials, such a current has not been observed in\nexperiments. In this article, we suggest an alternative probe in the form of\nimpurity-induced chiral currents. We first demonstrate that a single\nnon-magnetic impurity induces an encircling chiral current. Its direction\ndepends on the chirality of the order parameter and the sign of the impurity\npotential. Building on this observation, we consider the case of multiple\nimpurities, e.g., realized as adatoms deposited on the surface of a candidate\nchiral superconductor. We contrast the response that is obtained in two cases:\n(a) when the impurities are all identical in sign and (b) when the impurities\nhave mixed positive and negative signs. The former leads to coherent currents\nwithin the sample, arising from the fusion of individual current loops. The\nlatter produces loops of random chirality that lead to incoherent local\ncurrents. These two scenarios can be distinguished by measuring the induced\nmagnetic field using recent probes such as diamond NV centres. We argue that\nimpurity-induced currents may be easier to observe than edge currents, as they\ncan be tuned by varying impurity strength and concentration. We demonstrate\nthese results using a toy model for $p_x \\pm i p_y$ superconductivity on a\nsquare lattice. We develop an improved scheme for Bogoliubov deGennes (BdG)\nsimulations where both the order parameter as well as the magnetic field are\ndetermined self-consistently.\n"}
+{"abstract": "  In this paper we are interested in positive classical solutions of\n\\begin{equation} \\label{eqx} \\left\\{\\begin{array}{ll} -\\Delta u = a(x) u^{p-1}\n& \\mbox{ in } \\Omega, \\\\ u>0 & \\mbox{ in } \\Omega, \\\\ u= 0 & \\mbox{ on } \\pOm,\n\\end {array}\\right. \\end{equation} where $\\Omega$ is a bounded annular domain\n(not necessarily an annulus) in $\\IR^N$ $(N \\ge3)$\n  and $ a(x)$ is a nonnegative continuous function. We show the existence of a\nclassical positive solution for a range of supercritical values of $p$ when the\nproblem enjoys certain mild symmetry and monotonicity conditions. As a\nconsequence of our results, we shall show that (\\ref{eqx}) has\n$\\Bigl\\lfloor\\frac{N}{2} \\Bigr\\rfloor$ (the floor of $\\frac{N}{2}$) positive\nnonradial solutions when $ a(x)=1$ and $\\Omega$ is an annulus with certain\nassumptions on the radii. We also obtain the existence of positive solutions in\nthe case of toroidal domains. Our approach is based on a new variational\nprinciple that allows one to deal with supercritical problems variationally by\nlimiting the corresponding functional on a proper convex subset instead of the\nwhole space at the expense of a mild invariance property.\n"}
+{"abstract": "  The L-subshell ionization mechanism is studied in an ultra-thin osmium target\nbombarded by 4-6 MeV/u fluorine ions. Multiple ionization effects in the\ncollisions are considered through the change of fluorescence and Coster-Kronig\nyields while determining L-subshell ionization cross sections from L-line x-ray\nproduction cross sections. The L-subshell ionization, as well as L-shell x-ray\nproduction cross sections so obtained, are compared with various theoretical\napproximations. The Coulomb direct ionization contributions is studied by (i)\nthe relativistic semi-classical approximations (RSCA), (ii) the shellwise local\nplasma approximation (SLPA), and (iii) the ECUSAR theory, along with the\ninclusion of the vacancy sharing among the subshells by the coupled-states\nmodel (CSM) and the electron capture (EC) by a standard formalism. We find that\nthe ECUSAR-CSM-EC describes the measured excitation function curves the best.\nHowever, the theoretical calculations are still about a factor of two smaller\nthan the measured values. Such differences are resolved by re-evaluating the\nfluorescence and the Coster-Kronig yields. This work demonstrates that, in the\npresent energy range, the heavy-ion induced inner-shell ionization of heavy\natoms can be understood by combining the basic mechanisms of the direct Coulomb\nionization, the electron capture, the multiple ionization, and the vacancy\nsharing among subshells, together with optimized atomic parameters.\n"}
+{"abstract": "  The theory of path homology for digraphs was developed by Alexander\nGrigor'yan, Yong Lin, Yuri Muranov, and Shing-Tung Yau. In this paper, we\ngeneralize the path homology for digraphs. We prove that for any digraph $G$,\nany $t\\geq 0$, any $0\\leq q\\leq 2t$, and any $(2t+1)$-dimensional element\n$\\alpha$ in the differential algebra on the set of the vertices, we always have\nan $(\\alpha,q)$-path homology for $G$. In particular, if $t=0$, then the\n$(\\alpha,0)$-path homology gives the weighted path homology for vertex-weighted\ndigraphs.\n"}
+{"abstract": "  Cross-validation is a well-known and widely used bandwidth selection method\nin nonparametric regression estimation. However, this technique has two\nremarkable drawbacks: (i) the large variability of the selected bandwidths, and\n(ii) the inability to provide results in a reasonable time for very large\nsample sizes. To overcome these problems, bagging cross-validation bandwidths\nare analyzed in this paper. This approach consists in computing the\ncross-validation bandwidths for a finite number of subsamples and then\nrescaling the averaged smoothing parameters to the original sample size. Under\na random-design regression model, asymptotic expressions up to a second-order\nfor the bias and variance of the leave-one-out cross-validation bandwidth for\nthe Nadaraya--Watson estimator are obtained. Subsequently, the asymptotic bias\nand variance and the limit distribution are derived for the bagged\ncross-validation selector. Suitable choices of the number of subsamples and the\nsubsample size lead to an $n^{-1/2}$ rate for the convergence in distribution\nof the bagging cross-validation selector, outperforming the rate $n^{-3/10}$ of\nleave-one-out cross-validation. Several simulations and an illustration on a\nreal dataset related to the COVID-19 pandemic show the behavior of our proposal\nand its better performance, in terms of statistical efficiency and computing\ntime, when compared to leave-one-out cross-validation.\n"}
+{"abstract": "  In this study, we focus on identifying solution and an unknown\nspace-dependent coefficient in a space-time fractional differential equation by\nemploying fractional Taylor series method. The substantial advantage of this\nmethod is that we don't take any over-measured data into account. Consequently,\nwe determine the solution and unknown coefficient more precisely. The presented\nexamples illustrate that outcomes of this method are in high agreement with the\nexact ones of the corresponding problem. Moreover, it can be implemented and\napplied effectively comparing with other methods.\n"}
+{"abstract": "  The IceCube collaboration relies on GPU compute for many of its needs,\nincluding ray tracing simulation and machine learning activities. GPUs are\nhowever still a relatively scarce commodity in the scientific resource provider\ncommunity, so we expanded the available resource pool with GPUs provisioned\nfrom the commercial Cloud providers. The provisioned resources were fully\nintegrated into the normal IceCube workload management system through the Open\nScience Grid (OSG) infrastructure and used CloudBank for budget management. The\nresult was an approximate doubling of GPU wall hours used by IceCube over a\nperiod of 2 weeks, adding over 3.1 fp32 EFLOP hours for a price tag of about\n$58k. This paper describes the setup used and the operational experience.\n"}
+{"abstract": "  Natural Language Processing (NLP) relies heavily on training data.\nTransformers, as they have gotten bigger, have required massive amounts of\ntraining data. To satisfy this requirement, text augmentation should be looked\nat as a way to expand your current dataset and to generalize your models. One\ntext augmentation we will look at is translation augmentation. We take an\nEnglish sentence and translate it to another language before translating it\nback to English. In this paper, we look at the effect of 108 different language\nback translations on various metrics and text embeddings.\n"}
+{"abstract": "  Hand pose estimation is a crucial part of a wide range of augmented reality\nand human-computer interaction applications. Predicting the 3D hand pose from a\nsingle RGB image is challenging due to occlusion and depth ambiguities.\nGCN-based (Graph Convolutional Networks) methods exploit the structural\nrelationship similarity between graphs and hand joints to model kinematic\ndependencies between joints. These techniques use predefined or globally\nlearned joint relationships, which may fail to capture pose-dependent\nconstraints. To address this problem, we propose a two-stage GCN-based\nframework that learns per-pose relationship constraints. Specifically, the\nfirst phase quantizes the 2D/3D space to classify the joints into 2D/3D blocks\nbased on their locality. This spatial dependency information guides this phase\nto estimate reliable 2D and 3D poses. The second stage further improves the 3D\nestimation through a GCN-based module that uses an adaptative nearest neighbor\nalgorithm to determine joint relationships. Extensive experiments show that our\nmulti-stage GCN approach yields an efficient model that produces accurate 2D/3D\nhand poses and outperforms the state-of-the-art on two public datasets.\n"}
+{"abstract": "  In many-body quantum systems with spatially local interactions, quantum\ninformation propagates with a finite velocity, reminiscent of the ``light cone\"\nof relativity. In systems with long-range interactions which decay with\ndistance $r$ as $1/r^\\alpha$, however, there are multiple light cones which\ncontrol different information theoretic tasks. We show an optimal (up to\nlogarithms) ``Frobenius light cone\" obeying $t\\sim r^{\\min(\\alpha-1,1)}$ for\n$\\alpha>1$ in one-dimensional power-law interacting systems with finite local\ndimension: this controls, among other physical properties, the butterfly\nvelocity characterizing many-body chaos and operator growth. We construct an\nexplicit random Hamiltonian protocol that saturates the bound and settles the\noptimal Frobenius light cone in one dimension. We partially extend our\nconstraints on the Frobenius light cone to a several operator $p$-norms, and\nshow that Lieb-Robinson bounds can be saturated in at most an exponentially\nsmall $e^{-\\Omega(r)}$ fraction of the many-body Hilbert space.\n"}
+{"abstract": "  Hydrodynamic problems with stagnation points are of particular importance in\nfluid mechanics as they allow study and investigation of elongational flows. In\nthis article, the uniaxial elongational flow appearing at the surface of a\nviscoelastic drop and its role on the deformation of the droplet at low\ninertial regimes is studied. In studies related to viscoelastic droplets\nfalling/raising in an immiscible Newtonian fluids, it is well known that by\nincreasing the Deborah number (the ratio of the relaxation time of the interior\nfluid to a reference time scale) the droplet might lose its sphericity and\nobtain a dimple at the rear end. In this work, the drop deformation is\ninvestigated in detail to study the reason behind this transformation. We will\nshow that as the contribution of elastic and inertial forces are increased, the\nstagnation points at the rear and front sides of the droplet are expanded to\ncreate a region of elongational dominated flows. At this stage, due to a\ncombined effect of the shear thickening behavior of the elongational viscosity\nin viscoelastic fluids and the contribution of the inertial force, the interior\nphase is squeezed and consequently the droplet finds a shape similar to an\noblate. As these non-linear forces are increased further, an additional\ncircular stagnation line appears on the droplet surface in the external field,\npulling the droplet surface outward and therefore creating a dimple shape at\nthe rear end. Furthermore, the influence of inertia and viscoelastic properties\nare also studied on the motion, the drag coefficient and terminal velocity of\ndrops.\n"}
+{"abstract": "  We prove sharp $L^p$ regularity results for a class of generalized Radon\ntransforms for families of curves in a three-dimensional manifold associated to\na canonical relation with fold and blowdown singularities. The proof relies on\ndecoupling inequalities by Wolff and Bourgain-Demeter for plate decompositions\nof thin neighborhoods of cones and $L^2$ estimates for related oscillatory\nintegrals.\n"}
+{"abstract": "  Due to the limit of mesh density, the improvement of the spatial precision of\nnumerical computation always leads to a decrease in computing efficiency.\nAiming at this inability of numerical computation, we propose a novel method\nfor boosting the mesh density in numerical computation within the 2D domain.\nBased on the low mesh-density stress field in the 2D plane strain problem\ncomputed by the finite element method, this method utilizes a deep neural\nnetwork named SuperMeshingNet to learn the non-linear mapping from low\nmesh-density to high mesh-density stress field, and realizes the improvement of\nnumerical computation accuracy and efficiency simultaneously. We adopt residual\ndense blocks to our mesh-density boost model called SuperMeshingNet for\nextracting abundant local features and enhancing the prediction capacity of the\nmodel. Experimental results show that the SuperMeshingNet proposed in this work\ncan effectively boost the spatial resolution of the stress field under the\nmultiple scaling factors: 2X, 4X, 8X. Compared to the results of the finite\nelement method, the predicted stress field error of SuperMeshingNet is only\n0.54%, which is within the acceptable range of stress field estimation, and the\nSuperMeshingNet predicts the maximum stress value also without significant\naccuracy loss. We publicly share our work with full detail of implementation at\nhttps://github.com/zhenguonie/2021_SuperMeshing_2D_Plane_Strain.\n"}
+{"abstract": "  We propose a segmentation-based bounding box generation method for\nomnidirectional pedestrian detection that enables detectors to tightly fit\nbounding boxes to pedestrians without omnidirectional images for training. Due\nto the wide angle of view, omnidirectional cameras are more cost-effective than\nstandard cameras and hence suitable for large-scale monitoring. The problem of\nusing omnidirectional cameras for pedestrian detection is that the performance\nof standard pedestrian detectors is likely to be substantially degraded because\npedestrians' appearance in omnidirectional images may be rotated to any angle.\nExisting methods mitigate this issue by transforming images during inference.\nHowever, the transformation substantially degrades the detection accuracy and\nspeed. A recently proposed method obviates the transformation by training\ndetectors with omnidirectional images, which instead incurs huge annotation\ncosts. To obviate both the transformation and annotation works, we leverage an\nexisting large-scale object detection dataset. We train a detector with rotated\nimages and tightly fitted bounding box annotations generated from the\nsegmentation annotations in the dataset, resulting in detecting pedestrians in\nomnidirectional images with tightly fitted bounding boxes. We also develop\npseudo-fisheye distortion augmentation, which further enhances the performance.\nExtensive analysis shows that our detector successfully fits bounding boxes to\npedestrians and demonstrates substantial performance improvement.\n"}
+{"abstract": "  Sentinel-1 is a synthetic aperture radar (SAR) platform with an operational\nmode called extra wide (EW) that offers large regions of ocean areas to be\nobserved. A major issue with EW images is that the cross-polarized HV and VH\nchannels have prominent additive noise patterns relative to low backscatter\nintensity, which disrupts tasks that require manual or automated\ninterpretation. The European Space Agency (ESA) provides a method for removing\nthe additive noise pattern by means of lookup tables, but applying them\ndirectly produces unsatisfactory results because characteristics of the noise\nstill remain. Furthermore, evidence suggests that the magnitude of the additive\nnoise dynamically depends on factors that are not considered by the ESA\nestimated noise field.\n  To address these issues we propose a quadratic objective function to model\nthe mis-scale of the provided noise field on an image. We consider a linear\ndenoising model that re-scales the noise field for each subswath, whose\nparameters are found from a least-squares solution over the objective function.\nThis method greatly reduces the presence of additive noise while not requiring\na set of training images, is robust to heterogeneity in images, dynamically\nestimates parameters for each image, and finds parameters using a closed-form\nsolution.\n  Two experiments were performed to validate the proposed method. The first\nexperiment simulated noise removal on a set of RADARSAT-2 images with noise\nfields artificially imposed on them. The second experiment conducted noise\nremoval on a set of Sentinel-1 images taken over the five oceans. Afterwards,\nquality of the noise removal was evaluated based on the appearance of\nopen-water. The two experiments indicate that the proposed method marks an\nimprovement both visually and through numerical measures.\n"}
+{"abstract": "  We present a new overview of the life of very massive stars (VMS) in terms of\nneutrino emission from thermal processes: pair annihilation, plasmon decay,\nphotoneutrino process, bremsstrahlung and recombination processes in burning\nstages of selected VMS models. We use the realistic conditions of temperature,\ndensity, electron fraction and nuclear isotropic composition of the VMS.\nResults are presented for a set of progenitor stars with mass of 150, 200 and\n300 M$_\\odot$ Z=0.002 and 500 M$_\\odot$ Z=0.006 rotating models which are\nexpected to explode as a pair instability supernova at the end of their life\nexcept the 300 M$_\\odot$ would end up as a black hole. It is found that for\nVMS, thermal neutrino emission occurs as early as towards the end of hydrogen\nburning stage due to the high initial temperature and density of these VMS. We\ncalculate the total neutrino emissivity, $Q_\\nu$ and luminosity, $L_\\nu$ using\nthe structure profile of each burning stages of the models and observed the\ncontribution of photoneutrino at early burning stages (H and He) and pair\nannihilation at the advanced stages. Pair annihilation and photoneutrino\nprocesses are the most dominant neutrino energy loss mechanisms throughout the\nevolutionary track of the VMS. At the O-burning stage, the neutrino luminosity\n$\\sim 10^{47-48}$ erg/s depending on their initial mass and metallicity are\nslightly higher than the neutrino luminosity from massive stars. This could\nshed light on the possibility of using detection of neutrinos to locate the\ncandidates for pair instability supernova in our local universe.\n"}
+{"abstract": "  Noisy labels are very common in deep supervised learning. Although many\nstudies tend to improve the robustness of deep training for noisy labels, rare\nworks focus on theoretically explaining the training behaviors of learning with\nnoisily labeled data, which is a fundamental principle in understanding its\ngeneralization. In this draft, we study its two phenomena, clean data first and\nphase transition, by explaining them from a theoretical viewpoint.\nSpecifically, we first show that in the first epoch training, the examples with\nclean labels will be learned first. We then show that after the learning from\nclean data stage, continuously training model can achieve further improvement\nin testing error when the rate of corrupted class labels is smaller than a\ncertain threshold; otherwise, extensively training could lead to an increasing\ntesting error.\n"}
+{"abstract": "  This study uses the semantic brand score, a novel measure of brand importance\nin big textual data, to forecast elections based on online news. About 35,000\nonline news articles were transformed into networks of co-occurring words and\nanalyzed by combining methods and tools from social network analysis and text\nmining. Forecasts made for four voting events in Italy provided consistent\nresults across different voting systems: a general election, a referendum, and\na municipal election in two rounds. This work contributes to the research on\nelectoral forecasting by focusing on predictions based on online big data; it\noffers new perspectives regarding the textual analysis of online news through a\nmethodology which is relatively fast and easy to apply. This study also\nsuggests the existence of a link between the brand importance of political\ncandidates and parties and electoral results.\n"}
+{"abstract": "  We study minimal $\\mathbb{Z}^d$-Cantor systems and the relationship between\ntheir speedups, their collections of invariant Borel measures, their associated\nunital dimension groups, and their orbit equivalence classes. In the particular\ncase of minimal $\\mathbb{Z}^d$-odometers, we show that their bounded speedups\nmust again be odometers but, contrary to the 1-dimensional case, they need not\nbe conjugate, or even isomorphic, to the original.\n"}
+{"abstract": "  The non-Hermitian skin effect (NHSE) in non-Hermitian lattice systems depicts\nthe exponential localization of eigenstates at system's boundaries. It has led\nto a number of counter-intuitive phenomena and challenged our understanding of\nbulk-boundary correspondence in topological systems. This work aims to\ninvestigate how the NHSE localization and topological localization of in-gap\nedge states compete with each other, with several representative static and\nperiodically driven 1D models, whose topological properties are protected by\ndifferent symmetries. The emerging insight is that at critical system\nparameters, even topologically protected edge states can be perfectly\ndelocalized. In particular, it is discovered that this intriguing\ndelocalization occurs if the real spectrum of the system's edge states falls on\nthe same system's complex spectral loop obtained under the periodic boundary\ncondition. We have also performed sample numerical simulation to show that such\ndelocalized topological edge states can be safely reconstructed from\ntime-evolving states. Possible applications of delocalized topological edge\nstates are also briefly discussed.\n"}
+{"abstract": "  The equilibration of sinusoidally modulated distribution of the kinetic\ntemperature is analyzed in the $\\beta$-Fermi-Pasta-Ulam-Tsingou chain with\ndifferent degrees of nonlinearity and for different wavelengths of temperature\nmodulation. Two different types of initial conditions are used to show that\neither one gives the same result as the number of realizations increases and\nthat the initial conditions that are closer to the state of thermal equilibrium\ngive faster convergence. The kinetics of temperature equilibration is monitored\nand compared to the analytical solution available for the linear chain in the\ncontinuum limit. The transition from ballistic to diffusive thermal\nconductivity with an increase in the degree of anharmonicity is shown. In the\nballistic case, the energy equilibration has an oscillatory character with an\namplitude decreasing in time, and in the diffusive case, it is monotonous in\ntime. For smaller wavelength of temperature modulation, the oscillatory\ncharacter of temperature equilibration remains for a larger degree of\nanharmonicity. For a given wavelength of temperature modulation, there is such\na value of the anharmonicity parameter at which the temperature equilibration\noccurs most rapidly.\n"}
+{"abstract": "  PSR B1259-63 is a gamma-ray binary system hosting a radio pulsar orbiting\naround a O9.5Ve star, LS 2883, with a period of ~3.4 years. The interaction of\nthe pulsar wind with the LS 2883 outflow leads to unpulsed broadband emission\nin the radio, X-ray, GeV, and TeV domains. One of the most unusual features of\nthe system is an outburst at GeV energies around the periastron, during which\nthe energy release substantially exceeds the spin down luminosity under the\nassumption of the isotropic energy release. In this paper, we present the first\nresults of a recent multi-wavelength campaign (radio, optical, and X-ray bands)\naccompanied by the analysis of publicly available GeV Fermi/LAT data. The\ncampaign covered a period of more than 100 days around the 2021 periastron and\nrevealed substantial differences from previously observed passages. We report a\nmajor delay of the GeV flare, weaker X-ray flux during the peaks, which are\ntypically attributed to the times when the pulsar crosses the disk, and the\nappearance of a third X-ray peak never observed before. We argue that these\nfeatures are consistent with the emission cone model of Chernyakova et al\n(2020) in the case of a sparser and clumpier disk of the Be star.\n"}
+{"abstract": "  Social media such as Twitter, Facebook, etc. has led to a generated growing\nnumber of comments that contains users opinions. Sentiment analysis research\ndeals with these comments to extract opinions which are positive or negative.\nArabic language is a rich morphological language; thus, classical techniques of\nEnglish sentiment analysis cannot be used for Arabic. Word embedding technique\ncan be considered as one of successful methods to gaping the morphological\nproblem of Arabic. Many works have been done for Arabic sentiment analysis\nbased on word embedding, but there is no study focused on variable parameters.\nThis study will discuss three parameters (Window size, Dimension of vector and\nNegative Sample) for Arabic sentiment analysis using DBOW and DMPV\narchitectures. A large corpus of previous works generated to learn word\nrepresentations and extract features. Four binary classifiers (Logistic\nRegression, Decision Tree, Support Vector Machine and Naive Bayes) are used to\ndetect sentiment. The performance of classifiers evaluated based on; Precision,\nRecall and F1-score.\n"}
+{"abstract": "  Quasiconformal maps are homeomorphisms with useful local distortion\ninequalities; infinitesimally, they map balls to ellipsoids with bounded\neccentricity. This leads to a number of useful regularity properties, including\nquantitative H\\\"older continuity estimates; on the other hand, one can use the\nradial stretches to characterize the extremizers for H\\\"older continuity. In\nthis work, given any bounded countable set in $\\mathbb{R}^d$, we will construct\nan example of a $K$-quasiconformal map which exhibits the maximum stretching at\neach point of the set. This will provide an example of a quasiconformal map\nthat exhibits the worst-case regularity on a surprisingly large set, and\ngeneralizes constructions from the planar setting into $\\mathbb{R}^d$.\n"}
+{"abstract": "  Quantum communication networks are emerging as a promising technology that\ncould constitute a key building block in future communication networks in the\n6G era and beyond. These networks have an inherent feature of parallelism that\nallows them to boost the capacity and enhance the security of communication\nsystems. Recent advances led to the deployment of small- and large-scale\nquantum communication networks with real quantum hardware. In quantum networks,\nentanglement is a key resource that allows for data transmission between\ndifferent nodes. However, to reap the benefits of entanglement and enable\nefficient quantum communication, the number of generated entangled pairs must\nbe optimized. Indeed, if the entanglement generation rates are not optimized,\nthen some of these valuable resources will be discarded and lost. In this\npaper, the problem of optimizing the entanglement generation rates and their\ndistribution over a quantum memory is studied. In particular, a quantum network\nin which users have heterogeneous distances and applications is considered.\nThis problem is posed as a mixed integer nonlinear programming optimization\nproblem whose goal is to efficiently utilize the available quantum memory by\ndistributing the quantum entangled pairs in a way that maximizes the user\nsatisfaction. An interior point optimization method is used to solve the\noptimization problem and extensive simulations are conducted to evaluate the\neffectiveness of the proposed system. Simulation results show the key design\nconsiderations for efficient quantum networks, and the effect of different\nnetwork parameters on the network performance.\n"}
+{"abstract": "  We present new X-ray and UV observations of the Wolf-Rayet + black hole\nbinary system NGC 300 X-1 with the Chandra X-ray Observatory and the Hubble\nSpace Telescope Cosmic Origins Spectrograph. When combined with archival X-ray\nobservations, our X-ray and UV observations sample the entire binary orbit,\nproviding clues to the system geometry and interaction between the black hole\naccretion disk and the donor star wind. We measure a binary orbital period of\n32.7921$\\pm$0.0003 hr, in agreement with previous studies, and perform\nphase-resolved spectroscopy using the X-ray data. The X-ray light curve reveals\na deep eclipse, consistent with inclination angles of $i=60-75^{\\circ}$, and a\npre-eclipse excess consistent with an accretion stream impacting the disk edge.\nWe further measure radial velocity variations for several prominent FUV\nspectral lines, most notably He II $\\lambda$1640 and C IV $\\lambda$1550. We\nfind that the He II emission lines systematically lag the expected Wolf-Rayet\nstar orbital motion by a phase difference $\\Delta \\phi\\sim0.3$, while C IV\n$\\lambda$1550 matches the phase of the anticipated radial velocity curve of the\nWolf-Rayet donor. We assume the C IV $\\lambda$1550 emission line follows a\nsinusoidal radial velocity curve (semi-amplitude = 250 km s$^{-1}$) and infer a\nBH mass of 17$\\pm$4 M$_{\\odot}$. Our observations are consistent with the\npresence of a wind-Roche lobe overflow accretion disk, where an accretion\nstream forms from gravitationally focused wind material and impacts the edge of\nthe black hole accretion disk.\n"}
+{"abstract": "  We provide a unified interpretation of both paramagnon and plasmon modes in\nhigh-$T_c$ copper-oxides, and verify it quantitatively against available\nresonant inelastic $x$-ray scattering (RIXS) data across the hole-doped phase\ndiagram. Three-dimensional extended Hubbard model, with included long-range\nCoulomb interactions and doping-independent microscopic parameters for both\nclasses of quantum fluctuations, is used. Collective modes are studied using\nVWF+$1/\\mathcal{N}_f$ approach which extends variational wave function (VWF)\nscheme by means of an expansion in inverse number of fermionic flavors\n($1/\\mathcal{N}_f$). We show that intense paramagnons persist along the\nanti-nodal line from the underdoped to overdoped regime and undergo rapid\noverdamping in the nodal direction. Plasmons exhibit a three-dimensional\ncharacter, with minimal energy corresponding to anti-phase oscillations on\nneighboring $\\mathrm{CuO_2}$ planes. The theoretical spin- and charge\nexcitation energies reproduce semi-quantitatively RIXS data for $\\mathrm{(Bi,\nPb)_2 (Sr, La)_2 CuO_{6+\\delta}}$. The present VWF+$1/\\mathcal{N}_f$ analysis\nof dynamics and former VWF results for static quantities combine into a\nconsistent description of the principal properties of hole-doped high-$T_c$\ncuprates as strongly correlated systems.\n"}
+{"abstract": "  The atmospheric neutrino flux includes a component from the prompt decay of\ncharmed hadrons that becomes significant only at $E\\ge 10$ TeV. At these\nenergies, however, the diffuse flux of cosmic neutrinos discovered by IceCube\nseems to be larger than the atmospheric one. Here we study the possibility to\ndetect a neutrino interaction in down-going atmospheric events at km$^3$\ntelescopes. The neutrino signal will always appear together with a muon bundle\nthat reveals its atmospheric origin and, generically, it implies an increase in\nthe detector activity with the slant depth. We propose a simple algorithm that\ncould separate these events from regular muon bundles.\n"}
+{"abstract": "  We compute the motive of the variety of representations of the torus knot of\ntype (m,n) into the affine groups $AGL_1$ and $AGL_2$ for an arbitrary field\n$k$. In the case that $k = F_q$ is a finite field this gives rise to the count\nof the number of points of the representation variety, while for $k = C$ this\ncalculation returns the E-polynomial of the representation variety. We discuss\nthe interplay between these two results in sight of Katz theorem that relates\nthe point count polynomial with the E-polynomial. In particular, we shall show\nthat several point count polynomials exist for these representation varieties,\ndepending on the arithmetic between m,n and the characteristic of the field,\nwhereas only one of them agrees with the actual E-polynomial.\n"}
+{"abstract": "  We study {\\alpha}-attractor models with both E-model and T-model potential in\nan extended Non-Minimal Derivative (NMD) inflation where a canonical scalar\nfield and its derivatives are non-minimally coupled to gravity. We calculate\nthe evolution of perturbations during this regime. Then by adopting inflation\npotentials of the model we show that in the large N and small {\\alpha} limit,\nvalue of the scalar spectral index ns and tensor to scalar ratio r are\nuniversal. Next, we study reheating after inflation in this formalism. We\nobtain some constraints on the model's parameter space by adopting the results\nwith Planck 2018.\n"}
+{"abstract": "  We study the Nash Social Welfare problem: Given $n$ agents with valuation\nfunctions $v_i:2^{[m]} \\rightarrow {\\mathbb R}$, partition $[m]$ into\n$S_1,\\ldots,S_n$ so as to maximize $(\\prod_{i=1}^{n} v_i(S_i))^{1/n}$. The\nproblem has been shown to admit a constant-factor approximation for additive,\nbudget-additive, and piecewise linear concave separable valuations; the case of\nsubmodular valuations is open.\n  We provide a $\\frac{1}{e} (1-\\frac{1}{e})^2$-approximation of the {\\em\noptimal value} for several classes of submodular valuations: coverage, sums of\nmatroid rank functions, and certain matching-based valuations.\n"}
+{"abstract": "  This note extends a recently proposed algorithm for model identification and\nrobust MPC of asymptotically stable, linear time-invariant systems subject to\nprocess and measurement disturbances. Independent output predictors for\ndifferent steps ahead are estimated with Set Membership methods. It is here\nshown that the corresponding prediction error bounds are the least conservative\nin the considered model class. Then, a new multi-rate robust MPC algorithm is\ndeveloped, employing said multi-step predictors to robustly enforce constraints\nand stability against disturbances and model uncertainty, and to reduce\nconservativeness. A simulation example illustrates the effectiveness of the\napproach.\n"}
+{"abstract": "  Let $\\mathfrak q$ be a Lie algebra over a field $\\mathbb K$ and $p,\\tilde\np\\in\\mathbb K[t]$ two different normalised polynomials of degree at least 2. As\nvector spaces both quotient Lie algebras $\\mathfrak q[t]/(p)$ and $\\mathfrak\nq[t]/(\\tilde p)$ can be identified with $W=\\mathfrak q{\\cdot}1\\oplus\\mathfrak\nq\\bar t\\oplus\\ldots\\oplus\\mathfrak q\\bar t^{n-1}$. If $\\mathrm{deg}\\,(p-\\tilde\np)$ is at most 1, then the Lie brackets $[\\,\\,,\\,]_p$, $[\\,\\,,\\,]_{\\tilde p}$\ninduced on $W$ by $p$ and $\\tilde p$, respectively, are compatible. By a\ngeneral method, known as the Lenard-Magri scheme, we construct a subalgebra\n$Z=Z(p,\\tilde p)\\subset {\\mathcal S}(W)^{\\mathfrak q{\\cdot}1}$ such that\n$\\{Z,Z\\}_p=\\{Z,Z\\}_{\\tilde p}=0$. If ${\\mathrm{tr.deg\\,}}{\\mathcal S}(\\mathfrak\nq)^{\\mathfrak q}=\\mathrm{ind}\\,\\mathfrak q$ and $\\mathfrak q$ has the codim-$2$\nproperty, then ${\\mathrm{tr.deg\\,}} Z$ takes the maximal possible value, which\nis $((n-1)\\dim\\mathfrak q)/2+((n+1)\\mathrm{ind}\\,\\mathfrak q)/2$. If $\\mathfrak\nq=\\mathfrak g$ is semisimple, then $Z$ contains the Hamiltonians of a suitably\nchosen Gaudin model. Therefore, in a non-reductive case, we obtain a completely\nintegrable generalisation of Gaudin models.\n"}
+{"abstract": "  Interactions in biology and social systems are not restricted to pairwise but\ncan take arbitrary sizes. Extensive studies have revealed that the\narbitrary-sized interactions significantly affect the spreading dynamics on\nnetworked systems. Competing spreading dynamics, i.e., several epidemics spread\nsimultaneously and compete with each other, have been widely observed in the\nreal world, yet the way arbitrary-sized interactions affect competing spreading\ndynamics still lacks systematic study. This study presents a model of two\ncompeting simplicial susceptible-infected-susceptible epidemics on a\nhigher-order system represented by simplicial complex and analyzes the model's\ncritical phenomena. In the proposed model, a susceptible node can only be\ninfected by one of the two epidemics, and the transmission of infection to\nneighbors can occur through pairwise (i.e., an edge) and high-order (e.g.,\n2-simplex) interactions simultaneously. Through a mean-field (MF) theory\nanalysis and numerical simulations, we show that the model displays rich\ndynamical behavior depending on the 2-simplex infection strength. When the\n2-simplex infection strength is weak, the model's phase diagram is consistent\nwith the simple graph, consisting of three regions: the absolute dominant\nregions for each epidemic and the epidemic-free region. With the increase of\nthe 2-simplex infection strength, a new phase region called the alternative\ndominant region emerges. In this region, the survival of one epidemic depends\non the initial conditions. Our theoretical analysis can reasonably predict the\ntime evolution and steady-state outbreak size in each region. In addition, we\nfurther explore the model's phase diagram both when the 2-simplex infection\nstrength is symmetrical and asymmetrical. The results show that the 2-simplex\ninfection strength has a significant impact on the system phase diagram.\n"}
+{"abstract": "  We consider online coordinated precoding design for downlink wireless network\nvirtualization (WNV) in a multi-cell multiple-input multiple-output (MIMO)\nnetwork with imperfect channel state information (CSI). In our WNV framework,\nan infrastructure provider (InP) owns each base station that is shared by\nseveral service providers (SPs) oblivious of each other. The SPs design their\nprecoders as virtualization demands for user services, while the InP designs\nthe actual precoding solution to meet the service demands from the SPs. Our aim\nis to minimize the long-term time-averaged expected precoding deviation over\nMIMO fading channels, subject to both per-cell long-term and short-term\ntransmit power limits. We propose an online coordinated precoding algorithm for\nvirtualization, which provides a fully distributed semi-closed-form precoding\nsolution at each cell, based only on the current imperfect CSI without any CSI\nexchange across cells. Taking into account the two-fold impact of imperfect CSI\non both the InP and the SPs, we show that our proposed algorithm is within an\n$O(\\delta)$ gap from the optimum over any time horizon, where $\\delta$ is a CSI\ninaccuracy indicator. Simulation results validate the performance of our\nproposed algorithm under two commonly used precoding techniques in a typical\nurban micro-cell network environment.\n"}
+{"abstract": "  In 2021, a new track has been initiated in the Challenge for Learned Image\nCompression~: the video track. This category proposes to explore technologies\nfor the compression of short video clips at 1 Mbit/s. This paper proposes to\ngenerate coded videos using the latest standardized video coders, especially\nVersatile Video Coding (VVC). The objective is not only to measure the progress\nmade by learning techniques compared to the state of the art video coders, but\nalso to quantify their progress from years to years. With this in mind, this\npaper documents how to generate the video sequences fulfilling the requirements\nof this challenge, in a reproducible way, targeting the maximum performance for\nVVC.\n"}
+{"abstract": "  The Quantum Fisher Information (QFI) plays a crucial role in quantum\ninformation theory and in many practical applications such as quantum\nmetrology. However, computing the QFI is generally a computationally demanding\ntask. In this work we analyze a lower bound on the QFI which we call the\nsub-Quantum Fisher Information (sub-QFI). The bound can be efficiently\nestimated on a quantum computer for an $n$-qubit state using $2n$ qubits. The\nsub-QFI is based on the super-fidelity, an upper bound on Uhlmann's fidelity.\nWe analyze the sub-QFI in the context of unitary families, where we derive\nseveral crucial properties including its geometrical interpretation. In\nparticular, we prove that the QFI and the sub-QFI are maximized for the same\noptimal state, which implies that the sub-QFI is faithful to the QFI in the\nsense that both quantities share the same global extrema. Based on this\nfaithfulness, the sub-QFI acts as an efficiently computable surrogate for the\nQFI for quantum sensing and quantum metrology applications. Finally, we provide\nadditional meaning to the sub-QFI as a measure of coherence, asymmetry, and\npurity loss.\n"}
+{"abstract": "  We consider the problem of steering a multi-agent system to multi-consensus,\nnamely a regime where groups of agents agree on a given value which may be\ndifferent from group to group. We first address the problem by using\ndistributed proportional controllers that implement additional links in the\nnetwork modeling the communication protocol among agents and introduce a\nprocedure for the optimal selection of them. Both the cases of single\nintegrators and of second-order dynamics are taken into account and the\nstability for the multi-consensus state is studied, ultimately providing\nconditions for the gain of the controllers. We then extend the approach to\ncontrollers that either add or remove links in the original structure, by\npreserving eventually the weak connectedness of the resulting graph.\n"}
+{"abstract": "  Let $\\Gamma$ be a graph, $A$ an abelian group, $\\mathcal{D}$ a given\norientation of $\\Gamma$ and $R$ a unital subring of the endomorphism ring of\n$A$. It is shown that the set of all maps $\\varphi$ from $E(\\Gamma)$ to $A$\nsuch that $(\\mathcal{D},\\varphi)$ is an $A$-flow forms a left $R$-module. Let\n$\\Gamma$ be a union of two subgraphs $\\Gamma_{1}$ and $\\Gamma_{2}$, and $p^n$ a\nprime power. It is proved that $\\Gamma$ admits a nowhere-zero $p^n$-flow if\n$\\Gamma_{1}$ and $\\Gamma_{2}$ have at most $p^n-2$ common edges and both have\nnowhere-zero $p^n$-flows. More important, it is proved that $\\Gamma$ admits a\nnowhere-zero $4$-flow if $\\Gamma_{1}$ and $\\Gamma_{2}$ both have nowhere-zero\n$4$-flows and their common edges induce a connected subgraph of $\\Gamma$ of\nsize at most $3$. This covers a result of Catlin that a graph admits a\nnowhere-zero $4$-flow if it is a union of a $4$-cycle and a subgraph admiting a\nnowhere-zero $4$-flow.\n"}
+{"abstract": "  We study automata-theoretic classes of string-to-string functions whose\noutput may grow faster than linearly in their input. Our central contribution\nis to introduce a new such class, with polynomial growth and three equivalent\ndefinitions: the smallest class containing regular functions and closed under a\n\"composition by substitution\" operation; a restricted variant of pebble\ntransducers; a $\\lambda$-calculus with a linear type system. As their name\nsuggests, these comparison-free polyregular functions form a subclass of\npolyregular functions; we prove that the inclusion is strict. Other properties\nof our new function class that we show are incomparability with HDT0L\ntransductions and closure under composition. Finally, we look at the recently\nintroduced layered streaming string transducers (SSTs), or equivalently\nk-marble transducers. We prove that a function can be obtained by composing\nsuch transducers together if and only if it is polyregular, and that k-layered\nSSTs (or k-marble transducers) are equivalent to a corresponding notion of\n(k+1)-layered HDT0L systems.\n"}
+{"abstract": "  There are many sectors which have moved to Cloud and are planning\naggressively to move their workloads to Cloud since the world entered Covid-19\npandemic. There are various reasons why Cloud is an essential irresistible\ntechnology and serves as an ultimate solution to access IT software and\nsystems. It has become a new essential catalyst for Enterprise Organisations\nwhich are looking for Digital Transformation. Remote working is a common\nphenomenon now across all the IT companies making the services available all\nthe time. Covid-19 has made cloud adoption an immediate priority for\nOrganisation rather than a slowly approached future transformation. The\nbenefits of Cloud lies in the fact that employees rather engineers of an\nenterprise are no more dependent on the closed hardware-based IT infrastructure\nand hence eliminates the necessity of working from the networked office\npremises. This has raised a huge demand for skilled Cloud specialist who can\nmanage and support the systems running on cloud across different regions of the\nworld. In this research, the reasons for growing Cloud adoption after pandemic\nCovid-19 has been described and the challenges which Organization will face is\nalso explained. This study also details the most used cloud services during the\npandemic considering Amazon Web Services as the cloud provider.\n"}
+{"abstract": "  Relationships between people constantly evolve, altering interpersonal\nbehavior and defining social groups. Relationships between nodes in social\nnetworks can be represented by a tie strength, often empirically assessed using\nsurveys. While this is effective for taking static snapshots of relationships,\nsuch methods are difficult to scale to dynamic networks. In this paper, we\npropose a system that allows for the continuous approximation of relationships\nas they evolve over time. We evaluate this system using the NetSense study,\nwhich provides comprehensive communication records of students at the\nUniversity of Notre Dame over the course of four years. These records are\ncomplemented by semesterly ego network surveys, which provide discrete samples\nover time of each participant's true social tie strength with others. We\ndevelop a pair of powerful machine learning models (complemented by a suite of\nbaselines extracted from past works) that learn from these surveys to interpret\nthe communications records as signals. These signals represent dynamic tie\nstrengths, accurately recording the evolution of relationships between the\nindividuals in our social networks. With these evolving tie values, we are able\nto make several empirically derived observations which we compare to past\nworks.\n"}
+{"abstract": "  Mean-variance portfolio decisions that combine prediction and optimisation\nhave been shown to have poor empirical performance. Here, we consider the\nperformance of various shrinkage methods by their efficient frontiers under\ndifferent distributional assumptions to study the impact of reasonable\ndepartures from Normality. Namely, we investigate the impact of first-order\nauto-correlation, second-order auto-correlation, skewness, and excess kurtosis.\nWe show that the shrinkage methods tend to re-scale the sample efficient\nfrontier, which can change based on the nature of local perturbations from\nNormality. This re-scaling implies that the standard approach of comparing\ndecision rules for a fixed level of risk aversion is problematic, and more so\nin a dynamic market setting. Our results suggest that comparing efficient\nfrontiers has serious implications which oppose the prevailing thinking in the\nliterature. Namely, that sample estimators out-perform Stein type estimators of\nthe mean, and that improving the prediction of the covariance has greater\nimportance than improving that of the means.\n"}
+{"abstract": "  Transition metal dichalcogenides (TMDs) are known to support complex\nexcitonic states. Revealing the differences in relaxation dynamics among\ndifferent excitonic species and elucidating the transition dynamics between\nthem may provide important guidelines for designing TMD-based excitonic\ndevices. Combining photoluminescence (PL) and reflectance contrast measurements\nwith ultrafast pump-probe spectroscopy under cryogenic temperatures, we herein\nstudy the relaxation dynamics of neutral and charged excitons in a\nback-gate-controlled monolayer device. Pump-probe results reveal quite\ndifferent relaxation dynamics of excitonic states under different interfacial\nconditions: while neutral excitons experience much longer lifetime than trions\nin monolayer WS2, the opposite is true in the WS2/h-BN heterostructure. It is\nfound that the insertion of h-BN layer between the TMD monolayer and the\nsubstrate has a great influence on the lifetimes of different excitonic states.\nThe h-BN flakes can not only screen the effects of impurities and defects at\nthe interface, but also help establish a non-radiative transition from neutral\nexcitons to trions to be the dominant relaxation pathway, under cryogenic\ntemperature. Our findings highlight the important role interface may play in\ngoverning the transient properties of carriers in 2D semiconductors, and may\nalso have implications for designing light-emitting and photo-detecting devices\nbased on TMDs.\n"}
+{"abstract": "  Submillimeter/millimeter observations of dusty star-forming galaxies with the\nAtacama Large Millimeter/submillimeter Array (ALMA) have shown that dust\ncontinuum emission generally occurs in compact regions smaller than the stellar\ndistribution. However, it remains to be understood how systematic these\nfindings are. Studies often lack homogeneity in the sample selection, target\ndiscontinuous areas with inhomogeneous sensitivities, and suffer from modest\n$uv$ coverage coming from single array configurations. GOODS-ALMA is a 1.1mm\ngalaxy survey over a continuous area of 72.42arcmin$^2$ at a homogeneous\nsensitivity. In this version 2.0, we present a new low resolution dataset and\nits combination with the previous high resolution dataset from the survey,\nimproving the $uv$ coverage and sensitivity reaching an average of $\\sigma =\n68.4\\mu$Jy beam$^{-1}$. A total of 88 galaxies are detected in a blind search\n(compared to 35 in the high resolution dataset alone), 50% at $S/N_{peak} \\geq\n5$ and 50% at $3.5 \\leq S/N_{peak} \\leq 5$ aided by priors. Among them, 13 out\nof the 88 are optically dark or faint sources ($H$- or $K$-band dropouts). The\nsample dust continuum sizes at 1.1mm are generally compact, with a median\neffective radius of $R_{e} = 0\"10 \\pm 0\"05$ (a physical size of $R_{e} = 0.73\n\\pm 0.29$kpc at the redshift of each source). Dust continuum sizes evolve with\nredshift and stellar mass resembling the trends of the stellar sizes measured\nat optical wavelengths, albeit a lower normalization compared to those of\nlate-type galaxies. We conclude that for sources with flux densities $S_{1.1mm}\n> 1$mJy, compact dust continuum emission at 1.1mm prevails, and sizes as\nextended as typical star-forming stellar disks are rare. The $S_{1.1mm} < 1$mJy\nsources appear slightly more extended at 1.1mm, although they are still\ngenerally compact below the sizes of typical star-forming stellar disks.\n"}
+{"abstract": "  Web servers scaled across distributed systems necessitate complex runtime\ncontrols for providing quality of service (QoS) guarantees as well as\nminimizing the energy costs under dynamic workloads. This paper presents a\nQoS-aware runtime controller using horizontal scaling (node allocation) and\nvertical scaling (resource allocation within nodes) methods synergistically to\nprovide adaptation to workloads while minimizing the power consumption under\nQoS constraint (i.e., response time). A horizontal scaling determines the\nnumber of active nodes based on workload demands and the required QoS according\nto a set of rules. Then, it is coupled with vertical scaling using transfer\nQ-learning, which further tunes power/performance based on workload profile\nusing dynamic voltage/frequency scaling (DVFS). It transfers Q-values within\nminimally explored states reducing exploration requirements. In addition, the\napproach exploits a scalable architecture of the many-core server allowing to\nreuse available knowledge from fully or partially explored nodes. When\ncombined, these methods allow to reduce the exploration time and QoS violations\nwhen compared to model-free Q-learning. The technique balances design-time and\nruntime costs to maximize the portability and operational optimality\ndemonstrated through persistent power reductions with minimal QoS violations\nunder different workload scenarios on heterogeneous multi-processing nodes of a\nserver cluster.\n"}
+{"abstract": "  We prove that the restriction map from the subspace of regular points of the\nholonomy perturbed SU(2) traceless flat moduli space of a tangle in a\n3-manifold to the traceless flat moduli space of its boundary marked surface is\na Lagrangian immersion.\n  A key ingredient in our proof is the use of composition in the Weinstein\ncategory, combined with the fact that SU(2) holonomy perturbations in a\ncylinder induce Hamiltonian isotopies. In addition, we show that $(S^2,4)$, the\n2-sphere with four marked points, is its own traceless flat SU(2) moduli space.\n"}
+{"abstract": "  Consider an energy harvesting node where generation of a status update\nmessage takes non-negligible time due to sensing, computing and analytics\noperations performed before making update transmissions. The node has to\nharmonize its (re)transmission strategy with the sensing/computing. We call\nthis general set of problems intermittent status updating. In this paper, we\nconsider intermittent status updating through non-preemptive sensing/computing\n(S/C) and transmission (Tx) operations, each costing a single energy recharge\nof the node, through an erasure channel with (a) perfect channel feedback and\n(b) no channel feedback. The S/C time for each update is independent with a\ngeneral distribution. The Tx queue has a single data buffer to save the latest\npacket generated after the S/C operation and a single transmitter where\ntransmission time is deterministic. Once energy is harvested, the node has to\ndecide whether to activate S/C to generate a new update or to (re)send the\nexisting update (if any) to the receiver. We prove that when feedback is\navailable average peak age of information (AoI) at the receiver is minimized by\na threshold-based policy that allows only young packets to be (re)sent or else\ngenerates a new update. We additionally propose window based and probabilistic\nretransmission schemes for both cases (a) and (b) and obtain closed form\naverage peak AoI expressions. Our numerical results show average peak AoI\nperformance comparisons and improvements.\n"}
+{"abstract": "  We introduce a simple, rigorous, and unified framework for solving nonlinear\npartial differential equations (PDEs), and for solving inverse problems (IPs)\ninvolving the identification of parameters in PDEs, using the framework of\nGaussian processes. The proposed approach: (1) provides a natural\ngeneralization of collocation kernel methods to nonlinear PDEs and IPs; (2) has\nguaranteed convergence for a very general class of PDEs, and comes equipped\nwith a path to compute error bounds for specific PDE approximations; (3)\ninherits the state-of-the-art computational complexity of linear solvers for\ndense kernel matrices. The main idea of our method is to approximate the\nsolution of a given PDE as the maximum a posteriori (MAP) estimator of a\nGaussian process conditioned on solving the PDE at a finite number of\ncollocation points. Although this optimization problem is infinite-dimensional,\nit can be reduced to a finite-dimensional one by introducing additional\nvariables corresponding to the values of the derivatives of the solution at\ncollocation points; this generalizes the representer theorem arising in\nGaussian process regression. The reduced optimization problem has the form of a\nquadratic objective function subject to nonlinear constraints; it is solved\nwith a variant of the Gauss--Newton method. The resulting algorithm (a) can be\ninterpreted as solving successive linearizations of the nonlinear PDE, and (b)\nin practice is found to converge in a small number of iterations (2 to 10), for\na wide range of PDEs. Most traditional approaches to IPs interleave parameter\nupdates with numerical solution of the PDE; our algorithm solves for both\nparameter and PDE solution simultaneously. Experiments on nonlinear elliptic\nPDEs, Burgers' equation, a regularized Eikonal equation, and an IP for\npermeability identification in Darcy flow illustrate the efficacy and scope of\nour framework.\n"}
+{"abstract": "  By using worldline and diagrammatic quantum Monte Carlo techniques, matrix\nproduct state and a variational approach \\`a la Feynman, we investigate the\nequilibrium properties and relaxation features of a quantum system of $N$ spins\nantiferromagnetically interacting with each other, with strength $J$, and\ncoupled to a common bath of bosonic oscillators, with strength $\\alpha$. We\nshow that, in the Ohmic regime, a Beretzinski-Thouless-Kosterlitz quantum phase\ntransition occurs. While for $J=0$ the critical value of $\\alpha$ decreases\nasymptotically with $1/N$ by increasing $N$, for nonvanishing $J$ it turns out\nto be practically independent on $N$, allowing to identify a finite range of\nvalues of $\\alpha$ where spin phase coherence is preserved also for large $N$.\nThen, by using matrix product state simulations, and the Mori formalism and the\nvariational approach \\`a la Feynman jointly, we unveil the features of the\nrelaxation, that, in particular, exhibits a non monotonic dependence on the\ntemperature reminiscent of the Kondo effect. For the observed quantum phase\ntransition we also establish a criterion analogous to that of the\nmetal-insulator transition in solids.\n"}
+{"abstract": "  A seemingly simple oxide with a rutile structure, RuO2 has been shown to\npossess several intriguing properties ranging from strain-stabilized\nsuperconductivity to a strong catalytic activity. Much interest has arisen\nsurrounding the controlled synthesis of RuO2 films but, unfortunately,\nutilizing atomically-controlled deposition techniques like molecular beam\nepitaxy (MBE) has been difficult due to the ultra-low vapor pressure and low\noxidation potential of Ru. Here, we demonstrate the growth of epitaxial,\nsingle-crystalline RuO2 films on different substrate orientations using the\nnovel solid-source metal-organic (MO) MBE. This approach circumvents these\nissues by supplying Ru using a pre-oxidized solid metal-organic precursor\ncontaining Ru. High-quality epitaxial RuO2 films with bulk-like\nroom-temperature resistivity of 55 micro-ohm-cm were obtained at a substrate\ntemperature as low as 300 C. By combining X-ray diffraction, transmission\nelectron microscopy, and electrical measurements, we discuss the effect of\nsubstrate temperature, orientation, film thickness, and strain on the structure\nand electrical properties of these films. Our results illustrating the use of\nnovel solid-source MOMBE approach paves the way to the atomic-layer controlled\nsynthesis of complex oxides of stubborn metals, which are not only difficult to\nevaporate but also hard to oxidize.\n"}
+{"abstract": "  We analyze the conclusions of the influence of a Coulomb-type potential on\nthe Klein-Gordon oscillator. We show that the truncation method proposed by the\nauthors do not yield all the eigenvalues of the radial equation but just one of\nthem for a particular value of a model parameter. Besides, the existence of\nallowed oscillator frequencies that depend on the quantum numbers is an\nartifact of the truncation method.\n"}
+{"abstract": "  Recently, most siamese network based trackers locate targets via object\nclassification and bounding-box regression. Generally, they select the\nbounding-box with maximum classification confidence as the final prediction.\nThis strategy may miss the right result due to the accuracy misalignment\nbetween classification and regression. In this paper, we propose a novel\nsiamese tracking algorithm called SiamRCR, addressing this problem with a\nsimple, light and effective solution. It builds reciprocal links between\nclassification and regression branches, which can dynamically re-weight their\nlosses for each positive sample. In addition, we add a localization branch to\npredict the localization accuracy, so that it can work as the replacement of\nthe regression assistance link during inference. This branch makes the training\nand inference more consistent. Extensive experimental results demonstrate the\neffectiveness of SiamRCR and its superiority over the state-of-the-art\ncompetitors on GOT-10k, LaSOT, TrackingNet, OTB-2015, VOT-2018 and VOT-2019.\nMoreover, our SiamRCR runs at 65 FPS, far above the real-time requirement.\n"}
+{"abstract": "  We propose a Nitsche method for multiscale partial differential equations,\nwhich retrieves the macroscopic information and the local microscopic\ninformation at one stroke. We prove the convergence of the method for second\norder elliptic problem with bounded and measurable coefficients. The rate of\nconvergence may be derived for coefficients with further structures such as\nperiodicity and ergodicity. Extensive numerical results confirm the theoretical\npredictions.\n"}
+{"abstract": "  We report new measurements of the branching fraction $\\cal B(D_s^+\\to\n\\ell^+\\nu)$, where $\\ell^+$ is either $\\mu^+$ or\n$\\tau^+(\\to\\pi^+\\bar{\\nu}_\\tau)$, based on $6.32$ fb$^{-1}$ of\nelectron-positron annihilation data collected by the BESIII experiment at six\ncenter-of-mass energy points between $4.178$ and $4.226$ GeV. Simultaneously\nfloating the $D_s^+\\to\\mu^+\\nu_\\mu$ and $D_s^+\\to\\tau^+\\nu_\\tau$ components\nyields $\\cal B(D_s^+\\to \\tau^+\\nu_\\tau) = (5.21\\pm0.25\\pm0.17)\\times10^{-2}$,\n$\\cal B(D_s^+\\to \\mu^+\\nu_\\mu) = (5.35\\pm0.13\\pm0.16)\\times10^{-3}$, and the\nratio of decay widths $R=\\frac{\\Gamma(D_s^+\\to \\tau^+\\nu_\\tau)}{\\Gamma(D_s^+\\to\n\\mu^+\\nu_\\mu)} = 9.73^{+0.61}_{-0.58}\\pm 0.36$, where the first uncertainties\nare statistical and the second systematic. No evidence of ${\\it CP}$ asymmetry\nis observed in the decay rates $D_s^\\pm\\to\\mu^\\pm\\nu_\\mu$ and\n$D_s^\\pm\\to\\tau^\\pm\\nu_\\tau$: $A_{\\it CP}(\\mu^\\pm\\nu) = (-1.2\\pm2.5\\pm1.0)\\%$\nand $A_{\\it CP}(\\tau^\\pm\\nu) = (+2.9\\pm4.8\\pm1.0)\\%$. Constraining our\nmeasurement to the Standard Model expectation of lepton universality\n($R=9.75$), we find the more precise results $\\cal B(D_s^+\\to \\tau^+\\nu_\\tau) =\n(5.22\\pm0.10\\pm 0.14)\\times10^{-2}$ and $A_{\\it CP}(\\tau^\\pm\\nu_\\tau) =\n(-0.1\\pm1.9\\pm1.0)\\%$. Combining our results with inputs external to our\nanalysis, we determine the $c\\to \\bar{s}$ quark mixing matrix element, $D_s^+$\ndecay constant, and ratio of the decay constants to be $|V_{cs}| =\n0.973\\pm0.009\\pm0.014$, $f_{D^+_s} = 249.9\\pm2.4\\pm3.5~\\text{MeV}$, and\n$f_{D^+_s}/f_{D^+} = 1.232\\pm0.035$, respectively.\n"}
+{"abstract": "  Various attention mechanisms are being widely applied to acoustic scene\nclassification. However, we empirically found that the attention mechanism can\nexcessively discard potentially valuable information, despite improving\nperformance. We propose the attentive max feature map that combines two\neffective techniques, attention and a max feature map, to further elaborate the\nattention mechanism and mitigate the above-mentioned phenomenon. We also\nexplore various joint training methods, including multi-task learning, that\nallocate additional abstract labels for each audio recording. Our proposed\nsystem demonstrates state-of-the-art performance for single systems on Subtask\nA of the DCASE 2020 challenge by applying the two proposed techniques using\nrelatively fewer parameters. Furthermore, adopting the proposed attentive max\nfeature map, our team placed fourth in the recent DCASE 2021 challenge.\n"}
+{"abstract": "  Determining the aqueous solubility of molecules is a vital step in many\npharmaceutical, environmental, and energy storage applications. Despite efforts\nmade over decades, there are still challenges associated with developing a\nsolubility prediction model with satisfactory accuracy for many of these\napplications. The goal of this study is to develop a general model capable of\npredicting the solubility of a broad range of organic molecules. Using the\nlargest currently available solubility dataset, we implement deep\nlearning-based models to predict solubility from molecular structure and\nexplore several different molecular representations including molecular\ndescriptors, simplified molecular-input line-entry system (SMILES) strings,\nmolecular graphs, and three-dimensional (3D) atomic coordinates using four\ndifferent neural network architectures - fully connected neural networks\n(FCNNs), recurrent neural networks (RNNs), graph neural networks (GNNs), and\nSchNet. We find that models using molecular descriptors achieve the best\nperformance, with GNN models also achieving good performance. We perform\nextensive error analysis to understand the molecular properties that influence\nmodel performance, perform feature analysis to understand which information\nabout molecular structure is most valuable for prediction, and perform a\ntransfer learning and data size study to understand the impact of data\navailability on model performance.\n"}
+{"abstract": "  The $^{23}$Na($\\alpha,p$)$^{26}$Mg reaction has been identified as having a\nsignificant impact on the nucleosynthesis of several nuclei between Ne and Ti\nin type-Ia supernovae, and of $^{23}$Na and $^{26}$Al in massive stars. The\nreaction has been subjected to renewed experimental interest recently,\nmotivated by high uncertainties in early experimental data and in the\nstatistical Hauser-Feshbach models used in reaction rate compilations. Early\nexperiments were affected by target deterioration issues and unquantifiable\nuncertainties. Three new independent measurements instead are utilizing inverse\nkinematics and Rutherford scattering monitoring to resolve this. In this work\nwe present directly measured angular distributions of the emitted protons to\neliminate a discrepancy in the assumptions made in the recent reaction rate\nmeasurements, which results in cross sections differing by a factor of 3. We\nderive a new combined experimental reaction rate for the\n$^{23}$Na($\\alpha,p$)$^{26}$Mg reaction with a total uncertainty of 30% at\nrelevant temperatures. Using our new $^{23}$Na($\\alpha,p$)$^{26}$Mg rate, the\n$^{26}$Al and $^{23}$Na production uncertainty is reduced to within 8%. In\ncomparison, using the factor of 10 uncertainty previously recommended by the\nrate compilation STARLIB, $^{26}$Al and $^{23}$Na production was changing by\nmore than a factor of 2. In type-Ia supernova conditions, the impact on\nproduction of $^{23}$Na is constrained to within 15%.\n"}
+{"abstract": "  J-PAS will soon start imaging 8000 deg2 of the northern sky with its unique\nset of 56 filters (R $\\sim$ 60). Before, we observed 1 deg2 on the AEGIS field\nwith an interim camera with all the J-PAS filters. With this data (miniJPAS),\nwe aim at proving the scientific potential of J-PAS to identify and\ncharacterize the galaxy populations with the goal of performing galaxy\nevolution studies across cosmic time. Several SED-fitting codes are used to\nconstrain the stellar population properties of a complete flux-limited sample\n(rSDSS <= 22.5 AB) of miniJPAS galaxies that extends up to z = 1. We find\nconsistent results on the galaxy properties derived from the different codes,\nindependently of the galaxy spectral-type or redshift. For galaxies with\nSNR>=10, we estimate that the J-PAS photometric system allows to derive stellar\npopulation properties with a precision that is equivalent to that obtained with\nspectroscopic surveys of similar SNR. By using the dust-corrected (u-r)\ncolour-mass diagram, a powerful proxy to characterize galaxy populations, we\nfind that the fraction of red and blue galaxies evolves with cosmic time, with\nred galaxies being $\\sim$ 38% and $\\sim$ 18% of the whole population at z = 0.1\nand z = 0.5, respectively. At all redshifts, the more massive galaxies belong\nto the red sequence and these galaxies are typically older and more metal rich\nthan their counterparts in the blue cloud. Our results confirm that with J-PAS\ndata we will be able to analyze large samples of galaxies up to z $\\sim$ 1,\nwith galaxy stellar masses above of log(M$_*$/M$_{\\odot}$) $\\sim$ 8.9, 9.5, and\n9.9 at z = 0.3, 0.5, and 0.7, respectively. The SFH of a complete sub-sample of\ngalaxies selected at z $\\sim$ 0.1 with log(M$_*$/M$_{\\odot}$) > 8.3 constrain\nthe cosmic evolution of the star formation rate density up to z $\\sim$ 3 in\ngood agreement with results from cosmological surveys.\n"}
+{"abstract": "  We have developed a torsion balance with a sensitivity about ten times better\nthan those of previously operating balances for the study of long range forces\ncoupling to baryon and lepton number. We present here the details of the design\nand expected characteristics of this balance. Operation of this balance for a\nyear will also result in improved bounds on long range interactions of dark\nmatter violating Einstein's equivalence principle.\n"}
+{"abstract": "  Synchronous model is a type of formal models for modelling and specifying\nreactive systems. It has a great advantage over other real-time models that its\nmodelling paradigm supports a deterministic concurrent behaviour of systems.\nVarious approaches have been utilized for verification of synchronous models\nbased on different techniques, such as model checking, SAT/SMT sovling, term\nrewriting, type inference and so on. In this paper, we propose a verification\napproach for synchronous models based on compositional reasoning and term\nrewriting. Specifically, we initially propose a variation of dynamic logic,\ncalled synchronous dynamic logic (SDL). SDL extends the regular program model\nof first-order dynamic logic (FODL) with necessary primitives to capture the\nnotion of synchrony and synchronous communication between parallel programs,\nand enriches FODL formulas with temporal dynamic logical formulas to specify\nsafety properties -- a type of properties mainly concerned in reactive systems.\nTo rightly capture the synchronous communications, we define a constructive\nsemantics for the program model of SDL. We build a sound and relatively\ncomplete proof system for SDL. Compared to previous verification approaches,\nSDL provides a divide and conquer way to analyze and verify synchronous models\nbased on compositional reasoning of the syntactic structure of the programs of\nSDL. To illustrate the usefulness of SDL, we apply SDL to specify and verify a\nsmall example in the synchronous model SyncChart, which shows the potential of\nSDL to be used in practice.\n"}
+{"abstract": "  A limitation for collaborative robots (cobots) is their lack of ability to\nadapt to human partners, who typically exhibit an immense diversity of\nbehaviors. We present an autonomous framework as a cobot's real-time\ndecision-making mechanism to anticipate a variety of human characteristics and\nbehaviors, including human errors, toward a personalized collaboration. Our\nframework handles such behaviors in two levels: 1) short-term human behaviors\nare adapted through our novel Anticipatory Partially Observable Markov Decision\nProcess (A-POMDP) models, covering a human's changing intent (motivation),\navailability, and capability; 2) long-term changing human characteristics are\nadapted by our novel Adaptive Bayesian Policy Selection (ABPS) mechanism that\nselects a short-term decision model, e.g., an A-POMDP, according to an estimate\nof a human's workplace characteristics, such as her expertise and collaboration\npreferences. To design and evaluate our framework over a diversity of human\nbehaviors, we propose a pipeline where we first train and rigorously test the\nframework in simulation over novel human models. Then, we deploy and evaluate\nit on our novel physical experiment setup that induces cognitive load on humans\nto observe their dynamic behaviors, including their mistakes, and their\nchanging characteristics such as their expertise. We conduct user studies and\nshow that our framework effectively collaborates non-stop for hours and adapts\nto various changing human behaviors and characteristics in real-time. That\nincreases the efficiency and naturalness of the collaboration with a higher\nperceived collaboration, positive teammate traits, and human trust. We believe\nthat such an extended human adaptation is key to the long-term use of cobots.\n"}
+{"abstract": "  In a resource-constrained IoT network, end nodes like WSN, RFID, and embedded\nsystems are used which have memory, processing, and energy limitations. One of\nthe key distribution solutions in these types of networks is to use the key\npre-distribution scheme, which accomplishes the key distribution operation\noffline before the resource-constrained devices deployment in the environment.\nAlso, in order to reduce the shared key discovery computing and communication\noverhead, the use of combinatorial design in key pre-distribution has been\nproposed as a solution in recent years. In this study, a ${\\mu}$-PBIBD\ncombinatorial design is introduced and constructed and the mapping of such\ndesign as a key pre-distribution scheme in the resource-constrained IoT network\nis explained. Through using such key pre-distribution scheme, more keys are\nobtained for communication between two devices in the IoT network. This means\nthat there will be a maximum of q + 2 keys between the two devices in the\nnetwork, where q is the prime power, that is, instead of having a common key\nfor a direct secure connection, the two devices can have q + 2 common keys in\ntheir key chain. Accordingly, we would increase the resilience of the key\npre-distribution scheme compared to the SBIBD, TD, Trade-KP, UKP *, RD * and\n2-D ${\\mu}$-PBIBD designs.\n  Keywords: resource-constrained IoT network; combinatorial design;\n${\\mu}$-PBIBD; resilience.\n"}
+{"abstract": "  We discuss various aspects of a neutrino physics program that can be carried\nout with the neutrino Beam-Dump eXperiment DRIFT ($\\nu$BDX-DRIFT) detector\nusing neutrino beams produced in next generation neutrino facilities.\n$\\nu$BDX-DRIFT is a directional low-pressure TPC detector suitable for\nmeasurements of coherent elastic neutrino-nucleus scattering (CE$\\nu$NS) using\na variety of gaseous target materials which include carbon disulfide, carbon\ntetrafluoride and tetraethyllead, among others. The neutrino physics program\nincludes standard model (SM) measurements and beyond the standard model (BSM)\nphysics searches. Focusing on the Long Baseline Neutrino Facility (LBNF)\nbeamline at Fermilab, we first discuss basic features of the detector and\nestimate backgrounds, including beam-induced neutron backgrounds. We then\nquantify the CE$\\nu$NS signal in the different target materials and study the\nsensitivity of $\\nu$BDX-DRIFT to measurements of the weak mixing angle and\nneutron density distributions. We consider as well prospects for new physics\nsearches, in particular sensitivities to effective neutrino non-standard\ninteractions.\n"}
+{"abstract": "  Existing studies in weakly-supervised semantic segmentation (WSSS) using\nimage-level weak supervision have several limitations: sparse object coverage,\ninaccurate object boundaries, and co-occurring pixels from non-target objects.\nTo overcome these challenges, we propose a novel framework, namely Explicit\nPseudo-pixel Supervision (EPS), which learns from pixel-level feedback by\ncombining two weak supervisions; the image-level label provides the object\nidentity via the localization map and the saliency map from the off-the-shelf\nsaliency detection model offers rich boundaries. We devise a joint training\nstrategy to fully utilize the complementary relationship between both\ninformation. Our method can obtain accurate object boundaries and discard\nco-occurring pixels, thereby significantly improving the quality of\npseudo-masks. Experimental results show that the proposed method remarkably\noutperforms existing methods by resolving key challenges of WSSS and achieves\nthe new state-of-the-art performance on both PASCAL VOC 2012 and MS COCO 2014\ndatasets.\n"}
+{"abstract": "  The number of biomedical literature on new biomedical concepts is rapidly\nincreasing, which necessitates a reliable biomedical named entity recognition\n(BioNER) model for identifying new and unseen entity mentions. However, it is\nquestionable whether existing BioNER models can effectively handle them. In\nthis work, we systematically analyze the three types of recognition abilities\nof BioNER models: memorization, synonym generalization, and concept\ngeneralization. We find that although BioNER models achieve state-of-the-art\nperformance on BioNER benchmarks based on overall performance, they have\nlimitations in identifying synonyms and new biomedical concepts such as\nCOVID-19. From this observation, we conclude that existing BioNER models are\noverestimated in terms of their generalization abilities. Also, we identify\nseveral difficulties in recognizing unseen mentions in BioNER and make the\nfollowing conclusions: (1) BioNER models tend to exploit dataset biases, which\nhinders the models' abilities to generalize, and (2) several biomedical names\nhave novel morphological patterns with little name regularity such as COVID-19,\nand models fail to recognize them. We apply a current statistics-based\ndebiasing method to our problem as a simple remedy and show the improvement in\ngeneralization to unseen mentions. We hope that our analyses and findings would\nbe able to facilitate further research into the generalization capabilities of\nNER models in a domain where their reliability is of utmost importance.\n"}
+{"abstract": "  We investigate how entanglement can enhance two-photon absorption in a\nthree-level system. First, we employ the Schmidt decomposition to determine the\nentanglement properties of the optimal two-photon state to drive such a\ntransition, and the maximum enhancement which can be achieved in comparison to\nthe optimal classical pulse. We then adapt the optimization problem to\nrealistic experimental constraints, where photon pairs from a down-conversion\nsource are manipulated by local operations such as spatial light modulators. We\nderive optimal pulse shaping functions to enhance the absorption efficiency,\nand compare the maximal enhancement achievable by entanglement to the yield of\noptimally shaped, separable pulses.\n"}
+{"abstract": "  We design and demonstrate a novel technique for the active stabilization of\nthe relative phase between seed and pump in an optical parametric oscillator\n(OPO). We show that two error signals for the stabilization of the OPO\nfrequency, based on Pound-Drever-Hall (PDH), and of the seed-pump relative\nphase can be obtained just from the reflected beam of the OPO cavity, without\nthe necessity of two different modulation and demodulation stages. We also\nanalyze the effect of the pump in the cavity stabilization for different\nseed-pump relative phase configurations, resulting in an offset in the PDH\nerror signal, which has to be compensated. Finally, an application of our\ntechnique in the reliable generation of squeezed coherent states is presented.\n"}
+{"abstract": "  Glucose biosensors play an important role in the diagnosis and continued\nmonitoring of the disease, diabetes mellitus. This report proposes the\ndevelopment of a novel enzymatic electrochemical glucose biosensor based on\nTiO$_2$ nanotubes modified by AgO and Prussian blue (PB) nanoparticles (NPs),\nwhich has an additional advantage of possessing antimicrobial properties for\nimplantable biosensor applications. In this study, we developed two high\nperformance glucose biosensors based on the immobilization of glucose oxidase\n(GOx) onto Prussian blue (PB) modified TiO$_2$ nanotube arrays functionalized\nby Au and AgO NPs. AgO-deposited TiO$_2$ nanotubes were synthesized through an\nelectrochemical anodization process followed by Ag electroplating process in\nthe same electrolyte. Deposition of PB particles was performed from an acidic\nferricyanide solution. The surface morphology and elemental composition of the\ntwo fabricated biosensors were investigated by scanning electron microscopy\n(SEM) and energy-dispersive X-ray spectroscopy (EDS) which indicate the\nsuccessful deposition of Au and AgO nanoparticles as well as PB nanocrystals.\nCyclic voltammetry and chronoamperometry were used to investigate the\nperformance of the modified electrochemical biosensors. The results show that\nthe developed electrochemical biosensors display excellent properties in terms\nof electron transmission, low detection limit as well as high stability for the\ndetermination of glucose. Under the optimized conditions, the amperometric\nresponse shows a linear dependence on the glucose concentration to a detection\nlimit down to 4.91 $\\mu$M with sensitivity of 185.1 mA M$^{-1}$ cm$^{-2]$ in Au\nmodified biosensor and detection limit of 58.7 $\\mu$M with 29.1 mA M$^{-1}$\ncm$^{-2]$ sensitivity in AgO modified biosensor.\n"}
+{"abstract": "  We present a dialogue elicitation study to assess how users envision\nconversations with a perfect voice assistant (VA). In an online survey, N=205\nparticipants were prompted with everyday scenarios, and wrote the lines of both\nuser and VA in dialogues that they imagined as perfect. We analysed the\ndialogues with text analytics and qualitative analysis, including number of\nwords and turns, social aspects of conversation, implied VA capabilities, and\nthe influence of user personality. The majority envisioned dialogues with a VA\nthat is interactive and not purely functional; it is smart, proactive, and has\nknowledge about the user. Attitudes diverged regarding the assistant's role as\nwell as it expressing humour and opinions. An exploratory analysis suggested a\nrelationship with personality for these aspects, but correlations were low\noverall. We discuss implications for research and design of future VAs,\nunderlining the vision of enabling conversational UIs, rather than single\ncommand \"Q&As\".\n"}
+{"abstract": "  We establish some limit theorems for quasi-arithmetic means of random\nvariables. This class of means contains the arithmetic, geometric and harmonic\nmeans. Our feature is that the generators of quasi-arithmetic means are allowed\nto be complex-valued, which makes considerations for quasi-arithmetic means of\nrandom variables which could take negative values possible. Our motivation for\nthe limit theorems is finding simple estimators of the parameters of the Cauchy\ndistribution. By applying the limit theorems, we obtain some closed-form\nunbiased strongly-consistent estimators for the joint of the location and scale\nparameters of the Cauchy distribution, which are easy to compute and analyze.\n"}
+{"abstract": "  Quantum spin-1/2 antiferromagnetic Heisenberg trimerized chain with strong\nintradimer and weak monomer-dimer coupling constants is studied using the novel\nmany-body perturbation expansion, which is developed from the exactly solved\nspin-1/2 Ising-Heisenberg diamond chain preserving correlations between all\ninteracting spins of the trimerized chain unlike the standard perturbation\nscheme developed on the grounds of noninteracting spin monomers and dimers. The\nHeisenberg trimerized chain shows the intermediate one-third plateau, which was\nalso observed in the magnetization curve of the polymeric compound\nCu$_3$(P$_2$O$_6$OH)$_2$ affording its experimental realization. Within the\nmodified strong-coupling method we have obtained the effective Hamiltonians for\nthe magnetic-field range from zero to one-third plateau, and from one-third\nplateau to the saturation magnetization. The second-order perturbation theory\nshows reliable results even for the moderate ratio between weaker dimer-monomer\nand stronger intradimer coupling constants. We have also examined thermodynamic\nproperties and recovered the low-temperature peak in the specific heat. The\naccuracy of the developed method is tested through a comparison with numerical\ndensity-matrix renormalization group and quantum Monte Carlo simulations. Using\nthe results for the effective Hamiltonian we suggest straightforward procedure\nfor finding the microscopic parameters of one-dimensional trimerized magnetic\ncompounds with strong intradimer and weak monomer-dimer couplings. We found the\nrefined values for the coupling constants of Cu$_3$(P$_2$O$_6$OH)$_2$ by\nmatching the theoretical results with the available experimental data for the\nmagnetization and magnetic susceptibility in a wide range of temperatures and\nmagnetic fields.\n"}
+{"abstract": "  In circumstellar disks, the size of dust particles varies from submicron to\nseveral centimeters, while planetesimals have sizes of hundreds of kilometers.\nTherefore, various regimes for the aerodynamic drag between solid bodies and\ngas can be realized in these disks, depending on the grain sizes and\nvelocities: Epstein, Stokes, and Newton, as well as transitional regimes\nbetween them. For small bodies moving in the Epstein regime, the time required\nto establish the constant relative velocity between the gas and bodies can be\nmuch less than the dynamical time scale for the problem - the time for the\nrotation of the disk about the central body. In addition, the dust may be\nconcentrated in individual regions of the disk, making it necessary to take\ninto account the transfer of momentum between the dust and gas. It is shown\nthat, for a system of equations for gas and monodisperse dust, a semi-implicit\nfirst-order approximation scheme in time in which the interphase interaction is\ncalculated implicitly, while other forces, such as the pressure gradient and\ngravity are calculated explicitly, is suitable for stiff problems with intense\ninterphase interactions and for computations of the drag in non-linear regimes.\nThe piece-wise drag coefficient widely used in astrophysical simulations has a\ndiscontinuity at some values of the Mach and Knudsen numbers that are realized\nin a circumstellar disk. A continuous drag coefficient is presented, which\ncorresponds to experimental dependences obtained for various drag regimes.\n"}
+{"abstract": "  Hydrodynamic interactions are crucial for determining the cooperative\nbehavior of microswimmers at low Reynolds numbers. Here we provide a\ncomprehensive analysis of the scaling and strength of the interactions in the\ncase of a pair of three-sphere swimmers with intrinsic elasticity. Both\nstroke-based and force-based microswimmers are analyzed using an analytic\nperturbative approach. Following a detailed analysis of the passive\ninteractions, as well as active translations and rotations, we find that the\nmapping between the stroke-based and force-based swimmers is only possible in a\nlow driving frequency regime where the characteristic time scale is smaller\nthan the viscous one. Furthermore, we find that for swimmers separated by up to\nhundreds of swimmer lengths, swimming in pairs speeds up the self propulsion,\ndue to the dominant quadrupolar hydrodynamic interactions. Finally, we find\nthat the long term behavior of the swimmers, while sensitive to initial\nrelative positioning, does not depend on the pusher or puller nature of the\nswimmer.\n"}
+{"abstract": "  Oftentimes, patterns can be represented through different modalities. For\nexample, leaf data can be in the form of images or contours. Handwritten\ncharacters can also be either online or offline. To exploit this fact, we\npropose the use of self-augmentation and combine it with multi-modal feature\nembedding. In order to take advantage of the complementary information from the\ndifferent modalities, the self-augmented multi-modal feature embedding employs\na shared feature space. Through experimental results on classification with\nonline handwriting and leaf images, we demonstrate that the proposed method can\ncreate effective embeddings.\n"}
+{"abstract": "  Previous work has shown that early resolution of issues detected by static\ncode analyzers can prevent major cost later on. However, developers often\nignore such issues for two main reasons. First, many issues should be\ninterpreted to determine if they correspond to actual flaws in the program.\nSecond, static analyzers often do not present the issues in a way that makes it\napparent how to fix them. To address these problems, we present Sorald: a novel\nsystem that adopts a set of predefined metaprogramming templates to transform\nthe abstract syntax trees of programs to suggest fixes for static issues. Thus,\nthe burden on the developer is reduced from both interpreting and fixing static\nissues, to inspecting and approving solutions for them. Sorald fixes violations\nof 10 rules from SonarQube, one of the most widely used static analyzers for\nJava. We also implement an effective mechanism to integrate Sorald into\ndevelopment workflows based on pull requests. We evaluate Sorald on a dataset\nof 161 popular repositories on Github. Our analysis shows the effectiveness of\nSorald as it fixes 94\\% (1,153/1,223) of the violations that it attempts to\nfix. Overall, our experiments show it is possible to automatically fix\nviolations of static analysis rules produced by the state-of-the-art static\nanalyzer SonarQube.\n"}
+{"abstract": "  In this paper we develop an optimisation based approach to multivariate\nChebyshev approximation on a finite grid. We consider two models: multivariate\npolynomial approximation and multivariate generalised rational approximation.\nIn the second case the approximations are ratios of linear forms and the basis\nfunctions are not limited to monomials. It is already known that in the case of\nmultivariate polynomial approximation on a finite grid the corresponding\noptimisation problems can be reduced to solving a linear programming problem,\nwhile the area of multivariate rational approximation is not so well\nunderstood.In this paper we demonstrate that in the case of multivariate\ngeneralised rational approximation the corresponding optimisation problems are\nquasiconvex. This statement remains true even when the basis functions are not\nlimited to monomials. Then we apply a bisection method, which is a general\nmethod for quasiconvex optimisation. This method converges to an optimal\nsolution with given precision. We demonstrate that the convex feasibility\nproblems appearing in the bisection method can be solved using linear\nprogramming. Finally, we compare the deviation error and computational time for\nmultivariate polynomial and generalised rational approximation with the same\nnumber of decision variables.\n"}
+{"abstract": "  In this study, we investigate the attentiveness exhibited by participants\nsourced through Amazon Mechanical Turk (MTurk), thereby discovering a\nsignificant level of inattentiveness amongst the platform's top crowd workers\n(those classified as 'Master', with an 'Approval Rate' of 98% or more, and a\n'Number of HITS approved' value of 1,000 or more). A total of 564 individuals\nfrom the United States participated in our experiment. They were asked to read\na vignette outlining one of four hypothetical technology products and then\ncomplete a related survey. Three forms of attention check (logic, honesty, and\ntime) were used to assess attentiveness. Through this experiment we determined\nthat a total of 126 (22.3%) participants failed at least one of the three forms\nof attention check, with most (94) failing the honesty check - followed by the\nlogic check (31), and the time check (27). Thus, we established that\nsignificant levels of inattentiveness exist even among the most elite MTurk\nworkers. The study concludes by reaffirming the need for multiple forms of\ncarefully crafted attention checks, irrespective of whether participant quality\nis presumed to be high according to MTurk criteria such as 'Master', 'Approval\nRate', and 'Number of HITS approved'. Furthermore, we propose that researchers\nadjust their proposals to account for the effort and costs required to address\nparticipant inattentiveness.\n"}
+{"abstract": "  A new model of anisotropic compact star is obtained in our present paper by\nassuming the pressure anisotropy. The proposed model is singularity free. The\nmodel is obtained by considering a physically reasonable choice for the metric\npotential $g_{rr}$ which depends on a dimensionless parameter `n'. The effect\nof $n$ is discussed numerically, analytically and through plotting. We have\nconcentrated a wide range for n ($10\\leq n \\leq 1000$) for drawing the profiles\nof different physical parameters. The maximum allowable mass for different\nvalues of $n$ have been obtained by M-R plot. We have checked that the\nstability of the model is increased for larger value of $n$. For the viability\nof the model we have considered two compact stars PSR J1614-2230 and EXO\n1785-248. We have shown that the expressions for the anisotropy factor and the\nmetric component may serve as generating functions for uncharged stellar models\nin the context of the general theory of relativity.\n"}
+{"abstract": "  A unified expression for topological invariants has been proposed recently to\ndescribe the topological order in Dirac models belonging to any dimension and\nsymmetry class. We uncover a correspondence between the curvature function that\nintegrates to this unified topological invariant and the quantum metric that\nmeasures the distance between properly defined many-body Bloch states in\nmomentum space. Based on this metric-curvature correspondence, a time-resolved\nand angle-resolved photoemission spectroscopy experiment is proposed to measure\nthe violation of spectral sum rule caused by a pulse electric field to detect\nthe quantum metric, from which the topological properties of the system may be\nextracted.\n"}
+{"abstract": "  Here we propose a local earthquake tomography method that applies a\nstructured regularization technique to determine sharp changes in the Earth's\nseismic velocity structure with travel time data of direct waves. Our approach\nfocuses on the ability to better image two common features that are observed\nthe Earth's seismic velocity structure: velocity jumps that correspond to\nmaterial boundaries, such as the Conrad and Moho discontinuities, and gradual\nvelocity changes that are associated with the pressure and temperature\ndistributions in the crust and mantle. We employ different penalty terms in the\nvertical and horizontal directions to refine the imaging process. We utilize a\nvertical-direction (depth) penalty term that takes the form of the l1-sum of\nthe l2-norm of the second-order differences of the horizontal units in the\nvertical direction. This penalty is intended to represent sharp velocity jumps\ndue to discontinuities by creating a piecewise linear depth profile of the\naverage velocity structure. We set a horizontal-direction penalty term on the\nbasis of the l2-norm to express gradual velocity tendencies in the horizontal\ndirection. We use a synthetic dataset to demonstrate that our method provides\nsignificant improvements over the estimated velocity structures from\nconventional methods by obtaining stable estimates of both the velocity jumps\nand gradual velocity changes. We also demonstrate that our proposed method is\nrelatively robust against variations in the amplitude of the velocity jump,\ninitial velocity model, and the number of observed travel times. Furthermore,\nwe present a considerable potential for detecting a velocity discontinuity\nusing the observed travel times from only a small number of direct-wave\nobservations.\n"}
+{"abstract": "  Arbitrary-oriented objects exist widely in natural scenes, and thus the\noriented object detection has received extensive attention in recent years. The\nmainstream rotation detectors use oriented bounding boxes (OBB) or\nquadrilateral bounding boxes (QBB) to represent the rotating objects. However,\nthese methods suffer from the representation ambiguity for oriented object\ndefinition, which leads to suboptimal regression optimization and the\ninconsistency between the loss metric and the localization accuracy of the\npredictions. In this paper, we propose a Representation Invariance Loss (RIL)\nto optimize the bounding box regression for the rotating objects. Specifically,\nRIL treats multiple representations of an oriented object as multiple\nequivalent local minima, and hence transforms bounding box regression into an\nadaptive matching process with these local minima. Then, the Hungarian matching\nalgorithm is adopted to obtain the optimal regression strategy. We also propose\na normalized rotation loss to alleviate the weak correlation between different\nvariables and their unbalanced loss contribution in OBB representation.\nExtensive experiments on remote sensing datasets and scene text datasets show\nthat our method achieves consistent and substantial improvement. The source\ncode and trained models are available at https://github.com/ming71/RIDet.\n"}
+{"abstract": "  The production cross section of a top quark pair in association with a photon\nis measured in proton-proton collisions at a center-of-mass energy of 13 TeV.\nThe data set, corresponding to an integrated luminosity of 137 fb$^{-1}$, was\nrecorded by the CMS experiment during the 2016-2018 data taking of the LHC. The\nmeasurements are performed in a fiducial volume defined at the particle level.\nEvents with an isolated, highly energetic lepton, at least three jets from the\nhadronization of quarks, among which at least one is b tagged, and one isolated\nphoton are selected. The inclusive fiducial $\\mathrm{t\\overline{t}}\\gamma$\ncross section, for a photon with transverse momentum greater than 20 GeV and\npseudorapidity $\\lvert \\eta\\rvert$ $\\lt$ 1.4442, is measured to be 798 $\\pm$ 7\n(stat) $\\pm$ 48 (syst) fb, in good agreement with the prediction from the\nstandard model at next-to-leading order in quantum chromodynamics. The\ndifferential cross sections are also measured as a function of several\nkinematic observables and interpreted in the framework of the standard model\neffective field theory (EFT), leading to the most stringent direct limits to\ndate on anomalous electromagnetic dipole moment interactions of the top quark\nand the photon.\n"}
+{"abstract": "  We introduce Platform for Situated Intelligence, an open-source framework\ncreated to support the rapid development and study of multimodal,\nintegrative-AI systems. The framework provides infrastructure for sensing,\nfusing, and making inferences from temporal streams of data across different\nmodalities, a set of tools that enable visualization and debugging, and an\necosystem of components that encapsulate a variety of perception and processing\ntechnologies. These assets jointly provide the means for rapidly constructing\nand refining multimodal, integrative-AI systems, while retaining the efficiency\nand performance characteristics required for deployment in open-world settings.\n"}
+{"abstract": "  In OTC markets, one of the main tasks of dealers / market makers consists in\nproviding prices at which they agree to buy and sell the assets and securities\nthey have in their scope. With ever increasing trading volume, this quoting\ntask has to be done algorithmically. Over the last ten years, many market\nmaking models have been designed that can be the basis of quoting algorithms in\nOTC markets. Nevertheless, in most (if not all) OTC market making models, the\nmarket maker is a pure internalizer, setting quotes and waiting for clients.\nHowever, on many markets such as foreign exchange cash markets, market makers\nhave access to liquidity pools where they can hedge part of their inventory. In\nthis paper, we propose a model taking this possibility into account, therefore\nallowing market makers to externalize part of their risk by trading in a\nliquidity pool. The model displays an important feature well known to\npractitioners that within a certain inventory range the market maker\ninternalizes the flow by appropriately adjusting the quotes and externalize\noutside of that range. The larger the market making franchise, the wider is the\ninventory range suitable for internalization. The model is illustrated\nnumerically with realistic parameters for USDCNH spot market.\n"}
+{"abstract": "  An accurate experimental characterization of finite antiferromagnetic (AF)\nspin chains is crucial for controlling and manipulating their magnetic\nproperties and quantum states for potential applications in spintronics or\nquantum computation. In particular, finite AF chains are expected to show a\ndifferent magnetic behaviour depending on their length and topology. Molecular\nAF rings are able to combine the quantum-magnetic behaviour of AF chains with a\nvery remarkable tunability of their topological and geometrical properties. In\nthis work we measure the $^{53}$Cr-NMR spectra of the Cr$_{8}$Cd ring to study\nthe local spin densities on the Cr sites. Cr$_{8}$Cd can in fact be considered\na model system of a finite AF open chain with an even number of spins. The NMR\nresonant frequencies are in good agreement with the theoretical local spin\ndensities, by assuming a core polarization feld AC = -12.7 T/$\\mu_B$. Moreover,\nthese NMR results confirm the theoretically predicted non-collinear spin\narrangement along the Cr$_{8}$Cd ring, which is typical of an even-open AF spin\nchain.\n"}
+{"abstract": "  We establish semiclassical asymptotics and estimates for the Schwartz kernel\n$e_h(x,y;\\tau)$ of spectral projector for a second order elliptic operator on\nthe manifold with a boundary. While such asymptotics for its restriction to the\ndiagonal $e_h(x,x,\\tau)$ and, especially, for its trace $\\mathsf{N}_h(\\tau)=\n\\int e_h(x,x,\\tau)\\,dx$ are well-known, the out-of-diagonal asymptotics are\nmuch less explored.\n  Our main tools: microlocal methods, improved successive approximations and\ngeometric optics methods.\n  Our results would also lead to classical asymptotics of $e_h(x,y,\\tau)$ for\nfixed $h$ (say, $h=1$) and $\\tau\\to \\infty$.\n"}
+{"abstract": "  We study the thermal transport properties of three CaF$_{2}$ polymorphs up to\na pressure of 30 GPa using first-principle calculations and an interatomic\npotential based on machine learning. The lattice thermal conductivity $\\kappa$\nis computed by iteratively solving the linearized Boltzmann transport equation\n(BTE) and by taking into account three-phonon scattering. Overall, $\\kappa$\nincreases nearly linearly with pressure, and we show that the recently\ndiscovered $\\delta$-phase with $P\\bar{6}2m$ symmetry and the previously known\n$\\gamma$-CaF$_{2}$ high-pressure phase have significantly lower lattice thermal\nconductivities than the ambient-thermodynamic cubic fluorite ($Fm\\bar{3}m$)\nstructure. We argue that the lower $\\kappa$ of these two high-pressure phases\nstems mainly due to a lower contribution of acoustic modes to $\\kappa$ as a\nresult of their small group velocities. We further show that the phonon mean\nfree paths are very short for the $P\\bar{6}2m$ and $Pnma$ structures at high\ntemperatures, and resort to the Cahill-Pohl model to assess the lower limit of\nthermal conductivity in these domains.\n"}
+{"abstract": "  This paper treats a distributed optimal control problem for a tumor growth\nmodel of Cahn-Hilliard type including chemotaxis. The evolution of the tumor\nfraction is governed by a variational inequality corresponding to a double\nobstacle nonlinearity occurring in the associated potential. In addition, the\ncontrol and state variables are nonlinearly coupled and, furthermore, the cost\nfunctional contains a nondifferentiable term like the $L^1$-norm in order to\ninclude sparsity effects which is of utmost relevance, especially time\nsparsity, in the context of cancer therapies as applying a control to the\nsystem reflects in exposing the patient to an intensive medical treatment. To\ncope with the difficulties originating from the variational inequality in the\nstate system, we employ the so-called \"deep quench approximation\" in which the\nconvex part of the double obstacle potential is approximated by logarithmic\nfunctions. For such functions, first-order necessary conditions of optimality\ncan be established by invoking recent results. We use these results to derive\ncorresponding optimality conditions also for the double obstacle case, by\ndeducing a variational inequality in terms of the associated adjoint state\nvariables. The resulting variational inequality can be exploited to also obtain\nsparsity results for the optimal controls.\n"}
+{"abstract": "  A new model has been developed to estimate the dielectric constant of the\nlunar surface using Synthetic Aperture Radar (SAR) data. Continuous\ninvestigation on the dielectric constant of the lunar surface is a high\npriority task due to future lunar mission's goals and possible exploration of\nhuman outposts. For this purpose, derived anisotropy and backscattering\ncoefficients of SAR images are used. The SAR images are obtained from Miniature\nRadio Frequency (MiniRF) radar onboard Lunar Reconnaissance Orbiter (LRO).\nThese images are available in the form of Stokes parameters, which are used to\nderive the coherency matrix. The derived coherency matrix is further\nrepresented in terms of particle anisotropy. This coherency matrix's elements\ncompared with Cloud's coherency matrix, which results in the new relationship\nbetween particle anisotropy and coherency matrix elements (backscattering\ncoefficients). Following this, estimated anisotropy is used to determine the\ndielectric constant. Our model estimates the dielectric constant of the lunar\nsurface without parallax error. The produce results are also comparable with\nthe earlier estimate. As an advantageous, our method estimates the dielectric\nconstant without any apriori information about the density or composition of\nlunar surface materials. The proposed approach can also be useful for\ndetermining the dielectric properties of Mars and other celestial bodies.\n"}
+{"abstract": "  A multimode optical receiver for free space optical communications (FSOC)\nbased on a photonic lantern and adaptive optics coherent beam combining (CBC)\nof the lantern's single-mode outputs is proposed and demonstrated for the first\ntime. The use of optical coherent combining in fiber serves to increase the\nsignal to noise ratio compared to similar receivers based on electrically\ncombined signals, and represents an all-fiber approach to low-order adaptive\noptics. This optical receiver is demonstrated using a photonic lantern with\nthree outputs, fibre couplers and active phase locking, and further\ninvestigated under atmospheric conditions with and without turbulence.\n"}
+{"abstract": "  We exhibit a closed aspherical 5-manifold of nonpositive curvature that\nfibers over a circle whose fundamental group is hyperbolic relative to abelian\nsubgroups such that the fiber is a closed aspherical 4-manifold whose\nfundamental group is not hyperbolic relative to abelian subgroups.\n"}
+{"abstract": "  We consider 2d QFTs as relevant deformations of CFTs in the thermodynamic\nlimit. Using causality and KPZ universality, we place a lower bound on the\ntimescale characterizing the onset of hydrodynamics. The bound is determined\nparametrically in terms of the temperature and the scale associated with the\nrelevant deformation. This bound is typically much stronger than $\\frac{1}{T}$,\nthe expected quantum equilibration time. Subluminality of sound further allows\nus to define a thermodynamic $C$-function, and constrain the sign of the\n$\\mathcal T\\bar{\\mathcal T}$ term in EFTs.\n"}
+{"abstract": "  Robots are widespread across diverse application contexts. Teaching robots to\nperform tasks, in their respective contexts, demands a high domain and\nprogramming expertise. However, robot programming faces high entry barriers due\nto the complexity of robot programming itself. Even for experts robot\nprogramming is a cumbersome and error-prone task where faulty robot programs\ncan be created, causing damage when being executed on a real robot. To simplify\nthe process of robot programming, we combine Augmented Reality (AR) with\nprinciples of end-user development. By combining them, the real environment is\nextended with useful virtual artifacts that can enable experts as well as\nnon-professionals to perform complex robot programming tasks. Therefore, Simple\nProgramming Environment in Augmented Reality with Enhanced Debugging (SPEARED)\nwas developed as a prototype for an AR-assisted robot programming environment.\nSPEARED makes use of AR to project a robot as well as a programming environment\nonto the target working space. To evaluate our approach, expert interviews with\ndomain experts from the area of industrial automation, robotics, and AR were\nperformed. The experts agreed that SPEARED has the potential to enrich and ease\ncurrent robot programming processes.\n"}
+{"abstract": "  Using a data sample of 980~fb$^{-1}$ collected with the Belle detector\noperating at the KEKB asymmetric-energy $e^+e^-$ collider, we present evidence\nfor the $\\Omega(2012)^-$ in the resonant substructure of $\\Omega_{c}^{0} \\to\n\\pi^+ (\\bar{K}\\Xi)^{-}$ ($(\\bar{K}\\Xi)^{-}$ = $K^-\\Xi^0$ + $\\bar{K}^0 \\Xi^-$)\ndecays. The significance of the $\\Omega(2012)^-$ signal is 4.2$\\sigma$ after\nconsidering the systematic uncertainties. The ratio of the branching fraction\nof $\\Omega_{c}^{0} \\to \\pi^{+} \\Omega(2012)^- \\to \\pi^+ (\\bar{K}\\Xi)^{-}$\nrelative to that of $\\Omega_{c}^{0} \\to \\pi^{+} \\Omega^-$ is calculated to be\n0.220 $\\pm$ 0.059(stat.) $\\pm$ 0.035(syst.). The individual ratios of the\nbranching fractions of the two isospin modes are also determined, and found to\nbe ${\\cal B}(\\Omega_{c}^0 \\to \\pi^+ \\Omega(2012)^-) \\times {\\cal\nB}(\\Omega(2012)^- \\to K^-\\Xi^0)/{\\cal B}(\\Omega_{c}^0 \\to \\pi^+ K^- \\Xi^0)$ =\n(9.6 $\\pm$ 3.2(stat.) $\\pm$ 1.8(syst.))\\% and ${\\cal B}(\\Omega_{c}^0 \\to \\pi^+\n\\Omega(2012)^-) \\times {\\cal B}(\\Omega(2012)^- \\to \\bar{K}^0 \\Xi^-)/{\\cal\nB}(\\Omega_{c}^0 \\to \\pi^+ \\bar{K}^0 \\Xi^-)$ = (5.5 $\\pm$ 2.8(stat.) $\\pm$\n0.7(syst.))\\%.\n"}
+{"abstract": "  Writers, poets, singers usually do not create their compositions in just one\nbreath. Text is revisited, adjusted, modified, rephrased, even multiple times,\nin order to better convey meanings, emotions and feelings that the author wants\nto express. Amongst the noble written arts, Poetry is probably the one that\nneeds to be elaborated the most, since the composition has to formally respect\npredefined meter and rhyming schemes. In this paper, we propose a framework to\ngenerate poems that are repeatedly revisited and corrected, as humans do, in\norder to improve their overall quality. We frame the problem of revising poems\nin the context of Reinforcement Learning and, in particular, using Proximal\nPolicy Optimization. Our model generates poems from scratch and it learns to\nprogressively adjust the generated text in order to match a target criterion.\nWe evaluate this approach in the case of matching a rhyming scheme, without\nhaving any information on which words are responsible of creating rhymes and on\nhow to coherently alter the poem words. The proposed framework is general and,\nwith an appropriate reward shaping, it can be applied to other text generation\nproblems.\n"}
+{"abstract": "  A mesoscale model with molecular resolutions is presented for the\ndipalmitoyl-phosphatidylcholine (DPPC) and\n1-palmitoyl-2-oleyl-sn-glycero-3-phosphocholine (POPC) monolayer simulations at\nthe air-water interface using many-body dissipative particle dynamics (MDPD).\nThe parameterization scheme is rigorously based on reproducing the physical\nproperties of water and alkane and the interfacial property of the phospholipid\nmonolayer by comparing with our experimental results. The MDPD model yields a\nsimilar surface pressure-area isotherm as well as the similar pressure-related\nmorphologies compared with the all-atomistic simulations and experiments.\nMoreover, the compressibility modulus, order parameter of lipid tails, and\nthickness of the MDPD phospholipid monolayer are quantitatively in line with\nthe all-atomistic simulations and experiments. This model can also capture the\nsensitive changes in the pressure-area isotherms of the mixed DPPC/POPC\nmonolayers with altered mixed ratios by comparing with the experiments,\nindicating that our model scheme is promising in applications for complex\nnatural phospholipid monolayers. These results demonstrate a significant\nimprovement on quantitative phospholipid monolayer simulations over the\nprevious coarse-grained models.\n"}
+{"abstract": "  Cost functions have the potential to provide compact and understandable\ngeneralizations of motion. The goal of Inverse Optimal Control (IOC) is to\nanalyze an observed behavior which is assumed to be optimal with respect to an\nunknown cost function, and infer this cost function. Here we develop a method\nfor characterizing cost functions of legged locomotion, with the goal of\nrepresenting complex humanoid behavior with simple models. To test this\nmethodology we simulate walking gaits of a simple 5 link planar walking model\nwhich optimize known cost functions, and assess the ability of our IOC method\nto recover them. In particular, the IOC method uses an iterative trajectory\noptimization process to infer cost function weightings consistent with those\nused to generate a single demonstrated optimal trial. We also explore\nsensitivity of the IOC to sensor noise in the observed trajectory, imperfect\nknowledge of the model or task, as well as uncertainty in the components of the\ncost function used. With appropriate modeling, these methods may help infer\ncost functions from human data, yielding a compact and generalizable\nrepresentation of human-like motion for use in humanoid robot controllers, as\nwell as providing a new tool for experimentally exploring human preferences.\n"}
+{"abstract": "  A moplex is a natural graph structure that arises when lifting Dirac's\nclassical theorem from chordal graphs to general graphs. However, while every\nnon-complete graph has at least two moplexes, little is known about structural\nproperties of graphs with a bounded number of moplexes. The study of these\ngraphs is motivated by the parallel between moplexes in general graphs and\nsimplicial modules in chordal graphs: Unlike in the moplex setting, properties\nof chordal graphs with a bounded number of simplicial modules are well\nunderstood. For instance, chordal graphs having at most two simplicial modules\nare interval. In this work we initiate an investigation of $k$-moplex graphs,\nwhich are defined as graphs containing at most $k$ moplexes. Of particular\ninterest is the smallest nontrivial case $k=2$, which forms a counterpart to\nthe class of interval graphs. As our main structural result, we show that the\nclass of connected $2$-moplex graphs is sandwiched between the classes of\nproper interval graphs and cocomparability graphs; moreover, both inclusions\nare tight for hereditary classes. From a complexity theoretic viewpoint, this\nleads to the natural question of whether the presence of at most two moplexes\nguarantees a sufficient amount of structure to efficiently solve problems that\nare known to be intractable on cocomparability graphs, but not on proper\ninterval graphs. We develop new reductions that answer this question negatively\nfor two prominent problems fitting this profile, namely Graph Isomorphism and\nMax-Cut. On the other hand, we prove that every connected $2$-moplex graph\ncontains a Hamiltonian path, generalising the same property of connected proper\ninterval graphs. Furthermore, for graphs with a higher number of moplexes, we\nlift the previously known result that graphs without asteroidal triples have at\nmost two moplexes to the more general setting of larger asteroidal sets.\n"}
+{"abstract": "  Modern sentence encoders are used to generate dense vector representations\nthat capture the underlying linguistic characteristics for a sequence of words,\nincluding phrases, sentences, or paragraphs. These kinds of representations are\nideal for training a classifier for an end task such as sentiment analysis,\nquestion answering and text classification. Different models have been proposed\nto efficiently generate general purpose sentence representations to be used in\npretraining protocols. While averaging is the most commonly used efficient\nsentence encoder, Discrete Cosine Transform (DCT) was recently proposed as an\nalternative that captures the underlying syntactic characteristics of a given\ntext without compromising practical efficiency compared to averaging. However,\nas with most other sentence encoders, the DCT sentence encoder was only\nevaluated in English. To this end, we utilize DCT encoder to generate universal\nsentence representation for different languages such as German, French, Spanish\nand Russian. The experimental results clearly show the superior effectiveness\nof DCT encoding in which consistent performance improvements are achieved over\nstrong baselines on multiple standardized datasets.\n"}
+{"abstract": "  Collapsing process is studied in special type of inhomogeneous spherically\nsymmetric space-time model (known as IFRW model), having no time-like Killing\nvector field. The matter field for collapse dynamics is considered to be\nperfect fluid with anisotropic pressure. The main issue of the present\ninvestigation is to examine whether the end state of the collapse to be a naked\nsingularity or a black hole. Finally, null geodesics is studied near the\nsingularity.\n"}
+{"abstract": "  Based on a view of current flavour physics and motivated by the hierarchy\nproblem and by the pattern of quark masses and mixings, I describe a picture of\nflavour physics that should give rise in a not too distant future to observable\ndeviations from the SM in Higgs compositeness and/or in B-decays with\nviolations of Lepton Flavour Universality, as hinted by current data, or\nperhaps even in supersymmetry, depending on the specific realisation.\n"}
+{"abstract": "  In astronomical spectroscopy, optical fibres are abundantly used for\nmultiplexing and decoupling the spectrograph from the telescope to provide\nstability in a controlled environment. However, fibres are less than perfect\noptical components and introduce complex effects that diminish the overall\nthroughput, efficiency, and stability of the instrument.\n  We present a novel numerical field propagation model that emulates the\neffects of modal noise, scrambling, and focal ratio degradation with a rigorous\ntreatment of wave optics. We demonstrate that the simulation of the near- and\nfar-field output of a fiber, injected into a ray-tracing model of the\nspectrograph, allows to assess performance at the detector level.\n"}
+{"abstract": "  Federated Learning is a promising machine learning paradigm when multiple\nparties collaborate to build a high-quality machine learning model.\nNonetheless, these parties are only willing to participate when given enough\nincentives, such as a fair reward based on their contributions. Many studies\nexplored Shapley value based methods to evaluate each party's contribution to\nthe learned model. However, they commonly assume a semi-trusted server to train\nthe model and evaluate the data owners' model contributions, which lacks\ntransparency and may hinder the success of federated learning in practice. In\nthis work, we propose a blockchain-based federated learning framework and a\nprotocol to transparently evaluate each participant's contribution. Our\nframework protects all parties' privacy in the model building phase and\ntransparently evaluates contributions based on the model updates. The\nexperiment with the handwritten digits dataset demonstrates that the proposed\nmethod can effectively evaluate the contributions.\n"}
+{"abstract": "  This paper investigates the problem of impact-time-control and proposes a\nlearning-based computational guidance algorithm to solve this problem. The\nproposed guidance algorithm is developed based on a general\nprediction-correction concept: the exact time-to-go under proportional\nnavigation guidance with realistic aerodynamic characteristics is estimated by\na deep neural network and a biased command to nullify the impact time error is\ndeveloped by utilizing the emerging reinforcement learning techniques. The deep\nneural network is augmented into the reinforcement learning block to resolve\nthe issue of sparse reward that has been observed in typical reinforcement\nlearning formulation. Extensive numerical simulations are conducted to support\nthe proposed algorithm.\n"}
+{"abstract": "  We address the task of converting a floorplan and a set of associated photos\nof a residence into a textured 3D mesh model, a task which we call Plan2Scene.\nOur system 1) lifts a floorplan image to a 3D mesh model; 2) synthesizes\nsurface textures based on the input photos; and 3) infers textures for\nunobserved surfaces using a graph neural network architecture. To train and\nevaluate our system we create indoor surface texture datasets, and augment a\ndataset of floorplans and photos from prior work with rectified surface crops\nand additional annotations. Our approach handles the challenge of producing\ntileable textures for dominant surfaces such as floors, walls, and ceilings\nfrom a sparse set of unaligned photos that only partially cover the residence.\nQualitative and quantitative evaluations show that our system produces\nrealistic 3D interior models, outperforming baseline approaches on a suite of\ntexture quality metrics and as measured by a holistic user study.\n"}
+{"abstract": "  In 1888, Heinrich Schroeter provided a ruler construction for points on cubic\ncurves based on line involutions. Using Chasles' Theorem and the terminology of\nelliptic curves, we give a simple proof of Schroeter's construction. In\naddition, we show how to construct tangents and additional points on the curve\nusing another ruler construction which is also based on line involutions.\n"}
+{"abstract": "  We prove that there exists a function $f(k)=\\mathcal{O}(k^2 \\log k)$ such\nthat for every $C_4$-free graph $G$ and every $k \\in \\mathbb{N}$, $G$ either\ncontains $k$ vertex-disjoint holes of length at least $6$, or a set $X$ of at\nmost $f(k)$ vertices such that $G-X$ has no hole of length at least $6$. This\nanswers a question of Kim and Kwon [Erd\\H{o}s-P\\'osa property of chordless\ncycles and its applications. JCTB 2020].\n"}
+{"abstract": "  In this paper we examine the concept of complexity as it applies to\ngenerative art and design. Complexity has many different, discipline specific\ndefinitions, such as complexity in physical systems (entropy), algorithmic\nmeasures of information complexity and the field of \"complex systems\". We apply\na series of different complexity measures to three different generative art\ndatasets and look at the correlations between complexity and individual\naesthetic judgement by the artist (in the case of two datasets) or the\nphysically measured complexity of 3D forms. Our results show that the degree of\ncorrelation is different for each set and measure, indicating that there is no\noverall \"better\" measure. However, specific measures do perform well on\nindividual datasets, indicating that careful choice can increase the value of\nusing such measures. We conclude by discussing the value of direct measures in\ngenerative and evolutionary art, reinforcing recent findings from neuroimaging\nand psychology which suggest human aesthetic judgement is informed by many\nextrinsic factors beyond the measurable properties of the object being judged.\n"}
+{"abstract": "  There is a growing body of malware samples that evade automated analysis and\ndetection tools. Malware may measure fingerprints (\"artifacts\") of the\nunderlying analysis tool or environment and change their behavior when\nartifacts are detected. While analysis tools can mitigate artifacts to reduce\nexposure, such concealment is expensive. However, not every sample checks for\nevery type of artifact-analysis efficiency can be improved by mitigating only\nthose artifacts most likely to be used by a sample. Using that insight, we\npropose MIMOSA, a system that identifies a small set of \"covering\" tool\nconfigurations that collectively defeat most malware samples with increased\nefficiency. MIMOSA identifies a set of tool configurations that maximize\nanalysis throughput and detection accuracy while minimizing manual effort,\nenabling scalable automation to analyze stealthy malware. We evaluate our\napproach against a benchmark of 1535 labeled stealthy malware samples. Our\napproach increases analysis throughput over state of the art on over 95% of\nthese samples. We also investigate cost-benefit tradeoffs between the fraction\nof successfully-analyzed samples and computing resources required. MIMOSA\nprovides a practical, tunable method for efficiently deploying analysis\nresources.\n"}
+{"abstract": "  Efficient video processing is a critical component in many IoMT applications\nto detect events of interest. Presently, many window optimization techniques\nhave been proposed in event processing with an underlying assumption that the\nincoming stream has a structured data model. Videos are highly complex due to\nthe lack of any underlying structured data model. Video stream sources such as\nCCTV cameras and smartphones are resource-constrained edge nodes. At the same\ntime, video content extraction is expensive and requires computationally\nintensive Deep Neural Network (DNN) models that are primarily deployed at\nhigh-end (or cloud) nodes. This paper presents VID-WIN, an adaptive 2-stage\nallied windowing approach to accelerate video event analytics in an edge-cloud\nparadigm. VID-WIN runs parallelly across edge and cloud nodes and performs the\nquery and resource-aware optimization for state-based complex event matching.\nVID-WIN exploits the video content and DNN input knobs to accelerate the video\ninference process across nodes. The paper proposes a novel content-driven\nmicro-batch resizing, queryaware caching and micro-batch based utility\nfiltering strategy of video frames under resource-constrained edge nodes to\nimprove the overall system throughput, latency, and network usage. Extensive\nevaluations are performed over five real-world datasets. The experimental\nresults show that VID-WIN video event matching achieves ~2.3X higher throughput\nwith minimal latency and ~99% bandwidth reduction compared to other baselines\nwhile maintaining query-level accuracy and resource bounds.\n"}
+{"abstract": "  We propose DeepMetaHandles, a 3D conditional generative model based on mesh\ndeformation. Given a collection of 3D meshes of a category and their\ndeformation handles (control points), our method learns a set of meta-handles\nfor each shape, which are represented as combinations of the given handles. The\ndisentangled meta-handles factorize all the plausible deformations of the\nshape, while each of them corresponds to an intuitive deformation. A new\ndeformation can then be generated by sampling the coefficients of the\nmeta-handles in a specific range. We employ biharmonic coordinates as the\ndeformation function, which can smoothly propagate the control points'\ntranslations to the entire mesh. To avoid learning zero deformation as\nmeta-handles, we incorporate a target-fitting module which deforms the input\nmesh to match a random target. To enhance deformations' plausibility, we employ\na soft-rasterizer-based discriminator that projects the meshes to a 2D space.\nOur experiments demonstrate the superiority of the generated deformations as\nwell as the interpretability and consistency of the learned meta-handles.\n"}
+{"abstract": "  The single electron double quantum dot architecture is a versatile qubit\ncandidate, profiting from the advantages of both the spin qubit and the charge\nqubit, while also offering ways to mitigate their drawbacks. By carefully\ncontrolling the electrical parameters of the device, it can be imparted greater\nspinlike or greater chargelike characteristics, yielding long coherence times\nwith the former and, with the latter, allowing electrically driven spin\nrotations or coherent interaction with a microwave photon. In this work, we\ndemonstrate that applying the GRAPE algorithm to design the control pulses\nneeded to alter the operating regime of this device while preserving the\nlogical state encoded within can yield higher fidelity transfers than can be\nachieved using standard linear methods.\n"}
+{"abstract": "  A nuclear excitation following the capture of an electron in an empty orbital\nhas been recently observed for the first time. So far, the evaluation of the\ncross section of the process has been carried out widely using the assumption\nthat the ion is in its electronic ground state prior to the capture. We show\nthat by lifting this restriction new capture channels emerge resulting in a\nboost of various orders of magnitude to the electron capture resonance\nstrength. The present study also suggests the possibility to externally select\nthe capture channels by means of vortex electron beams.\n"}
+{"abstract": "  Video summarization technologies aim to create a concise and complete\nsynopsis by selecting the most informative parts of the video content. Several\napproaches have been developed over the last couple of decades and the current\nstate of the art is represented by methods that rely on modern deep neural\nnetwork architectures. This work focuses on the recent advances in the area and\nprovides a comprehensive survey of the existing deep-learning-based methods for\ngeneric video summarization. After presenting the motivation behind the\ndevelopment of technologies for video summarization, we formulate the video\nsummarization task and discuss the main characteristics of a typical\ndeep-learning-based analysis pipeline. Then, we suggest a taxonomy of the\nexisting algorithms and provide a systematic review of the relevant literature\nthat shows the evolution of the deep-learning-based video summarization\ntechnologies and leads to suggestions for future developments. We then report\non protocols for the objective evaluation of video summarization algorithms and\nwe compare the performance of several deep-learning-based approaches. Based on\nthe outcomes of these comparisons, as well as some documented considerations\nabout the amount of annotated data and the suitability of evaluation protocols,\nwe indicate potential future research directions.\n"}
+{"abstract": "  Quantum machine learning is one of the most promising applications of quantum\ncomputing in the Noisy Intermediate-Scale Quantum(NISQ) era. Here we propose a\nquantum convolutional neural network(QCNN) inspired by convolutional neural\nnetworks(CNN), which greatly reduces the computing complexity compared with its\nclassical counterparts, with $O((log_{2}M)^6) $ basic gates and $O(m^2+e)$\nvariational parameters, where $M$ is the input data size, $m$ is the filter\nmask size and $e$ is the number of parameters in a Hamiltonian. Our model is\nrobust to certain noise for image recognition tasks and the parameters are\nindependent on the input sizes, making it friendly to near-term quantum\ndevices. We demonstrate QCNN with two explicit examples. First, QCNN is applied\nto image processing and numerical simulation of three types of spatial\nfiltering, image smoothing, sharpening, and edge detection are performed.\nSecondly, we demonstrate QCNN in recognizing image, namely, the recognition of\nhandwritten numbers. Compared with previous work, this machine learning model\ncan provide implementable quantum circuits that accurately corresponds to a\nspecific classical convolutional kernel. It provides an efficient avenue to\ntransform CNN to QCNN directly and opens up the prospect of exploiting quantum\npower to process information in the era of big data.\n"}
+{"abstract": "  We give a criterion for the existence for pseudo-horizontal surfaces in small\nSeifert fibered manifolds. We calculate the genuses for such surfaces and\ndetect their $\\mathbb{Z}_2$-homology classes. Using such pseudo-horizontal\nsurfaces, we can determine the $\\mathbb{Z}_2$-Thurston norm for every\n$\\mathbb{Z}_2$-homology classes in small Seifert manifolds. We find several\nfamilies of examples that in the same $\\mathbb{Z}_2$-homology class the genus\nof a pseudo-horizontal surface is less than the genus of the pseudo-vertical\nsurface. Hence the pseudo-vertical surfaces is not $\\mathbb{Z}_2$-taut.\n"}
+{"abstract": "  A uniform in space, oscillatory in time plasma equilibrium sustained by a\ntime-dependent current density is analytically and numerically studied\nresorting to particle-in-cell simulations. The dispersion relation is derived\nfrom the Vlasov equation for oscillating equilibrium distribution functions,\nand used to demonstrate that the plasma has an infinite number of unstable\nkinetic modes. This instability represents a new kinetic mechanism for the\ndecay of the initial mode of infinite wavelength (or equivalently null\nwavenumber), for which no classical wave breaking or Landau damping exists. The\nrelativistic generalization of the instability is discussed. In this regime,\nthe growth rate of the fastest growing unstable modes scales with\n$\\gamma_T^{-1/2}$, where $\\gamma_T$ is the largest Lorentz factor of the plasma\ndistribution. This result hints that this instability is not as severely\nsuppressed for large Lorentz factor flows as purely streaming instabilities.\nThe relevance of this instability in inductive electric field oscillations\ndriven in pulsar magnetospheres is discussed.\n"}
+{"abstract": "  End-to-end models with auto-regressive decoders have shown impressive results\nfor automatic speech recognition (ASR). These models formulate the\nsequence-level probability as a product of the conditional probabilities of all\nindividual tokens given their histories. However, the performance of locally\nnormalised models can be sub-optimal because of factors such as exposure bias.\nConsequently, the model distribution differs from the underlying data\ndistribution. In this paper, the residual energy-based model (R-EBM) is\nproposed to complement the auto-regressive ASR model to close the gap between\nthe two distributions. Meanwhile, R-EBMs can also be regarded as\nutterance-level confidence estimators, which may benefit many downstream tasks.\nExperiments on a 100hr LibriSpeech dataset show that R-EBMs can reduce the word\nerror rates (WERs) by 8.2%/6.7% while improving areas under precision-recall\ncurves of confidence scores by 12.6%/28.4% on test-clean/test-other sets.\nFurthermore, on a state-of-the-art model using self-supervised learning\n(wav2vec 2.0), R-EBMs still significantly improves both the WER and confidence\nestimation performance.\n"}
+{"abstract": "  The DESERT Underwater framework (http://desert-underwater.dei.unipd.it/),\noriginally designed for simulating and testing underwater acoustic networks in\nsea trials, has recently been extended to support real payload data\ntransmission through underwater multimodal networks. Specifically, the new\nversion of the framework is now able to transmit data in real time through the\nEvoLogics S2C low-rate and high-rate acoustic modems, the SmartPORT low-cost\nacoustic underwater modem prototype (AHOI) for IoT applications, as well as\nEthernet, surface WiFi, and the BlueComm optical modem. The system can also be\ntested in the lab by employing a simulated channel, and the EvoLogics S2C DMAC\nEmulator (DMACE)\n"}
+{"abstract": "  The classical Poincar{\\'e} conjecture that every homotopy 3-sphere is\ndiffeomorphic to the 3-sphere is proved by G. Perelman by solving Thurston's\nprogram on geometrizations of 3-manifolds. A new confirmation of this\nconjecture is given by combining R. H. Bing's result on this conjecture with\nSmooth Unknotting Conjecutre for an $S^2$-knot and Smooth 4D Poincar{\\'e}\nConjecture.\n"}
+{"abstract": "  We describe a geometric and symmetry-based formulation of the equivalence\nprinciple in non-relativistic physics. It applies both on the classical and\nquantum levels and states that the Newtonian potential can be eliminated in\nfavor of a curved and time-dependent spatial metric. It is this requirement\nthat forces the gravitational mass to be equal to the inertial mass. We\nidentify the symmetry responsible for the equivalence principle as the remnant\nof time-reparameterization symmetry of the relativistic theory. We also clarify\nthe transformation properties of the Schroedinger wave-function under arbitrary\nchanges of frame.\n"}
+{"abstract": "  We study the energy-momentum relations of the Nambu-Goldstone modes in\nquantum antiferromagnetic Heisenberg models on a hypercubic lattice. This work\nis a sequel to the previous series about the models. We prove that the\nNambu-Goldstone modes show a linear dispersion relation for the small momenta,\ni.e., the low energies of the Nambu-Goldstone excitations are proportional to\nthe momentum of the spin wave above the infinite-volume ground states with\nsymmetry breaking. Our method relies on the upper bounds for the\nsusceptibilities which are derived from the reflection positivity of the\nquantum Heisenberg antiferromagnets. The method is also applicable to other\nsystems when the corresponding upper bounds for the susceptibilities are\njustified.\n"}
+{"abstract": "  In this paper, we present first-order accurate numerical methods for solution\nof the heat equation with uncertain temperature-dependent thermal conductivity.\nEach algorithm yields a shared coefficient matrix for the ensemble set\nimproving computational efficiency. Both mixed and Robin-type boundary\nconditions are treated. In contrast with alternative, related methodologies,\nstability and convergence are unconditional. In particular, we prove\nunconditional, energy stability and optimal-order error estimates. A battery of\nnumerical tests are presented to illustrate both the theory and application of\nthese algorithms.\n"}
+{"abstract": "  Sentiment analysis (SA) is an important research area in cognitive\ncomputation-thus in-depth studies of patterns of sentiment analysis are\nnecessary. At present, rich resource data-based SA has been well developed,\nwhile the more challenging and practical multi-source unsupervised SA (i.e. a\ntarget domain SA by transferring from multiple source domains) is seldom\nstudied. The challenges behind this problem mainly locate in the lack of\nsupervision information, the semantic gaps among domains (i.e., domain shifts),\nand the loss of knowledge. However, existing methods either lack the\ndistinguishable capacity of the semantic gaps among domains or lose private\nknowledge. To alleviate these problems, we propose a two-stage domain\nadaptation framework. In the first stage, a multi-task methodology-based\nshared-private architecture is employed to explicitly model the domain common\nfeatures and the domain-specific features for the labeled source domains. In\nthe second stage, two elaborate mechanisms are embedded in the shared private\narchitecture to transfer knowledge from multiple source domains. The first\nmechanism is a selective domain adaptation (SDA) method, which transfers\nknowledge from the closest source domain. And the second mechanism is a\ntarget-oriented ensemble (TOE) method, in which knowledge is transferred\nthrough a well-designed ensemble method. Extensive experiment evaluations\nverify that the performance of the proposed framework outperforms unsupervised\nstate-of-the-art competitors. What can be concluded from the experiments is\nthat transferring from very different distributed source domains may degrade\nthe target-domain performance, and it is crucial to choose the proper source\ndomains to transfer from.\n"}
+{"abstract": "  Consider a bipartite quantum system consisting of two subsystems A and B. The\nreduced density matrix ofA a is obtained by taking the partial trace with\nrespect to B. In this Letter we show that the Wigner distribution of this\nreduced density matrix is obtained by integrating the total Wigner distribution\nwith respect to the phase space variables corresponding to the subsystem B. Our\nproof makes use of the Weyl--Wigner--Moyal phase space formalism. Our result is\napplied to general Gaussian mixed states of which i gives a particularly simple\nand precise description. We also briefly discuss purification from the Wigner\npoint of view.\n"}
+{"abstract": "  Softening material models are known to trigger spurious localizations.This\nmay be shown theoretically by the existence of solutions with zero dissipation\nwhen localization occurs and numerically with spurious mesh dependency and\nlocalization in a single layer of elements. We introduce in this paper a new\nway to avoid spurious localization. The idea is to enforce a Lipschitz\nregularity on the internal variables responsible for the material softening.\nThe regularity constraint introduces the needed length scale in the material\nformulation. Moreover, we prove bounds on the domain affected by this\nconstraint. A first one-dimensional finite element implementation is proposed\nfor softening elasticity and softening plasticity.\n"}
+{"abstract": "  Autonomous missions of small unmanned aerial vehicles (UAVs) are prone to\ncollisions owing to environmental disturbances and localization errors.\nConsequently, a UAV that can endure collisions and perform recovery control in\ncritical aerial missions is desirable to prevent loss of the vehicle and/or\npayload. We address this problem by proposing a novel foldable quadrotor system\nwhich can sustain collisions and recover safely. The quadrotor is designed with\nintegrated mechanical compliance using a torsional spring such that the impact\ntime is increased and the net impact force on the main body is decreased. The\npost-collision dynamics is analysed and a recovery controller is proposed which\nstabilizes the system to a hovering location without additional collisions.\nFlight test results on the proposed and a conventional quadrotor demonstrate\nthat for the former, integrated spring-damper characteristics reduce the\nrebound velocity and lead to simple recovery control algorithms in the event of\nunintended collisions as compared to a rigid quadrotor of the same dimension.\n"}
+{"abstract": "  When an epidemic spreads into a population, it is often unpractical or\nimpossible to have a continuous monitoring of all subjects involved. As an\nalternative, algorithmic solutions can be used to infer the state of the whole\npopulation from a limited amount of measures. We analyze the capability of deep\nneural networks to solve this challenging task. Our proposed architecture is\nbased on Graph Convolutional Neural Networks. As such it can reason on the\neffect of the underlying social network structure, which is recognized as the\nmain component in the spreading of an epidemic. We test the proposed\narchitecture with two scenarios modeled on the CoVid-19 pandemic: a generic\nhomogeneous population, and a toy model of Boston metropolitan area.\n"}
+{"abstract": "  We introduce a generalized concept of quantum teleportation in the framework\nof quantum measurement and reversing operation. Our framework makes it possible\nto find an optimal protocol for quantum teleportation enabling a faithful\ntransfer of unknown quantum states with maximum success probability up to the\nfundamental limit of the no-cloning theorem. Moreover, an optimized protocol in\nthis generalized approach allows us to overcome noise in quantum channel beyond\nthe reach of existing teleportation protocols without requiring extra qubit\nresources. Our proposed framework is applicable to multipartite quantum\ncommunications and primitive functionalities in scalable quantum architectures.\n"}
+{"abstract": "  Optical cavities find diverse uses in lasers, frequency combs, optomechanics,\nand optical signal processors. Complete reconfigurability of the cavities\nenables development of generic field programmable cavities for achieving the\ndesired performance in all these applications. We propose and demonstrate a\nsimple and generic interferometer in a cavity structure that enables periodic\nmodification of the internal cavity loss and the cavity resonance to\nreconfigure the Q-factor, transmission characteristics, and group delay of the\nhybrid cavity, with simple engineering of the interferometer. We also\ndemonstrate methods to decouple the tuning of the loss from the\nresonance-shift, for resonance-locked reconfigurability. Such devices can be\nimplemented in any guided-wave platform (on-chip or fiber-optic) with potential\napplications in programmable photonics and reconfigurable optomechanics.\n"}
+{"abstract": "  The Marathi language is one of the prominent languages used in India. It is\npredominantly spoken by the people of Maharashtra. Over the past decade, the\nusage of language on online platforms has tremendously increased. However,\nresearch on Natural Language Processing (NLP) approaches for Marathi text has\nnot received much attention. Marathi is a morphologically rich language and\nuses a variant of the Devanagari script in the written form. This works aims to\nprovide a comprehensive overview of available resources and models for Marathi\ntext classification. We evaluate CNN, LSTM, ULMFiT, and BERT based models on\ntwo publicly available Marathi text classification datasets and present a\ncomparative analysis. The pre-trained Marathi fast text word embeddings by\nFacebook and IndicNLP are used in conjunction with word-based models. We show\nthat basic single layer models based on CNN and LSTM coupled with FastText\nembeddings perform on par with the BERT based models on the available datasets.\nWe hope our paper aids focused research and experiments in the area of Marathi\nNLP.\n"}
+{"abstract": "  For fixed $m$ and $R\\subseteq \\{0,1,\\ldots,m-1\\}$, take $A$ to be the set of\npositive integers congruent modulo $m$ to one of the elements of $R$, and let\n$p_A(n)$ be the number of ways to write $n$ as a sum of elements of $A$.\nNathanson proved that $\\log p_A(n) \\leq (1+o(1)) \\pi \\sqrt{2n|R|/3m}$ using a\nvariant of a remarkably simple method devised by Erd\\H{o}s in order to bound\nthe partition function. In this short note we describe a simpler and shorter\nproof of Nathanson's bound.\n"}
+{"abstract": "  Conversational interfaces to Business Intelligence (BI) applications enable\ndata analysis using a natural language dialog in small incremental steps. To\ntruly unleash the power of conversational BI to democratize access to data, a\nsystem needs to provide effective and continuous support for data analysis. In\nthis paper, we propose BI-REC, a conversational recommendation system for BI\napplications to help users accomplish their data analysis tasks.\n  We define the space of data analysis in terms of BI patterns, augmented with\nrich semantic information extracted from the OLAP cube definition, and use\ngraph embeddings learned using GraphSAGE to create a compact representation of\nthe analysis state. We propose a two-step approach to explore the search space\nfor useful BI pattern recommendations. In the first step, we train a\nmulti-class classifier using prior query logs to predict the next high-level\nactions in terms of a BI operation (e.g., {\\em Drill-Down} or {\\em Roll-up})\nand a measure that the user is interested in. In the second step, the\nhigh-level actions are further refined into actual BI pattern recommendations\nusing collaborative filtering. This two-step approach allows us to not only\ndivide and conquer the huge search space, but also requires less training data.\nOur experimental evaluation shows that BI-REC achieves an accuracy of 83% for\nBI pattern recommendations and up to 2X speedup in latency of prediction\ncompared to a state-of-the-art baseline. Our user study further shows that\nBI-REC provides recommendations with a precision@3 of 91.90% across several\ndifferent analysis tasks.\n"}
+{"abstract": "  Sea fishing is a highly mobile activity, favoured by the vastness of the\noceans, the absence of physical boundaries and the abstraction of legislative\nboundaries. Understanding and anticipating this mobility is a major challenge\nfor fisheries management issues, both at the national and international levels.\n''FisherMob'' is a free Gama tool designed to study the effect of economic and\nbiological factors on the dynamics of connected fisheries. It incorporate the\nmost important processes involved in fisheries dynamics: fish abundance\nvariability, price of the fishing effort and ex-vessel fish market price that\nwhich depends on the ratio between offer and demand. The tool uses as input a\nscheme of a coastal area with delimited fishing sites, fish biological\nparameters and fisheries parameters. It runs with a userfriendly graphic\ninterface and generates output files that can be post-processed easily using\ngraphic and statistical software.\n"}
+{"abstract": "  It is generally accepted that the pulsar magnetic field converts most of its\nrotational energy losses into radiation. In this paper, we propose an\nalternative emission mechanism, in which neither the pulsar rotational energy\nnor its magnetic field is involved. The emission mechanism proposed here is\nbased on the hypothesis that the pulsar matter is stable only when moving with\nrespect to the ambient medium at a velocity exceeding some threshold value. A\ndecrease in velocity below this threshold value leads to the decay of matter\nwith the emission of electromagnetic radiation. It is shown that decay regions\non the pulsar surface in which the velocities of pulsar particles drops to\narbitrarily small values are formed under simple driving condition. It is also\nshown that for the majority of pulsars having measured transverse velocities,\nsuch a condition is quite possible. Thus, the pulsar radiation carries away not\nthe pulsar rotational energy, but its mass, while the magnitude of the\nrotational energy does not play any role. At the end of the paper, we consider\nthe reason for the possible short-period precession of the pulsar.\n"}
+{"abstract": "  Digital tools have long been used for supporting children's creativity.\nDigital games that allow children to create artifacts and express themselves in\na playful environment serve as efficient Creativity Support Tools (or CSTs).\nCreativity is also scaffolded by social interactions with others in their\nenvironment. In our work, we explore the use of game-based interactions with a\nsocial agent to scaffold children's creative expression as game players. We\ndesigned three collaborative games and play-tested with 146 5-10 year old\nchildren played with the social robot Jibo, which affords three different kinds\nof creativity: verbal creativity, figural creativity and divergent thinking\nduring creative problem solving. In this paper, we reflect on game mechanic\npractices that we incorporated to design for stimulating creativity in\nchildren. These strategies may be valuable to game designers and HCI\nresearchers designing games and social agents for supporting children's\ncreativity.\n"}
+{"abstract": "  Pre-training models such as BERT have achieved great success in many natural\nlanguage processing tasks. However, how to obtain better sentence\nrepresentation through these pre-training models is still worthy to exploit.\nPrevious work has shown that the anisotropy problem is an critical bottleneck\nfor BERT-based sentence representation which hinders the model to fully utilize\nthe underlying semantic features. Therefore, some attempts of boosting the\nisotropy of sentence distribution, such as flow-based model, have been applied\nto sentence representations and achieved some improvement. In this paper, we\nfind that the whitening operation in traditional machine learning can similarly\nenhance the isotropy of sentence representations and achieve competitive\nresults. Furthermore, the whitening technique is also capable of reducing the\ndimensionality of the sentence representation. Our experimental results show\nthat it can not only achieve promising performance but also significantly\nreduce the storage cost and accelerate the model retrieval speed.\n"}
+{"abstract": "  Quantum uncertainty is a well-known property of quantum mechanics that states\nthe impossibility of predicting measurement outcomes of multiple incompatible\nobservables simultaneously. In contrast, the uncertainty in the classical\ndomain comes from the lack of information about the exact state of the system.\nOne may naturally ask, whether the quantum uncertainty is indeed a fully\nintrinsic property of the quantum theory, or whether similar to the classical\ndomain lack of knowledge about specific parts of the physical system might be\nthe source of this uncertainty. This question has been addressed in [New J.\nPhys.19 023038 (2017)] where the authors argue that in the entropic formulation\nof the uncertainty principle that can be illustrated using the so-called,\nguessing games, indeed such lack of information has a significant contribution\nto the arising quantum uncertainty. Here we investigate this issue\nexperimentally by implementing the corresponding two-dimensional and\nthree-dimensional guessing games. Our results confirm that within the\nguessing-game framework, the quantum uncertainty to a large extent relies on\nthe fact that quantum information determining the key properties of the game is\nstored in the degrees of freedom that remain inaccessible to the guessing\nparty.\n"}
+{"abstract": "  Tabular data underpins numerous high-impact applications of machine learning\nfrom fraud detection to genomics and healthcare. Classical approaches to\nsolving tabular problems, such as gradient boosting and random forests, are\nwidely used by practitioners. However, recent deep learning methods have\nachieved a degree of performance competitive with popular techniques. We devise\na hybrid deep learning approach to solving tabular data problems. Our method,\nSAINT, performs attention over both rows and columns, and it includes an\nenhanced embedding method. We also study a new contrastive self-supervised\npre-training method for use when labels are scarce. SAINT consistently improves\nperformance over previous deep learning methods, and it even outperforms\ngradient boosting methods, including XGBoost, CatBoost, and LightGBM, on\naverage over a variety of benchmark tasks.\n"}
+{"abstract": "  In collaborative software development, program merging is the mechanism to\nintegrate changes from multiple programmers. Merge algorithms in modern version\ncontrol systems report a conflict when changes interfere textually. Merge\nconflicts require manual intervention and frequently stall modern continuous\nintegration pipelines. Prior work found that, although costly, a large majority\nof resolutions involve re-arranging text without writing any new code. Inspired\nby this observation we propose the first data-driven approach to resolve merge\nconflicts with a machine learning model. We realize our approach in a tool\nDeepMerge that uses a novel combination of (i) an edit-aware embedding of merge\ninputs and (ii) a variation of pointer networks, to construct resolutions from\ninput segments. We also propose an algorithm to localize manual resolutions in\na resolved file and employ it to curate a ground-truth dataset comprising 8,719\nnon-trivial resolutions in JavaScript programs. Our evaluation shows that, on a\nheld out test set, DeepMerge can predict correct resolutions for 37% of\nnon-trivial merges, compared to only 4% by a state-of-the-art semistructured\nmerge technique. Furthermore, on the subset of merges with upto 3 lines\n(comprising 24% of the total dataset), DeepMerge can predict correct\nresolutions with 78% accuracy.\n"}
+{"abstract": "  We propose a framework using contrastive learning as a pre-training task to\nperform image classification in the presence of noisy labels. Recent strategies\nsuch as pseudo-labeling, sample selection with Gaussian Mixture models,\nweighted supervised contrastive learning have been combined into a fine-tuning\nphase following the pre-training. This paper provides an extensive empirical\nstudy showing that a preliminary contrastive learning step brings a significant\ngain in performance when using different loss functions: non-robust, robust,\nand early-learning regularized. Our experiments performed on standard\nbenchmarks and real-world datasets demonstrate that: i) the contrastive\npre-training increases the robustness of any loss function to noisy labels and\nii) the additional fine-tuning phase can further improve accuracy but at the\ncost of additional complexity.\n"}
+{"abstract": "  The literature in modern machine learning has only negative results for\nlearning to communicate between competitive agents using standard RL. We\nintroduce a modified sender-receiver game to study the spectrum of\npartially-competitive scenarios and show communication can indeed emerge in a\ncompetitive setting. We empirically demonstrate three key takeaways for future\nresearch. First, we show that communication is proportional to cooperation, and\nit can occur for partially competitive scenarios using standard learning\nalgorithms. Second, we highlight the difference between communication and\nmanipulation and extend previous metrics of communication to the competitive\ncase. Third, we investigate the negotiation game where previous work failed to\nlearn communication between independent agents (Cao et al., 2018). We show\nthat, in this setting, both agents must benefit from communication for it to\nemerge; and, with a slight modification to the game, we demonstrate successful\ncommunication between competitive agents. We hope this work overturns\nmisconceptions and inspires more research in competitive emergent\ncommunication.\n"}
+{"abstract": "  Delta-orthogonal multiple access (D-OMA) has been recently investigated as a\npotential technique to enhance the spectral efficiency in the sixth-generation\n(6G) networks. D-OMA enables partial overlapping of the adjacent sub-channels\nthat are assigned to different clusters of users served by non-orthogonal\nmultiple access (NOMA), at the expense of additional interference. In this\npaper, we analyze the performance of D-OMA in the uplink and develop a\nmulti-objective optimization framework to maximize the uplink energy efficiency\n(EE) in a multi-access point (AP) network enabled by D-OMA. Specifically, we\noptimize the sub-channel and transmit power allocations of the users as well as\nthe overlapping percentage of the spectrum between the adjacent sub-channels.\nThe formulated problem is a mixed binary non-linear programming problem.\nTherefore, to address the challenge we first transform the problem into a\nsingle-objective problem using Tchebycheff method. Then, we apply the monotonic\noptimization (MO) to explore the hidden monotonicity of the objective function\nand constraints, and reformulate the problem into a standard MO in canonical\nform. The reformulated problem is then solved by applying the outer polyblock\napproximation method. Our numerical results show that D-OMA outperforms the\nconventional non-orthogonal multiple access (NOMA) and orthogonal frequency\ndivision multiple access (OFDMA) when the adjacent sub-channel overlap and\nscheduling are optimized jointly.\n"}
+{"abstract": "  With the costs of renewable energy technologies declining, new forms of urban\nenergy systems are emerging that can be established in a cost-effective way.\nThe SolarEV City concept has been proposed that uses rooftop Photovoltaics (PV)\nto its maximum extent, combined with Electric Vehicle (EV) with bi-directional\ncharging for energy storage. Urban environments consist of various areas, such\nas residential and commercial districts, with different energy consumption\npatterns, building structures, and car parks. The cost effectiveness and\ndecarbonization potentials of PV + EV and PV (+ battery) systems vary across\nthese different urban environments and change over time as cost structures\ngradually shift. To evaluate these characteristics, we performed\ntechno-economic analyses of PV, battery, and EV technologies for a residential\narea in Shinchi, Fukushima and the central commercial district of Kyoto, Japan\nbetween 2020 and 2040. We found that PV + EV and PV only systems in 2020 are\nalready cost competitive relative to existing energy systems (grid electricity\nand gasoline car). In particular, the PV + EV system rapidly increases its\neconomic advantage over time, particularly in the residential district which\nhas larger PV capacity and EV battery storage relative to the size of energy\ndemand. Electricity exchanges between neighbors (e.g., peer-to-peer or\nmicrogrid) further enhanced the economic value (net present value) and\ndecarbonization potential of PV + EV systems up to 23 percent and 7 percent in\n2030, respectively. These outcomes have important strategic implications for\nurban decarbonization over the coming decades.\n"}
+{"abstract": "  Gravitational lensing has long been used to measure or constrain cosmology\nmodels. Although the lensing effect of gravitational waves has not been\nobserved by LIGO/Virgo, it is expected that there can be a few to a few\nhundreds lensed events to be detected by the future Japanese space-borne\ninterferometers DECIGO and B-DECIGO, if they are running for 4 years. Given the\npredicted lensed gravitational wave events, one can estimate the constraints on\nthe cosmological parameters via the lensing statistics and the time delay\nmethods. With the lensing statistics method, the knowledge of the lens\nredshifts, even with the moderate uncertainty, will set the tight bound on the\nenergy density parameter $\\Omega_M$ for matter, that is,\n$0.288\\lesssim\\Omega_M\\lesssim0.314$ at best. The constraint on the Hubble\nconstant $H_0$ can be determined using the time delay method. It is found out\nthat at $5\\sigma$, $|\\delta H_0|/H_0$ ranges from $3\\%$ to $11\\%$ for DECIGO,\nand B-DECIGO will give less constrained results, $8\\%-15\\%$. In this work, the\nuncertainties on the luminosity distance and the time delay distance are set to\nbe $10\\%$ and $20\\%$, respectively. The improvement on measuring these\ndistances will tighten the bounds.\n"}
+{"abstract": "  PyArmadillo is a linear algebra library for the Python language, with the aim\nof closely mirroring the programming interface of the widely used Armadillo C++\nlibrary, which in turn is deliberately similar to Matlab. PyArmadillo hence\nfacilitates algorithm prototyping with Matlab-like syntax directly in Python,\nand relatively straightforward conversion of PyArmadillo-based Python code into\nperformant Armadillo-based C++ code. The converted code can be used for\npurposes such as speeding up Python-based programs in conjunction with\npybind11, or the integration of algorithms originally prototyped in Python into\nlarger C++ codebases. PyArmadillo provides objects for matrices and cubes, as\nwell as over 200 associated functions for manipulating data stored in the\nobjects. Integer, floating point and complex numbers are supported. Various\nmatrix factorisations are provided through integration with LAPACK, or one of\nits high performance drop-in replacements such as Intel MKL or OpenBLAS.\nPyArmadillo is open-source software, distributed under the Apache 2.0 license;\nit can be obtained at https://pyarma.sourceforge.io or via the Python Package\nIndex in precompiled form.\n"}
+{"abstract": "  In this paper, we propose an efficient, high order accurate and\nasymptotic-preserving (AP) semi-Lagrangian (SL) method for the BGK model with\nconstant or spatially dependent Knudsen number. The spatial discretization is\nperformed by a mass conservative nodal discontinuous Galerkin (NDG) method,\nwhile the temporal discretization of the stiff relaxation term is realized by\nstiffly accurate diagonally implicit Runge-Kutta (DIRK) methods along\ncharacteristics. Extra order conditions are enforced for asymptotic accuracy\n(AA) property of DIRK methods when they are coupled with a semi-Lagrangian\nalgorithm in solving the BGK model. A local maximum principle preserving (LMPP)\nlimiter is added to control numerical oscillations in the transport step.\nThanks to the SL and implicit nature of time discretization, the time stepping\nconstraint is relaxed and it is much larger than that from an Eulerian\nframework with explicit treatment of the source term. Extensive numerical tests\nare presented to verify the high order AA, efficiency and shock capturing\nproperties of the proposed schemes.\n"}
+{"abstract": "  Scene text removal (STR) contains two processes: text localization and\nbackground reconstruction. Through integrating both processes into a single\nnetwork, previous methods provide an implicit erasure guidance by modifying all\npixels in the entire image. However, there exists two problems: 1) the implicit\nerasure guidance causes the excessive erasure to non-text areas; 2) the\none-stage erasure lacks the exhaustive removal of text region. In this paper,\nwe propose a ProgrEssively Region-based scene Text eraser (PERT), introducing\nan explicit erasure guidance and performing balanced multi-stage erasure for\naccurate and exhaustive text removal. Firstly, we introduce a new region-based\nmodification strategy (RegionMS) to explicitly guide the erasure process.\nDifferent from previous implicitly guided methods, RegionMS performs targeted\nand regional erasure on only text region, and adaptively perceives stroke-level\ninformation to improve the integrity of non-text areas with only bounding box\nlevel annotations. Secondly, PERT performs balanced multi-stage erasure with\nseveral progressive erasing stages. Each erasing stage takes an equal step\ntoward the text-erased image to ensure the exhaustive erasure of text regions.\nCompared with previous methods, PERT outperforms them by a large margin without\nthe need of adversarial loss, obtaining SOTA results with high speed (71 FPS)\nand at least 25% lower parameter complexity. Code is available at\nhttps://github.com/wangyuxin87/PERT.\n"}
+{"abstract": "  At most 1-2% of the global virome has been sampled to date. Here, we develop\na novel method that combines Linear Filtering (LF) and Singular Value\nDecomposition (SVD) to infer host-virus associations. Using this method, we\nrecovered highly plausible undiscovered interactions with a strong signal of\nviral coevolutionary history, and revealed a global hotspot of unusually unique\nbut unsampled (or unrealized) host-virus interactions in the Amazon rainforest.\nWe finally show that graph embedding of the imputed network can be used to\nimprove predictions of human infection from viral genome features, showing that\nthe global structure of the mammal-virus network provides additional insights\ninto human disease emergence.\n"}
+{"abstract": "  Model predictive control (MPC) schemes have a proven track record for\ndelivering aggressive and robust performance in many challenging control tasks,\ncoping with nonlinear system dynamics, constraints, and observational noise.\nDespite their success, these methods often rely on simple control\ndistributions, which can limit their performance in highly uncertain and\ncomplex environments. MPC frameworks must be able to accommodate changing\ndistributions over system parameters, based on the most recent measurements. In\nthis paper, we devise an implicit variational inference algorithm able to\nestimate distributions over model parameters and control inputs on-the-fly. The\nmethod incorporates Stein Variational gradient descent to approximate the\ntarget distributions as a collection of particles, and performs updates based\non a Bayesian formulation. This enables the approximation of complex\nmulti-modal posterior distributions, typically occurring in challenging and\nrealistic robot navigation tasks. We demonstrate our approach on both simulated\nand real-world experiments requiring real-time execution in the face of\ndynamically changing environments.\n"}
+{"abstract": "  Automatic speech recognition (ASR) systems for young children are needed due\nto the importance of age-appropriate educational technology. Because of the\nlack of publicly available young child speech data, feature extraction\nstrategies such as feature normalization and data augmentation must be\nconsidered to successfully train child ASR systems. This study proposes a novel\ntechnique for child ASR using both feature normalization and data augmentation\nmethods based on the relationship between formants and fundamental frequency\n($f_o$). Both the $f_o$ feature normalization and data augmentation techniques\nare implemented as a frequency shift in the Mel domain. These techniques are\nevaluated on a child read speech ASR task. Child ASR systems are trained by\nadapting a BLSTM-based acoustic model trained on adult speech. Using both $f_o$\nnormalization and data augmentation results in a relative word error rate (WER)\nimprovement of 19.3% over the baseline when tested on the OGI Kids' Speech\nCorpus, and the resulting child ASR system achieves the best WER currently\nreported on this corpus.\n"}
+{"abstract": "  We present an implementation of the Atiyah-Bott residue formula for\n$\\overline{M}_{0,m}(\\mathbb{P}^{n},d)$. We use this implementation to compute a\nlarge number of Gromov-Witten invariants of genus $0$, including characteristic\nnumbers of rational curves on general complete intersections. We also compute\nsome characteristic numbers of rational contact curves. Our computations\nconfirm known predictions made by Mirror Symmetry. The code we developed for\nthese computations is publicly available and can be used for other types of\ncomputationsions.\n"}
+{"abstract": "  A theorem of Glasner from 1979 shows that if $A \\subset \\mathbb{T} =\n\\mathbb{R}/\\mathbb{Z}$ is infinite then for each $\\epsilon > 0$ there exists an\ninteger $n$ such that $nA$ is $\\epsilon$-dense and Berend-Peres later showed\nthat in fact one can take $n$ to be of the form $f(m)$ for any non-constant\n$f(x) \\in \\mathbb{Z}[x]$. Alon and Peres provided a general framework for this\nproblem that has been used by Kelly-L\\^{e} and Dong to show that the same\nproperty holds for various linear actions on $\\mathbb{T}^d$. We complement the\nresult of Kelly-L\\^{e} on the $\\epsilon$-dense images of integer polynomial\nmatrices in some subtorus of $\\mathbb{T}^d$ by classifying those integer\npolynomial matrices that have the Glasner property in the full torus\n$\\mathbb{T}^d$. We also extend a recent result of Dong by showing that if\n$\\Gamma \\leq \\operatorname{SL}_d(\\mathbb{Z})$ is generated by finitely many\nunipotents and acts irreducibly on $\\mathbb{R}^d$ then the action $\\Gamma\n\\curvearrowright \\mathbb{T}^d$ has a uniform Glasner property.\n"}
+{"abstract": "  A hypergraph $H=(V(H), E(H))$ is a Berge copy of a graph $F$, if $V(F)\\subset\nV(H)$ and there is a bijection $f:E(F)\\rightarrow E(H)$ such that for any $e\\in\nE(F)$ we have $e\\subset f(e)$. A hypergraph is Berge-$F$-free if it does not\ncontain any Berge copies of $F$. We address the saturation problem concerning\nBerge-$F$-free hypergraphs, i.e., what is the minimum number $sat_r(n,F)$ of\nhyperedges in an $r$-uniform Berge-$F$-free hypergraph $H$ with the property\nthat adding any new hyperedge to $H$ creates a Berge copy of $F$. We prove that\n$sat_r(n,F)$ grows linearly in $n$ if $F$ is either complete multipartite or it\npossesses the following property: if $d_1\\le d_2\\le \\dots \\le d_{|V(F)|}$ is\nthe degree sequence of $F$, then $F$ contains two adjacent vertices $u,v$ with\n$d_F(u)=d_1$, $d_F(v)=d_2$. In particular, the Berge-saturation number of\nregular graphs grows linearly in $n$.\n"}
+{"abstract": "  Following an approach originally suggested by Balland in the context of the\nSABR model, we derive an ODE that is satisfied by normalized volatility smiles\nfor short maturities under a rough volatility extension of the SABR model that\nextends also the rough Bergomi model. We solve this ODE numerically and further\npresent a very accurate approximation to the numerical solution that we dub the\nrough SABR formula.\n"}
+{"abstract": "  Surgical data science is revolutionizing minimally invasive surgery by\nenabling context-aware applications. However, many challenges exist around\nsurgical data (and health data, more generally) needed to develop context-aware\nmodels. This work - presented as part of the Endoscopic Vision (EndoVis)\nchallenge at the Medical Image Computing and Computer Assisted Intervention\n(MICCAI) 2020 conference - seeks to explore the potential for visual domain\nadaptation in surgery to overcome data privacy concerns. In particular, we\npropose to use video from virtual reality (VR) simulations of surgical\nexercises in robotic-assisted surgery to develop algorithms to recognize tasks\nin a clinical-like setting. We present the performance of the different\napproaches to solve visual domain adaptation developed by challenge\nparticipants. Our analysis shows that the presented models were unable to learn\nmeaningful motion based features form VR data alone, but did significantly\nbetter when small amount of clinical-like data was also made available. Based\non these results, we discuss promising methods and further work to address the\nproblem of visual domain adaptation in surgical data science. We also release\nthe challenge dataset publicly at https://www.synapse.org/surgvisdom2020.\n"}
+{"abstract": "  Pristine graphene interacts relatively weakly with Al, which is a specie of\nimportance for novel generations of metal-ion batteries. We employ DFT\ncalculations to explore the possibility of enhancing Al interaction with\ngraphene. We investigate non-doped and N-doped graphene nanoribbons, address\nthe impact of the edge sites, which are always present to some extent in real\nsamples, and N-containing defects on the material's reactivity towards Al. The\nresults are compared to that of pristine graphene. We show that introduction of\nedges does not affect the energetics of Al adsorption significantly by itself.\nOn the other hand, N-doping of graphene nanoribbons is found to affect the\nadsorption energy of Al to the extent that strongly depends on the type of\nN-containing defect. While graphitic and pyrrolic N induce minimal changes, the\nintroduction of edge NO group and doping with in plane pyridinic N result in Al\nadsorption nearly twice as strong as on pristine graphene. The obtained results\ncould guide the further design of advanced materials for Al-ion rechargeable\nbatteries.\n"}
+{"abstract": "  Golan and Sapir \\cite{MR3978542} proved that the Thompson's groups $F$, $T$\nand $V$ have linear divergence. In the current paper, we focus on the\ndivergence properties of several generalisation of the Thompson's groups, we\nfirst consider the Brown-Thompson's groups $F_n$, $T_n$ and $V_n$ and found out\nthat these groups also have linear divergence function. We then consider the\nbraided Thompson's groups $BF$ and $\\widehat{BF}$ and $\\widehat{BV}$ together\nwith the result in \\cite{Kodama:2020to} we conclude that theses groups have\nlinear divergence.\n"}
+{"abstract": "  Euler's three-body problem is the problem of solving for the motion of a\nparticle moving in a Newtonian potential generated by two point sources fixed\nin space. This system is integrable in the Liouville sense. We consider the\nEuler problem with the inverse-square potential, which can be seen as a natural\ngeneralization of the three-body problem to higher-dimensional Newtonian\ntheory. We identify a family of stable stationary orbits in the generalized\nEuler problem. These orbits guarantee the existence of stable bound orbits.\nApplying the Poincar\\'e map method to these orbits, we show that stable bound\nchaotic orbits appear. As a result, we conclude that the generalized Euler\nproblem is nonintegrable.\n"}
+{"abstract": "  Non-Hermitian skin effect, namely that the eigenvalues and eigenstates of a\nnon-Hermitian tight-binding Hamiltonian have significant differences under open\nor periodic boundary conditions, is a remarkable phenomenon of non-Hermitian\nsystems. Inspired by the presence of the non-Hermitian skin effect, we study\nthe evolution of wave-packets in non-Hermitian systems, which can be determined\nusing the single-particle Green's function. Surprisingly, we find that in the\nthermodynamical limit, the Green's function does not depend on boundary\nconditions, despite the presence of skin effect. We proffer a general proof for\nthis statement in arbitrary dimension with finite hopping range, with an\nexplicit illustration in the non-Hermitian Su-Schrieffer-Heeger model. We also\nexplore its applications in non-interacting open quantum systems described by\nthe master equation, where we demonstrate that the evolution of the density\nmatrix is independent of the boundary condition.\n"}
+{"abstract": "  Binary neutron star mergers are thought to be one of the dominant sites of\nproduction for rapid neutron capture elements, including platinum and gold.\nSince the discovery of the binary neutron star merger GW170817, and its\nassociated kilonova AT2017gfo, numerous works have attempted to determine the\ncomposition of its outflowing material, but they have been hampered by the lack\nof complete atomic data. Here, we demonstrate how inclusion of new atomic data\nin synthetic spectra calculations can provide insights and constraints on the\nproduction of the heaviest elements. We employ theoretical atomic data\n(obtained using GRASP$^0$) for neutral, singly- and doubly-ionised platinum and\ngold, to generate photospheric and simple nebular phase model spectra for\nkilonova-like ejecta properties. We make predictions for the locations of\nstrong transitions, which could feasibly appear in the spectra of kilonovae\nthat are rich in these species. We identify low-lying electric quadrupole and\nmagnetic dipole transitions that may give rise to forbidden lines when the\nejecta becomes optically thin. The strongest lines lie beyond $8000\\,\\r{A}$,\nmotivating high quality near-infrared spectroscopic follow-up of kilonova\ncandidates. We compare our model spectra to the observed spectra of AT2017gfo,\nand conclude that no platinum or gold signatures are prominent in the ejecta.\nFrom our nebular phase modelling, we place tentative upper limits on the\nplatinum and gold mass of $\\lesssim$ a few $10^{-3}\\,\\rm{M}_{\\odot}$, and\n$\\lesssim 10^{-2}\\,\\rm{M}_{\\odot}$, respectively. This work demonstrates how\nnew atomic data of heavy elements can be included in radiative transfer\ncalculations, and motivates future searches for elemental signatures.\n"}
+{"abstract": "  The fate of relativistic pair beams produced in the intergalactic medium by\nvery high energy emission from blazars remains controversial in the literature.\nThe possible role of resonance beam plasma instability has been studied both\nanalytically and numerically but no consensus has been reached. In this paper,\nwe thoroughly analyze the development of this type of instability. This\nanalysis takes into account that a highly relativistic beam loses energy only\ndue to interactions with the plasma waves propagating within the opening angle\nof the beam (we call them parallel waves), whereas excitation of oblique waves\nresults merely in an angular spreading of the beam, which reduces the\ninstability growth rate. For parallel waves, the growth rate is a few times\nlarger than for oblique ones, so they grow faster than oblique waves and drain\nenergy from the beam before it expands. However, the specific property of\nextragalactic beams is that they are extraordinarily narrow; the opening angle\nis only $\\Delta\\theta\\sim 10^{-6}-10^{-5}$. In this case, the width of the\nresonance for parallel waves, $\\propto\\Delta\\theta^2$, is too small for them to\ngrow in realistic conditions. We perform both analytical estimates and\nnumerical simulations in the quasilinear regime. These show that for\nextragalactic beams, the growth of the waves is incapable of taking a\nsignificant portion of the beam's energy. This type of instability could at\nbest lead to an expansion of the beam by some factor but the beam's energy\nremains nearly intact.\n"}
+{"abstract": "  Ultraviolet (UV) and X-ray photons from active galactic nuclei (AGNs) can\nionize hydrogen in the intergalactic medium (IGM). We solve radiative transfer\naround AGNs in high redshift to evaluate the 21-cm line emission from the\nneutral hydrogen in the IGM and obtain the radial profile of the brightness\ntemperature in the epoch of reionization. The ionization profile extends over\n10 [Mpc] comoving distance which can be observed in the order of 10 [arcmin].\nFrom estimation of the radio galaxy number counts with high sensitivity\nobservation through the Square Kilometre Array (SKA), we investigate the\ncapability of parameter constrains for AGN luminosity function with Fisher\nanalysis for three evolution model through cosmic time. We find that the errors\nfor each parameter are restricted to a few percent when AGNs are sufficiently\nbright at high redshifts. We also investigate the possibility of further\nparameter constraints with future observation beyond the era of SKA.\n"}
+{"abstract": "  We determine the masses, the singlet and octet decay constants as well as the\nanomalous matrix elements of the $\\eta$ and $\\eta^\\prime$ mesons in $N_f=2+1$\nQCD\\@. The results are obtained using twenty-one CLS ensembles of\nnon-perturbatively improved Wilson fermions that span four lattice spacings\nranging from $a\\approx 0.086\\,$fm down to $a\\approx 0.050\\,$fm. The pion masses\nvary from $M_{\\pi}=420\\,$MeV to $126\\,$MeV and the spatial lattice extents\n$L_s$ are such that $L_sM_\\pi\\gtrsim 4$, avoiding significant finite volume\neffects. The quark mass dependence of the data is tightly constrained by\nemploying two trajectories in the quark mass plane, enabling a thorough\ninvestigation of U($3$) large-$N_c$ chiral perturbation theory (ChPT). The\ncontinuum limit extrapolated data turn out to be reasonably well described by\nthe next-to-leading order ChPT parametrization and the respective low energy\nconstants are determined. The data are shown to be consistent with the singlet\naxial Ward identity and, for the first time, also the matrix elements with the\ntopological charge density are computed. We also derive the corresponding\nnext-to-leading order large-$N_{c}$ ChPT formulae. We find $F^8 =\n115.0(2.8)~\\text{MeV}$, $\\theta_{8} = -25.8(2.3)^{\\circ}$, $\\theta_0 =\n-8.1(1.8)^{\\circ}$ and, in the $\\overline{\\mathrm{MS}}$ scheme for $N_f=3$,\n$F^{0}(\\mu = 2\\,\\mathrm{GeV}) = 100.1(3.0)~\\text{MeV}$, where the decay\nconstants read $F^8_\\eta=F^8\\cos \\theta_8$, $F^8_{\\eta^\\prime}=F^8\\sin\n\\theta_8$, $F^0_\\eta=-F^0\\sin \\theta_0$ and $F^0_{\\eta^\\prime}=F^0\\cos\n\\theta_0$. For the gluonic matrix elements, we obtain $a_{\\eta}(\\mu =\n2\\,\\mathrm{GeV}) = 0.0170(10)\\,\\mathrm{GeV}^{3}$ and $a_{\\eta^{\\prime}}(\\mu =\n2\\,\\mathrm{GeV}) = 0.0381(84)\\,\\mathrm{GeV}^{3}$, where statistical and all\nsystematic errors are added in quadrature.\n"}
+{"abstract": "  This paper considers the length of resolution proofs when using\nKrishnamurthy's classic symmetry rules. We show that inconsistent linear\nequation systems of bounded width over a fixed finite field $\\mathbb{F}_p$ with\n$p$ a prime have, in their standard encoding as CNFs, polynomial length\nresolutions when using the local symmetry rule (SRC-II).\n  As a consequence it follows that the multipede instances for the graph\nisomorphism problem encoded as CNF formula have polynomial length resolution\nproofs. This contrasts exponential lower bounds for\nindividualization-refinement algorithms on these graphs.\n  For the Cai-F\\\"urer-Immerman graphs, for which Tor\\'an showed exponential\nlower bounds for resolution proofs (SAT 2013), we also show that already the\nglobal symmetry rule (SRC-I) suffices to allow for polynomial length proofs.\n"}
+{"abstract": "  We report multi-frequency observations of large radio galaxies 3C 35 and 3C\n284. The low-frequency observations were done with Giant Metrewave Radio\nTelescope starting from $\\sim$150 MHz, and the high-frequency observations were\ndone with the Very Large Array. We have studied the radio morphology of these\ntwo sources at different frequencies. We present the spectral ageing map using\ntwo of the most widely used models, the Kardashev-Pacholczyk and Jaffe-Perola\nmodels. Another more realistic and complex Tribble model is also used. We also\ncalculate the jet-power and the speed of the radio lobes of these galaxies. We\ncheck for whether any episodic jet activity is present or not in these galaxies\nand found no sign of such kind of activity.\n"}
+{"abstract": "  Humans learn compositional and causal abstraction, \\ie, knowledge, in\nresponse to the structure of naturalistic tasks. When presented with a\nproblem-solving task involving some objects, toddlers would first interact with\nthese objects to reckon what they are and what can be done with them.\nLeveraging these concepts, they could understand the internal structure of this\ntask, without seeing all of the problem instances. Remarkably, they further\nbuild cognitively executable strategies to \\emph{rapidly} solve novel problems.\nTo empower a learning agent with similar capability, we argue there shall be\nthree levels of generalization in how an agent represents its knowledge:\nperceptual, conceptual, and algorithmic. In this paper, we devise the very\nfirst systematic benchmark that offers joint evaluation covering all three\nlevels. This benchmark is centered around a novel task domain, HALMA, for\nvisual concept development and rapid problem-solving. Uniquely, HALMA has a\nminimum yet complete concept space, upon which we introduce a novel paradigm to\nrigorously diagnose and dissect learning agents' capability in understanding\nand generalizing complex and structural concepts. We conduct extensive\nexperiments on reinforcement learning agents with various inductive biases and\ncarefully report their proficiency and weakness.\n"}
+{"abstract": "  Automatic extraction of product attribute values is an important enabling\ntechnology in e-Commerce platforms. This task is usually modeled using sequence\nlabeling architectures, with several extensions to handle multi-attribute\nextraction. One line of previous work constructs attribute-specific models,\nthrough separate decoders or entirely separate models. However, this approach\nconstrains knowledge sharing across different attributes. Other contributions\nuse a single multi-attribute model, with different techniques to embed\nattribute information. But sharing the entire network parameters across all\nattributes can limit the model's capacity to capture attribute-specific\ncharacteristics. In this paper we present AdaTag, which uses adaptive decoding\nto handle extraction. We parameterize the decoder with pretrained attribute\nembeddings, through a hypernetwork and a Mixture-of-Experts (MoE) module. This\nallows for separate, but semantically correlated, decoders to be generated on\nthe fly for different attributes. This approach facilitates knowledge sharing,\nwhile maintaining the specificity of each attribute. Our experiments on a\nreal-world e-Commerce dataset show marked improvements over previous methods.\n"}
+{"abstract": "  This note is devoted to establish some new arithmetic properties of the\ngeneralized Genocchi numbers $G_{n , a}$ ($n \\in \\mathbb{N}$, $a \\geq 2$). The\nresulting properties for the usual Genocchi numbers $G_n = G_{n , 2}$ are then\nderived. We show for example that for any even positive integer $n$, the\nGenocchi number $G_n$ is a multiple of the odd part of $n$.\n"}
+{"abstract": "  Self-attention has become increasingly popular in a variety of sequence\nmodeling tasks from natural language processing to recommendation, due to its\neffectiveness. However, self-attention suffers from quadratic computational and\nmemory complexities, prohibiting its applications on long sequences. Existing\napproaches that address this issue mainly rely on a sparse attention context,\neither using a local window, or a permuted bucket obtained by\nlocality-sensitive hashing (LSH) or sorting, while crucial information may be\nlost. Inspired by the idea of vector quantization that uses cluster centroids\nto approximate items, we propose LISA (LInear-time Self Attention), which\nenjoys both the effectiveness of vanilla self-attention and the efficiency of\nsparse attention. LISA scales linearly with the sequence length, while enabling\nfull contextual attention via computing differentiable histograms of codeword\ndistributions. Meanwhile, unlike some efficient attention methods, our method\nposes no restriction on casual masking or sequence length. We evaluate our\nmethod on four real-world datasets for sequential recommendation. The results\nshow that LISA outperforms the state-of-the-art efficient attention methods in\nboth performance and speed; and it is up to 57x faster and 78x more memory\nefficient than vanilla self-attention.\n"}
+{"abstract": "  We employ kernel-based approaches that use samples from a probability\ndistribution to approximate a Kolmogorov operator on a manifold. The\nself-tuning variable-bandwidth kernel method [Berry & Harlim, Appl. Comput.\nHarmon. Anal., 40(1):68--96, 2016] computes a large, sparse matrix that\napproximates the differential operator. Here, we use the eigendecomposition of\nthe discretization to (i) invert the operator, solving a differential equation,\nand (ii) represent gradient vector fields on the manifold. These methods only\nrequire samples from the underlying distribution and, therefore, can be applied\nin high dimensions or on geometrically complex manifolds when spatial\ndiscretizations are not available. We also employ an efficient $k$-$d$ tree\nalgorithm to compute the sparse kernel matrix, which is a computational\nbottleneck.\n"}
+{"abstract": "  In image editing, the most common task is pasting objects from one image to\nthe other and then eventually adjusting the manifestation of the foreground\nobject with the background object. This task is called image compositing. But\nimage compositing is a challenging problem that requires professional editing\nskills and a considerable amount of time. Not only these professionals are\nexpensive to hire, but the tools (like Adobe Photoshop) used for doing such\ntasks are also expensive to purchase making the overall task of image\ncompositing difficult for people without this skillset. In this work, we aim to\ncater to this problem by making composite images look realistic. To achieve\nthis, we are using Generative Adversarial Networks (GANS). By training the\nnetwork with a diverse range of filters applied to the images and special loss\nfunctions, the model is able to decode the color histogram of the foreground\nand background part of the image and also learns to blend the foreground object\nwith the background. The hue and saturation values of the image play an\nimportant role as discussed in this paper. To the best of our knowledge, this\nis the first work that uses GANs for the task of image compositing. Currently,\nthere is no benchmark dataset available for image compositing. So we created\nthe dataset and will also make the dataset publicly available for benchmarking.\nExperimental results on this dataset show that our method outperforms all\ncurrent state-of-the-art methods.\n"}
+{"abstract": "  Observing the Rossiter-McLaughlin effect during a planetary transit allows\nthe determination of the angle $\\lambda$ between the sky projections of the\nstar's spin axis and the planet's orbital axis. Such observations have revealed\na large population of well-aligned systems and a smaller population of\nmisaligned systems, with values of $\\lambda$ ranging up to 180$^\\circ$. For a\nsubset of 57 systems, we can now go beyond the sky projection and determine the\n3-d obliquity $\\psi$ by combining the Rossiter-McLaughlin data with constraints\non the line-of-sight inclination of the spin axis. Here we show that the\nmisaligned systems do not span the full range of obliquities; they show a\npreference for nearly-perpendicular orbits ($\\psi=80-125^\\circ$) that seems\nunlikely to be a statistical fluke. If confirmed by further observations, this\npile-up of polar orbits is a clue about the unknown processes of obliquity\nexcitation and evolution.\n"}
+{"abstract": "  We investigate quantum entanglement in an analogue black hole realized in the\nflow of a Bose-Einstein condensate. The system is described by a three-mode\nGaussian state and we construct the corresponding covariance matrix at zero and\nfinite temperature. We study associated bipartite and tripartite entanglement\nmeasures and discuss their experimental observation. We identify a simple\noptical setup equivalent to the analogue Bose-Einstein black hole which\nsuggests a new way of determining the Hawking temperature and grey-body factor\nof the system.\n"}
+{"abstract": "  Diam-mean equicontinuity is a dynamical property that has been of use in the\nstudy of non-periodic order. Using some type of \"local\" skew product between a\nshift and an odometer looking cellular automaton (CA) we will show there exists\nan almost diam-mean equicontinuous CA that is not almost equicontinuous, and\nhence not almost locally periodic.\n"}
+{"abstract": "  Hydrodynamic self-similar solutions, as obtained by Chi [J. Math. Phys. 24,\n2532 (1983)] have been generalized by introducing new variables in place of the\nold space and time variables. A systematic procedure of obtaining a complete\nset of solutions has been suggested. The Newtonian analogs of all homogeneous\nisotropic Friedmann dust universes with spatial curvature $k = 0, \\pm 1$ have\nbeen given.\n"}
+{"abstract": "  In this paper we characterize magnitude-dependent systematics in the proper\nmotions of the Gaia EDR3 catalog and provide a prescription for their removal.\nThe reference frame of bright stars (G<13) in EDR3 is known to rotate with\nrespect to extragalactic objects, but this rotation has proven difficult to\ncharacterize and correct. We employ a sample of binary stars and a sample of\nopen cluster members to characterize this proper motion bias as a\nmagnitude-dependent spin of the reference frame. We show that the bias varies\nwith G magnitude, reaching up to 80 {\\mu}as/yr for sources in the range G = 11\n- 13, several times the formal EDR3 proper motion uncertainties. We also show\nevidence for an additional dependence on the color of the source, with a\nmagnitude up to 10 {\\mu}as/yr. However, a color correction proportional to the\neffective wavenumber is unsatisfactory for very red or very blue stars and we\ndo not recommend its use. We provide a recipe for a magnitude-dependent\ncorrection to align the proper motion of the Gaia EDR3 sources brighter than\nG=13 with the International Celestial Reference Frame.\n"}
+{"abstract": "  We study canonical and affine versions of the quantized covariant Euclidean\nfree real scalar field-theory on four dimensional lattices through the Monte\nCarlo method. We calculate the two-point function at small values of the bare\ncoupling constant and near the continuum limit at finite volume. Our\ninvestigation shows that affine quantization is able to give meaningful results\nfor the two-point function for which is not available an exact analytic result\nand therefore numerical methods are necessary.\n"}
+{"abstract": "  This letter describes a method for estimating regions of attraction and\nbounds on permissible perturbation amplitudes in nonlinear fluids systems. The\nproposed approach exploits quadratic constraints between the inputs and outputs\nof the nonlinearity on elliptical sets. This approach reduces conservatism and\nimproves estimates for regions of attraction and bounds on permissible\nperturbation amplitudes over related methods that employ quadratic constraints\non spherical sets. We present and investigate two algorithms for performing the\nanalysis: an iterative method that refines the analysis by solving a sequence\nof semi-definite programs, and another based on solving a generalized\neigenvalue problem with lower computational complexity, but at the cost of some\nprecision in the final solution. The proposed algorithms are demonstrated on\nlow-order mechanistic models of transitional flows. We further compare accuracy\nand computational complexity with analysis based on sum-of-squares optimization\nand direct-adjoint looping methods.\n"}
+{"abstract": "  Since the 1980s, technology business incubators (TBIs), which focus on\naccelerating businesses through resource sharing, knowledge agglomeration, and\ntechnology innovation, have become a booming industry. As such, research on\nTBIs has gained international attention, most notably in the United States,\nEurope, Japan, and China. The present study proposes an entrepreneurial\necosystem framework with four key components, i.e., people, technology,\ncapital, and infrastructure, to investigate which factors have an impact on the\nperformance of TBIs. We also empirically examine this framework based on\nunique, three-year panel survey data from 857 national TBIs across China. We\nimplemented factor analysis and panel regression models on dozens of variables\nfrom 857 national TBIs between 2015 and 2017 in all major cities in China and\nfound that a number of factors associated with people, technology, capital, and\ninfrastructure components have various statistically significant impacts on the\nperformance of TBIs at either national model or regional models.\n"}
+{"abstract": "  Quantifying long-term statistical properties of satellite trajectories\ntypically entails time-consuming trajectory propagation. We present a fast,\nergodic\\cite{Arnold} method of analytically estimating these for\n$J_2-$perturbed elliptical orbits, broadly agreeing with trajectory\npropagation-derived results. We extend the approach in Graven and Lo (2019) to\nestimate: (1) Satellite-ground station coverage with limited satellite field of\nview and ground station elevation angle with numerically optimized formulae,\nand (2) long-term averages of general functions of satellite position. This\nmethod is fast enough to facilitate real-time, interactive tools for satellite\nconstellation and network design, with an approximate $1000\\times$ GPU speedup.\n"}
+{"abstract": "  The orientation of the disk of material accreting onto supermassive black\nholes that power quasars is one of most important quantities that are needed to\nunderstand quasars -- both individually and in the ensemble average. We present\na hypothesis for determining comparatively edge-on orientation in a subset of\nquasars (both radio loud and radio quiet). If confirmed, this orientation\nindicator could be applicable to individual quasars without reference to radio\nor X-ray data and could identify some 10-20% of quasars as being more edge-on\nthan average, based only on moderate resolution and signal-to-noise\nspectroscopy covering the CIV 1549A emission feature. We present a test of said\nhypothesis using X-ray observations and identify additional data that are\nneeded to confirm this hypothesis and calibrate the metric.\n"}
+{"abstract": "  Determinantal consensus clustering is a promising and attractive alternative\nto partitioning about medoids and k-means for ensemble clustering. Based on a\ndeterminantal point process or DPP sampling, it ensures that subsets of similar\npoints are less likely to be selected as centroids. It favors more diverse\nsubsets of points. The sampling algorithm of the determinantal point process\nrequires the eigendecomposition of a Gram matrix. This becomes computationally\nintensive when the data size is very large. This is particularly an issue in\nconsensus clustering, where a given clustering algorithm is run several times\nin order to produce a final consolidated clustering. We propose two efficient\nalternatives to carry out determinantal consensus clustering on large datasets.\nThey consist in DPP sampling based on sparse and small kernel matrices whose\neigenvalue distributions are close to that of the original Gram matrix.\n"}
+{"abstract": "  This manuscript is aimed at addressing several long standing limitations of\ndynamic mode decompositions in the application of Koopman analysis. Principle\namong these limitations are the convergence of associated Dynamic Mode\nDecomposition algorithms and the existence of Koopman modes. To address these\nlimitations, two major modifications are made, where Koopman operators are\nremoved from the analysis in light of Liouville operators (known as Koopman\ngenerators in special cases), and these operators are shown to be compact for\ncertain pairs of Hilbert spaces selected separately as the domain and range of\nthe operator. While eigenfunctions are discarded in the general analysis, a\nviable reconstruction algorithm is still demonstrated, and the sacrifice of\neigenfunctions realizes the theoretical goals of DMD analysis that have yet to\nbe achieved in other contexts. However, in the case where the domain is\nembedded in the range, an eigenfunction approach is still achievable, where a\nmore typical DMD routine is established, but that leverages a finite rank\nrepresentation that converges in norm. The manuscript concludes with the\ndescription of two Dynamic Mode Decomposition algorithms that converges when a\ndense collection of occupation kernels, arising from the data, are leveraged in\nthe analysis.\n"}
+{"abstract": "  In this paper, a numerical study on the melting behavior of phase change\nmaterial (PCM) with gradient porous media has been carried out at the pore\nscales. In order to solve the governing equations, a pore-scale lattice\nBoltzmann method with the double distribution functions is used, in which a\nvolumetric LB scheme is employed to handle the boundary. The Monte Carlo random\nsampling is adopted to generate a microstructure of two-dimensional gradient\nfoam metal which are then used to simulate the solid-liquid phase transition in\nthe cavity. The effect of several factors, such as gradient porosity structure,\ngradient direction, Rayleigh number and particle diameters on the liquid\nfraction of PCM are systematically investigated. It is observed that the\npresence of gradient media affect significantly the melting rate and shortens\nfull melting time compared to that for constant porosity by enhancing natural\nconvection. The melting time of positive and negative gradients will change\nwith Rayleigh number, and there is a critical value for Rayleigh number.\nSpecifically, when Rayleigh number is below the critical value, the positive\ngradient is more advantageous, and when Rayleigh number exceeds the critical\nvalue, the negative gradient is more advantageous. Moreover, smaller particle\ndiameters would lead to lower permeability and larger internal surfaces for\nheat transfer.\n"}
+{"abstract": "  In this paper, we use $K$-means clustering to analyze various relationships\nbetween malware samples. We consider a dataset comprising~20 malware families\nwith~1000 samples per family. These families can be categorized into seven\ndifferent types of malware. We perform clustering based on pairs of families\nand use the results to determine relationships between families. We perform a\nsimilar cluster analysis based on malware type. Our results indicate that\n$K$-means clustering can be a powerful tool for data exploration of malware\nfamily relationships.\n"}
+{"abstract": "  We study protostellar envelope and outflow evolution using Hubble Space\nTelescope NICMOS or WFC3 images of 304 protostars in the Orion Molecular\nclouds. These near-IR images resolve structures in the envelopes delineated by\nthe scattered light of the central protostars with 80 AU resolution and they\ncomplement the 1.2-870 micron spectral energy distributions obtained with the\nHerschel Orion Protostar Survey program (HOPS). Based on their 1.60 micron\nmorphologies, we classify the protostars into five categories: non-detections,\npoint sources without nebulosity, bipolar cavity sources, unipolar cavity\nsources, and irregulars. We find point sources without associated nebulosity\nare the most numerous, and show through monochromatic Monte Carlo radiative\ntransfer modeling that this morphology occurs when protostars are observed at\nlow inclinations or have low envelope densities. We also find that the\nmorphology is correlated with the SED-determined evolutionary class with Class\n0 protostars more likely to be non-detections, Class I protostars to show\ncavities and flat-spectrum protostars to be point sources. Using an edge\ndetection algorithm to trace the projected edges of the cavities, we fit\npower-laws to the resulting cavity shapes, thereby measuring the cavity\nhalf-opening angles and power-law exponents. We find no evidence for the growth\nof outflow cavities as protostars evolve through the Class I protostar phase,\nin contradiction with previous studies of smaller samples. We conclude that the\ndecline of mass infall with time cannot be explained by the progressive\nclearing of envelopes by growing outflow cavities. Furthermore, the low star\nformation efficiency inferred for molecular cores cannot be explained by\nenvelope clearing alone.\n"}
+{"abstract": "  We consider genuine type IIB string theory (supersymmetric) brane\nintersections that preserve $(1+1)$D Lorentz symmetry. We provide the full\nsupergravity solutions in their analytic form and discuss their physical\nproperties. The Ansatz for the spacetime dependence of the different brane warp\nfactors goes beyond the harmonic superposition principle. By studying the\nassociated near-horizon geometry, we construct interesting classes of AdS$_3$\nvacua in type IIB and highlight their relation to the existing classifications\nin the literature. Finally, we discuss their holographic properties.\n"}
+{"abstract": "  Given any $f$ a locally finitely piecewise affine homeomorphism of $\\Omega\n\\subset \\rn$ onto $\\Delta \\subset \\rn$ in $W^{1,p}$, $1\\leq p < \\infty$ and any\n$\\epsilon >0$ we construct a smooth injective map $\\tilde{f}$ such that\n$\\|f-\\tilde{f}\\|_{W^{1,p}(\\Omega,\\rn)} < \\epsilon$.\n"}
+{"abstract": "  For many relevant statistics of multivariate time series, no valid frequency\ndomain bootstrap procedures exist. This is mainly due to the fact that the\ndistribution of such statistics depends on the fourth-order moment structure of\nthe underlying process in nearly every scenario, except for some special cases\nlike Gaussian time series. In contrast to the univariate case, even additional\nstructural assumptions such as linearity of the multivariate process or a\nstandardization of the statistic of interest do not solve the problem. This\npaper focuses on integrated periodogram statistics as well as functions thereof\nand presents a new frequency domain bootstrap procedure for multivariate time\nseries, the multivariate frequency domain hybrid bootstrap (MFHB), to fill this\ngap. Asymptotic validity of the MFHB procedure is established for general\nclasses of periodogram-based statistics and for stationary multivariate\nprocesses satisfying rather weak dependence conditions. A simulation study is\ncarried out which compares the finite sample performance of the MFHB with that\nof the moving block bootstrap.\n"}
+{"abstract": "  Recent advancements in data-to-text generation largely take on the form of\nneural end-to-end systems. Efforts have been dedicated to improving text\ngeneration systems by changing the order of training samples in a process known\nas curriculum learning. Past research on sequence-to-sequence learning showed\nthat curriculum learning helps to improve both the performance and convergence\nspeed. In this work, we delve into the same idea surrounding the training\nsamples consisting of structured data and text pairs, where at each update, the\ncurriculum framework selects training samples based on the model's competence.\nSpecifically, we experiment with various difficulty metrics and put forward a\nsoft edit distance metric for ranking training samples. Our benchmarks show\nfaster convergence speed where training time is reduced by 38.7% and\nperformance is boosted by 4.84 BLEU.\n"}
+{"abstract": "  Manufacturing of medical devices is strictly controlled by authorities, and\nmanufacturers must conform to the regulatory requirements of the region in\nwhich a medical device is being marketed for use. In general, these\nrequirements make no difference between the physical device, embedded software\nrunning inside a physical device, or software that constitutes the device in\nitself. As a result, standalone software with intended medical use is\nconsidered to be a medical device. Consequently, its development must meet the\nsame requirements as the physical medical device manufacturing. This practice\ncreates a unique challenge for organizations developing medical software. In\nthis paper, we pinpoint a number of regulatory requirement mismatches between\nphysical medical devices and standalone medical device software. The view is\nbased on experiences from industry, from the development of all-software\nmedical devices as well as from defining the manufacturing process so that it\nmeets the regulatory requirements.\n"}
+{"abstract": "  We report a massive quiescent galaxy at $z_{\\rm\nspec}=3.0922^{+0.008}_{-0.004}$ spectroscopically confirmed at a protocluster\nin the SSA22 field by detecting the Balmer and Ca {\\footnotesize II} absorption\nfeatures with multi-object spectrometer for infrared exploration (MOSFIRE) on\nthe Keck I telescope. This is the most distant quiescent galaxy confirmed in a\nprotocluster to date. We fit the optical to mid-infrared photometry and\nspectrum simultaneously with spectral energy distribution (SED) models of\nparametric and nonparametric star formation histories (SFH). Both models fit\nthe observed SED well and confirm that this object is a massive quiescent\ngalaxy with the stellar mass of $\\log(\\rm M_{\\star}/M_{\\odot}) =\n11.26^{+0.03}_{-0.04}$ and $11.54^{+0.03}_{-0.00}$, and star formation rate of\n$\\rm SFR/M_{\\odot}~yr^{-1} <0.3$ and $=0.01^{+0.03}_{-0.01}$ for parametric and\nnonparametric models, respectively. The SFH from the former modeling is\ndescribed as an instantaneous starburst while that of the latter modeling is\nlonger-lived but both models agree with a sudden quenching of the star\nformation at $\\sim0.6$ Gyr ago. This massive quiescent galaxy is confirmed in\nan extremely dense group of galaxies predicted as a progenitor of a brightest\ncluster galaxy formed via multiple mergers in cosmological numerical\nsimulations. We newly find three plausible [O III]$\\lambda$5007 emitters at\n$3.0791\\leq z_{\\rm spec}\\leq3.0833$ happened to be detected around the target.\nTwo of them just between the target and its nearest massive galaxy are possible\nevidence of their interactions. They suggest the future strong size and stellar\nmass evolution of this massive quiescent galaxy via mergers.\n"}
+{"abstract": "  The paper contains a review on recent progress in the deformational\nproperties of smooth maps from compact surfaces $M$ to a one-dimensional\nmanifold $P$. It covers description of homotopy types of stabilizers and orbits\nof a large class of smooth functions on surfaces obtained by the author, E.\nKudryavtseva, B. Feshchenko, I. Kuznietsova, Yu. Soroka, A. Kravchenko. We also\npresent here a new direct proof of the fact that for generic Morse maps the\nconnected components their orbits are homotopy equivalent to finite products of\ncircles.\n"}
+{"abstract": "  Complex systems, such as Artificial Intelligence (AI) systems, are comprised\nof many interrelated components. In order to represent these systems,\ndemonstrating the relations between components is essential. Perhaps because of\nthis, diagrams, as \"icons of relation\", are a prevalent medium for signifying\ncomplex systems. Diagrams used to communicate AI system architectures are\ncurrently extremely varied. The diversity in diagrammatic conceptual modelling\nchoices provides an opportunity to gain insight into the aspects which are\nbeing prioritised for communication. In this philosophical exploration of AI\nsystems diagrams, we integrate theories of conceptual models, communication\ntheory, and semiotics. We discuss consequences of standardised diagrammatic\nlanguages for AI systems, concluding that while we expect engineers\nimplementing systems to benefit from standards, researchers would have a larger\nbenefit from guidelines.\n"}
+{"abstract": "  Optical spectrometers have propelled scientific and technological\nadvancements in a wide range of fields. While sophisticated systems with\nexcellent performance metrics are serving well in controlled laboratory\nenvironments, many applications require systems that are portable, economical,\nand robust to optical misalignment. Here, we propose and demonstrate a\nspectrometer that uses a planar one-dimensional photonic crystal cavity as a\ndispersive element and a reconstructive computational algorithm to extract\nspectral information from spatial patterns. The simple fabrication and planar\narchitecture of the photonic crystal cavity render our spectrometry platform\neconomical and robust to optical misalignment. The reconstructive algorithm\nallows miniaturization and portability. The intensity transmitted by the\nphotonic crystal cavity has a wavelength-dependent spatial profile. We generate\nthe spatial transmittance function of the system using finite-difference\ntime-domain method and also estimate the dispersion relation. The transmittance\nfunction serves as a transfer function in our reconstructive algorithm. We show\naccurate estimation of various kinds of input spectra. We also show that the\nspectral resolution of the system depends on the cavity linewidth that can be\nimproved by increasing the number of periodic layers in distributed Bragg\nmirrors. Finally, we experimentally estimate the center wavelength and\nlinewidth of the spectrum of an unknown light emitting diode. The estimated\nvalues are in good agreement with the values measured using a commercial\nspectrometer.\n"}
+{"abstract": "  In this paper we study the density in the real line of oscillating sequences\nof the form\n  $$ (g(k)\\cdot F(k\\alpha))_{k \\in \\mathbb{N}} ,$$ where $g$ is a positive\nincreasing function and $F$ a real continuous 1-periodic function. This extends\nwork by Berend, Boshernitzan and Kolesnik who established differential\nproperties on the function $F$ ensuring that the oscillating sequence is dense\nmodulo $1$.\n  More precisely, when $F$ has finitely many roots in $[0,1)$, we provide\nnecessary and also sufficient conditions for the oscillating sequence under\nconsideration to be dense in $\\mathbb{R}$. All the results are stated in terms\nof the Diophantine properties of $\\alpha$, with the help of the theory of\ncontinued fractions.\n"}
+{"abstract": "  The noncentrosymmetric transition metal monopnictides NbP, TaP, NbAs and TaAs\nare a family of Weyl semimetals in which pairs of protected linear crossings of\nspin-resolved bands occur. These so-called Weyl nodes are characterized by\ninteger topological charges of opposite sign associated with singular points of\nBerry curvature in momentum space. In such a system anomalous magnetoelectric\nresponses are predicted, which should only occur if the crossing points are\nclose to the Fermi level and enclosed by Fermi surface pockets penetrated by an\ninteger flux of Berry curvature, dubbed Weyl pockets. TaAs was shown to possess\nWeyl pockets whereas TaP and NbP have trivial pockets enclosing zero net flux\nof Berry curvature. Here, via measurements of the magnetic torque, resistivity\nand magnetisation, we present a comprehensive quantum oscillation study of\nNbAs, the last member of this family where the precise shape and nature of the\nFermi surface pockets is still unknown. We detect six distinct frequency\nbranches, two of which have not been observed before. A comparison to density\nfunctional theory calculations suggests that the two largest pockets are\ntopologically trivial, whereas the low frequencies might stem from tiny Weyl\npockets. The enclosed Weyl nodes are within a few meV of the Fermi energy.\n"}
+{"abstract": "  Deep neural networks (DNNs) have achieved great success in various machine\nlearning tasks. However, most existing powerful DNN models are computationally\nexpensive and memory demanding, hindering their deployment in devices with low\nmemory and computational resources or in applications with strict latency\nrequirements. Thus, several resource-adaptable or flexible approaches were\nrecently proposed that train at the same time a big model and several\nresource-specific sub-models. Inplace knowledge distillation (IPKD) became a\npopular method to train those models and consists in distilling the knowledge\nfrom a larger model (teacher) to all other sub-models (students). In this work\na novel generic training method called IPKD with teacher assistant (IPKD-TA) is\nintroduced, where sub-models themselves become teacher assistants teaching\nsmaller sub-models. We evaluated the proposed IPKD-TA training method using two\nstate-of-the-art flexible models (MSDNet and Slimmable MobileNet-V1) with two\npopular image classification benchmarks (CIFAR-10 and CIFAR-100). Our results\ndemonstrate that the IPKD-TA is on par with the existing state of the art while\nimproving it in most cases.\n"}
+{"abstract": "  We investigate the charge and heat electronic noise in a generic two-terminal\nmesoscopic conductor in the absence of the corresponding charge and heat\ncurrents. Despite these currents being zero, shot noise is generated in the\nsystem. We show that, irrespective of the conductor's details and the specific\nnonequilibrium conditions, the charge shot noise never exceeds its thermal\ncounterpart, thus establishing a general bound. Such a bound does not exist in\nthe case of heat noise, which reveals a fundamental difference between charge\nand heat transport under zero-current conditions.\n"}
+{"abstract": "  In optical communication systems, orthogonal frequency division multiplexing\n(OFDM) is widely used to combat inter-symbol interference (ISI) caused by\nmultipath propagation. Optical systems which use intensity modulation and\ndirect detection (IM/DD) can only transmit real valued symbols, but the inverse\ndiscrete Fourier transform (IDFT) or its computationally efficient form\ninverse-fast Fourier transform (IFFT) required for the OFDM waveform\nconstruction produces complex values. Hermitian symmetry is often used to\nobtain real valued symbols. For this purpose, some trigonometric\ntransformations such as discrete cosine transform (DCT) are also used, however\nthese transformations can eliminate the ISI only under certain conditions. In\nthis paper, we propose a completely different method for the construction of\nOFDM waveform with IFFT to obtain real valued symbols by combining the real and\nimaginary parts (CRIP) of IFFT output electrically (E-CRIP) or optically\n(O-CRIP). Analytical analysis and simulation works are presented to show that\ncompared to the Hermitian symmetric system, the proposed method slightly\nincreases the spectral efficiency, eliminates ISI, significantly reduces the\namount of needed calculation and does not effect the error performance. In\naddition, the O-CRIP method is less affected by clipping noise that may occur\ndue to the imperfections of the transmitter front-ends.\n"}
+{"abstract": "  Techniques for training artificial neural networks (ANNs) and convolutional\nneural networks (CNNs) using simulated dynamical electron diffraction patterns\nare described. The premise is based on the following facts. First, given a\nsuitable crystal structure model and scattering potential, electron diffraction\npatterns can be simulated accurately using dynamical diffraction theory.\nSecondly, using simulated diffraction patterns as input, ANNs can be trained\nfor the determination of crystal structural properties, such as crystal\norientation and local strain. Further, by applying the trained ANNs to\nfour-dimensional diffraction datasets (4D-DD) collected using the scanning\nelectron nanodiffraction (SEND) or 4D scanning transmission electron microscopy\n(4D-STEM) techniques, the crystal structural properties can be mapped at high\nspatial resolution. Here, we demonstrate the ANN-enabled possibilities for the\nanalysis of crystal orientation and strain at high precision and benchmark the\nperformance of ANNs and CNNs by comparing with previous methods. A factor of\nthirty improvement in angular resolution at 0.009 degrees (0.16 mrad) for\norientation mapping, sensitivity at 0.04% or less for strain mapping, and\nimprovements in computational performance are demonstrated.\n"}
+{"abstract": "  The quark mass function is computed both by solving the quark propagator\nDyson-Schwinger equation and from lattice simulations implementing overlap and\nDomain-Wall fermion actions for valence and sea quarks, respectively. The\nresults are confronted and seen to produce a very congruent picture, showing a\nremarkable agreement for the explored range of current-quark masses. The\neffective running-interaction is based on a process-independent charge rooted\non a particular truncation of the Dyson-Schwinger equations in the gauge\nsector, establishing thus a link from there to the quark sector and inspiring a\ncorrelation between the emergence of gluon and hadron masses.\n"}
+{"abstract": "  A stochastic approach is implemented to address the problem of a marine\nstructure exposed to water wave impacts. The focus is on (i) the average\nfrequency of wave impacts, and (ii) the related probability distribution of\nimpact kinematic variables. The wave field is assumed to be Gaussian. The\nseakeeping motions of the considered body are taken into account in the\nanalysis. The coupling of the stochastic model with a water entry model is\ndemonstrated through the case study of a foil exposed to wave impacts.\n"}
+{"abstract": "  A classical model for sources and sinks in a two-dimensional perfect\nincompressible fluid occupying a bounded domain dates back to Yudovich in 1966.\n  In this model, on the one hand, the normal component of the fluid velocity is\nprescribed on the boundary and is nonzero on an open subset of the boundary,\ncorresponding either to sources (where the flow is incoming) or to sinks (where\nthe flow is outgoing). On the other hand the vorticity of the fluid which is\nentering into the domain from the sources is prescribed.\n  In this paper we investigate the existence of weak solutions to this system\nby relying on \\textit{a priori} bounds of the vorticity, which satisfies a\ntransport equation associated with the fluid velocity vector field. Our results\ncover the case where the vorticity has a $L^p$ integrability in space, with $p\n$ in $[1,+\\infty]$, and prove the existence of solutions obtained by\ncompactness methods from viscous approximations. More precisely we prove the\nexistence of solutions which satisfy the vorticity equation in the\ndistributional sense in the case where $p >\\frac43$, in the renormalized sense\nin the case where $p >1$, and in a symmetrized sense in the case where $p =1$.\n"}
+{"abstract": "  We report the detection of a massive neutral gas outflow in the z=2.09\ngravitationally lensed Dusty Star-Forming Galaxy HATLASJ085358.9+015537\n(G09v1.40), seen in absorption with the OH+(1_1-1_0) transition using spatially\nresolved (0.5\"x0.4\") Atacama Large Millimeter/submillimeter Array (ALMA)\nobservations. The blueshifted OH+ line is observed simultaneously with the\nCO(9-8) emission line and underlying dust continuum. These data are\ncomplemented by high angular resolution (0.17\"x0.13\") ALMA observations of\nCH+(1-0) and underlying dust continuum, and Keck 2.2 micron imaging tracing the\nstellar emission. The neutral outflow, dust, dense molecular gas and stars all\nshow spatial offsets from each other. The total atomic gas mass of the observed\noutflow is 6.7x10^9 M_sun, >25% as massive as the gas mass of the galaxy. We\nfind that a conical outflow geometry best describes the OH+ kinematics and\nmorphology and derive deprojected outflow properties as functions of possible\ninclination (0.38 deg-64 deg). The neutral gas mass outflow rate is between\n83-25400 M_sun/yr, exceeding the star formation rate (788+/-300 M_sun/yr) if\nthe inclination is >3.6 deg (mass-loading factor = 0.3-4.7). Kinetic energy and\nmomentum fluxes span 4.4-290x10^9 L_sun and 0.1-3.7x10^37 dyne, respectively\n(energy-loading factor = 0.013-16), indicating that the feedback mechanisms\nrequired to drive the outflow depend on the inclination assumed. We derive a\ngas depletion time between 29 and 1 Myr, but find that the neutral outflow is\nlikely to remain bound to the galaxy, unless the inclination is small, and may\nbe re-accreted if additional feedback processes do not occur.\n"}
+{"abstract": "  The grain boundary (GB) microchemistry and precipitation behaviour in\nhigh-strength Al-Zn-Mg-Cu alloys has an important influence on their mechanical\nand electrochemical properties. Simulation of the GB segregation,\nprecipitation, and solute distribution in these alloys requires an accurate\ndescription of the thermodynamics and kinetics of this multi-component system.\nCALPHAD databases have been successfully developed for equilibrium\nthermodynamic calculations in complex multi-component systems, and in recent\nyears have been combined with diffusion simulations. In this work, we have\ndirectly incorporated a CALPHAD database into a phase-field framework, to\nsimulate, with high fidelity, the complex kinetics of the non-equilibrium GB\nmicrostructures that develop in these important commercial alloys during heat\ntreatment. In particular, the influence of GB solute segregation, GB diffusion,\nprecipitate number density, and far-field matrix composition, on the growth of\na population of GB precipitates, was systematically investigated in a model\nAl-Zn-Mg-Cu alloy of near AA7050 composition. The simulation results were\ncompared with scanning transmission electron microscopy and atom probe\ntomography characterisation of alloys of the similar composition, with good\nagreement.\n"}
+{"abstract": "  State governments in the U.S. have been facing difficult decisions involving\ntradeoffs between economic and health-related outcomes during the COVID-19\npandemic. Despite evidence of the effectiveness of government-mandated\nrestrictions mitigating the spread of contagion, these orders are stigmatized\ndue to undesirable economic consequences. This tradeoff resulted in state\ngovernments employing mandates in widely different ways. We compare the\ndifferent policies states implemented during periods of restriction (lockdown)\nand reopening with indicators of COVID-19 spread and consumer card spending at\neach state during the first wave of the pandemic in the U.S. between March and\nAugust 2020. We find that while some states enacted reopening decisions when\nthe incidence rate of COVID-19 was minimal or sustained in its relative\ndecline, other states relaxed socioeconomic restrictions near their highest\nincidence and prevalence rates experienced so far. Nevertheless, all states\nexperienced similar trends in consumer card spending recovery, which was\nstrongly correlated with reopening policies following the lockdowns and\nrelatively independent from COVID-19 incidence rates at the time. Our findings\nsuggest that consumer card spending patterns can be attributed to government\nmandates rather than COVID-19 incidence in the states. We estimate the recovery\nin states that reopened in late April was more than the recovery in states that\ndid not reopen in the same period - 15% for consumer card spending and 18% for\nspending by high income households. This result highlights the important role\nof state policies in minimizing health impacts while promoting economic\nrecovery and helps planning effective interventions in subsequent waves and\nimmunization efforts.\n"}
+{"abstract": "  In the framework of the Einstein-Dirac-axion-aether theory we consider the\nquartet of self-interacting cosmic fields, which includes the dynamic aether,\npresented by the unit timelike vector field, the axionic dark mater, described\nby the pseudoscalar field, the spinor field associated with fermion particles,\nand the gravity field. The key, associated with the mechanism of\nself-interaction, is installed into the modified periodic potential of the\npseudoscalar (axion) field constructed on the base of a guiding function, which\ndepends on one invariant, one pseudo-invariant and two cross-invariants\ncontaining the spinor and vector fields. The total system of the field\nequations related to the isotropic homogeneous cosmological model is solved; we\nhave found the exact solutions for the guiding function for three cases:\nnonzero, vanishing and critical values of the cosmological constant. Based on\nthese solutions, we obtained the expressions for the effective mass of spinor\nparticles, interacting with the axionic dark matter and dynamic aether. This\neffective mass is shown to bear imprints of the cosmological epoch and of the\nstate of the cosmic dark fluid in that epoch.\n"}
+{"abstract": "  Continuous, automated surveillance systems that incorporate machine learning\nmodels are becoming increasingly more common in healthcare environments. These\nmodels can capture temporally dependent changes across multiple patient\nvariables and can enhance a clinician's situational awareness by providing an\nearly warning alarm of an impending adverse event such as sepsis. However, most\ncommonly used methods, e.g., XGBoost, fail to provide an interpretable\nmechanism for understanding why a model produced a sepsis alarm at a given\ntime. The black-box nature of many models is a severe limitation as it prevents\nclinicians from independently corroborating those physiologic features that\nhave contributed to the sepsis alarm. To overcome this limitation, we propose a\ngeneralized linear model (GLM) approach to fit a Granger causal graph based on\nthe physiology of several major sepsis-associated derangements (SADs). We adopt\na recently developed stochastic monotone variational inequality-based estimator\ncoupled with forwarding feature selection to learn the graph structure from\nboth continuous and discrete-valued as well as regularly and irregularly\nsampled time series. Most importantly, we develop a non-asymptotic upper bound\non the estimation error for any monotone link function in the GLM. We conduct\nreal-data experiments and demonstrate that our proposed method can achieve\ncomparable performance to popular and powerful prediction methods such as\nXGBoost while simultaneously maintaining a high level of interpretability.\n"}
+{"abstract": "  Combinatorial optimization is a well-established area in operations research\nand computer science. Until recently, its methods have focused on solving\nproblem instances in isolation, ignoring the fact that they often stem from\nrelated data distributions in practice. However, recent years have seen a surge\nof interest in using machine learning, especially graph neural networks (GNNs),\nas a key building block for combinatorial tasks, either directly as solvers or\nby enhancing exact solvers. The inductive bias of GNNs effectively encodes\ncombinatorial and relational input due to their invariance to permutations and\nawareness of input sparsity. This paper presents a conceptual review of recent\nkey advancements in this emerging field, aiming at researchers in both\noptimization and machine learning.\n"}
+{"abstract": "  This paper develops a novel second order cone relaxation of the semidefinite\nprogramming formulation of optimal power flow, that does not imply the `angle\nrelaxation'. We build on a technique developed by Kim et al., extend it for\ncomplex matrices, and apply it to 3x3 positive semidefinite matrices to\ngenerate novel second-order cone constraints that augment upon the well-known\n2x2 principal-minor based second-order cone constraints. Finally, we apply it\nto optimal power flow in meshed networks and provide numerical illustrations.\n"}
+{"abstract": "  Self-supervised or weakly supervised models trained on large-scale datasets\nhave shown sample-efficient transfer to diverse datasets in few-shot settings.\nWe consider how upstream pretrained models can be leveraged for downstream\nfew-shot, multilabel, and continual learning tasks. Our model CLIPPER (CLIP\nPERsonalized) uses image representations from CLIP, a large-scale image\nrepresentation learning model trained using weak natural language supervision.\nWe developed a technique, called Multi-label Weight Imprinting (MWI), for\nmulti-label, continual, and few-shot learning, and CLIPPER uses MWI with image\nrepresentations from CLIP. We evaluated CLIPPER on 10 single-label and 5\nmulti-label datasets. Our model shows robust and competitive performance, and\nwe set new benchmarks for few-shot, multi-label, and continual learning. Our\nlightweight technique is also compute-efficient and enables privacy-preserving\napplications as the data is not sent to the upstream model for fine-tuning.\n"}
+{"abstract": "  The adaptive traffic signal control (ATSC) problem can be modeled as a\nmultiagent cooperative game among urban intersections, where intersections\ncooperate to optimize their common goal. Recently, reinforcement learning (RL)\nhas achieved marked successes in managing sequential decision making problems,\nwhich motivates us to apply RL in the ASTC problem. Here we use independent\nreinforcement learning (IRL) to solve a complex traffic cooperative control\nproblem in this study. One of the largest challenges of this problem is that\nthe observation information of intersection is typically partially observable,\nwhich limits the learning performance of IRL algorithms. To this, we model the\ntraffic control problem as a partially observable weak cooperative traffic\nmodel (PO-WCTM) to optimize the overall traffic situation of a group of\nintersections. Different from a traditional IRL task that averages the returns\nof all agents in fully cooperative games, the learning goal of each\nintersection in PO-WCTM is to reduce the cooperative difficulty of learning,\nwhich is also consistent with the traffic environment hypothesis. We also\npropose an IRL algorithm called Cooperative Important Lenient Double DQN\n(CIL-DDQN), which extends Double DQN (DDQN) algorithm using two mechanisms: the\nforgetful experience mechanism and the lenient weight training mechanism. The\nformer mechanism decreases the importance of experiences stored in the\nexperience reply buffer, which deals with the problem of experience failure\ncaused by the strategy change of other agents. The latter mechanism increases\nthe weight experiences with high estimation and `leniently' trains the DDQN\nneural network, which improves the probability of the selection of cooperative\njoint strategies. Experimental results show that CIL-DDQN outperforms other\nmethods in almost all performance indicators of the traffic control problem.\n"}
+{"abstract": "  COVID-19 pandemic has generated what public health officials called an\ninfodemic of misinformation. As social distancing and stay-at-home orders came\ninto effect, many turned to social media for socializing. This increase in\nsocial media usage has made it a prime vehicle for the spreading of\nmisinformation. This paper presents a mechanism to detect COVID-19\nhealth-related misinformation in social media following an interdisciplinary\napproach. Leveraging social psychology as a foundation and existing\nmisinformation frameworks, we defined misinformation themes and associated\nkeywords incorporated into the misinformation detection mechanism using applied\nmachine learning techniques. Next, using the Twitter dataset, we explored the\nperformance of the proposed methodology using multiple state-of-the-art machine\nlearning classifiers. Our method shows promising results with at most 78%\naccuracy in classifying health-related misinformation versus true information\nusing uni-gram-based NLP feature generations from tweets and the Decision Tree\nclassifier. We also provide suggestions on alternatives for countering\nmisinformation and ethical consideration for the study.\n"}
+{"abstract": "  Accurate tracking is still a challenging task due to appearance variations,\npose and view changes, and geometric deformations of target in videos. Recent\nanchor-free trackers provide an efficient regression mechanism but fail to\nproduce precise bounding box estimation. To address these issues, this paper\nrepurposes a Transformer-alike regression branch, termed as Target Transformed\nRegression (TREG), for accurate anchor-free tracking. The core to our TREG is\nto model pair-wise relation between elements in target template and search\nregion, and use the resulted target enhanced visual representation for accurate\nbounding box regression. This target contextualized representation is able to\nenhance the target relevant information to help precisely locate the box\nboundaries, and deal with the object deformation to some extent due to its\nlocal and dense matching mechanism. In addition, we devise a simple online\ntemplate update mechanism to select reliable templates, increasing the\nrobustness for appearance variations and geometric deformations of target in\ntime. Experimental results on visual tracking benchmarks including VOT2018,\nVOT2019, OTB100, GOT10k, NFS, UAV123, LaSOT and TrackingNet demonstrate that\nTREG obtains the state-of-the-art performance, achieving a success rate of\n0.640 on LaSOT, while running at around 30 FPS. The code and models will be\nmade available at https://github.com/MCG-NJU/TREG.\n"}
+{"abstract": "  Batch Normalization (BN) is one of the key components for accelerating\nnetwork training, and has been widely adopted in the medical image analysis\nfield. However, BN only calculates the global statistics at the batch level,\nand applies the same affine transformation uniformly across all spatial\ncoordinates, which would suppress the image contrast of different semantic\nstructures. In this paper, we propose to incorporate the semantic class\ninformation into normalization layers, so that the activations corresponding to\ndifferent regions (i.e., classes) can be modulated differently. We thus develop\na novel DualNorm-UNet, to concurrently incorporate both global image-level\nstatistics and local region-wise statistics for network normalization.\nSpecifically, the local statistics are integrated by adaptively modulating the\nactivations along different class regions via the learned semantic masks in the\nnormalization layer. Compared with existing methods, our approach exploits\nsemantic knowledge at normalization and yields more discriminative features for\nrobust segmentation results. More importantly, our network demonstrates\nsuperior abilities in capturing domain-invariant information from multiple\ndomains (institutions) of medical data. Extensive experiments show that our\nproposed DualNorm-UNet consistently improves the performance on various\nsegmentation tasks, even in the face of more complex and variable data\ndistributions. Code is available at https://github.com/lambert-x/DualNorm-Unet.\n"}
+{"abstract": "  With increasing usage of clickbaits in Indonesian Online News, newsworthy\narticles sometimes get buried among clickbaity news. A reliable and lightweight\ntool is needed to detect such clickbaits on-the-go. Leveraging state-of-the-art\nnatural language processing model BERT, a RESTful API based application is\ndeveloped. This study offloaded the computing resources needed to train the\nmodel on the cloud server, while the client-side application only needs to send\na request to the API and the cloud server will handle the rest. This study\nproposed the design and developed a web-based application to detect clickbait\nin Indonesian using IndoBERT as a language model. The application usage is\ndiscussed and available for public use with a performance of mean ROC-AUC of\n89%.\n"}
+{"abstract": "  Context: Technical Debt requirements are related to the distance between the\nideal value of the specification and the system's actual implementation, which\nare consequences of strategic decisions for immediate gains, or unintended\nchanges in context. To ensure the evolution of the software, it is necessary to\nkeep it managed. Identification and measurement are the first two stages of the\nmanagement process; however, they are little explored in academic research in\nrequirements engineering. Objective: We aimed at investigating which evidence\nhelps to strengthen the process of TD requirements management, including\nidentification and measurement. Method: We conducted a Systematic Literature\nReview through manual and automatic searches considering 7499 studies from 2010\nto 2020, and including 61 primary studies. Results: We identified some causes\nrelated to Technical Debt requirements, existing strategies to help in the\nidentification and measurement, and metrics to support the measurement stage.\nConclusion: Studies on TD requirements are still preliminary, especially on\nmanagement tools. Yet, not enough attention is given to interpersonal issues,\nwhich are difficulties encountered when performing such activities, and\ntherefore also require research. Finally, the provision of metrics to help\nmeasure TD is part of this work's contribution, providing insights into the\napplication in the requirements context.\n"}
+{"abstract": "  This paper introduces the first release of Pytearcat, a Python package\ndeveloped to compute tensor algebra operations in the context of theoretical\nphysics, for instance, in general relativity. Given that working with tensors\ncan become a complex task, people often rely on computational tools to perform\ntensor calculations. We aim to build a tensor calculator based on Python, which\nbenefits from being free and easy to use. Pytearcat syntax resembles the usual\nphysics notation for tensor calculus, such as the Einstein notation for index\ncontraction. This version allows the user to perform many tensor operations,\nincluding derivatives and series expansions, along with routines to obtain the\ntypical General Relativity tensors. A particular concern was put in the\nexecution times, leading to incorporate an alternative core for the symbolic\ncalculations, enabling to reach much faster execution times. The syntax and the\nversatility of Pytearcat are the most important features of this package, where\nthe latter can be used to extend Pytearcat to other areas of theoretical\nphysics.\n"}
+{"abstract": "  The black hole information paradox has been with us for some time. We outline\nthe nature of the paradox. We then propose a resolution based on an examination\nof the properties of quantum gravity under circumstances that give rise to a\nclassical singularity. We show that the gravitational wavefunction vanishes as\none gets close to the classical singularity. This results in a future boundary\ncondition inside the black hole that allows for quantum information to be\nrecovered in the evaporation process.\n"}
+{"abstract": "  In this paper we consider a class of boundary value problems for third order\nnonlinear functional differential equation. By the reduction of the problem to\noperator equation we establish the existence and uniqueness of solution and\nconstruct a numerical method for solving it. We prove that the method is of\nsecond order accuracy and obtain an estimate for total error. Some examples\ndemonstrate the validity of the obtained theoretical results and the efficiency\nof the numerical method. The approach used for the third order nonlinear\nfunctional differential equation can be applied to functional differential\nequations of any orders.\n"}
+{"abstract": "  Due to their long-standing reputation as excellent off-the-shelf predictors,\nrandom forests continue remain a go-to model of choice for applied\nstatisticians and data scientists. Despite their widespread use, however, until\nrecently, little was known about their inner-workings and about which aspects\nof the procedure were driving their success. Very recently, two competing\nhypotheses have emerged -- one based on interpolation and the other based on\nregularization. This work argues in favor of the latter by utilizing the\nregularization framework to reexamine the decades-old question of whether\nindividual trees in an ensemble ought to be pruned. Despite the fact that\ndefault constructions of random forests use near full depth trees in most\npopular software packages, here we provide strong evidence that tree depth\nshould be seen as a natural form of regularization across the entire procedure.\nIn particular, our work suggests that random forests with shallow trees are\nadvantageous when the signal-to-noise ratio in the data is low. In building up\nthis argument, we also critique the newly popular notion of \"double descent\" in\nrandom forests by drawing parallels to U-statistics and arguing that the\nnoticeable jumps in random forest accuracy are the result of simple averaging\nrather than interpolation.\n"}
+{"abstract": "  The nitrogen-vacancy (NV) centre in diamond has emerged as a candidate to\nnon-invasively hyperpolarise nuclear spins in molecular systems to improve the\nsensitivity of nuclear magnetic resonance (NMR) experiments. Several promising\nproof of principle experiments have demonstrated small-scale polarisation\ntransfer from single NVs to hydrogen spins outside the diamond. However, the\nscaling up of these results to the use of a dense NV ensemble, which is a\nnecessary prerequisite for achieving realistic NMR sensitivity enhancement, has\nnot yet been demonstrated. In this work, we present evidence for a polarising\ninteraction between a shallow NV ensemble and external nuclear targets over a\nmicrometre scale, and characterise the challenges in achieving useful\npolarisation enhancement. In the most favourable example of the interaction\nwith hydrogen in a solid state target, a maximum polarisation transfer rate of\n$\\approx 7500$ spins per second per NV is measured, averaged over an area\ncontaining order $10^6$ NVs. Reduced levels of polarisation efficiency are\nfound for liquid state targets, where molecular diffusion limits the transfer.\nThrough analysis via a theoretical model, we find that our results suggest\nimplementation of this technique for NMR sensitivity enhancement is feasible\nfollowing realistic diamond material improvements.\n"}
+{"abstract": "  Computing dynamical distributions in quantum many-body systems represents one\nof the paradigmatic open problems in theoretical condensed matter physics.\nDespite the existence of different techniques both in real-time and frequency\nspace, computational limitations often dramatically constrain the physical\nregimes in which quantum many-body dynamics can be efficiently solved. Here we\nshow that the combination of machine learning methods and complementary\nmany-body tensor network techniques substantially decreases the computational\ncost of quantum many-body dynamics. We demonstrate that combining kernel\npolynomial techniques and real-time evolution, together with deep neural\nnetworks, allows to compute dynamical quantities faithfully. Focusing on\nmany-body dynamical distributions, we show that this hybrid neural-network\nmany-body algorithm, trained with single-particle data only, can efficiently\nextrapolate dynamics for many-body systems without prior knowledge.\nImportantly, this algorithm is shown to be substantially resilient to numerical\nnoise, a feature of major importance when using this algorithm together with\nnoisy many-body methods. Ultimately, our results provide a starting point\ntowards neural-network powered algorithms to support a variety of quantum\nmany-body dynamical methods, that could potentially solve computationally\nexpensive many-body systems in a more efficient manner.\n"}
+{"abstract": "  An Adversarial Swarm model consists of two swarms that are interacting with\neach other in a competing manner. In the present study, an agent-based\nAdversarial swarm model is developed comprising of two competing swarms, the\nAttackers and the Defenders, respectively. The Defender's aim is to protect a\npoint of interest in unbounded 2D Euclidean space referred to as the Goal. In\ncontrast, the Attacker's main task is to intercept the Goal while continually\ntrying to evade the Defenders, which gets attracted to it when they are in a\ncertain vicinity of the Goal termed as the sphere of influence, essentially a\ncircular perimeter. The interaction of the two swarms was studied from a\nDynamical systems perspective by changing the number of Agents making up each\nrespective swarm. The simulations were strongly investigated for the presence\nof chaos by evaluating the Largest Lyapunov Exponent (LLE), implementing phase\nspace reconstruction. The source of chaos in the system was observed to be\ninduced by the passively constrained motion of the Defender agents around the\nGoal. Multiple local equilibrium points existed for the Defenders in all the\ncases and some instances for the Attackers, indicating complex dynamics. LLEs\nfor all the trials of the Monte Carlo analysis in all the cases revealed the\npresence of chaotic and non-chaotic solutions in each case, respectively, with\nthe majority of the Defenders indicating chaotic behavior. Overall, the swarms\nexist in the 'Edge of chaos', thus revealing complex dynamical behavior. The\nfinal system state (i,e, the outcome of the interaction between the swarms in a\nparticular simulation) is studied for all the cases, which indicated the\npresence of binary final states in some. Finally, to evaluate the complexity of\nindividual swarms, Multiscale Entropy is employed, which revealed a greater\ndegree of randomness for the Defenders when compared to Attackers.\n"}
+{"abstract": "  Starting from the moment sequences of classical orthogonal polynomials we\nderive the orthogonality purely algebraically. We consider also the moments of\n($q=1$) classical orthogonal polynomials, and study those cases in which the\nexponential generating function has a nice form. In the opposite direction, we\nshow that the generalized Dumont-Foata polynomials with six parameters are the\nmoments of rescaled continuous dual Hahn polynomials.\n"}
+{"abstract": "  Shift invariance is a critical property of CNNs that improves performance on\nclassification. However, we show that invariance to circular shifts can also\nlead to greater sensitivity to adversarial attacks. We first characterize the\nmargin between classes when a shift-invariant linear classifier is used. We\nshow that the margin can only depend on the DC component of the signals. Then,\nusing results about infinitely wide networks, we show that in some simple\ncases, fully connected and shift-invariant neural networks produce linear\ndecision boundaries. Using this, we prove that shift invariance in neural\nnetworks produces adversarial examples for the simple case of two classes, each\nconsisting of a single image with a black or white dot on a gray background.\nThis is more than a curiosity; we show empirically that with real datasets and\nrealistic architectures, shift invariance reduces adversarial robustness.\nFinally, we describe initial experiments using synthetic data to probe the\nsource of this connection.\n"}
+{"abstract": "  There are described equations coupling a completely symmetric conformal\nKilling or Codazzi tensor to the Einstein equations for a metric, in a manner\nanalogous to that used to obtain the Einstein-Maxwell equations by coupling a\ntwo-form to the metric. Examples of solutions are constructed from mean\ncurvature zero immersions, affine spheres, isoparametric polynomials, and\nregular graphs. There are deduced some constraints on the scalar curvature of\nthe metric occurring in a solution. Along the way, there are reviewed\nWeitzenb\\\"ock formulas, vanishing theorems, and related results for conformal\nKilling and divergence free Codazzi tensors.\n"}
+{"abstract": "  We initiate the study of the heterogeneous facility location problem with\nlimited resources. We mainly focus on the fundamental case where a set of\nagents are positioned in the line segment [0,1] and have approval preferences\nover two available facilities. A mechanism takes as input the positions and the\npreferences of the agents, and chooses to locate a single facility based on\nthis information. We study mechanisms that aim to maximize the social welfare\n(the total utility the agents derive from facilities they approve), under the\nconstraint of incentivizing the agents to truthfully report their positions and\npreferences. We consider three different settings depending on the level of\nagent-related information that is public or private. For each setting, we\ndesign deterministic and randomized strategyproof mechanisms that achieve a\ngood approximation of the optimal social welfare, and complement these with\nnearly-tight impossibility results.\n"}
+{"abstract": "  The main challenge in visible light communications (VLC) is the low\nmodulation bandwidth of light-emitting diodes (LEDs). This forms a barrier\ntowards achieving high data rates. Moreover, the implementation of high order\nmodulation schemes is restricted by the requirements of intensity modulation\n(IM) and direct detection (DD), which demand the use of real unipolar signals.\nIn this paper, we propose a novel amplitude, phase and quadrant (APQ)\nmodulation scheme that fits into the IM/DD restrictions in VLC systems. The\nproposed scheme decomposes the complex and bipolar symbols of high order\nmodulations into three different symbols that carry the amplitude, phase and\nquadrant information of the intended symbol. The constructed symbols are\nassigned different power levels and are transmitted simultaneously, i.e.\nexploiting the entire bandwidth and time resources. The receiving terminal\nperforms successive interference cancellation to extract and decode the three\ndifferent symbols, and then uses them to decide the intended complex bipolar\nsymbol. We evaluate the performance of the proposed APQ scheme in terms of\nsymbol-error-rate and achievable system throughput for different setup\nscenarios. The obtained results are compared with generalized spatial shift\nkeying (GSSK). The presented results show that APQ offers a higher reliability\ncompared to GSSK across the simulation area, while providing lower hardware\ncomplexity.\n"}
+{"abstract": "  For artificially intelligent learning systems to have widespread\napplicability in real-world settings, it is important that they be able to\noperate decentrally. Unfortunately, decentralized control is difficult --\ncomputing even an epsilon-optimal joint policy is a NEXP complete problem.\nNevertheless, a recently rediscovered insight -- that a team of agents can\ncoordinate via common knowledge -- has given rise to algorithms capable of\nfinding optimal joint policies in small common-payoff games. The Bayesian\naction decoder (BAD) leverages this insight and deep reinforcement learning to\nscale to games as large as two-player Hanabi. However, the approximations it\nuses to do so prevent it from discovering optimal joint policies even in games\nsmall enough to brute force optimal solutions. This work proposes CAPI, a novel\nalgorithm which, like BAD, combines common knowledge with deep reinforcement\nlearning. However, unlike BAD, CAPI prioritizes the propensity to discover\noptimal joint policies over scalability. While this choice precludes CAPI from\nscaling to games as large as Hanabi, empirical results demonstrate that, on the\ngames to which CAPI does scale, it is capable of discovering optimal joint\npolicies even when other modern multi-agent reinforcement learning algorithms\nare unable to do so. Code is available at https://github.com/ssokota/capi .\n"}
+{"abstract": "  Direct correlation functions (DCFs), linked to the second functional\nderivative of the free energy with respect to the one-particle density, play a\nfundamental role in a statistical mechanics description of matter. This holds\nin particular for the ordered phases: DCFs contain information about the local\nstructure including defects and encode the thermodynamic properties of\ncrystalline solids; they open a route to the elastic constants beyond low\ntemperature expansions. Via a numerical tour de force we have explicitly\ncalculated for the first time the DCF of a solid: based on the fundamental\nmeasure concept we provide results for the DCF of a hard sphere crystal. We\ndemonstrate that this function differs at coexistence significantly from its\nliquid counterpart - both in shape as well as in its order of magnitude -\nbecause it is dominated by vacancies. We provide evidence that the traditional\nuse of liquid DCFs in functional Taylor expansions of the free energy is\nconceptually wrong and show that the emergent elastic constants are in good\nagreement with simulation-based results.\n"}
+{"abstract": "  This paper is concerned with reconstruction issue of some typical inverse\nproblems and consists of three parts. First a framework of the enclosure method\nfor an inverse source problem governed by the Helmholtz equation at a fixed\nwave number in three dimensions is introduced. It is based on the nonvanishing\nof the coefficient of the leading profile of an oscillatory integral over a\ndomain having a conical singularity. Second an explicit formula of the\ncoefficient for a domain having a circular cone singularity and its implication\nunder the framework are given. Third, an application under the framework to an\ninverse obstacle problem governed by an inhomogeneous Helmholtz equation at a\nfixed wave number in three dimensions is given.\n"}
+{"abstract": "  We consider a fractal refinement of the Carleson problem for the\nSchr\\\"odinger equation, that is to identify the minimal regularity needed by\nthe solutions to converge pointwise to their initial data almost everywhere\nwith respect to the $\\alpha$-Hausdorff measure ($\\alpha$-a.e.). We extend to\nthe fractal setting ($\\alpha < n$) a recent counterexample of Bourgain\n\\cite{Bourgain2016}, which is sharp in the Lebesque measure setting ($\\alpha =\nn$). In doing so we recover the necessary condition from \\cite{zbMATH07036806}\nfor pointwise convergence~$\\alpha$-a.e. and we extend it to the range\n$n/2<\\alpha \\leq (3n+1)/4$.\n"}
+{"abstract": "  Purpose: We implemented the Machine Learning (ML) aided k-t SENSE\nreconstruction to enable high resolution quantitative real-time phase contrast\nMR (PCMR). Methods: A residual U-net and our U-net M were used to generate the\nhigh resolution x-f space estimate for k-t SENSE regularisation prior. The\nnetworks were judged on their ability to generalise to real undersampled data.\nThe in-vivo validation was done on 20 real-time 18x prospectively undersmapled\nGASperturbed PCMR data. The ML aided k-t SENSE reconstruction results were\ncompared against the free-breathing Cartesian retrospectively gated sequence\nand the compressed sensing (CS) reconstruction of the same data. Results: In\ngeneral, the ML aided k-t SENSE generated flow curves that were visually\nsharper than those produced using CS. In two exceptional cases, U-net M\npredictions exhibited blurring which propagated to the extracted velocity\ncurves. However, there were no statistical differences in the measured peak\nvelocities and stroke volumes between the tested methods. The ML aided k-t\nSENSE was estimated to be ~3.6x faster in processing than CS. Conclusion: The\nML aided k-t SENSE reconstruction enables artefact suppression on a par with CS\nwith no significant differences in quantitative measures. The timing results\nsuggest the on-line implementation could deliver a substantial increase in\nclinical throughput.\n"}
+{"abstract": "  Expected to operate in the imminent future, air taxi service (ATS) is an\naerial on-demand transport for a single passenger or a small group of riders,\nwhich seeks to transform the method of everyday commute. This uncharted\nterritory in the emerging transportation world is anticipated to enable\nconsumers bypass traffic congestion in urban road networks. By adopting an\nelectric vertical takeoff and landing concept (eVTOL), air taxis could be\noperational from skyports retrofitted on building rooftops, thus gaining\nadvantage from an implementation standpoint. Motivated by the potential impact\nof ATS, this study provides a review of air taxi systems and associated\noperations. We first discuss the current developments in the ATS (demand\nprediction, air taxi network design, and vehicle configuration). Next, we\nanticipate potential future challenges of ATS from an operations management\nperspective, and review the existing literature that could be leveraged to\ntackle these problems (ride-matching, pricing strategies, vehicle maintenance\nscheduling, and pilot training and recruitment). Finally, we detail future\nresearch opportunities in the air taxi domain.\n"}
+{"abstract": "  Multi-modal learning, which focuses on utilizing various modalities to\nimprove the performance of a model, is widely used in video recognition. While\ntraditional multi-modal learning offers excellent recognition results, its\ncomputational expense limits its impact for many real-world applications. In\nthis paper, we propose an adaptive multi-modal learning framework, called\nAdaMML, that selects on-the-fly the optimal modalities for each segment\nconditioned on the input for efficient video recognition. Specifically, given a\nvideo segment, a multi-modal policy network is used to decide what modalities\nshould be used for processing by the recognition model, with the goal of\nimproving both accuracy and efficiency. We efficiently train the policy network\njointly with the recognition model using standard back-propagation. Extensive\nexperiments on four challenging diverse datasets demonstrate that our proposed\nadaptive approach yields 35%-55% reduction in computation when compared to the\ntraditional baseline that simply uses all the modalities irrespective of the\ninput, while also achieving consistent improvements in accuracy over the\nstate-of-the-art methods.\n"}
+{"abstract": "  We present the relation between the star formation rate surface density,\n$\\Sigma_{\\rm SFR}$, and the hydrostatic mid-plane pressure, P$_{\\rm h}$, for\n4260 star-forming regions of kpc size located in 96 galaxies included in the\nEDGE-CALIFA survey covering a wide range of stellar masses and morphologies. We\nfind that these two parameters are tightly correlated, exhibiting smaller\nscatter and strong correlation in comparison to other star-forming scaling\nrelations. A power-law, with a slightly sub-linear index, is a good\nrepresentation of this relation. Locally, the residuals of this correlation\nshow a significant anti-correlation with both the stellar age and metallicity\nwhereas the total stellar mass may also play a secondary role in shaping the\n$\\Sigma_{\\rm SFR}$ - P$_{\\rm h}$ relation. For our sample of active\nstar-forming regions (i.e., regions with large values of H$\\alpha$ equivalent\nwidth), we find that the effective feedback momentum per unit stellar mass\n($p_\\ast/m_\\ast$),measured from the P$_{\\rm h}$ / $\\Sigma_{\\rm SFR}$ ratio\nincreases with P$_{\\rm h}$. The median value of this ratio for all the sampled\nregions is larger than the expected momentum just from supernovae explosions.\nMorphology of the galaxies, including bars, does not seem to have a significant\nimpact in the $\\Sigma_{\\rm SFR}$ - P$_{\\rm h}$ relation. Our analysis suggests\nthat self regulation of the $\\Sigma_{\\rm SFR}$ at kpc scales comes mainly from\nmomentum injection to the interstellar medium from supernovae explosions.\nHowever, other mechanism in disk galaxies may also play a significant role in\nshaping the $\\Sigma_{\\rm SFR}$ at local scales. Our results also suggest that\nP$_{\\rm h}$ can be considered as the main parameter that modulates star\nformation at kpc scales, rather than individual components of the baryonic\nmass.\n"}
+{"abstract": "  A graph is Helly if every family of pairwise intersecting balls has a\nnonempty common intersection. The class of Helly graphs is the discrete\nanalogue of the class of hyperconvex metric spaces. It is also known that every\ngraph isometrically embeds into a Helly graph, making the latter an important\nclass of graphs in Metric Graph Theory. We study diameter, radius and all\neccentricity computations within the Helly graphs. Under plausible complexity\nassumptions, neither the diameter nor the radius can be computed in truly\nsubquadratic time on general graphs. In contrast to these negative results, it\nwas recently shown that the radius and the diameter of an $n$-vertex $m$-edge\nHelly graph $G$ can be computed with high probability in $\\tilde{\\mathcal\nO}(m\\sqrt{n})$ time (i.e., subquadratic in $n+m$). In this paper, we improve\nthat result by presenting a deterministic ${\\mathcal O}(m\\sqrt{n})$ time\nalgorithm which computes not only the radius and the diameter but also all\nvertex eccentricities in a Helly graph. Furthermore, we give a parameterized\nlinear-time algorithm for this problem on Helly graphs, with the parameter\nbeing the Gromov hyperbolicity $\\delta$. More specifically, we show that the\nradius and a central vertex of an $m$-edge $\\delta$-hyperbolic Helly graph $G$\ncan be computed in $\\mathcal O(\\delta m)$ time and that all vertex\neccentricities in $G$ can be computed in $\\mathcal O(\\delta^2 m)$ time. To show\nthis more general result, we heavily use our new structural properties obtained\nfor Helly graphs.\n"}
+{"abstract": "  We present a graph-convolution-reinforced transformer, named Mesh Graphormer,\nfor 3D human pose and mesh reconstruction from a single image. Recently both\ntransformers and graph convolutional neural networks (GCNNs) have shown\npromising progress in human mesh reconstruction. Transformer-based approaches\nare effective in modeling non-local interactions among 3D mesh vertices and\nbody joints, whereas GCNNs are good at exploiting neighborhood vertex\ninteractions based on a pre-specified mesh topology. In this paper, we study\nhow to combine graph convolutions and self-attentions in a transformer to model\nboth local and global interactions. Experimental results show that our proposed\nmethod, Mesh Graphormer, significantly outperforms the previous\nstate-of-the-art methods on multiple benchmarks, including Human3.6M, 3DPW, and\nFreiHAND datasets. Code and pre-trained models are available at\nhttps://github.com/microsoft/MeshGraphormer\n"}
+{"abstract": "  A staged tree model is a discrete statistical model encoding relationships\nbetween events. These models are realised by directed trees with coloured\nvertices. In algebro-geometric terms, the model consists of points inside a\ntoric variety. For certain trees, called balanced, the model is in fact the\nintersection of the toric variety and the probability simplex. This gives the\nmodel a straightforward description, and has computational advantages. In this\npaper we show that the class of staged tree models with a toric structure\nextends far outside of the balanced case, if we allow a change of coordinates.\nIt is an open problem whether all staged tree models have toric structure.\n"}
+{"abstract": "  In this work, we firstly investigate how to reproduce and how well one can\nreproduce the Woods-Saxon density distribution of initial nuclei in the\nframework of the improved quantum molecular dynamics model. Then, we propose a\nnew treatment for the initialization of nuclei which is correlated with the\nnucleonic mean-field potential by using the same potential energy density\nfunctional. In the mean field potential, the three-body force term is\naccurately calculated. Based on the new version of the model, the influences of\nprecise calculations of the three-body force term, the slope of symmetry\nenergy, the neutron-proton effective mass splitting, and the width of the wave\npacket on heavy ion collision observables, such as the neutron to proton yield\nratios for emitted free nucleons [$R(n/p)$] and for coalescence invariant\nnucleons [$R_{ci}(n/p)$] for $^{124}$Sn+$^{112}$Sn at the beam energy of 200\nMeV per nucleon, are discussed. Our calculations show that the spectra of\nneutron to proton yield ratios [$R(n/p)$] can be used to probe the slope of\nsymmetry energy ($L$) and the neutron-proton effective mass splitting. In\ndetail, the $R(n/p)$ in the low kinetic energy region can be used to probe the\nslope of symmetry energy ($L$). With a given $L$, the inclination of $R(n/p)$\nto kinetic energy ($E_k$) can be used to probe the effective mass splitting. In\nthe case where the neutron-proton effective mass splitting is fixed, $R(n/p)$\nat high kinetic energy can also be used to learn the symmetry energy at\nsuprasaturation density.\n"}
+{"abstract": "  We study a natural generalization of that given in [arXiv:2005.13198\n[hep-th]] to heterotic string. Namely, starting from the generic Gepner models\nfor Calabi-Yau 3-folds, we construct the non-SUSY heterotic string vacua with\nthe vanishing cosmological constant at the one loop. We especially focus on the\nasymmetric orbifolding based on some discrete subgroup of the chiral\n$U(1)$-action which acts on both of the Gepner model and the $SO(32)$ or\n$E_8\\times E_8$-sector. We present a classification of the relevant orbifold\nmodels leading to the string vacua with the properties mentioned above. In some\ncases, the desired vacua can be constructed in the manner quite similar to\nthose given in [arXiv:2005.13198 [hep-th]] for the type II string, in which the\norbifold groups contain two generators with the discrete torsions. On the other\nhand, we also have simpler models that are just realized as the asymmetric\norbifolds of cyclic groups with only one generator.\n"}
+{"abstract": "  We consider the Hamiltonian renormalisation group flow of discretised\none-dimensional physical theories. In particular, we investigate the influence\nthe choice of different embedding maps has on the RG flow and the resulting\ncontinuum limit, and show in which sense they are, and in which sense they are\nnot equivalent as physical theories. We are furthermore elucidating the\ninterplay of the RG flow and the algebras operators satisfy, both on the\ndiscrete and the continuum. Further, we propose preferred renormalisation\nprescriptions for operator algebras guaranteeing to arrive at preferred\nalgebraic relations in the continuum, if suitable extension properties are\nassumed. Finally, we introduce a weaker form of distributional equivalence, and\nshow how unitarily inequivalent continuum limits, which arise due to a choice\nof different embedding maps, can still be weakly equivalent in that sense.\n"}
+{"abstract": "  Software Quality Assurance (SQA) planning aims to define proactive plans,\nsuch as defining maximum file size, to prevent the occurrence of software\ndefects in future releases. To aid this, defect prediction models have been\nproposed to generate insights as the most important factors that are associated\nwith software quality. Such insights that are derived from traditional defect\nmodels are far from actionable-i.e., practitioners still do not know what they\nshould do or avoid to decrease the risk of having defects, and what is the risk\nthreshold for each metric. A lack of actionable guidance and risk threshold can\nlead to inefficient and ineffective SQA planning processes. In this paper, we\ninvestigate the practitioners' perceptions of current SQA planning activities,\ncurrent challenges of such SQA planning activities, and propose four types of\nguidance to support SQA planning. We then propose and evaluate our AI-Driven\nSQAPlanner approach, a novel approach for generating four types of guidance and\ntheir associated risk thresholds in the form of rule-based explanations for the\npredictions of defect prediction models. Finally, we develop and evaluate an\ninformation visualization for our SQAPlanner approach. Through the use of\nqualitative survey and empirical evaluation, our results lead us to conclude\nthat SQAPlanner is needed, effective, stable, and practically applicable. We\nalso find that 80% of our survey respondents perceived that our visualization\nis more actionable. Thus, our SQAPlanner paves a way for novel research in\nactionable software analytics-i.e., generating actionable guidance on what\nshould practitioners do and not do to decrease the risk of having defects to\nsupport SQA planning.\n"}
+{"abstract": "  The threat from ransomware continues to grow both in the number of affected\nvictims as well as the cost incurred by the people and organisations impacted\nin a successful attack. In the majority of cases, once a victim has been\nattacked there remain only two courses of action open to them; either pay the\nransom or lose their data. One common behaviour shared between all crypto\nransomware strains is that at some point during their execution they will\nattempt to encrypt the users' files. Previous research Penrose et al. (2013);\nZhao et al. (2011) has highlighted the difficulty in differentiating between\ncompressed and encrypted files using Shannon entropy as both file types exhibit\nsimilar values. One of the experiments described in this paper shows a unique\ncharacteristic for the Shannon entropy of encrypted file header fragments. This\ncharacteristic was used to differentiate between encrypted files and other high\nentropy files such as archives. This discovery was leveraged in the development\nof a file classification model that used the differential area between the\nentropy curve of a file under analysis and one generated from random data. When\ncomparing the entropy plot values of a file under analysis against one\ngenerated by a file containing purely random numbers, the greater the\ncorrelation of the plots is, the higher the confidence that the file under\nanalysis contains encrypted data.\n"}
+{"abstract": "  Learning node representation on dynamically-evolving, multi-relational graph\ndata has gained great research interest. However, most of the existing models\nfor temporal knowledge graph forecasting use Recurrent Neural Network (RNN)\nwith discrete depth to capture temporal information, while time is a continuous\nvariable. Inspired by Neural Ordinary Differential Equation (NODE), we extend\nthe idea of continuum-depth models to time-evolving multi-relational graph\ndata, and propose a novel Temporal Knowledge Graph Forecasting model with NODE.\nOur model captures temporal information through NODE and structural information\nthrough a Graph Neural Network (GNN). Thus, our graph ODE model achieves a\ncontinuous model in time and efficiently learns node representation for future\nprediction. We evaluate our model on six temporal knowledge graph datasets by\nperforming link forecasting. Experiment results show the superiority of our\nmodel.\n"}
+{"abstract": "  Existing skin attributes detection methods usually initialize with a\npre-trained Imagenet network and then fine-tune the medical target task.\nHowever, we argue that such approaches are suboptimal because medical datasets\nare largely different from ImageNet and often contain limited training samples.\nIn this work, we propose Task Agnostic Transfer Learning (TATL), a novel\nframework motivated by dermatologists' behaviors in the skincare context. TATL\nlearns an attribute-agnostic segmenter that detects lesion skin regions and\nthen transfers this knowledge to a set of attribute-specific classifiers to\ndetect each particular region's attributes. Since TATL's attribute-agnostic\nsegmenter only detects abnormal skin regions, it enjoys ample data from all\nattributes, allows transferring knowledge among features, and compensates for\nthe lack of training data from rare attributes. We extensively evaluate TATL on\ntwo popular skin attributes detection benchmarks and show that TATL outperforms\nstate-of-the-art methods while enjoying minimal model and computational\ncomplexity. We also provide theoretical insights and explanations for why TATL\nworks well in practice.\n"}
+{"abstract": "  Protein function may be modulated by an event occurring far away from the\nfunctional site, a phenomenon termed allostery. While classically allostery\ninvolves conformational changes, we recently observed that charge\nredistribution within an antibody can also lead to an allosteric effect,\nmodulating the kinetics of binding to target antigen. In the present study, we\nstudy the association of a poly-histidine tagged enzyme (phosphoglycerate\nkinase, PGK) to surface-immobilized anti-His antibodies, finding a significant\nCharge-Reorganization Allostery (CRA) effect. We further observe that the\nnegatively charged nucleotide substrates of PGK modulate CRA substantially,\neven though they bind far away from the His-tag-antibody interaction interface.\nIn particular, binding of ATP reduces CRA by more than 50%. The results\nindicate that CRA may be affected by charged substrates bound to a protein and\nprovide further insight into the role of charge redistribution in protein\nfunction.\n"}
+{"abstract": "  We study a methodology to tackle the NASA Langley Uncertainty Quantification\nChallenge, a model calibration problem under both aleatory and epistemic\nuncertainties. Our methodology is based on an integration of robust\noptimization, more specifically a recent line of research known as\ndistributionally robust optimization, and importance sampling in Monte Carlo\nsimulation. The main computation machinery in this integrated methodology\namounts to solving sampled linear programs. We present theoretical statistical\nguarantees of our approach via connections to nonparametric hypothesis testing,\nand numerical performances including parameter calibration and downstream\ndecision and risk evaluation tasks.\n"}
+{"abstract": "  Continual or lifelong learning has been a long-standing challenge in machine\nlearning to date, especially in natural language processing (NLP). Although\nstate-of-the-art language models such as BERT have ushered in a new era in this\nfield due to their outstanding performance in multitask learning scenarios,\nthey suffer from forgetting when being exposed to a continuous stream of data\nwith shifting data distributions. In this paper, we introduce DRILL, a novel\ncontinual learning architecture for open-domain text classification. DRILL\nleverages a biologically inspired self-organizing neural architecture to\nselectively gate latent language representations from BERT in a\ntask-incremental manner. We demonstrate in our experiments that DRILL\noutperforms current methods in a realistic scenario of imbalanced,\nnon-stationary data without prior knowledge about task boundaries. To the best\nof our knowledge, DRILL is the first of its kind to use a self-organizing\nneural architecture for open-domain lifelong learning in NLP.\n"}
+{"abstract": "  Interacting many-body quantum systems show a rich array of physical phenomena\nand dynamical properties, but are notoriously difficult to study: they are\nchallenging analytically and exponentially difficult to simulate on classical\ncomputers. Small-scale quantum information processors hold the promise to\nefficiently emulate these systems, but characterizing their dynamics is\nexperimentally challenging, requiring probes beyond simple correlation\nfunctions and multi-body tomographic methods. Here, we demonstrate the\nmeasurement of out-of-time-ordered correlators (OTOCs), one of the most\neffective tools for studying quantum system evolution and processes like\nquantum thermalization. We implement a 3x3 two-dimensional hard-core\nBose-Hubbard lattice with a superconducting circuit, study its\ntime-reversibility by performing a Loschmidt echo, and measure OTOCs that\nenable us to observe the propagation of quantum information. A central\nrequirement for our experiments is the ability to coherently reverse time\nevolution, which we achieve with a digital-analog simulation scheme. In the\npresence of frequency disorder, we observe that localization can partially be\novercome with more particles present, a possible signature of many-body\nlocalization in two dimensions.\n"}
+{"abstract": "  Although recent inpainting approaches have demonstrated significant\nimprovements with deep neural networks, they still suffer from artifacts such\nas blunt structures and abrupt colors when filling in the missing regions. To\naddress these issues, we propose an external-internal inpainting scheme with a\nmonochromic bottleneck that helps image inpainting models remove these\nartifacts. In the external learning stage, we reconstruct missing structures\nand details in the monochromic space to reduce the learning dimension. In the\ninternal learning stage, we propose a novel internal color propagation method\nwith progressive learning strategies for consistent color restoration.\nExtensive experiments demonstrate that our proposed scheme helps image\ninpainting models produce more structure-preserved and visually compelling\nresults.\n"}
+{"abstract": "  We derive the equations for the odd and even parity perturbations of coupled\nelectromagnetic and gravitational fields of a black hole with an electric\ncharge within the context of general nonlinear electrodynamics. The Lagrangian\ndensity is a generic function of the Lorentz invariant scalar quantities of the\nelectromagnetic fields. We include the Hodge dual of the electromagnetic field\ntensor and the cosmological constant in our calculations. For each type of\nparity, we reduce the system of Einstein field equations coupled to nonlinear\nelectrodynamics to two coupled Schr\\\"odinger-type wave equations, one for the\ngravitational field and one for the electromagnetic field. The stability\nconditions in the presence of the Hodge dual of the electromagnetic field are\nderived.\n"}
+{"abstract": "  Terrestrial animals must often negotiate heterogeneous, varying environments.\nAccordingly, their locomotive strategies must adapt to a wide range of terrain,\nas well as to a range of speeds in order to accomplish different behavioral\ngoals. Studies in \\textit{Drosophila} have found that inter-leg coordination\npatterns (ICPs) vary smoothly with walking speed, rather than switching between\ndistinct gaits as in vertebrates (e.g., horses transitioning between trotting\nand galloping). Such a continuum of stepping patterns implies that separate\nneural controllers are not necessary for each observed ICP. Furthermore, the\nspectrum of \\textit{Drosophila} stepping patterns includes all canonical\ncoordination patterns observed during forward walking in insects. This raises\nthe exciting possibility that the controller in \\textit{Drosophila} is common\nto all insects, and perhaps more generally to panarthropod walkers. Here, we\nsurvey and collate data on leg kinematics and inter-leg coordination\nrelationships during forward walking in a range of arthropod species, as well\nas include data from a recent behavioral investigation into the tardigrade\n\\textit{Hypsibius exemplaris}. Using this comparative dataset, we point to\nseveral functional and morphological features that are shared amongst\npanarthropods. The goal of the framework presented in this review is to\nemphasize the importance of comparative functional and morphological analyses\nin understanding the origins and diversification of walking in Panarthropoda.\n"}
+{"abstract": "  Most of the works on the dispersion of droplets and their COVID-19\n(Coronavirus disease) implications address droplets' dynamics in quiescent\nenvironments. As most droplets in a common situation are immersed in external\nflows (such as ambient flows), we consider the effect of canonical flow\nprofiles namely, shear flow, Poiseuille flow, and unsteady shear flow on the\ntransport of spherical droplets of radius ranging from 5$\\mu$m to 100 $\\mu $m,\nwhich are characteristic lengths in human talking, coughing or sneezing\nprocesses. The dynamics we employ satisfies the Maxey-Riley (M-R) equation. An\norder-of-magnitude estimate allows us to solve the M-R equation to leading\norder analytically, and to higher order (accounting for the Boussinesq-Basset\nmemory term) numerically. Discarding evaporation, our results to leading order\nindicate that the maximum travelled distance for small droplets ($5\\mu m$\nradius) under a shear/Poiseuille external flow with a maximum flow speed of\n$1m/s$ may easily reach more than 250 meters, since those droplets remain in\nthe air for around 600 seconds. The maximum travelled distance was also\ncalculated to leading and higher orders, and it is observed that there is a\nsmall difference between the leading and higher order results, and that it\ndepends on the strength of the flow. For example, this difference for droplets\nof radius $5\\mu m$ in a shear flow, and with a maximum wind speed of $5m/s$, is\nseen to be around $2m$. In general, higher order terms are observed to slightly\nenhance droplets' dispersion and their flying time.\n"}
+{"abstract": "  This paper concerns the structural stability of smooth cylindrically\nsymmetric transonic flows in a concentric cylinder. Both cylindrical and\naxi-symmetric perturbations are considered. The governing system here is of\nmixed elliptic-hyperbolic and changes type and the suitable formulation of\nboundary conditions at the boundaries is of great importance. First, we\nestablish the existence and uniqueness of smooth cylindrical transonic spiral\nsolutions with nonzero angular velocity and vorticity which are close to the\nbackground transonic flow with small perturbations of the Bernoulli's function\nand the entropy at the outer cylinder and the flow angles at both the inner and\nouter cylinders independent of the symmetric axis, and it is shown that in this\ncase, the sonic points of the flow are nonexceptional and noncharacteristically\ndegenerate, and form a cylindrical surface. Second, we also prove the existence\nand uniqueness of axi-symmetric smooth transonic rotational flows which are\nadjacent to the background transonic flow, whose sonic points form an\naxi-symmetric surface. The key elements in our analysis are to utilize the\ndeformation-curl decomposition for the steady Euler system introduced in\n\\cite{WengXin19} to deal with the hyperbolicity in subsonic regions and to find\nan appropriate multiplier for the linearized second order mixed type equations\nwhich are crucial to identify the suitable boundary conditions and to yield the\nimportant basic energy estimates.\n"}
+{"abstract": "  Consider a measure $\\mu$ on $\\R^n$ generating a natural exponential family\n$F(\\mu)$ with variance function $V_{F(\\mu)}(m)$ and Laplace transform $$\n\\exp(\\ell_{\\mu}(s))=\\int_{\\R^n} \\exp(-\\<s,x\\>)\\mu(dx).$$ A dual measure $\\mu^*$\nsatisfies $-\\ell'_{\\mu^*}(-\\ell'_{\\mu}(s))=s.$ Such a dual measure does not\nalways exist. One important property is\n$\\ell\"_{\\mu^*}(m)=(V_{F(\\mu)}(m))^{-1},$ leading to the notion of duality among\nexponential families (or rather among the extended notion of T exponential\nfamilies $T\\hskip-2pt F$ obtained by considering all translations of a given\nexponential family $F$).\n"}
+{"abstract": "  The recently introduced polar codes constitute a breakthrough in coding\ntheory due to their capacityachieving property. This goes hand in hand with a\nquasilinear construction, encoding, and successive cancellation list decoding\nprocedures based on the Plotkin construction. The decoding algorithm can be\napplied with slight modifications to Reed-Muller or eBCH codes, that both\nachieve the capacity of erasure channels, although the list size needed for\ngood performance grows too fast to make the decoding practical even for\nmoderate block lengths. The key ingredient for proving the capacity-achieving\nproperty of Reed-Muller and eBCH codes is their group of symmetries. It can be\nplugged into the concept of Plotkin decomposition to design various permutation\ndecoding algorithms. Although such techniques allow to outperform the\nstraightforward polar-like decoding, the complexity stays impractical. In this\npaper, we show that although invariance under a large automorphism group is\nvaluable in a theoretical sense, it also ensures that the list size needed for\ngood performance grows exponentially. We further establish the bounds that\narise if we sacrifice some of the symmetries. Although the theoretical analysis\nof the list decoding algorithm remains an open problem, our result provides an\ninsight into the factors that impact the decoding complexity.\n"}
+{"abstract": "  Transfer learning eases the burden of training a well-performed model from\nscratch, especially when training data is scarce and computation power is\nlimited. In deep learning, a typical strategy for transfer learning is to\nfreeze the early layers of a pre-trained model and fine-tune the rest of its\nlayers on the target domain. Previous work focuses on the accuracy of the\ntransferred model but neglects the transfer of adversarial robustness. In this\nwork, we first show that transfer learning improves the accuracy on the target\ndomain but degrades the inherited robustness of the target model. To address\nsuch a problem, we propose a novel cooperative adversarially-robust transfer\nlearning (CARTL) by pre-training the model via feature distance minimization\nand fine-tuning the pre-trained model with non-expansive fine-tuning for target\ndomain tasks. Empirical results show that CARTL improves the inherited\nrobustness by about 28% at most compared with the baseline with the same degree\nof accuracy. Furthermore, we study the relationship between the batch\nnormalization (BN) layers and the robustness in the context of transfer\nlearning, and we reveal that freezing BN layers can further boost the\nrobustness transfer.\n"}
+{"abstract": "  The outbreak of novel coronavirus pneumonia (COVID-19) has caused mortality\nand morbidity worldwide. Oropharyngeal-swab (OP-swab) sampling is widely used\nfor the diagnosis of COVID-19 in the world. To avoid the clinical staff from\nbeing affected by the virus, we developed a 9-degree-of-freedom (DOF)\nrigid-flexible coupling (RFC) robot to assist the COVID-19 OP-swab sampling.\nThis robot is composed of a visual system, UR5 robot arm, micro-pneumatic\nactuator and force-sensing system. The robot is expected to reduce risk and\nfree up the clinical staff from the long-term repetitive sampling work.\nCompared with a rigid sampling robot, the developed force-sensing RFC robot can\nfacilitate OP-swab sampling procedures in a safer and softer way. In addition,\na varying-parameter zeroing neural network-based optimization method is also\nproposed for motion planning of the 9-DOF redundant manipulator. The developed\nrobot system is validated by OP-swab sampling on both oral cavity phantoms and\nvolunteers.\n"}
+{"abstract": "  Deep learning has been broadly applied to imaging in scattering applications.\nA common framework is to train a \"descattering\" neural network for image\nrecovery by removing scattering artifacts. To achieve the best results on a\nbroad spectrum of scattering conditions, individual \"expert\" networks have to\nbe trained for each condition. However, the performance of the expert sharply\ndegrades when the scattering level at the testing time differs from the\ntraining. An alternative approach is to train a \"generalist\" network using data\nfrom a variety of scattering conditions. However, the generalist generally\nsuffers from worse performance as compared to the expert trained for each\nscattering condition. Here, we develop a drastically different approach, termed\ndynamic synthesis network (DSN), that can dynamically adjust the model weights\nand adapt to different scattering conditions. The adaptability is achieved by a\nnovel architecture that enables dynamically synthesizing a network by blending\nmultiple experts using a gating network. Notably, our DSN adaptively removes\nscattering artifacts across a continuum of scattering conditions regardless of\nwhether the condition has been used for the training, and consistently\noutperforms the generalist. By training the DSN entirely on a\nmultiple-scattering simulator, we experimentally demonstrate the network's\nadaptability and robustness for 3D descattering in holographic 3D particle\nimaging. We expect the same concept can be adapted to many other imaging\napplications, such as denoising, and imaging through scattering media. Broadly,\nour dynamic synthesis framework opens up a new paradigm for designing highly\nadaptive deep learning and computational imaging techniques.\n"}
+{"abstract": "  We prove that sufficiently low-entropy hypersurfaces can be perturbed so that\ntheir mean curvature flow encounters only spherical and cylindrical\nsingularities.\n"}
+{"abstract": "  We take a deeper dive into the geometry and the number theory that underlay\nthe butterfly graphs of the Harper and the generalized Harper models of Bloch\nelectrons in a magnetic field. Root of the number theoretical characteristics\nof the fractal spectrum is traced to a close relationship between the Farey\ntree -- the hierarchical tree that generates all rationals and the Wannier\ndiagram -- a graph that labels all the gaps of the butterfly graph. The\nresulting Farey-Wannier hierarchical lattice of trapezoids provides geometrical\nrepresentation of the nested pattern of butterflies in the butterfly graph.\nSome features of the energy spectrum such as absence of some of the Wannier\ntrajectories in the butterfly graph fall outside the number theoretical\nframework, can be stated as a simple rule of \"minimal violation of mirror\nsymmetry\". In a generalized Harper model, Farey-Wannier representation prevails\nas the lattice regroups to form some hexagonal unit cells creating new {\\it\nspecies} of butterflies\n"}
+{"abstract": "  The ethical consequences of, constraints upon and regulation of algorithms\narguably represent the defining challenges of our age, asking us to reckon with\nthe rise of computational technologies whose potential to radically\ntransforming social and individual orders and identity in unforeseen ways is\nalready being realised. Yet despite the multidisciplinary impact of this\nalgorithmic turn, there remains some way to go in motivating the\ncrossdisciplinary collaboration that is crucial to advancing feasible proposals\nfor the ethical design, implementation and regulation of algorithmic and\nautomated systems. In this work, we provide a framework to assist\ncross-disciplinary collaboration by presenting a Four C's Framework covering\nkey computational considerations researchers across such diverse fields should\nconsider when approaching these questions: (i) computability, (ii) complexity,\n(iii) consistency and (iv) controllability. In addition, we provide examples of\nhow insights from ethics, philosophy and population ethics are relevant to and\ntranslatable within sciences concerned with the study and design of algorithms.\nOur aim is to set out a framework which we believe is useful for fostering\ncross-disciplinary understanding of pertinent issues in ethical algorithmic\nliterature which is relevant considering the feasibility of ethical algorithmic\ngovernance, especially the impact of computational constraints upon algorithmic\ngovernance.\n"}
+{"abstract": "  Topological defects are one of the most conspicuous features of liquid\ncrystals. In two dimensional nematics, they have been shown to behave\neffectively as particles with both, charge and orientation, which dictate their\ninteractions. Here, we study \"twisted\" defects that have a radially dependent\norientation. We find that twist can be partially relaxed through the creation\nand annihilation of defect pairs. By solving the equations for defect motion\nand calculating the forces on defects, we identify four distinct elements that\ngovern the relative relaxational motion of interacting topological defects,\nnamely attraction, repulsion, co-rotation and co-translation. The interaction\nof these effects can lead to intricate defect trajectories, which can be\ncontrolled by setting relevant timescales.\n"}
+{"abstract": "  This article explores the existing normalizing and variance-stabilizing\n(NoVaS) method on predicting squared log-returns of financial data. First, we\nexplore the robustness of the existing NoVaS method for long-term\ntime-aggregated predictions. Then we develop a more parsimonious variant of the\nexisting method. With systematic justification and extensive data analysis, our\nnew method shows better performance than current NoVaS and standard GARCH(1,1)\nmethods on both short- and long-term time-aggregated predictions.\n"}
+{"abstract": "  Agent-based models of disease transmission involve stochastic rules that\nspecify how a number of individuals would infect one another, recover or be\nremoved from the population. Common yet stringent assumptions stipulate\ninterchangeability of agents and that all pairwise contact are equally likely.\nUnder these assumptions, the population can be summarized by counting the\nnumber of susceptible and infected individuals, which greatly facilitates\nstatistical inference. We consider the task of inference without such\nsimplifying assumptions, in which case, the population cannot be summarized by\nlow-dimensional counts. We design improved particle filters, where each\nparticle corresponds to a specific configuration of the population of agents,\nthat take either the next or all future observations into account when\nproposing population configurations. Using simulated data sets, we illustrate\nthat orders of magnitude improvements are possible over bootstrap particle\nfilters. We also provide theoretical support for the approximations employed to\nmake the algorithms practical.\n"}
+{"abstract": "  Learning to flexibly follow task instructions in dynamic environments poses\ninteresting challenges for reinforcement learning agents. We focus here on the\nproblem of learning control flow that deviates from a strict step-by-step\nexecution of instructions -- that is, control flow that may skip forward over\nparts of the instructions or return backward to previously completed or skipped\nsteps. Demand for such flexible control arises in two fundamental ways:\nexplicitly when control is specified in the instructions themselves (such as\nconditional branching and looping) and implicitly when stochastic environment\ndynamics require re-completion of instructions whose effects have been\nperturbed, or opportunistic skipping of instructions whose effects are already\npresent. We formulate an attention-based architecture that meets these\nchallenges by learning, from task reward only, to flexibly attend to and\ncondition behavior on an internal encoding of the instructions. We test the\narchitecture's ability to learn both explicit and implicit control in two\nillustrative domains -- one inspired by Minecraft and the other by StarCraft --\nand show that the architecture exhibits zero-shot generalization to novel\ninstructions of length greater than those in a training set, at a performance\nlevel unmatched by two baseline recurrent architectures and one ablation\narchitecture.\n"}
+{"abstract": "  We analyze the axiomatic strength of the following theorem due to Rival and\nSands in the style of reverse mathematics. \"Every infinite partial order $P$ of\nfinite width contains an infinite chain $C$ such that every element of $P$ is\neither comparable with no element of $C$ or with infinitely many elements of\n$C$.\" Our main results are the following. The Rival-Sands theorem for infinite\npartial orders of arbitrary finite width is equivalent to $\\mathsf{I}\\Sigma^0_2\n+ \\mathsf{ADS}$ over $\\mathsf{RCA}_0$. For each fixed $k \\geq 3$, the\nRival-Sands theorem for infinite partial orders of width $\\leq\\! k$ is\nequivalent to $\\mathsf{ADS}$ over $\\mathsf{RCA}_0$. The Rival-Sands theorem for\ninfinite partial orders that are decomposable into the union of two chains is\nequivalent to $\\mathsf{SADS}$ over $\\mathsf{RCA}_0$. Here $\\mathsf{RCA}_0$\ndenotes the recursive comprehension axiomatic system, $\\mathsf{I}\\Sigma^0_2$\ndenotes the $\\Sigma^0_2$ induction scheme, $\\mathsf{ADS}$ denotes the\nascending/descending sequence principle, and $\\mathsf{SADS}$ denotes the stable\nascending/descending sequence principle. To our knowledge, these versions of\nthe Rival-Sands theorem for partial orders are the first examples of theorems\nfrom the general mathematics literature whose strength is exactly characterized\nby $\\mathsf{I}\\Sigma^0_2 + \\mathsf{ADS}$, by $\\mathsf{ADS}$, and by\n$\\mathsf{SADS}$. Furthermore, we give a new purely combinatorial result by\nextending the Rival-Sands theorem to infinite partial orders that do not have\ninfinite antichains, and we show that this extension is equivalent to\narithmetical comprehension over $\\mathsf{RCA}_0$.\n"}
+{"abstract": "  Cyber Physical Systems (CPSs) are often black box systems for which no exact\nmodel exists. Automata learning allows to build abstract models of CPSs and is\nused in several scenarios, i.e. simulation, monitoring, and test case\ngeneration. Real time localization systems (RTLSs) are an example of\nparticularly complex and often safety critical CPSs. We present a procedure for\nautomatic test case generation with automata learning and apply this approach\nin a case study to a localization system.\n"}
+{"abstract": "  This is a survey on stated skein algebras and their representations.\n"}
+{"abstract": "  Flares are known to play an important role for the evolution of the\natmospheres of young planets. In order to understand the evolution of planets,\nit is thus important to study the flare-activity of young stars. This is\nparticularly the case for young M-stars, because they are very active. We study\nphotometrically and spectroscopically the highly active M-star 2MASS\nJ16111534-1757214. We show that it is a member of the Upper Sco OB association,\nwhich has an age of 5-10 Myrs. We also re-evaluate the status of other\nbona-fide M-stars in this region and identify 42 members. Analyzing the\nK2-light curves, we find that 2MASS J16111534-1757214 has, on average, one\nsuper-flare with E > 1.0E35 erg every 620 hours, and one with E >1.0E34 erg\nevery 52 hours. Although this is the most active M-star in the Upper Sco\nassociation, the power-law index of its flare-distribution is similar to that\nof other M-stars in this region. 2MASS J16111534-1757214 as well as other\nM-stars in this region show a broken power-law distribution in the\nflare-frequency diagram. Flares larger than E >3E34 erg have a power-law index\nbeta=-1.3+/-0.1 and flares smaller than that beta=-0.8+/-0.1. We furthermore\nconclude that the flare-energy distribution for young M-stars is not that\ndifferent from solar-like stars.\n"}
+{"abstract": "  We report on the study of the magnetic ratchet effect in AlGaN/GaN\nheterostructures superimposed with lateral superlattice formed by dual-grating\ngate structure. We demonstrate that irradiation of the superlattice with\nterahertz beam results in the dc ratchet current, which shows giant\nmagneto-oscillations in the regime of Shubnikov de Haas oscillations. The\noscillations have the same period and are in phase with the resistivity\noscillations. Remarkably, their amplitude is greatly enhanced as compared to\nthe ratchet current at zero magnetic field, and the envelope of these\noscillations exhibits large beatings as a function of the magnetic field. We\ndemonstrate that the beatings are caused by the spin-orbit splitting of the\nconduction band. We develop a theory which gives a good qualitative explanation\nof all experimental observations and allows us to extract the spin-orbit\nsplitting constant \\alpha_{\\rm SO}= 7.5 \\pm 1.5 meV \\unicode{x212B}. We also\ndiscuss how our results are modified by plasmonic effects and show that these\neffects become more pronounced with decreasing the period of the gating gate\nstructures down to sub-microns.\n"}
+{"abstract": "  Spectroscopic Amplitudes (SA) in the Interacting Boson Fermion Fermion Model\n(IBFFM) are necessary for the computation of $0\\nu\\beta\\beta$ decays but also\nfor cross sections of heavy-ion reactions, in particular, Double Charge\nExchange reactions for the NUMEN collaboration, if one does not want to use the\nclosure limit. We present for the first time: i) the formalism and operators to\ncompute in a general case the spectroscopic amplitudes in the scheme IBFFM from\nan even-even to odd-odd nuclei, in a way suited to be used in reaction code,\ni.e., extracting the contribution of each orbital; 2) the odd-odd nuclei as\ndescribed by the old IBFFM are obtained for the first time with the new\nimplementation of Machine Learning (ML) techniques for fitting the parameters,\ngetting a more realistic description. The one body transition densities for\n$^{116}$Cd $\\rightarrow$ $^{116}$In and $^{116}$In $\\rightarrow$ $^{116}$Sn are\npart of the experimental program of the NUMEN experiment, which aims to find\nconstraints on Neutrinoless double beta decay matrix elements.\n"}
+{"abstract": "  We derive Kubo formulae for first-order spin hydrodynamics based on\nnon-equilibrium statistical operators method. In first-order spin\nhydrodynamics, there are two new transport coefficients besides the ordinary\nones appearing in first-order viscous hydrodynamics. They emerge due to the\nincorporation of the spin degree of freedom into fluids and the spin-orbital\ncoupling. Zubarev's non-equilibrium statistical operator method can be well\napplied to investigate these quantum effects in fluids. The Kubo formulae,\nbased on the method of non-equilibrium statistical operators, are related to\nequilibrium (imaginary-time) infrared Green's functions, and all the transport\ncoefficients can be determined when the microscopic theory is specified.\n"}
+{"abstract": "  As a promising lensless imaging method for distance objects, intensity\ninterferometry imaging (III) had been suffering from the unreliable phase\nretrieval process, hindering the development of III for decades. Recently, the\nintroduction of the ptychographic detection in III overcame this challenge, and\na method called ptychographic III (PIII) was proposed. We here experimentally\ndemonstrate that PIII can image a dynamic distance object. A reasonable image\nfor the moving object can be retrieved with only two speckle patterns for each\nprobe, and only 10 to 20 iterations are needed. Meanwhile, PIII exhibits robust\nto the inaccurate information of the probe. Furthermore, PIII successfully\nrecovers the image through a fog obfuscating the imaging light path, under\nwhich a conventional camera relying on lenses fails to provide a recognizable\nimage.\n"}
+{"abstract": "  We consider the possibility that the Milky Way's dark matter halo possesses a\nnon vanishing equation of state. Consequently, we evaluate the contribution due\nto the speed of sound, assuming that the dark matter content of the galaxy\nbehaves like a fluid with pressure. In particular, we model the dark matter\ndistribution via an exponential sphere profile in the galactic core, and inner\nparts of the galaxy whereas we compare the exponential sphere with three\nwidely-used profiles for the halo, i.e. the Einasto, Burkert and Isothermal\nprofile. For the galactic core we also compare the effects due to a dark matter\ndistribution without black hole with the case of a supermassive black hole in\nvacuum and show that present observations are unable to distinguish them.\nFinally we investigate the expected experimental signature provided by\ngravitational lensing due to the presence of dark matter in the core.\n"}
+{"abstract": "  Pre-trained multilingual language models (LMs) have achieved state-of-the-art\nresults in cross-lingual transfer, but they often lead to an inequitable\nrepresentation of languages due to limited capacity, skewed pre-training data,\nand sub-optimal vocabularies. This has prompted the creation of an ever-growing\npre-trained model universe, where each model is trained on large amounts of\nlanguage or domain specific data with a carefully curated, linguistically\ninformed vocabulary. However, doing so brings us back full circle and prevents\none from leveraging the benefits of multilinguality. To address the gaps at\nboth ends of the spectrum, we propose MergeDistill, a framework to merge\npre-trained LMs in a way that can best leverage their assets with minimal\ndependencies, using task-agnostic knowledge distillation. We demonstrate the\napplicability of our framework in a practical setting by leveraging\npre-existing teacher LMs and training student LMs that perform competitively\nwith or even outperform teacher LMs trained on several orders of magnitude more\ndata and with a fixed model capacity. We also highlight the importance of\nteacher selection and its impact on student model performance.\n"}
+{"abstract": "  We define and study two new kinds of \"effective resistances\" based on\nhubs-biased -- hubs-repelling and hubs-attracting -- models of navigating a\ngraph/network. We prove that these effective resistances are squared Euclidean\ndistances between the vertices of a graph. They can be expressed in terms of\nthe Moore-Penrose pseudoinverse of the hubs-biased Laplacian matrices of the\ngraph. We define the analogous of the Kirchhoff indices of the graph based of\nthese resistance distances. We prove several results for the new resistance\ndistances and the Kirchhoff indices based on spectral properties of the\ncorresponding Laplacians. After an intensive computational search we conjecture\nthat the Kirchhoff index based on the hubs-repelling resistance distance is not\nsmaller than that based on the standard resistance distance, and that the last\nis not smaller than the one based on the hubs-attracting resistance distance.\nWe also observe that in real-world brain and neural systems the efficiency of\nstandard random walk processes is as high as that of hubs-attracting schemes.\nOn the contrary, infrastructures and modular software networks seem to be\ndesigned to be navigated by using their hubs.\n"}
+{"abstract": "  This is a continuation of a previous study initiated by one of us on nonlocal\nvertex bialgebras and smash product nonlocal vertex algebras. In this paper, we\nstudy a notion of right $H$-comodule nonlocal vertex algebra for a nonlocal\nvertex bialgebra $H$ and give a construction of deformations of vertex algebras\nwith a right $H$-comodule nonlocal vertex algebra structure and a compatible\n$H$-module nonlocal vertex algebra structure. We also give a construction of\n$\\phi$-coordinated quasi modules for smash product nonlocal vertex algebras. As\nan example, we give a family of quantum vertex algebras by deforming the vertex\nalgebras associated to non-degenerate even lattices.\n"}
+{"abstract": "  We study the ground state of the Hubbard model on a square lattice with two\ndegenerate orbitals per site and at integer fillings as a function of onsite\nHubbard repulsion $U$ and Hund's intra-atomic exchange coupling $J$. We use a\nvariational slave-spin mean field (VSSMF) method which allows symmetry broken\nstates to be studied within the computationally less intensive slave-spin mean\nfield formalism, thus making the method more powerful to study strongly\ncorrelated electron physics. The results show that at half-filling, the ground\nstate at smaller $U$ is a Slater antiferromagnet (AF) with substantial local\ncharge fluctuations. As $U$ is increased, the AF state develops a Heisenberg\nbehavior, finally undergoing a first order transition to a Mott insulating AF\nstate at a critical interaction $U_c$ which is of the order of the bandwidth.\nIntroducing the Hund's coupling $J$ correlates the system more and reduces\n$U_c$ drastically. At quarter-filling with one electron per site, the ground\nstate at smaller $U$ is paramagnetic metallic. At finite Hund's coupling $J$,\nas interaction is increased above a lower critical value $U_{c1}$, it goes to a\nfully spin polarized ferromagnetic state coexisting with an antiferro-orbital\norder. The system eventually becomes Mott insulating at a higher critical value\n$U_{c2}$. The results as a function of $U$ and $J$ are thoroughly discussed.\n"}
+{"abstract": "  We present an analysis of the $R\\lesssim 1.5$ kpc core regions of seven\nsimulated Milky Way mass galaxies, from the FIRE-2 (Feedback in Realistic\nEnvironments) cosmological zoom-in simulation suite, for a finely sampled\nperiod ($\\Delta t = 2.2$ Myr) of 22 Myr at $z \\approx 0$, and compare them with\nstar formation rate (SFR) and gas surface density observations of the Milky\nWay's Central Molecular Zone (CMZ). Despite not being tuned to reproduce the\ndetailed structure of the CMZ, we find that four of these galaxies are\nconsistent with CMZ observations at some point during this 22 Myr period. The\ngalaxies presented here are not homogeneous in their central structures,\nroughly dividing into two morphological classes; (a) several of the galaxies\nhave very asymmetric gas and SFR distributions, with intense (compact)\nstarbursts occurring over a period of roughly 10 Myr, and structures on highly\neccentric orbits through the CMZ, whereas (b) others have smoother gas and SFR\ndistributions, with only slowly varying SFRs over the period analyzed. In class\n(a) centers, the orbital motion of gas and star-forming complexes across small\napertures ($R \\lesssim 150$pc, analogously $|l|<1^\\circ$ in the CMZ\nobservations) contributes as much to tracers of star formation/dense gas\nappearing in those apertures, as the internal evolution of those structures\ndoes. These asymmetric/bursty galactic centers can simultaneously match CMZ gas\nand SFR observations, demonstrating that time-varying star formation can\nexplain the CMZ's low star formation efficiency.\n"}
+{"abstract": "  Detecting anomalies in large complex systems is a critical and challenging\ntask. The difficulties arise from several aspects. First, collecting ground\ntruth labels or prior knowledge for anomalies is hard in real-world systems,\nwhich often lead to limited or no anomaly labels in the dataset. Second,\nanomalies in large systems usually occur in a collective manner due to the\nunderlying dependency structure among devices or sensors. Lastly, real-time\nanomaly detection for high-dimensional data requires efficient algorithms that\nare capable of handling different types of data (i.e. continuous and discrete).\nWe propose a correlation structure-based collective anomaly detection (CSCAD)\nmodel for high-dimensional anomaly detection problem in large systems, which is\nalso generalizable to semi-supervised or supervised settings. Our framework\nutilize graph convolutional network combining a variational autoencoder to\njointly exploit the feature space correlation and reconstruction deficiency of\nsamples to perform anomaly detection. We propose an extended mutual information\n(EMI) metric to mine the internal correlation structure among different data\nfeatures, which enhances the data reconstruction capability of CSCAD. The\nreconstruction loss and latent standard deviation vector of a sample obtained\nfrom reconstruction network can be perceived as two natural anomalous degree\nmeasures. An anomaly discriminating network can then be trained using low\nanomalous degree samples as positive samples, and high anomalous degree samples\nas negative samples. Experimental results on five public datasets demonstrate\nthat our approach consistently outperforms all the competing baselines.\n"}
+{"abstract": "  Time-periodic driving fields could endow a system with peculiar topological\nand transport features. In this work, we find dynamically controlled\nlocalization transitions and mobility edges in non-Hermitian quasicrystals via\nshaking the lattice periodically. The driving force dresses the hopping\namplitudes between lattice sites, yielding alternate transitions between\nlocalized, mobility edge and extended non-Hermitian quasicrystalline phases. We\napply our Floquet engineering approach to five representative models of\nnon-Hermitian quasicrystals, obtain the conditions of photon-assisted\nlocalization transitions and mobility edges, and find the expressions of\nLyapunov exponents for some models. We further introduce topological winding\nnumbers of Floquet quasienergies to distinguish non-Hermitian quasicrystalline\nphases with different localization nature. Our discovery thus extend the study\nof quasicrystals to non-Hermitian Floquet systems, and provide an efficient way\nof modulating the topological and transport properties of these unique phases.\n"}
+{"abstract": "  Understanding the behavior of learned classifiers is an important task, and\nvarious black-box explanations, logical reasoning approaches, and\nmodel-specific methods have been proposed. In this paper, we introduce\nprobabilistic sufficient explanations, which formulate explaining an instance\nof classification as choosing the \"simplest\" subset of features such that only\nobserving those features is \"sufficient\" to explain the classification. That\nis, sufficient to give us strong probabilistic guarantees that the model will\nbehave similarly when all features are observed under the data distribution. In\naddition, we leverage tractable probabilistic reasoning tools such as\nprobabilistic circuits and expected predictions to design a scalable algorithm\nfor finding the desired explanations while keeping the guarantees intact. Our\nexperiments demonstrate the effectiveness of our algorithm in finding\nsufficient explanations, and showcase its advantages compared to Anchors and\nlogical explanations.\n"}
+{"abstract": "  In this article, we consider the family of functions $f$ analytic in the unit\ndisk $|z|<1$ with the normalization $f(0)=0=f'(0)-1$ and satisfying the\ncondition $\\big |\\big (z/f(z)\\big )^{2}f'(z)-1\\big |<\\lambda $ for some\n$0<\\lambda \\leq 1$. We denote this class by $\\mathcal{U}(\\lambda)$ and we are\ninterested in the relations between $\\mathcal{U}(\\lambda)$ and other families\nof functions holomorphic or harmonic in the unit disk. Our first example in\nthis direction is the family of functions convex in one direction. Then we are\nconcerned with the subordinates to the function $1/((1-z)(1-\\lambda z))$. We\nprove that not all functions $f(z)/z$ $(f \\in \\mathcal{U}(\\lambda))$ belong to\nthis family. This disproves an assertion from \\cite{OPW}. Further, we disprove\na related coefficient conjecture for $\\mathcal{U}(\\lambda)$. We consider the\nintersection of the class of the above subordinates and $\\mathcal{U}(\\lambda)$\nconcerning the boundary behaviour of its functions. At last, with the help of\nfunctions from $\\mathcal{U}(\\lambda)$, we construct functions harmonic and\nclose-to-convex in the unit disk.\n"}
+{"abstract": "  In two-dimensional geometric knapsack problem, we are given a set of n\naxis-aligned rectangular items and an axis-aligned square-shaped knapsack. Each\nitem has integral width, integral height and an associated integral profit. The\ngoal is to find a (non-overlapping axis-aligned) packing of a maximum profit\nsubset of rectangles into the knapsack. A well-studied and frequently used\nconstraint in practice is to allow only packings that are guillotine separable,\ni.e., every rectangle in the packing can be obtained by recursively applying a\nsequence of edge-to-edge axis-parallel cuts that do not intersect any item of\nthe solution. In this paper we study approximation algorithms for the geometric\nknapsack problem under guillotine cut constraints. We present polynomial time\n(1 + {\\epsilon})-approximation algorithms for the cases with and without\nallowing rotations by 90 degrees, assuming that all input numeric data are\npolynomially bounded in n. In comparison, the best-known approximation factor\nfor this setting is 3 + {\\epsilon} [Jansen-Zhang, SODA 2004], even in the\ncardinality case where all items have the same profit. Our main technical\ncontribution is a structural lemma which shows that any guillotine packing can\nbe converted into another structured guillotine packing with almost the same\nprofit. In this packing, each item is completely contained in one of a constant\nnumber of boxes and L-shaped regions, inside which the items are placed by a\nsimple greedy routine. In particular, we provide a clean sufficient condition\nwhen such a packing obeys the guillotine cut constraints which might be useful\nfor other settings where these constraints are imposed.\n"}
+{"abstract": "  Layered two-dimensional (2D) materials MoTe2 have been paid special attention\ndue to the rich optoelectronic properties with various phases. The\nnonequilibrium carrier dynamics as well as its temperature dependence in MoTe2\nare of prime importance, as it can shed light on understanding the anomalous\noptical response and potential applications in far infrared (IR)\nphotodetection. Hereby, we employ time-resolved terahertz (THz) spectroscopy to\nstudy the temperature dependent nonequilibrium carrier dynamics in MoTe2 films.\nAfter photoexcitation of 1.59 eV, the 1T'-phase MoTe2 at high temperature\nbehaves only THz positive photoconductivity (PPC) with relaxation time of less\nthan 1 ps. In contrast, the Td-phase MoTe2 at low temperature shows ultrafast\nTHz PPC initially followed by emerging THz negative photoconductivity (NPC),\nand the THz NPC signal relaxes to the equilibrium state in hundreds of ps time\nscale. Small polaron formation induced by hot carrier has been proposed to be\nascribed to the THz NPC in the polar semimetal MoTe2 at low temperature. The\npolaron formation time after photoexcitation increases slightly with\ntemperature, which is determined to be ~0.4 ps at 5 K and 0.5 ps at 100 K. Our\nexperimental result demonstrates for the first time the dynamical formation of\nsmall poalron in MoTe2 Weyl semimetal, this is fundamental importance on the\nunderstanding the temperature dependent electron-phonon coupling and quantum\nphase transition, as well as the designing the MoTe2-based far IR\nphotodetector.\n"}
+{"abstract": "  Here we explore the structural, magnetic and dielectric properties of Co\nbased compound Na$_5$Co$_{15.5}$Te$_6$O$_{36}$ as a candidate of short-range\nmagnetic correlations driven development of dielectric anomaly above\nN$\\acute{e}$el temperature of ($T_N$=) 50 K. Low temperature neutron powder\ndiffraction (NPD) in zero applied magnetic field clearly indicates that the\ncanted spin structure is responsible for the antiferromagnetic transition and\npartially occupied Co form short range magnetic correlation with other Co,\nwhich further facilitates the structural distortion and consequent development\nof dielectric anomaly above antiferromagnetic transition. Additionally, the\ntemperature dependent magnetic heat capacity and electron spin resonance\nmeasurements reveal the presence of short-range magnetic correlations which\ncoincides with an anomaly in the dielectric constant vs temperature curve.\nMoreover, significant changes in the lattice parameters are also observed\naround the same temperature, indicating presence of noticeable spin-lattice\ncoupling. Further, sharp jump in the magnetic field dependent magnetization\nclearly indicates the presence of metamagnetic transition and magnetic field\ndependent NPD confirms that rotations of Co spins with applied magnetic field\nare responsible for this metamagnetic phase transition. As a result, this\ntransition causes the magnetocaloric effect to be developed in the system,\nwhich is suitable for the application in low temperature refrigeration.\n"}
+{"abstract": "  Scene graphs are a compact and explicit representation successfully used in a\nvariety of 2D scene understanding tasks. This work proposes a method to\nincrementally build up semantic scene graphs from a 3D environment given a\nsequence of RGB-D frames. To this end, we aggregate PointNet features from\nprimitive scene components by means of a graph neural network. We also propose\na novel attention mechanism well suited for partial and missing graph data\npresent in such an incremental reconstruction scenario. Although our proposed\nmethod is designed to run on submaps of the scene, we show it also transfers to\nentire 3D scenes. Experiments show that our approach outperforms 3D scene graph\nprediction methods by a large margin and its accuracy is on par with other 3D\nsemantic and panoptic segmentation methods while running at 35 Hz.\n"}
+{"abstract": "  A novel user friendly method is proposed for calibrating a procam system from\na single pose of a planar chessboard target. The user simply needs to orient\nthe chessboard in a single appropriate pose. A sequence of Gray Code patterns\nare projected onto the chessboard, which allows correspondences between the\ncamera, projector and the chessboard to be automatically extracted. These\ncorrespondences are fed as input to a nonlinear optimization method that models\nthe projector of the principle points onto the chessboard, and accurately\ncalculates the intrinsic and extrinsic parameters of both the camera and the\nprojector, as well as the camera's distortion coefficients. The method is\nexperimentally validated on the procam system, which is shown to be comparable\nin accuracy with existing multi-pose approaches. The impact of the orientation\nof the chessboard with respect to the procam imaging places is also explored\nthrough extensive simulation.\n"}
+{"abstract": "  Exploration in environments with sparse rewards is difficult for artificial\nagents. Curiosity driven learning -- using feed-forward prediction errors as\nintrinsic rewards -- has achieved some success in these scenarios, but fails\nwhen faced with action-dependent noise sources. We present aleatoric mapping\nagents (AMAs), a neuroscience inspired solution modeled on the cholinergic\nsystem of the mammalian brain. AMAs aim to explicitly ascertain which dynamics\nof the environment are unpredictable, regardless of whether those dynamics are\ninduced by the actions of the agent. This is achieved by generating separate\nforward predictions for the mean and variance of future states and reducing\nintrinsic rewards for those transitions with high aleatoric variance. We show\nAMAs are able to effectively circumvent action-dependent stochastic traps that\nimmobilise conventional curiosity driven agents. The code for all experiments\npresented in this paper is open sourced:\nhttp://github.com/self-supervisor/Escaping-Stochastic-Traps-With-Aleatoric-Mapping-Agents.\n"}
+{"abstract": "  We present two a posteriori error estimators for the virtual element method\n(VEM) based on global and local flux reconstruction in the spirit of [5]. The\nproposed error estimators are reliable and efficient for the $h$-, $p$-, and\n$hp$-versions of the VEM. This solves a partial limitation of our former\napproach in [6], which was based on solving local nonhybridized mixed problems.\nDifferently from the finite element setting, the proof of the efficiency turns\nout to be simpler, as the flux reconstruction in the VEM does not require the\nexistence of polynomial, stable, divergence right-inverse operators. Rather, we\nonly need to construct right-inverse operators in virtual element spaces,\nexploiting only the implicit definition of virtual element functions. The\ntheoretical results are validated by some numerical experiments on a benchmark\nproblem.\n"}
+{"abstract": "  We consider the steady-state nonequilibrium behavior of mesoscopic\nsuperconducting wires connected to normal-metal reservoirs. Going beyond the\ndiffusive limit, we utilize the quasiclassical theory and perform a\nself-consistent calculation that guarantees current conservation through the\nentire system. Going from the ballistic to the diffusive limit, we investigate\nseveral crucial phenomena such as charge imbalance, momentum-resolved\nnonequilbrium distributions, and the current-to-superflow conversion.\nConnecting to earlier results for the diffusive case, we find that\nsuperconductivity can break down at a critical bias voltage $V_\\mathrm{c}$. We\nfind that $V_\\mathrm{c}$ generally increases as the interface transparency is\nreduced, while the dependence on the mean-free path is non-monotonous. We\ndiscussthe key differences of the ballistic and semi-ballistic regimes to the\nfully diffusive case.\n"}
+{"abstract": "  We experimentally and theoretically investigate the non-degenerate two-photon\nabsorption coefficient $\\beta(\\omega_1,\\omega_2)$ in the prototypical\nsemiconductor ZnSe. In particular, we provide a comprehensive data set on the\ndependence of $\\beta(\\omega_1,\\omega_2)$ on the non-degeneracy parameter\n$\\omega_1/\\omega_2$ with the total frequency sum $\\omega_1+\\omega_2$ kept\nconstant. We find a substantial increase of the two-photon absorption strength\nwith increasing $\\omega_1/\\omega_2$. In addition, different crystallographic\norientations and polarization configurations are investigated. The nonlinear\noptical response is analyzed theoretically by evaluating the multiband\nsemiconductor Bloch equations including inter- and intraband excitations in the\nlength gauge. The band structure and the matrix elements are taken an\neight-band k.p model. The simulation results are in very good agreement with\nthe experiment.\n"}
+{"abstract": "  We explore the use of a topological manifold, represented as a collection of\ncharts, as the target space of neural network based representation learning\ntasks. This is achieved by a simple adjustment to the output of an encoder's\nnetwork architecture plus the addition of a maximal mean discrepancy (MMD)\nbased loss function for regularization. Most algorithms in representation and\nmetric learning are easily adaptable to our framework and we demonstrate its\neffectiveness by adjusting SimCLR (for representation learning) and standard\ntriplet loss training (for metric learning) to have manifold encoding spaces.\nOur experiments show that we obtain a substantial performance boost over the\nbaseline for low dimensional encodings. In the case of triplet training, we\nalso find, independent of the manifold setup, that the MMD loss alone (i.e.\nkeeping a flat, euclidean target space but using an MMD loss to regularize it)\nincreases performance over the baseline in the typical, high-dimensional\nEuclidean target spaces. Code for reproducing experiments is provided at\nhttps://github.com/ekorman/neurve .\n"}
+{"abstract": "  Scale-free percolation is a spatial random graph model with vertex set\n$\\mathbb{Z}^d$. Vertices $x$ and $y$ are connected with probability depending\non i.i.d. vertex weights and the Euclidean distance. Depending on the various\nparameters involved, we get a rich phase diagram. We study graph distances (in\ncomparison to Euclidean distances). Our main attention is on a regime where\ngraph distances are (poly-)logarithmic in the Euclidean distance. We obtain\nimproved bounds on the logarithmic exponents. In the light tail regime, the\ncorrect exponent is identified.\n"}
+{"abstract": "  There is an analogy between the ResNet (Residual Network) architecture for\ndeep neural networks and an Euler solver for an ODE. The transformation\nperformed by each layer resembles an Euler step in solving an ODE. We consider\nthe Heun Method, which involves a single predictor-corrector cycle, and\ncomplete the analogy, building a predictor-corrector variant of ResNet, which\nwe call a HeunNet. Just as Heun's method is more accurate than Euler's,\nexperiments show that HeunNet achieves high accuracy with low computational\n(both training and test) time compared to both vanilla recurrent neural\nnetworks and other ResNet variants.\n"}
+{"abstract": "  Central compact objects are young neutron stars emitting thermal X-rays with\nbolometric luminosities $L_X$ in the range $10^{32}$-$10^{34}$ erg/s.\nGourgouliatos, Hollerbach and Igoshev recently suggested that peculiar emission\nproperties of central compact objects can be explained by tangled magnetic\nfield configurations formed in a stochastic dynamo during the proto-neutron\nstar stage. In this case the magnetic field consists of multiple small-scale\ncomponents with negligible contribution of global dipolar field. We study\nnumerically three-dimensional magneto-thermal evolution of tangled crustal\nmagnetic fields in neutron stars. We find that all configurations produce\ncomplicated surface thermal patterns which consist of multiple small hot\nregions located at significant separations from each other. The configurations\nwith initial magnetic energy of $2.5-10\\times 10^{47}$ erg have temperatures of\nhot regions that reach $\\approx 0.2$ keV, to be compared with the bulk\ntemperature of $\\approx 0.1$ keV in our simulations with no cooling. A factor\nof two in temperature is also seen in observations of central compact objects.\nThe hot spots produce periodic modulations in light curve with typical\namplitudes of $\\leq 9-11$ %. Therefore, the tangled magnetic field\nconfiguration can explain thermal emission properties of some central compact\nobjects.\n"}
+{"abstract": "  We investigate the concept of cylindrical Wiener process subordinated to a\nstrictly $\\alpha$-stable L\\'evy process, with $\\alpha\\in\\left(0,1\\right)$, in\nan infinite dimensional, separable Hilbert space, and consider the related\nstochastic convolution. We then introduce the corresponding Ornstein-Uhlenbeck\nprocess, focusing on the regularizing properties of the Markov transition\nsemigroup defined by it. In particular, we provide an explicit, original\nformula -- which is not of Bismut-Elworthy-Li's type -- for the Gateaux\nderivatives of the functions generated by the operators of the semigroup, as\nwell as an upper bound for the norm of their gradients. In the case\n$\\alpha\\in\\left(\\frac{1}{2},1\\right)$, this estimate represents the starting\npoint for studying the Kolmogorov equation in its mild formulation.\n"}
+{"abstract": "  Urdu is a widely spoken language in South Asia. Though immoderate literature\nexists for the Urdu language still the data isn't enough to naturally process\nthe language by NLP techniques. Very efficient language models exist for the\nEnglish language, a high resource language, but Urdu and other under-resourced\nlanguages have been neglected for a long time. To create efficient language\nmodels for these languages we must have good word embedding models. For Urdu,\nwe can only find word embeddings trained and developed using the skip-gram\nmodel. In this paper, we have built a corpus for Urdu by scraping and\nintegrating data from various sources and compiled a vocabulary for the Urdu\nlanguage. We also modify fasttext embeddings and N-Grams models to enable\ntraining them on our built corpus. We have used these trained embeddings for a\nword similarity task and compared the results with existing techniques.\n"}
+{"abstract": "  As robots interact with a broader range of end-users, end-user robot\nprogramming has helped democratize robot programming by empowering end-users\nwho may not have experience in robot programming to customize robots to meet\ntheir individual contextual needs. This article surveys work on end-user robot\nprogramming, with a focus on end-user program specification. It describes the\nprimary domains, programming phases, and design choices represented by the\nend-user robot programming literature. The survey concludes by highlighting\nopen directions for further investigation to enhance and widen the reach of\nend-user robot programming systems.\n"}
+{"abstract": "  We study an approximation method of stationary characters of a\ntwo-dimensional Markov chain via the Stein method. For this purpose, innovative\nmethods are developed to estimate the moments of the Markov chain, as well as\nthe solution to the Poisson equation with a partial differential operator.\n"}
+{"abstract": "  The (non-uniform) sparsest cut problem is the following graph-partitioning\nproblem: given a \"supply\" graph, and demands on pairs of vertices, delete some\nsubset of supply edges to minimize the ratio of the supply edges cut to the\ntotal demand of the pairs separated by this deletion. Despite much effort,\nthere are only a handful of nontrivial classes of supply graphs for which\nconstant-factor approximations are known.\n  We consider the problem for planar graphs, and give a\n$(2+\\varepsilon)$-approximation algorithm that runs in quasipolynomial time.\nOur approach defines a new structural decomposition of an optimal solution\nusing a \"patching\" primitive. We combine this decomposition with a\nSherali-Adams-style linear programming relaxation of the problem, which we then\nround. This should be compared with the polynomial-time approximation algorithm\nof Rao (1999), which uses the metric linear programming relaxation and\n$\\ell_1$-embeddings, and achieves an $O(\\sqrt{\\log n})$-approximation in\npolynomial time.\n"}
+{"abstract": "  The fifth-generation (5G) mobile/cellular technology is a game changer for\nindustrial systems. Private 5G deployments are promising to address the\nchallenges faced by industrial networks. Programmability and open-source are\ntwo key aspects which bring unprecedented flexibility and customizability to\nprivate 5G networks. Recent regulatory initiatives are removing barriers for\nindustrial stakeholders to deploy their own local 5G networks with dedicated\nequipment. To this end, this demonstration showcases an open and programmable\n5G network-in-a-box solution for private deployments. The network-in-a-box\nprovides an integrated solution, based on open-source software stack and\ngeneral-purpose hardware, for operation in 5G non-standalone (NSA) as well as\n4G long-term evolution (LTE) modes. The demonstration also shows the capability\nof operation in different sub-6 GHz frequency bands, some of which are\nspecifically available for private networks. Performance results, in terms of\nend-to-end latency and data rates, with a commercial off-the-shelf (COTS) 5G\ndevice are shown as well.\n"}
+{"abstract": "  We study the impact of the recently computed mixed QCD-electroweak\ncorrections to the production of $W$ and $Z$ bosons at the LHC on the value of\nthe $W$ mass extracted from the transverse momentum distribution of charged\nleptons from $W$ decays. Using the average lepton transverse momenta in $W$ and\n$Z$ decays as simplified observables for the determination of the $W$ mass, we\nestimate that mixed QCD-electroweak corrections can shift the extracted value\nof the $W$ mass by up to ${\\cal O}(20)~{\\rm MeV}$, depending on the kinematic\ncuts employed to define fiducial cross sections for $Z$ and $W$ production.\nSince the target precision of the $W$-mass measurement at the LHC is ${\\cal\nO}(10)~{\\rm MeV}$, our results emphasize the need for fully-differential\ncomputations of mixed QCD-electroweak corrections and a careful analysis of\ntheir potential impact on the determination of the $W$ mass.\n"}
+{"abstract": "  Principal loading analysis is a dimension reduction method that discards\nvariables which have only a small distorting effect on the covariance matrix.\nWe complement principal loading analysis and propose to rather use a mix of\nboth, the correlation and covariance matrix instead. Further, we suggest to use\nrescaled eigenvectors and provide updated algorithms for all proposed changes.\n"}
+{"abstract": "  In recent years, graph neural networks (GNNs) have shown powerful ability in\ncollaborative filtering, which is a widely adopted recommendation scenario.\nWhile without any side information, existing graph neural network based methods\ngenerally learn a one-hot embedding for each user or item as the initial input\nrepresentation of GNNs. However, such one-hot embedding is intrinsically\ntransductive, making these methods with no inductive ability, i.e., failing to\ndeal with new users or new items that are unseen during training. Besides, the\nnumber of model parameters depends on the number of users and items, which is\nexpensive and not scalable. In this paper, we give a formal definition of\ninductive recommendation and solve the above problems by proposing Inductive\nrepresentation based Graph Convolutional Network (IGCN) for collaborative\nfiltering. Specifically, we design an inductive representation layer, which\nutilizes the interaction behavior with core users or items as the initial\nrepresentation, improving the general recommendation performance while bringing\ninductive ability. Note that, the number of parameters of IGCN only depends on\nthe number of core users or items, which is adjustable and scalable. Extensive\nexperiments on three public benchmarks demonstrate the state-of-the-art\nperformance of IGCN in both transductive and inductive recommendation\nscenarios, while with remarkably fewer model parameters. Our implementations\nare available here in PyTorch.\n"}
+{"abstract": "  The three key elements of a quantum simulation are state preparation, time\nevolution, and measurement. While the complexity scaling of dynamics and\nmeasurements are well known, many state preparation methods are strongly\nsystem-dependent and require prior knowledge of the system's eigenvalue\nspectrum. Here, we report on a quantum-classical implementation of the\ncoupled-cluster Green's function (CCGF) method, which replaces explicit ground\nstate preparation with the task of applying unitary operators to a simple\nproduct state. While our approach is broadly applicable to a wide range of\nmodels, we demonstrate it here for the Anderson impurity model (AIM). The\nmethod requires a number of T gates that grows as $ \\mathcal{O} \\left(N^5\n\\right)$ per time step to calculate the impurity Green's function in the time\ndomain, where $N$ is the total number of energy levels in the AIM. For\ncomparison, a classical CCGF calculation of the same order would require\ncomputational resources that grow as $ \\mathcal{O} \\left(N^6 \\right)$ per time\nstep.\n"}
+{"abstract": "  At the end of April 20, 2020, there were only a few new COVID-19 cases\nremaining in China, whereas the rest of the world had shown increases in the\nnumber of new cases. It is of extreme importance to develop an efficient\nstatistical model of COVID-19 spread, which could help in the global fight\nagainst the virus. We propose a clustering-segmented autoregressive sigmoid\n(CSAS) model to explore the space-time pattern of the log-transformed\ninfectious count. Four key characteristics are included in this CSAS model,\nincluding unknown clusters, change points, stretched S-curves, and\nautoregressive terms, in order to understand how this outbreak is spreading in\ntime and in space, to understand how the spread is affected by epidemic control\nstrategies, and to apply the model to updated data from an extended period of\ntime. We propose a nonparametric graph-based clustering method for discovering\ndissimilarity of the curve time series in space, which is justified with\ntheoretical support to demonstrate how the model works under mild and easily\nverified conditions. We propose a very strict purity score that penalizes\noverestimation of clusters. Simulations show that our nonparametric graph-based\nclustering method is faster and more accurate than the parametric clustering\nmethod regardless of the size of data sets. We provide a Bayesian information\ncriterion (BIC) to identify multiple change points and calculate a confidence\ninterval for a mean response. By applying the CSAS model to the collected data,\nwe can explain the differences between prevention and control policies in China\nand selected countries.\n"}
+{"abstract": "  In this paper, we study the quantum fluctuation dynamics in a Bose gas on a\ntorus $\\Lambda=([-L/2,L/2]^3/\\sim)$ that exhibits Bose-Einstein condensation,\nbeyond the leading order Hartree-Fock-Bogoliubov (HFB) fluctuations. Given a\nmean-field Hamiltonian and Bose-Einstein condensate (BEC) with density $N$, we\nextract a quantum Boltzmann type dynamics from a second-order Duhamel expansion\nupon subtracting both the HFB dynamics and the BEC dynamics. Using a Fock-space\napproach, we provide explicit error bounds. Given an approximately quasi-free\ninitial state, we determine the time evolution of the centered correlation\nfunctions $\\langle a\\rangle$, $\\langle aa\\rangle-\\langle a\\rangle^2$, $\\langle\na^+a\\rangle-|\\langle a\\rangle|^2$ at mesoscopic time scales. For large but\nfinite $N$, we consider both the case of fixed system size $|\\Lambda|\\sim1$,\nand the case $|\\Lambda|\\sim (\\log(N)/\\log\\log(N))^{\\frac78}$. In the case\n$|\\Lambda|\\sim1$, we show that the Boltzmann collision operator contains\nsubleading terms that can become dominant, depending on time-dependent\ncoefficients assuming particular values in $\\mathbb{Q}$; this phenomenon is\nreminiscent of the Talbot effect. For the case $|\\Lambda|\\sim\n(\\log(N)/\\log\\log(N))^{\\frac78}$, we prove that the collision operator is well\napproximated by the expression predicted in the literature.\n"}
+{"abstract": "  By inverting the time-dependent Kohn-Sham equation for a numerically exact\ndynamics of the helium atom, we show that the dynamical step and peak features\nof the exact correlation potential found previously in one-dimensional models\npersist for real three-dimensional systems. We demonstrate that the Kohn-Sham\nand true current-densities differ by a rotational component. The results have\ndirect implications for approximate TDDFT calculations of atoms and molecules\nin strong fields, emphasizing the need to go beyond the adiabatic\napproximation, and highlighting caution in quantitative use of the Kohn-Sham\ncurrent.\n"}
+{"abstract": "  A large fraction of known exoplanets have short orbital periods where tidal\nexcitation of gravity waves within the host star causes the planets' orbits to\ndecay. We study the effects of tidal resonance locking, in which the planet\nlocks into resonance with a tidally excited stellar gravity mode. Because a\nstar's gravity mode frequencies typically increase as the star evolves, the\nplanet's orbital frequency increases in lockstep, potentially causing much\nfaster orbital decay than predicted by other tidal theories. Due to nonlinear\nmode damping, resonance locking in Sun-like stars likely only operates for\nlow-mass planets ($M \\lesssim 0.1 \\, M_{\\rm Jup}$), but in stars with\nconvective cores it can likely operate for all planetary masses. The orbital\ndecay timescale with resonance locking is typically comparable to the star's\nmain-sequence lifetime, corresponding to a wide range in effective stellar\nquality factor ($10^3 \\lesssim Q' \\lesssim 10^9$), depending on the planet's\nmass and orbital period. We make predictions for several individual systems and\nexamine the orbital evolution resulting from both resonance locking and\nnonlinear wave dissipation. Our models demonstrate how short-period massive\nplanets can be quickly destroyed by nonlinear mode damping, while short-period\nlow-mass planets can survive, even though they undergo substantial inward tidal\nmigration via resonance locking.\n"}
+{"abstract": "  A vertex labeling of a hypergraph is sum distinguishing if it uses positive\nintegers and the sums of labels taken over the distinct hyperedges are\ndistinct. Let s(H) be the smallest integer N such that there is a\nsum-distinguishing labeling of H with each label at most N. The largest value\nof s(H) over all hypergraphs on n vertices and m hyperedges is denoted s(n,m).\nWe prove that s(n,m) is almost-quadratic in m as long as m is not too large.\nMore precisely, the following holds: If n < m < n^{O(1)} then s(n,m)= m^2/w(m),\nwhere w(m) is a function that goes to infinity and is smaller than any\npolynomial in m.\n  The parameter s(n,m) has close connections to several other graph and\nhypergraph functions, such as the irregularity strength of hypergraphs. Our\nresult has several applications, notably:\n  1. We answer a question of Gyarfas et al. whether there are n-vertex\nhypergraphs with irregularity strength greater than 2n. In fact we show that\nthere are n-vertex hypergraphs with irregularity strength at least n^{2-o(1)}.\n  2. Our results imply that s*(n)=n^2/w(n) where s*(n) is the distinguishing\nclosed-neighborhood number, i.e., the smallest integer N such that any n-vertex\ngraph allows for a vertex labeling with positive integers at most N so that the\nsums of labels on distinct closed neighborhoods of vertices are distinct.\n"}
+{"abstract": "  Molybdenum trioxide (MoO$_3$) in-plane anisotropy has increasingly attracted\nthe attention of the scientific community in the last few years. Many of the\nobserved in-plane anisotropic properties stem from the anisotropic refractive\nindex and elastic constants of the material but a comprehensive analysis of\nthese fundamental properties is still lacking. Here we employ Raman and\nmicro-reflectance measurements, using polarized light, to determine the angular\ndependence of the refractive index of thin MoO$_3$ flakes and we study the\ndirectional dependence of the MoO$_3$ Young's modulus using the buckling\nmetrology method. We found that MoO$_3$ displays one of the largest in-plane\nanisotropic mechanical properties reported for 2D materials so far.\n"}
+{"abstract": "  We present a study on the relationship between the ratio of the depth of a\ncrater to its diameter and the diameter for lunar craters both on the maria and\non the highlands. We consider craters younger than 1.1 billion years in age,\ni.e. of Copernican period. The aim of this work is to improve our understanding\nof such relationships based on our new estimates of the craters's depth and\ndiameter. Previous studies considered similar relationships for much older\ncraters (up to 3.2 billion years). We calculated the depths of craters with\ndiameters from 10 to 100 km based on the altitude profiles derived from data\nobtained by the Lunar Orbiter Laser Altimeter (LOLA) onboard the Lunar\nReconnaissance Orbiter (LRO). The ratio h/D of the depth h of a crater to its\ndiameter D can diverge by up to a factor of two for craters with almost the\nsame diameters. The linear and power approximations (regressions) of the\ndependence of h/D on D were made for simple and complex Copernican craters\nselected from the data from Mazrouei et al. (2019) and Losiak et al. (2015).\nFor the separation of highland craters into two groups based only on their\ndependences of h/D on D, at D<18 km these are mostly simple craters, although\nsome complex craters can have diameters D>16 km. Depths of mare craters with\nD<14 km are greater than 0.15D. Following Pike's (1981) classification, we\ngroup mare craters of D<15 km as simple craters. Mare craters with 15<D<18 km\nfit both approximation curves for simple and complex craters. Depths of mare\ncraters with D>18 km are in a better agreement with the approximation curve of\nh/D vs. D for complex craters than for simple craters. At the same diameter,\nmare craters are deeper than highland craters at a diameter smaller than 30-40\nkm. For greater diameters, highland craters are deeper.\n"}
+{"abstract": "  The minimal $U(1)_X$ extension of the Standard Model (SM) is a well-motivated\nnew physics scenario, where the anomaly cancellation requirement dictates the\nnew neutral gauge boson ($Z^\\prime$) couplings with the SM fermions in terms of\ntwo scalar charges ($x_H$ and $x_\\Phi$). In this paper, we investigate the SM\ncharged fermion pair production mechanism for different values of these scalar\ncharges in the $U(1)_X$ scenario at future electron-positron colliders, i.e.\n$e^+e^-\\to f\\bar{f}$. Apart from the standard photon and $Z$ boson exchange for\nthis process, this model features a $t$-channel (or both $s$ and $t$-channel\nfor $f=e^-$) $Z^\\prime$-boson exchange, which interferes with the SM processes.\nConsidering the dilepton and dijet signatures from the heavy resonance we\nestimate the bounds on the U$(1)_X$ coupling $(g^\\prime)$ and the $Z^\\prime$\nmass $(M_{Z^\\prime})$. Considering the LEP-II results and prospective\nInternational Linear Collider (ILC) bounds on the effective scale for the four\nfermion interaction we estimate the reach on $M_{Z^\\prime}/g^\\prime$ for\ndifferent center of mass energies. We study the angular distributions,\nforward-backward $(\\mathcal{A}_{\\rm{FB}})$, left-right\n$(\\mathcal{A}_{\\rm{LR}})$ and left-right forward-backward\n$(\\mathcal{A}_{\\rm{LR, FB}})$ asymmetries of the $f\\bar{f}$ final states which\ncan show substantial deviations from the SM results, even for a multi-TeV $Z'$.\nThis provides a powerful complementary way to probe the heavy $Z'$ parameter\nspace beyond the direct reach of the Large Hadron Collider (LHC), as well as an\neffective way to determine the $U(1)_X$ charges.\n"}
+{"abstract": "  In our previous study, we successfully reproduced the illusory motion of the\nrotating snake illusion using deep neural networks incorporating predictive\ncoding theory. In the present study, we further examined the properties of the\nnetworks using a set of 1500 images, including ordinary static images of\npaintings and photographs and images of various types of motion illusions.\nResults showed that the networks clearly classified illusory images and others\nand reproduced illusory motions against various types of illusions similar to\nhuman perception. Notably, the networks occasionally detected anomalous motion\nvectors, even in ordinally static images where humans were unable to perceive\nany illusory motion. Additionally, illusion-like designs with repeating\npatterns were generated using areas where anomalous vectors were detected, and\npsychophysical experiments were conducted, in which illusory motion perception\nin the generated designs was detected. The observed inaccuracy of the networks\nwill provide useful information for further understanding information\nprocessing associated with human vision.\n"}
+{"abstract": "  In this paper, we propose a spectral-spatial graph reasoning network (SSGRN)\nfor hyperspectral image (HSI) classification. Concretely, this network contains\ntwo parts that separately named spatial graph reasoning subnetwork (SAGRN) and\nspectral graph reasoning subnetwork (SEGRN) to capture the spatial and spectral\ngraph contexts, respectively. Different from the previous approaches\nimplementing superpixel segmentation on the original image or attempting to\nobtain the category features under the guide of label image, we perform the\nsuperpixel segmentation on intermediate features of the network to adaptively\nproduce the homogeneous regions to get the effective descriptors. Then, we\nadopt a similar idea in spectral part that reasonably aggregating the channels\nto generate spectral descriptors for spectral graph contexts capturing. All\ngraph reasoning procedures in SAGRN and SEGRN are achieved through graph\nconvolution. To guarantee the global perception ability of the proposed\nmethods, all adjacent matrices in graph reasoning are obtained with the help of\nnon-local self-attention mechanism. At last, by combining the extracted spatial\nand spectral graph contexts, we obtain the SSGRN to achieve a high accuracy\nclassification. Extensive quantitative and qualitative experiments on three\npublic HSI benchmarks demonstrate the competitiveness of the proposed methods\ncompared with other state-of-the-art approaches.\n"}
+{"abstract": "  Despite the rapid growth of online advertisement in developing countries,\nexisting highly over-parameterized Click-Through Rate (CTR) prediction models\nare difficult to be deployed due to the limited computing resources. In this\npaper, by bridging the relationship between CTR prediction task and tabular\nlearning, we present that tabular learning models are more efficient and\neffective in CTR prediction than over-parameterized CTR prediction models.\nExtensive experiments on eight public CTR prediction datasets show that tabular\nlearning models outperform twelve state-of-the-art CTR prediction models.\nFurthermore, compared to over-parameterized CTR prediction models, tabular\nlearning models can be fast trained without expensive computing resources\nincluding high-performance GPUs. Finally, through an A/B test on an actual\nonline application, we show that tabular learning models improve not only\noffline performance but also the CTR of real users.\n"}
+{"abstract": "  In this paper, we propose a novel spoken-text-style conversion method that\ncan simultaneously execute multiple style conversion modules such as\npunctuation restoration and disfluency deletion without preparing matched\ndatasets. In practice, transcriptions generated by automatic speech recognition\nsystems are not highly readable because they often include many disfluencies\nand do not include punctuation marks. To improve their readability, multiple\nspoken-text-style conversion modules that individually model a single\nconversion task are cascaded because matched datasets that simultaneously\nhandle multiple conversion tasks are often unavailable. However, the cascading\nis unstable against the order of tasks because of the chain of conversion\nerrors. Besides, the computation cost of the cascading must be higher than the\nsingle conversion. To execute multiple conversion tasks simultaneously without\npreparing matched datasets, our key idea is to distinguish individual\nconversion tasks using the on-off switch. In our proposed zero-shot joint\nmodeling, we switch the individual tasks using multiple switching tokens,\nenabling us to utilize a zero-shot learning approach to executing simultaneous\nconversions. Our experiments on joint modeling of disfluency deletion and\npunctuation restoration demonstrate the effectiveness of our method.\n"}
+{"abstract": "  In this paper we give few expressions and asymptotics of ruin probabilities\nfor a Markov modulated risk process for various regimes of a time horizon,\ninitial reserves and a claim size distribution. We also consider few versions\nof the ruin time.\n"}
+{"abstract": "  Cadmium Zinc Telluride Imager (CZTI) onboard AstroSat has been a prolific\nGamma-Ray Burst (GRB) monitor. While the 2-pixel Compton scattered events (100\n- 300 keV) are used to extract sensitive spectroscopic information, the\ninclusion of the low-gain pixels (around 20% of the detector plane) after\ncareful calibration extends the energy range of Compton energy spectra to 600\nkeV. The new feature also allows single-pixel spectroscopy of the GRBs to the\nsub-MeV range which is otherwise limited to 150 keV. We also introduced a new\nnoise rejection algorithm in the analysis ('Compton noise'). These new\nadditions not only enhances the spectroscopic sensitivity of CZTI, but the\nsub-MeV spectroscopy will also allow proper characterization of the GRBs not\ndetected by Fermi. This article describes the methodology of single, Compton\nevent and veto spectroscopy in 100 - 600 keV for the GRBs detected in the first\nyear of operation. CZTI in last five years has detected around 20 bright GRBs.\nThe new methodologies, when applied on the spectral analysis for this large\nsample of GRBs, has the potential to improve the results significantly and help\nin better understanding the prompt emission mechanism.\n"}
+{"abstract": "  Spin-orbit interactions which couple spin of a particle with its momentum\ndegrees of freedom lie at the center of spintronic applications. Of special\ninterest in semiconductor physics are Rashba and Dresselhaus spin-orbit\ncoupling (SOC). When equal in strength, the Rashba and Dresselhaus fields\nresult in SU(2) spin rotation symmetry and emergence of the persistent spin\nhelix (PSH) only investigated for charge carriers in semiconductor quantum\nwells. Recently, a synthetic Rashba-Dresselhaus Hamiltonian was shown to\ndescribe cavity photons confined in a microcavity filled with optically\nanisotropic liquid crystal. In this work, we present a purely optical\nrealisation of two types of spin patterns corresponding to PSH and the\nStern-Gerlach experiment in such a cavity. We show how the symmetry of the\nHamiltonian results in spatial oscillations of the spin orientation of photons\ntravelling in the plane of the cavity.\n"}
+{"abstract": "  Monocular 3D object detection is a key problem for autonomous vehicles, as it\nprovides a solution with simple configuration compared to typical multi-sensor\nsystems. The main challenge in monocular 3D detection lies in accurately\npredicting object depth, which must be inferred from object and scene cues due\nto the lack of direct range measurement. Many methods attempt to directly\nestimate depth to assist in 3D detection, but show limited performance as a\nresult of depth inaccuracy. Our proposed solution, Categorical Depth\nDistribution Network (CaDDN), uses a predicted categorical depth distribution\nfor each pixel to project rich contextual feature information to the\nappropriate depth interval in 3D space. We then use the computationally\nefficient bird's-eye-view projection and single-stage detector to produce the\nfinal output bounding boxes. We design CaDDN as a fully differentiable\nend-to-end approach for joint depth estimation and object detection. We\nvalidate our approach on the KITTI 3D object detection benchmark, where we rank\n1st among published monocular methods. We also provide the first monocular 3D\ndetection results on the newly released Waymo Open Dataset. We provide a code\nrelease for CaDDN which is made available.\n"}
+{"abstract": "  We consider warm inflation with a Dirac-Born-Infeld (DBI) kinetic term in\nwhich both the nonequilibrium dissipative particle production and the sound\nspeed parameter slow the motion of the inflaton field. We find that a low sound\nspeed parameter removes, or at least strongly suppresses, the growing function\nappearing in the scalar of curvature power spectrum of warm inflation, which\nappears due to the temperature dependence in the dissipation coefficient. As a\nconsequence of that, a low sound speed helps to push warm inflation into the\nstrong dissipation regime, which is an attractive regime from a model building\nand phenomenological perspective. In turn, the strong dissipation regime of\nwarm inflation softens the microscopic theoretical constraints on cold DBI\ninflation. The present findings, along with the recent results from swampland\ncriteria, give a strong hint that warm inflation may consistently be embedded\ninto string theory.\n"}
+{"abstract": "  We deform the moment map picture on the space of symplectic connections on a\nsymplectic manifold. To do that, we study a vector bundle of Fedosov star\nproduct algebras on the space of symplectic connections. We describe a natural\nformal connection on this bundle adapted to the star product algebras on the\nfibers. We study its curvature and show the star product trace of the curvature\nis a formal symplectic form on the space of symplectic connections. The action\nof Hamiltonian diffeomorphisms on symplectic connections preserves the formal\nsymplectic structure and we show the star product trace can be interpreted as a\nformal moment map for this action. Finally, we apply this picture to study\nautomorphisms of star products and Hamiltonian diffeomorphisms.\n"}
+{"abstract": "  We consider variants of the classical Frank-Wolfe algorithm for constrained\nsmooth convex minimization, that instead of access to the standard oracle for\nminimizing a linear function over the feasible set, have access to an oracle\nthat can find an extreme point of the feasible set that is closest in Euclidean\ndistance to a given vector. We first show that for many feasible sets of\ninterest, such an oracle can be implemented with the same complexity as the\nstandard linear optimization oracle. We then show that with such an oracle we\ncan design new Frank-Wolfe variants which enjoy significantly improved\ncomplexity bounds in case the set of optimal solutions lies in the convex hull\nof a subset of extreme points with small diameter (e.g., a low-dimensional face\nof a polytope). In particular, for many $0\\text{--}1$ polytopes, under\nquadratic growth and strict complementarity conditions, we obtain the first\nlinearly convergent variant with rate that depends only on the dimension of the\noptimal face and not on the ambient dimension.\n"}
+{"abstract": "  This paper proposes a robust beamforming scheme to enhance the physical layer\nsecurity (PLS) of multicast transmission in a cognitive satellite and aerial\nnetwork (CSAN) operating in the millimeter wave frequency band. Based on\nimperfect channel state information (CSI) of both eavesdroppers (Eves) and\nprimary users (PUs), we maximize the minimum achievable secrecy rate (ASR) of\nthe secondary users (SUs) in the aerial network under the constraints of the\ninterference to the PUs in the satellite network, the quality of service (QoS)\nrequirements of the SUs and per-antenna power budget of the aerial platform. To\ntackle this mathematically intractable problem, we first introduce an auxiliary\nvariable and outage constraints to simplify the complex objective function. We\nthen convert the non-convex outage constraints into deterministic forms and\nadopt penalty function approach to obtain a semi-definite problem such that it\ncan be solved in an iterative fashion. Finally, simulation results show that\nwith the transmit power increase, the minimal ASR of SUs obtained from the\nproposed BF scheme well approximate the optimal value.\n"}
+{"abstract": "  When transferring a control policy from simulation to a physical system, the\npolicy needs to be robust to variations in the dynamics to perform well.\nCommonly, the optimal policy overfits to the approximate model and the\ncorresponding state-distribution, often resulting in failure to trasnfer\nunderlying distributional shifts. In this paper, we present Robust Fitted Value\nIteration, which uses dynamic programming to compute the optimal value function\non the compact state domain and incorporates adversarial perturbations of the\nsystem dynamics. The adversarial perturbations encourage a optimal policy that\nis robust to changes in the dynamics. Utilizing the continuous-time perspective\nof reinforcement learning, we derive the optimal perturbations for the states,\nactions, observations and model parameters in closed-form. Notably, the\nresulting algorithm does not require discretization of states or actions.\nTherefore, the optimal adversarial perturbations can be efficiently\nincorporated in the min-max value function update. We apply the resulting\nalgorithm to the physical Furuta pendulum and cartpole. By changing the masses\nof the systems we evaluate the quantitative and qualitative performance across\ndifferent model parameters. We show that robust value iteration is more robust\ncompared to deep reinforcement learning algorithm and the non-robust version of\nthe algorithm. Videos of the experiments are shown at\nhttps://sites.google.com/view/rfvi\n"}
+{"abstract": "  We present a systematic study on multilingual and cross-lingual intent\ndetection from spoken data. The study leverages a new resource put forth in\nthis work, termed MInDS-14, a first training and evaluation resource for the\nintent detection task with spoken data. It covers 14 intents extracted from a\ncommercial system in the e-banking domain, associated with spoken examples in\n14 diverse language varieties. Our key results indicate that combining machine\ntranslation models with state-of-the-art multilingual sentence encoders (e.g.,\nLaBSE) can yield strong intent detectors in the majority of target languages\ncovered in MInDS-14, and offer comparative analyses across different axes:\ne.g., zero-shot versus few-shot learning, translation direction, and impact of\nspeech recognition. We see this work as an important step towards more\ninclusive development and evaluation of multilingual intent detectors from\nspoken data, in a much wider spectrum of languages compared to prior work.\n"}
+{"abstract": "  This work is a probabilistic study of the 'primes' of the Cram\\'er model. We\nprove that there exists a set of integers $\\mathcal S$ of density 1 such that\n\\begin{equation}\\liminf_{ \\mathcal S\\ni n\\to\\infty} (\\log n)\\mathbb{P} \\{S_n\\\n\\hbox{prime} \\} \\ge \\frac{1}{\\sqrt{2\\pi e}\\, }, \\end{equation} and that for\n$b>\\frac12$, the formula \\begin{equation} \\mathbb{P} \\{S_n\\ \\text{prime}\\, \\}\n\\, =\\, \\frac{ (1+ o( 1) )}{ \\sqrt{2\\pi B_n } } \\int_{m_n-\\sqrt{ 2bB_n\\log\nn}}^{m_n+\\sqrt{ 2bB_n\\log n}} \\, e^{-\\frac{(t - m_n)^2}{ 2 B_n } }\\, {\\rm\nd}\\pi(t), \\end{equation} in which $m_n=\\mathbb{E} S_n,B_n={\\rm Var }\\,S_n$,\nholds true for all $n\\in \\mathcal S$, $n\\to \\infty$. Further we prove that for\nany $0<\\eta<1$, and all $n$ large enough and $ \\zeta_0\\le \\zeta\\le \\exp\\big\\{\n\\frac{c\\log n}{\\log\\log n}\\big\\}$, letting $S'_n= \\sum_{j= 8}^n \\xi_j$,\n\\begin{eqnarray*} \\mathbb{P}\\big\\{ S'_n\\hbox{\\ $\\zeta$-quasiprime}\\big\\} \\,\\ge\n\\, (1-\\eta) \\frac{ e^{-\\gamma} }{ \\log \\zeta }, \\end{eqnarray*} according to\nPintz's terminology, where $c>0$ and $\\gamma$ is Euler's constant. We also test\nwhich infinite sequences of primes are ultimately avoided by the 'primes' of\nthe Cram\\'er model, with probability 1. Moreover we show that the Cram\\'er\nmodel has incidences on the Prime Number Theorem, since it predicts that the\nerror term is sensitive to subsequences. We obtain sharp results on the length\nand the number of occurrences of intervals $I$ such as for some $z>0$,\n\\begin{equation}\\sup_{n\\in I} \\frac{|S_n-m_n|}{ \\sqrt{B_n}}\\le z,\n\\end{equation} which are tied with the spectrum of the Sturm-Liouville\nequation.\n"}
+{"abstract": "  We give an explicit correspondance between Costantino$-$L\\^e's stated skein\nalgebras, which are defined via explicit relations on stated tangles, and\nGunningham$-$Jordan$-$Safronov's internal skein algebras, a.k.a\nBen-Zvi$-$Brochier$-$Jordan's moduli algebras, which are defined as internal\nendomorphism algebras. For the sake of accessibility, we do not use\nfactorisation homology but compute it using Cooke's skein categories. Stated\nskein algebras are defined on surfaces with multiple boundary edges and we\ngeneralise internal skein algebras in this context. We prove excision\nproperties of multi-edges internal skein algebras using excision properties of\nskein categories, and agreeing with excision properties of stated skein\nalgebras when $\\mathcal{V} = \\mathcal{U}_{q^2}(SL_2)\\text{--}mod^{fin}$.\n"}
+{"abstract": "  Active longitudinal beam optics can help FEL facilities achieve cutting edge\nperformance by optimizing the beam to: produce multi-color pulses, suppress\ncaustics, or support attosecond lasing. As the next generation of\nsuperconducting accelerators comes online, there is a need to find new elements\nwhich can both operate at high beam power and which offer multiplexing\ncapabilities at Mhz repetition rate. Laser heater shaping promises to satisfy\nboth criteria by imparting a programmable slice-energy spread on a shot-by-shot\nbasis. We use a simple kinetic analysis to show how control of the slice energy\nspread translates into control of the bunch current profile, and then we\npresent a collection of start-to-end simulations at LCLS-II in order to\nillustrate the technique.\n"}
+{"abstract": "  A general-purpose model combining concepts from rational continuum mechanics,\nfracture and damage mechanics, plasticity, and poromechanics is devised in\nEulerian coordinates, involving objective time derivatives. The model complies\nwith mass, momentum, and energy conservation as well as entropy inequality and\nobjectivity. It is devised to cover many diverse phenomena, specifically\nrupture of existing lithospheric faults, tectonic earthquakes, generation and\npropagation of seismic waves, birth of new tectonic faults, or volcanic\nactivity, aseismic creep, folding of rocks, aging of rocks, long-distance\nsaturated water transport and flow in poroelastic rocks, melting of rocks and\nformation of magma chambers, or solidification of magma.\n"}
+{"abstract": "  We present the results of a measurement of isotopic concentrations and atomic\nnumber ratio of a double-sided actinide target with alpha-spectroscopy and mass\nspectrometry. The double-sided actinide target, with primarily Pu-239 on one\nside and U-235 on the other, was used in the fission Time Projection Chamber\n(fissionTPC) for a measurement of the neutron-induced fission cross-section\nratio between the two isotopes. The measured atomic number ratio is intended to\nprovide an absolute normalization of the measured fission cross-section ratio.\nThe Pu-239/U-235 atom number ratio was measured with a combination of mass\nspectrometry and alpha-spectroscopy with a planar silicon detector with\nuncertainties of less than 1%.\n"}
+{"abstract": "  Therapeutic protons acting on O18-substituted thymidine increase cytotoxicity\nin radio-resistant human cancer cells. We consider here the physics behind the\nirradiation during proton beam therapy and diagnosis using O18-enriched thymine\nin DNA, with attention to the effect of the presence of thymine-18 on cancer\ncell death.\n"}
+{"abstract": "  We present a collaborative visual simultaneous localization and mapping\n(SLAM) framework for service robots. With an edge server maintaining a map\ndatabase and performing global optimization, each robot can register to an\nexisting map, update the map, or build new maps, all with a unified interface\nand low computation and memory cost. We design an elegant communication\npipeline to enable real-time information sharing between robots. With a novel\nlandmark organization and retrieval method on the server, each robot can\nacquire landmarks predicted to be in its view, to augment its local map. The\nframework is general enough to support both RGB-D and monocular cameras, as\nwell as robots with multiple cameras, taking the rigid constraints between\ncameras into consideration. The proposed framework has been fully implemented\nand verified with public datasets and live experiments.\n"}
+{"abstract": "  Single photons exhibit inherently quantum and unintuitive properties such as\nthe Hong-ou-Mandel effect, demonstrating their bosonic and quantized nature,\nyet at the same time may correspond to single excitations of spatial or\ntemporal modes with a very complex structure. Those two features are rarely\nseen together. Here we experimentally demonstrate how the Hong-Ou-Mandel effect\ncan be spectrally-resolved and harnessed to characterize a complex temporal\nmode of a single-photon \\textendash{} a zero-area pulse \\textendash{} obtained\nvia a resonant interaction of a terahertz-bandwidth photon with a narrow\ngigahertz-wide atomic transition of atomic vapor. The combination of bosonic\nquantum behavior with bandwidth-mismatched light-atom interaction is of\nfundamental importance for a deeper understanding of both phenomena, as well as\ntheir engineering offering applications in the characterization of ultra-fast\ntransient processes.\n"}
+{"abstract": "  We present a new technique to obtain outer-bounds on the capacity region of\nnetworks with ultra low-rate feedback. We establish a connection between the\nachievable rates in the forward channel and the minimum distortion that can be\nattained over the feedback channel.\n"}
+{"abstract": "  When discussing future concerns within socio-technical systems in work\ncontexts, we often find descriptions of missed technology development and\nintegration. The experience of technology that fails whilst being integrated is\noften rooted in dysfunctional epistemological approaches within the research\nand development process. Thus, ultimately leading to sustainable\ntechnology-distrust in work contexts. This is true for organizations that\nintegrate new technologies and for organizations that invent them.\nOrganizations in which we find failed technology development and integrations\nare, in their very nature, social systems. Nowadays, those complex social\nsystems act within an even more complex environment. This urges the development\nof new anticipation methods for technology development and integration.\nGathering of and dealing with complex information in the described context is\nwhat we call Anticipation Next. This explorative work uses existing literature\nfrom the adjoining research fields of system theory, organizational theory, and\nsocio-technical research to combine various concepts. We deliberately aim at a\nnetworked way of thinking in scientific contexts and thus combine\nmultidisciplinary subject areas in one paper to present an innovative way to\ndeal with multi-faceted problems in a human-centred way. We end with suggesting\na conceptual framework that should be used in the very early stages of\ntechnology development and integration in work contexts.\n"}
+{"abstract": "  Photovoltaic effect of neutral atoms using inhomogeneous light in double-trap\nopened system is studied theoretically. Using asymmetric external driving field\nto replacing original asymmetric chemical potential of atoms, we create\npolarization of atom population in the double-trap system. The polarization of\natom number distribution induces net current of atoms and works as collected\ncarriers in the cell. The cell can work even under partially coherent light.\nThe whole configuration is described by quantum master equation considering\nweak tunneling between the system and its reservoirs at finite temperature. The\nmodel of neutral atoms could be extended to more general quantum particles in\nprinciple.\n"}
+{"abstract": "  Deep learning-based segmentation methods are vulnerable to unforeseen data\ndistribution shifts during deployment, e.g. change of image appearances or\ncontrasts caused by different scanners, unexpected imaging artifacts etc. In\nthis paper, we present a cooperative framework for training image segmentation\nmodels and a latent space augmentation method for generating hard examples.\nBoth contributions improve model generalization and robustness with limited\ndata. The cooperative training framework consists of a fast-thinking network\n(FTN) and a slow-thinking network (STN). The FTN learns decoupled image\nfeatures and shape features for image reconstruction and segmentation tasks.\nThe STN learns shape priors for segmentation correction and refinement. The two\nnetworks are trained in a cooperative manner. The latent space augmentation\ngenerates challenging examples for training by masking the decoupled latent\nspace in both channel-wise and spatial-wise manners. We performed extensive\nexperiments on public cardiac imaging datasets. Using only 10 subjects from a\nsingle site for training, we demonstrated improved cross-site segmentation\nperformance and increased robustness against various unforeseen imaging\nartifacts compared to strong baseline methods. Particularly, cooperative\ntraining with latent space data augmentation yields 15% improvement in terms of\naverage Dice score when compared to a standard training method.\n"}
+{"abstract": "  A late (t $\\sim$ 1,500 days) multi-wavelength (UV, optical, IR, and X-ray)\nflare was found in PS1-10adi, a tidal disruption event (TDE) candidate that\ntook place in an active galactic nucleus (AGN). TDEs usually involve\nsuper-Eddington accretion, which drives fast mass outflow (disk wind). So here\nwe explore a possible scenario that such a flare might be produced by the\ninteraction of the disk wind with a dusty torus for TDEs in AGN. Due to the\nhigh velocity of the disk wind, strong shocks will emerge and convert the bulk\nof the kinetic energy of the disk wind to radiation. We calculate the dynamics\nand then predict the associated radiation signatures, taking into account the\nwidths of the wind and torus. We compare our model with the bolometric light\ncurve of the late flare in PS1-10adi constructed from observations. We find\nfrom our modeling that the disk wind has a total kinetic energy of about\n$10^{51}$ erg and a velocity of 0.1 c (i.e., a mass of 0.3 $M_{\\odot}$); the\ngas number density of the clouds in the torus is $3\\times 10^{7}$ $\\rm\ncm^{-3}$. Observation of such a late flare can be an evidence of the disk wind\nin TDEs and can be used as a tool to explore the nuclear environment of the\nhost.\n"}
+{"abstract": "  During a solar eclipse the solar irradiance reaching the top-of-atmosphere\n(TOA) is reduced in the Moon shadow. The solar irradiance is commonly measured\nby Earth observation satellites before the start of the solar eclipse and is\nnot corrected for this reduction, which results in a decrease of the computed\nTOA reflectances. Consequently, air quality products that are derived from TOA\nreflectance spectra, such as the ultraviolet (UV) Absorbing Aerosol Index\n(AAI), are distorted or undefined in the shadow of the Moon. The availability\nof air quality satellite data in the penumbral and antumbral shadow during\nsolar eclipses, however, is of particular interest to users studying the\natmospheric response to solar eclipses. Given the time and location of a point\non the Earth's surface, we explain how to compute the obscuration during a\nsolar eclipse taking into account wavelength-dependent solar limb darkening.\nWith the calculated obscuration fractions, we restore the TOA reflectances and\nthe AAI in the penumbral shadow during the annular solar eclipses on 26\nDecember 2019 and 21 June 2020 measured by the TROPOMI/S5P instrument. In the\ncorrected products, the signature of the Moon shadow disappeared, but only if\nwavelength-dependent solar limb darkening is taken into account. We conclude\nthat the correction method of this paper can be used to detect real AAI rising\nphenomena during a solar eclipse and has the potential to restore any other\nproduct that is derived from TOA reflectance spectra. This would resolve the\nsolar eclipse anomalies in satellite air quality measurements and would allow\nfor studying the effect of the eclipse obscuration on the composition of the\nEarth's atmosphere from space.\n"}
+{"abstract": "  Absorption spectroscopy studies the absorption of electromagnetic wave in a\nmaterial sample under test. The interacting electromagnetic wave can be\npropagating in free space or in a waveguide. The waveguide-based absorption\nspectroscopy method has several advantages compared to the free space setup.\nThe effect of the waveguide cross section on the interaction between the\nwaveguide mode and the sample can be expressed by a factor, called interaction\nfactor. In this article, a new formulation for the interaction factor is\nderived. It is shown that this factor is inversely proportional to the energy\nvelocity of the waveguide mode.\n"}
+{"abstract": "  Through the use of examples, we explain one way in which applied topology has\nevolved since the birth of persistent homology in the early 2000s. The first\napplications of topology to data emphasized the global shape of a dataset, such\nas the three-circle model for $3 \\times 3$ pixel patches from natural images,\nor the configuration space of the cyclo-octane molecule, which is a sphere with\na Klein bottle attached via two circles of singularity. In these studies of\nglobal shape, short persistent homology bars are disregarded as sampling noise.\nMore recently, however, persistent homology has been used to address questions\nabout the local geometry of data. For instance, how can local geometry be\nvectorized for use in machine learning problems? Persistent homology and its\nvectorization methods, including persistence landscapes and persistence images,\nprovide popular techniques for incorporating both local geometry and global\ntopology into machine learning. Our meta-hypothesis is that the short bars are\nas important as the long bars for many machine learning tasks. In defense of\nthis claim, we survey applications of persistent homology to shape recognition,\nagent-based modeling, materials science, archaeology, and biology.\nAdditionally, we survey work connecting persistent homology to geometric\nfeatures of spaces, including curvature and fractal dimension, and various\nmethods that have been used to incorporate persistent homology into machine\nlearning.\n"}
+{"abstract": "  The training of deep neural networks (DNNs) is usually memory-hungry due to\nthe limited device memory capacity of DNN accelerators. Characterizing the\nmemory behaviors of DNN training is critical to optimize the device memory\npressures. In this work, we pinpoint the memory behaviors of each device memory\nblock of GPU during training by instrumenting the memory allocators of the\nruntime system. Our results show that the memory access patterns of device\nmemory blocks are stable and follow an iterative fashion. These observations\nare useful for the future optimization of memory-efficient training from the\nperspective of raw memory access patterns.\n"}
+{"abstract": "  To identify the causes of performance problems or to predict process\nbehavior, it is essential to have correct and complete event data. This is\nparticularly important for distributed systems with shared resources, e.g., one\ncase can block another case competing for the same machine, leading to\ninter-case dependencies in performance. However, due to a variety of reasons,\nreal-life systems often record only a subset of all events taking place. To\nunderstand and analyze the behavior and performance of processes with shared\nresources, we aim to reconstruct bounds for timestamps of events in a case that\nmust have happened but were not recorded by inference over events in other\ncases in the system. We formulate and solve the problem by systematically\nintroducing multi-entity concepts in event logs and process models. We\nintroduce a partial-order based model of a multi-entity event log and a\ncorresponding compositional model for multi-entity processes. We define\nPQR-systems as a special class of multi-entity processes with shared resources\nand queues. We then study the problem of inferring from an incomplete event log\nunobserved events and their timestamps that are globally consistent with a\nPQR-system. We solve the problem by reconstructing unobserved traces of\nresources and queues according to the PQR-model and derive bounds for their\ntimestamps using a linear program. While the problem is illustrated for\nmaterial handling systems like baggage handling systems in airports, the\napproach can be applied to other settings where recording is incomplete. The\nideas have been implemented in ProM and were evaluated using both synthetic and\nreal-life event logs.\n"}
+{"abstract": "  The next frontier for the Internet leading by innovations in mobile\ncomputing, in particular, 5G, together with blockchains' transparency,\nimmutability, provenance, and authenticity, indicates the potentials of running\na new generation of applications on the mobile internet. A 5G-enabled\nblockchain system is structured as a hierarchy and needs to deal with different\nchallenges such as maintaining blockchain ledger at different spatial domains\nand various levels of the networks, efficient processing of cross-domain\ntransactions, establishing consensus among heterogeneous nodes, and supporting\ndelay-tolerant mobile transactions. In this paper, we present Saguaro, a\nhierarchical permissioned blockchain designed specifically for Internet-scale\nmobile networks. Saguaro benefits from the hierarchical structure of mobile\nnetwork infrastructure to address the aforementioned challenges. Our extensive\nexperimental results demonstrate the high potential of Saguaro being the first\n5G-enabled permissioned blockchain system in the community.\n"}
+{"abstract": "  In this paper, we present CogNet, a knowledge base (KB) dedicated to\nintegrating three types of knowledge: (1) linguistic knowledge from FrameNet,\nwhich schematically describes situations, objects and events. (2) world\nknowledge from YAGO, Freebase, DBpedia and Wikidata, which provides explicit\nknowledge about specific instances. (3) commonsense knowledge from ConceptNet,\nwhich describes implicit general facts. To model these different types of\nknowledge consistently, we introduce a three-level unified frame-styled\nrepresentation architecture. To integrate free-form commonsense knowledge with\nother structured knowledge, we propose a strategy that combines automated\nlabeling and crowdsourced annotation. At present, CogNet integrates 1,000+\nsemantic frames from linguistic KBs, 20,000,000+ frame instances from world\nKBs, as well as 90,000+ commonsense assertions from commonsense KBs. All these\ndata can be easily queried and explored on our online platform, and free to\ndownload in RDF format for utilization under a CC-BY-SA 4.0 license. The demo\nand data are available at http://cognet.top/.\n"}
+{"abstract": "  In this paper, we introduce a proximal-proximal majorization-minimization\n(PPMM) algorithm for nonconvex tuning-free robust regression problems. The\nbasic idea is to apply the proximal majorization-minimization algorithm to\nsolve the nonconvex problem with the inner subproblems solved by a sparse\nsemismooth Newton (SSN) method based proximal point algorithm (PPA). We must\nemphasize that the main difficulty in the design of the algorithm lies in how\nto overcome the singular difficulty of the inner subproblem. Furthermore, we\nalso prove that the PPMM algorithm converges to a d-stationary point. Due to\nthe Kurdyka-Lojasiewicz (KL) property of the problem, we present the\nconvergence rate of the PPMM algorithm. Numerical experiments demonstrate that\nour proposed algorithm outperforms the existing state-of-the-art algorithms.\n"}
+{"abstract": "  Compiling commonsense knowledge is traditionally an AI topic approached by\nmanual labor. Recent advances in web data processing have enabled automated\napproaches. In this demonstration we will showcase three systems for automated\ncommonsense knowledge base construction, highlighting each time one aspect of\nspecific interest to the data management community. (i) We use Quasimodo to\nillustrate knowledge extraction systems engineering, (ii) Dice to illustrate\nthe role that schema constraints play in cleaning fuzzy commonsense knowledge,\nand (iii) Ascent to illustrate the relevance of conceptual modelling. The demos\nare available online at https://quasimodo.r2.enst.fr,\nhttps://dice.mpi-inf.mpg.de and ascent.mpi-inf.mpg.de.\n"}
+{"abstract": "  We consider random singlet phases of spin-$\\frac{1}{2}$, random,\nantiferromagnetic spin chains, in which the universal leading-order divergence\n$\\frac{\\ln 2}{3}\\ln\\ell$ of the average entanglement entropy of a block of\n$\\ell$ spins, as well as the closely related leading term $\\frac{2}{3}l^{-2}$\nin the distribution of singlet lengths are well known by the strong-disorder\nrenormalization group (SDRG) method. Here, we address the question of how large\nthe subleading terms of the above quantities are. By an analytical calculation\nperformed along a special SDRG trajectory of the random XX chain, we identify a\nseries of integer powers of $1/l$ in the singlet-length distribution with the\nsubleading term $\\frac{4}{3}l^{-3}$. Our numerical SDRG analysis shows that,\nfor the XX fixed point, the subleading term is generally $O(l^{-3})$ with a\nnon-universal coefficient and also reveals terms with half-integer powers:\n$l^{-7/2}$ and $l^{-5/2}$ for the XX and XXX fixed points, respectively. We\nalso present how the singlet lengths originating in the SDRG approach can be\ninterpreted and calculated in the XX chain from the one-particle states of the\nequivalent free-fermion model. These results imply that the subleading term\nnext to the logarithmic one in the entanglement entropy is $O(\\ell^{-1})$ for\nthe XX fixed point and $O(\\ell^{-1/2})$ for the XXX fixed point with\nnon-universal coefficients. For the XX model, where a comparison with exact\ndiagonalization is possible, the order of the subleading term is confirmed but\nwe find that the SDRG fails to provide the correct non-universal coefficient.\n"}
+{"abstract": "  We consider certain systems of three linked simultaneous diagonal equations\nin ten variables with total degree exceeding five. By means of a complification\nargument, we obtain an asymptotic formula for the number of integral solutions\nof this system of bounded height that resolves the associated paucity problem.\n"}
+{"abstract": "  The coherent-state qubit is a promising candidate for optical quantum\ninformation processing due to its nearly-deterministic nature of the Bell-state\nmeasurement (BSM). However, its non-orthogonality incurs difficulties such as\nfailure of the BSM. One may use a large amplitude ($\\alpha$) for the coherent\nstate to minimize the failure probability, but the qubit then becomes more\nvulnerable to dephasing by photon loss. We propose a hardware-efficient\nconcatenated BSM (CBSM) scheme with modified parity encoding using coherent\nstates with reasonably small amplitudes ($|\\alpha| \\lessapprox 2$), which\nsimultaneously suppresses both failures and dephasing in the BSM procedure. We\nnumerically show that the CBSM scheme achieves a success probability\narbitrarily close to unity for appropriate values of $\\alpha$ and sufficiently\nlow photon loss rates (e.g., $\\lessapprox 5\\%$). Furthermore, we verify that\nthe quantum repeater scheme exploiting the CBSM scheme for quantum error\ncorrection enables one to carry out efficient long-range quantum communication\nover 1000 km. We show that the performance is comparable to those of other\nup-to-date methods or even outperforms them for some cases. Finally, we present\nmethods to prepare logical qubits under modified parity encoding and implement\nelementary logical operations, which consist of several physical-level\ningredients such as generation of Schr\\\"odinger's cat state and elementary\ngates under coherent-state basis. Our work demonstrates that the encoded\ncoherent-state qubits in free-propagating fields provide an alternative route\nto fault-tolerant information processing, especially long-range quantum\ncommunication.\n"}
+{"abstract": "  It has been hypothesized that quantum computers may lend themselves well to\napplications in machine learning. In the present work, we analyze function\nclasses defined via quantum kernels. Quantum computers offer the possibility to\nefficiently compute inner products of exponentially large density operators\nthat are classically hard to compute. However, having an exponentially large\nfeature space renders the problem of generalization hard. Furthermore, being\nable to evaluate inner products in high dimensional spaces efficiently by\nitself does not guarantee a quantum advantage, as already classically tractable\nkernels can correspond to high- or infinite-dimensional reproducing kernel\nHilbert spaces (RKHS).\n  We analyze the spectral properties of quantum kernels and find that we can\nexpect an advantage if their RKHS is low dimensional and contains functions\nthat are hard to compute classically. If the target function is known to lie in\nthis class, this implies a quantum advantage, as the quantum computer can\nencode this inductive bias, whereas there is no classically efficient way to\nconstrain the function class in the same way. However, we show that finding\nsuitable quantum kernels is not easy because the kernel evaluation might\nrequire exponentially many measurements.\n  In conclusion, our message is a somewhat sobering one: we conjecture that\nquantum machine learning models can offer speed-ups only if we manage to encode\nknowledge about the problem at hand into quantum circuits, while encoding the\nsame bias into a classical model would be hard. These situations may plausibly\noccur when learning on data generated by a quantum process, however, they\nappear to be harder to come by for classical datasets.\n"}
+{"abstract": "  The Internet of Things (IoT) and smart city paradigm includes ubiquitous\ntechnology to extract context information in order to return useful services to\nusers and citizens. An essential role in this scenario is often played by\ncomputer vision applications, requiring the acquisition of images from specific\ndevices. The need for high-end cameras often penalizes this process since they\nare power-hungry and ask for high computational resources to be processed.\nThus, the availability of novel low-power vision sensors, implementing advanced\nfeatures like in-hardware motion detection, is crucial for computer vision in\nthe IoT domain. Unfortunately, to be highly energy-efficient, these sensors\nmight worsen the perception performance (e.g., resolution, frame rate, color).\nTherefore, domain-specific pipelines are usually delivered in order to exploit\nthe full potential of these cameras. This paper presents the development,\nanalysis, and embedded implementation of a realtime detection, classification\nand tracking pipeline able to exploit the full potential of background\nfiltering Smart Vision Sensors (SVS). The power consumption obtained for the\ninference - which requires 8ms - is 7.5 mW.\n"}
+{"abstract": "  This paper investigates how a disturbance in the power network affects the\nnodal frequencies of certain network buses. To begin with, we show that the\ninertia of a single generator is in inverse proportion to the initial rate of\nchange of frequency (RoCoF) under disturbances. Then, we present how the\ninitial RoCoF of the nodal frequencies are related to the inertia constants of\nmultiple generators in a power network, which leads to a performance metric to\nanalyze nodal frequency performance. To be specific, the proposed metric\nevaluates the impact of disturbances on the nodal frequency performance. The\nvalidity and effectiveness of the proposed metric are illustrated via\nsimulations on a multi-machine power system.\n"}
+{"abstract": "  In this article we have studied bicomplex valued measurable functions on an\narbitrary measurable space. We have established the bicomplex version of\nLebesgue's dominated convergence theorem and some other results related to this\ntheorem. Also we have proved the bicomplex version of Lebesgue-Radon-Nikodym\ntheorem. Finally we have introduced the idea of hyperbolic version of invariant\nmeasure.\n"}
+{"abstract": "  Many exoplanets are discovered in binary star systems in internal or in\ncircumbinary orbits. Whether the planet can be habitable or not depends on the\npossibility to maintain liquid water on its surface, and therefore on the\nluminosity of its host stars and on the dynamical properties of the planetary\norbit. The trajectory of a planet in a double star system can be determined,\napproximating stars and planets with point masses, by solving numerically the\nequations of motion of the classical three-body system. In this study, we\nanalyze a large data set of planetary orbits, made up with high precision long\nintegration at varying: the mass of the planet, its distance from the primary\nstar, the mass ratio for the two stars in the binary system, and the\neccentricity of the star motion. To simulate the gravitational dynamics, we use\na 15th order integration scheme (IAS15, available within the REBOUND\nframework), that provides an optimal solution for long-term integration. In our\ndata analysis, we evaluate if an orbit is stable or not and also provide the\nstatistics of different types of instability: collisions with the primary or\nsecondary star and planets ejected away from the binary star system. Concerning\nthe stability, we find a significant number of orbits that are only marginally\nstable, according to the classification introduced by Musielak et al. 2005. For\nplanets of negligible mass, we estimate the critical semi-major axis $a_c$ as a\nfunction of the mass ratio and the eccentricity of the binary, in agreement\nwith the results of Holman and Wiegert 1999. However, we find that for very\nmassive planets (Super-Jupiters) the critical semi-major axis decrease in some\ncases by a few percent, compared to cases in which the mass of the planet is\nnegligible.\n"}
+{"abstract": "  We obtain local estimates, also called propagation of smallness or Remez-type\ninequalities, for analytic functions in several variables. Using Carleman\nestimates, we obtain a three sphere-type inequality, where the outer two\nspheres can be any sets satisfying a boundary separation property, and the\ninner sphere can be any set of positive Lebesgue measure. We apply this local\nresult to characterize the dominating sets for Bergman spaces on strongly\npseudoconvex domains in terms of a density condition or a testing condition on\nthe reproducing kernels. Our methods also yield a sufficient condition for\narbitrary domains and lower-dimensional sets.\n"}
+{"abstract": "  Recently, the LHCb Collaboration reported a new structure $P_{cs}(4459)$ with\na mass of 19 MeV below the $\\Xi_c \\bar{D}^{*} $ threshold. It may be a\ncandidate of molecular state from the $\\Xi_c \\bar{D}^{*} $ interaction. In the\ncurrent work, we perform a coupled-channel study of the $\\Xi_c^*\\bar{D}^*$,\n$\\Xi'_c\\bar{D}^*$, $\\Xi^*_c\\bar{D}$, $\\Xi_c\\bar{D}^*$, $\\Xi'_c\\bar{D}$, and\n$\\Xi_c\\bar{D}$ interactions in the quasipotential Bethe-Salpeter equation\napproach. With the help of the heavy quark chiral effective Lagrangian, the\npotential is constructed by light meson exchanges. Two $\\Xi_c \\bar{D}^{*} $\nmolecular states are produced with spin parities $ J^P=1/2^-$ and $3/2^- $. The\nlower state with $3/2^-$ can be related to the observed $P_{cs}(4450)$ while\ntwo-peak structure cannot be excluded. Within the same model, other strange\nhidden-charm pentaquarks are also predicted. Two states with spin parities\n$1/2^-$ and $3/2^-$ are predicted near the $\\Xi'_c\\bar{D}$, $\\Xi_c\\bar{D}$, and\n$\\Xi_c^*\\bar{D}$ thresholds, respectively. As two states near $\\Xi_c\n\\bar{D}^{*}$ threshold, two states are produced with $1/2^-$ and $3/2^-$ near\nthe $\\Xi'_c\\bar{D}^*$ threshold. The couplings of the molecular states to the\nconsidered channels are also discussed. The experimental research of those\nstates are helpful to understand the origin and internal structure of the\n$P_{cs}$ and $P_c$ states.\n"}
+{"abstract": "  We study the morphology of the stellar periphery of the Magellanic Clouds in\nsearch of substructure using near-infrared imaging data from the VISTA\nHemisphere Survey (VHS). Based on the selection of different stellar\npopulations using the ($J-K_\\mathrm{s}$, $K_\\mathrm{s}$) colour-magnitude\ndiagram, we confirm the presence of substructures related to the interaction\nhistory of the Clouds and find new substructures on the easter side of the LMC\ndisc which may be owing to the influence of the Milky Way, and on the northern\nside of the SMC, which is probably associated to the ellipsoidal structure of\nthe galaxy. We also study the luminosity function of red clump stars in the SMC\nand confirm the presence of a bi-modal distance distribution, in the form of a\nforeground population. We find that this bi-modality is still detectable in the\neastern regions of the galaxy out to a 10 deg distance from its centre.\nAdditionally, a background structure is detected in the North between 7 and 10\ndeg from the centre which might belong to the Counter Bridge, and a foreground\nstructure is detected in the South between 6 and 8 deg from the centre which\nmight be linked to the Old Bridge.\n"}
+{"abstract": "  This article introduces a representation of dynamic meshes, adapted to some\nnumerical simulations that require controlling the volume of objects with free\nboundaries, such as incompressible fluid simulation, some astrophysical\nsimulations at cosmological scale, and shape/topology optimization. The\nalgorithm decomposes the simulated object into a set of convex cells called a\nLaguerre diagram, parameterized by the position of $N$ points in 3D and $N$\nadditional parameters that control the volumes of the cells. These parameters\nare found as the (unique) solution of a convex optimization problem --\nsemi-discrete Monge-Amp\\`ere equation -- stemming from optimal transport\ntheory. In this article, this setting is extended to objects with free\nboundaries and arbitrary topology, evolving in a domain of arbitrary shape, by\nsolving a partial optimal transport problem. The resulting Lagrangian scheme\nmakes it possible to accurately control the volume of the object, while\nprecisely tracking interfaces, interactions, collisions, and topology changes.\n"}
+{"abstract": "  As the deep neural networks are being applied to complex tasks, the size of\nthe networks and architecture increases and their topology becomes more\ncomplicated too. At the same time, training becomes slow and at some instances\ninefficient. This motivated the introduction of various normalization\ntechniques such as Batch Normalization and Layer Normalization. The\naforementioned normalization methods use arithmetic operations to compute an\napproximation statistics (mainly the first and second moments) of the layer's\ndata and use it to normalize it. The aforementioned methods use plain Monte\nCarlo method to approximate the statistics and such method fails when\napproximating the statistics whose distribution is complex. Here, we propose an\napproach that uses weighted sum, implemented using depth-wise convolutional\nneural networks, to not only approximate the statistics, but to learn the\ncoefficients of the sum.\n"}
+{"abstract": "  A significant problem with immersive virtual reality (IVR) experiments is the\nability to compare research conditions. VR kits and IVR environments are\ncomplex and diverse but researchers from different fields, e.g. ICT,\npsychology, or marketing, often neglect to describe them with a level of detail\nsufficient to situate their research on the IVR landscape. Careful reporting of\nthese conditions may increase the applicability of research results and their\nimpact on the shared body of knowledge on HCI and IVR. Based on literature\nreview, our experience, practice and a synthesis of key IVR factors, in this\narticle we present a reference checklist for describing research conditions of\nIVR experiments. Including these in publications will contribute to the\ncomparability of IVR research and help other researchers decide to what extent\nreported results are relevant to their own research goals. The compiled\nchecklist is a ready-to-use reference tool and takes into account key hardware,\nsoftware and human factors as well as diverse factors connected to visual,\naudio, tactile, and other aspects of interaction.\n"}
+{"abstract": "  We propose a computational design tool to enable casual end-users to easily\ndesign, fabricate, and assemble flat-pack furniture with guaranteed\nmanufacturability. Using our system, users select parameterized components from\na library and constrain their dimensions. Then they abstractly specify\nconnections among components to define the furniture. Once fabrication\nspecifications (e.g. materials) designated, the mechanical implementation of\nthe furniture is automatically handled by leveraging encoded domain expertise.\nAfterwards, the system outputs 3D models for visualization and mechanical\ndrawings for fabrication. We demonstrate the validity of our approach by\ndesigning, fabricating, and assembling a variety of flat-pack (scaled)\nfurniture on demand.\n"}
+{"abstract": "  We present results from \\textsc{GigaEris}, a cosmological, $N$-body\nhydrodynamical ``zoom-in'' simulation of the formation of a Milky Way-sized\ngalaxy with unprecedented resolution, encompassing of order a billion particles\nwithin the refined region. The simulation employs a modern implementation of\nsmoothed-particle hydrodynamics, including metal-line cooling and metal and\nthermal diffusion. We focus on the early assembly of the galaxy, down to\nredshift $z=4.4$. The simulated galaxy has properties consistent with\nextrapolations of the main sequence of star-forming galaxies to higher\nredshifts and levels off to a star formation rate of $\\sim$60$\\,\nM_{\\odot}$yr$^{-1}$ at $z=4.4$. A compact, thin rotating stellar disk with\nproperties analogous to those of low-redshift systems arises already at $z \\sim\n8$-9. The galaxy rapidly develops a multi-component structure, and the disk, at\nleast at these early stages, does not grow upside-down as often reported in the\nliterature. Rather, at any given time, newly born stars contribute to sustain a\nthin disk, while the thick disk grows from stars that are primarily added\nthrough accretion and mergers. The kinematics reflect the early, ubiquitous\npresence of a thin disk, as a stellar disk component with $v_\\phi/\\sigma_R$\nlarger than unity is already present at $z \\sim 9$-10. Our results suggest that\nhigh-resolution spectro-photometric observations of very high-redshift galaxies\nshould find thin rotating disks, consistent with the recent discovery of cold\nrotating gas disks by ALMA. Finally, we present synthetic images for the JWST\nNIRCam camera, showing how the early disk would be easily detectable already at\n$z \\sim 7$.\n"}
+{"abstract": "  Gaps in protoplanetary disks have long been hailed as signposts of planet\nformation. However, a direct link between exoplanets and disks remains hard to\nidentify. We present a large sample study of ALMA disk surveys of nearby\nstar-forming regions to disentangle this connection. All disks are classified\nas either structured (transition, ring, extended) or non-structured (compact)\ndisks. Although low-resolution observations may not identify large scale\nsubstructure, we assume that an extended disk must contain substructure from a\ndust evolution argument. A comparison across ages reveals that structured disks\nretain high dust masses up to at least 10 Myr, whereas the dust mass of\ncompact, non-structured disks decreases over time. This can be understood if\nthe dust mass evolves primarily by radial drift, unless drift is prevented by\npressure bumps. We identify a stellar mass dependence of the fraction of\nstructured disks. We propose a scenario linking this dependence with that of\ngiant exoplanet occurrence rates. We show that there are enough exoplanets to\naccount for the observed disk structures if transitional disks are created by\nexoplanets more massive than Jupiter, and ring disks by exoplanets more massive\nthan Neptune, under the assumption that most of those planets eventually\nmigrate inwards. On the other hand, the known anti-correlation between\ntransiting super-Earths and stellar mass implies those planets must form in the\ndisks without observed structure, consistent with formation through pebble\naccretion in drift-dominated disks. These findings support an evolutionary\nscenario where the early formation of giant planets determines the disk's dust\nevolution and its observational appearance.\n"}
+{"abstract": "  We present a new family $\\left\\{ S_{n}(x;q)\\right\\} _{n\\geq 0}$ of monic\npolynomials in $x$, orthogonal with respect to a Sobolev-type inner product\nrelated to the $q$-Hermite I orthogonal polynomials, involving a first-order\n$q$-derivative on a mass-point $\\alpha \\in \\mathbb{R}$ located out of the\ncorresponding orthogonality interval $[-1,1]$, for some fixed real number $q\n\\in (0, 1)$. We present connection formulas, and the annihilation operator for\nthis non-standard orthogonal polynomial family.\n"}
+{"abstract": "  We study the dependence of some relevant tokamak equilibrium quantities on\nthe toroidal plasma rotation. The Grad-Shafranov equation generalised to the\nrotating case is analytically solved employing two different representations\nfor the homogenous solution. Using an expression in terms of polynomials, we\ndescribe the separatrix shape by a few geometrical parameters, reproducing\ndifferent plasma scenarios such as double-null and inverse triangularity. In\nthis setting, the introduction of toroidal rotation corresponds to variations\non relevant plasma quantities, most notably an enhancement of the poloidal\nbeta. Using a more general expression in terms of Bessel functions, we\nreconstruct the full plasma boundary of the double-null configuration proposed\nfor the upcoming DTT experiment, demonstrating how said configuration is\ncompatible with different values of the plasma velocity.\n"}
+{"abstract": "  Infertility is becoming an issue for an increasing number of couples. The\nmost common solution, in vitro fertilization, requires embryologists to\ncarefully examine light microscopy images of human oocytes to determine their\ndevelopmental potential. We propose an automatic system to improve the speed,\nrepeatability, and accuracy of this process. We first localize individual\noocytes and identify their principal components using CNN (U-Net) segmentation.\nNext, we calculate several descriptors based on geometry and texture. The final\nstep is an SVM classifier. Both the segmentation and classification training is\nbased on expert annotations. The presented approach leads to a classification\naccuracy of 70%.\n"}
+{"abstract": "  The well-known Landau-Yang (LY) theorem on the decay of a neutral particle\ninto two photons is generalized for analyzing the decay of a neutral or charged\nparticle into two identical massless particles of any spin. Selection rules\ncategorized by discrete parity invariance and Bose/Fermi symmetry are worked\nout in the helicity formulation. The general form of the Lorentz-covariant\ntriple vertices are derived and the corresponding decay helicity amplitudes are\nexplicitly calculated in the Jacob-Wick convention. After checking the\nconsistency of all the analytic results obtained by two complementary\napproaches, we extract out the key aspects of the generalized LY theorem.\n"}
+{"abstract": "  There is considerable interest in the pH-dependent switchable biocatalytic\nproperties of cerium oxide nanoparticles (CeNPs) in biomedicine, where these\nmaterials exhibit beneficial antioxidant activity against reactive oxygen\nspecies at neutral and basic physiological pH but cytotoxic prooxidant activity\nat acidic pathological pH. Oxygen vacancies play a key role in such\nbiocatalytic activities. While the general characteristics of the role of\noxygen vacancies are known, the mechanism of their action at the atomic scale\nunder different pH conditions has yet to be elucidated. The present work\napplies density functional theory (DFT) calculations to interpret the\npH-induced behavior of the stable {111} surface of CeO2 at the atomic scale.\nAnalysis of the surface-adsorbed media species reveals the critical role of pH\non the reversibility of the Ce3+ and Ce4+ redox equilibria and the formation\nand annihilation of the oxygen vacancies. Under acidic conditions, this\nreversible switching is hindered owing to incomplete volumetric filling of the\noxygen vacancies by the oxygen in the water molecules, hence effective\nretention of the oxygen vacancies, and consequent inhibition of redox\nbiomimetic reactions. Under neutral and basic conditions, the capacity for this\nreversible switching is preserved due to complete filling of the oxygen\nvacancies by the OH ions owing to their ready size accommodation, thereby\nretaining the capacity for performing redox biomimetic reactions cyclically.\n"}
+{"abstract": "  The mass of a supermassive black hole ($M_\\mathrm{BH}$) is a fundamental\nproperty that can be obtained through observational methods. Constraining\n$M_\\mathrm{BH}$ through multiple methods for an individual galaxy is important\nfor verifying the accuracy of different techniques, and for investigating the\nassumptions inherent in each method. NGC 4151 is one of those rare galaxies for\nwhich multiple methods can be used: stellar and gas dynamical modeling because\nof its proximity ($D=15.8\\pm0.4$ Mpc from Cepheids), and reverberation mapping\nbecause of its active accretion. In this work, we re-analyzed $H-$band integral\nfield spectroscopy of the nucleus of NGC 4151 from Gemini NIFS, improving the\nanalysis at several key steps. We then constructed a wide range of axisymmetric\ndynamical models with the new orbit-superposition code Forstand. One of our\nprimary goals is to quantify the systematic uncertainties in $M_\\mathrm{BH}$\narising from different combinations of the deprojected density profile,\ninclination, intrinsic flattening, and mass-to-light ratio. As a consequence of\nuncertainties on the stellar luminosity profile arising from the presence of\nthe AGN, our constraints on \\mbh are rather weak. Models with a steep central\ncusp are consistent with no black hole; however, in models with more moderate\ncusps, the black hole mass lies within the range of $0.25\\times10^7\\,M_\\odot\n\\lesssim M_\\mathrm{BH} \\lesssim 3\\times10^7\\,M_\\odot$. This measurement is\nsomewhat smaller than the earlier analysis presented by Onken et al., but\nagrees with previous $M_\\mathrm{BH}$ values from gas dynamical modeling and\nreverberation mapping. Future dynamical modeling of reverberation data, as well\nas IFU observations with JWST, will aid in further constraining $M_\\mathrm{BH}$\nin NGC 4151.\n"}
+{"abstract": "  We extend studies of micro-solvation of carbon monoxide by a combination of\nhigh-resolution IR spectroscopy and ab initio calculations. Spectra of the\n(H2O)4-CO and (D2O)4-CO pentamers are observed in the C-O stretch fundamental\nregion (~2150 cm-1). The H2O containing spectrum is broadened by\npredissociation, but that of D2O is sharp, enabling detailed analysis which\ngives a precise band origin and rotational parameters. Ab initio calculations\nare employed to confirm the assignment to (water)4-CO and to determine the\nstructure, in which the geometry of the (water)4 fragment is a cyclic ring very\nsimilar to the isolated water tetramer. The CO fragment is located \"above\" the\nring plane, with a partial hydrogen bond between the C atom and one of the\n\"free\" protons (deuterons) of the water tetramer. Together with previous\nresults on D2O-CO, (D2O)2-CO, and (D2O)3-CO, this represents a probe of the\nfour initial steps in the solvation of carbon monoxide at high resolution.\n"}
+{"abstract": "  In this work we study the spatio-temporal correlations of photons produced by\nspontaneous parametric down conversion. In particular, we study how the waists\nof the detection and pump beams impact on the spectral bandwidth of the\nphotons. Our results indicate that this parameter is greatly affected by the\nspatial properties of the detection beam, while not as much by the pump beam.\nThis allows for a simple experimental implementation to control the bandwidth\nof the biphoton spectra, which only entails modifying the optical configuration\nto collect the photons. Moreover, we have performed Hong-Ou-Mandel\ninterferometry measurements that also provide the phase of the biphoton\nwavefunction, and thereby its temporal shape. We explain all these results with\na toy model derived under certain approximations, which accurately recovers\nmost of the interesting experimental details.\n"}
+{"abstract": "  Recently, quantum oscillation of the resistance in insulating monolayer\nWTe$_2$ was reported. An explanation in terms of gap modulation in the\nhybridized Landau levels of an excitonic insulator was also proposed by one of\nus. However, the previous picture of gap modulation in the Landau levels\nspectrum was built on a pair of well nested electron and hole Fermi surfaces,\nwhile the monolayer WTe$_2$ has one hole and two electron Fermi pockets with\nrelative anisotropy. Here we demonstrate that for system like monolayer\nWTe$_2$, the excitonic insulating state arising from the coupled one hole and\ntwo electron pockets possesses a finite region in interaction parameter space\nthat shows gap modulation in a magnetic field. In this region, the thermally\nactivated conductivity displays the $1/B$ periodic oscillation and it can\nfurther develop into discrete peaks at low temperature, in agreement with the\nexperimental observation. We show that the relative anisotropy of the bands is\na key parameter and the qunatum oscillations decrease rapidly if the anisotropy\nincreases further than the realistic value for monolayer WTe$_2$.\n"}
+{"abstract": "  This paper and [29] treat the existence and nonexistence of stable weak\nsolutions to a fractional Hardy--H\\'enon equation $(-\\Delta)^s u = |x|^\\ell\n|u|^{p-1} u$ in $\\mathbb{R}^N$, where $0 < s < 1$, $\\ell > -2s$, $p>1$, $N \\geq\n1$ and $N > 2s$. In this paper, when $p$ is critical or supercritical in the\nsense of the Joseph--Lundgren, we prove the existence of a family of positive\nradial stable solutions, which satisfies the separation property. We also show\nthe multiple existence of the Joseph--Lundgren critical exponent for some $\\ell\n\\in (0,\\infty)$ and $s \\in (0,1)$, and this property does not hold in the case\n$s=1$.\n"}
+{"abstract": "  The Facility for Antiproton and Ion Research (FAIR) in Darmstadt, Germany,\nprovides unique possibilities for a new generation of hadron-, nuclear- and\natomic physics experiments. The future antiProton ANnihilations at DArmstadt\n(PANDA or $\\overline{\\rm P}$ANDA) experiment at FAIR will offer a broad physics\nprogramme, covering different aspects of the strong interaction. Understanding\nthe latter in the non-perturbative regime remains one of the greatest\nchallenges in contemporary physics. The antiproton-nucleon interaction studied\nwith PANDA provides crucial tests in this area. Furthermore, the\nhigh-intensity, low-energy domain of PANDA allows for searches for physics\nbeyond the Standard Model, e.g. through high precision symmetry tests. This\npaper takes into account a staged approach for the detector setup and for the\ndelivered luminosity from the accelerator. The available detector setup at the\ntime of the delivery of the first antiproton beams in the HESR storage ring is\nreferred to as the \\textit{Phase One} setup. The physics programme that is\nachievable during Phase One is outlined in this paper.\n"}
+{"abstract": "  In this paper, we review data mining approaches for health applications. Our\nfocus is on hardware-centric approaches. Modern computers consist of multiple\nprocessors, each equipped with multiple cores, each with a set of\narithmetic/logical units. Thus, a modern computer may be composed of several\nthousand units capable of doing arithmetic operations like addition and\nmultiplication. Graphic processors, in addition may offer some thousand such\nunits. In both cases, single instruction multiple data and multiple instruction\nmultiple data parallelism must be exploited. We review the principles of\nalgorithms which exploit this parallelism and focus also on the memory issues\nwhen multiple processing units access main memory through caches. This is\nimportant for many applications of health, such as ECG, EEG, CT, SPECT, fMRI,\nDTI, ultrasound, microscopy, dermascopy, etc.\n"}
+{"abstract": "  Time-lapse fluorescence microscopy (TLFM) is an important and powerful tool\nin synthetic biological research. Modeling TLFM experiments based on real data\nmay enable researchers to repeat certain experiments with minor effort. This\nthesis is a study towards deep learning-based modeling of TLFM experiments on\nthe image level. The modeling of TLFM experiments, by way of the example of\ntrapped yeast cells, is split into two tasks. The first task is to generate\nsynthetic image data based on real image data. To approach this problem, a\nnovel generative adversarial network, for conditionalized and unconditionalized\nimage generation, is proposed. The second task is the simulation of brightfield\nmicroscopy images over multiple discrete time-steps. To tackle this simulation\ntask an advanced future frame prediction model is introduced. The proposed\nmodels are trained and tested on a novel dataset that is presented in this\nthesis. The obtained results showed that the modeling of TLFM experiments, with\ndeep learning, is a proper approach, but requires future research to\neffectively model real-world experiments.\n"}
+{"abstract": "  The goal of quantum circuit transformation is to map a logical circuit to a\nphysical device by inserting additional gates as few as possible in an\nacceptable amount of time. We present an effective approach called TSA to\nconstruct the mapping. It consists of two key steps: one makes use of a\ncombined subgraph isomorphism and completion to initialize some candidate\nmappings, the other dynamically modifies the mappings by using tabu\nsearch-based adjustment. Our experiments show that, compared with\nstate-of-the-art methods GA, SABRE and FiDLS proposed in the literature, TSA\ncan generate mappings with a smaller number of additional gates and it has a\nbetter scalability for large-scale circuits.\n"}
+{"abstract": "  We study the action of symmetries on geodesics of a control problem on a\nCarnot group with growth vector $(4,7)$. We show that there is a subgroup of\nsymmetries isomorphic to $SO(3)$ and a set of points in the Carnot group with a\nnontrivial stabilizer of this action. We prove that each geodesic either lies\nin this set or do not intersect this set. In the former case the optimality of\ngeodesics is solved completely through the identification of the quotient with\nHeisenberg group.\n"}
+{"abstract": "  Training Deep neural networks (DNNs) on noisy labeled datasets is a\nchallenging problem, because learning on mislabeled examples deteriorates the\nperformance of the network. As the ground truth availability is limited with\nreal-world noisy datasets, previous papers created synthetic noisy datasets by\nrandomly modifying the labels of training examples of clean datasets. However,\nno final conclusions can be derived by just using this random noise, since it\nexcludes feature-dependent noise. Thus, it is imperative to generate\nfeature-dependent noisy datasets that additionally provide ground truth.\nTherefore, we propose an intuitive approach to creating feature-dependent noisy\ndatasets by utilizing the training predictions of DNNs on clean datasets that\nalso retain true label information. We refer to these datasets as \"Pseudo Noisy\ndatasets\". We conduct several experiments to establish that Pseudo noisy\ndatasets resemble feature-dependent noisy datasets across different conditions.\nWe further randomly generate synthetic noisy datasets with the same noise\ndistribution as that of Pseudo noise (referred as \"Randomized Noise\") to\nempirically show that i) learning is easier with feature-dependent label noise\ncompared to random noise, ii) irrespective of noise distribution, Pseudo noisy\ndatasets mimic feature-dependent label noise and iii) current training methods\nare not generalizable to feature-dependent label noise. Therefore, we believe\nthat Pseudo noisy datasets will be quite helpful to study and develop robust\ntraining methods.\n"}
+{"abstract": "  We consider the insulated conductivity problem with two unit balls as\ninsulating inclusions, a distance of order $\\varepsilon$ apart. The solution\n$u$ represents the electric potential. In dimensions $n \\ge 3$ it is an open\nproblem to find the optimal bound on the gradient of $u$, the electric field,\nin the narrow region between the insulating bodies. Li-Yang recently proved a\nbound of order $\\varepsilon^{-(1-\\gamma)/2}$ for some $\\gamma>0$. In this paper\nwe use a direct maximum principle argument to sharpen the Li-Yang estimate for\n$n \\ge 4$. Our method gives effective lower bounds on the best constant\n$\\gamma$, which in particular approach $1$ as $n$ tends to infinity.\n"}
+{"abstract": "  We develop an algorithm that combines the advantages of priority promotion -\none of the leading approaches to solving large parity games in practice - with\nthe quasi-polynomial time guarantees offered by Parys' algorithm. Hybridising\nthese algorithms sounds both natural and difficult, as they both generalise the\nclassic recursive algorithm in different ways that appear to be irreconcilable:\nwhile the promotion transcends the call structure, the guarantees change on\neach level. We show that an interface that respects both is not only effective,\nbut also efficient.\n"}
+{"abstract": "  We give an efficient classical algorithm that recovers the distribution of a\nnon-interacting fermion state over the computational basis. For a system of $n$\nnon-interacting fermions and $m$ modes, we show that $O(m^2 n^4 \\log(m/\\delta)/\n\\varepsilon^4)$ samples and $O(m^4 n^4 \\log(m/\\delta)/ \\varepsilon^4)$ time are\nsufficient to learn the original distribution to total variation distance\n$\\varepsilon$ with probability $1 - \\delta$. Our algorithm empirically\nestimates the one- and two-mode correlations and uses them to reconstruct a\nsuccinct description of the entire distribution efficiently.\n"}
+{"abstract": "  In this work we show that, in the class of\n$L^\\infty((0,T);L^2(\\mathbb{T}^3))$ distributional solutions of the\nincompressible Navier-Stokes system, the ones which are smooth in some open\ninterval of times are meagre in the sense of Baire category, and the Leray ones\nare a nowhere dense set.\n"}
+{"abstract": "  The evolution of satellite galaxies is shaped by their constant interaction\nwith the circum galactic medium surrounding central galaxies, which in turn may\nbe affected by gas and energy ejected from the central supermassive black hole.\nHowever, the nature of this coupling between black holes and galaxies is highly\ndebated and observational evidence remains scarce. Here we report an analysis\nof archival data on 124,163 satellite galaxies in the potential wells of 29,631\ndark matter halos with masses between 10$^{12}$ and $10^{14}$ solar masses. We\nfind that quiescent satellites are relatively less frequent along the minor\naxis of their central galaxies. This observation might appear counterintuitive\nas black hole activity is expected to eject mass and energy preferentially in\nthe direction of the minor axis of the host galaxy. However, we show that the\nobserved signal results precisely from the ejective nature of black hole\nfeedback in massive halos, as active galactic nuclei-powered outflows clear out\nthe circumgalactic medium, reducing the ram pressure and thus preserving star\nformation. This interpretation is supported by the IllustrisTNG suite of\ncosmological numerical simulations, where a similar modulation is observed even\nthough the sub-grid implementation of black hole feedback is effectively\nisotropic. Our results provide compelling observational evidence for the role\nof black holes in regulating galaxy evolution over spatial scales differing by\nseveral orders of magnitude.\n"}
+{"abstract": "  This paper integrates non-orthogonal multiple access (NOMA) and over-the-air\nfederated learning (AirFL) into a unified framework using one simultaneous\ntransmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The\nSTAR-RIS plays an important role in adjusting the decoding order of hybrid\nusers for efficient interference mitigation and omni-directional coverage\nextension. To capture the impact of non-ideal wireless channels on AirFL, a\nclosed-form expression for the optimality gap (a.k.a. convergence upper bound)\nbetween the actual loss and the optimal loss is derived. This analysis reveals\nthat the learning performance is significantly affected by active and passive\nbeamforming schemes as well as wireless noise. Furthermore, when the learning\nrate diminishes as the training proceeds, the optimality gap is explicitly\ncharacterized to converge with a linear rate. To accelerate convergence while\nsatisfying QoS requirements, a mixed-integer non-linear programming (MINLP)\nproblem is formulated by jointly designing the transmit power at users and the\nconfiguration mode of STAR-RIS. Next, a trust region-based successive convex\napproximation method and a penalty-based semidefinite relaxation approach are\nproposed to handle the decoupled non-convex subproblems iteratively. An\nalternating optimization algorithm is then developed to find a suboptimal\nsolution for the original MINLP problem. Extensive simulation results show that\ni) the proposed framework can efficiently support NOMA and AirFL users via\nconcurrent uplink communications, ii) our algorithms can achieve a faster\nconvergence rate on IID and non-IID settings compared to existing baselines,\nand iii) both the spectrum efficiency and learning performance can be\nsignificantly improved with the aid of the well-tuned STAR-RIS.\n"}
+{"abstract": "  We explore the phenomenology of a two-fluid cosmological model, where the\nfield equations of general relativity (GR) are sourced by baryonic and cold\ndark matter. We find that the model allows for a unified description of small\nand large scale, late-time cosmological dynamics. Specifically, in the static\nregime we recover the flattening of galactic rotation curves by requiring the\nmatter density profile to scale as $1/r^2$. The same behavior describes matter\ninhomogeneities distribution at small cosmological scales. This traces galactic\ndynamics back to structure formation. At large cosmological scales, we focus on\nback reaction effects of the spacetime geometry to the presence of matter\ninhomogeneities. We find that a cosmological constant with the observed order\nof magnitude, emerges by averaging the back reaction term on spatial scales of\norder 100 Mpc and it is related in a natural way to matter distribution. This\nprovides a resolution to both the cosmological constant and the coincidence\nproblems and shows the existence of an intriguing link between the small and\nlarge scale behavior in cosmology.\n"}
+{"abstract": "  In this study, we considered a moving particle with a magnetic quadrupole\nmoment in an elastic medium in the presence of a screw dislocation. We assumed\na radial electric field in a rotating frame that leads a uniform effective\nmagnetic field perpendicular to the plane of motion. We solved the\nSchr\\\"odinger equation to derive wave and energy eigenvalue functions by\nemploying analytical methods for two interaction configurations: in the absence\nof potential and in the presence of a static scalar potential. Due to the\ntopological defect in the medium, we observed a shift in the angular momentum\nquantum number which affects the energy eigenvalues and the wave function of\nthe system.\n"}
+{"abstract": "  We developed a systematic non-perturbative method base on Dyson-Schwinger\ntheory and the $\\Phi$-derivable theory for Ising model at broken phase. Based\non these methods, we obtain critical temperature and spin spin correlation\nbeyond mean field theory. The spectrum of Green function obtained from our\nmethods become gapless at critical point, so the susceptibility become\ndivergent at Tc. The critical temperature of Ising model obtained from this\nmethod is fairly good in comparison with other non-cluster methods. It is\nstraightforward to extend this method to more complicate spin models for\nexample with continue symmetry.\n"}
+{"abstract": "  Active systems evade the rules of equilibrium thermodynamics by constantly\ndissipating energy at the level of their microscopic components. This energy\nflux stems from the conversion of a fuel, present in the environment, into\nsustained individual motion. It can lead to collective effects without any\nequilibrium equivalent, such as a phase separation for purely repulsive\nparticles, or a collective motion (flocking) for aligning particles. Some of\nthese effects can be rationalized by using equilibrium tools to recapitulate\nnonequilibrium transitions. An important challenge is then to delineate\nsystematically to which extent the character of these active transitions is\ngenuinely distinct from equilibrium analogs. We review recent works that use\nstochastic thermodynamics tools to identify, for active systems, a measure of\nirreversibility comprising a coarse-grained or informatic entropy production.\nWe describe how this relates to the underlying energy dissipation or\nthermodynamic entropy production, and how it is influenced by collective\nbehavior. Then, we review the possibility to construct thermodynamic ensembles\nout-of-equilibrium, where trajectories are biased towards atypical values of\nnonequilibrium observables. We show that this is a generic route to discovering\nunexpected phase transitions in active matter systems, which can also inform\ntheir design.\n"}
+{"abstract": "  Model brittleness is a key concern when deploying deep learning models in\nreal-world medical settings. A model that has high performance at one\ninstitution may suffer a significant decline in performance when tested at\nother institutions. While pooling datasets from multiple institutions and\nretraining may provide a straightforward solution, it is often infeasible and\nmay compromise patient privacy. An alternative approach is to fine-tune the\nmodel on subsequent institutions after training on the original institution.\nNotably, this approach degrades model performance at the original institution,\na phenomenon known as catastrophic forgetting. In this paper, we develop an\napproach to address catastrophic forget-ting based on elastic weight\nconsolidation combined with modulation of batch normalization statistics under\ntwo scenarios: first, for expanding the domain from one imaging system's data\nto another imaging system's, and second, for expanding the domain from a large\nmulti-institutional dataset to another single institution dataset. We show that\nour approach outperforms several other state-of-the-art approaches and provide\ntheoretical justification for the efficacy of batch normalization modulation.\nThe results of this study are generally applicable to the deployment of any\nclinical deep learning model which requires domain expansion.\n"}
+{"abstract": "  The determination of the isothermal adsorption curves represents a mechanism\nthat allows ob-taining information on the process of adsorption of water in\norganic and inorganic materials. In addition, it is a measure to be considered\nwhen characterizing the physicochemical and structural properties of the\nmaterials. We want to present an approach to the state of knowledge about the\nmethods to characterize seeds and materials associated with food products\nphysically and struc-turally, and to relate this knowledge to biophysical\nprocesses in these materials. This review considers the papers available since\n2001 associated with water adsorption studies on seeds and other food products\nas well as the approach of different authors to to technical and experimental\nmodels and processes that are needed for the development of this topic. From\nthese articles the applied experimental methodologies (obtaining samples,\nenvironmental conditions and labor-atory equipment) and the mathematical models\nused to give physical, chemical and biological meaning to the results were\nanalyzed and discussed, concluding in the methodologies that have best adapted\nto the advance of the technology for obtaining isothermal curves in the last\nyears.\n"}
+{"abstract": "  Recent research has used deep learning to develop partial differential\nequation (PDE) models in science and engineering. The functional form of the\nPDE is determined by a neural network, and the neural network parameters are\ncalibrated to available data. Calibration of the embedded neural network can be\nperformed by optimizing over the PDE. Motivated by these applications, we\nrigorously study the optimization of a class of linear elliptic PDEs with\nneural network terms. The neural network parameters in the PDE are optimized\nusing gradient descent, where the gradient is evaluated using an adjoint PDE.\nAs the number of parameters become large, the PDE and adjoint PDE converge to a\nnon-local PDE system. Using this limit PDE system, we are able to prove\nconvergence of the neural network-PDE to a global minimum during the\noptimization. The limit PDE system contains a non-local linear operator whose\neigenvalues are positive but become arbitrarily small. The lack of a spectral\ngap for the eigenvalues poses the main challenge for the global convergence\nproof. Careful analysis of the spectral decomposition of the coupled PDE and\nadjoint PDE system is required. Finally, we use this adjoint method to train a\nneural network model for an application in fluid mechanics, in which the neural\nnetwork functions as a closure model for the Reynolds-averaged Navier-Stokes\n(RANS) equations. The RANS neural network model is trained on several datasets\nfor turbulent channel flow and is evaluated out-of-sample at different Reynolds\nnumbers.\n"}
+{"abstract": "  A majority of coded matrix-matrix computation literature has broadly focused\nin two directions: matrix partitioning for computing a single computation task\nand batch processing of multiple distinct computation tasks. While these works\nprovide codes with good straggler resilience and fast decoding for their\nproblem spaces, these codes would not be able to take advantage of the natural\nredundancy of re-using matrices across batch jobs. In this paper, we introduce\nthe Variable Coded Distributed Batch Matrix Multiplication (VCDBMM) problem\nwhich tasks a distributed system to perform batch matrix multiplication where\nmatrices are not necessarily distinct among batch jobs. Inspired in part by\nCross-Subspace Alignment codes, we develop Flexible Cross-Subspace Alignments\n(FCSA) codes that are flexible enough to utilize this redundancy. We provide a\nfull characterization of FCSA codes which allow for a wide variety of system\ncomplexities including good straggler resilience and fast decoding. We\ntheoretically demonstrate that, under certain practical conditions, FCSA codes\nare within a factor of 2 of the optimal solution when it comes to straggler\nresilience. Furthermore, our simulations demonstrate that our codes can achieve\neven better optimality gaps in practice, even going as low as 1.7.\n"}
+{"abstract": "  In this paper, we use local fraction derivative to show the H\\\"older\ncontinuity of the solution to the following nonlinear time-fractional slow and\nfast diffusion equation:\n  $$\\left(\\partial^\\beta+\\frac{\\nu}{2}(-\\Delta)^{\\alpha/2}\\right)u(t,x) =\nI_t^\\gamma\\left[\\sigma\\left(u(t,x)\\right)\\dot{W}(t,x)\\right],\\quad t>0,\\:\nx\\in\\mathbb{R}^d,$$ where $\\dot{W}$ is the space-time white noise,\n$\\alpha\\in(0,2]$, $\\beta\\in(0,2)$, $\\gamma\\ge 0$ and $\\nu>0$, under the\ncondition that $2(\\beta+\\gamma)-1-d\\beta/\\alpha>0$. The case when\n$\\beta+\\gamma\\le 1$ has been obtained in \\cite{ChHuNu19}. In this paper, we\nhave removed this extra condition, which in particular includes all cases for\n$\\beta\\in(0,2)$.\n"}
+{"abstract": "  For $S \\subset \\mathbb{R}^n$ and $d > 0$, denote by $G(S, d)$ the graph with\nvertex set $S$ with any two vertices being adjacent if and only if they are at\na Euclidean distance $d$ apart. Deem such a graph to be ``non-trivial\" if $d$\nis actually realized as a distance between points of $S$. In a 2015 article,\nthe author asked if there exist distinct $d_1, d_2$ such that the non-trivial\ngraphs $G(\\mathbb{Z}^2, d_1)$ and $G(\\mathbb{Z}^2, d_2)$ are isomorphic. In our\ncurrent work, we offer a straightforward geometric construction to show that a\nnegative answer holds for this question.\n"}
+{"abstract": "  Adaptive control architectures often make use of Lyapunov functions to design\nadaptive laws. We are specifically interested in adaptive control methods, such\nas the well-known L1 adaptive architecture, which employ a parameter observer\nfor this purpose. In such architectures, the observation error plays a critical\nrole in determining analytical bounds on the tracking error as well as\nrobustness. In this paper, we show how the non-existence of coercive Lyapunov\noperators can impact the analytical bounds, and with it the performance and the\nrobustness of such adaptive systems.\n"}
+{"abstract": "  High-resolution remote sensing images can provide abundant appearance\ninformation for ship detection. Although several existing methods use image\nsuper-resolution (SR) approaches to improve the detection performance, they\nconsider image SR and ship detection as two separate processes and overlook the\ninternal coherence between these two correlated tasks. In this paper, we\nexplore the potential benefits introduced by image SR to ship detection, and\npropose an end-to-end network named ShipSRDet. In our method, we not only feed\nthe super-resolved images to the detector but also integrate the intermediate\nfeatures of the SR network with those of the detection network. In this way,\nthe informative feature representation extracted by the SR network can be fully\nused for ship detection. Experimental results on the HRSC dataset validate the\neffectiveness of our method. Our ShipSRDet can recover the missing details from\nthe input image and achieves promising ship detection performance.\n"}
+{"abstract": "  Detection of individual molecules is the ultimate goal of any chemical\nsensor. In the case of gas detection, such resolution has been achieved in\nadvanced nanoscale electronic solid-state sensors, but it has not been possible\nso far in integrated photonic devices, where the weak light-molecule\ninteraction is typically hidden by noise. Here, we demonstrate a scheme to\ngenerate ultrasensitive down-conversion four-wave-mixing (FWM) in a graphene\nbipolar-junction-transistor heterogeneous D-shaped fiber. In the communication\nband, the FWM conversion efficiency can change steeply when the graphene Fermi\nlevel approaches 0.4 eV. In this condition, we exploit our unique two-step\noptoelectronic heterodyne detection scheme, and we achieve real-time individual\ngas molecule detection in vacuum. Such combination of graphene strong\nnonlinearities, electrical tunability, and all-fiber integration paves the way\ntoward the design of versatile high-performance graphene photonic devices.\n"}
+{"abstract": "  In this paper, we study problem of efficient service relocation (i.e.,\nchanging assigned data center for a selected client node) in elastic optical\nnetworks (EONs) in order to increase network performance (measured by the\nvolume of accepted traffic). To this end, we first propose novel traffic model\nfor cloud ready transport networks. The model takes into account four flow\ntypes (i.e., city-to-city, city-to-data center, data center-to-data center and\ndata center-to-data center) while the flow characteristics are based on real\neconomical and geographical parameters of the cities related to network nodes.\nThen, we propose dedicated flow allocation algorithm that can be supported by\nthe service relocation process. We also introduce 21 different relocation\npolicies, which use three types of data for decision making - network\ntopological characteristics, rejection history and traffic prediction.\nEventually, we perform extensive numerical experiments in order to: (i) tune\nproposed optimization approaches and (ii) evaluate and compare their efficiency\nand select the best one. The results of the investigation prove high efficiency\nof the proposed policies. The propoerly designed relocation policy allowed to\nallocate up to 3% more traffic (compared to the allocation without that\npolicy). The results also reveal that the most efficient relocation policy\nbases its decisions on two types of data simultaneously - the rejection history\nand traffic prediction.\n"}
+{"abstract": "  Predictive modeling is the key factor for saving time and resources with\nrespect to manufacturing processes such as fermentation processes arising e.g.\\\nin food and chemical manufacturing processes. According to Zhang et al. (2002),\nthe open-loop dynamics of yeast are highly dependent on the initial cell mass\ndistribution. This can be modeled via population balance models describing the\nsingle-cell behavior of the yeast cell. There have already been several\npopulation balance models for wine fermentation in the literature. However, the\nnew model introduced in this paper is much more detailed than the ones studied\npreviously. This new model for the white wine fermentation process is based on\na combination of components previously introduced in literature. It turns it\ninto a system of highly nonlinear weakly hyperbolic partial/ordinary\nintegro-differential equations. This model becomes very challenging from a\ntheoretical and numerical point of view. Existence and uniqueness of solutions\nto a simplified version of the introduced problem is studied based on semigroup\ntheory. For its numerical solution a numerical methodology based on a finite\nvolume scheme combined with a time implicit scheme is derived. The impact of\nthe initial cell mass distribution on the solution is studied and underlined\nwith numerical results. The detailed model is compared to a simpler model based\non ordinary differential equations. The observed differences for different\ninitial distributions and the different models turn out to be smaller than\nexpected. The outcomes of this paper are very interesting and useful for\napplied mathematicians, winemakers and process engineers.\n"}
+{"abstract": "  Non-line-of-sight (NLOS) imaging enables monitoring around corners and is\npromising for diverse applications. The resolution of transient NLOS imaging is\nlimited to a centimeter scale, mainly by the temporal resolution of the\ndetectors. Here, we construct an up-conversion single-photon detector with a\nhigh temporal resolution of ~1.4 ps and a low noise count rate of 5 counts per\nsecond (cps). Notably, the detector operates at room temperature, near-infrared\nwavelength. Using this detector, we demonstrate high-resolution and low-noise\nNLOS imaging. Our system can provide a 180 {\\mu}m axial resolution and a 2 mm\nlateral resolution, which is more than one order of magnitude better than that\nin previous experiments. These results open avenues for high-resolution NLOS\nimaging techniques in relevant applications.\n"}
+{"abstract": "  Card shuffling models have provided simple motivating examples for the\nmathematical theory of mixing times for Markov chains. As a complement, we\nintroduce a more intricate realistic model of a certain observable real-world\nscheme for mixing human players onto teams. We quantify numerically the\neffectiveness of this mixing scheme over the 7 or 8 steps performed in\npractice. We give a combinatorial proof of the non-trivial fact that the chain\nis indeed irreducible.\n"}
+{"abstract": "  Contemporary approaches to perception, planning, estimation, and control have\nallowed robots to operate robustly as our remote surrogates in uncertain,\nunstructured environments. This progress now creates an opportunity for robots\nto operate not only in isolation, but also with and alongside humans in our\ncomplex environments. Realizing this opportunity requires an efficient and\nflexible medium through which humans can communicate with collaborative robots.\nNatural language provides one such medium, and through significant progress in\nstatistical methods for natural-language understanding, robots are now able to\ninterpret a diverse array of free-form commands. However, most contemporary\napproaches require a detailed, prior spatial-semantic map of the robot's\nenvironment that models the space of possible referents of an utterance.\nConsequently, these methods fail when robots are deployed in new, previously\nunknown, or partially-observed environments, particularly when mental models of\nthe environment differ between the human operator and the robot. This paper\nprovides a comprehensive description of a novel learning framework that allows\nfield and service robots to interpret and correctly execute natural-language\ninstructions in a priori unknown, unstructured environments. Integral to our\napproach is its use of language as a \"sensor\" -- inferring spatial,\ntopological, and semantic information implicit in the utterance and then\nexploiting this information to learn a distribution over a latent environment\nmodel. We incorporate this distribution in a probabilistic, language grounding\nmodel and infer a distribution over a symbolic representation of the robot's\naction space. We use imitation learning to identify a belief-space policy that\nreasons over the environment and behavior distributions. We evaluate our\nframework through a variety navigation and mobile-manipulation experiments.\n"}
+{"abstract": "  This paper presents a model predictive control (MPC)-based online real-time\nadaptive control scheme for emergency voltage control in power systems. Despite\ntremendous success in various applications, real-time implementation of MPC for\ncontrol in power systems has not been successful due to its online\ncomputational burden for large-sized systems that takes more time than\navailable between the two control decisions. This long-standing problem is\naddressed here by developing a novel MPC-based adaptive control framework which\n(i) adapts the nominal offline computed control, by successive control\ncorrections, at each control decision point using the latest measurements, (ii)\nutilizes data-driven approach for prediction of voltage trajectory and its\nsensitivity with respect to control using trained deep neural networks (DNNs).\nIn addition, a realistic coordination scheme among control inputs of static var\ncompensators (SVC), load-shedding (LS), and load tap-changers (LTC) is\npresented with a goal of maintaining bus voltages within a predefined\npermissible range, where the delayed effect of LTC action is also incorporated\nin a novel way. The performance of the proposed scheme is validated for IEEE\n9-bus as well as 39-bus systems, with $\\pm 20\\%$ variations in nominal loading\nconditions. We also show that the proposed new scheme speeds up the online\ncomputation by a factor of 20 bringing it down to under one-tenth the control\ninterval, making the MPC-based power system control practically feasible.\n"}
+{"abstract": "  Graphene oxide (GO) is reduced by Joule heating using in-situ transmission\nelectron microscopy (TEM). The approach allows the simultaneous study of GO\nconductivity by electrical measurements and of its composition and structural\nproperties throughout the reduction process by TEM, electron diffraction and\nelectron energy-loss spectroscopy. The small changes of GO properties observed\nat low applied electric currents are attributed to the promotion of diffusion\nprocesses. The actual reduction process starts from an applied power density of\nabout 2 1014 Wm-3 and occurs in a highly uniform and localized manner. The\nconductivity increases more than 4 orders of magnitude reaching a value of 3\n103 Sm-1 with a final O content of less than 1%. We discuss differences between\nthe reduction by thermal annealing and Joule heating.\n"}
+{"abstract": "  We have recently proposed a Lagrangian in trace dynamics, to describe a\npossible unification of gravity, Yang-Mills fields, and fermions, at the Planck\nscale. This Lagrangian for the unified entity - called the aikyon - is\ninvariant under global unitary transformations, and as a result possesses a\nnovel conserved charge, known as the Adler-Millard charge. In the present\npaper, we derive an eigenvalue equation, analogous to the time-independent\nSchr\\\"{o}dinger equation, for the Hamiltonian of the theory. We show that in\nthe emergent quantum theory, the energy eigenvalues of the aikyon are\ncharacterised in terms of a fundamental frequency times Planck's constant. The\neigenvalues of this equation can, in principle, determine the values of the\nparameters of the standard model. We also report a ground state, in this theory\nof spontaneous quantum gravity, which could characterise a non-singular initial\nepoch in quantum cosmology.\n"}
+{"abstract": "  Probing classifiers have emerged as one of the prominent methodologies for\ninterpreting and analyzing deep neural network models of natural language\nprocessing. The basic idea is simple -- a classifier is trained to predict some\nlinguistic property from a model's representations -- and has been used to\nexamine a wide variety of models and properties. However, recent studies have\ndemonstrated various methodological limitations of this approach. This article\ncritically reviews the probing classifiers framework, highlighting their\npromises, shortcomings, and advances.\n"}
+{"abstract": "  We propose experimental measurements of the logarithmic negativity, which\nquantifies quantum correlations using Gouy phase measurements in an asymmetric\ndouble-slit interference experiment for twin photons. This is possible because\nboth quantities have analogous dependence with the spatial confinement by the\nslits and enables one to manipulate the portion of entanglement by the Gouy\nphase. In order to obtain those measurements, we need to work in a regime where\nthe position correlations between particles are strong, therefore we\ninvestigate such correlations for biphotons. Since we would like to handle\nentanglement quantifiers through the Gouy phase, we analyze the Gouy phase\ndifference for two entangled photons in an asymmetric double-slit interference\nexperiment.\n"}
+{"abstract": "  Majorana zero modes are expected to arise in semiconductor-superconductor\nhybrid systems, with potential topological quantum computing applications. One\nlimitation of this approach is the need for a relatively high external magnetic\nfield that should also change direction at nanoscale. This proposal considers\ndevices that incorporate micromagnets to address this challenge. We perform\nnumerical simulations of stray magnetic fields from different micromagnet\nconfigurations, which are then used to solve for Majorana wavefunctions.\nSeveral devices are proposed, starting with the basic four-magnet design to\nalign magnetic field with the nanowire and scaling up to nanowire T-junctions.\nThe feasibility of the approach is assessed by performing magnetic imaging of\nprototype patterns.\n"}
+{"abstract": "  Network Intrusion Detection Systems (NIDSs) are important tools for the\nprotection of computer networks against increasingly frequent and sophisticated\ncyber attacks. Recently, a lot of research effort has been dedicated to the\ndevelopment of Machine Learning (ML) based NIDSs. As in any ML-based\napplication, the availability of high-quality datasets is critical for the\ntraining and evaluation of ML-based NIDS. One of the key problems with the\ncurrently available datasets is the lack of a standard feature set. The use of\na unique and proprietary set of features for each of the publicly available\ndatasets makes it virtually impossible to compare the performance of ML-based\ntraffic classifiers on different datasets, and hence to evaluate the ability of\nthese systems to generalise across different network scenarios. To address that\nlimitation, this paper proposes and evaluates standard NIDS feature sets based\non the NetFlow network meta-data collection protocol and system. We evaluate\nand compare two NetFlow-based feature set variants, a version with 12 features,\nand another one with 43 features.\n"}
+{"abstract": "  The study of fracture propagation is an essential topic for several\ndisciplines in engineering and material sciences. Different mathematical\napproaches and numerical methods have been applied to simulate brittle\nfractures. Materials, naturally, present random properties that contribute its\nphysical properties, durability, and resistance, for this reason, stochastic\nmodeling is critical to obtain realistic simulations for fractures. In this\narticle, we propose applying a Gaussian random field with a Mat\\'ern covariance\nfunction to simulate a non-homogeneous energy release rate ($G_c$) of a\nmaterial. We propose a surrogate mathematical model based on a\nweighted-variational model to reduce numerical complexity and execution times\nfor simulations in the hybrid phase-field model. The FEniCS open-source\nsoftware is used to obtain numerical solutions to the variational and hybrid\nphase-field models with Gaussian random fields on the parameter $G_c$. Results\nhave shown that the weighted-variational model as a surrogate model is a\ncompetitive tool to emulate brittle fractures for real structures, reducing\nexecution times by 90\\%.\n"}
+{"abstract": "  Chase's lemma provides a powerful tool for translating properties of\n(co)products in abelian categories into chain conditions. This note discusses\nthe context in which the lemma is used, making explicit what is often neglected\nin the literature because of its technical nature.\n"}
+{"abstract": "  High-Efficiency Video Coding (HEVC) surpasses its predecessors in encoding\nefficiency by introducing new coding tools at the cost of an increased encoding\ntime-complexity. The Coding Tree Unit (CTU) is the main building block used in\nHEVC. In the HEVC standard, frames are divided into CTUs with the predetermined\nsize of up to 64x64 pixels. Each CTU is then divided recursively into a number\nof equally sized square areas, known as Coding Units (CUs). Although this\ndiversity of frame partitioning increases encoding efficiency, it also causes\nan increase in the time complexity due to the increased number of ways to find\nthe optimal partitioning. To address this complexity, numerous algorithms have\nbeen proposed to eliminate unnecessary searches during partitioning CTUs by\nexploiting the correlation in the video. In this paper, existing CTU depth\ndecision algorithms for HEVC are surveyed. These algorithms are categorized\ninto two groups, namely statistics and machine learning approaches. Statistics\napproaches are further subdivided into neighboring and inherent approaches.\nNeighboring approaches exploit the similarity between adjacent CTUs to limit\nthe depth range of the current CTU, while inherent approaches use only the\navailable information within the current CTU. Machine learning approaches try\nto extract and exploit similarities implicitly. Traditional methods like\nsupport vector machines or random forests use manually selected features, while\nrecently proposed deep learning methods extract features during training.\nFinally, this paper discusses extending these methods to more recent video\ncoding formats such as Versatile Video Coding (VVC) and AOMedia Video 1(AV1).\n"}
+{"abstract": "  Simulation techniques based on accurate and efficient representations of\npotential energy surfaces are urgently needed for the understanding of complex\naqueous systems such as solid-liquid interfaces. Here, we present a machine\nlearning framework that enables the efficient development and validation of\nmodels for complex aqueous systems. Instead of trying to deliver a\nglobally-optimal machine learning potential, we propose to develop models\napplicable to specific thermodynamic state points in a simple and user-friendly\nprocess. After an initial ab initio simulation, a machine learning potential is\nconstructed with minimum human effort through a data-driven active learning\nprotocol. Such models can afterwards be applied in exhaustive simulations to\nprovide reliable answers for the scientific question at hand. We showcase this\nmethodology on a diverse set of aqueous systems with increasing degrees of\ncomplexity. The systems chosen here comprise bulk water with different ions in\nsolution, water on a titanium dioxide surface, as well as water confined in\nnanotubes and between molybdenum disulfide sheets. Highlighting the accuracy of\nour approach with respect to the underlying ab initio reference, the resulting\nmodels are evaluated in detail with an automated validation protocol that\nincludes structural and dynamical properties and the precision of the force\nprediction of the models. Finally, we demonstrate the capabilities of our\napproach for the description of water on the rutile titanium dioxide (110)\nsurface to analyze the structure and mobility of water on this surface. Such\nmachine learning models provide a straightforward and uncomplicated but\naccurate extension of simulation time and length scales for complex systems.\n"}
+{"abstract": "  A line bundle on a smooth curve $C$ with two marked points determines a rank\nfunction $r(a,b) = h^0(C, L(-ap-bq))$. This paper studies Brill-Noether\ndegeneracy loci; such a locus is defined to be the closure in\n$\\operatorname{Pic}^d(C)$ of the locus of line bundles with a specified rank\nfunction. These loci generalize the classical Brill-Noether loci $W^r_d(C)$ as\nwell as Brill-Noether loci with imposed ramification. For general $(C,p,q)$ we\ndetermine the dimension, singular locus, and intersection class of\nBrill-Noether degeneracy loci, generalizing classical results about $W^r_d(C)$.\nThe intersection class has a combinatorial interpretation in terms of the\nnumber of reduced words for a permutation associated to the rank function, or\nalternatively the number of saturated chains in the Bruhat order. The essential\ntool is a versality theorem for a certain pair of flags on\n$\\operatorname{Pic}^d(C)$, conjectured by Melody Chan and the author. We prove\nthis versality theorem by showing the injectivity of a generalized Petri map,\nalong the lines of Eisenbud and Harris's proof of the Gieseker-Petri theorem.\n"}
+{"abstract": "  This paper develops an improved distributed finite-time control algorithm for\nmultiagent-based ac microgrids with battery energy storage systems (BESSs)\nutilizing a low-width communication network. The proposed control algorithm can\nsimultaneously coordinate BESSs to eliminate any deviation from the nominal\nfrequency as well as solving the state of charge (SoC) balancing problem. The\nstability of the proposed control algorithm is established using the Lyapunov\nmethod and homogeneous approximation theory, which guarantees an accelerated\nconvergence within a settling time that does not dependent on initial\nconditions. Based on this, to significantly reduce the communication burdens,\nan event-triggered communication mechanism is designed which can also avoid\nZeno behavior. Then sufficient conditions on the event-triggered boundary are\nderived to guarantee the stability and reliability of the whole system.\nPractical local constraints are imposed to implement the control protocol, and\nthe theoretical results are applied to a test system consisting of five DGs and\nfive BESSs, which verifies the effectiveness of the proposed strategy.\n"}
+{"abstract": "  It is essential for an automated vehicle in the field to perform\ndiscretionary lane changes with appropriate roadmanship - driving safely and\nefficiently without annoying or endangering other road users - under a wide\nrange of traffic cultures and driving conditions. While deep reinforcement\nlearning methods have excelled in recent years and been applied to automated\nvehicle driving policy, there are concerns about their capability to quickly\nadapt to unseen traffic with new environment dynamics. We formulate this\nchallenge as a multi-Markov Decision Processes (MDPs) adaptation problem and\ndeveloped Meta Reinforcement Learning (MRL) driving policies to showcase their\nquick learning capability. Two types of distribution variation in environments\nwere designed and simulated to validate the fast adaptation capability of\nresulting MRL driving policies which significantly outperform a baseline RL.\n"}
+{"abstract": "  In recent years, conversational agents have provided a natural and convenient\naccess to useful information in people's daily life, along with a broad and new\nresearch topic, conversational question answering (QA). Among the popular\nconversational QA tasks, conversational open-domain QA, which requires to\nretrieve relevant passages from the Web to extract exact answers, is more\npractical but less studied. The main challenge is how to well capture and fully\nexplore the historical context in conversation to facilitate effective\nlarge-scale retrieval. The current work mainly utilizes history questions to\nrefine the current question or to enhance its representation, yet the relations\nbetween history answers and the current answer in a conversation, which is also\ncritical to the task, are totally neglected. To address this problem, we\npropose a novel graph-guided retrieval method to model the relations among\nanswers across conversation turns. In particular, it utilizes a passage graph\nderived from the hyperlink-connected passages that contains history answers and\npotential current answers, to retrieve more relevant passages for subsequent\nanswer extraction. Moreover, in order to collect more complementary information\nin the historical context, we also propose to incorporate the multi-round\nrelevance feedback technique to explore the impact of the retrieval context on\ncurrent question understanding. Experimental results on the public dataset\nverify the effectiveness of our proposed method. Notably, the F1 score is\nimproved by 5% and 11% with predicted history answers and true history answers,\nrespectively.\n"}
+{"abstract": "  The variations in feedstock characteristics such as moisture and particle\nsize distribution lead to an inconsistent flow of feedstock from the biomass\npre-processing system to the reactor in-feed system. These inconsistencies\nresult in low on-stream times at the reactor in-feed equipment. This research\ndevelops an optimal process control method for a biomass pre-processing system\ncomprised of milling and densification operations to provide the consistent\nflow of feedstock to a reactor's throat. This method uses a mixed-integer\noptimization model to identify optimal bale sequencing, equipment in-feed rate,\nand buffer location and size in the biomass pre-processing system. This method,\nreferred to as the hybrid process control (HPC), aims to maximize throughput\nover time. We compare HPC with a baseline feed forward process control. Our\ncase study based on switchgrass finds that HPC reduces the variation of a\nreactor's feeding rate by 100\\% without increasing the operating cost of the\nbiomass pre-processing system for biomass with moisture ranging from 10 to\n25\\%. A biorefinery can adapt HPC to achieve its design capacity.\n"}
+{"abstract": "  Purpose: To characterize regional pulmonary function on CT images using a\nradiomic filtering approach. Methods: We develop a radiomic filtering technique\nto capture the image encoded regional pulmonary ventilation information on CT.\nThe lung volumes were first segmented on 46 CT images. Then, a 3D sliding\nwindow kernel is implemented to map the impulse response of radiomic features.\nSpecifically, for each voxel in the lungs, 53 radiomic features were calculated\nin such a rotationally-invariant 3D kernel to capture spatially-encoded\ninformation. Accordingly, each voxel coordinate is represented as a\n53-dimensional feature vector, and each image is represented as an image tensor\nthat we refer to as a feature map. To test the technique as a potential\npulmonary biomarker, the Spearman correlation analysis is performed between the\nfeature map and matched nuclear imaging measurements (Galligas PET or\nDTPA-SPECT) of lung ventilation. Results: Two features were found to be highly\ncorrelated with benchmark pulmonary ventilation function results based on the\nmedian of Spearman correlation coefficient () distribution. In particular,\nfeature GLRLM-based Run Length Non-uniformity and GLCOM-based Sum Average\nachieved robust high correlation across 46 patients and both Galligas PET or\nDTPA-SPECT nuclear imaging modalities, with the range (median) of [0.05, 0.67]\n(0.46) and [0.21, 0.65] (0.45), respectively. Such results are comparable to\nother image-based pulmonary function quantification techniques. Conclusions:\nOur results provide evidence that local regions of sparsely encoded homogenous\nlung parenchyma on CT are associated with diminished radiotracer uptake and\nmeasured lung ventilation defects on PET/SPECT imaging. This finding\ndemonstrates the potential of radiomics to serve as a non-invasive surrogate of\nregional lung function and provides hypothesis-generating data for future\nstudies.\n"}
+{"abstract": "  Diamond heat-spreaders for gallium nitride (GaN) devices currently depend\nupon a robust wafer bonding process. Bonding-free membrane methods demonstrate\npotential, however, chemical vapour deposition (CVD) of diamond directly onto a\nIII-nitride (III-N) heterostructure membrane induces significant thermal\nstresses. In this work, these thermal stresses are investigated using an\nanalytical approach, a numerical model and experimental validation. The thermal\nstresses are caused by the mismatch in the coefficient of thermal expansion\n(CTE) between the GaN/III-N stack, silicon (Si) and the diamond from room\ntemperature to CVD growth temperatures. Simplified analytical wafer bow models\nunderestimate the membrane bow for small sizes while numerical models replicate\nthe stresses and bows with increased accuracy using temperature gradients. The\nlargest tensile stress measured using Raman spectroscopy at room temperature\nwas approximately 1.0 $\\pm0.2$ GPa while surface profilometry shows membrane\nbows as large as \\SI{58}{\\micro\\metre}. This large bow is caused by additional\nstresses from the Si frame in the initial heating phase which are held in place\nby the diamond and highlights challenges for any device fabrication using\ncontact lithography. However, the bow can be reduced if the membrane is\npre-stressed to become flat at CVD temperatures. In this way, a sufficient\nplatform to grow diamond on GaN/III-N structures without wafer bonding can be\nrealised.\n"}
+{"abstract": "  Scientific workflows are a cornerstone of modern scientific computing, and\nthey have underpinned some of the most significant discoveries of the last\ndecade. Many of these workflows have high computational, storage, and/or\ncommunication demands, and thus must execute on a wide range of large-scale\nplatforms, from large clouds to upcoming exascale HPC platforms. Workflows will\nplay a crucial role in the data-oriented and post-Moore's computing landscape\nas they democratize the application of cutting-edge research techniques,\ncomputationally intensive methods, and use of new computing platforms. As\nworkflows continue to be adopted by scientific projects and user communities,\nthey are becoming more complex. Workflows are increasingly composed of tasks\nthat perform computations such as short machine learning inference, multi-node\nsimulations, long-running machine learning model training, amongst others, and\nthus increasingly rely on heterogeneous architectures that include CPUs but\nalso GPUs and accelerators. The workflow management system (WMS) technology\nlandscape is currently segmented and presents significant barriers to entry due\nto the hundreds of seemingly comparable, yet incompatible, systems that exist.\nAnother fundamental problem is that there are conflicting theoretical bases and\nabstractions for a WMS. Systems that use the same underlying abstractions can\nlikely be translated between, which is not the case for systems that use\ndifferent abstractions. More information:\nhttps://workflowsri.org/summits/technical\n"}
+{"abstract": "  In our analysis, we show that what Cottenden et al. accomplish is the\nderivation of the ordinary capstan equation, and a solution to a dynamic\nmembrane with both a zero-Poisson's ratio and a zero-mass density on a rigid\nright-circular cone. The authors states that the capstan equation holds true\nfor an elastic obstacle, and thus, it can be used to calculate the coefficient\nof friction between human skin and fabrics. However, using data that we\ngathered from human trials, we show that this claim cannot be substantiated as\nit is unwise to use the capstan equation (i.e. belt-friction models in general)\nto calculate the friction between in-vivo skin and fabrics. This is due to the\nfact that such models assume a rigid foundation, while human soft-tissue is\ndeformable, and thus, a portion of the applied force to the fabric is expended\non deforming the soft-tissue, which in turn leads to the illusion of a higher\ncoefficient of friction when using belt-friction models.\n"}
+{"abstract": "  This paper considers joint learning of multiple sparse Granger graphical\nmodels to discover underlying common and differential Granger causality (GC)\nstructures across multiple time series. This can be applied to drawing\ngroup-level brain connectivity inferences from a homogeneous group of subjects\nor discovering network differences among groups of signals collected under\nheterogeneous conditions. By recognizing that the GC of a single multivariate\ntime series can be characterized by common zeros of vector autoregressive (VAR)\nlag coefficients, a group sparse prior is included in joint regularized\nleast-squares estimations of multiple VAR models. Group-norm regularizations\nbased on group- and fused-lasso penalties encourage a decomposition of multiple\nnetworks into a common GC structure, with other remaining parts defined in\nindividual-specific networks. Prior information about sparseness and sparsity\npatterns of desired GC networks are incorporated as relative weights, while a\nnon-convex group norm in the penalty is proposed to enhance the accuracy of\nnetwork estimation in low-sample settings. Extensive numerical results on\nsimulations illustrated our method's improvements over existing sparse\nestimation approaches on GC network sparsity recovery. Our methods were also\napplied to available resting-state fMRI time series from the ADHD-200 data sets\nto learn the differences of causality mechanisms, called effective brain\nconnectivity, between adolescents with ADHD and typically developing children.\nOur analysis revealed that parts of the causality differences between the two\ngroups often resided in the orbitofrontal region and areas associated with the\nlimbic system, which agreed with clinical findings and data-driven results in\nprevious studies.\n"}
+{"abstract": "  Gaining insight into course choices holds significant value for universities,\nespecially those who aim for flexibility in their programs and wish to adapt\nquickly to changing demands of the job market. However, little emphasis has\nbeen put on utilizing the large amount of educational data to understand these\ncourse choices. Here, we use network analysis of the course selection of all\nstudents who enrolled in an undergraduate program in engineering, psychology,\nbusiness or computer science at a Nordic university over a five year period.\nWith these methods, we have explored student choices to identify their distinct\nfields of interest. This was done by applying community detection to a network\nof courses, where two courses were connected if a student had taken both. We\ncompared our community detection results to actual major specializations within\nthe computer science department and found strong similarities. To compliment\nthis analysis, we also used directed networks to identify the \"typical\"\nstudent, by looking at students' general course choices by semester. We found\nthat course choices diversify as programs progress, meaning that attempting to\nunderstand course choices by identifying a \"typical\" student gives less insight\nthan understanding what characterizes course choice diversity. Analysis with\nour proposed methodology can be used to offer more tailored education, which in\nturn allows students to follow their interests and adapt to the ever-changing\ncareer market.\n"}
+{"abstract": "  We study the entanglement wedge cross-section (EWCS) in holographic massive\ngravity theory, in which a first and second-order phase transition can occur.\nWe find that the mixed state entanglement measures, the EWCS and mutual\ninformation (MI) can characterize the phase transitions. The EWCS and MI show\nexactly the opposite behavior in the critical region, which suggests that the\nEWCS captures distinct degrees of freedom from that of the MI. More\nimportantly, EWCS, MI and HEE all show the same scaling behavior in the\ncritical region. We give an analytical understanding of this phenomenon. By\ncomparing the quantum information behavior in the thermodynamic phase\ntransition of holographic superconductors, we analyze the relationship and\ndifference between them, and provide two mechanisms of quantum information\nscaling behavior in the thermodynamic phase transition.\n"}
+{"abstract": "  The valleys in hexagonal two-dimensional systems with broken inversion\nsymmetry carry an intrinsic orbital magnetic moment. Despite this, such systems\npossess zero net magnetization unless additional symmetries are broken, since\nthe contributions from both valleys cancel. A nonzero net magnetization can be\ninduced through applying both uniaxial strain to break the rotational symmetry\nof the lattice and an in-plane electric field to break time-reversal symmetry\nowing to the resulting current. This creates a magnetoelectric effect whose\nstrength is characterized by a magnetoelectric susceptibility, which describes\nthe induced magnetization per unit applied in-plane electric field. Here, we\npredict the strength of this magnetoelectric susceptibility for Bernal-stacked\nbilayer graphene as a function of the magnitude and direction of strain, the\nchemical potential, and the interlayer electric field. We estimate that an\norbital magnetization of ~5400 $\\mu_{\\text{B}}/\\mu\\text{m}^2$ can be achieved\nfor 1% uniaxial strain and a 10 $\\mu\\text{A}$ bias current, which is almost\nthree orders of magnitude larger than previously probed experimentally in\nstrained monolayer MoS$_2$. We also identify regimes in which the\nmagnetoelectric susceptibility not only switches sign upon reversal of the\ninterlayer electric field but also in response to small changes in the carrier\ndensity. Taking advantage of this reversibility, we further show that it is\nexperimentally feasible to probe the effect using scanning magnetometry.\n"}
+{"abstract": "  We develop a description of defect loops in three-dimensional active nematics\nbased on a multipole expansion of the far-field director and show how this\nleads to a self-dynamics dependent on the loop's geometric type. The dipole\nterm leads to active stresses that generate a global self-propulsion for splay\nand bend loops. The quadrupole moment is non-zero only for non-planar loops and\ngenerates a net `active torque', such that defect loops are both self-motile\nand self-orienting. Our analysis identifies right- and left-handed twist loops\nas the only force and torque free geometries, suggesting a mechanism for\ngenerating an excess of twist loops. Finally, we determine the Stokesian flows\ncreated by defect loops and describe qualitatively their hydrodynamics.\n"}
+{"abstract": "  Time of flight based Non-line-of-sight (NLOS) imaging approaches require\nprecise calibration of illumination and detector positions on the visible scene\nto produce reasonable results. If this calibration error is sufficiently high,\nreconstruction can fail entirely without any indication to the user. In this\nwork, we highlight the necessity of building autocalibration into NLOS\nreconstruction in order to handle mis-calibration. We propose a forward model\nof NLOS measurements that is differentiable with respect to both, the hidden\nscene albedo, and virtual illumination and detector positions. With only a mean\nsquared error loss and no regularization, our model enables joint\nreconstruction and recovery of calibration parameters by minimizing the\nmeasurement residual using gradient descent. We demonstrate our method is able\nto produce robust reconstructions using simulated and real data where the\ncalibration error applied causes other state of the art algorithms to fail.\n"}
+{"abstract": "  Recently, Liu et al. reported that Ti2CTx MXene have ultra-high hydrogen\nstorage capacity (8.8 wt.%) at room temperature. For the purpose to clearly\nunderstand the hydrogen storage (H-storage), the composition of studied samples\nshould be clearly characterized and the H-storage structure need be explored.\nTo achieve 8.8 wt.% capacity, 3 layers of H2 molecules need be stored in the\ninterlayer space of MXene with the structure of Ti2CF2H14. The H2 layers with\ngraphene-like 2D structure are in solid/liquid state at room temperature, which\nis significant in the explore new materials with surprising properties.\n"}
+{"abstract": "  The representation learning on textual graph is to generate low-dimensional\nembeddings for the nodes based on the individual textual features and the\nneighbourhood information. Recent breakthroughs on pretrained language models\nand graph neural networks push forward the development of corresponding\ntechniques. The existing works mainly rely on the cascaded model architecture:\nthe textual features of nodes are independently encoded by language models at\nfirst; the textual embeddings are aggregated by graph neural networks\nafterwards. However, the above architecture is limited due to the independent\nmodeling of textual features. In this work, we propose GraphFormers, where\nlayerwise GNN components are nested alongside the transformer blocks of\nlanguage models. With the proposed architecture, the text encoding and the\ngraph aggregation are fused into an iterative workflow, making each node's\nsemantic accurately comprehended from the global perspective. In addition, a\nprogressive learning strategy is introduced, where the model is successively\ntrained on manipulated data and original data to reinforce its capability of\nintegrating information on graph. Extensive evaluations are conducted on three\nlarge-scale benchmark datasets, where GraphFormers outperform the SOTA\nbaselines with comparable running efficiency.\n"}
+{"abstract": "  This paper is a continuation of our Dwork crystals series. Here we exploit\nthe Cartier operation to prove supercongruences for expansion coefficients of\nrational functions. We also define excellent Frobenius lifts and show that for\nDwork's families of hypersurfaces such lifts can be approximated p-adically by\nrational functions with powers of the first and second Hasse-Witt determinants\nin denominators.\n"}
+{"abstract": "  A significant fraction of white dwarfs (WDs) exhibit signs of ongoing\naccretion of refractory elements at rates $\\sim10^3$--$10^7$ kg s$^{-1}$, among\nwhich, 37 WDs were detected to harbor dusty debris disks. Such a concurrence\nrequires not only fertile reservoirs of planetary material, but also a high\nduty cycle of metal delivery. It has been commonly suggested that this material\ncould be supplied by Solar System analogs of Main Belt asteroids or Kuiper Belt\nobjects. Here we consider the primary progenitors of WD pollutants as a\npopulation of residual high-eccentricity planetesimals, de-volatilized during\nthe stellar giant phases. Equivalent to the Solar System's long-period comets,\nthey are scattered to the proximity of WDs by perturbations from remaining\nplanets, Galactic tides, passing molecular clouds, and nearby stars. These\nobjects undergo downsizing when they venture within the tidal disruption limit.\nWe show quantitatively how the breakup condition and fragment sizes are\ndetermined by material strength and gravity. Thereafter, the fragments'\nsemi-major axes need to decay by at least $\\sim$6 orders of magnitude before\ntheir constituents are eventually accreted onto the surface of WDs. We\ninvestigate the orbital evolution of these fragments around WDs and show that\nWDs' magnetic fields induce an Alfv\\'en-wave drag during their periastron\npassages and rapidly circularize their orbits. This process could be\nresponsible for the observed accretion rates of heavy-elements and the\ngeneration of circum-WD debris disks. A speculative implication is that giant\nplanets may be common around WDs' progenitors and they may still be bound to\nsome WDs today.\n"}
+{"abstract": "  Polarons with different types of electron-phonon coupling have fundamentally\ndifferent properties. When the dominant interaction is between the electron\ndensity and lattice displacement, the momentum of the ground state does not\nchange and the polaron gets exponentially heavy at strong coupling. In\ncontrast, one-dimensional Peierls/Su-Schrieffer-Heeger (PSSH) polarons with\ninteraction originating from displacement-modulated hopping feature a shift of\nthe ground-state momentum to finite values and moderate values of effective\nmass as coupling is increased REF[Phys. Rev. Lett. 105, 266605 (2010)]. Based\non Diagrammatic Monte Carlo method, we investigate whether unusual properties\nof PSSH polarons depend on the type of the displacement-modulated hopping and\nto what degree they survive in higher dimension. We study two different PSSH\nmodels: with bosonic degrees of freedom residing on sites (model A) and bonds\n(model B) of the two-dimensional square lattice. For model A, we find that in\nboth adiabatic and intermediate regimes, the momentum of the ground state\nexperiences a continuous transition from zero to a finite value as a function\nof coupling strength. The transition is driven by quadratic instability of the\ndispersion function, implying that effective mass diverges at the critical\npoint, and then decreases in an anisotropic fashion with increasing coupling.\nUnexpectedly, for model B, the momentum of the ground state always stays at\nzero and the effective mass increases monotonously with coupling. The increase\nis far from exponential and tends to level-off at strong interaction, resulting\nin relatively light polarons. Having light polarons in the strong coupling\nregime is crucial for the bi-polaron mechanism of high-temperature\nsuperconductivity REF[Phys. Rev. Lett. 121, 247001 (2018)].\n"}
+{"abstract": "  In the 35 years since the discovery of cuprate superconductors, we have not\nyet reached a unified understanding of their properties, including their\nmaterial dependence of the superconducting transition temperature\n$T_{\\text{c}}$. The preceding theoretical and experimental studies have\nprovided an overall picture of the phase diagram, and some important parameters\nfor the $T_{\\text{c}}$, such as the contribution of the Cu $d_{z^2}$ orbital to\nthe Fermi surface and the site-energy difference $\\Delta_{dp}$ between the Cu\n$d_{x^2-y^2}$ and O $p$ orbitals. However, they are somewhat empirical and\nlimited in scope, always including exceptions, and do not provide a\ncomprehensive view of the series of cuprates. Here we propose a four-band\n$d$-$p$ model as a minimal model to study material dependence in cuprates.\nUsing the variational Monte Carlo method, we theoretically investigate the\nphase diagram for the La$_2$CuO$_4$ and HgBa$_2$CuO$_4$ systems and the\ncorrelation between the key parameters and the superconductivity. Our results\ncomprehensively account for the empirical correlation between $T_{\\text{c}}$\nand model parameters, and thus can provide a guideline for new material design.\nWe also show that the effect of the nearest-neighbor $d$-$d$ Coulomb\ninteraction $V_{dd}$ is actually quite important for the stability of\nsuperconductivity and phase competition.\n"}
+{"abstract": "  The multigrid algorithm is an efficient numerical method for solving a\nvariety of elliptic partial differential equations (PDEs). The method damps\nerrors at progressively finer grid scales, resulting in faster convergence\ncompared to standard iterative methods such as Gauss-Seidel. The prolongation,\nor coarse-to-fine interpolation operator within the multigrid algorithm lends\nitself to a data-driven treatment with ML super resolution, commonly used to\nincrease the resolution of images. We (i) propose the novel integration of a\nsuper resolution generative adversarial network (GAN) model with the multigrid\nalgorithm as the prolongation operator and (ii) show that the GAN-interpolation\nimproves the convergence properties of the multigrid in comparison to cubic\nspline interpolation on a class of multiscale PDEs typically solved in physics\nand engineering simulations.\n"}
+{"abstract": "  A striking discovery in the field of network science is that the majority of\nreal networked systems have some universal structural properties. In generally,\nthey are simultaneously sparse, scale-free, small-world, and loopy. In this\npaper, we investigate the second-order consensus of dynamic networks with such\nuniversal structures subject to white noise at vertices. We focus on the\nnetwork coherence $H_{\\rm SO}$ characterized in terms of the\n$\\mathcal{H}_2$-norm of the vertex systems, which measures the mean deviation\nof vertex states from their average value. We first study numerically the\ncoherence of some representative real-world networks. We find that their\ncoherence $H_{\\rm SO}$ scales sublinearly with the vertex number $N$. We then\nstudy analytically $H_{\\rm SO}$ for a class of iteratively growing networks --\npseudofractal scale-free webs (PSFWs), and obtain an exact solution to $H_{\\rm\nSO}$, which also increases sublinearly in $N$, with an exponent much smaller\nthan 1. To explain the reasons for this sublinear behavior, we finally study\n$H_{\\rm SO}$ for Sierpin\\'ski gaskets, for which $H_{\\rm SO}$ grows\nsuperlinearly in $N$, with a power exponent much larger than 1. Sierpin\\'ski\ngaskets have the same number of vertices and edges as the PSFWs, but do not\ndisplay the scale-free and small-world properties. We thus conclude that the\nscale-free and small-world, and loopy topologies are jointly responsible for\nthe observed sublinear scaling of $H_{\\rm SO}$.\n"}
+{"abstract": "  Model quantization is a promising approach to compress deep neural networks\nand accelerate inference, making it possible to be deployed on mobile and edge\ndevices. To retain the high performance of full-precision models, most existing\nquantization methods focus on fine-tuning quantized model by assuming training\ndatasets are accessible. However, this assumption sometimes is not satisfied in\nreal situations due to data privacy and security issues, thereby making these\nquantization methods not applicable. To achieve zero-short model quantization\nwithout accessing training data, a tiny number of quantization methods adopt\neither post-training quantization or batch normalization statistics-guided data\ngeneration for fine-tuning. However, both of them inevitably suffer from low\nperformance, since the former is a little too empirical and lacks training\nsupport for ultra-low precision quantization, while the latter could not fully\nrestore the peculiarities of original data and is often low efficient for\ndiverse data generation. To address the above issues, we propose a zero-shot\nadversarial quantization (ZAQ) framework, facilitating effective discrepancy\nestimation and knowledge transfer from a full-precision model to its quantized\nmodel. This is achieved by a novel two-level discrepancy modeling to drive a\ngenerator to synthesize informative and diverse data examples to optimize the\nquantized model in an adversarial learning fashion. We conduct extensive\nexperiments on three fundamental vision tasks, demonstrating the superiority of\nZAQ over the strong zero-shot baselines and validating the effectiveness of its\nmain components. Code is available at <https://git.io/Jqc0y>.\n"}
+{"abstract": "  Several recent end-to-end text-to-speech (TTS) models enabling single-stage\ntraining and parallel sampling have been proposed, but their sample quality\ndoes not match that of two-stage TTS systems. In this work, we present a\nparallel end-to-end TTS method that generates more natural sounding audio than\ncurrent two-stage models. Our method adopts variational inference augmented\nwith normalizing flows and an adversarial training process, which improves the\nexpressive power of generative modeling. We also propose a stochastic duration\npredictor to synthesize speech with diverse rhythms from input text. With the\nuncertainty modeling over latent variables and the stochastic duration\npredictor, our method expresses the natural one-to-many relationship in which a\ntext input can be spoken in multiple ways with different pitches and rhythms. A\nsubjective human evaluation (mean opinion score, or MOS) on the LJ Speech, a\nsingle speaker dataset, shows that our method outperforms the best publicly\navailable TTS systems and achieves a MOS comparable to ground truth.\n"}
+{"abstract": "  Purpose: Iterative Convolutional Neural Networks (CNNs) which resemble\nunrolled learned iterative schemes have shown to consistently deliver\nstate-of-the-art results for image reconstruction problems across different\nimaging modalities. However, because these methodes include the forward model\nin the architecture, their applicability is often restricted to either\nrelatively small reconstruction problems or to problems with operators which\nare computationally cheap to compute. As a consequence, they have so far not\nbeen applied to dynamic non-Cartesian multi-coil reconstruction problems.\nMethods: In this work, we propose a CNN-architecture for image reconstruction\nof accelerated 2D radial cine MRI with multiple receiver coils. The network is\nbased on a computationally light CNN-component and a subsequent conjugate\ngradient (CG) method which can be jointly trained end-to-end using an efficient\ntraining strategy. We investigate the proposed training-strategy and compare\nour method to other well-known reconstruction techniques with learned and\nnon-learned regularization methods. Results: Our proposed method outperforms\nall other methods based on non-learned regularization. Further, it performs\nsimilar or better than a CNN-based method employing a 3D U-Net and a method\nusing adaptive dictionary learning. In addition, we empirically demonstrate\nthat even by training the network with only iteration, it is possible to\nincrease the length of the network at test time and further improve the\nresults. Conclusions: End-to-end training allows to highly reduce the number of\ntrainable parameters of and stabilize the reconstruction network. Further,\nbecause it is possible to change the length of the network at test time, the\nneed to find a compromise between the complexity of the CNN-block and the\nnumber of iterations in each CG-block becomes irrelevant.\n"}
+{"abstract": "  In this paper, we address inter-beam inter-cell interference mitigation in 5G\nnetworks that employ millimeter-wave (mmWave), beamforming and non-orthogonal\nmultiple access (NOMA) techniques. Those techniques play a key role in\nimproving network capacity and spectral efficiency by multiplexing users on\nboth spatial and power domains. In addition, the coverage area of multiple\nbeams from different cells can intersect, allowing more flexibility in\nuser-cell association. However, the intersection of coverage areas also implies\nincreased inter-beam inter-cell interference, i.e. interference among beams\nformed by nearby cells. Therefore, joint user-cell association and inter-beam\npower allocation stand as a promising solution to mitigate inter-beam,\ninter-cell interference. In this paper, we consider a 5G mmWave network and\npropose a reinforcement learning algorithm to perform joint user-cell\nassociation and inter-beam power allocation to maximize the sum rate of the\nnetwork. The proposed algorithm is compared to a uniform power allocation that\nequally divides power among beams per cell. Simulation results present a\nperformance enhancement of 13-30% in network's sum-rate corresponding to the\nlowest and highest traffic loads, respectively.\n"}
+{"abstract": "  The present paper concerns filtered de la Vall\\'ee Poussin (VP) interpolation\nat the Chebyshev nodes of the four kinds. This approximation model is\ninteresting for applications because it combines the advantages of the\nclassical Lagrange polynomial approximation (interpolation and polynomial\npreserving) with the ones of filtered approximation (uniform boundedness of the\nLebesgue constants and reduction of the Gibbs phenomenon). Here we focus on\nsome additional features that are useful in the applications of filtered VP\ninterpolation. In particular, we analyze the simultaneous approximation\nprovided by the derivatives of the VP interpolation polynomials. Moreover, we\nstate the uniform boundedness of VP approximation operators in some Sobolev and\nH\\\"older--Zygmund spaces where several integro--differential models are\nuniquely and stably solvable.\n"}
+{"abstract": "  The concept of compressing deep Convolutional Neural Networks (CNNs) is\nessential to use limited computation, power, and memory resources on embedded\ndevices. However, existing methods achieve this objective at the cost of a drop\nin inference accuracy in computer vision tasks. To address such a drawback, we\npropose a framework that leverages knowledge distillation along with\ncustomizable block-wise optimization to learn a lightweight CNN structure while\npreserving better control over the compression-performance tradeoff.\nConsidering specific resource constraints, e.g., floating-point operations per\ninference (FLOPs) or model-parameters, our method results in a state of the art\nnetwork compression while being capable of achieving better inference accuracy.\nIn a comprehensive evaluation, we demonstrate that our method is effective,\nrobust, and consistent with results over a variety of network architectures and\ndatasets, at negligible training overhead. In particular, for the already\ncompact network MobileNet_v2, our method offers up to 2x and 5.2x better model\ncompression in terms of FLOPs and model-parameters, respectively, while getting\n1.05% better model performance than the baseline network.\n"}
+{"abstract": "  The impact of the ongoing COVID-19 pandemic is being felt in all spheres of\nour lives -- cutting across the boundaries of nation, wealth, religions or\nrace. From the time of the first detection of infection among the public, the\nvirus spread though almost all the countries in the world in a short period of\ntime. With humans as the carrier of the virus, the spreading process\nnecessarily depends on the their mobility after being infected. Not only in the\nprimary spreading process, but also in the subsequent spreading of the mutant\nvariants, human mobility plays a central role in the dynamics. Therefore, on\none hand travel restrictions of varying degree were imposed and are still being\nimposed, by various countries both nationally and internationally. On the other\nhand, these restrictions have severe fall outs in businesses and livelihood in\ngeneral. Therefore, it is an optimization process, exercised on a global scale,\nwith multiple changing variables. Here we review the techniques and their\neffects on optimization or proposed optimizations of human mobility in\ndifferent scales, carried out by data driven, machine learning and model\napproaches.\n"}
+{"abstract": "  We show that a bounded domain in a Euclidean space is a $W^{1,1}$-extension\ndomain if and only if it is a strong $BV$-extension domain. In the planar case,\nbounded and strong $BV$-extension domains are shown to be exactly those\n$BV$-extension domains for which the set $\\partial\\Omega \\setminus \\bigcup_{i}\n\\overline{\\Omega}_i$ is purely $1$-unrectifiable, where $\\Omega_i$ are the open\nconnected components of $\\mathbb{R}^2\\setminus\\overline{\\Omega}$.\n"}
+{"abstract": "  In this paper, we prove the stability of shear flows of Prandtl type as $\n\\big(U(y/\\sqrt{\\nu}),0\\big)$ for the steady Navier-Stokes equations under a\nnatural spectral assumption on the linearized NS operator. We develop a direct\nenergy method combined with compact method to solve the Orr-Sommerfeld\nequation.\n"}
+{"abstract": "  The soliton resolution for the Harry Dym equation is established for initial\nconditions in weighted Sobolev space $H^{1,1}(\\mathbb{R})$. Combining the\nnonlinear steepest descent method and $\\bar{\\partial}$-derivatives condition,\nwe obtain that when $\\frac{y}{t}<-\\epsilon(\\epsilon>0)$ the long time\nasymptotic expansion of the solution $q(x,t)$ in any fixed cone\n\\begin{equation}\n  C\\left(y_{1}, y_{2}, v_{1}, v_{2}\\right)=\\left\\{(y, t) \\in R^{2} \\mid\ny=y_{0}+v t, y_{0} \\in\\left[y_{1}, y_{2}\\right], v \\in\\left[v_{1},\nv_{2}\\right]\\right\\} \\end{equation} up to an residual error of order\n$\\mathcal{O}(t^{-1})$. The expansion shows the long time asymptotic behavior\ncan be described as an $N(I)$-soliton on discrete spectrum whose parameters are\nmodulated by a sum of localized soliton-soliton interactions as one moves\nthrough the cone and the second term coming from soliton-radiation\ninteractionson on continuous spectrum.\n"}
+{"abstract": "  There are many applications of multiphase flow in important fields such as\nbiological, chemical and power processes. Bubble coalescence is of a\nsignificant importance in simulating multiphase fluid flows. Weber number\n($W_e$), Reynolds number (Re) and collision parameter play important role in\nthe coalescence of bubbles. In the present work, front-tracking method is\napplied to simulate bubble coalescence. Moreover, the results are presented for\ndifferent collision parameters and changes in the coalescence of bubbles are\ndiscussed\n"}
+{"abstract": "  A downconversion receiver employing a switch-based N-path filter with reduced\nharmonic response around the third- and fifth- LO harmonics is presented. The\nN-path filter is placed in a frequency-translation feedback loop that is\neffective at the 3rd and the 5th LO harmonics to mitigate harmonic\ndownconversion. A pulse-width-modulated LO (PWM-LO) clocking scheme is used in\nthe feedback upconverter to reduce the noise injected around the LO harmonic at\nthe input of N-path downconverter. The compression resulting from blockers\naround the 3rd and the 5th LO harmonics is also suppressed as a result of\nreduced harmonic response. Compensation of peak frequency shift of the N-path\nresponse due to parasitic input capacitance is also described.\n"}
+{"abstract": "  I examine the regime of forward scattering of an energetic particle in a\nPlasma medium in thermal equilibrium. Treating the particle as an open quantum\nsystem interacting with a bath, I look at the time evolution of the reduced\ndensity matrix of the system. The kinematic and dynamical time scales that\nemerge can exist in several possible hierarchies which can lead to different\nEFT formulations. I show that in certain hierarchies, it becomes necessary to\naccount for arbitrary number of coherent exchanges between the system and the\nbath going beyond the independent scattering paradigm. Analytic results are\nobtained in certain limits and the formalism is applied for the measurement of\ntransverse momentum broadening of a quark in a Quark Gluon Plasma medium.\n"}
+{"abstract": "  In this paper, we develop an in-memory analog computing (IMAC) architecture\nrealizing both synaptic behavior and activation functions within non-volatile\nmemory arrays. Spin-orbit torque magnetoresistive random-access memory\n(SOT-MRAM) devices are leveraged to realize sigmoidal neurons as well as\nbinarized synapses. First, it is shown the proposed IMAC architecture can be\nutilized to realize a multilayer perceptron (MLP) classifier achieving orders\nof magnitude performance improvement compared to previous mixed-signal and\ndigital implementations. Next, a heterogeneous mixed-signal and mixed-precision\nCPU-IMAC architecture is proposed for convolutional neural networks (CNNs)\ninference on mobile processors, in which IMAC is designed as a co-processor to\nrealize fully-connected (FC) layers whereas convolution layers are executed in\nCPU. Architecture-level analytical models are developed to evaluate the\nperformance and energy consumption of the CPU-IMAC architecture. Simulation\nresults exhibit 6.5% and 10% energy savings for CPU-IMAC based realizations of\nLeNet and VGG CNN models, for MNIST and CIFAR-10 pattern recognition tasks,\nrespectively.\n"}
+{"abstract": "  Few-shot object detection (FSOD) aims to strengthen the performance of novel\nobject detection with few labeled samples. To alleviate the constraint of few\nsamples, enhancing the generalization ability of learned features for novel\nobjects plays a key role. Thus, the feature learning process of FSOD should\nfocus more on intrinsical object characteristics, which are invariant under\ndifferent visual changes and therefore are helpful for feature generalization.\nUnlike previous attempts of the meta-learning paradigm, in this paper, we\nexplore how to enhance object features with intrinsical characteristics that\nare universal across different object categories. We propose a new prototype,\nnamely universal prototype, that is learned from all object categories. Besides\nthe advantage of characterizing invariant characteristics, the universal\nprototypes alleviate the impact of unbalanced object categories. After\nenhancing object features with the universal prototypes, we impose a\nconsistency loss to maximize the agreement between the enhanced features and\nthe original ones, which is beneficial for learning invariant object\ncharacteristics. Thus, we develop a new framework of few-shot object detection\nwith universal prototypes ({FSOD}^{up}) that owns the merit of feature\ngeneralization towards novel objects. Experimental results on PASCAL VOC and MS\nCOCO show the effectiveness of {FSOD}^{up}. Particularly, for the 1-shot case\nof VOC Split2, {FSOD}^{up} outperforms the baseline by 6.8% in terms of mAP.\n"}
+{"abstract": "  The Injury Severity Score (ISS) is a standard aggregate indicator of the\noverall severity of multiple injuries to the human body. This score is\ncalculated by summing the squares of the three highest values of the\nAbbreviated Injury Scale (AIS) grades across six body regions of a trauma\nvictim. Despite its widespread usage over the past four decades, little is\nknown in the (mostly medical) literature on the subject about the axiomatic and\nstatistical properties of this quadratic aggregation score. To bridge this gap,\nthe present paper studies the ISS from the perspective of recent advances in\ndecision science. We demonstrate some statistical and axiomatic properties of\nthe ISS as a multicrtieria aggregation procedure. Our study highlights some\nunintended, undesirable properties that stem from arbitrary choices in its\ndesign and that call lead to bias in its use as a patient triage criterion.\n"}
+{"abstract": "  We consider symmetric second-order differential operators with real\ncoefficients such that the corresponding differential equation is in the limit\ncircle case at infinity. Our goal is to construct the theory of self-adjoint\nrealizations of such operators by an analogy with the case of Jacobi operators.\nWe introduce a new object, the quasiresolvent of the maximal operator, and use\nit to obtain a very explicit formula for the resolvents of all self-adjoint\nrealizations. In particular, this yields a simple representation for the\nCauchy-Stieltjes transforms of the spectral measures playing the role of the\nclassical Nevanlinna formula in the theory of Jacobi operators.\n"}
+{"abstract": "  We postulate that non-perturbative QCD evolution of a single parton in the\nvacuum will develop the long-range collective effects of a multi-parton system,\nreminiscent of those observed in high-energy hadronic or nuclear interactions\nwith large final-state particle multiplicity final-state particles.\nProton-Proton collisions at the Large Hadron Collider showed surprising\nsignatures of a strongly interacting, thermalized quark-gluon plasma, which was\nthought only to form in collisions of large nuclear systems. Another puzzle\nobserved earlier in $e^{+}e^{-}$ collisions is that production yields of\nvarious hadron species appear to follow a thermal-like distribution with a\ncommon temperature. We propose searches for thermal and collective properties\nof a single parton propagating in the vacuum using high multiplicity jets in\nhigh-energy elementary collisions. Several observables are studied using the\nPYTHIA 8 Monte Carlo event generator. Experimental observation of such\nlong-range collectivity will offer a new view of non-perturbative QCD dynamics\nof multi-parton systems at the smallest scales. Absence of any collective\neffect may offer new insights into the role of quantum entanglement in the\nobserved thermal behavior of particle production in high energy collisions.\n"}
+{"abstract": "  Real-world applications of machine learning tools in high-stakes domains are\noften regulated to be fair, in the sense that the predicted target should\nsatisfy some quantitative notion of parity with respect to a protected\nattribute. However, the exact tradeoff between fairness and accuracy with a\nreal-valued target is not entirely clear. In this paper, we characterize the\ninherent tradeoff between statistical parity and accuracy in the regression\nsetting by providing a lower bound on the error of any fair regressor. Our\nlower bound is sharp, algorithm-independent, and admits a simple\ninterpretation: when the moments of the target differ between groups, any fair\nalgorithm has to make an error on at least one of the groups. We further extend\nthis result to give a lower bound on the joint error of any (approximately)\nfair algorithm, using the Wasserstein distance to measure the quality of the\napproximation. With our novel lower bound, we also show that the price paid by\na fair regressor that does not take the protected attribute as input is less\nthan that of a fair regressor with explicit access to the protected attribute.\nOn the upside, we establish the first connection between individual fairness,\naccuracy parity, and the Wasserstein distance by showing that if a regressor is\nindividually fair, it also approximately verifies the accuracy parity, where\nthe gap is given by the Wasserstein distance between the two groups. Inspired\nby our theoretical results, we develop a practical algorithm for fair\nregression through the lens of representation learning, and conduct experiments\non a real-world dataset to corroborate our findings.\n"}
+{"abstract": "  We correct the faulty formulas given in a previous article and we compute the\ndefect group for the Iwasawa $\\lambda$ invariants attached to the S-ramified\nT-decomposed a belian pro-{\\ell}-extensions on the Z{\\ell}-cyclotomic\nextensionof a number field. As a consequence, we extend the results of Itoh,\nMizusawa and Ozaki on tamely ramified Iwasawa modules for the cyclotomic\nZ{\\ell}-extension of abelian fields.\n"}
+{"abstract": "  Chip-firing and rotor-routing are two well-studied examples of Abelian\nnetworks. We study the complexity of their respective reachability problems. We\nshow that the rotor-routing reachability problem is decidable in polynomial\ntime, and we give a simple characterization of when a chip-and-rotor\nconfiguration is reachable from another one. For chip-firing, it has been known\nthat the reachability problem is in P if we have a class of graphs whose period\nlength is polynomial (for example, Eulerian digraphs). Here we show that in the\ngeneral case, chip-firing reachability is hard in the sense that if the\nchip-firing reachability problem were in P for general digraphs, then the\npolynomial hierarchy would collapse to NP.\n"}
+{"abstract": "  We study an in-flight actuator failure recovery problem for a hexrotor UAV.\nThe hexrotor may experience external disturbances and modeling error, which are\naccounted for in the control design and distinguished from an actuator failure.\nA failure of any one actuator occurs during flight and must be identified\nquickly and accurately. This is achieved through the use of a multiple-model,\nmultiple extended high-gain observer (EHGO) based output feedback control\nstrategy. The family of EHGOs are responsible for estimating states,\ndisturbances, and are used to select the appropriate model based on the system\ndynamics after a failure has occurred. The proposed method is theoretically\nanalyzed and validated through simulations and experiments.\n"}
+{"abstract": "  In this short note I restate and simplify the proof of the impossibility of\nprobabilistic induction from Popper (1992). Other proofs are possible (cf.\nPopper (1985)).\n"}
+{"abstract": "  We propose a trust-region method that solves a sequence of linear integer\nprograms to tackle integer optimal control problems regularized with a total\nvariation penalty.\n  The total variation penalty allows us to prove the existence of minimizers of\nthe integer optimal control problem. We introduce a local optimality concept\nfor the problem, which arises from the infinite-dimensional perspective. In the\ncase of a one-dimensional domain of the control function, we prove convergence\nof the iterates produced by our algorithm to points that satisfy first-order\nstationarity conditions for local optimality. We demonstrate the theoretical\nfindings on a computational example.\n"}
+{"abstract": "  In this study we present a dynamical agent-based model to investigate the\ninterplay between the socio-economy of and SEIRS-type epidemic spreading over a\ngeographical area, divided to smaller area districts and further to smallest\narea cells. The model treats the populations of cells and authorities of\ndistricts as agents, such that the former can reduce their economic activity\nand the latter can recommend economic activity reduction both with the overall\ngoal to slow down the epidemic spreading. The agents make decisions with the\naim of attaining as high socio-economic standings as possible relative to other\nagents of the same type by evaluating their standings based on the local and\nregional infection rates, compliance to the authorities' regulations, regional\ndrops in economic activity, and efforts to mitigate the spread of epidemic. We\nfind that the willingness of population to comply with authorities'\nrecommendations has the most drastic effect on the epidemic spreading: periodic\nwaves spread almost unimpeded in non-compliant populations, while in compliant\nones the spread is minimal with chaotic spreading pattern and significantly\nlower infection rates. Health and economic concerns of agents turn out to have\nlesser roles, the former increasing their efforts and the latter decreasing\nthem.\n"}
+{"abstract": "  Recent work has made significant progress on using implicit functions, as a\ncontinuous representation for 3D rigid object shape reconstruction. However,\nmuch less effort has been devoted to modeling general articulated objects.\nCompared to rigid objects, articulated objects have higher degrees of freedom,\nwhich makes it hard to generalize to unseen shapes. To deal with the large\nshape variance, we introduce Articulated Signed Distance Functions (A-SDF) to\nrepresent articulated shapes with a disentangled latent space, where we have\nseparate codes for encoding shape and articulation. We assume no prior\nknowledge on part geometry, articulation status, joint type, joint axis, and\njoint location. With this disentangled continuous representation, we\ndemonstrate that we can control the articulation input and animate unseen\ninstances with unseen joint angles. Furthermore, we propose a Test-Time\nAdaptation inference algorithm to adjust our model during inference. We\ndemonstrate our model generalize well to out-of-distribution and unseen data,\ne.g., partial point clouds and real-world depth images.\n"}
+{"abstract": "  The rapid early spread of COVID-19 in the U.S. was experienced very\ndifferently by different socioeconomic groups and business industries. In this\nstudy, we study aggregate mobility patterns of New York City and Chicago to\nidentify the relationship between the amount of interpersonal contact between\npeople in urban neighborhoods and the disparity in the growth of positive cases\namong these groups. We introduce an aggregate Contact Exposure Index (CEI) to\nmeasure exposure due to this interpersonal contact and combine it with social\ndistancing metrics to show its effect on positive case growth. With the help of\nstructural equations modeling, we find that the effect of exposure on case\ngrowth was consistently positive and that it remained consistently higher in\nlower-income neighborhoods, suggesting a causal path of income on case growth\nvia contact exposure. Using the CEI, schools and restaurants are identified as\nhigh-exposure industries, and the estimation suggests that implementing\nspecific mobility restrictions on these point-of-interest categories are most\neffective. This analysis can be useful in providing insights for government\nofficials targeting specific population groups and businesses to reduce\ninfection spread as reopening efforts continue to expand across the nation.\n"}
+{"abstract": "  This study analyses the actual effect of a representative low-emission zone\n(LEZ) in terms of shifting vehicle registrations towards alternative fuel\ntechnologies and its effectiveness for reducing vehicle fleet CO2 emissions.\nVehicle registration data is combined with real life fuel consumption values on\nindividual vehicle model level, and the impact of the LEZ is then determined\nvia an econometric approach. The increase in alternative fuel vehicles (AFV)\nregistration shares due to the LEZ is found to be significant but fosters\nrather fossil fuel powered AFV and plug-in hybrid electric vehicles than zero\nemission vehicles. This is reflected in the average CO2 emissions of newly\nregistered vehicles, which do not decrease significantly. In consequence, while\nthe LEZ is an effective measure for stimulating the shift towards low emission\nvehicles, the support of non-electric AFV as low emission vehicles jeopardizes\nits effectiveness for decarbonizing the vehicle fleet.\n"}
+{"abstract": "  The Internet of Things (IoT) devices are highly reliant on cloud systems to\nmeet their storage and computational demands. However, due to the remote\nlocation of cloud servers, IoT devices often suffer from intermittent Wide Area\nNetwork (WAN) latency which makes execution of delay-critical IoT applications\ninconceivable. To overcome this, service providers (SPs) often deploy multiple\nfog nodes (FNs) at the network edge that helps in executing offloaded\ncomputations from IoT devices with improved user experience. As the FNs have\nlimited resources, matching IoT services to FNs while ensuring minimum latency\nand energy from an end-user's perspective and maximizing revenue and tasks\nmeeting deadlines from an SP's standpoint is challenging. Therefore in this\npaper, we propose a student project allocation (SPA) based efficient task\noffloading strategy called SPATO that takes into account key parameters from\ndifferent stakeholders. Thorough simulation analysis shows that SPATO is able\nto reduce the offloading energy and latency respectively by 29% and 40% and\nimproves the revenue by 25% with 99.3% of tasks executing within their\ndeadline.\n"}
+{"abstract": "  In the process of decarbonization, the global energy mix is shifting from\nfossil fuels to renewables. To study decarbonization pathways, large-scale\nenergy system models are utilized. These models require accurate data on\nrenewable generation to develop their full potential. Using different data can\nlead to conflicting results and policy advice. In this work, we compare several\ndatasets that are commonly used to study the transition towards highly\nrenewable European power system. We find significant differences between these\ndatasets and cost-difference of about 10% result in the different energy mix.\nWe conclude that much more attention must be paid to the large uncertainties of\nthe input data.\n"}
+{"abstract": "  We propose a novel blocked version of the continuous-time bouncy particle\nsampler of [Bouchard-C\\^ot\\'e et al., 2018] which is applicable to any\ndifferentiable probability density. This alternative implementation is\nmotivated by blocked Gibbs sampling for state space models [Singh et al., 2017]\nand leads to significant improvement in terms of effective sample size per\nsecond, and furthermore, allows for significant parallelization of the\nresulting algorithm. The new algorithms are particularly efficient for latent\nstate inference in high-dimensional state space models, where blocking in both\nspace and time is necessary to avoid degeneracy of MCMC. The efficiency of our\nblocked bouncy particle sampler, in comparison with both the standard\nimplementation of the bouncy particle sampler and the particle Gibbs algorithm\nof Andrieu et al. [2010], is illustrated numerically for both simulated data\nand a challenging real-world financial dataset.\n"}
+{"abstract": "  Squeezed light is a key quantum resource that enables quantum advantages for\nsensing, networking, and computing applications. The scalable generation and\nmanipulation of squeezed light with integrated platforms are highly desired for\nthe development of quantum technology with continuous variables. In this\nletter, we demonstrate squeezed light generation with thin-film lithium niobate\nintegrated photonics. Para-metric down-conversion is realized with quasi-phase\nmatching using ferroelectric domain engineering. With sub-wavelength mode\nconfinement, efficient nonlinear processes can be observed with single-pass\nconfiguration. We measure0.56+-0.09dB quadrature squeezing(~3 dB inferred\non-chip). The single-pass configuration further enables the generation of\nsqueezed light with large spectral bandwidth up to 7 THz. This work represents\na significant step towards the on-chip implementation of continuous-variable\nquantum information processing\n"}
+{"abstract": "  This paper considers the data association problem for multi-target tracking.\nMultiple hypothesis tracking is a popular algorithm for solving this problem\nbut it is NP-hard and is is quite complicated for a large number of targets or\nfor tracking maneuvering targets. To improve tracking performance and enhance\nrobustness, we propose a randomized multiple model multiple hypothesis tracking\nmethod, which has three distinctive advantages. First, it yields a randomized\ndata association solution which maximizes the expectation of the logarithm of\nthe posterior probability and can be solved efficiently by linear programming.\nNext, the state estimation performance is improved by the random coefficient\nmatrices Kalman filter, which mitigates the difficulty introduced by randomized\ndata association, i.e., where the coefficient matrices of the dynamic system\nare random. Third, the probability that the target follows a specific dynamic\nmodel is derived by jointly optimizing the multiple possible models and data\nassociation hypotheses, and it does not require prior mode transition\nprobabilities. Thus, it is more robust for tracking multiple maneuvering\ntargets. Simulations demonstrate the efficiency and superior results of the\nproposed algorithm over interacting multiple model multiple hypothesis\ntracking.\n"}
+{"abstract": "  A new similarity measure for two sets of S-parameters is proposed. It is\nconstructed with the modified Hausdorff distance applied to S-parameter points\nin 3D space with real, imaginary and normalized frequency axes. New\nS-parameters similarity measure facilitates automation of the analysis to\nmeasurement validation, comparison of models and measurements obtained with\ndifferent tools, as well as finding similar S-parameter models or similar\nelements within S-matrices.\n"}
+{"abstract": "  Brain-Computer Interfaces (BCI) based on motor imagery translate mental motor\nimages recognized from the electroencephalogram (EEG) to control commands. EEG\npatterns of different imagination tasks, e.g. hand and foot movements, are\neffectively classified with machine learning techniques using band power\nfeatures. Recently, also Convolutional Neural Networks (CNNs) that learn both\neffective features and classifiers simultaneously from raw EEG data have been\napplied. However, CNNs have two major drawbacks: (i) they have a very large\nnumber of parameters, which thus requires a very large number of training\nexamples; and (ii) they are not designed to explicitly learn features in the\nfrequency domain. To overcome these limitations, in this work we introduce\nSinc-EEGNet, a lightweight CNN architecture that combines learnable band-pass\nand depthwise convolutional filters. Experimental results obtained on the\npublicly available BCI Competition IV Dataset 2a show that our approach\noutperforms reference methods in terms of classification accuracy.\n"}
+{"abstract": "  We study map lattices coupled by collision and show how perturbations of\ntransfer operators associated with the spatially periodic approximation of the\nmodel can be used to extract information about collisions per lattice unit.\nMore precisely, we study a map on a finite box of $L$ sites with periodic\nboundary conditions, coupled by collision. We derive, via a non-trivial first\norder approximation for the leading eigenvalue of the rare event transfer\noperator, a formula for the first collision rate and a corresponding first\nhitting time law. For the former we show that the formula scales at the order\nof $L\\cdot\\varepsilon^2$, where $\\varepsilon$ is the coupling strength, and for\nthe latter, by tracking the $L$ dependency in our arguments, we show that the\nerror in the law is of order $O\\left(C(L)L\\varepsilon^2\\cdot|\\ln\nL\\varepsilon^2|\\right)$ for a specific function $C(L)$. Finally, we derive an\nexplicit formula for the first collision rate per lattice unit.\n"}
+{"abstract": "  Pretrained language models have achieved state-of-the-art performance when\nadapted to a downstream NLP task. However, theoretical analysis of these models\nis scarce and challenging since the pretraining and downstream tasks can be\nvery different. We propose an analysis framework that links the pretraining and\ndownstream tasks with an underlying latent variable generative model of text --\nthe downstream classifier must recover a function of the posterior distribution\nover the latent variables. We analyze head tuning (learning a classifier on top\nof the frozen pretrained model) and prompt tuning in this setting. The\ngenerative model in our analysis is either a Hidden Markov Model (HMM) or an\nHMM augmented with a latent memory component, motivated by long-term\ndependencies in natural language. We show that 1) under certain non-degeneracy\nconditions on the HMM, simple classification heads can solve the downstream\ntask, 2) prompt tuning obtains downstream guarantees with weaker non-degeneracy\nconditions, and 3) our recovery guarantees for the memory-augmented HMM are\nstronger than for the vanilla HMM because task-relevant information is easier\nto recover from the long-term memory. Experiments on synthetically generated\ndata from HMMs back our theoretical findings.\n"}
+{"abstract": "  Densest subgraph detection is a fundamental graph mining problem, with a\nlarge number of applications. There has been a lot of work on efficient\nalgorithms for finding the densest subgraph in massive networks. However, in\nmany domains, the network is private, and returning a densest subgraph can\nreveal information about the network. Differential privacy is a powerful\nframework to handle such settings. We study the densest subgraph problem in the\nedge privacy model, in which the edges of the graph are private. We present the\nfirst sequential and parallel differentially private algorithms for this\nproblem. We show that our algorithms have an additive approximation guarantee.\nWe evaluate our algorithms on a large number of real-world networks, and\nobserve a good privacy-accuracy tradeoff when the network has high density.\n"}
+{"abstract": "  Protein-protein interactions are the basis of many important physiological\nprocesses and are currently promising, yet difficult, targets for drug\ndiscovery. In this context, inhibitor of apoptosis proteins (IAPs)-mediated\ninteractions are pivotal for cancer cell survival; the interaction of the BIR1\ndomain of cIAP2 with TRAF2 was shown to lead the recruitment of cIAPs to the\nTNF receptor, promoting the activation of the NF-\\kappa B survival pathway. In\nthis work, using a combined in silico-in vitro approach, we identified a\ndrug-like molecule, NF023, able to disrupt cIAP2 interaction with TRAF2. We\ndemonstrated in vitro its ability to interfere with the assembly of the\ncIAP2-BIR1/TRAF2 complex and performed a thorough characterization of the\ncompound's mode of action through 248 parallel unbiased molecular dynamics\nsimulations of 300 ns (totaling almost 75 {\\mu}s of all-atom sampling), which\nidentified multiple binding modes to the BIR1 domain of cIAP2 via clustering\nand ensemble docking. NF023 is, thus, a promising protein-protein interaction\ndisruptor, representing a starting point to develop modulators of NF-\\kappa\nB-mediated cell survival in cancer. This study represents a model procedure\nthat shows the use of large-scale molecular dynamics methods to typify\npromiscuous interactors.\n"}
+{"abstract": "  We analysed the shadow cast by charged rotating black hole (BH) in presence\nof perfect fluid dark matter (PFDM). We studied the null geodesic equations and\nobtained the shadow of the charged rotating BH to see the effects of PFDM\nparameter $\\gamma$, charge $Q$ and rotation parameter $a$, and it is noticed\nthat the size as well as the shape of BH shadow is affected due to PFDM\nparameter, charge and rotation parameter. Thus, it is seen that the presence of\ndark matter around a BH affects its spacetime. We also investigated the\ninfluence of all the parameters (PFDM parameter $\\gamma$, BHs charge $Q$ and\nrotational parameter $a$) on effective potential, energy emission by graphical\nrepresentation, and compare all the results with the non rotating case in usual\ngeneral relativity. To this end, we have also explored the effect of PFDM on\nthe deflection angle and the size of Einstein rings.\n"}
+{"abstract": "  The year 2020 will be remembered for two events of global significance: the\nCOVID-19 pandemic and 2020 U.S. Presidential Election. In this chapter, we\nsummarize recent studies using large public Twitter data sets on these issues.\nWe have three primary objectives. First, we delineate epistemological and\npractical considerations when combining the traditions of computational\nresearch and social science research. A sensible balance should be struck when\nthe stakes are high between advancing social theory and concrete, timely\nreporting of ongoing events. We additionally comment on the computational\nchallenges of gleaning insight from large amounts of social media data. Second,\nwe characterize the role of social bots in social media manipulation around the\ndiscourse on the COVID-19 pandemic and 2020 U.S. Presidential Election. Third,\nwe compare results from 2020 to prior years to note that, although bot accounts\nstill contribute to the emergence of echo-chambers, there is a transition from\nstate-sponsored campaigns to domestically emergent sources of distortion.\nFurthermore, issues of public health can be confounded by political\norientation, especially from localized communities of actors who spread\nmisinformation. We conclude that automation and social media manipulation pose\nissues to a healthy and democratic discourse, precisely because they distort\nrepresentation of pluralism within the public sphere.\n"}
+{"abstract": "  We present a new numerical approach for wave induced dynamic fracture. The\nmethod is based on a discontinuous Galerkin approximation of the first-order\nhyperbolic system for elastic waves and a phase-field approximation of brittle\nfracture driven by the maximum tension. The algorithm is staggered in time and\ncombines an implicit midpoint rule for the wave propagation followed by an\nimplicit Euler step for the phase-field evolution. At fracture, the material is\ndegraded, and the waves are reflected at the diffusive interfaces. Two and\nthree-dimensional examples demonstrate the advantages of the proposed method\nfor the computation of crack growth and spalling initiated by reflected and\nsuperposed waves.\n"}
+{"abstract": "  Experimental measurements in deep-inelastic scattering and lepton-pair\nproduction on deuterium targets play an important role in the flavor separation\nof $u$ and $d$ (anti)quarks in global QCD analyses of the parton distribution\nfunctions (PDFs) of the nucleon. We investigate the impact of theoretical\ncorrections accounting for the light-nuclear structure of the deuteron upon the\nfitted $u, d$-quark, gluon, and other PDFs in the CJ15 and CT18 families of\nnext-to-leading order CTEQ global analyses. The investigation is done using the\n$L_2$ sensitivity statistical method, which provides a common metric to\nquantify the strength of experimental constraints on various PDFs and ratios of\nPDFs in the two distinct fitting frameworks. Using the $L_2$ sensitivity and\nother approaches, we examine the compatibility of deuteron data sets with other\nfitted experiments under varied implementations of the deuteron corrections. We\nfind that freely-fitted deuteron corrections modify the PDF uncertainty at\nlarge momentum fractions and will be relevant for future PDFs affecting\nelectroweak precision measurements.\n"}
+{"abstract": "  The Carina Nebula harbors a large population of high-mass stars, including at\nleast 75 O-type and Wolf-Rayet stars, but the current census is not complete\nsince further high-mass stars may be hidden in or behind the dense dark clouds\nthat pervade the association. With the aim of identifying optically obscured O-\nand early B-type stars in the Carina Nebula, we performed the first infrared\nspectroscopic study of stars in the optically obscured stellar cluster Tr\n16-SE, located behind a dark dust lane south of eta Car. We used the\nintegral-field spectrograph KMOS at the ESO VLT to obtain H- and K-band spectra\nwith a resolution of R sim 4000 (Delta lambda sim 5 A) for 45 out of the 47\npossible OB candidate stars in Tr 16-SE, and we derived spectral types for\nthese stars. We find 15 stars in Tr 16-SE with spectral types between O5 and B2\n(i.e., high-mass stars with M >= 8 Msun, only two of which were known before.\nAn additional nine stars are classified as (Ae)Be stars (i.e.,\nintermediate-mass pre-main-sequence stars), and most of the remaining targets\nshow clear signatures of being late-type stars and are thus most likely\nforeground stars or background giants unrelated to the Carina Nebula. Our\nestimates of the stellar luminosities suggest that nine of the 15 O- and early\nB-type stars are members of Tr 16-SE, whereas the other six seem to be\nbackground objects. Our study increases the number of spectroscopically\nidentified high-mass stars (M >= 8 Msun) in Tr 16-SE from two to nine and shows\nthat Tr 16-SE is one of the larger clusters in the Carina Nebula. Our\nidentification of three new stars with spectral types between O5 and O7 and\nfour new stars with spectral types O9 to B1 significantly increases the number\nof spectroscopically identified O-type stars in the Carina Nebula.\n"}
+{"abstract": "  Probabilistic deep learning is deep learning that accounts for uncertainty,\nboth model uncertainty and data uncertainty. It is based on the use of\nprobabilistic models and deep neural networks. We distinguish two approaches to\nprobabilistic deep learning: probabilistic neural networks and deep\nprobabilistic models. The former employs deep neural networks that utilize\nprobabilistic layers which can represent and process uncertainty; the latter\nuses probabilistic models that incorporate deep neural network components which\ncapture complex non-linear stochastic relationships between the random\nvariables. We discuss some major examples of each approach including Bayesian\nneural networks and mixture density networks (for probabilistic neural\nnetworks), and variational autoencoders, deep Gaussian processes and deep mixed\neffects models (for deep probabilistic models). TensorFlow Probability is a\nlibrary for probabilistic modeling and inference which can be used for both\napproaches of probabilistic deep learning. We include its code examples for\nillustration.\n"}
+{"abstract": "  The scale of small-field inflation cannot be constrained via primordial\ngravitational waves through measurement of tensor-to-scalar ratio $r$. In this\nstudy, I show that if cosmic strings are produced after symmetry breaking at\nthe end of hilltop supernatural inflation, this small-field inflation model can\nbe tested through the production of gravitational waves from cosmic strings.\nFuture experiments of gravitational wave detectors will determine or further\nconstrain the parameter space in the model.\n"}
+{"abstract": "  In this work, the structural, electrical, and optical properties of bilayer\nSiX (X= N, P, As, and Sb) are studied using density functional theory (DFT).\nFive different stacking orders are considered for every compound and their\nstructural properties are presented. The band structure of these materials\ndemonstrates that they are indirect semiconductors. The out-of-plane strain has\nbeen applied to tune the bandgap and its electrical properties. The bandgap\nincreases with tensile strain, whereas, compressive strain leads to\nsemiconductor-to-metal transition. The sensitivity of the bandgap to the\npressure is investigated and bilayer SiSb demonstrates the highest bandgap\nsensitivity to the pressure. These structures exhibit Mexican hat-like valence\nband dispersion that can be approved by a singularity in the density of states.\nThe Mexican-hat coefficient can be tuned by out-of-plane strain. Optical\nabsorption of these compounds shows that the second and lower valence bands due\nto the high density of states display a higher contribution to optical\ntransitions.\n"}
+{"abstract": "  Signal propagation in an optical fiber can be described by the nonlinear\nSchr\\\"odinger equation (NLSE). The NLSE has no known closed-form solution,\nmostly due to the interaction of dispersion and nonlinearities. In this paper,\nwe present a novel closed-form approximate model for the nonlinear optical\nchannel, with applications to passive optical networks. The proposed model is\nderived using logarithmic perturbation in the frequency domain on the\ngroup-velocity dispersion (GVD) parameter of the NLSE. The model can be seen as\nan improvement of the recently proposed regular perturbation (RP) on the GVD\nparameter. RP and logarithmic perturbation (LP) on the nonlinear coefficient\nhave already been studied in the literature, and are hereby compared with RP on\nthe GVD parameter and the proposed LP model. As an application of the model, we\nfocus on passive optical networks. For a 20 km PON at 10 Gbaud, the proposed\nmodel improves upon LP on the nonlinear coefficient by 1.5 dB. For the same\nsystem, a detector based on the proposed LP model reduces the uncoded\nbit-error-rate by up to 5.4 times at the same input power or reduces the input\npower by 0.4 dB at the same information rate.\n"}
+{"abstract": "  Rhombohedral dense forms of carbon, rh-C2 (or hexagonal h-C6), and boron\nnitride, rh-BN (or hexagonal h-B3N3), are derived from rhombohedral 3R graphite\nbased on original crystal chemistry scheme backed with full cell geometry\noptimization to minimal energy ground state computations within the quantum\ndensity functional theory. Considering throughout hexagonal settings featuring\nextended lattices, the calculation of the hexagonal set of elastic constants,\nprovide results of large bulk moduli i.e. B0(rh-C2) = 438 GPa close to that of\ndiamond, and B0(rh-BN) = 369 GPa close to that of cubic BN. The hardness\nassessment in the framework of three contemporary models enables both phases to\nbe considered as ultra-hard. From the electronic band structures calculated in\nthe hexagonal Brillouin zones, 3R graphite is a small-gap semiconductor,\noppositely to rh-C2 that is characterized by a large band gap close to 5 eV, as\nwell as the two BN phases.\n"}
+{"abstract": "  Currently, there are more than a dozen Russian-language corpora for sentiment\nanalysis, differing in the source of the texts, domain, size, number and ratio\nof sentiment classes, and annotation method. This work examines publicly\navailable Russian-language corpora, presents their qualitative and quantitative\ncharacteristics, which make it possible to get an idea of the current landscape\nof the corpora for sentiment analysis. The ranking of corpora by annotation\nquality is proposed, which can be useful when choosing corpora for training and\ntesting. The influence of the training dataset on the performance of sentiment\nanalysis is investigated based on the use of the deep neural network model\nBERT. The experiments with review corpora allow us to conclude that on average\nthe quality of models increases with an increase in the number of training\ncorpora. For the first time, quality scores were obtained for the corpus of\nreviews of ROMIP seminars based on the BERT model. Also, the study proposes the\ntask of the building a universal model for sentiment analysis.\n"}
+{"abstract": "  An LBYL (`Look Before You Leap') Network is proposed for end-to-end trainable\none-stage visual grounding. The idea behind LBYL-Net is intuitive and\nstraightforward: we follow a language's description to localize the target\nobject based on its relative spatial relation to `Landmarks', which is\ncharacterized by some spatial positional words and some descriptive words about\nthe object. The core of our LBYL-Net is a landmark feature convolution module\nthat transmits the visual features with the guidance of linguistic description\nalong with different directions. Consequently, such a module encodes the\nrelative spatial positional relations between the current object and its\ncontext. Then we combine the contextual information from the landmark feature\nconvolution module with the target's visual features for grounding. To make\nthis landmark feature convolution light-weight, we introduce a dynamic\nprogramming algorithm (termed dynamic max pooling) with low complexity to\nextract the landmark feature. Thanks to the landmark feature convolution\nmodule, we mimic the human behavior of `Look Before You Leap' to design an\nLBYL-Net, which takes full consideration of contextual information. Extensive\nexperiments show our method's effectiveness in four grounding datasets.\nSpecifically, our LBYL-Net outperforms all state-of-the-art two-stage and\none-stage methods on ReferitGame. On RefCOCO and RefCOCO+, Our LBYL-Net also\nachieves comparable results or even better results than existing one-stage\nmethods.\n"}
+{"abstract": "  When faced with learning challenging new tasks, humans often follow sequences\nof steps that allow them to incrementally build up the necessary skills for\nperforming these new tasks. However, in machine learning, models are most often\ntrained to solve the target tasks directly.Inspired by human learning, we\npropose a novel curriculum learning approach which decomposes challenging tasks\ninto sequences of easier intermediate goals that are used to pre-train a model\nbefore tackling the target task. We focus on classification tasks, and design\nthe intermediate tasks using an automatically constructed label hierarchy. We\ntrain the model at each level of the hierarchy, from coarse labels to fine\nlabels, transferring acquired knowledge across these levels. For instance, the\nmodel will first learn to distinguish animals from objects, and then use this\nacquired knowledge when learning to classify among more fine-grained classes\nsuch as cat, dog, car, and truck. Most existing curriculum learning algorithms\nfor supervised learning consist of scheduling the order in which the training\nexamples are presented to the model. In contrast, our approach focuses on the\noutput space of the model. We evaluate our method on several established\ndatasets and show significant performance gains especially on classification\nproblems with many labels. We also evaluate on a new synthetic dataset which\nallows us to study multiple aspects of our method.\n"}
+{"abstract": "  Given a monoid $S$ with $E$ any non-empty subset of its idempotents, we\npresent a novel one-sided version of idempotent completion we call left\n$E$-completion. In general, the construction yields a one-sided variant of a\nsmall category called a constellation by Gould and Hollings. Under certain\nconditions, this constellation is inductive, meaning that its partial\nmultiplication may be extended to give a left restriction semigroup, a type of\nunary semigroup whose unary operation models domain. We study the properties of\nthose pairs $S,E$ for which this happens, and characterise those left\nrestriction semigroups that arise as such left $E$-completions of their monoid\nof elements having domain $1$. As first applications, we decompose the left\nrestriction semigroup of partial functions on the set $X$ and the right\nrestriction semigroup of left total partitions on $X$ as left and right\n$E$-completions respectively of the transformation semigroup $T_X$ on $X$, and\ndecompose the left restriction semigroup of binary relations on $X$ under\ndemonic composition as a left $E$-completion of the left-total binary\nrelations. In many cases, including these three examples, the construction\nembeds in a semigroup Zappa-Sz\\'{e}p product.\n"}
+{"abstract": "  Synthesizing data for semantic parsing has gained increasing attention\nrecently. However, most methods require handcrafted (high-precision) rules in\ntheir generative process, hindering the exploration of diverse unseen data. In\nthis work, we propose a generative model which features a (non-neural) PCFG\nthat models the composition of programs (e.g., SQL), and a BART-based\ntranslation model that maps a program to an utterance. Due to the simplicity of\nPCFG and pre-trained BART, our generative model can be efficiently learned from\nexisting data at hand. Moreover, explicitly modeling compositions using PCFG\nleads to a better exploration of unseen programs, thus generate more diverse\ndata. We evaluate our method in both in-domain and out-of-domain settings of\ntext-to-SQL parsing on the standard benchmarks of GeoQuery and Spider,\nrespectively. Our empirical results show that the synthesized data generated\nfrom our model can substantially help a semantic parser achieve better\ncompositional and domain generalization.\n"}
+{"abstract": "  Detecting pedestrians is a crucial task in autonomous driving systems to\nensure the safety of drivers and pedestrians. The technologies involved in\nthese algorithms must be precise and reliable, regardless of environment\nconditions. Relying solely on RGB cameras may not be enough to recognize road\nenvironments in situations where cameras cannot capture scenes properly. Some\napproaches aim to compensate for these limitations by combining RGB cameras\nwith TOF sensors, such as LIDARs. However, there are few works that address\nthis problem using exclusively the 3D geometric information provided by LIDARs.\nIn this paper, we propose a PointNet++ based architecture to detect pedestrians\nin dense 3D point clouds. The aim is to explore the potential contribution of\ngeometric information alone in pedestrian detection systems. We also present a\nsemi-automatic labeling system that transfers pedestrian and non-pedestrian\nlabels from RGB images onto the 3D domain. The fact that our datasets have RGB\nregistered with point clouds enables label transferring by back projection from\n2D bounding boxes to point clouds, with only a light manual supervision to\nvalidate results. We train PointNet++ with the geometry of the resulting 3D\nlabelled clusters. The evaluation confirms the effectiveness of the proposed\nmethod, yielding precision and recall values around 98%.\n"}
+{"abstract": "  Being generated, the relic neutrino background contained equal fractions of\nelectron $\\nu_e$, muon $\\nu_\\mu$, and taon $\\nu_\\tau$ neutrinos. We show that\nthe gravitational field of our Galaxy and other nearby cosmic objects changes\nthis composition near the Solar System, enriching it with the heaviest neutrino\n$nu_3$. This mass state is almost free of the electron component (only $\\sim\n2\\%$ of $\\nu_e$) and contains more muon component than the tau one. As a\nresult, the relic background becomes enriched with taon and particularly muon\nneutrinos. The electron relic neutrinos are the rarest for a terrestrial\nobserver: instead of $1/3$, the relic background may contain only $\\gtrsim\n20\\%$ of them.\n"}
+{"abstract": "  If $(X, \\le_X)$ is a partially ordered set satisfying certain necessary\nconditions for $X$ to be order-isomorphic to the spectrum of a Noetherian\ndomain of dimension two, we describe a new poset $(\\text{str } X,\n\\le_{\\text{str } X})$ that completely determines $X$ up to isomorphism. The\norder relation $\\le_{\\text{str } X}$ imposed on $\\text{str } X$ is modeled\nafter R. Wiegand's well-known \"P5\" condition that can be used to determine when\na given partially ordered set $(U, \\le_U)$ of a certain type is\norder-isomorphic to $(\\text{Spec } \\mathbb Z[x], \\subseteq).$\n"}
+{"abstract": "  We demonstrate a new approach to supercontinuum generation and\ncarrier-envelope-offset detection in dispersion-engineered nanophotonic\nwaveguides based on saturated second-harmonic generation of femtosecond pulses.\nIn contrast with traditional approaches based on self-phase modulation, this\ntechnique simultaneously broadens both harmonics by generating rapid amplitude\nmodulations of the field envelopes. The generated supercontinuum produces\ncoherent carrier-envelope-offset beatnotes in the overlap region that remain in\nphase across 100's of nanometers of bandwidth while requiring $<$10 picojoules\nof pulse energy.\n"}
+{"abstract": "  Egocentric segmentation has attracted recent interest in the computer vision\ncommunity due to their potential in Mixed Reality (MR) applications. While most\nprevious works have been focused on segmenting egocentric human body parts\n(mainly hands), little attention has been given to egocentric objects. Due to\nthe lack of datasets of pixel-wise annotations of egocentric objects, in this\npaper we contribute with a semantic-wise labeling of a subset of 2124 images\nfrom the RGB-D THU-READ Dataset. We also report benchmarking results using\nThundernet, a real-time semantic segmentation network, that could allow future\nintegration with end-to-end MR applications.\n"}
+{"abstract": "  The connection of single-phase microgrids (MG) and loads to three-phase MGs\ncreates power quality problems such as unbalanced voltage and voltage rise at\nthe point of common coupling (PCC) of the MGs. In this paper, a modified\nreverse droop control (MRDC) scheme in the Energy Storage System (ESS) is\nproposed to improve the three-phase PCC voltage quality in multi-microgrids\n(MMG). The MRDC consists of a reactive power compensator (RPC) and a voltage\ncompensator. The controller regulates the reactive power and voltage unbalance\nof the MMG by using the reactive power produced by the ESS. The effectiveness\nof this proposed scheme is verified in real-time simulation using the Opal-RT\nOP5600 real-time simulator. The voltage unbalance factor (VUF) at the PCC is\ndecreased from 3.6 percent to 0.25 percent, while the reactive power is reduced\nsignificantly at the single-phase load.\n"}
+{"abstract": "  Generative Adversarial Networks (GANs) are machine learning networks based\naround creating synthetic data. Voice Conversion (VC) is a subset of voice\ntranslation that involves translating the paralinguistic features of a source\nspeaker to a target speaker while preserving the linguistic information. The\naim of non-parallel conditional GANs for VC is to translate an acoustic speech\nfeature sequence from one domain to another without the use of paired data. In\nthe study reported here, we investigated the interpretability of\nstate-of-the-art implementations of non-parallel GANs in the domain of VC. We\nshow that the learned representations in the repeating layers of a particular\nGAN architecture remain close to their original random initialised parameters,\ndemonstrating that it is the number of repeating layers that is more\nresponsible for the quality of the output. We also analysed the learned\nrepresentations of a model trained on one particular dataset when used during\ntransfer learning on another dataset. This showed extremely high levels of\nsimilarity across the entire network. Together, these results provide new\ninsight into how the learned representations of deep generative networks change\nduring learning and the importance in the number of layers.\n"}
+{"abstract": "  Time-resolved mapping of lattice dynamics in real- and momentum-space is\nessential to understand better several ubiquitous phenomena such as heat\ntransport, displacive phase transition, thermal conductivity, and many more. In\nthis regard, time-resolved diffraction and microscopy methods are employed to\nimage the induced lattice dynamics within a pump-probe configuration. In this\nwork, we demonstrate that inelastic scattering methods, with the aid of\ntheoretical simulation, are competent to provide similar information as one\ncould obtain from the time-resolved diffraction and imaging measurements. To\nillustrate the robustness of the proposed method, our simulated result of\nlattice dynamics in germanium is in excellent agreement with the time-resolved\nx-ray diffuse scattering measurement performed using x-ray free-electron laser.\nFor a given inelastic scattering data in energy and momentum space, the\nproposed method is useful to image in-situ lattice dynamics under different\nenvironmental conditions of temperature, pressure, and magnetic field.\nMoreover, the technique will profoundly impact where time-resolved diffraction\nwithin the pump-probe setup is not feasible, for instance, in inelastic neutron\nscattering.\n"}
+{"abstract": "  In clinical trials, there often exist multiple historical studies for the\nsame or related treatment investigated in the current trial. Incorporating\nhistorical data in the analysis of the current study is of great importance, as\nit can help to gain more information, improve efficiency, and provide a more\ncomprehensive evaluation of treatment. Enlightened by the unit information\nprior (UIP) concept in the reference Bayesian test, we propose a new\ninformative prior called UIP from an information perspective that can\nadaptively borrow information from multiple historical datasets. We consider\nboth binary and continuous data and also extend the new UIP methods to linear\nregression settings. Extensive simulation studies demonstrate that our method\nis comparable to other commonly used informative priors, while the\ninterpretation of UIP is intuitive and its implementation is relatively easy.\nOne distinctive feature of UIP is that its construction only requires summary\nstatistics commonly reported in the literature rather than the patient-level\ndata. By applying our UIP methods to phase III clinical trials for\ninvestigating the efficacy of memantine in Alzheimer's disease, we illustrate\nits ability of adaptively borrowing information from multiple historical\ndatasets in the real application.\n"}
+{"abstract": "  The initial period of vaccination shows strong heterogeneity between\ncountries' vaccinations rollout, both in the terms of the start of the\nvaccination process and in the dynamics of the number of people that are\nvaccinated. A predominant thesis in the ongoing debate on the drivers of this\nobserved heterogeneity is that a key determinant of the swift and extensive\nvaccine rollout is state capacity. Here, we utilize two measures that quantify\ndifferent aspects of the state capacity: i) the external capacity (measured\nthrough the soft power and the economic power of the country) and ii) the\ninternal capacity (measured via the country's government effectiveness) and\ninvestigate their relationship with the coronavirus vaccination outcome in the\ninitial period (up to 30th January 2021). By using data on 189 countries and a\ntwo-step Heckman approach, we find that the economic power of the country and\nits soft power are robust determinants of whether a country has started with\nthe vaccination process. In addition, the government effectiveness is a key\nfactor that determines vaccine roll-out. Altogether, our findings are in line\nwith the hypothesis that state capacity determines the observed heterogeneity\nbetween countries in the initial period of COVID-19 vaccines rollout.\n"}
+{"abstract": "  Background: Due to the finite size of the development sample, predicted\nprobabilities from a risk prediction model are inevitably uncertain. We apply\nValue of Information methodology to evaluate the decision-theoretic\nimplications of prediction uncertainty.\n  Methods: Adopting a Bayesian perspective, we extend the definition of the\nExpected Value of Perfect Information (EVPI) from decision analysis to net\nbenefit calculations in risk prediction. In the context of model development,\nEVPI is the expected gain in net benefit by using the correct predictions as\nopposed to predictions from a proposed model. We suggest bootstrap methods for\nsampling from the posterior distribution of predictions for EVPI calculation\nusing Monte Carlo simulations. In a case study, we used subsets of data of\nvarious sizes from a clinical trial for predicting mortality after myocardial\ninfarction to show how EVPI changes with sample size.\n  Results: With a sample size of 1,000 and at the pre-specified threshold of 2%\non predicted risks, the gain in net benefit by using the proposed and the\ncorrect models were 0.0006 and 0.0011, respectively, resulting in an EVPI of\n0.0005 and a relative EVPI of 87%. EVPI was zero only at unrealistically high\nthresholds (>85%). As expected, EVPI declined with larger samples. We summarize\nan algorithm for incorporating EVPI calculations into the commonly used\nbootstrap method for optimism correction.\n  Conclusion: Value of Information methods can be applied to explore\ndecision-theoretic consequences of uncertainty in risk prediction and can\ncomplement inferential methods when developing risk prediction models. R code\nfor implementing this method is provided.\n"}
+{"abstract": "  Specific topological excitations of energetically stable \"core-and-mantle\"\nconfigurations of trapped two-component immiscible Bose-Einstein condensates\nare studied numerically within the coupled Gross-Pitaevskii equations.\nNon-stationary long-lived coherent structures, that consist of several quantum\nvortex filaments penetrating the \"mantle\" from outside to inside and vice-versa\nand demonstrate quite nontrivial dynamics, are observed in simulations for the\nfirst time. The ends of filaments can remain attached to the interface between\nthe \"mantle\" and the \"core\" if the latter is large enough while the surface\ntension is not small. The shapes of such \"bubbles\" are strongly affected by the\nvortices and sometimes are far from being spherical.\n"}
+{"abstract": "  We extend previous work concerning rest-frame partial-wave mixing in\nHamiltonian effective field theory to both elongated and moving systems, where\ntwo particles are in a periodic elongated cube or have nonzero total momentum,\nrespectively. We also consider the combination of the two systems when\ndirections of the elongation and the moving momentum are aligned. This\nextension should also be applicable in any Hamiltonian formalism. As a\ndemonstration, we analyze lattice QCD results for the spectrum of an isospin-2\n$\\pi\\pi$ scattering system and determine the $s$, $d$, and $g$ partial-wave\nscattering information. The inclusion of lattice simulation results from moving\nframes significantly improves the uncertainty in the scattering information.\n"}
+{"abstract": "  We study Smith-Purcell radiation from a conducting grating generated by a\nvortex electron with an orbital angular momentum $\\ell \\hbar$, described as a\ngeneralized Laguerre-Gaussian packet, which has an intrinsic magnetic dipole\nmoment and an electric quadrupole moment. By using a multipole expansion of the\nelectromagnetic field of such an electron, we employ a generalized\nsurface-current method, applicable for a wide range of parameters. The radiated\nenergy contains contributions from the charge, from the magnetic moment, and\nfrom the electric quadrupole moment, as well as from their interference. The\nquadrupole contribution grows as the packet spreads while propagating, and it\nis enhanced for large $\\ell$. In contrast to the linear growth of the radiation\nintensity from the charge with a number of strips $N$, the quadrupole\ncontribution reveals an $N^3$ dependence, which puts a limit on the maximal\ngrating length for which the radiation losses stay small. We study\nspectral-angular distributions of the Smith-Purcell radiation both analytically\nand numerically and demonstrate that the electron's vorticity can give rise to\ndetectable effects for non-relativistic and moderately relativistic electrons.\nOn a practical side, preparing the incoming electron's state in a form of a\nnon-Gaussian packet with a quadrupole moment -- such as the vortex electron, an\nAiry beam, a Schr\\\"odinger cat state, and so on -- one can achieve quantum\nenhancement of the radiation power compared to the classical linear regime.\nSuch an enhancement would be a hallmark of a previously unexplored quantum\nregime of radiation, in which non-Gaussianity of the packet influences the\nradiation properties much stronger than the quantum recoil.\n"}
+{"abstract": "  The QED initial state corrections are calculated to the forward-backward\nasymmetry for $e^+e^- \\rightarrow \\gamma^*/{Z^{0}}^*$ in the leading\nlogarithmic approximation to $O(\\alpha^6 L^6)$ extending the known corrections\nup to $O(\\alpha^2 L^2)$ in analytic form. We use the method of massive on-shell\noperator matrix elements and present the radiators both in Mellin-$N$ and\nmomentum fraction $z$-space. Numerical results are presented for various\nenergies around the $Z$-peak by also including energy cuts. These corrections\nare of relevance for the precision measurements at the FCC$\\_$ee.\n"}
+{"abstract": "  Based on a recent work on traveling waves in spatially nonlocal\nreaction-diffusion equations, we investigate the existence of traveling fronts\nin reaction-diffusion equations with a memory term. We will explain how such\nmemory terms can arise from reduction of reaction-diffusion systems if the\ndiffusion constants of the other species can be neglected. In particular, we\nshow that two-scale homogenization of spatially periodic systems can induce\nspatially homogeneous systems with temporal memory.\n  The existence of fronts is proved using comparison principles as well as a\nreformulation trick involving an auxiliary speed that allows us to transform\nmemory terms into spatially nonlocal terms. Deriving explicit bounds and\nmonotonicity properties of the wave speed of the arising travelingfront, we are\nable to establish the existence of true traveling fronts for the original\nproblem with memory. Our results are supplemented by numerical simulations.\n"}
+{"abstract": "  In recent years, constant applied potential molecular dynamics has allowed to\nstudy the structure and dynamics of the electrochemical double-layer of a large\nvariety of nanoscale capacitors. Nevertheless it remained impossible to\nsimulate polarized electrodes at fixed total charge. Here we show that\ncombining a constant potential electrode with a finite electric displacement\nfills this gap by allowing to simulate open circuit conditions. The method can\nbe extended by applying an electric displacement ramp to perform computational\namperometry experiments at different current intensities. As in experiments,\nthe full capacitance of the system is obtained at low intensity, but this\nquantity decreases when the applied ramp becomes too fast with respect to the\nmicroscopic dynamics of the liquid.\n"}
+{"abstract": "  The main difficulty that arises in the analysis of most machine learning\nalgorithms is to handle, analytically and numerically, a large number of\ninteracting random variables. In this Ph.D manuscript, we revisit an approach\nbased on the tools of statistical physics of disordered systems. Developed\nthrough a rich literature, they have been precisely designed to infer the\nmacroscopic behavior of a large number of particles from their microscopic\ninteractions. At the heart of this work, we strongly capitalize on the deep\nconnection between the replica method and message passing algorithms in order\nto shed light on the phase diagrams of various theoretical models, with an\nemphasis on the potential differences between statistical and algorithmic\nthresholds. We essentially focus on synthetic tasks and data generated in the\nteacher-student paradigm. In particular, we apply these mean-field methods to\nthe Bayes-optimal analysis of committee machines, to the worst-case analysis of\nRademacher generalization bounds for perceptrons, and to empirical risk\nminimization in the context of generalized linear models. Finally, we develop a\nframework to analyze estimation models with structured prior informations,\nproduced for instance by deep neural networks based generative models with\nrandom weights.\n"}
+{"abstract": "  Influence competition finds its significance in many applications, such as\nmarketing, politics and public events like COVID-19. Existing work tends to\nbelieve that the stronger influence will always win and dominate nearly the\nwhole network, i.e., \"winner takes all\". However, this finding somewhat\ncontradicts with our common sense that many competing products are actually\ncoexistent, e.g., Android vs. iOS. This contradiction naturally raises the\nquestion: will the winner take all?\n  To answer this question, we make a comprehensive study into influence\ncompetition by identifying two factors frequently overlooked by prior art: (1)\nthe incomplete observation of real diffusion networks; (2) the existence of\ninformation overload and its impact on user behaviors. To this end, we attempt\nto recover possible diffusion links based on user similarities, which are\nextracted by embedding users into a latent space. Following this, we further\nderive the condition under which users will be overloaded, and formulate the\ncompeting processes where users' behaviors differ before and after information\noverload. By establishing the explicit expressions of competing dynamics, we\ndisclose that information overload acts as the critical \"boundary line\", before\nwhich the \"winner takes all\" phenomenon will definitively occur, whereas after\ninformation overload the share of influences gradually stabilizes and is\njointly affected by their initial spreading conditions, influence powers and\nthe advent of overload. Numerous experiments are conducted to validate our\ntheoretical results where favorable agreement is found. Our work sheds light on\nthe intrinsic driving forces behind real-world dynamics, thus providing useful\ninsights into effective information engineering.\n"}
+{"abstract": "  With the increasing adoption of private blockchain platforms, consortia\noperating in various sectors such as trade, finance, logistics, etc., are\nbecoming common. Despite having the benefits of a completely decentralized\narchitecture which supports transparency and distributed control, existing\nprivate blockchains limit the data, assets, and processes within its closed\nboundary, which restricts secure and verifiable service provisioning to the\nend-consumers. Thus, platforms such as e-commerce with multiple sellers or\ncloud federation with a collection of cloud service providers cannot be\ndecentralized with the existing blockchain platforms. This paper proposes a\ndecentralized gateway architecture interfacing private blockchain with\nend-users by leveraging the unique combination of public and private blockchain\nplatforms through interoperation. Through the use case of decentralized cloud\nfederations, we have demonstrated the viability of the solution. Our testbed\nimplementation with Ethereum and Hyperledger Fabric, with three service\nproviders, shows that such consortium can operate within an acceptable response\nlatency while scaling up to 64 parallel requests per second for cloud\ninfrastructure provisioning. Further analysis over the Mininet emulation\nplatform indicates that the platform can scale well with minimal impact over\nthe latency as the number of participating service providers increases.\n"}
+{"abstract": "  While Machine Comprehension (MC) has attracted extensive research interests\nin recent years, existing approaches mainly belong to the category of Machine\nReading Comprehension task which mines textual inputs (paragraphs and\nquestions) to predict the answers (choices or text spans). However, there are a\nlot of MC tasks that accept audio input in addition to the textual input, e.g.\nEnglish listening comprehension test. In this paper, we target the problem of\nAudio-Oriented Multimodal Machine Comprehension, and its goal is to answer\nquestions based on the given audio and textual information. To solve this\nproblem, we propose a Dynamic Inter- and Intra-modality Attention (DIIA) model\nto effectively fuse the two modalities (audio and textual). DIIA can work as an\nindependent component and thus be easily integrated into existing MC models.\nMoreover, we further develop a Multimodal Knowledge Distillation (MKD) module\nto enable our multimodal MC model to accurately predict the answers based only\non either the text or the audio. As a result, the proposed approach can handle\nvarious tasks including: Audio-Oriented Multimodal Machine Comprehension,\nMachine Reading Comprehension and Machine Listening Comprehension, in a single\nmodel, making fair comparisons possible between our model and the existing\nunimodal MC models. Experimental results and analysis prove the effectiveness\nof the proposed approaches. First, the proposed DIIA boosts the baseline models\nby up to 21.08% in terms of accuracy; Second, under the unimodal scenarios, the\nMKD module allows our multimodal MC model to significantly outperform the\nunimodal models by up to 18.87%, which are trained and tested with only audio\nor textual data.\n"}
+{"abstract": "  Geometry problem solving has attracted much attention in the NLP community\nrecently. The task is challenging as it requires abstract problem understanding\nand symbolic reasoning with axiomatic knowledge. However, current datasets are\neither small in scale or not publicly available. Thus, we construct a new\nlarge-scale benchmark, Geometry3K, consisting of 3,002 geometry problems with\ndense annotation in formal language. We further propose a novel geometry\nsolving approach with formal language and symbolic reasoning, called\nInterpretable Geometry Problem Solver (Inter-GPS). Inter-GPS first parses the\nproblem text and diagram into formal language automatically via rule-based text\nparsing and neural object detecting, respectively. Unlike implicit learning in\nexisting methods, Inter-GPS incorporates theorem knowledge as conditional rules\nand performs symbolic reasoning step by step. Also, a theorem predictor is\ndesigned to infer the theorem application sequence fed to the symbolic solver\nfor the more efficient and reasonable searching path. Extensive experiments on\nthe Geometry3K and GEOS datasets demonstrate that Inter-GPS achieves\nsignificant improvements over existing methods. The project with code and data\nis available at https://lupantech.github.io/inter-gps.\n"}
+{"abstract": "  Heterogeneous multi-task learning (HMTL) is an important topic in multi-task\nlearning (MTL). Most existing HMTL methods usually solve either scenario where\nall tasks reside in the same input (feature) space yet unnecessarily the\nconsistent output (label) space or scenario where their input (feature) spaces\nare heterogeneous while the output (label) space is consistent. However, to the\nbest of our knowledge, there is limited study on twofold heterogeneous MTL\n(THMTL) scenario where the input and the output spaces are both inconsistent or\nheterogeneous. In order to handle this complicated scenario, in this paper, we\ndesign a simple and effective multi-task adaptive learning (MTAL) network to\nlearn multiple tasks in such THMTL setting. Specifically, we explore and\nutilize the inherent relationship between tasks for knowledge sharing from\nsimilar convolution kernels in individual layers of the MTAL network. Then in\norder to realize the sharing, we weightedly aggregate any pair of convolutional\nkernels with their similarity greater than some threshold $\\rho$, consequently,\nour model effectively performs cross-task learning while suppresses the\nintra-redundancy of the entire network. Finally, we conduct end-to-end\ntraining. Our experimental results demonstrate the effectiveness of our method\nin comparison with the state-of-the-art counterparts.\n"}
+{"abstract": "  eBPF is a new technology which allows dynamically loading pieces of code into\nthe Linux kernel. It can greatly speed up networking since it enables the\nkernel to process certain packets without the involvement of a userspace\nprogram. So far eBPF has been used for simple packet filtering applications\nsuch as firewalls or Denial of Service protection. We show that it is possible\nto develop a flow based network intrusion detection system based on machine\nlearning entirely in eBPF. Our solution uses a decision tree and decides for\neach packet whether it is malicious or not, considering the entire previous\ncontext of the network flow. We achieve a performance increase of over 20\\%\ncompared to the same solution implemented as a userspace program.\n"}
+{"abstract": "  We explore the interplay of electron-electron correlations and surface\neffects in the prototypical correlated insulating material, NiO. In particular,\nwe compute the electronic structure, magnetic properties, and surface energies\nof the $(001)$ and $(110)$ surfaces of paramagnetic NiO using a fully charge\nself-consistent DFT+DMFT method. Our results reveal a complex interplay between\nelectronic correlations and surface effects in NiO, with the electronic\nstructure of the $(001)$ and $(110)$ NiO surfaces being significantly different\nfrom that in bulk NiO. We obtain a sizeable reduction of the band gap at the\nsurface of NiO, which is most significant for the $(110)$ NiO surface. This\nsuggests a higher catalytic activity of the $(110)$ NiO surface than that of\nthe $(001)$ NiO one. Our results reveal a charge-transfer character of the\n$(001)$ and $(110)$ surfaces of NiO. Most notably, for the $(110)$ NiO surface\nwe observe a remarkable electronic state characterized by an alternating\ncharge-transfer and Mott-Hubbard character of the band gap in the surface and\nsubsurface NiO layers, respectively. This novel form of electronic order\nstabilized by strong correlations is not driven by lattice reconstructions but\nof purely electronic origin. We notice the importance of\norbital-differentiation of the Ni $e_g$ states to characterize the Mott-Hubbard\ninsulating state of the $(001)$ and $(110)$ NiO surfaces. The unoccupied Ni\n$e_g$ surface states are seen to split from the lower edge of the conduction\nband to form strongly localized states in the fundamental gap of bulk NiO. Our\nresults for the surface energies of the $(001)$ and $(110)$ NiO surfaces show\nthat the $(001)$ facet of NiO has significantly lower energy. This implies that\nthe relative stability of different surfaces, at least from a purely energetic\npoint of view, does not depend on the presence or absence of magnetic order in\nNiO.\n"}
+{"abstract": "  The foundation for the research of summarization in the Czech language was\nlaid by the work of Straka et al. (2018). They published the SumeCzech, a large\nCzech news-based summarization dataset, and proposed several baseline\napproaches. However, it is clear from the achieved results that there is a\nlarge space for improvement. In our work, we focus on the impact of named\nentities on the summarization of Czech news articles. First, we annotate\nSumeCzech with named entities. We propose a new metric ROUGE_NE that measures\nthe overlap of named entities between the true and generated summaries, and we\nshow that it is still challenging for summarization systems to reach a high\nscore in it. We propose an extractive summarization approach Named Entity\nDensity that selects a sentence with the highest ratio between a number of\nentities and the length of the sentence as the summary of the article. The\nexperiments show that the proposed approach reached results close to the solid\nbaseline in the domain of news articles selecting the first sentence. Moreover,\nwe demonstrate that the selected sentence reflects the style of reports\nconcisely identifying to whom, when, where, and what happened. We propose that\nsuch a summary is beneficial in combination with the first sentence of an\narticle in voice applications presenting news articles. We propose two\nabstractive summarization approaches based on Seq2Seq architecture. The first\napproach uses the tokens of the article. The second approach has access to the\nnamed entity annotations. The experiments show that both approaches exceed\nstate-of-the-art results previously reported by Straka et al. (2018), with the\nlatter achieving slightly better results on SumeCzech's out-of-domain testing\nset.\n"}
+{"abstract": "  Lattice reconstruction in twisted transition-metal dichalcogenide (TMD)\nbilayers gives rise to piezo- and ferroelectric moir\\'e potentials for\nelectrons and holes, as well as a modulation of the hybridisation across the\nbilayer. Here, we develop hybrid $\\mathbf{k}\\cdot \\mathbf{p}$ tight-binding\nmodels to describe electrons and holes in the relevant valleys of twisted TMD\nhomobilayers with parallel (P) and anti-parallel (AP) orientations of the\nmonolayer unit cells. We apply these models to describe moir\\'e superlattice\neffects in twisted WSe${}_2$ bilayers, in conjunction with microscopic \\emph{ab\ninitio} calculations, and considering the influence of encapsulation, pressure\nand an electric displacement field. Our analysis takes into account mesoscale\nlattice relaxation, interlayer hybridisation, piezopotentials, and a weak\nferroelectric charge transfer between the layers, and describes a multitude of\npossibilities offered by this system, depending on the choices of P or AP\norientation, twist angle magnitude, and electron/hole valley.\n"}
+{"abstract": "  We consider the following time-independent nonlinear $L^2$-critical\nSchr\\\"{o}dinger equation \\[ -\\Delta\nu(x)+V(x)u(x)-a|x|^{-b}|u|^{1+\\frac{4-2b}{N}}=\\mu u(x)\\,\\ \\hbox{in}\\,\\\n\\mathbb{R}^N, \\] where $\\mu\\in\\mathbb{R}$, $a>0$, $N\\geq 1$, $0<b<\\min\\{2,N\\}$,\nand $V(x)$ is an external potential. It is shown that ground states of the\nabove equation can be equivalently described by minimizers of the corresponding\nminimization problem. In this paper, we prove that there is a threshold $a^*>0$\nsuch that minimizer exists for $0<a<a^*$ and minimizer does not exist for any\n$a>a^*$. However if $a=a^*$, it is proved that whether minimizer exists depends\nsensitively on the value of $V(0)$. Moreover, when there is no minimizer at\nthreshold $a^*$, we give a detailed concentration behavior of minimizers as\n$a\\nearrow a^*$, based on which we finally prove that there is a unique\nminimizer as $a\\nearrow a^*$.\n"}
+{"abstract": "  We study the compactness of composition operators on the Bergman spaces of\ncertain bounded convex domains in $\\mathbb{C}^n$ with non-trivial analytic\ndiscs contained in the boundary. As a consequence we characterize that\ncompactness of the composition operator with a continuous symbol (up to the\nclosure) on the Bergman space of the polydisc.\n"}
+{"abstract": "  We examine the possibility of \"soft cosmology\", namely small deviations from\nthe usual framework due to the effective appearance of soft-matter properties\nin the Universe sectors. One effect of such a case would be the dark energy to\nexhibit a different equation-of-state parameter at large scales (which\ndetermine the universe expansion) and at intermediate scales (which determine\nthe sub-horizon clustering and the large scale structure formation). Concerning\nsoft dark matter, we show that it can effectively arise due to the dark-energy\nclustering, even if dark energy is not soft. We propose a novel parametrization\nintroducing the \"softness parameters\" of the dark sectors. As we see, although\nthe background evolution remains unaffected, due to the extreme sensitivity and\nsignificant effects on the global properties even a slightly non-trivial\nsoftness parameter can improve the clustering behavior and alleviate e.g. the\n$f\\sigma_8$ tension. Lastly, an extension of the cosmological perturbation\ntheory and a detailed statistical mechanical analysis, in order to incorporate\ncomplexity and estimate the scale-dependent behavior from first principles, is\nnecessary and would provide a robust argumentation in favour of soft cosmology.\n"}
+{"abstract": "  Recently, several universal methods have been proposed for online convex\noptimization, and attain minimax rates for multiple types of convex functions\nsimultaneously. However, they need to design and optimize one surrogate loss\nfor each type of functions, which makes it difficult to exploit the structure\nof the problem and utilize the vast amount of existing algorithms. In this\npaper, we propose a simple strategy for universal online convex optimization,\nwhich avoids these limitations. The key idea is to construct a set of experts\nto process the original online functions, and deploy a meta-algorithm over the\n\\emph{linearized} losses to aggregate predictions from experts. Specifically,\nwe choose Adapt-ML-Prod to track the best expert, because it has a second-order\nbound and can be used to leverage strong convexity and exponential concavity.\nIn this way, we can plug in off-the-shelf online solvers as black-box experts\nto deliver problem-dependent regret bounds. Furthermore, our strategy inherits\nthe theoretical guarantee of any expert designed for strongly convex functions\nand exponentially concave functions, up to a double logarithmic factor. For\ngeneral convex functions, it maintains the minimax optimality and also achieves\na small-loss bound.\n"}
+{"abstract": "  An important limitation of standard multiple testing procedures is that the\nnull distribution should be known. Here, we consider a\n  null distribution-free approach for multiple testing in the following\nsemi-supervised setting: the user does not know the null distribution, but has\nat hand a sample drawn from this null distribution. In practical situations,\nthis null training sample (NTS) can come from previous experiments, from a part\nof the data under test, from specific simulations, or from a sampling process.\nIn this work, we present theoretical results that handle such a framework, with\na focus on the false discovery rate (FDR) control and the Benjamini-Hochberg\n(BH) procedure. First, we provide upper and lower bounds for the FDR of the BH\nprocedure based on empirical $p$-values. These bounds match when $\\alpha\n(n+1)/m$ is an integer, where $n$ is the NTS sample size and $m$ is the number\nof tests. Second, we give a power analysis for that procedure suggesting that\nthe price to pay for ignoring the null distribution is low when $n$ is\nsufficiently large in front of $m$; namely $n\\gtrsim m/(\\max(1,k))$, where $k$\ndenotes the number of ``detectable'' alternatives. Third, to complete the\npicture, we also present a negative result that evidences an intrinsic\ntransition phase to the general semi-supervised multiple testing problem {and\nshows that the empirical BH method is optimal in the sense that its performance\nboundary follows this transition phase}. Our theoretical properties are\nsupported by numerical experiments, which also show that the delineated\nboundary is of correct order without further tuning any constant. Finally, we\ndemonstrate that our work provides a theoretical ground for standard practice\nin astronomical data analysis, and in particular for the procedure proposed in\n\\cite{Origin2020} for galaxy detection.\n"}
+{"abstract": "  Classically, data interpolation with a parametrized model class is possible\nas long as the number of parameters is larger than the number of equations to\nbe satisfied. A puzzling phenomenon in deep learning is that models are trained\nwith many more parameters than what this classical theory would suggest. We\npropose a theoretical explanation for this phenomenon. We prove that for a\nbroad class of data distributions and model classes, overparametrization is\nnecessary if one wants to interpolate the data smoothly. Namely we show that\nsmooth interpolation requires $d$ times more parameters than mere\ninterpolation, where $d$ is the ambient data dimension. We prove this universal\nlaw of robustness for any smoothly parametrized function class with polynomial\nsize weights, and any covariate distribution verifying isoperimetry. In the\ncase of two-layers neural networks and Gaussian covariates, this law was\nconjectured in prior work by Bubeck, Li and Nagaraj. We also give an\ninterpretation of our result as an improved generalization bound for model\nclasses consisting of smooth functions.\n"}
+{"abstract": "  Motivated by the recent interest in cyber-physical and autonomous robotic\nsystems, we study the problem of dynamically coupled multi-agent systems under\na set of signal temporal logic tasks. In particular, the satisfaction of each\nof these signal temporal logic tasks depends on the behavior of a distinct set\nof agents. Instead of abstracting the agent dynamics and the temporal logic\ntasks into a discrete domain and solving the problem therein or using\noptimization-based methods, we derive collaborative feedback control laws.\nThese control laws are based on a decentralized control barrier function\ncondition that results in discontinuous control laws, as opposed to a\ncentralized condition resembling the single-agent case. The benefits of our\napproach are inherent robustness properties typically present in feedback\ncontrol as well as satisfaction guarantees for continuous-time multi-agent\nsystems. More specifically, time-varying control barrier functions are used\nthat account for the semantics of the signal temporal logic tasks at hand. For\na certain fragment of signal temporal logic tasks, we further propose a\nsystematic way to construct such control barrier functions. Finally, we show\nthe efficacy and robustness of our framework in an experiment including a group\nof three omnidirectional robots.\n"}
+{"abstract": "  We present the first searches for gravitational waves from r-modes of the\nCrab pulsar, coherently and separately integrating data from three stretches of\nthe first two observing runs of Advanced LIGO using the F-statistic. The second\nrun was divided in two by a glitch of the pulsar roughly halfway through. The\nfrequencies and derivatives searched were based on radio measurements of the\npulsar's spin-down parameters as described in Caride et al., Phys. Rev. D 100,\n064013 (2019). We did not find any evidence of gravitational waves. Our best\n90% confidence upper limits on gravitational wave intrinsic strain were 1.5e-25\nfor the first run, 1.3e-25 for the first stretch of the second run, and 1.1e-25\nfor the second stretch of the second run. These are the first upper limits on\ngravitational waves from r-modes of a known pulsar to beat its spin-down limit,\nand they do so by more than an order of magnitude in amplitude or two orders of\nmagnitude in luminosity.\n"}
+{"abstract": "  Probabilistic models such as Gaussian processes (GPs) are powerful tools to\nlearn unknown dynamical systems from data for subsequent use in control design.\nWhile learning-based control has the potential to yield superior performance in\ndemanding applications, robustness to uncertainty remains an important\nchallenge. Since Bayesian methods quantify uncertainty of the learning results,\nit is natural to incorporate these uncertainties into a robust design. In\ncontrast to most state-of-the-art approaches that consider worst-case\nestimates, we leverage the learning method's posterior distribution in the\ncontroller synthesis. The result is a more informed and, thus, more efficient\ntrade-off between performance and robustness. We present a novel controller\nsynthesis for linearized GP dynamics that yields robust controllers with\nrespect to a probabilistic stability margin. The formulation is based on a\nrecently proposed algorithm for linear quadratic control synthesis, which we\nextend by giving probabilistic robustness guarantees in the form of credibility\nbounds for the system's stability.Comparisons to existing methods based on\nworst-case and certainty-equivalence designs reveal superior performance and\nrobustness properties of the proposed method.\n"}
+{"abstract": "  Competitive board games have provided a rich and diverse testbed for\nartificial intelligence. This paper contends that collaborative board games\npose a different challenge to artificial intelligence as it must balance\nshort-term risk mitigation with long-term winning strategies. Collaborative\nboard games task all players to coordinate their different powers or pool their\nresources to overcome an escalating challenge posed by the board and a\nstochastic ruleset. This paper focuses on the exemplary collaborative board\ngame Pandemic and presents a rolling horizon evolutionary algorithm designed\nspecifically for this game. The complex way in which the Pandemic game state\nchanges in a stochastic but predictable way required a number of specially\ndesigned forward models, macro-action representations for decision-making, and\nrepair functions for the genetic operations of the evolutionary algorithm.\nVariants of the algorithm which explore optimistic versus pessimistic game\nstate evaluations, different mutation rates and event horizons are compared\nagainst a baseline hierarchical policy agent. Results show that an evolutionary\napproach via short-horizon rollouts can better account for the future dangers\nthat the board may introduce, and guard against them. Results highlight the\ntypes of challenges that collaborative board games pose to artificial\nintelligence, especially for handling multi-player collaboration interactions.\n"}
+{"abstract": "  Hazy images are often subject to color distortion, blurring, and other\nvisible quality degradation. Some existing CNN-based methods have great\nperformance on removing homogeneous haze, but they are not robust in\nnon-homogeneous case. The reasons are mainly in two folds. Firstly, due to the\ncomplicated haze distribution, texture details are easy to be lost during the\ndehazing process. Secondly, since the training pairs are hard to be collected,\ntraining on limited data can easily lead to over-fitting problem. To tackle\nthese two issues, we introduce a novel dehazing network using 2D discrete\nwavelet transform, namely DW-GAN. Specifically, we propose a two-branch network\nto deal with the aforementioned problems. By utilizing wavelet transform in DWT\nbranch, our proposed method can retain more high-frequency knowledge in feature\nmaps. In order to prevent over-fitting, ImageNet pre-trained Res2Net is adopted\nin the knowledge adaptation branch. Owing to the robust feature representations\nof ImageNet pre-training, the generalization ability of our network is improved\ndramatically. Finally, a patch-based discriminator is used to reduce artifacts\nof the restored images. Extensive experimental results demonstrate that the\nproposed method outperforms the state-of-the-arts quantitatively and\nqualitatively.\n"}
+{"abstract": "  This expository survey is based on my online talk at the ICCM 2020. It aims\nto sketch key steps of the recent proof of the uniform Mordell-Lang conjecture\nfor curves embedded into Jacobians (a question of Mazur). The full version of\nthis conjecture is proved by combining Dimitrov-Gao-Habegger\n(https://annals.math.princeton.edu/articles/17715) and K\\\"{u}hne\n(arXiv:2101.10272). We include in this survey a detailed proof on how to\ncombine these two results, which was implicitly done in another short paper of\nDimitrov-Gao-Habegger (arXiv:2009.08505) but not explicitly written in existing\nliterature. At the end of the survey we state some future aspects.\n"}
+{"abstract": "  In this paper, we study the long-time behavior of global solutions to the\nSchr\\\"odinger-Choquard equation $$i\\partial_tu+\\Delta\nu=-(I_\\alpha\\ast|\\cdot|^b|u|^{p})|\\cdot|^b|u|^{p-2}u.$$\n  Inspired by Murphy, who gave a simple proof of scattering for the non-radial\ninhomogeneous NLS, we prove scattering theory below the ground state for the\nintercritical case in energy space without radial assumption.\n"}
+{"abstract": "  We prove that the free energy of directed polymer in Bernoulli environment\nconverges to the growth rate for the number of open paths in super-critical\noriented percolation as the temperature tends to zero. Our proof is based on\nrate of convergence results which hold uniformly in the temperature. We also\nprove that the convergence rate is locally uniform in the percolation parameter\ninside the super-critical phase, which implies that the growth rate depends\ncontinuously on the percolation parameter.\n"}
+{"abstract": "  We propose a new method for the visual quality assessment of 360-degree\n(omnidirectional) videos. The proposed method is based on computing multiple\nspatio-temporal objective quality features on viewports extracted from\n360-degree videos. A new model is learnt to properly combine these features\ninto a metric that closely matches subjective quality scores. The main\nmotivations for the proposed approach are that: 1) quality metrics computed on\nviewports better captures the user experience than metrics computed on the\nprojection domain; 2) the use of viewports easily supports different projection\nmethods being used in current 360-degree video systems; and 3) no individual\nobjective image quality metric always performs the best for all types of visual\ndistortions, while a learned combination of them is able to adapt to different\nconditions. Experimental results, based on both the largest available\n360-degree videos quality dataset and a cross-dataset validation, demonstrate\nthat the proposed metric outperforms state-of-the-art 360-degree and 2D video\nquality metrics.\n"}
+{"abstract": "  In the context of scalar-tensor theories, the inclusion of new degrees of\nfreedom coupled non-minimally to the gravitational sector might produce\nadditional imprints on cosmological observables. We investigate this premise by\nincluding a canonical SU(2) Yang-Mills field to the total energy budget of the\nuniverse coupled to the standard quintessential field by a disformal\ntransformation. The background dynamics study is addressed by a dynamical\nsystem analysis from which novel anisotropic scaling solutions with a\nnon-vanishing gauge field, supporting a preferred spatial direction, are\nobtained. After establishing the dynamical character of the fixed points, the\nphenomenological consequences of the model on the background evolution of the\nuniverse are assessed by means of numerical analysis. As an interesting result,\nthe disformal coupling changes the equation of state of the gauge field from\nradiation to matter at some stages of the evolution of the universe, thereby\nthe gauge field can contribute to some fraction of the total dark matter. We\nhave also quantified the redshift-dependent contribution of the gauge field in\nthe form of dark radiation during the radiation era to the effective number of\nrelativistic species. This depends essentially on the initial conditions and,\nmore importantly, on the disformal coupling function. Phenomenological\ncouplings and the Abelian version of the model are discussed in order to check\nthe generality of our results. Finally, the phenomenological advantages of this\nmodel are discussed in the light of the current tensions in the $\\Lambda$CDM\nmodel.\n"}
+{"abstract": "  Let $\\mathbb{Z}^{ab}$ be the ring of integers of $\\mathbb{Q}^{ab}$, the\nmaximal abelian extension of $\\mathbb{Q}$. We show that there exists an\nalgorithm to decide whether a system of equations and inequations, with integer\ncoefficients, has a solution in $\\mathbb{Z}^{ab}$ modulo every rational prime.\n"}
+{"abstract": "  High temperature superconductivity in cuprates arises from doping a parent\nMott insulator by electrons or holes. A central issue is how the Mott gap\nevolves and the low-energy states emerge with doping. Here we report\nangle-resolved photoemission spectroscopy measurements on a cuprate parent\ncompound by sequential in situ electron doping. The chemical potential jumps to\nthe bottom of the upper Hubbard band upon a slight electron doping, making it\npossible to directly visualize the charge transfer band and the full Mott gap\nregion. With increasing doping, the Mott gap rapidly collapses due to the\nspectral weight transfer from the charge transfer band to the gapped region and\nthe induced low-energy states emerge in a wide energy range inside the Mott\ngap. These results provide key information on the electronic evolution in\ndoping a Mott insulator and establish a basis for developing microscopic\ntheories for cuprate superconductivity.\n"}
+{"abstract": "  A $(d,k)$-set is a subset of $\\mathbb{R}^d$ containing a $k$-dimensional unit\nball of all possible orientations. Using an approach of D.~Oberlin we prove\nvarious Fourier dimension estimates for compact $(d,k)$-sets. Our main interest\nis in restricted $(d,k)$-sets, where the set only contains unit balls with a\nrestricted set of possible orientations $\\Gamma$. In this setting our estimates\ndepend on the Hausdorff dimension of $\\Gamma$ and can sometimes be improved if\nadditional geometric properties of $\\Gamma$ are assumed. We are led to consider\ncones and prove that the cone in $\\mathbb{R}^{d+1}$ has Fourier dimension\n$d-1$, which may be of interest in its own right.\n"}
+{"abstract": "  We give an explicit formula for the Waring rank of every binary binomial form\nwith complex coefficients. We give several examples to illustrate this, and\ncompare the Waring rank and the real Waring rank for binary binomial forms.\n"}
+{"abstract": "  Open Information Extraction (OIE) systems seek to compress the factual\npropositions of a sentence into a series of n-ary tuples. These tuples are\nuseful for downstream tasks in natural language processing like knowledge base\ncreation, textual entailment, and natural language understanding. However,\ncurrent OIE datasets are limited in both size and diversity. We introduce a new\ndataset by converting the QA-SRL 2.0 dataset to a large-scale OIE dataset\n(LSOIE). Our LSOIE dataset is 20 times larger than the next largest\nhuman-annotated OIE dataset. We construct and evaluate several benchmark OIE\nmodels on LSOIE, providing baselines for future improvements on the task. Our\nLSOIE data, models, and code are made publicly available\n"}
+{"abstract": "  Software products have become an integral part of human lives, and therefore\nneed to account for human values such as privacy, fairness, and equality.\nIgnoring human values in software development leads to biases and violations of\nhuman values: racial biases in recidivism assessment and facial recognition\nsoftware are well-known examples of such issues. One of the most critical steps\nin software development is Software Release Planning (SRP), where decisions are\nmade about the presence or absence of the requirements (features) in the\nsoftware. Such decisions are primarily guided by the economic value of the\nrequirements, ignoring their impacts on a broader range of human values. That\nmay result in ignoring (selecting) requirements that positively (negatively)\nimpact human values, increasing the risk of value breaches in the software. To\naddress this, we have proposed an Integer Programming approach to considering\nhuman values in software release planning. In this regard, an Integer Linear\nProgramming (ILP) model has been proposed, that explicitly accounts for human\nvalues in finding an \"optimal\" subset of the requirements. The ILP model\nexploits the algebraic structure of fuzzy graphs to capture dependencies and\nconflicts among the values of the requirements.\n"}
+{"abstract": "  Without contamination from the final state interactions, the calculation of\nthe branching ratios of semileptonic decays $\\Xi^{(')}_{c}\\to\\Xi+e^+\\nu_e$ may\nprovide us more information about the inner structure of charmed baryons.\nMoreover, by studying those processes, one can better determine the form\nfactors of $\\Xi_c\\to\\Xi$ which can be further applied to relevant estimates. In\nthis work, we use the light-front quark model to carry out the computations\nwhere the three-body vertex functions for $\\Xi_c$ and $\\Xi$ are employed. To\nfit the new data of the Belle II, we re-adjust the model parameters and obtain\n$\\beta_{s[sq]}=1.07$ GeV which is 2.9 times larger than $\\beta_{s\\bar s}=0.366$\nGeV. This value may imply that the $ss$ pair in $\\Xi$ constitutes a more\ncompact subsystem. Furthermore, we also investigate the non-leptonic decays of\n$\\Xi^{(')}_c\\to \\Xi$ which will be experimentally measured soon, so our model\nwould be tested by consistency with the new data.\n"}
+{"abstract": "  Node influence metrics have been applied to many applications, including\nranking web pages on internet, or locations on spatial networks. PageRank is a\npopular and effective algorithm for estimating node influence. However,\nconventional PageRank method considers neither the heterogeneity of network\nstructures nor additional network information, causing a major impedance to\nperformance improvement and an underestimation of non-hub nodes' importance. As\nthese problems are only partially studied, existing solutions are still not\nsatisfying. This paper addresses the problems by presenting a general\nPageRank-based model framework, dubbed Hetero-NodeRank, that accounts for\nheterogeneous network topology and incorporates node attribute information to\ncapture both link- and node-based effects in measuring node influence.\nMoreover, the framework enables the calibration of the proposed model against\nempirical data, which transforms the original deductive approach into an\ninductive one that could be useful for hypothesis testing and causal-effect\nanalysis. As the original unsupervised task becomes a supervised one,\noptimization methods can be leveraged for model calibrations. Experiments on\nreal data from the national city network of China demonstrated that the\nproposed model outperforms several widely used algorithms.\n"}
+{"abstract": "  This review summarizes more than 100 years of research on spinel compounds,\nmainly focusing on the progress in understanding their magnetic, electronic,\nand polar properties during the last two decades. Many spinel compounds are\nmagnetic insulators or semiconductors; however, a number of spinel-type metals\nexists including superconductors and some rare examples of d-derived\nheavy-fermion compounds. In the early days, they gained importance as\nferrimagnetic or even ferromagnetic insulators with relatively high saturation\nmagnetization and high ordering temperatures, with magnetite being the first\nmagnetic mineral known to mankind. However, spinels played an outstanding role\nin the development of concepts of magnetism, in testing and verifying the\nfundamentals of magnetic exchange, in understanding orbital-ordering and\ncharge-ordering phenomena. In addition, the A- site as well as the B-site\ncations in the spinel structure form lattices prone to strong frustration\neffects resulting in exotic ground-state properties. In case the A-site cation\nis Jahn-Teller active, additional entanglements of spin and orbital degrees of\nfreedom appear, which can give rise to a spin-orbital liquid or an orbital\nglass state. The B-site cations form a pyrochlore lattice, one of the strongest\ncontenders of frustration in three dimensions. In addition, in spinels with\nboth cation lattices carrying magnetic moments, competing magnetic exchange\ninteractions become important, yielding ground states like the time-honoured\ntriangular Yafet-Kittel structure. Finally, yet importantly, there exists a\nlong-standing dispute about the possibility of a polar ground state in spinels,\ndespite their reported overall cubic symmetry. Indeed, over the years number of\nmultiferroic spinels were identified.\n"}
+{"abstract": "  We study a class of gauge groups that can automatically yield a\nperturbatively exact Peccei-Quinn symmetry, and we outline a model in which the\naxion quality problem is solved at all operator dimensions. Gauge groups\nbelonging to this class can also enforce and protect accidental symmetries of\nthe clockwork type, and we present a toy model where an `invisible' axion\narises from a single breaking of the gauge and global symmetries.\n"}
+{"abstract": "  I briefly review several important formal theory developments in quantum\nfield theory and string theory that were reported at ICHEP conferences in past\ndecades, and explain how they underlie a new research area referred to as\nphysical or quantum mathematics. To illustrate these ideas in some specific\ncontext, I discuss certain aspects of topological string theory and a recently\ndiscovered knots-quivers correspondence.\n"}
+{"abstract": "  Convolution is one of the basic building blocks of CNN architectures. Despite\nits common use, standard convolution has two main shortcomings:\nContent-agnostic and Computation-heavy. Dynamic filters are content-adaptive,\nwhile further increasing the computational overhead. Depth-wise convolution is\na lightweight variant, but it usually leads to a drop in CNN performance or\nrequires a larger number of channels. In this work, we propose the Decoupled\nDynamic Filter (DDF) that can simultaneously tackle both of these shortcomings.\nInspired by recent advances in attention, DDF decouples a depth-wise dynamic\nfilter into spatial and channel dynamic filters. This decomposition\nconsiderably reduces the number of parameters and limits computational costs to\nthe same level as depth-wise convolution. Meanwhile, we observe a significant\nboost in performance when replacing standard convolution with DDF in\nclassification networks. ResNet50 / 101 get improved by 1.9% and 1.3% on the\ntop-1 accuracy, while their computational costs are reduced by nearly half.\nExperiments on the detection and joint upsampling networks also demonstrate the\nsuperior performance of the DDF upsampling variant (DDF-Up) in comparison with\nstandard convolution and specialized content-adaptive layers.\n"}
+{"abstract": "  School performance measures are published annually in England to hold schools\nto account and to support parental school choice. This article reviews and\nevaluates the Progress 8 secondary school accountability system for\nstate-funded schools. We assess the statistical strengths and weaknesses of\nProgress 8 relating to: choice of pupil outcome attainment measures; potential\nadjustments for pupil input attainment and background characteristics;\ndecisions around which schools and pupils are excluded from the measure;\npresentation of Progress 8 to users, choice of statistical model, and\ncalculation of statistical uncertainty; and issues related to the volatility of\nschool performance over time, including scope for reporting multi-year\naverages. We then discuss challenges for Progress 8 raised by the COVID-19\npandemic. Six simple recommendations follow to improve Progress 8 and school\naccountability in England.\n"}
+{"abstract": "  Designing broadband enhanced chirality is of strong interest to the emerging\nfields of chiral chemistry and sensing, or to control the spin orbital momentum\nof photons in recently introduced nanophotonic chiral quantum and classical\noptical applications. However, chiral light-matter interactions have an\nextremely weak nature, are difficult to be controlled and enhanced, and cannot\nbe made tunable or broadband. In addition, planar ultrathin nanophotonic\nstructures to achieve strong, broadband, and tunable chirality at the\ntechnologically important visible to ultraviolet spectrum still remain elusive.\nHere, we tackle these important problems by experimentally demonstrating and\ntheoretically verifying spectrally tunable, extremely large, and broadband\nchiroptical response by nanohelical metamaterials. The reported new designs of\nall-dielectric and dielectric-metallic (hybrid) plasmonic metamaterials permit\nthe largest and broadest ever measured chiral Kuhn dissymmetry factor achieved\nby a large-scale nanophotonic structure. In addition, the strong circular\ndichroism of the presented bottom-up fabricated optical metamaterials can be\ntuned by varying their dimensions and proportions between their dielectric and\nplasmonic helical subsections. The currently demonstrated ultrathin optical\nmetamaterials are expected to provide a substantial boost to the developing\nfield of chiroptics leading to significantly enhanced and broadband chiral\nlight-matter interactions at the nanoscale.\n"}
+{"abstract": "  Recent works find that AI algorithms learn biases from data. Therefore, it is\nurgent and vital to identify biases in AI algorithms. However, the previous\nbias identification pipeline overly relies on human experts to conjecture\npotential biases (e.g., gender), which may neglect other underlying biases not\nrealized by humans. To help human experts better find the AI algorithms'\nbiases, we study a new problem in this work -- for a classifier that predicts a\ntarget attribute of the input image, discover its unknown biased attribute.\n  To solve this challenging problem, we use a hyperplane in the generative\nmodel's latent space to represent an image attribute; thus, the original\nproblem is transformed to optimizing the hyperplane's normal vector and offset.\nWe propose a novel total-variation loss within this framework as the objective\nfunction and a new orthogonalization penalty as a constraint. The latter\nprevents trivial solutions in which the discovered biased attribute is\nidentical with the target or one of the known-biased attributes. Extensive\nexperiments on both disentanglement datasets and real-world datasets show that\nour method can discover biased attributes and achieve better disentanglement\nw.r.t. target attributes. Furthermore, the qualitative results show that our\nmethod can discover unnoticeable biased attributes for various object and scene\nclassifiers, proving our method's generalizability for detecting biased\nattributes in diverse domains of images. The code is available at\nhttps://git.io/J3kMh.\n"}
+{"abstract": "  We study a fundamental problem in computational chemistry known as molecular\nconformation generation, trying to predict stable 3D structures from 2D\nmolecular graphs. Existing machine learning approaches usually first predict\ndistances between atoms and then generate a 3D structure satisfying the\ndistances, where noise in predicted distances may induce extra errors during 3D\ncoordinate generation. Inspired by the traditional force field methods for\nmolecular dynamics simulation, in this paper, we propose a novel approach\ncalled ConfGF by directly estimating the gradient fields of the log density of\natomic coordinates. The estimated gradient fields allow directly generating\nstable conformations via Langevin dynamics. However, the problem is very\nchallenging as the gradient fields are roto-translation equivariant. We notice\nthat estimating the gradient fields of atomic coordinates can be translated to\nestimating the gradient fields of interatomic distances, and hence develop a\nnovel algorithm based on recent score-based generative models to effectively\nestimate these gradients. Experimental results across multiple tasks show that\nConfGF outperforms previous state-of-the-art baselines by a significant margin.\n"}
+{"abstract": "  This paper will suggest a new finite element method to find a $P^4$-velocity\nand a $P^3$-pressure solving Stokes equations. The method solves first the\ndecoupled equation for the $P^4$-velocity. Then, four kinds of local\n$P^3$-pressures and one $P^0$-pressure will be calculated in a successive way.\nIf we superpose them, the resulting $P^3$-pressure shows the optimal order of\nconvergence same as a $P^3$-projection. The chief time cost of the new method\nis on solving two linear systems for the $P^4$-velocity and $P^0$-pressure,\nrespectively.\n"}
+{"abstract": "  Based on the fact that the constituent quark model reproduces the recent\nlattice result on baryon-baryon repulsion at short distance and that it\nincludes the quark dynamics with confinement, we analyze to what extent the\nquarkyonic modes appear in the phase space of baryons as one increases the\ndensity before only quark dynamics and hence deconfinement occurs. We find that\nas one increases the baryon density, the initial quark mode that appears will\ninvolve the $d(u)$-quark from a neutron (proton), which will leave the most\nattractive ($ud$) diquark intact.\n"}
+{"abstract": "  Since the outbreak of Coronavirus Disease 2019 (COVID-19), most of the\nimpacted patients have been diagnosed with high fever, dry cough, and soar\nthroat leading to severe pneumonia. Hence, to date, the diagnosis of COVID-19\nfrom lung imaging is proved to be a major evidence for early diagnosis of the\ndisease. Although nucleic acid detection using real-time reverse-transcriptase\npolymerase chain reaction (rRT-PCR) remains a gold standard for the detection\nof COVID-19, the proposed approach focuses on the automated diagnosis and\nprognosis of the disease from a non-contrast chest computed tomography (CT)scan\nfor timely diagnosis and triage of the patient. The prognosis covers the\nquantification and assessment of the disease to help hospitals with the\nmanagement and planning of crucial resources, such as medical staff,\nventilators and intensive care units (ICUs) capacity. The approach utilises\ndeep learning techniques for automated quantification of the severity of\nCOVID-19 disease via measuring the area of multiple rounded ground-glass\nopacities (GGO) and consolidations in the periphery (CP) of the lungs and\naccumulating them to form a severity score. The severity of the disease can be\ncorrelated with the medicines prescribed during the triage to assess the\neffectiveness of the treatment. The proposed approach shows promising results\nwhere the classification model achieved 93% accuracy on hold-out data.\n"}
+{"abstract": "  We investigate errors in tangents and adjoints of implicit functions\nresulting from errors in the primal solution due to approximations computed by\na numerical solver.\n  Adjoints of systems of linear equations turn out to be unconditionally\nnumerically stable. Tangents of systems of linear equations can become instable\nas well as both tangents and adjoints of systems of nonlinear equations, which\nextends to optima of convex unconstrained objectives. Sufficient conditions for\nnumerical stability are derived.\n"}
+{"abstract": "  Recently, it was argued in [Phys. Rev. Lett. {\\bf126}, 031102 (2021)] that\nthe WCCC can serve as a constraint to high-order effective field theories.\nHowever, we find there exists a key error in their approximate black hole\nsolution. After correcting it, their calculation cannot show the ability of\nWCCC to constrain the gravitational theories.\n"}
+{"abstract": "  We present a dynamical description of (anti)proton number fluctuations\ncumulants and correlation functions in central Au-Au collisions at\n$\\sqrt{s_{\\rm NN}} = 7.7-200$ GeV by utilizing viscous hydrodynamics\nsimulations. The cumulants of proton and baryon number are calculated in a\ngiven momentum acceptance analytically, via an appropriately extended\nCooper-Frye procedure describing particlization of an interacting hadron\nresonance gas. The effects of global baryon number conservation are taken into\naccount using a generalized subensemble acceptance method. The experimental\ndata of the STAR collaboration are consistent at $\\sqrt{s_{\\rm NN}} \\gtrsim 20$\nGeV with simultaneous effects of global baryon number conservation and\nrepulsive interactions in baryon sector, the latter being in line with the\nbehavior of baryon number susceptibilities observed in lattice QCD. The data at\nlower collision energies show possible indications for sizable attractive\ninteractions among baryons. The data also indicate sizable negative\ntwo-particle correlations between antiprotons that are not satisfactorily\ndescribed by baryon conservation and excluded volume effects. We also discuss\ndifferences between cumulants and correlation functions (factorial cumulants)\nof (anti)proton number distribution, proton versus baryon number fluctuations,\nand effects of hadronic afterburner.\n"}
+{"abstract": "  We consider the problem where $n$ clients transmit $d$-dimensional\nreal-valued vectors using $d(1+o(1))$ bits each, in a manner that allows the\nreceiver to approximately reconstruct their mean. Such compression problems\nnaturally arise in distributed and federated learning. We provide novel\nmathematical results and derive computationally efficient algorithms that are\nmore accurate than previous compression techniques. We evaluate our methods on\na collection of distributed and federated learning tasks, using a variety of\ndatasets, and show a consistent improvement over the state of the art.\n"}
+{"abstract": "  Recently, the SARS-CoV-2 variants from the United Kingdom (UK), South Africa,\nand Brazil have received much attention for their increased infectivity,\npotentially high virulence, and possible threats to existing vaccines and\nantibody therapies. The question remains if there are other more infectious\nvariants transmitted around the world. We carry out a large-scale study of\n252,874 SARS-CoV-2 genome isolates from patients to identify many other rapidly\ngrowing mutations on the spike (S) protein receptor-binding domain (RDB). We\nreveal that 88 out of 95 significant mutations that were observed more than 10\ntimes strengthen the binding between the RBD and the host\nangiotensin-converting enzyme 2 (ACE2), indicating the virus evolves toward\nmore infectious variants. In particular, we discover new fast-growing RBD\nmutations N439K, L452R, S477N, S477R, and N501T that also enhance the RBD and\nACE2 binding. We further unveil that mutation N501Y involved in United Kingdom\n(UK), South Africa, and Brazil variants may moderately weaken the binding\nbetween the RBD and many known antibodies, while mutations E484K and K417N\nfound in South Africa and Brazilian variants can potentially disrupt the\nbinding between the RDB and many known antibodies. Among three newly identified\nfast-growing RBD mutations, L452R, which is now known as part of the California\nvariant B.1.427, and N501T are able to effectively weaken the binding of many\nknown antibodies with the RBD. Finally, we hypothesize that RBD mutations that\ncan simultaneously make SARS-CoV-2 more infectious and disrupt the existing\nantibodies, called vaccine escape mutations, will pose an imminent threat to\nthe current crop of vaccines. A list of most likely vaccine escape mutations is\ngiven, including N501Y, L452R, E484K, N501T, S494P, and K417N.\n"}
+{"abstract": "  We relate the geometry of Schubert varieties in twisted affine Grassmannian\nand the nilpotent varieties in symmetric spaces. This extends some results of\nAchar-Henderson in the twisted setting. We also get some applications to the\ngeometry of the order 2 nilpotent varieties in certain classical symmetric\nspaces.\n"}
+{"abstract": "  The optomechanical character of molecules was discovered by Raman about one\ncentury ago. Today, molecules are promising contenders for high-performance\nquantum optomechanical platforms because their small size and large\nenergy-level separations make them intrinsically robust against thermal\nagitations. Moreover, the precision and throughput of chemical synthesis can\nensure a viable route to quantum technological applications. The challenge,\nhowever, is that the coupling of molecular vibrations to environmental phonons\nlimits their coherence to picosecond time scales. Here, we improve the\noptomechanical quality of a molecule by several orders of magnitude through\nphononic engineering of its surrounding. By dressing a molecule with long-lived\nhigh-frequency phonon modes of its nanoscopic environment, we achieve storage\nand retrieval of photons at millisecond time scales and allow for the emergence\nof single-photon strong coupling in optomechanics. Our strategy can be extended\nto the realization of molecular optomechanical networks.\n"}
+{"abstract": "  In this paper we propose $\\epsilon$-Consistent Mixup ($\\epsilon$mu).\n$\\epsilon$mu is a data-based structural regularization technique that combines\nMixup's linear interpolation with consistency regularization in the Mixup\ndirection, by compelling a simple adaptive tradeoff between the two. This\nlearnable combination of consistency and interpolation induces a more flexible\nstructure on the evolution of the response across the feature space and is\nshown to improve semi-supervised classification accuracy on the SVHN and\nCIFAR10 benchmark datasets, yielding the largest gains in the most challenging\nlow label-availability scenarios. Empirical studies comparing $\\epsilon$mu and\nMixup are presented and provide insight into the mechanisms behind\n$\\epsilon$mu's effectiveness. In particular, $\\epsilon$mu is found to produce\nmore accurate synthetic labels and more confident predictions than Mixup.\n"}
+{"abstract": "  We present a novel phase locking scheme for the coherent combination of beam\narrays in the filled aperture configuration. Employing a phase dithering\nmechanism for the different beams similar to LOCSET, dithering frequencies for\nsequential combination steps are reused. By applying an additional phase\nalternating scheme, this allows to use standard synchronized multichannel\nlock-in electronics for phase locking a large number of channels even when the\nfrequency bandwidth of the employed phase actuators is limited.\n"}
+{"abstract": "  Short text is a popular avenue of sharing feedback, opinions and reviews on\nsocial media, e-commerce platforms, etc. Many companies need to extract\nmeaningful information (which may include thematic content as well as semantic\npolarity) out of such short texts to understand users' behaviour. However,\nobtaining high quality sentiment-associated and human interpretable themes\nstill remains a challenge for short texts. In this paper we develop ELJST, an\nembedding enhanced generative joint sentiment-topic model that can discover\nmore coherent and diverse topics from short texts. It uses Markov Random Field\nRegularizer that can be seen as a generalisation of skip-gram based models.\nFurther, it can leverage higher-order semantic information appearing in word\nembedding, such as self-attention weights in graphical models. Our results show\nan average improvement of 10% in topic coherence and 5% in topic\ndiversification over baselines. Finally, ELJST helps understand users'\nbehaviour at more granular levels which can be explained. All these can bring\nsignificant values to the service and healthcare industries often dealing with\ncustomers.\n"}
+{"abstract": "  Using $980~\\rm fb^{-1}$ of data on and around the $\\Upsilon(nS)(n=1,2,3,4,5)$\nresonances collected with the Belle detector at the KEKB asymmetric-energy\n$e^+e^-$ collider, the two-photon process $\\gamma\\gamma\\to \\gamma\\psi(2S)$ is\nstudied from $\\sqrt{s} = 3.7$ to $4.2~{\\rm GeV}$ for the first time. Evidence\nis found for a structure in the invariant mass distribution of $\\gamma\\psi(2S)$\nat $M_1 = 3921.3\\pm 2.4 \\pm 1.6~{\\rm MeV}/c^2$ with a width of $\\Gamma_1 =\n0.0\\pm 5.3 \\pm 2.0~{\\rm MeV}$ and a significance of $4.0\\sigma$ including\nsystematic uncertainties, and another structure is seen at $M_2 = 4014.4\\pm 4.1\n\\pm 0.5~{\\rm MeV}/c^2$ with a width of $\\Gamma_2 = 6\\pm 16 \\pm 6~{\\rm MeV}$ and\na global significance of $2.8\\sigma$ including the look-elsewhere effect, if\nthe mass spectrum is parametrized with the incoherent sum of two Breit-Wigner\nfunctions. The upper limits of the widths are determined to be $\\Gamma_1^{\\rm\nUL} = 11.5~{\\rm MeV}$ and $\\Gamma_2^{\\rm UL} = 39.3~{\\rm MeV}$ at 90\\%\nconfidence level. The production rates are determined to be\n$\\Gamma_{\\gamma\\gamma}{\\cal B}(R_1\\to\\gamma\\psi(2S)) = 8.2\\pm 2.3\\pm 0.9~{\\rm\neV}$ assuming $(J^{PC}, |\\lambda|) =(0^{++}, 0)$ and $1.6\\pm 0.5\\pm 0.2~{\\rm\neV}$ with $(2^{++}, 2)$ for the first structure and $\\Gamma_{\\gamma\\gamma}{\\cal\nB}(R_2\\to\\gamma\\psi(2S)) = 5.3\\pm 2.7\\pm 2.5~{\\rm eV}$ with $(0^{++}, 0)$ and\n$1.1\\pm 0.5\\pm 0.5~{\\rm eV}$ with $(2^{++}, 2)$ for the second one. Here, the\nfirst errors are statistical and the second systematic.\n"}
+{"abstract": "  The influence of ligands on the low frequency vibration of different\nthicknesses cadmium selenide colloidal nanoplatelets is investigated using\nresonant low frequency Raman scattering. The strong vibration frequency shifts\ninduced by ligand modifications as well as the sharp spectral linewidths make\nlow frequency Raman scattering a tool of choice to follow ligand exchange as\nwell as the nano-mechanical properties of the NPLs, as evidenced by a\ncarboxylate to thiolate exchange study. Apart from their molecular weight, the\nnature of the ligands, such as the sulfur to metal bond of thiols, induces a\nmodification of the NPLs as a whole, increasing the thickness by one monolayer.\nMoreover, as the weight of the ligands increases, the discrepancy between the\nmassload model and the experimental measurements increase. These effects are\nall the more important when the number of layers is small and can only be\nexplained by a modification of the longitudinal sound velocity. This\nmodification takes its origin in a change of lattice structure of the NPLs,\nthat reflects on its elastic properties. These nanobalances are finally used to\ncharacterize ligands affinity with the surface using binary thiols mixtures,\nillustrating the potential of low frequency Raman scattering to finely\ncharacterize nanocrystals surfaces.\n"}
+{"abstract": "  Motivated by entropic optimal transport, we investigate an extended notion of\nsolution to the parabolic equation $( \\partial_t + b\\cdot \\nabla + \\Delta _{\na}/2 +V)g\n  =0$ with a final boundary condition. It is well-known that the viscosity\nsolution $g$ of this PDE is represented by the Feynman-Kac formula when the\ndrift $b$, the diffusion matrix $a$ and the scalar potential $V$ are regular\nenough and not growing too fast. In this article, $b$ and $V$ are not assumed\nto be regular and their growth is controlled by a finite entropy condition,\nallowing for instance $V$ to belong to some Kato class. We show that the\nFeynman-Kac formula represents a solution, in an extended sense, to the\nparabolic equation. This notion of solution is trajectorial and expressed with\nthe semimartingale extension of the Markov generator $ b\\cdot \\nabla + \\Delta\n_{ a}/2.$ Our probabilistic approach relies on stochastic derivatives,\nsemimartingales, Girsanov's theorem and the Hamilton-Jacobi-Bellman equation\nsatisfied by $\\log g$.\n"}
+{"abstract": "  In continuous-variable quantum information processing, quantum error\ncorrection of Gaussian errors requires simultaneous estimation of both\nquadrature components of displacements on phase space. However, quadrature\noperators $x$ and $p$ are non-commutative conjugate observables, whose\nsimultaneous measurement is prohibited by the uncertainty principle.\nGottesman-Kitaev-Preskill (GKP) error correction deals with this problem using\ncomplex non-Gaussian states called GKP states. On the other hand, simultaneous\nestimation of displacement using experimentally feasible non-Gaussian states\nhas not been well studied. In this paper, we consider a multi-parameter\nestimation problem of displacements assuming an isotropic Gaussian prior\ndistribution and allowing post-selection of measurement outcomes. We derive a\nlower bound for the estimation error when only Gaussian operations are used,\nand show that even simple non-Gaussian states such as single-photon states can\nbeat this bound. Based on Ghosh's bound, we also obtain a lower bound for the\nestimation error when the maximum photon number of the input state is given.\nOur results reveal the role of non-Gaussianity in the estimation of\ndisplacements, and pave the way toward the error correction of Gaussian errors\nusing experimentally feasible non-Gaussian states.\n"}
+{"abstract": "  In this paper we discuss two canonical transformations that turn St\\\"{a}ckel\nseparable Hamiltonians of Benenti type into polynomial form: transformation to\nVi\\`ete coordinates and transformation to Newton coordinates. Transformation to\nNewton coordinates has been applied to these systems only very recently and in\nthis paper we present a new proof that this transformation indeed leads to\npolynomial form of St\\\"{a}ckel Hamiltonians of Benenti type. Moreover we\npresent all geometric ingredients of these Hamiltonians in both Vi\\`ete and\nNewton coordinates.\n"}
+{"abstract": "  In this article we obtained the harmonic oscillator solution for quaternionic\nquantum mechanics ($\\mathbbm{H}$QM) in the real Hilbert space, both in the\nanalytic method and in the algebraic method. The quaternionic solutions have\nmany additional possibilities if compared to complex quantum mechanics\n($\\mathbbm{C}$QM), and thus there are many possible applications to these\nresults in future research.\n"}
+{"abstract": "  Recently unpolarized and polarized $J/\\psi \\,(\\Upsilon)$ production at the\nElectron-Ion Collider (EIC) has been proposed as a new way to extract two\npoorly known color-octet NRQCD long-distance matrix elements:\n$\\langle0\\vert{\\cal O}_{8}^{J/\\psi}(^{1}S_{0})\\vert0\\rangle$ and\n$\\langle0\\vert{\\cal O}_{8}^{J/\\psi}(^{3}P_{0})\\vert0\\rangle$. The proposed\nmethod is based on a comparison to open heavy-quark pair production ideally\nperformed at the same kinematics. In this paper we analyze this proposal in\nmore detail and provide predictions for the EIC based on the available\ndeterminations of the color-octet matrix elements. We also propose two\nadditional methods that do not require comparison to open heavy-quark pair\nproduction.\n"}
+{"abstract": "  Computational models based on the depth-averaged shallow water equations\n(SWE) offer an efficient choice to analyse velocity fields around hydraulic\nstructures. Second-order finite volume (FV2) solvers have often been used for\nthis purpose subject to adding an eddy viscosity term at sub-meter resolution,\nbut have been shown to fall short of capturing small-scale field transients\nemerging from wave-structure interactions. The second-order discontinuous\nGalerkin (DG2) alternative is significantly more resistant to the growth of\nnumerical diffusion and leads to faster convergence rates. These properties\nmake the DG2 solver a promising modelling tool for detailed velocity field\npredictions. This paper focuses on exploring this DG2 capability with reference\nto an FV2 counterpart for a selection of test cases that require well-resolved\nvelocity field predictions. The findings of this work lead to identifying a\nparticular setting for the DG2 solver that allows for obtaining more accurate\nand efficient depth-averaged velocity fields incorporating small-scale\ntransients.\n"}
+{"abstract": "  We develop a variational framework to understand the properties of functions\nlearned by fitting deep neural networks with rectified linear unit activations\nto data. We propose a new function space, which is reminiscent of classical\nbounded variation-type spaces, that captures the compositional structure\nassociated with deep neural networks. We derive a representer theorem showing\nthat deep ReLU networks are solutions to regularized data fitting problems over\nfunctions from this space. The function space consists of compositions of\nfunctions from the Banach spaces of second-order bounded variation in the Radon\ndomain. These are Banach spaces with sparsity-promoting norms, giving insight\ninto the role of sparsity in deep neural networks. The neural network solutions\nhave skip connections and rank bounded weight matrices, providing new\ntheoretical support for these common architectural choices. The variational\nproblem we study can be recast as a finite-dimensional neural network training\nproblem with regularization schemes related to the notions of weight decay and\npath-norm regularization. Finally, our analysis builds on techniques from\nvariational spline theory, providing new connections between deep neural\nnetworks and splines.\n"}
+{"abstract": "  The next wave of wireless technologies is proliferating in connecting things\namong themselves as well as to humans. In the era of the Internet of things\n(IoT), billions of sensors, machines, vehicles, drones, and robots will be\nconnected, making the world around us smarter. The IoT will encompass devices\nthat must wirelessly communicate a diverse set of data gathered from the\nenvironment for myriad new applications. The ultimate goal is to extract\ninsights from this data and develop solutions that improve quality of life and\ngenerate new revenue. Providing large-scale, long-lasting, reliable, and near\nreal-time connectivity is the major challenge in enabling a smart connected\nworld. This paper provides a comprehensive survey on existing and emerging\ncommunication solutions for serving IoT applications in the context of\ncellular, wide-area, as well as non-terrestrial networks. Specifically,\nwireless technology enhancements for providing IoT access in fifth-generation\n(5G) and beyond cellular networks, and communication networks over the\nunlicensed spectrum are presented. Aligned with the main key performance\nindicators of 5G and beyond 5G networks, we investigate solutions and standards\nthat enable energy efficiency, reliability, low latency, and scalability\n(connection density) of current and future IoT networks. The solutions include\ngrant-free access and channel coding for short-packet communications,\nnon-orthogonal multiple access, and on-device intelligence. Further, a vision\nof new paradigm shifts in communication networks in the 2030s is provided, and\nthe integration of the associated new technologies like artificial\nintelligence, non-terrestrial networks, and new spectra is elaborated. Finally,\nfuture research directions toward beyond 5G IoT networks are pointed out.\n"}
+{"abstract": "  Human gesture recognition has drawn much attention in the area of computer\nvision. However, the performance of gesture recognition is always influenced by\nsome gesture-irrelevant factors like the background and the clothes of\nperformers. Therefore, focusing on the regions of hand/arm is important to the\ngesture recognition. Meanwhile, a more adaptive architecture-searched network\nstructure can also perform better than the block-fixed ones like Resnet since\nit increases the diversity of features in different stages of the network\nbetter. In this paper, we propose a regional attention with\narchitecture-rebuilt 3D network (RAAR3DNet) for gesture recognition. We replace\nthe fixed Inception modules with the automatically rebuilt structure through\nthe network via Neural Architecture Search (NAS), owing to the different shape\nand representation ability of features in the early, middle, and late stage of\nthe network. It enables the network to capture different levels of feature\nrepresentations at different layers more adaptively. Meanwhile, we also design\na stackable regional attention module called dynamic-static Attention (DSA),\nwhich derives a Gaussian guidance heatmap and dynamic motion map to highlight\nthe hand/arm regions and the motion information in the spatial and temporal\ndomains, respectively. Extensive experiments on two recent large-scale RGB-D\ngesture datasets validate the effectiveness of the proposed method and show it\noutperforms state-of-the-art methods. The codes of our method are available at:\nhttps://github.com/zhoubenjia/RAAR3DNet.\n"}
+{"abstract": "  Neural networks (NNs) and linear stochastic estimation (LSE) have widely been\nutilized as powerful tools for fluid-flow regressions. We investigate\nfundamental differences between them considering two canonical fluid-flow\nproblems: 1. the estimation of high-order proper orthogonal decomposition\ncoefficients from low-order their counterparts for a flow around a\ntwo-dimensional cylinder, and 2. the state estimation from wall characteristics\nin a turbulent channel flow. In the first problem, we compare the performance\nof LSE to that of a multi-layer perceptron (MLP). With the channel flow\nexample, we capitalize on a convolutional neural network (CNN) as a nonlinear\nmodel which can handle high-dimensional fluid flows. For both cases, the\nnonlinear NNs outperform the linear methods thanks to nonlinear activation\nfunctions. We also perform error-curve analyses regarding the estimation error\nand the response of weights inside models. Our analysis visualizes the\nrobustness against noisy perturbation on the error-curve domain while revealing\nthe fundamental difference of the covered tools for fluid-flow regressions.\n"}
+{"abstract": "  In-volume ultrafast laser direct writing of silicon is generally limited by\nstrong nonlinear propagation effects preventing the initiation of\nmodifications. By employing a triple-optimization procedure in the spectral,\ntemporal and spatial domains, we demonstrate that modifications can be\nrepeatably produced inside silicon. Our approach relies on irradiation at\n$\\approx 2$-$\\mu$m wavelength with temporally-distorted femtosecond pulses.\nThese pulses are focused in a way that spherical aberrations of different\norigins counterbalance, as predicted by point spread function analyses and in\ngood agreement with nonlinear propagation simulations. We also establish the\nlaws governing modification growth on a pulse-to-pulse basis, which allows us\nto demonstrate transverse inscription inside silicon with various line\nmorphologies depending on the irradiation conditions. We finally show that the\nproduction of single-pulse repeatable modifications is a necessary condition\nfor reliable transverse inscription inside silicon.\n"}
+{"abstract": "  In the past decade, remarkable progress has been achieved in deep learning\nrelated systems and applications. In the post Moore's Law era, however, the\nlimit of semiconductor fabrication technology along with the increasing data\nsize have slowed down the development of learning algorithms. In parallel, the\nfast development of quantum computing has pushed it to the new ear. Google\nillustrates quantum supremacy by completing a specific task (random sampling\nproblem), in 200 seconds, which is impracticable for the largest classical\ncomputers. Due to the limitless potential, quantum based learning is an area of\ninterest, in hopes that certain systems might offer a quantum speedup. In this\nwork, we propose a novel architecture QuClassi, a quantum neural network for\nboth binary and multi-class classification. Powered by a quantum\ndifferentiation function along with a hybrid quantum-classic design, QuClassi\nencodes the data with a reduced number of qubits and generates the quantum\ncircuit, pushing it to the quantum platform for the best states, iteratively.\nWe conduct intensive experiments on both the simulator and IBM-Q quantum\nplatform. The evaluation results demonstrate that QuClassi is able to\noutperform the state-of-the-art quantum-based solutions, Tensorflow-Quantum and\nQuantumFlow by up to 53.75% and 203.00% for binary and multi-class\nclassifications. When comparing to traditional deep neural networks, QuClassi\nachieves a comparable performance with 97.37% fewer parameters.\n"}
+{"abstract": "  We consider a linear minimum mean squared error (LMMSE) estimation framework\nwith model mismatch where the assumed model order is smaller than that of the\nunderlying linear system which generates the data used in the estimation\nprocess. By modelling the regressors of the underlying system as random\nvariables, we analyze the average behaviour of the mean squared error (MSE).\nOur results quantify how the MSE depends on the interplay between the number of\nsamples and the number of parameters in the underlying system and in the\nassumed model. In particular, if the number of samples is not sufficiently\nlarge, neither increasing the number of samples nor the assumed model\ncomplexity is sufficient to guarantee a performance improvement.\n"}
+{"abstract": "  We discuss the anatomy of the $L_{VV}$ observable designed as a ratio of the\nlongitudinal components of $B_s \\to VV$ versus $B_d \\to VV$ decays. We focus on\nthe particular case of $B_{d,s} \\to K^{*0} {\\bar K^{*0}}$ where we find for the\nSM prediction $L_{K^* \\bar{K}^*}=19.5^{+9.3}_{-6.8}$ implying a 2.6$\\sigma$\ntension with respect to data. The interpretation of this tension in a model\nindependent way identifies two Wilson coefficients ${\\cal C}_{4}$ and ${\\cal\nC}_{8g}$ as possible sources. The example of one simplified model including a\nKaluza-Klein (KK) gluon is discussed. This KK gluon embedded inside a\ncomposite/extra dimensional model combined with a $Z^\\prime$ can explain also\nthe $b\\to s\\ell\\ell$ anomalies albeit with a significant amount of fine-tuning.\n"}
+{"abstract": "  We review the calculation of the solar axion flux from axion-photon and\naxion-electron interactions and discuss the size of various effects neglected\nin current calculations. For the Primakoff flux we then explicitly include the\npartial degeneracy of electrons. We survey the available solar models and\nopacity codes and develop a publicly available C++/Python code to quantify the\nassociated systematic differences and statistical uncertainties. The number of\naxions emitted in helioseismological solar models is systematically larger by\nabout 5% compared to photospheric models, while the overall statistical\nuncertainties in solar models are typically at the percent level in both\nhelioseismological and photospheric models. However, for specific energies, the\nstatistical fluctuations can reach up to about 5% as well. Taking these\nuncertainties into account, we investigate the ability of the upcoming\nhelioscope IAXO to discriminate KSVZ axion models. Such a discrimination is\npossible for a number of models, and a discovery of KSVZ axions with high $E/N$\nratios could potentially help to solve the solar abundance problem. We discuss\nlimitations of the axion emission calculations and identify potential\nimprovements, which would help to determine axion model parameters more\naccurately.\n"}
+{"abstract": "  In this paper, we prove existence and regularity of positive solutions for\nsingular quasilinear elliptic systems involving gradient terms. Our approach is\nbased on comparison properties, a priori estimates and Schauder's fixed point\ntheorem.\n"}
+{"abstract": "  A novel and compact tri-band planar antenna for 2.4/5.2/5.8-GHz wireless\nlocal area network (WLAN), 2.3/3.5/5.5GHz Worldwide Interoperability for\nMicrowave Access (WiMAX) and Bluetooth applications is proposed and studied in\nthis paper. The antenna comprises of a L-shaped element which is coupled with a\nground shorted parasitic resonator to generate three resonant modes for\ntri-band operation. The L-shaped element which is placed on top of the\nsubstrate is fed by a 50$\\Omega$ microstrip feed line and is responsible for\nthe generation of a wide band at 5.5 GHz. The parasitic resonator is placed on\nthe other side of the substrate and is directly connected to the ground plane.\nThe presence of the parasitic resonator gives rise to two additional resonant\nbands at 2.3 GHz and 3.5 GHz. Thus, together the two elements generate three\nresonant bands to cover WLAN, WiMAX and Bluetooth bands of operation. A\nthorough parametric study has been performed on the antenna and it has been\nfound that the three bands can be tuned by varying certain dimensions of the\nantenna. Hence, the same design can be used for frequencies in adjacent bands\nas well with minor changes in its dimensions. Important antenna parameters such\nas return loss, radiation pattern and peak gains in the operating bands have\nbeen studied in detail to prove that the proposed design is a promising\ncandidate for the aforementioned wireless technologies.\n"}
+{"abstract": "  We present a novel mapping approach for WENO schemes through the use of an\napproximate constant mapping function which is constructed by employing an\napproximation of the classic signum function. The new approximate constant\nmapping function is designed to meet the overall criteria for a proper mapping\nfunction required in the design of the WENO-PM6 scheme. The WENO-PM6 scheme was\nproposed to overcome the potential loss of accuracy of the WENO-M scheme which\nwas developed to recover the optimal convergence order of the WENO-JS scheme at\ncritical points. Our new mapped WENO scheme, denoted as WENO-ACM, maintains\nalmost all advantages of the WENO-PM6 scheme, including low dissipation and\nhigh resolution, while decreases the number of mathematical operations\nremarkably in every mapping process leading to a significant improvement of\nefficiency. The convergence rates of the WENO-ACM scheme have been shown\nthrough one-dimensional linear advection equation with various initial\nconditions. Numerical results of one-dimensional Euler equations for the\nRiemann problems, the Mach 3 shock-density wave interaction and the\nWoodward-Colella interacting blastwaves are improved in comparison with the\nresults obtained by the WENO-JS, WENO-M and WENO-PM6 schemes. Numerical\nexperiments with two-dimensional problems as the 2D Riemann problem, the\nshock-vortex interaction, the 2D explosion problem, the double Mach reflection\nand the forward-facing step problem modeled via the two dimensional Euler\nequations have been conducted to demonstrate the high resolution and the\neffectiveness of the WENO-ACM scheme. The WENO-ACM scheme provides\nsignificantly better resolution than the WENO-M scheme and slightly better\nresolution than the WENO-PM6 scheme, and compared to the WENO-M and WENO-PM6\nschemes, the extra computational cost is reduced by more than 83% and 93%,\nrespectively.\n"}
+{"abstract": "  Hundreds of millions of speakers of bidirectional (BiDi) languages rely on\nwriting systems that mix the native right-to-left script with left-to-right\nstrings. The global reach of interactive digital technologies requires special\nattention to these people, whose perception of interfaces is affected by this\nscript mixture. However, empirical research on this topic is scarce. Although\nleading software vendors provide guidelines for BiDi design, bidirectional\ninterfaces demonstrate inconsistent and incorrect directionality of UI\nelements, which may cause user confusion and errors. Through a websites'\nreview, we identified problematic UI items and considered reasons for their\nexistence. In an online survey with 234 BiDi speakers, we observed that in many\ncases, users' direction preferences were inconsistent with the guidelines. The\nfindings provide potential insights for design rules and empirical evidence for\nthe problem's complexity, suggesting the need for further empirical research\nand greater attention by the HCI community to the BiDi design problem.\n"}
+{"abstract": "  Grasping objects in cluttered scenarios is a challenging task in robotics.\nPerforming pre-grasp actions such as pushing and shifting to scatter objects is\na way to reduce clutter. Based on deep reinforcement learning, we propose a\nFast-Learning Grasping (FLG) framework, that can integrate pre-grasping actions\nalong with grasping to pick up objects from cluttered scenarios with reduced\nreal-world training time. We associate rewards for performing moving actions\nwith the change of environmental clutter and utilize a hybrid triggering\nmethod, leading to data-efficient learning and synergy. Then we use the output\nof an extended fully convolutional network as the value function of each pixel\npoint of the workspace and establish an accurate estimation of the grasp\nprobability for each action. We also introduce a mask function as prior\nknowledge to enable the agents to focus on the accurate pose adjustment to\nimprove the effectiveness of collecting training data and, hence, to learn\nefficiently. We carry out pre-training of the FLG over simulated environment,\nand then the learnt model is transferred to the real world with minimal\nfine-tuning for further learning during actions. Experimental results\ndemonstrate a 94% grasp success rate and the ability to generalize to novel\nobjects. Compared to state-of-the-art approaches in the literature, the\nproposed FLG framework can achieve similar or higher grasp success rate with\nlesser amount of training in the real world. Supplementary video is available\nat https://youtu.be/e04uDLsxfDg.\n"}
+{"abstract": "  Structured matrices, such as those derived from Kronecker products (KP), are\neffective at compressing neural networks, but can lead to unacceptable accuracy\nloss when applied to large models. In this paper, we propose the notion of\ndoping -- addition of an extremely sparse matrix to a structured matrix. Doping\nfacilitates additional degrees of freedom for a small number of parameters,\nallowing them to independently diverge from the fixed structure. To train LSTMs\nwith doped structured matrices, we introduce the additional parameter matrix\nwhile slowly annealing its sparsity level. However, we find that performance\ndegrades as we slowly sparsify the doping matrix, due to co-matrix adaptation\n(CMA) between the structured and the sparse matrices. We address this over\ndependence on the sparse matrix using a co-matrix dropout regularization (CMR)\nscheme. We provide empirical evidence to show that doping, CMA and CMR are\nconcepts generally applicable to multiple structured matrices (Kronecker\nProduct, LMF, Hybrid Matrix Decomposition). Additionally, results with doped\nkronecker product matrices demonstrate state-of-the-art accuracy at large\ncompression factors (10 - 25x) across 4 natural language processing\napplications with minor loss in accuracy. Doped KP compression technique\noutperforms previous state-of-the art compression results by achieving 1.3 -\n2.4x higher compression factor at a similar accuracy, while also beating strong\nalternatives like pruning and low-rank methods by a large margin (8% or more).\nAdditionally, we show that doped KP can be deployed on commodity hardware using\nthe current software stack and achieve 2.5 - 5.5x inference run-time speed-up\nover baseline.\n"}
+{"abstract": "  Recently, deep neural network (DNN)-based speech enhancement (SE) systems\nhave been used with great success. During training, such systems require clean\nspeech data - ideally, in large quantity with a variety of acoustic conditions,\nmany different speaker characteristics and for a given sampling rate (e.g.,\n48kHz for fullband SE). However, obtaining such clean speech data is not\nstraightforward - especially, if only considering publicly available datasets.\nAt the same time, a lot of material for automatic speech recognition (ASR) with\nthe desired acoustic/speaker/sampling rate characteristics is publicly\navailable except being clean, i.e., it also contains background noise as this\nis even often desired in order to have ASR systems that are noise-robust.\nHence, using such data to train SE systems is not straightforward. In this\npaper, we propose two improvements to train SE systems on noisy speech data.\nFirst, we propose several modifications of the loss functions, which make them\nrobust against noisy speech targets. In particular, computing the median over\nthe sample axis before averaging over time-frequency bins allows to use such\ndata. Furthermore, we propose a noise augmentation scheme for mixture-invariant\ntraining (MixIT), which allows using it also in such scenarios. For our\nexperiments, we use the Mozilla Common Voice dataset and we show that using our\nrobust loss function improves PESQ by up to 0.19 compared to a system trained\nin the traditional way. Similarly, for MixIT we can see an improvement of up to\n0.27 in PESQ when using our proposed noise augmentation.\n"}
+{"abstract": "  Temporal, spectral, and sample-to-sample fluctuations in coherence properties\nof qubits form an outstanding challenge for the development of upscaled\nfault-tolerant quantum computers. A ubiquitous source for these fluctuations in\nsuperconducting qubits is a set of atomic-scale defects with a two-level\nstructure. Here we propose a way to mitigate these fluctuations and stabilize\nthe qubit performance. We show that frequency modulation of a qubit or,\nalternatively, of the two-level defects, leads to averaging of the qubit\nrelaxation rate over a wide interval of frequencies.\n"}
+{"abstract": "  In this work, we introduce NU-Wave, the first neural audio upsampling model\nto produce waveforms of sampling rate 48kHz from coarse 16kHz or 24kHz inputs,\nwhile prior works could generate only up to 16kHz. NU-Wave is the first\ndiffusion probabilistic model for audio super-resolution which is engineered\nbased on neural vocoders. NU-Wave generates high-quality audio that achieves\nhigh performance in terms of signal-to-noise ratio (SNR), log-spectral distance\n(LSD), and accuracy of the ABX test. In all cases, NU-Wave outperforms the\nbaseline models despite the substantially smaller model capacity (3.0M\nparameters) than baselines (5.4-21%). The audio samples of our model are\navailable at https://mindslab-ai.github.io/nuwave, and the code will be made\navailable soon.\n"}
+{"abstract": "  We obtain the asymptotic formula with an error term $O(X^{\\frac{1}{2} +\n\\varepsilon})$ for the smoothed first moment of quadratic twists of modular\n$L$-functions. We also give a similar result for the smoothed first moment of\nthe first derivative of quadratic twists of modular $L$-functions. The argument\nis largely based on Young's recursive method [19,20].\n"}
+{"abstract": "  Convolutional neural network training can suffer from diverse issues like\nexploding or vanishing gradients, scaling-based weight space symmetry and\ncovariant-shift. In order to address these issues, researchers develop weight\nregularization methods and activation normalization methods. In this work we\npropose a weight soft-regularization method based on the Oblique manifold. The\nproposed method uses a loss function which pushes each weight vector to have a\nnorm close to one, i.e. the weight matrix is smoothly steered toward the\nso-called Oblique manifold. We evaluate our method on the very popular\nCIFAR-10, CIFAR-100 and ImageNet 2012 datasets using two state-of-the-art\narchitectures, namely the ResNet and wide-ResNet. Our method introduces\nnegligible computational overhead and the results show that it is competitive\nto the state-of-the-art and in some cases superior to it. Additionally, the\nresults are less sensitive to hyperparameter settings such as batch size and\nregularization factor.\n"}
+{"abstract": "  In the online balanced graph repartitioning problem, one has to maintain a\nclustering of $n$ nodes into $\\ell$ clusters, each having $k = n / \\ell$ nodes.\nDuring runtime, an online algorithm is given a stream of communication requests\nbetween pairs of nodes: an inter-cluster communication costs one unit, while\nthe intra-cluster communication is free. An algorithm can change the\nclustering, paying unit cost for each moved node.\n  This natural problem admits a simple $O(\\ell^2 \\cdot k^2)$-competitive\nalgorithm COMP, whose performance is far apart from the best known lower bound\nof $\\Omega(\\ell \\cdot k)$. One of open questions is whether the dependency on\n$\\ell$ can be made linear; this question is of practical importance as in the\ntypical datacenter application where virtual machines are clustered on physical\nservers, $\\ell$ is of several orders of magnitude larger than $k$. We answer\nthis question affirmatively, proving that a simple modification of COMP is\n$(\\ell \\cdot 2^{O(k)})$-competitive.\n  On the technical level, we achieve our bound by translating the problem to a\nsystem of linear integer equations and using Graver bases to show the existence\nof a ``small'' solution.\n"}
+{"abstract": "  We prove a pair of sharp reverse isoperimetric inequalities for domains in\nnonpositively curved surfaces: (1) metric disks centered at the vertex of a\nEuclidean cone of angle at least $2\\pi$ have minimal area among all\nnonpositively curved disks of the same perimeter and the same total curvature;\n(2) geodesic triangles in a Euclidean (resp. hyperbolic) cone of angle at least\n$2\\pi$ have minimal area among all nonpositively curved geodesic triangles\n(resp. all geodesic triangles of curvature at most $-1$) with the same side\nlengths and angles.\n"}
+{"abstract": "  We derive bounds on the error, in high-order Sobolev norms, incurred in the\napproximation of Sobolev-regular as well as analytic functions by neural\nnetworks with the hyperbolic tangent activation function. These bounds provide\nexplicit estimates on the approximation error with respect to the size of the\nneural networks. We show that tanh neural networks with only two hidden layers\nsuffice to approximate functions at comparable or better rates than much deeper\nReLU neural networks.\n"}
+{"abstract": "  This paper addresses the problem of time-varying bearing formation control in\n$d$ $(d\\ge 2)$-dimensional Euclidean space by exploring Persistence of\nExcitation (PE) of the desired bearing reference. A general concept of Bearing\nPersistently Exciting (BPE) formation defined in $d$-dimensional space is here\nfully developed. By providing a desired formation that is BPE, distributed\ncontrol laws for multi-agent systems under both single- and double-integrator\ndynamics are proposed using bearing measurements (along with velocity\nmeasurements when the agents are described by double-integrator dynamics),\nwhich guarantee uniform exponential stabilization of the desired formation in\nterms of shape and scale. A key contribution of this work is to show that the\nclassical bearing rigidity condition on the graph topology, required for\nachieving the stabilization of a formation up to a scaling factor, is relaxed\nand extended in a natural manner by exploring PE conditions imposed either on a\nspecific set of desired bearing vectors or on the whole desired formation.\nSimulation results are provided to illustrate the performance of the proposed\ncontrol method.\n"}
+{"abstract": "  By investigating the exam scores of introductory economics in a business\nschool in Taiwan between 2008 and 2019, we find three sets of results: First,\nwe find no significant difference between genders in the exam scores. Second,\nstudents' majors are significantly associated with their exam scores, which\nlikely reflects their academic ability measured at college admission. Third,\nthe exam scores are strong predictors of students' future academic performance.\n"}
+{"abstract": "  We explicitly determine the defining relations of all quantum symmetric pair\ncoideal subalgebras of quantized enveloping algebras of Kac-Moody type. Our\nmethods are based on star products on noncommutative $\\mathbb{N}$-graded\nalgebras. The resulting defining relations are expressed in terms of continuous\nq-Hermite polynomials and a new family of deformed Chebyshev polynomials.\n"}
+{"abstract": "  The usage of smartphone-collected respiratory sound, trained with deep\nlearning models, for detecting and classifying COVID-19 becomes popular\nrecently. It removes the need for in-person testing procedures especially for\nrural regions where related medical supplies, experienced workers, and\nequipment are limited. However, existing sound-based diagnostic approaches are\ntrained in a fully supervised manner, which requires large scale well-labelled\ndata. It is critical to discover new methods to leverage unlabelled respiratory\ndata, which can be obtained more easily. In this paper, we propose a novel\nself-supervised learning enabled framework for COVID-19 cough classification. A\ncontrastive pre-training phase is introduced to train a Transformer-based\nfeature encoder with unlabelled data. Specifically, we design a random masking\nmechanism to learn robust representations of respiratory sounds. The\npre-trained feature encoder is then fine-tuned in the downstream phase to\nperform cough classification. In addition, different ensembles with varied\nrandom masking rates are also explored in the downstream phase. Through\nextensive evaluations, we demonstrate that the proposed contrastive\npre-training, the random masking mechanism, and the ensemble architecture\ncontribute to improving cough classification performance.\n"}
+{"abstract": "  We report the discovery of a circumstellar debris disk viewed nearly edge-on\nand associated with the young, K1 star BD+45$^{\\circ}$598 using high-contrast\nimaging at 2.2$\\mu$m obtained at the W.M.~Keck Observatory. We detect the disk\nin scattered light with a peak significance of $\\sim$5$\\sigma$ over three\nepochs, and our best-fit model of the disk is an almost edge-on $\\sim$70 AU\nring, with inclination angle $\\sim$87$^\\circ$. Using the NOEMA interferometer\nat the Plateau de Bure Observatory operating at 1.3mm, we find resolved\ncontinuum emission aligned with the ring structure seen in the 2.2$\\mu$m\nimages. We estimate a fractional infrared luminosity of $L_{IR}/L_{tot}$\n$\\simeq6^{+2}_{-1}$$\\times$$10^{-4}$, higher than that of the debris disk\naround AU Mic. Several characteristics of BD+45$^{\\circ}$598, such as its\ngalactic space motion, placement in a color-magnitude diagram, and strong\npresence of Lithium, are all consistent with its membership in the $\\beta$\nPictoris Moving Group with an age of 23$\\pm$3 Myr. However, the galactic\nposition for BD+45$^{\\circ}$598 is slightly discrepant from previously-known\nmembers of the $\\beta$ Pictoris Moving Group, possibly indicating an extension\nof members of this moving group to distances of at least 70pc.\nBD+45$^{\\circ}$598 appears to be an example from a population of young\ncircumstellar debris systems associated with newly identified members of young\nmoving groups that can be imaged in scattered light, key objects for mapping\nout the early evolution of planetary systems from $\\sim$10-100 Myr. This target\nwill also be ideal for northern-hemisphere, high-contrast imaging platforms to\nsearch for self-luminous, planetary mass companions residing in this system.\n"}
+{"abstract": "  We study the fair division problem on divisible heterogeneous resources (the\ncake cutting problem) with strategic agents, where each agent can manipulate\nhis/her private valuation in order to receive a better allocation. A\n(direct-revelation) mechanism takes agents' reported valuations as input, and\noutputs an allocation that satisfies a given fairness requirement. A natural\nand fundamental open problem, first raised by [Chen et al., 2010] and\nsubsequently raised by [Procaccia, 2013] [Aziz and Ye, 2014] [Branzei and\nMiltersen, 2015] [Menon and Larson, 2017] [Bei et al., 2017] [Bei et al.,\n2020], etc., is whether there exists a deterministic, truthful and envy-free\n(or even proportional) cake cutting mechanism. In this paper, we resolve this\nopen problem by proving that there does not exist a deterministic, truthful and\nproportional cake cutting mechanism, even in the special case where all of the\nfollowings hold: 1. there are only two agents; 2. each agent's valuation is a\npiecewise-constant function; 3. each agent is hungry: each agent has a strictly\npositive value on any part of the cake. The impossibility result extends to the\ncase where the mechanism is allowed to leave some part of the cake unallocated.\n  To circumvent this impossibility result, we aim to design mechanisms that\npossess certain degree of truthfulness. Motivated by the kind of truthfulness\npossessed by the classical I-cut-you-choose protocol, we define a weaker notion\nof truthfulness: the risk-averse truthfulness. We show that the well-known\nmoving-knife procedure and Even-Paz algorithm do not have this truthful\nproperty. We propose a mechanism that is risk-averse truthful and envy-free,\nand a mechanism that is risk-averse truthful and proportional that always\noutputs allocations with connected pieces.\n"}
+{"abstract": "  The Deep Extragalactic VIsible Legacy Survey (DEVILS) is an ongoing\nhigh-completeness, deep spectroscopic survey of $\\sim$60,000 galaxies to\nY$<$21.2 mag, over $\\sim$6 deg2 in three well-studied deep extragalactic\nfields: D10 (COSMOS), D02 (XMM-LSS) and D03 (ECDFS). Numerous DEVILS projects\nall require consistent, uniformly-derived and state-of-the-art photometric data\nwith which to measure galaxy properties. Existing photometric catalogues in\nthese regions either use varied photometric measurement techniques for\ndifferent facilities/wavelengths leading to inconsistencies, older imaging data\nand/or rely on source detection and photometry techniques with known problems.\nHere we use the ProFound image analysis package and state-of-the-art imaging\ndatasets (including Subaru-HSC, VST-VOICE, VISTA-VIDEO and UltraVISTA-DR4) to\nderive matched-source photometry in 22 bands from the FUV to 500{\\mu}m. This\nphotometry is found to be consistent, or better, in colour-analysis to previous\napproaches using fixed-size apertures (which are specifically tuned to derive\ncolours), but produces superior total source photometry, essential for the\nderivation of stellar masses, star-formation rates, star-formation histories,\netc. Our photometric catalogue is described in detail and, after internal\nDEVILS team projects, will be publicly released for use by the broader\nscientific community.\n"}
+{"abstract": "  The Kepler and TESS missions delivered high-precision, long-duration\nphotometric time series for hundreds of main-sequence stars with\ngravito-inertial (g) pulsation modes. This high precision allows us to evaluate\nincreasingly detailed theoretical stellar models. Recent theoretical work\nextended the traditional approximation of rotation (TAR), a framework to\nevaluate the effect of the Coriolis acceleration on g-modes, to include the\neffects of the centrifugal acceleration in the approximation of slightly\ndeformed stars, which so far had mostly been neglected in asteroseismology.\nThis extension of the TAR was conceived by rederiving the TAR in a\ncentrifugally deformed, spheroidal coordinate system. We explore the effect of\nthe centrifugal acceleration on g modes and assess its detectability in\nspace-based photometry. We implement the new framework to calculate the\ncentrifugal deformation of precomputed 1D spherical stellar structure models\nand compute the corresponding g-mode frequencies, assuming uniform rotation.\nThe framework is evaluated for a grid of stellar structure models covering a\nrelevant parameter space for observed g-mode pulsators. The centrifugal\nacceleration modifies the effect of the Coriolis acceleration on g modes,\nnarrowing the equatorial band in which they are trapped. Furthermore, the\ncentrifugal acceleration causes the pulsation periods and period spacings of\nthe most common g modes (prograde dipole modes and r modes) to increase with\nvalues similar to the observational uncertainties in Kepler and TESS data. The\neffect of the centrifugal acceleration on g~modes is formally detectable in\nmodern space photometry. Implementation of the new theoretical framework in\nstellar structure and pulsation codes will allow for more precise asteroseismic\nmodelling of centrifugally deformed stars, to assess its effect on mode\nexcitation, -trapping and -damping.\n"}
+{"abstract": "  The analysis of Magnetic Resonance Imaging (MRI) sequences enables clinical\nprofessionals to monitor the progression of a brain tumor. As the interest for\nautomatizing brain volume MRI analysis increases, it becomes convenient to have\neach sequence well identified. However, the unstandardized naming of MRI\nsequences makes their identification difficult for automated systems, as well\nas makes it difficult for researches to generate or use datasets for machine\nlearning research. In the face of that, we propose a system for identifying\ntypes of brain MRI sequences based on deep learning. By training a\nConvolutional Neural Network (CNN) based on 18-layer ResNet architecture, our\nsystem can classify a volumetric brain MRI as a FLAIR, T1, T1c or T2 sequence,\nor whether it does not belong to any of these classes. The network was\nevaluated on publicly available datasets comprising both, pre-processed (BraTS\ndataset) and non-pre-processed (TCGA-GBM dataset), image types with diverse\nacquisition protocols, requiring only a few slices of the volume for training.\nOur system can classify among sequence types with an accuracy of 96.81%.\n"}
+{"abstract": "  Neural machine translation (NMT) has recently gained widespread attention\nbecause of its high translation accuracy. However, it shows poor performance in\nthe translation of long sentences, which is a major issue in low-resource\nlanguages. It is assumed that this issue is caused by insufficient number of\nlong sentences in the training data. Therefore, this study proposes a simple\ndata augmentation method to handle long sentences. In this method, we use only\nthe given parallel corpora as the training data and generate long sentences by\nconcatenating two sentences. Based on the experimental results, we confirm\nimprovements in long sentence translation by the proposed data augmentation\nmethod, despite its simplicity. Moreover, the translation quality is further\nimproved by the proposed method, when combined with back-translation.\n"}
+{"abstract": "  It is commonly known that Killing vectors and tensors are in one-to-one\ncorrespondence with polynomial first integrals of the geodesic equation. In\nthis work, metrics admitting nonpolynomial first integrals of the geodesic\nequation are constructed, each of which revealing a chain of generalised\nKilling vectors.\n"}
+{"abstract": "  Motivated by the philosophy that $C^*$-algebras reflect noncommutative\ntopology, we investigate the stable homotopy theory of the (opposite) category\nof $C^*$-algebras. We focus on $C^*$-algebras which are non-commutative\nCW-complexes in the sense of [ELP]. We construct the stable $\\infty$-category\nof noncommutative CW-spectra, which we denote by $\\mathtt{NSp}$. Let\n$\\mathcal{M}$ be the full spectral subcategory of $\\mathtt{NSp}$ spanned by\n\"noncommutative suspension spectra\" of matrix algebras. Our main result is that\n$\\mathtt{NSp}$ is equivalent to the $\\infty$-category of spectral presheaves on\n$\\mathcal{M}$.\n  To prove this we first prove a general result which states that any compactly\ngenerated stable $\\infty$-category is naturally equivalent to the\n$\\infty$-category of spectral presheaves on a full spectral subcategory spanned\nby a set of compact generators. This is an $\\infty$-categorical version of a\nresult by Schwede and Shipley [ScSh1]. In proving this we use the language of\nenriched $\\infty$-categories as developed by Hinich [Hin2,Hin3].\n  We end by presenting a \"strict\" model for $\\mathcal{M}$. That is, we define a\ncategory $\\mathcal{M}_s$ strictly enriched in a certain monoidal model category\nof spectra $\\mathtt{Sp^M}$. We give a direct proof that the category of\n$\\mathtt{Sp^M}$-enriched presheaves $\\mathcal{M}_s^{op}\\to\\mathtt{Sp^M}$ with\nthe projective model structure models $\\mathtt{NSp}$ and conclude that\n$\\mathcal{M}_s$ is a strict model for $\\mathcal{M}$.\n"}
+{"abstract": "  We consider a hierarchy of four typed call-by-value languages with either\nhigher-order or ground-type references and with either callcc or no control\noperator.Our first result is a fully abstract trace model for the most\nexpressive setting, featuring both higher-order references and callcc,\nconstructed in the spirit of operational game semantics. Next we examine the\nimpact of suppressing higher-order references and callcc in contexts and\nprovide an operational explanation for the game-semantic conditions known as\nvisibility and bracketing respectively.This allows us to refine the original\nmodel to provide fully abstract trace models of interaction with contexts that\nneed not use higher-order references or callcc. Along the way, we discuss the\nrelationship between error- and termination-based contextual testing in each\ncase, and relate the two to trace and complete trace equivalence\nrespectively.Overall, the paper provides a systematic development of\noperational game semantics for all four cases, which represent the state-based\nface of the so-called semantic cube.\n"}
+{"abstract": "  We describe a functional framework suitable to the analysis of the\nCahn-Hilliard equation on an evolving surface whose evolution is assumed to be\ngiven \\textit{a priori}. The model is derived from balance laws for an order\nparameter with an associated Cahn-Hilliard energy functional and we establish\nwell-posedness for general regular potentials, satisfying some prescribed\ngrowth conditions, and for two singular nonlinearities -- the thermodynamically\nrelevant logarithmic potential and a double obstacle potential. We identify,\nfor the singular potentials, necessary conditions on the initial data and the\nevolution of the surfaces for global-in-time existence of solutions, which\narise from the fact that integrals of solutions are preserved over time, and\nprove well-posedness for initial data on a suitable set of admissible initial\nconditions. We then briefly describe an alternative derivation leading to a\nmodel that instead preserves a weighted integral of the solution, and explain\nhow our arguments can be adapted in order to obtain global-in-time existence\nwithout restrictions on the initial conditions. Some illustrative examples and\nfurther research directions are given in the final sections.\n"}
+{"abstract": "  While unbiased machine learning models are essential for many applications,\nbias is a human-defined concept that can vary across tasks. Given only\ninput-label pairs, algorithms may lack sufficient information to distinguish\nstable (causal) features from unstable (spurious) features. However, related\ntasks often share similar biases -- an observation we may leverage to develop\nstable classifiers in the transfer setting. In this work, we explicitly inform\nthe target classifier about unstable features in the source tasks.\nSpecifically, we derive a representation that encodes the unstable features by\ncontrasting different data environments in the source task. We achieve\nrobustness by clustering data of the target task according to this\nrepresentation and minimizing the worst-case risk across these clusters. We\nevaluate our method on both text and image classifications. Empirical results\ndemonstrate that our algorithm is able to maintain robustness on the target\ntask, outperforming the best baseline by 22.9% in absolute accuracy across 12\ntransfer settings. Our code is available at https://github.com/YujiaBao/Tofu.\n"}
+{"abstract": "  A comb domain is defined to be the entire complex plain with a collection of\nvertical slits, symmetric over the real axis, removed. In this paper, we\nconsider the question of determining whether the exit time of planar Brownian\nmotion from such a domain has finite $p$-th moment. This question has been\naddressed before in relation to starlike domains, but these previous results do\nnot apply to comb domains. Our main result is a sufficient condition on the\nlocation of the slits which ensures that the $p$-th moment of the exit time is\nfinite. Several auxiliary results are also presented, including a construction\nof a comb domain whose exit time has infinite $p$-th moment for all $p \\geq\n1/2$.\n"}
+{"abstract": "  Word embeddings are often used in natural language processing as a means to\nquantify relationships between words. More generally, these same word embedding\ntechniques can be used to quantify relationships between features. In this\npaper, we first consider multiple different word embedding techniques within\nthe context of malware classification. We use hidden Markov models to obtain\nembedding vectors in an approach that we refer to as HMM2Vec, and we generate\nvector embeddings based on principal component analysis. We also consider the\npopular neural network based word embedding technique known as Word2Vec. In\neach case, we derive feature embeddings based on opcode sequences for malware\nsamples from a variety of different families. We show that we can obtain better\nclassification accuracy based on these feature embeddings, as compared to HMM\nexperiments that directly use the opcode sequences, and serve to establish a\nbaseline. These results show that word embeddings can be a useful feature\nengineering step in the field of malware analysis.\n"}
+{"abstract": "  Machine learning inference is increasingly being executed locally on mobile\nand embedded platforms, due to the clear advantages in latency, privacy and\nconnectivity. In this paper, we present approaches for online resource\nmanagement in heterogeneous multi-core systems and show how they can be applied\nto optimise the performance of machine learning workloads. Performance can be\ndefined using platform-dependent (e.g. speed, energy) and platform-independent\n(accuracy, confidence) metrics. In particular, we show how a Deep Neural\nNetwork (DNN) can be dynamically scalable to trade-off these various\nperformance metrics. Achieving consistent performance when executing on\ndifferent platforms is necessary yet challenging, due to the different\nresources provided and their capability, and their time-varying availability\nwhen executing alongside other workloads. Managing the interface between\navailable hardware resources (often numerous and heterogeneous in nature),\nsoftware requirements, and user experience is increasingly complex.\n"}
+{"abstract": "  Basic physics of drift-wave turbulence and zonal flows has long been studied\nwithin the framework of wave-kinetic theory. Recently, this framework has been\nre-examined from first principles, which has led to more accurate yet still\ntractable \"improved\" wave-kinetic equations. In particular, these equations\nreveal an important effect of the zonal-flow \"curvature\" (the second radial\nderivative of the flow velocity) on dynamics and stability of drift waves and\nzonal flows. We overview these recent findings and present a consolidated\nhigh-level picture of (mostly quasilinear) zonal-flow physics within reduced\nmodels of drift-wave turbulence.\n"}
+{"abstract": "  A search for the single material system that simultaneously exhibits\ntopological phase and intrinsic superconductivity has been largely limited,\nalthough such a system is far more favorable especially for the quantum device\napplications. Except artificially engineered topological superconductivity in\nheterostructure systems, another alternative is to have superconductivity\narising from the topological materials by pressure or other clean technology.\nHere, based on first-principles calculations, we first show that\nquasi-one-dimensional compound (NbSe4)2I represents a rare example of a chiral\nWeyl semimetal in which the set of symmetry-related Weyl points (WPs) exhibit\nthe same chiral charge at a certain energy. The net chiral charge (NCC) of the\nbelow Fermi level EF (or a certain energy) can be tuned by pressure. In\naddition, a partial disorder induced by pressure accompanied with\nsuperconductivity emerges. Although amorphization of the iodine sub-lattice\nunder high pressure, the one-dimensional NbSe4 chains in (NbSe4)2I remain\nintact and provide a superconducting channel in one dimension. Our combined\ntheoretical and experimental research provide critical insight into a new phase\nof the one-dimensional system, in which distinctive phase transitions and\ncorrelated topological states emerge upon compression.\n"}
+{"abstract": "  Most chatbot literature that focuses on improving the fluency and coherence\nof a chatbot, is dedicated to making chatbots more human-like. However, very\nlittle work delves into what really separates humans from chatbots -- humans\nintrinsically understand the effect their responses have on the interlocutor\nand often respond with an intention such as proposing an optimistic view to\nmake the interlocutor feel better. This paper proposes an innovative framework\nto train chatbots to possess human-like intentions. Our framework includes a\nguiding chatbot and an interlocutor model that plays the role of humans. The\nguiding chatbot is assigned an intention and learns to induce the interlocutor\nto reply with responses matching the intention, for example, long responses,\njoyful responses, responses with specific words, etc. We examined our framework\nusing three experimental setups and evaluated the guiding chatbot with four\ndifferent metrics to demonstrate flexibility and performance advantages.\nAdditionally, we performed trials with human interlocutors to substantiate the\nguiding chatbot's effectiveness in influencing the responses of humans to a\ncertain extent. Code will be made available to the public.\n"}
+{"abstract": "  We study the key features of the Josephson transport through a curved\nsemiconducting nanowire. Based on numerical simulations and analytical\nestimates within the framework of the Bogoliubov-de Gennes equations we find\nthe ground-state phase difference $\\varphi_0$ between the superconducting leads\ntuned by the spin splitting field $h$ driving the system from the topologically\ntrivial to the nontrivial superconducting state. The phase $\\varphi_0$ vanishes\nfor rather small $h$, grows in a certain field range around the topological\ntransition, and then saturates at large $h$ in the Kitaev regime. Both the\nsubgap and the continuum quasiparticle levels are responsible for the above\nbehavior of the anomalous Josephson phase. It is demonstrated that the\ncrossover region on $\\varphi_0(h)$ dependencies reveals itself in the\nsuperconducting diode effect. The resulting tunable phase battery can be used\nas a probe of topological transitions in Majorana networks and can become a\nuseful element of various quantum computation devices.\n"}
+{"abstract": "  In this paper we give an alternative construction of a certain class of\nDeformed Double Current Algebras. These algebras are deformations of $U({\\rm\nEnd}(\\Bbbk^r)[x,y])$ and they were initially defined and studied by N.Guay in\nhis papers. Here we construct them as algebras of endomorphisms in Deligne\ncategory. We do this by taking an ultraproduct of spherical subalgebras of the\nextended Cherednik algebras of finite rank.\n"}
+{"abstract": "  Call centers, in which human operators attend clients using textual chat, are\nvery common in modern e-commerce. Training enough skilled operators who are\nable to provide good service is a challenge. We suggest an algorithm and a\nmethod to train and implement an assisting agent that provides on-line advice\nto operators while they attend clients. The agent is domain-independent and can\nbe introduced to new domains without major efforts in design, training and\norganizing structured knowledge of the professional discipline. We demonstrate\nthe applicability of the system in an experiment that realizes its full\nlife-cycle on a specific domain and analyze its capabilities.\n"}
+{"abstract": "  Currently several Bayesian approaches are available to estimate large sparse\nprecision matrices, including Bayesian graphical Lasso (Wang, 2012), Bayesian\nstructure learning (Banerjee and Ghosal, 2015), and graphical horseshoe (Li et\nal., 2019). Although these methods have exhibited nice empirical performances,\nin general they are computationally expensive. Moreover, we have limited\nknowledge about the theoretical properties, e.g., posterior contraction rate,\nof graphical Bayesian Lasso and graphical horseshoe. In this paper, we propose\na new method that integrates some commonly used continuous shrinkage priors\ninto a quasi-Bayesian framework featured by a pseudo-likelihood. Under mild\nconditions, we establish an optimal posterior contraction rate for the proposed\nmethod. Compared to existing approaches, our method has two main advantages.\nFirst, our method is computationally more efficient while achieving similar\nerror rate; second, our framework is more amenable to theoretical analysis.\nExtensive simulation experiments and the analysis on a real data set are\nsupportive of our theoretical results.\n"}
+{"abstract": "  3D face recognition has shown its potential in many application scenarios.\nAmong numerous 3D face recognition methods, deep-learning-based methods have\ndeveloped vigorously in recent years. In this paper, an end-to-end deep\nlearning network entitled Sur3dNet-Face for point-cloud-based 3D face\nrecognition is proposed. The network uses PointNet as the backbone, which is a\nsuccessful point cloud classification solution but does not work properly in\nface recognition. Supplemented with modifications in network architecture and a\nfew-data guided learning framework based on Gaussian process morphable model,\nthe backbone is successfully modified for 3D face recognition. Different from\nexisting methods training with a large amount of data in multiple datasets, our\nmethod uses Spring2003 subset of FRGC v2.0 for training which contains only 943\nfacial scans, and the network is well trained with the guidance of such a small\namount of real data. Without fine-tuning on the test set, the Rank-1\nRecognition Rate (RR1) is achieved as follows: 98.85% on FRGC v2.0 dataset and\n99.33% on Bosphorus dataset, which proves the effectiveness and the\npotentiality of our method.\n"}
+{"abstract": "  In this perspective we discuss recent theoretical and experimental concepts\ngiving a route to a better understanding of conventional and unconventional\npairing mechanisms between opposite-spin fermions arising in one-dimensional\nmesoscopic systems. With special attention, we focus on the problem of\nexperimental detectability of correlations between particles. We argue that\nstate-of-the-art experiments with few ultracold fermions may finally break an\nimpasse and give pioneering and unquestionable verification of the existence of\ncorrelated pairs with non-zero center-of-mass momentum.\n"}
+{"abstract": "  Current intent classification approaches assign binary intent class\nmemberships to natural language utterances while disregarding the inherent\nvagueness in language and the corresponding vagueness in intent class\nboundaries. In this work, we propose a scheme to address the ambiguity in\nsingle-intent as well as multi-intent natural language utterances by creating\ndegree memberships over fuzzified intent classes. To our knowledge, this is the\nfirst work to address and quantify the impact of the fuzzy nature of natural\nlanguage utterances over intent category memberships. Additionally, our\napproach overcomes the sparsity of multi-intent utterance data to train\nclassification models by using a small database of single intent utterances to\ngenerate class memberships over multi-intent utterances. We evaluate our\napproach over two task-oriented dialog datasets, across different fuzzy\nmembership generation techniques and approximate string similarity measures.\nOur results reveal the impact of lexical overlap between utterances of\ndifferent intents, and the underlying data distributions, on the fuzzification\nof intent memberships. Moreover, we evaluate the accuracy of our approach by\ncomparing the defuzzified memberships to their binary counterparts, across\ndifferent combinations of membership functions and string similarity measures.\n"}
+{"abstract": "  We study neologism use in two samples of early English correspondence, from\n1640--1660 and 1760--1780. Of especial interest are the early adopters of new\nvocabulary, the social groups they represent, and the types and functions of\ntheir neologisms. We describe our computer-assisted approach and note the\ndifficulties associated with massive variation in the corpus. Our findings\ninclude that while male letter-writers tend to use neologisms more frequently\nthan women, the eighteenth century seems to have provided more opportunities\nfor women and the lower ranks to participate in neologism use as well. In both\nsamples, neologisms most frequently occur in letters written between close\nfriends, which could be due to this less stable relationship triggering more\ncreative language use. In the seventeenth-century sample, we observe the\ninfluence of the English Civil War, while the eighteenth-century sample appears\nto reflect the changing functions of letter-writing, as correspondence is\nincreasingly being used as a tool for building and maintaining social\nrelationships in addition to exchanging information.\n"}
+{"abstract": "  With the aim of matching a pair of instances from two different modalities,\ncross modality mapping has attracted growing attention in the computer vision\ncommunity. Existing methods usually formulate the mapping function as the\nsimilarity measure between the pair of instance features, which are embedded to\na common space. However, we observe that the relationships among the instances\nwithin a single modality (intra relations) and those between the pair of\nheterogeneous instances (inter relations) are insufficiently explored in\nprevious approaches. Motivated by this, we redefine the mapping function with\nrelational reasoning via graph modeling, and further propose a GCN-based\nRelational Reasoning Network (RR-Net) in which inter and intra relations are\nefficiently computed to universally resolve the cross modality mapping problem.\nConcretely, we first construct two kinds of graph, i.e., Intra Graph and Inter\nGraph, to respectively model intra relations and inter relations. Then RR-Net\nupdates all the node features and edge features in an iterative manner for\nlearning intra and inter relations simultaneously. Last, RR-Net outputs the\nprobabilities over the edges which link a pair of heterogeneous instances to\nestimate the mapping results. Extensive experiments on three example tasks,\ni.e., image classification, social recommendation and sound recognition,\nclearly demonstrate the superiority and universality of our proposed model.\n"}
+{"abstract": "  High-altitude balloon experiments are becoming very popular among\nuniversities and research institutes as they can be used for testing\ninstruments eventually intended for space, and for simple astronomical\nobservations of Solar System objects like the Moon, comets, and asteroids,\ndifficult to observe from the ground due to atmosphere. Further, they are one\nof the best platforms for atmospheric studies. In this experiment, we build a\nsimple 1U CubeSat and, by flying it on a high-altitude balloon to an altitude\nof about 30 km, where the total payload weighted 4.9 kg and examine how some\nparameters, such as magnetic field, humidity, temperature or pressure, vary as\na function of altitude. We also calibrate the magnetometer to remove the hard\niron and soft iron errors. Such experiments and studies through a stratospheric\nballoon flights can also be used to study the performance of easily available\ncommercial sensors in extreme conditions as well. We present the results of the\nfirst flight, which helped us study the functionality of the various sensors\nand electronics at low temperatures reaching about -40 degrees Celsius. Further\nthe motion of the payload has been tracked throughout this flight. This\nexperiment took place on 8 March 2020 from the CREST campus of the Indian\nInstitute of Astrophysics, Bangalore. Using the results from this flight, we\nidentify and rectify the errors to obtain better results from the subsequent\nflights.\n"}
+{"abstract": "  Matrix elements between nonorthogonal Slater determinants represent an\nessential component of many emerging electronic structure methods. However,\nevaluating nonorthogonal matrix elements is conceptually and computationally\nharder then their orthogonal counterparts. While several different approaches\nhave been developed, these are predominantly derived from the first-quantised\ngeneralised Slater-Condon rules and usually require biorthogonal occupied\norbitals to be computed for each matrix element. For coupling terms between\nnonorthogonal excited configurations, a second-quantised approach such as the\nnonorthogonal Wick's theorem is more desirable, but this fails when the two\nreference determinants have a zero many-body overlap. In this contribution, we\nderive an entirely generalised extension to the nonorthogonal Wick's theorem\nthat is applicable to all pairs of determinants with nonorthogonal orbitals.\nOur approach creates a universal methodology for evaluating any nonorthogonal\nmatrix element and allows Wick's theorem and the generalised Slater-Condon\nrules to be unified for the first time. Furthermore, we present a simple\nwell-defined protocol for deriving arbitrary coupling terms between\nnonorthogonal excited configurations. In the case of overlap and one-body\noperators, this protocol recovers efficient formulae with reduced scaling,\npromising significant computational acceleration for methods that rely on such\nterms.\n"}
+{"abstract": "  We present a new class of Langevin based algorithms, which overcomes many of\nthe known shortcomings of popular adaptive optimizers that are currently used\nfor the fine tuning of deep learning models. Its underpinning theory relies on\nrecent advances of Euler's polygonal approximations for stochastic differential\nequations (SDEs) with monotone coefficients. As a result, it inherits the\nstability properties of tamed algorithms, while it addresses other known\nissues, e.g. vanishing gradients in neural networks. In particular, we provide\na nonasymptotic analysis and full theoretical guarantees for the convergence\nproperties of an algorithm of this novel class, which we named TH$\\varepsilon$O\nPOULA (or, simply, TheoPouLa). Finally, several experiments are presented with\ndifferent types of deep learning models, which show the superior performance of\nTheoPouLa over many popular adaptive optimization algorithms.\n"}
+{"abstract": "  Distributing points on a (possibly high-dimensional) sphere with minimal\nenergy is a long-standing problem in and outside the field of mathematics. This\npaper considers a novel energy function that arises naturally from statistics\nand combinatorial optimization, and studies its theoretical properties. Our\nresult solves both the exact optimal spherical point configurations in certain\ncases and the minimal energy asymptotics under general assumptions. Connections\nbetween our results and the L1-Principle Component Analysis and Quasi-Monte\nCarlo methods are also discussed.\n"}
+{"abstract": "  Detecting cancers at early stages can dramatically reduce mortality rates.\nTherefore, practical cancer screening at the population level is needed. Here,\nwe develop a comprehensive detection system to classify all common cancer\ntypes. By integrating artificial intelligence deep learning neural network and\nnoncoding RNA biomarkers selected from massive data, our system can accurately\ndetect cancer vs healthy object with 96.3% of AUC of ROC (Area Under Curve of a\nReceiver Operating Characteristic curve). Intriguinely, with no more than 6\nbiomarkers, our approach can easily discriminate any individual cancer type vs\nnormal with 99% to 100% AUC. Furthermore, a comprehensive marker panel can\nsimultaneously multi-classify all common cancers with a stable 78% of accuracy\nat heterological cancerous tissues and conditions. This provides a valuable\nframework for large scale cancer screening. The AI models and plots of results\nwere available in https://combai.org/ai/cancerdetection/\n"}
+{"abstract": "  We present a multi-instrument spectroscopic analysis of the unique Li/Na-rich\ngiant star 25664 in Omega Centauri using spectra acquired with FLAMES-GIRAFFE,\nX-SHOOTER, UVES and HARPS. Li and Na abundances have been derived from the UVES\nspectrum using transitions weakly sensitive to non-local thermodynamic\nequilibrium and assumed isotopic ratio. This new analysis confirms the\nsurprising Li and Na abundances of this star (A(Li) =+2.71+-0.07 dex,\n[Na/Fe]=+1.00+-0.05 dex). Additionally, we provide new pieces of evidence for\nits chemical characterisation. The 12C/13C isotopic ratio (15+-2) shows that\nthis star has not yet undergone the extra-mixing episode usually associated\nwith the red giant branch bump. Therefore, we can rule out the scenario of\nefficient deep extra-mixing during the red giant branch phase envisaged to\nexplain the high Li and Na abundances. Also, the star exhibits high abundances\nof both C and N ([C/Fe]=+0.45+-0.16 dex and [N/Fe]=+0.99+-0.20 dex), not\ncompatible with the typical C-N anticorrelation observed in globular cluster\nstars. We found evidence of a radial velocity variability in 25664, suggesting\nthat the star could be part of a binary system, likely having accreted material\nfrom a more massive companion when the latter was evolving in the AGB phase.\nViable candidates for the donor star are AGB stars with 3-4 Msun and super-AGB\nstars (~7-8 Msun), both able to produce Li- and Na-rich material.\nAlternatively, the star could be formed from the pure ejecta of a super-AGB\nstars, before the dilution with primordial gas occurs.\n"}
+{"abstract": "  A sample of 46 stars, host of exoplanets, is used to search for a connection\nbetween their formation process and the formation of the planets rotating\naround them. Separating our sample in two, stars hosting high-mass exoplanets\n(HMEs) and low-mass exoplanets (LMEs), we found the former to be more massive\nand to rotate faster than the latter. We also found the HMEs to have higher\norbital angular momentum than the LMEs and to have lost more angular momentum\nthrough migration. These results are consistent with the view that the more\nmassive the star and higher its rotation, the more massive was its\nprotoplanetarys disk and rotation, and the more efficient the extraction of\nangular momentum from the planets.\n"}
+{"abstract": "  We present 63 new multi-site radial velocity measurements of the K1III giant\nHD 76920, which was recently reported to host the most eccentric planet known\nto orbit an evolved star. We focussed our observational efforts on the time\naround the predicted periastron passage and achieved near-continuous phase\ncoverage of the corresponding radial velocity peak. By combining our radial\nvelocity measurements from four different instruments with previously published\nones, we confirm the highly eccentric nature of the system, and find an even\nhigher eccentricity of $e=0.8782 \\pm 0.0025$, an orbital period of\n$415.891^{+0.043}_{-0.039}\\,\\mathrm{d}$, and a minimum mass of\n$3.13^{+0.41}_{-0.43}\\,\\mathrm{M_J}$ for the planet. The uncertainties in the\norbital elements are greatly reduced, especially for the period and\neccentricity. We also performed a detailed spectroscopic analysis to derive\natmospheric stellar parameters, and thus the fundamental stellar parameters\n($M_*, R_*, L_*$), taking into account the parallax from Gaia DR2, and\nindependently determined the stellar mass and radius using asteroseismology.\nIntriguingly, at periastron the planet comes to within 2.4 stellar radii of its\nhost star's surface. However, we find that the planet is not currently\nexperiencing any significant orbital decay and will not be engulfed by the\nstellar envelope for at least another $50-80$ Myr. Finally, while we calculate\na relatively high transit probability of $16\\%$, we did not detect a transit in\nthe TESS photometry.\n"}
+{"abstract": "  In this paper, we first address the general fractional integrals and\nderivatives with the Sonine kernels that possess the integrable singularities\nof power function type at the point zero. Both particular cases and\ncompositions of these operators are discussed. Then we proceed with a\nconstruction of an operational calculus of the Mikusi\\'nski type for the\ngeneral fractional derivatives with the Sonine kernels. This operational\ncalculus is applied for analytical treatment of some initial value problems for\nthe fractional differential equations with the general fractional derivatives.\nThe solutions are expressed in form of the convolution series that generalize\nthe power series for the exponential and the Mittag-Leffler functions.\n"}
+{"abstract": "  Beam matching is a common technique that is routinely employed in accelerator\ndesign with the aim of minimizing beam losses and preservation of beam\nbrightness. Despite being widely used, a full theoretical understanding of beam\nmatching in 6D remains elusive. Here, we present an analytical treatment of 6D\nbeam matching of a high-intensity beam onto an RF structure. We begin our\nanalysis within the framework of a linear model, and apply the averaging method\nto a set of 3D beam envelope equations. Accordingly, we obtain a matched\nsolution that is comprised of smoothed envelopes and periodic terms, describing\nenvelope oscillations with the period of the focusing structure. We then\nconsider the nonlinear regime, where the beam size is comparable with the\nseparatrix size. Stating with a Hamiltonian analysis in 6D phase space, we\nattain a self-consistent beam profile and show that it is significantly\ndifferent from the commonly used ellipsoidal shape. Subsequently, we analyze\nthe special case of an equilibrium with equal space charge depression between\nall degrees of freedom. Comparison of beam dynamics for equipartitioned, equal\nspace charge depression, and equal emittances beams is given. Finally, we\npresent experimental results on beam matching in the LANSCE linac.\n"}
+{"abstract": "  Due to the global pandemic, in March 2020 we in academia and industry were\nabruptly forced into working from home. Yet teaching never stopped, and neither\ndid developing software, fixing software, and expanding into new markets.\nDemands for flexible ways of working, responding to new requirements, have\nnever been so high. How did we manage to continue working, when we had to\nsuddenly switch all communication to online and virtual forms of contact? In\nthis short paper we describe how Ocuco Ltd., a medium-sized organization\nheadquartered in Ireland, managed our software development teams--distributed\nthroughout Ireland, Europe, Asia and America during the COVID-19 pandemic. We\ndescribe how we expanded, kept our customers happy, and our teams motivated. We\nmade changes, some large, such as providing emergency financial support; others\nsmall, like implementing regular online social pizza evenings. Technology and\nprocess changes were minor, an advantage of working in globally distributed\nteams since 2016, when development activities were coordinated according to the\nScaled Agile Framework (SAFe). The results of implementing the changes were\nsatisfying; productivity went up, we gained new customers, and preliminary\nresults from our wellness survey indicate that everyone feels extremely\nwell-supported by management to achieve their goals. However, the anonymised\nsurvey responses did show some developers' anxiety levels were slightly raised,\nand many are working longer hours. Administering this survey is very\nbeneficial, as now we know, so we can act.\n"}
+{"abstract": "  We show that the cross section for diffractive dissociation of a small onium\noff a large nucleus at total rapidity $Y$ and requiring a minimum rapidity gap\n$Y_{\\text{gap}}$ can be identified, in a well-defined parametric limit, with a\nsimple classical observable on the stochastic process representing the\nevolution of the state of the onium, as its rapidity increases, in the form of\ncolor dipole branchings: It formally coincides with twice the probability that\nan even number of these dipoles effectively participate in the scattering, when\nviewed in a frame in which the onium is evolved to the rapidity\n$Y-Y_{\\text{gap}}$. Consequently, finding asymptotic solutions to the\nKovchegov-Levin equation, which rules the $Y$-dependence of the diffractive\ncross section, boils down to solving a probabilistic problem. Such a\nformulation authorizes the derivation of a parameter-free analytical expression\nfor the gap distribution. Interestingly enough, events in which many dipoles\ninteract simultaneously play an important role, since the distribution of the\nnumber $k$ of dipoles participating in the interaction turns out to be\nproportional to $1/[k(k-1)]$.\n"}
+{"abstract": "  In this paper, the effect on the ultrasonic attenuation of the grain size\nheterogeneity in polycrystals is analyzed. First, new analytical developments\nallowing the extension of the unified theory of Stanke and Kino to general\ngrain size distributions are presented. It is then shown that one can\nadditively decompose the attenuation coefficient provided that groups of grains\nare defined. Second, the study is specialized to a bimodal distribution of the\ngrain size for which microstructures are numerically modeled by means of the\nsoftware Neper. The additive partition of the attenuation coefficient into\ncontributions coming from large and small grains motivates the derivation of an\noptimization procedure for characterizing the grain size distribution. The\naforementioned approach, which is based on a least squares minimization, is at\nlast presented and illustrated on both analytical and numerical attenuation\ndata. It is thus shown that the method provides satisfying approximations of\nvolume fractions of large grains and modal equivalent diameters from the\nfrequency-dependent attenuation coefficient.\n"}
+{"abstract": "  This paper proposes an enhanced coarray transformation model (EDCTM) and a\nmixed greedy maximum likelihood algorithm called List-Based Maximum Likelihood\nOrthogonal Matching Pursuit (LBML-OMP) for direction-of-arrival estimation with\nnon-uniform linear arrays (NLAs). The proposed EDCTM approach obtains improved\nestimates when Khatri-Rao product-based models are used to generate difference\ncoarrays under the assumption of uncorrelated sources. In the proposed LBML-OMP\ntechnique, for each iteration a set of candidates is generated based on the\ncorrelation-maximization between the dictionary and the residue vector.\nLBML-OMP then chooses the best candidate based on a reduced-complexity\nasymptotic maximum likelihood decision rule. Simulations show the improved\nresults of EDCTM over existing approaches and that LBML-OMP outperforms\nexisting sparse recovery algorithms as well as Spatial Smoothing Multiple\nSignal Classification with NLAs.\n"}
+{"abstract": "  Let $0 \\in \\Gamma$ and $\\Gamma \\setminus \\{0\\}$ be an abelian group under\nmultiplication, where $\\Gamma \\setminus \\{0\\} \\subseteq \\{ z\\in \\mathbb{C}:\n|z|=1 \\}$. Define $\\mathcal{H}_{n}(\\Gamma)$ to be the set of all $n\\times n$\nHermitian matrices with entries in $\\Gamma$, whose diagonal entries are zero.\nWe introduce the notion of switching equivalence on $\\mathcal{H}_{n}(\\Gamma)$.\nWe find a characterization, in terms of fundamental cycles of graphs, of\nswitching equivalence of matrices in $\\mathcal{H}_{n}(\\Gamma)$. We give\nsufficient conditions to characterize the cospectral matrices in\n$\\mathcal{H}_{n}(\\Gamma)$. We find bounds on the number of switching\nequivalence classes of all mixed graphs with the same underlying graph. We also\nprovide the size of all switching equivalence classes of mixed cycles, and give\na formula that calculates the size of a switching equivalence class of a mixed\nplanar graph. We also discuss the action of automorphism group of a graph on\nswitching equivalence classes.\n"}
+{"abstract": "  In photosynthetic organisms, the energy of light during illumination is\nabsorbed by the antenna complexes, which is transmitted by excitons and is\neither absorbed by the reaction centers (RCs), which have been closed in this\nway, or emitted by fluorescence. The basic components of the dynamics of light\nabsorption have been integrated into a simple model of exciton migration, which\ncontains two parameters: the exciton hopping probability and the exciton\nlifetime. During continuous radiation with light the fraction of closed RCs,\n$x$, continuously increases and at a critical threshold, $x_c$, a percolation\ntransition takes place. Performing extensive Monte Carlo simulations we study\nthe properties of the transition in this correlated percolation model. We\nmeasure the spanning probability in the vicinity of $x_c$, as well as the\nfractal properties of the critical percolating cluster, both in the bulk and at\nthe surface.\n"}
+{"abstract": "  Consider a truck filled with boxes of varying size and unknown mass and an\nindustrial robot with end-effectors that can unload multiple boxes from any\nreachable location. In this work, we investigate how would the robot with the\nhelp of a simulator, learn to maximize the number of boxes unloaded by each\naction. Most high-fidelity robotic simulators like ours are time-consuming.\nTherefore, we investigate the above learning problem with a focus on minimizing\nthe number of simulation runs required. The optimal decision-making problem\nunder this setting can be formulated as a multi-class classification problem.\nHowever, to obtain the outcome of any action requires us to run the\ntime-consuming simulator, thereby restricting the amount of training data that\ncan be collected. Thus, we need a data-efficient approach to learn the\nclassifier and generalize it with a minimal amount of data. A high-fidelity\nphysics-based simulator is common in general for complex manipulation tasks\ninvolving multi-body interactions. To this end, we train an optimal decision\ntree as the classifier, and for each branch of the decision tree, we reason\nabout the confidence in the decision using a Probably Approximately Correct\n(PAC) framework to determine whether more simulator data will help reach a\ncertain confidence level. This provides us with a mechanism to evaluate when\nsimulation can be avoided for certain decisions, and when simulation will\nimprove the decision making. For the truck unloading problem, our experiments\nshow that a significant reduction in simulator runs can be achieved using the\nproposed method as compared to naively running the simulator to collect data to\ntrain equally performing decision trees.\n"}
+{"abstract": "  We report on a Rosenbluth separation using previously published data by the\nCLAS collaboration in Hall B, Jefferson Lab for exclusive $\\pi^{0}$ deeply\nvirtual electroproduction (DVEP) from the proton at a mean $Q^{2}$ of $\\approx$\n2 (GeV/c)$^{2}$. The central question we address is the applicability of\nfactorization in $\\pi^0$ DVEP at these kinematics. The results of our\nRosenbluth separation clearly demonstrate the dominance of the longitudinal\ncontribution to the cross section. The extracted longitudinal and transverse\ncontributions are in agreement with previous data from Hall A at Jefferson Lab,\nbut over a much wider $-t$ range (0.12 - 1.8 (GeV/c)$^{2}$). The measured\ndominance of the longitudinal contribution at $Q^{2} \\approx$ 2 (GeV/c)$^{2}$\nis consistent with the expectation of the handbag factorization theorem. We\nfind that $\\sigma_L(t) \\sim 1/(-t)$ for $-t >$ 0.5 (GeV/c)$^2$. Determination\nof both longitudinal and transverse contributions to the deeply virtual\n$\\pi^{0}$ electroproduction cross section allows extraction of additional GPDs.\n"}
+{"abstract": "  Accurate segmentation for medical images is important for clinical diagnosis.\nExisting automatic segmentation methods are mainly based on fully supervised\nlearning and have an extremely high demand for precise annotations, which are\nvery costly and time-consuming to obtain. To address this problem, we proposed\nan automatic CT segmentation method based on weakly supervised learning, by\nwhich one could train an accurate segmentation model only with weak annotations\nin the form of bounding boxes. The proposed method is composed of two steps: 1)\ngenerating pseudo masks with bounding box annotations by k-means clustering,\nand 2) iteratively training a 3D U-Net convolutional neural network as a\nsegmentation model. Some data pre-processing methods are used to improve\nperformance. The method was validated on four datasets containing three types\nof organs with a total of 627 CT volumes. For liver, spleen and kidney\nsegmentation, it achieved an accuracy of 95.19%, 92.11%, and 91.45%,\nrespectively. Experimental results demonstrate that our method is accurate,\nefficient, and suitable for clinical use.\n"}
+{"abstract": "  A key open issue in condensed matter physics is how quantum and classical\ncorrelations emerge in an unconventional superconductor from the underlying\nnormal state. We study this problem in a doped Mott insulator with information\ntheory tools on the two-dimensional Hubbard model at finite temperature with\ncluster dynamical mean-field theory. We find that the local entropy detects the\nsuperconducting state and that the difference in the local entropy between the\nsuperconducting and normal states follows the same difference in the potential\nenergy. We find that the thermodynamic entropy is suppressed in the\nsuperconducting state and monotonically decreases with decreasing doping. The\nmaximum in entropy found in the normal state above the overdoped region of the\nsuperconducting dome is obliterated by superconductivity. The total mutual\ninformation, which quantifies quantum and classical correlations, is amplified\nin the superconducting state of the doped Mott insulator for all doping levels,\nand shows a broad peak versus doping, as a result of competing quantum and\nclassical effects.\n"}
+{"abstract": "  Knowledge Graph (KG) alignment aims at finding equivalent entities and\nrelations (i.e., mappings) between two KGs. The existing approaches utilize\neither reasoning-based or semantic embedding-based techniques, but few studies\nexplore their combination. In this demonstration, we present PRASEMap, an\nunsupervised KG alignment system that iteratively computes the Mappings with\nboth Probabilistic Reasoning (PR) And Semantic Embedding (SE) techniques.\nPRASEMap can support various embedding-based KG alignment approaches as the SE\nmodule, and enables easy human computer interaction that additionally provides\nan option for users to feed the mapping annotations back to the system for\nbetter results. The demonstration showcases these features via a stand-alone\nWeb application with user friendly interfaces. The demo is available at\nhttps://prasemap.qizhy.com.\n"}
+{"abstract": "  Face forgery detection is raising ever-increasing interest in computer vision\nsince facial manipulation technologies cause serious worries. Though recent\nworks have reached sound achievements, there are still unignorable problems: a)\nlearned features supervised by softmax loss are separable but not\ndiscriminative enough, since softmax loss does not explicitly encourage\nintra-class compactness and interclass separability; and b) fixed filter banks\nand hand-crafted features are insufficient to capture forgery patterns of\nfrequency from diverse inputs. To compensate for such limitations, a novel\nfrequency-aware discriminative feature learning framework is proposed in this\npaper. Specifically, we design a novel single-center loss (SCL) that only\ncompresses intra-class variations of natural faces while boosting inter-class\ndifferences in the embedding space. In such a case, the network can learn more\ndiscriminative features with less optimization difficulty. Besides, an adaptive\nfrequency feature generation module is developed to mine frequency clues in a\ncompletely data-driven fashion. With the above two modules, the whole framework\ncan learn more discriminative features in an end-to-end manner. Extensive\nexperiments demonstrate the effectiveness and superiority of our framework on\nthree versions of the FF++ dataset.\n"}
+{"abstract": "  When collaborating with an AI system, we need to assess when to trust its\nrecommendations. If we mistakenly trust it in regions where it is likely to\nerr, catastrophic failures may occur, hence the need for Bayesian approaches\nfor probabilistic reasoning in order to determine the confidence (or epistemic\nuncertainty) in the probabilities in light of the training data. We propose an\napproach to overcome the independence assumption behind most of the approaches\ndealing with a large class of probabilistic reasoning that includes Bayesian\nnetworks as well as several instances of probabilistic logic. We provide an\nalgorithm for Bayesian learning from sparse, albeit complete, observations, and\nfor deriving inferences and their confidences keeping track of the dependencies\nbetween variables when they are manipulated within the unifying computational\nformalism provided by probabilistic circuits. Each leaf of such circuits is\nlabelled with a beta-distributed random variable that provides us with an\nelegant framework for representing uncertain probabilities. We achieve better\nestimation of epistemic uncertainty than state-of-the-art approaches, including\nhighly engineered ones, while being able to handle general circuits and with\njust a modest increase in the computational effort compared to using point\nprobabilities.\n"}
+{"abstract": "  We propose and demonstrate a single-photon sensitive technique for optical\nvibrometry. It uses high speed photon counting to sample the modulated\nbackscattering from a vibrating target. Designed for remote vibration sensing\nwith ultralow photon flux, we show that this technique can detect small\ndisplacements down to 110 nm and resolve vibration frequencies from DC up to\nseveral kilohertz, with less than 0.01 detected photons per pulse. This\nsingle-photon sensitive optical vibrometry may find important applications in\nacousto-optic sensing and imaging, especially in photon-starved environments.\n"}
+{"abstract": "  We present a novel algorithm to solve a non-linear system of equations, whose\nsolution can be interpreted as a tight lower bound on the vector of expected\nhitting times of a Markov chain whose transition probabilities are only\npartially specified. We also briefly sketch how this method can be modified to\nsolve a conjugate system of equations that gives rise to the corresponding\nupper bound. We prove the correctness of our method, and show that it converges\nto the correct solution in a finite number of steps under mild conditions on\nthe system. We compare the runtime complexity of our method to a previously\npublished method from the literature, and identify conditions under which our\nnovel method is more efficient.\n"}
+{"abstract": "  In its most traditional setting, the main concern of optimization theory is\nthe search for optimal solutions for instances of a given computational\nproblem. A recent trend of research in artificial intelligence, called solution\ndiversity, has focused on the development of notions of optimality that may be\nmore appropriate in settings where subjectivity is essential. The idea is that\ninstead of aiming at the development of algorithms that output a single optimal\nsolution, the goal is to investigate algorithms that output a small set of\nsufficiently good solutions that are sufficiently diverse from one another. In\nthis way, the user has the opportunity to choose the solution that is most\nappropriate to the context at hand. It also displays the richness of the\nsolution space.\n  When combined with techniques from parameterized complexity theory, the\nparadigm of diversity of solutions offers a powerful algorithmic framework to\naddress problems of practical relevance. In this work, we investigate the\nimpact of this combination in the field of Kemeny Rank Aggregation, a\nwell-studied class of problems lying in the intersection of order theory and\nsocial choice theory and also in the field of order theory itself. In\nparticular, we show that the Kemeny Rank Aggregation problem is fixed-parameter\ntractable with respect to natural parameters providing natural formalizations\nof the notions of diversity and of the notion of a sufficiently good solution.\nOur main results work both when considering the traditional setting of\naggregation over linearly ordered votes, and in the more general setting where\nvotes are partially ordered.\n"}
+{"abstract": "  In this paper we present a novel approach to emulating a universal quantum\ncomputer with a classical system, one that uses a signal of bounded duration\nand amplitude to represent an arbitrary quantum state. The signal may be of any\nmodality (e.g., acoustic, electromagnetic, etc), but we focus our discussion\nhere on electronic signals. Unitary gate operations are performed using analog\nelectronic circuit devices, such as four-quadrant multipliers, operational\namplifiers, and analog filters, although non-unitary operations may be\nperformed as well. In this manner, the Hilbert space structure of the quantum\nstate, as well as a universal set of gate operations, may be fully emulated\nclassically. The required bandwidth scales exponentially with the number of\nqubits, however, thereby limiting the scalability of the approach, but the\nintrinsic parallelism, ease of construction, and classical robustness to\ndecoherence may nevertheless lead to capabilities and efficiencies rivaling\nthat of current high performance computers.\n"}
+{"abstract": "  We present a scalable technique for upper bounding the Lipschitz constant of\ngenerative models. We relate this quantity to the maximal norm over the set of\nattainable vector-Jacobian products of a given generative model. We approximate\nthis set by layerwise convex approximations using zonotopes. Our approach\ngeneralizes and improves upon prior work using zonotope transformers and we\nextend to Lipschitz estimation of neural networks with large output dimension.\nThis provides efficient and tight bounds on small networks and can scale to\ngenerative models on VAE and DCGAN architectures.\n"}
+{"abstract": "  Despite quantum networking concepts, designs, and hardware becoming\nincreasingly mature, there is no consensus on the optimal wavelength for\nfree-space systems. We present an in-depth analysis of a daytime free-space\nquantum channel as a function of wavelength and atmospheric spatial coherence\n(Fried coherence length). We choose decoy-state quantum key distribution bit\nyield as a performance metric in order to reveal the ideal wavelength choice\nfor an actual qubit-based protocol under realistic atmospheric conditions. Our\nanalysis represents a rigorous framework to analyze requirements for spatial,\nspectral, and temporal filtering. These results will help guide the development\nof free-space quantum communication and networking systems. In particular, our\nresults suggest that shorter wavelengths in the optical band should be\nconsidered for free-space quantum communication systems. Our results are also\ninterpreted in the context of atmospheric compensation by higher-order adaptive\noptics.\n"}
+{"abstract": "  Duality in the entanglement of identical particles manifests that\nentanglement in only one variable can be revealed at a time. We demonstrate\nthis using polarization and orbital angular momentum (OAM) variables of\nindistinguishable photons generated from parametric down conversion. We show\npolarization entanglement by sorting photons in even and odd OAM basis, while\nsorting them in two orthogonal polarization modes reveals the OAM entanglement.\nThe duality assisted observation of entanglement can be used as a verification\nfor the preservation of quantum indistinguishability over communication\nchannels. Indistinguishable photons entangled in complementary variables could\nalso evoke interest in distributed quantum sensing protocols and remote\nentanglement generation.\n"}
+{"abstract": "  Customer product reviews play a role in improving the quality of products and\nservices for business organizations or their brands. Complaining is an attitude\nthat expresses dissatisfaction with an event or a product not meeting customer\nexpectations. In this paper, we build a Open-domain Complaint Detection dataset\n(UIT-ViOCD), including 5,485 human-annotated reviews on four categories about\nproduct reviews on e-commerce sites. After the data collection phase, we\nproceed to the annotation task and achieve the inter-annotator agreement Am of\n87%. Then, we present an extensive methodology for the research purposes and\nachieve 92.16% by F1-score for identifying complaints. With the results, in the\nfuture, we aim to build a system for open-domain complaint detection in\nE-commerce websites.\n"}
+{"abstract": "  We construct a new set of asymptotically flat, static vacuum solutions to the\nEinstein equations in dimensions 4 and 5, which may be interpreted as a\nsuperposition of positive and negative mass black holes. The resulting\nspacetimes are axisymmetric in 4-dimensions and bi-axisymmetric in\n5-dimensions, and are regular away from the negative mass singularities, for\ninstance conical singularities are absent along the axes. In 5-dimensions, the\ntopologies of signed mass black holes used in the construction may be either\nspheres $S^3$ or rings $S^1 \\times S^2$; in particular, the negative mass\nstatic black ring solution is introduced. A primary observation that\nfacilitates the superposition is the fact that, in Weyl-Papapetrou coordinates,\nnegative mass singularities arise as overlapping singular support for a\nparticular type of Green's function. Furthermore, a careful analysis of conical\nsingularities along axes is performed, and formulas are obtained for their\npropagation across horizons, negative mass singularities, and corners. The\nmethods are robust, and may be used to construct a multitude of further\nexamples. Lastly, we show that balancing does not occur between any two signed\nmass black holes of the type studied here in 4 dimensions, while in 5\ndimensions two-body balancing is possible.\n"}
+{"abstract": "  We study static and spherically symmetric charged stars with a nontrivial\nprofile of the scalar field $\\phi$ in Einstein-Maxwell-scalar theories. The\nscalar field is coupled to a $U(1)$ gauge field $A_{\\mu}$ with the form\n$-\\alpha(\\phi)F_{\\mu \\nu}F^{\\mu \\nu}/4$, where $F_{\\mu\n\\nu}=\\partial_{\\mu}A_{\\nu}-\\partial_{\\nu} A_{\\mu}$ is the field strength\ntensor. Analogous to the case of charged black holes, we show that this type of\ninteraction can induce spontaneous scalarization of charged stars under the\nconditions $({\\rm d}\\alpha/{\\rm d}\\phi) (0)=0$ and $({\\rm d}^2\\alpha/{\\rm\nd}\\phi^2) (0)>0$. For the coupling $\\alpha (\\phi)=\\exp (-\\beta \\phi^2/M_{\\rm\npl}^2)$, where $\\beta~(<0)$ is a coupling constant and $M_{\\rm pl}$ is a\nreduced Planck mass, there is a branch of charged star solutions with a\nnontrivial profile of $\\phi$ approaching $0$ toward spatial infinity, besides a\nbranch of general relativistic solutions with a vanishing scalar field, i.e.,\nsolutions in the Einstein-Maxwell model. As the ratio $\\rho_c/\\rho_m$ between\ncharge density $\\rho_c$ and matter density $\\rho_m$ increases toward its\nmaximum value, the mass $M$ of charged stars in general relativity tends to be\nenhanced due to the increase of repulsive Coulomb force against gravity. In\nthis regime, the appearance of nontrivial branches induced by negative $\\beta$\nof order $-1$ effectively reduces the Coulomb force for a wide range of central\nmatter densities, leading to charged stars with smaller masses and radii in\ncomparison to those in the general relativistic branch. Our analysis indicates\nthat spontaneous scalarization of stars can be induced not only by the coupling\nto curvature invariants but also by the scalar-gauge coupling in Einstein\ngravity.\n"}
+{"abstract": "  The purpose of this study is to explore the relationship between the first\naffiliation and the corresponding affiliation at the different levels via the\nscientometric analysis We select over 18 million papers in the core collection\ndatabase of Web of Science (WoS) published from 2000 to 2015, and measure the\npercentage of match between the first and the corresponding affiliation at the\ncountry and institution level. We find that a paper's the first affiliation and\nthe corresponding affiliation are highly consistent at the country level, with\nover 98% of the match on average. However, the match at the institution level\nis much lower, which varies significantly with time and country. Hence, for\nstudies at the country level, using the first and corresponding affiliations\nare almost the same. But we may need to take more cautions to select\naffiliation when the institution is the focus of the investigation. In the\nmeanwhile, we find some evidence that the recorded corresponding information in\nthe WoS database has undergone some changes since 2013, which sheds light on\nfuture studies on the comparison of different databases or the affiliation\naccuracy of WoS. Our finding relies on the records of WoS, which may not be\nentirely accurate. Given the scale of the analysis, our findings can serve as a\nuseful reference for further studies when country allocation or institute\nallocation is needed. Existing studies on comparisons of straight counting\nmethods usually cover a limited number of papers, a particular research field\nor a limited range of time. More importantly, using the number counted can not\nsufficiently tell if the corresponding and first affiliation are similar. This\npaper uses a metric similar to Jaccard similarity to measure the percentage of\nthe match and performs a comprehensive analysis based on a large-scale\nbibliometric database.\n"}
+{"abstract": "  The development of intelligent tutoring system has greatly influenced the way\nstudents learn and practice, which increases their learning efficiency. The\nintelligent tutoring system must model learners' mastery of the knowledge\nbefore providing feedback and advices to learners, so one class of algorithm\ncalled \"knowledge tracing\" is surely important. This paper proposed Deep\nSelf-Attentive Knowledge Tracing (DSAKT) based on the data of PTA, an online\nassessment system used by students in many universities in China, to help these\nstudents learn more efficiently. Experimentation on the data of PTA shows that\nDSAKT outperforms the other models for knowledge tracing an improvement of AUC\nby 2.1% on average, and this model also has a good performance on the ASSIST\ndataset.\n"}
+{"abstract": "  The Landau-Lifshitz equation governing magnetization dynamics is written in\nterms of the amplitudes of normal modes associated with the micromagnetic\nsystem's appropriate ground state. This results in a system of nonlinear\nordinary differential equations (ODEs), the right-hand side of which can be\nexpressed as the sum of a linear term and nonlinear terms with increasing order\nof nonlinearity (quadratic, cubic, etc.). The application of the method to\nnanostructured magnetic systems demonstrates that the accurate description of\nmagnetization dynamics requires a limited number of normal modes, which results\nin a considerable improvement in computational speed. The proposed method can\nbe used to obtain a reduced-order dynamical description of magnetic\nnanostructures which allows to adjust the accuracy between low-dimensional\nmodels, such as macrospin, and micromagnetic models with full spatial\ndiscretization. This new paradigm for micromagnetic simulations is tested for\nthree problems relevant to the areas of spintronics and magnonics: directional\nspin-wave coupling in magnonic waveguides, high power ferromagnetic resonance\nin a magnetic nanodot, and injection-locking in spin-torque nano-oscillators.\nThe case studies considered demonstrate the validity of the proposed approach\nto systematically obtain an intermediate order dynamical model based on normal\nmodes for the analysis of magnetic nanosystems. The time-consuming calculation\nof the normal modes has to be done only one time for the system. These modes\ncan be used to optimize and predict the system response for all possible\ntime-varying external excitations (magnetic fields, spin currents). This is of\nutmost importance for applications where fast and accurate system simulations\nare required, such as in electronic circuits including magnetic devices.\n"}
+{"abstract": "  We investigate the motivation and means through which individuals expand\ntheir skill-set by analyzing a survey of applicants from the Facebook Jobs\nproduct. Individuals who report being influenced by their networks or local\neconomy are over 29% more likely to have a postsecondary degree, but peer\neffects still exist among those who do not acknowledge such influences. Users\nwith postsecondary degrees are more likely to upskill in general, by continuing\ncoursework or applying to higher-skill jobs, though the latter is more common\namong users across all education backgrounds. These findings indicate that\npolicies aimed at connecting individuals with different educational backgrounds\ncan encourage upskilling. Policies that encourage users to enroll in coursework\nmay not be as effective among individuals with a high school degree or less.\nInstead, connecting such individuals to opportunities that value skills\nacquired outside of a formal education, and allow for on-the-job training, may\nbe more effective.\n"}
+{"abstract": "  Single-photon emitters are essential for enabling several emerging\napplications in quantum information technology, quantum sensing and quantum\ncommunication. Scalable photonic platforms capable of hosting intrinsic or\ndirectly embedded sources of single-photon emission are of particular interest\nfor the realization of integrated quantum photonic circuits. Here, we report on\nthe first-time observation of room-temperature single-photon emitters in\nsilicon nitride (SiN) films grown on silicon dioxide substrates. As SiN has\nrecently emerged as one of the most promising materials for integrated quantum\nphotonics, the proposed platform is suitable for scalable fabrication of\nquantum on-chip devices. Photophysical analysis reveals bright (>$10^5$\ncounts/s), stable, linearly polarized, and pure quantum emitters in SiN films\nwith the value of the second-order autocorrelation function at zero time delay\n$g^{(2)}(0)$ below 0.2 at room temperatures. The emission is suggested to\noriginate from a specific defect center in silicon nitride due to the narrow\nwavelength distribution of the observed luminescence peak. Single-photon\nemitters in silicon nitride have the potential to enable direct, scalable and\nlow-loss integration of quantum light sources with the well-established\nphotonic on-chip platform.\n"}
+{"abstract": "  Quantum techniques can be used to enhance the signal-to-noise ratio in\noptical imaging. Leveraging the latest advances in single photon avalanche\ndiode array cameras and multi-photon detection techniques, here we introduce a\nsuper-sensitive phase imager, which uses space-polarization hyper-entanglement\nto operate over a large field-of-view without the need of scanning operation.\nWe show quantum-enhanced imaging of birefringent and non-birefringent phase\nsamples over large areas, with sensitivity improvements over equivalent\nclassical measurements carried out with equal number of photons. The practical\napplicability is demonstrated by imaging a biomedical protein microarray\nsample. Our quantum-enhanced phase imaging technology is inherently scalable to\nhigh resolution images, and represents an essential step towards practical\nquantum imaging.\n"}
+{"abstract": "  Interactive Task Learning (ITL) is an emerging research agenda that studies\nthe design of complex intelligent robots that can acquire new knowledge through\nnatural human teacher-robot learner interactions. ITL methods are particularly\nuseful for designing intelligent robots whose behavior can be adapted by humans\ncollaborating with them. Various research communities are contributing methods\nfor ITL and a large subset of this research is \\emph{robot-centered} with a\nfocus on developing algorithms that can learn online, quickly. This paper\nstudies the ITL problem from a \\emph{human-centered} perspective to provide\nguidance for robot design so that human teachers can naturally teach ITL\nrobots. In this paper, we present 1) a qualitative bidirectional analysis of an\ninteractive teaching study (N=10) through which we characterize various aspects\nof actions intended and executed by human teachers when teaching a robot; 2) an\nin-depth discussion of the teaching approach employed by two participants to\nunderstand the need for personal adaptation to individual teaching styles; and\n3) requirements for ITL robot design based on our analyses and informed by a\ncomputational theory of collaborative interactions, SharedPlans.\n"}
+{"abstract": "  The Debye sheath is shown to vanish completely in magnetised plasmas for a\nsufficiently small electron gyroradius and small angle between the magnetic\nfield and the wall. This angle depends on the current onto the wall. When the\nDebye sheath vanishes, there is still a potential drop between the wall and the\nplasma across the magnetic presheath. The magnetic field angle corresponding to\nsheath collapse is shown to be much smaller than previous estimates, scaling\nwith the electron-ion mass ratio and not with the square root of the mass\nratio. This is shown to be a consequence of the finite ion orbit width effects,\nwhich are not captured by fluid models. The wall potential with respect to the\nbulk plasma at which the Debye sheath vanishes is calculated. Above this wall\npotential, it is possible that the Debye sheath will invert.\n"}
+{"abstract": "  Beloved Curry--Howard correspondence tells that types are intuitionistic\npropositions, and in constructive math, a proof of proposition can be seen as\nsome kind of a construction, or witness, conveying the information of the\nproposition. We demonstrate how useful this point of view is as the guiding\nprinciple for developing dependently-typed programs.\n"}
+{"abstract": "  Manifold learning algorithms are valuable tools for the analysis of\nhigh-dimensional data, many of which include a step where nearest neighbors of\nall observations are found. This can present a computational bottleneck when\nthe number of observations is large or when the observations lie in more\ngeneral metric spaces, such as statistical manifolds, which require all\npairwise distances between observations to be computed. We resolve this problem\nby using a broad range of approximate nearest neighbor algorithms within\nmanifold learning algorithms and evaluating their impact on embedding accuracy.\nWe use approximate nearest neighbors for statistical manifolds by exploiting\nthe connection between Hellinger/Total variation distance for discrete\ndistributions and the L2/L1 norm. Via a thorough empirical investigation based\non the benchmark MNIST dataset, it is shown that approximate nearest neighbors\nlead to substantial improvements in computational time with little to no loss\nin the accuracy of the embedding produced by a manifold learning algorithm.\nThis result is robust to the use of different manifold learning algorithms, to\nthe use of different approximate nearest neighbor algorithms, and to the use of\ndifferent measures of embedding accuracy. The proposed method is applied to\nlearning statistical manifolds data on distributions of electricity usage. This\napplication demonstrates how the proposed methods can be used to visualize and\nidentify anomalies and uncover underlying structure within high-dimensional\ndata in a way that is scalable to large datasets.\n"}
+{"abstract": "  With the rapid advancement of technology, different biometric user\nauthentication, and identification systems are emerging. Traditional biometric\nsystems like face, fingerprint, and iris recognition, keystroke dynamics, etc.\nare prone to cyber-attacks and suffer from different disadvantages.\nElectroencephalography (EEG) based authentication has shown promise in\novercoming these limitations. However, EEG-based authentication is less\naccurate due to signal variability at different psychological and physiological\nconditions. On the other hand, keystroke dynamics-based identification offers\nhigh accuracy but suffers from different spoofing attacks. To overcome these\nchallenges, we propose a novel multimodal biometric system combining EEG and\nkeystroke dynamics. Firstly, a dataset was created by acquiring both keystroke\ndynamics and EEG signals from 10 users with 500 trials per user at 10 different\nsessions. Different statistical, time, and frequency domain features were\nextracted and ranked from the EEG signals and key features were extracted from\nthe keystroke dynamics. Different classifiers were trained, validated, and\ntested for both individual and combined modalities for two different\nclassification strategies - personalized and generalized. Results show that\nvery high accuracy can be achieved both in generalized and personalized cases\nfor the combination of EEG and keystroke dynamics. The identification and\nauthentication accuracies were found to be 99.80% and 99.68% for Extreme\nGradient Boosting (XGBoost) and Random Forest classifiers, respectively which\noutperform the individual modalities with a significant margin (around 5\npercent). We also developed a binary template matching-based algorithm, which\ngives 93.64% accuracy 6X faster. The proposed method is secured and reliable\nfor any kind of biometric authentication.\n"}
+{"abstract": "  We report here the discovery of a hot Jupiter at an orbital period of\n$3.208666\\pm0.000016$ days around TOI-1789 (TYC 1962-00303-1, $TESS_{mag}$ =\n9.1) based on the TESS photometry, ground-based photometry, and high-precision\nradial velocity observations. The high-precision radial velocity observations\nwere obtained from the high-resolution spectrographs, PARAS at Physical\nResearch Laboratory (PRL), India, and TCES at Th\\\"uringer Landessternwarte\nTautenburg (TLS), Germany, and the ground-based transit observations were\nobtained using the 0.43~m telescope at PRL with the Bessel-$R$ filter. The host\nstar is a slightly evolved ($\\log{g_*}$ = $3.939^{+0.024}_{-0.046}$), late\nF-type ($T_{eff}$ = $5984^{+55}_{-57}$ K), metal-rich star ([Fe/H] =\n$0.370^{+0.073}_{-0.089}$ dex) with a radius of {\\ensuremath{$R_{*}$}} =\n$2.172^{+0.037}_{-0.035}$ \\(R_\\odot\\) located at a distance of\n$223.56^{+0.91}_{-0.90}$ pc. The simultaneous fitting of the multiple light\ncurves and the radial velocity data of TOI-1789 reveals that TOI-1789b has a\nmass of $M_{P}$ = $0.70\\pm0.16 $ $M_{J}$, a radius of $R_{P}$ =\n$1.40^{+0.22}_{-0.13}$ $R_{J}$, and a bulk density of $\\rho_P$ =\n$0.31^{+0.15}_{-0.13}$ g cm$^{-3}$ with an orbital separation of a =\n$0.04873^{+0.00065}_{-0.0016}$ AU. This puts TOI-1789b in the category of\ninflated hot Jupiters. It is one of the few nearby evolved stars with a\nclose-in planet. The detection of such systems will contribute to our\nunderstanding of mechanisms responsible for inflation in hot Jupiters and also\nprovide an opportunity to understand the evolution of planets around stars\nleaving the main sequence branch.\n"}
+{"abstract": "  We present a method for approximating outcomes of road traffic simulations\nusing BERT-based models, which may find applications in, e.g., optimizing\ntraffic signal settings, especially with the presence of autonomous and\nconnected vehicles. The experiments were conducted on a dataset generated using\nthe Traffic Simulation Framework software runs on a realistic road network. The\nBERT-based models were compared with 4 other types of machine learning models\n(LightGBM, fully connected neural networks and 2 types of graph neural\nnetworks) and gave the best results in terms of all the considered metrics.\n"}
+{"abstract": "  Optical antennas made of low-loss dielectrics have several advantages over\nplasmonic antennas, including high radiative quantum efficiency, negligible\nheating and excellent photostability. However, due to weak spatial confinement,\nconventional dielectric antennas fail to offer light-matter interaction\nstrengths on par with those of plasmonic antennas. We propose here an\nall-dielectric antenna configuration that can support strongly confined modes\n($V\\sim10^{-4}\\lambda_{0}^3$) while maintaining unity antenna quantum\nefficiency. This configuration consists of a high-index pillar structure with a\ntransverse gap that is filled with a low-index material, where the contrast of\nindices induces a strong enhancement of the electric field perpendicular to the\ngap. We provide a detailed explanation of the operation principle of such\nPhotonic Gap Antennas (PGAs) based on the dispersion relation of symmetric and\nasymmetric horizontal slot-waveguides. To discuss the properties of PGAs, we\nconsider silicon pillars with air or CYTOP as the gap-material. We show by\nfull-wave simulations that PGAs with an emitter embedded in the gap can enhance\nthe spontaneous emission rate by a factor of $\\sim$1000 for air gaps and\n$\\sim$400 for CYTOP gaps over a spectral bandwidth of $\\Delta\\lambda\\approx300$\nnm at $\\lambda=1.25$ \\textmu m. Furthermore, the PGAs can be designed to\nprovide unidirectional out-of-plane radiation across a substantial portion of\ntheir spectral bandwidth. This is achieved by setting the position of the gap\nat an optimized off-centered position of the pillar so as to properly break the\nvertical symmetry of the structure. We also demonstrate that, when acting as\nreceivers, PGAs can lead to a near-field intensity enhancement by a factor of\n$\\sim$3000 for air gaps and $\\sim$1200 for CYTOP gaps.\n"}
+{"abstract": "  We study the multiple populations of $\\omega$ Cen by using the abundances of\nFe, C, N, O, Mg, Al, Si, K, Ca, and Ce from the high-resolution, high\nsignal-to-noise (S/N$>$70) spectra of 982 red giant stars observed by the\nSDSS-IV/APOGEE-2 survey. We find that the shape of the Al-Mg and N-C\nanticorrelations changes as a function of metallicity, continuous for the\nmetal-poor groups, but bimodal (or unimodal) at high metallicities. There are\nfour Fe populations, similar to what has been found in previously published\ninvestigations, but we find seven populations based on Fe, Al, and Mg\nabundances. The evolution of Al in $\\omega$ Cen is compared to its evolution in\nthe Milky Way and in five representative globular clusters. We find that the\ndistribution of Al in metal-rich stars of $\\omega$ Cen closely follows what is\nobserved in the Galaxy. Other $\\alpha-$elements and C, N, O, and Ce are also\ncompared to the Milky Way, and significantly elevated abundances are observed\nover what is found in the thick disk for almost all elements. However, we also\nfind some stars with high metallicity and low [Al/Fe], suggesting that $\\omega$\nCen could be the remnant core of a dwarf galaxy, but the existence of these\npeculiar stars needs an independent confirmation. We also confirm the increase\nin the sum of CNO as a function of metallicity previously reported in the\nliterature and find that the [C/N] ratio appears to show opposite correlations\nbetween Al-poor and Al-rich stars as a function of metallicity.\n"}
+{"abstract": "  A weak topological insulator in dimension $3$ is known to have a\ntopologically protected gapless mode along the screw dislocation. In this paper\nwe formulate and prove this fact with the language of C*-algebra K-theory. The\nproof is based on the coarse index theory of the helical surface.\n"}
+{"abstract": "  A study of the dynamical formation of networks of friends and enemies in\nsocial media, in this case Twitter, is presented. We characterise the single\nnode properties of such networks, as the clustering coefficient and the degree,\nto investigate the structure of links. The results indicate that the network is\nmade from three kinds of nodes: one with high clustering coefficient but very\nsmall degree, a second group has zero clustering coefficient with variable\ndegree, and finally, a third group in which the clustering coefficient as a\nfunction of the degree decays as a power law. This third group represents\n$\\sim2\\%$ of the nodes and is characteristic of dynamical networks with\nfeedback. This part of the lattice seemingly represents strongly interacting\nfriends in a real social network.\n"}
+{"abstract": "  We consider the problem of recovering equations of motion from multivariate\ntime series of oscillators interacting on sparse networks. We reconstruct the\nnetwork from an initial guess which can include expert knowledge about the\nsystem such as main motifs and hubs. When sparsity is taken into account the\nnumber of data points needed is drastically reduced when compared to the\nleast-squares recovery. We show that the sparse solution is stable under basis\nextensions, that is, once the correct network topology is obtained, the result\ndoes not change if further motifs are considered.\n"}
+{"abstract": "  This paper focuses on finite-time in-network computation of linear transforms\nof distributed graph data. Finite-time transform computation problems are of\ninterest in graph-based computing and signal processing applications in which\nthe objective is to compute, by means of distributed iterative methods, various\n(linear) transforms of the data distributed at the agents or nodes of the\ngraph. While finite-time computation of consensus-type or more generally\nrank-one transforms have been studied, systematic approaches toward scalable\ncomputing of general linear transforms, specifically in the case of\nheterogeneous agent objectives in which each agent is interested in obtaining a\ndifferent linear combination of the network data, are relatively less explored.\nIn this paper, by employing ideas from algebraic geometry, we develop a\nsystematic characterization of linear transforms that are amenable to\ndistributed in-network computation in finite-time using linear iterations.\nFurther, we consider the general case of directed inter-agent communication\ngraphs. Specifically, it is shown that \\emph{almost all} linear transformations\nof data distributed on the nodes of a digraph containing a Hamiltonian cycle\nmay be computed using at most $N$ linear distributed iterations. Finally, by\nstudying an associated matrix factorization based reformulation of the\ntransform computation problem, we obtain, as a by-product, certain results and\ncharacterizations on sparsity-constrained matrix factorization that are of\nindependent interest.\n"}
+{"abstract": "  Not only does cold climate pose a problem for outdoor plants during winter in\nthe northern hemisphere, but for indoor plants as well: low sunlight, low\nhumidity, and simultaneous cold breezes from windows and heat from radiators\nall cause problems for indoor plants. People often treat their indoor plants\nlike mere decoration, which can often lead to health issues for the plant or\neven death of the plant, especially during winter. A plant monitoring system\nwas developed to solve this problem, collecting information on plants' indoor\nenvironmental conditions (light, humidity, and temperature) and providing this\ninformation in an accessible format for the user. Preliminary functional tests\nwere conducted in similar settings where the system would be used. In addition,\nthe concept was evaluated by interviewing an expert in the field of\nhorticulture.\n  The evaluation results indicate that this kind of system could prove useful;\nhowever, the tests indicated that the system requires further development to\nachieve more practical value and wider usage.\n"}
+{"abstract": "  Most work in NLP makes the assumption that it is desirable to develop\nsolutions in the native language in question. There is consequently a strong\ntrend towards building native language models even for low-resource languages.\nThis paper questions this development, and explores the idea of simply\ntranslating the data into English, thereby enabling the use of pretrained, and\nlarge-scale, English language models. We demonstrate empirically that a large\nEnglish language model coupled with modern machine translation outperforms\nnative language models in most Scandinavian languages. The exception to this is\nFinnish, which we assume is due to inferior translation quality. Our results\nsuggest that machine translation is a mature technology, which raises a serious\ncounter-argument for training native language models for low-resource\nlanguages. This paper therefore strives to make a provocative but important\npoint. As English language models are improving at an unprecedented pace, which\nin turn improves machine translation, it is from an empirical and environmental\nstand-point more effective to translate data from low-resource languages into\nEnglish, than to build language models for such languages.\n"}
+{"abstract": "  In neural machine translation, cross entropy (CE) is the standard loss\nfunction in two training methods of auto-regressive models, i.e., teacher\nforcing and scheduled sampling. In this paper, we propose mixed cross entropy\nloss (mixed CE) as a substitute for CE in both training approaches. In teacher\nforcing, the model trained with CE regards the translation problem as a\none-to-one mapping process, while in mixed CE this process can be relaxed to\none-to-many. In scheduled sampling, we show that mixed CE has the potential to\nencourage the training and testing behaviours to be similar to each other, more\neffectively mitigating the exposure bias problem. We demonstrate the\nsuperiority of mixed CE over CE on several machine translation datasets, WMT'16\nRo-En, WMT'16 Ru-En, and WMT'14 En-De in both teacher forcing and scheduled\nsampling setups. Furthermore, in WMT'14 En-De, we also find mixed CE\nconsistently outperforms CE on a multi-reference set as well as a challenging\nparaphrased reference set. We also found the model trained with mixed CE is\nable to provide a better probability distribution defined over the translation\noutput space. Our code is available at https://github.com/haorannlp/mix.\n"}
+{"abstract": "  Current storytelling systems focus more ongenerating stories with coherent\nplots regard-less of the narration style, which is impor-tant for controllable\ntext generation. There-fore, we propose a new task, stylized story gen-eration,\nnamely generating stories with speci-fied style given a leading context. To\ntacklethe problem, we propose a novel generationmodel that first plans the\nstylized keywordsand then generates the whole story with theguidance of the\nkeywords. Besides, we pro-pose two automatic metrics to evaluate theconsistency\nbetween the generated story andthe specified style. Experiments\ndemonstratesthat our model can controllably generateemo-tion-driven\norevent-driven stories based onthe ROCStories dataset (Mostafazadeh et\nal.,2016). Our study presents insights for stylizedstory generation in further\nresearch.\n"}
+{"abstract": "  In this paper, we completely solve the problem when a Cantor dynamical system\n$(X,f)$ can be embedded in $\\mathbb{R}$ with vanishing derivative everywhere.\nFor this purpose, we construct a refining sequence of marked clopen partitions\nof $X$ which is adapted to a dynamical system of this kind. It turns out that\nthere is a huge class of such systems.\n"}
+{"abstract": "  Recently, distributed controller architectures have been quickly gaining\npopularity in Software-Defined Networking (SDN). However, the use of\ndistributed controllers introduces a new and important Request Dispatching (RD)\nproblem with the goal for every SDN switch to properly dispatch their requests\namong all controllers so as to optimize network performance. This goal can be\nfulfilled by designing an RD policy to guide distribution of requests at each\nswitch. In this paper, we propose a Multi-Agent Deep Reinforcement Learning\n(MA-DRL) approach to automatically design RD policies with high adaptability\nand performance. This is achieved through a new problem formulation in the form\nof a Multi-Agent Markov Decision Process (MA-MDP), a new adaptive RD policy\ndesign and a new MA-DRL algorithm called MA-PPO. Extensive simulation studies\nshow that our MA-DRL technique can effectively train RD policies to\nsignificantly outperform man-made policies, model-based policies, as well as RD\npolicies learned via single-agent DRL algorithms.\n"}
+{"abstract": "  We extend Fring-Tenney approach of constructing invariants of constant mass\ntime-dependent system to the case of a time-dependent mass particle. From a\ncoupled set of equations described in terms of guiding parameter functions, we\ntrack down a modified Ermakov-Pinney equation involving a time-dependent mass\nfunction. As a concrete example we focus on an exponential choice of the mass\nfunction.\n"}
+{"abstract": "  We consider the problem of collective exploration of a known $n$-node\nedge-weighted graph by $k$ mobile agents that have limited energy but are\ncapable of energy transfers. The agents are initially placed at an arbitrary\nsubset of nodes in the graph, and each agent has an initial, possibly\ndifferent, amount of energy. The goal of the exploration problem is for every\nedge in the graph to be traversed by at least one agent. The amount of energy\nused by an agent to travel distance $x$ is proportional to $x$. In our model,\nthe agents can {\\em share} energy when co-located: when two agents meet, one\ncan transfer part of its energy to the other.\n  For an $n$-node path, we give an $O(n+k)$ time algorithm that either finds an\nexploration strategy, or reports that one does not exist. For an $n$-node tree\nwith $\\ell $ leaves, we give an $O(n+ \\ell k^2)$ algorithm that finds an\nexploration strategy if one exists. Finally, for the general graph case, we\nshow that the problem of deciding if exploration is possible by energy-sharing\nagents is NP-hard, even for 3-regular graphs. In addition, we show that it is\nalways possible to find an exploration strategy if the total energy of the\nagents is at least twice the total weight of the edges; moreover, this is\nasymptotically optimal.\n"}
+{"abstract": "  We study some semi-linear equations for the $(m,p)$-Laplacian operator on\nlocally finite weighted graphs. We prove existence of weak solutions for all\n$m\\in\\mathbb{N}$ and $p\\in(1,+\\infty)$ via a variational method already known\nin the literature by exploiting the continuity properties of the energy\nfunctionals involved. When $m=1$, we also establish a uniqueness result in the\nspirit of the Brezis-Strauss Theorem. We finally provide some applications of\nour main results by dealing with some Yamabe-type and Kazdan-Warner-type\nequations on locally finite weighted graphs.\n"}
+{"abstract": "  Recent work in AI safety has highlighted that in sequential decision making,\nobjectives are often underspecified or incomplete. This gives discretion to the\nacting agent to realize the stated objective in ways that may result in\nundesirable outcomes. We contend that to learn to act safely, a reinforcement\nlearning (RL) agent should include contemplation of the impact of its actions\non the wellbeing and agency of others in the environment, including other\nacting agents and reactive processes. We endow RL agents with the ability to\ncontemplate such impact by augmenting their reward based on expectation of\nfuture return by others in the environment, providing different criteria for\ncharacterizing impact. We further endow these agents with the ability to\ndifferentially factor this impact into their decision making, manifesting\nbehavior that ranges from self-centred to self-less, as demonstrated by\nexperiments in gridworld environments.\n"}
+{"abstract": "  Most modern reinforcement learning algorithms optimize a cumulative\nsingle-step cost along a trajectory. The optimized motions are often\n'unnatural', representing, for example, behaviors with sudden accelerations\nthat waste energy and lack predictability. In this work, we present a novel\nparadigm of controlling nonlinear systems via the minimization of the Koopman\nspectrum cost: a cost over the Koopman operator of the controlled dynamics.\nThis induces a broader class of dynamical behaviors that evolve over stable\nmanifolds such as nonlinear oscillators, closed loops, and smooth movements. We\ndemonstrate that some dynamics realizations that are not possible with a\ncumulative cost are feasible in this paradigm. Moreover, we present a provably\nefficient online learning algorithm for our problem that enjoys a sub-linear\nregret bound under some structural assumptions.\n"}
+{"abstract": "  Bayesian optimization (BO) is a popular method for optimizing\nexpensive-to-evaluate black-box functions. BO budgets are typically given in\niterations, which implicitly assumes each evaluation has the same cost. In\nfact, in many BO applications, evaluation costs vary significantly in different\nregions of the search space. In hyperparameter optimization, the time spent on\nneural network training increases with layer size; in clinical trials, the\nmonetary cost of drug compounds vary; and in optimal control, control actions\nhave differing complexities. Cost-constrained BO measures convergence with\nalternative cost metrics such as time, money, or energy, for which the sample\nefficiency of standard BO methods is ill-suited. For cost-constrained BO, cost\nefficiency is far more important than sample efficiency. In this paper, we\nformulate cost-constrained BO as a constrained Markov decision process (CMDP),\nand develop an efficient rollout approximation to the optimal CMDP policy that\ntakes both the cost and future iterations into account. We validate our method\non a collection of hyperparameter optimization problems as well as a sensor set\nselection application.\n"}
+{"abstract": "  Annotated datasets have become one of the most crucial preconditions for the\ndevelopment and evaluation of machine learning-based methods designed for the\nautomated interpretation of remote sensing data. In this paper, we review the\nhistoric development of such datasets, discuss their features based on a few\nselected examples, and address open issues for future developments.\n"}
+{"abstract": "  We investigate special solutions to the Bethe Ansatz equations (BAE) for open\nintegrable $XXZ$ Heisenberg spin chains containing phantom (infinite) Bethe\nroots. The phantom Bethe roots do not contribute to the energy of the Bethe\nstate, so the energy is determined exclusively by the remaining regular\nexcitations. We rederive the phantom Bethe roots criterion and focus on BAE\nsolutions for mixtures of phantom roots and regular (finite) Bethe roots. We\nprove that in the presence of phantom Bethe roots, all eigenstates are split\nbetween two invariant subspaces, spanned by chiral shock states. Bethe\neigenstates are described by two complementary sets of Bethe Ansatz equations\nfor regular roots, one for each invariant subspace. The respective\n\"semi-phantom\" Bethe vectors are states of chiral nature, with chirality\nproperties getting less pronounced when more regular Bethe roots are added. For\nthe easy plane case \"semi-phantom\" Bethe states carry nonzero magnetic current,\nand are characterized by quasi-periodic modulation of the magnetization\nprofile, the most prominent example being the spin helix states (SHS). We\nillustrate our results investigating \"semi-phantom\" Bethe states generated by\none regular Bethe root (the other Bethe roots being phantom), with simple\nstructure of the invariant subspace, in all details. We obtain the explicit\nexpressions for Bethe vectors, and calculate the simplest correlation\nfunctions, including the spin-current for all the states in the single particle\nmultiplet.\n"}
+{"abstract": "  Lunar explorations have provided us with information about its abundant\nresources that can be utilized in orbiting-resource depots as lunar-derived\ncommodities. To reduce the energy requirements of a launcher to send these\ncommodities from the lunar surface to the space depots, this paper explores the\napplication of the electromagnetic acceleration principle and provides an\nassessment of the actual technical characteristics of the launcher's\ninstallation to ensure the acceleration of a payload with a mass of 1,500 kg to\na speed of 2,200 m/s (circumlunar orbit speed). To fulfill a lightweight (fewer\nmaterials and less energy) support structure for the electromagnetic launcher\nwith strength requirements, the tensegrity structure minimum mass principle\nwithout global buckling has been developed and applied to support the\nelectromagnetic acceleration device. Therefore, this paper proposes and\ndevelops a minimal mass electromagnetic tensegrity lunar launcher. We first\ndemonstrate the mechanics of launcher and payload, how a payload can be\naccelerated to a specific velocity, and how a payload carrier can be recycled\nfor another launch. Then, a detailed discussion on the lunar launch system,\nprocedures of propulsion, the required mass, and energy of the launch barrel\nare given. The governing equations of tensegrity minimal mass tensegrity design\nalgorithm with gravity and without global buckling. Finally, a case study is\nconducted to show a feasible structure design, the required mass, and energy.\nThe principles developed in this paper are also applicable to the rocket launch\nsystem, space elevator, space train transportation, interstellar payload\npackage delivery, etc.\n"}
+{"abstract": "  We present high-fidelity numerical simulations of expiratory biosol transport\nduring normal breathing under indoor, stagnant air conditions with and without\na facial mask. We investigate mask efficacy to suppress the spread of saliva\nparticles that is underpinnings existing social distancing recommendations. The\npresent simulations incorporate the effect of human anatomy and consider a\nspectrum of saliva particulate sizes that ranges from 0.1 micrometers to 10\nmicrometers while accounting also for their evaporation. The simulations\nelucidate the vorticity dynamics of human breathing and show that without a\nfacial mask, saliva particulates could travel over 2.2 m away from the person.\nHowever, a non-medical grade face mask can drastically reduce saliva\nparticulate propagation to 0.72 m away from the person. This study provides new\nquantitative evidence that facial masks can successfully suppress the spreading\nof saliva particulates due to normal breathing in indoor environments.\n"}
+{"abstract": "  Domain walls in fractional quantum Hall ferromagnets are gapless helical\none-dimensional channels formed at the boundaries of topologically distinct\nquantum Hall (QH) liquids. Na\\\"{i}vely, these helical domain walls (hDWs)\nconstitute two counter-propagating chiral states with opposite spins. Coupled\nto an s-wave superconductor, helical channels are expected to lead to\ntopological superconductivity with high order non-Abelian excitations. Here we\ninvestigate transport properties of hDWs in the $\\nu=2/3$ fractional QH regime.\nExperimentally we found that current carried by hDWs is substantially smaller\nthan the prediction of the na\\\"{i}ve model. Luttinger liquid theory of the\nsystem reveals redistribution of currents between quasiparticle charge, spin\nand neutral modes, and predicts the reduction of the hDW current. Inclusion of\nspin-non-conserving tunneling processes reconciles theory with experiment. The\ntheory confirms emergence of spin modes required for the formation of\nfractional topological superconductivity.\n"}
+{"abstract": "  We calculate the vacuum (Casimir) energy for a scalar field with $\\phi^4$\nself-interaction in (1+1) dimensions non perturbatively, i.e., in all orders of\nthe self-interaction. We consider massive and massless fields in a finite box\nwith Dirichlet boundary conditions and on the whole axis as well. For strong\ncoupling, the vacuum energy is negative indicating some instability.\n"}
+{"abstract": "  Multi input multi output (MIMO) systems\\' capability of using seperate\nsignals brings many advantages to radar signal processing and time frequency\nanalysis. In this paper, a variety of properties of MIMO ambiguity functions\nrelated with representations of Heisenberg group are given. Some of the\nexisting results for SIMO ambiguity functions are generalized to MIMO case.\nCombined effect of seperate signals is investigated.\n"}
+{"abstract": "  Recently, autonomous driving has made substantial progress in addressing the\nmost common traffic scenarios like intersection navigation and lane changing.\nHowever, most of these successes have been limited to scenarios with\nwell-defined traffic rules and require minimal negotiation with other vehicles.\nIn this paper, we introduce a previously unconsidered, yet everyday,\nhigh-conflict driving scenario requiring negotiations between agents of equal\nrights and priorities. There exists no centralized control structure and we do\nnot allow communications. Therefore, it is unknown if other drivers are willing\nto cooperate, and if so to what extent. We train policies to robustly negotiate\nwith opposing vehicles of an unobservable degree of cooperativeness using\nmulti-agent reinforcement learning (MARL). We propose Discrete Asymmetric Soft\nActor-Critic (DASAC), a maximum-entropy off-policy MARL algorithm allowing for\ncentralized training with decentralized execution. We show that using DASAC we\nare able to successfully negotiate and traverse the scenario considered over\n99% of the time. Our agents are robust to an unknown timing of opponent\ndecisions, an unobservable degree of cooperativeness of the opposing vehicle,\nand previously unencountered policies. Furthermore, they learn to exhibit\nhuman-like behaviors such as defensive driving, anticipating solution options\nand interpreting the behavior of other agents.\n"}
+{"abstract": "  Reservoir computers (RC) are a form of recurrent neural network (RNN) used\nfor forecasting timeseries data. As with all RNNs, selecting the\nhyperparameters presents a challenge when training onnew inputs. We present a\nmethod based on generalized synchronization (GS) that gives direction in\ndesigning and evaluating the architecture and hyperparameters of an RC. The\n'auxiliary method' for detecting GS provides a computationally efficient\npre-training test that guides hyperparameterselection. Furthermore, we provide\na metric for RC using the reproduction of the input system's Lyapunov\nexponentsthat demonstrates robustness in prediction.\n"}
+{"abstract": "  Structural defects and chemical impurities exist in organic semiconductors\nacting as trap centers for the excited states. This work presents a novel\nanalytical model to calculate the trapping and detrapping rates between two\nGaussian density of states. Miller-Abrahams rate and Fermi-Dirac statistics are\nemployed in this model. The introduction of effective filled and empty sites\nfor correlated bands greatly simplifies the expression of recombination rate. A\ntechnology computer-aided design simulator was used to simulate the donor-like\ntraps in an organic semiconductor DPP-DTT based thin-film transistor, showing\ngood agreement with the measured transfer characteristic.\n"}
+{"abstract": "  Recent work has shown that in a dataset of user ratings on items there exists\na group of Core Users who hold most of the information necessary for\nrecommendation. This set of Core Users can be as small as 20 percent of the\nusers. Core Users can be used to make predictions for out-of-sample users\nwithout much additional work. Since Core Users substantially shrink a ratings\ndataset without much loss of information, they can be used to improve\nrecommendation efficiency. We propose a method, combining latent factor models,\nensemble boosting and K-means clustering, to generate a small set of Artificial\nCore Users (ACUs) from real Core User data. Our ACUs have dense rating\ninformation, and improve the recommendation performance of real Core Users\nwhile remaining interpretable.\n"}
+{"abstract": "  Asteroseismic measurements enable inferences of the underlying stellar\nstructure, such as the density and the speed of sound at various points within\nthe interior of the star. This provides an opportunity to test stellar\nevolution theory by assessing whether the predicted structure of a star agrees\nwith the measured structure. Thus far, this kind of inverse analysis has only\nbeen applied to the Sun and three solar-like main-sequence stars. Here we\nextend the technique to stars on the subgiant branch, and apply it to one of\nthe best-characterized subgiants of the Kepler mission, HR 7322. The\nobservation of mixed oscillation modes in this star facilitates inferences of\nthe conditions of its inert helium core, nuclear-burning hydrogen shell, and\nthe deeper parts of its radiative envelope. We find that despite significant\ndifferences in the mode frequencies, the structure near to the center of this\nstar does not differ significantly from the predicted structure.\n"}
+{"abstract": "  In many engineering systems operating with a working fluid, the best\nefficiency is reached close to a condition of flow separation, which makes its\nprediction very crucial in industry. Providing that wall-based quantities can\nbe measured, we know today how to obtain good predictions for two and\nthree-dimensional steady and periodic flows. In these flows, the separation is\ndefined on a fixed line attached to a material surface. The last case to\nelucidate is the one where this line is no longer attached to the wall but on\nthe contrary is contained within the flow. This moving separation is probably,\nhowever, the most common case of separation in natural flows and industrial\napplications. Since this case has received less attention during the past few\nyears, we propose in this study to examine some properties of moving separation\nin two-dimensional, unsteady flows where the separation does not leave a\nsignature on the wall. Since in this framework separation can be extracted by\nusing a Lagrangian frame where the separation profile can be viewed as a\nhyperbolic unstable manifold, we propose a method to extract the separation\npoint defined by the Lagrangian saddle point that belongs to this unstable\nmanifold. In practice, the separation point and profile are initially extracted\nby detecting the most attracting Lagrangian coherent structure near the wall,\nand can then be advected in time for following instants. It is found that\nsaddle points, which initially act as separation points in the viscous wall\nflow region, remarkably preserve their hyperbolicity even if they are ejected\nfrom the wall toward the inviscid region. Two test cases are studied, the\ncreeping flow of a rotating and translating cylinder close to a wall, and the\nunsteady separation in the boundary layer generated by a planar jet impinging\nonto a plane wall.\n"}
+{"abstract": "  In this paper we continue a long line of work on representing the cut\nstructure of graphs. We classify the types minimum vertex cuts, and the\npossible relationships between multiple minimum vertex cuts.\n  As a consequence of these investigations, we exhibit a simple $O(\\kappa\nn)$-space data structure that can quickly answer pairwise\n$(\\kappa+1)$-connectivity queries in a $\\kappa$-connected graph. We also show\nhow to compute the \"closest\" $\\kappa$-cut to every vertex in near linear\n$\\tilde{O}(m+poly(\\kappa)n)$ time.\n"}
+{"abstract": "  In this paper we prove the soliton resolution conjecture for all times, for\nall solutions in the energy space, of the co-rotational wave map equation. To\nour knowledge this is the first such result for all initial data in the energy\nspace for a wave-type equation. We also prove the corresponding results for\nradial solutions, which remain bounded in the energy norm, of the cubic\n(energy-critical) nonlinear wave equation in space dimension 4.\n"}
+{"abstract": "  Magnetoelectric (ME) effect refers to the coupling between electric and\nmagnetic fields in a medium resulting in electric polarization induced by\nmagnetic fields and magnetization induced by electric fields. The linear ME\neffect in certain magnetoelectric materials such as multiferroics has been of\ngreat interest due to its application in the fabrication of spintronics\ndevices, memories, and magnetic sensors. However, the exclusive studies on the\nnonlinear ME effect are mostly centered on the investigation of second-harmonic\ngeneration in chiral materials. Here, we report the demonstration of nonlinear\nwave mixing of optical electric fields and radio-frequency (rf) magnetic fields\nin thermal atomic vapor, which is the consequence of the higher-order nonlinear\nME effect in the medium. The experimental results are explained by comparing\nwith density matrix calculations of the system. We also experimentally verify\nthe expected dependence of the generated field amplitudes on the rf field\nmagnitude as evidence of the magnetoelectric effect. This study can open up the\npossibility for precision rf-magnetometry due to its advantage in terms of\nlarger dynamic range and arbitrary frequency resolution.\n"}
+{"abstract": "  This paper studies point identification of the distribution of the\ncoefficients in some random coefficients models with exogenous regressors when\ntheir support is a proper subset, possibly discrete but countable. We exhibit\ntrade-offs between restrictions on the distribution of the random coefficients\nand the support of the regressors. We consider linear models including those\nwith nonlinear transforms of a baseline regressor, with an infinite number of\nregressors and deconvolution, the binary choice model, and panel data models\nsuch as single-index panel data models and an extension of the Kotlarski lemma.\n"}
+{"abstract": "  We report a precision measurement of the parity-violating asymmetry $A_{PV}$\nin the elastic scattering of longitudinally polarized electrons from\n$^{208}$Pb. We measure $A_{PV}=550\\pm 16 {\\rm (stat)}\\pm 8\\ {\\rm (syst)}$ parts\nper billion, leading to an extraction of the neutral weak form factor $F_W(Q^2\n= 0.00616\\ {\\rm GeV}^2) = 0.368 \\pm 0.013$. Combined with our previous\nmeasurement, the extracted neutron skin thickness is $R_n-R_p=0.283 \\pm\n0.071$~fm. The result also yields the first significant direct measurement of\nthe interior weak density of $^{208}$Pb: $\\rho^0_W = -0.0796\\pm0.0036\\ {\\rm\n(exp.)}\\pm0.0013\\ {\\rm (theo.)}\\ {\\rm fm}^{-3}$ leading to the interior baryon\ndensity $\\rho^0_b = 0.1480\\pm0.0036\\ {\\rm (exp.)}\\pm0.0013\\ {\\rm (theo.)}\\ {\\rm\nfm}^{-3}$. The measurement accurately constrains the density dependence of the\nsymmetry energy of nuclear matter near saturation density, with implications\nfor the size and composition of neutron stars.\n"}
+{"abstract": "  Connected vehicles, whether equipped with advanced driver-assistance systems\nor fully autonomous, are currently constrained to visual information in their\nlines-of-sight. A cooperative perception system among vehicles increases their\nsituational awareness by extending their perception ranges. Existing solutions\nimply significant network and computation load, as well as high flow of\nnot-always-relevant data received by vehicles. To address such issues, and thus\naccount for the inherently diverse informativeness of the data, we present\nAugmented Informative Cooperative Perception (AICP) as the first fast-filtering\nsystem which optimizes the informativeness of shared data at vehicles. AICP\ndisplays the filtered data to the drivers in augmented reality head-up display.\nTo this end, an informativeness maximization problem is presented for vehicles\nto select a subset of data to display to their drivers. Specifically, we\npropose (i) a dedicated system design with custom data structure and\nlight-weight routing protocol for convenient data encapsulation, fast\ninterpretation and transmission, and (ii) a comprehensive problem formulation\nand efficient fitness-based sorting algorithm to select the most valuable data\nto display at the application layer. We implement a proof-of-concept prototype\nof AICP with a bandwidth-hungry, latency-constrained real-life augmented\nreality application. The prototype realizes the informative-optimized\ncooperative perception with only 12.6 milliseconds additional latency. Next, we\ntest the networking performance of AICP at scale and show that AICP effectively\nfilter out less relevant packets and decreases the channel busy time.\n"}
+{"abstract": "  In recent years, programming has witnessed a shift towards using standard\nlibraries as a black box. However, there has not been a synchronous development\nof tools that can help demonstrate the working of such libraries in general\nprograms, which poses an impediment to improved learning outcomes and makes\ndebugging exasperating. We introduce Eye, an interactive pedagogical tool that\nvisualizes a program's execution as it runs. It demonstrates properties and\nusage of data structures in a general environment, thereby helping in learning,\nlogical debugging, and code comprehension. Eye provides a comprehensive\noverview at each stage during run time including the execution stack and the\nstate of data structures. The modular implementation allows for extension to\nother languages and modification of the graphics as desired.\n  Eye opens up a gateway for CS2 students to more easily understand myriads of\nprograms that are available on online programming websites, lowering the\nbarrier towards self-learning of coding. It expands the scope of visualizing\ndata structures from standard algorithms to general cases, benefiting both\nteachers as well as programmers who face issues in debugging. Line by line\ninterpreting allows Eye to describe the execution and not only the current\nstate. We also conduct experiments to evaluate the efficacy of Eye for\ndebugging and comprehending a new piece of code. Our findings show that it\nbecomes faster and less frustrating to debug certain problems using this tool,\nand also makes understanding new code a much more pleasant experience.\n"}
+{"abstract": "  The classic Riesz representation theorem characterizes all linear and\nincreasing functionals on the space $C_{c}(X)$ of continuous compactly\nsupported functions. A geometric version of this result, which characterizes\nall linear increasing functionals on the set of convex bodies in\n$\\mathbb{R}^{n}$, was essentially known to Alexandrov. This was used by\nAlexandrov to prove the existence of mixed area measures in convex geometry.\n  In this paper we characterize linear and increasing functionals on the class\nof log-concave functions on $\\mathbb{R}^{n}$. Here \"linear\" means linear with\nrespect to the natural addition on log-concave functions which is the\nsup-convolution. Equivalently, we characterize pointwise-linear and increasing\nfunctionals on the class of convex functions. For some choices of the exact\nclass of functions we prove that there are no non-trivial such functionals. For\nanother choice we obtain the expected analogue of the result for convex bodies.\nAnd most interestingly, for yet another choice we find a new unexpected family\nof such functionals.\n  Finally, we explain the connection between our results and recent work done\nin convex geometry regarding the surface area measure of a log-concave\nfunctions. An application of our results in this direction is also given.\n"}
+{"abstract": "  We have analyzed the ALMA archival data of the SO ($J_N=6_5-5_4$ and\n$J_N=7_6-6_5$), CO ($J=2-1$), and CCH ($N=3-2, J=7/2-5/2, F=4-3$) lines from\nthe class 0 protobinary system, NGC1333 IRAS 4A. The images of SO ($J_N =\n6_5-5_4$) and CO ($J=2-1$) successfully separate two northern outflow lobes\nconnected to each protostar, IRAS 4A1 and IRAS 4A2. The outflow from IRAS 4A2\nshows an S-shaped morphology, consisting of a flattened envelope around IRAS\n4A2 with two outflow lobes connected to both edges of the envelope. The\nflattened envelope surrounding IRAS 4A2 has an opposite velocity gradient to\nthat of the circumbinary envelope. The observed features are reproduced by the\nmagnetohydrodynamic simulation of the collapsing core whose magnetic field\ndirection is misaligned to the rotational axis. Our simulation shows that the\nintensity of the outflow lobes is enhanced on one side, resulting in the\nformation of S-shaped morphology. The S-shaped outflow can also be explained by\nthe precessing outflow launched from an unresolved binary with a separation\nlarger than 12 au (0.04arcsec). Additionally, we discovered a previously\nunknown extremely high velocity component at $\\sim$45-90 km/s near IRAS 4A2\nwith CO. CCH ($J_{N,F}=7/2_{3,4}-5/2_{2,3}$) emission shows two pairs of blobs\nattaching to the bottom of shell like feature, and the morphology is\nsignificantly different from those of SO and CO lines. Toward IRAS 4A2, the\nS-shaped outflow shown in SO is overlapped with the edges of CCH shells, while\nCCH shells have the velocity gradients opposite to the flattened structure\naround IRAS 4A2.\n"}
+{"abstract": "  We prove the existence of small amplitude time quasi-periodic solutions of\nthe pure gravity water waves equations with constant vorticity, for a\nbidimensional fluid over a flat bottom delimited by a space periodic free\ninterface. Using a Nash-Moser implicit function iterative scheme we construct\ntraveling nonlinear waves which pass through each other slightly deforming and\nretaining forever a quasiperiodic structure. These solutions exist for any\nfixed value of depth and gravity and restricting the vorticity parameter to a\nBorel set of asymptotically full Lebesgue measure.\n"}
+{"abstract": "  Quantum annealing is an emerging new platform for combinatorial optimization,\nrequiring an Ising model formulation for optimization problems. The formulation\ncan be an essential obstacle to the permeation of this innovation into broad\nareas of everyday life. Our research is aimed at the proposal of a Petri net\nmodeling approach for an Ising model formulation. Although the proposed method\nrequires users to model their optimization problems with Petri nets, this\nprocess can be carried out in a relatively straightforward manner if we know\nthe target problem and the simple Petri net modeling rules. With our method,\nthe constraints and objective functions in the target optimization problems are\nrepresented as fundamental characteristics of Petri net models, extracted\nsystematically from Petri net models, and then converted into binary quadratic\nnets, equivalent to Ising models. The proposed method can drastically reduce\nthe difficulty of the Ising model formulation.\n"}
+{"abstract": "  We consider the classical molecular beam epitaxy (MBE) model with logarithmic\ntype potential known as no-slope-selection. We employ a third order backward\ndifferentiation (BDF3) in time with implicit treatment of the surface diffusion\nterm. The nonlinear term is approximated by a third order explicit\nextrapolation (EP3) formula. We exhibit mild time step constraints under which\nthe modified energy dissipation law holds. We break the second Dahlquist\nbarrier and develop a new theoretical framework to prove unconditional uniform\nenergy boundedness with no size restrictions on the time step. This is the\nfirst unconditional result for third order BDF methods applied to the MBE\nmodels without introducing any stabilization terms or fictitious variables. A\nnovel theoretical framework is also established for the error analysis of high\norder methods.\n"}
+{"abstract": "  In this draft paper, we introduce a novel architecture for graph networks\nwhich is equivariant to the Euclidean group in $n$-dimensions. The model is\ndesigned to work with graph networks in their general form and can be shown to\ninclude particular variants as special cases. Thanks to its equivariance\nproperties, we expect the proposed model to be more data efficient with respect\nto classical graph architectures and also intrinsically equipped with a better\ninductive bias. We defer investigating this matter to future work.\n"}
+{"abstract": "  Let $\\Sigma_{g}$ be a closed surface of genus $g\\geq 2$ and $\\Gamma_{g}$\ndenote the fundamental group of $\\Sigma_{g}$. We establish a generalization of\nVoiculescu's theorem on the asymptotic $*$-freeness of Haar unitary matrices\nfrom free groups to $\\Gamma_{g}$. We prove that for a random representation of\n$\\Gamma_{g}$ into $\\mathsf{SU}(n)$, with law given by the volume form arising\nfrom the Atiyah-Bott-Goldman symplectic form on moduli space, the expected\nvalue of the trace of a fixed non-identity element of $\\Gamma_{g}$ is bounded\nas $n\\to\\infty$. The proof involves an interplay between Dehn's work on the\nword problem in $\\Gamma_{g}$ and classical invariant theory.\n"}
+{"abstract": "  We prove some Strichartz estimates for the massless, radial Dirac-Coulomb\nequation in 3D. The main tools in our argument are the use of a \"relativistic\nHankel transform\" together with some precise estimates on the generalized\neigenfunctions of the Dirac-Coulomb operator.\n"}
+{"abstract": "  We derive $q$-versions of Green's theorem from the Leibniz rules of partial\nderivatives for the $q$-deformed Euclidean space. Using these results and the\nSchr\\\"{o}dinger equations for a $q$-deformed nonrelativistic particle, we\nderive continuity equations for the probability density, the energy density,\nand the momentum density of a $q$-deformed nonrelativistic particle.\n"}
+{"abstract": "  Given a smooth convex cone in the Euclidean $(n+1)$-space ($n\\geq2$), we\nconsider strictly mean convex hypersurfaces with boundary which are star-shaped\nwith respect to the center of the cone and which meet the cone perpendicularly.\nIf those hypersurfaces inside the cone evolve by a class of inverse curvature\nflows, then, by using the convexity of the cone in the derivation of the\ngradient and H\\\"{o}lder estimates, we can prove that this evolution exists for\nall the time and the evolving hypersurfaces converge smoothly to a piece of a\nround sphere as time tends to infinity.\n"}
+{"abstract": "  Emitted photons stemming from the radiative recombination of electron-hole\npairs carry chemical potential in radiative energy converters. This luminescent\neffect can substantially alter the local net photogeneration in near-field\nthermophotovoltaic cells. Several assumptions involving the luminescent effect\nare commonly made in modeling photovoltaic devices; in particular, the photon\nchemical potential is assumed to be zero or a constant prescribed by the bias\nvoltage. The significance of photon chemical potential depends upon the emitter\ntemperature, the semiconductor properties, and the injection level. Hence,\nthese assumptions are questionable in thermophotovoltaic devices operating in\nthe near-field regime. In the present work, an iterative solver that combines\nfluctuational electrodynamics with the drift-diffusion model is developed to\ntackle the coupled photon and charge transport problem, enabling the\ndetermination of the spatial profile of photon chemical potential beyond the\ndetailed balance approach. The difference between the results obtained by\nallowing the photon chemical potential to vary spatially and by assuming a\nconstant value demonstrates the limitations of the conventional approaches.\nThis study is critically important for performance evaluation of near-field\nthermophotovoltaic systems.\n"}
+{"abstract": "  We propose to use tensor diagrams and the Fomin-Pylyavskyy conjectures to\nexplore the connection between symbol alphabets of $n$-particle amplitudes in\nplanar $\\mathcal{N}=4$ Yang-Mills theory and certain polytopes associated to\nthe Grassmannian G(4, $n$). We show how to assign a web (a planar tensor\ndiagram) to each facet of these polytopes. Webs with no inner loops are\nassociated to cluster variables (rational symbol letters). For webs with a\nsingle inner loop we propose and explicitly evaluate an associated web series\nthat contains information about algebraic symbol letters. In this manner we\nreproduce the results of previous analyses of $n \\le 8$, and find that the\npolytope $\\mathcal{C}^\\dagger(4,9)$ encodes all rational letters, and all\nsquare roots of the algebraic letters, of known nine-particle amplitudes.\n"}
+{"abstract": "  In this study, the stability dependence of turbulent Prandtl number ($Pr_t$)\nis quantified via a novel and simple analytical approach. Based on the variance\nand flux budget equations, a hybrid length scale formulation is first proposed\nand its functional relationships to well-known length scales are established.\nNext, the ratios of these length scales are utilized to derive an explicit\nrelationship between $Pr_t$ and gradient Richardson number. In addition,\ntheoretical predictions are made for several key turbulence variables (e.g.,\ndissipation rates, normalized fluxes). The results from our proposed approach\nare compared against other competing formulations as well as published\ndatasets. Overall, the agreement between the different approaches is rather\ngood despite their different theoretical foundations and assumptions.\n"}
+{"abstract": "  The problem of estimating the angular speed of a solid body from attitude\nmeasurements is addressed. To solve this problem, we propose an observer whose\ndynamics are not constrained to evolve on any specific manifold. This\ndrastically simplifies the analysis of the proposed observer. Using Lyapunov\nanalysis, sufficient conditions for global asymptotic stability of a set\nwherein the estimation error is equal to zero are established. In addition, the\nproposed methodology is adapted to deal with angular speed estimation for\nsystems evolving on the unit circle. The approach is illustrated through\nseveral numerical simulations.\n"}
+{"abstract": "  Exoplanet detection in the past decade by efforts including NASA's Kepler and\nTESS missions has discovered many worlds that differ substantially from planets\nin our own Solar system, including more than 400 exoplanets orbiting binary or\nmulti-star systems. This not only broadens our understanding of the diversity\nof exoplanets, but also promotes our study of exoplanets in the complex binary\nand multi-star systems and provides motivation to explore their habitability.\nIn this study, we analyze orbital stability of exoplanets in non-coplanar\ncircumbinary systems using a numerical simulation method, with which a large\nnumber of circumbinary planet samples are generated in order to quantify the\neffects of various orbital parameters on orbital stability. We also train a\nmachine learning model that can quickly determine the stability of the\ncircumbinary planetary systems. Our results indicate that larger inclinations\nof the planet tend to increase the stability of its orbit, but change in the\nplanet's mass range between Earth and Jupiter has little effect on the\nstability of the system. In addition, we find that Deep Neural Networks (DNNs)\nhave higher accuracy and precision than other machine learning algorithms.\n"}
+{"abstract": "  Let $M_n$ be a random $n\\times n$ matrix with i.i.d. $\\text{Bernoulli}(1/2)$\nentries. We show that for fixed $k\\ge 1$, \\[\\lim_{n\\to\n\\infty}\\frac{1}{n}\\log_2\\mathbb{P}[\\text{corank }M_n\\ge k] = -k.\\]\n"}
+{"abstract": "  Any binary string can be associated with a unary predicate $P$ on\n$\\mathbb{N}$. In this paper we investigate subsets named by a predicate $P$\nsuch that the relation $P(x+y)$ has finite VC dimension. This provides a\nmeasure of complexity for binary strings with different properties than the\nstandard string complexity function (based on diversity of substrings). We\nprove that strings of bounded VC dimension are meagre in the topology of the\nreals, provide simple rules for bounding the VC dimension of a string, and show\nthat the bi-infinite strings of VC dimension $d$ are a non-sofic shift space.\nAdditionally we characterize the irreducible strings of low VC dimension (0,1\nand 2), and provide connections to mathematical logic.\n"}
+{"abstract": "  The goal of this paper is extend Kottwitz's theory of $B(G)$ for global\nfields. In particular, we show how to extend the definition of ``$B(G)$ with\nadelic coefficients'' from tori to all connected reductive groups. As an\napplication, we give an explicit construction of certain transfer factors for\nnon-regular semisimple elements of non-quasisplit groups. This generalizes some\nresults of Kaletha and Taibi. These formulas are used in the stabilization of\nthe cohomology of Shimura and Igusa varieties.\n"}
+{"abstract": "  As an extension of previous ungraded work, we define a graded $p$-polar ring\nto be an analog of a graded commutative ring where multiplication is only\nallowed on $p$-tuples (instead of pairs) of elements of equal degree. We show\nthat the free affine $p$-adic group scheme functor, as well as the free formal\ngroup functor, defined on $k$-algebras for a perfect field $k$ of\ncharacteristic $p$, factors through $p$-polar $k$-algebras. It follows that the\nsame is true for any affine $p$-adic or formal group functor, in particular for\nthe functor of $p$-typical Witt vectors. As an application, we show that the\nlatter is free on the $p$-polar affine line.\n"}
+{"abstract": "  Bismuth ferrite is one of the most widely studied multiferroic materials\nbecause of its large ferroelectric polarisation coexisting with magnetic order\nat room temperature. Using density functional theory (DFT), we identify several\npreviously unknown polar and non-polar structures within the low-energy phase\nspace of perovskite-structure bismuth ferrite, BiFeO$_3$. Of particular\ninterest is a series of non-centrosymmetric structures with polarisation along\none lattice vector, combined with anti-polar distortions, reminiscent of\nferroelectric domains, along a perpendicular direction. We discuss possible\nroutes to stabilising the new phases using biaxial heteroepitaxial strain or\ninterfacial electrostatic control in heterostructures.\n"}
+{"abstract": "  Primordial black holes may have been produced in the early stages of the\nthermal history of the Universe after cosmic inflation. If so, dark matter in\nthe form of elementary particles can be subsequently accreted around these\nobjects, in particular when it gets non-relativistic and further streams freely\nin the primordial plasma. A dark matter mini-spike builds up gradually around\neach black hole, with density orders of magnitude larger than the cosmological\none. We improve upon previous work by carefully inspecting the computation of\nthe mini-spike radial profile as a function of black hole mass, dark matter\nparticle mass and temperature of kinetic decoupling. We identify a phase-space\ncontribution that has been overlooked and that leads to changes in the final\nresults. We also derive complementary analytical formulae using convenient\nasymptotic regimes, which allows us to bring out peculiar power-law behaviors\nfor which we provide tentative physical explanations.\n"}
+{"abstract": "  The $k$-mappability problem has two integers parameters $m$ and $k$. For\nevery subword of size $m$ in a text $S$, we wish to report the number of\nindices in $S$ in which the word occurs with at most $k$ mismatches.\n  The problem was lately tackled by Alzamel et al. For a text with constant\nalphabet $\\Sigma$ and $k \\in O(1)$, they present an algorithm with linear space\nand $O(n\\log^{k+1}n)$ time. For the case in which $k = 1$ and a constant size\nalphabet, a faster algorithm with linear space and $O(n\\log(n)\\log\\log(n))$\ntime was presented in a 2020 paper by Alzamel et al.\n  In this work, we enhance the techniques of Alzamel et al.'s 2020 paper to\nobtain an algorithm with linear space and $O(n \\log(n))$ time for $k = 1$. Our\nalgorithm removes the constraint of the alphabet being of constant size. We\nalso present linear algorithms for the case of $k=1$, $|\\Sigma|\\in O(1)$ and\n$m=\\Omega(\\sqrt{n})$.\n"}
+{"abstract": "  In the past few years, the detection of gravitational waves from compact\nbinary coalescences with the Advanced LIGO and Advanced Virgo detectors has\nbecome routine. Future observatories will detect even larger numbers of\ngravitational-wave signals, which will also spend a longer time in the\ndetectors' sensitive band. This will eventually lead to overlapping signals,\nespecially in the case of Einstein Telescope (ET) and Cosmic Explorer (CE).\nUsing realistic distributions for the merger rate as a function of redshift as\nwell as for component masses in binary neutron star and binary black hole\ncoalescences, we map out how often signal overlaps of various types will occur\nin an ET-CE network over the course of a year. We find that a binary neutron\nstar signal will typically have tens of overlapping binary black hole and\nbinary neutron star signals. Moreover, it will happen up to tens of thousands\nof times per year that two signals will have their end times within seconds of\neach other. In order to understand to what extent this would lead to\nmeasurement biases with current parameter estimation methodology, we perform\ninjection studies with overlapping signals from binary black hole and/or binary\nneutron star coalescences. Varying the signal-to-noise ratios, the durations of\noverlap, and the kinds of overlapping signals, we find that in most scenarios\nthe intrinsic parameters can be recovered with negligible bias. However, biases\ndo occur for a short binary black hole or a quieter binary neutron star signal\noverlapping with a long and louder binary neutron star event when the merger\ntimes are sufficiently close. Hence our studies show where improvements are\nrequired to ensure reliable estimation of source parameters for all detected\ncompact binary signals as we go from second-generation to third-generation\ndetectors.\n"}
+{"abstract": "  Computer vision (CV) techniques try to mimic human capabilities of visual\nperception to support labor-intensive and time-consuming tasks like the\nrecognition and localization of critical objects. Nowadays, CV increasingly\nrelies on artificial intelligence (AI) to automatically extract useful\ninformation from images that can be utilized for decision support and business\nprocess automation. However, the focus of extant research is often exclusively\non technical aspects when designing AI-based CV systems while neglecting\nsocio-technical facets, such as trust, control, and autonomy. For this purpose,\nwe consider the design of such systems from a hybrid intelligence (HI)\nperspective and aim to derive prescriptive design knowledge for CV-based HI\nsystems. We apply a reflective, practice-inspired design science approach and\naccumulate design knowledge from six comprehensive CV projects. As a result, we\nidentify four design-related mechanisms (i.e., automation, signaling,\nmodification, and collaboration) that inform our derived meta-requirements and\ndesign principles. This can serve as a basis for further socio-technical\nresearch on CV-based HI systems.\n"}
+{"abstract": "  Plasmon-free surface-enhanced Raman scattering (SERS) substrates have\nattracted tremendous attention for their abundant sources, excellent chemical\nstability, superior biocompatibility, good signal uniformity, and unique\nselectivity to target molecules. Recently, researchers have made great progress\nin fabricating novel plasmon-free SERS substrates and exploring new enhancement\nstrategies to improve their sensitivity. This review summarizes the recent\ndevelopments of plasmon-free SERS substrates and specially focuses on the\nenhancement mechanisms and strategies. Furthermore, the promising applications\nof plasmon-free SERS substrates in biomedical diagnosis, metal ions and organic\npollutants sensing, chemical and biochemical reactions monitoring, and\nphotoelectric characterization are introduced. Finally, current challenges and\nfuture research opportunities in plasmon-free SERS substrates are briefly\ndiscussed.\n"}
+{"abstract": "  This paper studies bandit algorithms under data poisoning attacks in a\nbounded reward setting. We consider a strong attacker model in which the\nattacker can observe both the selected actions and their corresponding rewards,\nand can contaminate the rewards with additive noise. We show that \\emph{any}\nbandit algorithm with regret $O(\\log T)$ can be forced to suffer a regret\n$\\Omega(T)$ with an expected amount of contamination $O(\\log T)$. This amount\nof contamination is also necessary, as we prove that there exists an $O(\\log\nT)$ regret bandit algorithm, specifically the classical UCB, that requires\n$\\Omega(\\log T)$ amount of contamination to suffer regret $\\Omega(T)$. To\ncombat such poising attacks, our second main contribution is to propose a novel\nalgorithm, Secure-UCB, which uses limited \\emph{verification} to access a\nlimited number of uncontaminated rewards. We show that with $O(\\log T)$\nexpected number of verifications, Secure-UCB can restore the order optimal\n$O(\\log T)$ regret \\emph{irrespective of the amount of contamination} used by\nthe attacker. Finally, we prove that for any bandit algorithm, this number of\nverifications $O(\\log T)$ is necessary to recover the order-optimal regret. We\ncan then conclude that Secure-UCB is order-optimal in terms of both the\nexpected regret and the expected number of verifications, and can save\nstochastic bandits from any data poisoning attack.\n"}
+{"abstract": "  The close-packed AB$_2$ structures called Laves phases constitute the largest\ngroup of intermetallic compounds. In this paper we computationally investigated\nthe pseudo-binary Laves phase system Y$_{1-x}$Gd$_x$(Fe$_{1-y}$Co$_y$)$_2$\nspanning between the YFe$_2$, YCo$_2$, GdFe$_2$, and GdCo$_2$ vertices. While\nthe vast majority of the Y$_{1-x}$Gd$_x$(Fe$_{1-y}$Co$_y$)$_2$ phase diagram is\nthe ferrimagnetic phase, YCo$_2$ along with a narrow range of concentrations\naround it is the paramagnetic phase. We presented results obtained by Monte\nCarlo simulations of the Heisenberg model with parameters derived from\nfirst-principles calculations. For calculations, we used the Uppsala atomistic\nspin dynamics (UppASD) code together with the spin-polarized relativistic\nKorringa-Kohn-Rostoker (SPR-KKR) code. From first principles we calculated the\nmagnetic moments and exchange integrals for the considered pseudo-binary\nsystem, together with spin-polarized densities of states for boundary\ncompositions. Furthermore, we showed how the compensation point with the\neffective zero total moment depends on the concentration in the considered\nferrimagnetic phases. However, the main result of our study was the\ndetermination of the Curie temperature dependence for the system\nY$_{1-x}$Gd$_x$(Fe$_{1-y}$Co$_y$)$_2$. Except for the paramagnetic region\naround YCo$_2$, the predicted temperatures were in good qualitative and\nquantitative agreement with experimental results, which confirmed the ability\nof the method to predict magnetic transition temperatures for systems\ncontaining up to three different magnetic elements (Fe, Co, and Gd)\nsimultaneously. For the Y(Fe$_{1-y}$Co$_y$)$_2$ and Gd(Fe$_{1-y}$Co$_y$)$_2$\nsystems our calculations matched the experimentally-confirmed\nSlater-Pauling-like behavior of T$_C$ dependence on the Co concentration.\n"}
+{"abstract": "  Quantum steering refers to correlations that can be classified as\nintermediate between entanglement and Bell nonlocality. Every state exhibiting\nBell nonlocality exhibits also quantum steering and every state exhibiting\nquantum steering is also entangled. In low dimensional cases similar\nhierarchical relations have been observed between the temporal counterparts of\nthese correlations. Here, we study the hierarchy of such temporal correlations\nfor a general multilevel quantum system. We demonstrate that the same hierarchy\nholds for two definitions of state over time. In order to compare different\ntypes of temporal correlations, we show that temporal counterparts of Bell\nnonlocality and entanglement can be quantified with a temporal nonlocality\nrobustness and temporal entanglement robustness. Our numerical result reveal\nthat in contrast to temporal steering, for temporal nonlocality to manifest\nitself we require the initial state not to be in a completely mixed state.\n"}
+{"abstract": "  The development of respiratory failure is common among patients in intensive\ncare units (ICU). Large data quantities from ICU patient monitoring systems\nmake timely and comprehensive analysis by clinicians difficult but are ideal\nfor automatic processing by machine learning algorithms. Early prediction of\nrespiratory system failure could alert clinicians to patients at risk of\nrespiratory failure and allow for early patient reassessment and treatment\nadjustment. We propose an early warning system that predicts moderate/severe\nrespiratory failure up to 8 hours in advance. Our system was trained on\nHiRID-II, a data-set containing more than 60,000 admissions to a tertiary care\nICU. An alarm is typically triggered several hours before the beginning of\nrespiratory failure. Our system outperforms a clinical baseline mimicking\ntraditional clinical decision-making based on pulse-oximetric oxygen saturation\nand the fraction of inspired oxygen. To provide model introspection and\ndiagnostics, we developed an easy-to-use web browser-based system to explore\nmodel input data and predictions visually.\n"}
+{"abstract": "  Herein we shall consider Lorentz boosts and Wigner rotations from a\n(complexified) quaternionic point of view. We shall demonstrate that for a\nsuitably defined self-adjoint complex quaternionic 4-velocity, pure Lorentz\nboosts can be phrased in terms of the quaternion square root of the relative\n4-velocity connecting the two inertial frames. Straightforward computations\nthen lead to quite explicit and relatively simple algebraic formulae for the\ncomposition of 4-velocities and the Wigner angle. We subsequently relate the\nWigner rotation to the generic non-associativity of the composition of three\n4-velocities, and develop a necessary and sufficient condition for\nassociativity to hold. Finally, we relate the composition of 4-velocities to a\nspecific implementation of the Baker-Campbell-Hausdorff theorem. As compared to\nordinary 4x4 Lorentz transformations, the use of self-adjoint complexified\nquaternions leads, from a computational view, to storage savings and more rapid\ncomputations, and from a pedagogical view to to relatively simple and explicit\nformulae.\n"}
+{"abstract": "  C. elegans shows chemotaxis using klinokinesis where the worm senses the\nconcentration based on a single concentration sensor to compute the\nconcentration gradient to perform foraging through gradient ascent/descent\ntowards the target concentration followed by contour tracking. The biomimetic\nimplementation requires complex neurons with multiple ion channel dynamics as\nwell as interneurons for control. While this is a key capability of autonomous\nrobots, its implementation on energy-efficient neuromorphic hardware like\nIntel's Loihi requires adaptation of the network to hardware-specific\nconstraints, which has not been achieved. In this paper, we demonstrate the\nadaptation of chemotaxis based on klinokinesis to Loihi by implementing\nnecessary neuronal dynamics with only LIF neurons as well as a complete\nspike-based implementation of all functions e.g. Heaviside function and\nsubtractions. Our results show that Loihi implementation is equivalent to the\nsoftware counterpart on Python in terms of performance - both during foraging\nand contour tracking. The Loihi results are also resilient in noisy\nenvironments. Thus, we demonstrate a successful adaptation of chemotaxis on\nLoihi - which can now be combined with the rich array of SNN blocks for SNN\nbased complex robotic control.\n"}
+{"abstract": "  Deep neural networks (DNNs) have shown to perform very well on large scale\nobject recognition problems and lead to widespread use for real-world\napplications, including situations where DNN are implemented as \"black boxes\".\nA promising approach to secure their use is to accept decisions that are likely\nto be correct while discarding the others. In this work, we propose DOCTOR, a\nsimple method that aims to identify whether the prediction of a DNN classifier\nshould (or should not) be trusted so that, consequently, it would be possible\nto accept it or to reject it. Two scenarios are investigated: Totally Black Box\n(TBB) where only the soft-predictions are available and Partially Black Box\n(PBB) where gradient-propagation to perform input pre-processing is allowed.\nEmpirically, we show that DOCTOR outperforms all state-of-the-art methods on\nvarious well-known images and sentiment analysis datasets. In particular, we\nobserve a reduction of up to $4\\%$ of the false rejection rate (FRR) in the PBB\nscenario. DOCTOR can be applied to any pre-trained model, it does not require\nprior information about the underlying dataset and is as simple as the simplest\navailable methods in the literature.\n"}
+{"abstract": "  A search is presented for a heavy vector resonance decaying into a Z boson\nand the standard model Higgs boson, where the Z boson is identified through its\nleptonic decays to electrons, muons, or neutrinos, and the Higgs boson is\nidentified through its hadronic decays. The search is performed in a\nLorentz-boosted regime and is based on data collected from 2016 to 2018 at the\nCERN LHC, corresponding to an integrated luminosity of 137 fb$^{-1}$. Upper\nlimits are derived on the production of a narrow heavy resonance Z', and a mass\nbelow 3.5 and 3.7 TeV is excluded at 95% confidence level in models where the\nheavy vector boson couples exclusively to fermions and to bosons, respectively.\nThese are the most stringent limits placed on the Heavy Vector Triplet Z' model\nto date. If the heavy vector boson couples exclusively to standard model\nbosons, upper limits on the product of the cross section and branching fraction\nare set between 23 and 0.3 fb for a Z' mass between 0.8 and 4.6 TeV,\nrespectively. This is the first limit set on a heavy vector boson coupling\nexclusively to standard model bosons in its production and decay.\n"}
+{"abstract": "  Obtaining labeled data for machine learning tasks can be prohibitively\nexpensive. Active learning mitigates this issue by exploring the unlabeled data\nspace and prioritizing the selection of data that can best improve the model\nperformance. A common approach to active learning is to pick a small sample of\ndata for which the model is most uncertain. In this paper, we explore the\nefficacy of Bayesian neural networks for active learning, which naturally\nmodels uncertainty by learning distribution over the weights of neural\nnetworks. By performing a comprehensive set of experiments, we show that\nBayesian neural networks are more efficient than ensemble based techniques in\ncapturing uncertainty. Our findings also reveal some key drawbacks of the\nensemble techniques, which was recently shown to be more effective than Monte\nCarlo dropouts.\n"}
+{"abstract": "  The advent of automated vehicles operating at SAE levels 4 and 5 poses high\nfault tolerance demands for all functions contributing to the driving task. At\nthe actuator level, fault-tolerant vehicle motion control, which exploits\nfunctional redundancies among the actuators, is one means to achieve the\nrequired degree of fault tolerance. Therefore, we give a comprehensive overview\nof the state of the art in actuator fault-tolerant vehicle motion control with\na focus on drive, brake, and steering degradations, as well as tire blowouts.\nThis review shows that actuator fault-tolerant vehicle motion is a widely\nstudied field; yet, the presented approaches differ with respect to many\naspects. To provide a starting point for future research, we survey the\nemployed actuator topologies, the tolerated degradations, the presented control\napproaches, as well as the experiments conducted for validation. Overall, and\ndespite the large number of different approaches, the covered literature\nreveals the potential of increasing fault tolerance by fault-tolerant vehicle\nmotion control. Thus, besides developing novel approaches or demonstrating\nreal-time applicability, future research should aim at investigating\nlimitations and enabling comparison of fault-tolerant motion control approaches\nin order to allow for a thorough safety argumentation.\n"}
+{"abstract": "  The odd isotopologues of ytterbium monohydroxide, $^{171,173}$YbOH, have been\nidentified as promising molecules in which to measure parity (P) and time\nreversal (T) violating physics. Here we characterize the\n$\\tilde{A}^{2}\\Pi_{1/2}(0,0,0)-\\tilde{X}^2\\Sigma^+(0,0,0)$ band near 577 nm for\nthese odd isotopologues. Both laser-induced fluorescence (LIF) excitation\nspectra of a supersonic molecular beam sample and absorption spectra of a\ncryogenic buffer-gas cooled sample were recorded. Additionally, a novel\nspectroscopic technique based on laser-enhanced chemical reactions is\ndemonstrated and utilized in the absorption measurements. This technique is\nespecially powerful for disentangling congested spectra. An effective\nHamiltonian model is used to extract the fine and hyperfine parameters for the\n$\\tilde{A}^{2}\\Pi_{1/2}(0,0,0)$ and $\\tilde{X}^2\\Sigma^+(0,0,0)$ states. A\ncomparison of the determined $\\tilde{X}^2\\Sigma^+(0,0,0)$ hyperfine parameters\nwith recently predicted values (M. Denis, et al., J. Chem. Phys. $\\bf{152}$,\n084303 (2020), K. Gaul and R. Berger, Phys. Rev. A $\\bf{101}$, 012508 (2020),\nJ. Liu et al., J. Chem. Phys. $\\bf{154}$, 064110 (2021)) is made. The measured\nhyperfine parameters provide experimental confirmation of the computational\nmethods used to compute the P,T-violating coupling constants $W_d$ and $W_M$,\nwhich correlate P,T-violating physics to P,T-violating energy shifts in the\nmolecule. The dependence of the fine and hyperfine parameters of the\n$\\tilde{A}^{2}\\Pi_{1/2}(0,0,0)$ and $\\tilde{X}^2\\Sigma^+(0,0,0)$ states for all\nisotopologues of YbOH are discussed and a comparison to isoelectronic YbF is\nmade.\n"}
+{"abstract": "  The two-dimensional Helmholtz equation separates in elliptic coordinates\nbased on two distinct foci, a limit case of which includes polar coordinate\nsystems when the two foci coalesce. This equation is invariant under the\nEuclidean group of translations and orthogonal transformations; we replace the\nlatter by the discrete dihedral group of N discrete rotations and reflections.\nThe separation of variables in polar and elliptic coordinates is then used to\ndefine discrete Bessel and Mathieu functions, as approximants to the well-known\ncontinuous Bessel and Mathieu functions, as N-point Fourier transforms\napproximate the Fourier transform over the circle, with integrals replaced by\nfinite sums. We find that these 'discrete' functions approximate the numerical\nvalues of their continuous counterparts very closely and preserve some key\nspecial function relations.\n"}
+{"abstract": "  A schizophrenia relapse has severe consequences for a patient's health, work,\nand sometimes even life safety. If an oncoming relapse can be predicted on\ntime, for example by detecting early behavioral changes in patients, then\ninterventions could be provided to prevent the relapse. In this work, we\ninvestigated a machine learning based schizophrenia relapse prediction model\nusing mobile sensing data to characterize behavioral features. A\npatient-independent model providing sequential predictions, closely\nrepresenting the clinical deployment scenario for relapse prediction, was\nevaluated. The model uses the mobile sensing data from the recent four weeks to\npredict an oncoming relapse in the next week. We used the behavioral rhythm\nfeatures extracted from daily templates of mobile sensing data, self-reported\nsymptoms collected via EMA (Ecological Momentary Assessment), and demographics\nto compare different classifiers for the relapse prediction. Naive Bayes based\nmodel gave the best results with an F2 score of 0.083 when evaluated in a\ndataset consisting of 63 schizophrenia patients, each monitored for up to a\nyear. The obtained F2 score, though low, is better than the baseline\nperformance of random classification (F2 score of 0.02 $\\pm$ 0.024). Thus,\nmobile sensing has predictive value for detecting an oncoming relapse and needs\nfurther investigation to improve the current performance. Towards that end,\nfurther feature engineering and model personalization based on the behavioral\nidiosyncrasies of a patient could be helpful.\n"}
+{"abstract": "  We propose a vector dark matter model with an exotic dark SU(2) gauge group.\nTwo Higgs triplets are introduced to spontaneously break the symmetry. All of\nthe dark gauge bosons become massive, and the lightest one is a viable vector\nDM candidate. Its stability is guaranteed by a remaining Z_2 symmetry. We study\nthe parameter space constrained by the Higgs measurement data, the dark matter\nrelic density, and direct and indirect detection experiments. We find numerous\nparameter points satisfying all the constraints, and they could be further\ntested in future experiments. Similar methodology can be used to construct\nvector dark matter models from an arbitrary SO(N) gauge group.\n"}
+{"abstract": "  Following E. Wigner's original vision, we prove that sampling the eigenvalue\ngaps within the bulk spectrum of a .fixed (deformed) Wigner matrix $H$ yields\nthe celebrated Wigner-Dyson-Mehta universal statistics with high probability.\nSimilarly, we prove universality for a monoparametric family of deformed Wigner\nmatrices $H+xA$ with a deterministic Hermitian matrix $A$ and a fixed Wigner\nmatrix $H$, just using the randomness of a single scalar real random variable\n$x$. Both results constitute quenched versions of bulk universality that has so\nfar only been proven in annealed sense with respect to the probability space of\nthe matrix ensemble.\n"}
+{"abstract": "  The demand for streaming media and live video conferencing is at peak and\nexpected to grow further, thereby the need for low-cost streaming services with\nbetter quality and lower latency is essential. Therefore, in this paper, we\npropose a novel peer-to-peer (P2P) live streaming platform, called fybrrStream,\nwhere a logical mesh and physical tree i.e., hybrid topology-based approach is\nleveraged for low latency streaming. fybrrStream distributes the load on\nparticipating peers in a hierarchical manner by considering their network\nbandwidth, network latency, and node stability. fybrrStream costs as low as the\ncost of just hosting a light-weight website and the performance is comparable\nto the existing state-of-the-art media streaming services. We evaluated and\ntested the proposed fybrrStream platform with real-field experiments using 50+\nusers spread across India and results obtained show significant improvements in\nthe live streaming performance over other schemes.\n"}
+{"abstract": "  We conjecture the existence of hidden Onsager algebra symmetries in two\ninteracting quantum integrable lattice models, i.e. spin-1/2 XXZ model and\nspin-1 Zamolodchikov-Fateev model at arbitrary root of unity values of the\nanisotropy. The conjectures relate the Onsager generators to the conserved\ncharges obtained from semi-cyclic transfer matrices. The conjectures are\nmotivated by two examples which are spin-1/2 XX model and spin-1 U(1)-invariant\nclock model. A novel construction of the semi-cyclic transfer matrices of\nspin-1 Zamolodchikov-Fateev model at arbitrary root of unity value of the\nanisotropy is carried out via transfer matrix fusion procedure.\n"}
+{"abstract": "  Robust multi-agent trajectory prediction is essential for the safe control of\nrobots and vehicles that interact with humans. Many existing methods treat\nsocial and temporal information separately and therefore fall short of\nmodelling the joint future trajectories of all agents in a socially consistent\nway. To address this, we propose a new class of Latent Variable Sequential Set\nTransformers which autoregressively model multi-agent trajectories. We refer to\nthese architectures as \"AutoBots\". AutoBots model the contents of sets (e.g.\nrepresenting the properties of agents in a scene) over time and employ\nmulti-head self-attention blocks over these sequences of sets to encode the\nsociotemporal relationships between the different actors of a scene. This\nproduces either the trajectory of one ego-agent or a distribution over the\nfuture trajectories for all agents under consideration. Our approach works for\ngeneral sequences of sets and we provide illustrative experiments modelling the\nsequential structure of the multiple strokes that make up symbols in the\nOmniglot data. For the single-agent prediction case, we validate our model on\nthe NuScenes motion prediction task and achieve competitive results on the\nglobal leaderboard. In the multi-agent forecasting setting, we validate our\nmodel on TrajNet. We find that our method outperforms physical extrapolation\nand recurrent network baselines and generates scene-consistent trajectories.\n"}
+{"abstract": "  We present the results of radio observations from the eMERLIN telescope\ncombined with X-ray data from Swift for the short-duration Gamma-ray burst\n(GRB) 200826A, located at a redshift of 0.71. The radio light curve shows\nevidence of a sharp rise, a peak around 4-5 days post-burst, followed by a\nrelatively steep decline. We provide two possible interpretations based on the\ntime at which the light curve reached its peak. (1) If the light curve peaks\nearlier, the peak is produced by the synchrotron self-absorption frequency\nmoving through the radio band, resulting from the forward shock propagating\ninto a wind medium and (2) if the light curve peaks later, the turn over in the\nlight curve is caused by a jet break. In the former case, we find a minimum\nequipartition energy of ~3x10^47 erg and bulk Lorentz factor of ~5, while in\nthe latter case we estimate the jet opening angle of ~9-16 degrees. Due to the\nlack of data, it is impossible to determine which is the correct\ninterpretation, however, due to its relative simplicity and consistency with\nother multi-wavelength observations which hint at the possibility that GRB\n200826A is in fact a long GRB, we prefer scenario one over scenario two.\n"}
+{"abstract": "  In this paper, we present a positivity-preserving limiter for nodal\nDiscontinuous Galerkin disctretizations of the compressible Euler equations. We\nuse a Legendre-Gauss-Lobatto (LGL) Discontinuous Galerkin Spectral Element\nMethod (DGSEM) and blend it locally with a consistent LGL-subcell Finite Volume\n(FV) discretization using a hybrid FV/DGSEM scheme that was recently proposed\nfor entropy stable shock capturing. We show that our strategy is able to ensure\nrobust simulations with positive density and pressure when using the standard\nand the split-form DGSEM. Furthermore, we show the applicability of our FV\npositivity limiter in extremely under-resolved vortex dominated simulations and\nin problems with shocks.\n"}
+{"abstract": "  We present covariant symmetry operators for the conformal wave equation in\nthe (off-shell) Kerr-NUT-AdS spacetimes. These operators, that are constructed\nfrom the principal Killing-Yano tensor, its `symmetry descendants', and the\ncurvature tensor, guarantee separability of the conformal wave equation in\nthese spacetimes. We next discuss how these operators give rise to a full set\nof conformally invariant mutually commuting operators for the conformally\nrescaled spacetimes and underlie the $R$-separability of the conformal wave\nequation therein. Finally, by employing the WKB approximation we derive the\nassociated Hamilton-Jacobi equation with a scalar curvature potential term and\nshow its separability in the Kerr-NUT-AdS spacetimes.\n"}
+{"abstract": "  We study the problem of repeatedly auctioning off an item to one of $k$\nbidders where: a) bidders have a per-round individual rationality constraint,\nb) bidders may leave the mechanism at any point, and c) the bidders' valuations\nare adversarially chosen (the prior-free setting). Without these constraints,\nthe auctioneer can run a second-price auction to \"sell the business\" and\nreceive the second highest total value for the entire stream of items. We show\nthat under these constraints, the auctioneer can attain a constant fraction of\nthe \"sell the business\" benchmark, but no more than $2/e$ of this benchmark.\n  In the course of doing so, we design mechanisms for a single bidder problem\nof independent interest: how should you repeatedly sell an item to a (per-round\nIR) buyer with adversarial valuations if you know their total value over all\nrounds is $V$ but not how their value changes over time? We demonstrate a\nmechanism that achieves revenue $V/e$ and show that this is tight.\n"}
+{"abstract": "  Chemical surfactants are omnipresent in consumers' products but they suffer\nfrom environmental concerns. For this reason, complete replacement of\npetrochemical surfactants by biosurfactants constitute a holy grail but this is\nfar from occurring any soon. If the \"biosurfactants revolution\" has not\noccurred, yet, mainly due to the higher cost and lower availability of\nbiosurfactants, another reason explains this fact: the poor knowledge of their\nproperties in solution. This tutorial review aims at reviewing the\nself-assembly properties and phase behavior, experimental (sections 2.3 and\n2.4) and from molecular modelling (section 5), in water of the most important\nmicrobial biosurfactants (sophorolipids, rhamnolipids, surfacting,\ncellobioselipids, glucolipids) as well as their major derivatives. A critical\ndiscussion of such properties in light of the well-known packing parameter of\nsurfactants is also provided (section 2.5). The relationship between the\nnanoscale self-assembly and macroscopic materials properties, including\nhydrogelling, solid foaming, templating or encapsulation is specifically\ndiscussed (section 2.7). We also present their self-assembly and adsorption at\nflat and complex air/liquid (e.g., foams), air/solid (adhesion), liquid/solid\n(nanoparticles) and liquid/liquid (e.g., emulsions) interfaces (section 3). A\ncritical discussion on the use of biosurfactants as capping agents for the\ndevelopment of stable nanoparticles is specifically provided (section 3.2.4).\nFinally, we discuss the major findings involving biosurfactants and\nmacromolecules, including proteins, enzymes, polymers and polyelectrolytes.\n"}
+{"abstract": "  We have deduced the structure of the \\ce{bromobenzene}--\\ce{I2} heterodimer\nand the \\ce{(bromobenzene)2} homodimer inside helium droplets using a\ncombination of laser-induced alignment, Coulomb explosion imaging, and\nthree-dimensional ion imaging. The complexes were fixed in a variety of\norientations in the laboratory frame, then in each case multiply ionized by an\nintense laser pulse. A three dimensional ion imaging detector, including a\nTimepix3 detector allowed us to measure the correlations between velocity\nvectors of different fragments and, in conjunction with classical simulations,\nwork backward to the initial structure of the complex prior to explosion. For\nthe heterodimer, we find that the \\ce{I2} molecular axis intersects the phenyl\nring of the bromobenzene approximately perpendicularly. The homodimer has a\nstacked parallel structure, with the two bromine atoms pointing in opposite\ndirections. These results illustrate the ability of Coulomb explosion imaging\nto determine the structure of large complexes, and point the way toward\nreal-time measurements of bimolecular reactions inside helium droplets.\n"}
+{"abstract": "  We present a theoretical investigation of anisotropic superconducting spin\ntransport at a magnetic interface between a p-wave superconductor and a\nferromagnetic insulator. Our formulation describes the ferromagnetic resonance\nmodulations due to spin-triplet current generation, including the frequency\nshift and enhanced Gilbert damping, in a unified manner. We find that the\nCooper pair symmetry is detectable from the qualitative behavior of the\nferromagnetic resonance modulation. Our theory paves the way toward anisotropic\nsuperconducting spintronics.\n"}
+{"abstract": "  In this paper, we develop a new free-stream preserving (FP) method for\nhigh-order upwind conservative finite-difference (FD) schemes on the\ncurvilinear grids. This FP method is constrcuted by subtracting a reference\ncell-face flow state from each cell-center value in the local stencil of the\noriginal upwind conservative FD schemes, which effectively leads to a\nreformulated dissipation. It is convenient to implement this method, as it does\nnot require to modify the original forms of the upwind schemes. In addition,\nthe proposed method removes the constraint in the traditional FP conservative\nFD schemes that require a consistent discretization of the mesh metrics and the\nfluxes. With this, the proposed method is more flexible in simulating the\nengineering problems which usually require a low-order scheme for their\nlow-quality mesh, while the high-order schemes can be applied to approximate\nthe flow states to improve the resolution. After demonstrating the strict FP\nproperty and the order of accuracy by two simple test cases, we consider\nvarious validation cases, including the supersonic flow around the cylinder,\nthe subsonic flow past the three-element airfoil, and the transonic flow around\nthe ONERA M6 wing, etc., to show that the method is suitable for a wide range\nof fluid dynamic problems containing complex geometries. Moreover, these test\ncases also indicate that the discretization order of the metrics have no\nsignificant influences on the numerical results if the mesh resolution is not\nsufficiently large.\n"}
+{"abstract": "  The traditional setup of link prediction in networks assumes that a test set\nof node pairs, which is usually balanced, is available over which to predict\nthe presence of links. However, in practice, there is no test set: the\nground-truth is not known, so the number of possible pairs to predict over is\nquadratic in the number of nodes in the graph. Moreover, because graphs are\nsparse, most of these possible pairs will not be links. Thus, link prediction\nmethods, which often rely on proximity-preserving embeddings or heuristic\nnotions of node similarity, face a vast search space, with many pairs that are\nin close proximity, but that should not be linked. To mitigate this issue, we\nintroduce LinkWaldo, a framework for choosing from this quadratic,\nmassively-skewed search space of node pairs, a concise set of candidate pairs\nthat, in addition to being in close proximity, also structurally resemble the\nobserved edges. This allows it to ignore some high-proximity but\nlow-resemblance pairs, and also identify high-resemblance, lower-proximity\npairs. Our framework is built on a model that theoretically combines Stochastic\nBlock Models (SBMs) with node proximity models. The block structure of the SBM\nmaps out where in the search space new links are expected to fall, and the\nproximity identifies the most plausible links within these blocks, using\nlocality sensitive hashing to avoid expensive exhaustive search. LinkWaldo can\nuse any node representation learning or heuristic definition of proximity, and\ncan generate candidate pairs for any link prediction method, allowing the\nrepresentation power of current and future methods to be realized for link\nprediction in practice. We evaluate LinkWaldo on 13 networks across multiple\ndomains, and show that on average it returns candidate sets containing 7-33%\nmore missing and future links than both embedding-based and heuristic\nbaselines' sets.\n"}
+{"abstract": "  We report in this paper the analysis for the linear and nonlinear version of\nthe flux corrected transport (FEM-FCT) scheme in combination with the backward\nEuler time-stepping scheme applied to time-dependent\nconvection-diffusion-reaction problems. We present the stability and error\nestimates for the linear and nonlinear FEM-FCT scheme. Numerical results\nconfirm the theoretical predictions.\n"}
+{"abstract": "  This paper proposes a new reinforcement learning with hyperbolic discounting.\nCombining a new temporal difference error with the hyperbolic discounting in\nrecursive manner and reward-punishment framework, a new scheme to learn the\noptimal policy is derived. In simulations, it is found that the proposal\noutperforms the standard reinforcement learning, although the performance\ndepends on the design of reward and punishment. In addition, the averages of\ndiscount factors w.r.t. reward and punishment are different from each other,\nlike a sign effect in animal behaviors.\n"}
+{"abstract": "  The discrepancy between theory and experiment severely limits the development\nof quantum key distribution (QKD). Reference-frame-independent (RFI) protocol\nhas been proposed to avoid alignment of the reference frame. However, multiple\noptical modes caused by Trojan horse attacks and equipment loopholes lead to\nthe imperfect emitted signal unavoidably. In this paper, we analyzed the\nsecurity of the RFI-QKD protocol with non-qubit sources based on generalizing\nloss-tolerant techniques. The simulation results show that our work can\neffectively defend against non-qubit sources including a misaligned reference\nframe, state preparation flaws, multiple optical modes, and Trojan horse\nattacks. Moreover, it only requires the preparation of four quantum states,\nwhich reduces the complexity of the experiment in the future.\n"}
+{"abstract": "  Humans are arguably one of the most important subjects in video streams, many\nreal-world applications such as video summarization or video editing workflows\noften require the automatic search and retrieval of a person of interest.\nDespite tremendous efforts in the person reidentification and retrieval\ndomains, few works have developed audiovisual search strategies. In this paper,\nwe present the Audiovisual Person Search dataset (APES), a new dataset composed\nof untrimmed videos whose audio (voices) and visual (faces) streams are densely\nannotated. APES contains over 1.9K identities labeled along 36 hours of video,\nmaking it the largest dataset available for untrimmed audiovisual person\nsearch. A key property of APES is that it includes dense temporal annotations\nthat link faces to speech segments of the same identity. To showcase the\npotential of our new dataset, we propose an audiovisual baseline and benchmark\nfor person retrieval. Our study shows that modeling audiovisual cues benefits\nthe recognition of people's identities. To enable reproducibility and promote\nfuture research, the dataset annotations and baseline code are available at:\nhttps://github.com/fuankarion/audiovisual-person-search\n"}
+{"abstract": "  The COVID-19 pandemic has influenced the lives of people globally. In the\npast year many researchers have proposed different models and approaches to\nexplore in what ways the spread of the disease could be mitigated. One of the\nmodels that have been used a great deal is the\nSusceptible-Exposed-Infectious-Recovered (SEIR) model. Some researchers have\nmodified the traditional SEIR model, and proposed new versions of it. However,\nto the best of our knowledge, the state-of-the-art papers have not considered\nthe effect of different vaccine types, meaning single shot and double shot\nvaccines, in their SEIR model. In this paper, we propose a modified version of\nthe SEIR model which takes into account the effect of different vaccine types.\nWe compare how different policies for the administration of the vaccine can\ninfluence the rate at which people are exposed to the disease, get infected,\nrecover, and pass away. Our results suggest that taking the double shot vaccine\nsuch as Pfizer-BioNTech and Moderna does a better job at mitigating the spread\nand fatality rate of the disease compared to the single shot vaccine, due to\nits higher efficacy.\n"}
+{"abstract": "  We study the potential of gravitational wave astronomy to observe the quantum\naspects of black holes. According to Bekenstein's quantization, we find that\nblack hole area discretization can have observable imprints on the\ngravitational wave signal from an inspiraling binary black hole. We study the\nimpact of quantization on tidal heating. We model the absorption lines and\ncompute gravitational wave flux due to tidal heating in such a case. By\nincluding the quantization we find the dephasing of the gravitational wave, to\nour knowledge it has never been done before. We discuss the observability of\nthe phenomena in different parameter ranges of the binary. We show that in the\ninspiral, it leads to vanishing tidal heating for the high spin values.\nTherefore measuring non-zero tidal heating can rule out area quantization. We\nalso argue that if area quantization is present in nature then our current\nmodeling with reflectivity can possibly probe the Hawking radiation which may\nbring important information regarding the quantum nature of gravity.\n"}
+{"abstract": "  Summarization evaluation remains an open research problem: current metrics\nsuch as ROUGE are known to be limited and to correlate poorly with human\njudgments. To alleviate this issue, recent work has proposed evaluation metrics\nwhich rely on question answering models to assess whether a summary contains\nall the relevant information in its source document. Though promising, the\nproposed approaches have so far failed to correlate better than ROUGE with\nhuman judgments.\n  In this paper, we extend previous approaches and propose a unified framework,\nnamed QuestEval. In contrast to established metrics such as ROUGE or BERTScore,\nQuestEval does not require any ground-truth reference. Nonetheless, QuestEval\nsubstantially improves the correlation with human judgments over four\nevaluation dimensions (consistency, coherence, fluency, and relevance), as\nshown in the extensive experiments we report.\n"}
+{"abstract": "  One of the best ways to understand the gravitation of a massive object is by\nstudying the photon's motion around it. We study the null geodesic of a regular\nblack hole in anti-de Sitter spacetime, including a Gaussian matter\ndistribution. Obtaining the effective potential and possible motions of the\nphoton are discussed for different energy levels. The nature of the effective\npotential implies that the photon is prevented from reaching the black hole's\ncenter. Different types of possible orbits are considered. A photon with\nnegative energy is trapped in a potential hole and has a back and forth motion\nbetween two horizons of the metric. However, for specific values of positive\nenergy, the trapped photon still has a back and forth motion; however, it\ncrosses the horizons in every direction. The effective potential has an\nunstable point outside the horizons, which indicates the possible circular\nmotion of the photon. The closest approach of the photon and the bending angle\nare also investigated.\n"}
+{"abstract": "  We discuss excitation of string oscillation modes by an initial singularity\nof inflation. The initial singularity of inflation is known to occur with a\nfinite Hubble parameter, which is generally lower than the string scale, and\nhence it is not clear that stringy effects become significant around it. With\nthe help of Penrose limit, we find that infinitely heavy oscillation modes get\nexcited when a singularity is strong in the sense of Krolak's classification.\nWe demonstrate that the initial singularities of Starobinsky and hill top\ninflation, assuming the slow roll inflation to the past infinity, are strong.\nHence stringy corrections are inevitable in the very early stage of these\ninflation models. We also find that the initial singularity of the hill top\ninflation could be weak for non-slow roll case.\n"}
+{"abstract": "  The leading-order approximation to a Filippov system $f$ about a generic\nboundary equilibrium $x^*$ is a system $F$ that is affine one side of the\nboundary and constant on the other side. We prove $x^*$ is exponentially stable\nfor $f$ if and only if it is exponentially stable for $F$ when the constant\ncomponent of $F$ is not tangent to the boundary. We then show exponential\nstability and asymptotic stability are in fact equivalent for $F$. We also show\nexponential stability is preserved under small perturbations to the pieces of\n$F$. Such results are well known for homogeneous systems. To prove the results\nhere additional techniques are required because the two components of $F$ have\ndifferent degrees of homogeneity. The primary function of the results is to\nreduce the problem of the stability of $x^*$ from the general Filippov system\n$f$ to the simpler system $F$. Yet in general this problem remains difficult.\nWe provide a four-dimensional example of $F$ for which orbits appear to\nconverge to $x^*$ in a chaotic fashion. By utilising the presence of both\nhomogeneity and sliding motion the dynamics of $F$ can in this case be reduced\nto the combination of a one-dimensional return map and a scalar function.\n"}
+{"abstract": "  Network design, a cornerstone of mathematical optimization, is about defining\nthe main characteristics of a network satisfying requirements on connectivity,\ncapacity, and level-of-service. It finds applications in logistics and\ntransportation, telecommunications, data sharing, energy distribution, and\ndistributed computing. In multi-commodity network design, one is required to\ndesign a network minimizing the installation cost of its arcs and the\noperational cost to serve a set of point-to-point connections. The definition\nof this prototypical problem was recently enriched by additional constraints\nimposing that each origin-destination of a connection is served by a single\npath satisfying one or more level-of-service requirements, thus defining the\nNetwork Design with Service Requirements [Balakrishnan, Li, and Mirchandani.\nOperations Research, 2017]. These constraints are crucial, e.g., in\ntelecommunications and computer networks, in order to ensure reliable and\nlow-latency communication. In this paper we provide a new formulation for the\nproblem, where variables are associated with paths satisfying the end-to-end\nservice requirements. We present a fast algorithm for enumerating all the\nexponentially-many feasible paths and, when this is not viable, we provide a\ncolumn generation scheme that is embedded into a branch-and-cut-and-price\nalgorithm. Extensive computational experiments on a large set of instances show\nthat our approach is able to move a step further in the solution of the Network\nDesign with Service Requirements, compared with the current state-of-the-art.\n"}
+{"abstract": "  To ensure protection of the intellectual property rights of DNN models,\nwatermarking techniques have been investigated to insert side-information into\nthe models without seriously degrading the performance of original task. One of\nthe threats for the DNN watermarking is the pruning attack such that less\nimportant neurons in the model are pruned to make it faster and more compact as\nwell as to remove the watermark. In this study, we investigate a channel coding\napproach to resist the pruning attack. As the channel model is completely\ndifferent from conventional models like digital images, it has been an open\nproblem what kind of encoding method is suitable for DNN watermarking. A novel\nencoding approach by using constant weight codes to immunize the effects of\npruning attacks is presented. To the best of our knowledge, this is the first\nstudy that introduces an encoding technique for DNN watermarking to make it\nrobust against pruning attacks.\n"}
+{"abstract": "  Three-dimensional line-nodal superconductors exhibit nontrivial topology,\nwhich is protected by the time-reversal symmetry. Here we investigate four\ntypes of short-range interaction between the gapless line-nodal fermionic\nquasiparticles by carrying renormalization group analysis. We find that such\ninteractions can induce the dynamical breaking of time-reversal symmetry, which\nalters the topology and might lead to six possible distinct superconducting\nstates, distinguished by the group representations. After computing the\nsusceptibilities for all the possible phase-transition instabilities, we\nestablish that the superconducting pairing characterized by $id_{xz}$-wave gap\nsymmetry is the leading instability in noncentrosymmetric superconductors.\nAppropriate extension of this approach is promising to pick out the most\nfavorable superconducting pairing during similar topology-changing transition\nin the polar phase of $^3$He.\n"}
+{"abstract": "  Malnutrition is a major public health concern in low-and-middle-income\ncountries (LMICs). Understanding food and nutrient intake across communities,\nhouseholds and individuals is critical to the development of health policies\nand interventions. To ease the procedure in conducting large-scale dietary\nassessments, we propose to implement an intelligent passive food intake\nassessment system via egocentric cameras particular for households in Ghana and\nUganda. Algorithms are first designed to remove redundant images for minimising\nthe storage memory. At run time, deep learning-based semantic segmentation is\napplied to recognise multi-food types and newly-designed handcrafted features\nare extracted for further consumed food weight monitoring. Comprehensive\nexperiments are conducted to validate our methods on an in-the-wild dataset\ncaptured under the settings which simulate the unique LMIC conditions with\nparticipants of Ghanaian and Kenyan origin eating common Ghanaian/Kenyan\ndishes. To demonstrate the efficacy, experienced dietitians are involved in\nthis research to perform the visual portion size estimation, and their\npredictions are compared to our proposed method. The promising results have\nshown that our method is able to reliably monitor food intake and give feedback\non users' eating behaviour which provides guidance for dietitians in regular\ndietary assessment.\n"}
+{"abstract": "  Increased connectivity has made us all more vulnerable. Cyberspace, besides\nall its benefits, spawned more devices to hack and more opportunities to commit\ncybercrime. Criminals have found it lucrative to target both individuals and\nbusinesses, by holding or stealing their assets via different types of cyber\nattacks. The cyber-enabled theft of Intellectual Property (IP), as one of the\nmost important and critical intangible assets of nations, organizations and\nindividuals, by foreign countries has been a devastating challenge of the\nUnited States (U.S.) in the past decades. In this study, we conduct a\nsocio-technical root cause analysis to investigate one of the recent cases of\nIP theft by employing a holistic approach. It concludes with a list of root\ncauses and some corrective actions to stop the impact and prevent the\nrecurrence of the problem in the future. Building upon the findings of this\nstudy, the U.S. requires a detailed revision of IP strategies bringing the\nwhole socio-technical regulatory system into focus and strengthen IP rights\nprotection considering China's indigenous innovation policies. It is critical\nthat businesses and other organizations take steps to reduce their exposure to\ncyber attacks. It is particularly important to train employees on how to spot\npotential threats, and to institute policies that encourage workers to report\npotential security failures so that action can be taken quickly. Finally, we\ndiscuss how cyber ranges can provide an efficient and safe platform for dealing\nwith such challenges. The results of this study can be expanded to other\ncountries in order to protect their IP rights and deter or prevent and respond\nto future incidents.\n"}
+{"abstract": "  Braiding Majorana zero modes (MZMs) is the key procedure toward topological\nquantum computation. However, the complexity of the braiding manipulation\nhinders its experimental realization. Here we propose an experimental setup\ncomposing of MZMs and a quantum dot state which can substantially simplify the\nbraiding protocol of MZMs. Such braiding scheme, which corresponds to a\nspecific closed loop in the parameter space, is quite universal and can be\nrealized in various platforms. Moreover, the braiding results can be directly\nmeasured and manifested through electric current, which provides a simple and\nnovel way to detect the non-Abelian statistics of MZMs.\n"}
+{"abstract": "  Dense optical flow estimation is challenging when there are large\ndisplacements in a scene with heterogeneous motion dynamics, occlusion, and\nscene homogeneity. Traditional approaches to handle these challenges include\nhierarchical and multiresolution processing methods. Learning-based optical\nflow methods typically use a multiresolution approach with image warping when a\nbroad range of flow velocities and heterogeneous motion is present. Accuracy of\nsuch coarse-to-fine methods is affected by the ghosting artifacts when images\nare warped across multiple resolutions and by the vanishing problem in smaller\nscene extents with higher motion contrast. Previously, we devised strategies\nfor building compact dense prediction networks guided by the effective\nreceptive field (ERF) characteristics of the network (DDCNet). The DDCNet\ndesign was intentionally simple and compact allowing it to be used as a\nbuilding block for designing more complex yet compact networks. In this work,\nwe extend the DDCNet strategies to handle heterogeneous motion dynamics by\ncascading DDCNet based sub-nets with decreasing extents of their ERF. Our\nDDCNet with multiresolution capability (DDCNet-Multires) is compact without any\nspecialized network layers. We evaluate the performance of the DDCNet-Multires\nnetwork using standard optical flow benchmark datasets. Our experiments\ndemonstrate that DDCNet-Multires improves over the DDCNet-B0 and -B1 and\nprovides optical flow estimates with accuracy comparable to similar lightweight\nlearning-based methods.\n"}
+{"abstract": "  Convergence to equilibrium of underdamped Langevin dynamics is studied under\ngeneral assumptions on the potential $U$ allowing for singularities. By\nmodifying the direct approach to convergence in $L^2$ pioneered by F. H\\'erau\nand developped by Dolbeault, Mouhot and Schmeiser, we show that the dynamics\nconverges exponentially fast to equilibrium in the topologies $L^2(d\\mu)$ and\n$L^2(W^* d\\mu)$, where $\\mu$ denotes the invariant probability measure and\n$W^*$ is a suitable Lyapunov weight. In both norms, we make precise how the\nexponential convergence rate depends on the friction parameter $\\gamma$ in\nLangevin dynamics, by providing a lower bound scaling as $\\min(\\gamma,\n\\gamma^{-1})$. The results hold for usual polynomial-type potentials as well as\npotentials with singularities such as those arising from pairwise Lennard-Jones\ninteractions between particles.\n"}
+{"abstract": "  Mobile communication networks were designed to mainly support ubiquitous\nwireless communications, yet they are expected to also achieve radio sensing\ncapabilities in the near future. Most prior studies on radar sensing focus on\ndistant targets, which usually rely on far-field assumption with uniform plane\nwave (UPW) models. However, with ever-increasing antenna size, together with\nthe growing need to also sense nearby targets, the far-field assumption may\nbecome invalid. This paper studies radar sensing with extremely large-scale\n(XL) antenna arrays, where a generic model that takes into account both\nspherical wavefront and amplitude variations across array elements is\ndeveloped. Furthermore, new closed-form expressions of the sensing\nsignal-to-noise ratios (SNRs) are derived for both XL-MIMO radar and\nXL-phased-array radar modes. Our results reveal that different from the\nconventional UPW model where the SNR scales linearly and unboundedly with N for\nMIMO radar and with MN for phased-array radar, with M and N being the transmit\nand receive antenna numbers, respectively, more practical SNR scaling laws are\nobtained. For XL-phased-array radar with optimal power allocation, the SNR\nincreases with M and N with diminishing returns, governed by new parameters\ncalled the transmit and receive angular spans. On the other hand, for XL-MIMO\nradar, while the same SNR scaling as XL-phased-array radar is obeyed for N, the\nSNR first increases and then decreases with M.\n"}
+{"abstract": "  In this paper LQG control over unreliable communication links is derived.\nThat is to say, the communication channels between the controller and the\nactuators and between the sensors and the controller are unreliable. Previous\nsolutions to finite horizon discrete time hold-input LQG control for this case\ndo not fully utilize the available information. Here a new solution is\npresented which resolves this limitation. The focus is to derive and present a\nfull mathematical proof to derive the optimal control sequence.\n"}
+{"abstract": "  We add non-linear and state-dependent terms to quantum field theory. We show\nthat the resulting low-energy theory, non-linear quantum mechanics, is causal,\npreserves probability and permits a consistent description of the process of\nmeasurement. We explore the consequences of such terms and show that non-linear\nquantum effects can be observed in macroscopic systems even in the presence of\nde-coherence. We find that current experimental bounds on these non-linearities\nare weak and propose several experimental methods to significantly probe these\neffects. The locally exploitable effects of these non-linearities have enormous\ntechnological implications. For example, they would allow large scale\nparallelization of computing (in fact, any other effort) and enable quantum\nsensing beyond the standard quantum limit. We also expose a fundamental\nvulnerability of any non-linear modification of quantum mechanics - these\nmodifications are highly sensitive to cosmic history and their locally\nexploitable effects can dynamically disappear if the observed universe has a\ntiny overlap with the overall quantum state of the universe, as is predicted in\nconventional inflationary cosmology. We identify observables that persist in\nthis case and discuss opportunities to detect them in cosmic ray experiments,\ntests of strong field general relativity and current probes of the equation of\nstate of the universe. Non-linear quantum mechanics also enables novel\ngravitational phenomena and may open new directions to solve the black hole\ninformation problem and uncover the theory underlying quantum field theory and\ngravitation.\n"}
+{"abstract": "  In 2021, Dzhunusov and Zaitseva classified two-dimensional normal affine\ncommutative algebraic monoids. In this work, we extend this classification to\nnoncommutative monoid structures on normal affine surfaces. We prove that\ntwo-dimensional algebraic monoids are toric. We also show how to find all\nmonoid structures on a normal toric surface. Every such structure is induced by\na comultiplication formula involving Demazure roots. We also give descriptions\nof opposite monoids, quotient monoids, and boundary divisors.\n"}
+{"abstract": "  We study nonlinear pantograph-type reaction-diffusion PDEs, which, in\naddition to the unknown $u=u(x,t)$, also contain the same functions with\ndilated or contracted arguments of the form $w=u(px,t)$, $w=u(x,qt)$, and\n$w=u(px,qt)$, where $p$ and $q$ are the free scaling parameters (for equations\nwith proportional delay we have $0<p<1$, $0<q<1$). A brief review of\npublications on pantograph-type ODEs and PDEs and their applications is given.\nExact solutions and reductions of various types of such nonlinear partial\nfunctional differential equations are described for the first time. We present\nexamples of nonlinear pantograph-type PDEs with proportional delay, which admit\ntraveling-wave and self-similar solutions (note that PDEs with constant delay\ndo not have self-similar solutions). Additive, multiplicative and functional\nseparable solutions, as well as some other exact solutions are also obtained.\nSpecial attention is paid to nonlinear pantograph-type PDEs of a rather general\nform, which contain one or two arbitrary functions. In total, more than forty\nnonlinear pantograph-type reaction-diffusion PDEs with dilated or contracted\narguments, admitting exact solutions, have been considered. Multi-pantograph\nnonlinear PDEs are also discussed. The principle of analogy is formulated,\nwhich makes it possible to efficiently construct exact solutions of nonlinear\npantograph-type PDEs. A number of exact solutions of more complex nonlinear\nfunctional differential equations with varying delay, which arbitrarily depends\non time or spatial coordinate, are also described. The presented equations and\ntheir exact solutions can be used to formulate test problems designed to\nevaluate the accuracy of numerical and approximate analytical methods for\nsolving the corresponding nonlinear initial-boundary value problems for PDEs\nwith varying delay.\n"}
+{"abstract": "  The regulatory framework of cryptocurrencies (and, in general, blockchain\ntokens) is of paramount importance. This framework drives nearly all key\ndecisions in the respective business areas. In this work, a computational model\nis proposed for quantitatively estimating the regulatory stance of countries\nwith respect to cryptocurrencies. This is conducted via web mining utilizing\nweb search engines. The proposed model is experimentally validated. In\naddition, unsupervised learning (clustering) is applied for better analyzing\nthe automatically derived estimations. Overall, very good performance is\nachieved by the proposed algorithmic approach.\n"}
+{"abstract": "  Automatic speech recognition systems have been largely improved in the past\nfew decades and current systems are mainly hybrid-based and end-to-end-based.\nThe recently proposed CTC-CRF framework inherits the data-efficiency of the\nhybrid approach and the simplicity of the end-to-end approach. In this paper,\nwe further advance CTC-CRF based ASR technique with explorations on modeling\nunits and neural architectures. Specifically, we investigate techniques to\nenable the recently developed wordpiece modeling units and Conformer neural\nnetworks to be succesfully applied in CTC-CRFs. Experiments are conducted on\ntwo English datasets (Switchboard, Librispeech) and a German dataset from\nCommonVoice. Experimental results suggest that (i) Conformer can improve the\nrecognition performance significantly; (ii) Wordpiece-based systems perform\nslightly worse compared with phone-based systems for the target language with a\nlow degree of grapheme-phoneme correspondence (e.g. English), while the two\nsystems can perform equally strong when such degree of correspondence is high\nfor the target language (e.g. German).\n"}
+{"abstract": "  We state and prove in modern terms a Splitting Principle first claimed by\nBeniamino Segre in 1938, which should be regarded as a strong form of the\nclassical Principle of Connectedness.\n"}
+{"abstract": "  The flex locus parameterizes plane cubics with three collinear cocritical\npoints under a projection, and the gothic locus arises from quadratic\ndifferentials with zeros at a fiber of the projection and with poles at the\ncocritical points. The flex and gothic loci provide the first example of a\nprimitive, totally geodesic subvariety of moduli space and new ${\\rm\nSL}_2(\\mathbb{R})$-invariant varieties in Teichm\\\"uller dynamics, as discovered\nby McMullen-Mukamel-Wright. In this paper we determine the divisor class of the\nflex locus as well as various tautological intersection numbers on the gothic\nlocus. For the case of the gothic locus our result confirms numerically a\nconjecture of Chen-M\\\"oller-Sauvaget about computing sums of Lyapunov exponents\nfor ${\\rm SL}_2(\\mathbb{R})$-invariant varieties via intersection theory.\n"}
+{"abstract": "  The concept of E-learning in Universities has grown rapidly over the years to\ninclude not just only a learning management system but also tools initially not\ndesigned for learning such as Facebook and advanced learning tools, for example\ngames, simulations and virtualization. As a result, Cloud-based LMS is being\ntouted as the next evolution of the traditional LMS. It is hoped that Cloud\nbased LMS will resolve some of the challenges associated with the traditional\nLMS implementation process. In a previous study, we reported that lack of\ninvolvement of faculty and students in the LMS implementation process results\nin the limited use of the LMS by faculty and students. The question then is,\nWill the cloud-based LMS resolve these issues? We conducted a review of\nliterature and presented an overview of the traditional LMS, cloud computing\nand the cloudbased LMS and we described how the cloud computing LMS resolve\nissues raised by faculty and students. we find that even though, cloud-based\nLMS resolve most of the technical issues associated with the traditional LMS,\nsome of the human issues were not resolved. We hope that this study draws\nattention to non-technical issues associated with the LMS implementation\nprocess.\n"}
+{"abstract": "  Deep learning is vulnerable to adversarial examples. Many defenses based on\nrandomized neural networks have been proposed to solve the problem, but fail to\nachieve robustness against attacks using proxy gradients such as the\nExpectation over Transformation (EOT) attack. We investigate the effect of the\nadversarial attacks using proxy gradients on randomized neural networks and\ndemonstrate that it highly relies on the directional distribution of the loss\ngradients of the randomized neural network. We show in particular that proxy\ngradients are less effective when the gradients are more scattered. To this\nend, we propose Gradient Diversity (GradDiv) regularizations that minimize the\nconcentration of the gradients to build a robust randomized neural network. Our\nexperiments on MNIST, CIFAR10, and STL10 show that our proposed GradDiv\nregularizations improve the adversarial robustness of randomized neural\nnetworks against a variety of state-of-the-art attack methods. Moreover, our\nmethod efficiently reduces the transferability among sample models of\nrandomized neural networks.\n"}
+{"abstract": "  This paper is devoted to show that the last quarter of the past century can\nbe considered as the golden age of the Mathematical Finance. In this period the\ncollaboration of great economists and the best generation of probabilists, most\nof them from the Strasbourg's School led by Paul Andr\\'e Meyer, gave rise to\nthe foundations of this discipline. They established the two fundamentals\ntheorems of arbitrage theory, close formulas for options, the main modelling a\n"}
+{"abstract": "  Two dimensional SrTiO3-based interfaces stand out among non-centrosymmetric\nsuperconductors due to their intricate interplay of gate tunable Rashba\nspin-orbit coupling and multi-orbital electronic occupations, whose combination\ntheoretically prefigures various forms of non-standard superconductivity.\nHowever, a convincing demonstration by phase sensitive measurements has been\nelusive so far. Here, by employing superconducting transport measurements in\nnano-devices we present clear-cut experimental evidences of unconventional\nsuperconductivity in the LaAlO3/SrTiO3 interface. The central observations are\nthe substantial anomalous enhancement of the critical current by small magnetic\nfields applied perpendicularly to the plane of electron motion, and the\nasymmetric response with respect to the magnetic field direction. These\nfeatures have a unique trend in intensity and sign upon electrostatic gating\nthat, together with their dependence on temperature and nanowire dimensions,\ncannot be accommodated within a scenario of canonical spin-singlet\nsuperconductivity. We theoretically demonstrate that the hall-marks of the\nexperimental observations unambiguously indicate a coexistence of Josephson\nchannels with sign difference and intrinsic phase shift. The character of these\nfindings establishes the occurrence of independent components of unconventional\npairing in the superconducting state due to inversion symmetry breaking. The\noutcomes open new venues for the investigation of multi-orbital\nnon-centrosymmetric superconductivity and Josephson-based devices for quantum\ntechnologies.\n"}
+{"abstract": "  We clarify the undecided case $c_2 = 3$ of a theorem of Ein, Hartshorne and\nVogelaar [Math. Ann. 259 (1982), 541--569] about the restriction of a stable\nrank 3 vector bundle with $c_1 = 0$ on the projective 3-space to a general\nplane. It turns out that there are more exceptions to the stable restriction\nproperty than those conjectured by the three authors. One of them is a\nSchwarzenberger bundle (twisted by $-1$); it has $c_3 = 6$. There are also some\nexceptions with $c_3 = 2$ (plus, of course, their duals). We also prove, for\ncompleteness, the basic properties of the corresponding moduli spaces; they are\nall nonsingular and connected, of dimension 28.\n"}
+{"abstract": "  The Variational Quantum Eigensolver (VQE) is a promising algorithm for Noisy\nIntermediate Scale Quantum (NISQ) computation. Verification and validation of\nNISQ algorithms' performance on NISQ devices is an important task. We consider\nthe exactly-diagonalizable Lipkin-Meshkov-Glick (LMG) model as a candidate for\nbenchmarking NISQ computers. We use the Bethe ansatz to construct eigenstates\nof the trigonometric LMG model using quantum circuits inspired by the LMG's\nunderlying algebraic structure. We construct circuits with depth\n$\\mathcal{O}(N)$ and $\\mathcal{O}(\\log_2N)$ that can prepare any trigonometric\nLMG eigenstate of $N$ particles. The number of gates required for both circuits\nis $\\mathcal{O}(N)$. The energies of the eigenstates can then be measured and\ncompared to the exactly-known answers.\n"}
+{"abstract": "  In this article, we deal with the order of growth of solutions of\nnon-homogeneous linear differential-difference equation \\begin{equation*}\n\\sum_{i=0}^{n}\\sum_{j=0}^{m}A_{ij}f^{(j)}(z+c_{i})=F(z), \\end{equation*} where\n$A_{ij},$ $F\\left( z\\right) $ are entire or meromorphic functions and $c_{i}$\n$\\left( 0,1,...,n\\right) $ are non-zero distinct complex numbers. Under the\nsufficient condition that there exists one coefficient having the maximal lower\norder or having the maximal lower type strictly greater than the order or the\ntype of other coefficients, we obtain estimates of the lower bound of the order\nof meromorphic solutions of the above equation.\n"}
+{"abstract": "  Labeling objects at a subordinate level typically requires expert knowledge,\nwhich is not always available when using random annotators. As such, learning\ndirectly from web images for fine-grained recognition has attracted broad\nattention. However, the presence of label noise and hard examples in web images\nare two obstacles for training robust fine-grained recognition models.\nTherefore, in this paper, we propose a novel approach for removing irrelevant\nsamples from real-world web images during training, while employing useful hard\nexamples to update the network. Thus, our approach can alleviate the harmful\neffects of irrelevant noisy web images and hard examples to achieve better\nperformance. Extensive experiments on three commonly used fine-grained datasets\ndemonstrate that our approach is far superior to current state-of-the-art\nweb-supervised methods.\n"}
+{"abstract": "  In this paper, we investigate joint information-theoretic security and covert\ncommunication on a network in the presence of a single transmitter (Alice), a\nfriendly jammer, a single untrusted user, two legitimate users, and a single\nwarden of the channel (Willie). In the considered network, one of the\nauthorized users, Bob, needs a secure and covert communication, and therefore\nhis message must be sent securely, and at the same time, the existence of his\ncommunication with the transmitter should not be detected by the channel's\nwarden, Willie, Meanwhile, another authorized user, Carol, needs covert\ncommunication. The purpose of secure communication is to prevent the message\nbeing decoded by the untrusted user who is present on the network, which leads\nus to use one of the physical layer security methods, named the secure\ntransmission of information theory. In some cases, in addition to protecting\nthe content of the message, it is important for the user that the existence of\nthe transmission not being detected by an adversary, which leads us to covert\ncommunication. In the proposed network model, it is assumed that for covert\ncommunication requirements, Alice will not send any messages to legitimate\nusers in one time slot and in another time slot will send to them both (Bob and\nCarol). One of the main challenges in covert communication is low transmission\nrate, because we have to reduce the transmission power such that the main\nmessage get hide in background noise.\n"}
+{"abstract": "  Permutation Mastermind is a version of the classical mastermind game in which\nthe number of positions $n$ is equal to the number of colors $k$, and\nrepetition of colors is not allowed, neither in the codeword nor in the\nqueries. In this paper we solve the main open question from Glazik, J\\\"ager,\nSchiemann and Srivastav (2021), who asked whether their bound of $O(n^{1.525})$\nfor the static version can be improved to $O(n \\log n)$, which would be best\npossible. By using a simple probabilistic argument we show that this is indeed\nthe case.\n"}
+{"abstract": "  This is the second in a sequence of three papers investigating the question\nfor which positive integers $m$ there exists a maximal antichain of size $m$ in\nthe Boolean lattice $B_n$ (the power set of $[n]:=\\{1,2,\\dots,n\\}$, ordered by\ninclusion). In the previous paper we characterized those $m$ between\n$\\binom{n}{\\lceil n/2\\rceil}-\\lceil n/2\\rceil^2$ and the maximum size\n$\\binom{n}{\\lceil n/2 \\rceil}$ that are not sizes of maximal antichains. In\nthis paper we show that all smaller $m$ are sizes of maximal antichains.\n"}
+{"abstract": "  The Planetary Instrument for X-ray Lithochemistry (PIXL) is a micro-focus\nX-ray fluorescence spectrometer mounted on the robotic arm of NASA's\nPerseverance rover. PIXL will acquire high spatial resolution observations of\nrock and soil chemistry, rapidly analyzing the elemental chemistry of a target\nsurface. In 10 seconds, PIXL can use its powerful 120 micrometer diameter X-ray\nbeam to analyze a single, sand-sized grain with enough sensitivity to detect\nmajor and minor rock-forming elements, as well as many trace elements. Over a\nperiod of several hours, PIXL can autonomously scan an area of the rock surface\nand acquire a hyperspectral map comprised of several thousand individual\nmeasured points.\n"}
+{"abstract": "  We review some recent results concerning the Hartle--Hawking wavefunction of\nthe universe. We focus on pure Einstein theory of gravity in the presence of a\npositive cosmological constant. We carefully implement the gauge-fixing\nprocedure for the minisuperspace path integral, by identifying the single\nmodulus and by using diffeomorphism-invariant measures for the ghosts and the\nscale factor. Field redefinitions of the scale factor yield different\nprescriptions for computing the no-boundary ground-state wavefunction. They\ngive rise to an infinite set of ground-state wavefunctions, each satisfying a\ndifferent Wheeler--DeWitt equation, at the semi-classical level. The\ndifferences in the form of the Wheeler--DeWitt equations can be traced to\nordering ambiguities in constructing the Hamiltonian upon canonical\nquantization. However, the inner products of the corresponding Hilbert spaces\nturn out to be equivalent, at least semi-classically. Thus, the model yields\nuniversal quantum predictions.\n"}
+{"abstract": "  The virtual try-on task is so attractive that it has drawn considerable\nattention in the field of computer vision. However, presenting the\nthree-dimensional (3D) physical characteristic (e.g., pleat and shadow) based\non a 2D image is very challenging. Although there have been several previous\nstudies on 2D-based virtual try-on work, most 1) required user-specified target\nposes that are not user-friendly and may not be the best for the target\nclothing, and 2) failed to address some problematic cases, including facial\ndetails, clothing wrinkles and body occlusions. To address these two\nchallenges, in this paper, we propose an innovative template-free try-on image\nsynthesis (TF-TIS) network. The TF-TIS first synthesizes the target pose\naccording to the user-specified in-shop clothing. Afterward, given an in-shop\nclothing image, a user image, and a synthesized pose, we propose a novel model\nfor synthesizing a human try-on image with the target clothing in the best\nfitting pose. The qualitative and quantitative experiments both indicate that\nthe proposed TF-TIS outperforms the state-of-the-art methods, especially for\ndifficult cases.\n"}
+{"abstract": "  We study piece-wise constant signals corrupted by additive Gaussian noise\nover a $d$-dimensional lattice. Data of this form naturally arise in a host of\napplications, and the tasks of signal detection or testing, de-noising and\nestimation have been studied extensively in the statistical and signal\nprocessing literature. In this paper we consider instead the problem of\npartition recovery, i.e.~of estimating the partition of the lattice induced by\nthe constancy regions of the unknown signal, using the\ncomputationally-efficient dyadic classification and regression tree (DCART)\nmethodology proposed by \\citep{donoho1997cart}. We prove that, under\nappropriate regularity conditions on the shape of the partition elements, a\nDCART-based procedure consistently estimates the underlying partition at a rate\nof order $\\sigma^2 k^* \\log (N)/\\kappa^2$, where $k^*$ is the minimal number of\nrectangular sub-graphs obtained using recursive dyadic partitions supporting\nthe signal partition, $\\sigma^2$ is the noise variance, $\\kappa$ is the minimal\nmagnitude of the signal difference among contiguous elements of the partition\nand $N$ is the size of the lattice. Furthermore, under stronger assumptions,\nour method attains a sharper estimation error of order\n$\\sigma^2\\log(N)/\\kappa^2$, independent of $k^*$, which we show to be minimax\nrate optimal. Our theoretical guarantees further extend to the partition\nestimator based on the optimal regression tree estimator (ORT) of\n\\cite{chatterjee2019adaptive} and to the one obtained through an NP-hard\nexhaustive search method. We corroborate our theoretical findings and the\neffectiveness of DCART for partition recovery in simulations.\n"}
+{"abstract": "  This paper investigates the problem of correcting multiple criss-cross\ninsertions and deletions in arrays. More precisely, we study the unique\nrecovery of $n \\times n$ arrays affected by $t$-criss-cross deletions defined\nas any combination of $t_r$ row and $t_c$ column deletions such that $t_r + t_c\n= t$ for a given $t$. We show an equivalence between correcting $t$-criss-cross\ndeletions and $t$-criss-cross insertions and show that a code correcting\n$t$-criss-cross insertions/deletions has redundancy at least $tn + t \\log n -\n\\log(t!)$. Then, we present an existential construction of $t$-criss-cross\ninsertion/deletion correcting code with redundancy bounded from above by $tn +\n\\mathcal{O}(t^2 \\log^2 n)$. The main ingredients of the presented code\nconstruction are systematic binary $t$-deletion correcting codes and Gabidulin\ncodes. The first ingredient helps locating the indices of the inserted/deleted\nrows and columns, thus transforming the insertion/deletion-correction problem\ninto a row/column erasure-correction problem which is then solved using the\nsecond ingredient.\n"}
+{"abstract": "  The concept of mean inactivity time plays a crucial role in reliability, risk\ntheory and life testing. In this regard, we introduce a weighted mean\ninactivity time function by considering a non-negative weight function. Based\non this function, we provide expressions for the variance of transformed random\nvariable and the weighted generalized cumulative entropy. The latter concept is\nan important measure of uncertainty which is shift-dependent and is of interest\nin certain applied contexts, such as reliability or mathematical neurobiology.\nMoreover, based on the comparison of mean inactivity times of a certain\nfunction of two lifetime random variables, we introduce and study a new\nstochastic order in terms of the weighted mean inactivity time function.\nSeveral characterizations and preservation properties of the new order under\nshock models, random maxima and renewal theory are discussed.\n"}
+{"abstract": "  Prepositional supersense annotation is time-consuming and requires expert\ntraining. Here, we present two sensible methods for obtaining prepositional\nsupersense annotations by eliciting surface substitution and similarity\njudgments. Four pilot studies suggest that both methods have potential for\nproducing prepositional supersense annotations that are comparable in quality\nto expert annotations.\n"}
+{"abstract": "  Deep learning methods have reached state-of-the-art performance in cardiac\nimage segmentation. Currently, the main bottleneck towards their effective\ntranslation into clinics requires assuring continuous high model performance\nand segmentation results. In this work, we present a novel learning framework\nto monitor the performance of heart segmentation models in the absence of\nground truth. Formulated as an anomaly detection problem, the monitoring\nframework allows deriving surrogate quality measures for a segmentation and\nallows flagging suspicious results. We propose two different types of quality\nmeasures, a global score and a pixel-wise map. We demonstrate their use by\nreproducing the final rankings of a cardiac segmentation challenge in the\nabsence of ground truth. Results show that our framework is accurate, fast, and\nscalable, confirming it is a viable option for quality control monitoring in\nclinical practice and large population studies.\n"}
+{"abstract": "  We introduce a non-standard model for percolation on the integer lattice\n$\\mathbb Z^2$. Randomly assign to each vertex $a \\in \\mathbb Z^2$ a potential,\ndenoted $\\phi_a$, chosen independently and uniformly from the interval $[0,\n1]$. For fixed $\\epsilon \\in [0,1]$, draw a directed edge from vertex $a$ to a\nnearest-neighbor vertex $b$ if $\\phi_b < \\phi_a + \\epsilon$, yielding a\ndirected subgraph of the infinite directed graph $\\overrightarrow{G}$ whose\nvertex set is $\\mathbb Z^2$, with nearest-neighbor edge set. We define notions\nof weak and strong percolation for our model, and observe that when $\\epsilon =\n0$ the model fails to percolate weakly, while for $\\epsilon = 1$ it percolates\nstrongly. We show that there is a positive $\\epsilon_0$ so that for $0 \\le\n\\epsilon \\le \\epsilon_0$, the model fails to percolate weakly, and that when\n$\\epsilon > p_\\text{site}$, the critical probability for standard site\npercolation in $\\mathbb Z^2$, the model percolates strongly. We study the\nnumber of infinite strongly connected clusters occurring in a typical\nconfiguration. We show that for these models of percolation on directed graphs,\nthere are some subtle issues that do not arise for undirected percolation.\nAlthough our model does not have the finite energy property, we are able to\nshow that, as in the standard model, the number of infinite strongly connected\nclusters is almost surely 0, 1 or $\\infty$.\n"}
+{"abstract": "  This paper describes a method for using Transformer-based Language Models\n(TLMs) to understand public opinion from social media posts. In this approach,\nwe train a set of GPT models on several COVID-19 tweet corpora that reflect\npopulations of users with distinctive views. We then use prompt-based queries\nto probe these models to reveal insights into the biases and opinions of the\nusers. We demonstrate how this approach can be used to produce results which\nresemble polling the public on diverse social, political and public health\nissues. The results on the COVID-19 tweet data show that transformer language\nmodels are promising tools that can help us understand public opinions on\nsocial media at scale.\n"}
+{"abstract": "  In 2017 April, the Event Horizon Telescope (EHT) observed the near-horizon\nregion around the supermassive black hole at the core of the M87 galaxy. These\n1.3 mm wavelength observations revealed a compact asymmetric ring-like source\nmorphology. This structure originates from synchrotron emission produced by\nrelativistic plasma located in the immediate vicinity of the black hole. Here\nwe present the corresponding linear-polarimetric EHT images of the center of\nM87. We find that only a part of the ring is significantly polarized. The\nresolved fractional linear polarization has a maximum located in the southwest\npart of the ring, where it rises to the level of about 15%. The polarization\nposition angles are arranged in a nearly azimuthal pattern. We perform\nquantitative measurements of relevant polarimetric properties of the compact\nemission and find evidence for the temporal evolution of the polarized source\nstructure over one week of EHT observations. The details of the polarimetric\ndata reduction and calibration methodology are provided. We carry out the data\nanalysis using multiple independent imaging and modeling techniques, each of\nwhich is validated against a suite of synthetic data sets. The gross\npolarimetric structure and its apparent evolution with time are insensitive to\nthe method used to reconstruct the image. These polarimetric images carry\ninformation about the structure of the magnetic fields responsible for the\nsynchrotron emission. Their physical interpretation is discussed in an\naccompanying publication.\n"}
+{"abstract": "  The dense plasma focus is a plasma discharge powered by a capacitor bank.\nStandard diagnostics include measurement of the time derivative of the current\nthrough and the voltage across its connections with the capacitor bank.\nInterpretation of this diagnostic data often involves some assumptions\nregarding the representation of the dense plasma focus as a time varying\ninductance. One of the characteristic features of the current derivative\nwaveform is a relatively sharp dip and an associated sharp voltage spike. This\nhas often been interpreted as a result of a rapid rise in the time varying\ninductance of the plasma. Sometimes, an anomalous plasma impedance is invoked.\nThis Letter discusses instances where such interpretation creates conceptual\ndifficulties. A first principles approach to the representation of the dense\nplasma focus as a circuit element reveals some fundamental problems with the\ntraditional representation of plasma focus as a time varying inductance. The\nanomalous impedance is shown to be necessary to account for the difference in\nthe motional impedance implied by a time-varying inductance in the circuit\nelement representation and a first principles description based on Poynting's\nTheorem. Dynamo effects that convert post-stagnation local motion of plasma\ninto 3-dimensional magnetic fields are shown to contribute to the effective\ninductance of the plasma focus and resolve the observed conceptual difficulties\n"}
+{"abstract": "  We discuss the solvability of a fairly general class of systems of perturbed\nHammerstein integral equations with functional terms that depend on several\nparameters. The nonlinearities and the functionals are allowed to depend on the\ncomponents of the system and their derivatives. The results are applicable to\nsystems of nonlocal second order ordinary differential equations subject to\nfunctional boundary conditions, this is illustrated in an example. Our approach\nis based on the classical fixed point index.\n"}
+{"abstract": "  We deal with the as yet unresolved exponential stability problem for a\nstretched Euler-Bernoulli beam on a star-shaped geometric graph with three\nidentical edges. The edges are hinged with respect to the boundary vertices.\nThe inner vertex is capable of both translation and rotation, the latter of\nwhich is subject to a combination of elastic and frictional effects. We present\ndetailed results on the asymptotic location and structure of the spectrum of\nthe linear operator associated with the spectral problem in Hilbert space.\nWithin this framework it is shown that the eigenvectors have the property of\nforming an unconditional or Riesz basis, which makes it possible to directly\ndeduce the exponential stability of the corresponding $C_0$-semigroup. As an\naside it is shown that the particular choice of connectivity conditions ensures\nthe exponential stability even when the elasticity acting on the slopes of the\nedges is absent.\n"}
+{"abstract": "  In classical set theory, there are many equivalent ways to introduce\nordinals. In a constructive setting, however, the different notions split\napart, with different advantages and disadvantages for each. We consider three\ndifferent notions of ordinals in homotopy type theory, and show how they relate\nto each other: A notation system based on Cantor normal forms, a refined notion\nof Brouwer trees (inductively generated by zero, successor and countable\nlimits), and wellfounded extensional orders. For Cantor normal forms, most\nproperties are decidable, whereas for wellfounded extensional transitive\norders, most are undecidable. Formulations for Brouwer trees are usually\npartially decidable. We demonstrate that all three notions have properties\nexpected of ordinals: their order relations, although defined differently in\neach case, are all extensional and wellfounded, and the usual arithmetic\noperations can be defined in each case. We connect these notions by\nconstructing structure preserving embeddings of Cantor normal forms into\nBrouwer trees, and of these in turn into wellfounded extensional orders. We\nhave formalised most of our results in cubical Agda.\n"}
+{"abstract": "  Given a closed connected spin manifold M with non-negative and somewhere\npositive scalar curvature, we show that the Dirac operator twisted with any\nflat Hilbert module bundle is invertible.\n"}
+{"abstract": "  In a recent Letter [Phys. Rev. Lett. 125, 180604 (2020)], we introduced a\nclosed-form analytic expression for the average bipartite von Neumann\nentanglement entropy of many-body eigenstates of random quadratic Hamiltonians.\nNamely, of Hamiltonians whose single-particle eigenstates have random\ncoefficients in the position basis. A paradigmatic Hamiltonian for which the\nexpression is valid is the quadratic Sachdev-Ye-Kitaev (SYK2) model in its\nDirac fermion formulation. Here we show that the applicability of our result is\nmuch broader. Most prominently, it is also relevant for local Hamiltonians such\nas the three-dimensional (3D) Anderson model at weak disorder. Moreover, it\ndescribes the average entanglement entropy in Hamiltonians without\nparticle-number conservation, such as the SYK2 model in the Majorana fermion\nformulation and the 3D Anderson model with additional terms that break\nparticle-number conservation. We extend our analysis to the average bipartite\nsecond R\\'enyi entanglement entropy of eigenstates of the same quadratic\nHamiltonians, which is derived analytically and tested numerically. We\nconjecture that our results for the entanglement entropies of many-body\neigenstates apply to quadratic Hamiltonians whose single-particle eigenstates\nexhibit quantum chaos, to which we refer as quantum-chaotic quadratic\nHamiltonians.\n"}
+{"abstract": "  The paper proposes an optimal management strategy for a system composed by a\nbattery and a photovoltaic power plant. This integrated system is called to\ndeliver the photovoltaic power and to simultaneously provide droop-based\nprimary frequency regulation to the main grid. The battery state-of-energy is\ncontrolled by power offset signals, which are determined using photovoltaic\nenergy generation forecasts and predictions of the energy required to operate\nfrequency regulation. A two level control architecture is developed. A\nday-ahead planning algorithm schedules the energy profile which is traded at\nthe day-ahead market and defines the primary control reserve that the\nintegrated system is able to provide in the considered day. During the day\noperations, a second level algorithm corrects the dispatched plan using updated\ninformation, in order to guarantee a continuous and reliable service. Both\ncontrol algorithms take into account the uncertainties of the photovoltaic\ngeneration and of the frequency dynamics using stochastic optimization.\n"}
+{"abstract": "  One of the most widely used methods for solving large-scale stochastic\noptimization problems is distributed asynchronous stochastic gradient descent\n(DASGD), a family of algorithms that result from parallelizing stochastic\ngradient descent on distributed computing architectures (possibly)\nasychronously. However, a key obstacle in the efficient implementation of DASGD\nis the issue of delays: when a computing node contributes a gradient update,\nthe global model parameter may have already been updated by other nodes several\ntimes over, thereby rendering this gradient information stale. These delays can\nquickly add up if the computational throughput of a node is saturated, so the\nconvergence of DASGD may be compromised in the presence of large delays. Our\nfirst contribution is that, by carefully tuning the algorithm's step-size,\nconvergence to the critical set is still achieved in mean square, even if the\ndelays grow unbounded at a polynomial rate. We also establish finer results in\na broad class of structured optimization problems (called variationally\ncoherent), where we show that DASGD converges to a global optimum with\nprobability $1$ under the same delay assumptions. Together, these results\ncontribute to the broad landscape of large-scale non-convex stochastic\noptimization by offering state-of-the-art theoretical guarantees and providing\ninsights for algorithm design.\n"}
+{"abstract": "  Anatomical motion and deformation pose challenges to the understanding of the\ndelivered dose distribution during radiotherapy treatments. Hence, deformable\nimage registration (DIR) algorithms are increasingly used to map contours and\ndose distributions from one image set to another. However, the lack of\nvalidation tools slows their clinical adoption, despite their commercial\navailability. This work presents a novel water-equivalent deformable dosimeter\nthat simultaneously measures the dose distribution and tracks deformation\nvector fields (DVF). The dosimeter in made of an array of 19 scintillating\nfiber detectors embedded in a cylindrical elastomer matrix. It is imaged by two\npairs of stereoscopic cameras tracking the position and angulation of the\nscintillators, while measuring the dose. The resulting system provides a\nprecision of 0.3 mm on DVF measurements. The dosimeter was irradiated with\n5$\\times$3, 4$\\times$3 and 3$\\times$3 cm$^2$ 6 MV photon beams in both fixed\nand deformed conditions. The measured DVF was compared to the one computed with\na DIR algorithm (Plastimatch). The deviations between the computed and measured\nDVFs was below 1.5 mm. As for dose measurements, the dosimeter acquired the\ndose distribution in fixed and deformed conditions within 1\\% of the treatment\nplanning system calculation and complementary dose validation using the\nHyperscint dosimetry system. Using the demonstrated qualities of scintillating\ndetectors, we developed a real-time, water-equivalent deformable dosimeter.\nGiven it's sensor tracking position precision and dose measurements accuracy,\nthe developed detector is a promising tools for the validation of DIR\nalgorithms as well as dose distribution measurements under fixed and deformed\nconditions.\n"}
+{"abstract": "  Sparse Principal Component Analysis (SPCA) is widely used in data processing\nand dimension reduction; it uses the lasso to produce modified principal\ncomponents with sparse loadings for better interpretability. However, sparse\nPCA never considers an additional grouping structure where the loadings share\nsimilar coefficients (i.e., feature grouping), besides a special group with all\ncoefficients being zero (i.e., feature selection). In this paper, we propose a\nnovel method called Feature Grouping and Sparse Principal Component Analysis\n(FGSPCA) which allows the loadings to belong to disjoint homogeneous groups,\nwith sparsity as a special case. The proposed FGSPCA is a subspace learning\nmethod designed to simultaneously perform grouping pursuit and feature\nselection, by imposing a non-convex regularization with naturally adjustable\nsparsity and grouping effect. To solve the resulting non-convex optimization\nproblem, we propose an alternating algorithm that incorporates the\ndifference-of-convex programming, augmented Lagrange and coordinate descent\nmethods. Additionally, the experimental results on real data sets show that the\nproposed FGSPCA benefits from the grouping effect compared with methods without\ngrouping effect.\n"}
+{"abstract": "  The resource constraints and accuracy requirements for Internet of Things\n(IoT) memory chips need three-dimensional (3D) monolithic integrated circuits,\nof which the increasing stack layers (currently more than 176) also cause\nexcessive energy consumption and increasing wire length. In this paper, a novel\n3D wireless network on chips (3DWiNoCs) model transmitting signal directly to\nthe destination in arbitrary layer is proposed and characterized. However, due\nto the the reflection and refraction characteristics in each layer, the complex\nand diverse wireless paths in 3DWiNoC add great difficulty to the channel\ncharacterization. To facilitate the modeling in massive layer NoC situation,\nboth boundary-less model boundary-constrained 3DWiNoC model are proposed, of\nwhich the channel gain can be obtained by a computational efficient approximate\nalgorithm. These 3DWiNoC models with approximation algorithm can well\ncharacterize the 3DWiNoC channel in aspect of complete reflection and\nrefraction characteristics, and avoid massive wired connections, high power\nconsumption of cross-layer communication and high-complexity of 3DWiNoC channel\ncharacterization. Numerical results show that: 1) The difference rate between\nthe two models is lower than 0.001% (signal transmit through 20 layers); 2) the\nchannel gain decreases sharply if refract time increases; and 3) the\napproximate algorithm can achieve an acceptable accuracy (error rate lower than\n0.1%).\n"}
+{"abstract": "  We present Atacama Large Millimeter/submillimeter Array (ALMA) observations\nof $\\mathrm{^{13}CO(J=1-0)}$ line and 104 GHz continuum emission from NGC 604,\na giant HII region (GHR) in the nearby spiral galaxy M33. Our high spatial\nresolution images ( 3.2\"$\\times$ 2.4\", corresponding to $13 \\times 10$ pc\nphysical scale) allow us to detect fifteen molecular clouds. We find spatial\noffsets between the $^{13}CO$ and 104 GHz continuum emission and also detect\ncontinuum emission near the centre of the GHR. The identified molecular clouds\nhave sizes ranging from 5-21 pc, linewidths of 0.3-3.0 $\\mathrm{kms^{-1}}$ and\nluminosity-derived masses of (0.4-80.5) $\\times 10^3$ M$_{\\bigodot}$. These\nmolecular clouds are in near virial equilibrium, with a spearman correlation\ncoefficient of 0.98. The linewidth-size relationship for these clouds is offset\nfrom the corresponding relations for the Milky Way and for NGC 300, although\nthis may be an artefact of the dendrogram process.\n"}
+{"abstract": "  Despite that deep neural networks (DNNs) have achieved enormous success in\nmany domains like natural language processing (NLP), they have also been proven\nto be vulnerable to maliciously generated adversarial examples. Such inherent\nvulnerability has threatened various real-world deployed DNNs-based\napplications. To strength the model robustness, several countermeasures have\nbeen proposed in the English NLP domain and obtained satisfactory performance.\nHowever, due to the unique language properties of Chinese, it is not trivial to\nextend existing defenses to the Chinese domain. Therefore, we propose AdvGraph,\na novel defense which enhances the robustness of Chinese-based NLP models by\nincorporating adversarial knowledge into the semantic representation of the\ninput. Extensive experiments on two real-world tasks show that AdvGraph\nexhibits better performance compared with previous work: (i) effective - it\nsignificantly strengthens the model robustness even under the adaptive attacks\nsetting without negative impact on model performance over legitimate input;\n(ii) generic - its key component, i.e., the representation of connotative\nadversarial knowledge is task-agnostic, which can be reused in any\nChinese-based NLP models without retraining; and (iii) efficient - it is a\nlight-weight defense with sub-linear computational complexity, which can\nguarantee the efficiency required in practical scenarios.\n"}
+{"abstract": "  CoSi single crystal is a known realization of a chiral topological semimetal\nwith simultaneously broken mirror and inversion symmetries. In addition to the\nsymmetry-induced spin-orbit coupling, surface ferromagnetism is known in\nnominally diamagnetic CoSi structures, which appears due to the distorted bonds\nand ordered vacancies near the surface. We experimentally investigate electron\ntransport through a thin CoSi flake at high current density. Surprisingly, we\ndemonstrate $dV/dI(I)$ curves which are qualitatively similar to ones for\nferromagnetic multilayers with characteristic $dV/dI$ magnon peaks and\nunconventional magnetic field evolution of the peaks' positions. We understand\nthese observations as a result of current-induced spin polarization due to the\nsignificant spin-orbit coupling in CoSi. Scattering of non-equilibrium\nspin-polarized carriers within the surface ferromagnetic layer is responsible\nfor the precessing spin-wave excitations, so the observed magnon modes are the\njoint effect of surface ferromagnetism and spin-orbit coupling in a CoSi chiral\ntopological semimetal. Thus, thin CoSi flakes behave as magnetic conductors\nwith broken inversion symmetry, which is important for different spintronic\nphenomena.\n"}
+{"abstract": "  The communication technology revolution in this era has increased the use of\nsmartphones in the world of transportation. In this paper, we propose to\nleverage IoT device data, capturing passengers' smartphones' Wi-Fi data in\nconjunction with weather conditions to predict the expected number of\npassengers waiting at a bus stop at a specific time using deep learning models.\nOur study collected data from the transit bus system at James Madison\nUniversity (JMU) in Virginia, USA. This paper studies the correlation between\nthe number of passengers waiting at bus stops and weather conditions.\nEmpirically, an experiment with several bus stops in JMU, was utilized to\nconfirm a high precision level. We compared our Deep Neural Network (DNN) model\nagainst two baseline models: Linear Regression (LR) and a Wide Neural Network\n(WNN). The gap between the baseline models and DNN was 35% and 14% better Mean\nSquared Error (MSE) scores for predictions in favor of the DNN compared to LR\nand WNN, respectively.\n"}
+{"abstract": "  Despite rapid progress, current deep learning methods face a number of\ncritical challenges. These include high energy consumption, catastrophic\nforgetting, dependance on global losses, and an inability to reason\nsymbolically. By combining concepts from information bottleneck theory and\nvector-symbolic architectures, we propose and implement a novel information\nprocessing architecture, the 'Bridge network.' We show this architecture\nprovides unique advantages which can address the problem of global losses and\ncatastrophic forgetting. Furthermore, we argue that it provides a further basis\nfor increasing energy efficiency of execution and the ability to reason\nsymbolically.\n"}
+{"abstract": "  Federated Learning (FL) is a promising framework that has great potentials in\nprivacy preservation and in lowering the computation load at the cloud. FedAvg\nand FedProx are two widely adopted algorithms. However, recent work raised\nconcerns on these two methods: (1) their fixed points do not correspond to the\nstationary points of the original optimization problem, and (2) the common\nmodel found might not generalize well locally.\n  In this paper, we alleviate these concerns. Towards this, we adopt the\nstatistical learning perspective yet allow the distributions to be\nheterogeneous and the local data to be unbalanced. We show, in the general\nkernel regression setting, that both FedAvg and FedProx converge to the\nminimax-optimal error rates. Moreover, when the kernel function has a finite\nrank, the convergence is exponentially fast. Our results further analytically\nquantify the impact of the model heterogeneity and characterize the federation\ngain - the reduction of the estimation error for a worker to join the federated\nlearning compared to the best local estimator. To the best of our knowledge, we\nare the first to show the achievability of minimax error rates under FedAvg and\nFedProx, and the first to characterize the gains in joining FL. Numerical\nexperiments further corroborate our theoretical findings on the statistical\noptimality of FedAvg and FedProx and the federation gains.\n"}
+{"abstract": "  Conventional indirect dark matter (DM) searches look for an excess in the\nelectromagnetic emission from the sky that cannot be attributed to known\nastrophysical sources. Here, we argue that the photon polarisation is an\nimportant feature to understand new physics interactions and can be exploited\nto improve our sensitivity to DM. In particular, circular polarisation can be\ngenerated from Beyond the Standard Model interactions if they violate parity\nand there is an asymmetry in the number of particles which participate in the\ninteraction. In this work, we consider a simplified model for fermionic\n(Majorana) DM and study the circularly polarised gamma rays below 10 GeV from\nthe scattering of cosmic ray electrons on DM. We calculate the differential\nflux of positive and negative polarised photons from the Galactic Center and\nshow that the degree of circular polarization can reach up to 90%. Finally,\nonce collider and DM constraints have been taken into account, we estimate the\nrequired sensitivity from future experiments to detect this signal finding\nthat, although a distinctive peak will be present in the photon flux spectrum,\na near future observation is unlikely. However, different sources or models not\nconsidered in this work could provide higher intensity fluxes, leading to a\npossible detection by e-ASTROGAM. In the event of a discovery, we argue that\nthe polarisation fraction is a valuable characterisation feature of the new\nsector.\n"}
+{"abstract": "  Inspired by the studies on the influence of transition metal impurities in\nhigh Tc superconductors and what is already known about nonmagnetic suppression\nof Tc in unconventional superconductors, we set out to investigate the behavior\nof the nonmagnetic disordered elastic scattering for a realistic 2D anisotropic\nhigh Tc superconductor with line nodes and a Fermi surface in the tight-binding\napproximation. For this purpose, we performed a detailed self-consistent 2D\nnumerical study of the disordered averaged scattering matrix with nonmagnetic\nimpurities and a singlet line nodes order parameter, varying the concentration\nand the strength of the impurities potential in the Born, intermediate and\nunitary limits. In a high Tc anisotropic superconductor with a tight binding\ndispersion law averaging over the Fermi surface, including hopping parameters\nand an order parameter in agreement with experimental data, the tight-binding\napproximation reflects the anisotropic effects. In this study, we also included\na detailed visualization of the behavior of the scattering matrix with\ndifferent sets of physical parameters involved in the nonmagnetic disorder,\nwhich allowed us to model the dressed scattering behavior in different regimes\nfor very low and high energies. With this study, we demonstrate that the\nscattering elastic matrix is affected by the non-magnetic disorder, as well as\nthe importance of an order parameter and a Fermi surface in agreement with\nexperiments when studying this effect in unconventional superconductors.\n"}
+{"abstract": "  Let $X$ be a complex space and $M$ a pure Hodge module with strict support\n$X$. We introduce a kind of coherent subsheaf $S(M,\\varphi)$ of M. Saito's\n$S(M)$ which is a combination of $S(M)$ and the multiplier ideal sheaf\n$\\mathscr{I}(\\varphi)$. An $L^2$-resolution of $S(M,\\varphi)$ is constructed.\nThis generalizes MacPherson's conjecture on the $L^2$-representation of the\nGrauert-Riemenschneider sheaf. Various vanishing theorems for $S(M)$ (Saito's\nvanishing, Kawamata-Viehweg vanishing and some new ones like Nadel vanishing,\npartial vanishing) are proved via standard differential geometric arguments.\nSome applications on the relative version of Fujita's conjecture are presented.\n"}
+{"abstract": "  Exposing a solution to a temperature gradient can lead to the accumulation of\nparticles on either the cold or warm side. This phenomenon, known as\nthermophoresis, has been discovered more than a century ago, and yet its\nmicroscopic origin is still debated. Here, we show that thermophoresis can be\nobserved in any system such that the transitions between different internal\nstates are modulated by temperature and such that different internal states\nhave different transport properties. We establish thermophoresis as a genuine\nnon-equilibrium effect, whereby a system of currents in real and internal space\nthat is consistent with the thermodynamic necessity of transporting heat from\nwarm to cold regions. Our approach also provides an expression for the Soret\ncoefficient, which decides whether particles accumulate on the cold or on the\nwarm side, that is associated with the correlation between the energies of the\ninternal states and their transport properties, that instead remain\nsystem-specific quantities. Finally, we connect our results to previous\napproaches based on close-to-equilibrium energetics. Our thermodynamically\nconsistent approach thus encompasses and generalizes previous findings.\n"}
+{"abstract": "  The assessment of program functionality can generally be accomplished with\nstraight-forward unit tests. However, assessing the design quality of a program\nis a much more difficult and nuanced problem. Design quality is an important\nconsideration since it affects the readability and maintainability of programs.\nAssessing design quality and giving personalized feedback is very time\nconsuming task for instructors and teaching assistants. This limits the scale\nof giving personalized feedback to small class settings. Further, design\nquality is nuanced and is difficult to concisely express as a set of rules. For\nthese reasons, we propose a neural network model to both automatically assess\nthe design of a program and provide personalized feedback to guide students on\nhow to make corrections. The model's effectiveness is evaluated on a corpus of\nstudent programs written in Python. The model has an accuracy rate from 83.67%\nto 94.27%, depending on the dataset, when predicting design scores as compared\nto historical instructor assessment. Finally, we present a study where students\ntried to improve the design of their programs based on the personalized\nfeedback produced by the model. Students who participated in the study improved\ntheir program design scores by 19.58%.\n"}
+{"abstract": "  A growing area of research in epidemiology is the identification of\nhealth-related sibling spillover effects, or the effect of one individual's\nexposure on their sibling's outcome. The health and health care of family\nmembers may be inextricably confounded by unobserved factors, rendering\nidentification of spillover effects within families particularly challenging.\nWe demonstrate a gain-score regression method for identifying\nexposure-to-outcome spillover effects within sibling pairs in a linear fixed\neffects framework. The method can identify the exposure-to-outcome spillover\neffect if only one sibling's exposure affects the other's outcome; and it\nidentifies the difference between the spillover effects if both siblings'\nexposures affect the others' outcomes. The method fails in the presence of\noutcome-to-exposure spillover and outcome-to-outcome spillover. Analytic\nresults and Monte Carlo simulations demonstrate the method and its limitations.\nTo exercise this method, we estimate the spillover effect of a child's preterm\nbirth on an older sibling's literacy skills, measured by the Phonological\nAwarenesses Literacy Screening-Kindergarten test. We analyze 20,010 sibling\npairs from a population-wide, Wisconsin-based (United States) birth cohort.\nWithout covariate adjustment, we estimate that preterm birth modestly decreases\nan older sibling's test score (-2.11 points; 95% confidence interval: -3.82,\n-0.40 points). In conclusion, gain-scores are a promising strategy for\nidentifying exposure-to-outcome spillovers in sibling pairs while controlling\nfor sibling-invariant unobserved confounding in linear settings.\n"}
+{"abstract": "  We propose a supervised machine learning algorithm, decision trees, to\nanalyze molecular dynamics output. The approach aims to identify the\npredominant geometric features which correlate with trajectories that\ntransition between two arbitrarily defined states. The data-based algorithm\naims to identify such features in an approach which is unbiased by human\n\"chemical intuition\". We demonstrate the method by analyzing proton exchange\nreactions in formic acid (FA) solvated in small water clusters. The simulations\nwere performed with ab initio molecular dynamics combined with a method for\ngenerating rare events, specifically path sampling. Our machine learning\nanalysis identified mechanistic descriptions of the proton transfer reaction\nfor the different water clusters.\n"}
+{"abstract": "  In a recent Letter, Dornheim et al. [PRL 125, 085001 (2020)] have\ninvestigated the nonlinear density response of the uniform electron gas in the\nwarm dense matter regime. More specifically, they have studied the cubic\nresponse function at the first harmonic, which cannot be neglected in many\nsituations of experimental relevance. In this work, we go one step further and\nstudy the full spectrum of excitations at the higher harmonics of the original\nperturbation based on extensive new ab initio path integral Monte Carlo (PIMC)\nsimulations. We find that the dominant contribution to the density response\nbeyond linear response theory is given by the quadratic response function at\nthe second harmonic in the moderately nonlinear regime. Furthermore, we show\nthat the nonlinear density response is highly sensitive to exchange-correlation\neffects, which makes it a potentially valuable new tool of diagnostics. To this\nend, we present a new theoretical description of the nonlinear electronic\ndensity response based on the recent effective static approximation to the\nlocal field correction [PRL 125, 235001 (2020)], which accurately reproduces\nour PIMC data with negligible computational cost.\n"}
+{"abstract": "  Message passing Graph Neural Networks (GNNs) provide a powerful modeling\nframework for relational data. However, the expressive power of existing GNNs\nis upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test,\nwhich means GNNs that are not able to predict node clustering coefficients and\nshortest path distances, and cannot differentiate between different d-regular\ngraphs. Here we develop a class of message passing GNNs, named Identity-aware\nGraph Neural Networks (ID-GNNs), with greater expressive power than the 1-WL\ntest. ID-GNN offers a minimal but powerful solution to limitations of existing\nGNNs. ID-GNN extends existing GNN architectures by inductively considering\nnodes' identities during message passing. To embed a given node, ID-GNN first\nextracts the ego network centered at the node, then conducts rounds of\nheterogeneous message passing, where different sets of parameters are applied\nto the center node than to other surrounding nodes in the ego network. We\nfurther propose a simplified but faster version of ID-GNN that injects node\nidentity information as augmented node features. Altogether, both versions of\nID-GNN represent general extensions of message passing GNNs, where experiments\nshow that transforming existing GNNs to ID-GNNs yields on average 40% accuracy\nimprovement on challenging node, edge, and graph property prediction tasks; 3%\naccuracy improvement on node and graph classification benchmarks; and 15% ROC\nAUC improvement on real-world link prediction tasks. Additionally, ID-GNNs\ndemonstrate improved or comparable performance over other task-specific graph\nnetworks.\n"}
+{"abstract": "  Lithium niobate (LN), an outstanding and versatile material, has influenced\nour daily life for decades: from enabling high-speed optical communications\nthat form the backbone of the Internet to realizing radio-frequency filtering\nused in our cell phones. This half-century-old material is currently embracing\na revolution in thin-film LN integrated photonics. The success of manufacturing\nwafer-scale, high-quality, thin films of LN on insulator (LNOI), accompanied\nwith breakthroughs in nanofabrication techniques, have made high-performance\nintegrated nanophotonic components possible. With rapid development in the past\nfew years, some of these thin-film LN devices, such as optical modulators and\nnonlinear wavelength converters, have already outperformed their legacy\ncounterparts realized in bulk LN crystals. Furthermore, the nanophotonic\nintegration enabled ultra-low-loss resonators in LN, which unlocked many novel\napplications such as optical frequency combs and quantum transducers. In this\nReview, we cover -- from basic principles to the state of the art -- the\ndiverse aspects of integrated thin-film LN photonics, including the materials,\nbasic passive components, and various active devices based on electro-optics,\nall-optical nonlinearities, and acousto-optics. We also identify challenges\nthat this platform is currently facing and point out future opportunities. The\nfield of integrated LNOI photonics is advancing rapidly and poised to make\ncritical impacts on a broad range of applications in communication, signal\nprocessing, and quantum information.\n"}
+{"abstract": "  Infrared divergences in perturbative gravitational scattering amplitudes have\nbeen recently argued to be governed by the two-point function of the\nsupertranslation Goldstone mode on the celestial sphere. We show that the form\nof this celestial two-point function simply derives from an effective action\nthat also controls infrared divergences in the symplectic structure of General\nRelativity with asymptotically flat boundary conditions. This effective action\nfinds its natural place in a path integral formulation of a celestial conformal\nfield theory, as we illustrate by re-deriving the infrared soft factors in\nterms of celestial correlators. Our analysis relies on a well-posed action\nprinciple close to spatial infinity introduced by Comp\\`ere and Dehouck.\n"}
+{"abstract": "  In this paper, we prove the existence of full dimensional tori for\n$d$-dimensional nonlinear Schr$\\ddot{\\mbox{o}}$dinger equation with periodic\nboundary conditions \\begin{equation*}\\label{L1} \\sqrt{-1}u_{t}+\\Delta\nu+V*u\\pm\\epsilon |u|^2u=0,\\hspace{12pt}x\\in\\mathbb{T}^d,\\quad d\\geq 1,\n\\end{equation*} where $V*$ is the convolution potential. Here the radius of the\ninvariant torus satisfies a slower decay, i.e. \\begin{equation*}\\label{031601}\nI_{\\textbf n}\\sim e^{-r\\ln^{\\sigma}\\left\\|\\textbf n\\right\\|},\\qquad \\mbox{as}\\\n\\left\\|\\textbf n\\right\\|\\rightarrow\\infty, \\end{equation*}for any $\\sigma>2$\nand $r\\geq 1$. This result confirms a conjecture by Bourgain [J. Funct. Anal.\n229 (2005), no. 1, 62-94].\n"}
+{"abstract": "  Estimating camera wearer's body pose from an egocentric view (egopose) is a\nvital task in augmented and virtual reality. Existing approaches either use a\nnarrow field of view front facing camera that barely captures the wearer, or an\nextruded head-mounted top-down camera for maximal wearer visibility. In this\npaper, we tackle the egopose estimation from a more natural human vision span,\nwhere camera wearer can be seen in the peripheral view and depending on the\nhead pose the wearer may become invisible or has a limited partial view. This\nis a realistic visual field for user-centric wearable devices like glasses\nwhich have front facing wide angle cameras. Existing solutions are not\nappropriate for this setting, and so, we propose a novel deep learning system\ntaking advantage of both the dynamic features from camera SLAM and the body\nshape imagery. We compute 3D head pose, 3D body pose, the figure/ground\nseparation, all at the same time while explicitly enforcing a certain geometric\nconsistency across pose attributes. We further show that this system can be\ntrained robustly with lots of existing mocap data so we do not have to collect\nand annotate large new datasets. Lastly, our system estimates egopose in real\ntime and on the fly while maintaining high accuracy.\n"}
+{"abstract": "  Transition-metal chalcogenides (TMCs) materials have attracted increasing\ninterest both for fundamental research and industrial applications. Among all\nthese materials, two-dimensional (2D) compounds with honeycomb-like structure\npossess exotic electronic structures. Here, we report a systematic study of TMC\nmonolayer AgTe fabricated by direct depositing Te on the surface of Ag(111) and\nannealing. Few intrinsic defects are observed and studied by scanning tunneling\nmicroscopy, indicating that there are two kinds of AgTe domains and they can\nform gliding twin-boundary. Then, the monolayer AgTe can serve as the template\nfor the following growth of Te film. Meanwhile, some Te atoms are observed in\nthe form of chains on the top of the bottom Te film. Our findings in this work\nmight provide insightful guide for the epitaxial growth of 2D materials for\nstudy of novel physical properties and for future quantum devices.\n"}
+{"abstract": "  In quantum electrodynamics with charged chiral fermions, a background\nelectric field is the source of the chiral anomaly which creates a chirally\nimbalanced state of fermions. This chiral state is realized through the\nproduction of entangled pairs of right-moving fermions and left-moving\nantifermions (or vice versa, depending on the orientation of the electric\nfield). Here we show that the statistical Gibbs entropy associated with these\npairs is equal to the entropy of entanglement between the right-moving\nparticles and left-moving antiparticles. We then derive an asymptotic expansion\nfor the entanglement entropy in terms of the cumulants of the multiplicity\ndistribution of produced particles and explain how to re-sum this asymptotic\nexpansion. Finally, we study the time dependence of the entanglement entropy in\na specific time-dependent pulsed background electric field, the so-called\n\"Sauter pulse\", and illustrate how our re-summation method works in this\nspecific case. We also find that short pulses (such as the ones created by high\nenergy collisions) result in an approximately thermal distribution for the\nproduced particles.\n"}
+{"abstract": "  This article outlines a novel interpretation of quantum theory: the Q-based\ninterpretation. The core idea underlying this interpretation, recently\nsuggested for quantum field theories by Drummond and Reid [2020], is to\ninterpret the phase space function Q -- a transform of the better known Wigner\nfunction -- as a proper probability distribution, roughly analogous to the\nprobability distribution \\rho in classical statistical mechanics.\n  Here I motivate the Q-based interpretation, investigate whether it is\nempirically adequate, and outline some of its key conceptual features. I argue\nthat the Q-based interpretation is attractive in that it promises having no\nmeasurement problem, is conceptually parsimonious and has the potential to\napply elegantly to relativistic and field-theoretic contexts.\n"}
+{"abstract": "  In this paper, we establish a structure theorem for projective klt pairs\n$(X,\\Delta)$ with nef anti-log canonical divisor; specifically, we prove that,\nup to replacing $X$ with a finite quasi-\\'etale cover, $X$ admits a locally\ntrivial rationally connected fibration onto a projective klt variety with\nnumerically trivial canonical divisor. This structure theorem generalizes\nprevious works for smooth projective varieties and reduces several structure\nproblems to the singular Beauville-Bogomolov decomposition for Calabi-Yau\nvarieties. As an application, projective varieties of klt Calabi-Yau type,\nwhich naturally appear as an outcome of the Log Minimal Model Program, are\ndecomposed into building block varieties: rationally connected varieties and\nCalabi-Yau varieties.\n"}
+{"abstract": "  Physical systems that dissipate, mix and develop turbulence also irreversibly\ntransport statistical density. In statistical physics, laws for these processes\nhave a mathematical form and tractability that depends on whether the\ndescription is classical or quantum mechanical. Here, we establish a theory for\ndensity transport in any classical dynamical system that is analogous to the\ndensity matrix formulation of quantum mechanics. Defining states in terms of a\nclassical density matrix leads to generalizations of Liouville's theorem and\nLiouville's equation, establishing an alternative computationally-tractable\nbasis for nonequilibrium statistical mechanics. The formalism is complete with\nclassical commutators and anti-commutators that embed measures of local\ninstability and chaos and are directly related to Poisson brackets when the\ndynamics are Hamiltonian. It also recovers the traditional Liouville equation\nand the Liouville theorem by imposing trace preservation or Hamiltonian\ndynamics. Applying to systems that are driven, transient, dissipative, regular,\nand chaotic, this formalism has the potential for broad applications.\n"}
+{"abstract": "  Hyper-parameters of time series models play an important role in time series\nanalysis. Slight differences in hyper-parameters might lead to very different\nforecast results for a given model, and therefore, selecting good\nhyper-parameter values is indispensable. Most of the existing generic\nhyper-parameter tuning methods, such as Grid Search, Random Search, Bayesian\nOptimal Search, are based on one key component - search, and thus they are\ncomputationally expensive and cannot be applied to fast and scalable\ntime-series hyper-parameter tuning (HPT). We propose a self-supervised learning\nframework for HPT (SSL-HPT), which uses time series features as inputs and\nproduces optimal hyper-parameters. SSL-HPT algorithm is 6-20x faster at getting\nhyper-parameters compared to other search based algorithms while producing\ncomparable accurate forecasting results in various applications.\n"}
+{"abstract": "  Microquasars with high-mass companion stars are promising very-high-energy\n(VHE; 0.1-100 TeV) gamma-ray emitters, but their behaviors above 10 TeV are\npoorly known. Using the High Altitude Water Cherenkov (HAWC) observatory, we\nsearch for excess gamma-ray emission coincident with the positions of known\nhigh-mass microquasars (HMMQs). No significant emission is observed for LS\n5039, Cygnus X-1, Cygnus X-3, and SS 433 with 1,523 days of HAWC data. We set\nthe most stringent limit above 10 TeV obtained to date on each individual\nsource. Under the assumption that HMMQs produce gamma rays via a common\nmechanism, we have performed source-stacking searches, considering two\ndifferent scenarios: I) gamma-ray luminosity is a fraction $\\epsilon_\\gamma$ of\nthe microquasar jet luminosity, and II) very-high-energy gamma rays are\nproduced by relativistic electrons up-scattering the radiation field of the\ncompanion star in a magnetic field $B$. We obtain $\\epsilon_\\gamma < 5.4\\times\n10^{-6}$ for scenario I, which tightly constrains models that suggest\nobservable high-energy neutrino emission by HMMQs. In the case of scenario II,\nthe non-detection of VHE gamma rays yields a strong magnetic field, which\nchallenges synchrotron radiation as the dominant mechanism of the microquasar\nemission between 10 keV and 10 MeV.\n"}
+{"abstract": "  In this reply, we address the comment [arXiv:2105.14908] to our recent paper\n[arXiv:2105.09328], where we argued that the Thakurta metric does not describe\ncosmological black holes. We clarify that the mass growth of Thakurta black\nholes is due to an influx of energy (i.e. accretion), which, by definition, is\nnot a feature of geometry. The conclusions of [arXiv:2105.09328] are\nindependent of the interpretation of this energy flux. We show that the average\nenergy density of primordial Thakurta black holes scales as $a^{-2}$ and\nrequires an unrealistic and fine-tuned energy transfer from a smooth dark\nmatter component to the primordial black hole sector.\n"}
+{"abstract": "  Quantum Optical Coherence Tomography (Q-OCT) uses quantum properties of light\nto provide several advantages over its classical counterpart, OCT: it achieves\na twice better axial resolution with the same spectral bandwidth and it is\nimmune to even orders of dispersion. Since these features are very sought-after\nin OCT imaging, many hardware and software techniques have been created to\nmimic the quantum behaviour of light and achieve these features using\ntraditional OCT systems. The most recent, purely algorithmic scheme - an\nimproved version of Intensity Correlation Spectral Domain OCT named ICA-SD-OCT\nshowed even-order dispersion cancellation and reduction of artefacts. The true\ncapabilities of this method were unfortunately severely undermined, both in\nterms of its relation to Q-OCT and in terms of its main performance parameters.\nIn this work, we provide experimental demonstrations as well as numerical and\nanalytical arguments to show that ICA-SD-OCT is a true classical equivalent of\nQ-OCT, more specifically its Fourier domain version, and therefore it enables a\ntrue two-fold axial resolution improvement. We believe that clarification of\nall the misconceptions about this very promising algorithm will highlight the\ngreat value of this method for OCT and consequently lead to its practical\napplications for resolution- and quality-enhanced OCT imaging.\n"}
+{"abstract": "  Short-read DNA sequencing instruments can yield over 1e+12 bases per run,\ntypically composed of reads 150 bases long. Despite this high throughput, de\nnovo assembly algorithms have difficulty reconstructing contiguous genome\nsequences using short reads due to both repetitive and difficult-to-sequence\nregions in these genomes. Some of the short read assembly challenges are\nmitigated by scaffolding assembled sequences using paired-end reads. However,\nunresolved sequences in these scaffolds appear as \"gaps\". Here, we introduce\nGapPredict, a tool that uses a character-level language model to predict\nunresolved nucleotides in scaffold gaps. We benchmarked GapPredict against the\nstate-of-the-art gap-filling tool Sealer, and observed that the former can fill\n65.6% of the sampled gaps that were left unfilled by the latter, demonstrating\nthe practical utility of deep learning approaches to the gap-filling problem in\ngenome sequence assembly.\n"}
+{"abstract": "  We study the Cauchy problem for a class of third order linear anisotropic\nevolution equations with complex valued lower order terms depending both on\ntime and space variables. Under suitable decay assumptions for $|x| \\to \\infty$\non these coefficients, we prove a well posedness result in Gevrey-type spaces.\n"}
+{"abstract": "  In this work, we have considered the recently proposed new Tsallis Agegraphic\nDark Energy model (NTADE) (Mod. Phys. Lett. A 34, 1950086, 2019) within the\nframework of a flat Friedmann-Robertson-Walker(FRW)\n  Universe by taking various values of the parameter $\\delta$. The NTADE model\nshows the current phase transition of the Universe from\n  decelerated to accelerated phase. The NTADE EoS parameter shows a rich\nbehaviour as it can be quintessence-like or phantom-like depending on the value\nof $\\delta$. For discriminating the NTADE model from $\\Lambda$CDM, we have\nplotted the statefinder parameters $r(z)$, $s(z)$ and $(r, s)$, $(r, q)$ pair.\nThe NTADE model shows distinct evolutionary trajectories of their evolution in\n($ r, s$) and ($ r, q$) plane. An analysis using the snap parameter and\n  the $\\omega_{D}-\\omega_{D}^{'}$ pair dynamical analysis have also been\nperformed.\n"}
+{"abstract": "  Functional magnetic resonance imaging (fMRI) is a non-invasive and in-vivo\nimaging technique essential for measuring brain activity. Functional\nconnectivity is used to study associations between brain regions either at rest\nor while study subjects perform tasks. In this paper, we propose a rigorous\ndefinition of task-evoked functional connectivity at the population level\n(ptFC). Importantly, our proposed ptFC is interpretable in the context of\ntask-fMRI studies. An algorithm for estimating ptFC is provided. We present the\nperformance of the proposed algorithm compared to existing functional\nconnectivity estimation approaches using simulations. Lastly, we apply the\nproposed framework to estimate task-evoked functional connectivity in a\nmotor-task study from the Human Connectome Project. We show that the proposed\nalgorithm identifies associations regions of the brain related to the\nperformance of motor tasks as expected.\n"}
+{"abstract": "  Sazdanovic and Yip defined a categorification of Stanley's chromatic function\ncalled the chromatic symmetric homology. In this paper we prove that (as\nconjectured by Chandler, Sazdanovic, Stella and Yip), if a graph $G$ is\nnon-planar, then its chromatic symmetric homology in bidegree (1,0) contains\n$\\mathbb{Z}_2$-torsion. Our proof follows a recursive argument based on\nKuratowsky's theorem.\n"}
+{"abstract": "  Detecting similar code fragments, usually referred to as code clones, is an\nimportant task. In particular, code clone detection can have significant uses\nin the context of vulnerability discovery, refactoring and plagiarism\ndetection. However, false positives are inevitable and always require manual\nreviews. In this paper, we propose Twin-Finder+, a novel closed-loop approach\nfor pointer-related code clone detection that integrates machine learning and\nsymbolic execution techniques to achieve precision. Twin-Finder+ introduces a\nformal verification mechanism to automate such manual reviews process. Our\nexperimental results show Twin-Finder+ that can remove 91.69% false positives\nin average. We further conduct security analysis for memory safety using\nreal-world applications, Links version 2.14 and libreOffice-6.0.0.1.\nTwin-Finder+ is able to find 6 unreported bugs in Links version 2.14 and one\npublic patched bug in libreOffice-6.0.0.1.\n"}
+{"abstract": "  Lithium niobate on insulator (LNOI), as an emerging and promising optical\nintegration platform, faces shortages of on-chip active devices including\nlasers and amplifiers. Here, we report the fabrication on-chip erbium-doped\nLNOI waveguide amplifiers based on electron beam lithography and inductively\ncoupled plasma reactive ion etching. A net internal gain of ~30 dB/cm in\ncommunication band was achieved in the fabricated waveguide amplifiers under\nthe pump of a 974-nm continuous laser. This work develops new active devices on\nLNOI and will promote the development of LNOI integrated photonics.\n"}
+{"abstract": "  The scientific image integrity area presents a challenging research\nbottleneck, the lack of available datasets to design and evaluate forensic\ntechniques. Its data sensitivity creates a legal hurdle that prevents one to\nrely on real tampered cases to build any sort of accessible forensic benchmark.\nTo mitigate this bottleneck, we present an extendable open-source library that\nreproduces the most common image forgery operations reported by the research\nintegrity community: duplication, retouching, and cleaning. Using this library\nand realistic scientific images, we create a large scientific forgery image\nbenchmark (39,423 images) with an enriched ground-truth. In addition, concerned\nabout the high number of retracted papers due to image duplication, this work\nevaluates the state-of-the-art copy-move detection methods in the proposed\ndataset, using a new metric that asserts consistent match detection between the\nsource and the copied region. The dataset and source-code will be freely\navailable upon acceptance of the paper.\n"}
+{"abstract": "  Visual interpretability of Convolutional Neural Networks (CNNs) has gained\nsignificant popularity because of the great challenges that CNN complexity\nimposes to understanding their inner workings. Although many techniques have\nbeen proposed to visualize class features of CNNs, most of them do not provide\na correspondence between inputs and the extracted features in specific layers.\nThis prevents the discovery of stimuli that each layer responds better to. We\npropose an approach to visually interpret CNN features given a set of images by\ncreating corresponding images that depict the most informative features of a\nspecific layer. Exploring features in this class-agnostic manner allows for a\ngreater focus on the feature extractor of CNNs. Our method uses a\ndual-objective activation maximization and distance minimization loss, without\nrequiring a generator network nor modifications to the original model. This\nlimits the number of FLOPs to that of the original network. We demonstrate the\nvisualization quality on widely-used architectures.\n"}
+{"abstract": "  Exploding granules have drawn renewed interest because of their interaction\nwith the magnetic field. Especially the newly forming downflow lanes developing\nin their centre seem to be eligible candidates for the intensification of\nmagnetic fields. We analyse spectroscopic data from two different instruments\nin order to study the intricate velocity pattern within the newly forming\ndownflow lanes in detail. We aim to examine general properties of a number of\nexploding granules. To gain a better understanding of the formation process of\nthe developing intergranular lane in exploding granules, we study the temporal\nevolution and height dependence of the line-of-sight velocities at their\nformation location. Additionally, we search for evidence that exploding\ngranules act as acoustic sources. We investigated the evolution of several\nexploding granules using data taken with the Interferometric Bidimensional\nSpectrometer and the Imaging Magnetograph eXperiment. Velocities for different\nheights of the solar atmosphere were determined by computing bisectors of the\nFe I 6173.0{\\AA} and the Fe I 5250.2{\\AA} lines. We performed a wavelet\nanalysis to study the intensity and velocity oscillations within and around\nexploding granules. We also compared our findings with predictions of numerical\nsimulations. We found that exploding granules have significantly longer\nlifetimes than regular granules. Exploding granules larger than 3.8 arcsec form\nan independent intergranular lane during their decay phase, while smaller\ngranules usually fade away or disappear into the intergranular area. For all\nexploding granules that form a new intergranular downflow lane, we find a\ntemporal height-dependent shift with respect to the maximum of the downflow\nvelocity. Our suggestion that this results from a complex atmospheric structure\nwithin the newly forming downflow lane is supported by the simulations.\n"}
+{"abstract": "  Designing agents that acquire knowledge autonomously and use it to solve new\ntasks efficiently is an important challenge in reinforcement learning.\nKnowledge acquired during an unsupervised pre-training phase is often\ntransferred by fine-tuning neural network weights once rewards are exposed, as\nis common practice in supervised domains. Given the nature of the reinforcement\nlearning problem, we argue that standard fine-tuning strategies alone are not\nenough for efficient transfer in challenging domains. We introduce Behavior\nTransfer (BT), a technique that leverages pre-trained policies for exploration\nand that is complementary to transferring neural network weights. Our\nexperiments show that, when combined with large-scale pre-training in the\nabsence of rewards, existing intrinsic motivation objectives can lead to the\nemergence of complex behaviors. These pre-trained policies can then be\nleveraged by BT to discover better solutions than without pre-training, and\ncombining BT with standard fine-tuning strategies results in additional\nbenefits. The largest gains are generally observed in domains requiring\nstructured exploration, including settings where the behavior of the\npre-trained policies is misaligned with the downstream task.\n"}
+{"abstract": "  The American Physical Society calls on its members to improve the diversity\nof physics by supporting an inclusive culture that encourages women and Black,\nIndigenous, and people of color to become physicists. In the current\neducational system, it is unlikely for a student to become a physicist if they\ndo not share the same attitudes about what it means to learn and do physics as\nthose held by most professional physicists. Evidence shows college physics\ncourses and degree programs do not support students in developing these\nattitudes. Rather physics education filters out students who do not enter\ncollege physics courses with these attitudes. To better understand the role of\nattitudes in the lack of diversity in physics, we investigated the intersecting\nrelationships between racism and sexism in inequities in student attitudes\nabout learning and doing physics using a critical quantitative framework. The\nanalyses used hierarchical linear models to examine students attitudes as\nmeasured by the Colorado learning attitudes about science survey. The data came\nfrom the LASSO database and included 2170 students in 46 calculus-based\nmechanics courses and 2503 students in 49 algebra-based mechanics courses\ntaught at 18 institutions. Like prior studies, we found that attitudes either\ndid not change or slightly decreased for most groups. Results identified large\ndifferences across intersecting race and gender groups representing educational\ndebts society owes these students. White students, particularly White men in\ncalculus-based courses, tended to have more expert-like attitudes than any\nother group of students. Instruction that addresses society's educational debts\ncan help move physics toward an inclusive culture supportive of diverse\nstudents and professionals.\n"}
+{"abstract": "  Efficient control of a magnetization without an application of the external\nmagnetic fields is the ultimate goal of spintronics. We demonstrate, that in\nmonolayers of $\\text{CrI}_3$, magnetization can be switched all optically, by\napplication of the resonant pulses of circularly polarized light. This happens\nbecause of the efficient coupling of the lattice magnetization with bright\nexcitonic transition. $\\text{CrI}_3$ is thus perspective functional material\nwith high potential for applications in the domains of spintronics and\nultra-fast magnetic memory.\n"}
+{"abstract": "  Phenotype transition takes place in many biological processes such as\ndifferentiation, and understanding how a cell reprograms its global gene\nexpression profile is a problem of rate theories. A cell phenotype transition\naccompanies with switching of expression rates of clusters of genes, analogous\nto domain flipping in an Ising system. Here through analyzing single cell RNA\nsequencing data in the framework of transition path theory, we set to study how\nsuch a genome-wide expression program switching proceeds in three different\ncell transition processes. For each process after reconstructing a Markov\ntransition model in the cell state space, we formed an ensemble of shortest\npaths connecting the initial and final cell states, reconstructed a reaction\ncoordinate describing the transition progression, and inferred the gene\nregulation network (GRN) along the reaction coordinate. In all three processes\nwe observed common pattern that the frustration of gene regulatory network\n(GRN), defined as overall confliction between the regulation received by genes\nand their expression states, first increases then decreases when approaching a\nnew phenotype. The results support a mechanism of concerted silencing of genes\nthat are active in the initial phenotype and activation of genes that are\nactive in the final phenotype.\n"}
+{"abstract": "  With the advent of continuous health monitoring with wearable devices, users\nnow generate their unique streams of continuous data such as minute-level step\ncounts or heartbeats. Summarizing these streams via scalar summaries often\nignores the distributional nature of wearable data and almost unavoidably leads\nto the loss of critical information. We propose to capture the distributional\nnature of wearable data via user-specific quantile functions (QF) and use these\nQFs as predictors in scalar-on-quantile-function-regression (SOQFR). As an\nalternative approach, we also propose to represent QFs via user-specific\nL-moments, robust rank-based analogs of traditional moments, and use L-moments\nas predictors in SOQFR (SOQFR-L). These two approaches provide two mutually\nconsistent interpretations: in terms of quantile levels by SOQFR and in terms\nof L-moments by SOQFR-L. We also demonstrate how to deal with multi-modal\ndistributional data via Joint and Individual Variation Explained (JIVE) using\nL-moments. The proposed methods are illustrated in a study of association of\ndigital gait biomarkers with cognitive function in Alzheimer's disease (AD).\nOur analysis shows that the proposed methods demonstrate higher predictive\nperformance and attain much stronger associations with clinical cognitive\nscales compared to simple distributional summaries.\n"}
+{"abstract": "  We present H-TD2: Hybrid Temporal Difference Learning for Taxi Dispatch, a\nmodel-free, adaptive decision-making algorithm to coordinate a large fleet of\nautomated taxis in a dynamic urban environment to minimize expected customer\nwaiting times. Our scalable algorithm exploits the natural transportation\nnetwork company topology by switching between two behaviors: distributed\ntemporal-difference learning computed locally at each taxi and infrequent\ncentralized Bellman updates computed at the dispatch center. We derive a regret\nbound and design the trigger condition between the two behaviors to explicitly\ncontrol the trade-off between computational complexity and the individual taxi\npolicy's bounded sub-optimality; this advances the state of the art by enabling\ndistributed operation with bounded-suboptimality. Additionally, unlike recent\nreinforcement learning dispatch methods, this policy estimation is adaptive and\nrobust to out-of-training domain events. This result is enabled by a two-step\nmodelling approach: the policy is learned on an agent-agnostic, cell-based\nMarkov Decision Process and individual taxis are coordinated using the learned\npolicy in a distributed game-theoretic task assignment. We validate our\nalgorithm against a receding horizon control baseline in a Gridworld\nenvironment with a simulated customer dataset, where the proposed solution\ndecreases average customer waiting time by 50% over a wide range of parameters.\nWe also validate in a Chicago city environment with real customer requests from\nthe Chicago taxi public dataset where the proposed solution decreases average\ncustomer waiting time by 26% over irregular customer distributions during a\n2016 Major League Baseball World Series game.\n"}
+{"abstract": "  Image virtual try-on replaces the clothes on a person image with a desired\nin-shop clothes image. It is challenging because the person and the in-shop\nclothes are unpaired. Existing methods formulate virtual try-on as either\nin-painting or cycle consistency. Both of these two formulations encourage the\ngeneration networks to reconstruct the input image in a self-supervised manner.\nHowever, existing methods do not differentiate clothing and non-clothing\nregions. A straight-forward generation impedes virtual try-on quality because\nof the heavily coupled image contents. In this paper, we propose a Disentangled\nCycle-consistency Try-On Network (DCTON). The DCTON is able to produce\nhighly-realistic try-on images by disentangling important components of virtual\ntry-on including clothes warping, skin synthesis, and image composition. To\nthis end, DCTON can be naturally trained in a self-supervised manner following\ncycle consistency learning. Extensive experiments on challenging benchmarks\nshow that DCTON outperforms state-of-the-art approaches favorably.\n"}
+{"abstract": "  Model-free off-policy actor-critic methods are an efficient solution to\ncomplex continuous control tasks. However, these algorithms rely on a number of\ndesign tricks and hyperparameters, making their application to new domains\ndifficult and computationally expensive. This paper creates an evolutionary\napproach that automatically tunes these design decisions and eliminates the\nRL-specific hyperparameters from the Soft Actor-Critic algorithm. Our design is\nsample efficient and provides practical advantages over baseline approaches,\nincluding improved exploration, generalization over multiple control\nfrequencies, and a robust ensemble of high-performance policies. Empirically,\nwe show that our agent outperforms well-tuned hyperparameter settings in\npopular benchmarks from the DeepMind Control Suite. We then apply it to less\ncommon control tasks outside of simulated robotics to find high-performance\nsolutions with minimal compute and research effort.\n"}
+{"abstract": "  In this paper, we estimate the high dimensional precision matrix under the\nweak sparsity condition where many entries are nearly zero. We study a\nLasso-type method for high dimensional precision matrix estimation and derive\ngeneral error bounds under the weak sparsity condition. The common\nirrepresentable condition is relaxed and the results are applicable to the weak\nsparse matrix. As applications, we study the precision matrix estimation for\nthe heavy-tailed data, the non-paranormal data, and the matrix data with the\nLasso-type method.\n"}
+{"abstract": "  We construct a model of type theory enjoying parametricity from an arbitrary\none. A type in the new model is a semi-cubical type in the old one,\nillustrating the correspondence between parametricity and cubes.\n  Our construction works not only for parametricity, but also for similar\ninterpretations of type theory and in fact similar interpretations of any\ngeneralized algebraic theory. To be precise we consider a functor forgetting\nunary operations and equations defining them recursively in a generalized\nalgebraic theory. We show that it has a right adjoint.\n  We use techniques from locally presentable category theory, as well as from\nquotient inductive-inductive types.\n"}
+{"abstract": "  The broad range of requirements of Internet of Things applications has lead\nto the development of several dedicated communication technologies, each\ntailored to meet a specific feature set. A solution combining different\nwireless technologies in one device, can overcome the disadvantages of any\nindividual technology. The design of such Multiple Radio Access Technology\nsolutions based on the diverse characteristics of the technologies offers\ninteresting opportunities. In this work we analyze the potential of combining\nLoRaWAN and NB-IoT in a Multi-RAT solution for IoT. To that end we evaluate key\nIoT node requirements in function of payload size and link quality: (1) energy\nefficiency, (2) coverage, (3) payload size, (4) latency performance, (5)\nQuality of Service, and (6) cost efficiency. Our theoretical assessment and\nexperimental validation of these IoT features show the merits of a Multi-RAT\nsolution. Notably, energy consumption in use cases with only sporadic large\npayload requirements, can be improved by a factor of at least 4 with respect to\neither single-mode technologies. Moreover, latency-critical messages can get\ndelivered on time and coverage can be extended elegantly where needed.\n"}
+{"abstract": "  In this article we establish an asymptotic formula for the number of rational\npoints, with bounded denominators, within a given distance to a compact\nsubmanifold $\\mathcal{M}$ of $\\mathbb{R}^M$ with a certain curvature condition.\nOur result generalises earlier work of Huang for hypersurfaces [J.-J. Huang,\nThe density of rational points near hypersurfaces, Duke Math. J. 169 (2020),\n2045--2077.], as our curvature condition reduces to Gaussian curvature being\nbounded away from $0$ when $M - dim \\mathcal{M} = 1$. An interesting feature of\nour result is that the asymptotic formula holds beyond the conjectured range of\nthe distance to $\\mathcal{M}$. Furthermore, we obtain an upper bound for the\nnumber of rational points on $\\mathcal{M}$ with additional power saving to the\nbound in the analogue of Serre's dimension growth conjecture for compact\nsubmanifolds of $\\mathbb{R}^M$ when $M - dim \\mathcal{M} > 1$.\n"}
+{"abstract": "  In recent years, locomotion mechanisms exhibited by vertebrate animals have\nbeen the inspiration for the improvement in the performance of robotic systems.\nThese mechanisms include the adaptability of their locomotion to any change\nregistered in the environment through their biological sensors. In this regard,\nwe aim to replicate such kind of adaptability in legged robots through a\nSpiking Central Pattern Generator. This Spiking Central Pattern Generator\ngenerates different locomotion (rhythmic) patterns which are driven by an\nexternal stimulus, that is, the output of a Force Sensitive Resistor connected\nto the robot to provide feedback. The Spiking Central Pattern Generator\nconsists of a network of five populations of Leaky Integrate-and-Fire neurons\ndesigned with a specific topology in such a way that the rhythmic patterns can\nbe generated and driven by the aforementioned external stimulus. Therefore, the\nlocomotion of the end robotic platform (any-legged robot) can be adapted to the\nterrain by using any sensor as input. The Spiking Central Pattern Generator\nwith adaptive learning has been numerically validated at software and hardware\nlevel, using the Brian 2 simulator and the SpiNNaker neuromorphic platform for\nthe latest. In particular, our experiments clearly show an adaptation in the\noscillation frequencies between the spikes produced in the populations of the\nSpiking Central Pattern Generator while the input stimulus varies. To validate\nthe robustness and adaptability of the Spiking Central Pattern Generator, we\nhave performed several tests by variating the output of the sensor. These\nexperiments were carried out in Brian 2 and SpiNNaker; both implementations\nshowed a similar behavior with a Pearson correlation coefficient of 0.905.\n"}
+{"abstract": "  The content of two additional Ward identities exhibited by the $U(1)$ Higgs\nmodel is exploited. These novel Ward identities can be derived only when a pair\nof local composite operators providing a gauge invariant setup for the Higgs\nparticle and the massive vector boson is introduced in the theory from the\nbeginning. Among the results obtained from the above mentioned Ward identities,\nwe underline a new exact relationship between the stationary condition for the\nvacuum energy, the vanishing of the tadpoles and the vacuum expectation value\nof the gauge invariant scalar operator. We also present a characterization of\nthe two-point correlation function of the composite operator corresponding to\nthe vector boson in terms of the two-point function of the elementary gauge\nfields. Finally, a discussion on the connection between the cartesian and the\npolar parametrization of the complex scalar field is presented in the light of\nthe Equivalence Theorem. The latter can in the current case be understood in\nthe language of a constrained cohomology, which also allows to rewrite the\naction in terms of the aforementioned gauge invariant operators. We also\ncomment on the diminished role of the global $U(1)$ symmetry and its breaking.\n"}
+{"abstract": "  A quantum stabilizer code over GF$(q)$ corresponds to a classical additive\ncode over GF$(q^2)$ that is self-orthogonal with respect to a symplectic inner\nproduct. We study the decoding of quantum low-density parity-check (LDPC) codes\nover binary finite fields GF$(q=2^l)$ by the sum-product algorithm, also known\nas belief propagation (BP). Conventionally, a message in a nonbinary BP for\nquantum codes over GF$(2^l)$ represents a probability vector over GF$(2^{2l})$,\ninducing high decoding complexity. In this paper, we explore the property of\nthe symplectic inner product and show that scalar messages suffice for BP\ndecoding of nonbinary quantum codes, rather than vector messages necessary for\nthe conventional BP. Consequently, we propose a BP decoding algorithm for\nquantum codes over GF$(2^l)$ by passing scalar messages so that it has low\ncomputation complexity. The algorithm is specified in log domain by using\nlog-likelihood ratios (LLRs) of the channel statistics to have a low\nimplementation cost. Moreover, techniques such as message normalization or\noffset can be naturally applied in this algorithm to mitigate the effects of\nshort cycles to improve BP performance. This is important for nonbinary quantum\ncodes since they may have more short cycles compared to binary quantum codes.\nSeveral computer simulations are provided to demonstrate these advantages. The\nscalar-based strategy can also be used to improve the BP decoding of classical\nlinear codes over GF$(2^l)$ with many short cycles.\n"}
+{"abstract": "  Collective excitations in topologically non-trivial systems have attracted\nconsiderable attention in recent years. Here we study plasmons in the\nSu-Schrieffer-Heeger model whose low-energy electronic band is only partially\nfilled, such that the system is metallic. Using the random phase approximation,\nwe calculate the intra- and inter-band polarization functions and determine the\nbulk plasmonic dispersion from the dielectric function within the random phase\napproximation. We find that the sub-lattice basis states strongly affect the\npolarization functions and therefore control the system's plasmonic\nexcitations. By varying the real-space separation of these local orbitals, one\ncan thus selectively enhance or suppress the plasmonic energies via a tunable\ntrade-off between intra-band and inter-band screening processes. Specifically,\nthis mechanism can be used to stabilize undamped high energy plasmons that have\nalready been reported in related models. We propose scenarios on how to control\nand observe these effects in experiments.\n"}
+{"abstract": "  Totally symmetric sets are a recently introduced tool for studying\nhomomorphisms between groups. In this paper, we give full classifications of\ntotally symmetric sets in certain families of groups and bound their sizes in\nothers. As a consequence, we derive restrictions on possible homomorphisms\nbetween these groups. One sample application of our results is that any\nhomomorphism of a braid group to a direct product of solvable groups must have\ncyclic image.\n"}
+{"abstract": "  We study in this work the 2D dynamics of an experimental system of\ndisk-shaped rotors, fluidized by a turbulent upflow. Our experiments show a\ncomplex chirality behavior. In particular, as average kinetic energy increases,\nthe system evolves from positive chirality (one vortex rotating in the same\ndirection as particles spin), to complex chirality (several vortexes of both\nsigns) and negative chirality (one vortex in opposite sense to particle spin).\nWe find that these transitions are determined by the combined action of heat\ndissipation at the boundaries and statistical correlations between particles\nspin and translational velocities. Moreover, we show that the decay to negative\nchirality is produced as a consequence of particles spin syncronization.\nTherefore, we elucidate a control mechanism of chirality, via the adjustment of\nspin in a system of active rotors.\n"}
+{"abstract": "  A polynomial threshold function (PTF) $f:\\mathbb{R}^n \\rightarrow \\mathbb{R}$\nis a function of the form $f(x) = \\mathsf{sign}(p(x))$ where $p$ is a\npolynomial of degree at most $d$. PTFs are a classical and well-studied\ncomplexity class with applications across complexity theory, learning theory,\napproximation theory, quantum complexity and more. We address the question of\ndesigning pseudorandom generators (PRG) for polynomial threshold functions\n(PTFs) in the gaussian space: design a PRG that takes a seed of few bits of\nrandomness and outputs a $n$-dimensional vector whose distribution is\nindistinguishable from a standard multivariate gaussian by a degree $d$ PTF.\n  Our main result is a PRG that takes a seed of $d^{O(1)}\\log ( n /\n\\varepsilon)\\log(1/\\varepsilon)/\\varepsilon^2$ random bits with output that\ncannot be distinguished from $n$-dimensional gaussian distribution with\nadvantage better than $\\varepsilon$ by degree $d$ PTFs. The best previous\ngenerator due to O'Donnell, Servedio, and Tan (STOC'20) had a quasi-polynomial\ndependence (i.e., seedlength of $d^{O(\\log d)}$) in the degree $d$. Along the\nway we prove a few nearly-tight structural properties of restrictions of PTFs\nthat may be of independent interest.\n"}
+{"abstract": "  Bound-states-in-the-continuum (BIC)is a wave-mechanical concept that\ngenerates resonances with vanishing spectral linewidths. It has many practical\napplications in Optics, such as narrow-band filters, mirror-less lasing, and\nnonlinear harmonic generation. As true BIC optical modes non-radiative and\nconfined to the near field of nanostructures, they cannot be excited using\npropagating light. As a result, their direct experimental observation has been\nelusive. Rather than using light, we demonstrate probing BIC modes on arrays of\nsilicon nanoantennas using a focused beam of electrons in a tranmission\nelectron microscope. By combining cathodoluminescence (CL) and monochromated\nelectron energy-loss spectroscopy (EELS) with controlled nanofabrication, we\nprovide direct experimental evidence of \"true\" BIC modes, and demonstrate a BIC\nmode in the visible spectrum at 720 nm. The ability to observe and quantify\nthese guided resonances with a spatial precision more than two orders of\nmagnitude higher than previous far-field measurements allows the probing of\nindividual elements in the nano-antenna arrays. The high-resolution\nexperimental results are supported by numerical simulations as well as\nmultipolar decomposition analysis, allowing us to demonstrate that the coherent\ninteraction length of the quasi-BIC resonance requires at least 6 neighboring\nantenna elements, achieving over 60 times higher emissivity than for\nunpatterned silicon.\n"}
+{"abstract": "  Let $G$ be a finite abelian group viewed a $\\mathbb{Z}$-module and let\n$\\mathcal{G} = (V, E)$ be a simple graph. In this paper, we consider a graph\n$\\Gamma(G)$ called as a \\textit{group-annihilator} graph. The vertices of\n$\\Gamma(G)$ are all elements of $G$ and two distinct vertices $x$ and $y$ are\nadjacent in $\\Gamma(G)$ if and only if $[x : G][y : G]G = \\{0\\}$, where $x,\ny\\in G$ and $[x : G] = \\{r\\in\\mathbb{Z} : rG \\subseteq \\mathbb{Z}x\\}$ is an\nideal of a ring $\\mathbb{Z}$. We discuss in detail the graph structure realised\nby the group $G$. Moreover, we study the creation sequence, hyperenergeticity\nand hypoenergeticity of group-annihilator graphs. Finally, we conclude the\npaper with a discussion on Laplacian eigen values of the group-annhilator\ngraph. We show that the Laplacian eigen values are representatives of orbits of\nthe group action: $Aut(\\Gamma(G)) \\times G \\rightarrow G$.\n"}
+{"abstract": "  In this paper, we outline an approach for automatic generation of challenging\nroad networks for virtual testing of an automated lane keep system. Based on a\nset of control points, we construct a parametric curve that represents a road\nnetwork, which defines the dynamic driving task an automated lane keep system\nequipped vehicle has to perform. Changing control points has global influence\non the resulting road geometry. Our approach uses search to find a set of\ncontrol points that results in a challenging road geometry, eventually forcing\nthe vehicle to leave the intended path. We evaluated our approach in three\ndifferent search-configurations regarding test efficiency and practical\napplicability for automatic virtual testing of an automated lane keep system.\n"}
+{"abstract": "  LHAASO detected 12 gamma-ray sources above 100 TeV which are the possible\norigins of Galactic cosmic-rays. We summarize the neutrino measurements by\nIceCube and ANTARES in the vicinity of LHAASO sources to constrain the\ncontribution of hadronic gamma-rays in these sources. We find that the current\nobservations constrain that the hadronic gamma-rays contribute no more than\n~60% of the gamma-rays from Crab Nebula. Gamma-rays from two LHAASO sources,\nLHAASO J1825-1326 and LHAASO J1907+0626, are dominated by leptonic components\nup to ~200 TeV, under the hypotheses in the analysis by IceCube. The\nuncertainties of the constraint on the hadronic gamma-ray emission are\ndiscussed. We also constrain the total 100 TeV gamma-ray emission from TeV PWNe\nrelying on the remarkable sensitivity of LHAASO at that energies.\n"}
+{"abstract": "  CAV platooning technology has received considerable attention in the past few\nyears, driven by the next generation smart transportation systems. Unlike most\nof the existing platooning methods that focus on linear vehicle dynamics of\nCAVs, this paper considers nonlinear vehicle dynamics and develops fully\ndistributed optimization based CAV platooning control schemes via the model\npredictive control (MPC) approach for a possibly heterogeneous CAV platoon. The\nnonlinear vehicle dynamics leads to several major difficulties in distributed\nalgorithm development and control analysis and design. Specifically, the\nunderlying MPC optimization problem is nonconvex and densely coupled. Further,\nthe closed loop dynamics becomes a time-varying nonlinear system subject to\nexternal perturbations, making closed loop stability analysis rather\ncomplicated. To overcome these difficulties, we formulate the underlying MPC\noptimization problem as a locally coupled, albeit nonconvex, optimization\nproblem and develop a sequential convex programming based fully distributed\nscheme for a general MPC horizon. Such a scheme can be effectively implemented\nfor real-time computing using operator splitting methods. To analyze the closed\nloop stability, we apply various tools from global implicit function theorems,\nstability of linear time-varying systems, and Lyapunov theory for\ninput-to-state stability to show that the closed loop system is locally\ninput-to-state stable uniformly in all small coefficients pertaining to the\nnonlinear dynamics. Numerical tests on homogeneous and heterogeneous CAV\nplatoons demonstrate the effectiveness of the proposed fully distributed\nschemes and CAV platooning control.\n"}
+{"abstract": "  The Continual Learning (CL) problem involves performing well on a sequence of\ntasks under limited compute. Current algorithms in the domain are either slow,\noffline or sensitive to hyper-parameters. La-MAML, an optimization-based\nmeta-learning algorithm claims to be better than other replay-based,\nprior-based and meta-learning based approaches. According to the MER paper [1],\nmetrics to measure performance in the continual learning arena are Retained\nAccuracy (RA) and Backward Transfer-Interference (BTI). La-MAML claims to\nperform better in these values when compared to the SOTA in the domain. This is\nthe main claim of the paper, which we shall be verifying in this report.\n"}
+{"abstract": "  There is a big difference in the tone of color of skin between dark and light\nskinned people. Despite this fact, most face recognition tasks almost all\nclassical state-of-the-art models are trained on datasets containing an\noverwhelming majority of light skinned face images. It is tedious to collect a\nhuge amount of data for dark skinned faces and train a model from scratch. In\nthis paper, we apply transfer learning on VGGFace to check how it works on\nrecognising dark skinned mainly Ethiopian faces. The dataset is of low quality\nand low resource. Our experimental results show above 95\\% accuracy which\nindicates that transfer learning in such settings works.\n"}
+{"abstract": "  In this paper we begin mapping out the space of rank-2 $\\mathcal{N}=2$\nsuperconformal field theories (SCFTs) in four dimensions. This represents an\nideal set of theories which can be potentially classified using purely quantum\nfield-theoretic tools, thus providing a precious case study to probe the\ncompleteness of the current understanding of SCFTs, primarily derived from\nstring theory constructions. Here, we collect and systematize a large amount of\nfield theoretic data characterizing each theory. We also provide a detailed\ndescription of each case and determine the theories' Coulomb, Higgs and Mixed\nbranch stratification. The theories naturally organize themselves into series\nconnected by RG flows but which have gaps suggesting that our current\nunderstanding is not complete.\n"}
+{"abstract": "  Let the group $G$ act transitively on the finite set $\\Omega$. We show that\nrandom Schreier graphs on $O(\\log|\\Omega|)$ elements are expanders with high\nprobability, magnifying a famous theorem of Alon and Roichman. On the other\nside, depending on the particular action of $G$ on $\\Omega$, we give a lower\nbound on the number of elements which are necessary to provide expansion. We\napply this method to estimate the spectral gap in the case where $G$ is\nnilpotent.\n"}
+{"abstract": "  We present lambda layers -- an alternative framework to self-attention -- for\ncapturing long-range interactions between an input and structured contextual\ninformation (e.g. a pixel surrounded by other pixels). Lambda layers capture\nsuch interactions by transforming available contexts into linear functions,\ntermed lambdas, and applying these linear functions to each input separately.\nSimilar to linear attention, lambda layers bypass expensive attention maps, but\nin contrast, they model both content and position-based interactions which\nenables their application to large structured inputs such as images. The\nresulting neural network architectures, LambdaNetworks, significantly\noutperform their convolutional and attentional counterparts on ImageNet\nclassification, COCO object detection and COCO instance segmentation, while\nbeing more computationally efficient. Additionally, we design LambdaResNets, a\nfamily of hybrid architectures across different scales, that considerably\nimproves the speed-accuracy tradeoff of image classification models.\nLambdaResNets reach excellent accuracies on ImageNet while being 3.2 - 4.4x\nfaster than the popular EfficientNets on modern machine learning accelerators.\nWhen training with an additional 130M pseudo-labeled images, LambdaResNets\nachieve up to a 9.5x speed-up over the corresponding EfficientNet checkpoints.\n"}
+{"abstract": "  Beamforming technology is widely used in millimeter wave systems to combat\npath losses, and beamformers are usually selected from a predefined codebook.\nUnfortunately, the traditional codebook design neglects the beam squint effect,\nand this will cause severe performance degradation when the bandwidth is large.\nIn this letter, we consider that a codebook with fixed size is adopted in the\nwideband beamforming system. First, we analyze how beam squint affects system\nperformance when all beams have the same width. The expression of average\nspectrum efficiency is derived based on the ideal beam pattern. Next, we\nformulate the optimization problem to design the optimal codebook. Simulation\nresults demonstrate that the proposed codebook deals with beam squint by\nspreading the beam coverage and significantly mitigates the performance\ndegradation.\n"}
+{"abstract": "  While the predictive performance of modern statistical dependency parsers\nrelies heavily on the availability of expensive expert-annotated treebank data,\nnot all annotations contribute equally to the training of the parsers. In this\npaper, we attempt to reduce the number of labeled examples needed to train a\nstrong dependency parser using batch active learning (AL). In particular, we\ninvestigate whether enforcing diversity in the sampled batches, using\ndeterminantal point processes (DPPs), can improve over their diversity-agnostic\ncounterparts. Simulation experiments on an English newswire corpus show that\nselecting diverse batches with DPPs is superior to strong selection strategies\nthat do not enforce batch diversity, especially during the initial stages of\nthe learning process. Additionally, our diversityaware strategy is robust under\na corpus duplication setting, where diversity-agnostic sampling strategies\nexhibit significant degradation.\n"}
+{"abstract": "  We present BIEBER (Byte-IdEntical Binary parsER), the first system to model\nand regenerate a full working parser from instrumented program executions. To\nachieve this, BIEBER exploits the regularity (e.g., header fields and\narray-like data structures) that is commonly found in file formats. Key\ngeneralization steps derive strided loops that parse input file data and\nrewrite concrete loop bounds with expressions over input file header bytes.\nThese steps enable BIEBER to generalize parses of specific input files to\nobtain parsers that operate over input files of arbitrary size. BIEBER also\nincrementally and efficiently infers a decision tree that reads file header\nbytes to route input files of different types to inferred parsers of the\nappropriate type. The inferred parsers and decision tree are expressed in an\nIR; separate backends (C and Perl in our prototype) can translate the IR into\nthe same language as the original program (for a safer drop-in replacement), or\nautomatically port to a different language. An empirical evaluation shows that\nBIEBER can successfully regenerate parsers for six file formats (waveform audio\n[1654 files], MT76x0 .BIN firmware containers [5 files], OS/2 1.x bitmap images\n[9 files], Windows 3.x bitmaps [9971 files], Windows 95/NT4 bitmaps [133\nfiles], and Windows 98/2000 bitmaps [859 files]), correctly parsing 100% (>=\n99.98% when using standard held-out cross-validation) of the corresponding\ncorpora. The regenerated parsers contain automatically inserted safety checks\nthat eliminate common classes of errors such as memory errors. We find that\nBIEBER can help reverse-engineer file formats, because it automatically\nidentifies predicates for the decision tree that relate to key semantics of the\nfile format. We also discuss how BIEBER helped us detect and fix two new bugs\nin stb_image as well as independently rediscover and fix a known bug.\n"}
+{"abstract": "  Irreversibility is usually captured by a comparison between the process that\nhappens and a corresponding \"reverse process\". In the last decades, this\ncomparison has been extensively studied through fluctuation relations. Here we\nrevisit fluctuation relations from the standpoint, suggested decades ago by\nWatanabe, that the comparison should involve the prediction and the\nretrodiction on the unique process, rather than two processes. We identify a\nnecessary and sufficient condition for a retrodictive reading of a fluctuation\nrelation. The retrodictive narrative also brings to the fore the possibility of\nderiving fluctuation relations based on various statistical divergences, and\nclarifies some of the traditional assumptions as arising from the choice of a\nreference prior.\n"}
+{"abstract": "  Let $GP(q,d)$ be the $d$-Paley graph defined on the finite field\n$\\mathbb{F}_q$. It is notoriously difficult to improve the trivial upper bound\n$\\sqrt{q}$ on the clique number of $GP(q,d)$. In this paper, we investigate the\nconnection between Gauss sums over a finite field and the maximum cliques of\ntheir corresponding generalized Paley graphs. We show that the trivial upper\nbound on the clique number of $GP(q,d)$ is tight if and only if $d \\mid\n(\\sqrt{q}+1)$, which strengthens the previous related results by\nBroere-D\\\"oman-Ridley and Schneider-Silva. We also obtain a new simple proof of\nStickelberger's theorem on evaluating semi-primitive Gauss sums.\n"}
+{"abstract": "  The nearest prototype classification is a less computationally intensive\nreplacement for the $k$-NN method, especially when large datasets are\nconsidered. In metric spaces, centroids are often used as prototypes to\nrepresent whole clusters. The selection of cluster prototypes in non-metric\nspaces is more challenging as the idea of computing centroids is not directly\napplicable.\n  In this paper, we present CRS, a novel method for selecting a small yet\nrepresentative subset of objects as a cluster prototype. Memory and\ncomputationally efficient selection of representatives is enabled by leveraging\nthe similarity graph representation of each cluster created by the NN-Descent\nalgorithm. CRS can be used in an arbitrary metric or non-metric space because\nof the graph-based approach, which requires only a pairwise similarity measure.\nAs we demonstrate in the experimental evaluation, our method outperforms the\nstate of the art techniques on multiple datasets from different domains.\n"}
+{"abstract": "  We report on the results of multi-wavelength follow-up observations with\nGemini, VLA, and ATCA, to search for a host galaxy and any persistent radio\nemission associated with FRB 180309. This FRB is among the most luminous FRB\ndetections to date, with a luminosity of $> 8.7\\times 10^{32}$ erg Hz$^{-1}$ at\nthe dispersion-based redshift upper limit of 0.32. We used the\nhigh-significance detection of FRB 180309 with the Parkes Telescope and a beam\nmodel of the Parkes Multibeam Receiver to improve the localization of the FRB\nto a region spanning approximately $\\sim2'\\times2'$. We aimed to seek bright\ngalaxies within this region to determine the strongest candidates as the\noriginator of this highly luminous FRB. We identified optical sources within\nthe localization region above our r-band magnitude limit of 24.27, fourteen of\nwhich have photometric redshifts whose fitted mean is consistent with the\nredshift upper limit ($z < 0.32$) of our FRB. Two of these galaxies are\ncoincident with marginally detected \"persistent\" radio sources of flux density\n24.3$\\mu$Jy beam$^{-1}$ and 22.1$\\mu$Jy beam$^{-1}$ respectively. Our\nredshift-dependent limit on the luminosity of any associated persistent radio\nsource is comparable to the luminosity limits for other localized FRBs. We\nanalyze several properties of the candidate hosts we identified, including\nchance association probability, redshift, and presence of radio emission,\nhowever it remains possible that any of these galaxies could be the host of\nthis FRB. Follow-up spectroscopy on these objects to explore their H$\\alpha$\nemission and ionization contents, as well as to obtain more precisely measured\nredshifts, may be able to isolate a single host for this luminous FRB.\n"}
+{"abstract": "  Spin waves are promising chargeless information carriers for the future,\nenergetically efficient beyond-CMOS systems. Among many advantages there are\nthe ease of achieving nonlinearity, the variety of possible interactions, and\nexcitation types. Although the rapidly developing magnonic research has already\nyielded impressive realizations, multi-mode nonlinear effects, particularly\npropagating waves and their nanoscale realizations, are still an open research\nproblem. We study theoretically the dynamic interactions of the spin waves\nconfined to the edge of a thin ferromagnetic film with the spin-wave beam\nincident at this edge. We found the inelastically scattered spin-wave beams at\nfrequencies increased and decreased by the frequency of the edge spin-wave\nrelative to the specularly reflected beam. We observed a strong dependence of\nthe angular shift of the inelastic scattered spin-wave beam on the edge-mode\nfrequency, which allowed us to propose a magnonic demultiplexing of the signal\nencoded in spin waves propagating along the edge. Since dynamic magnetostatic\ninteractions, which are ubiquitous in the spin-wave dynamics, are decisive in\nthis process, this indicates the possibility of implementing the presented\neffects, also in other configurations and their use in magnonic systems.\n"}
+{"abstract": "  Spectral clustering is a popular algorithm that clusters points using the\neigenvalues and eigenvectors of Laplacian matrices derived from the data. For\nyears, spectral clustering has been working mysteriously. This paper explains\nspectral clustering by dividing it into two categories based on whether the\ngraph Laplacian is fully connected or not. For a fully connected graph, this\npaper demonstrates the dimension reduction part by offering an objective\nfunction: the covariance between the original data points' similarities and the\nmapped data points' similarities. For a multi-connected graph, this paper\nproves that with a proper $k$, the first $k$ eigenvectors are the indicators of\nthe connected components. This paper also proves there is an equivalence\nbetween spectral embedding and PCA.\n"}
+{"abstract": "  The new two-dimensional (2D) kagome superconductor CsV$_3$Sb$_5$ has\nattracted much recent attention due to the coexistence of superconductivity,\ncharge order, topology and kagome physics. A key issue in this field is to\nunveil the unique reconstructed electronic structure, which successfully\naccommodates different orders and interactions to form a fertile ground for\nemergent phenomena. Here, we report angle-resolved photoemission spectroscopy\n(ARPES) evidence for two distinct band reconstructions in CsV$_3$Sb$_5$. The\nfirst one is characterized by the appearance of new electron energy band at low\ntemperature. The new band is theoretically reproduced when the three\ndimensionality of the charge order is considered for a band-folding along the\nout-of-plane direction. The second reconstruction is identified as a surface\ninduced orbital-selective shift of the electron energy band. Our results\nprovide the first evidence for the three dimensionality of the charge order in\nsingle-particle spectral function, highlighting the importance of long-range\nout-of-plane electronic correlations in this layered kagome superconductor.\nThey also point to the feasibility of orbital-selective control of the band\nstructure via surface modification, which would open a new avenue for\nmanipulating exotic phenomena in this system, including superconductivity.\n"}
+{"abstract": "  Transformer networks are effective at modeling long-range contextual\ninformation and have recently demonstrated exemplary performance in the natural\nlanguage processing domain. Conventionally, the temporal action proposal\ngeneration (TAPG) task is divided into two main sub-tasks: boundary prediction\nand proposal confidence prediction, which rely on the frame-level dependencies\nand proposal-level relationships separately. To capture the dependencies at\ndifferent levels of granularity, this paper intuitively presents a unified\ntemporal action proposal generation framework with original Transformers,\ncalled TAPG Transformer, which consists of a Boundary Transformer and a\nProposal Transformer. Specifically, the Boundary Transformer captures long-term\ntemporal dependencies to predict precise boundary information and the Proposal\nTransformer learns the rich inter-proposal relationships for reliable\nconfidence evaluation. Extensive experiments are conducted on two popular\nbenchmarks: ActivityNet-1.3 and THUMOS14, and the results demonstrate that TAPG\nTransformer outperforms state-of-the-art methods. Equipped with the existing\naction classifier, our method achieves remarkable performance on the temporal\naction localization task. Codes and models will be available.\n"}
+{"abstract": "  Infrastructure systems, such as power, transportation, telecommunication, and\nwater systems, are composed of multiple components which are interconnected and\ninterdependent to produce and distribute essential goods and services. So, the\nrobustness of infrastructure systems to resist disturbances is crucial for the\ndurable performance of modern societies. Multilayer networks have been used to\nmodel the multiplicity and interrelation of infrastructure systems and\npercolation theory is the most common approach to quantify the robustness of\nsuch networks. This survey systematically reviews literature published between\n2010 and 2021, on applying percolation theory to assess the robustness of\ninfrastructure systems modeled as multilayer networks. We discussed all network\nproperties applied to build infrastructure models. Among all properties,\ninterdependency strength and communities were the most common network property\nwhilst very few studies considered realistic attributes of infrastructure\nsystems such as directed links and feedback conditions. The review highlights\nthat the properties produced approximately similar model outcomes, in terms of\ndetecting improvement or deterioration in the robustness of multilayer\ninfrastructure networks, with few exceptions. Most of the studies focused on\nhighly simpliffied synthetic models rather than models built by real datasets.\nThus, this review suggests analyzing multiple properties in a single model to\nassess whether they boost or weaken the impact of each other. In addition, the\neffect size of different properties on the robustness of infrastructure systems\nshould be quantiffied. It can support the design and planning of robust\ninfrastructure systems by arranging and prioritizing the most effective\nproperties.\n"}
+{"abstract": "  We study the energy-density dynamics at finite momentum of the\ntwo-dimensional Kitaev spin-model on the honeycomb lattice. Due to\nfractionalization of magnetic moments, the energy relaxation occurs through\nmobile Majorana matter, coupled to a static $\\mathbb{Z}_2$ gauge field. At\nfinite temperatures, the $\\mathbb{Z}_2$ flux excitations act as an emergent\ndisorder, which strongly affects the energy dynamics. We show that sufficiently\nfar above the flux proliferation temperature, but not yet in the classical\nregime, gauge disorder modifies the coherent low-temperature energy-density\ndynamics into a form which is almost diffusive, with hydrodynamic momentum\nscaling of a diffusion-kernel, which however remains retarded, primarily due to\nthe presence of two distinct relaxation channels of particle-hole and\nparticle-particle nature. Relations to thermal conductivity are clarified. Our\nanalysis is based on complementary calculations in the low-temperature\nhomogeneous gauge and a mean-field treatment of thermal gauge fluctuations,\nvalid at intermediate and high temperatures.\n"}
+{"abstract": "  The electronic behaviour in graphene under arbitrary uniaxial deformations,\nsuch as foldings or flexural fields, is studied by including in the Dirac\nequation pseudo-electromagnetic fields. General foldings are thus studied by\nshowing that uniaxial deformations can be considered as pseudo-magnetic fields\nin the Coulomb gauge norm. This allows to give an expression for the Fermi\n(zero) energy modes wavefunctions. For random deformations, contact is made\nwith previous works on the quantum Hall effect under random magnetic fields,\nshowing that the density of states has a power law behaviour and that the zero\nenergy modes wavefunctions are multifractal. This hints of an unusual electron\nvelocity distribution. Also, it is shown that a strong Aharonov-Bohm\npseudo-effect is produced. For more general non-uniaxial general flexural\nstrain, it is not possible to use the Coulomb gauge. The results presented here\nallow to tailor-made graphene uniaxial deformations to achieve specific\nwave-functions and electronic properties.\n"}
+{"abstract": "  The direct linearisation framework is presented for the two-dimensional Toda\nequations associated with the infinite-dimensional Lie algebras $A_\\infty$,\n$B_\\infty$ and $C_\\infty$, as well as the Kac--Moody algebras $A_{r}^{(1)}$,\n$A_{2r}^{(2)}$, $C_{r}^{(1)}$ and $D_{r+1}^{(2)}$ for arbitrary integers\n$r\\in\\mathbb{Z}^+$, from the aspect of a set of linear integral equations in a\ncertain form. Such a scheme not only provides a unified perspective to\nunderstand the underlying integrability structure, but also induces the direct\nlinearising type solution potentially leading to the universal solution space,\nfor each class of the two-dimensional Toda system. As particular applications\nof this framework to the two-dimensional Toda lattices, we rediscover the Lax\npairs and the adjoint Lax pairs and simultaneously construct the generalised\nCauchy matrix solutions.\n"}
+{"abstract": "  Mobile contact tracing apps are -- in principle -- a perfect aid to condemn\nthe human-to-human spread of an infectious disease such as COVID-19 due to the\nwide use of smartphones worldwide. Yet, the unknown accuracy of contact\nestimation by wireless technologies hinders the broader use. We address this\nchallenge by conducting a measurement study with a custom testbed to show the\ncapabilities and limitations of Bluetooth Low Energy (BLE) in different\nscenarios. Distance estimation is based on interpreting the signal pathloss\nwith a basic linear and a logarithmic model. Further, we compare our results\nwith accurate ultra-wideband (UWB) distance measurements. While the results\nindicate that distance estimation by BLE is not accurate enough, a contact\ndetector can detect contacts below 2.5 m with a true positive rate of 0.65 for\nthe logarithmic and of 0.54 for the linear model. Further, the measurements\nreveal that multi-path signal propagation reduces the effect of body shielding\nand thus increases detection accuracy in indoor scenarios.\n"}
+{"abstract": "  The interstellar turbulence is magnetized and thus anisotropic. The\nanisotropy of turbulent magnetic fields and velocities is imprinted in the\nrelated observables, rotation measures (RMs), and velocity centroids (VCs).\nThis anisotropy provides valuable information on both the direction and\nstrength of the magnetic field. However, its measurement is difficult\nespecially in highly supersonic turbulence in cold interstellar phases due to\nthe distortions by isotropic density fluctuations. By using 3D simulations of\nsupersonic and sub-Alfv\\'enic magnetohydrodynamic(MHD) turbulence, we find that\nthe problem can be alleviated when we selectively sample the volume-filling\nlow-density regions in supersonic MHD turbulence. Our results show that in\nthese low-density regions, the anisotropy of RM and VC fluctuations depends on\nthe Alfv\\'enic Mach number as $\\rm M_A^{-4/3}$. This anisotropy-$\\rm M_A$\nrelation is theoretically expected for sub-Alfv 'enic MHD turbulence and\nconfirmed by our synthetic observations of $^{12}$CO emission. It provides a\nnew method for measuring the plane-of-the-sky magnetic fields in cold\ninterstellar phases.\n"}
+{"abstract": "  Robotic nursing aid is one of the heavily researched areas in robotics\nnowadays. Several robotic assistants exist that only focus on a specific\nfunction related to nurses assistance or functions related to patient aid.\nThere is a need for a unified system that not only performs tasks that would\nassist nurses and reduce their burden but also perform tasks that help a\npatient. In recent times, due to the COVID-19 pandemic, there is also an\nincrease in the need for robotic assistants that have teleoperation\ncapabilities to provide better protection against the virus spread. To address\nthese requirements, we propose a novel Multi-purpose Intelligent Nurse Aid\n(MINA) robotic system that is capable of providing walking assistance to the\npatients and perform teleoperation tasks with an easy-to-use and intuitive\nGraphical User Interface (GUI). This paper also presents preliminary results\nfrom the walking assistant task that improves upon the current state-of-the-art\nmethods and shows the developed GUI for teleoperation.\n"}
+{"abstract": "  In this paper, we propose a direct Eulerian generalized Riemann problem (GRP)\nscheme for a blood flow model in arteries. It is an extension of the Eulerian\nGRP scheme, which is developed by Ben-Artzi, et. al. in J. Comput. Phys.,\n218(2006). By using the Riemann invariants, we diagonalize the blood flow\nsystem into a weakly coupled system, which is used to resolve rarefaction wave.\nWe also use Rankine-Hugoniot condition to resolve the local GRP formulation. We\npay special attention to the acoustic case as well as the sonic case. The\nextension to the two dimensional case is carefully obtained by using the\ndimensional splitting technique. We test that the derived GRP scheme is second\norder accuracy.\n"}
+{"abstract": "  We study the Seebeck effect in the three-dimensional Dirac electron system\nbased on the linear response theory with Luttinger's gravitational potential.\nThe Seebeck coefficient $S$ is defined by $S = L_{12} / L_{11} T$, where $T$ is\nthe temperature, and $L_{11}$ and $L_{12}$ are the longitudinal response\ncoefficients of the charge current to the electric field and to the temperature\ngradient, respectively; $L_{11}$ is the electric conductivity and $L_{12}$ is\nthe thermo-electric conductivity. We consider randomly-distributed impurity\npotentials as the source of the momentum relaxation of electrons and\nmicroscopically calculate the relaxation rate and the vertex corrections of\n$L_{11}$ and $L_{12}$ due to the impurities. It is confirmed that $L_{11}$ and\n$L_{12}$ are related through Mott's formula in low temperatures when the\nchemical potential lies above the gap ($|\\mu| > \\Delta$), irrespective of the\nlinear dispersion of the Dirac electrons and unconventional energy dependence\nof the lifetime of electrons. On the other hand, when the chemical potential\nlies in the band gap ($|\\mu| < \\Delta$), Seebeck coefficient behaves just as in\nconventional semiconductors: Its dependences on the chemical potential $\\mu$\nand the temperature $T$ are partially captured by $S \\propto (\\Delta - \\mu) /\n\\kB T$ for $\\mu > 0$. The Seebeck coefficient takes the relatively large value\n$|S| \\simeq 1.7 \\,\\mathrm{m V/K}$ at $T \\simeq 8.7\\,\\mathrm{K}$ for $\\Delta =\n15 \\,\\mathrm{m eV}$ by assuming doped bismuth.\n"}
+{"abstract": "  Replacing a fossil fuel-powered car with an electric model can halve\ngreenhouse gas emissions over the course of the vehicle's lifetime and reduce\nthe noise pollution in urban areas. In green logistics, a well-scheduled\ncharging ensures an efficient operation of transportation and power systems\nand, at the same time, provides economical and satisfactory charging services\nfor drivers. This paper presents a taxonomy of current electric vehicle\ncharging scheduling problems in green logistics by analyzing its unique\nfeatures with some typical use cases, such as space assignment, routing and\nenergy management; discusses the challenges, i.e., the information availability\nand stakeholders' strategic behaviors that arise in stochastic and\ndecentralized environments; and classifies the existing approaches, as\ncentralized, distributed and decentralized ones, that apply to these\nchallenges. Moreover, we discuss research opportunities in applying\nmarket-based mechanisms, which shall be coordinated with stochastic\noptimization and machine learning, to the decentralized, dynamic and\ndata-driven charging scheduling problems for the management of the future green\nlogistics.\n"}
+{"abstract": "  We consider an analog of particle production in a quartic $O(N)$ quantum\noscillator with time-dependent frequency, which is a toy model of particle\nproduction in the dynamical Casimir effect and de Sitter space. We calculate\nexact quantum averages, Keldysh propagator, and particle number using two\ndifferent methods. First, we employ a kind of rotating wave approximation to\nestimate these quantities for small deviations from stationarity. Second, we\nextend these results to arbitrarily large deviations using the\nSchwinger-Keldysh diagrammatic technique. We show that in strongly\nnonstationary situations, including resonant oscillations, loop corrections to\nthe tree-level expressions effectively result in an additional degree of\nfreedom, $N \\to N + \\frac{3}{2}$, which modifies the average number and energy\nof created particles.\n"}
+{"abstract": "  In this paper we investigate the commutator relations for prenilpotent roots\nwhich are nested. These commutator relations are trivial in a lot of cases.\n"}
+{"abstract": "  We extend a recent breakthrough result relating expectation thresholds and\nactual thresholds to include rainbow versions.\n"}
+{"abstract": "  A symmetry-preserving treatment of mesons, within a Dyson-Schwinger and\nBethe-Salpeter equations approach, demands an interconnection between the\nkernels of the quark gap equation and meson Bethe-Salpeter equation. Appealing\nto those symmetries expressed by the vector and axial-vector\nWard-Green-Takahashi identitiges (WGTI), we construct a two-body Bethe-Salpeter\nkernel and study its implications in the vector channel; particularly, we\nanalyze the structure of the quark-photon vertex, which explicitly develops a\nvector meson pole in the timelike axis and the quark anomlaous magnetic moment\nterm, as well as a variety of $\\rho$ meson properties: mass and decay\nconstants, electromagnetic form factors, and valence-quark distribution\namplitudes.\n"}
+{"abstract": "  An equation is derived for analyzing the self-action of a wave packets with\nfew optical cycles in multicore fibers (MCF). A new class of stable\nout-of-phase spatio-temporal solitons with few cycle durations in the MCF with\ncores located in a ring is found and analyzed. The stability boundary of the\nobtained solutions is determined. As an example of using such solitons, we\nconsidered the problem of their self-compression in the process of multisoliton\ndynamics in the MCF. The formation of laser pulses with a duration of few\noptical cycles at the output of a ten-core MCF is shown.\n"}
+{"abstract": "  The least squares problem with L1-regularized regressors, called Lasso, is a\nwidely used approach in optimization problems where sparsity of the regressors\nis desired. This formulation is fundamental for many applications in signal\nprocessing, machine learning and control. As a motivating problem, we\ninvestigate a sparse data predictive control problem, run at a cloud service to\ncontrol a system with unknown model, using L1-regularization to limit the\nbehavior complexity. The input-output data collected for the system is\nprivacy-sensitive, hence, we design a privacy-preserving solution using\nhomomorphically encrypted data. The main challenges are the non-smoothness of\nthe L1-norm, which is difficult to evaluate on encrypted data, as well as the\niterative nature of the Lasso problem. We use a distributed ADMM formulation\nthat enables us to exchange substantial local computation for little\ncommunication between multiple servers. We first give an encrypted multi-party\nprotocol for solving the distributed Lasso problem, by approximating the\nnon-smooth part with a Chebyshev polynomial, evaluating it on encrypted data,\nand using a more cost effective distributed bootstrapping operation. For the\nexample of data predictive control, we prefer a non-homogeneous splitting of\nthe data for better convergence. We give an encrypted multi-party protocol for\nthis non-homogeneous splitting of the Lasso problem to a non-homogeneous set of\nservers: one powerful server and a few less powerful devices, added for\nsecurity reasons. Finally, we provide numerical results for our proposed\nsolutions.\n"}
+{"abstract": "  Many platforms for benchmarking optimization algorithms offer users the\npossibility of sharing their experimental data with the purpose of promoting\nreproducible and reusable research. However, different platforms use different\ndata models and formats, which drastically inhibits identification of relevant\ndata sets, their interpretation, and their interoperability. Consequently, a\nsemantically rich, ontology-based, machine-readable data model is highly\ndesired.\n  We report in this paper on the development of such an ontology, which we name\nOPTION (OPTImization algorithm benchmarking ONtology). Our ontology provides\nthe vocabulary needed for semantic annotation of the core entities involved in\nthe benchmarking process, such as algorithms, problems, and evaluation\nmeasures. It also provides means for automated data integration, improved\ninteroperability, powerful querying capabilities and reasoning, thereby\nenriching the value of the benchmark data. We demonstrate the utility of OPTION\nby annotating and querying a corpus of benchmark performance data from the BBOB\nworkshop data - a use case which can be easily extended to cover other\nbenchmarking data collections.\n"}
+{"abstract": "  Transmission electron microscopy (TEM) is one of the primary tools to show\nmicrostructural characterization of materials as well as film thickness.\nHowever, manual determination of film thickness from TEM images is\ntime-consuming as well as subjective, especially when the films in question are\nvery thin and the need for measurement precision is very high. Such is the case\nfor head overcoat (HOC) thickness measurements in the magnetic hard disk drive\nindustry. It is therefore necessary to develop software to automatically\nmeasure HOC thickness. In this paper, for the first time, we propose a HOC\nlayer segmentation method using NASNet-Large as an encoder and then followed by\na decoder architecture, which is one of the most commonly used architectures in\ndeep learning for image segmentation. To further improve segmentation results,\nwe are the first to propose a post-processing layer to remove irrelevant\nportions in the segmentation result. To measure the thickness of the segmented\nHOC layer, we propose a regressive convolutional neural network (RCNN) model as\nwell as orthogonal thickness calculation methods. Experimental results\ndemonstrate a higher dice score for our model which has lower mean squared\nerror and outperforms current state-of-the-art manual measurement.\n"}
+{"abstract": "  Objectives: Federal open data initiatives that promote increased sharing of\nfederally collected data are important for transparency, data quality, trust,\nand relationships with the public and state, tribal, local, and territorial\n(STLT) partners. These initiatives advance understanding of health conditions\nand diseases by providing data to more researchers, scientists, and\npolicymakers for analysis, collaboration, and valuable use outside CDC\nresponders. This is particularly true for emerging conditions such as COVID-19\nwhere we have much to learn and have evolving data needs. Since the beginning\nof the outbreak, CDC has collected person-level, de-identified data from\njurisdictions and currently has over 8 million records, increasing each day.\nThis paper describes how CDC designed and produces two de-identified public\ndatasets from these collected data.\n  Materials and Methods: Data elements were included based on the usefulness,\npublic request, and privacy implications; specific field values were suppressed\nto reduce risk of reidentification and exposure of confidential information.\nDatasets were created and verified for privacy and confidentiality using data\nmanagement platform analytic tools as well as R scripts.\n  Results: Unrestricted data are available to the public through Data.CDC.gov\nand restricted data, with additional fields, are available with a data use\nagreement through a private repository on GitHub.com.\n  Practice Implications: Enriched understanding of the available public data,\nthe methods used to create these data, and the algorithms used to protect\nprivacy of de-identified individuals allow for improved data use. Automating\ndata generation procedures allows greater and more timely sharing of data.\n"}
+{"abstract": "  Non-orthogonal multiple access (NOMA) is a key technology to enable massive\nmachine type communications (mMTC) in 5G networks and beyond. In this paper,\nNOMA is applied to improve the random access efficiency in high-density\nspatially-distributed multi-cell wireless IoT networks, where IoT devices\ncontend for accessing the shared wireless channel using an adaptive\np-persistent slotted Aloha protocol. To enable a capacity-optimal network, a\nnovel formulation of random channel access management is proposed, in which the\ntransmission probability of each IoT device is tuned to maximize the geometric\nmean of users' expected capacity. It is shown that the network optimization\nobjective is high dimensional and mathematically intractable, yet it admits\nfavourable mathematical properties that enable the design of efficient\ndata-driven algorithmic solutions which do not require a priori knowledge of\nthe channel model or network topology. A centralized model-based algorithm and\na scalable distributed model-free algorithm, are proposed to optimally tune the\ntransmission probabilities of IoT devices and attain the maximum capacity. The\nconvergence of the proposed algorithms to the optimal solution is further\nestablished based on convex optimization and game-theoretic analysis. Extensive\nsimulations demonstrate the merits of the novel formulation and the efficacy of\nthe proposed algorithms.\n"}
+{"abstract": "  The performance of wireless networks is fundamentally limited by the\naggregate interference, which depends on the spatial distributions of the\ninterferers, channel conditions, and user traffic patterns (or queueing\ndynamics). These factors usually exhibit spatial and temporal correlations and\nthus make the performance of large-scale networks environment-dependent (i.e.,\ndependent on network topology, locations of the blockages, etc.). The\ncorrelation can be exploited in protocol designs (e.g., spectrum-, load-,\nlocation-, energy-aware resource allocations) to provide efficient wireless\nservices. For this, accurate system-level performance characterization and\nevaluation with spatio-temporal correlation are required. In this context,\nstochastic geometry models and random graph techniques have been used to\ndevelop analytical frameworks to capture the spatio-temporal interference\ncorrelation in large-scale wireless networks. The objective of this article is\nto provide a tutorial on the stochastic geometry analysis of large-scale\nwireless networks that captures the spatio-temporal interference correlation\n(and hence the signal-to-interference ratio (SIR) correlation). We first\ndiscuss the importance of spatio-temporal performance analysis, different\nparameters affecting the spatio-temporal correlation in the SIR, and the\ndifferent performance metrics for spatio-temporal analysis. Then we describe\nthe methodologies to characterize spatio-temporal SIR correlations for\ndifferent network configurations (independent, attractive, repulsive\nconfigurations), shadowing scenarios, user locations, queueing behavior,\nrelaying, retransmission, and mobility. We conclude by outlining future\nresearch directions in the context of spatio-temporal analysis of emerging\nwireless communications scenarios.\n"}
+{"abstract": "  Vortex based spin torque nano oscillators (STVOs) can present more complex\ndynamics than the spin torque induced gyrotropic (G) motion of the vortex core.\nThe respective dynamic modes and the transition between them can be controlled\nby experimental parameters such as the applied dc current. An interesting\nbehavior is the stochastic transition from the G- to a dynamic C-state\noccurring for large current densities. Moreover, the C-state oscillations\nexhibit a constant active magnetic volume. We present noise measurements in the\ndifferent dynamic states that allow accessing specific properties of the\nstochastic transition, such as the characteristic state transition frequency.\nFurthermore,we confirm, as theoretically predicted, an increase of flicker\nnoise with $I_{dc}^2$ when the oscillation volume remains constant with the\ncurrent. These results bring insight into the potential optimization of noise\nproperties sought for many potential rf applications with spin torque\noscillators. Furthermore, the investigated stochastic characteristics open up\nnew potentialities, for instance in the emerging field of neuromorphic\ncomputing schemes.\n"}
+{"abstract": "  Temperature is a deceptively simple concept that still raises deep questions\nat the forefront of quantum physics research. The observation of thermalisation\nin completely isolated quantum systems, such as cold-atom quantum simulators,\nimplies that a temperature can be assigned even to individual, pure quantum\nstates. Here, we propose a scheme to measure the temperature of such pure\nstates through quantum interference. Our proposal involves interferometry of an\nauxiliary qubit probe, which is prepared in a superposition state and\nsubsequently undergoes decoherence due to weak coupling with a closed,\nthermalised many-body system. Using only a few basic assumptions about chaotic\nquantum systems -- namely, the eigenstate thermalisation hypothesis and the\nemergence of hydrodynamics at long times -- we show that the qubit undergoes\npure exponential decoherence at a rate that depends on the temperature of its\nsurroundings. We verify our predictions by numerical experiments on a quantum\nspin chain that thermalises after absorbing energy from a periodic drive. Our\nwork provides a general method to measure the temperature of isolated, strongly\ninteracting systems under minimal assumptions.\n"}
+{"abstract": "  The study of hyper-compact (HC) or ultra-compact (UC) HII regions is\nfundamental to understanding the process of massive (> 8 M_sun) star formation.\nWe employed Atacama Large Millimeter/submillimeter Array (ALMA) 1.4 mm Cycle 6\nobservations to investigate at high angular resolution (~0.050\", corresponding\nto 330 au) the HC HII region inside molecular core A1 of the high-mass\nstar-forming cluster G24.78+0.08. We used the H30alpha emission and different\nmolecular lines of CH3CN and 13CH3CN to study the kinematics of the ionized and\nmolecular gas, respectively. At the center of the HC HII region, at radii <~500\nau, we observe two mutually perpendicular velocity gradients, which are\ndirected along the axes at PA = 39 deg and PA = 133 deg, respectively. The\nvelocity gradient directed along the axis at PA = 39 deg has an amplitude of 22\nkm/s mpc^(-1), which is much larger than the other's, 3 km/s mpc^(-1). We\ninterpret these velocity gradients as rotation around, and expansion along, the\naxis at PA = 39 deg. We propose a scenario where the H30alpha line traces the\nionized heart of a disk-jet system that drives the formation of the massive\nstar (~20 M_sun) responsible for the HC HII region. Such a scenario is also\nsupported by the position-velocity plots of the CH3CN and 13CH3CN lines along\nthe axis at PA = 133 deg, which are consistent with Keplerian rotation around a\n20 M_sun star. Toward the HC HII region in G24.78+0.08, the coexistence of mass\ninfall (at radii of ~5000 au), an outer molecular disk (from <~4000 au to >~500\nau), and an inner ionized disk (<~500 au) indicates that the massive ionizing\nstar is still actively accreting from its parental molecular core. To our\nknowledge, this is the first example of a molecular disk around a high-mass\nforming star that, while becoming internally ionized after the onset of the HII\nregion, continues to accrete mass onto the ionizing star.\n"}
+{"abstract": "  Numerous congruences for partitions with designated summands have been proven\nsince first being introduced and studied by Andrews, Lewis, and Lovejoy. This\npaper explicitly characterizes the number of partitions with designated\nsummands whose parts are not divisible by $2^\\ell$, $2$, and $3^\\ell$ working\nmodulo $2,\\ 4,$ and $3$, respectively, greatly extending previous results on\nthe subject. We provide a few applications of our characterizations throughout\nin the form of congruences and a computationally fast recurrence. Moreover, we\nillustrate a previously undocumented connection between the number of\npartitions with designated summands and the number of partitions with odd\nmultiplicities.\n"}
+{"abstract": "  The quadratic rough Heston model provides a natural way to encode Zumbach\neffect in the rough volatility paradigm. We apply multi-factor approximation\nand use deep learning methods to build an efficient calibration procedure for\nthis model. We show that the model is able to reproduce very well both SPX and\nVIX implied volatilities. We typically obtain VIX option prices within the\nbid-ask spread and an excellent fit of the SPX at-the-money skew. Moreover, we\nalso explain how to use the trained neural networks for hedging with\ninstantaneous computation of hedging quantities.\n"}
+{"abstract": "  Compact objects inspiraling into supermassive black holes, known as\nextreme-mass-ratio inspirals, are an important source for future space-borne\ngravitational-wave detectors. When constructing waveform templates, usually the\nadiabatic approximation is employed to treat the compact object as a test\nparticle for a short duration, and the radiation reaction is reflected in the\nchanges of the constants of motion. However, the mass of the compact object\nshould have contributions to the background. In the present paper, employing\nthe effective-one-body formalism, we analytically calculate the trajectories of\na compact object around a massive Kerr black hole with generally\nthree-dimensional orbits and express the fundamental orbital frequencies in\nexplicit forms. In addition, by constructing an approximate \"constant\" similar\nto the Carter constant, we transfer the dynamical quantities such as energy,\nangular momentum, and the \"Carter constant\" to the semilatus rectum,\neccentricity, and orbital inclination with mass-ratio corrections. The linear\nmass-ratio terms in the formalism may not be sufficient for accurate waveforms,\nbut our analytical method for solving the equations of motion could be useful\nin various approaches to building waveform models.\n"}
+{"abstract": "  We study the electronic phase diagram of the excitonic insulator candidates\nTa$_2$Ni(Se$_{1-x}$S$_x$)$_5$ [x=0, ... ,1] using Raman spectroscopy. Critical\nexcitonic fluctuations are observed, that diminish with $x$ and ultimately\nshift to high energies, characteristic of a quantum phase transition.\nNonetheless, a symmetry-breaking transition at finite temperatures is detected\nfor all $x$, exposing a cooperating lattice instability that takes over for\nlarge $x$. Our study reveals a failed excitonic quantum phase transition,\nmasked by a preemptive structural order.\n"}
+{"abstract": "  Spectropolarimetric measurements of gamma-ray burst (GRB) optical afterglows\ncontain polarization information for both continuum and absorption lines. Based\non the Zeeman effect, an absorption line in a strong magnetic field is\npolarized and split into a triplet. In this paper, we solve the polarization\nradiative transfer equations of the absorption lines, and obtain the degree of\nlinear polarization of the absorption lines as a function of the optical depth.\nIn order to effectively measure the degree of linear polarization for the\nabsorption lines, a magnetic field strength of at least $10^3$ G is required.\nThe metal elements that produce the polarized absorption lines should be\nsufficiently abundant and have large oscillation strengths or Einstein\nabsorption coefficients. We encourage both polarization measurements and\nhigh-dispersion observations of the absorption lines in order to detect the\ntriplet structure in early GRB optical afterglows.\n"}
+{"abstract": "  Existing deterministic variational inference approaches for diffusion\nprocesses use simple proposals and target the marginal density of the\nposterior. We construct the variational process as a controlled version of the\nprior process and approximate the posterior by a set of moment functions. In\ncombination with moment closure, the smoothing problem is reduced to a\ndeterministic optimal control problem. Exploiting the path-wise Fisher\ninformation, we propose an optimization procedure that corresponds to a natural\ngradient descent in the variational parameters. Our approach allows for richer\nvariational approximations that extend to state-dependent diffusion terms. The\nclassical Gaussian process approximation is recovered as a special case.\n"}
+{"abstract": "  In this paper, a distributed formation flight control topology for\nLeader-Follower formation structure is presented. Such topology depends in the\nfirst place on online generation of the trajectories that should be followed by\nthe agents in the formation. The trajectory of each agent is planned during\nexecution depending on its neighbors and considering that the desired reference\ntrajectory is only given to the leader. Simulation using MATLAB/SIMULINK is\ndone on a formation of quadrotor UAVs to illustrate the proposed method. The\ntopology shows very good results in achieving the formation and following the\nreference trajectory.\n"}
+{"abstract": "  A theory of a pseudogap phase of high-temperature superconductors where\ncurrent carriers are translation invariant bipolarons is developed. A\ntemperature T* of a transition from a pseudogap phase to a normal one is\ncalculated. For the temperature of a transition to the pseudogap phase, the\nisotope coefficient is found. It is shown that the results obtained, in\nparticular, the possibility of negative values of the isotope coefficient are\nconsistent with the experiment. New experiments on the influence of the\nmagnetic field on the isotope coefficient are proposed.\n"}
+{"abstract": "  FloodNet is a high-resolution image dataset acquired by a small UAV platform,\nDJI Mavic Pro quadcopters, after Hurricane Harvey. The dataset presents a\nunique challenge of advancing the damage assessment process for post-disaster\nscenarios using unlabeled and limited labeled dataset. We propose a solution to\naddress their classification and semantic segmentation challenge. We approach\nthis problem by generating pseudo labels for both classification and\nsegmentation during training and slowly incrementing the amount by which the\npseudo label loss affects the final loss. Using this semi-supervised method of\ntraining helped us improve our baseline supervised loss by a huge margin for\nclassification, allowing the model to generalize and perform better on the\nvalidation and test splits of the dataset. In this paper, we compare and\ncontrast the various methods and models for image classification and semantic\nsegmentation on the FloodNet dataset.\n"}
+{"abstract": "  Change detection from synthetic aperture radar (SAR) imagery is a critical\nyet challenging task. Existing methods mainly focus on feature extraction in\nspatial domain, and little attention has been paid to frequency domain.\nFurthermore, in patch-wise feature analysis, some noisy features in the\nmarginal region may be introduced. To tackle the above two challenges, we\npropose a Dual-Domain Network. Specifically, we take features from the discrete\ncosine transform domain into consideration and the reshaped DCT coefficients\nare integrated into the proposed model as the frequency domain branch. Feature\nrepresentations from both frequency and spatial domain are exploited to\nalleviate the speckle noise. In addition, we further propose a multi-region\nconvolution module, which emphasizes the central region of each patch. The\ncontextual information and central region features are modeled adaptively. The\nexperimental results on three SAR datasets demonstrate the effectiveness of the\nproposed model. Our codes are available at\nhttps://github.com/summitgao/SAR_CD_DDNet.\n"}
+{"abstract": "  Domain mismatch is a noteworthy issue in acoustic event detection tasks, as\nthe target domain data is difficult to access in most real applications. In\nthis study, we propose a novel CNN-based discriminative training framework as a\ndomain compensation method to handle this issue. It uses a parallel CNN-based\ndiscriminator to learn a pair of high-level intermediate acoustic\nrepresentations. Together with a binary discriminative loss, the discriminators\nare forced to maximally exploit the discrimination of heterogeneous acoustic\ninformation in each audio clip with target events, which results in a robust\npaired representations that can well discriminate the target events and\nbackground/domain variations separately. Moreover, to better learn the\ntransient characteristics of target events, a frame-wise classifier is designed\nto perform the final classification. In addition, a two-stage training with the\nCNN-based discriminator initialization is further proposed to enhance the\nsystem training. All experiments are performed on the DCASE 2018 Task3\ndatasets. Results show that our proposal significantly outperforms the official\nbaseline on cross-domain conditions in AUC by relative $1.8-12.1$% without any\nperformance degradation on in-domain evaluation conditions.\n"}
+{"abstract": "  We introduce a novel inferential framework for marked point processes that\nenjoys both scalability and interpretability. The framework is based on\nvariational inference and it aims to speed up inference for a flexible family\nof marked point processes where the joint distribution of times and marks can\nbe specified in terms of the conditional distribution of times given the\nprocess filtration, and of the conditional distribution of marks given the\nprocess filtration and the current time. We assess the predictive ability of\nour proposed method over four real-world datasets where results show its\ncompetitive performance against other baselines. The attractiveness of our\nframework for the modelling of marked point processes is illustrated through a\ncase study of association football data where scalability and interpretability\nare exploited for extracting useful informative patterns.\n"}
+{"abstract": "  Decision-based attacks (DBA), wherein attackers perturb inputs to spoof\nlearning algorithms by observing solely the output labels, are a type of severe\nadversarial attacks against Deep Neural Networks (DNNs) requiring minimal\nknowledge of attackers. State-of-the-art DBA attacks relying on zeroth-order\ngradient estimation require an excessive number of queries. Recently, Bayesian\noptimization (BO) has shown promising in reducing the number of queries in\nscore-based attacks (SBA), in which attackers need to observe real-valued\nprobability scores as outputs. However, extending BO to the setting of DBA is\nnontrivial because in DBA only output labels instead of real-valued scores, as\nneeded by BO, are available to attackers. In this paper, we close this gap by\nproposing an efficient DBA attack, namely BO-DBA. Different from existing\napproaches, BO-DBA generates adversarial examples by searching so-called\n\\emph{directions of perturbations}. It then formulates the problem as a BO\nproblem that minimizes the real-valued distortion of perturbations. With the\noptimized perturbation generation process, BO-DBA converges much faster than\nthe state-of-the-art DBA techniques. Experimental results on pre-trained\nImageNet classifiers show that BO-DBA converges within 200 queries while the\nstate-of-the-art DBA techniques need over 15,000 queries to achieve the same\nlevel of perturbation distortion. BO-DBA also shows similar attack success\nrates even as compared to BO-based SBA attacks but with less distortion.\n"}
+{"abstract": "  In this chapter we give an overview of the consensus-based global\noptimization algorithm and its recent variants. We recall the formulation and\nanalytical results of the original model, then we discuss variants using\ncomponent-wise independent or common noise. In combination with mini-batch\napproaches those variants were tailored for machine learning applications.\nMoreover, it turns out that the analytical estimates are dimension independent,\nwhich is useful for high-dimensional problems. We discuss the relationship of\nconsensus-based optimization with particle swarm optimization, a method widely\nused in the engineering community. Then we survey a variant of consensus-based\noptimization that is proposed for global optimization problems constrained to\nhyper-surfaces. We conclude the chapter with remarks on applications, preprints\nand open problems.\n"}
+{"abstract": "  With the growth of the open-source data science community, both the number of\ndata science libraries and the number of versions for the same library are\nincreasing rapidly. To match the evolving APIs from those libraries,\nopen-source organizations often have to exert manual effort to refactor the\nAPIs used in the code base. Moreover, due to the abundance of similar\nopen-source libraries, data scientists working on a certain application may\nhave an abundance of libraries to choose, maintain and migrate between. The\nmanual refactoring between APIs is a tedious and error-prone task. Although\nrecent research efforts were made on performing automatic API refactoring\nbetween different languages, previous work relies on statistical learning with\ncollected pairwise training data for the API matching and migration. Using\nlarge statistical data for refactoring is not ideal because such training data\nwill not be available for a new library or a new version of the same library.\nWe introduce Synthesis for Open-Source API Refactoring (SOAR), a novel\ntechnique that requires no training data to achieve API migration and\nrefactoring. SOAR relies only on the documentation that is readily available at\nthe release of the library to learn API representations and mapping between\nlibraries. Using program synthesis, SOAR automatically computes the correct\nconfiguration of arguments to the APIs and any glue code required to invoke\nthose APIs. SOAR also uses the interpreter's error messages when running\nrefactored code to generate logical constraints that can be used to prune the\nsearch space. Our empirical evaluation shows that SOAR can successfully\nrefactor 80% of our benchmarks corresponding to deep learning models with up to\n44 layers with an average run time of 97.23 seconds, and 90% of the data\nwrangling benchmarks with an average run time of 17.31 seconds.\n"}
+{"abstract": "  This paper considers (partial) identification of a variety of counterfactual\nparameters in binary response models with possibly endogenous regressors. Our\nframework allows for nonseparable index functions with multi-dimensional latent\nvariables, and does not require parametric distributional assumptions. We\nleverage results on hyperplane arrangements and cell enumeration from the\nliterature on computational geometry in order to provide a tractable means of\ncomputing the identified set. We demonstrate how various functional form,\nindependence, and monotonicity assumptions can be imposed as constraints in our\noptimization procedure to tighten the identified set, and we show how these\nassumptions can be assigned meaningful interpretations in terms of restrictions\non latent response types. Finally, we apply our method to study the effects of\nhealth insurance on the decision to seek medical treatment.\n"}
+{"abstract": "  Scalar metric fluctuations generically source a spectrum of gravitational\nwaves at second order in perturbation theory, poising gravitational wave\nexperiments as potentially powerful probes of the small-scale curvature power\nspectrum. We perform a detailed study of the imprint of primordial\nnon-Gaussianity on these induced gravitational waves, emphasizing the role of\nboth the disconnected and connected components of the primoridal trispectrum.\nSpecializing to local-type non-Gaussianity, we numerically compute all\ncontributions and present results for a variety of enhanced primordial\ncurvature power spectra.\n"}
+{"abstract": "  The disruption of asteroids and comets produces cm-sized meteoroids that end\nup impacting the Earth's atmosphere and producing bright fireballs that might\nhave associated shock waves or, in geometrically-favorable occasions excavate\ncraters that put them into unexpected hazardous scenarios. The astrometric\nreduction of meteors and fireballs to infer their atmospheric trajectories and\nheliocentric orbits involves a complex and tedious process that generally\nrequires many manual tasks. To streamline the process, we present a software\npackage called SPMN 3D Fireball Trajectory and Orbit Calculator (3D-FireTOC),\nan automatic Python code for detection, trajectory reconstruction of meteors,\nand heliocentric orbit computation from video recordings. The automatic\n3D-FireTOC package comprises of a user interface and a graphic engine that\ngenerates a realistic 3D representation model, which allows users to easily\ncheck the geometric consistency of the results and facilitates scientific\ncontent production for dissemination. The software automatically detects\nmeteors from digital systems, completes the astrometric measurements, performs\nphotometry, computes the meteor atmospheric trajectory, calculates the velocity\ncurve, and obtains the radiant and the heliocentric orbit, all in all\nquantifying the error measurements in each step. The software applies\ncorrections such as light aberration, refraction, zenith attraction, diurnal\naberration and atmospheric extinction. It also characterizes the atmospheric\nflight and consequently determines fireball fates by using the $\\alpha - \\beta$\ncriterion that analyses the ability of a fireball to penetrate deep into the\natmosphere and produce meteorites. We demonstrate the performance of the\nsoftware by analyzing two bright fireballs recorded by the Spanish Fireball and\nMeteorite Network (SPMN).\n"}
+{"abstract": "  The fermionic quantum emulator (FQE) is a collection of protocols for\nemulating quantum dynamics of fermions efficiently taking advantage of common\nsymmetries present in chemical, materials, and condensed-matter systems. The\nlibrary is fully integrated with the OpenFermion software package and serves as\nthe simulation backend. The FQE reduces memory footprint by exploiting number\nand spin symmetry along with custom evolution routines for sparse and dense\nHamiltonians, allowing us to study significantly larger quantum circuits at\nmodest computational cost when compared against qubit state vector simulators.\nThis release paper outlines the technical details of the simulation methods and\nkey advantages.\n"}
+{"abstract": "  Musculoskeletal models have the potential to improve diagnosis and optimize\nclinical treatment by predicting accurate outcomes on an individual basis.\nHowever, the subject-specific modeling of spinal alignment is often strongly\nsimplified or is based on radiographic assessments, exposing subjects to\nunnecessary radiation. We therefore developed a novel skin marker-based\napproach for modeling subject-specific spinal alignment and evaluated its\nfeasibility by comparing the predicted with the actual intervertebral joint\n(IVJ) locations/orientations (ground truth) using lateral-view radiographic\nimages. Moreover, the predictive performance of the subject-specific models was\nevaluated by comparing the predicted L1/L2 spinal loads during various\nfunctional activities with in vivo measured data obtained from the OrthoLoad\ndatabase. IVJ locations/orientations were predicted closer to ground truth as\nopposed to standard model scaling, with average location prediction errors of\n0.99+/-0.68 cm on the frontal and 1.21+/-0.97 cm on the transverse axis as well\nas an average orientation prediction error of 4.74{\\deg}+/-2.80{\\deg}.\nSimulated spinal loads showed similar curve patterns but considerably larger\nvalues as compared to in vivo measured data. Differences in spinal loads\nbetween generic and subject-specific models become only apparent on an\nindividual subject level. These results underline the feasibility of the\nproposed method and associated workflow for inter- and intra-subject\ninvestigations using musculoskeletal simulations. When implemented into\nstandard model scaling workflows, it is expected to improve the accuracy of\nmuscle activity and joint loading simulations, which is crucial for\ninvestigations of treatment effects or pathology-dependent deviations.\n"}
+{"abstract": "  We identify Whittaker vectors for $\\mathcal{W}_k(\\mathfrak{g})$-modules with\npartition functions of higher Airy structures. This implies that Gaiotto\nvectors, describing the fundamental class in the equivariant cohomology of a\nsuitable compactification of the moduli space of $G$-bundles over\n$\\mathbb{P}^2$ for $G$ a complex simple Lie group, can be computed by a\nnon-commutative version of the Chekhov-Eynard-Orantin topological recursion. We\nformulate the connection to higher Airy structures for Gaiotto vectors of type\nA, B, C, and D, and explicitly construct the topological recursion for type A\n(at arbitrary level) and type B (at self-dual level). On the physics side, it\nmeans that the Nekrasov partition function for pure $\\mathcal{N} = 2$\nfour-dimensional supersymmetric gauge theories can be accessed by topological\nrecursion methods.\n"}
+{"abstract": "  I present a strategy for unsupervised manifold learning on local atomic\nenvironments in molecular simulations based on simple rotation- and\npermutation-invariant three-body features. These features are highly\ndescriptive, generalize to multiple chemical species, and are\nhuman-interpretable. The low-dimensional embeddings of each atomic environment\ncan be used to understand and quantify messy crystal structures such as those\nnear interfaces and defects or well-ordered crystal lattices such as in bulk\nmaterials without modification. The same method can also yield collective\nvariables describing collections of particles such as for an entire simulation\ndomain. I demonstrate the method on colloidal crystallization, ice crystals,\nand binary mesophases to illustrate its broad applicability. In each case, the\nlearned latent space yields insights into the details of the observed\nmicrostructures. For ices and mesophases, supervised classifiers are trained\nbased on the learned manifolds and directly compared against a recent\nneural-network-based approach. Notably, while this method provides comparable\nclassification performance, it can also be deployed on even a handful of\nobserved environments without labels or \\textit{a priori} knowledge. Thus, the\ncurrent approach provides an incredibly versatile strategy to characterize and\nclassify local atomic environments, and may unlock insights in a wide variety\nof molecular simulation contexts.\n"}
+{"abstract": "  The Weisfeiler-Leman (WL) algorithm is a well-known combinatorial procedure\nfor detecting symmetries in graphs and it is widely used in graph-isomorphism\ntests. It proceeds by iteratively refining a colouring of vertex tuples. The\nnumber of iterations needed to obtain the final output is crucial for the\nparallelisability of the algorithm.\n  We show that there is a constant k such that every planar graph can be\nidentified (that is, distinguished from every non-isomorphic graph) by the\nk-dimensional WL algorithm within a logarithmic number of iterations. This\ngeneralises a result due to Verbitsky (STACS 2007), who proved the same for\n3-connected planar graphs.\n  The number of iterations needed by the k-dimensional WL algorithm to identify\na graph corresponds to the quantifier depth of a sentence that defines the\ngraph in the (k+1)-variable fragment C^{k+1} of first-order logic with counting\nquantifiers. Thus, our result implies that every planar graph is definable with\na C^{k+1}-sentence of logarithmic quantifier depth.\n"}
+{"abstract": "  We present a methodical study of the thermal and nuclear properties for the\nhot nuclear matter using relativistic-mean field theory. We examine the effects\nof temperature on the binding energy, pressure, thermal index, symmetry energy,\nand its derivative for the symmetric nuclear matter using temperature-dependent\nrelativistic mean-field formalism for the well-known G2$^{*}$ and recently\ndeveloped IOPB-I parameter sets. The critical temperature for the liquid-gas\nphase transition in an asymmetric nuclear matter system has also been\ncalculated and collated with the experimentally available data. We investigate\nthe approach of the thermal index as a function of nucleon density in the wake\nof relativistic and non-relativistic formalism. The computation of neutrino\nemissivity through the direct Urca process for the supernovae remnants has also\nbeen performed, which manifests some exciting results about the thermal\nstabilization and evolution of the newly born proto-neutron star. The central\ntemperature and the maximum mass of the proto-neutron star have also been\ncalculated for different entropy values.\n"}
+{"abstract": "  Let $E/\\mathbb{Q}$ be an elliptic curve having multiplicative reduction at a\nprime $p$. Let $(g,h)$ be a pair of eigenforms of weight $1$ arising as the\ntheta series of an imaginary quadratic field $K$, and assume that the\ntriple-product $L$-function $L(f,g,h,s)$ is self-dual and does not vanish at\nthe central critical point $s=1$. The main result of this article is a formula\nexpressing the $p$-adic iterated integrals introduced in [DLR] to the Kolyvagin\nclasses associated by Bertolini and Darmon to a system of Heegner points on\n$E$.\n"}
+{"abstract": "  This paper is the first paper of a series that will present the derivation of\nthe modal mineralogy of Mars (M3 project) at a global scale from the\nnear-infrared dataset acquired by the imaging spectrometer OMEGA (Observatoire\npour la Min\\'eralogie, l'Eau, les Glaces et l'Activit\\'e) on board ESA/Mars\nExpress. The objective is to create and provide a global 3-D image-cube of Mars\nat 32px/{\\deg} covering most of Mars surface. This product has several\nadvantages. First, it can be used to instantaneously extract atmospheric- and\naerosol-corrected near-infrared (NIR) spectra from any location on Mars.\nSecond, several new data maps can be built as discussed here. That includes new\nglobal mineral distributions, quantitative mineral abundance distributions and\nmaps of Martian surface chemistry (wt % oxide) detailed in a companion paper\n(Riu et al., submitted). Here we present the method to derive the global\nhyperspectral cube from several hundred millions of spectra. Global maps of\nsome mafic minerals are then shown, and compared to previous works.\n"}
+{"abstract": "  Learning from demonstration (LfD) is commonly considered to be a natural and\nintuitive way to allow novice users to teach motor skills to robots. However,\nit is important to acknowledge that the effectiveness of LfD is heavily\ndependent on the quality of teaching, something that may not be assured with\nnovices. It remains an open question as to the most effective way of guiding\ndemonstrators to produce informative demonstrations beyond ad hoc advice for\nspecific teaching tasks. To this end, this paper investigates the use of\nmachine teaching to derive an index for determining the quality of\ndemonstrations and evaluates its use in guiding and training novices to become\nbetter teachers. Experiments with a simple learner robot suggest that guidance\nand training of teachers through the proposed approach can lead to up to 66.5%\ndecrease in error in the learnt skill.\n"}
+{"abstract": "  Financial markets are difficult to predict due to its complex systems\ndynamics. Although there have been some recent studies that use machine\nlearning techniques for financial markets prediction, they do not offer\nsatisfactory performance on financial returns. We propose a novel\none-dimensional convolutional neural networks (CNN) model to predict financial\nmarket movement. The customized one-dimensional convolutional layers scan\nfinancial trading data through time, while different types of data, such as\nprices and volume, share parameters (kernels) with each other. Our model\nautomatically extracts features instead of using traditional technical\nindicators and thus can avoid biases caused by selection of technical\nindicators and pre-defined coefficients in technical indicators. We evaluate\nthe performance of our prediction model with strictly backtesting on historical\ntrading data of six futures from January 2010 to October 2017. The experiment\nresults show that our CNN model can effectively extract more generalized and\ninformative features than traditional technical indicators, and achieves more\nrobust and profitable financial performance than previous machine learning\napproaches.\n"}
+{"abstract": "  The natural world is long-tailed: rare classes are observed orders of\nmagnitudes less frequently than common ones, leading to highly-imbalanced data\nwhere rare classes can have only handfuls of examples. Learning from few\nexamples is a known challenge for deep learning based classification\nalgorithms, and is the focus of the field of low-shot learning. One potential\napproach to increase the training data for these rare classes is to augment the\nlimited real data with synthetic samples. This has been shown to help, but the\ndomain shift between real and synthetic hinders the approaches' efficacy when\ntested on real data.\n  We explore the use of image-to-image translation methods to close the domain\ngap between synthetic and real imagery for animal species classification in\ndata collected from camera traps: motion-activated static cameras used to\nmonitor wildlife. We use low-level feature alignment between source and target\ndomains to make synthetic data for a rare species generated using a graphics\nengine more \"realistic\". Compared against a system augmented with unaligned\nsynthetic data, our experiments show a considerable decrease in classification\nerror rates on a rare species.\n"}
+{"abstract": "  Efficient discovery of a speaker's emotional states in a multi-party\nconversation is significant to design human-like conversational agents. During\na conversation, the cognitive state of a speaker often alters due to certain\npast utterances, which may lead to a flip in their emotional state. Therefore,\ndiscovering the reasons (triggers) behind the speaker's emotion-flip during a\nconversation is essential to explain the emotion labels of individual\nutterances. In this paper, along with addressing the task of emotion\nrecognition in conversations (ERC), we introduce a novel task - Emotion-Flip\nReasoning (EFR), that aims to identify past utterances which have triggered\none's emotional state to flip at a certain time. We propose a masked memory\nnetwork to address the former and a Transformer-based network for the latter\ntask. To this end, we consider MELD, a benchmark emotion recognition dataset in\nmulti-party conversations for the task of ERC, and augment it with new\nground-truth labels for EFR. An extensive comparison with five state-of-the-art\nmodels suggests improved performances of our models for both tasks. We further\npresent anecdotal evidence and both qualitative and quantitative error analyses\nto support the superiority of our models compared to the baselines.\n"}
+{"abstract": "  Neural keyphrase generation models have recently attracted much interest due\nto their ability to output absent keyphrases, that is, keyphrases that do not\nappear in the source text. In this paper, we discuss the usefulness of absent\nkeyphrases from an Information Retrieval (IR) perspective, and show that the\ncommonly drawn distinction between present and absent keyphrases is not made\nexplicit enough. We introduce a finer-grained categorization scheme that sheds\nmore light on the impact of absent keyphrases on scientific document retrieval.\nUnder this scheme, we find that only a fraction (around 20%) of the words that\nmake up keyphrases actually serves as document expansion, but that this small\nfraction of words is behind much of the gains observed in retrieval\neffectiveness. We also discuss how the proposed scheme can offer a new angle to\nevaluate the output of neural keyphrase generation models.\n"}
+{"abstract": "  The radio emission in many pulsars show sudden changes, usually within a\nperiod, that cannot be related to the steady state processes within the inner\nacceleration region (IAR) above the polar cap. These changes are often\nquasi-periodic in nature, where regular transitions between two or more stable\nemission states are seen. The durations of these states show a wide variety\nranging from several seconds to hours at a time. There are strong, small scale\nmagnetic field structures and huge temperature gradients present at the polar\ncap surface. We have considered several processes that can cause temporal\nmodifications of the local magnetic field structure and strength at the surface\nof the polar cap. Using different magnetic field strengths and scales, and also\nassuming realistic scales of the temperature gradients, the evolutionary\ntimescales of different phenomena affecting the surface magnetic field was\nestimated. We find that the Hall drift results in faster changes in comparison\nto both Ohmic decay and thermoelectric effects. A mechanism based on the\nPartially Screened Gap (PSG) model of the IAR has been proposed, where the Hall\nand thermoelectric oscillations perturb the polar cap magnetic field to alter\nthe sparking process in the PSG. This is likely to affect the observed radio\nemission resulting in the observed state changes.\n"}
+{"abstract": "  Robots are used for collecting samples from natural environments to create\nmodels of, for example, temperature or algae fields in the ocean. Adaptive\ninformative sampling is a proven technique for this kind of spatial field\nmodeling. This paper compares the performance of humans versus adaptive\ninformative sampling algorithms for selecting informative waypoints. The humans\nand simulated robot are given the same information for selecting waypoints, and\nboth are evaluated on the accuracy of the resulting model. We developed a\ngraphical user interface for selecting waypoints and visualizing samples.\nEleven participants iteratively picked waypoints for twelve scenarios. Our\nsimulated robot used Gaussian Process regression with two entropy-based\noptimization criteria to iteratively choose waypoints. Our results show that\nthe robot can on average perform better than the average human, and\napproximately as good as the best human, when the model assumptions correspond\nto the actual field. However, when the model assumptions do not correspond as\nwell to the characteristics of the field, both human and robot performance are\nno better than random sampling.\n"}
+{"abstract": "  We present a two-dimensional continuum model of tumor growth, which treats\nthe tissue as a composition of six distinct fluid phases; their dynamics are\ngoverned by the equations of mass and momentum conservation. Our model divides\nthe cancer cells phase into two sub-phases depending on their maturity state.\nThe same approach is also applied for the vasculature phase, which is divided\ninto young sprouts (products of angiogenesis), and fully formed-mature vessels.\nThe remaining two phases correspond to healthy cells and extracellular material\n(ECM). Furthermore, the model foresees the existence of nutrient chemical\nspecies, which are transferred within the tissue through diffusion or supplied\nby the vasculature (blood vessels). The model is numerically solved with the\nFinite Elements Method and computations are performed with the commercial\nsoftware Comsol Multiphysics. The numerical simulations predict that mature\ncancer cells are well separated from young cancer cells, which form a\nprotective shield for the growing tumor. We study the effect of different\nmitosis and death rates for mature and young cancer cells on the tumor growth\nrate, and predict accelerated rates when the mitosis rate of young cancer cells\nis higher compared to mature cancer cells.\n"}
+{"abstract": "  This paper presents the equivariant systems theory and observer design for\nsecond order kinematic systems on matrix Lie groups. The state of a second\norder kinematic system on a matrix Lie group is naturally posed on the tangent\nbundle of the group with the inputs lying in the tangent of the tangent bundle\nknown as the double tangent bundle. We provide a simple parameterization of\nboth the tangent bundle state space and the input space (the fiber space of the\ndouble tangent bundle) and then introduce a semi-direct product group and group\nactions onto both the state and input spaces. We show that with the proposed\ngroup actions the second order kinematics are equivariant. An equivariant lift\nof the kinematics onto the symmetry group is defined and used to design a\nnonlinear observer on the lifted state space using nonlinear constructive\ndesign techniques. A simple hovercraft simulation verifies the performance of\nour observer.\n"}
+{"abstract": "  This paper investigates the problem of co-synthesis of edit function and\nsupervisor for opacity enforcement in the supervisory control of discrete-event\nsystems (DES), assuming the presence of an external (passive) intruder, where\nthe following goals need to be achieved: 1) the external intruder should never\ninfer the system secret, i.e., the system is opaque, and never be sure about\nthe existence of the edit function, i.e., the edit function remains covert; 2)\nthe controlled plant behaviors should satisfy some safety and nonblockingness\nrequirements, in the presence of the edit function. We focus on the class of\nedit functions that satisfy the following properties: 1) the observation\ncapability of the edit function in general can be different from those of the\nsupervisor and the intruder; 2) the edit function can implement insertion,\ndeletion, and replacement operations; 3) the edit function performs bounded\nedit operations, i.e., the length of each string output of the edit function is\nupper bounded by a given constant. We propose an approach to solve this\nco-synthesis problem by modeling it as a distributed supervisor synthesis\nproblem in the Ramadge-Wonham supervisory control framework. By taking the\nspecial structure of this distributed supervisor synthesis problem into\nconsideration and to improve the possibility of finding a non-empty distributed\nsupervisor, we propose two novel synthesis heuristics that incrementally\nsynthesize the supervisor and the edit function. The effectiveness of our\napproach is illustrated on an example in the enforcement of the location\nprivacy.\n"}
+{"abstract": "  In this work we shall study the implications of a subclass of $E$-models\ncosmological attractors, namely of $a$-attractors, on hydrodynamically stable\nslowly rotating neutron stars. Specifically, we shall present the Jordan frame\ntheory of the $a$-attractors, and by using a conformal transformation we shall\nderive the Einstein frame theory. We discuss the inflationary context of\n$a$-attractors in order to specify the allowed range of values for the free\nparameters of the model based on the latest cosmic-microwave-background-based\nPlanck 2018 data. Accordingly, using the notation and physical units frequently\nused in theoretical astrophysics contexts, we shall derive the\nTolman-Oppenheimer-Volkoff equations in the Einstein frame. Assuming a\npiecewise polytropic equation of state, the lowest density part of which shall\nbe chosen to be the WFF1, or APR or the SLy EoS, we numerically solve the\nTolman-Oppenheimer-Volkoff equations using a double shooting python-based\n\"LSODA\" numerical code. The resulting picture depends on the value of the\nparameter $a$ characterizing the $a$-attractors. As we show, for large values\nof $a$, which do not produce a viable inflationary era, the $M-R$ graphs are\nnearly identical to the general relativistic result, and these two are\ndiscriminated at large central densities values. Also, for large $a$-values,\nthe WFF1 equation of state is excluded, due to the GW170817 constraints. In\naddition, the small $a$ cases produce larger masses and radii compared to the\ngeneral relativistic case and are compatible with the GW170817 constraints on\nthe radii of neutron stars. Our results indicate deep and not yet completely\nunderstood connections between non-minimal inflationary attractors and neutron\nstars phenomenology in scalar-tensor theory.\n"}
+{"abstract": "  The observation of Majorana fermions as collective excitations in\ncondensed-matter systems is an ongoing quest, and several state-of-the-art\nexperiments have been performed in the last decade. As a potential avenue in\nthis direction, we simulate the high-harmonic spectrum of Kitaev's\nsuperconducting chain model that hosts Majorana edge modes in its topological\nphase, and find their fingerprints in the spectral profiles. It is well-known\nthat this system exhibits a topological--trivial superconducting phase\ntransition. We demonstrate that high-harmonic spectroscopy is sensitive to the\nphase transition in presence of open boundary conditions due to the presence or\nabsence of these edge modes. The population dynamics of the Majorana edge modes\nare different from the bulk modes, which is the underlying reason for the\ndistinct harmonic profile of both the phases. On the contrary, in presence of\nperiodic boundary conditions with only bulk modes, high-harmonic spectroscopy\nbecomes insensitive to the phase transition with similar harmonic profiles in\nboth phases.\n"}
+{"abstract": "  Skyrmions are important in topological quantum field theory for being soliton\nsolutions of a nonlinear sigma model and in information technology for their\nattractive applications. Skyrmions are believed to be circular and stripy spin\ntextures appeared in the vicinity of skyrmion crystals are termed spiral,\nhelical, and cycloid spin orders, but not skyrmions. Here we present convincing\nevidences showing that those stripy spin textures are skyrmions, \"siblings\" of\ncircular skyrmions in skyrmion crystals and \"cousins\" of isolated circular\nskyrmions. Specifically, isolated skyrmions are excitations when skyrmion\nformation energy is positive. The skyrmion morphologies are various stripy\nstructures when the ground states of chiral magnetic films are skyrmions. The\ndensity of skyrmion number determines the morphology of condensed skyrmion\nstates. At the extreme of one skyrmion in the whole sample, the skyrmion is a\nramified stripe. As the skyrmion number density increases, individual skyrmion\nshapes gradually change from ramified stripes to rectangular stripes, and\neventually to disk-like objects. At a low skyrmion number density, the natural\nwidth of stripes is proportional to the ratio between the exchange stiffness\nconstant and Dzyaloshinskii-Moriya interaction coefficient. At a high skyrmion\nnumber density, skyrmion crystals are the preferred states. Our findings reveal\nthe nature and properties of stripy spin texture, and open a new avenue for\nmanipulating skyrmions, especially condensed skyrmions such as skyrmion\ncrystals.\n"}
+{"abstract": "  Theoretical understanding of evolutionary dynamics in spatially structured\npopulations often relies on non-spatial models. Biofilms are among such\npopulations where a more accurate understanding is of theoretical interest and\ncan reveal new solutions to existing challenges. Here, we studied how the\ngeometry of the environment affects the evolutionary dynamics of expanding\npopulations, using the Eden model. Our results show that fluctuations of\nsub-populations during range expansion in 2D and 3D environments are not\nBrownian. Furthermore, we found that the substrate's geometry interferes with\nthe evolutionary dynamics of populations that grow upon it. Inspired by these\nfindings, we propose a periodically wedged pattern on surfaces prone to develop\nbiofilms. On such patterned surfaces, natural selection becomes less effective\nand beneficial mutants would have a harder time establishing. Additionally,\nthis modification accelerates genetic drift and leads to less diverse biofilms.\nBoth interventions are highly desired for biofilms.\n"}
+{"abstract": "  Transformer-based language model approaches to automated story generation\ncurrently provide state-of-the-art results. However, they still suffer from\nplot incoherence when generating narratives over time, and critically lack\nbasic commonsense reasoning. Furthermore, existing methods generally focus only\non single-character stories, or fail to track characters at all. To improve the\ncoherence of generated narratives and to expand the scope of character-centric\nnarrative generation, we introduce Commonsense-inference Augmented neural\nStoryTelling (CAST), a framework for introducing commonsense reasoning into the\ngeneration process while modeling the interaction between multiple characters.\nWe find that our CAST method produces significantly more coherent and on-topic\ntwo-character stories, outperforming baselines in dimensions including plot\nplausibility and staying on topic. We also show how the CAST method can be used\nto further train language models that generate more coherent stories and reduce\ncomputation cost.\n"}
+{"abstract": "  Prolonged power outages debilitate the economy and threaten public health.\nExisting research is generally limited in its scope to a single event, an\noutage cause, or a region. Here, we provide one of the most comprehensive\nanalyses of U.S. power outages for 2002--2019. We categorized all outage data\ncollected under U.S. federal mandates into four outage causes and computed\nindustry-standard reliability metrics. Our spatiotemporal analysis reveals six\nof the most resilient U.S. states since 2010, improvement of power resilience\nagainst natural hazards in the south and northeast regions, and a\ndisproportionately large number of human attacks for its population in the\nWestern Electricity Coordinating Council region. Our regression analysis\nidentifies several statistically significant predictors and hypotheses for\npower resilience. Furthermore, we propose a novel framework for analyzing\noutage data using differential weighting and influential points to better\nunderstand power resilience. We share curated data and code as Supplementary\nMaterials.\n"}
+{"abstract": "  The cross-correlation sensitivity of two identical balanced photodiode\nheterodyne receivers is characterized. Both balanced photodiodes receive the\nsame weak signal split up equally, a situation equivalent to an astronomical\nspatial interferometer. A common local oscillator (LO) is also split up equally\nand its phase difference between both receivers is stabilized. We show by\nsemi-classical photon deletion theory that the post-detection laser shot noise\ncontributions on both receivers must be completely uncorrelated in this case of\npassing three power splitters. We measured the auto- and cross-correlation\noutputs as a function of weak signal power (system noise temperature\nmeasurement), and obtain a cross-correlation system noise temperature up to 20\ntimes lower than for the auto-correlation system noise temperature of each\nreceiver separately. This is supported by Allan plot measurements showing\ncross-correlation standard deviations 30 times lower than in auto-correlation.\nCareful calibration of the source power shows that the auto-correlation\n(regular) noise temperature of the single balanced receivers is already very\nnear to the quantum limit as expected, which suggests a cross-correlation\nsystem noise temperature below the quantum limit. If validated further, this\nexperimentally clear finding will not only be relevant for astronomical\ninstrumentation but also for other fields like telecommunications and medical\nimaging.\n"}
+{"abstract": "  We present a systematic analysis of our ability to tune chiral\nDzyaloshinskii-Moriya Interactions (DMI) in compensated ferrimagnetic\nPt/GdCo/Pt1-xWx trilayers by cap layer composition. Using first principles\ncalculations, we show that the DMI increases rapidly for only ~ 10% W and\nsaturates thereafter, in agreement with experiments. The calculated DMI shows a\nspread in values around the experimental mean, depending on the atomic\nconfiguration of the cap layer interface. The saturation is attributed to the\nvanishing of spin orbit coupling energy at the cap layer and the simultaneous\nconstancy at the bottom interface. Additionally, we predict the DMI in\nPt/GdCo/X (X=Ta, W, Ir) and find that W in the cap layer favors a higher DMI\nthan Ta and Ir that can be attributed to the difference in d-band alignment\naround the Fermi level. Our results open up exciting combinatorial\npossibilities for controlling the DMI in ferrimagnets towards nucleating and\nmanipulating ultrasmall high-speed skyrmions.\n"}
+{"abstract": "  Many speech processing methods based on deep learning require an automatic\nand differentiable audio metric for the loss function. The DPAM approach of\nManocha et al. learns a full-reference metric trained directly on human\njudgments, and thus correlates well with human perception. However, it requires\na large number of human annotations and does not generalize well outside the\nrange of perturbations on which it was trained. This paper introduces CDPAM, a\nmetric that builds on and advances DPAM. The primary improvement is to combine\ncontrastive learning and multi-dimensional representations to build robust\nmodels from limited data. In addition, we collect human judgments on triplet\ncomparisons to improve generalization to a broader range of audio\nperturbations. CDPAM correlates well with human responses across nine varied\ndatasets. We also show that adding this metric to existing speech synthesis and\nenhancement methods yields significant improvement, as measured by objective\nand subjective tests.\n"}
+{"abstract": "  In upcoming years, the number of Internet-of-Things (IoT) devices is expected\nto surge up to tens of billions of physical objects. However, while the IoT is\noften presented as a promising solution to tackle environmental challenges, the\ndirect environmental impacts generated over the life cycle of the physical\ndevices are usually overlooked. It is implicitly assumed that their\nenvironmental burden is negligible compared to the positive impacts they can\ngenerate. In this paper, we present a parametric framework based on hardware\nprofiles to evaluate the cradle-to-gate carbon footprint of IoT edge devices.\nWe exploit our framework in three ways. First, we apply it on four use cases to\nevaluate their respective production carbon footprint. Then, we show that the\nheterogeneity inherent to IoT edge devices must be considered as the production\ncarbon footprint between simple and complex devices can vary by a factor of\nmore than 150x. Finally, we estimate the absolute carbon footprint induced by\nthe worldwide production of IoT edge devices through a macroscopic analysis\nover a 10-year period. Results range from 22 to 562 MtCO2-eq/year in 2027\ndepending on the deployment scenarios. However, the truncation error\nacknowledged for LCA bottom-up approaches usually lead to an undershoot of the\nenvironmental impacts. We compared the results of our use cases with the few\nreports available from Google and Apple, which suggest that our estimates could\nbe revised upwards by a factor around 2x to compensate for the truncation\nerror. Worst-case scenarios in 2027 would therefore reach more than 1000\nMtCO2-eq/year. This truly stresses the necessity to consider environmental\nconstraints when designing and deploying IoT edge devices.\n"}
+{"abstract": "  Visual search, recommendation, and contrastive similarity learning power a\nwide breadth of technologies that impact billions of users across the world.\nThe best-performing approaches are often complex and difficult to interpret,\nand there are several competing techniques one can use to explain a search\nengine's behavior. We show that the theory of fair credit assignment provides a\nunique axiomatic solution that generalizes several existing recommendation- and\nmetric-explainability techniques in the literature. Using this formalism, we\nare able to determine in what regimes existing approaches fall short of\nfairness and provide variations that are fair in more situations and handle\ncounterfactual information. More specifically, we show existing approaches\nimplicitly approximate second-order Shapley-Taylor indices and use this\nperspective to extend CAM, GradCAM, LIME, SHAP, SBSM, and other methods to\nsearch engines. These extensions can extract pairwise correspondences between\nimages from trained black-box models. We also introduce a fast kernel-based\nmethod for estimating Shapley-Taylor indices that require orders of magnitude\nfewer function evaluations to converge. Finally, we evaluate these methods and\nshow that these game-theoretic measures yield more consistent explanations for\nimage similarity architectures.\n"}
+{"abstract": "  Recently discovered simple quantitative relations, known as bacterial growth\nlaws, hint on the existence of simple underlying principles at the heart of\nbacterial growth. In this work, we provide a unifying picture on how these\nknown relations, as well as new relations that we derive, stems from a\nuniversal autocatalytic network common to all bacteria, facilitating balanced\nexponential growth of individual cells. We show that the core of the cellular\nautocatalytic network is the transcription -- translation machinery -- in\nitself an autocatalytic network comprising several coupled autocatalytic\ncycles, including the ribosome, RNA polymerase, and tRNA charging cycles. We\nderive two types of growth laws per autocatalytic cycle, one relating growth\nrate to the relative fraction of the catalyst and its catalysis rate, and the\nother relating growth rate to all the time scales in the cycle. The structure\nof the autocatalytic network generates numerous regimes in state space,\ndetermined by the limiting components, while the number of growth laws can be\nmuch smaller. We also derive a growth law that accounts for the RNA polymerase\nautocatalytic cycle, which we use to explain how growth rate depends on the\ninducible expression of the rpoB and rpoC genes, which code for the RpoB and C\nprotein subunits of RNA polymerase, and how the concentration of rifampicin,\nwhich targets RNA polymerase, affects growth rate without changing the\nRNA-to-protein ratio. We derive growth laws for tRNA synthesis and charging,\nand predict how growth rate depends on temperature, perturbation to ribosome\nassembly, and membrane synthesis.\n"}
+{"abstract": "  Image classification is a common step in image recognition for machine\nlearning in overhead applications. When applying popular model architectures\nlike MobileNetV2, known vulnerabilities expose the model to counter-attacks,\neither mislabeling a known class or altering box location. This work proposes\nan automated approach to defend these models. We evaluate the use of\nmulti-spectral image arrays and ensemble learners to combat adversarial\nattacks. The original contribution demonstrates the attack, proposes a remedy,\nand automates some key outcomes for protecting the model's predictions against\nadversaries. In rough analogy to defending cyber-networks, we combine\ntechniques from both offensive (\"red team\") and defensive (\"blue team\")\napproaches, thus generating a hybrid protective outcome (\"green team\"). For\nmachine learning, we demonstrate these methods with 3-color channels plus\ninfrared for vehicles. The outcome uncovers vulnerabilities and corrects them\nwith supplemental data inputs commonly found in overhead cases particularly.\n"}
+{"abstract": "  While the particle-in-cell (PIC) method is quite mature, verification and\nvalidation of both newly developed methods and individual codes has largely\nfocused on an idiosyncratic choice of a few test cases. Many of these test\ncases involve either one- or two-dimensional simulations. This is either due to\navailability of (quasi) analytic solutions or historical reasons. Additionally,\ntests often focus on investigation of particular physics problems, such as\nparticle emission or collisions, and do not necessarily study the combined\nimpact of the suite of algorithms necessary for a full featured PIC code. As\nthree dimensional (3D) codes become the norm, there is a lack of benchmarks\ntest that can establish the validity of these codes; existing papers either do\nnot delve into the details of the numerical experiment or provide other\nmeasurable numeric metrics (such as noise) that are outcomes of the simulation.\nThis paper seeks to provide several test cases that can be used for validation\nand bench-marking of particle in cell codes in 3D. We focus on examples that\nare collisionless, and can be run with a reasonable amount of computational\npower. Four test cases are presented in significant detail; these include,\nbasic particle motion, beam expansion, adiabatic expansion of plasma, and two\nstream instability. All presented cases are compared either against existing\nanalytical data or other codes. We anticipate that these cases should help fill\nthe void of bench-marking and validation problems and help the development of\nnew particle in cell codes.\n"}
+{"abstract": "  The unscented transform uses a weighted set of samples called sigma points to\npropagate the means and covariances of nonlinear transformations of random\nvariables. However, unscented transforms developed using either the Gaussian\nassumption or a minimum set of sigma points typically fall short when the\nrandom variable is not Gaussian distributed and the nonlinearities are\nsubstantial. In this paper, we develop the generalized unscented transform\n(GenUT), which uses 2n+1 sigma points to accurately capture up to the diagonal\ncomponents of the skewness and kurtosis tensors of most probability\ndistributions. Constraints can be analytically enforced on the sigma points\nwhile guaranteeing at least second-order accuracy. The GenUT uses the same\nnumber of sigma points as the original unscented transform while also being\napplicable to non-Gaussian distributions, including the assimilation of\nobservations in the modeling of infectious diseases such as coronavirus\n(SARS-CoV-2) causing COVID-19.\n"}
+{"abstract": "  The hard-sphere model is one of the most extensively studied models in\nstatistical physics. It describes the continuous distribution of spherical\nparticles, governed by hard-core interactions. An important quantity of this\nmodel is the normalizing factor of this distribution, called the partition\nfunction. We propose a Markov chain Monte Carlo algorithm for approximating the\ngrand-canonical partition function of the hard-sphere model in $d$ dimensions.\nUp to a fugacity of $\\lambda < \\text{e}/2^d$, the runtime of our algorithm is\npolynomial in the volume of the system. This covers the entire known\nreal-valued regime for the uniqueness of the Gibbs measure.\n  Key to our approach is to define a discretization that closely approximates\nthe partition function of the continuous model. This results in a discrete\nhard-core instance that is exponential in the size of the initial hard-sphere\nmodel. Our approximation bound follows directly from the correlation decay\nthreshold of an infinite regular tree with degree equal to the maximum degree\nof our discretization. To cope with the exponential blow-up of the discrete\ninstance we use clique dynamics, a Markov chain that was recently introduced in\nthe setting of abstract polymer models. We prove rapid mixing of clique\ndynamics up to the tree threshold of the univariate hard-core model. This is\nachieved by relating clique dynamics to block dynamics and adapting the\nspectral expansion method, which was recently used to bound the mixing time of\nGlauber dynamics within the same parameter regime.\n"}
+{"abstract": "  Novel emergent phenomena are expected to occur under conditions exceeding the\nQED critical electric field, where the vacuum becomes unstable to\nelectron-positron pair production. The required intensity to reach this regime,\n$\\sim10^{29}\\,\\mathrm{Wcm^{-2}}$, cannot be achieved even with the most intense\nlasers now being planned/constructed without a sizeable Lorentz boost provided\nby interactions with ultrarelativistic particles. Seeded laser-laser collisions\nmay access this strong-field QED regime at laser intensities as low as\n$\\sim10^{24}\\,\\mathrm{Wcm^{-2}}$. Counterpropagating e-beam--laser interactions\nexceed the QED critical field at still lower intensities\n($\\sim10^{20}\\,\\mathrm{Wcm^{-2}}$ at $\\sim10\\,\\mathrm{GeV}$). Novel emergent\nphenomena are predicted to occur in the \"QED plasma regime\", where strong-field\nquantum and collective plasma effects play off one another. Here the electron\nbeam density becomes a decisive factor. Thus, the challenge is not just to\nexceed the QED critical field, but to do so with high quality, approaching\nsolid-density electron beams. Even though laser wakefield accelerators (LWFA)\nrepresent a very promising research field, conventional accelerators still\nprovide orders of magnitude higher charge densities at energies\n$\\gtrsim10\\,\\mathrm{GeV}$. Co-location of extremely dense and highly energetic\nelectron beams with a multi-petawatt laser system would therefore enable\nseminal research opportunities in high-field physics and laboratory\nastrophysics. This white paper elucidates the potential scientific impact of\nmulti-beam capabilities that combine a multi-PW optical laser,\nhigh-energy/density electron beam, and high-intensity x rays and outlines how\nto achieve such capabilities by co-locating a 3-10 PW laser with a\nstate-of-the-art linear accelerator.\n"}
+{"abstract": "  The key feature of nonlocal kinetic energy functionals is their ability to\nreduce to the Thomas-Fermi functional in the regions of high density and to the\nvon Weizs\\\"acker functional in the region of low density/high density gradient.\nThis behavior is crucial when these functionals are employed in subsystem DFT\nsimulations to approximate the nonadditive kinetic energy. We propose a GGA\nnonadditive kinetic energy functional which mimics the good behavior of\nnonlocal functionals retaining the computational complexity of typical\nsemilocal functionals. The new functional reproduces Kohn-Sham DFT and\nbenchmark CCSD(T) interaction energies of weakly interacting dimers in the\nS22-5 and S66 test sets with a mean absolute deviation well below 1 kcal/mol.\n"}
+{"abstract": "  Federated Bayesian learning offers a principled framework for the definition\nof collaborative training algorithms that are able to quantify epistemic\nuncertainty and to produce trustworthy decisions. Upon the completion of\ncollaborative training, an agent may decide to exercise her legal \"right to be\nforgotten\", which calls for her contribution to the jointly trained model to be\ndeleted and discarded. This paper studies federated learning and unlearning in\na decentralized network within a Bayesian framework. It specifically develops\nfederated variational inference (VI) solutions based on the decentralized\nsolution of local free energy minimization problems within exponential-family\nmodels and on local gossip-driven communication. The proposed protocols are\ndemonstrated to yield efficient unlearning mechanisms.\n"}
+{"abstract": "  We deploy and demonstrate the CoinTossX low-latency, high-throughput,\nopen-source matching engine with orders sent using the Julia and Python\nlanguages. We show how this can be deployed for small-scale local desk-top\ntesting and discuss a larger scale, but local hosting, with multiple traded\ninstruments managed concurrently and managed by multiple clients. We then\ndemonstrate a cloud based deployment using Microsoft Azure, with large-scale\nindustrial and simulation research use cases in mind. The system is exposed and\ninteracted with via sockets using UDP SBE message protocols and can be\nmonitored using a simple web browser interface using HTTP. We give examples\nshowing how orders can be be sent to the system and market data feeds monitored\nusing the Julia and Python languages. The system is developed in Java with\norders submitted as binary encodings (SBE) via UDP protocols using the Aeron\nMedia Driver as the low-latency, high throughput message transport. The system\nseparates the order-generation and simulation environments e.g. agent-based\nmodel simulation, from the matching of orders, data-feeds and various\nmodularised components of the order-book system. This ensures a more natural\nand realistic asynchronicity between events generating orders, and the events\nassociated with order-book dynamics and market data-feeds. We promote the use\nof Julia as the preferred order submission and simulation environment.\n"}
+{"abstract": "  Training GANs on videos is even more sophisticated than on images because\nvideos have a distinguished dimension: time. While recent methods designed a\ndedicated architecture considering time, generated videos are still far from\nindistinguishable from real videos. In this paper, we introduce ArrowGAN\nframework, where the discriminators learns to classify arrow of time as an\nauxiliary task and the generators tries to synthesize forward-running videos.\nWe argue that the auxiliary task should be carefully chosen regarding the\ntarget domain. In addition, we explore categorical ArrowGAN with recent\ntechniques in conditional image generation upon ArrowGAN framework, achieving\nthe state-of-the-art performance on categorical video generation. Our extensive\nexperiments validate the effectiveness of arrow of time as a self-supervisory\ntask, and demonstrate that all our components of categorical ArrowGAN lead to\nthe improvement regarding video inception score and Frechet video distance on\nthree datasets: Weizmann, UCFsports, and UCF-101.\n"}
+{"abstract": "  Understanding the 3D world from 2D projected natural images is a fundamental\nchallenge in computer vision and graphics. Recently, an unsupervised learning\napproach has garnered considerable attention owing to its advantages in data\ncollection. However, to mitigate training limitations, typical methods need to\nimpose assumptions for viewpoint distribution (e.g., a dataset containing\nvarious viewpoint images) or object shape (e.g., symmetric objects). These\nassumptions often restrict applications; for instance, the application to\nnon-rigid objects or images captured from similar viewpoints (e.g., flower or\nbird images) remains a challenge. To complement these approaches, we propose\naperture rendering generative adversarial networks (AR-GANs), which equip\naperture rendering on top of GANs, and adopt focus cues to learn the depth and\ndepth-of-field (DoF) effect of unlabeled natural images. To address the\nambiguities triggered by unsupervised setting (i.e., ambiguities between smooth\ntexture and out-of-focus blurs, and between foreground and background blurs),\nwe develop DoF mixture learning, which enables the generator to learn real\nimage distribution while generating diverse DoF images. In addition, we devise\na center focus prior to guiding the learning direction. In the experiments, we\ndemonstrate the effectiveness of AR-GANs in various datasets, such as flower,\nbird, and face images, demonstrate their portability by incorporating them into\nother 3D representation learning GANs, and validate their applicability in\nshallow DoF rendering.\n"}
+{"abstract": "  Near-field imaging experiments exist both in optics and microwaves with often\ndifferent methods and theoretical supports. For millimeter waves or THz waves,\ntechniques from both fields can be merged to identify materials at the micron\nscale on the surface or in near-surface volumes. The principle of such\nnear-field vector imaging at the frequency of 60 GHz is discussed in detail\nhere. We develop techniques for extracting vector voltages and methods for\nextracting the normalized near-field vector reflection on the sample. In\nparticular, the subharmonic IQ mixer imbalance, which produced corrupted\noutputs either due to amplitude or phase differences, must be taken into\naccount and compensated for to avoid any systematic errors. We provide a method\nto fully characterize these imperfections and to isolate the only contribution\nof the near-field interaction between the probe and the sample. The effects of\nthe mechanical modulation waveform and harmonic rank used for signal\nacquisition are also discussed.\n"}
+{"abstract": "  The characteristic electron densities, temperatures, and thermal\ndistributions of 1MK active region loops are now fairly well established, but\ntheir coronal magnetic field strengths remain undetermined. Here we present\nmeasurements from a sample of coronal loops observed by the Extreme-ultraviolet\nImaging Spectrometer (EIS) on Hinode. We use a recently developed diagnostic\ntechnique that involves atomic radiation modeling of the contribution of a\nmagnetically induced transition (MIT) to the Fe X 257.262A spectral line\nintensity. We find coronal magnetic field strengths in the range of 60--150G.\nWe discuss some aspects of these new results in the context of previous\nmeasurements using different spectropolarimetric techniques, and their\ninfluence on the derived Alfv\\'{e}n speeds and plasma $\\beta$ in coronal loops.\n"}
+{"abstract": "  In this paper, we study the existence of traveling waves for a fourth order\nSchr\\\" odinger equations with mixed dispersion, that is, solutions to\n$$\\Delta^2 u +\\beta \\Delta u +i V \\nabla u +\\alpha u =|u|^{p-2} u,\\ in\\ \\R^N ,\\\nN\\geq 2.$$ We consider this equation in the Helmholtz regime, when the Fourier\nsymbol $P$ of our operator is strictly negative at some point. Under suitable\nassumptions, we prove the existence of solution using the dual method of\nEvequoz and Weth provided that $p\\in (p_1 , 2N/(N-4)_+)$. The real number $p_1$\ndepends on the number of principal curvature of $M$ staying bounded away from\n$0$, where $M$ is the hypersurface defined by the roots of $P$. We also\nobtained estimates on the Green function of our operator and a $L^p - L^q$\nresolvent estimate which can be of independent interest and can be applied to\nother operators.\n"}
+{"abstract": "  Polyphenols are natural molecules of crucial importance in many applications,\nof which tannic acid (TA) is one of the most abundant and established. Most\nhigh-value applications require precise control of TA interactions with the\nsystem of interest. However, the molecular structure of TA is still not\ncomprehended at the atomic level, of which all electronic and reactivity\nproperties depend. Here, we combine an enhanced sampling global optimization\nmethod with density functional theory (DFT)-based calculations to explore the\nconformational space of TA assisted by unsupervised machine learning\nvisualization, and then investigate its lowest energy conformers. We study the\nexternal environment's effect on the TA structure and properties. We find that\nvacuum favors compact structures by stabilizing peripheral atoms' weak\ninteractions, while in water, the molecule adopts more open conformations. The\nfrontier molecular orbitals of the conformers with lowest harmonic vibrational\nfree energy have a HOMO-LUMO energy gap of 2.21 (3.27) eV, increasing to 2.82\n(3.88) eV in water, at the DFT generalized gradient approximation (and hybrid)\nlevel of theory. Structural differences also change the distribution of\npotential reactive sites. We establish the fundamental importance of accurate\nstructural consideration in determining TA and related polyphenols interactions\nin relevant technological applications.\n"}
+{"abstract": "  In this paper we state two quantitative Sylvester-Gallai results for high\ndegree curves. Moreover we give two constructions which show that these results\nare not trivial.\n"}
+{"abstract": "  The main goal of this paper is to develop the MRA theory along with wavelet\ntheory in L2(Qp). Generalized scaling sets are important in wavelet theory\nbecause it determine multiwavelet sets. Although the theory of scaling set and\ngeneralized scaling set on R and many other local field of positive\ncharacteristic are available but not on Qp. This article contains discussion of\nsome necessary conditions of scaling set and characterize generalized scaling\nset with examples.\n"}
+{"abstract": "  Lattice QCD calculations of scattering phaseshifts and resonance parameters\nin the two-body sector are becoming precision studies. Early calculations\nemployed L\\\"uscher's formula for extracting these quantities at lowest order.\nAs the calculations become more ambitious, higher-order relations are required.\nIn this study we present a way to validate the higher-order quantization\nconditions. This is an important step given the involved derivations of these\nformulae. We derive and validate quantization conditions up to $\\ell=5$ partial\nwaves in both cubic and elongated geometries, and for states zero and non-zero\ntotal momentum. For all 45 quantization conditions we considered (22 in cubic\nbox, 23 in elongated box) we find perfect agreement.\n"}
+{"abstract": "  When analysing multiple time series that may be subject to changepoints, it\nis sometimes possible to specify a priori, by means of a graph G, which pairs\nof time series are likely to be impacted by simultaneous changepoints. This\narticle proposes a novel Bayesian changepoint model for multiple time series\nthat borrows strength across clusters of connected time series in G to detect\nweak signals for synchronous changepoints. The graphical changepoint model is\nfurther extended to allow dependence between nearby but not necessarily\nsynchronous changepoints across neighbour time series in G. A novel reversible\njump MCMC algorithm making use of auxiliary variables is proposed to sample\nfrom the graphical changepoint model. The merit of the proposed model is\ndemonstrated via a changepoint analysis of real network authentication data\nfrom Los Alamos National Laboratory (LANL), with some success at detecting weak\nsignals for network intrusions across users that are linked by network\nconnectivity, whilst limiting the number of false alerts.\n"}
+{"abstract": "  We use Volterra-Hamilton systems theory and their associated cost functional\nto study the population dynamics and productive processes of coral reefs in\nrecovery from bleaching and show that the cost of production remains the same\nafter the process. The KCC-theory geometrical invariants are determined for the\nmodel proposed to describe the renewed symbiotic interaction between coral and\nalgae.\n"}
+{"abstract": "  In this work we consider a multidimensional KdV type equation, the\nZakharov-Kuznetsov (ZK) equation. We derive the 3-wave kinetic equation from\nboth the stochastic ZK equation and the deterministic ZK equation with random\ninitial condition. The equation is given on a hypercubic lattice of size $L$.\nIn the case of the stochastic ZK equation, we show that the two point\ncorrelation function can be asymptotically expressed as the solution of the\n3-wave kinetic equation at the kinetic limit under very general assumptions, in\nwhich the initial condition is out of equilibrium and the size $L$ of the\ndomain is fixed. In the case of the deterministic ZK equation with random\ninitial condition, the kinetic equation can also be derived at the kinetic\nlimit, but under more restrictive assumptions.\n"}
+{"abstract": "  The commercialization of Virtual Reality (VR) headsets has made immersive and\n360-degree video streaming the subject of intense interest in the industry and\nresearch communities. While the basic principles of video streaming are the\nsame, immersive video presents a set of specific challenges that need to be\naddressed. In this survey, we present the latest developments in the relevant\nliterature on four of the most important ones: (i) omnidirectional video coding\nand compression, (ii) subjective and objective Quality of Experience (QoE) and\nthe factors that can affect it, (iii) saliency measurement and Field of View\n(FoV) prediction, and (iv) the adaptive streaming of immersive 360-degree\nvideos. The final objective of the survey is to provide an overview of the\nresearch on all the elements of an immersive video streaming system, giving the\nreader an understanding of their interplay and performance.\n"}
+{"abstract": "  We introduce twisted arrow categories of operads and of algebras over\noperads. Up to equivalence of categories, the simplex category $\\Delta$,\nSegal's category $\\Gamma$, Connes cyclic category $\\Lambda$, Moerdijk--Weiss\ndendroidal category $\\Omega$, and categories similar to graphical categories of\nHackney--Robertson--Yau are twisted arrow categories of symmetric or cyclic\noperads. Twisted arrow categories of operads admit Segal presheaves and 2-Segal\npresheaves, or decomposition spaces. Twisted arrow category of an operad $P$ is\nthe $(\\infty, 1)$-localization of the corresponding category $\\Omega/P$ by the\nboundary preserving morphisms. Under mild assumptions, twisted arrow categories\nof operads, and closely related universal enveloping categories, are\ngeneralized Reedy. We also introduce twisted arrow operads, which are related\nto Baez--Dolan plus construction.\n"}
+{"abstract": "  In this work we explore the new catalog of galactic open clusters that became\navailable recently, containing 1750 clusters that have been re-analysed using\nthe Gaia DR2 catalog to determine the stellar memberships. We used the young\nopen clusters as tracers of spiral arms and determined the spiral pattern\nrotation speed of the Galaxy and the corotation radius, the strongest Galactic\nresonance. The sample of open clusters used here increases the last one from\nDias et al. (2019) used in the previous determination of the pattern speed by\ndozens objects. In addition, the distances and ages values are better\ndetermined, using improvements to isochrone fitting and including an updated\nextinction polynomial for the Gaia DR2 photometric band-passes, and the\nGalactic abundance gradient as a prior for metallicity. In addition to the\nbetter age determinations, the catalog contains better positions in the\nGalactic plane and better proper motions. This allow us to discuss not only the\npresent space distribution of the clusters, but also the space distribution of\nthe clusters's birthplaces, obtained by integration of the orbits for a time\nequal to their age. The value of the rotation velocity of the arms ($28.5 \\pm\n1.0$ km s$^{-1}$ kpc$^{-1}$) implies that the corotation radius ($R_c$) is\nclose to the solar Galactic orbit ($R_c/R_0 = 1.01\\pm0.08$), which is supported\nby other observational evidence discussed in this text. A simulation is\npresented, illustrating the motion of the clusters in the reference frame of\ncorotation. We also present general statistics of the catalog of clusters, like\nspatial distribution, distribution relative to height from the Galactic plane,\nand distribution of ages and metallicity. An important feature of the space\ndistribution, the corotation gap in the gas distribution and its consequences\nfor the young clusters, is discussed.\n"}
+{"abstract": "  We numerically investigate the dynamical properties of $\\kappa$-type\nmolecular conductors in their antiferromagnetic Mott insulating state. By\ntreating the extended Hubbard model on the two-dimensional $\\kappa$-type\nlattice within the Lanzcos exact diagonalization method, we calculate the\none-particle spectral function $A(\\boldsymbol{k}, \\omega)$ and the optical\nabsorption spectra taking advantage of twisted boundary conditions. We find\nspin splitting in $A(\\boldsymbol{k}, \\omega)$ predicted by a previous\nHartree-Fock study [M. Naka et al., Nat. Commun. 10, 4305 (2019)]; namely,\ntheir up- and down-spin components become different in the general\n$\\boldsymbol{k}$-points of the Brillouin zone, even without the spin-orbit\ncoupling. Furthermore, we demonstrate the variation in the optical properties\nnear the Mott gap by the presence or absence of the antiferromagnetic order,\ntuned by a small staggered magnetic field.\n"}
+{"abstract": "  The leap eccentric connectivity index of $G$ is defined as\n$$L\\xi^{C}(G)=\\sum_{v\\in V(G)}d_{2}(v|G)e(v|G)$$ where $d_{2}(v|G) $ be the\nsecond degree of the vertex $v$ and $e(v|G)$ be the eccentricity of the vertex\n$v$ in $G$. In this paper, we first give a counterexample for that if $G$ be a\ngraph and $S(G)$ be its the subdivision graph, then each vertex $v\\in V(G)$,\n$e(v|S(G))=2e(v|G)$ by Yarahmadi in \\cite{yar14} in Theorem 3.1. And we\ndescribe the upper and lower bounds of the leap eccentric connectivity index of\nfour graphs based on subdivision edges, and then give the expressions of the\nleap eccentric connectivity index of join graph based on subdivision, finally,\ngive the bounds of the leap eccentric connectivity index of four variants of\nthe corona graph.\n"}
+{"abstract": "  Radially self-accelerating light exhibits an intensity pattern that describes\na spiraling trajectory around the optical axis as the beam propagates. In this\narticle, we show in simulation and experiment how such beams can be used to\nperform a high-accuracy distance measurement with respect to a reference using\nsimple off-axis intensity detection. We demonstrate that generating beams whose\nintensity pattern simultaneously spirals with fast and slow rotation components\nenables a distance measurement with high accuracy over a broad range, using the\nhigh and low rotation frequency, respectively. In our experiment, we achieve an\naccuracy of around 2~$\\mu$m over a longitudinal range of more than 2~mm using a\nsingle beam and only two quadrant detectors. As our method relies on\nsingle-beam interference and only requires a static generation and simple\nintensity measurements, it is intrinsically stable and might find applications\nin high-speed measurements of longitudinal position.\n"}
+{"abstract": "  The parallel strong-scaling of Krylov iterative methods is largely determined\nby the number of global reductions required at each iteration. The GMRES and\nKrylov-Schur algorithms employ the Arnoldi algorithm for nonsymmetric matrices.\nThe underlying orthogonalization scheme is left-looking and processes one\ncolumn at a time. Thus, at least one global reduction is required per\niteration. The traditional algorithm for generating the orthogonal Krylov basis\nvectors for the Krylov-Schur algorithm is classical Gram Schmidt applied twice\nwith reorthogonalization (CGS2), requiring three global reductions per step. A\nnew variant of CGS2 that requires only one reduction per iteration is applied\nto the Arnoldi-QR iteration. Strong-scaling results are presented for finding\neigenvalue-pairs of nonsymmetric matrices. A preliminary attempt to derive a\nsimilar algorithm (one reduction per Arnoldi iteration with a robust\northogonalization scheme) was presented by Hernandez et al.(2007). Unlike our\napproach, their method is not forward stable for eigenvalues.\n"}
+{"abstract": "  Recently, the magnetic topological insulator MnBi$_2$Te$_4$ emerged as a\ncompetitive platform to realize quantum anomalous Hall (QAH) states. We report\na Berry-curvature splitting mechanism to realize the QAH effect in the\ndisordered magnetic TI multilayers when switching from an antiferromagnetic\norder to a ferromagnetic order. We reveal that the splitting of spin-resolved\nBerry curvature, originating from the separation of the mobility edge during\nthe magnetic switching, can give rise to a QAH insulator even \\emph{without}\nclosing the band gap. We present a global phase diagram, and also provide a\nphenomenological picture to elucidate the Berry curvature splitting mechanism\nby the evolution of topological charges. At last, we predict that the Berry\ncurvature splitting mechanism will lead to a reentrant QAH effect, which can be\ndetected by tuning gate voltage. Our theory will be instructive for the studies\nof the QAH effect in MnBi$_2$Te$_4$ in future experiments.\n"}
+{"abstract": "  We study the effect of Dzyaloshinskii-Moriya (DM) interaction on the\ntriangular lattice $U(1)$ quantum spin liquid (QSL) which is stabilized by\nring-exchange interactions. A weak DM interaction introduces a staggered flux\nto the $U(1)$ QSL state and changes the density of states at the spinon fermi\nsurface. If the DM vector contains in-plane components, then the spinons gain\nnonzero Berry phase. The resultant thermal conductances $\\kappa_{xx}$ and\n$\\kappa_{xy}$ qualitatively agree with the experimental results on the material\nEtMe$_3$Sb[Pd(dmit)$_2]_2$. Furthermore, owing to perfect nesting of the fermi\nsurface, a spin density wave state is triggered by larger DM interactions. On\nthe other hand, when the ring-exchange interaction decreases, another\nanti-ferromagnetic (AFM) phase with $120^\\circ$ order shows up which is\nproximate to a $U(1)$ Dirac QSL. We discuss the difference of the two AFM\nphases from their static structure factors and excitation spectra.\n"}
+{"abstract": "  We propose a principled method for projecting an arbitrary square matrix to\nthe non-convex set of asymptotically stable matrices. Leveraging ideas from\nlarge deviations theory, we show that this projection is optimal in an\ninformation-theoretic sense and that it simply amounts to shifting the initial\nmatrix by an optimal linear quadratic feedback gain, which can be computed\nexactly and highly efficiently by solving a standard linear quadratic regulator\nproblem. The proposed approach allows us to learn the system matrix of a stable\nlinear dynamical system from a single trajectory of correlated state\nobservations. The resulting estimator is guaranteed to be stable and offers\nexplicit statistical bounds on the estimation error.\n"}
+{"abstract": "  In this paper we shed light on the impact of fine-tuning over social media\ndata in the internal representations of neural language models. We focus on bot\ndetection in Twitter, a key task to mitigate and counteract the automatic\nspreading of disinformation and bias in social media. We investigate the use of\npre-trained language models to tackle the detection of tweets generated by a\nbot or a human account based exclusively on its content. Unlike the general\ntrend in benchmarks like GLUE, where BERT generally outperforms generative\ntransformers like GPT and GPT-2 for most classification tasks on regular text,\nwe observe that fine-tuning generative transformers on a bot detection task\nproduces higher accuracies. We analyze the architectural components of each\ntransformer and study the effect of fine-tuning on their hidden states and\noutput representations. Among our findings, we show that part of the\nsyntactical information and distributional properties captured by BERT during\npre-training is lost upon fine-tuning while the generative pre-training\napproach manage to preserve these properties.\n"}
+{"abstract": "  An Unmanned Aerial Vehicle (UAV) is a promising technology for providing\nwireless coverage to ground user devices. For all the infrastructure\ncommunication networks destroyed in disasters, UAVs battery life is challenging\nduring service delivery in a post-disaster scenario. Therefore, selecting\ncluster heads among user devices plays a vital role in detecting UAV signals\nand processing data for improving UAV energy efficacy and reliable\nConnectivity. This paper focuses on the performance evaluation of the\nclustering approach performance in detecting wireless coverage services with\nimproving energy efficiency. The evaluation performance is a realistic\nsimulation for the ground to air channel Line of Sight (LoS). The results show\nthat the cluster head can effectively link the UAVs and cluster members at\nminimal energy expenditure. The UAVs altitudes and path loss exponent affected\nuser devices for detecting wireless coverage. Moreover, the bit error rate in\nthe cluster heads is considered for reliable Connectivity in post disaster.\nClustering stabilizes the clusters linking the uncovered nodes to the UAV, and\nits effectiveness in doing so resulted in its ubiquity in emergency\ncommunication systems.\n"}
+{"abstract": "  We construct regular, asymptotically flat black holes of higher order scalar\ntensor (DHOST) theories, which are obtained by making use of a generalized\nKerr-Schild solution generating method. The solutions depend on a mass\nintegration constant, admit a smooth core of chosen regularity, and generically\nhave an inner and outer event horizon. In particular, below a certain mass\nthreshold, we find massive, horizonless, particle-like solutions. We scan\nthrough possible observational signatures ranging from weak to strong gravity\nand study the thermodynamics of our regular solutions, comparing them, when\npossible, to General Relativity black holes and their thermodynamic laws.\n"}
+{"abstract": "  We calculate the superconformal indices of a class of six-dimensional ${\\cal\nN}=(1,0)$ superconformal field theories realized on M5-branes at\n$\\mathbb{C}^2/\\mathbb{Z}_k$ singularity by using the method developed in\nprevious works of the authors and collaborators. We use the AdS/CFT\ncorrespondence, and finite $N$ corrections are included as the contribution of\nM2-branes wrapped on two-cycles in $S^4/\\mathbb{Z}_k$. We confirm that the\nindices are consistent with the expected flavor symmetries.\n"}
+{"abstract": "  There is a growing desire to create computer systems that can communicate\neffectively to collaborate with humans on complex, open-ended activities.\nAssessing these systems presents significant challenges. We describe a\nframework for evaluating systems engaged in open-ended complex scenarios where\nevaluators do not have the luxury of comparing performance to a single right\nanswer. This framework has been used to evaluate human-machine creative\ncollaborations across story and music generation, interactive block building,\nand exploration of molecular mechanisms in cancer. These activities are\nfundamentally different from the more constrained tasks performed by most\ncontemporary personal assistants as they are generally open-ended, with no\nsingle correct solution, and often no obvious completion criteria.\n  We identified the Key Properties that must be exhibited by successful\nsystems. From there we identified \"Hallmarks\" of success -- capabilities and\nfeatures that evaluators can observe that would be indicative of progress\ntoward achieving a Key Property. In addition to being a framework for\nassessment, the Key Properties and Hallmarks are intended to serve as goals in\nguiding research direction.\n"}
+{"abstract": "  We consider the problem of efficient ultra-massive multiple-input\nmultiple-output (UM-MIMO) data detection in terahertz (THz)-band non-orthogonal\nmultiple access (NOMA) systems. We argue that the most common THz NOMA\nconfiguration is power-domain superposition coding over quasi-optical\ndoubly-massive MIMO channels. We propose spatial tuning techniques that modify\nantenna subarray arrangements to enhance channel conditions. Towards recovering\nthe superposed data at the receiver side, we propose a family of data detectors\nbased on low-complexity channel matrix puncturing, in which higher-order\ndetectors are dynamically formed from lower-order component detectors. We first\ndetail the proposed solutions for the case of superposition coding of multiple\nstreams in point-to-point THz MIMO links. We then extend the study to\nmulti-user NOMA, in which randomly distributed users get grouped into narrow\ncell sectors and are allocated different power levels depending on their\nproximity to the base station. We show that successive interference\ncancellation is carried with minimal performance and complexity costs under\nspatial tuning. We derive approximate bit error rate (BER) equations, and we\npropose an architectural design to illustrate complexity reductions. Under\ntypical THz conditions, channel puncturing introduces more than an order of\nmagnitude reduction in BER at high signal-to-noise ratios while reducing\ncomplexity by approximately 90%.\n"}
+{"abstract": "  We analyze the correlation coefficient T(E_e), which was introduced by Ebel\nand Feldman (Nucl. Phys. 4, 213 (1957)). The correlation coefficient T(E_e) is\ninduced by the correlations of the neutron spin with the antineutrino\n3-momentum and the electron spin with the electron 3-momentum. Such a\ncorrelation structure is invariant under discrete P, C and T symmetries. The\ncorrelation coefficient T(E_e), calculated to leading order in the large\nnucleon mass m_N expansion, is equal to T(E_e) = - 2 g_A(1 + g_A)/(1 + 3 g^2_A)\n= - B_0, i.e. of order |T(E_e)| ~ 1, where $g_A$ is the axial coupling\nconstant. Within the Standard Model (SM) we describe the correlation\ncoefficient $T(E_e)$ at the level of 10^{-3} by taking into the radiative\ncorrections of order O(\\alpha/\\pi) or the outer model-independent radiative\ncorrections, where \\alpha is the fine-structure constant, and the corrections\nof order O(E_e/m_N), caused by weak magnetism and proton recoil. We calculate\nalso the contributions of interactions beyond the SM, including the\ncontributions of the second class currents.\n"}
+{"abstract": "  Interactions between the intra- and inter-domain routing protocols received\nlittle attention despite playing an important role in forwarding transit\ntraffic. More precisely, by default, IGP distances are taken into account by\nBGP to select the closest exit gateway for the transit traffic (hot-potato\nrouting). Upon an IGP update, the new best gateway may change and should be\nupdated through the (full) re-convergence of BGP, causing superfluous BGP\nprocessing and updates in many cases. We propose OPTIC (Optimal Protection\nTechnique for Inter-intra domain Convergence), an efficient way to assemble\nboth protocols without losing the hot-potato property. OPTIC pre-computes sets\nof gateways (BGP next-hops) shared by groups of prefixes. Such sets are\nguaranteed to contain the post-convergence gateway after any single IGP event\nfor the grouped prefixes. The new optimal exits can be found through a single\nwalk-through of each set, allowing the transit traffic to benefit from optimal\nBGP routes almost as soon as the IGP converges. Compared to vanilla BGP,\nOPTIC's structures allow it to consider a reduced number of entries: this\nnumber can be reduced by 99\\% for stub networks. The update of OPTIC's\nstructures, which is not required as long as border routers remain at least\nbi-connected, scales linearly in time with its number of groups.\n"}
+{"abstract": "  Immigration to the United States is certainly not a new phenomenon, and it is\ntherefore natural for immigration, culture and identity to be given due\nattention by the public and policy makers. However, current discussion of\nimmigration, legal and illegal, and the philosophical underpinnings is lost in\ntranslation, not necessarily on ideological lines, but on political\norientation. In this paper we reexamine the philosophical underpinnings of the\nmelting pot versus multiculturalism as antecedents and precedents of current\nimmigration debate and how the core issues are lost in translation. We take a\nbrief look at immigrants and the economy to situate the current immigration\ndebate. We then discuss the two philosophical approaches to immigration and how\nthe understanding of the philosophical foundations can help streamline the\ncurrent immigration debate.\n"}
+{"abstract": "  How the solar electromagnetic energy entering the Earth's atmosphere varied\nsince pre-industrial times is an important consideration in the climate change\ndebate. Detrimental to this debate, estimates of the change in total solar\nirradiance (TSI) since the Maunder minimum, an extended period of weak solar\nactivity preceding the industrial revolution, differ markedly, ranging from a\ndrop of 0.75 Wm-2 to a rise of 6.3 Wm-2. Consequently, the exact contribution\nby solar forcing to the rise in global temperatures over the past centuries\nremains inconclusive. Adopting a novel approach based on state-of-the-art solar\nimagery and numerical simulations, we establish the TSI level of the Sun when\nit is in its least-active state to be 2.0 +/- 0.7 Wm-2 below the 2019 level.\nThis means TSI could not have risen since the Maunder minimum by more than this\namount, thus restricting the possible role of solar forcing in global warming.\n"}
+{"abstract": "  We calculate the lifetime of the deuteron from dimension-six quark operators\nthat violate baryon number by one unit. We construct an effective field theory\nfor $|\\Delta B|=1$ interactions that give rise to nucleon and $\\Delta B=1$\ndeuteron decay in a systematic expansion. We show that up to and including\nnext-to-leading order the deuteron decay rate is given by the sum of the decay\nrates of the free proton and neutron. The first nuclear correction is expected\nto contribute at the few-percent level and comes with an undetermined\nlow-energy constant. We discuss its relation to earlier potential-model\ncalculations.\n"}
+{"abstract": "  Quantum resources and protocols are known to outperform their classical\ncounterparts in variety of communication and information processing tasks.\nRandom Access Codes (RACs) are one such cryptographically significant family of\nbipartite communication tasks, wherein, the sender encodes a data set\n(typically a string of input bits) onto a physical system of bounded dimension\nand transmits it to the receiver, who then attempts to guess a randomly chosen\npart of the sender's data set (typically one of the sender's input bits). In\nthis work, we introduce a generalization of this task wherein the receiver, in\naddition to the individual input bits, aims to retrieve randomly chosen\nfunctions of sender's input string. Specifically, we employ sets of mutually\nunbiased balanced functions (MUBS), such that perfect knowledge of any one of\nthe constituent functions yields no knowledge about the others. We investigate\nand bound the performance of (i.) classical, (ii.) quantum prepare and measure,\nand (iii.) entanglement assisted classical communication (EACC) protocols for\nthe resultant generalized RACs (GRACs). Finally, we detail the case of GRACs\nwith three input bits, find maximal success probabilities for classical,\nquantum and EACC protocols, along with the effect of noisy quantum channels on\nthe performance of quantum protocols. Moreover, with the help of this case\nstudy, we reveal several characteristic properties of the GRACs which deviate\nfrom the standard RACs.\n"}
+{"abstract": "  Machine translation models have discrete vocabularies and commonly use\nsubword segmentation techniques to achieve an 'open vocabulary.' This approach\nrelies on consistent and correct underlying unicode sequences, and makes models\nsusceptible to degradation from common types of noise and variation. Motivated\nby the robustness of human language processing, we propose the use of visual\ntext representations, which dispense with a finite set of text embeddings in\nfavor of continuous vocabularies created by processing visually rendered text\nwith sliding windows. We show that models using visual text representations\napproach or match performance of traditional text models on small and larger\ndatasets. More importantly, models with visual embeddings demonstrate\nsignificant robustness to varied types of noise, achieving e.g., 25.9 BLEU on a\ncharacter permuted German-English task where subword models degrade to 1.9.\n"}
+{"abstract": "  Spine-related diseases have high morbidity and cause a huge burden of social\ncost. Spine imaging is an essential tool for noninvasively visualizing and\nassessing spinal pathology. Segmenting vertebrae in computed tomography (CT)\nimages is the basis of quantitative medical image analysis for clinical\ndiagnosis and surgery planning of spine diseases. Current publicly available\nannotated datasets on spinal vertebrae are small in size. Due to the lack of a\nlarge-scale annotated spine image dataset, the mainstream deep learning-based\nsegmentation methods, which are data-driven, are heavily restricted. In this\npaper, we introduce a large-scale spine CT dataset, called CTSpine1K, curated\nfrom multiple sources for vertebra segmentation, which contains 1,005 CT\nvolumes with over 11,100 labeled vertebrae belonging to different spinal\nconditions. Based on this dataset, we conduct several spinal vertebrae\nsegmentation experiments to set the first benchmark. We believe that this\nlarge-scale dataset will facilitate further research in many spine-related\nimage analysis tasks, including but not limited to vertebrae segmentation,\nlabeling, 3D spine reconstruction from biplanar radiographs, image\nsuper-resolution, and enhancement.\n"}
+{"abstract": "  A classical branch of graph algorithms is graph transversals, where one seeks\na minimum-weight subset of nodes in a node-weighted graph $G$ which intersects\nall copies of subgraphs~$F$ from a fixed family $\\mathcal F$. Many such graph\ntransversal problems have been shown to admit polynomial-time approximation\nschemes (PTAS) for planar input graphs $G$, using a variety of techniques like\nthe shifting technique (Baker, J. ACM 1994), bidimensionality (Fomin et al.,\nSODA 2011), or connectivity domination (Cohen-Addad et al., STOC 2016). These\ntechniques do not seem to apply to graph transversals with parity constraints,\nwhich have recently received significant attention, but for which no PTASs are\nknown. In the even-cycle transversal (\\ECT) problem, the goal is to find a\nminimum-weight hitting set for the set of even cycles in an undirected graph.\n  For ECT, Fiorini et al. (IPCO 2010) showed that the integrality gap of the\nstandard covering LP relaxation is $\\Theta(\\log n)$, and that adding sparsity\ninequalities reduces the integrality gap to~10. Our main result is a\nprimal-dual algorithm that yields a $47/7\\approx6.71$-approximation for ECT on\nnode-weighted planar graphs, and an integrality gap of the same value for the\nstandard LP relaxation on node-weighted planar graphs.\n"}
+{"abstract": "  Geo-indistinguishability and expected inference error are two complementary\nnotions for location privacy. The joint guarantee of differential privacy\n(indistinguishability) and distortion privacy (inference error) limits the\ninformation leakage. In this paper, we analyze the differential privacy of PIVE\ndynamic location obfuscation mechanism proposed by Yu, Liu and Pu (ISOC Network\nand Distributed System Security Symposium, 2017) and show that PIVE fails to\noffer differential privacy guarantees on adaptive protection location set as\nclaimed. Specifically, we demonstrate that different protection location sets\ncould intersect with one another due to the defined search algorithm and then\ndifferent locations in the same protection location set could have different\nprotection diameters. As a result, we can show that the proof of differential\nprivacy for PIVE is incorrect. We also make some detailed discussions on\nfeasible privacy frameworks with achieving personalized error bounds.\n"}
+{"abstract": "  We introduced a generalized Wilson line gauge link that reproduces both\nstaple and near straight links in different limits. We then studied the\ngauge-invariant bi-local orbital angular momentum operator with such a general\ngauge link, in the framework of Chen et. al. decomposition of gauge fields. At\nthe appropriate combination of limits, the operator reproduces both\nJaffe-Manohar and Ji's operator structure and offers a continuous analytical\ninterpolation between the two in the small-$x$ limit. We also studied the\npotential OAM which is defined as the difference between the two, and how it\ndepends on the geometry or orientation of the gauge links.\n"}
+{"abstract": "  Graphs naturally lend themselves to model the complexities of Hyperspectral\nImage (HSI) data as well as to serve as semi-supervised classifiers by\npropagating given labels among nearest neighbours. In this work, we present a\nnovel framework for the classification of HSI data in light of a very limited\namount of labelled data, inspired by multi-view graph learning and graph signal\nprocessing. Given an a priori superpixel-segmented hyperspectral image, we seek\na robust and efficient graph construction and label propagation method to\nconduct semi-supervised learning (SSL). Since the graph is paramount to the\nsuccess of the subsequent classification task, particularly in light of the\nintrinsic complexity of HSI data, we consider the problem of finding the\noptimal graph to model such data. Our contribution is two-fold: firstly, we\npropose a multi-stage edge-efficient semi-supervised graph learning framework\nfor HSI data which exploits given label information through pseudo-label\nfeatures embedded in the graph construction. Secondly, we examine and enhance\nthe contribution of multiple superpixel features embedded in the graph on the\nbasis of pseudo-labels in an extension of the previous framework, which is less\nreliant on excessive parameter tuning. Ultimately, we demonstrate the\nsuperiority of our approaches in comparison with state-of-the-art methods\nthrough extensive numerical experiments.\n"}
+{"abstract": "  The quasiparticle spectra of atomically thin semiconducting transition metal\ndichalcogenides (TMDCs) and their response to an ultrafast optical excitation\ncritically depend on interactions with the underlying substrate. Here, we\npresent a comparative time- and angle-resolved photoemission spectroscopy\n(TR-ARPES) study of the transient electronic structure and ultrafast carrier\ndynamics in the single- and bilayer TMDCs MoS$_2$ and WS$_2$ on three different\nsubstrates: Au(111), Ag(111) and graphene/SiC. The photoexcited quasiparticle\nbandgaps are observed to vary over the range of 1.9-2.3 eV between our systems.\nThe transient conduction band signals decay on a sub-100 fs timescale on the\nmetals, signifying an efficient removal of photoinduced carriers into the bulk\nmetallic states. On graphene, we instead observe two timescales on the order of\n200 fs and 50 ps, respectively, for the conduction band decay in MoS$_2$. These\nmultiple timescales are explained by Auger recombination involving MoS$_2$ and\nin-gap defect states. In bilayer TMDCs on metals we observe a complex\nredistribution of excited holes along the valence band that is substantially\naffected by interactions with the continuum of bulk metallic states.\n"}
+{"abstract": "  In a domain $\\Omega\\subseteq \\mathbb{R}^\\mathbf{N}$ we consider compact,\nBirman-Schwinger type, operators of the form\n$\\mathbf{T}_{P,\\mathfrak{A}}=\\mathfrak{A}^*P\\mathfrak{A}$; here $P$ is a\nsingular Borel measure in $\\Omega$ and $\\mathfrak{A}$ is a noncritical order\n$-l\\ne -\\mathbf{N}/2$ pseudodifferential operator. For a class of such\noperators, we obtain estimates and a proper version of H.Weyl's asymptotic law\nfor eigenvalues, with order depending on dimensional characteristics of the\nmeasure. A version of the CLR estimate for singular measures is proved. For\nnon-selfadjoint operators of the form $P_2 \\mathfrak{A} P_1$ and\n$\\mathfrak{A}_2 P \\mathfrak{A}_1$ with singular measures $P,P_1,P_2$ and\nnegative order pseudodifferential operators\n$\\mathfrak{A},\\mathfrak{A}_1,\\mathfrak{A}_2$ we obtain estimates for singular\nnumbers.\n"}
+{"abstract": "  In this paper we study the deterministic and stochastic homogenisation of\nfree-discontinuity functionals under \\emph{linear} growth and coercivity\nconditions. The main novelty of our deterministic result is that we work under\nvery general assumptions on the integrands which, in particular, are not\nrequired to be periodic in the space variable. Combining this result with the\npointwise Subadditive Ergodic Theorem by Akcoglu and Krengel, we prove a\nstochastic homogenisation result, in the case of stationary random integrands.\nIn particular, we characterise the limit integrands in terms of asymptotic cell\nformulas, as in the classical case of periodic homogenisation.\n"}
+{"abstract": "  Phase-locked laser arrays have been extensively investigated in terms of\ntheir stability and nonlinear dynamics. Specifically, enhancing the\nphase-locking stability allows laser arrays to generate high-power and\nsteerable coherent optical beams for a plethora of applications, including\nremote sensing and optical communications. Compared to other coupling\narchitectures, laterally coupled lasers are especially desirable since they\nallow for denser integration and simpler fabrication process. Here, we present\nthe theoretical effects of varying the spontaneous emission factor $\\beta$, an\nimportant parameter for micro- and nanoscale lasers, on the stability\nconditions of phase-locking for two laterally coupled semiconductor lasers.\nThrough bifurcation analyses, we observe that increasing $\\beta$ contributes to\nthe expanding of the in-phase stability region under all scenarios considered,\nincluding complex coupling coefficients, varying pump rates, and frequency\ndetuning. Moreover, the effect is more pronounced for $\\beta$ approaching 1,\nthus underlining the significant advantages of implementing nanolasers with\nintrinsically high $\\beta$ in phase-locked laser arrays for high-power\ngeneration. We also show that the steady-state phase differences can be widely\ntuned - up to $\\pi$ radians - by asymmetrically pumping high-$\\beta$ coupled\nlasers. This demonstrates the potential of high-$\\beta$ nanolasers in building\nnext-generation optical phased arrays requiring wide scanning angles with\nultra-high resolution.\n"}
+{"abstract": "  Mutations on Brauer configurations are introduced and associated with some\nsuitable automata in order to solve generalizations of the Chicken McNugget\nproblem. Besides, based on marked order polytopes a new class of diophantine\nequations called Gelfand-Tsetlin equations are also solved. The approach allows\ngiving an algebraic description of the schedule of an AES key via some suitable\nnon-deterministic finite automata (NFA).\n"}
+{"abstract": "  This paper reports on a new analysis of archival ALMA $870\\,\\mu$m dust\ncontinuum observations. Along with the previously observed bright inner ring\n($r \\sim 20-40\\,$au), two addition substructures are evident in the new\ncontinuum image: a wide dust gap, $r \\sim 40-150\\,$au, and a faint outer ring\nranging from $r \\sim 150\\,$au to $r \\sim 250\\,$au and whose presence was\nformerly postulated in low-angular-resolution ALMA cycle 0 observations but\nnever before observed. Notably, the dust emission of the outer ring is not\nhomogeneous, and it shows two prominent azimuthal asymmetries that resemble an\neccentric ring with eccentricity $e = 0.07 $. The characteristic double-ring\ndust structure of HD 100546 is likely produced by the interaction of the disk\nwith multiple giant protoplanets. This paper includes new\nsmoothed-particle-hydrodynamic simulations with two giant protoplanets, one\ninside of the inner dust cavity and one in the dust gap. The simulations\nqualitatively reproduce the observations, and the final masses and orbital\ndistances of the two planets in the simulations are 3.1 $M_{J}$ at 15 au and\n8.5 $M_{J}$ at 110 au, respectively. The massive outer protoplanet\nsubstantially perturbs the disk surface density distribution and gas dynamics,\nproducing multiple spiral arms both inward and outward of its orbit. This can\nexplain the observed perturbed gas dynamics inward of 100 au as revealed by\nALMA observations of CO.\n  Finally, the reduced dust surface density in the $\\sim 40-150\\,$au dust gap\ncan nicely clarify the origin of the previously detected H$_2$O gas and ice\nemission.\n"}
+{"abstract": "  In this paper, we give the calculation of the jumps of the ramification\ngroups of some finite non-abelian Galois extensions of over complete discrete\nvaluation fields of positive characteristic $p$ of perfect residue field. The\nGalois group is of order $p^{n+1}$, where $2\\leq n\\leq p$.\n"}
+{"abstract": "  Copositive optimization is a special case of convex conic programming, and it\noptimizes a linear function over the cone of all completely positive matrices\nunder linear constraints. Copositive optimization provides powerful relaxations\nof NP-hard quadratic problems or combinatorial problems, but there are still\nmany open problems regarding copositive or completely positive matrices. In\nthis paper, we focus on one of such open problems; finding a completely\npositive (CP) factorization for a given completely positive matrix. We treat it\nas a nonsmooth Riemannian optimization, i.e., a minimization of a nonsmooth\nfunction over the Riemannian manifolds. To solve this problem, we present a\ngeneral smoothing framework for nonsmooth Riemannian optimization and guarantee\nconvergence to a stationary point of the original problem. An advantage is that\nwe can implement it quickly with minimal effort by directly using the existing\nstandard smooth Riemannian solvers, such as Manopt. Numerical experiments show\nthe efficiency of our method especially for large-scale CP factorizations.\n"}
+{"abstract": "  Adversarial training is among the most effective techniques to improve the\nrobustness of models against adversarial perturbations. However, the full\neffect of this approach on models is not well understood. For example, while\nadversarial training can reduce the adversarial risk (prediction error against\nan adversary), it sometimes increase standard risk (generalization error when\nthere is no adversary). Even more, such behavior is impacted by various\nelements of the learning problem, including the size and quality of training\ndata, specific forms of adversarial perturbations in the input, model\noverparameterization, and adversary's power, among others. In this paper, we\nfocus on \\emph{distribution perturbing} adversary framework wherein the\nadversary can change the test distribution within a neighborhood of the\ntraining data distribution. The neighborhood is defined via Wasserstein\ndistance between distributions and the radius of the neighborhood is a measure\nof adversary's manipulative power. We study the tradeoff between standard risk\nand adversarial risk and derive the Pareto-optimal tradeoff, achievable over\nspecific classes of models, in the infinite data limit with features dimension\nkept fixed. We consider three learning settings: 1) Regression with the class\nof linear models; 2) Binary classification under the Gaussian mixtures data\nmodel, with the class of linear classifiers; 3) Regression with the class of\nrandom features model (which can be equivalently represented as two-layer\nneural network with random first-layer weights). We show that a tradeoff\nbetween standard and adversarial risk is manifested in all three settings. We\nfurther characterize the Pareto-optimal tradeoff curves and discuss how a\nvariety of factors, such as features correlation, adversary's power or the\nwidth of two-layer neural network would affect this tradeoff.\n"}
+{"abstract": "  We present a simple $O(n^4)$-time algorithm for computing optimal search\ntrees with two-way comparisons. The only previous solution to this problem, by\nAnderson et al., has the same running time, but is significantly more\ncomplicated and is restricted to the variant where only successful queries are\nallowed. Our algorithm extends directly to solve the standard full variant of\nthe problem, which also allows unsuccessful queries and for which no\npolynomial-time algorithm was previously known. The correctness proof of our\nalgorithm relies on a new structural theorem for two-way-comparison search\ntrees.\n"}
+{"abstract": "  Flight delays impose challenges that impact any flight transportation system.\nPredicting when they are going to occur is an important way to mitigate this\nissue. However, the behavior of the flight delay system varies through time.\nThis phenomenon is known in predictive analytics as concept drift. This paper\ninvestigates the prediction performance of different drift handling strategies\nin aviation under different scales (models trained from flights related to a\nsingle airport or the entire flight system). Specifically, two research\nquestions were proposed and answered: (i) How do drift handling strategies\ninfluence the prediction performance of delays? (ii) Do different scales change\nthe results of drift handling strategies? In our analysis, drift handling\nstrategies are relevant, and their impacts vary according to scale and machine\nlearning models used.\n"}
+{"abstract": "  Swimming bacteria in passive nematics in the form of lyotropic liquid\ncrystals are defined as a new class of active matter known as living liquid\ncrystals in recent studies. It has also been shown that liquid crystal\nsolutions are promising candidates for trapping and detecting bacteria. We ask\nthe question, can a similar class of matter be designed for background nematics\nwhich are also active? Hence, we developed a minimal model for the mixture of\npolar particles in active nematics. It is found that the active nematics in\nsuch a mixture are highly sensitive to the presence of polar particles, and\nshow the formation of large scale higher order structures for a relatively low\npolar particle density. Upon increasing the density of polar particles,\ndifferent phases of active nematics are found and it is observed that the\nsystem shows two phase transitions. The first phase transition is a first order\ntransition from quasi-long ranged ordered active nematics to disordered active\nnematics with larger scale structures. On further increasing density of polar\nparticles, the system transitions to a third phase, where polar particles form\nlarge, mutually aligned clusters. These clusters sweep the whole system and\nenforce local order in the nematics. The current study can be helpful for\ndetecting the presence of very low densities of polar swimmers in active\nnematics and can be used to design and control different structures in active\nnematics.\n"}
+{"abstract": "  Sequential plateau transitions of quantum spin chains ($S$=1,3/2,2 and 3) are\ndemonstrated by a spin pump using dimerization and staggered magnetic field as\nsynthetic dimensions. The bulk is characterized by the Chern number associated\nwith the boundary twist and the pump protocol as a time. It counts the number\nof critical points in the loop that is specified by the $Z_2$ Berry phases.\nWith open boundary condition, discontinuity of the spin weighted center of mass\ndue to emergent effective edge spins also characterizes the pump as the bulk\nedge correspondence. It requires extra level crossings in the pump as a\nsuper-selection rule that is consistent with the Valence Bond Solid (VBS)\npicture.\n"}
+{"abstract": "  Chest computed tomography (CT) has played an essential diagnostic role in\nassessing patients with COVID-19 by showing disease-specific image features\nsuch as ground-glass opacity and consolidation. Image segmentation methods have\nproven to help quantify the disease burden and even help predict the outcome.\nThe availability of longitudinal CT series may also result in an efficient and\neffective method to reliably assess the progression of COVID-19, monitor the\nhealing process and the response to different therapeutic strategies. In this\npaper, we propose a new framework to identify infection at a voxel level\n(identification of healthy lung, consolidation, and ground-glass opacity) and\nvisualize the progression of COVID-19 using sequential low-dose non-contrast CT\nscans. In particular, we devise a longitudinal segmentation network that\nutilizes the reference scan information to improve the performance of disease\nidentification. Experimental results on a clinical longitudinal dataset\ncollected in our institution show the effectiveness of the proposed method\ncompared to the static deep neural networks for disease quantification.\n"}
+{"abstract": "  High-quality automatic speech recognition (ASR) is essential for virtual\nassistants (VAs) to work well. However, ASR often performs poorly on VA\nrequests containing named entities. In this work, we start from the observation\nthat many ASR errors on named entities are inconsistent with real-world\nknowledge. We extend previous discriminative n-gram language modeling\napproaches to incorporate real-world knowledge from a Knowledge Graph (KG),\nusing features that capture entity type-entity and entity-entity relationships.\nWe apply our model through an efficient lattice rescoring process, achieving\nrelative sentence error rate reductions of more than 25% on some synthesized\ntest sets covering less popular entities, with minimal degradation on a\nuniformly sampled VA test set.\n"}
+{"abstract": "  Valtancoli in his paper entitled [P. Valtancoli, Canonical transformations,\nand minimal length J. Math. Phys. 56, 122107 (2015)] has shown how the\ndeformation of the canonical transformations can be made compatible with the\ndeformed Poisson brackets. Based on this work and through an appropriate\ncanonical transformation, we solve the problem of one dimensional (1D) damped\nharmonic oscillator at the classical limit of the Snyder-de Sitter (SdS) space.\nWe show that the equations of the motion can be described by trigonometric\nfunctions with frequency and period depending on the deformed and the damped\nparameters. We eventually discuss the influences of these parameters on the\nmotion of the system.\n"}
+{"abstract": "  Most sensor setups for onboard autonomous perception are composed of LiDARs\nand vision systems, as they provide complementary information that improves the\nreliability of the different algorithms necessary to obtain a robust scene\nunderstanding. However, the effective use of information from different sources\nrequires an accurate calibration between the sensors involved, which usually\nimplies a tedious and burdensome process. We present a method to calibrate the\nextrinsic parameters of any pair of sensors involving LiDARs, monocular or\nstereo cameras, of the same or different modalities. The procedure is composed\nof two stages: first, reference points belonging to a custom calibration target\nare extracted from the data provided by the sensors to be calibrated, and\nsecond, the optimal rigid transformation is found through the registration of\nboth point sets. The proposed approach can handle devices with very different\nresolutions and poses, as usually found in vehicle setups. In order to assess\nthe performance of the proposed method, a novel evaluation suite built on top\nof a popular simulation framework is introduced. Experiments on the synthetic\nenvironment show that our calibration algorithm significantly outperforms\nexisting methods, whereas real data tests corroborate the results obtained in\nthe evaluation suite. Open-source code is available at\nhttps://github.com/beltransen/velo2cam_calibration\n"}
+{"abstract": "  We propose a quantum enhanced interferometric protocol for gravimetry and\nforce sensing using cold atoms in an optical lattice supported by a\nstanding-wave cavity. By loading the atoms in partially delocalized\nWannier-Stark states, it is possible to cancel the undesirable inhomogeneities\narising from the mismatch between the lattice and cavity fields and to generate\nspin squeezed states via a uniform one-axis twisting model. The quantum\nenhanced sensitivity of the states is combined with the subsequent application\nof a compound pulse sequence that allows to separate atoms by several lattice\nsites. This, together with the capability to load small atomic clouds in the\nlattice at micrometric distances from a surface, make our setup ideal for\nsensing short-range forces. We show that for arrays of $10^4$ atoms, our\nprotocol can reduce the required averaging time by a factor of $10$ compared to\nunentangled lattice-based interferometers after accounting for primary sources\nof decoherence.\n"}
+{"abstract": "  Given a graph, the densest subgraph problem asks for a set of vertices such\nthat the average degree among these vertices is maximized. Densest subgraph has\nnumerous applications in learning, e.g., community detection in social\nnetworks, link spam detection, correlation mining, bioinformatics, and so on.\nAlthough there are efficient algorithms that output either exact or approximate\nsolutions to the densest subgraph problem, existing algorithms may violate the\nprivacy of the individuals in the network, e.g., leaking the\nexistence/non-existence of edges.\n  In this paper, we study the densest subgraph problem in the framework of the\ndifferential privacy, and we derive the first upper and lower bounds for this\nproblem. We show that there exists a linear-time $\\epsilon$-differentially\nprivate algorithm that finds a $2$-approximation of the densest subgraph with\nan extra poly-logarithmic additive error. Our algorithm not only reports the\napproximate density of the densest subgraph, but also reports the vertices that\nform the dense subgraph.\n  Our upper bound almost matches the famous $2$-approximation by Charikar both\nin performance and in approximation ratio, but we additionally achieve\ndifferential privacy. In comparison with Charikar's algorithm, our algorithm\nhas an extra poly-logarithmic additive error. We partly justify the additive\nerror with a new lower bound, showing that for any differentially private\nalgorithm that provides a constant-factor approximation, a sub-logarithmic\nadditive error is inherent.\n  We also practically study our differentially private algorithm on real-world\ngraphs, and we show that in practice the algorithm finds a solution which is\nvery close to the optimal\n"}
+{"abstract": "  We introduce the notion of Drinfeld twists for both set-theoretical YBE\nsolutions and skew braces. We give examples of such twists and show that all\ntwists between skew braces come from families of isomorphisms between their\nadditive groups. We then describe the relation between these definitions and\nco-twists on FRT-type Hopf algebras in the category $\\mathrm{SupLat}$, and\nprove that any co-twist on a co-quasitriangular Hopf algebra in\n$\\mathrm{SupLat}$ induces a Drinfeld twist on its remnant skew brace. We go on\nto classify co-twists on bicrossproduct Hopf algebras coming from groups with\nunique factorisation, and the twists which they induce on skew braces.\n"}
+{"abstract": "  We establish a construction for the entanglement wedge in asymptotically flat\nbulk geometries for subsystems in dual $(1+1)$-dimensional Galilean conformal\nfield theories in the context of flat space holography. In this connection we\npropose a definition for the bulk entanglement wedge cross section for\nbipartite states of such dual non relativistic conformal field theories.\nUtilizing our construction for the entanglement wedge cross section we compute\nthe entanglement negativity for such bipartite states through the\ngeneralization of an earlier proposal, in the context of the usual $AdS/CFT$\nscenario, to flat space holography. The entanglement negativity obtained from\nour construction exactly reproduces earlier holographic results and match with\nthe corresponding field theory replica technique results in the large central\ncharge limit.\n"}
+{"abstract": "  The gravity model has been a useful framework to describe macroscopic flow\npatterns in geographically correlated systems. In the general framework of the\ngravity model, the flow between two nodes decreases with distance and has been\nset to be proportional to the suitably defined mass of each node. Despite the\nfrequent successful applications of the gravity model and its alternatives, the\nexisting models certainly possess a serious logical drawback from a theoretical\nperspective. In particular, the mass in the gravity model has been either\nassumed to be proportional to the total in- and out-flow of the corresponding\nnode or simply assigned the other node attribute external to the gravity model\nformulation. In the present work, we propose a general novel framework in which\nthe mass as well as the distance-dependent deterrence function can be computed\niteratively in a self-consistent manner within the framework only based on the\nflow data as input. We validate our suggested methodology in an artificial\nsynthetic flow data to find the near-perfect agreement between the input\ninformation and the results from our framework. We also apply our method to the\nreal international trade network data and discuss implications of the results.\n"}
+{"abstract": "  In this chapter we argue that discourses on AI must transcend the language of\n'ethics' and engage with power and political economy in order to constitute\n'Good Data'. In particular, we must move beyond the depoliticised language of\n'ethics' currently deployed (Wagner 2018) in determining whether AI is 'good'\ngiven the limitations of ethics as a frame through which AI issues can be\nviewed. In order to circumvent these limits, we use instead the language and\nconceptualisation of 'Good Data', as a more expansive term to elucidate the\nvalues, rights and interests at stake when it comes to AI's development and\ndeployment, as well as that of other digital technologies. Good Data\nconsiderations move beyond recurring themes of data protection/privacy and the\nFAT (fairness, transparency and accountability) movement to include explicit\npolitical economy critiques of power. Instead of yet more ethics principles\n(that tend to say the same or similar things anyway), we offer four 'pillars'\non which Good Data AI can be built: community, rights, usability and politics.\nOverall we view AI's 'goodness' as an explicly political (economy) question of\npower and one which is always related to the degree which AI is created and\nused to increase the wellbeing of society and especially to increase the power\nof the most marginalized and disenfranchised. We offer recommendations and\nremedies towards implementing 'better' approaches towards AI. Our strategies\nenable a different (but complementary) kind of evaluation of AI as part of the\nbroader socio-technical systems in which AI is built and deployed.\n"}
+{"abstract": "  In this work, we develop an optimal transport (OT) based framework to select\ninformative prototypical examples that best represent a given target dataset.\nSummarizing a given target dataset via representative examples is an important\nproblem in several machine learning applications where human understanding of\nthe learning models and underlying data distribution is essential for decision\nmaking. We model the prototype selection problem as learning a sparse\n(empirical) probability distribution having the minimum OT distance from the\ntarget distribution. The learned probability measure supported on the chosen\nprototypes directly corresponds to their importance in representing the target\ndata. We show that our objective function enjoys a key property of\nsubmodularity and propose an efficient greedy method that is both\ncomputationally fast and possess deterministic approximation guarantees.\nEmpirical results on several real world benchmarks illustrate the efficacy of\nour approach.\n"}
+{"abstract": "  Isolated neutron stars are prime targets for continuous-wave (CW) searches by\nground-based gravitational$-$wave interferometers. Results are presented from a\nCW search targeting ten pulsars. The search uses a semicoherent algorithm,\nwhich combines the maximum-likelihood $\\mathcal{F}$-statistic with a hidden\nMarkov model (HMM) to efficiently detect and track quasi$-$monochromatic\nsignals which wander randomly in frequency. The targets, which are associated\nwith TeV sources detected by the High Energy Stereoscopic System (H.E.S.S.),\nare chosen to test for gravitational radiation from young, energetic pulsars\nwith strong $\\mathrm{\\gamma}$-ray emission, and take maximum advantage of the\nfrequency tracking capabilities of HMM compared to other CW search algorithms.\nThe search uses data from the second observing run of the Advanced Laser\nInterferometer Gravitational-Wave Observatory (aLIGO). It scans 1$-$Hz\nsub-bands around $f_*$, 4$f_*$/3, and 2$f_*$, where $f_*$ denotes the star's\nrotation frequency, in order to accommodate a physically plausible frequency\nmismatch between the electromagnetic and gravitational-wave emission. The 24\nsub-bands searched in this study return 5,256 candidates above the Gaussian\nthreshold with a false alarm probability of 1$\\%$ per sub-band per target. Only\n12 candidates survive the three data quality vetoes which are applied to\nseparate non$-$Gaussian artifacts from true astrophysical signals. CW searches\nusing the data from subsequent observing runs will clarify the status of the\nremaining candidates.\n"}
+{"abstract": "  The real-space Green's function code FEFF has been extensively developed and\nused for calculations of x-ray and related spectra, including x-ray absorption\n(XAS), x-ray emission (XES), inelastic x-ray scattering, and electron energy\nloss spectra (EELS). The code is particularly useful for the analysis and\ninterpretation of the XAS fine-structure (EXAFS) and the near-edge structure\n(XANES) in materials throughout the periodic table. Nevertheless, many\napplications, such as non-equilibrium systems, and the analysis of ultra-fast\npump-probe experiments, require extensions of the code including\nfinite-temperature and auxiliary calculations of structure and vibrational\nproperties. To enable these extensions, we have developed in tandem, a new\nversion FEFF10, and new FEFF based workflows for the Corvus workflow manager,\nwhich allow users to easily augment the capabilities of FEFF10 via auxiliary\ncodes. This coupling facilitates simplified input and automated calculations of\nspectra based on advanced theoretical techniques. The approach is illustrated\nwith examples of high temperature behavior, vibrational properties, many-body\nexcitations in XAS, super-heavy materials, and fits of calculated spectra to\nexperiment.\n"}
+{"abstract": "  In a topological insulator (TI)/magnetic insulator (MI) hetero-structure,\nlarge spin-orbit coupling of the TI and inversion symmetry breaking at the\ninterface could foster non-planar spin textures such as skyrmions at the\ninterface. This is observed as topological Hall effect in a conventional Hall\nset-up. While this effect has been observed at the interface of TI/MI, where MI\nbeholds perpendicular magnetic anisotropy, non-trivial spin-textures that\ndevelop in interfacial MI with in-plane magnetic anisotropy is under-reported.\nIn this work, we study Bi$_2$Te$_3$/EuS hetero-structure using planar Hall\neffect (PHE). We observe planar topological Hall and spontaneous planar Hall\nfeatures that are characteristic of non-trivial in-plane spin textures at the\ninterface. We find that the latter is minimum when the current and magnetic\nfield directions are aligned parallel, and maximum when they are aligned\nperpendicularly within the sample plane, which maybe attributed to the\nunderlying planar anisotropy of the spin-texture. These results demonstrate the\nimportance of PHE for sensitive detection and characterization of non-trivial\nmagnetic phase that has evaded exploration in the TI/MI interface.\n"}
+{"abstract": "  Recently, the problem of spin and orbital angular momentum (AM) separation\nhas widely been discussed. Nowadays, all discussions about the possibility to\nseparate the spin AM from the orbital AM in the gauge invariant manner are\nbased on the ansatz that the gluon field can be presented in form of the\ndecomposition where the physical gluon components are additive to the pure\ngauge gluon components, i.e. $A_\\mu = A_\\mu^{\\text{phys}}+A_\\mu^{\\text{pure}}$.\nIn the present paper, we show that in the non-Abelian gauge theory this gluon\ndecomposition has a strong mathematical evidence in the frame of the contour\ngauge conception. In other words, we reformulate the gluon decomposition ansatz\nas a theorem on decomposition and, then, we use the contour gauge to prove this\ntheorem. In the first time, we also demonstrate that the contour gauge\npossesses the special kind of residual gauge related to the boundary field\nconfigurations and expressed in terms of the pure gauge fields. As a result,\nthe trivial boundary conditions lead to the inference that the decomposition\nincludes the physical gluon configurations only provided the contour gauge\ncondition.\n"}
+{"abstract": "  We present a study of 41 dwarf galaxies hosting active massive black holes\n(BHs) using Hubble Space Telescope observations. The host galaxies have stellar\nmasses in the range of $M_\\star \\sim 10^{8.5}-10^{9.5}~M_\\odot$ and were\nselected to host active galactic nuclei (AGNs) based on narrow emission line\nratios derived from Sloan Digital Sky Survey spectroscopy. We find a wide range\nof morphologies in our sample including both regular and irregular dwarf\ngalaxies. We fit the HST images of the regular galaxies using GALFIT and find\nthat the majority are disk-dominated with small pseudobulges, although we do\nfind a handful of bulge-like/elliptical dwarf galaxies. We also find an\nunresolved source of light in all of the regular galaxies, which may indicate\nthe presence of a nuclear star cluster and/or the detection of AGN continuum.\nThree of the galaxies in our sample appear to be Magellanic-type dwarf\nirregulars and two galaxies exhibit clear signatures of interactions/mergers.\nThis work demonstrates the diverse nature of dwarf galaxies hosting\noptically-selected AGNs. It also has implications for constraining the origin\nof the first BH seeds using the local BH occupation fraction at low masses --\nwe must account for the various types of dwarf galaxies that may host BHs.\n"}
+{"abstract": "  The results of the investigation of the core-envelope model presented in Negi\net al. \\cite{Ref1} have been discussed in view of the reference \\cite{Ref2} .\nIt is seen that there are significant changes in the results to be addressed.\nIn addition, I have also calculated the gravitational binding energy, causality\nand pulsational stability of the structures which were not considered in Negi\net al. \\cite{Ref1} . The modified results have important consequences to model\nneutron stars and pulsars. The maximum neutron star mass obtained in this study\ncorresponds to the mean value of the classical results obtained by Rhodes \\&\nRuffini \\cite {Ref3} and the upper bound on neutron star mass obtained by\nKalogera \\& Byam \\cite {Ref4} and is much closer to the most recent theoretical\nestimate made by Sotani \\cite{Ref5}. On one hand, when there are only few\nequations of state (EOSs) available in the literature which can fulfil the\nrecent observational constraint imposed by the largest neutron star masses\naround 2$M_\\odot$\\cite{Ref6}, \\cite{Ref7}, \\cite{Ref8}, the present analytic\nmodels, on the other hand, can comfortably satisfy this constraint.\nFurthermore, the maximum allowed value of compactness parameter $u(\\equiv M/a$;\nmass to size ratio in geometrized units) $ \\leq 0.30$ obtained in this study is\nalso consistent with an absolute maximum value of $ u_{\\rm max} =\n0.333^{+0.001}_{-0.005}$ resulting from the observation of binary neutron stars\nmerger GW170817 (see, e.g.\\cite{Ref9}).\n"}
+{"abstract": "  Traditional power system frequency dynamics are driven by Newtonian physics,\nwhere the ubiquitous synchronous generator (SG) maps second order frequency\ntrajectories following power imbalances. The integration of sustainable,\nrenewable energy resources is primarily accomplished with inverters that\nconvert DC power into AC power, which hitherto use grid-following control\nstrategies that require an established voltage waveform. A 100\\% integration of\nthis particular control strategy is untenable and attention has recently\nshifted to grid-forming (GFM) control, where the inverter directly regulates\nfrequency and voltage. We compare and analyze the frequency interactions of\nmulti-loop droop GFMs and SGs. Full order dynamical models are reduced to\nhighlight the disparate power conversion processes, and singular perturbation\ntheory is applied to illuminate an order reduction in frequency response of the\nGFM. Extensive electromagnetic transient domain simulations of the 9- and\n39-bus test systems confirm the order reduction and the associated decoupling\nof the nadir and rate of change of frequency. Finally, matrix pencil analysis\nof the system oscillatory modes show a general increase in primary mode damping\nwith GFMs, although an unexpected, substantial increase in mode frequency and\ndecrease in damping is observed for high penetrations of GFMs in the 39-bus\nsystem.\n"}
+{"abstract": "  When interacting motile units self-organize into flocks, they realize one of\nthe most robust ordered state found in nature. However, after twenty five years\nof intense research, the very mechanism controlling the ordering dynamics of\nboth living and artificial flocks has remained unsettled. Here, combining\nactive-colloid experiments, numerical simulations and analytical work, we\nexplain how flocking liquids heal their spontaneous flows initially plagued by\ncollections of topological defects to achieve long-ranged polar order even in\ntwo dimensions. We demonstrate that the self-similar ordering of flocking\nmatter is ruled by a living network of domain walls linking all $\\pm 1$\nvortices, and guiding their annihilation dynamics. Crucially, this singular\norientational structure echoes the formation of extended density patterns in\nthe shape of interconnected bow ties. We establish that this double structure\nemerges from the interplay between self-advection and density gradients\ndressing each $-1$ topological charges with four orientation walls. We then\nexplain how active Magnus forces link all topological charges with extended\ndomain walls, while elastic interactions drive their attraction along the\nresulting filamentous network of polarization singularities. Taken together our\nexperimental, numerical and analytical results illuminate the suppression of\nall flow singularities, and the emergence of pristine unidirectional order in\nflocking matter.\n"}
+{"abstract": "  We present an approach to studying and predicting the spatio-temporal\nprogression of infectious diseases. We treat the problem by adopting a partial\ndifferential equation (PDE) version of the Susceptible, Infected, Recovered,\nDeceased (SIRD) compartmental model of epidemiology, which is achieved by\nreplacing compartmental populations by their densities. Building on our recent\nwork (Computational Mechanics, 66, 1177, 2020), we replace our earlier use of\nglobal polynomial basis functions with those having local support, as\nepitomized in the finite element method, for the spatial representation of the\nSIRD parameters. The time dependence is treated by inferring constant\nparameters over time intervals that coincide with the time step in\nsemi-discrete numerical implementations. In combination, this amounts to a\nscheme of field inversion of the SIRD parameters over each time step. Applied\nto data over ten months of 2020 for the pandemic in the US state of Michigan\nand to all of Mexico, our system inference via field inversion infers\nspatio-temporally varying PDE SIRD parameters that replicate the progression of\nthe pandemic with high accuracy. It also produces accurate predictions, when\ncompared against data, for a three week period into 2021. Of note is the\ninsight that is suggested on the spatio-temporal variation of infection,\nrecovery and death rates, as well as patterns of the population's mobility\nrevealed by diffusivities of the compartments.\n"}
+{"abstract": "  In semi-supervised domain adaptation, a few labeled samples per class in the\ntarget domain guide features of the remaining target samples to aggregate\naround them. However, the trained model cannot produce a highly discriminative\nfeature representation for the target domain because the training data is\ndominated by labeled samples from the source domain. This could lead to\ndisconnection between the labeled and unlabeled target samples as well as\nmisalignment between unlabeled target samples and the source domain. In this\npaper, we propose a novel approach called Cross-domain Adaptive Clustering to\naddress this problem. To achieve both inter-domain and intra-domain adaptation,\nwe first introduce an adversarial adaptive clustering loss to group features of\nunlabeled target data into clusters and perform cluster-wise feature alignment\nacross the source and target domains. We further apply pseudo labeling to\nunlabeled samples in the target domain and retain pseudo-labels with high\nconfidence. Pseudo labeling expands the number of ``labeled\" samples in each\nclass in the target domain, and thus produces a more robust and powerful\ncluster core for each class to facilitate adversarial learning. Extensive\nexperiments on benchmark datasets, including DomainNet, Office-Home and Office,\ndemonstrate that our proposed approach achieves the state-of-the-art\nperformance in semi-supervised domain adaptation.\n"}
+{"abstract": "  We numerically investigate the energy and arrival-time noise of ultrashort\nlaser pulses produced via resonant dispersive wave emission in gas-filled\nhollow-core waveguides under the influence of pump-laser instability. We find\nthat for low pump energy, fluctuations in the pump energy are strongly\namplified. However, when the generation process is saturated, the energy of the\nresonant dispersive wave can be significantly less noisy than that of the pump\npulse. This holds for a variety of generation conditions and while still\nproducing few-femtosecond pulses. We further find that the arrival-time jitter\nof the generated pulse remains well below one femtosecond even for a\nconservative estimate of the pump pulse energy noise, and that photoionisation\nand plasma dynamics can lead to exceptional stability for some generation\nconditions. By applying our analysis to a scaled-down system, we demonstrate\nthat our results hold for frequency conversion schemes based on both small-core\nmicrostructured fibre and large-core hollow capillary fibre.\n"}
+{"abstract": "  Over the past decade, Deep Convolutional Neural Networks have been widely\nadopted for medical image segmentation and shown to achieve adequate\nperformance. However, due to the inherent inductive biases present in the\nconvolutional architectures, they lack understanding of long-range dependencies\nin the image. Recently proposed Transformer-based architectures that leverage\nself-attention mechanism encode long-range dependencies and learn\nrepresentations that are highly expressive. This motivates us to explore\nTransformer-based solutions and study the feasibility of using\nTransformer-based network architectures for medical image segmentation tasks.\nMajority of existing Transformer-based network architectures proposed for\nvision applications require large-scale datasets to train properly. However,\ncompared to the datasets for vision applications, for medical imaging the\nnumber of data samples is relatively low, making it difficult to efficiently\ntrain transformers for medical applications. To this end, we propose a Gated\nAxial-Attention model which extends the existing architectures by introducing\nan additional control mechanism in the self-attention module. Furthermore, to\ntrain the model effectively on medical images, we propose a Local-Global\ntraining strategy (LoGo) which further improves the performance. Specifically,\nwe operate on the whole image and patches to learn global and local features,\nrespectively. The proposed Medical Transformer (MedT) is evaluated on three\ndifferent medical image segmentation datasets and it is shown that it achieves\nbetter performance than the convolutional and other related transformer-based\narchitectures. Code: https://github.com/jeya-maria-jose/Medical-Transformer\n"}
+{"abstract": "  In this paper, we attempt to propose Ekeland's variational principle for\ninterval-valued functions (IVFs). To develop the variational principle, we\nstudy the concept of sequence of intervals. In the sequel, the idea of\ngH-semicontinuity for IVFs is explored. A necessary and sufficient condition\nfor an IVF to be gH-continuous in terms of gH-lower and upper semicontinuity is\ngiven. Moreover, we prove a characterization for gH-lower semicontinuity by the\nlevel sets of the IVF. With the help of this characterization result, we ensure\nthe existence of a minimum for an extended gH-lower semicontinuous,\nlevel-bounded and proper IVF. To find an approximate minima of a gH-lower\nsemicontinuous and gH-Gateaux differentiable IVF, the proposed Ekeland's\nvariational principle is used.\n"}
+{"abstract": "  In this paper, we address risk aggregation and capital allocation problems in\nthe presence of dependence between risks. The dependence structure is defined\nby a mixed Bernstein copula which represents a generalization of the well-known\nArchimedean copulas. Using this new copula, the probability density function\nand the cumulative distribution function of the aggregate risk are obtained.\nThen, closed-form expressions for basic risk measures, such as tail\nvalue-at-risk(TVaR) and TVaR-based allocations, are derived.\n"}
+{"abstract": "  Word emphasis in textual content aims at conveying the desired intention by\nchanging the size, color, typeface, style (bold, italic, etc.), and other\ntypographical features. The emphasized words are extremely helpful in drawing\nthe readers' attention to specific information that the authors wish to\nemphasize. However, performing such emphasis using a soft keyboard for social\nmedia interactions is time-consuming and has an associated learning curve. In\nthis paper, we propose a novel approach to automate the emphasis word detection\non short written texts. To the best of our knowledge, this work presents the\nfirst lightweight deep learning approach for smartphone deployment of emphasis\nselection. Experimental results show that our approach achieves comparable\naccuracy at a much lower model size than existing models. Our best lightweight\nmodel has a memory footprint of 2.82 MB with a matching score of 0.716 on\nSemEval-2020 public benchmark dataset.\n"}
+{"abstract": "  Physicists are starting to work in areas where noisy signal analysis is\nrequired. In these fields, such as Economics, Neuroscience, and Physics, the\nnotion of causality should be interpreted as a statistical measure. We\nintroduce to the lay reader the Granger causality between two time series and\nillustrate ways of calculating it: a signal $X$ ``Granger-causes'' a signal $Y$\nif the observation of the past of $X$ increases the predictability of the\nfuture of $Y$ when compared to the same prediction done with the past of $Y$\nalone. In other words, for Granger causality between two quantities it suffices\nthat information extracted from the past of one of them improves the forecast\nof the future of the other, even in the absence of any physical mechanism of\ninteraction. We present derivations of the Granger causality measure in the\ntime and frequency domains and give numerical examples using a non-parametric\nestimation method in the frequency domain. Parametric methods are addressed in\nthe Appendix. We discuss the limitations and applications of this method and\nother alternatives to measure causality.\n"}
+{"abstract": "  Reverberation mapping (RM) is an efficient method to investigate the physical\nsizes of the broad line region (BLR) and dusty torus in an active galactic\nnucleus (AGN). The Spectro-Photometer for the History of the Universe, Epoch of\nReionization and Ices Explorer (SPHEREx) mission will provide multi-epoch\nspectroscopic data at optical and near-infrared wavelengths. These data can be\nused for RM experiments for bright AGNs. We present results of a feasibility\ntest using SPHEREx data in the SPHEREx deep regions for the torus RM\nmeasurements. We investigate the physical properties of bright AGNs in the\nSPHEREx deep field. Based on this information, we compute the efficiency of\ndetecting torus time lags in simulated light curves. We demonstrate that, in\ncombination with the complementary optical data with a depth of $\\sim20$ mag in\n$B-$band, lags of $\\le 750$ days for tori can be measured for more than\n$\\sim200$ bright AGNs. If high signal-to-noise ratio photometric data with a\ndepth of $\\sim21-22$ mag are available, RM measurements can be applied for up\nto $\\sim$900 objects. When complemented by well-designed early optical\nobservations, SPHEREx can provide a unique dataset for studies of the physical\nproperties of dusty tori in bright AGNs.\n"}
+{"abstract": "  Unrestricted particle transport through microfluidic channels is of paramount\nimportance to a wide range of applications, including lab-on-a-chip devices. In\nthis article, we study using video microscopy the electro-osmotic aggregation\nof colloidal particles at the opening of a micrometer-sized silica channel in\npresence of a salt gradient. Particle aggregation eventually leads to clogging\nof the channel, which may be undone by a time-adjusted reversal of the applied\nelectric potential. We numerically model our system via the\nStokes-Poisson-Nernst-Planck equations in a geometry that approximates the real\nsample. This allows us to identify the transport processes induced by the\nelectric field and salt gradient and to provide evidence that a balance thereof\nleads to aggregation. We further demonstrate experimentally that a net flow of\ncolloids through the channel may be achieved by applying a square-waveform\nelectric potential with an appropriately tuned duty cycle. Our results serve to\nguide the design of microfluidic and nanofluidic pumps that allow for\ncontrolled particle transport and provide new insights for anti-fouling in\nultra-filtration.\n"}
+{"abstract": "  Let $R$ be a commutative ring with nonzero identity, and $\\delta\n:\\mathcal{I(R)}\\rightarrow\\mathcal{I(R)}$ be an ideal expansion where\n$\\mathcal{I(R)}$ the set of all ideals of $R$. In this paper, we introduce the\nconcept of $\\delta$-$n$-ideals which is an extension of $n$-ideals in\ncommutative rings. We call a proper ideal $I$ of $R$ a $\\delta$-$n$-ideal if\nwhenever $a,b\\in R$ with$\\ ab\\in I$ and $a\\notin\\sqrt{0}$, then $b\\in\n\\delta(I)$. For example, $\\delta_{1}$ is defined by $\\delta_{1}(I)=\\sqrt{I}.$ A\nnumber of results and characterizations related to $\\delta$-$n$-ideals are\ngiven. Furthermore, we present some results related to quasi $n$-ideals which\nis for the particular case $\\delta=\\delta_{1}.$\n"}
+{"abstract": "  The possibility of targeting the causal genes along with the mechanisms of\npathogenically complex diseases has led to numerous studies on the genetic\netiology of some diseases. In particular, studies have added more genes to the\nlist of type 1 diabetes mellitus (T1DM) suspect genes, necessitating an update\nfor the interest of all stakeholders. Therefore this review articulates T1DM\nsuspect genes and their pathophysiology. Notable electronic databases,\nincluding Medline, Scopus, PubMed, and Google-Scholar were searched for\nrelevant information. The search identified over 73 genes suspected in the\npathogenesis of T1DM, with human leukocyte antigen, insulin gene, and cytotoxic\nT lymphocyte-associated antigen 4 accounting for most of the cases. Mutations\nin these genes, along with environmental factors, may produce a defective\nimmune response in the pancreas, resulting in \\b{eta}-cell autoimmunity,\ninsulin deficiency, and hyperglycemia. The mechanisms leading to these cellular\nreactions are gene-specific and, if targeted in diabetic individuals, may lead\nto improved treatment. Medical practitioners are advised to formulate treatment\nprocedures that target these genes in patients with T1DM.\n"}
+{"abstract": "  Unsupervised cross-lingual pretraining has achieved strong results in neural\nmachine translation (NMT), by drastically reducing the need for large parallel\ndata. Most approaches adapt masked-language modeling (MLM) to\nsequence-to-sequence architectures, by masking parts of the input and\nreconstructing them in the decoder. In this work, we systematically compare\nmasking with alternative objectives that produce inputs resembling real (full)\nsentences, by reordering and replacing words based on their context. We\npretrain models with different methods on English$\\leftrightarrow$German,\nEnglish$\\leftrightarrow$Nepali and English$\\leftrightarrow$Sinhala monolingual\ndata, and evaluate them on NMT. In (semi-) supervised NMT, varying the\npretraining objective leads to surprisingly small differences in the finetuned\nperformance, whereas unsupervised NMT is much more sensitive to it. To\nunderstand these results, we thoroughly study the pretrained models using a\nseries of probes and verify that they encode and use information in different\nways. We conclude that finetuning on parallel data is mostly sensitive to few\nproperties that are shared by most models, such as a strong decoder, in\ncontrast to unsupervised NMT that also requires models with strong\ncross-lingual abilities.\n"}
+{"abstract": "  In this article we consider zero and non-zero sum risk-sensitive average\ncriterion games for semi-Markov processes with a finite state space. For the\nzero-sum case, under suitable assumptions we show that the game has a value. We\nalso establish the existence of a stationary saddle point equilibrium. For the\nnon-zero sum case, under suitable assumptions we establish the existence of a\nstationary Nash equilibrium.\n"}
+{"abstract": "  Understanding the water oxidation mechanism in Photosystem II (PSII)\nstimulates the design of biomimetic artificial systems that can convert solar\nenergy into hydrogen fuel efficiently. The Sr2+ substituted PSII is active but\nslower than with the native Ca2+ as an oxygen evolving catalyst. Here, we use\nDensity Functional Theory (DFT) to compare the energetics of the S2 to S3\ntransition in the Mn4O5Ca2+ and Mn4O5Sr2+ clusters. The calculations show that\ndeprotonation of the water bound to Ca2+ (W3), required for the S2 to S3\ntransition, is energetically more favorable in Mn4O5Ca2+ than Mn4O5Sr2+. In\naddition, we have calculated the pKa of the water that bridges Mn4 and the\nCa2+/Sr2+ in the S2 using continuum electrostatics. The calculations show that\nthe pKa is higher by 4 pH units in the Mn4O5Sr2+.\n"}
+{"abstract": "  Microphone array techniques can improve the acoustic sensing performance on\ndrones, compared to the use of a single microphone. However, multichannel sound\nacquisition systems are not available in current commercial drone platforms. To\nencourage the research in drone audition, we present an embedded sound\nacquisition and recording system with eight microphones and a multichannel\nsound recorder mounted on a quadcopter. In addition to recording and storing\nlocally the sound from multiple microphones simultaneously, the embedded system\ncan connect wirelessly to a remote terminal to transfer audio files for further\nprocessing. This will be the first stage towards creating a fully embedded\nsolution for drone audition. We present experimental results obtained by\nstate-of-the-art drone audition algorithms applied to the sound recorded by the\nembedded system.\n"}
+{"abstract": "  We evaluate the effectiveness of semi-supervised learning (SSL) on a\nrealistic benchmark where data exhibits considerable class imbalance and\ncontains images from novel classes. Our benchmark consists of two fine-grained\nclassification datasets obtained by sampling classes from the Aves and Fungi\ntaxonomy. We find that recently proposed SSL methods provide significant\nbenefits, and can effectively use out-of-class data to improve performance when\ndeep networks are trained from scratch. Yet their performance pales in\ncomparison to a transfer learning baseline, an alternative approach for\nlearning from a few examples. Furthermore, in the transfer setting, while\nexisting SSL methods provide improvements, the presence of out-of-class is\noften detrimental. In this setting, standard fine-tuning followed by\ndistillation-based self-training is the most robust. Our work suggests that\nsemi-supervised learning with experts on realistic datasets may require\ndifferent strategies than those currently prevalent in the literature.\n"}
+{"abstract": "  In this paper we investigate the algebraic structure of the truth tables of\nall bracketed formulae with n distinct variables connected by the binary\nconnective of implication.\n"}
+{"abstract": "  Character linking, the task of linking mentioned people in conversations to\nthe real world, is crucial for understanding the conversations. For the\nefficiency of communication, humans often choose to use pronouns (e.g., \"she\")\nor normal phrases (e.g., \"that girl\") rather than named entities (e.g.,\n\"Rachel\") in the spoken language, which makes linking those mentions to real\npeople a much more challenging than a regular entity linking task. To address\nthis challenge, we propose to incorporate the richer context from the\ncoreference relations among different mentions to help the linking. On the\nother hand, considering that finding coreference clusters itself is not a\ntrivial task and could benefit from the global character information, we\npropose to jointly solve these two tasks. Specifically, we propose C$^2$, the\njoint learning model of Coreference resolution and Character linking. The\nexperimental results demonstrate that C$^2$ can significantly outperform\nprevious works on both tasks. Further analyses are conducted to analyze the\ncontribution of all modules in the proposed model and the effect of all\nhyper-parameters.\n"}
+{"abstract": "  We continue the line of work initiated by Goldreich and Ron (Journal of the\nACM, 2017) on testing dynamic environments and propose to pursue a systematic\nstudy of the complexity of testing basic dynamic environments and local rules.\nAs a first step, in this work we focus on dynamic environments that correspond\nto elementary cellular automata that evolve according to threshold rules.\n  Our main result is the identification of a set of conditions on local rules,\nand a meta-algorithm that tests evolution according to local rules that satisfy\nthe conditions. The meta-algorithm has query complexity poly$ (1/\\epsilon) $,\nis non-adaptive and has one-sided error. We show that all the threshold rules\nsatisfy the set of conditions, and therefore are poly$ (1/\\epsilon) $-testable.\nWe believe that this is a rich area of research and suggest a variety of open\nproblems and natural research directions that may extend and expand our\nresults.\n"}
+{"abstract": "  Multi-party machine learning is a paradigm in which multiple participants\ncollaboratively train a machine learning model to achieve a common learning\nobjective without sharing their privately owned data. The paradigm has recently\nreceived a lot of attention from the research community aimed at addressing its\nassociated privacy concerns. In this work, we focus on addressing the concerns\nof data privacy, model privacy, and data quality associated with\nprivacy-preserving multi-party machine learning, i.e., we present a scheme for\nprivacy-preserving collaborative learning that checks the participants' data\nquality while guaranteeing data and model privacy. In particular, we propose a\nnovel metric called weight similarity that is securely computed and used to\ncheck whether a participant can be categorized as a reliable participant (holds\ngood quality data) or not. The problems of model and data privacy are tackled\nby integrating homomorphic encryption in our scheme and uploading encrypted\nweights, which prevent leakages to the server and malicious participants,\nrespectively. The analytical and experimental evaluations of our scheme\ndemonstrate that it is accurate and ensures data and model privacy.\n"}
+{"abstract": "  We study $^5$He variationally as the first $p$-shell nucleus in the\ntensor-optimized antisymmetrized molecular dynamics (TOAMD) using the bare\nnucleon--nucleon interaction without any renormalization. In TOAMD, the central\nand tensor correlation operators promote the AMD's Gaussian wave function to a\nsophisticated many-body state including the short-range and tensor correlations\nwith high-momentum nucleon pairs. We develop a successive approach by applying\nthese operators successively with up to double correlation operators to get\nconverging results. We obtain satisfactory results for $^5$He, not only for the\nground state but also for the excited state, and discuss explicitly the\ncorrelated Hamiltonian components in each state. We also show the importance of\nthe independent optimization of the correlation functions in the variation of\nthe total energy beyond the condition assuming common correlation forms used in\nthe Jastrow approach.\n"}
+{"abstract": "  Maintaining a $k$-core decomposition quickly in a dynamic graph is an\nimportant problem in many applications, including social network analytics,\ngraph visualization, centrality measure computations, and community detection\nalgorithms. The main challenge for designing efficient $k$-core decomposition\nalgorithms is that a single change to the graph can cause the decomposition to\nchange significantly.\n  We present the first parallel batch-dynamic algorithm for maintaining an\napproximate $k$-core decomposition that is efficient in both theory and\npractice. Given an initial graph with $m$ edges, and a batch of $B$ updates,\nour algorithm maintains a $(2 + \\delta)$-approximation of the coreness values\nfor all vertices (for any constant $\\delta > 0$) in $O(B\\log^2 m)$ amortized\nwork and $O(\\log^2 m \\log\\log m)$ depth (parallel time) with high probability.\nOur algorithm also maintains a low out-degree orientation of the graph in the\nsame bounds. We implemented and experimentally evaluated our algorithm on a\n30-core machine with two-way hyper-threading on $11$ graphs of varying\ndensities and sizes. Compared to the state-of-the-art algorithms, our algorithm\nachieves up to a 114.52x speedup against the best multicore implementation and\nup to a 497.63x speedup against the best sequential algorithm, obtaining\nresults for graphs that are orders-of-magnitude larger than those used in\nprevious studies.\n  In addition, we present the first approximate static $k$-core algorithm with\nlinear work and polylogarithmic depth. We show that on a 30-core machine with\ntwo-way hyper-threading, our implementation achieves up to a 3.9x speedup in\nthe static case over the previous state-of-the-art parallel algorithm.\n"}
+{"abstract": "  Salient object detection (SOD) is viewed as a pixel-wise saliency modeling\ntask by traditional deep learning-based methods. A limitation of current SOD\nmodels is insufficient utilization of inter-pixel information, which usually\nresults in imperfect segmentation near edge regions and low spatial coherence.\nAs we demonstrate, using a saliency mask as the only label is suboptimal. To\naddress this limitation, we propose a connectivity-based approach called\nbilateral connectivity network (BiconNet), which uses connectivity masks\ntogether with saliency masks as labels for effective modeling of inter-pixel\nrelationships and object saliency. Moreover, we propose a bilateral voting\nmodule to enhance the output connectivity map, and a novel edge feature\nenhancement method that efficiently utilizes edge-specific features. Through\ncomprehensive experiments on five benchmark datasets, we demonstrate that our\nproposed method can be plugged into any existing state-of-the-art\nsaliency-based SOD framework to improve its performance with negligible\nparameter increase.\n"}
+{"abstract": "  Reinforcement Learning (RL) has been able to solve hard problems such as\nplaying Atari games or solving the game of Go, with a unified approach. Yet\nmodern deep RL approaches are still not widely used in real-world applications.\nOne reason could be the lack of guarantees on the performance of the\nintermediate executed policies, compared to an existing (already working)\nbaseline policy. In this paper, we propose an online model-free algorithm that\nsolves conservative exploration in the policy optimization problem. We show\nthat the regret of the proposed approach is bounded by\n$\\tilde{\\mathcal{O}}(\\sqrt{T})$ for both discrete and continuous parameter\nspaces.\n"}
+{"abstract": "  Rap generation, which aims to produce lyrics and corresponding singing beats,\nneeds to model both rhymes and rhythms. Previous works for rap generation\nfocused on rhyming lyrics but ignored rhythmic beats, which are important for\nrap performance. In this paper, we develop DeepRapper, a Transformer-based rap\ngeneration system that can model both rhymes and rhythms. Since there is no\navailable rap dataset with rhythmic beats, we develop a data mining pipeline to\ncollect a large-scale rap dataset, which includes a large number of rap songs\nwith aligned lyrics and rhythmic beats. Second, we design a Transformer-based\nautoregressive language model which carefully models rhymes and rhythms.\nSpecifically, we generate lyrics in the reverse order with rhyme representation\nand constraint for rhyme enhancement and insert a beat symbol into lyrics for\nrhythm/beat modeling. To our knowledge, DeepRapper is the first system to\ngenerate rap with both rhymes and rhythms. Both objective and subjective\nevaluations demonstrate that DeepRapper generates creative and high-quality\nraps with rhymes and rhythms. Code will be released on GitHub.\n"}
+{"abstract": "  The simulation of multi-body systems with frictional contacts is a\nfundamental tool for many fields, such as robotics, computer graphics, and\nmechanics. Hard frictional contacts are particularly troublesome to simulate\nbecause they make the differential equations stiff, calling for computationally\ndemanding implicit integration schemes. We suggest to tackle this issue by\nusing exponential integrators, a long-standing class of integration schemes\n(first introduced in the 60's) that in recent years has enjoyed a resurgence of\ninterest. We show that this scheme can be easily applied to multi-body systems\nsubject to stiff viscoelastic contacts, producing accurate results at lower\ncomputational cost than \\changed{classic explicit or implicit schemes}. In our\ntests with quadruped and biped robots, our method demonstrated stable behaviors\nwith large time steps (10 ms) and stiff contacts ($10^5$ N/m). Its excellent\nproperties, especially for fast and coarse simulations, make it a valuable\ncandidate for many applications in robotics, such as simulation, Model\nPredictive Control, Reinforcement Learning, and controller design.\n"}
+{"abstract": "  The crossover in solving linear programs is a procedure to recover an optimal\ncorner/extreme point from an approximately optimal inner point generated by\ninterior-point method or emerging first-order methods. Unfortunately it is\noften observed that the computation time of this procedure can be much longer\nthan the time of the former stage. Our work shows that this bottleneck can be\nsignificantly improved if the procedure can smartly take advantage of the\nproblem characteristics and implement customized strategies. For the problem\nwith the network structure, our approach can even start from an inexact\nsolution of interior-point method as well as other emerging first-order\nalgorithms. It fully exploits the network structure to smartly evaluate\ncolumns' potential of forming the optimal basis and efficiently identifies a\nnearby basic feasible solution. For the problem with a large optimal face, we\npropose a perturbation crossover approach to find a corner point of the optimal\nface. The comparison experiments with state-of-art commercial LP solvers on\nclassical linear programming problem benchmarks, network flow problem\nbenchmarks and MINST datasets exhibit its considerable advantages in practice.\n"}
+{"abstract": "  Synthetic Magnetic Resonance (MR) imaging predicts images at new design\nparameter settings from a few observed MR scans. Model-based methods, that use\nboth the physical and statistical properties underlying the MR signal and its\nacquisition, can predict images at any setting from as few as three scans,\nallowing it to be used in individualized patient- and anatomy-specific\ncontexts. However, the estimation problem in model-based synthetic MR imaging\nis ill-posed and so regularization, in the form of correlated Gaussian Markov\nRandom Fields, is imposed on the voxel-wise spin-lattice relaxation time,\nspin-spin relaxation time and the proton density underlying the MR image. We\ndevelop theoretically sound but computationally practical matrix-free\nestimation methods for synthetic MR imaging. Our evaluations demonstrate\nexcellent ability of our methods to synthetize MR images in a clinical\nframework and also estimation and prediction accuracy and consistency. An added\nstrength of our model-based approach, also developed and illustrated here, is\nthe accurate estimation of standard errors of regional means in the synthesized\nimages.\n"}
+{"abstract": "  Universal Domain Adaptation (UNDA) aims to handle both domain-shift and\ncategory-shift between two datasets, where the main challenge is to transfer\nknowledge while rejecting unknown classes which are absent in the labeled\nsource data but present in the unlabeled target data. Existing methods manually\nset a threshold to reject unknown samples based on validation or a pre-defined\nratio of unknown samples, but this strategy is not practical. In this paper, we\npropose a method to learn the threshold using source samples and to adapt it to\nthe target domain. Our idea is that a minimum inter-class distance in the\nsource domain should be a good threshold to decide between known or unknown in\nthe target. To learn the inter-and intra-class distance, we propose to train a\none-vs-all classifier for each class using labeled source data. Then, we adapt\nthe open-set classifier to the target domain by minimizing class entropy. The\nresulting framework is the simplest of all baselines of UNDA and is insensitive\nto the value of a hyper-parameter yet outperforms baselines with a large\nmargin.\n"}
+{"abstract": "  Following Demidovich's concept and definition of convergent systems, we\nanalyze the optimal nonlinear damping control, recently proposed [1] for the\nsecond-order systems. Targeting the problem of output regulation,\ncorrespondingly tracking of $\\mathcal{C}^1$-trajectories, it is shown that all\nsolutions of the control system are globally uniformly asymptotically stable.\nThe existence of the unique limit solution in the origin of the control error\nand its time derivative coordinates are shown in the sense of Demidovich's\nconvergent dynamics. Explanative numerical examples are also provided along\nwith analysis.\n"}
+{"abstract": "  We review the development of thermodynamic protein hydropathic scaling\ntheory, starting from backgrounds in mathematics and statistical mechanics, and\nleading to biomedical applications. Darwinian evolution has organized each\nprotein family in different ways, but dynamical hydropathic scaling theory is\nboth simple and effective in providing readily transferable dynamical insights\nfor many proteins represented in the uncounted amino acid sequences, as well as\nthe 90 thousand static structures contained in the online Protein Data Base.\nCritical point theory is general, and recently it has proved to be the most\neffective way of describing protein networks that have evolved towards nearly\nperfect functionality in given environments, self-organized criticality.\nDarwinian evolutionary patterns are governed by common dynamical hydropathic\nscaling principles, which can be quantified using scales that have been\ndeveloped bioinformatically by studying thousands of static PDB structures. The\nmost effective dynamical scales involve hydropathic globular sculpting\ninteractions averaged over length scales centered on domain dimensions. A\ncentral feature of dynamical hydropathic scaling theory is the characteristic\ndomain length associated with a given protein functionality. Evolution has\nfunctioned in such a way that the minimal critical length scale established so\nfar is about nine amino acids, but in some cases it is much larger. Some\ningenuity is needed to find this primary length scale, as shown by the examples\ndiscussed here. Often a survey of the Darwinian evolution of a protein sequence\nsuggests a means of determining the critical length scale. The evolution of\nCoronavirus is an interesting application; it identifies critical mutations.\n"}
+{"abstract": "  In functional analysis, there are different notions of limit for a bounded\nsequence of $L^1$ functions. Besides the pointwise limit, that does not always\nexist, the behaviour of a bounded sequence of $L^1$ functions can be described\nin terms of its weak-$\\star$ limit or by introducing a measure-valued notion of\nlimit in the sense of Young measures. Working in Robinson's framework of\nanalysis with infinitesimals, we show that for every bounded sequence\n$\\{z_n\\}_{n \\in \\mathbb{N}}$ of $L^1$ functions there exists a function of a\nhyperfinite domain (i.e.\\ a grid function) that represents both the\nweak-$\\star$ and the Young measure limits of the sequence. This result has\nrelevant applications to the study of nonlinear PDEs. We discuss the example of\nan ill-posed forward-backward parabolic equation.\n"}
+{"abstract": "  We resolve several puzzles related to the electromagnetic response of\ntopological superconductors in 3+1 dimensions. In particular we show by an\nanalytical calculation that the interface between a topological and normal\nsuperconductor does not exhibit any quantum Hall effect as long as time\nreversal invariance is preserved. We contrast this with the analogous case of a\ntopological insulator to normal insulator interface. The difference is that in\nthe topological insulator the electromagnetic vector potential couples to a\nvector current in a theory with a Dirac mass, while in the superconductor a\npair of Weyl fermions are gapped by Majorana masses and the electromagnetic\nvector potential couples to their axial currents.\n"}
+{"abstract": "  A novel scheme is proposed for generating a polarized positron beam via\nmultiphoton Breit-Wheeler process during the collision of a 10 GeV, pC seeding\nelectron beam with the other 1 GeV, nC driving electron beam. The driving beam\nprovides the strong self-generated field, and a suitable transverse deviation\ndistance between two beams enables the field experienced by the seeding beam to\nbe unipolar, which is crucial for realizing the positron polarization. We\nemploy the particle simulation with a Monte-Carlo method to calculate the spin-\nand polarization-resolved photon emission and electron-positron pair production\nin the local constant field approximation. Our simulation results show that a\nhighly polarized positron beam with polarization above $40\\%$ can be generated\nin several femtoseconds, which is robust with respect to parameters of two\nelectron beams. Based on an analysis of the influence of $\\gamma$-photon\npolarization on the polarized pair production, we find that a polarized seeding\nbeam of the proper initial polarization can further improve the positron\npolarization to $60\\%$.\n"}
+{"abstract": "  Financial portfolio management is one of the most applicable problems in\nreinforcement learning (RL) owing to its sequential decision-making nature.\nExisting RL-based approaches, while inspiring, often lack scalability,\nreusability, or profundity of intake information to accommodate the\never-changing capital markets. In this paper, we propose MSPM, a modularized\nand scalable, multi-agent RL-based system for financial portfolio management.\nMSPM involves two asynchronously updated units: an Evolving Agent Module (EAM)\nand Strategic Agent Module (SAM). A self-sustained EAM produces\nsignal-comprised information for a specific asset using heterogeneous data\ninputs, and each EAM employs its reusability to have connections to multiple\nSAMs. An SAM is responsible for asset reallocation in a portfolio using\nprofound information from the connected EAMs. With the elaborate architecture\nand the multi-step condensation of volatile market information, MSPM aims to\nprovide a customizable, stable, and dedicated solution to portfolio management,\nunlike existing approaches. We also tackle the data-shortage issue of\nnewly-listed stocks by transfer learning, and validate the indispensability of\nEAM with four different portfolios. Experiments on 8-year U.S. stock market\ndata prove the effectiveness of MSPM in profit accumulation, by its\noutperformance over existing benchmarks.\n"}
+{"abstract": "  We present the upper bound of the essential norm of the composition operator\nover the Polylogarithmic Hardy space PL2(D;s).The results involve the\nNevanlinna counting function for PL2(D;s). We first prove the Littlewood-Paley\nIdentity for PL2(D;s) which leads to the Nevanlinna counting function for\nPL2(D;s). With all these results, not only we get the upper bound of the\nessential norm of the composition operator over PL2(D;s) but also we get an\nupper bound in terms of the angular derivative and essential norm of\ncomposition operator over the Hardy space H2.\n"}
+{"abstract": "  We present an optimization-based method to efficiently calculate accurate\nnonlinear models of Taylor vortex flow. We use the resolvent formulation of\nMcKeon & Sharma (2010) to model these Taylor vortex solutions by treating the\nnonlinearity not as an inherent part of the governing equations but rather as a\ntriadic constraint which must be satisfied by the model solution. We exploit\nthe low rank linear dynamics of the system to calculate an efficient basis for\nour solution, the coefficients of which are then calculated through an\noptimization problem where the cost function to be minimized is the triadic\nconsistency of the solution with itself as well as with the input mean flow.\nOur approach constitutes, what is to the best of our knowledge, the first fully\nnonlinear and self-sustaining, resolvent-based model described in the\nliterature. We compare our results to direct numerical simulation of Taylor\nCouette flow at up to five times the critical Reynolds number, and show that\nour model accurately captures the structure of the flow. Additionally, we find\nthat as the Reynolds number increases the flow undergoes a fundamental\ntransition from a classical weakly nonlinear regime, where the forcing cascade\nis strictly down scale, to a fully nonlinear regime characterized by the\nemergence of an inverse (up scale) forcing cascade. Triadic contributions from\nthe inverse and traditional cascade destructively interfere implying that the\naccurate modeling of a certain Fourier mode requires knowledge of its immediate\nharmonic and sub-harmonic. We show analytically that this finding is a direct\nconsequence of the structure of the quadratic nonlinearity of the governing\nequations formulated in Fourier space. Finally, we show that using our model\nsolution as an initial condition to a higher Reynolds number DNS significantly\nreduces the time to convergence.\n"}
+{"abstract": "  In this paper, we study the problem of federated learning over a wireless\nchannel with user sampling, modeled by a Gaussian multiple access channel,\nsubject to central and local differential privacy (DP/LDP) constraints. It has\nbeen shown that the superposition nature of the wireless channel provides a\ndual benefit of bandwidth efficient gradient aggregation, in conjunction with\nstrong DP guarantees for the users. Specifically, the central DP privacy\nleakage has been shown to scale as $\\mathcal{O}(1/K^{1/2})$, where $K$ is the\nnumber of users. It has also been shown that user sampling coupled with\northogonal transmission can enhance the central DP privacy leakage with the\nsame scaling behavior. In this work, we show that, by join incorporating both\nwireless aggregation and user sampling, one can obtain even stronger privacy\nguarantees. We propose a private wireless gradient aggregation scheme, which\nrelies on independently randomized participation decisions by each user. The\ncentral DP leakage of our proposed scheme scales as $\\mathcal{O}(1/K^{3/4})$.\nIn addition, we show that LDP is also boosted by user sampling. We also present\nanalysis for the convergence rate of the proposed scheme and study the\ntradeoffs between wireless resources, convergence, and privacy theoretically\nand empirically for two scenarios when the number of sampled participants are\n$(a)$ known, or $(b)$ unknown at the parameter server.\n"}
+{"abstract": "  We introduce a novel hybrid algorithm to simulate the real-time evolution of\nquantum systems using parameterized quantum circuits. The method, named\n\"projected - Variational Quantum Dynamics\" (p-VQD) realizes an iterative,\nglobal projection of the exact time evolution onto the parameterized manifold.\nIn the small time-step limit, this is equivalent to the McLachlan's variational\nprinciple. Our approach is efficient in the sense that it exhibits an optimal\nlinear scaling with the total number of variational parameters. Furthermore, it\nis global in the sense that it uses the variational principle to optimize all\nparameters at once. The global nature of our approach then significantly\nextends the scope of existing efficient variational methods, that instead\ntypically rely on the iterative optimization of a restricted subset of\nvariational parameters. Through numerical experiments, we also show that our\napproach is particularly advantageous over existing global optimization\nalgorithms based on the time-dependent variational principle that, due to a\ndemanding quadratic scaling with parameter numbers, are unsuitable for large\nparameterized quantum circuits.\n"}
+{"abstract": "  Crises like the COVID-19 pandemic pose a serious challenge to health-care\ninstitutions. They need to plan the resources required for handling the\nincreased load, for instance, hospital beds and ventilators. To support the\nresource planning of local health authorities from the Cologne region,\nBaBSim.Hospital, a tool for capacity planning based on discrete event\nsimulation, was created. The predictive quality of the simulation is determined\nby 29 parameters. Reasonable default values of these parameters were obtained\nin detailed discussions with medical professionals. We aim to investigate and\noptimize these parameters to improve BaBSim.Hospital. First approaches with\n\"out-of-the-box\" optimization algorithms failed. Implementing a surrogate-based\noptimization approach generated useful results in a reasonable time. To\nunderstand the behavior of the algorithm and to get valuable insights into the\nfitness landscape, an in-depth sensitivity analysis was performed. The\nsensitivity analysis is crucial for the optimization process because it allows\nfocusing the optimization on the most important parameters. We illustrate how\nthis reduces the problem dimension without compromising the resulting accuracy.\nThe presented approach is applicable to many other real-world problems, e.g.,\nthe development of new elevator systems to cover the last mile or simulation of\nstudent flow in academic study periods.\n"}
+{"abstract": "  The recent investigation of chains of Rydberg atoms has brought back the\nproblem of commensurate-incommensurate transitions into the focus of current\nresearch. In 2D classical systems, or in 1D quantum systems, the commensurate\nmelting of a period-p phase with p larger than 4 is known to take place through\nan intermediate floating phase where correlations between domain walls or\nparticles decay only as a power law, but when p is equal to 3 or 4, it has been\nargued by Huse and Fisher that the transition could also be direct and\ncontinuous in a non-conformal chiral universality class with a dynamical\nexponent larger than 1. This is only possible however if the floating phase\nterminates at a Lifshitz point before reaching the conformal point, a\npossibility debated since then. Here we argue that this is a generic feature of\nmodels where the number of particles is not conserved because the exponent of\nthe floating phase changes along the Pokrovsky-Talapov transition and can thus\nreach the value at which the floating phase becomes unstable. Furthermore, we\nshow numerically that this scenario is realized in an effective model of the\nperiod-3 phase of Rydberg chains in which hard-core bosons are created and\nannihilated three by three: The Luttinger liquid parameter reaches the critical\nvalue $p^2/8=9/8$ along the Pokrovsky-Talapov transition, leading to a Lifshitz\npoint that separates the floating phase from a chiral transition. Implications\nbeyond Rydberg atoms are briefly discussed.\n"}
+{"abstract": "  Two-dimensional (2D) magnetic materials are essential for the development of\nthe next-generation spintronic technologies. Recently, layered van der Waals\n(vdW) compound MnBi2Te4 (MBT) has attracted great interest, and its 2D\nstructure has been reported to host coexisting magnetism and topology. Here, we\ndesign several conceptual nanodevices based on MBT monolayer (MBT-ML) and\nreveal their spin-dependent transport properties by means of the\nfirst-principles calculations. The pn-junction diodes and sub-3-nm pin-junction\nfield-effect transistors (FETs) show a strong rectifying effect and a spin\nfiltering effect, with an ideality factor n close to 1 even at a reasonably\nhigh temperature. In addition, the pip- and nin-junction FETs give an\ninteresting negative differential resistive (NDR) effect. The gate voltages can\ntune currents through these FETs in a large range. Furthermore, the MBT-ML has\na strong response to light. Our results uncover the multifunctional nature of\nMBT-ML, pave the road for its applications in diverse next-generation\nsemiconductor spin electric devices.\n"}
+{"abstract": "  This is a brief survey of classical and recent results about the typical\nbehavior of eigenvalues of large random matrices, written for mathematicians\nand others who study and use matrices but may not be accustomed to thinking\nabout randomness.\n"}
+{"abstract": "  Inspection of available data on the decay exponent for the kinetic energy of\nhomogeneous and isotropic turbulence (HIT) shows that it varies by as much as\n100\\%. Measurements and simulations often show no correspondence with\ntheoretical arguments, which are themselves varied. This situation is\nunsatisfactory given that HIT is a building block of turbulence theory and\nmodeling. We take recourse to a large base of direct numerical simulations and\nstudy decaying HIT for a variety of initial conditions. We show that the\nKolmogorov decay exponent and the Birkhoff-Saffman decay are both readily\nobserved, albeit approximately, for long periods of time if the initial\nconditions are appropriately arranged. We also present, for both cases, other\nturbulent statistics such as the velocity derivative skewness, energy spectra\nand dissipation, and show that the decay and growth laws are approximately as\nexpected theoretically, though the wavenumber spectrum near the origin begins\nto change relatively quickly, suggesting that the invariants do not strictly\nexist. We comment briefly on why the decay exponent has varied so widely in\npast experiments and simulations.\n"}
+{"abstract": "  This research paper proposes a novel Neighbourhood Rough Set based approach\nfor supervised Multi-document Text Summarization (MDTS) with analysis and\nimpact on the summarization results for MDTS. Here, Rough Set based LERS\nalgorithm is improved using Neighborhood Rough Set which is itself a novel\ncombination called Neighborhood-LERS to be experimented for evaluations of\nefficacy and efficiency. In this paper, we shall apply and evaluate the\nproposed Neighborhood-LERS for Multi-document Summarization which here is\nproved experimentally to be superior to the base LERS technique for MDTS.\n"}
+{"abstract": "  We experimentally demonstrate the efficient generation of circularly\npolarized pulses tunable from the vacuum to deep ultraviolet (160-380 nm)\nthrough resonant dispersive wave emission from optical solitons in a gas-filled\nhollow capillary fiber. In the deep ultraviolet we measure up to 13 microjoule\nof pulse energy, and from numerical simulations, we estimate the shortest\noutput pulse duration to be 8.5 fs. We also experimentally verify that simply\nscaling the pulse energy by 3/2 between linearly and circularly polarized\npumping closely reproduces the soliton and dispersive wave dynamics. Based on\nprevious results with linearly polarized self-compression and resonant\ndispersive wave emission, we expect our technique to be extended to produce\ncircularly polarized few-fs pulses further into the vacuum ultraviolet, and few\nto sub-fs circularly polarized pulses in the near-infrared.\n"}
+{"abstract": "  As the representations output by Graph Neural Networks (GNNs) are\nincreasingly employed in real-world applications, it becomes important to\nensure that these representations are fair and stable. In this work, we\nestablish a key connection between counterfactual fairness and stability and\nleverage it to propose a novel framework, NIFTY (uNIfying Fairness and\nstabiliTY), which can be used with any GNN to learn fair and stable\nrepresentations. We introduce a novel objective function that simultaneously\naccounts for fairness and stability and develop a layer-wise weight\nnormalization using the Lipschitz constant to enhance neural message passing in\nGNNs. In doing so, we enforce fairness and stability both in the objective\nfunction as well as in the GNN architecture. Further, we show theoretically\nthat our layer-wise weight normalization promotes counterfactual fairness and\nstability in the resulting representations. We introduce three new graph\ndatasets comprising of high-stakes decisions in criminal justice and financial\nlending domains. Extensive experimentation with the above datasets demonstrates\nthe efficacy of our framework.\n"}
+{"abstract": "  The estimation of the covariance function of a stochastic process, or signal,\nis of integral importance for a multitude of signal processing applications. In\nthis work, we derive closed-form expressions for the variance of covariance\nestimates for mixed-spectrum signals, i.e., spectra containing both absolutely\ncontinuous and singular parts. The results cover both finite-sample and\nasymptotic regimes, allowing for assessing the exact speed of convergence of\nestimates to their expectations, as well as their limiting behavior. As is\nshown, such covariance estimates may converge even for non-ergodic processes.\nFurthermore, we consider approximating signals with arbitrary spectral\ndensities by sequences of singular spectrum, i.e., sinusoidal, processes, and\nderive the limiting behavior of covariance estimates as both the sample size\nand the number of sinusoidal components tend to infinity. We show that the\nasymptotic regime variance can be described by a time-frequency resolution\nproduct, with dramatically different behavior depending on how the sinusoidal\napproximation is constructed. In a few numerical examples we illustrate the\ntheory and the corresponding implications for direction of arrival estimation.\n"}
+{"abstract": "  Successful navigation of a rigid-body traveling with six degrees of freedom\n(6 DoF) requires accurate estimation of attitude , position, and linear\nvelocity. The true navigation dynamics are highly nonlinear and are modeled on\nthe matrix Lie group of SE2(3). This paper presents novel geometric nonlinear\ncontinuous stochastic navigation observers on SE2(3) capturing the true\nnonlinearity of the problem. The proposed observers combines IMU and landmark\nmeasurements. It efficiently handles the IMU measurement noise. The proposed\nobservers are guaranteed to be almost semi-globally uniformly ultimately\nbounded in the mean square. Quaternion representation is provided. A real-world\nquadrotor measurement dataset is used to validate the effectiveness of the\nproposed observers in its discrete form. Keywords: Inertial navigation,\nstochastic system, Brownian motion process, stochastic filter algorithm,\nstochastic differential equation, Lie group, SE(3), SO(3), pose estimator,\nposition, attitude, feature measurement, inertial measurement unit, IMU.\n"}
+{"abstract": "  We study a graph search problem in which a team of searchers attempts to find\na mobile target located in a graph. Assuming that (a) the visibility field of\nthe searchers is limited, (b) the searchers have unit speed and (c) the target\nhas infinite speed, we formulate the Limited Visibility Graph Search (LVGS)\nproblem and present the LVGS algorithm, which produces a search schedule\nguaranteed to find the target in the minimum possible number of steps. Our LVGS\nalgorithm is a conversion of Guibas and Lavalle's polygonal region search\nalgorithm.\n"}
+{"abstract": "  The objective of this paper is to analyze the existence of equilibria for a\nclass of deterministic mean field games of controls. The interaction between\nplayers is due to both a congestion term and a price function which depends on\nthe distributions of the optimal strategies. Moreover, final state and mixed\nstate-control constraints are considered, the dynamics being nonlinear and\naffine with respect to the control. The existence of equilibria is obtained by\nKakutani's theorem, applied to a fixed point formulation of the problem.\nFinally, uniqueness results are shown under monotonicity assumptions.\n"}
+{"abstract": "  This paper deals with perturbation theory for discrete spectra of linear\noperators. To simplify exposition we consider here self-adjoint operators. This\ntheory is based on the Feshbach-Schur map and it has advantages with respect to\nthe standard perturbation theory in three aspects: (a) it readily produces\nrigorous estimates on eigenvalues and eigenfunctions with explicit constants;\n(b) it is compact and elementary (it uses properties of norms and the\nfundamental theorem of algebra about solutions of polynomial equations); and\n(c) it is based on a self-contained formulation of a fixed point problem for\nthe eigenvalues and eigenfunctions, allowing for easy iterations. We apply our\nabstract results to obtain rigorous bounds on the ground states of Helium-type\nions.\n"}
+{"abstract": "  We prove various low-energy decomposition results, showing that we can\ndecompose a finite set $A\\subset \\mathbb{F}_p$ satisfying $|A|<p^{5/8}$, into\n$A = S\\sqcup T$ so that, for a non-degenerate quadratic $f\\in\n\\mathbb{F}_p[x,y]$, we have\n  \\[ |\\{(s_1,s_2,s_3,s_4)\\in S^4 : s_1 + s_2 = s_3 + s_4\\}| \\ll |A|^{3 -\n\\frac15 + \\varepsilon}\n  \\] and\n  \\[\n  |\\{(t_1,t_2,t_3,t_4)\\in T^4 : f(t_1, t_2) = f(t_3, t_4)\\}|\\ll |A|^{3 -\n\\frac15 + \\varepsilon}\\,.\n  \\]\n  Variations include extending this result to large $A$ and a low-energy\ndecomposition involving additive energy of images of rational functions. This\ngives a quantitative improvement to a result of Roche-Newton, Shparlinski and\nWinterhof as well as a generalisation of a result of Rudnev, Shkredov and\nStevens.\n  We consider applications to conditional expanders, exponential sum estimates\nand the finite field Littlewood problem. In particular, we improve results of\nMirzaei, Swaenepoel and Winterhof and Garcia.\n"}
+{"abstract": "  To improve the viewer's Quality of Experience (QoE) and optimize computer\ngraphics applications, 3D model quality assessment (3D-QA) has become an\nimportant task in the multimedia area. Point cloud and mesh are the two most\nwidely used digital representation formats of 3D models, the visual quality of\nwhich is quite sensitive to lossy operations like simplification and\ncompression. Therefore, many related studies such as point cloud quality\nassessment (PCQA) and mesh quality assessment (MQA) have been carried out to\nmeasure the caused visual quality degradations. However, a large part of\nprevious studies utilizes full-reference (FR) metrics, which means they may\nfail to predict the quality level with the absence of the reference 3D model.\nFurthermore, few 3D-QA metrics are carried out to consider color information,\nwhich significantly restricts the effectiveness and scope of application. In\nthis paper, we propose a no-reference (NR) quality assessment metric for\ncolored 3D models represented by both point cloud and mesh. First, we project\nthe 3D models from 3D space into quality-related geometry and color feature\ndomains. Then, the natural scene statistics (NSS) and entropy are utilized to\nextract quality-aware features. Finally, the Support Vector Regressor (SVR) is\nemployed to regress the quality-aware features into quality scores. Our method\nis mainly validated on the colored point cloud quality assessment database\n(SJTU-PCQA) and the colored mesh quality assessment database (CMDM). The\nexperimental results show that the proposed method outperforms all the\nstate-of-art NR 3D-QA metrics and obtains an acceptable gap with the\nstate-of-art FR 3D-QA metrics.\n"}
+{"abstract": "  In the highly non-equilibrium conditions of laser induced spin dynamics\nmagnetic moments can only be obtained from the spectral information, most\ncommonly from the spectroscopy of semi-core states using the so-called x-ray\nmagnetic circular dichroism (XMCD) sum rules. The validity of the these sum\nrules in tracking femtosecond spin dynamics remains, however, an open question.\nEmploying the time dependent extension of density functional theory (TD-DFT) we\ncompare spectroscopically obtained moments with those directly calculated from\nthe TD-DFT densities. We find that for experimentally typical pump pulses these\ntwo very distinct routes to the spin moment are, for Co and Ni, in excellent\nagreement, validating the experimental approach. However, for short and intense\npulses or high fluence pulses of long duration the XMCD sum rules fail, with\nerrors exceeding 50\\%. This failure persists only during the pulse and occurs\nwhen the pump pulse excites charge out of the $d$-band and into $sp$-character\nbands, invalidating the semi-core to $d$-state transitions assumed by the XMCD\nsum rules.\n"}
+{"abstract": "  Under stereo settings, the problem of image super-resolution (SR) and\ndisparity estimation are interrelated that the result of each problem could\nhelp to solve the other. The effective exploitation of correspondence between\ndifferent views facilitates the SR performance, while the high-resolution (HR)\nfeatures with richer details benefit the correspondence estimation. According\nto this motivation, we propose a Stereo Super-Resolution and Disparity\nEstimation Feedback Network (SSRDE-FNet), which simultaneously handles the\nstereo image super-resolution and disparity estimation in a unified framework\nand interact them with each other to further improve their performance.\nSpecifically, the SSRDE-FNet is composed of two dual recursive sub-networks for\nleft and right views. Besides the cross-view information exploitation in the\nlow-resolution (LR) space, HR representations produced by the SR process are\nutilized to perform HR disparity estimation with higher accuracy, through which\nthe HR features can be aggregated to generate a finer SR result. Afterward, the\nproposed HR Disparity Information Feedback (HRDIF) mechanism delivers\ninformation carried by HR disparity back to previous layers to further refine\nthe SR image reconstruction. Extensive experiments demonstrate the\neffectiveness and advancement of SSRDE-FNet.\n"}
+{"abstract": "  The open string field theory of Witten (SFT) has a close formal similarity\nwith Chern-Simons theory in three dimensions. This similarity is due to the\nfact that the former theory has concepts corresponding to forms, exterior\nderivative, wedge product and integration over the manifold. In this paper, we\nintroduce the interior product and the Lie derivative in the $KBc$ subsector of\nSFT. The interior product in SFT is specified by a two-component \"tangent\nvector\" and lowers the ghost number by one (like the ordinary interior product\nmaps a $p$-form to $(p-1)$-form). The Lie derivative in SFT is defined as the\nanti-commutator of the interior product and the BRST operator. The important\nproperty of these two operations is that they respect the $KBc$ algebra.\nDeforming the original $(K,B,c)$ by using the Lie derivative, we can consider\nan infinite copies of the $KBc$ algebra, which we call the $KBc$ manifold. As\nan application, we construct the Wilson line on the manifold, which could play\na role in reproducing degenerate fluctuation modes around a multi-brane\nsolution.\n"}
+{"abstract": "  A common approach to solving physical reasoning tasks is to train a value\nlearner on example tasks. A limitation of such an approach is that it requires\nlearning about object dynamics solely from reward values assigned to the final\nstate of a rollout of the environment. This study aims to address this\nlimitation by augmenting the reward value with self-supervised signals about\nobject dynamics. Specifically, we train the model to characterize the\nsimilarity of two environment rollouts, jointly with predicting the outcome of\nthe reasoning task. This similarity can be defined as a distance measure\nbetween the trajectory of objects in the two rollouts, or learned directly from\npixels using a contrastive formulation. Empirically, we find that this approach\nleads to substantial performance improvements on the PHYRE benchmark for\nphysical reasoning (Bakhtin et al., 2019), establishing a new state-of-the-art.\n"}
+{"abstract": "  Network spaces have been known as a critical factor in both handcrafted\nnetwork designs or defining search spaces for Neural Architecture Search (NAS).\nHowever, an effective space involves tremendous prior knowledge and/or manual\neffort, and additional constraints are required to discover efficiency-aware\narchitectures. In this paper, we define a new problem, Network Space Search\n(NSS), as searching for favorable network spaces instead of a single\narchitecture. We propose an NSS method to directly search for efficient-aware\nnetwork spaces automatically, reducing the manual effort and immense cost in\ndiscovering satisfactory ones. The resultant network spaces, named Elite\nSpaces, are discovered from Expanded Search Space with minimal human expertise\nimposed. The Pareto-efficient Elite Spaces are aligned with the Pareto front\nunder various complexity constraints and can be further served as NAS search\nspaces, benefiting differentiable NAS approaches (e.g. In CIFAR-100, an\naveragely 2.3% lower error rate and 3.7% closer to target constraint than the\nbaseline with around 90% fewer samples required to find satisfactory networks).\nMoreover, our NSS approach is capable of searching for superior spaces in\nfuture unexplored spaces, revealing great potential in searching for network\nspaces automatically. Website: https://minhungchen.netlify.app/publication/nss.\n"}
+{"abstract": "  This paper describes MagicPai's system for SemEval 2021 Task 7, HaHackathon:\nDetecting and Rating Humor and Offense. This task aims to detect whether the\ntext is humorous and how humorous it is. There are four subtasks in the\ncompetition. In this paper, we mainly present our solution, a multi-task\nlearning model based on adversarial examples, for task 1a and 1b. More\nspecifically, we first vectorize the cleaned dataset and add the perturbation\nto obtain more robust embedding representations. We then correct the loss via\nthe confidence level. Finally, we perform interactive joint learning on\nmultiple tasks to capture the relationship between whether the text is humorous\nand how humorous it is. The final result shows the effectiveness of our system.\n"}
+{"abstract": "  Mobile edge computing (MEC) has become a promising solution to utilize\ndistributed computing resources for supporting computation-intensive vehicular\napplications in dynamic driving environments. To facilitate this paradigm, the\nonsite resource trading serves as a critical enabler. However, dynamic\ncommunications and resource conditions could lead unpredictable trading\nlatency, trading failure, and unfair pricing to the conventional resource\ntrading process. To overcome these challenges, we introduce a novel\nfutures-based resource trading approach in edge computing-enabled internet of\nvehicles (IoV), where a forward contract is used to facilitate resource trading\nrelated negotiations between an MEC server (seller) and a vehicle (buyer) in a\ngiven future term. Through estimating the historical statistics of future\nresource supply and network condition, we formulate the futures-based resource\ntrading as the optimization problem aiming to maximize the seller's and the\nbuyer's expected utility, while applying risk evaluations to relieve possible\nlosses incurred by the uncertainties in the system. To tackle this problem, we\npropose an efficient bilateral negotiation approach which facilitates the\nparticipants reaching a consensus. Extensive simulations demonstrate that the\nproposed futures-based resource trading brings considerable utilities to both\nparticipants, while significantly outperforming the baseline methods on\ncritical factors, e.g., trading failures and fairness, negotiation latency and\ncost.\n"}
+{"abstract": "  The silicon pixel sensor is the core component of the vertex detector for the\nCircular Electron Positron Collider~(CEPC). The JadePix3 is a full-function\nlarge-size CMOS chip designed for the CEPC vertex detector. To test all the\nfunctions and the performance of this chip, we designed a test system based on\nthe IPbus framework. The test system controls the parameters and monitors the\nstatus of the pixel chip. By integrating the jumbo frame feature into the IPbus\nsuite, the block read/write speed is further extended in order to meet the\nspecifications of the JadePix3. The robustness, scalability, and portability of\nthis system have been verified by pulse test, cosmic test and laser test in the\nlaboratory. This paper summarizes the DAQ and control system of the JadePix3\nand presents the first results of the tests.\n"}
+{"abstract": "  We study the thermal phase transitions of a generic real scalar field,\nwithout a $Z_2$-symmetry, referred to variously as an inert, sterile or singlet\nscalar, or $\\phi^3+\\phi^4$ theory. Such a scalar field arises in a wide range\nof models, including as the inflaton, or as a portal to the dark sector. At\nhigh temperatures, we perform dimensional reduction, matching to an effective\ntheory in three dimensions, which we then study both perturbatively to\nthree-loop order and on the lattice. For strong first-order transitions, with\nlarge tree-level cubic couplings, our lattice Monte-Carlo simulations agree\nwith perturbation theory within error. However, as the size of the cubic\ncoupling decreases, relative to the quartic coupling, perturbation theory\nbecomes less and less reliable, breaking down completely in the approach to the\n$Z_2$-symmetric limit, in which the transition is of second order.\nNotwithstanding, the renormalisation group is shown to significantly extend the\nvalidity of perturbation theory. Throughout, our calculations are made as\nexplicit as possible so that this article may serve as a guide for similar\ncalculations in other theories.\n"}
+{"abstract": "  With increasing adoption of face recognition systems, it is important to\nensure adequate performance of these technologies across demographic groups.\nRecently, phenotypes such as skin-tone, have been proposed as superior\nalternatives to traditional race categories when exploring performance\ndifferentials. However, there is little consensus regarding how to\nappropriately measure skin-tone in evaluations of biometric performance or in\nAI more broadly. In this study, we explore the relationship between\nface-area-lightness-measures (FALMs) estimated from images and ground-truth\nskin readings collected using a device designed to measure human skin. FALMs\nestimated from different images of the same individual varied significantly\nrelative to ground-truth FALM. This variation was only reduced by greater\ncontrol of acquisition (camera, background, and environment). Next, we compare\nground-truth FALM to Fitzpatrick Skin Types (FST) categories obtained using the\nstandard, in-person, medical survey and show FST is poorly predictive of\nskin-tone. Finally, we show how noisy estimation of FALM leads to errors\nselecting explanatory factors for demographic differentials. These results\ndemonstrate that measures of skin-tone for biometric performance evaluations\nmust come from objective, characterized, and controlled sources. Further,\ndespite this being a currently practiced approach, estimating FST categories\nand FALMs from uncontrolled imagery does not provide an appropriate measure of\nskin-tone.\n"}
+{"abstract": "  The quantum gravity vacuum must contain virtual fluctuations of black hole\nmicrostates. These extended-size fluctuations get `crushed' when a closed\ntrapped surface forms, and turn into on-shell `fuzzball' states that resolve\nthe information puzzle. We argue that these same fluctuations can get\n`stretched' by the anti-trapped surfaces in an expanding cosmology, and that\nthis stretching generates vacuum energy. The stretching happen when the Hubble\ndeceleration reduces quickly, which happens whenever the pressure drops\nquickly. We thus get an inflation-scale vacuum energy when the heavy GUTS\nparticles become nonrelativistic, and again a small vacuum energy when the\nradiation phase turns to dust. The expansion law in the radiation phase does\nnot allow stretching, in agreement with the observed irrelevance of vacuum\nenergy in that phase. The extra energy induced when the radiation phase changes\nto dust may explain the tension in the Hubble constant between low and high\nredshift data.\n"}
+{"abstract": "  Bloch states of electrons in honeycomb two-dimensional crystals with\nmulti-valley band structure and broken inversion symmetry have orbital magnetic\nmoments of a topological nature. In crystals with two degenerate valleys, a\nperpendicular magnetic field lifts the valley degeneracy via a Zeeman effect\ndue to these magnetic moments, leading to magnetoelectric effects which can be\nleveraged for creating valleytronic devices. In this work, we demonstrate that\ntrilayer graphene with Bernal stacking, (ABA TLG) hosts topological magnetic\nmoments with a large and widely tunable valley g-factor, reaching a value 1050\nat the extreme of the studied parametric range. The reported experiment\nconsists in sublattice-resolved scanning tunneling spectroscopy under\nperpendicular electric and magnetic fields that control the TLG bands. The\ntunneling spectra agree very well with the results of theoretical modeling that\nincludes the full details of the TLG tight-binding model and accounts for a\nquantum-dot-like potential profile formed electrostatically under the scanning\ntunneling microscope tip.\n"}
+{"abstract": "  Time-dependent orbital-free DFT is an efficient method for calculating the\ndynamic properties of large scale quantum systems due to the low computational\ncost compared to standard time-dependent DFT. We formalize this method by\nmapping the real system of interacting fermions onto a fictitious system of\nnon-interacting bosons. The dynamic Pauli potential and associated kernel\nemerge as key ingredients of time-tependent orbital-free DFT. Using the uniform\nelectron gas as a model system, we derive an approximate frequency-dependent\nPauli kernel. Pilot calculations suggest that space nonlocality is a key\nfeature for this kernel. Nonlocal terms arise already in the second order\nexpansion with respect to unitless frequency and reciprocal space variable\n($\\frac{\\omega}{q\\, k_F}$ and $\\frac{q}{2\\, k_F}$, respectively). Given the\nencouraging performance of the proposed kernel, we expect it will lead to more\naccurate orbital-free DFT simulations of nanoscale systems out of equilibrium.\nAdditionally, the proposed path to formulate nonadiabatic Pauli kernels\npresents several avenues for further improvements which can be exploited in\nfuture works to improve the results.\n"}
+{"abstract": "  Recognizing human grasping strategies is an important factor in robot\nteaching as these strategies contain the implicit knowledge necessary to\nperform a series of manipulations smoothly. This study analyzed the effects of\nobject affordance-a prior distribution of grasp types for each object-on\nconvolutional neural network (CNN)-based grasp-type recognition. To this end,\nwe created datasets of first-person grasping-hand images labeled with grasp\ntypes and object names, and tested a recognition pipeline leveraging object\naffordance. We evaluated scenarios with real and illusory objects to be\ngrasped, to consider a teaching condition in mixed reality where the lack of\nvisual object information can make the CNN recognition challenging. The results\nshow that object affordance guided the CNN in both scenarios, increasing the\naccuracy by 1) excluding unlikely grasp types from the candidates and 2)\nenhancing likely grasp types. In addition, the \"enhancing effect\" was more\npronounced with high degrees of grasp-type heterogeneity. These results\nindicate the effectiveness of object affordance for guiding grasp-type\nrecognition in robot teaching applications.\n"}
+{"abstract": "  The field of two-dimensional topological semimetals, which emerged at the\nintersection of two-dimensional materials and topological materials, have been\nrapidly developing in recent years. In this article, we briefly review the\nprogress in this field. Our focus is on the basic concepts and notions, in\norder to convey a coherent overview of the field. Some material examples are\ndiscussed to illustrate the concepts. We discuss the outstanding problems in\nthe field that need to be addressed in future research.\n"}
+{"abstract": "  Purpose. Early squamous cell neoplasia (ESCN) in the oesophagus is a highly\ntreatable condition. Lesions confined to the mucosal layer can be curatively\ntreated endoscopically. We build a computer-assisted detection (CADe) system\nthat can classify still images or video frames as normal or abnormal with high\ndiagnostic accuracy. Methods. We present a new benchmark dataset containing 68K\nbinary labeled frames extracted from 114 patient videos whose imaged areas have\nbeen resected and correlated to histopathology. Our novel convolutional network\n(CNN) architecture solves the binary classification task and explains what\nfeatures of the input domain drive the decision-making process of the network.\nResults. The proposed method achieved an average accuracy of 91.7 % compared to\nthe 94.7 % achieved by a group of 12 senior clinicians. Our novel network\narchitecture produces deeply supervised activation heatmaps that suggest the\nnetwork is looking at intrapapillary capillary loop (IPCL) patterns when\npredicting abnormality. Conclusion. We believe that this dataset and baseline\nmethod may serve as a reference for future benchmarks on both video frame\nclassification and explainability in the context of ESCN detection. A future\nwork path of high clinical relevance is the extension of the classification to\nESCN types.\n"}
+{"abstract": "  Mennicke--Newman lemma for unimodular rows was used by W. van der Kallen to\ngive a group structure on the orbit set $\\frac{Um_{n}(R)}{E_{n}(R)}$ for a\ncommutative noetherian ring of dimension $d\\leq 2n-4.$ In this paper, we\ngeneralise the Mennicke--Newman lemma for $m\\times n $ right invertible\nmatrices.\n"}
+{"abstract": "  We consider the two dimensional surface quasi-geostrophic equations with\nsuper-critical dissipation. For large initial data in critical Sobolev and\nBesov spaces, we prove optimal Gevrey regularity with the same decay exponent\nas the linear part. This settles several open problems in \\cite{Bis14, BMS15}.\n"}
+{"abstract": "  Aldous-Broder algorithm is a famous algorithm used to sample a uniform\nspanning tree of any finite connected graph G, but it is more general: it\nstates that given a reversible M Markov chain on G started at r, the tree\nrooted at r formed by the steps of successive first entrance in each node\n(different from the root) has a probability proportional to\n$\\prod_{e=(e1,e2)\\in{\\sf Edges}(t,r)} M_{e1,e2}$ , where the edges are directed\ntoward r. As stated, it allows to sample many distributions on the set of\nspanning trees. In this paper we extend Aldous-Broder theorem by dropping the\nreversibility condition on M. We highlight that the general statement we prove\nis not the same as the original one (but it coincides in the reversible case\nwith that of Aldous and Broder). We prove this extension in two ways: an\nadaptation of the classical argument, which is purely probabilistic, and a new\nproof based on combinatorial arguments. On the way we introduce a new\ncombinatorial object that we call the golf sequences.\n"}
+{"abstract": "  Predictor screening rules, which discard predictors from the design matrix\nbefore fitting a model, have had sizable impacts on the speed with which\n$\\ell_1$-regularized regression problems, such as the lasso, can be solved.\nCurrent state-of-the-art screening rules, however, have difficulties in dealing\nwith highly-correlated predictors, often becoming too conservative. In this\npaper, we present a new screening rule to deal with this issue: the Hessian\nScreening Rule. The rule uses second-order information from the model in order\nto provide more accurate screening as well as higher-quality warm starts. In\nour experiments on $\\ell_1$-regularized least-squares (the lasso) and logistic\nregression, we show that the rule outperforms all other alternatives in\nsimulated experiments with high correlation, as well as in the majority of real\ndatasets that we study.\n"}
+{"abstract": "  It is not yet fully understood how planet formation affects the properties of\nhost stars, in or out of a cluster; however, abundance trends can help us\nunderstand these processes. We present a detailed chemical abundance analysis\nof six stars in Praesepe, a planet-hosting open cluster. Pr0201 is known to\nhost a close-in (period of 4.4 days) giant planet (mass of 0.54$\\rm\nM_{\\rm{J}}$), while the other five cluster members in our sample (Pr0133,\nPr0081, Pr0208, Pr0051, and Pr0076) have no detected planets according to RV\nmeasurements. Using high-resolution, high signal-to-noise echelle spectra\nobtained with Keck/HIRES and a novel approach to equivalent width measurements\n(XSpect-EW), we derived abundances of up to 20 elements spanning a range of\ncondensation temperatures (Tc). We find a mean cluster metallicity of [Fe/H] =\n+0.21$\\pm$0.02 dex, in agreement with most previous determinations. We find\nmost of our elements show a [X/Fe] scatter of $\\sim$0.02-0.03 dex and conclude\nthat our stellar sample is chemically homogeneous. The Tc slope for the cluster\nmean abundances is consistent with zero and none of the stars in our sample\nexhibit individually a statistically significant Tc slope. Using a planet\nengulfment model, we find that the planet-host, Pr0201, shows no evidence of\nsignificant enrichment in its refractory elements when compared to the cluster\nmean that would be consistent with a planetary accretion scenario.\n"}
+{"abstract": "  The systematic trend in nuclear charge radii is of great interest due to the\nuniversal shell effects and odd-even staggering (OES). Modified root mean\nsquare (rms) charge radius formula to phenomenologically account for the\nformation of neutron-proton ($np$) correlations is firstly applied to study the\nodd-$Z$ copper and indium isotopes. Theoretical results obtained by\nrelativistic mean field (RMF) model with NL3, PK1 and NL3$^{*}$ parameter sets\nare compared with the experimental data. Our results show that both OES and the\nabrupt changes across $N=50$ and $82$ shell closures are reproduced evidently\nin nuclear charge radii. The inverted parabolic-like behaviors of rms charge\nradii can also be remarkably reproduced between two neutron magic numbers,\nnamely $N=28$ to $50$ for copper isotopes and $N=50$ to $82$ for indium\nisotopes. Meanwhile, our conclusions show almost no dependence on the effective\nforces. This means the $np$-correlations play an indispensable role in\nquantitatively determining the fine structures of nuclear charge radii along\nodd-$Z$ isotopic chains. The underlying mechanism is discussed.\n"}
+{"abstract": "  For general spin systems, we prove that a contractive coupling for any local\nMarkov chain implies optimal bounds on the mixing time and the modified\nlog-Sobolev constant for a large class of Markov chains including the Glauber\ndynamics, arbitrary heat-bath block dynamics, and the Swendsen-Wang dynamics.\nThis reveals a novel connection between probabilistic techniques for bounding\nthe convergence to stationarity and analytic tools for analyzing the decay of\nrelative entropy. As a corollary of our general results, we obtain\n$O(n\\log{n})$ mixing time and $\\Omega(1/n)$ modified log-Sobolev constant of\nthe Glauber dynamics for sampling random $q$-colorings of an $n$-vertex graph\nwith constant maximum degree $\\Delta$ when $q > (11/6 - \\epsilon_0)\\Delta$ for\nsome fixed $\\epsilon_0>0$. We also obtain $O(\\log{n})$ mixing time and\n$\\Omega(1)$ modified log-Sobolev constant of the Swendsen-Wang dynamics for the\nferromagnetic Ising model on an $n$-vertex graph of constant maximum degree\nwhen the parameters of the system lie in the tree uniqueness region. At the\nheart of our results are new techniques for establishing spectral independence\nof the spin system and block factorization of the relative entropy. On one hand\nwe prove that a contractive coupling of a local Markov chain implies spectral\nindependence of the Gibbs distribution. On the other hand we show that spectral\nindependence implies factorization of entropy for arbitrary blocks,\nestablishing optimal bounds on the modified log-Sobolev constant of the\ncorresponding block dynamics.\n"}
+{"abstract": "  Distillation is the technique of training a \"student\" model based on examples\nthat are labeled by a separate \"teacher\" model, which itself is trained on a\nlabeled dataset. The most common explanations for why distillation \"works\" are\npredicated on the assumption that student is provided with \\emph{soft} labels,\n\\eg probabilities or confidences, from the teacher model. In this work, we\nshow, that, even when the teacher model is highly overparameterized, and\nprovides \\emph{hard} labels, using a very large held-out unlabeled dataset to\ntrain the student model can result in a model that outperforms more\n\"traditional\" approaches.\n  Our explanation for this phenomenon is based on recent work on \"double\ndescent\". It has been observed that, once a model's complexity roughly exceeds\nthe amount required to memorize the training data, increasing the complexity\n\\emph{further} can, counterintuitively, result in \\emph{better} generalization.\nResearchers have identified several settings in which it takes place, while\nothers have made various attempts to explain it (thus far, with only partial\nsuccess). In contrast, we avoid these questions, and instead seek to\n\\emph{exploit} this phenomenon by demonstrating that a highly-overparameterized\nteacher can avoid overfitting via double descent, while a student trained on a\nlarger independent dataset labeled by this teacher will avoid overfitting due\nto the size of its training set.\n"}
+{"abstract": "  The learning and usage of an API is supported by official documentation. Like\nsource code, API documentation is itself a software product. Several research\nresults show that bad design in API documentation can make the reuse of API\nfeatures difficult. Indeed, similar to code smells or code antipatterns, poorly\ndesigned API documentation can also exhibit 'smells'. Such documentation smells\ncan be described as bad documentation styles that do not necessarily produce an\nincorrect documentation but nevertheless make the documentation difficult to\nproperly understand and to use. Recent research on API documentation has\nfocused on finding content inaccuracies in API documentation and to complement\nAPI documentation with external resources (e.g., crowd-shared code examples).\nWe are aware of no research that focused on the automatic detection of API\ndocumentation smells. This paper makes two contributions. First, we produce a\ncatalog of five API documentation smells by consulting literature on API\ndocumentation presentation problems. We create a benchmark dataset of 1,000 API\ndocumentation units by exhaustively and manually validating the presence of the\nfive smells in Java official API reference and instruction documentation.\nSecond, we conduct a survey of 21 professional software developers to validate\nthe catalog. The developers agreed that they frequently encounter all five\nsmells in API official documentation and 95.2% of them reported that the\npresence of the documentation smells negatively affects their productivity. The\nparticipants wished for tool support to automatically detect and fix the smells\nin API official documentation. We develop a suite of rule-based, deep and\nshallow machine learning classifiers to automatically detect the smells. The\nbest performing classifier BERT, a deep learning model, achieves F1-scores of\n0.75 - 0.97.\n"}
+{"abstract": "  Given a countable metric space, we can consider its end. Then a basis of a\nHilbert space indexed by the metric space defines an end of the Hilbert space,\nwhich is a new notion and different from an end as a metric space.\n  Such an indexed basis also defines unitary operators of finite propagation,\nand these operators preserve an end of a Hilbert space. Then, we can define a\nHilbert bundle with end, which lightens up new structures of Hilbert bundles.\nIn a special case, we can define characteristic classes of Hilbert bundles with\nends, which are new invariants of Hilbert bundles. We show Hilbert bundles with\nends appear in natural contexts. First, we generalize the pushforward of a\nvector bundle along a finite covering to an infinite covering, which is a\nHilbert bundle with end under a mild condition. Then we compute characteristic\nclasses of some pushforwards along infinite coverings. Next, we will show the\nspectral decompositions of nice differential operators give rise to Hilbert\nbundles with ends, which elucidate new features of spectral decompositions. The\nspectral decompositions we will consider are the Fourier transform and the\nharmonic oscillators.\n"}
+{"abstract": "  Federated Learning (FL) is a paradigm in Machine Learning (ML) that addresses\ndata privacy, security, access rights and access to heterogeneous information\nissues by training a global model using distributed nodes. Despite its\nadvantages, there is an increased potential for cyberattacks on FL-based ML\ntechniques that can undermine the benefits. Model-poisoning attacks on FL\ntarget the availability of the model. The adversarial objective is to disrupt\nthe training. We propose attestedFL, a defense mechanism that monitors the\ntraining of individual nodes through state persistence in order to detect a\nmalicious worker. A fine-grained assessment of the history of the worker\npermits the evaluation of its behavior in time and results in innovative\ndetection strategies. We present three lines of defense that aim at assessing\nif the worker is reliable by observing if the node is really training,\nadvancing towards a goal. Our defense exposes an attacker's malicious behavior\nand removes unreliable nodes from the aggregation process so that the FL\nprocess converge faster. Through extensive evaluations and against various\nadversarial settings, attestedFL increased the accuracy of the model between\n12% to 58% under different scenarios such as attacks performed at different\nstages of convergence, attackers colluding and continuous attacks.\n"}
+{"abstract": "  In many information extraction applications, entity linking (EL) has emerged\nas a crucial task that allows leveraging information about named entities from\na knowledge base. In this paper, we address the task of multimodal entity\nlinking (MEL), an emerging research field in which textual and visual\ninformation is used to map an ambiguous mention to an entity in a knowledge\nbase (KB). First, we propose a method for building a fully annotated Twitter\ndataset for MEL, where entities are defined in a Twitter KB. Then, we propose a\nmodel for jointly learning a representation of both mentions and entities from\ntheir textual and visual contexts. We demonstrate the effectiveness of the\nproposed model by evaluating it on the proposed dataset and highlight the\nimportance of leveraging visual information when it is available.\n"}
+{"abstract": "  Recent advances in multi-task peer prediction have greatly expanded our\nknowledge about the power of multi-task peer prediction mechanisms. Various\nmechanisms have been proposed in different settings to elicit different types\nof information. But we still lack understanding about when desirable mechanisms\nwill exist for a multi-task peer prediction problem. In this work, we study the\nelicitability of multi-task peer prediction problems. We consider a designer\nwho has certain knowledge about the underlying information structure and wants\nto elicit certain information from a group of participants. Our goal is to\ninfer the possibility of having a desirable mechanism based on the primitives\nof the problem.\n  Our contribution is twofold. First, we provide a characterization of the\nelicitable multi-task peer prediction problems, assuming that the designer only\nuses scoring mechanisms. Scoring mechanisms are the mechanisms that reward\nparticipants' reports for different tasks separately. The characterization uses\na geometric approach based on the power diagram characterization in the\nsingle-task setting ([Lambert and Shoham, 2009, Frongillo and Witkowski,\n2017]). For general mechanisms, we also give a necessary condition for a\nmulti-task problem to be elicitable.\n  Second, we consider the case when the designer aims to elicit some properties\nthat are linear in the participant's posterior about the state of the world. We\nfirst show that in some cases, the designer basically can only elicit the\nposterior itself. We then look into the case when the designer aims to elicit\nthe participants' posteriors. We give a necessary condition for the posterior\nto be elicitable. This condition implies that the mechanisms proposed by Kong\nand Schoenebeck are already the best we can hope for in their setting, in the\nsense that their mechanisms can solve any problem instance that can possibly be\nelicitable.\n"}
+{"abstract": "  Semantic segmentation of building facade is significant in various\napplications, such as urban building reconstruction and damage assessment. As\nthere is a lack of 3D point clouds datasets related to the fine-grained\nbuilding facade, we construct the first large-scale building facade point\nclouds benchmark dataset for semantic segmentation. The existing methods of\nsemantic segmentation cannot fully mine the local neighborhood information of\npoint clouds. Addressing this problem, we propose a learnable attention module\nthat learns Dual Local Attention features, called DLA in this paper. The\nproposed DLA module consists of two blocks, including the self-attention block\nand attentive pooling block, which both embed an enhanced position encoding\nblock. The DLA module could be easily embedded into various network\narchitectures for point cloud segmentation, naturally resulting in a new 3D\nsemantic segmentation network with an encoder-decoder architecture, called\nDLA-Net in this work. Extensive experimental results on our constructed\nbuilding facade dataset demonstrate that the proposed DLA-Net achieves better\nperformance than the state-of-the-art methods for semantic segmentation.\n"}
+{"abstract": "  Academic plagiarism is a serious problem nowadays. Due to the existence of\ninexhaustible sources of digital information, today it is easier to plagiarize\nmore than ever before. The good thing is that plagiarism detection techniques\nhave improved and are powerful enough to detect attempts of plagiarism in\neducation. We are now witnessing efficient plagiarism detection software in\naction, such as Turnitin, iThenticate or SafeAssign. In the introduction we\nexplore software that is used within the Croatian academic community for\nplagiarism detection in universities and/or in scientific journals. The\nquestion is: is this enough? Current software has proven to be successful,\nhowever the problem of identifying paraphrasing or obfuscation plagiarism\nremains unresolved. In this paper we present a report of how semantic\nsimilarity measures can be used in the plagiarism detection task.\n"}
+{"abstract": "  The thermodynamic limit for the efficiency of solar cells is predominantly\ndefined by the energy bandgap of the used semiconductor. In case of organic\nsolar cells both energetics and kinetics of three different species play role:\nexcitons, charge transfer states and charge separated states. In this work, we\nclarify the effect of the relative energetics and kinetics of these species on\nthe recombination and generation dynamics. Making use of detailed balance, we\ndevelop an analytical framework describing how the intricate interplay between\nthe different species influence the photocurrent generation, the recombination,\nand the open-circuit voltage in organic solar cells. Furthermore, we clarify\nthe essential requirements for equilibrium between excitons, CT states and\ncharge carriers to occur. Finally, we find that the photovoltaic parameters are\nnot only determined by the relative energy level between the different states\nbut also by the kinetic rate constants. These findings provide vital insights\ninto the operation of state-of-art non-fullerene organic solar cells with low\noffsets.\n"}
+{"abstract": "  We investigated the electronic structure of the Si(111)--7$\\times$7 surface\nbelow 20 K by scanning tunneling and photoemission spectroscopies and by\ndensity functional theory calculations. Previous experimental studies have\nquestioned the ground state of this surface, which is expected to be metallic\nin a band picture because of the odd number of electrons per unit cell. Our\ndifferential conductance spectra instead show the opening of an energy gap at\nthe Fermi level and a significant temperature dependence of the electronic\nproperties, especially for the adatoms at the center of the unfaulted half of\nthe unit cell. Complementary photoemission spectra with improved correction of\nthe surface photovoltage shift corroborate the differential conductance data\nand demonstrate the absence of surface bands crossing the Fermi level at 17 K.\nThese consistent experimental observations point to an insulating ground state\nand contradict the prediction of a metallic surface obtained by density\nfunctional theory in the generalized gradient approximation. The calculations\nindicate that this surface has or is near a magnetic instability, but remains\nmetallic in the magnetic phases even including correlation effects at\nmean-field level. We discuss possible origins of the observed discrepancies\nbetween experiments and calculations.\n"}
+{"abstract": "  Challenging space mission scenarios include those in low altitude orbits,\nwhere the atmosphere creates significant drag to the S/C and forces their orbit\nto an early decay. For drag compensation, propulsion systems are needed,\nrequiring propellant to be carried on-board. An atmosphere-breathing electric\npropulsion system (ABEP) ingests the residual atmosphere particles through an\nintake and uses them as propellant for an electric thruster. Theoretically\napplicable to any planet with atmosphere, the system might allow to orbit for\nunlimited time without carrying propellant. A new range of altitudes for\ncontinuous operation would become accessible, enabling new scientific missions\nwhile reducing costs. Preliminary studies have shown that the collectible\npropellant flow for an ion thruster (in LEO) might not be enough, and that\nelectrode erosion due to aggressive gases, such as atomic oxygen, will limit\nthe thruster lifetime. In this paper an inductive plasma thruster (IPT) is\nconsidered for the ABEP system. The starting point is a small scale inductively\nheated plasma generator IPG6-S. These devices are electrodeless and have\nalready shown high electric-to-thermal coupling efficiencies using O2 and CO2.\nThe system analysis is integrated with IPG6-S tests to assess mean\nmass-specific energies of the plasma plume and estimate exhaust velocities.\n"}
+{"abstract": "  Recent progress in artificial intelligence (AI) raises a wide array of\nethical and societal concerns. Accordingly, an appropriate policy approach is\nneeded today. While there has been a wave of scholarship in this field, the\nresearch community at times appears divided amongst those who emphasize\nnear-term concerns, and those focusing on long-term concerns and corresponding\npolicy measures. In this paper, we seek to map and critically examine this\nalleged gulf, with a view to understanding the practical space for\ninter-community collaboration on AI policy. This culminates in a proposal to\nmake use of the legal notion of an incompletely theorized agreement. We propose\nthat on certain issue areas, scholars working with near-term and long-term\nperspectives can converge and cooperate on selected mutually beneficial AI\npolicy projects all the while maintaining divergent perspectives.\n"}
+{"abstract": "  Understanding how events are semantically related to each other is the\nessence of reading comprehension. Recent event-centric reading comprehension\ndatasets focus mostly on event arguments or temporal relations. While these\ntasks partially evaluate machines' ability of narrative understanding,\nhuman-like reading comprehension requires the capability to process event-based\ninformation beyond arguments and temporal reasoning. For example, to understand\ncausality between events, we need to infer motivation or purpose; to establish\nevent hierarchy, we need to understand the composition of events. To facilitate\nthese tasks, we introduce ESTER, a comprehensive machine reading comprehension\n(MRC) dataset for Event Semantic Relation Reasoning. The dataset leverages\nnatural language queries to reason about the five most common event semantic\nrelations, provides more than 6K questions and captures 10.1K event relation\npairs. Experimental results show that the current SOTA systems achieve 22.1%,\n63.3%, and 83.5% for token-based exact-match, F1, and event-based HIT@1 scores,\nwhich are all significantly below human performances (36.0%, 79.6%, 100%\nrespectively), highlighting our dataset as a challenging benchmark.\n"}
+{"abstract": "  Artificial Recurrent Neural Networks are a powerful information processing\nabstraction, and Reservoir Computing provides an efficient strategy to build\nrobust implementations by projecting external inputs into high dimensional\ndynamical system trajectories. In this paper, we propose an extension of the\noriginal approach, a local unsupervised learning mechanism we call Phase\nTransition Adaptation, designed to drive the system dynamics towards the `edge\nof stability'. Here, the complex behavior exhibited by the system elicits an\nenhancement in its overall computational capacity. We show experimentally that\nour approach consistently achieves its purpose over several datasets.\n"}
+{"abstract": "  In the early universe it is important to take into account quantum effect of\nthe gravity to explain the feature of the inflation. In this paper, we consider\nthe M-theory effective action which consists of 11 dimensional supergravity and\n(Weyl)$^4$ terms. The equations of motion are solved perturbatively, and the\nsolution describes the inflation-like expansion in 4 dimensional spacetime.\nScalar and tensor perturbations around this background are evaluated\nanalytically and their behaviors are investigated numerically. If we assume\nthat these perturbations are constant at the beginning of the inflation,\nspectral indices for scalar and tensor perturbations become almost scale\ninvariant.\n"}
+{"abstract": "  We revisit the contour dynamics (CD) simulation method which is applicable to\nlarge deformation of distribution function in the Vlasov-Poisson plasma with\nthe periodic boundary, where contours of distribution function are traced\nwithout using spatial grids. Novelty of this study lies in application of CD to\nthe one-dimensional Vlasov-Poisson plasma with the periodic boundary condition.\nA major difficulty in application of the periodic boundary is how to deal with\ncontours when they cross the boundaries. It has been overcome by virtue of a\nperiodic Green's function, which effectively introduces the periodic boundary\ncondition without cutting nor reallocating the contours. The simulation results\nare confirmed by comparing with an analytical solution for the piece-wise\nconstant distribution function in the linear regime and a linear analysis of\nthe Landau damping. Also, particle trapping by Langmuir wave is successfully\nreproduced in the nonlinear regime.\n"}
+{"abstract": "  This is the second one in a series of papers classifying the factorizations\nof almost simple groups with nonsolvable factors. In this paper we deal with\nalmost simple unitary groups.\n"}
+{"abstract": "  We derive the general continuum model for a bilayer system of staggered-flux\nsquare lattices, with arbitrary elastic deformation in each layer. Applying\nthis general continuum model to the case where the two layers are rigidly\nrotated relative to each other by a small angle, we obtain the band structure\nof the twisted bilayer staggered-flux square lattice. We show that this band\nstructure exhibits a \"magic continuum\" in the sense that an exponential\nreduction of the Dirac velocity and bandwidths occurs in a large parameter\nregime. We show that the continuum model of the twisted bilayer system\neffectively describes a massless Dirac fermion in a spatially modulating\nmagnetic field, whose renormalized Dirac velocity can be exactly calculated. We\nfurther give an intuitive argument for the emergence of flattened bands near\nhalf filling in the magic continuum and provide an estimation of the large\nnumber of associated nearly-zero-energy states. We also show that the entire\nband structure of the twisted bilayer system is free of band gaps due to\nsymmetry constraints.\n"}
+{"abstract": "  White dwarf spectroscopy shows that nearly half of white dwarf atmospheres\ncontain metals that must have been accreted from planetary material that\nsurvived the red giant phases of stellar evolution. We can use metal pollution\nin white dwarf atmospheres as flags, signalling recent accretion, in order to\nprioritize an efficient sample of white dwarfs to search for transiting\nmaterial. We present a search for planetesimals orbiting six nearby white\ndwarfs with the CHEOPS spacecraft. The targets are relatively faint for CHEOPS,\n$11$ mag $< G < 12.8$ mag. We use aperture photometry data products from the\nCHEOPS mission as well as custom PSF photometry to search for periodic\nvariations in flux due to transiting planetesimals. We detect no significant\nvariations in flux that cannot be attributed to spacecraft systematics, despite\nreaching a photometric precision of $<2$ ppt in 60 s exposures on each target.\nWe simulate observations to show that the small survey is sensitive primarily\nto Moon-sized transiting objects with periods $3$ hr $< P < 10$ hr, with radii\n$R \\gtrsim 1000$ km.\n"}
+{"abstract": "  Recent performance analysis of dual-function radar communications (DFRC)\nsystems, which embed information using phase shift keying (PSK) into\nmultiple-input multiple-output (MIMO) frequency hopping (FH) radar pulses,\nshows promising results for addressing spectrum sharing issues between radar\nand communications. However, the problem of decoding information at the\ncommunication receiver remains challenging, since the DFRC transmitter is\ntypically assumed to transmit only information embedded radar waveforms and not\nthe training sequence. We propose a novel method for decoding information at\nthe communication receiver without using training data, which is implemented\nusing a software-defined radio (SDR). The performance of the SDR implementation\nis examined in terms of bit error rate (BER) as a function of signal-to-noise\nratio (SNR) for differential binary and quadrature phase shift keying\nmodulation schemes and compared with the BER versus SNR obtained with numerical\nsimulations.\n"}
+{"abstract": "  Automatically configuring a robotic prosthesis to fit its user's needs and\nphysical conditions is a great technical challenge and a roadblock to the\nadoption of the technology. Previously, we have successfully developed\nreinforcement learning (RL) solutions toward addressing this issue. Yet, our\ndesigns were based on using a subjectively prescribed target motion profile for\nthe robotic knee during level ground walking. This is not realistic for\ndifferent users and for different locomotion tasks. In this study for the first\ntime, we investigated the feasibility of RL enabled automatic configuration of\nimpedance parameter settings for a robotic knee to mimic the intact knee motion\nin a co-adapting environment. We successfully achieved such tracking control by\nan online policy iteration. We demonstrated our results in both OpenSim\nsimulations and two able-bodied (AB) subjects.\n"}
+{"abstract": "  We present a systematic spectral-timing analysis of a fast\nappearance/disappearance of a type-B quasi-periodic oscillation (QPO), observed\nin four NICER observations of MAXI J1348-630. By comparing the spectra of the\nperiod with and without the type-B QPO, we found that the main difference\nappears at energy bands above ~2 keV, suggesting that the QPO emission is\ndominated by the hard Comptonised component. During the transition, a change in\nthe relative contribution of the disk and Comptonised emission was observed.\nThe disk flux decreased while the Comptonised flux increased from non-QPO to\ntype-B QPO. However, the total flux did not change too much in the NICER band.\nOur results reveal that the type-B QPO is associated with a redistribution of\naccretion power between the disk and Comptonised emission. When the type-B QPO\nappears, more accretion power is dissipated into the Comptonised region than in\nthe disk. Our spectral fits give a hint that the increased Comptonised emission\nmay come from an additional component that is related to the base of the jet.\n"}
+{"abstract": "  Blockchain (BC) technology can revolutionize the future of communications by\nenabling decentralized and open sharing networks. In this paper, we propose the\napplication of BC to facilitate Mobile Network Operators (MNOs) and other\nplayers such as Verticals or Over-The-Top (OTT) service providers to exchange\nRadio Access Network (RAN) resources (e.g., infrastructure, spectrum) in a\nsecure, flexible and autonomous manner. In particular, we propose a BC-enabled\nreverse auction mechanism for RAN sharing and dynamic users' service provision\nin Beyond 5G networks, and we analyze its potential advantages with respect to\ncurrent service provisioning and RAN sharing schemes. Moreover, we study the\ndelay and overheads incurred by the BC in the whole process, when running over\nboth wireless and wired interfaces.\n"}
+{"abstract": "  Human can infer the 3D geometry of a scene from a sketch instead of a\nrealistic image, which indicates that the spatial structure plays a fundamental\nrole in understanding the depth of scenes. We are the first to explore the\nlearning of a depth-specific structural representation, which captures the\nessential feature for depth estimation and ignores irrelevant style\ninformation. Our S2R-DepthNet (Synthetic to Real DepthNet) can be well\ngeneralized to unseen real-world data directly even though it is only trained\non synthetic data. S2R-DepthNet consists of: a) a Structure Extraction (STE)\nmodule which extracts a domaininvariant structural representation from an image\nby disentangling the image into domain-invariant structure and domain-specific\nstyle components, b) a Depth-specific Attention (DSA) module, which learns\ntask-specific knowledge to suppress depth-irrelevant structures for better\ndepth estimation and generalization, and c) a depth prediction module (DP) to\npredict depth from the depth-specific representation. Without access of any\nreal-world images, our method even outperforms the state-of-the-art\nunsupervised domain adaptation methods which use real-world images of the\ntarget domain for training. In addition, when using a small amount of labeled\nreal-world data, we achieve the state-ofthe-art performance under the\nsemi-supervised setting. The code and trained models are available at\nhttps://github.com/microsoft/S2R-DepthNet.\n"}
+{"abstract": "  A search for Heavy Neutral Leptons has been performed with the ArgoNeuT\ndetector exposed to the NuMI neutrino beam at Fermilab. We search for the decay\nsignature $N \\to \\nu \\mu^+ \\mu^-$, considering decays occurring both inside\nArgoNeuT and in the upstream cavern. In the data, corresponding to an exposure\nto $1.25 \\times 10^{20}$ POT, zero passing events are observed consistent with\nthe expected background. This measurement leads to a new constraint at 90\\%\nconfidence level on the mixing angle $\\left\\vert U_{\\tau N}\\right\\rvert^2$ of\ntau-coupled Dirac Heavy Neutral Leptons with masses $m_N =$ 280 - 970 MeV,\nassuming $\\left\\vert U_{eN}\\right\\rvert^2 = \\left\\vert U_{\\mu N}\\right\\rvert^2\n= 0$.\n"}
+{"abstract": "  Cognitive Diagnosis Models (CDMs) are a special family of discrete latent\nvariable models widely used in educational, psychological and social sciences.\nIn many applications of CDMs, certain hierarchical structures among the latent\nattributes are assumed by researchers to characterize their dependence\nstructure. Specifically, a directed acyclic graph is used to specify\nhierarchical constraints on the allowable configurations of the discrete latent\nattributes. In this paper, we consider the important yet unaddressed problem of\ntesting the existence of latent hierarchical structures in CDMs. We first\nintroduce the concept of testability of hierarchical structures in CDMs and\npresent sufficient conditions. Then we study the asymptotic behaviors of the\nlikelihood ratio test (LRT) statistic, which is widely used for testing nested\nmodels. Due to the irregularity of the problem, the asymptotic distribution of\nLRT becomes nonstandard and tends to provide unsatisfactory finite sample\nperformance under practical conditions. We provide statistical insights on such\nfailures, and propose to use parametric bootstrap to perform the testing. We\nalso demonstrate the effectiveness and superiority of parametric bootstrap for\ntesting the latent hierarchies over non-parametric bootstrap and the na\\\"ive\nChi-squared test through comprehensive simulations and an educational\nassessment dataset.\n"}
+{"abstract": "  Hypergraphs are a generalization of graphs in which edges can connect any\nnumber of vertices. They allow the modeling of complex networks with\nhigher-order interactions, and their spectral theory studies the qualitative\nproperties that can be inferred from the spectrum, i.e. the multiset of the\neigenvalues, of an operator associated to a hypergraph. It is expected that a\nsmall perturbation of a hypergraph, such as the removal of a few vertices or\nedges, does not lead to a major change of the eigenvalues. In particular, it is\nexpected that the eigenvalues of the original hypergraph interlace the\neigenvalues of the perturbed hypergraph. Here we work on hypergraphs where, in\naddition, each vertex--edge incidence is given a real number, and we prove\ninterlacing results for the adjacency matrix, the Kirchoff Laplacian and the\nnormalized Laplacian. Tightness of the inequalities is also shown.\n"}
+{"abstract": "  Recent FDA guidance on adaptive clinical trial designs defines bias as \"a\nsystematic tendency for the estimate of treatment effect to deviate from its\ntrue value\", and states that it is desirable to obtain and report estimates of\ntreatment effects that reduce or remove this bias. In many adaptive designs,\nthe conventional end-of-trial point estimates of the treatment effects are\nprone to bias, because they do not take into account the potential and realised\ntrial adaptations. While much of the methodological developments on adaptive\ndesigns have tended to focus on control of type I error rates and power\nconsiderations, in contrast the question of biased estimation has received less\nattention. This article addresses this issue by providing a comprehensive\noverview of proposed approaches to remove or reduce the potential bias in point\nestimation of treatment effects in an adaptive design, as well as illustrating\nhow to implement them. We first discuss how bias can affect standard estimators\nand critically assess the negative impact this can have. We then describe and\ncompare proposed unbiased and bias-adjusted estimators of treatment effects for\ndifferent types of adaptive designs. Furthermore, we illustrate the computation\nof different estimators in practice using a real trial example. Finally, we\npropose a set of guidelines for researchers around the choice of estimators and\nthe reporting of estimates following an adaptive design.\n"}
+{"abstract": "  In this paper, we present a Bayesian multipath-based simultaneous\nlocalization and mapping (SLAM) algorithm that continuously adapts interacting\nmultiple models (IMM) parameters to describe the mobile agent state dynamics.\nThe time-evolution of the IMM parameters is described by a Markov chain and the\nparameters are incorporated into the factor graph structure that represents the\nstatistical structure of the SLAM problem. The proposed belief propagation\n(BP)-based algorithm adapts, in an online manner, to time-varying system models\nby jointly inferring the model parameters along with the agent and map feature\nstates. The performance of the proposed algorithm is finally evaluating with a\nsimulated scenario. Our numerical simulation results show that the proposed\nmultipath-based SLAM algorithm is able to cope with strongly changing agent\nstate dynamics.\n"}
+{"abstract": "  When two resonantly interacting modes are in contact with a thermostat, their\nstatistics is exactly Gaussian and the modes are statistically independent\ndespite strong interaction. Considering noise-driven system, we show that when\none mode is pumped and another dissipates, the statistics (of such cascades) is\nnever close to Gaussian no matter the interaction/noise relation. One finds\nsubstantial phase correlation in the limit of strong interaction (weak noise).\nSurprisingly, for both cascades, the mutual information between modes increases\nand entropy further decreases when interaction strength decreases. We use the\nmodel to elucidate the fundamental problem of far-from equilibrium physics:\nwhere the information (entropy deficit) is encoded and how singular measures\nform. For an instability-driven system (a laser), even a small added noise\nleads to large fluctuations of the relative phase near the stability threshold,\nwhile far from it we show that the conversion into the second harmonic is\nweakly affected by noise.\n"}
+{"abstract": "  We consider the jump telegraph process when switching intensities depend on\nexternal shocks also accompanying with jumps. The incomplete financial market\nmodel based on this process is studied. The Esscher transform, which changes\nonly unobservable parameters, is considered in detail. The financial market\nmodel based on this transform can price switching risks as well as jump risks\nof the model.\n"}
+{"abstract": "  The Legacy Survey of Space and Time (LSST) by the Vera C. Rubin Observatory\nis expected to discover tens of millions of quasars. A significant fraction of\nthese could be powered by coalescing massive black hole (MBH) binaries, since\nmany quasars are believed to be triggered by mergers. We show that under\nplausible assumptions about the luminosity functions, lifetimes, and binary\nfractions of quasars, we expect the full LSST quasar catalogue to contain\nbetween 20-100 million compact MBH binaries with masses $M=10^{5-9}M_{\\odot}$,\nredshifts $z=0-6$, and orbital periods $P=1-70$ days. Their light-curves are\nexpected to be distinctly periodic, which can be confidently distinguished from\nstochastic red-noise variability, because LSST will cover dozens, or even\nhundreds of cycles. A very small subset of 10-150 ultra-compact ($P\\lesssim1$\nday) binary quasars among these will, over $\\sim$5-15 years, evolve into the\nmHz gravitational-wave (GW) frequency band and can be detected by\n$\\textit{LISA}$. They can therefore be regarded as \"$\\textit{LISA}$\nverification binaries\", analogous to short-period Galactic compact-object\nbinaries. The practical question is how to find these handful of \"needles in\nthe haystack\" among the large number of quasars: this will likely require a\ntailored co-adding analysis optimised for this purpose.\n"}
+{"abstract": "  Quantum spin models find applications in many different areas, such as\nspintronics, high-Tc superconductivity, and even complex optimization problems.\nHowever, studying their many-body behaviour, especially in the presence of\nfrustration, represents an outstanding computational challenge. To overcome it,\nquantum simulators based on cold, trapped atoms and ions have been built,\nshedding light already on many non-trivial phenomena. Unfortunately, the models\ncovered by these simulators are limited by the type of interactions that appear\nnaturally in these systems. Waveguide QED setups have recently been pointed out\nas a powerful alternative due to the possibility of mediating more versatile\nspin-spin interactions with tunable sign, range, and even chirality. Yet,\ndespite their potential, the many-body phases emerging from these systems have\nonly been scarcely explored. In this manuscript, we fill this gap analyzing the\nground states of a general class of spin models that can be obtained in such\nwaveguide QED setups. Importantly, we find novel many-body phases different\nfrom the ones obtained in other platforms, e.g., symmetry-protected topological\nphases with large-period magnetic orderings, and explain the measurements\nneeded to probe them.\n"}
+{"abstract": "  We establish uniform with respect to the Mach number regularity estimates for\nthe isentropic compressible Navier-Stokes system in smooth domains with\nNavier-slip condition on the boundary in the general case of ill-prepared\ninitial data. To match the boundary layer effects due to the fast oscillations\nand the ill-prepared initial data assumption, we prove uniform estimates in an\nanisotropic functional framework with only one normal derivative close to the\nboundary. This allows to prove the local existence of a strong solution on a\ntime interval independent of the Mach number and to justify the incompressible\nlimit through a simple compactness argument.\n"}
+{"abstract": "  C/2020 F3 (NEOWISE) was discovered in images from the Near Earth Object\nprogram of the Wide-Field Infrared Survey Explorer (NEOWISE) taken on 27 March\n2020 and has become the Great Comet of 2020. The Solar Wind ANisotropies (SWAN)\ncamera on the Solar and Heliospheric Observatory (SOHO) spacecraft, located in\na halo orbit around the Earth-Sun L1 Lagrange point, makes daily full-sky\nimages of hydrogen Lyman-alpha. Water production rates were determined from the\nSWAN hydrogen Lyman-alpha brightness and spatial distribution of the comet\nmeasured over a 4-month period of time on either side of the comet's perihelion\non 3 July 2020. The water production rate in s^-1 was moderately asymmetric\naround perihelion and varied with the heliocentric distance, r, in au as\n(6.9+/-0.5) x 10^28 r^-2.5+/-0.2 and (10.1+/-0.5) x 10^28 r^-3.5+/-0.1 before\nand after perihelion, respectively. This is consistent with the comet having\nbeen through the planetary region of the solar system on one or more previous\napparitions. A water production rates as large as 5.27 x 10^30 s^-1 were\ndetermined shortly after perihelion, once the comet was outside the solar\navoidance area of SWAN, when the comet was 0.324 au from the Sun.\n"}
+{"abstract": "  With the ChemCam instrument, laser-induced breakdown spectroscopy (LIBS) has\nsuccessively contributed to Mars exploration by determining elemental\ncompositions of the soil, crust and rocks. Two new lunched missions, Chinese\nTianwen 1 and American Perseverance, will further increase the number of LIBS\ninstruments on Mars after the planned landings in spring 2021. Such\nunprecedented situation requires a reinforced research effort on the methods of\nLIBS spectral data treatment. Although the matrix effects correspond to a\ngeneral issue in LIBS, they become accentuated in the case of rock analysis for\nMars exploration, because of the large variation of rock composition leading to\nthe chemical matrix effect, and the difference in morphology between laboratory\nstandard samples (in pressed pellet, glass or ceramics) used to establish\ncalibration models and natural rocks encountered on Mars, leading to the\nphysical matric effect. The chemical matrix effect has been tackled in the\nChemCam project with large sets of laboratory standard samples offering a good\nrepresentation of various compositions of Mars rocks. The present work deals\nwith the physical matrix effect which is still expecting a satisfactory\nsolution. The approach consists in introducing transfer learning in LIBS data\ntreatment. For the specific case of total alkali-silica (TAS) classification of\nnatural rocks, the results show a significant improvement of the prediction\ncapacity of pellet sample-based models when trained together with suitable\ninformation from rocks in a procedure of transfer learning. The correct\nclassification rate of rocks increases from 33.3% with a machine learning model\nto 83.3% with a transfer learning model.\n"}
+{"abstract": "  We propose a Multiscale Invertible Generative Network (MsIGN) and associated\ntraining algorithm that leverages multiscale structure to solve\nhigh-dimensional Bayesian inference. To address the curse of dimensionality,\nMsIGN exploits the low-dimensional nature of the posterior, and generates\nsamples from coarse to fine scale (low to high dimension) by iteratively\nupsampling and refining samples. MsIGN is trained in a multi-stage manner to\nminimize the Jeffreys divergence, which avoids mode dropping in\nhigh-dimensional cases. On two high-dimensional Bayesian inverse problems, we\nshow superior performance of MsIGN over previous approaches in posterior\napproximation and multiple mode capture. On the natural image synthesis task,\nMsIGN achieves superior performance in bits-per-dimension over baseline models\nand yields great interpret-ability of its neurons in intermediate layers.\n"}
+{"abstract": "  The axion-like particles with ultralight mass ($\\sim10^{-22}$eV) can be a\npossibile candidate of dark matter, known as the fuzzy dark matter (FDM). These\nparticles form Bose-Einstein condensate in the early universe which can explain\nthe dark matter density distribution in galaxies at the present time. We study\nthe time evolution of ultralight axion-like field in the near region of a\nstrong gravitational wave (GW) source, such as binary black hole merger. We\nshow that GWs can lead to the generation of field excitations in a spherical\nshell about the source that eventually propagate out of the shell to minimize\nthe energy density of the field configuration. These excitations are generated\ntowards the end of the merger and in some cases even in the ringdown phase of\nthe merger, therefore it can provide a qualitatively distinct prediction for\nchanges in the GW waveform due to presence of FDM. This would be helpful in\ninvestigating the existence of FDM in galaxies.\n"}
+{"abstract": "  Because of surface structural constraint and thermal management requirement,\nvisible - infrared compatible camouflage is still a great challenge. In this\nstudy, we introduce a 2D periodic aperture array into ZnO/Ag/ZnO film to\nrealize visible-infrared compatible camouflage with a performance of thermal\nmanagement by utilizing the extraordinary optical transmission in a\ndielectric/metal/dielectric (D/M/D) structure. Because of the high visible\ntransmittance of the D/M/D structure, when applied on a visible camouflage\ncoating, the beneath coating can be observed, realizing arbitrary visible\ncamouflage. Due to the perforated Ag layer, both low emittances in 3~5 {\\mu}m,\n8~14 {\\mu}m for infrared camouflage and high emittance in 5~8 {\\mu}m for heat\ndissipation by radiation are achieved theoretically and experimentally. The\nfabricated photonic crystal exhibits high-temperature infrared camouflage in\ntwo atmospheric windows. With the same heating power of 0.40 W/cm2, this\nphotonic crystal is 12.2 K cooler than a sample with a low-emittance surface.\nThe proposed visible - infrared compatible camouflage photonic crystal with the\nperformance of thermal management provides a guideline on coordinated control\nof light and heat, indicating a potential application in energy & thermal\ntechnologies.\n"}
+{"abstract": "  Historically, to bound the mean for small sample sizes, practitioners have\nhad to choose between using methods with unrealistic assumptions about the\nunknown distribution (e.g., Gaussianity) and methods like Hoeffding's\ninequality that use weaker assumptions but produce much looser (wider)\nintervals. In 1969, Anderson (1969) proposed a mean confidence interval\nstrictly better than or equal to Hoeffding's whose only assumption is that the\ndistribution's support is contained in an interval $[a,b]$. For the first time\nsince then, we present a new family of bounds that compares favorably to\nAnderson's. We prove that each bound in the family has {\\em guaranteed\ncoverage}, i.e., it holds with probability at least $1-\\alpha$ for all\ndistributions on an interval $[a,b]$. Furthermore, one of the bounds is tighter\nthan or equal to Anderson's for all samples. In simulations, we show that for\nmany distributions, the gain over Anderson's bound is substantial.\n"}
+{"abstract": "  Suppose that a statistician observes two independent variates $X_1$ and $X_2$\nhaving densities $f_i(\\cdot;\\theta)\\equiv f_i(\\cdot-\\theta)\\ ,\\ i=1,2$ ,\n$\\theta\\in\\mathbb{R}$. His purpose is to conduct a test for\n  \\begin{equation*}\n  H:\\theta=0 \\ \\ \\text{vs.}\\ \\ K:\\theta\\in\\mathbb{R}\\setminus\\{0\\}\n  \\end{equation*}\n  with a pre-defined significance level $\\alpha\\in(0,1)$.\n  Moran (1973) suggested a test which is based on a single split of the data,\n\\textit{i.e.,} to use $X_2$ in order to conduct a one-sided test in the\ndirection of $X_1$. Specifically, if $b_1$ and $b_2$ are the $(1-\\alpha)$'th\nand $\\alpha$'th quantiles associated with the distribution of $X_2$ under $H$,\nthen Moran's test has a rejection zone\n  \\begin{equation*}\n  (a,\\infty)\\times(b_1,\\infty)\\cup(-\\infty,a)\\times(-\\infty,b_2)\n  \\end{equation*}\n  where $a\\in\\mathbb{R}$ is a design parameter.\n  Motivated by this issue, the current work includes an analysis of a new\nnotion, \\textit{regular admissibility} of tests. It turns out that the theory\nregarding this kind of admissibility leads to a simple sufficient condition on\n$f_1(\\cdot)$ and $f_2(\\cdot)$ under which Moran's test is inadmissible.\nFurthermore, the same approach leads to a formal proof for the conjecture of\nDiCiccio (2018) addressing that the multi-dimensional version of Moran's test\nis inadmissible when the observations are $d$-dimensional Gaussians.\n"}
+{"abstract": "  We derive for the first time an effective neutrino evolution Hamiltonian\naccounting for neutrino interactions with external magnetic field due to\nneutrino charge radii and anapole moment. The results are interesting for\npossible applications in astrophysics.\n"}
+{"abstract": "  We consider agents in a social network competing to be selected as partners\nin collaborative, mutually beneficial activities. We study this through a model\nin which an agent i can initiate a limited number k_i>0 of games and selects\nthe ideal partners from its one-hop neighborhood. On the flip side it can\naccept as many games offered from its neighbors. Each game signifies a\nproductive joint economic activity, and players attempt to maximize their\nindividual utilities. Unsurprisingly, more trustworthy agents are more\ndesirable as partners. Trustworthiness is measured by the game theoretic\nconcept of Limited-Trust, which quantifies the maximum cost an agent is willing\nto incur in order to improve the net utility of all agents. Agents learn about\ntheir neighbors' trustworthiness through interactions and their behaviors\nevolve in response. Empirical trials performed on realistic social networks\nshow that when given the option, many agents become highly trustworthy; most or\nall become highly trustworthy when knowledge of their neighbors'\ntrustworthiness is based on past interactions rather than known a priori. This\ntrustworthiness is not the result of altruism, instead agents are intrinsically\nmotivated to become trustworthy partners by competition. Two insights are\npresented: first, trustworthy behavior drives an increase in the utility of all\nagents, where maintaining a relatively modest level of trustworthiness may\neasily improve net utility by as much as 14.5%. If only one agent exhibits\nmodest trust among self-centered ones, it can increase its average utility by\nup to 25% in certain cases! Second, and counter-intuitively, when partnership\nopportunities are abundant agents become less trustworthy.\n"}
+{"abstract": "  In supervised learning, it is known that overparameterized neural networks\nwith one hidden layer provably and efficiently learn and generalize, when\ntrained using stochastic gradient descent with sufficiently small learning rate\nand suitable initialization. In contrast, the benefit of overparameterization\nin unsupervised learning is not well understood. Normalizing flows (NFs)\nconstitute an important class of models in unsupervised learning for sampling\nand density estimation. In this paper, we theoretically and empirically analyze\nthese models when the underlying neural network is one-hidden-layer\noverparameterized network. Our main contributions are two-fold: (1) On the one\nhand, we provide theoretical and empirical evidence that for a class of NFs\ncontaining most of the existing NF models, overparametrization hurts training.\n(2) On the other hand, we prove that unconstrained NFs, a recently introduced\nmodel, can efficiently learn any reasonable data distribution under minimal\nassumptions when the underlying network is overparametrized.\n"}
+{"abstract": "  In this paper, we study the galactic cosmic ray (GCR) variations over the\nsolar cycles 23 and 24, with measurements from the NASA's ACE/CRIS instrument\nand the ground-based neutron monitors (NMs). The results show that the maximum\nGCR intensities of heavy nuclei (nuclear charge 5-28, 50-500 MeV/nuc) at 1 AU\nduring the solar minimum in 2019-2020 break their previous records, exceeding\nthose recorded in 1997 and 2009 by ~25% and ~6%, respectively, and are at the\nhighest levels since the space age. However, the peak NM count rates are lower\nthan those in late 2009. The difference between GCR intensities and NM count\nrates still remains to be explained. Furthermore, we find that the GCR\nmodulation environment during the solar minimum P24/25 are significantly\ndifferent from previous solar minima in several aspects, including remarkably\nlow sunspot numbers, extremely low inclination of the heliospheric current\nsheet, rare coronal mass ejections, weak interplanetary magnetic field and\nturbulence. These changes are conducive to reduce the level of solar\nmodulation, providing a plausible explanation for the record-breaking GCR\nintensities in interplanetary space.\n"}
+{"abstract": "  Quantum teleportation, the faithful transfer of an unknown input state onto a\nremote quantum system, is a key component in long distance quantum\ncommunication protocols and distributed quantum computing. At the same time,\nhigh frequency nano-optomechanical systems hold great promise as nodes in a\nfuture quantum network, operating on-chip at low-loss optical telecom\nwavelengths with long mechanical lifetimes. Recent demonstrations include\nentanglement between two resonators, a quantum memory and microwave to optics\ntransduction. Despite these successes, quantum teleportation of an optical\ninput state onto a long-lived optomechanical memory is an outstanding\nchallenge. Here we demonstrate quantum teleportation of a polarization-encoded\noptical input state onto the joint state of a pair of nanomechanical\nresonators. Our protocol also allows for the first time to store and retrieve\nan arbitrary qubit state onto a dual-rail encoded optomechanical quantum\nmemory. This work demonstrates the full functionality of a single quantum\nrepeater node, and presents a key milestone towards applications of\noptomechanical systems as quantum network nodes.\n"}
+{"abstract": "  We investigate both linear and nonlinear stability aspects of rigid motions\n(resp. M\\\"obius transformations) of $\\mathbb{S}^{n-1}$ among Sobolev maps from\n$\\mathbb{S}^{n-1}$ into $\\mathbb{R}^n$. Unlike similar in flavour results for\nmaps defined on domains of $\\mathbb{R}^n$ and mapping into $\\mathbb{R}^n$, not\nonly an isometric (resp. conformal) deficit is necessary in this more flexible\nsetting, but also a deficit measuring the distortion of $\\mathbb{S}^{n-1}$\nunder the maps in consideration. The latter is defined as an associated\nisoperimetric type of deficit. We mostly focus on the case $n=3$, where we also\nexplain why the estimates are optimal in their corresponding settings. In the\nisometric case the estimate holds true also when $n=2$ and generalizes in\ndimensions $n\\geq 4$ as well, if one requires apriori boundedness in a certain\nhigher Sobolev norm. We also obtain linear stability estimates for both cases\nin all dimensions. These can be regarded as Korn-type inequalities for the\ncombination of the quadratic form associated with the isometric (resp.\nconformal) deficit on $\\mathbb{S}^{n-1}$ and the isoperimetric one.\n"}
+{"abstract": "  It was Fano who first classified Enriques-Fano threefolds. However his\narguments appear to contain several gaps. In this paper, we will verify some of\nhis assertions through the use of modern techniques.\n"}
+{"abstract": "  We investigate the Nichols algebra $\\mathfrak{B}(V_{abe})$ which are from the\nYetter-Drinfeld category of Suzuki algebras. The $4n$ and $n^2$ dimensional\nNichols algebras, first appeared in \\cite{Andruskiewitsch2018}, are obtained\nagain via a different method. And the connection between the Nichols algebra\n$\\mathfrak{B}(V_{abe})$ and a class of combinatorial numbers on the subgroups\nof symmetric groups is established.\n"}
+{"abstract": "  We present a bulk data collection service, Harvest, for energy constrained\nwireless sensor nodes. To increase spatial reuse and thereby decrease latency,\nHarvest performs concurrent, pipelined exfiltration from multiple nodes to a\nbase station. To this end, it uses a distance-k coloring of the nodes, notably\nwith a constant number of colors, which yields a TDMA schedule whereby nodes\ncan communicate concurrently with low packet losses due to collision. This\ncoloring is based on a randomized CSMA approach which does not exploit location\nknowledge. Given a bounded degree of the network, each node waits only O$(1)$\ntime to obtain a unique color among its distance-k neighbors, in contrast to\nthe traditional deterministic distributed distance-k vertex coloring wherein\neach node waits O$(\\Delta^{2})$ time to obtain a color.\n  Harvest offers the option of limiting memory use to only a small constant\nnumber of bytes or of improving latency with increased memory use; it can be\nused with or without additional mechanisms for reliability of message\nforwarding. We experimentally evaluate the performance of Harvest using 51\nmotes in the Kansei testbed. We also provide theoretical as well as\nTOSSIM-based comparison of Harvest with Straw, an extant data collection\nservice implemented for TinyOS platforms that use one-node at a time\nexfiltration. For networks with more than 3-hops, Harvest reduces the latency\nby at least 33% as compared to that of Straw.\n"}
+{"abstract": "  We present a sparse Gauss-Newton solver for accelerated sensitivity analysis\nwith applications to a wide range of equilibrium-constrained optimization\nproblems. Dense Gauss-Newton solvers have shown promising convergence rates for\ninverse problems, but the cost of assembling and factorizing the associated\nmatrices has so far been a major stumbling block. In this work, we show how the\ndense Gauss-Newton Hessian can be transformed into an equivalent sparse matrix\nthat can be assembled and factorized much more efficiently. This leads to\ndrastically reduced computation times for many inverse problems, which we\ndemonstrate on a diverse set of examples. We furthermore show links between\nsensitivity analysis and nonlinear programming approaches based on Lagrange\nmultipliers and prove equivalence under specific assumptions that apply for our\nproblem setting.\n"}
+{"abstract": "  Phase-field modeling -- a continuous approach to discontinuities -- is\ngaining popularity for simulating rock fractures due to its ability to handle\ncomplex, discontinuous geometry without an explicit surface tracking algorithm.\nNone of the existing phase-field models, however, incorporates the impact of\nsurface roughness on the mechanical response of fractures -- such as elastic\ndeformability and shear-induced dilation -- despite the importance of this\nbehavior for subsurface systems. To fill this gap, here we introduce the first\nframework for phase-field modeling of rough rock fractures. The framework\ntransforms a displacement-jump-based discrete constitutive model for\ndiscontinuities into a strain-based continuous model, without any additional\nparameter, and then casts it into a phase-field formulation for frictional\ninterfaces. We illustrate the framework by constructing a particular\nphase-field form employing a rock joint model originally formulated for\ndiscrete modeling. The results obtained by the new formulation show excellent\nagreement with those of a well-established discrete method for a variety of\nproblems ranging from shearing of a single discontinuity to compression of\nfractured rocks. It is further demonstrated that the phase-field formulation\ncan well simulate complex crack growth from rough discontinuities.\nConsequently, our phase-field framework provides an unprecedented bridge\nbetween a discrete constitutive model for rough discontinuities -- common in\nrock mechanics -- and the continuous finite element method -- standard in\ncomputational mechanics -- without any algorithm to explicitly represent\ndiscontinuity geometry.\n"}
+{"abstract": "  The bottleneck for an attosecond science experiment is concluded to be the\nlack of a high-peak-power isolated attosecond pulse source. Therefore,\ncurrently, generating an intense attosecond pulse would be one of the highest\npriority goals. In this paper, we review a TW-class parallel three-channel\nwaveform synthesizer for generating a gigawatt-scale soft-x-ray isolated\nattosecond pulse (IAP) using high-order harmonics generation (HHG).\nSimultaneously, using several stabilization methods, namely, the\nlow-repetition-rate laser carrier-envelope phase stabilization, Mach-Zehnder\ninterferometer, balanced optical cross-correlator, and beam-pointing\nstabilizer, we demonstrate a stable 50-mJ three-channel optical-waveform\nsynthesizer with a peak power at the multi-TW level. This optical-waveform\nsynthesizer is capable of creating a stable intense optical field for\ngenerating an intense continuum harmonic beam thanks to the successful\nstabilization of all the parameters. Furthermore, the precision control of\nshot-to-shot reproducible synthesized waveforms is achieved. Through the HHG\nprocess employing a loose-focusing geometry, an intense shot-to-shot stable\nsupercontinuum (50-70 eV) is generated in an argon gas cell. This continuum\nspectrum supports an IAP with a transform-limited duration of 170 as and a\nsubmicrojoule pulse energy, which allows the generation of a GW-scale IAP.\nAnother supercontinuum in the soft-x-ray region with higher photon energy of\napproximately 100-130 eV is also generated in neon gas from the synthesizer.\nThe transform-limited pulse duration is 106 as. According to this work, the\nenhancement of HHG output through optimized waveform synthesis is\nexperimentally proved. The high-energy multicycle pulse with 10-Hz repetition\nrate is proved to have the same controllability for optimized waveform\nsynthesis for HHG as few- or subcycle pulses from a 1-kHz laser.\n"}
+{"abstract": "  In a previous work I constructed R-negative scissors states in the Two-Rotors\nModel in which the rotors are two-sided classical bodies. Such states have |K|\n= 1; 0 components and negative space parity. Here I extend this work including\nreflections in a plane through the rotors symmetry axes obtaining also\nR-negative states of positive parity. I then associate reflections and\nR-operations with corresponding operations on internal noncollective variables\nof the rotors. I evaluate the em transition strengths of these states and\ncompare them with the corresponding strengths of R-positive scissors modes.\n"}
+{"abstract": "  We comment on two formal proofs of Fermat's sum of two squares theorem,\nwritten using the Mathematical Components libraries of the Coq proof assistant.\nThe first one follows Zagier's celebrated one-sentence proof; the second\nfollows David Christopher's more recent proof relying on partition-theoretic\narguments. Both formal proofs rely on a general property of involutions of\nfinite sets, of independent interest. The proof technique consists for the most\npart of automating recurrent tasks (such as case distinctions and computations\non natural numbers) via ad hoc tactics. With the same method, we also provide a\nformal proof of another classical result on primes of the form $a^2 + 2 b^2$.\n"}
+{"abstract": "  Networks describe the, often complex, relationships between individual\nactors. In this work, we address the question of how to determine whether a\nparametric model, such as a stochastic block model or latent space model, fits\na dataset well and will extrapolate to similar data. We use recent results in\nrandom matrix theory to derive a general goodness-of-fit test for dyadic data.\nWe show that our method, when applied to a specific model of interest, provides\nan straightforward, computationally fast way of selecting parameters in a\nnumber of commonly used network models. For example, we show how to select the\ndimension of the latent space in latent space models. Unlike other network\ngoodness-of-fit methods, our general approach does not require simulating from\na candidate parametric model, which can be cumbersome with large graphs, and\neliminates the need to choose a particular set of statistics on the graph for\ncomparison. It also allows us to perform goodness-of-fit tests on partial\nnetwork data, such as Aggregated Relational Data. We show with simulations that\nour method performs well in many situations of interest. We analyze several\nempirically relevant networks and show that our method leads to improved\ncommunity detection algorithms. R code to implement our method is available on\nGithub.\n"}
+{"abstract": "  We studied simple random-walk models with asymmetric time delays. Stochastic\nsimulations were performed for hyperbolic-tangent fitness functions and to\nobtain analytical results we approximated them by step functions. A novel\nbehavior has been observed. Namely, the mean position of a walker depends on\ntime delays. This is a joint effect of both stochasticity and time delays\npresent in the system. We also observed that by shifting appropriately fitness\nfunctions we may reverse the effect of time delays - the mean position of the\nwalker changes the sign.\n"}
+{"abstract": "  Travel time or speed estimation are part of many intelligent transportation\napplications. Existing estimation approaches rely on either function fitting or\naggregation and represent different trade-offs between generalizability and\naccuracy. Function-fitting approaches learn functions that map feature vectors\nof, e.g., routes, to travel time or speed estimates, which enables\ngeneralization to unseen routes. However, mapping functions are imperfect and\noffer poor accuracy in practice. Aggregation-based approaches instead form\nestimates by aggregating historical data, e.g., traversal data for routes. This\nenables very high accuracy given sufficient data. However, they rely on\nsimplistic heuristics when insufficient data is available, yielding poor\ngeneralizability. We present a Unifying approach to Travel time and speed\nEstimation (UniTE) that combines function-fitting and aggregation-based\napproaches into a unified framework that aims to achieve the generalizability\nof function-fitting approaches and the accuracy of aggregation-based\napproaches. An empirical study finds that an instance of UniTE can improve the\naccuracies of travel speed distribution and travel time estimation by $40-64\\%$\nand $3-23\\%$, respectively, compared to using function fitting or aggregation\nalone\n"}
+{"abstract": "  We present a theoretical calculation of the influence of ultraviolet\nradiative pumping on the excitation of the rotational levels of the ground\nvibrational state for HD molecules under conditions of the cold diffuse\ninterstellar medium (ISM). Two main excitation mechanisms have been taken into\naccount in our analysis: (i) collisions with atoms and molecules and (ii)\nradiative pumping by the interstellar ultraviolet (UV) radiation field. The\ncalculation of the radiative pumping rate coefficients $\\Gamma_{\\rm ij}$\ncorresponding to Drane's model of the field of interstellar UV radiation,\ntaking into account the self-shielding of HD molecules, is performed. We found\nthat the population of the first HD rotational level ($J = 1$) is determined\nmainly by radiative pumping rather than by collisions if the thermal gas\npressure $p_{\\rm\nth}\\le10^4\\left(\\frac{I_{\\rm{UV}}}{1}\\right)\\,\\mbox{K\\,cm}^{-3}$ and the column\ndensity of HD is lower than $\\log N({\\rm{HD}})<15$. Under this constraint the\npopulations of rotational levels of HD turns out to be as well a more sensitive\nindicator of the UV radiation intensity than the fine-structure levels of\natomic carbon. We suggest that taking into account radiative pumping of HD\nrotational levels may be important for the problem of the cooling of primordial\ngas at high redshift: ultraviolet radiation from first stars can increase the\nrate of HD cooling of the primordial gas in the early Universe.\n"}
+{"abstract": "  This submission has been removed by arXiv administrators as the submitter did\nnot have the right to agree to the license at the time of submission.\n"}
+{"abstract": "  This study aims at revealing the effect of composition on mechanical and\nantibacterial properties of hydroxyapatite/gray Titania coating for biomedical\napplications. HAp is a bioceramic material used as a plasma-sprayed coating to\npromote osseointegra-tion of femoral stems. Biomaterial coatings are fabricated\nmostly using atmospheric plasma spray (APS). However, the conventional plasma\nspray process requires hydrox-yapatite powder with good flow, and to prepare\nsuch free-flowing powder, agglomer-ation, such as spray drying, fusing, and\ncrushing is used. Therefore, it is impossible to spray nano-powder using\nconventional methods. Here, we designed a suspension-feeding system to feed\nnanoparticles using a liquid carrier. Suspension plasma spray (SPS)\nsuccessfully deposited homogeneous HAp/Gray Titania coating with less poros-ity\non the surface of titanium substrates. The microstructure of coatings with\ndiffer-ent compositions was then characterized using scanning electron\nmicroscopy, X-ray diffraction, and Raman spectroscopy to identify the crystal\nstructure. All results con-sistently demonstrated that SPS could transform Ti2\nO3 into TiO2 with mixed Magneli phases, such as Ti4 O7 and Ti3 O5, which\nusually show photocatalytic activity. Interfacial strength, hardness, young\nmodulus, and fracture toughness were also improved with a high concentration of\nTiO2. Antibacterial test for E.Coli under LED light re-vealed that SPSed\nHAp/Gray Titania coating could significantly enhance antibacterial properties.\nThe possible underlying mechanism of enhanced antibacterial properties can be\nattributed to increased Magneli phases and better bacterial adhesion caused by\nhydrophilic properties due to submicron size particles. SPS can fabricate\nvisible light-responsive antibacterial coating, which can be used for medical\ndevices.\n"}
+{"abstract": "  In this paper, we investigate the hemodynamics of a left atrium (LA) by\nproposing a computational model suitable to provide physically meaningful fluid\ndynamics indications and detailed blood flow characterization. In particular,\nwe consider the incompressible Navier-Stokes equations in Arbitrary Lagrangian\nEulerian (ALE) formulation to deal with the LA domain under prescribed motion.\nA Variational Multiscale (VMS) method is adopted to obtain a stable formulation\nof the Navier-Stokes equations discretized by means of the Finite Element\nmethod and to account for turbulence modeling based on Large Eddy Simulation\n(LES). The aim of this paper is twofold: on one hand to improve the general\nunderstanding of blood flow in the human LA in normal conditions; on the other,\nto analyse the effects of the turbulence VMS-LES method on a situation of blood\nflow which is neither laminar, nor fully turbulent, but rather transitional as\nin LA. Our results suggest that if relatively coarse meshes are adopted, the\nadditional stabilization terms introduced by the VMS-LES method allow to better\npredict transitional effects and cycle-to-cycle blood flow variations than the\nstandard SUPG stabilization method.\n"}
+{"abstract": "  The problem of showing the existence of localised modes in nonlinear lattices\nhas attracted considerable efforts from the physical but also from the\nmathematical viewpoint where a rich variety of methods has been employed. In\nthis paper we prove that a fixed point theory approach based on the celebrated\nSchauder's Fixed Point Theorem may provide a general method to establish\nconcisely not only the existence of localised structures but also a required\nrate of spatial localisation. As a case study we consider lattices of coupled\nparticles with nonlinear nearest neighbour interaction and prove the existence\nof exponentially spatially localised breathers exhibiting either even-parity or\nodd-parity symmetry under necessary non-resonant conditions accompanied with\nthe proof of energy bounds of the solutions.\n"}
+{"abstract": "  We have developed several autotuning benchmarks in CUDA that take into\naccount performance-relevant source-code parameters and reach near\npeak-performance on various GPU architectures. We have used them during the\ndevelopment and evaluation of a novel search method for tuning space proposed\nin [1]. With our framework Kernel Tuning Toolkit, freely available at Github,\nwe measured computation times and hardware performance counters on several GPUs\nfor the complete tuning spaces of five benchmarks. These data, which we provide\nhere, might benefit research of search algorithms for the tuning spaces of GPU\ncodes or research of relation between applied code optimization, hardware\nperformance counters, and GPU kernels' performance.\n  Moreover, we describe the scripts we used for robust evaluation of our\nsearcher and comparison to others in detail. In particular, the script that\nsimulates the tuning, i.e., replaces time-demanding compiling and executing the\ntuned kernels with a quick reading of the computation time from our measured\ndata, makes it possible to inspect the convergence of tuning search over a\nlarge number of experiments. These scripts, freely available with our other\ncodes, make it easier to experiment with search algorithms and compare them in\na robust way.\n  During our research, we generated models for predicting values of performance\ncounters from values of tuning parameters of our benchmarks. Here, we provide\nthe models themselves and describe the scripts we implemented for their\ntraining. These data might benefit researchers who want to reproduce or build\non our research.\n"}
+{"abstract": "  We first consider a $d$-dimensional branching Brownian motion (BBM) evolving\nin an expanding ball, where the particles are killed at the boundary of the\nball, and the expansion is subdiffusive in time. We study the large-time\nasymptotic behavior of the mass inside the ball, and obtain a large-deviation\n(LD) result as time tends to infinity on the probability that the mass is\naytpically small. Then, we consider the problem of BBM among mild Poissonian\nobstacles, where a random `trap field' in $\\mathbb{R}^d$ is created via a\nPoisson point process. The trap field consists of balls of fixed radius\ncentered at the atoms of the Poisson point process. The mild obstacle rule is\nthat when a particle is inside a trap, it branches at a lower rate, which is\nallowed to be zero, whereas when outside the trap field it branches at the\nnormal rate. As an application of our LD result on the mass of BBM inside\nexpanding balls, we prove upper bounds on the LD probabilities for the mass of\nBBM among mild obstacles, which we then use along with the Borel-Cantelli lemma\nto prove the corresponding strong law of large numbers. Our results are\nquenched, that is, they hold in almost every environment with respect to the\nPoisson point process.\n"}
+{"abstract": "  Exact solutions are obtained in the quadratic theory of gravity with a scalar\nfield for wave-like models of space-time with spatial homogeneity symmetry and\nallowing the integration of the equations of motion of test particles in the\nHamilton-Jacobi formalism by the method of separation of variables with\nseparation of wave variables (Shapovalov spaces of type II). The form of the\nscalar field and the scalar field functions included in the Lagrangian of the\ntheory is found. The obtained exact solutions can describe the primary\ngravitational wave disturbances in the Universe (primary gravitational waves).\n"}
+{"abstract": "  Massive scalar fields provide excellent dark matter candidates, whose\ndynamics are often explored analytically and numerically using nonrelativistic\nSchr\\\"{o}dinger-Poisson (SP) equations in a cosmological context. In this\npaper, starting from the nonlinear and fully relativistic Klein-Gordon-Einstein\n(KGE) equations in an expanding universe, we provide a systematic framework for\nderiving the SP equations, as well as relativistic corrections to them, by\nintegrating out `fast modes' and including nonlinear metric and matter\ncontributions. We provide explicit equations for the leading-order relativistic\ncorrections, which provide insight into deviations from the SP equations as the\nsystem approaches the relativistic regime. Upon including the leading-order\ncorrections, our equations are applicable beyond the domain of validity of the\nSP system, and are simpler to use than the full KGE case in some contexts. As a\nconcrete application, we calculate the mass-radius relationship of solitons in\nscalar dark matter and accurately capture the deviations of this relationship\nfrom the SP system towards the KGE one.\n"}
+{"abstract": "  The relationship between classical and quantum mechanics is usually\nunderstood via the limit $\\hbar \\rightarrow 0$. This is the underlying idea\nbehind the quantization of classical objects. The apparent incompatibility of\ngeneral relativity with quantum mechanics and quantum field theory has\nchallenged for many decades this basic idea. We recently showed the emergence\nof classical dynamics for very general quantum lattice systems with mean-field\ninteractions, without (complete) supression of its quantum features, in the\ninfinite volume limit. This leads to a theoretical framework in which the\nclassical and quantum worlds are entangled. Such an entanglement is noteworthy\nand is a consequence of the highly non-local character of mean-field\ninteractions. Therefore, this phenomenon should not be restricted to systems\nwith mean-field interactions only, but should also appear in presence of\ninteractions that are sufficiently long-range, yielding effective, classical\nbackground fields, in the spirit of the Higgs mechanism of quantum field\ntheory. In order to present the result in a less abstract way than in its\noriginal version, here we apply it to a concrete, physically relevant, example\nand discuss, by this means, various important aspects of our general approach.\nThe model we consider is not exactly solvable and the particular results\nobtained are new.\n"}
+{"abstract": "  Aims: We investigate a new method to for obtaining the plasma parameters of\nsolar prominences observed in the Mgii h&k spectral lines by comparing line\nprofiles from the IRIS satellite to a bank of profiles computed with a\none-dimensional non-local thermodynamic equilibrium (non-LTE) radiative\ntransfer code.\n  Methods: Using a grid of 1007 one-dimensional non-LTE radiative transfer\nmodels we carry out this new method to match computed spectra to observed line\nprofiles while accounting for line core shifts not present in the models. The\nprominence observations were carried out by the IRIS satellite on 19 April\n2018.\n  Results: The prominence is very dynamic with many flows. The models are able\nto recover satisfactory matches in areas of the prominence where single line\nprofiles are observed. We recover: mean temperatures of 6000 to 50,000K; mean\npressures of 0.01 to 0.5 dyne cm$^{-2}$; column masses of 3.7$\\times10^{-8}$ to\n5$\\times10^{-4}$ g cm$^{-2}$; a mean electron density of 7.3$\\times10^{8}$ to\n1.8$\\times10^{11}$ cm$^{-3}$; and an ionisation degree\n${n_\\text{HII}}/{n_\\text{HI}}=0.03 - 4500$. The highest values for the\nionisation degree are found in areas where the line of sight crosses mostly\nplasma from the PCTR, correlating with high mean temperatures and\ncorrespondingly no H$\\alpha$ emission.\n  Conclusions: This new method naturally returns information on how closely the\nobserved and computed profiles match, allowing the user to identify areas where\nno satisfactory match between models and observations can be obtained. Regions\nwhere satisfactory fits were found were more likely to contain a model\nencompassing a PCTR. The line core shift can also be recovered from this new\nmethod, and it shows a good qualitative match with that of the line core shift\nfound by the quantile method. This demonstrates the effectiveness of the\napproach to line core shifts in the new method.\n"}
+{"abstract": "  The objective of this work is to find temporal boundaries between signs in\ncontinuous sign language. Motivated by the paucity of annotation available for\nthis task, we propose a simple yet effective algorithm to improve segmentation\nperformance on unlabelled signing footage from a domain of interest. We make\nthe following contributions: (1) We motivate and introduce the task of\nsource-free domain adaptation for sign language segmentation, in which labelled\nsource data is available for an initial training phase, but is not available\nduring adaptation. (2) We propose the Changepoint-Modulated Pseudo-Labelling\n(CMPL) algorithm to leverage cues from abrupt changes in motion-sensitive\nfeature space to improve pseudo-labelling quality for adaptation. (3) We\nshowcase the effectiveness of our approach for category-agnostic sign\nsegmentation, transferring from the BSLCORPUS to the BSL-1K and\nRWTH-PHOENIX-Weather 2014 datasets, where we outperform the prior state of the\nart.\n"}
+{"abstract": "  This paper presents a mathematic dynamic model of a quadrotor unmanned aerial\nvehicle (QUAV) by using the symbolic regression approach and then proposes a\nhierarchical control scheme for trajectory tracking. The symbolic regression\napproach is capable of constructing analytical quadrotor dynamic equations only\nthrough the collected data, which relieves the burden of first principle\nmodeling. To facilitate position tracking control of a QUAV, the design of\ncontroller can be decomposed into two parts: a proportional-integral controller\nfor the position subsystem is first designed to obtain the desired horizontal\nposition and the backstepping method for the attitude subsystem is developed to\nensure that the Euler angles and the altitude can fast converge to the\nreference values. The effectiveness is verified through experiments on a\nbenchmark multicopter simulator.\n"}
+{"abstract": "  We link conditional weak mixing and ergodicity of the tensor product in Riesz\nspaces. In particular, we characterise conditional weak mixing of a conditional\nexpectation preserving system by the ergodicity of its tensor product with\nitself or other ergodic systems. In order to achieve this we characterise the\ncomponents of the weak order units in the tensor product of two Dedekind\ncomplete Riesz spaces with weak order units.\n"}
+{"abstract": "  Sum rules for structure functions and their twist-2 relations have important\nroles in constraining their magnitudes and $x$ dependencies and in studying\nhigher-twist effects. The Wandzura-Wilczek (WW) relation and the\nBurkhardt-Cottingham (BC) sum rule are such examples for the polarized\nstructure functions $g_1$ and $g_2$. Recently, new twist-3 and twist-4 parton\ndistribution functions were proposed for spin-1 hadrons, so that it became\npossible to investigate spin-1 structure functions including higher-twist ones.\nWe show in this work that an analogous twist-2 relation and a sum rule exist\nfor the tensor-polarized parton distribution functions $f_{1LL}$ and $f_{LT}$,\nwhere $f_{1LL}$ is a twist-2 function and $f_{LT}$ is a twist-3 one. Namely,\nthe twist-2 part of $f_{LT}$ is expressed by an integral of $f_{1LL}$ (or\n$b_1$) and the integral of the function $f_{2LT} = (2/3) f_{LT} -f_{1LL}$ over\n$x$ vanishes. If the parton-model sum rule for $f_{1LL}$ ($b_1$) is applied by\nassuming vanishing tensor-polarized antiquark distributions, another sum rule\nalso exists for $f_{LT}$ itself. These relations should be valuable for\nstudying tensor-polarized distribution functions of spin-1 hadrons and for\nseparating twist-2 components from higher-twist terms, as the WW relation and\nBC sum rule have been used for investigating $x$ dependence and higher-twist\neffects in $g_2$. In deriving these relations, we indicate that four twist-3\nmultiparton distribution functions $F_{LT}$, $G_{LT}$, $H_{LL}^\\perp$, and\n$H_{TT}$ exist for tensor-polarized spin-1 hadrons. These multiparton\ndistribution functions are also interesting to probe multiparton correlations\nin spin-1 hadrons.\n"}
+{"abstract": "  We present a work-in-progress approach to improving driver attentiveness in\ncars provided with automated driving systems. The approach is based on a\ncontrol loop that monitors the driver's biometrics (eye movement, heart rate,\netc.) and the state of the car; analyses the driver's attentiveness level using\na deep neural network; plans driver alerts and changes in the speed of the car\nusing a formally verified controller; and executes this plan using actuators\nranging from acoustic and visual to haptic devices. The paper presents (i) the\nself-adaptive system formed by this monitor-analyse-plan-execute (MAPE) control\nloop, the car and the monitored driver, and (ii) the use of probabilistic model\nchecking to synthesise the controller for the planning step of the MAPE loop.\n"}
+{"abstract": "  Traditional telesurgery relies on the surgeon's full control of the robot on\nthe patient's side, which tends to increase surgeon fatigue and may reduce the\nefficiency of the operation. This paper introduces a Robotic Partner (RP) to\nfacilitate intuitive bimanual telesurgery, aiming at reducing the surgeon\nworkload and enhancing surgeon-assisted capability. An interval type-2\npolynomial fuzzy-model-based learning algorithm is employed to extract expert\ndomain knowledge from surgeons and reflect environmental interaction\ninformation. Based on this, a bimanual shared control is developed to interact\nwith the other robot teleoperated by the surgeon, understanding their control\nand providing assistance. As prior information of the environment model is not\nrequired, it reduces reliance on force sensors in control design. Experimental\nresults on the DaVinci Surgical System show that the RP could assist\npeg-transfer tasks and reduce the surgeon's workload by 51\\% in\nforce-sensor-free scenarios.\n"}
+{"abstract": "  Self-driving vehicles must perceive and predict the future positions of\nnearby actors in order to avoid collisions and drive safely. A learned deep\nlearning module is often responsible for this task, requiring large-scale,\nhigh-quality training datasets. As data collection is often significantly\ncheaper than labeling in this domain, the decision of which subset of examples\nto label can have a profound impact on model performance. Active learning\ntechniques, which leverage the state of the current model to iteratively select\nexamples for labeling, offer a promising solution to this problem. However,\ndespite the appeal of this approach, there has been little scientific analysis\nof active learning approaches for the perception and prediction (P&P) problem.\nIn this work, we study active learning techniques for P&P and find that the\ntraditional active learning formulation is ill-suited for the P&P setting. We\nthus introduce generalizations that ensure that our approach is both cost-aware\nand allows for fine-grained selection of examples through partially labeled\nscenes. Our experiments on a real-world, large-scale self-driving dataset\nsuggest that fine-grained selection can improve the performance across\nperception, prediction, and downstream planning tasks.\n"}
+{"abstract": "  We study the problem of determining the minimum number $f(n,k,d)$ of affine\nsubspaces of codimension $d$ that are required to cover all points of\n$\\mathbb{F}_2^n\\setminus \\{\\vec{0}\\}$ at least $k$ times while covering the\norigin at most $k-1$ times. The case $k=1$ is a classic result of Jamison,\nwhich was independently obtained by Brouwer and Schrijver for $d = 1$. The\nvalue of $f(n,1,1)$ also follows from a well-known theorem of Alon and F\\\"uredi\nabout coverings of finite grids in affine spaces over arbitrary fields. Here we\ndetermine the value of this function exactly in various ranges of the\nparameters. In particular, we prove that for $k \\ge 2^{n-d-1}$ we have\n$f(n,k,d)=2^d k - \\left \\lfloor \\frac{k}{2^{n-d}} \\right \\rfloor$, while for $n\n> 2^{2^d k-k-d+1}$ we have $f(n,k,d)= n + 2^dk-d-2$, and also study the\ntransition between these two ranges. While previous work in this direction has\nprimarily employed the polynomial method, we prove our results through more\ndirect combinatorial and probabilistic arguments, and also exploit a connection\nto coding theory.\n"}
+{"abstract": "  Automatic speaker verification (ASV) is one of the core technologies in\nbiometric identification. With the ubiquitous usage of ASV systems in\nsafety-critical applications, more and more malicious attackers attempt to\nlaunch adversarial attacks at ASV systems. In the midst of the arms race\nbetween attack and defense in ASV, how to effectively improve the robustness of\nASV against adversarial attacks remains an open question. We note that the\nself-supervised learning models possess the ability to mitigate superficial\nperturbations in the input after pretraining. Hence, with the goal of effective\ndefense in ASV against adversarial attacks, we propose a standard and\nattack-agnostic method based on cascaded self-supervised learning models to\npurify the adversarial perturbations. Experimental results demonstrate that the\nproposed method achieves effective defense performance and can successfully\ncounter adversarial attacks in scenarios where attackers may either be aware or\nunaware of the self-supervised learning models.\n"}
+{"abstract": "  Geographic ranges of communities of species evolve in response to\nenvironmental, ecological, and evolutionary forces. Understanding the effects\nof these forces on species' range dynamics is a major goal of spatial ecology.\nPrevious mathematical models have jointly captured the dynamic changes in\nspecies' population distributions and the selective evolution of\nfitness-related phenotypic traits in the presence of an environmental gradient.\nThese models inevitably include some unrealistic assumptions, and biologically\nreasonable ranges of values for their parameters are not easy to specify. As a\nresult, simulations of the seminal models of this type can lead to markedly\ndifferent conclusions about the behavior of such populations, including the\npossibility of maladaptation setting stable range boundaries. Here, we\nharmonize such results by developing and simulating a continuum model of range\nevolution in a community of species that interact competitively while diffusing\nover an environmental gradient. Our model extends existing models by\nincorporating both competition and freely changing intraspecific trait\nvariance. Simulations of this model predict a spatial profile of species' trait\nvariance that is consistent with experimental measurements available in the\nliterature. Moreover, they reaffirm interspecific competition as an effective\nfactor in limiting species' ranges, even when trait variance is not\nartificially constrained. These theoretical results can inform the design of,\nas yet rare, empirical studies to clarify the evolutionary causes of range\nstabilization.\n"}
+{"abstract": "  A natural choice for quantum communication is to use the relative phase\nbetween two paths of a single-photon for information encoding. This method was\nnevertheless quickly identified as impractical over long distances and thus a\nmodification based on single-photon time-bins has then become widely adopted.\nIt however, introduces a fundamental loss, which increases with the dimension\nand that limits its application over long distances. Here, we are able to solve\nthis long-standing hurdle by employing a few-mode fiber space-division\nmultiplexing platform working with orbital angular momentum modes. In our\nscheme, we maintain the practicability provided by the time-bin scheme, while\nthe quantum states are transmitted through a few-mode fiber in a configuration\nthat does not introduce post-selection losses. We experimentally demonstrate\nour proposal by successfully transmitting phase-encoded single-photon states\nfor quantum cryptography over 500 m of few-mode fiber, showing the feasibility\nof our scheme.\n"}
+{"abstract": "  We study the possibility to realize Majorana zero mode that's robust and may\nbe easily manipulated for braiding in quantum computing in the ground state of\nthe Kitaev model in this work. To achieve this we first apply a uniform [111]\nmagnetic field to the gapless Kitaev model and turn the Kitaev model to an\neffective p + ip topological superconductor of spinons. We then study possible\nvortex binding in such system to a topologically trivial spot in the ground\nstate. We consider two cases in the system: one is a vacancy and the other is a\nfully polarized spin. We show that in both cases, the system binds a vortex\nwith the defect and a robust Majorana zero mode in the ground state at a weak\nuniform [111] magnetic field. The distribution and asymptotic behavior of these\nMajorana zero modes is studied. The Majorana zero modes in both cases decay\nexponentially in space, and are robust against local perturbations and other\nMajorana zero modes far away, which makes them promising candidate for braiding\nin topological quantum computing.\n"}
+{"abstract": "  Timed automata (TA) is used for modeling systems with timing aspects. A TA\nextends a finite automaton with a set of real valued variables called clocks,\nthat measure the time and constraints over the clocks guard the transitions. A\nparametric TA (PTA) is a TA extension that allows parameters in clock\nconstraints. In this paper, we focus on synthesis of a control strategy and\nparameter valuation for a PTA such that each run of the resulting TA reaches a\ntarget location within the given amount of time while avoiding unsafe\nlocations. We propose an algorithm based on depth first analysis combined with\nan iterative feasibility check. The algorithm iteratively constructs a symbolic\nrepresentation of the possible solutions, and employs a feasibility check to\nterminate the exploration along infeasible directions. Once the construction is\ncompleted, a mixed integer linear program is solved for each candidate strategy\nto generate a parameter valuation and a control strategy pair. We present a\nrobotic planning example to motivate the problem and to illustrate the results.\n"}
+{"abstract": "  Logarithmic number systems (LNS) are used to represent real numbers in many\napplications using a constant base raised to a fixed-point exponent making its\ndistribution exponential. This greatly simplifies hardware multiply, divide and\nsquare root. LNS with base-2 is most common, but in this paper we show that for\nlow-precision LNS the choice of base has a significant impact.\n  We make four main contributions. First, LNS is not closed under addition and\nsubtraction, so the result is approximate. We show that choosing a suitable\nbase can manipulate the distribution to reduce the average error. Second, we\nshow that low-precision LNS addition and subtraction can be implemented\nefficiently in logic rather than commonly used ROM lookup tables, the\ncomplexity of which can be reduced by an appropriate choice of base. A similar\neffect is shown where the result of arithmetic has greater precision than the\ninput. Third, where input data from external sources is not expected to be in\nLNS, we can reduce the conversion error by selecting a LNS base to match the\nexpected distribution of the input. Thus, there is no one base which gives the\nglobal optimum, and base selection is a trade-off between different factors.\nFourth, we show that circuits realized in LNS require lower area and power\nconsumption for short word lengths.\n"}
+{"abstract": "  Ramanujan provided several results involving the modified Bessel function\n$K_z(x)$ in his Lost Notebook. One of them is the famous Ramanujan-Guinand\nformula, equivalent to the functional equation of the non-holomorphic\nEiesenstien series on $SL_2(z)$. Recently, this formula was generalized by\nDixit, Kesarwani, and Moll. In this article, we first obtain a generalization\nof a theorem of Watson and, as an application of it, give a new proof of the\nresult of Dixit, Kesarwani, and Moll. Watson's theorem is also generalized in a\ndifferent direction using ${}_\\mu K_z(x,\\lambda)$ which is itself a\ngeneralization of $K_z(x)$. Analytic continuation of all these results are also\ngiven.\n"}
+{"abstract": "  When bars form within galaxy formation simulations in the standard\ncosmological context, dynamical friction with dark matter (DM) causes them to\nrotate rather slowly. However, almost all observed galactic bars are fast in\nterms of the ratio between corotation radius and bar length. Here, we\nexplicitly display an $8\\sigma$ tension between the observed distribution of\nthis ratio and that in the EAGLE simulation at redshift 0. We also compare the\nevolution of Newtonian galactic discs embedded in DM haloes to their evolution\nin three extended gravity theories: Milgromian Dynamics (MOND), a model of\nnon-local gravity, and a scalar-tensor-vector gravity theory (MOG). Although\nour models start with the same initial baryonic distribution and rotation\ncurve, the long-term evolution is different. The bar instability happens more\nviolently in MOND compared to the other models. There are some common features\nbetween the extended gravity models, in particular the negligible role played\nby dynamical friction $-$ which plays a key role in the DM model. Partly for\nthis reason, all extended gravity models predict weaker bars and faster bar\npattern speeds compared to the DM case. Although the absence of strong bars in\nour idealized, isolated extended gravity simulations is in tension with\nobservations, they reproduce the strong observational preference for `fast' bar\npattern speeds, which we could not do with DM. We confirm previous findings\nthat apparently `ultrafast' bars can be due to bar-spiral arm alignment leading\nto an overestimated bar length, especially in extended gravity scenarios where\nthe bar is already fast.\n"}
+{"abstract": "  In recent years, quantitative investment methods combined with artificial\nintelligence have attracted more and more attention from investors and\nresearchers. Existing related methods based on the supervised learning are not\nvery suitable for learning problems with long-term goals and delayed rewards in\nreal futures trading. In this paper, therefore, we model the price prediction\nproblem as a Markov decision process (MDP), and optimize it by reinforcement\nlearning with expert trajectory. In the proposed method, we employ more than\n100 short-term alpha factors instead of price, volume and several technical\nfactors in used existing methods to describe the states of MDP. Furthermore,\nunlike DQN (deep Q-learning) and BC (behavior cloning) in related methods, we\nintroduce expert experience in training stage, and consider both the\nexpert-environment interaction and the agent-environment interaction to design\nthe temporal difference error so that the agents are more adaptable for\ninevitable noise in financial data. Experimental results evaluated on share\nprice index futures in China, including IF (CSI 300) and IC (CSI 500), show\nthat the advantages of the proposed method compared with three typical\ntechnical analysis and two deep leaning based methods.\n"}
+{"abstract": "  Understanding the origin and mechanism of the transverse polarization of\nhyperons produced in unpolarized proton-proton collision, $pp\\to\n\\Lambda^\\uparrow X$, has been one of the long-standing issues in high-energy\nspin physics. In the framework of the collinear factorization applicable to\nlarge-$p_T$ hadron productions, this phenomenon is a twist-3 observable which\nis caused by multi-parton correlations either in the initial protons or in the\nprocess of fragmentation into the hyperon. We derive the twist-3 gluon\nfragmentation function (FF) contribution to this process in the leading order\n(LO) with respect to the QCD coupling constant. Combined with the known results\nfor the contribution from the twist-3 distribution function and the twist-3\nquark FF, this completes the LO twist-3 cross section. We also found that the\nmodel independent relations among the twist-3 gluon FFs based on the QCD\nequation of motion and the Lorentz invariance property of the correlation\nfunctions guarantee the color gauge invariance and the frame-independence of\nthe cross section.\n"}
+{"abstract": "  Semi-local DFT methods exhibit significant errors for the phase diagrams of\ntransition-metal oxides that are caused by an incorrect description of\nmolecular oxygen and the large self-interaction error in materials with\nstrongly localized electronic orbitals. Empirical and semiempirical corrections\nbased on the DFT+U method can reduce these errors, but the parameterization and\nvalidation of the correction terms remains an on-going challenge. We develop a\nsystematic methodology to determine the parameters and to statistically assess\nthe results by considering thermochemical data across a set of transition metal\ncompounds. We consider three interconnected levels of correction terms: (1) a\nconstant oxygen binding correction, (2) Hubbard-U correction, and (3) DFT/DFT+U\ncompatibility correction. The parameterization is expressed as a unified\noptimization problem. We demonstrate this approach for 3d transition metal\noxides, considering a target set of binary and ternary oxides. With a total of\n37 measured formation enthalpies taken from the literature, the dataset is\naugmented by the reaction energies of 1,710 unique reactions that were derived\nfrom the formation energies by systematic enumeration. To ensure a balanced\ndataset across the available data, the reactions were grouped by their\nsimilarity using clustering and suitably weighted. The parameterization is\nvalidated using leave-one-out cross-validation (CV), a standard technique for\nthe validation of statistical models. We apply the methodology to the SCAN\ndensity functional. Based on the CV score, the error of binary (ternary) oxide\nformation energies is reduced by 40% (75%) to 0.10 (0.03) eV/atom. The method\nand tools demonstrated here can be applied to other classes of materials or to\nparameterize the corrections to optimize DFT+U performance for other target\nphysical properties.\n"}
+{"abstract": "  We analyze the solution of the Schr\\\"odinger equation arising in the\ntreatment of a geometric model introduced to explain the origin of the observed\nshallow levels in semiconductors threaded by a dislocation density. We show\n(contrary to what the authors claimed) that the model does not support bound\nstates for any chosen set of model parameters. Assuming a fictitious motion in\nthe $x-y$ plane there are bound states provided that $k\\neq 0$ and not only for\n$k>0$ as the authors believed. The truncation condition proposed by the authors\nyields only one particular energy for a given value of a chosen model parameter\nand misses all the others (conditionally solvable problem)\n"}
+{"abstract": "  In this paper, we conjecture a connection between the $A$-polynomial of a\nknot in $\\mathbb{S}^{3}$ and the hyperbolic volume of its exterior\n$\\mathcal{M}_{K}$ : the knots with zero hyperbolic volume are exactly the knots\nwith an $A$-polynomial where every irreducible factor is the sum of two\nmonomials in $L$ and $M$. Herein, we show the forward implication and examine\ncases that suggest the converse may also be true. Since the $A$-polynomial of\nhyperbolic knots are known to have at least one irreducible factor which is not\nthe sum of two monomials in $L$ and $M$, this paper considers satellite knots\nwhich are graph knots and some with positive hyperbolic volume.\n"}
+{"abstract": "  Artificial intelligence (AI) has been successful at solving numerous problems\nin machine perception. In radiology, AI systems are rapidly evolving and show\nprogress in guiding treatment decisions, diagnosing, localizing disease on\nmedical images, and improving radiologists' efficiency. A critical component to\ndeploying AI in radiology is to gain confidence in a developed system's\nefficacy and safety. The current gold standard approach is to conduct an\nanalytical validation of performance on a generalization dataset from one or\nmore institutions, followed by a clinical validation study of the system's\nefficacy during deployment. Clinical validation studies are time-consuming, and\nbest practices dictate limited re-use of analytical validation data, so it is\nideal to know ahead of time if a system is likely to fail analytical or\nclinical validation. In this paper, we describe a series of sanity tests to\nidentify when a system performs well on development data for the wrong reasons.\nWe illustrate the sanity tests' value by designing a deep learning system to\nclassify pancreatic cancer seen in computed tomography scans.\n"}
+{"abstract": "  Science gateways are user-centric, end-to-end cyberinfrastructure for\nmanaging scientific data and executions of computational software on\ndistributed resources. In order to simplify the creation and management of\nscience gateways, we have pursued a multi-tenanted, platform-as-a-service\napproach that allows multiple gateway front-ends (portals) to be integrated\nwith a consolidated middleware that manages the movement of data and the\nexecution of workflows on multiple back-end scientific computing resources. An\nimportant challenge for this approach is to provide an end-to-end data movement\nand management solution that allows gateway users to integrate their own data\nstores with the gateway platform. These user-provided data stores may include\ncommercial cloud-based object store systems, third-party data stores accessed\nthrough APIs such as REST endpoints, and users' own local storage resources. In\nthis paper, we present a solution design and implementation based on the\nintegration of a managed file transfer (MFT) service (Airavata MFT) into the\nplatform.\n"}
+{"abstract": "  Conformal Field Theories (CFTs) have rich dynamics in heavy states. We\ndescribe the constraints due to spontaneously broken boost and dilatation\nsymmetries in such states. The spontaneously broken boost symmetries require\nthe existence of new low-lying primaries whose scaling dimension gap, we argue,\nscales as $O(1)$. We demonstrate these ideas in various states, including\nfluid, superfluid, mean field theory, and Fermi surface states. We end with\nsome remarks about the large charge limit in 2d and discuss a theory of a\nsingle compact boson with an arbitrary conformal anomaly.\n"}
+{"abstract": "  This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method,\nwhich projects KG entities and relations into the Hamming space based on a\ncomputationally tractable discrete optimization algorithm, to solve the\nformidable storage and computation cost challenges in traditional continuous\ngraph embedding methods. The convergence of DKGE can be guaranteed\ntheoretically. Extensive experiments demonstrate that DKGE achieves superior\naccuracy than classical hashing functions that map the effective continuous\nembeddings into discrete codes. Besides, DKGE reaches comparable accuracy with\nmuch lower computational complexity and storage compared to many continuous\ngraph embedding methods.\n"}
+{"abstract": "  Two of the most sensitive probes of the large scale structure of the universe\nare the clustering of galaxies and the tangential shear of background galaxy\nshapes produced by those foreground galaxies, so-called galaxy-galaxy lensing.\nCombining the measurements of these two two-point functions leads to\ncosmological constraints that are independent of the galaxy bias factor. The\noptimal choice of foreground, or lens, galaxies is governed by the joint, but\nconflicting requirements to obtain accurate redshift information and large\nstatistics. We present cosmological results from the full 5000 sq. deg. of the\nDark Energy Survey first three years of observations (Y3) combining those\ntwo-point functions, using for the first time a magnitude-limited lens sample\n(MagLim) of 11 million galaxies especially selected to optimize such\ncombination, and 100 million background shapes. We consider two cosmological\nmodels, flat $\\Lambda$CDM and $w$CDM. In $\\Lambda$CDM we obtain for the matter\ndensity $\\Omega_m = 0.320^{+0.041}_{-0.034}$ and for the clustering amplitude\n$S_8 = 0.778^{+0.037}_{-0.031}$, at 68\\% C.L. The latter is only 1$\\sigma$\nsmaller than the prediction in this model informed by measurements of the\ncosmic microwave background by the Planck satellite. In $w$CDM we find\n$\\Omega_m = 0.32^{+0.044}_{-0.046}$, $S_8=0.777^{+0.049}_{-0.051}$, and dark\nenergy equation of state $w=-1.031^{+0.218}_{-0.379}$. We find that including\nsmaller scales while marginalizing over non-linear galaxy bias improves the\nconstraining power in the $\\Omega_m-S_8$ plane by $31\\%$ and in the\n$\\Omega_m-w$ plane by $41\\%$ while yielding consistent cosmological parameters\nfrom those in the linear bias case. These results are combined with those from\ncosmic shear in a companion paper to present full DES-Y3 constraints from the\nthree two-point functions (3x2pt).\n"}
+{"abstract": "  Object detection in aerial images is an important task in environmental,\neconomic, and infrastructure-related tasks. One of the most prominent\napplications is the detection of vehicles, for which deep learning approaches\nare increasingly used. A major challenge in such approaches is the limited\namount of data that arises, for example, when more specialized and rarer\nvehicles such as agricultural machinery or construction vehicles are to be\ndetected. This lack of data contrasts with the enormous data hunger of deep\nlearning methods in general and object recognition in particular. In this\narticle, we address this issue in the context of the detection of road vehicles\nin aerial images. To overcome the lack of annotated data, we propose a\ngenerative approach that generates top-down images by overlaying artificial\nvehicles created from 2D CAD drawings on artificial or real backgrounds. Our\nexperiments with a modified RetinaNet object detection network show that adding\nthese images to small real-world datasets significantly improves detection\nperformance. In cases of very limited or even no real-world images, we observe\nan improvement in average precision of up to 0.70 points. We address the\nremaining performance gap to real-world datasets by analyzing the effect of the\nimage composition of background and objects and give insights into the\nimportance of background.\n"}
+{"abstract": "  While autoregressive models excel at image compression, their sample quality\nis often lacking. Although not realistic, generated images often have high\nlikelihood according to the model, resembling the case of adversarial examples.\nInspired by a successful adversarial defense method, we incorporate randomized\nsmoothing into autoregressive generative modeling. We first model a smoothed\nversion of the data distribution, and then reverse the smoothing process to\nrecover the original data distribution. This procedure drastically improves the\nsample quality of existing autoregressive models on several synthetic and\nreal-world image datasets while obtaining competitive likelihoods on synthetic\ndatasets.\n"}
+{"abstract": "  The framework of Baikov-Gazizov-Ibragimov approximate symmetries has proven\nuseful for many examples where a small perturbation of an ordinary differential\nequation (ODE) destroys its local symmetry group. For the perturbed model, some\nof the local symmetries of the unperturbed equation may (or may not) re-appear\nas approximate symmetries, and new approximate symmetries can appear.\nApproximate symmetries are useful as a tool for the construction of approximate\nsolutions. We show that for algebraic and first-order differential equations,\nto every point symmetry of the unperturbed equation, there corresponds an\napproximate point symmetry of the perturbed equation. For second and\nhigher-order ODEs, this is not the case: some point symmetries of the original\nODE may be unstable, that is, they do not arise in the approximate point\nsymmetry classification of the perturbed ODE. We show that such unstable point\nsymmetries correspond to higher-order approximate symmetries of the perturbed\nODE, and can be systematically computed. Two detailed examples, including a\nfourth-order nonlinear Boussinesq equation, are presented. Examples of the use\nof higher-order approximate symmetries and approximate integrating factors to\nobtain approximate solutions of higher-order ODEs are provided.\n"}
+{"abstract": "  Citations are used for research evaluation, and it is therefore important to\nknow which factors influence or associate with citation impact of articles.\nSeveral citation factors have been studied in the literature. In this study we\npropose a new factor, topic growth, that no previous study has taken into\nconsideration. The growth rate of topics may influence future citation counts,\nbecause a high growth in a topic means there are more publications citing\nprevious publications in that topic. We construct topics using community\ndetection in a citation network and use a two-part regression model is used to\nstudy the association between topic growth and citation counts in eight broad\ndisciplines. The first part of the model uses quantile regression to estimate\nthe effect of growth ratio on citation counts for publications with more than\nthree citations. The second part of the model uses logistic regression to model\nthe influence of the independent variables on the probability of being lowly\ncited versus being modestly or highly cited. Both models control for three\nvariables that may distort the association between the topic growth and\ncitations: journal impact, number of references, and number of authors. The\nregression model clearly shows that publications in fast-growing topics have a\ncitation advantage compared to publications in slow-growing or declining topics\nin all of the eight disciplines. Using citation indicators for research\nevaluation may give incentives for researchers to publish in fast-growing\ntopics, but they may cause research to be less diversified. The results have\nalso some implications for citation normalization.\n"}
+{"abstract": "  We have studied spin excitations in a single-domain crystal of\nantiferromagnetic LiCoPO4 by THz absorption spectroscopy. By analyzing the\nselection rules and comparing the strengths of the absorption peaks in the\ndifferent antiferromagnetic domains, we found electromagnons and\nmagnetoelectric spin resonances besides conventional magnetic-dipole active\nspin-wave excitations. Using the sum rule for the magnetoelectric\nsusceptibility we determined the contribution of the spin excitations to all\nthe different off-diagonal elements of the static magnetoelectric\nsusceptibility tensor in zero as well as in finite magnetic fields. We conclude\nthat the magnetoelectric spin resonances are responsible for the static\nmagnetoelectric response of the bulk when the magnetic field is along the\nx-axis, and the symmetric part of the magnetoelectric tensor with zero diagonal\nelements dominates over the antisymmetric components.\n"}
+{"abstract": "  In the next decades, the gravitational-wave (GW) standard siren observations\nand the neutral hydrogen 21-cm intensity mapping (IM) surveys, as two promising\ncosmological probes, will play an important role in precisely measuring\ncosmological parameters. In this work, we make a forecast for cosmological\nparameter estimation with the synergy between the GW standard siren\nobservations and the 21-cm IM surveys. We choose the Einstein Telescope (ET)\nand the Taiji observatory as the representatives of the GW detection projects\nand choose the Square Kilometre Array (SKA) phase I mid-frequency array as the\nrepresentative of the 21-cm IM experiments. In the simulation of the 21-cm IM\ndata, we assume perfect foreground removal and calibration. We find that the\nsynergy of the GW standard siren observations and the 21-cm IM survey could\nbreak the cosmological parameter degeneracies. The joint ET+Taiji+SKA data give\n$\\sigma(H_0)=0.28\\ {\\rm km\\ s^{-1}\\ Mpc^{-1}}$ in the $\\Lambda$CDM model,\n$\\sigma(w)=0.028$ in the $w$CDM model, which are better than the results of\n$Planck$+BAO+SNe, and $\\sigma(w_0)=0.077$ and $\\sigma(w_a)=0.295$ in the CPL\nmodel, which are comparable with the results of $Planck$+BAO+SNe. In the\n$\\Lambda$CDM model, the constraint precision of $H_0$ and $\\Omega_{\\rm m}$ is\nless than or rather close to 1%, indicating that the magnificent prospects for\nprecision cosmology with these two promising cosmological probes are worth\nexpecting.\n"}
+{"abstract": "  Many reinforcement learning algorithms rely on value estimation. However, the\nmost widely used algorithms -- namely temporal difference algorithms -- can\ndiverge under both off-policy sampling and nonlinear function approximation.\nMany algorithms have been developed for off-policy value estimation which are\nsound under linear function approximation, based on the linear mean-squared\nprojected Bellman error (PBE). Extending these methods to the non-linear case\nhas been largely unsuccessful. Recently, several methods have been introduced\nthat approximate a different objective, called the mean-squared Bellman error\n(BE), which naturally facilities nonlinear approximation. In this work, we\nbuild on these insights and introduce a new generalized PBE, that extends the\nlinear PBE to the nonlinear setting. We show how this generalized objective\nunifies previous work, including previous theory, and obtain new bounds for the\nvalue error of the solutions of the generalized objective. We derive an\neasy-to-use, but sound, algorithm to minimize the generalized objective which\nis more stable across runs, is less sensitive to hyperparameters, and performs\nfavorably across four control domains with neural network function\napproximation.\n"}
+{"abstract": "  We present a simple quantum description of the gravitational collapse of a\nball of dust which excludes those states whose width is arbitrarily smaller\nthan the gravitational radius of the matter source and supports the conclusion\nthat black holes are macroscopic extended objects. We also comment briefly on\nthe relevance of this result for the ultraviolet self-completion of gravity and\nthe corpuscular picture of black holes.\n"}
+{"abstract": "  Here we deal with the stabilization problem of non-diagonal systems by\nboundary control. In the studied setting, the boundary control input is subject\nto a constant delay. We use the spectral decomposition method and split the\nsystem into two components: an unstable and a stable one. To stabilize the\nunstable part of the system, we connect, for the first time in the literature,\nthe famous backstepping control design technique with the direct-proportional\ncontrol design. More precisely, we construct a proportional open-loop\nstabilizer, then, by means of the Artstein transformation we close the loop. At\nthe end of the paper, an example is provided in order to illustrate the\nacquired results.\n"}
+{"abstract": "  In this paper, a new framework, named as graphical state space model, is\nproposed for the real time optimal estimation of a class of nonlinear state\nspace model. By discretizing this kind of system model as an equation which can\nnot be solved by Extended Kalman filter, factor graph optimization can\noutperform Extended Kalman filter in some cases. A simple nonlinear example is\ngiven to demonstrate the efficiency of this framework.\n"}
+{"abstract": "  We consider four-point correlation functions of protected single-trace scalar\noperators in planar N = 4 supersymmetric Yang-Mills (SYM). We conjecture that\nall loop corrections derive from an integrand which enjoys a ten-dimensional\nsymmetry. This symmetry combines spacetime and R-charge transformations. By\nconsidering a 10D light-like limit, we extend the correlator/amplitude duality\nby equating large R-charge octagons with Coulomb branch scattering amplitudes.\nUsing results from integrability, this predicts new finite amplitudes as well\nas some Feynman integrals.\n"}
+{"abstract": "  The interfacial behavior of quantum materials leads to emergent phenomena\nsuch as two dimensional electron gases, quantum phase transitions, and\nmetastable functional phases. Probes for in situ and real time surface\nsensitive characterization are critical for active monitoring and control of\nepitaxial synthesis, and hence the atomic-scale engineering of heterostructures\nand superlattices. Termination switching, especially as an interfacial process\nin ternary complex oxides, has been studied using a variety of probes, often ex\nsitu; however, direct observation of this phenomena is lacking. To address this\nneed, we establish in situ and real time reflection high energy electron\ndiffraction and Auger electron spectroscopy for pulsed laser deposition, which\nprovide structural and compositional information of the surface during film\ndeposition. Using this unique capability, we show, for the first time, the\ndirect observation and control of surface termination in complex oxide\nheterostructures of SrTiO3 and SrRuO3. Density-functional-theory calculations\ncapture the energetics and stability of the observed structures and elucidate\ntheir electronic behavior. This demonstration opens up a novel approach to\nmonitor and control the composition of materials at the atomic scale to enable\nnext-generation heterostructures for control over emergent phenomena, as well\nas electronics, photonics, and energy applications.\n"}
+{"abstract": "  This paper explores serverless cloud computing for double machine learning.\nBeing based on repeated cross-fitting, double machine learning is particularly\nwell suited to exploit the high level of parallelism achievable with serverless\ncomputing. It allows to get fast on-demand estimations without additional cloud\nmaintenance effort. We provide a prototype Python implementation\n\\texttt{DoubleML-Serverless} for the estimation of double machine learning\nmodels with the serverless computing platform AWS Lambda and demonstrate its\nutility with a case study analyzing estimation times and costs.\n"}
+{"abstract": "  Bayesian neural networks (BNNs) have shown success in the areas of\nuncertainty estimation and robustness. However, a crucial challenge prohibits\ntheir use in practice: Bayesian NNs require a large number of predictions to\nproduce reliable results, leading to a significant increase in computational\ncost. To alleviate this issue, we propose spatial smoothing, a method that\nensembles neighboring feature map points of CNNs. By simply adding a few blur\nlayers to the models, we empirically show that the spatial smoothing improves\naccuracy, uncertainty estimation, and robustness of BNNs across a whole range\nof ensemble sizes. In particular, BNNs incorporating the spatial smoothing\nachieve high predictive performance merely with a handful of ensembles.\nMoreover, this method also can be applied to canonical deterministic neural\nnetworks to improve the performances. A number of evidences suggest that the\nimprovements can be attributed to the smoothing and flattening of the loss\nlandscape. In addition, we provide a fundamental explanation for prior works -\nnamely, global average pooling, pre-activation, and ReLU6 - by addressing to\nthem as special cases of the spatial smoothing. These not only enhance\naccuracy, but also improve uncertainty estimation and robustness by making the\nloss landscape smoother in the same manner as the spatial smoothing. The code\nis available at https://github.com/xxxnell/spatial-smoothing.\n"}
+{"abstract": "  The generalized Brillouin zone (GBZ), which is the core concept of the\nnon-Bloch band theory to rebuild the bulk boundary correspondence in the\nnon-Hermitian topology, appears as a closed loop generally. In this work, we\nfind that even if the GBZ itself collapses into a point, the recovery of the\nopen boundary energy spectrum by the continuum bands remains unchanged.\nContrastively, if the bizarreness of the GBZ occurs, the winding number will\nbecome illness. Namely, we find that the bulk boundary correspondence can still\nbe established whereas the GBZ has singularities from the perspective of the\nenergy, but not from the topological invariants. Meanwhile, regardless of the\nfact that the GBZ comes out with the closed loop, the bulk boundary\ncorrespondence can not be well characterized yet because of the ill-definition\nof the topological number. Here, the results obtained may be useful for\nimproving the existing non-Bloch band theory.\n"}
+{"abstract": "  The Vlasov-Poisson-Boltzmann equation is a classical equation governing the\ndynamics of charged particles with the electric force being self-imposed. We\nconsider the system in a convex domain with the Cercignani-Lampis boundary\ncondition. We construct a uniqueness local-in-time solution based on an\n$L^\\infty$-estimate and $W^{1,p}$-estimate. In particular, we develop a new\niteration scheme along the characteristic with the Cercignani-Lampis boundary\nfor the $L^\\infty$-estimate, and an intrinsic decomposition of boundary\nintegral for $W^{1,p}$-estimate.\n"}
+{"abstract": "  In this paper, we generalize the embedded homology in \\cite{hg1} for\nhypergraphs and study the relative embedded homology for hypergraph pairs. We\nstudy the topology for sub-hypergraphs. Using the relative embedded homology\nand the topology for sub-hypergraphs, we discuss persistent relative embedded\nhomology for hypergraph pairs.\n"}
+{"abstract": "  We report on the trapping of single Rb atoms in tunable arrays of optical\ntweezers in a cryogenic environment at $\\sim 4$ K. We describe the design and\nconstruction of the experimental apparatus, based on a custom-made, UHV\ncompatible, closed-cycle cryostat with optical access. We demonstrate the\ntrapping of single atoms in cryogenic arrays of optical tweezers, with\nlifetimes in excess of $\\sim6000$ s, despite the fact that the vacuum system\nhas not been baked out. These results open the way to large arrays of single\natoms with extended coherence, for applications in large-scale quantum\nsimulation of many-body systems, and more generally in quantum science and\ntechnology.\n"}
+{"abstract": "  We introduce a new isometric strain model for the study of the dynamics of\ncloth garments in a moderate stress environment, such as robotic manipulation\nin the neighborhood of humans. This model treats textiles as surfaces which are\ninextensible, admitting only isometric motions. Inextensibility is imposed in a\ncontinuous setting, prior to any discretization, which gives consistency with\nrespect to re-meshing and prevents the problem of locking even with coarse\nmeshes. The simulations of robotic manipulation using the model are compared to\nthe actual manipulation in the real world, finding that the error between the\nsimulated and real position of each point in the garment is lower than 1cm in\naverage, even when a coarse mesh is used. Aerodynamic contributions to motion\nare incorporated to the model through the virtual uncoupling of the inertial\nand gravitational mass of the garment. This approach results in an accurate, as\ncompared to reality, description of cloth motion incorporating aerodynamic\neffects by using only two parameters.\n"}
+{"abstract": "  An abstract theory of Fourier series in locally convex topological vector\nspaces is developed. An analog of Fej\\'{e}r's theorem is proved for these\nseries. The theory is applied to distributional solutions of Cauchy-Riemann\nequations to recover basic results of complex analysis. Some classical results\nof function theory are also shown to be consequences of the series expansion.\n"}
+{"abstract": "  We study the fundamental question of the sample complexity of learning a good\npolicy in finite Markov decision processes (MDPs) when the data available for\nlearning is obtained by following a logging policy that must be chosen without\nknowledge of the underlying MDP. Our main results show that the sample\ncomplexity, the minimum number of transitions necessary and sufficient to\nobtain a good policy, is an exponential function of the relevant quantities\nwhen the planning horizon $H$ is finite. In particular, we prove that the\nsample complexity of obtaining $\\epsilon$-optimal policies is at least\n$\\Omega(\\mathrm{A}^{\\min(\\mathrm{S}-1, H+1)})$ for $\\gamma$-discounted\nproblems, where $\\mathrm{S}$ is the number of states, $\\mathrm{A}$ is the\nnumber of actions, and $H$ is the effective horizon defined as $H=\\lfloor\n\\tfrac{\\ln(1/\\epsilon)}{\\ln(1/\\gamma)} \\rfloor$; and it is at least\n$\\Omega(\\mathrm{A}^{\\min(\\mathrm{S}-1, H)}/\\varepsilon^2)$ for finite horizon\nproblems, where $H$ is the planning horizon of the problem. This lower bound is\nessentially matched by an upper bound. For the average-reward setting we show\nthat there is no algorithm finding $\\epsilon$-optimal policies with a finite\namount of data.\n"}
+{"abstract": "  Under voltage load shedding has been considered as a standard and effective\nmeasure to recover the voltage stability of the electric power grid under\nemergency and severe conditions. However, this scheme usually trips a massive\namount of load which can be unnecessary and harmful to customers. Recently,\ndeep reinforcement learning (RL) has been regarded and adopted as a promising\napproach that can significantly reduce the amount of load shedding. However,\nlike most existing machine learning (ML)-based control techniques, RL control\nusually cannot guarantee the safety of the systems under control. In this\npaper, we introduce a novel safe RL method for emergency load shedding of power\nsystems, that can enhance the safe voltage recovery of the electric power grid\nafter experiencing faults. Unlike the standard RL method, the safe RL method\nhas a reward function consisting of a Barrier function that goes to minus\ninfinity when the system state goes to the safety bounds. Consequently, the\noptimal control policy, that maximizes the reward function, can render the\npower system to avoid the safety bounds. This method is general and can be\napplied to other safety-critical control problems. Numerical simulations on the\n39-bus IEEE benchmark is performed to demonstrate the effectiveness of the\nproposed safe RL emergency control, as well as its adaptive capability to\nfaults not seen in the training.\n"}
+{"abstract": "  Nambu-Goldstone bosons, or axions, may be ubiquitous. Some of the axions may\nhave small masses and thus serve as mediators of long-range forces. In this\npaper, we study the force mediated by an extremely light axion, $\\phi$, between\nthe visible sector and the dark sector, where dark matter lives. Since nature\ndoes not preserve the CP symmetry, the coupling between dark matter and $\\phi$\nis generically CP-violating. In this case, the induced force is extremely\nlong-range and behaves as an effective magnetic field. If the force acts on\nelectrons or nucleons, the spins of them on Earth precess around a fixed\ndirection towards the galactic center. This provides an experimental\nopportunity for $\\phi$ with mass, $m_\\phi$, and decay constant, $f_\\phi$,\nsatisfying $m_\\phi\\lesssim 10^{-25}\\,$ eV, $f_\\phi\\lesssim 10^{14}\\,$GeV if the\ndaily modulation of the effective magnetic field signals in magnetometers is\nmeasured by using the coherent averaging method. The effective magnetic field\ninduced by an axionic compact object, such as an axion domain wall, is also\ndiscussed.\n"}
+{"abstract": "  Texture can be defined as the change of image intensity that forms repetitive\npatterns, resulting from physical properties of the object's roughness or\ndifferences in a reflection on the surface. Considering that texture forms a\ncomplex system of patterns in a non-deterministic way, biodiversity concepts\ncan help texture characterization in images. This paper proposes a novel\napproach capable of quantifying such a complex system of diverse patterns\nthrough species diversity and richness and taxonomic distinctiveness. The\nproposed approach considers each image channel as a species ecosystem and\ncomputes species diversity and richness measures as well as taxonomic measures\nto describe the texture. The proposed approach takes advantage of ecological\npatterns' invariance characteristics to build a permutation, rotation, and\ntranslation invariant descriptor. Experimental results on three datasets of\nnatural texture images and two datasets of histopathological images have shown\nthat the proposed texture descriptor has advantages over several texture\ndescriptors and deep methods.\n"}
+{"abstract": "  A pulse oximeter is an optical device that monitors tissue oxygenation\nlevels. Traditionally, these devices estimate the oxygenation level by\nmeasuring the intensity of the transmitted light through the tissue and are\nembedded into everyday devices such as smartphones and smartwatches. However,\nthese sensors require prior information and are susceptible to unwanted changes\nin the intensity, including ambient light, skin tone, and motion artefacts.\nPrevious experiments have shown the potential of Time-of-Flight (ToF)\ntechniques in measurements of tissue hemodynamics. Our proposed technology uses\nhistograms of photon flight paths within the tissue to obtain tissue\noxygenation, regardless of the changes in the intensity of the source. Our\ndevice is based on a 45ps time-to-digital converter (TDC) which is implemented\nin a Xilinx Zynq UltraScale+ field programmable gate array (FPGA), a CMOS\nSingle Photon Avalanche Diode (SPAD) detector, and a low-cost compact laser\nsource. All these components including the SPAD detector are manufactured using\nthe latest commercially available technology, which leads to increased\nlinearity, accuracy, and stability for ToF measurements. This proof-of-concept\nsystem is approximately 10cmx8cmx5cm in size, with a high potential for\nshrinkage through further system development and component integration. We\ndemonstrate preliminary results of ToF pulse measurements and report the\nengineering details, trade-offs, and challenges of this design. We discuss the\npotential for mass adoption of ToF based pulse oximeters in everyday devices\nsuch as smartphones and wearables.\n"}
+{"abstract": "  The present study proposed a method for numerical solution of linear Volterra\nintegral equations (VIEs) of the third kind, before only analytical solution\nmethods had been discussed with reference to previous research and review of\nthe related literature. Given that such analytical solutions are not almost\nalways feasible, it is required to provide a numerical method for solving the\nmentioned equations. Accordingly, Krall-Laguerre polynomials were utilized for\nnumerical solution of these equations. The main purpose of this method is to\napproximate the unknown functions through Krall-Laguerre polynomials. Moreover,\nan error analysis is performed on the proposed method.\n"}
+{"abstract": "  Traffic violation and the flexible and changeable nature of pedestrians make\nit more difficult to predict pedestrian behavior or intention, which might be a\npotential safety hazard on the road. Pedestrian motion state (such as walking\nand standing) directly affects or reflects its intention. In combination with\npedestrian motion state and other influencing factors, pedestrian intention can\nbe predicted to avoid unnecessary accidents. In this paper, pedestrian is\ntreated as non-rigid object, which can be represented by a set of\ntwo-dimensional key points, and the movement of key point relative to the torso\nis introduced as micro motion. Static and dynamic micro motion features, such\nas position, angle and distance, and their differential calculations in time\ndomain, are used to describe its motion pattern. Gated recurrent neural network\nbased seq2seq model is used to learn the dependence of motion state transition\non previous information, finally the pedestrian motion state is estimated via a\nsoftmax classifier. The proposed method only needs the previous hidden state of\nGRU and current feature to evaluate the probability of current motion state,\nand it is computation efficient to deploy on vehicles. This paper verifies the\nproposed algorithm on the JAAD public dataset, and the accuracy is improved by\n11.6% compared with the existing method.\n"}
+{"abstract": "  We study the impact of the inter-level energy constraints imposed by Haldane\nExclusion Statistics on relaxation processes in 1-dimensional systems coupled\nto a bosonic bath. By formulating a second-quantized description of the\nrelevant Fock space, we identify certain universal features of this relaxation\ndynamics, and show that it is generically slower than that of spinless\nfermions. Our study focuses on the Calogero-Sutherland model, which realizes\nHaldane Exclusion statistics exactly in one dimension; however our results\napply to any system that has the associated pattern of inter-level occupancy\nconstraints in Fock space.\n"}
+{"abstract": "  There is a number of contradictory findings with regard to whether the theory\ndescribing easy-plane quantum antiferromagnets undergoes a second-order phase\ntransition. The traditional Landau-Ginzburg-Wilson approach suggests a\nfirst-order phase transition, as there are two different competing order\nparameters. On the other hand, it is known that the theory has the property of\nself-duality which has been connected to the existence of a deconfined quantum\ncritical point. The latter regime suggests that order parameters are not the\nelementary building blocks of the theory, but rather consist of fractionalized\nparticles that are confined in both phases of the transition and only appear -\ndeconfine - at the critical point. Nevertheless, numerical Monte Carlo\nsimulations disagree with the claim of deconfined quantum criticality in the\nsystem, indicating instead a first-order phase transition. Here these\ncontradictions are resolved by demonstrating via a duality transformation that\na new critical regime exists analogous to the zero temperature limit of a\ncertain classical statistical mechanics system. Because of this analogy, we dub\nthis critical regime \"frozen\". A renormalization group analysis bolsters this\nclaim, allowing us to go beyond it and align previous numerical predictions of\nthe first-order phase transition with the deconfined criticality in a\nconsistent framework.\n"}
+{"abstract": "  In the upcoming process to overcome the limitations of the standard von\nNeumann architecture, synaptic electronics is gaining a primary role for the\ndevelopment of in-memory computing. In this field, Ge-based compounds have been\nproposed as switching materials for nonvolatile memory devices and for\nselectors. By employing the classical molecular dynamics, we study the\nstructural features of both the liquid states at 1500K and the amorphous phase\nat 300K of Ge-rich and Se-rich chalcogenides binary GexSe1-x systems in the\nrange x 0.4-0.6. The simulations rely on a model of interatomic potentials\nwhere ions interact through steric repulsion, as well as Coulomb and\ncharge-dipole interactions given by the large electronic polarizability of Se\nions. Our results indicate the formation of temperature-dependent hierarchical\nstructures with short-range local orders and medium-range structures, which\nvary with the Ge content. Our work demonstrates that nanosecond-long\nsimulations, not accessible via ab initio techniques, are required to obtain a\nrealistic amorphous phase from the melt. Our classical molecular dynamics\nsimulations are able to describe the profound structural differences between\nthe melt and the glassy structures of GeSe chalcogenides. These results open to\nthe understanding of the interplay between chemical composition, atomic\nstructure, and electrical properties in switching materials.\n"}
+{"abstract": "  Medication recommendation is an essential task of AI for healthcare. Existing\nworks focused on recommending drug combinations for patients with complex\nhealth conditions solely based on their electronic health records. Thus, they\nhave the following limitations: (1) some important data such as drug molecule\nstructures have not been utilized in the recommendation process. (2) drug-drug\ninteractions (DDI) are modeled implicitly, which can lead to sub-optimal\nresults. To address these limitations, we propose a DDI-controllable drug\nrecommendation model named SafeDrug to leverage drugs' molecule structures and\nmodel DDIs explicitly. SafeDrug is equipped with a global message passing\nneural network (MPNN) module and a local bipartite learning module to fully\nencode the connectivity and functionality of drug molecules. SafeDrug also has\na controllable loss function to control DDI levels in the recommended drug\ncombinations effectively. On a benchmark dataset, our SafeDrug is relatively\nshown to reduce DDI by 19.43% and improves 2.88% on Jaccard similarity between\nrecommended and actually prescribed drug combinations over previous approaches.\nMoreover, SafeDrug also requires much fewer parameters than previous deep\nlearning-based approaches, leading to faster training by about 14% and around\n2x speed-up in inference.\n"}
+{"abstract": "  The analysis of animal movement has gained attention recently, and new\ncontinuous-time models and statistical methods have been developed. All of them\nare based on the assumption that this movement can be recorded over a long\nperiod of time, which is sometimes infeasible, for instance when the battery\nlife of the GPS is short. We prove that the estimation of its home range\nimproves if periods when the GPS is on are alternated with periods when the GPS\nis turned off. This is illustrated through a simulation study, and real life\ndata. We also provide estimators of the stationary distribution, level sets\n(which provides estimators of the core area) and the drift function.\n"}
+{"abstract": "  Bug detection and prevention is one of the most important goals of software\nquality assurance. Nowadays, many of the major problems faced by developers can\nbe detected or even fixed fully or partially with automatic tools. However,\nrecent works explored that there exists a substantial amount of simple yet very\nannoying errors in code-bases, which are easy to fix, but hard to detect as\nthey do not hinder the functionality of the given product in a major way.\nProgrammers introduce such errors accidentally, mostly due to inattention.\nUsing the ManySStuBs4J dataset, which contains many simple, stupid bugs, found\nin GitHub repositories written in the Java programming language, we\ninvestigated the history of such bugs. We were interested in properties such\nas: How long do such bugs stay unnoticed in code-bases? Whether they are\ntypically fixed by the same developer who introduced them? Are they introduced\nwith the addition of new code or caused more by careless modification of\nexisting code? We found that most of such stupid bugs lurk in the code for a\nlong time before they get removed. We noticed that the developer who made the\nmistake seems to find a solution faster, however less then half of SStuBs are\nfixed by the same person. We also examined PMD's performance when to came to\nflagging lines containing SStuBs, and found that similarly to SpotBugs, it is\ninsufficient when it comes to finding these types of errors. Examining the\nlife-cycle of such bugs allows us to better understand their nature and adjust\nour development processes and quality assurance methods to better support\navoiding them.\n"}
+{"abstract": "  Semantic image synthesis, translating semantic layouts to photo-realistic\nimages, is a one-to-many mapping problem. Though impressive progress has been\nrecently made, diverse semantic synthesis that can efficiently produce\nsemantic-level multimodal results, still remains a challenge. In this paper, we\npropose a novel diverse semantic image synthesis framework from the perspective\nof semantic class distributions, which naturally supports diverse generation at\nsemantic or even instance level. We achieve this by modeling class-level\nconditional modulation parameters as continuous probability distributions\ninstead of discrete values, and sampling per-instance modulation parameters\nthrough instance-adaptive stochastic sampling that is consistent across the\nnetwork. Moreover, we propose prior noise remapping, through linear\nperturbation parameters encoded from paired references, to facilitate\nsupervised training and exemplar-based instance style control at test time.\nExtensive experiments on multiple datasets show that our method can achieve\nsuperior diversity and comparable quality compared to state-of-the-art methods.\nCode will be available at \\url{https://github.com/tzt101/INADE.git}\n"}
+{"abstract": "  Image compression using colour densities is historically impractical to\ndecompress losslessly. We examine the use of conditional generative adversarial\nnetworks in making this transformation more feasible, through learning a\nmapping between the images and a loss function to train on. We show that this\nmethod is effective at producing visually lossless generations, indicating that\nefficient colour compression is viable.\n"}
+{"abstract": "  Discovering new materials with ultrahigh thermal conductivity has been a\ncritical research frontier and driven by many important technological\napplications ranging from thermal management to energy science. Here we have\nrigorously investigated the fundamental lattice vibrational spectra in ternary\ncompounds and determined the thermal conductivity using a predictive ab initio\napproach. Phonon transport in B-X-C (X = N, P, As) groups is systematically\nquantified with different crystal structures and high-order anharmonicity\ninvolving a four-phonon process. Our calculation found an ultrahigh\nroom-temperature thermal conductivity through strong carbon-carbon bonding up\nto 2100 W/mK beyond most common materials and the recently discovered boron\narsenide. This study provides fundamental insight into the atomistic design of\nthermal conductivity and opens up opportunities in new materials searching\ntowards complicated compound structures.\n  DOI: 10.1103/PhysRevB.103.L041203\n"}
+{"abstract": "  Path planning has long been one of the major research areas in robotics, with\nPRM and RRT being two of the most effective classes of path planners. Though\ngenerally very efficient, these sampling-based planners can become\ncomputationally expensive in the important case of \"narrow passages\". This\npaper develops a path planning paradigm specifically formulated for narrow\npassage problems. The core is based on planning for rigid-body robots\nencapsulated by unions of ellipsoids. The environmental features are enclosed\ngeometrically using convex differentiable surfaces (e.g., superquadrics). The\nmain benefit of doing this is that configuration-space obstacles can be\nparameterized explicitly in closed form, thereby allowing prior knowledge to be\nused to avoid sampling infeasible configurations. Then, by characterizing a\ntight volume bound for multiple ellipsoids, robot transitions involving\nrotations are guaranteed to be collision-free without traditional collision\ndetection. Furthermore, combining the stochastic sampling strategy, the\nproposed planning framework can be extended to solving higher dimensional\nproblems in which the robot has a moving base and articulated appendages.\nBenchmark results show that, remarkably, the proposed framework outperforms the\npopular sampling-based planners in terms of computational time and success rate\nin finding a path through narrow corridors and in higher dimensional\nconfiguration spaces.\n"}
+{"abstract": "  In the class of strictly convex smooth boundaries, each of which not having\nstrip around its boundary foliated by invariant curves, we prove that the\nTaylor coefficients of the \"normalized\" Mather's $\\beta$-function are\ninvariants under $C^\\infty$-conjugacies. In contrast, we prove that any two\nelliptic billiard maps are $C^0$-conjugated near their respective boundaries,\nand $C^\\infty$-conjugated in the open cylinder, near the boundary and away from\na plain passing through the center of the underlying ellipse. We also prove\nthat if the billiard maps corresponding to two ellipses are topologically\nconjugated then the two ellipses are similar.\n"}
+{"abstract": "  According to the Probability Ranking Principle (PRP), ranking documents in\ndecreasing order of their probability of relevance leads to an optimal document\nranking for ad-hoc retrieval. The PRP holds when two conditions are met: [C1]\nthe models are well calibrated, and, [C2] the probabilities of relevance are\nreported with certainty. We know however that deep neural networks (DNNs) are\noften not well calibrated and have several sources of uncertainty, and thus\n[C1] and [C2] might not be satisfied by neural rankers. Given the success of\nneural Learning to Rank (L2R) approaches-and here, especially BERT-based\napproaches-we first analyze under which circumstances deterministic, i.e.\noutputs point estimates, neural rankers are calibrated. Then, motivated by our\nfindings we use two techniques to model the uncertainty of neural rankers\nleading to the proposed stochastic rankers, which output a predictive\ndistribution of relevance as opposed to point estimates. Our experimental\nresults on the ad-hoc retrieval task of conversation response ranking reveal\nthat (i) BERT-based rankers are not robustly calibrated and that stochastic\nBERT-based rankers yield better calibration; and (ii) uncertainty estimation is\nbeneficial for both risk-aware neural ranking, i.e.taking into account the\nuncertainty when ranking documents, and for predicting unanswerable\nconversational contexts.\n"}
+{"abstract": "  In this paper, we propose a new class of operator factorization methods to\ndiscretize the integral fractional Laplacian $(-\\Delta)^\\frac{\\alpha}{2}$ for\n$\\alpha \\in (0, 2)$. The main advantage of our method is to easily increase\nnumerical accuracy by using high-degree Lagrange basis functions, but remain\nthe scheme structure and computer implementation unchanged. Moreover, our\ndiscretization of the fractional Laplacian results in a symmetric (multilevel)\nToeplitz differentiation matrix, which not only saves memory cost in\nsimulations but enables efficient computations via the fast Fourier transforms.\nThe performance of our method in both approximating the fractional Laplacian\nand solving the fractional Poisson problems was detailedly examined. It shows\nthat our method has an optimal accuracy of ${\\mathcal O}(h^2)$ for constant or\nlinear basis functions, while ${\\mathcal O}(h^4)$ if quadratic basis functions\nare used, with $h$ a small mesh size. Note that this accuracy holds for any\n$\\alpha \\in (0, 2)$ and can be further increased if higher-degree basis\nfunctions are used. If the solution of fractional Poisson problem satisfies $u\n\\in C^{m, l}(\\bar{\\Omega})$ for $m \\in {\\mathbb N}$ and $0 < l < 1$, then our\nmethod has an accuracy of ${\\mathcal O}\\big(h^{\\min\\{m+l,\\, 2\\}}\\big)$ for\nconstant and linear basis functions, while ${\\mathcal O}\\big(h^{\\min\\{m+l,\\,\n4\\}}\\big)$ for quadratic basis functions. Additionally, our method can be\nreadily applied to study generalized fractional Laplacians with a symmetric\nkernel function, and numerical study on the tempered fractional Poisson problem\ndemonstrates its efficiency.\n"}
+{"abstract": "  The eccentricity of a planet's orbit and the inclination of its orbital plane\nencode important information about its formation and history. However,\nexoplanets detected via direct-imaging are often only observed over a very\nsmall fraction of their period, making it challenging to perform reliable\nphysical inferences given wide, unconstrained posteriors. The aim of this\nproject is to investigate biases (deviation of the median and mode of the\nposterior from the true values of orbital parameters, and the width and\ncoverage of their credible intervals) in the estimation of orbital parameters\nof directly-imaged exoplanets, particularly their eccentricities, and to define\ngeneral guidelines to perform better estimations of uncertainty. For this, we\nconstructed various orbits and generated mock data for each spanning $\\sim 0.5\n\\%$ of the orbital period. We used the Orbits For The Impatient (OFTI)\nalgorithm to compute orbit posteriors, and compared those to the true values of\nthe orbital parameters. We found that the inclination of the orbital plane is\nthe parameter that most affects our estimations of eccentricity, with orbits\nthat appear near edge-on producing eccentricity distributions skewed away from\nthe true values, and often bi-modal. We also identified a degeneracy between\neccentricity and inclination that makes it difficult to distinguish posteriors\nof face-on, eccentric orbits and edge-on, circular orbits. For the\nexoplanet-imaging community, we propose practical recommendations, guidelines\nand warnings relevant to orbit-fitting.\n"}
+{"abstract": "  Time-resolved XUV-IR photoion mass spectroscopy of naphthalene conducted with\nbroadband, as well as with wavelength-selected narrowband XUV pulses reveals a\nrising probability of fragmentation characterized by a lifetime of $92\\pm4$~fs.\nThis lifetime is independent of the XUV excitation wavelength and is the same\nfor all low appearance energy fragments recorded in the experiment. Analysis of\nthe experimental data in conjunction with a statistical multi-state vibronic\nmodel suggests that the experimental signals track vibrational energy\nredistribution on the potential energy surface of the ground state cation. In\nparticular, populations of the out-of-plane ring twist and the out-of-plane\nwave bending modes could be responsible for opening new IR absorption channels\nleading to enhanced fragmentation.\n"}
+{"abstract": "  D$_2$ molecules, excited by linearly cross-polarized femtosecond extreme\nultraviolet (XUV) and near-infrared (NIR) light pulses, reveal highly\nstructured D$^+$ ion fragment momenta and angular distributions that originate\nfrom two different 4-step dissociative ionization pathways after four photon\nabsorption (1 XUV + 3 NIR). We show that, even for very low dissociation\nkinetic energy release $\\le$~240~meV, specific electronic excitation pathways\ncan be identified and isolated in the final ion momentum distributions. With\nthe aid of {\\it ab initio} electronic structure and time-dependent\nSchr\\\"odinger equation calculations, angular momentum, energy, and parity\nconservation are used to identify the excited neutral molecular states and\nmolecular orientations relative to the polarization vectors in these different\nphotoexcitation and dissociation sequences of the neutral D$_2$ molecule and\nits D$_2^+$ cation. In one sequential photodissociation pathway, molecules\naligned along either of the two light polarization vectors are excluded, while\nanother pathway selects molecules aligned parallel to the light propagation\ndirection. The evolution of the nuclear wave packet on the intermediate \\Bstate\nelectronic state of the neutral D$_2$ molecule is also probed in real time.\n"}
+{"abstract": "  We prove that for every $N\\ge 3$, the group $\\mathrm{Out}(F_N)$ of outer\nautomorphisms of a free group of rank $N$ is superrigid from the point of view\nof measure equivalence: any countable group that is measure equivalent to\n$\\mathrm{Out}(F_N)$, is in fact virtually isomorphic to $\\mathrm{Out}(F_N)$.\n  We introduce three new constructions of canonical splittings associated to a\nsubgroup of $\\mathrm{Out}(F_N)$ of independent interest. They encode\nrespectively the collection of invariant free splittings, invariant cyclic\nsplittings, and maximal invariant free factor systems. Our proof also relies on\nthe following improvement of an amenability result by Bestvina and the authors:\ngiven a free factor system $\\mathcal{F}$ of $F_N$, the action of\n$\\mathrm{Out}(F_N,\\mathcal{F})$ (the subgroup of $\\mathrm{Out}(F_N)$ that\npreserves $\\mathcal{F}$) on the space of relatively arational trees with\namenable stabilizer is a Borel amenable action.\n"}
+{"abstract": "  Let $T$ denote a positive operator with spectral radius $1$ on, say, an\n$L^p$-space. A classical result in infinite dimensional Perron--Frobenius\ntheory says that, if $T$ is irreducible and power bounded, then its peripheral\npoint spectrum is either empty or a subgroup of the unit circle.\n  In this note we show that the analogous assertion for the entire peripheral\nspectrum fails. More precisely, for every finite union $U$ of finite subgroups\nof the unit circle we construct an irreducible stochastic operator on $\\ell^1$\nwhose peripheral spectrum equals $U$.\n  We also give a similar construction for the $C_0$-semigroup case.\n"}
+{"abstract": "  The sensitivity to blockages is a key challenge for the high-frequency (5G\nmillimeter wave and 6G sub-terahertz) wireless networks. Since these networks\nmainly rely on line-of-sight (LOS) links, sudden link blockages highly threaten\nthe reliability of the networks. Further, when the LOS link is blocked, the\nnetwork typically needs to hand off the user to another LOS basestation, which\nmay incur critical time latency, especially if a search over a large codebook\nof narrow beams is needed. A promising way to tackle the reliability and\nlatency challenges lies in enabling proaction in wireless networks. Proaction\nbasically allows the network to anticipate blockages, especially dynamic\nblockages, and initiate user hand-off beforehand. This paper presents a\ncomplete machine learning framework for enabling proaction in wireless networks\nrelying on visual data captured, for example, by RGB cameras deployed at the\nbase stations. In particular, the paper proposes a vision-aided wireless\ncommunication solution that utilizes bimodal machine learning to perform\nproactive blockage prediction and user hand-off. The bedrock of this solution\nis a deep learning algorithm that learns from visual and wireless data how to\npredict incoming blockages. The predictions of this algorithm are used by the\nwireless network to proactively initiate hand-off decisions and avoid any\nunnecessary latency. The algorithm is developed on a vision-wireless dataset\ngenerated using the ViWi data-generation framework. Experimental results on two\nbasestations with different cameras indicate that the algorithm is capable of\naccurately detecting incoming blockages more than $\\sim 90\\%$ of the time. Such\nblockage prediction ability is directly reflected in the accuracy of proactive\nhand-off, which also approaches $87\\%$. This highlights a promising direction\nfor enabling high reliability and low latency in future wireless networks.\n"}
+{"abstract": "  For a smooth surface $S$, Porta-Sala defined a categorical Hall algebra\ngeneralizing previous work in K-theory of Zhao and Kapranov-Vasserot. We\nconstruct semi-orthogonal decompositions for categorical Hall algebras of\npoints on $S$. We refine these decompositions in K-theory for a topological\nK-theoretic Hall algebra.\n"}
+{"abstract": "  Despite constant improvements in efficiency, today's data centers and\nnetworks consume enormous amounts of energy and this demand is expected to rise\neven further. An important research question is whether and how fog computing\ncan curb this trend. As real-life deployments of fog infrastructure are still\nrare, a significant part of research relies on simulations. However, existing\npower models usually only target particular components such as compute nodes or\nbattery-constrained edge devices.\n  Combining analytical and discrete-event modeling, we develop a holistic but\ngranular energy consumption model that can determine the power usage of compute\nnodes as well as network traffic and applications over time. Simulations can\nincorporate thousands of devices that execute complex application graphs on a\ndistributed, heterogeneous, and resource-constrained infrastructure. We\nevaluated our publicly available prototype LEAF within a smart city traffic\nscenario, demonstrating that it enables research on energy-conserving fog\ncomputing architectures and can be used to assess dynamic task placement\nstrategies and other energy-saving mechanisms.\n"}
+{"abstract": "  This monograph introduces key concepts and problems in the new research area\nof Periodic Geometry and Topology for materials applications.Periodic\nstructures such as solid crystalline materials or textiles were previously\nclassified in discrete and coarse ways that depend on manual choices or are\nunstable under perturbations. Since crystal structures are determined in a\nrigid form, their finest natural equivalence is defined by rigid motion or\nisometry, which preserves inter-point distances. Due to atomic vibrations,\nisometry classes of periodic point sets form a continuous space whose geometry\nand topology were unknown. The key new problem in Periodic Geometry is to\nunambiguously parameterize this space of isometry classes by continuous\ncoordinates that allow a complete reconstruction of any crystal. The major part\nof this manuscript reviews the recently developed isometry invariants to\nresolve the above problem: (1) density functions computed from higher order\nVoronoi zones, (2) distance-based invariants that allow ultra-fast\nvisualizations of huge crystal datasets, and (3) the complete invariant isoset\n(a DNA-type code) with a first continuous metric on all periodic crystals. The\nmain goal of Periodic Topology is to classify textiles up to periodic isotopy,\nwhich is a continuous deformation of a thickened plane without a fixed lattice\nbasis. This practical problem substantially differs from past research focused\non links in a fixed thickened torus.\n"}
+{"abstract": "  Aims. The purpose of this paper is to describe a new post-processing\nalgorithm dedicated to the reconstruction of the spatial distribution of light\nreceived from off-axis sources, in particular from circumstellar disks.\n  Methods. Built on the recent PACO algorithm dedicated to the detection of\npoint-like sources, the proposed method is based on the local learning of patch\ncovariances capturing the spatial fluctuations of the stellar leakages. From\nthis statistical modeling, we develop a regularized image reconstruction\nalgorithm (REXPACO) following an inverse problem approach based on a forward\nimage formation model of the off-axis sources in the ADI sequences.\n  Results. Injections of fake circumstellar disks in ADI sequences from the\nVLT/SPHERE-IRDIS instrument show that both the morphology and the photometry of\nthe disks are better preserved by REXPACO compared to standard postprocessing\nmethods like cADI. In particular, the modeling of the spatial covariances\nproves usefull in reducing typical ADI artifacts and in better disentangling\nthe signal of these sources from the residual stellar contamination. The\napplication to stars hosting circumstellar disks with various morphologies\nconfirms the ability of REXPACO to produce images of the light distribution\nwith reduced artifacts. Finally, we show how REXPACO can be combined with PACO\nto disentangle the signal of circumstellar disks from the signal of candidate\npoint-like sources.\n  Conclusions. REXPACO is a novel post-processing algorithm producing\nnumerically deblurred images of the circumstellar environment. It exploits the\nspatial covariances of the stellar leakages and of the noise to efficiently\neliminate this nuisance term.\n"}
+{"abstract": "  Anomalous electric currents along a magnetic field, first predicted to emerge\nduring large heavy ion collision experiments, were also observed a few years\nago in condensed matter environments, exploring the fact that charge carriers\nin Dirac/Weyl semi-metals exhibit a relativistic-like behavior. The mechanism\nthrough which such currents are generated relies on an imbalance in the\nchirality of systems immersed in a magnetic background, leading to the\nso-called chiral magnetic effect (CME). While chiral magnetic currents have\nbeen observed in materials in three space dimensions, in this work we propose\nthat an analog of the chiral magnetic effect can be constructed in two space\ndimensions, corresponding to a novel type of intrinsic half-integer Quantum\nHall effect, thereby also offering a topological protection mechanism for the\ncurrent. While the 3D chiral anomaly underpins the CME, its 2D cousin is\nemerging from the 2D parity anomaly, we thence call it the parity magnetic\neffect (PME). It can occur in disturbed honeycomb lattices where both spin\ndegeneracy and time reversal symmetry are broken. These configurations harbor\ntwo distinct gap-opening mechanisms that, when occurring simultaneously, drive\nslightly different gaps in each valley, establishing an analog of the necessary\nchiral imbalance. Some examples of promising material setups that fulfill the\nprerequisites of our proposal are also listed.\n"}
+{"abstract": "  This paper presents a thoroughgoing interpretation of a weak relevant logic\nbuilt over the Dunn-Belnap four-valued semantics in terms of the communication\nof information in a network of sites of knowledge production (laboratories).\nThe knowledge communicated concerns experimental data and the regularities\ntested using it. There have been many nods to interpretations similar to ours -\nfor example, in Dunn (1976), Belnap (1977). The laboratory interpretation was\noutlined in Bilkova et al. (2010).\n  Our system is built on the Routley--Meyer semantics for relevant logic\nequipped with a four-valued valuation of formulas, where labs stand in for\nsituations, and the four values reflect the complexity of assessing results of\nexperiments. This semantics avoids using the Routley star, on the cost of\nintroducing a further relation, required in evaluating falsity assignments of\nimplication. We can however provide a natural interpretation of two\naccessibility relations - confirmation and refutation of hypotheses are two\nindependent processes in our laboratory setup. This setup motivates various\nbasic properties of the accessibility relations, as well as a number of other\npossible restrictions. This gives us a flexible modular system which can be\nadjusted to specific epistemic contexts.\n  As perfect regularities are rarely, or perhaps never, actually observed, we\nadd probabilities to the logical framework. As our logical framework is\nnon-classical, the probability is non-classical as well, satisfying a weaker\nversion of Kolmogorov axioms (cf. Priest 2006). We show that these\nprobabilities allow for a relative frequency as well as for a subjective\ninterpretation (we provide a Dutch book argument). We further show how to\nupdate the probabilities and to distinguish conditional probabilities from the\nprobability of conditionals.\n"}
+{"abstract": "  In 1981 Wyman classified the solutions of the Einstein--Klein--Gordon\nequations with static spherically symmetric spacetime metric and vanishing\nscalar potential. For one of these classes, the scalar field linearly grows\nwith time. We generalize this symmetry noninheriting solution, perturbatively,\nto a rotating one and extend the static solution exactly to arbitrary spacetime\ndimensions. Furthermore, we investigate the existence of nonminimally coupled,\ntime-dependent real scalar fields on top of static black holes, and prove a\nno-hair theorem for stealth scalar fields on the Schwarzschild background.\n"}
+{"abstract": "  In his work on representations of Thompson's group $F$, Vaughan Jones defined\nand studied the $3$-colorable subgroup $\\mathcal{F}$ of $F$. Later, Ren showed\nthat it is isomorphic with the Brown-Thompson group $F_4$. In this paper we\ncontinue with the study of the $3$-colorable subgroup and prove that the\nquasi-regular representation of $F$ associated with the $3$-colorable subgroup\nis irreducible. We show moreover that the preimage of $\\mathcal{F}$ under a\ncertain injective endomorphism of $F$ is contained in three (explicit) maximal\nsubgroups of $F$ of infinite index. These subgroups are different from the\npreviously known infinite index maximal subgroups of $F$, namely the parabolic\nsubgroups that fix a point in $(0,1)$, (up to isomorphism) the Jones' oriented\nsubgroup $\\vec{F}$, and the explicit examples found by Golan.\n"}
+{"abstract": "  Hybrid entangled states prove to be necessary for quantum information\nprocessing within heterogeneous quantum networks. A method with irreducible\nnumber of consumed resources that firmly provides hybrid CV-DV entanglement for\nany input conditions of the experimental setup is proposed. Namely, a family of\nCV states is introduced. Each of such CV states is first superimposed on a\nbeam-splitter with a delocalized photon and then detected by a photo-detector\nbehind the beam-splitter. Detection of any photon number heralds generation of\na hybrid CV-DV entangled state in the outputs, independent of\ntransmission/reflection coefficients of the beam-splitter and size of the input\nCV state. Nonclassical properties of the generated state are studied and their\nentanglement degree in terms of negativity is calculated. There are wide\ndomains of values of input parameters of the experimental setup that can be\nchosen to make the generated state maximally entangled. The proposed method is\nalso applicable to truncated versions of the input CV states. We also propose a\nsimple method to produce even/odd CV states.\n"}
+{"abstract": "  We present optical follow-up observations for candidate clusters in the\nClusters Hiding in Plain Sight (CHiPS) survey, which is designed to find new\ngalaxy clusters with extreme central galaxies that were misidentified as bright\nisolated sources in the ROSAT All-Sky Survey catalog. We identify 11 cluster\ncandidates around X-ray, radio, and mid-IR bright sources, including six\nwell-known clusters, two false associations of foreground and background\nclusters, and three new candidates which are observed further with Chandra. Of\nthe three new candidates, we confirm two newly discovered galaxy clusters:\nCHIPS1356-3421 and CHIPS1911+4455. Both clusters are luminous enough to be\ndetected in the ROSAT All Sky-Survey data if not because of their bright\ncentral cores. CHIPS1911+4455 is similar in many ways to the Phoenix cluster,\nbut with a highly-disturbed X-ray morphology on large scales. We find the\noccurrence rate for clusters that would appear to be X-ray bright point sources\nin the ROSAT All-Sky Survey (and any surveys with similar angular resolution)\nto be 2+/-1%, and the occurrence rate of clusters with runaway cooling in their\ncores to be <1%, consistent with predictions of Chaotic Cold Accretion. With\nthe number of new groups and clusters predicted to be found with eROSITA, the\npopulation of clusters that appear to be point sources (due to a central QSO or\na dense cool core) could be around 2000. Finally, this survey demonstrates that\nthe Phoenix cluster is likely the strongest cool core at z<0.7 -- anything more\nextreme would have been found in this survey.\n"}
+{"abstract": "  Pedestrian attribute recognition in surveillance scenarios is still a\nchallenging task due to the inaccurate localization of specific attributes. In\nthis paper, we propose a novel view-attribute localization method based on\nattention (VALA), which utilizes view information to guide the recognition\nprocess to focus on specific attributes and attention mechanism to localize\nspecific attribute-corresponding areas. Concretely, view information is\nleveraged by the view prediction branch to generate four view weights that\nrepresent the confidences for attributes from different views. View weights are\nthen delivered back to compose specific view-attributes, which will participate\nand supervise deep feature extraction. In order to explore the spatial location\nof a view-attribute, regional attention is introduced to aggregate spatial\ninformation and encode inter-channel dependencies of the view feature.\nSubsequently, a fine attentive attribute-specific region is localized, and\nregional weights for the view-attribute from different spatial locations are\ngained by the regional attention. The final view-attribute recognition outcome\nis obtained by combining the view weights with the regional weights.\nExperiments on three wide datasets (RAP, RAPv2, and PA-100K) demonstrate the\neffectiveness of our approach compared with state-of-the-art methods.\n"}
+{"abstract": "  Delaunay triangulation is a well-known geometric combinatorial optimization\nproblem with various applications. Many algorithms can generate Delaunay\ntriangulation given an input point set, but most are nontrivial algorithms\nrequiring an understanding of geometry or the performance of additional\ngeometric operations, such as the edge flip. Deep learning has been used to\nsolve various combinatorial optimization problems; however, generating Delaunay\ntriangulation based on deep learning remains a difficult problem, and very few\nresearch has been conducted due to its complexity. In this paper, we propose a\nnovel deep-learning-based approach for learning Delaunay triangulation using a\nnew attention mechanism based on self-attention and domain knowledge. The\nproposed model is designed such that the model efficiently learns\npoint-to-point relationships using self-attention in the encoder. In the\ndecoder, a new attention score function using domain knowledge is proposed to\nprovide a high penalty when the geometric requirement is not satisfied. The\nstrength of the proposed attention score function lies in its ability to extend\nits application to solving other combinatorial optimization problems involving\ngeometry. When the proposed neural net model is well trained, it is simple and\nefficient because it automatically predicts the Delaunay triangulation for an\ninput point set without requiring any additional geometric operations. We\nconduct experiments to demonstrate the effectiveness of the proposed model and\nconclude that it exhibits better performance compared with other\ndeep-learning-based approaches.\n"}
+{"abstract": "  This paper gives necessary and sufficient conditions for the Tanner graph of\na quasi-cyclic (QC) low-density parity-check (LDPC) code based on the all-one\nprotograph to have girth 6, 8, 10, and 12, respectively, in the case of\nparity-check matrices with column weight 4. These results are a natural\nextension of the girth results of the already-studied cases of column weight 2\nand 3, and it is based on the connection between the girth of a Tanner graph\ngiven by a parity-check matrix and the properties of powers of the product\nbetween the matrix and its transpose. The girth conditions can be easily\nincorporated into fast algorithms that construct codes of desired girth between\n6 and 12; our own algorithms are presented for each girth, together with\nconstructions obtained from them and corresponding computer simulations. More\nimportantly, this paper emphasizes how the girth conditions of the Tanner graph\ncorresponding to a parity-check matrix composed of circulants relate to the\nmatrix obtained by adding (over the integers) the circulant columns of the\nparity-check matrix. In particular, we show that imposing girth conditions on a\nparity-check matrix is equivalent to imposing conditions on a square circulant\nsubmatrix of size 4 obtained from it.\n"}
+{"abstract": "  The online technical Q&A site Stack Overflow (SO) is popular among developers\nto support their coding and diverse development needs. To address shortcomings\nin API official documentation resources, several research has thus focused on\naugmenting official API documentation with insights (e.g., code examples) from\nSO. The techniques propose to add code examples/insights about APIs into its\nofficial documentation. Reviews are opinionated sentences with\npositive/negative sentiments. However, we are aware of no previous research\nthat attempts to automatically produce API documentation from SO by considering\nboth API code examples and reviews. In this paper, we present two novel\nalgorithms that can be used to automatically produce API documentation from SO\nby combining code examples and reviews towards those examples. The first\nalgorithm is called statistical documentation, which shows the distribution of\npositivity and negativity around the code examples of an API using different\nmetrics (e.g., star ratings). The second algorithm is called concept-based\ndocumentation, which clusters similar and conceptually relevant usage\nscenarios. An API usage scenario contains a code example, a textual description\nof the underlying task addressed by the code example, and the reviews (i.e.,\nopinions with positive and negative sentiments) from other developers towards\nthe code example. We deployed the algorithms in Opiner, a web-based platform to\naggregate information about APIs from online forums. We evaluated the\nalgorithms by mining all Java JSON-based posts in SO and by conducting three\nuser studies based on produced documentation from the posts.\n"}
+{"abstract": "  Constraint programming (CP) is a paradigm used to model and solve constraint\nsatisfaction and combinatorial optimization problems. In CP, problems are\nmodeled with constraints that describe acceptable solutions and solved with\nbacktracking tree search augmented with logical inference. In this paper, we\nshow how quantum algorithms can accelerate CP, at both the levels of inference\nand search. Leveraging existing quantum algorithms, we introduce a\nquantum-accelerated filtering algorithm for the $\\texttt{alldifferent}$ global\nconstraint and discuss its applicability to a broader family of global\nconstraints with similar structure. We propose frameworks for the integration\nof quantum filtering algorithms within both classical and quantum backtracking\nsearch schemes, including a novel hybrid classical-quantum backtracking search\nmethod. This work suggests that CP is a promising candidate application for\nearly fault-tolerant quantum computers and beyond.\n"}
+{"abstract": "  In this paper, we discuss interesting potential implications for the\nsupersymmetric (SUSY) universe in light of cosmological problems on (1) the\nnumber of the satellite galaxies of the Milky Way (missing satellite problem)\nand (2) a value of the matter density fluctuation at the scale around\n8$h^{-1}$Mpc ($S_{8}$ tension). The implications are extracted by assuming that\nthe gravitino of a particular mass can be of help to alleviate the cosmological\ntension. We consider two gravitino mass regimes vastly separated, that is,\n$m_{3/2}\\simeq100{\\rm eV}$ and $m_{3/2}\\simeq100{\\rm GeV}$. We discuss\nnon-trivial features of each supersymmetric universe associated with a specific\ngravitino mass by projecting potential resolutions of the cosmological problems\non each of associated SUSY models.\n"}
+{"abstract": "  Special point defects in semiconductors have been envisioned as suitable\ncomponents for quantum-information technology. The identification of new deep\ncenters in silicon that can be easily activated and controlled is a main target\nof the research in the field. Vacancy-related complexes are suitable to provide\ndeep electronic levels but they are hard to control spatially. With the spirit\nof investigating solid state devices with intentional vacancy-related defects\nat controlled position, here we report on the functionalization of silicon\nvacancies by implanting Ge atoms through single-ion implantation, producing\nGe-vacancy (GeV) complexes. We investigate the quantum transport through an\narray of GeV complexes in a silicon-based transistor. By exploiting a model\nbased on an extended Hubbard Hamiltonian derived from ab-initio results we find\nanomalous activation energy values of the thermally activated conductance of\nboth quasi-localized and delocalized many-body states, compared to conventional\ndopants. We identify such states, forming the upper Hubbard band, as\nresponsible of the experimental sub-threshold transport across the transistor.\nThe combination of our model with the single-ion implantation method enables\nfuture research for the engineering of GeV complexes towards the creation of\nspatially controllable individual defects in silicon for applications in\nquantum information technologies.\n"}
+{"abstract": "  From social interactions to the human brain, higher-order networks are key to\ndescribe the underlying network geometry and topology of many complex systems.\nWhile it is well known that network structure strongly affects its function,\nthe role that network topology and geometry has on the emerging dynamical\nproperties of higher-order networks is yet to be clarified. In this\nperspective, the spectral dimension plays a key role since it determines the\neffective dimension for diffusion processes on a network. Despite its\nrelevance, a theoretical understanding of which mechanisms lead to a finite\nspectral dimension, and how this can be controlled, represents nowadays still a\nchallenge and is the object of intense research. Here we introduce two\nnon-equilibrium models of hyperbolic higher-order networks and we characterize\ntheir network topology and geometry by investigating the interwined appearance\nof small-world behavior, $\\delta$-hyperbolicity and community structure. We\nshow that different topological moves determining the non-equilibrium growth of\nthe higher-order hyperbolic network models induce tunable values of the\nspectral dimension, showing a rich phenomenology which is not displayed in\nrandom graph ensembles. In particular, we observe that, if the topological\nmoves used to construct the higher-order network increase the area$/$volume\nratio, the spectral dimension continuously decreases, while the opposite effect\nis observed if the topological moves decrease the area$/$volume ratio. Our work\nreveals a new link between the geometry of a network and its diffusion\nproperties, contributing to a better understanding of the complex interplay\nbetween network structure and dynamics.\n"}
+{"abstract": "  Variable active galactic nuclei showing periodic light curves have been\nproposed as massive black hole binary (MBHB) candidates. In such scenarios the\nperiodicity can be due to relativistic Doppler-boosting of the emitted light.\nThis hypothesis can be tested through the timing of scattered polarized light.\nFollowing the results of polarization studies in type I nuclei and of dynamical\nstudies of MBHBs with circumbinary discs, we assume a coplanar equatorial\nscattering ring, whose elements contribute differently to the total polarized\nflux, due to different scattering angles, levels of Doppler boost, and\nline-of-sight time delays. We find that in the presence of a MBHB, both the\ndegree of polarization and the polarization angle have periodic modulations.\nThe minimum of the polarization degree approximately coincides with the peak of\nthe light curve, regardless of the scattering ring size. The polarization angle\noscillates around the semi-minor axis of the projected MBHB orbital ellipse,\nwith a frequency equal either to the binary's orbital frequency (for large\nscattering screen radii), or twice this value (for smaller scattering\nstructures). These distinctive features can be used to probe the nature of\nperiodic MBHB candidates and to compile catalogs of the most promising sub-pc\nMBHBs. The identification of such polarization features in gravitational-wave\ndetected MBHBs would enormously increase the amount of physical information\nabout the sources, allowing the measurement of the individual masses of the\nbinary components, and the orientation of the line of nodes on the sky, even\nfor monochromatic gravitational wave signals.\n"}
+{"abstract": "  This article is part of a comprehensive research project on liquidity risk in\nasset management, which can be divided into three dimensions. The first\ndimension covers liability liquidity risk (or funding liquidity) modeling, the\nsecond dimension focuses on asset liquidity risk (or market liquidity)\nmodeling, and the third dimension considers asset-liability liquidity risk\nmanagement (or asset-liability matching). The purpose of this research is to\npropose a methodological and practical framework in order to perform liquidity\nstress testing programs, which comply with regulatory guidelines (ESMA, 2019)\nand are useful for fund managers. The review of the academic literature and\nprofessional research studies shows that there is a lack of standardized and\nanalytical models. The aim of this research project is then to fill the gap\nwith the goal to develop mathematical and statistical approaches, and provide\nappropriate answers.\n  In this first part that focuses on liability liquidity risk modeling, we\npropose several statistical models for estimating redemption shocks. The\nhistorical approach must be complemented by an analytical approach based on\nzero-inflated models if we want to understand the true parameters that\ninfluence the redemption shocks. Moreover, we must also distinguish aggregate\npopulation models and individual-based models if we want to develop behavioral\napproaches. Once these different statistical models are calibrated, the second\nbig issue is the risk measure to assess normal and stressed redemption shocks.\nFinally, the last issue is to develop a factor model that can translate stress\nscenarios on market risk factors into stress scenarios on fund liabilities.\n"}
+{"abstract": "  Van der Waals heterostructures obtained by artificially stacking\ntwo-dimensional crystals represent the frontier of material engineering,\ndemonstrating properties superior to those of the starting materials. Fine\ncontrol of the interlayer twist angle has opened new possibilities for\ntailoring the optoelectronic properties of these heterostructures. Twisted\nbilayer graphene with a strong interlayer coupling is a prototype of twisted\nheterostructure inheriting the intriguing electronic properties of graphene.\nUnderstanding the effects of the twist angle on its out-of-equilibrium optical\nproperties is crucial for devising optoelectronic applications. With this aim,\nwe here combine excitation-resolved hot photoluminescence with femtosecond\ntransient absorption microscopy. The hot charge carrier distribution induced by\nphoto-excitation results in peaked absorption bleaching and photo-induced\nabsorption bands, both with pronounced twist angle dependence. Theoretical\nsimulations of the electronic band structure and of the joint density of states\nenable to assign these bands to the blocking of interband transitions at the\nvan Hove singularities and to photo-activated intersubband transitions. The\ntens of picoseconds relaxation dynamics of the observed bands is attributed to\nthe angle-dependence of electron and phonon heat capacities of twisted bilayer\ngraphene.\n"}
+{"abstract": "  The paper investigates a discrete time Binomial risk model with different\ntypes of polices and shock events may influence some of the claim sizes. It is\nshown that this model can be considered as a particular case of the classical\ncompound Binomial model. As far as we work with parallel Binomial counting\nprocesses in infinite time, if we consider them as independent, the probability\nof the event they to have at least once simultaneous jumps would be equal to\none. We overcome this problem by using thinning instead of convolution\noperation. The bivariate claim counting processes are expressed in two\ndifferent ways. The characteristics of the total claim amount processes are\nderived. The risk reserve process and the probabilities of ruin are discussed.\nThe deficit at ruin is thoroughly investigated when the initial capital is\nzero. Its mean, probability mass function and probability generating function\nare obtained. We show that although the probability generating function of the\nglobal maxima of the random walk is uniquely determined via its probability\nmass function and vice versa, any compound geometric distribution with\nnon-negative summands has uncountably many stochastically equivalent compound\ngeometric presentations. The probability to survive in much more general\nsettings, than those, discussed here, for example in the Anderson risk model,\nhas uncountably many Beekman's convolution series presentations.\n"}
+{"abstract": "  Anticipating the quantity of new associated or affirmed cases with novel\ncoronavirus ailment 2019 (COVID-19) is critical in the counteraction and\ncontrol of the COVID-19 flare-up. The new associated cases with COVID-19\ninformation were gathered from 20 January 2020 to 21 July 2020. We filtered out\nthe countries which are converging and used those for training the network. We\nutilized the SARIMAX, Linear regression model to anticipate new suspected\nCOVID-19 cases for the countries which did not converge yet. We predict the\ncurve of non-converged countries with the help of proposed Statistical SARIMAX\nmodel (SSM). We present new information investigation-based forecast results\nthat can assist governments with planning their future activities and help\nclinical administrations to be more ready for what's to come. Our framework can\nforesee peak corona cases with an R-Squared value of 0.986 utilizing linear\nregression and fall of this pandemic at various levels for countries like\nIndia, US, and Brazil. We found that considering more countries for training\ndegrades the prediction process as constraints vary from nation to nation.\nThus, we expect that the outcomes referenced in this work will help individuals\nto better understand the possibilities of this pandemic.\n"}
+{"abstract": "  We study how conserved quantities such as angular momentum and center of mass\nevolve with respect to the retarded time at null infinity, which is described\nin terms of a Bondi-Sachs coordinate system. These evolution formulae\ncomplement the classical Bondi mass loss formula for gravitational radiation.\nThey are further expressed in terms of the potentials of the shear and news\ntensors. The consequences that follow from these formulae are (1)\nSupertranslation invariance of the fluxes of the CWY conserved quantities. (2)\nA conservation law of angular momentum \\`a la Christodoulou. (3) A duality\nparadigm for null infinity. In particular, the supertranslation invariance\ndistinguishes the CWY angular momentum and center of mass from the classical\ndefinitions.\n"}
+{"abstract": "  The distributed denial of service (DDoS) attack is detrimental to businesses\nand individuals as people are heavily relying on the Internet. Due to\nremarkable profits, crackers favor DDoS as cybersecurity weapons to attack a\nvictim. Even worse, edge servers are more vulnerable. Current solutions lack\nadequate consideration to the expense of attackers and inter-defender\ncollaborations. Hence, we revisit the DDoS attack and defense, clarifying the\nadvantages and disadvantages of both parties. We further propose a joint\ndefense framework to defeat attackers by incurring a significant increment of\nrequired bots and enlarging attack expenses. The quantitative evaluation and\nexperimental assessment showcase that such expense can surge up to thousands of\ntimes. The skyrocket of expenses leads to heavy loss to the cracker, which\nprevents further attacks.\n"}
+{"abstract": "  Nondeterministic automata may be viewed as succinct programs implementing\ndeterministic automata, i.e. complete specifications. Converting a given\ndeterministic automaton into a small nondeterministic one is known to be\ncomputationally very hard; in fact, the ensuing decision problem is\nPSPACE-complete. This paper stands in stark contrast to the status quo. We\nrestrict attention to subatomic nondeterministic automata, whose individual\nstates accept unions of syntactic congruence classes. They are general enough\nto cover almost all structural results concerning nondeterministic\nstate-minimality. We prove that converting a monoid recognizing a regular\nlanguage into a small subatomic acceptor corresponds to an NP-complete problem.\nThe NP certificates are solutions of simple equations involving relations over\nthe syntactic monoid. We also consider the subclass of atomic nondeterministic\nautomata introduced by Brzozowski and Tamm. Given a deterministic automaton and\nanother one for the reversed language, computing small atomic acceptors is\nshown to be NP-complete with analogous certificates. Our complexity results\nemerge from an algebraic characterization of (sub)atomic acceptors in terms of\ndeterministic automata with semilattice structure, combined with an equivalence\nof categories leading to succinct representations.\n"}
+{"abstract": "  In this paper we consider high dimension models based on dependent\nobservations defined through autoregressive processes. For such models we\ndevelop an adaptive efficient estimation method via the robust sequential model\nselection procedures. To this end, firstly, using the Van Trees inequality, we\nobtain a sharp lower bound for robust risks in an explicit form given by the\nfamous Pinsker constant. It should be noted, that for such models this constant\nis calculated for the first time. Then, using the weighted least square method\nand sharp non asymptotic oracle inequalities we provide the efficiency property\nin the minimax sense for the proposed estimation procedure, i.e. we establish,\nthat the upper bound for its risk coincides with the obtained lower bound. It\nshould be emphasized that this property is obtained without using sparse\nconditions and in the adaptive setting when the parameter dimension and model\nregularity are unknown.\n"}
+{"abstract": "  Despite recent advances in natural language generation, it remains\nchallenging to control attributes of generated text. We propose DExperts:\nDecoding-time Experts, a decoding-time method for controlled text generation\nthat combines a pretrained language model with \"expert\" LMs and/or\n\"anti-expert\" LMs in a product of experts. Intuitively, under the ensemble,\ntokens only get high probability if they are considered likely by the experts,\nand unlikely by the anti-experts. We apply DExperts to language detoxification\nand sentiment-controlled generation, where we outperform existing controllable\ngeneration methods on both automatic and human evaluations. Moreover, because\nDExperts operates only on the output of the pretrained LM, it is effective with\n(anti-)experts of smaller size, including when operating on GPT-3. Our work\nhighlights the promise of tuning small LMs on text with (un)desirable\nattributes for efficient decoding-time steering.\n"}
+{"abstract": "  We revisit the problem of permuting an array of length $n$ according to a\ngiven permutation in place, that is, using only a small number of bits of extra\nstorage. Fich, Munro and Poblete [FOCS 1990, SICOMP 1995] obtained an elegant\n$\\mathcal{O}(n\\log n)$-time algorithm using only $\\mathcal{O}(\\log^{2}n)$ bits\nof extra space for this basic problem by designing a procedure that scans the\npermutation and outputs exactly one element from each of its cycles. However,\nin the strict sense in place should be understood as using only an\nasymptotically optimal $\\mathcal{O}(\\log n)$ bits of extra space, or storing a\nconstant number of indices. The problem of permuting in this version is, in\nfact, a well-known interview question, with the expected solution being a\nquadratic-time algorithm. Surprisingly, no faster algorithm seems to be known\nin the literature.\n  Our first contribution is a strictly in-place generalisation of the method of\nFich et al. that works in $\\mathcal{O}_{\\varepsilon}(n^{1+\\varepsilon})$ time,\nfor any $\\varepsilon > 0$. Then, we build on this generalisation to obtain a\nstrictly in-place algorithm for inverting a given permutation on $n$ elements\nworking in the same complexity. This is a significant improvement on a recent\nresult of Gu\\'spiel [arXiv 2019], who designed an $\\mathcal{O}(n^{1.5})$-time\nalgorithm.\n"}
+{"abstract": "  We address a blind source separation (BSS) problem in a noisy reverberant\nenvironment in which the number of microphones $M$ is greater than the number\nof sources of interest, and the other noise components can be approximated as\nstationary and Gaussian distributed. Conventional BSS algorithms for the\noptimization of a multi-input multi-output convolutional beamformer have\nsuffered from a huge computational cost when $M$ is large. We here propose a\ncomputationally efficient method that integrates a weighted prediction error\n(WPE) dereverberation method and a fast BSS method called independent vector\nextraction (IVE), which has been developed for less reverberant environments.\nWe show that, given the power spectrum for each source, the optimization\nproblem of the new method can be reduced to that of IVE by exploiting the\nstationary condition, which makes the optimization easy to handle and\ncomputationally efficient. An experiment of speech signal separation shows\nthat, compared to a conventional method that integrates WPE and independent\nvector analysis, our proposed method achieves much faster convergence while\nmaintaining its separation performance.\n"}
+{"abstract": "  Obtaining samples from the posterior distribution of inverse problems with\nexpensive forward operators is challenging especially when the unknowns involve\nthe strongly heterogeneous Earth. To meet these challenges, we propose a\npreconditioning scheme involving a conditional normalizing flow (NF) capable of\nsampling from a low-fidelity posterior distribution directly. This conditional\nNF is used to speed up the training of the high-fidelity objective involving\nminimization of the Kullback-Leibler divergence between the predicted and the\ndesired high-fidelity posterior density for indirect measurements at hand. To\nminimize costs associated with the forward operator, we initialize the\nhigh-fidelity NF with the weights of the pretrained low-fidelity NF, which is\ntrained beforehand on available model and data pairs. Our numerical\nexperiments, including a 2D toy and a seismic compressed sensing example,\ndemonstrate that thanks to the preconditioning considerable speed-ups are\nachievable compared to training NFs from scratch.\n"}
+{"abstract": "  Rock-salt lead selenide nanocrystals can be used as building blocks for large\nscale square superlattices via two-dimensional assembly of nanocrystals at a\nliquid-air interface followed by oriented attachment. Here we report\nmeasurements of the local density of states of an atomically coherent\nsuperlattice with square geometry made from PbSe nanocrystals. Controlled\nannealing of the sample permits the imaging of a clean structure and to\nreproducibly probe the band gap and the valence hole and conduction electron\nstates. The measured band gap and peak positions are compared to the results of\noptical spectroscopy and atomistic tight-binding calculations of the square\nsuperlattice band structure. In spite of the crystalline connections between\nnanocrystals that induce significant electronic couplings, the electronic\nstructure of the superlattices remains very strongly influenced by the effects\nof disorder and variability.\n"}
+{"abstract": "  We evaluated the generalization capability of deep neural networks (DNNs),\ntrained to classify chest X-rays as COVID-19, normal or pneumonia, using a\nrelatively small and mixed dataset. We proposed a DNN to perform lung\nsegmentation and classification, stacking a segmentation module (U-Net), an\noriginal intermediate module and a classification module (DenseNet201). To\nevaluate generalization, we tested the DNN with an external dataset (from\ndistinct localities) and used Bayesian inference to estimate probability\ndistributions of performance metrics. Our DNN achieved 0.917 AUC on the\nexternal test dataset, and a DenseNet without segmentation, 0.906. Bayesian\ninference indicated mean accuracy of 76.1% and [0.695, 0.826] 95% HDI (high\ndensity interval, which concentrates 95% of the metric's probability mass) with\nsegmentation and, without segmentation, 71.7% and [0.646, 0.786]. We proposed a\nnovel DNN evaluation technique, using Layer-wise Relevance Propagation (LRP)\nand Brixia scores. LRP heatmaps indicated that areas where radiologists found\nstrong COVID-19 symptoms and attributed high Brixia scores are the most\nimportant for the stacked DNN classification. External validation showed\nsmaller accuracies than internal, indicating difficulty in generalization,\nwhich segmentation improves. Performance in the external dataset and LRP\nanalysis suggest that DNNs can be trained in small and mixed datasets and\ndetect COVID-19.\n"}
+{"abstract": "  We show how phase-space simulations of Gaussian quantum states in a photonic\nnetwork permit verification of measurable correlations of Gaussian boson\nsampling (GBS) quantum computers. Our results agree with experiments for up to\n100-th order correlations, provided decoherence is included. We extend this to\nmore than 16,000 modes, and describe how to simulate genuine multipartite\nentanglement.\n"}
+{"abstract": "  A fundamental task in AI is to assess (in)dependence between mixed-type\nvariables (text, image, sound). We propose a Bayesian kernelised correlation\ntest of (in)dependence using a Dirichlet process model. The new measure of\n(in)dependence allows us to answer some fundamental questions: Based on data,\nare (mixed-type) variables independent? How likely is dependence/independence\nto hold? How high is the probability that two mixed-type variables are more\nthan just weakly dependent? We theoretically show the properties of the\napproach, as well as algorithms for fast computation with it. We empirically\ndemonstrate the effectiveness of the proposed method by analysing its\nperformance and by comparing it with other frequentist and Bayesian approaches\non a range of datasets and tasks with mixed-type variables.\n"}
+{"abstract": "  Superconductivity in a crystalline lattice without inversion is subject to\ncomplex spin-orbit-coupling effects, which can lead to mixed-parity pairing and\nan unusual magnetic response. In this study, the properties of a layered\nsuperconductor with alternating Rashba spin-orbit coupling in the stacking of\nlayers, hence (globally) possessing a center of inversion, is analyzed in an\napplied magnetic field, using a generalized Ginzburg-Landau model. The\nsuperconducting order parameter consists of an even- and an odd-parity pairing\ncomponent which exchange their roles as dominant pairing channel upon\nincreasing the magnetic field. This leads to an unusual kink feature in the\nupper critical field and a first-order phase transition within the mixed phase.\nWe investigate various signatures of this internal phase transition. The\nphysics we discuss here could explain the recently found $H$--$T$ phase diagram\nof the heavy Fermion superconductor CeRh$_2$As$_2$.\n"}
+{"abstract": "  Recent studies strive to incorporate various human rationales into neural\nnetworks to improve model performance, but few pay attention to the quality of\nthe rationales. Most existing methods distribute their models' focus to\ndistantly-labeled rationale words entirely and equally, while ignoring the\npotential important non-rationale words and not distinguishing the importance\nof different rationale words. In this paper, we propose two novel auxiliary\nloss functions to make better use of distantly-labeled rationales, which\nencourage models to maintain their focus on important words beyond labeled\nrationales (PINs) and alleviate redundant training on non-helpful rationales\n(NoIRs). Experiments on two representative classification tasks show that our\nproposed methods can push a classification model to effectively learn crucial\nclues from non-perfect rationales while maintaining the ability to spread its\nfocus to other unlabeled important words, thus significantly outperform\nexisting methods.\n"}
+{"abstract": "  A novel framework is proposed to extract near-threshold resonant states from\nfinite-volume energy levels of lattice QCD and is applied to elucidate\nstructures of the positive parity $D_s$. The quark model, the\nquark-pair-creation mechanism and $D^{(*)}K$ interaction are incorporated into\nthe Hamiltonian effective field theory. The bare $1^+$ $c\\bar s$ states are\nalmost purely given by the states with heavy-quark spin bases. The physical\n$D^*_{s0}(2317)$ and $D^*_{s1}(2460)$ are the mixtures of bare $c\\bar s$ core\nand $D^{(*)}K$ component, while the $D^*_{s1}(2536)$ and $D^*_{s2}(2573)$ are\nalmost dominated by bare $c\\bar{s}$. Furthermore, our model reproduces the\nclear level crossing of the $D^*_{s1}(2536)$ with the scattering state at a\nfinite volume.\n"}
+{"abstract": "  As the worldwide population gets increasingly aged, in-home telemedicine and\nmobile-health solutions represent promising services to promote active and\nindependent aging and to contribute to a paradigm shift towards patient-centric\nhealthcare. In this work, we present ACTA (Advanced Cognitive Training for\nAging), a prototype mobile-health solution to provide advanced cognitive\ntraining for senior citizens with mild cognitive impairments. We disclose here\nthe conceptualization of ACTA as the integration of two promising\nrehabilitation strategies: the \"Nudge theory\", from the cognitive domain, and\nthe neurofeedback, from the neuroscience domain. Moreover, in ACTA we exploit\nthe most advanced machine learning techniques to deliver customized and fully\nadaptive support to the elderly, while training in an ecological environment.\nACTA represents the next-step beyond SENIOR, an earlier mobile-health project\nfor cognitive training based on Nudge theory, currently ongoing in Lombardy\nRegion. Beyond SENIOR, ACTA represents a highly-usable, accessible, low-cost,\nnew-generation mobile-health solution to promote independent aging and\neffective motor-cognitive training support, while empowering the elderly in\ntheir own aging.\n"}
+{"abstract": "  Analysis of large observational data sets generated by a reactive system is a\ncommon challenge in debugging system failures and determining their root cause.\nOne of the major problems is that these observational data suffer from\nsurvivorship bias. Examples include analyzing traffic logs from networks, and\nsimulation logs from circuit design. In such applications, users want to detect\nnon-spurious correlations from observational data and obtain actionable\ninsights about them. In this paper, we introduce log to Neuro-symbolic\n(Log2NS), a framework that combines probabilistic analysis from machine\nlearning (ML) techniques on observational data with certainties derived from\nsymbolic reasoning on an underlying formal model. We apply the proposed\nframework to network traffic debugging by employing the following steps. To\ndetect patterns in network logs, we first generate global embedding vector\nrepresentations of entities such as IP addresses, ports, and applications.\nNext, we represent large log flow entries as clusters that make it easier for\nthe user to visualize and detect interesting scenarios that will be further\nanalyzed. To generalize these patterns, Log2NS provides an ability to query\nfrom static logs and correlation engines for positive instances, as well as\nformal reasoning for negative and unseen instances. By combining the strengths\nof deep learning and symbolic methods, Log2NS provides a very powerful\nreasoning and debugging tool for log-based data. Empirical evaluations on a\nreal internal data set demonstrate the capabilities of Log2NS.\n"}
+{"abstract": "  Gaze estimation methods learn eye gaze from facial features. However, among\nrich information in the facial image, real gaze-relevant features only\ncorrespond to subtle changes in eye region, while other gaze-irrelevant\nfeatures like illumination, personal appearance and even facial expression may\naffect the learning in an unexpected way. This is a major reason why existing\nmethods show significant performance degradation in cross-domain/dataset\nevaluation. In this paper, we tackle the cross-domain problem in gaze\nestimation. Different from common domain adaption methods, we propose a domain\ngeneralization method to improve the cross-domain performance without touching\ntarget samples. The domain generalization is realized by gaze feature\npurification. We eliminate gaze-irrelevant factors such as illumination and\nidentity to improve the cross-domain performance. We design a plug-and-play\nself-adversarial framework for the gaze feature purification. The framework\nenhances not only our baseline but also existing gaze estimation methods\ndirectly and significantly. To the best of our knowledge, we are the first to\npropose domain generalization methods in gaze estimation. Our method achieves\nnot only state-of-the-art performance among typical gaze estimation methods but\nalso competitive results among domain adaption methods. The code is released in\nhttps://github.com/yihuacheng/PureGaze.\n"}
+{"abstract": "  The High Altitude Water Cherenkov (HAWC) Gamma-Ray Observatory surveys the\nvery high energy sky in the 300 GeV to $>100$ TeV energy range. HAWC has\ndetected two blazars above $11\\sigma$, Markarian 421 (Mrk 421) and Markarian\n501 (Mrk 501). The observations are comprised of data taken in the period\nbetween June 2015 and July 2018, resulting in a $\\sim 1038$ days of exposure.\nIn this work we report the time-averaged spectral analysis for both sources\nabove 0.5 TeV. Taking into account the flux attenuation due to the\nextragalactic background light (EBL), the intrinsic spectrum of Mrk 421 is\ndescribed by a power law with an exponential energy cut-off with index\n$\\alpha=2.26\\pm(0.12)_{stat}(_{-0.2}^{+0.17})_{sys}$ and energy cut-off\n$E_c=5.1\\pm(1.6)_{stat}(_{-2.5}^{+1.4})_{sys}$ TeV, while the intrinsic\nspectrum of Mrk 501 is better described by a simple power law with index\n$\\alpha=2.61\\pm(0.11)_{stat}(_{-0.07}^{+0.01})_{sys}$. The maximum energies at\nwhich the Mrk 421 and Mrk 501 signals are detected are 9 and 12 TeV,\nrespectively. This makes these some of the highest energy detections to date\nfor spectra averaged over years-long timescales. Since the observation of gamma\nradiation from blazars provides information about the physical processes that\ntake place in their relativistic jets, it is important to study the broad-band\nspectral energy distribution (SED) of these objects. To this purpose,\ncontemporaneous data from the Large Area Telescope on board the {\\em Fermi}\nsatellite and literature data, in the radio to X-ray range, were used to build\ntime-averaged SEDs that were modeled within a synchrotron self-Compton leptonic\nscenario to derive the physical parameters that describe the nature of the\nrespective jets.\n"}
+{"abstract": "  We describe the structure of finite Boolean inverse monoids and apply our\nresults to the representation theory of finite inverse semigroups. We then\ngeneralize to semisimple Boolean inverse semigroups.\n"}
+{"abstract": "  The score of a vertex $x$ in an oriented graph is defined to be its\noutdegree, \\emph{i.e.}, the number of arcs with initial vertex $x$. The score\nsequence of an oriented graph is the sequence of all scores arranged in\nnondecreasing order. An oriented complete bipartite graph is called a\nbitournament. The score sequence of a bitournament consists of two\nnondecreasing sequences of nonnegative integers, one for each of the two\npartite sets. Moon has characterized the score sequences of bitournaments. This\npaper introduces the concept of trimming a sequence and gives a\ncharacterization of score sequences of bitournaments utilizing this concept.\n"}
+{"abstract": "  In the present work, $k_T$-factorization formalism is applied to compute the\nexclusive dilepton production by timelike Compton scattering (TCS) in $eA$,\n$pA$ and $AA$ collisions. The nuclear effects are investigated considering\nheavy and light ions. The production cross section in terms of invariant mass\nand rapidity distribution of the lepton pair is shown. The analysis is done for\nelectron-ion collisions at the Large Hadron-Electron Collider (LHeC), its\nhigh-energy upgrade (HE-LHeC) and at the Future Circular Collider (FCC) in\nlepton-hadron mode. Additionally, ultraperipheral heavy ion collisions at\nfuture runs of the Large Hadron Collider (LHC) and at the FCC (hadron-hadron\nmode) are also considered.\n"}
+{"abstract": "  This paper surveys 60 English Machine Reading Comprehension datasets, with a\nview to providing a convenient resource for other researchers interested in\nthis problem. We categorize the datasets according to their question and answer\nform and compare them across various dimensions including size, vocabulary,\ndata source, method of creation, human performance level, and first question\nword. Our analysis reveals that Wikipedia is by far the most common data source\nand that there is a relative lack of why, when, and where questions across\ndatasets.\n"}
+{"abstract": "  The security of mobile robotic networks (MRNs) has been an active research\ntopic in recent years. This paper demonstrates that the observable interaction\nprocess of MRNs under formation control will present increasingly severe\nthreats. Specifically, we find that an external attack robot, who has only\npartial observation over MRNs while not knowing the system dynamics or access,\ncan learn the interaction rules from observations and utilize them to replace a\ntarget robot, destroying the cooperation performance of MRNs. We call this\nnovel attack as sneak, which endows the attacker with the intelligence of\nlearning knowledge and is hard to be tackled by traditional defense techniques.\nThe key insight is to separately reveal the internal interaction structure\nwithin robots and the external interaction mechanism with the environment, from\nthe coupled state evolution influenced by the model-unknown rules and\nunobservable part of the MRN. To address this issue, we first provide general\ninteraction process modeling and prove the learnability of the interaction\nrules. Then, with the learned rules, we design an Evaluate-Cut-Restore (ECR)\nattack strategy considering the partial interaction structure and geometric\npattern. We also establish the sufficient conditions for a successful sneak\nwith maximum control impacts over the MRN. Extensive simulations illustrate the\nfeasibility and effectiveness of the proposed attack.\n"}
+{"abstract": "  We present a fully Eulerian hybrid immersed-boundary/phase-field model to\nsimulate wetting and contact line motion over any arbitrary geometry. The solid\nwall is described with a volume-penalisation ghost-cell immersed boundary\nwhereas the interface between the two fluids by a diffuse-interface method. The\ncontact line motion on the complex wall is prescribed via slip velocity in the\nmomentum equation and static/dynamic contact angle condition for the order\nparameter of the Cahn-Hilliard model. This combination requires accurate\ncomputations of the normal and tangential gradients of the scalar order\nparameter and of the components of the velocity. However, the present algorithm\nrequires the computation of averaging weights and other geometrical variables\nas a preprocessing step. Several validation tests are reported in the\nmanuscript, together with 2D simulations of a droplet spreading over a\nsinusoidal wall with different contact angles and slip length and a spherical\ndroplet spreading over a sphere, showing that the proposed algorithm is capable\nto deal with the three-phase contact line motion over any complex wall. The\nEulerian feature of the algorithm facilitates the implementation and provides a\nstraight-forward and potentially highly scalable parallelisation. The employed\nparallelisation of the underlying Navier-Stokes solver can be efficiently used\nfor the multiphase part as well. The procedure proposed here can be directly\nemployed to impose any types of boundary conditions (Neumann, Dirichlet and\nmixed) for any field variable evolving over a complex geometry, modelled with\nan immersed-boundary approach (for instance, modelling deformable biological\nmembranes, red blood cells, solidification, evaporation and boiling, to name a\nfew)\n"}
+{"abstract": "  Many systems nowadays require protection against security or safety threats.\nA physical protection system (PPS) integrates people, procedures, and equipment\nto protect assets or facilities. PPSs have targeted various systems, including\nairports, rail transport, highways, hospitals, bridges, the electricity grid,\ndams, power plants, seaports, oil refineries, and water systems. Hence, PPSs\nare characterized by a broad set of features, from which part is common, while\nother features are variant and depend on the particular system to be developed.\nThe notion of PPS has been broadly addressed in the literature, and even\ndomain-specific PPS development methods have been proposed. However, the common\nand variant features are fragmented across many studies. This situation\nseriously impedes the identification of the required features and likewise the\nguidance of the systems engineering process of PPSs. To enhance the\nunderstanding and support the guidance of the development of PPS, in this\npaper, we provide a feature-driven survey of PPSs. The approach applies a\nsystematic domain analysis process based on the state-of-the-art of PPSs. It\npresents a family feature model that defines the common and variant features\nand herewith the configuration space of PPSs\n"}
+{"abstract": "  Multi-task learning is an important trend of machine learning in facing the\nera of artificial intelligence and big data. Despite a large amount of\nresearches on learning rate estimates of various single-task machine learning\nalgorithms, there is little parallel work for multi-task learning. We present\nmathematical analysis on the learning rate estimate of multi-task learning\nbased on the theory of vector-valued reproducing kernel Hilbert spaces and\nmatrix-valued reproducing kernels. For the typical multi-task regularization\nnetworks, an explicit learning rate dependent both on the number of sample data\nand the number of tasks is obtained. It reveals that the generalization ability\nof multi-task learning algorithms is indeed affected as the number of tasks\nincreases.\n"}
+{"abstract": "  We present multi-scale and multi-wavelength data of the Galactic HII region\nG25.4-0.14 (hereafter G25.4NW, distance ~5.7 kpc). The SHARC-II 350 micron\ncontinuum map displays a hub-filament configuration containing five parsec\nscale filaments and a central compact hub. Through the 5 GHz radio continuum\nmap, four ionized clumps (i.e., Ia-Id) are identified toward the central hub,\nand are powered by massive OB-stars. The Herschel temperature map depicts the\nwarm dust emission (i.e., Td ~23-39 K) toward the hub. High resolution Atacama\nLarge Millimeter/submillimeter Array (ALMA) 1.3 mm continuum map (resolution\n~0\".82 X 0\".58) reveals three cores (c1-c3; mass ~80-130 Msun) toward the\nionized clumps Ia, and another one (c4; mass ~70 Msun) toward the ionized clump\nIb. A compact near-infrared (NIR) emission feature (extent ~0.2 pc) is\ninvestigated toward the ionized clump Ia excited by an O8V-type star, and\ncontains at least three embedded K-band stars. In the direction of the ionized\nclump Ia, the ALMA map also shows an elongated feature (extent ~0.2 pc) hosting\nthe cores c1-c3. All these findings together illustrate the existence of a\nsmall cluster of massive stars in the central hub. Considering the detection of\nthe hub-filament morphology and the spatial locations of the mm cores, a global\nnon-isotropic collapse (GNIC) scenario appears to be applicable in G25.4NW,\nwhich includes the basic ingredients of the global hierarchical collapse and\nclump-fed accretion models. Overall, the GNIC scenario explains the birth of\nmassive stars in G25.4NW.\n"}
+{"abstract": "  In this work, we introduce a control variate approximation technique for low\nerror approximate Deep Neural Network (DNN) accelerators. The control variate\ntechnique is used in Monte Carlo methods to achieve variance reduction. Our\napproach significantly decreases the induced error due to approximate\nmultiplications in DNN inference, without requiring time-exhaustive retraining\ncompared to state-of-the-art. Leveraging our control variate method, we use\nhighly approximated multipliers to generate power-optimized DNN accelerators.\nOur experimental evaluation on six DNNs, for Cifar-10 and Cifar-100 datasets,\ndemonstrates that, compared to the accurate design, our control variate\napproximation achieves same performance and 24% power reduction for a merely\n0.16% accuracy loss.\n"}
+{"abstract": "  We study unbendable rational curves, i.e., nonsingular rational curves in a\ncomplex manifold of dimension $n$ with normal bundles isomorphic to\n$\\mathcal{O}_{\\mathbb{P}^1}(1)^{\\oplus p} \\oplus\n\\mathcal{O}_{\\mathbb{P}^1}^{\\oplus (n-1-p)}$ for some nonnegative integer $p$.\nWell-known examples arise from algebraic geometry as general minimal rational\ncurves of uniruled projective manifolds. After describing the relations between\nthe differential geometric properties of the natural distributions on the\ndeformation spaces of unbendable rational curves and the projective geometric\nproperties of their varieties of minimal rational tangents, we concentrate on\nthe case of $p=1$ and $n \\leq 5$, which is the simplest nontrivial situation.\nIn this case, the families of unbendable rational curves fall essentially into\ntwo classes: Goursat type or Cartan type. Those of Goursat type arise from\nordinary differential equations and those of Cartan type have special features\nrelated to contact geometry. We show that the family of lines on any\nnonsingular cubic 4-fold is of Goursat type, whereas the family of lines on a\ngeneral quartic 5-fold is of Cartan type, in the proof of which the projective\ngeometry of varieties of minimal rational tangents plays a key role.\n"}
+{"abstract": "  Deep Neural Networks (DNNs) are witnessing increased adoption in multiple\ndomains owing to their high accuracy in solving real-world problems. However,\nthis high accuracy has been achieved by building deeper networks, posing a\nfundamental challenge to the low latency inference desired by user-facing\napplications. Current low latency solutions trade-off on accuracy or fail to\nexploit the inherent temporal locality in prediction serving workloads.\n  We observe that caching hidden layer outputs of the DNN can introduce a form\nof late-binding where inference requests only consume the amount of computation\nneeded. This enables a mechanism for achieving low latencies, coupled with an\nability to exploit temporal locality. However, traditional caching approaches\nincur high memory overheads and lookup latencies, leading us to design learned\ncaches - caches that consist of simple ML models that are continuously updated.\nWe present the design of GATI, an end-to-end prediction serving system that\nincorporates learned caches for low-latency DNN inference. Results show that\nGATI can reduce inference latency by up to 7.69X on realistic workloads.\n"}
+{"abstract": "  India has a maternal mortality ratio of 113 and child mortality ratio of 2830\nper 100,000 live births. Lack of access to preventive care information is a\nmajor contributing factor for these deaths, especially in low resource\nhouseholds. We partner with ARMMAN, a non-profit based in India employing a\ncall-based information program to disseminate health-related information to\npregnant women and women with recent child deliveries. We analyze call records\nof over 300,000 women registered in the program created by ARMMAN and try to\nidentify women who might not engage with these call programs that are proven to\nresult in positive health outcomes. We built machine learning based models to\npredict the long term engagement pattern from call logs and beneficiaries'\ndemographic information, and discuss the applicability of this method in the\nreal world through a pilot validation. Through a pilot service quality\nimprovement study, we show that using our model's predictions to make\ninterventions boosts engagement metrics by 61.37%. We then formulate the\nintervention planning problem as restless multi-armed bandits (RMABs), and\npresent preliminary results using this approach.\n"}
+{"abstract": "  The evolution of deformation from plasticity to localization to damage is\ninvestigated in ferritic-pearlitic steel through nanometer-resolution\nmicrostructure-correlated SEM-DIC (u-DIC) strain mapping, enabled through\nhighly accurate microstructure-to-strain alignment. We reveal the key\nplasticity mechanisms in ferrite and pearlite as well as their evolution into\nlocalization and damage and their relation to the microstructural arrangement.\nNotably, two contrasting mechanisms were identified that control whether damage\ninitiation in pearlite occurs and, through connection of localization hotspots\nin ferrite grains, potentially results in macroscale fracture: (i) cracking of\npearlite bridges with relatively clean lamellar structure by brittle fracture\nof cementite lamellae due to build-up of strain concentrations in nearby\nferrite, versus (ii) large plasticity without damage in pearlite bridges with a\nmore \"open\", chaotic pearlite morphology, which enables plastic percolation\npaths in the interlamellar ferrite channels. Based on these insights,\nrecommendations for damage resistant ferritic-pearlitic steels are proposed.\n"}
+{"abstract": "  This is a survey of recent results on central and non-central limit theorems\nfor quadratic functionals of stationary processes. The underlying processes are\nGaussian, linear or L\\'evy-driven linear processes with memory, and are defined\neither in discrete or continuous time. We focus on limit theorems for Toeplitz\nand tapered Toeplitz type quadratic functionals of stationary processes with\napplications in parametric and nonparametric statistical estimation theory. We\ndiscuss questions concerning Toeplitz matrices and operators, Fej\\'er-type\nsingular integrals, and L\\'evy-It\\^o-type and Stratonovich-type multiple\nstochastic integrals. These are the main tools for obtaining limit theorems.\n"}
+{"abstract": "  Although deep convolution neural networks (DCNN) have achieved excellent\nperformance in human pose estimation, these networks often have a large number\nof parameters and computations, leading to the slow inference speed. For this\nissue, an effective solution is knowledge distillation, which transfers\nknowledge from a large pre-trained network (teacher) to a small network\n(student). However, there are some defects in the existing approaches: (I) Only\na single teacher is adopted, neglecting the potential that a student can learn\nfrom multiple teachers. (II) The human segmentation mask can be regarded as\nadditional prior information to restrict the location of keypoints, which is\nnever utilized. (III) A student with a small number of parameters cannot fully\nimitate heatmaps provided by datasets and teachers. (IV) There exists noise in\nheatmaps generated by teachers, which causes model degradation. To overcome\nthese defects, we propose an orderly dual-teacher knowledge distillation (ODKD)\nframework, which consists of two teachers with different capabilities.\nSpecifically, the weaker one (primary teacher, PT) is used to teach keypoints\ninformation, the stronger one (senior teacher, ST) is utilized to transfer\nsegmentation and keypoints information by adding the human segmentation mask.\nTaking dual-teacher together, an orderly learning strategy is proposed to\npromote knowledge absorbability. Moreover, we employ a binarization operation\nwhich further improves the learning ability of the student and reduces noise in\nheatmaps. Experimental results on COCO and OCHuman keypoints datasets show that\nour proposed ODKD can improve the performance of different lightweight models\nby a large margin, and HRNet-W16 equipped with ODKD achieves state-of-the-art\nperformance for lightweight human pose estimation.\n"}
+{"abstract": "  RGB-D salient object detection (SOD) is usually formulated as a problem of\nclassification or regression over two modalities, i.e., RGB and depth. Hence,\neffective RGBD feature modeling and multi-modal feature fusion both play a\nvital role in RGB-D SOD. In this paper, we propose a depth-sensitive RGB\nfeature modeling scheme using the depth-wise geometric prior of salient\nobjects. In principle, the feature modeling scheme is carried out in a\ndepth-sensitive attention module, which leads to the RGB feature enhancement as\nwell as the background distraction reduction by capturing the depth geometry\nprior. Moreover, to perform effective multi-modal feature fusion, we further\npresent an automatic architecture search approach for RGB-D SOD, which does\nwell in finding out a feasible architecture from our specially designed\nmulti-modal multi-scale search space. Extensive experiments on seven standard\nbenchmarks demonstrate the effectiveness of the proposed approach against the\nstate-of-the-art.\n"}
+{"abstract": "  This paper attempts to study the optimal stopping time for semi-Markov\nprocesses (SMPs) under the discount optimization criteria with unbounded cost\nrates. In our work, we introduce an explicit construction of the equivalent\nsemi-Markov decision processes (SMDPs). The equivalence is embodied in the\nvalue functions of SMPs and SMDPs, that is, every stopping time of SMPs can\ninduce a policy of SMDPs such that the value functions are equal, and vice\nversa. The existence of the optimal stopping time of SMPs is proved by this\nequivalence relation. Next, we give the optimality equation of the value\nfunction and develop an effective iterative algorithm for computing it.\nMoreover, we show that the optimal and {\\epsilon}-optimal stopping time can be\ncharacterized by the hitting time of the special sets. Finally, to illustrate\nthe validity of our results, an example of a maintenance system is presented in\nthe end.\n"}
+{"abstract": "  Autoregressive models are widely used for tasks such as image and audio\ngeneration. The sampling process of these models, however, does not allow\ninterruptions and cannot adapt to real-time computational resources. This\nchallenge impedes the deployment of powerful autoregressive models, which\ninvolve a slow sampling process that is sequential in nature and typically\nscales linearly with respect to the data dimension. To address this difficulty,\nwe propose a new family of autoregressive models that enables anytime sampling.\nInspired by Principal Component Analysis, we learn a structured representation\nspace where dimensions are ordered based on their importance with respect to\nreconstruction. Using an autoregressive model in this latent space, we trade\noff sample quality for computational efficiency by truncating the generation\nprocess before decoding into the original data space. Experimentally, we\ndemonstrate in several image and audio generation tasks that sample quality\ndegrades gracefully as we reduce the computational budget for sampling. The\napproach suffers almost no loss in sample quality (measured by FID) using only\n60\\% to 80\\% of all latent dimensions for image data. Code is available at\nhttps://github.com/Newbeeer/Anytime-Auto-Regressive-Model .\n"}
+{"abstract": "  A novel spin orientation mechanism - dynamic electron spin polarization has\nbeen recently suggested in Phys. Rev. Lett. $\\mathbf{125}$, 156801 (2020). It\ntakes place for unpolarized optical excitation in weak magnetic fields of the\norder of a few millitesla. In this paper we demonstrate experimentally and\ntheoretically that the dynamic electron spin polarization degree changes sign\nas a function of time, strength of the applied magnetic field and its\ndirection. The studies are performed on indirect band-gap (In,Al)As/AlAs\nquantum dots and their results are explained in the framework of a theoretical\nmodel developed for our experimental setting.\n"}
+{"abstract": "  We investigate the computability of algebraic closure and definable closure\nwith respect to a collection of formulas. We show that for a computable\ncollection of formulas of quantifier rank at most $n$, in any given computable\nstructure, both algebraic and definable closure with respect to that collection\nare $\\Sigma^0_{n+2}$ sets. We further show that these bounds are tight.\n"}
+{"abstract": "  We prove the existence of an eddy heat diffusion coefficient coming from an\nidealized model of turbulent fluid. A difficulty lies in the presence of a\nboundary, with also turbulent mixing and the eddy diffusion coefficient going\nto zero at the boundary. Nevertheless enhanced diffusion takes place.\n"}
+{"abstract": "  A simplification strategy for Segmented Mirror Splitters (SMS) used as beam\ncombiners is presented. These devices are useful for compact beam division and\ncombination of linear and 2-D arrays. However, the standard design requires\nunique thin-film coating sections for each input beam and thus potential for\nscaling to high beam-counts is limited due to manufacturing complexity. Taking\nadvantage of the relative insensitivity of the beam combination process to\namplitude variations, numerical techniques are used to optimize\nhighly-simplified designs with only one, two or three unique coatings. It is\ndemonstrated that with correctly chosen coating reflectivities, the simplified\noptics are capable of high combination efficiency for several tens of beams.\nThe performance of these optics as beamsplitters in multicore fiber amplifier\nsystems is analyzed, and inhomogeneous power distribution of the simplified\ndesigns is noted as a potential source of combining loss in such systems. These\nsimplified designs may facilitate further scaling of filled-aperture coherently\ncombined systems.\n"}
+{"abstract": "  In this paper, we provide causal evidence on abortions and risky health\nbehaviors as determinants of mental health development among young women. Using\nadministrative in- and outpatient records from Sweden, we apply a novel grouped\nfixed-effects estimator proposed by Bonhomme and Manresa (2015) to allow for\ntime-varying unobserved heterogeneity. We show that the positive association\nobtained from standard estimators shrinks to zero once we control for grouped\ntime-varying unobserved heterogeneity. We estimate the group-specific profiles\nof unobserved heterogeneity, which reflect differences in unobserved risk to be\ndiagnosed with a mental health condition. We then analyze mental health\ndevelopment and risky health behaviors other than unwanted pregnancies across\ngroups. Our results suggest that these are determined by the same type of\nunobserved heterogeneity, which we attribute to the same unobserved process of\ndecision-making. We develop and estimate a theoretical model of risky choices\nand mental health, in which mental health disparity across groups is generated\nby different degrees of self-control problems. Our findings imply that mental\nhealth concerns cannot be used to justify restrictive abortion policies.\nMoreover, potential self-control problems should be targeted as early as\npossible to combat future mental health consequences.\n"}
+{"abstract": "  Recent years have witnessed a renewed interest in Boolean function in\nexplaining binary classifiers in the field of explainable AI (XAI). The\nstandard approach of Boolean function is propositional logic. We study a family\nof classifier models, axiomatize it and show completeness of our axiomatics.\nMoreover, we prove that satisfiability checking for our modal language relative\nto such a class of models is NP-complete. We leverage the language to formalize\ncounterfactual conditional as well as a variety of notions of explanation\nincluding abductive, contrastive and counterfactual explanations, and biases.\nFinally, we present two extensions of our language: a dynamic extension by the\nnotion of assignment enabling classifier change and an epistemic extension in\nwhich the classifier's uncertainty about the actual input can be represented.\n"}
+{"abstract": "  We discuss a probe of the contribution of wind-related shocks to the radio\nemission in otherwise radio-quiet quasars. Given 1) the non-linear correlation\nbetween UV and X-ray luminosity in quasars, 2) that such correlation leads to\nhigher likelihood of radiation-line-driven winds in more luminous quasars, and\n3) that luminous quasars are more abundant at high redshift, deep radio\nobservations of high-redshift quasars are needed to probe potential\ncontributions from accretion disk winds. We target a sample of 50 $z\\simeq\n1.65$ color-selected quasars that span the range of expected accretion disk\nwind properties as traced by broad CIV emission. 3-GHz observations with the\nVery Large Array to an rms of $\\approx10\\mu$Jy beam$^{-1}$ probe to star\nformation rates of $\\approx400\\,M_{\\rm Sun}\\,{\\rm yr}^{-1}$, leading to 22\ndetections. Supplementing these pointed observations are survey data of 388\nsources from the LOFAR Two-metre Sky Survey Data Release 1 that reach\ncomparable depth (for a typical radio spectral index), where 123 sources are\ndetected. These combined observations reveal a radio detection fraction that is\na non-linear function of \\civ\\ emission-line properties and suggest that the\ndata may require multiple origins of radio emission in radio-quiet quasars. We\nfind evidence for radio emission from weak jets or coronae in radio-quiet\nquasars with low Eddingtion ratios, with either (or both) star formation and\naccretion disk winds playing an important role in optically luminous quasars\nand correlated with increasing Eddington ratio. Additional pointed radio\nobservations are needed to fully establish the nature of radio emission in\nradio-quiet quasars.\n"}
+{"abstract": "  Like adiabatic time-dependent density-functional theory (TD-DFT), the\nBethe-Salpeter equation (BSE) formalism of many-body perturbation theory, in\nits static approximation, is \"blind\" to double (and higher) excitations, which\nare ubiquitous, for example, in conjugated molecules like polyenes. Here, we\napply the spin-flip \\textit{ansatz} (which considers the lowest triplet state\nas the reference configuration instead of the singlet ground state) to the BSE\nformalism in order to access, in particular, double excitations. The present\nscheme is based on a spin-unrestricted version of the $GW$ approximation\nemployed to compute the charged excitations and screened Coulomb potential\nrequired for the BSE calculations. Dynamical corrections to the static BSE\noptical excitations are taken into account via an unrestricted generalization\nof our recently developed (renormalized) perturbative treatment. The\nperformance of the present spin-flip BSE formalism is illustrated by computing\nexcited-state energies of the beryllium atom, the hydrogen molecule at various\nbond lengths, and cyclobutadiene in its rectangular and square-planar\ngeometries.\n"}
+{"abstract": "  Question answering from semi-structured tables can be seen as a semantic\nparsing task and is significant and practical for pushing the boundary of\nnatural language understanding. Existing research mainly focuses on\nunderstanding contents from unstructured evidence, e.g., news, natural language\nsentences, and documents. The task of verification from structured evidence,\nsuch as tables, charts, and databases, is still less explored. This paper\ndescribes sattiy team's system in SemEval-2021 task 9: Statement Verification\nand Evidence Finding with Tables (SEM-TAB-FACT). This competition aims to\nverify statements and to find evidence from tables for scientific articles and\nto promote the proper interpretation of the surrounding article. In this paper,\nwe exploited ensemble models of pre-trained language models over tables, TaPas\nand TaBERT, for Task A and adjust the result based on some rules extracted for\nTask B. Finally, in the leaderboard, we attain the F1 scores of 0.8496 and\n0.7732 in Task A for the 2-way and 3-way evaluation, respectively, and the F1\nscore of 0.4856 in Task B.\n"}
+{"abstract": "  A generalized method of alternating resolvents was introduced by Boikanyo and\nMoro{\\c s}anu as a way to approximate common zeros of two maximal monotone\noperators. In this paper we analyse the strong convergence of this algorithm\nunder two different sets of conditions. As a consequence we obtain effective\nrates of metastability (in the sense of Terence Tao) and quasi-rates of\nasymptotic regularity. Furthermore, we bypass the need for sequential weak\ncompactness in the original proofs. Our quantitative results are obtained using\nproof-theoretical techniques in the context of the proof mining program.\n"}
+{"abstract": "  At the latest since the advent of the Internet, disinformation and conspiracy\ntheories have become ubiquitous. Recent examples like QAnon and Pizzagate prove\nthat false information can lead to real violence. In this motivation statement\nfor the Workshop on Human Aspects of Misinformation at CHI 2021, I explain my\nresearch agenda focused on 1. why people believe in disinformation, 2. how\npeople can be best supported in recognizing disinformation, and 3. what the\npotentials and risks of different tools designed to fight disinformation are.\n"}
+{"abstract": "  We investigate the asymptotic risk of a general class of overparameterized\nlikelihood models, including deep models. The recent empirical success of\nlarge-scale models has motivated several theoretical studies to investigate a\nscenario wherein both the number of samples, $n$, and parameters, $p$, diverge\nto infinity and derive an asymptotic risk at the limit. However, these theorems\nare only valid for linear-in-feature models, such as generalized linear\nregression, kernel regression, and shallow neural networks. Hence, it is\ndifficult to investigate a wider class of nonlinear models, including deep\nneural networks with three or more layers. In this study, we consider a\nlikelihood maximization problem without the model constraints and analyze the\nupper bound of an asymptotic risk of an estimator with penalization.\nTechnically, we combine a property of the Fisher information matrix with an\nextended Marchenko-Pastur law and associate the combination with empirical\nprocess techniques. The derived bound is general, as it describes both the\ndouble descent and the regularized risk curves, depending on the penalization.\nOur results are valid without the linear-in-feature constraints on models and\nallow us to derive the general spectral distributions of a Fisher information\nmatrix from the likelihood. We demonstrate that several explicit models, such\nas parallel deep neural networks, ensemble learning, and residual networks, are\nin agreement with our theory. This result indicates that even large and deep\nmodels have a small asymptotic risk if they exhibit a specific structure, such\nas divisibility. To verify this finding, we conduct a real-data experiment with\nparallel deep neural networks. Our results expand the applicability of the\nasymptotic risk analysis, and may also contribute to the understanding and\napplication of deep learning.\n"}
+{"abstract": "  We study Lagrangian systems with a finite number of degrees of freedom that\nare non-local in time. We obtain an extension of Noether theorem and Noether\nidentities to this kind of Lagrangians. A Hamiltonian formalism is then set up\nfor this systems. $n$-order local Lagrangians can be treated as a particular\ncase and the standard results for them are recovered. The method is then\napplied to several other cases, namely two examples of non-local oscillators\nand the p-adic particle.\n"}
+{"abstract": "  Quantum algorithms for computing classical nonlinear maps are widely known\nfor toy problems but might not suit potential applications to realistic physics\nsimulations. Here, we propose how to compute a general differentiable\ninvertible nonlinear map on a quantum computer using only linear unitary\noperations. The price of this universality is that the original map is\nrepresented adequately only on a finite number of iterations. More iterations\nproduce spurious echos, which are unavoidable in any finite unitary emulation\nof generic non-conservative dynamics. Our work is intended as the first survey\nof these issues and possible ways to overcome them in the future. We propose\nhow to monitor spurious echos via auxiliary measurements, and we illustrate our\nresults with numerical simulations.\n"}
+{"abstract": "  Assessing the exploitability of software vulnerabilities at the time of\ndisclosure is difficult and error-prone, as features extracted via technical\nanalysis by existing metrics are poor predictors for exploit development.\nMoreover, exploitability assessments suffer from a class bias because \"not\nexploitable\" labels could be inaccurate.\n  To overcome these challenges, we propose a new metric, called Expected\nExploitability (EE), which reflects, over time, the likelihood that functional\nexploits will be developed. Key to our solution is a time-varying view of\nexploitability, a departure from existing metrics, which allows us to learn EE\nusing data-driven techniques from artifacts published after disclosure, such as\ntechnical write-ups, proof-of-concept exploits, and social media discussions.\nOur analysis reveals that prior features proposed for related exploit\nprediction tasks are not always beneficial for predicting functional exploits,\nand we design novel feature sets to capitalize on previously under-utilized\nartifacts.\n  This view also allows us to investigate the effect of the label biases on the\nclassifiers. We characterize the noise-generating process for exploit\nprediction, showing that our problem is subject to class- and feature-dependent\nlabel noise, considered the most challenging type. By leveraging\ndomain-specific observations, we then develop techniques to incorporate noise\nrobustness into learning EE.\n  On a dataset of 103,137 vulnerabilities, we show that EE increases precision\nfrom 49\\% to 86\\% over existing metrics, including two state-of-the-art exploit\nclassifiers, while the performance of our metric also improving over time. EE\nscores capture exploitation imminence, by distinguishing exploits which are\ngoing to be developed in the near future.\n"}
+{"abstract": "  We introduce the \"inverse bandit\" problem of estimating the rewards of a\nmulti-armed bandit instance from observing the learning process of a low-regret\ndemonstrator. Existing approaches to the related problem of inverse\nreinforcement learning assume the execution of an optimal policy, and thereby\nsuffer from an identifiability issue. In contrast, our paradigm leverages the\ndemonstrator's behavior en route to optimality, and in particular, the\nexploration phase, to obtain consistent reward estimates. We develop simple and\nefficient reward estimation procedures for demonstrations within a class of\nupper-confidence-based algorithms, showing that reward estimation gets\nprogressively easier as the regret of the algorithm increases. We match these\nupper bounds with information-theoretic lower bounds that apply to any\ndemonstrator algorithm, thereby characterizing the optimal tradeoff between\nexploration and reward estimation. Extensive empirical evaluations on both\nsynthetic data and simulated experimental design data from the natural sciences\ncorroborate our theoretical results.\n"}
+{"abstract": "  Interevent times in temporal contact data from humans and animals typically\nobey heavy-tailed distributions, and this property impacts contagion and other\ndynamical processes on networks. We theoretically show that distributions of\ninterevent times heavier-tailed than exponential distributions are a\nconsequence of the most basic metapopulation model used in epidemiology and\necology, in which individuals move from a patch to another according to the\nsimple random walk. Our results hold true irrespectively of the network\nstructure and also for more realistic mobility rules such as high-order random\nwalks and the recurrent mobility patterns used for modeling human dynamics.\n"}
+{"abstract": "  We present observations of a region of the Galactic plane taken during the\nEarly Science Program of the Australian Square Kilometre Array Pathfinder\n(ASKAP). In this context, we observed the SCORPIO field at 912 MHz with an\nuncompleted array consisting of 15 commissioned antennas. The resulting map\ncovers a square region of ~40 deg^2, centred on (l, b)=(343.5{\\deg},\n0.75{\\deg}), with a synthesized beam of 24\"x21\" and a background rms noise of\n150-200 {\\mu}Jy/beam, increasing to 500-600 {\\mu}Jy/beam close to the Galactic\nplane. A total of 3963 radio sources were detected and characterized in the\nfield using the CAESAR source finder. We obtained differential source counts in\nagreement with previously published data after correction for source extraction\nand characterization uncertainties, estimated from simulated data. The ASKAP\npositional and flux density scale accuracy were also investigated through\ncomparison with previous surveys (MGPS, NVSS) and additional observations of\nthe SCORPIO field, carried out with ATCA at 2.1 GHz and 10\" spatial resolution.\nThese allowed us to obtain a measurement of the spectral index for a subset of\nthe catalogued sources and an estimated fraction of (at least) 8% of resolved\nsources in the reported catalogue. We cross-matched our catalogued sources with\ndifferent astronomical databases to search for possible counterparts, finding\n~150 associations to known Galactic objects. Finally, we explored a\nmultiparametric approach for classifying previously unreported Galactic sources\nbased on their radio-infrared colors.\n"}
+{"abstract": "  In this paper, a general class of mixture of some densities is proposed. The\nproposed class contains some of classical and weighted distributions as special\ncases. Formulas for each of cumulative distribution function, reliability\nfunction, hazard rate function, rth raw moments function, characteristic\nfunction, stress-strength reliability and Tsallis entropy of order are derived.\n"}
+{"abstract": "  Attribution methods provide an insight into the decision-making process of\nmachine learning models, especially deep neural networks, by assigning\ncontribution scores to each individual feature. However, the attribution\nproblem has not been well-defined, which lacks a unified guideline to the\ncontribution assignment process. Furthermore, existing attribution methods\noften built upon various empirical intuitions and heuristics. There still lacks\na general theoretical framework that not only can offer a good description of\nthe attribution problem, but also can be applied to unifying and revisiting\nexisting attribution methods. To bridge the gap, in this paper, we propose a\nTaylor attribution framework, which models the attribution problem as how to\ndecide individual payoffs in a coalition. Then, we reformulate fourteen\nmainstream attribution methods into the Taylor framework and analyze these\nattribution methods in terms of rationale, fidelity, and limitation in the\nframework. Moreover, we establish three principles for a good attribution in\nthe Taylor attribution framework, i.e., low approximation error, correct Taylor\ncontribution assignment, and unbiased baseline selection. Finally, we\nempirically validate the Taylor reformulations and reveal a positive\ncorrelation between the attribution performance and the number of principles\nfollowed by the attribution method via benchmarking on real-world datasets.\n"}
+{"abstract": "  We develop some basic concepts in the theory of higher categories internal to\nan arbitrary $\\infty$-topos. We define internal left and right fibrations and\nprove a version of the Grothendieck construction and of Yoneda's lemma for\ninternal categories.\n"}
+{"abstract": "  Much of interesting complex biological behaviour arises from collective\nproperties. Important information about collective behaviour lies in the time\nand space structure of fluctuations around average properties, and two-point\ncorrelation functions are a fundamental tool to study these fluctuations. We\ngive a self-contained presentation of definitions and techniques for\ncomputation of correlation functions aimed at providing students and\nresearchers outside the field of statistical physics a practical guide to\ncalculating correlation functions from experimental and simulation data. We\ndiscuss some properties of correlations in critical systems, and the effect of\nfinite system size, which is particularly relevant for most biological\nexperimental systems. Finally we apply these to the case of the dynamical\ntransition in a simple neuronal model,\n"}
+{"abstract": "  In this paper, we propose a time-dependent\nSusceptible-Exposed-Infectious-Recovered-Died (SEIRD) reaction-diffusion system\nfor the COVID-19 pandemic and we deal with its derivation from a kinetic model.\nThe derivation is obtained by mathematical description delivered at the\nmicro-scale of individuals. Our approach is based on the micro-macro\ndecomposition which leads to an equivalent formulation of the kinetic model\nwhich couples the microscopic equations with the macroscopic equations. We\ndevelop a numerical asymptotic preservation scheme to solve the kinetic model.\nThe proposed approach is validated by various numerical tests where particular\nattention is paid to the Moroccan situation against the actual pandemic.\n"}
+{"abstract": "  Luminescent multifunctional nanomaterials are important because of their\npotential impact on the development of key technologies such as smart\nluminescent sensors and solid-state lightings. To be technologically viable,\nthe luminescent material needs to fulfil a number of requirements such as\nfacile and cost-effective fabrication, a high quantum yield, structural\nrobustness, and long-term material stability. To achieve these requirements, an\neco-friendly and scalable synthesis of a highly photoluminescent, multistimuli\nresponsive and electroluminescent silver-based metal-organic framework\n(Ag-MOF), termed \"OX-2\" was developed. Its exceptional photophysical and\nmechanically resilient properties that can be reversibly switched by\ntemperature and pressure make this material stood out over other competing\nluminescent materials. The potential use of OX-2 MOF as a good\nelectroluminescent material was tested by constructing a proof-of-concept\nMOF-LED (light emitting diode) device, further contributing to the rare\nexamples of electroluminescent MOFs. The results reveal the huge potential for\nexploiting the Ag MOF as a multitasking platform to engineer innovative\nphotonic technologies.\n"}
+{"abstract": "  I find that several models for information sharing in social networks can be\ninterpreted as age-dependent multi-type branching processes, and build them\nindependently following Sewastjanow. This allows to characterize criticality in\n(real and random) social networks. For random networks, I develop a\nmoment-closure method that handles the high-dimensionality of these models: By\nmodifying the timing of sharing with followers, all users can be represented by\na single representative, while leaving the total progeny unchanged. Thus I\ncompute the exact popularity distribution, revealing a viral character of\ncritical models expressed by fat tails of order minus three half.\n"}
+{"abstract": "  We address the task of domain generalization, where the goal is to train a\npredictive model such that it is able to generalize to a new, previously unseen\ndomain. We choose a hierarchical generative approach within the framework of\nvariational autoencoders and propose a domain-unsupervised algorithm that is\nable to generalize to new domains without domain supervision. We show that our\nmethod is able to learn representations that disentangle domain-specific\ninformation from class-label specific information even in complex settings\nwhere domain structure is not observed during training. Our interpretable\nmethod outperforms previously proposed generative algorithms for domain\ngeneralization as well as other non-generative state-of-the-art approaches in\nseveral hierarchical domain settings including sequential overlapped near\ncontinuous domain shift. It also achieves competitive performance on the\nstandard domain generalization benchmark dataset PACS compared to\nstate-of-the-art approaches which rely on observing domain-specific information\nduring training, as well as another domain unsupervised method. Additionally,\nwe proposed model selection purely based on Evidence Lower Bound (ELBO) and\nalso proposed weak domain supervision where implicit domain information can be\nadded into the algorithm.\n"}
+{"abstract": "  Generative adversarial networks (GAN) is a framework for generating fake data\nbased on given reals but is unstable in the optimization. In order to stabilize\nGANs, the noise enlarges the overlap of the real and fake distributions at the\ncost of significant variance. The data smoothing may reduce the dimensionality\nof data but suppresses the capability of GANs to learn high-frequency\ninformation. Based on these observations, we propose a data representation for\nGANs, called noisy scale-space, that recursively applies the smoothing with\nnoise to data in order to preserve the data variance while replacing\nhigh-frequency information by random data, leading to a coarse-to-fine training\nof GANs. We also present a synthetic data-set using the Hadamard bases that\nenables us to visualize the true distribution of data. We experiment with a\nDCGAN with the noise scale-space (NSS-GAN) using major data-sets in which\nNSS-GAN overtook state-of-the-arts in most cases independent of the image\ncontent.\n"}
+{"abstract": "  This paper discusses the design, implementation and field trials of WiMesh -\na resilient Wireless Mesh Network (WMN) based disaster communication system\npurpose-built for underdeveloped and rural parts of the world. Mesh networking\nis a mature area, and the focus of this paper is not on proposing novel models,\nprotocols or other mesh solutions. Instead, the paper focuses on the\nidentification of important design considerations and justifications for\nseveral design trade offs in the context of mesh networking for disaster\ncommunication in developing countries with very limited resources. These\ntrade-offs are discussed in the context of key desirable traits including\nsecurity, low cost, low power, size, availability, customization, portability,\nease of installation and deployment, and coverage area among others. We discuss\nat length the design, implementation, and field trial results of the WiMesh\nsystem which enables users spread over large geographical regions, to\ncommunicate with each other despite the lack of cellular coverage, power, and\nother communication infrastructure by leveraging multi-hop mesh networking and\nWi-Fi equipped handheld devices. Lessons learned along with real-world results\nare shared for WiMesh deployment in a remote rural mountainous village of\nPakistan, and the source code is shared with the research community.\n"}
+{"abstract": "  Learning by interaction is the key to skill acquisition for most living\norganisms, which is formally called Reinforcement Learning (RL). RL is\nefficient in finding optimal policies for endowing complex systems with\nsophisticated behavior. All paradigms of RL utilize a system model for finding\nthe optimal policy. Modeling dynamics can be done by formulating a mathematical\nmodel or system identification. Dynamic models are usually exposed to aleatoric\nand epistemic uncertainties that can divert the model from the one acquired and\ncause the RL algorithm to exhibit erroneous behavior. Accordingly, the RL\nprocess sensitive to operating conditions and changes in model parameters and\nlose its generality. To address these problems, Intensive system identification\nfor modeling purposes is needed for each system even if the model dynamics\nstructure is the same, as the slight deviation in the model parameters can\nrender the model useless in RL. The existence of an oracle that can adaptively\npredict the rest of the trajectory regardless of the uncertainties can help\nresolve the issue. The target of this work is to present a framework for\nfacilitating the system identification of different instances of the same\ndynamics class by learning a probability distribution of the dynamics\nconditioned on observed data with variational inference and show its\nreliability in robustly solving different instances of control problems with\nthe same model in model-based RL with maximum sample efficiency.\n"}
+{"abstract": "  Distributed quantum metrology can enhance the sensitivity for sensing\nspatially distributed parameters beyond the classical limits. Here we\ndemonstrate distributed quantum phase estimation with discrete variables to\nachieve Heisenberg limit phase measurements. Based on parallel entanglement in\nmodes and particles, we demonstrate distributed quantum sensing for both\nindividual phase shifts and an averaged phase shift, with an error reduction up\nto 1.4 dB and 2.7 dB below the shot-noise limit. Furthermore, we demonstrate a\ncombined strategy with parallel mode entanglement and multiple passes of the\nphase shifter in each mode. In particular, our experiment uses six entangled\nphotons with each photon passing the phase shifter up to six times, and\nachieves a total number of photon passes N=21 at an error reduction up to 4.7\ndB below the shot-noise limit. Our research provides a faithful verification of\nthe benefit of entanglement and coherence for distributed quantum sensing in\ngeneral quantum networks.\n"}
+{"abstract": "  We consider a simple model of a stochastic heat engine, which consists of a\nsingle Brownian particle moving in a one-dimensional periodically breathing\nharmonic potential. Overdamped limit is assumed. Expressions of second moments\n(variances and covariances ) of heat and work are obtained in the form of\nintegrals, whose integrands contain functions satisfying certain differential\nequations. The results in the quasi-static limit are simple functions of\ntemperatures of hot and cold thermal baths. The coefficient of variation of the\nwork is suggested to give an approximate probability for the work to exceeds a\ncertain threshold. During derivation, we get the expression of the\ncumulant-generating function.\n"}
+{"abstract": "  We apply general moment identities for Poisson stochastic integrals with\nrandom integrands to the computation of the moments of Markovian\ngrowth-collapse processes. This extends existing formulas for mean and variance\navailable in the literature to closed form moments expressions of all orders.\nIn comparison with other methods based on differential equations, our approach\nyields polynomial expressions in the time parameter. We also treat the case of\nthe associated embedded chain.\n"}
+{"abstract": "  Studies on stratospheric ozone have attracted much attention due to its\nserious impacts on climate changes and its important role as a tracer of\nEarth's global circulation. Tropospheric ozone as a main atmospheric pollutant\ndamages human health as well as the growth of vegetation. Yet there is still a\nlack of a theoretical framework to fully describe the variation of ozone. To\nunderstand ozone's spatiotemporal variance, we introduce the eigen microstate\nmethod to analyze the global ozone mass mixing ratio (OMMR) between 1979-01-01\nand 2020-06-30 at 37 pressure layers. We find that eigen microstates at\ndifferent geopotential heights can capture different climate phenomena and\nmodes. Without deseasonalization, the first eigen microstates capture the\nseasonal effect and reveal that the phase of the intra-annual cycle moves with\nthe geopotential heights. After deseasonalization, by contrast, the collective\npatterns from the overall trend, ENSO, QBO, and tropopause pressure are\nidentified by the first few significant eigen microstates. The theoretical\nframework proposed here can also be applied to other complex Earth systems.\n"}
+{"abstract": "  Effective and causal observable functions for low-order lifting linearization\nof nonlinear controlled systems are learned from data by using neural networks.\nWhile Koopman operator theory allows us to represent a nonlinear system as a\nlinear system in an infinite-dimensional space of observables, exact\nlinearization is guaranteed only for autonomous systems with no input, and\nfinding effective observable functions for approximation with a low-order\nlinear system remains an open question. Dual-Faceted Linearization uses a set\nof effective observables for low-order lifting linearization, but the method\nrequires knowledge of the physical structure of the nonlinear system. Here, a\ndata-driven method is presented for generating a set of nonlinear observable\nfunctions that can accurately approximate a nonlinear control system to a\nlow-order linear control system. A caveat in using data of measured variables\nas observables is that the measured variables may contain input to the system,\nwhich incurs a causality contradiction when lifting the system, i.e. taking\nderivatives of the observables. The current work presents a method for\neliminating such anti-causal components of the observables and lifting the\nsystem using only causal observables. The method is applied to excavation\nautomation, a complex nonlinear dynamical system, to obtain a low-order lifted\nlinear model for control design.\n"}
+{"abstract": "  With the widespread use and adoption of mobile platforms like Android a new\nsoftware quality concern has emerged -- energy consumption. However, developing\nenergy-efficient software and applications requires knowledge and likewise\nproper tooling to support mobile developers. To this aim, we present an\napproach to examine the energy evolution of software revisions based on their\nAPI interactions. The approach stems from the assumption that the utilization\nof an API has direct implications on the energy being consumed during runtime.\nBased on an empirical evaluation, we show initial results that API interactions\nserve as a flexible, lightweight, and effective way to compare software\nrevisions regarding their energy evolution. Given our initial results we\nenvision that in future using our approach mobile developers will be able to\ngain insights on the energy implications of changes in source code in the\ncourse of the software development life-cycle.\n"}
+{"abstract": "  In this work we develop a novel characterization of marginal causal effect\nand causal bias in the continuous treatment setting. We show they can be\nexpressed as an expectation with respect to a conditional probability\ndistribution, which can be estimated via standard statistical and probabilistic\nmethods. All terms in the expectations can be computed via automatic\ndifferentiation, also for highly non-linear models. We further develop a new\ncomplete criterion for identifiability of causal effects via covariate\nadjustment, showing the bias equals zero if the criterion is met. We study the\neffectiveness of our framework in three different scenarios: linear models\nunder confounding, overcontrol and endogenous selection bias; a non-linear\nmodel where full identifiability cannot be achieved because of missing data; a\nsimulated medical study of statins and atherosclerotic cardiovascular disease.\n"}
+{"abstract": "  In weather disasters, first responders access dedicated communication\nchannels different from civilian commercial channels to facilitate rescues.\nHowever, rescues in recent disasters have increasingly involved civilian and\nvolunteer forces, requiring civilian channels not to be overloaded with\ntraffic. We explore seven enhancements to the wording of Wireless Emergency\nAlerts (WEAs) and their effectiveness in getting smartphone users to comply,\nincluding reducing frivolous mobile data consumption during critical weather\ndisasters. We conducted a between-subjects survey (N=898), in which\nparticipants were either assigned no alert (control) or an alert framed as\nBasic Information, Altruism, Multimedia, Negative Feedback, Positive Feedback,\nReward, or Punishment. We find that Basic Information alerts resulted in the\nlargest reduction of multimedia and video services usage; we also find that\nPunishment alerts have the lowest absolute compliance. This work has\nimplications for creating more effective WEAs and providing a better\nunderstanding of how wording can affect emergency alert compliance.\n"}
+{"abstract": "  Malaria is an infectious disease with an immense global health burden.\nPlasmodium vivax is the most geographically widespread species of malaria.\nRelapsing infections, caused by the activation of liver-stage parasites known\nas hypnozoites, are a critical feature of the epidemiology of Plasmodium vivax.\nHypnozoites remain dormant in the liver for weeks or months after inoculation,\nbut cause relapsing infections upon activation. Here, we introduce a dynamic\nprobability model of the activation-clearance process governing both potential\nrelapses and the size of the hypnozoite reservoir. We begin by modelling\nactivation-clearance dynamics for a single hypnozoite using a continuous-time\nMarkov chain. We then extend our analysis to consider activation-clearance\ndynamics for a single mosquito bite, which can simultaneously establish\nmultiple hypnozoites, under the assumption of independent hypnozoite behaviour.\nWe derive analytic expressions for the time to first relapse and the time to\nhypnozoite clearance for mosquito bites establishing variable numbers of\nhypnozoites, both of which are quantities of epidemiological significance. Our\nresults extend those in the literature, which were limited due to an assumption\nof non-independence. Our within-host model can be embedded readily in\nmulti-scale models and epidemiological frameworks, with analytic solutions\nincreasing the tractability of statistical inference and analysis. Our work\ntherefore provides a foundation for further work on immune development and\nepidemiological-scale analysis, both of which are important for achieving the\ngoal of malaria elimination.\n"}
+{"abstract": "  Academic neural models for coreference resolution (coref) are typically\ntrained on a single dataset, OntoNotes, and model improvements are benchmarked\non that same dataset. However, real-world applications of coref depend on the\nannotation guidelines and the domain of the target dataset, which often differ\nfrom those of OntoNotes. We aim to quantify transferability of coref models\nbased on the number of annotated documents available in the target dataset. We\nexamine eleven target datasets and find that continued training is consistently\neffective and especially beneficial when there are few target documents. We\nestablish new benchmarks across several datasets, including state-of-the-art\nresults on PreCo.\n"}
+{"abstract": "  In many randomized clinical trials of therapeutics for COVID-19, the primary\noutcome is an ordinal categorical variable, and interest focuses on the odds\nratio (active agent vs. control) under the assumption of a proportional odds\nmodel. Although at the final analysis the outcome will be determined for all\nsubjects, at an interim analysis, the status of some participants may not yet\nbe determined, e.g., because ascertainment of the outcome may not be possible\nuntil some pre-specified follow-up time. Accordingly, the outcome from these\nsubjects can be viewed as censored. A valid interim analysis can be based on\ndata only from those subjects with full follow up; however, this approach is\ninefficient, as it does not exploit additional information that may be\navailable on those for whom the outcome is not yet available at the time of the\ninterim analysis. Appealing to the theory of semiparametrics, we propose an\nestimator for the odds ratio in a proportional odds model with censored,\ntime-lagged categorical outcome that incorporates additional baseline and\ntime-dependent covariate information and demonstrate that it can result in\nconsiderable gains in efficiency relative to simpler approaches. A byproduct of\nthe approach is a covariate-adjusted estimator for the odds ratio based on the\nfull data that would be available at a final analysis.\n"}
+{"abstract": "  Scoring rules measure the deviation between a probabilistic forecast and\nreality. Strictly proper scoring rules have the property that for any forecast,\nthe mathematical expectation of the score of a forecast p by the lights of p is\nstrictly better than the mathematical expectation of any other forecast q by\nthe lights of p. Probabilistic forecasts need not satisfy the axioms of the\nprobability calculus, but Predd, et al. (2009) have shown that given a finite\nsample space and any strictly proper additive and continuous scoring rule, the\nscore for any forecast that does not satisfy the axioms of probability is\nstrictly dominated by the score for some probabilistically consistent forecast.\nRecently, this result has been extended to non-additive continuous scoring\nrules. In this paper, a condition weaker than continuity is given that suffices\nfor the result, and the condition is proved to be optimal.\n"}
+{"abstract": "  We prove that, if $\\mathcal{GP}$ is the class of all Gorenstein projective\nmodules over a ring $R$, then $\\mathfrak{GP}=(\\mathcal{GP},\\mathcal{GP}^\\perp)$\nis a cotorsion pair. Moreover, $\\mathfrak{GP}$ is complete when all projective\nmodules are $\\lambda$-pure-injective for some infinite regular cardinal\n$\\lambda$ (in particular, if $R$ is right $\\Sigma$-pure-injective).\n  We obtain these results, on the one hand, studying the class of totally\nacyclic complexes over $R$. We prove that, when $R$ is $\\Sigma$-pure-injective,\nthis class is deconstructible and forms a coreflective subcategory of the\nhomotopy category of the projective modules. On the other hand, we use results\nabout $\\lambda$-pure-injective modules for infinite regular cardinals\n$\\lambda$.\n  Finally, under different set-theoretical hypotheses, we show that for an\narbitrary ring $R$, the following hold: (1) There exists an infinite regular\ncardinal number $\\lambda$ such that every projective module is\n$\\lambda$-pure-injective (and $\\mathfrak{GP}$ is complete). (2) $R$ is right\npure-semisimple if and only if there exists a regular uncountable $\\lambda$\nsuch that $\\mathrm{Mod}$-$R$ has enough $\\lambda$-pure-injective objects.\n"}
+{"abstract": "  Intensity mapping of the 21cm signal of neutral hydrogen will yield exciting\ninsights into the Epoch of Reionisation and the nature of the first galaxies.\nHowever, the large amount of data that will be generated by the next generation\nof radio telescopes, such as the Square Kilometre Array (SKA), as well as the\nnumerous observational obstacles to overcome, require analysis techniques tuned\nto extract the reionisation history and morphology. In this context, we\nintroduce a one-point statistic, to which we refer as the local variance,\n$\\sigma_\\mathrm{loc}$, that describes the distribution of the mean differential\n21cm brightness temperatures measured in two-dimensional maps along the\nfrequency direction of a light-cone. The local variance takes advantage of what\nis usually considered an observational bias, the sample variance. We find the\nredshift-evolution of the local variance to not only probe the reionisation\nhistory of the observed patches of the sky, but also trace the ionisation\nmorphology. This estimator provides a promising tool to constrain the midpoint\nof reionisation as well as gaining insight into the ionising properties of\nearly galaxies.\n"}
+{"abstract": "  Temperature fluctuations of a finite system follows the Landau bound $\\delta\nT^2 = T^2/C(T)$ where $C(T)$ is the heat capacity of the system. In turn, the\nsame bound sets a limit to the precision of temperature estimation when the\nsystem itself is used as a thermometer. In this paper, we employ graph theory\nand the concept of Fisher information to assess the role of topology on the\nthermometric performance of a given system. We find that low connectivity is a\nresource to build precise thermometers working at low temperatures, whereas\nhighly connected systems are suitable for higher temperatures. Upon modelling\nthe thermometer as a set of vertices for the quantum walk of an excitation, we\ncompare the precision achievable by position measurement to the optimal one,\nwhich itself corresponds to energy measurement.\n"}
+{"abstract": "  Deep neural networks have been widely used for feature learning in facial\nexpression recognition systems. However, small datasets and large intra-class\nvariability can lead to overfitting. In this paper, we propose a method which\nlearns an optimized compact network topology for real-time facial expression\nrecognition utilizing localized facial landmark features. Our method employs a\nspatio-temporal bilinear layer as backbone to capture the motion of facial\nlandmarks during the execution of a facial expression effectively. Besides, it\ntakes advantage of Monte Carlo Dropout to capture the model's uncertainty which\nis of great importance to analyze and treat uncertain cases. The performance of\nour method is evaluated on three widely used datasets and it is comparable to\nthat of video-based state-of-the-art methods while it has much less complexity.\n"}
+{"abstract": "  Accurate evaluation of the treatment result on X-ray images is a significant\nand challenging step in root canal therapy since the incorrect interpretation\nof the therapy results will hamper timely follow-up which is crucial to the\npatients' treatment outcome. Nowadays, the evaluation is performed in a manual\nmanner, which is time-consuming, subjective, and error-prone. In this paper, we\naim to automate this process by leveraging the advances in computer vision and\nartificial intelligence, to provide an objective and accurate method for root\ncanal therapy result assessment. A novel anatomy-guided multi-branch\nTransformer (AGMB-Transformer) network is proposed, which first extracts a set\nof anatomy features and then uses them to guide a multi-branch Transformer\nnetwork for evaluation. Specifically, we design a polynomial curve fitting\nsegmentation strategy with the help of landmark detection to extract the\nanatomy features. Moreover, a branch fusion module and a multi-branch structure\nincluding our progressive Transformer and Group Multi-Head Self-Attention\n(GMHSA) are designed to focus on both global and local features for an accurate\ndiagnosis. To facilitate the research, we have collected a large-scale root\ncanal therapy evaluation dataset with 245 root canal therapy X-ray images, and\nthe experiment results show that our AGMB-Transformer can improve the diagnosis\naccuracy from 57.96% to 90.20% compared with the baseline network. The proposed\nAGMB-Transformer can achieve a highly accurate evaluation of root canal\ntherapy. To our best knowledge, our work is the first to perform automatic root\ncanal therapy evaluation and has important clinical value to reduce the\nworkload of endodontists.\n"}
+{"abstract": "  Inference of population structure from genetic data plays an important role\nin population and medical genetics studies. The traditional EIGENSTRAT method\nhas been widely used for computing and selecting top principal components that\ncapture population structure information (Price et al., 2006). With the\nadvancement and decreasing cost of sequencing technology, whole-genome\nsequencing data provide much richer information about the underlying population\nstructures. However, the EIGENSTRAT method was originally developed for\nanalyzing array-based genotype data and thus may not perform well on sequencing\ndata for two reasons. First, the number of genetic variants $p$ is much larger\nthan the sample size $n$ in sequencing data such that the sample-to-marker\nratio $n/p$ is nearly zero, violating the assumption of the Tracy-Widom test\nused in the EIGENSTRAT method. Second, the EIGENSTRAT method might not be able\nto handle the linkage disequilibrium (LD) well in sequencing data. To resolve\nthose two critical issues, we propose a new statistical method called ERStruct\nto estimate the number of latent sub-populations based on sequencing data. We\npropose to use the ratio of successive eigenvalues as a more robust testing\nstatistic, and then we approximate the null distribution of our proposed test\nstatistic using modern random matrix theory. Simulation studies found that our\nproposed ERStruct method has outperformed the traditional Tracy-Widom test on\nsequencing data. We further use two public data sets from the HapMap 3 and the\n1000 Genomes Projects to demonstrate the performance of our ERStruct method. We\nalso implement our ERStruct in a MATLAB toolbox which is now publicly available\non GitHub through https://github.com/bglvly/ERStruct.\n"}
+{"abstract": "  It is well known that the classic Allen-Cahn equation satisfies the maximum\nbound principle (MBP), that is, the absolute value of its solution is uniformly\nbounded for all time by certain constant under suitable initial and boundary\nconditions. In this paper, we consider numerical solutions of the modified\nAllen-Cahn equation with a Lagrange multiplier of nonlocal and local effects,\nwhich not only shares the same MBP as the original Allen-Cahn equation but also\nconserves the mass exactly. We reformulate the model equation with a linear\nstabilizing technique, then construct first- and second-order exponential time\ndifferencing schemes for its time integration. We prove the unconditional MBP\npreservation and mass conservation of the proposed schemes in the time discrete\nsense and derive their error estimates under some regularity assumptions.\nVarious numerical experiments in two and three dimensions are also conducted to\nverify the theoretical results.\n"}
+{"abstract": "  We prove that the set of Segre-degenerate points of a real-analytic\nsubvariety $X$ in ${\\mathbb{C}}^n$ is a closed semianalytic set. It is a\nsubvariety if $X$ is coherent. More precisely, the set of points where the germ\nof the Segre variety is of dimension $k$ or greater is a closed semianalytic\nset in general, and for a coherent $X$, it is a real-analytic subvariety of\n$X$. For a hypersurface $X$ in ${\\mathbb{C}}^n$, the set of Segre-degenerate\npoints, $X_{[n]}$, is a semianalytic set of dimension at most $2n-4$. If $X$ is\ncoherent, then $X_{[n]}$ is a complex subvariety of (complex) dimension $n-2$.\nExample hypersurfaces are given showing that $X_{[n]}$ need not be a subvariety\nand that it also needs not be complex; $X_{[n]}$ can, for instance, be a real\nline.\n"}
+{"abstract": "  In the house credit process, banks and lenders rely on a fast and accurate\nestimation of a real estate price to determine the maximum loan value. Real\nestate appraisal is often based on relational data, capturing the hard facts of\nthe property. Yet, models benefit strongly from including image data, capturing\nadditional soft factors. The combination of the different data types requires a\nmulti-view learning method. Therefore, the question arises which strengths and\nweaknesses different multi-view learning strategies have. In our study, we test\nmulti-kernel learning, multi-view concatenation and multi-view neural networks\non real estate data and satellite images from Asheville, NC. Our results\nsuggest that multi-view learning increases the predictive performance up to 13%\nin MAE. Multi-view neural networks perform best, however result in\nintransparent black-box models. For users seeking interpretability, hybrid\nmulti-view neural networks or a boosting strategy are a suitable alternative.\n"}
+{"abstract": "  The electronic density of states (DOS) highlights fundamental properties of\nmaterials that oftentimes dictate their properties, such as the band gap and\nVan Hove singularities. In this short note, we discuss how sharp features of\nthe density of states can be obscured by smearing methods (such as the Gaussian\nand Fermi smearing methods) when calculating the DOS. While the common approach\nto reach a \"converged\" density of states of a material is to increase the\ndiscrete k-point mesh density, we show that the DOS calculated by smearing\nmethods can appear to converge but not to the correct DOS. Employing the\ntetrahedron method for Brillouin zone integration resolves key features of the\ndensity of states far better than smearing methods.\n"}
+{"abstract": "  In this study, the in-plane Bloch wave propagation and bandgaps in a finitely\nstretched square lattice were investigated numerically and theoretically. To be\nspecific, the elastic band diagram was calculated for an infinite periodic\nstructure with a cruciform hyperelastic unit cell under uniaxial or biaxial\ntension. In addition, an elastodynamic \"tight binding\" model was proposed to\ninvestigate the formation and evolution of the band structure. The elastic\nwaves were found to propagate largely under \"easy\" modes in the pre-stretched\nsoft lattice, and finite stretch tuned the symmetry of the band structure, but\nalso \"purify\" the propagation modes. Moreover, the uniaxial stretch exhibits\nthe opposite impacts on the two \"easy\" modes. The effect of the biaxial stretch\nwas equated with the superposition of the uniaxial stretches in the\ntessellation directions. The mentioned effects on the band structure could be\nattributed to the competition between the effective shear moduli and lengths\nfor different beam components. Next, the finite stretch could tune the\ndirectional bandgap of the soft lattice, and the broadest elastic wave bandgaps\ncould be anticipated in an equi-biaxial stretch. In this study, an avenue was\nopened to design and implement elastic wave control devices with weight\nefficiency and tunability. Furthermore, the differences between the physical\nsystem and the corresponding simplified theoretical model (e.g., the\ntheoretically predicted flat bands) did not exist in the numerical\ncalculations.\n"}
+{"abstract": "  In this paper, a sparse Kronecker-product (SKP) coding scheme is proposed for\nunsourced multiple access. Specifically, the data of each active user is\nencoded as the Kronecker product of two component codewords with one being\nsparse and the other being forward-error-correction (FEC) coded. At the\nreceiver, an iterative decoding algorithm is developed, consisting of matrix\nfactorization for the decomposition of the Kronecker product and soft-in\nsoft-out decoding for the component sparse code and the FEC code. The cyclic\nredundancy check (CRC) aided interference cancellation technique is further\nincorporated for performance improvement. Numerical results show that the\nproposed scheme outperforms the state-of-the-art counterparts, and approaches\nthe random coding bound within a gap of only 0.1 dB at the code length of 30000\nwhen the number of active users is less than 75, and the error rate can be made\nvery small even if the number of active users is relatively large.\n"}
+{"abstract": "  Deep neural networks for medical image reconstruction are traditionally\ntrained using high-quality ground-truth images as training targets. Recent work\non Noise2Noise (N2N) has shown the potential of using multiple noisy\nmeasurements of the same object as an alternative to having a ground-truth.\nHowever, existing N2N-based methods are not suitable for learning from the\nmeasurements of an object undergoing nonrigid deformation. This paper addresses\nthis issue by proposing the deformation-compensated learning (DeCoLearn) method\nfor training deep reconstruction networks by compensating for object\ndeformations. A key component of DeCoLearn is a deep registration module, which\nis jointly trained with the deep reconstruction network without any\nground-truth supervision. We validate DeCoLearn on both simulated and\nexperimentally collected magnetic resonance imaging (MRI) data and show that it\nsignificantly improves imaging quality.\n"}
+{"abstract": "  This paper develops a theory of polynomial maps from commutative semigroups\nto arbitrary groups and proves that it has desirable formal properties when the\ntarget group is locally nilpotent. We apply this theory to solve Waring's\nProblem for Heisenberg groups in a sequel to this paper.\n"}
+{"abstract": "  Conditional image synthesis aims to create an image according to some\nmulti-modal guidance in the forms of textual descriptions, reference images,\nand image blocks to preserve, as well as their combinations. In this paper,\ninstead of investigating these control signals separately, we propose a new\ntwo-stage architecture, M6-UFC, to unify any number of multi-modal controls. In\nM6-UFC, both the diverse control signals and the synthesized image are\nuniformly represented as a sequence of discrete tokens to be processed by\nTransformer. Different from existing two-stage autoregressive approaches such\nas DALL-E and VQGAN, M6-UFC adopts non-autoregressive generation (NAR) at the\nsecond stage to enhance the holistic consistency of the synthesized image, to\nsupport preserving specified image blocks, and to improve the synthesis speed.\nFurther, we design a progressive algorithm that iteratively improves the\nnon-autoregressively generated image, with the help of two estimators developed\nfor evaluating the compliance with the controls and evaluating the fidelity of\nthe synthesized image, respectively. Extensive experiments on a newly collected\nlarge-scale clothing dataset M2C-Fashion and a facial dataset Multi-Modal\nCelebA-HQ verify that M6-UFC can synthesize high-fidelity images that comply\nwith flexible multi-modal controls.\n"}
+{"abstract": "  The scaling up of quantum hardware is the fundamental challenge ahead in\norder to realize the disruptive potential of quantum technology in information\nscience. Among the plethora of hardware platforms, photonics stands out by\noffering a modular approach, where the main challenge is to construct\nsufficiently high-quality building blocks and develop methods to efficiently\ninterface them. Importantly, the subsequent scaling-up will make full use of\nthe mature integrated photonic technology provided by photonic foundry\ninfrastructure to produce small foot-print quantum processors of immense\ncomplexity. A fully coherent and deterministic photon-emitter interface is a\nkey enabler of quantum photonics, and can today be realized with solid-state\nquantum emitters with specifications reaching the quantitative benchmark\nreferred to as Quantum Advantage. This light-matter interaction primer realizes\na range of quantum photonic resources and functionalities, including on-demand\nsingle-photon and multi-photon entanglement sources, and photon-photon\nnonlinear quantum gates. We will present the current state-of-the-art in\nsingle-photon quantum hardware and the main photonic building blocks required\nin order to scale up. Furthermore, we will point out specific promising\napplications of the hardware building blocks within quantum communication and\nphotonic quantum computing, laying out the road ahead for quantum photonics\napplications that could offer a genuine quantum advantage.\n"}
+{"abstract": "  This paper presents an NLP (Natural Language Processing) approach to\ndetecting spoilers in book reviews, using the University of California San\nDiego (UCSD) Goodreads Spoiler dataset. We explored the use of LSTM, BERT, and\nRoBERTa language models to perform spoiler detection at the sentence-level.\nThis was contrasted with a UCSD paper which performed the same task, but using\nhandcrafted features in its data preparation. Despite eschewing the use of\nhandcrafted features, our results from the LSTM model were able to slightly\nexceed the UCSD team's performance in spoiler detection.\n"}
+{"abstract": "  An iterative numerical method to compute the conformal mapping in the context\nof propagating water waves over uneven topographies is investigated. The map\nflattens the fluid domain onto a canonical strip in which computations are\nperformed. The accuracy of the method is tested by using the MATLAB\nSchwarz-Christoffel toolbox mapping as a benchmark. Besides, we give a\nnumerical alternative to compute the inverse of the conformal map.\n"}
+{"abstract": "  We consider an investment process that includes a number of features, each of\nwhich can be active or inactive. Our goal is to attribute or decompose an\nachieved performance to each of these features, plus a baseline value. There\nare many ways to do this, which lead to potentially different attributions in\nany specific case. We argue that a specific attribution method due to Shapley\nis the preferred method, and discuss methods that can be used to compute this\nattribution exactly, or when that is not practical, approximately.\n"}
+{"abstract": "  The evolution of circumstellar discs is highly influenced by their\nsurroundings, in particular by external photoevaporation due to nearby stars\nand dynamical truncations. The impact of these processes on disc populations\ndepends on the dynamical evolution of the star-forming region. Here we\nimplement a simple model of molecular cloud collapse and star formation to\nobtain primordial positions and velocities of young stars and follow their\nevolution in time, including that of their circumstellar discs. Our disc model\ntakes into account viscous evolution, internal and external photoevaporation,\ndust evolution, and dynamical truncations. The disc evolution is resolved\nsimultaneously with the star cluster dynamics and stellar evolution. Our\nresults show that an extended period of star formation allows for massive discs\nformed later in the simulations to survive for several million years. This\ncould explain massive discs surviving in regions of high UV radiation.\n"}
+{"abstract": "  This article proposes a framework for the study of periodic maps $T$ from a\n(typically finite) set $X$ to itself when the set $X$ is equipped with one or\nmore real- or complex-valued functions. The main idea, inspired by the\ntime-evolution operator construction from ergodic theory, is the introduction\nof a vector space that contains the given functions and is closed under\ncomposition with $T$, along with a time-evolution operator on that vector\nspace. I show that the invariant functions and 0-mesic functions span\ncomplementary subspaces associated respectively with the eigenvalue 1 and the\nother eigenvalues. Alongside other examples, I give an explicit description of\nthe spectrum of the evolution operator when $X$ is the set of $k$-element\nmultisets with elements in $\\{0,1,\\dots,n-1\\}$, $T$ increments each element of\na multiset by 1 mod $n$, and $g_i: X \\rightarrow \\mathbb{R}$ (with $1 \\leq i\n\\leq k$) maps a multiset to its $i$th smallest element.\n"}
+{"abstract": "  In the upcoming decades large facilities, such as the SKA, will provide\nresolved observations of the kinematics of millions of galaxies. In order to\nassist in the timely exploitation of these vast datasets we explore the use of\na self-supervised, physics aware neural network capable of Bayesian kinematic\nmodelling of galaxies. We demonstrate the network's ability to model the\nkinematics of cold gas in galaxies with an emphasis on recovering physical\nparameters and accompanying modelling errors. The model is able to recover\nrotation curves, inclinations and disc scale lengths for both CO and HI data\nwhich match well with those found in the literature. The model is also able to\nprovide modelling errors over learned parameters thanks to the application of\nquasi-Bayesian Monte-Carlo dropout. This work shows the promising use of\nmachine learning, and in particular self-supervised neural networks, in the\ncontext of kinematically modelling galaxies. This work represents the first\nsteps in applying such models for kinematic fitting and we propose that\nvariants of our model would seem especially suitable for enabling emission-line\nscience from upcoming surveys with e.g. the SKA, allowing fast exploitation of\nthese large datasets.\n"}
+{"abstract": "  Context: Petri net slicing is a technique to reduce the size of a Petri net\nso that it can ease the analysis or understanding of the original Petri net.\n  Objective: Presenting two new Petri net slicing algorithms to isolate those\nplaces and transitions of a Petri net (the slice) that may contribute tokens to\none or more places given (the slicing criterion).\n  Method: The two algorithms proposed are formalized. The completeness of the\nfirst algorithm and the minimality of the second algorithm are formally proven.\nBoth algorithms together with other three state-of-the-art algorithms have been\nimplemented and integrated into a single tool so that we have been able to\ncarry out a fair empirical evaluation.\n  Results: Besides the two new Petri net slicing algorithms, a public, free,\nand open-source implementation of five algorithms is reported. The results of\nan empirical evaluation of the new algorithms and the slices that they produce\nare also presented.\n  Conclusions: The first algorithm collects all places and transitions that may\ninfluence (in any computation) the slicing criterion, while the second\nalgorithm collects a minimum set of places and transitions that may influence\n(in some computation) the slicing criterion. Therefore, the net computed by the\nfirst algorithm can reproduce any computation that contributes tokens to any\nplace of interest. In contrast, the second algorithm loses this possibility but\nit often produces a much more reduced subnet (which still can reproduce some\ncomputations that contribute tokens to some places of interest). The first\nalgorithm is proven complete, and the second one is proven minimal.\n"}
+{"abstract": "  The recently developed generalized Fourier-Galerkin method is complemented by\na numerical continuation with respect to the kinetic energy, which extends the\nframework to the investigation of modal interactions resulting in folds of the\nnonlinear modes. In order to enhance the practicability regarding the\ninvestigation of complex large-scale systems, it is proposed to provide\nanalytical gradients and exploit sparsity of the nonlinear part of the\ngoverning algebraic equations. A novel reduced order model (ROM) is developed\nfor those regimes where internal resonances are absent. The approach allows for\nan accurate approximation of the multi-harmonic content of the resonant mode\nand accounts for the contributions of the off-resonant modes in their\nlinearized forms. The ROM facilitates the efficient analysis of self-excited\nlimit cycle oscillations, frequency response functions and the direct tracing\nof forced resonances. The ROM is equipped with a large parameter space\nincluding parameters associated with linear damping and near-resonant harmonic\nforcing terms. An important objective of this paper is to demonstrate the broad\napplicability of the proposed overall methodology. This is achieved by selected\nnumerical examples including finite element models of structures with strongly\nnonlinear, non-conservative contact constraints.\n"}
+{"abstract": "  In this position paper, we explore the adoption of a Smart City with a\nsocio-technical perspective. A Smart city is a transformational technological\nprocess leading to profound modifications of existing urban regimes and\ninfrastructure components. In this study, we consider a Smart City as a\nsocio-technical system where the interplay between technologies and users\nensures the sustainable development of smart city initiatives that improve the\nquality of life and solve important socio-economic problems. The adoption of a\nSmart City required a participative approach where users are involved during\nthe adoption process to joint optimise both systems. Thus, we contribute to\nsocio-technical research showing how a participative approach based on press\nrelationships to facilitate information exchange between municipal actors and\ncitizens worked as a success factor for the smart city adoption. We also\ndiscuss the limitations of this approach.\n"}
+{"abstract": "  We investigate the role played by density inhomogeneities and dissipation on\nthe final outcome of collapse of a self-gravitating sphere. By imposing a\nperturbative scheme on the thermodynamical variables and gravitational\npotentials we track the evolution of the collapse process starting off with an\ninitially static perfect fluid sphere which is shear-free. The collapsing core\ndissipates energy in the form of a radial heat flux with the exterior spacetime\nbeing filled with a superposition of null energy and an anisotropic string\ndistribution. The ensuing dynamical process slowly evolves into a shear-like\nregime with contributions from the heat flux and density fluctuations. We show\nthat the anisotropy due to the presence of the strings drives the stellar fluid\ntowards instability with this effect being enhanced by the density\ninhomogeneity. An interesting and novel consequence of this collapse scenario\nis the delay in the formation of the horizon.\n"}
+{"abstract": "  Shapley Values, a solution to the credit assignment problem in cooperative\ngame theory, are a popular type of explanation in machine learning, having been\nused to explain the importance of features, embeddings, and even neurons. In\nNLP, however, leave-one-out and attention-based explanations still predominate.\nCan we draw a connection between these different methods? We formally prove\nthat -- save for the degenerate case -- attention weights and leave-one-out\nvalues cannot be Shapley Values. $\\textit{Attention flow}$ is a post-processed\nvariant of attention weights obtained by running the max-flow algorithm on the\nattention graph. Perhaps surprisingly, we prove that attention flows are indeed\nShapley Values, at least at the layerwise level. Given the many desirable\ntheoretical qualities of Shapley Values -- which has driven their adoption\namong the ML community -- we argue that NLP practitioners should, when\npossible, adopt attention flow explanations alongside more traditional ones.\n"}
+{"abstract": "  Currently there has been increasing demand for real-time training on\nresource-limited IoT devices such as smart sensors, which realizes standalone\nonline adaptation for streaming data without data transfers to remote servers.\nOS-ELM (Online Sequential Extreme Learning Machine) has been one of promising\nneural-network-based online algorithms for on-chip learning because it can\nperform online training at low computational cost and is easy to implement as a\ndigital circuit. Existing OS-ELM digital circuits employ fixed-point data\nformat and the bit-widths are often manually tuned, however, this may cause\noverflow or underflow which can lead to unexpected behavior of the circuit. For\non-chip learning systems, an overflow/underflow-free design has a great impact\nsince online training is continuously performed and the intervals of\nintermediate variables will dynamically change as time goes by. In this paper,\nwe propose an overflow/underflow-free bit-width optimization method for\nfixed-point digital circuits of OS-ELM. Experimental results show that our\nmethod realizes overflow/underflow-free OS-ELM digital circuits with 1.0x -\n1.5x more area cost compared to the baseline simulation method where overflow\nor underflow can happen.\n"}
+{"abstract": "  In 1914 Bohr proved that there is an $r_0 \\in(0,1)$ such that if a power\nseries $\\sum_{m=0}^\\infty c_m z^m$ is convergent in the open unit disc and\n$|\\sum_{m=0}^\\infty c_m z^m|<1$ then, $\\sum_{m=0}^\\infty |c_m z^m|<1$ for\n$|z|<r_0$. The largest value of such $r_0$ is called the Bohr radius. In this\narticle, we find Bohr radius for some univalent harmonic mappings having\ndifferent dilatations and in addition, also compute Bohr radius for the\nfunctions convex in one direction.\n"}
+{"abstract": "  Edge computing was introduced as a technical enabler for the demanding\nrequirements of new network technologies like 5G. It aims to overcome\nchallenges related to centralized cloud computing environments by distributing\ncomputational resources to the edge of the network towards the customers. The\ncomplexity of the emerging infrastructures increases significantly, together\nwith the ramifications of outages on critical use cases such as self-driving\ncars or health care. Artificial Intelligence for IT Operations (AIOps) aims to\nsupport human operators in managing complex infrastructures by using machine\nlearning methods. This paper describes the system design of an AIOps platform\nwhich is applicable in heterogeneous, distributed environments. The overhead of\na high-frequency monitoring solution on edge devices is evaluated and\nperformance experiments regarding the applicability of three anomaly detection\nalgorithms on edge devices are conducted. The results show, that it is feasible\nto collect metrics with a high frequency and simultaneously run specific\nanomaly detection algorithms directly on edge devices with a reasonable\noverhead on the resource utilization.\n"}
+{"abstract": "  Pushing is an essential non-prehensile manipulation skill used for tasks\nranging from pre-grasp manipulation to scene rearrangement, reasoning about\nobject relations in the scene, and thus pushing actions have been widely\nstudied in robotics. The effective use of pushing actions often requires an\nunderstanding of the dynamics of the manipulated objects and adaptation to the\ndiscrepancies between prediction and reality. For this reason, effect\nprediction and parameter estimation with pushing actions have been heavily\ninvestigated in the literature. However, current approaches are limited because\nthey either model systems with a fixed number of objects or use image-based\nrepresentations whose outputs are not very interpretable and quickly accumulate\nerrors. In this paper, we propose a graph neural network based framework for\neffect prediction and parameter estimation of pushing actions by modeling\nobject relations based on contacts or articulations. Our framework is validated\nboth in real and simulated environments containing different shaped multi-part\nobjects connected via different types of joints and objects with different\nmasses. Our approach enables the robot to predict and adapt the effect of a\npushing action as it observes the scene. Further, we demonstrate 6D effect\nprediction in the lever-up action in the context of robot-based hard-disk\ndisassembly.\n"}
+{"abstract": "  The Ly$\\alpha$ emission line is one of the most promising probes of cosmic\nreionisation but isolating the signature of a change in the ionisation state of\nthe IGM is challenging because of intrinsic evolution and internal radiation\ntransfer effects. We present the first study of the evolution of Ly$\\alpha$\nemitters (LAE) during the epoch of reionisation based on a full\nradiation-hydrodynamics cosmological simulation that is able to capture both\nthe large-scale process of reionisation and the small-scale properties of\ngalaxies. We predict the Ly$\\alpha$ emission of galaxies in the $10^3$ cMpc$^3$\nSPHINX simulation at $6\\leq z\\leq9$ by computing the full Ly$\\alpha$ radiation\ntransfer from ISM to IGM scales. SPHINX is able to reproduce many observational\nconstraints such as the UV/Ly$\\alpha$ luminosity functions and stellar mass\nfunctions at z $\\geq$ 6 for the dynamical range probed by our simulation\n($M_{\\rm 1500}\\gtrsim-18$, $L_{\\rm Ly\\alpha}\\lesssim10^{42}$ erg/s,\n$M_{\\star}\\lesssim10^9$ M$_{\\odot}$). As intrinsic Ly$\\alpha$ emission and\ninternal Ly$\\alpha$ escape fractions barely evolve from $z=6$ to 9, the\nobserved suppression of Ly$\\alpha$ luminosities with increasing redshift is\nfully attributed to IGM absorption. For most observable galaxies ($M_{\\rm\n1500}\\lesssim-16$), the Ly$\\alpha$ line profiles are slightly shifted to the\nred due to internal radiative transfer effects which mitigates the effect of\nIGM absorption. Overall, the enhanced Ly$\\alpha$ suppression during\nreionisation traces the IGM neutral fraction $x_{\\rm HI}$ well but the\npredicted amplitude of this reduction is a strong function of the Ly$\\alpha$\npeak shift, which is set at ISM/CGM scales. We find that a large number of LAEs\ncould be detectable in very deep surveys during reionisation when $x_{\\rm HI}$\nis still $\\approx 50\\%$.\n"}
+{"abstract": "  We present our 500 pc distance-limited study of stellar fares using the Dark\nEnergy Camera as part of the Deeper, Wider, Faster Program. The data was\ncollected via continuous 20-second cadence g band imaging and we identify\n19,914 sources with precise distances from Gaia DR2 within twelve, ~3\nsquare-degree, fields over a range of Galactic latitudes. An average of ~74\nminutes is spent on each field per visit. All light curves were accessed\nthrough a novel unsupervised machine learning technique designed for anomaly\ndetection. We identify 96 flare events occurring across 80 stars, the majority\nof which are M dwarfs. Integrated are energies range from $\\sim\n10^{31}-10^{37}$ erg, with a proportional relationship existing between\nincreased are energy with increased distance from the Galactic plane,\nrepresentative of stellar age leading to declining yet more energetic are\nevents. In agreement with previous studies we observe an increase in flaring\nfraction from M0 -> M6 spectral types. Furthermore, we find a decrease in the\nflaring fraction of stars as vertical distance from the galactic plane is\nincreased, with a steep decline present around ~100 pc. We find that ~70% of\nidentified flares occur on short timescales of ~8 minutes. Finally we present\nour associated are rates, finding a volumetric rate of $2.9 \\pm 0.3 \\times\n10^{-6}$ flares pc$^{-3}$ hr$^{-1}$.\n"}
+{"abstract": "  We combine NLO predictions with full top-quark mass dependence with\napproximate NNLO predictions for Higgs-boson pair production in gluon fusion,\nincluding the possibility to vary coupling parameters within a non-linear\nEffective Field Theory framework containing five anomalous couplings for this\nprocess. We study the impact of the anomalous couplings on various observables,\nand present Higgs-pair invariant-mass distributions at seven benchmark points\ncharacterising different $m_{hh}$ shape types. We also provide numerical\ncoefficients for the approximate NNLO cross section as a function of the\nanomalous couplings at $\\sqrt{s}=14$ TeV.\n"}
+{"abstract": "  Detection and classification of ships based on their silhouette profiles in\nnatural imagery is an important undertaking in computer science. This problem\ncan be viewed from a variety of perspectives, including security, traffic\ncontrol, and even militarism. Therefore, in each of the aforementioned\napplications, specific processing is required. In this paper, by applying the\n\"bag of words\" (BoW), a new method is presented that its words are the features\nthat are obtained using pre-trained models of deep convolutional networks. ,\nThree VGG models are utilized which provide superior accuracy in identifying\nobjects. The regions of the image that are selected as the initial proposals\nare derived from a greedy algorithm on the key points generated by the Scale\nInvariant Feature Transform (SIFT) method. Using the deep features in the BOW\nmethod provides a good improvement in the recognition and classification of\nships. Eventually, we obtained an accuracy of 91.8% in the classification of\nthe ships which shows the improvement of about 5% compared to previous methods.\n"}
+{"abstract": "  Recent data on the nuclear modification of W and Z boson production measured\nby the ATLAS collaboration in PbPb collisions at $\\sqrt{s_{\\rm nn}}=5.02$ TeV\nshow an enhancement in peripheral collisions, seemingly contradicting\npredictions of the Glauber model. The data were previously explained by arguing\nthat the nucleon-nucleon cross section may be shadowed in nucleus-nucleus\ncollisions, and hence suppressed compared to the proton-proton cross section at\nthe same collision energy. This interpretation has quite significant\nconsequences for the understanding of heavy-ion data, in particular in the\ncontext of the Glauber model. Instead, we provide an alternative explanation of\nthe data by assuming that there is a mild bias present in the centrality\ndetermination of the measurement; on the size of the related systematic\nuncertainty. Using this assumption, we show that the data is in agreement with\ntheoretical calculations using nuclear parton distribution functions. Finally,\nwe speculate that the centrality dependence of the W$^-$/W$^{+}$ ratio may\npoint to the relevance of a larger skin thickness of the Pb nucleus, which, if\npresent, would result in a few percent larger PbPb cross section than currently\naccounted for in the Glauber model and may hence be the root of the centrality\nbias.\n"}
+{"abstract": "  A complete understanding of solar radio bursts requires developing numerical\ntechniques which can connect large-scale activities with kinetic plasma\nprocesses. As a starting point, this study presents a numerical scheme\ncombining three different techniques: (1) extrapolation of magnetic field\noverlying a specific active region in order to derive the background field, (2)\nguiding-center simulation of dynamics of millions of particles within a\nselected loop to reveal the integral velocity distribution function (VDF)\naround certain sections of the loop, and (3) particle-in-cell (PIC) simulation\nof kinetic instabilities driven by energetic electrons initiated by the\nobtained distributions. Scattering effects at various levels (weak, moderate,\nand strong) due to wave/turbulence-particle interaction are considered using\nprescribed time scales of scattering. It was found that the obtained VDFs\ncontain strip-like and loss-cone features with positive gradient, and both\nfeatures are capable of driving electron cyclotron maser emission (ECME), which\nis a viable radiation mechanism for some solar radio bursts, in particular,\nsolar radio spikes. The strip-like feature is important in driving the harmonic\nX mode, while the loss-cone feature can be important in driving the fundamental\nX mode. In the weak-scattering case, the rate of energy conversion from\nenergetic electrons to X2 can reach up to ~2.9 * 10^-3 Ek0, where Ek0 is the\ninitial kinetic energy of energetic electrons. The study demonstrates a novel\nway of exciting X2 mode in the corona during solar flares, and provides new\nsight into how escaping radiation can be generated within a coronal loop during\nsolar flares.\n"}
+{"abstract": "  We show that the QRAT simulation algorithm of $\\forall$Exp+Res from [B. Kiesl\nand M. Seidl, 2019] cannot be lifted to IR-calc.\n"}
+{"abstract": "  William Cranch Bond, director of the Harvard College Observatory in mid-19th\ncentury, carried out detailed sunspot observations during the period 1847-1849.\nWe highlight Bond was the observer with the highest daily number of sunspot\ngroups observed in Solar Cycle 9 recording 18 groups on 26 December 1848\naccording to the current sunspot group database. However, we have detected\nsignificant mistakes in these counts due to the use of sunspot position tables\ninstead of solar drawings. Therefore, we have revisited the sunspot\nobservations made by Bond, establishing a new group counting. Our new counts of\nthe sunspot groups from Bond's drawings indicate that solar activity was\npreviously overestimated. Moreover, after this new counting, Bond would not be\nthe astronomer who recorded the highest daily group number for Solar Cycle 9\nbut Schmidt with 16 groups on 14 February 1849. We have also indicated the new\nhighest annual group numbers recorded by any observer for the period 1847-1849\nin order to correct those values applied in the \"brightest star\" method, which\nis used as a rough indicator of the solar activity level. Furthermore, a\ncomparison between Bond's sunspot records and the sunspot observations made by\nSchwabe and Wolf is shown. We conclude that the statistics of Wolf and Bond are\nsimilar regarding to the group count. Additionally, Schwabe was able to observe\nsmaller groups than Bond.\n"}
+{"abstract": "  Implementing embedded neural network processing at the edge requires\nefficient hardware acceleration that couples high computational performance\nwith low power consumption. Driven by the rapid evolution of network\narchitectures and their algorithmic features, accelerator designs are\nconstantly updated and improved. To evaluate and compare hardware design\nchoices, designers can refer to a myriad of accelerator implementations in the\nliterature. Surveys provide an overview of these works but are often limited to\nsystem-level and benchmark-specific performance metrics, making it difficult to\nquantitatively compare the individual effect of each utilized optimization\ntechnique. This complicates the evaluation of optimizations for new accelerator\ndesigns, slowing-down the research progress. This work provides a survey of\nneural network accelerator optimization approaches that have been used in\nrecent works and reports their individual effects on edge processing\nperformance. It presents the list of optimizations and their quantitative\neffects as a construction kit, allowing to assess the design choices for each\nbuilding block separately. Reported optimizations range from up to 10'000x\nmemory savings to 33x energy reductions, providing chip designers an overview\nof design choices for implementing efficient low power neural network\naccelerators.\n"}
+{"abstract": "  The speed-accuracy Pareto curve of object detection systems have advanced\nthrough a combination of better model architectures, training and inference\nmethods. In this paper, we methodically evaluate a variety of these techniques\nto understand where most of the improvements in modern detection systems come\nfrom. We benchmark these improvements on the vanilla ResNet-FPN backbone with\nRetinaNet and RCNN detectors. The vanilla detectors are improved by 7.7% in\naccuracy while being 30% faster in speed. We further provide simple scaling\nstrategies to generate family of models that form two Pareto curves, named\nRetinaNet-RS and Cascade RCNN-RS. These simple rescaled detectors explore the\nspeed-accuracy trade-off between the one-stage RetinaNet detectors and\ntwo-stage RCNN detectors. Our largest Cascade RCNN-RS models achieve 52.9% AP\nwith a ResNet152-FPN backbone and 53.6% with a SpineNet143L backbone. Finally,\nwe show the ResNet architecture, with three minor architectural changes,\noutperforms EfficientNet as the backbone for object detection and instance\nsegmentation systems.\n"}
+{"abstract": "  We are concerned with the linear stability of the Couette flow for the\nnon-isentropic compressible Navier-Stokes equations with vanished shear\nviscosity in a domain $\\mathbb{T}\\times \\mathbb{R}$. For a general initial data\nsettled in Sobolev spaces, we obtain a Lyapunov type instability of the\ndensity, the temperature, the compressible part of the velocity field, and also\nobtain an inviscid damping for the incompressible part of the velocity field.\nMoreover, if the initial density, the initial temperature and the\nincompressible part of the initial velocity field satisfy some quality\nrelation, we can prove the enhanced dissipation phenomenon for the velocity\nfield.\n"}
+{"abstract": "  In 2021, the Sombor index was introduced by Gutman, which is a new\ndegree-based topological molecular descriptors. The Sombor index of a graph $G$\nis defined as $SO(G) =\\sum_{uv\\in E(G)}\\sqrt{d^2_G(u)+d^2_G(v)}$, where\n$d_G(v)$ is the degree of the vertex $v$ in $G$. Let $\\mathscr{T}_{n,m}$ and\n$\\mathscr{U}_{n,m}$ be the set of trees and unicyclic graphs on $n$ vertices\nwith fixed matching number $m$, respectively. In this paper, the tree and the\nunicyclic graph with the maximum Sombor index are determined among\n$\\mathscr{T}_{n,m}$ and $\\mathscr{U}_{n,m}$, respectively.\n"}
+{"abstract": "  Purpose: Diffusion MRI (dMRI) suffers from eddy currents induced by strong\ndiffusion gradients, which introduce artefacts that can impair subsequent\ndiffusion metric analysis. Existing retrospective correction techniques that\ncorrect for diffusion gradient induced eddy currents do not account for eddy\ncurrent decay, which is generally effective for traditional Pulsed Gradient\nSpin Echo (PGSE) diffusion encoding. However, these techniques do not\nnecessarily apply to advanced forms of dMRI that require substantial gradient\nslewing, such as Oscillating Gradient Spin Echo (OGSE).\n  Methods: An in-house algorithm (TVEDDY), that for the first time\nretrospectively models eddy current decay, was tested on PGSE and OGSE brain\nimages acquired at 7T. Correction performance was compared to conventional\ncorrection methods by evaluating the mean-squared error (MSE) between\ndiffusion-weighted images acquired with opposite polarity diffusion gradients.\nAs a ground truth comparison, images were corrected using field dynamics up to\nthird order in space measured using a field monitoring system.\n  Results: Time-varying eddy currents were observed for OGSE, which introduced\nblurring that was not reduced using the traditional approach but was diminished\nconsiderably with TVEDDY and model-based reconstruction. No MSE difference was\nobserved between the conventional approach and TVEDDY for PGSE, but for OGSE\nTVEDDY resulted in significantly lower MSE than the conventional approach. The\nfield-monitoring-informed model-based reconstruction had the lowest MSE for\nboth PGSE and OGSE.\n  Conclusion: This work establishes that it is possible to estimate\ntime-varying eddy currents from the diffusion data itself, which provides\nsubstantial image quality improvements for gradient-intensive dMRI acquisitions\nlike OGSE.\n"}
+{"abstract": "  We consider two popular Graph Representation Learning (GRL) methods: message\npassing for node classification and network embedding for link prediction. For\neach, we pick a popular model that we: (i) linearize and (ii) and switch its\ntraining objective to Frobenius norm error minimization. These simplifications\ncan cast the training into finding the optimal parameters in closed-form. We\nprogram in TensorFlow a functional form of Truncated Singular Value\nDecomposition (SVD), such that, we could decompose a dense matrix $\\mathbf{M}$,\nwithout explicitly computing $\\mathbf{M}$. We achieve competitive performance\non popular GRL tasks while providing orders of magnitude speedup. We\nopen-source our code at http://github.com/samihaija/tf-fsvd\n"}
+{"abstract": "  We describe and contrast two distinct problem areas for statistical\ncausality: studying the likely effects of an intervention (\"effects of\ncauses\"), and studying whether there is a causal link between the observed\nexposure and outcome in an individual case (\"causes of effects\"). For each of\nthese, we introduce and compare various formal frameworks that have been\nproposed for that purpose, including the decision-theoretic approach,\nstructural equations, structural and stochastic causal models, and potential\noutcomes. It is argued that counterfactual concepts are unnecessary for\nstudying effects of causes, but are needed for analysing causes of effects.\nThey are however subject to a degree of arbitrariness, which can be reduced,\nthough not in general eliminated, by taking account of additional structure in\nthe problem.\n"}
+{"abstract": "  The breaking of chiral symmetry in holographic light-front QCD is encoded in\nits longitudinal dynamics with its chiral limit protected by the superconformal\nalgebraic structure which governs its transverse dynamics. The scale in the\nlongitudinal light-front Hamiltonian determines the confinement strength in\nthis direction: It is also responsible for most of the light meson ground state\nmass consistent with the Gell-Mann-Oakes-Renner constraint. Longitudinal\nconfinement and the breaking of chiral symmetry are found to be different\nmanifestations of the same underlying dynamics as found in 't Hooft large $N_C$\nQCD(1 + 1) model.\n"}
+{"abstract": "  We study the benefits of complex-valued weights for neural networks. We prove\nthat shallow complex neural networks with quadratic activations have no\nspurious local minima. In contrast, shallow real neural networks with quadratic\nactivations have infinitely many spurious local minima under the same\nconditions. In addition, we provide specific examples to demonstrate that\ncomplex-valued weights turn poor local minima into saddle points. The\nactivation function CReLU is also discussed to illustrate the superiority of\nanalytic activations in complex-valued neural networks.\n"}
+{"abstract": "  In the development of governmental policy for artificial intelligence (AI)\nthat is informed by ethics, one avenue currently pursued is that of drawing on\nAI Ethics Principles. However, these AI Ethics Principles often fail to be\nactioned in governmental policy. This paper proposes a novel framework for the\ndevelopment of Actionable Principles for AI. The approach acknowledges the\nrelevance of AI Ethics Principles and homes in on methodological elements to\nincrease their practical implementability in policy processes. As a case study,\nelements are extracted from the development process of the Ethics Guidelines\nfor Trustworthy AI of the European Commissions High Level Expert Group on AI.\nSubsequently, these elements are expanded on and evaluated in light of their\nability to contribute to a prototype framework for the development of\nActionable Principles for AI. The paper proposes the following three\npropositions for the formation of such a prototype framework: (1) preliminary\nlandscape assessments; (2) multi-stakeholder participation and cross-sectoral\nfeedback; and, (3) mechanisms to support implementation and\noperationalizability.\n"}
+{"abstract": "  We describe the asymptotic behavior of positive solutions $u_\\epsilon$ of the\nequation $-\\Delta u + au = 3\\,u^{5-\\epsilon}$ in $\\Omega\\subset\\mathbb{R}^3$\nwith a homogeneous Dirichlet boundary condition. The function $a$ is assumed to\nbe critical in the sense of Hebey and Vaugon and the functions $u_\\epsilon$ are\nassumed to be an optimizing sequence for the Sobolev inequality. Under a\nnatural nondegeneracy assumption we derive the exact rate of the blow-up and\nthe location of the concentration point, thereby proving a conjecture of\nBr\\'ezis and Peletier (1989). Similar results are also obtained for solutions\nof the equation $-\\Delta u + (a+\\epsilon V) u = 3\\,u^5$ in $\\Omega$.\n"}
+{"abstract": "  The multiple-input multiple-output (MIMO) detection problem, a fundamental\nproblem in modern digital communications, is to detect a vector of transmitted\nsymbols from the noisy outputs of a fading MIMO channel. The maximum likelihood\ndetector can be formulated as a complex least-squares problem with discrete\nvariables, which is NP-hard in general. Various semidefinite relaxation (SDR)\nmethods have been proposed in the literature to solve the problem due to their\npolynomial-time worst-case complexity and good detection error rate\nperformance. In this paper, we consider two popular classes of SDR-based\ndetectors and study the conditions under which the SDRs are tight and the\nrelationship between different SDR models. For the enhanced complex and real\nSDRs proposed recently by Lu et al., we refine their analysis and derive the\nnecessary and sufficient condition for the complex SDR to be tight, as well as\na necessary condition for the real SDR to be tight. In contrast, we also show\nthat another SDR proposed by Mobasher et al. is not tight with high probability\nunder mild conditions. Moreover, we establish a general theorem that shows the\nequivalence between two subsets of positive semidefinite matrices in different\ndimensions by exploiting a special \"separable\" structure in the constraints.\nOur theorem recovers two existing equivalence results of SDRs defined in\ndifferent settings and has the potential to find other applications due to its\ngenerality.\n"}
+{"abstract": "  The joint detection of the gravitational wave GW170817, of the short\n$\\gamma$-ray burst GRB170817A and of the kilonova AT2017gfo, generated by the\nthe binary neutron star merger observed on August 17, 2017, is a milestone in\nmultimessenger astronomy and provides new constraints on the neutron star\nequation of state. We perform Bayesian inference and model selection on\nAT2017gfo using semi-analytical, multi-components models that also account for\nnon-spherical ejecta. Observational data favor anisotropic geometries to\nspherically symmetric profiles, with a log-Bayes' factor of ${\\sim}10^{4}$, and\nfavor multi-component models against single-component ones. The best fitting\nmodel is an anisotropic three-component composed of dynamical ejecta plus\nneutrino and viscous winds. Using the dynamical ejecta parameters inferred from\nthe best-fitting model and numerical-relativity relations connecting the ejecta\nproperties to the binary properties, we constrain the binary mass ratio to\n$q<1.54$ and the reduced tidal parameter to $120<\\tilde\\Lambda<1110$. Finally,\nwe combine the predictions from AT2017gfo with those from GW170817,\nconstraining the radius of a neutron star of $1.4~{\\rm M}_\\odot$ to\n$12.2\\pm0.5~{\\rm km}$ ($1\\sigma$ level). This prediction could be further\nstrengthened by improving kilonova models with numerical-relativity\ninformation.\n"}
+{"abstract": "  In this paper, we discuss possible color palletes, prediction and analysis of\noriginality of the colors that Artists used on the Renaissance oil paintings.\nThis framework goal is to help to use the color symbology and image enhancement\ntools, to predict the historical color palletes of the Renaissance oil\nartworks. This work is only the start of a development to explore the\npossibilities of prediction of color palletes of the Renaissance oil artworks.\nWe believe that framework might be very useful in the prediction of color\npalletes of the Renaissance oil artworks and other artworks. The images in\nnumber 105 have been taken from the paintings of three well-known artists,\nRafael, Leonardo Da Vinci, and Rembrandt that are available in the Olga's\nGallery. Images are processed in the frequency domain to enhance a quality of\nimages and ratios of primary colors are calculated and analyzed by using new\nmeasurements of color-ratios.\n"}
+{"abstract": "  The control of domain walls is central to nearly all magnetic technologies,\nparticularly for information storage and spintronics. Creative attempts to\nincrease storage density need to overcome volatility due to thermal\nfluctuations of nanoscopic domains and heating limitations. Topological\ndefects, such as solitons, skyrmions, and merons, may be much less susceptible\nto fluctuations, owing to topological constraints, while also being\ncontrollable with low current densities. Here, we present the first evidence\nfor soliton/soliton and soliton/antisoliton domain walls in the hexagonal\nchiral magnet Mn1/3NbS2 that respond asymmetrically to magnetic fields and\nexhibit pair-annihilation. This is important because it suggests the\npossibility of controlling the occurrence of soliton pairs and the use of small\nfields or small currents to control nanoscopic magnetic domains. Specifically,\nour data suggest that either soliton/soliton or soliton/antisoliton pairs can\nbe stabilized by tuning the balance between intrinsic exchange interactions and\nlong-range magnetostatics in restricted geometries\n"}
+{"abstract": "  How many neurons are needed to approximate a target probability distribution\nusing a neural network with a given input distribution and approximation error?\nThis paper examines this question for the case when the input distribution is\nuniform, and the target distribution belongs to the class of histogram\ndistributions. We obtain a new upper bound on the number of required neurons,\nwhich is strictly better than previously existing upper bounds. The key\ningredient in this improvement is an efficient construction of the neural nets\nrepresenting piecewise linear functions. We also obtain a lower bound on the\nminimum number of neurons needed to approximate the histogram distributions.\n"}
+{"abstract": "  The development of recommender systems that optimize multi-turn interaction\nwith users, and model the interactions of different agents (e.g., users,\ncontent providers, vendors) in the recommender ecosystem have drawn increasing\nattention in recent years. Developing and training models and algorithms for\nsuch recommenders can be especially difficult using static datasets, which\noften fail to offer the types of counterfactual predictions needed to evaluate\npolicies over extended horizons. To address this, we develop RecSim NG, a\nprobabilistic platform for the simulation of multi-agent recommender systems.\nRecSim NG is a scalable, modular, differentiable simulator implemented in\nEdward2 and TensorFlow. It offers: a powerful, general probabilistic\nprogramming language for agent-behavior specification; tools for probabilistic\ninference and latent-variable model learning, backed by automatic\ndifferentiation and tracing; and a TensorFlow-based runtime for running\nsimulations on accelerated hardware. We describe RecSim NG and illustrate how\nit can be used to create transparent, configurable, end-to-end models of a\nrecommender ecosystem, complemented by a small set of simple use cases that\ndemonstrate how RecSim NG can help both researchers and practitioners easily\ndevelop and train novel algorithms for recommender systems.\n"}
+{"abstract": "  Experimentally synthesized perovskite-type YRh$_{3}$B with a $Pm\\bar{3}m$\ntype structure was proposed as a novel topological material (TM) via\nfirst-principles calculations and the low-energy $k\\cdot p$ effective\nHamiltonian, which has a quadratic contact triple point (QCTP) at point\n$\\Gamma$ and six pairs of open nodal lines (NLs) of the hybrid type. Clear\nsurface states observed in the surface spectrum confirmed the topological\nstates. When spin-orbit coupling was considered, the QCTP at $\\Gamma$\ntransferred to the quadratic-type Dirac nodal point (NP). Under 1$\\%$\ntetragonal strained lattice constants, YRh$_{3}$B hosted richer topological\nstates, including a quadratic-type two-fold degenerate NP, six pairs of open\nNLs of the hybrid type, and two closed NLs of type I and hybrid type. Moreover,\nit was proved that the NLs of YRh$_{3}$B at its strained lattice constants\ncontain all types of band-crossing points (BCPs) (i.e., type I, type II, and\ncritical type). Such rich types of NP and NL states in one compound make it\npotentially applicable for multifunctional electronic devices as well as an\nappropriate platform to study entanglement among topological states.\n"}
+{"abstract": "  A couple of dozen Earth-like planets orbiting M dwarfs have been discovered\nso far. Some of them have attracted interest because of their potential\nlong-term habitability; such a possibility is currently vigorously debated in\nthe literature. I show that post-Keplerian (pK) orbit precessions may impact\nthe habitability of a fictitious telluric planet orbiting an oblate late-type M\ndwarf of spectral class M9V with $M_\\star=0.08\\,M_\\odot$ at\n$a=0.02\\,\\mathrm{au}$, corresponding to an orbital period $P_\\mathrm{b}\\simeq\n4\\,\\mathrm{d}$, inducing long-term variations of the planetary obliquity\n$\\varepsilon$ which, under certain circumstances, may not be deemed as\nnegligible from the point of view of life's sustainability. I resume the\nanalytical orbit-averaged equations of the pK precessions, both classical and\ngeneral relativistic, of the unit vectors\n$\\boldsymbol{\\hat{S}},\\,\\boldsymbol{\\hat{h}}$ of both the planet's spin and\norbital angular momenta $\\boldsymbol S,\\,\\boldsymbol{L}$ entering\n$\\varepsilon$, and numerically integrate them by producing time series of the\npK changes $\\Delta\\varepsilon(t)$ of the obliquity. For rapidly rotating M\ndwarfs with rotational periods of the order of $P_\\star \\simeq\n0.1-1\\,\\mathrm{d}$, the planet's obliquity $\\varepsilon$ can undergo classical\npK large variations $\\Delta\\varepsilon(t)$ up to tens of degrees over\ntimescales $\\Delta t \\simeq 20-200\\,\\mathrm{kyr}$, depending on the mutual\norientations of the star's spin ${\\boldsymbol J}_\\star$, of $\\boldsymbol S$,\nand of $\\boldsymbol L$. Instead, $\\Delta\\varepsilon(t)$ are $\\lesssim\n1-1.5^\\circ$ for the planet b of the Teegarden's Star. In certain\ncircumstances, the M dwarf's oblateness $J_2^\\star$ should be considered as one\nof the key dynamical features to be taken into account in compiling budgets of\nthe long-term habitability of rocky planets around fast spinning late M dwarfs.\n(Abridged)\n"}
+{"abstract": "  Multipartite entangled states are significant resources for both quantum\ninformation processing and quantum metrology. In particular, non-Gaussian\nentangled states are predicted to achieve a higher sensitivity of precision\nmeasurements than Gaussian states. On the basis of metrological sensitivity,\nthe conventional linear Ramsey squeezing parameter (RSP) efficiently\ncharacterises the Gaussian entangled atomic states but fails for much wider\nclasses of highly sensitive non-Gaussian states. These complex non-Gaussian\nentangled states can be classified by the nonlinear squeezing parameter (NLSP),\nas a generalisation of the RSP with respect to nonlinear observables, and\nidentified via the Fisher information. However, the NLSP has never been\nmeasured experimentally. Using a 19-qubit programmable superconducting\nprocessor, here we report the characterisation of multiparticle entangled\nstates generated during its nonlinear dynamics. First, selecting 10 qubits, we\nmeasure the RSP and the NLSP by single-shot readouts of collective spin\noperators in several different directions. Then, by extracting the Fisher\ninformation of the time-evolved state of all 19 qubits, we observe a large\nmetrological gain of 9.89$^{+0.28}_{-0.29}$ dB over the standard quantum limit,\nindicating a high level of multiparticle entanglement for quantum-enhanced\nphase sensitivity. Benefiting from high-fidelity full controls and addressable\nsingle-shot readouts, the superconducting processor with interconnected qubits\nprovides an ideal platform for engineering and benchmarking non-Gaussian\nentangled states that are useful for quantum-enhanced metrology.\n"}
+{"abstract": "  Convolutional Neural Networks (CNN) are used mainly to treat problems with\nmany images characteristic of Deep Learning. In this work, we propose a hybrid\nimage classification model to take advantage of quantum and classical\ncomputing. The method will use the potential that convolutional networks have\nshown in artificial intelligence by replacing classical filters with\nvariational quantum filters. Similarly, this work will compare with other\nclassification methods and the system's execution on different servers. The\nalgorithm's quantum feasibility is modelled and tested on Amazon Braket\nNotebook instances and experimented on the Pennylane's philosophy and\nframework.\n"}
+{"abstract": "  We derive a distribution function for the position of a tagged active\nparticle in a slowly varying in space external potential, in a system of\ninteracting active particles. The tagged particle distribution has the form of\nthe Boltzmann distribution but with an effective temperature that replaces the\ntemperature of the heat bath. We show that the effective temperature that\nenters the tagged particle distribution is the same as the effective\ntemperature defined through the Einstein relation, i.e. it is equal to the\nratio of the self-diffusion and tagged particle mobility coefficients. This\nshows that this effective temperature, which is defined through a\nfluctuation-dissipation ratio, is relevant beyond the linear response regime.\nWe verify our theoretical findings through computer simulations. Our theory\nfails when an additional large length scale appears in our active system. This\nlength scale is associated with long-wavelength density fluctuations that\nemerge upon approaching motility-induced phase separation.\n"}
+{"abstract": "  We study all the possible spin asymmetries that can arise in back-to-back\nelectron-jet production, $ep\\rightarrow e+\\text{jet}+X$, as well as the\nassociated jet fragmentation process, $ep\\rightarrow e+ \\text{jet} (h)+X$, in\nelectron-proton collisions. We derive the factorization formalism for these\nspin asymmetries and perform the corresponding phenomenology for the kinematics\nrelevant to the future electron ion collider. In the case of unpolarized\nelectron-proton scattering, we also give predictions for azimuthal asymmetries\nfor the HERA experiment. This demonstrates that electron-jet production is an\noutstanding process for probing unpolarized and polarized transverse momentum\ndependent parton distribution functions and fragmentation functions.\n"}
+{"abstract": "  In this paper, combinatorial quantitative group testing (QGT) with noisy\nmeasurements is studied. The goal of QGT is to detect defective items from a\ndata set of size $n$ with counting measurements, each of which counts the\nnumber of defects in a selected pool of items. While most literatures consider\neither probabilistic QGT with random noise or combinatorial QGT with noiseless\nmeasurements, our focus is on the combinatorial QGT with noisy measurements\nthat might be adversarially perturbed by additive bounded noises. Since perfect\ndetection is impossible, a partial detection criterion is adopted. With the\nadversarial noise being bounded by $d_n = \\Theta(n^\\delta)$ and the detection\ncriterion being to ensure no more than $k_n = \\Theta(n^\\kappa)$ errors can be\nmade, our goal is to characterize the fundamental limit on the number of\nmeasurement, termed \\emph{pooling complexity}, as well as provide explicit\nconstruction of measurement plans with optimal pooling complexity and efficient\ndecoding algorithms. We first show that the fundamental limit is\n$\\frac{1}{1-2\\delta}\\frac{n}{\\log n}$ to within a constant factor not depending\non $(n,\\kappa,\\delta)$ for the non-adaptive setting when $0<2\\delta\\leq \\kappa\n<1$, sharpening the previous result by Chen and Wang [1]. We also provide an\nexplicit construction of a non-adaptive deterministic measurement plan with\n$\\frac{1}{1-2\\delta}\\frac{n}{\\log_{2} n}$ pooling complexity up to a constant\nfactor, matching the fundamental limit, with decoding complexity being\n$o(n^{1+\\rho})$ for all $\\rho > 0$, nearly linear in $n$, the size of the data\nset.\n"}
+{"abstract": "  In SPECT, list-mode (LM) format allows storing data at higher precision\ncompared to binned data. There is significant interest in investigating whether\nthis higher precision translates to improved performance on clinical tasks.\nTowards this goal, in this study, we quantitatively investigated whether\nprocessing data in LM format, and in particular, the energy attribute of the\ndetected photon, provides improved performance on the task of absolute\nquantification of region-of-interest (ROI) uptake in comparison to processing\nthe data in binned format. We conducted this evaluation study using a DaTscan\nbrain SPECT acquisition protocol, conducted in the context of imaging patients\nwith Parkinson's disease. This study was conducted with a synthetic phantom. A\nsignal-known exactly/background-known-statistically (SKE/BKS) setup was\nconsidered. An ordered-subset expectation-maximization algorithm was used to\nreconstruct images from data acquired in LM format, including the\nscatter-window data, and including the energy attribute of each LM event. Using\na realistic 2-D SPECT system simulation, quantification tasks were performed on\nthe reconstructed images. The results demonstrated improved quantification\nperformance when LM data was used compared to binning the attributes in all the\nconducted evaluation studies. Overall, we observed that LM data, including the\nenergy attribute, yielded improved performance on absolute quantification tasks\ncompared to binned data.\n"}
+{"abstract": "  In the last few years, the interest in knowledge bases has grown\nexponentially in both the research community and the industry due to their\nessential role in AI applications. Entity alignment is an important task for\nenriching knowledge bases. This paper provides a comprehensive tutorial-type\nsurvey on representative entity alignment techniques that use the new approach\nof representation learning. We present a framework for capturing the key\ncharacteristics of these techniques, propose two datasets to address the\nlimitation of existing benchmark datasets, and conduct extensive experiments\nusing the proposed datasets. The framework gives a clear picture of how the\ntechniques work. The experiments yield important results about the empirical\nperformance of the techniques and how various factors affect the performance.\nOne important observation not stressed by previous work is that techniques\nmaking good use of attribute triples and relation predicates as features stand\nout as winners.\n"}
+{"abstract": "  Micrometer sized alkane-in-water emulsion drops, stabilized by appropriate\nlong-chain surfactants, spontaneously break symmetry upon cooling and transform\nconsecutively into series of regular shapes (Denkov et al., Nature 2015, 528,\n392). Two mechanisms were proposed to explain this phenomenon of drop\n\"self-shaping\". One of these mechanisms assumes that thin layers of plastic\nrotator phase form at the drop surface around the freezing temperature of the\noil. This mechanism has been supported by several indirect experimental\nfindings but direct structural characterization has not been reported so far.\nWe combine small- and wide-angle X-ray scattering (SAXS/WAXS) with optical\nmicroscopy and DSC measurements of self-shaping drops in emulsions. In the\nemulsions exhibiting drop self-shaping, the scattering spectra reveal the\nformation of intermediate, metastable rotator phases in the alkane drops before\ntheir crystallization. In addition, shells of rotator phase were observed to\nform in hexadecane drops, stabilized by C16EO10 surfactant. This rotator phase\nmelts at ca. 16.6 {\\deg}C which is significantly lower than the melting\ntemperature of crystalline hexadecane, 18 {\\deg}C. The scattering results are\nin a very good agreement with the complementary optical observations and DSC\nmeasurements.\n"}
+{"abstract": "  We present a method that generalizes the periodic orbit dividing surface\nconstruction for Hamiltonian systems with three or more degrees of freedom. We\nconstruct a torus using as a basis a periodic orbit and we extend this to a\n$2n-2$ dimensional object in the $2n-1$ dimensional energy surface. We present\nour methods using benchmark examples for two and three degree of freedom\nHamiltonian systems to illustrate the corresponding algorithm for this\nconstruction. Towards this end we use the normal form quadratic Hamiltonian\nsystem with two and three degrees of freedom. We found that the periodic orbit\ndividing surface can provide us the same dynamical information as the dividing\nsurface constructed using normally hyperbolic invariant manifolds. This is\nsignificant because, in general, computations of normally hyperbolic invariant\nmanifolds are very difficult in Hamiltonian systems with three or more degrees\nof freedom. However, our method avoids this computation and the only\ninformation that we need is the location of one periodic orbit.\n"}
+{"abstract": "  We perform the maximal twist of eleven-dimensional supergravity. This twist\nis partially topological and exists on manifolds of $G_2 \\times SU(2)$\nholonomy. Our derivation starts with an explicit description of the\nBatalin-Vilkovisky complex associated to the three-form multiplet in the pure\nspinor superfield formalism. We then determine the $L_\\infty$ module structure\nof the supersymmetry algebra on the component fields. We twist the theory by\nmodifying the differential of the Batalin-Vilkovisky complex to incorporate the\naction of a scalar supercharge. We find that the resulting free twisted theory\nis given by the tensor product of the de Rham and Dolbeault complexes of the\nrespective $G_2$ and $SU(2)$ holonomy manifolds as conjectured by Costello.\n"}
+{"abstract": "  A numerical method is developed to solve linear semi-infinite programming\nproblem (LSIP) in which the iterates produced by the algorithm are feasible for\nthe original problem. This is achieved by constructing a sequence of standard\nlinear programming problems with respect to the successive discretization of\nthe index set such that the approximate regions are included in the original\nfeasible region. The convergence of the approximate solutions to the solution\nof the original problem is proved and the associated optimal objective function\nvalues of the approximate problems are monotonically decreasing and converge to\nthe optimal value of LSIP. An adaptive refinement procedure is designed to\ndiscretize the index set and update the constraints for the approximate\nproblem. Numerical experiments demonstrate the performance of the proposed\nalgorithm.\n"}
+{"abstract": "  Searches for the lepton number violating $K^{+} \\rightarrow \\pi^{-} \\mu^{+}\ne^{+}$ decay and the lepton flavour violating $K^{+} \\rightarrow \\pi^{+}\n\\mu^{-} e^{+}$ and $\\pi^{0} \\rightarrow \\mu^{-} e^{+}$ decays are reported\nusing data collected by the NA62 experiment at CERN in $2017$-$2018$. No\nevidence for these decays is found and upper limits of the branching ratios are\nobtained at 90% confidence level:\n$\\mathcal{B}(K^{+}\\rightarrow\\pi^{-}\\mu^{+}e^{+})<4.2\\times 10^{-11}$,\n$\\mathcal{B}(K^{+}\\rightarrow\\pi^{+}\\mu^{-}e^{+})<6.6\\times10^{-11}$ and\n$\\mathcal{B}(\\pi^{0}\\rightarrow\\mu^{-}e^{+})<3.2\\times 10^{-10}$. These results\nimprove by one order of magnitude over previous results for these decay modes.\n"}
+{"abstract": "  The phenomenon of population interference, where a treatment assigned to one\nexperimental unit affects another experimental unit's outcome, has received\nconsiderable attention in standard randomized experiments. The complications\nproduced by population interference in this setting are now readily recognized,\nand partial remedies are well known. Much less understood is the impact of\npopulation interference in panel experiments where treatment is sequentially\nrandomized in the population, and the outcomes are observed at each time step.\nThis paper proposes a general framework for studying population interference in\npanel experiments and presents new finite population estimation and inference\nresults. Our findings suggest that, under mild assumptions, the addition of a\ntemporal dimension to an experiment alleviates some of the challenges of\npopulation interference for certain estimands. In contrast, we show that the\npresence of carryover effects -- that is, when past treatments may affect\nfuture outcomes -- exacerbates the problem. Revisiting the special case of\nstandard experiments with population interference, we prove a central limit\ntheorem under weaker conditions than previous results in the literature and\nhighlight the trade-off between flexibility in the design and the interference\nstructure.\n"}
+{"abstract": "  One of the difficulties related to the COVID-19 pandemic is the shifting from\nface-to-face to distance teaching. Both schools and universities had suddenly\nto organize on-line lectures. To perform laboratory practice even in this\nperiod, easily accessible materials, smartphones physics apps, on-line tools\nand devices can be used. In this paper a method to measure the gravitational\nacceleration studying the free falling body using Arduino board is presented.\n"}
+{"abstract": "  The presence of multiple talkers in the surrounding environment poses a\ndifficult challenge for real-time speech communication systems considering the\nconstraints on network size and complexity. In this paper, we present\nPersonalized PercepNet, a real-time speech enhancement model that separates a\ntarget speaker from a noisy multi-talker mixture without compromising on\ncomplexity of the recently proposed PercepNet. To enable speaker-dependent\nspeech enhancement, we first show how we can train a perceptually motivated\nspeaker embedder network to produce a representative embedding vector for the\ngiven speaker. Personalized PercepNet uses the target speaker embedding as\nadditional information to pick out and enhance only the target speaker while\nsuppressing all other competing sounds. Our experiments show that the proposed\nmodel significantly outperforms PercepNet and other baselines, both in terms of\nobjective speech enhancement metrics and human opinion scores.\n"}
+{"abstract": "  Composite quantum compounds (CQC) are classic example of quantum materials\nwhich host more than one apparently distinct quantum phenomenon in physics.\nMagnetism, topological superconductivity, Rashba physics etc. are few such\nquantum phenomenon which are ubiquitously observed in several functional\nmaterials and can co-exist in CQCs. In this letter, we use {\\it ab-initio}\ncalculations to predict the co-existence of two incompatible phenomena, namely\ntopologically non-trivial Weyl semimetal and spin gapless semiconducting (SGS)\nbehavior, in a single crystalline system. SGS belong to a special class of\nspintronics material which exhibit a unique band structure involving a\nsemiconducting state for one spin channel and a gapless state for the other. We\nreport such a SGS behavior in conjunction with the topologically non-trivial\nmulti-Weyl Fermions in MnPO$_4$. Interestingly, these Weyl nodes are located\nvery close to the Fermi level with the minimal trivial band density. A drumhead\nlike surface state originating from a nodal loop around Y-point in the\nBrillouin zone is observed. A large value of the simulated anomalous Hall\nconductivity (1265 $\\Omega^{-1} cm^{-1}$) indirectly reflects the topological\nnon-trivial behavior of this compound. Such co-existent quantum phenomena are\nnot common in condensed matter systems and hence it opens up a fertile ground\nto explore and achieve newer functional materials.\n"}
+{"abstract": "  We present the structural and magnetic properties of KNaCuP$_2$O$_7$\ninvestigated via x-ray diffraction, magnetization, specific heat, and $^{31}$P\nNMR and $^{23}$Na NMR measurements and complementary electronic structure\ncalculations. The temperature dependent magnetic susceptibility and $^{31}$P\nNMR shift could be modeled very well by the uniform spin-$1/2$ Heisenberg\nantiferromagnetic chain model with nearest-neighbour interaction $J/k_{\\rm\nB}\\simeq 58.7$ K. The corresponding mapping using first principles electronic\nstructure calculations leads to $J^{\\rm DFT}/k_{\\rm B} \\simeq 59$ K with\nnegligibly small inter-chain couplings ($J^{\\prime}/k_{\\rm B}$, $J^{\\prime\n\\prime}/k_{\\rm B} < 0.1$ K), further confirming that the system is indeed an\none-dimensional uniform spin-$1/2$ Heisenberg antiferromagnet. The\ntemperature-dependent unit cell volume could be described well using the Debye\napproximation with a Debye temperature of $\\Theta_{\\rm D} \\simeq 294$ K,\nconsistent with the heat capacity data. The diverging trend of the NMR\nspin-lattice relaxation rates ($^{31}1/T_1$ and $^{23}1/T_1$) imply the onset\nof a magnetic long-range-ordering at very low temperatures supporting the\nanticipated $T_{\\rm N} \\simeq 0.38$ K from the inter-chain couplings. Moreover,\nthe NMR spin-lattice relaxation rates show the dominant contributions from\nuniform ($q=0$) and staggered ($q = \\pm \\pi/a$) spin fluctuations in the high\nand low temperature regimes, respectively mimicking one-dimensionality of the\nspin-lattice. We have also demonstrated that $^{31}1/T_1$ in high temperatures\nvaries linearly with $1/\\sqrt{H}$ reflecting the effect of spin diffusion on\nthe dynamic susceptibility. Further, the inter-chain frustration also\nsubstantially impede the magnetic ordering rendering the spin-lattice a perfect\none-dimensional uniform spin-$1/2$ Heisenberg antiferromagnet over a wide\ntemperature range.\n"}
+{"abstract": "  Internet of Things (IoT) devices are becoming ubiquitous in our lives, with\napplications spanning from the consumer domain to commercial and industrial\nsystems. The steep growth and vast adoption of IoT devices reinforce the\nimportance of sound and robust cybersecurity practices during the device\ndevelopment life-cycles. IoT-related vulnerabilities, if successfully exploited\ncan affect, not only the device itself, but also the application field in which\nthe IoT device operates. Evidently, identifying and addressing every single\nvulnerability is an arduous, if not impossible, task. Attack taxonomies can\nassist in classifying attacks and their corresponding vulnerabilities. Security\ncountermeasures and best practices can then be leveraged to mitigate threats\nand vulnerabilities before they emerge into catastrophic attacks and ensure\noverall secure IoT operation. Therefore, in this paper, we provide an attack\ntaxonomy which takes into consideration the different layers of IoT stack,\ni.e., device, infrastructure, communication, and service, and each layer's\ndesignated characteristics which can be exploited by adversaries. Furthermore,\nusing nine real-world cybersecurity incidents, that had targeted IoT devices\ndeployed in the consumer, commercial, and industrial sectors, we describe the\nIoT-related vulnerabilities, exploitation procedures, attacks, impacts, and\npotential mitigation mechanisms and protection strategies. These (and many\nother) incidents highlight the underlying security concerns of IoT systems and\ndemonstrate the potential attack impacts of such connected ecosystems, while\nthe proposed taxonomy provides a systematic procedure to categorize attacks\nbased on the affected layer and corresponding impact.\n"}
+{"abstract": "  Many deep learning based methods are designed to remove non-uniform\n(spatially variant) motion blur caused by object motion and camera shake\nwithout knowing the blur kernel. Some methods directly output the latent sharp\nimage in one stage, while others utilize a multi-stage strategy (\\eg\nmulti-scale, multi-patch, or multi-temporal) to gradually restore the sharp\nimage. However, these methods have the following two main issues: 1) The\ncomputational cost of multi-stage is high; 2) The same convolution kernel is\napplied in different regions, which is not an ideal choice for non-uniform\nblur. Hence, non-uniform motion deblurring is still a challenging and open\nproblem. In this paper, we propose a new architecture which consists of\nmultiple Atrous Spatial Pyramid Deformable Convolution (ASPDC) modules to\ndeblur an image end-to-end with more flexibility. Multiple ASPDC modules\nimplicitly learn the pixel-specific motion with different dilation rates in the\nsame layer to handle movements of different magnitude. To improve the training,\nwe also propose a reblurring network to map the deblurred output back to the\nblurred input, which constrains the solution space. Our experimental results\nshow that the proposed method outperforms state-of-the-art methods on the\nbenchmark datasets.\n"}
+{"abstract": "  Constant function market makers (CFMMs) such as Uniswap, Balancer, Curve, and\nmStable, among many others, make up some of the largest decentralized exchanges\non Ethereum and other blockchains. Because all transactions are public in\ncurrent implementations, a natural next question is if there exist similar\ndecentralized exchanges which are privacy-preserving; i.e., if a transaction's\nquantities are hidden from the public view, then an adversary cannot correctly\nreconstruct the traded quantities from other public information. In this note,\nwe show that privacy is impossible with the usual implementations of CFMMs\nunder most reasonable models of an adversary and provide some mitigating\nstrategies.\n"}
+{"abstract": "  Motivated by recent results of Corwin and Knizel on stationary measures for\nthe open KPZ equation on the spatial interval [0, 1], we study a pair of Markov\nprocesses with Laplace transforms that have dual representations, with the\narguments of the Laplace transforms and the time parameters of the processes\nswapped. Combined with the results of Corwin and Knizel, our formula identifies\nthe law of the stationary solutions for the open KPZ in terms of a Markov\nprocess which is a Doob's h transform of the Brownian motion killed at an\nexponential rate.\n"}
+{"abstract": "  The entropy principle shows that, for self-gravitating perfect fluid, the\nEinstein field equations can be derived from the extrema of the total entropy,\nand the thermodynamical stability criterion are equivalent to the dynamical\nstability criterion. In this paper, we recast the dynamical criterion for the\ncharged self-gravitating perfect fluid in Einstein-Maxwell theory, and further\ngive the criterion of the star with barotropic condition. In order to obtain\nthe thermodynamical stability criterion, first we get the general formula of\nthe second variation of the total entropy for charged perfect fluid case, and\nthen obtain the thermodynamical criterion for radial perturbation. We show that\nthese two stability criterion are the same, which suggest that the inherent\nconnection between gravity and thermodynamic even when the electric field is\ntaken into account.\n"}
+{"abstract": "  In this paper, we first consider two scalar nonlocal diffusion problems with\na free boundary and a fixed boundary. We obtain the global existence,\nuniqueness and longtime behaviour of solution of these two problems. The\nspreading-vanishing dichotomy and sharp criteria for spreading and vanishing\nare established. We also prove that accelerated spreading could happen if and\nonly if a threshold condition is violated by kernel function. Then we discuss a\nclassical Lotka-Volterra predator-prey model with nonlocal diffusions and a\nfree boundary which can be seen as nonlocal diffusion counterpart of the model\nin the work of Wang (2014 J. Differential Equations \\textbf{256}, 3365-3394).\n"}
+{"abstract": "  We provide a setting and a general approach to fair online learning with\nstochastic sensitive and non-sensitive contexts. The setting is a repeated game\nbetween the Player and Nature, where at each stage both pick actions based on\nthe contexts. Inspired by the notion of unawareness, we assume that the Player\ncan only access the non-sensitive context before making a decision, while we\ndiscuss both cases of Nature accessing the sensitive contexts and Nature\nunaware of the sensitive contexts. Adapting Blackwell's approachability theory\nto handle the case of an unknown contexts' distribution, we provide a general\nnecessary and sufficient condition for learning objectives to be compatible\nwith some fairness constraints. This condition is instantiated on (group-wise)\nno-regret and (group-wise) calibration objectives, and on demographic parity as\nan additional constraint. When the objective is not compatible with the\nconstraint, the provided framework permits to characterise the optimal\ntrade-off between the two.\n"}
+{"abstract": "  In this paper, we propose a new deep neural network classifier that\nsimultaneously maximizes the inter-class separation and minimizes the\nintra-class variation by using the polyhedral conic classification function.\nThe proposed method has one loss term that allows the margin maximization to\nmaximize the inter-class separation and another loss term that controls the\ncompactness of the class acceptance regions. Our proposed method has a nice\ngeometric interpretation using polyhedral conic function geometry. We tested\nthe proposed method on various visual classification problems including\nclosed/open set recognition and anomaly detection. The experimental results\nshow that the proposed method typically outperforms other state-of-the art\nmethods, and becomes a better choice compared to other tested methods\nespecially for open set recognition type problems.\n"}
+{"abstract": "  We embed natural inflation in an explict string theory model and derive\nobservables in cosmology. We achieve this by compactifying the type IIB string\non a Calabi-Yau orientifold, stabilizing moduli via the Large Volume Scenario,\nand configuring axions using D7-brane stacks. In order to obtain a large\neffective decay constant, we employ the Kim-Nilles-Peloso alignment mechanism,\nwith the required multiple axions arising naturally from anisotropic bulk\ngeometries. The bulk volumes, and hence the axion decay constants, are\nstabilized by generalized one-loop corrections and subject to various\nconditions: the K\\\"ahler cone condition on the string geometry; the convex hull\ncondition of the weak gravity conjecture; and the constraint from the power\nspectrum of scalar perturbations. We find that all constraints can be satisfied\nin a geometry with relatively small volume and thus heavy bulk axion mass. We\nalso covariantize the convex hull condition for the axion-dilaton-instanton\nsystem and verify the normalization of the extremal bound.\n"}
+{"abstract": "  The authors of the article have reviewed the scientific literature on the\ndevelopment of the Russian-Chinese cooperation in the field of combining\neconomic and logistics projects of the Eurasian Economic Union and the Silk\nRoad Economic Belt. The opinions of not only Russian, but also Chinese experts\non these projects are indicated, which provides the expansion of the vision of\nthe concept of the New Silk Road in both countries.\n"}
+{"abstract": "  [This paper was initially published in PHME conference in 2016, selected for\nfurther publication in International Journal of Prognostics and Health\nManagement.]\n  This paper describes an Autoregressive Partially-hidden Markov model (ARPHMM)\nfor fault detection and prognostics of equipments based on sensors' data. It is\na particular dynamic Bayesian network that allows to represent the dynamics of\na system by means of a Hidden Markov Model (HMM) and an autoregressive (AR)\nprocess. The Markov chain assumes that the system is switching back and forth\nbetween internal states while the AR process ensures a temporal coherence on\nsensor measurements. A sound learning procedure of standard ARHMM based on\nmaximum likelihood allows to iteratively estimate all parameters\nsimultaneously. This paper suggests a modification of the learning procedure\nconsidering that one may have prior knowledge about the structure which becomes\npartially hidden. The integration of the prior is based on the Theory of\nWeighted Distributions which is compatible with the Expectation-Maximization\nalgorithm in the sense that the convergence properties are still satisfied. We\nshow how to apply this model to estimate the remaining useful life based on\nhealth indicators. The autoregressive parameters can indeed be used for\nprediction while the latent structure can be used to get information about the\ndegradation level. The interest of the proposed method for prognostics and\nhealth assessment is demonstrated on CMAPSS datasets.\n"}
+{"abstract": "  We study the limit behaviour of upper and lower bounds on expected time\naverages in imprecise Markov chains; a generalised type of Markov chain where\nthe local dynamics, traditionally characterised by transition probabilities,\nare now represented by sets of `plausible' transition probabilities. Our first\nmain result is a necessary and sufficient condition under which these upper and\nlower bounds, called upper and lower expected time averages, will converge as\ntime progresses towards infinity to limit values that do not depend on the\nprocess' initial state. Our condition is considerably weaker than that needed\nfor ergodic behaviour; a similar notion which demands that marginal upper and\nlower expectations of functions at a single time instant converge to so-called\nlimit-or steady state-upper and lower expectations. For this reason, we refer\nto our notion as `weak ergodicity'. Our second main result shows that, as far\nas this weakly ergodic behaviour is concerned, one should not worry about which\ntype of independence assumption to adopt-epistemic irrelevance, complete\nindependence or repetition independence. The characterisation of weak\nergodicity as well as the limit values of upper and lower expected time\naverages do not depend on such a choice. Notably, this type of robustness is\nnot exhibited by the notion of ergodicity and the related inferences of limit\nupper and lower expectations. Finally, though limit upper and lower\nexpectations are often used to provide approximate information about the limit\nbehaviour of time averages, we show that such an approximation is sub-optimal\nand that it can be significantly improved by directly using upper and lower\nexpected time averages.\n"}
+{"abstract": "  We consider a generalization of the recursive utility model by adding a new\ncomponent that represents utility of investment gains and losses. We also study\nthe utility process in this generalized model with constant elasticity of\nintertemporal substitution and relative risk aversion degree, and with infinite\ntime horizon. In a specific, finite-state Markovian setting, we prove that the\nutility process uniquely exists when the agent derives nonnegative gain-loss\nutility, and that it can be non-existent or non-unique otherwise. Moreover, we\nprove that the utility process, when it uniquely exists, can be computed by\nstarting from any initial guess and applying the recursive equation that\ndefines the utility process repeatedly. We then consider a portfolio selection\nproblem with gain-loss utility and solve it by proving that the corresponding\ndynamic programming equation has a unique solution. Finally, we extend certain\nprevious results to the case in which the state space is infinite.\n"}
+{"abstract": "  A stochastic sewing lemma which is applicable for processes taking values in\nBanach spaces is introduced. Applications to additive functionals of fractional\nBrownian motion of distributional type are discussed.\n"}
+{"abstract": "  Compositional Zero-Shot learning (CZSL) aims to recognize unseen compositions\nof state and object visual primitives seen during training. A problem with\nstandard CZSL is the assumption of knowing which unseen compositions will be\navailable at test time. In this work, we overcome this assumption operating on\nthe open world setting, where no limit is imposed on the compositional space at\ntest time, and the search space contains a large number of unseen compositions.\nTo address this problem, we propose a new approach, Compositional Cosine Graph\nEmbeddings (Co-CGE), based on two principles. First, Co-CGE models the\ndependency between states, objects and their compositions through a graph\nconvolutional neural network. The graph propagates information from seen to\nunseen concepts, improving their representations. Second, since not all unseen\ncompositions are equally feasible, and less feasible ones may damage the\nlearned representations, Co-CGE estimates a feasibility score for each unseen\ncomposition, using the scores as margins in a cosine similarity-based loss and\nas weights in the adjacency matrix of the graphs. Experiments show that our\napproach achieves state-of-the-art performances in standard CZSL while\noutperforming previous methods in the open world scenario.\n"}
+{"abstract": "  In this paper, we present a technique for balancing predictive relevance\nmodels related to supervised modelling ligand biochemical activities to\nbiological targets. We train uncalibrated models employing conventional\nsupervised machine learning technique, namely Support Vector Machines.\nUnfortunately, SVMs have a serious drawback. They are sensitive to imbalanced\ndatasets, outliers and high multicollinearity among training samples, which\ncould be a cause of preferencing one group over another. Thus, an additional\ncalibration could be required for balancing a predictive relevance of models.\nAs a technique for this balancing, we propose the Platt's scaling. The achieved\nresults were demonstrated on single-target models trained on datasets exported\nfrom the ExCAPE database. Unlike traditional used machine techniques, we focus\non decreasing uncertainty employing deterministic solvers.\n"}
+{"abstract": "  We use symplectic self-dual additive codes over $\\mathbb{F}_4$ obtained from\nmetacirculant graphs to construct, for the first time, $[[\\ell, 0, d ]]$ qubit\ncodes with parameters $(\\ell,d) \\in \\{(78, 20), (90, 21), (91, 22),\n(93,21),(96,21)\\}$. Secondary constructions applied to the qubit codes result\nin many qubit codes that perform better than the previous best-known.\n"}
+{"abstract": "  The bidomain equations have been widely used to mathematically model the\nelectrical activity of the cardiac tissue. In this work, we present a potential\ntheory-based Cartesian grid method which is referred as the kernel-free\nboundary integral (KFBI) method which works well on complex domains to\nefficiently simulate the linear diffusion part of the bidomain equation. After\na proper temporal discretization, the KFBI method is applied to solve the\nresulting homogeneous Neumann boundary value problems with a second-order\naccuracy. According to the potential theory, the boundary integral equations\nreformulated from the boundary value problems can be solved iteratively with\nthe simple Richardson iteration or the Krylov subspace iteration method. During\nthe iteration, the boundary and volume integrals are evaluated by limiting the\nstructured grid-based discrete solutions of the equivalent interface problems\nat quasi-uniform interface nodes without the need to know the analytical\nexpression of Green's functions. In particular, the discrete linear system of\nthe equivalent interface problem obtained from the standard finite difference\nschemes or the finite element schemes can be efficiently solved by fast\nelliptic solvers such as the fast Fourier transform based solvers or those\nbased on geometric multigrid iterations after an appropriate modification at\nthe irregular grid nodes. Numerical results for solving the FitzHugh-Nagumo\nbidomain equations in both two- and three-dimensional spaces are presented to\ndemonstrate the numerical performance of the KFBI method such as the\nsecond-order accuracy and the propagation and scroll wave of the voltage\nsimulated on the real human left ventricle model.\n"}
+{"abstract": "  Beginning programmers struggle with the complex grammar of modern programming\nlanguages like Java, and make lot of syntax errors. The diagnostic syntax error\nmessages from compilers and IDEs are sometimes useful, but often the messages\nare cryptic and puzzling. Students could be helped, and instructors' time\nsaved, by automated repair suggestions when dealing with syntax errors. Large\nsamples of student errors and fixes are now available, offering the possibility\nof data-driven machine-learning approaches to help students fix syntax errors.\nCurrent machine-learning approaches do a reasonable job fixing syntax errors in\nshorter programs, but don't work as well even for moderately longer programs.\nWe introduce SYNFIX, a machine-learning based tool that substantially improves\non the state-of-the-art, by learning to use compiler diagnostics, employing a\nvery large neural model that leverages unsupervised pre-training, and relying\non multi-label classification rather than autoregressive synthesis to generate\nthe (repaired) output. We describe SYNFIX's architecture in detail, and provide\na detailed evaluation. We have built SYNFIX into a free, open-source version of\nVisual Studio Code; we make all our source code and models freely available.\n"}
+{"abstract": "  Multiple-input multiple-output (MIMO) is an enabling technology to meet the\ngrowing demand for faster and more reliable communications in wireless networks\nwith a large number of terminals, but it can also be applied for position\nestimation of a terminal exploiting multipath propagation from multiple\nantennas. In this paper, we investigate new convolutional neural network (CNN)\nstructures for exploiting MIMO-based channel state information (CSI) to improve\nindoor positioning. We evaluate and compare the performance of three variants\nof the proposed CNN structure to five NN structures proposed in the scientific\nliterature using the same sets of training-evaluation data. The results\ndemonstrate that the proposed residual convolutional NN structure improves the\naccuracy of position estimation and keeps the total number of weights lower\nthan the published NN structures. The proposed CNN structure yields from 2cm to\n10cm better position accuracy than known NN structures used as a reference.\n"}
+{"abstract": "  In this paper, one uses a damped potential to present a description of the\nrunning coupling constant of QCD in the confinement phase. Based on a\nphenomenological perspective for the Debye screening length, one compares the\nrunning coupling obtained here with both the Brodsky-de T\\'eramond-Deur and the\nRichardson approaches. The results seem to indicate the model introduced here\ncorroborate the Richardson approach. Moreover, the Debye screening mass in the\nconfinement phase depends on a small parameter, which tends to vanish in the\nnon-confinement phase of QCD.\n"}
+{"abstract": "  Measurements of single Higgs production and its decays are in good agreement\nwith the Standard Model. There is still room for large modifications in double\nHiggs production at LHC, though these effects may be correlated with large\ncorrections to other observables, in particular single Higgs production. In\nthis work we address the issue of enhancing double Higgs production in the\npresence of scalar leptoquarks while satisfying all experimental constraints.\nWe show at leading order that including more than one species of leptoquarks,\nlarge cubic interactions with the Higgs can lead to sizable enhancement of\ndi-Higgs production cross section at LHC, while at the same time keeping other\nHiggs observables and precision measurements under control. For masses above\n800 GeV these corrections are in general below 30%, whereas in a viable\nscenario in which one of the leptoquarks can be light, specifically in the mass\nrange $400-600$ GeV, we show that it is possible to roughly double the SM cross\nsection for di-Higgs production, implying that possible first hints of it may\nbe probed at the high luminosity LHC at $\\mathcal{L}\\sim 2$ ab$^{-1}$.\n"}
+{"abstract": "  We investigate a quantum non-relativistic system describing the interaction\nof two particles with spin 1/2 and spin 0, respectively. Assuming that the\nHamiltonian is rotationally invariant and parity conserving we identify all\nsuch systems which allow additional (pseudo)tensor integrals of motion that are\nsecond order matrix polynomials in the momenta. Previously we found all the\n(pseudo)scalar and (axial)vector integrals of motion. No non-obvious tensor\nintegrals exist. However, nontrivial pseudo-tensor integrals do exist. Together\nwith our earlier results we give a complete list of such superintegrable\nHamiltonian systems allowing second-order integrals of motion.\n"}
+{"abstract": "  Graph coloring is often used in parallelizing scientific computations that\nrun in distributed and multi-GPU environments; it identifies sets of\nindependent data that can be updated in parallel. Many algorithms exist for\ngraph coloring on a single GPU or in distributed memory, but to the best of our\nknowledge, hybrid MPI+GPU algorithms have been unexplored until this work. We\npresent several MPI+GPU coloring approaches based on the distributed coloring\nalgorithms of Gebremedhin et al. and the shared-memory algorithms of Deveci et\nal. . The on-node parallel coloring uses implementations in KokkosKernels,\n  which provide parallelization for both multicore CPUs and GPUs. We further\nextend our approaches to compute distance-2 and partial distance-2 colorings,\ngiving the first known distributed, multi-GPU algorithm for these problems. In\naddition, we propose a novel heuristic to reduce communication for recoloring\nin distributed graph coloring. Our experiments show that our approaches operate\nefficiently on inputs too large to fit on a single GPU and scale up to graphs\nwith 76.7 billion edges running on 128 GPUs.\n"}
+{"abstract": "  It is often assumed that atoms are hard spheres in the estimation of local\nlattice distortion (LLD) in high-entropy alloys (HEAs). However, our study\ndemonstrates that the hard sphere model misses the key effect, charge transfer\namong atoms with different electronegativities, in the understanding of the\nstabilization of severely-distorted HEAs. Through the characterization and\nsimulations of the local structure of the HfNbTiZr HEA, we found that the\ncharge transfer effect competes with LLD to significantly reduce the average\natomic-size mismatch. Our finding may form the basis for the design of severely\ndistorted, but stable HEAs.\n"}
+{"abstract": "  Byzantine fault-tolerant systems have been researched for more than four\ndecades, and although shown possible early, the solutions were impractical for\na long time. With PBFT the first practical solution was proposed in 1999 and\nspawned new research which culminated in novel applications using it today.\nAlthough the safety and liveness properties of PBFT-type protocols have been\nrigorously analyzed, when it comes to practical performance only empirical\nresults - often in artificial settings - are known and imperfections on the\ncommunication channels are not specifically considered. In this work we present\nthe first performance model for PBFT specifically considering the impact of\nunreliable channels and the use of different transport protocols over them. We\nalso did extensive simulations to verify the model and to gain more insight on\nthe impact of deployment parameters on the overall transaction time. We show\nthat the usage of UDP can lead to significant speedup for PBFT protocols\ncompared to TCP when tuned accordingly even over lossy channels. Finally, we\ncompared the simulation to a real implementation and measure the benefits of a\ndeveloped improvement directly. We found that the impact on the design of the\nnetwork layer has been overlooked in the past but offers some additional room\nfor improvement when it comes to practical performance. In this work we are\nfocusing on the optimistic case with no node failures, as this is hopefully the\nmost relevant situation.\n"}
+{"abstract": "  The polarization of Cosmic Microwave Background (CMB) photons is rotated as\nthey pass through (ultralight-) axion string loops. Studying this birefringence\ncan reveal valuable information about the axion-photon coupling and the\nstructure of the string network. We develop an approximate analytic formalism\nand identify a kernel function that can be used to calculate the two-point\ncorrelation function for CMB birefringence induced by an arbitrary axion string\nnetwork. Using this formalism, we evaluate the birefringence signal for some\nsimple loop distributions (including scaling and network collapse). We find\nthat the angular correlation function has a characteristic angular scale set by\n$\\theta_\\mathrm{min}$, which corresponds to the angular extent of the loops at\nthe time of recombination. This results in a peak in the birefringence power\nspectrum around $\\ell_p \\sim 1/\\theta_\\mathrm{min}$. An additional scale,\ncontrolled by the axion's mass, is introduced if the network collapses before\ntoday.\n"}
+{"abstract": "  High-power and narrow-linewidth laser light is a vital tool for atomic\nphysics, being used for example in laser cooling and trapping and precision\nspectroscopy. Here we produce Watt-level laser radiation at 457.49 nm and\n460.86 nm of respective relevance for the cooling transitions of cadmium and\nstrontium atoms. This is achieved via the frequency doubling of a kHz-linewidth\nvertical-external-cavity surface-emitting laser (VECSEL), which is based on a\nnovel gain chip design enabling lasing at > 2 W in the 915-928 nm region.\nFollowing an additional doubling stage, spectroscopy of the $^1S_0\\to{}^1P_1$\ncadmium transition at 228.89 nm is performed on an atomic beam, with all the\ntransitions from all eight natural isotopes observed in a single continuous\nsweep of more than 4 GHz in the deep ultraviolet. The absolute value of the\ntransition frequency of Cd-114 and the isotope shifts relative to this\ntransition are determined, with values for some of these shifts provided for\nthe first time\n"}
+{"abstract": "  Holographic acoustical tweezers (HAT) based on Archimedes-Fermat spiraling\nInterDigitated Transducers (S-IDTs) are a versatile tool for the selective\nmanipulation of microparticles [Baudoin et. al., Sci. Adv., 5: eaav1967 (2019)]\nand cells [Baudoin et. al., Nat. Commu., 11, 4244 (2020)] in a standard\nmicrofluidic environment. These binary active holograms produce some focused\nhelical wave, with the ability to trap particles at the vortex core. Yet, all\nthe studies conducted with S-IDTs have so far been restricted to 2D\nmanipulation only. Here we show (i) that 3D radiation trap for microparticles\nand cells can be obtained with spiraling tweezers with sufficiently large\naperture and (ii) that the particles can be displaced axially by simply tuning\nthe driving frequency, without any motion of the transducer. This work opens\nperspectives for 3D cells and microparticles manipulation with single-beam\nacoustical tweezers.\n"}
+{"abstract": "  To assess generalization, machine learning scientists typically either (i)\nbound the generalization gap and then (after training) plug in the empirical\nrisk to obtain a bound on the true risk; or (ii) validate empirically on\nholdout data. However, (i) typically yields vacuous guarantees for\noverparameterized models. Furthermore, (ii) shrinks the training set and its\nguarantee erodes with each re-use of the holdout set. In this paper, we\nintroduce a method that leverages unlabeled data to produce generalization\nbounds. After augmenting our (labeled) training set with randomly labeled fresh\nexamples, we train in the standard fashion. Whenever classifiers achieve low\nerror on clean data and high error on noisy data, our bound provides a tight\nupper bound on the true risk. We prove that our bound is valid for 0-1\nempirical risk minimization and with linear classifiers trained by gradient\ndescent. Our approach is especially useful in conjunction with deep learning\ndue to the early learning phenomenon whereby networks fit true labels before\nnoisy labels but requires one intuitive assumption. Empirically, on canonical\ncomputer vision and NLP tasks, our bound provides non-vacuous generalization\nguarantees that track actual performance closely. This work provides\npractitioners with an option for certifying the generalization of deep nets\neven when unseen labeled data is unavailable and provides theoretical insights\ninto the relationship between random label noise and generalization.\n"}
+{"abstract": "  The increasing digitization and interconnection of legacy Industrial Control\nSystems (ICSs) open new vulnerability surfaces, exposing such systems to\nmalicious attackers. Furthermore, since ICSs are often employed in critical\ninfrastructures (e.g., nuclear plants) and manufacturing companies (e.g.,\nchemical industries), attacks can lead to devastating physical damages. In\ndealing with this security requirement, the research community focuses on\ndeveloping new security mechanisms such as Intrusion Detection Systems (IDSs),\nfacilitated by leveraging modern machine learning techniques. However, these\nalgorithms require a testing platform and a considerable amount of data to be\ntrained and tested accurately. To satisfy this prerequisite, Academia,\nIndustry, and Government are increasingly proposing testbed (i.e., scaled-down\nversions of ICSs or simulations) to test the performances of the IDSs.\nFurthermore, to enable researchers to cross-validate security systems (e.g.,\nsecurity-by-design concepts or anomaly detectors), several datasets have been\ncollected from testbeds and shared with the community. In this paper, we\nprovide a deep and comprehensive overview of ICSs, presenting the architecture\ndesign, the employed devices, and the security protocols implemented. We then\ncollect, compare, and describe testbeds and datasets in the literature,\nhighlighting key challenges and design guidelines to keep in mind in the design\nphases. Furthermore, we enrich our work by reporting the best performing IDS\nalgorithms tested on every dataset to create a baseline in state of the art for\nthis field. Finally, driven by knowledge accumulated during this survey's\ndevelopment, we report advice and good practices on the development, the\nchoice, and the utilization of testbeds, datasets, and IDSs.\n"}
+{"abstract": "  Strong gravitational lensing of gravitational wave sources offers a novel\nprobe of both the lens galaxy and the binary source population. In particular,\nthe strong lensing event rate and the time delay distribution of\nmultiply-imaged gravitational-wave binary coalescence events can be used to\nconstrain the mass distribution of the lenses as well as the intrinsic\nproperties of the source population. We calculate the strong lensing event rate\nfor a range of second (2G) and third generation (3G) detectors, including\nAdvanced LIGO/Virgo, A+, Einstein Telescope (ET), and Cosmic Explorer (CE). For\n3G detectors, we find that {$\\sim0.1\\%$} of observed events are expected to be\nstrongly lensed. We predict detections of {$\\sim 1$} lensing pair per year with\nA+, and {$\\sim 50$} pairs {per year} with ET/CE. These rates are highly\nsensitive to the characteristic galaxy velocity dispersion, $\\sigma_*$,\nimplying that observations of the rates will be a sensitive probe of lens\nproperties. We explore using the time delay distribution between\nmultiply-imaged gravitational-wave sources to constrain properties of the\nlenses. We find that 3G detectors would constrain $\\sigma_*$ to {$\\sim21\\%$\nafter 5 years}. Finally, we show that the presence or absence of strong lensing\n{within the detected population} provides useful insights into the source\nredshift and mass distribution out to redshifts beyond the peak of the star\nformation rate, which can be used to constrain formation channels and their\nrelation to the star formation rate and delay time distributions for these\nsystems.\n"}
+{"abstract": "  Two new classes of skew codes over a finite field $\\F$ are proposed, called\nskew convolutional codes and skew trellis codes. These two classes are defined\nby, respectively, left or right sub-modules over the skew fields of fractions\nof skew polynomials over $\\F$. The skew convolutional codes can be represented\nas periodic time-varying ordinary convolutional codes. The skew trellis codes\nare in general nonlinear over $\\F$. Every code from both classes has a code\ntrellis and can be decoded by Viterbi or BCJR algorithms.\n"}
+{"abstract": "  A key challenge for abstractive summarization is ensuring factual consistency\nof the generated summary with respect to the original document. For example,\nstate-of-the-art models trained on existing datasets exhibit entity\nhallucination, generating names of entities that are not present in the source\ndocument. We propose a set of new metrics to quantify the entity-level factual\nconsistency of generated summaries and we show that the entity hallucination\nproblem can be alleviated by simply filtering the training data. In addition,\nwe propose a summary-worthy entity classification task to the training process\nas well as a joint entity and summary generation approach, which yield further\nimprovements in entity level metrics.\n"}
+{"abstract": "  This paper considers distributed estimation of linear systems when the state\nobservations are corrupted with Gaussian noise of unbounded support and under\npossible random adversarial attacks. We consider sensors equipped with single\ntime-scale estimators and local chi-square ($\\chi^2$) detectors to\nsimultaneously opserve the states, share information, fuse the\nnoise/attack-corrupted data locally, and detect possible anomalies in their own\nobservations. While this scheme is applicable to a wide variety of systems\nassociated with full-rank (invertible) matrices, we discuss it within the\ncontext of distributed inference in social networks. The proposed technique\noutperforms existing results in the sense that: (i) we consider Gaussian noise\nwith no simplifying upper-bound assumption on the support; (ii) all existing\n$\\chi^2$-based techniques are centralized while our proposed technique is\ndistributed, where the sensors \\textit{locally} detect attacks, with no central\ncoordinator, using specific probabilistic thresholds; and (iii) no\nlocal-observability assumption at a sensor is made, which makes our method\nfeasible for large-scale social networks. Moreover, we consider a Linear Matrix\nInequalities (LMI) approach to design block-diagonal gain (estimator) matrices\nunder appropriate constraints for isolating the attacks.\n"}
+{"abstract": "  A double-phase argon Time Projection Chamber (TPC), with an active mass of\n185 g, has been designed and constructed for the Recoil Directionality (ReD)\nexperiment. The aim of the ReD project is to investigate the directional\nsensitivity of argon-based TPCs via columnar recombination to nuclear recoils\nin the energy range of interest (20-200 keV$_{nr}$) for direct dark matter\nsearches. The key novel feature of the ReD TPC is a readout system based on\ncryogenic Silicon Photomultipliers, which are employed and operated\ncontinuously for the first time in an argon TPC. Over the course of six months,\nthe ReD TPC was commissioned and characterised under various operating\nconditions using $\\gamma$-ray and neutron sources, demonstrating remarkable\nstability of the optical sensors and reproducibility of the results. The\nscintillation gain and ionisation amplification of the TPC were measured to be\n$g_1 = (0.194 \\pm 0.013)$ PE/photon and $g_2 = (20.0 \\pm 0.9)$ PE/electron,\nrespectively. The ratio of the ionisation to scintillation signals (S2/S1),\ninstrumental for the positive identification of a candidate directional signal\ninduced by WIMPs, has been investigated for both nuclear and electron recoils.\nAt a drift field of 183 V/cm, an S2/S1 dispersion of 12% was measured for\nnuclear recoils of approximately 60-90 keV$_{nr}$, as compared to 18% for\nelectron recoils depositing 60 keV of energy. The detector performance reported\nhere meets the requirements needed to achieve the principal scientific goals of\nthe ReD experiment in the search for a directional effect due to columnar\nrecombination. A phenomenological parameterisation of the recombination\nprobability in LAr is presented and employed for modeling the dependence of\nscintillation quenching and charge yield on the drift field for electron\nrecoils between 50-500 keV and fields up to 1000 V/cm.\n"}
+{"abstract": "  Although distributed machine learning has opened up numerous frontiers of\nresearch, the separation of large models across different devices, nodes, and\nsites can invite significant communication overhead, making reliable training\ndifficult.\n  The focus on gradients as the primary shared statistic during training has\nled to a number of intuitive algorithms for distributed deep learning; however,\ngradient-based algorithms for training large deep neural networks (DNNs) are\ncommunication-heavy, often requiring additional modifications via sparsity\nconstraints, compression, quantization, and other similar approaches, to lower\nbandwidth.\n  We introduce a surprisingly simple statistic for training distributed DNNs\nthat is more communication-friendly than the gradient. The error\nbackpropagation process can be modified to share these smaller intermediate\nvalues instead of the gradient, reducing communication overhead with no impact\non accuracy. The process provides the flexibility of averaging gradients during\nbackpropagation, enabling novel flexible training schemas while leaving room\nfor further bandwidth reduction via existing gradient compression methods.\nFinally, consideration of the matrices used to compute the gradient inspires a\nnew approach to compression via structured power iterations, which can not only\nreduce bandwidth but also enable introspection into distributed training\ndynamics, without significant performance loss.\n"}
+{"abstract": "  Field observations form the basis of many scientific studies, especially in\necological and social sciences. Despite efforts to conduct such surveys in a\nstandardized way, observations can be prone to systematic measurement errors.\nThe removal of systematic variability introduced by the observation process, if\npossible, can greatly increase the value of this data. Existing non-parametric\ntechniques for correcting such errors assume linear additive noise models. This\nleads to biased estimates when applied to generalized linear models (GLM). We\npresent an approach based on residual functions to address this limitation. We\nthen demonstrate its effectiveness on synthetic data and show it reduces\nsystematic detection variability in moth surveys.\n"}
+{"abstract": "  We present an approach based on density-functional theory for the calculation\nof fundamental gaps of both finite and periodic two-dimensional (2D) electronic\nsystems. The computational cost of our approach is comparable to that of total\nenergy calculations performed via standard semi-local forms. We achieve this by\nreplacing the 2D local density approximation with a more sophisticated -- yet\ncomputationally simple -- orbital-dependent modeling of the exchange potential\nwithin the procedure by Guandalini et al. [Phys. Rev. B 99, 125140 (2019)]. We\nshowcase promising results for semiconductor 2D quantum dots and artificial\ngraphene systems, where the band structure can be tuned through, e.g., Kekul\\'e\ndistortion.\n"}
+{"abstract": "  Many weakly supervised classification methods employ a noise transition\nmatrix to capture the class-conditional label corruption. To estimate the\ntransition matrix from noisy data, existing methods often need to estimate the\nnoisy class-posterior, which could be unreliable due to the overconfidence of\nneural networks. In this work, we propose a theoretically grounded method that\ncan estimate the noise transition matrix and learn a classifier simultaneously,\nwithout relying on the error-prone noisy class-posterior estimation.\nConcretely, inspired by the characteristics of the stochastic label corruption\nprocess, we propose total variation regularization, which encourages the\npredicted probabilities to be more distinguishable from each other. Under mild\nassumptions, the proposed method yields a consistent estimator of the\ntransition matrix. We show the effectiveness of the proposed method through\nexperiments on benchmark and real-world datasets.\n"}
+{"abstract": "  I suggest a novel solution of the inflation and reheating problems of the\nvery early universe. My start point is directly to solve the evolution equation\nsystem of the slow-roll parameters rather than build an inflaton potential. My\nmodel can completely calculate the time evolutions of the inflation and\nreheating processes provided a few boundary values. The numerical results of\nthe model not only clearly show the slow-roll characteristic of the inflation\nand the unconventional mechanism of the inflaton mass generation, but also\nperfectly reproduce all of the measured data of the inflation. In addition, the\nmodel establishes the relationships among the inflation, the reheating and the\nparticle physics, in particular, it predicts that the reheating duration is\n$\\approx1.1$ times the inflaton lifetime, $r_{0.002}$ is one to two order of\nmagnitude smaller than its current upper bound, and so on. Finally, it is very\npossible to test the model in the near future.\n"}
+{"abstract": "  A popular way to create detailed yet easily controllable 3D shapes is via\nprocedural modeling, i.e. generating geometry using programs. Such programs\nconsist of a series of instructions along with their associated parameter\nvalues. To fully realize the benefits of this representation, a shape program\nshould be compact and only expose degrees of freedom that allow for meaningful\nmanipulation of output geometry. One way to achieve this goal is to design\nhigher-level macro operators that, when executed, expand into a series of\ncommands from the base shape modeling language. However, manually authoring\nsuch macros, much like shape programs themselves, is difficult and largely\nrestricted to domain experts. In this paper, we present ShapeMOD, an algorithm\nfor automatically discovering macros that are useful across large datasets of\n3D shape programs. ShapeMOD operates on shape programs expressed in an\nimperative, statement-based language. It is designed to discover macros that\nmake programs more compact by minimizing the number of function calls and free\nparameters required to represent an input shape collection. We run ShapeMOD on\nmultiple collections of programs expressed in a domain-specific language for 3D\nshape structures. We show that it automatically discovers a concise set of\nmacros that abstract out common structural and parametric patterns that\ngeneralize over large shape collections. We also demonstrate that the macros\nfound by ShapeMOD improve performance on downstream tasks including shape\ngenerative modeling and inferring programs from point clouds. Finally, we\nconduct a user study that indicates that ShapeMOD's discovered macros make\ninteractive shape editing more efficient.\n"}
+{"abstract": "  In some scientific fields, it is common to have certain variables of interest\nthat are of particular importance and for which there are many studies\nindicating a relationship with a different explanatory variable. In such cases,\nparticularly those where no relationships are known among explanatory\nvariables, it is worth asking under what conditions it is possible for all such\nclaimed effects to exist simultaneously. This paper addresses this question by\nreviewing some theorems from multivariate analysis that show, unless the\nexplanatory variables also have sizable effects on each other, it is impossible\nto have many such large effects. We also discuss implications for the\nreplication crisis in social science.\n"}
+{"abstract": "  Convolutions are the core operation of deep learning applications based on\nConvolutional Neural Networks (CNNs). Current GPU architectures are highly\nefficient for training and deploying deep CNNs, and hence, these are largely\nused in production for this purpose. State-of-the-art implementations, however,\npresent a lack of efficiency for some commonly used network configurations.\n  In this paper we propose a GPU-based implementation of the convolution\noperation for CNN inference that favors coalesced accesses, without requiring\nprior data transformations. Our experiments demonstrate that our proposal\nyields notable performance improvements in a range of common CNN forward\npropagation convolution configurations, with speedups of up to 2.29x with\nrespect to the best implementation of convolution in cuDNN, hence covering a\nrelevant region in currently existing approaches.\n"}
+{"abstract": "  In this paper we prove two results pertaining to the (unramified and global)\ngeometric Langlands program. The first result is an analogue of the Ramanujan\nconjecture: any cuspidal D-module on Bun_G is tempered. We actually prove a\nmore general statement: any D-module that is *-extended from a quasi-compact\nopen substack of Bun_G is tempered. Then the assertion about cuspidal objects\nis an immediate consequence of a theorem of Drinfeld-Gaitsgory. Building up on\nthis, we prove our second main result, the automorphic gluing theorem for the\ngroup SL_2: it states that any D-module on Bun_{SL_2} is determined by its\ntempered part and its constant term. This theorem (vaguely speaking, an\nanalogue of Langlands' classification for the group SL_2(R)) corresponds under\ngeometric Langlands to the spectral gluing theorem of Arinkin-Gaitsgory and the\nauthor.\n"}
+{"abstract": "  We find all solutions to the parametrized family of norm-form equations\n$x^3-(t^3-1)y^3+3(t^3-1)xy+(t^3-1)^2 = \\pm 1$ studied by Amoroso, Masser and\nZannier. Our proof relies upon an appeal to lower bounds for linear forms in\nlogarithms and various elementary arguments.\n"}
+{"abstract": "  In this paper, we have designed and employed a suspended-wall silo to remove\nJanssen effect in order to explore directly the local pressure dependence of\nGranular Orifice Flow (GOF) systematically. We find that as Janssen effect is\nremoved, the flow rate Q changes linearly with the external pressure. The slope\n{\\alpha} of the linear change decays exponentially with the ratio of the silo\nsize and the size of the orifice {\\Phi}/D, which suggests the existence of a\ncharacteristic ratio {\\lambda} (~2.4). When {\\Phi}/D > {\\lambda}, {\\alpha}\ngradually decays to zero, and the effect of external pressure on the GOF\nbecomes negligible, where the Beverloo law retrieves. Our results show that\nJanssen effect is not a determining factor of the constant rate of GOF,\nalthough it may contribute to shield the top load. The key parameter in GOF is\n{\\Phi}/D. In small {\\Phi}/D, the flow rate of GOF can be directly adjusted by\nthe external pressure via our suspended-wall setup, which may be useful to the\ntransportation of granules in microgravity environment where the gravity-driven\nBeverloo law is disabled.\n"}
+{"abstract": "  Recent experiments in quantum simulators have provided evidence for the\nMany-Body Localized (MBL) phase in 1D and 2D bosonic quantum matter. The\ntheoretical study of such bosonic MBL, however, is a daunting task due to the\nunbounded nature of its Hilbert space. In this work, we introduce a method to\ncompute the long-time real-time evolution of 1D and 2D bosonic systems in an\nMBL phase at strong disorder and weak interactions. We focus on local dynamical\nindicators that are able to distinguish an MBL phase from an Anderson localized\none. In particular, we consider the temporal fluctuations of local observables,\nthe spatiotemporal behavior of two-time correlators and Out-Of-Time-Correlators\n(OTOCs). We show that these few-body observables can be computed with a\ncomputational effort that depends only polynomially on system size but is\nindependent of the target time, by extending a recently proposed numerical\nmethod [Phys. Rev. B 99, 241114 (2019)] to mixed states and bosons. Our method\nalso allows us to surrogate our numerical study with analytical considerations\nof the time-dependent behavior of the studied quantities.\n"}
+{"abstract": "  Benford's Law (BL) or the Significant Digit Law defines the probability\ndistribution of the first digit of numerical values in a data sample. This Law\nis observed in many naturally occurring datasets. It can be seen as a measure\nof naturalness of a given distribution and finds its application in areas like\nanomaly and fraud detection. In this work, we address the following question:\nIs the distribution of the Neural Network parameters related to the network's\ngeneralization capability? To that end, we first define a metric, MLH (Model\nEnthalpy), that measures the closeness of a set of numbers to Benford's Law and\nwe show empirically that it is a strong predictor of Validation Accuracy.\nSecond, we use MLH as an alternative to Validation Accuracy for Early Stopping,\nremoving the need for a Validation set. We provide experimental evidence that\neven if the optimal size of the validation set is known before-hand, the peak\ntest accuracy attained is lower than not using a validation set at all.\nFinally, we investigate the connection of BL to Free Energy Principle and First\nLaw of Thermodynamics, showing that MLH is a component of the internal energy\nof the learning system and optimization as an analogy to minimizing the total\nenergy to attain equilibrium.\n"}
+{"abstract": "  Predicting the densest random disc packing fraction is an unsolved paradigm\nproblem relevant to a number of disciplines and technologies. One difficulty is\nthat it is ill-defined without setting a criterion for the disorder. Another is\nthat the density depends on the packing protocol and the multitude of possible\nprotocol parameters has so far hindered a general solution. A new approach is\nproposed here. After formulating a well-posed form of the general\nprotocol-independent problem for planar packings of discs, a systematic\ncriterion is proposed to avoid crystalline hexagonal order as well as further\ntopological order. The highest possible random packing fraction is then derived\nexactly: $\\phi_{RCP}=0.852525...$. The solution is based on the cell order\ndistribution that is shown to: (i) yield directly the packing fraction; (ii)\nparameterise all possible packing protocols; (iii) make it possible to define\nand limit all topological disorder. The method is further useful for predicting\nthe highest packing fraction in specific protocols, which is illustrated for a\nfamily of simply-sheared packings that generate maximum-entropy cell order\ndistributions.\n"}
+{"abstract": "  Quantum state tomography (QST) is a crucial ingredient for almost all aspects\nof experimental quantum information processing. As an analog of the \"imaging\"\ntechnique in the quantum settings, QST is born to be a data science problem,\nwhere machine learning techniques, noticeably neural networks, have been\napplied extensively. In this work, we build an integrated all-optical setup for\nneural network QST, based on an all-optical neural network (AONN). Our AONN is\nequipped with built-in nonlinear activation function, which is based on\nelectromagnetically induced transparency. Experiment results demonstrate the\nvalidity and efficiency of the all-optical setup, indicating that AONN can\nmitigate the state-preparation-and-measurement error and predict the phase\nparameter in the quantum state accurately. Given that optical setups are highly\ndesired for future quantum networks, our all-optical setup of integrated\nAONN-QST may shed light on replenishing the all-optical quantum network with\nthe last brick.\n"}
+{"abstract": "  Recent advances in training deep learning models have demonstrated the\npotential to provide accurate chest X-ray interpretation and increase access to\nradiology expertise. However, poor generalization due to data distribution\nshifts in clinical settings is a key barrier to implementation. In this study,\nwe measured the diagnostic performance for 8 different chest X-ray models when\napplied to (1) smartphone photos of chest X-rays and (2) external datasets\nwithout any finetuning. All models were developed by different groups and\nsubmitted to the CheXpert challenge, and re-applied to test datasets without\nfurther tuning. We found that (1) on photos of chest X-rays, all 8 models\nexperienced a statistically significant drop in task performance, but only 3\nperformed significantly worse than radiologists on average, and (2) on the\nexternal set, none of the models performed statistically significantly worse\nthan radiologists, and five models performed statistically significantly better\nthan radiologists. Our results demonstrate that some chest X-ray models, under\nclinically relevant distribution shifts, were comparable to radiologists while\nother models were not. Future work should investigate aspects of model training\nprocedures and dataset collection that influence generalization in the presence\nof data distribution shifts.\n"}
+{"abstract": "  In this work we characterise the properties of the object SDSS\nJ020536.84-081424.7, an extended nebular region with projected extension of $14\n\\times 14$ kpc$^{2}$ in the line of sight of the ETG Mrk 1172, using\nunprecedented spectroscopic data from MUSE. We perform a spatially resolved\nstellar population synthesis and estimate the stellar mass for both Mrk 1172\n($1 \\times 10^{11} M_{\\odot}$) and our object of study ($3 \\times 10^{9}\nM_{\\odot}$). While the stellar content of Mrk 1172 is dominated by an old\n($\\sim 10$ Gyr) stellar population, the extended nebular emission has its light\ndominated by young to intermediate age populations (from $\\sim 100$ Myr to\n$\\sim 1$ Gyr) and presents strong emission lines such as: H${\\beta}$, [O III]\n${\\lambda}{\\lambda}$4959,5007, H${\\alpha}$, [N II]\n${\\lambda}{\\lambda}$6549,6585 and [S II] ${\\lambda}{\\lambda}$6717,6732. Using\nthese emission lines we find that it is metal-poor (with $Z \\sim$ 1/3\n$Z_{\\odot}$, comparable to the LMC) and is actively forming stars ($0.70$\nM$_{\\odot}$ yr$^{-1}$), especially in a few bright clumpy knots that are\nreadily visible in H${\\alpha}$. The object has an ionised gas mass $\\geq 3.8\n\\times 10^{5}$ M$_{\\odot}$. Moreover, the motion of the gas is well described\nby a gas in circular orbit in the plane of a disk and is being affected by\ninteraction with Mrk 1172. We conclude that SDSS J020536.84-081424.7 is most\nlikely a dwarf irregular galaxy (dIGal).\n"}
+{"abstract": "  We construct new examples of exceptional Hahn and Jacobi polynomials.\nExceptional polynomials are orthogonal polynomials with respect to a measure\nwhich are also eigenfunctions of a second order difference or differential\noperator. The most apparent difference between classical or classical discrete\northogonal polynomials and their exceptional counterparts is that the\nexceptional families have gaps in their degrees, in the sense that not all\ndegrees are present in the sequence of polynomials. The new examples have the\nnovelty that they depend on an arbitrary number of continuous parameters.\n"}
+{"abstract": "  Weak instruments present a major setback to empirical work. This paper\nintroduces an estimator that admits weak, uncorrelated, or mean-independent\ninstruments that are non-independent of endogenous covariates. Relative to\nconventional instrumental variable methods, the proposed estimator weakens the\nrelevance condition considerably without imposing a stronger exclusion\nrestriction. Identification mainly rests on (1) a weak conditional median\nexclusion restriction imposed on pairwise differences in disturbances and (2)\nnon-independence between covariates and instruments. Under mild conditions, the\nestimator is consistent and asymptotically normal. Monte Carlo experiments\nshowcase an excellent performance of the estimator, and two empirical examples\nillustrate its practical utility.\n"}
+{"abstract": "  We propose a novel thermal production mechanism for dark matter based on the\nidea that dark matter particles $\\chi$ can transform (`infect') heat bath\nparticles $\\psi$: $\\chi \\psi \\rightarrow \\chi \\chi$. For a small initial\nabundance of $\\chi$ this induces an exponential growth in the dark matter\nnumber density, closely resembling the epidemic curves of a spreading pathogen\nafter an initial outbreak. To quantify this relation we present a sharp duality\nbetween the Boltzmann equation for the dark matter number density and\nepidemiological models for the spread of infectious diseases. Finally we\ndemonstrate that the exponential growth naturally stops before $\\chi$\nthermalizes with the heat bath, corresponding to a triumphant `flattening of\nthe curve' that matches the observed dark matter abundance.\n"}
+{"abstract": "  We analyze the optimal information design in a click-through auction with\nfixed valuations per click, but stochastic click-through rates. While the\nauctioneer takes as given the auction rule of the click-through auction, namely\nthe generalized second-price auction, the auctioneer can design the information\nflow regarding the click-through rates among the bidders. A natural requirement\nin this context is to ask for the information structure to be calibrated in the\nlearning sense. With this constraint, the auction needs to rank the ads by a\nproduct of the bid and an unbiased estimator of the click-through rates, and\nthe task of designing an optimal information structure is thus reduced to the\ntask of designing an optimal unbiased estimator.\n  We show that in a symmetric setting with uncertainty about the click-through\nrates, the optimal information structure attains both social efficiency and\nsurplus extraction. The optimal information structure requires private (rather\nthan public) signals to the bidders. It also requires correlated (rather than\nindependent) signals, even when the underlying uncertainty regarding the\nclick-through rates is independent. Beyond symmetric settings, we show that the\noptimal information structure requires partial information disclosure.\n"}
+{"abstract": "  Abstract We study the analytic expression for four flavor neutrino\noscillation in the presence of matter. We calculate the time evolution operator\non flavor and mass basis. We find the matter dependent mass square difference\nand neutrino transition probabilities for (3+1) four flavor neutrino\noscillation.\n"}
+{"abstract": "  We study dynanics of $SU(N-4)$ gauge theories with fermions in rank-2\nsymmetric tensor and $N$ anti-fundamental representations, by perturbing\nsupersymmetric theories with anomaly-mediated supersymmetry breaking. We find\nthe $SU(N)\\times U(1)$ global symmetry is dynamically broken to $SO(N)$ for\n$N\\geq 17$, a different result from conjectures in the literature. For $N<17$,\ntheories flow to infrared fixed points.\n"}
+{"abstract": "  In this paper we discuss contrastive explanations for formal argumentation -\nthe question why a certain argument (the fact) can be accepted, whilst another\nargument (the foil) cannot be accepted under various extension-based semantics.\nThe recent work on explanations for argumentation-based conclusions has mostly\nfocused on providing minimal explanations for the (non-)acceptance of\narguments. What is still lacking, however, is a proper argumentation-based\ninterpretation of contrastive explanations. We show under which conditions\ncontrastive explanations in abstract and structured argumentation are\nmeaningful, and how argumentation allows us to make implicit foils explicit.\n"}
+{"abstract": "  Deep learning has demonstrated its strengths in numerous binary analysis\ntasks, including function boundary detection, binary code search, function\nprototype inference, value set analysis, etc. When applying deep learning to\nbinary analysis tasks, we need to decide what input should be fed into the\nneural network model. More specifically, we need to answer how to represent an\ninstruction in a fixed-length vector. The idea of automatically learning\ninstruction representations is intriguing, however the existing schemes fail to\ncapture the unique characteristics of disassembly. These schemes ignore the\ncomplex intra-instruction structures and mainly rely on control flow in which\nthe contextual information is noisy and can be influenced by compiler\noptimizations.\n  In this paper, we propose to pre-train an assembly language model called\nPalmTree for generating general-purpose instruction embeddings by conducting\nself-supervised training on large-scale unlabeled binary corpora. PalmTree\nutilizes three pre-training tasks to capture various characteristics of\nassembly language. These training tasks overcome the problems in existing\nschemes, thus can help to generate high-quality representations. We conduct\nboth intrinsic and extrinsic evaluations, and compare PalmTree with other\ninstruction embedding schemes. PalmTree has the best performance for intrinsic\nmetrics, and outperforms the other instruction embedding schemes for all\ndownstream tasks.\n"}
+{"abstract": "  Recent work (Takanobu et al., 2020) proposed the system-wise evaluation on\ndialog systems and found that improvement on individual components (e.g., NLU,\npolicy) in prior work may not necessarily bring benefit to pipeline systems in\nsystem-wise evaluation. To improve the system-wise performance, in this paper,\nwe propose new joint system-wise optimization techniques for the pipeline\ndialog system. First, we propose a new data augmentation approach which\nautomates the labeling process for NLU training. Second, we propose a novel\nstochastic policy parameterization with Poisson distribution that enables\nbetter exploration and offers a principled way to compute policy gradient.\nThird, we propose a reward bonus to help policy explore successful dialogs. Our\napproaches outperform the competitive pipeline systems from Takanobu et al.\n(2020) by big margins of 12% success rate in automatic system-wise evaluation\nand of 16% success rate in human evaluation on the standard multi-domain\nbenchmark dataset MultiWOZ 2.1, and also outperform the recent state-of-the-art\nend-to-end trained model from DSTC9.\n"}
+{"abstract": "  Orthogonal frequency division multiplexing (OFDM) is one of the dominant\nwaveforms in wireless communication systems due to its efficient\nimplementation. However, it suffers from a loss of spectral efficiency as it\nrequires a cyclic prefix (CP) to mitigate inter-symbol interference (ISI) and\npilots to estimate the channel. We propose in this work to address these\ndrawbacks by learning a neural network (NN)-based receiver jointly with a\nconstellation geometry and bit labeling at the transmitter, that allows CP-less\nand pilotless communication on top of OFDM without a significant loss in bit\nerror rate (BER). Our approach enables at least 18% throughput gains compared\nto a pilot and CP-based baseline, and at least 4% gains compared to a system\nthat uses a neural receiver with pilots but no CP.\n"}
+{"abstract": "  Theory of choreographic languages typically includes a number of complex\nresults that are proved by structural induction. The high number of cases and\nthe subtle details in some of them lead to long reviewing processes, and\noccasionally to errors being found in published proofs. In this work, we take a\npublished proof of Turing completeness of a choreographic language and\nformalise it in Coq. Our development includes formalising the choreographic\nlanguage and its basic properties, Kleene's theory of partial recursive\nfunctions, the encoding of these functions as choreographies, and proving this\nencoding correct.\n  With this effort, we show that theorem proving can be a very useful tool in\nthe field of choreographic languages: besides the added degree of confidence\nthat we get from a mechanised proof, the formalisation process led us to a\nsignificant simplification of the underlying theory. Our results offer a\nfoundation for the future formal development of choreographic languages.\n"}
+{"abstract": "  In this paper, we propose a novel fault attack termed as Single Event\nTransient Fault Analysis (SETFA) attack, which is well suited for hardware\nimplementations. The proposed approach pinpoints hotspots in the cypher's Sbox\ncombinational logic circuit that significantly reduce the key entropy when\nsubjected to faults. ELEPHANT is a parallel authenticated encryption and\nassociated data (AEAD) scheme targeted to hardware implementations, a finalist\nin the Lightweight cryptography (LWC) competition launched by NIST. In this\nwork, we investigate vulnerabilities of ELEPHANT against fault analysis. We\nobserve that the use of 128-bit random nonce makes it resistant against many\ncryptanalysis techniques like differential, linear, etc., and their variants.\nHowever, the relaxed nature of Statistical Fault Analysis (SFA) methods makes\nthem widely applicable in restrictive environments. We propose a SETFA-based\nkey recovery attack on Elephant. We performed Single experiments with random\nplaintexts and keys, on Dumbo, a Sponge-based instance of the Elephant-AEAD\nscheme. Our proposed approach could recover the secret key in 85-250\nciphertexts. In essence, this work investigates new vulnerabilities towards\nfault analysis that may require to be addressed to ensure secure computations\nand communications in IoT scenarios.\n"}
+{"abstract": "  This paper proposes a robust beamforming (BF) scheme to enhance physical\nlayer security (PLS) of the downlink of a multibeam satellite system in the\npresence of either uncoordinated or coordinated eavesdroppers (Eves).\nSpecifically, with knowing only the approximate locations of the Eves, we aim\nat maximizing the worst-case achievable secrecy rate (ASR) of the legitimate\nuser (LU), subject to the constraints of per-antenna transmit power and quality\nof service (QoS) requirement of the LU. Since the optimization problem is\nnon-convex, we first adopt the discretization method to deal with the unknown\nregions of the Eves and then exploit the log-sum-exp function to approximate\nthe objective function. Afterwards, a BF method joint alternating direction\nmethod of multipliers (ADMM) with Dinkelbach iteration is presented to solve\nthis non-convex problem. Finally, simulation results verify that our robust BF\nalgorithm can effectively improve the security of multibeam satellite systems.\n"}
+{"abstract": "  We investigate the role of the Higgs \\emph{doublet} in the thermal decoupling\nof multi-TeV dark matter coupled to the Weak interactions of the Standard Model\nand the Higgs. The Higgs doublet can mediate a long-range force that affects\nthe annihilation processes and binds dark matter into bound states. More\nimportantly, the emission of a Higgs doublet by a pair of dark matter particles\ncan give rise to extremely rapid monopole bound-state formation processes and\nbound-to-bound transitions. We compute these effects in the unbroken\nelectroweak phase. To this end, we consider the simplest renormalisable\nfermionic model, consisting of a singlet and a doublet under $SU_{L}(2)$ that\nare stabilised by a $\\mathbb{Z}_2$ symmetry, in the regime where the two\nmultiplets coannihilate. In a companion paper, we use the results to show that\nthe formation of metastable bound states via Higgs-doublet emission and their\ndecay decrease the relic density very significantly.\n"}
+{"abstract": "  The proton radiography diagnostic is widely used in laser-plasma experiments\nto make magnetic field measurements. Recent developments in analysis have\nenabled quantitative reconstruction of path-integrated magnetic field values,\nbut making conclusions about the three-dimensional structure of the fields\nremains challenging. In this Letter we propose and demonstrate in kinetic\nsimulations a novel target geometry which makes possible the production of\nmultiple proton beams from a single laser pulse, enabling the application of\ntomographic methods to proton radiography.\n"}
+{"abstract": "  In the current work, we consider diversion of childbirth patients who arrive\nseeking emergency admission to public primary health centers (PHCs). PHCs are\nthe first point of contact for an Indian patient with formal medical care, and\noffer medical care on an outpatient basis, and limited inpatient and childbirth\ncare. In this context, real-time prediction of the wait time of the arriving\npatient becomes important in order to determine whether the patient must be\ndiverted to another PHC or not. We study this problem using a discrete event\nsimulation that we develop of medical care operations in two PHCs in India. We\napproximate the labour room service at each PHC as an M/G/1 queueing system and\nshow how the accuracy of real-time delay predictors impacts the extent of the\nchange in operational outcomes at each PHC. We simulate patient diversion using\nactual delays as well as the delay estimates generated by various delay\npredictors based on the state of the system such as queue-length, elapsed\nservice time, and observed delay histories. The simulation of the diversion\nprocess also incorporates travel time between the PHCs. We also propose a new\ndelay predictor that incorporates information regarding the system state as\nwell as the service time distribution. We compare the operational outcomes at\nboth PHCs without diversion and with diversion using the above delay\npredictors. We show numerically that more accurate delay predictors lead to\nmore equitable distribution of resources involved in provision of childbirth\ncare across both PHCs.\n"}
+{"abstract": "  We study eight different gamma-ray burst (GRB) data sets to examine whether\ncurrent GRB measurements -- that probe a largely unexplored part of\ncosmological redshift ($z$) space -- can be used to reliably constrain\ncosmological model parameters. We use three Amati-correlation samples and five\nCombo-correlation samples to simultaneously derive correlation and cosmological\nmodel parameter constraints. The intrinsic dispersion of each GRB data set is\ntaken as a goodness measurement. We examine the consistency between the\ncosmological bounds from GRBs with those determined from better-established\ncosmological probes, such as baryonic acoustic oscillation (BAO) and Hubble\nparameter $H(z)$ measurements. We use the Markov chain Monte Carlo method\nimplemented in \\textsc{MontePython} to find best-fit correlation and\ncosmological parameters, in six different cosmological models, for the eight\nGRB samples, alone or in conjunction with BAO and $H(z)$ data. For the Amati\ncorrelation case, we compile a data set of 118 bursts, the A118 sample, which\nis the largest -- about half of the total Amati-correlation GRBs -- current\ncollection of GRBs suitable for constraining cosmological parameters. This\nupdated GRB compilation has the smallest intrinsic dispersion of the three\nAmati-correlation GRB data sets we examined. We are unable to define a\ncollection of reliable bursts for current Combo-correlation GRB data.\nCosmological constraints determined from the A118 sample are consistent with --\nbut significantly weaker than -- those from BAO and $H(z)$ data. They also are\nconsistent with the spatially-flat $\\Lambda$CDM model as well as with dynamical\ndark energy models and non-spatially-flat models. Since GRBs probe a largely\nunexplored region of $z$, it is well worth acquiring more and better-quality\nburst data which will give a more definitive answer to the question of the\ntitle.\n"}
+{"abstract": "  Interactive technologies are getting closer to our bodies and permeate the\ninfrastructure of our homes. While such technologies offer many benefits, they\ncan also cause an initial feeling of unease in users. It is important for\nHuman-Computer Interaction to manage first impressions and avoid designing\ntechnologies that appear creepy. To that end, we developed the Perceived\nCreepiness of Technology Scale (PCTS), which measures how creepy a technology\nappears to a user in an initial encounter with a new artefact. The scale was\ndeveloped based on past work on creepiness and a set of ten focus groups\nconducted with users from diverse backgrounds. We followed a structured process\nof analytically developing and validating the scale. The PCTS is designed to\nenable designers and researchers to quickly compare interactive technologies\nand ensure that they do not design technologies that produce initial feelings\nof creepiness in users.\n"}
+{"abstract": "  Understanding player strategies is a key question when analyzing player\nbehavior both for academic researchers and industry practitioners. For game\ndesigners and game user researchers, it is important to gauge the distance\nbetween intended strategies and emergent strategies; this comparison allows\nidentification of glitches or undesirable behaviors. For academic researchers\nusing games for serious purposes such as education, the strategies adopted by\nplayers are indicative of their cognitive progress in relation to serious\ngoals, such as learning process. Current techniques and systems created to\naddress these needs present a few drawbacks. Qualitative methods are difficult\nto scale upwards to include large number of players and are prone to subjective\nbiases. Other approaches such as visualization and analytical tools are either\ndesigned to provide an aggregated overview of the data, losing the nuances of\nindividual player behaviors, or, in the attempt of accounting for individual\nbehavior, are not specifically designed to reduce the visual cognitive load. In\nthis work, we propose a novel visualization technique that specifically\naddresses the tasks of comparing behavior sequences in order to capture an\noverview of the strategies enacted by players and at the same time examine\nindividual player behaviors to identify differences and outliers. This approach\nallows users to form hypotheses about player strategies and verify them. We\ndemonstrate the effectiveness of the technique through a case study: utilizing\na prototype system to investigate data collected from a commercial educational\npuzzle game. While the prototype's usability can be improved, initial testing\nresults show that core features of the system proved useful to our potential\nusers for understanding player strategies.\n"}
+{"abstract": "  In this analysis, we work with the data set that was compiled by Darren\nLinvill and Patrick Warren, along with a representative sample of Facebook ads\nthat were released by the House Intelligence Committee Minority. The goal of\nthis analysis is to use the categories defined by Linvill and Warren in the\nTwitter data and investigate if these categories exist in Facebook ads. This\nbegin to give us insights to the tactics used between the two social media\nservices. Further, we try to replicate Linvill and Warren's original\ncategorization of the Twitter data. Lastly, we investigate what categories may\nexist in the Facebook data.\n"}
+{"abstract": "  This paper presents a novel hierarchical motion planning approach based on\nRapidly-Exploring Random Trees (RRT) for global planning and Model Predictive\nControl (MPC) for local planning. The approach targets a three-wheeled cycle\nrickshaw (trishaw) used for autonomous urban transportation in shared spaces.\nDue to the nature of the vehicle, the algorithms had to be adapted in order to\nadhere to non-holonomic kinematic constraints using the Kinematic Single-Track\nModel.\n  The vehicle is designed to offer transportation for people and goods in\nshared environments such as roads, sidewalks, bicycle lanes but also open\nspaces that are often occupied by other traffic participants. Therefore, the\nalgorithm presented in this paper needs to anticipate and avoid dynamic\nobstacles, such as pedestrians or bicycles, but also be fast enough in order to\nwork in real-time so that it can adapt to changes in the environment. Our\napproach uses an RRT variant for global planning that has been modified for\nsingle-track kinematics and improved by exploiting dead-end nodes. This allows\nus to compute global paths in unstructured environments very fast. In a second\nstep, our MPC-based local planner makes use of the global path to compute the\nvehicle's trajectory while incorporating dynamic obstacles such as pedestrians\nand other road users.\n  Our approach has shown to work both in simulation as well as first real-life\ntests and can be easily extended for more sophisticated behaviors.\n"}
+{"abstract": "  AlphaZero has achieved impressive performance in deep reinforcement learning\nby utilizing an architecture that combines search and training of a neural\nnetwork in self-play. Many researchers are looking for ways to reproduce and\nimprove results for other games/tasks. However, the architecture is designed to\nlearn from scratch, tabula rasa, accepting a cold-start problem in self-play.\nRecently, a warm-start enhancement method for Monte Carlo Tree Search was\nproposed to improve the self-play starting phase. It employs a fixed parameter\n$I^\\prime$ to control the warm-start length. Improved performance was reported\nin small board games. In this paper we present results with an adaptive switch\nmethod. Experiments show that our approach works better than the fixed\n$I^\\prime$, especially for \"deep,\" tactical, games (Othello and Connect Four).\nWe conjecture that the adaptive value for $I^\\prime$ is also influenced by the\nsize of the game, and that on average $I^\\prime$ will increase with game size.\nWe conclude that AlphaZero-like deep reinforcement learning benefits from\nadaptive rollout based warm-start, as Rapid Action Value Estimate did for\nrollout-based reinforcement learning 15 years ago.\n"}
+{"abstract": "  For $n\\geq s> r\\geq 1$ and $k\\geq 2$, write $n \\rightarrow (s)_{k}^r$ if\nevery hyperedge colouring with $k$ colours of the complete $r$-uniform\nhypergraph on $n$ vertices has a monochromatic subset of size $s$. Improving\nupon previous results by \\textcite{AGLM14} and \\textcite{EHMR84} we show that\n\\[ \\text{if } r \\geq 3 \\text{ and } n \\nrightarrow (s)_k^r \\text{ then } 2^n\n\\nrightarrow (s+1)_{k+3}^{r+1}. \\] This yields an improvement for some of the\nknown lower bounds on multicolour hypergraph Ramsey numbers.\n  Given a hypergraph $H=(V,E)$, we consider the Ramsey-like problem of\ncolouring all $r$-subsets of $V$ such that no hyperedge of size $\\geq r+1$ is\nmonochromatic. We provide upper and lower bounds on the number of colours\nnecessary in terms of the chromatic number $\\chi(H)$. In particular we show\nthat this number is $O(\\log^{(r-1)} (r \\chi(H)) + r)$.\n"}
+{"abstract": "  Reflecting our experiences in areas, like Algebraic Specifications, Abstract\nModel Theory, Graph Transformations, and Model Driven Software Engineering\n(MDSE), we present a general, category independent approach to Logics of\nFirst-Order Constraints (LFOC). Traditional First-Order Logic, Description\nLogic and the sketch framework are discussed as examples. We use the concept of\ninstitution [Diaconescu08,GoguenBurstall92] as a guideline to describe LFOC's.\nThe main result states that any choice of the six parameters, we are going to\ndescribe, gives us a corresponding \"institution of constraints\" at hand. The\n\"presentations\" for an institution of constraints can be characterized as\n\"first-order sketches\". As a corresponding variant of the \"sketch-entailments\"\nin [Makkai97], we finally introduce \"sketch rules\" to equip LFOC's with the\nnecessary expressive power.\n"}
+{"abstract": "  Podcasts are spoken documents across a wide-range of genres and styles, with\ngrowing listenership across the world, and a rapidly lowering barrier to entry\nfor both listeners and creators. The great strides in search and recommendation\nin research and industry have yet to see impact in the podcast space, where\nrecommendations are still largely driven by word of mouth. In this perspective\npaper, we highlight the many differences between podcasts and other media, and\ndiscuss our perspective on challenges and future research directions in the\ndomain of podcast information access.\n"}
+{"abstract": "  Pairwise comparison matrices are increasingly used in settings where some\npairs are missing. However, there exist few inconsistency indices for similar\nincomplete data sets and no reasonable measure has an associated threshold.\nThis paper generalises the famous rule of thumb for the acceptable level of\ninconsistency, proposed by Saaty, to incomplete pairwise comparison matrices.\nThe extension is based on choosing the missing elements such that the maximal\neigenvalue of the incomplete matrix is minimised. Consequently, the\nwell-established values of the random index cannot be adopted: the\ninconsistency of random matrices is found to be the function of matrix size and\nthe number of missing elements, with a nearly linear dependence in the case of\nthe latter variable. Our results can be directly built into decision-making\nsoftware and used by practitioners as a statistical criterion for accepting or\nrejecting an incomplete pairwise comparison matrix.\n"}
+{"abstract": "  The recent discovery of AV$_3$Sb$_5$ (A=K,Rb,Cs) has uncovered an intriguing\narena for exotic Fermi surface instabilities in a kagome metal. Among them,\nsuperconductivity is found in the vicinity of multiple van Hove singularities,\nexhibiting indications of unconventional pairing. We show that the sublattice\ninterference mechanism is central to understanding the formation of\nsuperconductivity in a kagome metal. Starting from an appropriately chosen\nminimal tight-binding model with multiple with multiple van Hove singularities\nclose to the Fermi level for AV$_3$Sb$_5$, we provide a random phase\napproximation analysis of superconducting instabilities. Non-local Coulomb\nrepulsion, the sublattice profile of the van Hove bands, and the bare\ninteraction strength turn out to be the crucial parameters to determine the\npreferred pairing symmetry. Implications for potentially topological surface\nstates are discussed, along with a proposal for additional measurements to pin\ndown the nature of superconductivity in AV$_3$Sb$_5$.\n"}
+{"abstract": "  Silicon can be isotopically enriched, allowing for the fabrication of highly\ncoherent semiconductor spin qubits. However, the conduction band of bulk Si\nexhibits a six-fold valley degeneracy, which may adversely impact the\nperformance of silicon quantum devices. To date, the spatial characterization\nof valley states in Si remains limited. Moreover, techniques for probing valley\nstates in functional electronic devices are needed. We describe here a\ncryogen-free scanning gate microscope for the characterization of\nSi/Si$_{0.7}$Ge$_{0.3}$ quantum devices at mK temperatures. The microscope is\nbased on the Pan-walker design, with coarse positioning piezo stacks and a fine\nscanning piezo tube. A tungsten microscope tip is attached to a tuning fork for\nactive control of the tip-to-sample distance. To reduce vibration noise from\nthe pulse tube cooler, we utilize both active and passive vibration isolation\nmechanisms, and achieve a root-mean-square noise in $z$ of $\\sim$ 2 nm. Our\nmicroscope is designed to characterize fully functioning\nSi/Si$_{0.7}$Ge$_{0.3}$ quantum devices. As a proof of concept, we use the\nmicroscope to manipulate the charge occupation of a Si quantum dot, opening up\na range of possibilities for the exploration of quantum devices and materials.\n"}
+{"abstract": "  We develop a theory of the spin battery effect in\nsuperconductor/ferromagnetic insulator (SC/FI) systems taking into account the\nmagnetic proximity effect. We demonstrate that the spin-energy mixing enabled\nby the superconductivity leads to the enhancement of spin accumulation by\nseveral orders of magnitude relative to the normal state. This finding can\nexplain the recently observed giant inverse spin Hall effect generated by\nthermal magnons in the SC/FI system. We suggest a non-local electrical\ndetection scheme which can directly probe the spin accumulation driven by the\nmagnetization dynamics. We predict a giant Seebeck effect converting the magnon\ntemperature bias into the non-local voltage signal. We also show how this can\nbe used to enhance the sensitivity of magnon detection even up to the\nsingle-magnon level.\n"}
+{"abstract": "  Deep neural networks (DNNs) have been widely used for medical image analysis.\nHowever, the lack of access a to large-scale annotated dataset poses a great\nchallenge, especially in the case of rare diseases, or new domains for the\nresearch society. Transfer of pre-trained features, from the relatively large\ndataset is a considerable solution. In this paper, we have explored supervised\nsegmentation using domain adaptation for optic nerve and orbital tumor, when\nonly small sampled CT images are given. Even the lung image database consortium\nimage collection (LIDC-IDRI) is a cross-domain to orbital CT, but the proposed\ndomain adaptation method improved the performance of attention U-Net for the\nsegmentation in public optic nerve dataset and our clinical orbital tumor\ndataset. The code and dataset are available at https://github.com/cmcbigdata.\n"}
+{"abstract": "  The increasing amount of distributed energy resources including renewable\nenergy systems and electric vehicles is expected to change electric power grids\nsignificantly, where conventional consumers are transformed to prosumers since\nthey can produce electricity as well. In such an ecosystem, prosumers can start\noffering their excess energy to supply demands of the other customers on the\ngrids behind the meter without interference of distribution system operators\n(DSO). Besides, DSOs require more accurate and more frequent data form\nprosumers' net demand to be able to operate their network efficiently. The main\nchallenge in these new distribution grids is the amount of data that needs to\nbe collected in this platform is unbelievably high, and more immortally,\nprosumers will likely refuse to share their information with DSOs due to their\npotential privacy and economic concerns. Blockchain technology as an efficient\ndistributed solution for management of data and financial transactions, has\nbeen considered to solve this trust issue. With blockchain-based solutions,\ndata and financial transactions between all parties will take placed through\ndistributed ledgers without any interference from an intermediary. In this\npaper, impacts of blockchain technologies on electric power industry is\nstudied. The paper specifically focuses on LO3 Energy -- one of startups\napplying blockchain to electric power grids -- their blockchain-based solution\ncalled Exergy, and their use cases to implement such solutions.\n"}
+{"abstract": "  Mean dimension is a topological invariant of dynamical systems, which\noriginates with Mikhail Gromov in 1999 and which was studied with deep\napplications around 2000 by Elon Lindenstrauss and Benjamin Weiss within the\nframework of amenable group actions. Let a countable discrete amenable group\n$G$ act continuously on compact metrizable spaces $X$ and $Y$. Consider the\nproduct action of $G$ on the product space $X\\times Y$. The product inequality\nfor mean dimension is well known: $\\mathrm{mdim}(X\\times\nY,G)\\le\\mathrm{mdim}(X,G)+\\mathrm{mdim}(Y,G)$, while it was unknown for a long\ntime if the product inequality could be an equality. In 2019, Masaki Tsukamoto\nconstructed the first example of two different continuous actions of $G$ on\ncompact metrizable spaces $X$ and $Y$, respectively, such that the product\ninequality becomes strict. However, there is still one longstanding problem\nwhich remains open in this direction, asking if there exists a continuous\naction of $G$ on some compact metrizable space $X$ such that\n$\\mathrm{mdim}(X\\times X,G)<2\\cdot\\mathrm{mdim}(X,G)$. We solve this problem.\nSomewhat surprisingly, we prove, in contrast to (topological) dimension theory,\na rather satisfactory theorem: If an infinite (countable discrete) amenable\ngroup $G$ acts continuously on a compact metrizable space $X$, then we have\n$\\mathrm{mdim}(X^n,G)=n\\cdot\\mathrm{mdim}(X,G)$, for any positive integer $n$.\nOur product formula for mean dimension, together with the example and\ninequality (stated previously), eventually allows mean dimension of product\nactions to be fully understood.\n"}
+{"abstract": "  We study the homogenous quenching processes in a holographic s+p model with\nreentrant phase transitions. We first realize the reentrant phase transition in\nthe holographic model in probe limit and draw the phase diagram. Next, we\ncompare the time evolution of the two condensates in two groups of numerical\nquenching experiments across the reentrant region, with different quenching\nspeed as well as different width of the reentrant region, respectively. We also\nstudy the dynamical competition between the two orders in quenching processes\nfrom the normal phase to the superconductor phase.\n"}
+{"abstract": "  We propose and discuss sensitivity metrics for reliability analysis, which\nare based on the value of information. These metrics are easier to interpret\nthan other existing sensitivity metrics in the context of a specific decision\nand they are applicable to any type of reliability assessment, including those\nwith dependent inputs. We develop computational strategies that enable\nefficient evaluation of these metrics, in some scenarios without additional\nruns of the deterministic model. The metrics are investigated by application to\nnumerical examples.\n"}
+{"abstract": "  Synonymous keyword retrieval has become an important problem for sponsored\nsearch ever since major search engines relax the exact match product's matching\nrequirement to a synonymous level. Since the synonymous relations between\nqueries and keywords are quite scarce, the traditional information retrieval\nframework is inefficient in this scenario. In this paper, we propose a novel\nquotient space-based retrieval framework to address this problem. Considering\nthe synonymy among keywords as a mathematical equivalence relation, we can\ncompress the synonymous keywords into one representative, and the corresponding\nquotient space would greatly reduce the size of the keyword repository. Then an\nembedding-based retrieval is directly conducted between queries and the keyword\nrepresentatives. To mitigate the semantic gap of the quotient space-based\nretrieval, a single semantic siamese model is utilized to detect both the\nkeyword--keyword and query-keyword synonymous relations. The experiments show\nthat with our quotient space-based retrieval method, the synonymous keyword\nretrieving performance can be greatly improved in terms of memory cost and\nrecall efficiency. This method has been successfully implemented in Baidu's\nonline sponsored search system and has yielded a significant improvement in\nrevenue.\n"}
+{"abstract": "  We compute the motivic Euler characteristic of Ayoub's nearby cycles by\nstrata of a semi-stable reduction, for a degeneration to multiple isolated\nquasi-homogeneous singularities resolved by a single weighted blow-up. This\nallows to compare the local picture at the singularities with the global\nconductor formula for hypersurfaces developed by Levine, Pepin Lehalleur and\nSrinivas, revealing that the formula is local in nature, thus extending it to\nthe more general setting considered in this paper. This gives a quadratic\nrefinement for the classical Milnor number formula with multiple singularities\nof a certain type.\n"}
+{"abstract": "  We propose a robust in-time predictor for in-hospital COVID-19 patient's\nprobability of requiring mechanical ventilation. A challenge in the risk\nprediction for COVID-19 patients lies in the great variability and irregular\nsampling of patient's vitals and labs observed in the clinical setting.\nExisting methods have strong limitations in handling time-dependent features'\ncomplex dynamics, either oversimplifying temporal data with summary statistics\nthat lose information or over-engineering features that lead to less robust\noutcomes. We propose a novel in-time risk trajectory predictive model to handle\nthe irregular sampling rate in the data, which follows the dynamics of risk of\nperforming mechanical ventilation for individual patients. The model\nincorporates the Multi-task Gaussian Process using observed values to learn the\nposterior joint multi-variant conditional probability and infer the missing\nvalues on a unified time grid. The temporal imputed data is fed into a\nmulti-objective self-attention network for the prediction task. A novel\npositional encoding layer is proposed and added to the network for producing\nin-time predictions. The positional layer outputs a risk score at each\nuser-defined time point during the entire hospital stay of an inpatient. We\nframe the prediction task into a multi-objective learning framework, and the\nrisk scores at all time points are optimized altogether, which adds robustness\nand consistency to the risk score trajectory prediction. Our experimental\nevaluation on a large database with nationwide in-hospital patients with\nCOVID-19 also demonstrates that it improved the state-of-the-art performance in\nterms of AUC (Area Under the receiver operating characteristic Curve) and AUPRC\n(Area Under the Precision-Recall Curve) performance metrics, especially at\nearly times after hospital admission.\n"}
+{"abstract": "  Interference is the cornerstone of Huygens source design for reshaping and\ncontrolling scattering patterns. The conventional underpinning principle, such\nas for the Kerker effect, is the interference of electric and magnetic dipole\nand quadrupole modes. Here a route to realize transverse Kerker scattering\nthrough employing only the interference between the electric dipole and\nmagnetic quadrupole is demonstrated. The proposed approach is numerically\nvalidated in an ultra-thin Silicon square nanoplate metasurface, and is further\nverified by multipole decomposition. The metasurface is shown to be invisible\nfornear-infrared wavelengths and with an enhanced electric field in the region\nof the nanoparticle. Additionally, we develop further the proposed approach\nwith practical implementation for invisibility applications by exploring the\neffects of the aspect ratio of the square plate nanoresonator, the\ninter-particle separation, and the presence of a substrate. Further it is\ndemonstrated that invisibility can be observed at oblique incidence up to\n60{\\deg} for a transverse magnetic plane wave. The results are relevant for\nHuygens metasurface design for perfect reflectors, invisibility and devices for\nharmonic generation manipulation.\n"}
+{"abstract": "  We study a one dimensional quantum XY spin chain driven by a local noisy spin\nimpurity with finite correlation time, along the transverse field direction. We\nrecover the celebrated Zeno crossover and we show that entanglement can be used\nas a proxy for the heating and strong-measurement regimes. We compute the\nentanglement entropy of a block of spins and we observe that its velocity\nspreading decreases at strong dissipation, as a result of the Zeno effect. Upon\nincreasing the correlation time of the noise, the location of the Zeno\ncrossover shifts at stronger dissipation rates opening up a broader heating\nphase. We offer insight on the mechanisms underlying the dynamics of the\nentanglement entropy by monitoring different time traces of the local\ntransverse magnetisation profile. Our results aim at starting a complementary\nviewpoint on the field of dissipative quantum impurities, based on a\ntheoretical quantum information perspective.\n"}
+{"abstract": "  In this short article we show a particular version of the Hedberg inequality\nwhich can be used to derive, in a very simple manner, functional inequalities\ninvolving Sobolev and Besov spaces in the general setting of Lebesgue spaces of\nvariable exponents and in the framework of Orlicz spaces.\n"}
+{"abstract": "  Recently, two-dimensional monolayer MoSi2N4 with hexagonal structure was\nsuccessfully synthesized in experiment (Hong et al 2020 Science 369, 670). The\nfabricated monolayer MoSi2N4 is predicted to have excellent mechanical\nproperties. Motived by the experiment, we perform first-principles calculations\nto investigate the mechanical properties of monolayer MoSi2N4, including its\nideal tensile strengths, critical strains, and failure mechanisms. Our results\ndemonstrate that monolayer MoSi2N4 can withstand stresses up to 51.6 and 49.2\nGPa along zigzag and armchair directions, respectively. The corresponding\ncritical strains are 26.5% and 17.5%, respectively. For biaxial strain, the\nideal tensile strength is 50.6 GPa with a critical strain of 19.5%. Compared\nwith monolayer MoS2, monolayer MoSi2N4 possesses much higher elastic moduli and\nideal tensile strengths for both uniaxial and biaxial strains. Interestingly,\nthe critical strain and failure mechanism of zigzag direction in MoSi2N4 are\nalmost the same as those of armchair direction in MoS2, while the critical\nstrain and failure mechanism of armchair direction for MoSi2N4 are similar to\nthe ones of zigzag direction for MoS2. Our work reveals the remarkable\nmechanical characteristics of monolayer MoSi2N4.\n"}
+{"abstract": "  We present a new learning-based method for identifying safe and navigable\nregions in off-road terrains and unstructured environments from RGB images. Our\napproach consists of classifying groups of terrains based on their navigability\nlevels using coarse-grained semantic segmentation. We propose a bottleneck\ntransformer-based deep neural network architecture that uses a novel group-wise\nattention mechanism to distinguish between navigability levels of different\nterrains. Our group-wise attention heads enable the network to explicitly focus\non the different groups and improve the accuracy. We show through extensive\nevaluations on the RUGD and RELLIS-3D datasets that our learning algorithm\nimproves visual perception accuracy in off-road terrains for navigation. We\ncompare our approach with prior work on these datasets and achieve an\nimprovement over the state-of-the-art mIoU by 6.74-39.1% on RUGD and\n3.82-10.64% on RELLIS-3D. In addition, we deploy our method on a Clearpath\nJackal robot. Our approach improves the performance of the navigation algorithm\nin terms of average progress towards the goal by 54.73% and the false positives\nin terms of forbidden region by 29.96%.\n"}
+{"abstract": "  This paper introduces the \\emph{Simultaneous Assignment Problem}. Here, we\nare given an assignment problem on some of the subgraphs of a given graph, and\nwe are looking for a heaviest assignment which is feasible when restricted to\nany of the assignment problems. More precisely, we are given a graph with a\nweight- and a capacity function on its edges and a set of its subgraphs\n$H_1,\\dots,H_k$ along with a degree upper bound function for each of them. In\naddition, we are also given a laminar system on the node set with an upper\nbound on the degree-sum of the nodes in each set in the system. We want to\nassign each edge a non-negative integer below its capacity such that the total\nweight is maximized, the degrees in each subgraph are below the degree upper\nbound associated with the subgraph, and the degree-sum bound is respected in\neach set of the laminar system.\n  The problem is shown to be APX-hard in the unweighted case even if the graph\nis a forest and $k=2$. This also implies that the Distance matching problem is\nAPX-hard in the weighted case and that the Cyclic distance matching problem is\nAPX-hard in the unweighted case. We identify multiple special cases when the\nproblem can be solved in strongly polynomial time. One of these cases, the\nso-called locally laminar case, is a common generalization of the Hierarchical\nb-matching problem and the Laminar matchoid problem, and it implies that both\nof these problems can be solved efficiently in the weighted, capacitated case\n-- improving upon the most general polynomial-time algorithms for these\nproblems. The problem can be constant approximated when $k$ is a constant, and\nwe show that the approximation factor matches the integrality gap of a\nstrengthened LP-relaxation for small $k$. We give improved approximation\nalgorithms for special cases, for example, when the degree bounds are uniform\nor the graph is sparse.\n"}
+{"abstract": "  Let $V$ be an $n$-dimensional vector space over a finite field\n$\\mathbb{F}_q$, where $q$ is a prime power. Define the \\emph{generalized\n$q$-Kneser graph} $K_q(n,k,t)$ to be the graph whose vertices are the\n$k$-dimensional subspaces of $V$ and two vertices $F_1$ and $F_2$ are adjacent\nif $\\dim(F_1\\cap F_2)<t$. Then $K_q(n,k,1)$ is the well-known $q$-Kneser graph.\nIn this paper, we determine the treewidth of $K_q(n,k,t)$ for $n\\geq\n2t(k-t+1)+k+1$ and $t\\ge 1$ exactly. Note that $K_q(n,k,k-1)$ is the complement\nof the Grassmann graph $G_q(n,k)$. We give a more precise result for the\ntreewidth of $\\overline{G_q(n,k)}$ for any possible $n$, $k$ and $q$.\n"}
+{"abstract": "  The creation of an electron-positron pair in the collision of two real\nphotons, namely the linear Breit-Wheeler process, has never been detected\ndirectly in the laboratory since its prediction in 1934 despite its fundamental\nimportance in quantum electrodynamics and astrophysics. In the last few years,\nseveral experimental setup have been proposed to observe this process in the\nlaboratory, relying either on thermal radiation, Bremsstrahlung, linear or\nmultiphoton inverse Compton scattering photons sources created by lasers or by\nthe mean of a lepton collider coupled with lasers. In these propositions, the\ninfluence of the photons' energy distribution on the total number of produced\npairs has been taken into account with an analytical model only for two of\nthese cases. We hereafter develop a general and original, semi-analytical model\nto estimate the influence of the photons energy distribution on the total\nnumber of pairs produced by the collision of two such photon beams, and give\noptimum energy parameters for some of the proposed experimental configurations.\nOur results shows that the production of optimum Bremsstrahlung and linear\ninverse Compton sources are, only from energy distribution considerations,\nalready reachable in today's facilities. Despite its less interesting energy\ndistribution features for the LBW pair production, the photon sources generated\nvia multiphoton inverse Compton scattering by the propagation of a laser in a\nmicro-channel can also be interesting, thank to the high collision luminosity\nthat could eventually be reached by such configurations. These results then\ngives important insights for the design of experiments intended to detect\nlinear Breit-Wheeler produced positrons in the laboratory for the first time.\n"}
+{"abstract": "  The quasiparticle formalism invented by Lev Landau for description of\nconventional Fermi liquids is generalized to exotic superconductivity\nattributed to Cooper pairing, whose measured properties defy explanation within\nthe standard BCS-Fermi Liquid description. We demonstrate that in such systems\nthe quasiparticle number remains equal to particle number, just as in common\nFermi liquids. We are then able to explain the puzzling relationship between\nthe variation with doping $x$ of two key properties of the family\nLa$_{2-x}$Sr$_x$Cu0$_4$ of exotic superconductors, namely the $T=0$ superfluid\ndensity $\\rho_{s0}(x)$ and the coefficient $A_1(x)$ in the linear-in-$T$\ncomponent of the normal-state low-$T$ resistivity $\\rho(T)=\\rho_0+A_1T+A_2T^2$,\nin terms of the presence of interaction-induced flat bands in the ground states\nof these metals.\n"}
+{"abstract": "  CTB 80 (G69.0+2.7) is a relatively old (50--80 kyr) supernova remnant (SNR)\nwith a complex radio morphology showing three extended radio arms and a radio\nand X-ray nebula near the location of the pulsar PSR B1951+32. We report on a\nstudy of the GeV emission in the region of CTB 80 with \\emph{Fermi}-LAT data.\nAn extended source with a size of 1.3$^\\circ$, matching the size of the\ninfrared shell associated to the SNR, was discovered. The GeV emission,\ndetected up to an energy of $\\sim 20$ GeV, is more significant at the location\nof the northern radio arm where previous observations imply that the SNR shock\nis interacting with ambient material. Both hadronic and leptonic scenarios can\nreproduce the multiwavelength data reasonably well. The hadronic cosmic ray\nenergy density required is considerably larger than the local Galactic value\nand the gamma-ray leptonic emission is mainly due to bremsstrahlung\ninteractions. We conclude that GeV particles are still trapped or accelerated\nby the SNR producing the observed high-energy emission when interacting with\nambient material.\n"}
+{"abstract": "  We prove unique weak solvability and Feller property for stochastic\ndifferential equations with drift in a large class of time-dependent vector\nfields. This class contains, in particular, the critical\nLadyzhenskaya-Prodi-Serrin class, the weak $L^d$ class as well as some vector\nfields that are not even in $L^{2+\\varepsilon}_{\\rm loc}$, $\\varepsilon>0$.\n"}
+{"abstract": "  For a digraph $G$ and $v \\in V(G)$, let $\\delta^+(v)$ be the number of\nout-neighbors of $v$ in $G$. The Caccetta-H\\\"{a}ggkvist conjecture states that\nfor all $k \\ge 1$, if $G$ is a digraph with $n = |V(G)|$ such that $\\delta^+(v)\n\\ge k$ for all $v \\in V(G)$, then $G$ contains a directed cycle of length at\nmost $\\lceil n/k \\rceil$. Aharoni proposed a generalization of this conjecture,\nthat a simple edge-colored graph on $n$ vertices with $n$ color classes, each\nof size $k$, has a rainbow cycle of length at most $\\lceil n/k \\rceil$. With\nPelik\\'anov\\'a and Pokorn\\'a, we showed that this conjecture is true if each\ncolor class has size ${\\Omega}(k\\log k)$. In this paper, we present a proof of\nthe conjecture if each color class has size ${\\Omega}(k)$, which improved the\nprevious result and is only a constant factor away from Aharoni's conjecture.\nWe also consider what happens when the condition on the number of colors is\nrelaxed.\n"}
+{"abstract": "  In this work we present a dual-mode mid-infrared workflow [6], for detecting\nsub-superficial mural damages in frescoes artworks. Due to the large nature of\nfrescoes, multiple thermal images are recorded. Thus, the experimental setup\nmay introduce measurements errors, seen as inter-frame changes in the image\ncontrast, after mosaicking. An approach to lowering errors is to post-process\nthe mosaic [10] via osmosis partial differential equation (PDE) [12, 13], which\npreserves details, mass and balance the lights: efficient numerical study for\nosmosis on large images is proposed [2, 11], based on operator splitting [8].\nOur range of Cultural Heritage applications include the detection of\nsub-superficial voids in Monocromo (L. Da Vinci, Castello Sforzesco, Milan)\n[5], the light-balance for multi-spectral imaging and the data integration on\nthe Archimedes Palimpsest [10].\n"}
+{"abstract": "  Machine learning technologies using deep neural networks (DNNs), especially\nconvolutional neural networks (CNNs), have made automated, accurate, and fast\nmedical image analysis a reality for many applications, and some DNN-based\nmedical image analysis systems have even been FDA-cleared. Despite the\nprogress, challenges remain to build DNNs as reliable as human expert doctors.\nIt is known that DNN classifiers may not be robust to noises: by adding a small\namount of noise to an input image, a DNN classifier may make a wrong\nclassification of the noisy image (i.e., in-distribution adversarial sample),\nwhereas it makes the right classification of the clean image. Another issue is\ncaused by out-of-distribution samples that are not similar to any sample in the\ntraining set. Given such a sample as input, the output of a DNN will become\nmeaningless. In this study, we investigated the in-distribution (IND) and\nout-of-distribution (OOD) adversarial robustness of a representative CNN for\nlumbar disk shape reconstruction from spine MR images. To study the\nrelationship between dataset size and robustness to IND adversarial attacks, we\nused a data augmentation method to create training sets with different levels\nof shape variations. We utilized the PGD-based algorithm for IND adversarial\nattacks and extended it for OOD adversarial attacks to generate OOD adversarial\nsamples for model testing. The results show that IND adversarial training can\nimprove the CNN robustness to IND adversarial attacks, and larger training\ndatasets may lead to higher IND robustness. However, it is still a challenge to\ndefend against OOD adversarial attacks.\n"}
+{"abstract": "  The Posner-Robinson Theorem states that for any reals $Z$ and $A$ such that\n$Z \\oplus 0' \\leq_\\mathrm{T} A$ and $0 <_\\mathrm{T} Z$, there exists $B$ such\nthat $A \\equiv_\\mathrm{T} B' \\equiv_\\mathrm{T} B \\oplus Z \\equiv_\\mathrm{T} B\n\\oplus 0'$. Consequently, any nonzero Turing degree\n$\\operatorname{deg}_\\mathrm{T}(Z)$ is a Turing jump relative to some $B$. Here\nwe prove the hyperarithmetical analog, based on an unpublished proof of Slaman,\nnamely that for any reals $Z$ and $A$ such that $Z \\oplus \\mathcal{O}\n\\leq_\\mathrm{T} A$ and $0 <_\\mathrm{HYP} Z$, there exists $B$ such that $A\n\\equiv_\\mathrm{T} \\mathcal{O}^B \\equiv_\\mathrm{T} B \\oplus Z \\equiv_\\mathrm{T}\nB \\oplus \\mathcal{O}$. As an analogous consequence, any nonhyperarithmetical\nTuring degree $\\operatorname{deg}_\\mathrm{T}(Z)$ is a hyperjump relative to\nsome $B$.\n"}
+{"abstract": "  We provide a sharp lower bound on the $p$-norm of a sum of independent\nuniform random variables in terms of its variance when $0 < p < 1$. We address\nan analogous question for $p$-R\\'enyi entropy for $p$ in the same range.\n"}
+{"abstract": "  Understanding turbulence is the key to our comprehension of many natural and\ntechnological flow processes. At the heart of this phenomenon lies its\nintricate multi-scale nature, describing the coupling between different-sized\neddies in space and time. Here we introduce a new paradigm for analyzing the\nstructure of turbulent flows by quantifying correlations between different\nlength scales using methods inspired from quantum many-body physics. We present\nresults for interscale correlations of two paradigmatic flow examples, and use\nthese insights along with tensor network theory to design a structure-resolving\nalgorithm for simulating turbulent flows. With this algorithm, we find that the\nincompressible Navier-Stokes equations can be accurately solved within a\ncomputational space reduced by over an order of magnitude compared to direct\nnumerical simulation. Our quantum-inspired approach provides a pathway towards\nconducting computational fluid dynamics on quantum computers.\n"}
+{"abstract": "  We investigate one-dimensional three-body systems composed of two identical\nbosons and one imbalanced atom (impurity) with two-body and three-body\nzero-range interactions. For the case in the absence of three-body interaction,\nwe give a complete phase diagram of the number of three-body bound states in\nthe whole region of mass ratio via the direct calculation of the\nSkornyakov-Ter-Martirosyan equations. We demonstrate that other low-lying\nthree-body bound states emerge when the mass of the impurity particle is not\nequal to another two identical particles. We can obtain not only the binding\nenergies but also the corresponding wave functions. When the mass of impurity\natom is vary large, there are at most three three-body bound states. We then\nstudy the effect of three-body zero-range interaction and unveil that it can\ninduces one more three-body bound state at a certain region of coupling\nstrength ratio under a fixed mass ratio.\n"}
+{"abstract": "  We consider an input-to-response (ItR) system characterized by (1)\nparameterized input with a known probability distribution and (2) stochastic\nItR function with heteroscedastic randomness. Our purpose is to efficiently\nquantify the extreme response probability when the ItR function is expensive to\nevaluate. The problem setup arises often in physics and engineering problems,\nwith randomness in ItR coming from either intrinsic uncertainties (say, as a\nsolution to a stochastic equation) or additional (critical) uncertainties that\nare not incorporated in a low-dimensional input parameter space (as a result of\ndimension reduction applied to the original high-dimensional input space). To\nreduce the required sampling numbers, we develop a sequential Bayesian\nexperimental design method leveraging the variational heteroscedastic Gaussian\nprocess regression (VHGPR) to account for the stochastic ItR, along with a new\ncriterion to select the next-best samples sequentially. The validity of our new\nmethod is first tested in two synthetic problems with the stochastic ItR\nfunctions defined artificially. Finally, we demonstrate the application of our\nmethod to an engineering problem of estimating the extreme ship motion\nprobability in irregular waves, where the uncertainty in ItR naturally\noriginates from standard wave group parameterization, which reduces the\noriginal high-dimensional wave field into a two-dimensional parameter space.\n"}
+{"abstract": "  It is a long-standing conjecture that any CFT with a large central charge and\na large gap $\\Delta_{\\text{gap}}$ in the spectrum of higher-spin single-trace\noperators must be dual to a local effective field theory in AdS. We prove a\nsharp form of this conjecture by deriving numerical bounds on bulk Wilson\ncoefficients in terms of $\\Delta_{\\text{gap}}$ using the conformal bootstrap.\nOur bounds exhibit the scaling in $\\Delta_{\\text{gap}}$ expected from\ndimensional analysis in the bulk. Our main tools are dispersive sum rules that\nprovide a dictionary between CFT dispersion relations and S-matrix dispersion\nrelations in appropriate limits. This dictionary allows us to apply\nrecently-developed flat-space methods to construct positive CFT functionals. We\nshow how AdS$_{4}$ naturally resolves the infrared divergences present in 4D\nflat-space bounds. Our results imply the validity of twice-subtracted\ndispersion relations for any S-matrix arising from the flat-space limit of\nAdS/CFT.\n"}
+{"abstract": "  We describe a new addition to the WebVectors toolkit which is used to serve\nword embedding models over the Web. The new ELMoViz module adds support for\ncontextualized embedding architectures, in particular for ELMo models. The\nprovided visualizations follow the metaphor of `two-dimensional text' by\nshowing lexical substitutes: words which are most semantically similar in\ncontext to the words of the input sentence. The system allows the user to\nchange the ELMo layers from which token embeddings are inferred. It also\nconveys corpus information about the query words and their lexical substitutes\n(namely their frequency tiers and parts of speech). The module is well\nintegrated into the rest of the WebVectors toolkit, providing lexical\nhyperlinks to word representations in static embedding models. Two web services\nhave already implemented the new functionality with pre-trained ELMo models for\nRussian, Norwegian and English.\n"}
+{"abstract": "  We present a novel binding mechanism where a neutral Rydberg atom and an\natomic ion form a molecular bound state at large internuclear distance. The\nbinding mechanism is based on Stark shifts and level crossings which are\ninduced in the Rydberg atom due to the electric field of the ion. At particular\ninternuclear distances between Rydberg atom and ion, potential wells occur\nwhich can hold atom-ion molecular bound states. Apart from the binding\nmechanism we describe important properties of the long-range atom-ion Rydberg\nmolecule, such as its lifetime and decay paths, its vibrational and rotational\nstructure, and its large dipole moment. Furthermore, we discuss methods how to\nproduce and detect it. The unusual properties of the long-range atom-ion\nRydberg molecule give rise to interesting prospects for studies of wave packet\ndynamics in engineered potential energy landscapes.\n"}
+{"abstract": "  In a previous study, we presented VT-Lane, a three-step framework for\nreal-time vehicle detection, tracking, and turn movement classification at\nurban intersections. In this study, we present a case study incorporating the\nhighly accurate trajectories and movement classification obtained via VT-Lane\nfor the purpose of speed estimation and driver behavior calibration for traffic\nat urban intersections. First, we use a highly instrumented vehicle to verify\nthe estimated speeds obtained from video inference. The results of the speed\nvalidation show that our method can estimate the average travel speed of\ndetected vehicles in real-time with an error of 0.19 m/sec, which is equivalent\nto 2% of the average observed travel speeds in the intersection of the study.\nInstantaneous speeds (at the resolution of 30 Hz) were found to be estimated\nwith an average error of 0.21 m/sec and 0.86 m/sec respectively for\nfree-flowing and congested traffic conditions. We then use the estimated speeds\nto calibrate the parameters of a driver behavior model for the vehicles in the\narea of study. The results show that the calibrated model replicates the\ndriving behavior with an average error of 0.45 m/sec, indicating the high\npotential for using this framework for automated, large-scale calibration of\ncar-following models from roadside traffic video data, which can lead to\nsubstantial improvements in traffic modeling via microscopic simulation.\n"}
+{"abstract": "  Visual attention mechanisms are a key component of neural network models for\ncomputer vision. By focusing on a discrete set of objects or image regions,\nthese mechanisms identify the most relevant features and use them to build more\npowerful representations. Recently, continuous-domain alternatives to discrete\nattention models have been proposed, which exploit the continuity of images.\nThese approaches model attention as simple unimodal densities (e.g. a\nGaussian), making them less suitable to deal with images whose region of\ninterest has a complex shape or is composed of multiple non-contiguous patches.\nIn this paper, we introduce a new continuous attention mechanism that produces\nmultimodal densities, in the form of mixtures of Gaussians. We use the EM\nalgorithm to obtain a clustering of relevant regions in the image, and a\ndescription length penalty to select the number of components in the mixture.\nOur densities decompose as a linear combination of unimodal attention\nmechanisms, enabling closed-form Jacobians for the backpropagation step.\nExperiments on visual question answering in the VQA-v2 dataset show competitive\naccuracies and a selection of regions that mimics human attention more closely\nin VQA-HAT. We present several examples that suggest how multimodal attention\nmaps are naturally more interpretable than their unimodal counterparts, showing\nthe ability of our model to automatically segregate objects from ground in\ncomplex scenes.\n"}
+{"abstract": "  Given a dynamic network, where edges appear and disappear over time, we are\ninterested in finding sets of edges that have similar temporal behavior and\nform a dense subgraph. Formally, we define the problem as the enumeration of\nthe maximal subgraphs that satisfy specific density and similarity thresholds.\nTo measure the similarity of the temporal behavior, we use the correlation\nbetween the binary time series that represent the activity of the edges. For\nthe density, we study two variants based on the average degree. For these\nproblem variants we enumerate the maximal subgraphs and compute a compact\nsubset of subgraphs that have limited overlap. We propose an approximate\nalgorithm that scales well with the size of the network, while achieving a high\naccuracy. We evaluate our framework on both real and synthetic datasets. The\nresults of the synthetic data demonstrate the high accuracy of the\napproximation and show the scalability of the framework.\n"}
+{"abstract": "  Fisher-KPP equation is proved to be the scaling limit of a system of Brownian\nparticles with local interaction. Particles proliferate and die depending on\nthe local concentration of other particles. Opposite to discrete models,\ncontrolling concentration of particles is a major difficulty in Brownian\nparticle interaction; local interactions instead of mean field or moderate ones\nmakes it more difficult to implement the law of large numbers properties. The\napproach taken here to overcome these difficulties is largely inspired by A.\nHammond and F. Rezakhanlou [10] implemented there in the mean free path case\ninstead of the local interaction regime.\n"}
+{"abstract": "  We extend previous works by considering two additional radio frequencies (K\nband and X/Ka band) with the aim to study the frequency dependence of the\nsource positions and its potential connection with the physical properties of\nthe underlying AGN. We compared the absolute source positions measured at four\ndifferent wavelengths, that is, the optical position from the Gaia Early Data\nRelease 3 (EDR3) and the radio positions at the dual S/X, X/Ka combinations and\nat K band, as available from the third realization of the International\nCelestial Reference Frame (ICRF3), for 512 common sources. We first aligned the\nthree ICRF3 individual catalogs onto the Gaia EDR3 frame and compare the\noptical-to-radio offsets before and after the alignment. Then we studied the\ncorrelation of optical-to-radio offsets with the observing (radio) frequency,\nsource morphology, magnitude, redshift, and source type. The deviation among\noptical-to-radio offsets determined in the different radio bands is less than\n0.5 mas, but there is statistical evidence that the optical-to-radio offset is\nsmaller at K band compared to S/X band for sources showing extended structures.\nThe optical-to-radio offset was found to statistically correlate with the\nstructure index. Large optical-to-radio offsets appear to favor faint sources\nbut are well explained by positional uncertainty, which is also larger for\nthese sources. We did not detect any statistically significant correlation\nbetween the optical-to-radio offset and the redshift. The radio source\nstructure might also be a major cause for the radio-to-optical offset. For the\nalignment of with the Gaia celestial reference frame, the S/X band frame\nremains the preferred choice at present.\n"}
+{"abstract": "  Robot manipulation of unknown objects in unstructured environments is a\nchallenging problem due to the variety of shapes, materials, arrangements and\nlighting conditions. Even with large-scale real-world data collection, robust\nperception and manipulation of transparent and reflective objects across\nvarious lighting conditions remain challenging. To address these challenges we\npropose an approach to performing sim-to-real transfer of robotic perception.\nThe underlying model, SimNet, is trained as a single multi-headed neural\nnetwork using simulated stereo data as input and simulated object segmentation\nmasks, 3D oriented bounding boxes (OBBs), object keypoints, and disparity as\noutput. A key component of SimNet is the incorporation of a learned stereo\nsub-network that predicts disparity. SimNet is evaluated on 2D car detection,\nunknown object detection, and deformable object keypoint detection and\nsignificantly outperforms a baseline that uses a structured light RGB-D sensor.\nBy inferring grasp positions using the OBB and keypoint predictions, SimNet can\nbe used to perform end-to-end manipulation of unknown objects in both easy and\nhard scenarios using our fleet of Toyota HSR robots in four home environments.\nIn unknown object grasping experiments, the predictions from the baseline RGB-D\nnetwork and SimNet enable successful grasps of most of the easy objects.\nHowever, the RGB-D baseline only grasps 35% of the hard (e.g., transparent)\nobjects, while SimNet grasps 95%, suggesting that SimNet can enable robust\nmanipulation of unknown objects, including transparent objects, in unknown\nenvironments.\n"}
+{"abstract": "  Internet of Things (IoT) is being considered as the growth engine for\nindustrial revolution 4.0. The combination of IoT, cloud computing and\nhealthcare can contribute in ensuring well-being of people. One important\nchallenge of IoT network is maintaining privacy and to overcome security\nthreats. This paper provides a systematic review of the security aspects of\nIoT. Firstly, the application of IoT in industrial and medical service\nscenarios are described, and the security threats are discussed for the\ndifferent layers of IoT healthcare architecture. Secondly, different types of\nexisting malware including spyware, viruses, worms, keyloggers, and trojan\nhorses are described in the context of IoT. Thirdly, some of the recent malware\nattacks such as Mirai, echobot and reaper are discussed. Next, a comparative\ndiscussion is presented on the effectiveness of different machine learning\nalgorithms in mitigating the security threats. It is found that the k-nearest\nneighbor (kNN) machine learning algorithm exhibits excellent accuracy in\ndetecting malware. This paper also reviews different tools for ransomware\ndetection, classification and analysis. Finally, a discussion is presented on\nthe existing security issues, open challenges and possible future scopes in\nensuring IoT security.\n"}
+{"abstract": "  Let $\\pi$ be a set of primes such that $|\\pi|\\geqslant 2$ and $\\pi$ differs\nfrom the set of all primes. Denote by $r$ the smallest prime which does not\nbelong to $\\pi$ and set $m=r$ if $r=2,3$ and $m=r-1$ if $r\\geqslant 5$. We\nstudy the following conjecture: a conjugacy class $D$ of a finite group $G$ is\ncontained in $O\\pi(G)$ if and only if every $m$ elements of $D$ generate a\n$\\pi$-subgroup. We confirm this conjecture for each group $G$ whose nonabelian\ncomposition factors are isomorphic to alternating, linear and unitary simple\ngroups.\n"}
+{"abstract": "  Isolated mechanical systems -- e.g., those floating in space, in free-fall,\nor on a frictionless surface -- are able to achieve net rotation by cyclically\nchanging their shape, even if they have no net angular momentum. Similarly,\nswimmers immersed in \"perfect fluids\" are able to use cyclic shape changes to\nboth translate and rotate even if the swimmer-fluid system has no net linear or\nangular momentum. Finally, systems fully constrained by direct nonholonomic\nconstraints (e.g., passive wheels) can push against these constraints to move\nthrough the world. Previous work has demonstrated that the net displacement\ninduced by these shape changes corresponds to the amount of *constraint\ncurvature* that the gaits enclose.\n  To properly assess or optimize the utility of a gait, however, we must also\nconsider the time or resources required to execute it: A gait that produces a\nsmall displacement per cycle, but that can be executed in a short time, may\nproduce a faster average velocity than a gait that produces a large\ndisplacement per cycle, but takes much longer to complete a cycle at the same\naverage instantaneous effort.\n  In this paper, we consider two effort-based cost functions for assessing the\ncosts associated with executing these cycles. For each of these cost functions,\nwe demonstrate that fixing the average instantaneous cost to a unit value\nallows us to transform the effort costs into time-to-execute costs for any\ngiven gait cycle. We then illustrate how the interaction between the constraint\ncurvature and these costs leads to characteristic geometries for optimal\ncycles, in which the gait trajectories resemble elastic hoops distended from\nwithin by internal pressures.\n"}
+{"abstract": "  Snapshot hyperspectral imaging can capture the 3D hyperspectral image (HSI)\nwith a single 2D measurement and has attracted increasing attention recently.\nRecovering the underlying HSI from the compressive measurement is an ill-posed\nproblem and exploiting the image prior is essential for solving this ill-posed\nproblem. However, existing reconstruction methods always start from modeling\nimage prior with the 1D vector or 2D matrix and cannot fully exploit the\nstructurally spectral-spatial nature in 3D HSI, thus leading to a poor\nfidelity. In this paper, we propose an effective high-order tensor optimization\nbased method to boost the reconstruction fidelity for snapshot hyperspectral\nimaging. We first build high-order tensors by exploiting the spatial-spectral\ncorrelation in HSI. Then, we propose a weight high-order singular value\nregularization (WHOSVR) based low-rank tensor recovery model to characterize\nthe structure prior of HSI. By integrating the structure prior in WHOSVR with\nthe system imaging process, we develop an optimization framework for HSI\nreconstruction, which is finally solved via the alternating minimization\nalgorithm. Extensive experiments implemented on two representative systems\ndemonstrate that our method outperforms state-of-the-art methods.\n"}
+{"abstract": "  Sparse matrices, more specifically SpGEMM kernels, are commonly found in a\nwide range of applications, spanning graph-based path-finding to machine\nlearning algorithms (e.g., neural networks). A particular challenge in\nimplementing SpGEMM kernels has been the pressure placed on DRAM memory. One\napproach to tackle this problem is to use an inner product method for the\nSpGEMM kernel implementation. While the inner product produces fewer\nintermediate results, it can end up saturating the memory bandwidth, given the\nhigh number of redundant fetches of the input matrix elements. Using an outer\nproduct-based SpGEMM kernel can reduce redundant fetches, but at the cost of\nincreased overhead due to extra computation and memory accesses for\nproducing/managing partial products.\n  In this thesis, we introduce a novel SpGEMM kernel implementation based on\nthe row-wise product approach. We leverage atomic instructions to merge\nintermediate partial products as they are generated. The use of atomic\ninstructions eliminates the need to create partial product matrices.\n  To evaluate our row-wise product approach, we map an optimized SpGEMM kernel\nto a custom accelerator designed to accelerate graph-based applications. The\ntargeted accelerator is an experimental system named PIUMA, being developed by\nIntel. PIUMA provides several attractive features, including fast context\nswitching, user-configurable caches, globally addressable memory, non-coherent\ncaches, and asynchronous pipelines. We tailor our SpGEMM kernel to exploit many\nof the features of the PIUMA fabric.\n  This thesis compares our SpGEMM implementation against prior solutions, all\nmapped to the PIUMA framework. We briefly describe some of the PIUMA\narchitecture features and then delve into the details of our optimized SpGEMM\nkernel. Our SpGEMM kernel can achieve 9.4x speedup as compared to competing\napproaches.\n"}
+{"abstract": "  The Gamma Factory (GF) is an ambitious proposal, currently explored within\nthe CERN Physics Beyond Colliders program, for a source of photons with\nenergies up to $\\approx 400\\,$MeV and photon fluxes (up to $\\approx 10^{17}$\nphotons per second) exceeding those of the currently available gamma sources by\norders of magnitude. The high-energy (secondary) photons are produced via\nresonant scattering of the primary laser photons by highly relativistic\npartially-stripped ions circulating in the accelerator. The secondary photons\nare emitted in a narrow cone and the energy of the beam can be monochromatized,\neventually down to the $\\approx1$ ppm level, via collimation, at the expense of\nthe photon flux. This paper surveys the new opportunities that may be afforded\nby the GF in nuclear physics and related fields.\n"}
+{"abstract": "  The application of strain to 2D materials allows manipulating the electronic,\nmagnetic, and thermoelectric properties. These physical properties are\nsensitive to slight variations induced by tensile and compressive strain and to\nthe uniaxial strain direction. Herein, we take advantage of the reversible\nsemiconductor-metal transition observed in certain monolayers to propose a\nhetero-bilayer device. We propose to pill up phosphorene (layered black\nphosphorus) and carbon monosulfide monolayers. In the first, such transition\nappears for positive strain, while the second appears for negative strain. Our\nfirst-principle calculations show that depending on the direction of the\napplied uniaxial strain; it is possible to achieve reversible control in the\nlayer that behaves as an electronic conductor while the other layer remains as\na thermal conductor. The described strain-controlled selectivity could be used\nin the design of novel devices.\n"}
+{"abstract": "  The recently proposed high-order TENO scheme [Fu et al., Journal of\nComputational Physics, 305, pp.333-359] has shown great potential in predicting\ncomplex fluids owing to the novel weighting strategy, which ensures the\nhigh-order accuracy, the low numerical dissipation, and the sharp\nshock-capturing capability. However, the applications are still restricted to\nsimple geometries with Cartesian or curvilinear meshes. In this work, a new\nclass of high-order shock-capturing TENO schemes for unstructured meshes are\nproposed. Similar to the standard TENO schemes and some variants of WENO\nschemes, the candidate stencils include one large stencil and several small\nthird-order stencils. Following a strong scale-separation procedure, a tailored\nnovel ENO-like stencil selection strategy is proposed such that the high-order\naccuracy is restored in smooth regions by selecting the candidate\nreconstruction on the large stencil while the ENO property is enforced near\ndiscontinuities by adopting the candidate reconstruction from smooth small\nstencils. The nonsmooth stencils containing genuine discontinuities are\nexplicitly excluded from the final reconstruction, leading to excellent\nnumerical stability. Different from the WENO concept, such unique sharp stencil\nselection retains the low numerical dissipation without sacrificing the\nshock-capturing capability. The newly proposed framework enables arbitrarily\nhigh-order TENO reconstructions on unstructured meshes. For conceptual\nverification, the TENO schemes with third- to sixth-order accuracy are\nconstructed. Without parameter tuning case by case, the performance of the\nproposed TENO schemes is demonstrated by examining a set of benchmark cases\nwith broadband flow length scales.\n"}
+{"abstract": "  Reading is a complex process which requires proper understanding of texts in\norder to create coherent mental representations. However, comprehension\nproblems may arise due to hard-to-understand sections, which can prove\ntroublesome for readers, while accounting for their specific language skills.\nAs such, steps towards simplifying these sections can be performed, by\naccurately identifying and evaluating difficult structures. In this paper, we\ndescribe our approach for the SemEval-2021 Task 1: Lexical Complexity\nPrediction competition that consists of a mixture of advanced NLP techniques,\nnamely Transformer-based language models, pre-trained word embeddings, Graph\nConvolutional Networks, Capsule Networks, as well as a series of hand-crafted\ntextual complexity features. Our models are applicable on both subtasks and\nachieve good performance results, with a MAE below 0.07 and a Person\ncorrelation of .73 for single word identification, as well as a MAE below 0.08\nand a Person correlation of .79 for multiple word targets. Our results are just\n5.46% and 6.5% lower than the top scores obtained in the competition on the\nfirst and the second subtasks, respectively.\n"}
+{"abstract": "  Negative viscosity seems to be an impossible parameter for any thermodynamic\nsystem. But for some special boundary conditions the viscosity of a fluid has\napparently become negative, like for secondary flow of a fluid or in a plasma\nflow interacting with a dominant magnetic field. This work studied the effect\nof negative viscosity for a fluid flow over a cylinder. Four different\nviscosities are considered, in which the positive viscosities of Air and CO2\nhas been considered at 300 K temperature and their negative pair of viscosities\nare considered in this work. The results show a vast difference in the vortex\nformation and pattern. General incompressible Navier Stokes equation has been\nemployed for the analysis. The thermodynamic feasibility, vortex formation,\nvariation of X direction velocity, variation of the VA factor and variation of\ndrag coefficient has been studied subsequently in this work. SimFlow CFD\nsoftware has been used in this work, which uses the OpenFOAM solver.\n"}
+{"abstract": "  We introduce operational quantum tasks based on betting with risk-aversion --\nor quantum betting tasks for short -- inspired by standard quantum state\ndiscrimination and classical horse betting with risk-aversion and side\ninformation. In particular, we introduce the operational tasks of quantum state\nbetting (QSB), noisy quantum state betting (nQSB), and quantum channel betting\n(QCB) played by gamblers with different risk tendencies. We prove that the\nadvantage that informative measurements (non-constant channels) provide in QSB\n(nQSB) is exactly characterised by Arimoto's $\\alpha$-mutual information, with\nthe order $\\alpha$ determining the risk aversion of the gambler. More\ngenerally, we show that Arimoto-type information-theoretic quantities\ncharacterise the advantage that resourceful objects offer at playing quantum\nbetting tasks when compared to resourceless objects, for general quantum\nresource theories (QRTs) of measurements, channels, states, and\nstate-measurement pairs, with arbitrary resources. In limiting cases, we show\nthat QSB (QCB) recovers the known tasks of quantum state (channel)\ndiscrimination when $\\alpha \\rightarrow \\infty$, and quantum state (channel)\nexclusion when $\\alpha \\rightarrow -\\infty$. Inspired by these connections, we\nalso introduce new quantum R\\'enyi divergences for measurements, and derive a\nnew family of resource monotones for the QRT of measurement informativeness.\nThis family of resource monotones recovers in the same limiting cases as above,\nthe generalised robustness and the weight of informativeness. Altogether, these\nresults establish a broad and continuous family of four-way correspondences\nbetween operational tasks, mutual information measures, quantum R\\'enyi\ndivergences, and resource monotones, that can be seen to generalise two\nlimiting correspondences that were recently discovered for the QRT of\nmeasurement informativeness.\n"}
+{"abstract": "  We present a perturbative approach to solving the three-nucleon continuum\nFaddeev equation. This approach is particularly well suited to dealing with\nvariable strengths of contact terms in a chiral three-nucleon force. We use\nexamples of observables in the elastic nucleon-deuteron scattering as well as\nin the deuteron breakup reaction to demonstrate high precision of the proposed\nprocedure and its capability to reproduce exact results. A significant\nreduction of computer time achieved by the perturbative approach in comparison\nto exact treatment makes this approach valuable for fine-tuning of the\nthree-nucleon Hamiltonian parameters.\n"}
+{"abstract": "  We study the limit behaviour of singularly-perturbed elliptic functionals of\nthe form \\[ \\mathcal F_k(u,v)=\\int_A v^2\\,f_k(x,\\nabla\nu)\\.dx+\\frac{1}{\\varepsilon_k}\\int_A g_k(x,v,\\varepsilon_k\\nabla v)\\.dx\\,, \\]\nwhere $u$ is a vector-valued Sobolev function, $v \\in [0,1]$ a phase-field\nvariable, and $\\varepsilon_k>0$ a singular-perturbation parameter, i.e.,\n$\\varepsilon_k \\to 0$, as $k\\to +\\infty$.\n  Under mild assumptions on the integrands $f_k$ and $g_k$, we show that if\n$f_k$ grows superlinearly in the gradient-variable, then the functionals\n$\\mathcal F_k$ $\\Gamma$-converge (up to subsequences) to a brittle\nenergy-functional, i.e., to a free-discontinuity functional whose surface\nintegrand does not depend on the jump-amplitude of $u$. This result is achieved\nby providing explicit asymptotic formulas for the bulk and surface integrands\nwhich show, in particular, that volume and surface term in $\\mathcal F_k$\ndecouple in the limit.\n  The abstract $\\Gamma$-convergence analysis is complemented by a stochastic\nhomogenisation result for stationary random integrands.\n"}
+{"abstract": "  We characterize stable differential-algebraic equations (DAEs) using a\ngeneralized Lyapunov inequality. The solution of this inequality is then used\nto rewrite stable DAEs as dissipative Hamiltonian (dH) DAEs on the subspace\nwhere the solutions evolve. Conversely, we give sufficient conditions\nguaranteeing stability of dH DAEs. Further, for stabilizable descriptor systems\nwe construct solutions of generalized algebraic Bernoulli equations which can\nthen be used to rewrite these systems as pH descriptor systems. Furthermore, we\nshow how to describe the stable and stabilizable systems using Dirac and\nLagrange structures.\n"}
+{"abstract": "  We consider the decay of the false vacuum, realised within a quantum quench\ninto an anti-confining regime of the Ising spin chain with a magnetic field\nopposite to the initial magnetisation. Although the effective linear potential\nbetween the domain walls is repulsive, the time evolution of correlations still\nshows a suppression of the light cone and a reduction of vacuum decay. The\nsuppressed decay is a lattice effect, and can be assigned to emergent Bloch\noscillations.\n"}
+{"abstract": "  For compact, isometrically embedded Riemannian manifolds $ N \\hookrightarrow\n\\mathbb{R}^L$, we introduce a fourth-order version of the wave map equation. By\nenergy estimates, we prove an priori estimate for smooth local solutions in the\nenergy subcritical dimension $ n = 1,2$. The estimate excludes blow-up of a\nSobolev norm in finite existence times. In particular, combining this with\nrecent work of local well-posedness of the Cauchy problem, it follows that for\nsmooth initial data with compact support, there exists a (smooth) unique global\nsolution in dimension $n = 1,2$. We also give a proof of the uniqueness of\nsolutions that are bounded in these Sobolev norms.\n"}
+{"abstract": "  Hjorth, assuming ${\\sf{AD+ZF+DC}}$, showed that there is no sequence of\nlength $\\omega_2$ consisting of distinct $\\Sigma^1_2$-sets. We show that the\nsame theory implies that for $n\\geq 0$, there is no sequence of length\n$\\delta^1_{2n+2}$ consisting of distinct $\\Sigma^1_{2n+2}$ sets. The theorem\nsettles Question 30.21 of Kanamori, which was also conjectured by Kechris.\n"}
+{"abstract": "  In this paper, we introduce a definition of Fenchel conjugate and Fenchel\nbiconjugate on Hadamard manifolds based on the tangent bundle. Our definition\novercomes the inconvenience that the conjugate depends on the choice of a\ncertain point on the manifold, as previous definitions required. On the other\nhand, this new definition still possesses properties known to hold in the\nEuclidean case. It even yields a broader interpretation of the Fenchel\nconjugate in the Euclidean case itself. Most prominently, our definition of the\nFenchel conjugate provides a Fenchel-Moreau Theorem for geodesically convex,\nproper, lower semicontinuous functions. In addition, this framework allows us\nto develop a theory of separation of convex sets on Hadamard manifolds, and a\nstrict separation theorem is obtained.\n"}
+{"abstract": "  Several areas have been improved with Deep Learning during the past years.\nFor non-safety related products adoption of AI and ML is not an issue, whereas\nin safety critical applications, robustness of such approaches is still an\nissue. A common challenge for Deep Neural Networks (DNN) occur when exposed to\nout-of-distribution samples that are previously unseen, where DNNs can yield\nhigh confidence predictions despite no prior knowledge of the input.\n  In this paper we analyse two supervisors on two well-known DNNs with varied\nsetups of training and find that the outlier detection performance improves\nwith the quality of the training procedure. We analyse the performance of the\nsupervisor after each epoch during the training cycle, to investigate\nsupervisor performance as the accuracy converges. Understanding the\nrelationship between training results and supervisor performance is valuable to\nimprove robustness of the model and indicates where more work has to be done to\ncreate generalized models for safety critical applications.\n"}
+{"abstract": "  The temperature dependencies of the excess conductivity $\\sigma'(T)$ and\npossible pseudogap (PG), in a Dy$_{0.6}$Y$_{0.4}$Rh$_{3.85}$Ru$_{0.15}$B$_4$\npolycrystal were studied for the first time. It was shown that $\\sigma'(T)$\nnear T$_{c}$ is well described by the Aslamazov Larkin fluctuation theory,\ndemonstrating a crossover with increasing temperature. Using the crossover\ntemperature $T_0$, the coherence length along the c axis $\\xi_c(0)$, was\ndetermined. Above the level of $T_{2D}>T_{0}$, an unusual dependence\n$\\sigma'(T)$ was found, which is not described by the fluctuation theories in\nthe range from $T_{0}$ to $T_{FM}$, at which a ferromagnetic transition occurs.\nThe range in which superconducting fluctuations exist is apparently quite\nnarrow and amounts to $\\Delta T_{fl}=2.8 K$. The resulting temperature\ndependence of the PG parameter $\\Delta^*(T)$ has the form typical of magnetic\nsuperconductors with features at $T_{max}=154 K$ and the temperature of a\npossible structural transition at $T_{s}=95 K$. Below $T_{s}$, dependence\n$\\Delta^*{T}$ has a shape typical for PG in cuprates, which suggests that the\nPG state can be realized in Dy$_{0.6}$Y$_{0.4}$Rh$_{3.85}$Ru$_{0.15}$B$_4$ in\nthis temperature range. Comparison of $\\Delta^*(T)$ with the Peters Bauer\ntheory made it possible to determine the density of local pairs ~0.35, near\nT$_{c}$, which is 1.17 times greater than in optimally doped\nYBa$_{2}$Cu$_{3}$O$_{7-\\delta}$ single crystals.\n"}
+{"abstract": "  Tracking multiple objects in videos relies on modeling the spatial-temporal\ninteractions of the objects. In this paper, we propose a solution named\nTransMOT, which leverages powerful graph transformers to efficiently model the\nspatial and temporal interactions among the objects. TransMOT effectively\nmodels the interactions of a large number of objects by arranging the\ntrajectories of the tracked objects as a set of sparse weighted graphs, and\nconstructing a spatial graph transformer encoder layer, a temporal transformer\nencoder layer, and a spatial graph transformer decoder layer based on the\ngraphs. TransMOT is not only more computationally efficient than the\ntraditional Transformer, but it also achieves better tracking accuracy. To\nfurther improve the tracking speed and accuracy, we propose a cascade\nassociation framework to handle low-score detections and long-term occlusions\nthat require large computational resources to model in TransMOT. The proposed\nmethod is evaluated on multiple benchmark datasets including MOT15, MOT16,\nMOT17, and MOT20, and it achieves state-of-the-art performance on all the\ndatasets.\n"}
+{"abstract": "  The orthant model is a directed percolation model on $\\mathbb{Z}^d$, in which\nall clusters are infinite. We prove a sharp threshold result for this model: if\n$p$ is larger than the critical value above which the cluster of $0$ is\ncontained in a cone, then the shift from $0$ that is required to contain the\ncluster of $0$ in that cone is exponentially small. As a consequence, above\nthis critical threshold, a shape theorem holds for the cluster of $0$, as well\nas ballisiticity of the random walk on this cluster.\n"}
+{"abstract": "  Having engaging and informative conversations with users is the utmost goal\nfor open-domain conversational systems. Recent advances in transformer-based\nlanguage models and their applications to dialogue systems have succeeded to\ngenerate fluent and human-like responses. However, they still lack control over\nthe generation process towards producing contentful responses and achieving\nengaging conversations. To achieve this goal, we present \\textbf{DiSCoL}\n(\\textbf{Di}alogue \\textbf{S}ystems through \\textbf{Co}versational\n\\textbf{L}ine guided response generation). DiSCoL is an open-domain dialogue\nsystem that leverages conversational lines (briefly \\textbf{convlines}) as\ncontrollable and informative content-planning elements to guide the generation\nmodel produce engaging and informative responses. Two primary modules in\nDiSCoL's pipeline are conditional generators trained for 1) predicting relevant\nand informative convlines for dialogue contexts and 2) generating high-quality\nresponses conditioned on the predicted convlines. Users can also change the\nreturned convlines to \\textit{control} the direction of the conversations\ntowards topics that are more interesting for them. Through automatic and human\nevaluations, we demonstrate the efficiency of the convlines in producing\nengaging conversations.\n"}
+{"abstract": "  There are multiple mappings that can be used to generate what we call the\n'edge geometry' of a regular N-gon, but they are all based on piecewise\nisometries acting on the extended edges of N to form a 'singularity' set W.\nThis singularity set is also known as the 'web' because it is connected and\nconsists of rays or line segments, with possible accumulation points in the\nlimit. We will use three such maps here, all of which appear to share the same\nlocal geometry of W. These mappings are the outer-billiards map Tau, the\ndigital-filter map Df and the 'dual-center' map Dc. In 'Outer-billiards,\ndigital filters and kicked Hamiltonians' (arXiv:1206.5223) we show that the Df\nand Dc maps are equivalent to a 'shear and rotation' in a toral space and in\nthe complex plane respectively, and in 'First Families of Regular Polygons and\ntheir Mutations' (arXiv:1612.09295) we show that the web for Tau can also be\nreduced to a shear and rotation. This equivalence of maps supports the premise\nthat this web geometry is inherent in the N-gon. Here we describe the edge\ngeometry up to N = 25 and in Part 2 this will be extended to N = 50. In all\ncases this geometry defines an invariant region local to N. Typically this\nregion contains multiple S[k] 'tiles' from the First Family of N, but our\nemphasis is on the S[1] and S[2] tiles adjacent to N. Since the web evolves in\na multi-step fashion, it is possible to make predictions about the\n'next-generation' tiles which will survive in the early web of S[1] and S[2].\nThe Edge Conjecture defines just 8 classes of N-gons based on this edge\ngeometry. Since the webs are recursive these predictions have long-term\nimplications.\n"}
+{"abstract": "  When solving a complex task, humans will spontaneously form teams and to\ncomplete different parts of the whole task, respectively. Meanwhile, the\ncooperation between teammates will improve efficiency. However, for current\ncooperative MARL methods, the cooperation team is constructed through either\nheuristics or end-to-end blackbox optimization. In order to improve the\nefficiency of cooperation and exploration, we propose a structured\ndiversification emergence MARL framework named {\\sc{Rochico}} based on\nreinforced organization control and hierarchical consensus learning.\n{\\sc{Rochico}} first learns an adaptive grouping policy through the\norganization control module, which is established by independent multi-agent\nreinforcement learning. Further, the hierarchical consensus module based on the\nhierarchical intentions with consensus constraint is introduced after team\nformation. Simultaneously, utilizing the hierarchical consensus module and a\nself-supervised intrinsic reward enhanced decision module, the proposed\ncooperative MARL algorithm {\\sc{Rochico}} can output the final diversified\nmulti-agent cooperative policy. All three modules are organically combined to\npromote the structured diversification emergence. Comparative experiments on\nfour large-scale cooperation tasks show that {\\sc{Rochico}} is significantly\nbetter than the current SOTA algorithms in terms of exploration efficiency and\ncooperation strength.\n"}
+{"abstract": "  Reducing the complexity of the pipeline of instance segmentation is crucial\nfor real-world applications. This work addresses this issue by introducing an\nanchor-box free and single-shot instance segmentation framework, termed\nPolarMask, which reformulates the instance segmentation problem as predicting\nthe contours of objects in the polar coordinate, with several appealing\nbenefits. (1) The polar representation unifies instance segmentation (masks)\nand object detection (bounding boxes) into a single framework, reducing the\ndesign and computational complexity. (2) Two modules are carefully designed\n(i.e. soft polar centerness and polar IoU loss) to sample high-quality center\nexamples and optimize polar contour regression, making the performance of\nPolarMask does not depend on the bounding box prediction results and thus\nbecomes more efficient in training. (3) PolarMask is fully convolutional and\ncan be easily embedded into most off-the-shelf detection methods. To further\nimprove the accuracy of the framework, a Refined Feature Pyramid is introduced\nto further improve the feature representation at different scales, termed\nPolarMask++. Extensive experiments demonstrate the effectiveness of both\nPolarMask and PolarMask++, which achieve competitive results on instance\nsegmentation in the challenging COCO dataset with single-model and single-scale\ntraining and testing, as well as new state-of-the-art results on rotate text\ndetection and cell segmentation. We hope the proposed polar representation can\nprovide a new perspective for designing algorithms to solve single-shot\ninstance segmentation. The codes and models are available at:\ngithub.com/xieenze/PolarMask.\n"}
+{"abstract": "  Ultra intense lasers are a promising source of energetic ions for various\napplications. An interesting approach described in Ferri et al. 2019 argues\nfrom Particle-in-Cell simulations that using two laser pulses of half energy\n(half intensity) arriving with close to 45 degrees angle of incidence is\nsignificantly more effective at accelerating ions than one pulse at full energy\n(full intensity). For a variety of reasons, at the time of this writing there\nhas not yet been a true experimental confirmation of this enhancement. In this\npaper we perform 2D Particle-in-Cell simulations to examine if a milliJoule\nclass, 5x10^18 W cm^-2 peak intensity laser system could be used for such a\ndemonstration experiment. Laser systems in this class can operate at a kHz rate\nwhich should be helpful for addressing some of the challenges of performing\nthis experiment. Despite investigating a 3.5 times lower intensity than Ferri\net al. 2019 did, we find that the double pulse approach enhances the peak\nproton energy and the energy conversion to protons by a factor of about three\ncompared to a single laser pulse with the same total laser energy. We also\ncomment on the nature of the enhancement and describe simulations that examine\nhow the enhancement may depend on the spatial or temporal alignment of the two\npulses.\n"}
+{"abstract": "  Ensuring performance robustness for a variety of situations that can occur in\nreal-world environments is one of the challenging tasks in sound event\nclassification. One of the unpredictable and detrimental factors in\nperformance, especially in indoor environments, is reverberation. To alleviate\nthis problem, we propose a conditioning method that provides room impulse\nresponse (RIR) information to help the network become less sensitive to\nenvironmental information and focus on classifying the desired sound.\nExperimental results show that the proposed method successfully reduced\nperformance degradation caused by the reverberation of the room. In particular,\nour proposed method works even with similar RIR that can be inferred from the\nroom type rather than the exact one, which has the advantage of potentially\nbeing used in real-world applications.\n"}
+{"abstract": "  The aims of this paper are: 1) to identify \"worst smells\", i.e., bad smells\nthat never have a good reason to exist, 2) to determine the frequency,\nchange-proneness, and severity associated with worst smells, and 3) to identify\nthe \"worst reasons\", i.e., the reasons for introducing these worst smells in\nthe first place. To achieve these aims we ran a survey with 71 developers. We\nlearned that 80 out of 314 catalogued code smells are \"worst\"; that is,\ndevelopers agreed that these 80 smells should never exist in any code base. We\nthen checked the frequency and change-proneness of these worst smells on 27\nlarge Apache open-source projects. Our results show insignificant differences,\nin both frequency and change proneness, between worst and non-worst smells.\nThat is to say, these smells are just as damaging as other smells, but there is\nnever any justifiable reason to introduce them. Finally, in follow-up phone\ninterviews with five developers we confirmed that these smells are indeed\nworst, and the interviewees proposed seven reasons for why they may be\nintroduced in the first place. By explicitly identifying these seven reasons,\nproject stakeholders can, through quality gates or reviews, ensure that such\nsmells are never accepted in a code base, thus improving quality without\ncompromising other goals such as agility or time to market.\n"}
+{"abstract": "  Content-based image retrieval (CBIR) systems on pixel domain use low-level\nfeatures, such as colour, texture and shape, to retrieve images. In this\ncontext, two types of image representations i.e. local and global image\nfeatures have been studied in the literature. Extracting these features from\npixel images and comparing them with images from the database is very\ntime-consuming. Therefore, in recent years, there has been some effort to\naccomplish image analysis directly in the compressed domain with lesser\ncomputations. Furthermore, most of the images in our daily transactions are\nstored in the JPEG compressed format. Therefore, it would be ideal if we could\nretrieve features directly from the partially decoded or compressed data and\nuse them for retrieval. Here, we propose a unified model for image retrieval\nwhich takes DCT coefficients as input and efficiently extracts global and local\nfeatures directly in the JPEG compressed domain for accurate image retrieval.\nThe experimental findings indicate that our proposed model performed similarly\nto the current DELG model which takes RGB features as an input with reference\nto mean average precision while having a faster training and retrieval speed.\n"}
+{"abstract": "  End-to-end (E2E) spoken language understanding (SLU) can infer semantics\ndirectly from speech signal without cascading an automatic speech recognizer\n(ASR) with a natural language understanding (NLU) module. However, paired\nutterance recordings and corresponding semantics may not always be available or\nsufficient to train an E2E SLU model in a real production environment. In this\npaper, we propose to unify a well-optimized E2E ASR encoder (speech) and a\npre-trained language model encoder (language) into a transformer decoder. The\nunified speech-language pre-trained model (SLP) is continually enhanced on\nlimited labeled data from a target domain by using a conditional masked\nlanguage model (MLM) objective, and thus can effectively generate a sequence of\nintent, slot type, and slot value for given input speech in the inference. The\nexperimental results on two public corpora show that our approach to E2E SLU is\nsuperior to the conventional cascaded method. It also outperforms the present\nstate-of-the-art approaches to E2E SLU with much less paired data.\n"}
+{"abstract": "  The mission statement (MS) is the most used organizational strategic planning\ntool worldwide. The relationship between an MS and an organizations financial\nperformance has been shown to be significantly positive, albeit small. However,\nan MSs relationship to the macroeconomic environment and to organizational\ninnovation has not been investigated. We implemented a Structural Equation\nModeling using the SCImago Institutional Ranking (SIR) as a global baseline\nsample and assessment of organizational research and innovation (RandI), an\nautomated MS content analysis, and the Economic Complexity Index (ECI) as a\ncomprehensive macroeconomic environment measure. We found that the median\nperformance of organizations that do not report an MS is significantly higher\nthan that of reporting organizations, and that a path-dependence driven by the\nState's long-term view and investment is a better explanatory variable for\norganizational RandI performance than the MS construct or the intermediate-term\nmacroeconomic environment.\n"}
+{"abstract": "  Neural architecture search (NAS) and hyperparameter optimization (HPO) make\ndeep learning accessible to non-experts by automatically finding the\narchitecture of the deep neural network to use and tuning the hyperparameters\nof the used training pipeline. While both NAS and HPO have been studied\nextensively in recent years, NAS methods typically assume fixed hyperparameters\nand vice versa - there exists little work on joint NAS + HPO. Furthermore, NAS\nhas recently often been framed as a multi-objective optimization problem, in\norder to take, e.g., resource requirements into account. In this paper, we\npropose a set of methods that extend current approaches to jointly optimize\nneural architectures and hyperparameters with respect to multiple objectives.\nWe hope that these methods will serve as simple baselines for future research\non multi-objective joint NAS + HPO. To facilitate this, all our code is\navailable at https://github.com/automl/multi-obj-baselines.\n"}
+{"abstract": "  The emission mechanism for hard $\\gamma$-ray spectra from supernova remnants\n(SNRs) is still a matter of debate. Recent multi-wavelength observations of TeV\nsource HESS J1912+101 show that it is associated with an SNR with an age of\n$\\sim 100$ kyrs, making it unlikely produce the TeV $\\gamma$-ray emission via\nleptonic processes. We analyzed Fermi observations of it and found an extended\nsource with a hard spectrum. HESS J1912+101 may represent a peculiar stage of\nSNR evolution that dominates the acceleration of TeV cosmic rays. By fitting\nthe multi-wavelength spectra of 13 SNRs with hard GeV $\\gamma$-ray spectra with\nsimple emission models with a density ratio of GeV electrons to protons of\n$\\sim 10^{-2}$, we obtain reasonable mean densities and magnetic fields with a\ntotal energy of $\\sim 10^{50}$ ergs for relativistic ions in each SNR. Among\nthese sources, only two of them, namely SN 1006 and RCW 86, favor a leptonic\norigin for the $\\gamma$-ray emission. The magnetic field energy is found to be\ncomparable to that of the accelerated relativistic ions and their ratio has a\ntendency of increase with the age of SNRs. These results suggest that TeV\ncosmic rays mainly originate from SNRs with hard $\\gamma$-ray spectra.\n"}
+{"abstract": "  \"No-till\" and cover cropping are often identified as the leading simple, best\nmanagement practices for carbon sequestration in agriculture. However, the root\nof the problem is more complex, with the potential benefits of these approaches\ndepending on numerous factors including a field's soil type(s), topography, and\nmanagement history. Instead of using computer vision approaches to simply\nclassify a field a still vs. no-till, we instead seek to identify the degree of\nresidue coverage across afield through a probabilistic deep learning\nsegmentation approach to enable more accurate analysis of carbon holding\npotential and realization. This approach will not only provide more precise\ninsights into currently implemented practices, but also enable a more accurate\nidentification process of fields with the greatest potential for adopting new\npractices to significantly impact carbon sequestration in agriculture.\n"}
+{"abstract": "  It was recently pointed out that so-called \"superhydrides\", hydrogen-rich\nmaterials that appear to become superconducting at high temperatures and\npressures, exhibit physical properties that are different from both\nconventional and unconventional standard type I and type II superconductors\n[1,2]. Here we consider magnetic field expulsion in the first material in this\nclass discovered in 2015, sulfur hydride [3]. A nuclear resonant scattering\nexperiment has been interpreted as demonstration that the Meissner effect takes\nplace in this material [4,5]. Here we point out that the observed effect, under\nthe assumption that the system is in thermodynamic equilibrium, implies a\nMeissner pressure [6] in this material that is {\\it much larger} than that of\nstandard superconductors. This suggests that hydride superconductors are\nqualitatively different from the known standard superconductors {\\it if} they\nare superconductors.\n"}
+{"abstract": "  Scaling arguments provide valuable analysis tools across physics and complex\nsystems yet are often employed as one generic method, without explicit\nreference to the various mathematical concepts underlying them. A careful\nunderstanding of these concepts empowers us to unlock their full potential.\n"}
+{"abstract": "  The number of units of a network dynamical system, its size, arguably\nconstitutes its most fundamental property. Many units of a network, however,\nare typically experimentally inaccessible such that the network size is often\nunknown. Here we introduce a \\emph{detection matrix }that suitably arranges\nmultiple transient time series from the subset of accessible units to detect\nnetwork size via matching rank constraints. The proposed method is model-free,\napplicable across system types and interaction topologies and applies to\nnon-stationary dynamics near fixed points, as well as periodic and chaotic\ncollective motion. Even if only a small minority of units is perceptible and\nfor systems simultaneously exhibiting nonlinearities, heterogeneities and\nnoise, \\emph{exact} size detection is feasible. We illustrate applicability for\na paradigmatic class of biochemical reaction networks.\n"}
+{"abstract": "  Delay differential equations are of great importance in science, engineering,\nmedicine and biological models. These type of models include time delay\nphenomena which is helpful for characterising the real-world applications in\nmachine learning, mechanics, economics, electrodynamics and so on. Besides,\nspecial classes of functional differential equations have been investigated in\nmany researches. In this study, a numerical investigation of retarded type of\nthese models together with initial conditions are introduced. The technique is\nbased on a polynomial approach along with collocation points which maintains an\napproximated solutions to the problem. Besides, an error analysis of the\napproximate solutions is given. Accuracy of the method is shown by the results.\nConsequently, illustrative examples are considered and detailed analysis of the\nproblem is acquired. Consequently, the future outlook is discussed in\nconclusion.\n"}
+{"abstract": "  We report the results of the analyses of the cosmic ray data collected with a\n4 tonne (3$\\times$1$\\times$1~m$^3$) active mass (volume) Liquid Argon\nTime-Projection Chamber (TPC) operated in a dual-phase mode. We present a\ndetailed study of the TPC's response, its main detector parameters and\nperformance. The results are important for the understanding and further\ndevelopments of the dual-phase technology, thanks to the verification of key\naspects, such as the extraction of electrons from liquid to gas and their\namplification through the entire one square metre readout plain, gain\nstability, purity and charge sharing between readout views.\n"}
+{"abstract": "  Modern vehicles are complex cyber-physical systems made of hundreds of\nelectronic control units (ECUs) that communicate over controller area networks\n(CANs). This inherited complexity has expanded the CAN attack surface which is\nvulnerable to message injection attacks. These injections change the overall\ntiming characteristics of messages on the bus, and thus, to detect these\nmalicious messages, time-based intrusion detection systems (IDSs) have been\nproposed. However, time-based IDSs are usually trained and tested on\nlow-fidelity datasets with unrealistic, labeled attacks. This makes difficult\nthe task of evaluating, comparing, and validating IDSs. Here we detail and\nbenchmark four time-based IDSs against the newly published ROAD dataset, the\nfirst open CAN IDS dataset with real (non-simulated) stealthy attacks with\nphysically verified effects. We found that methods that perform hypothesis\ntesting by explicitly estimating message timing distributions have lower\nperformance than methods that seek anomalies in a distribution-related\nstatistic. In particular, these \"distribution-agnostic\" based methods\noutperform \"distribution-based\" methods by at least 55% in area under the\nprecision-recall curve (AUC-PR). Our results expand the body of knowledge of\nCAN time-based IDSs by providing details of these methods and reporting their\nresults when tested on datasets with real advanced attacks. Finally, we develop\nan after-market plug-in detector using lightweight hardware, which can be used\nto deploy the best performing IDS method on nearly any vehicle.\n"}
+{"abstract": "  A graph $ G $ is said to be $ (H;k) $-vertex stable if $ G $ contains\na~subgraph isomorphic to $ H $ even after removing any $ k $ of its vertices\nalongside with their incident edges. We will denote by $ \\text{stab}(H;k) $ the\nminimum size among sizes of all $ (H;k) $-vertex stable graphs. In this paper\nwe consider a~case where the structure $ H $ is a~star graph $ K_{1,r} $ and\nthe the number of vertices in $ G $ is exact, \\ie equal to $ 1 + r + k $. We\nwill show that under the above assumptions $ \\text{stab}(K_{1,r};k) $ equals\neither $ \\frac{1}{2}(k + 1)(2r + k) $, $ \\frac{1}{2}\\big((r + k)^{2} - 1\\big) $\nor $ \\frac{1}{2}(r + k)^{2} $. Moreover, we will characterize all the extremal\ngraphs.\n"}
+{"abstract": "  We present a novel method for reliably explaining the predictions of neural\nnetworks. We consider an explanation reliable if it identifies input features\nrelevant to the model output by considering the input and the neighboring data\npoints. Our method is built on top of the assumption of smooth landscape in a\nloss function of the model prediction: locally consistent loss and gradient\nprofile. A theoretical analysis established in this study suggests that those\nlocally smooth model explanations are learned using a batch of noisy copies of\nthe input with the L1 regularization for a saliency map. Extensive experiments\nsupport the analysis results, revealing that the proposed saliency maps\nretrieve the original classes of adversarial examples crafted against both\nnaturally and adversarially trained models, significantly outperforming\nprevious methods. We further demonstrated that such good performance results\nfrom the learning capability of this method to identify input features that are\ntruly relevant to the model output of the input and the neighboring data\npoints, fulfilling the requirements of a reliable explanation.\n"}
+{"abstract": "  We study certain physically-relevant subgeometries of binary symplectic polar\nspaces $W(2N-1,2)$ of small rank $N$, when the points of these spaces\ncanonically encode $N$-qubit observables. Key characteristics of a subspace of\nsuch a space $W(2N-1,2)$ are: the number of its negative lines, the\ndistribution of types of observables, the character of the geometric hyperplane\nthe subspace shares with the distinguished (non-singular) quadric of\n$W(2N-1,2)$ and the structure of its Veldkamp space. In particular, we classify\nand count polar subspaces of $W(2N-1,2)$ whose rank is $N-1$. $W(3,2)$ features\nthree negative lines of the same type and its $W(1,2)$'s are of five different\ntypes. $W(5,2)$ is endowed with 90 negative lines of two types and its\n$W(3,2)$'s split into 13 types. 279 out of 480 $W(3,2)$'s with three negative\nlines are composite, i.\\,e. they all originate from the two-qubit $W(3,2)$.\nGiven a three-qubit $W(3,2)$ and any of its geometric hyperplanes, there are\nthree other $W(3,2)$'s possessing the same hyperplane. The same holds if a\ngeometric hyperplane is replaced by a `planar' tricentric triad. A hyperbolic\nquadric of $W(5,2)$ is found to host particular sets of seven $W(3,2)$'s, each\nof them being uniquely tied to a Conwell heptad with respect to the quadric.\nThere is also a particular type of $W(3,2)$'s, a representative of which\nfeatures a point each line through which is negative. Finally, $W(7,2)$ is\nfound to possess 1908 negative lines of five types and its $W(5,2)$'s fall into\nas many as 29 types. 1524 out of 1560 $W(5,2)$'s with 90 negative lines\noriginate from the three-qubit $W(5,2)$. Remarkably, the difference in the\nnumber of negative lines for any two distinct types of four-qubit $W(5,2)$'s is\na multiple of four.\n"}
+{"abstract": "  In this paper we studied a broader type of generalized balls which are\ndomains on the complex projective with possibly Levi-degenerate boundaries. We\nproved rigidity theorems for proper holomorphic mappings among them by\nexploring the structure of the moduli spaces of projective linear subspaces,\nwhich generalized some earlier results for the ordinary generalized balls with\nLevi-nondegenerate boundaries.\n"}
+{"abstract": "  Decentralized data storage systems like the Interplanetary Filesystem (IPFS)\nare becoming increasingly popular, e. g., as a data layer in blockchain\napplications and for sharing content in a censorship-resistant manner. In IPFS,\ndata is hosted by an open set of peers, requests to which are broadcast to all\ndirectly connected peers and routed via a distributed hash table (DHT). In this\npaper, we showcase how the monitoring of said data requests allows for profound\ninsights about the IPFS network while simultaneously breaching individual\nusers' privacy. To this end, we present a passive monitoring methodology that\nenables us to collect data requests of a significant and upscalable portion of\nthe total IPFS node population. Using a measurement setup implementing our\napproach and data collected over a period of fifteen months, we demonstrate the\nestimation of, among other things: the size of the IPFS network, activity\nlevels and structure, and content popularity distributions. We furthermore\npresent how our methodology can be abused for attacks on users' privacy. As a\ndemonstration, we identify and successfully surveil public IPFS/HTTP gateways,\nthereby also uncovering their (normally hidden) node identifiers. We find that\nthe number of requests by public gateways is substantial, suggesting\nsubstantial usage of these gateways. We give a detailed analysis of the\nmechanics and reasons behind implied privacy threats and discuss possible\ncountermeasures.\n"}
+{"abstract": "  The Kerr rotating black hole metric has unstable photon orbits that orbit\naround the hole at fixed values of the Boyer-Lindquist coordinate $r$ that\ndepend on the axial angular momentum of the orbit, as well as on the parameters\nof the hole. For zero orbital axial angular momentum, these orbits cross the\nrotational axes at a fixed value of $r$ that depends on the mass $M$ and\nangular momentum $J$ of the black hole. Nonzero angular momentum of the hole\ncauses the photon orbit to rotate so that its direction when crossing the north\npolar axis changes from one crossing to the next by an angle I shall call\n$\\Delta\\phi$, which depends on the black hole dimensionless rotation parameter\n$a/M = cJ/(GM^2)$ by an equation involving a complete elliptic integral of the\nfirst kind. When the black hole has $a/M \\approx 0.994\\,341\\,179\\,923\\,26$,\nwhich is nearly maximally rotating, a photon sent out in a constant-$r$\ndirection from the north polar axis at $r \\approx 2.423\\,776\\,210\\,035\\,73\\,\nGM/c^2$ returns to the north polar axis in precisely the opposite direction (in\na frame nonrotating with respect to the distant stars), a photon boomerang.\n"}
+{"abstract": "  Machine-learned potential energy surfaces (PESs) for molecules with more than\n10 atoms are typically forced to use lower-level electronic structure methods\nsuch as density functional theory and second-order Moller-Plesset perturbation\ntheory (MP2). While these are efficient and realistic, they fall short of the\naccuracy of the ``gold standard'' coupled-cluster method, especially with\nrespect to reaction and isomerization barriers. We report a major step forward\nin applying a $\\Delta$-machine learning method to the challenging case of\nacetylacetone, whose MP2 barrier height for H-atom transfer is low by roughly\n1.5 kcal/mol relative to the benchmark CCSD(T) barrier of 3.2 kcal/mol. From a\ndatabase of 2151 local CCSD(T) energies, and training with as few as 430\nenergies, we obtain a new PES with a barrier of 3.49 kcal/mol in agreement with\nthe LCCSD(T) one of 3.54 kcal/mol and close to the benchmark value. Tunneling\nsplittings due to H-atom transfer are calculated using this new PES, providing\nimproved estimates over previous ones obtained using an MP2-based PES.\n"}
+{"abstract": "  Numerical solutions to high-dimensional partial differential equations (PDEs)\nbased on neural networks have seen exciting developments. This paper derives\ncomplexity estimates of the solutions of $d$-dimensional second-order elliptic\nPDEs in the Barron space, that is a set of functions admitting the integral of\ncertain parametric ridge function against a probability measure on the\nparameters. We prove under some appropriate assumptions that if the\ncoefficients and the source term of the elliptic PDE lie in Barron spaces, then\nthe solution of the PDE is $\\epsilon$-close with respect to the $H^1$ norm to a\nBarron function. Moreover, we prove dimension-explicit bounds for the Barron\nnorm of this approximate solution, depending at most polynomially on the\ndimension $d$ of the PDE. As a direct consequence of the complexity estimates,\nthe solution of the PDE can be approximated on any bounded domain by a\ntwo-layer neural network with respect to the $H^1$ norm with a\ndimension-explicit convergence rate.\n"}
+{"abstract": "  Layout designs are encountered in a variety of fields. For problems with many\ndesign degrees of freedom, efficiency of design methods becomes a major\nconcern. In recent years, machine learning methods such as artificial neural\nnetworks have been used increasingly to speed up the design process. A main\nissue of many such approaches is the need for a large corpus of training data\nthat are generated using high-dimensional simulations. The high computational\ncost associated with training data generation largely diminishes the efficiency\ngained by using machine learning methods. In this work, an adaptive artificial\nneural network-based generative design approach is proposed and developed. This\nmethod uses a generative adversarial network to generate design candidates and\nthus the number of design variables is greatly reduced. To speed up the\nevaluation of the objective function, a convolutional neural network is\nconstructed as the surrogate model for function evaluation. The inverse design\nis carried out using the genetic algorithm in conjunction with two neural\nnetworks. A novel adaptive learning and optimization strategy is proposed,\nwhich allows the design space to be effectively explored for the search for\noptimal solutions. As such the number of training data needed is greatly\nreduced. The performance of the proposed design method is demonstrated on two\nheat source layout design problems. In both problems, optimal designs have been\nobtained. Compared with several existing approaches, the proposed approach has\nthe best performance in terms of accuracy and efficiency.\n"}
+{"abstract": "  Accountability is widely understood as a goal for well governed computer\nsystems, and is a sought-after value in many governance contexts. But how can\nit be achieved? Recent work on standards for governable artificial intelligence\nsystems offers a related principle: traceability. Traceability requires\nestablishing not only how a system worked but how it was created and for what\npurpose, in a way that explains why a system has particular dynamics or\nbehaviors. It connects records of how the system was constructed and what the\nsystem did mechanically to the broader goals of governance, in a way that\nhighlights human understanding of that mechanical operation and the decision\nprocesses underlying it. We examine the various ways in which the principle of\ntraceability has been articulated in AI principles and other policy documents\nfrom around the world, distill from these a set of requirements on software\nsystems driven by the principle, and systematize the technologies available to\nmeet those requirements. From our map of requirements to supporting tools,\ntechniques, and procedures, we identify gaps and needs separating what\ntraceability requires from the toolbox available for practitioners. This map\nreframes existing discussions around accountability and transparency, using the\nprinciple of traceability to show how, when, and why transparency can be\ndeployed to serve accountability goals and thereby improve the normative\nfidelity of systems and their development processes.\n"}
+{"abstract": "  Recent works have shown that a rich set of semantic directions exist in the\nlatent space of Generative Adversarial Networks (GANs), which enables various\nfacial attribute editing applications. However, existing methods may suffer\npoor attribute variation disentanglement, leading to unwanted change of other\nattributes when altering the desired one. The semantic directions used by\nexisting methods are at attribute level, which are difficult to model complex\nattribute correlations, especially in the presence of attribute distribution\nbias in GAN's training set. In this paper, we propose a novel framework (IALS)\nthat performs Instance-Aware Latent-Space Search to find semantic directions\nfor disentangled attribute editing. The instance information is injected by\nleveraging the supervision from a set of attribute classifiers evaluated on the\ninput images. We further propose a Disentanglement-Transformation (DT) metric\nto quantify the attribute transformation and disentanglement efficacy and find\nthe optimal control factor between attribute-level and instance-specific\ndirections based on it. Experimental results on both GAN-generated and\nreal-world images collectively show that our method outperforms\nstate-of-the-art methods proposed recently by a wide margin. Code is available\nat https://github.com/yxuhan/IALS.\n"}
+{"abstract": "  We discuss the recent results on the muon anomalous magnetic moment in the\ncontext of new physics models with light scalars. We propose a model in which\nthe one-loop contributions to g-2 of a scalar and a pseudoscalar naturally\ncancel in the massless limit due to the symmetry structure of the model. This\nmodel allows to interpolate between two possible interpretations. In the first\ninterpretation, the results provide a strong evidence of the existence of new\nphysics, dominated by the positive contribution of a CP-even scalar. In the\nsecond one, supported by the recent lattice result, the data provides a strong\nupper bound on new physics, specifically in the case of (negative) pseudoscalar\ncontributions. We emphasize that tree-level signatures of the new degrees of\nfreedom of the model are enhanced relative to conventional explanations of the\ndiscrepancy. As a result, this model can be tested in the near future with\naccelerator-based experiments and possibly also at the precision frontier.\n"}
+{"abstract": "  Probabilistic point cloud registration methods are becoming more popular\nbecause of their robustness. However, unlike point-to-plane variants of\niterative closest point (ICP) which incorporate local surface geometric\ninformation such as surface normals, most probabilistic methods (e.g., coherent\npoint drift (CPD)) ignore such information and build Gaussian mixture models\n(GMMs) with isotropic Gaussian covariances. This results in sphere-like GMM\ncomponents which only penalize the point-to-point distance between the two\npoint clouds. In this paper, we propose a novel method called CPD with Local\nSurface Geometry (LSG-CPD) for rigid point cloud registration. Our method\nadaptively adds different levels of point-to-plane penalization on top of the\npoint-to-point penalization based on the flatness of the local surface. This\nresults in GMM components with anisotropic covariances. We formulate point\ncloud registration as a maximum likelihood estimation (MLE) problem and solve\nit with the Expectation-Maximization (EM) algorithm. In the E step, we\ndemonstrate that the computation can be recast into simple matrix manipulations\nand efficiently computed on a GPU. In the M step, we perform an unconstrained\noptimization on a matrix Lie group to efficiently update the rigid\ntransformation of the registration. The proposed method outperforms\nstate-of-the-art algorithms in terms of accuracy and robustness on various\ndatasets captured with range scanners, RGBD cameras, and LiDARs. Also, it is\nsignificantly faster than modern implementations of CPD. The source code is\navailable at https://github.com/ChirikjianLab/LSG-CPD.git.\n"}
+{"abstract": "  Recent work has proven the effectiveness of transformers in many computer\nvision tasks. However, the performance of transformers in gaze estimation is\nstill unexplored. In this paper, we employ transformers and assess their\neffectiveness for gaze estimation. We consider two forms of vision transformer\nwhich are pure transformers and hybrid transformers. We first follow the\npopular ViT and employ a pure transformer to estimate gaze from images. On the\nother hand, we preserve the convolutional layers and integrate CNNs as well as\ntransformers. The transformer serves as a component to complement CNNs. We\ncompare the performance of the two transformers in gaze estimation. The Hybrid\ntransformer significantly outperforms the pure transformer in all evaluation\ndatasets with less parameters. We further conduct experiments to assess the\neffectiveness of the hybrid transformer and explore the advantage of\nself-attention mechanism. Experiments show the hybrid transformer can achieve\nstate-of-the-art performance in all benchmarks with pre-training.To facilitate\nfurther research, we release codes and models in\nhttps://github.com/yihuacheng/GazeTR.\n"}
+{"abstract": "  When fitting N-body models to astronomical data - including transit times,\nradial velocity, and astrometric positions at observed times - the derivatives\nof the model outputs with respect to the initial conditions can help with model\noptimization and posterior sampling. Here we describe a general-purpose\nsymplectic integrator for arbitrary orbital architectures, including those with\nclose encounters, which we have recast to maintain numerical stability and\nprecision for small step sizes. We compute the derivatives of the N-body\ncoordinates and velocities as a function of time with respect to the initial\nconditions and masses by propagating the Jacobian along with the N-body\nintegration. For the first time we obtain the derivatives of the transit times\nwith respect to the initial conditions and masses using the chain rule, which\nis quicker and more accurate than using finite differences or automatic\ndifferentiation. We implement this algorithm in an open source package,\nNbodyGradient.jl, written in the Julia language, which has been used in the\noptimization and error analysis of transit-timing variations in the TRAPPIST-1\nsystem. We present tests of the accuracy and precision of the code, and show\nthat it compares favorably in speed to other integrators which are written in\nC.\n"}
+{"abstract": "  This paper proposes a parallel computation strategy and a posterior-based\nlattice expansion algorithm for efficient lattice rescoring with neural\nlanguage models (LMs) for automatic speech recognition. First, lattices from\nfirst-pass decoding are expanded by the proposed posterior-based lattice\nexpansion algorithm. Second, each expanded lattice is converted into a minimal\nlist of hypotheses that covers every arc. Each hypothesis is constrained to be\nthe best path for at least one arc it includes. For each lattice, the neural LM\nscores of the minimal list are computed in parallel and are then integrated\nback to the lattice in the rescoring stage. Experiments on the Switchboard\ndataset show that the proposed rescoring strategy obtains comparable\nrecognition performance and generates more compact lattices than a competitive\nbaseline method. Furthermore, the parallel rescoring method offers more\nflexibility by simplifying the integration of PyTorch-trained neural LMs for\nlattice rescoring with Kaldi.\n"}
+{"abstract": "  Motivated by the appearance of fractional powers of line bundles in studies\nof vector-like spectra in 4d F-theory compactifications, we analyze the\nstructure and origin of these bundles. Fractional powers of line bundles are\nalso known as root bundles and can be thought of as generalizations of spin\nbundles. We explain how these root bundles are linked to inequivalent F-theory\ngauge potentials of a $G_4$-flux.\n  While this observation is interesting in its own right, it is particularly\nvaluable for F-theory Standard Model constructions. In aiming for MSSMs, it is\ndesired to argue for the absence of vector-like exotics. We work out the root\nbundle constraints on all matter curves in the largest class of currently-known\nF-theory Standard Model constructions without chiral exotics and gauge coupling\nunification. On each matter curve, we conduct a systematic \"bottom\"-analysis of\nall solutions to the root bundle constraints and all spin bundles. Thereby, we\nderive a lower bound for the number of combinations of root bundles and spin\nbundles whose cohomologies satisfy the physical demand of absence of\nvector-like pairs.\n  On a technical level, this systematic study is achieved by a well-known\ndiagrammatic description of root bundles on nodal curves. We extend this\ndescription by a counting procedure, which determines the cohomologies of\nso-called limit root bundles on full blow-ups of nodal curves. By use of\ndeformation theory, these results constrain the vector-like spectra on the\nsmooth matter curves in the actual F-theory geometry.\n"}
+{"abstract": "  The Bayesian approach is effective for inverse problems. The posterior\ndensity distribution provides useful information of the unknowns. However, for\nproblems with non-unique solutions, the classical estimators such as the\nmaximum a posterior (MAP) and conditional mean (CM) are not enough. We\nintroduce two new estimators, the local maximum a posterior (LMAP) and local\nconditional mean (LCM). Their applications are demonstrated by three inverse\nproblems: an inverse spectral problem, an inverse source problem, and an\ninverse medium problem.\n"}
+{"abstract": "  We derive the planar limit of 2- and 3-point functions of single-trace chiral\nprimary operators of ${\\cal N}=2$ SQCD on $S^4$, to all orders in the 't Hooft\ncoupling. In order to do so, we first obtain a combinatorial expression for the\nplanar free energy of a hermitian matrix model with an infinite number of\narbitrary single and double trace terms in the potential; this solution might\nhave applications in many other contexts. We then use these results to evaluate\nthe analogous planar correlation functions on ${\\mathbb R}^4$. Specifically, we\ncompute all the terms with a single value of the $\\zeta$ function for a few\nplanar 2- and 3-point functions, and conjecture general formulas for these\nterms for all 2- and 3-point functions on ${\\mathbb R}^4$.\n"}
+{"abstract": "  The rotational sublevels of the key (000) and (010) vibrational states of the\nH2S molecule were modeled with an accuracy close to experimental uncertainty\nusing the generating function and Euler approaches. The predictive ability of\nthe Hamiltonian parameters derived is tested against variational calculations.\nComparison of transitions wavenumbers obtained from the presently calculated\nset of the H2S (000) and (010) energy levels with simulated (000)-(000),\n(010)-(010) transitions included in HITRAN 2016 database revealed a large\ndiscrepancy up to 44 cm-1. Large sets of accurate rotational sublevels of the\n(000) and (010) states are calculated.\n"}
+{"abstract": "  The work is devoted to ways of modeling street traffic on a street layout\nwithout traffic lights of an established topology. The behavior of traffic\nparticipants takes into account the individual inclinations of drivers to\ncreatively interpret traffic rules. Participant interactions describe game\ntheory models that provide information for simulation algorithms based on\ncellular automata. Driver diversification comes down to two types often\nconsidered in such research: DE(fective)-agent and CO(operative)-agent. Various\nways of using the description of traffic participants to examine the impact of\nbehavior on street traffic dynamics were shown. Directions for the further\ndetailed analysis were indicated, which requires basic research in the field of\ngame theory models.\n"}
+{"abstract": "  We study the processes $\\gamma \\gamma \\to \\eta_c \\to \\eta' K^+ K^-$, $\\eta'\n\\pi^+ \\pi^-$, and $\\eta \\pi^+ \\pi^-$ using a data sample of 519 $fb^{-1}$\nrecorded with the BaBar detector operating at the SLAC PEP-II asymmetric-energy\n$e^+e^-$ collider at center-of-mass energies at and near the $\\Upsilon(nS)$ ($n\n= 2,3,4$) resonances. This is the first observation of the decay $\\eta_c \\to\n\\eta' K^+ K^-$ and we measure the branching fraction $\\Gamma(\\eta_c \\to \\eta'\nK^+ K^-)/(\\Gamma(\\eta_c \\to \\eta' \\pi^+ \\pi^-)=0.644\\pm 0.039_{\\rm stat}\\pm\n0.032_{\\rm sys}$. Significant interference is observed between $\\gamma \\gamma\n\\to \\eta_c\\to \\eta \\pi^+ \\pi^-$ and the non-resonant two-photon process $\\gamma\n\\gamma \\to \\eta \\pi^+ \\pi^-$. A Dalitz plot analysis is performed of $\\eta_c$\ndecays to $\\eta' K^+ K^-$, $\\eta' \\pi^+ \\pi^-$, and $\\eta \\pi^+ \\pi^-$.\nCombined with our previous analysis of $\\eta_c \\to K \\bar K \\pi$, we measure\nthe $K^*_0(1430)$ parameters and the ratio between its $\\eta' K$ and $\\pi K$\ncouplings. The decay $\\eta_c \\to \\eta' \\pi^+ \\pi^-$ is dominated by the\n$f_0(2100)$ resonance, also observed in $J/\\psi$ radiative decays. A new\n$a_0(1700) \\to \\eta \\pi$ resonance is observed in the $\\eta_c \\to \\eta \\pi^+\n\\pi^-$ channel. We also compare $\\eta_c$ decays to $\\eta$ and $\\eta'$ final\nstates in association with scalar mesons as they relate to the identification\nof the scalar glueball.\n"}
+{"abstract": "  Colloidal self-assembly -- the spontaneous organization of colloids into\nordered structures -- has been considered key to produce next-generation\nmaterials. However, the present-day staggering variety of colloidal building\nblocks and the limitless number of thermodynamic conditions make a systematic\nexploration intractable. The true challenge in this field is to turn this logic\naround, and to develop a robust, versatile algorithm to inverse design colloids\nthat self-assemble into a target structure. Here, we introduce a generic\ninverse design method to efficiently reverse-engineer crystals, quasicrystals,\nand liquid crystals by targeting their diffraction patterns. Our algorithm\nrelies on the synergetic use of an evolutionary strategy for parameter\noptimization, and a convolutional neural network as an order parameter, and\nprovides a new way forward for the inverse design of experimentally feasible\ncolloidal interactions, specifically optimized to stabilize the desired\nstructure.\n"}
+{"abstract": "  There has been recently a growing interest in studying adversarial examples\non natural language models in the black-box setting. These methods attack\nnatural language classifiers by perturbing certain important words until the\nclassifier label is changed. In order to find these important words, these\nmethods rank all words by importance by querying the target model word by word\nfor each input sentence, resulting in high query inefficiency. A new\ninteresting approach was introduced that addresses this problem through\ninterpretable learning to learn the word ranking instead of previous expensive\nsearch. The main advantage of using this approach is that it achieves\ncomparable attack rates to the state-of-the-art methods, yet faster and with\nfewer queries, where fewer queries are desirable to avoid suspicion towards the\nattacking agent. Nonetheless, this approach sacrificed the useful information\nthat could be leveraged from the target classifier for that sake of query\nefficiency. In this paper we study the effect of leveraging the target model\noutputs and data on both attack rates and average number of queries, and we\nshow that both can be improved, with a limited overhead of additional queries.\n"}
+{"abstract": "  A growing number of eclipsing binary systems of the \"HW Vir\" kind (i. e.,\ncomposed by a subdwarf-B/O primary star and an M dwarf secondary) show\nvariations in their orbital period, also called Eclipse Time Variations (ETVs).\nTheir physical origin is not yet known with certainty: while some ETVs have\nbeen claimed to arise from dynamical perturbations due to the presence of\ncircumbinary planetary companions, other authors suggest that the Applegate\neffect or other unknown stellar mechanisms could be responsible for them. In\nthis work, we present twenty-eight unpublished high-precision light curves of\none of the most controversial of these systems, the prototype HW Virginis. We\nhomogeneously analysed the new eclipse timings together with historical data\nobtained between 1983 and 2012, demonstrating that the planetary models\npreviously claimed do not fit the new photometric data, besides being\ndynamically unstable. In an effort to find a new model able to fit all the\navailable data, we developed a new approach based on a global-search genetic\nalgorithm and eventually found two new distinct families of solutions that fit\nthe observed timings very well, yet dynamically unstable at the 10^5-year time\nscale. This serves as a cautionary tale on the existence of formal solutions\nthat apparently explain ETVs but are not physically meaningful, and on the need\nof carefully testing their stability. On the other hand, our data confirm the\npresence of an ETV on HW Vir that known stellar mechanisms are unable to\nexplain, pushing towards further observing and modelling efforts.\n"}
+{"abstract": "  The electricity sector has tended to be one of the first industries to face\ntechnology change motivated by sustainability concerns. Whilst efficient market\ndesigns for electricity have tended to focus upon market power concerns,\nenvironmental externalities pose extra challenges for efficient solutions.\nThus, we show that ad hoc remedies for market power alongside administered\ncarbon prices are inefficient unless they are integrated. Accordingly, we\ndevelop an incentive-based market clearing design that can include\nexternalities as well as market power mitigation. A feature of the solution is\nthat it copes with incomplete information of the system operator regarding\ngeneration costs. It is uses a network representation of the power system and\nthe proposed incentive mechanism holds even with energy limited technologies\nhaving temporal constraints, e.g., storage. The shortcomings of price caps to\nmitigate market power, in the context of sustainability externalities, are\novercome under the proposed incentive mechanism.\n"}
+{"abstract": "  Full-field imaging through scattering media is fraught with many challenges.\nDespite many achievements in recent years, current imaging methods are too slow\nto deal with fast dynamics that occur for example in biomedical imaging. Here\nwe present an ultra-fast all-optical method, where the object to be imaged and\nthe scattering medium (diffuser) are inserted into a highly multimode\nself-imaging laser cavity. We show that the intra-cavity laser light from the\nobject is mainly focused onto specific regions of the scattering medium where\nthe phase variations are low. Thus, round trip loss within the laser cavity is\nminimized, thereby overcoming most of the scattering effects. The method is\nexploited to image objects through scattering media whose diffusion angle is\nlower than the numerical aperture of the laser cavity. As our method is based\non optical feedback inside a laser cavity, it can deal with temporal variations\nthat occur on timescales as short as several cavity round trips, with an upper\nbound of 200 ns.\n"}
+{"abstract": "  High capacity end-to-end approaches for human motion (behavior) prediction\nhave the ability to represent subtle nuances in human behavior, but struggle\nwith robustness to out of distribution inputs and tail events. Planning-based\nprediction, on the other hand, can reliably output decent-but-not-great\npredictions: it is much more stable in the face of distribution shift (as we\nverify in this work), but it has high inductive bias, missing important aspects\nthat drive human decisions, and ignoring cognitive biases that make human\nbehavior suboptimal. In this work, we analyze one family of approaches that\nstrive to get the best of both worlds: use the end-to-end predictor on common\ncases, but do not rely on it for tail events / out-of-distribution inputs --\nswitch to the planning-based predictor there. We contribute an analysis of\ndifferent approaches for detecting when to make this switch, using an\nautonomous driving domain. We find that promising approaches based on\nensembling or generative modeling of the training distribution might not be\nreliable, but that there very simple methods which can perform surprisingly\nwell -- including training a classifier to pick up on tell-tale issues in\npredicted trajectories.\n"}
+{"abstract": "  We prove the existence of immersed closed curves of constant geodesic\ncurvature in an arbitrary Riemannian 2-sphere for almost every prescribed\ncurvature. To achieve this, we develop a min-max scheme for a weighted length\nfunctional.\n"}
+{"abstract": "  In image anomaly detection, Autoencoders are the popular methods that\nreconstruct the input image that might contain anomalies and output a clean\nimage with no abnormalities. These Autoencoder-based methods usually calculate\nthe anomaly score from the reconstruction error, the difference between the\ninput image and the reconstructed image. On the other hand, the accuracy of the\nreconstruction is insufficient in many of these methods, so it leads to\ndegraded accuracy of anomaly detection. To improve the accuracy of the\nreconstruction, we consider defining loss function in the frequency domain. In\ngeneral, we know that natural images contain many low-frequency components and\nfew high-frequency components. Hence, to improve the accuracy of the\nreconstruction of high-frequency components, we introduce a new loss function\nnamed weighted frequency domain loss(WFDL). WFDL provides a sharper\nreconstructed image, which contributes to improving the accuracy of anomaly\ndetection. In this paper, we show our method's superiority over the\nconventional Autoencoder methods by comparing it with AUROC on the MVTec AD\ndataset.\n"}
+{"abstract": "  Background: Poverty among the population of a country is one of the most\ndisputable topics in social studies. Many researchers devote their work to\nidentifying the factors that influence it most. Bulgaria is one of the EU\nmember states with the highest poverty levels. Regional facets of social\nexclusion and risks of poverty among the population are a key priority of the\nNational Development Strategy for the third decade of 21st century. In order to\nmitigate the regional poverty levels it is necessary for the social policy\nmakers to pay more attention to the various factors expected to influence these\nlevels. Results: Poverty reduction is observed in most areas of the country.\nThe regions with obviously favorable developments are Sofia district, Pernik,\nPleven, Lovech, Gabrovo, Veliko Tarnovo, Silistra, Shumen, Stara Zagora,\nSmolyan, Kyustendil and others. Increased levels of poverty are found for\nRazgrad and Montana districts. It was fond that the reduction in the risk of\npoverty is associated to the increase in employment, investment, and housing.\nConclusion: The social policy making needs to be aware of the fact that the\ndegree of exposition to risk of poverty and social exclusion significantly\nrelates to the levels of regional employment, investment and housing.\n"}
+{"abstract": "  A natural way of increasing our understanding of NP-complete graph problems\nis to restrict the input to a special graph class. Classes of $H$-free graphs,\nthat is, graphs that do not contain some graph $H$ as an induced subgraph, have\nproven to be an ideal testbed for such a complexity study. However, if the\nforbidden graph $H$ contains a cycle or claw, then these problems often stay\nNP-complete. A recent complexity study on the $k$-Colouring problem shows that\nwe may still obtain tractable results if we also bound the diameter of the\n$H$-free input graph. We continue this line of research by initiating a\ncomplexity study on the impact of bounding the diameter for a variety of\nclassical vertex partitioning problems restricted to $H$-free graphs. We prove\nthat bounding the diameter does not help for Independent Set, but leads to new\ntractable cases for problems closely related to 3-Colouring. That is, we show\nthat Near-Bipartiteness, Independent Feedback Vertex Set, Independent Odd Cycle\nTransversal, Acyclic 3-Colouring and Star 3-Colouring are all polynomial-time\nsolvable for chair-free graphs of bounded diameter. To obtain these results we\nexploit a new structural property of 3-colourable chair-free graphs.\n"}
+{"abstract": "  The aim of this paper is to present an elementary computable theory of random\nvariables, based on the approach to probability via valuations. The theory is\nbased on a type of lower-measurable sets, which are controlled limits of open\nsets, and extends existing work in this area by providing a computable theory\nof conditional random variables. The theory is based within the framework of\ntype-two effectivity, so has an explicit direct link with Turing computation,\nand is expressed in a system of computable types and operations, so has a clean\nmathematical description.\n"}
+{"abstract": "  As the field of superconducting quantum computing approaches maturity,\noptimization of single-device performance is proving to be a promising avenue\ntowards large-scale quantum computers. However, this optimization is possible\nonly if performance metrics can be accurately compared among measurements,\ndevices, and laboratories. Currently such comparisons are inaccurate or\nimpossible due to understudied errors from a plethora of sources. In this\nPerspective, we outline the current state of error analysis for qubits and\nresonators in superconducting quantum circuits, and discuss what future\ninvestigations are required before superconducting quantum device optimization\ncan be realized.\n"}
+{"abstract": "  We address the issues of clustering and non-global logarithms for jet shapes\nin the process of production of a Higgs/vector boson associated with a single\nhard jet at hadron colliders. We perform an analytical fixed-order calculation\nup to second order in the coupling as well as an all-orders estimation for the\nspecific invariant mass distribution of the highest-$p_t$ jet, for various jet\nalgorithms. Our results are derived in the eikonal (soft) limit and are valid\nup to next-to-leading logarithmic accuracy. We perform a matching of the\nresummed distribution to next-to-leading order results from MCFM and compare\nour findings with the outputs of the Monte Carlo event generators Pythia 8 and\nHerwig 7. After accounting for non-perturbative effects we compare our results\nwith available experimental data from the CMS collaboration for the Z + jet\nproduction. We find good agreement over a wide range of the observable.\n"}
+{"abstract": "  Bioenergy with Carbon Capture and Sequestration (BECCS) is critical for\nstringent climate change mitigation, but is commercially and technologically\nimmature and resource-intensive. In California, state and federal fuel and\nclimate policies can drive first-markets for BECCS. We develop a spatially\nexplicit optimization model to assess niche markets for renewable natural gas\n(RNG) production with carbon capture and sequestration (CCS) from waste biomass\nin California. Existing biomass residues produce biogas and RNG and enable\nlow-cost CCS through the upgrading process and CO$_2$ truck transport. Under\ncurrent state and federal policy incentives, we could capture and sequester 2.9\nmillion MT CO$_2$/year (0.7% of California's 2018 CO$_2$ emissions) and produce\n93 PJ RNG/year (4% of California's 2018 natural gas demand) with a profit\nmaximizing objective. Existing federal and state policies produce profits of\n\\$11/GJ. Distributed RNG production with CCS potentially catalyzes markets and\ntechnologies for CO$_2$ capture, transport, and storage in California.\n"}
+{"abstract": "  Current and future generations of intensity mapping surveys promise dramatic\nimprovements in our understanding of galaxy evolution and large-scale\nstructure. An intensity map provides a census of the cumulative emission from\nall galaxies in a given region and redshift, including faint objects that are\nundetectable individually. Furthermore, cross-correlations between line\nintensity maps and galaxy redshift surveys are sensitive to the line intensity\nand clustering bias without the limitation of cosmic variance. Using the Fisher\ninformation matrix, we derive simple expressions describing sensitivities to\nthe intensity and bias obtainable for cross-correlation surveys, focusing on\ncosmic variance evasion. Based on these expressions, we conclude that the\noptimal sensitivity is obtained by matching the survey depth, defined by the\nratio of the clustering power spectrum to noise in a given mode, between the\ntwo surveys. We find that mid- to far-infrared space telescopes could benefit\nfrom this technique by cross-correlating with coming galaxy redshift surveys\nsuch as those planned for the Nancy Grace Roman Space Telescope, allowing for\nsensitivities beyond the cosmic variance limit. Our techniques can therefore be\napplied to survey design and requirements development to maximize the\nsensitivities of future intensity mapping experiments to tracers of galaxy\nevolution and large-scale structure cosmology.\n"}
+{"abstract": "  We study real steady state varieties of the dynamics of chemical reaction\nnetworks. The dynamics are derived using mass action kinetics with parametric\nreaction rates. The models studied are not inherently parametric in nature.\nRather, our interest in parameters is motivated by parameter uncertainty, as\nreaction rates are typically either measured with limited precision or\nestimated. We aim at detecting toricity and shifted toricity, using a framework\nthat has been recently introduced and studied for the non-parametric case over\nboth the real and the complex numbers. While toricity requires that the variety\nspecifies a subgroup of the direct power of the multiplicative group of the\nunderlying field, shifted toricity requires only a coset. In the non-parametric\ncase these requirements establish real decision problems. In the presence of\nparameters we must go further and derive necessary and sufficient conditions in\nthe parameters for toricity or shifted toricity to hold. Technically, we use\nreal quantifier elimination methods. Our computations on biological networks\nhere once more confirm shifted toricity as a relevant concept, while toricity\nholds only for degenerate parameter choices.\n"}
+{"abstract": "  Many-body localization of a disorder interacting boson system in one\ndimension is studied numerically at the filling factor being one-half, in terms\nof level statistics, local compressibility, correlation function and\nentanglement entropies. The von Neumann entanglement entropy is decoupled into\na particle number entropy and a configuration entropy. An anomalous volume-law\nbehavior is found for the configuration entanglement entropy to confirm a\nrecent experimental observation [A. Lukin, M. Rispoli, R. Schittko, et al.,\nScience 364, 256 (2019)] for sufficient strong disorder, while the particle\nnumber entropy fulfills an area-law corresponding to the total entropy for\ndisordered spin chain. The localization length are extracted from a two-body\ncorrelation function for many-body localization states and corresponding\ntime-evolutions states as well. A phase diagrams is established with consisting\nof an ergodic thermalized region and a many-body-localization region in a\nparameter space of the disorder strength and the energy density. Two regions\nare separated by a many-body mobility edge deducted from the standard deviation\nof the particle-number entanglement entropy, which appears consistent with that\nbased on the localization length. Slow dynamics characterized by a logarithmic\ntime-dependence is explicitly shown for both the particle number entropy and\nthe configuration entropy in an intermediate regime of their time-evolutions,\nwhich does not show up in the Anderson localization case, i.e. non-interacting\ndisorder systems.\n"}
+{"abstract": "  Existing work in counterfactual Learning to Rank (LTR) has focussed on\noptimizing feature-based models that predict the optimal ranking based on\ndocument features. LTR methods based on bandit algorithms often optimize\ntabular models that memorize the optimal ranking per query. These types of\nmodel have their own advantages and disadvantages. Feature-based models provide\nvery robust performance across many queries, including those previously unseen,\nhowever, the available features often limit the rankings the model can predict.\nIn contrast, tabular models can converge on any possible ranking through\nmemorization. However, memorization is extremely prone to noise, which makes\ntabular models reliable only when large numbers of user interactions are\navailable. Can we develop a robust counterfactual LTR method that pursues\nmemorization-based optimization whenever it is safe to do? We introduce the\nGeneralization and Specialization (GENSPEC) algorithm, a robust feature-based\ncounterfactual LTR method that pursues per-query memorization when it is safe\nto do so. GENSPEC optimizes a single feature-based model for generalization:\nrobust performance across all queries, and many tabular models for\nspecialization: each optimized for high performance on a single query. GENSPEC\nuses novel relative high-confidence bounds to choose which model to deploy per\nquery. By doing so, GENSPEC enjoys the high performance of successfully\nspecialized tabular models with the robustness of a generalized feature-based\nmodel. Our results show that GENSPEC leads to optimal performance on queries\nwith sufficient click data, while having robust behavior on queries with little\nor noisy data.\n"}
+{"abstract": "  Common domain shift problem formulations consider the integration of multiple\nsource domains, or the target domain during training. Regarding the\ngeneralization of machine learning models between different car interiors, we\nformulate the criterion of training in a single vehicle: without access to the\ntarget distribution of the vehicle the model would be deployed to, neither with\naccess to multiple vehicles during training. We performed an investigation on\nthe SVIRO dataset for occupant classification on the rear bench and propose an\nautoencoder based approach to improve the transferability. The autoencoder is\non par with commonly used classification models when trained from scratch and\nsometimes out-performs models pre-trained on a large amount of data. Moreover,\nthe autoencoder can transform images from unknown vehicles into the vehicle it\nwas trained on. These results are corroborated by an evaluation on real\ninfrared images from two vehicle interiors.\n"}
+{"abstract": "  Since its beginning in the 1950s, the field of artificial intelligence has\ncycled several times between periods of optimistic predictions and massive\ninvestment (\"AI spring\") and periods of disappointment, loss of confidence, and\nreduced funding (\"AI winter\"). Even with today's seemingly fast pace of AI\nbreakthroughs, the development of long-promised technologies such as\nself-driving cars, housekeeping robots, and conversational companions has\nturned out to be much harder than many people expected. One reason for these\nrepeating cycles is our limited understanding of the nature and complexity of\nintelligence itself. In this paper I describe four fallacies in common\nassumptions made by AI researchers, which can lead to overconfident predictions\nabout the field. I conclude by discussing the open questions spurred by these\nfallacies, including the age-old challenge of imbuing machines with humanlike\ncommon sense.\n"}
+{"abstract": "  Wildfire is one of the biggest disasters that frequently occurs on the west\ncoast of the United States. Many efforts have been made to understand the\ncauses of the increases in wildfire intensity and frequency in recent years. In\nthis work, we propose static and dynamic prediction models to analyze and\nassess the areas with high wildfire risks in California by utilizing a\nmultitude of environmental data including population density, Normalized\nDifference Vegetation Index (NDVI), Palmer Drought Severity Index (PDSI), tree\nmortality area, tree mortality number, and altitude. Moreover, we focus on a\nbetter understanding of the impacts of different factors so as to inform\npreventive actions. To validate our models and findings, we divide the land of\nCalifornia into 4,242 grids of 0.1 degrees $\\times$ 0.1 degrees in latitude and\nlongitude, and compute the risk of each grid based on spatial and temporal\nconditions. To verify the generalizability of our models, we further expand the\nscope of wildfire risk assessment from California to Washington without any\nfine tuning. By performing counterfactual analysis, we uncover the effects of\nseveral possible methods on reducing the number of high risk wildfires. Taken\ntogether, our study has the potential to estimate, monitor, and reduce the\nrisks of wildfires across diverse areas provided that such environment data is\navailable.\n"}
+{"abstract": "  Among the versatile forms of dynamical patterns of activity exhibited by the\nbrain, oscillations are one of the most salient and extensively studied, yet\nare still far from being well understood. In this paper, we provide various\nstructural characterizations of the existence of oscillatory behavior in neural\nnetworks using a classical neural mass model of mesoscale brain activity called\nlinear-threshold dynamics. Exploiting the switched-affine nature of this\ndynamics, we obtain various necessary and/or sufficient conditions on the\nnetwork structure and its external input for the existence of oscillations in\n(i) two-dimensional excitatory-inhibitory networks (E-I pairs), (ii) networks\nwith one inhibitory but arbitrary number of excitatory nodes, (iii) purely\ninhibitory networks with an arbitrary number of nodes, and (iv) networks of E-I\npairs. Throughout our treatment, and given the arbitrary dimensionality of the\nconsidered dynamics, we rely on the lack of stable equilibria as a system-based\nproxy for the existence of oscillations, and provide extensive numerical\nresults to support its tight relationship with the more standard, signal-based\ndefinition of oscillations in computational neuroscience.\n"}
+{"abstract": "  Thorstensen (2020) recently argued that the cataclysmic variable (CV) LAMOST\nJ024048.51+195226.9 may be a twin to the unique magnetic propeller system AE\nAqr. If this is the case, two predictions are that it should display a short\nperiod white dwarf spin modulation, and that it should be a bright radio\nsource. We obtained follow-up optical and radio observations of this CV, in\norder to see if this holds true. Our optical high-speed photometry does not\nreveal a white dwarf spin signal, but lacks the sensitivity to detect a\nmodulation similar to the 33-s spin signal seen in AE Aqr. We detect the source\nin the radio, and measure a radio luminosity similar to that of AE Aqr and\nclose to the highest so far reported for a CV. We also find good evidence for\nradio variability on a time scale of tens of minutes. Optical polarimetric\nobservations produce no detection of linear or circular polarization. While we\nare not able to provide compelling evidence, our observations are all\nconsistent with this object being a propeller system.\n"}
+{"abstract": "  With the growing prevalence of psychological interventions, it is vital to\nhave measures which rate the effectiveness of psychological care to assist in\ntraining, supervision, and quality assurance of services. Traditionally,\nquality assessment is addressed by human raters who evaluate recorded sessions\nalong specific dimensions, often codified through constructs relevant to the\napproach and domain. This is however a cost-prohibitive and time-consuming\nmethod that leads to poor feasibility and limited use in real-world settings.\nTo facilitate this process, we have developed an automated competency rating\ntool able to process the raw recorded audio of a session, analyzing who spoke\nwhen, what they said, and how the health professional used language to provide\ntherapy. Focusing on a use case of a specific type of psychotherapy called\nMotivational Interviewing, our system gives comprehensive feedback to the\ntherapist, including information about the dynamics of the session (e.g.,\ntherapist's vs. client's talking time), low-level psychological language\ndescriptors (e.g., type of questions asked), as well as other high-level\nbehavioral constructs (e.g., the extent to which the therapist understands the\nclients' perspective). We describe our platform and its performance using a\ndataset of more than 5,000 recordings drawn from its deployment in a real-world\nclinical setting used to assist training of new therapists. Widespread use of\nautomated psychotherapy rating tools may augment experts' capabilities by\nproviding an avenue for more effective training and skill improvement,\neventually leading to more positive clinical outcomes.\n"}
+{"abstract": "  Starting from a recently proposed comprehensive theory for the high-Tc\nsuperconductivity in cuprates, we derive a general analytic expression for the\nplanar resistivity, in the presence of an applied external magnetic field\n$\\textbf{H}$ and explore its consequences in the different phases of these\nmaterials. As an initial probe of our result, we show it compares very well\nwith experimental data for the resistivity of LSCO at different values of the\napplied field. We also apply our result to Bi2201 and show that the\nmagnetoresistivity in the strange metal phase of this material, exhibits the\n$H^2$ to $H$ crossover, as we move from the weak to the strong field regime.\nYet, despite of that, the magnetoresistivity does not present a quadrature\nscaling. Remarkably, the resistivity H-field derivative does scale as a\nfunction of $\\frac{H}{T}$, in complete agreement with recent magneto-transport\nmeasurements made in the strange metal phase of cuprates \\cite{Hussey2020}. We,\nfinally, address the issue of the $T$-power-law dependence of the resistivity\nof overdoped cuprates and compare our results with experimental data for\nTl2201. We show that this provides a simple method to determine whether the\nquantum critical point associated to the pseudogap temperature $T^*(x)$ belongs\nto the SC dome or not.\n"}
+{"abstract": "  Schooling fish provide a spectacular example of self-organization in Nature.\nThe most remarkable patterns they form are giant rotating clusters such as\nballs, tori, and rings, but the underlying mechanism remains largely unknown.\nHere we propose an agent-based model that limits the number of agents that can\ninteract with each other. We incorporate the characteristic behaviors of fish\nby (i) attraction that is weakened in a dense cluster of fish, and (ii)\nacceleration with finite duration (\"fast-start\") when the fish is out of the\ncluster. By three-dimensional numerical simulations, we show emergence of giant\nrotating clusters (balls, tori, and rings) that are much larger than the radius\nof interaction. We present a phase diagram of patterns including polarized\nschools and swarms, and propose a physical mechanism that determines the\ncluster shape in terms of the interaction capacity and strength of attraction.\nOur model also indicates that each fish randomly moves back and forth between\nthe inner and outer regions of a vortex on a large time-scale. These results\nshow that fish without inherent chirality form giant rotating clusters\nspontaneously and only by short-ranged interactions.\n"}
+{"abstract": "  The gold standard for COVID-19 is RT-PCR, testing facilities for which are\nlimited and not always optimally distributed. Test results are delayed, which\nimpacts treatment. Expert radiologists, one of whom is a co-author, are able to\ndiagnose COVID-19 positivity from Chest X-Rays (CXR) and CT scans, that can\nfacilitate timely treatment. Such diagnosis is particularly valuable in\nlocations lacking radiologists with sufficient expertise and familiarity with\nCOVID-19 patients. This paper has two contributions. One, we analyse literature\non CXR based COVID-19 diagnosis. We show that popular choices of dataset\nselection suffer from data homogeneity, leading to misleading results. We\ncompile and analyse a viable benchmark dataset from multiple existing\nheterogeneous sources. Such a benchmark is important for realistically testing\nmodels. Our second contribution relates to learning from imbalanced data.\nDatasets for COVID X-Ray classification face severe class imbalance, since most\nsubjects are COVID -ve. Twin Support Vector Machines (Twin SVM) and Twin Neural\nNetworks (Twin NN) have, in recent years, emerged as effective ways of handling\nskewed data. We introduce a state-of-the-art technique, termed as Twin\nAugmentation, for modifying popular pre-trained deep learning models. Twin\nAugmentation boosts the performance of a pre-trained deep neural network\nwithout requiring re-training. Experiments show, that across a multitude of\nclassifiers, Twin Augmentation is very effective in boosting the performance of\ngiven pre-trained model for classification in imbalanced settings.\n"}
+{"abstract": "  In this paper we study the time differential dual-phase-lag model of heat\nconduction incorporating the microstructural interaction effect in the\nfast-transient process of heat transport. We analyse the influence of the delay\ntimes upon some qualitative properties of the solutions of the initial boundary\nvalue problems associated to such a model. Thus, the uniqueness results are\nestablished under the assumption that the conductivity tensor is positive\ndefinite and the delay times $\\tau_q$ and $\\tau_T$ vary in the set $\\{0\\leq\n\\tau_q\\leq 2\\tau_T\\}\\cup \\{0<2\\tau_T< \\tau_q\\}$. For the continuous dependence\nproblem we establish two different estimates. The first one is obtained for the\ndelay times with $0\\leq \\tau_q \\leq 2\\tau_T$, which agrees with the\nthermodynamic restrictions on the model in concern, and the solutions are\nstable. The second estimate is established for the delay times with $0<2\\tau_T<\n\\tau_q$ and it allows the solutions to have an exponential growth in time. The\nspatial behavior of the transient solutions and the steady-state vibrations is\nalso addressed. For the transient solutions we establish a theorem of influence\ndomain, under the assumption that the delay times are in $\\left\\{0<\\tau_q\\leq\n2\\tau_T\\right\\}\\cup \\left\\{0<2\\tau_T<\\tau_q\\right\\}$. While for the amplitude\nof the harmonic vibrations we obtain an exponential decay estimate of\nSaint-Venant type, provided the frequency of vibration is lower than a critical\nvalue and without any restrictions upon the delay times.\n"}
+{"abstract": "  We prove an optimal $\\Omega(n^{-1})$ lower bound on the spectral gap of\nGlauber dynamics for anti-ferromagnetic two-spin systems with $n$ vertices in\nthe tree uniqueness regime. This spectral gap holds for all, including\nunbounded, maximum degree $\\Delta$. Consequently, we have the following mixing\ntime bounds for the models satisfying the uniqueness condition with a slack\n$\\delta\\in(0,1)$:\n  $\\bullet$ $C(\\delta) n^2\\log n$ mixing time for the hardcore model with\nfugacity $\\lambda\\le (1-\\delta)\\lambda_c(\\Delta)= (1-\\delta)\\frac{(\\Delta -\n1)^{\\Delta - 1}}{(\\Delta - 2)^\\Delta}$;\n  $\\bullet$ $C(\\delta) n^2$ mixing time for the Ising model with edge activity\n$\\beta\\in\\left[\\frac{\\Delta-2+\\delta}{\\Delta-\\delta},\\frac{\\Delta-\\delta}{\\Delta-2+\\delta}\\right]$;\nwhere the maximum degree $\\Delta$ may depend on the number of vertices $n$, and\n$C(\\delta)$ depends only on $\\delta$.\n  Our proof is built upon the recently developed connections between the\nGlauber dynamics for spin systems and the high-dimensional expander walks. In\nparticular, we prove a stronger notion of spectral independence, called the\ncomplete spectral independence, and use a novel Markov chain called the field\ndynamics to connect this stronger spectral independence to the rapid mixing of\nGlauber dynamics for all degrees.\n"}
+{"abstract": "  Future microcalorimeter X-ray observations will resolve spectral features in\nunmatched detail. Understanding the line formation processes in the X-rays\ndeserves much attention. The purpose of this paper is to discuss such processes\nin the presence of a photoionizing source. Line formation processes in one and\ntwo-electron species are broadly categorized into four cases. Case A occurs\nwhen the Lyman line optical depths are very small and photoexcitation does not\noccur. Line photons escape the cloud without any scattering. Case B occurs when\nthe Lyman-line optical depths are large enough for photons to undergo multiple\nscatterings. Case C occurs when a broadband continuum source strikes an\noptically thin cloud. The Lyman lines are enhanced by induced radiative\nexcitation of the atoms/ions by continuum photons, also known as continuum\npumping. A fourth less-studied scenario, where the Case B spectrum is enhanced\nby continuum pumping, is called Case D. Here, we establish the mathematical\nfoundation of Cases A, B, C, and D in an irradiated cloud with Cloudy. We also\nshow the total X-ray emission spectrum for all four cases within the energy\nrange 0.1 - 10 keV at the resolving power of XRISM around 6 keV. Additionally,\nwe show that a combined effect of electron scattering and partial blockage of\ncontinuum pumping reduces the resonance line intensities. Such reduction\nincreases with column density and can serve as an important tool to measure the\ncolumn density/optical depth of the cloud.\n"}
+{"abstract": "  Little is known about the spin-flip diffusion length $l_{\\rm sf}$, one of the\nmost important material parameters in the field of spintronics. We use a\ndensity-functional-theory based scattering approach to determine values of\n$l_{\\rm sf}$ that result from electron-phonon scattering as a function of\ntemperature for all 5d transition metal elements. $l_{\\rm sf}$ does not\ndecrease monotonically with the atomic number Z but is found to be inversely\nproportional to the density of states at the Fermi level. By using the same\nlocal current methodology to calculate the spin Hall angle $\\Theta_{\\rm sH}$\nthat characterizes the efficiency of the spin Hall effect, we show that the\nproducts $\\rho(T)l_{\\rm sf}(T)$ and $\\Theta_{\\rm sH}(T)l_{\\rm sf}(T)$ are\nconstant.\n"}
+{"abstract": "  We propose a predictive $Q_4$ flavored 2HDM model, where the scalar sector is\nenlarged by the inclusion of several gauge singlet scalars and the fermion\nsector by the inclusion of right handed Majorana neutrinos. In our model, the\n$Q_4$ family symmetry is supplemented by several auxiliary cyclic symmetries,\nwhose spontaneous breaking produces the observed pattern of SM charged fermion\nmasses and quark mixing angles. The light active neutrino masses are generated\nfrom an inverse seesaw mechanism at one loop level thanks to a remnant\npreserved $Z_2$ symmetry. Our model succesfully reproduces the measured dark\nmatter relic abundance only for masses of the DM candidate below $\\sim$ 0.8\nTeV. Furthermore, our model is also consistent with the lepton and baryon\nasymmetries of the Universe as well as with the muon anomalous magnetic moment.\n"}
+{"abstract": "  We use the modified Richardson-Lucy deconvolution algorithm to reconstruct\nthe Primordial Power Spectrum from the Weak Lensing Power spectrum\nreconstructed from the CMB anisotropies. This provides an independent window to\nobserve and constrain the PPS $P_R(k)$ along different $k$ scales as compared\nto CMB Temperature Power Spectrum. The Weak Lensing Power spectrum does not\ncontain secondary variations in power and hence is cleaner, unlike the\nTemperature Power spectrum which suffers from lensing which is visible in its\nPPS reconstructions. We demonstrate that the physical behaviour of the weak\nlensing kernel is different from the temperature kernel and reconstructs broad\nfeatures over $k$. We provide an in-depth analysis of the error propagation\nusing simulated data and Monte-Carlo sampling, based on Planck best-fit\ncosmological parameters to simulate the data and cosmic variance limited error\nbars. The error and initial condition analysis provides a clear picture of the\noptimal reconstruction region for the estimator and we provide and algorithm\nfor $P_R(k)$ sampling to be used based on the given data, errors and its\nbinning properties. Eventually we plan to use this method on actual mission\ndata and provide a cross reference to PPS reconstructed from other sectors and\nany possible features in them.\n"}
+{"abstract": "  Data deduplication saves storage space by identifying and removing repeats in\nthe data stream. Compared with traditional compression methods, data\ndeduplication schemes are more time efficient and are thus widely used in large\nscale storage systems. In this paper, we provide an information-theoretic\nanalysis on the performance of deduplication algorithms on data streams in\nwhich repeats are not exact. We introduce a source model in which probabilistic\nsubstitutions are considered. More precisely, each symbol in a repeated string\nis substituted with a given edit probability. Deduplication algorithms in both\nthe fixed-length scheme and the variable-length scheme are studied. The\nfixed-length deduplication algorithm is shown to be unsuitable for the proposed\nsource model as it does not take into account the edit probability. Two\nmodifications are proposed and shown to have performances within a constant\nfactor of optimal with the knowledge of source model parameters. We also study\nthe conventional variable-length deduplication algorithm and show that as\nsource entropy becomes smaller, the size of the compressed string vanishes\nrelative to the length of the uncompressed string, leading to high compression\nratios.\n"}
+{"abstract": "  We report multi-epoch radial velocities, rotational velocities, and\natmospheric parameters for 37 T-type brown dwarfs observed with Keck/NIRSPEC.\nUsing a Markov Chain Monte Carlo forward-modeling method, we achieve median\nprecisions of 0.5 km s$^{-1}$ and 0.9 km s$^{-1}$ for radial and rotational\nvelocities, respectively. All of the T dwarfs in our sample are thin disk brown\ndwarfs. We confirm previously reported moving group associations for four T\ndwarfs. However, the lack of spectral indicators of youth in two of these\nsources suggests that these are chance alignments. We confirm two previously\nun-resolved binary candidates, the T0+T4.5 2MASS J11061197+2754225 and the\nL7+T3.5 2MASS J21265916+7617440, with orbital periods of 4 yr and 12 yr,\nrespectively. We find a kinematic age of 3.5$\\pm$0.3 Gyr for local T dwarfs,\nconsistent with nearby late-M dwarfs (4.1$\\pm$0.3 Gyr). Removal of thick disk L\ndwarfs in the local ultracool dwarf sample gives a similar age for L dwarfs\n(4.2$\\pm$0.3 Gyr), largely resolving the local L dwarf age anomaly. The\nkinematic ages of local late-M, L, and T dwarfs can be accurately reproduced\nwith population simulations incorporating standard assumptions of the mass\nfunction, star formation rate, and brown dwarf evolutionary models. A kinematic\ndispersion break is found at the L4$-$L6 subtypes, likely reflecting the\nterminus of the stellar Main Sequence. We provide a compilation of precise\nradial velocities for 172 late-M, L, and T dwarfs within $\\sim$20 pc of the\nSun.\n"}
+{"abstract": "  One of the most intriguing phenomena in active matter has been the gas-liquid\nlike motility induced phase separation (MIPS) observed in repulsive active\nparticles. However, experimentally no particle can be a perfect sphere, and the\nasymmetric shape, mass distribution or catalysis coating can induce an active\ntorque on the particle, which makes it a chiral active particle. Here using\ncomputer simulations and dynamic mean-field theory, we demonstrate that the\nlarge enough torque of circle active Brownian particles (cABPs) in two\ndimensions generates a dynamical clustering state interrupting the conventional\nMIPS. Multiple clusters arise from the combination of the conventional MIPS\ncohesion, and the circulating current caused disintegration. The non-vanishing\ncurrent in non-equilibrium steady states microscopically originates from the\nmotility ``relieved'' by automatic rotation, which breaks the detailed balance\nat the continuum level. This suggests that no equilibrium-like phase separation\ntheory can be constructed for chiral active colloids even with tiny active\ntorque, in which no visible collective motion exists. This mechanism also sheds\nlight on the understanding of dynamic clusters observed in a variety of active\nmatter systems.\n"}
+{"abstract": "  In this study we investigate the potential of parametric images formed from\nultrasound B-mode scans using the Nakagami distribution for non-invasive\nclassification of breast lesions. Through a sliding window technique, we\ngenerated seven types of parametric images from each patient scan in our\ndataset using basic and as well as derived parameters of the Nakagami\ndistribution. To determine the most suitable window size for image generation,\nwe conducted an empirical analysis using three windows, and selected the best\none for our study. From the parametric images formed for each patient, we\nextracted a total of 72 features. Feature selection was performed to find the\noptimum subset of features for the best classification performance.\nIncorporating the selected subset of features with the Support Vector Machine\n(SVM) classifier, and by tuning the decision threshold, we obtained a maximum\nclassification accuracy of 93.08%, an Area under the ROC Curve (AUC) of 0.9712,\na False Negative Rate of 0%, and a very low False Positive Rate of 8.65%. Our\nresults indicate that the high accuracy of such a procedure may assist in the\ndiagnostic process associated with detection of breast cancer, as well as help\nto reduce false positive diagnosis.\n"}
+{"abstract": "  In a previous paper, we presented an extension of our reflection model\nRELXILL_NK to include the finite thickness of the accretion disk following the\nprescription in Taylor & Reynolds (2018). In this paper, we apply our model to\nfit the 2013 simultaneous observations by NuSTAR and XMM-Newton of the\nsupermassive black hole in MCG-06-30-15 and the 2019 NuSTAR observation of the\nGalactic black hole in EXO 1846-031. The high-quality data of these spectra had\npreviously led to precise black hole spin measurements and very stringent\nconstraints on possible deviations from the Kerr metric. We find that the disk\nthickness does not change previous spin results found with a model employing an\ninfinitesimally thin disk, which confirms the robustness of spin measurements\nin high radiative efficiency disks, where the impact of disk thickness is\nminimal. Similar analysis on lower accretion rate systems will be an important\ntest for measuring the effect of disk thickness on black hole spin\nmeasurements.\n"}
+{"abstract": "  Searches for new leptophobic resonances at high energy colliders usually\ntarget their decay modes into pairs of light quarks, top quarks, or standard\nmodel bosons. Additional decay modes may also be present, producing signatures\nto which current searches are not sensitive. We investigate the performance of\ngeneric searches that look for resonances decaying into two large-radius jets.\nAs benchmark for our analysis we use a supersymmetric $\\text{U}(1)'$ extension\nof the Standard Model, the so-called U$\\mu\\nu$SSM, where all the SM decay modes\nof the $Z'$ boson take place, plus additional (cascade) decays into new\nscalars. The generic searches use a generic multi-pronged jet tagger and take\nadvantage of the presence of $b$ quarks in the large-radius jets, and are\nsensitive to all these $Z'$ decay modes (except into light quarks) at once. For\ncouplings that are well below current experimental constraints, these generic\nsearches are sensitive at the $3\\sigma-4\\sigma$ level with Run 2 LHC data.\n"}
+{"abstract": "  Load side participation can provide support to the power network by\nappropriately adapting the demand when required. In addition, it enables an\neconomically improved power allocation. In this study, we consider the problem\nof providing an optimal power allocation among generation and on-off loads\nwithin the secondary frequency control timeframe. In particular, we consider a\nmixed integer optimization problem which ensures that the secondary frequency\ncontrol objectives (i.e. generation/demand balance and the frequency attaining\nits nominal value at steady state) are satisfied. We present analytical\nconditions on the generation and on-off load profiles such that an\n$\\epsilon$-optimality interpretation of the steady state power allocation is\nobtained, providing a non-conservative value for $\\epsilon$. Moreover, we\ndevelop a hierarchical control scheme that provides on-off load values that\nsatisfy the proposed conditions. We study the interaction of the proposed\ncontrol scheme with the physical dynamics of the power network and provide\nanalytic stability guarantees. Our results are verified with numerical\nsimulations on the Northeast Power Coordinating Council (NPCC) 140-bus system,\nwhere it is demonstrated that the proposed algorithm yields a close to optimal\npower allocation.\n"}
+{"abstract": "  Relational Hoare logics (RHL) provide rules for reasoning about relations\nbetween programs. Several RHLs include a rule we call sequential product that\ninfers a relational correctness judgment from judgments of ordinary Hoare logic\n(HL). Other rules embody sensible patterns of reasoning and have been found\nuseful in practice, but sequential product is relatively complete on its own\n(with HL). As a more satisfactory way to evaluate RHLs, a notion of alignment\ncompleteness is introduced, in terms of the inductive assertion method and\nproduct automata. Alignment completeness results are given to account for\nseveral different sets of rules. The notion may serve to guide the design of\nRHLs and relational verifiers for richer programming languages and alignment\npatterns.\n"}
+{"abstract": "  Antiferromagnetic PbMnTeO6, also known as mineral kuranakhite, has been\nreported recently to have all three cations in trigonal prismatic coordination,\nwhich is extremely unusual for both Mn(4+) and Te(6+). In this work, the phase\nwas reproduced with the same lattice parameters and N\\'eel temperature TN = 20\nK. However, powder neutron diffraction unambiguously determined octahedral\n(trigonal antiprismatic) coordination for all cations within the chiral space\ngroup P312. The same symmetry was proposed for SrMnTeO6 and PbGeTeO6, instead\nof the reported space groups P-62m and P31m, respectively. PbMnTeO6 was found\nto be a robust antiferromagnet with an assumingly substantial scale of exchange\ninteractions since the Neel temperature did not show any changes in external\nmagnetic fields up to 7 T. The determined effective magnetic moment meff = 3.78\nmB was in excellent agreement with the numerical estimation using the effective\ng-factor g = 1.95 directly measured here by electron spin resonance (ESR). Both\nspecific heat and ESR data indicated the two-dimensional character of magnetism\nin the compound under study. The combination of chirality with magnetic order\nmakes PbMnTeO6 a promising material with possible multiferroic properties.\n"}
+{"abstract": "  Beam-induced ionization injection (B-III) is currently being explored as a\nmethod for injecting an electron beam with a controlled density profile into a\nplasma wakefield accelerator (PWFA). This process is initiated by the fields of\nan unmatched drive beam where the slice envelope reaches its minimum value, the\n'pinch'. To control the injected beam's qualities, it is crucial to study the\nbeam-slice envelope oscillations, especially size and the location of the\npinch. In this proceeding, an ansatz based on the harmonic motion is proposed\nto find the analytical solution to beam-slice envelope evolution in the\nnonlinear regime. The size of the pinch is then found through the application\nof energy conservation in the transverse direction. The resulting analytical\nexpressions are shown to be in good agreement with numerical solutions.\n"}
+{"abstract": "  Topological superconductors (TSCs) are unconventional superconductors with\nbulk superconducting gap and in-gap Majorana states on the boundary that may be\nused as topological qubits for quantum computation. Despite their importance in\nboth fundamental research and applications, natural TSCs are very rare. Here,\ncombining state of the art synchrotron and laser-based angle-resolved\nphotoemission spectroscopy, we investigated a stoichiometric transition metal\ndichalcogenide (TMD), 2M-WS2 with a superconducting transition temperature of\n8.8 K (the highest among all TMDs in the natural form up to date) and observed\ndistinctive topological surface states (TSSs). Furthermore, in the\nsuperconducting state, we found that the TSSs acquired a nodeless\nsuperconducting gap with similar magnitude as that of the bulk states. These\ndiscoveries not only evidence 2M-WS2 as an intrinsic TSC without the need of\nsensitive composition tuning or sophisticated heterostructures fabrication, but\nalso provide an ideal platform for device applications thanks to its van der\nWaals layered structure.\n"}
+{"abstract": "  Each knot invariant can be extended to singular knots according to the skein\nrule. A Vassiliev invariant of order at most $n$ is defined as a knot invariant\nthat vanishes identically on knots with more than $n$ double points. A chord\ndiagram encodes the order of double points along a singular knot. A Vassiliev\ninvariant of order $n$ gives rise to a function on chord diagrams with $n$\nchords. Such a function should satisfy some conditions in order to come from a\nVassiliev invariant. A weight system is a function on chord diagrams that\nsatisfies so-called 4-term relations. Given a Lie algebra $\\mathfrak{g}$\nequipped with a non-degenerate invariant bilinear form, one can construct a\nweight system with values in the center of the universal enveloping algebra\n$U(\\mathfrak{g})$. In this paper, we calculate $\\mathfrak{sl}_3$ weight system\nfor chord diagram whose intersection graph is complete bipartite graph\n$K_{2,n}$.\n"}
+{"abstract": "  The interplay of interactions and disorder in low-dimensional superconductors\nsupports the formation of multiple quantum phases, as possible instabilities of\nthe Superconductor-Insulator Transition (SIT) at a singular quantum critical\npoint. We explore a one-dimensional model which exhibits such variety of phases\nin the strongly quantum fluctuations regime. Specifically, we study the effect\nof weak disorder on a two-leg Josephson ladder with comparable Josephson and\ncharging energies ($E_J$~$E_C$). An additional key feature of our model is the\nrequirement of perfect $\\mathbb{Z}_2$-symmetry, respected by all parameters\nincluding the disorder. Using a perturbative renormalization-group (RG)\nanalysis, we derive the phase diagram and identify at least one intermediate\nphase between a full-fledged superconductor and a disorder-dominated insulator.\nMost prominently, for repulsive interactions on the rungs we identify two\ndistinct mixed phases: in both of them the longitudinal charge mode is a\ngapless superconductor, however one phase exhibits a dipolar charge density\norder on the rungs, while the other is disordered. This latter phase is\ncharacterized by coexisting superconducting (phase-locked) and charge-ordered\nrungs, and encompasses the potential of evolving into a Griffith's phase\ncharacteristic of the random-field Ising model in the strong disorder limit.\n"}
+{"abstract": "  The goal of person search is to localize and match query persons from scene\nimages. For high efficiency, one-step methods have been developed to jointly\nhandle the pedestrian detection and identification sub-tasks using a single\nnetwork. There are two major challenges in the current one-step approaches. One\nis the mutual interference between the optimization objectives of multiple\nsub-tasks. The other is the sub-optimal identification feature learning caused\nby small batch size when end-to-end training. To overcome these problems, we\npropose a decoupled and memory-reinforced network (DMRNet). Specifically, to\nreconcile the conflicts of multiple objectives, we simplify the standard\ntightly coupled pipelines and establish a deeply decoupled multi-task learning\nframework. Further, we build a memory-reinforced mechanism to boost the\nidentification feature learning. By queuing the identification features of\nrecently accessed instances into a memory bank, the mechanism augments the\nsimilarity pair construction for pairwise metric learning. For better encoding\nconsistency of the stored features, a slow-moving average of the network is\napplied for extracting these features. In this way, the dual networks reinforce\neach other and converge to robust solution states. Experimentally, the proposed\nmethod obtains 93.2% and 46.9% mAP on CUHK-SYSU and PRW datasets, which exceeds\nall the existing one-step methods.\n"}
+{"abstract": "  Sequence-to-sequence models have lead to significant progress in keyphrase\ngeneration, but it remains unknown whether they are reliable enough to be\nbeneficial for document retrieval. This study provides empirical evidence that\nsuch models can significantly improve retrieval performance, and introduces a\nnew extrinsic evaluation framework that allows for a better understanding of\nthe limitations of keyphrase generation models. Using this framework, we point\nout and discuss the difficulties encountered with supplementing documents with\n-- not present in text -- keyphrases, and generalizing models across domains.\nOur code is available at https://github.com/boudinfl/ir-using-kg\n"}
+{"abstract": "  Despite the successes of deep neural networks on many challenging vision\ntasks, they often fail to generalize to new test domains that are not\ndistributed identically to the training data. The domain adaptation becomes\nmore challenging for cross-modality medical data with a notable domain shift.\nGiven that specific annotated imaging modalities may not be accessible nor\ncomplete. Our proposed solution is based on the cross-modality synthesis of\nmedical images to reduce the costly annotation burden by radiologists and\nbridge the domain gap in radiological images. We present a novel approach for\nimage-to-image translation in medical images, capable of supervised or\nunsupervised (unpaired image data) setups. Built upon adversarial training, we\npropose a learnable self-attentive spatial normalization of the deep\nconvolutional generator network's intermediate activations. Unlike previous\nattention-based image-to-image translation approaches, which are either\ndomain-specific or require distortion of the source domain's structures, we\nunearth the importance of the auxiliary semantic information to handle the\ngeometric changes and preserve anatomical structures during image translation.\nWe achieve superior results for cross-modality segmentation between unpaired\nMRI and CT data for multi-modality whole heart and multi-modal brain tumor MRI\n(T1/T2) datasets compared to the state-of-the-art methods. We also observe\nencouraging results in cross-modality conversion for paired MRI and CT images\non a brain dataset. Furthermore, a detailed analysis of the cross-modality\nimage translation, thorough ablation studies confirm our proposed method's\nefficacy.\n"}
+{"abstract": "  Dynamic Causal Modeling (DCM) is a Bayesian framework for inferring on hidden\n(latent) neuronal states, based on measurements of brain activity. Since its\nintroduction in 2003 for functional magnetic resonance imaging data, DCM has\nbeen extended to electrophysiological data, and several variants have been\ndeveloped. Their biophysically motivated formulations make these models\npromising candidates for providing a mechanistic understanding of human brain\ndynamics, both in health and disease. However, due to their complexity and\nreliance on concepts from several fields, fully understanding the mathematical\nand conceptual basis behind certain variants of DCM can be challenging. At the\nsame time, a solid theoretical knowledge of the models is crucial to avoid\npitfalls in the application of these models and interpretation of their\nresults. In this paper, we focus on one of the most advanced formulations of\nDCM, i.e. conductance-based DCM for cross-spectral densities, whose components\nare described across multiple technical papers. The aim of the present article\nis to provide an accessible exposition of the mathematical background, together\nwith an illustration of the model's behavior. To this end, we include\nstep-by-step derivations of the model equations, point to important aspects in\nthe software implementation of those models, and use simulations to provide an\nintuitive understanding of the type of responses that can be generated and the\nrole that specific parameters play in the model. Furthermore, all code utilized\nfor our simulations is made publicly available alongside the manuscript to\nallow readers an easy hands-on experience with conductance-based DCM.\n"}
+{"abstract": "  Direct simulation of physical processes on a kinetic level is prohibitively\nexpensive in aerospace applications due to the extremely high dimension of the\nsolution spaces. In this paper, we consider the moment system of the Boltzmann\nequation, which projects the kinetic physics onto the hydrodynamic scale. The\nunclosed moment system can be solved in conjunction with the entropy closure\nstrategy. Using an entropy closure provides structural benefits to the physical\nsystem of partial differential equations. Usually computing such closure of the\nsystem spends the majority of the total computational cost, since one needs to\nsolve an ill-conditioned constrained optimization problem. Therefore, we build\na neural network surrogate model to close the moment system, which preserves\nthe structural properties of the system by design, but reduces the\ncomputational cost significantly. Numerical experiments are conducted to\nillustrate the performance of the current method in comparison to the\ntraditional closure.\n"}
+{"abstract": "  Document grounded generation is the task of using the information provided in\na document to improve text generation. This work focuses on two different\ndocument grounded generation tasks: Wikipedia Update Generation task and\nDialogue response generation. Our work introduces two novel adaptations of\nlarge scale pre-trained encoder-decoder models focusing on building context\ndriven representation of the document and enabling specific attention to the\ninformation in the document. Additionally, we provide a stronger BART baseline\nfor these tasks. Our proposed techniques outperform existing methods on both\nautomated (at least 48% increase in BLEU-4 points) and human evaluation for\ncloseness to reference and relevance to the document. Furthermore, we perform\ncomprehensive manual inspection of the generated output and categorize errors\nto provide insights into future directions in modeling these tasks.\n"}
+{"abstract": "  Physics-Informed Machine Learning (PIML) has gained momentum in the last 5\nyears with scientists and researchers aiming to utilize the benefits afforded\nby advances in machine learning, particularly in deep learning. With large\nscientific data sets with rich spatio-temporal data and high-performance\ncomputing providing large amounts of data to be inferred and interpreted, the\ntask of PIML is to ensure that these predictions, categorizations, and\ninferences are enforced by, and conform to the limits imposed by physical laws.\nIn this work a new approach to utilizing PIML is discussed that deals with the\nuse of physics-based loss functions. While typical usage of physical equations\nin the loss function requires complex layers of derivatives and other functions\nto ensure that the known governing equation is satisfied, here we show that a\nsimilar level of enforcement can be found by implementing more simpler loss\nfunctions on specific kinds of output data. The generalizability that this\napproach affords is shown using examples of simple mechanical models that can\nbe thought of as sufficiently simplified surrogate models for a wide class of\nproblems.\n"}
+{"abstract": "  We discover a new minimality property of the absolute minimisers of supremal\nfunctionals (also known as $L^\\infty$ Calculus of Variations problems).\n"}
+{"abstract": "  We present a method to tune the resonantly enhanced harmonic emission from\nengineered potentials, which would be experimentally feasible in the purview of\nthe recent advances in atomic and condensed matter physics. The recombination\nof the electron from the potential dependent excited state to the ground state\ncauses the emission of photons with a specific energy. The energy of the\nemitted photons can be controlled by appropriately tweaking the potential\nparameters. The resonant enhancement in high-harmonic generation enables the\nemission of very intense extreme ultra-violet or soft x-ray radiations. The\nscaling law of the resonant harmonic emission with the model parameter of the\npotential is also obtained by numerically solving the time-dependent\nSchr\\\"odinger equation in two dimensions.\n"}
+{"abstract": "  We prove equidistribution theorems for a family of holomorphic Siegel cusp\nforms of general degree in the level aspect. Our main contribution is to\nestimate unipotent contributions for general degree in the geometric side of\nArthur's invariant trace formula in terms of Shintani zeta functions. Several\napplications including the vertical Sato-Tate theorem and low-lying zeros for\nstandard $L$-functions of holomorphic Siegel cusp forms are discussed. We also\nshow that the ``non-genuine forms\" which come from non-trivial endoscopic\ncontributions by Langlands functoriality classified by Arthur are negligible.\n"}
+{"abstract": "  Image decomposition is a crucial subject in the field of image processing. It\ncan extract salient features from the source image. We propose a new image\ndecomposition method based on convolutional neural network. This method can be\napplied to many image processing tasks. In this paper, we apply the image\ndecomposition network to the image fusion task. We input infrared image and\nvisible light image and decompose them into three high-frequency feature images\nand a low-frequency feature image respectively. The two sets of feature images\nare fused using a specific fusion strategy to obtain fusion feature images.\nFinally, the feature images are reconstructed to obtain the fused image.\nCompared with the state-of-the-art fusion methods, this method has achieved\nbetter performance in both subjective and objective evaluation.\n"}
+{"abstract": "  Exascale computing systems will exhibit high degrees of hierarchical\nparallelism, with thousands of computing nodes and hundreds of cores per node.\n  Efficiently exploiting hierarchical parallelism is challenging due to load\nimbalance that arises at multiple levels.\n  OpenMP is the most widely-used standard for expressing and exploiting the\never-increasing node-level parallelism.\n  The scheduling options in OpenMP are insufficient to address the load\nimbalance that arises during the execution of multithreaded applications.\n  The limited scheduling options in OpenMP hinder research on novel scheduling\ntechniques which require comparison with others from the literature.\n  This work introduces LB4OMP, an open-source dynamic load balancing library\nthat implements successful scheduling algorithms from the literature.\n  LB4OMP is a research infrastructure designed to spur and support present and\nfuture scheduling research, for the benefit of multithreaded applications\nperformance.\n  Through an extensive performance analysis campaign, we assess the\neffectiveness and demystify the performance of all loop scheduling techniques\nin the library.\n  We show that, for numerous applications-systems pairs, the scheduling\ntechniques in LB4OMP outperform the scheduling options in OpenMP.\n  Node-level load balancing using LB4OMP leads to reduced cross-node load\nimbalance and to improved MPI+OpenMP applications performance, which is\ncritical for Exascale computing.\n"}
+{"abstract": "  We study the steady-state patterns of population of the coupled oscillators\nthat sync and swarm, where the interaction distances among oscillators have\nfinite-cutoff in interaction distance. We examine how the static patterns known\nin the infinite-cutoff are reproduced or deformed, and explore a new static\npattern that does not appear until a finite-cutoff is considered. All\nsteady-state patterns of the infinite-cutoff, static sync, static async, and\nstatic phase wave are respectively repeated in space for proper finite-cutoff\nranges. Their deformation in shape and density takes place for the other\nfinite-cutoff ranges. Bar-like phase wave states are observed, which has not\nbeen the case for the infinite-cutoff. All the patterns are investigated via\nnumerical and theoretical analysis.\n"}
+{"abstract": "  We consider linear parameter-dependent systems $A(\\mu) x(\\mu) = b$ for many\ndifferent $\\mu$, where $A$ is large and sparse, and depends nonlinearly on\n$\\mu$. Solving such systems individually for each $\\mu$ would require great\ncomputational effort. In this work we propose to compute a partial\nparameterization $\\tilde{x} \\approx x(\\mu)$ where $\\tilde{x}(\\mu)$ is cheap to\ncompute for many different $\\mu$. Our methods are based on the observation that\na companion linearization can be formed where the dependence on $\\mu$ is only\nlinear. In particular, we develop methods which combine the well-established\nKrylov subspace method for linear systems, GMRES, with algorithms for nonlinear\neigenvalue problems (NEPs) to generate a basis for the Krylov subspace. Within\nthis new approach, the basis matrix is constructed in three different ways,\nusing a tensor structure and exploiting that certain problems have low-rank\nproperties. We show convergence factor bounds obtained similarly to those for\nthe method GMRES for linear systems. More specifically, a bound is obtained\nbased on the magnitude of the parameter $\\mu$ and the spectrum of the linear\ncompanion matrix, which corresponds to the reciprocal solutions to the\ncorresponding NEP. Numerical experiments illustrate the competitiveness of our\nmethods for large-scale problems. The simulations are reproducible and publicly\navailable online.\n"}
+{"abstract": "  We prove a folklore conjecture concerning the sum-of-digits functions in\nbases two and three: there are infinitely many positive integers $n$ such that\nthe binary sum of digits of $n$ equals its ternary sum of digits.\n"}
+{"abstract": "  Context. Radiation-driven mass loss is key to our understanding of\nmassive-star evolution. However, for low-luminosity O-type stars there are big\ndiscrepancies between theoretically predicted and empirically derived mass-loss\nrates (called the weak-wind problem). Aims. We compute radiation-line-driven\nwind models of a typical weak-wind star to determine its temperature structure\nand the corresponding impact on ultra-violet (UV) line formation. Methods. We\ncarried out hydrodynamic simulations of the line-deshadowing instability (LDI)\nfor a weak-wind star in the Galaxy. Subsequently, we used this LDI model as\ninput in a short-characteristics radiative transfer code to compute synthetic\nUV line profiles. Results. We find that the line-driven weak wind is\nsignificantly shock heated to high temperatures and is unable to cool down\neffciently. This results in a complex temperature structure where more than\nhalf of the wind volume has temperatures significantly higher than the stellar\neffective temperature. Therefore, a substantial portion of the weak wind will\nbe more ionised, resulting in a reduction of the UV line opacity and therefore\nin weaker line profiles for a given mass-loss rate. Quantifying this, we find\nthat weak-wind mass-loss rates derived from unsaturated UV lines could be\nunderestimated by a factor of between 10 and 100 if the high-temperature gas is\nnot properly taken into account in the spectroscopic analysis. This offers a\ntentative basic explanation for the weak-wind problem: line-driven weak winds\nare not really weaker than theoretically expected, but rather a large portion\nof their wind volume is much hotter than the stellar effective temperature.\n"}
+{"abstract": "  In end-to-end optimized learned image compression, it is standard practice to\nuse a convolutional variational autoencoder with generalized divisive\nnormalization (GDN) to transform images into a latent space. Recently,\nOperational Neural Networks (ONNs) that learn the best non-linearity from a set\nof alternatives, and their self-organized variants, Self-ONNs, that approximate\nany non-linearity via Taylor series have been proposed to address the\nlimitations of convolutional layers and a fixed nonlinear activation. In this\npaper, we propose to replace the convolutional and GDN layers in the\nvariational autoencoder with self-organized operational layers, and propose a\nnovel self-organized variational autoencoder (Self-VAE) architecture that\nbenefits from stronger non-linearity. The experimental results demonstrate that\nthe proposed Self-VAE yields improvements in both rate-distortion performance\nand perceptual image quality.\n"}
+{"abstract": "  In wireless sensor networks (WSNs), designing a stable, low-power routing\nprotocol is a major challenge because successive changes in links or breakdowns\ndestabilize the network topology. Therefore, choosing the right route in this\ntype of network due to resource constraints and their operating environment is\none of the most important challenges in these networks. Therefore, the main\npurpose of these networks is to collect appropriate routing information about\nthe environment around the network sensors while observing the energy\nconsumption of the sensors. One of the important approaches to reduce energy\nconsumption in sensor networks is the use of the clustering technique, but in\nmost clustering methods, only the criterion of the amount of energy of the\ncluster or the distance of members to the cluster has been considered.\nTherefore, in this paper, a method is presented using the firefly algorithm and\nusing the four criteria of residual energy, noise rate, number of hops, and\ndistance. The proposed method called EM-FIREFLY is introduced which selects the\nbest cluster head with high attractiveness and based on the fitness function\nand transfers the data packets through these cluster head to the sink. The\nproposed method is evaluated with NS-2 simulator and compared with the\nalgorithm-PSO and optimal clustering methods. The evaluation results show the\nefficiency of the EM-FIREFLY method in maximum relative load and network\nlifetime criteria compared to other methods discussed in this article.\n"}
+{"abstract": "  Employing the time-dependent variational principle combined with the multiple\nDavydov $\\mathrm{D}_2$ Ansatz, we investigate Landau-Zener (LZ) transitions in\na qubit coupled to a photon mode with various initial photon states at zero\ntemperature. Thanks to the multiple Davydov trial states, exact photonic\ndynamics taking place in the course of the LZ transition is also studied\nefficiently. With the qubit driven by a linear external field and the photon\nmode initialized with Schr\\\"odinger-cat states, asymptotic behavior of the\ntransition probability beyond the rotating-wave approximation is uncovered for\na variety of initial states. Using a sinusoidal external driving field, we also\nexplore the photon-assisted dynamics of Landau-Zener-St\\\"{u}ckelberg-Majorana\ninterferometry. Transition pathways involving multiple energy levels are\nunveiled by analyzing the photon dynamics.\n"}
+{"abstract": "  In this paper, we propose a novel framework for multi-target multi-camera\ntracking (MTMCT) of vehicles based on metadata-aided re-identification\n(MA-ReID) and the trajectory-based camera link model (TCLM). Given a video\nsequence and the corresponding frame-by-frame vehicle detections, we first\naddress the isolated tracklets issue from single camera tracking (SCT) by the\nproposed traffic-aware single-camera tracking (TSCT). Then, after automatically\nconstructing the TCLM, we solve MTMCT by the MA-ReID. The TCLM is generated\nfrom camera topological configuration to obtain the spatial and temporal\ninformation to improve the performance of MTMCT by reducing the candidate\nsearch of ReID. We also use the temporal attention model to create more\ndiscriminative embeddings of trajectories from each camera to achieve robust\ndistance measures for vehicle ReID. Moreover, we train a metadata classifier\nfor MTMCT to obtain the metadata feature, which is concatenated with the\ntemporal attention based embeddings. Finally, the TCLM and hierarchical\nclustering are jointly applied for global ID assignment. The proposed method is\nevaluated on the CityFlow dataset, achieving IDF1 76.77%, which outperforms the\nstate-of-the-art MTMCT methods.\n"}
+{"abstract": "  We investigate latent-space scalability for multi-task collaborative\nintelligence, where one of the tasks is object detection and the other is input\nreconstruction. In our proposed approach, part of the latent space can be\nselectively decoded to support object detection while the remainder can be\ndecoded when input reconstruction is needed. Such an approach allows reduced\ncomputational resources when only object detection is required, and this can be\nachieved without reconstructing input pixels. By varying the scaling factors of\nvarious terms in the training loss function, the system can be trained to\nachieve various trade-offs between object detection accuracy and input\nreconstruction quality. Experiments are conducted to demonstrate the adjustable\nsystem performance on the two tasks compared to the relevant benchmarks.\n"}
+{"abstract": "  Observatory publications comprise the work of local astronomers from\nobservatories around the world and are traditionally exchanged between\nobservatories through libraries. However, large collections of observatory\npublications seem to be rare; or at the least rarely digitally described or\naccessible on the Internet. Notable examples to the contrary are the Woodman\nAstronomical Library at Wisconsin-Madison and the Dudley Observatory in\nLoudonville, New York both in the US. Due to the irregularities in receiving\nmaterial, the collections are generally often incomplete both with respect to\nthe observatories included as well as volumes. In order to assess the unique\nproperties of the collections, we summarize and compare observatories present\nin our own as well as the collections from the Woodman Library and the Dudley\nObservatory.\n"}
+{"abstract": "  We present a detailed comparison of several recent and new approaches to\nmultigrid solver algorithms suitable for the solution of 5d chiral fermion\nactions such as Domain Wall fermions in the Shamir formulation, and also for\nthe Partial Fraction and Continued Fraction overlap. Our focus is on the\nacceleration of gauge configuration sampling, and a compact nearest neighbour\nstencil is required to limit the calculational cost of obtaining a coarse\noperator. This necessitates the coarsening of a nearest neighbour operator to\npreserve sparsity in coarsened grids, unlike HDCG. We compare the approaches of\nHDCR and the Multigrid algorithm and also several new hybrid schemes. In this\nwork we introduce a new recursive Chebyshev polynomial based setup scheme. We\nfind that the HDCR approach, can both setup, and solve standard Shamir Domain\nWall Fermions faster than a single solve with red-black preconditioned\nConjugate Gradients on large volumes and for modern GPU systems such as the\nSummit supercomputer. This is promising for the acceleration of HMC,\nparticularly if setup costs are shared across multiple Hasenbusch determinant\nfactors. The setup scheme is likely generally applicable to other Fermion\nactions.\n"}
+{"abstract": "  Let $P,Q$ be longest paths in a simple graph. We analyze the possible\nconnections between the components of $P\\cup Q\\setminus (V(P)\\cap V(Q))$ and\nintroduce the notion of a bi-traceable graph. We use the results for all the\npossible configurations of the intersection points when $\\#V(P)\\cap V(Q)\\le 5$\nin order to prove that if the intersection of three longest paths $P,Q,R$ is\nempty, then $\\#(V(P)\\cap V(Q))\\ge 6$. We also prove Hippchen's conjecture for\n$k\\le 6$: If a graph $G$ is $k$-connected for $k\\le 6$, and $P$ and $Q$ are\nlongest paths in $G$, then $\\#(V(P)\\cap V(Q))\\ge 6$.\n"}
+{"abstract": "  This paper presents a framework for the design and analysis of an\n$\\mathcal{L}_1$ adaptive controller with a switching reference system. The use\nof a switching reference system allows the desired behavior to be scheduled\nacross the operating envelope, which is often required in aerospace\napplications. The analysis uses a switched reference system that assumes\nperfect knowledge of uncertainties and uses a corresponding non-adaptive\ncontroller. Provided that this switched reference system is stable, it is shown\nthat the closed-loop system with unknown parameters and disturbances and the\n$\\mathcal{L}_1$ adaptive controller can behave arbitrarily close to this\nreference system. Simulations of the short period dynamics of a transport class\naircraft during the approach phase illustrate the theoretical results.\n"}
+{"abstract": "  Music streaming services heavily rely on recommender systems to improve their\nusers' experience, by helping them navigate through a large musical catalog and\ndiscover new songs, albums or artists. However, recommending relevant and\npersonalized content to new users, with few to no interactions with the\ncatalog, is challenging. This is commonly referred to as the user cold start\nproblem. In this applied paper, we present the system recently deployed on the\nmusic streaming service Deezer to address this problem. The solution leverages\na semi-personalized recommendation strategy, based on a deep neural network\narchitecture and on a clustering of users from heterogeneous sources of\ninformation. We extensively show the practical impact of this system and its\neffectiveness at predicting the future musical preferences of cold start users\non Deezer, through both offline and online large-scale experiments. Besides, we\npublicly release our code as well as anonymized usage data from our\nexperiments. We hope that this release of industrial resources will benefit\nfuture research on user cold start recommendation.\n"}
+{"abstract": "  Government agencies always need to carefully consider potential risks of\ndisclosure whenever they publish statistics based on their data or give\nexternal researchers access to the collected data. For this reason, research on\ndisclosure avoiding techniques has a long tradition at statistical agencies. In\nthis context, the promise of formal privacy guarantees offered by concepts such\nas differential privacy seem to be the panacea enabling the agencies to exactly\nquantify and control the privacy loss incurred by any data release. Still,\ndespite the excitement in academia and industry, most agencies-with the\nprominent exception of the U.S. Census Bureau-have been reluctant to even\nconsider the concept for their data release strategy.\n  This paper aims to shed some light on potential reasons for this. We argue\nthat the requirements when implementing differential privacy approaches at\ngovernment agencies are often fundamentally different from the requirements in\nindustry. This raises many challenging problems and open questions that still\nneed to be addressed before the concept might be used as an overarching\nprinciple when sharing data with the public. The paper will not offer any\nsolutions to these challenges. Instead, we hope to stimulate some collaborative\nresearch efforts, as we believe that many of the problems can only be addressed\nby inter-disciplinary collaborations.\n"}
+{"abstract": "  To characterize entanglement of tripartite\n$\\mathbb{C}^d\\otimes\\mathbb{C}^d\\otimes\\mathbb{C}^d$ systems, we employ\nalgebraic-geometric tools that are invariants under Stochastic Local Operation\nand Classical Communication (SLOCC), namely $k$-secant varieties and\none-multilinear ranks. Indeed, by means of them, we present a classification of\ntripartite pure states in terms of a finite number of families and subfamilies.\nAt the core of it stands out a fine-structure grouping of three-qutrit\nentanglement.\n"}
+{"abstract": "  Electric currents carrying a net spin polarization are widely used in\nspintronics, whereas globally spin-neutral currents are expected to play no\nrole in spin-dependent phenomena. Here we show that, in contrast to this common\nexpectation, spin-independent conductance in compensated antiferromagnets and\nnormal metals can be efficiently exploited in spintronics, provided their\nmagnetic space group symmetry supports a non-spin-degenerate Fermi surface. Due\nto their momentum-dependent spin polarization, such antiferromagnets can be\nused as active elements in antiferromagnetic tunnel junctions (AFMTJs) and\nproduce a giant tunneling magnetoresistance (TMR) effect. Using RuO$_{2}$ as a\nrepresentative compensated antiferromagnet exhibiting spin-independent\nconductance along the [001] direction but a non-spin-degenerate Fermi surface,\nwe design a RuO$_{2}$/TiO$_{2}$/RuO$_{2}$ (001) AFMTJ, where a globally\nspin-neutral charge current is controlled by the relative orientation of the\nN\\'eel vectors of the two RuO$_{2}$ electrodes, resulting in the TMR effect as\nlarge as ~500%. These results are expanded to normal metals which can be used\nas a counter electrode in AFMTJs with a single antiferromagnetic layer or other\nelements in spintronic devices. Our work uncovers an unexplored potential of\nthe materials with no global spin polarization for utilizing them in\nspintronics.\n"}
+{"abstract": "  Predictive uncertainty estimation is an essential next step for the reliable\ndeployment of deep object detectors in safety-critical tasks. In this work, we\nfocus on estimating predictive distributions for bounding box regression output\nwith variance networks. We show that in the context of object detection,\ntraining variance networks with negative log likelihood (NLL) can lead to high\nentropy predictive distributions regardless of the correctness of the output\nmean. We propose to use the energy score as a non-local proper scoring rule and\nfind that when used for training, the energy score leads to better calibrated\nand lower entropy predictive distributions than NLL. We also address the\nwidespread use of non-proper scoring metrics for evaluating predictive\ndistributions from deep object detectors by proposing an alternate evaluation\napproach founded on proper scoring rules. Using the proposed evaluation tools,\nwe show that although variance networks can be used to produce high quality\npredictive distributions, ad-hoc approaches used by seminal object detectors\nfor choosing regression targets during training do not provide wide enough data\nsupport for reliable variance learning. We hope that our work helps shift\nevaluation in probabilistic object detection to better align with predictive\nuncertainty evaluation in other machine learning domains. Code for all models,\nevaluation, and datasets is available at:\nhttps://github.com/asharakeh/probdet.git.\n"}
+{"abstract": "  Numerous missions planned for the next decade are likely to target a handful\nof smal sites of interest on the Moon's surface, creating risks of crowding and\ninterference at these locations. The Moon presents finite and scarce areas with\nrare topography or concentrations of resources of special value. Locations of\ninterest to science, notably for astronomy, include the Peaks of Eternal Light,\nthe coldest of the cold traps and smooth areas on the far side. Regions richest\nin physical resources could also be uniquely suited to settlement and commerce.\nSuch sites of interest are both few and small. Typically, there are fewer than\nten key sites of each type, each site spanning a few kilometres across. We\nsurvey the implications for different kins of mission and find that the diverse\nactors pursuing incomptible ends at these sites could soon crowd and interfere\nwith each other, leaving almost all actors worse off. Without proactive\nmeasures to prevent these outcomes, lunar actors are likely to experience\nsignificant losses of opportunity. We highlight the legal, policy, and ethical\nramifications. Insights from research on comparable sites on Earth present a\npath toward managing lunar crowding and interference grounded in ethical and\npractical near-term considerations. This article is part of a discussion\nmeeting issue 'Astronomy from the Moon: the next decades'.\n"}
+{"abstract": "  To improve online search results, clarification questions can be used to\nelucidate the information need of the user. This research aims to predict the\nuser engagement with the clarification pane as an indicator of relevance based\non the lexical information: query, question, and answers. Subsequently, the\npredicted user engagement can be used as a feature to rank the clarification\npanes. Regression and classification are applied for predicting user engagement\nand compared to naive heuristic baselines (e.g. mean) on the new MIMICS dataset\n[20]. An ablation study is carried out using a RankNet model to determine\nwhether the predicted user engagement improves clarification pane ranking\nperformance. The prediction models were able to improve significantly upon the\nnaive baselines, and the predicted user engagement feature significantly\nimproved the RankNet results in terms of NDCG and MRR. This research\ndemonstrates the potential for ranking clarification panes based on lexical\ninformation only and can serve as a first neural baseline for future research\nto improve on. The code is available online.\n"}
+{"abstract": "  The NLP community has witnessed steep progress in a variety of tasks across\nthe realms of monolingual and multilingual language processing recently. These\nsuccesses, in conjunction with the proliferating mixed language interactions on\nsocial media have boosted interest in modeling code-mixed texts. In this work,\nwe present CodemixedNLP, an open-source library with the goals of bringing\ntogether the advances in code-mixed NLP and opening it up to a wider machine\nlearning community. The library consists of tools to develop and benchmark\nversatile model architectures that are tailored for mixed texts, methods to\nexpand training sets, techniques to quantify mixing styles, and fine-tuned\nstate-of-the-art models for 7 tasks in Hinglish. We believe this work has a\npotential to foster a distributed yet collaborative and sustainable ecosystem\nin an otherwise dispersed space of code-mixing research. The toolkit is\ndesigned to be simple, easily extensible, and resourceful to both researchers\nas well as practitioners.\n"}
+{"abstract": "  In the work we use integral formulas for calculating the monodromy data for\nthe Painlev\\'e-2 equation. The perturbation theory for the auxiliary linear\nsystem is constructed and formulas for the variation of the monodromy data are\nobtained. We also derive a formula for solving the linearized Painlev\\'e-2\nequation based on the Fourier-type integral of the squared solutions of the\nauxiliary linear system of equations.\n"}
+{"abstract": "  A liquid droplet impacting on a solvophobic surface normally rebounds. The\nrebound is suppressed by a small amount of dissolved polymer. In this work,\nusing multi-body dissipative particle dynamics simulations, two anti-rebound\nmechanisms, the slow-retraction and the slow-hopping mechanisms, are\nidentified. Which of them dominates depends on the polymer-surface attraction\nstrength. However, these two mechanisms are not excluding each other but may\ncoexist. During the droplet rebound, the surface-adsorbed polymer acts in two\nways: the adsorbed beads mediate solvent-surface interactions, and highly\nstretching unadsorbed polymer segment exerts a retraction force on the liquid.\nBoth actions increase the friction against retraction and the resistance\nagainst hopping. We also investigate the effects of the molecular weight and\nthe concentration of the polymer additive, the droplet size, and the impact\nvelocity on the rebound tendency. As the first work to provide a microscopic\nexplanation of the anti-rebound mechanism by polymer additives, this study\nallows better understanding of wetting behavior by polymer-solution droplets.\n"}
+{"abstract": "  The successful deployment of artificial intelligence (AI) in many domains\nfrom healthcare to hiring requires their responsible use, particularly in model\nexplanations and privacy. Explainable artificial intelligence (XAI) provides\nmore information to help users to understand model decisions, yet this\nadditional knowledge exposes additional risks for privacy attacks. Hence,\nproviding explanation harms privacy. We study this risk for image-based model\ninversion attacks and identified several attack architectures with increasing\nperformance to reconstruct private image data from model explanations. We have\ndeveloped several multi-modal transposed CNN architectures that achieve\nsignificantly higher inversion performance than using the target model\nprediction only. These XAI-aware inversion models were designed to exploit the\nspatial knowledge in image explanations. To understand which explanations have\nhigher privacy risk, we analyzed how various explanation types and factors\ninfluence inversion performance. In spite of some models not providing\nexplanations, we further demonstrate increased inversion performance even for\nnon-explainable target models by exploiting explanations of surrogate models\nthrough attention transfer. This method first inverts an explanation from the\ntarget prediction, then reconstructs the target image. These threats highlight\nthe urgent and significant privacy risks of explanations and calls attention\nfor new privacy preservation techniques that balance the dual-requirement for\nAI explainability and privacy.\n"}
+{"abstract": "  This paper explores supply chain viability through empirical network-level\nanalysis of supplier reachability under various scenarios. Specifically, this\nstudy investigates the effect of multi-tier random failures across different\nscales, as well as intelligent attacks on the global supply chain of medical\nequipment, an industry whose supply chain's viability was put under a crucial\ntest during the COVID-19 pandemic. The global supply chain data was mined and\nanalyzed from about 45,000 firms with about 115,000 intertwined relationships\nspanning across 10 tiers of the backward supply chain of medical equipment.\nThis complex supply chain network was analyzed at four scales, namely: firm,\ncountry-industry, industry, and country. A notable contribution of this study\nis the application of a supply chain tier optimization tool to identify the\nlowest tier of the supply chain that can provide adequate resolution for the\nstudy of the supply chain pattern in the medical equipment sector. We also\ndeveloped data-driven-tools to identify the thresholds for the breakdown and\nfragmentation of the medical equipment supply chain when faced with random\nfailures, or different intelligent attack scenarios. The novel network analysis\ntools utilized in the study can be applied to the study of supply chain\nreachability and viability in other industries.\n"}
+{"abstract": "  We revisit the dynamic relationship between stock market and domestic\neconomic policy uncertainty (EPU) with the symmetric thermal optimal path\n(TOPS) method. We observe totally different interaction pattern in emerging and\ndeveloped markets. Economic policy uncertainty can drive stock market in China,\nwhile stock market plays a leading role in the UK and the US. Meanwhile, the\nlead-lag relationship of the three countries react significantly when extreme\nevents happen. Our findings have important implications for investors and\npolicy makers.\n"}
+{"abstract": "  We provide necessary and sufficient conditions for generic n-qubit states to\nbe equivalent under Stochastic Local Operations with Classical Communication\n(SLOCC) using a single polynomial entanglement measure. SLOCC operations may be\nrepresented geometrically by M\\\"obius transformations on the roots of the\nentanglement measure on the Bloch sphere. Moreover, we show how the roots of\nthe 3-tangle measure classify 4-qubit generic states and propose a method to\nobtain the normal form of a 4-qubit state which bypasses the possibly infinite\niterative procedure.\n"}
+{"abstract": "  One of the leading single-channel speech separation (SS) models is based on a\nTasNet with a dual-path segmentation technique, where the size of each segment\nremains unchanged throughout all layers. In contrast, our key finding is that\nmulti-granularity features are essential for enhancing contextual modeling and\ncomputational efficiency. We introduce a self-attentive network with a novel\nsandglass-shape, namely Sandglasset, which advances the state-of-the-art (SOTA)\nSS performance at significantly smaller model size and computational cost.\nForward along each block inside Sandglasset, the temporal granularity of the\nfeatures gradually becomes coarser until reaching half of the network blocks,\nand then successively turns finer towards the raw signal level. We also unfold\nthat residual connections between features with the same granularity are\ncritical for preserving information after passing through the bottleneck layer.\nExperiments show our Sandglasset with only 2.3M parameters has achieved the\nbest results on two benchmark SS datasets -- WSJ0-2mix and WSJ0-3mix, where the\nSI-SNRi scores have been improved by absolute 0.8 dB and 2.4 dB, respectively,\ncomparing to the prior SOTA results.\n"}
+{"abstract": "  The anomalous magnetic and electric dipole moments in spin motion equation\nacquire pseudoscalar corrections if the $T(CP)$-noninvariance is admitted. It\nallows to explain the discrepancy between experimental and theoretical values\nof muon $(g-2)$ factor under assumption that the pseudoscalar correction is the\ndominant source of this discrepancy.\n"}
+{"abstract": "  To be able to predict a molecular graph structure ($W$) given a 2D image of a\nchemical compound ($U$) is a challenging problem in machine learning. We are\ninterested to learn $f: U \\rightarrow W$ where we have a fully mediating\nrepresentation $V$ such that $f$ factors into $U \\rightarrow V \\rightarrow W$.\nHowever, observing V requires detailed and expensive labels. We propose graph\naligning approach that generates rich or detailed labels given normal labels\n$W$. In this paper we investigate the scenario of domain adaptation from the\nsource domain where we have access to the expensive labels $V$ to the target\ndomain where only normal labels W are available. Focusing on the problem of\npredicting chemical compound graphs from 2D images the fully mediating layer is\nrepresented using the planar embedding of the chemical graph structure we are\npredicting. The use of a fully mediating layer implies some assumptions on the\nmechanism of the underlying process. However if the assumptions are correct it\nshould allow the machine learning model to be more interpretable, generalize\nbetter and be more data efficient at training time. The empirical results show\nthat, using only 4000 data points, we obtain up to 4x improvement of\nperformance after domain adaptation to target domain compared to pretrained\nmodel only on the source domain. After domain adaptation, the model is even\nable to detect atom types that were never seen in the original source domain.\nFinally, on the Maybridge data set the proposed self-labeling approach reached\nhigher performance than the current state of the art.\n"}
+{"abstract": "  The paper is an introduction to intuitionistic mathematics.\n"}
+{"abstract": "  As machine learning black boxes are increasingly being deployed in critical\ndomains such as healthcare and criminal justice, there has been a growing\nemphasis on developing techniques for explaining these black boxes in a post\nhoc manner. In this work, we analyze two popular post hoc interpretation\ntechniques: SmoothGrad which is a gradient based method, and a variant of LIME\nwhich is a perturbation based method. More specifically, we derive explicit\nclosed form expressions for the explanations output by these two methods and\nshow that they both converge to the same explanation in expectation, i.e., when\nthe number of perturbed samples used by these methods is large. We then\nleverage this connection to establish other desirable properties, such as\nrobustness, for these techniques. We also derive finite sample complexity\nbounds for the number of perturbations required for these methods to converge\nto their expected explanation. Finally, we empirically validate our theory\nusing extensive experimentation on both synthetic and real world datasets.\n"}
+{"abstract": "  The purpose of this note is twofold: firstly to characterize all the\nsequences of orthogonal polynomials $(P_n)_{n\\geq 0}$ such that $$\n\\frac{\\triangle}{{\\bf \\triangle} x(s-1/2)}P_{n+1}(x(s-1/2))=c_n(\\triangle\n+2\\,\\mathrm{I})P_n(x(s-1/2)), $$ where $\\mathrm{I}$ is the identity operator,\n$x$ defines a class of lattices with, generally, nonuniform step-size, and\n$\\triangle f(s)=f(s+1)-f(s)$; and secondly to present, in a friendly way, a\nmethod to deal with these kind of problems.\n"}
+{"abstract": "  The addition of an external starshade to the {\\it Nancy Grace Roman Space\nTelescope} will enable the direct imaging of Earth-radius planets orbiting at\n$\\sim$1 AU. Classification of any detected planets as Earth-like requires both\nspectroscopy to characterize their atmospheres and multi-epoch imaging to trace\ntheir orbits. We consider here the ability of the Starshade Rendezvous Probe to\nconstrain the orbits of directly imaged Earth-like planets. The target list for\nthis proposed mission consists of the 16 nearby stars best suited for direct\nimaging. The field of regard for a starshade mission is constrained by solar\nexclusion angles, resulting in four observing windows during a two-year\nmission. We find that for habitable-zone planetary orbits that are detected at\nleast three times during the four viewing opportunities, their semi-major axes\nare measured with a median precision of 7 mas, or a median fractional precision\nof 3\\%. Habitable-zone planets can be correctly identified as such 96.7\\% of\nthe time, with a false positive rate of 2.8\\%. If a more conservative criteria\nis used for habitable-zone classification (95\\% probability), the false\npositive rate drops close to zero, but with only 81\\% of the truly Earth-like\nplanets correctly classified as residing in the habitable zone.\n"}
+{"abstract": "  We consider the incompressible Euler equations in $R^2$ when the initial\nvorticity is bounded, radially symmetric and non-increasing in the radial\ndirection. Such a radial distribution is stationary, and we show that the\nmonotonicity produces stability in some weighted norm related to the angular\nimpulse. For instance, it covers the cases of circular vortex patches and\nGaussian distributions. Our stability does not depend on $L^\\infty$-bound or\nsupport size of perturbations. The proof is based on the fact that such a\nradial monotone distribution minimizes the impulse of functions having the same\nlevel set measure.\n"}
+{"abstract": "  This paper presents a novel method for clustering surfaces. The proposal\ninvolves first using basis functions in a tensor product to smooth the data and\nthus reduce the dimension to a finite number of coefficients, and then using\nthese estimated coefficients to cluster the surfaces via the k-means algorithm.\nAn extension of the algorithm to clustering tensors is also discussed. We show\nthat the proposed algorithm exhibits the property of strong consistency, with\nor without measurement errors, in correctly clustering the data as the sample\nsize increases. Simulation studies suggest that the proposed method outperforms\nthe benchmark k-means algorithm which uses the original vectorized data. In\naddition, an EGG real data example is considered to illustrate the practical\napplication of the proposal.\n"}
+{"abstract": "  Acoustic transparency is the capability of a medium to transmit mechanical\nwaves to adjacent media, without scattering. This characteristic can be\nachieved by carefully engineering the acoustic impedance of the medium -- a\ncombination of wave speed and density, to match that of the surroundings. Owing\nto the strong correlation between acoustic wave speed and static stiffness, it\nis challenging to design acoustically transparent materials in a fluid, while\nmaintaining their high structural rigidity. In this work, we propose a method\nto design architected lattices with independent control of the elastic wave\nspeed at a chosen frequency, the mass density, and the static stiffness, along\na chosen loading direction. We provide a sensitivity analysis to optimize these\nproperties with respect to design parameters of the structure, that include\nlocalized masses at specific positions. We demonstrate the method on five\ndifferent periodic, three dimensional lattices, to calculate bounds on the\nlongitudinal wave speed as a function of their density and stiffness. We then\nperform experiments on 3-D printed structures, to validate our numerical\nsimulations. The tools developed in this work can be used to design lightweight\nand stiff materials with optimized acoustic impedance for a plethora of\napplications, including ultrasound imaging, wave filtering and waveguiding.\n"}
+{"abstract": "  We present a short review of possible applications of the Wheeler-De Witt\nequation to cosmological models based on the low-energy string effective\naction, and characterised by an initial regime of asymptotically flat, low\nenergy, weak coupling evolution. Considering in particular a class of\nduality-related (but classically disconnected) background solutions, we shall\ndiscuss the possibility of quantum transitions between the phases of pre-big\nbang and post-big bang evolution. We will show that it is possible, in such a\ncontext, to represent the birth of our Universe as a quantum process of\ntunneling or \"anti-tunneling\" from an initial state asymptotically approaching\nthe string perturbative vacuum.\n"}
+{"abstract": "  Finite-difference (FD) modeling of seismic waves in the vicinity of dipping\ninterfaces gives rise to artifacts. Examples are phase and amplitude errors, as\nwell as staircase diffractions. Such errors can be reduced in two general ways.\nIn the first approach, the interface can be anti-aliased (i.e., with an\nanti-aliased step-function, or a lowpass filter). Alternatively, the interface\nmay be replaced with an equivalent medium (i.e., using Schoenberg \\& Muir (SM)\ncalculus or orthorhombic averaging). We test these strategies in acoustic,\nelastic isotropic, and elastic anisotropic settings. Computed FD solutions are\ncompared to analytical solutions. We find that in acoustic media, anti-aliasing\nmethods lead to the smallest errors. Conversely, in elastic media, the SM\ncalculus provides the best accuracy. The downside of the SM calculus is that it\nrequires an anisotropic FD solver even to model an interface between two\nisotropic materials. As a result, the computational cost increases compared to\nwhen using isotropic FD solvers. However, since coarser grid spacings can be\nused to represent the dipping interfaces, the two effects (an expensive FD\nsolver on a coarser FD grid) equal out. Hence, the SM calculus can provide an\nefficient means to reduce errors, also in elastic isotropic media.\n"}
+{"abstract": "  The singlemode condition is one of the most important design rules for\noptical waveguides in guided-wave optics. The reason following the singlemode\ncondition is that higher-order modes might be excited and thus introduce some\nundesired mode-mismatching loss as well as inter-mode crosstalk when light\npropagates along an optical waveguide beyond the singlemode regime. As a\nresult, multimode photonic waveguides are usually not allowed. In this paper,\nwe propose the concept of silicon photonics beyond the singlemode regime,\ndeveloped with low-loss and low-crosstalk light propagation in multimode\nphotonic waveguides with broadened silicon cores. In particular, silicon\nphotonic waveguides with a broadened core region have shown an ultra-low-loss\nof ~0.1 dB/cm for the fundamental mode even without any special fabrication\nprocess. A micro-racetrack resonator fabricated with standard 220-nm-SOI\nMPW-foundry processes shows a record intrinsic Q-factor as high as 1.02*107 for\nthe first time, corresponding to ultra-low waveguide propagation loss of only\n0.065 dB/cm. A high-performance microwave photonic filter on silicon is then\nrealized with an ultra-narrow 3-dB bandwidth of 20.6 MHz as well as a tuning\nrange of ~20 GHz for the first time. An on-chip 100-cm-long delayline is also\ndemonstrated by using the present broadened SOI photonic waveguides with\ncompact Euler-curve bends, the measured propagation loss is ~0.14 dB/cm. The\nproposed concept of silicon photonics beyond the singlemode regime helps solve\nthe issue of high propagation loss and also significantly reduces the random\nphase errors of light due to the random variations of waveguide dimensions. In\nparticularity it enables silicon photonic devices with enhanced performances,\nwhich paves the way for new-generation silicon photonics realizing the\nlarge-scale photonic integration.\n"}
+{"abstract": "  Useful materials must satisfy multiple objectives, where the optimization of\none objective is often at the expense of another. The Pareto front reports the\noptimal trade-offs between competing objectives. Here we report a self-driving\nlaboratory, \"Ada\", that defines the Pareto front of conductivities and\nprocessing temperatures for palladium films formed by combustion synthesis. Ada\nidentified previously untested combustion synthesis conditions that resulted in\nthe discovery of lower processing temperatures (below 200 {\\deg}C) relative to\nthe prior art for this technique (250 {\\deg}C), a temperature difference that\nmakes the coating of different commodity plastic materials possible (e.g.,\nNafion, polyethersulfone). These conditions enabled us to use combustion\nsynthesis to spray coat uniform palladium films with moderate conductivity (1.1\n$\\times$ 10$^5$ S m$^{-1}$) at 191 {\\deg}C. Spray coating at 226 {\\deg}C\nyielded films with conductivities (2.0 $\\times$ 10$^6$ S m$^{-1}$) comparable\nto those of sputtered films (2.0 to 5.8 $\\times$ 10$^6$ S m$^{-1}$). This work\nshows how self-driving laboratories can discover materials satisfying multiple\nobjectives.\n"}
+{"abstract": "  Observations suggest that satellite quenching plays a major role in the\nbuild-up of passive, low-mass galaxies at late cosmic times. Studies of\nlow-mass satellites, however, are limited by the ability to robustly\ncharacterize the local environment and star-formation activity of faint\nsystems. In an effort to overcome the limitations of existing data sets, we\nutilize deep photometry in Stripe 82 of the Sloan Digital Sky Survey, in\nconjunction with a neural network classification scheme, to study the\nsuppression of star formation in low-mass satellite galaxies in the local\nUniverse. Using a statistically-driven approach, we are able to push beyond the\nlimits of existing spectroscopic data sets, measuring the satellite quenched\nfraction down to satellite stellar masses of ${\\sim}10^7~{\\rm M}_{\\odot}$ in\ngroup environments (${M}_{\\rm{halo}} = 10^{13-14}~h^{-1}~{\\rm M}_{\\odot}$). At\nhigh satellite stellar masses ($\\gtrsim 10^{10}~{\\rm M}_{\\odot}$), our analysis\nsuccessfully reproduces existing measurements of the quenched fraction based on\nspectroscopic samples. Pushing to lower masses, we find that the fraction of\npassive satellites increases, potentially signaling a change in the dominant\nquenching mechanism at ${M}_{\\star} \\sim 10^{9}~{\\rm M}_{\\odot}$. Similar to\nthe results of previous studies of the Local Group, this increase in the\nquenched fraction at low satellite masses may correspond to an increase in the\nefficacy of ram-pressure stripping as a quenching mechanism in groups.\n"}
+{"abstract": "  We are concerned with the global bifurcation analysis of positive solutions\nto free boundary problems arising in plasma physics. We show that in general,\nin the sense of domain variations, the following alternative holds: either the\nshape of the branch of solutions resembles the monotone one of the model case\nof the two-dimensional disk, or it is a continuous simple curve without\nbifurcation points which ends up at a point where the boundary density\nvanishes. On the other hand, we deduce a general criterion ensuring the\nexistence of a free boundary in the interior of the domain. Application to a\nclassic nonlinear eigenvalue problem is also discussed.\n"}
+{"abstract": "  Flexible optical network is a promising technology to accommodate\nhigh-capacity demands in next-generation networks. To ensure uninterrupted\ncommunication, existing lightpath provisioning schemes are mainly done with the\nassumption of worst-case resource under-provisioning and fixed channel spacing,\nwhich preserves an excessive signal-to-noise ratio (SNR) margin. However, under\na resource over-provisioning scenario, the excessive SNR margin restricts the\ntransmission bit-rate or transmission reach, leading to physical layer resource\nwaste and stranded transmission capacity. To tackle this challenging problem,\nwe leverage an iterative feedback tuning algorithm to provide a just-enough SNR\nmargin, so as to maximize the network throughput. Specifically, the proposed\nalgorithm is implemented in three steps. First, starting from the high SNR\nmargin setup, we establish an integer linear programming model as well as a\nheuristic algorithm to maximize the network throughput by solving the problem\nof routing, modulation format, forward error correction, baud-rate selection,\nand spectrum assignment. Second, we optimize the channel spacing of the\nlightpaths obtained from the previous step, thereby increasing the available\nphysical layer resources. Finally, we iteratively reduce the SNR margin of each\nlightpath until the network throughput cannot be increased. Through numerical\nsimulations, we confirm the throughput improvement in different networks and\nwith different baud-rates. In particular, we find that our algorithm enables\nover 20\\% relative gain when network resource is over-provisioned, compared to\nthe traditional method preserving an excessive SNR margin.\n"}
+{"abstract": "  We prove limit equalities between the sharp constants in weighted\nNikolskii-type inequalities for multivariate polynomials on an $m$-dimensional\ncube and ball and the corresponding constants for entire functions of\nexponential type.\n"}
+{"abstract": "  Developing the flocking behavior for a dynamic squad of fixed-wing UAVs is\nstill a challenge due to kinematic complexity and environmental uncertainty. In\nthis paper, we deal with the decentralized flocking and collision avoidance\nproblem through deep reinforcement learning (DRL). Specifically, we formulate a\ndecentralized DRL-based decision making framework from the perspective of every\nfollower, where a collision avoidance mechanism is integrated into the flocking\ncontroller. Then, we propose a novel reinforcement learning algorithm PS-CACER\nfor training a shared control policy for all the followers. Besides, we design\na plug-n-play embedding module based on convolutional neural networks and the\nattention mechanism. As a result, the variable-length system state can be\nencoded into a fixed-length embedding vector, which makes the learned DRL\npolicy independent with the number and the order of followers. Finally,\nnumerical simulation results demonstrate the effectiveness of the proposed\nmethod, and the learned policies can be directly transferred to semi-physical\nsimulation without any parameter finetuning.\n"}
+{"abstract": "  Motivated by the recent advances in the categorification of the cluster\nstructure on the coordinate rings of Grassmannians of $k$-subspaces in\n$n$-space, we investigate a particular construction of root systems of type\n$\\mathsf{T}_{2,p,q}$, including the type $\\mathsf{E}_n$. This construction\ngeneralizes Manin's ``hyperbolic construction'' of $\\mathsf{E}_8$ and reveals a\nlot of otherwise hidden regularities in this family of root systems.\n"}
+{"abstract": "  We study the evolutionary dynamics of a phenotypically structured population\nin a changing environment , where the environmental conditions vary with a\nlinear trend but in an oscillatory manner. Such phenomena can be described by\nparabolic Lotka-Volterra type equations with non-local competition and a time\ndependent growth rate. We first study the long time behavior of the solution to\nthis problem. Next, using an approach based on Hamilton-Jacobi equations we\nstudy asymptotically such long time solutions when the effects of the mutations\nare small. We prove that, as the effect of the mutations vanishes, the\nphenotypic density of the population concentrates on a single trait which\nvaries linearly with time, while the size of the population oscillates\nperiodically. In contrast with the case of an environment without linear shift,\nsuch dominant trait does not have the maximal growth rate in the averaged\nenvironment and there is a cost on the growth rate due to the climate shift. We\nalso provide an asymptotic expansion for the average size of the population and\nfor the critical speed above which the population goes extinct, which is\nclosely related to the derivation of an asymptotic expansion for the Floquet\neigenvalue in terms of the diffusion rate. By mean of a biological example,\nthis expansion allows to show that the fluctuations on the environment may help\nthe population to follow the climatic shift in a better way.\n"}
+{"abstract": "  The paper was suggested by a brief note of the second author about the\napplication of the Hubbert curve to predict decay of resource exploitation. A\nfurther suggestion came from the interpretation of the Hubbert curve in terms\nof a specific Lotka Volterra (LV) equation. The link with population dynamics\nwas obvious as logistic function and LV equation were proposed within the\ndemography science field. Mathematical population dynamics has a history of\nabout two centuries. The first principle and model of population dynamics can\nbe regarded the exponential law of Malthus. In the XIX century, the Malthusian\ndemographic model was first refined to include mortality rate by Gompertz. In\nthe early XIX century the model was further refined by Verhulst by introducing\nthe standard logistic function. The previous models only concern the population\nof a single species. In the early XX century, the American demographer Lotka\nand the Italian mathematician Volterra proposed a pair of state equations which\ndescribe the population dynamics of two competing species, the predator and the\nprey. This paper is concerned with the single and two-species fundamental\nequations: the logistic and LV equation. The paper starts with the generalized\nlogistic equation whose free response is derived together with equilibrium\npoints and stability properties. The parameter estimation of the logistic\nfunction is applied to the raw data of the US crude oil production. The paper\nproceeds with the Lotka Volterra equation of the competition between two\nspecies, with the goal of applying it to resource exploitation. At the end, a\nlimiting version of the LV equation is studied since it describes a competition\nmodel between the production rate of exploited resources and the relevant\ncapital stock employed in the exploitation.\n"}
+{"abstract": "  We conduct spectral observations of 138 superthin galaxies (STGs) with high\nradial-to-vertical stellar disk scales ratio with the Dual Imaging Spectrograph\n(DIS) on the 3.5m telescope at the Apache Point Observatory (APO) to obtain the\nionized gas rotation curves with R ~ 5000 resolution. We also performed near\ninfrared (NIR) H and Ks photometry for 18 galaxies with the NICFPS camera on\nthe 3.5m telescope. The spectra, the NIR photometry and published optical and\nNIR photometry are used for modeling that utilizes the thickness of the stellar\ndisk and rotation curves simultaneously. The projection and dust extinction\neffects are taken into account. We evaluate eight models that differ by their\nfree parameters and constraints. As a result, we estimated masses and scale\nlengths of the galactic dark halos. We find systematic differences between the\nproperties of our red and blue STGs. The blue STGs have a large fraction of\ndynamically under-evolved galaxies whose vertical velocity dispersion is low in\nboth gas and stellar disks. The dark halo-to-disk scale ratio is shorter in the\nred STGs than in the blue ones, but in a majority of all STGs this ratio is\nunder 2. The optical color $(r-i)$ of the superthin galaxies correlates with\ntheir rotation curve maximum, vertical velocity dispersion in stellar disks,\nand mass of the dark halo. We conclude that there is a threshold central\nsurface density of 50 $M_{\\odot}$\\,pc$^{-2}$ below which we do not observe very\nthin, rotationally supported galactic disks.\n"}
+{"abstract": "  UV radiation has been used as a disinfection strategy to deactivate a wide\nrange of pathogens, but existing irradiation strategies do not ensure\nsufficient exposure of all environmental surfaces and/or require long\ndisinfection times. We present a near-optimal coverage planner for mobile UV\ndisinfection robots. The formulation optimizes the irradiation time efficiency,\nwhile ensuring that a sufficient dosage of radiation is received by each\nsurface. The trajectory and dosage plan are optimized taking collision and\nlight occlusion constraints into account. We propose a two-stage scheme to\napproximate the solution of the induced NP-hard optimization, and, for\nefficiency, perform key irradiance and occlusion calculations on a GPU.\nEmpirical results show that our technique achieves more coverage for the same\nexposure time as strategies for existing UV robots, can be used to compare UV\nrobot designs, and produces near-optimal plans. This is an extended version of\nthe paper originally contributed to ICRA2021.\n"}
+{"abstract": "  A good joint training framework is very helpful to improve the performances\nof weakly supervised audio tagging (AT) and acoustic event detection (AED)\nsimultaneously. In this study, we propose three methods to improve the best\nteacher-student framework of DCASE2019 Task 4 for both AT and AED tasks. A\nframe-level target-events based deep feature distillation is first proposed, it\naims to leverage the potential of limited strong-labeled data in weakly\nsupervised framework to learn better intermediate feature maps. Then we propose\nan adaptive focal loss and two-stage training strategy to enable an effective\nand more accurate model training, in which the contribution of\ndifficult-to-classify and easy-to-classify acoustic events to the total cost\nfunction can be automatically adjusted. Furthermore, an event-specific post\nprocessing is designed to improve the prediction of target event time-stamps.\nOur experiments are performed on the public DCASE2019 Task4 dataset, and\nresults show that our approach achieves competitive performances in both AT\n(49.8% F1-score) and AED (81.2% F1-score) tasks.\n"}
+{"abstract": "  We present a novel large-scale dataset and accompanying machine learning\nmodels aimed at providing a detailed understanding of the interplay between\nvisual content, its emotional effect, and explanations for the latter in\nlanguage. In contrast to most existing annotation datasets in computer vision,\nwe focus on the affective experience triggered by visual artworks and ask the\nannotators to indicate the dominant emotion they feel for a given image and,\ncrucially, to also provide a grounded verbal explanation for their emotion\nchoice. As we demonstrate below, this leads to a rich set of signals for both\nthe objective content and the affective impact of an image, creating\nassociations with abstract concepts (e.g., \"freedom\" or \"love\"), or references\nthat go beyond what is directly visible, including visual similes and\nmetaphors, or subjective references to personal experiences. We focus on visual\nart (e.g., paintings, artistic photographs) as it is a prime example of imagery\ncreated to elicit emotional responses from its viewers. Our dataset, termed\nArtEmis, contains 439K emotion attributions and explanations from humans, on\n81K artworks from WikiArt. Building on this data, we train and demonstrate a\nseries of captioning systems capable of expressing and explaining emotions from\nvisual stimuli. Remarkably, the captions produced by these systems often\nsucceed in reflecting the semantic and abstract content of the image, going\nwell beyond systems trained on existing datasets. The collected dataset and\ndeveloped methods are available at https://artemisdataset.org.\n"}
+{"abstract": "  Finding the mean square averages of the Dirichlet $L$-functions over\nDirichlet characters $\\chi$ of same parity is an active problem in number\ntheory. Here we explicitly evaluate such averages of $L(3,\\chi)$ and\n$L(4,\\chi)$ using certain trigonometric sums and Bernoulli polynomials and\nexpress them in terms of the Euler totient function $\\phi$ and the Jordan\ntotient function $J_s$.\n"}
+{"abstract": "  Direct measurements of three-dimensional magnetic fields in the interstellar\nmedium (ISM) are not achievable. However, the anisotropic nature of\nmagnetohydrodynamic (MHD) turbulence provides a novel way of tracing the\nmagnetic fields. Guided by the advanced understanding of turbulence's\nanisotropy in the Position-Position-Velocity (PPV) space, we extend the\nStructure-Function Analysis (SFA) to measure both the three-dimensional\nmagnetic field orientation and Alfven Mach number $M_A$, which provides the\ninformation on magnetic field strength. Following the theoretical framework\ndeveloped in Kandel et al. (2016), we find that the anisotropy in a given\nvelocity channel is affected by the inclination angle between the 3D magnetic\nfield direction and the line-of-sight as well as media magnetization. We\nanalyze the synthetic PPV cubes generated by incompressible and compressible\nMHD simulations. We confirm that the PPV channel's intensity fluctuations\nmeasured in various position angles reveal plane-of-the-sky magnetic field\norientation. We show that by varying the channel width, the anisotropies of the\nintensity fluctuations in PPV space can be used to simultaneously estimate both\nmagnetic field inclination angle and strength of total magnetic fields.\n"}
+{"abstract": "  We develop a general framework to significantly reduce the degree of\nsum-of-squares proofs by introducing new variables. To illustrate the power of\nthis framework, we use it to speed up previous algorithms based on\nsum-of-squares for two important estimation problems, clustering and robust\nmoment estimation. The resulting algorithms offer the same statistical\nguarantees as the previous best algorithms but have significantly faster\nrunning times. Roughly speaking, given a sample of $n$ points in dimension $d$,\nour algorithms can exploit order-$\\ell$ moments in time $d^{O(\\ell)}\\cdot\nn^{O(1)}$, whereas a naive implementation requires time $(d\\cdot n)^{O(\\ell)}$.\nSince for the aforementioned applications, the typical sample size is\n$d^{\\Theta(\\ell)}$, our framework improves running times from $d^{O(\\ell^2)}$\nto $d^{O(\\ell)}$.\n"}
+{"abstract": "  Let $\\mathbf{P}$ be a parabolic subgroup with Levi $\\mathbf{M}$ of a\nconnected reductive group defined over a locally compact non-archimedean field\n$F$. Given a certain compact open subgroup $\\Gamma$ of $\\mathbf{P}(F)$, this\nnote proves that the Hecke algebra $\\mathcal{H}(\\mathbf{M}(F))$ of\n$\\mathbf{M}(F)$ with respect to $\\Gamma\\cap \\mathbf{M}(F)$ is a left ring of\nfractions of the Hecke algebra $\\mathcal{H}(\\mathbf{P}(F))$ of $\\mathbf{P}(F)$\nwith respect to $\\Gamma$. This leads to a characterization of\n$\\mathcal{H}(\\mathbf{P}(F))$-modules that come from\n$\\mathcal{H}(\\mathbf{M}(F))$-modules.\n"}
+{"abstract": "  The presence of stars on retrograde orbits in disc galaxies is usually\nattributed to accretion events, both via direct accretion, as well as through\nthe heating of the disc stars. Recent studies have shown that retrograde orbits\ncan also be produced via scattering by dense clumps, which are often present in\nthe early stages of a galaxy's evolution. However, so far it has been unclear\nwhether other internally-driven mechanisms, such as bars, are also capable of\ndriving retrograde motion. Therefore, in this paper, we investigate the\nefficiencies with which bars and clumps produce retrograde orbits in disc\ngalaxies. We do this by comparing the retrograde fractions and the spatial\ndistributions of the retrograde populations in four $N$-body$+$smooth particle\nhydrodynamics (SPH) simulations of isolated disc galaxies spanning a range of\nevolutionary behaviours. We find that both bars and clumps are capable of\ngenerating significant retrograde populations of order $\\sim 10\\%$ of all\nstars. We also find that while clump-driven retrograde stars may be found at\nlarge galactocentric radii, bar-driven retrograde stars remain in the vicinity\nof the bar, even if the bar dissolves. Consequently, we find that retrograde\nstars in the Solar Neighbourhood in the clumpy models are exclusively\nclump-driven, but this is a trace population, constituting $0.01-0.04\\%$ of the\ntotal stellar population in this region. Finally, we find that neither bars\n(including dissolving ones) nor clumps in the models are able to produce\nrotationally supported counter-rotating discs.\n"}
+{"abstract": "  A reinforcement learning (RL) policy trained in a nominal environment could\nfail in a new/perturbed environment due to the existence of dynamic variations.\nExisting robust methods try to obtain a fixed policy for all envisioned dynamic\nvariation scenarios through robust or adversarial training. These methods could\nlead to conservative performance due to emphasis on the worst case, and often\ninvolve tedious modifications to the training environment. We propose an\napproach to robustifying a pre-trained non-robust RL policy with\n$\\mathcal{L}_1$ adaptive control. Leveraging the capability of an\n$\\mathcal{L}_1$ control law in the fast estimation of and active compensation\nfor dynamic variations, our approach can significantly improve the robustness\nof an RL policy trained in a standard (i.e., non-robust) way, either in a\nsimulator or in the real world. Numerical experiments are provided to validate\nthe efficacy of the proposed approach.\n"}
+{"abstract": "  The study of anisotropic harmonic flow coefficients $ v_{n}$(n=2,3,4) is\nperformed in Xe-Xe collisions at $\\sqrt{s_{NN}}$ = 5.44 TeV under Monte Carlo\nHYDJET++ model (HYDrodynamics plus JETs) framework. Anisotropic flow of\nidentified particles and correlation between the azimuthal harmonic flow\namplitudes is presented. Here, we have considered body-body and tip-tip type of\ngeometrical configurations for Xe-Xe collision systems. The kinematic ranges\n$|\\eta|<0.8$, $0<p_{T}<5.0$ GeV/c, and $|\\delta / \\eta|> 2$ are considered. The\nresults have been shown for seven classes of centrality and compared with the\nALICE experimental data. The anisotropic flow of identified charged particles\nshow a strong centrality dependence. Mass ordering is observed for\n$v_{2},v_{3}$ and $v_{4}$. Mass ordering is different for different ranges of\ntransverse momentum $p_{T}$. Strong correlation is observed between\n$v_{3}-v_{2}$, $v_{4}-v_{2}$, and $v_{4}-v_{3}$. Such correlation is centrality\ndependent and is different in different centrality windows. The anisotropic\nflow coefficients show a clear dependence on the total charged particle\nmultiplicity. HYDJET++ model justifies experimental data well enough.\n"}
+{"abstract": "  Here we investigate the temperature dependence of anomalous Hall effect in\nHf/GdFeCo/MgO sheet film and Hall bar device. The magnetic compensation\ntemperature ($T_{comp}$) for the sheet film and device is found to be ~240 K\nand ~118 K, respectively. In sheet film, spin-flopping is witnessed at a\nconsiderably lower field, 0.6 T, close to $T_{comp}$. The AHE hysteresis loops\nin the sheet film have a single loop whereas in the Hall bar device, hystereses\nconsist of triple loops are observed just above the Tcomp. Moreover, the\ntemperature-dependent anomalous Hall resistance ($R_\\mathrm{AHE}$) responds\nunusually when a perpendicular magnetic field is applied while recording the\n$R_\\mathrm{AHE}$. The zero-field $R_\\mathrm{AHE}$ scan suggests the Hall signal\ngenerates solely from the FeCo moment. However, the behavior of 3 T-field\n$R_\\mathrm{AHE}$ scan in which the $R_\\mathrm{AHE}$ drops close to zero near\nthe $T_{comp}$ seems to be following the net magnetization response of the\ndevice, is explained by considering the low field spin-flopping around the\ncompensation temperature. The results presented here give important insight to\nunderstand the complex AHE behavior of ferrimagnets for their spintronic\napplications.\n"}
+{"abstract": "  Significant efforts have been expended in the research and development of a\ndatabase management system (DBMS) that has a wide range of applications for\nmanaging an enormous collection of multisource, heterogeneous, complex, or\ngrowing data. Besides the primary function (i.e., create, delete, and update),\na practical and impeccable DBMS can interact with users through information\nselection, that is, querying with their targets. Previous querying algorithms,\nsuch as frequent itemset querying and sequential pattern querying (SPQ) have\nfocused on the measurement of frequency, which does not involve the concept of\nutility, which is helpful for users to discover more informative patterns. To\napply the querying technology for wider applications, we incorporate utility\ninto target-oriented SPQ and formulate the task of targeted utility-oriented\nsequence querying. To address the proposed problem, we develop a novel\nalgorithm, namely targeted high-utility sequence querying (TUSQ), based on two\nnovel upper bounds suffix remain utility and terminated descendants utility as\nwell as a vertical Last Instance Table structure. For further efficiency, TUSQ\nrelies on a projection technology utilizing a compact data structure called the\ntargeted chain. An extensive experimental study conducted on several real and\nsynthetic datasets shows that the proposed algorithm outperformed the designed\nbaseline algorithm in terms of runtime, memory consumption, and candidate\nfiltering.\n"}
+{"abstract": "  Wikidata has been increasingly adopted by many communities for a wide variety\nof applications, which demand high-quality knowledge to deliver successful\nresults. In this paper, we develop a framework to detect and analyze\nlow-quality statements in Wikidata by shedding light on the current practices\nexercised by the community. We explore three indicators of data quality in\nWikidata, based on: 1) community consensus on the currently recorded knowledge,\nassuming that statements that have been removed and not added back are\nimplicitly agreed to be of low quality; 2) statements that have been\ndeprecated; and 3) constraint violations in the data. We combine these\nindicators to detect low-quality statements, revealing challenges with\nduplicate entities, missing triples, violated type rules, and taxonomic\ndistinctions. Our findings complement ongoing efforts by the Wikidata community\nto improve data quality, aiming to make it easier for users and editors to find\nand correct mistakes.\n"}
+{"abstract": "  Graph convolution networks (GCN), which recently becomes new state-of-the-art\nmethod for graph node classification, recommendation and other applications,\nhas not been successfully applied to industrial-scale search engine yet. In\nthis proposal, we introduce our approach, namely SearchGCN, for embedding-based\ncandidate retrieval in one of the largest e-commerce search engine in the\nworld. Empirical studies demonstrate that SearchGCN learns better embedding\nrepresentations than existing methods, especially for long tail queries and\nitems. Thus, SearchGCN has been deployed into JD.com's search production since\nJuly 2020.\n"}
+{"abstract": "  We study the hop-constrained s-t path enumeration (HcPE) problem, which takes\na graph $G$, two distinct vertices $s,t$ and a hop constraint $k$ as input, and\noutputs all paths from $s$ to $t$ whose length is at most $k$. The\nstate-of-the-art algorithms suffer from severe performance issues caused by the\ncostly pruning operations during enumeration for the workloads with the large\nsearch space. Consequently, these algorithms hardly meet the real-time\nconstraints of many online applications. In this paper, we propose PathEnum, an\nefficient index-based algorithm towards real-time HcPE. For an input query,\nPathEnum first builds a light-weight index aiming to reduce the number of edges\ninvolved in the enumeration, and develops efficient index-based approaches for\nenumeration, one based on depth-first search and the other based on joins. We\nfurther develop a query optimizer based on a join-based cost model to optimize\nthe search order. We conduct experiments with 15 real-world graphs. Our\nexperiment results show that PathEnum outperforms the state-of-the-art\napproaches by orders of magnitude in terms of the query time, throughput and\nresponse time.\n"}
+{"abstract": "  In this paper, we investigate two dimensional subsonic and subsonic-sonic\nspiral flows outside a porous body. The existence and uniqueness of the\nsubsonic spiral flow are obtained via variational formulation. The optimal\ndecay rate at far fields is also derived by the Kelvin's transformation and\nsome elliptic estimates. By extracting spiral subsonic solutions as the\napproximate sequences, we obtain the spiral subsonic-sonic limit solution. The\nmain ingredients of our analysis are methods of calculus of variations, the\ntheory of second-order quasilinear equations and the compactness framework.\n"}
+{"abstract": "  We study a non-Hermitian and non-unitary version of the two-dimensional\nChalker-Coddington network model with balanced gain and loss. This model\nbelongs to the class D^dagger with particle-hole symmetry^dagger and hosts both\nthe non-Hermitian skin effect as well as exceptional points. By calculating its\ntwo-terminal transmission, we find a novel contact effect induced by the skin\neffect, which results in a non-quantized transmission for chiral edge states.\nIn addition, the model exhibits an insulator to 'supermetal' transition, across\nwhich the transmission changes from exponentially decaying with system size to\nexponentially growing with system size. In the clean system, the critical point\nseparating insulator from supermetal is characterized by a non-Hermitian Dirac\npoint that produces a quantized critical transmission of 4, instead of the\nvalue of 1 expected in Hermitian systems. This change in critical transmission\nis a consequence of the balanced gain and loss. When adding disorder to the\nsystem, we find a critical exponent for the divergence of the localization\nlength \\nu \\approx 1, which is the same as that characterizing the universality\nclass of two-dimensional Hermitian systems in class D. Our work provides a\nnovel way of exploring the localization behavior of non-Hermitian systems, by\nusing network models, which in the past proved versatile tools to describe\nHermitian physics.\n"}
+{"abstract": "  Road accident can be triggered by wet road because it decreases skid\nresistance. To prevent the road accident, detecting road surface abnomality is\nhighly useful. In this paper, we propose the deep learning based cost-effective\nreal-time anomaly detection architecture, naming with non-compression\nauto-encoder (NCAE). The proposed architecture can reflect forward and backward\ncausality of time series information via convolutional operation. Moreover, the\nabove architecture shows higher anomaly detection performance of published\nanomaly detection model via experiments. We conclude that NCAE as a\ncutting-edge model for road surface anomaly detection with 4.20\\% higher AUROC\nand 2.99 times faster decision than before.\n"}
+{"abstract": "  We outline the construction of a molecular system that could, in principle,\nimplement a thermodynamically reversible Universal Turing Machine (UTM). By\nproposing a concrete-albeit idealised-design and operational protocol, we\nreveal fundamental challenges that arise when attempting to implement arbitrary\ncomputations reversibly. Firstly, the requirements of thermodynamic\nreversibility inevitably lead to an intricate design. Secondly,\nthermodynamically reversible UTMs, unlike simpler devices, must also be\nlogically reversible. Finally, implementing multiple distinct computations in\nparallel is necessary to take the cost of external control per computation to\nzero, but this approach is complicated the distinct halting times of different\ncomputations.\n"}
+{"abstract": "  Quantum annealing is an emerging technology with the potential to solve some\nof the computational challenges that remain unresolved as we approach an era\nbeyond Moore's Law. In this work, we investigate the capabilities of the\nquantum annealers of D-Wave Systems, Inc., for computing a certain type of\nBoolean tensor decomposition called Boolean Hierarchical Tucker Network (BHTN).\nBoolean tensor decomposition problems ask for finding a decomposition of a\nhigh-dimensional tensor with categorical, [true, false], values, as a product\nof smaller Boolean core tensors. As the BHTN decompositions are usually not\nexact, we aim to approximate an input high-dimensional tensor by a product of\nlower-dimensional tensors such that the difference between both is minimized in\nsome norm. We show that BHTN can be calculated as a sequence of optimization\nproblems suitable for the D-Wave 2000Q quantum annealer. Although current\ntechnology is still fairly restricted in the problems they can address, we show\nthat a complex problem such as BHTN can be solved efficiently and accurately.\n"}
+{"abstract": "  With the aim of understanding the role of outflows in star formation, we\nperformed a statistical study of the physical parameters of outflows in eleven\nmassive protoclusters associated with ultra-compact HII regions. A total of 106\noutflow lobes are identified in these protoclusters using the ALMA CO (3-2),\nHCN (4-3) and HCO+ (4-3) line observations. Although the position angles of\noutflow lobes do not differ in these three tracers, HCN and HCO+ tend to detect\nlower terminal velocity of the identified outflows compared to CO. The majority\nof the outflows in our targets are young with typical dynamical time-scales of\n10^2-10^4 years, and are mostly composed of low-mass outflows along with at\nleast one high-mass outflow in each target. An anti-correlation of outflow rate\nwith dynamical time-scale indicates that the outflow rate possibly decreases\nwith time. Also, a rising trend of dynamical time-scale with the mass of the\nassociated core hints that the massive cores might have longer accretion\nhistories than the low mass cores. Estimation of different energies in these\nprotoclusters shows that outflows studied here cannot account for the\ngeneration of the observed turbulence, but can sustain the turbulence at the\ncurrent epoch as the energy injection rate from the outflows is similar to the\nestimated dissipation rate.\n"}
+{"abstract": "  In recent years, deep learning-based automated personality trait detection\nhas received a lot of attention, especially now, due to the massive digital\nfootprints of an individual. Moreover, many researchers have demonstrated that\nthere is a strong link between personality traits and emotions. In this paper,\nwe build on the known correlation between personality traits and emotional\nbehaviors, and propose a novel multitask learning framework, SoGMTL that\nsimultaneously predicts both of them. We also empirically evaluate and discuss\ndifferent information-sharing mechanisms between the two tasks. To ensure the\nhigh quality of the learning process, we adopt a MAML-like framework for model\noptimization. Our more computationally efficient CNN-based multitask model\nachieves the state-of-the-art performance across multiple famous personality\nand emotion datasets, even outperforming Language Model based models.\n"}
+{"abstract": "  Most of the recent deep reinforcement learning advances take an RL-centric\nperspective and focus on refinements of the training objective. We diverge from\nthis view and show we can recover the performance of these developments not by\nchanging the objective, but by regularising the value-function estimator.\nConstraining the Lipschitz constant of a single layer using spectral\nnormalisation is sufficient to elevate the performance of a Categorical-DQN\nagent to that of a more elaborated \\rainbow{} agent on the challenging Atari\ndomain. We conduct ablation studies to disentangle the various effects\nnormalisation has on the learning dynamics and show that is sufficient to\nmodulate the parameter updates to recover most of the performance of spectral\nnormalisation. These findings hint towards the need to also focus on the neural\ncomponent and its learning dynamics to tackle the peculiarities of Deep\nReinforcement Learning.\n"}
+{"abstract": "  We revisit a supersymmetric string model for space-time foam, in which\nbosonic open-string states, such as photons, can possess\nquantum-gravity-induced velocity fluctuations in vacuum. We argue that the\nsuggestion of light speed variation with lower bound from gamma-ray burst\nphoton time delays can serve as a support for this string-inspired framework,\nthrough connecting the experimental finding with model predictions. We also\nderive the value of the effective quantum-gravity mass in this framework, and\ngive a qualitative study on the model-dependent coefficients. Constraints from\nbirefringent effects and/or photon decays, including the novel $\\gamma$-decay\nconstraint obtained here from the latest Tibet AS$\\gamma$ near-PeV photon, are\nalso found to be consistent with predictions in such a quantum-gravity scheme.\nFuture observation that can testify further the theory is suggested.\n"}
+{"abstract": "  Few-shot object detection is an imperative and long-lasting problem due to\nthe inherent long-tail distribution of real-world data. Its performance is\nlargely affected by the data scarcity of novel classes. But the semantic\nrelation between the novel classes and the base classes is constant regardless\nof the data availability. In this work, we investigate utilizing this semantic\nrelation together with the visual information and introduce explicit relation\nreasoning into the learning of novel object detection. Specifically, we\nrepresent each class concept by a semantic embedding learned from a large\ncorpus of text. The detector is trained to project the image representations of\nobjects into this embedding space. We also identify the problems of trivially\nusing the raw embeddings with a heuristic knowledge graph and propose to\naugment the embeddings with a dynamic relation graph. As a result, our few-shot\ndetector, termed SRR-FSD, is robust and stable to the variation of shots of\nnovel objects. Experiments show that SRR-FSD can achieve competitive results at\nhigher shots, and more importantly, a significantly better performance given\nboth lower explicit and implicit shots. The benchmark protocol with implicit\nshots removed from the pretrained classification dataset can serve as a more\nrealistic setting for future research.\n"}
+{"abstract": "  The paper considers a distributed version of deep reinforcement learning\n(DRL) for multi-agent decision-making process in the paradigm of federated\nlearning. Since the deep neural network models in federated learning are\ntrained locally and aggregated iteratively through a central server, frequent\ninformation exchange incurs a large amount of communication overheads. Besides,\ndue to the heterogeneity of agents, Markov state transition trajectories from\ndifferent agents are usually unsynchronized within the same time interval,\nwhich will further influence the convergence bound of the aggregated deep\nneural network models. Therefore, it is of vital importance to reasonably\nevaluate the effectiveness of different optimization methods. Accordingly, this\npaper proposes a utility function to consider the balance between reducing\ncommunication overheads and improving convergence performance. Meanwhile, this\npaper develops two new optimization methods on top of variation-aware periodic\naveraging methods: 1) the decay-based method which gradually decreases the\nweight of the model's local gradients within the progress of local updating,\nand 2) the consensus-based method which introduces the consensus algorithm into\nfederated learning for the exchange of the model's local gradients. This paper\nalso provides novel convergence guarantees for both developed methods and\ndemonstrates their effectiveness and efficiency through theoretical analysis\nand numerical simulation results.\n"}
+{"abstract": "  Scaling the cyber hunt problem poses several key technical challenges.\nDetecting and characterizing cyber threats at scale in large enterprise\nnetworks is hard because of the vast quantity and complexity of the data that\nmust be analyzed as adversaries deploy varied and evolving tactics to\naccomplish their goals. There is a great need to automate all aspects, and,\nindeed, the workflow of cyber hunting. AI offers many ways to support this. We\nhave developed the WILEE system that automates cyber threat hunting by\ntranslating high-level threat descriptions into many possible concrete\nimplementations. Both the (high-level) abstract and (low-level) concrete\nimplementations are represented using a custom domain specific language (DSL).\nWILEE uses the implementations along with other logic, also written in the DSL,\nto automatically generate queries to confirm (or refute) any hypotheses tied to\nthe potential adversarial workflows represented at various layers of\nabstraction.\n"}
+{"abstract": "  Few-shot learning aims to correctly recognize query samples from unseen\nclasses given a limited number of support samples, often by relying on global\nembeddings of images. In this paper, we propose to equip the backbone network\nwith an attention agent, which is trained by reinforcement learning. The policy\ngradient algorithm is employed to train the agent towards adaptively localizing\nthe representative regions on feature maps over time. We further design a\nreward function based on the prediction of the held-out data, thus helping the\nattention mechanism to generalize better across the unseen classes. The\nextensive experiments show, with the help of the reinforced attention, that our\nembedding network has the capability to progressively generate a more\ndiscriminative representation in few-shot learning. Moreover, experiments on\nthe task of image classification also show the effectiveness of the proposed\ndesign.\n"}
+{"abstract": "  A few years ago, the first example of a closed manifold admitting an Anosov\ndiffeomorphism but no expanding map was given. Unfortunately, this example is\nnot explicit and is high-dimensional, although its exact dimension is unknown\ndue to the type of construction. In this paper, we present a family of concrete\n12-dimensional nilmanifolds with an Anosov diffeomorphism but no expanding map,\nwhere nilmanifolds are defined as the quotient of a 1-connected nilpotent Lie\ngroup by a cocompact lattice. We show that this family has the smallest\npossible dimension in the class of infra-nilmanifolds, which is conjectured to\nbe the only type of manifolds admitting Anosov diffeomorphisms up to\nhomeomorphism. The proof shows how to construct positive gradings from the\neigenvalues of the Anosov diffeomorphism under some additional assumptions\nrelated to the rank, using the action of the Galois group on these algebraic\nunits.\n"}
+{"abstract": "  3D snapshot microscopy enables fast volumetric imaging by capturing a 3D\nvolume in a single 2D camera image, and has found a variety of biological\napplications such as whole brain imaging of fast neural activity in larval\nzebrafish. The optimal microscope design for this optical 3D-to-2D encoding is\nboth sample- and task-dependent, with no general solution known. Highly\nprogrammable optical elements create new possibilities for sample-specific\ncomputational optimization of microscope parameters, e.g. tuning the collection\nof light for a given sample structure. We perform such optimization with deep\nlearning, using a differentiable wave-optics simulation of light propagation\nthrough a programmable microscope and a neural network to reconstruct volumes\nfrom the microscope image. We introduce a class of global kernel Fourier\nconvolutional neural networks which can efficiently decode information from\nmultiple depths in the volume, globally encoded across a 3D snapshot image. We\nshow that our proposed networks succeed in large field of view volume\nreconstruction and microscope parameter optimization where traditional networks\nfail. We also show that our networks outperform the state-of-the-art learned\nreconstruction algorithms for lensless computational photography.\n"}
+{"abstract": "  Chiral symmetry represents a fundamental concept lying at the core of\nparticle and nuclear physics. Its spontaneous breaking in vacuum can be\nexploited to distinguish chiral hadronic partners, whose masses differ. In\nfact, the features of this breaking serve as guiding principles for the\nconstruction of effective approaches of QCD at low energies, e.g., the chiral\nperturbation theory, the linear sigma model, the\n(Polyakov)--Nambu--Jona-Lasinio model, etc. At high temperatures/densities\nchiral symmetry can be restored bringing the chiral partners to be nearly\ndegenerated in mass. At vanishing baryochemical potential, such restoration\nfollows a smooth transition, and the chiral companions reach this degeneration\nabove the transition temperature. In this work I review how different\nrealizations of chiral partner degeneracy arise in different effective\ntheories/models of QCD. I distinguish the cases where the chiral states are\neither fundamental degrees of freedom or (dynamically-generated) composed\nstates. In particular, I discuss the intriguing case in which chiral symmetry\nrestoration involves more than two chiral partners, recently addressed in the\nliterature.\n"}
+{"abstract": "  The interplay between time-reversal symmetry (TRS) and band topology plays a\ncrucial role in topological states of quantum matter. In\ntime-reversal-invariant (TRI) systems, the inversion of spin-degenerate bands\nwith opposite parity leads to nontrivial topological states, such as\ntopological insulators and Dirac semimetals. When the TRS is broken, the\nexchange field induces spin splitting of the bands. The inversion of a pair of\nspin-splitting subbands can generate more exotic topological states, such as\nquantum anomalous Hall insulators and magnetic Weyl semimetals. So far, such\ntopological phase transitions driven by the TRS breaking have not been\nvisualized. In this work, using angle-resolved photoemission spectroscopy, we\nhave demonstrated that the TRS breaking induces a band inversion of a pair of\nspin-splitting subbands at the TRI points of Brillouin zone in EuB$_6$, when a\nlong-range ferromagnetic order is developed. The dramatic changes in the\nelectronic structure result in a topological phase transition from a TRI\nordinary insulator state to a TRS-broken topological semimetal (TSM) state.\nRemarkably, the magnetic TSM state has an ideal electronic structure, in which\nthe band crossings are located at the Fermi level without any interference from\nother bands. Our findings not only reveal the topological phase transition\ndriven by the TRS breaking, but also provide an excellent platform to explore\nnovel physical behavior in the magnetic topological states of quantum matter.\n"}
+{"abstract": "  The exploitation of syntactic graphs (SyGs) as a word's context has been\nshown to be beneficial for distributional semantic models (DSMs), both at the\nlevel of individual word representations and in deriving phrasal\nrepresentations via composition. However, notwithstanding the potential\nperformance benefit, the syntactically-aware DSMs proposed to date have huge\nnumbers of parameters (compared to conventional DSMs) and suffer from data\nsparsity. Furthermore, the encoding of the SyG links (i.e., the syntactic\nrelations) has been largely limited to linear maps. The knowledge graphs'\nliterature, on the other hand, has proposed light-weight models employing\ndifferent geometric transformations (GTs) to encode edges in a knowledge graph\n(KG). Our work explores the possibility of adopting this family of models to\nencode SyGs. Furthermore, we investigate which GT better encodes syntactic\nrelations, so that these representations can be used to enhance phrase-level\ncomposition via syntactic contextualisation.\n"}
+{"abstract": "  We present a new insight into the propagation of ion magnetoacoustic and\nneutral acoustic waves in a magnetic arcade in the lower solar atmosphere. By\nmeans of numerical simulations, we aim to: (a) study two-fluid waves\npropagating in a magnetic arcade embedded in the partially-ionized, lower solar\natmosphere; and (b) investigate the impact of the background magnetic field\nconfiguration on the observed wave-periods. We consider a 2D approximation of\nthe gravitationally stratified and partially-ionized lower solar atmosphere\nconsisting of ion+electron and neutral fluids that are coupled by ion-neutral\ncollisions. In this model, the convection below the photosphere is responsible\nfor the excitation of ion magnetoacoustic-gravity and neutral acoustic-gravity\nwaves. We find that in the solar photosphere, where ions and neutrals are\nstrongly coupled by collisions, ion magnetoacoustic-gravity and neutral\nacoustic-gravity waves have periods ranging from 250 s to 350 s. In the\nchromosphere, where the collisional coupling is weak, the wave characteristics\nstrongly depend on the magnetic field configuration. Above the foot-points of\nthe considered arcade, the plasma is dominated by a vertical magnetic field\nalong which ion magnetoacoustic-gravity waves propagate. These waves exhibit a\nbroad range of periods with the most prominent periods of 180 s, 220 s, and 300\ns. Above the main loop of the solar arcade, where mostly horizontal magnetic\nfield lines guide ion magnetoacoustic-gravity waves, the main spectral power\nreduces to the period of about 180 s and longer wave-periods do not exist. Our\nresults are in agreement with the recent observational data reported by\nWi\\'sniewska et al. (2016) and Kayshap et al. (2018).\n"}
+{"abstract": "  Let $\\Sigma$ be a compact convex hypersurface in ${\\bf R}^{2n}$ which is\nP-cyclic symmetric, i.e., $x\\in \\Sigma$ implies $Px\\in\\Sigma$ with P being a\n$2n\\times2n$ symplectic orthogonal matrix and satisfying $P^k=I_{2n}$,\n$ker(P^l-I_{2n})=0$ for $1\\leq l< k$, where $n, k\\geq2$. In this paper, we\nprove that there exist at least $n$ geometrically distinct closed\ncharacteristics on $\\Sigma$, which solves a longstanding conjecture about the\nmultiplicity of closed characteristics for a broad class of compact convex\nhypersurfaces with symmetries(cf.,Page 235 of \\cite{Eke1}). Based on the proof,\nwe further prove that if the number of geometrically distinct closed\ncharacteristics on $\\Sigma$ is finite, then at least $2[\\frac{n}{2}]$ of them\nare non-hyperbolic; and if the number of geometrically distinct closed\ncharacteristics on $\\Sigma$ is exactly $n$ and $k\\geq3$, then all of them are\nP-cyclic symmetric, where a closed characteristic $(\\tau, y)$ on $\\Sigma$ is\ncalled P-cyclic symmetric if $y({\\bf R})=Py({\\bf R})$.\n"}
+{"abstract": "  In this paper, we obtain the weighted boundedness for the local\nmulti(sub)linear Hardy-Littlewood maximal operators and local multilinear\nfractional integral operators associated with the local Muckenhoupt weights on\nGaussian measure spaces. We deal with these problems by introducing a new\npointwise equivalent \"radial\" definitions of these local operators. Moreover\nusing a similar approach, we also get the weighted boundedness for the local\nfractional maximal operators with rough kernel and local fractional integral\noperators with rough kernel on Gaussian measure spaces.\n"}
+{"abstract": "  Synchronization has been the subject of intense research during decades\nmainly focused on determining the structural and dynamical conditions driving a\nset of interacting units to a coherent state globally stable. However, little\nattention has been paid to the description of the dynamical development of each\nindividual networked unit in the process towards the synchronization of the\nwhole ensemble. In this paper, we show how in a network of identical dynamical\nsystems, nodes belonging to the same degree class differentiate in the same\nmanner visiting a sequence of states of diverse complexity along the route to\nsynchronization independently on the global network structure. In particular,\nwe observe, just after interaction starts pulling orbits from the initially\nuncoupled attractor, a general reduction of the complexity of the dynamics of\nall units being more pronounced in those with higher connectivity. In the weak\ncoupling regime, when synchronization starts to build up, there is an increase\nin the dynamical complexity whose maximum is achieved, in general, first in the\nhubs due to their earlier synchronization with the mean field. For very strong\ncoupling, just before complete synchronization, we found a hierarchical\ndynamical differentiation with lower degree nodes being the ones exhibiting the\nlargest complexity departure. We unveil how this differentiation route holds\nfor several models of nonlinear dynamics including toroidal chaos and how it\ndepends on the coupling function. This study provides new insights to\nunderstand better strategies for network identification and control or to\ndevise effective methods for network inference.\n"}
+{"abstract": "  This project is based on a mathematical model of erythropoiesis for anemia,\nwhich consists of five hyperbolic population equations describing the\nproduction of red blood cells under treatment with epoetin-alfa (EPO). Extended\ndynamic mode decomposition (EDMD) is utilized to approximate the non-linear\ndynamical systems by linear ones. This allows for efficient and reliable\nstrategies based on a combination of EDMD and model predictive control (MPC),\nwhich produces results comparable with the one obtained in past publications\nfor the original model.\n"}
+{"abstract": "  We propose a simple and efficient real-space approach for the calculation of\nthe ground-state energies of Wigner crystals in 1, 2, and 3 dimensions. To be\nprecise, we calculate the first two terms in the asymptotic expansion of the\ntotal energy per electron which correspond to the classical energy and the\nharmonic correction due to the zero-point motion of the Wigner crystals,\nrespectively. Our approach employs Clifford periodic boundary conditions to\nsimulate the infinite electron gas and a renormalized distance to evaluate the\nCoulomb potential. This allows us to calculate the energies unambiguously and\nwith a higher precision than those reported in the literature. Our results are\nin agreement with the literature values with the exception of harmonic\ncorrection of the 2-dimensional Wigner crystal for which we find a significant\ndifference. Although we focus on the ground state, i.e., the triangular lattice\nand the body-centered cubic lattice, in two and three dimensions, respectively,\nwe also report the classical energies of several other common lattice\nstructures.\n"}
+{"abstract": "  Large matrices are often accessed as a row-order stream. We consider the\nsetting where rows are time-sensitive (i.e. they expire), which can be\ndescribed by the sliding-window row-order model, and provide the first\n$(1+\\epsilon)$-approximation of Schatten $p$-norms in this setting. Our main\ntechnical contribution is a proof that Schatten $p$-norms in row-order streams\nare smooth, and thus fit the smooth-histograms technique of Braverman and\nOstrovsky (FOCS 2007) for sliding-window streams.\n"}
+{"abstract": "  Smart devices, such as smartphones, wearables, robots, and others, can\ncollect vast amounts of data from their environment. This data is suitable for\ntraining machine learning models, which can significantly improve their\nbehavior, and therefore, the user experience. Federated learning is a young and\npopular framework that allows multiple distributed devices to train deep\nlearning models collaboratively while preserving data privacy. Nevertheless,\nthis approach may not be optimal for scenarios where data distribution is\nnon-identical among the participants or changes over time, causing what is\nknown as concept drift. Little research has yet been done in this field, but\nthis kind of situation is quite frequent in real life and poses new challenges\nto both continual and federated learning. Therefore, in this work, we present a\nnew method, called Concept-Drift-Aware Federated Averaging (CDA-FedAvg). Our\nproposal is an extension of the most popular federated algorithm, Federated\nAveraging (FedAvg), enhancing it for continual adaptation under concept drift.\nWe empirically demonstrate the weaknesses of regular FedAvg and prove that\nCDA-FedAvg outperforms it in this type of scenario.\n"}
+{"abstract": "  Exotic tiling patterns of quasicrystals have motivated extensive studies of\nquantum phenomena such as critical states and phasons. Nevertheless, the\nsystematic understanding of the Landau levels of quasicrystals in the presence\nof the magnetic field has not been established yet. One of the main obstacles\nis the complication of the quasiperiodic tilings without periodic length\nscales, thus it has been thought that the system cannot possess any universal\nfeatures of the Landau levels. In this paper, contrary to these assertions, we\ndevelop a generic theory of the Landau levels for quasicrystals. Focusing on\nthe two dimensional quasicrystals with rotational symmetries, we highlight that\nquasiperiodic tilings induce anomalous Landau levels where electrons are\nlocalized near the rotational symmetry centers. Interestingly, the localization\nlength of these Landau levels has a universal dependence on n for quasicrystals\nwith n-fold rotational symmetry. Furthermore, macroscopically degenerate zero\nenergy Landau levels are present due to the chiral symmetry of the rhombic\ntilings. In this case, each Landau level forms an independent island where\nelectrons are trapped at given fields, but with field control, the interference\nbetween the islands gives rise to an abrupt change in the local density of\nstates. Our work provide a general scheme to understand the electron\nlocalization behavior of the Landau levels in quasicrystals.\n"}
+{"abstract": "  The gas content of the complete compilation of Local Group dwarf galaxies\n(119 within 2 Mpc) is presented using HI survey data. Within the virial radius\nof the Milky Way (224 kpc here), 53 of 55 dwarf galaxies are devoid of gas to\nlimits of M$_{\\rm HI}<10^4$ M$_\\odot$. Within the virial radius of M31 (266\nkpc), 27 of 30 dwarf galaxies are devoid of gas (with limits typically $<10^5$\nM$_\\odot$). Beyond the virial radii of the Milky Way and M31, the majority of\nthe dwarf galaxies have detected HI gas and have HI masses higher than the\nlimits. When the relationship between gas content and distance is investigated\nusing a Local Group virial radius, more of the non-detected dwarf galaxies are\nwithin this radius (85$\\pm1$ of the 93 non-detected dwarf galaxies) than within\nthe virial radii of the Milky Way and M31. Using the Gaia proper motion\nmeasurements available for 38 dwarf galaxies, the minimum gas density required\nto completely strip them of gas is calculated. Halo densities between $10^{-5}$\nand $5 \\times 10^{-4}$ cm$^{-3}$ are typically required for instantaneous\nstripping at perigalacticon. When compared to halo density with radius\nexpectations from simulations and observations, 80% of the dwarf galaxies with\nproper motions are consistent with being stripped by ram pressure at Milky Way\npericenter. The results suggest a diffuse gaseous galactic halo medium is\nimportant in quenching dwarf galaxies, and that a Local Group medium also\npotentially plays a role.\n"}
+{"abstract": "  We introduce a visual motion segmentation method employing spherical geometry\nfor fisheye cameras and automoated driving. Three commonly used geometric\nconstraints in pin-hole imagery (the positive height, positive depth and\nepipolar constraints) are reformulated to spherical coordinates, making them\ninvariant to specific camera configurations as long as the camera calibration\nis known. A fourth constraint, known as the anti-parallel constraint, is added\nto resolve motion-parallax ambiguity, to support the detection of moving\nobjects undergoing parallel or near-parallel motion with respect to the host\nvehicle. A final constraint constraint is described, known as the spherical\nthree-view constraint, is described though not employed in our proposed\nalgorithm. Results are presented and analyzed that demonstrate that the\nproposal is an effective motion segmentation approach for direct employment on\nfisheye imagery.\n"}
+{"abstract": "  Most of the functions performed by astrocytes in brain information processing\nare related to calcium waves. Experimental studies involving calcium waves\npresent discrepant results, leading to gaps in the full understanding of the\nfunctions of these cells. The use of mathematical models help to understand the\nexperimental results, identifying chemical mechanisms involved in calcium waves\nand the limits of experimental methods. The model is diffusion-based and uses\nreceptors and channels as boundary conditions. The computer program developed\nwas prepared to allow the study of complex geometries, with several astrocytes,\neach of them with several branches. The code structure allows easy adaptation\nto various experimental situations in which the model can be compared. The code\nwas deposited in the ModelDB repository, and will be under number 266795 after\npublication. A sensitivity analysis showed the relative significance of the\nparameters and identifies the ideal range of values for each one. We showed\nthat several sets of values can lead to the same calcium signaling dynamics.\nThis encourages the questioning of parameters to model calcium signaling in\nastrocytes that are commonly used in the literature, and it suggests better\nexperimental planning. In the final part of the work, the effects produced by\nthe endoplasmic reticulum when located close to the extremities of the branches\nwere evaluated. We conclude that when they are located close to the region of\nthe glutamatergic stimulus, they favor local calcium dynamics. By contrast,\nwhen they are located at points away from the stimulated region, they\naccelerate the global spread of signaling.\n"}
+{"abstract": "  Result relevance prediction is an essential task of e-commerce search engines\nto boost the utility of search engines and ensure smooth user experience. The\nlast few years eyewitnessed a flurry of research on the use of\nTransformer-style models and deep text-match models to improve relevance.\nHowever, these two types of models ignored the inherent bipartite network\nstructures that are ubiquitous in e-commerce search logs, making these models\nineffective. We propose in this paper a novel Second-order Relevance, which is\nfundamentally different from the previous First-order Relevance, to improve\nresult relevance prediction. We design, for the first time, an end-to-end\nFirst-and-Second-order Relevance prediction model for e-commerce item\nrelevance. The model is augmented by the neighborhood structures of bipartite\nnetworks that are built using the information of user behavioral feedback,\nincluding clicks and purchases. To ensure that edges accurately encode\nrelevance information, we introduce external knowledge generated from BERT to\nrefine the network of user behaviors. This allows the new model to integrate\ninformation from neighboring items and queries, which are highly relevant to\nthe focus query-item pair under consideration. Results of offline experiments\nshowed that the new model significantly improved the prediction accuracy in\nterms of human relevance judgment. An ablation study showed that the\nFirst-and-Second-order model gained a 4.3% average gain over the First-order\nmodel. Results of an online A/B test revealed that the new model derived more\ncommercial benefits compared to the base model.\n"}
+{"abstract": "  We apply the multi-particle fields model to calculate the differential\ncross-section d{\\sigma}/dt of elastic proton-proton scattering. This problem\nincludes the calculation of multidimensional integrals arising from the loop\nFeynman diagrams. We demonstrated how these integrals can be reduced with\nLaplace's method to one- and two-dimensional integrals which can be calculated\nnumerically. The obtained result qualitatively describe the minimum in\ndifferential cross-section dependency d{\\sigma}/dt(t).\n"}
+{"abstract": "  Let $p(z)$ be a nonconstant polynomial and $\\beta(z)$ be a small entire\nfunction of $e^{p(z)}$ in the sense of Nevanlinna. We first describe the growth\nbehavior of the entire function $H(z):=e^{p(z)}\\int_0^{z}\\beta(t)e^{-p(t)}dt$\non the complex plane $\\mathbb{C}$. As an application, we solve entire solutions\nof Tumura--Clunie type differential equation\n$f(z)^n+P(z,f)=b_1(z)e^{p_1(z)}+b_2(z)e^{p_2(z)}$, where $b_1(z)$ and $b_2(z)$\nare nonzero polynomials, $p_1(z)$ and $p_2(z)$ are two polynomials of the same\ndegree~$k\\geq 1$ and $P(z,f)$ is a differential polynomial in $f$ of degree\n$\\leq n-1$ with meromorphic functions of order~$<k$ as coefficients. These\nresults allow us to determine all solutions with relatively few zeros of the\nsecond-order differential equation\n$f''-[b_1(z)e^{p_1(z)}+b_2(z)e^{p_2(z)}+b_3(z)]f=0$, where $b_3(z)$ is a\npolynomial. We also prove a theorem on certain first-order linear differential\nequation related to complex dynamics.\n"}
+{"abstract": "  This work is focused on the system-level performance of a broadcast network.\nSince all transmitters in a broadcast network transmit the identical signal,\nreceived signals from multiple transmitters can be combined to improve system\nperformance. We develop a stochastic geometry based analytical framework to\nderive the coverage of a typical receiver. We show that there may exist an\noptimal connectivity radius that maximizes the rate coverage. Our analysis\nincludes the fact that users may have their individual content/advertisement\npreferences. We assume that there are multiple classes of users with each user\nclass prefers a particular type of content/advertisements and the users will\npay the network only when then can see content aligned with their interest. The\noperator may choose to transmit multiple contents simultaneously to cater more\nusers' interests to increase its revenue. We present revenue models to study\nthe impact of the number of contents on the operator revenue. We consider two\nscenarios for users' distribution: one where users' interest depends on their\ngeographical location and the one where it doesn't. With the help of numerical\nresults and analysis, we show the impact of various parameters including\ncontent granularity, connectivity radius, and rate threshold and present\nimportant design insights.\n"}
+{"abstract": "  Differential cross sections for the Drell-Yan process, including Z boson\nproduction, using the dimuon decay channel are measured in proton-lead (pPb)\ncollisions at a nucleon-nucleon centre-of-mass energy of 8.16 TeV. A data\nsample recorded with the CMS detector at the LHC is used, corresponding to an\nintegrated luminosity of 173 nb$^{-1}$. The differential cross section as a\nfunction of the dimuon mass is measured in the range 15-600 GeV, for the first\ntime in proton-nucleus collisions. It is also reported as a function of dimuon\nrapidity over the mass ranges 15-60 GeV and 60-120 GeV, and ratios for the\np-going over the Pb-going beam directions are built. In both mass ranges, the\ndifferential cross sections as functions of the dimuon transverse momentum\n$p_\\mathrm{T}$ and of a geometric variable $\\phi^*$ are measured, where\n$\\phi^*$ highly correlates with $p_\\mathrm{T}$ but is determined with higher\nprecision. In the Z mass region, the rapidity dependence of the data indicate a\nmodification of the distribution of partons within a lead nucleus as compared\nto the proton case. The data are more precise than predictions based upon\ncurrent models of parton distributions.\n"}
+{"abstract": "  In this paper, we study {\\bf twisted Milnor hypersurfaces} and compute their\n$\\hat A$-genus and Atiyah-Singer-Milnor $\\alpha$-invariant. Our tool to compute\nthe $\\alpha$-invariant is Zhang's analytic Rokhlin congruence formula. We also\ngive some applications about group actions and metrics of positive scalar\ncurvature on twisted Milnor hypersurfaces.\n"}
+{"abstract": "  We propose magnetically arrested disks (MADs) in quiescent black-hole (BH)\nbinaries as the origin of the multiwavelength emission, and argue that this\nclass of sources can dominate the cosmic-ray spectrum around the knee. X-ray\nluminosities of Galactic BH binaries in the quiescent state are far below the\nEddington luminosity, and thus, radiatively inefficient accretion flows (RIAFs)\nare formed in the inner region. Strong thermal and turbulent pressures in RIAFs\nproduce outflows, which can create large-scale poloidal magnetic fields. These\nfields are carried to the vicinity of the BH by the rapid inflow motion,\nforming a MAD. Inside the MAD, non-thermal protons and electrons are naturally\naccelerated by magnetic reconnections or stochastic acceleration by turbulence.\nBoth thermal and non-thermal electrons emit broadband photons via synchrotron\nemission, which are broadly consistent with the optical and X-ray data of the\nquiescent BH X-ray binaries. Moreover, protons are accelerated up to PeV\nenergies and diffusively escape from these MADs, which can account for the\ncosmic-ray intensity around the knee energy.\n"}
+{"abstract": "  In this paper, a hybrid model for single-crystal Shape Memory Alloy (SMA)\nwire actuators is presented. The result is based on a mathematical\nreformulation of the M\\\"uller-Achenbach-Seelecke (MAS) model, which provides an\naccurate and interconnection-oriented description of the SMA hysteretic\nresponse. The strong nonlinearity and high numerical stiffness of the MAS\nmodel, however, hinder its practical use for simulation and control of complex\nSMA-driven systems. The main idea behind the hybrid reformulation is based on\ndividing the mechanical hysteresis of the SMA into five operating modes, each\none representing a different physical state of the material. By properly\nderiving the switching conditions among those modes in a physically-consistent\nway, the MAS model is effectively reformulated within a hybrid dynamical\nsetting. The main advantage of the hybrid reformulation is the possibility of\ndescribing the material dynamics with a simplified set of state equations while\nmaintaining all benefits of the physics-based description offered by the MAS\nmodel After describing the novel approach, simulation studies are conducted on\na flexible robotic module actuated by protagonist-antagonist SMA wires. Through\ncomparative numerical analysis, it is shown how the hybrid model provides the\nsame accuracy as the MAS model while saving up to 80% of the simulation time.\nMoreover, the new modeling framework opens up the possibility of addressing SMA\ncontrol from a hybrid systems perspective.\n"}
+{"abstract": "  We determine the Weierstrass semigroup $H(P_\\infty,P_1,\\ldots,P_m)$ at\nseveral rational points on the maximal curves which cannot be covered by the\nHermitian curve introduced by Tafazolian, Teher\\'an-Herrera, and Torres.\nFurthermore, we present some conditions to find pure gaps. We use this\nsemigroup to obtain AG codes with better relative parameters than comparable\none-point AG codes arising from these curves.\n"}
+{"abstract": "  We present here a simple mathematical model that provides a successful\nstrategy, quantitatively, to ending the continued championship futility\nexperienced by Canadian Hockey Teams. Competitive Intransitivity is used here\nas a simple predictive framework to capture how investing strategically, under\na uniform salary cap, in just 3 independently variable aspects of the sport\n(such as Offence, Defence, and a Goaltender), by just 3 Hockey Teams applying\ndiffering salary priorities (such as Montreal, Boston, and New York), can lead\nto rich and perhaps surprisingly unexpected outcomes in play, similar to\nrolling intransitive dice together in a series of head-to-head games. A\npossibly fortunate conclusion of this analysis is the prediction that for any\nTeam's chosen strategy (such as New York's), a counter strategy within the same\nsalary cap can be adopted by a playoff opponent (such as Montreal) which will\nprove victorious over a long playoff series, enabling a pathway to end\nprolonged championship futility.\n"}
+{"abstract": "  We introduce Dynabench, an open-source platform for dynamic dataset creation\nand model benchmarking. Dynabench runs in a web browser and supports\nhuman-and-model-in-the-loop dataset creation: annotators seek to create\nexamples that a target model will misclassify, but that another person will\nnot. In this paper, we argue that Dynabench addresses a critical need in our\ncommunity: contemporary models quickly achieve outstanding performance on\nbenchmark tasks but nonetheless fail on simple challenge examples and falter in\nreal-world scenarios. With Dynabench, dataset creation, model development, and\nmodel assessment can directly inform each other, leading to more robust and\ninformative benchmarks. We report on four initial NLP tasks, illustrating these\nconcepts and highlighting the promise of the platform, and address potential\nobjections to dynamic benchmarking as a new standard for the field.\n"}
+{"abstract": "  Due to the wide adoption of social media platforms like Facebook, Twitter,\netc., there is an emerging need of detecting online posts that can go against\nthe community acceptance standards. The hostility detection task has been well\nexplored for resource-rich languages like English, but is unexplored for\nresource-constrained languages like Hindidue to the unavailability of large\nsuitable data. We view this hostility detection as a multi-label multi-class\nclassification problem. We propose an effective neural network-based technique\nfor hostility detection in Hindi posts. We leverage pre-trained multilingual\nBidirectional Encoder Representations of Transformer (mBERT) to obtain the\ncontextual representations of Hindi posts. We have performed extensive\nexperiments including different pre-processing techniques, pre-trained models,\nneural architectures, hybrid strategies, etc. Our best performing neural\nclassifier model includes One-vs-the-Rest approach where we obtained 92.60%,\n81.14%,69.59%, 75.29% and 73.01% F1 scores for hostile, fake, hate, offensive,\nand defamation labels respectively. The proposed model outperformed the\nexisting baseline models and emerged as the state-of-the-art model for\ndetecting hostility in the Hindi posts.\n"}
+{"abstract": "  We study the Wishart-Sachdev-Ye-Kitaev (WSYK) model consisting of two\n$\\hat{q}$-body Sachdev-Ye-Kitaev (SYK) models with general complex couplings,\none the Hermitian conjugate of the other, living in off-diagonal blocks of a\nlarger WSYK Hamiltonian. The spectrum is positive with a hard edge at zero\nenergy. We employ diagrammatic and combinatorial techniques to compute\nanalytically the low-order moments of the Hamiltonian. In the limit of large\nnumber $N$ of Majoranas, we have found striking similarities with the moments\nof the weight function of the Al-Salam-Chihara $Q$-Laguerre polynomials. For\n$\\hat{q} = 3, 4$, the $Q$-Laguerre prediction, with $Q=Q(\\hat{q},N)$ also\ncomputed analytically, agrees well with exact diagonalization results for $30 <\nN \\leq 34$ while we observe some deviations for $\\hat q = 2$. The most salient\nfeature of the spectral density is that, for odd $\\hat{q}$, low-energy\nexcitations grow as a stretched exponential, with a functional form different\nfrom that of the supersymmetric SYK model. For $\\hat q = 4$, a detailed\nanalysis of level statistics reveals quantum chaotic dynamics even for time\nscales substantially shorter than the Heisenberg time. More specifically, the\nspacing ratios in the bulk of the spectrum and the microscopic spectral density\nand the number variance close to the hard edge are very well approximated by\nthat of an ensemble of random matrices that, depending on $N$, belong to the\nchiral or superconducting universality classes. In particular, we report the\nfirst realization of level statistics belonging to the chGUE universality\nclass, which completes the tenfold-way classification in the SYK model.\n"}
+{"abstract": "  Data from clinical real-world settings is characterized by variability in\nquality, machine-type, setting, and source. One of the primary goals of medical\ncomputer vision is to develop and validate artificial intelligence (AI) based\nalgorithms on real-world data enabling clinical translations. However, despite\nthe exponential growth in AI based applications in healthcare, specifically in\nophthalmology, translations to clinical settings remain challenging. Limited\naccess to adequate and diverse real-world data inhibits the development and\nvalidation of translatable algorithms. In this paper, we present a new\nmulti-modal longitudinal ophthalmic imaging dataset, the Illinois Ophthalmic\nDatabase Atlas (I-ODA), with the goal of advancing state-of-the-art computer\nvision applications in ophthalmology, and improving upon the translatable\ncapacity of AI based applications across different clinical settings. We\npresent the infrastructure employed to collect, annotate, and anonymize images\nfrom multiple sources, demonstrating the complexity of real-world retrospective\ndata and its limitations. I-ODA includes 12 imaging modalities with a total of\n3,668,649 ophthalmic images of 33,876 individuals from the Department of\nOphthalmology and Visual Sciences at the Illinois Eye and Ear Infirmary of the\nUniversity of Illinois Chicago (UIC) over the course of 12 years.\n"}
+{"abstract": "  We explore an anomaly-free ${\\textrm U}(1)$ gauge extended beyond the\nStandard model (BSM) framework, to account for the baryon asymmetry of the\nUniverse, along with arranging for tiny neutrino mass. Neutrino masses are\ngenerated via higher-dimensional operators (HDOs) involving three right-handed\nneutrinos (RHNs) with gauge charges ($4$, $4$ and $-5$ respectively) and two\nBSM scalars. This is an attractive framework as it can accommodate a keV scale\ndark matter, with the lightest RHN being the candidate. The remaining two RHNs\nare quasi-degenerate at the TeV-scale, actively participating in the process of\nresonant leptogenesis through their decay governed by the same set of HDOs. The\nRHNs being at the TeV scale, make this framework relevant for studying flavored\nresonant leptogenesis. This TeV-scale resonant leptogenesis, after satisfying\nthe neutrino oscillation data, leads to interesting predictions on the Yukawa\nsector of the model HDOs. The thermal evolution of the baryon asymmetry has\nfollowed the experimental results rather accurately in that corner of parameter\nspace. As a matter of fact, this TeV-scale framework which in principle relies\non the low scale resonant leptogenesis typically leads to predictions that\npotentially can be tested at the colliders. In particular, we consider the\nsame-sign dilepton signature that arises from the RHN pair production through\nthe decay of heavy gauge boson of the extra ${\\textrm U}(1)$.\n"}
+{"abstract": "  We prove that random hypergraphs are asymptotically almost surely resiliently\nHamiltonian. Specifically, for any $\\gamma>0$ and $k\\ge3$, we show that\nasymptotically almost surely, every subgraph of the binomial random $k$-uniform\nhypergraph $G^{(k)}\\big(n,n^{\\gamma-1}\\big)$ in which all $(k-1)$-sets are\ncontained in at least $\\big(\\tfrac12+2\\gamma\\big)pn$ edges has a tight Hamilton\ncycle. This is a cyclic ordering of the $n$ vertices such that each consecutive\n$k$ vertices forms an edge.\n"}
+{"abstract": "  Language resources are necessary for language processing,but building them is\ncostly, involves many researches from different areas and needs constant\nupdating. In this paper, we describe the crosslingual framework used for\ndeveloping the Multilingual Central Repository (MCR), a multilingual knowledge\nbase that includes wordnets of Basque, Catalan, English, Galician, Portuguese,\nSpanish and the following ontologies: Base Concepts, Top Ontology, WordNet\nDomains and Suggested Upper Merged Ontology. We present the story of MCR, its\nstate in 2017 and the developed tools.\n"}
+{"abstract": "  Contrastive learning has delivered impressive results for various tasks in\nthe self-supervised regime. However, existing approaches optimize for learning\nrepresentations specific to downstream scenarios, i.e., \\textit{global}\nrepresentations suitable for tasks such as classification or \\textit{local}\nrepresentations for tasks such as detection and localization. While they\nproduce satisfactory results in the intended downstream scenarios, they often\nfail to generalize to tasks that they were not originally designed for. In this\nwork, we propose to learn video representations that generalize to both the\ntasks which require global semantic information (e.g., classification) and the\ntasks that require local fine-grained spatio-temporal information (e.g.,\nlocalization). We achieve this by optimizing two contrastive objectives that\ntogether encourage our model to learn global-local visual information given\naudio signals. We show that the two objectives mutually improve the\ngeneralizability of the learned global-local representations, significantly\noutperforming their disjointly learned counterparts. We demonstrate our\napproach on various tasks including action/sound classification, lip reading,\ndeepfake detection, event and sound localization\n(https://github.com/yunyikristy/global\\_local).\n"}
+{"abstract": "  When applying imitation learning techniques to fit a policy from expert\ndemonstrations, one can take advantage of prior stability/robustness\nassumptions on the expert's policy and incorporate such control-theoretic prior\nknowledge explicitly into the learning process. In this paper, we formulate the\nimitation learning of linear policies as a constrained optimization problem,\nand present efficient methods which can be used to enforce stability and\nrobustness constraints during the learning processes. Specifically, we show\nthat one can guarantee the closed-loop stability and robustness by posing\nlinear matrix inequality (LMI) constraints on the fitted policy. Then both the\nprojected gradient descent method and the alternating direction method of\nmultipliers (ADMM) method can be applied to solve the resulting constrained\npolicy fitting problem. Finally, we provide numerical results to demonstrate\nthe effectiveness of our methods in producing linear polices with various\nstability and robustness guarantees.\n"}
+{"abstract": "  We explore the presence of active galactic nuclei (AGN)/black hole (BH) in\nGreen Pea galaxies (GPs), motivated by the presence of high ionization emission\nlines such as HeII and [NeIII] in their optical spectra. In order to identify\nAGN candidates, we used mid-infrared (MIR) photometric observations from the\nall-sky Wide-field Infrared Survey Explorer (WISE) mission for a sample of 516\nGPs. We select 58 GPs as candidate AGN based on a stringent 3-band WISE color\ndiagnostic. Using multi-epoch photometry of W1 and W2 bands from the\nWISE/NEOWISE-R observations, we find 38 GPs showing significant variability in\nboth the WISE bands. Four of these were selected as AGN by the WISE 3-band\ncolor diagnostic as well. Interestingly, we find a high fraction of MIR\nvariable sources among GPs which demonstrates the uniqueness and importance of\nstudying these extreme objects. Through this work, we demonstrate that\nphotometric variability is a promising tool to select AGN that may be missed by\nother selection techniques (including optical emission-line ratios and X-ray\nemission) in star-formation dominated, low-mass, low-metallicity galaxies.\n"}
+{"abstract": "  Second language (L2) English learners often find it difficult to improve\ntheir pronunciations due to the lack of expressive and personalized corrective\nfeedback. In this paper, we present Pronunciation Teacher (PTeacher), a\nComputer-Aided Pronunciation Training (CAPT) system that provides personalized\nexaggerated audio-visual corrective feedback for mispronunciations. Though the\neffectiveness of exaggerated feedback has been demonstrated, it is still\nunclear how to define the appropriate degrees of exaggeration when interacting\nwith individual learners. To fill in this gap, we interview 100 L2 English\nlearners and 22 professional native teachers to understand their needs and\nexperiences. Three critical metrics are proposed for both learners and teachers\nto identify the best exaggeration levels in both audio and visual modalities.\nAdditionally, we incorporate the personalized dynamic feedback mechanism given\nthe English proficiency of learners. Based on the obtained insights, a\ncomprehensive interactive pronunciation training course is designed to help L2\nlearners rectify mispronunciations in a more perceptible, understandable, and\ndiscriminative manner. Extensive user studies demonstrate that our system\nsignificantly promotes the learners' learning efficiency.\n"}
+{"abstract": "  Applying the concept of S-convergence, based on averaging in the spirit of\nStrong Law of Large Numbers, the vanishing viscosity solutions of the Euler\nsystem are studied. We show how to efficiently compute a viscosity solution of\nthe Euler system as the S-limit of numerical solutions obtained by the\nViscosity Finite Volume method. Theoretical results are illustrated by\nnumerical simulations of the Kelvin--Helmholtz instability problem.\n"}
+{"abstract": "  In this paper, we propose our enhanced approach to create a dedicated corpus\nfor Algerian Arabic newspapers comments. The developed approach has to enhance\nan existing approach by the enrichment of the available corpus and the\ninclusion of the annotation step by following the Model Annotate Train Test\nEvaluate Revise (MATTER) approach. A corpus is created by collecting comments\nfrom web sites of three well know Algerian newspapers. Three classifiers,\nsupport vector machines, na{\\\"i}ve Bayes, and k-nearest neighbors, were used\nfor classification of comments into positive and negative classes. To identify\nthe influence of the stemming in the obtained results, the classification was\ntested with and without stemming. Obtained results show that stemming does not\nenhance considerably the classification due to the nature of Algerian comments\ntied to Algerian Arabic Dialect. The promising results constitute a motivation\nfor us to improve our approach especially in dealing with non Arabic sentences,\nespecially Dialectal and French ones.\n"}
+{"abstract": "  We describe a technique to measure photon pair joint spectra by detecting the\ntime-correlation beat note when non-degenerate photon pairs interfere at a\nbeamsplitter. The technique implements a temporal analog of the Ghosh-Mandel\neffect with one photon counter and a time-resolved Hong-Ou-Mandel interference\nwith two. It is well suited to characterize pairs of photons, each of which can\ninteract with a single atomic species, as required to study recently predicted\nphoton-photon interaction in sub-wavelength atomic arrays. With this technique,\nwe characterize photon pairs from cavity-enhanced parametric downconversion\nwith a bandwidth $\\approx$ 5 MHz and frequency separation of $\\sim$ 200 MHz\nnear the D$_1$ line of atomic Rb.\n"}
+{"abstract": "  We consider the problem of linear regression from strategic data sources with\na public good component, i.e., when data is provided by strategic agents who\nseek to minimize an individual provision cost for increasing their data's\nprecision while benefiting from the model's overall precision. In contrast to\nprevious works, our model tackles the case where there is uncertainty on the\nattributes characterizing the agents' data -- a critical aspect of the problem\nwhen the number of agents is large. We provide a characterization of the game's\nequilibrium, which reveals an interesting connection with optimal design.\nSubsequently, we focus on the asymptotic behavior of the covariance of the\nlinear regression parameters estimated via generalized least squares as the\nnumber of data sources becomes large. We provide upper and lower bounds for\nthis covariance matrix and we show that, when the agents' provision costs are\nsuperlinear, the model's covariance converges to zero but at a slower rate\nrelative to virtually all learning problems with exogenous data. On the other\nhand, if the agents' provision costs are linear, this covariance fails to\nconverge. This shows that even the basic property of consistency of generalized\nleast squares estimators is compromised when the data sources are strategic.\n"}
+{"abstract": "  The supernova remnant (SNR) 3C 397 is thought to originate from a Type Ia\nsupernova (SN Ia) explosion of a near-Chandrasekhar-mass ($M_{\\rm Ch}$)\nprogenitor, based on the enhanced abundances of Mn and Ni revealed by previous\nX-ray study with Suzaku. Here we report follow-up XMM-Newton observations of\nthis SNR, conducted with the aim of investigating the detailed spatial\ndistribution of the Fe-peak elements. We have discovered an ejecta clump with\nextremely high abundances of Ti and Cr, in addition to Mn, Fe, and Ni, in the\nsouthern part of the SNR. The Fe mass of this ejecta clump is estimated to be\n$\\sim$ 0.06 $M_{\\odot}$, under the assumption of a typical Fe yield for SNe Ia\n(i.e., $\\sim$ 0.8 $M_{\\odot}$). The observed mass ratios among the Fe-peak\nelements and Ti require substantial neutronization that is achieved only in the\ninnermost regions of a near-$M_{\\rm Ch}$ SN Ia with a central density of\n$\\rho_c \\sim 5 \\times 10^9$ g cm$^{-3}$, significantly higher than typically\nassumed for standard near-$M_{\\rm Ch}$ SNe Ia ($\\rho_c \\sim 2 \\times 10^9$ g\ncm$^{-3}$). The overproduction of the neutron-rich isotopes (e.g., $^{50}$Ti\nand $^{54}$Cr) is significant in such high-$\\rho_c$ SNe Ia, with respect to the\nsolar composition. Therefore, if 3C 397 is a typical high-$\\rho_c$ near-$M_{\\rm\nCh}$ SN Ia remnant, the solar abundances of these isotopes could be reproduced\nby the mixture of the high- and low-$\\rho_c$ near-$M_{\\rm Ch}$ and sub-$M_{\\rm\nCh}$ Type Ia events, with $\\lesssim$ 20 % being high-$\\rho_c$ near-$M_{\\rm\nCh}$.\n"}
+{"abstract": "  We present the results of the X-ray flaring activity of 1ES 1959+650 during\nOctober 25-26, 2017 using AstroSat observations. The source was variable in the\nX-ray. We investigated the evolution of the X-ray spectral properties of the\nsource by dividing the total observation period ($\\sim 130$ ksecs) into time\nsegments of 5 ksecs, and fitting the SXT and LAXPC spectra for each segment.\nSynchrotron emission of a broken power-law particle density model provided a\nbetter fit than the log-parabola one. The X-ray flux and the normalised\nparticle density at an energy less than the break one, were found to\nanti-correlate with the index before the break. However, a stronger correlation\nbetween the density and index was obtained when a delay of $\\sim 60$ ksec was\nintroduced. The amplitude of the normalised particle density variation $|\\Delta\nn_\\gamma/n_\\gamma| \\sim 0.1$ was found to be less than that of the index\n$\\Delta \\Gamma \\sim 0.5$. We model the amplitudes and the time delay in a\nscenario where the particle acceleration time-scale varies on a time-scale\ncomparable to itself. In this framework, the rest frame acceleration time-scale\nis estimated to be $\\sim 1.97\\times10^{5}$ secs and the emission region size to\nbe $\\sim 6.73\\times 10^{15}$ cms.\n"}
+{"abstract": "  In this paper we consider the problem to reconstruct a $k$-uniform hypergraph\nfrom its line graph. In general this problem is hard. We solve this problem\nwhen the number of hyperedges containing any pair of vertices is bounded. Given\nan integer sequence, constructing a $k$-uniform hypergraph with that as its\ndegree sequence is NP-complete. Here we show that for constant integer\nsequences the question can be answered in polynomial time using Baranyai's\ntheorem.\n"}
+{"abstract": "  We quantitatively investigate the dependence of central galaxy HI mass\n($M_{\\rm HI}$) on the stellar mass ($M_\\ast$), halo mass ($M_{\\rm h}$), star\nformation rate (SFR), and central stellar surface density within 1 kpc\n($\\Sigma_1$), taking advantage of the HI spectra stacking technique using both\nthe Arecibo Fast Legacy ALFA Survey and the Sloan Digital Sky Survey. We find\nthat the shapes of $M_{\\rm HI}$-$M_{\\rm h}$ and $M_{\\rm HI}$-$M_\\ast$ relations\nare remarkably similar for both star-forming and quenched galaxies, with\nmassive quenched galaxies having constantly lower HI masses of around 0.6 dex.\nThis similarity strongly suggests that neither halo mass nor stellar mass is\nthe direct cause of quenching, but rather the depletion of HI reservoir. While\nthe HI reservoir for low-mass galaxies of $M_\\ast<10^{10.5}M_\\odot$ strongly\nincreases with $M_{\\rm h}$, more massive galaxies show no significant\ndependence of $M_{\\rm HI}$ on $M_{\\rm h}$, indicating the effect of halo to\ndetermine the smooth cold gas accretion. We find that the star formation and\nquenching of central galaxies are directly regulated by the available HI\nreservoir, with an average relation of ${\\rm SFR}\\propto M_{\\rm\nHI}^{2.75}/M_\\ast^{0.40}$, implying a quasi-steady state of star formation. We\nfurther confirm that galaxies are depleted of their HI reservoir once they drop\noff the star-formation main sequence and there is a very tight and consistent\ncorrelation between $M_{\\rm HI}$ and $\\Sigma_1$ in this phase, with $M_{\\rm\nHI}\\propto\\Sigma_1^{-2}$. This result is in consistent with the\ncompaction-triggered quenching scenario, with galaxies going through three\nevolutionary phases of cold gas accretion, compaction and post-compaction, and\nquenching.\n"}
+{"abstract": "  The competent programmer hypothesis states that most programmers are\ncompetent enough to create correct or almost correct source code. Because this\nimplies that bugs should usually manifest through small variations of the\ncorrect code, the competent programmer hypothesis is one of the fundamental\nassumptions of mutation testing. Unfortunately, it is still unclear if the\ncompetent programmer hypothesis holds and past research presents contradictory\nclaims. Within this article, we provide a new perspective on the competent\nprogrammer hypothesis and its relation to mutation testing. We try to re-create\nreal-world bugs through chains of mutations to understand if there is a direct\nlink between mutation testing and bugs. The lengths of these paths help us to\nunderstand if the source code is really almost correct, or if large variations\nare required. Our results indicate that while the competent programmer\nhypothesis seems to be true, mutation testing is missing important operators to\ngenerate representative real-world bugs.\n"}
+{"abstract": "  Demanding that charged Nariai black holes in (quasi-)de Sitter space decay\nwithout becoming super-extremal implies a lower bound on the masses of charged\nparticles, known as the Festina Lente (FL) bound. In this paper we fix the\n$\\mathcal{O}(1)$ constant in the bound and elucidate various aspects of it, as\nwell as extensions to $d>4$ and to situations with scalar potentials and\ndilatonic couplings. We also discuss phenomenological implications of FL\nincluding an explanation of why the Higgs potential cannot have a local minimum\nat the origin, thus explaining why the weak force must be broken. For\nconstructions of meta-stable dS involving anti-brane uplift scenarios, even\nthough the throat region is consistent with FL, the bound implies that we\ncannot have any light charged matter fields coming from any far away region in\nthe compactified geometry, contrary to the fact that they are typically\nexpected to arise in these scenarios. This strongly suggests that introduction\nof warped anti-branes in the throat cannot be decoupled from the bulk dynamics\nas is commonly assumed. Finally, we provide some evidence that in certain\nsituations the FL bound can have implications even with gravity decoupled and\nillustrate this in the context of non-compact throats.\n"}
+{"abstract": "  From small screenshots to large videos, documents take up a bulk of space in\na modern smartphone. Documents in a phone can accumulate from various sources,\nand with the high storage capacity of mobiles, hundreds of documents are\naccumulated in a short period. However, searching or managing documents remains\nan onerous task, since most search methods depend on meta-information or only\ntext in a document. In this paper, we showcase that a single modality is\ninsufficient for classification and present a novel pipeline to classify\ndocuments on-device, thus preventing any private user data transfer to server.\nFor this task, we integrate an open-source library for Optical Character\nRecognition (OCR) and our novel model architecture in the pipeline. We optimise\nthe model for size, a necessary metric for on-device inference. We benchmark\nour classification model with a standard multimodal dataset FOOD-101 and\nshowcase competitive results with the previous State of the Art with 30% model\ncompression.\n"}
+{"abstract": "  We consider a novel formulation of the dynamic pricing and demand learning\nproblem, where the evolution of demand in response to posted prices is governed\nby a stochastic variant of the popular Bass model with parameters $\\alpha,\n\\beta$ that are linked to the so-called \"innovation\" and \"imitation\" effects.\nUnlike the more commonly used i.i.d. and contextual demand models, in this\nmodel the posted price not only affects the demand and the revenue in the\ncurrent round but also the future evolution of demand, and hence the fraction\nof potential market size $m$ that can be ultimately captured. In this paper, we\nconsider the more challenging incomplete information problem where dynamic\npricing is applied in conjunction with learning the unknown parameters, with\nthe objective of optimizing the cumulative revenues over a given selling\nhorizon of length $T$. Equivalently, the goal is to minimize the regret which\nmeasures the revenue loss of the algorithm relative to the optimal expected\nrevenue achievable under the stochastic Bass model with market size $m$ and\ntime horizon $T$. Our main contribution is the development of an algorithm that\nsatisfies a high probability regret guarantee of order $\\tilde O(m^{2/3})$;\nwhere the market size $m$ is known a priori. Moreover, we show that no\nalgorithm can incur smaller order of loss by deriving a matching lower bound.\nUnlike most regret analysis results, in the present problem the market size $m$\nis the fundamental driver of the complexity; our lower bound in fact, indicates\nthat for any fixed $\\alpha, \\beta$, most non-trivial instances of the problem\nhave constant $T$ and large $m$. We believe that this insight sets the problem\nof dynamic pricing under the Bass model apart from the typical i.i.d. setting\nand multi-armed bandit based models for dynamic pricing, which typically focus\nonly on the asymptotics with respect to time horizon $T$.\n"}
+{"abstract": "  We focus on the realistic maximization of the uplink minimum\nsignal-to-interference-plus-noise ratio (SINR) of a general multiple-input\nsingle-output (MISO) system assisted by an intelligent reflecting surface (IRS)\nin the large system limit accounting for HIs. In particular, we introduce the\nHIs at both the IRS (IRS-HIs) and the transceiver HIs (AT-HIs), usually\nneglected despite their inevitable impact. Specifically, the deterministic\nequivalent analysis enables the derivation of the asymptotic weighted\nmaximum-minimum SINR with HIs by jointly optimizing the HIs-aware receiver, the\ntransmit power, and the reflect beamforming matrix (RBM). Notably, we obtain\nthe optimal power allocation and reflect beamforming matrix with low overhead\ninstead of their frequent necessary computation in conventional MIMO systems\nbased on the instantaneous channel information. Monte Carlo simulations verify\nthe analytical results which show the insightful interplay among the key\nparameters and the degradation of the performance due to HIs.\n"}
+{"abstract": "  It was suggested that a programmable matter system (composed of multiple\ncomputationally weak mobile particles) should remain connected at all times\nsince otherwise, reconnection is difficult and may be impossible. At the same\ntime, it was not clear that allowing the system to disconnect carried a\nsignificant advantage in terms of time complexity. We demonstrate for a\nfundamental task, that of leader election, an algorithm where the system\ndisconnects and then reconnects automatically in a non-trivial way (particles\ncan move far away from their former neighbors and later reconnect to others).\nMoreover, the runtime of the temporarily disconnecting deterministic leader\nelection algorithm is linear in the diameter. Hence, the disconnecting --\nreconnecting algorithm is as fast as previous randomized algorithms. When\ncomparing to previous deterministic algorithms, we note that some of the\nprevious work assumed weaker schedulers. Still, the runtime of all the previous\ndeterministic algorithms that did not assume special shapes of the particle\nsystem (shapes with no holes) was at least quadratic in $n$, where $n$ is the\nnumber of particles in the system. (Moreover, the new algorithm is even faster\nin some parameters than the deterministic algorithms that did assume special\ninitial shapes.)\n  Since leader election is an important module in algorithms for various other\ntasks, the presented algorithm can be useful for speeding up other algorithms\nunder the assumption of a strong scheduler. This leaves open the question: \"can\na deterministic algorithm be as fast as the randomized ones also under weaker\nschedulers?\"\n"}
+{"abstract": "  We discuss the prospects of gravitational lensing of gravitational waves\n(GWs) coming from core-collapse supernovae (CCSN). As the CCSN GW signal can\nonly be detected from within our own Galaxy and the local group by current and\nupcoming ground-based GW detectors, we focus on microlensing. We introduce a\nnew technique based on analysis of the power spectrum and association of peaks\nof the power spectrum with the peaks of the amplification factor to identify\nlensed signals. We validate our method by applying it on the CCSN-like mock\nsignals lensed by a point mass lens. We find that the lensed and unlensed\nsignal can be differentiated using the association of peaks by more than one\nsigma for lens masses larger than 150 solar masses. We also study the\ncorrelation integral between the power spectra and corresponding amplification\nfactor. This statistical approach is able to differentiate between unlensed and\nlensed signals for lenses as small as 15 solar masses. Further, we demonstrate\nthat this method can be used to estimate the mass of a lens in case the signal\nis lensed. The power spectrum based analysis is general and can be applied to\nany broad band signal and is especially useful for incoherent signals.\n"}
+{"abstract": "  Recently superconductivity was discovered in the Kagome metal AV3Sb5 (A = K,\nRb, and Cs), which has an ideal Kagome lattice of vanadium. These V-based\nsuperconductors also host charge density wave (CDW) and topological nontrivial\nband structure. Here we report the ultralow-temperature thermal conductivity\nand high pressure resistance measurements on CsV3Sb5 with Tc = 2.5 K, the\nhighest among AV3Sb5. A finite residual linear term of thermal conductivity at\nzero magnetic field and its rapid increase in fields suggest nodal\nsuperconductivity. By applying pressure, the Tc of CsV3Sb5 increases first,\nthen decreases to lower than 0.3 K at 11.4 GPa, showing a clear first\nsuperconducting dome peaked around 0.8 GPa. Above 11.4 GPa, superconductivity\nre-emerges, suggesting a second superconducting dome. Both nodal\nsuperconductivity and superconducting domes point to unconventional\nsuperconductivity in this V-based superconductor. While our finding of nodal\nsuperconductivity puts a strong constrain on the pairing state of the first\ndome, which should be related to the CDW instability, the superconductivity of\nthe second dome may present another exotic pairing state in this ideal Kagome\nlattice of vanadium.\n"}
+{"abstract": "  Scientific journals are currently the primary medium used by researchers to\nreport their research findings. The transformation of print journals into\ne-journals has simplified the process of submissions to journals and also their\naccess has become wider. Journals are usually published by commercial\npublishers, learned societies as well as Universities. There are different\nnumber of journals published from different countries. This paper attempts to\nexplore whether the number of journals published from a country influences its\nresearch output. Scopus master journal list is analysed to identify journals\npublished from 50 selected countries with significant volume of research\noutput. The following relationship are analysed: (a) number of journals from a\ncountry and its research output, (b) growth rate of journals and research\noutput for different countries, (c) global share of journals and research\noutput for different countries, and (d) subject area-wise number of journals\nand research output in that subject area for different countries. Factors like\njournal packing density are also analysed. The results obtained show that for\nmajority of the countries, the number of journals is positively correlated to\ntheir research output volume, though some other factors also play a role in\ngrowth of research output. The study at the end presents a discussion of the\nanalytical outcomes and provides useful suggestions on policy perspectives for\ndifferent countries.\n"}
+{"abstract": "  Superluminal tunneling of light through a barrier has attracted broad\ninterest in the last several decades. Despite the observation of such phenomena\nin various systems, it has been under intensive debate whether the transmitted\nlight truly carry the information of the original pulse. Here we report\nobservation of anomalous time response for terahertz electromagnetic pulses\npassing through thin metal films, with the pulse shape of the transmitted beam\nfaithfully resembling that of the incident beam. A causal theoretical analysis\nis developed to explain the experiments, though the theory of Special\nRelativity may confront a challenge in this exceptional circumstance. These\nfindings may facilitate future applications in high-speed optical communication\nor signal transmission, and may reshape our fundamental understanding about the\ntunneling of light.\n"}
+{"abstract": "  Deep Neural Networks have achieved unprecedented success in the field of face\nrecognition such that any individual can crawl the data of others from the\nInternet without their explicit permission for the purpose of training\nhigh-precision face recognition models, creating a serious violation of\nprivacy. Recently, a well-known system named Fawkes (published in USENIX\nSecurity 2020) claimed this privacy threat can be neutralized by uploading\ncloaked user images instead of their original images. In this paper, we present\nOriole, a system that combines the advantages of data poisoning attacks and\nevasion attacks, to thwart the protection offered by Fawkes, by training the\nattacker face recognition model with multi-cloaked images generated by Oriole.\nConsequently, the face recognition accuracy of the attack model is maintained\nand the weaknesses of Fawkes are revealed. Experimental results show that our\nproposed Oriole system is able to effectively interfere with the performance of\nthe Fawkes system to achieve promising attacking results. Our ablation study\nhighlights multiple principal factors that affect the performance of the Oriole\nsystem, including the DSSIM perturbation budget, the ratio of leaked clean user\nimages, and the numbers of multi-cloaks for each uncloaked image. We also\nidentify and discuss at length the vulnerabilities of Fawkes. We hope that the\nnew methodology presented in this paper will inform the security community of a\nneed to design more robust privacy-preserving deep learning models.\n"}
+{"abstract": "  Quantum correlations, in particular those, which enable to violate a Bell\ninequality \\cite{BELL}, open a way to advantage in certain communication tasks.\nHowever, the main difficulty in harnessing quantumness is its fragility to,\ne.g, noise or loss of particles. We study the persistency of Bell correlations\nof GHZ based mixtures and Dicke states. For the former, we consider quantum\ncommunication complexity reduction (QCCR) scheme, and propose new Bell\ninequalities (BIs), which can be used in that scheme for higher persistency in\nthe limit of large number of particles $N$. In case of Dicke states, we show\nthat persistency can reach $0.482N$, significantly more than reported in\nprevious studies.\n"}
+{"abstract": "  Neural network modules conditioned by known priors can be effectively trained\nand combined to represent systems with nonlinear dynamics. This work explores a\nnovel formulation for data-efficient learning of deep control-oriented\nnonlinear dynamical models by embedding local model structure and constraints.\nThe proposed method consists of neural network blocks that represent input,\nstate, and output dynamics with constraints placed on the network weights and\nsystem variables. For handling partially observable dynamical systems, we\nutilize a state observer neural network to estimate the states of the system's\nlatent dynamics. We evaluate the performance of the proposed architecture and\ntraining methods on system identification tasks for three nonlinear systems: a\ncontinuous stirred tank reactor, a two tank interacting system, and an\naerodynamics body. Models optimized with a few thousand system state\nobservations accurately represent system dynamics in open loop simulation over\nthousands of time steps from a single set of initial conditions. Experimental\nresults demonstrate an order of magnitude reduction in open-loop simulation\nmean squared error for our constrained, block-structured neural models when\ncompared to traditional unstructured and unconstrained neural network models.\n"}
+{"abstract": "  Third-generation gravitational wave detectors, such as the Einstein Telescope\nand Cosmic Explorer, will detect a bunch of gravitational-wave (GW) signals\noriginating from the coalescence of binary neutron star (BNS) and binary black\nhole (BBH) systems out to the higher redshifts, $z\\sim 5-10$. There is a\npotential concern that some of the GW signals detected at a high statistical\nsignificance eventually overlap with each other, and the parameter estimation\nof such an overlapping system can differ from the one expected from a single\nevent. Also, there are certainly overlapping systems in which one of the\noverlapping events has a low signal-to-noise ratio $\\lesssim 4$, and is thus\nunable to be clearly detected. Those system will potentially be misidentified\nwith a single GW event, and the estimated parameters of binary GWs can be\nbiased. We estimate the occurrence rate of those overlapping events. We find\nthat the numbers of overlapping events are $\\sim 200$ per day for BNSs and a\nfew per hour for BBHs. Then we study the statistical impacts of these\noverlapping GWs on a parameter estimation based on the Fisher matrix analysis.\nOur finding is that the overlapping signals produce neither large statistical\nerrors nor serious systematic biases on the parameters of binary systems,\nunless the coalescence time and the redshifted chirp masses of the two\noverlapping GWs are very close to each other, i.e.,\n$|\\mathcal{M}_{z1}-\\mathcal{M}_{z2}|\\lesssim10^{-4} \\,(10^{-1})\\,M_\\odot$ and\n$|t_{\\rm c1}-t_{\\rm c2}|\\lesssim10^{-2}\\,(10^{-1})$\\,s for BNSs (BBHs). The\noccurrence rate of such a closely overlapping event is shown to be much smaller\nthan one per year with the third-generation detectors.\n"}
+{"abstract": "  We present the concept of a magnetless Reflective Gyrotropic Spatial Isolator\n(RGSI) metasurface. This is a birefringent metasurface that reflects vertically\npolarized incident waves into a horizontally polarized waves, and absorbs\nhorizontally polarized incident waves, hence providing isolation between the\ntwo orthogonal polarization. We first synthesize the metasurface using surface\nsusceptibility-based Generalized Sheet Transition Conditions~(GSTCs). We then\npropose a mirror-backed metaparticle implementation of this metasurface, where\ntransistor-loaded resonators provide the desired magnetless nonreciprocal\nresponse. Finally, we demonstrate the metasurface by full-wave simulation\nresults. The proposed RGSI metasurface may be used in various electromagnetic\napplications, and may also serve as a step towards more sophisticated\nmagnetless nonreciprocal metasurface systems.\n"}
+{"abstract": "  In this article, we aim to recover locally conservative and $H(div)$\nconforming fluxes for the linear Cut Finite Element Solution with Nitsche's\nmethod for Poisson problems with Dirichlet boundary condition. The computation\nof the conservative flux in the Raviart-Thomas space is completely local and\ndoes not require to solve any mixed problem. The $L^2$-norm of the difference\nbetween the numerical flux and the recovered flux can then be used as a\nposteriori error estimator in the adaptive mesh refinement procedure.\nTheoretically we are able to prove the global reliability and local efficiency.\nThe theoretical results are verified in the numerical results. Moreover, in the\nnumerical results we also observe optimal convergence rate for the flux error.\n"}
+{"abstract": "  We perform explorative analyses of the 3D gluon content of the proton via a\nstudy of (un)polarized twist-2 gluon TMDs, calculated in a spectator model for\nthe parent nucleon. Our approach encodes a flexible parametrization for the\nspectator-mass density, suited to describe both moderate and small-$x$ effects.\nAll these prospective developments are relevant in the investigation of the\ngluon dynamics inside nucleons and nuclei, which constitutes one of the major\ngoals of new-generation colliding machines, as the EIC, the HL-LHC and NICA.\n"}
+{"abstract": "  The strong alignment of small-scale turbulent Alfv\\'enic motions with the\ndirection of the magnetic field that percolates the small-scale eddies and\nimprints the direction of the magnetic field is a property that follows from\nthe MHD theory and the theory of turbulent reconnection. The Alfv\\'enic eddies\nmix magnetic fields perpendicular to the direction of the local magnetic field,\nand this type of motion is used to trace magnetic fields with the velocity\ngradient technique (VGT). The other type of turbulent motion, fast modes,\ninduces anisotropies orthogonal to Alfv\\'enic eddies and interferes with the\ntracing of the magnetic field with the VGT. We report a new effect, i.e., in a\nmagnetically dominated low-\\beta subsonic medium, fast modes are very\nintermittent, and in a volume, with a small filling factor the fast modes\ndominate other turbulent motions. We identify these localized regions as the\ncause of the occasional change of direction of gradients in our synthetic\nobservations. We show that the new technique of measuring the gradients of\ngradient amplitudes suppresses the contribution from the fast-mode-dominated\nregions, improving the magnetic field tracing. In addition, we show that the\ndistortion of the gradient measurements by fast modes is also applicable to the\nsynchrotron intensity gradients, but the effect is reduced compared to the VGT.\n"}
+{"abstract": "  This paper presents an approach to improve the forecast of computational\nfluid dynamics (CFD) simulations of urban air pollution using deep learning,\nand most specifically adversarial training. This adversarial approach aims to\nreduce the divergence of the forecasts from the underlying physical model. Our\ntwo-step method integrates a Principal Components Analysis (PCA) based\nadversarial autoencoder (PC-AAE) with adversarial Long short-term memory (LSTM)\nnetworks. Once the reduced-order model (ROM) of the CFD solution is obtained\nvia PCA, an adversarial autoencoder is used on the principal components time\nseries. Subsequentially, a Long Short-Term Memory network (LSTM) is\nadversarially trained on the latent space produced by the PC-AAE to make\nforecasts. Once trained, the adversarially trained LSTM outperforms a LSTM\ntrained in a classical way. The study area is in South London, including\nthree-dimensional velocity vectors in a busy traffic junction.\n"}
+{"abstract": "  Promising searches for new physics beyond the current Standard Model (SM) of\nparticle physics are feasible through isotope-shift spectroscopy, which is\nsensitive to a hypothetical fifth force between the neutrons of the nucleus and\nthe electrons of the shell. Such an interaction would be mediated by a new\nparticle which could in principle be associated with dark matter. In so-called\nKing plots, the mass-scaled frequency shifts of two optical transitions are\nplotted against each other for a series of isotopes. Subtle deviations from the\nexpected linearity could reveal such a fifth force. Here, we study\nexperimentally and theoretically six transitions in highly charged ions of Ca,\nan element with five stable isotopes of zero nuclear spin. Some of the\ntransitions are suitable for upcoming high-precision coherent laser\nspectroscopy and optical clocks. Our results provide a sufficient number of\nclock transitions for -- in combination with those of singly charged Ca$^+$ --\napplication of the generalized King plot method. This will allow future\nhigh-precision measurements to remove higher-order SM-related nonlinearities\nand open a new door to yet more sensitive searches for unknown forces and\nparticles.\n"}
+{"abstract": "  This paper and accompanying Python and C++ Framework is the product of the\nauthors perceived problems with narrow (Discrimination based) AI. (Artificial\nIntelligence) The Framework attempts to develop a genetic transfer of\nexperience through potential structural expressions using a common\nregulation/exchange value (energy) to create a model whereby neural\narchitecture and all unit processes are co-dependently developed by genetic and\nreal time signal processing influences; successful routes are defined by\nstability of the spike distribution per epoch which is influenced by\ngenetically encoded morphological development biases.These principles are aimed\ntowards creating a diverse and robust network that is capable of adapting to\ngeneral tasks by training within a simulation designed for transfer learning to\nother mediums at scale.\n"}
+{"abstract": "  Relativistic runaway electron avalanches (RREAs) imply a large multiplication\nof high energy electrons (~1 MeV). Two factors are necessary for this\nphenomenon: a high electric field sustained over a large distance and an\nenergetic particle to serve as a seed. The former sustains particle energies as\nthey keep colliding and lose energy randomly, and the latter serves as a\nmultiplication starting point that promotes avalanches. RREA is usually\nconnected to both terrestrial gamma-ray flashes (TGFs) and gamma-ray glows\n(also known as Thunderstorm Ground Enhancement (TGE) when detected at ground\nlevel) as possible generation mechanism of both events, but the current\nknowledge does not provide a clear relationship between these events (TGF and\nTGE), beyond their possible common source mechanism, still as they have\ndifferent characteristics. In particular, their timescales differ by several\norders of magnitude. This work shows that chain reactions by TGF byproducts can\ncontinue for the timescale of gamma-ray glows and even provide energetic\nparticles as seeds for RREAs of gamma-ray glows.\n"}
+{"abstract": "  We generalize the correspondence between theories and monads with arities of\narXiv:1101.3064 to $\\infty$-categories. Additionally, we introduce the notion\nof complete theories that is unique to the $\\infty$-categorical case and\nprovide a completion construction for a certain class of theories. Along the\nway we also develop the necessary technical material related to the flagged\nbicategory of correspondences and lax functor in the $\\infty$-categorical\ncontext.\n"}
+{"abstract": "  We report on the experimental observation of mirror enhanced directional\nsurface enhanced Raman scattering (SERS) from a self-assembled monolayer of\nmolecules coupled to a nanowire-nanoparticle (NW-NP) junction on a mirror in\nremote excitation configuration. Placing NW-NP junction on a metallic mirror\ngenerates multiple gap plasmon modes which have unique momentum space\nscattering signatures. We perform Fourier plane imaging of SERS from NW-NP on a\nmirror to understand the effect of multiple hotspots on molecular emission. We\nsystematically study the effect of ground plane on the directionality of\nemission from NW-NP junction and show that the presence of a mirror drastically\nreduces angular spread of emission. The effect of multiple hotspots in the\ngeometry on directionality of molecular emission is studied using 3D numerical\nsimulations. The results presented here will have implications in understanding\nplasmon hybridization in the momentum space and its effects on molecular\nemission.\n"}
+{"abstract": "  In the steady-state contingency analysis, the traditional Newton-Raphson\nmethod suffers from non-convergence issues when solving post-outage power flow\nproblems, which hinders the integrity and accuracy of security assessment. In\nthis paper, we propose a novel robust contingency analysis approach based on\nholomorphic embedding (HE). The HE-based simulator guarantees convergence if\nthe true power flow solution exists, which is desirable because it avoids the\ninfluence of numerical issues and provides a credible security assessment\nconclusion. In addition, based on the multi-area characteristics of real-world\npower systems, a partitioned HE (PHE) method is proposed with an\ninterface-based partitioning of HE formulation. The PHE method does not\nundermine the numerical robustness of HE and significantly reduces the\ncomputation burden in large-scale contingency analysis. The PHE method is\nfurther enhanced by parallel or distributed computation to become parallel PHE\n(P${}^\\mathrm{2}$HE). Tests on a 458-bus system, a synthetic 419-bus system and\na large-scale 21447-bus system demonstrate the advantages of the proposed\nmethods in robustness and efficiency.\n"}
+{"abstract": "  The $\\alpha$-spectral radius of a connected graph $G$ is the spectral radius\nof $A_\\alpha$-matrix of $G$. In this paper, we discuss the methods for\ncomparing $\\alpha$-spectral radius of graphs. As applications, we characterize\nthe graphs with the maximal $\\alpha$-spectral radius among all unicyclic and\nbicyclic graphs of order $n$ with diameter $d$, respectively. Finally, we\ndetermine the unique graph with maximal signless Laplacian spectral radius\namong bicyclic graphs of order $n$ with diameter $d$.\n"}
+{"abstract": "  Machine learning models are known to be vulnerable to adversarial attacks,\nnamely perturbations of the data that lead to wrong predictions despite being\nimperceptible. However, the existence of \"universal\" attacks (i.e., unique\nperturbations that transfer across different data points) has only been\ndemonstrated for images to date. Part of the reason lies in the lack of a\ncommon domain, for geometric data such as graphs, meshes, and point clouds,\nwhere a universal perturbation can be defined. In this paper, we offer a change\nin perspective and demonstrate the existence of universal attacks for geometric\ndata (shapes). We introduce a computational procedure that operates entirely in\nthe spectral domain, where the attacks take the form of small perturbations to\nshort eigenvalue sequences; the resulting geometry is then synthesized via\nshape-from-spectrum recovery. Our attacks are universal, in that they transfer\nacross different shapes, different representations (meshes and point clouds),\nand generalize to previously unseen data.\n"}
+{"abstract": "  The large radii of many hot Jupiters can only be matched by models that have\nhot interior adiabats, and recent theoretical work has shown that the interior\nevolution of hot Jupiters has a significant impact on their atmospheric\nstructure. Due to its inflated radius, low gravity, and ultra-hot equilibrium\ntemperature, WASP-76b is an ideal case study for the impact of internal\nevolution on observable properties. Hot interiors should most strongly affect\nthe non-irradiated side of the planet, and thus full phase curve observations\nare critical to ascertain the effect of the interior on the atmospheres of hot\nJupiters. In this work, we present the first Spitzer phase curve observations\nof WASP-76b. We find that WASP-76b has an ultra-hot day side and relatively\ncold nightside with brightness temperatures of $2471 \\pm 27~\\mathrm{K}$/$1518\n\\pm 61~\\mathrm{K}$ at $3.6~\\micron$ and $2699 \\pm 32~\\mathrm{K}$/$1259 \\pm\n44~\\mathrm{K}$ at $4.5~\\micron$, respectively. These results provide evidence\nfor a dayside thermal inversion. Both channels exhibit small phase offsets of\n$0.68 \\pm 0.48^{\\circ}$ at $3.6~\\micron$ and $0.67 \\pm 0.2^{\\circ}$ at\n$4.5~\\mu\\mathrm{m}$. We compare our observations to a suite of general\ncirculation models that consider two end-members of interior temperature along\nwith a broad range of frictional drag strengths. Strong frictional drag is\nnecessary to match the small phase offsets and cold nightside temperatures\nobserved. From our suite of cloud-free GCMs, we find that only cases with a\ncold interior can reproduce the cold nightsides and large phase curve amplitude\nat $4.5~\\micron$, hinting that the hot interior adiabat of WASP-76b does not\nsignificantly impact its atmospheric dynamics or that clouds blanket its\nnightside.\n"}
+{"abstract": "  Many researchers have been concerned with whether social media has a negative\nimpact on the well-being of their audience. With the popularity of social\nnetworking sites (SNS) steadily increasing, psychological and social sciences\nhave shown great interest in their effects and consequences on humans. In this\nwork, we investigate Facebook using the tools of HCI to find connections\nbetween interface features and the concerns raised by these domains. Using an\nempirical design analysis, we identify interface interferences impacting users'\nonline privacy. Through a subsequent survey (n=116), we find usage behaviour\nchanges due to increased privacy concerns and report individual cases of\naddiction and mental health issues. These observations are the results of a\nrapidly changing SNS creating a gap of understanding between users'\ninteractions with the platform and future consequences. We explore how HCI can\nhelp close this gap and work towards more ethical user interfaces in the\nfuture.\n"}
+{"abstract": "  The full physics potential of the next-generation Deep Underground Neutrino\nExperiment (DUNE) is still being explored. In particular, there have been some\nrecent studies on the possibility of improving DUNE's neutrino energy\nreconstruction. The main motivation is that a better determination of the\nneutrino energy in an event-by-event basis will translate into an improved\nmeasurement of the Dirac $CP$ phase and other neutrino oscillation parameters.\nTo further motivate studies and improvements on the neutrino energy\nreconstruction, we evaluate the impact of energy resolution at DUNE on an\nillustrative new physics scenario, viz. non-standard interactions (NSI) of\nneutrinos with matter. We show that a better energy resolution in comparison to\nthe ones given in the DUNE conceptual and technical design reports may\nsignificantly enhance the experimental sensitivity to NSI, particularly when\ndegeneracies are present. While a better reconstruction of the first\noscillation peak helps disentangling standard $CP$ effects from those coming\nfrom NSIs, we find that the second oscillation peak also plays a nontrivial\nrole in improving DUNE's sensitivity.\n"}
+{"abstract": "  Transition metal dichalcogenide (TMD) moir\\'e heterostructures provide an\nideal platform to explore the extended Hubbard model1 where long-range Coulomb\ninteractions play a critical role in determining strongly correlated electron\nstates. This has led to experimental observations of Mott insulator states at\nhalf filling2-4 as well as a variety of extended Wigner crystal states at\ndifferent fractional fillings5-9. Microscopic understanding of these emerging\nquantum phases, however, is still lacking. Here we describe a novel scanning\ntunneling microscopy (STM) technique for local sensing and manipulation of\ncorrelated electrons in a gated WS2/WSe2 moir\\'e superlattice that enables\nexperimental extraction of fundamental extended Hubbard model parameters. We\ndemonstrate that the charge state of local moir\\'e sites can be imaged by their\ninfluence on STM tunneling current, analogous to the charge-sensing mechanism\nin a single-electron transistor. In addition to imaging, we are also able to\nmanipulate the charge state of correlated electrons. Discharge cascades of\ncorrelated electrons in the moir\\'e superlattice are locally induced by ramping\nthe STM bias, thus enabling the nearest-neighbor Coulomb interaction (UNN) to\nbe estimated. 2D mapping of the moir\\'e electron charge states also enables us\nto determine onsite energy fluctuations at different moir\\'e sites. Our\ntechnique should be broadly applicable to many semiconductor moir\\'e systems,\noffering a powerful new tool for microscopic characterization and control of\nstrongly correlated states in moir\\'e superlattices.\n"}
+{"abstract": "  The idea of coded caching for content distribution networks was introduced by\nMaddah-Ali and Niesen, who considered the canonical $(N, K)$ cache network in\nwhich a server with $N$ files satisfy the demands of $K$ users (equipped with\nindependent caches of size $M$ each). Among other results, their work provided\na characterization of the exact rate memory tradeoff for the problem when\n$M\\geq\\frac{N}{K}(K-1)$. In this paper, we improve this result for large caches\nwith $M\\geq \\frac{N}{K}(K-2)$. For the case\n$\\big\\lceil\\frac{K+1}{2}\\big\\rceil\\leq N \\leq K$, we propose a new coded\ncaching scheme, and derive a matching lower bound to show that the proposed\nscheme is optimal. This extends the characterization of the exact rate memory\ntradeoff to the case $M\\geq \\frac{N}{K}\\Big(K-2+\\frac{(K-2+1/N)}{(K-1)}\\Big)$.\nFor the case $1\\leq N\\leq \\big\\lceil\\frac{K+1}{2}\\big\\rceil$, we derive a new\nlower bound, which demonstrates that the scheme proposed by Yu et al. is\noptimal and thus extend the characterization of the exact rate memory tradeoff\nto the case $M\\geq \\frac{N}{K}(K-2)$.\n"}
+{"abstract": "  Segmentation-based methods are widely used for scene text detection due to\ntheir superiority in describing arbitrary-shaped text instances. However, two\nmajor problems still exist: 1) current label generation techniques are mostly\nempirical and lack theoretical support, discouraging elaborate label design; 2)\nas a result, most methods rely heavily on text kernel segmentation which is\nunstable and requires deliberate tuning. To address these challenges, we\npropose a human cognition-inspired framework, termed, Conceptual Text Region\nNetwork (CTRNet). The framework utilizes Conceptual Text Regions (CTRs), which\nis a class of cognition-based tools inheriting good mathematical properties,\nallowing for sophisticated label design. Another component of CTRNet is an\ninference pipeline that, with the help of CTRs, completely omits the need for\ntext kernel segmentation. Compared with previous segmentation-based methods,\nour approach is not only more interpretable but also more accurate.\nExperimental results show that CTRNet achieves state-of-the-art performance on\nbenchmark CTW1500, Total-Text, MSRA-TD500, and ICDAR 2015 datasets, yielding\nperformance gains of up to 2.0%. Notably, to the best of our knowledge, CTRNet\nis among the first detection models to achieve F-measures higher than 85.0% on\nall four of the benchmarks, with remarkable consistency and stability.\n"}
+{"abstract": "  Improving the image resolution and acquisition speed of magnetic resonance\nimaging (MRI) is a challenging problem. There are mainly two strategies dealing\nwith the speed-resolution trade-off: (1) $k$-space undersampling with\nhigh-resolution acquisition, and (2) a pipeline of lower resolution image\nreconstruction and image super-resolution. However, these approaches either\nhave limited performance at certain high acceleration factor or suffer from the\nerror accumulation of two-step structure. In this paper, we combine the idea of\nMR reconstruction and image super-resolution, and work on recovering HR images\nfrom low-resolution under-sampled $k$-space data directly. Particularly, the\nSR-involved reconstruction can be formulated as a variational problem, and a\nlearnable network unrolled from its solution algorithm is proposed. A\ndiscriminator was introduced to enhance the detail refining performance.\nExperiment results using in-vivo HR multi-coil brain data indicate that the\nproposed SRR-Net is capable of recovering high-resolution brain images with\nboth good visual quality and perceptual quality.\n"}
+{"abstract": "  In the absence of an initial seed, the Biermann battery term of a non-ideal\ninduction equation acts as a source that generates weak magnetic fields. These\nfields are then amplified via a dynamo mechanism. The Kelvin-Helmholtz\ninstability is a fluid phenomenon that takes place in many astrophysical\nscenarios and can trigger the action of the Biermann battery and dynamo\nprocesses. We aim to investigate the effect that the ionisation degree of the\nplasma and the interaction between the charged and neutral species have on the\ngeneration and amplification of magnetic fields during the different stages of\nthe instability. We use the two-fluid model implemented in the numerical code\nMancha-2F. We perform 2D simulations starting from a configuration with no\ninitial magnetic field and which is unstable due to a velocity shear. We vary\nthe ionisation degree of the plasma and we analyse the role that the different\ncollisional terms included in the equations of the model play on the evolution\nof the instability and the generation of magnetic field. We find that when no\ncollisional coupling is considered between the two fluids, the effect of the\nBiermann battery mechanism does not depend on the ionisation degree. However,\nwhen elastic collisions are taken into account, the generation of magnetic\nfield is increased as the ionisation degree is reduced. This behaviour is\nslightly enhanced if the process of charge-exchange is also considered. We also\nfind a dependence on the total density of the plasma related to the dependence\non the coupling degree between the two fluids. As the total density is\nincreased, the results from the two-fluid model converge to the predictions of\nsingle-fluid models.\n"}
+{"abstract": "  High-angular-resolution cosmic microwave background experiments provide a\nunique opportunity to conduct a survey of time-variable sources at millimeter\nwavelengths, a population which has primarily been understood through follow-up\nmeasurements of detections in other bands. Here we report the first results of\nan astronomical transient survey with the South Pole Telescope (SPT) using the\nSPT-3G camera to observe 1500 square degrees of the southern sky. The\nobservations took place from March to November 2020 in three bands centered at\n95, 150, and 220 GHz. This survey yielded the detection of fifteen transient\nevents from sources not previously detected by the SPT. The majority are\nassociated with variable stars of different types, expanding the number of such\ndetected flares by more than a factor of two. The stellar flares are\nunpolarized and bright, in some cases exceeding 1 Jy, and have durations from a\nfew minutes to several hours. Another population of detected events last for\n2--3 weeks and appear to be extragalactic in origin. Though data availability\nat other wavelengths is limited, we find evidence for concurrent optical\nactivity for two of the stellar flares. Future data from SPT-3G and forthcoming\ninstruments will provide real-time detection of millimeter-wave transients on\ntimescales of minutes to months.\n"}
+{"abstract": "  The Fermilab Muon $g-2$ collaboration recently announced the first result of\nmeasurement of the muon anomalous magnetic moment ($g-2$), which confirmed the\nprevious result at the Brookhaven National Laboratory and thus the discrepancy\nwith its Standard Model prediction. We revisit low-scale supersymmetric models\nthat are naturally capable to solve the muon $g-2$ anomaly, focusing on two\ndistinct scenarios: chargino-contribution dominated and pure-bino-contribution\ndominated scenarios. It is shown that the slepton pair-production searches have\nexcluded broad parameter spaces for both two scenarios, but they are not closed\nyet. For the chargino-dominated scenario, the models with $m_{\\tilde{\\mu}_{\\rm\nL}}\\gtrsim m_{\\tilde{\\chi}^{\\pm}_1}$ are still widely allowed. For the\nbino-dominated scenario, we find that, although slightly non-trivial, the\nregion with low $\\tan \\beta$ with heavy higgsinos is preferred. In the case of\nuniversal slepton masses, the low mass regions with $m_{\\tilde{\\mu}}\\lesssim\n230$ GeV can explain the $g-2$ anomaly while satisfying the LHC constraints.\nFurthermore, we checked that the stau-bino coannihilation works properly to\nrealize the bino thermal relic dark matter. We also investigate heavy staus\ncase for the bino-dominated scenario, where the parameter region that can\nexplain the muon $g-2$ anomaly is stretched to $m_{\\tilde{\\mu}}\\lesssim 1.3$\nTeV.\n"}
+{"abstract": "  Overparametrized neural networks, where the number of active parameters is\nlarger than the sample size, prove remarkably effective in modern deep learning\npractice. From the classical perspective, however, much fewer parameters are\nsufficient for optimal estimation and prediction, whereas overparametrization\ncan be harmful even in the presence of explicit regularization. To reconcile\nthis conflict, we present a generalization theory for overparametrized ReLU\nnetworks by incorporating an explicit regularizer based on the scaled variation\nnorm. Interestingly, this regularizer is equivalent to the ridge from the angle\nof gradient-based optimization, but is similar to the group lasso in terms of\ncontrolling model complexity. By exploiting this ridge-lasso duality, we show\nthat overparametrization is generally harmless to two-layer ReLU networks. In\nparticular, the overparametrized estimators are minimax optimal up to a\nlogarithmic factor. By contrast, we show that overparametrized random feature\nmodels suffer from the curse of dimensionality and thus are suboptimal.\n"}
+{"abstract": "  Since planet occurrence and primordial atmospheric retention probability\nincrease with period, the occurrence-weighted median planets discovered by\ntransit surveys may bear little resemblance to the low-occurrence, short-period\nplanets sculpted by atmospheric escape ordinarily used to calibrate\nmass--radius relations and planet formation models. An occurrence-weighted\nmass--radius relation for the low-mass planets discovered so far by transit\nsurveys orbiting solar-type stars requires both occurrence-weighted median\nEarth-mass and Neptune-mass planets to have a few percent of their masses in\nhydrogen/helium (H/He) atmospheres. Unlike the Earth that finished forming long\nafter the protosolar nebula was dissipated, these occurrence-weighted median\nEarth-mass planets must have formed early in their systems' histories. The\nexistence of significant H/He atmospheres around Earth-mass planets confirms an\nimportant prediction of the core-accretion model of planet formation. It also\nimplies core masses $M_{\\text{c}}$ in the range $2~M_{\\oplus}\\lesssim\nM_{\\text{c}}\\lesssim 8~M_{\\oplus}$ that can retain their primordial\natmospheres. If atmospheric escape is driven by photoevaporation due to extreme\nultraviolet (EUV) flux, then our observation requires a reduction in the\nfraction of incident EUV flux converted into work usually assumed in\nphotoevaporation models. If atmospheric escape is core driven, then the\noccurrence-weighted median Earth-mass planets must have large Bond albedos. In\ncontrast to Uranus and Neptune that have at least 10% of their masses in H/He\natmospheres, these occurrence-weighted median Neptune-mass planets are H/He\npoor. The implication is that they experienced collisions or formed in much\nshorter-lived and/or hotter parts of their parent protoplanetary disks than\nUranus and Neptune's formation location in the protosolar nebula.\n"}
+{"abstract": "  Subgradient and Newton algorithms for nonsmooth optimization require\ngeneralized derivatives to satisfy subtle approximation properties:\nconservativity for the former and semismoothness for the latter. Though these\ntwo properties originate in entirely different contexts, we show that in the\nsemi-algebraic setting they are equivalent. Both properties for a generalized\nderivative simply require it to coincide with the standard directional\nderivative on the tangent spaces of some partition of the domain into smooth\nmanifolds. An appealing byproduct is a new short proof that semi-algebraic maps\nare semismooth relative to the Clarke Jacobian.\n"}
+{"abstract": "  Lung nodule detection from 3D Computed Tomography scans plays a vital role in\nefficient lung cancer screening. Despite the SOTA performance obtained by\nrecent anchor-based detectors using CNNs for this task, they require\npredetermined anchor parameters such as the size, number, and aspect ratio of\nanchors, and have limited robustness when dealing with lung nodules with a\nmassive variety of sizes. To overcome these problems, we propose a 3D sphere\nrepresentation-based center-points matching detection network that is\nanchor-free and automatically predicts the position, radius, and offset of\nnodules without the manual design of nodule/anchor parameters. The SCPM-Net\nconsists of two novel components: sphere representation and center points\nmatching. First, to match the nodule annotation in clinical practice, we\nreplace the commonly used bounding box with our proposed bounding sphere to\nrepresent nodules with the centroid, radius, and local offset in 3D space. A\ncompatible sphere-based intersection over-union loss function is introduced to\ntrain the lung nodule detection network stably and efficiently. Second, we\nempower the network anchor-free by designing a positive center-points selection\nand matching process, which naturally discards pre-determined anchor boxes. An\nonline hard example mining and re-focal loss subsequently enable the CPM\nprocess to be more robust, resulting in more accurate point assignment and\nmitigation of class imbalance. In addition, to better capture spatial\ninformation and 3D context for the detection, we propose to fuse multi-level\nspatial coordinate maps with the feature extractor and combine them with 3D\nsqueeze-and-excitation attention modules. Experimental results on the LUNA16\ndataset showed that our proposed framework achieves superior performance\ncompared with existing anchor-based and anchor-free methods for lung nodule\ndetection.\n"}
+{"abstract": "  The idea of possible modification to gravity theory, whether it is in the\nNewtonian or general relativistic premises, is there for quite sometime. Based\non it, astrophysical and cosmological problems are targeted to solve. But none\nof the Newtonian theories of modification has been performed from the first\nprinciple. Here, we modify Poisson's equation and propose two possible ways to\nmodify the law gravitation which, however, reduces to Newton's law far away\nfrom the center of source. Based on these modified Newton's laws, we attempt to\nsolve problems lying with white dwarfs. There are observational evidences for\npossible violation of the Chandrasekhar mass-limit significantly: it could be\nsub- as well as super-Chandrasekhar. We show that modified Newton's law, either\nby modifying LHS or RHS of Poisson's equation, can explain them.\n"}
+{"abstract": "  Within the framework of canonical type-I seesaw, a feebly interacting massive\nparticle (FIMP) $\\chi$ is introduced as a dark matter candidate. The\nleptogenesis mechanism and dark matter relic density share a common origin via\ndecays of Majorana neutrinos $N$. Provided an additional species $\\varphi$\nwhose energy density red-shifts as $\\rho_{\\varphi}\\propto a^{-(4+n)}$, the\nHubble expansion rate is larger than the standard scenario, i.e., the Universe\nexpands faster. The consequences of such a fast expanding Universe on\nleptogenesis as well as FIMP dark matter are investigated in detail. We\ndemonstrate a significant impact on the final baryon asymmetry and dark matter\nabundance due to the existence of $\\varphi$ for the strong washout scenario.\nWhile for the weak washout scenario, the effects of FEU are relatively small.\nWe introduce scale factors $F_L$ and $F_\\chi$ to describe the corresponding\neffects of FEU. A semi-analytical approach to derive the efficiency factors\n$\\eta_L$ and $\\eta_\\chi$ in FEU is also discussed. The viable parameter space\nfor success thermal leptogenesis and correct FIMP DM relic density is obtained\nfor standard cosmology and FEU. Our results show that it is possible to\ndistinguish different cosmology scenarios for strong washout cases.\n"}
+{"abstract": "  In this paper, we study important Schr\\\"{o}dinger systems with linear and\nnonlinear couplings \\begin{equation}\\label{eq:diricichlet} \\begin{cases}\n-\\Delta u_1-\\lambda_1 u_1=\\mu_1 |u_1|^{p_1-2}u_1+r_1\\beta\n|u_1|^{r_1-2}u_1|u_2|^{r_2}+\\kappa (x)u_2~\\hbox{in}~\\mathbb{R}^N,\\\\ -\\Delta\nu_2-\\lambda_2 u_2=\\mu_2 |u_2|^{p_2-2}u_2+r_2\\beta\n|u_1|^{r_1}|u_2|^{r_2-2}u_2+\\kappa (x)u_1~ \\hbox{in}~\\mathbb{R}^N,\\\\ u_1\\in\nH^1(\\mathbb{R}^N), u_2\\in H^1(\\mathbb{R}^N),\\nonumber \\end{cases}\n\\end{equation} with the condition $$\\int_{\\mathbb{R}^N} u_1^2=a_1^2,\n\\int_{\\mathbb{R}^N} u_2^2=a_2^2,$$ where $N\\geq 2$, $\\mu_1,\\mu_2,a_1,a_2>0$,\n$\\beta\\in\\mathbb{R}$, $2<p_1,p_2<2^*$, $2<r_1+r_2<2^*$, $\\kappa(x)\\in\nL^{\\infty}(\\mathbb{R}^N)$ with fixed sign and $\\lambda_1,\\lambda_2$ are\nLagrangian multipliers. We use Ekland variational principle to prove this\nsystem has a normalized radially symmetric solution for $L^2-$subcritical case\nwhen $N\\geq 2$, and use minimax method to prove this system has a normalized\nradially symmetric positive solution for $L^2-$supercritical case when $N=3$,\n$p_1=p_2=4,\\ r_1=r_2=2$.\n"}
+{"abstract": "  We present radiative hydrodynamic simulations of solar flares generated by\nthe RADYN and RH codes to study the perturbations induced in photospheric Fe I\nlines by electron beam heating. We investigate how variations in the beam\nparameters result in discernible differences in the induced photospheric\nvelocities. Line synthesis revealed a significant chromospheric contribution to\nthe line profiles resulting in an apparent red asymmetry by as much as 40 m/s\nclose to the time of maximum beam heating which was not reflective of the\nupflow velocities that arose from the radiative hydrodynamic simulations at\nthose times. The apparent redshift to the overall line profile was produced by\nsignificant chromospheric emission that was blueshifted by as much as 400 m/s\nand fills in the blue side of the near stationary photospheric absorption\nprofile. The velocity information that can be retrieved from photospheric line\nprofiles during flares must therefore be treated with care to mitigate the\neffects of higher parts of the atmosphere providing an erroneous velocity\nsignal.\n"}
+{"abstract": "  We develop a theory of vector spaces spanned by orbit-finite sets. Using this\ntheory, we give a decision procedure for equivalence of weighted register\nautomata, which are the common generalization of weighted automata and register\nautomata for infinite alphabets. The algorithm runs in exponential time, and in\npolynomial time for a fixed number of registers. As a special case, we can\ndecide, with the same complexity, language equivalence for unambiguous register\nautomata, which improves previous results in three ways: (a) we allow for order\ncomparisons on atoms, and not just equality; (b) the complexity is\nexponentially better; and (c) we allow automata with guessing.\n"}
+{"abstract": "  In this paper, we present the submitted system for the third DIHARD Speech\nDiarization Challenge from the DKU-Duke-Lenovo team. Our system consists of\nseveral modules: voice activity detection (VAD), segmentation, speaker\nembedding extraction, attentive similarity scoring, agglomerative hierarchical\nclustering. In addition, the target speaker VAD (TSVAD) is used for the phone\ncall data to further improve the performance. Our final submitted system\nachieves a DER of 15.43% for the core evaluation set and 13.39% for the full\nevaluation set on task 1, and we also get a DER of 21.63% for core evaluation\nset and 18.90% for full evaluation set on task 2.\n"}
+{"abstract": "  The local stability of ion-temperature gradient driven mode (ITG) in the\npresence of a given radial electric field is investigated using gyrokinetic\ntheory and ballooning mode formalism with toroidal effect accounted for. It is\nfound that, zero frequency radial electric field induced poloidal rotation can\nsignificantly stabilize ITG, while the associated density perturbation has\nlittle effect on ITG stability due to the modification of finite-orbit-width\neffect. However, the parallel mode structure is slightly affected due to the\nevenly symmetric density modulation of ZFZF.\n"}
+{"abstract": "  Despite the prominence of neural abstractive summarization models, we know\nlittle about how they actually form summaries and how to understand where their\ndecisions come from. We propose a two-step method to interpret summarization\nmodel decisions. We first analyze the model's behavior by ablating the full\nmodel to categorize each decoder decision into one of several generation modes:\nroughly, is the model behaving like a language model, is it relying heavily on\nthe input, or is it somewhere in between? After isolating decisions that do\ndepend on the input, we explore interpreting these decisions using several\ndifferent attribution methods. We compare these techniques based on their\nability to select content and reconstruct the model's predicted token from\nperturbations of the input, thus revealing whether highlighted attributions are\ntruly important for the generation of the next token. While this machinery can\nbe broadly useful even beyond summarization, we specifically demonstrate its\ncapability to identify phrases the summarization model has memorized and\ndetermine where in the training pipeline this memorization happened, as well as\nstudy complex generation phenomena like sentence fusion on a per-instance\nbasis.\n"}
+{"abstract": "  Over the past decade, many approaches have been introduced for traffic speed\nprediction. However, providing fine-grained, accurate, time-efficient, and\nadaptive traffic speed prediction for a growing transportation network where\nthe size of the network keeps increasing and new traffic detectors are\nconstantly deployed has not been well studied. To address this issue, this\npaper presents DistTune based on Long Short-Term Memory (LSTM) and the\nNelder-Mead method. Whenever encountering an unprocessed detector, DistTune\ndecides if it should customize an LSTM model for this detector by comparing the\ndetector with other processed detectors in terms of the normalized traffic\nspeed patterns they have observed. If similarity is found, DistTune directly\nshares an existing LSTM model with this detector to achieve time-efficient\nprocessing. Otherwise, DistTune customizes an LSTM model for the detector to\nachieve fine-grained prediction. To make DistTune even more time-efficient,\nDistTune performs on a cluster of computing nodes in parallel. To achieve\nadaptive traffic speed prediction, DistTune also provides LSTM re-customization\nfor detectors that suffer from unsatisfactory prediction accuracy due to for\ninstance traffic speed pattern change. Extensive experiments based on traffic\ndata collected from freeway I5-N in California are conducted to evaluate the\nperformance of DistTune. The results demonstrate that DistTune provides\nfine-grained, accurate, time-efficient, and adaptive traffic speed prediction\nfor a growing transportation network.\n"}
+{"abstract": "  This paper develops a fractional stochastic partial differential equation\n(SPDE) to model the evolution of a random tangent vector field on the unit\nsphere. The SPDE is governed by a fractional diffusion operator to model the\nL\\'{e}vy-type behaviour of the spatial solution, a fractional derivative in\ntime to depict the intermittency of its temporal solution, and is driven by\nvector-valued fractional Brownian motion on the unit sphere to characterize its\ntemporal long-range dependence. The solution to the SPDE is presented in the\nform of the Karhunen-Lo\\`{e}ve expansion in terms of vector spherical\nharmonics. Its covariance matrix function is established as a tensor field on\nthe unit sphere that is an expansion of Legendre tensor kernels. The variance\nof the increments and approximations to the solutions are studied and\nconvergence rates of the approximation errors are given. It is demonstrated how\nthese convergence rates depend on the decay of the power spectrum and variances\nof the fractional Brownian motion.\n"}
+{"abstract": "  Since the advent of graphene ushered the era of two-dimensional materials,\nmany forms of hydrogenated graphene have been reported, exhibiting diverse\nproperties ranging from a tunable band gap to ferromagnetic ordering. Patterned\nhydrogenated graphene with micron-scale patterns has been fabricated by\nlithographic means. Here we report successful millimeter-scale synthesis of an\nintrinsically honeycomb patterned form of hydrogenated graphene on Ru(0001) by\nepitaxial growth followed by hydrogenation. Combining scanning tunneling\nmicroscopy observations with density-functional-theory (DFT) calculations, we\nreveal that an atomic-hydrogen layer intercalates between graphene and\nRu(0001). The result is a hydrogen honeycomb structure that serves as a\ntemplate for the final hydrogenation, which converts the graphene into graphane\nonly over the template, yielding honeycomb-patterned hydrogenated graphene\n(HPHG). In effect, HPHG is a form of patterned graphane. DFT calculations find\nthat the unhydrogenated graphene regions embedded in the patterned graphane\nexhibit spin-polarized edge states. This type of growth mechanism provides new\npathways for the fabrication of intrinsically patterned graphene-based\nmaterials.\n"}
+{"abstract": "  The interlayer van der Waals interaction in twisted bilayer graphene (tBLG)\ninduces both in-plane and out-of-plane atomic displacements showing complex\npatterns that depend on the twist angle. In particular, for small twist angles,\nwithin each graphene layer, the relaxations give rise to a vortex-like\ndisplacement pattern which is known to affect the dispersion of the flat bands.\nHere, we focus on yet another structural property, the chirality of the twisted\nbilayer. We perform first-principles calculations based on density functional\ntheory to investigate the properties induced by twist chirality in both real\nand momentum space. In real space, we study the interplay between twist\nchirality and atomic relaxation patterns. In momentum space, we investigate the\nspin textures around the $K$ points of the Brillouin zone, showing that\nalternating vortex-like textures are correlated with the chirality of tBLG.\nInterestingly, the helicity of each vortex is inverted by changing the\nchirality while the different twist angles also modify the spin textures. We\ndiscuss the origin of the spin textures by calculating the layer weights and\nusing plot regression models.\n"}
+{"abstract": "  Super-resolution (SR) is a coveted image processing technique for mobile apps\nranging from the basic camera apps to mobile health. Existing SR algorithms\nrely on deep learning models with significant memory requirements, so they have\nyet to be deployed on mobile devices and instead operate in the cloud to\nachieve feasible inference time. This shortcoming prevents existing SR methods\nfrom being used in applications that require near real-time latency. In this\nwork, we demonstrate state-of-the-art latency and accuracy for on-device\nsuper-resolution using a novel hybrid architecture called SplitSR and a novel\nlightweight residual block called SplitSRBlock. The SplitSRBlock supports\nchannel-splitting, allowing the residual blocks to retain spatial information\nwhile reducing the computation in the channel dimension. SplitSR has a hybrid\ndesign consisting of standard convolutional blocks and lightweight residual\nblocks, allowing people to tune SplitSR for their computational budget. We\nevaluate our system on a low-end ARM CPU, demonstrating both higher accuracy\nand up to 5 times faster inference than previous approaches. We then deploy our\nmodel onto a smartphone in an app called ZoomSR to demonstrate the first-ever\ninstance of on-device, deep learning-based SR. We conducted a user study with\n15 participants to have them assess the perceived quality of images that were\npost-processed by SplitSR. Relative to bilinear interpolation -- the existing\nstandard for on-device SR -- participants showed a statistically significant\npreference when looking at both images (Z=-9.270, p<0.01) and text (Z=-6.486,\np<0.01).\n"}
+{"abstract": "  We present GeoSP, a parallel method that creates a parcellation of the\ncortical mesh based on a geodesic distance, in order to consider gyri and sulci\ntopology. The method represents the mesh with a graph and performs a K-means\nclustering in parallel. It has two modes of use, by default, it performs the\ngeodesic cortical parcellation based on the boundaries of the anatomical\nparcels provided by the Desikan-Killiany atlas. The other mode performs the\ncomplete parcellation of the cortex. Results for both modes and with different\nvalues for the total number of sub-parcels show homogeneous sub-parcels.\nFurthermore, the execution time is 82 s for the whole cortex mode and 18 s for\nthe Desikan-Killiany atlas subdivision, for a parcellation into 350\nsub-parcels. The proposed method will be available to the community to perform\nthe evaluation of data-driven cortical parcellations. As an example, we\ncompared GeoSP parcellation with Desikan-Killiany and Destrieux atlases in 50\nsubjects, obtaining more homogeneous parcels for GeoSP and minor differences in\nstructural connectivity reproducibility across subjects.\n"}
+{"abstract": "  Neural network classifiers are vulnerable to misclassification of adversarial\nsamples, for which the current best defense trains classifiers with adversarial\nsamples. However, adversarial samples are not optimal for steering attack\nconvergence, based on the minimization at the core of adversarial attacks. The\nminimization perturbation term can be minimized towards $0$ by replacing\nadversarial samples in training with duplicated original samples, labeled\ndifferently only for training. Using only original samples, Target Training\neliminates the need to generate adversarial samples for training against all\nattacks that minimize perturbation. In low-capacity classifiers and without\nusing adversarial samples, Target Training exceeds both default CIFAR10\naccuracy ($84.3$%) and current best defense accuracy (below $25$%) with $84.8$%\nagainst CW-L$_2$($\\kappa=0$) attack, and $86.6$% against DeepFool. Using\nadversarial samples against attacks that do not minimize perturbation, Target\nTraining exceeds current best defense ($69.1$%) with $76.4$% against\nCW-L$_2$($\\kappa=40$) in CIFAR10.\n"}
+{"abstract": "  Entity linking -- the task of identifying references in free text to relevant\nknowledge base representations -- often focuses on single languages. We\nconsider multilingual entity linking, where a single model is trained to link\nreferences to same-language knowledge bases in several languages. We propose a\nneural ranker architecture, which leverages multilingual transformer\nrepresentations of text to be easily applied to a multilingual setting. We then\nexplore how a neural ranker trained in one language (e.g. English) transfers to\nan unseen language (e.g. Chinese), and find that while there is a consistent\nbut not large drop in performance. How can this drop in performance be\nalleviated? We explore adding an adversarial objective to force our model to\nlearn language-invariant representations. We find that using this approach\nimproves recall in several datasets, often matching the in-language\nperformance, thus alleviating some of the performance loss occurring from\nzero-shot transfer.\n"}
+{"abstract": "  We analyze Hegselmann-Krause opinion formation models with leadership in\npresence of time delay effects. In particular, we consider a model with\npointwise time variable time delay and a model with a distributed delay. In\nboth cases we show that, when the delays satisfy suitable smallness conditions,\nthen the leader can control the system, leading the group to any prefixed\nstate. Some numerical tests illustrate our analytical results.\n"}
+{"abstract": "  In this work we prove the uniqueness of solutions to the nonlocal linear\nequation $L \\varphi - c(x)\\varphi = 0$ in $\\mathbb{R}$, where $L$ is an\nelliptic integro-differential operator, in the presence of a positive solution\nor of an odd solution vanishing only at zero. As an application, we deduce the\nnondegeneracy of layer solutions (bounded and monotone solutions) to the\nsemilinear problem $L u = f(u)$ in $\\mathbb{R}$ when the nonlinearity is of\nAllen-Cahn type. To our knowledge, this is the first work where such uniqueness\nand nondegeneracy results are proven in the nonlocal framework when the\nCaffarelli-Silvestre extension technique is not available. Our proofs are based\non a nonlocal Liouville-type method developed by Hamel, Ros-Oton, Sire, and\nValdinoci for nonlinear problems in dimension two.\n"}
+{"abstract": "  Predicting the final folded structure of protein molecules and simulating\ntheir folding pathways is of crucial importance for designing viral drugs and\nstudying diseases such as Alzheimer's at the molecular level. To this end, this\npaper investigates the problem of protein conformation prediction under the\nconstraint of avoiding high-entropy-loss routes during folding. Using the\nwell-established kinetostatic compliance (KCM)-based nonlinear dynamics of a\nprotein molecule, this paper formulates the protein conformation prediction as\na pointwise optimal control synthesis problem cast as a quadratic program (QP).\nIt is shown that the KCM torques in the protein folding literature can be\nutilized for defining a reference vector field for the QP-based control\ngeneration problem. The resulting kinetostatic control torque inputs will be\nclose to the KCM-based reference vector field and guaranteed to be constrained\nby a predetermined bound; hence, high-entropy-loss routes during folding are\navoided while the energy of the molecule is decreased.\n"}
+{"abstract": "  In a recent Letter [T.~Dornheim \\emph{et al.}, Phys.~Rev.~Lett.~\\textbf{125},\n085001 (2020)], we have presented the first \\emph{ab initio} results for the\nnonlinear density response of electrons in the warm dense matter regime. In the\npresent work, we extend these efforts by carrying out extensive new path\nintegral Monte Carlo (PIMC) simulations of a \\emph{ferromagnetic} electron gas\nthat is subject to an external harmonic perturbation. This allows us to\nunambiguously quantify the impact of spin-effects on the nonlinear density\nresponse of the warm dense electron gas. In addition to their utility for the\ndescription of warm dense matter in an external magnetic field, our results\nfurther advance our current understanding of the uniform electron gas as a\nfundamental model system, which is important in its own right.\n"}
+{"abstract": "  This article presents a method that uses turn-by-turn beam position data and\nk-modulation data to measure the calibration factors of beam position monitors\nin high energy accelerators. In this method, new algorithms have been developed\nto reduce the effect of coupling and other sources of uncertainty, allowing\naccurate estimates of the calibration factors. Simulations with known sources\nof errors indicate that calibration factors can be recovered with an accuracy\nof 0.7% rms for arc beam position monitors and an accuracy of 0.4% rms for\ninteraction region beam position monitors. The calibration factors are also\nobtained from LHC experimental data and are used to evaluate the effect this\ncalibration has on a quadrupole correction estimated with the action and phase\njump method for a interaction region of the LHC.\n"}
+{"abstract": "  Point cloud registration is a common step in many 3D computer vision tasks\nsuch as object pose estimation, where a 3D model is aligned to an observation.\nClassical registration methods generalize well to novel domains but fail when\ngiven a noisy observation or a bad initialization. Learning-based methods, in\ncontrast, are more robust but lack in generalization capacity. We propose to\nconsider iterative point cloud registration as a reinforcement learning task\nand, to this end, present a novel registration agent (ReAgent). We employ\nimitation learning to initialize its discrete registration policy based on a\nsteady expert policy. Integration with policy optimization, based on our\nproposed alignment reward, further improves the agent's registration\nperformance. We compare our approach to classical and learning-based\nregistration methods on both ModelNet40 (synthetic) and ScanObjectNN (real\ndata) and show that our ReAgent achieves state-of-the-art accuracy. The\nlightweight architecture of the agent, moreover, enables reduced inference time\nas compared to related approaches. In addition, we apply our method to the\nobject pose estimation task on real data (LINEMOD), outperforming\nstate-of-the-art pose refinement approaches.\n"}
+{"abstract": "  We construct non-Abelian analogs for some KdV type equations, including the\n(rational form of) exponential Calogero--Degasperis equation and\ngeneralizations of the Schwarzian KdV equation. Equations and differential\nsubstitutions under study contain arbitrary non-Abelian parameters.\n"}
+{"abstract": "  1I/'Oumuamua (or 1I) and 2I/Borisov (or 2I), the first InterStellar Objects\n(ISOs) discovered passing through the solar system, have opened up entirely new\nareas of exobody research. Finding additional ISOs and planning missions to\nintercept or rendezvous with these bodies will greatly benefit from knowledge\nof their likely orbits and arrival rates. Here, we use the local velocity\ndistribution of stars from the Gaia Early Data Release 3 Catalogue of Nearby\nStars and a standard gravitational focusing model to predict the velocity\ndependent flux of ISOs entering the solar system. With an 1I-type ISO number\ndensity of $\\sim$0.1 AU$^{-3}$, we predict that a total of $\\sim$6.9 such\nobjects per year should pass within 1 AU of the Sun. There will be a fairly\nlarge high-velocity tail to this flux, with half of the incoming ISOs predicted\nto have a velocity at infinity, v$_{\\infty}$, $>$ 40 km s$^{-1}$. Our model\npredicts that $\\sim$92\\% of incoming ISOs will be residents of the galactic\nthin disk, $\\sim$6\\% ($\\sim$4 per decade) will be from the thick disk, $\\sim$1\nper decade will be from the halo and at most $\\sim$3 per century will be\nunbound objects, ejected from our galaxy or entering the Milky Way from another\ngalaxy. The rate of ISOs with very low v$_{\\infty}$ $\\lesssim$ 1.5 km s$^{-1}$\nis so low in our model that any incoming very low velocity ISOs are likely to\nbe previously lost solar system objects. Finally, we estimate a cometary ISO\nnumber density of $\\sim$7 $\\times$ 10$^{-5}$ AU$^{-3}$ for 2I type ISOs,\nleading to discovery rates for these objects possibly approaching once per\ndecade with future telescopic surveys.\n"}
+{"abstract": "  In game-theoretic learning, several agents are simultaneously following their\nindividual interests, so the environment is non-stationary from each player's\nperspective. In this context, the performance of a learning algorithm is often\nmeasured by its regret. However, no-regret algorithms are not created equal in\nterms of game-theoretic guarantees: depending on how they are tuned, some of\nthem may drive the system to an equilibrium, while others could produce cyclic,\nchaotic, or otherwise divergent trajectories. To account for this, we propose a\nrange of no-regret policies based on optimistic mirror descent, with the\nfollowing desirable properties: i) they do not require any prior tuning or\nknowledge of the game; ii) they all achieve O(\\sqrt{T}) regret against\narbitrary, adversarial opponents; and iii) they converge to the best response\nagainst convergent opponents. Also, if employed by all players, then iv) they\nguarantee O(1) social regret; while v) the induced sequence of play converges\nto Nash equilibrium with O(1) individual regret in all variationally stable\ngames (a class of games that includes all monotone and convex-concave zero-sum\ngames).\n"}
+{"abstract": "  Intelligent robots provide a new insight into efficiency improvement in\nindustrial and service scenarios to replace human labor. However, these\nscenarios include dense and dynamic obstacles that make motion planning of\nrobots challenging. Traditional algorithms like A* can plan collision-free\ntrajectories in static environment, but their performance degrades and\ncomputational cost increases steeply in dense and dynamic scenarios.\nOptimal-value reinforcement learning algorithms (RL) can address these problems\nbut suffer slow speed and instability in network convergence. Network of policy\ngradient RL converge fast in Atari games where action is discrete and finite,\nbut few works have been done to address problems where continuous actions and\nlarge action space are required. In this paper, we modify existing advantage\nactor-critic algorithm and suit it to complex motion planning, therefore\noptimal speeds and directions of robot are generated. Experimental results\ndemonstrate that our algorithm converges faster and stable than optimal-value\nRL. It achieves higher success rate in motion planning with lesser processing\ntime for robot to reach its goal.\n"}
+{"abstract": "  Thermodynamic uncertainty relation (TUR) provides a stricter bound for\nentropy production (EP) than that of the thermodynamic second law. This\nstricter bound can be utilized to infer the EP and derive other trade-off\nrelations. Though the validity of the TUR has been verified in various\nstochastic systems, its application to general Langevin dynamics has not been\nsuccessful in a unified way, especially for underdamped Langevin dynamics,\nwhere odd parity variables in time-reversal operation such as velocity get\ninvolved. Previous TURs for underdamped Langevin dynamics is neither\nexperimentally accessible nor reduced to the original form of the overdamped\nLangevin dynamics in the zero-mass limit. Here, we find an operationally\naccessible TUR for underdamped Langevin dynamics with an arbitrary\ntime-dependent protocol. We show that the original TUR is a consequence of our\nunderdamped TUR in the zero-mass limit. This indicates that the TUR formulation\npresented here can be regarded as the universal form of the TUR for general\nLangevin dynamics. The validity of our result is examined and confirmed for\nthree prototypical underdamped Langevin systems and their zero-mass limits;\nfree diffusion dynamics, charged Brownian particle in a magnetic field, and\nmolecular refrigerator.\n"}
+{"abstract": "  The use of Bayesian information criterion (BIC) in the model selection\nprocedure is under the assumption that the observations are independent and\nidentically distributed (i.i.d.). However, in practice, we do not always have\ni.i.d. samples. For example, clustered observations tend to be more similar\nwithin the same group, and longitudinal data is collected by measuring the same\nsubject repeatedly. In these scenarios, the assumption in BIC is not satisfied.\nThe concept of effective sample size is brought up and improved BIC is defined\nby replacing the sample size in the original BIC expression with the effective\nsample size, which will give us a better theoretical foundation in the\ncircumstance that mixed-effects models involve. Numerical experiment results\nare also given by comparing the performance of our new BIC with other widely\nused BICs.\n"}
+{"abstract": "  Based on relative energy estimates, we study the stability of solutions to\nthe Cahn-Hilliard equation with concentration dependent mobility with respect\nto perturbations. As a by-product of our analysis, we obtain a weak-strong\nuniqueness principle on the continuous level under realistic regularity\nassumptions on strong solutions. We then show that the stability estimates can\nbe further inherited almost verbatim by appropriate Galerkin approximations in\nspace and time. This allows us to derive sharp bounds for the discretization\nerror in terms of certain projection errors and to establish order-optimal\na-priori error estimates for semi- and fully discrete approximation schemes.\n"}
+{"abstract": "  Performing imperfect or noisy measurements on a quantum system both impacts\nthe measurement outcome and the state of the system after the measurement. In\nthis paper we are concerned with imperfect calorimetric measurements. In\ncalorimetric measurements one typically measures the energy of a thermal\nenvironment to extract information about the system. The measurement is\nimperfect in the sense that we simultaneously measure the energy of the\ncalorimeter and an additional noise bath. Under weak coupling assumptions, we\nfind that the presence of the noise bath manifests itself by modifying the jump\nrates of the reduced system dynamics. We study an example of a driven qubit\ninteracting with resonant bosons calorimeter and find increasing the noise\nleads to a reduction in the power flowing from qubit to calorimeter and thus an\napparent heating up of the calorimeter.\n"}
+{"abstract": "  Let $\\mathcal{G}$ be a connected reductive almost simple group over the Witt\nring $W(\\mathbb{F})$ for $\\mathbb{F}$ a finite field of characteristic $p$. Let\n$R$ and $R'$ be complete noetherian local $W(\\mathbb{F})$ -algebras with\nresidue field $\\mathbb{F}$. Under a mild condition on $p$ in relation to\nstructural constants of $\\mathcal{G}$, we show the following results: (1) Every\nclosed subgroup $H$ of $\\mathcal{G}(R)$ with full residual image\n$\\mathcal{G}(\\mathbb{F})$ is a conjugate of a group $\\mathcal{G}(A)$ for\n$A\\subset R$ a closed subring that is local and has residue field $\\mathbb{F}$\n. (2) Every surjective homomorphism $\\mathcal{G}(R)\\to\\mathcal{G}(R')$ is, up\nto conjugation, induced from a ring homomorphism $R\\to R'$. (3) The identity\nmap on $\\mathcal{G}(R)$ represents the universal deformation of the\nrepresentation of the profinite group $\\mathcal{G}(R)$ given by the reduction\nmap $\\mathcal{G}(R)\\to\\mathcal{G}(\\mathbb{F})$. This generalizes results of\nDorobisz and Eardley-Manoharmayum and of Manoharmayum, and in addition provides\nan abstract classification result for closed subgroups of $\\mathcal{G}(R)$ with\nresidually full image.\n  We provide an axiomatic framework to study this type of question, also for\nslightly more general $\\mathcal{G}$, and we study in the case at hand in great\ndetail what conditions on $\\mathbb{F}$ or on $p$ in relation to $\\mathcal{G}$\nare necessary for the above results to hold.\n"}
+{"abstract": "  Optical isolators are indispensible components in nearly all photonic systems\nas they help ensure unidirectionality and provide crucial protection from\nundesirable reflections. While commercial isolators are exclusively built on\nmagneto-optic (MO) principles they are not readily implemented within photonic\nintegrated circuits due to the need for specialized materials. Importantly, the\nMO effect is generally weak, especially at shorter wavelengths. These\nchallenges as a whole have motivated extensive research on non-MO alternatives.\nTo date, however, no alternative technology has managed to simultaneously\ncombine linearity (i.e. no frequency shift), linear response (i.e. input-output\nscaling), ultralow insertion loss, and large directional contrast on-chip. Here\nwe demonstrate an optical isolator design that leverages the unbeatable\ntransparency of a short, high quality dielectric waveguide, with the\nnear-perfect attenuation from a critically-coupled absorber. Our design concept\nis implemented using a lithium niobate racetrack resonator in which phonon\nmediated Autler-Townes splitting (ATS) breaks the chiral symmetry of the\nresonant modes. We demonstrate on-chip optical isolators at wavelengths one\noctave apart near 1550 nm and 780 nm, fabricated from the same lithium\nniobate-on-insulator wafer. Linear optical isolation is demonstrated with\nsimultaneously <1 dB insertion loss, >39 dB contrast, and bandwidth as wide as\nthe optical mode that is used. Our results outperform the current best-in-class\nMO isolator on-chip on both insertion loss and isolator figures-of-merit, and\ndemonstrate a lithographically defined wavelength adaptability that cannot yet\nbe achieved with any MO isolator.\n"}
+{"abstract": "  The High Energy Rapid Modular Ensemble of Satellites (HERMES) Technological\nand Scientific pathfinder is a space borne mission based on a constellation of\nLEO nanosatellites. The payloads of these CubeSats consist of miniaturized\ndetectors designed for bright high-energy transients such as Gamma-Ray Bursts\n(GRBs). This platform aims to impact Gamma Ray Burst (GRB) science and enhance\nthe detection of Gravitational Wave (GW) electromagnetic counterparts. This\ngoal will be achieved with a field of view of several steradians, arcmin\nprecision and state of the art timing accuracy. The localization performance\nfor the whole constellation is proportional to the number of components and\ninversely proportional to the average baseline between them, and therefore is\nexpected to increase as more. In this paper we describe the Payload Data\nHandling Unit (PDHU) for the HERMES-TP and HERMES SP mission. The PDHU is the\nmain interface between the payload and the satellite bus. The PDHU is also in\ncharge of the on-board control and monitoring of the scintillating crystal\ndetectors. We will explain the TM/TC design and the distinct modes of\noperation. We also discuss the on-board data processing carried out by the PDHU\nand its impact on the output data of the detector.\n"}
+{"abstract": "  For an inverse temperature $\\beta>0$, we define the $\\beta$-circular Riesz\ngas on $\\mathbb{R}^d$ as any microscopic thermodynamic limit of Gibbs particle\nsystems on the torus interacting via the Riesz potential $g(x) = \\Vert x\n\\Vert^{-s}$. We focus on the non integrable case $d-1<s<d$. Our main result\nensures, for any dimension $d\\ge 1$ and inverse temperature $\\beta>0$, the\nexistence of a $\\beta$-circular Riesz gas which is not number-rigid. Recall\nthat a point process is said number rigid if the number of points in a bounded\nBorel set $\\Delta$ is a function of the point configuration outside $\\Delta$.\nIt is the first time that the non number-rigidity is proved for a Gibbs point\nprocess interacting via a non integrable potential. We follow a statistical\nphysics approach based on the canonical DLR equations. It is inspired by\nDereudre-Hardy-Lebl\\'e and Ma\\\"ida (2021) where the authors prove the\nnumber-rigidity of the $\\text{Sine}_\\beta$ process.\n"}
+{"abstract": "  We consider Morrey's open question whether rank-one convexity already implies\nquasiconvexity in the planar case. For some specific families of energies,\nthere are precise conditions known under which rank-one convexity even implies\npolyconvexity. We will extend some of these findings to the more general family\nof energies $W:\\operatorname{GL}^+(n)\\rightarrow\\mathbb{R}$ with an additive\nvolumetric-isochoric split, i.e. \\[\n  W(F)=W_{\\rm iso}(F)+W_{\\rm vol}(\\det F)=\\widetilde W_{\\rm\niso}\\bigg(\\frac{F}{\\sqrt{\\det F}}\\bigg)+W_{\\rm vol}(\\det F)\\,, \\] which is the\nnatural finite extension of isotropic linear elasticity. Our approach is based\non a condition for rank-one convexity which was recently derived from the\nclassical two-dimensional criterion by Knowles and Sternberg and consists of a\nfamily of one-dimensional coupled differential inequalities. We identify a\nnumber of \\enquote{least} rank-one convex energies and, in particular, show\nthat for planar volumetric-isochorically split energies with a concave\nvolumetric part, the question of whether rank-one convexity implies\nquasiconvexity can be reduced to the open question of whether the rank-one\nconvex energy function \\[\n  W_{\\rm magic}^+(F)=\\frac{\\lambda_{\\rm max}}{\\lambda_{\\rm\nmin}}-\\log\\frac{\\lambda_{\\rm max}}{\\lambda_{\\rm min}}+\\log\\det\nF=\\frac{\\lambda_{\\rm max}}{\\lambda_{\\rm min}}-2\\log\\lambda_{\\rm min} \\] is\nquasiconvex. In addition, we demonstrate that under affine boundary conditions,\n$W_{\\rm magic}^+(F)$ allows for non-trivial inhomogeneous deformations with the\nsame energy level as the homogeneous solution, and show a surprising connection\nto the work of Burkholder and Iwaniec in the field of complex analysis.\n"}
+{"abstract": "  The Uhlmann process is built on the density matrix of a mixed quantum state\nand offers a way to characterize topological properties at finite temperatures.\nWe analyze an ideal spin-j quantum paramagnet in a magnetic field undergoing an\nUhlmann process and derive general formulae of the Uhlmann phase and Loschmidt\namplitude for arbitrary j as the system traverses a great circle in the\nparameter space. A quantized jump of the Uhlmann phase signifies a topological\nquantum phase transition (TQPT) of the underlying process, which is accompanied\nby a zero of the Loschmidt amplitude. The exact results of j=1/2 and j=1\nsystems show topological regimes that only survive at finite temperatures but\nnot at zero temperature, and the number of TQPTs is associated with the winding\nnumber in the parameter space. Our results pave the way for future studies on\nfinite-temperature topological properties, and possible experimental protocols\nand implications for atomic simulators and digital simulations are discussed.\n"}
+{"abstract": "  Processing astronomical data often comes with huge challenges with regards to\ndata management as well as data processing. MeerKAT telescope is one of the\nprecursor telescopes of the World's largest observatory - Square Kilometre\nArray. So far, MeerKAT data was processed using the South African computing\nfacility i.e. IDIA, and exploited to make ground-breaking discoveries. However,\nto process MeerKAT data on UK's IRIS computing facility requires new\nimplementation of the MeerKAT pipeline. This paper focuses on how to transfer\nMeerKAT data from the South African site to UK's IRIS systems for processing.\nWe discuss about our RapifXfer Data transfer framework for transferring the\nMeerKAT data from South Africa to the UK, and the MeerKAT job processing\nframework pertaining to the UK's IRIS resources.\n"}
+{"abstract": "  We study analytic properties of \"$q$-deformed real numbers\", a notion\nrecently introduced by two of us. A $q$-deformed positive real number is a\npower series with integer coefficients in one formal variable~$q$. We study the\nradius of convergence of these power series assuming that $q \\in \\C.$ Our main\nconjecture, which can be viewed as a $q$-analogue of Hurwitz's Irrational\nNumber Theorem, provides a lower bound for these radii, given by the radius of\nconvergence of the $q$-deformed golden ratio. The conjecture is proved in\nseveral particular cases and confirmed by a number of computer experiments. For\nan interesting sequence of \"Pell polynomials\", we obtain stronger bounds.\n"}
+{"abstract": "  Abstractive summarization, the task of generating a concise summary of input\ndocuments, requires: (1) reasoning over the source document to determine the\nsalient pieces of information scattered across the long document, and (2)\ncomposing a cohesive text by reconstructing these salient facts into a shorter\nsummary that faithfully reflects the complex relations connecting these facts.\nIn this paper, we adapt TP-TRANSFORMER (Schlag et al., 2019), an architecture\nthat enriches the original Transformer (Vaswani et al., 2017) with the\nexplicitly compositional Tensor Product Representation (TPR), for the task of\nabstractive summarization. The key feature of our model is a structural bias\nthat we introduce by encoding two separate representations for each token to\nrepresent the syntactic structure (with role vectors) and semantic content\n(with filler vectors) separately. The model then binds the role and filler\nvectors into the TPR as the layer output. We argue that the structured\nintermediate representations enable the model to take better control of the\ncontents (salient facts) and structures (the syntax that connects the facts)\nwhen generating the summary. Empirically, we show that our TP-TRANSFORMER\noutperforms the Transformer and the original TP-TRANSFORMER significantly on\nseveral abstractive summarization datasets based on both automatic and human\nevaluations. On several syntactic and semantic probing tasks, we demonstrate\nthe emergent structural information in the role vectors and improved syntactic\ninterpretability in the TPR layer outputs. Code and models are available at\nhttps://github.com/jiangycTarheel/TPT-Summ.\n"}
+{"abstract": "  We generalize Mertens' product theorem to Chebotarev sets of prime ideals in\nGalois extensions of number fields. Using work of Rosen, we extend an argument\nof Williams from cyclotomic extensions to this more general case. Additionally,\nwe compute these products for Cheboratev sets in abelian extensions, $S_3$\nsextic extensions, and sets of primes represented by some quadratic forms.\n"}
+{"abstract": "  The problem of quantifying uncertainty about the locations of multiple change\npoints by means of confidence intervals is addressed. The asymptotic\ndistribution of the change point estimators obtained as the local maximisers of\nmoving sum statistics is derived, where the limit distributions differ\ndepending on whether the corresponding size of changes is local, i.e. tends to\nzero as the sample size increases, or fixed. A bootstrap procedure for\nconfidence interval generation is proposed which adapts to the unknown\nmagnitude of changes and guarantees asymptotic validity both for local and\nfixed changes. Simulation studies show good performance of the proposed\nbootstrap procedure, and some discussions about how it can be extended to\nserially dependent errors is provided.\n"}
+{"abstract": "  We propose a nonlinear acoustic echo cancellation system, which aims to model\nthe echo path from the far-end signal to the near-end microphone in two parts.\nInspired by the physical behavior of modern hands-free devices, we first\nintroduce a novel neural network architecture that is specifically designed to\nmodel the nonlinear distortions these devices induce between receiving and\nplaying the far-end signal. To account for variations between devices, we\nconstruct this network with trainable memory length and nonlinear activation\nfunctions that are not parameterized in advance, but are rather optimized\nduring the training stage using the training data. Second, the network is\nsucceeded by a standard adaptive linear filter that constantly tracks the echo\npath between the loudspeaker output and the microphone. During training, the\nnetwork and filter are jointly optimized to learn the network parameters. This\nsystem requires 17 thousand parameters that consume 500 Million floating-point\noperations per second and 40 Kilo-bytes of memory. It also satisfies hands-free\ncommunication timing requirements on a standard neural processor, which renders\nit adequate for embedding on hands-free communication devices. Using 280 hours\nof real and synthetic data, experiments show advantageous performance compared\nto competing methods.\n"}
+{"abstract": "  We investigate a possibility to describe the non-Debye relaxation processes\nusing the Volterra-type equations with kernels given by the Prabhakar functions\nwith the upper parameter $\\nu$ being negative. Proposed integro-differential\nequations mimic the fading memory effects and are explicitly solved using the\numbral calculus and the Laplace transform methods. Both approaches lead to the\nsame results valid for admissible domain of the parameters $\\alpha$, $\\mu$ and\n$\\nu$ characterizing the Prabhakar function. For the special case $\\alpha\\in\n(0,1]$, $\\mu=0$ and $\\nu=-1$ we recover the Cole-Cole model, in general having\na residual polarization. We also show that our scheme gives results equivalent\nto those obtained using the stochastic approach to relaxation phenomena merged\nwith integral equations involving kernels given by the Prabhakar functions with\nthe positive upper parameter.\n"}
+{"abstract": "  Billions of X-ray images are taken worldwide each year. Machine learning, and\ndeep learning in particular, has shown potential to help radiologists triage\nand diagnose images. However, deep learning requires large datasets with\nreliable labels. The CheXpert dataset was created with the participation of\nboard-certified radiologists, resulting in the strong ground truth needed to\ntrain deep learning networks. Following the structured format of Datasheets for\nDatasets, this paper expands on the original CheXpert paper and other sources\nto show the critical role played by radiologists in the creation of reliable\nlabels and to describe the different aspects of the dataset composition in\ndetail. Such structured documentation intends to increase the awareness in the\nmachine learning and medical communities of the strengths, applications, and\nevolution of CheXpert, thereby advancing the field of medical image analysis.\nAnother objective of this paper is to put forward this dataset datasheet as an\nexample to the community of how to create detailed and structured descriptions\nof datasets. We believe that clearly documenting the creation process, the\ncontents, and applications of datasets accelerates the creation of useful and\nreliable models.\n"}
+{"abstract": "  Knowledge distillation aims to transfer representation ability from a teacher\nmodel to a student model. Previous approaches focus on either individual\nrepresentation distillation or inter-sample similarity preservation. While we\nargue that the inter-sample relation conveys abundant information and needs to\nbe distilled in a more effective way. In this paper, we propose a novel\nknowledge distillation method, namely Complementary Relation Contrastive\nDistillation (CRCD), to transfer the structural knowledge from the teacher to\nthe student. Specifically, we estimate the mutual relation in an anchor-based\nway and distill the anchor-student relation under the supervision of its\ncorresponding anchor-teacher relation. To make it more robust, mutual relations\nare modeled by two complementary elements: the feature and its gradient.\nFurthermore, the low bound of mutual information between the anchor-teacher\nrelation distribution and the anchor-student relation distribution is maximized\nvia relation contrastive loss, which can distill both the sample representation\nand the inter-sample relations. Experiments on different benchmarks demonstrate\nthe effectiveness of our proposed CRCD.\n"}
+{"abstract": "  Error-bounded lossy compression is a critical technique for significantly\nreducing scientific data volumes. With ever-emerging heterogeneous\nhigh-performance computing (HPC) architecture, GPU-accelerated error-bounded\ncompressors (such as cuSZ+ and cuZFP) have been developed. However, they suffer\nfrom either low performance or low compression ratios. To this end, we propose\ncuSZ+ to target both high compression ratios and throughputs. We identify that\ndata sparsity and data smoothness are key factors for high compression\nthroughputs. Our key contributions in this work are fourfold: (1) We propose an\nefficient compression workflow to adaptively perform run-length encoding and/or\nvariable-length encoding. (2) We derive Lorenzo reconstruction in decompression\nas multidimensional partial-sum computation and propose a fine-grained Lorenzo\nreconstruction algorithm for GPU architectures. (3) We carefully optimize each\nof cuSZ+ kernels by leveraging state-of-the-art CUDA parallel primitives. (4)\nWe evaluate cuSZ+ using seven real-world HPC application datasets on V100 and\nA100 GPUs. Experiments show cuSZ+ improves the compression throughputs and\nratios by up to 18.4X and 5.3X, respectively, over cuSZ on the tested datasets.\n"}
+{"abstract": "  In the high energy limit of hadron collisions, the evolution of the gluon\ndensity in the longitudinal momentum fraction can be deduced from the Balitsky\nhierarchy of equations or, equivalently, from the nonlinear\nJalilian-Marian-Iancu-McLerran-Weigert-Leonidov-Kovner (JIMWLK) equation. The\nsolutions of the latter can be studied numerically by using its reformulation\nin terms of a Langevin equation. In this paper, we present a comprehensive\nstudy of systematic effects associated with the numerical framework, in\nparticular the ones related to the inclusion of the running coupling. We\nconsider three proposed ways in which the running of the coupling constant can\nbe included: \"square root\" and \"noise\" prescriptions and the recent proposal by\nHatta and Iancu. We implement them both in position and momentum spaces and we\ninvestigate and quantify the differences in the resulting evolved gluon\ndistributions. We find that the systematic differences associated with the\nimplementation technicalities can be of a similar magnitude as differences in\nrunning coupling prescriptions in some cases, or much smaller in other cases.\n"}
+{"abstract": "  We prove a formula for the polar degree of projective hypersurfaces in terms\nof the Milnor data of the singularities, extending to 1-dimensional\nsingularities the Dimca-Papadima result for isolated singularities. We discuss\nthe semi-continuity of the polar degree in deformations, and we classify the\nhomaloidal cubic surfaces with 1-dimensional singular locus. Some open\nquestions are pointed out along the way.\n"}
+{"abstract": "  Postive semidefinite (PSD) cone is the cone of positive semidefinite\nmatrices, and is the object of interest in semidefinite programming (SDP). A\ncomputational efficient approximation of the PSD cone is the $k$-PSD closure,\n$1 \\leq k < n$, cone of $n\\times n$ real symmetric matrices such that all of\ntheir $k\\times k$ principal submatrices are positive semidefinite. For $k=1$,\none obtains a polyhedral approximation, while $k=2$ yields a second order conic\n(SOC) approximation of the PSD cone. These approximations of the PSD cone have\nbeen used extensively in real-world applications such as AC Optimal Power Flow\n(ACOPF) to address computational inefficiencies where SDP relaxations are\nutilized for convexification the non-convexities. In a recent series of\narticles Blekharman et al. provided bounds on the quality of these\napproximations. In this work, we revisit some of their results and also propose\na new dominant bound on quality of the $k$-PSD closure approximation of the PSD\ncone. In addition, we characterize the extreme rays of the $2$-PSD closure.\n"}
+{"abstract": "  Despite many of the most common chaotic dynamical systems being continuous in\ntime, it is through discrete time mappings that much of the understanding of\nchaos is formed. Henri Poincar\\'e first made this connection by tracking\nconsecutive iterations of the continuous flow with a lower-dimensional,\ntransverse subspace. The mapping that iterates the dynamics through consecutive\nintersections of the flow with the subspace is now referred to as a Poincar\\'e\nmap, and it is the primary method available for interpreting and classifying\nchaotic dynamics. Unfortunately, in all but the simplest systems, an explicit\nform for such a mapping remains outstanding. This work proposes a method for\nobtaining explicit Poincar\\'e mappings by using deep learning to construct an\ninvertible coordinate transformation into a conjugate representation where the\ndynamics are governed by a relatively simple chaotic mapping. The invertible\nchange of variable is based on an autoencoder, which allows for dimensionality\nreduction, and has the advantage of classifying chaotic systems using the\nequivalence relation of topological conjugacies. Indeed, the enforcement of\ntopological conjugacies is the critical neural network regularization for\nlearning the coordinate and dynamics pairing. We provide expository\napplications of the method to low-dimensional systems such as the R\\\"ossler and\nLorenz systems, while also demonstrating the utility of the method on\ninfinite-dimensional systems, such as the Kuramoto--Sivashinsky equation.\n"}
+{"abstract": "  Multimodal generative models should be able to learn a meaningful latent\nrepresentation that enables a coherent joint generation of all modalities\n(e.g., images and text). Many applications also require the ability to\naccurately sample modalities conditioned on observations of a subset of the\nmodalities. Often not all modalities may be observed for all training data\npoints, so semi-supervised learning should be possible. In this study, we\npropose a novel product-of-experts (PoE) based variational autoencoder that\nhave these desired properties. We benchmark it against a mixture-of-experts\n(MoE) approach and an approach of combining the modalities with an additional\nencoder network. An empirical evaluation shows that the PoE based models can\noutperform the contrasted models. Our experiments support the intuition that\nPoE models are more suited for a conjunctive combination of modalities.\n"}
+{"abstract": "  We explore the interplay of New Physics (NP) effects in $(g-2)_\\ell$ and $h\n\\to \\ell^+ \\ell^-$ within the Standard Model Effective Field Theory (SMEFT)\nframework, including one-loop Renormalization Group (RG) evolution of the\nWilson coefficients as well as matching to the observables below the\nelectroweak symmetry breaking scale. We include both the leading dimension six\nchirality flipping operators including a Higgs and $SU(2)_L$ gauge bosons as\nwell as four-fermion scalar and tensor operators, forming a closed operator set\nunder the SMEFT RG equations. We compare present and future experimental\nsensitivity to different representative benchmark scenarios. We also consider\ntwo simple UV completions, a Two Higgs Doublet Model and a single scalar\nLeptoQuark extension of the SM, and show how tree level matching to SMEFT\nfollowed by the one-loop RG evolution down to the electroweak scale can\nreproduce with high accuracy the $(g-2)_\\ell$ and $h \\to \\ell^+ \\ell^-$\ncontributions obtained by the complete one- and even two-loop calculations in\nthe full models.\n"}
+{"abstract": "  In this paper, it is established finite active-set identification of an\nalmost cyclic 2-coordinate descent method for problems with one linear coupling\nconstraint and simple bounds. First, general active-set identification results\nare stated for non-convex objective functions. Then, under convexity and a\nquadratic growth condition (satisfied by any strongly convex function),\ncomplexity results on the number of iterations required to identify the active\nset are given. In our analysis, a simple Armijo line search is used to compute\nthe stepsize, thus not requiring exact minimizations or additional information.\n"}
+{"abstract": "  A program is created to compute recursively the Moshinsky brackets. It is\nvery fast and provides highly accurate results. In the case of the\ndouble-precision computations with a single-processor consumer notebook, the\ncomputing time per bracket at any not small oscillator excitations is on the\nscale of 10^{-8} s and the accuracy is very good for the total number of quanta\nup to 80. The program is easy to handle.\n"}
+{"abstract": "  Device identification is one way to secure a network of IoT devices, whereby\ndevices identified as suspicious can subsequently be isolated from a network.\nIn this study, we present a machine learning-based method, IoTDevID, that\nrecognizes devices through characteristics of their network packets. As a\nresult of using a rigorous feature analysis and selection process, our study\noffers a generalizable and realistic approach to modelling device behavior,\nachieving high predictive accuracy across two public datasets. The model's\nunderlying feature set is shown to be more predictive than existing feature\nsets used for device identification, and is shown to generalize to data unseen\nduring the feature selection process. Unlike most existing approaches to IoT\ndevice identification, IoTDevID is able to detect devices using non-IP and\nlow-energy protocols.\n"}
+{"abstract": "  Weakly interacting Bose gas with an impurity is analyzed in one dimension.\nThe local spectral density is evaluated and an analytic expression valid for\nall strengths of the impurity coupling to the background bosons, at all\ndistances and at all frequencies is obtained using the Bogoliubov-de Gennes\nformalism.\n"}
+{"abstract": "  Intense transient electric ({\\bf E}) and magnetic ({\\bf B}) fields are\nproduced in the high energy heavy-ion collisions. The electromagnetic fields\nproduced in such high-energy heavy-ion collisions are proposed to give rise to\na multitude of exciting phenomenon including the Chiral Magnetic Effect. We use\na Monte Carlo (MC) Glauber model to calculate the electric and magnetic fields,\nmore specifically their scalar product $\\bf{E}\\cdot\\bf{B}$, as a function of\nspace-time on an event-by-event basis for the Au+Au collisions at\n$\\sqrt{s_{NN}}=200$ GeV for different centrality classes. We also calculate the\nsame for the isobars Ruthenium and Zirconium at $\\sqrt{s_{NN}}=200$ GeV. In the\nQED sector $\\bf{E}\\cdot\\bf{B}$ acts as a source of Chiral Separation Effect,\nChiral Magnetic Wave, etc., which are associated phenomena to the Chiral\nMagnetic Effect. We also study the relationships between the electromagnetic\nsymmetry plane angle defined by $\\bf{E}\\cdot\\bf{B}$ ($\\psi_{E.B}$) and the\nparticipant plane angle $\\psi_{P}$ defined from the participating nucleons for\nthe second-fifth order harmonics.\n"}
+{"abstract": "  Results of an analysis of the BRITE-Constellation and SMEI photometry and\nradial-velocity observations, archival and new, of two single-lined\nspectroscopic binary systems $\\nu$ Centauri and $\\gamma$ Lupi are reported. In\nthe case of $\\gamma$ Lup AB, a visual binary, an examination of the light-time\neffect shows that component A is the spectroscopic binary. Both $\\nu$ Cen and\n$\\gamma$ Lup exhibit light variations with the orbital period. The variations\nare caused by the reflection effect, i.e. heating of the secondary's hemisphere\nby the early-B main sequence (MS) primary component's light. The modelling of\nthe light curves augmented with the fundamental parameters of the primary\ncomponents obtained from the literature photometric data and Hipparcos\nparallaxes, shows that the secondary components are pre-MS stars, in the\nprocess of contracting onto the MS. $\\nu$ Cen and $\\gamma$ Lup A are thus found\nto be non-eclipsing counterparts of the B2 IV eclipsing binary (and a $\\beta$\nCephei variable) 16 (EN) Lac, the B5 IV eclipsing binary (and an SPB variable)\n$\\mu$ Eri, and the recently discovered LMC nascent eclipsing binaries.\n"}
+{"abstract": "  A composable infrastructure is defined as resources, such as compute,\nstorage, accelerators and networking, that are shared in a pool and that can be\ngrouped in various configurations to meet application requirements. This\nfreedom to 'mix and match' resources dynamically allows for experimentation\nearly in the design cycle, prior to the final architectural design or hardware\nimplementation of a system. This design provides flexibility to serve a variety\nof workloads and provides a dynamic co-design platform that allows experiments\nand measurements in a controlled manner. For instance, key performance\nbottlenecks can be revealed early on in the experimentation phase thus avoiding\ncostly and time consuming mistakes. Additionally, various system-level\ntopologies can be evaluated when experimenting with new System on Chip (SoCs)\nand new accelerator types. This paper details the design of an enterprise\ncomposable infrastructure that we have implemented and made available to our\npartners in the IBM Research AI Hardware Center (AIHC). Our experimental\nevaluations on the composable system give insights into how the system works\nand evaluates the impact of various resource aggregations and reconfigurations\non representative deep learning benchmarks.\n"}
+{"abstract": "  End-to-end (E2E) spoken language understanding (SLU) systems avoid an\nintermediate textual representation by mapping speech directly into intents\nwith slot values. This approach requires considerable domain-specific training\ndata. In low-resource scenarios this is a major concern, e.g., in the present\nstudy dealing with SLU for dysarthric speech. Pretraining part of the SLU model\nfor automatic speech recognition targets helps but no research has shown to\nwhich extent SLU on dysarthric speech benefits from knowledge transferred from\nother dysarthric speech tasks. This paper investigates the efficiency of\npre-training strategies for SLU tasks on dysarthric speech. The designed SLU\nsystem consists of a TDNN acoustic model for feature encoding and a capsule\nnetwork for intent and slot decoding. The acoustic model is pre-trained in two\nstages: initialization with a corpus of normal speech and finetuning on a\nmixture of dysarthric and normal speech. By introducing the intelligibility\nscore as a metric of the impairment severity, this paper quantitatively\nanalyzes the relation between generalization and pathology severity for\ndysarthric speech.\n"}
+{"abstract": "  Finite-element simulations of magnetostatic fields are performed in terms of\nmagnetic vector and total scalar potentials and compared for purpose of\nmodeling the accelerator magnets. The potentials represent the unknown\nvariables associated with the A and V formulations of magnetostatics to\ndescribe the magnetic fields. The simulations are carried out with a single\nsoftware package, the COMSOL Multiphysics, where both formulations are\nimplemented. The numerical performance of these methods is illustrated with the\nmodel example of a superconducting dipole magnet recently developed for\noperation in the isochronous cyclotron SC200. The results of calculations are\nanalyzed and compared in terms of the relevant FEM parameters accounting for\nperformance of computation as well as the computational cost. We show in\nparticular that the use of scalar potential as compared to its vector\ncounterpart substantially reduces the number of degrees of freedom, the usage\nof computer memory and the computational time for a similar relative error.\n"}
+{"abstract": "  We examine the magneto-optical response of chiral multifold fermions.\nSpecifically, we show that they are ideal candidates for observing the Helical\nMagnetic Effect (HME) previously predicted for simple Weyl fermions. Unlike\nWeyl fermions, the HME is present in multifold fermions even in the simplest\ncase where the low-energy dispersion is linear and spherically symmetric. In\nthis ideal case, we derive an analytical expression for the HME and find it is\nproportional to the circular photogalvanic effect; for realistic parameters and\naccounting for the geometry of the setup, the HME photocurrent could be roughly\nthe same order of magnitude as the circular photogalvanic effect observed in\nmultifold fermions. Additional non-linear and symmetry-breaking terms will ruin\nthe quantization but not hurt the observation of the HME.\n"}
+{"abstract": "  We propose a neutrino model based on a $\\Delta(54)$ flavor symmetry suitable\nfor explaining the current neutrino oscillation data. Neutrino masses arise\nfrom the type II seesaw mechanism where the $\\Delta(54)$ field assignments has\nled to a simple neutrino mass matrix with one texture zero and which satisfies\nthe magic symmetry consistent with the well-known trimaximal mixing matrix. We\nfound interesting predictions concerning neutrino masses and mixing. In\nparticular, only the normal neutrino mass hierarchy and the lower octant of the\natmospheric angles are allowed in this model. The model predicts as well that\nthe $CP$ conserving values for the Dirac $CP$ phase $\\delta _{CP}$ are not\nallowed and thus, $CP$ is always violated in the neutrino sector. We have also\ninvestigated the sum of absolute neutrino masses from cosmological\nobservations, the effective Majorana mass $m_{\\beta \\beta }$\\ from neutrinoless\ndouble beta decay experiments, and the electron neutrino mass $m_{\\beta }$\\\nfrom beta decays where we found that the obtained range of $m_{\\beta \\beta }$\\\ncan be tested by several experiments in the near future.\n"}
+{"abstract": "  Pre-trained language models have been successful on text classification\ntasks, but are prone to learning spurious correlations from biased datasets,\nand are thus vulnerable when making inferences in a new domain. Prior work\nreveals such spurious patterns via post-hoc explanation algorithms which\ncompute the importance of input features. Further, the model is regularized to\nalign the importance scores with human knowledge, so that the unintended model\nbehaviors are eliminated. However, such a regularization technique lacks\nflexibility and coverage, since only importance scores towards a pre-defined\nlist of features are adjusted, while more complex human knowledge such as\nfeature interaction and pattern generalization can hardly be incorporated. In\nthis work, we propose to refine a learned language model for a target domain by\ncollecting human-provided compositional explanations regarding observed biases.\nBy parsing these explanations into executable logic rules, the human-specified\nrefinement advice from a small set of explanations can be generalized to more\ntraining examples. We additionally introduce a regularization term allowing\nadjustments for both importance and interaction of features to better rectify\nmodel behavior. We demonstrate the effectiveness of the proposed approach on\ntwo text classification tasks by showing improved performance in target domain\nas well as improved model fairness after refinement.\n"}
+{"abstract": "  We utilize the Legendre-Fenchel transform and weak geodesics for\nplurisubharmonic functions to construct a weight function that can be used in\nthe Berndtsson-Lempert method, to give an Ohsawa-Takegoshi extension type of\nresult. Theorem 4.1 and Theorem 0.1 in \\cite{OT2017} (Theorem \\ref{Theorem A}\nand \\ref{Theorem B} below) follow as two special cases of this result, thus\nanswering affirmatively a question posed by Ohsawa in remark 4.1 in\n\\cite{OT2017}, on the Berndtsson-Lempert method.\n"}
+{"abstract": "  Public earthquake early warning systems have the potential to reduce\nindividual risk by warning people of an incoming tremor but their development\nhas been hampered by costly infrastructure. Furthermore, users' understanding\nof such a service and their reactions to warnings remains poorly studied. The\nsmartphone app of the Earthquake Network initiative turns users' smartphones\ninto motion detectors and provides the first example of purely smartphone-based\nearthquake early warnings, without the need for dedicated seismic station\ninfrastructure and operating in multiple countries. We demonstrate here that\nearly warnings have been emitted in multiple countries even for damaging\nshaking levels and so this offers an alternative in the many regions unlikely\nto be covered by conventional early warning systems in the foreseeable future.\nWe also show that although warnings are understood and appreciated by users,\nnotably to get psychologically prepared, only a fraction take protective\nactions such as \"drop, cover and hold\".\n"}
+{"abstract": "  Code-switching in automatic speech recognition (ASR) is an important\nchallenge due to globalization. Recent research in multilingual ASR shows\npotential improvement over monolingual systems. We study key issues related to\nmultilingual modeling for ASR through a series of large-scale ASR experiments.\nOur innovative framework deploys a multi-graph approach in the weighted finite\nstate transducers (WFST) framework. We compare our WFST decoding strategies\nwith a transformer sequence to sequence system trained on the same data. Given\na code-switching scenario between Arabic and English languages, our results\nshow that the WFST decoding approaches were more suitable for the\nintersentential code-switching datasets. In addition, the transformer system\nperformed better for intrasentential code-switching task. With this study, we\nrelease an artificially generated development and test sets, along with\necological code-switching test set, to benchmark the ASR performance.\n"}
+{"abstract": "  Channel pruning is formulated as a neural architecture search (NAS) problem\nrecently. However, existing NAS-based methods are challenged by huge\ncomputational cost and inflexibility of applications. How to deal with multiple\nsparsity constraints simultaneously and speed up NAS-based channel pruning are\nstill open challenges. In this paper, we propose a novel Accurate and Automatic\nChannel Pruning (AACP) method to address these problems. Firstly, AACP\nrepresents the structure of a model as a structure vector and introduces a\npruning step vector to control the compressing granularity of each layer.\nSecondly, AACP utilizes Pruned Structure Accuracy Estimator (PSAE) to speed up\nthe performance estimation process. Thirdly, AACP proposes Improved\nDifferential Evolution (IDE) algorithm to search the optimal structure vector\neffectively. Because of IDE, AACP can deal with FLOPs constraint and model size\nconstraint simultaneously and efficiently. Our method can be easily applied to\nvarious tasks and achieve state of the art performance. On CIFAR10, our method\nreduces $65\\%$ FLOPs of ResNet110 with an improvement of $0.26\\%$ top-1\naccuracy. On ImageNet, we reduce $42\\%$ FLOPs of ResNet50 with a small loss of\n$0.18\\%$ top-1 accuracy and reduce $30\\%$ FLOPs of MobileNetV2 with a small\nloss of $0.7\\%$ top-1 accuracy. The source code will be released after\npublication.\n"}
+{"abstract": "  Direct numerical simulations are performed to contrast turbulent boundary\nlayers over a concave wall without and with free-stream turbulence. Adverse\npressure gradient near the onset of curvature leads to sharp decrease in skin\nfriction and intermittent separation. The presence of free-stream turbulence\nreduces the probability of reverse flow, accelerates the recovery of the\nboundary layer in the downstream zero-pressure gradient region, and leads to a\nsustained and appreciable increase in the skin friction. The forcing also\npromotes the amplification of coherent G\\\"ortler structures in the logarithmic\nlayer of the curved-wall boundary layer. Statistically, the spanwise and\nwall-normal Reynolds stresses intensify and the radial distance between their\npeaks increases downstream as the G\\\"ortler structures expand. The Reynolds\nshear stress coefficient also increases in the logarithmic layer in contrast to\na decrease when a flat-plate boundary layer is exposed to free-stream\nturbulence. In addition, the more coherent and energetic roll motions in the\nforced flow promote mixing of free-stream and boundary-layer fluids, where the\nformer is seen more often deep within the buffer layer.\n"}
+{"abstract": "  Probabilistic programming languages allow programmers to write down\nconditional probability distributions that represent statistical and machine\nlearning models as programs that use observe statements. These programs are run\nby accumulating likelihood at each observe statement, and using the likelihood\nto steer random choices and weigh results with inference algorithms such as\nimportance sampling or MCMC. We argue that naive likelihood accumulation does\nnot give desirable semantics and leads to paradoxes when an observe statement\nis used to condition on a measure-zero event, particularly when the observe\nstatement is executed conditionally on random data. We show that the paradoxes\ndisappear if we explicitly model measure-zero events as a limit of positive\nmeasure events, and that we can execute these type of probabilistic programs by\naccumulating infinitesimal probabilities rather than probability densities. Our\nextension improves probabilistic programming languages as an executable\nnotation for probability distributions by making it more well-behaved and more\nexpressive, by allowing the programmer to be explicit about which limit is\nintended when conditioning on an event of measure zero.\n"}
+{"abstract": "  In parametric Bayesian learning, a prior is assumed on the parameter $W$\nwhich determines the distribution of samples. In this setting, Minimum Excess\nRisk (MER) is defined as the difference between the minimum expected loss\nachievable when learning from data and the minimum expected loss that could be\nachieved if $W$ was observed. In this paper, we build upon and extend the\nrecent results of (Xu & Raginsky, 2020) to analyze the MER in Bayesian learning\nand derive information-theoretic bounds on it. We formulate the problem as a\n(constrained) rate-distortion optimization and show how the solution can be\nbounded above and below by two other rate-distortion functions that are easier\nto study. The lower bound represents the minimum possible excess risk\nachievable by any process using $R$ bits of information from the parameter $W$.\nFor the upper bound, the optimization is further constrained to use $R$ bits\nfrom the training set, a setting which relates MER to information-theoretic\nbounds on the generalization gap in frequentist learning. We derive\ninformation-theoretic bounds on the difference between these upper and lower\nbounds and show that they can provide order-wise tight rates for MER under\ncertain conditions. This analysis gives more insight into the\ninformation-theoretic nature of Bayesian learning as well as providing novel\nbounds.\n"}
+{"abstract": "  As the motions of nonconservative autonomous systems are typically not\nperiodic, the definition of nonlinear modes as periodic motions cannot be\napplied in the classical sense. In this paper, it is proposed 'make the motions\nperiodic' by introducing an additional damping term of appropriate sign and\nmagnitude. It is shown that this generalized definition is particularly suited\nto reflect the periodic vibration behavior induced by harmonic external forcing\nor negative linear damping. In a large range, the energy dependence of modal\nfrequency, damping ratio and stability is reproduced well. The limitation to\nisolated or weakly-damped modes is discussed.\n"}
+{"abstract": "  We consider regular variation for marked point processes with independent\nheavy-tailed marks and prove a single large point heuristic: the limit measure\nis concentrated on the cone of point measures with one single point. We then\ninvestigate successive hidden regular variation removing the cone of point\nmeasures with at most $k$ points, $k\\geq 1$, and prove a multiple large point\nphenomenon: the limit measure is concentrated on the cone of point measures\nwith $k+1$ points. We show how these results imply hidden regular variation in\nSkorokhod space of the associated risk process. Finally, we provide an\napplication to risk theory in a reinsurance model where the $k$ largest claims\nare covered and we study the asymptotic behavior of the residual risk.\n"}
+{"abstract": "  Enhancing interoperability and information exchange between domain-specific\nsoftware products for BIM is an important aspect in the Architecture,\nEngineering, Construction and Operations industry. Recent research started\ninvestigating methods from the areas of machine and deep learning for semantic\nenrichment of BIM models. However, training and evaluation of these machine\nlearning algorithms requires sufficiently large and comprehensive datasets.\nThis work presents IFCNet, a dataset of single-entity IFC files spanning a\nbroad range of IFC classes containing both geometric and semantic information.\nUsing only the geometric information of objects, the experiments show that\nthree different deep learning models are able to achieve good classification\nperformance.\n"}
+{"abstract": "  Programming digital devices and developing software is an important\nprofessional qualification, which contributes to employment opportunities.\nDespite this fact, there is a remarkable shortage in suitable human resources.\nIn this context, research studies focus on issues of programming didactic,\nteaching models, programming paradigms, which are meant to enhance and optimize\nprogrammers' skills. Recent development of brain imaging techniques such as\nelectroencephalography and the functional magnetic resonance imaging, have\nprovided additional opportunity for neuroscientists to explore the functional\norganization of the human brain. With the use of these techniques, this\nresearch is an approach to supporting learning in the field of learning and\nteaching computer programming. On one hand, there is an attempt to connect\ntheoretical neurosciences with cognitive science; on the other hand, the\nobtained research data will contribute to the identification of practices that\ncan be applied to formal and informal programming education.\n"}
+{"abstract": "  We prove a concentration phenomenon on the empirical eigenvalue distribution\n(EED) of the principal submatrix in a random hermitian matrix whose\ndistribution is invariant under unitary conjugacy; for example, this class\nincludes GUE (Gaussian Unitary Ensemble) and Wishart matrices. More precisely,\nif the EED of the whole matrix converges to some deterministic probability\nmeasure $\\mathfrak{m}$, then its fluctuation from the EED of the principal\nsubmatrix, after a rescaling, concentrates at the Rayleigh measure (in general,\na Schwartz distribution) associated with $\\mathfrak{m}$ by the Markov--Krein\ncorrespondence. For the proof, we use the moment method with Weingarten\ncalculus and free probability. At some stage of calculations, the proof\nrequires a relation between the moments of the Rayleigh measure and free\ncumulants of $\\mathfrak{m}$. This formula is more or less known, but we provide\na different proof by observing a combinatorial structure of non-crossing\npartitions.\n"}
+{"abstract": "  With the ability to transfer and process quantum information, large-scale\nquantum networks will enable a suite of fundamentally new applications, from\nquantum communications to distributed sensing, metrology, and computing. This\nperspective reviews requirements for quantum network nodes and color centers in\ndiamond as suitable node candidates. We give a brief overview of\nstate-of-the-art quantum network experiments employing color centers in\ndiamond, and discuss future research directions, focusing in particular on the\ncontrol and coherence of qubits that distribute and store entangled states, and\non efficient spin-photon interfaces. We discuss a route towards large-scale\nintegrated devices combining color centers in diamond with other photonic\nmaterials and give an outlook towards realistic future quantum network protocol\nimplementations and applications.\n"}
+{"abstract": "  Due to the large cross section of Ly$\\alpha$ photons with hydrogen, Lyman\nAlpha Emitters (LAEs) are sensitive to the presence of neutral hydrogen in the\nintergalactic medium (IGM) during the Epoch of Reionization (EoR): the period\nin the Universe's history where neutral hydrogen in the IGM is ionized. The\ncorrelation of the ionized regions in the IGM with respect to the underlying\nintrinsic LAEs has a pronounced effect on the number of observed LAEs and their\napparent clustering. As a result, observations of LAEs during the EoR can be\nused as a probe of the EoR morphology. Here we build on previous works where we\nparametrize the density-ionisation correlation during the EoR, and study how\nthe observed number density and angular correlation function (ACF) of LAEs\ndepends on this parametrization. Using Subaru measurements of the number\ndensity of LAEs and their ACF at z = 6.6, we place constraints on the EoR\nmorphology. We find that measurements of LAEs at z = 6.6 alone cannot\ndistinguish between different density-ionization models at $68\\%$ credibility.\nHowever, adding information regarding the number density, and ACF, of LAEs at\n$z = 6.6$ to 21cm power spectrum measurements using the hydrogen Epoch of\nReionization Array (HERA) at the midpoint of reionization can rule out\nuncorrelated and outside-in reionization at $99\\%$ credibility.\n"}
+{"abstract": "  The laser intensity dependence of the recoil energies from the Coulomb\nexplosion of small argon clusters has been investigated by resolving the\ncontributions of the individual charge states to the ion recoil energy spectra.\nBetween $10^{14}$ and $10^{15}$ W/cm$^2$ the high-energy tail of the ion energy\nspectra changes its shape and develops into the well-known knee feature, which\nresults from the cluster size distribution, laser focal averaging, and\nionization saturation. Resolving the contributions of the different charge\nstates to the recoil energies, the experimental data reveal that the basic\nassumption of an exploding homogeneously charged sphere cannot be maintained in\ngeneral. In fact, the energy spectra of the high-$q$ show distinct gaps in the\nyields at low kinetic energies, which hints at more complex radial ion charge\ndistributions developing during the laser pulse impact.\n"}
+{"abstract": "  Quadrupeds are strong candidates for navigating challenging environments\nbecause of their agile and dynamic designs. This paper presents a methodology\nthat extends the range of exploration for quadrupedal robots by creating an\nend-to-end navigation framework that exploits walking and jumping modes. To\nobtain a dynamic jumping maneuver while avoiding obstacles,\ndynamically-feasible trajectories are optimized offline through\ncollocation-based optimization where safety constraints are imposed. Such\noptimization schematic allows the robot to jump through window-shaped obstacles\nby considering both obstacles in the air and on the ground. The resulted\njumping mode is utilized in an autonomous navigation pipeline that leverages a\nsearch-based global planner and a local planner to enable the robot to reach\nthe goal location by walking. A state machine together with a decision making\nstrategy allows the system to switch behaviors between walking around obstacles\nor jumping through them. The proposed framework is experimentally deployed and\nvalidated on a quadrupedal robot, a Mini Cheetah, to enable the robot to\nautonomously navigate through an environment while avoiding obstacles and\njumping over a maximum height of 13 cm to pass through a window-shaped opening\nin order to reach its goal.\n"}
+{"abstract": "  In recent years, several systems have been developed to regulate the spread\nof negativity and eliminate aggressive, offensive or abusive contents from the\nonline platforms. Nevertheless, a limited number of researches carried out to\nidentify positive, encouraging and supportive contents. In this work, our goal\nis to identify whether a social media post/comment contains hope speech or not.\nWe propose three distinct models to identify hope speech in English, Tamil and\nMalayalam language to serve this purpose. To attain this goal, we employed\nvarious machine learning (support vector machine, logistic regression,\nensemble), deep learning (convolutional neural network + long short term\nmemory) and transformer (m-BERT, Indic-BERT, XLNet, XLM-Roberta) based methods.\nResults indicate that XLM-Roberta outdoes all other techniques by gaining a\nweighted $f_1$-score of $0.93$, $0.60$ and $0.85$ respectively for English,\nTamil and Malayalam language. Our team has achieved $1^{st}$, $2^{nd}$ and\n$1^{st}$ rank in these three tasks respectively.\n"}
+{"abstract": "  Weak field gravitational wave solutions are investigated in Brans-Dicke (BD)\ntheory in the presence of a cosmological constant. In this setting the\nbackground geometry is not flat but asymptotically de-Sitter. We investigate\nthe linearised field equations, and their gravitational wave solutions in a\ncertain gauge choice. We will show that this theory leads to massless scalar\nwaves as in original BD theory and in contrast to massive BD theory. The\neffects of these waves on free particles and their polarization properties are\nstudied extensively and effects of the cosmological constant is analyzed in\nthese phenomena in detail. The energy flux of these waves are also discussed in\nthis background. By analyzing this flux, we obtain a critical distance where\nthe waves cannot propagate further, which extends Cosmic no Hair Conjecture\n(CNC) to BD theory with a cosmological constant.\n"}
+{"abstract": "  This paper explores the impacts of decarbonisation of heat on demand and\nsubsequently on the generation capacity required to secure against system\nadequacy standards. Gas demand is explored as a proxy variable for modelling\nthe electrification of heating demand in existing housing stock, with a focus\non impacts on timescales of capacity markets (up to four years ahead). The work\nconsiders the systemic changes that electrification of heating could introduce,\nincluding biases that could be introduced if legacy modelling approaches\ncontinue to prevail. Covariates from gas and electrical regression models are\ncombined to form a novel, time-collapsed system model, with demand-weather\nsensitivities determined using lasso-regularized linear regression. It is\nshown, using a GB case study with one million domestic heat pump installations\nper year, that the sensitivity of electrical system demand to temperature (and\nsubsequently sensitivities to cold/warm winter seasons) could increase by 50%\nfollowing four years of heat demand electrification. A central estimate of 1.75\nkW additional peak demand per heat pump is estimated, with variability across\nthree published heat demand profiles leading to a range of more than 14 GW in\nthe most extreme cases. It is shown that the legacy approach of scaling\nhistoric demand, as compared to the explicit modelling of heat, could lead to\nover-procurement of 0.79 GW due to bias in estimates of additional capacity to\nsecure. Failure to address this issue could lead to {\\pounds}100m overspend on\ncapacity over ten years.\n"}
+{"abstract": "  The theory of Frobenius groups with Frobenius complements of even order\nlargely reduces to tractable algebraic number theory. If we consider only\nFrobenius complements with an upper bound $s$ on the number of distinct primes\ndividing the order of their commutator subgroups, then the proportion of these\nwith odd order is less than $1/2^{s}$. A positive lower bound is also given.\n"}
+{"abstract": "  Using Gaia EDR3, we study the most spectacular and photogenic cluster of\nPtolemy. After deriving its membership, we identify in its colour-magnitude\ndiagram a star that definitively decided to straggle and dress in blue. Further\nanalysis with the FARCE telescope allows us to discover in its light curve the\nsecret of its rejuvenation, which we gladly share in this paper. This research\nis an important contribution to attain the ultimate goal of astronomy as\nprofessed by DJ Format.\n"}
+{"abstract": "  Implied volatility is at the very core of modern finance, notwithstanding\nstandard option pricing models continue to derive option prices starting from\nthe joint dynamics of the underlying asset price and the spot volatility. These\nmodels often cause difficulties: no closed formulas for prices, demanding\ncalibration techniques, unclear maps between spot and implied volatility.\nInspired by the practice of using implied volatility as quoting system for\noption prices, models for the joint dynamics of the underlying asset price and\nthe implied volatility have been proposed to replace standard option pricing\nmodels. Starting from Carr and Sun (2014), we develop a framework based on the\nImplied Remaining Variance where minimal conditions for absence of arbitrage\nare identified, and smile bubbles are dealt with. The key concepts arising from\nthe new IRV framework are those of locally consistent dynamics and sandwiched\nmartingale. Within the new IRV framework, the results of Schweizer and Wissel\n(2008b) are reformulated, while those of El Amrani, Jacquier and Martini (2021)\nare independently derived.\n"}
+{"abstract": "  We propose new strategies to handle polygonal grids refinement based on\nConvolutional Neural Networks (CNNs). We show that CNNs can be successfully\nemployed to identify correctly the \"shape\" of a polygonal element so as to\ndesign suitable refinement criteria to be possibly employed within adaptive\nrefinement strategies. We propose two refinement strategies that exploit the\nuse of CNNs to classify elements' shape, at a low computational cost. We test\nthe proposed idea considering two families of finite element methods that\nsupport arbitrarily shaped polygonal elements, namely Polygonal Discontinuous\nGalerkin (PolyDG) methods and Virtual Element Methods (VEMs). We demonstrate\nthat the proposed algorithms can greatly improve the performance of the\ndiscretization schemes both in terms of accuracy and quality of the underlying\ngrids. Moreover, since the training phase is performed off-line and is\nindependent of the differential model the overall computational costs are kept\nlow.\n"}
+{"abstract": "  We study the dynamic sensitivity of the quantum Rabi model, which exhibits\nquantum criticality in the finite-component-system case. This dynamic\nsensitivity can be detected by introducing an auxiliary two-level atom\nfar-off-resonantly coupled to the cavity field of the quantum Rabi model. We\nfind that when the quantum Rabi model goes through the critical point, the\nauxiliary atom experiences a sudden decoherence, which can be characterised by\na sharp decay of the Loschmidt echo. Our scheme will provide a reliable way to\nobserve quantum phase transition in ultrastrongly coupled quantum systems.\n"}
+{"abstract": "  Software fault localization is one of the most expensive, tedious, and\ntime-consuming activities in program debugging. This activity becomes even much\nmore challenging in Software Product Line (SPL) systems due to the variability\nof failures in SPL systems. These unexpected behaviors are caused by\nvariability faults which can only be exposed under some combinations of system\nfeatures. Although localizing bugs in non-configurable code has been\ninvestigated in-depth, variability fault localization in SPL systems still\nremains mostly unexplored. To approach this challenge, we propose a benchmark\nfor variability fault localization with a large set of 1,570 buggy versions of\nsix SPL systems and baseline variability fault localization performance\nresults. Our hope is to engage the community to propose new and better\napproaches to the problem of variability fault localization in SPL systems.\n"}
+{"abstract": "  Machine understanding of user utterances in conversational systems is of\nutmost importance for enabling engaging and meaningful conversations with\nusers. Entity Linking (EL) is one of the means of text understanding, with\nproven efficacy for various downstream tasks in information retrieval. In this\npaper, we study entity linking for conversational systems. To develop a better\nunderstanding of what EL in a conversational setting entails, we analyze a\nlarge number of dialogues from existing conversational datasets and annotate\nreferences to concepts, named entities, and personal entities using\ncrowdsourcing. Based on the annotated dialogues, we identify the main\ncharacteristics of conversational entity linking. Further, we report on the\nperformance of traditional EL systems on our Conversational Entity Linking\ndataset, ConEL, and present an extension to these methods to better fit the\nconversational setting. The resources released with this paper include\nannotated datasets, detailed descriptions of crowdsourcing setups, as well as\nthe annotations produced by various EL systems. These new resources allow for\nan investigation of how the role of entities in conversations is different from\nthat in documents or isolated short text utterances like queries and tweets,\nand complement existing conversational datasets.\n"}
+{"abstract": "  Recent work (Xu et al., 2020) has suggested that numeral systems in different\nlanguages are shaped by a functional need for efficient communication in an\ninformation-theoretic sense. Here we take a learning-theoretic approach and\nshow how efficient communication emerges via reinforcement learning. In our\nframework, two artificial agents play a Lewis signaling game where the goal is\nto convey a numeral concept. The agents gradually learn to communicate using\nreinforcement learning and the resulting numeral systems are shown to be\nefficient in the information-theoretic framework of Regier et al. (2015);\nGibson et al. (2017). They are also shown to be similar to human numeral\nsystems of same type. Our results thus provide a mechanistic explanation via\nreinforcement learning of the recent results in Xu et al. (2020) and can\npotentially be generalized to other semantic domains.\n"}
+{"abstract": "  Learning an effective representation of 3D point clouds requires a good\nmetric to measure the discrepancy between two 3D point sets, which is\nnon-trivial due to their irregularity. Most of the previous works resort to\nusing the Chamfer discrepancy or Earth Mover's distance, but those metrics are\neither ineffective in measuring the differences between point clouds or\ncomputationally expensive. In this paper, we conduct a systematic study with\nextensive experiments on distance metrics for 3D point clouds. From this study,\nwe propose to use sliced Wasserstein distance and its variants for learning\nrepresentations of 3D point clouds. In addition, we introduce a new algorithm\nto estimate sliced Wasserstein distance that guarantees that the estimated\nvalue is close enough to the true one. Experiments show that the sliced\nWasserstein distance and its variants allow the neural network to learn a more\nefficient representation compared to the Chamfer discrepancy. We demonstrate\nthe efficiency of the sliced Wasserstein metric and its variants on several\ntasks in 3D computer vision including training a point cloud autoencoder,\ngenerative modeling, transfer learning, and point cloud registration.\n"}
+{"abstract": "  We present a streamlined, slightly modified version, in the two-variable\nsituation, of a beautiful, but not so well known, theory by B\\\"{o}gel, already\nfrom the 1930s, on an alternative higher dimensional calculus of real\nfunctions, a double calculus, which includes two-variable extensions of many\nclassical results from single variable calculus, such as Rolle's theorem,\nLagrange's mean value theorem, Cauchy's mean value theorem, Fermat's extremum\ntheorem, the first derivative test, and the first and second fundamental\ntheorems of calculus.\n"}
+{"abstract": "  The technology of dynamic map fusion among networked vehicles has been\ndeveloped to enlarge sensing ranges and improve sensing accuracies for\nindividual vehicles. This paper proposes a federated learning (FL) based\ndynamic map fusion framework to achieve high map quality despite unknown\nnumbers of objects in fields of view (FoVs), various sensing and model\nuncertainties, and missing data labels for online learning. The novelty of this\nwork is threefold: (1) developing a three-stage fusion scheme to predict the\nnumber of objects effectively and to fuse multiple local maps with fidelity\nscores; (2) developing an FL algorithm which fine-tunes feature models (i.e.,\nrepresentation learning networks for feature extraction) distributively by\naggregating model parameters; (3) developing a knowledge distillation method to\ngenerate FL training labels when data labels are unavailable. The proposed\nframework is implemented in the Car Learning to Act (CARLA) simulation\nplatform. Extensive experimental results are provided to verify the superior\nperformance and robustness of the developed map fusion and FL schemes.\n"}
+{"abstract": "  The isospin chemical potential region is known as the sign-problem free\nregion of quantum chromodynamics (QCD). In this paper, we introduce the isospin\nchemical potential to the three-dimensional three-state Potts model to mimic\nthe dense QCD; e.g., the QCD effective model with heavy quarks at finite\ndensity. We call it as QCD-like Potts model. The QCD-like Potts model does not\nhave the sign problem, but we can expect that it shares some properties with\nQCD. Since we can obtain the non-approximated Potts spin configuration at\nfinite isospin chemical potential where the simple Metropolis algorithm can\nwork, we perform the persistent homology analysis towards exploring the dense\nspatial structure of QCD. We show that the averaged birth-death ratio has the\nsame information with the Polyakov loop, but the maximum birth-death ratio has\nadditional information near the phase transition.\n"}
+{"abstract": "  The agricultural sector is still lagging behind from all other sectors in\nterms of using the newest technologies. For production, the latest machines are\nbeing introduced and adopted. However, pre-harvest and post-harvest processing\nare still done by following traditional methodologies while tracing, storing,\nand publishing agricultural data. As a result, farmers are not getting deserved\npayment, consumers are not getting enough information before buying their\nproduct, and intermediate person/processors are increasing retail prices. Using\nblockchain, smart contracts, and IoT devices, we can fully automate the process\nwhile establishing absolute trust among all these parties. In this research, we\nexplored the different aspects of using blockchain and smart contracts with the\nintegration of IoT devices in pre-harvesting and post-harvesting segments of\nagriculture. We proposed a system that uses blockchain as the backbone while\nIoT devices collect data from the field level, and smart contracts regulate the\ninteraction among all these contributing parties. The system implementation has\nbeen shown in diagrams and with proper explanations. Gas costs of every\noperation have also been attached for a better understanding of the costs. We\nalso analyzed the system in terms of challenges and advantages. The overall\nimpact of this research was to show the immutable, available, transparent, and\nrobustly secure characteristics of blockchain in the field of agriculture while\nalso emphasizing the vigorous mechanism that the collaboration of blockchain,\nsmart contract, and IoT presents.\n"}
+{"abstract": "  In multi-robot motion planning (MRMP) the aim is to plan the motion of\nseveral robots operating in a common workspace, while avoiding collisions with\nobstacles or with fellow robots. The main contribution of this paper is a\nsimple construction that serves as a lower bound for the computational cost of\na variety of prevalent MRMP problems. In particular we show that optimal\ndecoupling of multi-robot motion -- decoupling being a standard approach to\npractically addressing MRMP -- is NP-hard. The basic problem for which we\npresent our construction is monotone MRMP, a restricted and natural MRMP\nvariant, where robots move one by one to their targets with no intermediate\nstops. Observing the hardness of these restricted versions of MRMP is\nsignificant as it guides the search for efficient solutions towards techniques\nthat can cope with intractable problems. Furthermore, our construction\nhighlights structural properties of difficult instances, such as the need of\nrobots to pass through many start and target positions of other robots. These\ninsights can lead to useful problem relaxations resulting in efficient\nsolutions and to suitable engineering of workspaces.\n"}
+{"abstract": "  Face Anti-spoofing (FAS) is a challenging problem due to complex serving\nscenarios and diverse face presentation attack patterns. Especially when\ncaptured images are low-resolution, blurry, and coming from different domains,\nthe performance of FAS will degrade significantly. The existing multi-modal FAS\ndatasets rarely pay attention to the cross-domain problems under deployment\nscenarios, which is not conducive to the study of model performance. To solve\nthese problems, we explore the fine-grained differences between multi-modal\ncameras and construct a cross-domain multi-modal FAS dataset under surveillance\nscenarios called GREAT-FASD-S. Besides, we propose an Attention based Face\nAnti-spoofing network with Feature Augment (AFA) to solve the FAS towards\nlow-quality face images. It consists of the depthwise separable attention\nmodule (DAM) and the multi-modal based feature augment module (MFAM). Our model\ncan achieve state-of-the-art performance on the CASIA-SURF dataset and our\nproposed GREAT-FASD-S dataset.\n"}
+{"abstract": "  Contrastive self-supervised learning has outperformed supervised pretraining\non many downstream tasks like segmentation and object detection. However,\ncurrent methods are still primarily applied to curated datasets like ImageNet.\nIn this paper, we first study how biases in the dataset affect existing\nmethods. Our results show that current contrastive approaches work surprisingly\nwell across: (i) object- versus scene-centric, (ii) uniform versus long-tailed\nand (iii) general versus domain-specific datasets. Second, given the generality\nof the approach, we try to realize further gains with minor modifications. We\nshow that learning additional invariances -- through the use of multi-scale\ncropping, stronger augmentations and nearest neighbors -- improves the\nrepresentations. Finally, we observe that MoCo learns spatially structured\nrepresentations when trained with a multi-crop strategy. The representations\ncan be used for semantic segment retrieval and video instance segmentation\nwithout finetuning. Moreover, the results are on par with specialized models.\nWe hope this work will serve as a useful study for other researchers. The code\nand models are available at\nhttps://github.com/wvangansbeke/Revisiting-Contrastive-SSL.\n"}
+{"abstract": "  Network regression models, where the outcome comprises the valued edge in a\nnetwork and the predictors are actor or dyad-level covariates, are used\nextensively in the social and biological sciences. Valid inference relies on\naccurately modeling the residual dependencies among the relations. Frequently\nhomogeneity assumptions are placed on the errors which are commonly incorrect\nand ignore critical, natural clustering of the actors. In this work, we present\na novel regression modeling framework that models the errors as resulting from\na community-based dependence structure and exploits the subsequent\nexchangeability properties of the error distribution to obtain parsimonious\nstandard errors for regression parameters.\n"}
+{"abstract": "  Major advances in public health have resulted from disease prevention.\nHowever, prevention of a new infectious disease by vaccination or\npharmaceuticals is made difficult by the slow process of vaccine and drug\ndevelopment. We propose an additional intervention that allows rapid control of\nemerging infectious diseases, and can also be used to eradicate diseases that\nrely almost exclusively on human-to-human transmission. The intervention is\nbased on (1) testing every individual for the disease, (2) repeatedly, and (3)\nisolation of infected individuals. We show here that at a sufficient rate of\ntesting, the reproduction number is reduced below 1.0 and the epidemic will\nrapidly collapse. The approach does not rely on strong or unrealistic\nassumptions about test accuracy, isolation compliance, population structure or\nepidemiological parameters, and its success can be monitored in real time by\nfollowing the test positivity rate. In addition to the compliance rate and\nfalse negatives, the required rate of testing depends on the design of the\ntesting regime, with concurrent testing outperforming random sampling. Provided\nthat results are obtained rapidly, the test frequency required to suppress an\nepidemic is monotonic and near-linear with respect to R0, the infectious\nperiod, and the fraction of susceptible individuals. The testing regime is\neffective against both early phase and established epidemics, and additive to\nother interventions (e.g. contact tracing and social distancing). It is also\nrobust to failure: any rate of testing reduces the number of infections,\nimproving both public health and economic conditions. These conclusions are\nbased on rigorous analysis and simulations of appropriate epidemiological\nmodels. A mass-produced, disposable test that could be used at home would be\nideal, due to the optimal performance of concurrent tests that return immediate\nresults.\n"}
+{"abstract": "  Online prediction for streaming time series data has practical use for many\nreal-world applications where downstream decisions depend on accurate forecasts\nfor the future. Deployment in dynamic environments requires models to adapt\nquickly to changing data distributions without overfitting. We propose POLA\n(Predicting Online by Learning rate Adaptation) to automatically regulate the\nlearning rate of recurrent neural network models to adapt to changing time\nseries patterns across time. POLA meta-learns the learning rate of the\nstochastic gradient descent (SGD) algorithm by assimilating the prequential or\ninterleaved-test-then-train evaluation scheme for online prediction. We\nevaluate POLA on two real-world datasets across three commonly-used recurrent\nneural network models. POLA demonstrates overall comparable or better\npredictive performance over other online prediction methods.\n"}
+{"abstract": "  Quantum systems composed of $N$ distinct particles in $\\R^2$ with two-body\ncontact interactions of TMS type are shown to arise as limits - in the norm\nresolvent sense - of Schr\\\"odinger operators with suitably rescaled potentials.\n"}
+{"abstract": "  We investigate the dynamics of cohesive particles in homogeneous isotropic\nturbulence, based on one-way coupled simulations that include Stokes drag,\nlubrication, cohesive and direct contact forces. We observe a transient\nflocculation phase characterized by a growing average floc size, followed by a\nstatistically steady equilibrium phase. We analyze the temporal evolution of\nfloc size and shape due to aggregation, breakage, and deformation. Larger\nturbulent shear and weaker cohesive forces yield elongated flocs that are\nsmaller in size. Flocculation proceeds most rapidly when the fluid and particle\ntime scales are balanced and a suitably defined Stokes number is \\textit{O}(1).\nDuring the transient stage, cohesive forces of intermediate strength produce\nflocs of the largest size, as they are strong enough to cause aggregation, but\nnot so strong as to pull the floc into a compact shape. Small Stokes numbers\nand weak turbulence delay the onset of the equilibrium stage. During\nequilibrium, stronger cohesive forces yield flocs of larger size. The\nequilibrium floc size distribution exhibits a preferred size that depends on\nthe cohesive number. We observe that flocs are generally elongated by turbulent\nstresses before breakage. Flocs of size close to the Kolmogorov length scale\npreferentially align themselves with the intermediate strain direction and the\nvorticity vector. Flocs of smaller size tend to align themselves with the\nextensional strain direction. More generally, flocs are aligned with the\nstrongest Lagrangian stretching direction. The Kolmogorov scale is seen to\nlimit floc growth. We propose a new flocculation model with a variable fractal\ndimension that predicts the temporal evolution of the floc size and shape.\n"}
+{"abstract": "  In star-forming galaxies, the far-infrared (FIR) and radio-continuum\nluminosities obey a tight empirical relation over a large range of\nstar-formation rates (SFR). To understand the physics, we examine\nmagneto-hydrodynamic galaxy simulations, which follow the genesis of cosmic ray\n(CR) protons at supernovae and their advective and anisotropic diffusive\ntransport. We show that gravitational collapse of the proto-galaxy drives a\nsmall-scale dynamo, which exponentially amplifies weak seed magnetic fields.\nAfter saturation at small scales, they grow in scale to reach equipartition\nwith thermal and CR energy densities in Milky Way-mass galaxies. In small\ngalaxies, the magnetic energy saturates at the turbulent energy while it fails\nto reach equipartition with thermal and CR energies. We solve for steady-state\nspectra of CR protons, secondary electrons/positrons from hadronic CR-proton\ninteractions with the interstellar medium, and primary shock-accelerated\nelectrons at supernovae. The modeled radio-synchrotron emission is dominated by\nprimary electrons, irradiates the magnetised disc and bulge of our simulated\nMilky Way-mass galaxy and weakly traces bubble-shaped magnetically-loaded\noutflows. Our star-forming and star-bursting galaxies with saturated magnetic\nfields match the global FIR-radio correlation (FRC) across four orders of\nmagnitude. Its intrinsic scatter arises due to (i) different magnetic\nsaturation levels that result from different seed magnetic fields, (ii)\ndifferent radio synchrotron luminosities for different specific SFRs at fixed\nSFR and (iii) a varying radio intensity with galactic inclination. In agreement\nwith observations, several 100-pc-sized regions within star-forming galaxies\nalso obey the FRC, while the centres of starbursts exceed the FRC by a\nsubstantial amount.\n"}
+{"abstract": "  Multimodal imaging has transformed neuroscience research. While it presents\nunprecedented opportunities, it also imposes serious challenges. Particularly,\nit is difficult to combine the merits of the interpretability attributed to a\nsimple association model with the flexibility achieved by a highly adaptive\nnonlinear model. In this article, we propose an orthogonalized kernel debiased\nmachine learning approach, which is built upon the Neyman orthogonality and a\nform of decomposition orthogonality, for multimodal data analysis. We target\nthe setting that naturally arises in almost all multimodal studies, where there\nis a primary modality of interest, plus additional auxiliary modalities. We\nestablish the root-$N$-consistency and asymptotic normality of the estimated\nprimary parameter, the semi-parametric estimation efficiency, and the\nasymptotic validity of the confidence band of the predicted primary modality\neffect. Our proposal enjoys, to a good extent, both model interpretability and\nmodel flexibility. It is also considerably different from the existing\nstatistical methods for multimodal data integration, as well as the\northogonality-based methods for high-dimensional inferences. We demonstrate the\nefficacy of our method through both simulations and an application to a\nmultimodal neuroimaging study of Alzheimer's disease.\n"}
+{"abstract": "  The aerial manipulator (AM) is a systematic operational robotic platform in\nhigh standard on algorithm robustness. Directly deploying the algorithms to the\npractical system will take numerous trial and error costs and even cause\ndestructive results. In this paper, a new modular simulation platform is\ndesigned to evaluate aerial manipulation related algorithms before deploying.\nIn addition, to realize a fully autonomous aerial grasping, a series of\nalgorithm modules consisting a complete workflow are designed and integrated in\nthe simulation platform, including perception, planning and control modules.\nThis framework empowers the AM to autonomously grasp remote targets without\ncolliding with surrounding obstacles relying only on on-board sensors.\nBenefiting from its modular design, this software architecture can be easily\nextended with additional algorithms. Finally, several simulations are performed\nto verify the effectiveness of the proposed system.\n"}
+{"abstract": "  Efficient machine learning implementations optimized for inference in\nhardware have wide-ranging benefits, depending on the application, from lower\ninference latency to higher data throughput and reduced energy consumption. Two\npopular techniques for reducing computation in neural networks are pruning,\nremoving insignificant synapses, and quantization, reducing the precision of\nthe calculations. In this work, we explore the interplay between pruning and\nquantization during the training of neural networks for ultra low latency\napplications targeting high energy physics use cases. Techniques developed for\nthis study have potential applications across many other domains. We study\nvarious configurations of pruning during quantization-aware training, which we\nterm quantization-aware pruning, and the effect of techniques like\nregularization, batch normalization, and different pruning schemes on\nperformance, computational complexity, and information content metrics. We find\nthat quantization-aware pruning yields more computationally efficient models\nthan either pruning or quantization alone for our task. Further,\nquantization-aware pruning typically performs similar to or better in terms of\ncomputational efficiency compared to other neural architecture search\ntechniques like Bayesian optimization. Surprisingly, while networks with\ndifferent training configurations can have similar performance for the\nbenchmark application, the information content in the network can vary\nsignificantly, affecting its generalizability.\n"}
+{"abstract": "  Part segmentations provide a rich and detailed part-level description of\nobjects, but their annotation requires an enormous amount of work. In this\npaper, we introduce CGPart, a comprehensive part segmentation dataset that\nprovides detailed annotations on 3D CAD models, synthetic images, and real test\nimages. CGPart includes $21$ 3D CAD models covering $5$ vehicle categories,\neach with detailed per-mesh part labeling. The average number of parts per\ncategory is $24$, which is larger than any existing datasets for part\nsegmentation on vehicle objects. By varying the rendering parameters, we make\n$168,000$ synthetic images from these CAD models, each with automatically\ngenerated part segmentation ground-truth. We also annotate part segmentations\non $200$ real images for evaluation purposes. To illustrate the value of\nCGPart, we apply it to image part segmentation through unsupervised domain\nadaptation (UDA). We evaluate several baseline methods by adapting\ntop-performing UDA algorithms from related tasks to part segmentation.\nMoreover, we introduce a new method called Geometric-Matching Guided domain\nadaptation (GMG), which leverages the spatial object structure to guide the\nknowledge transfer from the synthetic to the real images. Experimental results\ndemonstrate the advantage of our new algorithm and reveal insights for future\nimprovement. We will release our data and code.\n"}
+{"abstract": "  We describe Hilbert's spacefilling curve in several different ways: as an\nautomatic sequence of directions,as a regular and synchronized sequence of\ncoordinates of lattice points encountered, and as an automatic bitmap image.\n"}
+{"abstract": "  Multi-column dependencies in relational databases come associated with two\ndifferent computational tasks. The detection problem is to decide whether a\ndependency of a certain type and size holds in a given database, the discovery\nproblem asks to enumerate all valid dependencies of that type. We settle the\ncomplexity of both of these problems for unique column combinations (UCCs),\nfunctional dependencies (FDs), and inclusion dependencies (INDs). We show that\nthe detection of UCCs and FDs is W[2]-complete when parameterized by the\nsolution size. The discovery of inclusion-wise minimal UCCs is proven to be\nequivalent under parsimonious reductions to the transversal hypergraph problem\nof enumerating the minimal hitting sets of a hypergraph. The discovery of FDs\nis equivalent to the simultaneous enumeration of the hitting sets of multiple\ninput hypergraphs. We further identify the detection of INDs as one of the\nfirst natural W[3]-complete problems. The discovery of maximal INDs is shown to\nbe equivalent to enumerating the maximal satisfying assignments of\nantimonotone, 3-normalized Boolean formulas.\n"}
+{"abstract": "  This paper presents a new joint radar and communication technique based on\nthe classical stepped frequency radar waveform. The randomization in the\nwaveform, which is achieved by using permutations of the sequence of frequency\ntones, is utilized for data transmission. A new signaling scheme is proposed in\nwhich the mapping between incoming data and waveforms is performed based on an\nefficient combinatorial transform called the Lehmer code. Considering the\noptimum maximum likelihood (ML) detection, the union bound and the nearest\nneighbour approximation on the communication block error probability is derived\nfor communication in an additive white Gaussian noise (AWGN) channel. The\nresults are further extended to incorporate the Rician fading channel model, of\nwhich the Rayleigh fading channel model is presented as a special case.\nFurthermore, an efficient communication receiver implementation is discussed\nbased on the Hungarian algorithm which achieves optimum performance with much\nless operational complexity when compared to an exhaustive search. From the\nradar perspective, two key analytical tools, namely, the ambiguity function\n(AF) and the Fisher information matrix are derived. Furthermore, accurate\napproximations to the Cramer-Rao lower bounds (CRLBs) on the delay and Doppler\nestimation errors are derived based on which the range and velocity estimation\naccuracy of the waveform is analysed. Numerical examples are used to highlight\nthe accuracy of the analysis and to illustrate the performance of the proposed\nwaveform.\n"}
+{"abstract": "  Given a monogenic function on the quaternionic algebra $\\mathbb{H}$, the\nClifford algebra $\\mathbb{R}_n$ or the octonionic algebra $\\mathbb{O}$ we prove\nthat $|\\nabla^m f|^\\alpha$ is subharmonic for some $\\alpha>0$ where $\\nabla^m\nf$ is the $m$-th order gradient of $f$. We find also the optimal value of\n$\\alpha$. This is generalization of a result of Calderon and Zygmund.\n"}
+{"abstract": "  An algorithm for the unbiased simulation of max-(or min-)id stochastic\nprocesses is developed. Our algorithm only requires the simulation of finite\nPoisson random measures and avoids the necessity of computing conditional\ndistributions of infinite (exponent)measures. Special emphasis is put on the\nsimulation of exchangeable max-(or min-)id sequences, in particular\nexchangeable Sato-frailty sequences. Additionally, exact simulation schemes of\nhigh-dimensional exchangeable exogenous shock models and exchangeable\nmax-stable sequences are sketched.\n"}
+{"abstract": "  In this paper, we investigate the simplest wormhole solution - the\nEllis-Bronnikov one - in the context of the Asymptotically Safe Gravity (ASG)\nat the Planck scale. We work with three models, which employ Ricci scalar,\nKretschmann scalar, and squared Ricci tensor to improve the field equations by\nturning the Newton constant into a running coupling constant. For all the\ncases, we check the radial energy conditions of the wormhole solution and\ncompare them with those valid in General Relativity (GR). We verify that\nasymptotic safety guarantees that the Ellis-Bronnikov wormhole can satisfy the\nradial energy conditions at the throat radius, $r_0$, within an interval of\nvalues of this latter. That is quite different from the result found in GR.\nFollowing, we evaluate the effective radial state parameter, $\\omega(r)$, at\n$r_0$, showing that the quantum gravitational effects modify Einstein's field\nequations in such a way that it is necessary a very exotic source of matter to\ngenerate the wormhole spacetime -- phantom or quintessence-like. That occurs\nwithin some ranges of throat radii, even though the energy conditions are or\nnot violated there. Finally, we find that, although at $r_0$ we have a\nquintessence-like matter, on growing of $r$ we necessarily come across\nphantom-like regions. We speculate if such a phantom fluid must always be\npresent in wormholes in the ASG context or even in more general quantum gravity\nscenarios.\n"}
+{"abstract": "  Hydrogen, the smallest and most abundant element in nature, can be\nefficiently incorporated within a solid and drastically modify its electronic\nstate - it has been known to induce novel magnetoelectric effects in complex\nperovskites and modulate insulator-to-metal transition in a correlated Mott\noxide. Here we demonstrate that hydrogenation resolves an outstanding challenge\nin chalcogenide classes of three-dimensional (3D) topological insulators and\nmagnets - the control of intrinsic bulk conduction that denies access to\nquantum surface transport. With electrons donated by a reversible binding of H+\nions to Te(Se) chalcogens, carrier densities are easily changed by over 10^20\ncm^-3, allowing tuning the Fermi level into the bulk bandgap to enter\nsurface/edge current channels. The hydrogen-tuned topological materials are\nstable at room temperature and tunable disregarding bulk size, opening a\nbreadth of platforms for harnessing emergent topological states.\n"}
+{"abstract": "  In the present work, we propose new tensor Krylov subspace method for ill\nposed linear tensor problems such as in color or video image restoration. Those\nmethods are based on the tensor-tensor discrete cosine transform that gives\nfast tensor-tensor product computations. In particular, we will focus on the\ntensor discrete cosine versions of GMRES, Golub-Kahan bidiagonalisation and\nLSQR methods. The presented numerical tests show that the methods are very fast\nand give good accuracies when solving some linear tensor ill-posed problems.\n"}
+{"abstract": "  We explain how logarithmic structures select natural principal components in\nan intersection of schemes. These manifest in Chow homology and can be\nunderstood using the geometry of strict transforms under logarithmic blowups.\nOur motivation comes from Gromov--Witten theory. The toric contact cycles in\nthe moduli space of curves parameterize curves that admit a map to a fixed\ntoric variety with prescribed contact orders. We show that they are\nintersections of virtual strict transforms of double ramification cycles in\nblowups of the moduli space of curves. We supply a calculation scheme for the\nvirtual strict transforms, and deduce that toric contact cycles lie in the\ntautological ring of the moduli space of curves. This is a higher dimensional\nanalogue of a result of Faber--Pandharipande. The operational Chow rings of\nArtin fans play a basic role, and are shown to be isomorphic to rings of\npiecewise polynomials on associated cone complexes. The ingredients in our\nanalysis are Fulton's blowup formula, Aluffi's formulas for Segre classes of\nmonomial schemes, piecewise polynomials, and degeneration methods. A model\ncalculation in toric intersection theory is treated without logarithmic methods\nand may be read independently.\n"}
+{"abstract": "  We study stationary Stokes systems in divergence form with piecewise Dini\nmean oscillation coefficients and data in a bounded domain containing a finite\nnumber of subdomains with $C^{1,\\rm{Dini}}$ boundaries. We prove that if $(u,\np)$ is a weak solution of the system, then $(Du, p)$ is bounded and piecewise\ncontinuous. The corresponding results for stationary Navier-Stokes systems are\nalso established, from which the Lipschitz regularity of the stationary\n$H^1$-weak solution in dimensions $d=2,3,4$ is obtained.\n"}
+{"abstract": "  Levitated nanodiamonds containing negatively charged nitrogen vacancy centers\n(${\\text{NV}}^{-}$) have been proposed as a platform to generate macroscopic\nspatial superpositions. Requirements for this include having a long\n${\\text{NV}}^{-}$ spin coherence time, which necessitates formulating a\ndynamical decoupling strategy in which the regular spin flips do not cancel the\ngrowth of the superposition through the Stern-Gerlach effect in an\ninhomogeneous magnetic field. Here, we propose a scheme to place a\n$250$-nm-diameter diamond in a superposition with spatial separation of over\n$250$ nm, while incorporating dynamical decoupling. We achieve this by letting\na diamond fall for $2.4$ m through a magnetic structure, including $1.13$ m in\nan inhomogeneous region generated by magnetic teeth.\n"}
+{"abstract": "  Following the Unlimited Sampling strategy to alleviate the omnipresent\ndynamic range barrier, we study the problem of recovering a bandlimited signal\nfrom point-wise modulo samples, aiming to connect theoretical guarantees with\nhardware implementation considerations. Our starting point is a class of\nnon-idealities that we observe in prototyping an unlimited sampling based\nanalog-to-digital converter. To address these non-idealities, we provide a new\nFourier domain recovery algorithm. Our approach is validated both in theory and\nvia extensive experiments on our prototype analog-to-digital converter,\nproviding the first demonstration of unlimited sampling for data arising from\nreal hardware, both for the current and previous approaches. Advantages of our\nalgorithm include that it is agnostic to the modulo threshold and it can handle\narbitrary folding times. We expect that the end-to-end realization studied in\nthis paper will pave the path for exploring the unlimited sampling methodology\nin a number of real world applications.\n"}
+{"abstract": "  Predicting student performance is a fundamental task in Intelligent Tutoring\nSystems (ITSs), by which we can learn about students' knowledge level and\nprovide personalized teaching strategies for them. Researchers have made plenty\nof efforts on this task. They either leverage educational psychology methods to\npredict students' scores according to the learned knowledge proficiency, or\nmake full use of Collaborative Filtering (CF) models to represent latent\nfactors of students and exercises. However, most of these methods either\nneglect the exercise-specific characteristics (e.g., exercise materials), or\ncannot fully explore the high-order interactions between students, exercises,\nas well as knowledge concepts. To this end, we propose a Graph-based Exercise-\nand Knowledge-Aware Learning Network for accurate student score prediction.\nSpecifically, we learn students' mastery of exercises and knowledge concepts\nrespectively to model the two-fold effects of exercises and knowledge concepts.\nThen, to model the high-order interactions, we apply graph convolution\ntechniques in the prediction process. Extensive experiments on two real-world\ndatasets prove the effectiveness of our proposed Graph-EKLN.\n"}
+{"abstract": "  Growing up in an artificial intelligence-filled world, with Siri and Amazon\nAlexa often within arm's - or speech's - reach, could have significant impact\non children. Conversational agents could influence how students\nanthropomorphize computer systems or develop a theory of mind. Previous\nresearch has explored how conversational agents are used and perceived by\nchildren within and outside of learning contexts. This study investigates how\nmiddle and high school students' perceptions of Alexa change through\nprogramming their own conversational agents in week-long AI education\nworkshops. Specifically, we investigate the workshops' influence on student\nperceptions of Alexa's intelligence, friendliness, aliveness, safeness,\ntrustworthiness, human-likeness, and feelings of closeness. We found that\nstudents felt Alexa was more intelligent and felt closer to Alexa after the\nworkshops. We also found strong correlations between students' perceptions of\nAlexa's friendliness and trustworthiness, and safeness and trustworthiness.\nFinally, we explored how students tended to more frequently use computer\nscience-related diction and ideas after the workshops. Based on our findings,\nwe recommend designers carefully consider personification, transparency,\nplayfulness and utility when designing CAs for learning contexts.\n"}
+{"abstract": "  The discrepancy between the cost function used for training a speech\nenhancement model and human auditory perception usually makes the quality of\nenhanced speech unsatisfactory. Objective evaluation metrics which consider\nhuman perception can hence serve as a bridge to reduce the gap. Our previously\nproposed MetricGAN was designed to optimize objective metrics by connecting the\nmetric with a discriminator. Because only the scores of the target evaluation\nfunctions are needed during training, the metrics can even be\nnon-differentiable. In this study, we propose a MetricGAN+ in which three\ntraining techniques incorporating domain-knowledge of speech processing are\nproposed. With these techniques, experimental results on the VoiceBank-DEMAND\ndataset show that MetricGAN+ can increase PESQ score by 0.3 compared to the\nprevious MetricGAN and achieve state-of-the-art results (PESQ score = 3.15).\n"}
+{"abstract": "  In this work, we present a framework for product quality inspection based on\ndeep learning techniques. First, we categorize several deep learning models\nthat can be applied to product inspection systems. Also we explain entire steps\nfor building a deep learning-based inspection system in great detail. Second,\nwe address connection schemes that efficiently link the deep learning models to\nthe product inspection systems. Finally, we propose an effective method that\ncan maintain and enhance the deep learning models of the product inspection\nsystem. It has good system maintenance and stability due to the proposed\nmethods. All the proposed methods are integrated in a unified framework and we\nprovide detailed explanations of each proposed method. In order to verify the\neffectiveness of the proposed system, we compared and analyzed the performance\nof methods in various test scenarios.\n"}
+{"abstract": "  A concept of drone launched short range rockets (DLSRR) is presented. A drone\nor an aircraft rises DLSRR to a release altitude of up to 20 $km$. At the\nrelease altitude, the drone or an aircraft is moving at a velocity of up to 700\n$m/s$ and a steep angle of up to 68$^o$ to the horizontal. After DLSRRs are\nreleased, their motors start firing. DLSRRs use slow burning motors to gain\naltitude and velocity. At the apogee of their flight DLSRRs release projectiles\nwhich fly to the target and strike it at high impact velocity. The projectiles\nreach a target at ranges of up to 442 $km$ and impact velocities up to 1.88\n$km/s$. We show that a rocket launched at high altitude and high initial\nvelocity does not need expensive thermal protection to survive ascent. Delivery\nof munitions to target by DLSRRs should be much less expensive than delivery by\na conventional rocket. %% Even though delivery of munitions by bomber aircraft\nis even less expensive, a bomber needs to fly close to the target, while a\nDLSRR carrier releases the rockets from a distance of at least 200 $km$ from\nthe target. %% All parameters of DLSRRs, and their trajectories are calculated\nbased on theoretical (mechanical and thermodynamical) analysis and on several\nMatLab programs.\n"}
+{"abstract": "  We investigate effects of non-zero Dirac and Majorana CP violating phases on\nneutrino-antineutrino oscillations in a magnetic field of astrophysical\nenvironments. It is shown that in the presence of strong magnetic fields and\ndense matter, non-zero CP phases can induce new resonances in the oscillations\nchannels $\\nu_e \\leftrightarrow \\bar{\\nu}_e$, $\\nu_e \\leftrightarrow\n\\bar{\\nu}_\\mu$ and $\\nu_e \\leftrightarrow \\bar{\\nu}_{\\tau}$. We also consider\nall other possible oscillation channels with $\\nu_\\mu$ and $\\nu_\\tau$ in the\ninitial state. The resonances can potentially lead to significant phenomena in\nneutrino oscillations accessible for observation in experiments. In particular,\nwe show that neutrino-antineutrino oscillations combined with Majorana-type CP\nviolation can affect the $\\bar{\\nu}_e$/$\\nu_e$ ratio for neutrinos coming from\nthe supernovae explosion. This effect is more prominent for the normal neutrino\nmass ordering. The detection of supernovae neutrino fluxes in the future\nexperiments, such as JUNO, DUNE and Hyper-Kamiokande, can give an insight into\nthe nature of CP violation and, consequently, provides a tool for\ndistinguishing the Dirac or Majorana nature of neutrinos.\n"}
+{"abstract": "  We show that $k$-point correlation measurements on output of a\nnon-interacting, multimode, random unitary allow to quantify the $k$-particle\ncoherence of $N\\geq k$ identical (bosonic or fermionic) particles. For\nseparable many-particle input states, we establish an on average strictly\nmonotonic relationship between such $k$-particle coherences, the interference\ncontrast in the experimentally accessible counting statistics and the degree of\nthe particles' mutual distinguishability, as controlled by their internal\ndegrees of freedom. Non-separability on input can be unveiled by comparison of\ncorrelation measurements of different orders.\n"}
+{"abstract": "  Deep learning is very data hungry, and supervised learning especially\nrequires massive labeled data to work well. Machine listening research often\nsuffers from limited labeled data problem, as human annotations are costly to\nacquire, and annotations for audio are time consuming and less intuitive.\nBesides, models learned from labeled dataset often embed biases specific to\nthat particular dataset. Therefore, unsupervised learning techniques become\npopular approaches in solving machine listening problems. Particularly, a\nself-supervised learning technique utilizing reconstructions of multiple\nhand-crafted audio features has shown promising results when it is applied to\nspeech domain such as emotion recognition and automatic speech recognition\n(ASR). In this paper, we apply self-supervised and multi-task learning methods\nfor pre-training music encoders, and explore various design choices including\nencoder architectures, weighting mechanisms to combine losses from multiple\ntasks, and worker selections of pretext tasks. We investigate how these design\nchoices interact with various downstream music classification tasks. We find\nthat using various music specific workers altogether with weighting mechanisms\nto balance the losses during pre-training helps improve and generalize to the\ndownstream tasks.\n"}
+{"abstract": "  Pre-trained language models have recently advanced abstractive summarization.\nThese models are further fine-tuned on human-written references before summary\ngeneration in test time. In this work, we propose the first application of\ntransductive learning to summarization. In this paradigm, a model can learn\nfrom the test set's input before inference. To perform transduction, we propose\nto utilize input document summarizing sentences to construct references for\nlearning in test time. These sentences are often compressed and fused to form\nabstractive summaries and provide omitted details and additional context to the\nreader. We show that our approach yields state-of-the-art results on CNN/DM and\nNYT datasets. For instance, we achieve over 1 ROUGE-L point improvement on\nCNN/DM. Further, we show the benefits of transduction from older to more recent\nnews. Finally, through human and automatic evaluation, we show that our\nsummaries become more abstractive and coherent.\n"}
+{"abstract": "  Context. Particles in protoplanetary disks go through a number of phases that\nare dominated by collisions. In each of these events, grains exchange\nelectrical charge via triboelectric effects. This enhances the stability of\nparticle aggregates. Aim. Dielectric grains are easily charged by collisions.\nHere, we investigate whether a charge is capable of inducing an aggregation of\nparticles and we consider how collision properties, such as ticking velocities\nand collisional cross-sections, are altered. Methods. We explored aggregation\nin microgravity experiments based on the observation of the motion of\nsubmillimeter (submm) grains following many collisions. In the process, grains\nattract each other, collide, stick, and ultimately form small aggregates.\nResults. We observed a bottom-up formation of irregular aggregates from submm\ngrains. While some of the observed trajectories during the approach of grains\nreflect the presence of a pure Coulomb potential, the motion is not always in\nagreement with pure Kepler motion. Higher-order potentials of multipole charge\ndistributions stand as a plausible explanation for this behavior. An immediate\nconsequence of charging is that the particles continue to stick to each other\nat velocities of $\\sim 10 \\, \\rm cm/s,$ while surface forces of neutral grains\nare only expected to allow sticking below $\\sim 1 \\, \\rm mm/s$. No bouncing\ncollision was observed among hundreds of collisions in the given parameter\nrange. Applied to early phases of planet formation, the forming aggregates are\ntherefore the first steps in a new growth phase beyond the traditional bouncing\nbarrier in planet formation.\n"}
+{"abstract": "  We show that if $E/\\mathbb{Q}$ is an elliptic curve with a rational\n$p$-torsion for $p=2$ or $3$, then there is a congruence relation between\nRamanujan's tau function and $E$ modulo $p$. We make use of such congruences to\ncompute the Iwasawa invariants of $2$-adic and $3$-adic Mazur--Tate elements\nattached to Ramanujan's tau function. We also investigate numerically the\nIwasawa invariants of the Mazur--Tate elements attached to an elliptic curve\nwith additive reduction at a fixed prime number.\n"}
+{"abstract": "  Bug localization is an important aspect of software maintenance because it\ncan locate modules that should be changed to fix a specific bug. Our previous\nstudy showed that the accuracy of the information retrieval (IR)-based bug\nlocalization technique improved when used in combination with code smell\ninformation. Although this technique showed promise, the study showed limited\nusefulness because of the small number of: 1) projects in the dataset, 2) types\nof smell information, and 3) baseline bug localization techniques used for\nassessment. This paper presents an extension of our previous experiments on\nBench4BL, the largest bug localization benchmark dataset available for bug\nlocalization. In addition, we generalized the smell-aware bug localization\ntechnique to allow different configurations of smell information, which were\ncombined with various bug localization techniques. Our results confirmed that\nour technique can improve the performance of IR-based bug localization\ntechniques for the class level even when large datasets are processed.\nFurthermore, because of the optimized configuration of the smell information,\nour technique can enhance the performance of most state-of-the-art bug\nlocalization techniques.\n"}
+{"abstract": "  Employing a combination of symmetry analysis, low-energy modeling, and ab\ninitio simulations, we predict the presence of magnetic-field-induced Weyl\npoints close to the Fermi level in CaKFe$_4$As$_4$. Depending on the relative\nstrengths of the magnetic field and of the spin-orbit coupling, the Weyl\nfermions can carry a topological charge of $\\pm1$ or $\\pm2$, making\nCaKFe$_4$As$_4$ a rare realization of a double-Weyl semimetal. We further\npredict experimental manifestations of these Weyl points, both in bulk\nproperties, such as the anomalous Hall effect, and in surface properties, such\nas the emergence of prominent Fermi arcs. Because CaKFe$_4$As$_4$ displays\nunconventional fully-gapped superconductivity below 30 K, our findings open a\nnovel route to investigate the interplay between superconductivity and Weyl\nfermions.\n"}
+{"abstract": "  Siamese deep-network trackers have received significant attention in recent\nyears due to their real-time speed and state-of-the-art performance. However,\nSiamese trackers suffer from similar looking confusers, that are prevalent in\naerial imagery and create challenging conditions due to prolonged occlusions\nwhere the tracker object re-appears under different pose and illumination. Our\nwork proposes SiamReID, a novel re-identification framework for Siamese\ntrackers, that incorporates confuser rejection during prolonged occlusions and\nis well-suited for aerial tracking. The re-identification feature is trained\nusing both triplet loss and a class balanced loss. Our approach achieves\nstate-of-the-art performance in the UAVDT single object tracking benchmark.\n"}
+{"abstract": "  In the present work, we consider the industrial problem of estimating in\nreal-time the mold-steel heat flux in continuous casting mold. We approach this\nproblem by first considering the mold modeling problem (direct problem). Then,\nwe plant the heat flux estimation problem as the inverse problem of estimating\na Neumann boundary condition having as data pointwise temperature measurements\nin the interior of the mold domain. We also consider the case of having a total\nheat flux measurement together with the temperature measurements. We develop\ntwo methodologies for solving this inverse problem. The first one is the\ntraditional Alifanov's regularization, the second one exploits the\nparameterization of the heat flux. We develop the latter method to have an\noffline-online decomposition with a computationally efficient online part to be\nperformed in real-time. In the last part of this work, we test these methods on\nacademic and industrial benchmarks. The results show that the parameterization\nmethod outclasses Alifanov's regularization both in performance and\ncomputational cost. Moreover, it proves to be robust with respect to the\nmeasurements noise. Finally, the tests confirm that the computational cost is\nsuitable for real-time estimation of the heat flux.\n"}
+{"abstract": "  Spintronic heterostructures are considered to be the new generation THz\nsources for their capability in producing high power and broadband THz\nradiation. Here, we provide a brief review on the state-of-the-art in this\nfield. The optically excited bi- and tri-layer combinations of ferromagnetic\nand nonmagnetic thin films have become increasingly popular. Towards optimizing\nthe THz conversion efficiency and broadband gapless spectrum from these THz\nemitters, various control parameters need to be taken into consideration. The\ninverse spin Hall effect in the heavy metal layer of the heterostructure is\nprimarily responsible for the generation of THz pulses. A few new results on\niron, platinum and tantalum based heterostructures have also been reported\nhere. It is observed that the Ta(2nm)/Fe(2nm)/Pt(2nm) tri-layer heterostructure\ngenerates ~40(250)% stronger THz signal as compared to the counterpart\nFe(2nm)/Pt(2nm) (Fe(3nm)/Ta(2nm)) bi-layer heterostructure.\n"}
+{"abstract": "  Width-based planning methods deal with conjunctive goals by decomposing\nproblems into subproblems of low width. Algorithms like SIW thus fail when the\ngoal is not easily serializable in this way or when some of the subproblems\nhave a high width. In this work, we address these limitations by using a simple\nbut powerful language for expressing finer problem decompositions introduced\nrecently by Bonet and Geffner, called policy sketches. A policy sketch over a\nset of Boolean and numerical features is a set of sketch rules that express how\nthe values of these features are supposed to change. Like general policies,\npolicy sketches are domain general, but unlike policies, the changes captured\nby sketch rules do not need to be achieved in a single step. We show that many\nplanning domains that cannot be solved by SIW are provably solvable in low\npolynomial time with the SIW_R algorithm, the version of SIW that employs\nuser-provided policy sketches. Policy sketches are thus shown to be a powerful\nlanguage for expressing domain-specific knowledge in a simple and compact way\nand a convenient alternative to languages such as HTNs or temporal logics.\nFurthermore, they make it easy to express general problem decompositions and\nprove key properties of them like their width and complexity.\n"}
+{"abstract": "  In the majority of population synthesis calculations of close binary stars,\nthe common envelope (CE) phase is modeled using a standard prescription based\nupon the conservation of energy, known as the alpha prescription. In this\nprescription, the orbital separation of the secondary and giant core at the end\nof the CE phase is taken to be the orbital separation when the envelope becomes\nunbound. However, recent observations of planetary nebulae with binary cores\n(BPNe), believed to be the immediate products of CE evolution, indicate orbital\nperiods that are significantly shorter than predicted by population synthesis\nmodels using the alpha prescription. We argue that unbinding the envelope\nprovides a necessary, but not sufficient, condition to escape a merger during\nCE evolution. The spiral-in of the secondary must also be halted. This requires\nthe additional dynamical constraint that the frictional torque on the secondary\nbe reduced to approximately zero. In this paper, we undertake a preliminary\nexamination of the effect of adding this dynamical constraint in population\nsynthesis calculations of BPNe. We assume that the frictional torque will be\nsufficiently reduced when the secondary enters a region within the giant where\nthe mass-radius profile is flat. We crudely estimate the location of this\nregion as a function of the core mass based upon existing stellar models of AGB\nstars between 1 and 7 solar masses. We calculate a theoretical orbital period\ndistribution of BPNe using a population synthesis code that incorporates this\ndynamical constraint along with the alpha prescription.\n"}
+{"abstract": "  In machine learning, the term active learning regroups techniques that aim at\nselecting the most useful data to label from a large pool of unlabelled\nexamples. While supervised deep learning techniques have shown to be\nincreasingly efficient on many applications, they require a huge number of\nlabelled examples to reach operational performances. Therefore, the labelling\neffort linked to the creation of the datasets required is also increasing. When\nworking on defense-related remote sensing applications, labelling can be\nchallenging due to the large areas covered and often requires military experts\nwho are rare and whose time is primarily dedicated to operational needs.\nLimiting the labelling effort is thus of utmost importance. This study aims at\nreviewing the most relevant active learning techniques to be used for object\ndetection on very high resolution imagery and shows an example of the value of\nsuch techniques on a relevant operational use case: aircraft detection.\n"}
+{"abstract": "  Although deep convolutional networks have achieved great performance in face\nrecognition tasks, the challenge of domain discrepancy still exists in real\nworld applications. Lack of domain coverage of training data (source domain)\nmakes the learned models degenerate in a testing scenario (target domain). In\nface recognition tasks, classes in two domains are usually different, so\nclassical domain adaptation approaches, assuming there are shared classes in\ndomains, may not be reasonable solutions for this problem. In this paper,\nself-supervised learning is adopted to learn a better embedding space where the\nsubjects in target domain are more distinguishable. The learning goal is\nmaximizing the similarity between the embeddings of each image and its mirror\nin both domains. The experiments show its competitive results compared with\nprior works. To know the reason why it can achieve such performance, we further\ndiscuss how this approach affects the learning of embeddings.\n"}
+{"abstract": "  In this work we apply methods for describing 3D images to the problem of\nencoding atomic environments in a way that is invariant to rotations,\ntranslations, and permutations of the atoms and, crucially, can be decoded back\ninto the original environment modulo global orientation without the need for\ntraining a model. From the point of view of decoding, the descriptor is\noptimally complete and can be extended to arbitrary order, allowing for a\nsystematic convergence of the fidelity of the description. In experiments on\nmolecules ranging from 3 to 29 atoms in size, we demonstrate that positions can\nbe decoded with a 97% success rate and positions plus species with a 70% rate\nof success, rising to 95% if a second fingerprint is used. In all cases,\nconsistent recovery is observed for molecules with 17 or fewer atoms.\nAdditionally, we evaluate the descriptor's performance in predicting the\nenergies and forces of bulk Ni, Cu, Li, Mo, Si and Ge by means of a neural\nnetwork model trained on DFT data. When comparing to six machine learning\ninteraction potential methods that use various descriptors and regression\nschemes our descriptor is found be to competitive, in several cases\noutperforming well established methods. The combined ability to both decode and\nmake property predictions from a representation that does not need to be\nlearned lays the foundations for a novel way of building generative models that\nare tasked with solving the inverse problem of predicting atomic arrangements\nthat are statistically likely to have certain desired properties.\n"}
+{"abstract": "  In education and intervention programs, person's engagement has been\nidentified as a major factor in successful program completion. Automatic\nmeasurement of person's engagement provides useful information for instructors\nto meet program objectives and individualize program delivery. In this paper,\nwe present a novel approach for video-based engagement measurement in virtual\nlearning programs. We propose to use affect states, continuous values of\nvalence and arousal extracted from consecutive video frames, along with a new\nlatent affective feature vector and behavioral features for engagement\nmeasurement. Deep learning-based temporal, and traditional\nmachine-learning-based non-temporal models are trained and validated on\nframe-level, and video-level features, respectively. In addition to the\nconventional centralized learning, we also implement the proposed method in a\ndecentralized federated learning setting and study the effect of model\npersonalization in engagement measurement. We evaluated the performance of the\nproposed method on the only two publicly available video engagement measurement\ndatasets, DAiSEE and EmotiW, containing videos of students in online learning\nprograms. Our experiments show a state-of-the-art engagement level\nclassification accuracy of 63.3% and correctly classifying disengagement videos\nin the DAiSEE dataset and a regression mean squared error of 0.0673 on the\nEmotiW dataset. Our ablation study shows the effectiveness of incorporating\naffect states in engagement measurement. We interpret the findings from the\nexperimental results based on psychology concepts in the field of engagement.\n"}
+{"abstract": "  This doctoral dissertation proposes a novel approach to enhance the\ndevelopment of smart services for the Internet of Things (IoT) and smart\nCyber-Physical Systems (CPS). The proposed approach offers abstraction and\nautomation to the software engineering processes, as well as the Data Analytics\n(DA) and Machine Learning (ML) practices. This is realized in an integrated and\nseamless manner. We implement and validate the proposed approach by extending\nan open source modeling tool, called ThingML. ThingML is a domain-specific\nlanguage and modeling tool with code generation for the IoT/CPS domain. Neither\nThingML nor any other IoT/CPS modeling tool supports DA/ML at the modeling\nlevel. Therefore, as the primary contribution of the doctoral dissertation, we\nadd the necessary syntax and semantics concerning DA/ML methods and techniques\nto the modeling language of ThingML. Moreover, we support the APIs of several\nML libraries and frameworks for the automated generation of the source code of\nthe target software in Python and Java. Our approach enables\nplatform-independent, as well as platform-specific models. Further, we assist\nin carrying out semiautomated DA/ML tasks by offering Automated ML (AutoML), in\nthe background (in expert mode), and through model-checking constraints and\nhints at design-time. Finally, we consider three use case scenarios from the\ndomains of network security, smart energy systems and energy exchange markets.\n"}
+{"abstract": "  Dynamical radiation pressure effects in cavity optomechanical systems give\nrise to self-sustained oscillations or `phonon lasing' behavior, producing\nstable oscillators up to GHz frequencies in nanoscale devices. Like in photonic\nlasers, phonon lasing normally occurs in a single mechanical mode. We show here\nthat phase-locked, multimode phonon lasing can be established in a multimode\noptomechanical system through Floquet dynamics induced by a temporally\nmodulated laser drive. We demonstrate this concept in a suitably engineered\nsilicon photonic nanocavity coupled to multiple GHz-frequency mechanical modes.\nWe find that the long-term frequency stability is significantly improved in the\nmultimode lasing state as a result of the phase locking. These results provide\na path towards highly stable ultra-compact oscillators, pulsed phonon lasing,\ncoherent waveform synthesis, and emergent many-mode phenomena in oscillator\narrays.\n"}
+{"abstract": "  Graph matching, also known as network alignment, refers to finding a\nbijection between the vertex sets of two given graphs so as to maximally align\ntheir edges. This fundamental computational problem arises frequently in\nmultiple fields such as computer vision and biology. Recently, there has been a\nplethora of work studying efficient algorithms for graph matching under\nprobabilistic models. In this work, we propose a new algorithm for graph\nmatching: Our algorithm associates each vertex with a signature vector using a\nmultistage procedure and then matches a pair of vertices from the two graphs if\ntheir signature vectors are close to each other. We show that, for two\nErd\\H{o}s--R\\'enyi graphs with edge correlation $1-\\alpha$, our algorithm\nrecovers the underlying matching exactly with high probability when $\\alpha \\le\n1 / (\\log \\log n)^C$, where $n$ is the number of vertices in each graph and $C$\ndenotes a positive universal constant. This improves the condition $\\alpha \\le\n1 / (\\log n)^C$ achieved in previous work.\n"}
+{"abstract": "  We studied the general advective accretion solutions around the Kerr black\nhole (BH) with investigating two types of inflow gases at the outer accretion\nboundary (AB). We classified these two types of gases as a \\cm and a \\hm inflow\ngas at the outer AB on the basis of their temperatures and solutions. We found\nthat the \\hm gas is more efficient for angular momentum transportation around\nthe outer AB than the \\cm gas. The \\hm gas can give global multiple\n\\cite[popular as shock solution][]{c89} or single sonic point solutions and the\n\\cm can give smooth global solution \\cite[popular as ADAF][]{ny94} or two sonic\npoint solutions. These solutions also represented on a plane of energy and\nangular momentum ($\\be-L_0$) parameter space. Theoretically for the first time,\nwe explored the relation between the nature of accretion solutions with the\nnature of initial accreting gas at the AB with detail computational and\npossible physical analysis. We also found that the surface density of the flow\nis highly affected with changing of the temperature at the AB, which can alter\nthe radiative emissivities of the flow. The flow variables of various advective\nsolutions also compared. On the basis of those results, we plotted some inner\ndisk structures around the BHs. Doing so, we conjectured about the\npersistent/transient nature of spectral states, soft-excess and time scales of\nvariabilities around the black hole $X-$ ray binaries (BXBs) and active\ngalactic nuclei (AGNs).\n"}
+{"abstract": "  We find that the Schwinger Boson mean field theory(SBMFT) supplemented with\nGutzwiller projection provides an exceedingly accurate description for the\nground state of the spin-$\\frac{1}{2}$ triangular lattice antiferromagnetic\nHeisenberg model(spin-$\\frac{1}{2}$ TLHAF). However, we find the SBMFT fails\neven qualitatively in the description of the dynamical behavior of the system.\nIn particular, the SBMFT fails to predict the Goldstone mode in the magnetic\nordered phase. We show that the coherent peak in the two-spinon continuum in\nthe presence of spinon condensate should not be interpreted as a magnon mode.\nThe SBMFT also predicts incorrectly a gapless longitudinal spin fluctuation\nmode in the magnetic ordered phase. We show that these failures are related to\nthe following facts: (1)Spinon condensation fails to provide a consistent\ndescription of the order parameter manifold of the 120 degree ordered phase.\n(2)There lacks in the SBMFT the coupling between the uncondensed spinon and the\nspinon condensate, which breaks both the spin rotational and the translational\nsymmetry. (3)There lacks in the SBMFT the rigidity that is related to the no\ndouble occupancy constraint on the spinon system. We show that such failures of\nthe SBMFT is neither restricted to the spin-$\\frac{1}{2}$ TLHAF nor to the\nmagnetic ordered phase. We proposed a generalized SBMFT to resolve the first\ntwo issues and a new formalism to address the third issue.\n"}
+{"abstract": "  When it comes to studying the impacts of decision making, the research has\nbeen largely focused on examining the fairness of the decisions, the long-term\neffects of the decision pipelines, and utility-based perspectives considering\nboth the decision-maker and the individuals. However, there has hardly been any\nfocus on precarity which is the term that encapsulates the instability in\npeople's lives. That is, a negative outcome can overspread to other decisions\nand measures of well-being. Studying precarity necessitates a shift in focus -\nfrom the point of view of the decision-maker to the perspective of the decision\nsubject. This centering of the subject is an important direction that unlocks\nthe importance of parting with aggregate measures to examine the long-term\neffects of decision making. To address this issue, in this paper, we propose a\nmodeling framework that simulates the effects of compounded decision-making on\nprecarity over time. Through our simulations, we are able to show the\nheterogeneity of precarity by the non-uniform ruinous aftereffects of negative\ndecisions on different income classes of the underlying population and how\npolicy interventions can help mitigate such effects.\n"}
+{"abstract": "  Freezing of ice has been largely reported from many aspects, especially its\ncomplex pattern formation. Ice grown from liquid phase is usually\ncharacteristic of lamellar morphology which plays a significant role in various\ndomains. However, tilted growth of ice via transition from coplanar to\nnon-coplanar growth in directional solidification has been paid little\nattention in previous studies and there is misleading explanation of the\nformation of tilted lamellar ice. Here, we in-situ investigated the variations\nof tilting behavior of lamellar ice tip under different conditions within a\nsingle ice crystal with manipulated orientation via unidirectional freezing of\naqueous solutions. It is found that tilted growth of ice tips is sensitive to\npulling velocity and solute type. These experimental results reveal intrinsic\ntilted growth behavior of lamellar ice and enrich our understanding in pattern\nformation of ice.\n"}
+{"abstract": "  Because of the recent applications to distributed storage systems,\nresearchers have introduced a new class of block codes, i.e., locally\nrecoverable (LRC) codes. LRC codes can recover information from erasure(s) by\naccessing a small number of erasure-free code symbols and increasing the\nefficiency of repair processes in large-scale distributed storage systems. In\nthis context, Tamo and Barg first gave a breakthrough by cleverly introducing a\ngood polynomial notion. Constructing good polynomials for locally recoverable\ncodes achieving Singleton-type bound (called optimal codes) is challenging and\nhas attracted significant attention in recent years. This article aims to\nincrease our knowledge on good polynomials for optimal LRC codes. Using tools\nfrom algebraic function fields and Galois theory, we continue investigating\nthose polynomials and studying them by developing the Galois theoretical\napproach initiated by Micheli in 2019. Specifically, we push further the study\nof a crucial parameter $\\mathcal G(f)$ (of a given polynomial $f$), which\nmeasures how much a polynomial is \"good\" in the sense of LRC codes. We provide\nsome characterizations of polynomials with minimal Galois groups and prove some\nproperties of finite fields where polynomials exist with a specific size of\nGalois groups. We also present some explicit shapes of polynomials with small\nGalois groups. For some particular polynomials $f$, we give the exact formula\nof $\\mathcal G(f)$.\n"}
+{"abstract": "  Let $\\Sigma$ be a closed subset of $\\mathbb{R}^ {n+1}$ which is parabolic\nAhlfors-David regular and assume that $\\Sigma$ satisfies a 2-sided corkscrew\ncondition. Assume, in addition, that $\\Sigma$ is either time-forwards\nAhlfors-David regular, time-backwards Ahlfors-David regular, or parabolic\nuniform rectifiable. We then first prove that\n  $\\Sigma$ satisfies a {\\it weak synchronized two cube condition}. Based on\nthis we are able to revisit the argument in \\cite{NS} and prove that $\\Sigma$\ncontains {\\it uniform big pieces of Lip(1,1/2) graphs}. When $\\Sigma$ is\nparabolic uniformly rectifiable the construction can be refined and in this\ncase we prove that $\\Sigma$ contains {\\it uniform big pieces of regular\nparabolic Lip(1,1/2) graphs}. Similar results hold if $\\Omega\\subset\\mathbb\nR^{n+1}$ is a connected component of $\\mathbb R^{n+1}\\setminus\\Sigma$ and in\nthis context we also give a parabolic counterpart of the main result in\n\\cite{AHMNT} by proving that if $\\Omega$ is a one-sided parabolic chord arc\ndomain, and if $\\Sigma$ is parabolic uniformly rectifiable, then $\\Omega$ is in\nfact a parabolic chord arc domain. Our results give a flexible parabolic\nversion of the classical (elliptic) result of G. David and D. Jerison\nconcerning the existence of uniform big pieces of Lipschitz graphs for sets\nsatisfying a two disc condition.\n"}
+{"abstract": "  I study a population model in which the reproduction rate lambda is inherited\nwith mutation, favoring fast reproducers in the short term, but conflicting\nwith a process that eliminates agglomerations of individuals. The model is a\nvariant of the triplet annihilation model introduced several decades ago [R.\nDickman, Phys. Rev. B~{\\bf 40}, 7005 (1989)] in which organisms (\"particles\")\nreproduce and diffuse on a lattice, subject to annihilation when (and only\nwhen) occupying three consecutive sites. For diffusion rates below a certain\nvalue, the population possesses two \"survival strategies\": (i) rare\nreproduction (0 < lambda < lambda_{c,1}), in which a low density of diffusing\nparticles renders triplets exceedingly rare, and (ii) frequent reproduction\n(lambda > lambda_{c,2}). For lambda between lambda_{c,1} and lambda_{c,2} there\nis no active steady state. In the rare-reproduction regime, a mutating\n$\\lambda$ leads to stochastic boom-and-bust cycles in which the reproduction\nrate fluctuates upward in certain regions, only to lead to extinction as the\nlocal value of lambda becomes excessive. The global population can nevertheless\nsurvive due to the presence of other regions, with reproduction rates that have\nyet to drift upward.\n"}
+{"abstract": "  We perform a Koopman spectral analysis of elementary cellular automata (ECA).\nBy lifting the system dynamics using a one-hot representation of the system\nstate, we derive a matrix representation of the Koopman operator as a transpose\nof the adjacency matrix of the state-transition network. The Koopman\neigenvalues are either zero or on the unit circle in the complex plane, and the\nassociated Koopman eigenfunctions can be explicitly constructed. From the\nKoopman eigenvalues, we can judge the reversibility, determine the number of\nconnected components in the state-transition network, evaluate the periods of\nasymptotic orbits, and derive the conserved quantities for each system. We\nnumerically calculate the Koopman eigenvalues of all rules of ECA on a\none-dimensional lattice of 13 cells with periodic boundary conditions. It is\nshown that the spectral properties of the Koopman operator reflect Wolfram's\nclassification of ECA.\n"}
+{"abstract": "  We use quantum kinetic theory to calculate the thermoelectric transport\nproperties of the 2D single band Fermi-Hubbard model in the weak coupling\nlimit. For generic filling, we find that the high-temperature limiting\nbehaviors of the electrical ($\\sim T$) and thermal ($\\sim T^2$) resistivities\npersist down to temperatures of order the hopping matrix element $T\\sim t$,\nalmost an order of magnitude below the bandwidth. At half filling, perfect\nnesting leads to anomalous low temperature scattering and nearly $T$-linear\nelectrical resistivity at all temperatures. We hypothesize that the $T$-linear\nresistivity observed in recent cold atom experiments is continuously connected\nto this weak coupling physics and suggest avenues for experimental\nverification. We find a number of other novel thermoelectric results, such as a\nlow-temperature Wiedemann-Franz law with Lorenz coefficient $5\\pi^2/36$.\n"}
+{"abstract": "  An innovative approach for the synthesis of inexpensive holographic smart\nelectromagnetic (EM) skins with advanced beamforming features is proposed. The\ncomplex multiscale smart skin design is formulated within the Generalized Sheet\nTransition Condition (GSTC) framework as a combination of a mask-constrained\nisophoric inverse source problem and a micro-scale susceptibility dyadic\noptimization. The solution strategy integrates a local search procedure based\non the iterative projection technique (IPT) and a System-by-Design (SbD)-based\noptimization loop for the identification of optimal metasurface descriptors\nmatching the desired surface currents. The performance and the efficiency of\nthe proposed approach are assessed in a set of representative test cases\nconcerned with different smart skin apertures and target pattern masks.\n"}
+{"abstract": "  We report on the observation of bona fide stochastic resonance (SR) in a\nnonGaussian active bath without any periodic forcing. Particles hopping in a\nnanoscale double-well potential under the influence of correlated Poisson noise\ndisplay a series of equally-spaced peaks in the residence time distribution.\nMaximal peaks are measured when the mean residence time matches a double\ncondition on the interval and correlation timescales of the noise,\ndemonstrating a new type of SR. The experimental findings agree with a simple\nmodel that explains the emergence of SR without periodicity. Correlated\nnonGaussian noise is common in living systems, suggesting that this type of SR\nis widespread in this regime.\n"}
+{"abstract": "  We generalize Birkhoff's Theorem in the following fashion. We find necessary\nand sufficient conditions for any spherically symmetric space-time to be static\nin terms of the eigenvalues of the stress-energy tensor. In particular, we\ngeneralize the Tolman-Oppenheimer-Volkoff equation and prove that Birkhoff's\ntheorem holds under the weaker hypothesis of no pressure (with respect to an\nappropriate frame.) We provide equations that show how the coefficients of the\nmetric relate to the eigenvalues of the stress-energy tensor. These involve\nintegrals that are simple functions of those eigenvalues. We also determine\namong all static spherically symmetric space-times those that are\nasymptotically flat. A few examples are presented taking advantage of the\nresults. The calculations are done by viewing the space-times as warped\nproducts and the computations are done using Cartan's moving frames approach.\n"}
+{"abstract": "  Deep Reinforcement Learning has emerged as an efficient dynamic obstacle\navoidance method in highly dynamic environments. It has the potential to\nreplace overly conservative or inefficient navigation approaches. However, the\nintegration of Deep Reinforcement Learning into existing navigation systems is\nstill an open frontier due to the myopic nature of\nDeep-Reinforcement-Learning-based navigation, which hinders its widespread\nintegration into current navigation systems. In this paper, we propose the\nconcept of an intermediate planner to interconnect novel\nDeep-Reinforcement-Learning-based obstacle avoidance with conventional global\nplanning methods using waypoint generation. Therefore, we integrate different\nwaypoint generators into existing navigation systems and compare the joint\nsystem against traditional ones. We found an increased performance in terms of\nsafety, efficiency and path smoothness especially in highly dynamic\nenvironments.\n"}
+{"abstract": "  Correlation matrices are used in many domains of neurosciences such as fMRI,\nEEG, MEG. However, statistical analyses often rely on embeddings into a\nEuclidean space or into Symmetric Positive Definite matrices which do not\nprovide intrinsic tools. The quotient-affine metric was recently introduced as\nthe quotient of the affine-invariant metric on SPD matrices by the action of\ndiagonal matrices. In this work, we provide most of the fundamental Riemannian\noperations of the quotient-affine metric: the expression of the metric itself,\nthe geodesics with initial tangent vector, the Levi-Civita connection and the\ncurvature.\n"}
+{"abstract": "  We present a systematic study of holographic correlators in a vast array of\nSCFTs with non-maximal superconformal symmetry. These theories include 4d\n$\\mathcal{N}=2$ SCFTs from D3-branes near F-theory singularities, 5d Seiberg\nexceptional theories and 6d E-string theory, as well as 3d and 4d\nphenomenological models with probe flavor branes. We consider current\nmultiplets and their generalizations with higher weights, dual to massless and\nmassive super gluons in the bulk. At leading order in the inverse central\ncharge expansion, connected four-point functions of these operators correspond\nto tree-level gluon scattering amplitudes in AdS. We show that all such\ntree-level four-point amplitudes in all these theories are fully fixed by\nsymmetries and consistency conditions and explicitly construct them. Our\nresults encode a wealth of SCFT data and exhibit various interesting emergent\nstructures. These include Parisi-Sourlas-like dimensional reductions, hidden\nconformal symmetry and an AdS version of the color-kinematic duality.\n"}
+{"abstract": "  New MMT/Hectospec spectroscopy centered on the galaxy cluster A2626 and\ncovering a ${\\sim} 1.8\\,\\text{deg}^2$ area out to $z \\sim 0.46$ more than\ndoubles the number of galaxy redshifts in this region. The spectra confirm four\nclusters previously identified photometrically. A2625, which was previously\nthought to be a close neighbor of A2626, is in fact much more distant. The new\ndata show six substructures associated with A2626 and five more associated with\nA2637. There is also a highly collimated collection of galaxies and galaxy\ngroups between A2626 and A2637 having at least three and probably four\nsubstructures. At larger scales, the A2626--A2637 complex is not connected to\nthe Pegasus--Perseus filament.\n"}
+{"abstract": "  In order to safely deploy Deep Neural Networks (DNNs) within the perception\npipelines of real-time decision making systems, there is a need for safeguards\nthat can detect out-of-training-distribution (OoD) inputs both efficiently and\naccurately. Building on recent work leveraging the local curvature of DNNs to\nreason about epistemic uncertainty, we propose Sketching Curvature of OoD\nDetection (SCOD), an architecture-agnostic framework for equipping any trained\nDNN with a task-relevant epistemic uncertainty estimate. Offline, given a\ntrained model and its training data, SCOD employs tools from matrix sketching\nto tractably compute a low-rank approximation of the Fisher information matrix,\nwhich characterizes which directions in the weight space are most influential\non the predictions over the training data. Online, we estimate uncertainty by\nmeasuring how much perturbations orthogonal to these directions can alter\npredictions at a new test input. We apply SCOD to pre-trained networks of\nvarying architectures on several tasks, ranging from regression to\nclassification. We demonstrate that SCOD achieves comparable or better OoD\ndetection performance with lower computational burden relative to existing\nbaselines.\n"}
+{"abstract": "  For many of the 700 million illiterate people around the world, speech\nrecognition technology could provide a bridge to valuable information and\nservices. Yet, those most in need of this technology are often the most\nunderserved by it. In many countries, illiterate people tend to speak only\nlow-resource languages, for which the datasets necessary for speech technology\ndevelopment are scarce. In this paper, we investigate the effectiveness of\nunsupervised speech representation learning on noisy radio broadcasting\narchives, which are abundant even in low-resource languages. We make three core\ncontributions. First, we release two datasets to the research community. The\nfirst, West African Radio Corpus, contains 142 hours of audio in more than 10\nlanguages with a labeled validation subset. The second, West African Virtual\nAssistant Speech Recognition Corpus, consists of 10K labeled audio clips in\nfour languages. Next, we share West African wav2vec, a speech encoder trained\non the noisy radio corpus, and compare it with the baseline Facebook speech\nencoder trained on six times more data of higher quality. We show that West\nAfrican wav2vec performs similarly to the baseline on a multilingual speech\nrecognition task, and significantly outperforms the baseline on a West African\nlanguage identification task. Finally, we share the first-ever speech\nrecognition models for Maninka, Pular and Susu, languages spoken by a combined\n10 million people in over seven countries, including six where the majority of\nthe adult population is illiterate. Our contributions offer a path forward for\nethical AI research to serve the needs of those most disadvantaged by the\ndigital divide.\n"}
+{"abstract": "  Many-access channel (MnAC) model allows the number of users in the system and\nthe number of active users to scale as a function of the blocklength and as\nsuch is suited for dynamic communication systems with massive number of users\nsuch as the Internet of Things. Existing MnAC models assume a priori knowledge\nof channel gains which is impractical since acquiring Channel State Information\n(CSI) for massive number of users can overwhelm the available radio resources.\nThis paper incorporates Rayleigh fading effects to the MnAC model and derives\nan upper bound on the symmetric message-length capacity of the Rayleigh-fading\nGaussian MnAC. Furthermore, a lower bound on the minimum number of channel uses\nfor discovering the active users is established. In addition, the performance\nof Noisy Combinatorial Orthogonal Matching Pursuit (N-COMP) based group testing\n(GT) is studied as a practical strategy for active device discovery.\nSimulations show that, for a given SNR, as the number of users increase, the\nrequired number of channel uses for N-COMP GT scales approximately the same way\nas the lower bound on minimum user identification cost. Moreover, in the low\nSNR regime, for sufficiently large population sizes, the number of channel uses\nrequired by N-COMP GT was observed to be within a factor of two of the lower\nbound when the expected number of active users scales sub-linearly with the\ntotal population size.\n"}
+{"abstract": "  A long march of fifty years of successive theoretical progress and new\nphysics discovered using observations of gamma-ray bursts, has finally led to\nthe formulation of an efficient mechanism able to extract the rotational energy\nof a Kerr black hole to power these most energetic astrophysical sources and\nactive galactic nuclei. We here present the salient features of this\nlong-sought mechanism, based on gravito-electrodynamics, and which represents\nan authentic shift of paradigm of black holes as forever \"alive\" astrophysical\nobjects.\n"}
+{"abstract": "  Graph-based subspace clustering methods have exhibited promising performance.\nHowever, they still suffer some of these drawbacks: encounter the expensive\ntime overhead, fail in exploring the explicit clusters, and cannot generalize\nto unseen data points. In this work, we propose a scalable graph learning\nframework, seeking to address the above three challenges simultaneously.\nSpecifically, it is based on the ideas of anchor points and bipartite graph.\nRather than building a $n\\times n$ graph, where $n$ is the number of samples,\nwe construct a bipartite graph to depict the relationship between samples and\nanchor points. Meanwhile, a connectivity constraint is employed to ensure that\nthe connected components indicate clusters directly. We further establish the\nconnection between our method and the K-means clustering. Moreover, a model to\nprocess multi-view data is also proposed, which is linear scaled with respect\nto $n$. Extensive experiments demonstrate the efficiency and effectiveness of\nour approach with respect to many state-of-the-art clustering methods.\n"}
+{"abstract": "  The paper considers the input-constrained binary erasure channel (BEC) with\ncausal, noiseless feedback. The channel input sequence respects the\n$(d,\\infty)$-runlength limited (RLL) constraint, i.e., any pair of successive\n$1$s must be separated by at least $d$ $0$s. We derive upper and lower bounds\non the feedback capacity of this channel, for all $d\\geq 1$, given by:\n$\\max\\limits_{\\delta \\in [0,\\frac{1}{d+1}]}R(\\delta) \\leq\nC^{\\text{fb}}_{(d\\infty)}(\\epsilon) \\leq \\max\\limits_{\\delta \\in\n[0,\\frac{1}{1+d\\epsilon}]}R(\\delta)$, where the function $R(\\delta) =\n\\frac{h_b(\\delta)}{d\\delta + \\frac{1}{1-\\epsilon}}$, with $\\epsilon\\in [0,1]$\ndenoting the channel erasure probability, and $h_b(\\cdot)$ being the binary\nentropy function. We note that our bounds are tight for the case when $d=1$\n(see Sabag et al. (2016)), and, in addition, we demonstrate that for the case\nwhen $d=2$, the feedback capacity is equal to the capacity with non-causal\nknowledge of erasures, for $\\epsilon \\in [0,1-\\frac{1}{2\\log(3/2)}]$. For\n$d>1$, our bounds differ from the non-causal capacities (which serve as upper\nbounds on the feedback capacity) derived in Peled et al. (2019) in only the\ndomains of maximization. The approach in this paper follows Sabag et al.\n(2017), by deriving single-letter bounds on the feedback capacity, based on\noutput distributions supported on a finite $Q$-graph, which is a directed graph\nwith edges labelled by output symbols.\n"}
+{"abstract": "  The optical properties of bulk ZrSiS nodal-line semimetal are theoretically\nstudied within a many-body formalism. The G0W0 bands are similar to those\ncalculated within the density functional theory, except near the {\\Gamma}\npoint; in particular, no significant differences are found around the Fermi\nenergy. On the other hand, the solution of the Bethe-Salpeter equation reveals\na significant excitonic activity, mostly as dark excitons which appear in a\nwide energy range. Bright excitons, on the contrary, are less numerous, but\ntheir location and intensity depend greatly on the polarization of the incident\nelectric field, as the absorption coefficient itself does. The binding energy\nof these excitons correlate well with their spatial distribution functions. In\nany case, a good agreement with available experimental data for\nabsorption-reflection is achieved. Finally, the possible activation of plasma\noscillations at low energies is discarded, because these are damped by\nproducing electron-hole pairs, more importantly for q along the {\\Gamma}-M\npath.\n"}
+{"abstract": "  We theoretically study magnetic field, temperature, and energy band-gap\ndependences of magnetizations in the Dirac fermions. We use the zeta function\nregularization to obtain analytical expressions of thermodynamic potential,\nfrom which the magnetization of graphene for strong field/low temperature and\nweak field/high temperature limits are calculated. Further, we generalize the\nresult by considering the effects of impurity on orbital susceptibility of\ngraphene. In particular, we show that in the presence of impurity, the\nsusceptibility follows a scaling law which can be approximated by the Faddeeva\nfunction. In the case of the massive Dirac fermions, we show that a large\nband-gap gives a robust magnetization with respect to temperature and impurity.\nIn the doped Dirac fermion, we discuss the dependence of the band-gap on the\nperiod and amplitude of the de Haas-van Alphen effect.\n"}
+{"abstract": "  Data quality monitoring is critical to all experiments impacting the quality\nof any physics results. Traditionally, this is done through an alarm system,\nwhich detects low level faults, leaving higher level monitoring to human crews.\nArtificial Intelligence is beginning to find its way into scientific\napplications, but comes with difficulties, relying on the acquisition of new\nskill sets, either through education or acquisition, in data science. This\npaper will discuss the development and deployment of the Hydra monitoring\nsystem in production at Gluex. It will show how \"off-the-shelf\" technologies\ncan be rapidly developed, as well as discuss what sociological hurdles must be\novercome to successfully deploy such a system. Early results from production\nrunning of Hydra will also be shared as well as a future outlook for\ndevelopment of Hydra.\n"}
+{"abstract": "  Motivated by the recent discovery that the interpretation maps of CNNs could\neasily be manipulated by adversarial attacks against network interpretability,\nwe study the problem of interpretation robustness from a new perspective of\n\\Renyi differential privacy (RDP). The advantages of our Renyi-Robust-Smooth\n(RDP-based interpretation method) are three-folds. First, it can offer provable\nand certifiable top-$k$ robustness. That is, the top-$k$ important attributions\nof the interpretation map are provably robust under any input perturbation with\nbounded $\\ell_d$-norm (for any $d\\geq 1$, including $d = \\infty$). Second, our\nproposed method offers $\\sim10\\%$ better experimental robustness than existing\napproaches in terms of the top-$k$ attributions. Remarkably, the accuracy of\nRenyi-Robust-Smooth also outperforms existing approaches. Third, our method can\nprovide a smooth tradeoff between robustness and computational efficiency.\nExperimentally, its top-$k$ attributions are {\\em twice} more robust than\nexisting approaches when the computational resources are highly constrained.\n"}
+{"abstract": "  Today, artificial neural networks are one of the major innovators pushing the\nprogress of machine learning. This has particularly affected the development of\nneural network accelerating hardware. However, since most of these\narchitectures require specialized toolchains, there is a certain amount of\nadditional effort for developers each time they want to make use of a new deep\nlearning accelerator. Furthermore the flexibility of the device is bound to the\narchitecture itself, as well as to the functionality of the runtime\nenvironment.\n  In this paper we propose a toolflow using TensorFlow as frontend, thus\noffering developers the opportunity of using a familiar environment. On the\nbackend we use an FPGA, which is addressable via an HSA runtime environment. In\nthis way we are able to hide the complexity of controlling new hardware from\nthe user, while at the same time maintaining a high amount of flexibility. This\ncan be achieved by our HSA toolflow, since the hardware is not statically\nconfigured with the structure of the network. Instead, it can be dynamically\nreconfigured during runtime with the respective kernels executed by the network\nand simultaneously from other sources e.g. OpenCL/OpenMP.\n"}
+{"abstract": "  Software of Unknown Provenance, SOUP, refers to a software component that is\nalready developed and widely available from a 3rd party, and that has not been\ndeveloped, to be integrated into a medical device. From regulatory perspective,\nSOUP software requires special considerations, as the developers' obligations\nrelated to design and implementation are not applied to it. In this paper, we\nconsider the implications of extending the concept of SOUP to machine learning\n(ML) models. As the contribution, we propose practical means to manage the\nadded complexity of 3rd party ML models in regulated development.\n"}
+{"abstract": "  The set \\[ \\overline{\\mathbb{E}}= \\{ x \\in {\\mathbb{C}}^3: \\quad 1-x_1 z -\nx_2 w + x_3 zw \\neq 0 \\mbox{ whenever } |z| < 1, |w| < 1 \\} \\] is called the\ntetrablock and has intriguing complex-geometric properties. It is polynomially\nconvex, nonconvex and starlike about $0$. It has a group of automorphisms\nparametrised by ${\\mathrm{Aut}~} {\\mathbb{D}} \\times {\\mathrm{Aut}~}\n{\\mathbb{D}} \\times {\\mathbb{Z}}_2$ and its distinguished boundary\n$b\\overline{\\mathbb{E}}$ is homeomorphic to the solid torus\n$\\overline{\\mathbb{D}} \\times {\\mathbb{T}}$. It has a special subvariety\n\\[\\mathcal{R}_{\\mathbb{\\overline{E}}} = \\big\\{ (x_{1}, x_{2}, x_{3}) \\in\n\\overline{\\mathbb{E}} : x_{1}x_{2}=x_{3} \\big\\}, \\] called the royal variety of\n$\\overline{\\mathbb{E}}$, which is a complex geodesic of ${\\mathbb{E}}$ that is\ninvariant under all automorphisms of ${\\mathbb{E}}$. We exploit this geometry\nto develop an explicit and detailed structure theory for the rational maps from\nthe unit disc ${\\mathbb{D}}$ to $\\overline{\\mathbb{E}}$ that map the unit\ncircle ${\\mathbb{T}}$ to the distinguished boundary $b\\overline{\\mathbb{E}}$ of\n$\\overline{\\mathbb{E}}$. Such maps are called rational $\\mathbb{ \\overline{\nE}}$-inner functions. We show that, for each nonconstant rational $\\mathbb{\n\\overline{ E}}$-inner function $x$, either $x(\\overline{\\mathbb{D}}) \\subseteq\n\\mathcal{R}_{\\mathbb{\\overline{E}}} \\cap \\overline{\\mathbb{E}}$ or\n$x(\\overline{\\mathbb{D}})$ meets $\\mathcal{R}_{\\mathbb{\\overline{E}}}$ exactly\n$deg(x)$ times.\n  We study convex subsets of the set $\\mathcal{J}$ of all rational $\\mathbb{\n\\overline{ E}}$-inner functions and extreme points of $\\mathcal{J}$.\n"}
+{"abstract": "  A strong backdoor in a formula $\\phi$ of propositional logic to a tractable\nclass $\\mathcal{C}$ of formulas is a set $B$ of variables of $\\phi$ such that\nevery assignment of the variables in $B$ results in a formula from\n$\\mathcal{C}$. Strong backdoors of small size or with a good structure, e.g.\nwith small backdoor treewidth, lead to efficient solutions for the\npropositional satisfiability problem SAT. In this paper we propose the new\nnotion of recursive backdoors, which is inspired by the observation that in\norder to solve SAT we can independently recurse into the components that are\ncreated by partial assignments of variables. The quality of a recursive\nbackdoor is measured by its recursive backdoor depth. Similar to the concept of\nbackdoor treewidth, recursive backdoors of bounded depth include backdoors of\nunbounded size that have a certain treelike structure. However, the two\nconcepts are incomparable and our results yield new tractability results for\nSAT.\n"}
+{"abstract": "  We study the action of the multiplicative group generated by two prime\nnumbers in $\\mathbf{Z}/Q\\mathbf{Z}$. More specifically, we study returns to the\nset $([-Q^\\varepsilon,Q^\\varepsilon]\\cap \\mathbf{Z})/Q\\mathbf{Z}$. This is\nintimately related to the problem of bounding the greatest common divisor of\n$S$-unit differences, which we revisit. Our main tool is the $S$-adic subspace\ntheorem.\n"}
+{"abstract": "  We study cardinality-constrained optimization problems (CCOP) in general\nposition, i. e. those optimization-related properties that are fulfilled for a\ndense and open subset of their defining functions. We show that the well-known\ncardinality-constrained linear independence constraint qualification (CC-LICQ)\nis generic in this sense. For M-stationary points we define nondegeneracy and\nshow that it is a generic property too. In particular, the sparsity constraint\nturns out to be active at all minimizers of a generic CCOP. Moreover, we\ndescribe the global structure of CCOP in the sense of Morse theory, emphasizing\nthe strength of the generic approach. Here, we prove that multiple cells need\nto be attached, each of dimension coinciding with the proposed M-index of\nnondegenerate M-stationary points. Beyond this generic viewpoint, we study\nsingularities of CCOP. For that, the relation between nondegeneracy and strong\nstability in the sense of Kojima (1980) is examined. We show that nondegeneracy\nimplies the latter, while the reverse implication is in general not true. To\nfill the gap, we fully characterize the strong stability of M-stationary points\nunder CC-LICQ by first- and second-order information of CCOP defining\nfunctions. Finally, we compare nondegeneracy and strong stability of\nM-stationary points with second-order sufficient conditions recently introduced\nin the literature.\n"}
+{"abstract": "  The ultraconfined light of plasmonic modes put their effective wavelength\nclose to the mean free path of electrons inside the metal electron gas. The\nDrude model, which can not take the repulsive interactions of electrons into\naccount, then clearly begins to show its limits. In an intermediate length\nscale where a full quantum treatment is computationally prohibitive, the\nsemiclassical hydrodynamic model, instrinsically non-local, has proven\nsuccessful. Here we generalize the expression for the absorption volume density\nand the reciprocity theorem in the framework of this hydrodynamic model. We\nvalidate numerically these generalized theorems and show that using classical\nexpressions instead leads to large discrepancies.\n"}
+{"abstract": "  We study multi-agent reinforcement learning (MARL) in infinite-horizon\ndiscounted zero-sum Markov games. We focus on the practical but challenging\nsetting of decentralized MARL, where agents make decisions without coordination\nby a centralized controller, but only based on their own payoffs and local\nactions executed. The agents need not observe the opponent's actions or\npayoffs, possibly being even oblivious to the presence of the opponent, nor be\naware of the zero-sum structure of the underlying game, a setting also referred\nto as radically uncoupled in the literature of learning in games. In this\npaper, we develop a radically uncoupled Q-learning dynamics that is both\nrational and convergent: the learning dynamics converges to the best response\nto the opponent's strategy when the opponent follows an asymptotically\nstationary strategy; when both agents adopt the learning dynamics, they\nconverge to the Nash equilibrium of the game. The key challenge in this\ndecentralized setting is the non-stationarity of the environment from an\nagent's perspective, since both her own payoffs and the system evolution depend\non the actions of other agents, and each agent adapts her policies\nsimultaneously and independently. To address this issue, we develop a\ntwo-timescale learning dynamics where each agent updates her local Q-function\nand value function estimates concurrently, with the latter happening at a\nslower timescale.\n"}
+{"abstract": "  Recently normalizing flows (NFs) have demonstrated state-of-the-art\nperformance on modeling 3D point clouds while allowing sampling with arbitrary\nresolution at inference time. However, these flow-based models still require\nlong training times and large models for representing complicated geometries.\nThis work enhances their representational power by applying mixtures of NFs to\npoint clouds. We show that in this more general framework each component learns\nto specialize in a particular subregion of an object in a completely\nunsupervised fashion. By instantiating each mixture component with a\ncomparatively small NF we generate point clouds with improved details compared\nto single-flow-based models while using fewer parameters and considerably\nreducing the inference runtime. We further demonstrate that by adding data\naugmentation, individual mixture components can learn to specialize in a\nsemantically meaningful manner. We evaluate mixtures of NFs on generation,\nautoencoding and single-view reconstruction based on the ShapeNet dataset.\n"}
+{"abstract": "  Over the past several decades, single photon-based path interference in\ndouble-slit experiment has been well demonstrated for the particle nature of\nphotons satisfying complementarity theory, where the quantum mechanical\ninterpretation of the single photon interference fringe is given by Born rule\nfor complex amplitudes in measurements. Unlike most conventional methods using\nentangled photon pairs, here, a classical coherent light source is directly\napplied for the proof of the same self-interference phenomenon. Instead of\ndouble slits, we use a Mach-Zehnder interferometer as usual, where the\nresulting self-interference fringe of coherent single photons is exactly the\nsame as that of the coherent photon ensemble of the laser, demonstrating that\nthe classical coherence based on the wave nature is rooted in the single photon\nself-interference. This unexpected result seems to contradict our common\nunderstanding of decoherence phenomena caused by multi-wave interference among\nbandwidth distributed photons in coherence optics.\n"}
+{"abstract": "  Let G be a simple complex algebraic group and let K be a reductive subgroup\nof G such that the coordinate ring of G/K is a multiplicity free G-module. We\nconsider the G-algebra structure of C[G/K], and study the decomposition into\nirreducible summands of the product of irreducible G-submodules in C[G/K]. When\nthe spherical roots of G/K generate a root system of type A we propose a\nconjectural decomposition rule, which relies on a conjecture of Stanley on the\nmultiplication of Jack symmetric functions. With the exception of one case, we\nshow that the rule holds true whenever the root system generated by the\nspherical roots of G/K is direct sum of subsystems of rank one.\n"}
+{"abstract": "  We study the decay phase of solar flares in several spectral bands using a\nmethod based on that successfully applied to white light flares observed on an\nM4 dwarf. We selected and processed 102 events detected in the Sun-as-a-star\nflux obtained with SDO/AIA images in the 1600~{\\AA} and 304~{\\AA} channels and\n54 events detected in the 1700~{\\AA} channel. The main criterion for the\nselection of time profiles was a slow, continuous flux decay without\nsignificant new bursts. The obtained averaged time profiles were fitted with\nanalytical templates, using different time intervals, that consisted of a\ncombination of two independent exponents or a broken power law. The average\nflare profile observed in the 1700~{\\AA} channel decayed more slowly than the\naverage flare profile observed on the M4 dwarf. As the 1700~{\\AA} emission is\nassociated with a similar temperature to that usually ascribed to M dwarf\nflares, this implies that the M dwarf flare emission comes from a more dense\nlayer than solar flare emission in the 1700~{\\AA} band. The cooling processes\nin solar flares were best described by the two exponents model, fitted over the\nintervals t1=[0, 0.5]$t_{1/2}$ and t2=[3, 10]$t_{1/2}$ where $t_{1/2}$ is time\ntaken for the profile to decay to half the maximum value. The broken power law\nmodel provided a good fit to the first decay phase, as it was able to account\nfor the impact of chromospheric plasma evaporation, but it did not successfully\nfit the second decay phase.\n"}
+{"abstract": "  As various databases of facial expressions have been made accessible over the\nlast few decades, the Facial Expression Recognition (FER) task has gotten a lot\nof interest. The multiple sources of the available databases raised several\nchallenges for facial recognition task. These challenges are usually addressed\nby Convolution Neural Network (CNN) architectures. Different from CNN models, a\nTransformer model based on attention mechanism has been presented recently to\naddress vision tasks. One of the major issue with Transformers is the need of a\nlarge data for training, while most FER databases are limited compared to other\nvision applications. Therefore, we propose in this paper to learn a vision\nTransformer jointly with a Squeeze and Excitation (SE) block for FER task. The\nproposed method is evaluated on different publicly available FER databases\nincluding CK+, JAFFE,RAF-DB and SFEW. Experiments demonstrate that our model\noutperforms state-of-the-art methods on CK+ and SFEW and achieves competitive\nresults on JAFFE and RAF-DB.\n"}
+{"abstract": "  In this work, we provide the design and implementation of a switch-assisted\ncongestion control algorithm for data center networks (DCNs). In particular, we\nprovide a prototype of the switch-driven congestion control algorithm and\ndeploy it in a real data center. The prototype is based on few simple\nmodifications to the switch software. The modifications imposed by the\nalgorithm on the switch are to enable the switch to modify the TCP\nreceive-window field in the packet headers. By doing so, the algorithm can\nenforce a pre-calculated (or target rate) to limit the sending rate at the\nsources. Therefore, the algorithm requires no modifications to the TCP source\nor receiver code which considered out of the DCN operators' control (e.g., in\nthe public cloud where the VM is maintained by the tenant). This paper\ndescribes in detail two implementations, one as a Linux kernel module and the\nsecond as an added feature to the well-known software switch, Open vSwitch.\nThen we present evaluation results based on experiments of the deployment of\nboth designs in a small testbed to demonstrate the effectiveness of the\nproposed technique in achieving high throughput, good fairness, and short flow\ncompletion times for delay-sensitive flows.\n"}
+{"abstract": "  We introduce a set of algorithms (Het-node2vec) that extend the original\nnode2vec node-neighborhood sampling method to heterogeneous multigraphs, i.e.\nnetworks characterized by multiple types of nodes and edges. The resulting\nrandom walk samples capture both the structural characteristics of the graph\nand the semantics of the different types of nodes and edges. The proposed\nalgorithms can focus their attention on specific node or edge types, allowing\naccurate representations also for underrepresented types of nodes/edges that\nare of interest for the prediction problem under investigation. These rich and\nwell-focused representations can boost unsupervised and supervised learning on\nheterogeneous graphs.\n"}
+{"abstract": "  Understanding the internals of Integrated Circuits (ICs), referred to as\nHardware Reverse Engineering (HRE), is of interest to both legitimate and\nmalicious parties. HRE is a complex process in which semi-automated steps are\ninterwoven with human sense-making processes. Currently, little is known about\nthe technical and cognitive processes which determine the success of HRE.\n  This paper performs an initial investigation on how reverse engineers solve\nproblems, how manual and automated analysis methods interact, and which\ncognitive factors play a role. We present the results of an exploratory\nbehavioral study with eight participants that was conducted after they had\ncompleted a 14-week training. We explored the validity of our findings by\ncomparing them with the behavior (strategies applied and solution time) of an\nHRE expert. The participants were observed while solving a realistic HRE task.\nWe tested cognitive abilities of our participants and collected large sets of\nbehavioral data from log files. By comparing the least and most efficient\nreverse engineers, we were able to observe successful strategies. Moreover, our\nanalyses suggest a phase model for reverse engineering, consisting of three\nphases. Our descriptive results further indicate that the cognitive factor\nWorking Memory (WM) might play a role in efficiently solving HRE problems. Our\nexploratory study builds the foundation for future research in this topic and\noutlines ideas for designing cognitively difficult countermeasures (\"cognitive\nobfuscation\") against HRE.\n"}
+{"abstract": "  We present a systematic numerical modeling investigation of magnetization\ndynamics and thermal magnetic moment fluctuations of single magnetic domain\nnanoparticles in a configuration applicable to enhancing inductive magnetic\nresonance detection signal to noise ratio (SNR). Previous proposals for\noriented anisotropic single magnetic domain nanoparticle amplification of\nmagnetic flux in MRI coil focused only on the coil pick-up voltage signal\nenhancement. Here we extend the analysis to the numerical evaluation of the SNR\nby modeling the inherent thermal magnetic noise introduced into the detection\ncoil by the insertion of such anisotropic nanoparticle-filled coil core. We\nutilize the Landau-Lifshitz-Gilbert equation under the Stoner-Wohlfarth single\nmagnetic domain (macrospin) assumption to simulate the magnetization dynamics\nin such nanoparticles due to AC drive field as well as thermal noise. These\nsimulations are used to evaluate the nanoparticle configurations and shape\neffects on enhancing SNR. Finally, we explore the effect of narrow band\nfiltering of the broadband magnetic moment thermal fluctuation noise on the\nSNR. Our results provide the impetus for relatively simple modifications to\nexisting MRI systems for achieving enhanced detection SNR in scanners with\nmodest polarizing magnetic fields.\n"}
+{"abstract": "  We consider the problem of constrained Markov Decision Process (CMDP) where\nan agent interacts with a unichain Markov Decision Process. At every\ninteraction, the agent obtains a reward. Further, there are $K$ cost functions.\nThe agent aims to maximize the long-term average reward while simultaneously\nkeeping the $K$ long-term average costs lower than a certain threshold. In this\npaper, we propose CMDP-PSRL, a posterior sampling based algorithm using which\nthe agent can learn optimal policies to interact with the CMDP. Further, for\nMDP with $S$ states, $A$ actions, and diameter $D$, we prove that following\nCMDP-PSRL algorithm, the agent can bound the regret of not accumulating rewards\nfrom optimal policy by $\\Tilde{O}(poly(DSA)\\sqrt{T})$. Further, we show that\nthe violations for any of the $K$ constraints is also bounded by\n$\\Tilde{O}(poly(DSA)\\sqrt{T})$. To the best of our knowledge, this is the first\nwork which obtains a $\\Tilde{O}(\\sqrt{T})$ regret bounds for ergodic MDPs with\nlong-term average constraints.\n"}
+{"abstract": "  Network pruning is an effective approach to reduce network complexity with\nacceptable performance compromise. Existing studies achieve the sparsity of\nneural networks via time-consuming weight tuning or complex search on networks\nwith expanded width, which greatly limits the applications of network pruning.\nIn this paper, we show that high-performing and sparse sub-networks without the\ninvolvement of weight tuning, termed ''lottery jackpots'', exist in pre-trained\nmodels with unexpanded width. For example, we obtain a lottery jackpot that has\nonly 10% parameters and still reaches the performance of the original dense\nVGGNet-19 without any modifications on the pre-trained weights on CIFAR-10.\nFurthermore, we observe that the sparse masks derived from many existing\npruning criteria have a high overlap with the searched mask of our lottery\njackpot, among which, the magnitude-based pruning results in the most similar\nmask with ours. Based on this insight, we initialize our sparse mask using the\nmagnitude-based pruning, resulting in at least 3x cost reduction on the lottery\njackpot search while achieving comparable or even better performance.\nSpecifically, our magnitude-based lottery jackpot removes 90% weights in\nResNet-50, while it easily obtains more than 70% top-1 accuracy using only 10\nsearching epochs on ImageNet. Our code is available at\nhttps://github.com/zyxxmu/lottery-jackpots.\n"}
+{"abstract": "  Density inhomogeneities are ubiquitous in space and astrophysical plasmas, in\nparticular at contact boundaries between different media. They often correspond\nto regions that exhibits strong dynamics on a wide range of spatial and\ntemporal scales. Indeed, density inhomogeneities are a source of free energy\nthat can drive various instabilities such as, for instance, the\nlower-hybrid-drift instability which in turn transfers energy to the particles\nthrough wave-particle interactions and eventually heat the plasma. We aim at\nquantifying the efficiency of the lower-hybrid-drift instability to accelerate\nand/or heat electrons parallel to the ambient magnetic field. We combine two\ncomplementary methods: full-kinetic and quasilinear models. We report\nself-consistent evidence of electron acceleration driven by the development of\nthe lower-hybrid-drift instability using 3D-3V full-kinetic numerical\nsimulations. The efficiency of the observed acceleration cannot be explained by\nstandard quasilinear theory. For this reason, we develop an extended\nquasilinear model able to quantitatively predict the interaction between\nlower-hybrid fluctuations and electrons on long time scales, now in agreement\nwith full-kinetic simulations results. Finally, we apply this new, extended\nquasilinear model to a specific inhomogeneous space plasma boundary: the\nmagnetopause of Mercury, and we discuss our quantitative predictions of\nelectron acceleration in support to future BepiColombo observations.\n"}
+{"abstract": "  Deep learning and data-driven approaches have shown great potential in\nscientific domains. The promise of data-driven techniques relies on the\navailability of a large volume of high-quality training datasets. Due to the\nhigh cost of obtaining data through expensive physical experiments,\ninstruments, and simulations, data augmentation techniques for scientific\napplications have emerged as a new direction for obtaining scientific data\nrecently. However, existing data augmentation techniques originating from\ncomputer vision, yield physically unacceptable data samples that are not\nhelpful for the domain problems that we are interested in. In this paper, we\ndevelop new physics-informed data augmentation techniques based on\nconvolutional neural networks. Specifically, our generative models leverage\ndifferent physics knowledge (such as governing equations, observable\nperception, and physics phenomena) to improve the quality of the synthetic\ndata. To validate the effectiveness of our data augmentation techniques, we\napply them to solve a subsurface seismic full-waveform inversion using\nsimulated CO$_2$ leakage data. Our interest is to invert for subsurface\nvelocity models associated with very small CO$_2$ leakage. We validate the\nperformance of our methods using comprehensive numerical tests. Via comparison\nand analysis, we show that data-driven seismic imaging can be significantly\nenhanced by using our physics-informed data augmentation techniques.\nParticularly, the imaging quality has been improved by 15% in test scenarios of\ngeneral-sized leakage and 17% in small-sized leakage when using an augmented\ntraining set obtained with our techniques.\n"}
+{"abstract": "  The bimodal behavior of the order parameter is studied in the framework of\nBoltzmann-Uehling-Uhlenbeck (BUU) transport model. In order to do that,\nsimplified yet accurate method of BUU model is used which allow calculation of\nfluctuations in systems much larger than what was considered feasible in a\nwell-known and already existing model. It is observed that depending on the\nprojectile energy and centrality of the reaction, both entrance channel and\nexit channel effects can be at the origin of the experimentally observed\nbimodal behavior. Both dynamical and statistical bimodality mechanisms are\nassociated in the theoretical model to different time scales of the reaction,\nand to different energy regimes.\n"}
+{"abstract": "  We provide a generic algorithm for constructing formulae that distinguish\nbehaviourally inequivalent states in systems of various transition types such\nas nondeterministic, probabilistic or weighted; genericity over the transition\ntype is achieved by working with coalgebras for a set functor in the paradigm\nof universal coalgebra. For every behavioural equivalence class in a given\nsystem, we construct a formula which holds precisely at the states in that\nclass. The algorithm instantiates to deterministic finite automata, transition\nsystems, labelled Markov chains, and systems of many other types. The ambient\nlogic is a modal logic featuring modalities that are generically extracted from\nthe functor; these modalities can be systematically translated into custom sets\nof modalities in a postprocessing step. The new algorithm builds on an existing\ncoalgebraic partition refinement algorithm. It runs in time $\\mathcal{O}((m+n)\n\\log n)$ on systems with $n$ states and $m$ transitions, and the same\nasymptotic bound applies to the dag size of the formulae it constructs. This\nimproves the bounds on run time and formula size compared to previous\nalgorithms even for previously known specific instances, viz. transition\nsystems and Markov chains; in particular, the best previous bound for\ntransition systems was $\\mathcal{O}(m n)$.\n"}
+{"abstract": "  We present a new lower bound on the spectral gap of the Glauber dynamics for\nthe Gibbs distribution of a spectrally independent $q$-spin system on a graph\n$G = (V,E)$ with maximum degree $\\Delta$. Notably, for several interesting\nexamples, our bound covers the entire regime of $\\Delta$ excluded by arguments\nbased on coupling with the stationary distribution. As concrete applications,\nby combining our new lower bound with known spectral independence computations\nand known coupling arguments:\n  (1) We show that for a triangle-free graph $G = (V,E)$ with maximum degree\n$\\Delta \\geq 3$, the Glauber dynamics for the uniform distribution on proper\n$k$-colorings with $k \\geq (1.763\\dots + \\delta)\\Delta$ colors has spectral gap\n$\\tilde{\\Omega}_{\\delta}(|V|^{-1})$. Previously, such a result was known either\nif the girth of $G$ is at least $5$ [Dyer et.~al, FOCS 2004], or under\nrestrictions on $\\Delta$ [Chen et.~al, STOC 2021; Hayes-Vigoda, FOCS 2003].\n  (2) We show that for a regular graph $G = (V,E)$ with degree $\\Delta \\geq 3$\nand girth at least $6$, and for any $\\varepsilon, \\delta > 0$, the partition\nfunction of the hardcore model with fugacity $\\lambda \\leq\n(1-\\delta)\\lambda_{c}(\\Delta)$ may be approximated within a\n$(1+\\varepsilon)$-multiplicative factor in time\n$\\tilde{O}_{\\delta}(n^{2}\\varepsilon^{-2})$. Previously, such a result was\nknown if the girth is at least $7$ [Efthymiou et.~al, SICOMP 2019].\n  (3) We show for the binomial random graph $G(n,d/n)$ with $d = O(1)$, with\nhigh probability, an approximately uniformly random matching may be sampled in\ntime $O_{d}(n^{2+o(1)})$. This improves the corresponding running time of\n$\\tilde{O}_{d}(n^{3})$ due to [Jerrum-Sinclair, SICOMP 1989; Jerrum, 2003].\n"}
+{"abstract": "  Myntra is an online fashion e-commerce company based in India. At Myntra, a\nmarket leader in fashion e-commerce in India, customer experience is paramount\nand a significant portion of our resources are dedicated to it. Here we\ndescribe an algorithm that identifies eligible customers to enable preferential\nproduct return processing for them by Myntra. We declare the group of\naforementioned eligible customers on the platform as elite customers. Our\nalgorithm to identify eligible/elite customers is based on sound principles of\ngame theory. It is simple, easy to implement and scalable.\n"}
+{"abstract": "  We present a new end-to-end learning framework to obtain detailed and\nspatially coherent reconstructions of multiple people from a single image.\nExisting multi-person methods suffer from two main drawbacks: they are often\nmodel-based and therefore cannot capture accurate 3D models of people with\nloose clothing and hair; or they require manual intervention to resolve\nocclusions or interactions. Our method addresses both limitations by\nintroducing the first end-to-end learning approach to perform model-free\nimplicit reconstruction for realistic 3D capture of multiple clothed people in\narbitrary poses (with occlusions) from a single image. Our network\nsimultaneously estimates the 3D geometry of each person and their 6DOF spatial\nlocations, to obtain a coherent multi-human reconstruction. In addition, we\nintroduce a new synthetic dataset that depicts images with a varying number of\ninter-occluded humans and a variety of clothing and hair styles. We demonstrate\nrobust, high-resolution reconstructions on images of multiple humans with\ncomplex occlusions, loose clothing and a large variety of poses and scenes. Our\nquantitative evaluation on both synthetic and real-world datasets demonstrates\nstate-of-the-art performance with significant improvements in the accuracy and\ncompleteness of the reconstructions over competing approaches.\n"}
+{"abstract": "  In this work, we investigate the question of how knowledge about expectations\n$\\mathbb{E}(f_i(X))$ of a random vector $X$ translate into inequalities for\n$\\mathbb{E}(g(X))$ for given functions $f_i$, $g$ and a random vector $X$ whose\nsupport is contained in some set $S\\subseteq \\mathbb{R}^n$. We show that there\nis a connection between the problem of obtaining tight expectation inequalities\nin this context and properties of convex hulls, allowing us to rewrite it as an\noptimization problem. The results of these optimization problems not only\narrive at sharp bounds for $\\mathbb{E}(g(X))$ but in some cases also yield\ndiscrete probability measures where equality holds.\n  We develop an analytical approach that is particularly suited for studying\nthe Jensen gap problem when the known information are the average and variance,\nas well as a numerical approach for the general case, that reduces the problem\nto a convex optimization; which in a sense extends known results about the\nmoment problem.\n"}
+{"abstract": "  Linear time-varying (LTV) systems are widely used for modeling real-world\ndynamical systems due to their generality and simplicity. Providing stability\nguarantees for LTV systems is one of the central problems in control theory.\nHowever, existing approaches that guarantee stability typically lead to\nsignificantly sub-optimal cumulative control cost in online settings where only\ncurrent or short-term system information is available. In this work, we propose\nan efficient online control algorithm, COvariance Constrained Online Linear\nQuadratic (COCO-LQ) control, that guarantees input-to-state stability for a\nlarge class of LTV systems while also minimizing the control cost. The proposed\nmethod incorporates a state covariance constraint into the semi-definite\nprogramming (SDP) formulation of the LQ optimal controller. We empirically\ndemonstrate the performance of COCO-LQ in both synthetic experiments and a\npower system frequency control example.\n"}
+{"abstract": "  The circular restricted three body problem, which considers the dynamics of\nan infinitesimal particle in the presence of the gravitational interaction with\ntwo massive bodies moving on circular orbits about their common center of mass,\nis a very useful model for investigating the behavior of real astronomical\nobjects in the Solar System. In such a system, there are five Lagrangian\nequilibrium points, and one important characteristic of the motion is the\nexistence of linearly stable equilibria at the two equilibrium points that form\nequilateral triangles with the primaries, in the plane of the primaries' orbit.\nWe analyze the stability of motion in the restricted three body problem by\nusing the concept of Jacobi stability, as introduced and developed in the\nKosambi-Cartan-Chern (KCC) theory. The KCC theory is a differential geometric\napproach to the variational equations describing the deviation of the whole\ntrajectory of a dynamical system with respect to the nearby ones. We obtain the\ngeneral result that, from the point of view of the KCC theory and of Jacobi\nstability, all five Lagrangian equilibrium points of the restricted three body\nproblem are unstable.\n"}
+{"abstract": "  Grain boundaries (GBs), an important constituent of polycrystalline\nmaterials, have a wide range of manifestion and significantly affect the\nproperties of materials. Fully understanding the effects of GBs is stalemated\ndue to lack of complete knowledge of their structures and energetics. Here, for\nthe first time, by taking graphene as an example, we propose an analytical\nenergy functional of GBs in angle space. We find that an arbitrary GB can be\ncharacterized by a geometric combination of symmetric GBs that follow the\nprinciple of uniform distribution of their dislocation cores in straight lines.\nFurthermore, we determine the elusive kinetic effects on GBs from the\ndifference between experimental statistics and energy-dependent thermodynamic\neffects. This study not only presents an analytical energy functional of GBs\nwhich could also be extended to other two-dimensional materials, but also sheds\nlight on understanding the kinetic effects of GBs in material synthesizing\nprocesses.\n"}
+{"abstract": "  The subclass of magnetic Cataclysmic Variables (CV), known as asynchronous\npolars, are still relatively poorly understood. An asynchronous polar is a\npolar in which the spin period of the white dwarf is either shorter or longer\nthan the binary orbital period (typically within a few percent). The\nasynchronous polars have been disproportionately detected in soft gamma-ray\nobservations, leading us to consider the possibility that they have\nintrinsically harder X-ray spectra. We compared standard and asynchronous\npolars in order to examine the relationship between a CV's synchronization\nstatus and its spectral shape. Using the entire sample of asynchronous polars,\nwe find that the asynchronous polars may, indeed, have harder spectra, but that\nthe result is not statistically significant.\n"}
+{"abstract": "  Borrowing ideas from elliptic complex geometry, we approach M-theory\ncompactifications on real toric fibrations. Precisely, we explore real toric\nequations rather than complex ones exploited in F-theory and related dual\nmodels. These geometries have been built by moving real circles over real\nbases. Using topological changing behaviors, we unveil certain data associated\nwith gauge sectors relying on affine Lie symmetries.\n"}
+{"abstract": "  Machine Learning (ML)-based network intrusion detection systems bring many\nbenefits for enhancing the cybersecurity posture of an organisation. Many\nsystems have been designed and developed in the research community, often\nachieving a close to perfect detection rate when evaluated using synthetic\ndatasets. However, the high number of academic research has not often\ntranslated into practical deployments. There are several causes contributing\ntowards the wide gap between research and production, such as the limited\nability of comprehensive evaluation of ML models and lack of understanding of\ninternal ML operations. This paper tightens the gap by evaluating the\ngeneralisability of a common feature set to different network environments and\nattack scenarios. Therefore, two feature sets (NetFlow and CICFlowMeter) have\nbeen evaluated in terms of detection accuracy across three key datasets, i.e.,\nCSE-CIC-IDS2018, BoT-IoT, and ToN-IoT. The results show the superiority of the\nNetFlow feature set in enhancing the ML models detection accuracy of various\nnetwork attacks. In addition, due to the complexity of the learning models,\nSHapley Additive exPlanations (SHAP), an explainable AI methodology, has been\nadopted to explain and interpret the classification decisions of ML models. The\nShapley values of two common feature sets have been analysed across multiple\ndatasets to determine the influence contributed by each feature towards the\nfinal ML prediction.\n"}
+{"abstract": "  This paper analyzes Floquet topological insulators resulting from the\ntime-harmonic irradiation of electromagnetic waves on two dimensional materials\nsuch as graphene. We analyze the bulk and edge topologies of approximations to\nthe evolution of the light-matter interaction. Topologically protected\ninterface states are created by spatial modulations of the drive polarization\nacross an interface. In the high-frequency modulation regime, we obtain a\nsequence of topologies that apply to different time scales. Bulk-difference\ninvariants are computed in detail and a bulk-interface correspondence is shown\nto apply. We also analyze a high-frequency high-amplitude modulation resulting\nin a large-gap effective topology topologically that remains valid only for\nmoderately long times.\n"}
+{"abstract": "  Africa has amazing potential due to natural (such as dark sky) and human\nresources for scientific research in astronomy and space science. At the same\ntime, the continent is still facing many difficulties, and its countries are\nnow recognising the importance of astronomy, space science and satellite\ntechnologies for improving some of their principal socio-economic challenges.\nThe development of astronomy in Africa (including Ethiopia) has grown\nsignificantly over the past few years, and never before it was more possible to\nuse astronomy for education, outreach, and development as it is now. However,\nmuch still remains to be done. This paper will summarise the recent\ndevelopments in astronomy research and education in Africa and Ethiopia and\nwill focus on how working together on the development of science and education\ncan we fight poverty in the long term and increase our possibilities of\nattaining the United Nations Sustainable Development Goals in future for\nbenefit of all.\n"}
+{"abstract": "  Let $D$ be a compact K\\\"ahler manifold with trivial canonical bundle and\n$\\Gamma$ be a finite cyclical group of order $m$ acting on $\\mathbb{C} \\times\nD$ by biholomorphisms, where the action on the first factor is generated by\nrotation of angle $2\\pi /m$. Furthermore, suppose that $\\Omega_D$ is a\ntrivialisation of the canonical bundle such that $\\Gamma$ preserves the\nholomorphic form $dz \\wedge \\Omega_D$ on $\\mathbb C \\times D$, with $z$\ndenoting the coordinate on $\\mathbb{C}$. The main result of this article is the\nconstruction of new examples of gradient steady K\\\"ahler-Ricci solitons on\ncertain crepant resolutions of the orbifolds $\\left( \\mathbb{C}\\times D \\right)\n/ \\Gamma$. These new solitons converge exponentially to a Ricci-flat cylinder\n$\\mathbb{R} \\times(\\mathbb{S}^1 \\times D) / \\Gamma$.\n"}
+{"abstract": "  We consider the difference-of-convex (DC) programming problems whose\nobjective function is level-bounded. The classical DC algorithm (DCA) is\nwell-known for solving this kind of problems, which returns a critical point.\nRecently, de Oliveira and Tcheo incorporated the inertial-force procedure into\nDCA (InDCA) for potential acceleration and preventing the algorithm from\nconverging to a critical point which is not d(directional)-stationary. In this\npaper, based on InDCA, we propose two refined inertial DCA (RInDCA) with\nenlarged inertial step-sizes for better acceleration. We demonstrate the\nsubsequential convergence of our refined versions to a critical point. In\naddition, by assuming the Kurdyka-Lojasiewicz (KL) property of the objective\nfunction, we establish the sequential convergence of RInDCA. Numerical\nsimulations on image restoration problem show the benefit of enlarged\nstep-size.\n"}
+{"abstract": "  Since the Covid-19 pandemic is a global threat to health that few can fully\nescape, it has given a unique opportunity to study international reactions to a\ncommon problem. Such reactions can be partly obtained from public posts to\nTwitter, allowing investigations of changes in interest over time. This study\nanalysed English-language Covid-19 tweets mentioning cures, treatments, or\nvaccines from 1 January 2020 to 8 April 2021, seeking trends and international\ndifferences. The results have methodological limitations but show a tendency\nfor countries with a lower human development index score to tweet more about\ncures, although they were a minor topic for all countries. Vaccines were\ndiscussed about as much as treatments until July 2020, when they generated more\ninterest because of developments in Russia. The November 2020 Pfizer-BioNTech\npreliminary Phase 3 trials results generated an immediate and sustained sharp\nincrease, however, followed by a continuing roughly linear increase in interest\nfor vaccines until at least April 2021. Against this background, national\ndeviations from the average were triggered by country-specific news about\ncures, treatments or vaccines. Nevertheless, interest in vaccines in all\ncountries increased in parallel to some extent, despite substantial\ninternational differences in national regulatory approval and availability. The\nresults also highlight that unsubstantiated claims about alternative medicine\nremedies gained traction in several countries, apparently posing a threat to\npublic health.\n"}
+{"abstract": "  In this paper, we study multiple-input multiple-output (MIMO) wireless power\ntransfer (WPT) systems, where the energy harvester (EH) node is equipped with\nmultiple nonlinear rectennas. We characterize the optimal transmit strategy by\nthe optimal distribution of the transmit symbol vector that maximizes the\naverage harvested power at the EH subject to a constraint on the power budget\nof the transmitter. We show that the optimal transmit strategy employs scalar\nunit-norm input symbols with arbitrary phase and two beamforming vectors, which\nare determined as solutions of a non-convex optimization problem. To solve this\nproblem, we propose an iterative algorithm based on a two-dimensional grid\nsearch, semidefinite relaxation, and successive convex approximation. Our\nsimulation results reveal that the proposed MIMO WPT design significantly\noutperforms two baseline schemes based on a linear EH model and a single\nbeamforming vector, respectively. Finally, we show that the average harvested\npower grows linearly with the number of rectennas at the EH node and saturates\nfor a large number of TX antennas.\n"}
+{"abstract": "  Many real-world decision-making tasks require learning casual relationships\nbetween a set of variables. Typical causal discovery methods, however, require\nthat all variables are observed, which might not be realistic in practice.\nUnfortunately, in the presence of latent confounding, recovering casual\nrelationships from observational data without making additional assumptions is\nan ill-posed problem. Fortunately, in practice, additional structure among the\nconfounders can be expected, one such example being pervasive confounding,\nwhich has been exploited for consistent causal estimation in the special case\nof linear causal models. In this paper, we provide a proof and method to\nestimate causal relationships in the non-linear, pervasive confounding setting.\nThe heart of our procedure relies on the ability to estimate the pervasive\nconfounding variation through a simple spectral decomposition of the observed\ndata matrix. We derive a DAG score function based on this insight, and\nempirically compare our method to existing procedures. We show improved\nperformance on both simulated and real datasets by explicitly accounting for\nboth confounders and non-linear effects.\n"}
+{"abstract": "  In the framework of mean field approach, we study topological Mott transition\nin a two band model of spinless fermions on a square lattice at half filling.\nWe consider the combined effect of the on-site Coulomb repulsion and the\nspin-orbit Rashba coupling. The ground state phase diagram is calculated as a\nfunction of the strength of the spin-orbit Rashba coupling and the Coulomb\nrepulsion. The spin-orbit Rashba coupling leads to a distinct phase of matter,\nthe topological semimetal. We study a new type of phase transition between the\nnon-topological insulator and topological semimetal states. Topological phase\nstate is characterized by the zero energy Majorana states, which there are in\ndefined region of the wave vectors and are localized at the boundaries of the\nsample. The region of existence of the zero energy Majorana states tends to\nzero at the point of the Mott phase transition. The zero energy Majorana states\nare dispersionless (they can be considered as flat bands), the Chern number and\nHall conductance are equal to zero (note in two dimensional model\n"}
+{"abstract": "  Polar ice cores play a central role in studies of the earth's climate system\nthrough natural archives. A pressing issue is the analysis of the oldest,\nhighly thinned ice core sections, where the identification of paleoclimate\nsignals is particularly challenging. For this, state-of-the-art imaging by\nlaser-ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) has the\npotential to be revolutionary due to its combination of micron-scale 2D\nchemical information with visual features. However, the quantitative study of\nrecord preservation in chemical images raises new questions that call for the\nexpertise of the computer vision community. To illustrate this new\ninter-disciplinary frontier, we describe a selected set of key questions. One\ncritical task is to assess the paleoclimate significance of single line\nprofiles along the main core axis, which we show is a scale-dependent problem\nfor which advanced image analysis methods are critical. Another important issue\nis the evaluation of post-depositional layer changes, for which the chemical\nimages provide rich information. Accordingly, the time is ripe to begin an\nintensified exchange among the two scientific communities of computer vision\nand ice core science. The collaborative building of a new framework for\ninvestigating high-resolution chemical images with automated image analysis\ntechniques will also benefit the already wide-spread application of LA-ICP-MS\nchemical imaging in the geosciences.\n"}
+{"abstract": "  A number of recent, low-redshift, lensing measurements hint at a universe in\nwhich the amplitude of lensing is lower than that predicted from the\n$\\Lambda$CDM model fit to the data of the Planck CMB mission. Here we use the\nauto- and cross-correlation signal of unWISE galaxies and Planck CMB lensing\nmaps to infer cosmological parameters at low redshift. In particular, we\nconsider three unWISE samples (denoted as \"blue\", \"green\" and \"red\") at median\nredshifts $z \\sim 0.6$, $1.1$ and 1.5, which fully cover the Dark Energy\ndominated era. Our cross-correlation measurements, with combined significance\n$S/N \\sim 80$, are used to infer the amplitude of low-redshift fluctuations,\n$\\sigma_8$; the fraction of matter in the Universe, $\\Omega_m$; and the\ncombination $S_8 \\equiv \\sigma_8 (\\Omega_m / 0.3)^{0.5}$ to which these\nlow-redshift lensing measurements are most sensitive. The combination of blue,\ngreen and red samples gives a value $S_8=0.784\\pm 0.015$, that is fully\nconsistent with other low-redshift lensing measurements and in 2.4$\\sigma$\ntension with the CMB predictions from Planck. This is noteworthy, because CMB\nlensing probes the same physics as previous galaxy lensing measurements, but\nwith very different systematics, thus providing an excellent complement to\nprevious measurements.\n"}
+{"abstract": "  We study the secure stochastic convex optimization problem. A learner aims to\nlearn the optimal point of a convex function through sequentially querying a\n(stochastic) gradient oracle. In the meantime, there exists an adversary who\naims to free-ride and infer the learning outcome of the learner from observing\nthe learner's queries. The adversary observes only the points of the queries\nbut not the feedback from the oracle. The goal of the learner is to optimize\nthe accuracy, i.e., obtaining an accurate estimate of the optimal point, while\nsecuring her privacy, i.e., making it difficult for the adversary to infer the\noptimal point. We formally quantify this tradeoff between learner's accuracy\nand privacy and characterize the lower and upper bounds on the learner's query\ncomplexity as a function of desired levels of accuracy and privacy. For the\nanalysis of lower bounds, we provide a general template based on information\ntheoretical analysis and then tailor the template to several families of\nproblems, including stochastic convex optimization and (noisy) binary search.\nWe also present a generic secure learning protocol that achieves the matching\nupper bound up to logarithmic factors.\n"}
+{"abstract": "  Augmenting the body with artificial limbs controlled concurrently to the\nnatural limbs has long appeared in science fiction, but recent technological\nand neuroscientific advances have begun to make this vision possible. By\nallowing individuals to achieve otherwise impossible actions, this movement\naugmentation could revolutionize medical and industrial applications and\nprofoundly change the way humans interact with their environment. Here, we\nconstruct a movement augmentation taxonomy through what is augmented and how it\nis achieved. With this framework, we analyze augmentation that extends the\nnumber of degrees-of-freedom, discuss critical features of effective\naugmentation such as physiological control signals, sensory feedback and\nlearning, and propose a vision for the field.\n"}
+{"abstract": "  Recent works on click-based interactive segmentation have demonstrated\nstate-of-the-art results by using various inference-time optimization schemes.\nThese methods are considerably more computationally expensive compared to\nfeedforward approaches, as they require performing backward passes through a\nnetwork during inference and are hard to deploy on mobile frameworks that\nusually support only forward passes. In this paper, we extensively evaluate\nvarious design choices for interactive segmentation and discover that new\nstate-of-the-art results can be obtained without any additional optimization\nschemes. Thus, we propose a simple feedforward model for click-based\ninteractive segmentation that employs the segmentation masks from previous\nsteps. It allows not only to segment an entirely new object, but also to start\nwith an external mask and correct it. When analyzing the performance of models\ntrained on different datasets, we observe that the choice of a training dataset\ngreatly impacts the quality of interactive segmentation. We find that the\nmodels trained on a combination of COCO and LVIS with diverse and high-quality\nannotations show performance superior to all existing models. The code and\ntrained models are available at\nhttps://github.com/saic-vul/ritm_interactive_segmentation.\n"}
+{"abstract": "  We improve upon the two-stage sparse vector autoregression (sVAR) method in\nDavis et al. (2016) by proposing an alternative two-stage modified sVAR method\nwhich relies on time series graphical lasso to estimate sparse inverse spectral\ndensity in the first stage, and the second stage refines non-zero entries of\nthe AR coefficient matrices using a false discovery rate (FDR) procedure. Our\nmethod has the advantage of avoiding the inversion of the spectral density\nmatrix but has to deal with optimization over Hermitian matrices with\ncomplex-valued entries. It significantly improves the computational time with a\nlittle loss in forecasting performance. We study the properties of our proposed\nmethod and compare the performance of the two methods using simulated and a\nreal macro-economic dataset. Our simulation results show that the proposed\nmodification or msVAR is a preferred choice when the goal is to learn the\nstructure of the AR coefficient matrices while sVAR outperforms msVAR when the\nultimate task is forecasting.\n"}
+{"abstract": "  Artificial Intelligence (AI) and Machine Learning (ML) are pervasive in the\ncurrent computer science landscape. Yet, there still exists a lack of software\nengineering experience and best practices in this field. One such best\npractice, static code analysis, can be used to find code smells, i.e.,\n(potential) defects in the source code, refactoring opportunities, and\nviolations of common coding standards. Our research set out to discover the\nmost prevalent code smells in ML projects. We gathered a dataset of 74\nopen-source ML projects, installed their dependencies and ran Pylint on them.\nThis resulted in a top 20 of all detected code smells, per category. Manual\nanalysis of these smells mainly showed that code duplication is widespread and\nthat the PEP8 convention for identifier naming style may not always be\napplicable to ML code due to its resemblance with mathematical notation. More\ninterestingly, however, we found several major obstructions to the\nmaintainability and reproducibility of ML projects, primarily related to the\ndependency management of Python projects. We also found that Pylint cannot\nreliably check for correct usage of imported dependencies, including prominent\nML libraries such as PyTorch.\n"}
+{"abstract": "  Kinetic models of biochemical systems used in the modern literature often\ncontain hundreds or even thousands of variables. While these models are\nconvenient for detailed simulations, their size is often an obstacle to\nderiving mechanistic insights. One way to address this issue is to perform an\nexact model reduction by finding a self-consistent lower-dimensional projection\nof the corresponding dynamical system. Recently, a new algorithm CLUE has been\ndesigned and implemented, which allows one to construct an exact linear\nreduction of the smallest possible dimension such that the fixed variables of\ninterest are preserved. It turned out that allowing arbitrary linear\ncombinations (as opposed to zero-one combinations used in the prior approaches)\nmay yield a much smaller reduction. However, there was a drawback: some of the\nnew variables did not have clear physical meaning, thus making the reduced\nmodel harder to interpret. We design and implement an algorithm that, given an\nexact linear reduction, re-parametrizes it by performing an invertible\ntransformation of the new coordinates to improve the interpretability of the\nnew variables. We apply our algorithm to three case studies and show that\n\"uninterpretable\" variables disappear entirely in all the case studies. The\nimplementation of the algorithm and the files for the case studies are\navailable at https://github.com/xjzhaang/LumpingPostiviser.\n"}
+{"abstract": "  Heterogeneity of brain diseases is a challenge for precision\ndiagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised\ncLustEring-Generative Adversarial Network), a novel semi-supervised\ndeep-clustering method, which dissects neuroanatomical heterogeneity, enabling\nidentification of disease subtypes via their imaging signatures relative to\ncontrols. When applied to MRIs (2 studies; 2,832 participants; 8,146 scans)\nincluding cognitively normal individuals and those with cognitive impairment\nand dementia, Smile-GAN identified 4 neurodegenerative patterns/axes: P1,\nnormal anatomy and highest cognitive performance; P2, mild/diffuse atrophy and\nmore prominent executive dysfunction; P3, focal medial temporal atrophy and\nrelatively greater memory impairment; P4, advanced neurodegeneration. Further\napplication to longitudinal data revealed two distinct progression pathways:\nP1$\\rightarrow$P2$\\rightarrow$P4 and P1$\\rightarrow$P3$\\rightarrow$P4. Baseline\nexpression of these patterns predicted the pathway and rate of future\nneurodegeneration. Pattern expression offered better yet complementary\nperformance in predicting clinical progression, compared to amyloid/tau. These\ndeep-learning derived biomarkers offer promise for precision diagnostics and\ntargeted clinical trial recruitment.\n"}
+{"abstract": "  Spectral imaging is the acquisition of multiple images of an object at\ndifferent energy spectra. In mammography, dual-energy imaging (spectral imaging\nwith two energy levels) has been investigated for several applications, in\nparticular material decomposition, which allows for quantitative analysis of\nbreast composition and quantitative contrast-enhanced imaging. Material\ndecomposition with dual-energy imaging is based on the assumption that there\nare two dominant photon interaction effects that determine linear attenuation:\nthe photoelectric effect and Compton scattering. This assumption limits the\nnumber of basis materials, i.e. the number of materials that are possible to\ndifferentiate between, to two. However, Rayleigh scattering may account for\nmore than 10% of the linear attenuation in the mammography energy range. In\nthis work, we show that a modified version of a scanning multi-slit spectral\nphoton-counting mammography system is able to acquire three images at different\nspectra and can be used for triple-energy imaging. We further show that\ntriple-energy imaging in combination with the efficient scatter rejection of\nthe system enables measurement of Rayleigh scattering, which adds an additional\nenergy dependency to the linear attenuation and enables material decomposition\nwith three basis materials. Three available basis materials have the potential\nto improve virtually all applications of spectral imaging.\n"}
+{"abstract": "  We study existence and convergence properties of least-energy symmetric\nsolutions (l.e.s.s.) to the pure critical problem \\begin{equation*}\n(-\\Delta)^su_s=|u_s|^{2^\\star_s-2}u_s, \\quad u_s\\in D^s_0(\\Omega),\\quad\n2^\\star_s:=\\frac{2N}{N-2s}, \\end{equation*} where $s$ is any positive number,\n$\\Omega$ is either $\\mathbb{R}^N$ or a smooth symmetric bounded domain, and\n$D^s_0(\\Omega)$ is the homogeneous Sobolev space. Depending on the kind of\nsymmetry considered, solutions can be sign changing. We show that, up to a\nsubsequence, a l.e.s.s. $u_s$ converges to a l.e.s.s. $u_{t}$ as $s$ goes to\nany $t>0$. In bounded domains, this convergence can be characterized in terms\nof an homogeneous fractional norm of order $t-\\varepsilon$. A similar\ncharacterization is no longer possible in unbounded domains due to scaling\ninvariance and an incompatibility with the functional spaces; to circumvent\nthese difficulties, we use a suitable rescaling and characterize the\nconvergence via cut-off functions. If $t$ is an integer, these results describe\nin a precise way the nonlocal-to-local transition. Finally, we also include a\nnonexistence result of nontrivial nonnegative solutions in a ball for any\n$s>1$.\n"}
+{"abstract": "  Despite their contributions to the financial efficiency and environmental\nsustainability of industrial processes, robotic assembly and disassembly have\nbeen understudied in the existing literature. This is in contradiction to their\nimportance in realizing the Fourth Industrial Revolution. More specifically,\nalthough most of the literature has extensively discussed how to optimally\nassemble or disassemble given products, the role of other factors has been\noverlooked. For example, the types of robots involved in implementing the\nsequence plans, which should ideally be taken into account throughout the whole\nchain consisting of design, assembly, disassembly and reassembly. Isolating the\nforegoing operations from the rest of the components of the relevant ecosystems\nmay lead to erroneous inferences toward both the necessity and efficiency of\nthe underlying procedures. In this paper we try to alleviate these shortcomings\nby comprehensively investigating the state-of-the-art in robotic assembly and\ndisassembly. We consider and review various aspects of manufacturing and\nremanufacturing frameworks while particularly focusing on their desirability\nfor supporting a circular economy.\n"}
+{"abstract": "  Current value-based multi-agent reinforcement learning methods optimize\nindividual Q values to guide individuals' behaviours via centralized training\nwith decentralized execution (CTDE). However, such expected, i.e.,\nrisk-neutral, Q value is not sufficient even with CTDE due to the randomness of\nrewards and the uncertainty in environments, which causes the failure of these\nmethods to train coordinating agents in complex environments. To address these\nissues, we propose RMIX, a novel cooperative MARL method with the Conditional\nValue at Risk (CVaR) measure over the learned distributions of individuals' Q\nvalues. Specifically, we first learn the return distributions of individuals to\nanalytically calculate CVaR for decentralized execution. Then, to handle the\ntemporal nature of the stochastic outcomes during executions, we propose a\ndynamic risk level predictor for risk level tuning. Finally, we optimize the\nCVaR policies with CVaR values used to estimate the target in TD error during\ncentralized training and the CVaR values are used as auxiliary local rewards to\nupdate the local distribution via Quantile Regression loss. Empirically, we\nshow that our method significantly outperforms state-of-the-art methods on\nchallenging StarCraft II tasks, demonstrating enhanced coordination and\nimproved sample efficiency.\n"}
+{"abstract": "  For complex molecules, nuclear degrees of freedom can act as an environment\nfor the electronic `system' variables, allowing the theory and concepts of open\nquantum systems to be applied. However, when molecular system-environment\ninteractions are non-perturbative and non-Markovian, numerical simulations of\nthe complete system-environment wave function become necessary. These many body\ndynamics can be very expensive to simulate, and extracting finite-temperature\nresults - which require running and averaging over many such simulations -\nbecomes especially challenging. Here, we present numerical simulations that\nexploit a recent theoretical result that allows dissipative environmental\neffects at finite temperature to be extracted efficiently from a single,\nzero-temperature wave function simulation. Using numerically exact\ntime-dependent variational matrix product states, we verify that this approach\ncan be applied to vibronic tunneling systems and provide insight into the\npractical problems lurking behind the elegance of the theory, such as the\nrapidly growing numerical demands that can appear for high temperatures over\nthe length of computations.\n"}
+{"abstract": "  In this paper, we analyze the secrecy outage performance for more realistic\neavesdropping scenario of free-space optical (FSO) communications, where the\nmain and wiretap links are correlated. The FSO fading channels are modeled by\nthe well-known M\\'alaga distribution. Exact expressions for the secrecy\nperformance metrics such as secrecy outage probability (SOP) and probability of\nthe non zero secrecy capacity (PNZSC) are derived, and asymptotic analysis on\nthe SOP is also conducted. The obtained results reveal useful insights on the\neffect of channel correlation on FSO communications. Counterintuitively, it is\nfound that the secrecy outage performance demonstrates a non-monotonic behavior\nwith the increase of correlation. More specifically, there is an SNR penalty\nfor achieving a target SOP as the correlation increases within some range.\nHowever, when the correlation is further increased beyond some threshold, the\nSOP performance improves significantly.\n"}
+{"abstract": "  The classical Clarke subdifferential alone is inadequate for understanding\nautomatic differentiation in nonsmooth contexts. Instead, we can sometimes rely\non enlarged generalized gradients called \"conservative fields\", defined through\nthe natural path-wise chain rule: one application is the convergence analysis\nof gradient-based deep learning algorithms. In the semi-algebraic case, we show\nthat all conservative fields are in fact just Clarke subdifferentials plus\nnormals of manifolds in underlying Whitney stratifications.\n"}
+{"abstract": "  Global navigation satellite systems (GNSS) are one of the utterly popular\nsources for providing globally referenced positioning for autonomous systems.\nHowever, the performance of the GNSS positioning is significantly challenged in\nurban canyons, due to the signal reflection and blockage from buildings. Given\nthe fact that the GNSS measurements are highly environmentally dependent and\ntime-correlated, the conventional filtering-based method for GNSS positioning\ncannot simultaneously explore the time-correlation among historical\nmeasurements. As a result, the filtering-based estimator is sensitive to\nunexpected outlier measurements. In this paper, we present a factor graph-based\nformulation for GNSS positioning and real-time kinematic (RTK). The formulated\nfactor graph framework effectively explores the time-correlation of\npseudorange, carrier-phase, and doppler measurements, and leads to the\nnon-minimal state estimation of the GNSS receiver. The feasibility of the\nproposed method is evaluated using datasets collected in challenging urban\ncanyons of Hong Kong and significantly improved positioning accuracy is\nobtained, compared with the filtering-based estimator.\n"}
+{"abstract": "  We present a self-supervised learning method to learn audio and video\nrepresentations. Prior work uses the natural correspondence between audio and\nvideo to define a standard cross-modal instance discrimination task, where a\nmodel is trained to match representations from the two modalities. However, the\nstandard approach introduces two sources of training noise. First, audio-visual\ncorrespondences often produce faulty positives since the audio and video\nsignals can be uninformative of each other. To limit the detrimental impact of\nfaulty positives, we optimize a weighted contrastive learning loss, which\ndown-weighs their contribution to the overall loss. Second, since\nself-supervised contrastive learning relies on random sampling of negative\ninstances, instances that are semantically similar to the base instance can be\nused as faulty negatives. To alleviate the impact of faulty negatives, we\npropose to optimize an instance discrimination loss with a soft target\ndistribution that estimates relationships between instances. We validate our\ncontributions through extensive experiments on action recognition tasks and\nshow that they address the problems of audio-visual instance discrimination and\nimprove transfer learning performance.\n"}
+{"abstract": "  We study the topological phase transitions induced by Coulomb engineering in\nthree triangular-lattice Hubbard models $AB_2$, $AC_3$ and $B_2C_3$, each of\nwhich consists of two types of magnetic atoms with opposite magnetic moments.\nThe energy bands are calculated using the Schwinger boson method. We find that\na topological phase transition can be triggered by the second-order\n(three-site) virtual processes between the two types of magnetic atoms, the\nstrengths of which are controlled by the on-site Coulomb interaction $U$. This\nnew class of topological phase transitions have been rarely studied and may be\nrealized in a variety of real magnetic materials.\n"}
+{"abstract": "  Let $G$ be a finite group and $H$ a normal subgroup of prime index $p$. Let\n$V$ be an irreducible ${\\mathbb F}H$-module and $U$ a quotient of the induced\n${\\mathbb F}G$-module $V\\kern-3pt\\uparrow$. We describe the structure of $U$,\nwhich is semisimple when ${\\rm char}({\\mathbb F})\\ne p$ and uniserial if ${\\rm\nchar}({\\mathbb F})=p$. Furthermore, we describe the division rings arising as\nendomorphism algebras of the simple components of $U$. We use techniques from\nnoncommutative ring theory to study ${\\rm End}_{{\\mathbb\nF}G}(V\\kern-3pt\\uparrow)$ and relate the right ideal structure of ${\\rm\nEnd}_{{\\mathbb F}G}(V\\kern-3pt\\uparrow)$ to the submodule structure of\n$V\\kern-3pt\\uparrow$.\n"}
+{"abstract": "  As machine learning becomes more widely used for critical applications, the\nneed to study its implications in privacy turns to be urgent. Given access to\nthe target model and auxiliary information, the model inversion attack aims to\ninfer sensitive features of the training dataset, which leads to great privacy\nconcerns. Despite its success in grid-like domains, directly applying model\ninversion techniques on non-grid domains such as graph achieves poor attack\nperformance due to the difficulty to fully exploit the intrinsic properties of\ngraphs and attributes of nodes used in Graph Neural Networks (GNN). To bridge\nthis gap, we present \\textbf{Graph} \\textbf{M}odel \\textbf{I}nversion attack\n(GraphMI), which aims to extract private graph data of the training graph by\ninverting GNN, one of the state-of-the-art graph analysis tools. Specifically,\nwe firstly propose a projected gradient module to tackle the discreteness of\ngraph edges while preserving the sparsity and smoothness of graph features.\nThen we design a graph auto-encoder module to efficiently exploit graph\ntopology, node attributes, and target model parameters for edge inference. With\nthe proposed methods, we study the connection between model inversion risk and\nedge influence and show that edges with greater influence are more likely to be\nrecovered. Extensive experiments over several public datasets demonstrate the\neffectiveness of our method. We also show that differential privacy in its\ncanonical form can hardly defend our attack while preserving decent utility.\n"}
+{"abstract": "  The question whether the Higgs boson is connected to additional CP violation\nis one of the driving forces behind precision studies at the Large Hadron\nCollider. In this work, we investigate the CP structure of the top quark Yukawa\ninteraction-one of the most prominent places for searching for New\nPhysics-through Higgs boson loops in top quark pair production. We calculate\nthe electroweak corrections including arbitrary CP mixtures at\nnext-to-leading-order in the Standard Model Effective Field Theory. This\napproach of probing Higgs boson degrees of freedom relies on the large\n$t\\bar{t}$ cross section and the excellent perturbative control. In addition,\nwe consider all direct probes with on-shell Higgs boson production in\nassociation with a single top quark or top quark pair. This allows us to\ncontrast loop sensitivity versus on-shell sensitivity in these fundamentally\ndifferent process dynamics. We find that loop sensitivity in $t\\bar{t}$\nproduction and on-shell sensitivity in $t\\bar{t}H$ and $tH$ provide\ncomplementary handles over a wide range of parameter space.\n"}
+{"abstract": "  Accelerated degradation testing (ADT) is one of the major approaches in\nreliability engineering which allows accurate estimation of reliability\ncharacteristics of highly reliable systems within a relatively short time. The\ntesting data are extrapolated through a physically reasonable statistical model\nto obtain estimates of lifetime quantiles at normal use conditions. The Gamma\nprocess is a natural model for degradation, which exhibits a monotone and\nstrictly increasing degradation path. In this work, optimal experimental\ndesigns are derived for ADT with two response components. We consider the\nsituations of independent as well as dependent marginal responses where the\nobservational times are assumed to be fixed and known. The marginal degradation\npaths are assumed to follow a Gamma process where a copula function is utilized\nto express the dependence between both components. For the case of independent\nresponse components the optimal design minimizes the asymptotic variance of an\nestimated quantile of the failure time distribution at the normal use\nconditions. For the case of dependent response components the $D$-criterion is\nadopted to derive $D$-optimal designs. Further, $D$- and $c$-optimal designs\nare developed when the copula-based models are reduced to bivariate binary\noutcomes.\n"}
+{"abstract": "  Negative sampling is a limiting factor w.r.t. the generalization of\nmetric-learned neural networks. We show that uniform negative sampling provides\nlittle information about the class boundaries and thus propose three novel\ntechniques for efficient negative sampling: drawing negative samples from (1)\nthe top-$k$ most semantically similar classes, (2) the top-$k$ most\nsemantically similar samples and (3) interpolating between contrastive latent\nrepresentations to create pseudo negatives. Our experiments on CIFAR-10,\nCIFAR-100 and Tiny-ImageNet-200 show that our proposed \\textit{Semantically\nConditioned Negative Sampling} and Latent Mixup lead to consistent performance\nimprovements. In the standard supervised learning setting, on average we\nincrease test accuracy by 1.52\\% percentage points on CIFAR-10 across various\nnetwork architectures. In the knowledge distillation setting, (1) the\nperformance of student networks increase by 4.56\\% percentage points on\nTiny-ImageNet-200 and 3.29\\% on CIFAR-100 over student networks trained with no\nteacher and (2) 1.23\\% and 1.72\\% respectively over a \\textit{hard-to-beat}\nbaseline (Hinton et al., 2015).\n"}
+{"abstract": "  We use the recipe of arXiv:1003.2974 to find half-BPS near-horizon geometries\nin the t$^3$ model of $N=2$, $D=4$ gauged supergravity, and explicitely\nconstruct some new examples. Among these are black holes with noncompact\nhorizons, but also with spherical horizons that have conical singularities\n(spikes) at one of the two poles. A particular family of them is extended to\nthe full black hole geometry. Applying a double-Wick rotation to the\nnear-horizon region, we obtain solutions with NUT charge that asymptote to\ncurved domain walls with AdS$_3$ world volume. These new solutions may provide\ninteresting testgrounds to address fundamental questions related to quantum\ngravity and holography.\n"}
+{"abstract": "  The results of speckle interferometric observations at the 4.1 m Southern\nAstrophysical Research Telescope (SOAR) in 2020, as well as earlier unpublished\ndata, are given, totaling 1735 measurements of 1288 resolved pairs and\nnon-resolutions of 1177 targets. We resolved for the first time 59 new pairs or\nsubsystems in known binaries, mostly among nearby dwarf stars. This work\ncontinues our long-term speckle program. Its main goal is to monitor orbital\nmotion of close binaries, including members of high-order hierarchies and\nHipparcos pairs in the solar neighborhood. We also report observations of 892\nmembers of young moving groups and associations, where we resolved 103 new\npairs.\n"}
+{"abstract": "  A unique feature of the complex band structures of moir\\'e materials is the\npresence of minivalleys, their hybridization, and scattering between them. Here\nwe investigate magneto-transport oscillations caused by scattering between\nminivalleys - a phenomenon analogous to magneto-intersubband oscillations - in\na twisted double bilayer graphene sample with a twist angle of 1.94{\\deg}. We\nstudy and discuss the potential scattering mechanisms and find an\nelectron-phonon mechanism and valley conserving scattering to be likely.\nFinally, we discuss the relevance of our findings for different materials and\ntwist angles.\n"}
+{"abstract": "  Computer-Aided Design (CAD) applications are used in manufacturing to model\neverything from coffee mugs to sports cars. These programs are complex and\nrequire years of training and experience to master. A component of all CAD\nmodels particularly difficult to make are the highly structured 2D sketches\nthat lie at the heart of every 3D construction. In this work, we propose a\nmachine learning model capable of automatically generating such sketches.\nThrough this, we pave the way for developing intelligent tools that would help\nengineers create better designs with less effort. Our method is a combination\nof a general-purpose language modeling technique alongside an off-the-shelf\ndata serialization protocol. We show that our approach has enough flexibility\nto accommodate the complexity of the domain and performs well for both\nunconditional synthesis and image-to-sketch translation.\n"}
+{"abstract": "  We consider the linear regression model along with the process of its\n$\\alpha$-regression quantile, $0<\\alpha<1$. We are interested mainly in the\nslope components of $\\alpha$-regression quantile and in their dependence on the\nchoice of $\\alpha.$ While they are invariant to the location, and only the\nintercept part of the $\\alpha$-regression quantile estimates the quantile\n$F^{-1}(\\alpha)$ of the model errors, their dispersion depends on $\\alpha$ and\nis infinitely increasing as $\\alpha\\rightarrow 0,1$, in the same rate as for\nthe ordinary quantiles. We study the process of $R$-estimators of the slope\nparameters over $\\alpha\\in[0,1]$, generated by the H\\'{a}jek rank scores. We\nshow that this process, standardized by $f(F ^{-1}(\\alpha))$ under\nexponentially tailed $F$, converges to the vector of independent Brownian\nbridges. The same course is true for the process of the slope components of\n$\\alpha$-regression quantile.\n"}
+{"abstract": "  We derive a generalized Knizhnik-Zamolodchikov equation for the correlation\nfunction of the intertwiners of the vector and the MacMahon representations of\nDing-Iohara-Miki algebra. These intertwiners are cousins of the refined\ntopological vertex which is regarded as the intertwining operator of the Fock\nrepresentation. The shift of the spectral parameter of the intertwiners is\ngenerated by the operator which is constructed from the universal $R$ matrix.\nThe solutions to the generalized KZ equation are factorized into the ratio of\ntwo point functions which are identified with generalizations of the Nekrasov\nfactor for supersymmetric quiver gauge theories\n"}
+{"abstract": "  Rats and mice use their whiskers to probe the environment. By rhythmically\nswiping their whiskers back and forth they can detect the existence of an\nobject, locate it, and identify its texture. Localization can be accomplished\nby inferring the position of the whisker. Rhythmic neurons that track the phase\nof the whisking cycle encode information about the azimuthal location of the\nwhisker. These neurons are characterized by preferred phases of firing that are\nnarrowly distributed. Consequently, pooling the rhythmic signal from several\nupstream neurons is expected to result in a much narrower distribution of\npreferred phases in the downstream population, which however has not been\nobserved empirically. Here, we show how spike-timing-dependent plasticity\n(STDP) can provide a solution to this conundrum. We investigated the effect of\nSTDP on the utility of a neural population to transmit rhythmic information\ndownstream using the framework of a modeling study. We found that under a wide\nrange of parameters, STDP facilitated the transfer of rhythmic information\ndespite the fact that all the synaptic weights remained dynamic. As a result,\nthe preferred phase of the downstream neuron was not fixed, but rather drifted\nin time at a drift velocity that depended on the preferred phase, thus inducing\na distribution of preferred phases. We further analyzed how the STDP rule\ngoverns the distribution of preferred phases in the downstream population. This\nlink between the STDP rule and the distribution of preferred phases constitutes\na natural test for our theory.\n"}
+{"abstract": "  Classically, Jensen's Inequality asserts that if $X$ is a compact convex set,\nand $f:K\\to \\mathbb{R}$ is a convex function, then for any probability measure\n$\\mu$ on $K$, that $f(\\text{bar}(\\mu))\\le \\int f\\;d\\mu$, where\n$\\text{bar}(\\mu)$ is the barycenter of $\\mu$. Recently, Davidson and Kennedy\nproved a noncommutative (\"nc\") version of Jensen's inequality that applies to\nnc convex functions, which take matrix values, with probability measures\nreplaced by ucp maps. In the classical case, if $f$ is only a separately convex\nfunction, then $f$ still satisfies the Jensen inequality for any probability\nmeasure which is a product measure. We prove a noncommutative Jensen inequality\nfor functions which are separately nc convex in each variable. The inequality\nholds for a large class of ucp maps which satisfy a noncommutative analogue of\nFubini's theorem. This class of ucp maps includes any free product of ucp maps\nbuilt from Boca's theorem, or any ucp map which is conditionally free in the\nfree-probabilistic sense of M{\\l}otkowski. As an application to free\nprobability, we obtain some operator inequalities for conditionally free ucp\nmaps applied to free semicircular families.\n"}
+{"abstract": "  Recent advances in neural multi-speaker text-to-speech (TTS) models have\nenabled the generation of reasonably good speech quality with a single model\nand made it possible to synthesize the speech of a speaker with limited\ntraining data. Fine-tuning to the target speaker data with the multi-speaker\nmodel can achieve better quality, however, there still exists a gap compared to\nthe real speech sample and the model depends on the speaker. In this work, we\npropose GANSpeech, which is a high-fidelity multi-speaker TTS model that adopts\nthe adversarial training method to a non-autoregressive multi-speaker TTS\nmodel. In addition, we propose simple but efficient automatic scaling methods\nfor feature matching loss used in adversarial training. In the subjective\nlistening tests, GANSpeech significantly outperformed the baseline\nmulti-speaker FastSpeech and FastSpeech2 models, and showed a better MOS score\nthan the speaker-specific fine-tuned FastSpeech2.\n"}
+{"abstract": "  We consider auction environments in which at the time of the auction bidders\nobserve signals about their ex-post value. We introduce a model of novice\nbidders who do not know know the joint distribution of signals and instead\nbuild a statistical model relating others' bids to their own ex post value from\nthe data sets accessible from past similar auctions. Crucially, we assume that\nonly ex post values and bids are accessible while signals observed by bidders\nin past auctions remain private. We consider steady-states in such\nenvironments, and importantly we allow for correlation in the signal\ndistribution. We first observe that data-driven bidders may behave suboptimally\nin classical auctions such as the second-price or first-price auctions whenever\nthere are correlations. Allowing for a mix of rational (or experienced) and\ndata-driven (novice) bidders results in inefficiencies in such auctions, and we\nshow the inefficiency extends to all auction-like mechanisms in which bidders\nare restricted to submit one-dimensional (real-valued) bids.\n"}
+{"abstract": "  Recent trends in Web development demonstrate an increased interest in\nserverless applications, i.e. applications that utilize computational resources\nprovided by cloud services on demand instead of requiring traditional server\nmanagement. This approach enables better resource management while being\nscalable, reliable, and cost-effective. However, it comes with a number of\norganizational and technical difficulties which stem from the interaction\nbetween the application and the cloud infrastructure, for example, having to\nset up a recurring task of reuploading updated files. In this paper, we present\nKotless - a Kotlin Serverless Framework. Kotless is a cloud-agnostic toolkit\nthat solves these problems by interweaving the deployed application into the\ncloud infrastructure and automatically generating the necessary deployment\ncode. This relieves developers from having to spend their time integrating and\nmanaging their applications instead of developing them. Kotless has proven its\ncapabilities and has been used to develop several serverless applications\nalready in production. Its source code is available at\nhttps://github.com/JetBrains/kotless, a tool demo can be found at\nhttps://www.youtube.com/watch?v=IMSakPNl3TY\n"}
+{"abstract": "  The intention of this research is to study and design an automated\nagriculture commodity price prediction system with novel machine learning\ntechniques. Due to the increasing large amounts historical data of agricultural\ncommodity prices and the need of performing accurate prediction of price\nfluctuations, the solution has largely shifted from statistical methods to\nmachine learning area. However, the selection of proper set from historical\ndata for forecasting still has limited consideration. On the other hand, when\nimplementing machine learning techniques, finding a suitable model with optimal\nparameters for global solution, nonlinearity and avoiding curse of\ndimensionality are still biggest challenges, therefore machine learning\nstrategies study are needed. In this research, we propose a web-based automated\nsystem to predict agriculture commodity price. In the two series experiments,\nfive popular machine learning algorithms, ARIMA, SVR, Prophet, XGBoost and LSTM\nhave been compared with large historical datasets in Malaysia and the most\noptimal algorithm, LSTM model with an average of 0.304 mean-square error has\nbeen selected as the prediction engine of the proposed system.\n"}
+{"abstract": "  The need for robust, secure and private machine learning is an important goal\nfor realizing the full potential of the Internet of Things (IoT). Federated\nlearning has proven to help protect against privacy violations and information\nleakage. However, it introduces new risk vectors which make machine learning\nmodels more difficult to defend against adversarial samples. In this study, we\nexamine the role of differential privacy and self-normalization in mitigating\nthe risk of adversarial samples specifically in a federated learning\nenvironment. We introduce DiPSeN, a Differentially Private Self-normalizing\nNeural Network which combines elements of differential privacy noise with\nself-normalizing techniques. Our empirical results on three publicly available\ndatasets show that DiPSeN successfully improves the adversarial robustness of a\ndeep learning classifier in a federated learning environment based on several\nevaluation metrics.\n"}
+{"abstract": "  The performance of modern algorithms on certain computer vision tasks such as\nobject recognition is now close to that of humans. This success was achieved at\nthe price of complicated architectures depending on millions of parameters and\nit has become quite challenging to understand how particular predictions are\nmade. Interpretability methods propose to give us this understanding. In this\npaper, we study LIME, perhaps one of the most popular. On the theoretical side,\nwe show that when the number of generated examples is large, LIME explanations\nare concentrated around a limit explanation for which we give an explicit\nexpression. We further this study for elementary shape detectors and linear\nmodels. As a consequence of this analysis, we uncover a connection between LIME\nand integrated gradients, another explanation method. More precisely, the LIME\nexplanations are similar to the sum of integrated gradients over the\nsuperpixels used in the preprocessing step of LIME.\n"}
+{"abstract": "  Solar-thermal evaporation, a traditional steam generation method for solar\ndesalination, has received numerous attentions in recent years due to the\nsignificant increase in efficiency by adopting interfacial evaporation. While\nmost of the previous studies focus on improving the evaporation efficiency by\nmaterials innovation and system design, the underlying mechanisms of its energy\nefficiency are less explored, leading to many confusions and misunderstandings.\nHerein, we clarify these mechanisms with a detailed thermal analysis model.\nUsing this model, we elucidate the advantages of interfacial evaporation over\nthe traditional evaporation method. Furthermore, we clarify the role of tuning\nthe solar flux and surface area on the evaporation efficiency. Moreover, we\nquantitatively prove that the influence of environmental conditions on\nevaporation efficiency could not be eliminated by subtracting the dark\nevaporation rate from evaporation rate under solar. We also find that\ninterfacial evaporation in a solar still does not have the high overall solar\ndesalination efficiency as expected, but further improvement is possible from\nthe system design part. Our analysis gains insights to the thermal processes\ninvolved in interfacial solar evaporation and offers perspectives to the\nfurther development of interfacial solar desalination technology.\n"}
+{"abstract": "  The purpose of marine seismic experiments is to provide information of the\nstructure and physical properties of the subsurface. The p-wave velocity\ndistribution is the most commonly modelled property, usually by inversion of\nwaveform attributes. In wide-angle reflection/refraction (WAS) experiments,\narrival times of seismic phases identified in Ocean Bottom Seismometers (OBS),\nare used to image relatively deep structures. Most experiments have relatively\nlow redundancy and produce limited resolution velocity models. As alternative\nto WAS experiments, the shallow subsurface is commonly studied with\nMulti-Channel Seismic (MCS) data collected in towed streamers. In this case,\nthe recording of refractions as first arrivals is limited primarily by the\nstreamer length and by structural features like water depth and P-wave velocity\nstructure and, in general, a considerable amount of the refractions are masked\nby reflections and noise. The most widely used tool to extract refraction\ninformation from MCS data is the so-called downward continuation technique,\nwhich is designed to redatuming streamer field data to the seafloor. In this\nnew virtual configuration, the early refractions transform to first arrivals\nbecoming visible from zero offset, which facilitates identification and use in\ntravel-time tomography.\n  This work presents a user friendly open source HPC software for redatuming 2D\nstreamer field data to the sea bottom for any seafloor relief. The main\ningredient is the acoustic wave equation used backward in time, allowing first\nthe redatuming of the receivers, and after, the redatuming of the sources.\nAssessment tools are provided to evaluate the information available after\nredatuming for specific data acquisition configurations. Also, we present a\nstep-by-step analysis that defines the most important features that influence\nthe quality of the redatumed, virtual recordings.\n"}
+{"abstract": "  3D point cloud registration is a fundamental problem in computer vision and\nrobotics. There has been extensive research in this area, but existing methods\nmeet great challenges in situations with a large proportion of outliers and\ntime constraints, but without good transformation initialization. Recently, a\nseries of learning-based algorithms have been introduced and show advantages in\nspeed. Many of them are based on correspondences between the two point clouds,\nso they do not rely on transformation initialization. However, these\nlearning-based methods are sensitive to outliers, which lead to more incorrect\ncorrespondences. In this paper, we propose a novel deep graph matchingbased\nframework for point cloud registration. Specifically, we first transform point\nclouds into graphs and extract deep features for each point. Then, we develop a\nmodule based on deep graph matching to calculate a soft correspondence matrix.\nBy using graph matching, not only the local geometry of each point but also its\nstructure and topology in a larger range are considered in establishing\ncorrespondences, so that more correct correspondences are found. We train the\nnetwork with a loss directly defined on the correspondences, and in the test\nstage the soft correspondences are transformed into hard one-to-one\ncorrespondences so that registration can be performed by singular value\ndecomposition. Furthermore, we introduce a transformer-based method to generate\nedges for graph construction, which further improves the quality of the\ncorrespondences. Extensive experiments on registering clean, noisy,\npartial-to-partial and unseen category point clouds show that the proposed\nmethod achieves state-of-the-art performance. The code will be made publicly\navailable at https://github.com/fukexue/RGM.\n"}
+{"abstract": "  We present a purely geometric method for constructing a rank two Killing\ntensor in a spacetime with a codimension one foliation that lifts the trivial\nKilling tensors from slices to the entire manifold. The resulting Killing\ntensor can be nontrivial. A deep connection is found between the existence of\nsuch a Killing tensor and the presence of generalized photon surfaces in\nspacetime with two Killing vector fields. This technique generates Killing\ntensors in a purely algebraic way, without solving differential equations. The\nuse of our method is demonstrated for Kerr, Kerr-Newman-NUT-AdS metrics and\nKerr-NUT-AdS multicharge gauged supergravity solution.\n"}
+{"abstract": "  Most existing image privacy protection works focus mainly on the privacy of\nphoto owners and their friends, but lack the consideration of other people who\nare in the background of the photos and the related location privacy issues. In\nfact, when a person is in the background of someone else's photos, he/she may\nbe unintentionally exposed to the public when the photo owner shares the photo\nonline. Not only a single visited place could be exposed, attackers may also be\nable to piece together a person's travel route from images. In this paper, we\npropose a novel image privacy protection system, called LAMP, which aims to\nlight up the location awareness for people during online image sharing. The\nLAMP system is based on a newly designed location-aware multi-party image\naccess control model. The LAMP system will automatically detect the user's\noccurrences on photos regardless the user is the photo owner or not. Once a\nuser is identified and the location of the photo is deemed sensitive according\nto the user's privacy policy, the LAMP system will intelligently replace the\nuser's face. A prototype of the system was implemented and evaluated to\ndemonstrate its applicability in the real world.\n"}
+{"abstract": "  Learning from Demonstration (LfD) has been established as the dominant\nparadigm for efficiently transferring skills from human teachers to robots. In\nthis context, the Federated Learning (FL) conceptualization has very recently\nbeen introduced for developing large-scale human-robot collaborative\nenvironments, targeting to robustly address, among others, the critical\nchallenges of multi-agent learning and long-term autonomy. In the current work,\nthe latter scheme is further extended and enhanced, by designing and\nintegrating a novel user profile formulation for providing a fine-grained\nrepresentation of the exhibited human behavior, adopting a Deep Learning\n(DL)-based formalism. In particular, a hierarchically organized set of key\ninformation sources is considered, including: a) User attributes (e.g.\ndemographic, anthropomorphic, educational, etc.), b) User state (e.g. fatigue\ndetection, stress detection, emotion recognition, etc.) and c)\nPsychophysiological measurements (e.g. gaze, electrodermal activity, heart\nrate, etc.) related data. Then, a combination of Long Short-Term Memory (LSTM)\nand stacked autoencoders, with appropriately defined neural network\narchitectures, is employed for the modelling step. The overall designed scheme\nenables both short- and long-term analysis/interpretation of the human behavior\n(as observed during the feedback capturing sessions), so as to adaptively\nadjust the importance of the collected feedback samples when aggregating\ninformation originating from the same and different human teachers,\nrespectively.\n"}
+{"abstract": "  Quantum networks are a new paradigm of complex networks, allowing us to\nharness networked quantum technologies and to develop a quantum internet. But\nhow robust is a quantum network when its links and nodes start failing? We show\nthat quantum networks based on typical noisy quantum-repeater nodes are prone\nto discontinuous phase transitions with respect to the random loss of operating\nlinks and nodes, abruptly compromising the connectivity of the network, and\nthus significantly limiting the reach of its operation. Furthermore, we\ndetermine the critical quantum-repeater efficiency necessary to avoid this\ncatastrophic loss of connectivity as a function of the network topology, the\nnetwork size, and the distribution of entanglement in the network. In\nparticular, our results indicate that a scale-free topology is a crucial design\nprinciple to establish a robust large-scale quantum internet.\n"}
+{"abstract": "  This paper outlines a rigorous variational-based multilevel Global-Local\nformulation for ductile fracture. Here, a phase-field formulation is used to\nresolve failure mechanisms by regularizing the sharp crack topology on the\nlocal state. The coupling of plasticity to the crack phase-field is realized by\na constitutive work density function, which is characterized through a degraded\nstored elastic energy and the accumulated dissipated energy due to plasticity\nand damage. Two different Global-Local approaches based on the idea of\nmultiplicative Schwarz' alternating method are proposed: (i) A global\nconstitutive model with an elastic-plastic behavior is first proposed, while it\nis enhanced with a single local domain, which, in turn, describes an\nelastic-plastic fracturing response. (ii) The main objective of the second\nmodel is to introduce an adoption of the Global-Local approach toward the\nmultilevel local setting. To this end, an elastic-plastic global constitutive\nmodel is augmented with two distinct local domains; in which, the first local\ndomain behaves as an elastic-plastic material and the next local domain is\nmodeled due to the fracture state. To further reduce the computational cost,\npredictor-corrector adaptivity within Global-Local concept is introduced. An\nadaptive scheme is devised through the evolution of the effective global\nplastic flow (for only elastic-plastic adaptivity), and through the evolution\nof the local crack phase-field state (for only fracture adaptivity). Thus, two\nlocal domains are dynamically updated during the computation, resulting with\ntwo-way adaptivity procedure. The overall response of the Global-Local approach\nin terms of accuracy/robustness and efficiency is verified using single-scale\nproblems. The resulting framework is algorithmically described in detail and\nsubstantiated with numerical examples.\n"}
+{"abstract": "  While it is generally accepted that the magnetic field and its non-ideal\neffects play important roles during the stellar formation, simple models of\npure hydrodynamics and angular momentum conservation are still widely employed\nin the studies of disk assemblage in the framework of the so-called\n\"alpha-disk\" model due to their simplicity. There has only been a few efforts\ntrying to bridge the gap between a collapsing prestellar core and a developed\ndisk. The goal of the present work is to revisit the assemblage of the\nprotoplanetary disk (PPD), by performing 3D MHD simulations with ambipolar\ndiffusion and full radiative transfer. We follow the global evolution of the\nPPD from the prestellar core collapse for 100 kyr, with resolution of one AU.\nThe formed disk is more realistic and is in agreement with recent observations\nof disks around class-0 young stellar objects. The mass flux arriving onto the\ndisk and the radial mass accretion rate within the disk are measured and\ncompared to analytical self-similar models. The surface mass flux is very\ncentrally peaked, implying that most of the mass falling onto the star does not\ntransit through the mid-plane of the disk. The disk mid-plane is almost dead to\nturbulence, whereas upper layers and the disk outer edge are very turbulent.\nThe snow-line is significantly further away than in a passive disk. We\ndeveloped a zoomed rerun technique to quickly obtain a reasonable disk that is\nhighly stratified, weakly magnetized inside, and strongly magnetized outside.\nDuring the class-0 phase of PPD formation, the interaction between the disk and\nthe infalling envelope is important and ought not be neglected. Accretion onto\nthe star is found to mostly depend on dynamics of the collapsing envelope,\nrather than the detailed disk structure.\n"}
+{"abstract": "  Thumbnail is the face of online videos. The explosive growth of videos both\nin number and variety underpins the importance of a good thumbnail because it\nsaves potential viewers time to choose videos and even entice them to click on\nthem. A good thumbnail should be a frame that best represents the content of a\nvideo while at the same time capturing viewers' attention. However, the\ntechniques and models in the past only focus on frames within a video, and we\nbelieve such narrowed focus leave out much useful information that are part of\na video. In this paper, we expand the definition of content to include title,\ndescription, and audio of a video and utilize information provided by these\nmodalities in our selection model. Specifically, our model will first sample\nframes uniformly in time and return the top 1,000 frames in this subset with\nthe highest aesthetic scores by a Double-column Convolutional Neural Network,\nto avoid the computational burden of processing all frames in downstream task.\nThen, the model incorporates frame features extracted from VGG16, text features\nfrom ELECTRA, and audio features from TRILL. These models were selected because\nof their results on popular datasets as well as their competitive performances.\nAfter feature extraction, the time-series features, frames and audio, will be\nfed into Transformer encoder layers to return a vector representing their\ncorresponding modality. Each of the four features (frames, title, description,\naudios) will pass through a context gating layer before concatenation. Finally,\nour model will generate a vector in the latent space and select the frame that\nis most similar to this vector in the latent space. To the best of our\nknowledge, we are the first to propose a multi-modal deep learning model to\nselect video thumbnail, which beats the result from the previous\nState-of-The-Art models.\n"}
+{"abstract": "  For every field $F$ which has a quadratic extension $E$ we show there are\nnon-metabelian infinite-dimensional thin graded Lie algebras all of whose\nhomogeneous components, except the second one, have dimension $2$. We construct\nsuch Lie algebras as $F$-subalgebras of Lie algebras $M$ of maximal class over\n$E$. We characterise the thin Lie $F$-subalgebras of $M$ generated in degree\n$1$. Moreover we show that every thin Lie algebra $L$ whose ring of graded\nendomorphisms of degree zero of $L^3$ is a quadratic extension of $F$ can be\nobtained in this Lie algebra of maximal class over $E$ which are ideally\n$r$-constrained for a positive integer $r$.\n"}
+{"abstract": "  We survey functional analytic methods for studying subwavelength resonator\nsystems. In particular, rigorous discrete approximations of Helmholtz\nscattering problems are derived in an asymptotic subwavelength regime. This is\nachieved by re-framing the Helmholtz equation as a non-linear eigenvalue\nproblem in terms of integral operators. In the subwavelength limit, resonant\nstates are described by the eigenstates of the generalized capacitance matrix,\nwhich appears by perturbing the elements of the kernel of the limiting\noperator. Using this formulation, we are able to describe subwavelength\nresonance and related phenomena. In particular, we demonstrate large-scale\neffective parameters with exotic values. We also show that these systems can\nexhibit localized and guided waves on very small length scales. Using the\nconcept of topologically protected edge modes, such localization can be made\nrobust against structural imperfections.\n"}
+{"abstract": "  This letter reports the findings of the late time behavior of the\nout-of-time-ordered correlators (OTOCs) via a quantum kicked rotor model with\n$\\cal{PT}$-symmetric driving potential. An analytical expression of the OTOCs'\nquadratic growth with time is yielded as $C(t)=G(K)t^2$. Interestingly, the\ngrowth rate $G$ features a quantized response to the increase of the kick\nstrength $K$, which indicates the chaos-assisted quantization in the OTOCs'\ndynamics. The physics behind this is the quantized absorption of energy from\nthe non-Hermitian driving potential. This discovery and the ensuing\nestablishment of the quantization mechanism in the dynamics of quantum chaos\nwith non-Hermiticity will provide insights in chaotic dynamics, promising\nunprecedented observations in updated experiments.\n"}
+{"abstract": "  We consider a family of deep neural networks consisting of two groups of\nconvolutional layers, a downsampling operator, and a fully connected layer. The\nnetwork structure depends on two structural parameters which determine the\nnumbers of convolutional layers and the width of the fully connected layer. We\nestablish an approximation theory with explicit approximation rates when the\napproximated function takes a composite form $f\\circ Q$ with a feature\npolynomial $Q$ and a univariate function $f$. In particular, we prove that such\na network can outperform fully connected shallow networks in approximating\nradial functions with $Q(x) =|x|^2$, when the dimension $d$ of data from\n$\\mathbb{R}^d$ is large. This gives the first rigorous proof for the\nsuperiority of deep convolutional neural networks in approximating functions\nwith special structures. Then we carry out generalization analysis for\nempirical risk minimization with such a deep network in a regression framework\nwith the regression function of the form $f\\circ Q$. Our network structure\nwhich does not use any composite information or the functions $Q$ and $f$ can\nautomatically extract features and make use of the composite nature of the\nregression function via tuning the structural parameters. Our analysis provides\nan error bound which decreases with the network depth to a minimum and then\nincreases, verifying theoretically a trade-off phenomenon observed for network\ndepths in many practical applications.\n"}
+{"abstract": "  We revisit the evaluation of the chiral vortical effect in the accelerated\nmatter. To first order in the acceleration the corresponding matrix element of\nthe axial current can be reconstructed from the flat-space limit. A crucial\npoint is the existence in the this case of an extra conservation law of fluid\nhelicity. As a result, the chirality of the microscopic degrees of freedom and\nhelicity of the macroscopic motion are separately conserved. This separation\npersists also in presence of gravity. Implications for the thermal chiral\nvortical effect are discussed.\n"}
+{"abstract": "  We present a numerical approach to efficiently calculate spin-wave\ndispersions and spatial mode profiles in magnetic waveguides of arbitrarily\nshaped cross section with any non-collinear equilibrium magnetization which is\ntranslationally invariant along the waveguide. Our method is based on the\npropagating-wave dynamic-matrix approach by Henry et al. and extends it to\narbitrary cross sections using a finite-element method. We solve the linearized\nequation of motion of the magnetization only in a single waveguide cross\nsection which drastically reduces computational effort compared to common\nthree-dimensional micromagnetic simulations. In order to numerically obtain the\ndipolar potential of individual spin-wave modes, we present a plane-wave\nversion of the hybrid finite-element/boundary-element method by Frekdin and\nKoehler which, for the first time, we extend to a modified version of the\nPoisson equation. Our method is applied to several important examples of\nmagnonic waveguides including systems with surface curvature, such as magnetic\nnanotubes, where the curvature leads to an asymmetric spin-wave dispersion. In\nall cases, the validity of our approach is confirmed by other methods. Our\nmethod is of particular interest for the study of curvature-induced or\nmagnetochiral effects on spin-wave transport but also serves as an efficient\ntool to investigate standard magnonic problems.\n"}
+{"abstract": "  Business processes are continuously evolving in order to adapt to changes due\nto various factors. One type of process changes are branching frequency\nchanges, which are related to changes in frequencies between different options\nwhen there is an exclusive choice. Existing methods either cannot detect such\nchanges or cannot provide accurate and comprehensive results. In this paper, we\npropose a method which takes both event logs and process models as input and\ngenerates a choice sequence for each exclusive choice in the process model. The\nmethod then identifies change points based on the choice sequences. We evaluate\nour method on a real-life event log. Results show that our method can identify\nbranching frequency changes in process models and provide comprehensive results\nto users.\n"}
+{"abstract": "  We analyze the LIGO/Virgo GWTC-2 catalog to study the primary mass\ndistribution of the merging black holes. We perform hierarchical Bayesian\nanalysis, and examine whether the mass distribution has a sharp cutoff for\nprimary black hole masses below $65 M_\\odot$, as predicted in pulsational pair\ninstability supernova model. We construct two empirical mass functions. One is\na piece-wise function with two power-law segments jointed by a sudden drop. The\nother consists of a main truncated power-law component, a Gaussian component,\nand a third very massive component. Both models can reasonably fit the data and\na sharp drop of the mass distribution is found at $\\sim 50M_\\odot$, suggesting\nthat the majority of the observed black holes can be explained by the stellar\nevolution scenarios in which the pulsational pair-instability process takes\nplace. On the other hand, the very massive sub-population, which accounts for\nat most several percents of the total, may be formed through hierarchical\nmergers or other processes.\n"}
+{"abstract": "  This study examines the mechanism design problem for public-good provision in\na large economy with $n$ independent agents. We propose a class of\ndominant-strategy incentive compatible (DSIC) and ex post individual rational\n(EPIR) mechanisms which we call the adjusted mean-thresholding (AMT)\nmechanisms. We show that when the cost of provision grows slower than the\n$\\sqrt{n}$ rate, the AMT mechanisms are both asymptotically ex ante budget\nbalanced (AEABB) and asymptotically efficient (AE). When the cost grows faster\nthan the $\\sqrt{n}$ rate, in contrast, we show that any DSIC, EPIR, and AEABB\nmechanism must have provision probability converging to zero and hence cannot\nbe AE. Lastly, the AMT mechanisms are more informationally robust when compared\nto, for example, the second-best mechanism. This is because the construction of\nAMT mechanisms depends only on the first moments of the valuation\ndistributions.\n"}
+{"abstract": "  Social Network Analysis is the use of Network and Graph Theory to study\nsocial phenomena, which was found to be highly relevant in areas like\nCriminology. This chapter provides an overview of key methods and tools that\nmay be used for the analysis of criminal networks, which are presented in a\nreal-world case study. Starting from available juridical acts, we have\nextracted data on the interactions among suspects within two Sicilian Mafia\nclans, obtaining two weighted undirected graphs. Then, we have investigated the\nroles of these weights on the criminal network's properties, focusing on two\nkey features: weight distribution and shortest path length. We also present an\nexperiment that aims to construct an artificial network that mirrors criminal\nbehaviours. To this end, we have conducted a comparative degree distribution\nanalysis between the real criminal networks, using some of the most popular\nartificial network models: Watts-Strogatz, Erd\\H{o}s-R\\'{e}nyi, and\nBarab\\'{a}si-Albert, with some topology variations. This chapter will be a\nvaluable tool for researchers who wish to employ social network analysis within\ntheir own area of interest.\n"}
+{"abstract": "  As more exoplanets are being discovered around ultracool dwarfs,\nunderstanding their magnetic activity -- and the implications for habitability\n-- is of prime importance. To find stellar flares and photometric signatures\nrelated to starspots, continuous monitoring is necessary, which can be achieved\nwith spaceborn observatories like the Transiting Exoplanet Survey Satellite\n(TESS). We present an analysis of TRAPPIST-1 like ultracool dwarfs with TESS\nfull-frame image photometry from the first two years of the primary mission. A\nvolume-limited sample up to 50 pc is constructed consisting of 339 stars closer\nthan 0.5 mag to TRAPPIST-1 on the Gaia colour-magnitude diagram. The 30-min\ncadence TESS light curves of 248 stars were analysed, searching for flares and\nrotational modulation caused by starspots. The composite flare frequency\ndistribution of the 94 identified flares shows a power law index similar to\nTRAPPIST-1, and contains flares up to $E_\\mathrm{TESS} = 3 \\times 10^{33}$ erg.\nRotational periods shorter than 5 days were determined for 42 stars, sampling\nthe regime of fast rotators. The ages of 88 stars from the sample were\nestimated using kinematic information. A weak correlation between rotational\nperiod and age is observed, which is consistent with magnetic braking.\n"}
+{"abstract": "  5G and future cellular networks intend to incorporate low earth orbit (LEO)\nsatellite communication systems (SatCom) to solve the coverage and availability\nproblems that cannot be addressed by satellite-based or ground-based\ninfrastructure alone. This integration of terrestrial and non terrestrial\nnetworks poses many technical challenges which need to be identified and\naddressed. To this aim, we design and simulate the downlink of a LEO SatCom\ncompatible with 5G NR, with a special focus on the design of the beamforming\ncodebook at the satellite side. The performance of this approach is evaluated\nfor the link between a LEO satellite and a mobile terminal in the Ku band,\nassuming a realistic channel model and commercial antenna array designs, both\nat the satellite and the terminal. Simulation results provide insights on open\nresearch challenges related to analog codebook design and hybrid beamforming\nstrategies, requirements of the antenna terminals to provide a given SNR, or\nrequired beam reconfiguration capabilities among others.\n"}
+{"abstract": "  In this study, we numerically investigated the mechanical responses and\ntrajectories of frictional granular particles under oscillatory shear in the\nreversible phase where particle trajectories form closed loops below the\nyielding point. When the friction coefficient is small, the storage modulus\nexhibits softening, and the loss modulus remains finite in the quasi-static\nlimit. As the friction coefficient increases, the softening and residual loss\nmodulus are suppressed. The storage and loss moduli satisfy scaling laws if\nthey are plotted as functions of the areas of the loop trajectories divided by\nthe strain amplitude and diameter of grains, at least for their small values.\n"}
+{"abstract": "  It is shown that each non-compact locally compact second countable non-(T)\ngroup $G$ possesses non-strongly ergodic weakly mixing IDPFT Poisson actions of\narbitrary Krieger's type. These actions are amenable if and only if $G$ is\namenable. If $G$ has the Haagerup property then (and only then) these actions\ncan be chosen of 0-type. If $G$ is amenable and unimodular then $G$ has weakly\nmixing Bernoulli actions of any possible Krieger's type.\n"}
+{"abstract": "  It was recently proposed that there is a phase in thermal QCD (IR phase) at\ntemperatures well above the chiral crossover, featuring elements of scale\ninvariance in the infrared (IR). Here we study the effective spatial\ndimensions, $d_{IR}$, of Dirac low-energy modes in this phase, in the context\nof pure-glue QCD. Our $d_{IR}$ is based on the scaling of mode support toward\nthermodynamic limit, and hence is an IR probe. Ordinary extended modes, such as\nthose at high energy, have $d_{IR}=3$. We find $d_{IR}<3$ in the spectral range\nwhose lower edge coincides with $\\lambda_{IR}=0$, the singularity of spectral\ndensity defining the IR phase, and the upper edge with $\\lambda_A$, the\npreviously identified Anderson-like non-analyticity. Details near\n$\\lambda_{IR}$ are unexpected in that only exact zero modes are $d_{IR}=3$,\nwhile a thin spectral layer near zero is $d_{IR}=2$, followed by an extended\nlayer of $d_{IR}=1$ modes. With only integer values appearing, $d_{IR}$ may\nhave topological origin. We find similar structure at $\\lambda_A$, and\nassociate its adjacent thin layer ($d_{IR} >\\approx 2$) with Anderson-like\ncriticality. Our analysis reveals the manner in which non-analyticities at\n$\\lambda_{IR}$ and $\\lambda_A$, originally identified in other quantities,\nappear in $d_{IR}(\\lambda)$. This dimension structure may be important for\nunderstanding the near-perfect fluidity of the quark-gluon medium seen in\naccelerator experiments. The role of $\\lambda_A$ in previously conjectured\ndecoupling of IR component is explained.\n"}
+{"abstract": "  ART-XC (Astronomical Roentgen Telescope - X-ray Concentrator) is the hard\nX-ray instrument with grazing incidence imaging optics on board the\nSpektr-Roentgen-Gamma (SRG) observatory. The SRG observatory is the flagship\nastrophysical mission of the Russian Federal Space Program, which was\nsuccessively launched into orbit around the second Lagrangian point (L2) of the\nEarth-Sun system with a Proton rocket from the Baikonur cosmodrome on 13 July\n2019. The ART-XC telescope will provide the first ever true imaging all-sky\nsurvey performed with grazing incidence optics in the 4-30 keV energy band and\nwill obtain the deepest and sharpest map of the sky in the energy range of 4-12\nkeV. Observations performed during the early calibration and performance\nverification phase as well as during the on-going all-sky survey that started\non 12 Dec. 2019 have demonstrated that the in-flight characteristics of the\nART-XC telescope are very close to expectations based on the results of ground\ncalibrations. Upon completion of its 4-year all-sky survey, ART-XC is expected\nto detect ~5000 sources (~3000 active galactic nuclei, including heavily\nobscured ones, several hundred clusters of galaxies, ~1000 cataclysmic\nvariables and other Galactic sources), and to provide a high-quality map of the\nGalactic background emission in the 4-12 keV energy band. ART-XC is also well\nsuited for discovering transient X-ray sources. In this paper, we describe the\ntelescope, results of its ground calibrations, major aspects of the mission,\nthe in-flight performance of ART-XC and first scientific results.\n"}
+{"abstract": "  Linear global instability of the three-dimensional (3-D),\nspanwise-homogeneous laminar separation bubble (LSB) induced by\nshock-wave/boundary-layer interaction (SBLI) in a Mach 7 flow of nitrogen over\na $30^{\\circ}-55^{\\circ}$ double wedge is studied. At these conditions\ncorresponding to a freestream unit Reynolds number, $Re_1=5.2\\times 10^{4}$\nm$^{-1}$, the flow exhibits rarefaction effects and comparable\nshock-thicknesses to the size of the boundary-layer at separation. This, in\nturn, requires the use of the high-fidelity Direct Simulation Monte Carlo\n(DSMC) method to accurately resolve unsteady flow features.\n  We show for the first time that the LSB sustains self-excited,\nsmall-amplitude, 3-D perturbations that lead to spanwise-periodic flow\nstructures not only in and downstream of the separated region, as seen in a\nmultitude of experiments and numerical simulations, but also in the internal\nstructure of the separation and detached shock layers. The spanwise-periodicity\nlength and growth rate of the structures in the two zones are found to be\nidentical. It is shown that the linear global instability leads to\nlow-frequency unsteadiness of the triple point formed by the intersection of\nseparation and detached shocks, corresponding to a Strouhal number of\n$St\\sim0.02$. Linear superposition of the spanwise-homogeneous base flow and\nthe leading 3-D flow eigenmode provides further evidence of the strong coupling\nbetween linear instability in the LSB and the shock layer.\n"}
+{"abstract": "  Ensuring reliable operation of large power systems subjected to multiple\noutages is a challenging task because of the combinatorial nature of the\nproblem. Traditional approaches for security assessment are often limited by\ntheir scope and/or speed, resulting in missing of critical contingencies that\ncould lead to cascading failures. This paper proposes a two-component\nmethodology to enhance power system security. The first component combines an\nefficient algorithm to detect cut-set saturation (called the feasibility test\n(FT) algorithm) with real-time contingency analysis (RTCA) to create an\nintegrated corrective action (iCA), whose goal is to secure the system against\ncut-set saturation as well as critical branch overloads. The second component\nonly employs the results of the FT to create a relaxed corrective action (rCA)\nto secure the system against post-contingency cut-set saturation. The first\ncomponent is more comprehensive, but the latter is computationally more\nefficient. The effectiveness of the two components is evaluated based upon the\nnumber of cascade triggering contingencies alleviated, and the computation\ntime. The results obtained by analyzing different case-studies on the IEEE\n118-bus and 2000-bus synthetic Texas systems indicate that the proposed\ntwo-component methodology successfully enhances the scope and speed of power\nsystem security assessment during multiple outages.\n"}
+{"abstract": "  We analyze the performance of the best-response dynamic across all\nnormal-form games using a random games approach. The playing sequence -- the\norder in which players update their actions -- is essentially irrelevant in\ndetermining whether the dynamic converges to a Nash equilibrium in certain\nclasses of games (e.g. in potential games) but, when evaluated across all\npossible games, convergence to equilibrium depends on the playing sequence in\nan extreme way. Our main asymptotic result shows that the best-response dynamic\nconverges to a pure Nash equilibrium in a vanishingly small fraction of all\n(large) games when players take turns according to a fixed cyclic order. By\ncontrast, when the playing sequence is random, the dynamic converges to a pure\nNash equilibrium if one exists in almost all (large) games.\n"}
+{"abstract": "  Natural Human-Robot Interaction (HRI) is one of the key components for\nservice robots to be able to work in human-centric environments. In such\ndynamic environments, the robot needs to understand the intention of the user\nto accomplish a task successfully. Towards addressing this point, we propose a\nsoftware architecture that segments a target object from a crowded scene,\nindicated verbally by a human user. At the core of our system, we employ a\nmulti-modal deep neural network for visual grounding. Unlike most grounding\nmethods that tackle the challenge using pre-trained object detectors via a\ntwo-stepped process, we develop a single stage zero-shot model that is able to\nprovide predictions in unseen data. We evaluate the performance of the proposed\nmodel on real RGB-D data collected from public scene datasets. Experimental\nresults showed that the proposed model performs well in terms of accuracy and\nspeed, while showcasing robustness to variation in the natural language input.\n"}
+{"abstract": "  In this paper we propose an improved fast iterative method to solve the\nEikonal equation, which can be implemented in parallel. We improve the fast\niterative method for Eikonal equation in two novel ways, in the value update\nand in the error correction. The new value update is very similar to the fast\niterative method in that we selectively update the points, chosen by a\nconvergence measure, in the active list. However, in order to reduce running\ntime, the improved algorithm does not run a convergence check of the\nneighboring points of the narrow band as the fast iterative method usually\ndoes. The additional error correction step is to correct the errors that the\nprevious value update step may cause. The error correction step consists of\nfinding and recalculating the point values in a separate remedy list which is\nquite easy to implement on a GPU. In contrast to the fast marching method and\nthe fast sweeping method for the Eikonal equation, our improved method does not\nneed to compute the solution with any special ordering in neither the remedy\nlist nor the active list. Therefore, our algorithm can be implemented in\nparallel. In our experiments, we implemente our new algorithm in parallel on a\nGPU and compare the elapsed time with other current algorithms. The improved\nfast iterative method runs faster than the other algorithms in most cases\nthrough our numercal studies.\n"}
+{"abstract": "  Point forecast reconciliation of collection of time series with linear\naggregation constraints has evolved substantially over the last decade. A few\ncommonly used methods are GLS (generalized least squares), OLS (ordinary least\nsquares), WLS (weighted least squares), and MinT (minimum trace). GLS and MinT\nhave similar mathematical expressions, but they differ by the covariance matrix\nused. OLS and WLS can be considered as special cases of MinT where they differ\nby the assumptions made about the structure of the covariance matrix. All these\nmethods ensure that the reconciled forecasts are unbiased, provided that the\nbase forecasts are unbiased. The ERM (empirical risk minimizer) approach was\nproposed to relax the assumption of unbiasedness.\n  This paper proves that (a) GLS and MinT reduce to the same solution; (b) on\naverage, a method similar to ERM (which we refer to as MinT-U) can produce\nbetter forecasts than MinT (lowest total mean squared error) which is then\nfollowed by OLS and then by base; and (c) the mean squared error of each series\nin the structure for MinT-U is smaller than that for MinT which is then\nfollowed by that for either OLS or base forecasts. We show these theoretical\nresults using a set of simulation studies. We also evaluate them using the\nAustralian domestic tourism data set.\n"}
+{"abstract": "  It has been shown previously that the presence of a Dzyaloshinskii-Moriya\ninteraction in perpendicularly magnetized thin films stabilizes N\\'eel type\ndomain walls. We demonstrate, using micromagnetic simulations and analytical\nmodeling, that the presence of a uniaxial in-plane magnetic anisotropy can also\nlead to the formation of N\\'eel walls in the absence of a Dzyaloshinskii-Moriya\ninteraction. It is possible to abruptly switch between Bloch and N\\'eel walls\nvia a small modulation of both the in-plane, but also the perpendicular\nmagnetic anisotropy. This opens up a route towards electric field control of\nthe domain wall type with small applied voltages through electric field\ncontrolled anisotropies.\n"}
+{"abstract": "  3D action recognition is referred to as the classification of action\nsequences which consist of 3D skeleton joints. While many research work are\ndevoted to 3D action recognition, it mainly suffers from three problems: highly\ncomplicated articulation, a great amount of noise, and a low implementation\nefficiency. To tackle all these problems, we propose a real-time 3D action\nrecognition framework by integrating the locally aggregated kinematic-guided\nskeletonlet (LAKS) with a supervised hashing-by-analysis (SHA) model. We first\ndefine the skeletonlet as a few combinations of joint offsets grouped in terms\nof kinematic principle, and then represent an action sequence using LAKS, which\nconsists of a denoising phase and a locally aggregating phase. The denoising\nphase detects the noisy action data and adjust it by replacing all the features\nwithin it with the features of the corresponding previous frame, while the\nlocally aggregating phase sums the difference between an offset feature of the\nskeletonlet and its cluster center together over all the offset features of the\nsequence. Finally, the SHA model which combines sparse representation with a\nhashing model, aiming at promoting the recognition accuracy while maintaining a\nhigh efficiency. Experimental results on MSRAction3D, UTKinectAction3D and\nFlorence3DAction datasets demonstrate that the proposed method outperforms\nstate-of-the-art methods in both recognition accuracy and implementation\nefficiency.\n"}
+{"abstract": "  Secant defectivity of projective varieties is classically approached via\ndimensions of linear systems with multiple base points in general position. The\nlatter can be studied via degenerations. We exploit a technique that allows\nsome of the base points to collapse together. We deduce a general result which\nwe apply to prove a conjecture by Abo and Brambilla: for $c \\geq 3$ and $d \\geq\n3$, the Segre-Veronese embedding of $\\mathbb{P}^m\\times\\mathbb{P}^n$ in\nbidegree $(c,d)$ is non-defective.\n"}
+{"abstract": "  Memes are one of the most popular types of content used to spread information\nonline. They can influence a large number of people through rhetorical and\npsychological techniques. The task, Detection of Persuasion Techniques in Texts\nand Images, is to detect these persuasive techniques in memes. It consists of\nthree subtasks: (A) Multi-label classification using textual content, (B)\nMulti-label classification and span identification using textual content, and\n(C) Multi-label classification using visual and textual content. In this paper,\nwe propose a transfer learning approach to fine-tune BERT-based models in\ndifferent modalities. We also explore the effectiveness of ensembles of models\ntrained in different modalities. We achieve an F1-score of 57.0, 48.2, and 52.1\nin the corresponding subtasks.\n"}
+{"abstract": "  Quasi-periodic oscillations, often present in the power density spectrum of\naccretion disk around black holes, are useful probes for the understanding of\ngravitational interaction in the near-horizon regime of black holes. Since the\npresence of an extra spatial dimension modifies the near horizon geometry of\nblack holes, it is expected that the study of these quasi-periodic oscillations\nmay shed some light on the possible existence of these extra dimensions.\nIntriguingly, most of the extra dimensional models, which are of significant\ninterest to the scientific community, predicts the existence of a tidal charge\nparameter in black hole spacetimes. This tidal charge parameter can have an\noverall negative sign and is a distinctive signature of the extra dimensions.\nMotivated by this, we have studied the quasi-periodic oscillations for a\nrotating braneworld black hole using the available theoretical models.\nSubsequently, we have used the observations of the quasi-periodic oscillations\nfrom available black hole sources, e.g., GRO J1655 -- 40, XTE J1550 -- 564, GRS\n1915 + 105, H 1743 + 322 and Sgr A* and have compared them with the predictions\nfrom the relevant theoretical models, in order to estimate the tidal charge\nparameter. It turns out that among the 11 theoretical models considered here, 8\nof them predict a negative value for the tidal charge parameter, while for the\nothers negative values of the tidal charge parameter are also well within the\n1-$\\sigma$ confidence interval.\n"}
+{"abstract": "  We derive a continuum mechanical model to capture the morphological changes\noccurring at the pretumoral stage of epithelial tissues. The proposed model\naims to investigate the competition between the bulk elasticity of the\nepithelium and the surface tensions of the apical and basal sides. According to\nthis model, when the apico-basal tension imbalance reaches a critical value, a\nsubcritical bifurcation is triggered and the epithelium attains its\nphysiological folded shape. Based on data available in the literature, our\nmodel predicts that pretumoral cells are softer than healthy cells.\n"}
+{"abstract": "  The 21cm line of neutral hydrogen (HI) opens a new avenue in our exploration\nof the structure and evolution of the Universe. It provides complementary data\nto the current large-scale structure observations with different systematics,\nand thus it will be used to improve our understanding of the $\\Lambda$CDM\nmodel. Among several radio cosmological surveys designed to measure this line,\nBINGO is a single-dish telescope mainly designed to detect baryon acoustic\noscillations (BAOs) at low redshifts ($0.127< z<0.449$). Our goal is to assess\nthe fiducial BINGO setup and its capabilities of constraining the cosmological\nparameters, and to analyze the effect of different instrument configurations.\nWe used the Phase 1 fiducial configuration of the BINGO telescope to perform\nour cosmological forecasts. In addition, we investigated the impact of several\ninstrumental setups, taking into account some instrumental systematics, and\ndifferent cosmological models. Combining BINGO with Planck temperature and\npolarization data, the projected constraint improves from a $13\\%$ and $25\\%$\nprecision measurement at the $68\\%$ confidence level with Planck only to $1\\%$\nand $3\\%$ for the Hubble constant and the dark energy equation of state (EoS),\nrespectively, within the wCDM model. Assuming a Chevallier-Polarski-Linder\nparameterization, the EoS parameters have standard deviations given by\n$\\sigma_{w_0} = 0.30$ and $\\sigma_{w_a} = 1.2$, which are improvements on the\norder of $30\\%$ with respect to Planck alone. Also, we can access information\nabout the HI density and bias, obtaining $\\sim 8.5\\%$ and $\\sim 6\\%$ precision,\nrespectively, assuming they vary with redshift at three independent bins. The\nfiducial BINGO configuration will be able to extract significant cosmological\ninformation from the HI distribution and provide constraints competitive with\ncurrent and future cosmological surveys. (Abridged)\n"}
+{"abstract": "  In cases of pressure or volume overload, probing cardiac function may be\ndifficult because of the interactions between shape and deformations.In this\nwork, we use the LDDMM framework and parallel transport to estimate and\nreorient deformations of the right ventricle. We then propose a normalization\nprocedure for the amplitude of the deformation, and a second-order spline model\nto represent the full cardiac contraction. The method is applied to 3D meshes\nof the right ventricle extracted from echocardiographic sequences of 314\npatients divided into three disease categories and a control group. We find\nsignificant differences between pathologies in the model parameters, revealing\ninsights into the dynamics of each disease.\n"}
+{"abstract": "  In this paper, we mainly deal with sequences of bounded linear operators on\nHilbert space. The main result is the so-called squeeze theorem (or sandwich\nrule) for convergent sequences of self-adjoint operators. We show that this\ntheorem remains valid for all three main topologies on $B(H)$.\n"}
+{"abstract": "  A growing effort in NLP aims to build datasets of human explanations.\nHowever, the term explanation encompasses a broad range of notions, each with\ndifferent properties and ramifications. Our goal is to provide an overview of\ndiverse types of explanations and human limitations, and discuss implications\nfor collecting and using explanations in NLP. Inspired by prior work in\npsychology and cognitive sciences, we group existing human explanations in NLP\ninto three categories: proximal mechanism, evidence, and procedure. These three\ntypes differ in nature and have implications for the resultant explanations.\nFor instance, procedure is not considered explanations in psychology and\nconnects with a rich body of work on learning from instructions. The diversity\nof explanations is further evidenced by proxy questions that are needed for\nannotators to interpret and answer open-ended why questions. Finally,\nexplanations may require different, often deeper, understandings than\npredictions, which casts doubt on whether humans can provide useful\nexplanations in some tasks.\n"}
+{"abstract": "  An electron beam is deflected when it passes over a silicon nitride surface,\nif the surface is illuminated by a low-power continuous-wave diode laser. A\ndeflection angle of up-to $1.2 \\,\\textrm{mrad}$ is achieved for an electron\nbeam of $29 \\,\\mu\\textrm{rad}$ divergence. A mechanical beam-stop is used to\ndemonstrate that the effect can act as an optical electron switch with a rise\nand fall time of $6 \\,\\mu\\textrm{s}$. Such a switch provides an alternative\nmeans to control electron beams, which may be useful in electron lithography\nand microscopy.\n"}
+{"abstract": "  Our main result is to establish stability of martingale couplings: suppose\nthat $\\pi$ is a martingale coupling with marginals $\\mu, \\nu$. Then, given\napproximating marginal measures $\\tilde \\mu \\approx \\mu, \\tilde \\nu\\approx \\nu$\nin convex order, we show that there exists an approximating martingale coupling\n$\\tilde\\pi \\approx \\pi$ with marginals $\\tilde \\mu, \\tilde \\nu$.\n  In mathematical finance, prices of European call / put option yield\ninformation on the marginal measures of the arbitrage free pricing measures.\nThe above result asserts that small variations of call / put prices lead only\nto small variations on the level of arbitrage free pricing measures.\n  While these facts have been anticipated for some time, the actual proof\nrequires somewhat intricate stability results for the adapted Wasserstein\ndistance. Notably the result has consequences for a several related problems.\nSpecifically, it is relevant for numerical approximations, it leads to a new\nproof of the monotonicity principle of martingale optimal transport and it\nimplies stability of weak martingale optimal transport as well as optimal\nSkorokhod embedding. On the mathematical finance side this yields continuity of\nthe robust pricing problem for exotic options and VIX options with respect to\nmarket data. These applications will be detailed in two companion papers.\n"}
+{"abstract": "  In present paper, we search the existence of dark energy scalar field models\nwithin in $f(R, T)$ gravity theory established by Harko et al. (Phys. Rev. D\n84, 024020, 2011) in a flat FRW universe. The correspondence between scalar\nfield models have been examined by employing new generalized dynamical\ncosmological term $ \\Lambda(t) $. In this regards, the best fit observational\nvalues of parameters from three distinct sets data are applied. To decide the\nsolution to field equations, a scale factor $ a= \\left(\\sinh(\\beta\nt)\\right)^{1/n} $ has been considered, where $ \\beta$ \\& $n $ are constants.\nHere, we employ the recent ensues ($H_{0}=69.2$ and $q_{0}=-0.52)$ from\n(OHD+JLA) observation (Yu et al., Astrophys. J. 856, 3, 2018). Through the\nnumerical estimation and graphical assessing of various cosmological\nparameters, it has been experienced that findings are comparable with\nkinematics and physical properties of universe and compatible with recent\ncosmological ensues. The dynamics and potentials of scalar fields are clarified\nin FRW scenario in the present model. Potentials reconstruction is highly\nreasonable and shows a periodic establishment and in agreement with latest\nobservations.\n"}
+{"abstract": "  We demonstrate the transfer of $^{23}$Na$^{40}$K molecules from a\nclosed-channel dominated Feshbach-molecule state to the absolute ground state.\nThe Feshbach molecules are initially created from a gas of sodium and potassium\natoms via adiabatic ramping over a Feshbach resonance at 78.3$\\,$G. The\nmolecules are then transferred to the absolute ground state using stimulated\nRaman adiabatic passage with an intermediate state in the spin-orbit-coupled\ncomplex $|c^3 \\Sigma^+, v=35, J=1 \\rangle \\sim |B^1\\Pi, v=12, J=1\\rangle$. Our\nmeasurements show that the pump transition dipole moment linearly increases\nwith the closed-channel fraction. Thus, the pump-beam intensity can be two\norders of magnitude lower than is necessary with open-channel dominated\nFeshbach molecules. We also demonstrate that the phase noise of the Raman\nlasers can be reduced by filter cavities, significantly improving the transfer\nefficiency.\n"}
+{"abstract": "  We propose HERMES, a scalable, secure, and privacy-enhancing system for users\nto share and access vehicles. HERMES securely outsources operations of vehicle\naccess token generation to a set of untrusted servers. It builds on an earlier\nproposal, namely SePCAR [1], and extends the system design for improved\nefficiency and scalability. To cater to system and user needs for secure and\nprivate computations, HERMES utilizes and combines several cryptographic\nprimitives with secure multiparty computation efficiently. It conceals secret\nkeys of vehicles and transaction details from the servers, including vehicle\nbooking details, access token information, and user and vehicle identities. It\nalso provides user accountability in case of disputes. Besides, we provide\nsemantic security analysis and prove that HERMES meets its security and privacy\nrequirements. Last but not least, we demonstrate that HERMES is efficient and,\nin contrast to SePCAR, scales to a large number of users and vehicles, making\nit practical for real-world deployments. We build our evaluations with two\ndifferent multiparty computation protocols: HtMAC-MiMC and CBC-MAC-AES. Our\nresults demonstrate that HERMES with HtMAC-MiMC requires only approx 1,83 ms\nfor generating an access token for a single-vehicle owner and approx 11,9 ms\nfor a large branch of rental companies with over a thousand vehicles. It\nhandles 546 and 84 access token generations per second, respectively. This\nresults in HERMES being 696 (with HtMAC-MiMC) and 42 (with CBC-MAC-AES) times\nfaster compared to in SePCAR for a single-vehicle owner access token\ngeneration. Furthermore, we show that HERMES is practical on the vehicle side,\ntoo, as access token operations performed on a prototype vehicle on-board unit\ntake only approx 62,087 ms.\n"}
+{"abstract": "  Context. HI filaments are closely related to dusty magnetized structures that\nare observable in the far infrared (FIR). Recently it was proposed that the\ncoherence of oriented HI structures in velocity traces the line of sight\nmagnetic field tangling. Aims. We study the velocity-dependent coherence\nbetween FIR emission at 857 GHz and HI on angular scales of 18 arcmin. Methods.\nWe use HI4PI HI data and Planck FIR data and apply the Hessian operator to\nextract filaments. For coherence, we require that local orientation angles\n{\\theta} in the FIR at 857 GHz along the filaments be correlated with the HI.\nResults. We find some correlation for HI column densities at |v_LSR | < 50 km/,\nbut a tight agreement between FIR and HI orientation angles {\\theta} exists\nonly in narrow velocity intervals of 1 km/s. Accordingly, we assign velocities\nto FIR filaments. Along the line of sight these HI structures show a high\ndegree of the local alignment with {\\theta}, as well as in velocity space.\nInterpreting these aligned structures in analogy to the polarization of dust\nemission defines an HI polarization. We observe polarization fractions of up to\n80%, with averages of 30%. Orientation angles {\\theta} along the filaments,\nprojected perpendicular to the line of sight, are fluctuating systematically\nand allow a characteristic distribution of filament curvatures to be\ndetermined. Conclusions. Local HI and FIR filaments identified by the Hessian\nanalysis are coherent structures with well-defined radial velocities. HI\nstructures are also organized along the line of sight with a high degree of\ncoherence. The observed bending of these structures in the plane of the sky is\nconsistent with models for magnetic field curvatures induced by a Galactic\nsmall-scale turbulent dynamo.\n"}
+{"abstract": "  Recent years have witnessed the prosperity of legal artificial intelligence\nwith the development of technologies. In this paper, we propose a novel legal\napplication of legal provision prediction (LPP), which aims to predict the\nrelated legal provisions of affairs. We formulate this task as a challenging\nknowledge graph completion problem, which requires not only text understanding\nbut also graph reasoning. To this end, we propose a novel text-guided graph\nreasoning approach. We collect amounts of real-world legal provision data from\nthe Guangdong government service website and construct a legal dataset called\nLegalLPP. Extensive experimental results on the dataset show that our approach\nachieves better performance compared with baselines. The code and dataset are\navailable in \\url{https://github.com/zxlzr/LegalPP} for reproducibility.\n"}
+{"abstract": "  Local SGD is a promising approach to overcome the communication overhead in\ndistributed learning by reducing the synchronization frequency among worker\nnodes. Despite the recent theoretical advances of local SGD in empirical risk\nminimization, the efficiency of its counterpart in minimax optimization remains\nunexplored. Motivated by large scale minimax learning problems, such as\nadversarial robust learning and training generative adversarial networks\n(GANs), we propose local Stochastic Gradient Descent Ascent (local SGDA), where\nthe primal and dual variables can be trained locally and averaged periodically\nto significantly reduce the number of communications. We show that local SGDA\ncan provably optimize distributed minimax problems in both homogeneous and\nheterogeneous data with reduced number of communications and establish\nconvergence rates under strongly-convex-strongly-concave and\nnonconvex-strongly-concave settings. In addition, we propose a novel variant\nlocal SGDA+, to solve nonconvex-nonconcave problems. We give corroborating\nempirical evidence on different distributed minimax problems.\n"}
+{"abstract": "  In [Frobenius1896] it was shown that many important properties of a finite\ngroup could be examined using formulas involving the character ratios of group\nelements, i.e., the trace of the element acting in a given irreducible\nrepresentation, divided by the dimension of the representation. In\n[Gurevich-Howe15] and [Gurevich-Howe17], the current authors introduced the\nnotion of rank of an irreducible representation of a finite classical group.\nOne of the motivations for studying rank was to clarify the nature of character\nratios for certain elements in these groups. In fact in the above cited papers,\ntwo notions of rank were given. The first is the Fourier theoretic based notion\nof U-rank of a representation, which comes up when one looks at its\nrestrictions to certain abelian unipotent subgroups. The second is the more\nalgebraic based notion of tensor rank which comes up naturally when one\nattempts to equip the representation ring of the group with a grading that\nreflects the central role played by the few \"smallest\" possible representations\nof the group. In [Gurevich-Howe17] we conjectured that the two notions of rank\nmentioned just above agree on a suitable collection called \"low rank\"\nrepresentations. In this note we review the development of the theory of rank\nfor the case of the general linear group GL_n over a finite field F_q, and give\na proof of the \"agreement conjecture\" that holds true for sufficiently large q.\nOur proof is Fourier theoretic in nature, and uses a certain curious positivity\nproperty of the Fourier transform of the set of matrices of low enough fixed\nrank in the vector space of matrices of size m x n over F_q. In order to make\nthe story we are trying to tell clear, we choose in this note to follow a\nparticular example that shows how one might apply the theory of rank to certain\ncounting problems.\n"}
+{"abstract": "  This study proposes an efficient data collection strategy exploiting a team\nof Unmanned Aerial Vehicles (UAVs) to monitor and collect the data of a large\ndistributed sensor network usually used for environmental monitoring,\nmeteorology, agriculture, and renewable energy applications. The study develops\na collaborative mission planning system that enables a team of UAVs to conduct\nand complete the mission of sensors' data collection collaboratively while\nconsidering existing constraints of the UAV payload and battery capacity. The\nproposed mission planner system employs the Differential Evolution (DE)\noptimization algorithm enabling UAVs to maximize the number of visited sensor\nnodes given the priority of the sensors and avoiding the redundant collection\nof sensors' data. The proposed mission planner is evaluated through extensive\nsimulation and comparative analysis. The simulation results confirm the\neffectiveness and fidelity of the proposed mission planner to be used for the\ndistributed sensor network monitoring and data collection.\n"}
+{"abstract": "  Pauli spin blockade (PSB) in double quantum dots (DQDs) has matured into a\nprime technique for precise measurements of nanoscale system parameters. In\nthis work we demonstrate that systems with site-dependent g-tensors and\nspin-polarized leads allow for a complete characterization of the g-tensors in\nthe dots by magnetotransport experiments alone. Additionally, we show that\nspecial polarization configurations can enhance the often elusive\nmagnetotransport signal, rendering the proposed technique robust against noise\nin the system, and inducing a giant magnetoresistance effect. Finally, we\nincorporate the effects of the spin-orbit interaction (SOI) and show that in\nthis case the leakage current contains information about the degree of spin\npolarization in the leads.\n"}
+{"abstract": "  We study the packing fraction of clusters in free-falling streams of\nspherical and irregularly shaped particles using flash X-ray radiography. The\nestimated packing fraction of clusters is low enough to correspond to\ncoordination numbers less than 6. Such coordination numbers in numerical\nsimulations correspond to aggregates that collide and grow without bouncing.\nMoreover, the streams of irregular particles evolved faster and formed clusters\nof larger sizes with lower packing fraction. This result on granular streams\nsuggests that particle shape has a significant effect on the agglomeration\nprocess of granular materials.\n"}
+{"abstract": "  In this paper we propose a theoretical model including a\nsusceptible-infected-recovered-dead (SIRD) model of epidemic in a dynamic\nmacroeconomic general equilibrium framework with agents' mobility. The latter\naffect both their income (and consumption) and their probability of infecting\nand of being infected. Strategic complementarities among individual mobility\nchoices drive the evolution of aggregate economic activity, while infection\nexternalities caused by individual mobility affect disease diffusion. Rational\nexpectations of forward looking agents on the dynamics of aggregate mobility\nand epidemic determine individual mobility decisions. The model allows to\nevaluate alternative scenarios of mobility restrictions, especially policies\ndependent on the state of epidemic. We prove the existence of an equilibrium\nand provide a recursive construction method for finding equilibrium(a), which\nalso guides our numerical investigations. We calibrate the model by using\nItalian experience on COVID-19 epidemic in the period February 2020 - May 2021.\nWe discuss how our economic SIRD (ESIRD) model produces a substantially\ndifferent dynamics of economy and epidemic with respect to a SIRD model with\nconstant agents' mobility. Finally, by numerical explorations we illustrate how\nthe model can be used to design an efficient policy of\nstate-of-epidemic-dependent mobility restrictions, which mitigates the epidemic\npeaks stressing health system, and allows for trading-off the economic losses\ndue to reduced mobility with the lower death rate due to the lower spread of\nepidemic.\n"}
+{"abstract": "  New ideas and technologies adopted by a small number of individuals\noccasionally spread globally through a complex web of social ties. Here, we\npresent a simple and general approximation method, namely, a message-passing\napproach, that allows us to describe the diffusion processes on complex\nnetworks in an almost exact manner. We consider two classes of binary-action\ngames in each of which the best pure strategy for individual players is\ncharacterized as a variant of the threshold rule. We show that the dynamics of\ndiffusion observed on synthetic networks are accurately replicated by the\nmessage-passing equation, whose fixed point corresponds to a Nash equilibrium.\nIn contrast, the mean-field method tends to overestimate the size and frequency\nof diffusion. We extend the framework to analyze multiplex networks in which\nsocial interactions take place in multiple layers.\n"}
+{"abstract": "  The modular design of planar phased arrays arranged on orthogonal\npolygon-shaped apertures is addressed and a new method is proposed to\nsynthesize domino-tiled arrays fitting multiple, generally conflicting,\nrequirements. Starting from an analytic procedure to check the\ndomino-tileability of the aperture, two multi-objective optimization techniques\nare derived to efficiently and effectively deal with small and medium/large\narrays depending on the values of the bounds for the cardinality of the\nsolution space of the admissible clustered solutions. A set of representative\nnumerical examples is reported to assess the effectiveness of the proposed\nsynthesis approach also through full-wave simulations when considering\nnon-ideal models for the radiating elements of the array.\n"}
+{"abstract": "  The prediction of the intensity, location and time of the landfall of a\ntropical cyclone well advance in time and with high accuracy can reduce human\nand material loss immensely. In this article, we develop a Long Short-Term\nmemory based Recurrent Neural network model to predict intensity (in terms of\nmaximum sustained surface wind speed), location (latitude and longitude), and\ntime (in hours after the observation period) of the landfall of a tropical\ncyclone which originates in the North Indian ocean. The model takes as input\nthe best track data of cyclone consisting of its location, pressure, sea\nsurface temperature, and intensity for certain hours (from 12 to 36 hours)\nanytime during the course of the cyclone as a time series and then provide\npredictions with high accuracy. For example, using 24 hours data of a cyclone\nanytime during its course, the model provides state-of-the-art results by\npredicting landfall intensity, time, latitude, and longitude with a mean\nabsolute error of 4.24 knots, 4.5 hours, 0.24 degree, and 0.37 degree\nrespectively, which resulted in a distance error of 51.7 kilometers from the\nlandfall location. We further check the efficacy of the model on three recent\ndevastating cyclones Bulbul, Fani, and Gaja, and achieved better results than\nthe test dataset.\n"}
+{"abstract": "  This proof of concept (PoC) assesses the ability of machine learning (ML)\nclassifiers to predict the presence of a stenosis in a three vessel arterial\nsystem consisting of the abdominal aorta bifurcating into the two common\niliacs. A virtual patient database (VPD) is created using one-dimensional pulse\nwave propagation model of haemodynamics. Four different machine learning (ML)\nmethods are used to train and test a series of classifiers -- both binary and\nmulticlass -- to distinguish between healthy and unhealthy virtual patients\n(VPs) using different combinations of pressure and flow-rate measurements. It\nis found that the ML classifiers achieve specificities larger than 80% and\nsensitivities ranging from 50-75%. The most balanced classifier also achieves\nan area under the receiver operative characteristic curve of 0.75,\noutperforming approximately 20 methods used in clinical practice, and thus\nplacing the method as moderately accurate. Other important observations from\nthis study are that: i) few measurements can provide similar classification\naccuracies compared to the case when more/all the measurements are used; ii)\nsome measurements are more informative than others for classification; and iii)\na modification of standard methods can result in detection of not only the\npresence of stenosis, but also the stenosed vessel.\n"}
+{"abstract": "  Unlabelled data appear in many domains and are particularly relevant to\nstreaming applications, where even though data is abundant, labelled data is\nrare. To address the learning problems associated with such data, one can\nignore the unlabelled data and focus only on the labelled data (supervised\nlearning); use the labelled data and attempt to leverage the unlabelled data\n(semi-supervised learning); or assume some labels will be available on request\n(active learning). The first approach is the simplest, yet the amount of\nlabelled data available will limit the predictive performance. The second\nrelies on finding and exploiting the underlying characteristics of the data\ndistribution. The third depends on an external agent to provide the required\nlabels in a timely fashion. This survey pays special attention to methods that\nleverage unlabelled data in a semi-supervised setting. We also discuss the\ndelayed labelling issue, which impacts both fully supervised and\nsemi-supervised methods. We propose a unified problem setting, discuss the\nlearning guarantees and existing methods, explain the differences between\nrelated problem settings. Finally, we review the current benchmarking practices\nand propose adaptations to enhance them.\n"}
+{"abstract": "  We present CSWin Transformer, an efficient and effective Transformer-based\nbackbone for general-purpose vision tasks. A challenging issue in Transformer\ndesign is that global self-attention is very expensive to compute whereas local\nself-attention often limits the field of interactions of each token. To address\nthis issue, we develop the Cross-Shaped Window self-attention mechanism for\ncomputing self-attention in the horizontal and vertical stripes in parallel\nthat form a cross-shaped window, with each stripe obtained by splitting the\ninput feature into stripes of equal width. We provide a detailed mathematical\nanalysis of the effect of the stripe width and vary the stripe width for\ndifferent layers of the Transformer network which achieves strong modeling\ncapability while limiting the computation cost. We also introduce\nLocally-enhanced Positional Encoding (LePE), which handles the local positional\ninformation better than existing encoding schemes. LePE naturally supports\narbitrary input resolutions, and is thus especially effective and friendly for\ndownstream tasks. Incorporated with these designs and a hierarchical structure,\nCSWin Transformer demonstrates competitive performance on common vision tasks.\nSpecifically, it achieves 85.4% Top-1 accuracy on ImageNet-1K without any extra\ntraining data or label, 53.9 box AP and 46.4 mask AP on the COCO detection\ntask, and 51.7 mIOU on the ADE20K semantic segmentation task, surpassing\nprevious state-of-the-art Swin Transformer backbone by +1.2, +2.0, +1.4, and\n+2.0 respectively under the similar FLOPs setting. By further pretraining on\nthe larger dataset ImageNet-21K, we achieve 87.5% Top-1 accuracy on ImageNet-1K\nand state-of-the-art segmentation performance on ADE20K with 55.7 mIoU. The\ncode and models will be available at\nhttps://github.com/microsoft/CSWin-Transformer.\n"}
+{"abstract": "  In this paper, we discuss asymptotic behavior of the capacity of the range of\nsymmetric simple random walks on finitely generated groups. We show the\ncorresponding strong law of large numbers and central limit theorem.\n"}
+{"abstract": "  Periodic outbursts are observed in several changing-look (CL) active galactic\nnuclei (AGNs). \\citet{sniegowska_possible_2020} suggested a model to explain\nthe repeating CL in these AGNs, where the periodic outbursts are triggered in a\nnarrow unstable zone between an inner ADAF and outer thin disk. In this work,\nwe intend to investigate the effects of large-scale magnetic fields on the\nlimit cycle behaviors of CL AGNs. The winds driven by magnetic fields can\nsignificantly change the structure of thin disk by taking away the angular\nmomentum and energy of the disk. It is found that the period of outburst in\nrepeating CL AGNs can be substantially reduced by the magnetic fields.\nConversely, if we keep the period unchanged, the outburst intensity can be\nraised for several times. These results can help to explain the observational\nproperties of multiple CL AGNs. Besides the magnetic fields, the effects of\ntransition radius $R_{\\rm tr}$, the width of transition zone $\\Delta R$ and\nShakura-Sunyaev parameter $\\alpha$ are also explored in this work.\n"}
+{"abstract": "  In this paper, we study the long time asymptotic behavior for the initial\nvalue problem of the modified Camassa-Holm (mCH) equation in the solitonic\nregion \\begin{align}\n  &m_{t}+\\left(m\\left(u^{2}-u_{x}^{2}\\right)\\right)_{x}+\\kappa u_{x}=0, \\quad\nm=u-u_{x x}, \\nonumber\n  &u(x, 0)=u_{0}(x),\\nonumber \\end{align} where $\\kappa$ is a positive\nconstant. Based on the spectral analysis of the Lax pair associated with the\nmCH equation and scattering matrix, the solution of the Cauchy problem is\ncharacterized via the solution of a Riemann-Hilbert (RH) problem. Further using\nthe $\\overline\\partial$ generalization of Deift-Zhou steepest descent method,\nwe derive different long time asymptotic expansion of the solution $u(x,t)$ in\ndifferent space-time solitonic region of $x/t$. These asymptotic approximations\ncan be characterized with an $N(\\Lambda)$-soliton whose parameters are\nmodulated by a sum of localized soliton-soliton interactions as one moves\nthrough the region with diverse residual error order from $\\overline\\partial$\nequation: $\\mathcal{O}(|t|^{-1+2\\rho})$ for\n$\\xi=\\frac{y}{t}\\in(-\\infty,-0.25)\\cup(2,+\\infty)$ and\n$\\mathcal{O}(|t|^{-3/4})$ for $\\xi=\\frac{y}{t}\\in(-0.25,2)$. Our results also\nconfirm the soliton resolution conjecture and asymptotically stability of\nN-soliton solutions for the mCH equation.\n"}
+{"abstract": "  Mean-reverting portfolios with few assets, but high variance, are of great\ninterest for investors in financial markets. Such portfolios are\nstraightforwardly profitable because they include a small number of assets\nwhose prices not only oscillate predictably around a long-term mean but also\npossess enough volatility. Roughly speaking, sparsity minimizes trading costs,\nvolatility provides arbitrage opportunities, and mean-reversion property equips\ninvestors with ideal investment strategies. Finding such favorable portfolios\ncan be formulated as a nonconvex quadratic optimization problem with an\nadditional sparsity constraint. To the best of our knowledge, there is no\nmethod for solving this problem and enjoying favorable theoretical properties\nyet. In this paper, we develop an effective two-stage algorithm for this\nproblem. In the first stage, we apply a tailored penalty decomposition method\nfor finding a stationary point of this nonconvex problem. For a fixed penalty\nparameter, the block coordinate descent method is utilized to find a stationary\npoint of the associated penalty subproblem. In the second stage, we improve the\nresult from the first stage via a greedy scheme that solves restricted\nnonconvex quadratically constrained quadratic programs (QCQPs). We show that\nthe optimal value of such a QCQP can be obtained by solving their semidefinite\nrelaxations. Numerical experiments on S\\&P 500 are conducted to demonstrate the\neffectiveness of the proposed algorithm.\n"}
+{"abstract": "  Generalized self-concordance is a key property present in the objective\nfunction of many important learning problems. We establish the convergence rate\nof a simple Frank-Wolfe variant that uses the open-loop step size strategy\n$\\gamma_t = 2/(t+2)$, obtaining a $\\mathcal{O}(1/t)$ convergence rate for this\nclass of functions in terms of primal gap and Frank-Wolfe gap, where $t$ is the\niteration count. This avoids the use of second-order information or the need to\nestimate local smoothness parameters of previous work. We also show improved\nconvergence rates for various common cases, e.g., when the feasible region\nunder consideration is uniformly convex or polyhedral.\n"}
+{"abstract": "  Starphenes are attractive compounds due to their characteristic\nphysicochemical properties that are inherited from acenes, making them\ninteresting compounds for organic electronics and optics. However, the\ninstability and low solubility of larger starphene homologs make their\nsynthesis extremely challenging. Herein, we present a new strategy leading to\npristine [16]starphene in preparative scale. Our approach is based on a\nsynthesis of a carbonyl-protected starphene precursor that is thermally\nconverted in a solid-state form to the neat [16]starphene, which is then\ncharacterised with a variety of analytical methods, such as 13C CP-MAS NMR,\nTGA, MS MALDI, UV-Vis and FTIR spectroscopy. Furthermore, high-resolution STM\nexperiments unambiguously confirm its expected structure and reveal a moderate\nelectronic delocalisation between the pentacene arms. Nucleus-independent\nchemical shifts NICS(1) are also calculated to survey its aromatic character.\n"}
+{"abstract": "  An extended Designed regression analysis of experimental data on density and\nrefractive indices of several classes of ionic liquids yielded statistically\naveraged atomic volumes and polarizabilities of the constituting atoms. These\nvalues can be used to predict the molecular volume and polarizability of an\nunknown ionic liquid as well as its mass density and refractive index. Our\napproach does not need information on the molecular structure of the ionic\nliquid, but it turned out that the discrimination of the hybridization state of\nthe carbons improved the overall result. Our results are not only compared to\nexperimental data but also to quantum-chemical calculations. Furthermore,\nfractional charges of ionic liquid ions and their relation to polarizability\nare discussed.\n"}
+{"abstract": "  We propose an efficient semi-Lagrangian Characteristic Mapping (CM) method\nfor solving the three-dimensional (3D) incompressible Euler equations. This\nmethod evolves advected quantities by discretizing the flow map associated with\nthe velocity field. Using the properties of the Lie group of volume preserving\ndiffeomorphisms SDiff, long-time deformations are computed from a composition\nof short-time submaps which can be accurately evolved on coarse grids. This\nmethod is a fundamental extension to the CM method for two-dimensional\nincompressible Euler equations [51]. We take a geometric approach in the 3D\ncase where the vorticity is not a scalar advected quantity, but can be computed\nas a differential 2-form through the pullback of the initial condition by the\ncharacteristic map. This formulation is based on the Kelvin circulation theorem\nand gives point-wise a Lagrangian description of the vorticity field. We\ndemonstrate through numerical experiments the validity of the method and show\nthat energy is not dissipated through artificial viscosity and small scales of\nthe solution are preserved. We provide error estimates and numerical\nconvergence tests showing that the method is globally third-order accurate.\n"}
+{"abstract": "  The first phase of table recognition is to detect the tabular area in a\ndocument. Subsequently, the tabular structures are recognized in the second\nphase in order to extract information from the respective cells. Table\ndetection and structural recognition are pivotal problems in the domain of\ntable understanding. However, table analysis is a perplexing task due to the\ncolossal amount of diversity and asymmetry in tables. Therefore, it is an\nactive area of research in document image analysis. Recent advances in the\ncomputing capabilities of graphical processing units have enabled deep neural\nnetworks to outperform traditional state-of-the-art machine learning methods.\nTable understanding has substantially benefited from the recent breakthroughs\nin deep neural networks. However, there has not been a consolidated description\nof the deep learning methods for table detection and table structure\nrecognition. This review paper provides a thorough analysis of the modern\nmethodologies that utilize deep neural networks. This work provided a thorough\nunderstanding of the current state-of-the-art and related challenges of table\nunderstanding in document images. Furthermore, the leading datasets and their\nintricacies have been elaborated along with the quantitative results. Moreover,\na brief overview is given regarding the promising directions that can serve as\na guide to further improve table analysis in document images.\n"}
+{"abstract": "  General Relativity and the $\\Lambda$CDM framework are currently the standard\nlore and constitute the concordance paradigm. Nevertheless, long-standing open\ntheoretical issues, as well as possible new observational ones arising from the\nexplosive development of cosmology the last two decades, offer the motivation\nand lead a large amount of research to be devoted in constructing various\nextensions and modifications. All extended theories and scenarios are first\nexamined under the light of theoretical consistency, and then are applied to\nvarious geometrical backgrounds, such as the cosmological and the spherical\nsymmetric ones. Their predictions at both the background and perturbation\nlevels, and concerning cosmology at early, intermediate and late times, are\nthen confronted with the huge amount of observational data that astrophysics\nand cosmology are able to offer recently. Theories, scenarios and models that\nsuccessfully and efficiently pass the above steps are classified as viable and\nare candidates for the description of Nature. We list the recent developments\nin the fields of gravity and cosmology, presenting the state of the art,\nhigh-lighting the open problems, and outlining the directions of future\nresearch. Its realization is performed in the framework of the COST European\nAction \"Cosmology and Astrophysics Network for Theoretical Advances and\nTraining Actions\".\n"}
+{"abstract": "  The free monoid $A^*$ on a finite totally ordered alphabet $A$ acts at the\nleft on columns, by Schensted left insertion. This defines a finite monoid,\ndenoted $Styl(A)$ and called the stylic monoid. It is canonically a quotient of\nthe plactic monoid. Main results are: the cardinality of $Styl(A)$ is equal to\nthe number of partitions of a set on $|A|+1$ elements. We give a bijection with\nso-called $N$-tableaux, similar to Schensted's algorithm, explaining this fact.\nPresentation of $Styl(A)$: it is generated by $A$ subject to the plactic\n(Knuth) relations and the idempotent relations $a^2=a$, $a\\in A$. The canonical\ninvolutive anti-automorphism on $A^*$, which reverses the order on $A$, induces\nan involution of $Styl(A)$, which similarly to the corresponding involution of\nthe plactic monoid, may be computed by an evacuation-like operation\n(Sch\\\"utzenberger involution on tableaux) on so-called standard immaculate\ntableaux (which are in bijection with partitions). The monoid $Styl(A)$ is\n$J$-trivial, and the $J$-order of $Styl(A)$ is graded: the co-rank is given by\nthe number of elements in the $N$-tableau. The monoid $Styl(A)$ is the\nsyntactic monoid for the the function which associates to each word $w\\in A^*$\nthe length of its longest strictly decreasing subword.\n"}
+{"abstract": "  Uniaxial anisotropy in nonlinear birefringent crystals limits the efficiency\nof nonlinear optical interactions and breaks the spatial symmetry of light\ngenerated in the parametric down-conversion (PDC) process. Therefore, this\neffect is usually undesirable and must be compensated for. However, high gain\nmay be used to overcome the destructive role of anisotropy and instead to use\nit for the generation of bright two-mode correlated twin-beams. In this work,\nwe provide a rigorous theoretical description of the spatial properties of\nbright squeezed light in the presence of strong anisotropy. We investigate a\nsingle-crystal and a two-crystal configuration and demonstrate the generation\nof bright correlated twin-beams in such systems at high gain due to anisotropy.\nWe explore the mode structure of the generated light and show how anisotropy,\ntogether with crystal spacing, can be used for radiation shaping.\n"}
+{"abstract": "  Today, permissioned blockchains are being adopted by large organizations for\nbusiness critical operations. Consequently, they are subject to attacks by\nmalicious actors. Researchers have discovered and enumerated a number of\nattacks that could threaten availability, integrity and confidentiality of\nblockchain data. However, currently it remains difficult to detect these\nattacks. We argue that security experts need appropriate visualizations to\nassist them in detecting attacks on blockchain networks. To achieve this, we\ndevelop HyperSec, a visual analytics monitoring tool that provides relevant\ninformation at a glance to detect ongoing attacks on Hyperledger Fabric. For\nevaluation, we connect the HyperSec prototype to a Hyperledger Fabric test\nnetwork. The results show that common attacks on Fabric can be detected by a\nsecurity expert using HyperSec's visualizations.\n"}
+{"abstract": "  We present a novel approach for temporal contrast enhancement of energetic\nlaserpulses by filtered SPM broadened spectra. A measured temporal contrast\nenhancement by at least 7 orders of magnitude in a simple setup has been\nachieved. This technique is applicable to a wide range of laser parameters and\nposes a highly efficient alternative to existing contrast-enhancement methods.\n"}
+{"abstract": "  Zero-knowledge succinct non-interactive argument of knowledge (zkSNARK)\nallows a party, known as the prover, to convince another party, known as the\nverifier, that he knows a private value $v$, without revealing it, such that\n$F(u,v)=y$ for some function $F$ and public values $u$ and $y$. There are\nvarious versions of zk-SNARK, among them, Quadratic Arithmetic Program\n(QAP)-based zk-SNARK has been widely used in practice, specially in Blockchain\ntechnology. This is attributed to two desirable features; its fixed-size proof\nand the very light computation load of the verifier. However, the computation\nload of the prover in QAP-based zkSNARKs, is very heavy, even-though it is\ndesigned to be very efficient. This load can be beyond the prover's computation\npower to handle, and has to be offloaded to some external servers. In the\nexisting offloading solutions, either (i) the load of computation, offloaded to\neach sever, is a fraction of the prover's primary computation (e.g., DZIK),\nhowever the servers need to be trusted, (ii) the servers are not required to be\ntrusted, but the computation complexity imposed to each one is the same as the\nprover's primary computation (e.g., Trinocchio). In this paper, we present a\nscheme, which has the benefits of both solutions. In particular, we propose a\nsecure multi-party proof generation algorithm where the prover can delegate its\ntask to $N $ servers, where (i) even if a group of $T \\in \\mathbb{N}$ servers,\n$T\\le N$, collude, they cannot gain any information about the secret value $v$,\n(ii) the computation complexity of each server is less than $1/(N-T)$ of the\nprover's primary computation. The design is such that we don't lose the\nefficiency of the prover's algorithm in the process of delegating the tasks to\nexternal servers.\n"}
+{"abstract": "  We investigate the impact of rotation and magnetic fields on the dynamics and\ngravitational wave emission in 2D core-collapse supernova simulations with\nneutrino transport. We simulate 17 different models of $15\\,M_\\odot$ and\n$39\\,M_\\odot$ progenitor stars with various initial rotation profiles and\ninitial magnetic fields strengths up to $10^{12}\\, \\mathrm{G}$, assuming a\ndipolar field geometry in the progenitor. Strong magnetic fields generally\nprove conducive to shock revival, though this trend is not without exceptions.\nThe impact of rotation on the post-bounce dynamics is more variegated, in line\nwith previous studies. A significant impact on the time-frequency structure of\nthe gravitational wave signal is found only for rapid rotation or strong\ninitial fields. For rapid rotation, the angular momentum gradient at the\nproto-neutron star surface can appreciably affect the frequency of the dominant\nmode, so that known analytic relations for the high-frequency emission band no\nlonger hold. In case of two magnetorotational explosion models, the deviation\nfrom these analytic relations is even more pronounced. One of the\nmagnetorotational explosions has been evolved to more than half a second after\nthe onset of the explosion and shows a subsidence of high-frequency emission at\nlate times. Its most conspicuous gravitational wave signature is a\nhigh-amplitude tail signal. We also estimate the maximum detection distances\nfor our waveforms. The magnetorotational models do not stick out for higher\ndetectability during the post-bounce and explosion phase.\n"}
+{"abstract": "  Existing coordinated cyber-attack detection methods have low detection\naccuracy and efficiency and poor generalization ability due to difficulties\ndealing with unbalanced attack data samples, high data dimensionality, and\nnoisy data sets. This paper proposes a model for cyber and physical data fusion\nusing a data link for detecting attacks on a Cyber-Physical Power System\n(CPPS). Two-step principal component analysis (PCA) is used for classifying the\nsystem's operating status. An adaptive synthetic sampling algorithm is used to\nreduce the imbalance in the categories' samples. The loss function is improved\naccording to the feature intensity difference of the attack event, and an\nintegrated classifier is established using a classification algorithm based on\nthe cost-sensitive gradient boosting decision tree (CS-GBDT). The simulation\nresults show that the proposed method provides higher accuracy, recall, and\nF-Score than comparable algorithms.\n"}
+{"abstract": "  We examine the general problem of inter-domain Gaussian Processes (GPs):\nproblems where the GP realization and the noisy observations of that\nrealization lie on different domains. When the mapping between those domains is\nlinear, such as integration or differentiation, inference is still closed form.\nHowever, many of the scaling and approximation techniques that our community\nhas developed do not apply to this setting. In this work, we introduce the\nhierarchical inducing point GP (HIP-GP), a scalable inter-domain GP inference\nmethod that enables us to improve the approximation accuracy by increasing the\nnumber of inducing points to the millions. HIP-GP, which relies on inducing\npoints with grid structure and a stationary kernel assumption, is suitable for\nlow-dimensional problems. In developing HIP-GP, we introduce (1) a fast\nwhitening strategy, and (2) a novel preconditioner for conjugate gradients\nwhich can be helpful in general GP settings. Our code is available at https:\n//github.com/cunningham-lab/hipgp.\n"}
+{"abstract": "  Causally consistent distributed storage systems have received significant\nrecent attention due to the potential for providing a low latency data access\nas compared with linearizability. Current causally consistent data stores use\npartial or full replication to ensure data access to clients over a distributed\nsetting.\n  In this paper, we develop, for the first time, an erasure coding based\nalgorithm called CausalEC that ensures causal consistency for a collection of\nread-write objects stored in a distributed set of nodes over an asynchronous\nmessage passing system. CausalEC can use an arbitrary linear erasure code for\ndata storage, and ensures liveness and storage properties prescribed by the\nerasure code.\n  CausalEC retains a key benefit of previously designed replication-based\nalgorithms - every write operation is local, that is, a server performs only\nlocal actions before returning to a client that issued a write operation. For\nservers that store certain objects in an uncoded manner, read operations to\nthose objects also return locally. In general, a read operation to an object\ncan be returned by a server on contacting a small subset of other servers so\nlong as the underlying erasure code allows for the object to be decoded from\nthat subset. As a byproduct, we develop EventualEC, a new eventually consistent\nerasure coding based data storage algorithm.\n  A novel technical aspect of CausalEC is the use of cross-object erasure\ncoding, where nodes encode values across multiple objects, unlike previous\nconsistent erasure coding based solutions. CausalEC navigates the technical\nchallenges of cross-object erasure coding, in particular, pertaining to\nre-encoding the objects when writes update the values and ensuring that reads\nare served in the transient state where the system transitions to storing the\ncodeword symbols corresponding to the new object versions.\n"}
+{"abstract": "  This paper proposes a general enhancement to the Normalizing Flows (NF) used\nin neural vocoding. As a case study, we improve expressive speech vocoding with\na revamped Parallel Wavenet (PW). Specifically, we propose to extend the affine\ntransformation of PW to the more expressive invertible non-affine function. The\ngreater expressiveness of the improved PW leads to better-perceived signal\nquality and naturalness in the waveform reconstruction and text-to-speech (TTS)\ntasks. We evaluate the model across different speaking styles on a\nmulti-speaker, multi-lingual dataset. In the waveform reconstruction task, the\nproposed model closes the naturalness and signal quality gap from the original\nPW to recordings by $10\\%$, and from other state-of-the-art neural vocoding\nsystems by more than $60\\%$. We also demonstrate improvements in objective\nmetrics on the evaluation test set with L2 Spectral Distance and Cross-Entropy\nreduced by $3\\%$ and $6\\unicode{x2030}$ comparing to the affine PW.\nFurthermore, we extend the probability density distillation procedure proposed\nby the original PW paper, so that it works with any non-affine invertible and\ndifferentiable function.\n"}
+{"abstract": "  We obtain an exact spherically symmetric and magnetically charged black hole\nsolution in 4D Einstein-Gauss-Bonnet gravity coupled with rational nonlinear\nelectrodynamics. The thermodynamics of our model is studied. We calculate the\nHawking temperature and the heat capacity of the black hole. The phase\ntransitions occur in the point where the Hawking temperature possesses an\nextremum. We show that black holes are thermodynamically stable at some range\nof event horizon radii when the heat capacity is positive. The logarithmic\ncorrection to the Bekenstein-Hawking entropy is obtained.\n"}
+{"abstract": "  As photovoltaic (PV) penetration continues to rise and smart inverter\nfunctionality continues to expand, smart inverters and other distributed energy\nresources (DERs) will play increasingly important roles in distribution system\npower management and security. In this paper, it is demonstrated that a\nconstellation of smart inverters in a simulated distribution circuit can enable\nprecise voltage predictions using an asynchronous and decentralized prediction\nalgorithm. Using simulated data and a constellation of 15 inverters in a ring\ncommunication topology, the COLA algorithm is shown to accomplish the learning\ntask required for voltage magnitude prediction with far less communication\noverhead than fully connected P2P learning protocols. Additionally, a dynamic\nstopping criterion is proposed that does not require a regularizer like the\noriginal COLA stopping criterion.\n"}
+{"abstract": "  We describe a formal approach based on graphical causal models to identify\nthe \"root causes\" of the change in the probability distribution of variables.\nAfter factorizing the joint distribution into conditional distributions of each\nvariable, given its parents (the \"causal mechanisms\"), we attribute the change\nto changes of these causal mechanisms. This attribution analysis accounts for\nthe fact that mechanisms often change independently and sometimes only some of\nthem change. Through simulations, we study the performance of our distribution\nchange attribution method. We then present a real-world case study identifying\nthe drivers of the difference in the income distribution between men and women.\n"}
+{"abstract": "  Electroencephalography (EEG) signals signals are often used to learn about\nbrain structure and to learn what thinking. EEG signals can be easily affected\nby external factors. For this reason, they should be applied various\npre-process during their analysis. In this study, it is used the EEG signals\nreceived from 109 subjects when opening and closing their right or left fists\nand performing hand and foot movements and imagining the same movements. The\nrelationship between motor activities and imaginary of that motor activities\nwere investigated. Algorithms with high performance rates have been used for\nfeature extraction , selection and classification using the nearest neighbour\nalgorithm.\n"}
+{"abstract": "  Motivated by the LHCb-group discovery of exotic hadrons in the range (6.2\n$\\sim$ 6.9) GeV, we present new results for the masses and couplings of\n$0^{++}$ fully heavy $(\\bar{Q}Q)(Q\\bar{Q})$ molecules and $(QQ)(\\overline{QQ})$\ntetraquaks states from relativistic QCD Laplace Sum Rule (LSR) within stability\ncriteria where Next-to-Leading Order (NLO) Factorized (F) Perturbative (PT)\ncorrections is included. As the Operator Product Expansion (OPE) usually\nconverges for $d\\leqslant 6-8$, we evaluated the QCD spectral functions at\nLowest Order (LO) of PT QCD and up to $\\langle G^3 \\rangle$. We also emphasize\nthe importance of PT radiative corrections for heavy quark sum rules in order\nto justify the use of the running heavy quark mass value in the analysis. We\ncompare our predictions in Table 3 with the ones from ratio of Moments (MOM).\nThe broad structure arround (6.2 $\\sim$ 6.9) GeV can be described by the\n$\\overline{\\eta}_c\\eta_c$, $\\overline{J/\\psi}J/\\psi$ and\n$\\overline{\\chi}_{c1}\\chi_{c1}$ molecules or/and $\\overline{S}_c S_c$,\n$\\overline{A}_c A_c$ and $\\overline{V}_c V_c$ tetraquarks lowest mass ground\nstates. The narrow structure at (6.8 $\\sim$ 6.9) GeV if it is a $0^{++}$ state\ncan be a $\\overline{\\chi}_{c0}\\chi_{c0}$ molecules or/and its analogue\n$\\overline{P}_c P_c$ tetraquark. The $\\overline{\\chi}_{c1}\\chi_{c1}$ predicted\nmass is found to be below the $\\chi_{c1}\\chi_{c1}$ threshold while for the\nbeauty states, all of the estimated masses are above the $\\eta_b \\eta_b$ and\n$\\Upsilon(1S)\\Upsilon(1S)$ threshold.\n"}
+{"abstract": "  We consider quantum effects of gravitational and electromagnetic fields in\nspherically symmetric black hole spacetimes in the asymptotic safety scenario.\nIntroducing both the running gravitational and electromagnetic couplings from\nthe renormalization group equations and applying a physically sensible scale\nidentification scheme based on the Kretschmann scalar, we construct a quantum\nmechanically corrected, or quantum improved Reissner-Nordstrom metric. We study\nthe global structure of the quantum improved geometry and show, in particular,\nthat the central singularity is resolved, being generally replaced with a\nregular Minkowski-core, where the curvature tensor vanishes. Exploring cases\nwith more general scale identifications, we further find that the space of\nquantum improved geometries is divided into two regions: one for geometries\nwith a regular Minkowski-core and the other for those with a weak singularity\nat the center. At the boundary of the two regions, the geometry has either\nMinkowski-, de Sitter-, or anti-de Sitter(AdS)-core.\n"}
+{"abstract": "  We investigate phases of 3d ${\\cal N}=2$ Chern-Simons-matter theories,\nextending to three dimensions the celebrated correspondence between 2d gauged\nWess-Zumino-Witten (GWZW) models and non-linear sigma models (NLSMs) with\ngeometric targets. We find that although the correspondence in 3d and 2d are\nclosely related by circle compactification, an important subtlety arises in\nthis process, changing the phase structure of the 3d theory. Namely, the\neffective theory obtained from the circle compactification of a phase of a 3d\n${\\cal N}=2$ gauge theory is, in general, different from the phase of the 3d\n${\\cal N}=2$ theory on ${\\mathbb R}^2\\times S^{1}$, which means taking phases\nof a 3d gauge theory does not necessarily commute with compactification. We\ncompute the Witten index of each effective theory to check this observation.\nFurthermore, when the matter fields have the same non-minimal charges, the 3d\n${\\cal N}=2$ Chern-Simons-matter theory with a proper Chern-Simons level will\ndecompose into several identical 2d gauged linear sigma models (GLSMs) for the\nsame target upon reduction to 2d. To illustrate this phenomenon, we investigate\nhow vacua of the 3d gauge theory for a weighted projective space\n$W\\mathbb{P}_{[l,\\cdots,l]}$ move on the field space when we change the radius\nof $S^{1}$.\n"}
+{"abstract": "  Ransomware has emerged as an infamous malware that has not escaped a lot of\nmyths and inaccuracies from media hype. Victims are not sure whether or not to\npay a ransom demand without fully understanding the lurking consequences. In\nthis paper, we present a ransomware classification framework based on\nfile-deletion and file-encryption attack structures that provides a deeper\ncomprehension of potential flaws and inadequacies exhibited in ransomware. We\nformulate a threat and attack model representative of a typical ransomware\nattack process from which we derive the ransomware categorization framework\nbased on a proposed classification algorithm. The framework classifies the\nvirulence of a ransomware attack to entail the overall effectiveness of\npotential ways of recovering the attacked data without paying the ransom demand\nas well as the technical prowess of the underlying attack structures. Results\nof the categorization, in increasing severity from CAT1 through to CAT5, show\nthat many ransomwares exhibit flaws in their implementation of encryption and\ndeletion attack structures which make data recovery possible without paying the\nransom. The most severe categories CAT4 and CAT5 are better mitigated by\nexploiting encryption essentials while CAT3 can be effectively mitigated via\nreverse engineering. CAT1 and CAT2 are not common and are easily mitigated\nwithout any decryption essentials.\n"}
+{"abstract": "  Measuring the solar neutrino flux over gigayear timescales could provide a\nnew window to inform the Solar Standard Model as well as studies of the Earth's\nlong-term climate. We demonstrate the feasibility of measuring the\ntime-evolution of the $^8$B solar neutrino flux over gigayear timescales using\npaleo detectors, naturally occurring minerals which record neutrino-induced\nrecoil tracks over geological times. We explore suitable minerals and identify\ntrack lengths of 15--30 nm to be a practical window to detect the $^8$B solar\nneutrino flux. A collection of ultra-radiopure minerals of different ages, each\nsome 0.1 kg by mass, can be used to probe the rise of the $^8$B solar neutrino\nflux over the recent gigayear of the Sun's evolution. We also show that models\nof the solar abundance problem can be distinguished based on the\ntime-integrated tracks induced by the $^8$B solar neutrino flux.\n"}
+{"abstract": "  Metal-organic species can be designed to self-assemble in large-scale,\natomically defined, supramolecular architectures. Hybrid quantum wells, where\ninorganic two-dimensional (2D) planes are separated by organic ligands, are a\nparticular example. The ligands effectively provide an intralayer confinement\nfor charge carriers resulting in a 2D electronic structure, even in\nmultilayered assemblies. Air-stable metal organic chalcogenides hybrid quantum\nwells have recently been found to host tightly bound 2D excitons with strong\noptical anisotropy in a bulk matrix. Here, we investigate the excited carrier\ndynamics in the prototypical metal organic chalcogenide [AgSePh], disentangling\nthree excitonic resonances by low temperature transient absorption\nspectroscopy. Our analysis suggests a complex relaxation cascade comprising\nultrafast screening and renormalization, inter-exciton relaxation, and\nself-trapping of excitons within few picoseconds. The ps-decay provided by the\nself-trapping mechanism may be leveraged to unlock the material's potential for\nultrafast optoelectronic applications.\n"}
+{"abstract": "  A deeply rooted view in classical and quantum information is that\n\"information is physical\", i.e., to store and process information, we need a\nphysical body. Here we ask whether quantum information can remain without a\nphysical body. We answer this question in the affirmative, i.e., we argue that\nquantum information can exist without a physical body in volatile form. We\nintroduce the notion of the volatility of quantum information and show that\nindeed the conditions for it are naturally satisfied in the quantum\nteleportation protocol. We argue that even if special relativity principles are\nnot assumed, it is possible to make quantum information volatile. We also\ndiscuss the classical limit of the phenomenon, as well as the multiparty\nscenario.\n"}
+{"abstract": "  One can hardly believe that there is still something to be said about cubic\nequations. To dodge this doubt, we will instead try and say something about\nSylvester. He doubtless found a way to solve cubic equations. As mentioned by\nRota, it was the only method in this vein that he could remember. We realize\nthat Sylvester's magnificent approach for reduced cubic equations boils down to\nan easy identity.\n"}
+{"abstract": "  'Magic'-angle twisted bilayer graphene has received a lot of interest due to\nits flat bands with potentially non-trivial topology that lead to intricate\ncorrelated phases. A spectrum with flat bands, however, does not require a\ntwist between multiple sheets of van der Waals materials, but rather can be\nrealized with the application of an appropriate periodic potential. Here, we\npropose the imposition of a tailored periodic potential onto a single graphene\nlayer through local perturbations that could be created via lithography or\nadatom manipulation, which also results in an energy spectrum featuring flat\nbands. Our first-principle calculations for an appropriate decoration of\ngraphene with adatoms indeed show the presence of flat bands in the spectrum.\nFurthermore, we reveal the topological nature of the flat bands through a\nsymmetry-indicator analysis. This non-trivial topology manifests itself in\ncorner-localized states with a filling anomaly as we show using a tight-binding\nmodel. Our proposal of a single decorated graphene sheet provides a new\nversatile route to study correlated phases in topologically non-trivial, flat\nband structures.\n"}
+{"abstract": "  Data augmentation is a key element of deep learning pipelines, as it informs\nthe network during training about transformations of the input data that keep\nthe label unchanged. Manually finding adequate augmentation methods and\nparameters for a given pipeline is however rapidly cumbersome. In particular,\nwhile intuition can guide this decision for images, the design and choice of\naugmentation policies remains unclear for more complex types of data, such as\nneuroscience signals. Besides, class-dependent augmentation strategies have\nbeen surprisingly unexplored in the literature, although it is quite intuitive:\nchanging the color of a car image does not change the object class to be\npredicted, but doing the same to the picture of an orange does. This paper\ninvestigates gradient-based automatic data augmentation algorithms amenable to\nclass-wise policies with exponentially larger search spaces. Motivated by\nsupervised learning applications using EEG signals for which good augmentation\npolicies are mostly unknown, we propose a new differentiable relaxation of the\nproblem. In the class-agnostic setting, results show that our new relaxation\nleads to optimal performance with faster training than competing gradient-based\nmethods, while also outperforming gradient-free methods in the class-wise\nsetting. This work proposes also novel differentiable augmentation operations\nrelevant for sleep stage classification.\n"}
+{"abstract": "  The Internet of Things, also known as the IoT, refers to the billions of\ndevices around the world that are now connected to the Internet, collecting and\nsharing data. The amount of data collected through IoT sensors must be\ncompletely securely controlled. To protect the information collected by IoT\nsensors, a lightweight method called Discover the Flooding Attack-RPL (DFA-RPL)\nhas been proposed. The proposed DFA-RPL method identifies intrusive nodes in\nseveral steps to exclude them from continuing routing operations. Thus, in the\nDFA-RPL method, it first builds a cluster and selects the most appropriate node\nas a cluster head in DODAG, then, due to the vulnerability of the RPL protocol\nto Flooding attacks, it uses an ant colony algorithm (ACO) using five steps to\ndetect attacks. Use Flooding to prevent malicious activity on the IoT network.\nIn other words, if it detects a node as malicious, it puts that node on the\ndetention list and quarantines it for a certain period of time. The results\nobtained from the simulation show the superiority of the proposed method in\nterms of Packet Delivery Rate, Detection Rate, False Positive Rate, and False\nNegative Rate compared to IRAD and REATO methods.\n"}
+{"abstract": "  This paper presents an approach to improve computational fluid dynamics\nsimulations forecasts of air pollution using deep learning. Our method, which\nintegrates Principal Components Analysis (PCA) and adversarial training, is a\nway to improve the forecast skill of reduced order models obtained from the\noriginal model solution. Once the reduced-order model (ROM) is obtained via\nPCA, a Long Short-Term Memory network (LSTM) is adversarially trained on the\nROM to make forecasts. Once trained, the adversarially trained LSTM outperforms\na LSTM trained in a classical way. The study area is in London, including\nvelocities and a concentration tracer that replicates a busy traffic junction.\nThis adversarially trained LSTM-based approach is used on the ROM in order to\nproduce faster forecasts of the air pollution tracer.\n"}
+{"abstract": "  In this work, we investigate how quickly local perturbations propagate in\ninteracting boson systems with Bose-Hubbard-type Hamiltonians. In general,\nthese systems have unbounded local energies, and arbitrarily fast information\npropagation may occur. We focus on a specific but experimentally natural\nsituation in which the number of bosons at any one site in the unperturbed\ninitial state is approximately limited. We rigorously prove the existence of an\nalmost-linear information-propagation light-cone, thus establishing a\nLieb--Robinson bound: the wave-front grows at most as $t\\log^2 (t)$. We prove\nthe clustering theorem for gapped ground states and study the time complexity\nof classically simulating one-dimensional quench dynamics, a topic of great\npractical interest.\n"}
+{"abstract": "  Massive multiple-input multiple-output is a very important technology for\nfuture fifth-generation systems. However, massive massive multiple input\nmultiple output systems are still limited because of pilot contamination,\nimpacting the data rate due to the non-orthogonality of pilot sequences\ntransmitted by users in the same cell to the neighboring cells. We propose a\nchannel estimation with complete knowledge of large-scale fading by using an\northogonal pilot reuse sequence to eliminate PC in edge users with poor channel\nquality based on the estimation of large-scale fading and performance analysis\nof maximum ratio transmission and zero forcing precoding methods. We derived\nthe lower bounds on the achievable downlink DR and signal-to-interference noise\nratio based on assigning PRS to a user grouping that mitigated this problem\nwhen the number of antenna elements approaches infinity The simulation results\nshowed that a high DR can be achieved due to better channel estimation and\nreduced performance loss\n"}
+{"abstract": "  The goal of this paper is to investigate the validity of a hybrid\nembedded/homogenized in-silico approach for modeling perfusion through solid\ntumors. The rationale behind this novel idea is that only the larger blood\nvessels have to be explicitly resolved while the smaller scales of the\nvasculature are homogenized. As opposed to typical discrete or fully-resolved\n1D-3D models, the required data can be obtained with in-vivo imaging techniques\nsince the morphology of the smaller vessels is not necessary. By contrast, the\nlarger vessels, whose topology and structure is attainable non-invasively, are\nresolved and embedded as one-dimensional inclusions into the three-dimensional\ntissue domain which is modeled as a porous medium. A sound mortar-type\nformulation is employed to couple the two representations of the vasculature.\nWe validate the hybrid model and optimize its parameters by comparing its\nresults to a corresponding fully-resolved model based on several well-defined\nmetrics. These tests are performed on a complex data set of three different\ntumor types with heterogeneous vascular architectures. The correspondence of\nthe hybrid model in terms of mean representative elementary volume blood and\ninterstitial fluid pressures is excellent with relative errors of less than 4%.\nLarger, but less important and explicable errors are present in terms of blood\nflow in the smaller, homogenized vessels. We finally discuss and demonstrate\nhow the hybrid model can be further improved to apply it for studies on tumor\nperfusion and the efficacy of drug delivery.\n"}
+{"abstract": "  We study online change point detection problems under the constraint of local\ndifferential privacy (LDP) where, in particular, the statistician does not have\naccess to the raw data. As a concrete problem, we study a multivariate\nnonparametric regression problem. At each time point $t$, the raw data are\nassumed to be of the form $(X_t, Y_t)$, where $X_t$ is a $d$-dimensional\nfeature vector and $Y_t$ is a response variable. Our primary aim is to detect\nchanges in the regression function $m_t(x)=\\mathbb{E}(Y_t |X_t=x)$ as soon as\nthe change occurs. We provide algorithms which respect the LDP constraint,\nwhich control the false alarm probability, and which detect changes with a\nminimal (minimax rate-optimal) delay. To quantify the cost of privacy, we also\npresent the optimal rate in the benchmark, non-private setting. These\nnon-private results are also new to the literature and thus are interesting\n\\emph{per se}. In addition, we study the univariate mean online change point\ndetection problem, under privacy constraints. This serves as the blueprint of\nstudying more complicated private change point detection problems.\n"}
+{"abstract": "  Self-force methods can be applied in calculations of the scatter angle in\ntwo-body hyperbolic encounters, working order by order in the mass ratio\n(assumed small) but with no recourse to a weak-field approximation. This, in\nturn, can inform ongoing efforts to construct an accurate model of the\ngeneral-relativistic binary dynamics via an effective-one-body description and\nother semi-analytical approaches. Existing self-force methods are to a large\nextent specialised to bound, inspiral orbits. Here we develop a technique for\n(numerical) self-force calculations that can efficiently tackle scatter orbits.\nThe method is based on a time-domain reconstruction of the metric perturbation\nfrom a scalar-like Hertz potential that satisfies the Teukolsky equation, an\nidea pursued so far only for bound orbits. The crucial ingredient in this\nformulation are certain jump conditions that (each multipole mode of) the Hertz\npotential must satisfy along the orbit, in a 1+1-dimensional multipole\nreduction of the problem. We obtain a closed-form expression for these jumps,\nfor an arbitrary geodesic orbit in Schwarzschild spacetime, and present a full\nnumerical implementation for a scatter orbit. In this paper we focus on method\ndevelopment, and go only as far as calculating the Hertz potential; a\ncalculation of the self-force and its physical effects on the scatter orbit\nwill be the subject of forthcoming work.\n"}
+{"abstract": "  This study aimed to provide a framework to evaluate team attacking\nperformances in rugby league using 59,233 plays from 180 Super League matches\nvia expected possession value (EPV) models. The EPV-308 split the pitch into\n308 5m x 5m zones, the EPV-77 split the pitch into 77 10m x 10m zones and the\nEPV-19 split the pitch in 19 zones of variable size dependent on the total zone\nvalue generated during a match. Attacking possessions were considered as Markov\nChains, allowing the value of each zone visited to be estimated based on the\noutcome of the possession. The Kullback-Leibler Divergence was used to evaluate\nthe reproducibility of the value generated from each zone (the reward\ndistribution) by teams between matches. The EPV-308 had the greatest\nvariability and lowest reproducibility, compared to EPV-77 and EPV-19. When six\nprevious matches were considered, the team's subsequent match attacking\nperformances had a similar reward distribution for EPV-19, EPV-77 and EPV-308\non 95 +/- 4%, 51 +/- 12% and 0 +/- 0% of occasions. This study supports the use\nof EPV-19 to evaluate team attacking performance in rugby league and provides a\nsimple framework through which attacking performances can be compared between\nteams.\n"}
+{"abstract": "  Given $X$ a finite nilpotent simplicial set, consider the classifying\nfibrations $$ X\\to Baut_G^*(X)\\to Baut_G(X),\\qquad X\\to Z\\to Baut_{\\pi}^*(X),\n$$\n  where $G$ and $\\pi$ denote, respectively, subgroups of the free and pointed\nhomotopy classes of free and pointed self homotopy equivalences of $X$ which\nact nilpotently on $H_*(X)$ and $\\pi_*(X)$.\n  We give algebraic models, in terms of complete differential graded Lie\nalgebras (cdgl's), of the rational homotopy type of these fibrations.\nExplicitly, if $L$ is a cdgl model of $X$, there are connected sub cdgl's\n$Der^G L$ and $Der^{\\pi} L$ of the Lie algebra $Der L$ of derivations of $L$\nsuch that the geometrical realization of the sequences of cdgl morphisms\n  $$\n  L\\stackrel{ad}{\\to} Der^G L\\to Der^G L\\widetilde\\times sL,\\qquad L\\to\nL\\widetilde\\times Der^{\\pi} L\\to Der^{\\pi} L\n  $$\n  have the rational homotopy type of the above classifying fibrations. Among\nthe consequences we also describe in cdgl terms the Malcev $Q$-completion of\n$G$ and $\\pi$ together with the rational homotopy type of the classifying\nspaces $BG $ and $B\\pi$.\n"}
+{"abstract": "  When people observe events, they are able to abstract key information and\nbuild concise summaries of what is happening. These summaries include\ncontextual and semantic information describing the important high-level details\n(what, where, who and how) of the observed event and exclude background\ninformation that is deemed unimportant to the observer. With this in mind, the\ndescriptions people generate for videos of different dynamic events can greatly\nimprove our understanding of the key information of interest in each video.\nThese descriptions can be captured in captions that provide expanded attributes\nfor video labeling (e.g. actions/objects/scenes/sentiment/etc.) while allowing\nus to gain new insight into what people find important or necessary to\nsummarize specific events. Existing caption datasets for video understanding\nare either small in scale or restricted to a specific domain. To address this,\nwe present the Spoken Moments (S-MiT) dataset of 500k spoken captions each\nattributed to a unique short video depicting a broad range of different events.\nWe collect our descriptions using audio recordings to ensure that they remain\nas natural and concise as possible while allowing us to scale the size of a\nlarge classification dataset. In order to utilize our proposed dataset, we\npresent a novel Adaptive Mean Margin (AMM) approach to contrastive learning and\nevaluate our models on video/caption retrieval on multiple datasets. We show\nthat our AMM approach consistently improves our results and that models trained\non our Spoken Moments dataset generalize better than those trained on other\nvideo-caption datasets.\n"}
+{"abstract": "  The problem of uniqueness of universal formulae for (quantum) dimensions of\nsimple Lie algebras is investigated. We present generic functions, which\nmultiplied by a universal (quantum) dimension formula, preserve both its\nstructure and its values at the points from Vogel's table. Connection of some\nof these functions with geometrical configurations, such as the famous\nPappus-Brianchon-Pascal $(9_3)_1$ configuration of points and lines, is\nestablished. Particularly, the appropriate realizable configuration\n$(144_336_{12})$ (yet to be found) will provide a symmetric non-uniqueness\nfactor for any universal dimension formula.\n"}
+{"abstract": "  We introduce a new approach for the study of the Problem of Iterates using\nthe theory on general ultradifferentiable structures developed in the last\nyears. Our framework generalizes many of the previous settings including the\nGevrey case and enables us, for the first time, to prove non-analytic Theorems\nof Iterates for non-elliptic differential operators. In particular, by\ngeneralizing a Theorem of Baouendi and Metivier we obtain the Theorem of\nIterates for hypoelliptic analytic operators of principal type with respect to\nseveral non-analytic ultradifferentiable structures.\n"}
+{"abstract": "  Digitalization is forging its path in the architecture, construction,\nengineering, operation (AECO) industry. This trend demands not only solutions\nfor data governance but also sophisticated cyber-physical systems with a high\nvariety of stakeholder background and very complex requirements. Existing\napproaches to general requirements engineering ignore the context of the AECO\nindustry. This makes it harder for the software engineers usually lacking the\nknowledge of the industry context to elicit, analyze and structure the\nrequirements and to effectively communicate with AECO professionals. To live up\nto that task, we present an approach and a tool for collecting AECO-specific\nsoftware requirements with the aim to foster reuse and leverage domain\nknowledge. We introduce a common scenario space, propose a novel choice of an\nubiquitous language well-suited for this particular industry and develop a\nsystematic way to refine the scenario ontologies based on the exploration of\nthe scenario space. The viability of our approach is demonstrated on an\nontology of 20 practical scenarios from a large project aiming to develop a\ndigital twin of a construction site.\n"}
+{"abstract": "  In this communication we test the hypothesis that for some initial conditions\nthe time evolution of surface waves according to the extended KdV equation\n(KdV2) exhibits signatures of the deterministic chaos.\n"}
+{"abstract": "  A novel class of integrable $\\sigma$-models interpolating between exact coset\nconformal field theories in the IR and hyperbolic spaces in the UV is\nconstructed. We demonstrate the relation to the asymptotic limit of\n$\\lambda$-deformed models for cosets of non-compact groups. An integrable model\ninterpolating between two spacetimes with cosmological and black hole\ninterpretations and exact conformal field theory descriptions is also provided.\nIn the process of our work, a new zoom-in limit, distinct from the well known\nnon-Abelian T-duality limit, is found.\n"}
+{"abstract": "  In this paper, we first study the well-posedness of a class of McKean-Vlasov\nstochastic partial differential equations driven by cylindrical $\\alpha$-stable\nprocess, where $\\alpha\\in(1,2)$. Then by the method of the Khasminskii's time\ndiscretization, we prove the averaging principle of a class of multiscale\nMcKean-Vlasov stochastic partial differential equations driven by cylindrical\n$\\alpha$-stable processes. Meanwhile, we obtain a specific strong convergence\nrate.\n"}
+{"abstract": "  The existence of moments of first downwards passage times of a spectrally\nnegative L\\'evy process is governed by the general dynamics of the L\\'evy\nprocess, i.e. whether it is drifting to $+\\infty$, $-\\infty$ or oscillates.\nWhenever the L\\'evy process drifts to $+\\infty$, we prove that the $\\kappa$-th\nmoment of the first passage time (conditioned to be finite) exists if and only\nif the $(\\kappa+1)$-th moment of the L\\'evy jump measure exists, thus\ngeneralizing a result shown earlier by Delbaen for Cram\\'er-Lundberg risk\nprocesses \\cite{Delbaen1990}. Whenever the L\\'evy process drifts to $-\\infty$\nwe prove that all moments of the passage time exist, while for an oscillating\nL\\'evy process we derive conditions for non-existence of the moments and in\nparticular we show that no integer moments exist. Moreover we provide general\nformulae for integer moments of the first passage time (whenever they exist) in\nterms of the scale function of the L\\'evy process and its derivatives and\nantiderivatives.\n"}
+{"abstract": "  We present a direct speech-to-speech translation (S2ST) model that translates\nspeech from one language to speech in another language without relying on\nintermediate text generation. Previous work addresses the problem by training\nan attention-based sequence-to-sequence model that maps source speech\nspectrograms into target spectrograms. To tackle the challenge of modeling\ncontinuous spectrogram features of the target speech, we propose to predict the\nself-supervised discrete representations learned from an unlabeled speech\ncorpus instead. When target text transcripts are available, we design a\nmultitask learning framework with joint speech and text training that enables\nthe model to generate dual mode output (speech and text) simultaneously in the\nsame inference pass. Experiments on the Fisher Spanish-English dataset show\nthat predicting discrete units and joint speech and text training improve model\nperformance by 11 BLEU compared with a baseline that predicts spectrograms and\nbridges 83% of the performance gap towards a cascaded system. When trained\nwithout any text transcripts, our model achieves similar performance as a\nbaseline that predicts spectrograms and is trained with text data.\n"}
+{"abstract": "  In this contribution, we present the implementation of a second-order CASSCF\nalgorithm in conjunction with the Cholesky decomposition of the two-electron\nrepulsion integrals. The algorithm, called Norm-Extended Optimization,\nguarantees convergence of the optimization, but it involves the full Hessian of\nthe wavefunction and is therefore computationally expensive. Coupling the\nsecond-order procedure with the Cholesky decomposition leads to a significant\nreduction in the computational cost, reduced memory requirements, and an\nimproved parallel performance. As a result, CASSCF calculations of larger\nmolecular systems become possible as a routine task. The performance of the new\nimplementation is illustrated by means of benchmark calculations on molecules\nof increasing size, with up to about 3000 basis functions and 14 active\norbitals.\n"}
+{"abstract": "  Connected and Automated Vehicles (CAVs) are envisioned to transform the\nfuture industrial and private transportation sectors. Due to the complexity of\nthe systems, functional verification and validation of safety aspects are\nessential before the technology merges into the public domain. In recent years,\na scenario-driven approach has gained acceptance for CAVs emphasizing the\nrequirement of a solid data basis of scenarios. The large-scale research\nfacility Test Bed Lower Saxony (TFNDS) enables the provision of substantial\ninformation for a database of scenarios on motorways. For that purpose,\nhowever, the scenarios of interest must be identified and categorized in the\ncollected trajectory data. This work addresses this problem and proposes a\nframework for on-ramp scenario identification that also enables for scenario\ncategorization and assessment. The efficacy of the framework is shown with a\ndataset collected on the TFNDS.\n"}
+{"abstract": "  In this paper, we present an efficient spatial-temporal representation for\nvideo person re-identification (reID). Firstly, we propose a Bilateral\nComplementary Network (BiCnet) for spatial complementarity modeling.\nSpecifically, BiCnet contains two branches. Detail Branch processes frames at\noriginal resolution to preserve the detailed visual clues, and Context Branch\nwith a down-sampling strategy is employed to capture long-range contexts. On\neach branch, BiCnet appends multiple parallel and diverse attention modules to\ndiscover divergent body parts for consecutive frames, so as to obtain an\nintegral characteristic of target identity. Furthermore, a Temporal Kernel\nSelection (TKS) block is designed to capture short-term as well as long-term\ntemporal relations by an adaptive mode. TKS can be inserted into BiCnet at any\ndepth to construct BiCnetTKS for spatial-temporal modeling. Experimental\nresults on multiple benchmarks show that BiCnet-TKS outperforms\nstate-of-the-arts with about 50% less computations. The source code is\navailable at https://github.com/ blue-blue272/BiCnet-TKS.\n"}
+{"abstract": "  Leddar PixSet is a new publicly available dataset (dataset.leddartech.com)\nfor autonomous driving research and development. One key novelty of this\ndataset is the presence of full-waveform data from the Leddar Pixell sensor, a\nsolid-state flash LiDAR. Full-waveform data has been shown to improve the\nperformance of perception algorithms in airborne applications but is yet to be\ndemonstrated for terrestrial applications such as autonomous driving. The\nPixSet dataset contains approximately 29k frames from 97 sequences recorded in\nhigh-density urban areas, using a set of various sensors (cameras, LiDARs,\nradar, IMU, etc.) Each frame has been manually annotated with 3D bounding\nboxes.\n"}
+{"abstract": "  Deep neural networks are susceptible to poisoning attacks by purposely\npolluted training data with specific triggers. As existing episodes mainly\nfocused on attack success rate with patch-based samples, defense algorithms can\neasily detect these poisoning samples. We propose DeepPoison, a novel\nadversarial network of one generator and two discriminators, to address this\nproblem. Specifically, the generator automatically extracts the target class'\nhidden features and embeds them into benign training samples. One discriminator\ncontrols the ratio of the poisoning perturbation. The other discriminator works\nas the target model to testify the poisoning effects. The novelty of DeepPoison\nlies in that the generated poisoned training samples are indistinguishable from\nthe benign ones by both defensive methods and manual visual inspection, and\neven benign test samples can achieve the attack. Extensive experiments have\nshown that DeepPoison can achieve a state-of-the-art attack success rate, as\nhigh as 91.74%, with only 7% poisoned samples on publicly available datasets\nLFW and CASIA. Furthermore, we have experimented with high-performance defense\nalgorithms such as autodecoder defense and DBSCAN cluster detection and showed\nthe resilience of DeepPoison.\n"}
+{"abstract": "  We consider the system of sticky-reflected Brownian particles on the real\nline proposed in [arXiv:1711.03011]. The model is a modification of the\nHowitt-Warren flow but now the diffusion rate of particles is inversely\nproportional to the mass which they transfer. It is known that the system\nconsists of a finite number of distinct particles for almost all times. In this\npaper, we show that the system also admits an infinite number of distinct\nparticles on a dense subset of the time interval if and only if the function\nresponsible for the splitting of particles takes an infinite number of values.\n"}
+{"abstract": "  In this paper, we present LookOut, a novel autonomy system that perceives the\nenvironment, predicts a diverse set of futures of how the scene might unroll\nand estimates the trajectory of the SDV by optimizing a set of contingency\nplans over these future realizations. In particular, we learn a diverse joint\ndistribution over multi-agent future trajectories in a traffic scene that\ncovers a wide range of future modes with high sample efficiency while\nleveraging the expressive power of generative models. Unlike previous work in\ndiverse motion forecasting, our diversity objective explicitly rewards sampling\nfuture scenarios that require distinct reactions from the self-driving vehicle\nfor improved safety. Our contingency planner then finds comfortable and\nnon-conservative trajectories that ensure safe reactions to a wide range of\nfuture scenarios. Through extensive evaluations, we show that our model\ndemonstrates significantly more diverse and sample-efficient motion forecasting\nin a large-scale self-driving dataset as well as safer and less-conservative\nmotion plans in long-term closed-loop simulations when compared to current\nstate-of-the-art models.\n"}
+{"abstract": "  High-dimensional, low sample-size (HDLSS) data problems have been a topic of\nimmense importance for the last couple of decades. There is a vast literature\nthat proposed a wide variety of approaches to deal with this situation, among\nwhich variable selection was a compelling idea. On the other hand, a deep\nneural network has been used to model complicated relationships and\ninteractions among responses and features, which is hard to capture using a\nlinear or an additive model. In this paper, we discuss the current status of\nvariable selection techniques with the neural network models. We show that the\nstage-wise algorithm with neural network suffers from disadvantages such as the\nvariables entering into the model later may not be consistent. We then propose\nan ensemble method to achieve better variable selection and prove that it has\nprobability tending to zero that a false variable is selected. Then, we discuss\nadditional regularization to deal with over-fitting and make better regression\nand classification. We study various statistical properties of our proposed\nmethod. Extensive simulations and real data examples are provided to support\nthe theory and methodology.\n"}
+{"abstract": "  Delays in the availability of vaccines are costly as the pandemic continues.\nHowever, in the presence of adjustment costs firms have an incentive to\nincrease production capacity only gradually. The existing contracts specify\nonly a fixed quantity to be supplied over a certain period and thus provide no\nincentive for an accelerated buildup in capacity. A high price does not change\nthis. The optimal contract would specify a decreasing price schedule over time\nwhich can replicate the social optimum.\n"}
+{"abstract": "  Reaction-Diffusion equations can present solutions in the form of traveling\nwaves. Such solutions evolve in different spatial and temporal scales and it is\ndesired to construct numerical methods that can adopt a spatial refinement at\nlocations with large gradient solutions. In this work we develop a high order\nadaptive mesh method based on Chebyshev polynomials with a multidomain approach\nfor the traveling wave solutions of reaction-diffusion systems, where the\nproposed method uses the non-conforming and non-overlapping spectral\nmultidomain method with the temporal adaptation of the computational mesh.\nContrary to the existing multidomain spectral methods for reaction-diffusion\nequations, the proposed multidomain spectral method solves the given PDEs in\neach subdomain locally first and the boundary and interface conditions are\nsolved in a global manner. In this way, the method can be parallelizable and is\nefficient for the large reaction-diffusion system. We show that the proposed\nmethod is stable and provide both the one- and two-dimensional numerical\nresults that show the efficacy of the proposed method.\n"}
+{"abstract": "  Sentence insertion is a delicate but fundamental NLP problem. Current\napproaches in sentence ordering, text coherence, and question answering (QA)\nare neither suitable nor good at solving it. In this paper, We propose\nInsertGNN, a simple yet effective model that represents the problem as a graph\nand adopts the graph Neural Network (GNN) to learn the connection between\nsentences. It is also supervised by both the local and global information that\nthe local interactions of neighboring sentences can be considered. To the best\nof our knowledge, this is the first recorded attempt to apply a supervised\ngraph-structured model in sentence insertion. We evaluate our method in our\nnewly collected TOEFL dataset and further verify its effectiveness on the\nlarger arXivdataset using cross-domain learning. The experiments show that\nInsertGNN outperforms the unsupervised text coherence method, the topological\nsentence ordering approach, and the QA architecture. Specifically, It achieves\nan accuracy of 70%, rivaling the average human test scores.\n"}
+{"abstract": "  We provide a queueing-theoretic framework for job replication schemes based\non the principle \"\\emph{replicate a job as soon as the system detects it as a\n\\emph{straggler}}\". This is called job \\emph{speculation}. Recent works have\nanalyzed {replication} on arrival, which we refer to as \\emph{replication}.\nReplication is motivated by its implementation in Google's BigTable. However,\nsystems such as Apache Spark and Hadoop MapReduce implement speculative job\nexecution. The performance and optimization of speculative job execution is not\nwell understood. To this end, we propose a queueing network model for load\nbalancing where each server can speculate on the execution time of a job.\nSpecifically, each job is initially assigned to a single server by a frontend\ndispatcher. Then, when its execution begins, the server sets a timeout. If the\njob completes before the timeout, it leaves the network, otherwise the job is\nterminated and relaunched or resumed at another server where it will complete.\nWe provide a necessary and sufficient condition for the stability of\nspeculative queueing networks with heterogeneous servers, general job sizes and\nscheduling disciplines. We find that speculation can increase the stability\nregion of the network when compared with standard load balancing models and\nreplication schemes. We provide general conditions under which timeouts\nincrease the size of the stability region and derive a formula for the optimal\nspeculation time, i.e., the timeout that minimizes the load induced through\nspeculation. We compare speculation with redundant-$d$ and\nredundant-to-idle-queue-$d$ rules under an $S\\& X$ model. For light loaded\nsystems, redundancy schemes provide better response times. However, for\nmoderate to heavy loadings, redundancy schemes can lose capacity and have\nmarkedly worse response times when compared with a speculative scheme.\n"}
+{"abstract": "  We present the first intensive continuum reverberation mapping study of the\nhigh accretion rate Seyfert galaxy Mrk 110. The source was monitored almost\ndaily for more than 200 days with the Swift X-ray and UV/optical telescopes,\nsupported by ground-based observations from Las Cumbres Observatory, the\nLiverpool Telescope, and the Zowada Observatory, thus extending the wavelength\ncoverage to 9100 \\r{A}. Mrk 110 was found to be significantly variable at all\nwavebands. Analysis of the intraband lags reveals two different behaviours,\ndepending on the timescale. On timescales shorter than 10 days the lags,\nrelative to the shortest UV waveband ($\\sim1928$ \\r{A}), increase with\nincreasing wavelength up to a maximum of $\\sim2$days lag for the longest\nwaveband ($\\sim9100$ \\r{A}), consistent with the expectation from disc\nreverberation. On longer timescales, however, the g-band lags the Swift BAT\nhard X-rays by $\\sim10$ days, with the z-band lagging the g-band by a similar\namount, which cannot be explained in terms of simple reprocessing from the\naccretion disc. We interpret this result as an interplay between the emission\nfrom the accretion disc and diffuse continuum radiation from the broad line\nregion.\n"}
+{"abstract": "  Coronal loop observations have existed for many decades yet the precise shape\nof these fundamental coronal structures is still widely debated since the\ndiscovery that they appear to undergo negligible expansion between their\nfootpoints and apex. In this work a selection of eight EUV loops and their\ntwenty-two sub-element strands are studied from the second successful flight of\nNASA's High resolution Coronal Imager (Hi-C 2.1). Four of the loops correspond\nto open fan structures with the other four considered to be magnetically closed\nloops. Width analysis is performed on the loops and their sub-resolution\nstrands using our method of fitting multiple Gaussian profiles to\ncross-sectional intensity slices. It is found that whilst the magnetically\nclosed loops and their sub-element strands do not expand along their observable\nlength, open fan structures may expand an additional 150% of their initial\nwidth. Following recent work, the Pearson correlation coefficient between peak\nintensity and loop/strand width are found to be predominantly positively\ncorrelated for the loops (~88%) and their sub-element strands (~80%). These\nresults align with the hypothesis of Klimchuk & DeForest that loops and - for\nthe first time - their sub-element strands have approximately circular\ncross-sectional profiles.\n"}
+{"abstract": "  Searching for novel antiferromagnetic materials with large magnetotransport\nresponse is highly demanded for constructing future spintronic devices with\nhigh stability, fast switching speed, and high density. Here we report a\ncolossal anisotropic magnetoresistance effect in an antiferromagnetic binary\ncompound with layered structure rare-earth dichalcogenide EuTe2. The AMR\nreaches 40000%, which is 4 orders of magnitude larger than that in conventional\nantiferromagnetic alloys. Combined magnetization, resistivity, and theoretical\nanalysis reveal that the colossal AMR effect is attributed to a novel mechanism\nof vector-field tunable band structure, rather than the conventional spin-orbit\ncoupling mechanism. Moreover, it is revealed that the strong hybridization\nbetween orbitals of Eu-layer with localized spin and Te-layer with itinerant\ncarriers is extremely important for the large AMR effect. Our results suggest a\nnew direction towards exploring AFM materials with prominent magnetotransport\nproperties, which creates an unprecedented opportunity for AFM spintronics\napplications.\n"}
+{"abstract": "  Atomically precise dopant arrays in Si are being pursued for solid-state\nquantum computing applications. We propose a guided self-assembly process to\nproduce atomically precise arrays of single dopant atoms in lieu of\nlithographic patterning. We leverage the self-assembled c(4x2) structure formed\non Br- and I-Si(100) and investigate molecular precursor adsorption into the\ngenerated array of single-dimer window (SDW) adsorption sites with density\nfunctional theory (DFT). The adsorption of several technologically relevant\ndopant precursors (PH$_3$, BCl$_3$, AlCl$_3$, GaCl$_3$) into SDWs formed with\nvarious resists (H, Cl, Br, I) are explored to identify the effects of steric\ninteractions. PH$_3$ adsorbed without barrier on all resists studied, while\nBCl$_3$ exhibited the largest adsorption barrier, 0.34 eV, with an I resist.\nDense arrays of AlCl$_3$ were found to form within experimentally realizable\nconditions demonstrating the potential for the proposed use of guided\nself-assembly for atomically precise fabrication of dopant-based devices.\n"}
+{"abstract": "  Fractionalization is a phenomenon in which strong interactions in a quantum\nsystem drive the emergence of excitations with quantum numbers that are absent\nin the building blocks. Outstanding examples are excitations with charge e/3 in\nthe fractional quantum Hall effect, solitons in one-dimensional conducting\npolymers and Majorana states in topological superconductors. Fractionalization\nis also predicted to manifest itself in low-dimensional quantum magnets, such\nas one-dimensional antiferromagnetic S = 1 chains. The fundamental features of\nthis system are gapped excitations in the bulk and, remarkably, S = 1/2 edge\nstates at the chain termini, leading to a four-fold degenerate ground state\nthat reflects the underlying symmetry-protected topological order. Here, we use\non-surface synthesis to fabricate one-dimensional spin chains that contain the\nS = 1 polycyclic aromatic hydrocarbon triangulene as the building block. Using\nscanning tunneling microscopy and spectroscopy at 4.5 K, we probe\nlength-dependent magnetic excitations at the atomic scale in both open-ended\nand cyclic spin chains, and directly observe gapped spin excitations and\nfractional edge states therein. Exact diagonalization calculations provide\nconclusive evidence that the spin chains are described by the S = 1\nbilinear-biquadratic Hamiltonian in the Haldane symmetry-protected topological\nphase. Our results open a bottom-up approach to study strongly correlated\nquantum spin liquid phases in purely organic materials, with the potential for\nthe realization of measurement-based quantum computation.\n"}
+{"abstract": "  We compute the 3d N = 2 superconformal indices for 3d/1d coupled systems,\nwhich arise as the worldvolume theories of intersecting surface defects\nengineered by Higgsing 5d N = 1 gauge theories. We generalize some known 3d\ndualities, including non-Abelian 3d mirror symmetry and 3d/3d correspondence,\nto some of the simple 3d/1d coupled systems. Finally we propose a q-Virasoro\nconstruction for the superconformal indices.\n"}
+{"abstract": "  We study termination of higher-order probabilistic functional programs with\nrecursion, stochastic conditioning and sampling from continuous distributions.\n  Reasoning about the termination probability of programs with continuous\ndistributions is hard, because the enumeration of terminating executions cannot\nprovide any non-trivial bounds. We present a new operational semantics based on\ntraces of intervals, which is sound and complete with respect to the standard\nsampling-based semantics, in which (countable) enumeration can provide\narbitrarily tight lower bounds. Consequently we obtain the first proof that\ndeciding almost-sure termination (AST) for programs with continuous\ndistributions is $\\Pi^0_2$-complete. We also provide a compositional\nrepresentation of our semantics in terms of an intersection type system.\n  In the second part, we present a method of proving AST for non-affine\nprograms, i.e., recursive programs that can, during the evaluation of the\nrecursive body, make multiple recursive calls (of a first-order function) from\ndistinct call sites. Unlike in a deterministic language, the number of\nrecursion call sites has direct consequences on the termination probability.\nOur framework supports a proof system that can verify AST for programs that are\nwell beyond the scope of existing methods.\n  We have constructed prototype implementations of our method of computing\nlower bounds of termination probability, and AST verification.\n"}
+{"abstract": "  Conventional imaging only records photons directly sent from the object to\nthe detector, while non-line-of-sight (NLOS) imaging takes the indirect light\ninto account. Most NLOS solutions employ a transient scanning process, followed\nby a physical based algorithm to reconstruct the NLOS scenes. However, the\ntransient detection requires sophisticated apparatus, with long scanning time\nand low robustness to ambient environment, and the reconstruction algorithms\nare typically time-consuming and computationally expensive. Here we propose a\nnew NLOS solution to address the above defects, with innovations on both\nequipment and algorithm. We apply inexpensive commercial Lidar for detection,\nwith much higher scanning speed and better compatibility to real-world imaging.\nOur reconstruction framework is deep learning based, with a generative two-step\nremapping strategy to guarantee high reconstruction fidelity. The overall\ndetection and reconstruction process allows for millisecond responses, with\nreconstruction precision of millimeter level. We have experimentally tested the\nproposed solution on both synthetic and real objects, and further demonstrated\nour method to be applicable to full-color NLOS imaging.\n"}
+{"abstract": "  In this paper we revisit the memristor concept within circuit theory. We\nstart from the definition of the basic circuit elements, then we introduce the\noriginal formulation of the memristor concept and summarize some of the\ncontroversies on its nature. We also point out the ambiguities resulting from a\nnon rigorous usage of the flux linkage concept. After concluding that the\nmemristor is not a fourth basic circuit element, prompted by recent claims in\nthe memristor literature, we look into the application of the memristor concept\nto electrophysiology, realizing that an approach suitable to explain the\nobserved inductive behavior of the giant squid axon had already been developed\nin the 1960s, with the introduction of \"time-variant resistors.\" We also\ndiscuss a recent memristor implementation in which the magnetic flux plays a\ndirect role, concluding that it cannot strictly qualify as a memristor, because\nits $v-i$ curve cannot exactly pinch at the origin. Finally, we present\nnumerical simulations of a few memristors and memristive systems, focusing on\nthe behavior in the $\\varphi-q$ plane. We show that, contrary to what happens\nfor the most basic memristor concept, for general memristive systems the\n$\\varphi-q$ curve is not single-valued or not even closed.\n"}
+{"abstract": "  Concept drift detectors allow learning systems to maintain good accuracy on\nnon-stationary data streams. Financial time series are an instance of\nnon-stationary data streams whose concept drifts (market phases) are so\nimportant to affect investment decisions worldwide. This paper studies how\nconcept drift detectors behave when applied to financial time series. General\nresults are: a) concept drift detectors usually improve the runtime over\ncontinuous learning, b) their computational cost is usually a fraction of the\nlearning and prediction steps of even basic learners, c) it is important to\nstudy concept drift detectors in combination with the learning systems they\nwill operate with, and d) concept drift detectors can be directly applied to\nthe time series of raw financial data and not only to the model's accuracy one.\nMoreover, the study introduces three simple concept drift detectors, tailored\nto financial time series, and shows that two of them can be at least as\neffective as the most sophisticated ones from the state of the art when applied\nto financial time series. Currently submitted to Pattern Recognition\n"}
+{"abstract": "  One of the pillars of any machine learning model is its concepts. Using\nsoftware engineering, we can engineer these concepts and then develop and\nexpand them. In this article, we present a SELM framework for Software\nEngineering of machine Learning Models. We then evaluate this framework through\na case study. Using the SELM framework, we can improve a machine learning\nprocess efficiency and provide more accuracy in learning with less processing\nhardware resources and a smaller training dataset. This issue highlights the\nimportance of an interdisciplinary approach to machine learning. Therefore, in\nthis article, we have provided interdisciplinary teams' proposals for machine\nlearning.\n"}
+{"abstract": "  Bures distance holds a special place among various distance measures due to\nits several distinguished features and finds applications in diverse problems\nin quantum information theory. It is related to fidelity and, among other\nthings, it serves as a bona fide measure for quantifying the separability of\nquantum states. In this work, we calculate exact analytical results for the\nmean root fidelity and mean square Bures distance between a fixed density\nmatrix and a random density matrix, and also between two random density\nmatrices. In the course of derivation, we also obtain spectral density for\nproduct of above pairs of density matrices. We corroborate our analytical\nresults using Monte Carlo simulations. Moreover, we compare these results with\nthe mean square Bures distance between reduced density matrices generated using\ncoupled kicked tops and find very good agreement.\n"}
+{"abstract": "  Let $\\mathcal{L}=-\\Delta+\\mathit{V}(x)$ be a Schr\\\"{o}dinger operator, where\n$\\Delta$ is the Laplacian operator on $\\mathbb{R}^{d}$ $(d\\geq 3)$, while the\nnonnegative potential $\\mathit{V}(x)$ belongs to the reverse H\\\"{o}lder class\n$B_{q}, q>d/2$. In this paper, we study weighted compactness of commutators of\nsome Schr\\\"{o}dinger operators, which include Riesz transforms, standard\nCalder\\'{o}n-Zygmund operatos and Littlewood-Paley functions. These results\ngeneralize substantially some well-know results.\n"}
+{"abstract": "  Dual decomposition is widely utilized in distributed optimization of\nmulti-agent systems. In practice, the dual decomposition algorithm is desired\nto admit an asynchronous implementation due to imperfect communication, such as\ntime delay and packet drop. In addition, computational errors also exist when\nindividual agents solve their own subproblems. In this paper, we analyze the\nconvergence of the dual decomposition algorithm in distributed optimization\nwhen both the asynchrony in communication and the inexactness in solving\nsubproblems exist. We find that the interaction between asynchrony and\ninexactness slows down the convergence rate from $\\mathcal{O} ( 1 / k )$ to\n$\\mathcal{O} ( 1 / \\sqrt{k} )$. Specifically, with a constant step size, the\nvalue of objective function converges to a neighborhood of the optimal value,\nand the solution converges to a neighborhood of the exact optimal solution.\nMoreover, the violation of the constraints diminishes in $\\mathcal{O} ( 1 /\n\\sqrt{k} )$. Our result generalizes and unifies the existing ones that only\nconsider either asynchrony or inexactness. Finally, numerical simulations\nvalidate the theoretical results.\n"}
+{"abstract": "  Benford's law is widely used for fraud-detection nowadays. The underlying\nassumption for using the law is that a \"regular\" dataset follows the\nsignificant digit phenomenon. In this paper, we address the scenario where a\nshrewd fraudster manipulates a list of numbers in such a way that still\ncomplies with Benford's law. We develop a general family of distributions that\nprovides several degrees of freedom to such a fraudster such as minimum,\nmaximum, mean and size of the manipulated dataset. The conclusion further\ncorroborates the idea that Benford's law should be used with utmost discretion\nas a means for fraud detection.\n"}
+{"abstract": "  We study removable sets for Newtonian Sobolev functions in metric measure\nspaces satisfying the usual (local) assumptions of a doubling measure and a\nPoincar\\'e inequality. In particular, when restricted to Euclidean spaces, a\nclosed set $E\\subset \\mathbf{R}^n$ with zero Lebesgue measure is shown to be\nremovable for $W^{1,p}(\\mathbf{R}^n \\setminus E)$ if and only if $\\mathbf{R}^n\n\\setminus E$ supports a $p$-Poincar\\'e inequality as a metric space. When\n$p>1$, this recovers Koskela's result (Ark. Mat. 37 (1999), 291--304), but for\n$p=1$, as well as for metric spaces, it seems to be new. We also obtain the\ncorresponding characterization for the Dirichlet spaces $L^{1,p}$. To be able\nto include $p=1$, we first study extensions of Newtonian Sobolev functions in\nthe case $p=1$ from a noncomplete space $X$ to its completion $\\widehat{X}$.\n  In these results, $p$-path almost open sets play an important role, and we\nprovide a characterization of them by means of $p$-path open, $p$-quasiopen and\n$p$-finely open sets. We also show that there are nonmeasurable $p$-path almost\nopen subsets of $\\mathbf{R}^n$, $n \\geq 2$, provided that the continuum\nhypothesis is assumed to be true.\n  Furthermore, we extend earlier results about measurability of functions with\n$L^p$-integrable upper gradients, about $p$-quasiopen, $p$-path and $p$-finely\nopen sets, and about Lebesgue points for $N^{1,1}$-functions, to spaces that\nonly satisfy local assumptions.\n"}
+{"abstract": "  Context. The astrometric satellite Gaia is expected to significantly increase\nour knowledge as to the properties of the Milky Way. The Gaia Early Data\nRelease 3 (Gaia EDR3) provides the most precise parallaxes for many OB stars,\nwhich can be used to delineate the Galactic spiral structure. Aims. We\ninvestigate the local spiral structure with the largest sample of\nspectroscopically confirmed young OB stars available to date, and we compare it\nwith what was traced by the parallax measurements of masers. Methods. A sample\nconsisting of three different groups of massive young stars, including O-B2\nstars, O-B0 stars and O-type stars with parallax accuracies better than 10% was\ncompiled and used in our analysis. Results. The local spiral structures in all\nfour Galactic quadrants within $\\approx$5 kpc of the Sun are clearly delineated\nin detail. The revealed Galactic spiral pattern outlines a clear sketch of\nnearby spiral arms, especially in the third and fourth quadrants where the\nmaser parallax data are still absent. These O-type stars densify and extend the\nspiral structure constructed by using the Very Long Baseline Interferometry\n(VLBI) maser data alone. The clumped distribution of O-type stars also\nindicates that the Galaxy spiral structure is inhomogeneous.\n"}
+{"abstract": "  We present computer simulations about the spatial and temporal evolution of a\n1-MeV proton microbeam transmitted through an insulating macrocapillary with\nthe length of 45 mm and with the inner diameter of 800 {\\mu}m. The axis of the\ncapillary was tilted to 1{\\deg} relative to the axis of the incident beam,\nwhich ensured geometrical nontransparency. The simulation is based on the\ncombination of stochastic (Monte Carlo) and deterministic methods. It involves\n(1) random sampling of the initial conditions, according to distributions\ngenerated by the widely used and freely available computer software packages,\nSRIM and WINTRAX, (2) the numerical solution of the governing equations for\nfollowing the classical trajectory of the projectiles, and (3) the description\nof the field-driven charge migration on the surface and in the bulk of the\ninsulator material. We found that our simulation describes reasonably all of\nour previous experimental observations, indicating the functionality and\nreliability of the applied model. In addition, we found that at different\nphases of the beam transmission, different atomic processes result in the\nevolution of the beam distribution. First, in a scattering phase, the multiple\nsmall angle atomic scattering dominates in the beam transmission, resulting in\nan outgoing beam into a wide angular range and in a wide energy window. Later,\nin a mixed phase, scattering and guiding happens simultaneously, with a\ncontinuously increasing contribution of guiding. Finally, in the phase of the\nstabilized, guided transmission, a quadrupolelike focusing effect is observed,\ni.e., the transmitted beam is concentrated into a small spot, and the\ntransmitted protons keep their initial kinetic energy.\n"}
+{"abstract": "  Large scale projects increasingly operate in complicated settings whilst\ndrawing on an array of complex data-points, which require precise analysis for\naccurate control and interventions to mitigate possible project failure.\nCoupled with a growing tendency to rely on new information systems and\nprocesses in change projects, 90% of megaprojects globally fail to achieve\ntheir planned objectives. Renewed interest in the concept of Artificial\nIntelligence (AI) against a backdrop of disruptive technological innovations,\nseeks to enhance project managers cognitive capacity through the project\nlifecycle and enhance project excellence. However, despite growing interest\nthere remains limited empirical insights on project managers ability to\nleverage AI for cognitive load enhancement in complex settings. As such this\nresearch adopts an exploratory sequential linear mixed methods approach to\naddress unresolved empirical issues on transient adaptations of AI in complex\nprojects, and the impact on cognitive load enhancement. Initial thematic\nfindings from semi-structured interviews with domain experts, suggest that in\norder to leverage AI technologies and processes for sustainable cognitive load\nenhancement with complex data over time, project managers require improved\nknowledge and access to relevant technologies that mediate data processes in\ncomplex projects, but equally reflect application across different project\nphases. These initial findings support further hypothesis testing through a\nlarger quantitative study incorporating structural equation modelling to\nexamine the relationship between artificial intelligence and project managers\ncognitive load with project data in complex contexts.\n"}
+{"abstract": "  The fluid/gravity correspondence establishes how gravitational dynamics, as\ndictated by Einstein's field equations, are related to the fluid dynamics,\ngoverned by the relativistic Navier-Stokes equations. In this work the\ncorrespondence is extended, where the duality between incompressible fluids and\ngravitational backgrounds with soft hair excitations is implemented. This\nconstruction is set through appropriate boundary conditions to the\ngravitational background, leading to a correspondence between generalized\nincompressible Navier-Stokes equations and soft hairy horizons.\n"}
+{"abstract": "  We present an analysis of the lightcurve extracted from Transiting Exoplanet\nSurvey Satellite Full Frame Images of the double-mode RR Lyrae V338 Boo. We\nfind that the fundamental mode pulsation is changing in amplitude across the 54\ndays of observations. The first overtone mode pulsation also changes, but on a\nmuch smaller scale. Harmonics and combinations of the primary pulsation modes\nalso exhibit unusual behavior. Possible connections with other changes in RR\nLyrae pulsations are discussed, but a full understanding of the cause of the\nchanges seen in V338 Boo should shed light on some of the most difficult and\nunanswered questions in stellar pulsation theory, and astrophysics more\ngenerally.\n"}
+{"abstract": "  Strontium titanate (SrTiO3) is widely used as a promising photocatalyst due\nto its unique band edge alignment with respect to the oxidation and reduction\npotential corresponding to oxygen evolution reaction (OER) and hydrogen\nevolution reaction (HER). However, further enhancement of the photocatalytic\nactivity in this material could be envisaged through the effective control of\noxygen vacancy states. This could substantially tune the photoexcited charge\ncarrier trapping under the influence of elemental functionalization in SrTiO3,\ncorresponding to the defect formation energy. The charge trapping states in\nSrTiO3 decrease through the substitutional doping in Ti sites with p-block\nelements like Aluminium (Al) with respect to the relative oxygen vacancies.\nWith the help of electronic structure calculations based on density functional\ntheory (DFT) formalism, we have explored the synergistic effect of doping with\nboth Al and Iridium (Ir) in SrTiO3 from the perspective of defect formation\nenergy, band edge alignment and the corresponding charge carrier recombination\nprobability to probe the photoexcited charge carrier trapping that primarily\ngoverns the photocatalytic water splitting process. We have also systematically\ninvestigated the ratio-effect of Ir:Al functionalization on the position of\nacceptor levels lying between Fermi and conduction band in oxygen deficient\nSrTiO3, which governs the charge carrier recombination and therefore the\ncorresponding photocatalytic efficiency.\n"}
+{"abstract": "  We combine $SU(5)$ Grand Unified Theories (GUTs) with $A_4$ modular symmetry\nand present a comprehensive analysis of the resulting quark and lepton mass\nmatrices for all the simplest cases. Classifying the models according to the\nrepresentation assignments of the matter fields under $A_4$, we find that there\nare seven types of $SU(5)$ models with $A_4$ modular symmetry. We present 53\nbenchmark models with the fewest free parameters. The parameter space of each\nmodel is scanned to optimize the agreement between predictions and experimental\ndata, and predictions for the masses and mixing parameters of quarks and\nleptons are given at the best fitting points. The best fit predictions for the\nleptonic CP violating Dirac phase, the lightest neutrino mass and the\nneutrinoless double beta decay parameter when displayed graphically are\nobserved to cover a wide range of possible values, but are clustered around\nparticular regions, allowing future neutrino experiments to discriminate\nbetween the different types of models.\n"}
+{"abstract": "  In this paper, we present an updated version of the NELA-GT-2019 dataset,\nentitled NELA-GT-2020. NELA-GT-2020 contains nearly 1.8M news articles from 519\nsources collected between January 1st, 2020 and December 31st, 2020. Just as\nwith NELA-GT-2018 and NELA-GT-2019, these sources come from a wide range of\nmainstream news sources and alternative news sources. Included in the dataset\nare source-level ground truth labels from Media Bias/Fact Check (MBFC) covering\nmultiple dimensions of veracity. Additionally, new in the 2020 dataset are the\nTweets embedded in the collected news articles, adding an extra layer of\ninformation to the data. The NELA-GT-2020 dataset can be found at\nhttps://doi.org/10.7910/DVN/CHMUYZ.\n"}
+{"abstract": "  Studying the collective pairing phenomena in a two-component Fermi gas, we\npredict the appearance near the transition temperature $T_c$ of a well-resolved\ncollective mode of quadratic dispersion. The mode is visible both above and\nbelow $T_c$ in the system's response to a driving pairing field. When\napproaching $T_c$ from below, the phononic and pair-breaking branches,\ncharacteristic of the zero temperature behavior, reduce to a very low\nenergy-momentum region when the pair correlation length reaches its critical\ndivergent behavior $\\xi_{\\rm pair}\\propto|T_c-T|^{-1/2}$; elsewhere, they are\nreplaced by the quadratically-dispersed pairing resonance, which thus acts as a\nprecursor of the phase transition. In the strong-coupling and Bose-Einstein\nCondensate regime, this mode is a weakly-damped propagating mode associated to\na Lorentzian resonance. Conversely, in the BCS limit it is a relaxation mode of\npure imaginary eigenenergy. At large momenta, the resonance disappears when it\nis reabsorbed by the lower-edge of the pairing continuum. At intermediate\ntemperatures between 0 and $T_c$, we unify the newly found collective phenomena\nnear $T_c$ with the phononic and pair-breaking branches predicted from previous\nstudies, and we exhaustively classify the roots of the analytically continued\ndispersion equation, and show that they provided a very good summary of the\npair spectral functions.\n"}
+{"abstract": "  We study the problem of convergence of the normalized Ricci flow evolving on\na compact manifold $\\Omega$ without boundary. In \\cite{KS10, KS15} we derived,\nvia PDE techniques, global-in-time existence of the classical solution and\npre-compactness of the orbit. In this work we show its convergence to\nsteady-states, using a gradient inequality of {\\L}ojasiewicz type. We have thus\nan alternative proof of \\cite{ha}, but for general manifold $\\Omega$ and not\nonly for unit sphere. As a byproduct of that approach we also derive the rate\nof convergence according to this steady-sate being either degenerate or\nnon-degenerate as a critical point of a related energy functional.\n"}
+{"abstract": "  Stern's diatomic sequence is a well-studied and simply defined sequence with\nmany fascinating characteristics. The binary signed-digit (BSD) representation\nof integers is used widely in efficient computation, coding theory and other\napplications. We link these two objects, showing that the number of $i$-bit\nbinary signed-digit representations of an integer $n<2^i$ is the\n$(2^i-n)^\\text{th}$ element in Stern's diatomic sequence.\n  This correspondence makes the vast range of results known about the Stern\ndiatomic sequence available for consideration in the study of binary\nsigned-digit integers, and vice versa. Applications of this relationship\ndiscussed in this paper include a weight-distribution theorem for BSD\nrepresentations, linking these representations to Stern polynomials, a\nrecursion for the number of optimal BSD representations of an integer along\nwith their Hamming weight, stemming from an easy recursion for the leading\ncoefficients and degrees of Stern polynomials, and the identification of all\nintegers having a maximal number of such representations.\n"}
+{"abstract": "  The electrical behavior of Ni Schottky barrier formed onto heavily doped\n(ND>1019 cm-3) n-type phosphorous implanted silicon carbide (4H-SiC) was\ninvestigated, with a focus on the current transport mechanisms in both forward\nand reverse bias. The forward current-voltage characterization of Schottky\ndiodes showed that the predominant current transport is a thermionic-field\nemission mechanism. On the other hand, the reverse bias characteristics could\nnot be described by a unique mechanism. In fact, under moderate reverse bias,\nimplantation-induced damage is responsible for the temperature increase of the\nleakage current, while a pure field emission mechanism is approached with bias\nincreasing. The potential application of metal/4H-SiC contacts on heavily doped\nlayers in real devices are discussed.\n"}
+{"abstract": "  We present an original approach for predicting the static recrystallization\ntexture development during annealing of deformed crystalline materials. The\nmicrostructure is considered as a population of subgrains and grains whose\nsizes and boundary properties determine their growth rates. The model input\nparameters are measured directly on orientation maps maps of the deformed\nmicrostructure measured by electron backscattered diffraction. The anisotropy\nin subgrain properties then drives a competitive growth giving rise to the\nrecrystallization texture development. The method is illustrated by a\nsimulation of the static recrystallization texture development in a hot rolled\nferritic stainless steel. The model predictions are found to be in good\nagreement with the experimental measurements, and allow for an in-depth\ninvestigation of the formation sequence of the recrystallization texture. A\ndistinction is established between the texture components which develop due to\nfavorable growth conditions and those developing due to their predominance in\nthe prior deformed state. The high fraction of alpha fibre orientations in the\nrecrystallized state is shown to be a consequence of their predominance in the\ndeformed microstructure rather than a preferred growth mechanism. A close\ncontrol of the fraction of these orientations before annealing is thus required\nto minimize their presence in the recrystallized state.\n"}
+{"abstract": "  With the increased interest in machine learning, and deep learning in\nparticular, the use of automatic differentiation has become more wide-spread in\ncomputation. There have been two recent developments to provide the theoretical\nsupport for this types of structure. One approach, due to Abadi and Plotkin,\nprovides a simple differential programming language. Another approach is the\nnotion of a reverse differential category. In the present paper we bring these\ntwo approaches together. In particular, we show how an extension of reverse\nderivative categories models Abadi and Plotkin's language, and describe how\nthis categorical model allows one to consider potential improvements to the\noperational semantics of the language.\n"}
+{"abstract": "  As a result of 33 intercontinental Zoom calls, we characterise big Ramsey\ndegrees of the generic partial order in a similar way as Devlin characterised\nbig Ramsey degrees of the generic linear order (the order of rationals).\n"}
+{"abstract": "  Traditional smart grid energy auctions cannot directly be integrated in\nblockchain due to its decentralized nature. Therefore, research works are being\ncarried out to propose efficient decentralized auctions for energy trading.\nSince, blockchain is a novel paradigm which ensures trust, but it also comes up\nwith a curse of high computation and communication complexity which eventually\ncauses resource scarcity. Therefore, there is a need to develop and encourage\ndevelopment of greener and computational-friendly auctions to carry out\ndecentralized energy trading. In this paper, we first provide a thorough\nmotivation of decentralized auctions over traditional auctions. Afterwards, we\nprovide in-depth design requirements that can be taken into consideration while\ndeveloping such auctions. After that, we analyze technical works that have\ndeveloped blockchain based energy auctions from green perspective. Finally, we\nsummarize the article by providing challenges and possible future research\ndirections of blockchain based energy auction from green viewpoint.\n"}
+{"abstract": "  We study the mean-field Ising spin glass model with external field, where the\nrandom symmetric couplings matrix is orthogonally invariant in law. For\nsufficiently high temperature, we prove that the replica-symmetric prediction\nis correct for the first-order limit of the free energy. Our analysis is an\nadaption of a \"conditional quenched equals annealed\" argument used by\nBolthausen to analyze the high-temperature regime of the\nSherrington-Kirkpatrick model. We condition on a sigma-field that is generated\nby the iterates of an Approximate Message Passing algorithm for solving the TAP\nequations in this model, whose rigorous state evolution was recently\nestablished.\n"}
+{"abstract": "  The objective of the paper is to put canonical Lyapunov function(CLF),\ncanonizing diffeomorphism (CD) and canonical form of dynamical systems (CFDS),\nwhich have led to the generalization of the Lyapunov second method, in\nperspective of their high efficiency for Mathematical Modelling and Control\nDesign. We show how the symbiosis of the ideas of Henri Poincare and Nikolay\nChetaev leads us to CD, CFDS and CLF. Our approach successfully translates into\nmathematical modelling and control design for special two-angles synchronized\nlongitudinal maneuvering of a thrust-vectored aircraft. The essentially\nnonlinear five-dimensional mathematical model of the longitudinal flight\ndynamics of a thrust-vectored aircraft in a wing-body coordinate system with\ntwo controls, namely the angular deflections of a movable horizontal stabilizer\nand a turbojet engine nozzle, is investigated. The wide-sense robust and stable\nin the large tracking control law is designed. Its core is the hierarchical\ncascade of two controlling attractor-mediators and two controlling terminal\nattractors embedded in the extended phase space of the mathematical model of\nthe aircraft longitudinal motion. The detailed demonstration of the elaborated\ntechnique of designing wide-sense robust tracking control for the nonlinear\nmultidimensional mathematical model constitutes the quintessence of the paper.\n"}
+{"abstract": "  In this paper we consider the symmetric Kolmogorov operator $L=\\Delta\n+\\frac{\\nabla \\mu}{\\mu}\\cdot \\nabla$ on $L^2(\\mathbb R^N,d\\mu)$, where $\\mu$ is\nthe density of a probability measure on $\\mathbb R^N$. Under general conditions\non $\\mu$ we prove first weighted Rellich's inequalities with optimal constants\nand deduce that the operators $L$ and $-L^2$ with domain $H^2(\\mathbb\nR^N,d\\mu)$ and $H^4(\\mathbb R^N,d\\mu)$ respectively, generate analytic\nsemigroups of contractions on $L^2(\\mathbb R^N,d\\mu)$. We observe that $d\\mu$\nis the unique invariant measure for the semigroup generated by $-L^2$ and as a\nconsequence we describe the asymptotic behaviour of such semigroup and obtain\nsome local positivity properties. As an application we study the\nbi-Ornstein-Uhlenbeck operator and its semigroup on $L^2(\\mathbb R^N,d\\mu)$.\n"}
+{"abstract": "  We show that the stochastic Schr\\\"odinger equation (SSE) provides an ideal\nway to simulate the quantum mechanical spin dynamics of radical pairs. Electron\nspin relaxation effects arising from fluctuations in the spin Hamiltonian are\nstraightforward to include in this approach, and their treatment can be\ncombined with a highly efficient stochastic evaluation of the trace over\nnuclear spin states that is required to compute experimental observables. These\nfeatures are illustrated in example applications to a flavin-tryptophan radical\npair of interest in avian magnetoreception, and to a problem involving\nspin-selective radical pair recombination along a molecular wire. In the first\nof these examples, the SSE is shown to be both more efficient and more widely\napplicable than a recent stochastic implementation of the Lindblad equation,\nwhich only provides a valid treatment of relaxation in the extreme-narrowing\nlimit. In the second, the exact SSE results are used to assess the accuracy of\na recently-proposed combination of Nakajima-Zwanzig theory for the spin\nrelaxation and Schulten-Wolynes theory for the spin dynamics, which is\napplicable to radical pairs with many more nuclear spins. An appendix analyses\nthe efficiency of trace sampling in some detail, highlighting the particular\nadvantages of sampling with SU(N) coherent states.\n"}
+{"abstract": "  This paper presents a novel approach using sensitivity analysis for\ngeneralizing Differential Dynamic Programming (DDP) to systems characterized by\nimplicit dynamics, such as those modelled via inverse dynamics and variational\nor implicit integrators. It leads to a more general formulation of DDP,\nenabling for example the use of the faster recursive Newton-Euler inverse\ndynamics. We leverage the implicit formulation for precise and exact contact\nmodelling in DDP, where we focus on two contributions: (1) Contact dynamics in\nacceleration level that enables high-order integration schemes; (2) Formulation\nusing an invertible contact model in the forward pass and a closed form\nsolution in the backward pass to improve the numerical resolution of contacts.\nThe performance of the proposed framework is validated (1) by comparing\nimplicit versus explicit DDP for the swing-up of a double pendulum, and (2) by\nplanning motions for two tasks using a single leg model making multi-body\ncontacts with the environment: standing up from ground, where a priori contact\nenumeration is challenging, and maintaining balance under an external\nperturbation.\n"}
+{"abstract": "  An emerging amount of intelligent applications have been developed with the\nsurge of Machine Learning (ML). Deep Neural Networks (DNNs) have demonstrated\nunprecedented performance across various fields such as medical diagnosis and\nautonomous driving. While DNNs are widely employed in security-sensitive\nfields, they are identified to be vulnerable to Neural Trojan (NT) attacks that\nare controlled and activated by the stealthy trigger. We call this vulnerable\nmodel adversarial artificial intelligence (AI). In this paper, we target to\ndesign a robust Trojan detection scheme that inspects whether a pre-trained AI\nmodel has been Trojaned before its deployment. Prior works are oblivious of the\nintrinsic property of trigger distribution and try to reconstruct the trigger\npattern using simple heuristics, i.e., stimulating the given model to incorrect\noutputs. As a result, their detection time and effectiveness are limited. We\nleverage the observation that the pixel trigger typically features spatial\ndependency and propose TAD, the first trigger approximation based Trojan\ndetection framework that enables fast and scalable search of the trigger in the\ninput space. Furthermore, TAD can also detect Trojans embedded in the feature\nspace where certain filter transformations are used to activate the Trojan. We\nperform extensive experiments to investigate the performance of the TAD across\nvarious datasets and ML models. Empirical results show that TAD achieves a\nROC-AUC score of 0:91 on the public TrojAI dataset 1 and the average detection\ntime per model is 7:1 minutes.\n"}
+{"abstract": "  The 4f-electron delocalization plays a key role in the low-temperature\nproperties of rare-earth metals and intermetallics, including heavy fermions\nand mix-valent compounds, and is normally realized by the many-body Kondo\ncoupling between 4f and conduction electrons. Due to the large onsite Coulomb\nrepulsion of 4f electrons, the bandwidth-control Mott-type delocalization,\ncommonly observed in d-electron systems, is difficult in 4f-electron systems\nand remains elusive in spectroscopic experiments. Here we demonstrate that the\nbandwidth-control orbital-selective delocalization of 4f electrons can be\nrealized in epitaxial Ce films by thermal annealing, which results in a\nmetastable surface phase with a reduced layer spacing. The resulting\nquasiparticle bands exhibit large dispersion with exclusive 4f character near\nE_F and extend reasonably far below the Fermi energy, which can be explained\nfrom the Mott physics. The experimental quasiparticle dispersion agrees\nsurprisingly well with density-functional theory calculation and also exhibits\nunusual temperature dependence, which could be a direct consequence of the\ndelicate interplay between the bandwidth-control Mott physics and the\ncoexisting Kondo hybridization. Our work therefore opens up the opportunity to\nstudy the interaction between two well-known localization-delocalization\nmechanisms in correlation physics, i.e., Kondo vs Mott, which can be important\nfor a fundamental understanding of 4f-electron systems.\n"}
+{"abstract": "  Field transformation rules of the standard fermionic T-duality require\nfermionic isometries to anticommute, which leads to complexification of the\nKilling spinors and results in complex valued dual backgrounds. We generalize\nthe field transformations to the setting with non-anticommuting fermionic\nisometries and show that the resulting backgrounds are solutions of double\nfield theory. Explicit examples of non-abelian fermionic T-dualities that\nproduce real backgrounds are given. Some of our examples can be bosonic\nT-dualized into usual supergravity solutions, while the others are genuinely\nnon-geometric. Comparison with alternative treatment based on sigma models on\nsupercosets shows consistency.\n"}
+{"abstract": "  Hybrid data combining both tabular and textual content (e.g., financial\nreports) are quite pervasive in the real world. However, Question Answering\n(QA) over such hybrid data is largely neglected in existing research. In this\nwork, we extract samples from real financial reports to build a new large-scale\nQA dataset containing both Tabular And Textual data, named TAT-QA, where\nnumerical reasoning is usually required to infer the answer, such as addition,\nsubtraction, multiplication, division, counting, comparison/sorting, and the\ncompositions. We further propose a novel QA model termed TAGOP, which is\ncapable of reasoning over both tables and text. It adopts sequence tagging to\nextract relevant cells from the table along with relevant spans from the text\nto infer their semantics, and then applies symbolic reasoning over them with a\nset of aggregation operators to arrive at the final answer. TAGOPachieves 58.0%\ninF1, which is an 11.1% absolute increase over the previous best baseline\nmodel, according to our experiments on TAT-QA. But this result still lags far\nbehind performance of expert human, i.e.90.8% in F1. It is demonstrated that\nour TAT-QA is very challenging and can serve as a benchmark for training and\ntesting powerful QA models that address hybrid form data.\n"}
+{"abstract": "  We discuss transport through an interferometer formed by helical edge states\nof the quantum spin Hall insulator. Focusing on effects induced by a strong\nmagnetic impurity placed in one of the arms of interferometer, we consider the\nexperimentally relevant case of relatively high temperature as compared to the\nlevel spacing. We obtain the conductance and the spin polarization in the\nclosed form for arbitrary tunneling amplitude of the contacts and arbitrary\nstrength of the magnetic impurity. We demonstrate the existence of quantum\neffects which do not show up in previously studied case of weak magnetic\ndisorder. We find optimal conditions for spin filtering and demonstrate that\nthe spin polarization of outgoing electrons can reach 100%.\n"}
+{"abstract": "  This paper describes the IDLab submission for the text-independent task of\nthe Short-duration Speaker Verification Challenge 2021 (SdSVC-21). This speaker\nverification competition focuses on short duration test recordings and\ncross-lingual trials, along with the constraint of limited availability of\nin-domain DeepMine Farsi training data. Currently, both Time Delay Neural\nNetworks (TDNNs) and ResNets achieve state-of-the-art results in speaker\nverification. These architectures are structurally very different and the\nconstruction of hybrid networks looks a promising way forward. We introduce a\n2D convolutional stem in a strong ECAPA-TDNN baseline to transfer some of the\nstrong characteristics of a ResNet based model to this hybrid CNN-TDNN\narchitecture. Similarly, we incorporate absolute frequency positional encodings\nin an SE-ResNet34 architecture. These learnable feature map biases along the\nfrequency axis offer this architecture a straightforward way to exploit\nfrequency positional information. We also propose a frequency-wise variant of\nSqueeze-Excitation (SE) which better preserves frequency-specific information\nwhen rescaling the feature maps. Both modified architectures significantly\noutperform their corresponding baseline on the SdSVC-21 evaluation data and the\noriginal VoxCeleb1 test set. A four system fusion containing the two improved\narchitectures achieved a third place in the final SdSVC-21 Task 2 ranking.\n"}
+{"abstract": "  The lack of comprehensive sources of accurate vulnerability data represents a\ncritical obstacle to studying and understanding software vulnerabilities (and\ntheir corrections). In this paper, we present an approach that combines\nheuristics stemming from practical experience and machine-learning (ML) -\nspecifically, natural language processing (NLP) - to address this problem. Our\nmethod consists of three phases. First, an advisory record containing key\ninformation about a vulnerability is extracted from an advisory (expressed in\nnatural language). Second, using heuristics, a subset of candidate fix commits\nis obtained from the source code repository of the affected project by\nfiltering out commits that are known to be irrelevant for the task at hand.\nFinally, for each such candidate commit, our method builds a numerical feature\nvector reflecting the characteristics of the commit that are relevant to\npredicting its match with the advisory at hand. The feature vectors are then\nexploited for building a final ranked list of candidate fixing commits. The\nscore attributed by the ML model to each feature is kept visible to the users,\nallowing them to interpret of the predictions.\n  We evaluated our approach using a prototype implementation named Prospector\non a manually curated data set that comprises 2,391 known fix commits\ncorresponding to 1,248 public vulnerability advisories. When considering the\ntop-10 commits in the ranked results, our implementation could successfully\nidentify at least one fix commit for up to 84.03% of the vulnerabilities (with\na fix commit on the first position for 65.06% of the vulnerabilities). In\nconclusion, our method reduces considerably the effort needed to search OSS\nrepositories for the commits that fix known vulnerabilities.\n"}
+{"abstract": "  Concatenated modal interferometers based multipoint sensing system for\ndetection of instantaneous amplitude, frequency, and phase of mechanical\nvibrations is proposed and demonstrated. The sensor probes are fabricated using\nidentical photonic crystal fiber (PCF) sections and integrated along a single\nfiber channel to act as a compact and efficient sensing system. Individual\nprobes operate independently producing a resultant signal that is a\nsuperposition of each interferometer response signal. By analyzing the\nresultant signals, information about the measurand field at each location is\nrealized. Such a sensing system would find wide applications at industrial,\ninfrastructural, and medical fronts for monitoring various unsteady physical\nphenomena.\n"}
+{"abstract": "  Mantaci et al. [TCS 2007] defined the eBWT to extend the definition of the\nBWT to a collection of strings, however, since this introduction, it has been\nused more generally to describe any BWT of a collection of strings and the\nfundamental property of the original definition (i.e., the independence from\nthe input order) is frequently disregarded. In this paper, we propose a simple\nlinear-time algorithm for the construction of the original eBWT, which does not\nrequire the preprocessing of Bannai et al. [CPM 2021]. As a byproduct, we\nobtain the first linear-time algorithm for computing the BWT of a single string\nthat uses neither an end-of-string symbol nor Lyndon rotations. We combine our\nnew eBWT construction with a variation of prefix-free parsing to allow for\nscalable construction of the eBWT. We evaluate our algorithm (pfpebwt) on sets\nof human chromosomes 19, Salmonella, and SARS-CoV2 genomes, and demonstrate\nthat it is the fastest method for all collections, with a maximum speedup of\n7.6x on the second best method. The peak memory is at most 2x larger than the\nsecond best method. Comparing with methods that are also, as our algorithm,\nable to report suffix array samples, we obtain a 57.1x improvement in peak\nmemory. The source code is publicly available at\nhttps://github.com/davidecenzato/PFP-eBWT.\n"}
+{"abstract": "  Path sets are spaces of one-sided infinite symbol sequences corresponding to\nthe one-sided infinite walks beginning at a fixed initial vertex in a directed\nlabeled graph. Path sets are a generalization of one-sided sofic shifts. This\npaper studies decimation operations $\\psi_{j, n}(\\cdot)$ which extract symbol\nsequences in infinite arithmetic progressions (mod n). starting with the symbol\nat position j. It also studies a family of n-ary interleaving operations, one\nfor each arity n, which act on an ordered set $(X_0, X_1, ..., X_{n-1})$ of\none-sided symbol sequences on a finite alphabet A, to produce a set $X$ of all\noutput sequences obtained by interleaving the symbols of words $x_i$ in each\n$X_i$ in arithmetic progressions (mod n). It studies a set of closure\noperations relating interleaving and decimation. It reviews basic algorithmic\nresults on presentations of path sets and existence of a minimal\nright-resolving presentation. It gives an algorithm for computing presentations\nof decimations of path sets from presentations of path sets, showing the\nminimal right-resolving presentation of $\\psi_{j,n}(X)$ has at most one more\nvertex than a minimal right-resolving presentation of X. It shows that a path\nset has only finitely many distinct decimations. It shows the class of path\nsets on a fixed alphabet is closed under all interleaving operations, and gives\nalgorithms for computing presentations of n-fold interleavings of given sets\n$X_i$. It studies interleaving factorizations and classifies path sets that\nhave infinite interleaving factorizations, and gives an algorithm to recognize\nthem. It shows a finiteness of a process of iterated interleaving\nfactorizations, which \"freezes\" factors that have infinite interleavings.\n"}
+{"abstract": "  We study linear perturbations about static and spherically symmetric black\nhole solutions with stealth scalar hair in degenerate higher-order\nscalar-tensor (DHOST) theories. We clarify master variables and derive the\nquadratic Lagrangian for both odd- and even-parity perturbations. It is shown\nthat the even modes are in general plagued by gradient instabilities, or\notherwise the perturbations would be strongly coupled. Several possible ways\nout are also discussed.\n"}
+{"abstract": "  Sparse regression is frequently employed in diverse scientific settings as a\nfeature selection method. A pervasive aspect of scientific data that hampers\nboth feature selection and estimation is the presence of strong correlations\nbetween predictive features. These fundamental issues are often not appreciated\nby practitioners, and jeapordize conclusions drawn from estimated models. On\nthe other hand, theoretical results on sparsity-inducing regularized regression\nsuch as the Lasso have largely addressed conditions for selection consistency\nvia asymptotics, and disregard the problem of model selection, whereby\nregularization parameters are chosen. In this numerical study, we address these\nissues through exhaustive characterization of the performance of several\nregression estimators, coupled with a range of model selection strategies.\nThese estimators and selection criteria were examined across correlated\nregression problems with varying degrees of signal to noise, distribution of\nthe non-zero model coefficients, and model sparsity. Our results reveal a\nfundamental tradeoff between false positive and false negative control in all\nregression estimators and model selection criteria examined. Additionally, we\nare able to numerically explore a transition point modulated by the\nsignal-to-noise ratio and spectral properties of the design covariance matrix\nat which the selection accuracy of all considered algorithms degrades. Overall,\nwe find that SCAD coupled with BIC or empirical Bayes model selection performs\nthe best feature selection across the regression problems considered.\n"}
+{"abstract": "  The aim of this paper is to study in details the regular holonomic $D-$module\nintroduced in \\cite{[B.19]} whose local solutions outside the polar\nhyper-surface $\\{\\Delta(\\sigma).\\sigma_k = 0 \\}$ are given by the local system\ngenerated by the local branches of the multivalued function which is the root\nof the universal degree $k$ equation $z^k + \\sum_{h=1}^k\n(-1)^h.\\sigma_h.z^{k-h} = 0 $. Note that it is surprising that this regular\nholonomic $D-$module is given by the quotient of $D$ by a left ideal which has\nvery simple explicit generators despite the fact it necessary encodes the\nanalogous systems for any root of the universal degree $l$ equation for each $l\n\\leq k$. Our main result is to relate this $D-$module with the minimal\nextension of the irreducible local system associated to the difference of two\nbranches of the multivalued function defined above. Then we obtain again a very\nsimple explicit description of this minimal extension in term of the generators\nof its left ideal in the Weyl algebra. As an application we show how these\nresults allow to compute the Taylor expansion of the root near $-1$ of the\nequation $z^k + \\sum_{h=-1}^k (-1)^h.\\sigma_h.z^{k-h} - (-1)^k = 0 $.\n"}
+{"abstract": "  We consider backward filtrations generated by processes coming from\ndeterministic and probabilistic cellular automata. We prove that these\nfiltrations are standard in the classical sense of Vershik's theory, but we\nalso study them from another point of view that takes into account the\nmeasurepreserving action of the shift map, for which each sigma-algebra in the\nfiltrations is invariant. This initiates what we call the dynamical\nclassification of factor filtrations, and the examples we study show that this\nclassification leads to different results.\n"}
+{"abstract": "  Quantum state transfer is a very important process in building a quantum\nnetwork when information from flying Qubit is transferred to the stationary\nQubit in a node via a quantum state transfer. NV centers due to their long\ncoherence time and the presence of nearby $13_C$ nuclear spin is an excellent\ncandidate for multi-Qubit quantum memory. Here we propose a theoretical\ndescription for such a quantum state transfer from a cavity to a nearest\nneighbour $13_C$ nuclear spin of a single Nitrogen vacancy center in diamond;\nit shows great potential in realizing scalable quantum networks and quantum\nsimulation. The full Hamiltonian was considered with the zeroth-order and\ninteraction terms in the Hamiltonian and the theory of effective hamiltonian\ntheory was applied. We study the time evolution of the combined cavity-$13_C$\nstate through analytical calculation and simulation using QuTip. Graphs for\nstate transfer and fidelity measurement are presented here. We show that our\ntheoretical description verifies a high fidelity quantum state transfer from\nthe cavity to $13_C$ center by choosing suitable system parameters.\n"}
+{"abstract": "  It has been proved in [J.-D. Hardtke, J. Math. Phys. Anal. Geom. 16, no.2,\n119--137 (2020)] that a K\\\"othe-Bochner space $E(X)$ is locally\noctahedral/locally almost square if $X$ has the respective property and the\nsimple functions are dense in $E(X)$. Here we show that the result still holds\ntrue without the density assumption. The proof makes use of the\nKuratowski-Ryll-Nardzewski Theorem on measurable selections.\n"}
+{"abstract": "  Textual escalation detection has been widely applied to e-commerce companies'\ncustomer service systems to pre-alert and prevent potential conflicts.\nSimilarly, in public areas such as airports and train stations, where many\nimpersonal conversations frequently take place, acoustic-based escalation\ndetection systems are also useful to enhance passengers' safety and maintain\npublic order. To this end, we introduce a system based on acoustic-lexical\nfeatures to detect escalation from speech, Voice Activity Detection (VAD) and\nlabel smoothing are adopted to further enhance the performance in our\nexperiments. Considering a small set of training and development data, we also\nemploy transfer learning on several wellknown emotional detection datasets,\ni.e. RAVDESS, CREMA-D, to learn advanced emotional representations that is then\napplied to the conversational escalation detection task. On the development\nset, our proposed system achieves 81.5% unweighted average recall (UAR) which\nsignificantly outperforms the baseline with 72.2% UAR.\n"}
+{"abstract": "  We propose a novel end-to-end solution for video instance segmentation (VIS)\nbased on transformers. Recently, the per-clip pipeline shows superior\nperformance over per-frame methods leveraging richer information from multiple\nframes. However, previous per-clip models require heavy computation and memory\nusage to achieve frame-to-frame communications, limiting practicality. In this\nwork, we propose Inter-frame Communication Transformers (IFC), which\nsignificantly reduces the overhead for information-passing between frames by\nefficiently encoding the context within the input clip. Specifically, we\npropose to utilize concise memory tokens as a mean of conveying information as\nwell as summarizing each frame scene. The features of each frame are enriched\nand correlated with other frames through exchange of information between the\nprecisely encoded memory tokens. We validate our method on the latest benchmark\nsets and achieved the state-of-the-art performance (AP 44.6 on YouTube-VIS 2019\nval set using the offline inference) while having a considerably fast runtime\n(89.4 FPS). Our method can also be applied to near-online inference for\nprocessing a video in real-time with only a small delay. The code will be made\navailable.\n"}
+{"abstract": "  Subscription services face a difficult problem when estimating the causal\nimpact of content launches on acquisition. Customers buy subscriptions, not\nindividual pieces of content, and once subscribed they may consume many pieces\nof content in addition to the one(s) that drew them to the service. In this\npaper, we propose a scalable methodology to estimate the incremental\nacquisition impact of content launches in a subscription business model when\nrandomized experimentation is not feasible. Our approach uses simple\nassumptions to transform the problem into an equivalent question: what is the\nexpected consumption rate for new subscribers who did not join due to the\ncontent launch? We estimate this counterfactual rate using the consumption rate\nof new subscribers who joined just prior to launch, while making adjustments\nfor variation related to subscriber attributes, the in-product experience, and\nseasonality. We then compare our counterfactual consumption to the actual rate\nin order to back out an acquisition estimate. Our methodology provides top-line\nimpact estimates at the content / day / region grain. Additionally, to enable\nsubscriber-level attribution, we present an algorithm that assigns specific\nindividual accounts to add up to the top-line estimate. Subscriber-level\nattribution is derived by solving an optimization problem to minimize the\nnumber of subscribers attributed to more than one piece of content, while\nmaximizing the average propensity to be incremental for subscribers attributed\nto each piece of content. Finally, in the absence of definitive ground truth,\nwe present several validation methods which can be used to assess the\nplausibility of impact estimates generated by these methods.\n"}
+{"abstract": "  We report the effect of 4f electron doping on structural, electrical and\nmagneto-transport properties of Dy doped half Heusler Y1-x(Dy)xPdBi (x =0, 0.2,\n0.5, 1) thin films grown by pulsed laser deposition. The Dy doping leads to\nlattice contraction which increases from 0% for the parent x =0 sample to\napprox 1.3% for x=1 sample with increase in Dy doping. The electrical transport\nmeasurements show a typical semi-metallic behaviour in the temperature range 3K\nto 300K and a sharp drop in resistivity at low temperatures (less than 3K) for\nall the samples. Magnetotransport measurements and Shubnikov de-Hass\noscillations at high magnetic fields demonstrate that for these topologically\nnon-trivial samples, Dy doping induced lattice contraction plays an active role\nin modifying the Fermi surface, carrier concentration and the effective\nelectron mass. There is an uniform suppression of the onset of\nsuperconductivity with increased Dy doping which is possibly related to the\nincreasing local exchange field arising from the 4f electrons in Dy. Our\nresults indicate that we can tune various band structure parameters of YPdBi by\nf electron doping and strained thin films of Y1-x(Dy)xPdBi show surface\ndominated relativistic carrier transport at low temperatures.\n"}
+{"abstract": "  Asteroseismology using space-based telescopes is vital to our understanding\nof stellar structure and evolution. {\\textit{CoRoT}}, {\\textit{Kepler}}, and\n{\\textit{TESS}} space telescopes have detected large numbers of solar-like\noscillating evolved stars. %(kaynaklar, Kallinger, vb ). Solar-like oscillation\nfrequencies have an important role in the determination of fundamental stellar\nparameters; in the literature, the relations between the two is established by\nthe so-called scaling relations. % These scaling relations are in better\nagreement with mass and radius of main-sequence stars with large separation\n($\\Delta\\nu$) and frequency of maximum amplitude (${\\nu_{\\rm max}}$). In this\nstudy, we analyse data obtained from the observation of 15 evolved solar-like\noscillating stars using the {\\textit{Kepler}} and ground-based %\\textit{CoRoT}\ntelescopes.\n  The main purpose of the study is to determine very precisely the fundamental\nparameters of evolved stars by constructing interior models using asteroseismic\nparameters. We also fit the reference frequencies of models to the\nobservational reference frequencies caused by the He {\\scriptsize II}\nionization zone.\n  The 15 evolved stars are found to have masses and radii within ranges of\n$0.79$-$1.47$ $M_{\\rm sun}$ and $1.60$-$3.15$ $R_{\\rm sun}$, respectively.\nTheir model ages range from $2.19$ to $12.75$ Gyr. %Using a number of methods\nbased on conventional and modified scaling relations and evolutionary models\nconstructed with using the {\\small {MESA}} code, we determine stellar radii,\nmasses and ages. It is revealed that fitting reference frequencies typically\nincrease the accuracy of asteroseismic radius, mass, and age. The typical\nuncertainties of mass and radius are $\\sim$ 3-6 and $\\sim$ 1-2 per cent,\nrespectively. Accordingly, the differences between the model and literature\nages are generally only a few Gyr.\n"}
+{"abstract": "  Over-the-air computation (AirComp) has recently been recognized as a\npromising scheme for a fusion center to achieve fast distributed data\naggregation in wireless networks via exploiting the superposition property of\nmultiple-access channels. Since it is challenging to provide reliable data\naggregation for a large number of devices using AirComp, in this paper, we\npropose to enable AirComp via the cloud radio access network (Cloud-RAN)\narchitecture, where a large number of antennas are deployed at separate sites\ncalled remote radio heads (RRHs). However, the potential densification gain\nprovided by Cloud-RAN is generally bottlenecked by the limited capacity of the\nfronthaul links connecting the RRHs and the fusion center. To this end, we\nformulate a joint design problem for AirComp transceivers and quantization bits\nallocation and propose an efficient algorithm to tackle this problem. Our\nnumerical results shows the advantages of the proposed architecture compared\nwith the state-of-the-art solutions.\n"}
+{"abstract": "  This early work aims to allow organizations to diagnose their capacity to\nproperly adopt microservices through initial milestones of a Microservice\nMaturity Model (MiMMo). The objective is to prepare the way towards a general\nframework to help companies and industries to determine their microservices\nmaturity. Organizations lean more and more on distributed web applications and\nLine of Business software. This is particularly relevant during the current\nCovid-19 crisis, where companies are even more challenged to offer their\nservices online, targeting a very high level of responsiveness in the face of\nrapidly increasing and diverse demands. For this, microservices remain the most\nsuitable delivery application architectural style. They allow agility not only\non the technical application, as often considered, but on the enterprise\narchitecture as a whole, influencing the actual financial business of the\ncompany. However, microservices adoption is highly risk-prone and complex.\nBefore they establish an appropriate migration plan, first and foremost,\ncompanies must assess their degree of readiness to adopt microservices. For\nthis, MiMMo, a Microservices Maturity Model framework assessment, is proposed\nto help companies assess their readiness for the microservice architectural\nstyle, based on their actual situation. MiMMo results from observations of and\nexperience with about thirty organizations writing software. It conceptualizes\nand generalizes the progression paths they have followed to adopt microservices\nappropriately. Using the model, an organization can evaluate itself in two\ndimensions and five maturity levels and thus: (i) benchmark itself on its\ncurrent use of microservices; (ii) project the next steps it needs to achieve a\nhigher maturity level and (iii) analyze how it has evolved and maintain a\nglobal coherence between technical and business stakes.\n"}
+{"abstract": "  In this paper we study a neighborhood of generic singularities formed by mean\ncurvature flow (MCF). We limit our consideration to the singularities modelled\non $\\mathbb{S}^3\\times\\mathbb{R}$ because, compared to the cases\n$\\mathbb{S}^k\\times \\mathbb{R}^{l}$ with $l\\geq 2$, the present case has the\nfewest possibilities to be considered. For various possibilities, we provide a\ndetailed description for a small, but fixed, neighborhood of singularity, and\nprove that a small neighborhood of the singularity is mean convex, and the\nsingularity is isolated. For the remaining possibilities, we conjecture that an\nentire neighborhood of the singularity becomes singular at the time of blowup,\nand present evidences to support this conjecture. A key technique is that, when\nlooking for a dominating direction for the rescaled MCF, we need a normal form\ntransformation, as a result, the rescaled MCF is parametrized over some chosen\ncurved cylinder, instead over a standard straight one.\n  This is a long paper. The introduction is carefully written to present the\nkey steps and ideas.\n"}
+{"abstract": "  The evidence for benzonitrile (C$_6$H$_5$CN}) in the starless cloud core\nTMC-1 makes high-resolution studies of other aromatic nitriles and their\nring-chain derivatives especially timely. One such species is\nphenylpropiolonitrile (3-phenyl-2-propynenitrile, C$_6$H$_5$C$_3$N), whose\nspectroscopic characterization is reported here for the first time. The low\nresolution (0.5 cm$^{-1}$) vibrational spectrum of C$_6$H$_5$C$_3$N} has been\nrecorded at far- and mid-infrared wavelengths (50 - 3500 cm$^{-1}$) using a\nFourier Transform interferometer, allowing for the assignment of band centers\nof 14 fundamental vibrational bands. The pure rotational spectrum of the\nspecies has been investigated using a chirped-pulse Fourier transform microwave\n(FTMW) spectrometer (6 - 18 GHz), a cavity enhanced FTMW instrument (6 - 20\nGHz), and a millimeter-wave one (75 - 100 GHz, 140 - 214 GHz). Through the\nassignment of more than 6200 lines, accurate ground state spectroscopic\nconstants (rotational, centrifugal distortion up to octics, and nuclear\nquadrupole hyperfine constants) have been derived from our measurements, with a\nplausible prediction of the weaker bands through calculations. Interstellar\nsearches for this highly polar species can now be undertaken with confidence\nsince the astronomically most interesting radio lines have either been measured\nor can be calculated to very high accuracy below 300 GHz.\n"}
+{"abstract": "  In this work, the order parameter or average magnetization expressions are\nobtained for the square and the honeycomb lattices based on recently obtained\nmagnetization relation, $<\\sigma_{0,i}>=\n  <\\!\\!\\tanh[ \\kappa(\\sigma_{1,i}+\\sigma_{2,i}+\\dots +\\sigma_{z,i})+H]\\!\\!> $.\nWhere, $\\kappa$ is the coupling strength and $z$ is the number of nearest\nneighbors. $\\sigma_{0,i}$ denotes the central spin at the $i^{th}$ site while\n$\\sigma_{l,i}$, $l=1,2,\\dots,z$, are the nearest neighbor spins around the\ncentral spin. In our investigation, inevitably we have to make a conjecture\nabout the three site correlation function appearing in the obtained relation of\nthis paper. The conjectured form of the the three spin correlation function is\ngiven by the relation,\n$<\\!\\!\\sigma_{1}\\sigma_{2}\\sigma_{3}\\!\\!>=a<\\sigma>+(1-a)<\\sigma>^{(1+\\beta^{-1})}$,\nhere $\\beta$ denotes the critical exponent for the average magnetization and\n$a$ is positive real number less than one. The relevance of this conjecture is\nbased on fundamental physical reasoning. In addition, it is tested and\ninvestigated by comparing the obtained relations of this paper with the\npreviously obtained exact results for the square and honeycomb lattices. It is\nseen that the agreements of the obtained average magnetization relations with\nthose of the previously obtained exact results are unprecedentedly perfect.\n"}
+{"abstract": "  We consider a node where packets of fixed size are generated at arbitrary\nintervals. The node is required to maintain the peak age of information (AoI)\nat the monitor below a threshold by transmitting potentially a subset of the\ngenerated packets. At any time, depending on packet availability and current\nAoI, the node can choose the packet to transmit, and its transmission speed. We\nconsider a power function (rate of energy consumption) that is increasing and\nconvex in transmission speed, and the objective is to minimize the energy\nconsumption under the peak AoI constraint at all times. For this problem, we\npropose a (customized) greedy policy, and analyze its competitive ratio (CR) by\ncomparing it against an optimal offline policy by deriving some structural\nresults. We show that for polynomial power functions, the CR upper bound for\nthe greedy policy is independent of the system parameters, such as the peak\nAoI, packet size, time horizon, or the number of packets generated. Also, we\nderive a lower bound on the competitive ratio of any causal policy, and show\nthat for exponential power functions (e.g., Shannon rate function), the\ncompetitive ratio of any causal policy grows exponentially with increase in the\nratio of packet size to peak AoI.\n"}
+{"abstract": "  By a quasi-connected reductive group (a term of Labesse) over an arbitrary\nfield we mean an almost direct product of a connected semisimple group and a\nquasi-torus (a smooth group of multiplicative type). We show that a linear\nalgebraic group is quasi-connected reductive if and only if it is isomorphic to\na smooth normal subgroup of a connected reductive group. We compute the first\nGalois cohomology set H^1(R,G) of a quasi-connected reductive group G over the\nfield R of real numbers in terms of a certain action of a subgroup of the Weyl\ngroup on the Galois cohomology of a fundamental quasi-torus of G.\n"}
+{"abstract": "  We evaluate three leading dependency parser systems from different paradigms\non a small yet diverse subset of languages in terms of their\naccuracy-efficiency Pareto front. As we are interested in efficiency, we\nevaluate core parsers without pretrained language models (as these are\ntypically huge networks and would constitute most of the compute time) or other\naugmentations that can be transversally applied to any of them. Biaffine\nparsing emerges as a well-balanced default choice, with sequence-labelling\nparsing being preferable if inference speed (but not training energy cost) is\nthe priority.\n"}
+{"abstract": "  We identify an effective proxy for the analytically-unknown second integral\nof motion (I_2) for rotating barred or tri-axial potentials. Planar orbits of a\ngiven energy follow a tight sequence in the space of the time-averaged angular\nmomentum and its amplitude of fluctuation. The sequence monotonically traces\nthe main orbital families in the Poincare map, even in the presence of resonant\nand chaotic orbits. This behavior allows us to define the \"Calibrated Angular\nMomentum,\" the average angular momentum normalized by the amplitude of its\nfluctuation, as a numerical proxy for I_2. It also implies that the amplitude\nof fluctuation in L_z, previously under-appreciated, contains valuable\ninformation. This new proxy allows one to classify orbital families easily and\naccurately, even for real orbits in N-body simulations of barred galaxies. It\nis a good diagnostic tool of dynamical systems, and may facilitate the\nconstruction of equilibrium models.\n"}
+{"abstract": "  Vertex connectivity is a well-studied concept in graph theory with numerous\napplications. A graph is $k$-connected if it remains connected after removing\nany $k-1$ vertices. The vertex connectivity of a graph is the maximum $k$ such\nthat the graph is $k$-connected. There is a long history of algorithmic\ndevelopment for efficiently computing vertex connectivity. Recently, two near\nlinear-time algorithms for small k were introduced by [Forster et al. SODA\n2020]. Prior to that, the best known algorithm was one by [Henzinger et al.\nFOCS'96] with quadratic running time when k is small.\n  In this paper, we study the practical performance of the algorithms by\nForster et al. In addition, we introduce a new heuristic on a key subroutine\ncalled local cut detection, which we call degree counting. We prove that the\nnew heuristic improves space-efficiency (which can be good for caching\npurposes) and allows the subroutine to terminate earlier. According to\nexperimental results on random graphs with planted vertex cuts, random\nhyperbolic graphs, and real world graphs with vertex connectivity between 4 and\n15, the degree counting heuristic offers a factor of 2-4 speedup over the\noriginal non-degree counting version for most of our data. It also outperforms\nthe previous state-of-the-art algorithm by Henzinger et al. even on relatively\nsmall graphs.\n"}
+{"abstract": "  Smart Cities are developing in parallel with the global trend towards\nurbanization. The ultimate goal of Smart City projects is to deliver a positive\nimpact for the citizens and the socio-economic and ecological environment. This\ninvolves the challenge to derive concrete requirements for (technical) projects\nfrom overarching concepts like Quality of Life (QoL) and Subjective Well-Being\n(SWB). Linking long-term, impact oriented goals with project outputs and\noutcomes is a complex problem. Decision making on requirements and resulting\nfeatures of single Smart City projects (or systems) is even more complex since\ncities are not like monolithic, hierarchical and well structured systems.\nNevertheless, systems engineering provides concepts which support decision\nmaking in such situations. Complex socio-technical systems such as smart cities\ncan be characterized as systems of systems (SoS). A SoS is composed of\nindependently developed systems that nevertheless provide a higher-level\nintegrated functionality. To add new functionality to a SoS, either existing\nsystems must be extended or new systems must be developed and integrated. In\nboth cases, the extension of functionality is usually done in small increments\nand structured via software releases. However, the decision which features to\ninclude in the next release is complex and difficult to manage when done\nmanually. To address this, we make use of the multi-objective next release\nproblem (MONRP) to search for an optimal set of features for a software release\nin a SoS context. In order to refine the search in an early planning phase, we\npropose a technique to model and validate the features using the scenario\nmodeling language for Kotlin (SMLK). This is demonstrated with a\nproof-of-concept implementation.\n"}
+{"abstract": "  As a 3D topological insulator, bismuth selenide (Bi2Se3) has potential\napplications for electrically and optically controllable magnetic and\noptoelectronic devices. How the carriers interact with lattice is important to\nunderstand the coupling with its topological phase. It is essential to measure\nwith a time scale smaller than picoseconds for initial interaction. Here we use\nan X-ray free-electron laser to perform time-resolved diffraction to study\nultrafast carrier-induced lattice contractions and interlayer modulations in\nBi2Se3 thin films. The lattice contraction depends on the carrier concentration\nand is followed by an interlayer expansion accompanied by oscillations. Using\ndensity functional theory (DFT) and the Lifshitz model, the initial contraction\ncan be explained by van der Waals force modulation of the confined free carrier\nlayers. Band inversion, related to a topological phase transition, is modulated\nby the expansion of the interlayer distance. These results provide insight into\ninstantaneous topological phases on ultrafast timescales.\n"}
+{"abstract": "  We investigate artificial compressibility (AC) techniques for the time\ndiscretization of the incompressible Navier-Stokes equations. The space\ndiscretization is based on a lowest-order face-based scheme supporting\npolytopal meshes, namely discrete velocities are attached to the mesh faces and\ncells, whereas discrete pressures are attached to the mesh cells. This\nface-based scheme can be embedded into the framework of hybrid mixed mimetic\nschemes and gradient schemes, and has close links to the lowest-order version\nof hybrid high-order methods devised for the steady incompressible\nNavier-Stokes equations. The AC timestepping uncouples at each time step the\nvelocity update from the pressure update. The performances of this approach are\ncompared against those of the more traditional monolithic approach which\nmaintains the velocity-pressure coupling at each time step. We consider both\nfirst-order and second-order time schemes and either an implicit or an explicit\ntreatment of the nonlinear convection term. We investigate numerically the CFL\nstability restriction resulting from an explicit treatment, both on Cartesian\nand polytopal meshes. Finally, numerical tests on large 3D polytopal meshes\nhighlight the efficiency of the AC approach and the benefits of using\nsecond-order schemes whenever accurate discrete solutions are to be attained.\n"}
+{"abstract": "  A deluge of recent work has explored equivalences between wide neural\nnetworks and kernel methods. A central theme is that one can analytically find\nthe kernel corresponding to a given wide network architecture, but despite\nmajor implications for architecture design, no work to date has asked the\nconverse question: given a kernel, can one find a network that realizes it? We\naffirmatively answer this question for fully-connected architectures,\ncompletely characterizing the space of achievable kernels. Furthermore, we give\na surprising constructive proof that any kernel of any wide, deep,\nfully-connected net can also be achieved with a network with just one hidden\nlayer and a specially-designed pointwise activation function. We experimentally\nverify our construction and demonstrate that, by just choosing the activation\nfunction, we can design a wide shallow network that mimics the generalization\nperformance of any wide, deep, fully-connected network.\n"}
+{"abstract": "  Nowadays, developers often reuse existing APIs to implement their programming\ntasks. A lot of API usage patterns are mined to help developers learn API usage\nrules. However, there are still many missing variables to be synthesized when\ndevelopers integrate the patterns into their programming context. To deal with\nthis issue, we propose a comprehensive approach to integrate API usage patterns\nin this paper. We first perform an empirical study by analyzing how API usage\npatterns are integrated in real-world projects. We find the expressions for\nvariable synthesis is often non-trivial and can be divided into 5 syntax types.\nBased on the observation, we promote an approach to help developers\ninteractively complete API usage patterns. Compared to the existing code\ncompletion techniques, our approach can recommend infrequent expressions\naccompanied with their real-world usage examples according to the user intent.\nThe evaluation shows that our approach could assist users to integrate APIs\nmore efficiently and complete the programming tasks faster than existing works.\n"}
+{"abstract": "  We are interested in martingale rearrangement couplings. As introduced by\nWiesel [37] in order to prove the stability of Martingale Optimal Transport\nproblems, these are projections in adapted Wasserstein distance of couplings\nbetween two probability measures on the real line in the convex order onto the\nset of martingale couplings between these two marginals. In reason of the lack\nof relative compactness of the set of couplings with given marginals for the\nadapted Wasserstein topology, the existence of such a projection is not clear\nat all. Under a barycentre dispersion assumption on the original coupling which\nis in particular satisfied by the Hoeffding-Fr\\'echet or comonotone coupling,\nWiesel gives a clear algorithmic construction of a martingale rearrangement\nwhen the marginals are finitely supported and then gets rid of the finite\nsupport assumption by relying on a rather messy limiting procedure to overcome\nthe lack of relative compactness. Here, we give a direct general construction\nof a martingale rearrangement coupling under the barycentre dispersion\nassumption. This martingale rearrangement is obtained from the original\ncoupling by an approach similar to the construction we gave in [24] of the\ninverse transform martingale coupling, a member of a family of martingale\ncouplings close to the Hoeffding-Fr\\'echet coupling, but for a slightly\ndifferent injection in the set of extended couplings introduced by Beiglb\\\"ock\nand Juillet [9] and which involve the uniform distribution on [0, 1] in\naddition to the two marginals. We last discuss the stability in adapted\nWassertein distance of the inverse transform martingale coupling with respect\nto the marginal distributions.\n"}
+{"abstract": "  Gravitationally lensed extragalactic sources are often subject to statistical\nmicrolensing by stars in the galaxy or cluster lens. Accurate models of the\nflux statistics are required for inferring source and lens properties from flux\nobservations. We derive an accurate semi-analytic approximation for calculating\nthe mean and variance of the magnification factor, which are applicable to\nGaussian source profiles and arbitrary non-uniform macro lens models, and hence\ncan save the need to perform expensive numerical simulations. The results are\ngiven as single and double lens-plane integrals with simple, non-oscillatory\nintegrands, and hence are fast computable using common Monte Carlo integrators.\nEmploying numerical ray-shooting experiments, we examine the case of a highly\nmagnified source near a macro fold caustic, and demonstrate the excellent\naccuracy of this semi-analytic approximation in the regime of multiple micro\nimages. Additionally, we point out how the maximum persistent magnification\nachievable near a macro caustic is fundamentally limited by the masses and\nnumber density of the foreground microlenses, in addition to the source's\nphysical size.\n"}
+{"abstract": "  Electrically interfacing atomically thin transition metal dichalcogenide\nsemiconductors (TMDSCs) with metal leads is challenging because of undesired\ninterface barriers, which have drastically constrained the electrical\nperformance of TMDSC devices for exploring their unconventional physical\nproperties and realizing potential electronic applications. Here we demonstrate\na strategy to achieve nearly barrier-free electrical contacts with few-layer\nTMDSCs by engineering interfacial bonding distortion. The carrier-injection\nefficiency of such electrical junction is substantially increased with robust\nohmic behaviors from room to cryogenic temperatures. The performance\nenhancements of TMDSC field-effect transistors are well reflected by the\nultralow contact resistance (down to 90 Ohm um in MoS2, towards the quantum\nlimit), the ultrahigh field-effect mobility (up to 358,000 cm2V-1s-1 in WSe2)\nand the prominent transport characteristics at cryogenic temperatures. This\nmethod also offers new possibilities of the local manipulation of structures\nand electronic properties for TMDSC device design.\n"}
+{"abstract": "  Let $G$ be a non-compact connected semisimple real Lie group with finite\ncenter. Suppose $L$ is a non-compact connected closed subgroup of $G$ acting\ntransitively on a symmetric space $G/H$ such that $L\\cap H$ is compact. We\nstudy the action on $L/L\\cap H$ of a Dirac operator $D_{G/H}(E)$ acting on\nsections of an $E$-twist of the spin bundle over $G/H$. As a byproduct, in the\ncase of $(G,H,L)=(SL(2,{\\mathbb R})\\times SL(2,{\\mathbb\nR}),\\Delta(SL(2,{\\mathbb R})\\times SL(2,{\\mathbb R})),SL(2,{\\mathbb R})\\times\nSO(2))$, we identify certain representations of $L$ which lie in the kernel of\n$D_{G/H}(E)$.\n"}
+{"abstract": "  Many real-world systems can be expressed in temporal networks with nodes\nplaying far different roles in structure and function and edges representing\nthe relationships between nodes. Identifying critical nodes can help us control\nthe spread of public opinions or epidemics, predict leading figures in\nacademia, conduct advertisements for various commodities, and so on. However,\nit is rather difficult to identify critical nodes because the network structure\nchanges over time in temporal networks. In this paper, considering the sequence\ntopological information of temporal networks, a novel and effective learning\nframework based on the combination of special GCNs and RNNs is proposed to\nidentify nodes with the best spreading ability. The effectiveness of the\napproach is evaluated by weighted Susceptible-Infected-Recovered model.\nExperimental results on four real-world temporal networks demonstrate that the\nproposed method outperforms both traditional and deep learning benchmark\nmethods in terms of the Kendall $\\tau$ coefficient and top $k$ hit rate.\n"}
+{"abstract": "  It has recently been shown that two-loop kite-type diagrams can be computed\nanalytically in terms of iterated integrals with algebraic kernels. This result\nwas obtained using a new integral representation for two-loop sunset subgraphs.\nIn this paper, we have developed a similar representation for a three-loop\nbanana integral in $d = 2-2\\varepsilon$ dimensions. This answer can be\ngeneralized up to any given order in the $\\varepsilon$-expansion and can be\ncalculated numerically both below and above the threshold. We also demonstrate\nhow this result can be used to compute more complex three-loop integrals\ncontaining the three-loop banana as a subgraph.\n"}
+{"abstract": "  We develop a trust-region method for minimizing the sum of a smooth term $f$\nand a nonsmooth term $h$), both of which can be nonconvex. Each iteration of\nour method minimizes a possibly nonconvex model of $f + h$ in a trust region.\nThe model coincides with $f + h$ in value and subdifferential at the center. We\nestablish global convergence to a first-order stationary point when $f$\nsatisfies a smoothness condition that holds, in particular, when it has\nLipschitz-continuous gradient, and $h$ is proper and lower semi-continuous. The\nmodel of $h$ is required to be proper, lower-semi-continuous and prox-bounded.\nUnder these weak assumptions, we establish a worst-case $O(1/\\epsilon^2)$\niteration complexity bound that matches the best known complexity bound of\nstandard trust-region methods for smooth optimization. We detail a special\ninstance, named TR-PG, in which we use a limited-memory quasi-Newton model of\n$f$ and compute a step with the proximal gradient method, resulting in a\npractical proximal quasi-Newton method. We establish similar convergence\nproperties and complexity bound for a quadratic regularization variant, named\nR2, and provide an interpretation as a proximal gradient method with adaptive\nstep size for nonconvex problems. R2 may also be used to compute steps inside\nthe trust-region method, resulting in an implementation named TR-R2. We\ndescribe our Julia implementations and report numerical results on inverse\nproblems from sparse optimization and signal processing. Both TR-PG and TR-R2\nexhibit promising performance and compare favorably with two linesearch\nproximal quasi-Newton methods based on convex models.\n"}
+{"abstract": "  Coronavirus disease 2019 (COVID-19) is a Public Health Emergency of\nInternational Concern infecting more than 40 million people across 188\ncountries and territories. Chest computed tomography (CT) imaging technique\nbenefits from its high diagnostic accuracy and robustness, it has become an\nindispensable way for COVID-19 mass testing. Recently, deep learning approaches\nhave become an effective tool for automatic screening of medical images, and it\nis also being considered for COVID-19 diagnosis. However, the high infection\nrisk involved with COVID-19 leads to relative sparseness of collected labeled\ndata limiting the performance of such methodologies. Moreover, accurately\nlabeling CT images require expertise of radiologists making the process\nexpensive and time-consuming. In order to tackle the above issues, we propose a\nsupervised domain adaption based COVID-19 CT diagnostic method which can\nperform effectively when only a small samples of labeled CT scans are\navailable. To compensate for the sparseness of labeled data, the proposed\nmethod utilizes a large amount of synthetic COVID-19 CT images and adjusts the\nnetworks from the source domain (synthetic data) to the target domain (real\ndata) with a cross-domain training mechanism. Experimental results show that\nthe proposed method achieves state-of-the-art performance on few-shot COVID-19\nCT imaging based diagnostic tasks.\n"}
+{"abstract": "  We provide moment bounds for expressions of the type $(X^{(1)} \\otimes \\dots\n\\otimes X^{(d)})^T A (X^{(1)} \\otimes \\dots \\otimes X^{(d)})$ where $\\otimes$\ndenotes the Kronecker product and $X^{(1)}, \\dots, X^{(d)}$ are random vectors\nwith independent, mean 0, variance 1, subgaussian entries. The bounds are tight\nup to constants depending on $d$ for the case of Gaussian random vectors. Our\nproof also provides a decoupling inequality for expressions of this type. Using\nthese bounds, we obtain new, improved concentration inequalities for\nexpressions of the form $\\|B (X^{(1)} \\otimes \\dots \\otimes X^{(d)})\\|_2$.\n"}
+{"abstract": "  This paper argues that continual learning methods can benefit by splitting\nthe capacity of the learner across multiple models. We use statistical learning\ntheory and experimental analysis to show how multiple tasks can interact with\neach other in a non-trivial fashion when a single model is trained on them. The\ngeneralization error on a particular task can improve when it is trained with\nsynergistic tasks, but can also deteriorate when trained with competing tasks.\nThis theory motivates our method named Model Zoo which, inspired from the\nboosting literature, grows an ensemble of small models, each of which is\ntrained during one episode of continual learning. We demonstrate that Model Zoo\nobtains large gains in accuracy on a variety of continual learning benchmark\nproblems.\n"}
+{"abstract": "  Pretrained language models are now of widespread use in Natural Language\nProcessing. Despite their success, applying them to Low Resource languages is\nstill a huge challenge. Although Multilingual models hold great promise,\napplying them to specific low-resource languages e.g. Roman Urdu can be\nexcessive. In this paper, we show how the code-switching property of languages\nmay be used to perform cross-lingual transfer learning from a corresponding\nhigh resource language. We also show how this transfer learning technique\ntermed Bilingual Language Modeling can be used to produce better performing\nmodels for Roman Urdu. To enable training and experimentation, we also present\na collection of novel corpora for Roman Urdu extracted from various sources and\nsocial networking sites, e.g. Twitter. We train Monolingual, Multilingual, and\nBilingual models of Roman Urdu - the proposed bilingual model achieves 23%\naccuracy compared to the 2% and 11% of the monolingual and multilingual models\nrespectively in the Masked Language Modeling (MLM) task.\n"}
+{"abstract": "  With the consolidation of deep learning in drug discovery, several novel\nalgorithms for learning molecular representations have been proposed. Despite\nthe interest of the community in developing new methods for learning molecular\nembeddings and their theoretical benefits, comparing molecular embeddings with\neach other and with traditional representations is not straightforward, which\nin turn hinders the process of choosing a suitable representation for QSAR\nmodeling. A reason behind this issue is the difficulty of conducting a fair and\nthorough comparison of the different existing embedding approaches, which\nrequires numerous experiments on various datasets and training scenarios. To\nclose this gap, we reviewed the literature on methods for molecular embeddings\nand reproduced three unsupervised and two supervised molecular embedding\ntechniques recently proposed in the literature. We compared these five methods\nconcerning their performance in QSAR scenarios using different classification\nand regression datasets. We also compared these representations to traditional\nmolecular representations, namely molecular descriptors and fingerprints. As\nopposed to the expected outcome, our experimental setup consisting of over\n25,000 trained models and statistical tests revealed that the predictive\nperformance using molecular embeddings did not significantly surpass that of\ntraditional representations. While supervised embeddings yielded competitive\nresults compared to those using traditional molecular representations,\nunsupervised embeddings tended to perform worse than traditional\nrepresentations. Our results highlight the need for conducting a careful\ncomparison and analysis of the different embedding techniques prior to using\nthem in drug design tasks, and motivate a discussion about the potential of\nmolecular embeddings in computer-aided drug design.\n"}
+{"abstract": "  We employ neural networks for classification of data of the TUS fluorescence\ntelescope, the world's first orbital detector of ultra-high energy cosmic rays.\nWe focus on two particular types of signals in the TUS data: track-like flashes\nproduced by cosmic ray hits of the photodetector and flashes that originated\nfrom distant lightnings. We demonstrate that even simple neural networks\ncombined with certain conventional methods of data analysis can be highly\neffective in tasks of classification of data of fluorescence telescopes.\n"}
+{"abstract": "  The performance of natural language generation systems has improved\nsubstantially with modern neural networks. At test time they typically employ\nbeam search to avoid locally optimal but globally suboptimal predictions.\nHowever, due to model errors, a larger beam size can lead to deteriorating\nperformance according to the evaluation metric. For this reason, it is common\nto rerank the output of beam search, but this relies on beam search to produce\na good set of hypotheses, which limits the potential gains. Other alternatives\nto beam search require changes to the training of the model, which restricts\ntheir applicability compared to beam search. This paper proposes incremental\nbeam manipulation, i.e. reranking the hypotheses in the beam during decoding\ninstead of only at the end. This way, hypotheses that are unlikely to lead to a\ngood final output are discarded, and in their place hypotheses that would have\nbeen ignored will be considered instead. Applying incremental beam manipulation\nleads to an improvement of 1.93 and 5.82 BLEU points over vanilla beam search\nfor the test sets of the E2E and WebNLG challenges respectively. The proposed\nmethod also outperformed a strong reranker by 1.04 BLEU points on the E2E\nchallenge, while being on par with it on the WebNLG dataset.\n"}
+{"abstract": "  We consider the problem of soft scattering for the analogue of pion states in\ngauge-fermion theories which approach a conformal fixed point in the infrared\nlimit. Introducing a fermion mass into such a theory will explicitly break both\nscale invariance and chiral symmetry, leading to confinement and a spectrum of\nbound states. We argue that in such a theory, the pion scattering length\ndiverges in the limit of zero fermion mass, in sharp contrast to QCD-like\ntheories where the chiral Lagrangian predicts a vanishing scattering length. We\ndemonstrate this effect both with a simple dimensional argument, and in a\ngeneralized linear sigma model which we argue can be used to describe the\ninteractions of light scalar and pseudoscalar bound states in the soft limit of\na mass-deformed infrared-conformal theory. As a result, lattice calculations of\npion scattering lengths could be a sensitive probe for infrared scale\ninvariance in gauge-fermion theories.\n"}
+{"abstract": "  Context: Interest in software engineering (SE) methodologies and tools has\nbeen complemented in recent years by research efforts oriented towards\nunderstanding the human processes involved in software development. This shift\nhas been imperative given reports of inadequately performing teams and the\nconsequent growing emphasis on individuals and team relations in contemporary\nSE methods. Objective: While software repositories have frequently been studied\nwith a view to explaining such human processes, research has tended to use\nprimarily quantitative analysis approaches. There is concern, however, that\nsuch approaches can provide only a partial picture of the software process.\nGiven the way human behavior is nuanced within psychological and social\ncontexts, it has been asserted that a full understanding may only be achieved\nthrough deeper contextual enquiries. Method: We have followed such an approach\nand have applied data mining, SNA, psycholinguistic analysis and directed\ncontent analysis (CA) to study the way core developers at IBM Rational Jazz\ncontribute their social and intellectual capital, and have compared the\nattitudes, interactions and activities of these members to those of their less\nactive counterparts. Results: Among our results, we uncovered that Jazz's core\ndevelopers worked across multiple roles, and were crucial to their teams'\norganizational, intra-personal and inter-personal processes. Additionally,\nalthough these individuals were highly task- and achievement-focused, they were\nalso largely responsible for maintaining positive team atmosphere, and for\nproviding context awareness in support of their colleagues. Conclusion: Our\nresults suggest that high-performing distributed agile teams rely on both\nindividual and collective efforts, as well as organizational environments that\npromote informal and organic work structures.(Abridged)\n"}
+{"abstract": "  Thanks to missions like Kepler and TESS, we now have access to tens of\nthousands of high precision, fast cadence, and long baseline stellar\nphotometric observations. In principle, these light curves encode a vast amount\nof information about stellar variability and, in particular, about the\ndistribution of starspots and other features on their surfaces. Unfortunately,\nthe problem of inferring stellar surface properties from a rotational light\ncurve is famously ill-posed, as it often does not admit a unique solution.\nInference about the number, size, contrast, and location of spots can therefore\ndepend very strongly on the assumptions of the model, the regularization\nscheme, or the prior. The goal of this paper is twofold: (1) to explore the\nvarious degeneracies affecting the stellar light curve \"inversion\" problem and\ntheir effect on what can and cannot be learned from a stellar surface given\nunresolved photometric measurements; and (2) to motivate ensemble analyses of\nthe light curves of many stars at once as a powerful data-driven alternative to\ncommon priors adopted in the literature. We further derive novel results on the\ndependence of the null space on stellar inclination and limb darkening and show\nthat single-band photometric measurements cannot uniquely constrain quantities\nlike the total spot coverage without the use of strong priors. This is the\nfirst in a series of papers devoted to the development of novel algorithms and\ntools for the analysis of stellar light curves and spectral time series, with\nthe explicit goal of enabling statistically robust inference about their\nsurface properties.\n"}
+{"abstract": "  Graph representation of structured data can facilitate the extraction of\nstereoscopic features, and it has demonstrated excellent ability when working\nwith deep learning systems, the so-called Graph Neural Networks (GNNs).\nChoosing a promising architecture for constructing GNNs can be transferred to a\nhyperparameter optimisation problem, a very challenging task due to the size of\nthe underlying search space and high computational cost for evaluating\ncandidate GNNs. To address this issue, this research presents a novel genetic\nalgorithm with a hierarchical evaluation strategy (HESGA), which combines the\nfull evaluation of GNNs with a fast evaluation approach. By using full\nevaluation, a GNN is represented by a set of hyperparameter values and trained\non a specified dataset, and root mean square error (RMSE) will be used to\nmeasure the quality of the GNN represented by the set of hyperparameter values\n(for regression problems). While in the proposed fast evaluation process, the\ntraining will be interrupted at an early stage, the difference of RMSE values\nbetween the starting and interrupted epochs will be used as a fast score, which\nimplies the potential of the GNN being considered. To coordinate both types of\nevaluations, the proposed hierarchical strategy uses the fast evaluation in a\nlower level for recommending candidates to a higher level, where the full\nevaluation will act as a final assessor to maintain a group of elite\nindividuals. To validate the effectiveness of HESGA, we apply it to optimise\ntwo types of deep graph neural networks. The experimental results on three\nbenchmark datasets demonstrate its advantages compared to Bayesian\nhyperparameter optimization.\n"}
+{"abstract": "  Building on the success of the ADReSS Challenge at Interspeech 2020, which\nattracted the participation of 34 teams from across the world, the ADReSSo\nChallenge targets three difficult automatic prediction problems of societal and\nmedical relevance, namely: detection of Alzheimer's Dementia, inference of\ncognitive testing scores, and prediction of cognitive decline. This paper\npresents these prediction tasks in detail, describes the datasets used, and\nreports the results of the baseline classification and regression models we\ndeveloped for each task. A combination of acoustic and linguistic features\nextracted directly from audio recordings, without human intervention, yielded a\nbaseline accuracy of 78.87% for the AD classification task, an MMSE prediction\nroot mean squared (RMSE) error of 5.28, and 68.75% accuracy for the cognitive\ndecline prediction task.\n"}
+{"abstract": "  A solution is proposed to a longstanding open problem in kinetic theory,\nnamely, given any set of realizable velocity moments up to order 2n, a closure\nfor the moment of order 2n+1 is constructed for which the moment system found\nfrom the free-transport term in the one-dimensional (1-D) kinetic equation is\nglobally hyperbolic and in conservative form. In prior work, the hyperbolic\nquadrature method of moments (HyQMOM) was introduced to close this moment\nsystem up to fourth order (n $\\le$ 2). Here, HyQMOM is reformulated and\nextended to arbitrary even-order moments. The HyQMOM closure is defined based\non the properties of the monic orthogonal polynomials Qn that are uniquely\ndefined by the velocity moments up to order 2n -- 1. Thus, HyQMOM is strictly a\nmoment closure and does not rely on the reconstruction of a velocity\ndistribution function with the same moments. On the boundary of moment space, n\ndouble roots of the characteristic polynomial P2n+1 are the roots of Qn, while\nin the interior, P 2n+1 and Qn share n roots. The remaining n + 1 roots of\nP2n+1 bound and separate the roots of Qn. An efficient algorithm, based on the\nChebyshev algorithm, for computing the moment of order 2n + 1 from the moments\nup to order 2n is developed. The analytical solution to a 1-D Riemann problem\nis used to demonstrate convergence of the HyQMOM closure with increasing n.\n"}
+{"abstract": "  Renormalisation group (RG) methods provide one of the most important\ntechniques for analysing the physics of many-body systems, both analytically\nand numerically. By iterating an RG map, which \"course-grains\" the description\nof a many-body system and generates a flow in the parameter space, physical\nproperties of interest can be extracted even for complex models. RG analysis\nalso provides an explanation of physical phenomena such as universality. Many\nsystems exhibit simple RG flows, but more complicated -- even chaotic --\nbehaviour is also known. Nonetheless, the structure of such RG flows can still\nbe analysed, elucidating the physics of the system, even if specific\ntrajectories may be highly sensitive to the initial point. In contrast, recent\nwork has shown that important physical properties of quantum many-body systems,\nsuch as its spectral gap and phase diagram, can be uncomputable.\n  In this work, we show that such undecidable systems exhibit a novel type of\nRG flow, revealing a qualitatively different and more extreme form of\nunpredictability than chaotic RG flows. In contrast to chaotic RG flows in\nwhich initially close points can diverge exponentially, trajectories under\nthese novel uncomputable RG flows can remain arbitrarily close together for an\nuncomputable number of iterations, before abruptly diverging to different fixed\npoints that are in separate phases. The structure of such uncomputable RG flows\nis so complex that it cannot be computed or approximated, even in principle. We\ngive a mathematically rigorous construction of the block-renormalisation-group\nmap for the original undecidable many-body system that appeared in the\nliterature (Cubitt, P\\'erez-Garcia, Wolf, Nature 528, 207-211 (2015)). We prove\nthat each step of this RG map is computable, and that it converges to the\ncorrect fixed points, yet the resulting RG flow is uncomputable.\n"}
+{"abstract": "  This article studies estimation of a stationary autocovariance structure in\nthe presence of an unknown number of mean shifts. Here, a Yule-Walker moment\nestimator for the autoregressive parameters in a dependent time series\ncontaminated by mean shift changepoints is proposed and studied. The estimator\nis based on first order differences of the series and is proven consistent and\nasymptotically normal when the number of changepoints $m$ and the series length\n$N$ satisfies $m/N \\rightarrow 0$ as $N \\rightarrow \\infty$\n"}
+{"abstract": "  The quality of controlling a system of optical cavities in the\nTavis-Cummings-Hubbard (TCH) model is estimated with the examples of quantum\ngates, quantum walks on graphs, and of the detection of singlet states. This\ntype of control of complex systems is important for quantum computing, for the\noptical interpretation of mechanical movements, and for quantum cryptography,\nwhere singlet states of photons and charges play an essential role. It has been\nfound that the main reason for the decrease of the control quality in the THC\nmodel is due to the finite width of the atomic spectral lines, which is itself\nrelated to the time energy uncertainty relation. This paper evaluates the\nquality of a CSign-type quantum gate based on asynchronous atomic excitations\nand on the optical interpretation of the motion of a free particle.\n"}
+{"abstract": "  It is important to calculate and analyze temperature and humidity prediction\naccuracies among quantitative meteorological forecasting. This study\nmanipulates the extant neural network methods to foster the predictive\naccuracy. To achieve such tasks, we analyze and explore the predictive accuracy\nand performance in the neural networks using two combined meteorological\nfactors (temperature and humidity). Simulated studies are performed by applying\nthe artificial neural network (ANN), deep neural network (DNN), extreme\nlearning machine (ELM), long short-term memory (LSTM), and long short-term\nmemory with peephole connections (LSTM-PC) machine learning methods, and the\naccurate prediction value are compared to that obtained from each other\nmethods. Data are extracted from low frequency time-series of ten metropolitan\ncities of South Korea from March 2014 to February 2020 to validate our\nobservations. To test the robustness of methods, the error of LSTM is found to\noutperform that of the other four methods in predictive accuracy. Particularly,\nas testing results, the temperature prediction of LSTM in summer in Tongyeong\nhas a root mean squared error (RMSE) value of 0.866 lower than that of other\nneural network methods, while the mean absolute percentage error (MAPE) value\nof LSTM for humidity prediction is 5.525 in summer in Mokpo, significantly\nbetter than other metropolitan cities.\n"}
+{"abstract": "  The nonequilibrium dynamics of vortices in 2D quantum fluids can be predicted\nby accounting for the way in which vortex ellipticity is coupled to the\ngradient in background fluid density. In the absence of nonlinear interactions,\na harmonically trapped fluid can be analyzed analytically to show that single\nvortices will move in an elliptic trajectory that has the same orientation and\naspect ratio as the vortex projection itself. This allows the vortex\nellipticity to be estimated through observation of its trajectory. A\ncombination of analysis and numerical simulation is then used to show that\nnonlinear interactions cause the vortex orientation to precess, and that the\nrate of vortex precession is once again mimicked by a precession of the\nelliptical trajectory. Both vortex ellipticity and rate of precession can\ntherefore be inferred by observing its motion in a trap. An ability to\nanticipate and control local vortex structure and vortex trajectory is expected\nto prove useful in designing few-vortex systems in which ellipticity is a\nubiquitous, as-yet-unharnessed feature.\n"}
+{"abstract": "  Identifying the mutations that drive cancer growth is key in clinical\ndecision making and precision oncology. As driver mutations confer selective\nadvantage and thus have an increased likelihood of occurrence, frequency-based\nstatistical models are currently favoured. These methods are not suited to\nrare, low frequency, driver mutations. The alternative approach to address this\nis through functional-impact scores, however methods using this approach are\nhighly prone to false positives. In this paper, we propose a novel combination\nmethod for driver mutation identification, which uses the power of both\nstatistical modelling and functional-impact based methods. Initial results show\nthis approach outperforms the state-of-the-art methods in terms of precision,\nand provides comparable performance in terms of area under receiver operating\ncharacteristic curves (AU-ROC). We believe that data-driven systems based on\nmachine learning, such as these, will become an integral part of precision\noncology in the near future.\n"}
+{"abstract": "  Considering a non-centrosymmetric, non-magnetic double Weyl semimetal (WSM)\nSrSi$_2$, we investigate the electron and hole pockets in bulk Fermi surface\nbehavior that enables us to characterize the material as a type-I WSM. We study\nthe structural handedness of the material and correlate it with the distinct\nsurface Fermi surface at two opposite surfaces following an energy evolution.\nThe Fermi arc singlet becomes doublet with the onset of spin orbit coupling\nthat is in accordance with the topological charge of the Weyl Nodes (WNs). A\nfinite energy separation between WNs of opposite chirality in SrSi$_2$ allows\nus to compute circular photogalvanic effect (CPGE). Followed by the three band\nformula, we show that CPGE is only quantized for Fermi level chosen in the\nvicinity of WN residing at higher value of energy. Surprisingly, for the other\nWN of opposite chirality in the lower value of energy, CPGE is not found to be\nquantized. Such a behavior of CPGE is in complete contrast to the time reversal\nbreaking WSM where CPGE is quantized to two opposite plateau depending on the\ntopological charge of the activated WN. We further analyze our finding by\nexamining the momentum resolved CPGE. Finally we show that two band formula for\nCPGE is not able to capture the quantization that is apprehended by the three\nband formula.\n"}
+{"abstract": "  In this article a surprising result is demonstrated using the neural tangent\nkernel. This kernel is defined as the inner product of the vector of the\ngradient of an underlying model evaluated at training points. This kernel is\nused to perform kernel regression. The surprising thing is that the accuracy of\nthat regression is independent of the accuracy of the underlying network.\n"}
+{"abstract": "  Theoretical models show that the power of relativistic jets of active\ngalactic nuclei depends on the spin and mass of the central supermassive black\nholes, as well as the accretion. Here we report an analysis of archival\nobservations of a sample of blazars. We find a significant correlation between\njet kinetic power and the spin of supermassive black holes. At the same time,\nwe use multiple linear regression to analyze the relationship between jet\nkinetic power and accretion, spin and black hole mass. We find that the spin of\nsupermassive black holes and accretion are the most important contribution to\nthe jet kinetic power. The contribution rates of both the spin of supermassive\nblack holes and accretion are more than 95\\%. These results suggest that the\nspin energy of supermassive black holes powers the relativistic jets. The jet\nproduction efficiency of almost all Fermi blazars can be explained by\nmoderately thin magnetically arrested accretion disks around rapidly spinning\nblack holes.\n"}
+{"abstract": "  By viewing $\\tilde{A}$ and $\\tilde{D}$ type cluster algebras as triangulated\nsurfaces, we find all cluster variables in terms of either (i) the frieze\npattern (or bipartite belt) or (ii) the periodic quantities previously found\nfor the cluster map associated with these frieze patterns. We show that these\ncluster variables form friezes which are precisely the ones found in [1] by\napplying the cluster character to the associated cluster category.\n"}
+{"abstract": "  The representation of ground states of fermionic quantum impurity problems as\nsuperpositions of Gaussian states has recently been given a rigorous\nmathematical foundation. [S. Bravyi and D. Gosset, Comm. Math. Phys. 356, 451\n(2017)]. It is natural to ask how many parameters are required for an efficient\nvariational scheme based on this representation. An upper bound is $\\mathcal\nO(N^2)$, where $N$ is the system size, which corresponds to the number\nparameters needed to specify an arbitrary Gaussian state. We provide an\nalternative representation, with more favorable scaling, only requiring\n$\\mathcal O(N)$ parameters, that we illustrate for the interacting resonant\nlevel model. We achieve the reduction by associating mean-field-like parent\nHamiltonians with the individual terms in the superposition, using physical\ninsight to retain only the most relevant channels in each parent Hamiltonian.\nWe benchmark our variational ansatz against the Numerical Renormalization\nGroup, and compare our results to existing variational schemes of a similar\nnature to ours. Apart from the ground state energy, we also study the spectrum\nof the correlation matrix -- a very stringent measure of accuracy. Our approach\noutperforms some existing schemes and remains quantitatively accurate in the\nnumerically challenging near-critical regime.\n"}
+{"abstract": "  In this manuscript we show that the metric mean dimension of a free semigroup\naction satisfies three variational principles: (a) the first one is based on a\ndefinition of Shapira's entropy, introduced in \\cite{SH} for a singles dynamics\nand extended for a semigroup action in this note; (b) the second one treats\nabout a definition of Katok's entropy for a free semigroup action introduced in\n\\cite{CRV-IV}; (c) lastly we consider the local entropy function for a free\nsemigroup action and show that the metric mean dimension satisfies a\nvariational principle in terms of such function. Our results are inspired in\nthe ones obtained by \\cite{LT2019}, \\cite{VV}, \\cite{GS1} and \\cite{RX}.\n"}
+{"abstract": "  Machine learning is a modern approach to problem-solving and task automation.\nIn particular, machine learning is concerned with the development and\napplications of algorithms that can recognize patterns in data and use them for\npredictive modeling. Artificial neural networks are a particular class of\nmachine learning algorithms and models that evolved into what is now described\nas deep learning. Given the computational advances made in the last decade,\ndeep learning can now be applied to massive data sets and in innumerable\ncontexts. Therefore, deep learning has become its own subfield of machine\nlearning. In the context of biological research, it has been increasingly used\nto derive novel insights from high-dimensional biological data. To make the\nbiological applications of deep learning more accessible to scientists who have\nsome experience with machine learning, we solicited input from a community of\nresearchers with varied biological and deep learning interests. These\nindividuals collaboratively contributed to this manuscript's writing using the\nGitHub version control platform and the Manubot manuscript generation toolset.\nThe goal was to articulate a practical, accessible, and concise set of\nguidelines and suggestions to follow when using deep learning. In the course of\nour discussions, several themes became clear: the importance of understanding\nand applying machine learning fundamentals as a baseline for utilizing deep\nlearning, the necessity for extensive model comparisons with careful\nevaluation, and the need for critical thought in interpreting results generated\nby deep learning, among others.\n"}
+{"abstract": "  We study the barotropic compressible Navier-Stokes equations with Navier-type\nboundary condition in a two-dimensional simply connected bounded domain with\n$C^{\\infty}$ boundary $\\partial\\Omega.$ By some new estimates on the boundary\nrelated to the Navier-type slip boundary condition, the classical solution to\nthe initial-boundary-value problem of this system exists globally in time\nprovided the initial energy is suitably small even if the density has large\noscillations and contains vacuum states. Futhermore, we also prove that the\noscillation of the density will grow unboundedly in the long run with an\nexponential rate provided vacuum (even a point) appears initially. As we known,\nthis is the first result concerning the global existence of classical solutions\nto the compressible Navier-Stokes equations with Navier-type slip boundary\ncondition and the density containing vacuum initially for general 2D bounded\nsmooth domains.\n"}
+{"abstract": "  Community detection is a key task to further understand the function and the\nstructure of complex networks. Therefore, a strategy used to assess this task\nmust be able to avoid biased and incorrect results that might invalidate\nfurther analyses or applications that rely on such communities. Two widely used\nstrategies to assess this task are generally known as structural and\nfunctional. The structural strategy basically consists in detecting and\nassessing such communities by using multiple methods and structural metrics. On\nthe other hand, the functional strategy might be used when ground truth data\nare available to assess the detected communities. However, the evaluation of\ncommunities based on such strategies is usually done in experimental\nconfigurations that are largely susceptible to biases, a situation that is\ninherent to algorithms, metrics and network data used in this task.\nFurthermore, such strategies are not systematically combined in a way that\nallows for the identification and mitigation of bias in the algorithms, metrics\nor network data to converge into more consistent results. In this context, the\nmain contribution of this article is an approach that supports a robust quality\nevaluation when detecting communities in real-world networks. In our approach,\nwe measure the quality of a community by applying the structural and functional\nstrategies, and the combination of both, to obtain different pieces of\nevidence. Then, we consider the divergences and the consensus among the pieces\nof evidence to identify and overcome possible sources of bias in community\ndetection algorithms, evaluation metrics, and network data. Experiments\nconducted with several real and synthetic networks provided results that show\nthe effectiveness of our approach to obtain more consistent conclusions about\nthe quality of the detected communities.\n"}
+{"abstract": "  In this note, we consider the complexity of maintaining the longest\nincreasing subsequence (LIS) of an array under (i) inserting an element, and\n(ii) deleting an element of an array. We show that no algorithm can support\nqueries and updates in time $\\mathcal{O}(n^{1/2-\\epsilon})$ and\n$\\mathcal{O}(n^{1/3-\\epsilon})$ for the dynamic LIS problem, for any constant\n$\\epsilon>0$, when the elements are weighted or the algorithm supports\n1D-queries (on subarrays), respectively, assuming the All-Pairs Shortest Paths\n(APSP) conjecture or the Online Boolean Matrix-Vector Multiplication (OMv)\nconjecture. The main idea in our construction comes from the work of Abboud and\nDahlgaard [FOCS 2016], who proved conditional lower bounds for dynamic planar\ngraph algorithm. However, this needs to be appropriately adjusted and\ntranslated to obtain an instance of the dynamic LIS problem.\n"}
+{"abstract": "  We present a high-resolution (R=140,000) spectrum of the bright quasar\nHE0001-2340 (z=2.26), obtained with ESPRESSO at the Very Large Telescope. We\nanalyse three systems at z=0.45, z=1.65, and z=2.19 using multiple-component\nVoigt-profile fitting. We also compare our spectrum with those obtained with\nVLT/UVES, covering a total period of 17 years. We disentangle turbulent and\nthermal broadening in many components spread over about 400 km/s in the z~2.19\nsub-DLA system. We derive an average temperature of 16000+/-1300 K, i.e., about\ntwice the canonical value of the warm neutral medium in the Galactic\ninterstellar medium. A comparison with other high-z, low-metallicity absorbers\nreveals an anti-correlation between gas temperature and total HI column\ndensity. Although requiring confirmation, this could be the first observational\nevidence of a thermal decrease with galacto-centric distance, i.e., we may be\nwitnessing a thermal transition between the circum-galactic medium and the\ncooler ISM. We revisit the Mg isotopic ratios at z=0.45 and z=1.65 and\nconstrain them to be xi = (26Mg+25Mg)/24Mg <0.6 and <1.4 in these two systems,\nrespectively. These values are consistent with the standard Solar ratio, i.e.,\nwe do not confirm strong enhancement of heavy isotopes previously inferred from\nUVES data. Finally, we confirm the partial coverage of the quasar emission-line\nregion by a FeI-bearing cloud in the z=0.45 system and present evidence for\nvelocity sub-structure of the gas that has Doppler parameters of the order of\nonly ~0.3 km/s. This work demonstrates the uniqueness of high-fidelity,\nhigh-resolution optical spectrographs on large telescopes as tools to\ninvestigate the thermal state of the gas in and around galaxies as well as its\nspatial and velocity structure on small scales, and to constrain the associated\nstellar nucleosynthetic history. [abridged]\n"}
+{"abstract": "  The anomalous dimensions for the interpolating currents of baryons are\nindispensable inputs in a serious analysis of baryon QCD sum rules. However,\nthe results in the literature are vague. In view of this, in this work, we\ninvestigate the one-loop anomalous dimensions for some interpolating currents\nsuch as those of $\\Lambda_{Q}$ and proton. This work has more significance in\npedagogy.\n"}
+{"abstract": "  Four adaptations of the smoothed aggregation algebraic multigrid (SA-AMG)\nmethod are proposed with an eye towards improving the convergence and\nrobustness of the solver in situations when the discretization matrix contains\nmany weak connections. These weak connections can cause higher than expected\nlevels of fill-in within the coarse discretization matrices and can also give\nrise to sub-optimal smoothing within the prolongator smoothing phase. These\nsmoothing drawbacks are due to the relatively small size of some diagonal\nentries within the filtered matrix that one obtains after dropping the weak\nconnections. The new algorithms consider modifications to the Jacobi-like step\nthat defines the prolongator smoother, modifications to the filtered matrix,\nand also direct modifications to the resulting grid transfer operators.\nNumerical results are given illustrating the potential benefits of the proposed\nadaptations.\n"}
+{"abstract": "  Materials with strong magnetoresistive responses are the backbone of\nspintronic technology, magnetic sensors, and hard drives. Among them, manganese\noxides with a mixed valence and a cubic perovskite structure stand out due to\ntheir colossal magnetoresistance (CMR). A double exchange interaction underlies\nthe CMR in manganates, whereby charge transport is enhanced when the spins on\nneighboring Mn3+ and Mn4+ ions are parallel. Prior efforts to find different\nmaterials or mechanisms for CMR resulted in a much smaller effect. Here we show\nan enormous CMR at low temperatures in EuCd2P2 without manganese, oxygen, mixed\nvalence, or cubic perovskite structure. EuCd2P2 has a layered trigonal lattice\nand exhibits antiferromagnetic ordering at 11 K. The magnitude of CMR (104\npercent) in as-grown crystals of EuCd2P2 rivals the magnitude in optimized thin\nfilms of manganates. Our magnetization, transport, and synchrotron X-ray data\nsuggest that strong magnetic fluctuations are responsible for this phenomenon.\nThe realization of CMR at low temperatures without heterovalency leads to a new\nregime for materials and technologies related to antiferromagnetic spintronics.\n"}
+{"abstract": "  Group sequential design (GSD) is widely used in clinical trials in which\ncorrelated tests of multiple hypotheses are used. Multiple primary objectives\nresulting in tests with known correlations include evaluating 1) multiple\nexperimental treatment arms, 2) multiple populations, 3) the combination of\nmultiple arms and multiple populations, or 4) any asymptotically multivariate\nnormal tests. In this paper, we focus on the first 3 of these and extend the\nframework of the weighted parametric multiple test procedure from fixed designs\nwith a single analysis per objective to a GSD setting where different\nobjectives may be assessed at the same or different times, each in a group\nsequential fashion. Pragmatic methods for design and analysis of weighted\nparametric group sequential design(WPGSD) under closed testing procedures are\nproposed to maintain the strong control of familywise Type I error rate (FWER)\nwhen correlations between tests are incorporated. This results in the ability\nto relax testing bounds compared to designs not fully adjusting for known\ncorrelations, increasing power or allowing decreased sample size. We illustrate\nthe proposed methods using clinical trial examples and conduct a simulation\nstudy to evaluate the operating characteristics.\n"}
+{"abstract": "  The equilibrium of magneto-elastic rods, formed of an elastic matrix\ncontaining a uniform distribution of paramagnetic particles, that are subject\nto terminal loads and are immersed in a uniform magnetic field, is studied. The\ndeduced nonlinear equilibrium equations are fully consistent with Kirchhoff's\ntheory in the sense that they hold at the same order of magnitude. Exact\nsolutions of those equations in terms of Weierstrass elliptic functions are\npresented with reference to magneto-elastic cantilevers that undergo planar\ndeformations under the action of a terminal force and a magnetic field whose\ndirections are either parallel or orthogonal. The exact solutions are applied\nto the study of a problem of remotely controlled deformation of a rod and to a\nbifurcation problem in which the end force and the magnetic field act as an\nimperfection parameter and a bifurcation parameter, respectively.\n"}
+{"abstract": "  The likelihood-informed subspace (LIS) method offers a viable route to\nreducing the dimensionality of high-dimensional probability distributions\narising in Bayesian inference. LIS identifies an intrinsic low-dimensional\nlinear subspace where the target distribution differs the most from some\ntractable reference distribution. Such a subspace can be identified using the\nleading eigenvectors of a Gram matrix of the gradient of the log-likelihood\nfunction. Then, the original high-dimensional target distribution is\napproximated through various forms of marginalization of the likelihood\nfunction, in which the approximated likelihood only has support on the\nintrinsic low-dimensional subspace. This approximation enables the design of\ninference algorithms that can scale sub-linearly with the apparent\ndimensionality of the problem. Intuitively, the accuracy of the approximation,\nand hence the performance of the inference algorithms, are influenced by three\nfactors -- the dimension truncation error in identifying the subspace, Monte\nCarlo error in estimating the Gram matrices, and Monte Carlo error in\nconstructing marginalizations. %This work establishes a unified framework to\nanalyze each of these three factors and their interplay. Under mild technical\nassumptions, we establish error bounds for a range of existing dimension\nreduction techniques based on the principle of LIS. Our error bounds also\nprovide useful insights into the accuracy of these methods. In addition, we\nanalyze the integration of LIS with sampling methods such as Markov Chain Monte\nCarlo (MCMC) and sequential Monte Carlo (SMC). We also demonstrate the\napplicability of our analysis on a linear inverse problem with Gaussian prior,\nwhich shows that all the estimates can be dimension-independent if the prior\ncovariance is a trace-class operator.\n"}
+{"abstract": "  The asteroid (16) Psyche is the largest of the M-type asteroids, which have\nbeen hypothesized to be the cores of disrupted planetesimals and the parent\nbodies of the iron meteorites. While recent evidence has collected against a\npure metal composition for Psyche, its spectrum and radar properties remain\nanomalous. We observed (16) Psyche in thermal emission with the Atacama Large\n(sub-)Millimeter Array (ALMA) at a resolution of 30 km over 2/3 of its\nrotation. The diurnal temperature variations are at the $\\sim$10 K level over\nmost of the surface and are best fit by a smooth surface with a thermal inertia\nof 280$\\pm$100 J m$^{-2}$ K$^{-1}$ s$^{-1/2}$. We measure a millimeter\nemissivity of 0.61$\\pm$0.02, which we interpret via a model that treats the\nsurface as a porous mixture of silicates and metals, where the latter may take\nthe form of iron sulfides/oxides or alternatively as conducting metallic\ninclusions. The emissivity indicates a metal content of no less than 20\\% and\npotentially much higher, but the polarized emission that should be present for\na surface with $\\geq$20\\% metal content is almost completely absent. This\nrequires a highly scattering surface, which may be due to the presence of\nreflective metallic inclusions. If such is the case, a consequence is that\nmetal-rich asteroids may produce less polarized emission than metal-poor\nasteroids, exactly the opposite prediction from standard theory, arising from\nthe dominance of scattering over the bulk material properties.\n"}
+{"abstract": "  For a Lie group $G$, let $B_{com} G$ be the classifying space for\ncommutativity. Let $E_{com} G$ be the total space of the principal $G$-bundle\nassociated to $B_{com} G$. In this article, we present a computation of the\ncohomology of $E_{com} U(3)$ using the spectral sequence associated to a\nhomotopy colimit. As a part of our computation we will also compute the\nintegral cohomology of $U(3)/N(T)$ and $(U(3)/T) \\times_{\\Sigma_3} (U(3)/T)$\nwhere $T$ is a maximal torus of $U(3)$ with normalizer $N(T)$.\n"}
+{"abstract": "  In this paper, the well-known Faulkner construction is revisited and adapted\nto include the super case, which gives a natural correspondence between\ngeneralized Jordan (super)pairs and faithful Lie (super)algebra (super)modules,\nunder certain constraints (bilinear forms with properties analogous to the ones\nof a Killing form are required, and only finite-dimensional objects are\nconsidered). We always assume that the base field has characteristic different\nfrom $2$.\n  It is also proven that associated objects in this Faulkner correspondence\nhave isomorphic automorphism group schemes. Finally, this correspondence will\nbe used to transfer the construction of the tensor product to the class of\ngeneralized Jordan (super)pairs with \"good\" bilinear forms.\n"}
+{"abstract": "  The important, and often dominant, role of tunneling in low temperature\nkinetics has resulted in numerous theoretical explorations into the methodology\nfor predicting it. Nevertheless, there are still key aspects of the derivations\nthat are lacking, particularly for non-separable systems in the low temperature\nregime, and further explorations of the physical factors affecting the\ntunneling rate are warranted. In this work we obtain a closed-form rate\nexpression for the tunneling rate constant that is a direct analog of the\nrigid-rotor-harmonic-oscillator expression. This expression introduces a novel\n\"entanglement factor\" that modulates the reaction rate. Furthermore, we are\nable to extend this expression, which is valid for non-separable systems at low\ntemperatures, to properly account for the conservation of angular momentum. In\ncontrast, previous calculations have considered only vibrational transverse\nmodes and so effectively employ a decoupled rotational partition function for\nthe orientational modes. We also suggest a simple theoretical model to describe\nthe tunneling effects in the vicinity of the crossover temperature (the\ntemperature where tunneling becomes the dominating mechanism). This model\nallows one to naturally classify, interpret, and predict experimental data.\nAmong other things, it quantitatively explains in simple terms the so-called\n\"quantum bobsled\" effect, also known as the negative centrifugal effect, which\nis related to curvature of the reaction path. Taken together, the expressions\nobtained here allow one to predict the thermal and $E$-resolved rate constants\nover broad ranges of temperatures and energies.\n"}
+{"abstract": "  Convolutional Neural Networks (CNNs) are the state-of-the-art algorithms for\nthe processing of images. However the configuration and training of these\nnetworks is a complex task requiring deep domain knowledge, experience and much\ntrial and error. Using genetic algorithms, competitive CNN topologies for image\nrecognition can be produced for any specific purpose, however in previous work\nthis has come at high computational cost. In this work two novel approaches are\npresented to the utilisation of these algorithms, effective in reducing\ncomplexity and training time by nearly 20%. This is accomplished via\nregularisation directly on training time, and the use of partial training to\nenable early ranking of individual architectures. Both approaches are validated\non the benchmark CIFAR10 data set, and maintain accuracy.\n"}
+{"abstract": "  In this paper, the structure of the second relative homology and the relative\nstem cover of the direct sum of two pairs of Leibniz algebras are determined by\nmeans of the non-abelian tensor product of Leibniz algebras. We also\ncharacterize all pairs of finite dimensional nilpotent Leibniz algebras such\nthat...\n"}
+{"abstract": "  We study two actions of the (degree 0) Picard group on the set of the\nspanning trees of a finite ribbon graph. It is known that these two actions,\ndenoted $\\beta_q$ and $\\rho_q$ respectively, are independent of the base vertex\n$q$ if and only if the ribbon graph is planar. Baker and Wang conjectured that\nin a nonplanar ribbon graph without multiple edges there always exists a vertex\n$q$ for which $\\rho_q\\neq\\beta_q$. We prove the conjecture and extend it to a\nclass of ribbon graphs with multiple edges. We also give explicit examples\nexploring the relationship between the two torsor structures in the nonplanar\ncase.\n"}
+{"abstract": "  In this paper, we consider Legendre trajectories of trans-$S$-manifolds. We\nobtain curvature characterizations of these curves and give a classification\ntheorem. We also investigate Legendre curves whose Frenet frame fields are\nlinearly dependent with certain combination of characteristic vector fields of\nthe trans-$S$-manifold.\n"}
+{"abstract": "  Recent research has shown that it is possible to find interpretable\ndirections in the latent spaces of pre-trained Generative Adversarial Networks\n(GANs). These directions enable controllable image generation and support a\nwide range of semantic editing operations, such as zoom or rotation. The\ndiscovery of such directions is often done in a supervised or semi-supervised\nmanner and requires manual annotations which limits their use in practice. In\ncomparison, unsupervised discovery allows finding subtle directions that are\ndifficult to detect a priori. In this work, we propose a contrastive\nlearning-based approach to discover semantic directions in the latent space of\npre-trained GANs in a self-supervised manner. Our approach finds semantically\nmeaningful dimensions comparable with state-of-the-art methods.\n"}
+{"abstract": "  The confirmation of the discrepancy with the Standard Model predictions in\nthe anomalous magnetic moment by the Muon g-2 experiment at Fermilab points to\na low scale of new physics. Flavour symmetries broken at low energies can\naccount for this discrepancy but these models are much more restricted, as they\nwould also generate off-diagonal entries in the dipole moment matrix.\nTherefore, if we assume that the observed discrepancy in the muon $g-2$ is\nexplained by the contributions of a low-energy flavor symmetry, lepton flavour\nviolating processes can constrain the structure of the lepton mass matrices and\ntherefore the flavour symmetries themselves predicting these structures. We\napply these ideas to several discrete flavour symmetries popular in the\nleptonic sector, such as $\\Delta (27)$, $A_4$, and $A_5 \\ltimes {\\rm CP}$.\n"}
+{"abstract": "  In this paper, we propose a novel discriminative model for online behavioral\nanalysis with application to emotion state identification. The proposed model\nis able to extract more discriminative characteristics from behavioral data\neffectively and find the direction of optimal projection efficiently to satisfy\nrequirements of online data analysis, leading to better utilization of the\nbehavioral information to produce more accurate recognition results.\n"}
+{"abstract": "  Explainable artificial intelligence is the attempt to elucidate the workings\nof systems too complex to be directly accessible to human cognition through\nsuitable side-information referred to as \"explanations\". We present a trainable\nexplanation module for convolutional image classifiers we call bounded logit\nattention (BLA). The BLA module learns to select a subset of the convolutional\nfeature map for each input instance, which then serves as an explanation for\nthe classifier's prediction. BLA overcomes several limitations of the\ninstancewise feature selection method \"learning to explain\" (L2X) introduced by\nChen et al. (2018): 1) BLA scales to real-world sized image classification\nproblems, and 2) BLA offers a canonical way to learn explanations of variable\nsize. Due to its modularity BLA lends itself to transfer learning setups and\ncan also be employed as a post-hoc add-on to trained classifiers. Beyond\nexplainability, BLA may serve as a general purpose method for differentiable\napproximation of subset selection. In a user study we find that BLA\nexplanations are preferred over explanations generated by the popular\n(Grad-)CAM method.\n"}
+{"abstract": "  Deep Neural Networks are known to be vulnerable to small, adversarially\ncrafted, perturbations. The current most effective defense methods against\nthese adversarial attacks are variants of adversarial training. In this paper,\nwe introduce a radically different defense trained only on clean images: a\nsparse coding based frontend which significantly attenuates adversarial attacks\nbefore they reach the classifier. We evaluate our defense on CIFAR-10 dataset\nunder a wide range of attack types (including Linf , L2, and L1 bounded\nattacks), demonstrating its promise as a general-purpose approach for defense.\n"}
+{"abstract": "  A classical theorem on character degrees states that if a finite group has\nfewer than four character degrees, then the group is solvable. We prove a\ncorresponding result on character values by showing that if a finite group has\nfewer than eight character values in its character table, then the group is\nsolvable. This confirms a conjecture of T. Sakurai. We also classify\nnon-solvable groups with exactly eight character values.\n"}
+{"abstract": "  We show that weak equivalences in a (cofibrantly generated) left Bousfield\nlocalization of the projective model category of simplicial presheaves can be\ncharacterized by a local lifting property if and only if the localization is\nexact.\n"}
+{"abstract": "  Accurate liver and lesion segmentation from computed tomography (CT) images\nare highly demanded in clinical practice for assisting the diagnosis and\nassessment of hepatic tumor disease. However, automatic liver and lesion\nsegmentation from contrast-enhanced CT volumes is extremely challenging due to\nthe diversity in contrast, resolution, and quality of images. Previous methods\nbased on UNet for 2D slice-by-slice or 3D volume-by-volume segmentation either\nlack sufficient spatial contexts or suffer from high GPU computational cost,\nwhich limits the performance. To tackle these issues, we propose a novel\ncontext-aware PolyUNet for accurate liver and lesion segmentation. It jointly\nexplores structural diversity and consecutive t-adjacent slices to enrich\nfeature expressive power and spatial contextual information while avoiding the\noverload of GPU memory consumption. In addition, we utilize zoom out/in and\ntwo-stage refinement strategy to exclude the irrelevant contexts and focus on\nthe specific region for the fine-grained segmentation. Our method achieved very\ncompetitive performance at the MICCAI 2017 Liver Tumor Segmentation (LiTS)\nChallenge among all tasks with a single model and ranked the $3^{rd}$,\n$12^{th}$, $2^{nd}$, and $5^{th}$ places in the liver segmentation, lesion\nsegmentation, lesion detection, and tumor burden estimation, respectively.\n"}
+{"abstract": "  Fusing live fluoroscopy images with a 3D rotational reconstruction of the\nvasculature allows to navigate endovascular devices in minimally invasive\nneuro-vascular treatment, while reducing the usage of harmful iodine contrast\nmedium. The alignment of the fluoroscopy images and the 3D reconstruction is\ninitialized using the sensor information of the X-ray C-arm geometry. Patient\nmotion is then corrected by an image-based registration algorithm, based on a\ngradient difference similarity measure using digital reconstructed radiographs\nof the 3D reconstruction. This algorithm does not require the vessels in the\nfluoroscopy image to be filled with iodine contrast agent, but rather relies on\ngradients in the image (bone structures, sinuses) as landmark features. This\npaper investigates the accuracy, robustness and computation time aspects of the\nimage-based registration algorithm. Using phantom experiments 97% of the\nregistration attempts passed the success criterion of a residual registration\nerror of less than 1 mm translation and 3{\\deg} rotation. The paper establishes\na new method for validation of 2D-3D registration without requiring changes to\nthe clinical workflow, such as attaching fiducial markers. As a consequence,\nthis method can be retrospectively applied to pre-existing clinical data. For\nclinical data experiments, 87% of the registration attempts passed the\ncriterion of a residual translational error of < 1 mm, and 84% possessed a\nrotational error of < 3{\\deg}.\n"}
+{"abstract": "  We determine the Waring ranks of all sextic binary forms using a Geometric\nInvariant Theory approach. In particular, we shed new light on a claim by E. B.\nElliott at the end of the 19th century concerning the binary sextics with\nWaring rank 3.\n"}
+{"abstract": "  We demonstrate that single crystals of methylammonium lead bromide (MAPbBr3)\ncould be grown directly on vertically aligned carbon nanotube (VACNT) forests.\nThe fast-growing MAPbBr3 single crystals engulfed the protogenetic inclusions\nin the form of individual CNTs, thus resulting in a three-dimensionally\nenlarged photosensitive interface. Photodetector devices were obtained,\ndetecting low light intensities (~20 nW) from UV range to 550 nm. Moreover, a\nphotocurrent was recorded at zero external bias voltage which points to the\nplausible formation of a p-n junction resulting from interpenetration of\nMAPbBr3 single crystals into the VACNT forest. This reveals that vertically\naligned CNTs can be used as electrodes in operationally stable perovskite-based\noptoelectronic devices and can serve as a versatile platform for future\nselective electrode development.\n"}
+{"abstract": "  We present the Galactic Radio Explorer (GReX), an all-sky monitor to probe\nthe brightest bursts in the radio sky. Building on the success of STARE2, we\nwill search for fast radio bursts (FRBs) emitted from Galactic magnetars as\nwell as bursts from nearby galaxies. GReX will search down to ten microseconds\ntime resolution, allowing us to find new super giant radio pulses from Milky\nWay pulsars and study their broadband emission. The proposed instrument will\nemploy ultra-wide band (0.7-2 GHz) feeds coupled to a high performance\n(receiver temperature 10 K) low noise amplifier (LNA) originally developed for\nthe DSA-110 and DSA-2000 projects. In GReX Phase I (GReX-I), unit systems will\nbe deployed at Owens Valley Radio Observatory (OVRO) and Big Smoky Valley,\nNevada. Phase II will expand the array, placing feeds in India, Australia, and\nelsewhere in order to build up to continuous coverage of nearly 4$\\pi$\nsteradians and to increase our exposure to the Galactic plane. We model the\nlocal magnetar population to forecast for GReX, finding the improved\nsensitivity and increased exposure to the Galactic plane could lead to dozens\nof FRB-like bursts per year.\n"}
+{"abstract": "  Although travelling faster than the speed of light in vacuum is not\nphysically allowed, the analogous bound in medium can be exceeded by a moving\nparticle. For an electron in dielectric material this leads to emission of\nphotons which is usually referred to as Cherenkov radiation. In this article a\nrelated mathematical system for waves in inhomogeneous anisotropic medium with\na maximum of three polarisation directions is studied. The waves are assumed to\nsatisfy $P^k_j u_k (x,t) = S_j(x,t)$, where $P$ is a vector-valued wave\noperator that depends on a Riemannian metric and $S $ is a point source that\nmoves at speed $\\beta < c$ in given direction $\\theta \\in \\mathbb{S}^2$. The\nphase velocity $v_{\\text{phase}}$ is described by the metric and depends on\nboth location and direction of motion. In regions where\n$v_{\\text{phase}}(x,\\theta) < \\beta <c $ holds the source generates a\ncone-shaped front of singularities that propagate according to the underlying\ngeometry. We introduce a model for a measurement setup that applies the\nmechanism and show that the Riemannian metric inside a bounded region can be\nreconstructed from partial boundary measurements. The result suggests that\nCherenkov type radiation can be applied to detect internal geometric properties\nof an inhomogeneous anisotropic target from a distance.\n"}
+{"abstract": "  Camera pose estimation in known scenes is a 3D geometry task recently tackled\nby multiple learning algorithms. Many regress precise geometric quantities,\nlike poses or 3D points, from an input image. This either fails to generalize\nto new viewpoints or ties the model parameters to a specific scene. In this\npaper, we go Back to the Feature: we argue that deep networks should focus on\nlearning robust and invariant visual features, while the geometric estimation\nshould be left to principled algorithms. We introduce PixLoc, a scene-agnostic\nneural network that estimates an accurate 6-DoF pose from an image and a 3D\nmodel. Our approach is based on the direct alignment of multiscale deep\nfeatures, casting camera localization as metric learning. PixLoc learns strong\ndata priors by end-to-end training from pixels to pose and exhibits exceptional\ngeneralization to new scenes by separating model parameters and scene geometry.\nThe system can localize in large environments given coarse pose priors but also\nimprove the accuracy of sparse feature matching by jointly refining keypoints\nand poses with little overhead. The code will be publicly available at\nhttps://github.com/cvg/pixloc.\n"}
+{"abstract": "  High precision CCD observations of six totally eclipsing contact binaries\nwere presented and analyzed. It is found that only one target is an A-type\ncontact binary (V429 Cam), while the others are W-type contact ones. By\nanalyzing the times of light minima, we discovered that two of them exhibit\nsecular period increase while three manifest long-term period decrease. For\nV1033 Her, a cyclic variation superimposed on the long-term increase was\ndiscovered. By comparing the Gaia distances with those calculated by the\nabsolute parameters of 173 contact binaries, we found that Gaia distance can be\napplied to estimate absolute parameters for most contact binaries. The absolute\nparameters of our six targets were estimated by using their Gaia distances. The\nevolutionary status of contact binaries was studied, we found that the A- and\nW- subtype contact binaries may have different formation channels. The\nrelationship between the spectroscopic and photometric mass ratios for 101\ncontact binaries was presented. It is discovered that the photometric mass\nratios are in good agreement with the spectroscopic ones for almost all the\ntotally eclipsing systems, which is corresponding to the results derived by\nPribulla et al. and Terrell & Wilson.\n"}
+{"abstract": "  User activities generate a significant number of poor-quality or irrelevant\nimages and data vectors that cannot be processed in the main data processing\npipeline or included in the training dataset. Such samples can be found with\nmanual analysis by an expert or with anomalous detection algorithms. There are\nseveral formal definitions for the anomaly samples. For neural networks, the\nanomalous is usually defined as out-of-distribution samples. This work proposes\nmethods for supervised and semi-supervised detection of out-of-distribution\nsamples in image datasets. Our approach extends a typical neural network that\nsolves the image classification problem. Thus, one neural network after\nextension can solve image classification and anomalous detection problems\nsimultaneously. Proposed methods are based on the center loss and its effect on\na deep feature distribution in a last hidden layer of the neural network. This\npaper provides an analysis of the proposed methods for the LeNet and\nEfficientNet-B0 on the MNIST and ImageNet-30 datasets.\n"}
+{"abstract": "  Deep learning (DL) has emerged as a powerful tool for accelerated MRI\nreconstruction, but these methods often necessitate a database of fully-sampled\nmeasurements for training. Recent self-supervised and unsupervised learning\napproaches enable training without fully-sampled data. However, a database of\nundersampled measurements may not be available in many scenarios, especially\nfor scans involving contrast or recently developed translational acquisitions.\nMoreover, database-trained models may not generalize well when the unseen\nmeasurements differ in terms of sampling pattern, acceleration rate, SNR, image\ncontrast, and anatomy. Such challenges necessitate a new methodology that can\nenable scan-specific DL MRI reconstruction without any external training\ndatasets. In this work, we propose a zero-shot self-supervised learning\napproach to perform scan-specific accelerated MRI reconstruction to tackle\nthese issues. The proposed approach splits available measurements for each scan\ninto three disjoint sets. Two of these sets are used to enforce data\nconsistency and define loss during training, while the last set is used to\nestablish an early stopping criterion. In the presence of models pre-trained on\na database with different image characteristics, we show that the proposed\napproach can be combined with transfer learning to further improve\nreconstruction quality.\n"}
+{"abstract": "  We study the collision frequencies of particles in the weakly and highly\nionized plasmas with the power-law q-distributions in nonextensive statistics.\nWe derive the average collision frequencies of neutral-neutral particle,\nelectron-neutral particle, ion-neutral particle, electron-electron, ion-ion and\nelectron-ion, respectively, in the q-distributed plasmas. We show that the\naverage collision frequencies depend strongly on the q-parameter in a complex\nform and thus their properties are significantly different from that in\nMaxwell-distributed plasmas. These new average collision frequencies are\nimportant for us to study accurately the transport property in the complex\nplasmas with non-Maxwell/power-law velocity distributions.\n"}
+{"abstract": "  Generalized zero-shot learning (GZSL) aims to classify samples under the\nassumption that some classes are not observable during training. To bridge the\ngap between the seen and unseen classes, most GZSL methods attempt to associate\nthe visual features of seen classes with attributes or to generate unseen\nsamples directly. Nevertheless, the visual features used in the prior\napproaches do not necessarily encode semantically related information that the\nshared attributes refer to, which degrades the model generalization to unseen\nclasses. To address this issue, in this paper, we propose a novel semantics\ndisentangling framework for the generalized zero-shot learning task (SDGZSL),\nwhere the visual features of unseen classes are firstly estimated by a\nconditional VAE and then factorized into semantic-consistent and\nsemantic-unrelated latent vectors. In particular, a total correlation penalty\nis applied to guarantee the independence between the two factorized\nrepresentations, and the semantic consistency of which is measured by the\nderived relation network. Extensive experiments conducted on four GZSL\nbenchmark datasets have evidenced that the semantic-consistent features\ndisentangled by the proposed SDGZSL are more generalizable in tasks of\ncanonical and generalized zero-shot learning. Our source code is available at\nhttps://github.com/uqzhichen/SDGZSL.\n"}
+{"abstract": "  While symmetry has been exploited to analyze synchronization patterns in\ncomplex networks, the identification of symmetries in large-size network\nremains as a challenge. We present in the present work a new method, namely the\nmethod of eigenvector-based analysis, to identify symmetries in general complex\nnetworks and, incorporating the method of eigenvalue analysis, investigate the\nformation and transition of synchronization patterns. The efficiency of the\nproposed method is validated by both artificial and empirical network models\nconsisting of coupled chaotic oscillators. Furthermore, we generalize the\nmethod to networks of weighted couplings in which no strict symmetry exists but\nsynchronization clusters are still well organized, with the predications\nagreeing with the results of direct simulations. Our study provides a new\napproach for identifying network symmetries, and paves a way to the\ninvestigation of synchronization patterns in large-size complex network.\n"}
+{"abstract": "  We revisit the notions of the quantum-mechanical sojourn time in the context\nof the quantum clocks to enquire whether the sojourn time be clocked without\nthe clock affecting the dynamics of the wave motion. Upon recognizing that the\npositivity of conditional sojourn time is not ensured even in the case of\nphysically co-evolving clock mechanisms, we trace its origins to the\nnon-trivial inadvertent scattering arising from the disparity, however weak,\nengendered by the very clock potential. Specifically, our investigations focus\non the Larmor spin rotation-based unitary clock where the alleviation of these\nunphysical contributions has been achieved by correcting the mathematical\napparatus of extracting the sojourn times. The corrections have been obtained\nfor both the spin precession-based and spin alignment-based scenarios. The\nsojourn times so obtained are found to have proper high- and low-energy limits\nand turn out to be positive definite for an arbitrary potential. The regimen\nprovided here is general and appeals equivalently for unitary as well as\nnon-unitary clocks where the clock-induced perturbations couple to the system\nHamiltonian.\n"}
+{"abstract": "  The damage mechanisms and load redistribution of high strength TC17 titanium\nalloy/unidirectional SiC fibre composite (fibre diameter = 100 $\\mu$m) under\nhigh temperature (350 {\\deg}C) fatigue cycling have been investigated in situ\nusing synchrotron X-ray computed tomography (CT) and X-ray diffraction (XRD)\nfor high cycle fatigue (HCF) under different stress amplitudes. The\nthree-dimensional morphology of the crack and fibre fractures has been mapped\nby CT. During stable growth, matrix cracking dominates with the crack\ndeflecting (by 50-100 $\\mu$m in height) when bypassing bridging fibres. A small\nnumber of bridging fibres have fractured close to the matrix crack plane\nespecially under relatively high stress amplitude cycling. Loading to the peak\nstress led to rapid crack growth accompanied by a burst of fibre fractures.\nMany of the fibre fractures occurred 50-300 $\\mu$m from the matrix crack plane\nduring rapid growth, in contrast to that in the stable growth stage, leading to\nextensive fibre pull-out on the fracture surface. The changes in fibre loading,\ninterfacial stress, and the extent of fibre-matrix debonding in the vicinity of\nthe crack have been mapped for the fatigue cycle and after the rapid growth by\nhigh spatial resolution XRD. The fibre/matrix interfacial sliding extends up to\n600 $\\mu$m (in the stable growth zone) or 700 $\\mu$m (in the rapid growth zone)\neither side of the crack plane. The direction of interfacial shear stress\nreverses with the loading cycle, with the maximum frictional sliding stress\nreaching ~55 MPa in both the stable growth and rapid growth regimes.\n"}
+{"abstract": "  HardWare-aware Neural Architecture Search (HW-NAS) has recently gained\ntremendous attention by automating the design of DNNs deployed in more\nresource-constrained daily life devices. Despite its promising performance,\ndeveloping optimal HW-NAS solutions can be prohibitively challenging as it\nrequires cross-disciplinary knowledge in the algorithm, micro-architecture, and\ndevice-specific compilation. First, to determine the hardware-cost to be\nincorporated into the NAS process, existing works mostly adopt either\npre-collected hardware-cost look-up tables or device-specific hardware-cost\nmodels. Both of them limit the development of HW-NAS innovations and impose a\nbarrier-to-entry to non-hardware experts. Second, similar to generic NAS, it\ncan be notoriously difficult to benchmark HW-NAS algorithms due to their\nsignificant required computational resources and the differences in adopted\nsearch spaces, hyperparameters, and hardware devices. To this end, we develop\nHW-NAS-Bench, the first public dataset for HW-NAS research which aims to\ndemocratize HW-NAS research to non-hardware experts and make HW-NAS research\nmore reproducible and accessible. To design HW-NAS-Bench, we carefully\ncollected the measured/estimated hardware performance of all the networks in\nthe search spaces of both NAS-Bench-201 and FBNet, on six hardware devices that\nfall into three categories (i.e., commercial edge devices, FPGA, and ASIC).\nFurthermore, we provide a comprehensive analysis of the collected measurements\nin HW-NAS-Bench to provide insights for HW-NAS research. Finally, we\ndemonstrate exemplary user cases to (1) show that HW-NAS-Bench allows\nnon-hardware experts to perform HW-NAS by simply querying it and (2) verify\nthat dedicated device-specific HW-NAS can indeed lead to optimal accuracy-cost\ntrade-offs. The codes and all collected data are available at\nhttps://github.com/RICE-EIC/HW-NAS-Bench.\n"}
+{"abstract": "  Tumour spheroids have the potential to be used as preclinical\nchemosensitivity assays. However, the production of three dimensional (3D)\ntumour spheroids remains challenging as not all tumour cell lines form\nspheroids with regular morphologies and spheroid transfer often induces\ndisaggregation. In the field of pancreatic cancer, the MiaPaCa-2 cell line is\nan interesting model for research but it is known for its difficulty to form\nstable spheroids; also, when formed, spheroids from this cell line are weak and\narduous to manage and to harvest for further analyses such as multiple staining\nand imaging. In this work, we compared different methods (i.e. hanging drop,\nround bottom wells and Matrigel embedding, each of them with or without\nmethylcellulose in the media) to evaluate which one allowed to better overpass\nthese limitations. Morphometric analysis indicated that hanging drop in\npresence of methylcellulose leaded to well-organized spheroids; interestingly,\nquantitative PCR (qPCR) analysis reflected the morphometric characterization,\nindicating that same spheroids expressed the highest values of CD44, VIMENTIN,\nTGF beta1 and Ki67. In addition, we investigated the generation of MiaPaCa-2\nspheroids when cultured on substrates of different hydrophobicity, in order to\nminimize the area in contact with the culture media and to further improve\nspheroid formation.\n"}
+{"abstract": "  We propose a method to probe chameleon particles predicted in the $F(R)$\ngravity as a model of the modified gravity based on the concept of a stimulated\npulsed-radar collider. We analyze the chameleon mechanism induced by an ambient\nenvironment consisting of focused photon beams and a dilute gas surrounding a\nplace where stimulated photon-photon scatterings occur. We then discuss how to\nextract the characteristic feature of the chameleon signal. We find that a\nchameleon with the varying mass around $(0.1-1)\\,\\mu$eV in a viable model of\nthe $F(R)$ gravity is testable by searching for the steep pressure dependence\nof the 10th-power for the signal yield.\n"}
+{"abstract": "  We propose a robust and efficient way to store and transport quantum\ninformation via one-dimensional discrete time quantum walks. We show how to\nattain an effective dispersionless wave packet evolution using only two types\nof local unitary operators (quantum coins or gates), properly engineered to act\nat predetermined times and at specific lattice sites during the system's time\nevolution. In particular, we show that a qubit initially localized about a\nGaussian distribution can be almost perfectly confined during long times or\nsent hundreds lattice sites away from its original location and later almost\nperfectly reconstructed using only Hadamard and $\\sigma_x$ gates.\n"}
+{"abstract": "  Nested stochastic modeling has been on the rise in many fields of the\nfinancial industry. Such modeling arises whenever certain components of a\nstochastic model are stochastically determined by other models. There are at\nleast two main areas of applications, including (1) portfolio risk management\nin the banking sector and (2) principle-based reserving and capital\nrequirements in the insurance sector. As financial instrument values often\nchange with economic fundamentals, the risk management of a portfolio (outer\nloop) often requires the assessment of financial positions subject to changes\nin risk factors in the immediate future. The valuation of financial position\n(inner loop) is based on projections of cashflows and risk factors into the\ndistant future. The nesting of such stochastic modeling can be computationally\nchallenging.\n  Most of existing techniques to speed up nested simulations are based on curve\nfitting. The main idea is to establish a functional relationship between inner\nloop estimator and risk factors by running a limited set of economic scenarios,\nand, instead of running inner loop simulations, inner loop estimations are made\nby feeding other scenarios into the fitted curve. This paper presents a\nnon-conventional approach based on the concept of sample recycling. Its essence\nis to run inner loop estimation for a small set of outer loop scenarios and to\nfind inner loop estimates under other outer loop scenarios by recycling those\nknown inner loop paths. This new approach can be much more efficient when\ntraditional techniques are difficult to implement in practice.\n"}
+{"abstract": "  Reconstruction of object or scene surfaces has tremendous applications in\ncomputer vision, computer graphics, and robotics. In this paper, we study a\nfundamental problem in this context about recovering a surface mesh from an\nimplicit field function whose zero-level set captures the underlying surface.\nTo achieve the goal, existing methods rely on traditional meshing algorithms;\nwhile promising, they suffer from loss of precision learned in the implicit\nsurface networks, due to the use of discrete space sampling in marching cubes.\nGiven that an MLP with activations of Rectified Linear Unit (ReLU) partitions\nits input space into a number of linear regions, we are motivated to connect\nthis local linearity with a same property owned by the desired result of\npolygon mesh. More specifically, we identify from the linear regions,\npartitioned by an MLP based implicit function, the analytic cells and analytic\nfaces that are associated with the function's zero-level isosurface. We prove\nthat under mild conditions, the identified analytic faces are guaranteed to\nconnect and form a closed, piecewise planar surface. Based on the theorem, we\npropose an algorithm of analytic marching, which marches among analytic cells\nto exactly recover the mesh captured by an implicit surface network. We also\nshow that our theory and algorithm are equally applicable to advanced MLPs with\nshortcut connections and max pooling. Given the parallel nature of analytic\nmarching, we contribute AnalyticMesh, a software package that supports\nefficient meshing of implicit surface networks via CUDA parallel computing, and\nmesh simplification for efficient downstream processing. We apply our method to\ndifferent settings of generative shape modeling using implicit surface\nnetworks. Extensive experiments demonstrate our advantages over existing\nmethods in terms of both meshing accuracy and efficiency.\n"}
+{"abstract": "  Faced with a considerable lack of resources in African languages to carry out\nwork in Natural Language Processing (NLP), Natural Language Understanding (NLU)\nand artificial intelligence, the research teams of NTeALan association has set\nitself the objective of building open-source platforms for the collaborative\nconstruction of lexicographic data in African languages. In this article, we\npresent our first reports after 2 years of collaborative construction of\nlexicographic resources useful for African NLP tools.\n"}
+{"abstract": "  A significant number of those killed in traffic accidents annually refers to\npedestrians. most of these accidents occur when a pedestrian is going to pass\nthe street. One of the most hazardous areas for pedestrians crossing the street\nis midblock crosswalks. These areas are often uncontrolled and there is no\nseparated phase for pedestrian crossing. As a result, using created gaps among\nvehicles is often the only opportunity for pedestrians to cross the street.\nThis research aims to investigate effective factors on pedestrian gap\nacceptance at uncontrolled mid-block crosswalks. In this regard, several\nvariables such as individual, environmental, and traffic variables were\nconsidered and their related data were extracted by filming the location. from\nvideo images, they were analyzed using logit regression model and the behavior\nAfter extracting the data of pedestrian while he is crossing the street was\nmodeled as the probabilistic function of pedestrian gap acceptance. The results\nillustrate that conflict flow rate, the temporal gap among vehicles, and driver\nyielding in the face of vehicles are the most important effective factors on\npedestrian behavior. The other one is that the speed and type of vehicles do\nnot affect the pedestrian's decision about rejecting or accepting the gap. The\nresults also show that individual characteristics such as age and gender of\npedestrians do not make a significant relationship with pedestrian gap\nacceptance.\n"}
+{"abstract": "  We construct for the first time conditionally decomposable $d$-polytopes for\n$d \\ge 4$. These examples have any number of vertices from $4d-4$ upwards. A\npolytope is said to be conditionally decomposable if one polytope\ncombinatorially equivalent to it is decomposable (with respect to the Minkowski\nsum) and another one combinatorially equivalent to it is indecomposable. In our\nexamples, one has $4d-2$ vertices and is the sum of a line segment and a\nbi-pyramid over a prism. The other ones have $4d-4$ vertices, and one of them\nis the sum of a line segment and a $2$-fold pyramid over a prism. We show that\nthe latter examples have the minimum number of vertices among all conditionally\ndecomposable $d$-polytopes that have a line segment for a summand.\n"}
+{"abstract": "  We consider various asymptotic scaling limits $N\\to\\infty$ for the $2N$\ncomplex eigenvalues of non-Hermitian random matrices in the symmetry class of\nthe symplectic Ginibre ensemble. These are known to be integrable, forming\nPfaffian point processes, and we obtain limiting expressions for the\ncorresponding kernel for different potentials. The first part is devoted to the\nsymplectic Ginibre ensemble with a Gaussian potential. We obtain the asymptotic\nat the edge of the spectrum in the vicinity of the real line. The unifying form\nof the kernel allows us to make contact with the bulk scaling along the real\nline and with the edge scaling away from the real line, where we recover the\nknown determinantal process of the complex Ginibre ensemble. Part two covers\nensembles of Mittag-Leffler type with a singularity at the origin. For\npotentials $Q(\\zeta)=|\\zeta|^{2\\lambda}-(2c/N)\\log|\\zeta|$, with $\\lambda>0$\nand $c>-1$, the limiting kernel obeys a linear differential equation of\nfractional order $1/\\lambda$ at the origin. For integer $m=1/\\lambda$ it can be\nsolved in terms of Mittag-Leffler functions. In the last part, we derive the\nWard's equation for a general class of potentials as a tool to investigate\nuniversality. This allows us to determine the functional form of kernels that\nare translation invariant up to its integration domain.\n"}
+{"abstract": "  We report novel observational evidence on the evolutionary status of\nlithium-rich giant stars by combining asteroseismic and lithium abundance data.\nComparing observations and models of the asteroseismic gravity-mode period\nspacing $\\Delta\\Pi_{1}$, we find that super-Li-rich giants (SLR, A(Li)~$>\n3.2$~dex) are almost exclusively young red-clump (RC) stars. Depending on the\nexact phase of evolution, which requires more data to refine, SLR stars are\neither (i) less than $\\sim 2$~Myr or (ii) less than $\\sim40$~Myr past the main\ncore helium flash (CHeF). Our observations set a strong upper limit for the\ntime of the inferred Li-enrichment phase of $< 40$~Myr post-CHeF, lending\nsupport to the idea that lithium is produced around the time of the CHeF. In\ncontrast, the more evolved RC stars ($> 40$~Myr post-CHeF) generally have low\nlithium abundances (A(Li)~$<1.0$~dex). Between the young, super-Li-rich phase,\nand the mostly old, Li-poor RC phase, there is an average reduction of lithium\nby about 3 orders of magnitude. This Li-destruction may occur rapidly. We find\nthe situation to be less clear with stars having Li abundances between the two\nextremes of super-Li-rich and Li-poor. This group, the `Li-rich' stars ($3.2\n>$~A(Li)~$> 1.0$~dex), shows a wide range of evolutionary states.\n"}
+{"abstract": "  The Coulomb Branch Formula conjecturally expresses the refined Witten index\nfor $N=4$ Quiver Quantum Mechanics as a sum over multi-centered collinear black\nhole solutions, weighted by so-called `single-centered' or `pure-Higgs'\nindices, and suitably modified when the quiver has oriented cycles. On the\nother hand, localization expresses the same index as an integral over the\ncomplexified Cartan torus and auxiliary fields, which by Stokes' theorem leads\nto the famous Jeffrey-Kirwan residue formula. Here, by evaluating the same\nintegral using steepest descent methods, we show the index is in fact given by\na sum over deformed multi-centered collinear solutions, which encompasses both\nregular and scaling collinear solutions. As a result, we confirm the Coulomb\nBranch Formula for Abelian quivers in the presence of oriented cycles, and\nidentify the origin of the pure-Higgs and minimal modification terms as coming\nfrom collinear scaling solutions. For cyclic Abelian quivers, we observe that\npart of the scaling contributions reproduce the stacky invariants for trivial\nstability, a mathematically well-defined notion whose physics significance had\nremained obscure.\n"}
+{"abstract": "  We present an S$_4$ flavour symmetric model within a minimal seesaw framework\nresulting in mass matrices that leads to TM$_1$ mixing. Minimal seesaw is\nrealized by adding two right-handed neutrinos to the Standard Model. The model\npredicts Normal Hierarchy (NH) for neutrino masses. Using the constrained\nsix-dimensional parameter space, we have evaluated the effective Majorana\nneutrino mass, which is the parameter of interest in neutrinoless double beta\ndecay experiments. The possibility of explaining baryogenesis via resonant\nleptogenesis is also examined within the model. A non-zero, resonantly enhanced\nCP asymmetry generated from the decay of right-handed neutrinos at the TeV\nscale is studied, considering flavour effects. The evolution of lepton\nasymmetry is discussed by solving the set of Boltzmann equations numerically\nand obtain the value of baryon asymmetry to be $\\lvert \\eta_B \\rvert = 6.3\n\\times 10^{-10}$.\n"}
+{"abstract": "  Automatic speech recognition (ASR) is widely used in consumer electronics.\nASR greatly improves the utility and accessibility of technology, but usually\nthe output is only word sequences without punctuation. This can result in\nambiguity in inferring user-intent. We first present a transformer-based\napproach for punctuation prediction that achieves 8% improvement on the IWSLT\n2012 TED Task, beating the previous state of the art [1]. We next describe our\nmultimodal model that learns from both text and audio, which achieves 8%\nimprovement over the text-only algorithm on an internal dataset for which we\nhave both the audio and transcriptions. Finally, we present an approach to\nlearning a model using contextual dropout that allows us to handle variable\namounts of future context at test time.\n"}
+{"abstract": "  Semiconductor quantum dots, where electrons or holes are isolated via\nelectrostatic potentials generated by surface gates, are promising building\nblocks for semiconductor-based quantum technology. Here, we investigate double\nquantum dot (DQD) charge qubits in GaAs, capacitively coupled to high-impedance\nSQUID array and Josephson junction array resonators. We tune the strength of\nthe electric dipole interaction between the qubit and the resonator in-situ\nusing surface gates. We characterize the qubit-resonator coupling strength,\nqubit decoherence, and detuning noise affecting the charge qubit for different\nelectrostatic DQD configurations. We find that all quantities can be tuned\nsystematically over more than one order of magnitude, resulting in reproducible\ndecoherence rates $\\Gamma_2/2\\pi<~5$ MHz in the limit of high interdot\ncapacitance. Conversely, by reducing the interdot capacitance, we can increase\nthe DQD electric dipole strength, and therefore its coupling to the resonator.\nBy employing a Josephson junction array resonator with an impedance of $\\sim4$\nk$\\Omega$ and a resonance frequency of $\\omega_r/2\\pi \\sim 5.6$ GHz, we observe\na coupling strength of $g/2\\pi \\sim 630$ MHz, demonstrating the possibility to\nachieve the ultrastrong coupling regime (USC) for electrons hosted in a\nsemiconductor DQD. These results are essential for further increasing the\ncoherence of quantum dot based qubits and investigating USC physics in\nsemiconducting QDs.\n"}
+{"abstract": "  The process of the excitation of an electromagnetic field by a relativistic\nelectron bunch at the input of a semi-infinite plasma waveguide is\ninvestigated. The shape and intensity of the short transition electromagnetic\npulse are determined and its evolution during propagating in the plasma\nwaveguide is studied. Besides, the influence of the plasma boundary on the\nspatial structure of plasma wake oscillations in plasma waveguide is\nconsidered.\n"}
+{"abstract": "  We revisit the classic Susceptible-Infected-Recovered (SIR) epidemic model\nand one of its nonlocal variations recently developed in \\cite{Guan}. We\nintroduce several new approaches to derive exact analytical solutions in the\nclassical situation and analyze the corresponding effective approximations in\nthe nonlocal setting. An interesting new feature of the nonlocal models,\ncompared with the classic SIR model, is the appearance of multiple peak\nsolutions for the infected population. We provide several rigorous results on\nthe existence and non-existence of peak solutions with sharp asymptotics.\n"}
+{"abstract": "  Brunel harmonics appear in the optical response of an atom in process of\nlaser-induced ionization, when the electron leaves the atom and is accelerated\nin the strong optical field. In contrast to recollision-based harmonics, the\nBrunel mechanism does not require the electron returning to the core. Here we\nshow that in the presence of a strong ionizing terahertz (THz) field, even a\nweak driving field at the optical frequencies allow for generating Brunel\nharmonics effectively. The strong ionizing THz pump suppresses recollisions,\nmaking Brunel dominant in a wide spectral range. High-order Brunel harmonics\nmay form a coherent carrier-envelope-phase insensitive supercontinuum,\ncompressible into an isolated pulse with the duration down to 100 attoseconds.\n"}
+{"abstract": "  Privacy Policies are the legal documents that describe the practices that an\norganization or company has adopted in the handling of the personal data of its\nusers. But as policies are a legal document, they are often written in\nextensive legal jargon that is difficult to understand. Though work has been\ndone on privacy policies but none that caters to the problem of verifying if a\ngiven privacy policy adheres to the data protection laws of a given country or\nstate. We aim to bridge that gap by providing a framework that analyzes privacy\npolicies in light of various data protection laws, such as the General Data\nProtection Regulation (GDPR). To achieve that, firstly we labeled both the\nprivacy policies and laws. Then a correlation scheme is developed to map the\ncontents of a privacy policy to the appropriate segments of law that a policy\nmust conform to. Then we check the compliance of privacy policy's text with the\ncorresponding text of the law using NLP techniques. By using such a tool, users\nwould be better equipped to understand how their personal data is managed. For\nnow, we have provided a mapping for the GDPR and PDPA, but other laws can\neasily be incorporated in the already built pipeline.\n"}
+{"abstract": "  We present a scalar-tensor theory of gravity on a torsion-free and metric\ncompatible Lyra manifold. This is obtained by generalizing the concept of\nphysical reference frame by considering a scale function defined over the\nmanifold. The choice of a specific frame induces a local base, naturally\nnon-holonomic, whose structure constants give rise to extra terms in the\nexpression of the connection coefficients and in the expression for the\ncovariant derivative. In the Lyra manifold, transformations between reference\nframes involving both coordinates and scale change the transformation law of\ntensor fields, when compared to those of the Riemann manifold. From a direct\ngeneralization of the Einstein-Hilbert minimal action coupled with a matter\nterm, it was possible to build a Lyra invariant action, which gives rise to the\nassociated Lyra Scalar-Tensor theory of gravity (LyST), with field equations\nfor $g_{\\mu\\nu}$ and $\\phi$. These equations have a well-defined Newtonian\nlimit, from which it can be seen that both metric and scale play a role in the\ndescription gravitational interaction. We present a spherically symmetric\nsolution for the LyST gravity field equations. It dependent on two parameters\n$m$ and $r_{L}$, whose physical meaning is carefully investigated. We highlight\nthe properties of LyST spherically symmetric line element and compare it to\nSchwarzchild solution.\n"}
+{"abstract": "  Tubercles are modifications to the leading edge of an airfoil in the form of\nblunt wave-like serrations. Several studies on the effect of tubercles on\nisolated airfoils have shown a beneficial effect in the post-stall regime, as\nreduced drag and increased lift, leading to a delay of stall. The prospect of\ndelaying stall is particularly attractive to designers of axial compressors in\ngas turbines, as this leads to designs with higher loading and therefore higher\npressure rise with fewer number of stages. In the present study, experiments\nwere performed on a cascade of airfoils with NACA 65209 profile with different\ntubercle geometries. The measurements were made over an exit plane using a\nfive-hole probe to compare the cascade performance parameters. Additionally,\nhot-wire measurements were taken near the blade surface to understand the\nnature of the flow in the region close to the tubercles. Oil-flow visualization\non the cascade end wall reveal the flow through the passage of blades with and\nwithout tubercles. For the cascade considered, the estimated stall angle for\nthe best performing set of blades is found to increase up to 8.6{\\deg} from\nthat of the unmodified blade of 6.0{\\deg}. Application of such structures in\naxial compressor blades may well lead to suppression of stall in axial\ncompressors and extend the operating range.\n"}
+{"abstract": "  Hexagonal rare-earth ferrite RFeO$_3$ family represents a unique class of\nmultiferroics exhibiting weak ferromagnetism, and a strong coupling between\nmagnetism and structural trimerization is predicted. However, the hexagonal\nstructure for RFeO$_3$ remains metastable in conventional condition. We have\nsucceeded in stabilizing the hexagonal structure of polycrystalline YbFeO$_3$\nby partial Sc substitution of Yb. Using bulk magnetometry and neutron\ndiffraction, we find that Yb$_{0.42}$Sc$_{0.58}$FeO$_3$ orders into a canted\nantiferromagnetic state with the Neel temperature $T_N$ ~ 165 K, below which\nthe $Fe^{3+}$ moments form the triangular configuration in the $ab$-plane and\ntheir in-plane projections are parallel to the [100] axis, consistent with\nmagnetic space group $P$6$_{3}$$c'm'$. It is determined that the spin-canting\nis aligned along the $c$-axis, giving rise to the weak ferromagnetism.\nFurthermore, the $Fe^{3+}$ moments reorient toward a new direction below\nreorientation temperature $T_R$ ~ 40 K, satisfying magnetic subgroup\n$P$6$_{3}$, while the $Yb^{3+}$ moments order independently and\nferrimagnetically along the $c$-axis at the characteristic temperature $T_{Yb}$\n~ 15 K. Interestingly, reproducible modulation of electric polarization induced\nby magnetic field at low temperature is achieved, suggesting that the delicate\nstructural distortion associated with two-up/one-down buckling of the\nYb/Sc-planes and tilting of the FeO$_5$ bipyramids may mediate the coupling\nbetween ferroelectric and magnetic orders under magnetic field. The present\nwork represents a substantial progress to search for high-temperature\nmultiferroics in hexagonal ferrites and related materials.\n"}
+{"abstract": "  High quality facial image editing is a challenging problem in the movie\npost-production industry, requiring a high degree of control and identity\npreservation. Previous works that attempt to tackle this problem may suffer\nfrom the entanglement of facial attributes and the loss of the person's\nidentity. Furthermore, many algorithms are limited to a certain task. To tackle\nthese limitations, we propose to edit facial attributes via the latent space of\na StyleGAN generator, by training a dedicated latent transformation network and\nincorporating explicit disentanglement and identity preservation terms in the\nloss function. We further introduce a pipeline to generalize our face editing\nto videos. Our model achieves a disentangled, controllable, and\nidentity-preserving facial attribute editing, even in the challenging case of\nreal (i.e., non-synthetic) images and videos. We conduct extensive experiments\non image and video datasets and show that our model outperforms other\nstate-of-the-art methods in visual quality and quantitative evaluation. Source\ncodes are available at https://github.com/InterDigitalInc/latent-transformer.\n"}
+{"abstract": "  Not all topics are equally \"flammable\" in terms of toxicity: a calm\ndiscussion of turtles or fishing less often fuels inappropriate toxic dialogues\nthan a discussion of politics or sexual minorities. We define a set of\nsensitive topics that can yield inappropriate and toxic messages and describe\nthe methodology of collecting and labeling a dataset for appropriateness. While\ntoxicity in user-generated data is well-studied, we aim at defining a more\nfine-grained notion of inappropriateness. The core of inappropriateness is that\nit can harm the reputation of a speaker. This is different from toxicity in two\nrespects: (i) inappropriateness is topic-related, and (ii) inappropriate\nmessage is not toxic but still unacceptable. We collect and release two\ndatasets for Russian: a topic-labeled dataset and an appropriateness-labeled\ndataset. We also release pre-trained classification models trained on this\ndata.\n"}
+{"abstract": "  Evolution equations for leading twist operators in high orders of\nperturbation theory can be restored from the spectrum of anomalous dimensions\nand the calculation of the special conformal anomaly at one order less using\nconformal symmetry of QCD at the Wilson-Fisher critical point at non-integer\n$d=4-2\\epsilon$ space-time dimensions. In this work we generalize this\ntechnique to axial-vector operators. We calculate the corresponding three-loop\nevolution kernels in Larin's scheme and derive explicit expressions for the\nfinite renormalization kernel that describes the difference to the vector case\nto restore the conventional ${\\overline{\\mathrm{MS}}}$-scheme. The results are\ndirectly applicable to deeply-virtual Compton scattering and the transition\nform factor $\\gamma^*\\gamma\\to\\pi$.\n"}
+{"abstract": "  In one complex variable, the cross ratio is a well-known quantity associated\nwith four given points in the complex plane that remains invariant under linear\nfractional maps. In particular, if one knows where three points in the complex\nplane are mapped under a linear fractional map, one can use this invariance to\nexplicitly determine the map and to show that linear fractional maps are\n$3$-transitive. In this paper, we define a generalized cross ratio and\ndetermine some of its basic properties. In particular, we determine which\nhypotheses must be made to guarantee that our generalized cross ratio is well\ndefined. We thus obtain a class of maps that obey similar transitivity\nproperties as in one complex dimension, under some more restrictive conditions.\n"}
+{"abstract": "  Automated story generation remains a difficult area of research because it\nlacks strong objective measures. Generated stories may be linguistically sound,\nbut in many cases suffer poor narrative coherence required for a compelling,\nlogically-sound story. To address this, we present Fabula Entropy Indexing\n(FEI), an evaluation method to assess story coherence by measuring the degree\nto which human participants agree with each other when answering true/false\nquestions about stories. We devise two theoretically grounded measures of\nreader question-answering entropy, the entropy of world coherence (EWC), and\nthe entropy of transitional coherence (ETC), focusing on global and local\ncoherence, respectively. We evaluate these metrics by testing them on\nhuman-written stories and comparing against the same stories that have been\ncorrupted to introduce incoherencies. We show that in these controlled studies,\nour entropy indices provide a reliable objective measure of story coherence.\n"}
+{"abstract": "  Learning individualized treatment rules (ITRs) is an important topic in\nprecision medicine. Current literature mainly focuses on deriving ITRs from a\nsingle source population. We consider the observational data setting when the\nsource population differs from a target population of interest. We assume\nsubject covariates are available from both populations, but treatment and\noutcome data are only available from the source population. Although adjusting\nfor differences between source and target populations can potentially lead to\nan improved ITR for the target population, it can substantially increase the\nvariability in ITR estimation. To address this dilemma, we develop a weighting\nframework that aims to tailor an ITR for a given target population and protect\nagainst high variability due to superfluous covariate shift adjustments. Our\nmethod seeks covariate balance over a nonparametric function class\ncharacterized by a reproducing kernel Hilbert space and can improve many ITR\nlearning methods that rely on weights. We show that the proposed method\nencompasses importance weights and the so-called overlap weights as two extreme\ncases, allowing for a better bias-variance trade-off in between. Numerical\nexamples demonstrate that the use of our weighting method can greatly improve\nITR estimation for the target population compared with other weighting methods.\n"}
+{"abstract": "  We calculate the mass spectrum and the structure of the positronium system at\na strong coupling in a basis light-front approach. We start from the\nlight-front QED Hamiltonian and retain one dynamical photon in our basis. We\nperform the fermion mass renormalization associated with the nonperturbative\nfermion self-energy correction. We present the resulting mass spectrum and wave\nfunctions for the selected low-lying states. Next, we apply this approach to\nQCD and calculate the heavy meson system with one dynamical gluon retained. We\nillustrate the obtained mass spectrum and wave functions for the selected\nlow-lying states.\n"}
+{"abstract": "  We study compressible types in the context of (local and global) NIP. By\nextending a result in machine learning theory (the existence of a bound on the\nrecursive teaching dimension), we prove density of compressible types. Using\nthis, we obtain explicit uniform honest definitions for NIP formulas (answering\na question of Eshel and the second author), and build compressible models in\ncountable NIP theories.\n"}
+{"abstract": "  The paper describes the construction of entropy-stable discontinuous Galerkin\ndifference (DGD) discretizations for hyperbolic conservation laws on\nunstructured grids. The construction takes advantage of existing theory for\nentropy-stable summation-by-parts (SBP) discretizations. In particular, the\npaper shows how DGD discretizations -- both linear and nonlinear -- can be\nconstructed by defining the SBP trial and test functions in terms of\ninterpolated DGD degrees of freedom. In the case of entropy-stable\ndiscretizations, the entropy variables rather than the conservative variables\nmust be interpolated to the SBP nodes. A fully-discrete entropy-stable scheme\nis obtained by adopting the relaxation Runge-Kutta version of the midpoint\nmethod. In addition, DGD matrix operators for the first derivative are shown to\nbe dense-norm SBP operators. Numerical results are presented to verify the\naccuracy and entropy-stability of the DGD discretization in the context of the\nEuler equations. The results suggest that DGD and SBP solution errors are\nsimilar for the same number of degrees of freedom. Finally, an investigation of\nthe DGD spectra shows that spectral radius is relatively insensitive to\ndiscretization order; however, the high-order methods do suffer from the linear\ninstability reported for other entropy-stable discretizations.\n"}
+{"abstract": "  Segmenting histology images into diagnostically relevant regions is\nimperative to support timely and reliable decisions by pathologists. To this\nend, computer-aided techniques have been proposed to delineate relevant regions\nin scanned histology slides. However, the techniques necessitate task-specific\nlarge datasets of annotated pixels, which is tedious, time-consuming,\nexpensive, and infeasible to acquire for many histology tasks. Thus,\nweakly-supervised semantic segmentation techniques are proposed to utilize weak\nsupervision that is cheaper and quicker to acquire. In this paper, we propose\nSegGini, a weakly supervised segmentation method using graphs, that can utilize\nweak multiplex annotations, i.e. inexact and incomplete annotations, to segment\narbitrary and large images, scaling from tissue microarray (TMA) to whole slide\nimage (WSI). Formally, SegGini constructs a tissue-graph representation for an\ninput histology image, where the graph nodes depict tissue regions. Then, it\nperforms weakly-supervised segmentation via node classification by using\ninexact image-level labels, incomplete scribbles, or both. We evaluated SegGini\non two public prostate cancer datasets containing TMAs and WSIs. Our method\nachieved state-of-the-art segmentation performance on both datasets for various\nannotation settings while being comparable to a pathologist baseline.\n"}
+{"abstract": "  Over the past decade, HCI researchers, design researchers, and practitioners\nhave increasingly addressed ethics-focused issues through a range of\ntheoretical, methodological and pragmatic contributions to the field. While\nmany forms of design knowledge have been proposed and described, we focus\nexplicitly on knowledge that has been codified as \"methods,\" which we define as\nany supports for everyday work practices of designers. In this paper, we\nidentify, analyze, and map a collection of 63 existing ethics-focused methods\nintentionally designed for ethical impact. We present a content analysis,\nproviding a descriptive record of how they operationalize ethics, their\nintended audience or context of use, their \"core\" or \"script,\" and the means by\nwhich these methods are formulated, articulated, and languaged. Building on\nthese results, we provide an initial definition of ethics-focused methods,\nidentifying potential opportunities for the development of future methods to\nsupport design practice and research.\n"}
+{"abstract": "  We present a novel neural network Maximum Mean Discrepancy (MMD) statistic by\nidentifying a new connection between neural tangent kernel (NTK) and MMD. This\nconnection enables us to develop a computationally efficient and\nmemory-efficient approach to compute the MMD statistic and perform NTK based\ntwo-sample tests towards addressing the long-standing challenge of memory and\ncomputational complexity of the MMD statistic, which is essential for online\nimplementation to assimilating new samples. Theoretically, such a connection\nallows us to understand the NTK test statistic properties, such as the Type-I\nerror and testing power for performing the two-sample test, by adapting\nexisting theories for kernel MMD. Numerical experiments on synthetic and\nreal-world datasets validate the theory and demonstrate the effectiveness of\nthe proposed NTK-MMD statistic.\n"}
+{"abstract": "  This paper contributes a closed-form linear IV estimator to a class of\nestimators that minimises the mean dependence of an error term on a set of\ninstruments. Subject to a weak uncorrelatedness exclusion restriction, root-n\nconsistency and asymptotic normality are achieved under a significantly weak\nrelevance condition in at least two respects: (1) consistent estimation without\nexcluded instruments is possible provided endogenous covariates are\nnon-linearly mean-dependent on exogenous covariates, and (2) the endogenous\ncovariates may be uncorrelated with but mean-dependent on instruments. In\naddition, this paper proposes a test of the weak relevance condition in the\ncase of a single endogenous with exogenous covariates. Monte Carlo simulations\nillustrate low bias relative to conventional IV methods when instruments are\nvery weak. An empirical example illustrates the practical usefulness of the\nestimator where, for instance, reasonable estimates are still achieved when no\nexcluded instrument is used.\n"}
+{"abstract": "  We propose an algorithm based on Hilbert space-filling curves to reorder mesh\nelements in memory for use with the Spectral Element Method, aiming to attain\nfewer cache misses, better locality of data reference and faster execution. We\npresent a technique to numerically simulate acoustic wave propagation in 2D\ndomains using the Spectral Element Method, and discuss computational\nperformance aspects of this procedure. We reorder mesh-related data via Hilbert\ncurves to achieve sizable reductions in execution time under several mesh\nconfigurations in shared-memory systems. Our experiments show that the Hilbert\ncurve approach works well with meshes of several granularities and also with\nsmall and large variations in element sizes, achieving reductions between 9%\nand 25% in execution time when compared to three other ordering schemes.\n"}
+{"abstract": "  We propose a novel approach to lifelong learning, introducing a compact\nencapsulated support structure which endows a network with the capability to\nexpand its capacity as needed to learn new tasks while preventing the loss of\nlearned tasks. This is achieved by splitting neurons with high semantic drift\nand constructing an adjacent network to encode the new tasks at hand. We call\nthis the Plastic Support Structure (PSS), it is a compact structure to learn\nnew tasks that cannot be efficiently encoded in the existing structure of the\nnetwork. We validate the PSS on public datasets against existing lifelong\nlearning architectures, showing it performs similarly to them but without prior\nknowledge of the task and in some cases with fewer parameters and in a more\nunderstandable fashion where the PSS is an encapsulated container for specific\nfeatures related to specific tasks, thus making it an ideal \"add-on\" solution\nfor endowing a network to learn more tasks.\n"}
+{"abstract": "  Entropy production characterizes irreversibility. This viewpoint allows us to\nconsider the thermodynamic uncertainty relation, which states that a higher\nprecision can be achieved at the cost of higher entropy production, as a\nrelation between precision and irreversibility. Considering the original and\nperturbed dynamics, we show that the precision of an arbitrary counting\nobservable in continuous measurement of quantum Markov processes is bounded\nfrom below by Loschmidt echo between the two dynamics, representing the\nirreversibility of quantum dynamics. When considering particular perturbed\ndynamics, our relation leads to several thermodynamic uncertainty relations,\nindicating that our relation provides a unified perspective on classical and\nquantum thermodynamic uncertainty relations.\n"}
+{"abstract": "  We study the repair problem for hyperproperties specified in the temporal\nlogic HyperLTL. Hyperproperties are system properties that relate multiple\ncomputation traces. This class of properties includes information flow policies\nlike noninterference and observational determinism. The repair problem is to\nfind, for a given Kripke structure, a substructure that satisfies a given\nspecification. We show that the repair problem is decidable for HyperLTL\nspecifications and finite-state Kripke structures. We provide a detailed\ncomplexity analysis for different fragments of HyperLTL and different system\ntypes: tree-shaped, acyclic, and general Kripke structures.\n"}
+{"abstract": "  New integrability properties of a family of sequences of ordinary\ndifferential equations, which contains the Riccati and Abel chains as the most\nsimple sequences, are studied. The determination of n generalized symmetries of\nthe nth-order equation in each chain provides, without any kind of integration,\nn-1 functionally independent first integrals of the equation. A remaining first\nintegral arises by a quadrature by using a Jacobi last multiplier that is\nexpressed in terms of the preceding equation in the corresponding sequence. The\ncomplete set of n first integrals is used to obtain the exact general solution\nof the nth-order equation of each sequence. The results are applied to derive\ndirectly the exact general solution of any equation in the Riccati and Abel\nchains.\n"}
+{"abstract": "  In a previous paper we presented the results of applying machine learning to\nclassify whether an HI 21-cm absorption spectrum arises in a source intervening\nthe sight-line to a more distant radio source or within the host of the radio\nsource itself. This is usually determined from an optical spectrum giving the\nsource redshift. However, not only will this be impractical for the large\nnumber of sources expected to be detected with the Square Kilometre Array, but\nbright optical sources are the most ultra-violet luminous at high redshift and\nso bias against the detection of cool, neutral gas. Adding another 44, mostly\nnewly detected absorbers, to the previous sample of 92, we test four different\nmachine learning algorithms, again using the line properties (width, depth and\nnumber of Gaussian fits) as features. Of these algorithms, three gave a some\nimprovement over the previous sample, with a logistic regression model giving\nthe best results. This suggests that the inclusion of further training data, as\nnew absorbers are detected, will further increase the prediction accuracy above\nthe current 80%. We use the logistic regression model to classify the z = 0.42\nabsorption towards PKS 1657-298 and find this to be associated, which is\nconsistent with a previous study which determined a similar redshift from the\nK-band magnitude-redshift relation.\n"}
+{"abstract": "  We study combinatorial problems with real world applications such as machine\nscheduling, routing, and assignment. We propose a method that combines\nReinforcement Learning (RL) and planning. This method can equally be applied to\nboth the offline, as well as online, variants of the combinatorial problem, in\nwhich the problem components (e.g., jobs in scheduling problems) are not known\nin advance, but rather arrive during the decision-making process. Our solution\nis quite generic, scalable, and leverages distributional knowledge of the\nproblem parameters. We frame the solution process as an MDP, and take a Deep\nQ-Learning approach wherein states are represented as graphs, thereby allowing\nour trained policies to deal with arbitrary changes in a principled manner.\nThough learned policies work well in expectation, small deviations can have\nsubstantial negative effects in combinatorial settings. We mitigate these\ndrawbacks by employing our graph-convolutional policies as non-optimal\nheuristics in a compatible search algorithm, Monte Carlo Tree Search, to\nsignificantly improve overall performance. We demonstrate our method on two\nproblems: Machine Scheduling and Capacitated Vehicle Routing. We show that our\nmethod outperforms custom-tailored mathematical solvers, state of the art\nlearning-based algorithms, and common heuristics, both in computation time and\nperformance.\n"}
+{"abstract": "  As weak lensing surveys become deeper, they reveal more non-Gaussian aspects\nof the convergence field which can only be extracted using statistics beyond\nthe power spectrum. In Cheng et al. (2020) we showed that the scattering\ntransform, a novel statistic borrowing mathematical concepts from convolutional\nneural networks, is a powerful tool for cosmological parameter estimation in\nthe non-Gaussian regime. Here, we extend that analysis to explore its\nsensitivity to dark energy and neutrino mass parameters with weak lensing\nsurveys. We first use image synthesis to show visually that, compared to the\npower spectrum and bispectrum, the scattering transform provides a better\nstatistical vocabulary to characterize the perceptual properties of lensing\nmass maps. We then show that it is also better suited for parameter inference:\n(i) it provides higher sensitivity in the noiseless regime, and (ii) at the\nnoise level of Rubin-like surveys, though the constraints are not significantly\ntighter than those of the bispectrum, the scattering coefficients have a more\nGaussian sampling distribution, which is an important property for likelihood\nparametrization and accurate cosmological inference. We argue that the\nscattering coefficients are preferred statistics considering both constraining\npower and likelihood properties.\n"}
+{"abstract": "  This study applies a new approach, the Theory of Functional Connections\n(TFC), to solve the two-point boundary-value problem (TPBVP) in non-Keplerian\norbit transfer. The perturbations considered are drag, solar radiation\npressure, higher-order gravitational potential harmonic terms, and multiple\nbodies. The proposed approach is applied to Earth-to-Moon transfers, and\nobtains exact boundary condition satisfaction and with very fast convergence.\nThanks to this highly efficient approach, perturbed pork-chop plots of\nEarth-to-Moon transfers are generated, and individual analyses on the\ntransfers' parameters are easily done at low computational costs. The minimum\nfuel analysis is provided in terms of the time of flight, thrust application\npoints, and relative geometry of the Moon and Sun. The transfer costs obtained\nare in agreement with the literature's best solutions, and in some cases are\neven slightly better.\n"}
+{"abstract": "  We propose a fast and flexible method to scale multivariate return volatility\npredictions up to high-dimensions using a dynamic risk factor model. Our\napproach increases parsimony via time-varying sparsity on factor loadings and\nis able to sequentially learn the use of constant or time-varying parameters\nand volatilities. We show in a dynamic portfolio allocation problem with 452\nstocks from the S&P 500 index that our dynamic risk factor model is able to\nproduce more stable and sparse predictions, achieving not just considerable\nportfolio performance improvements but also higher utility gains for the\nmean-variance investor compared to the traditional Wishart benchmark and the\npassive investment on the market index.\n"}
+{"abstract": "  With the increased interest in machine learning and big data problems, the\nneed for large amounts of labelled data has also grown. However, it is often\ninfeasible to get experts to label all of this data, which leads many\npractitioners to crowdsourcing solutions. In this paper, we present new\ntechniques to improve the quality of the labels while attempting to reduce the\ncost. The naive approach to assigning labels is to adopt a majority vote\nmethod, however, in the context of data labelling, this is not always ideal as\ndata labellers are not equally reliable. One might, instead, give higher\npriority to certain labellers through some kind of weighted vote based on past\nperformance. This paper investigates the use of more sophisticated methods,\nsuch as Bayesian inference, to measure the performance of the labellers as well\nas the confidence of each label. The methods we propose follow an iterative\nimprovement algorithm which attempts to use the least amount of workers\nnecessary to achieve the desired confidence in the inferred label. This paper\nexplores simulated binary classification problems with simulated workers and\nquestions to test the proposed methods. Our methods outperform the standard\nvoting methods in both cost and accuracy while maintaining higher reliability\nwhen there is disagreement within the crowd.\n"}
+{"abstract": "  Coded-caching is a promising technique to reduce the peak rate requirement of\nbackhaul links during high traffic periods. In this letter, we study the effect\nof adaptive transmission on the performance of coded-caching based networks.\nParticularly, concentrating on the reduction of backhaul peak load during the\nhigh traffic periods, we develop adaptive rate and power allocation schemes\nmaximizing the network successful transmission probability, which is defined as\nthe probability of the event with all cache nodes decoding their intended\nsignals correctly. Moreover, we study the effect of different message decoding\nand buffering schemes on the system performance. As we show, the performance of\ncoded-caching networks is considerably affected by rate/power allocation as\nwell as the message decoding/buffering schemes.\n"}
+{"abstract": "  Time series classification(TSC) has always been an important and challenging\nresearch task. With the wide application of deep learning, more and more\nresearchers use deep learning models to solve TSC problems. Since time series\nalways contains a lot of noise, which has a negative impact on network\ntraining, people usually filter the original data before training the network.\nThe existing schemes are to treat the filtering and training as two stages, and\nthe design of the filter requires expert experience, which increases the design\ndifficulty of the algorithm and is not universal. We note that the essence of\nfiltering is to filter out the insignificant frequency components and highlight\nthe important ones, which is similar to the attention mechanism. In this paper,\nwe propose an attention mechanism that acts on spectrum (SAM). The network can\nassign appropriate weights to each frequency component to achieve adaptive\nfiltering. We use L1 regularization to further enhance the frequency screening\ncapability of SAM. We also propose a segmented-SAM (SSAM) to avoid the loss of\ntime domain information caused by using the spectrum of the whole sequence. In\nwhich, a tumbling window is introduced to segment the original data. Then SAM\nis applied to each segment to generate new features. We propose a heuristic\nstrategy to search for the appropriate number of segments. Experimental results\nshow that SSAM can produce better feature representations, make the network\nconverge faster, and improve the robustness and classification accuracy.\n"}
+{"abstract": "  A high-order Flux reconstruction implementation of the hyperbolic formulation\nfor the incompressible Navier-Stokes equation is presented. The governing\nequations employ Chorin's classical artificial compressibility (AC) formulation\ncast in hyperbolic form. Instead of splitting the second-order conservation law\ninto two equations, one for the solution and another for the gradient, the\nNavier-Stokes equation is cast into a first-order hyperbolic system of\nequations. Including the gradients in the AC iterative process results in a\nsignificant improvement in accuracy for the pressure, velocity, and its\ngradients. Furthermore, this treatment allows for taking larger time-steps\nsince the hyperbolic formulation eliminates the restriction due to diffusion.\nTests using the method of manufactured solutions show that solving the\nconventional form of the Navier-Stokes equation lowers the order of accuracy\nfor gradients, while the hyperbolic method is shown to provide equal orders of\naccuracy for both the velocity and its gradients which may be beneficial in\nseveral applications. Two- and three-dimensional benchmark tests demonstrate\nthe superior accuracy and computational efficiency of the developed solver in\ncomparison to the conventional method and other published works. This study\nshows that the developed high-order hyperbolic solver for incompressible flows\nis attractive due to its accuracy, stability and efficiency in solving\ndiffusion dominated problems.\n"}
+{"abstract": "  In this investigation, force field-based molecular dynamics (MD) simulations\nhave been employed to generate detailed structural representations for a range\nof amorphous quaternary CaO-MgO-Al2O3-SiO2 (CMAS) and ternary CaO-Al2O3-SiO2\n(CAS) glasses. Comparison of the simulation results with select experimental\nX-ray and neutron total scattering and literature data reveals that the\nMD-generated structures have captured the key structural features of these CMAS\nand CAS glasses. Based on the MD-generated structural representations, we have\ndeveloped two structural descriptors, specifically (i) average metal oxide\ndissociation energy (AMODE) and (ii) average self-diffusion coefficient (ASDC)\nof all the atoms at melting. Both structural descriptors are seen to more\naccurately predict the relative glass reactivity than the commonly used degree\nof depolymerization parameter, especially for the eight synthetic CAS glasses\nthat span a wide compositional range. Hence these descriptors hold great\npromise for predicting CMAS and CAS glass reactivity in alkaline environments\nfrom compositional information.\n"}
+{"abstract": "  We make use of ALMA continuum observations of $15$ luminous Lyman-break\ngalaxies at $z$$\\sim$$7$$-$$8$ to probe their dust-obscured star-formation.\nThese observations are sensitive enough to probe to obscured SFRs of $20$\n$M_{\\odot}$$/$$yr$ ($3\\sigma$). Six of the targeted galaxies show significant\n($\\geq$$3$$\\sigma$) dust continuum detections, more than doubling the number of\nknown dust-detected galaxies at $z$$>$$6.5$. Their IR luminosities range from\n$2.7$$\\times$$10^{11}$ $L_{\\odot}$ to $1.1$$\\times$$10^{12}$ $L_{\\odot}$,\nequivalent to obscured SFRs of $20$ to $105$ $M_{\\odot}$$/$$yr$. We use our\nresults to quantify the correlation of the infrared excess IRX on the\nUV-continuum slope $\\beta_{UV}$ and stellar mass. Our results are most\nconsistent with an SMC attenuation curve for intrinsic $UV$-slopes\n$\\beta_{UV,intr}$ of $-2.63$ and most consistent with an attenuation curve\nin-between SMC and Calzetti for $\\beta_{UV,intr}$ slopes of $-2.23$, assuming a\ndust temperature $T_d$ of $50$ K. Our fiducial IRX-stellar mass results at\n$z$$\\sim$$7$$-$$8$ are consistent with marginal evolution from $z$$\\sim$$0$. We\nthen show how both results depend on $T_d$. For our six dust-detected sources,\nwe estimate their dust masses and find that they are consistent with dust\nproduction from SNe if the dust destruction is low ($<$$90$%). Finally we\ndetermine the contribution of dust-obscured star formation to the star\nformation rate density for $UV$ luminous ($<$$-$$21.5$ mag:\n$\\gtrsim$$1.7$$L_{UV} ^*$) $z$$\\sim$$7$$-$$8$ galaxies, finding that the total\nSFR density at $z$$\\sim$$7$ and $z$$\\sim$$8$ from bright galaxies is\n$0.18_{-0.10}^{+0.08}$ dex and $0.20_{-0.09}^{+0.05}$ dex higher, respectively,\ni.e. $\\sim$$\\frac{1}{3}$ of the star formation in $\\gtrsim$$1.7$$L_{UV} ^*$\ngalaxies at $z$$\\sim$$7$$-$$8$ is obscured by dust.\n"}
+{"abstract": "  Cycle polytopes of matroids have been introduced in combinatorial\noptimization as a generalization of important classes of polyhedral objects\nlike cut polytopes and Eulerian subgraph polytopes associated to graphs. Here\nwe start an algebraic and geometric investigation of these polytopes by\nstudying their toric algebras, called cycle algebras, and their defining\nideals. Several matroid operations are considered which determine faces of\ncycle polytopes that belong again to this class of polyhedral objects. As a key\ntechnique used in this paper, we study certain minors of given matroids which\nyield algebra retracts on the level of cycle algebras. In particular, that\nallows us to use a powerful algebraic machinery. As an application, we study\nhighest possible degrees in minimal homogeneous systems of generators of\ndefining ideals of cycle algebras as well as interesting cases of cut polytopes\nand Eulerian subgraph polytopes.\n"}
+{"abstract": "  We construct tame differential calculi coming from toral actions on a class\nof C*-algebras. Relying on the existence of a unique Levi-Civita connection on\nsuch a calculi, we prove a version of the Bianchi identity. A Gauss-Bonnet\ntheorem for the canonical rank 2-calculus is studied.\n"}
+{"abstract": "  H-principle (or homotopy principle) is the property that some solutions to a\npartial differential equation/inequality can be obtained as a deformed of a\nformal solution by a homotopy. Gromov defines the sheaf theoritic h-principle\nin his book and shows the existence of h-principle from a very abstract\nsetting. In this paper, we clarify a categorical structure behind Gromov\nh-principle. The main result is that a flexible sheaf can be understood as a\nfibrant object in some categories.\n"}
+{"abstract": "  We propose a route choice model in which traveler behavior is represented as\na utility maximizing assignment of flow across an entire network under a flow\nconservation constraint}. Substitution between routes depends on how much they\noverlap. {\\tr The model is estimated considering the full set of route\nalternatives, and no choice set generation is required. Nevertheless,\nestimation requires only linear regression and is very fast. Predictions from\nthe model can be computed using convex optimization, and computation is\nstraightforward even for large networks. We estimate and validate the model\nusing a large dataset comprising 1,337,096 GPS traces of trips in the Greater\nCopenhagen road network.\n"}
+{"abstract": "  Context. To investigate the source of a type III radio burst storm during\nencounter 2 of NASA's Parker Solar Probe (PSP) mission.\n  Aims. It was observed that in encounter 2 of NASA's Parker Solar Probe\nmission there was a large amount of radio activity, and in particular a noise\nstorm of frequent, small type III bursts from 31st March to 6th April 2019. Our\naim is to investigate the source of these small and frequent bursts.\n  Methods. In order to do this, we analysed data from the Hinode EUV Imaging\nSpectrometer (EIS), PSP FIELDS, and the Solar Dynamics Observatory (SDO)\nAtmospheric Imaging Assembly (AIA). We studied the behaviour of active region\n12737, whose emergence and evolution coincides with the timing of the radio\nnoise storm and determined the possible origins of the electron beams within\nthe active region. To do this, we probe the dynamics, Doppler velocity,\nnon-thermal velocity, FIP bias, densities, and carry out magnetic modelling.\n  Results. We demonstrate that although the active region on the disk produces\nno significant flares, its evolution indicates it is a source of the electron\nbeams causing the radio storm. They most likely originate from the area at the\nedge of the active region that shows strong blue-shifted plasma. We demonstrate\nthat as the active region grows and expands, the area of the blue-shifted\nregion at the edge increases, which is also consistent with the increasing area\nwhere large-scale or expanding magnetic field lines from our modelling are\nanchored. This expansion is most significant between 1 and 4 April 2019,\ncoinciding with the onset of the type III storm and the decrease of the\nindividual burst's peak frequency, indicating the height at which the peak\nradiation is emitted increases as the active region evolves.\n"}
+{"abstract": "  We provide a simple proof that the partial sums $\\sum_{n\\leq x}f(n)$ of a\nRademacher random multiplicative function $f$ change sign for an infinite\nnumber of $x>0$, almost surely.\n"}
+{"abstract": "  We study random digraphs on sequences of expanders with bounded average\ndegree and weak local limit. The threshold for the existence of a giant\nstrongly connected component, as well as the asymptotic fraction of nodes with\ngiant fan-in or giant fan-out are local, in the sense that they are the same\nfor two sequences with the same weak local limit. The digraph has a bow-tie\nstructure, with all but a vanishing fraction of nodes lying either in the\nunique strongly connected giant and its fan-in and fan-out, or in sets with\nsmall fan-in and small fan-out. All local quantities are expressed in terms of\npercolation on the limiting rooted graph, without any structural assumptions on\nthe limit, allowing, in particular, for non tree-like limits.\n  In the course of proving these results, we prove that for unoriented\npercolation, there is a unique giant above criticality, whose size and critical\nthreshold are again local. An application of our methods shows that the\ncritical threshold for bond percolation and random digraphs on preferential\nattachment graphs is $p_c=0$, with an infinite order phase transition at $p_c$.\n"}
+{"abstract": "  In this paper we propose a novel point cloud generator that is able to\nreconstruct and generate 3D point clouds composed of semantic parts. Given a\nlatent representation of the target 3D model, the generation starts from a\nsingle point and gets expanded recursively to produce the high-resolution point\ncloud via a sequence of point expansion stages. During the recursive procedure\nof generation, we not only obtain the coarse-to-fine point clouds for the\ntarget 3D model from every expansion stage, but also unsupervisedly discover\nthe semantic segmentation of the target model according to the\nhierarchical/parent-child relation between the points across expansion stages.\nMoreover, the expansion modules and other elements used in our recursive\ngenerator are mostly sharing weights thus making the overall framework light\nand efficient. Extensive experiments are conducted to demonstrate that our\nproposed point cloud generator has comparable or even superior performance on\nboth generation and reconstruction tasks in comparison to various baselines, as\nwell as provides the consistent co-segmentation among 3D instances of the same\nobject class.\n"}
+{"abstract": "  Given (small amounts of) time-series' data from a high-dimensional,\nfine-grained, multiscale dynamical system, we propose a generative framework\nfor learning an effective, lower-dimensional, coarse-grained dynamical model\nthat is predictive of the fine-grained system's long-term evolution but also of\nits behavior under different initial conditions. We target fine-grained models\nas they arise in physical applications (e.g. molecular dynamics, agent-based\nmodels), the dynamics of which are strongly non-stationary but their transition\nto equilibrium is governed by unknown slow processes which are largely\ninaccessible by brute-force simulations. Approaches based on domain knowledge\nheavily rely on physical insight in identifying temporally slow features and\nfail to enforce the long-term stability of the learned dynamics. On the other\nhand, purely statistical frameworks lack interpretability and rely on large\namounts of expensive simulation data (long and multiple trajectories) as they\ncannot infuse domain knowledge. The generative framework proposed achieves the\naforementioned desiderata by employing a flexible prior on the complex plane\nfor the latent, slow processes, and an intermediate layer of physics-motivated\nlatent variables that reduces reliance on data and imbues inductive bias. In\ncontrast to existing schemes, it does not require the a priori definition of\nprojection operators from the fine-grained description and addresses\nsimultaneously the tasks of dimensionality reduction and model estimation. We\ndemonstrate its efficacy and accuracy in multiscale physical systems of\nparticle dynamics where probabilistic, long-term predictions of phenomena not\ncontained in the training data are produced.\n"}
+{"abstract": "  In the context of large-angle cone-beam tomography (CBCT), we present a\npractical iterative reconstruction (IR) scheme designed for rapid convergence\nas required for large datasets. The robustness of the reconstruction is\nprovided by the \"space-filling\" source trajectory along which the experimental\ndata is collected. The speed of convergence is achieved by leveraging the\nhighly isotropic nature of this trajectory to design an approximate\ndeconvolution filter that serves as a pre-conditioner in a multi-grid scheme.\nWe demonstrate this IR scheme for CBCT and compare convergence to that of more\ntraditional techniques.\n"}
+{"abstract": "  Boundary controlled irreversible port-Hamiltonian systems (BC-IPHS) on\n1-dimensional spatial domains are defined by extending the formulation of\nreversible BC-PHS to irreversible thermodynamic systems controlled at the\nboundaries of their spatial domains. The structure of BC-IPHS has clear\nphysical interpretation, characterizing the coupling between energy storing and\nenergy dissipating elements. By extending the definition of boundary port\nvariables of BC-PHS to deal with the dissipative terms, a set of boundary port\nvariables are defined such that BC-IPHS are passive with respect to a given set\nof conjugated inputs and outputs. As for finite dimensional IPHS, the first and\nsecond principle are satisfied as a structural property. Several examples are\ngiven to illustrate the proposed approach.\n"}
+{"abstract": "  In this paper some new proposals for method comparison are presented. On the\none hand, two new robust regressions, the M-Deming and the MM-Deming, have been\ndeveloped by modifying Linnet's method of the weighted Deming regression. The\nM-Deming regression shows superior qualities to the Passing-Bablok regression;\nit does not suffer from bias when the data to be validated have a reduced\nprecision, and therefore turns out to be much more reliable. On the other hand,\na graphical method (box and ellipses) for validations has been developed which\nis also equipped with a unified statistical test. In this test the intercept\nand slope pairs obtained from a bootstrap process are combined into a\nmultinomial distribution by robust determination of the covariance matrix. The\nMahalanobis distance from the point representing the null hypothesis is\nevaluated using the $\\chi^{2}$ distribution. It is emphasized that the\ninterpretation of the graph is more important than the probability obtained\nfrom the test. The unified test has been evaluated through Monte Carlo\nsimulations, comparing the theoretical $\\alpha$ levels with the empirical rate\nof rejections (type-I errors). In addition, a power comparison of the various\n(new and old) methods was conducted using the same techniques. This unified\nmethod, regardless of the regression chosen, shows much higher power and allows\na significant reduction in the sample size required for validations.\n"}
+{"abstract": "  In this paper, we introduce a streaming keyphrase detection system that can\nbe easily customized to accurately detect any phrase composed of words from a\nlarge vocabulary. The system is implemented with an end-to-end trained\nautomatic speech recognition (ASR) model and a text-independent speaker\nverification model. To address the challenge of detecting these keyphrases\nunder various noisy conditions, a speaker separation model is added to the\nfeature frontend of the speaker verification model, and an adaptive noise\ncancellation (ANC) algorithm is included to exploit cross-microphone noise\ncoherence. Our experiments show that the text-independent speaker verification\nmodel largely reduces the false triggering rate of the keyphrase detection,\nwhile the speaker separation model and adaptive noise cancellation largely\nreduce false rejections.\n"}
+{"abstract": "  There are concerns that the ability of language models (LMs) to generate high\nquality synthetic text can be misused to launch spam, disinformation, or\npropaganda. Therefore, the research community is actively working on developing\napproaches to detect whether a given text is organic or synthetic. While this\nis a useful first step, it is important to be able to further fingerprint the\nauthor LM to attribute its origin. Prior work on fingerprinting LMs is limited\nto attributing synthetic text generated by a handful (usually < 10) of\npre-trained LMs. However, LMs such as GPT2 are commonly fine-tuned in a myriad\nof ways (e.g., on a domain-specific text corpus) before being used to generate\nsynthetic text. It is challenging to fingerprinting fine-tuned LMs because the\nuniverse of fine-tuned LMs is much larger in realistic scenarios. To address\nthis challenge, we study the problem of large-scale fingerprinting of\nfine-tuned LMs in the wild. Using a real-world dataset of synthetic text\ngenerated by 108 different fine-tuned LMs, we conduct comprehensive experiments\nto demonstrate the limitations of existing fingerprinting approaches. Our\nresults show that fine-tuning itself is the most effective in attributing the\nsynthetic text generated by fine-tuned LMs.\n"}
+{"abstract": "  We report on the analysis of a deep Chandra observation of the high-magnetic\nfield pulsar (PSR) J1119-6127 and its compact pulsar wind nebula (PWN) taken in\nOctober 2019, three years after the source went into outburst. The 0.5-7 keV\npost-outburst (2019) spectrum of the pulsar is best described by a\ntwo-component blackbody plus powerlaw model with a temperature of 0.2\\pm0.1\nkeV, photon index of 1.8\\pm0.4 and X-ray luminosity of ~1.9e33 erg s^{-1},\nconsistent with its pre-burst quiescent phase. We find that the pulsar has gone\nback to quiescence. The compact nebula shows a jet-like morphology elongated in\nthe north-south direction, similar to the pre-burst phase. The post-outburst\nPWN spectrum is best fit by an absorbed powerlaw with a photon index of\n2.3\\pm0.5 and flux of ~3.2e-14 erg cm^{-2} s^{-1} (0.5-7 keV). The PWN spectrum\nshows evidence of spectral softening in the post-outburst phase, with the\npre-burst photon index of 1.2\\pm0.4 changing to 2.3\\pm0.5, and pre-burst\nluminosity of ~1.5e32 erg s^{-1} changing to 2.7e32 erg s^{-1} in the 0.5-7 keV\nband, suggesting magnetar outbursts can impact PWNe. The observed timescale for\nreturning to quiescence, of just a few years, implies a rather fast cooling\nprocess and favors a scenario where J1119 is temporarily powered by magnetic\nenergy following the magnetar outburst, in addition to its spin-down energy.\n"}
+{"abstract": "  Inspired by biological molecular machines we explore the idea of an active\nquantum robot whose purpose is delaying decoherence. A conceptual model capable\nof partially protecting arbitrary logical qubit states against single physical\nqubit errors is presented. Implementation of an instance of that model - the\nentanglement qubot - is proposed using laser-dressed Rydberg atoms. Dynamics of\nthe system is studied using stochastic wavefunction methods.\n"}
+{"abstract": "  Experiments dedicated to the measurement of the electric dipole moment of the\nneutron require outstanding control of the magnetic field uniformity. The\nneutron electric dipole moment (nEDM) experiment at the Paul Scherrer Institute\nuses a 199Hg co-magnetometer to precisely monitor magnetic field variations.\nThis co-magnetometer, in the presence of field non-uniformity, is responsible\nfor the largest systematic effect of this measurement. To evaluate and correct\nthat effect, offline measurements of the field non-uniformity were performed\nduring mapping campaigns in 2013, 2014 and 2017. We present the results of\nthese campaigns, and the improvement the correction of this effect brings to\nthe neutron electric dipole moment measurement.\n"}
+{"abstract": "  Given a nonparametric Hidden Markov Model (HMM) with two states, the question\nof constructing efficient multiple testing procedures is considered, treating\none of the states as an unknown null hypothesis. A procedure is introduced,\nbased on nonparametric empirical Bayes ideas, that controls the False Discovery\nRate (FDR) at a user--specified level. Guarantees on power are also provided,\nin the form of a control of the true positive rate. One of the key steps in the\nconstruction requires supremum--norm convergence of preliminary estimators of\nthe emission densities of the HMM. We provide the existence of such estimators,\nwith convergence at the optimal minimax rate, for the case of a HMM with $J\\ge\n2$ states, which is of independent interest.\n"}
+{"abstract": "  In this paper, we demonstrate a Symbolic Reinforcement Learning (SRL)\narchitecture for safe control in Radio Access Network (RAN) applications. In\nour automated tool, a user can select a high-level safety specifications\nexpressed in Linear Temporal Logic (LTL) to shield an RL agent running in a\ngiven cellular network with aim of optimizing network performance, as measured\nthrough certain Key Performance Indicators (KPIs). In the proposed\narchitecture, network safety shielding is ensured through model-checking\ntechniques over combined discrete system models (automata) that are abstracted\nthrough reinforcement learning. We demonstrate the user interface (UI) helping\nthe user set intent specifications to the architecture and inspect the\ndifference in allowed and blocked actions.\n"}
+{"abstract": "  One of the distinct features of this century has been the population of older\nadults which has been on a constant rise. Elderly people have several needs and\nrequirements due to physical disabilities, cognitive issues, weakened memory\nand disorganized behavior, that they face with increasing age. The extent of\nthese limitations also differs according to the varying diversities in elderly,\nwhich include age, gender, background, experience, skills, knowledge and so on.\nThese varying needs and challenges with increasing age, limits abilities of\nolder adults to perform Activities of Daily Living (ADLs) in an independent\nmanner. To add to it, the shortage of caregivers creates a looming need for\ntechnology-based services for elderly people, to assist them in performing\ntheir daily routine tasks to sustain their independent living and active aging.\nTo address these needs, this work consists of making three major contributions\nin this field. First, it provides a rather comprehensive review of assisted\nliving technologies aimed at helping elderly people to perform ADLs. Second,\nthe work discusses the challenges identified through this review, that\ncurrently exist in the context of implementation of assisted living services\nfor elderly care in Smart Homes and Smart Cities. Finally, the work also\noutlines an approach for implementation, extension and integration of the\nexisting works in this field for development of a much-needed framework that\ncan provide personalized assistance and user-centered behavior interventions to\nelderly as per their varying and ever-changing needs.\n"}
+{"abstract": "  In solving optimization problems, objective functions generally need to be\nminimized or maximized. However, objective functions cannot always be\nformulated explicitly in a mathematical form for complicated problem settings.\nAlthough several regression techniques infer the approximate forms of objective\nfunctions, they are at times expensive to evaluate. Optimal points of\n\"black-box\" objective functions are computed in such scenarios, while\neffectively using a small number of clues. Recently, an efficient method by use\nof inference by sparse prior for a black-box objective function with binary\nvariables has been proposed. In this method, a surrogate model was proposed in\nthe form of a quadratic unconstrained binary optimization (QUBO) problem, and\nwas iteratively solved to obtain the optimal solution of the black-box\nobjective function. In the present study, we employ the D-Wave 2000Q quantum\nannealer, which can solve QUBO by driving the binary variables by quantum\nfluctuations. The D-Wave 2000Q quantum annealer does not necessarily output the\nground state at the end of the protocol due to freezing effect during the\nprocess. We investigate effects from the output of the D-Wave quantum annealer\nin performing black-box optimization. We demonstrate a benchmark test by\nemploying the sparse Sherrington-Kirkpatrick (SK) model as the black-box\nobjective function, by introducing a parameter controlling the sparseness of\nthe interaction coefficients. Comparing the results of the D-Wave quantum\nannealer to those of the simulated annealing (SA) and semidefinite programming\n(SDP), our results by the D-Wave quantum annealer and SA exhibit superiority in\nblack-box optimization with SDP. On the other hand, we did not find any\nadvantage of the D-Wave quantum annealer over the simulated annealing. As far\nas in our case, any effects by quantum fluctuation are not found.\n"}
+{"abstract": "  We describe a procedure to introduce general dependence structures on a set\nof random variables. These include order-$q$ moving average-type structures, as\nwell as seasonal, periodic, spatial and spatio-temporal dependences. The\ninvariant marginal distribution can be in any family that is conjugate to an\nexponential family with quadratic variance function. Dependence is induced via\na set of suitable latent variables whose conditional distribution mirrors the\nsampling distribution in a Bayesian conjugate analysis of such exponential\nfamilies. We obtain strict stationarity as a special case.\n"}
+{"abstract": "  Consider a geodesic triangle on a surface of constant curvature and subdivide\nit recursively into 4 triangles by joining the midpoints of its edges. We show\nthe existence of a uniform $\\delta>0$ such that, at any step of the\nsubdivision, all the triangle angles lie in the interval $(\\delta, \\pi\n-\\delta)$. Additionally, we exhibit stabilising behaviours for both angles and\nlengths as this subdivision progresses.\n"}
+{"abstract": "  (Abridged) We present a systematic investigation of physical conditions and\nelemental abundances in four optically thick Lyman-limit systems (LLSs) at\n$z=0.36-0.6$ discovered within the Cosmic Ultraviolet Baryon Survey (CUBS).\nCUBS LLSs exhibit multi-component kinematic structure and a complex mix of\nmultiphase gas, with associated metal transitions from multiple ionization\nstates that span several hundred km/s in line-of-sight velocity. Specifically,\nhigher column density components (log N(HI)>16) in all four absorbers comprise\ndynamically cool gas with $\\langle T \\rangle =(2\\pm1) \\times10^4\\,$K and modest\nnon-thermal broadening of $5\\pm3\\,$ km/s. The high quality of the QSO\nabsorption spectra allows us to infer the physical conditions of the gas, using\na detailed ionization modeling that takes into account the resolved component\nstructures of HI and metal transitions. The range of inferred gas densities\nindicates that these absorbers consist of spatially compact clouds with a\nmedian line-of-sight thickness of $160^{+140}_{-50}$ pc. While obtaining robust\nmetallicity constraints for the low-density, highly ionized phase remains\nchallenging due to the uncertain N(HI), we demonstrate that the cool-phase gas\nin LLSs has a median metallicity of\n$\\mathrm{[\\alpha/H]_{1/2}}=-0.7^{+0.1}_{-0.2}$, with a 16-84 percentile range\nof $\\mathrm{[\\alpha/H]}=(-1.3,-0.1)$. Furthermore, the wide range of inferred\nelemental abundance ratios ($\\mathrm{[C/\\alpha]}$, $\\mathrm{[N/\\alpha]}$, and\n$\\mathrm{[Fe/\\alpha]}$) indicate a diversity of chemical enrichment histories.\nCombining the absorption data with deep galaxy survey data characterizing the\ngalaxy environment of these absorbers, we discuss the physical connection\nbetween star-forming regions in galaxies and diffuse gas associated with\noptically thick absorption systems in the $z<1$ circumgalactic medium.\n"}
+{"abstract": "  Offline reinforcement learning (RL) aims at learning a good policy from a\nbatch of collected data, without extra interactions with the environment during\ntraining. However, current offline RL benchmarks commonly have a large reality\ngap, because they involve large datasets collected by highly exploratory\npolicies, and the trained policy is directly evaluated in the environment. In\nreal-world situations, running a highly exploratory policy is prohibited to\nensure system safety, the data is commonly very limited, and a trained policy\nshould be well validated before deployment. In this paper, we present a near\nreal-world offline RL benchmark, named NeoRL, which contains datasets from\nvarious domains with controlled sizes, and extra test datasets for policy\nvalidation. We evaluate existing offline RL algorithms on NeoRL and argue that\nthe performance of a policy should also be compared with the deterministic\nversion of the behavior policy, instead of the dataset reward. The empirical\nresults demonstrate that the tested offline RL algorithms become less\ncompetitive to the deterministic policy on many datasets, and the offline\npolicy evaluation hardly helps. The NeoRL suit can be found at\nhttp://polixir.ai/research/neorl. We hope this work will shed some light on\nfuture research and draw more attention when deploying RL in real-world\nsystems.\n"}
+{"abstract": "  In this paper we establish uniqueness in the inverse boundary value problem\nfor the two coefficients in the inhomogeneous porous medium equation\n$\\epsilon\\partial_tu-\\nabla\\cdot(\\gamma\\nabla u^m)=0$, with $m>1$, in dimension\n3 or higher, which is a degenerate parabolic type quasilinear PDE. Our approach\nrelies on using a Laplace transform to turn the original equation into a\ncoupled family of nonlinear elliptic equations, indexed by the frequency\nparameter ($1/h$ in our definition) of the transform. A careful analysis of the\nasymptotic expansion in powers of $h$, as $h\\to\\infty$, of the solutions to the\ntransformed equation, with special boundary data, allows us to obtain\nsufficient information to deduce the uniqueness result.\n"}
+{"abstract": "  Accurate estimation of cratering asymmetry on the Moon is crucial for\nunderstanding Moon evolution history. Early studies of cratering asymmetry have\nomitted the contributions of high lunar obliquity and inclination. Here, we\ninclude lunar obliquity and inclination as new controlling variables to derive\nthe cratering rate spatial variation as a function of longitude and latitude.\nWith examining the influence of lunar obliquity and inclination on the\nasteroids population encountered by the Moon, we then have derived general\nformulas of the cratering rate spatial variation based on the crater scaling\nlaw. Our formulas with addition of lunar obliquity and inclination can\nreproduce the lunar cratering rate asymmetry at the current Earth-Moon distance\nand predict the apex/ant-apex ratio and the pole/equator ratio of this lunar\ncratering rate to be 1.36 and 0.87, respectively. The apex/ant-apex ratio is\ndecreasing as the obliquity and inclination increasing. Combining with the\nevolution of lunar obliquity and inclination, our model shows that the\napex/ant-apex ratio does not monotonically decrease with Earth-Moon distance\nand hence the influences of obliquity and inclination are not negligible on\nevolution of apex/ant-apex ratio. This model is generalizable to other planets\nand moons, especially for different spin-orbit resonances.\n"}
+{"abstract": "  We present the N-body simulation techniques in EXP. EXP uses\nempirically-chosen basis functions to expand the potential field of an ensemble\nof particles. Unlike other basis function expansions, the derived basis\nfunctions are adapted to an input mass distribution, enabling accurate\nexpansion of highly non-spherical objects, such as galactic discs. We measure\nthe force accuracy in three models, one based on a spherical or aspherical\nhalo, one based on an exponential disc, and one based on a bar-based disc\nmodel. We find that EXP is as accurate as a direct-summation or tree-based\ncalculation, and in some ways is better, while being considerably less\ncomputationally intensive. We discuss optimising the computation of the basis\nfunction representation. We also detail numerical improvements for performing\norbit integrations, including timesteps.\n"}
+{"abstract": "  An analysis of 1,955 physics graduate students from 19 PhD programs shows\nthat undergraduate grade point average predicts graduate grades and PhD\ncompletion more effectively than GRE scores. Students' undergraduate GPA (UGPA)\nand GRE Physics (GRE-P) scores are small but statistically significant\npredictors of graduate course grades, while GRE quantitative and GRE verbal\nscores are not. We also find that males and females score equally well in their\ngraduate coursework despite a statistically significant 18 percentile point gap\nin median GRE-P scores between genders. A counterfactual mediation analysis\ndemonstrates that among admission metrics tested only UGPA is a significant\npredictor of overall PhD completion, and that UGPA predicts PhD completion\nindirectly through graduate grades. Thus UGPA measures traits linked to\ngraduate course grades, which in turn predict graduate completion. Although\nGRE-P scores are not significantly associated with PhD completion, our results\nsuggest that any predictive effect they may have are also linked indirectly\nthrough graduate GPA. Overall our results indicate that among commonly used\nquantitative admissions metrics, UGPA offers the most insight into two\nimportant measures of graduate school success, while posing fewer concerns for\nequitable admissions practices.\n"}
+{"abstract": "  We propose a general method for optimizing periodic input waveforms for\nglobal entrainment of weakly forced limit-cycle oscillators based on phase\nreduction and nonlinear programming. We derive averaged phase dynamics from the\nmathematical model of a limit-cycle oscillator driven by a weak periodic input\nand optimize the Fourier coefficients of the input waveform to maximize\nprescribed objective functions. In contrast to the optimization methods that\nrely on the calculus of variations, the proposed method can be applied to a\nwider class of optimization problems including global entrainment objectives.\nAs an illustration, we consider two optimization problems, one for achieving\nfast global convergence of the oscillator to the entrained state and the other\nfor realizing prescribed global phase distributions in a population of\nidentical uncoupled noisy oscillators. We show that the proposed method can\nsuccessfully yield optimal input waveforms to realize the desired states in\nboth cases.\n"}
+{"abstract": "  Using a generalized Madelung transformation, we derive the hydrodynamic\nrepresentation of the Dirac equation in arbitrary curved space-times coupled to\nan electromagnetic field. We obtain Dirac-Euler equations for fermions\ninvolving a continuity equation and a first integral of the Bernoulli equation.\nUsing the comparison of the Dirac and Klein-Gordon equations we obtain the\nbalance equation for fermion particles. We also use the correspondence between\nfermions and bosons to derive the hydrodynamic representation of the Weyl\nequation which is a chiral form of the Dirac equation.\n"}
+{"abstract": "  Hate speech and profanity detection suffer from data sparsity, especially for\nlanguages other than English, due to the subjective nature of the tasks and the\nresulting annotation incompatibility of existing corpora. In this study, we\nidentify profane subspaces in word and sentence representations and explore\ntheir generalization capability on a variety of similar and distant target\ntasks in a zero-shot setting. This is done monolingually (German) and\ncross-lingually to closely-related (English), distantly-related (French) and\nnon-related (Arabic) tasks. We observe that, on both similar and distant target\ntasks and across all languages, the subspace-based representations transfer\nmore effectively than standard BERT representations in the zero-shot setting,\nwith improvements between F1 +10.9 and F1 +42.9 over the baselines across all\ntested monolingual and cross-lingual scenarios.\n"}
+{"abstract": "  The debate on the role of school closures as a mitigation strategy against\nthe spread of Covid-19 is gaining relevance due to emerging variants in Europe.\nAccording to WHO, decisions on schools \"should be guided by a risk-based\napproach\". However, risk evaluation requires sound methods, transparent data\nand careful consideration of the context at the local level. We review a recent\nstudy by Gandini et al., on the role of school opening as a driver of the\nsecond COVID-19 wave in Italy, which concluded that there was no connection\nbetween school openings/closures and SARS-CoV-2 incidence. infections. This\nanalysis has been widely commented in Italian media as conclusive proof that\n\"schools are safe\". However the study presents severe oversights and careless\ninterpretation of data.\n"}
+{"abstract": "  As autonomous driving and augmented reality evolve, a practical concern is\ndata privacy. In particular, these applications rely on localization based on\nuser images. The widely adopted technology uses local feature descriptors,\nwhich are derived from the images and it was long thought that they could not\nbe reverted back. However, recent work has demonstrated that under certain\nconditions reverse engineering attacks are possible and allow an adversary to\nreconstruct RGB images. This poses a potential risk to user privacy. We take\nthis a step further and model potential adversaries using a privacy threat\nmodel. Subsequently, we show under controlled conditions a reverse engineering\nattack on sparse feature maps and analyze the vulnerability of popular\ndescriptors including FREAK, SIFT and SOSNet. Finally, we evaluate potential\nmitigation techniques that select a subset of descriptors to carefully balance\nprivacy reconstruction risk while preserving image matching accuracy; our\nresults show that similar accuracy can be obtained when revealing less\ninformation.\n"}
+{"abstract": "  The electric power grid is a critical societal resource connecting multiple\ninfrastructural domains such as agriculture, transportation, and manufacturing.\nThe electrical grid as an infrastructure is shaped by human activity and public\npolicy in terms of demand and supply requirements. Further, the grid is subject\nto changes and stresses due to solar weather, climate, hydrology, and ecology.\nThe emerging interconnected and complex network dependencies make such\ninteractions increasingly dynamic causing potentially large swings, thus\npresenting new challenges to manage the coupled human-natural system. This\npaper provides a survey of models and methods that seek to explore the\nsignificant interconnected impact of the electric power grid and interdependent\ndomains. We also provide relevant critical risk indicators (CRIs) across\ndiverse domains that may influence electric power grid risks, including\nclimate, ecology, hydrology, finance, space weather, and agriculture. We\ndiscuss the convergence of indicators from individual domains to explore\npossible systemic risk, i.e., holistic risk arising from cross-domains\ninterconnections. Our study provides an important first step towards\ndata-driven analysis and predictive modeling of risks in the coupled\ninterconnected systems. Further, we propose a compositional approach to risk\nassessment that incorporates diverse domain expertise and information, data\nscience, and computer science to identify domain-specific CRIs and their union\nin systemic risk indicators.\n"}
+{"abstract": "  One or more scalar leptoquarks with masses around a few TeV may provide a\nsolution to some of the flavor anomalies that have been observed. We discuss\nthe impact of such new degrees on baryon number violation when the theory is\nembedded in a Pati-Salam model. The Pati-Salam embedding can suppress\nrenormalizable and dimension-five baryon number violation in some cases. Our\nwork extends the results of Assad, Grinstein, and Fornal who considered the\nsame issue for vector leptoquarks.\n"}
+{"abstract": "  The inclusion of domain (point) sources into a three dimensional boundary\nelement method while solving the Helmholtz equation is described. The method is\nfully desingularized which allows for the use of higher order quadratic\nelements on the surfaces of the problem with ease. The effect of the monopole\nsources ends up on the right hand side of the resulting matrix system. Several\ncarefully chosen examples are shown, such as sources near and within a\nconcentric spherical core-shell scatterer as a verification case, a curved\nfocusing surface and a multi-scale acoustic lens.\n"}
+{"abstract": "  Single-crystal inorganic halide perovskites are attracting interest for\nquantum device applications. Here we present low-temperature quantum\nmagnetotransport measurements on thin film devices of epitaxial single-crystal\nCsSnBr$_{3}$, which exhibit two-dimensional Mott variable range hopping (VRH)\nand giant negative magnetoresistance. These findings are described by a model\nfor quantum interference between different directed hopping paths and we\nextract the temperature-dependent hopping length of charge carriers, their\nlocalization length, and a lower bound for their phase coherence length of ~100\nnm at low temperatures. These observations demonstrate that epitaxial halide\nperovskite devices are emerging as a material class for low-dimensional quantum\ncoherent transport devices.\n"}
+{"abstract": "  We present observations of quantum depletion in expanding condensates\nreleased from a harmonic trap. We confirm experimental observations of\nslowly-decaying tails in the far-field beyond the thermal component, consistent\nwith the survival of the quantum depletion. Our measurements support the\nhypothesis that the depletion survives the expansion, and even appears stronger\nin the far-field than expected before release based on the Bogoliubov theory.\nThis result is in conflict with the hydrodynamic theory which predicts that the\nin-situ depletion does not survive when atoms are released from a trap.\nSimulations of our experiment show that the depletion should indeed survive\ninto the far field and become stronger. However, while in qualitative\nagreement, the final depletion observed in the experiment is much larger than\nin the simulation. In light of the predicted power-law decay of the momentum\ndensity, we discuss general issues inherent in characterizing power laws.\n"}
+{"abstract": "  Precise theoretical predictions are a key ingredient for an accurate\ndetermination of the structure of the Langrangian of particle physics,\nincluding its free parameters, which summarizes our understanding of the\nfundamental interactions among particles. Furthermore, due to the absence of\nclear new-physics signals, precise theoretical calculations are required in\norder to pin down possible subtle deviations from the Standard Model\npredictions. The error associated with such calculations must be scrutinized,\nas non-perturbative power corrections, dubbed infrared renormalons, can limit\nthe ultimate precision of truncated perturbative expansions in quantum\nchromodynamics. In this review we focus on linear power corrections that can\narise in certain kinematic distributions relevant for collider phenomenology\nwhere an operator product expansion is missing, e.g. those obtained from the\ntop-quark decay products, shape observables and the transverse momentum of\nmassive gauge bosons. Only the last one is found to be free from such\ncorrections, while the mass of the system comprising the top decay products has\na larger power correction if the perturbative expansion is expressed in terms\nof a short-distance mass instead of the pole mass. A proper modelization of\nnon-perturbative corrections is crucial in the context of shape observables to\nobtain reliable strong coupling constant extractions.\n"}
+{"abstract": "  Mobile Crowdsourcing (MC) is an effective way of engaging large groups of\nsmart devices to perform tasks remotely while exploiting their built-in\nfeatures. It has drawn great attention in the areas of smart cities and urban\ncomputing communities to provide decentralized, fast, and flexible ubiquitous\ntechnological services. The vast majority of previous studies focused on\nnon-cooperative MC schemes in Internet of Things (IoT) systems. Advanced\ncollaboration strategies are expected to leverage the capability of MC services\nand enable the execution of more complicated crowdsourcing tasks. In this\ncontext, Collaborative Mobile Crowdsourcing (CMC) enables task requesters to\nhire groups of IoT devices' users that must communicate with each other and\ncoordinate their operational activities in order to accomplish complex tasks.\nIn this paper, we present and discuss the novel CMC paradigm in IoT. Then, we\nprovide a detailed taxonomy to classify the different components forming CMC\nsystems. Afterwards, we investigate the challenges in designing CMC tasks and\ndiscuss different team formation strategies involving the crowdsourcing\nplatform and selected team leaders. We also analyze and compare the\nperformances of certain proposed CMC recruitment algorithms. Finally, we shed\nthe light on open research directions to leverage CMC service design.\n"}
+{"abstract": "  A special place in climatology is taken by the so-called conceptual climate\nmodels. These, relatively simple, sets of differential equations can\nsuccessfully describe single mechanisms of the climate. We focus on one family\nof such models based on the global energy balance. This gives rise to a\ndegenerate nonlocal parabolic nonlinear partial differential equation for the\nzonally averaged temperature. We construct a fully discrete numerical method\nthat has an optimal spectral accuracy in space and second order in time. Our\nscheme is based on Galerkin formulation of the Legendre basis expansion which\nis particularly convenient for this setting. By using extrapolation the\nnumerical scheme is linear even though the original equation is strongly\nnonlinear. We also test our theoretical result during various numerical\nsimulations that confirm the aforementioned accuracy of the scheme. All\nimplementations are coded in Julia programming language with the use of\nparallelization (multi-threading).\n"}
+{"abstract": "  Effectively parsing the facade is essential to 3D building reconstruction,\nwhich is an important computer vision problem with a large amount of\napplications in high precision map for navigation, computer aided design, and\ncity generation for digital entertainments. To this end, the key is how to\nobtain the shape grammars from 2D images accurately and efficiently. Although\nenjoying the merits of promising results on the semantic parsing, deep learning\nmethods cannot directly make use of the architectural rules, which play an\nimportant role for man-made structures. In this paper, we present a novel\ntranslational symmetry-based approach to improving the deep neural networks.\nOur method employs deep learning models as the base parser, and a module taking\nadvantage of translational symmetry is used to refine the initial parsing\nresults. In contrast to conventional semantic segmentation or bounding box\nprediction, we propose a novel scheme to fuse segmentation with anchor-free\ndetection in a single stage network, which enables the efficient training and\nbetter convergence. After parsing the facades into shape grammars, we employ an\noff-the-shelf rendering engine like Blender to reconstruct the realistic\nhigh-quality 3D models using procedural modeling. We conduct experiments on\nthree public datasets, where our proposed approach outperforms the\nstate-of-the-art methods. In addition, we have illustrated the 3D building\nmodels built from 2D facade images.\n"}
+{"abstract": "  The reflexive completion of a category consists of the Set-valued functors on\nit that are canonically isomorphic to their double conjugate. After reviewing\nboth this construction and Isbell conjugacy itself, we give new examples and\nrevisit Isbell's main results from 1960 in a modern categorical context. We\nestablish the sense in which reflexive completion is functorial, and find\nconditions under which two categories have equivalent reflexive completions. We\ndescribe the relationship between the reflexive and Cauchy completions,\ndetermine exactly which limits and colimits exist in an arbitrary reflexive\ncompletion, and make precise the sense in which the reflexive completion of a\ncategory is the intersection of the categories of covariant and contravariant\nfunctors on it.\n"}
+{"abstract": "  Recent work by Jenkins and Sakellariadou claims that cusps on cosmic strings\nlead to black hole production. To derive this conclusion they use the hoop\nconjecture in the rest frame of the string loop, rather than in the rest frame\nof the proposed black hole. Most of the energy they include is the bulk motion\nof the string near the cusp. We redo the analysis taking this into account and\nfind that cusps on cosmic strings with realistic energy scale do not produce\nblack holes, unless the cusp parameters are extremely fine-tuned.\n"}
+{"abstract": "  Radio-frequency (14.6 MHz) AC magnetic susceptibility, $\\chi^{\\prime}_{AC}$,\nof \\dytio\\ was measured using a self-oscillating tunnel-diode resonator.\nMeasurements were made with the excitation AC field parallel to the\nsuperimposed DC magnetic field up 5 T in a wide temperature range from 50 mK to\n100 K. At 14.6 MHz a known broad peak of $\\chi^{\\prime}_{AC}(T)$ from kHz -\nrange audio-frequency measurements around 15~K for both [111] and [110]\ndirections shifts to 45~K, continuing the Arrhenius activated behavior with the\nsame activation energy barrier of $E_a \\approx 230$~K. Magnetic field\ndependence of $\\chi^{\\prime}_{AC}$ along [111] reproduces previously reported\nlow-temperature two-in-two-out to three-in-one-out spin configuration\ntransition at about 1~T, and an intermediate phase between 1 and 1.5~T. The\nboundaries of the intermediate phase show reasonable overlap with the\nliterature data and connect at a critical endpoint of the first-order\ntransition line, suggesting that these low-temperature features are frequency\nindependent. An unusual upturn of magnetic susceptibility at $T \\to 0$ was\nobserved in magnetic fields between 1.5~T and 2~T for both magnetic field\ndirections, before fully polarized configuration sets in above 2~T.\n"}
+{"abstract": "  Powder-based additive manufacturing techniques provide tools to construct\nintricate structures that are difficult to manufacture using conventional\nmethods. In Laser Powder Bed Fusion, components are built by selectively\nmelting specific areas of the powder bed, to form the two-dimensional\ncross-section of the specific part. However, the high occurrence of defects\nimpacts the adoption of this method for precision applications. Therefore, a\ncontrol policy for dynamically altering process parameters to avoid phenomena\nthat lead to defect occurrences is necessary. A Deep Reinforcement Learning\n(DRL) framework that derives a versatile control strategy for minimizing the\nlikelihood of these defects is presented. The generated control policy alters\nthe velocity of the laser during the melting process to ensure the consistency\nof the melt pool and reduce overheating in the generated product. The control\npolicy is trained and validated on efficient simulations of the continuum\ntemperature distribution of the powder bed layer under various laser\ntrajectories.\n"}
+{"abstract": "  Canonical Correlation Analysis (CCA) and its regularised versions have been\nwidely used in the neuroimaging community to uncover multivariate associations\nbetween two data modalities (e.g., brain imaging and behaviour). However, these\nmethods have inherent limitations: (1) statistical inferences about the\nassociations are often not robust; (2) the associations within each data\nmodality are not modelled; (3) missing values need to be imputed or removed.\nGroup Factor Analysis (GFA) is a hierarchical model that addresses the first\ntwo limitations by providing Bayesian inference and modelling modality-specific\nassociations. Here, we propose an extension of GFA that handles missing data,\nand highlight that GFA can be used as a predictive model. We applied GFA to\nsynthetic and real data consisting of brain connectivity and non-imaging\nmeasures from the Human Connectome Project (HCP). In synthetic data, GFA\nuncovered the underlying shared and specific factors and predicted correctly\nthe non-observed data modalities in complete and incomplete data sets. In the\nHCP data, we identified four relevant shared factors, capturing associations\nbetween mood, alcohol and drug use, cognition, demographics and\npsychopathological measures and the default mode, frontoparietal control,\ndorsal and ventral networks and insula, as well as two factors describing\nassociations within brain connectivity. In addition, GFA predicted a set of\nnon-imaging measures from brain connectivity. These findings were consistent in\ncomplete and incomplete data sets, and replicated previous findings in the\nliterature. GFA is a promising tool that can be used to uncover associations\nbetween and within multiple data modalities in benchmark datasets (such as,\nHCP), and easily extended to more complex models to solve more challenging\ntasks.\n"}
+{"abstract": "  A general framework for obtaining exact transition rate matrices for\nstochastic systems on networks is presented and applied to many well-known\ncompartmental models of epidemiology. The state of the population is described\nas a vector in the tensor product space of $N$ individual probability vector\nspaces, whose dimension equals the number of compartments of the\nepidemiological model $n_c$. The transition rate matrix for the\n$n_c^N$-dimensional Markov chain is obtained by taking suitable linear\ncombinations of tensor products of $n_c$-dimensional matrices. The resulting\ntransition rate matrix is a sum over bilocal linear operators, which gives\ninsight in the microscopic dynamics of the system. The more familiar and\nnon-linear node-based mean-field approximations are recovered by restricting\nthe exact models to uncorrelated (separable) states. We show how the exact\ntransition rate matrix for the susceptible-infected (SI) model can be used to\nfind analytic solutions for SI outbreaks on trees and the cycle graph for\nfinite $N$.\n"}
+{"abstract": "  Uncovering how inequality emerges from human interaction is imperative for\njust societies. Here we show that the way social groups interact in\nface-to-face situations can enable the emergence of degree inequality. We\npresent a mechanism that integrates group mixing dynamics with individual\npreferences, which reproduces group degree inequality found in six empirical\ndata sets of face-to-face interactions. We uncover the impact of group-size\nimbalance on degree inequality, revealing a critical minority group size that\nchanges social gatherings qualitatively. If the minority group is larger than\nthis 'critical mass' size, it can be a well-connected, cohesive group; if it is\nsmaller, minority cohesion widens degree inequality. Finally, we expose the\nunder-representation of social groups in degree rankings due to mixing dynamics\nand propose a way to reduce such biases.\n"}
+{"abstract": "  We study the Randic index for cactus graphs. It is conjectured to be bounded\nbelow by radius (for other than an even path), and it is known to obey several\nbounds based on diameter. We study radius and diameter for cacti then verify\nthe radius bound and strengthen two diameter bounds for cacti. Along the way,\nwe produce several other bounds for the Randic index in terms of graph size,\norder, and valency for several special classes of graphs, including chemical\nnontrivial cacti and cacti with starlike BC-trees.\n"}
+{"abstract": "  Artificial intelligence (AI) has witnessed a substantial breakthrough in a\nvariety of Internet of Things (IoT) applications and services, spanning from\nrecommendation systems to robotics control and military surveillance. This is\ndriven by the easier access to sensory data and the enormous scale of\npervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data\nstreams. Designing accurate models using such data streams, to predict future\ninsights and revolutionize the decision-taking process, inaugurates pervasive\nsystems as a worthy paradigm for a better quality-of-life. The confluence of\npervasive computing and artificial intelligence, Pervasive AI, expanded the\nrole of ubiquitous IoT systems from mainly data collection to executing\ndistributed computations with a promising alternative to centralized learning,\npresenting various challenges. In this context, a wise cooperation and resource\nscheduling should be envisaged among IoT devices (e.g., smartphones, smart\nvehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid\ncommunication and computation overheads and ensure maximum performance. In this\npaper, we conduct a comprehensive survey of the recent techniques developed to\novercome these resource challenges in pervasive AI systems. Specifically, we\nfirst present an overview of the pervasive computing, its architecture, and its\nintersection with artificial intelligence. We then review the background,\napplications and performance metrics of AI, particularly Deep Learning (DL) and\nonline learning, running in a ubiquitous system. Next, we provide a deep\nliterature review of communication-efficient techniques, from both algorithmic\nand system perspectives, of distributed inference, training and online learning\ntasks across the combination of IoT devices, edge devices and cloud servers.\nFinally, we discuss our future vision and research challenges.\n"}
+{"abstract": "  In this paper, we disprove EMSO(FO$^2$) convergence law for the binomial\nrandom graph $G(n,p)$ for any constant probability $p$. More specifically, we\nprove that there exists an existential monadic second order sentence with 2\nfirst order variables such that, for every $p\\in(0,1)$, the probability that it\nis true on $G(n,p)$ does not converge.\n"}
+{"abstract": "  The distributed optimization problem is set up in a collection of nodes\ninterconnected via a communication network. The goal is to find the minimizer\nof a global objective function formed by the addition of partial functions\nlocally known at each node. A number of methods are available for addressing\nthis problem, having different advantages. The goal of this work is to achieve\nthe maximum possible convergence rate. As the first step towards this end, we\npropose a new method which we show converges faster than other available\noptions. As with most distributed optimization methods, convergence rate\ndepends on a step size parameter. As the second step towards our goal we\ncomplement the proposed method with a fully distributed method for estimating\nthe optimal step size that maximizes convergence speed. We provide theoretical\nguarantees for the convergence of the resulting method in a neighborhood of the\nsolution. Also, for the case in which the global objective function has a\nsingle local minimum, we provide a different step size selection criterion\ntogether with theoretical guarantees for convergence. We present numerical\nexperiments showing that, when using the same step size, our method converges\nsignificantly faster than its rivals. Experiments also show that the\ndistributed step size estimation method achieves an asymptotic convergence rate\nvery close to the theoretical maximum.\n"}
+{"abstract": "  We investigate the phenomenology of light GeV-scale fermionic dark matter in\n$U(1)_{L_\\mu - L_{\\tau}}$ gauge extension of the Standard Model. Heavy neutral\nfermions alongside with a $S_1(\\overline{3}$,$1$,$1/3$) scalar leptoquark and\nan inert scalar doublet are added to address the flavor anomalies and light\nneutrino mass respectively. The light gauge boson associated with\n$U(1)_{L_\\mu-L_\\tau}$ gauge group mediates dark to visible sector and helps to\nobtain the correct relic density. Aided with a colored scalar, we constrain the\nnew model parameters by using the branching ratios of various $b \\to sll$ and\n$b \\to s \\gamma$ decay processes as well as the lepton flavour non-universality\nobservables $R_{K^{(*)}}$ and then show the implication on the branching ratios\nof some rare semileptonic $B \\to (K^{(*)}, \\phi)+$ missing energy, processes.\n"}
+{"abstract": "  In recent years, the implications of the generalized (GUP) and extended (EUP)\nuncertainty prin-ciples on Maxwell-Boltzmann distribution have been widely\ninvestigated. However, at high energy regimes, the validity of\nMaxwell-Boltzmann statistics is under debate and instead, the J\\\"{u}ttner\ndistribution is proposed as the distribution function in relativistic limit.\nMotivated by these considerations, in the present work, our aim is to study the\neffects of GUP and EUP on a system that obeys the J\\\"{u}ttner distribution. To\nachieve this goal, we address a method to get the distribution function by\nstarting from the partition function and its relation with thermal energy which\nfinally helps us in finding the corresponding energy density states.\n"}
+{"abstract": "  Supervised machine learning has several drawbacks that make it difficult to\nuse in many situations. Drawbacks include: heavy reliance on massive training\ndata, limited generalizability and poor expressiveness of high-level semantics.\nLow-shot Learning attempts to address these drawbacks. Low-shot learning allows\nthe model to obtain good predictive power with very little or no training data,\nwhere structured knowledge plays a key role as a high-level semantic\nrepresentation of human. This article will review the fundamental factors of\nlow-shot learning technologies, with a focus on the operation of structured\nknowledge under different low-shot conditions. We also introduce other\ntechniques relevant to low-shot learning. Finally, we point out the limitations\nof low-shot learning, the prospects and gaps of industrial applications, and\nfuture research directions.\n"}
+{"abstract": "  Relation extraction is a type of information extraction task that recognizes\nsemantic relationships between entities in a sentence. Many previous studies\nhave focused on extracting only one semantic relation between two entities in a\nsingle sentence. However, multiple entities in a sentence are associated\nthrough various relations. To address this issue, we propose a relation\nextraction model based on a dual pointer network with a multi-head attention\nmechanism. The proposed model finds n-to-1 subject-object relations using a\nforward object decoder. Then, it finds 1-to-n subject-object relations using a\nbackward subject decoder. Our experiments confirmed that the proposed model\noutperformed previous models, with an F1-score of 80.8% for the ACE-2005 corpus\nand an F1-score of 78.3% for the NYT corpus.\n"}
+{"abstract": "  Actor-critic style two-time-scale algorithms are very popular in\nreinforcement learning, and have seen great empirical success. However, their\nperformance is not completely understood theoretically. In this paper, we\ncharacterize the global convergence of an online natural actor-critic algorithm\nin the tabular setting using a single trajectory. Our analysis applies to very\ngeneral settings, as we only assume that the underlying Markov chain is ergodic\nunder all policies (the so-called Recurrence assumption). We employ\n$\\epsilon$-greedy sampling in order to ensure enough exploration.\n  For a fixed exploration parameter $\\epsilon$, we show that the natural actor\ncritic algorithm is $\\mathcal{O}(\\frac{1}{\\epsilon T^{1/4}}+\\epsilon)$ close to\nthe global optimum after $T$ iterations of the algorithm.\n  By carefully diminishing the exploration parameter $\\epsilon$ as the\niterations proceed, we also show convergence to the global optimum at a rate of\n$\\mathcal{O}(1/T^{1/6})$.\n"}
+{"abstract": "  Human machine interaction systems are those of much needed in the emerging\ntechnology to make the user aware of what is happening around. It is huge\ndomain in which the smart material enables the factor of convergence. One such\nis the piezoelectric crystals, is a class of smart material and this has an\nincredible property of self-sensing actuation (SSA). This property of SSA has\nadded an indescribable advantage to the robotic field by having the advantages\nof exhibiting both the functionality of sensing and actuating characteristics\nwith reduced devices, space and power. This paper focuses on integrating the\nSSA to drive an unmanned ground vehicle with wireless radio control system\nwhich will be of great use in all the automation field. The piezo electric\nplate will be used as an input device to send the signal to move the UGV in\ncertain direction and then, the same piezo-electric plate will be used as an\nactuator for haptic feedback with the help of drive circuit if obstacles or\ndanger is experienced by UGV.\n"}
+{"abstract": "  The COVID-19 pandemic, which spread rapidly in late 2019, has revealed that\nthe use of computing and communication technologies provides significant aid in\npreventing, controlling, and combating infectious diseases. With the ongoing\nresearch in next-generation networking (NGN), the use of secure and reliable\ncommunication and networking is of utmost importance when dealing with users'\nhealth records and other sensitive information. Through the adaptation of\nArtificial Intelligence (AI)-enabled NGN, the shape of healthcare systems can\nbe altered to achieve smart and secure healthcare capable of coping with\nepidemics that may emerge at any given moment. In this article, we envision a\ncooperative and distributed healthcare framework that relies on\nstate-of-the-art computing, communication, and intelligence capabilities,\nnamely, Federated Learning (FL), mobile edge computing (MEC), and Blockchain,\nto enable epidemic (or suspicious infectious disease) discovery, remote\nmonitoring, and fast health-authority response. The introduced framework can\nalso enable secure medical data exchange at the edge and between different\nhealth entities. Such a technique, coupled with the low latency and high\nbandwidth functionality of 5G and beyond networks, would enable mass\nsurveillance, monitoring and analysis to occur at the edge. Challenges, issues,\nand design guidelines are also discussed in this article with highlights on\nsome trending solutions.\n"}
+{"abstract": "  Let $\\mathcal{P}$ be a finite set of points in the plane in general position.\nFor any spanning tree $T$ on $\\mathcal{P}$, we denote by $|T|$ the Euclidean\nlength of $T$. Let $T_{\\text{OPT}}$ be a plane (that is, noncrossing) spanning\ntree of maximum length for $\\mathcal{P}$. It is not known whether such a tree\ncan be found in polynomial time. Past research has focused on designing\npolynomial time approximation algorithms, using low diameter trees. In this\nwork we initiate a systematic study of the interplay between the approximation\nfactor and the diameter of the candidate trees. Specifically, we show three\nresults. First, we construct a plane tree $T_{\\text{ALG}}$ with diameter at\nmost four that satisfies $|T_{\\text{ALG}}|\\ge \\delta\\cdot |T_{\\text{OPT}}|$ for\n$\\delta>0.546$, thereby substantially improving the currently best known\napproximation factor. Second, we show that the longest plane tree among those\nwith diameter at most three can be found in polynomial time. Third, for any\n$d\\ge 3$ we provide upper bounds on the approximation factor achieved by a\nlongest plane tree with diameter at most $d$ (compared to a longest general\nplane tree).\n"}
+{"abstract": "  Jerky flow in solids results from collective dynamics of dislocations which\ngives rise to serrated deformation curves and a complex evolution of the strain\nheterogeneity. A rich example of this phenomenon is the Portevin-Le Chatelier\neffect in alloys. The corresponding spatiotemporal patterns showed some\nuniversal features which provided a basis for a well-known phenomenological\nclassification. Recent studies revealed peculiar features in both the stress\nserration sequences and the kinematics of deformation bands in Al-based alloys\ncontaining fine microstructure elements, such as nanosize precipitates and (or)\nsubmicron grains. In the present work, jerky flow of an AlMgScZr alloy is\nstudied using statistical analysis of stress serrations and the accompanying\nacoustic emission. As in the case of coarse-grained binary AlMg alloys, the\namplitude distributions of acoustic events obey a power-law scaling which is\nusually considered as evidence of avalanchelike dynamics. However, the scaling\nexponents display specific dependences on the strain and strain rate for the\ninvestigated materials. The observed effects bear evidence to a competition\nbetween the phenomena of synchronization and randomization of dislocation\navalanches, which may shed light on the mechanisms leading to a high variety of\njerky flow patterns observed in applied alloys.\n"}
+{"abstract": "  Considering a common case where measurements are obtained from independent\nsensors, we present a novel outlier-robust filter for nonlinear dynamical\nsystems in this work. The proposed method is devised by modifying the\nmeasurement model and subsequently using the theory of Variational Bayes and\ngeneral Gaussian filtering. We treat the measurement outliers independently for\nindependent observations leading to selective rejection of the corrupted data\nduring inference. By carrying out simulations for variable number of sensors we\nverify that an implementation of the proposed filter is computationally more\nefficient as compared to the proposed modifications of similar baseline methods\nstill yielding similar estimation quality. In addition, experimentation results\nfor various real-time indoor localization scenarios using Ultra-wide Band (UWB)\nsensors demonstrate the practical utility of the proposed method.\n"}
+{"abstract": "  We present Magic Layouts; a method for parsing screenshots or hand-drawn\nsketches of user interface (UI) layouts. Our core contribution is to extend\nexisting detectors to exploit a learned structural prior for UI designs,\nenabling robust detection of UI components; buttons, text boxes and similar.\nSpecifically we learn a prior over mobile UI layouts, encoding common spatial\nco-occurrence relationships between different UI components. Conditioning\nregion proposals using this prior leads to performance gains on UI layout\nparsing for both hand-drawn UIs and app screenshots, which we demonstrate\nwithin the context an interactive application for rapidly acquiring digital\nprototypes of user experience (UX) designs.\n"}
+{"abstract": "  The synthesis of 3 and 4 abstract polymer chains divided into two sexes is\nconsidered, where the degree of kinship of the chains is determined by their\noverlap. It is shown that the use of some types of entangled bi-photon in\none-way control gives a difference in the degree of kinship between the legal\nand nonlegal pairs that is unattainable with classical control. This example\ndemonstrates the quantum superiority in distributed computing, coming from the\nviolation of the Bell inequality. It may be of interest for revealing the\nquantum mechanisms of synthesis of real biopolymers with directional\nproperties.\n"}
+{"abstract": "  Autonomous multi-robot optical inspection systems are increasingly applied\nfor obtaining inline measurements in process monitoring and quality control.\nNumerous methods for path planning and robotic coordination have been developed\nfor static and dynamic environments and applied to different fields. However,\nthese approaches may not work for the autonomous multi-robot optical inspection\nsystem due to fast computation requirements of inline optimization, unique\ncharacteristics on robotic end-effector orientations, and complex large-scale\nfree-form product surfaces. This paper proposes a novel task allocation\nmethodology for coordinated motion planning of multi-robot inspection.\nSpecifically, (1) a local robust inspection task allocation is proposed to\nachieve efficient and well-balanced measurement assignment among robots; (2)\ncollision-free path planning and coordinated motion planning are developed via\ndynamic searching in robotic coordinate space and perturbation of probe poses\nor local paths in the conflicting robots. A case study shows that the proposed\napproach can mitigate the risk of collisions between robots and environments,\nresolve conflicts among robots, and reduce the inspection cycle time\nsignificantly and consistently.\n"}
+{"abstract": "  We report on high power operation of Er:Y2O3 ceramic laser at ~1.6 {\\mu}m\nusing low scattering loss, 0.25 at.% Er3+ doped ceramic sample fabricated\nin-house via co-precipitation process. The laser is in-band pumped by an Er, Yb\nfiber laser at 1535.6 nm and generates 10.2 W of continuous-wave (CW) output\npower at 1640.4 nm with a slope efficiency of 25% with respect to the absorbed\npump power. To the best of our knowledge, this is the first demonstration of\n~1.6 {\\mu}m Er:Y2O3 laser at room temperature. The prospects for further\nscaling in output power and lasing efficiency via low Er3+ doping and reduced\nenergy-transfer upconversion are discussed.\n"}
+{"abstract": "  In this work, the thermodynamic properties of the organic superconductor\n$\\lambda$-(BETS)$_2$GaCl$_4$ are investigated to study a high-field\nsuperconducting state known as the putative Fulde-Ferrell-Larkin-Ovchinnikov\n(FFLO) phase. We observed a small thermodynamic anomaly in the field $H_{\\rm\nFFLO}$ $\\sim$ 10~T, which corresponds to the Pauli limiting field $H_{\\rm P}$.\nThis anomaly probably originates from a transition from a uniform\nsuperconducting state to the FFLO state. $H_{\\rm FFLO}$ does not show a strong\nfield-angular dependence due to a quasi-isotropic paramagnetic effect in\n$\\lambda$-(BETS)$_2$GaCl$_4$. The thermodynamic anomaly at $H_{\\rm FFLO}$ is\nsmeared out and low-temperature upper critical field $H_{\\rm c2}$ changes\nsignificantly if fields are not parallel to the conducting plane even for a\ndeviation of $\\sim$0.5$^{\\circ}$. This behavior indicates that the high-field\nstate is very unstable, as it is influenced by the strongly anisotropic orbital\neffect. Our results are consistent with the theoretical predictions on the FFLO\nstate, and show that the high-field superconductivity is probably an FFLO state\nin $\\lambda$-(BETS)$_2$GaCl$_4$ from a thermodynamic point of view.\n"}
+{"abstract": "  Given the recent development of rotating black-bounce-Kerr spacetimes, for\nboth theoretical and observational purposes it becomes interesting to see\nwhether it might be possible to construct black-bounce variants of the entire\nKerr-Newman family. Specifically, herein we shall consider\nblack-bounce-Reissner-Nordstr\\\"om and black-bounce-Kerr-Newman spacetimes as\nparticularly simple and clean everywhere-regular black hole \"mimickers\" that\ndeviate from the Kerr-Newman family in a precisely controlled and minimal\nmanner, and smoothly interpolate between regular black holes and traversable\nwormholes. While observationally the electric charges on astrophysical black\nholes are likely to be extremely low, $|Q|/m \\ll 1$, introducing any non-zero\nelectric charge has a significant theoretical impact. In particular, we verify\nthe existence of a Killing tensor (and associated Carter-like constant) but\nwithout the full Killing tower of principal tensor and Killing-Yano tensor,\nalso we discuss how, assuming general relativity, the black-bounce-Kerr-Newman\nsolution requires an interesting, non-trivial matter/energy content.\n"}
+{"abstract": "  In this manuscript we prove the Bernstein inequality and develop the theory\nof holonomic D-modules for rings of invariants of finite groups in\ncharacteristic zero, and for strongly F-regular finitely generated graded\nalgebras with FFRT in prime characteristic. In each of these cases, the ring\nitself, its localizations, and its local cohomology modules are holonomic. We\nalso show that holonomic D-modules, in this context, have finite length. We\nobtain these results using a more general version of Bernstein filtrations.\n"}
+{"abstract": "  Photoelectron circular dichroism (PECD) is a fascinating phenomenon both from\na fundamental science aspect but also due to its emerging role as a highly\nsensitive analytic tool for chiral recognition in the gas phase. PECD has been\nstudied with single-photon as well as multi-photon ionization. The latter has\nbeen investigated in the short pulse limit with femtosecond laser pulses, where\nionization can be thought of as an instantaneous process.\n  In this contribution, we demonstrate that multiphoton PECD still can be\nobserved when using an ultra-violet nanosecond pulse to ionize chiral showcase\nfenchone molecules. Compared to femtosecond ionization, the magnitude of PECD\nis similar, but the lifetime of intermediate molecular states imprints itself\nin the photoelectron spectra. Being able to use an industrial nanosecond laser\nto investigate PECD furthermore reduces the technical requirements to apply\nPECD in analytical chemistry.\n"}
+{"abstract": "  Inter-beat interval (IBI) measurement enables estimation of heart-rate\nvariability (HRV) which, in turns, can provide early indication of potential\ncardiovascular diseases. However, extracting IBIs from noisy signals is\nchallenging since the morphology of the signal is distorted in the presence of\nthe noise. Electrocardiogram (ECG) of a person in heavy motion is highly\ncorrupted with noise, known as motion-artifact, and IBI extracted from it is\ninaccurate. As a part of remote health monitoring and wearable system\ndevelopment, denoising ECG signals and estimating IBIs correctly from them have\nbecome an emerging topic among signal-processing researchers. Apart from\nconventional methods, deep-learning techniques have been successfully used in\nsignal denoising recently, and diagnosis process has become easier, leading to\naccuracy levels that were previously unachievable. We propose a deep-learning\napproach leveraging tiramisu autoencoder model to suppress motion-artifact\nnoise and make the R-peaks of the ECG signal prominent even in the presence of\nhigh-intensity motion. After denoising, IBIs are estimated more accurately\nexpediting diagnosis tasks. Results illustrate that our method enables IBI\nestimation from noisy ECG signals with SNR up to -30dB with average root mean\nsquare error (RMSE) of 13 milliseconds for estimated IBIs. At this noise level,\nour error percentage remains below 8% and outperforms other state of the art\ntechniques.\n"}
+{"abstract": "  Recommender Systems (RS) have employed knowledge distillation which is a\nmodel compression technique training a compact student model with the knowledge\ntransferred from a pre-trained large teacher model. Recent work has shown that\ntransferring knowledge from the teacher's intermediate layer significantly\nimproves the recommendation quality of the student. However, they transfer the\nknowledge of individual representation point-wise and thus have a limitation in\nthat primary information of RS lies in the relations in the representation\nspace. This paper proposes a new topology distillation approach that guides the\nstudent by transferring the topological structure built upon the relations in\nthe teacher space. We first observe that simply making the student learn the\nwhole topological structure is not always effective and even degrades the\nstudent's performance. We demonstrate that because the capacity of the student\nis highly limited compared to that of the teacher, learning the whole\ntopological structure is daunting for the student. To address this issue, we\npropose a novel method named Hierarchical Topology Distillation (HTD) which\ndistills the topology hierarchically to cope with the large capacity gap. Our\nextensive experiments on real-world datasets show that the proposed method\nsignificantly outperforms the state-of-the-art competitors. We also provide\nin-depth analyses to ascertain the benefit of distilling the topology for RS.\n"}
+{"abstract": "  Evidence-based fact checking aims to verify the truthfulness of a claim\nagainst evidence extracted from textual sources. Learning a representation that\neffectively captures relations between a claim and evidence can be challenging.\nRecent state-of-the-art approaches have developed increasingly sophisticated\nmodels based on graph structures. We present a simple model that can be trained\non sequence structures. Our model enables inter-sentence attentions at\ndifferent levels and can benefit from joint training. Results on a large-scale\ndataset for Fact Extraction and VERification (FEVER) show that our model\noutperforms the graph-based approaches and yields 1.09% and 1.42% improvements\nin label accuracy and FEVER score, respectively, over the best published model.\n"}
+{"abstract": "  A simple vibrational model of heat transfer in two-dimensional (2D) fluids\nrelates the heat conductivity coefficient to the longitudinal and transverse\nsound velocities, specific heat, and the mean interatomic separation. This\nmodel is demonstrated not to contradict the available experimental and\nnumerical data on heat transfer in 2D complex plasma layers. Additionally, the\nheat conductivity coefficient of a 2D one-component plasma with a logarithmic\ninteraction is evaluated.\n"}
+{"abstract": "  Neural Architecture Search (NAS) has enabled the possibility of automated\nmachine learning by streamlining the manual development of deep neural network\narchitectures defining a search space, search strategy, and performance\nestimation strategy. To solve the need for multi-platform deployment of\nConvolutional Neural Network (CNN) models, Once-For-All (OFA) proposed to\ndecouple Training and Search to deliver a one-shot model of sub-networks that\nare constrained to various accuracy-latency tradeoffs. We find that the\nperformance estimation strategy for OFA's search severely lacks\ngeneralizability of different hardware deployment platforms due to single\nhardware latency lookup tables that require significant amount of time and\nmanual effort to build beforehand. In this work, we demonstrate the framework\nfor building latency predictors for neural network architectures to address the\nneed for heterogeneous hardware support and reduce the overhead of lookup\ntables altogether. We introduce two generalizability strategies which include\nfine-tuning using a base model trained on a specific hardware and NAS search\nspace, and GPU-generalization which trains a model on GPU hardware parameters\nsuch as Number of Cores, RAM Size, and Memory Bandwidth. With this, we provide\na family of latency prediction models that achieve over 50% lower RMSE loss as\ncompared to with ProxylessNAS. We also show that the use of these latency\npredictors match the NAS performance of the lookup table baseline approach if\nnot exceeding it in certain cases.\n"}
+{"abstract": "  We give explicit formulas for the geodesic growth series of a Right Angled\nCoxeter Group (RACG) based on a link-regular graph that is 4-clique free, i.e.\nwithout tetrahedrons\n"}
+{"abstract": "  We consider some supervised binary classification tasks and a regression\ntask, whereas SVM and Deep Learning, at present, exhibit the best\ngeneralization performances. We extend the work [3] on a generalized quadratic\nloss for learning problems that examines pattern correlations in order to\nconcentrate the learning problem into input space regions where patterns are\nmore densely distributed. From a shallow methods point of view (e.g.: SVM),\nsince the following mathematical derivation of problem (9) in [3] is incorrect,\nwe restart from problem (8) in [3] and we try to solve it with one procedure\nthat iterates over the dual variables until the primal and dual objective\nfunctions converge. In addition we propose another algorithm that tries to\nsolve the classification problem directly from the primal problem formulation.\nWe make also use of Multiple Kernel Learning to improve generalization\nperformances. Moreover, we introduce for the first time a custom loss that\ntakes in consideration pattern correlation for a shallow and a Deep Learning\ntask. We propose some pattern selection criteria and the results on 4 UCI\ndata-sets for the SVM method. We also report the results on a larger binary\nclassification data-set based on Twitter, again drawn from UCI, combined with\nshallow Learning Neural Networks, with and without the generalized quadratic\nloss. At last, we test our loss with a Deep Neural Network within a larger\nregression task taken from UCI. We compare the results of our optimizers with\nthe well known solver SVMlight and with Keras Multi-Layers Neural Networks with\nstandard losses and with a parameterized generalized quadratic loss, and we\nobtain comparable results.\n"}
+{"abstract": "  We show that the symmetric radial decreasing rearrangement can increase the\nfractional Gagliardo semi-norm in domains.\n"}
+{"abstract": "  We consider two degenerate heat equations with a nonlocal space term,\nstudying, in particular, their null controllability property. To this aim, we\nfirst consider the associated nonhomogeneous degenerate heat equations: we\nstudy their well posedness, the Carleman estimates for the associated adjoint\nproblems and, finally, the null controllability. Then, as a consequence, using\nthe Kakutani's fixed point Theorem, we deduce the null controllability property\nfor the initial nonlocal problems.\n"}
+{"abstract": "  Decades of deindustrialization have led to economic decline and population\nloss throughout the U.S. Midwest, with the highest national poverty rates found\nin Detroit, Cleveland, and Buffalo. This poverty is often confined to core\ncities themselves, however, as many of their surrounding suburbs continue to\nprosper. Poverty can therefore be highly concentrated at the MSA level, but\nmore evenly distributed within the borders of the city proper. One result of\nthis disparity is that if suburbanites consider poverty to be confined to the\ncentral city, they might be less willing to devote resources to alleviate it.\nBut due to recent increases in suburban poverty, particularly since the 2008\nrecession, such urban-suburban gaps might be shrinking. Using Census\ntract-level data, this study quantifies poverty concentrations for four \"Rust\nBelt\" MSAs, comparing core-city and suburban concentrations in 2000, 2010, and\n2015. There is evidence of a large gap between core cities and outlying areas,\nwhich is closing in the three highest-poverty cities, but not in Milwaukee. A\nset of four comparison cities show a smaller, more stable city-suburban divide\nin the U.S. \"Sunbelt,\" while Chicago resembles a \"Rust Belt\" metro.\n"}
+{"abstract": "  Recent observations by the {\\it Juno} spacecraft have revealed that the tidal\nLove number $k_2$ of Jupiter is $4\\%$ lower than the hydrostatic value. We\npresent a simple calculation of the dynamical Love number of Jupiter that\nexplains the observed \"anomaly\". The Love number is usually dominated by the\nresponse of the (rotation-modified) f-modes of the planet. Our method also\nallows for efficient computation of high-order dynamical Love numbers. While\nthe inertial-mode contributions to the Love numbers are negligible, a\nsufficiently strong stratification in a large region of the planet's interior\nwould induce significant g-mode responses and influence the measured Love\nnumbers.\n"}
+{"abstract": "  Conformal predictors are an important class of algorithms that allow\npredictions to be made with a user-defined confidence level. They are able to\ndo this by outputting prediction sets, rather than simple point predictions.\nThe conformal predictor is valid in the sense that the accuracy of its\npredictions is guaranteed to meet the confidence level, only assuming\nexchangeability in the data. Since accuracy is guaranteed, the performance of a\nconformal predictor is measured through the efficiency of the prediction sets.\nTypically, a conformal predictor is built on an underlying machine learning\nalgorithm and hence its predictive power is inherited from this algorithm.\nHowever, since the underlying machine learning algorithm is not trained with\nthe objective of minimizing predictive efficiency it means that the resulting\nconformal predictor may be sub-optimal and not aligned sufficiently to this\nobjective. Hence, in this study we consider an approach to train the conformal\npredictor directly with maximum predictive efficiency as the optimization\nobjective, and we focus specifically on the inductive conformal predictor for\nclassification. To do this, the conformal predictor is approximated by a\ndifferentiable objective function and gradient descent used to optimize it. The\nresulting parameter estimates are then passed to a proper inductive conformal\npredictor to give valid prediction sets. We test the method on several real\nworld data sets and find that the method is promising and in most cases gives\nimproved predictive efficiency against a baseline conformal predictor.\n"}
+{"abstract": "  A recent experiment [K. H. Kim, et al., Science 370, 978 (2020)] showed that\nit may be possible to detect a liquid-liquid phase transition (LLPT) in\nsupercooled water by subjecting high density amorphous ice (HDA) to ultrafast\nheating, after which the sample reportedly undergoes spontaneous decompression\nfrom a high density liquid (HDL) to a low density liquid (LDL) via a\nfirst-order phase transition. Here we conduct computer simulations of the ST2\nwater model, in which a LLPT is known to occur. We subject various HDA samples\nof this model to a heating and decompression protocol that follows a\nthermodynamic pathway similar to that of the recent experiments. Our results\nshow that a signature of the underlying equilibrium LLPT can be observed in a\nstrongly out-of-equilibrium process that follows this pathway despite the very\nhigh heating and decompression rates employed here. Our results are also\nconsistent with the phase diagram of glassy ST2 water reported in previous\nstudies.\n"}
+{"abstract": "  In recent years, the notion of Quantum Materials has emerged as a powerful\nunifying concept across diverse fields of science and engineering, from\ncondensed-matter and cold atom physics to materials science and quantum\ncomputing. Beyond traditional quantum materials such as unconventional\nsuperconductors, heavy fermions, and multiferroics, the field has significantly\nexpanded to encompass topological quantum matter, two-dimensional materials and\ntheir van der Waals heterostructures, Moire materials, Floquet time crystals,\nas well as materials and devices for quantum computation with Majorana\nfermions. In this Roadmap collection we aim to capture a snapshot of the most\nrecent developments in the field, and to identify outstanding challenges and\nemerging opportunities. The format of the Roadmap, whereby experts in each\ndiscipline share their viewpoint and articulate their vision for quantum\nmaterials, reflects the dynamic and multifaceted nature of this research area,\nand is meant to encourage exchanges and discussions across traditional\ndisciplinary boundaries. It is our hope that this collective vision will\ncontribute to sparking new fascinating questions and activities at the\nintersection of materials science, condensed matter physics, device\nengineering, and quantum information, and to shaping a clearer landscape of\nquantum materials science as a new frontier of interdisciplinary scientific\ninquiry.\n"}
+{"abstract": "  The paper contains several theoretical results related to the weighted\nnonlinear least-squares problem for low-rank signal estimation, which can be\nconsidered as a Hankel structured low-rank approximation problem. A\nparameterization of the subspace of low-rank time series connected with\ngeneralized linear recurrence relations (GLRRs) is described and its features\nare investigated. It is shown how the obtained results help to describe the\ntangent plane, prove optimization problem features and construct stable\nalgorithms for solving low-rank approximation problems. For the latter, a\nstable algorithm for constructing the projection onto a subspace of time series\nthat satisfy a given GLRR is proposed and justified. This algorithm is used for\na new implementation of the known Gauss-Newton method using the variable\nprojection approach. The comparison by stability and computational cost is\nperformed theoretically and with the help of an example.\n"}
+{"abstract": "  We develop a new method for analyzing moduli problems related to the stack of\npure coherent sheaves on a polarized family of projective schemes. It is an\ninfinite-dimensional analogue of geometric invariant theory. We apply this to\ntwo familiar moduli problems: the stack of $\\Lambda$-modules and the stack of\npairs. In both examples, we construct a $\\Theta$-stratification of the stack,\ndefined in terms of a polynomial numerical invariant, and we construct good\nmoduli spaces for the open substacks of semistable points. One of the essential\ningredients is the construction of higher dimensional analogues of the affine\nGrassmannian for the moduli problems considered.\n"}
+{"abstract": "  Subset selection is a valuable tool for interpretable learning, scientific\ndiscovery, and data compression. However, classical subset selection is often\neschewed due to selection instability, computational bottlenecks, and lack of\npost-selection inference. We address these challenges from a Bayesian\nperspective. Given any Bayesian predictive model $\\mathcal{M}$, we elicit\npredictively-competitive subsets using linear decision analysis. The approach\nis customizable for (local) prediction or classification and provides\ninterpretable summaries of $\\mathcal{M}$. A key quantity is the acceptable\nfamily of subsets, which leverages the predictive distribution from\n$\\mathcal{M}$ to identify subsets that offer nearly-optimal prediction. The\nacceptable family spawns new (co-) variable importance metrics based on whether\nvariables (co-) appear in all, some, or no acceptable subsets. Crucially, the\nlinear coefficients for any subset inherit regularization and predictive\nuncertainty quantification via $\\mathcal{M}$. The proposed approach exhibits\nexcellent prediction, interval estimation, and variable selection for simulated\ndata, including $p=400 > n$. These tools are applied to a large education\ndataset with highly correlated covariates, where the acceptable family is\nespecially useful. Our analysis provides unique insights into the combination\nof environmental, socioeconomic, and demographic factors that predict\neducational outcomes, and features highly competitive prediction with\nremarkable stability.\n"}
+{"abstract": "  As infamous invaders to the North American ecosystem, the Asian giant hornet\n(Vespa mandarinia) is devastating not only to native bee colonies, but also to\nlocal apiculture. One of the most effective way to combat the harmful species\nis to locate and destroy their nests. By mobilizing the public to actively\nreport possible sightings of the Asian giant hornet, the governmentcould timely\nsend inspectors to confirm and possibly destroy the nests. However, such\nconfirmation requires lab expertise, where manually checking the reports one by\none is extremely consuming of human resources. Further given the limited\nknowledge of the public about the Asian giant hornet and the randomness of\nreport submission, only few of the numerous reports proved positive, i.e.\nexisting nests. How to classify or prioritize the reports efficiently and\nautomatically, so as to determine the dispatch of personnel, is of great\nsignificance to the control of the Asian giant hornet. In this paper, we\npropose a method to predict the priority of sighting reports based on machine\nlearning. We model the problem of optimal prioritization of sighting reports as\na problem of classification and prediction. We extracted a variety of rich\nfeatures in the report: location, time, image(s), and textual description.\nBased on these characteristics, we propose a classification model based on\nlogistic regression to predict the credibility of a certain report.\nFurthermore, our model quantifies the impact between reports to get the\npriority ranking of the reports. Extensive experiments on the public dataset\nfrom the WSDA (the Washington State Department of Agriculture) have proved the\neffectiveness of our method.\n"}
+{"abstract": "  Despite the recent success of reconciling spike-based coding with the error\nbackpropagation algorithm, spiking neural networks are still mostly applied to\ntasks stemming from sensory processing, operating on traditional data\nstructures like visual or auditory data. A rich data representation that finds\nwide application in industry and research is the so-called knowledge graph - a\ngraph-based structure where entities are depicted as nodes and relations\nbetween them as edges. Complex systems like molecules, social networks and\nindustrial factory systems can be described using the common language of\nknowledge graphs, allowing the usage of graph embedding algorithms to make\ncontext-aware predictions in these information-packed environments. We propose\na spike-based algorithm where nodes in a graph are represented by single spike\ntimes of neuron populations and relations as spike time differences between\npopulations. Learning such spike-based embeddings only requires knowledge about\nspike times and spike time differences, compatible with recently proposed\nframeworks for training spiking neural networks. The presented model is easily\nmapped to current neuromorphic hardware systems and thereby moves inference on\nknowledge graphs into a domain where these architectures thrive, unlocking a\npromising industrial application area for this technology.\n"}
+{"abstract": "  Mutations sometimes increase contagiousness for evolving pathogens. During an\nepidemic, scientists use viral genome data to infer a shared evolutionary\nhistory and connect this history to geographic spread. We propose a model that\ndirectly relates a pathogen's evolution to its spatial contagion dynamics --\neffectively combining the two epidemiological paradigms of phylogenetic\ninference and self-exciting process modeling -- and apply this\n\\emph{phylogenetic Hawkes process} to a Bayesian analysis of 23,422 viral cases\nfrom the 2014-2016 Ebola outbreak in West Africa. The proposed model is able to\ndetect individual viruses with significantly elevated rates of spatiotemporal\npropagation for a subset of 1,610 samples that provide genome data. Finally, to\nfacilitate model application in big data settings, we develop massively\nparallel implementations for the gradient and Hessian of the log-likelihood and\napply our high performance computing framework within an adaptively\npreconditioned Hamiltonian Monte Carlo routine.\n"}
+{"abstract": "  Understanding the interaction of massive black hole binaries with their\ngaseous environment is crucial since at sub-parsec scales the binary is too\nwide for gravitational wave emission to take over and to drive the two black\nholes to merge. We here investigate the interaction between a massive black\nhole binary and a self-gravitating circumbinary disc using 3D smoothed particle\nhydrodynamics simulations. We find that, when the disc self-gravity regulates\nthe angular momentum transport, the binary semi-major axis decreases regardless\nthe choice of disc masses and temperatures, within the range we explored. In\nparticular, we find that the disc initial temperature (hence the disc aspect\nratio) has little effect on the evolution of the binary since discs with the\nsame mass self-regulate towards the same temperature. Initially warmer discs\ncause the binary to shrink on a slightly shorter timescale until the disc has\nreached the self-regulated equilibrium temperature. More massive discs drive\nthe binary semi-major axis to decrease at a faster pace compared to less\nmassive discs and result in faster binary eccentricity growth even after the\ninitial-condition-dependent transient evolution. Finally we investigate the\neffect that the initial cavity size has on the binary-disc interaction and we\nfind that, in the self-gravitating regime, an initially smaller cavity leads to\na much faster binary shrinking, as expected. Our results are especially\nimportant for very massive black hole binaries such as those in the PTA band,\nfor which gas self gravity cannot be neglected.\n"}
+{"abstract": "  When wave scattering systems are subject to certain symmetries, resonant\nstates may decouple from the far-field continuum; they remain localized to the\nstructure and cannot be excited by incident waves from the far field. In this\nwork, we use layer-potential techniques to prove the existence of such states,\nknown as bound states in the continuum, in systems of subwavelength resonators.\nWhen the symmetry is slightly broken, this resonant state can be excited from\nthe far field. Remarkably, this may create asymmetric (Fano-type) scattering\nbehaviour where the transmission is fundamentally different for frequencies on\neither side of the resonant frequency. Using asymptotic analysis, we compute\nthe scattering matrix of the system explicitly, thereby characterizing this\nFano-type transmission anomaly.\n"}
+{"abstract": "  We study the problem of regularization of inverse problems adopting a purely\ndata driven approach, by using the similarity to the method of regularization\nby projection. We provide an application of a projection algorithm, utilized\nand applied in frames theory, as a data driven reconstruction procedure in\ninverse problems, generalizing the algorithm proposed by the authors in Inverse\nProblems 36 (2020), n. 12, 125009, based on an orthonormalization procedure for\nthe training pairs. We show some numerical experiments, comparing the different\nmethods.\n"}
+{"abstract": "  We enumerate the number of staircase diagrams over classically finite\n$E$-type Dynkin diagrams, extending the work of Richmond and Slofstra\n(Staircase Diagrams and Enumeration of smooth Schubert varieties) and\ncompleting the enumeration of staircase diagrams over finite type Dynkin\ndiagrams. The staircase diagrams are in bijection to smooth and rationally\nsmooth Schubert varieties over $E$-type thereby giving an enumeration of these\nvarieties.\n"}
+{"abstract": "  Machine learning, artificial intelligence, and deep learning have advanced\nsignificantly over the past decade. Nonetheless, humans possess unique\nabilities such as creativity, intuition, context and abstraction, analytic\nproblem solving, and detecting unusual events. To successfully tackle pressing\nscientific and societal challenges, we need the complementary capabilities of\nboth humans and machines. The Federal Government could accelerate its\npriorities on multiple fronts through judicious integration of citizen science\nand crowdsourcing with artificial intelligence (AI), Internet of Things (IoT),\nand cloud strategies.\n"}
+{"abstract": "  The affinoid envelope, $\\widehat{U(\\mathcal{L})}$ of a free, finitely\ngenerated $\\mathbb{Z}_p$-Lie algebra $\\mathcal{L}$ has proven to be useful\nwithin the representation theory of compact $p$-adic Lie groups. Our aim is to\nfurther understand the algebraic structure of $\\widehat{U(\\mathcal{L})}$, and\nto this end, we will define a Dixmier module over $\\widehat{U(\\mathcal{L})}$,\nand prove that this object is generally irreducible in case where $\\mathcal{L}$\nis nilpotent. Ultimately, we will prove that all primitive ideals in the\naffinoid envelope can be described in terms of the annihilators of Dixmier\nmodules, and using this, we aim towards proving that these algebras satisfy a\nversion of the classical Dixmier-Moeglin equivalence.\n"}
+{"abstract": "  We theoretically investigate electron-hole recollisions in high-harmonic\ngeneration (HHG) in band-gap solids irradiated by linearly and elliptically\npolarized drivers. We find that in many cases the emitted harmonics do not\noriginate in electron-hole pairs created at the minimum band gap, where the\ntunneling probability is maximized, but rather in pairs created across an\nextended region of the Brillouin zone (BZ). In these situations, the analogy to\ngas-phase HHG in terms of the short- and long-trajectory categorizations is\ninadequate. Our analysis methodology comprises three complementary levels of\ntheory: the numerical solutions to the semiconductor Bloch equations, an\nextended semiclassical recollision model, and a quantum wave packet approach.\nWe apply this methodology to two general material types with representative\nband structures: a bulk system and a hexagonal monolayer system. In the bulk,\nthe interband harmonics generated using elliptically-polarized drivers are\nfound to originate not from tunneling at the minimum band gap $\\Gamma$, but\nfrom regions away from it. In the monolayer system driven by linearly-polarized\npulses, tunneling regions near different symmetry points in the BZ lead to\ndistinct harmonic energies and emission profiles. We show that the imperfect\nrecollisions, where an electron-hole pair recollide while being spatially\nseparated, are important in both bulk and monolayer materials. The excellent\nagreement between our three levels of theory highlights and characterizes the\ncomplexity behind the HHG emission dynamics in solids, and expands on the\nnotion of interband HHG as always originating in trajectories tunnelled at the\nminimum band gap. Our work furthers the fundamental understanding of HHG in\nperiodic systems and will benefit the future design of experiments.\n"}
+{"abstract": "  Within the transport model evaluation project (TMEP) of simulations for\nheavy-ion collisions, the mean-field response is examined here. Specifically,\nzero-sound propagation is considered for neutron-proton symmetric matter\nenclosed in a periodic box, at zero temperature and around normal density. The\nresults of several transport codes belonging to two families (BUU-like and\nQMD-like) are compared among each other and to exact calculations. For BUU-like\ncodes, employing the test particle method, the results depend on the\ncombination of the number of test particles and the spread of the profile\nfunctions that weight integration over space. These parameters can be properly\nadapted to give a good reproduction of the analytical zero-sound features.\nQMD-like codes, using molecular dynamics methods, are characterized by large\ndamping effects, attributable to the fluctuations inherent in their phase-space\nrepresentation. Moreover, for a given nuclear effective interaction, they\ngenerally lead to slower density oscillations, as compared to BUU-like codes.\nThe latter problem is mitigated in the more recent lattice formulation of some\nof the QMD codes. The significance of these results for the description of real\nheavy-ion collisions is discussed.\n"}
+{"abstract": "  In this paper, we present a physics-constrained deep neural network (PCDNN)\nmethod for parameter estimation in the zero-dimensional (0D) model of the\nvanadium redox flow battery (VRFB). In this approach, we use deep neural\nnetworks (DNNs) to approximate the model parameters as functions of the\noperating conditions. This method allows the integration of the VRFB\ncomputational models as the physical constraints in the parameter learning\nprocess, leading to enhanced accuracy of parameter estimation and cell voltage\nprediction. Using an experimental dataset, we demonstrate that the PCDNN method\ncan estimate model parameters for a range of operating conditions and improve\nthe 0D model prediction of voltage compared to the 0D model prediction with\nconstant operation-condition-independent parameters estimated with traditional\ninverse methods. We also demonstrate that the PCDNN approach has an improved\ngeneralization ability for estimating parameter values for operating conditions\nnot used in the DNN training.\n"}
+{"abstract": "  Billiards in ellipses have a confocal ellipse or hyperbola as caustic. The\ngoal of this paper is to prove that for each billiard of one type there exists\nan isometric counterpart of the other type. Isometry means here that the\nlengths of corresponding sides are equal. The transition between these two\nisometric billiard can be carried out continuosly via isometric focal billiards\nin a fixed ellipsoid. The extended sides of these particular billiards in an\nellipsoid are focal axes, i.e., generators of confocal hyperboloids. This\ntransition enables to transfer properties of planar billiards to focal\nbilliards, in particular billiard motions and canonical parametrizations. A\nperiodic planar billiard and its associated Poncelet grid give rise to periodic\nfocal billiards and spatial Poncelet grids. If the sides of a focal billiard\nare materialized as thin rods with spherical joints at the vertices and other\ncrossing points between different sides, then we obtain Henrici's hyperboloid,\nwhich is flexible between the two planar limits.\n"}
+{"abstract": "  A search for laser light from Proxima Centauri was performed, including 107\nhigh-resolution, optical spectra obtained between 2004 and 2019. Among them, 57\nspectra contain multiple, confined spectral combs, each consisting of 10\nclosely-spaced frequencies of light. The spectral combs, as entities, are\nthemselves equally spaced with a frequency separation of 5800 GHz, rendering\nthem unambiguously technological in origin. However, the combs do not originate\nat Proxima Centauri. Otherwise, the 107 spectra of Proxima Centauri show no\nevidence of technological signals, including 29 observations between March and\nJuly 2019 when the candidate technological radio signal, BLC1, was captured by\nBreakthrough Listen. This search would have revealed lasers pointed toward\nEarth having a power of 20 to 120 kilowatts and located within the 1.3au field\nof view centered on Proxima Centauri, assuming a benchmark laser launcher\nhaving a 10-meter aperture.\n"}
+{"abstract": "  Agent-based modelling is a powerful tool when simulating human systems, yet\nwhen human behaviour cannot be described by simple rules or maximising one's\nown profit, we quickly reach the limits of this methodology. Machine learning\nhas the potential to bridge this gap by providing a link between what people\nobserve and how they act in order to reach their goal. In this paper we use a\nframework for agent-based modelling that utilizes human values like fairness,\nconformity and altruism. Using this framework we simulate a public goods game\nand compare to experimental results. We can report good agreement between\nsimulation and experiment and furthermore find that the presented framework\noutperforms strict reinforcement learning. Both the framework and the utility\nfunction are generic enough that they can be used for arbitrary systems, which\nmakes this method a promising candidate for a foundation of a universal\nagent-based model.\n"}
+{"abstract": "  Drug combination therapy has become a increasingly promising method in the\ntreatment of cancer. However, the number of possible drug combinations is so\nhuge that it is hard to screen synergistic drug combinations through wet-lab\nexperiments. Therefore, computational screening has become an important way to\nprioritize drug combinations. Graph neural network have recently shown\nremarkable performance in the prediction of compound-protein interactions, but\nit has not been applied to the screening of drug combinations. In this paper,\nwe proposed a deep learning model based on graph neural networks and attention\nmechanism to identify drug combinations that can effectively inhibit the\nviability of specific cancer cells. The feature embeddings of drug molecule\nstructure and gene expression profiles were taken as input to multi-layer\nfeedforward neural network to identify the synergistic drug combinations. We\ncompared DeepDDS with classical machine learning methods and other deep\nlearning-based methods on benchmark data set, and the leave-one-out\nexperimental results showed that DeepDDS achieved better performance than\ncompetitive methods. Also, on an independent test set released by well-known\npharmaceutical enterprise AstraZeneca, DeepDDS was superior to competitive\nmethods by more than 16\\% predictive precision. Furthermore, we explored the\ninterpretability of the graph attention network, and found the correlation\nmatrix of atomic features revealed important chemical substructures of drugs.\nWe believed that DeepDDS is an effective tool that prioritized synergistic drug\ncombinations for further wet-lab experiment validation.\n"}
+{"abstract": "  Bell inequality can provide a useful witness for device-independent\napplications with quantum (or post-quantum) eavesdroppers. This feature holds\nonly for single entangled systems. Our goal is to explore device-independent\nmodel for quantum networks. We firstly propose a Bell inequality to verify the\ngenuinely multipartite nonlocality of connected quantum networks including\ncyclic networks and universal quantum computational resources for\nmeasurement-based computation model. This is further used to construct new\nmonogamy relation in a fully device-independent model with multisource quantum\nresources. It is finally applied for multiparty quantum key distribution, blind\nquantum computation, and quantum secret sharing. The present model can inspire\nvarious large-scale applications on quantum networks in a device-independent\nmanner.\n"}
+{"abstract": "  In this paper the authors study quotients of the product of elliptic curves\nby a rigid diagonal action of a finite group $G$. It is shown that only for $G\n= \\operatorname{He(3)}, \\mathbb Z_3^2$, and only for dimension $\\geq 4$ such an\naction can be free. A complete classification of the singular quotients in\ndimension 3 and the smooth quotients in dimension $4$ is given. For the other\nfinite groups a strong structure theorem for rigid quotients is proven.\n"}
+{"abstract": "  Log-based cyber threat hunting has emerged as an important solution to\ncounter sophisticated cyber attacks. However, existing approaches require\nnon-trivial efforts of manual query construction and have overlooked the rich\nexternal knowledge about threat behaviors provided by open-source Cyber Threat\nIntelligence (OSCTI). To bridge the gap, we build ThreatRaptor, a system that\nfacilitates cyber threat hunting in computer systems using OSCTI. Built upon\nmature system auditing frameworks, ThreatRaptor provides (1) an unsupervised,\nlight-weight, and accurate NLP pipeline that extracts structured threat\nbehaviors from unstructured OSCTI text, (2) a concise and expressive\ndomain-specific query language, TBQL, to hunt for malicious system activities,\n(3) a query synthesis mechanism that automatically synthesizes a TBQL query\nfrom the extracted threat behaviors, and (4) an efficient query execution\nengine to search the big system audit logging data.\n"}
+{"abstract": "  We develop a theory for the susceptible-infected-susceptible (SIS) epidemic\nmodel on networks that incorporate both network structure and dynamic\ncorrelations. This theory can account for the multistage onset of the epidemic\nphase in scale-free networks. This phenomenon is characterized by multiple\npeaks in the susceptibility as a function of the infection rate. It can be\nexplained by that, even under the global epidemic threshold, a hub can sustain\nthe epidemics for an extended period. Moreover, our approach improves\ntheoretical calculations of prevalence close to the threshold in heterogeneous\nnetworks and also can predict the average risk of infection for neighbors of\nnodes with different degree and state on uncorrelated static networks.\n"}
+{"abstract": "  This paper describes a search for galaxy centers with clear indications of\nunusual stellar populations with an initial mass function flatter than Salpeter\nat high stellar masses. Out of a sample of 668 face-on galaxies with stellar\nmasses in the range 10^10- 10^11 M_sol, I identify 15 galaxies with young to\nintermediate age central stellar populations with unusual stellar population\ngradients in the inner regions of the galaxy. In these galaxies, the 4000\nAngstrom break is either flat or rising towards the center of the galaxy,\nindicating that the central regions host evolved stars, but the H$\\alpha$\nequivalent width also rises steeply in the central regions. The ionization\nparameter [OIII]/[OII] is typically low in these galactic centers, indicating\nthat ionizing sources are stellar rather than AGN. Wolf Rayet features\ncharacteristic of hot young stars are often found in the spectra and these also\nget progressively stronger at smaller galactocentric radii. These outliers are\ncompared to a control sample of galaxies of similar mass with young inner\nstellar populations, but where the gradients in Halpha equivalent width and\n4000 Angstrom break follow each other more closely. The outliers exhibit\ncentral Wolf Rayet red bump excesses much more frequently, they have higher\ncentral stellar and ionized gas metallicities, and they are also more\nfrequently detected at 20 cm radio wavelengths. I highlight one outlier where\nthe ionized gas is clearly being strongly perturbed and blown out either by\nmassive stars after they explode as supernovae, or by energy injection from\nmatter falling onto a black hole.\n"}
+{"abstract": "  We consider the problem of operator-valued kernel learning and investigate\nthe possibility of going beyond the well-known separable kernels. Borrowing\ntools and concepts from the field of quantum computing, such as partial trace\nand entanglement, we propose a new view on operator-valued kernels and define a\ngeneral family of kernels that encompasses previously known operator-valued\nkernels, including separable and transformable kernels. Within this framework,\nwe introduce another novel class of operator-valued kernels called entangled\nkernels that are not separable. We propose an efficient two-step algorithm for\nthis framework, where the entangled kernel is learned based on a novel\nextension of kernel alignment to operator-valued kernels. We illustrate our\nalgorithm with an application to supervised dimensionality reduction, and\ndemonstrate its effectiveness with both artificial and real data for\nmulti-output regression.\n"}
+{"abstract": "  Turbulence has the potential for creating gas density enhancements that\ninitiate cloud and star formation (SF), and it can be generated locally by SF.\nTo study the connection between turbulence and SF, we looked for relationships\nbetween SF traced by FUV images, and gas turbulence traced by kinetic energy\ndensity (KED) and velocity dispersion ($v_{disp}$) in the LITTLE THINGS sample\nof nearby dIrr galaxies. We performed 2D cross-correlations between FUV and KED\nimages, measured cross-correlations in annuli to produce correlation\ncoefficients as a function of radius, and determined the cumulative\ndistribution function of the cross correlation value. We also plotted on a\npixel-by-pixel basis the locally excess KED, $v_{disp}$, and HI mass surface\ndensity, $\\Sigma_{\\rm HI}$, as determined from the respective values with the\nradial profiles subtracted, versus the excess SF rate density $\\Sigma_{\\rm\nSFR}$, for all regions with positive excess $\\Sigma_{\\rm SFR}$. We found that\n$\\Sigma_{\\rm SFR}$ and KED are poorly correlated. The excess KED associated\nwith SF implies a $\\sim0.5$% efficiency for supernova energy to pump local HI\nturbulence on the scale of resolution here, which is a factor of $\\sim2$ too\nsmall for all of the turbulence on a galactic scale. The excess $v_{disp}$ in\nSF regions is also small, only $\\sim0.37$ km s$^{-1}$. The local excess in\n$\\Sigma_{\\rm HI}$ corresponding to an excess in $\\Sigma_{\\rm SFR}$ is\nconsistent with an HI consumption time of $\\sim1.6$ Gyr in the inner parts of\nthe galaxies. The similarity between this timescale and the consumption time\nfor CO implies that CO-dark molecular gas has comparable mass to HI in the\ninner disks.\n"}
+{"abstract": "  Through CaH2 chemical reduction of a parent R3+Ni3+O3 perovskite form,\nsuperconductivity was recently achieved in Sr-doped NdNiO2 on SrTiO3 substrate.\nUsing density functional theory (DFT) calculations, we find that stoichiometric\nNdNiO2 is significantly unstable with respect to decomposition into 1/2[Nd2O3 +\nNiO + Ni] with exothermic decomposition energy of +176 meV/atom, a considerably\nhigher instability than that for common ternary oxides. This poses the question\nif the stoichiometric NdNiO2 nickelate compound used extensively to model the\nelectronic band structure of Ni-based oxide analog to cuprates and found to be\nmetallic is the right model for this purpose. To examine this, we study via DFT\nthe role of the common H impurity expected to be present in the process of\nchemical reduction needed to obtain NdNiO2. We find that H can be incorporated\nexothermically, i.e., spontaneously in NdNiO2, even from H2 gas. In the\nconcentrated limit, such impurities can result in the formation of a hydride\ncompound NdNiO2H, which has significantly reduced instability relative to\nhydrogen-free NdNiO2. Interestingly, the hydrogenated form has a similar\nlattice constant as the pure form (leading to comparable XRD patterns), but\nunlike the metallic character of NdNiO2, the hydrogenated form is predicted to\nbe a wide gap insulator thus, requiring doping to create a metallic or\nsuperconducting state, just like cuprates, but unlike unhydrogenated\nnickelates. While it is possible that hydrogen would be eventually desorbed,\nthe calculation suggests that pristine NdNiO2 is hydrogen-stabilized. One must\nexercise caution with theories predicting new physics in pristine\nstoichiometric NdNiO2 as it might be an unrealizable compound. Experimental\nexamination of the composition of real NdNiO2 superconductors and the effect of\nhydrogen on the superconductivity is called for.\n"}
+{"abstract": "  The dynamics of water molecules plays a vital role in understanding water. We\ncombined computer simulation and deep learning to study the dynamics of H-bonds\nbetween water molecules. Based on ab initio molecular dynamics simulations and\na newly defined directed Hydrogen (H-) bond population operator, we studied a\ntypical dynamic process in bulk water: interchange, in which the H-bond donor\nreverses roles with the acceptor. By designing a recurrent neural network-based\nmodel, we have successfully classified the interchange and breakage processes\nin water. We have found that the ratio between them is approximately 1:4, and\nit hardly depends on temperatures from 280 to 360 K. This work implies that\ndeep learning has the great potential to help distinguish complex dynamic\nprocesses containing H-bonds in other systems.\n"}
+{"abstract": "  Understanding and simulating how a quantum system interacts and exchanges\ninformation or energy with its surroundings is a ubiquitous problem, one which\nmust be carefully addressed in order to establish a coherent framework to\ndescribe the dynamics and thermodynamics of quantum systems. Significant effort\nhas been invested in developing various methods for tackling this issue and in\nthis Perspective we focus on one such technique, namely collision models, which\nhave emerged as a remarkably flexible approach. We discuss their application to\nunderstanding non-Markovian dynamics and to studying the thermodynamics of\nquantum systems, two areas in which collision models have proven to be\nparticularly insightful. Their simple structure endows them with extremely\nbroad applicability which has spurred their recent experimental demonstrations.\nBy focusing on these areas, our aim is to provide a succinct entry point to\nthis remarkable framework.\n"}
+{"abstract": "  Nucleons (protons and neutrons) are the building blocks of atomic nuclei, and\nare responsible for more than 99\\% of the visible matter in the universe.\nDespite decades of efforts in studying its internal structure, there are still\na number of puzzles surrounding the proton such as its spin, and charge radius.\nAccurate knowledge about the proton charge radius is not only essential for\nunderstanding how quantum chromodynamics (QCD) works in the non-perturbative\nregion, but also important for bound state quantum electrodynamics (QED)\ncalculations of atomic energy levels. It also has an impact on the Rydberg\nconstant, one of the most precisely measured fundamental constants in nature.\nThis article reviews the latest situation concerning the proton charge radius\nin light of the new experimental results from both atomic hydrogen spectroscopy\nand electron scattering measurements, with particular focus on the latter. We\nalso present the related theoretical developments and backgrounds concerning\nthe determination of the proton charge radius using different experimental\ntechniques. We discuss upcoming experiments, and briefly mention the deuteron\ncharge radius puzzle at the end.\n"}
+{"abstract": "  Topological properties of the jacobian curve ${\\mathcal\nJ}_{\\mathcal{F},\\mathcal{G}}$ of two foliations $\\mathcal{F}$ and $\\mathcal{G}$\nare described in terms of invariants associated to the foliations. The main\nresult gives a decomposition of the jacobian curve ${\\mathcal\nJ}_{\\mathcal{F},\\mathcal{G}}$ which depends on how similar are the foliations\n$\\mathcal{F}$ and $\\mathcal{G}$. The similarity between foliations is codified\nin terms of the Camacho-Sad indices of the foliations with the notion of\ncollinear point or divisor. Our approach allows to recover the results\nconcerning the factorization of the jacobian curve of two plane curves and of\nthe polar curve of a curve or a foliation.\n"}
+{"abstract": "  Relativistic jets and disc-winds are typically observed in BH-XRBs and AGNs.\nHowever, many physical details of jet launching and the driving of disc winds\nfrom the underlying accretion disc are still not fully understood. In this\nstudy, we further investigate the role of the magnetic field strength and\nstructure in launching jets and disc winds. In particular, we explore the\nconnection between jet, wind, and the accretion disc around the central black\nhole. We perform axisymmetric GRMHD simulations of the accretion-ejection\nsystem using adaptive mesh refinement. Essentially, our simulations are\ninitiated with a thin accretion disc in equilibrium. An extensive parametric\nstudy by choosing different combinations of magnetic field strength and initial\nmagnetic field inclination is also performed. Our study finds relativistic jets\ndriven by the Blandford \\& Znajek (BZ) mechanism and the disc-wind driven by\nthe Blandford \\& Payne (BP) mechanism. We also find that plasmoids are formed\ndue to the reconnection events, and these plasmoids advect with disc-winds. As\na result, the tension force due to the poloidal magnetic field is enhanced in\nthe inner part of the accretion disc, resulting in disc truncation and\noscillation. These oscillations result in flaring activities in the jet mass\nflow rates. We find simulation runs with a lower value of the plasma-$\\beta$,\nand lower inclination angle parameters are more prone to the formation of\nplasmoids and subsequent inner disc oscillations. Our models provide a possible\ntemplate to understand spectral state transition phenomena in BH-XRBs.\n"}
+{"abstract": "  Space-borne optical frequency references based on spectroscopy of atomic\nvapors may serve as an integral part of compact optical atomic clocks, which\ncan advance global navigation systems, or can be utilized for earth observation\nmissions as part of laser systems for cold atom gradiometers. Nanosatellites\noffer low launch-costs, multiple deployment opportunities and short payload\ndevelopment cycles, enabling rapid maturation of optical frequency references\nand underlying key technologies in space. Towards an in-orbit demonstration on\nsuch a platform, we have developed a CubeSat-compatible prototype of an optical\nfrequency reference based on the D2-transition in rubidium. A frequency\ninstability of 1.7e-12 at 1 s averaging time is achieved. The optical module\noccupies a volume of 35 cm^3, weighs 73 g and consumes 780 mW of power.\n"}
+{"abstract": "  At the heart of all automated driving systems is the ability to sense the\nsurroundings, e.g., through semantic segmentation of LiDAR sequences, which\nexperienced a remarkable progress due to the release of large datasets such as\nSemanticKITTI and nuScenes-LidarSeg. While most previous works focus on sparse\nsegmentation of the LiDAR input, dense output masks provide self-driving cars\nwith almost complete environment information. In this paper, we introduce MASS\n- a Multi-Attentional Semantic Segmentation model specifically built for dense\ntop-view understanding of the driving scenes. Our framework operates on pillar-\nand occupancy features and comprises three attention-based building blocks: (1)\na keypoint-driven graph attention, (2) an LSTM-based attention computed from a\nvector embedding of the spatial input, and (3) a pillar-based attention,\nresulting in a dense 360-degree segmentation mask. With extensive experiments\non both, SemanticKITTI and nuScenes-LidarSeg, we quantitatively demonstrate the\neffectiveness of our model, outperforming the state of the art by 19.0% on\nSemanticKITTI and reaching 32.7% in mIoU on nuScenes-LidarSeg, where MASS is\nthe first work addressing the dense segmentation task. Furthermore, our\nmulti-attention model is shown to be very effective for 3D object detection\nvalidated on the KITTI-3D dataset, showcasing its high generalizability to\nother tasks related to 3D vision.\n"}
+{"abstract": "  Prediction tasks about students have practical significance for both student\nand college. Making multiple predictions about students is an important part of\na smart campus. For instance, predicting whether a student will fail to\ngraduate can alert the student affairs office to take predictive measures to\nhelp the student improve his/her academic performance. With the development of\ninformation technology in colleges, we can collect digital footprints which\nencode heterogeneous behaviors continuously. In this paper, we focus on\nmodeling heterogeneous behaviors and making multiple predictions together,\nsince some prediction tasks are related and learning the model for a specific\ntask may have the data sparsity problem. To this end, we propose a variant of\nLSTM and a soft-attention mechanism. The proposed LSTM is able to learn the\nstudent profile-aware representation from heterogeneous behavior sequences. The\nproposed soft-attention mechanism can dynamically learn different importance\ndegrees of different days for every student. In this way, heterogeneous\nbehaviors can be well modeled. In order to model interactions among multiple\nprediction tasks, we propose a co-attention mechanism based unit. With the help\nof the stacked units, we can explicitly control the knowledge transfer among\nmultiple tasks. We design three motivating behavior prediction tasks based on a\nreal-world dataset collected from a college. Qualitative and quantitative\nexperiments on the three prediction tasks have demonstrated the effectiveness\nof our model.\n"}
+{"abstract": "  We tackle the problem of predicting saliency maps for videos of dynamic\nscenes. We note that the accuracy of the maps reconstructed from the gaze data\nof a fixed number of observers varies with the frame, as it depends on the\ncontent of the scene. This issue is particularly pressing when a limited number\nof observers are available. In such cases, directly minimizing the discrepancy\nbetween the predicted and measured saliency maps, as traditional deep-learning\nmethods do, results in overfitting to the noisy data. We propose a noise-aware\ntraining (NAT) paradigm that quantifies and accounts for the uncertainty\narising from frame-specific gaze data inaccuracy. We show that NAT is\nespecially advantageous when limited training data is available, with\nexperiments across different models, loss functions, and datasets. We also\nintroduce a video game-based saliency dataset, with rich temporal semantics,\nand multiple gaze attractors per frame. The dataset and source code are\navailable at https://github.com/NVlabs/NAT-saliency.\n"}
+{"abstract": "  In this work, we present a comparative analysis of the trajectories estimated\nfrom various Simultaneous Localization and Mapping (SLAM) systems in a\nsimulation environment for vineyards. Vineyard environment is challenging for\nSLAM methods, due to visual appearance changes over time, uneven terrain, and\nrepeated visual patterns. For this reason, we created a simulation environment\nspecifically for vineyards to help studying SLAM systems in such a challenging\nenvironment. We evaluated the following SLAM systems: LIO-SAM, StaticMapping,\nORB-SLAM2, and RTAB-MAP in four different scenarios. The mobile robot used in\nthis study equipped with 2D and 3D lidars, IMU, and RGB-D camera (Kinect v2).\nThe results show good and encouraging performance of RTAB-MAP in such an\nenvironment.\n"}
+{"abstract": "  We present a study of the wrinkling modes, localized in the plane of single-\nand few-layer graphene sheets embedded in or placed on a compliant\ncompressively strained matrix. We provide the analytical model based on\nnonlinear elasticity of the graphene sheet, which shows that the compressive\nsurface stress results in spatial localization of the extended sinusoidal\nwrinkling mode with soliton-like envelope with localization length, decreasing\nwith the overcritical external strain. The parameters of the extended\nsinusoidal wrinkling modes are found from the conditions of anomalous softening\nof flexural surface acoustic wave propagating along the graphene sheet in or on\nthe matrix. For relatively small overcritical external strain, the continuous\ntransition occurs from the sinusoidal wrinkling modes with soliton-like\nenvelope to the strongly localized modes with approximately one-period\nsinusoidal profiles and amplitude- and external-strain-independent spatial\nwidths. Two types of graphene wrinkling modes with different symmetry are\ndescribed, when the in-plane antisymmetric or symmetric modes are presumably\nrealized in the graphene sheet embedded in or placed on a compliant strained\nmatrix. Strongly localized wrinkling modes can be realized without delamination\nof the graphene sheet from the compliant matrix and are not equivalent to the\nripplocations in layered solids. Molecular-dynamics modeling confirms the\nappearance of sinusoidal wrinkling modes in single- and few-layer graphene\nsheets embedded in polyethylene matrix at T = 300K.\n"}
+{"abstract": "  A new method is proposed for human motion predition by learning temporal and\nspatial dependencies in an end-to-end deep neural network. The joint\nconnectivity is explicitly modeled using a novel autoregressive structured\nprediction representation based on flow-based generative models. We learn a\nlatent space of complex body poses in consecutive frames which is conditioned\non the high-dimensional structure input sequence. To construct each latent\nvariable, the general and local smoothness of the joint positions are\nconsidered in a generative process using conditional normalizing flows. As a\nresult, all frame-level and joint-level continuities in the sequence are\npreserved in the model. This enables us to parameterize the inter-frame and\nintra-frame relationships and joint connectivity for robust long-term\npredictions as well as short-term prediction. Our experiments on two\nchallenging benchmark datasets of Human3.6M and AMASS demonstrate that our\nproposed method is able to effectively model the sequence information for\nmotion prediction and outperform other techniques in 42 of the 48 total\nexperiment scenarios to set a new state-of-the-art.\n"}
+{"abstract": "  We consider bootstrap percolation and diffusion in sparse random graphs with\nfixed degrees, constructed by configuration model. Every node has two states:\nit is either active or inactive. We assume that to each node is assigned a\nnonnegative (integer) threshold. The diffusion process is initiated by a subset\nof nodes with threshold zero which consists of initially activated nodes,\nwhereas every other node is inactive. Subsequently, in each round, if an\ninactive node with threshold $\\theta$ has at least $\\theta$ of its neighbours\nactivated, then it also becomes active and remains so forever. This is repeated\nuntil no more nodes become activated. The main result of this paper provides a\ncentral limit theorem for the final size of activated nodes. Namely, under\nsuitable assumptions on the degree and threshold distributions, we show that\nthe final size of activated nodes has asymptotically Gaussian fluctuations.\n"}
+{"abstract": "  A novel class of methods for combining $p$-values to perform aggregate\nhypothesis tests has emerged that exploit the properties of heavy-tailed Stable\ndistributions. These methods offer important practical advantages including\nrobustness to dependence and better-than-Bonferroni scaleability, and they\nreveal theoretical connections between Bayesian and classical hypothesis tests.\nThe harmonic mean $p$-value (HMP) procedure is based on the convergence of\nsummed inverse $p$-values to the Landau distribution, while the Cauchy\ncombination test (CCT) is based on the self-similarity of summed\nCauchy-transformed $p$-values. The CCT has the advantage that it is analytic\nand exact. The HMP has the advantage that it emulates a model-averaged Bayes\nfactor, is insensitive to $p$-values near 1, and offers multilevel testing via\na closed testing procedure. Here I investigate whether other Stable combination\ntests can combine these benefits, and identify a new method, the L\\'evy\ncombination test (LCT). The LCT exploits the self-similarity of sums of L\\'evy\nrandom variables transformed from $p$-values. Under arbitrary dependence, the\nLCT possesses better robustness than the CCT and HMP, with two-fold worst-case\ninflation at small significance thresholds. It controls the strong-sense\nfamilywise error rate through a multilevel test uniformly more powerful than\nBonferroni. Simulations show that the LCT behaves like Simes' test in some\nrespects, with power intermediate between the HMP and Bonferroni. The LCT\nrepresents an interesting and attractive addition to combined testing methods\nbased on heavy-tailed distributions.\n"}
+{"abstract": "  We shall present with examples how analysis of astronomy data can be used for\nan educational purpose to train students in methods of data analysis,\nstatistics, programming skills and research problems. Special reference will be\nmade to our IAU-OAD project `Astronomy from Archival Data' where we are in the\nprocess of building a repository of instructional videos and reading material\nfor undergraduate and postgraduate students. Virtual Observatory tools will\nalso be discussed and applied. As this is an ongoing project, by the time of\nthe conference we will have the projects and work done by students included in\nour presentation. The material produced can be freely used by the community.\n"}
+{"abstract": "  Maxwell's boundary conditions (MBCs) were long known insufficient to\ndetermine the optical responses of spatially dispersive medium. Supplementing\nMBCs with additional boundary conditions (ABCs) has become a normal yet\ncontroversial practice. Here the problem of ABCs is solved by analyzing some\nsubtle aspects of a physical surface. A generic theory is presented for\nhandling the interaction of light with the surfaces of an arbitrary medium and\napplied to study the traditional problem of exciton polaritons. We show that\nABCs can always be adjusted to fit the theory but they can by no means be\nconstrued as intrinsic surface characteristics, which are instead captured by a\n\\textit{surface response function} (SRF). Unlike any ABCs, a SRF describes\nessentially non-local boundary effects. Methods for experimentally extracting\nthe spatial profile of this function are proposed.\n"}
+{"abstract": "  These Monte Carlo studies describe the impact of higher order effects in both\nQCD and EW $t\\bar{t}W$ production. Both next-to-leading inclusive and multileg\nsetups are studied for $t\\bar{t}W$ QCD production.\n"}
+{"abstract": "  In this paper we show how rederive the Bogomolny's equations of generalized\nMaxwell-Chern-Simons-Higgs model presented in Ref. \\cite{Bazeia:2012ux} by\nusing BPS Lagrangian method. We also show that the other results\n(identification, potential terms, Gauss's law constraint) in there can be\nobtained rigorously with a particular form of the BPS Lagrangian density. In\nthis method, we find that the potential terms are the most general form that\ncould have the BPS vortex solutions. The Gauss's law constraint turns out to be\nthe Euler-Lagrange equations of the BPS Lagrangian density. We also find\nanother BPS vortex solutions by taking other identification between the neutral\nscalar field and the electric scalar potential field, $N=\\pm A_0$, which is\ndifferent by a relative sign to the identification in Ref.\n\\cite{Bazeia:2012ux}, $N=\\mp A_0$,. We find the BPS vortex solutions have\nnegative electric charge which are related to the corresponding BPS vortes\nsolutions in Ref. \\cite{Bazeia:2012ux} by tranforming the neutral scalar field\n$N\\to-N$. Other possible choice of BPS Lagrangian density might give different\nBogomolny's equations and the form of potential terms which will be discussed\nin another work.\n"}
+{"abstract": "  We introduce the magnon circular photogalvanic effect enabled by stimulated\nRaman scattering. This provides an all-optical pathway to the generation of\ndirected magnon currents with circularly polarized light in honeycomb\nantiferromagnetic insulators. The effect is the leading order contribution to\nmagnon photocurrent generation via optical fields. Control of the magnon\ncurrent by the polarization and angle of incidence of the laser is\ndemonstrated. Experimental detection by sizeable inverse spin Hall voltages in\nplatinum contacts is proposed.\n"}
+{"abstract": "  Let $F: T^{n} \\times I \\to T^{n}$ be a homotopy on a n-dimensional torus. The\nmain purpose of this paper is to present a formula for the one-parameter\nNielsen number $N(F)$ of $F$ in terms of induced homomorphism. If $L(F)$ is the\none-parameter Lefschetz class of $F$ then $L(F)$ is given by $L(F) = \\pm\nN(F)\\alpha,$ for some $\\alpha \\in H_{1}(\\pi_{1}(T^{n}),\\mathbb{Z}).$\n"}
+{"abstract": "  Neutrino non-standard interactions (NSI) can be constrained using coherent\nelastic neutrino-nucleus scattering. We discuss here two aspects in this\nrespect, namely the effects of (i) charged current NSI in neutrino production\nand (ii) CP-violating phases associated with neutral current NSI in neutrino\ndetection. Effects of CP-phases require the simultaneous presence of two\ndifferent flavor-changing neutral current NSI parameters. Applying these two\nscenarios to the COHERENT measurement, we derive limits on charged current NSI\nand find that more data is required to compete with the existing limits.\nRegarding CP-phases, we show how the limits on the NSI parameters depend\ndramatically on the values of the phases. Accidentally, the same parameters\ninfluencing coherent scattering also show up in neutrino oscillation\nexperiments. We find that COHERENT provides complementary constraints on the\nset of NSI parameters that can explain the discrepancy in the best-fit value of\nthe standard CP-phase obtained by T2K and NO$\\nu$A, while the significance with\nwhich the LMA-Dark solution is ruled out can be weakened by the presence of\nadditional NSI parameters introduced here.\n"}
+{"abstract": "  Scientific digital libraries play a critical role in the development and\ndissemination of scientific literature. Despite dedicated search engines,\nretrieving relevant publications from the ever-growing body of scientific\nliterature remains challenging and time-consuming. Indexing scientific articles\nis indeed a difficult matter, and current models solely rely on a small portion\nof the articles (title and abstract) and on author-assigned keyphrases when\navailable. This results in a frustratingly limited access to scientific\nknowledge. The goal of the DELICES project is to address this pitfall by\nexploiting semantic relations between scientific articles to both improve and\nenrich indexing. To this end, we will rely on the latest advances in semantic\nrepresentations to both increase the relevance of keyphrases extracted from the\ndocuments, and extend indexing to new terms borrowed from semantically similar\ndocuments.\n"}
+{"abstract": "  We give a brief account of the history of neutrino, and how that most aloof\nof all particles has shaped our search for a theory of fundamental interactions\never since it was theoretically proposed. We introduce the necessary concepts\nand phenomena in a non-technical language aimed at a physicist with some basic\nknowledge of quantum mechanics. In showing that neutrino mass could be the door\nto new physics beyond the Standard Model, we emphasize the need to frame the\nissue in the context of a complete theory, with testable predictions accessible\nto present and near future experiments. We argue in favor of the Minimal\nLeft-Right Symmetric theory as the strongest candidate for such theory,\nconnecting neutrino mass with parity breakdown in nature. This is the theory\nthat led originally to neutrino mass and the seesaw mechanism behind its\nsmallness, but even more important, the theory that sheds light on a\nfundamental question that touches us all: the symmetry between left and right.\n"}
+{"abstract": "  Contextuality and entanglement are valuable resources for quantum computing\nand quantum information. Bell inequalities are used to certify entanglement;\nthus, it is important to understand why and how they are violated. Quantum\nmechanics and behavioral sciences teach us that random variables measuring the\nsame content (the answer to the same Yes or No question) may vary, if measured\njointly with other random variables. Alice and Bob raw data confirm Einsteinian\nnon-signaling, but setting dependent experimental protocols are used to create\nsamples of coupled pairs of distant outcomes and to estimate correlations.\nMarginal expectations, estimated using these final samples, depend on distant\nsettings. Therefore, a system of random variables measured in Bell tests is\ninconsistently connected and it should be analyzed using a\nContextuality-by-Default approach, what is done for the first time in this\npaper. The violation of Bell inequalities and inconsistent connectedness may be\nexplained using a contextual locally causal probabilistic model in which\nsetting dependent variables describing measuring instruments are correctly\nincorporated. We prove that this model does not restrict experimenters freedom\nof choice which is a prerequisite of science. Contextuality seems to be the\nrule and not an exception; thus, it should be carefully tested.\n"}
+{"abstract": "  The agent-based Yard-Sale model of wealth inequality is generalized to\nincorporate exponential economic growth and its distribution. The distribution\nof economic growth is nonuniform and is determined by the wealth of each agent\nand a parameter $\\lambda$. Our numerical results indicate that the model has a\ncritical point at $\\lambda=1$ between a phase for $\\lambda < 1$ with economic\nmobility and exponentially growing wealth of all agents and a non-stationary\nphase for $\\lambda \\geq 1$ with wealth condensation and no mobility. We define\nthe energy of the system and show that the system can be considered to be in\nthermodynamic equilibrium for $\\lambda < 1$. Our estimates of various critical\nexponents are consistent with a mean-field theory (see following paper). The\nexponents do not obey the usual scaling laws unless a combination of parameters\nthat we refer to as the Ginzburg parameter is held fixed as the transition is\napproached. The model illustrates that both poorer and richer agents benefit\nfrom economic growth if its distribution does not favor the richer agents too\nstrongly. This work and the accompanying theory paper contribute to\nunderstanding whether the methods of equilibrium statistical mechanics can be\napplied to economic systems.\n"}
+{"abstract": "  We consider the (discrete) parabolic Anderson model $\\partial u(t,x)/\\partial\nt=\\Delta u(t,x) +\\xi_t(x) u(t,x)$, $t\\geq 0$, $x\\in \\mathbb{Z}^d$, where the\n$\\xi$-field is $\\mathbb{R}$-valued and plays the role of a dynamic random\nenvironment, and $\\Delta$ is the discrete Laplacian. We focus on the case in\nwhich $\\xi$ is given by a properly rescaled symmetric simple exclusion process\nunder which it converges to an Ornstein--Uhlenbeck process. Scaling the\nLaplacian diffusively and restricting ourselves to a torus, we show that in\ndimension $d=3$ upon considering a suitably renormalised version of the above\nequation, the sequence of solutions converges in law.\n  As a by-product of our main result we obtain precise asymptotics for the\nsurvival probability of a simple random walk that is killed at a scale\ndependent rate when meeting an exclusion particle. Our proof relies on the\ndiscrete theory of regularity structures of \\cite{ErhardHairerRegularity} and\non novel sharp estimates of joint cumulants of arbitrary large order for the\nexclusion process. We think that the latter is of independent interest and may\nfind applications elsewhere.\n"}
+{"abstract": "  We present an arithmetic circuit performing constant modular addition having\n$\\mathcal{O}(n)$ depth of Toffoli gates and using a total of $n+3$ qubits. This\nis an improvement by a factor of two compared to the width of the\nstate-of-the-art Toffoli-based constant modular adder. The advantage of our\nadder, compared to the ones operating in the Fourier-basis, is that it does not\nrequire small angle rotations and their Clifford+T decomposition. Our circuit\nuses a recursive adder combined with the modular addition scheme proposed by\nVedral et. al. The circuit is implemented and verified exhaustively with\nQUANTIFY, an open-sourced framework. We also report on the Clifford+T cost of\nthe circuit.\n"}
+{"abstract": "  The recently proposed end-to-end transformer detectors, such as DETR and\nDeformable DETR, have a cascade structure of stacking 6 decoder layers to\nupdate object queries iteratively, without which their performance degrades\nseriously. In this paper, we investigate that the random initialization of\nobject containers, which include object queries and reference points, is mainly\nresponsible for the requirement of multiple iterations. Based on our findings,\nwe propose Efficient DETR, a simple and efficient pipeline for end-to-end\nobject detection. By taking advantage of both dense detection and sparse set\ndetection, Efficient DETR leverages dense prior to initialize the object\ncontainers and brings the gap of the 1-decoder structure and 6-decoder\nstructure. Experiments conducted on MS COCO show that our method, with only 3\nencoder layers and 1 decoder layer, achieves competitive performance with\nstate-of-the-art object detection methods. Efficient DETR is also robust in\ncrowded scenes. It outperforms modern detectors on CrowdHuman dataset by a\nlarge margin.\n"}
+{"abstract": "  We introduce a class of $n$-dimensional (possibly inhomogeneous) spin-like\nlattice systems presenting modulated phases with possibly different textures.\nSuch systems can be parameterized according to the number of ground states, and\ncan be described by a phase-transition energy which we compute by means of\nvariational techniques. Degeneracies due to frustration are also discussed.\n"}
+{"abstract": "  We translate a closed text that is known in advance into a severely low\nresource language by leveraging massive source parallelism. In other words,\ngiven a text in 124 source languages, we translate it into a severely low\nresource language using only ~1,000 lines of low resource data without any\nexternal help. Firstly, we propose a systematic method to rank and choose\nsource languages that are close to the low resource language. We call the\nlinguistic definition of language family Family of Origin (FAMO), and we call\nthe empirical definition of higher-ranked languages using our metrics Family of\nChoice (FAMC). Secondly, we build an Iteratively Pretrained Multilingual\nOrder-preserving Lexiconized Transformer (IPML) to train on ~1,000 lines\n(~3.5%) of low resource data. To translate named entities correctly, we build a\nmassive lexicon table for 2,939 Bible named entities in 124 source languages,\nand include many that occur once and covers more than 66 severely low resource\nlanguages. Moreover, we also build a novel method of combining translations\nfrom different source languages into one. Using English as a hypothetical low\nresource language, we get a +23.9 BLEU increase over a multilingual baseline,\nand a +10.3 BLEU increase over our asymmetric baseline in the Bible dataset. We\nget a 42.8 BLEU score for Portuguese-English translation on the medical EMEA\ndataset. We also have good results for a real severely low resource Mayan\nlanguage, Eastern Pokomchi.\n"}
+{"abstract": "  Recent advance in deep offline reinforcement learning (RL) has made it\npossible to train strong robotic agents from offline datasets. However,\ndepending on the quality of the trained agents and the application being\nconsidered, it is often desirable to fine-tune such agents via further online\ninteractions. In this paper, we observe that state-action distribution shift\nmay lead to severe bootstrap error during fine-tuning, which destroys the good\ninitial policy obtained via offline RL. To address this issue, we first propose\na balanced replay scheme that prioritizes samples encountered online while also\nencouraging the use of near-on-policy samples from the offline dataset.\nFurthermore, we leverage multiple Q-functions trained pessimistically offline,\nthereby preventing overoptimism concerning unfamiliar actions at novel states\nduring the initial training phase. We show that the proposed method improves\nsample-efficiency and final performance of the fine-tuned robotic agents on\nvarious locomotion and manipulation tasks. Our code is available at:\nhttps://github.com/shlee94/Off2OnRL.\n"}
+{"abstract": "  In this paper, we present a new approach based on dynamic factor models\n(DFMs) to perform nowcasts for the percentage annual variation of the Mexican\nGlobal Economic Activity Indicator (IGAE in Spanish). The procedure consists of\nthe following steps: i) build a timely and correlated database by using\neconomic and financial time series and real-time variables such as social\nmobility and significant topics extracted by Google Trends; ii) estimate the\ncommon factors using the two-step methodology of Doz et al. (2011); iii) use\nthe common factors in univariate time-series models for test data; and iv)\naccording to the best results obtained in the previous step, combine the\nstatistically equal better nowcasts (Diebold-Mariano test) to generate the\ncurrent nowcasts. We obtain timely and accurate nowcasts for the IGAE,\nincluding those for the current phase of drastic drops in the economy related\nto COVID-19 sanitary measures. Additionally, the approach allows us to\ndisentangle the key variables in the DFM by estimating the confidence interval\nfor both the factor loadings and the factor estimates. This approach can be\nused in official statistics to obtain preliminary estimates for IGAE up to 50\ndays before the official results.\n"}
+{"abstract": "  Motivated by estimation of quantum noise models, we study the problem of\nlearning a Pauli channel, or more generally the Pauli error rates of an\narbitrary channel. By employing a novel reduction to the \"Population Recovery\"\nproblem, we give an extremely simple algorithm that learns the Pauli error\nrates of an $n$-qubit channel to precision $\\epsilon$ in $\\ell_\\infty$ using\njust $O(1/\\epsilon^2) \\log(n/\\epsilon)$ applications of the channel. This is\noptimal up to the logarithmic factors. Our algorithm uses only unentangled\nstate preparation and measurements, and the post-measurement classical runtime\nis just an $O(1/\\epsilon)$ factor larger than the measurement data size. It is\nalso impervious to a limited model of measurement noise where heralded\nmeasurement failures occur independently with probability $\\le 1/4$.\n  We then consider the case where the noise channel is close to the identity,\nmeaning that the no-error outcome occurs with probability $1-\\eta$. In the\nregime of small $\\eta$ we extend our algorithm to achieve multiplicative\nprecision $1 \\pm \\epsilon$ (i.e., additive precision $\\epsilon \\eta$) using\njust $O\\bigl(\\frac{1}{\\epsilon^2 \\eta}\\bigr) \\log(n/\\epsilon)$ applications of\nthe channel.\n"}
+{"abstract": "  We find that the Casimir pressure in peptide films deposited on metallic\nsubstrates is always repulsive which makes these films less stable. It is shown\nthat by adding a graphene sheet on top of peptide film one can change the sign\nof the Casimir pressure by making it attractive. For this purpose, the\nformalism of the Lifshitz theory is extended to the case when the film and\nsubstrate materials are described by the frequency-dependent dielectric\npermittivities, whereas the response of graphene to the electromagnetic field\nis governed by the polarization tensor in (2+1)-dimensional space-time found in\nthe framework of the Dirac model. Both pristine and gapped and doped graphene\nsheets are considered possessing some nonzero energy gap and chemical\npotential. According to our results, in all cases the presence of graphene\nsheet makes the Casimir pressure in peptide film deposited on a metallic\nsubstrate attractive starting from some minimum film thickness. The value of\nthis minimum thickness becomes smaller with increasing chemical potential and\nlarger with increasing energy gap and the fraction of water in peptide film.\nThe physical explanation for these results is provided, and their possible\napplications in organic electronics are discussed.\n"}
+{"abstract": "  Urban Air Mobility (UAM) has the potential to revolutionize transportation.\nIt will exploit the third dimension to help smooth ground traffic in densely\npopulated areas. To be successful, it will require an integrated approach able\nto balance efficiency and safety while harnessing common resources and\ninformation. In this work we focus on future urban air-taxi services, and\npresent the first methods and algorithms to efficiently operate air-taxi at\nscale. Our approach is twofold. First, we use a passenger-centric perspective\nwhich introduces traveling classes, and information sharing between transport\nmodes to differentiate quality of services. This helps smooth multimodal\njourneys and increase passenger satisfaction. Second, we provide a flight\nrouting and recharging solution which minimizes direct operational costs while\npreserving long term battery life through reduced energy-intense recharging.\nOur methods, which surpass the performance of a general state-of-the-art\ncommercial solver, are also used to gain meaningful insights on the design\nspace of the air-taxi problem, including solutions to hidden fairness issues.\n"}
+{"abstract": "  In this paper, we consider user selection and downlink precoding for an\nover-loaded single-cell massive multiple-input multiple-output (MIMO) system in\nfrequency division duplexing (FDD) mode, where the base station is equipped\nwith a dual-polarized uniform planar array (DP-UPA) and serves a large number\nof single-antenna users. Due to the absence of uplink-downlink channel\nreciprocity and the high-dimensionality of channel matrices, it is extremely\nchallenging to design downlink precoders using closed-loop channel probing and\nfeedback with limited spectrum resource. To address these issues, a novel\nmethodology -- active channel sparsification (ACS) -- has been proposed\nrecently in the literature for uniform linear array (ULA) to design sparsifying\nprecoders, which boosts spectral efficiency for multi-user downlink\ntransmission with substantially reduced channel feedback overhead. Pushing\nforward this line of research, we aim to facilitate the potential deployment of\nACS in practical FDD massive MIMO systems, by extending it from ULA to DP-UPA\nwith explicit user selection and making the current ACS implementation\nsimplified. To this end, by leveraging Toeplitz structure of channel covariance\nmatrices, we extend the original ACS using scale-weight bipartite graph\nrepresentation to the matrix-weight counterpart. Building upon this, we propose\na multi-dimensional ACS (MD-ACS) method, which is a generalization of original\nACS formulation and is more suitable for DP-UPA antenna configurations. The\nnonlinear integer program formulation of MD-ACS can be classified as a\ngeneralized multi-assignment problem (GMAP), for which we propose a simple yet\nefficient greedy algorithm to solve it. Simulation results demonstrate the\nperformance improvement of the proposed MD-ACS with greedy algorithm over the\nstate-of-the-art methods based on the QuaDRiGa channel models.\n"}
+{"abstract": "  The QCD$\\times$QED factorization is studied for two-body non-leptonic and\nsemi-leptonic $B$ decays with heavy-light final states. These non-leptonic\ndecays, like $\\bar{B}^0_{(s)}\\to D^+_{(s)} \\pi^-$ and $\\bar{B}_d^0 \\to D^+\nK^-$, are among the theoretically cleanest non-leptonic decays as penguin loops\ndo not contribute and colour-suppressed tree amplitudes are suppressed in the\nheavy-quark limit or even completely absent. Advancing the theoretical\ncalculations of such decays requires therefore also a careful analysis of QED\neffects. Including QED effects does not alter the general structure of\nfactorization which is analogous for both semi-leptonic and non-leptonic\ndecays. For the latter, we express our result as a correction of the tree\namplitude coefficient $a_1$. At the amplitude level, we find QED effects at the\nsub-percent level, which is of the same order as the QCD uncertainty. We\ndiscuss the phenomenological implications of adding QED effects in light of\ndiscrepancies observed between theory and experimental data, for ratios of\nnon-leptonic over semi-leptonic decay rates. At the level of the rate,\nultrasoft photon effects can produce a correction up to a few percent,\nrequiring a careful treatment of such effects in the experimental analyses.\n"}
+{"abstract": "  Accelerated multi-coil magnetic resonance imaging reconstruction has seen a\nsubstantial recent improvement combining compressed sensing with deep learning.\nHowever, most of these methods rely on estimates of the coil sensitivity\nprofiles, or on calibration data for estimating model parameters. Prior work\nhas shown that these methods degrade in performance when the quality of these\nestimators are poor or when the scan parameters differ from the training\nconditions. Here we introduce Deep J-Sense as a deep learning approach that\nbuilds on unrolled alternating minimization and increases robustness: our\nalgorithm refines both the magnetization (image) kernel and the coil\nsensitivity maps. Experimental results on a subset of the knee fastMRI dataset\nshow that this increases reconstruction performance and provides a significant\ndegree of robustness to varying acceleration factors and calibration region\nsizes.\n"}
+{"abstract": "  Learning from examples with noisy labels has attracted increasing attention\nrecently. But, this paper will show that the commonly used CIFAR-based datasets\nand the accuracy evaluation metric used in the literature are both\ninappropriate in this context. An alternative valid evaluation metric and new\ndatasets are proposed in this paper to promote proper research and evaluation\nin this area. Then, friends and foes are identified from existing methods as\ntechnical components that are either beneficial or detrimental to deep learning\nfrom noisy labeled examples, respectively, and this paper improves and combines\ntechnical components from the friends category, including self-supervised\nlearning, new warmup strategy, instance filtering and label correction. The\nresulting F&F method significantly outperforms existing methods on the proposed\nnCIFAR datasets and the real-world Clothing1M dataset.\n"}
+{"abstract": "  Signatures of superconductivity at elevated temperatures above $T_c$ in high\ntemperature superconductors have been observed near 1/8 hole doping for\nphotoexcitation with infrared or optical light polarized either in the\nCuO$_2$-plane or along the $c$-axis. While the use of in-plane polarization has\nbeen effective for incident energies aligned to specific phonons, $c$-axis\nlaser excitation in a broad range between 5 $\\mu$m and 400 nm was found to\naffect the superconducting dynamics in striped La$_{1.885}$Ba$_{0.115}$CuO$_4$,\nwith a maximum enhancement in the $1/\\omega$ dependence to the conductivity\nobserved at 800 nm. This broad energy range, and specifically 800 nm, is not\nresonant with any phonon modes, yet induced electronic excitations appear to be\nconnected to superconductivity at energy scales well above the typical gap\nenergies in the cuprates. A critical question is what can be responsible for\nsuch an effect at 800 nm? Using time-dependent exact diagonalization, we\ndemonstrate that the holes in the CuO$_2$ plane can be photoexcited into the\ncharge reservoir layers at resonant wavelengths within a multi-band Hubbard\nmodel. This orbitally selective photoinduced charge transfer effectively\nchanges the in-plane doping level, which can lead to an enhancement of $T_c$\nnear the 1/8 anomaly.\n"}
+{"abstract": "  A dominating set of a graph $G$ is a set of vertices that contains at least\none endpoint of every edge on the graph. The domination number of $G$ is the\norder of a minimum dominating set of $G$. The $(t,r)$ broadcast domination is a\ngeneralization of domination in which a set of broadcasting vertices emits\nsignals of strength $t$ that decrease by 1 as they traverse each edge, and we\nrequire that every vertex in the graph receives a cumulative signal of at least\n$r$ from its set of broadcasting neighbors. In this paper, we extend the study\nof $(t,r)$ broadcast domination to directed graphs. Our main result explores\nthe interval of values obtained by considering the directed $(t,r)$ broadcast\ndomination numbers of all orientations of a graph $G$. In particular, we prove\nthat in the cases $r=1$ and $(t,r) = (2,2)$, for every integer value in this\ninterval, there exists an orientation $\\vec{G}$ of $G$ which has directed\n$(t,r)$ broadcast domination number equal to that value. We also investigate\ndirected $(t,r)$ broadcast domination on the finite grid graph, the star graph,\nthe infinite grid graph, and the infinite triangular lattice graph. We conclude\nwith some directions for future study.\n"}
+{"abstract": "  Content feed, a type of product that recommends a sequence of items for users\nto browse and engage with, has gained tremendous popularity among social media\nplatforms. In this paper, we propose to study the diversity problem in such a\nscenario from an item sequence perspective using time series analysis\ntechniques. We derive a method called sliding spectrum decomposition (SSD) that\ncaptures users' perception of diversity in browsing a long item sequence. We\nalso share our experiences in designing and implementing a suitable item\nembedding method for accurate similarity measurement under long tail effect.\nCombined together, they are now fully implemented and deployed in Xiaohongshu\nApp's production recommender system that serves the main Explore Feed product\nfor tens of millions of users every day. We demonstrate the effectiveness and\nefficiency of the method through theoretical analysis, offline experiments and\nonline A/B tests.\n"}
+{"abstract": "  Test automation is common in software development; often one tests repeatedly\nto identify regressions. If the amount of test cases is large, one may select a\nsubset and only use the most important test cases. The regression test\nselection (RTS) could be automated and enhanced with Artificial Intelligence\n(AI-RTS). This however could introduce ethical challenges. While such\nchallenges in AI are in general well studied, there is a gap with respect to\nethical AI-RTS. By exploring the literature and learning from our experiences\nof developing an industry AI-RTS tool, we contribute to the literature by\nidentifying three challenges (assigning responsibility, bias in decision-making\nand lack of participation) and three approaches (explicability, supervision and\ndiversity). Additionally, we provide a checklist for ethical AI-RTS to help\nguide the decision-making of the stakeholders involved in the process.\n"}
+{"abstract": "  In this note, we show that the convolution of a discrete symmetric\nlog-concave distribution and a discrete symmetric bimodal distribution can have\nany strictly positive number of modes. A similar result is proved for smooth\ndistributions.\n"}
+{"abstract": "  Blood glucose (BG) management is crucial for type-1 diabetes patients\nresulting in the necessity of reliable artificial pancreas or insulin infusion\nsystems. In recent years, deep learning techniques have been utilized for a\nmore accurate BG level prediction system. However, continuous glucose\nmonitoring (CGM) readings are susceptible to sensor errors. As a result,\ninaccurate CGM readings would affect BG prediction and make it unreliable, even\nif the most optimal machine learning model is used. In this work, we propose a\nnovel approach to predicting blood glucose level with a stacked Long short-term\nmemory (LSTM) based deep recurrent neural network (RNN) model considering\nsensor fault. We use the Kalman smoothing technique for the correction of the\ninaccurate CGM readings due to sensor error. For the OhioT1DM dataset,\ncontaining eight weeks' data from six different patients, we achieve an average\nRMSE of 6.45 and 17.24 mg/dl for 30 minutes and 60 minutes of prediction\nhorizon (PH), respectively. To the best of our knowledge, this is the leading\naverage prediction accuracy for the ohioT1DM dataset. Different physiological\ninformation, e.g., Kalman smoothed CGM data, carbohydrates from the meal, bolus\ninsulin, and cumulative step counts in a fixed time interval, are crafted to\nrepresent meaningful features used as input to the model. The goal of our\napproach is to lower the difference between the predicted CGM values and the\nfingerstick blood glucose readings - the ground truth. Our results indicate\nthat the proposed approach is feasible for more reliable BG forecasting that\nmight improve the performance of the artificial pancreas and insulin infusion\nsystem for T1D diabetes management.\n"}
+{"abstract": "  The key challenge in multiple-object tracking task is temporal modeling of\nthe object under track. Existing tracking-by-detection methods adopt simple\nheuristics, such as spatial or appearance similarity. Such methods, in spite of\ntheir commonality, are overly simple and lack the ability to learn temporal\nvariations from data in an end-to-end manner. In this paper, we present MOTR, a\nfully end-to-end multiple-object tracking framework. It learns to model the\nlong-range temporal variation of the objects. It performs temporal association\nimplicitly and avoids previous explicit heuristics. Built upon DETR, MOTR\nintroduces the concept of \"track query\". Each track query models the entire\ntrack of an object. It is transferred and updated frame-by-frame to perform\niterative predictions in a seamless manner. Tracklet-aware label assignment is\nproposed for one-to-one assignment between track queries and object tracks.\nTemporal aggregation network together with collective average loss is further\nproposed to enhance the long-range temporal relation. Experimental results show\nthat MOTR achieves competitive performance and can serve as a strong\nTransformer-based baseline for future research. Code is available at\n\\url{https://github.com/megvii-model/MOTR}.\n"}
+{"abstract": "  The stiffness of the Hodgkin-Huxley (HH) equations during an action potential\n(spike) limits the use of large time steps. We observe that the neurons can be\nevolved independently between spikes, $i.e.,$ different neurons can be evolved\nwith different methods and different time steps. This observation motivates us\nto design fast algorithms to raise efficiency. We present an adaptive method,\nan exponential time differencing (ETD) method and a library-based method to\ndeal with the stiff period. All the methods can use time steps one order of\nmagnitude larger than the regular Runge-Kutta methods to raise efficiency while\nachieving precise statistical properties of the original HH neurons like the\nlargest Lyapunov exponent and mean firing rate. We point out that the ETD and\nlibrary methods can stably achieve maximum 8 and 10 times of speedup,\nrespectively.\n"}
+{"abstract": "  Singer voice classification is a meaningful task in the digital era. With a\nhuge number of songs today, identifying a singer is very helpful for music\ninformation retrieval, music properties indexing, and so on. In this paper, we\npropose a new method to identify the singer's name based on analysis of\nVietnamese popular music. We employ the use of vocal segment detection and\nsinging voice separation as the pre-processing steps. The purpose of these\nsteps is to extract the singer's voice from the mixture sound. In order to\nbuild a singer classifier, we propose a neural network architecture working\nwith Mel Frequency Cepstral Coefficient as extracted input features from said\nvocal. To verify the accuracy of our methods, we evaluate on a dataset of 300\nVietnamese songs from 18 famous singers. We achieve an accuracy of 92.84% with\n5-fold stratified cross-validation, the best result compared to other methods\non the same data set.\n"}
+{"abstract": "  Payment channel networks, such as Bitcoin's Lightning Network, promise to\nimprove the scalability of blockchain systems by processing the majority of\ntransactions off-chain. Due to the design, the positioning of nodes in the\nnetwork topology is a highly influential factor regarding the experienced\nperformance, costs, and fee revenue of network participants. As a consequence,\ntoday's Lightning Network is built around a small number of highly-connected\nhubs. Recent literature shows the centralizing tendencies to be\nincentive-compatible and at the same time detrimental to security and privacy.\nThe choice of attachment strategies therefore becomes a crucial factor for the\nfuture of such systems. In this paper, we provide an empirical study on the\n(local and global) impact of various attachment strategies for payment channel\nnetworks. To this end, we introduce candidate strategies from the field of\ngraph theory and analyze them with respect to their computational complexity as\nwell as their repercussions for end users and service providers. Moreover, we\nevaluate their long-term impact on the network topology.\n"}
+{"abstract": "  Spherically, plane, or hyperbolically symmetric spacetimes with an additional\nhypersurface orthogonal Killing vector are often called ``static'' spacetimes\neven if they contain regions where the Killing vector is non-timelike. It seems\nto be widely believed that an energy-momentum tenor for a matter field\ncompatible with these spacetimes in general relativity is of the Hawking-Ellis\ntype I everywhere. We show in arbitrary $n(\\ge 3)$ dimensions that, contrary to\npopular belief, a matter field on a Killing horizon is not necessarily of type\nI but can be of type II. Such a type-II matter field on a Killing horizon is\nrealized in the Gibbons-Maeda-Garfinkle-Horowitz-Strominger black hole in the\nEinstein-Maxwell-dilaton system and may be interpreted as a mixture of a\nparticular anisotropic fluid and a null dust fluid.\n"}
+{"abstract": "  Principal component analysis (PCA) defines a reduced space described by PC\naxes for a given multidimensional-data sequence to capture the variations of\nthe data. In practice, we need multiple data sequences that accurately obey\nindividual probability distributions and for a fair comparison of the sequences\nwe need PC axes that are common for the multiple sequences but properly capture\nthese multiple distributions. For these requirements, we present individual\nergodic samplings for these sequences and provide special reweighting for\nrecovering the target distributions.\n"}
+{"abstract": "  In many multiagent environments, a designer has some, but limited control\nover the game being played. In this paper, we formalize this by considering\nincompletely specified games, in which some entries of the payoff matrices can\nbe chosen from a specified set. We show that it is NP-hard for the designer to\nmake these choices optimally, even in zero-sum games. In fact, it is already\nintractable to decide whether a given action is (potentially or necessarily)\nplayed in equilibrium. We also consider incompletely specified symmetric games\nin which all completions are required to be symmetric. Here, hardness holds\neven in weak tournament games (symmetric zero-sum games whose entries are all\n-1, 0, or 1) and in tournament games (symmetric zero-sum games whose\nnon-diagonal entries are all -1 or 1). The latter result settles the complexity\nof the possible and necessary winner problems for a social-choice-theoretic\nsolution concept known as the bipartisan set. We finally give a mixed-integer\nlinear programming formulation for weak tournament games and evaluate it\nexperimentally.\n"}
+{"abstract": "  Introducing the notion of extended Schr\\\"odinger spaces, we define the\ncriticality and subcriticality of Schr\\\"odinger forms in the same manner as the\nrecurrence and transience of Dirichlet forms, and give a sufficient condition\nfor the subcriticality of Schr\\\"odinger forms in terms the bottom of spectrum.\nWe define a subclass of Hardy potentials and prove that Schr\\\"odinger forms\nwith potentials in this subclass are always critical, which leads us to optimal\nHardy type inequality. We show that this definition of criticality and\nsubcriticality is equivalent to that there exists an excessive function with\nrespect to Schr\\\"odinger semigroup and its generating Dirichlet form through\n$h$-transform is recurrent and transient respectively. As an application, we\ncan show the recurrence and transience of a family of Dirichlet forms by\nshowing the criticality and subcriticaly of Schr\\\"odinger forms and show the\nother way around through $h$-transform, We give a such example with fractional\nSchr\\\"odinger operators with Hardy potential.\n"}
+{"abstract": "  A splitting BIBD is a type of combinatorial design that can be used to\nconstruct splitting authentication codes with good properties. In this paper we\nshow that a design-theoretic approach is useful in the analysis of more general\nsplitting authentication codes. Motivated by the study of algebraic\nmanipulation detection (AMD) codes, we define the concept of a group generated\nsplitting authentication code. We show that all group-generated authentication\ncodes have perfect secrecy, which allows us to demonstrate that algebraic\nmanipulation detection codes can be considered to be a special case of an\nauthentication code with perfect secrecy.\n  We also investigate splitting BIBDs that can be \"equitably ordered\". These\nsplitting BIBDs yield authentication codes with splitting that also have\nperfect secrecy. We show that, while group generated BIBDs are inherently\nequitably ordered, the concept is applicable to more general splitting BIBDs.\nFor various pairs $(k,c)$, we determine necessary and sufficient (or almost\nsufficient) conditions for the existence of $(v, k \\times c,1)$-splitting BIBDs\nthat can be equitably ordered. The pairs for which we can solve this problem\nare $(k,c) = (3,2), (4,2), (3,3)$ and $(3,4)$, as well as all cases with $k =\n2$.\n"}
+{"abstract": "  Introduction: Mobile apps, through artificial vision, are capable of\nrecognizing vegetable species in real time. However, the existing species\nrecognition apps do not take in consideration the wide variety of endemic and\nnative (Chilean) species, which leads to wrong species predictions. This study\nintroduces the development of a chilean species dataset and an optimized\nclassification model implemented to a mobile app. Method: the data set was\nbuilt by putting together pictures of several species captured on the field and\nby selecting some pictures available from other datasets available online.\nConvolutional neural networks were used in order to develop the images\nprediction models. The networks were trained by performing a sensitivity\nanalysis, validating with k-fold cross validation and performing tests with\ndifferent hyper-parameters, optimizers, convolutional layers, and learning\nrates in order to identify and choose the best models and then put them\ntogether in one classification model. Results: The final data set was\ncompounded by 46 species, including native species, endemic and exotic from\nChile, with 6120 training pictures and 655 testing pictures. The best models\nwere implemented on a mobile app, obtaining a 95% correct prediction rate with\nrespect to the set of tests. Conclusion: The app developed in this study is\ncapable of classifying species with a high level of accuracy, depending on the\nstate of the art of the artificial vision and it can also show relevant\ninformation related to the classified species.\n"}
+{"abstract": "  We study quantum effects of the vacuum light-matter interaction in materials\nembedded in optical cavities. We focus on the electronic response of a\ntwo-dimensional semiconductor placed inside a planar cavity. By using a\ndiagrammatic expansion of the electron-photon interaction, we describe\nsignatures of light-matter hybridization characterized by large asymmetric\nshifts of the spectral weight at resonant frequencies. We follow the evolution\nof the light-dressing from the cavity to the free-space limit. In the cavity\nlimit, light-matter hybridization results in a modification of the optical gap\nwith sizeable spectral weight appearing below the bare gap edge. In the limit\nof large cavities, we find a residual redistribution of spectral weight which\nbecomes independent of the distance between the two mirrors. We show that the\nphoton dressing of the electronic response can be fully explained by using a\nclassical description of light. The classical description is found to hold up\nto a strong coupling regime of the light-matter interaction highlighted by the\nlarge modification of the photon spectra with respect to the empty cavity. We\nshow that, despite the strong coupling, quantum corrections are negligibly\nsmall and weakly dependent on the cavity confinement. As a consequence, in\ncontrast to the optical gap, the single particle electronic band gap is not\nsensibly modified by the strong-coupling. Our results show that quantum\ncorrections are dominated by off-resonant photon modes at high energy. As such,\ncavity confinement can hardly be seen as a knob to control the quantum effects\nof the light-matter interaction in vacuum.\n"}
+{"abstract": "  Recent discoveries of charge order and electronic nematic order in the\niron-based superconductors and cuprates have pointed towards the possibility of\nnematic and charge fluctuations playing a role in the enhancement of\nsuperconductivity. The Ba1-xSrxNi2As2 system, closely related in structure to\nthe BaFe2As2 system, has recently been shown to exhibit both types of ordering\nwithout the presence of any magnetic order. We report single crystal X-ray\ndiffraction, resistance transport measurements, and magnetization of \\BaSrLate,\nproviding evidence that the previously reported incommensurate charge order\nwith wavevector $(0,0.28,0)_{tet}$ in the tetragonal state of \\BaNi~vanishes by\nthis concentration of Sr substitution together with nematic order. Our\nmeasurements suggest that the nematic and incommensurate charge orders are\nclosely tied in the tetragonal state, and show that the $(0,0.33,0)_{tri}$\ncharge ordering in the triclinic phase of BaNi2As2 evolves to become\n$(0,0.5,0)_{tri}$ charge ordering at $x$=0.65 before vanishing at $x$=0.71.\n"}
+{"abstract": "  We study the one-level density for families of L-functions associated with\ncubic Dirichlet characters defined over the Eisenstein field. We show that the\nfamily of $L$-functions associated with the cubic residue symbols $\\chi_n$ with\n$n$ square-free and congruent to 1 modulo 9 satisfies the Katz-Sarnak\nconjecture for all test functions whose Fourier transforms are supported in\n$(-13/11, 13/11)$, under GRH. This is the first result extending the support\noutside the \\emph{trivial range} $(-1, 1)$ for a family of cubic L-functions.\nThis implies that a positive density of the L-functions associated with these\ncharacters do not vanish at the central point $s=1/2$. A key ingredient in our\nproof is a bound on an average of generalized cubic Gauss sums at prime\narguments, whose proof is based on the work of Heath-Brown and Patterson.\n"}
+{"abstract": "  To inhibit the spread of rumorous information and its severe consequences,\ntraditional fact checking aims at retrieving relevant evidence to verify the\nveracity of a given claim. Fact checking methods typically use knowledge graphs\n(KGs) as external repositories and develop reasoning mechanism to retrieve\nevidence for verifying the triple claim. However, existing methods only focus\non verifying a single claim. As real-world rumorous information is more complex\nand a textual statement is often composed of multiple clauses (i.e. represented\nas multiple claims instead of a single one), multiclaim fact checking is not\nonly necessary but more important for practical applications. Although previous\nmethods for verifying a single triple can be applied repeatedly to verify\nmultiple triples one by one, they ignore the contextual information implied in\na multi-claim statement and could not learn the rich semantic information in\nthe statement as a whole. In this paper, we propose an end-to-end knowledge\nenhanced learning and verification method for multi-claim fact checking. Our\nmethod consists of two modules, KG-based learning enhancement and multi-claim\nsemantic composition. To fully utilize the contextual information, the KG-based\nlearning enhancement module learns the dynamic context-specific representations\nvia selectively aggregating relevant attributes of entities. To capture the\ncompositional semantics of multiple triples, the multi-claim semantic\ncomposition module constructs the graph structure to model claim-level\ninteractions, and integrates global and salient local semantics with multi-head\nattention. Experimental results on a real-world dataset and two benchmark\ndatasets show the effectiveness of our method for multi-claim fact checking\nover KG.\n"}
+{"abstract": "  Ferroelectric tunneling junctions (FTJ) are considered to be the\nintrinsically most energy efficient memristors. In this work, specific\nelectrical features of ferroelectric hafnium-zirconium oxide based FTJ devices\nare investigated. Moreover, the impact on the design of FTJ-based circuits for\nedge computing applications is discussed by means of two example circuits.\n"}
+{"abstract": "  The number of non-isomorphic cubic fields L sharing a common discriminant\nd(L) = d is called the multiplicity m = m(d) of d. For an assigned value of d,\nthese fields are collected in a multiplet M(d) = (L(1) ,..., L(m)). In this\npaper, the information in all existing tables of totally real cubic number\nfields L with positive discriminants d(L) < 10000000 is extended by computing\nthe differential principal factorization types tau(L) in (alpha1, alpha2,\nalpha3, beta1, beta2, gamma, delta1, delta2, epsilon) of the members L of each\nmultiplet M(d) of non-cyclic fields, a new kind of arithmetical invariants\nwhich provide succinct information about ambiguous principal ideals and\ncapitulation in the normal closures N of non-Galois cubic fields L. The\nclassification is arranged with respect to increasing 3-class rank of the\nquadratic subfields K of the S3-fields N, and to ascending number of prime\ndivisors of the conductor f of N/K. The Scholz conjecture concerning the\ndistinguished index of subfield units (U(N) : U(0)) = 1 for ramified extensions\nN/K with conductor f > 1 is refined and verified.\n"}
+{"abstract": "  One of the major challenges for low-rank multi-fidelity (MF) approaches is\nthe assumption that low-fidelity (LF) and high-fidelity (HF) models admit\n\"similar\" low-rank kernel representations. Low-rank MF methods have\ntraditionally attempted to exploit low-rank representations of linear kernels,\nwhich are kernel functions of the form $K(u,v) = v^T u$ for vectors $u$ and\n$v$. However, such linear kernels may not be able to capture low-rank behavior,\nand they may admit LF and HF kernels that are not similar. Such a situation\nrenders a naive approach to low-rank MF procedures ineffective. In this paper,\nwe propose a novel approach for the selection of a near-optimal kernel function\nfor use in low-rank MF methods. The proposed framework is a two-step strategy\nwherein: (1) hyperparameters of a library of kernel functions are optimized,\nand (2) a particular combination of the optimized kernels is selected, through\neither a convex mixture (Additive Kernels) or through a data-driven\noptimization (Adaptive Kernels). The two resulting methods for this generalized\nframework both utilize only the available inexpensive low-fidelity data and\nthus no evaluation of high-fidelity simulation model is needed until a kernel\nis chosen. These proposed approaches are tested on five non-trivial problems\nincluding multi-fidelity surrogate modeling for one- and two-species molecular\nsystems, gravitational many-body problem, associating polymer networks,\nplasmonic nano-particle arrays, and an incompressible flow in channels with\nstenosis. The results for these numerical experiments demonstrate the numerical\nstability efficiency of both proposed kernel function selection procedures, as\nwell as high accuracy of their resultant predictive models for estimation of\nquantities of interest. Comparisons against standard linear kernel procedures\nalso demonstrate increased accuracy of the optimized kernel approaches.\n"}
+{"abstract": "  The investigation of the energy frontier in physics requires novel concepts\nfor future colliders. The idea of a muon collider is very appealing since it\nwould allow to study particle collisions at up to tens of TeV energy, while\noffering a cleaner experimental environment with respect to hadronic colliders.\nOne key element in the muon collider design is the low-emittance muon\nproduction. Recently,the Low EMittance Muon Accelerator (LEMMA) collaboration\nhas explored the muon pair production close to its kinematic threshold by\nannihilating 45 GeV positrons with electrons in a low Z material target. In\nthis configuration, muons are emerging from the target with a naturally\nlow-emittance. In this paper we describe the performance of a system, to study\nthis production mechanism, that consists in several segmented absorbers with\nalternating active layers composed of fast Cherenkov detectors together with a\nmuon identification technique based on this detector. Passive layers were made\nof tungsten. We collected data corresponding to muon and electron beams\nproduced at the H2 line in the North Area of the European Organization for\nNuclear Research (CERN) in September 2018.\n"}
+{"abstract": "  We explore equilibrium solutions of spherically symmetric boson stars in the\nPalatini formulation of $f(\\mathcal{R})$ gravity. We account for the\nmodifications introduced in the gravitational sector by using a recently\nestablished correspondence between modified gravity with scalar matter and\ngeneral relativity with modified scalar matter. We focus on the quadratic\ntheory $f(\\mathcal{R})=R+\\xi R^2$ and compare its solutions with those found in\ngeneral relativity, exploring both positive and negative values of the coupling\nparameter $\\xi$. As matter source, a complex, massive scalar field with and\nwithout self-interaction terms is considered. Our results show that the\nexistence curves of boson stars in Palatini $f(\\mathcal{R})$ gravity are fairly\nsimilar to those found in general relativity. Major differences are observed\nfor negative values of the coupling parameter which results in a repulsive\ngravitational component for high enough scalar field density distributions.\nAdding self-interactions makes the degeneracy between $f(\\mathcal{R})$ and\ngeneral relativity even more pronounced, leaving very little room for\nobservational discrimination between the two theories.\n"}
+{"abstract": "  With the widespread use of toxic language online, platforms are increasingly\nusing automated systems that leverage advances in natural language processing\nto automatically flag and remove toxic comments. However, most automated\nsystems -- when detecting and moderating toxic language -- do not provide\nfeedback to their users, let alone provide an avenue of recourse for these\nusers to make actionable changes. We present our work, RECAST, an interactive,\nopen-sourced web tool for visualizing these models' toxic predictions, while\nproviding alternative suggestions for flagged toxic language. Our work also\nprovides users with a new path of recourse when using these automated\nmoderation tools. RECAST highlights text responsible for classifying toxicity,\nand allows users to interactively substitute potentially toxic phrases with\nneutral alternatives. We examined the effect of RECAST via two large-scale user\nevaluations, and found that RECAST was highly effective at helping users reduce\ntoxicity as detected through the model. Users also gained a stronger\nunderstanding of the underlying toxicity criterion used by black-box models,\nenabling transparency and recourse. In addition, we found that when users focus\non optimizing language for these models instead of their own judgement (which\nis the implied incentive and goal of deploying automated models), these models\ncease to be effective classifiers of toxicity compared to human annotations.\nThis opens a discussion for how toxicity detection models work and should work,\nand their effect on the future of online discourse.\n"}
+{"abstract": "  The production of $\\Lambda$ baryons and ${\\rm K}^{0}_{\\rm S}$ mesons (${\\rm\nV}^{0}$ particles) was measured in p-Pb collisions at $\\sqrt{s_{\\rm NN}} = 5$\nTeV and pp collisions at $\\sqrt{s} = 7$ TeV with ALICE at the LHC. The\nproduction of these strange particles is studied separately for particles\nassociated with hard scatterings and the underlying event to shed light on the\nbaryon-to-meson ratio enhancement observed at intermediate transverse momentum\n($p_{\\rm T}$) in high multiplicity pp and p-Pb collisions. Hard scatterings are\nselected on an event-by-event basis with jets reconstructed with the\nanti-$k_{\\rm T}$ algorithm using charged particles. The production of strange\nparticles associated with jets $p_{\\rm T,\\;jet}^{\\rm ch}>10$ GeV/$c$ is\nreported as a function of $p_{\\rm T}$ in both systems; and its dependence on\n$p_{\\rm T}$ with jets $p_{\\rm T,\\;jet}^{\\rm ch}>20$ GeV/$c$ and on angular\ndistance from the jet axis, $R({\\rm V}^{0},\\;{\\rm jet})$, for jets with $p_{\\rm\nT,\\;jet}^{\\rm ch} > 10$ GeV/$c$ are reported in p-Pb collisions. The results\nare compared with the strange particle production in the underlying event. The\n$\\Lambda/{\\rm K}^{0}_{\\rm S}$ ratio associated with jets in p-Pb collisions for\n$R({\\rm V}^{0},\\;{\\rm jet})<0.4$ is consistent with the ratio measured in pp\ncollisions and with the expectation of jets fragmenting in vacuum given by the\nPYTHIA event generator.\n"}
+{"abstract": "  Recent results have demonstrated that samplers constructed with flow-based\ngenerative models are a promising new approach for configuration generation in\nlattice field theory. In this paper, we present a set of methods to construct\nflow models for targets with multiple separated modes (i.e. theories with\nmultiple vacua). We demonstrate the application of these methods to modeling\ntwo-dimensional real scalar field theory in its symmetry-broken phase. In this\ncontext we investigate the performance of different flow-based sampling\nalgorithms, including a composite sampling algorithm where flow-based proposals\nare occasionally augmented by applying updates using traditional algorithms\nlike HMC.\n"}
+{"abstract": "  We study the propagation of light under a strong electric field in\nBorn-Infeld electrrdynamics. The nonlinear effect can be described by the\neffective indices of refraction. Because the effective indices of refraction\ndepend on the background electric field, the path of light can be bent when the\nbackground field is non-uniform. We compute the bending angle of light by a\nBorn-Infeld-type Coulomb charge in the weak lensing limit using the trajectory\nequation based on geometric optics. We also compute the deflection angle of\nlight by the Einstein-Born-Infeld black hole using the geodesic equation and\nconfirm that the contribution of the electric charge to the total bending angle\nagree.\n"}
+{"abstract": "  In this paper, we investigate certain graded-commutative rings which are\nrelated to the reciprocal plane compactification of the coordinate ring of a\ncomplement of a hyperplane arrangement. We give a presentation of these rings\nby generators and defining relations. This presentation was used by Holler and\nI. Kriz to calculate the $\\mathbb{Z}$-graded coefficients of localizations of\nordinary $RO((\\mathbb{Z}/p)^n)$-graded equivariant cohomology at a given set of\nrepresentation spheres, and also more recently by the author in a\ngeneralization to the case of an arbitrary finite group. We also give an\ninterpretation of these rings in terms of superschemes, which can be used to\nfurther illuminate their structure.\n"}
+{"abstract": "  We present a framework for quantum process tomography of two-ion interactions\nthat leverages modulations of the trapping potential and composite pulses from\na global laser beam to achieve individual-ion addressing. Tomographic analysis\nof identity and delay processes reveals dominant error contributions from laser\ndecoherence and slow qubit frequency drift during the tomography experiment. We\nuse this framework on two co-trapped $^{40}$Ca$^+$ ions to analyze both an\noptimized and an overpowered M{\\o}lmer-S{\\o}rensen gate and to compare the\nresults of this analysis to a less informative Bell-state tomography\nmeasurement and to predictions based on a simplified noise model. These results\nshow that the technique is effective for the characterization of two-ion\nquantum processes and for the extraction of meaningful information about the\nerrors present in the system. The experimental convenience of this method will\nallow for more widespread use of process tomography for characterizing\nentangling gates in trapped-ion systems.\n"}
+{"abstract": "  Strain localization is responsible for mesh dependence in numerical analyses\nconcerning a vast variety of fields such as solid mechanics, dynamics,\nbiomechanics and geomechanics. Therefore, numerical methods that regularize\nstrain localization are paramount in the analysis and design of engineering\nproducts and systems. In this paper we revisit the elasto-viscoplastic,\nstrain-softening, strain-rate hardening model as a means to avoid strain\nlocalization on a mathematical plane in the case of a Cauchy continuum. Going\nbeyond previous works (de Borst and Duretz (2020); Needleman (1988); Sluys and\nde Borst (1992); Wang et al. (1997)), we assume that both the frequency\n{\\omega} and the wave number k belong to the complex plane. Therefore, a\ndifferent expression for the dispersion relation is derived. We prove then that\nunder these conditions strain localization on a mathematical plane is possible.\nThe above theoretical results are corroborated by extensive numerical analyses,\nwhere the total strain and plastic strain rate profiles exhibit mesh dependent\nbehavior.\n"}
+{"abstract": "  In this paper, we propose a new strategy for learning inertial robotic\nnavigation models. The proposed strategy enhances the generalisability of\nend-to-end inertial modelling, and is aimed at wheeled robotic deployments.\nConcretely, the paper describes the following. (1) Using precision robotics, we\nempirically characterise the effect of changing the sensor position during\nnavigation on the distribution of raw inertial signals, as well as the\ncorresponding impact on learnt latent spaces. (2) We propose neural\narchitectures and algorithms to assimilate knowledge from an indexed set of\nsensor positions in order to enhance the robustness and generalisability of\nrobotic inertial tracking in the field. Our scheme of choice uses continuous\ndomain adaptation (DA) and optimal transport (OT). (3) In our evaluation,\ncontinuous OT DA outperforms a continuous adversarial DA baseline, while also\nshowing quantifiable learning benefits over simple data augmentation. We will\nrelease our dataset to help foster future research.\n"}
+{"abstract": "  In this paper, for $1<p<\\infty$, we obtain the $L^p$-boundedness of the\nHilbert transform $H^{\\gamma}$ along a variable plane curve $(t,u(x_1,\nx_2)\\gamma(t))$, where $u$ is a Lipschitz function with small Lipschitz norm,\nand $\\gamma$ is a general curve satisfying some suitable smoothness and\ncurvature conditions.\n"}
+{"abstract": "  We compute the non-planar contribution to the universal anomalous dimension\nof twist-two operators in N=4 supersymmetric Yang-Mills theory at four loops\nthrough Lorentz spin eighteen. Exploiting the results of this and our previous\ncalculations along with recent analytic results for the cusp anomalous\ndimension and some expected analytic properties, we reconstruct a general\nexpression valid for arbitrary Lorentz spin. We study various properties of\nthis general result, such as its large-spin limit, its small-x limit, and\nothers. In particular, we present a prediction for the non-planar contribution\nto the anomalous dimension of the single-magnon operator in the beta-deformed\nversion of the theory.\n"}
+{"abstract": "  Deep neural networks have been well-known for their superb performance in\nhandling various machine learning and artificial intelligence tasks. However,\ndue to their over-parameterized black-box nature, it is often difficult to\nunderstand the prediction results of deep models. In recent years, many\ninterpretation tools have been proposed to explain or reveal the ways that deep\nmodels make decisions. In this paper, we review this line of research and try\nto make a comprehensive survey. Specifically, we introduce and clarify two\nbasic concepts-interpretations and interpretability-that people usually get\nconfused. First of all, to address the research efforts in interpretations, we\nelaborate the design of several recent interpretation algorithms, from\ndifferent perspectives, through proposing a new taxonomy. Then, to understand\nthe results of interpretation, we also survey the performance metrics for\nevaluating interpretation algorithms. Further, we summarize the existing work\nin evaluating models' interpretability using \"trustworthy\" interpretation\nalgorithms. Finally, we review and discuss the connections between deep models'\ninterpretations and other factors, such as adversarial robustness and data\naugmentations, and we introduce several open-source libraries for\ninterpretation algorithms and evaluation approaches.\n"}
+{"abstract": "  We have designed honeycomb lattices for microwave photons with a frequency\nimbalance between the two sites in the unit cell. This imbalance is the\nequivalent of a mass term that breaks the lattice inversion symmetry. At the\ninterface between two lattices with opposite imbalance, we observe topological\nvalley edge states. By imaging the spatial dependence of the modes along the\ninterface, we obtain their dispersion relation that we compare to the\npredictions of an ab initio tight-binding model describing our microwave\nphotonic lattices.\n"}
+{"abstract": "  The goal of this paper is to increase the membership list of the Chamaeleon\nstar forming region and the $\\epsilon$ Cha moving group, in particular for\nlow-mass stars and substellar objects. We extended the search region\nsignificantly beyond the dark clouds. Our sample has been selected based on\nproper motions and colours obtained from Gaia and 2MASS. We present and discuss\nthe optical spectroscopic follow-up of 18 low-mass stellar objects in Cha I and\n$\\epsilon$ Cha. We characterize the properties of objects by deriving their\nphysical parameters, both from spectroscopy and photometry. We add three more\nlow-mass members to the list of Cha I, and increase the census of known\n$\\epsilon$ Cha members by more than 40%, confirming spectroscopically 13 new\nmembers and relying on X-ray emission as youth indicator for 2 more. In most\ncases the best-fitting spectral template is from objects in the TW Hya\nassociation, indicating that $\\epsilon$ Cha has a similar age. The first\nestimate of the slope of the initial mass function in $\\epsilon$ Cha down to\nthe sub-stellar regime is consistent with that of other young clusters. We\nestimate our IMF to be complete down to $\\approx 0.03$M$_{\\odot}$. The IMF can\nbe represented by two power laws: for M $<$ 0.5 M$_{\\odot}$ $\\alpha = 0.42 \\pm\n0.11$ and for M $>$ 0.5 M$_{\\odot}$ $\\alpha = 1.44 \\pm 0.12$. We find\nsimilarities between $\\epsilon$ Cha and the southernmost part of Lower\nCentaurus Crux (LCC A0), both lying at similar distances and sharing the same\nproper motions. This suggests that $\\epsilon$ Cha and LCC A0 may have been born\nduring the same star formation event\n"}
+{"abstract": "  We observe the density wave angular pattern speed OMEGA-p to be near 12 to 17\nkm / s / kpc, by the separation between a typical optical HII region (from the\nspiral arm dust lane) and using a HII evolution time model to yield its\nrelative speed, and independently by the separation between a typical radio\nmaser (from the spiral arm dust lane) with a maser model.\n"}
+{"abstract": "  An instance of the super-stable matching problem with incomplete lists and\nties is an undirected bipartite graph $G = (A \\cup B, E)$, with an adjacency\nlist being a linearly ordered list of ties. Ties are subsets of vertices\nequally good for a given vertex. An edge $(x,y) \\in E \\backslash M$ is a\nblocking edge for a matching $M$ if by getting matched to each other neither of\nthe vertices $x$ and $y$ would become worse off. Thus, there is no disadvantage\nif the two vertices would like to match up. A matching $M$ is super-stable if\nthere is no blocking edge with respect to $M$. It has previously been shown\nthat super-stable matchings form a distributive lattice and the number of\nsuper-stable matchings can be exponential in the number of vertices. We give\ntwo compact representations of size $O(m)$ that can be used to construct all\nsuper-stable matchings, where $m$ denotes the number of edges in the graph. The\nconstruction of the second representation takes $O(mn)$ time, where $n$ denotes\nthe number of vertices in the graph, and gives an explicit rotation poset\nsimilar to the rotation poset in the classical stable marriage problem. We also\ngive a polyhedral characterisation of the set of all super-stable matchings and\nprove that the super-stable matching polytope is integral, thus solving an open\nproblem stated in the book by Gusfield and Irving .\n"}
+{"abstract": "  The relaxation of field-line tension during magnetic reconnection gives rise\nto a universal Fermi acceleration process involving the curvature drift of\nparticles. However, the efficiency of this mechanism is limited by the trapping\nof energetic particles within flux-ropes. Using 3D fully kinetic simulations,\nwe demonstrate that the flux-rope kink instability leads to strong field-line\nchaos in weak-guide-field regimes where the Fermi mechanism is most efficient,\nthus allowing particles to transport out of flux-ropes and undergo further\nacceleration. As a consequence, both ions and electrons develop clear power-law\nenergy spectra which contain a significant fraction of the released energy. The\nlow-energy bounds are determined by the injection physics, while the\nhigh-energy cutoffs are limited only by the system size. These results have\nstrong relevance to observations of nonthermal particle acceleration in space\nand astrophysics.\n"}
+{"abstract": "  Galactic Internet may already exist, if all stars are exploited as\ngravitational lenses. In fact, the gravitational lens of the Sun is a\nwell-known astrophysical phenomenon predicted by Einstein's general theory of\nrelativity. It implies that, if we can send a probe along any radial direction\naway from the Sun up to the minimal distance of 550 AU and beyond, the Sun's\nmass will act as a huge magnifying lens, letting us \"see\" detailed radio maps\nof whatever may lie on the other side of the Sun even at very large distances.\nThe 2009 book by this author, ref. [1], studies such future FOCAL space\nmissions to 550 AU and beyond. In this paper, however, we want to study another\npossibility yet: how to create the future interstellar radio links between the\nsolar system and any future interstellar probe by utilizing the gravitational\nlens of the Sun as a huge antenna. In particular, we study the Bit Error Rate\n(BER) across interstellar distances with and without using the gravitational\nlens effect of the Sun (ref. [2]). The conclusion is that only when we will\nexploit the Sun as a gravitational lens we will be able to communicate with our\nown probes (or with nearby Aliens) across the distances of even the nearest\nstars to us in the Galaxy, and that at a reasonable Bit Error Rate. We also\nstudy the radio bridge between the Sun and any other Star that is made up by\nthe two gravitational lenses of both the Sun and that Star. The alignment for\nthis radio bridge to work is very strict, but the power-saving is enormous, due\nto the huge contributions of the two stars' lenses to the overall antenna gain\nof the system. We study a few cases in detail. Finally, we find the information\nchannel capacity for each of those radio bridges, putting thus a physical\nconstraint to the amount of information transfer that will be possible even by\nexploiting the stars as gravitational lenses\n"}
+{"abstract": "  Gaia DR2 published positions, parallaxes and proper motions for an\nunprecedented 1,331,909,727 sources, revolutionising the field of Galactic\ndynamics. We complement this data with the Astrometry Spread Function (ASF),\nthe expected uncertainty in the measured positions, proper motions and parallax\nfor a non-accelerating point source. The ASF is a Gaussian function for which\nwe construct the 5D astrometric covariance matrix as a function of position on\nthe sky and apparent magnitude using the Gaia DR2 scanning law and demonstrate\nexcellent agreement with the observed data. This can be used to answer the\nquestion `What astrometric covariance would Gaia have published if my star was\na non-accelerating point source?'.\n  The ASF will enable characterisation of binary systems, exoplanet orbits,\nastrometric microlensing events and extended sources which add an excess\nastrometric noise to the expected astrometry uncertainty. By using the ASF to\nestimate the unit weight error (UWE) of Gaia DR2 sources, we demonstrate that\nthe ASF indeed provides a direct probe of the excess source noise.\n  We use the ASF to estimate the contribution to the selection function of the\nGaia astrometric sample from a cut on astrometric_sigma5d_max showing high\ncompleteness for $G<20$ dropping to $<1\\%$ in underscanned regions of the sky\nfor $G=21$.\n  We have added an ASF module to the Python package SCANNINGLAW\n(https://github.com/gaiaverse/scanninglaw) through which users can access the\nASF.\n"}
+{"abstract": "  We compare the capabilities of two approaches to approximating graph\nisomorphism using linear algebraic methods: the \\emph{invertible map tests}\n(introduced by Dawar and Holm) and proof systems with algebraic rules, namely\n\\emph{polynomial calculus}, \\emph{monomial calculus} and \\emph{Nullstellensatz\ncalculus}. In the case of fields of characteristic zero, these variants are all\nessentially equivalent to the the Weisfeiler-Leman algorithms. In positive\ncharacteristic we show that the invertible map method can simulate the monomial\ncalculus and identify a potential way to extend this to the monomial calculus.\n"}
+{"abstract": "  Relational semigroups with domain and range are a useful tool for modelling\nnondeterministic programs. We prove that the representation class of\ndomain-range semigroups with demonic composition is not finitely axiomatisable.\nWe extend the result for ordered domain algebras and show that any relation\nalgebra reduct signature containing domain, range, converse, and composition,\nbut no negation, meet, nor join has the finite representation property. That is\nany finite representable structure of such a signature is representable over a\nfinite base. We survey the results in the area of the finite representation\nproperty.\n"}
+{"abstract": "  Single-species reaction-diffusion equations, such as the Fisher-KPP and\nPorous-Fisher equations, support travelling wave solutions that are often\ninterpreted as simple mathematical models of biological invasion. Such\ntravelling wave solutions are thought to play a role in various applications\nincluding development, wound healing and malignant invasion. One criticism of\nthese single-species equations is that they do not explicitly describe\ninteractions between the invading population and the surrounding environment.\nIn this work we study a reaction-diffusion equation that describes malignant\ninvasion which has been used to interpret experimental measurements describing\nthe invasion of malignant melanoma cells into surrounding human skin tissues.\nThis model explicitly describes how the population of cancer cells degrade the\nsurrounding tissues, thereby creating free space into which the cancer cells\nmigrate and proliferate to form an invasion wave of malignant tissue that is\ncoupled to a retreating wave of skin tissue. We analyse travelling wave\nsolutions of this model using a combination of numerical simulation, phase\nplane analysis and perturbation techniques. Our analysis shows that the\ntravelling wave solutions involve a range of very interesting properties that\nresemble certain well-established features of both the Fisher-KPP and\nPorous-Fisher equations, as well as a range of novel properties that can be\nthought of as extensions of these well-studied single-species equations. Of\nparticular interest is that travelling wave solutions of the invasion model are\nvery well approximated by trajectories in the Fisher-KPP phase plane that are\nnormally disregarded. This observation establishes a previously unnoticed link\nbetween coupled multi-species reaction diffusion models of invasion and a\ndifferent class of models of invasion that involve moving boundary problems.\n"}
+{"abstract": "  Scientometric analysis of 146 and 59 research articles published in Indian\njournal of Information Sources and Services (IJISS) and Pakistan Journal of\nLibrary and Information Science has been carried out. Seven Volumes of the\nIJISS containing 14 issues and Seven volumes of PJLIS containing 8 issues from\n2011 - 2017 have been taken into consideration for the present study. The\nnumber of contributions, authorship pattern & author productivity, average\ncitations, average length of articles, average keywords and collaborative\npapers has been analyzed. Out of 146 of IJISS contributions, only 39 are single\nauthored and rest by multi authored with degree of collaboration 0.73 and week\ncollaboration among the authors and from 59 contributions of PJLIS only 18 are\nsingle authored and rest by multi authored with degree of collaboration 0.69\nand week collaboration among the authors. The study revealed that the author\nproductivity is 0.53 (IJISS) and 0.50 (PJLIS) and dominated by the Indian and\nPakistani authors.\n"}
+{"abstract": "  Load-generation balance and system inertia are essential for maintaining\nfrequency in power systems. Power grids are equipped with Rate-of-Change-of\nFrequency (ROCOF) and Load Shedding (LS) relays in order to keep\nload-generation balance. With the increasing penetration of renewables, the\ninertia of the power grids is declining, which results in a faster drop in\nsystem frequency in case of load-generation imbalance. In this context, we\nanalyze the feasibility of launching False Data Injection (FDI) in order to\ncreate False Relay Operations (FRO), which we refer to as FRO attack, in the\npower systems with high renewables. We model the frequency dynamics of the\npower systems and corresponding FDI attacks, including the impact of\nparameters, such as synchronous generators inertia, and governors time constant\nand droop, on the success of FRO attacks. We formalize the FRO attack as a\nConstraint Satisfaction Problem (CSP) and solve using Satisfiability Modulo\nTheories (SMT). Our case studies show that power grids with renewables are more\nsusceptible to FRO attacks and the inertia of synchronous generators plays a\ncritical role in reducing the success of FRO attacks in the power grids.\n"}
+{"abstract": "  Motivated by the phenomenon of Coherent Perfect Absorption, we study the\nshape of the deepest minima in the frequency-dependent single-channel\nreflection of waves from a cavity with spatially uniform losses. We show that\nit is largely determined by non-orthogonality factors $O_{nn}$ of the\neigenmodes associated with the non-selfadjoint effective Hamiltonian. For\ncavities supporting chaotic ray dynamics we then use random matrix theory to\nderive, fully non-perturbatively, the explicit probability density ${\\cal\nP}(O_{nn})$ of the non-orthogonality factors for systems with both broken and\npreserved time reversal symmetry. The results imply that $O_{nn}$ are\nheavy-tail distributed, with the universal tail ${\\cal P}(O_{nn}\\gg 1)\\sim\nO_{nn}^{-3}$.\n"}
+{"abstract": "  Expanding an idea of Raoul Bott, we propose a construction of canonical bases\nfor unitary representations that comes from big torus actions on families of\nBott-Samelson manifolds. The construction depends only on the choices of a\nmaximal torus, a Borel subgroup ,and a reduced expression for the longest\nelement of the Weyl group. It relies on a conjectural vanishing of higher\ncohomology of sheaves of holomorphic sections of certain line bundles on the\ntotal spaces of the families, hence the question mark in the title.\n"}
+{"abstract": "  We present an end-to-end model using streaming physiological time series to\naccurately predict near-term risk for hypoxemia, a rare, but life-threatening\ncondition known to cause serious patient harm during surgery. Our proposed\nmodel makes inference on both hypoxemia outcomes and future input sequences,\nenabled by a joint sequence autoencoder that simultaneously optimizes a\ndiscriminative decoder for label prediction, and two auxiliary decoders trained\nfor data reconstruction and forecast, which seamlessly learns future-indicative\nlatent representation. All decoders share a memory-based encoder that helps\ncapture the global dynamics of patient data. In a large surgical cohort of\n73,536 surgeries at a major academic medical center, our model outperforms all\nbaselines and gives a large performance gain over the state-of-the-art\nhypoxemia prediction system. With a high sensitivity cutoff at 80%, it presents\n99.36% precision in predicting hypoxemia and 86.81% precision in predicting the\nmuch more severe and rare hypoxemic condition, persistent hypoxemia. With\nexceptionally low rate of false alarms, our proposed model is promising in\nimproving clinical decision making and easing burden on the health system.\n"}
+{"abstract": "  An effective approach in meta-learning is to utilize multiple \"train tasks\"\nto learn a good initialization for model parameters that can help solve unseen\n\"test tasks\" with very few samples by fine-tuning from this initialization.\nAlthough successful in practice, theoretical understanding of such methods is\nlimited. This work studies an important aspect of these methods: splitting the\ndata from each task into train (support) and validation (query) sets during\nmeta-training. Inspired by recent work (Raghu et al., 2020), we view such\nmeta-learning methods through the lens of representation learning and argue\nthat the train-validation split encourages the learned representation to be\nlow-rank without compromising on expressivity, as opposed to the non-splitting\nvariant that encourages high-rank representations. Since sample efficiency\nbenefits from low-rankness, the splitting strategy will require very few\nsamples to solve unseen test tasks. We present theoretical results that\nformalize this idea for linear representation learning on a subspace\nmeta-learning instance, and experimentally verify this practical benefit of\nsplitting in simulations and on standard meta-learning benchmarks.\n"}
+{"abstract": "  Characterizing multipartite quantum correlations beyond two parties is of\nutmost importance for building cutting edge quantum technologies, although the\ncomprehensive picture is still missing. Here we investigate quantum\ncorrelations (QCs) present in a multipartite system by exploring connections\nbetween monogamy score (MS), localizable quantum correlations (LQC), and\ngenuine multipartite entanglement (GME) content of the state. We find that the\nfrequency distribution of GME for Dicke states with higher excitations\nresembles that of random states. We show that there is a critical value of GME\nbeyond which all states become monogamous and it is investigated by considering\ndifferent powers of MS which provide various layers of monogamy relations.\nInterestingly, such a relation between LQC and MS as well as GME does not hold.\nStates having a very low GME (low monogamy score, both positive and negative)\ncan localize a high amount of QCs in two parties. We also provide an upper\nbound to the sum of bipartite QC measures including LQC for random states and\nestablish a gap between the actual upper bound and the algebraic maximum.\n"}
+{"abstract": "  We present a novel weighted average model based on the mixture of experts\n(MoE) concept to provide robustness in Federated learning (FL) against the\npoisoned/corrupted/outdated local models. These threats along with the non-IID\nnature of data sets can considerably diminish the accuracy of the FL model. Our\nproposed MoE-FL setup relies on the trust between users and the server where\nthe users share a portion of their public data sets with the server. The server\napplies a robust aggregation method by solving the optimization problem or the\nSoftmax method to highlight the outlier cases and to reduce their adverse\neffect on the FL process. Our experiments illustrate that MoE-FL outperforms\nthe performance of the traditional aggregation approach for high rate of\npoisoned data from attackers.\n"}
+{"abstract": "  Primordial black holes (PBHs) as part of the Dark Matter (DM) would modify\nthe evolution of large-scale structures and the thermal history of the\nuniverse. Future 21 cm forest observations, sensitive to small scales and the\nthermal state of the Inter Galactic Medium (IGM), could probe the existence of\nsuch PBHs. In this article, we show that the shot noise isocurvature mode on\nsmall scales induced by the presence of PBHs can enhance the amount of low mass\nhalos, or minihalos, and thus, the number of 21 cm absorption lines. However,\nif the mass of PBHs is as large as $M_{\\rm PBH}\\gtrsim 10 \\, M_\\odot$, with an\nabundant enough fraction of PBHs as DM, $f_{\\rm PBH}$, the IGM heating due to\naccretion onto the PBHs counteracts the enhancement due to the isocurvature\nmode, reducing the number of absorption lines instead. The concurrence of both\neffects imprints distinctive signatures in the number of absorbers, allowing to\nbound the abundance of PBHs. We compute the prospects for constraining PBHs\nwith future 21 cm forest observations, finding achievable competitive upper\nlimits on the abundance as low as $f_{\\rm PBH} \\sim 10^{-3}$ at $M_{\\rm PBH}=\n100 \\, M_\\odot$, or even lower at larger masses, in unexplored regions of the\nparameter space by current probes. The impact of astrophysical X-ray sources on\nthe IGM temperature is also studied, which could potentially weaken the bounds.\n"}
+{"abstract": "  Self-adaptive systems continuously adapt to changes in their execution\nenvironment. Capturing all possible changes to define suitable behaviour\nbeforehand is unfeasible, or even impossible in the case of unknown changes,\nhence human intervention may be required. We argue that adapting to unknown\nsituations is the ultimate challenge for self-adaptive systems. Learning-based\napproaches are used to learn the suitable behaviour to exhibit in the case of\nunknown situations, to minimize or fully remove human intervention. While such\napproaches can, to a certain extent, generalize existing adaptations to new\nsituations, there is a number of breakthroughs that need to be achieved before\nsystems can adapt to general unknown and unforeseen situations. We posit the\nresearch directions that need to be explored to achieve unanticipated\nadaptation from the perspective of learning-based self-adaptive systems. At\nminimum, systems need to define internal representations of previously unseen\nsituations on-the-fly, extrapolate the relationship to the previously\nencountered situations to evolve existing adaptations, and reason about the\nfeasibility of achieving their intrinsic goals in the new set of conditions. We\nclose discussing whether, even when we can, we should indeed build systems that\ndefine their own behaviour and adapt their goals, without involving a human\nsupervisor.\n"}
+{"abstract": "  For a finite group $G$ and an inverse-closed generating set $C$ of $G$, let\n$Aut(G;C)$ consist of those automorphisms of $G$ which leave $C$ invariant. We\ndefine an $Aut(G;C)$-invariant normal subgroup $\\Phi(G;C)$ of $G$ which has the\nproperty that, for any $Aut(G;C)$-invariant normal set of generators for $G$,\nif we remove from it all the elements of $\\Phi(G;C)$, then the remaining set is\nstill an $Aut(G;C)$-invariant normal generating set for $G$. The subgroup\n$\\Phi(G;C)$ contains the Frattini subgroup $\\Phi(G)$ but the inclusion may be\nproper. The Cayley graph $Cay(G,C)$ is normal edge-transitive if $Aut(G;C)$\nacts transitively on the pairs $\\{c,c^{-1}\\}$ from $C$. We show that, for a\nnormal edge-transitive Cayley graph $Cay(G,C)$, its quotient modulo $\\Phi(G;C)$\nis the unique largest normal quotient which is isomorphic to a subdirect\nproduct of normal edge-transitive graphs of characteristically simple groups.\nIn particular, we may therefore view normal edge-transitive Cayley graphs of\ncharacteristically simple groups as building blocks for normal edge-transitive\nCayley graphs whenever the subgroup $\\Phi(G;C)$ is trivial. We explore several\nquestions which these results raise, some concerned with the set of all\ninverse-closed generating sets for groups in a given family. In particular we\nuse this theory to classify all $4$-valent normal edge-transitive Cayley graphs\nfor dihedral groups; this involves a new construction of an infinite family of\nexamples, and disproves a conjecture of Talebi.\n"}
+{"abstract": "  With the rapid development of E-commerce and the increase in the quantity of\nitems, users are presented with more items hence their interests broaden. It is\nincreasingly difficult to model user intentions with traditional methods, which\nmodel the user's preference for an item by combining a single user vector and\nan item vector. Recently, some methods are proposed to generate multiple user\ninterest vectors and achieve better performance compared to traditional\nmethods. However, empirical studies demonstrate that vectors generated from\nthese multi-interests methods are sometimes homogeneous, which may lead to\nsub-optimal performance. In this paper, we propose a novel method of Diversity\nRegularized Interests Modeling (DRIM) for Recommender Systems. We apply a\ncapsule network in a multi-interest extractor to generate multiple user\ninterest vectors. Each interest of the user should have a certain degree of\ndistinction, thus we introduce three strategies as the diversity regularized\nseparator to separate multiple user interest vectors. Experimental results on\npublic and industrial data sets demonstrate the ability of the model to capture\ndifferent interests of a user and the superior performance of the proposed\napproach.\n"}
+{"abstract": "  In the decade since 2010, successes in artificial intelligence have been at\nthe forefront of computer science and technology, and vector space models have\nsolidified a position at the forefront of artificial intelligence. At the same\ntime, quantum computers have become much more powerful, and announcements of\nmajor advances are frequently in the news.\n  The mathematical techniques underlying both these areas have more in common\nthan is sometimes realized. Vector spaces took a position at the axiomatic\nheart of quantum mechanics in the 1930s, and this adoption was a key motivation\nfor the derivation of logic and probability from the linear geometry of vector\nspaces. Quantum interactions between particles are modelled using the tensor\nproduct, which is also used to express objects and operations in artificial\nneural networks.\n  This paper describes some of these common mathematical areas, including\nexamples of how they are used in artificial intelligence (AI), particularly in\nautomated reasoning and natural language processing (NLP). Techniques discussed\ninclude vector spaces, scalar products, subspaces and implication, orthogonal\nprojection and negation, dual vectors, density matrices, positive operators,\nand tensor products. Application areas include information retrieval,\ncategorization and implication, modelling word-senses and disambiguation,\ninference in knowledge bases, and semantic composition.\n  Some of these approaches can potentially be implemented on quantum hardware.\nMany of the practical steps in this implementation are in early stages, and\nsome are already realized. Explaining some of the common mathematical tools can\nhelp researchers in both AI and quantum computing further exploit these\noverlaps, recognizing and exploring new directions along the way.\n"}
+{"abstract": "  Some implementations of variable neighborhood search based algorithms were\npresented in \\emph{C\\'ecilia Daquin, Hamid Allaoui, Gilles Goncalves and\nTient\\'e Hsu, Variable neighborhood search based algorithms for crossdock truck\nassignment, RAIRO-Oper. Res., 55 (2021) 2291-2323}. This work is based on model\nin \\emph{Zhaowei Miao, Andrew Lim, Hong Ma, Truck dock assignment problem with\noperational time constraint within crossdocks, European Journal of Operational\nResearch 192 (1), 2009, 105-115 }m which has been proven to be incorrect. We\nreiterate and elaborate on the deficiencies in the latter and show that the\nauthors in the former were already aware of the deficiencies in the latter and\nthe proposed minor amendment does not overcome any of such deficiencies.\n"}
+{"abstract": "  Gamma-ray data from the Fermi-Large Area Telescope reveal an unexplained,\napparently diffuse, signal from the Galactic bulge. The origin of this\n\"Galactic Center Excess\" (GCE) has been debated with proposed sources\nprominently including self-annihilating dark matter and a hitherto undetected\npopulation of millisecond pulsars (MSPs). We use a binary population synthesis\nforward model to demonstrate that an MSP population arising from the accretion\ninduced collapse of O-Ne white dwarfs in Galactic bulge binaries can naturally\nexplain the GCE. Synchrotron emission from MSP-launched cosmic ray electrons\nand positrons seems also to explain the mysterious \"haze\" of hard-spectrum,\nnon-thermal microwave emission from the inner Galaxy detected in WMAP and\nPlanck data.\n"}
+{"abstract": "  In HTTP Adaptive Streaming, video content is conventionally encoded by\nadapting its spatial resolution and quantization level to best match the\nprevailing network state and display characteristics. It is well known that the\ntraditional solution, of using a fixed bitrate ladder, does not result in the\nhighest quality of experience for the user. Hence, in this paper, we consider a\ncontent-driven approach for estimating the bitrate ladder, based on\nspatio-temporal features extracted from the uncompressed content. The method\nimplements a content-driven interpolation. It uses the extracted features to\ntrain a machine learning model to infer the curvature points of the Rate-VMAF\ncurves in order to guide a set of initial encodings. We employ the VMAF quality\nmetric as a means of perceptually conditioning the estimation. When compared to\nexhaustive encoding that produces the reference ladder, the estimated ladder is\ncomposed by 74.3% of identical Rate-VMAF points with the reference ladder. The\nproposed method offers a significant reduction of the number of encodes\nrequired, 77.4%, at a small average Bj{\\o}ntegaard Delta Rate cost, 1.12%.\n"}
+{"abstract": "  Recently, In the year 2020, Altun et al. \\cite{AL} introduced the notion of\n$p$-proximal contractions and discussed about best proximity point results for\nthis class of mappings. Then in the year 2021, Gabeleh and Markin \\cite{GB}\nshowed that the best proximity point theorem proved by Altun et al. in\n\\cite{AL} follows from the fixed point theory. In this short note, we show that\nif the $p$-proximal contraction constant $k<\\frac{1}{3}$ then the existence of\nbest proximity point for $p$-proximal contractions follows from the celebrated\nBanach contraction principle.\n"}
+{"abstract": "  Radiomic representations can quantify properties of regions of interest in\nmedical image data. Classically, they account for pre-defined statistics of\nshape, texture, and other low-level image features. Alternatively, deep\nlearning-based representations are derived from supervised learning but require\nexpensive annotations from experts and often suffer from overfitting and data\nimbalance issues. In this work, we address the challenge of learning\nrepresentations of 3D medical images for an effective quantification under data\nimbalance. We propose a \\emph{self-supervised} representation learning\nframework to learn high-level features of 3D volumes as a complement to\nexisting radiomics features. Specifically, we demonstrate how to learn image\nrepresentations in a self-supervised fashion using a 3D Siamese network. More\nimportantly, we deal with data imbalance by exploiting two unsupervised\nstrategies: a) sample re-weighting, and b) balancing the composition of\ntraining batches. When combining our learned self-supervised feature with\ntraditional radiomics, we show significant improvement in brain tumor\nclassification and lung cancer staging tasks covering MRI and CT imaging\nmodalities.\n"}
+{"abstract": "  We study decoupling theory for functions on $\\mathbb{R}$ with Fourier\ntransform supported in a neighborhood of short Dirichlet sequences $\\{\\log\nn\\}_{n=N+1}^{N+N^{1/2}}$, as well as sequences with similar convexity\nproperties. We utilize the wave packet structure of functions with frequency\nsupport near an arithmetic progression.\n"}
+{"abstract": "  This paper defines a methodology with in-depth data to identify the skills\nneeded by riders in the highest risk crash configurations to reduce casualty\nrates. We present a case study using in-depth data of 803 powered-two-wheeler\ncrashes. Seven high-risk crash configuration based on the pre-crash\ntrajectories of the road-users involved were considered to investigate the\nhuman errors as crash contributors. Primary crash contributing factor, evasive\nmanoeuvres performed, horizontal roadway alignment and speed-related factors\nwere identified, along with the most frequent configurations and those with the\ngreatest risk of severe injury. Straight Crossing Path/Lateral Direction was\nthe most frequent crash configuration and Turn Across Path/ Opposing Direction\nthat with the greatest risk of serious injury were identified. Multi-vehicle\ncrashes cannot be considered as a homogenous category of crashes to which the\nsame human failure is attributed, as different interactions between\nmotorcyclists and other road users are associated with both different types of\nhuman error and different rider reactions. Human error in multiple-vehicle\ncrashes related to crossing paths configurations were different from errors\nrelated to rear-end or head-on crashes. Multi-vehicle head-on crashes and\nsingle-vehicle collisions frequently occur along curves. The involved collision\navoidance manoeuvres of the riders differed significantly among the highest\nrisk crash configurations. The most relevant lack of skills are identified and\nlinked to their most representative context. In most cases a combination of\ndifferent skills was required simultaneously to avoid the crash. The findings\nunderline the need to group accident cases, beyond the usual single-vehicle\nversus multi-vehicle collision approach. Our methodology can also be applied to\nsupport preventive actions based on riders training and eventually ADAS design.\n"}
+{"abstract": "  We continue to study the optical properties of the solar gravitational lens\n(SGL). The aim is prospective applications of the SGL for imaging purposes. We\ninvestigate the solution of Maxwell's equations for the electromagnetic (EM)\nfield, obtained on the background of a static gravitational field of the Sun.\nWe now treat the Sun as an extended body with a gravitational field that can be\ndescribed using an infinite series of gravitational multipole moments. Studying\nthe propagation of monochromatic EM waves in this extended solar gravitational\nfield, we develop a wave-optical treatment of the SGL that allows us to study\nthe caustics formed in an image plane in the SGL's strong interference region.\nWe investigate the EM field in several important regions, namely i) the area in\nthe inner part of the caustic and close to the optical axis, ii) the region\noutside the caustic, and iii) the region in the immediate vicinity of the\ncaustic, especially around its cusps and folds. We show that in the first two\nregions the physical behavior of the EM field may be understood using the\nmethod of stationary phase. However, in the immediate vicinity of the caustic\nthe method of stationary phase is inadequate and a wave-optical treatment is\nnecessary. Relying on the angular eikonal method, we develop a new approach to\ndescribe the EM field accurately in all regions, including the immediate\nvicinity of the caustics and especially near the cusps and folds. The method\nallows us to investigate the EM field in this important region, which is\ncharacterized by rapidly oscillating behavior. Our results are new and can be\nused to describe gravitational lensing by realistic astrophysical objects, such\nas stars, spiral and elliptical galaxies.\n"}
+{"abstract": "  In two spatial dimensions, there are very few global existence results for\nthe Kuramoto-Sivashinsky equation. The majority of the few results in the\nliterature are strongly anisotropic, i.e. are results of thin-domain type. In\nthe spatially periodic case, the dynamics of the Kuramoto-Sivashinsky equation\nare in part governed by the size of the domain, as this determines how many\nlinearly growing Fourier modes are present. The strongly anisotropic results\nallow linearly growing Fourier modes in only one of the spatial directions. We\nprovide here the first proof of global solutions for the two-dimensional\nKuramoto-Sivashinsky equation with a linearly growing mode in both spatial\ndirections. We develop a new method to this end, categorizing wavenumbers as\nlow (linearly growing modes), intermediate (linearly decaying modes which serve\nas energy sinks for the low modes), and high (strongly linearly decaying\nmodes). The low and intermediate modes are controlled by means of a Lyapunov\nfunction, while the high modes are controlled with operator estimates in\nfunction spaces based on the Wiener algebra.\n"}
+{"abstract": "  Improving the predictive capability of molecular properties in ab initio\nsimulations is essential for advanced material discovery. Despite recent\nprogress making use of machine learning, utilizing deep neural networks to\nimprove quantum chemistry modelling remains severely limited by the scarcity\nand heterogeneity of appropriate experimental data. Here we show how training a\nneural network to replace the exchange-correlation functional within a\nfully-differentiable three-dimensional Kohn-Sham density functional theory\n(DFT) framework can greatly improve simulation accuracy. Using only eight\nexperimental data points on diatomic molecules, our trained\nexchange-correlation networks enable improved prediction accuracy of\natomization energies across a collection of 104 molecules containing new bonds\nand atoms that are not present in the training dataset.\n"}
+{"abstract": "  Background and Objective:Computer-aided diagnosis (CAD) systems promote\ndiagnosis effectiveness and alleviate pressure of radiologists. A CAD system\nfor lung cancer diagnosis includes nodule candidate detection and nodule\nmalignancy evaluation. Recently, deep learning-based pulmonary nodule detection\nhas reached satisfactory performance ready for clinical application. However,\ndeep learning-based nodule malignancy evaluation depends on heuristic inference\nfrom low-dose computed tomography volume to malignant probability, which lacks\nclinical cognition. Methods:In this paper, we propose a joint radiology\nanalysis and malignancy evaluation network (R2MNet) to evaluate the pulmonary\nnodule malignancy via radiology characteristics analysis. Radiological features\nare extracted as channel descriptor to highlight specific regions of the input\nvolume that are critical for nodule malignancy evaluation. In addition, for\nmodel explanations, we propose channel-dependent activation mapping to\nvisualize the features and shed light on the decision process of deep neural\nnetwork. Results:Experimental results on the LIDC-IDRI dataset demonstrate that\nthe proposed method achieved area under curve of 96.27% on nodule radiology\nanalysis and AUC of 97.52% on nodule malignancy evaluation. In addition,\nexplanations of CDAM features proved that the shape and density of nodule\nregions were two critical factors that influence a nodule to be inferred as\nmalignant, which conforms with the diagnosis cognition of experienced\nradiologists. Conclusion:Incorporating radiology analysis with nodule malignant\nevaluation, the network inference process conforms to the diagnostic procedure\nof radiologists and increases the confidence of evaluation results. Besides,\nmodel interpretation with CDAM features shed light on the regions which DNNs\nfocus on when they estimate nodule malignancy probabilities.\n"}
+{"abstract": "  We demonstrate that crystal defects can act as a probe of intrinsic\nnon-Hermitian topology. In particular, in point-gapped systems with periodic\nboundary conditions, a pair of dislocations may induce a non-Hermitian skin\neffect, where an extensive number of Hamiltonian eigenstates localize at only\none of the two dislocations. An example of such a phase are two-dimensional\nsystems exhibiting weak non-Hermitian topology, which are adiabatically related\nto a decoupled stack of Hatano-Nelson chains. Moreover, we show that strong\ntwo-dimensional point-gap topology may also result in a dislocation response,\neven when there is no skin effect present with open boundary conditions. For\nboth cases, we directly relate their bulk topology to a stable dislocation\nnon-Hermitian skin effect. Finally, and in stark contrast to the Hermitian\ncase, we find that gapless non-Hermitian systems hosting bulk exceptional\npoints also give rise to a well-localized dislocation response.\n"}
+{"abstract": "  After discussing the limitations inherent to all set-theoretic reflection\nprinciples akin to those studied by A. L\\'evy et. al. in the 1960's, we\nintroduce new principles of reflection based on the general notion of\n\\emph{Structural Reflection} and argue that they are in strong agreement with\nthe conception of reflection implicit in Cantor's original idea of the\nunknowability of the \\emph{Absolute}, which was subsequently developed in the\nworks of Ackermann, L\\'evy, G\\\"odel, Reinhardt, and others. We then present a\ncomprehensive survey of results showing that different forms of the new\nprinciples of Structural Reflection are equivalent to well-known large\ncardinals axioms covering all regions of the large-cardinal hierarchy, thereby\njustifying the naturalness of the latter.\n"}
+{"abstract": "  In this paper, we investigate the task of hallucinating an authentic\nhigh-resolution (HR) human face from multiple low-resolution (LR) video\nsnapshots. We propose a pure transformer-based model, dubbed VidFace, to fully\nexploit the full-range spatio-temporal information and facial structure cues\namong multiple thumbnails. Specifically, VidFace handles multiple snapshots all\nat once and harnesses the spatial and temporal information integrally to\nexplore face alignments across all the frames, thus avoiding accumulating\nalignment errors. Moreover, we design a recurrent position embedding module to\nequip our transformer with facial priors, which not only effectively\nregularises the alignment mechanism but also supplants notorious pre-training.\nFinally, we curate a new large-scale video face hallucination dataset from the\npublic Voxceleb2 benchmark, which challenges prior arts on tackling unaligned\nand tiny face snapshots. To the best of our knowledge, we are the first attempt\nto develop a unified transformer-based solver tailored for video-based face\nhallucination. Extensive experiments on public video face benchmarks show that\nthe proposed method significantly outperforms the state of the arts.\n"}
+{"abstract": "  To support faster and more efficient networks, mobile operators and service\nproviders are bringing 5G millimeter wave (mmWave) networks indoors. However,\ndue to their high directionality, mmWave links are extremely vulnerable to\nblockage by walls and human mobility. To address these challenges, we exploit\nadvances in artificially engineered metamaterials, introducing a wall-mounted\nsmart metasurface, called mmWall, that enables a fast mmWave beam relay through\nthe wall and redirects the beam power to another direction when a human body\nblocks a line-of-sight path. Moreover, our mmWall supports multiple users and\nfast beam alignment by generating multi-armed beams. We sketch the design of a\nreal-time system by considering (1) how to design a programmable,\nmetamaterial-based surface that refracts the incoming signal to one or more\narbitrary directions, and (2) how to split an incoming mmWave beam into\nmultiple outgoing beams and arbitrarily control the beam energy between these\nbeams. Preliminary results show the mmWall metasurface steers the outgoing beam\nin a full 360-degrees, with an 89.8% single-beam efficiency and 74.5%\ndouble-beam efficiency.\n"}
+{"abstract": "  Given a set P of n points in the plane, the unit-disk graph G_{r}(P) with\nrespect to a parameter r is an undirected graph whose vertex set is P such that\nan edge connects two points p, q \\in P if the Euclidean distance between p and\nq is at most r (the weight of the edge is 1 in the unweighted case and is the\ndistance between p and q in the weighted case). Given a value \\lambda>0 and two\npoints s and t of P, we consider the following reverse shortest path problem:\ncomputing the smallest r such that the shortest path length between s and t in\nG_r(P) is at most \\lambda. In this paper, we present an algorithm of O(\\lfloor\n\\lambda \\rfloor \\cdot n \\log n) time and another algorithm of O(n^{5/4}\n\\log^{7/4} n) time for the unweighted case, as well as an O(n^{5/4} \\log^{5/2}\nn) time algorithm for the weighted case. We also consider the L_1 version of\nthe problem where the distance of two points is measured by the L_1 metric; we\nsolve the problem in O(n \\log^3 n) time for both the unweighted and weighted\ncases.\n"}
+{"abstract": "  In recent years, deep neural networks (DNNs) achieved state-of-the-art\nperformance on several computer vision tasks. However, the one typical drawback\nof these DNNs is the requirement of massive labeled data. Even though few-shot\nlearning methods address this problem, they often use techniques such as\nmeta-learning and metric-learning on top of the existing methods. In this work,\nwe address this problem from a neuroscience perspective by proposing a\nhypothesis named Ikshana, which is supported by several findings in\nneuroscience. Our hypothesis approximates the refining process of conceptual\ngist in the human brain while understanding a natural scene/image. While our\nhypothesis holds no particular novelty in neuroscience, it provides a novel\nperspective for designing DNNs for vision tasks. By following the Ikshana\nhypothesis, we design a novel neural-inspired CNN architecture named\nIkshanaNet. The empirical results demonstrate the effectiveness of our method\nby outperforming several baselines on the entire and subsets of the Cityscapes\nand the CamVid semantic segmentation benchmarks.\n"}
+{"abstract": "  Deep learning (DL) has gained much attention and become increasingly popular\nin modern data science. Computer scientists led the way in developing deep\nlearning techniques, so the ideas and perspectives can seem alien to\nstatisticians. Nonetheless, it is important that statisticians become involved\n-- many of our students need this expertise for their careers. In this paper,\ndeveloped as part of a program on DL held at the Statistical and Applied\nMathematical Sciences Institute, we address this culture gap and provide tips\non how to teach deep learning to statistics graduate students. After some\nbackground, we list ways in which DL and statistical perspectives differ,\nprovide a recommended syllabus that evolved from teaching two iterations of a\nDL graduate course, offer examples of suggested homework assignments, give an\nannotated list of teaching resources, and discuss DL in the context of two\nresearch areas.\n"}
+{"abstract": "  The problem of finding near-stationary points in convex optimization has not\nbeen adequately studied yet, unlike other optimality measures such as\nminimizing function value. Even in the deterministic case, the optimal method\n(OGM-G, due to Kim and Fessler (2021)) has just been discovered recently. In\nthis work, we conduct a systematic study of the algorithmic techniques in\nfinding near-stationary points of convex finite-sums. Our main contributions\nare several algorithmic discoveries: (1) we discover a memory-saving variant of\nOGM-G based on the performance estimation problem approach (Drori and Teboulle,\n2014); (2) we design a new accelerated SVRG variant that can simultaneously\nachieve fast rates for both minimizing gradient norm and function value; (3) we\npropose an adaptively regularized accelerated SVRG variant, which does not\nrequire the knowledge of some unknown initial constants and achieves\nnear-optimal complexities. We put an emphasis on the simplicity and\npracticality of the new schemes, which could facilitate future developments.\n"}
+{"abstract": "  Digital pathology tasks have benefited greatly from modern deep learning\nalgorithms. However, their need for large quantities of annotated data has been\nidentified as a key challenge. This need for data can be countered by using\nunsupervised learning in situations where data are abundant but access to\nannotations is limited. Feature representations learned from unannotated data\nusing contrastive predictive coding (CPC) have been shown to enable classifiers\nto obtain state of the art performance from relatively small amounts of\nannotated computer vision data. We present a modification to the CPC framework\nfor use with digital pathology patches. This is achieved by introducing an\nalternative mask for building the latent context and using a multi-directional\nPixelCNN autoregressor. To demonstrate our proposed method we learn feature\nrepresentations from the Patch Camelyon histology dataset. We show that our\nproposed modification can yield improved deep classification of histology\npatches.\n"}
+{"abstract": "  Exotic high-rank multipolar order parameters have been found to be\nunexpectedly active in more and more correlated materials in recent years. Such\nmultipoles are usually dubbed as \"Hidden Orders\" since they are insensitive to\ncommon experimental probes. Theoretically, it is also difficult to predict\nmultipolar orders via \\textit{ab initio} calculations in real materials. Here,\nwe present an efficient method to predict possible multipoles in materials\nbased on linear response theory under random phase approximation. Using this\nmethod, we successfully predict two pure meta-stable magnetic octupolar states\nin monolayer $\\alpha$-\\ce{RuCl3}, which is confirmed by self-consistent\nunrestricted Hartree-Fock calculations. We then demonstrate that these\noctupolar states can be stabilized in monolayer $\\alpha$-\\ce{RuI3}, one of\nwhich becomes the octupolar ground state. Furthermore, we also predict a\nfingerprint of orthogonal magnetization pattern produced by the octupole\nmoment, which can be easily detected by experiment. The method and the example\npresented in this work serve as a guidance for searching multipolar order\nparameters in other correlated materials.\n"}
+{"abstract": "  With sequentially stacked self-attention, (optional) encoder-decoder\nattention, and feed-forward layers, Transformer achieves big success in natural\nlanguage processing (NLP), and many variants have been proposed. Currently,\nalmost all these models assume that the layer order is fixed and kept the same\nacross data samples. We observe that different data samples actually favor\ndifferent orders of the layers. Based on this observation, in this work, we\nbreak the assumption of the fixed layer order in the Transformer and introduce\ninstance-wise layer reordering into the model structure. Our Instance-wise\nOrdered Transformer (IOT) can model variant functions by reordered layers,\nwhich enables each sample to select the better one to improve the model\nperformance under the constraint of almost the same number of parameters. To\nachieve this, we introduce a light predictor with negligible parameter and\ninference cost to decide the most capable and favorable layer order for any\ninput sequence. Experiments on 3 tasks (neural machine translation, abstractive\nsummarization, and code generation) and 9 datasets demonstrate consistent\nimprovements of our method. We further show that our method can also be applied\nto other architectures beyond Transformer. Our code is released at Github.\n"}
+{"abstract": "  This article shows that achieving capacity region of a 2-users weak Gaussian\nInterference Channel (GIC) is equivalent to enlarging the core in a nested set\nof Polymatroids (each equivalent to capacity region of a multiple-access\nchannel) through maximizing a minimum rate, then projecting along its\northogonal span and continuing recursively. This formulation relies on defining\ndummy private messages to capture the effect of interference in GIC. It follows\nthat relying on independent Gaussian random code-books is optimum, and the\ncorresponding solution corresponds to achieving the boundary in HK constraints.\n"}
+{"abstract": "  In this paper, we represent the problem of selecting miners within a\nblockchain-based system as a subset selection problem. We formulate the problem\nof minimising blockchain energy consumption as an optimisation problem with two\nconflicting objectives: energy consumption and trust. The proposed model is\ncompared across different algorithms to demonstrate its performance.\n"}
+{"abstract": "  Edge computing offers the distinct advantage of harnessing compute\ncapabilities on resources located at the edge of the network to run workloads\nof relatively weak user devices. This is achieved by offloading computationally\nintensive workloads, such as deep learning from user devices to the edge. Using\nthe edge reduces the overall communication latency of applications as workloads\ncan be processed closer to where data is generated on user devices rather than\nsending them to geographically distant clouds. Specialised hardware\naccelerators, such as Graphics Processing Units (GPUs) available in the\ncloud-edge network can enhance the performance of computationally intensive\nworkloads that are offloaded from devices on to the edge. The underlying\napproach required to facilitate this is virtualization of GPUs. This paper\ntherefore sets out to investigate the potential of GPU accelerator\nvirtualization to improve the performance of deep learning workloads in a\ncloud-edge environment. The AVEC accelerator virtualization framework is\nproposed that incurs minimum overheads and requires no source-code modification\nof the workload. AVEC intercepts local calls to a GPU on a device and forwards\nthem to an edge resource seamlessly. The feasibility of AVEC is demonstrated on\na real-world application, namely OpenPose using the Caffe deep learning\nlibrary. It is observed that on a lab-based experimental test-bed AVEC delivers\nup to 7.48x speedup despite communication overheads incurred due to data\ntransfers.\n"}
+{"abstract": "  As herd size on dairy farms continues to increase, automatic health\nmonitoring of cows is gaining in interest. Lameness, a prevalent health\ndisorder in dairy cows, is commonly detected by analyzing the gait of cows. A\ncow's gait can be tracked in videos using pose estimation models because models\nlearn to automatically localize anatomical landmarks in images and videos. Most\nanimal pose estimation models are static, that is, videos are processed frame\nby frame and do not use any temporal information. In this work, a static\ndeep-learning model for animal-pose-estimation was extended to a temporal model\nthat includes information from past frames. We compared the performance of the\nstatic and temporal pose estimation models. The data consisted of 1059 samples\nof 4 consecutive frames extracted from videos (30 fps) of 30 different dairy\ncows walking through an outdoor passageway. As farm environments are prone to\nocclusions, we tested the robustness of the static and temporal models by\nadding artificial occlusions to the videos.The experiments showed that, on\nnon-occluded data, both static and temporal approaches achieved a Percentage of\nCorrect Keypoints (PCKh@0.2) of 99%. On occluded data, our temporal approach\noutperformed the static one by up to 32.9%, suggesting that using temporal data\nwas beneficial for pose estimation in environments prone to occlusions, such as\ndairy farms. The generalization capabilities of the temporal model was\nevaluated by testing it on data containing unknown cows (cows not present in\nthe training set). The results showed that the average PCKh@0.2 was of 93.8% on\nknown cows and 87.6% on unknown cows, indicating that the model was capable of\ngeneralizing well to new cows and that they could be easily fine-tuned to new\nherds. Finally, we showed that with harder tasks, such as occlusions and\nunknown cows, a deeper architecture was more beneficial.\n"}
+{"abstract": "  Document-level machine translation conditions on surrounding sentences to\nproduce coherent translations. There has been much recent work in this area\nwith the introduction of custom model architectures and decoding algorithms.\nThis paper presents a systematic comparison of selected approaches from the\nliterature on two benchmarks for which document-level phenomena evaluation\nsuites exist. We find that a simple method based purely on back-translating\nmonolingual document-level data performs as well as much more elaborate\nalternatives, both in terms of document-level metrics as well as human\nevaluation.\n"}
+{"abstract": "  The possibility that rotating black holes could be natural particle\naccelerators has been subject of intense debate. While it appears that for\nextremal Kerr black holes arbitrarily high center of mass energies could be\nachieved, several works pointed out that both theoretical as well as\nastrophysical arguments would severely dampen the attainable energies. In this\nwork we study particle collisions near Kerr--Newman black holes, by reviewing\nand extending previously proposed scenarios. Most importantly, we implement the\nhoop conjecture for all cases and we discuss the astrophysical relevance of\nthese collisional Penrose processes. The outcome of this investigation is that\nscenarios involving near-horizon target particles are in principle able to\nattain, sub-Planckian, but still ultra high, center of mass energies of the\norder of $10^{21}-10^{23}$ eV. Thus, these target particle collisional Penrose\nprocesses could contribute to the observed spectrum of ultra high-energy cosmic\nrays, even if the hoop conjecture is taken into account, and as such deserve\nfurther scrutiny in realistic settings.\n"}
+{"abstract": "  In this paper, we describe a method to tackle data sparsity and create\nrecommendations in domains with limited knowledge about user preferences. We\nexpand the variational autoencoder collaborative filtering from a single-domain\nto a multi-domain setting. The intuition is that user-item interactions in a\nsource domain can augment the recommendation quality in a target domain. The\nintuition can be taken to its extreme, where, in a cross-domain setup, the user\nhistory in a source domain is enough to generate high-quality recommendations\nin a target one. We thus create a Product-of-Experts (POE) architecture for\nrecommendations that jointly models user-item interactions across multiple\ndomains. The method is resilient to missing data for one or more of the\ndomains, which is a situation often found in real life. We present results on\ntwo widely-used datasets - Amazon and Yelp, which support the claim that\nholistic user preference knowledge leads to better recommendations.\nSurprisingly, we find that in some cases, a POE recommender that does not\naccess the target domain user representation can surpass a strong VAE\nrecommender baseline trained on the target domain.\n"}
+{"abstract": "  The KATRIN experiment is designed for a direct and model-independent\ndetermination of the effective electron anti-neutrino mass via a high-precision\nmeasurement of the tritium $\\beta$-decay endpoint region with a sensitivity on\n$m_\\nu$ of 0.2$\\,$eV/c$^2$ (90% CL). For this purpose, the $\\beta$-electrons\nfrom a high-luminosity windowless gaseous tritium source traversing an\nelectrostatic retarding spectrometer are counted to obtain an integral spectrum\naround the endpoint energy of 18.6$\\,$keV. A dominant systematic effect of the\nresponse of the experimental setup is the energy loss of $\\beta$-electrons from\nelastic and inelastic scattering off tritium molecules within the source. We\ndetermined the \\linebreak energy-loss function in-situ with a pulsed\nangular-selective and monoenergetic photoelectron source at various\ntritium-source densities. The data was recorded in integral and differential\nmodes; the latter was achieved by using a novel time-of-flight technique.\n  We developed a semi-empirical parametrization for the energy-loss function\nfor the scattering of 18.6-keV electrons from hydrogen isotopologs. This model\nwas fit to measurement data with a 95% T$_2$ gas mixture at 30$\\,$K, as used in\nthe first KATRIN neutrino mass analyses, as well as a D$_2$ gas mixture of 96%\npurity used in KATRIN commissioning runs. The achieved precision on the\nenergy-loss function has abated the corresponding uncertainty of\n$\\sigma(m_\\nu^2)<10^{-2}\\,\\mathrm{eV}^2$ [arXiv:2101.05253] in the KATRIN\nneutrino-mass measurement to a subdominant level.\n"}
+{"abstract": "  We present the results from a new search for candidate galaxies at z ~ 8.5-11\ndiscovered over the 850 arcmin^2 area probed by the Cosmic Assembly\nNear-Infrared Deep Extragalactic Legacy Survey (CANDELS). We use a photometric\nredshift selection including both Hubble and Spitzer Space Telescope photometry\nto robustly identify galaxies in this epoch at F160W < 26.6. We use a detailed\nvetting procedure, including screening for persistence, stellar contamination,\ninclusion of ground-based imaging, and followup space-based imaging to build a\nrobust sample of 11 candidate galaxies, three presented here for the first\ntime. The inclusion of Spitzer/IRAC photometry in the selection process reduces\ncontamination, and yields more robust redshift estimates than Hubble alone. We\nconstrain the evolution of the rest-frame ultraviolet luminosity function via a\nnew method of calculating the observed number densities without choosing a\nprior magnitude bin size. We find that the abundance at our brightest probed\nluminosities (M_UV=-22.3) is consistent with predictions from simulations which\nassume that galaxies in this epoch have gas depletion times at least as short\nas those in nearby starburst galaxies. Due to large Poisson and cosmic variance\nuncertainties we cannot conclusively rule out either a smooth evolution of the\nluminosity function continued from z=4-8, or an accelerate decline at z > 8. We\ncalculate that the presence of seven galaxies in a single field (EGS) is an\noutlier at the 2-sigma significance level, implying the discovery of a\nsignificant overdensity. These scenarios will be imminently testable to high\nconfidence within the first year of observations of the James Webb Space\nTelescope.\n"}
+{"abstract": "  This paper investigates the stability properties of the spectrum of the\nclassical Steklov problem under domain perturbation. We find conditions which\nguarantee the spectral stability and we show their optimality. We emphasize the\nfact that our spectral stability results also involve convergence of\neigenfunctions in a suitable sense according with the definition of connecting\nsystem by \\cite{Vainikko}. The convergence of eigenfunctions can be expressed\nin terms of the $H^1$ strong convergence. The arguments used in our proofs are\nbased on an appropriate definition of compact convergence of the resolvent\noperators associated with the Steklov problems on varying domains. In order to\nshow the optimality of our conditions we present alternative assumptions which\ngive rise to a degeneration of the spectrum or to a discontinuity of the\nspectrum in the sense that the eigenvalues converge to the eigenvalues of a\nlimit problem which does not coincide with the Steklov problem on the limiting\ndomain.\n"}
+{"abstract": "  In recent years, pi-conjugated polymers are attracting considerable interest\nin view of their light-dependent torsional reorganization around the\npi-conjugated backbone, which determines peculiar light-emitting properties.\nMotivated by the interest in designing conjugated polymers with tunable\nphotoswitchable pathways, we devised a computational framework to enhance the\nsampling of the torsional conformational space and at the same time estimate\nground to excited-state free-energy differences. This scheme is based on a\ncombination of Hamiltonian Replica Exchange (REM), Parallel Bias metadynamics,\nand free-energy perturbation theory. In our scheme, each REM replica samples an\nintermediate unphysical state between the ground and the first two excited\nstates, which are characterized by TD-DFT simulations at the B3LYP/6-31G* level\nof theory. We applied the method to a 5-mer of 9,9-dioctylfluorene and found\nthat upon irradiation this system can undergo a dihedral inversion from 155 to\n-155 degrees crossing a barrier that decreases from 0.1 eV in the ground state\n(S0) to 0.05 eV and 0.04 eV in the first (S1) and second (S2) excited states.\nFurthermore, S1 and even more S2 were predicted to stabilize coplanar\ndihedrals, with a local free-energy minimum located at +-44 degrees. The\npresence of a free-energy barrier of 0.08 eV for the S1 and 0.12 eV for the S2\nstate can trap this conformation in a basin far from the global free-energy\nminimum located at 155 degrees. The simulation results were compared with the\nexperimental emission spectrum, showing a quantitative agreement with the\npredictions provided by our framework.\n"}
+{"abstract": "  We explore variants of Erd\\H os' unit distance problem concerning dot\nproducts between successive pairs of points chosen from a large finite subset\nof either $\\mathbb F_q^d$ or $\\mathbb Z_q^d,$ where $q$ is a power of an odd\nprime. Specifically, given a large finite set of points $E$, and a sequence of\nelements of the base field (or ring) $(\\alpha_1,\\ldots,\\alpha_k)$, we give\nconditions guaranteeing the expected number of $(k+1)$-tuples of distinct\npoints $(x_1,\\dots, x_{k+1})\\in E^{k+1}$ satisfying $x_j \\cdot\nx_{j+1}=\\alpha_j$ for every $1\\leq j \\leq k$.\n"}
+{"abstract": "  The generation of high-order harmonics in finite, hexagonal nanoribbons is\nsimulated. Ribbons with armchair and zig-zag edges are investigated by using a\ntight-binding approach with only nearest neighbor hopping. By turning an\nalternating on-site potential off or on, the system describes for example\ngraphene or hexagonal boron nitride, respectively. The incoming laser pulse is\nlinearly polarized along the ribbons. The emitted light has a polarization\ncomponent parallel to the polarization of the incoming field. The presence or\nabsence of a polarization component perpendicular to the polarization of the\nincoming field can be explained by the symmetry of the ribbons. Characteristic\nfeatures in the harmonic spectra for the finite ribbons are analyzed with the\nhelp of the band structure for the corresponding periodic systems.\n"}
+{"abstract": "  Pions constitute nearly $70\\%$ of final state particles in ultra high energy\ncollisions. They act as a probe to understand the statistical properties of\nQuantum Chromodynamics (QCD) matter i.e. Quark Gluon Plasma (QGP) created in\nsuch relativistic heavy ion collisions (HIC). Apart from this, direct photons\nare the most versatile tools to study relativistic HIC. They are produced, by\nvarious mechanisms, during the entire space-time history of the strongly\ninteracting system. Direct photons provide measure of jet-quenching when\ncompared with other quark or gluon jets. The $\\pi^{0}$ decay into two photons\nmake the identification of non-correlated gamma coming from another process\ncumbersome in the Electromagnetic Calorimeter. We investigate the use of deep\nlearning architecture for reconstruction and identification of single as well\nas multi particles showers produced in calorimeter by particles created in high\nenergy collisions. We utilize the data of electromagnetic shower at calorimeter\ncell-level to train the network and show improvements for identification and\ncharacterization. These networks are fast and computationally inexpensive for\nparticle shower identification and reconstruction for current and future\nexperiments at particle colliders.\n"}
+{"abstract": "  We propose a three-terminal structure to probe robust signatures of Majorana\nzero modes. This structure consists of a quantum dot coupled to the normal\nmetal, s-wave superconducting and Majorana Y-junction leads. The zero-bias\ndifferential conductance at zero temperature of the normal-metal lead peaks at\n$2e^{2}/h$, which will be deflected after Majorana braiding. This quantized\nconductance can entirely arise from the Majorana-induced crossed Andreev\nreflection, protected by the energy gap of the superconducting lead. We find\nthat the effect of thermal broadening is significantly suppressed when the dot\nis on resonance. In the case that the energy level of the quantum dot is much\nlarger than the superconducting gap, tunneling processes are dominated by\nMajorana-induced crossed Andreev reflection. Particularly, a novel kind of\ncrossed Andreev reflection equivalent to the splitting of charge quanta $3e$\noccurs after Majorana braiding.\n"}
+{"abstract": "  Fuzzing is becoming more and more popular in the field of vulnerability\ndetection. In the process of fuzzing, seed selection strategy plays an\nimportant role in guiding the evolution direction of fuzzing. However, the SOTA\nfuzzers only focus on individual uncertainty, neglecting the multi-factor\nuncertainty caused by both randomization and evolution. In this paper, we\nconsider seed selection in fuzzing as a large-scale online planning problem\nunder uncertainty. We propose \\mytool which is a new intelligent seed selection\nstrategy. In Alpha-Fuzz, we leverage the MCTS algorithm to deal with the\neffects of the uncertainty of randomization and evolution of fuzzing.\nEspecially, we analyze the role of the evolutionary relationship between seeds\nin the process of fuzzing, and propose a new tree policy and a new default\npolicy to make the MCTS algorithm better adapt to the fuzzing. We compared\n\\mytool with four state-of-the-art fuzzers in 12 real-world applications and\nLAVA-M data set. The experimental results show that \\mytool could find more\nbugs on lava-M and outperforms other tools in terms of code coverage and number\nof bugs discovered in the real-world applications. In addition, we tested the\ncompatibility of \\mytool, and the results showed that \\mytool could improve the\nperformance of existing tools such as MOPT and QSYM.\n"}
+{"abstract": "  The Baikal Gigaton Volume Detector (Baikal-GVD) is a km$^3$-scale neutrino\ndetector currently under construction in Lake Baikal, Russia. The detector\nconsists of several thousand optical sensors arranged on vertical strings, with\n36 sensors per string. The strings are grouped into clusters of 8 strings each.\nEach cluster can operate as a stand-alone neutrino detector. The detector\nlayout is optimized for the measurement of astrophysical neutrinos with\nenergies of $\\sim$ 100 TeV and above. Events resulting from charged current\ninteractions of muon (anti-)neutrinos will have a track-like topology in\nBaikal-GVD. A fast $\\chi^2$-based reconstruction algorithm has been developed\nto reconstruct such track-like events. The algorithm has been applied to data\ncollected in 2019 from the first five operational clusters of Baikal-GVD,\nresulting in observations of both downgoing atmospheric muons and upgoing\natmospheric neutrinos. This serves as an important milestone towards\nexperimental validation of the Baikal-GVD design. The analysis is limited to\nsingle-cluster data, favoring nearly-vertical tracks.\n"}
+{"abstract": "  Using a three-dimensional active vertex model, we numerically study the\nshapes of strained unsupported epithelial monolayers subject to active\njunctional noise due to stochastic binding and unbinding of myosin. We find\nthat while uniaxial, biaxial, and isotropic in-plane compressive strains do\nlead to the formation of longitudinal, herringbone-pattern, and labyrinthine\nfolds, respectively, the villus morphology characteristic of, e.g., the small\nintestine appears only if junctional tension fluctuations are strong enough to\nfluidize the tissue. Moreover, the fluidized epithelium features villi even in\nabsence of compressive strain provided that the apico-basal differential\ntension is large enough. We analyze several details of the different epithelial\nforms including the role of strain rate and the modulation of tissue thickness\nacross folds. Our results show that nontrivial morphologies can form even in\nunsupported, non-patterned epithelia.\n"}
+{"abstract": "  In this paper we study the magnetic charges of the free massless\nRarita-Schwinger field in four dimensional asymptotically flat space-time. This\nis the first step towards extending the study of the dual BMS charges to\nsupergravity. The magnetic charges appear due to the addition of a boundary\nterm in the action. This term is similar to the theta term in Yang-Mills\ntheory. At null-infinity an infinite dimensional algebra is discovered, both\nfor the electric and magnetic charge.\n"}
+{"abstract": "  Graded modal types systems and coeffects are becoming a standard formalism to\ndeal with context-dependent computations where code usage plays a central role.\nThe theory of program equivalence for modal and coeffectful languages, however,\nis considerably underdeveloped if compared to the denotational and operational\nsemantics of such languages. This raises the question of how much of the theory\nof ordinary program equivalence can be given in a modal scenario. In this work,\nwe show that coinductive equivalences can be extended to a modal setting, and\nwe do so by generalising Abramsky's applicative bisimilarity to coeffectful\nbehaviours. To achieve this goal, we develop a general theory of ternary\nprogram relations based on the novel notion of a comonadic lax extension, on\ntop of which we define a modal extension of Abramsky's applicative bisimilarity\n(which we dub modal applicative bisimilarity). We prove such a relation to be a\ncongruence, this way obtaining a compositional technique for reasoning about\nmodal and coeffectful behaviours. But this is not the end of the story: we also\nestablish a correspondence between modal program relations and program\ndistances. This correspondence shows that modal applicative bisimilarity and (a\nproperly extended) applicative bisimilarity distance coincide, this way\nrevealing that modal program equivalences and program distances are just two\nsides of the same coin.\n"}
+{"abstract": "  We study multivariate approximation in the average case setting with the\nerror measured in the weighted $L_2$ norm. We consider algorithms that use\nstandard information $\\Lambda^{\\rm std}$ consisting of function values or\ngeneral linear information $\\Lambda^{\\rm all}$ consisting of arbitrary\ncontinuous linear functionals. We investigate the equivalences of various\nnotions of algebraic and exponential tractability for $\\Lambda^{\\rm std}$ and\n$\\Lambda^{\\rm all}$ for the absolute error criterion, and show that the power\nof $\\Lambda^{\\rm std}$ is the same as that of $\\Lambda^{\\rm all}$ for all\nnotions of algebraic and exponential tractability without any condition.\nSpecifically, we solve Open Problems 116-118 and almost solve Open Problem 115\nas posed by E.Novak and H.Wo\\'zniakowski in the book: Tractability of\nMultivariate Problems, Volume III: Standard Information for Operators, EMS\nTracts in Mathematics, Z\\\"urich, 2012.\n"}
+{"abstract": "  A topological pump enables robust transport of quantized particles when the\nsystem parameters are varied in a cyclic process. In previous studies,\ntopological pump was achieved inhomogeneous systems guaranteed by a topological\ninvariant of the bulk band structure when time is included as an additional\nsynthetic dimension. Recently, bulk-boundary correspondence has been\ngeneralized to the bulk-disclination correspondence, describing the emergence\nof topological bounded states in the crystallographic defects protected by the\nbulk topology. Here we show the topological pumping can happen between\ndifferent disclination states with different chiralities in an inhomogeneous\nstructure. Based on a generalized understanding of the charge pumping process,\nwe explain the topological disclination pump by tracing the motion of Wannier\ncenters in each unit cell. Besides, by constructing two disclination structures\nand introducing a symmetry-breaking perturbation, we achieve a topological\npumping between different dislocation cores. Our result opens a route to study\nthe topological pumping in inhomogeneous topological crystalline systems and\nprovides a flexible platform for robust energy transport.\n"}
+{"abstract": "  Person images captured by surveillance cameras are often occluded by various\nobstacles, which lead to defective feature representation and harm person\nre-identification (Re-ID) performance. To tackle this challenge, we propose to\nreconstruct the feature representation of occluded parts by fully exploiting\nthe information of its neighborhood in a gallery image set. Specifically, we\nfirst introduce a visible part-based feature by body mask for each person\nimage. Then we identify its neighboring samples using the visible features and\nreconstruct the representation of the full body by an outlier-removable graph\nneural network with all the neighboring samples as input. Extensive experiments\nshow that the proposed approach obtains significant improvements. In the\nlarge-scale Occluded-DukeMTMC benchmark, our approach achieves 64.2% mAP and\n67.6% rank-1 accuracy which outperforms the state-of-the-art approaches by\nlarge margins, i.e.,20.4% and 12.5%, respectively, indicating the effectiveness\nof our method on occluded Re-ID problem.\n"}
+{"abstract": "  This paper studies system theoretic properties of the class of difference\ninclusions of convex processes. We will develop a framework considering\neigenvalues and eigenvectors, weakly and strongly invariant cones, and a\ndecomposition of convex processes. This will allow us to characterize\nreachability, stabilizability and (null-)controllability of nonstrict convex\nprocesses in terms of spectral properties. These characterizations generalize\nall previously known results regarding for instance linear processes and\nspecific classes of nonstrict convex processes.\n"}
+{"abstract": "  In the international oil trade network (iOTN), trade shocks triggered by\nextreme events may spread over the entire network along the trade links of the\ncentral economies and even lead to the collapse of the whole system. In this\nstudy, we focus on the concept of \"too central to fail\" and use traditional\ncentrality indicators as strategic indicators for simulating attacks on\neconomic nodes, and simulates various situations in which the structure and\nfunction of the global oil trade network are lost when the economies suffer\nextreme trade shocks. The simulation results show that the global oil trade\nsystem has become more vulnerable in recent years. The regional aggregation of\noil trade is an essential source of iOTN's vulnerability. Maintaining global\noil trade stability and security requires a focus on economies with greater\ninfluence within the network module of the iOTN. International organizations\nsuch as OPEC and OECD established more trade links around the world, but their\ninfluence on the iOTN is declining. We improve the framework of oil security\nand trade risk assessment based on the topological index of iOTN, and provide a\nreference for finding methods to maintain network robustness and trade\nstability.\n"}
+{"abstract": "  Audio-visual speech enhancement system is regarded to be one of promising\nsolutions for isolating and enhancing speech of desired speaker. Conventional\nmethods focus on predicting clean speech spectrum via a naive convolution\nneural network based encoder-decoder architecture, and these methods a) not\nadequate to use data fully and effectively, b) cannot process features\nselectively. The proposed model addresses these drawbacks, by a) applying a\nmodel that fuses audio and visual features layer by layer in encoding phase,\nand that feeds fused audio-visual features to each corresponding decoder layer,\nand more importantly, b) introducing soft threshold attention into the model to\nselect the informative modality softly. This paper proposes attentional\naudio-visual multi-layer feature fusion model, in which soft threshold\nattention unit are applied on feature mapping at every layer of decoder. The\nproposed model demonstrates the superior performance of the network against the\nstate-of-the-art models.\n"}
+{"abstract": "  In this article we propose a shooting algorithm for partially-affine optimal\ncontrol problems, this is, systems in which the controls appear both linearly\nand nonlinearly in the dynamics. Since the shooting system generally has more\nequations than unknowns, the algorithm relies on the Gauss-Newton method. As a\nconsequence, the convergence is locally quadratic provided that the derivative\nof the shooting function is injective and Lipschitz continuous at the optimal\nsolution. We provide a proof of the convergence for the proposed algorithm\nusing recently developed second order sufficient conditions for weak optimality\nof partially-affine problems. We illustrate the applicability of the algorithm\nby solving an optimal treatment-vaccination epidemiological problem.\n"}
+{"abstract": "  We optimize a selection of eigenvalues of the Laplace operator with Dirichlet\nor Neumann boundary conditions by adjusting the shape of the domain on which\nthe eigenvalue problem is considered. Here, a phase-field function is used to\nrepresent the shapes over which we minimize. The idea behind this method is to\nmodify the Laplace operator by introducing phase-field dependent coefficients\nin order to extend the eigenvalue problem on a fixed design domain containing\nall admissible shapes. The resulting shape and topology optimization problem\ncan then be formulated as an optimal control problem with PDE constraints in\nwhich the phase-field function acts as the control. For this optimal control\nproblem, we establish first-order necessary optimality conditions and we\nrigorously derive its sharp interface limit. Eventually, we present and discuss\nseveral numerical simulations for our optimization problem.\n"}
+{"abstract": "  Numerous early warning systems based on rainfall measurements have been\ndesigned over the last decades to forecast the onset of rainfall-induced\nshallow landslides. However, their use over large areas poses challenges due to\nuncertainties related with the interaction among various controlling factors.\nWe propose a hybrid stochastic-mechanical approach to quantify the role of the\nhydro-mechanical factors influencing slope stability and rank their importance.\nThe proposed methodology relies on a physically-based model of landslide\ntriggering, and a stochastic approach treating selected model parameters as\ncorrelated aleatory variables. The features of the methodology are illustrated\nby referencing data for Campania, an Italian region characterized by\nlandslide-prone volcanic deposits. Synthetic intensity-duration (ID) thresholds\nare computed through Monte Carlo simulations. Several key variables are treated\nas aleatoric, constraining their statistical properties through available\nmeasurements. The variabilities of topographic features (e.g., slope angle),\nphysical and hydrological properties (e.g., porosity, dry unit weight\n${\\gamma}_d$, and saturated hydraulic conductivity, $K_s$), and pre-rainstorm\nsuction is evaluated to inspect its role on the resulting scatter of ID\nthresholds. We find that: i) $K_s$ is most significant for high-intensity\nstorms; ii) in steep slopes, changes in pressure head greatly reduce the\ntimescale of landslide triggering, making the system heavily reliant on initial\nconditions; iii) for events occurring at long failure times (gentle slopes\nand/or low intensity storms), the significance of the evolving stress level\n(through ${\\gamma}_d$) is highest. The proposed approach can be translated to\nother regions, expanded to encompass new aleatory variables, and combined with\nother hydro-mechanical triggering models.\n"}
+{"abstract": "  The design and performance of the inner detector trigger for the high level\ntrigger of the ATLAS experiment at the Large Hadron Collider during the 2016-18\ndata taking period is discussed. In 2016, 2017, and 2018 the ATLAS detector\nrecorded 35.6 fb$^{-1}$, 46.9 fb$^{-1}$, and 60.6 fb$^{-1}$ respectively of\nproton-proton collision data at a centre-of-mass energy of 13 TeV. In order to\ndeal with the very high interaction multiplicities per bunch crossing expected\nwith the 13 TeV collisions the inner detector trigger was redesigned during the\nlong shutdown of the Large Hadron Collider from 2013 until 2015. An overview of\nthese developments is provided and the performance of the tracking in the\ntrigger for the muon, electron, tau and $b$-jet signatures is discussed. The\nhigh performance of the inner detector trigger with these extreme interaction\nmultiplicities demonstrates how the inner detector tracking continues to lie at\nthe heart of the trigger performance and is essential in enabling the ATLAS\nphysics programme.\n"}
+{"abstract": "  Two-dimensional (2D) semiconductors are promising candidates for scaled\ntransistors because they are immune to mobility degradation at the monolayer\nlimit. However, sub-10 nm scaling of 2D semiconductors, such as MoS2, is\nlimited by the contact resistance. In this work, we show for the first time a\nstatistical study of Au contacts to chemical vapor deposited monolayer MoS2\nusing transmission line model (TLM) structures, before and after dielectric\nencapsulation. We report contact resistance values as low as 330 ohm-um, which\nis the lowest value reported to date. We further study the effect of Al2O3\nencapsulation on variability in contact resistance and other device metrics.\nFinally, we note some deviations in the TLM model for short-channel devices in\nthe back-gated configuration and discuss possible modifications to improve the\nmodel accuracy.\n"}
+{"abstract": "  In an unpublished work of Fasel-Rao-Swan the notion of the relative Witt\ngroup $W_E(R,I)$ is defined. In this article we will give the details of this\nconstruction. Then we studied the injectivity of the relative Vaserstein symbol\n$V_{R,I}: Um_3(R,I)/E_3(R,I)\\rightarrow W_E(R,I)$. We established injectivity\nof this symbol if $R$ is an affine non-singular algebra of dimension $3$ over a\nperfect $C_1$-field and $I$ is a local complete intersection ideal of $R$. It\nis believed that for a $3$-dimensional affine algebra non-singularity is not\nnecessary for establishing injectivity of the Vaserstein symbol . At the end of\nthe article we will give an example of a singular $3$-dimensional algebra over\na perfect $C_1$-field for which the Vaserstein symbol is injective.\n"}
+{"abstract": "  Using a variant of Caccioppoli's inequality involving small weights, i.e.\nweights of the form $(1+|\\nabla u|^2)^{-\\alpha/2}$ for some $\\alpha > 0$, we\nestablish several Liouville-type theorems under general non-standard growth\nconditions.\n"}
+{"abstract": "  As the COVID-19 virus spread over the world, governments restricted mobility\nto slow transmission. Public health measures had different intensities across\nEuropean countries but all had significant impact on peoples daily lives and\neconomic activities, causing a drop of CO2 emissions of about 10% for the whole\nyear 2020. Here, we analyze changes in natural gas use in the industry and\nbuilt environment sectors during the first half of year 2020 with daily gas\nflows data from pipeline and storage facilities in Europe. We find that\nreductions of industrial gas use reflect decreases in industrial production\nacross most countries. Surprisingly, natural gas use in buildings also\ndecreased despite most people being confined at home and cold spells in March\n2020. Those reductions that we attribute to the impacts of COVID-19 remain of\ncomparable magnitude to previous variations induced by cold or warm climate\nanomalies in the cold season. We conclude that climate variations played a\nlarger role than COVID-19 induced stay-home orders in natural gas consumption\nacross Europe.\n"}
+{"abstract": "  We demonstrate the stability of ferromagnetic order of one unit cell thick\noptimally doped manganite (La0.7Ba0.3MnO3, LBMO) epitaxially grown between two\nlayers of SrRuO3 (SRO) by using x-ray magnetic circular dichroism. At low\ntemperature LBMO shows an inverted hysteresis loop due to the strong\nantiferromagnetic coupling to SRO. Moreover, above SRO TC the manganite still\nexhibits magnetic remanence. Density Functional Theory calculations show that\ncoherent interfaces of LBMO with SRO hinder electronic confinement and the\nstrong magnetic coupling enables the increase of the LBMO TC. From the\nstructural point of view, interfacing with SRO enables LBMO to have octahedral\nrotations similar to bulk. All these factors jointly contribute for stable\nferromagnetism up to 130 K for a one unit cell LBMO film.\n"}
+{"abstract": "  We tackle the long-tailed visual recognition problem from the knowledge\ndistillation perspective by proposing a Distill the Virtual Examples (DiVE)\nmethod. Specifically, by treating the predictions of a teacher model as virtual\nexamples, we prove that distilling from these virtual examples is equivalent to\nlabel distribution learning under certain constraints. We show that when the\nvirtual example distribution becomes flatter than the original input\ndistribution, the under-represented tail classes will receive significant\nimprovements, which is crucial in long-tailed recognition. The proposed DiVE\nmethod can explicitly tune the virtual example distribution to become flat.\nExtensive experiments on three benchmark datasets, including the large-scale\niNaturalist ones, justify that the proposed DiVE method can significantly\noutperform state-of-the-art methods. Furthermore, additional analyses and\nexperiments verify the virtual example interpretation, and demonstrate the\neffectiveness of tailored designs in DiVE for long-tailed problems.\n"}
+{"abstract": "  We prove several statements about arithmetic hyperbolicity of certain blow-up\nvarieties. As a corollary we obtain multiple examples of simply connected\nquasi-projective varieties that are pseudo-arithmetically hyperbolic. This\ngeneralizes results of Corvaja and Zannier obtained in dimension 2 to arbitrary\ndimension. The key input is an application of the Ru-Vojta's strategy. We also\nobtain the analogue results for function fields and Nevanlinna theory with the\ngoal to apply them in a future paper in the context of Campana's conjectures.\n"}
+{"abstract": "  We show how one may classify all semisimple algebras containing the\n$\\mathfrak{su}(3)\\oplus \\mathfrak{su}(2) \\oplus \\mathfrak{u}(1)$ symmetry of\nthe Standard Model and acting on some given matter sector, enabling theories\nbeyond the Standard Model with unification (partial or total) of symmetries\n(gauge or global) to be catalogued. With just a single generation of Standard\nModel fermions plus a singlet neutrino, the only {gauge} symmetries correspond\nto the well-known algebras $\\mathfrak{su}(5),\\mathfrak{so}(10),$ and\n$\\mathfrak{su}(4)\\oplus \\mathfrak{su}(2) \\oplus \\mathfrak{su}(2)$, but with two\nor more generations a limited number of exotic symmetries mixing flavour,\ncolour, and electroweak degrees of freedom become possible. We provide a\ncomplete catalogue in the case of 3 generations or fewer and outline how our\nmethod generalizes to cases with additional matter.\n"}
+{"abstract": "  With the rapid development of Internet of Things (IoT), massive machine-type\ncommunication has become a promising application scenario, where a large number\nof devices transmit sporadically to a base station (BS). Reconfigurable\nintelligent surface (RIS) has been recently proposed as an innovative new\ntechnology to achieve energy efficiency and coverage enhancement by\nestablishing favorable signal propagation environments, thereby improving data\ntransmission in massive connectivity. Nevertheless, the BS needs to detect\nactive devices and estimate channels to support data transmission in\nRIS-assisted massive access systems, which yields unique challenges. This paper\nshall consider an RIS-assisted uplink IoT network and aims to solve the\nRIS-related activity detection and channel estimation problem, where the BS\ndetects the active devices and estimates the separated channels of the\nRIS-to-device link and the RIS-to-BS link. Due to limited scattering between\nthe RIS and the BS, we model the RIS-to-BS channel as a sparse channel. As a\nresult, by simultaneously exploiting both the sparsity of sporadic transmission\nin massive connectivity and the RIS-to-BS channels, we formulate the\nRIS-related activity detection and channel estimation problem as a sparse\nmatrix factorization problem. Furthermore, we develop an approximate message\npassing (AMP) based algorithm to solve the problem based on Bayesian inference\nframework and reduce the computational complexity by approximating the\nalgorithm with the central limit theorem and Taylor series arguments. Finally,\nextensive numerical experiments are conducted to verify the effectiveness and\nimprovements of the proposed algorithm.\n"}
+{"abstract": "  Time series forecasting is a relevant task that is performed in several\nreal-world scenarios such as product sales analysis and prediction of energy\ndemand. Given their accuracy performance, currently, Recurrent Neural Networks\n(RNNs) are the models of choice for this task. Despite their success in time\nseries forecasting, less attention has been paid to make the RNNs trustworthy.\nFor example, RNNs can not naturally provide an uncertainty measure to their\npredictions. This could be extremely useful in practice in several cases e.g.\nto detect when a prediction might be completely wrong due to an unusual pattern\nin the time series. Whittle Sum-Product Networks (WSPNs), prominent deep\ntractable probabilistic circuits (PCs) for time series, can assist an RNN with\nproviding meaningful probabilities as uncertainty measure. With this aim, we\npropose RECOWN, a novel architecture that employs RNNs and a discriminant\nvariant of WSPNs called Conditional WSPNs (CWSPNs). We also formulate a\nLog-Likelihood Ratio Score as better estimation of uncertainty that is tailored\nto time series and Whittle likelihoods. In our experiments, we show that\nRECOWNs are accurate and trustworthy time series predictors, able to \"know when\nthey do not know\".\n"}
+{"abstract": "  We present a new model-based interpolation procedure for satisfiability\nmodulo theories (SMT). The procedure uses a new mode of interaction with the\nSMT solver that we call solving modulo a model. This either extends a given\npartial model into a full model for a set of assertions or returns an\nexplanation (a model interpolant) when no solution exists. This mode of\ninteraction fits well into the model-constructing satisfiability (MCSAT)\nframework of SMT. We use it to develop an interpolation procedure for any\nMCSAT-supported theory. In particular, this method leads to an effective\ninterpolation procedure for nonlinear real arithmetic. We evaluate the new\nprocedure by integrating it into a model checker and comparing it with\nstate-of-art model-checking tools for nonlinear arithmetic.\n"}
+{"abstract": "  The Cox construction presents a toric variety as a quotient of affine space\nby a torus. The category of coherent sheaves on the corresponding stack thus\nhas an evident description as invariants in a quotient of the category of\nmodules over a polynomial ring. Here we give the mirror to this description,\nand in particular, a clean new proof of mirror symmetry for smooth toric\nstacks.\n"}
+{"abstract": "  This literature review focuses on the use of Natural Language Generation\n(NLG) to automatically detect and generate persuasive texts. Extending previous\nresearch on automatic identification of persuasion in text, we concentrate on\ngenerative aspects through conceptualizing determinants of persuasion in five\nbusiness-focused categories: benevolence, linguistic appropriacy, logical\nargumentation, trustworthiness, tools and datasets. These allow NLG to increase\nan existing message's persuasiveness. Previous research illustrates key aspects\nin each of the above mentioned five categories. A research agenda to further\nstudy persuasive NLG is developed. The review includes analysis of\nseventy-seven articles, outlining the existing body of knowledge and showing\nthe steady progress in this research field.\n"}
+{"abstract": "  If one proposes to use the theory of Eisenstein cohomology to prove\nalgebraicity results for the special values of automorphic L-functions as in my\nwork with Harder for Rankin-Selberg L-functions, or its generalizations as in\nmy work with Bhagwat for L-functions for orthogonal groups and independently\nwith Krishnamurthy on Asai L-functions, then in a key step, one needs to prove\nthat the normalised standard intertwining operator between induced\nrepresentations for p-adic groups has a certain arithmetic property. The\nprincipal aim of this article is to address this particular local problem in\nthe generality of the Langlands-Shahidi machinery. The main result of this\narticle is invoked in some of the works mentioned above, and I expect that it\nwill be useful in future investigations on the arithmetic properties of\nautomorphic L-functions.\n"}
+{"abstract": "  In 2007, Grytczuk conjecture that for any sequence $(\\ell_i)_{i\\ge1}$ of\nalphabets of size $3$ there exists a square-free infinite word $w$ such that\nfor all $i$, the $i$-th letter of $w$ belongs to $\\ell_i$. The result of Thue\nof 1906 implies that there is an infinite square-free word if all the $\\ell_i$\nare identical. On the other, hand Grytczuk, Przyby{\\l}o and Zhu showed in 2011\nthat it also holds if the $\\ell_i$ are of size $4$ instead of $3$.\n  In this article, we first show that if the lists are of size $4$, the number\nof square-free words is at least $2.45^n$ (the previous similar bound was\n$2^n$). We then show our main result: we can construct such a square-free word\nif the lists are subsets of size $3$ of the same alphabet of size $4$. Our\nproof also implies that there are at least $1.25^n$ square-free words of length\n$n$ for any such list assignment. This proof relies on the existence of a set\nof coefficients verified with a computer. We suspect that the full conjecture\ncould be resolved by this method with a much more powerful computer (but we\nmight need to wait a few decades for such a computer to be available).\n"}
+{"abstract": "  In recent years, Quantum Computing witnessed massive improvements both in\nterms of resources availability and algorithms development. The ability to\nharness quantum phenomena to solve computational problems is a long-standing\ndream that has drawn the scientific community's interest since the late '80s.\nIn such a context, we pose our contribution. First, we introduce basic concepts\nrelated to quantum computations, and then we explain the core functionalities\nof technologies that implement the Gate Model and Adiabatic Quantum Computing\nparadigms. Finally, we gather, compare and analyze the current state-of-the-art\nconcerning Quantum Perceptrons and Quantum Neural Networks implementations.\n"}
+{"abstract": "  Force chains, which are quasi-linear self-organised structures carrying large\nstresses, are ubiquitous in jammed amorphous materials, such as granular\nmaterials, foams, emulsions or even assemblies of cells. Predicting where they\nwill form upon mechanical deformation is crucial in order to describe the\nphysical properties of such materials, but remains an open question. Here we\ndemonstrate that graph neural networks (GNN) can accurately infer the location\nof these force chains in frictionless materials from the local structure prior\nto deformation, without receiving any information about the inter-particle\nforces. Once trained on a prototypical system, the GNN prediction accuracy\nproves to be robust to changes in packing fraction, mixture composition, amount\nof deformation, and the form of the interaction potential. The GNN is also\nscalable, as it can make predictions for systems much larger than those it was\ntrained on. Our results and methodology will be of interest for experimental\nrealizations of granular matter and jammed disordered systems, e.g. in cases\nwhere direct visualisation of force chains is not possible or contact forces\ncannot be measured.\n"}
+{"abstract": "  In a recent paper published in PNAS authors prove that locality and free\nchoice are equivalent resources which need to be relaxed in order to fully\nreproduce some statistics in Bell experiments (while always maintaining\nrealism). We explain that their assumption of free choice is simply\ncounterfactual definiteness or noncontextuality. Therefore the resource in Bell\nexperiments is contextuality and not the violations of locality and/or of free\nchoice. It is definitely less mind boggling conclusion because experimenters`\nfreedom of choice is a prerequisite of science,\n"}
+{"abstract": "  We present a solution to multi-robot distributed semantic mapping of novel\nand unfamiliar environments. Most state-of-the-art semantic mapping systems are\nbased on supervised learning algorithms that cannot classify novel observations\nonline. While unsupervised learning algorithms can invent labels for novel\nobservations, approaches to detect when multiple robots have independently\ndeveloped their own labels for the same new class are prone to erroneous or\ninconsistent matches. These issues worsen as the number of robots in the system\nincreases and prevent fusing the local maps produced by each robot into a\nconsistent global map, which is crucial for cooperative planning and joint\nmission summarization. Our proposed solution overcomes these obstacles by\nhaving each robot learn an unsupervised semantic scene model online and use a\nmultiway matching algorithm to identify consistent sets of matches between\nlearned semantic labels belonging to different robots. Compared to the state of\nthe art, the proposed solution produces 20-60% higher quality global maps that\ndo not degrade even as many more local maps are fused.\n"}
+{"abstract": "  Fashion is intertwined with external cultural factors, but identifying these\nlinks remains a manual process limited to only the most salient phenomena. We\npropose a data-driven approach to identify specific cultural factors affecting\nthe clothes people wear. Using large-scale datasets of news articles and\nvintage photos spanning a century, we present a multi-modal statistical model\nto detect influence relationships between happenings in the world and people's\nchoice of clothing. Furthermore, on two image datasets we apply our model to\nimprove the concrete vision tasks of visual style forecasting and photo\ntimestamping. Our work is a first step towards a computational, scalable, and\neasily refreshable approach to link culture to clothing.\n"}
+{"abstract": "  We calculate the response of a lead-based detector, such as the Helium and\nLead Observatory (HALO) or its planned upgrade HALO-1kt to a galactic\ncore-collapse supernova. We pay particular attention to the time dependence of\nthe reaction rates. All reaction rates decrease as the neutrino luminosity\nexponentially drops during the cooling period but the ratio of one-neutron (1n)\nto two-neutron (2n) event rates in HALO is independent of this overall\ndecrease. Nevertheless, we find that this ratio still changes with time due to\nthe changing character of neutrino flavor transformations with the evolving\nconditions in the supernova. In the case of inverted hierarchy, this is caused\nby the fact that the spectral splits become less and less sharp with the\ndecreasing luminosity. In the case of normal hierarchy, it is caused by the\npassage of the shock wave through the Mikheyev-Smirnov-Wolfenstein resonance\nregion. However, in both cases, we find that the change in the ratio of 1n to\n2n event rates is limited to a few percent.\n"}
+{"abstract": "  In this study, a variational method for the inverse problem of self-assembly,\ni.e., a reconstruction of the interparticle interaction potential of a given\nstructure, is applied to three-dimensional crystals. According to the method,\nthe interaction potential is derived as a function that maximizes the\nfree-energy functional of the one- and two-particle density distribution\nfunctions. The interaction potentials of the target crystals, including those\nwith face-centered cubic (fcc), body-centered cubic (bcc), and simple hexagonal\n(shx) lattices, are obtained by numerical maximization of the functional. Monte\nCarlo simulations for the systems of particles with these interactions were\ncarried out, and the self-assembly of the target crystals was confirmed for the\nbcc and shx cases. However, in the many-particle system with the predicted\ninteraction for the fcc lattice, the fcc lattice did not spontaneously form and\nwas metastable.\n"}
+{"abstract": "  A formalisation of G\\\"odel's incompleteness theorems using the Isabelle proof\nassistant is described. This is apparently the first mechanical verification of\nthe second incompleteness theorem. The work closely follows {\\'S}wierczkowski\n(2003), who gave a detailed proof using hereditarily finite set theory. The\nadoption of this theory is generally beneficial, but it poses certain technical\nissues that do not arise for Peano arithmetic. The formalisation itself should\nbe useful to logicians, particularly concerning the second incompleteness\ntheorem, where existing proofs are lacking in detail.\n"}
+{"abstract": "  We investigate experimentally the behavior of self-propelled water-in-oil\ndroplets, confined in capillaries of different square and circular\ncross-sections. The droplet's activity comes from the formation of swollen\nmicelles at its interface. In straight capillaries the velocity of the droplet\ndecreases with increasing confinement. However at very high confinement, the\nvelocity converges toward a non-zero value, so that even very long droplets\nswim. Stretched circular capillaries are then used to explore even higher\nconfinement. The lubrication layer around the droplet then takes a non-uniform\nthickness which constitutes a significant difference with usual flow-driven\npassive droplets. A neck forms at the rear of the droplet, deepens with\nincreasing confinement, and eventually undergoes successive spontaneous\nsplitting events for large enough confinement. Such observations stress the\ncritical role of the activity of the droplet interface on the droplet's\nbehavior under confinement. We then propose an analytical formulation by\nintegrating the interface activity and the swollen micelles transport problem\ninto the classical Bretherton approach. The model accounts for the convergence\nof the droplet's velocity to a finite value for large confinement, and for the\nnon-classical shape of the lubrication layer. We further discuss on the\nsaturation of the micelles concentration along the interface, which would\nexplain the divergence of the lubrication layer thickness for long enough\ndroplets, eventually leading to the spontaneous droplet division.\n"}
+{"abstract": "  In this methodological paper, we first review the classic cubic Diophantine\nequation $a^3 + b^3 + c^3 = d^3$, and consider the specific class of solutions\n$q_1^3 + q_2^3 + q_3^3 = q_4^3$ with each $q_i$ being a binary quadratic form.\nNext we turn our attention to the familiar sums of powers of the first $n$\npositive integers, $S_k = 1^k + 2^k + \\cdots + n^k$, and express the squares\n$S_k^2$, $S_m^2$, and the product $S_k S_m$ as a linear combination of power\nsums. These expressions, along with the above quadratic-form solution for the\ncubic equation, allows one to generate an infinite number of relations of the\nform $Q_1^3 + Q_2^3 + Q_3^3 = Q_4^3$, with each $Q_i$ being a linear\ncombination of power sums. Also, we briefly consider the quadratic Diophantine\nequations $a^2 + b^2 + c^2 = d^2$ and $a^2 + b^2 = c^2$, and give a family of\ncorresponding solutions $Q_1^2 + Q_2^2 + Q_3^2 = Q_4^2$ and $Q_1^2 + Q_2^2 =\nQ_3^2$ in terms of sums of powers of integers.\n"}
+{"abstract": "  While reinforcement learning (RL) has proven to be the approach of choice for\ntackling many complex problems, it remains challenging to develop and deploy RL\nagents in real-life scenarios successfully. This paper presents pH-RL\n(personalization in e-Health with RL) a general RL architecture for\npersonalization to bring RL to health practice. pH-RL allows for various levels\nof personalization in health applications and allows for online and batch\nlearning. Furthermore, we provide a general-purpose implementation framework\nthat can be integrated with various healthcare applications. We describe a\nstep-by-step guideline for the successful deployment of RL policies in a mobile\napplication. We implemented our open-source RL architecture and integrated it\nwith the MoodBuster mobile application for mental health to provide messages to\nincrease daily adherence to the online therapeutic modules. We then performed a\ncomprehensive study with human participants over a sustained period. Our\nexperimental results show that the developed policies learn to select\nappropriate actions consistently using only a few days' worth of data.\nFurthermore, we empirically demonstrate the stability of the learned policies\nduring the study.\n"}
+{"abstract": "  In this paper we discuss the optimal control of a quasilinear parabolic state\nequation. Its form is leaned on the kind of problems arising for example when\ncontrolling the anisotropic Allen-Cahn equation as a model for crystal growth.\nMotivated by this application we consider the state equation as a result of a\ngradient flow of an energy functional. The quasilinear term is strongly\nmonotone and obeys a certain growth condition and the lower order term is\nnon-monotone. The state equation is discretized implicitly in time with\npiecewise constant functions. The existence of the control-to-state operator\nand its Lipschitz-continuity is shown for the time discretized as well as for\nthe time continuous problem. Latter is based on the convergence proof of the\ndiscretized solutions. Finally we present for both the existence of global\nminimizers. Also convergence of a subsequence of time discrete optimal controls\nto a global minimizer of the time continuous problem can be shown. Our results\nhold in arbitrary space dimensions.\n"}
+{"abstract": "  We show that general time-local quantum master equations admit an unravelling\nin quantum trajectories with jumps. The sufficient condition is to weigh state\nvector Monte Carlo averages by a probability pseudo-measure which we call the\n\"influence martingale\". The influence martingale satisfies a $ 1d $ stochastic\ndifferential equation enslaved to the ones governing the quantum trajectories.\nOur interpretation is that the influence martingale models interference effects\nbetween distinct realizations of the quantum trajectories at strong\nsystem-environment coupling.\n  If the master equation generates a completely positive dynamical map, there\nis no interference. In such a case the influence martingale becomes positive\ndefinite and the Monte Carlo average straightforwardly reduces to the well\nknown unravelling of completely positive divisible dynamical maps.\n"}
+{"abstract": "  Sustainable urban design or planning is not a LEGO-like assembly of\nprefabricated elements, but an embryo-like growth with persistent\ndifferentiation and adaptation towards a coherent whole. The coherent whole has\na striking character - called living structure - that consists of far more\nsmall substructures than large ones. To detect the living structure, natural\nstreets or axial lines have been previously adopted to be topologically\nrepresent an urban environment as a coherent whole. This paper develops a new\napproach to detecting the underlying living structure of urban environments.\nThe approach takes an urban environment as a whole and recursively decomposes\nit into meaningful subwholes at different levels of hierarchy or scale ranging\nfrom the largest to the smallest. We compared the new approach to natural\nstreet and axial line approaches and demonstrated, through four case studies,\nthat the new approach is better and more powerful. Based on the study, we\nfurther discuss how the new approach can be used not only for understanding,\nbut also for effectively designing or planning the living structure of an urban\nenvironment to be more living or more livable. Keywords: Urban design or\nplanning, structural beauty, space syntax, natural streets, life, wholeness\n"}
+{"abstract": "  Tree-based models are among the most efficient machine learning techniques\nfor data mining nowadays due to their accuracy, interpretability, and\nsimplicity. The recent orthogonal needs for more data and privacy protection\ncall for collaborative privacy-preserving solutions. In this work, we survey\nthe literature on distributed and privacy-preserving training of tree-based\nmodels and we systematize its knowledge based on four axes: the learning\nalgorithm, the collaborative model, the protection mechanism, and the threat\nmodel. We use this to identify the strengths and limitations of these works and\nprovide for the first time a framework analyzing the information leakage\noccurring in distributed tree-based model learning.\n"}
+{"abstract": "  Let $\\K$ be a finite field and $X$ be a complete simplicial toric variety\nover $\\K$. We give an algorithm relying on elimination theory for finding\ngenerators of the vanishing ideal of a subgroup $Y_Q$ parameterized by a matrix\n$Q$ which can be used to study algebraic geometric codes arising from $Y_Q$. We\ngive a method to compute the lattice $L$ whose ideal $I_L$ is exactly $I(Y_Q)$\nunder a mild condition. As applications, we give precise descriptions for the\nlattices corresponding to some special subgroups. We also prove a\nNullstellensatz type theorem valid over finite fields, and share\n\\verb|Macaulay2| codes for our algorithms.\n"}
+{"abstract": "  Vanadium oxides have been highly attractive for over half a century since the\ndiscovery of the metal insulator transition near room temperatures. Here NaxVO2\nis studied through a systematic comparison with other layered sodium metal\noxides with early 3d transition metals, first disclosing a unified evolution\npattern of Na density waves through in situ XRD analysis. Combining ab-initio\nsimulations and theoretical modelings, a sodium-modulated Peierls-like\ntransition mechanism is then proposed for the bonding formation of metal ion\ndimers. More importantly, the unique trimer structure in NaxVO2 is shown to be\nvery sensitive to the onsite Coulomb repulsion value, suggesting a delicate\nbalance between strong electronic correlations and orbital effects that can be\nprecisely modulated by both Na compositions and atomic stackings. This unveils\na unique opportunity to design strongly correlated materials with tailored\nelectronic transitions through electrochemical modulations and crystallographic\ndesigns, to elegantly balance various competition effects. We think the\nunderstanding will also help further elucidate complicated electronic behaviors\nin other vanadium oxide systems.\n"}
+{"abstract": "  In Europe, 20% of road crashes occur at intersections. In recent years,\nevolving communication technologies are making V2V and V2I faster and more\nreliable; with such advancements, these crashes, as well as their economic\ncost, can be partially reduced. In this work, we concentrate on straight path\nintersection collisions. Connectivity-based algorithms relying on 5G technology\nand smart sensors are presented and compared to a commercial radar AEB logic in\norder to evaluate performances and effectiveness in collision avoidance or\nmitigation. The aforementioned novel safety systems are tested in a blind\nintersection and low adherence scenario. The first algorithm proposed is\nobtained by incorporating connectivity information to the original control\nscheme, while the second algorithm proposed is a novel control logic fully\ncapable of utilizing also adherence estimation provided by smart sensors. Test\nresults show an improvement in terms of safety for both the architecture and\nhigh prospects for future developments.\n"}
+{"abstract": "  This paper deals with the generalized spectrum of continuously invertible\nlinear operators defined on infinite dimensional Hilbert spaces. More\nprecisely, we consider two bounded, coercive, and self-adjoint operators\n$\\bc{A, B}: V\\mapsto V^{\\#}$, where $V^{\\#}$ denotes the dual of $V$, and\ninvestigate the conditions under which the whole spectrum of\n$\\bc{B}^{-1}\\bc{A}:V\\mapsto V$ can be approximated to an arbitrary accuracy by\nthe eigenvalues of the finite dimensional discretization\n$\\bc{B}_n^{-1}\\bc{A}_n$. Since $\\bc{B}^{-1}\\bc{A}$ is continuously invertible,\nsuch an investigation cannot use the concept of uniform (normwise) convergence,\nand it relies instead on the pointwise (strong) convergence of\n$\\bc{B}_n^{-1}\\bc{A}_n$ to $\\bc{B}^{-1}\\bc{A}$.\n  The paper is motivated by operator preconditioning which is employed in the\nnumerical solution of boundary value problems. In this context, $\\bc{A},\n\\bc{B}: H_0^1(\\Omega) \\mapsto H^{-1}(\\Omega)$ are the standard\nintegral/functional representations of the differential operators $ -\\nabla\n\\cdot (k(x)\\nabla u)$ and $-\\nabla \\cdot (g(x)\\nabla u)$, respectively, and\n$k(x)$ and $g(x)$ are scalar coefficient functions. The investigated question\ndiffers from the eigenvalue problem studied in the numerical PDE literature\nwhich is based on the approximation of the eigenvalues within the framework of\ncompact operators.\n  This work follows the path started by the two recent papers published in\n[SIAM J. Numer. Anal., 57 (2019), pp.~1369-1394 and 58 (2020), pp.~2193-2211]\nand addresses one of the open questions formulated at the end of the second\npaper.\n"}
+{"abstract": "  We prove that if there are $\\mathfrak c$ incomparable selective ultrafilters\nthen, for every infinite cardinal $\\kappa$ such that $\\kappa^\\omega=\\kappa$,\nthere exists a group topology on the free Abelian group of cardinality $\\kappa$\nwithout nontrivial convergent sequences and such that every finite power is\ncountably compact. In particular, there are arbitrarily large countably compact\ngroups. This answers a 1992 question of D. Dikranjan and D. Shakhmatov.\n"}
+{"abstract": "  For fixed $q\\in\\{3,7,11,19, 43,67,163\\}$, we consider the density of primes\n$p$ congruent to $1$ modulo $4$ such that the class group of the number field\n$\\mathbb{Q}(\\sqrt{-qp})$ has order divisible by $16$. We show that this density\nis equal to $1/8$, in line with a more general conjecture of Gerth.\nVinogradov's method is the key analytic tool for our work.\n"}
+{"abstract": "  The recent proposal of antidoping scheme breaks new ground in conceiving\nconversely functional materials and devices, yet the few available examples\nbelong to the correlated electron systems. Here we demonstrate both\ntheoretically and experimentally that the main group oxide BaBiO$_3$ is a model\nsystem for antidoping using oxygen vacancies. The first principles calculations\nshow that the band gap systematically increases due to the strongly enhanced\nBiO breathing distortions away from the vacancies and the annihilation of Bi 6s\nand O 2p hybridized conduction bands near the vacancies. The spectroscopic\nexperiments confirm the band gap increasing systematically with electron\ndoping, with a maximal gap enhancement of 75% when the film's stoichiometry is\nreduced to BaBiO$_{2.75}$. The Raman and diffraction experiments show the\nsuppression of the overall breathing distortion. The study unambiguously\ndemonstrates the remarkable antidoping effect in a material without strong\nelectron correlations and underscores the importance of bond disproportionation\nin realizing such an effect.\n"}
+{"abstract": "  We provide a tight result for a fundamental problem arising from packing\nsquares into a circular container: The critical density of packing squares into\na disk is $\\delta=\\frac{8}{5\\pi}\\approx 0.509$. This implies that any set of\n(not necessarily equal) squares of total area $A \\leq \\frac{8}{5}$ can always\nbe packed into a disk with radius 1; in contrast, for any $\\varepsilon>0$ there\nare sets of squares of total area $\\frac{8}{5}+\\varepsilon$ that cannot be\npacked, even if squares may be rotated. This settles the last (and arguably,\nmost elusive) case of packing circular or square objects into a circular or\nsquare container: The critical densities for squares in a square\n$\\left(\\frac{1}{2}\\right)$, circles in a square\n$\\left(\\frac{\\pi}{(3+2\\sqrt{2})}\\approx 0.539\\right)$ and circles in a circle\n$\\left(\\frac{1}{2}\\right)$ have already been established, making use of\nrecursive subdivisions of a square container into pieces bounded by straight\nlines, or the ability to use recursive arguments based on similarity of objects\nand container; neither of these approaches can be applied when packing squares\ninto a circular container. Our proof uses a careful manual analysis,\ncomplemented by a computer-assisted part that is based on interval arithmetic.\nBeyond the basic mathematical importance, our result is also useful as a\nblackbox lemma for the analysis of recursive packing algorithms. At the same\ntime, our approach showcases the power of a general framework for\ncomputer-assisted proofs, based on interval arithmetic.\n"}
+{"abstract": "  It is shown that the dipole moment of polar (water, methanol, formamide,\nacetone and acetonitrile) molecules in the neighborhood of a cation is\nincreased primarily by polarization from the bare electrostatic charge of the\ncation, although the effective value of the latter is somewhat reduced by \"back\ndonation\" of electrons from neighbouring polar molecules. In other words, the\nclassical picture may be viewed as if a point charge slightly smaller than the\nnominal charge of the cation would be placed at the cation site. It was found\nthat the geometrical arrangement of the polar molecules in the first solvation\nshell is such that their mutual polarization reduces the dipole moments of\nindividual molecules, so that in some cases they become smaller than the dipole\nmoment of the free protic or aprotic molecule. We conjecture that this behavior\nis essentially a manifestation of the Le Chatellier-Braun principle.\n"}
+{"abstract": "  It was recently discovered that atoms subject to a time-periodic drive can\ngive rise to a crystal structure in phase space. In this work, we point out the\natom-atom interactions give rise to collective phonon excitations of\nphase-space crystal via a pairing interaction with intrinsically complex phases\nthat can lead to a phonon Chern insulator, accompanied by topologically robust\nchiral transport along the edge of the phase-space crystal. This topological\nphase is realized even in scenarios where the time-reversal transformation is a\nsymmetry, which is surprising because the breaking of time-reversal symmetry is\na strict precondition for topological chiral transport in the standard setting\nof real-space crystals. Our work has also important implications for the\ndynamics of 2D charged particles in a strong magnetic field.\n"}
+{"abstract": "  Background: For its simplicity, the eikonal method is the tool of choice to\nanalyze nuclear reactions at high energies ($E>100$ MeV/nucleon), including\nknockout reactions. However, so far, the effective interactions used in this\nmethod are assumed to be fully local.\n  Purpose: Given the recent studies on non-local optical potentials, in this\nwork we assess whether non-locality in the optical potentials is expected to\nimpact reactions at high energies and then explore different avenues for\nextending the eikonal method to include non-local interactions.\n  Method: We compare angular distributions obtained for non-local interactions\n(using the exact R-matrix approach for elastic scattering and the adiabatic\ndistorted wave approximation for transfer) with those obtained using their\nlocal-equivalent interactions.\n  Results: Our results show that transfer observables are significantly\nimpacted by non-locality in the high-energy regime. Because knockout reactions\nare dominated by stripping (transfer to inelastic channels), non-locality is\nexpected to have a large effect on knockout observables too. Three approaches\nare explored for extending the eikonal method to non-local interactions,\nincluding an iterative method and a perturbation theory.\n  Conclusions: None of the derived extensions of the eikonal model provide a\ngood description of elastic scattering. This work suggests that non-locality\nremoves the formal simplicity associated with the eikonal model.\n"}
+{"abstract": "  Over the past decade the in-medium similarity renormalization group (IMSRG)\napproach has proven to be a powerful and versatile ab initio many-body method\nfor studying medium-mass nuclei. So far, the IMSRG was limited to the\napproximation in which only up to two-body operators are incorporated in the\nrenormalization group flow, referred to as the IMSRG(2). In this work, we\nextend the IMSRG(2) approach to fully include three-body operators yielding the\nIMSRG(3) approximation. We use a perturbative scaling analysis to estimate the\nimportance of individual terms in this approximation and introduce truncations\nthat aim to approximate the IMSRG(3) at a lower computational cost. The\nIMSRG(3) is systematically benchmarked for different nuclear Hamiltonians for\n${}^{4}\\text{He}$ and ${}^{16}\\text{O}$ in small model spaces. The IMSRG(3)\nsystematically improves over the IMSRG(2) relative to exact results.\nApproximate IMSRG(3) truncations constructed based on computational cost are\nable to reproduce much of the systematic improvement offered by the full\nIMSRG(3). We also find that the approximate IMSRG(3) truncations behave\nconsistently with expectations from our perturbative analysis, indicating that\nthis strategy may also be used to systematically approximate the IMSRG(3).\n"}
+{"abstract": "  We demonstrate that the reduced Hartree-Fock equation (REHF) with an Anderson\ntype background charge distribution has an unique stationary solution by\nexplicitly computing a screening mass at positive temperature.\n"}
+{"abstract": "  A* search is an informed search algorithm that uses a heuristic function to\nguide the order in which nodes are expanded. Since the computation required to\nexpand a node and compute the heuristic values for all of its generated\nchildren grows linearly with the size of the action space, A* search can become\nimpractical for problems with large action spaces. This computational burden\nbecomes even more apparent when heuristic functions are learned by general, but\ncomputationally expensive, deep neural networks. To address this problem, we\nintroduce DeepCubeAQ, a deep reinforcement learning and search algorithm that\nbuilds on the DeepCubeA algorithm and deep Q-networks. DeepCubeAQ learns a\nheuristic function that, with a single forward pass through a deep neural\nnetwork, computes the sum of the transition cost and the heuristic value of all\nof the children of a node without explicitly generating any of the children,\neliminating the need for node expansions. DeepCubeAQ then uses a novel variant\nof A* search, called AQ* search, that uses the deep Q-network to guide search.\nWe use DeepCubeAQ to solve the Rubik's cube when formulated with a large action\nspace that includes 1872 meta-actions and show that this 157-fold increase in\nthe size of the action space incurs less than a 4-fold increase in computation\ntime when performing AQ* search and that AQ* search is orders of magnitude\nfaster than A* search.\n"}
+{"abstract": "  The resolution of $4$-dimensional massless field operators of higher spins\nwas constructed by Eastwood-Penrose-Wells by using the twistor method. Recently\nphysicists are interested in $6$-dimensional physics including the massless\nfield operators of higher spins on Lorentzian space $\\Bbb R^{5,1}$. Its\nEuclidean version $\\mathscr{D}_0$ and their function theory are discussed in\n\\cite{wangkang3}. In this paper, we construct an exact sequence of Hilbert\nspaces as weighted $L^2$ spaces resolving $\\mathscr{D}_0$: $$L^2_\\varphi(\\Bbb\nR^6, \\mathscr{V}_0)\\overset{\\mathscr{D}_0}\\longrightarrow L^2_\\varphi(\\Bbb\nR^6,\\mathscr{V}_1)\\overset{\\mathscr{D}_1}\\longrightarrow L^2_\\varphi(\\Bbb R^6,\n\\mathscr{V}_2)\\overset{\\mathscr{D}_2}\\longrightarrow L^2_\\varphi(\\Bbb R^6,\n\\mathscr{V}_3)\\longrightarrow 0,$$ with suitable operators $\\mathscr{D}_l$ and\nvector spaces $\\mathscr{V}_l$. Namely, we can solve $\\mathscr{D}_{l}u=f$ in\n$L^2_\\varphi(\\Bbb R^6, \\mathscr{V}_{l})$ when $\\mathscr{D}_{l+1} f=0$ for $f\\in\nL^2_{\\varphi}(\\Bbb R^6, \\mathscr{V}_{l+1})$. This is proved by using the $L^2$\nmethod in the theory of several complex variables, which is a general framework\nto solve overdetermined PDEs under the compatibility condition. To apply this\nmethod here, it is necessary to consider weighted $L^2$ spaces, an advantage of\nwhich is that any polynomial is $L^2_{\\varphi}$ integrable. As a corollary, we\nprove that $$ P(\\Bbb R^6, \\mathscr{V}_0)\\overset{\\mathscr{D}_0}\\longrightarrow\nP(\\Bbb R^6,\\mathscr{V}_1)\\overset{\\mathscr{D}_1}\\longrightarrow P(\\Bbb R^6,\n\\mathscr{V}_2)\\overset{\\mathscr{D}_2}\\longrightarrow P(\\Bbb R^6,\n\\mathscr{V}_3)\\longrightarrow 0$$ is a resolution, where $P(\\Bbb R^6,\n\\mathscr{V}_l)$ is the space of all $\\mathscr{V}_l$-valued polynomials. This\nprovides an analytic way to construct a resolution of a differential operator\nacting on vector valued polynomials.\n"}
+{"abstract": "  Angular momentum plays a central role in a multitude of phenomena in quantum\nmechanics, recurring in every length scale from the microscopic interactions of\nlight and matter to the macroscopic behavior of superfluids. Vortex beams,\ncarrying intrinsic orbital angular momentum (OAM), are now regularly generated\nwith elementary particles such as photons and electrons, and harnessed for\nnumerous applications including microscopy and communication. Untapped\npossibilities remain hidden in vortices of non-elementary particles, as their\ncomposite structure can lead to coupling of OAM with internal degrees of\nfreedom. However, thus far, the creation of a vortex beam of a non-elementary\nparticle has never been demonstrated experimentally. We present the first\nvortex beams of atoms and molecules, formed by diffracting supersonic beams of\nhelium atoms and dimers, respectively, off binary masks made from transmission\ngratings. By achieving large particle coherence lengths and nanometric grating\nfeatures, we observe a series of vortex rings corresponding to different OAM\nstates in the accumulated images of particles impacting a detector. This method\nis general and can be applied to most atomic and molecular gases. Our results\nmay open new frontiers in atomic physics, utilizing the additional degree of\nfreedom of OAM to probe collisions and alter fundamental interactions.\n"}
+{"abstract": "  Using a tight-biding model, we elaborate that the previously discovered\nout-of-plane polarized helical edge spin current caused by Rashba spin-orbit\ncoupling can be attributed to the fact that in a strip geometry, a positive\nmomentum eigenstate does not always have the same spin polarization at the edge\nas the corresponding negative momentum eigenstate. In addition, in the presence\nof a magnetization pointing perpendicular to the edge, an edge charge current\nis produced, which can be chiral or nonchiral depending on whether the\nmagnetization lies in-plane or out-of-plane. The spin polarization near the\nedge develops a transverse component orthogonal to the magnetization, which is\nantisymmetric between the two edges and tends to cause a noncollinear magnetic\norder between the two edges. If the magnetization only occupies a region near\none edge, or in an irregular shaped quantum dot, this transverse component has\na nonzero average, rendering a gate voltage-induced magnetoelectric torque\nwithout the need of a bias voltage. We also argue that other types of\nspin-orbit coupling that can be obtained from the Rashba type through a unitary\ntransformation, such as the Dresselhaus spin-orbit coupling, will have similar\neffects too.\n"}
+{"abstract": "  Hydrodynamics is a general theoretical framework for describing the long-time\nlarge-distance behaviors of various macroscopic physical systems, with its\nequations based on conservation laws such as energy-momentum conservation and\ncharge conservation. Recently there has been significant interest in\nunderstanding the implications of angular momentum conservation for a\ncorresponding hydrodynamic theory. In this work, we examine the key conceptual\nissues for such a theory in the relativistic regime where the orbital and spin\ncomponents get entangled. We derive the equations for relativistic viscous\nhydrodynamics with angular momentum through Navier-Stokes type of gradient\nexpansion analysis.\n"}
+{"abstract": "  Split-learning (SL) has recently gained popularity due to its inherent\nprivacy-preserving capabilities and ability to enable collaborative inference\nfor devices with limited computational power. Standard SL algorithms assume an\nideal underlying digital communication system and ignore the problem of scarce\ncommunication bandwidth. However, for a large number of agents, limited\nbandwidth resources, and time-varying communication channels, the communication\nbandwidth can become the bottleneck. To address this challenge, in this work,\nwe propose a novel SL framework to solve the remote inference problem that\nintroduces an additional layer at the agent side and constrains the choices of\nthe weights and the biases to ensure over the air aggregation. Hence, the\nproposed approach maintains constant communication cost with respect to the\nnumber of agents enabling remote inference under limited bandwidth. Numerical\nresults show that our proposed algorithm significantly outperforms the digital\nimplementation in terms of communication-efficiency, especially as the number\nof agents grows large.\n"}
+{"abstract": "  Time series analysis is quickly proceeding towards long and complex tasks. In\nrecent years, fast approximate algorithms for discord search have been proposed\nin order to compensate for the increasing size of the time series. It is more\ninteresting, however, to find quick exact solutions. In this research, we\nimproved HOT SAX by exploiting two main ideas: the warm-up process, and the\nsimilarity between sequences close in time. The resulting algorithm, called HOT\nSAX Time (HST), has been validated with real and synthetic time series, and\nsuccessfully compared with HOT SAX, RRA, SCAMP, and DADD. The complexity of a\ndiscord search has been evaluated with a new indicator, the cost per sequence\n(cps), which allows one to compare searches on time series of different\nlengths. Numerical evidence suggests that two conditions are involved in\ndetermining the complexity of a discord search in a non-trivial way: the length\nof the discords, and the noise/signal ratio. In the case of complex searches,\nHST can be more than 100 times faster than HOT SAX, thus being at the forefront\nof the exact discord search.\n"}
+{"abstract": "  In this work, we revisit the adaptive L1 time-stepping scheme for solving the\ntime-fractional Allen-Cahn equation in the Caputo's form. The L1 implicit\nscheme is shown to preserve a variational energy dissipation law on arbitrary\nnonuniform time meshes by using the recent discrete analysis tools, i.e., the\ndiscrete orthogonal convolution kernels and discrete complementary convolution\nkernels. Then the discrete embedding techniques and the fractional Gr\\\"onwall\ninequality were applied to establish an $L^2$ norm error estimate on nonuniform\ntime meshes. An adaptive time-stepping strategy according to the dynamical\nfeature of the system is presented to capture the multi-scale behaviors and to\nimprove the computational performance.\n"}
+{"abstract": "  Extended decorations on naturally decorated trees were introduced in the work\nof Bruned, Hairer and Zambotti on algebraic renormalization of regularity\nstructures to provide a convenient framework for the renormalization of systems\nof singular stochastic PDEs within that setting. This non-dynamical feature of\nthe trees complicated the analysis of the dynamical counterpart of the\nrenormalization process. We provide a new proof of the renormalized system\nby-passing the use of extended decorations and working for a large class of\nrenormalization maps, with the BPHZ renormalization as a special case. The\nproof reveals important algebraic properties connected to preparation maps.\n"}
+{"abstract": "  PYROBOCOP is a lightweight Python-based package for control and optimization\nof robotic systems described by nonlinear Differential Algebraic Equations\n(DAEs). In particular, the package can handle systems with contacts that are\ndescribed by complementarity constraints and provides a general framework for\nspecifying obstacle avoidance constraints. The package performs direct\ntranscription of the DAEs into a set of nonlinear equations by performing\northogonal collocation on finite elements. The resulting optimization problem\nbelongs to the class of Mathematical Programs with Complementarity Constraints\n(MPCCs). MPCCs fail to satisfy commonly assumed constraint qualifications and\nrequire special handling of the complementarity constraints in order for\nNonLinear Program (NLP) solvers to solve them effectively. PYROBOCOP provides\nautomatic reformulation of the complementarity constraints that enables NLP\nsolvers to perform optimization of robotic systems. The package is interfaced\nwith ADOLC for obtaining sparse derivatives by automatic differentiation and\nIPOPT for performing optimization. We demonstrate the effectiveness of our\napproach in terms of speed and flexibility. We provide several numerical\nexamples for several robotic systems with collision avoidance as well as\ncontact constraints represented using complementarity constraints. We provide\ncomparisons with other open source optimization packages like CasADi and Pyomo .\n"}
+{"abstract": "  Demand response (DR) programs engage distributed demand-side resources, e.g.,\ncontrollable residential and commercial loads, in providing ancillary services\nfor electric power systems. Ensembles of these resources can help reducing\nsystem load peaks and meeting operational limits by adjusting their electric\npower consumption. To equip utilities or load aggregators with adequate\ndecision-support tools for ensemble dispatch, we develop a Markov Decision\nProcess (MDP) approach to optimally control load ensembles in a\nprivacy-preserving manner. To this end, the concept of differential privacy is\ninternalized into the MDP routine to protect transition probabilities and,\nthus, privacy of DR participants. The proposed approach also provides a\ntrade-off between solution optimality and privacy guarantees, and is analyzed\nusing real-world data from DR events in the New York University microgrid in\nNew York, NY.\n"}
+{"abstract": "  A 3D unit cell model containing eight different spherical particles embedded\nin a homogeneous strain gradient plasticity (SGP) matrix material is presented.\nThe interaction between particles and matrix is controlled by an interface\nmodel formulated within the higher order SGP theory used. Strengthening of the\nparticle reinforced material is investigated in terms of the increase in\nmacroscopic yield stress. The results are used to validate a closed form\nstrengthening relation proposed by the authors, which suggests that the\nincrease in macroscopic yield stress is governed by the interface strength\ntimes the total surface area of particles in the material volume.\n"}
+{"abstract": "  The Higgs boson decay modes to $b$ and $c$ quarks are crucial for many Higgs\nprecision measurements. The presence of semileptonic decays in the jets\noriginating from $b$ and $c$ quarks causes missing energy due to the\nundetectable neutrinos. A correction for the missing neutrino momenta can be\nderived from the kinematics of the decay up to a two-fold ambiguity. The\ncorrect solution can be identified by a kinematic fit, which exploits the\nwell-known initial state at an $e^{+}e^{-}$ collider by adjusting the measured\nquantities within their uncertainties to fulfill the kinematic constraints. The\nParticleFlow concept, based on the reconstruction of individual particles in a\njet allows understanding the individual jet-level uncertainties at an\nunprecedented level. The modeling of the jet uncertainties and the resulting\nfit performance will be discussed for the example of the ILD detector. Applied\nto $H\\rightarrow b\\bar{b}/c\\bar{c}$ events, the combination of the neutrino\ncorrection with the kinematic fit improves the Higgs mass reconstruction\nsignificantly, both in terms of resolution and peak position.\n"}
+{"abstract": "  A variational discrete element method is applied to simulate quasi-static\ncrack propagation. Cracks are considered to propagate between the mesh cells\nthrough the mesh facets. The elastic behaviour is parametrized by the\ncontinuous mechanical parameters (Young modulus and Poisson ratio). A discrete\nenergetic cracking criterion coupled to a discrete kinking criterion guide the\ncracking process. Two-dimensional numerical examples are presented to\nillustrate the robustness and versatility of the method.\n"}
+{"abstract": "  In this article, we consider convergence of stochastic gradient descent\nschemes (SGD) under weak assumptions on the underlying landscape. More\nexplicitly, we show that on the event that the SGD stays local we have\nconvergence of the SGD if there is only a countable number of critical points\nor if the target function/landscape satisfies Lojasiewicz-inequalities around\nall critical levels as all analytic functions do. In particular, we show that\nfor neural networks with analytic activation function such as softplus, sigmoid\nand the hyperbolic tangent, SGD converges on the event of staying local, if the\nrandom variables modeling the signal and response in the training are compactly\nsupported.\n"}
+{"abstract": "  We present a directed variant of Salop (1979) model to analyze bus transport\ndynamics. The players are operators competing in cooperative and\nnon-cooperative games. Utility, like in most bus concession schemes in emerging\ncountries, is proportional to the total fare collection. Competition for\npicking up passengers leads to well documented and dangerous driving practices\nthat cause road accidents, traffic congestion and pollution. We obtain\ntheoretical results that support the existence and implementation of such\npractices, and give a qualitative description of how they come to occur. In\naddition, our results allow to compare the current or base transport system\nwith a more cooperative one.\n"}
+{"abstract": "  In reinforcement learning (RL), the goal is to obtain an optimal policy, for\nwhich the optimality criterion is fundamentally important. Two major optimality\ncriteria are average and discounted rewards, where the later is typically\nconsidered as an approximation to the former. While the discounted reward is\nmore popular, it is problematic to apply in environments that have no natural\nnotion of discounting. This motivates us to revisit a) the progression of\noptimality criteria in dynamic programming, b) justification for and\ncomplication of an artificial discount factor, and c) benefits of directly\nmaximizing the average reward. Our contributions include a thorough examination\nof the relationship between average and discounted rewards, as well as a\ndiscussion of their pros and cons in RL. We emphasize that average-reward RL\nmethods possess the ingredient and mechanism for developing the general\ndiscounting-free optimality criterion (Veinott, 1969) in RL.\n"}
+{"abstract": "  We use the crystal isomorphisms of the Fock space to describe two maps on\npartitions and multipartitions which naturally appear in the crystal basis\ntheory for quantum groups in affine type A and in the representation theory of\nHecke algebras of type G(l, l, n).\n"}
+{"abstract": "  We prove that Strings-and-Coins -- the combinatorial two-player game\ngeneralizing the dual of Dots-and-Boxes -- is strongly PSPACE-complete on\nmultigraphs. This result improves the best previous result, NP-hardness, argued\nin Winning Ways. Our result also applies to the Nimstring variant, where the\nwinner is determined by normal play; indeed, one step in our reduction is the\nstandard reduction (also from Winning Ways) from Nimstring to\nStrings-and-Coins.\n"}
+{"abstract": "  Observed rotation curves in star-forming galaxies indicate a puzzling dearth\nof dark matter in extended flat cores within haloes of mass $\\geq\\!\n10^{12}M_\\odot$ at $z\\!\\sim\\! 2$. This is not reproduced by current\ncosmological simulations, and supernova-driven outflows are not effective in\nsuch massive haloes. We address a hybrid scenario where post-compaction merging\nsatellites heat up the dark-matter cusps by dynamical friction, allowing\nAGN-driven outflows to generate cores. Using analytic and semi-analytic models\n(SatGen), we estimate the dynamical-friction heating as a function of satellite\ncompactness for a cosmological sequence of mergers. Cosmological simulations\n(VELA) demonstrate that satellites of initial virial masses\n$>\\!10^{11.3}M_\\odot$, that undergo wet compactions, become sufficiently\ncompact for significant heating. Constituting a major fraction of the accretion\nonto haloes $\\geq\\!10^{12}M_\\odot$, these satellites heat-up the cusps in half\na virial time at $z\\!\\sim\\! 2$. Using a model for outflow-driven core formation\n(CuspCore), we demonstrate that the heated dark-matter cusps develop extended\ncores in response to removal of half the gas mass, while the more compact\nstellar systems remain intact. The mergers keep the dark matter hot, while the\ngas supply, fresh and recycled, is sufficient for the AGN outflows. AGN indeed\nbecome effective in haloes $\\geq\\!10^{12}M_\\odot$, where the black-hole growth\nis no longer suppressed by supernovae and its compaction-driven rapid growth is\nmaintained by a hot CGM. For simulations to reproduce the dynamical-friction\neffects, they should resolve the compaction of the massive satellites and avoid\nartificial tidal disruption. AGN feedback could be boosted by clumpy black-hole\naccretion and clumpy response to AGN.\n"}
+{"abstract": "  We studied the dynamics of an object sliding down on a semi-sphere with\nradius $R$. We consider the physical situation where the semi-sphere is free to\nmove over a horizontal surface. Also, we consider that all surfaces in contact\nare friction-less. We analyze the values for the last contact angle\n$\\theta^\\star$, corresponding to the angle when the object and the semi-sphere\ndetach one of each other, considering all possible scenarios with different\nvalues of $m_A$ and $m_B$. We found that the last contact angle only depends on\nthe ratio between the masses, and it is independent of the acceleration of\ngravity and semi-sphere radius. In addition, we found that the largest possible\nvalue of $\\theta^\\star$ occurs for the case when the semi-sphere does not move.\nOn the opposite case, the minimum value of the angle occurs for $m_A \\gg m_B$\noccurring at the top of the semi-sphere.\n"}
+{"abstract": "  In a legal system, judgment consistency is regarded as one of the most\nimportant manifestations of fairness. However, due to the complexity of factual\nelements that impact sentencing in real-world scenarios, few works have been\ndone on quantitatively measuring judgment consistency towards real-world data.\nIn this paper, we propose an evaluation metric for judgment inconsistency,\nLegal Inconsistency Coefficient (LInCo), which aims to evaluate inconsistency\nbetween data groups divided by specific features (e.g., gender, region, race).\nWe propose to simulate judges from different groups with legal judgment\nprediction (LJP) models and measure the judicial inconsistency with the\ndisagreement of the judgment results given by LJP models trained on different\ngroups. Experimental results on the synthetic data verify the effectiveness of\nLInCo. We further employ LInCo to explore the inconsistency in real cases and\ncome to the following observations: (1) Both regional and gender inconsistency\nexist in the legal system, but gender inconsistency is much less than regional\ninconsistency; (2) The level of regional inconsistency varies little across\ndifferent time periods; (3) In general, judicial inconsistency is negatively\ncorrelated with the severity of the criminal charges. Besides, we use LInCo to\nevaluate the performance of several de-bias methods, such as adversarial\nlearning, and find that these mechanisms can effectively help LJP models to\navoid suffering from data bias.\n"}
+{"abstract": "  Multiplying matrices is among the most fundamental and compute-intensive\noperations in machine learning. Consequently, there has been significant work\non efficiently approximating matrix multiplies. We introduce a learning-based\nalgorithm for this task that greatly outperforms existing methods. Experiments\nusing hundreds of matrices from diverse domains show that it often runs\n$100\\times$ faster than exact matrix products and $10\\times$ faster than\ncurrent approximate methods. In the common case that one matrix is known ahead\nof time, our method also has the interesting property that it requires zero\nmultiply-adds. These results suggest that a mixture of hashing, averaging, and\nbyte shuffling$-$the core operations of our method$-$could be a more promising\nbuilding block for machine learning than the sparsified, factorized, and/or\nscalar quantized matrix products that have recently been the focus of\nsubstantial research and hardware investment.\n"}
+{"abstract": "  The ability to automatically extract Knowledge Graphs (KG) from a given\ncollection of documents is a long-standing problem in Artificial Intelligence.\nOne way to assess this capability is through the task of slot filling. Given an\nentity query in form of [Entity, Slot, ?], a system is asked to `fill' the slot\nby generating or extracting the missing value from a relevant passage or\npassages. This capability is crucial to create systems for automatic knowledge\nbase population, which is becoming in ever-increasing demand, especially in\nenterprise applications. Recently, there has been a promising direction in\nevaluating language models in the same way we would evaluate knowledge bases,\nand the task of slot filling is the most suitable to this intent. The recent\nadvancements in the field try to solve this task in an end-to-end fashion using\nretrieval-based language models. Models like Retrieval Augmented Generation\n(RAG) show surprisingly good performance without involving complex information\nextraction pipelines. However, the results achieved by these models on the two\nslot filling tasks in the KILT benchmark are still not at the level required by\nreal-world information extraction systems. In this paper, we describe several\nstrategies we adopted to improve the retriever and the generator of RAG in\norder to make it a better slot filler. Our KGI0 system (available at\nhttps://github.com/IBM/retrieve-write-slot-filling) reached the top-1 position\non the KILT leaderboard on both T-REx and zsRE dataset with a large margin.\n"}
+{"abstract": "  Let $R$ be a commutative ring with non-zero identity. In this paper, we\nintroduce the concept of weakly $J$-ideals as a new generalization of\n$J$-ideals. We call a proper ideal $I$ of a ring $R$ a weakly $J$-ideal if\nwhenever $a,b\\in R$ with $0\\neq ab\\in I$ and $a\\notin J(R)$, then $a\\in I$.\nMany of the basic properties and characterizations of this concept are studied.\nWe investigate weakly $J$-ideals under various contexts of constructions such\nas direct products, localizations, homomorphic images. Moreover, a number of\nexamples and results on weakly $J$-ideals are discussed. Finally, the third\nsection is devoted to the characterizations of these constructions in an\namagamated ring along an ideal.\n"}
+{"abstract": "  We compute the Borel equivariant cohomology ring of the left $K$-action on a\nhomogeneous space $G/H$, where $G$ is a connected Lie group, $H$ and $K$ are\nclosed, connected subgroups and $2$ and the torsion primes of the Lie groups\nare units of the coefficient ring. As a special case, this gives the singular\ncohomology rings of biquotients $H \\backslash G / K$.\n  This depends on a version of the Eilenberg-Moore theorem developed in the\nappendix, where a novel multiplicative structure on the two-sided bar\nconstruction $\\mathbf{B}(A',A,A'')$ is defined, valid when $A' \\leftarrow A \\to\nA''$ is a pair of maps of homotopy Gerstenhaber algebras.\n"}
+{"abstract": "  Scaling relations are very useful tools for estimating unknown stellar\nquantities. Within this framework, eclipsing binaries are ideal for this goal\nbecause their mass and radius are known with a very good level of accuracy,\nleading to improved constraints on the models. We aim to provide empirical\nrelations for the mass and radius as function of luminosity, metallicity, and\nage. We investigate, in particular, the impact of metallicity and age on those\nrelations. We used a multi-dimensional fit approach based on the data from\nDEBCat, an updated catalogue of eclipsing binary observations such as mass,\nradius, luminosity, effective temperature, gravity, and metallicity. We used\nthe {PARAM web interface for the Bayesian estimation of stellar parameters,\nalong with} the stellar evolutionary code MESA to estimate the binary age,\nassuming a coeval hypothesis for both members. We derived the mass and\nradius-luminosity-metallicity-age relations using 56 stars, {with metallicity\nand mass in the range -0.34<[Fe/H]<0.27 and 0.66<M/M{_\\odot}<1.8}. With that,\nthe observed mass and radius are reproduced with an accuracy of 3.5% and 5.9%,\nrespectively, which is consistent with the other results in literature. We\nconclude that including the age in such relations increases the quality of the\nfit, particularly in terms of the mass, as compared to the radius. On the other\nhand, as other authors have noted, we observed an higher dispersion on the mass\nrelation than in that of the radius. We propose that this is due to a stellar\nage effect.\n"}
+{"abstract": "  Traditional normalization techniques (e.g., Batch Normalization and Instance\nNormalization) generally and simplistically assume that training and test data\nfollow the same distribution. As distribution shifts are inevitable in\nreal-world applications, well-trained models with previous normalization\nmethods can perform badly in new environments. Can we develop new normalization\nmethods to improve generalization robustness under distribution shifts? In this\npaper, we answer the question by proposing CrossNorm and SelfNorm. CrossNorm\nexchanges channel-wise mean and variance between feature maps to enlarge\ntraining distribution, while SelfNorm uses attention to recalibrate the\nstatistics to bridge gaps between training and test distributions. CrossNorm\nand SelfNorm can complement each other, though exploring different directions\nin statistics usage. Extensive experiments on different fields (vision and\nlanguage), tasks (classification and segmentation), settings (supervised and\nsemi-supervised), and distribution shift types (synthetic and natural) show the\neffectiveness. Code is available at\nhttps://github.com/amazon-research/crossnorm-selfnorm\n"}
+{"abstract": "  We use a combination of Hipparcos space mission data with the USNO dedicated\nground-based astrometric program URAT-Bright designed to complement and verify\nGaia results for the brightest stars in the south to estimate the small\nperturbations of observed proper motions caused by exoplanets. One of the 1423\nbright stars in the program, $\\delta$ Pav, stands out with a small proper\nmotion difference between our long-term estimate and Gaia EDR3 value, which\ncorresponds to a projected velocity of $(-17,+13)$ m s$^{-1}$. This difference\nis significant at a 0.994 confidence in the RA component, owing to the\nproximity of the star and the impressive precision of proper motions. The\neffect is confirmed by a comparison of long-term EDR3-Hipparcos and short-term\nGaia EDR3 proper motions at a smaller velocity, but with formally absolute\nconfidence. We surmise that the close Solar analog $\\delta$ Pav harbors a\nlong-period exoplanet similar to Jupiter.\n"}
+{"abstract": "  This paper proposes a strategy to assess the robustness of different machine\nlearning models that involve natural language processing (NLP). The overall\napproach relies upon a Search and Semantically Replace strategy that consists\nof two steps: (1) Search, which identifies important parts in the text; (2)\nSemantically Replace, which finds replacements for the important parts, and\nconstrains the replaced tokens with semantically similar words. We introduce\ndifferent types of Search and Semantically Replace methods designed\nspecifically for particular types of machine learning models. We also\ninvestigate the effectiveness of this strategy and provide a general framework\nto assess a variety of machine learning models. Finally, an empirical\ncomparison is provided of robustness performance among three different model\ntypes, each with a different text representation.\n"}
+{"abstract": "  In this paper, we study gapsets and we focus on obtaining information on how\nthe maximum distance between to consecutive elements influences the behaviour\nof the set. In particular, we prove that the cardinality of the set of gapsets\nwith genus $g$ such that the maximum distance between two consecutive elements\nis $\\k$ is equal to the cardinality of the set of gapsets with genus $g+1$ such\nthat the maximum distance between two consecutive elements is $\\k+1$, when $2g\n\\leq 3\\k$.\n"}
+{"abstract": "  Understanding the competition between superconductivity and other ordered\nstates (such as antiferromagnetic or charge-density-wave (CDW) state) is a\ncentral issue in condensed matter physics. The recently discovered layered\nkagome metal AV3Sb5 (A = K, Rb, and Cs) provides us a new playground to study\nthe interplay of superconductivity and CDW state by involving nontrivial\ntopology of band structures. Here, we conduct high-pressure electrical\ntransport and magnetic susceptibility measurements to study CsV3Sb5 with the\nhighest Tc of 2.7 K in AV3Sb5 family. While the CDW transition is monotonically\nsuppressed by pressure, superconductivity is enhanced with increasing pressure\nup to P1~0.7 GPa, then an unexpected suppression on superconductivity happens\nuntil pressure around 1.1 GPa, after that, Tc is enhanced with increasing\npressure again. The CDW is completely suppressed at a critical pressure P2~2\nGPa together with a maximum Tc of about 8 K. In contrast to a common dome-like\nbehavior, the pressure-dependent Tc shows an unexpected double-peak behavior.\nThe unusual suppression of Tc at P1 is concomitant with the rapidly damping of\nquantum oscillations, sudden enhancement of the residual resistivity and rapid\ndecrease of magnetoresistance. Our discoveries indicate an unusual competition\nbetween superconductivity and CDW state in pressurized kagome lattice.\n"}
+{"abstract": "  The missing data issue is ubiquitous in health studies. Variable selection in\nthe presence of both missing covariates and outcomes is an important\nstatistical research topic but has been less studied. Existing literature\nfocuses on parametric regression techniques that provide direct parameter\nestimates of the regression model. Flexible nonparametric machine learning\nmethods considerably mitigate the reliance on the parametric assumptions, but\ndo not provide as naturally defined variable importance measure as the\ncovariate effect native to parametric models. We investigate a general variable\nselection approach when both the covariates and outcomes can be missing at\nrandom and have general missing data patterns. This approach exploits the\nflexibility of machine learning modeling techniques and bootstrap imputation,\nwhich is amenable to nonparametric methods in which the covariate effects are\nnot directly available. We conduct expansive simulations investigating the\npractical operating characteristics of the proposed variable selection\napproach, when combined with four tree-based machine learning methods, XGBoost,\nRandom Forests, Bayesian Additive Regression Trees (BART) and Conditional\nRandom Forests, and two commonly used parametric methods, lasso and backward\nstepwise selection. Numeric results suggest that when combined with bootstrap\nimputation, XGBoost and BART have the overall best variable selection\nperformance with respect to the $F_1$ score and Type I error across various\nsettings. In general, there is no significant difference in the variable\nselection performance due to imputation methods. We further demonstrate the\nmethods via a case study of risk factors for 3-year incidence of metabolic\nsyndrome with data from the Study of Women's Health Across the Nation.\n"}
+{"abstract": "  We explore coherent control of Penning and associative ionization in cold\ncollisions of metastable He$^*({2}^3\\text{S})$ atoms via the quantum\ninterference between different states of the He$_2^*$ collision complex. By\ntuning the preparation coefficients of the initial atomic spin states, we can\nbenefit from the quantum interference between molecular channels to maximize or\nminimize the cross sections for Penning and associative ionization. In\nparticular, we find that we can enhance the ionization ratio by 30% in the cold\nregime. This work is significant for the coherent control of chemical reactions\nin the cold and ultracold regime.\n"}
+{"abstract": "  To make informed decisions in natural environments that change over time,\nhumans must update their beliefs as new observations are gathered. Studies\nexploring human inference as a dynamical process that unfolds in time have\nfocused on situations in which the statistics of observations are\nhistory-independent. Yet temporal structure is everywhere in nature, and yields\nhistory-dependent observations. Do humans modify their inference processes\ndepending on the latent temporal statistics of their observations? We\ninvestigate this question experimentally and theoretically using a change-point\ninference task. We show that humans adapt their inference process to fine\naspects of the temporal structure in the statistics of stimuli. As such, humans\nbehave qualitatively in a Bayesian fashion, but, quantitatively, deviate away\nfrom optimality. Perhaps more importantly, humans behave suboptimally in that\ntheir responses are not deterministic, but variable. We show that this\nvariability itself is modulated by the temporal statistics of stimuli. To\nelucidate the cognitive algorithm that yields this behavior, we investigate a\nbroad array of existing and new models that characterize different sources of\nsuboptimal deviations away from Bayesian inference. While models with 'output\nnoise' that corrupts the response-selection process are natural candidates,\nhuman behavior is best described by sampling-based inference models, in which\nthe main ingredient is a compressed approximation of the posterior, represented\nthrough a modest set of random samples and updated over time. This result comes\nto complement a growing literature on sample-based representation and learning\nin humans.\n"}
+{"abstract": "  Unemployment benefits in the US were extended by up to 73 weeks during the\nGreat Recession. Equilibrium labor market theory indicates that extensions of\nbenefit duration impact not only search decisions by job seekers but also job\nvacancy creations by employers. Most of the literature focused on the former to\nshow partial equilibrium effect that increment of unemployment benefits\ndiscourage job search and lead to a rise in unemployment. To study the total\neffect of UI benefit extensions on unemployment, I follow border county\nidentification strategy, take advantage of quasi-differenced specification to\ncontrol for changes in future benefit policies, apply interactive fixed effects\nmodel to deal with unobserved shocks so as to obtain unbiased and consistent\nestimation. I find that benefit extensions have a statistically significant\npositive effect on unemployment, which is consistent with the results of\nprevailing literature.\n"}
+{"abstract": "  Human motion characteristics are used to monitor the progression of\nneurological diseases and mood disorders. Since perceptions of emotions are\nalso interleaved with body posture and movements, emotion recognition from\nhuman gait can be used to quantitatively monitor mood changes. Many existing\nsolutions often use shallow machine learning models with raw positional data or\nmanually extracted features to achieve this. However, gait is composed of many\nhighly expressive characteristics that can be used to identify human subjects,\nand most solutions fail to address this, disregarding the subject's privacy.\nThis work introduces a novel deep neural network architecture to disentangle\nhuman emotions and biometrics. In particular, we propose a cross-subject\ntransfer learning technique for training a multi-encoder autoencoder deep\nneural network to learn disentangled latent representations of human motion\nfeatures. By disentangling subject biometrics from the gait data, we show that\nthe subject's privacy is preserved while the affect recognition performance\noutperforms traditional methods. Furthermore, we exploit Guided Grad-CAM to\nprovide global explanations of the model's decision across gait cycles. We\nevaluate the effectiveness of our method to existing methods at recognizing\nemotions using both 3D temporal joint signals and manually extracted features.\nWe also show that this data can easily be exploited to expose a subject's\nidentity. Our method shows up to 7% improvement and highlights the joints with\nthe most significant influence across the average gait cycle.\n"}
+{"abstract": "  Standard Model (SM) of particle physics has achieved enormous success in\ndescribing the interactions among the known fundamental constituents of nature,\nyet it fails to describe phenomena for which there is very strong experimental\nevidence, such as the existence of dark matter, and which point to the\nexistence of new physics not included in that model; beyond its existence,\nexperimental data, however, have not provided clear indications as to the\nnature of that new physics. The effective field theory (EFT) approach, the\nsubject of this review, is designed for this type of situations; it provides a\nconsistent and unbiased framework within which to study new physics effects\nwhose existence is expected but whose detailed nature is known very\nimperfectly. We will provide a description of this approach together with a\ndiscussion of some of its basic theoretical aspects. We then consider\napplications to high-energy phenomenology and conclude with a discussion of the\napplication of EFT techniques to the study of dark matter physics and it\npossible interactions with the SM. In several of the applications we also\nbriefly discuss specific models that are ultraviolet complete and may realize\nthe effects described by the EFT.\n"}
+{"abstract": "  To study the heavy quark production processes, we use the transverse momentum\ndependent (TMD, or unintegrated) gluon distribution function in a proton\nobtained recently using the Kimber-Martin-Ryskin prescription from the\nBessel-inspired behavior of parton densities at small Bjorken $x$ values. We\nobtained a good agreement of our results with the latest HERA experimental data\nfor reduced cross sections $\\sigma^{c\\overline{c}}_{\\rm red}(x,Q^2)$ and\n$\\sigma^{b\\overline{b}}_{\\rm red}(x,Q^2)$, and also for deep inelastic\nstructure functions $F_2^c(x,Q^2)$ and $F_2^b(x,Q^2)$ in a wide range of $x$\nand $Q^2$ values. Comparisons with the predictions based on the\nCiafaloni-Catani-Fiorani-Marchesini evolution equation and with the results of\nconventional pQCD calculations performed at first three orders of perturbative\nexpansion are presented.\n"}
+{"abstract": "  Fast and automated inference of binary-lens, single-source (2L1S)\nmicrolensing events with sampling-based Bayesian algorithms (e.g., Markov Chain\nMonte Carlo; MCMC) is challenged on two fronts: high computational cost of\nlikelihood evaluations with microlensing simulation codes, and a pathological\nparameter space where the negative-log-likelihood surface can contain a\nmultitude of local minima that are narrow and deep. Analysis of 2L1S events\nusually involves grid searches over some parameters to locate approximate\nsolutions as a prerequisite to posterior sampling, an expensive process that\noften requires human-in-the-loop domain expertise. As the next-generation,\nspace-based microlensing survey with the Roman Space Telescope is expected to\nyield thousands of binary microlensing events, a new fast and automated method\nis desirable. Here, we present a likelihood-free inference (LFI) approach named\namortized neural posterior estimation, where a neural density estimator (NDE)\nlearns a surrogate posterior $\\hat{p}(\\theta|x)$ as an observation-parametrized\nconditional probability distribution, from pre-computed simulations over the\nfull prior space. Trained on 291,012 simulated Roman-like 2L1S simulations, the\nNDE produces accurate and precise posteriors within seconds for any observation\nwithin the prior support without requiring a domain expert in the loop, thus\nallowing for real-time and automated inference. We show that the NDE also\ncaptures expected posterior degeneracies. The NDE posterior could then be\nrefined into the exact posterior with a downstream MCMC sampler with minimal\nburn-in steps.\n"}
+{"abstract": "  After disasters, distribution networks have to be restored by repair,\nreconfiguration, and power dispatch. During the restoration process, changes\ncan occur in real time that deviate from the situations considered in\npre-designed planning strategies. That may result in the pre-designed plan to\nbecome far from optimal or even unimplementable. This paper proposes a\ncentralized-distributed bi-level optimization method to solve the real-time\nrestoration planning problem. The first level determines integer variables\nrelated to routing of the crews and the status of the switches using a genetic\nalgorithm (GA), while the second level determines the dispatch of\nactive/reactive power by using distributed model predictive control (DMPC). A\nnovel Aitken- DMPC solver is proposed to accelerate convergence and to make the\nmethod suitable for real-time decision making. A case study based on the IEEE\n123-bus system is considered, and the acceleration performance of the proposed\nAitken-DMPC solver is evaluated and compared with the standard DMPC method.\n"}
+{"abstract": "  We present extensive, well-sampled optical and ultraviolet photometry and\noptical spectra of the Type Ia supernova (SN Ia) 2017hpa. The light curves\nindicate that SN 2017hpa is a normal SN Ia with an absolute peak magnitude of\n$M_{\\rm max}^{B} \\approx$ -19.12$\\pm$0.11 mag and a post-peak decline rate \\mb\\\n= 1.02$\\pm$0.07 mag. According to the quasibolometric light curve, we derive a\npeak luminosity of 1.25$\\times$10$^{43}$ erg s$^{-1}$ and a $^{56}$Ni mass of\n0.63$\\pm$0.02 $M_{\\odot}$. The spectral evolution of SN 2017hpa is similar to\nthat of normal SNe Ia, while it exhibits unusually rapid velocity evolution\nresembling that of SN 1991bg-like SNe Ia or the high-velocity subclass of SNe\nIa, with a post-peak velocity gradient of $\\sim$ 130$\\pm$7 km s$^{-1}$\nd$^{-1}$. Moreover, its early spectra ($t < -7.9$ d) show prominent\n\\CII~$\\lambda$6580 absorption feature, which disappeared in near-maximum-light\nspectra but reemerged at phases from $t \\sim +8.7$ d to $t \\sim +11.7$ d after\nmaximum light. This implies that some unburned carbon may mix deep into the\ninner layer, and is supported by the low \\CII~$\\lambda$6580 to\n\\SiII~$\\lambda$6355 velocity ratio ($\\sim 0.81$) observed in SN 2017hpa. The\n\\OI~$\\lambda$7774 line shows a velocity distribution like that of carbon. The\nprominent carbon feature, low velocity seen in carbon and oxygen, and large\nvelocity gradient make SN 2017hpa stand out from other normal SNe Ia, and are\nmore consistent with predictions from a violent merger of two white dwarfs.\nDetailed modelling is still needed to reveal the nature of SN 2017hpa.\n"}
+{"abstract": "  The law of a positive infinitely divisible process with no drift is\ncharacterized by its L\\'evy measure on the paths space. Based on recent results\nof the two authors, it is shown that even for simple examples of such\nprocesses, the knowledge of their L\\'evy measures allows to obtain remarkable\ndistributional identities.\n"}
+{"abstract": "  Gravitational waves (GWs) at ultra-low frequencies (${\\lesssim\n100\\,\\mathrm{nHz}}$) are key to understanding the assembly and evolution of\nastrophysical black hole (BH) binaries with masses $\\sim\n10^{6}-10^{9}\\,M_\\odot$ at low redshifts. These GWs also offer a unique window\ninto a wide variety of cosmological processes. Pulsar timing arrays (PTAs) are\nbeginning to measure this stochastic signal at $\\sim 1-100\\,\\mathrm{nHz}$ and\nthe combination of data from several arrays is expected to confirm a detection\nin the next few years. The dominant physical processes generating gravitational\nradiation at $\\mathrm{nHz}$ frequencies are still uncertain. PTA observations\nalone are currently unable to distinguish a binary BH astrophysical foreground\nfrom a cosmological background due to, say, a first order phase transition at a\ntemperature $\\sim 1-100\\,\\mathrm{MeV}$ in a weakly-interacting dark sector.\nThis letter explores the extent to which incorporating integrated bounds on the\nultra-low frequency GW spectrum from any combination of cosmic microwave\nbackground, big bang nucleosynethesis or astrometric observations can help to\nbreak this degeneracy.\n"}
+{"abstract": "  Fluctuations of conserved charges are sensitive to the QCD phase transition\nand a possible critical endpoint in the phase diagram at finite density. In\nthis work, we compute the baryon number fluctuations up to tenth order at\nfinite temperature and density. This is done in a QCD-assisted effective theory\nthat accurately captures the quantum- and in-medium effects of QCD at low\nenergies. A direct computation at finite density allows us to assess the\napplicability of expansions around vanishing density. By using different\nfreeze-out scenarios in heavy-ion collisions, we translate these results into\nbaryon number fluctuations as a function of collision energy. We show that a\nnon-monotonic energy dependence of baryon number fluctuations can arise in the\nnon-critical crossover region of the phase diagram. Our results compare well\nwith recent experimental measurements of the kurtosis and the sixth-order\ncumulant of the net-proton distribution from the STAR collaboration. They\nindicate that the experimentally observed non-monotonic energy dependence of\nfourth-order net-proton fluctuations is highly non-trivial. It could be an\nexperimental signature of an increasingly sharp chiral crossover and may\nindicate a QCD critical point. The physics implications and necessary upgrades\nof our analysis are discussed in detail.\n"}
+{"abstract": "  Contributions: This paper investigates the relations between undergraduate\nsoftware architecture students' self-confidence and their course expectations,\ncognitive levels, preferred learning methods, and critical thinking.\nBackground: these students, often, lack self-confidence in their ability to use\ntheir knowledge to design software architectures. Intended Outcomes:\nSelf-confidence is expected to be related to the students' course expectations,\ncognitive levels, preferred learning methods, and critical thinking.\nApplication Design: We developed a questionnaire with open-ended questions to\nassess the self-confidence levels and related factors, which was taken by\none-hundred ten students in two semesters. The students answers were coded and\nanalyzed afterward. Findings: We found that self-confidence is weakly\nassociated with the students' course expectations and critical thinking and\nindependent from their cognitive levels and preferred learning methods. The\nresults suggest that to improve the self-confidence of the students, the\ninstructors should ensure that the students' have \"correct\" course expectations\nand work on improving the students' critical thinking capabilities.\n"}
+{"abstract": "  High-quality 4D reconstruction of human performance with complex interactions\nto various objects is essential in real-world scenarios, which enables numerous\nimmersive VR/AR applications. However, recent advances still fail to provide\nreliable performance reconstruction, suffering from challenging interaction\npatterns and severe occlusions, especially for the monocular setting. To fill\nthis gap, in this paper, we propose RobustFusion, a robust volumetric\nperformance reconstruction system for human-object interaction scenarios using\nonly a single RGBD sensor, which combines various data-driven visual and\ninteraction cues to handle the complex interaction patterns and severe\nocclusions. We propose a semantic-aware scene decoupling scheme to model the\nocclusions explicitly, with a segmentation refinement and robust object\ntracking to prevent disentanglement uncertainty and maintain temporal\nconsistency. We further introduce a robust performance capture scheme with the\naid of various data-driven cues, which not only enables re-initialization\nability, but also models the complex human-object interaction patterns in a\ndata-driven manner. To this end, we introduce a spatial relation prior to\nprevent implausible intersections, as well as data-driven interaction cues to\nmaintain natural motions, especially for those regions under severe\nhuman-object occlusions. We also adopt an adaptive fusion scheme for temporally\ncoherent human-object reconstruction with occlusion analysis and human parsing\ncue. Extensive experiments demonstrate the effectiveness of our approach to\nachieve high-quality 4D human performance reconstruction under complex\nhuman-object interactions whilst still maintaining the lightweight monocular\nsetting.\n"}
+{"abstract": "  This graduate textbook on machine learning tells a story of how patterns in\ndata support predictions and consequential actions. Starting with the\nfoundations of decision making, we cover representation, optimization, and\ngeneralization as the constituents of supervised learning. A chapter on\ndatasets as benchmarks examines their histories and scientific bases.\nSelf-contained introductions to causality, the practice of causal inference,\nsequential decision making, and reinforcement learning equip the reader with\nconcepts and tools to reason about actions and their consequences. Throughout,\nthe text discusses historical context and societal impact. We invite readers\nfrom all backgrounds; some experience with probability, calculus, and linear\nalgebra suffices.\n"}
+{"abstract": "  System-level test, or SLT, is an increasingly important process step in\ntoday's integrated circuit testing flows. Broadly speaking, SLT aims at\nexecuting functional workloads in operational modes. In this paper, we\nconsolidate available knowledge about what SLT is precisely and why it is used\ndespite its considerable costs and complexities. We discuss the types or\nfailures covered by SLT, and outline approaches to quality assessment, test\ngeneration and root-cause diagnosis in the context of SLT. Observing that the\ntheoretical understanding for all these questions has not yet reached the level\nof maturity of the more conventional structural and functional test methods, we\noutline new and promising directions for methodical developments leveraging on\nrecent findings from software engineering.\n"}
+{"abstract": "  Novel Object Captioning is a zero-shot Image Captioning task requiring\ndescribing objects not seen in the training captions, but for which information\nis available from external object detectors. The key challenge is to select and\ndescribe all salient detected novel objects in the input images. In this paper,\nwe focus on this challenge and propose the ECOL-R model (Encouraging Copying of\nObject Labels with Reinforced Learning), a copy-augmented transformer model\nthat is encouraged to accurately describe the novel object labels. This is\nachieved via a specialised reward function in the SCST reinforcement learning\nframework (Rennie et al., 2017) that encourages novel object mentions while\nmaintaining the caption quality. We further restrict the SCST training to the\nimages where detected objects are mentioned in reference captions to train the\nECOL-R model. We additionally improve our copy mechanism via Abstract Labels,\nwhich transfer knowledge from known to novel object types, and a Morphological\nSelector, which determines the appropriate inflected forms of novel object\nlabels. The resulting model sets new state-of-the-art on the nocaps (Agrawal et\nal., 2019) and held-out COCO (Hendricks et al., 2016) benchmarks.\n"}
+{"abstract": "  The Internet of Things (IoT) is becoming an indispensable part of everyday\nlife, enabling a variety of emerging services and applications. However, the\npresence of rogue IoT devices has exposed the IoT to untold risks with severe\nconsequences. The first step in securing the IoT is detecting rogue IoT devices\nand identifying legitimate ones. Conventional approaches use cryptographic\nmechanisms to authenticate and verify legitimate devices' identities. However,\ncryptographic protocols are not available in many systems. Meanwhile, these\nmethods are less effective when legitimate devices can be exploited or\nencryption keys are disclosed. Therefore, non-cryptographic IoT device\nidentification and rogue device detection become efficient solutions to secure\nexisting systems and will provide additional protection to systems with\ncryptographic protocols. Non-cryptographic approaches require more effort and\nare not yet adequately investigated. In this paper, we provide a comprehensive\nsurvey on machine learning technologies for the identification of IoT devices\nalong with the detection of compromised or falsified ones from the viewpoint of\npassive surveillance agents or network operators. We classify the IoT device\nidentification and detection into four categories: device-specific pattern\nrecognition, Deep Learning enabled device identification, unsupervised device\nidentification, and abnormal device detection. Meanwhile, we discuss various\nML-related enabling technologies for this purpose. These enabling technologies\ninclude learning algorithms, feature engineering on network traffic traces and\nwireless signals, continual learning, and abnormality detection.\n"}
+{"abstract": "  Nonstationary signals are commonly analyzed and processed in the\ntime-frequency (T-F) domain that is obtained by the discrete Gabor transform\n(DGT). The T-F representation obtained by DGT is spread due to windowing, which\nmay degrade the performance of T-F domain analysis and processing. To obtain a\nwell-localized T-F representation, sparsity-aware methods using $\\ell_1$-norm\nhave been studied. However, they need to discretize a continuous parameter onto\na grid, which causes a model mismatch. In this paper, we propose a method of\nestimating a sparse T-F representation using atomic norm. The atomic norm\nenables sparse optimization without discretization of continuous parameters.\nNumerical experiments show that the T-F representation obtained by the proposed\nmethod is sparser than the conventional methods.\n"}
+{"abstract": "  This paper explores methods for constructing low multipole temperature and\npolarisation likelihoods from maps of the cosmic microwave background\nanisotropies that have complex noise properties and partial sky coverage. We\nuse Planck 2018 High Frequency Instrument (HFI) and updated SRoll2 temperature\nand polarisation maps to test our methods. We present three likelihood\napproximations based on quadratic cross spectrum estimators: (i) a variant of\nthe simulation-based likelihood (SimBaL) techniques used in the Planck legacy\npapers to produce a low multipole EE likelihood; (ii) a semi-analytical\nlikelihood approximation (momento) based on the principle of maximum entropy;\n(iii) a density-estimation `likelihood-free' scheme (DELFI). Approaches (ii)\nand (iii) can be generalised to produce low multipole joint\ntemperature-polarisation (TTTEEE) likelihoods. We present extensive tests of\nthese methods on simulations with realistic correlated noise. We then analyse\nthe Planck data and confirm the robustness of our method and likelihoods on\nmultiple inter- and intra-frequency detector set combinations of SRoll2 maps.\nThe three likelihood techniques give consistent results and support a low value\nof the optical depth to reoinization, tau, from the HFI. Our best estimate of\ntau comes from combining the low multipole SRoll2 momento (TTTEEE) likelihood\nwith the CamSpec high multipole likelihood and is tau = 0.0627+0.0050-0.0058.\nThis is consistent with the SRoll2 team's determination of tau, though slightly\nhigher by 0.5 sigma, mainly because of our joint treatment of temperature and\npolarisation.\n"}
+{"abstract": "  Drones are effective for reducing human activity and interactions by\nperforming tasks such as exploring and inspecting new environments, monitoring\nresources and delivering packages. Drones need a controller to maintain\nstability and to reach their goal. The most well-known drone controllers are\nproportional-integral-derivative (PID) and proportional-derivative (PD)\ncontrollers. However, the controller parameters need to be tuned and optimized.\nIn this paper, we introduce the use of two evolutionary algorithms,\nbiogeography-based optimization~(BBO) and particle swarm optimization (PSO),\nfor multi-objective optimization (MOO) to tune the parameters of the PD\ncontroller of a drone. The combination of MOO, BBO, and PSO results in various\nmethods for optimization: vector evaluated BBO and PSO, denoted as VEBBO and\nVEPSO; and non-dominated sorting BBO and PSO, denoted as NSBBO and NSPSO. The\nmulti-objective cost function is based on tracking errors for the four states\nof the system. Two criteria for evaluating the Pareto fronts of the\noptimization methods, normalized hypervolume and relative coverage, are used to\ncompare performance. Results show that NSBBO generally performs better than the\nother methods.\n"}
+{"abstract": "  Deep neural networks (DNNs) used for brain-computer-interface (BCI)\nclassification are commonly expected to learn general features when trained\nacross a variety of contexts, such that these features could be fine-tuned to\nspecific contexts. While some success is found in such an approach, we suggest\nthat this interpretation is limited and an alternative would better leverage\nthe newly (publicly) available massive EEG datasets. We consider how to adapt\ntechniques and architectures used for language modelling (LM), that appear\ncapable of ingesting awesome amounts of data, towards the development of\nencephalography modelling (EM) with DNNs in the same vein. We specifically\nadapt an approach effectively used for automatic speech recognition, which\nsimilarly (to LMs) uses a self-supervised training objective to learn\ncompressed representations of raw data signals. After adaptation to EEG, we\nfind that a single pre-trained model is capable of modelling completely novel\nraw EEG sequences recorded with differing hardware, and different subjects\nperforming different tasks. Furthermore, both the internal representations of\nthis model and the entire architecture can be fine-tuned to a variety of\ndownstream BCI and EEG classification tasks, outperforming prior work in more\ntask-specific (sleep stage classification) self-supervision.\n"}
+{"abstract": "  We investigate the behavior of the Lyapunov spectrum of a linear\ndiscrete-time system under the action of small perturbations in order to obtain\nsome verifiable conditions for stability and openness of the Lyapunov spectrum.\nTo this end we introduce the concepts of broken away solutions and splitted\nsystems. The main results obtained are a necessary condition for stability and\na sufficient condition for the openness of the Lyapunov spectrum, which is\ngiven in terms of the system itself. Finally, examples of using the obtained\nresults are presented.\n"}
+{"abstract": "  We prove fixed point theorems in a space with a distance function that takes\nvalues in a partially ordered monoid. On the one hand, such an approach allows\none to generalize some fixed point theorems in a broad class of spaces,\nincluding metric and uniform spaces. On the other hand, compared to the\nso-called cone metric spaces and $K$-metric spaces, we do not require that the\ndistance function range has a linear structure. We also consider several\napplications of the obtained fixed point theorems. In particular, we consider\nthe questions of the existence of solutions of the Fredholm integral equation\nin $L$-spaces.\n"}
+{"abstract": "  In this work, we have proposed augmented KRnets including both discrete and\ncontinuous models. One difficulty in flow-based generative modeling is to\nmaintain the invertibility of the transport map, which is often a trade-off\nbetween effectiveness and robustness. The exact invertibility has been achieved\nin the real NVP using a specific pattern to exchange information between two\nseparated groups of dimensions. KRnet has been developed to enhance the\ninformation exchange among data dimensions by incorporating the\nKnothe-Rosenblatt rearrangement into the structure of the transport map. Due to\nthe maintenance of exact invertibility, a full nonlinear update of all data\ndimensions needs three iterations in KRnet. To alleviate this issue, we will\nadd augmented dimensions that act as a channel for communications among the\ndata dimensions. In the augmented KRnet, a fully nonlinear update is achieved\nin two iterations. We also show that the augmented KRnet can be reformulated as\nthe discretization of a neural ODE, where the exact invertibility is kept such\nthat the adjoint method can be formulated with respect to the discretized ODE\nto obtain the exact gradient. Numerical experiments have been implemented to\ndemonstrate the effectiveness of our models.\n"}
+{"abstract": "  In this paper, we study 5d $\\mathcal{N}=1$ $Sp(N)$ gauge theory with $N_f (\n\\leq 2N + 3 )$ flavors based on 5-brane web diagram with $O5$-plane. On the one\nhand, we discuss Seiberg-Witten curve based on the dual graph of the 5-brane\nweb with $O5$-plane. On the other hand, we compute the Nekrasov partition\nfunction based on the topological vertex formalism with $O5$-plane. Rewriting\nit in terms of profile functions, we obtain the saddle point equation for the\nprofile function after taking thermodynamic limit. By introducing the\nresolvent, we derive the Seiberg-Witten curve and its boundary conditions as\nwell as its relation to the prepotential in terms of the cycle integrals. They\ncoincide with those directly obtained from the dual graph of the 5-brane web\nwith $O5$-plane. This agreement gives further evidence for mirror symmetry\nwhich relates Nekrasov partition function with Seiberg-Witten curve in the case\nwith orientifold plane and shed light on the non-toric Calabi-Yau 3-folds\nincluding D-type singularities.\n"}
+{"abstract": "  In this paper, we study the problem of physical layer security in the uplink\nof millimeter-wave massive multiple-input multiple-output (MIMO) networks and\npropose a jamming detection and suppression method. The proposed method is\nbased on directional information of the received signals at the base station\nantenna array. The proposed jamming detection method can accurately detect both\nthe existence and direction of the jammer using the received pilot signals in\nthe training phase. The obtained information is then exploited to develop a\nchannel estimator that excludes the jammer's angular subspace from received\ntraining signals. The estimated channel information is then used for designing\na combiner at the base station that is able to effectively cancel out the\ndeliberate interference of the jammer. By numerical simulations, we evaluate\nthe performance of the proposed jamming detection method in terms of correct\ndetection probability and false alarm probability and show its effectiveness\nwhen the jammer's power is substantially lower than the user's power. Also, our\nresults show that the proposed jamming suppression method can achieve a very\nclose spectral efficiency as the case of no jamming in the network\n"}
+{"abstract": "  In this study, using low-temperature scanning tunneling microscopy (STM), we\nfocus on understanding the native defects in pristine \\textit{1T}-TiSe$_2$ at\nthe atomic scale. We probe how they perturb the charge density waves (CDWs) and\nlead to local domain formation. These defects influence the correlation length\nof CDWs. We establish a connection between suppression of CDWs, Ti\nintercalation, and show how this supports the exciton condensation model of CDW\nformation in \\textit{1T}-TiSe$_2$.\n"}
+{"abstract": "  We developed recently [A. Fert\\'e, et al., J. Phys. Chem. Lett. 11, 4359\n(2020)] a method to compute single site double core hole (ssDCH or K$^{-2}$)\nspectra. We refer to that method as NOTA+CIPSI. In the present paper this\nmethod is applied to the O K$^{-2}$ spectrum of the CO$_2$ molecule, and we use\nthis as an example to discuss in detail its convergence properties. Using this\napproach, a theoretical spectra in excellent agreement with the experimental\none is obtained. Thanks to a thorough interpretation of the shake-up states\nresponsible for the main satellite peaks and with the help of a comparison with\nthe O K$^{-2}$ spectrum of CO, we can highlight the clear signature of the two\nnon equivalent carbon oxygen bonds in the oxygen ssDCH CO$_2$ dication.\n"}
+{"abstract": "  Non-negative matrix factorization (NMF) is a powerful tool for dimensionality\nreduction and clustering. Unfortunately, the interpretation of the clustering\nresults from NMF is difficult, especially for the high-dimensional biological\ndata without effective feature selection. In this paper, we first introduce a\nrow-sparse NMF with $\\ell_{2,0}$-norm constraint (NMF_$\\ell_{20}$), where the\nbasis matrix $W$ is constrained by the $\\ell_{2,0}$-norm, such that $W$ has a\nrow-sparsity pattern with feature selection. It is a challenge to solve the\nmodel, because the $\\ell_{2,0}$-norm is non-convex and non-smooth. Fortunately,\nwe prove that the $\\ell_{2,0}$-norm satisfies the Kurdyka-\\L{ojasiewicz}\nproperty. Based on the finding, we present a proximal alternating linearized\nminimization algorithm and its monotone accelerated version to solve the\nNMF_$\\ell_{20}$ model. In addition, we also present a orthogonal NMF with\n$\\ell_{2,0}$-norm constraint (ONMF_$\\ell_{20}$) to enhance the clustering\nperformance by using a non-negative orthogonal constraint. We propose an\nefficient algorithm to solve ONMF_$\\ell_{20}$ by transforming it into a series\nof constrained and penalized matrix factorization problems. The results on\nnumerical and scRNA-seq datasets demonstrate the efficiency of our methods in\ncomparison with existing methods.\n"}
+{"abstract": "  We propose a program at B-factories of inclusive, multi-track displaced\nvertex searches, which are expected to be low background and give excellent\nsensitivity to non-minimal hidden sectors. Multi-particle hidden sectors often\ninclude long-lived particles (LLPs) which result from approximate symmetries,\nand we classify the possible decays of GeV-scale LLPs in an effective field\ntheory framework. Considering several LLP production modes, including dark\nphotons and dark Higgs bosons, we study the sensitivity of LLP searches with\ndifferent number of displaced vertices per event and track requirements per\ndisplaced vertex, showing that inclusive searches can have sensitivity to a\nlarge range of hidden sector models that are otherwise unconstrained by current\nor planned searches.\n"}
+{"abstract": "  We present a derivation of the integral fluctuation theorem (IFT) for\nisolated quantum systems based on some natural assumptions on transition\nprobabilities. Under these assumptions of \"stiffness\" and \"smoothness\" the IFT\nimmediately follows for microcanonical and pure quantum states. We numerically\ncheck the IFT as well as the validity of our assumptions by analyzing two\nexemplary systems. We have been informed by T. Sagawa et al. that he and his\nco-workers found comparable numerical results and are preparing a corresponding\npaper, which should be available on the same day as the present text. We\nrecommend reading their submission.\n"}
+{"abstract": "  Erbium-doped lithium niobate on insulator (Er:LNOI) is a promising platform\nfor photonic integrated circuits as it adds gain to the LNOI system and enables\non-chip lasers and amplifiers. A challenge for Er:LNOI laser is to increase its\noutput power while maintaining single-frequency and single (-transverse)-mode\noperation. In this work, we demonstrate that single-frequency and single-mode\noperation can be achieved even in a single multi-mode Er:LNOI microring by\nintroducing mode-dependent loss and gain competition. In a single microring\nwith a free spectral range of 192 GHz, we have achieved single-mode lasing with\nan output power of 2.1 microwatt, a side-mode suppression of 35.5 dB, and a\nlinewidth of 1.27 MHz.\n"}
+{"abstract": "  In this paper, we study the response of large models from the BERT family to\nincoherent inputs that should confuse any model that claims to understand\nnatural language. We define simple heuristics to construct such examples. Our\nexperiments show that state-of-the-art models consistently fail to recognize\nthem as ill-formed, and instead produce high confidence predictions on them. As\na consequence of this phenomenon, models trained on sentences with randomly\npermuted word order perform close to state-of-the-art models. To alleviate\nthese issues, we show that if models are explicitly trained to recognize\ninvalid inputs, they can be robust to such attacks without a drop in\nperformance.\n"}
+{"abstract": "  Supergranules create a peak in the spatial spectrum of photospheric velocity\nfeatures. They have some properties of convection cells but their origin is\nstill being debated in the literature. The time-distance helioseismology\nconstitutes a method that is suitable for investigating the deep structure of\nsupergranules. Our aim is to construct the model of the flows in the average\nsupergranular cell using fully consistent time-distance inverse methodology. We\nused the Multi-Channel Subtractive Optimally Localised Averaging inversion\nmethod with regularisation of the cross-talk. We combined the difference and\nthe mean travel-time averaging geometries. We applied this methodology to\ntravel-time maps averaged over more than 10000 individual supergranular cells.\nThese cells were detected automatically in travel-time maps computed for 64\nquiet days around the disc centre. The ensemble averaging method allows us to\nsignificantly improve the signal-to-noise ratio and to obtain a clear picture\nof the flows in the average supergranule. We found near-surface divergent\nhorizontal flows which quickly and monotonously weakened with depth; they\nbecame particularly weak at the depth of about 7 Mm, where they even apparently\nswitched sign. To learn about the vertical component, we integrated the\ncontinuity equation from the surface. The derived estimates of the vertical\nflow depicted a sub-surface increase from about 5 m/s at the surface to about\n35 m/s at the depth of about 3 Mm followed by a monotonous decrease to greater\ndepths. The vertical flow remained positive (an upflow) and became\nindistinguishable from the background at the depth of about 15 Mm. We further\ndetected a systematic flow in the longitudinal direction. The course of this\nsystematic flow with depth agrees well with the model of the solar rotation in\nthe sub-surface layers.\n"}
+{"abstract": "  Statistical learning theory provides the foundation to applied machine\nlearning, and its various successful applications in computer vision, natural\nlanguage processing and other scientific domains. The theory, however, does not\ntake into account the unique challenges of performing statistical learning in\ngeospatial settings. For instance, it is well known that model errors cannot be\nassumed to be independent and identically distributed in geospatial (a.k.a.\nregionalized) variables due to spatial correlation; and trends caused by\ngeophysical processes lead to covariate shifts between the domain where the\nmodel was trained and the domain where it will be applied, which in turn harm\nthe use of classical learning methodologies that rely on random samples of the\ndata. In this work, we introduce the geostatistical (transfer) learning\nproblem, and illustrate the challenges of learning from geospatial data by\nassessing widely-used methods for estimating generalization error of learning\nmodels, under covariate shift and spatial correlation. Experiments with\nsynthetic Gaussian process data as well as with real data from geophysical\nsurveys in New Zealand indicate that none of the methods are adequate for model\nselection in a geospatial context. We provide general guidelines regarding the\nchoice of these methods in practice while new methods are being actively\nresearched.\n"}
+{"abstract": "  We show that the widely used relaxation time approximation to the\nrelativistic Boltzmann equation contains basic flaws, being incompatible with\nmicroscopic and macroscopic conservation laws. We propose a new approximation\nthat fixes such fundamental issues and maintains the basic properties of the\nlinearized Boltzmann collision operator. We show how this correction affects\ntransport coefficients, such as the bulk viscosity and particle diffusion.\n"}
+{"abstract": "  Estimating the data density is one of the challenging problems in deep\nlearning. In this paper, we present a simple yet effective method for\nestimating the data density using a deep neural network and the\nDonsker-Varadhan variational lower bound on the KL divergence. We show that the\noptimal critic function associated with the Donsker-Varadhan representation on\nthe KL divergence between the data and the uniform distribution can estimate\nthe data density. We also present the deep neural network-based modeling and\nits stochastic learning. The experimental results and possible applications of\nthe proposed method demonstrate that it is competitive with the previous\nmethods and has a lot of possibilities in applied to various applications.\n"}
+{"abstract": "  A unified framework for the Chevalley and equitable presentation of\n$U_q(sl_2)$ is introduced. It is given in terms of a system of Freidel-Maillet\ntype equations satisfied by a pair of quantum K-operators ${\\cal K}^\\pm$, whose\nentries are expressed in terms of either Chevalley or equitable generators. The\nHopf algebra structure is reconsidered in light of this presentation, and\ninterwining relations for K-operators are obtained. A K-operator solving a\nspectral parameter dependent Freidel-Maillet equation is also considered.\nSpecializations to $U_q(sl_2)$ admit a decomposition in terms of ${\\cal\nK}^\\pm$. Explicit examples of K-matrices are constructed.\n"}
+{"abstract": "  Automatic medical image segmentation based on Computed Tomography (CT) has\nbeen widely applied for computer-aided surgery as a prerequisite. With the\ndevelopment of deep learning technologies, deep convolutional neural networks\n(DCNNs) have shown robust performance in automated semantic segmentation of\nmedical images. However, semantic segmentation algorithms based on DCNNs still\nmeet the challenges of feature loss between encoder and decoder, multi-scale\nobject, restricted field of view of filters, and lack of medical image data.\nThis paper proposes a novel algorithm for automated vertebrae segmentation via\n3D volumetric spine CT images. The proposed model is based on the structure of\nencoder to decoder, using layer normalization to optimize mini-batch training\nperformance. To address the concern of the information loss between encoder and\ndecoder, we designed an Atrous Residual Path to pass more features from encoder\nto decoder instead of an easy shortcut connection. The proposed model also\napplied the attention module in the decoder part to extract features from\nvariant scales. The proposed model is evaluated on a publicly available dataset\nby a variety of metrics. The experimental results show that our model achieves\ncompetitive performance compared with other state-of-the-art medical semantic\nsegmentation methods.\n"}
+{"abstract": "  We introduce Robust Restless Bandits, a challenging generalization of\nrestless multi-arm bandits (RMAB). RMABs have been widely studied for\nintervention planning with limited resources. However, most works make the\nunrealistic assumption that the transition dynamics are known perfectly,\nrestricting the applicability of existing methods to real-world scenarios. To\nmake RMABs more useful in settings with uncertain dynamics: (i) We introduce\nthe Robust RMAB problem and develop solutions for a minimax regret objective\nwhen transitions are given by interval uncertainties; (ii) We develop a double\noracle algorithm for solving Robust RMABs and demonstrate its effectiveness on\nthree experimental domains; (iii) To enable our double oracle approach, we\nintroduce RMABPPO, a novel deep reinforcement learning algorithm for solving\nRMABs. RMABPPO hinges on learning an auxiliary \"$\\lambda$-network\" that allows\neach arm's learning to decouple, greatly reducing sample complexity required\nfor training; (iv) Under minimax regret, the adversary in the double oracle\napproach is notoriously difficult to implement due to non-stationarity. To\naddress this, we formulate the adversary oracle as a multi-agent reinforcement\nlearning problem and solve it with a multi-agent extension of RMABPPO, which\nmay be of independent interest as the first known algorithm for this setting.\nCode is available at https://github.com/killian-34/RobustRMAB.\n"}
+{"abstract": "  In this paper, we introduce a new framework for unsupervised deep homography\nestimation. Our contributions are 3 folds. First, unlike previous methods that\nregress 4 offsets for a homography, we propose a homography flow\nrepresentation, which can be estimated by a weighted sum of 8 pre-defined\nhomography flow bases. Second, considering a homography contains 8\nDegree-of-Freedoms (DOFs) that is much less than the rank of the network\nfeatures, we propose a Low Rank Representation (LRR) block that reduces the\nfeature rank, so that features corresponding to the dominant motions are\nretained while others are rejected. Last, we propose a Feature Identity Loss\n(FIL) to enforce the learned image feature warp-equivariant, meaning that the\nresult should be identical if the order of warp operation and feature\nextraction is swapped. With this constraint, the unsupervised optimization is\nachieved more effectively and more stable features are learned. Extensive\nexperiments are conducted to demonstrate the effectiveness of all the newly\nproposed components, and results show that our approach outperforms the\nstate-of-the-art on the homography benchmark datasets both qualitatively and\nquantitatively. Code is available at\nhttps://github.com/megvii-research/BasesHomo.\n"}
+{"abstract": "  Irregular cusp of an orthogonal modular variety is a cusp where the lattice\nfor Fourier expansion is strictly smaller than the lattice of translation.\nPresence of such a cusp affects the study of pluricanonical forms on the\nmodular variety using modular forms. We study toroidal compactification over an\nirregular cusp, and clarify there the cusp form criterion for the calculation\nof Kodaira dimension. At the same time, we show that irregular cusps do not\narise frequently: besides the cases when the group is neat or contains -1, we\nprove that the stable orthogonal groups of most (but not all) even lattices\nhave no irregular cusp.\n"}
+{"abstract": "  Collisions electrically charge grains which promotes growth by coagulation.\nWe present aggregation experiments with three large ensembles of basalt beads\n($150\\,\\mu\\mathrm{m} - 180\\,\\mu\\mathrm{m})$, two of which are charged, while\none remains almost neutral as control system. In microgravity experiments, free\ncollisions within these samples are induced with moderate collision velocities\n($0 - 0.2 \\,\\mathrm{m\\,s}^{-1}$). In the control system, coagulation stops at\n(sub-)mm size while the charged grains continue to grow. A maximum agglomerate\nsize of 5\\,cm is reached, limited only by bead depletion in the free volume.\nFor the first time, charge-driven growth well into the centimeter range is\ndirectly proven by experiments. In protoplanetary disks, this agglomerate size\nis well beyond the critical size needed for hydrodynamic particle concentration\nas, e.g., by the streaming instabilities.\n"}
+{"abstract": "  We offer an embedding of CPython that runs entirely in memory without\n\"touching\" the disk. This in-memory embedding can load Python scripts directly\nfrom memory instead these scripts having to be loaded from files on disk.\nMalware that resides only in memory is harder to detect or mitigate against. We\nintend for our work to be used by security researchers to rapidly develop and\ndeploy offensive techniques that is difficult for security products to analyze\ngiven these instructions are in bytecode and only translated to machine-code by\nthe interpreter immediately prior to execution. Our work helps security\nresearchers and enterprise Red Teams who play offense. Red Teams want to\nrapidly prototype malware for their periodic campaigns and do not want their\nmalware to be detected by the Incident Response (IR) teams prior to\naccomplishing objectives. Red Teams also have difficulty running malware in\nproduction from files on disk as modern enterprise security products emulate,\ninspect, or quarantine such executables given these files have no reputation.\nOur work also helps enterprise Hunt and IR teams by making them aware of the\nviability of this type of attack. Our approach has been in use in production\nfor over a year and meets our customers' needs to quickly emulate\nthreat-actors' tasks, techniques, and procedures (TTPs).\n"}
+{"abstract": "  The tracking and timely resolution of service requests is one of the major\nchallenges in agile project management. Having an efficient solution to this\nproblem is a key requirement for Walmart to facilitate seamless collaboration\nacross its different business units. The Jira software is one of the popular\nchoices in industries for monitoring such service requests. A service request\nonce logged into the system by a reporter is referred to as a (Jira) ticket\nwhich is assigned to an engineer for servicing. In this work, we explore how\nthe tickets which may arise in any of the Walmart stores and offices\ndistributed over several countries can be assigned to engineers efficiently.\nSpecifically, we will discuss how the introduction of a bot for automated\nticket assignment has helped in reducing the disparity in ticket assignment to\nengineers by human managers and also decreased the average ticket resolution\ntime - thereby improving the experience for both the reporters and the\nengineers. Additionally, the bot sends reminders and status updates over\ndifferent business communication platforms for timely tracking of tickets; it\ncan be suitably modified to provision for human intervention in case of special\nneeds by some teams. The current study conducted over data collected from\nvarious teams within Walmart shows the efficacy of our bot.\n"}
+{"abstract": "  We rewrite the numerical ansatz of the Method of Auxiliary Sources (MAS),\ntypically used in computational electromagnetics, as a neural network, i.e. as\na composed function of linear and activation layers. MAS is a numerical method\nfor Partial Differential Equations (PDEs) that employs point sources, which are\nalso exact solutions of the considered PDE, as radial basis functions to match\na given boundary condition. In the framework of neural networks we rely on\noptimization algorithms such as Adam to train MAS and find both its optimal\ncoefficients and positions of the central singularities of the sources. In this\nwork we also show that the MAS ansatz trained as a neural network can be used,\nin the case of an unknown function with a central singularity, to detect the\nposition of such singularity.\n"}
+{"abstract": "  The process of learning a manipulation task depends strongly on the action\nspace used for exploration: posed in the incorrect action space, solving a task\nwith reinforcement learning can be drastically inefficient. Additionally,\nsimilar tasks or instances of the same task family impose latent manifold\nconstraints on the most effective action space: the task family can be best\nsolved with actions in a manifold of the entire action space of the robot.\nCombining these insights we present LASER, a method to learn latent action\nspaces for efficient reinforcement learning. LASER factorizes the learning\nproblem into two sub-problems, namely action space learning and policy learning\nin the new action space. It leverages data from similar manipulation task\ninstances, either from an offline expert or online during policy learning, and\nlearns from these trajectories a mapping from the original to a latent action\nspace. LASER is trained as a variational encoder-decoder model to map raw\nactions into a disentangled latent action space while maintaining action\nreconstruction and latent space dynamic consistency. We evaluate LASER on two\ncontact-rich robotic tasks in simulation, and analyze the benefit of policy\nlearning in the generated latent action space. We show improved sample\nefficiency compared to the original action space from better alignment of the\naction space to the task space, as we observe with visualizations of the\nlearned action space manifold. Additional details:\nhttps://www.pair.toronto.edu/laser\n"}
+{"abstract": "  Cardiac imaging known as echocardiography is a non-invasive tool utilized to\nproduce data including images and videos, which cardiologists use to diagnose\ncardiac abnormalities in general and myocardial infarction (MI) in particular.\nEchocardiography machines can deliver abundant amounts of data that need to be\nquickly analyzed by cardiologists to help them make a diagnosis and treat\ncardiac conditions. However, the acquired data quality varies depending on the\nacquisition conditions and the patient's responsiveness to the setup\ninstructions. These constraints are challenging to doctors especially when\npatients are facing MI and their lives are at stake. In this paper, we propose\nan innovative real-time end-to-end fully automated model based on convolutional\nneural networks (CNN) to detect MI depending on regional wall motion\nabnormalities (RWMA) of the left ventricle (LV) from videos produced by\nechocardiography. Our model is implemented as a pipeline consisting of a 2D CNN\nthat performs data preprocessing by segmenting the LV chamber from the apical\nfour-chamber (A4C) view, followed by a 3D CNN that performs a binary\nclassification to detect if the segmented echocardiography shows signs of MI.\nWe trained both CNNs on a dataset composed of 165 echocardiography videos each\nacquired from a distinct patient. The 2D CNN achieved an accuracy of 97.18% on\ndata segmentation while the 3D CNN achieved 90.9% of accuracy, 100% of\nprecision and 95% of recall on MI detection. Our results demonstrate that\ncreating a fully automated system for MI detection is feasible and propitious.\n"}
+{"abstract": "  For a semimartingale with jumps, we propose a new estimation method for\nintegrated volatility, i.e., the quadratic variation of the continuous\nmartingale part, based on the global jump filter proposed by Inatsugu and\nYoshida [8]. To decide whether each increment of the process has jumps, the\nglobal jump filter adopts the upper $\\alpha$-quantile of the absolute\nincrements as the threshold. This jump filter is called global since it uses\nall the observations to classify one increment. We give a rate of convergence\nand prove asymptotic mixed normality of the global realized volatility and its\nvariant \"Winsorized global volatility\". By simulation studies, we show that our\nestimators outperform previous realized volatility estimators that use a few\nadjacent increments to mitigate the effects of jumps.\n"}
+{"abstract": "  The singular value decomposition going with many problems in medical imaging,\nnon-destructive testing, geophysics, is of central importance. Unfortunately\nthe effective numerical determination of the singular functions in question is\na very ill-posed problem. The best known remedy to this problem goes back to\nthe work of D. Slepian, H.Landau and H. Pollak, Bell Labs 1960-1965. We show\nthat the master symmetries of the Korteweg-de Vries equation give a way to\nextend the remarkable result of D. Slepian in connection with the Bessel\nintegral kernel and the existence of a differential operator that commutes with\nthe corresponding integral operator. The original results of the Bell Labs\ngroup has already played an important role in the study of the limited angle\nproblem in X-ray tomography as well as in Random Matrix theory.\n"}
+{"abstract": "  We present d3p, a software package designed to help fielding runtime\nefficient widely-applicable Bayesian inference under differential privacy\nguarantees. d3p achieves general applicability to a wide range of probabilistic\nmodelling problems by implementing the differentially private variational\ninference algorithm, allowing users to fit any parametric probabilistic model\nwith a differentiable density function. d3p adopts the probabilistic\nprogramming paradigm as a powerful way for the user to flexibly define such\nmodels. We demonstrate the use of our software on a hierarchical logistic\nregression example, showing the expressiveness of the modelling approach as\nwell as the ease of running the parameter inference. We also perform an\nempirical evaluation of the runtime of the private inference on a complex model\nand find a $\\sim$10 fold speed-up compared to an implementation using\nTensorFlow Privacy.\n"}
+{"abstract": "  The structural connectome is often represented by fiber bundles generated\nfrom various types of tractography. We propose a method of analyzing\nconnectomes by representing them as a Riemannian metric, thereby viewing them\nas points in an infinite-dimensional manifold. After equipping this space with\na natural metric structure, the Ebin metric, we apply object-oriented\nstatistical analysis to define an atlas as the Fr\\'echet mean of a population\nof Riemannian metrics. We demonstrate connectome registration and atlas\nformation using connectomes derived from diffusion tensors estimated from a\nsubset of subjects from the Human Connectome Project.\n"}
+{"abstract": "  Interest in physical therapy and individual exercises such as yoga/dance has\nincreased alongside the well-being trend. However, such exercises are hard to\nfollow without expert guidance (which is impossible to scale for personalized\nfeedback to every trainee remotely). Thus, automated pose correction systems\nare required more than ever, and we introduce a new captioning dataset named\nFixMyPose to address this need. We collect descriptions of correcting a\n\"current\" pose to look like a \"target\" pose (in both English and Hindi). The\ncollected descriptions have interesting linguistic properties such as\negocentric relations to environment objects, analogous references, etc.,\nrequiring an understanding of spatial relations and commonsense knowledge about\npostures. Further, to avoid ML biases, we maintain a balance across characters\nwith diverse demographics, who perform a variety of movements in several\ninterior environments (e.g., homes, offices). From our dataset, we introduce\nthe pose-correctional-captioning task and its reverse target-pose-retrieval\ntask. During the correctional-captioning task, models must generate\ndescriptions of how to move from the current to target pose image, whereas in\nthe retrieval task, models should select the correct target pose given the\ninitial pose and correctional description. We present strong cross-attention\nbaseline models (uni/multimodal, RL, multilingual) and also show that our\nbaselines are competitive with other models when evaluated on other\nimage-difference datasets. We also propose new task-specific metrics\n(object-match, body-part-match, direction-match) and conduct human evaluation\nfor more reliable evaluation, and we demonstrate a large human-model\nperformance gap suggesting room for promising future work. To verify the\nsim-to-real transfer of our FixMyPose dataset, we collect a set of real images\nand show promising performance on these images.\n"}
+{"abstract": "  Industrial Internet of Things (IIoT) applications can benefit from leveraging\nedge computing. For example, applications underpinned by deep neural networks\n(DNN) models can be sliced and distributed across the IIoT device and the edge\nof the network for improving the overall performance of inference and for\nenhancing privacy of the input data, such as industrial product images.\nHowever, low network performance between IIoT devices and the edge is often a\nbottleneck. In this study, we develop ScissionLite, a holistic framework for\naccelerating distributed DNN inference using the Transfer Layer (TL). The TL is\na traffic-aware layer inserted between the optimal slicing point of a DNN model\nslice in order to decrease the outbound network traffic without a significant\naccuracy drop. For the TL, we implement a new lightweight down/upsampling\nnetwork for performance-limited IIoT devices. In ScissionLite, we develop\nScissionTL, the Preprocessor, and the Offloader for end-to-end activities for\ndeploying DNN slices with the TL. They decide the optimal slicing point of the\nDNN, prepare pre-trained DNN slices including the TL, and execute the DNN\nslices on an IIoT device and the edge. Employing the TL for the sliced DNN\nmodels has a negligible overhead. ScissionLite improves the inference latency\nby up to 16 and 2.8 times when compared to execution on the local device and an\nexisting state-of-the-art model slicing approach respectively.\n"}
+{"abstract": "  The influence of implantation-induced point defects (PDs) on SiC oxidation is\ninvestigated via molecular dynamics simulations. PDs generally increase the\noxidation rate of crystalline grains. Particularly, accelerations caused by Si\nantisites and vacancies are comparable, and followed by Si interstitials, which\nare higher than those by C antisites and C interstitials. However, in the grain\nboundary (GB) region, defect contribution to oxidation is more complex, with C\nantisites decelerating oxidation. The underlying reason is the formation of a\nC-rich region along the oxygen diffusion pathway that blocks the access of O to\nSi and thus reduces the oxidation rate, as compared to the oxidation along a GB\nwithout defects.\n"}
+{"abstract": "  In this paper, we study the numerical stabilization of a 1D system of two\nwave equations coupled by velocities with an internal, local control acting on\nonly one equation. In the theoretical part of this study, we distinguished two\ncases. In the first one, the two waves assumed propagate at the same speed.\nUnder appropriate geometric conditions, we had proved that the energy decays\nexponentially. While in the second case, when the waves propagate at different\nspeeds, under appropriate geometric conditions, we had proved that the energy\ndecays only at a polynomial rate. In this paper, we confirmed these two results\nin a 1D numerical approximation. However, when the coupling region does not\nintersect the damping region, the stabilization of the system is still\ntheoretically an open problem. But, here in both cases, we observed an\nunpredicted behavior : the energy decays at an exponential rate when the\npropagation speeds are the same or at a polynomial rate when they are\ndifferent.\n"}
+{"abstract": "  Recently, a generative variational autoencoder (VAE) has been proposed for\nspeech enhancement to model speech statistics. However, this approach only uses\nclean speech in the training phase, making the estimation particularly\nsensitive to noise presence, especially in low signal-to-noise ratios (SNRs).\nTo increase the robustness of the VAE, we propose to include noise information\nin the training phase by using a noise-aware encoder trained on noisy-clean\nspeech pairs. We evaluate our approach on real recordings of different noisy\nenvironments and acoustic conditions using two different noise datasets. We\nshow that our proposed noise-aware VAE outperforms the standard VAE in terms of\noverall distortion without increasing the number of model parameters. At the\nsame time, we demonstrate that our model is capable of generalizing to unseen\nnoise conditions better than a supervised feedforward deep neural network\n(DNN). Furthermore, we demonstrate the robustness of the model performance to a\nreduction of the noisy-clean speech training data size.\n"}
+{"abstract": "  A long standing puzzle in the rheology of living cells is the origin of the\nexperimentally observed long time stress relaxation. The mechanics of the cell\nis largely dictated by the cytoskeleton, which is a biopolymer network\nconsisting of transient crosslinkers, allowing for stress relaxation over time.\nMoreover, these networks are internally stressed due to the presence of\nmolecular motors. In this work we propose a theoretical model that uses a\nmode-dependent mobility to describe the stress relaxation of such prestressed\ntransient networks. Our theoretical predictions agree favorably with\nexperimental data of reconstituted cytoskeletal networks and may provide an\nexplanation for the slow stress relaxation observed in cells.\n"}
+{"abstract": "  The classic censored regression model (tobit model) has been widely used in\nthe economic literature. This model assumes normality for the error\ndistribution and is not recommended for cases where positive skewness is\npresent. Moreover, in regression analysis, it is well-known that a quantile\nregression approach allows us to study the influences of the explanatory\nvariables on the dependent variable considering different quantiles. Therefore,\nwe propose in this paper a quantile tobit regression model based on\nquantile-based log-symmetric distributions. The proposed methodology allows us\nto model data with positive skewness (which is not suitable for the classic\ntobit model), and to study the influence of the quantiles of interest, in\naddition to accommodating heteroscedasticity. The model parameters are\nestimated using the maximum likelihood method and an elaborate Monte Carlo\nstudy is performed to evaluate the performance of the estimates. Finally, the\nproposed methodology is illustrated using two female labor supply data sets.\nThe results show that the proposed log-symmetric quantile tobit model has a\nbetter fit than the classic tobit model.\n"}
+{"abstract": "  We present the first study of cross-correlation between Cosmic Microwave\nBackground (CMB) gravitational lensing potential map measured by the $Planck$\nsatellite and $z\\geq 0.8$ galaxies from the photometric redshift catalogues\nfrom Herschel Extragalactic Legacy Project (HELP), divided into four sky\npatches: NGP, Herschel Stripe-82 and two halves of SGP field, covering in total\n$\\sim 660$ deg$^{2}$ of the sky. Contrary to previous studies exploiting only\nthe common area between galaxy surveys and CMB lensing data, we improve the\ncross-correlation measurements using the full available area of the CMB lensing\nmap. We estimate galaxy linear bias parameter, $b$, from joint analysis of\ncross-power spectrum and galaxy auto-power spectrum using Maximum Likelihood\nEstimation technique to obtain the value averaged over four fields as\n$b=2.06_{-0.02}^{+0.02}$, ranging from $1.94_{-0.03}^{+0.04}$ for SGP Part-2 to\n$3.03_{-0.09}^{+0.10}$ for NGP. We also estimate the amplitude of\ncross-correlation and find the averaged value to be $A=0.52_{-0.08}^{+0.08}$\nspanning from $0.34_{-0.19}^{+0.19}$ for NGP to $0.67_{-0.20}^{+0.21}$ for SGP\nPart-1 respectively, significantly lower than expected value for the standard\ncosmological model. We perform several tests on systematic errors that can\naccount for this discrepancy. We find that lower amplitude could be to some\nextent explained by the lower value of median redshift of the catalogue,\nhowever, we do not have any evidence that redshifts are systematically\noverestimated.\n"}
+{"abstract": "  In the theory of local fields we have the well-known filtration of unit\ngroups. In this short paper we compute the first cohomology groups of unit\ngorups for a finite Galois extension of local fields. We show that these\ncohomology groups are closely related to the ramification indices.\n"}
+{"abstract": "  We introduce DeepGLEAM, a hybrid model for COVID-19 forecasting. DeepGLEAM\ncombines a mechanistic stochastic simulation model GLEAM with deep learning. It\nuses deep learning to learn the correction terms from GLEAM, which leads to\nimproved performance. We further integrate various uncertainty quantification\nmethods to generate confidence intervals. We demonstrate DeepGLEAM on\nreal-world COVID-19 mortality forecasting tasks.\n"}
+{"abstract": "  In collaborative intelligence, an artificial intelligence (AI) model is\ntypically split between an edge device and the cloud. Feature tensors produced\nby the edge sub-model are sent to the cloud via an imperfect communication\nchannel. At the cloud side, parts of the feature tensor may be missing due to\npacket loss. In this paper we propose a method called Content-Adaptive Linear\nTensor Completion (CALTeC) to recover the missing feature data. The proposed\nmethod is fast, data-adaptive, does not require pre-training, and produces\nbetter results than existing methods for tensor data recovery in collaborative\nintelligence.\n"}
+{"abstract": "  GADTs can be represented either as their Church encodings \\`a la Atkey, or as\nfixpoints \\`a la Johann and Polonsky. While a GADT represented as its Church\nencoding need not support a map function satisfying the functor laws, the\nfixpoint representation of a GADT must support such a map function even to be\nwell-defined. The two representations of a GADT thus need not be the same in\ngeneral. This observation forces a choice of representation of data types in\nlanguages supporting GADTs. In this paper we show that choosing whether to\nrepresent data types as their Church encodings or as fixpoints determines\nwhether or not a language supporting GADTs can have parametric models. This\nchoice thus has important consequences for how we can program with, and reason\nabout, these advanced data types.\n"}
+{"abstract": "  Context. The North Ecliptic Pole (NEP) field provides a unique set of\npanchromatic data, well suited for active galactic nuclei (AGN) studies.\nSelection of AGN candidates is often based on mid-infrared (MIR) measurements.\nSuch method, despite its effectiveness, strongly reduces a catalog volume due\nto the MIR detection condition. Modern machine learning techniques can solve\nthis problem by finding similar selection criteria using only optical and\nnear-infrared (NIR) data. Aims. Aims of this work were to create a reliable AGN\ncandidates catalog from the NEP field using a combination of optical SUBARU/HSC\nand NIR AKARI/IRC data and, consequently, to develop an efficient alternative\nfor the MIR-based AKARI/IRC selection technique. Methods. A set of supervised\nmachine learning algorithms was tested in order to perform an efficient AGN\nselection. Best of the models were formed into a majority voting scheme, which\nused the most popular classification result to produce the final AGN catalog.\nAdditional analysis of catalog properties was performed in form of the spectral\nenergy distribution (SED) fitting via the CIGALE software. Results. The\nobtained catalog of 465 AGN candidates (out of 33 119 objects) is characterized\nby 73% purity and 64% completeness. This new classification shows consistency\nwith the MIR-based selection. Moreover, 76% of the obtained catalog can be\nfound only with the new method due to the lack of MIR detection for most of the\nnew AGN candidates. Training data, codes and final catalog are available via\nthe github repository. Final AGN candidates catalog will be also available via\nthe CDS service after publication.\n"}
+{"abstract": "  The note concerns the $\\bar\\partial$ problem on product domains in $\\mathbb\nC^2$. We show that there exists a bounded solution operator from $C^{k,\n\\alpha}$ into itself, $k\\in \\mathbb Z^+\\cup \\{0\\}, 0<\\alpha< 1$. The regularity\nresult is optimal in view of an example of Stein-Kerzman.\n"}
+{"abstract": "  It is shown numerically, in a chiral U(1) gauge Higgs theory in which the\nleft and right-handed fermion components have opposite U(1) charges, that the\nspectrum of gauge and Higgs fields surrounding a static fermion contains both a\nground state and at least one stable excited state. To bypass the difficulties\nassociated with dynamical fermions in a lattice chiral gauge theory we consider\nonly static fermion sources in a quenched approximation, at fixed lattice\nspacing and couplings, and with a lattice action along the lines suggested long\nago by Smit and Swift.\n"}
+{"abstract": "  We investigate the explicit implementation of quantum repeater protocols that\nrely on three-qubit repetition codes using nitrogen-vacancy (NV) centers in\ndiamond as quantum memories. NV centers offer a two-qubit register,\ncorresponding to their electron and nuclear spins, which makes it possible to\nperform deterministic two-qubit operations within one NV center. For quantum\nrepeater applications, we however need to do joint operations on two separate\nNV centers. Here, we study two NV-based repeater structures that enable such\ndeterministic joint operations. One structure offers less consumption of\nclassical communication, hence is more resilient to decoherence effects,\nwhereas the other one relies on fewer numbers of physical resources and\noperations. We assess and compare their performance for the task of secret key\ngeneration under the influence of noise and decoherence with current and\nnear-term experimental parameters. We quantify the regimes of operation, where\none structure outperforms the other, and find the regions where encoded QRs\noffer practical advantages over their non-encoded counterparts.\n"}
+{"abstract": "  Real-world videos contain many complex actions with inherent relationships\nbetween action classes. In this work, we propose an attention-based\narchitecture that models these action relationships for the task of temporal\naction localization in untrimmed videos. As opposed to previous works that\nleverage video-level co-occurrence of actions, we distinguish the relationships\nbetween actions that occur at the same time-step and actions that occur at\ndifferent time-steps (i.e. those which precede or follow each other). We define\nthese distinct relationships as action dependencies. We propose to improve\naction localization performance by modeling these action dependencies in a\nnovel attention-based Multi-Label Action Dependency (MLAD)layer. The MLAD layer\nconsists of two branches: a Co-occurrence Dependency Branch and a Temporal\nDependency Branch to model co-occurrence action dependencies and temporal\naction dependencies, respectively. We observe that existing metrics used for\nmulti-label classification do not explicitly measure how well action\ndependencies are modeled, therefore, we propose novel metrics that consider\nboth co-occurrence and temporal dependencies between action classes. Through\nempirical evaluation and extensive analysis, we show improved performance over\nstate-of-the-art methods on multi-label action localization\nbenchmarks(MultiTHUMOS and Charades) in terms of f-mAP and our proposed metric.\n"}
+{"abstract": "  As a basic building block, optical resonant cavities (ORCs) are widely used\nin light manipulation; they can confine electromagnetic waves and improve the\ninteraction between light and matter, which also plays an important role in\ncavity quantum electrodynamics, nonlinear optics and quantum optics. Especially\nin recent years, the rise of metamaterials, artificial materials composed of\nsubwavelength unit cells, greatly enriches the design and function of ORCs.\nHere, we review zero-index and hyperbolic metamaterials for constructing the\nnovel ORCs. Firstly, this paper introduces the classification and\nimplementation of zero-index and hyperbolic metamaterials. Secondly, the\ndistinctive properties of zero-index and hyperbolic cavities are summarized,\nincluding the geometry-invariance, homogeneous/inhomogeneous field\ndistribution, and the topological protection (anomalous scaling law, size\nindependence, continuum of high-order modes, and dispersionless modes) for the\nzero-index (hyperbolic) metacavities. Finally, the paper introduces some\ntypical applications of zero-index and hyperbolic metacavities, and prospects\nthe research of metacavities.\n"}
+{"abstract": "  Let $p(x)$ be an integer polynomial with $m\\ge 2$ distinct roots\n$\\rho_1,\\ldots,\\rho_m$ whose multiplicities are\n$\\boldsymbol{\\mu}=(\\mu_1,\\ldots,\\mu_m)$. We define the D-plus discriminant of\n$p(x)$ to be $D^+(p):= \\prod_{1\\le i<j\\le m}(\\rho_i-\\rho_j)^{\\mu_i+\\mu_j}$. We\nfirst prove a conjecture that $D^+(p)$ is a $\\boldsymbol{\\mu}$-symmetric\nfunction of its roots $\\rho_1,\\ldots,\\rho_m$. Our main result gives an explicit\nformula for $D^+(p)$, as a rational function of its coefficients. Our proof is\nideal-theoretic, based on re-casting the classic Poisson resultant as the\n\"symbolic Poisson formula\". The D-plus discriminant first arose in the\ncomplexity analysis of a root clustering algorithm from Becker et al. (ISSAC\n2016). The bit-complexity of this algorithm is proportional to a quantity\n$\\log(|D^+(p)|^{-1})$. As an application of our main result, we give an\nexplicit upper bound on this quantity in terms of the degree of $p$ and its\nleading coefficient.\n"}
+{"abstract": "  We introduce Robin boundary conditions for biharmonic operators, which are a\nmodel for elastically supported plates and are closely related to the study of\nspaces of traces of Sobolev functions. We study the dependence of the operator,\nits eigenvalues, and eigenfunctions on the Robin parameters. We show in\nparticular that when the parameters go to plus infinity the Robin problem\nconverges to other biharmonic problems, and obtain estimates on the rate of\ndivergence when the parameters go to minus infinity. We also analyse the\ndependence of the operator on smooth perturbations of the domain, computing the\nshape derivatives of the eigenvalues and giving a characterisation for critical\ndomains under volume and perimeter constraints. We include a number of open\nproblems arising in the context of our results.\n"}
+{"abstract": "  Let $h$ be the planar Gaussian free field and let $D_h$ be a supercritical\nLiouville quantum gravity (LQG) metric associated with $h$. Such metrics arise\nas subsequential scaling limits of supercritical Liouville first passage\npercolation (Gwynne-Ding, 2020) and correspond to values of the matter central\ncharge $\\mathbf{c}_{\\mathrm M} \\in (1,25)$. We show that a.s. the boundary of\neach complementary connected component of a $D_h$-metric ball is a Jordan curve\nand is compact and finite-dimensional with respect to $D_h$. This is in\ncontrast to the whole boundary of the $D_h$-metric ball, which is non-compact\nand infinite-dimensional with respect to $D_h$ (Pfeffer, 2021). Using our\nregularity results for boundary components of $D_h$-metric balls, we extend the\nconfluence of geodesics results of Gwynne-Miller (2019) to the case of\nsupercritical Liouville quantum gravity. These results show that two\n$D_h$-geodesics with the same starting point and different target points\ncoincide for a non-trivial initial time interval.\n"}
+{"abstract": "  Under the validity of the positive mass theorem, the Yamabe flow on a smooth\ncompact Riemannian manifold of dimension $N \\ge 3$ is known to exist for all\ntime $t$ and converges to a solution to the Yamabe problem as $t \\to \\infty$.\nWe prove that if a suitable perturbation, which may be smooth and arbitrarily\nsmall, is imposed on the Yamabe flow on any given Riemannian manifold $M$ of\ndimension $N \\ge 5$, the resulting flow may blow up at multiple points on $M$\nin the infinite time. Our proof is constructive, and indeed we construct such a\nflow by using solutions of the Yamabe problem on the unit sphere $\\mathbb{S}^N$\nas blow-up profiles. We also examine the stability of the blow-up phenomena\nunder a negativity condition on the Ricci curvature at blow-up points.\n"}
+{"abstract": "  The recent and upcoming releases of the 3rd Generation Partnership Project's\n5G New Radio specifications include features that are motivated by providing\nconnectivity services to a broad set of verticals, including the automotive,\nrail, and air transport industries. Currently, several radio access network\nfeatures are being further enhanced or newly introduced in NR to improve 5G's\ncapability to provide fast, reliable, and non-limiting connectivity for\ntransport applications. In this article, we review the most important\ncharacteristics and requirements of a wide range of services that are driven by\nthe desire to help the transport sector to become more sustainable,\neconomically viable, safe, and secure. These requirements will be supported by\nthe evolving and entirely new features of 5G NR systems, including accurate\npositioning, reference signal design to enable multi-transmission and reception\npoints, service-specific scheduling configuration, and service quality\nprediction.\n"}
+{"abstract": "  3D model generation from single 2D RGB images is a challenging and actively\nresearched computer vision task. Various techniques using conventional network\narchitectures have been proposed for the same. However, the body of research\nwork is limited and there are various issues like using inefficient 3D\nrepresentation formats, weak 3D model generation backbones, inability to\ngenerate dense point clouds, dependence of post-processing for generation of\ndense point clouds, and dependence on silhouettes in RGB images. In this paper,\na novel 2D RGB image to point cloud conversion technique is proposed, which\nimproves the state of art in the field due to its efficient, robust and simple\nmodel by using the concept of parallelization in network architecture. It not\nonly uses the efficient and rich 3D representation of point clouds, but also\nuses a novel and robust point cloud generation backbone in order to address the\nprevalent issues. This involves using a single-encoder multiple-decoder deep\nnetwork architecture wherein each decoder generates certain fixed viewpoints.\nThis is followed by fusing all the viewpoints to generate a dense point cloud.\nVarious experiments are conducted on the technique and its performance is\ncompared with those of other state of the art techniques and impressive gains\nin performance are demonstrated. Code is available at\nhttps://github.com/mueedhafiz1982/\n"}
+{"abstract": "  A large number of processes in the mesoscopic world occur out of equilibrium,\nwhere the time course of the system evolution becomes immensely important --\nthey being driven principally by dissipative effects. Non-equilibrium steady\nstates (NESS) represent a crucial category in such systems -- which are widely\nobserved in biological domains -- especially in chemical kinetics in cellular\nprocesses, and molecular motors. In this study, we employ a model NESS\nstochastic system which comprises of an colloidal microparticle, optically\ntrapped in a viscous fluid and externally driven by a temporally correlated\ncolored noise, and show that the work done on the system and the work\ndissipated by it -- both follow the three Levy arcsine laws. These statistics\nremain unchanged even in the presence of a perturbation generated by a\nmicrobubble at close proximity to the trapped particle. We confirm our\nexperimental findings with theoretical simulations of the systems. Our work\nprovides an interesting insight into the NESS statistics of the meso-regime,\nwhere stochastic fluctuations play a pivotal role.\n"}
+{"abstract": "  Precision medicine involves answering counterfactual questions such as \"Would\nthis patient respond better to treatment A or treatment B?\" These types of\nquestions are causal in nature and require the tools of causal inference to be\nanswered, e.g., with a structural causal model (SCM). In this work, we develop\nan SCM that models the interaction between demographic information, disease\ncovariates, and magnetic resonance (MR) images of the brain for people with\nmultiple sclerosis. Inference in the SCM generates counterfactual images that\nshow what an MR image of the brain would look like if demographic or disease\ncovariates are changed. These images can be used for modeling disease\nprogression or used for image processing tasks where controlling for\nconfounders is necessary.\n"}
+{"abstract": "  Analysis and clustering of multivariate time-series data attract growing\ninterest in immunological and clinical studies. In such applications,\nresearchers are interested in clustering subjects based on potentially\nhigh-dimensional longitudinal features, and in investigating how clinical\ncovariates may affect the clustering results. These studies are often\nchallenging due to high dimensionality, as well as the sparse and irregular\nnature of sample collection along the time dimension. We propose a smoothed\nprobabilistic PARAFAC model with covariates (SPACO) to tackle these two\nproblems while utilizing auxiliary covariates of interest. We provide intensive\nsimulations to test different aspects of SPACO and demonstrate its use on\nimmunological data sets from two recent cohorts of SARs-CoV-2 patients.\n"}
+{"abstract": "  This report formulates a conjectural combinatorial rule that positively\nexpands Grothendieck polynomials into Lascoux polynomials. It generalizes one\nsuch formula expanding Schubert polynomials into key polynomials, and refines\nanother one expanding stable Grothendieck polynomials.\n"}
+{"abstract": "  It is well known that many problems in image recovery, signal processing, and\nmachine learning can be modeled as finding zeros of the sum of maximal monotone\nand Lipschitz continuous monotone operators. Many papers have studied\nforward-backward splitting methods for finding zeros of the sum of two monotone\noperators in Hilbert spaces. Most of the proposed splitting methods in the\nliterature have been proposed for the sum of maximal monotone and\ninverse-strongly monotone operators in Hilbert spaces. In this paper, we\nconsider splitting methods for finding zeros of the sum of maximal monotone\noperators and Lipschitz continuous monotone operators in Banach spaces. We\nobtain weak and strong convergence results for the zeros of the sum of maximal\nmonotone and Lipschitz continuous monotone operators in Banach spaces. Many\nalready studied problems in the literature can be considered as special cases\nof this paper.\n"}
+{"abstract": "  Unstructured pruning reduces the memory footprint in deep neural networks\n(DNNs). Recently, researchers proposed different types of structural pruning\nintending to reduce also the computation complexity. In this work, we first\nsuggest a new measure called mask-diversity which correlates with the expected\naccuracy of the different types of structural pruning. We focus on the recently\nsuggested N:M fine-grained block sparsity mask, in which for each block of M\nweights, we have at least N zeros. While N:M fine-grained block sparsity allows\nacceleration in actual modern hardware, it can be used only to accelerate the\ninference phase. In order to allow for similar accelerations in the training\nphase, we suggest a novel transposable fine-grained sparsity mask, where the\nsame mask can be used for both forward and backward passes. Our transposable\nmask guarantees that both the weight matrix and its transpose follow the same\nsparsity pattern; thus, the matrix multiplication required for passing the\nerror backward can also be accelerated. We formulate the problem of finding the\noptimal transposable-mask as a minimum-cost flow problem. Additionally, to\nspeed up the minimum-cost flow computation, we also introduce a fast\nlinear-time approximation that can be used when the masks dynamically change\nduring training. Our experiments suggest a 2x speed-up in the matrix\nmultiplications with no accuracy degradation over vision and language models.\nFinally, to solve the problem of switching between different structure\nconstraints, we suggest a method to convert a pre-trained model with\nunstructured sparsity to an N:M fine-grained block sparsity model with little\nto no training. A reference implementation can be found at\nhttps://github.com/papers-submission/structured_transposable_masks.\n"}
+{"abstract": "  Skyrmion-containing devices have been proposed as a promising solution for\nlow energy data storage. These devices include racetrack or logic structures\nand require skyrmions to be confined in regions with dimensions comparable to\nthe size of a single skyrmion. Here we examine Bloch skyrmions in {FeGe} device\nshapes using Lorentz transmission electron microscopy (LTEM) to reveal the\nconsequences of skyrmion confinement in a device structure. Dumbbell-shaped\ndevices were created by focused ion beam (FIB) milling to provide regions where\nsingle skyrmions are confined adjacent to areas containing a skyrmion lattice.\nSimple block shapes of equivalent dimensions were prepared within the specimen\nto allow a direct comparison with skyrmion formation in a less complex, yet\nstill confined, device geometry. The impact of the application of an applied\nexternal field and varying the temperature on skyrmion formation within the\nshapes was examined and this revealed that it is not just confinement within a\nsmall device structure that controls the position and number of skyrmions, but\nthat a complex device geometry changes the skyrmion behaviour, including\nallowing formation of skyrmions at lower applied magnetic fields than in simple\nshapes. This could allow experimental methods to be developed to control the\npositioning and number of skyrmions within device shapes.\n"}
+{"abstract": "  Solar coronal rain is classified generally into two categories: flare-driven\nand quiescent coronal rain. The latter is observed to form along both closed\nand open magnetic field structures. Recently, we proposed that some of the\nquiescent coronal rain events, detected in the transition region and\nchromospheric diagnostics, along loop-like paths could be explained by the\nformation mechanism for quiescent coronal rain facilitated by interchange\nmagnetic reconnection between open and closed field lines. In this study, we\nrevisited 38 coronal rain reports from the literature. From these earlier\nworks, we picked 15 quiescent coronal rain events out of the solar limb, mostly\nsuggested to occur in active region closed loops due to thermal nonequilibrium,\nto scrutinize their formation mechanism. Employing the extreme ultraviolet\nimages and line-of-sight magnetograms, the evolution of the quiescent coronal\nrain events and their magnetic fields and context coronal structures is\nexamined. We find that 6, comprising 40%, of the 15 quiescent coronal rain\nevents could be totally or partially interpreted by the formation mechanism for\nquiescent coronal rain along open structures facilitated by interchange\nreconnection. The results suggest that the quiescent coronal rain facilitated\nby interchange reconnection between open and closed field lines deserves more\nattention.\n"}
+{"abstract": "  We characterize the soliton solutions and their interactions for a system of\ncoupled evolution equations of nonlinear Schr\\\"odinger (NLS) type that models\nthe dynamics in one-dimensional repulsive Bose-Einstein condensates with spin\none, taking advantage of the representation of such model as a special\nreduction of a 2 x 2 matrix NLS system. Specifically, we study in detail the\ncase in which solutions tend to a non-zero background at space infinities.\nFirst we derive a compact representation for the multi-soliton solutions in the\nsystem using the Inverse Scattering Transform (IST). We introduce the notion of\ncanonical form of a solution, corresponding to the case when the background is\nproportional to the identity. We show that solutions for which the asymptotic\nbehavior at infinity is not proportional to the identity, referred to as being\nin non-canonical form, can be reduced to canonical form by unitary\ntransformations that preserve the symmetric nature of the solution (physically\ncorresponding to complex rotations of the quantization axes). Then we give a\ncomplete characterization of the two families of one-soliton solutions arising\nin this problem, corresponding to ferromagnetic and to polar states of the\nsystem, and we discuss how the physical parameters of the solitons for each\nfamily are related to the spectral data in the IST. We also show that any\nferromagnetic one-soliton solution in canonical form can be reduced to a single\ndark soliton of the scalar NLS equation, and any polar one-soliton solution in\ncanonical form is unitarily equivalent to a pair of oppositely polarized\ndisplaced scalar dark solitons up to a rotation of the quantization axes.\nFinally, we discuss two-soliton interactions and we present a complete\nclassification of the possible scenarios that can arise depending on whether\neither soliton is of ferromagnetic or polar type.\n"}
+{"abstract": "  The volume of data moving through a network increases with new scientific\nexperiments and simulations. Network bandwidth requirements also increase\nproportionally to deliver data within a certain time frame. We observe that a\nsignificant portion of the popular dataset is transferred multiple times to\ndifferent users as well as to the same user for various reasons. In-network\ndata caching for the shared data has shown to reduce the redundant data\ntransfers and consequently save network traffic volume. In addition, overall\napplication performance is expected to improve with in-network caching because\naccess to the locally cached data results in lower latency. This paper shows\nhow much data was shared over the study period, how much network traffic volume\nwas consequently saved, and how much the temporary in-network caching increased\nthe scientific application performance. It also analyzes data access patterns\nin applications and the impacts of caching nodes on the regional data\nrepository. From the results, we observed that the network bandwidth demand was\nreduced by nearly a factor of 3 over the study period.\n"}
+{"abstract": "  Synthetic data generation is an appealing approach to generate novel traffic\nscenarios in autonomous driving. However, deep learning perception algorithms\ntrained solely on synthetic data encounter serious performance drops when they\nare tested on real data. Such performance drops are commonly attributed to the\ndomain gap between real and synthetic data. Domain adaptation methods that have\nbeen applied to mitigate the aforementioned domain gap achieve visually\nappealing results, but usually introduce semantic inconsistencies into the\ntranslated samples. In this work, we propose a novel, unsupervised, end-to-end\ndomain adaptation network architecture that enables semantically consistent\n\\textit{sim2real} image transfer. Our method performs content disentanglement\nby employing shared content encoder and fixed style code.\n"}
+{"abstract": "  By properly considering the propagation dynamics of the dipole field, we\nobtain the full magnetic dipolar interaction between two quantum dipoles for\ngeneral situations. With the help the Maxwell equation and the corresponding\nGreen function, this result applies for general boundary conditions, and\nnaturally unifies all the interaction terms between permanent dipoles, resonant\nor non-resonant transition dipoles, and even the counter-rotating interaction\nterms altogether. In particular, we study the dipolar interaction in a\nrectangular 3D cavity with discrete field modes. When the two dipoles are quite\nnear to each other and far from the cavity boundary, their interaction simply\nreturns the freespace result; when the distance between the two dipoles is\ncomparable to their distance to the cavity boundary and the field mode\nwavelength, the dipole images and near-resonant cavity modes bring in\nsignificant changes to the freespace interaction. This approach also provides a\ngeneral way to study the interaction mediated by other kinds of fields.\n"}
+{"abstract": "  We present a data-driven approach to construct entropy-based closures for the\nmoment system from kinetic equations. The proposed closure learns the entropy\nfunction by fitting the map between the moments and the entropy of the moment\nsystem, and thus does not depend on the space-time discretization of the moment\nsystem and specific problem configurations such as initial and boundary\nconditions. With convex and $C^2$ approximations, this data-driven closure\ninherits several structural properties from entropy-based closures, such as\nentropy dissipation, hyperbolicity, and H-Theorem. We construct convex\napproximations to the Maxwell-Boltzmann entropy using convex splines and neural\nnetworks, test them on the plane source benchmark problem for linear transport\nin slab geometry, and compare the results to the standard, optimization-based\nM$_N$ closures. Numerical results indicate that these data-driven closures\nprovide accurate solutions in much less computation time than the M$_N$\nclosures.\n"}
+{"abstract": "  Recently the LHAASO Collaboration published the detection of 12\nultra-high-energy gamma-ray sources above 100 TeV, with the highest energy\nphoton reaching 1.4 PeV. The first detection of PeV gamma rays from\nastrophysical sources may provide a very sensitive probe of the effect of the\nLorentz invariance violation (LIV), which results in decay of high-energy gamma\nrays in the superluminal scenario and hence a sharp cutoff of the energy\nspectrum. Two highest energy sources are studied in this work. No signature of\nthe existence of LIV is found in their energy spectra, and the lower limits on\nthe LIV energy scale are derived. Our results show that the first-order LIV\nenergy scale should be higher than about 10^5 times the Planck scale M_{pl} and\nthat the second-order LIV scale is >10^{-3}M_{pl}. Both limits improve by at\nleast one order of magnitude the previous results.\n"}
+{"abstract": "  In 1979 Pisier proved remarkably that a sequence of independent and\nidentically distributed standard Gaussian random variables determines, via\nrandom Fourier series, a homogeneous Banach algebra $\\mathscr{P}$ strictly\ncontained in $C(\\mathbb{T})$, the class of continuous functions on the unit\ncircle $\\mathbb{T}$ and strictly containing the classical Wiener algebra\n$\\mathbb{A}(\\mathbb{T})$, that is, $\\mathbb{A}(\\mathbb{T}) \\subsetneqq\n\\mathscr{P} \\subsetneqq C(\\mathbb{T}).$ This improved some previous results\nobtained by Zafran in solving a long-standing problem raised by Katznelson. In\nthis paper we extend Pisier's result by showing that any probability measure on\nthe unit circle defines a homogeneous Banach algebra contained in\n$C(\\mathbb{T})$. Thus Pisier algebra is not an isolated object but rather an\nelement in a large class of Pisier-type algebras. We consider the case of\nspectral measures of stationary sequences of Gaussian random variables and\nobtain a sufficient condition for the boundedness of the random Fourier series\n$\\sum_{n\\in \\mathbb{Z}}\\hat f(n) \\,\\xi_n \\exp(2\\pi i n t)$ in the general\nsetting of dependent random variables $(\\xi_n)$.\n"}
+{"abstract": "  We present MatchKAT, an algebraic language for modeling match-action packet\nprocessing in network switches. Although the match-action paradigm has remained\na popular low-level programming model for specifying packet forwarding\nbehavior, little has been done towards giving it formal semantics. With\nMatchKAT, we hope to embark on the first steps in exploring how network\nprograms compiled to match-action rules can be reasoned about formally in a\nreliable, algebraic way. In this paper, we give details of MatchKAT and its\nmetatheory, as well as a formal treatment of match expressions on binary\nstrings that form the basis of \"match\" in match-action. Through a\ncorrespondence with NetKAT, we show that MatchKAT's equational theory is sound\nand complete with regards to a similar packet filtering semantics. We also\ndemonstrate the complexity of deciding equivalence in MatchKAT is\nPSPACE-complete.\n"}
+{"abstract": "  We develop connections between the qualitative dynamics of Hamiltonian\nisotopies on a surface $\\Sigma$ and their chain-level Floer theory using ideas\ndrawn from Hofer-Wysocki-Zehnder's theory of finite energy foliations. We\nassociate to every collection of capped $1$-periodic orbits which is `maximally\nunlinked relative the Morse range' a singular foliation on $S^1 \\times \\Sigma$\nwhich is positively transverse to the vector field $\\partial_t \\oplus X^H$ and\nwhich is assembled in a straight-forward way from the relevant Floer moduli\nspaces. We derive a purely topological and Turing-computable characterization\nof the spectral invariant $c(H;[\\Sigma])$ for generic Hamiltonians on arbitrary\nclosed surfaces. This completes, for generic Hamiltonians, a project initiated\nby Humili\\`{e}re-Le Roux-Seyfaddini, in addition to fulfilling a desideratum\nexpressed by Gambaudo-Ghys seeking a topological characterization of the\nEntov-Polterovich quasi-morphism on $Ham(S^2)$.\n"}
+{"abstract": "  Placing robots outside controlled conditions requires versatile movement\nrepresentations that allow robots to learn new tasks and adapt them to\nenvironmental changes. The introduction of obstacles or the placement of\nadditional robots in the workspace, the modification of the joint range due to\nfaults or range-of-motion constraints are typical cases where the adaptation\ncapabilities play a key role for safely performing the robot's task.\nProbabilistic movement primitives (ProMPs) have been proposed for representing\nadaptable movement skills, which are modelled as Gaussian distributions over\ntrajectories. These are analytically tractable and can be learned from a small\nnumber of demonstrations. However, both the original ProMP formulation and the\nsubsequent approaches only provide solutions to specific movement adaptation\nproblems, e.g., obstacle avoidance, and a generic, unifying, probabilistic\napproach to adaptation is missing. In this paper we develop a generic\nprobabilistic framework for adapting ProMPs. We unify previous adaptation\ntechniques, for example, various types of obstacle avoidance, via-points,\nmutual avoidance, in one single framework and combine them to solve complex\nrobotic problems. Additionally, we derive novel adaptation techniques such as\ntemporally unbound via-points and mutual avoidance. We formulate adaptation as\na constrained optimisation problem where we minimise the Kullback-Leibler\ndivergence between the adapted distribution and the distribution of the\noriginal primitive while we constrain the probability mass associated with\nundesired trajectories to be low. We demonstrate our approach on several\nadaptation problems on simulated planar robot arms and 7-DOF Franka-Emika\nrobots in a dual robot arm setting.\n"}
+{"abstract": "  Many applications require accurate indoor localization. Fingerprint-based\nlocalization methods propose a solution to this problem, but rely on a radio\nmap that is effort-intensive to acquire. We automate the radio map acquisition\nphase using a software-defined radio (SDR) and a wheeled robot. Furthermore, we\nopen-source a radio map acquired with our automated tool for a 3GPP Long-Term\nEvolution (LTE) wireless link. To the best of our knowledge, this is the first\npublicly available radio map containing channel state information (CSI).\nFinally, we describe first localization experiments on this radio map using a\nconvolutional neural network to regress for location coordinates.\n"}
+{"abstract": "  This paper addresses the problem of identifying a linear time-varying (LTV)\nsystem characterized by a (possibly infinite) discrete set of delay-Doppler\nshifts without a lattice (or other geometry-discretizing) constraint on the\nsupport set. Concretely, we show that a class of such LTV systems is\nidentifiable whenever the upper uniform Beurling density of the delay-Doppler\nsupport sets, measured uniformly over the class, is strictly less than 1/2. The\nproof of this result reveals an interesting relation between LTV system\nidentification and interpolation in the Bargmann-Fock space. Moreover, we show\nthat this density condition is also necessary for classes of systems invariant\nunder time-frequency shifts and closed under a natural topology on the support\nsets. We furthermore show that identifiability guarantees robust recovery of\nthe delay-Doppler support set, as well as the weights of the individual\ndelay-Doppler shifts, both in the sense of asymptotically vanishing\nreconstruction error for vanishing measurement error.\n"}
+{"abstract": "  Topological effects exist from a macroscopic system such as the universe to a\nmicroscopic system described by quantum mechanics. We show here that an\ninteresting geometric structure can be created by the self replication\nprocedure of a square with an enclosed circle, in which the sum of the circles\narea will remain the same but the sum of the circumference will increase. It is\ndemonstrated by means of Monte Carlo simulations that these topological\nfeatures have great impacts to the vacuum pumping probability and the photon\nabsorption probability of the active surface. The results show significant\nimprovement of the system performance and have application potential in vacuum\npumping of large research facilities such as a nuclear fusion reactor,\nsynchrotron and in photovoltaic industry.\n"}
+{"abstract": "  The use of abundance ratios involving Y, or other slow-neutron capture\nelements, are routinely used to infer stellar ages.Aims.We aim to explain the\nobserved [Y/H] and [Y/Mg] abundance ratios of star clusters located in the\ninner disc with a new prescription for mixing in Asymptotic Giant Branch (AGB)\nstars. In a Galactic chemical evolution model, we adopt a new set of AGB\nstellar yields in which magnetic mixing is included. We compare the results of\nthe model with a sample of abundances and ages of open clusters located at\ndifferent Galactocentric distances. The magnetic mixing causes a less efficient\nproduction of Y at high metallicity. A non-negligible fraction of stars with\nsuper-solar metallicity is produced in the inner disc, and their Y abundances\nare affected by the reduced yields. The results of the new AGB model\nqualitatively reproduce the observed trends for both [Y/H] and [Y/Mg] vs age at\ndifferent Galactocetric distances. Our results confirm from a theoretical point\nof view that the relationship between [Y/Mg] and stellar age cannot be\nuniversal, i.e., the same in every part of the Galaxy. It has a strong\ndependence on the star formation rate, on the s-process yields and their\nrelation with metallicity, and thus it varies across the Galactic disc.\n"}
+{"abstract": "  In this paper, a multi-objective approach for the design of composite\ndata-driven mathematical models is proposed. It allows automating the\nidentification of graph-based heterogeneous pipelines that consist of different\nblocks: machine learning models, data preprocessing blocks, etc. The\nimplemented approach is based on a parameter-free genetic algorithm (GA) for\nmodel design called GPComp@Free. It is developed to be part of automated\nmachine learning solutions and to increase the efficiency of the modeling\npipeline automation. A set of experiments was conducted to verify the\ncorrectness and efficiency of the proposed approach and substantiate the\nselected solutions. The experimental results confirm that a multi-objective\napproach to the model design allows achieving better diversity and quality of\nobtained models. The implemented approach is available as a part of the\nopen-source AutoML framework FEDOT.\n"}
+{"abstract": "  Effective human-vehicle collaboration requires an appropriate un-derstanding\nof vehicle behavior for safety and trust. Improvingon our prior work by adding\na future prediction module, we in-troduce our framework, calledAutoPreview, to\nenable humans topreview autopilot behaviors prior to direct interaction with\nthevehicle. Previewing autopilot behavior can help to ensure\nsmoothhuman-vehicle collaboration during the initial exploration stagewith the\nvehicle. To demonstrate its practicality, we conducted acase study on\nhuman-vehicle collaboration and built a prototypeof our framework with the\nCARLA simulator. Additionally, weconducted a between-subject control experiment\n(n=10) to studywhether ourAutoPreviewframework can provide a deeper\nunder-standing of autopilot behavior compared to direct interaction. Ourresults\nsuggest that theAutoPreviewframework does, in fact, helpusers understand\nautopilot behavior and develop appropriate men-tal models\n"}
+{"abstract": "  In this paper, we prove sharp gradient estimates for positive solutions to\nthe weighted heat equation on smooth metric measure spaces with compact\nboundary. As an application, we prove Liouville theorems for ancient solutions\nsatisfying the Dirichlet boundary condition and some sharp growth restriction\nnear infinity. Our results can be regarded as a refinement of recent results\ndue to Kunikawa and Sakurai.\n"}
+{"abstract": "  We extend a semantic verification framework for hybrid systems with the\nIsabelle/HOL proof assistant by an algebraic model for hybrid program stores, a\nshallow expression model for hybrid programs and their correctness\nspecifications, and domain-specific deductive and calculational support. The\nnew store model yields clean separations and dynamic local views of variables,\ne.g. discrete/continuous, mutable/immutable, program/logical, and enhanced ways\nof manipulating them using combinators, projections and framing. This leads to\nmore local inference rules, procedures and tactics for reasoning with invariant\nsets, certifying solutions of hybrid specifications or calculating derivatives\nwith increased proof automation and scalability. The new expression model\nprovides more user-friendly syntax, better control of name spaces and\ninterfaces connecting the framework with real-world modelling languages.\n"}
+{"abstract": "  Let $(N,\\rho)$ be a Riemannian manifold, $S$ a surface of genus at least two\nand let $f\\colon S \\to N$ be a continuous map. We consider the energy spectrum\nof $(N,\\rho)$ (and $f$) which assigns to each point $[J]\\in \\mathcal{T}(S)$ in\nthe Teichm\\\"uller space of $S$ the infimum of the Dirichlet energies of all\nmaps $(S,J)\\to (N,\\rho)$ homotopic to $f$. We study the relation between the\nenergy spectrum and the simple length spectrum. Our main result is that if\n$N=S$, $f=id$ and $\\rho$ is a metric of non-positive curvature, then the energy\nspectrum determines the simple length spectrum. Furthermore, we prove that the\nconverse does not hold by exhibiting two metrics on $S$ with equal simple\nlength spectrum but different energy spectrum. As corollaries to our results we\nobtain that the set of hyperbolic metrics and the set of singular flat metrics\ninduced by quadratic differentials satisfy energy spectrum rigidity, i.e. a\nmetric in these sets is determined, up to isotopy, by its energy spectrum. We\nprove that analogous statements also hold true for Kleinian surface groups.\n"}
+{"abstract": "  We introduce Trankit, a light-weight Transformer-based Toolkit for\nmultilingual Natural Language Processing (NLP). It provides a trainable\npipeline for fundamental NLP tasks over 100 languages, and 90 pretrained\npipelines for 56 languages. Built on a state-of-the-art pretrained language\nmodel, Trankit significantly outperforms prior multilingual NLP pipelines over\nsentence segmentation, part-of-speech tagging, morphological feature tagging,\nand dependency parsing while maintaining competitive performance for\ntokenization, multi-word token expansion, and lemmatization over 90 Universal\nDependencies treebanks. Despite the use of a large pretrained transformer, our\ntoolkit is still efficient in memory usage and speed. This is achieved by our\nnovel plug-and-play mechanism with Adapters where a multilingual pretrained\ntransformer is shared across pipelines for different languages. Our toolkit\nalong with pretrained models and code are publicly available at:\nhttps://github.com/nlp-uoregon/trankit. A demo website for our toolkit is also\navailable at: http://nlp.uoregon.edu/trankit. Finally, we create a demo video\nfor Trankit at: https://youtu.be/q0KGP3zGjGc.\n"}
+{"abstract": "  I summarize evidence against the hypothesis that `Oumuamua is the artificial\ncreation of an advanced civilization. An appendix discusses the flaws and\ninconsistencies of the \"Breakthrough\" proposal for laser acceleration of\nspacecraft to semi-relativistic speeds. Reality is much more challenging, and\ninteresting.\n"}
+{"abstract": "  Shared mental models are critical to team success; however, in practice, team\nmembers may have misaligned models due to a variety of factors. In\nsafety-critical domains (e.g., aviation, healthcare), lack of shared mental\nmodels can lead to preventable errors and harm. Towards the goal of mitigating\nsuch preventable errors, here, we present a Bayesian approach to infer\nmisalignment in team members' mental models during complex healthcare task\nexecution. As an exemplary application, we demonstrate our approach using two\nsimulated team-based scenarios, derived from actual teamwork in cardiac\nsurgery. In these simulated experiments, our approach inferred model\nmisalignment with over 75% recall, thereby providing a building block for\nenabling computer-assisted interventions to augment human cognition in the\noperating room and improve teamwork.\n"}
+{"abstract": "  We study a high throughput satellite system, where the feeder link uses\nfree-space optical (FSO) and the user link uses radio frequency (RF)\ncommunication. In particular, we first propose a transmit diversity using\nAlamouti space time block coding to mitigate the atmospheric turbulence in the\nfeeder link. Then, based on the concept of average virtual\nsignal-to-interference-plus-noise ratio and one-bit feedback, we propose a\nbeamforming algorithm for the user link to maximize the ergodic capacity (EC).\nMoreover, by assuming that the FSO links follow the Malaga distribution whereas\nRF links undergo the shadowed-Rician fading, we derive a closed-form EC\nexpression of the considered system. Finally, numerical simulations validate\nthe accuracy of our theoretical analysis, and show that the proposed schemes\ncan achieve higher capacity compared with the reference schemes.\n"}
+{"abstract": "  Of the many modern approaches to calculating evolutionary distance via models\nof genome rearrangement, most are tied to a particular set of genomic modelling\nassumptions and to a restricted class of allowed rearrangements. The \"position\nparadigm\", in which genomes are represented as permutations signifying the\nposition (and orientation) of each region, enables a refined model-based\napproach, where one can select biologically plausible rearrangements and assign\nto them relative probabilities/costs. Here, one must further incorporate any\nunderlying structural symmetry of the genomes into the calculations and ensure\nthat this symmetry is reflected in the model. In our recently-introduced\nframework of {\\em genome algebras}, each genome corresponds to an element that\nsimultaneously incorporates all of its inherent physical symmetries. The\nrepresentation theory of these algebras then provides a natural model of\nevolution via rearrangement as a Markov chain. Whilst the implementation of\nthis framework to calculate distances for genomes with `practical' numbers of\nregions is currently computationally infeasible, we consider it to be a\nsignificant theoretical advance: one can incorporate different genomic\nmodelling assumptions, calculate various genomic distances, and compare the\nresults under different rearrangement models. The aim of this paper is to\ndemonstrate some of these features.\n"}
+{"abstract": "  The Institute of Materials and Processes, IMP, of the University of Applied\nSciences in Karlsruhe, Germany in cooperation with VDI Verein Deutscher\nIngenieure e.V, AEN Automotive Engineering Network and their cooperation\npartners present their competences of AI-based solution approaches in the\nproduction engineering field. The online congress KI 4 Industry on November 12\nand 13, 2020, showed what opportunities the use of artificial intelligence\noffers for medium-sized manufacturing companies, SMEs, and where potential\nfields of application lie. The main purpose of KI 4 Industry is to increase the\ntransfer of knowledge, research and technology from universities to small and\nmedium-sized enterprises, to demystify the term AI and to encourage companies\nto use AI-based solutions in their own value chain or in their products.\n"}
+{"abstract": "  Given a permutation $\\pi:[k] \\to [k]$, a function $f:[n] \\to \\mathbb{R}$\ncontains a $\\pi$-appearance if there exists $1 \\leq i_1 < i_2 < \\dots < i_k\n\\leq n$ such that for all $s,t \\in [k]$, it holds that $f(i_s) < f(i_t)$ if and\nonly if $\\pi(s) < \\pi(t)$. The function is $\\pi$-free if it has no\n$\\pi$-appearances. In this paper, we investigate the problem of testing whether\nan input function $f$ is $\\pi$-free or whether at least $\\varepsilon n$ values\nin $f$ need to be changed in order to make it $\\pi$-free. This problem is a\ngeneralization of the well-studied monotonicity testing and was first studied\nby Newman, Rabinovich, Rajendraprasad and Sohler (Random Structures and\nAlgorithms 2019). We show that for all constants $k \\in \\mathbb{N}$,\n$\\varepsilon \\in (0,1)$, and permutation $\\pi:[k] \\to [k]$, there is a\none-sided error $\\varepsilon$-testing algorithm for $\\pi$-freeness of functions\n$f:[n] \\to \\mathbb{R}$ that makes $\\tilde{O}(n^{o(1)})$ queries. We improve\nsignificantly upon the previous best upper bound $O(n^{1 - 1/(k-1)})$ by\nBen-Eliezer and Canonne (SODA 2018). Our algorithm is adaptive, while the\nearlier best upper bound is known to be tight for nonadaptive algorithms.\n"}
+{"abstract": "  In random quantum magnets, like the random transverse Ising chain, the low\nenergy excitations are localized in rare regions and there are only weak\ncorrelations between them. It is a fascinating question whether these\ncorrelations are completely irrelevant in the sense of the renormalization\ngroup. To answer this question, we calculate the distribution of the excitation\nenergy of the random transverse Ising chain in the disordered Griffiths phase\nwith high numerical precision by the strong disorder renormalization group\nmethod and - for shorter chains - by free-fermion techniques. Asymptotically,\nthe two methods give identical results, which are well fitted by the Fr\\'echet\nlimit law of the extremes of independent and identically distributed random\nnumbers. Given the finite size corrections, the two numerical methods give very\nsimilar results, but they differ from the correction term for uncorrelated\nrandom variables. This fact shows that the weak correlations between low-energy\nexcitations in random quantum magnets are not entirely irrelevant.\n"}
+{"abstract": "  New words are regularly introduced to communities, yet not all of these words\npersist in a community's lexicon. Among the many factors contributing to\nlexical change, we focus on the understudied effect of social networks. We\nconduct a large-scale analysis of over 80k neologisms in 4420 online\ncommunities across a decade. Using Poisson regression and survival analysis,\nour study demonstrates that the community's network structure plays a\nsignificant role in lexical change. Apart from overall size, properties\nincluding dense connections, the lack of local clusters and more external\ncontacts promote lexical innovation and retention. Unlike offline communities,\nthese topic-based communities do not experience strong lexical levelling\ndespite increased contact but accommodate more niche words. Our work provides\nsupport for the sociolinguistic hypothesis that lexical change is partially\nshaped by the structure of the underlying network but also uncovers findings\nspecific to online communities.\n"}
+{"abstract": "  It is well known that a classical Fubini theorem for Hausdorff dimension\ncannot hold; that is, the dimension of the intersections of a fixed set with a\nparallel family of planes do not determine the dimension of the set. Here we\nprove that a Fubini theorem for Hausdorff dimension does hold modulo sets that\nare small on all Lipschitz curves/surfaces.\n  We say that $G\\subset \\mathbb{R}^k\\times \\mathbb{R}^n$ is $\\Gamma_k$-null if\nfor every Lipschitz function $f:\\mathbb{R}^k\\to \\mathbb{R}^n$ the set $\\{t\\in\n\\mathbb{R}^k\\,:\\,(t,f(t))\\in G\\}$ has measure zero. We show that for every\ncompact set $E\\subset \\mathbb{R}^k\\times \\mathbb{R}^n$ there is a\n$\\Gamma_k$-null subset $G\\subset E$ such that $$\\dim (E\\setminus G) =\nk+\\text{ess-}\\sup(\\dim E_t)$$ where $\\text{ess-}\\sup(\\dim E_t)$ is the\nessential supremum of the Hausdorff dimension of the vertical sections\n$\\{E_t\\}_{t\\in \\mathbb{R}^k}$ of $E$, assuming that $proj_{\\mathbb{R}^k} E$ has\npositive measure.\n  We also obtain more general results by replacing $\\mathbb{R}^k$ by an Ahlfors\nregular set. Applications of our results include Fubini-type results for unions\nof affine subspaces and related projection theorems.\n"}
+{"abstract": "  Multi-scale, multi-fidelity numerical simulations form the pillar of\nscientific applications related to numerically modeling fluids. However,\nsimulating the fluid behavior characterized by the non-linear Navier Stokes\nequations are often times computational expensive. Physics informed machine\nlearning methods is a viable alternative and as such has seen great interest in\nthe community [refer to Kutz (2017); Brunton et al. (2020); Duraisamy et al.\n(2019) for a detailed review on this topic]. For full physics emulators, the\ncost of network inference is often trivial. However, in the current paradigm of\ndata-driven fluid mechanics models are built as surrogates for complex\nsub-processes. These models are then used in conjunction to the Navier Stokes\nsolvers, which makes ML model inference an important factor in the terms of\nalgorithmic latency. With the ever growing size of networks, and often times\noverparameterization, exploring effective network compression techniques\nbecomes not only relevant but critical for engineering systems design. In this\nstudy, we explore the applicability of pruning and quantization (FP32 to int8)\nmethods for one such application relevant to modeling fluid turbulence.\nPost-compression, we demonstrate the improvement in the accuracy of network\npredictions and build intuition in the process by comparing the compressed to\nthe original network state.\n"}
+{"abstract": "  We argue that deriving an effective field theory from string theory requires\na Wilsonian perspective with a physical cutoff. Employing proper time\nregularization we demonstrate the decoupling of states and contrast this with\nwhat happens in dimensional regularization. In particular we point out that\neven if the cosmological constant (CC) calculated from some classical action at\nsome ultra-violet scale is negative, this does not necessarily imply that the\nCC calculated at cosmological scales is also negative, and discuss the possible\ncriteria for achieving a positive CC starting with a CC at the string/KK scale\nwhich is negative. Obviously this has implications for swampland claims.\n"}
+{"abstract": "  A central problem in Binary Hypothesis Testing (BHT) is to determine the\noptimal tradeoff between the Type I error (referred to as false alarm) and Type\nII (referred to as miss) error. In this context, the exponential rate of\nconvergence of the optimal miss error probability -- as the sample size tends\nto infinity -- given some (positive) restrictions on the false alarm\nprobabilities is a fundamental question to address in theory. Considering the\nmore realistic context of a BHT with a finite number of observations, this\npaper presents a new non-asymptotic result for the scenario with monotonic\n(sub-exponential decreasing) restriction on the Type I error probability, which\nextends the result presented by Strassen in 2009. Building on the use of\nconcentration inequalities, we offer new upper and lower bounds to the optimal\nType II error probability for the case of finite observations. Finally, the\nderived bounds are evaluated and interpreted numerically (as a function of the\nnumber samples) for some vanishing Type I error restrictions.\n"}
+{"abstract": "  Molecular simulations of the forced unfolding and refolding of biomolecules\nor molecular complexes allow to gain important kinetic, structural and\nthermodynamic information about the folding process and the underlying energy\nlandscape. In force probe molecular dynamics (FPMD) simulations, one pulls one\nend of the molecule with a constant velocity in order to induce the relevant\nconformational transitions. Since the extended configuration of the system has\nto fit into the simulation box together with the solvent such simulations are\nvery time consuming. Here, we apply a hybrid scheme in which the solute is\ntreated with atomistic resolution and the solvent molecules far away from the\nsolute are described in a coarse-grained manner. We use the adaptive resolution\nscheme (AdResS) that has very successfully been applied to various examples of\nequilibrium simulations. We perform FPMD simulations using AdResS on a well\nstudied system, a dimer formed from mechanically interlocked calixarene\ncapsules. The results of the multiscale simulations are compared to all-atom\nsimulations of the identical system and we observe that the size of the region\nin which atomistic resolution is required depends on the pulling velocity, i.e.\nthe particular non-equilibrium situation. For large pulling velocities a larger\nall atom region is required. Our results show that multiscale simulations can\nbe applied also in the strong non-equilibrium situations that the system\nexperiences in FPMD simulations.\n"}
+{"abstract": "  Graph neural networks (GNNs) constitute a class of deep learning methods for\ngraph data. They have wide applications in chemistry and biology, such as\nmolecular property prediction, reaction prediction and drug-target interaction\nprediction. Despite the interest, GNN-based modeling is challenging as it\nrequires graph data pre-processing and modeling in addition to programming and\ndeep learning. Here we present DGL-LifeSci, an open-source package for deep\nlearning on graphs in life science. DGL-LifeSci is a python toolkit based on\nRDKit, PyTorch and Deep Graph Library (DGL). DGL-LifeSci allows GNN-based\nmodeling on custom datasets for molecular property prediction, reaction\nprediction and molecule generation. With its command-line interfaces, users can\nperform modeling without any background in programming and deep learning. We\ntest the command-line interfaces using standard benchmarks MoleculeNet, USPTO,\nand ZINC. Compared with previous implementations, DGL-LifeSci achieves a speed\nup by up to 6x. For modeling flexibility, DGL-LifeSci provides well-optimized\nmodules for various stages of the modeling pipeline. In addition, DGL-LifeSci\nprovides pre-trained models for reproducing the test experiment results and\napplying models without training. The code is distributed under an Apache-2.0\nLicense and is freely accessible at https://github.com/awslabs/dgl-lifesci.\n"}
+{"abstract": "  We report the discovery of three new pulsars in the Globular Cluster (GC)\nNGC6517, namely NGC 6517 E, F, and G, made with the Five-hundred-meter Aperture\nSpherical radio Telescope (FAST). The spin periods of NGC 6517 E, F, and G are\n7.60~ms, 24.89~ms, and 51.59~ms, respectively. Their dispersion measures are\n183.29, 183.713, and 185.3~pc~cm$^{-3}$, respectively, all slightly larger than\nthose of the previously known pulsars in this cluster. The spin period\nderivatives are at the level of 1$\\times$10$^{-18}$~s~s$^{-1}$, which suggests\nthese are recycled pulsars. In addition to the discovery of these three new\npulsars, we updated the timing solutions of the known isolated pulsars, NGC\n6517 A, C, and D. The solutions are consistent with those from Lynch et al.\n(2011) and with smaller timing residuals. From the timing solution, NGC 6517 A,\nB (position from Lynch et al. 2011), C, E, and F are very close to each other\non the sky and only a few arcseconds from the optical core of NGC 6517. With\ncurrently published and unpublished discoveries, NGC6517 now has 9 pulsars,\nranking 5$^{th}$ of the GCs with the most pulsars. The discoveries take\nadvantage of the high sensitivity of FAST and a new algorithm used to check and\nfilter possible candidate signals.\n"}
+{"abstract": "  We report $^{121/123}$Sb nuclear quadrupole resonance (NQR) and $^{51}$V\nnuclear magnetic resonance (NMR) measurements on kagome metal CsV$_3$Sb$_5$\nwith $T_{\\rm c}=2.5$ K. Both $^{51}$V NMR spectra and $^{121/123}$Sb NQR\nspectra split after a charge density wave (CDW) transition, which demonstrates\na commensurate CDW state. The coexistence of the high temperature phase and the\nCDW phase between $91$ K and $94$ K manifests that it is a first order phase\ntransition. At low temperature, electric-field-gradient fluctuations diminish\nand magnetic fluctuations become dominant. Superconductivity emerges in the\ncharge order state. Knight shift decreases and $1/T_{1}T$ shows a\nHebel--Slichter coherence peak just below $T_{\\rm c}$, indicating that\nCsV$_3$Sb$_5$ is an s-wave superconductor.\n"}
+{"abstract": "  Vibrational and electronic absorption spectra calculated at the\n(time-dependent) density functional theory level for the bismuth carbide\nclusters Bi$_{n}$C$_{2n}$$^+$ ($3 \\le n \\le 9$) indicate significant\ndifferences in types of bonding that depend on cluster geometry. Analysis of\nthe electronic charge densities of these clusters highlighted bonding trends\nconsistent with the spectroscopic information. The combined data suggest that\nlarger clusters ($n > 5$) are likely to be kinetically unstable in agreement\nwith the cluster mass distribution obtained in gas-aggregation source\nexperiments. The spectral fingerprints of the different clusters obtained from\nour calculations also suggest that identification of specific\nBi$_{n}$C$_{2n}$$^+$ isomers of should be possible based on infra-red and\noptical absorption spectroscopy.\n"}
+{"abstract": "  Observations carried out toward starless and pre-stellar cores have revealed\nthat complex organic molecules are prevalent in these objects, but it is\nunclear what chemical processes are involved in their formation. Recently, it\nhas been shown that complex organics are preferentially produced at an\nintermediate-density shell within the L1544 pre-stellar core at radial\ndistances of ~4000 au with respect to the core center. However, the spatial\ndistribution of complex organics has only been inferred toward this core and it\nremains unknown whether these species present a similar behaviour in other\ncores. We report high-sensitivity observations carried out toward two positions\nin the L1498 pre-stellar core, the dust peak and a position located at a\ndistance of ~11000 au from the center of the core where the emission of\nCH$_3$OH peaks. Similarly to L1544, our observations reveal that small\nO-bearing molecules and N-bearing species are enhanced by factors ~4-14 toward\nthe outer shell of L1498. However, unlike L1544, large O-bearing organics such\nas CH3CHO, CH3OCH3 or CH3OCHO are not detected within our sensitivity limits.\nFor N-bearing organics, these species are more abundant toward the outer shell\nof the L1498 pre-stellar core than toward the one in L1544. We propose that the\ndifferences observed between O-bearing and N-bearing species in L1498 and L1544\nare due to the different physical structure of these cores, which in turn is a\nconsequence of their evolutionary stage, with L1498 being younger than L1544.\n"}
+{"abstract": "  Successful active speaker detection requires a three-stage pipeline: (i)\naudio-visual encoding for all speakers in the clip, (ii) inter-speaker relation\nmodeling between a reference speaker and the background speakers within each\nframe, and (iii) temporal modeling for the reference speaker. Each stage of\nthis pipeline plays an important role for the final performance of the created\narchitecture. Based on a series of controlled experiments, this work presents\nseveral practical guidelines for audio-visual active speaker detection.\nCorrespondingly, we present a new architecture called ASDNet, which achieves a\nnew state-of-the-art on the AVA-ActiveSpeaker dataset with a mAP of 93.5%\noutperforming the second best with a large margin of 4.7%. Our code and\npretrained models are publicly available.\n"}
+{"abstract": "  We investigate the optical response of a hybrid electro-optomechanical system\ninteracting with a qubit. In our experimentally feasible system, tunable\nall-optical-switching, double-optomechanically induced transparency (OMIT) and\noptomechanically induced absorption (OMIA) can be realized. The proposed system\nis also shown to generate anomalous dispersion. Based on our theoretical\nresults, we provide a tunable switch between OMIT and OMIA of the probe field\nby manipulating the relevant system parameters. Also, the normal-mode-splitting\n(NMS) effect induced by the interactions between the subsystems are discussed\nin detail and the effects of varying the interactions on the NMS are clarified.\nThese rich optical properties of the probe field may provide a promising\nplatform for controllable all-optical-switch and various other quantum photonic\ndevices.\n"}
+{"abstract": "  During the early months of the current COVID-19 pandemic, social-distancing\nmeasures effectively slowed disease transmission in many countries in Europe\nand Asia, but the same benefits have not been observed in some developing\ncountries such as Brazil. In part, this is due to a failure to organise\nsystematic testing campaigns at nationwide or even regional levels. To gain\neffective control of the pandemic, decision-makers in developing countries,\nparticularly those with large populations, must overcome difficulties posed by\nan unequal distribution of wealth combined with low daily testing capacities.\nThe economic infrastructure of the country, often concentrated in a few cities,\nforces workers to travel from commuter cities and rural areas, which induces\nstrong nonlinear effects on disease transmission. In the present study, we\ndevelop a smart testing strategy to identify geographic regions where COVID-19\ntesting could most effectively be deployed to limit further disease\ntransmission. The strategy uses readily available anonymised mobility and\ndemographic data integrated with intensive care unit (ICU) occupancy data and\ncity-specific social-distancing measures. Taking into account the heterogeneity\nof ICU bed occupancy in differing regions and the stages of disease evolution,\nwe use a data-driven study of the Brazilian state of Sao Paulo as an example to\nshow that smart testing strategies can rapidly limit transmission while\nreducing the need for social-distancing measures, thus returning life to a\nso-called new normal, even when testing capacity is limited.\n"}
+{"abstract": "  Teaching collaborative argumentation is an advanced skill that many K-12\nteachers struggle to develop. To address this, we have developed Discussion\nTracker, a classroom discussion analytics system based on novel algorithms for\nclassifying argument moves, specificity, and collaboration. Results from a\nclassroom deployment indicate that teachers found the analytics useful, and\nthat the underlying classifiers perform with moderate to substantial agreement\nwith humans.\n"}
+{"abstract": "  As is well known, the smallest neutrino mass turns out to be vanishing in the\nminimal seesaw model, since the effective neutrino mass matrix $M^{}_\\nu$ is of\nrank two due to the fact that only two heavy right-handed neutrinos are\nintroduced. In this paper, we point out that the one-loop matching condition\nfor the effective dimension-five neutrino mass operator can make an important\ncontribution to the smallest neutrino mass. By using the available one-loop\nmatching condition and two-loop renormalization group equations in the\nsupersymmetric version of the minimal seesaw model, we explicitly calculate the\nsmallest neutrino mass in the case of normal neutrino mass ordering and find\n$m^{}_1 \\in [10^{-10}, 10^{-8}]~{\\rm eV}$ at the Fermi scale $\\Lambda^{}_{\\rm\nF} = 91.2~{\\rm GeV}$, where the range of $m^{}_1$ results from the\nuncertainties on the choice of the seesaw scale $\\Lambda^{}_{\\rm SS}$ and on\nthe input values of relevant parameters at $\\Lambda^{}_{\\rm SS}$.\n"}
+{"abstract": "  Hypergraphs are used to model higher-order interactions amongst agents and\nthere exist many practically relevant instances of hypergraph datasets. To\nenable efficient processing of hypergraph-structured data, several hypergraph\nneural network platforms have been proposed for learning hypergraph properties\nand structure, with a special focus on node classification. However, almost all\nexisting methods use heuristic propagation rules and offer suboptimal\nperformance on many datasets. We propose AllSet, a new hypergraph neural\nnetwork paradigm that represents a highly general framework for (hyper)graph\nneural networks and for the first time implements hypergraph neural network\nlayers as compositions of two multiset functions that can be efficiently\nlearned for each task and each dataset. Furthermore, AllSet draws on new\nconnections between hypergraph neural networks and recent advances in deep\nlearning of multiset functions. In particular, the proposed architecture\nutilizes Deep Sets and Set Transformer architectures that allow for significant\nmodeling flexibility and offer high expressive power. To evaluate the\nperformance of AllSet, we conduct the most extensive experiments to date\ninvolving ten known benchmarking datasets and three newly curated datasets that\nrepresent significant challenges for hypergraph node classification. The\nresults demonstrate that AllSet has the unique ability to consistently either\nmatch or outperform all other hypergraph neural networks across the tested\ndatasets. Our implementation and dataset will be released upon acceptance.\n"}
+{"abstract": "  The planet-metallicity correlation serves as a potential link between\nexoplanet systems as we observe them today and the effects of bulk composition\non the planet formation process. Many observers have noted a tendency for\nJovian planets to form around stars with higher metallicities; however, there\nis no consensus on a trend for smaller planets. Here, we investigate the\nplanet-metallicity correlation for rocky planets in single and multi-planet\nsystems around Kepler M-dwarf and late K-dwarf stars. Due to molecular\nblanketing and the dim nature of these low mass stars, it is difficult to make\ndirect elemental abundance measurements via spectroscopy. We instead use a\ncombination of accurate and uniformly measured parallaxes and photometry to\nobtain relative metallicities and validate this method with a subsample of\nspectroscopically determined metallicities. We use the Kolmogorov-Smirnov (KS)\ntest, Mann-Whitney U test, and Anderson-Darling test to compare the compact\nmultiple planetary systems with single transiting planet systems and systems\nwith no detected transiting planets. We find that the compact multiple\nplanetary systems are derived from a statistically more metal-poor population,\nwith a p-value of 0.015 in the KS test, a p-value of 0.005 in the Mann-Whitney\nU test, and a value of 2.574 in the Anderson-Darling test statistic, which\nexceeds the derived threshold for significance by a factor of 25. We conclude\nthat metallicity plays a significant role in determining the architecture of\nrocky planet systems. Compact multiples either form more readily, or are more\nlikely to survive on Gyr timescales, around metal-poor stars.\n"}
+{"abstract": "  We construct weight-preserving bijections between column strict shifted plane\npartitions with one row and alternating sign trapezoids with exactly one column\nin the left half that sums to $1$. Amongst other things, they relate the number\nof $-1$s in the alternating sign trapezoids to certain elements in the column\nstrict shifted plane partitions that generalise the notion of special parts in\ndescending plane partitions. The advantage of these bijections is that they\ninclude configurations with $-1$s, which is a feature that many of the\nbijections in the realm of alternating sign arrays lack.\n"}
+{"abstract": "  The nonlinear response associated with the current dependence of the\nsuperconducting kinetic inductance was studied in capacitively shunted NbTiN\nmicrostrip transmission lines. It was found that the inductance per unit length\nof one microstrip line could be changed by up to 20% by applying a DC current,\ncorresponding to a single pass time delay of 0.7 ns. To investigate nonlinear\ndissipation, Bragg reflectors were placed on either end of a section of this\ntype of transmission line, creating resonances over a range of frequencies.\nFrom the change in the resonance linewidth and amplitude with DC current, the\nratio of the reactive to the dissipative response of the line was found to be\n788. The low dissipation makes these transmission lines suitable for a number\nof applications that are microwave and millimeter-wave band analogues of\nnonlinear optical processes. As an example, by applying a millimeter-wave pump\ntone, very wide band parametric amplification was observed between about 3 and\n34 GHz. Use as a current variable delay line for an on-chip millimeter-wave\nFourier transform spectrometer is also considered.\n"}
+{"abstract": "  Determining the architecture of multi-planetary systems is one of the\ncornerstones of understanding planet formation and evolution. Resonant systems\nare especially important as the fragility of their orbital configuration\nensures that no significant scattering or collisional event has taken place\nsince the earliest formation phase when the parent protoplanetary disc was\nstill present. In this context, TOI-178 has been the subject of particular\nattention since the first TESS observations hinted at a 2:3:3 resonant chain.\nHere we report the results of observations from CHEOPS, ESPRESSO, NGTS, and\nSPECULOOS with the aim of deciphering the peculiar orbital architecture of the\nsystem. We show that TOI-178 harbours at least six planets in the super-Earth\nto mini-Neptune regimes, with radii ranging from 1.152(-0.070/+0.073) to\n2.87(-0.13/+0.14) Earth radii and periods of 1.91, 3.24, 6.56, 9.96, 15.23, and\n20.71 days. All planets but the innermost one form a 2:4:6:9:12 chain of\nLaplace resonances, and the planetary densities show important variations from\nplanet to planet, jumping from 1.02(+0.28/-0.23) to 0.177(+0.055/-0.061) times\nthe Earth's density between planets c and d. Using Bayesian interior structure\nretrieval models, we show that the amount of gas in the planets does not vary\nin a monotonous way, contrary to what one would expect from simple formation\nand evolution models and unlike other known systems in a chain of Laplace\nresonances. The brightness of TOI-178 allows for a precise characterisation of\nits orbital architecture as well as of the physical nature of the six presently\nknown transiting planets it harbours. The peculiar orbital configuration and\nthe diversity in average density among the planets in the system will enable\nthe study of interior planetary structures and atmospheric evolution, providing\nimportant clues on the formation of super-Earths and mini-Neptunes.\n"}
+{"abstract": "  We advance and experimentally implement a protocol to generate perfect\noptical coherence lattices (OCL) that are not modulated by an envelope field.\nStructuring the amplitude and phase of an input partially coherent beam in a\nFourier plane of an imaging system lies at the heart of our protocol. In the\nproposed approach, the OCL node profile depends solely on the degree of\ncoherence (DOC) of the input beam such that, in principle, any lattice\nstructure can be attained via proper manipulations in the Fourier plane.\nMoreover, any genuine partially coherent source can serve as an input to our\nlattice generating imaging system. Our results are anticipated to find\napplications to optical field engineering and multi-target probing among\nothers.\n"}
+{"abstract": "  We carried out a comprehensive study of electronic transport, thermal and\nthermodynamic properties in FeCr$_2$Te$_4$ single crystals. It exhibits\nbad-metallic behavior and anomalous Hall effect (AHE) below a weak-itinerant\nparamagentic-to-ferrimagnetic transition $T_c$ $\\sim$ 123 K. The linear scaling\nbetween the anomalous Hall resistivity $\\rho_{xy}$ and the longitudinal\nresistivity $\\rho_{xx}$ implies that the AHE in FeCr$_2$Te$_4$ is most likely\ndominated by extrinsic skew-scattering mechanism rather than intrinsic KL or\nextrinsic side-jump mechanism, which is supported by our Berry phase\ncalculations.\n"}
+{"abstract": "  In this article, we study the problem of air-to-ground ultra-reliable and\nlow-latency communication (URLLC) for a moving ground user. This is done by\ncontrolling multiple unmanned aerial vehicles (UAVs) in real time while\navoiding inter-UAV collisions. To this end, we propose a novel multi-agent deep\nreinforcement learning (MADRL) framework, coined a graph attention exchange\nnetwork (GAXNet). In GAXNet, each UAV constructs an attention graph locally\nmeasuring the level of attention to its neighboring UAVs, while exchanging the\nattention weights with other UAVs so as to reduce the attention mismatch\nbetween them. Simulation results corroborates that GAXNet achieves up to 4.5x\nhigher rewards during training. At execution, without incurring inter-UAV\ncollisions, GAXNet achieves 6.5x lower latency with the target 0.0000001 error\nrate, compared to a state-of-the-art baseline framework.\n"}
+{"abstract": "  Animals can quickly learn the timing of events with fixed intervals and their\nrate of acquisition does not depend on the length of the interval. In contrast,\nrecurrent neural networks that use gradient based learning have difficulty\npredicting the timing of events that depend on stimulus that occurred long ago.\nWe present the latent time-adaptive drift-diffusion model (LTDDM), an extension\nto the time-adaptive drift-diffusion model (TDDM), a model for animal learning\nof timing that exhibits behavioural properties consistent with experimental\ndata from animals. The performance of LTDDM is compared to that of a state of\nthe art long short-term memory (LSTM) recurrent neural network across three\ntiming tasks. Differences in the relative performance of these two models is\ndiscussed and it is shown how LTDDM can learn these events time series orders\nof magnitude faster than recurrent neural networks.\n"}
+{"abstract": "  In the last three decades, memory safety issues in system programming\nlanguages such as C or C++ have been one of the significant sources of security\nvulnerabilities. However, there exist only a few attempts with limited success\nto cope with the complexity of C++ program verification. Here we describe and\nevaluate a novel verification approach based on bounded model checking (BMC)\nand satisfiability modulo theories (SMT) to verify C++ programs formally. Our\nverification approach analyzes bounded C++ programs by encoding into SMT\nvarious sophisticated features that the C++ programming language offers, such\nas templates, inheritance, polymorphism, exception handling, and the Standard\nC++ Libraries. We formalize these features within our formal verification\nframework using a decidable fragment of first-order logic and then show how\nstate-of-the-art SMT solvers can efficiently handle that. We implemented our\nverification approach on top of ESBMC. We compare ESBMC to LLBMC and DIVINE,\nwhich are state-of-the-art verifiers to check C++ programs directly from the\nLLVM bitcode. Experimental results show that ESBMC can handle a wide range of\nC++ programs, presenting a higher number of correct verification results. At\nthe same time, it reduces the verification time if compared to LLBMC and DIVINE\ntools. Additionally, ESBMC has been applied to a commercial C++ application in\nthe telecommunication domain and successfully detected arithmetic overflow\nerrors, potentially leading to security vulnerabilities.\n"}
+{"abstract": "  We investigate the asymptotic symmetry group of a scalar field\nminimally-coupled to an abelian gauge field using the Hamiltonian formulation.\nThis extends previous work by Henneaux and Troessaert on the pure\nelectromagnetic case. We deal with minimally coupled massive and massless\nscalar fields and find that they behave differently insofar as the latter do\nnot allow for canonically implemented asymptotic boost symmetries. We also\nconsider the abelian Higgs model and show that its asymptotic canonical\nsymmetries reduce to the Poincar\\'e group in an unproblematic fashion.\n"}
+{"abstract": "  Despite the rich literature on scheduling algorithms for wireless networks,\nalgorithms that can provide deadline guarantees on packet delivery for general\ntraffic and interference models are very limited. In this paper, we study the\nproblem of scheduling real-time traffic under a conflict-graph interference\nmodel with unreliable links due to channel fading. Packets that are not\nsuccessfully delivered within their deadlines are of no value. We consider\ntraffic (packet arrival and deadline) and fading (link reliability) processes\nthat evolve as an unknown finite-state Markov chain. The performance metric is\nefficiency ratio which is the fraction of packets of each link which are\ndelivered within their deadlines compared to that under the optimal (unknown)\npolicy. We first show a conversion result that shows classical non-real-time\nscheduling algorithms can be ported to the real-time setting and yield a\nconstant efficiency ratio, in particular, Max-Weight Scheduling (MWS) yields an\nefficiency ratio of 1/2. We then propose randomized algorithms that achieve\nefficiency ratios strictly higher than 1/2, by carefully randomizing over the\nmaximal schedules. We further propose low-complexity and myopic distributed\nrandomized algorithms, and characterize their efficiency ratio. Simulation\nresults are presented that verify that randomized algorithms outperform\nclassical algorithms such as MWS and GMS.\n"}
+{"abstract": "  As the number of IoT devices has increased rapidly, IoT botnets have\nexploited the vulnerabilities of IoT devices. However, it is still challenging\nto detect the initial intrusion on IoT devices prior to massive attacks. Recent\nstudies have utilized power side-channel information to identify this intrusion\nbehavior on IoT devices but still lack accurate models in real-time for\nubiquitous botnet detection.\n  We proposed the first online intrusion detection system called DeepAuditor\nfor IoT devices via power auditing. To develop the real-time system, we\nproposed a lightweight power auditing device called Power Auditor. We also\ndesigned a distributed CNN classifier for online inference in a laboratory\nsetting. In order to protect data leakage and reduce networking redundancy, we\nthen proposed a privacy-preserved inference protocol via Packed Homomorphic\nEncryption and a sliding window protocol in our system. The classification\naccuracy and processing time were measured, and the proposed classifier\noutperformed a baseline classifier, especially against unseen patterns. We also\ndemonstrated that the distributed CNN design is secure against any distributed\ncomponents. Overall, the measurements were shown to the feasibility of our\nreal-time distributed system for intrusion detection on IoT devices.\n"}
+{"abstract": "  Vector control strategies are central to the mitigation and containment of\nCOVID-19 and have come in the form of municipal ordinances that restrict the\noperational status of public and private spaces and associated services. Yet,\nlittle is known about specific population responses in terms of risk behaviors.\nTo help understand the impact of those vector control variable strategies, a\nmulti-week, multi-site observational study was undertaken outside of 19 New\nYork City medical facilities during the peak of the city's initial COVID-19\nwave (03/22/20-05/19/20). The aim was to capture perishable data of the touch,\ndestination choice, and PPE usage behavior of individuals egressing hospitals\nand urgent care centers. A major goal was to establish an empirical basis for\nfuture research on the way people interact with three-dimensional vector\nenvironments. Anonymized data were collected via smart phones. Each data record\nincludes the time, data, and location of an individual leaving a healthcare\nfacility, their routing, interactions with the build environment, other\nindividuals, and themselves. Most records also note their PPE usage,\ndestination, intermediary stops, and transportation choices. The records were\nlinked with 61 socio-economic factors by the facility zip code and 7\ncontemporaneous weather factors and the merged in a unified shapefile in an\nARCGIS system. This paper describes the project team and protocols used to\nproduce over 5,100 publicly accessible observational records and an affiliated\ncodebook that can be used to study linkages between individual behaviors and\non-the-ground conditions.\n"}
+{"abstract": "  Deep Neural Networks (DNNs), despite their tremendous success in recent\nyears, could still cast doubts on their predictions due to the intrinsic\nuncertainty associated with their learning process. Ensemble techniques and\npost-hoc calibrations are two types of approaches that have individually shown\npromise in improving the uncertainty calibration of DNNs. However, the\nsynergistic effect of the two types of methods has not been well explored. In\nthis paper, we propose a truth discovery framework to integrate ensemble-based\nand post-hoc calibration methods. Using the geometric variance of the ensemble\ncandidates as a good indicator for sample uncertainty, we design an\naccuracy-preserving truth estimator with provably no accuracy drop.\nFurthermore, we show that post-hoc calibration can also be enhanced by truth\ndiscovery-regularized optimization. On large-scale datasets including CIFAR and\nImageNet, our method shows consistent improvement against state-of-the-art\ncalibration approaches on both histogram-based and kernel density-based\nevaluation metrics. Our codes are available at\nhttps://github.com/horsepurve/truly-uncertain.\n"}
+{"abstract": "  Space-based transit missions such as Kepler and TESS have demonstrated that\nplanets are ubiquitous. However, the success of these missions heavily depends\non ground-based radial velocity (RV) surveys, which combined with transit\nphotometry can yield bulk densities and orbital properties. While most Kepler\nhost stars are too faint for detailed follow-up observations, TESS is detecting\nplanets orbiting nearby bright stars that are more amenable to RV\ncharacterization. Here we introduce the TESS-Keck Survey (TKS), an RV program\nusing ~100 nights on Keck/HIRES to study exoplanets identified by TESS. The\nprimary survey aims are investigating the link between stellar properties and\nthe compositions of small planets; studying how the diversity of system\narchitectures depends on dynamical configurations or planet multiplicity;\nidentifying prime candidates for atmospheric studies with JWST; and\nunderstanding the role of stellar evolution in shaping planetary systems. We\npresent a fully-automated target selection algorithm, which yielded 103 planets\nin 86 systems for the final TKS sample. Most TKS hosts are inactive,\nsolar-like, main-sequence stars (4500 K < Teff < 6000 K) at a wide range of\nmetallicities. The selected TKS sample contains 71 small planets (Rp < 4 Re),\n11 systems with multiple transiting candidates, 6 sub-day period planets and 3\nplanets that are in or near the habitable zone of their host star. The target\nselection described here will facilitate the comparison of measured planet\nmasses, densities, and eccentricities to predictions from planet population\nmodels. Our target selection software is publicly available (at\nhttps://github.com/ashleychontos/sort-a-survey) and can be adapted for any\nsurvey which requires a balance of multiple science interests within a given\ntelescope allocation.\n"}
+{"abstract": "  We present an isomorphism test for graphs of Euler genus $g$ running in time\n$2^{O(g^4 \\log g)}n^{O(1)}$. Our algorithm provides the first explicit upper\nbound on the dependence on $g$ for an fpt isomorphism test parameterized by the\nEuler genus of the input graphs. The only previous fpt algorithm runs in time\n$f(g)n$ for some function $f$ (Kawarabayashi 2015). Actually, our algorithm\neven works when the input graphs only exclude $K_{3,h}$ as a minor. For such\ngraphs, no fpt isomorphism test was known before.\n  The algorithm builds on an elegant combination of simple group-theoretic,\ncombinatorial, and graph-theoretic approaches. In particular, we introduce\n$(t,k)$-WL-bounded graphs which provide a powerful tool to combine\ngroup-theoretic techniques with the standard Weisfeiler-Leman algorithm. This\nconcept may be of independent interest.\n"}
+{"abstract": "  Consider a branching random walk $(V_u)_{u\\in \\mathcal T^{IGW}}$ in $\\mathbb\nZ^d$ with the genealogy tree $\\mathcal T^{IGW}$ formed by a sequence of i.i.d.\ncritical Galton-Watson trees. Let $R_n $ be the set of points in $\\mathbb Z^d$\nvisited by $(V_u)$ when the index $u$ explores the first $n$ subtrees in\n$\\mathcal T^{IGW}$. Our main result states that for $d\\in \\{3, 4, 5\\}$, the\ncapacity of $R_n$ is almost surely equal to $n^{\\frac{d-2}{2}+o(1)}$ as $n \\to\n\\infty$.\n"}
+{"abstract": "  We investigate magnetic instabilities in charge-neutral twisted bilayer\ngraphene close to so-called \"magic angles\" using a combination of real-space\nHartree-Fock and dynamical mean-field theories. In view of the large size of\nthe unit cell close to magic angles, we examine a previously proposed rescaling\nthat permits to mimic the same underlying flat minibands at larger twist\nangles. We find that localized magnetic states emerge for values of the Coulomb\ninteraction $U$ that are significantly smaller than what would be required to\nrender an isolated layer antiferromagnetic. However, this effect is\noverestimated in the rescaled system, hinting at a complex interplay of\nflatness of the minibands close to the Fermi level and the spatial extent of\nthe corresponding localized states. Our findings shed new light on perspectives\nfor experimental realization of magnetic states in charge-neutral twisted\nbilayer graphene.\n"}
+{"abstract": "  Locating lesions is important in the computer-aided diagnosis of X-ray\nimages. However, box-level annotation is time-consuming and laborious. How to\nlocate lesions accurately with few, or even without careful annotations is an\nurgent problem. Although several works have approached this problem with\nweakly-supervised methods, the performance needs to be improved. One obstacle\nis that general weakly-supervised methods have failed to consider the\ncharacteristics of X-ray images, such as the highly-structural attribute. We\ntherefore propose the Cross-chest Graph (CCG), which improves the performance\nof automatic lesion detection by imitating doctor's training and\ndecision-making process. CCG models the intra-image relationship between\ndifferent anatomical areas by leveraging the structural information to simulate\nthe doctor's habit of observing different areas. Meanwhile, the relationship\nbetween any pair of images is modeled by a knowledge-reasoning module to\nsimulate the doctor's habit of comparing multiple images. We integrate\nintra-image and inter-image information into a unified end-to-end framework.\nExperimental results on the NIH Chest-14 database (112,120 frontal-view X-ray\nimages with 14 diseases) demonstrate that the proposed method achieves\nstate-of-the-art performance in weakly-supervised localization of lesions by\nabsorbing professional knowledge in the medical field.\n"}
+{"abstract": "  We study effects of higher-order antinematic interactions on the critical\nbehavior of the antiferromagnetic (AFM) $XY$ model on a triangular lattice,\nusing Monte Carlo simulations. The parameter $q$ of the generalized antinematic\n(ANq) interaction is found to have a pronounced effect on the phase diagram\ntopology by inducing new quasi-long-range ordered phases due to competition\nwith the conventional AFM interaction as well as geometrical frustration. For\nvalues of $q$ divisible by 3 the conflict between the two interactions results\nin a frustrated canted AFM phase appearing at low temperatures wedged between\nthe AFM and ANq phases. For $q$ nondivisible by 3 with the increase of $q$ one\ncan observe the evolution of the phase diagram topology featuring two ($q=2$),\nthree ($q=4,5$) and four ($q \\geq 7$) ordered phases. In addition to the two\nphases previously found for $q=2$, the first new phase with solely AFM ordering\narises for $q=4$ in the limit of strong AFM coupling and higher temperatures by\nseparating from the phase with the coexisting AFM and ANq orderings. For $q=7$\nanother phase with AFM ordering but multimodal spin distribution in each\nsublattice appears at intermediate temperatures. All these algebraic phases\nalso display standard and generalized chiral long-range orderings, which\ndecouple at higher temperatures in the regime of dominant ANq (AFM) interaction\nfor $q \\geq 4$ ($q \\geq 7$) preserving only the generalized (standard) chiral\nordering.\n"}
+{"abstract": "  Deep reinforcement learning (DRL) has great potential for acquiring the\noptimal action in complex environments such as games and robot control.\nHowever, it is difficult to analyze the decision-making of the agent, i.e., the\nreasons it selects the action acquired by learning. In this work, we propose\nMask-Attention A3C (Mask A3C), which introduces an attention mechanism into\nAsynchronous Advantage Actor-Critic (A3C), which is an actor-critic-based DRL\nmethod, and can analyze the decision-making of an agent in DRL. A3C consists of\na feature extractor that extracts features from an image, a policy branch that\noutputs the policy, and a value branch that outputs the state value. In this\nmethod, we focus on the policy and value branches and introduce an attention\nmechanism into them. The attention mechanism applies a mask processing to the\nfeature maps of each branch using mask-attention that expresses the judgment\nreason for the policy and state value with a heat map. We visualized\nmask-attention maps for games on the Atari 2600 and found we could easily\nanalyze the reasons behind an agent's decision-making in various game tasks.\nFurthermore, experimental results showed that the agent could achieve a higher\nperformance by introducing the attention mechanism.\n"}
+{"abstract": "  This paper presents Contrastive Reconstruction, ConRec - a self-supervised\nlearning algorithm that obtains image representations by jointly optimizing a\ncontrastive and a self-reconstruction loss. We showcase that state-of-the-art\ncontrastive learning methods (e.g. SimCLR) have shortcomings to capture\nfine-grained visual features in their representations. ConRec extends the\nSimCLR framework by adding (1) a self-reconstruction task and (2) an attention\nmechanism within the contrastive learning task. This is accomplished by\napplying a simple encoder-decoder architecture with two heads. We show that\nboth extensions contribute towards an improved vector representation for images\nwith fine-grained visual features. Combining those concepts, ConRec outperforms\nSimCLR and SimCLR with Attention-Pooling on fine-grained classification\ndatasets.\n"}
+{"abstract": "  Let $\\mathfrak{g}=\\mathfrak{g}_{\\bar{0}}+\\mathfrak{g}_{\\bar{1}}$ be a basic\nLie superalgebra, $\\mathcal{W}_0$ (resp.$\\mathcal{W}$) be the finite\nW-(resp.super-) algebras constructed from a fixed nilpotent element in\n$\\mathfrak{g}_{\\bar{0}}$. Based on a relation between finite W-algebra\n$\\mathcal{W}_0$ and W-superalgebra $\\mathcal{W}$ found recently by the author\nand Shu, we study the finite dimensional representations of finite\nW-superalgebras in this paper. We first formulate and prove a version of\nPremet's conjecture for the finite W-superalgebras from basic simple Lie\nsuperalgebras. As in the W-algebra case, the Premet's conjecture is very close\nto give a classification to the finite dimensional simple\n$\\mathcal{W}$-modules. In the case of $\\ggg$ is Lie superalgebras of basic type\n\\Rmnum{1}, we prove the set of simple $\\mathcal{W}$-supermodules is bijective\nwith that of simple $\\mathcal{W}_0$-modules; presenting a triangular\ndecomposition to the tensor product of $\\mathcal{W}$ with a Clifford algebra,\nwe also give an algorithm to compute the character of the finite dimensional\nsimple $\\mathcal{W}$-supermodules with integral central character.\n"}
+{"abstract": "  Pulsating ultra-luminous X-ray sources (PULXs) are characterised by an\nextremely large luminosity ($ > 10^{40} \\text{erg s}^{-1}$). While there is a\ngeneral consensus that they host an accreting, magnetized neutron star (NS),\nthe problem of how to produce luminosities $> 100$ times the Eddington limit,\n$L_E$, of a solar mass object is still debated. A promising explanation relies\non the reduction of the opacities in the presence of a strong magnetic field,\nwhich allows for the local flux to be much larger than the Eddington flux.\nHowever, avoiding the onset of the propeller effect may be a serious problem.\nHere, we reconsider the problem of column accretion onto a highly magnetized\nNS, extending previously published calculations by relaxing the assumption of a\npure dipolar field and allowing for more complex magnetic field topologies. We\nfind that the maximum luminosity is determined primarily by the magnetic field\nstrength near the NS surface. We also investigate other factors determining the\naccretion column geometry and the emergent luminosity, such as the assumptions\non the parameters governing the accretion flow at the disk-magnetosphere\nboundary. We conclude that a strongly magnetized NS with a dipole component of\n$\\sim 10^{13} \\text{G}$, octupole component of $\\sim10^{14} \\text{G}$ and spin\nperiod $\\sim1 \\text{s}$ can produce a luminosity of $\\sim 10^{41} \\text{erg\ns}^{-1}$ while avoiding the propeller regime. We apply our model to two PULXs,\nNGC 5907 ULX-1 and NGC 7793 P13, and discuss how their luminosity and spin\nperiod rate can be explained in terms of different configurations, either with\nor without multipolar magnetic components.\n"}
+{"abstract": "  Key to successfully deal with complex contemporary datasets is the\ndevelopment of tractable models that account for the irregular structure of the\ninformation at hand. This paper provides a comprehensive and unifying view of\nseveral sampling, reconstruction, and recovery problems for signals defined on\nirregular domains that can be accurately represented by a graph. The workhorse\nassumption is that the (partially) observed signals can be modeled as the\noutput of a graph filter to a structured (parsimonious) input graph signal.\nWhen either the input or the filter coefficients are known, this is tantamount\nto assuming that the signals of interest live on a subspace defined by the\nsupporting graph. When neither is known, the model becomes bilinear. Upon\nimposing different priors and additional structure on either the input or the\nfilter coefficients, a broad range of relevant problem formulations arise. The\ngoal is then to leverage those priors, the shift operator of the supporting\ngraph, and the samples of the signal of interest to recover: the signal at the\nnon-sampled nodes (graph-signal interpolation), the input (deconvolution), the\nfilter coefficients (system identification), or any combination thereof (blind\ndeconvolution).\n"}
+{"abstract": "  We study the boundary behaviour of solutions to second order parabolic linear\nequations in moving domains. Our main result is a higher order boundary Harnack\ninequality in $C^1$ and $C^{k,\\alpha}$ domains, providing that the quotient of\ntwo solutions vanishing on the boundary of the domain is as smooth as the\nboundary.\n  As a consequence of our result, we provide a new proof of higher order\nregularity of the free boundary in the parabolic obstacle problem.\n"}
+{"abstract": "  In this paper, we analyze the so-called Master Equation of the linear\nbackreaction of a plasma disk in the central object magnetic field, when small\nscale ripples are considered. This study allows to single out two relevant\nphysical properties of the linear disk backreaction: (i) the appearance of a\nvertical growth of the magnetic flux perturbations; (ii) the emergence of\nsequence of magnetic field O-points, crucial for the triggering of local plasma\ninstabilities. We first analyze a general Fourier approach to the solution of\nthe addressed linear partial differential problem. This technique allows to\nshow how the vertical gradient of the backreaction is, in general, inverted\nwith respect to the background one. Instead, the fundamental harmonic solution\nconstitutes a specific exception for which the background and the perturbed\nprofiles are both decaying. Then, we study the linear partial differential\nsystem from the point of view of a general variable separation method. The\nobtained profile describes the crystalline behavior of the disk. Using a simple\nrescaling, the governing equation is reduced to the second order differential\nWhittaker equation. The zeros of the radial magnetic field are found by using\nthe solution written in terms Kummer functions. The possible implications of\nthe obtained morphology of the disk magnetic profile are then discussed in view\nof the jet formation.\n"}
+{"abstract": "  Identifying academic plagiarism is a pressing problem, among others, for\nresearch institutions, publishers, and funding organizations. Detection\napproaches proposed so far analyze lexical, syntactical, and semantic text\nsimilarity. These approaches find copied, moderately reworded, and literally\ntranslated text. However, reliably detecting disguised plagiarism, such as\nstrong paraphrases, sense-for-sense translations, and the reuse of non-textual\ncontent and ideas, is an open research problem.\n  The thesis addresses this problem by proposing plagiarism detection\napproaches that implement a different concept: analyzing non-textual content in\nacademic documents, specifically citations, images, and mathematical content.\n  To validate the effectiveness of the proposed detection approaches, the\nthesis presents five evaluations that use real cases of academic plagiarism and\nexploratory searches for unknown cases.\n  The evaluation results show that non-textual content elements contain a high\ndegree of semantic information, are language-independent, and largely immutable\nto the alterations that authors typically perform to conceal plagiarism.\nAnalyzing non-textual content complements text-based detection approaches and\nincreases the detection effectiveness, particularly for disguised forms of\nacademic plagiarism.\n  To demonstrate the benefit of combining non-textual and text-based detection\nmethods, the thesis describes the first plagiarism detection system that\nintegrates the analysis of citation-based, image-based, math-based, and\ntext-based document similarity. The system's user interface employs\nvisualizations that significantly reduce the effort and time users must invest\nin examining content similarity.\n"}
+{"abstract": "  We introduce \"$t$-LC triangulated manifolds\" as those triangulations\nobtainable from a tree of $d$-simplices by recursively identifying two boundary\n$(d-1)$-faces whose intersection has dimension at least $d-t-1$. The $t$-LC\nnotion interpolates between the class of LC manifolds introduced by\nDurhuus--Jonsson (corresponding to the case $t=1$), and the class of all\nmanifolds (case $t=d$). Benedetti--Ziegler proved that there are at most\n$2^{d^2 \\, N}$ triangulated $1$-LC $d$-manifolds with $N$ facets. Here we prove\nthat there are at most $2^{\\frac{d^3}{2}N}$ triangulated $2$-LC $d$-manifolds\nwith $N$ facets. This extends to all dimensions an intuition by Mogami for\n$d=3$.\n  We also introduce \"$t$-constructible complexes\", interpolating between\nconstructible complexes (the case $t=1$) and all complexes (case $t=d$). We\nshow that all $t$-constructible pseudomanifolds are $t$-LC, and that all\n$t$-constructible complexes have (homotopical) depth larger than $d-t$. This\nextends the famous result by Hochster that constructible complexes are\n(homotopy) Cohen--Macaulay.\n"}
+{"abstract": "  This paper revisits the connection between the girth of a protograph-based\nLDPC code given by a parity-check matrix and the properties of powers of the\nproduct between the matrix and its transpose in order to obtain the necessary\nand sufficient conditions for a code to have given girth between 6 and 12, and\nto show how these conditions can be incorporated into simple algorithms to\nconstruct codes of that girth. To this end, we highlight the role that certain\nsubmatrices that appear in these products have in the construction of codes of\ndesired girth. In particular, we show that imposing girth conditions on a\nparity-check matrix is equivalent to imposing conditions on a square submatrix\nobtained from it and we show how this equivalence is particularly strong for a\nprotograph based parity-check matrix of variable node degree 2, where the\ncycles in its Tanner graph correspond one-to-one to the cycles in the Tanner\ngraph of a square submatrix obtained by adding the permutation matrices (or\nproducts of these) in the composition of the parity-check matrix. We end the\npaper with exemplary constructions of codes with various girths and computer\nsimulations. Although, we mostly assume the case of fully connected protographs\nof variable node degree 2 and 3, the results can be used for any parity-check\nmatrix/protograph-based Tanner graph.\n"}
+{"abstract": "  Two-dimensional electron systems subjected to a perpendicular magnetic field\nabsorb electromagnetic radiation via the cyclotron resonance (CR). Here we\nreport a qualitative breach of this well-known behaviour in graphene. Our study\nof the terahertz photoresponse reveals a resonant burst at the main overtone of\nthe CR, drastically exceeding the signal detected at the position of the\nordinary CR. In accordance with the developed theory, the photoresponse\ndependencies on the magnetic field, doping level, and sample geometry suggest\nthat the origin of this anomaly lies in the near-field magnetoabsorption\nfacilitated by the Bernstein modes, ultra-slow magnetoplasmonic excitations\nreshaped by nonlocal electron dynamics. Close to the CR harmonics, these modes\nare characterized by a flat dispersion and a diverging plasmonic density of\nstates that strongly amplifies the radiation absorption. Besides fundamental\ninterest, our experimental results and developed theory show that the radiation\nabsorption via nonlocal collective modes can facilitate a strong photoresponse,\na behaviour potentially useful for infrared and terahertz technology.\n"}
+{"abstract": "  We obtain results on the condensation principle called local club\ncondensation. We prove that in extender models an equivalence between the\nfailure of local club condensation and subcompact cardinals holds. This gives a\ncharacterization of $\\square_{\\kappa}$ in terms of local club condensation in\nextender models. Assuming $\\gch$, given an interval of ordinals $I$ we verify\nthat iterating the forcing defined by Holy-Welch-Wu, we can preserve $\\gch$,\ncardinals and cofinalities and obtain a model where local club condensation\nholds for every ordinal in $I$ modulo those ordinals which cardinality is a\nsingular cardinal.\n  We prove that if $\\kappa$ is a regular cardinal in an interval $I$, the above\niteration provides enough condensation for the combinatorial principle\n$\\Dl_{S}^{*}(\\Pi^{1}_{2})$, and in particular $\\diamondsuit(S)$, to hold for\nany stationary $S \\subseteq \\kappa$.\n"}
+{"abstract": "  In this paper we find analytical solutions for the scalar and gauge fields in\nthe Freedman-Robertson-Walker multiply warped braneworld scenario. With this we\nfind the precise mass spectra for these fields. We compare these spectra with\nthat previously found in the literature for the static case.\n"}
+{"abstract": "  The Mn(Bi$_{1-x}$Sb$_x$)$_2$Te$_4$ series is purported to span from\nantiferromagnetic (AF) topological insulator at x = 0 to a trivial AF insulator\nat x = 1. Here we report on neutron diffraction and inelastic neutron\nscattering studies of the magnetic interactions across this series. All\ncompounds measured possess ferromagnetic (FM) triangular layers and we find a\ncrossover from AF to FM interlayer coupling near x = 1 for our samples. The\nlarge spin gap at x = 0 closes rapidly and the average FM exchange interactions\nwithin the triangular layer increase with Sb substitution. Similar to a\nprevious study of MnBi$_2$Te$_4$, we find severe spectral broadening which\nincreases dramatically across the compositional series. In addition to\nbroadening, we observe an additional sharp magnetic excitation in\nMnSb$_2$Te$_4$ that may indicate the development of local magnetic modes based\non recent reports of antisite disorder between Mn and Sb sublattices. The\nresults suggest that both substitutional and antisite disorder contribute\nsubstantially to the magnetism in Mn(Bi$_{1-x}$Sb$_x$)$_2$Te$_4$.\n"}
+{"abstract": "  We have built a renormalizable $U(1)_X$ model with a $\\Sigma (18)\\times Z_4$\nsymmetry, whose spontaneous breaking yields the observed SM fermion masses and\nfermionic mixing parameters. The tiny masses of the light active neutrinos are\nproduced by the type I seesaw mechanism mediated by very heavy right handed\nMajorana neutrinos. To the best of our knowledge, this model is the first\nimplementation of the $\\Sigma (18)$ flavor symmetry in a renormalizable\n$U(1)_X$ model. Our model allows a successful fit for the SM fermion masses,\nfermionic mixing angles and CP phases for both quark and lepton sectors. The\nobtained values for the physical observables of both quark and lepton sectors\nare in accordance with the experimental data. We obtain an effective neutrino\nmass parameter of $\\langle m_{ee}\\rangle=1.51\\times 10^{-3}\\, \\mathrm{eV}$ for\nnormal ordering and $\\langle m_{ee}\\rangle =4.88\\times 10^{-2} \\, \\mathrm{eV}$\nfor inverted ordering which are well consistent with the recent experimental\nlimits on neutrinoless double beta decay.\n"}
+{"abstract": "  Spatial resolution is one of the most important specifications of an imaging\nsystem. Recent results in quantum parameter estimation theory reveal that an\narbitrarily small distance between two incoherent point sources can always be\nefficiently determined through the use of a spatial mode sorter. However,\nextending this procedure to a general object consisting of many incoherent\npoint sources remains challenging, due to the intrinsic complexity of\nmulti-parameter estimation problems. Here, we generalize the Richardson-Lucy\n(RL) deconvolution algorithm to address this challenge. We simulate its\napplication to an incoherent confocal microscope, with a Zernike spatial mode\nsorter replacing the pinhole used in a conventional confocal microscope. We\ntest different spatially incoherent objects of arbitrary geometry, and we find\nthat the resolution enhancement of sorter-based microscopy is on average over\n30% higher than that of a conventional confocal microscope using the standard\nRL deconvolution algorithm. Our method could potentially be used in diverse\napplications such as fluorescence microscopy and astronomical imaging.\n"}
+{"abstract": "  In this article, we study the phenomenology of a two dimensional dilute\nsuspension of active amphiphilic Janus particles. We analyze how the morphology\nof the aggregates emerging from their self-assembly depends on the strength and\nthe direction of the active forces. We systematically explore and contrast the\nphenomenologies resulting from particles with a range of attractive patch\ncoverages. Finally, we illustrate how the geometry of the colloids and the\ndirectionality of their interactions can be used to control the physical\nproperties of the assembled active aggregates and suggest possible strategies\nto exploit self-propulsion as a tunable driving force for self-assembly.\n"}
+{"abstract": "  Purpose Infectious agents, such as SARS-CoV-2, can be carried by droplets\nexpelled during breathing. The spatial dissemination of droplets varies\naccording to their initial velocity. After a short literature review, our goal\nwas to determine the velocity of the exhaled air during vocal exercises.\nMethods A propylene glycol cloud produced by 2 e-cigarettes' users allowed\nvisualization of the exhaled air emitted during vocal exercises. Airflow\nvelocities were measured during the first 200 ms of a long exhalation, a\nsustained vowel /a/ and varied vocal exercises. For the long exhalation and the\nsustained vowel /a/, the decrease of airflow velocity was measured until 3 s.\nResults were compared with a Computational Fluid Dynamics (CFD) study using\nboundary conditions consistent with our experimental study. Results Regarding\nthe production of vowels, higher velocities were found in loud and whispered\nvoices than in normal voice. Voiced consonants like /3/ or /v/ generated higher\nvelocities than vowels. Some voiceless consonants, e.g., /t/ generated high\nvelocities, but long exhalation had the highest velocities. Semi-occluded vocal\ntract exercises generated faster airflow velocities than loud speech, with a\ndecreased velocity during voicing. The initial velocity quickly decreased as\nwas shown during a long exhalation or a sustained vowel /a/. Velocities were\nconsistent with the CFD data. Conclusion Initial velocity of the exhaled air is\na key factor influencing droplets trajectory. Our study revealed that vocal\nexercises produce a slower airflow than long exhalation. Speech therapy should,\ntherefore, not be associated with an increased risk of contamination when\nimplementing standard recommendations.\n"}
+{"abstract": "  Various hardware accelerators have been developed for energy-efficient and\nreal-time inference of neural networks on edge devices. However, most training\nis done on high-performance GPUs or servers, and the huge memory and computing\ncosts prevent training neural networks on edge devices. This paper proposes a\nnovel tensor-based training framework, which offers orders-of-magnitude memory\nreduction in the training process. We propose a novel rank-adaptive tensorized\nneural network model, and design a hardware-friendly low-precision algorithm to\ntrain this model. We present an FPGA accelerator to demonstrate the benefits of\nthis training method on edge devices. Our preliminary FPGA implementation\nachieves $59\\times$ speedup and $123\\times$ energy reduction compared to\nembedded CPU, and $292\\times$ memory reduction over a standard full-size\ntraining.\n"}
+{"abstract": "  The reliable detection of neutrons in a harsh gamma-ray environment is an\nimportant aspect of establishing non-destructive methods for the\ncharacterization of spent nuclear fuel. In this study, we present results from\nextended in-situ monitoring of detector systems consisting of commercially\navailable components: EJ-426, a $^6$Li-enriched solid-state scintillator\nmaterial sensitive to thermal neutrons, and two different types of Hamamatsu\nphotomultiplier tubes (PMT). Over the period of eight months, these detectors\nwere operated in close vicinity to spent nuclear fuel stored at the interim\nstorage facility CLAB, Oskarshamn, Sweden. At the measurement position the\ndetectors were continuously exposed to an estimated neutron flux of approx. 280\nn/s $\\cdot$ cm$^2$ and a gamma-ray dose rate of approx. 6 Sv/h.\n  Using offline software algorithms, neutron pulses were identified in the\ndata. Over the entire investigated dose range of up to 35 kGr, the detector\nsystems were functioning and were delivering detectable neutron signals. Their\nperformance as measured by the number of identified neutrons degrades down to\nabout 30% of the initial value. Investigations of the irradiated components\nsuggest that this degradation is a result of reduced optical transparency of\nthe involved materials as well as a reduction of PMT gain due to the continuous\nhigh currents. Increasing the gain of the PMT through step-ups of the applied\nhigh voltage allowed to partially compensate for this loss in detection\nsensitivity.\n  The integrated neutron fluence during the measurement was experimentally\nverified to be in the order of $5 \\cdot 10^9$ n/cm$^2$. The results were\ninterpreted with the help of MCNP6.2 simulations of the setup and the neutron\nflux.\n"}
+{"abstract": "  This paper presents a novel three-degree-of-freedom (3-DOF) translational\nparallel manipulator (TPM) by using a topological design method of parallel\nmechanism (PM) based on position and orientation characteristic (POC)\nequations. The proposed PM is only composed of lower-mobility joints and\nactuated prismatic joints, together with the investigations on three kinematic\nissues of importance. The first aspect pertains to geometric modeling of the\nTPM in connection with its topological characteristics, such as the POC, degree\nof freedom and coupling degree, from which its symbolic direct kinematic\nsolutions are readily obtained. Moreover, the decoupled properties of\ninput-output motions are directly evaluated without Jacobian analysis.\nSequentially, based upon the inverse kinematics, the singular configurations of\nthe TPM are identified, wherein the singular surfaces are visualized by means\nof a Gr{\\\"o}bner based elimination operation. Finally, the workspace of the TPM\nis evaluated with a geometric approach. This 3-DOF TPM features less joints and\nlinks compared with the well-known Delta robot, which reduces the structural\ncomplexity. Its symbolic direct kinematics and partially-decoupled property\nwill ease path planning and dynamic analysis. The TPM can be used for\nmanufacturing large work pieces.\n"}
+{"abstract": "  We prove that the slice rank of a 3-tensor (a combinatorial notion introduced\nby Tao in the context of the cap-set problem), the analytic rank (a\nFourier-theoretic notion introduced by Gowers and Wolf), and the geometric rank\n(a recently introduced algebro-geometric notion) are all equivalent up to an\nabsolute constant. As a corollary, we obtain strong trade-offs on the\narithmetic complexity of a biased bililnear map, and on the separation between\ncomputing a bilinear map exactly and on average. Our result settles open\nquestions of Haramaty and Shpilka [STOC 2010], and of Lovett [Discrete Anal.,\n2019] for 3-tensors.\n"}
+{"abstract": "  The field of Energy System Analysis (ESA) has experienced exponential growth\nin the number of publications since at least the year 2000. This paper presents\na comprehensive bibliometric analysis on ESA by employing different algorithms\nin Matlab and R. The focus of results is on quantitative indicators relating to\nnumber and type of publication outputs, collaboration links between\ninstitutions, authors and countries, and dynamic trends within the field. The\nfive and twelve most productive countries have 50% and 80% of ESA publications\nrespectively. The dominant institutions are even more concentrated within a\nsmall number of countries. A significant concentration of published papers\nwithin countries and institutions was also confirmed by analysing collaboration\nnetworks. These show dominant collaboration within the same university or at\nleast the same country. There is also is a strong link among the most\nsuccessful journals, authors and institutions. The Energy journal has had the\nmost publications in the field, and its editor-in-chief Lund H is the author\nwith most of the publications in the field, as well as the author with most of\nthe highly cited publications in the field. In terms of the dynamics within the\nfield in the past decade, recent years have seen a higher impact of topics\nrelated to flexibility and hybrid/integrated energy systems alongside a decline\nin individual technologies. This paper provides a holistic overview of two\ndecades' research output and enables interested readers to obtain a\ncomprehensive overview of the key trends in this active field.\n"}
+{"abstract": "  Recently room temperature superconductivity with Tc=15 degrees Celsius has\nbeen discovered in a pressurized complex ternary hydride, CSHx, which is a\ncarbon doped H3S alloy. The nanoscale structure of H3S is a particular\nrealization of the 1993 patent claim of superlattice of quantum wires for room\ntemperature superconductors where the maximum Tc occurs at the top of a\nsuperconducting dome. Here we focus on the electronic structure of materials\nshowing nanoscale heterostructures at atomic limit made of a superlattice of\nquantum wires like hole doped cuprate perovskites, organics, A15 intermetallics\nand pressurized hydrides. We provide a perspective of the theory of room\ntemperature multigap superconductivity in heterogeneous materials tuned at a\nFano Feshbach resonance (called also shape resonance) in the superconducting\ngaps focusing on H3S where the maximum Tc occurs where the pressure tunes the\nchemical pressure near a topological Lifshitz transition. Here the\nsuperconductivity dome of Tc versus pressure is driven by both electron-phonon\ncoupling and contact exchange interaction. We show that the Tc amplification up\nto room temperature is driven by the Fano Feshbach resonance between a\nsuperconducting gap in the anti-adiabatic regime and other gaps in the\nadiabatic regime. In these cases the Tc amplification via contact exchange\ninteraction is the missing term in conventional multiband BCS and anisotropic\nMigdal-Eliashberg theories including only Cooper pairing\n"}
+{"abstract": "  This paper is concerned with a reaction--diffusion system modeling the\nfixation and the invasion in a population of a gene drive (an allele biasing\ninheritance, increasing its own transmission to offspring). In our model, the\ngene drive has a negative effect on the fitness of individuals carrying it, and\nis therefore susceptible of decreasing the total carrying capacity of the\npopulation locally in space. This tends to generate an opposing demographic\nadvection that the gene drive has to overcome in order to invade. While\nprevious reaction--diffusion models neglected this aspect, here we focus on it\nand try to predict the sign of the traveling wave speed. It turns out to be an\nanalytical challenge, only partial results being within reach, and we complete\nour theoretical analysis by numerical simulations. Our results indicate that\ntaking into account the interplay between population dynamics and population\ngenetics might actually be crucial, as it can effectively reverse the direction\nof the invasion and lead to failure. Our findings can be extended to other\nbistable systems, such as the spread of cytoplasmic incompatibilities caused by\nWolbachia.\n"}
+{"abstract": "  We present a new model of neural networks called Min-Max-Plus Neural Networks\n(MMP-NNs) based on operations in tropical arithmetic. In general, an MMP-NN is\ncomposed of three types of alternately stacked layers, namely linear layers,\nmin-plus layers and max-plus layers. Specifically, the latter two types of\nlayers constitute the nonlinear part of the network which is trainable and more\nsophisticated compared to the nonlinear part of conventional neural networks.\nIn addition, we show that with higher capability of nonlinearity expression,\nMMP-NNs are universal approximators of continuous functions, even when the\nnumber of multiplication operations is tremendously reduced (possibly to none\nin certain extreme cases). Furthermore, we formulate the backpropagation\nalgorithm in the training process of MMP-NNs and introduce an algorithm of\nnormalization to improve the rate of convergence in training.\n"}
+{"abstract": "  In this paper we study the general minimization vector problem (P),\nconcerning a perturbation mapping, defined in locally convex Hausdorff\ntopological vector spaces where the \"WInf\" stands for the weak infimum with\nrespect to an ordering generated by a convex cone $K$. Several representations\nof the epigraph of the conjugate mapping of the perturbation mapping are\nestablished. From these, variants vector Farkas lemmas are then proved. Armed\nwith these basic tools, the {\\it dual} and the so-called {\\it loose dual\nproblem} of (P) are defined, and then stable strong duality results between\nthese pairs of primal-dual problems are established. The results just obtained\nare then applied to a general class (CCCV) of composed vector optimization\nproblems with cone-constrained. For this classes of problems, four perturbation\nmappings are suggested. Each of these mappings yields several forms of vector\nFarkas lemmas and two forms of dual problems for (CCVP). Concretely, one of the\nsuggested perturbation mapping give rises to well-known {\\it Lagrange} and {\\it\nloose Lagrange dual problems} for (CCVP) while each of the three others, yields\ntwo kinds of Fenchel-Lagrange dual problems for (CCVP). Stable strong duality\nfor these pairs of primal-dual problems are proved. Several special cases of\n(CCVP) are also considered at the end of the paper, including: vector composite\nproblems (without constraints), cone-constrained vector problems, and scalar\ncomposed problems. The results obtained in this papers when specified to the\ntwo concrete mentioned vector problems go some Lagrange duality results\nappeared recently, and also lead to new results on stable strong\nFenchel-Lagrange duality results, which, to the best knowledge of the authors,\nappear for the first time in the literature.\n"}
+{"abstract": "  We prove the existence of elements of infinite order in the homotopy groups\nof the spaces $\\mathcal{R}_{Ric>0}(M)$ and $\\mathcal{R}_{sec>0}(M)$ of positive\nRicci and positive sectional curvature, provided that $M$ is high-dimensional\nand Spin, admits such a metric and has a non-vanishing rational Pontryagin\nclass.\n"}
+{"abstract": "  In this paper we design efficient quadrature rules for finite element\ndiscretizations of nonlocal diffusion problems with compactly supported kernel\nfunctions. Two of the main challenges in nonlocal modeling and simulations are\nthe prohibitive computational cost and the nontrivial implementation of\ndiscretization schemes, especially in three-dimensional settings. In this work\nwe circumvent both challenges by introducing a parametrized mollifying function\nthat improves the regularity of the integrand, utilizing an adaptive\nintegration technique, and exploiting parallelization. We first show that the\n\"mollified\" solution converges to the exact one as the mollifying parameter\nvanishes, then we illustrate the consistency and accuracy of the proposed\nmethod on several two- and three-dimensional test cases. Furthermore, we\ndemonstrate the good scaling properties of the parallel implementation of the\nadaptive algorithm and we compare the proposed method with recently developed\ntechniques for efficient finite element assembly.\n"}
+{"abstract": "  Localization is one of the most fundamental interference phenomena caused by\nrandomness, and its universal aspects have been extensively explored from the\nperspective of one-parameter scaling mainly for static properties. We\nnumerically study dynamics of fermions on disordered onedimensional potentials\nexhibiting localization and find dynamical one-parameter scaling for surface\nroughness, which represents particle-number fluctuations at a given\nlengthscale, and for entanglement entropy when the system is in delocalized\nphases. This dynamical scaling corresponds to the Family-Vicsek scaling\noriginally developed in classical surface growth, and the associated scaling\nexponents depend on the type of disorder. Notably, we find that partially\nlocalized states in the delocalized phase of the random-dimer model lead to\nanomalous scaling, where destructive interference unique to quantum systems\nleads to exponents unknown for classical systems and clean systems.\n"}
+{"abstract": "  We reemphasize that the ratio $R_{s\\mu} \\equiv\n\\overline{\\mathcal{B}}(B_s\\to\\mu\\bar\\mu)/\\Delta M_s$ is a measure of the\ntension of the Standard Model (SM) with latest measurements of\n$\\overline{\\mathcal{B}}(B_s\\to\\mu\\bar\\mu)$ that does not suffer from the\npersistent puzzle on the $|V_{cb}|$ determinations from inclusive versus\nexclusive $b\\to c\\ell\\bar\\nu$ decays and which affects the value of the CKM\nelement $|V_{ts}|$ that is crucial for the SM predictions of both\n$\\overline{\\mathcal{B}}(B_s\\to\\mu\\bar\\mu)$ and $\\Delta M_s$, but cancels out in\nthe ratio $R_{s\\mu}$. In our analysis we include higher order electroweak and\nQED corrections und adapt the latest hadronic input to find a tension of about\n$2\\sigma$ for $R_{s\\mu}$ measurements with the SM independently of $|V_{ts}|$.\nWe also discuss the ratio $R_{d\\mu}$ which could turn out, in particular in\ncorrelation with $R_{s\\mu}$, to be useful for the search for New Physics, when\nthe data on both ratios improves. Also $R_{d\\mu}$ is independent of $|V_{cb}|$\nor more precisely $|V_{td}|$.\n"}
+{"abstract": "  We prove a local version of the noncollapsing estimate for mean curvature\nflow. By combining our result with earlier work of X.-J. Wang, it follows that\ncertain ancient convex solutions that sweep out the entire space are\nnoncollapsed.\n"}
+{"abstract": "  The optical spectra of vertically stacked MoSe$_2$/WSe$_2$ heterostructures\ncontain additional 'interlayer' excitonic peaks that are absent in the\nindividual monolayer materials and exhibit a significant spatial charge\nseparation in out-of-plane direction. Extending on a previous study, we used a\nmany-body perturbation theory approach to simulate and analyse the excitonic\nspectra of MoSe$_2$/WSe$_2$ heterobilayers with three stacking orders,\nconsidering both momentum-direct and momentum-indirect excitons. We find that\nthe small oscillator strengths and corresponding optical responses of the\ninterlayer excitons are significantly stacking-dependent and give rise to high\nradiative lifetimes in the range of 5-200\\,ns (at T=4\\,K) for the 'bright'\ninterlayer excitons. Solving the finite-momentum Bethe-Salpeter Equation, we\npredict that the lowest-energy excitation should be an indirect exciton over\nthe fundamental indirect band gap (K$\\rightarrow$Q), with a binding energy of\n220\\,meV. However, in agreement with recent magneto-optics experiments and\nprevious theoretical studies, our simulations of the effective excitonic\nLand\\'e g-factors suggest that the low-energy momentum-indirect excitons are\nnot experimentally observed for MoSe$_2$/WSe$_2$ heterostructures. We further\nreveal the existence of 'interlayer' C excitons with significant exciton\nbinding energies and optical oscillator strengths, which are analogous to the\nprominent band nesting excitons in mono- and few-layer transition-metal\ndichalcogenides.\n"}
+{"abstract": "  Strong magnetic fields have a large impact on the dynamics of molecules. In\naddition to the changes of the electronic structure, the nuclei are exposed to\nthe Lorentz force with the magnetic field being screened by the electrons. In\nthis work, we explore these effects using ab-initio molecular dynamics\nsimulations based on an effective Hamiltonian calculated at the Hartree-Fock\nlevel of theory. To correctly include these non-conservative forces in the\ndynamics, we have designed a series of novel propagators that show both good\nefficiency and stability in test cases. As a first application, we analyze\nsimulations of He and H$_2$ at two field strengths characteristic of magnetic\nwhite dwarfs (0.1 $B_0 = 2.35 \\times 10^4$ T and $B_0 = 2.35 \\times 10^5$ T).\nWhile the He simulations clearly demonstrate the importance of electron\nscreening of the Lorentz force in the dynamics, the extracted rovibrational\nspectra of H$_2$ reveal a number of fascinating features not observed in the\nfield-free case: couplings of rotations/vibrations with the cyclotron rotation,\novertones with unusual selection rules, and hindered rotations that transmute\ninto librations with increasing field strength. We conclude that our presented\nframework is a powerful tool to investigate molecules in these extreme\nenvironments.\n"}
+{"abstract": "  The inverse spectral problem for the second-order differential pencil with\nquadratic dependence on the spectral parameter is studied. We obtain sufficient\nconditions for the global solvability of the inverse problem, prove its local\nsolvability and stability. The problem is considered in the general case of\ncomplex-valued pencil coefficients and arbitrary eigenvalue multiplicities.\nStudying local solvability and stability, we take the possible splitting of\nmultiple eigenvalues under a small perturbation of the spectrum into account.\nOur approach is constructive. It is based on the reduction of the nonlinear\ninverse problem to a linear equation in the Banach space of infinite sequences.\nThe theoretical results are illustrated by numerical examples.\n"}
+{"abstract": "  Sgr B1 is a luminous H II region in the Galactic Center immediately next to\nthe massive star-forming giant molecular cloud Sgr B2 and apparently connected\nto it from their similar radial velocities. In 2018 we showed from SOFIA\nFIFI-LS observations of the [O III] 52 and 88 micron lines that there is no\ncentral exciting star cluster and that the ionizing stars must be widely spread\nthroughout the region. Here we present SOFIA FIFI-LS observations of the [O I]\n146 and [C II] 158 micron lines formed in the surrounding photodissociation\nregions (PDRs). We find that these lines correlate neither with each other nor\nwith the [O III] lines although together they correlate better with the 70\nmicron Herschel PACS images from Hi-GAL. We infer from this that Sgr B1\nconsists of a number of smaller H II regions plus their associated PDRs, some\nseen face-on and the others seen more or less edge-on. We used the PDR Toolbox\nto estimate densities and the far-ultraviolet intensities exciting the PDRs.\nUsing models computed with Cloudy, we demonstrate possible appearances of\nedge-on PDRs and show that the density difference between the PDR densities and\nthe electron densities estimated from the [O III] line ratios is incompatible\nwith pressure equilibrium unless there is a substantial pressure contribution\nfrom either turbulence or magnetic field or both. We likewise conclude that the\nhot stars exciting Sgr B1 are widely spaced throughout the region at\nsubstantial distances from the gas with no evidence of current massive star\nformation.\n"}
+{"abstract": "  Abortion is one of the biggest causes of maternal deaths, accounting for 15%\nof maternal deaths in Southeast Asia. The increase in and effectiveness of\nusing contraception are still considered to be the effective method to reduce\nabortion rate. Data pertaining to abortion incidence and effective efforts to\nreduce abortion rate in Indonesia is limited and difficult to access. Meanwhile\nsuch supporting information is necessary to enable the planning and evaluation\nof abortion control programs. This paper exemplifies the use of a mathematical\nmodel to explain an abortion decline scenario. The model employs determinants\nproposed by Bongaarts, which include average reproductive period, contraceptive\nprevalence and effectiveness, total fertility rate (TFR), and intended total\nfertility rate (ITFR), as well as birth and abortion intervals. The data used\nis from the 1991-2007 Indonesian Demography and Health Survey (Survei Demografi\ndan Kesehatan Indonesia/SDKI), and the unit of analysis is women who had been\nmarried and aged 15-49 years old. Based on the current contraceptive prevalence\nlevel in Indonesia at 59-61%, the estimated total abortion rate is 1.9-2.2.\nBased on the plot of this total abortion rate, an abortion decline scenario can\nbe estimated. At the current TFR level of 2.6, the required contraceptive\nprevalence is 69% (9% increase) for a decrease of one abortion case per woman.\nWith a delay of one year in the age of the first marriage and a birth interval\nof three years, it is estimated that the abortion rate will decline from 3.05\nto 0.69 case per woman throughout her reproductive period. Based on the\nassumption of contraceptive prevalence growth at 1-1.4%, it can be estimated\nthat abortion rate will reach nearly 0 between 2018 and 2022.\n"}
+{"abstract": "  Topological Spatial Model Checking is a recent paradigm that combines Model\nChecking with the topological interpretation of Modal Logic. The Spatial Logic\nof Closure Spaces, SLCS, extends Modal Logic with reachability connectives\nthat, in turn, can be used for expressing interesting spatial properties, such\nas \"being near to\" or \"being surrounded by\". SLCS constitutes the kernel of a\nsolid logical framework for reasoning about discrete space, such as graphs and\ndigital images, interpreted as quasi discrete closure spaces. In particular,\nthe spatial model checker VoxLogicA, that uses an extended version of SLCS, has\nbeen used successfully in the domain of medical imaging. However, SLCS is not\nrestricted to discrete space. Following a recently developed geometric\nsemantics of Modal Logic, we show that it is possible to assign an\ninterpretation to SLCS in continuous space, admitting a model checking\nprocedure, by resorting to models based on polyhedra. In medical imaging such\nrepresentations of space are increasingly relevant, due to recent developments\nof 3D scanning and visualisation techniques that exploit mesh processing. We\ndemonstrate feasibility of our approach via a new tool, PolyLogicA, aimed at\nefficient verification of SLCS formulas on polyhedra, while inheriting some\nwell-established optimization techniques already adopted in VoxLogicA. Finally,\nwe cater for a geometric definition of bisimilarity, proving that it\ncharacterises logical equivalence.\n"}
+{"abstract": "  We show, in detail, that the only non-trivial black hole (BH) solutions for a\nneutral as well as a charged spherically symmetric space-times, using the class\n${\\textit F(R)}={\\textit R}\\pm{\\textit F_1 (R)} $, must-have metric potentials\nin the form $h(r)=\\frac{1}{2}-\\frac{2M}{r}$ and\n$h(r)=\\frac{1}{2}-\\frac{2M}{r}+\\frac{q^2}{r^2}$. These BHs have a non-trivial\nform of Ricci scalar, i.e., $R=\\frac{1}{r^2}$ and the form of ${\\textit F_1\n(R)}=\\mp\\frac{\\sqrt{\\textit R}} {3M} $. We repeat the same procedure for\n(Anti-)de Sitter, (A)dS, space-time and got the metric potentials of neutral as\nwell as charged in the form $h(r)=\\frac{1}{2}-\\frac{2M}{r}-\\frac{2\\Lambda r^2}\n{3} $ and $h(r)=\\frac{1}{2}-\\frac{2M}{r}+\\frac{q^2}{r^2}-\\frac{2\\Lambda r^2}\n{3} $, respectively. The Ricci scalar of the (A)dS space-times has the form\n${\\textit R}=\\frac{1+8r^2\\Lambda}{r^2}$ and the form of ${\\textit\nF_1(R)}=\\mp\\frac{\\textit 2\\sqrt{R-8\\Lambda}}{3M}$. We calculate the\nthermodynamical quantities, Hawking temperature, entropy, quasi-local energy,\nand Gibbs-free energy for all the derived BHs, that behaves asymptotically as\nflat and (A)dS, and show that they give acceptable physical thermodynamical\nquantities consistent with the literature. Finally, we prove the validity of\nthe first law of thermodynamics for those BHs.\n"}
+{"abstract": "  The energy demand is growing daily at an accelerated pace due to the\ninternationalization and development of civilization. Yet proper economic\nutilization of additional energy generated by the Islanded Hybrid Microgrid\nSystem (IHMS) that was not consumed by the load is a major global challenge. To\nresolve the above-stated summons, this research focuses on a multi-optimal\ncombination of IHMS for the Penang Hill Resort located on Penang Island,\nMalaysia, with effective use of redundant energy. To avail this excess energy\nefficiently, an electrical heater along with a storage tank has been designed\nconcerning diversion load having proper energy management. Furthermore, the\nsystem design has adopted the HOMER Pro software for profitable and practical\nanalysis. Alongside, MATLAB Simulink had stabilized the whole system by\nrepresenting the values of 2068 and 19,072 kW that have been determined as the\napproximated peak and average load per day for the resort. Moreover, the\noptimized IHMS is comprehended of Photovoltaic (PV) cells, Diesel Generator,\nWind Turbine, Battery, and Converter. Adjacent to this, the optimized system\nensued in having a Net Present Cost (NPC) of $21.66 million, Renewable Fraction\n(RF) of 27.8%, Cost of Energy (COE) of $0.165/kWh, CO2 of 1,735,836 kg/year,\nand excess energy of 517.29MWh per annum. Since the diesel generator lead\nsystem was included in the scheme, a COE of $0.217/kWh, CO2 of 5,124,879\nkg/year, and NPC of $23.25 million were attained. The amount of excess energy\nis effectively utilized with an electrical heater as a diversion load.\n"}
+{"abstract": "  We analyze a series of trials that randomly assigned Wikipedia users in\nGermany to different web banners soliciting donations. The trials varied\nframing or content of social information about how many other users are\ndonating. Framing a given number of donors in a negative way increased donation\nrates. Variations in the communicated social information had no detectable\neffects. The findings are consistent with the results from a survey experiment.\nIn line with donations being strategic substitutes, the survey documents that\nthe negative framing lowers beliefs about others' donations. Varying the social\ninformation, in contrast, is ineffective in changing average beliefs.\n"}
+{"abstract": "  The $\\Xi N$ interaction is investigated in the quark mean-field (QMF) model\nbased on recent observables of the $\\Xi^-+^{14}\\rm{N}$ ($_{\\Xi^-}^{15}\\rm{C}$)\nsystem. The experimental data about the binding energy of $1p$-state $\\Xi^-$\nhyperon in $_{\\Xi^-}^{15}\\rm{C}$ hypernuclei at KISO, IBUKI, E07-T011,\nE176-14-03-35 events are conflated as $B_{\\Xi^-}(1p)=1.14\\pm0.11$ MeV. With\nthis constraint, the coupling strengths between the vector meson and $\\Xi$\nhyperon are fixed in three QMF parameter sets. Meanwhile, the $\\Xi^-$ binding\nenergy of $1s$ state in $_{\\Xi^-}^{15}\\rm{C}$ is predicted as\n$B_{\\Xi^-}(1s)=5.66\\pm0.38$ MeV with the same interactions, which are\ncompletely consistent with the data from the KINKA and IRRAWADDY events.\nFinally, the single $\\Xi N$ potential is calculated in the symmetric nuclear\nmatter in the framework of QMF models. It is $U_{\\Xi N}=-11.96\\pm 0.85$ MeV at\nnuclear saturation density, which will contribute to the study on the\nstrangeness degree of freedom in compact star.\n"}
+{"abstract": "  Consider the Vlasov--Poisson--Landau system with Coulomb potential in the\nweakly collisional regime on a $3$-torus, i.e. $$\\begin{aligned} \\partial_t\nF(t,x,v) + v_i \\partial_{x_i} F(t,x,v) + E_i(t,x) \\partial_{v_i} F(t,x,v) = \\nu\nQ(F,F)(t,x,v),\\\\ E(t,x) = \\nabla \\Delta^{-1} (\\int_{\\mathbb R^3} F(t,x,v)\\,\n\\mathrm{d} v - \\frac{1}{(2\\pi)^3}\\int_{\\mathbb T^3} \\int_{\\mathbb R^3}\nF(t,x,v)\\, \\mathrm{d} v \\, \\mathrm{d} x), \\end{aligned}$$ with $\\nu\\ll 1$. We\nprove that for $\\epsilon>0$ sufficiently small (but independent of $\\nu$),\ninitial data which are $O(\\epsilon \\nu^{1/3})$-Sobolev space perturbations from\nthe global Maxwellians lead to global-in-time solutions which converge to the\nglobal Maxwellians as $t\\to \\infty$. The solutions exhibit uniform-in-$\\nu$\nLandau damping and enhanced dissipation.\n  Our main result is analogous to an earlier result of Bedrossian for the\nVlasov--Poisson--Fokker--Planck equation with the same threshold. However,\nunlike in the Fokker--Planck case, the linear operator cannot be inverted\nexplicitly due to the complexity of the Landau collision operator. For this\nreason, we develop an energy-based framework, which combines Guo's weighted\nenergy method with the hypocoercive energy method and the commuting vector\nfield method. The proof also relies on pointwise resolvent estimates for the\nlinearized density equation.\n"}
+{"abstract": "  A new class of sensing paradigm known as lab-onskin where stretchable and\nflexible smart sensor devices are integrated into the skin, provides direct\nmonitoring and diagnostic interfaces to the body. Distributed lab-on-skin\nwireless sensors have the ability to provide continuous long term assessment of\nthe skin health. This paper proposes a distributed skin health monitoring\nsystem using a wireless body area network. The system is responsive to the\ndynamic changes in the skin health, and remotely reports on the same. The\nproposed algorithm detects the abnormal skin and creates an energy efficient\ndata aggregation tree covering the affected area while putting the unnecessary\nsensors to sleep mode. The algorithm responds to the changing conditions of the\nskin by dynamically adapting the size and shape of the monitoring trees to that\nof the abnormal skin areas thus providing a comprehensive monitoring.\nSimulation results demonstrate the application and utility of the proposed\nalgorithm for changing wound shapes and sizes.\n"}
+{"abstract": "  Astrophysical time series often contain periodic signals. The large and\ngrowing volume of time series data from photometric surveys demands\ncomputationally efficient methods for detecting and characterizing such\nsignals. The most efficient algorithms available for this purpose are those\nthat exploit the $\\mathcal{O}(N\\log N)$ scaling of the Fast Fourier Transform\n(FFT). However, these methods are not optimal for non-sinusoidal signal shapes.\nTemplate fits (or periodic matched filters) optimize sensitivity for a priori\nknown signal shapes but at a significant computational cost. Current\nimplementations of template periodograms scale as $\\mathcal{O}(N_f N_{obs})$,\nwhere $N_f$ is the number of trial frequencies and $N_{obs}$ is the number of\nlightcurve observations, and due to non-convexity, they do not guarantee the\nbest fit at each trial frequency, which can lead to spurious results. In this\nwork, we present a non-linear extension of the Lomb-Scargle periodogram to\nobtain a template-fitting algorithm that is both accurate (globally optimal\nsolutions are obtained except in pathological cases) and computationally\nefficient (scaling as $\\mathcal{O}(N_f\\log N_f)$ for a given template). The\nnon-linear optimization of the template fit at each frequency is recast as a\npolynomial zero-finding problem, where the coefficients of the polynomial can\nbe computed efficiently with the non-equispaced fast Fourier transform. We show\nthat our method, which uses truncated Fourier series to approximate templates,\nis an order of magnitude faster than existing algorithms for small problems\n($N\\lesssim 10$ observations) and 2 orders of magnitude faster for long\nbase-line time series with $N_{obs} \\gtrsim 10^4$ observations. An open-source\nimplementation of the fast template periodogram is available at\nhttps://www.github.com/PrincetonUniversity/FastTemplatePeriodogram.\n"}
+{"abstract": "  Near the end of the 16th century Wilhelm IV, Landgraf von Hessen-Kassel, set\nup an observatory with the main goal to increase the accuracy of stellar\npositions primarily for use in astrology and for calendar purposes. A new star\ncatalogue was compiled from measurements of altitudes and angles between stars\nand a print ready version was prepared listing measurements as well as\nequatorial and ecliptic coordinates of stellar positions. Unfortunately, this\ncatalogue appeared in print not before 1666, long after the dissemination of\nBrahe's catalogue. With the data given in the manuscript we are able to analyze\nthe accuracy of measurements and computations. The measurements and the\ncomputations are very accurate, thanks to the instrument maker and\nmathematician Jost B\\\"urgi. The star catalogue is more accurate by a factor two\nthan the later catalogue of Tycho Brahe.\n"}
+{"abstract": "  With the rapid growth of data, how to extract effective information from data\nis one of the most fundamental problems. In this paper, based on Tikhonov\nregularization, we propose an effective method for reconstructing the function\nand its derivative from scattered data with random noise. Since the noise level\nis not assumed small, we will use the amount of data for reducing the random\nerror, and use a relatively small number of knots for interpolation. An\nindicator function for our algorithm is constructed. It indicates where the\nnumerical results are good or may not be good. The corresponding error\nestimates are obtained. We show how to choose the number of interpolation knots\nin the reconstruction process for balancing the random errors and interpolation\nerrors. Numerical examples show the effectiveness and rapidity of our method.\nIt should be remarked that the algorithm in this paper can be used for on-line\ndata.\n"}
+{"abstract": "  In the context of the longitudinally boost-invariant Bjorken flow with\ntransverse expansion, we use three different numerical methods to analyze the\nemergence of attractor solutions in an ideal gas of massless particles\nexhibiting constant shear viscosity to entropy density ratio $\\eta / s$. The\nfluid energy density is initialized using a Gaussian profile in the transverse\nplane, while the ratio $\\chi = \\mathcal{P}_L / \\mathcal{P}_T$ between the\nlongitudinal and transverse pressures is set at initial time $\\tau_0$ to a\nconstant value $\\chi_0$ throughout the system employing the\nRomatschke-Strickland distribution. We introduce the hydrodynamization time\n$\\delta \\tau_H = (\\tau_H - \\tau_0)/ \\tau_0$ based on the time $\\tau_H$ when the\nstandard deviation $\\sigma(\\chi)$ of a family of solutions with different\n$\\chi_0$ reaches a minimum value at the point of maximum convergence of the\nsolutions. In the $0+1{\\rm D}$ setup, $\\delta \\tau_H$ exhibits scale\ninvariance, being a function only of $(\\eta / s) / (\\tau_0 T_0)$. With\ntransverse expansion, we find a similar $\\delta \\tau_H$ computed with respect\nto the local initial temperature, $T_0(r)$. We highlight the transition between\nthe regimes where the longitudinal and transverse expansions dominate. We find\nthat the hydrodynamization time required for the attractor solution to be\nreached increases with the distance from the origin, as expected based on the\nproperties of the $0+1{\\rm D}$ system defined by the local initial conditions.\nWe argue that hydrodynamization is predominantly the effect of the longitudinal\nexpansion, being significantly influenced by the transverse dynamics only for\nsmall systems or for large values of $\\eta / s$.\n"}
+{"abstract": "  Continuum kinetic theories provide an important tool for the analysis and\nsimulation of particle suspensions. When those particles are anisotropic, the\naddition of a particle orientation vector to the kinetic description yields a\n$2d-1$ dimensional theory which becomes intractable to simulate, especially in\nthree dimensions or near states where the particles are highly aligned.\nCoarse-grained theories that track only moments of the particle distribution\nfunctions provide a more efficient simulation framework, but require closure\nassumptions. For the particular case where the particles are apolar, the\nBingham closure has been found to agree well with the underlying kinetic\ntheory; yet the closure is non-trivial to compute, requiring the solution of an\noften nearly-singular nonlinear equation at every spatial discretization point\nat every timestep. In this paper, we present a robust, accurate, and efficient\nnumerical scheme for evaluating the Bingham closure, with a controllable\nerror/efficiency tradeoff. To demonstrate the utility of the method, we carry\nout high-resolution simulations of a coarse-grained continuum model for a\nsuspension of active particles in parameter regimes inaccessible to kinetic\ntheories. Analysis of these simulations reveals that inaccurately computing the\nclosure can act to effectively limit spatial resolution in the coarse-grained\nfields. Pushing these simulations to the high spatial resolutions enabled by\nour method reveals a coupling between vorticity and topological defects in the\nsuspension director field, as well as signatures of energy transfer between\nscales in this active fluid model.\n"}
+{"abstract": "  We present forecasted cosmological constraints from combined measurements of\ngalaxy cluster abundances from the Simons Observatory and galaxy clustering\nfrom a DESI-like experiment on two well-studied modified gravity models, the\nchameleon-screened $f(R)$ Hu-Sawicki model and the nDGP braneworld Vainshtein\nmodel.\n  A Fisher analysis is conducted using $\\sigma_8$ constraints derived from\nthermal Sunyaev-Zel'dovich (tSZ) selected galaxy clusters, as well as linear\nand mildly non-linear redshift-space 2-point galaxy correlation functions. We\nfind that the cluster abundances drive the constraints on the nDGP model while\n$f(R)$ constraints are led by galaxy clustering. The two tracers of the\ncosmological gravitational field are found to be complementary, and their\ncombination significantly improves constraints on the $f(R)$ in particular in\ncomparison to each individual tracer alone.\n  For a fiducial model of $f(R)$ with $\\text{log}_{10}(f_{R0})=-6$ and $n=1$ we\nfind combined constraints of $\\sigma(\\text{log}_{10}(f_{R0}))=0.48$ and\n$\\sigma(n)=2.3$, while for the nDGP model with $n_{\\text{nDGP}}=1$ we find\n$\\sigma(n_{\\text{nDGP}})=0.087$. Around a fiducial General Relativity (GR)\nmodel, we find a $95\\%$ confidence upper limit on $f(R)$ of\n$f_{R0}\\leq5.68\\times 10^{-7}$. Our results present the exciting potential to\nutilize upcoming galaxy and CMB survey data available in the near future to\ndiscern and/or constrain cosmic deviations from GR.\n"}
+{"abstract": "  The measurement of the epicyclic frequencies is a widely used astrophysical\ntechnique to infer information on a given self-gravitating system and on the\nrelated gravity background. We derive their explicit expressions in static and\nspherically symmetric wormhole spacetimes. We discuss how these theoretical\nresults can be applied to: (1) detect the presence of a wormhole,\ndistinguishing it by a black hole; (2) reconstruct wormhole solutions through\nthe fit of the observational data, once we have them. Finally, we discuss the\nphysical implications of our proposed epicyclic method.\n"}
+{"abstract": "  We investigate quantitative estimates in homogenization of the locally\nperiodic parabolic operator with multiscales\n  $$ \\partial_t- \\text{div} (A(x,t,x/\\varepsilon,t/\\kappa^2) \\nabla ),\\qquad\n\\varepsilon>0,\\, \\kappa>0. $$ Under proper assumptions, we establish the\nfull-scale interior and boundary Lipschitz estimates. These results are new\neven for the case $\\kappa=\\varepsilon$, and for the periodic operators $\n  \\partial_t-\\text{div}(A(x/\\varepsilon, t/\\varepsilon^{\\ell}) \\nabla ),$\n$0<\\varepsilon,\\ell<\\infty, $ of which the large-scale Lipschitz estimate down\nto $\\varepsilon+\\varepsilon^{\\ell/2}$ was recently established by the first\nauthor and Shen in Arch. Ration. Mech. Anal. 236(1): 145--188 (2020). Due to\nthe non-self-similar structure, the full-scale estimates do not follow directly\nfrom the large-scale estimates and the blow-up argument. As a byproduct, we\nalso derive the convergence rates for the corresponding initial-Dirichlet\nproblems, which extend the results in the aforementioned literature to more\ngeneral settings.\n"}
+{"abstract": "  Contact integrators are a family of geometric numerical schemes which\nguarantee the conservation of the contact structure. In this work we review the\nconstruction of both the variational and Hamiltonian versions of these methods.\nWe illustrate some of the advantages of geometric integration in the\ndissipative setting by focusing on models inspired by recent studies in\ncelestial mechanics and cosmology.\n"}
+{"abstract": "  We present a numerical implementation of the recently developed\nunconditionally convergent representation of general Heun functions as integral\nseries. We produce two codes in Python available for download, one of which is\nespecially aimed at reproducing the output of Mathematica's HeunG function. We\nshow that the present code compares favorably with Mathematica's HeunG and with\nan Octave/Matlab code of Motygin, in particular when the Heun function is to be\nevaluated at a large number of points if less accuracy is sufficient. We\nsuggest further improvements concerning the accuracy and discuss the issue of\nsingularities.\n"}
+{"abstract": "  Thin film lithium niobate (LN) has recently emerged as a playground for\nchip-scale nonlinear optics and leads to highly efficient frequency conversions\nfrom near infrared to near-visible bands. For many nonlinear and quantum\nphotonics applications, it is desirable to operate deep into the visible band\nwithin LN's transparency window. However, the strong material dispersion at\nshort wavelengths makes phase-matching difficult, necessitating sub-micron\nscale control of domain structures for efficient quasi-phase-matching (QPM).\nHere we report the operation of thin film LN in the blue wavelength and high\nfidelity poling of thin-film LN waveguide to this regime. As a result,\nquasi-phase matching is realized between IR (871nm) and blue (435.5nm)\nwavelengths in a straight waveguide and prompts strong blue light generation\nwith a conversion efficiency $2900\\pm400\\%W^{-1}cm^{-2}$\n"}
+{"abstract": "  High dimensional categorical data are routinely collected in biomedical and\nsocial sciences. It is of great importance to build interpretable parsimonious\nmodels that perform dimension reduction and uncover meaningful latent\nstructures from such discrete data. Identifiability is a fundamental\nrequirement for valid modeling and inference in such scenarios, yet is\nchallenging to address when there are complex latent structures. In this\narticle, we propose a class of identifiable multilayer (potentially deep)\ndiscrete latent structure models for discrete data, termed Bayesian pyramids.\nWe establish the identifiability of Bayesian pyramids by developing novel\ntransparent conditions on the pyramid-shaped deep latent directed graph. The\nproposed identifiability conditions can ensure Bayesian posterior consistency\nunder suitable priors. As an illustration, we consider the two-latent-layer\nmodel and propose a Bayesian shrinkage estimation approach. Simulation results\nfor this model corroborate the identifiability and estimability of model\nparameters. Applications of the methodology to DNA nucleotide sequence data\nuncover useful discrete latent features that are highly predictive of sequence\ntypes. The proposed framework provides a recipe for interpretable unsupervised\nlearning of discrete data, and can be a useful alternative to popular machine\nlearning methods.\n"}
+{"abstract": "  Neural dialogue models suffer from low-quality responses when interacted in\npractice, demonstrating difficulty in generalization beyond training data.\nRecently, knowledge distillation has been used to successfully regularize the\nstudent by transferring knowledge from the teacher. However, the teacher and\nthe student are trained on the same dataset and tend to learn similar feature\nrepresentations, whereas the most general knowledge should be found through\ndifferences. The finding of general knowledge is further hindered by the\nunidirectional distillation, as the student should obey the teacher and may\ndiscard some knowledge that is truly general but refuted by the teacher. To\nthis end, we propose a novel training framework, where the learning of general\nknowledge is more in line with the idea of reaching consensus, i.e., finding\ncommon knowledge that is beneficial to different yet all datasets through\ndiversified learning partners. Concretely, the training task is divided into a\ngroup of subtasks with the same number of students. Each student assigned to\none subtask not only is optimized on the allocated subtask but also imitates\nmulti-view feature representation aggregated from other students (i.e., student\npeers), which induces students to capture common knowledge among different\nsubtasks and alleviates the over-fitting of students on the allocated subtasks.\nTo further enhance generalization, we extend the unidirectional distillation to\nthe bidirectional distillation that encourages the student and its student\npeers to co-evolve by exchanging complementary knowledge with each other.\nEmpirical results and analysis demonstrate that our training framework\neffectively improves the model generalization without sacrificing training\nefficiency.\n"}
+{"abstract": "  Visual explanation methods have an important role in the prognosis of the\npatients where the annotated data is limited or unavailable. There have been\nseveral attempts to use gradient-based attribution methods to localize\npathology from medical scans without using segmentation labels. This research\ndirection has been impeded by the lack of robustness and reliability. These\nmethods are highly sensitive to the network parameters. In this study, we\nintroduce a robust visual explanation method to address this problem for\nmedical applications. We provide an innovative visual explanation algorithm for\ngeneral purpose and as an example application, we demonstrate its effectiveness\nfor quantifying lesions in the lungs caused by the Covid-19 with high accuracy\nand robustness without using dense segmentation labels. This approach overcomes\nthe drawbacks of commonly used Grad-CAM and its extended versions. The premise\nbehind our proposed strategy is that the information flow is minimized while\nensuring the classifier prediction stays similar. Our findings indicate that\nthe bottleneck condition provides a more stable severity estimation than the\nsimilar attribution methods.\n"}
+{"abstract": "  The cuprate superconductors are characterized by numerous ordering\ntendencies, with the nematic order being the most distinct form of order. Here\nthe intertwinement of the electronic nematicity with superconductivity in\ncuprate superconductors is studied based on the kinetic-energy-driven\nsuperconductivity. It is shown that the optimized Tc takes a dome-like shape\nwith the weak and strong strength regions on each side of the optimal strength\nof the electronic nematicity, where the optimized Tc reaches its maximum. This\ndome-like shape nematic-order strength dependence of Tc indicates that the\nelectronic nematicity enhances superconductivity. Moreover, this nematic order\ninduces the anisotropy of the electron Fermi surface (EFS), where although the\noriginal EFS with the four-fold rotation symmetry is broken up into that with a\nresidual two-fold rotation symmetry, this EFS with the two-fold rotation\nsymmetry still is truncated to form the Fermi arcs with the most spectral\nweight that locates at the tips of the Fermi arcs. Concomitantly, these tips of\nthe Fermi arcs connected by the wave vectors ${\\bf q}_{i}$ construct an octet\nscattering model, however, the partial wave vectors and their respective\nsymmetry-corresponding partners occur with unequal amplitudes, leading to these\nordered states being broken both rotation and translation symmetries. As a\nnatural consequence, the electronic structure is inequivalent between the\n$k_{x}$ and $k_{y}$ directions. These anisotropic features of the electronic\nstructure are also confirmed via the result of the autocorrelation of the\nsingle-particle excitation spectra, where the breaking of the rotation symmetry\nis verified by the inequivalence on the average of the electronic structure at\nthe two Bragg scattering sites. Furthermore, the strong energy dependence of\nthe order parameter of the electronic nematicity is also discussed.\n"}
+{"abstract": "  The unsupervised task of aligning two or more distributions in a shared\nlatent space has many applications including fair representations, batch effect\nmitigation, and unsupervised domain adaptation. Existing flow-based approaches\nestimate multiple flows independently, which is equivalent to learning multiple\nfull generative models. Other approaches require adversarial learning, which\ncan be computationally expensive and challenging to optimize. Thus, we aim to\njointly align multiple distributions while avoiding adversarial learning.\nInspired by efficient alignment algorithms from optimal transport (OT) theory\nfor univariate distributions, we develop a simple iterative method to build\ndeep and expressive flows. Our method decouples each iteration into two\nsubproblems: 1) form a variational approximation of a distribution divergence\nand 2) minimize this variational approximation via closed-form invertible\nalignment maps based on known OT results. Our empirical results give evidence\nthat this iterative algorithm achieves competitive distribution alignment at\nlow computational cost while being able to naturally handle more than two\ndistributions.\n"}
+{"abstract": "  Situations in which immediate self-interest and long-term collective interest\nconflict often require some form of influence to prevent them from leading to\nundesirable or unsustainable outcomes. Next to sanctioning, social influence\nand social structure, it is possible that strategic solutions can exist for\nthese social dilemmas. However, the existence of strategies that enable a\nplayer to exert control in the long-run outcomes can be difficult to show and\ndifferent situations allow for different levels of strategic influence. Here,\nwe investigate the effect of threshold nonlinearities on the possibilities of\nexerting unilateral control in finitely repeated n-player public goods games\nand snowdrift games. These models can describe situations in which a collective\neffort is necessary in order for a benefit to be created. We identify\nconditions in terms of a cooperator threshold for the existence of generous,\nextortionate and equalizing zero-determinant (ZD) strategies. Our results show\nthat, for both games, the thresholds prevent equalizing ZD strategies from\nexisting. In the snowdrift game, introducing a cooperator threshold has no\neffect on the region of feasible extortionate ZD strategies. For extortionate\nstrategies in the public goods game, the threshold only restricts the region of\nenforceable strategies for small values of the public goods multiplier.\nGenerous ZD strategies exist for both games, but introducing a cooperator\nthreshold forces the slope more towards the value of a fair strategy, where the\nplayer has approximately the same payoff as the average payoff of his\nopponents.\n"}
+{"abstract": "  Selfie-based biometrics has great potential for a wide range of applications\nfrom marketing to higher security environments like online banking. This is now\nespecially relevant since e.g. periocular verification is contactless, and\nthereby safe to use in pandemics such as COVID-19. However, selfie-based\nbiometrics faces some challenges since there is limited control over the data\nacquisition conditions. Therefore, super-resolution has to be used to increase\nthe quality of the captured images. Most of the state of the art\nsuper-resolution methods use deep networks with large filters, thereby needing\nto train and store a correspondingly large number of parameters, and making\ntheir use difficult for mobile devices commonly used for selfie-based.\n  In order to achieve an efficient super-resolution method, we propose an\nEfficient Single Image Super-Resolution (ESISR) algorithm, which takes into\naccount a trade-off between the efficiency of the deep neural network and the\nsize of its filters. To that end, the method implements a novel loss function\nbased on the Sharpness metric. This metric turns out to be more suitable for\nincreasing the quality of the eye images. Our method drastically reduces the\nnumber of parameters when compared with Deep CNNs with Skip Connection and\nNetwork (DCSCN): from 2,170,142 to 28,654 parameters when the image size is\nincreased by a factor of x3. Furthermore, the proposed method keeps the sharp\nquality of the images, which is highly relevant for biometric recognition\npurposes. The results on remote verification systems with raw images reached an\nEqual Error Rate (EER) of 8.7% for FaceNet and 10.05% for VGGFace. Where\nembedding vectors were used from periocular images the best results reached an\nEER of 8.9% (x3) for FaceNet and 9.90% (x4) for VGGFace.\n"}
+{"abstract": "  In this work, we use a combination of formal upscaling and data-driven\nmachine learning for explicitly closing a nonlinear transport and reaction\nprocess in a multiscale tissue. The classical effectiveness factor model is\nused to formulate the macroscale reaction kinetics. We train a multilayer\nperceptron network using training data generated by direct numerical\nsimulations over microscale examples. Once trained, the network is used for\nnumerically solving the upscaled (coarse-grained) differential equation\ndescribing mass transport and reaction in two example tissues. The network is\ndescribed as being explicit in the sense that the network is trained using\nmacroscale concentrations and gradients of concentration as components of the\nfeature space.\n  Network training and solutions to the macroscale transport equations were\ncomputed for two different tissues. The two tissue types (brain and liver)\nexhibit markedly different geometrical complexity and spatial scale (cell size\nand sample size). The upscaled solutions for the average concentration are\ncompared with numerical solutions derived from the microscale concentration\nfields by a posteriori averaging. There are two outcomes of this work of\nparticular note: 1) we find that the trained network exhibits good\ngeneralizability, and it is able to predict the effectiveness factor with high\nfidelity for realistically-structured tissues despite the significantly\ndifferent scale and geometry of the two example tissue types; and 2) the\napproach results in an upscaled PDE with an effectiveness factor that is\npredicted (implicitly) via the trained neural network. This latter result\nemphasizes our purposeful connection between conventional averaging methods\nwith the use of machine learning for closure; this contrasts with some machine\nlearning methods for upscaling where the exact form of the macroscale equation\nremains unknown.\n"}
+{"abstract": "  In the last few years, Lopez-Permouth and several collaborators have\nintroduced a new approach in the study of the classical projectivity,\ninjectivity and flatness of modules. This way, they introduced subprojectivity\ndomains of modules as a tool to measure, somehow, the projectivity level of\nsuch a module (so not just to determine whether or not the module is\nprojective). In this paper we develop a new treatment of the subprojectivity in\nany abelian category which shed more light on some of its various important\naspects. Namely, in terms of subprojectivity, some classical results are\nunified and some classical rings are characterized. It is also shown that, in\nsome categories, the subprojectivity measures notions other than the\nprojectivity. Furthermore, this new approach allows, in addition to\nestablishing nice generalizations of known results, to construct various new\nexamples such as the subprojectivity domain of the class of Gorenstein\nprojective objects, the class of semi-projective complexes and particular types\nof representations of a finite linear quiver. The paper ends with a study\nshowing that the fact that a subprojectivity domain of a class coincides with\nits first right Ext-orthogonal class can be characterized in terms of the\nexistence of preenvelopes and precovers.\n"}
+{"abstract": "  Clustering is an unsupervised learning technique that is useful when working\nwith a large volume of unlabeled data. Complex dynamical systems in real life\noften entail data streaming from a large number of sources. Although it is\ndesirable to use all source variables to form accurate state estimates, it is\noften impractical due to large computational power requirements, and\nsufficiently robust algorithms to handle these cases are not common. We propose\na hierarchical time series clustering technique based on symbolic dynamic\nfiltering and Granger causality, which serves as a dimensionality reduction and\nnoise-rejection tool. Our process forms a hierarchy of variables in the\nmultivariate time series with clustering of relevant variables at each level,\nthus separating out noise and less relevant variables. A new distance metric\nbased on Granger causality is proposed and used for the time series clustering,\nas well as validated on empirical data sets. Experimental results from\noccupancy detection and building temperature estimation tasks show fidelity to\nthe empirical data sets while maintaining state-prediction accuracy with\nsubstantially reduced data dimensionality.\n"}
+{"abstract": "  With the advancement of IoT and artificial intelligence technologies, and the\nneed for rapid application growth in fields such as security entrance control\nand financial business trade, facial information processing has become an\nimportant means for achieving identity authentication and information security.\nIn this paper, we propose a multi-feature fusion algorithm based on integral\nhistograms and a real-time update tracking particle filtering module. First,\nedge and colour features are extracted, weighting methods are used to weight\nthe colour histogram and edge features to describe facial features, and fusion\nof colour and edge features is made adaptive by using fusion coefficients to\nimprove face tracking reliability. Then, the integral histogram is integrated\ninto the particle filtering algorithm to simplify the calculation steps of\ncomplex particles. Finally, the tracking window size is adjusted in real time\naccording to the change in the average distance from the particle centre to the\nedge of the current model and the initial model to reduce the drift problem and\nachieve stable tracking with significant changes in the target dimension. The\nresults show that the algorithm improves video tracking accuracy, simplifies\nparticle operation complexity, improves the speed, and has good\nanti-interference ability and robustness.\n"}
+{"abstract": "  In this article, we present the integral representations of the power series\ndiagonals. Such representations are obtained by lowering the integration\nmultiplicity for the previously known integral representation. The procedure is\ncarried out within the framework of Leray's residue theory. The concept of the\namoeba of the complex analytical hypersurface plays an essential role in the\nconstruction of new integral representations.\n"}
+{"abstract": "  This paper describes an adaptive method in continuous time for the estimation\nof external fields by a team of $N$ agents. The agents $i$ each explore\nsubdomains $\\Omega^i$ of a bounded subset of interest $\\Omega\\subset X :=\n\\mathbb{R}^d$. Ideal adaptive estimates $\\hat{g}^i_t$ are derived for each\nagent from a distributed parameter system (DPS) that takes values in the\nscalar-valued reproducing kernel Hilbert space $H_X$ of functions over $X$.\nApproximations of the evolution of the ideal local estimate $\\hat{g}^i_t$ of\nagent $i$ is constructed solely using observations made by agent $i$ on a fine\ntime scale. Since the local estimates on the fine time scale are constructed\nindependently for each agent, we say that the method is strictly decentralized.\nOn a coarse time scale, the individual local estimates $\\hat{g}^i_t$ are fused\nvia the expression $\\hat{g}_t:=\\sum_{i=1}^N\\Psi^i \\hat{g}^i_t$ that uses a\npartition of unity $\\{\\Psi^i\\}_{1\\leq i\\leq N}$ subordinate to the cover\n$\\{\\Omega^i\\}_{i=1,\\ldots,N}$ of $\\Omega$. Realizable algorithms are obtained\nby constructing finite dimensional approximations of the DPS in terms of\nscattered bases defined by each agent from samples along their trajectories.\nRates of convergence of the error in the finite dimensional approximations are\nderived in terms of the fill distance of the samples that define the scattered\ncenters in each subdomain. The qualitative performance of the convergence rates\nfor the decentralized estimation method is illustrated via numerical\nsimulations.\n"}
+{"abstract": "  Convolutional Neural Networks (CNNs) deployed in real-life applications such\nas autonomous vehicles have shown to be vulnerable to manipulation attacks,\nsuch as poisoning attacks and fine-tuning. Hence, it is essential to ensure the\nintegrity and authenticity of CNNs because compromised models can produce\nincorrect outputs and behave maliciously. In this paper, we propose a\nself-contained tamper-proofing method, called DeepiSign, to ensure the\nintegrity and authenticity of CNN models against such manipulation attacks.\nDeepiSign applies the idea of fragile invisible watermarking to securely embed\na secret and its hash value into a CNN model. To verify the integrity and\nauthenticity of the model, we retrieve the secret from the model, compute the\nhash value of the secret, and compare it with the embedded hash value. To\nminimize the effects of the embedded secret on the CNN model, we use a\nwavelet-based technique to transform weights into the frequency domain and\nembed the secret into less significant coefficients. Our theoretical analysis\nshows that DeepiSign can hide up to 1KB secret in each layer with minimal loss\nof the model's accuracy. To evaluate the security and performance of DeepiSign,\nwe performed experiments on four pre-trained models (ResNet18, VGG16, AlexNet,\nand MobileNet) using three datasets (MNIST, CIFAR-10, and Imagenet) against\nthree types of manipulation attacks (targeted input poisoning, output\npoisoning, and fine-tuning). The results demonstrate that DeepiSign is\nverifiable without degrading the classification accuracy, and robust against\nrepresentative CNN manipulation attacks.\n"}
+{"abstract": "  This article develops new closed-form variance expressions for power analyses\nfor commonly used difference-in-differences (DID) and comparative interrupted\ntime series (CITS) panel data estimators. The main contribution is to\nincorporate variation in treatment timing into the analysis. The power formulas\nalso account for other key design features that arise in practice:\nautocorrelated errors, unequal measurement intervals, and clustering due to the\nunit of treatment assignment. We consider power formulas for both\ncross-sectional and longitudinal models and allow for covariates. An\nillustrative power analysis provides guidance on appropriate sample sizes. The\nkey finding is that accounting for treatment timing increases required sample\nsizes. Further, DID estimators have considerably more power than standard CITS\nand ITS estimators. An available Shiny R dashboard performs the sample size\ncalculations for the considered estimators.\n"}
+{"abstract": "  In the graph signal processing (GSP) literature, it has been shown that\nsignal-dependent graph Laplacian regularizer (GLR) can efficiently promote\npiecewise constant (PWC) signal reconstruction for various image restoration\ntasks. However, for planar image patches, like total variation (TV), GLR may\nsuffer from the well-known \"staircase\" effect. To remedy this problem, we\ngeneralize GLR to gradient graph Laplacian regularizer (GGLR) that provably\npromotes piecewise planar (PWP) signal reconstruction for the image\ninterpolation problem -- a 2D grid with random missing pixels that requires\ncompletion. Specifically, we first construct two higher-order gradient graphs\nto connect local horizontal and vertical gradients. Each local gradient is\nestimated using structure tensor, which is robust using known pixels in a small\nneighborhood, mitigating the problem of larger noise variance when computing\ngradient of gradients. Moreover, unlike total generalized variation (TGV), GGLR\nretains the quadratic form of GLR, leading to an unconstrained quadratic\nprogramming (QP) problem per iteration that can be solved quickly using\nconjugate gradient (CG). We derive the means-square-error minimizing weight\nparameter for GGLR, trading off bias and variance of the signal estimate.\nExperiments show that GGLR outperformed competing schemes in interpolation\nquality for severely damaged images at a reduced complexity.\n"}
+{"abstract": "  In order to study the ram-pressure interaction between radio galaxies and the\nintracluster medium, we analyse a sample of 208 highly-bent narrow-angle tail\nradio sources (NATs) in clusters, detected by the LOFAR Two-metre Sky Survey.\nFor NATs within $7\\,R_{500}$ of the cluster centre, we find that their tails\nare distributed anisotropically with a strong tendency to be bent radially away\nfrom the cluster, which suggests that they are predominantly on radially\ninbound orbits. Within $0.5\\,R_{500}$, we also observe an excess of NATs with\ntheir jets bent towards the cluster core, indicating that these outbound\nsources fade away soon after passing pericentre. For the subset of NATs with\nspectroscopic redshifts, we find the radial bias in the jet angles exists even\nout to $10\\,R_{500}$, far beyond the virial radius. The presence of NATs at\nsuch large radii implies that significant deceleration of the accompanying\ninflowing intergalactic medium must be occurring there to create the ram\npressure that bends the jets, and potentially even triggers the radio source.\n"}
+{"abstract": "  We consider the problem of preprocessing two strings $S$ and $T$, of lengths\n$m$ and $n$, respectively, in order to be able to efficiently answer the\nfollowing queries: Given positions $i,j$ in $S$ and positions $a,b$ in $T$,\nreturn the optimal alignment of $S[i \\mathinner{.\\,.} j]$ and $T[a\n\\mathinner{.\\,.} b]$. Let $N=mn$. We present an oracle with preprocessing time\n$N^{1+o(1)}$ and space $N^{1+o(1)}$ that answers queries in $\\log^{2+o(1)}N$\ntime. In other words, we show that we can query the alignment of every two\nsubstrings in almost the same time it takes to compute just the alignment of\n$S$ and $T$. Our oracle uses ideas from our distance oracle for planar graphs\n[STOC 2019] and exploits the special structure of the alignment graph.\nConditioned on popular hardness conjectures, this result is optimal up to\nsubpolynomial factors. Our results apply to both edit distance and longest\ncommon subsequence (LCS).\n  The best previously known oracle with construction time and size\n$\\mathcal{O}(N)$ has slow $\\Omega(\\sqrt{N})$ query time [Sakai, TCS 2019], and\nthe one with size $N^{1+o(1)}$ and query time $\\log^{2+o(1)}N$ (using a planar\ngraph distance oracle) has slow $\\Omega(N^{3/2})$ construction time [Long &\nPettie, SODA 2021]. We improve both approaches by roughly a $\\sqrt N$ factor.\n"}
+{"abstract": "  The BINGO telescope was designed to measure the fluctuations of the 21-cm\nradiation arising from the hyperfine transition of neutral hydrogen and aims to\nmeasure the Baryon Acoustic Oscillations (BAO) from such fluctuations,\ntherefore serving as a pathfinder to future deeper intensity mapping surveys.\nThe requirements for the Phase 1 of the projects consider a large reflector\nsystem (two 40 m-class dishes in a crossed-Dragone configuration), illuminating\na focal plane with 28 horns to measure the sky with two circular polarisations\nin a drift scan mode to produce measurements of the radiation in intensity as\nwell as the circular polarisation. In this paper we present the optical design\nfor the instrument. We describe the intensity and polarisation properties of\nthe beams and the optical arrangement of the horns in the focal plane to\nproduce a homogeneous and well-sampled map after the end of Phase 1. Our\nanalysis provides an optimal model for the location of the horns in the focal\nplane, producing a homogeneous and Nyquist sampled map after the nominal survey\ntime. We arrive at an optimal configuration for the optical system, including\nthe focal plane positioning and the beam behavior of the instrument. We present\nan estimate of the expected side lobes both for intensity and polarisation, as\nwell as the effect of band averaging on the final side lobes. The cross\npolarisation leakage values for the final configuration allow us to conclude\nthat the optical arrangement meets the requirements of the project. We conclude\nthat the chosen optical design meets the requirements for the project in terms\nof polarisation purity, area coverage as well as homogeneity of coverage so\nthat BINGO can perform a successful BAO experiment. We further conclude that\nthe requirements on the placement and r.m.s. error on the mirrors are also\nachievable so that a successful experiment can be conducted.(Abridged)\n"}
+{"abstract": "  We provide a quantitative asymptotic analysis for the nonlinear\nVlasov--Poisson--Fokker--Planck system with a large linear friction force and\nhigh force-fields. The limiting system is a diffusive model with nonlocal\nvelocity fields often referred to as aggregation-diffusion equations. We show\nthat a weak solution to the Vlasov--Poisson--Fokker--Planck system strongly\nconverges to a strong solution to the diffusive model. Our proof relies on the\nmodulated macroscopic kinetic energy estimate based on the weak-strong\nuniqueness principle together with a careful analysis of the Poisson equation.\n"}
+{"abstract": "  Recent advances in unsupervised domain adaptation (UDA) show that\ntransferable prototypical learning presents a powerful means for class\nconditional alignment, which encourages the closeness of cross-domain class\ncentroids. However, the cross-domain inner-class compactness and the underlying\nfine-grained subtype structure remained largely underexplored. In this work, we\npropose to adaptively carry out the fine-grained subtype-aware alignment by\nexplicitly enforcing the class-wise separation and subtype-wise compactness\nwith intermediate pseudo labels. Our key insight is that the unlabeled subtypes\nof a class can be divergent to one another with different conditional and label\nshifts, while inheriting the local proximity within a subtype. The cases of\nwith or without the prior information on subtype numbers are investigated to\ndiscover the underlying subtype structure in an online fashion. The proposed\nsubtype-aware dynamic UDA achieves promising results on medical diagnosis\ntasks.\n"}
+{"abstract": "  Viscoelastic fluids are non-Newtonian fluids that exhibit both \"viscous\" and\n\"elastic\" characteristics in virtue of mechanisms to store energy and produce\nentropy. Usually the energy storage properties of such fluids are modelled\nusing the same concepts as in the classical theory of nonlinear solids.\nRecently new models for elastic solids have been successfully developed by\nappealing to implicit constitutive relations, and these new models offer a\ndifferent perspective to the old topic of elastic response of materials. In\nparticular, a sub-class of implicit constitutive relations, namely relations\nwherein the left Cauchy-Green tensor is expressed as a function of stress is of\ninterest. We show how to use this new perspective it the development of\nmathematical models for viscoelastic fluids, and we provide a discussion of the\nthermodynamic underpinnings of such models. We focus on the use of Gibbs free\nenergy instead of the Helmholtz free energy, and using the standard\nGiesekus/Oldroyd-B models, we show how the alternative approach works in the\ncase of well-known models. The proposed approach is straightforward to\ngeneralise to more complex setting wherein the classical approach might be\nimpractical of even inapplicable.\n"}
+{"abstract": "  Podcast episodes often contain material extraneous to the main content, such\nas advertisements, interleaved within the audio and the written descriptions.\nWe present classifiers that leverage both textual and listening patterns in\norder to detect such content in podcast descriptions and audio transcripts. We\ndemonstrate that our models are effective by evaluating them on the downstream\ntask of podcast summarization and show that we can substantively improve ROUGE\nscores and reduce the extraneous content generated in the summaries.\n"}
+{"abstract": "  All external electromagnetic fields in which the Klein-Gordon-Fock equation\nadmits the first-order symmetry operators are found, provided that in the\nspace-time $V_4$ a group of motion $G_3$ acts simply transitively on a non-null\nsubspace of transitivity $V_3$. It is shown that in the case of a Riemannian\nspace $V_n$, in which the group $G_r$ acts simply transitively, the algebra of\nsymmetry operators of the $n$-dimensional Klein-Gordon-Fock equation in an\nexternal admissible electromagnetic field coincides with the algebra of\noperators of the group $G_r$.\n"}
+{"abstract": "  Air pollution has long been a serious environmental health challenge,\nespecially in metropolitan cities, where air pollutant concentrations are\nexacerbated by the street canyon effect and high building density. Whilst\naccurately monitoring and forecasting air pollution are highly crucial,\nexisting data-driven models fail to fully address the complex interaction\nbetween air pollution and urban dynamics. Our Deep-AIR, a novel hybrid deep\nlearning framework that combines a convolutional neural network with a long\nshort-term memory network, aims to address this gap to provide fine-grained\ncity-wide air pollution estimation and station-wide forecast. Our proposed\nframework creates 1x1 convolution layers to strengthen the learning of\ncross-feature spatial interaction between air pollution and important urban\ndynamic features, particularly road density, building density/height, and\nstreet canyon effect. Using Hong Kong and Beijing as case studies, Deep-AIR\nachieves a higher accuracy than our baseline models. Our model attains an\naccuracy of 67.6%, 77.2%, and 66.1% in fine-grained hourly estimation, 1-hr,\nand 24-hr air pollution forecast for Hong Kong, and an accuracy of 65.0%,\n75.3%, and 63.5% for Beijing. Our saliency analysis has revealed that for Hong\nKong, street canyon and road density are the best estimators for NO2, while\nmeteorology is the best estimator for PM2.5.\n"}
+{"abstract": "  The counterintuitive fact that wave chaos appears in the bending spectrum of\nfree rectangular thin plates is presented. After extensive numerical\nsimulations, varying the ratio between the length of its sides, it is shown\nthat (i) frequency levels belonging to different symmetry classes cross each\nother and (ii) for levels within the same symmetry sector, only avoided\ncrossings appear. The consequence of anticrossings is studied by calculating\nthe distributions of the ratio of consecutive level spacings for each symmetry\nclass. The resulting ratio distribution disagrees with the expected Poissonian\nresult. They are then compared with some well-known transition distributions\nbetween Poisson and the Gaussian orthogonal random matrix ensemble. It is found\nthat the distribution of the ratio of consecutive level spacings agrees with\nthe prediction of the Rosenzweig-Porter model. Also, the normal-mode vibration\namplitudes are found experimentally on aluminum plates, before and after an\navoided crossing for symmetrical-symmetrical, symmetrical-antisymmetrical, and\nantisymmetrical-symmetrical classes. The measured modes show an excellent\nagreement with our numerical predictions. The expected Poissonian distribution\nis recovered for the simply supported rectangular plate.\n"}
+{"abstract": "  Dating back to Euler, in classical analysis and number theory, the Hurwitz\nzeta function $$ \\zeta(z,q)=\\sum_{n=0}^{\\infty}\\frac{1}{(n+q)^{z}}, $$ the\nRiemann zeta function $\\zeta(z)$, the generalized Stieltjes constants\n$\\gamma_k(q)$, the Euler constant $\\gamma$, Euler's gamma function $\\Gamma(q)$\nand the digamma function $\\psi(q)$ have many close connections on their\ndefinitions and properties. There are also many integrals, series or infinite\nproduct representations of them along the history.\n  In this note, we try to provide a parallel story for the alternating Hurwitz\nzeta function (also known as the Hurwitz-type Euler zeta function)\n$$\\zeta_{E}(z,q)=\\sum_{n=0}^\\infty\\frac{(-1)^{n}}{(n+q)^{z}},$$ the alternating\nzeta function $\\zeta_{E}(z)$ (also known as the Dirichlet's eta function\n$\\eta(z)$), the modified Stieltjes constants $\\tilde\\gamma_k(q)$, the modified\nEuler constant $\\tilde\\gamma_{0}$, the modified gamma function\n$\\tilde\\Gamma(q)$ and the modified digamma function $\\tilde\\psi(q)$ (also known\nas the Nielsen's $\\beta$ function). Many new integrals, series or infinite\nproduct representations of these constants and special functions have been\nfound. By the way, we also get two new series expansions of $\\pi:$\n\\begin{equation*} \\frac{\\pi^2}{12}=\\frac34-\\sum_{k=1}^\\infty(\\zeta_E(2k+2)-1)\n\\end{equation*} and \\begin{equation*} \\frac{\\pi}{2}=\n\\log2+2\\sum_{k=1}^\\infty\\frac{(-1)^k}{k!}\\tilde\\gamma_k(1)\\sum_{j=0}^kS(k,j)j!.\n\\end{equation*}\n"}
+{"abstract": "  Wearable devices hold great potential for promoting children's health and\nwell-being. However, research on kids' wearables is sparse and often focuses on\ntheir use in the context of parental surveillance. To gain insight into the\ncurrent landscape of kids' wearables, we surveyed 47 wearable devices marketed\nfor children. We collected rich data on the functionality of these devices and\nassessed how different features satisfy parents' information needs, and\nidentified opportunities for wearables to support children's needs and\ninterests. We found that many kids' wearables are technologically sophisticated\ndevices that focus on parents' ability to communicate with their children and\nkeep them safe, as well as encourage physical activity and nurture good habits.\nWe discuss how our findings could inform the design of wearables that serve as\nmore than monitoring devices, and instead support children and parents as equal\nstakeholders, providing implications for kids' agency, long-term development,\nand overall well-being. Finally, we identify future research efforts related to\ndesigning for kids' self-tracking and collaborative tracking with parents.\n"}
+{"abstract": "  Elucidating emergent regularities in intriguing crowd dynamics is a\nfundamental scientific problem arising in multiple fields. In this work, based\non the social force model, we simulate the typical scenario of collective\nescape towards a single exit and reveal the striking analogy of crowd dynamics\nand crystallisation. With the outflow of the pedestrians, crystalline order\nemerges in the compact crowd. In this process, the local misalignment and\nglobal rearrangement of pedestrians are well rationalized in terms of the\ncharacteristic motions of topological defects in the crystal. Exploiting the\nnotions from the physics of crystallisation further reveals the emergence of\nmultiple fast tracks in the collective escape.\n"}
+{"abstract": "  Supersonic gas jets produced by converging-diverging (C-D) nozzles are\ncommonly used as targets for laser-plasma acceleration (LPA) experiments. A\nmajor point of interest for these targets is the gas density at the region of\ninteraction where the laser ionizes the gas plume to create a plasma, providing\nthe acceleration structure. Tuning the density profiles at this interaction\nregion is crucial to LPA optimization. A \"flat-top\" density profile is desired\nat this line of interaction to control laser propagation and high energy\nelectron acceleration, while a short high-density profile is often preferred\nfor acceleration of lower-energy tightly-focused laser-plasma interactions. A\nparticular design parameter of interest is the curvature of the nozzle's\ndiverging section. We examine three nozzle designs with different curvatures:\nthe concave \"bell\", straight conical and convex \"trumpet\" nozzles. We\ndemonstrate that, at mm-scale distances from the nozzle exit, the trumpet and\nstraight nozzles, if optimized, produce \"flat-top\" density profiles whereas the\nbell nozzle creates focused regions of gas with higher densities. An\noptimization procedure for the trumpet nozzle is derived and compared to the\nstraight nozzle optimization process. We find that the trumpet nozzle, by\nproviding an extra parameter of control through its curvature, is more\nversatile for creating flat-top profiles and its optimization procedure is more\nrefined compared to the straight nozzle and the straight nozzle optimization\nprocess. We present results for different nozzle designs from computational\nfluid dynamics (CFD) simulations performed with the program ANSYS Fluent and\nverify them experimentally using neutral density interferometry.\n"}
+{"abstract": "  Almost 46% of the world's population resides in a rural landscape. Smart\nvillages, alongside smart cities, are in need of time for future economic\ngrowth, improved agriculture, better health, and education. The smart village\nis a concept that improves the traditional rural aspects with the help of\ndigital transformation. The smart village is built up using heterogeneous\ndigital technologies pillared around the Internet-of-Thing (IoT). There exist\nmany opportunities in research to design a low-cost, secure, and efficient\ntechnical ecosystem. This article identifies the key application areas, where\nthe IoT can be applied in the smart village. The article also presents a\ncomparative study of communication technology options.\n"}
+{"abstract": "  In many quantum materials, strong electron correlations lead to the emergence\nof new states of matter. In particular, the study in the last decades of the\ncomplex phase diagram of high temperature superconducting cuprates highlighted\nintra-unit-cell electronic instabilities breaking discrete Ising-like\nsymmetries, while preserving the lattice translation invariance. Polarized\nneutron diffraction experiments have provided compelling evidences supporting a\nnew form of intra-unit-cell magnetism, emerging concomitantly with the\nso-called pseudogap state of these materials. This observation is currently\ninterpreted as the magnetic hallmark of an intra-unit-cell loop current order,\nbreaking both parity and time-reversal symmetries. More generally, this\nmagneto-electric state is likely to exist in a wider class of quantum materials\nbeyond superconducting cuprates. For instance, it has been already observed in\nhole-doped Mott insulating iridates or in the spin liquid state of hole-doped\n2-leg ladder cuprates.\n"}
+{"abstract": "  We reconsider the thermodynamics of AdS black holes in the context of\ngauge-gravity duality. In this new setting where both the cosmological constant\n$\\Lambda$ and the gravitational Newton constant $G$ are varied in the bulk, we\nrewrite the first law in a new form containing both $\\Lambda$ (associated with\nthermodynamic pressure) and the central charge $C$ of the dual CFT theory and\ntheir conjugate variables. We obtain a novel thermodynamic volume, in turn\nleading to a new understanding of the Van der Waals behavior of the charged AdS\nblack holes, in which phase changes are governed by the degrees of freedom in\nthe CFT. Compared to the \"old\" $P-V$ criticality, this new criticality is\n\"universal\" (independent of the bulk pressure) and directly relates to the\nthermodynamics of the dual field theory and its central charge.\n"}
+{"abstract": "  The Standard Model (SM) is augmented with a $\\mathrm{U}(1)_{B-3L_\\mu} $ gauge\nsymmetry spontaneously broken above the TeV scale when an SM-singlet scalar\ncondenses. Scalar leptoquarks $S_{1(3)} = (\\overline{\\mathbf{3}},\\, \\mathbf{1}\n(\\mathbf{3}),\\, ^1\\!/_3)$ charged under $\\mathrm{U}(1)_{B-3L_\\mu} $ mediate the\nintriguing effects observed in muon $(g-2)$, $R_{K^{(*)}}$ and $b \\to s \\mu^+\n\\mu^-$, while generically evading all other phenomenological constraints. The\nfermionic sector is minimally extended with three right-handed neutrinos, and a\nsuccessful type-I seesaw mechanism is realized. Charged lepton flavor violation\nis effectively suppressed, and proton decay - a common prediction of\nleptoquarks - is postponed to the dimension-6 effective Lagrangian. Unavoidable\nradiative corrections in the Higgs mass and muon Yukawa favor leptoquark masses\ninteresting for collider searches. The parameters of the model are radiatively\nstable and can be evolved by the renormalization group to the Planck scale\nwithout inconsistencies. Alternative lepton-flavored gauge extensions of the\nSM, under which leptoquarks become muoquarks, are proposed for comparison.\n"}
+{"abstract": "  We present a novel spectral machine learning (SML) method in screening for\npancreatic mass using CT imaging. Our algorithm is trained with approximately\n30,000 images from 250 patients (50 patients with normal pancreas and 200\npatients with abnormal pancreas findings) based on public data sources. A test\naccuracy of 94.6 percents was achieved in the out-of-sample diagnosis\nclassification based on a total of approximately 15,000 images from 113\npatients, whereby 26 out of 32 patients with normal pancreas and all 81\npatients with abnormal pancreas findings were correctly diagnosed. SML is able\nto automatically choose fundamental images (on average 5 or 9 images for each\npatient) in the diagnosis classification and achieve the above mentioned\naccuracy. The computational time is 75 seconds for diagnosing 113 patients in a\nlaptop with standard CPU running environment. Factors that influenced high\nperformance of a well-designed integration of spectral learning and machine\nlearning included: 1) use of eigenvectors corresponding to several of the\nlargest eigenvalues of sample covariance matrix (spike eigenvectors) to choose\ninput attributes in classification training, taking into account only the\nfundamental information of the raw images with less noise; 2) removal of\nirrelevant pixels based on mean-level spectral test to lower the challenges of\nmemory capacity and enhance computational efficiency while maintaining superior\nclassification accuracy; 3) adoption of state-of-the-art machine learning\nclassification, gradient boosting and random forest. Our methodology showcases\npractical utility and improved accuracy of image diagnosis in pancreatic mass\nscreening in the era of AI.\n"}
+{"abstract": "  In Vaidya-Bonner de Sitter Black hole space-time, the tunneling radiation\ncharacteristics of fermions and bosons are corrected by taking Lorentz symmetry\nbreaking theory into account. The corresponding gamma matrices and ether-like\nfield vectors of the black hole are constructed, then the new modified form of\nDirac equation for the fermion with spin 1/2 and the new modified form of\nKlein-Gordon equation for boson in the curved space-time of the black hole are\nobtained. Through solving the two equations, new and corrected expressions of\nsurface gravity, Hawking temperature and tunneling rate of the black hole are\nobtained, and the results obtained are also discussed.\n"}
+{"abstract": "  While Moore's law has driven exponential computing power expectations, its\nnearing end calls for new avenues for improving the overall system performance.\nOne of these avenues is the exploration of new alternative brain-inspired\ncomputing architectures that promise to achieve the flexibility and\ncomputational efficiency of biological neural processing systems. Within this\ncontext, neuromorphic intelligence represents a paradigm shift in computing\nbased on the implementation of spiking neural network architectures tightly\nco-locating processing and memory. In this paper, we provide a comprehensive\noverview of the field, highlighting the different levels of granularity present\nin existing silicon implementations, comparing approaches that aim at\nreplicating natural intelligence (bottom-up) versus those that aim at solving\npractical artificial intelligence applications (top-down), and assessing the\nbenefits of the different circuit design styles used to achieve these goals.\nFirst, we present the analog, mixed-signal and digital circuit design styles,\nidentifying the boundary between processing and memory through time\nmultiplexing, in-memory computation and novel devices. Next, we highlight the\nkey tradeoffs for each of the bottom-up and top-down approaches, survey their\nsilicon implementations, and carry out detailed comparative analyses to extract\ndesign guidelines. Finally, we identify both necessary synergies and missing\nelements required to achieve a competitive advantage for neuromorphic edge\ncomputing over conventional machine-learning accelerators, and outline the key\nelements for a framework toward neuromorphic intelligence.\n"}
+{"abstract": "  This survey discusses the classical Bernstein and Markov inequalities for the\nderivatives of polynomials, as well as some of their extensions to general\nsets.\n"}
+{"abstract": "  Self-assembly of Janus (or `patchy') particles is dependent on the precise\ninteraction between neighbouring particles. Here, the orientations of two\namphiphilic Janus spheres within a dimer in an explicit fluid are studied with\nhigh geometric resolution. Molecular dynamics simulations and first-principles\nenergy calculations are used with hard- and soft-sphere Lennard-Jones\npotentials, and temperature and hydrophobicity are varied. The most probable\ncentre-centre-pole angles are in the range 40{\\deg} to 55{\\deg}, with\npole-to-pole alignment not observed due to orientational entropy. Angles near\n90{\\deg} are energetically unfavoured due to solvent exclusion, and we\nunexpectedly found that the relative azimuthal angle between the spheres is\naffected by solvent ordering.\n"}
+{"abstract": "  The atmospheric depth of the air shower maximum $X_{\\mathrm{max}}$ is an\nobservable commonly used for the determination of the nuclear mass composition\nof ultra-high energy cosmic rays. Direct measurements of $X_{\\mathrm{max}}$ are\nperformed using observations of the longitudinal shower development with\nfluorescence telescopes. At the same time, several methods have been proposed\nfor an indirect estimation of $X_{\\mathrm{max}}$ from the characteristics of\nthe shower particles registered with surface detector arrays. In this paper, we\npresent a deep neural network (DNN) for the estimation of $X_{\\mathrm{max}}$.\nThe reconstruction relies on the signals induced by shower particles in the\nground based water-Cherenkov detectors of the Pierre Auger Observatory. The\nnetwork architecture features recurrent long short-term memory layers to\nprocess the temporal structure of signals and hexagonal convolutions to exploit\nthe symmetry of the surface detector array. We evaluate the performance of the\nnetwork using air showers simulated with three different hadronic interaction\nmodels. Thereafter, we account for long-term detector effects and calibrate the\nreconstructed $X_{\\mathrm{max}}$ using fluorescence measurements. Finally, we\nshow that the event-by-event resolution in the reconstruction of the shower\nmaximum improves with increasing shower energy and reaches less than\n$25~\\mathrm{g/cm^{2}}$ at energies above $2\\times 10^{19}~\\mathrm{eV}$.\n"}
+{"abstract": "  We analyze the propagation dynamics of radially polarized symmetric Airy\nbeams (R-SABs) in a (2+1)-dimensional optical system with fractional\ndiffraction, modeled by the fractional Schr\\\"odinger equation (FSE)\ncharacterized by the L\\'evy index. The autofocusing effect featured by such\nbeams becomes stronger, while the focal length becomes shorter, with the\nincrease of . The effect of the intrinsic vorticity on the autofocusing\ndynamics of the beams is considered too. Then, the ability of R-SABs to capture\nnano-particles by means of radiation forces is explored, and multiple capture\npositions emerging in the course of the propagation are identified. Finally, we\nfind that the propagation of the vortical R-SABs with an off-axis shift leads\nto rupture of the ring-shaped pattern of the power-density distribution.\n"}
+{"abstract": "  Abstractive neural summarization models have seen great improvements in\nrecent years, as shown by ROUGE scores of the generated summaries. But despite\nthese improved metrics, there is limited understanding of the strategies\ndifferent models employ, and how those strategies relate their understanding of\nlanguage. To understand this better, we run several experiments to characterize\nhow one popular abstractive model, the pointer-generator model of See et al.\n(2017), uses its explicit copy/generation switch to control its level of\nabstraction (generation) vs extraction (copying). On an extractive-biased\ndataset, the model utilizes syntactic boundaries to truncate sentences that are\notherwise often copied verbatim. When we modify the copy/generation switch and\nforce the model to generate, only simple paraphrasing abilities are revealed\nalongside factual inaccuracies and hallucinations. On an abstractive-biased\ndataset, the model copies infrequently but shows similarly limited abstractive\nabilities. In line with previous research, these results suggest that\nabstractive summarization models lack the semantic understanding necessary to\ngenerate paraphrases that are both abstractive and faithful to the source\ndocument.\n"}
+{"abstract": "  In this article, a combinatorial characterization of the family of planes of\n$\\PG(3,q)$ which meet a hyperbolic quadric in an irreducible conic, using their\nintersection properties with the points and lines of $\\PG(3,q)$, is given.\n"}
+{"abstract": "  The upgraded LHCb detector, due to start datataking in 2022, will have to\nprocess an average data rate of 4~TB/s in real time. Because LHCb's physics\nobjectives require that the full detector information for every LHC bunch\ncrossing is read out and made available for real-time processing, this\nbandwidth challenge is equivalent to that of the ATLAS and CMS HL-LHC software\nread-out, but deliverable five years earlier. Over the past six years, the LHCb\ncollaboration has undertaken a bottom-up rewrite of its software\ninfrastructure, pattern recognition, and selection algorithms to make them\nbetter able to efficiently exploit modern highly parallel computing\narchitectures. We review the impact of this reoptimization on the energy\nefficiency of the real-time processing software and hardware which will be used\nfor the upgrade of the LHCb detector. We also review the impact of the decision\nto adopt a hybrid computing architecture consisting of GPUs and CPUs for the\nreal-time part of LHCb's future data processing. We discuss the implications of\nthese results on how LHCb's real-time power requirements may evolve in the\nfuture, particularly in the context of a planned second upgrade of the\ndetector.\n"}
+{"abstract": "  New surprises continue to be revealed about La$_2$CuO$_4$, the parent\ncompound of the original cuprate superconductor. Here we present neutron\nscattering evidence that the structural symmetry is lower than commonly\nassumed. The static distortion results in anisotropic Cu-O bonds within the\nCuO$_2$ planes; such anisotropy is relevant to pinning charge stripes in\nhole-doped samples. Associated with the extra structural modulation is a soft\nphonon mode. If this phonon were to soften completely, the resulting change in\nCuO$_6$ octahedral tilts would lead to weak ferromagnetism. Hence, we suggest\nthat this mode may be the \"chiral\" phonon inferred from recent studies of the\nthermal Hall effect. We also note the absence of interaction between the\nantiferromagnetic spin waves and low-energy optical phonons, in contrast to\nwhat is observed in hole-doped samples.\n"}
+{"abstract": "  Beyond the scope of conventional metasurface which necessitates plenty of\ncomputational resources and time, an inverse design approach using machine\nlearning algorithms promises an effective way for metasurfaces design. In this\npaper, benefiting from Deep Neural Network (DNN), an inverse design procedure\nof a metasurface in an ultra-wide working frequency band is presented where the\noutput unit cell structure can be directly computed by a specified design\ntarget. To reach the highest working frequency, for training the DNN, we\nconsider 8 ring-shaped patterns to generate resonant notches at a wide range of\nworking frequencies from 4 to 45 GHz. We propose two network architectures. In\none architecture, we restricted the output of the DNN, so the network can only\ngenerate the metasurface structure from the input of 8 ring-shaped patterns.\nThis approach drastically reduces the computational time, while keeping the\nnetwork's accuracy above 91\\%. We show that our model based on DNN can\nsatisfactorily generate the output metasurface structure with an average\naccuracy of over 90\\% in both network architectures. Determination of the\nmetasurface structure directly without time-consuming optimization procedures,\nhaving an ultra-wide working frequency, and high average accuracy equip an\ninspiring platform for engineering projects without the need for complex\nelectromagnetic theory.\n"}
+{"abstract": "  We obtain new separation results for the two-party external information\ncomplexity of boolean functions. The external information complexity of a\nfunction $f(x,y)$ is the minimum amount of information a two-party protocol\ncomputing $f$ must reveal to an outside observer about the input. We obtain the\nfollowing results:\n  1. We prove an exponential separation between external and internal\ninformation complexity, which is the best possible; previously no separation\nwas known.\n  2. We prove a near-quadratic separation between amortized zero-error\ncommunication complexity and external information complexity for total\nfunctions, disproving a conjecture of \\cite{Bravermansurvey}.\n  3. We prove a matching upper showing that our separation result is tight.\n"}
+{"abstract": "  We introduce TransformerFusion, a transformer-based 3D scene reconstruction\napproach. From an input monocular RGB video, the video frames are processed by\na transformer network that fuses the observations into a volumetric feature\ngrid representing the scene; this feature grid is then decoded into an implicit\n3D scene representation. Key to our approach is the transformer architecture\nthat enables the network to learn to attend to the most relevant image frames\nfor each 3D location in the scene, supervised only by the scene reconstruction\ntask. Features are fused in a coarse-to-fine fashion, storing fine-level\nfeatures only where needed, requiring lower memory storage and enabling fusion\nat interactive rates. The feature grid is then decoded to a higher-resolution\nscene reconstruction, using an MLP-based surface occupancy prediction from\ninterpolated coarse-to-fine 3D features. Our approach results in an accurate\nsurface reconstruction, outperforming state-of-the-art multi-view stereo depth\nestimation methods, fully-convolutional 3D reconstruction approaches, and\napproaches using LSTM- or GRU-based recurrent networks for video sequence\nfusion.\n"}
+{"abstract": "  We study the effects of additional cooling due to the emission of a dark\nmatter candidate particle, the dark photon, on the final phases of the\nevolution of a $15\\,M_\\odot$ star and resulting modifications of the\npre-supernova neutrino signal. For a substantial portion of the dark photon\nparameter space the extra cooling speeds up Si burning, which results in a\nreduced number of neutrinos emitted during the last day before core collapse.\nThis reduction can be described by a systematic acceleration of the relevant\ntimescales and the results can be estimated semi-analytically in good agreement\nwith the numerical simulations. Outside the semi-analytic regime we find more\ncomplicated effects. In a narrow parameter range, low-mass dark photons lead to\nan increase of the number of emitted neutrinos because of additional shell\nburning episodes that delay core collapse. Furthermore, relatively strong\ncouplings produce a thermonuclear runaway during O burning, which could result\nin a complete disruption of the star but requires more detailed simulations to\ndetermine the outcome. Our results show that pre-supernova neutrino signals are\na potential probe of the dark photon parameter space.\n"}
+{"abstract": "  Unsupervised contrastive learning achieves great success in learning image\nrepresentations with CNN. Unlike most recent methods that focused on improving\naccuracy of image classification, we present a novel contrastive learning\napproach, named DetCo, which fully explores the contrasts between global image\nand local image patches to learn discriminative representations for object\ndetection. DetCo has several appealing benefits. (1) It is carefully designed\nby investigating the weaknesses of current self-supervised methods, which\ndiscard important representations for object detection. (2) DetCo builds\nhierarchical intermediate contrastive losses between global image and local\npatches to improve object detection, while maintaining global representations\nfor image recognition. Theoretical analysis shows that the local patches\nactually remove the contextual information of an image, improving the lower\nbound of mutual information for better contrastive learning. (3) Extensive\nexperiments on PASCAL VOC, COCO and Cityscapes demonstrate that DetCo not only\noutperforms state-of-the-art methods on object detection, but also on\nsegmentation, pose estimation, and 3D shape prediction, while it is still\ncompetitive on image classification. For example, on PASCAL VOC, DetCo-100ep\nachieves 57.4 mAP, which is on par with the result of MoCov2-800ep. Moreover,\nDetCo consistently outperforms supervised method by 1.6/1.2/1.0 AP on Mask\nRCNN-C4/FPN/RetinaNet with 1x schedule. Code will be released at\n\\href{https://github.com/xieenze/DetCo}{\\color{blue}{\\tt\ngithub.com/xieenze/DetCo}}.\n"}
+{"abstract": "  Graphdiyne nanomaterials are low density and highly porous carbon-based\ntwo-dimensional (2D) materials, with outstanding application prospects for\nelectronic and energy storage/conversion systems. In two latest scientific\nadvances, large-area pyrenyl graphdiyne (Pyr-GDY) and pyrazinoquinoxaline\ngraphdiyne (PQ-GDY) nanosheets have been successfully fabricated. As the first\ntheoretical study, herein we conduct first-principles simulations to explore\nthe stability and electronic, optical and mechanical properties of Pyr-GDY,\nN-Pyr-GDY, PQ-GDY and N-Pyr-GYN monolayers. We particularly examine the\nintrinsic properties of PQ-graphyne (PQ-GYN) and Pyr-graphyne (Pyr-GYN)\nmonolayers. Acquired results confirm desirable dynamical and thermal stability\nand high mechanical strength of these novel nanosheets, owing to their strong\ncovalent networks. We show that Pyr-based lattices can show high\nstretchability. Analysis of optical results also confirm the suitability of\nPyr- and PQ-GDY/GYN nanosheets to adsorb in the near-IR, visible, and UV range\nof light. Notably, PQ-GDY is found to exhibit distorted Dirac cone and highly\nanisotropic fermi velocities. First-principles results reveal ultrahigh carrier\nmobilities along the considered nanoporous nanomembranes, particularly PQ-GYN\nmonolayer is predicted to outperform phosphorene and MoS2. Acquired results\nintroduce pyrenyl and pyrazinoquinoxaline graphyne/graphyne as promising\ncandidates to design novel nanoelectronics and energy storage/conversion\nsystems.\n"}
+{"abstract": "  Soft materials such as rubber and hydrogels are commonly used in industry for\ntheir excellent hyperelastic behaviour. There are various types of constitutive\nmodels for soft materials, and phenomenological models are very popular for\nfinite element method (FEM) simulations. However, it is not easy to construct a\nmodel that can precisely predict the complex behaviours of soft materials. In\nthis paper, we suggest that the strain energy functions should be expressed as\nfunctions of ordered principal stretches, which have more flexible expressions\nand are capable of matching various experimental curves. Moreover, the feasible\nregion is small, and simple experiments, such as uniaxial tension/compression\nand hydrostatic tests, are on its boundaries. Therefore, strain energy\nfunctions can be easily constructed by the interpolation of experimental\ncurves, which does not need initial guessing in the form of the strain energy\nfunction as most existing phenomenological models do. The proposed strain\nenergy functions are perfectly consistent with the available experimental\ncurves for interpolation. It is found that for incompressible materials, the\nfunction via an interpolation from two experimental curves can already predict\nother experimental curves reasonably well. To further improve the accuracy,\nadditional experiments can be used in the interpolation.\n"}
+{"abstract": "  In this paper, constant false alarm rate (CFAR) detector-based approaches are\nproposed for interference mitigation of Frequency modulated continuous wave\n(FMCW) radars. The proposed methods exploit the fact that after dechirping and\nlow-pass filtering operations the targets' beat signals of FMCW radars are\ncomposed of exponential sinusoidal components while interferences exhibit short\nchirp waves within a sweep. The spectra of interferences in the time-frequency\n($t$-$f$) domain are detected by employing a 1-D CFAR detector along each\nfrequency bin and then the detected map is dilated as a mask for interference\nsuppression. They are applicable to the scenarios in the presence of multiple\ninterferences. Compared to the existing methods, the proposed methods reduce\nthe power loss of useful signals and are very computationally efficient. Their\ninterference mitigation performances are demonstrated through both numerical\nsimulations and experimental results.\n"}
+{"abstract": "  Fully-heavy tetraquark states, i.e. $cc\\bar{c}\\bar{c}$, $bb\\bar{b}\\bar{b}$,\n$bb\\bar{c}\\bar{c}$ ($cc\\bar{b}\\bar{b}$), $cb\\bar{c}\\bar{c}$,\n$cb\\bar{b}\\bar{b}$, and $cb\\bar{c}\\bar{b}$, are systematically investigated by\nmeans of a non-relativistic quark model based on lattice-QCD studies of the\ntwo-body $Q\\bar{Q}$ interaction, which exhibits a spin-independent Cornell\npotential along with a spin-spin term. The four-body problem is solved using\nthe Gaussian expansion method; additionally, the so-called complex scaling\ntechnique is employed so that bound, resonance, and scattering states can be\ntreated on the same footing. Moreover, a complete set of four-body\nconfigurations, including meson-meson, diquark-antidiquark, and K-type\nconfigurations, as well as their couplings, are considered for spin-parity\nquantum numbers $J^{P(C)}=0^{+(+)}$, $1^{+(\\pm)}$, and $2^{+(+)}$ in the\n$S$-wave channel. Several narrow resonances, with two-meson strong decay widths\nless than 30 MeV, are found in all of the tetraquark systems studied.\nParticularly, the fully-charm resonances recently reported by the LHCb\nCollaboration, at the energy range between 6.2 and 7.2 GeV in the di-$J/\\psi$\ninvariant spectrum, can be well identified in our calculation. Focusing on the\nfully-bottom tetraquark spectrum, resonances with masses between 18.9 and 19.6\nGeV are found. For the remaining charm-bottom cases, the masses are obtained\nwithin a energy region from 9.8 GeV to 16.4 GeV. All these predicted resonances\ncan be further examined in future experiments.\n"}
+{"abstract": "  Relations among various musical concepts are investigated through a new\nconcept, musical icosahedron that is the regular icosahedron each of whose\nvertices has one of 12 tones. First, we found that there exist four musical\nicosahedra that characterize the topology of the chromatic scale and one of the\nwhole tone scales, and have the hexagon-icosahedron symmetry (an operation of\nraising all the tones of a given scale by two semitones corresponds to a\nsymmetry transformation of the regular icosahedron): chromatic/whole tone\nmusical icosahedra. The major triads or the minor triads are set on the golden\ntriangles of these musical icosahedra. Also, various dualities between musical\nconcepts are shown by these musical icosahedra: the major triads/scales and the\nminor triads/scales, the major/minor triads and the fundamental triads for the\nhexatonic major/minor scales, the major/minor scales and the Gregorian modes.\nSecond, we proposed Pythagorean/whole tone musical icosahedra that characterize\nthe topology of the Pythagorean chain and one of the whole tone scales, and\nhave the hexagon-icosahedron symmetry. The Pythagorean chain (chromatic scale)\nin the chromatic (Pythagorean)/whole tone musical icosahedron is constructed by\n\"middle\" lines of the regular icosahedron. While some golden triangles\ncorrespond to the major/minor triads in the chromatic/whole tone musical\nicosahedra, in the Pythagorean/whole tone musical icosahedra, some golden\ngnomons correspond to the minor/major triads. Third, we found four types of\nmusical icosahedra other than the chromatic/whole tone musical icosahedra and\nthe Pythagorean/whole tone musical icosahedra that have the hexagon-icosahedron\nsymmetry. All the major triads and minor triads are represented by the golden\ntriangles or the golden gnomons on each type. All of these musical icosahedra\nlead to generalizations of major/minor triads and scales.\n"}
+{"abstract": "  Silicon-based tracking detectors have been used in several important\napplications, such as in cancer therapy using particle beams, and for the\ndiscovery of new elementary particles at the Large Hadron Collider at CERN.\nIII-V semiconductor materials are an attractive alternative to silicon for this\napplication, as they have some superior physical properties. They could meet\nthe demands for fast timing detectors allowing time-of-flight measurements with\nps resolution while being radiation tolerant and cost-efficient. As a material\nwith a larger density, higher atomic number Z and much higher electron mobility\nthan silicon, GaAs exhibits faster signal collection and a larger signal per\n{\\mu}m of sensor thickness. In this work, we report on the fabrication of\nn-in-n GaAs thin-film devices intended to serve next-generation high-energy\nparticle tracking detectors. Molecular beam epitaxy (MBE) was used to grow\nhigh-quality GaAs films with doping levels sufficiently low to achieve full\ndepletion for detectors with an active thickness of 10 {\\mu}m. The signal\ncollection speed of the detector structures was assessed using the transient\ncurrent technique (TCT). To elucidate the structural properties of the\ndetector, Kelvin probe force microscopy (KPFM) was used, which confirmed the\nformation of the junction in the detector and revealed residual doping in the\nintrinsic layer. Our results suggest that GaAs thin films are suitable\ncandidates to achieve thin and radiation-tolerant tracking detectors.\n"}
+{"abstract": "  We propose a new example of the AdS/CFT correspondence between the system of\nmultiple giant gravitons in AdS${}_5 \\times {}$S${}^5$ and the operators with\n$O(N_c)$ dimensions in ${\\cal N}=4$ super Yang-Mills. We first extend the\nmixing of huge operators on the Gauss graph basis in the $su(2)$ sector to all\nloops of the 't Hooft coupling, by demanding the commutation of perturbative\nHamiltonians in an effective $U(p)$ theory, where $p$ corresponds to the number\nof giant gravitons. The all-loop dispersion relation remains gapless at any\n$\\lambda$, which suggests that harmonic oscillators of the effective $U(p)$\ntheory should correspond to the classical motion of the D3-brane that is\ncontinuously connected to non-maximal giant gravitons.\n"}
+{"abstract": "  The present article is devoted to representations of rational numbers in\nterms sign-variable Cantor expansions. The main attention is given to one of\nthe discussions given by J. Galambos in [4].\n"}
+{"abstract": "  Inspired by more detailed modeling of biological neurons, Spiking neural\nnetworks (SNNs) have been investigated both as more biologically plausible and\npotentially more powerful models of neural computation, and also with the aim\nof extracting biological neurons' energy efficiency; the performance of such\nnetworks however has remained lacking compared to classical artificial neural\nnetworks (ANNs). Here, we demonstrate how a novel surrogate gradient combined\nwith recurrent networks of tunable and adaptive spiking neurons yields\nstate-of-the-art for SNNs on challenging benchmarks in the time-domain, like\nspeech and gesture recognition. This also exceeds the performance of standard\nclassical recurrent neural networks (RNNs) and approaches that of the best\nmodern ANNs. As these SNNs exhibit sparse spiking, we show that they\ntheoretically are one to three orders of magnitude more computationally\nefficient compared to RNNs with comparable performance. Together, this\npositions SNNs as an attractive solution for AI hardware implementations.\n"}
+{"abstract": "  We will construct a confidence region of parameters for $N$ samples from\nCauchy distributed random variables. Although Cauchy distribution has two\nparameters, a location parameter $\\mu \\in \\mathbb{R}$ and a scale parameter\n$\\sigma > 0$, we will infer them at once by regarding them as a single complex\nparameter $\\gamma := \\mu + i \\sigma$. Therefore the region should be a domain\nin the complex plane and we will give a simple and concrete formula to give the\nregion as a disc.\n"}
+{"abstract": "  Aspect-based Sentiment analysis (ABSA) accomplishes a fine-grained analysis\nthat defines the aspects of a given document or sentence and the sentiments\nconveyed regarding each aspect. This level of analysis is the most detailed\nversion that is capable of exploring the nuanced viewpoints of the reviews. The\nbulk of study in ABSA focuses on English with very little work available in\nArabic. Most previous work in Arabic has been based on regular methods of\nmachine learning that mainly depends on a group of rare resources and tools for\nanalyzing and processing Arabic content such as lexicons, but the lack of those\nresources presents another challenge. In order to address these challenges,\nDeep Learning (DL)-based methods are proposed using two models based on Gated\nRecurrent Units (GRU) neural networks for ABSA. The first is a DL model that\ntakes advantage of word and character representations by combining\nbidirectional GRU, Convolutional Neural Network (CNN), and Conditional Random\nField (CRF) making up the (BGRU-CNN-CRF) model to extract the main opinionated\naspects (OTE). The second is an interactive attention network based on\nbidirectional GRU (IAN-BGRU) to identify sentiment polarity toward extracted\naspects. We evaluated our models using the benchmarked Arabic hotel reviews\ndataset. The results indicate that the proposed methods are better than\nbaseline research on both tasks having 39.7% enhancement in F1-score for\nopinion target extraction (T2) and 7.58% in accuracy for aspect-based sentiment\npolarity classification (T3). Achieving F1 score of 70.67% for T2, and accuracy\nof 83.98% for T3.\n"}
+{"abstract": "  Contrastive Learning has emerged as a powerful representation learning method\nand facilitates various downstream tasks especially when supervised data is\nlimited. How to construct efficient contrastive samples through data\naugmentation is key to its success. Unlike vision tasks, the data augmentation\nmethod for contrastive learning has not been investigated sufficiently in\nlanguage tasks. In this paper, we propose a novel approach to construct\ncontrastive samples for language tasks using text summarization. We use these\nsamples for supervised contrastive learning to gain better text representations\nwhich greatly benefit text classification tasks with limited annotations. To\nfurther improve the method, we mix up samples from different classes and add an\nextra regularization, named Mixsum, in addition to the cross-entropy-loss.\nExperiments on real-world text classification datasets (Amazon-5, Yelp-5, AG\nNews, and IMDb) demonstrate the effectiveness of the proposed contrastive\nlearning framework with summarization-based data augmentation and Mixsum\nregularization.\n"}
+{"abstract": "  The field of explainable AI (XAI) has quickly become a thriving and prolific\ncommunity. However, a silent, recurrent and acknowledged issue in this area is\nthe lack of consensus regarding its terminology. In particular, each new\ncontribution seems to rely on its own (and often intuitive) version of terms\nlike \"explanation\" and \"interpretation\". Such disarray encumbers the\nconsolidation of advances in the field towards the fulfillment of scientific\nand regulatory demands e.g., when comparing methods or establishing their\ncompliance with respect to biases and fairness constraints. We propose a\ntheoretical framework that not only provides concrete definitions for these\nterms, but it also outlines all steps necessary to produce explanations and\ninterpretations. The framework also allows for existing contributions to be\nre-contextualized such that their scope can be measured, thus making them\ncomparable to other methods. We show that this framework is compliant with\ndesiderata on explanations, on interpretability and on evaluation metrics. We\npresent a use-case showing how the framework can be used to compare LIME, SHAP\nand MDNet, establishing their advantages and shortcomings. Finally, we discuss\nrelevant trends in XAI as well as recommendations for future work, all from the\nstandpoint of our framework.\n"}
+{"abstract": "  Multi Agent Path Finding (MAPF) requires identification of conflict free\npaths for agents which could be point-sized or with dimensions. In this paper,\nwe propose an approach for MAPF for spatially-extended agents. These find\napplication in real world problems like Convoy Movement Problem, Train\nScheduling etc. Our proposed approach, Decentralised Multi Agent Path Finding\n(DeMAPF), handles MAPF as a sequence of pathplanning and allocation problems\nwhich are solved by two sets of agents Travellers and Routers respectively,\nover multiple iterations. The approach being decentralised allows an agent to\nsolve the problem pertinent to itself, without being aware of other agents in\nthe same set. This allows the agents to be executed on independent machines,\nthereby leading to scalability to handle large sized problems. We prove, by\ncomparison with other distributed approaches, that the approach leads to a\nfaster convergence to a conflict-free solution, which may be suboptimal, with\nlesser memory requirement.\n"}
+{"abstract": "  Coupled 3D-1D problems arise in many practical applications, in an attempt to\nreduce the computational burden in simulations where cylindrical inclusions\nwith a small section are embedded in a much larger domain. Nonetheless the\nresolution of such problems can be non trivial, both from a mathematical and a\ngeometrical standpoint. Indeed 3D-1D coupling requires to operate in non\nstandard function spaces, and, also, simulation geometries can be complex for\nthe presence of multiple intersecting domains. Recently, a PDE-constrained\noptimization based formulation has been proposed for such problems, proving a\nwell posed mathematical formulation and allowing for the use of non conforming\nmeshes for the discrete problem. Here an unconstrained optimization formulation\nof the problem is derived and an efficient gradient based solver is proposed\nfor such formulation. Some numerical tests on quite complex configurations are\ndiscussed to show the viability of the method.\n"}
+{"abstract": "  In this paper we examine necessary conditions for an inhomogeneity to be\nnon-scattering, or equivalently, by negation, sufficient conditions for it to\nbe scattering. These conditions are formulated in terms of the regularity of\nthe boundary of the inhomogeneity. We examine broad classes of incident waves\nin both two and three dimensions. Our analysis is greatly influenced by the\nanalysis carried out by Williams [28] in order to establish that a domain,\nwhich does not possess the Pompeiu Property, has a real analytic boundary. That\nanalysis, as well as ours, relies crucially on classical free boundary\nregularity results due to Kinderlehrer and Nirenberg [18], and Caffarelli [6].\n"}
+{"abstract": "  Stemming from de Finetti's work on finitely additive coherent probabilities,\nthe paradigm of coherence has been applied to many uncertainty calculi in order\nto remove structural restrictions on the domain of the assessment. Three\npossible approaches to coherence are available: coherence as a consistency\nnotion, coherence as fair betting scheme, and coherence in terms of penalty\ncriterion. Due to its intimate connection with (finitely additive) probability\ntheory, Dempster-Shafer theory allows notions of coherence in all the forms\nrecalled above, presenting evident similarities with probability theory. In\nthis chapter we present a systematic study of such coherence notions showing\ntheir equivalence.\n"}
+{"abstract": "  The simulation of chemical kinetics involving multiple scales constitutes a\nmodeling challenge (from ordinary differential equations to Markov chain) and a\ncomputational challenge (multiple scales, large dynamical systems, time step\nrestrictions). In this paper we propose a new discrete stochastic simulation\nalgorithm: the postprocessed second kind stabilized orthogonal $\\tau$-leap\nRunge-Kutta method (PSK-$\\tau$-ROCK). In the context of chemical kinetics this\nmethod can be seen as a stabilization of Gillespie's explicit $\\tau$-leap\ncombined with a postprocessor. The stabilized procedure allows to simulate\nproblems with multiple scales (stiff), while the postprocessing procedure\nallows to approximate the invariant measure (e.g. mean and variance) of ergodic\nstochastic dynamical systems. We prove stability and accuracy of the\nPSK-$\\tau$-ROCK. Numerical experiments illustrate the high reliability and\nefficiency of the scheme when compared to other $\\tau$-leap methods.\n"}
+{"abstract": "  Mesoporous bioactive glasses (MBGs) in the system SiO2-CaO-P2O5-Ga2O3 have\nbeen synthesized by the evaporation induced self-assembly method and subsequent\nimpregnation with Ga cations. Two different compositions have been prepared and\nthe local environment of Ga(III) has been characterized using 29Si, 71Ga and\n31P NMR analysis, demonstrating that Ga(III) is efficiently incorporated as\nboth, network former (GaO4 units) and network modifier (GaO6 units). In vitro\nbioactivity tests evidenced that Ga-containing MBGs retain their capability for\nnucleation and growth of an apatite-like layer in contact with a simulated body\nfluid with ion concentrations nearly equal to those of human blood plasma.\nFinally, in vitro cell culture tests evidenced that Ga incorporation results in\na selective effect on osteoblasts and osteoclasts. Indeed, the presence of this\nelement enhances the early differentiation towards osteoblast phenotype while\ndisturbing osteoclastogenesis. Considering these results, Ga-doped MBGs might\nbe proposed as bone substitutes, especially in osteoporosis scenarios.\n"}
+{"abstract": "  System optimum (SO) routing, wherein the total travel time of all users is\nminimized, is a holy grail for transportation authorities. However, SO routing\nmay discriminate against users who incur much larger travel times than others\nto achieve high system efficiency, i.e., low total travel times. To address the\ninherent unfairness of SO routing, we study the ${\\beta}$-fair SO problem whose\ngoal is to minimize the total travel time while guaranteeing a ${\\beta\\geq 1}$\nlevel of unfairness, which specifies the maximum possible ratio between the\ntravel times of different users with shared origins and destinations.\n  To obtain feasible solutions to the ${\\beta}$-fair SO problem while achieving\nhigh system efficiency, we develop a new convex program, the Interpolated\nTraffic Assignment Problem (I-TAP), which interpolates between a\nfairness-promoting and an efficiency-promoting traffic-assignment objective. We\nevaluate the efficacy of I-TAP through theoretical bounds on the total system\ntravel time and level of unfairness in terms of its interpolation parameter, as\nwell as present a numerical comparison between I-TAP and a state-of-the-art\nalgorithm on a range of transportation networks. The numerical results indicate\nthat our approach is faster by several orders of magnitude as compared to the\nbenchmark algorithm, while achieving higher system efficiency for all desirable\nlevels of unfairness. We further leverage the structure of I-TAP to develop two\npricing mechanisms to collectively enforce the I-TAP solution in the presence\nof selfish homogeneous and heterogeneous users, respectively, that\nindependently choose routes to minimize their own travel costs. We mention that\nthis is the first study of pricing in the context of fair routing for general\nroad networks (as opposed to, e.g., parallel road networks).\n"}
+{"abstract": "  Food texture is a complex property; various sensory attributes such as\nperceived crispiness and wetness have been identified as ways to quantify it.\nObjective and automatic recognition of these attributes has applications in\nmultiple fields, including health sciences and food engineering. In this work\nwe use an in-ear microphone, commonly used for chewing detection, and propose\nalgorithms for recognizing three food-texture attributes, specifically\ncrispiness, wetness (moisture), and chewiness. We use binary SVMs, one for each\nattribute, and propose two algorithms: one that recognizes each texture\nattribute at the chew level and one at the chewing-bout level. We evaluate the\nproposed algorithms using leave-one-subject-out cross-validation on a dataset\nwith 9 subjects. We also evaluate them using leave-one-food-type-out\ncross-validation, in order to examine the generalization of our approach to\nnew, unknown food types. Our approach performs very well in recognizing\ncrispiness (0.95 weighted accuracy on new subjects and 0.93 on new food types)\nand demonstrates promising results for objective and automatic recognition of\nwetness and chewiness.\n"}
+{"abstract": "  Streaks in the buffer layer of wall-bounded turbulence are tracked in time to\nstudy their life-cycle. Spatially and temporally resolved direct numerical\nsimulation data is used to analyze the strong wall-parallel movements\nconditioned to low-speed streamwise flow. The analysis of the streaks shows\nthat there is a clear distinction between wall-attached and detached streaks,\nand that the former can be further categorized into streaks that are contained\nin the buffer layer and the ones that reach the outer region. The results\nreveal that streaks are born in the buffer layer, coalescing with each other to\ncreate larger streaks that are still attached to the wall. Once the streak\nbecomes large enough, it starts to meander due to the large\nstreamwise-to-wall-normal aspect ratio, and consequently the elongation in the\nstreamwise direction, which makes it more difficult for the streak to be\noriented strictly in the streamwise direction. While the continuous interaction\nof the streaks allows the super-structure to span extremely long temporal and\nlength scales, individual streak components are relatively small and\nshort-lived. Tall-attached streaks eventually split into wall-attached and\nwall-detached components. These wall-detached streaks have a strong wall-normal\nvelocity away from the wall, similar to ejections or bursts observed in the\nliterature. Conditionally averaging the flow fields to these split events show\nthat the detached streak has not only a larger wall-normal velocity compared to\nthe wall-attached counterpart, it also has a larger (less negative) streamwise\nvelocity, similar to the velocity field at the tip of a vortex cluster.\n"}
+{"abstract": "  A graph $G$ is $H$-saturated if it contains no $H$ as a subgraph, but does\ncontain $H$ after the addition of any edge in the complement of $G$. The\nsaturation number, $sat (n, H)$, is the minimum number of edges of a graph in\nthe set of all $H$-saturated graphs with order $n$. In this paper, we determine\nthe saturation number $sat (n, P_6 + tP_2)$ for $n \\geq 10t/3 + 10$ and\ncharacterize the extremal graphs for $n >10t/3 + 20$.\n"}
+{"abstract": "  Quantum theories of gravity predict interesting phenomenological features\nsuch as a minimum measurable length and maximum momentum. We use the\nGeneralized Uncertainty Principle (GUP), which is an extension of the standard\nHeisenberg Uncertainty Principle motivated by Quantum Gravity, to model the\nabove features. In particular, we use a GUP with modelling maximum momentum to\nestablish a correspondence between the GUP-modified dynamics of a massless\nspin-2 field and quadratic (referred to as Stelle) gravity. In other words,\nStelle gravity can be regarded as the classical manifestation of a maximum\nmomentum and the related GUP. We explore the applications of Stelle gravity to\ncosmology and specifically show that Stelle gravity applied to a homogeneous\nand isotropic background leads to inflation with an exit. Using the above, we\nobtain strong bounds on the GUP parameter from CMB observations. Unlike\nprevious works, which fixed only upper bounds for GUP parameters, we obtain\nboth \\emph{lower and upper bounds} on the GUP parameter.\n"}
+{"abstract": "  The acceptance of Internet of Things (IoT) applications and services has seen\nan enormous rise of interest in IoT. Organizations have begun to create various\nIoT based gadgets ranging from small personal devices such as a smart watch to\na whole network of smart grid, smart mining, smart manufacturing, and\nautonomous driver-less vehicles. The overwhelming amount and ubiquitous\npresence have attracted potential hackers for cyber-attacks and data theft.\nSecurity is considered as one of the prominent challenges in IoT. The key scope\nof this research work is to propose an innovative model using machine learning\nalgorithm to detect and mitigate botnet-based distributed denial of service\n(DDoS) attack in IoT network. Our proposed model tackles the security issue\nconcerning the threats from bots. Different machine learning algorithms such as\nK- Nearest Neighbour (KNN), Naive Bayes model and Multi-layer Perception\nArtificial Neural Network (MLP ANN) were used to develop a model where data are\ntrained by BoT-IoT dataset. The best algorithm was selected by a reference\npoint based on accuracy percentage and area under the receiver operating\ncharacteristics curve (ROC AUC) score. Feature engineering and Synthetic\nminority oversampling technique (SMOTE) were combined with machine learning\nalgorithms (MLAs). Performance comparison of three algorithms used was done in\nclass imbalance dataset and on the class balanced dataset.\n"}
+{"abstract": "  Research on overlapped and discontinuous named entity recognition (NER) has\nreceived increasing attention. The majority of previous work focuses on either\noverlapped or discontinuous entities. In this paper, we propose a novel\nspan-based model that can recognize both overlapped and discontinuous entities\njointly. The model includes two major steps. First, entity fragments are\nrecognized by traversing over all possible text spans, thus, overlapped\nentities can be recognized. Second, we perform relation classification to judge\nwhether a given pair of entity fragments to be overlapping or succession. In\nthis way, we can recognize not only discontinuous entities, and meanwhile\ndoubly check the overlapped entities. As a whole, our model can be regarded as\na relation extraction paradigm essentially. Experimental results on multiple\nbenchmark datasets (i.e., CLEF, GENIA and ACE05) show that our model is highly\ncompetitive for overlapped and discontinuous NER.\n"}
+{"abstract": "  The idea of transfer in reinforcement learning (TRL) is intriguing: being\nable to transfer knowledge from one problem to another problem without learning\neverything from scratch. This promises quicker learning and learning more\ncomplex methods. To gain an insight into the field and to detect emerging\ntrends, we performed a database search. We note a surprisingly late adoption of\ndeep learning that starts in 2018. The introduction of deep learning has not\nyet solved the greatest challenge of TRL: generalization. Transfer between\ndifferent domains works well when domains have strong similarities (e.g.\nMountainCar to Cartpole), and most TRL publications focus on different tasks\nwithin the same domain that have few differences. Most TRL applications we\nencountered compare their improvements against self-defined baselines, and the\nfield is still missing unified benchmarks. We consider this to be a\ndisappointing situation. For the future, we note that: (1) A clear measure of\ntask similarity is needed. (2) Generalization needs to improve. Promising\napproaches merge deep learning with planning via MCTS or introduce memory\nthrough LSTMs. (3) The lack of benchmarking tools will be remedied to enable\nmeaningful comparison and measure progress. Already Alchemy and Meta-World are\nemerging as interesting benchmark suites. We note that another development, the\nincrease in procedural content generation (PCG), can improve both benchmarking\nand generalization in TRL.\n"}
+{"abstract": "  Semantic parsing, as an important approach to question answering over\nknowledge bases (KBQA), transforms a question into the complete query graph for\nfurther generating the correct logical query. Existing semantic parsing\napproaches mainly focus on relations matching with paying less attention to the\nunderlying internal structure of questions (e.g., the dependencies and\nrelations between all entities in a question) to select the query graph. In\nthis paper, we present a relational graph convolutional network (RGCN)-based\nmodel gRGCN for semantic parsing in KBQA. gRGCN extracts the global semantics\nof questions and their corresponding query graphs, including structure\nsemantics via RGCN and relational semantics (label representation of relations\nbetween entities) via a hierarchical relation attention mechanism. Experiments\nevaluated on benchmarks show that our model outperforms off-the-shelf models.\n"}
+{"abstract": "  We prove Engstr\\\"{o}m's conjecture that the independence complex of graphs\nwith no induced cycle of length divisible by $3$ is either contractible or\nhomotopy equivalent to a sphere. Our result strengthens a result by Zhang and\nWu, verifying a conjecture of Kalai and Meshulam which states that the total\nBetti number of the independence complex of such a graph is at most $1$. A\nweaker conjecture was proved earlier by Chudnovsky, Scott, Seymour, and Spikl,\nwho showed that in such a graph, the number of independent sets of even size\nminus the number of independent sets of odd size has values $0$, $1$, or $-1$.\n"}
+{"abstract": "  We predict that a photon condensate inside a dye-filled microcavity forms\nlong-lived spatial structures that resemble vortices when incoherently excited\nby a focused pump orbiting around the cavity axis. The finely structured\ndensity of the condensates have a discrete rotational symmetry that is\ncontrolled by the orbital frequency of the pump spot and is phase-coherent over\nits full spatial extent despite the absence of any effective photon-photon\ninteractions.\n"}
+{"abstract": "  Volumetric modulated arc therapy planning is a challenging problem in\nhigh-dimensional, non-convex optimization. Traditionally, heuristics such as\nfluence-map-optimization-informed segment initialization use locally optimal\nsolutions to begin the search of the full arc therapy plan space from a\nreasonable starting point. These routines facilitate arc therapy optimization\nsuch that clinically satisfactory radiation treatment plans can be created in\nabout 10 minutes. However, current optimization algorithms favor solutions near\ntheir initialization point and are slower than necessary due to plan\noverparameterization. In this work, arc therapy overparameterization is\naddressed by reducing the effective dimension of treatment plans with\nunsupervised deep learning. An optimization engine is then built based on\nlow-dimensional arc representations which facilitates faster planning times.\n"}
+{"abstract": "  Time-delay interferometry (TDI) is a post-processing technique used to reduce\nlaser noise in heterodyne interferometric measurements with unequal armlengths,\na situation characteristic of space gravitational detectors such as Laser\nInterferometer Space Antenna (LISA). This technique consists in properly\ntime-shifting and linearly combining the interferometric measurements in order\nto reduce the laser noise by several orders of magnitude and to detect\ngravitational waves. In this communication, we show that the Doppler shift due\nto the time evolution of the armlengths leads to an unacceptably large residual\nnoise when using interferometric measurements expressed in units of frequency\nand standard expressions of the TDI variables. We also present a technique to\nmitigate this effect by including a scaling of the interferometric measurements\nin addition to the usual time-shifting operation when constructing the TDI\nvariables. We demonstrate analytically and using numerical simulations that\nthis technique allows one to recover standard laser noise suppression which is\nnecessary to measure gravitational waves.\n"}
+{"abstract": "  3D object detection is a core component of automated driving systems.\nState-of-the-art methods fuse RGB imagery and LiDAR point cloud data\nframe-by-frame for 3D bounding box regression. However, frame-by-frame 3D\nobject detection suffers from noise, field-of-view obstruction, and sparsity.\nWe propose a novel Temporal Fusion Module (TFM) to use information from\nprevious time-steps to mitigate these problems. First, a state-of-the-art\nfrustum network extracts point cloud features from raw RGB and LiDAR point\ncloud data frame-by-frame. Then, our TFM module fuses these features with a\nrecurrent neural network. As a result, 3D object detection becomes robust\nagainst single frame failures and transient occlusions. Experiments on the\nKITTI object tracking dataset show the efficiency of the proposed TFM, where we\nobtain ~6%, ~4%, and ~6% improvements on Car, Pedestrian, and Cyclist classes,\nrespectively, compared to frame-by-frame baselines. Furthermore, ablation\nstudies reinforce that the subject of improvement is temporal fusion and show\nthe effects of different placements of TFM in the object detection pipeline.\nOur code is open-source and available at\nhttps://github.com/emecercelik/Temp-Frustum-Net.git.\n"}
+{"abstract": "  In this work we provide a framework that connects the co-rotating and counter\nrotating $f$-mode frequencies of rotating neutron stars with their stellar\nstructure. The accurate computation of these modes for realistic equations of\nstate has been presented recently and they are here used as input for a\nBayesian analysis of the inverse problem. This allows to quantitatively\nreconstruct basic neutron star parameters, such as the mass, radius, rotation\nrate or universal scaling parameters. We find that future observations of both\n$f$-mode frequencies, in combination with a Bayesian analysis, would provide a\npromising direction to solve the inverse stellar problem. We provide two\ncomplementary approaches, one that is equation of state dependent and one that\nonly uses universal scaling relations. We discuss advantages and disadvantages\nof each approach, such as possible bias and robustness. The focus is on\nastrophysically motivated scenarios in which informed prior information on the\nneutron star mass or rotation rate can be provided and study how they impact\nthe results.\n"}
+{"abstract": "  The discovery of gravitational waves, high-energy neutrinos or the\nvery-high-energy counterpart of gamma-ray bursts has revolutionized the\nhigh-energy and transient astrophysics community. The development of new\ninstruments and analysis techniques will allow the discovery and/or follow-up\nof new transient sources. We describe the prospects for the Cherenkov Telescope\nArray (CTA), the next-generation ground-based gamma-ray observatory, for\nmulti-messenger and transient astrophysics in the decade ahead. CTA will\nexplore the most extreme environments via very-high-energy observations of\ncompact objects, stellar collapse events, mergers and cosmic-ray accelerators.\n"}
+{"abstract": "  Recommender systems rely heavily on increasing computation resources to\nimprove their business goal. By deploying computation-intensive models and\nalgorithms, these systems are able to inference user interests and exhibit\ncertain ads or commodities from the candidate set to maximize their business\ngoals. However, such systems are facing two challenges in achieving their\ngoals. On the one hand, facing massive online requests, computation-intensive\nmodels and algorithms are pushing their computation resources to the limit. On\nthe other hand, the response time of these systems is strictly limited to a\nshort period, e.g. 300 milliseconds in our real system, which is also being\nexhausted by the increasingly complex models and algorithms.\n  In this paper, we propose the computation resource allocation solution (CRAS)\nthat maximizes the business goal with limited computation resources and\nresponse time. We comprehensively illustrate the problem and formulate such a\nproblem as an optimization problem with multiple constraints, which could be\nbroken down into independent sub-problems. To solve the sub-problems, we\npropose the revenue function to facilitate the theoretical analysis, and obtain\nthe optimal computation resource allocation strategy. To address the\napplicability issues, we devise the feedback control system to help our\nstrategy constantly adapt to the changing online environment. The effectiveness\nof our method is verified by extensive experiments based on the real dataset\nfrom Taobao.com. We also deploy our method in the display advertising system of\nAlibaba. The online results show that our computation resource allocation\nsolution achieves significant business goal improvement without any increment\nof computation cost, which demonstrates the efficacy of our method in real\nindustrial practice.\n"}
+{"abstract": "  We present a novel implementation of classification using the machine\nlearning / artificial intelligence method called boosted decision trees (BDT)\non field programmable gate arrays (FPGA). The firmware implementation of binary\nclassification requiring 100 training trees with a maximum depth of 4 using\nfour input variables gives a latency value of about 10 ns, independent of the\nclock speed from 100 to 320 MHz in our setup. The low timing values are\nachieved by restructuring the BDT layout and reconfiguring its parameters. The\nFPGA resource utilization is also kept low at a range from 0.01% to 0.2% in our\nsetup. A software package called fwXmachina achieves this implementation. Our\nintended user is an expert of custom electronics-based trigger systems in high\nenergy physics experiments or anyone that needs decisions at the lowest latency\nvalues for real-time event classification. Two problems from high energy\nphysics are considered, in the separation of electrons vs. photons and in the\nselection of vector boson fusion-produced Higgs bosons vs. the rejection of the\nmultijet processes.\n"}
+{"abstract": "  High-throughput computational imaging requires efficient processing\nalgorithms to retrieve multi-dimensional and multi-scale information. In\ncomputational phase imaging, phase retrieval (PR) is required to reconstruct\nboth amplitude and phase in complex space from intensity-only measurements. The\nexisting PR algorithms suffer from the tradeoff among low computational\ncomplexity, robustness to measurement noise and strong generalization on\ndifferent modalities. In this work, we report an efficient large-scale phase\nretrieval technique termed as LPR. It extends the plug-and-play\ngeneralized-alternating-projection framework from real space to nonlinear\ncomplex space. The alternating projection solver and enhancing neural network\nare respectively derived to tackle the measurement formation and statistical\nprior regularization. This framework compensates the shortcomings of each\noperator, so as to realize high-fidelity phase retrieval with low computational\ncomplexity and strong generalization. We applied the technique for a series of\ncomputational phase imaging modalities including coherent diffraction imaging,\ncoded diffraction pattern imaging, and Fourier ptychographic microscopy.\nExtensive simulations and experiments validate that the technique outperforms\nthe existing PR algorithms with as much as 17dB enhancement on signal-to-noise\nratio, and more than one order-of-magnitude increased running efficiency.\nBesides, we for the first time demonstrate ultra-large-scale phase retrieval at\nthe 8K level (7680$\\times$4320 pixels) in minute-level time.\n"}
+{"abstract": "  While quantum measurement theories are built around density matrices and\nobservables, the laws of thermodynamics are based on processes such as are used\nin heat engines and refrigerators. The study of quantum thermodynamics fuses\nthese two distinct paradigms. In this article, we highlight the usage of\nquantum process matrices as a unified language for describing thermodynamic\nprocesses in the quantum regime. We experimentally demonstrate this in the\ncontext of a quantum Maxwell's demon, where two major quantities are commonly\ninvestigated; the average work extraction $\\langle W \\rangle$ and the efficacy\n$\\gamma$ which measures how efficiently the feedback operation uses the\nobtained information. Using the tool of quantum process matrices, we develop\nthe optimal feedback protocols for these two quantities and experimentally\ninvestigate them in a superconducting circuit QED setup.\n"}
+{"abstract": "  Computing power, big data, and advancement of algorithms have led to a\nrenewed interest in artificial intelligence (AI), especially in deep learning\n(DL). The success of DL largely lies on data representation because different\nrepresentations can indicate to a degree the different explanatory factors of\nvariation behind the data. In the last few year, the most successful story in\nDL is supervised learning. However, to apply supervised learning, one challenge\nis that data labels are expensive to get, noisy, or only partially available.\nWith consideration that we human beings learn in an unsupervised way;\nself-supervised learning methods have garnered a lot of attention recently. A\ndominant force in self-supervised learning is the autoencoder, which has\nmultiple uses (e.g., data representation, anomaly detection, denoise). This\nresearch explored the application of an autoencoder to learn effective data\nrepresentation of helicopter flight track data, and then to support helicopter\ntrack identification. Our testing results are promising. For example, at\nPhoenix Deer Valley (DVT) airport, where 70% of recorded flight tracks have\nmissing aircraft types, the autoencoder can help to identify twenty-two times\nmore helicopters than otherwise detectable using rule-based methods; for Grand\nCanyon West Airport (1G4) airport, the autoencoder can identify thirteen times\nmore helicopters than a current rule-based approach. Our approach can also\nidentify mislabeled aircraft types in the flight track data and find true types\nfor records with pseudo aircraft type labels such as HELO. With improved\nlabelling, studies using these data sets can produce more reliable results.\n"}
+{"abstract": "  The convergence rate in Wasserstein distance is estimated for the empirical\nmeasures of symmetric semilinear SPDEs. Unlike in the finite-dimensional case\nthat the convergence is of algebraic order in time, in the present situation\nthe convergence is of log order with a power given by eigenvalues of the\nunderlying linear operator.\n"}
+{"abstract": "  Background: A global description of the ground-state properties of nuclei in\na wide mass range in a unified manner is desirable not only for understanding\nexotic nuclei but for providing nuclear data for applications. Purpose: We\ndemonstrate the KIDS functional describes the ground states appropriately with\nrespect to the existing data and predictions for a possible application of the\nfunctional to all the nuclei by taking Nd isotopes as examples. Method: The\nKohn-Sham-Bogoliubov equation is solved for the Nd isotopes with the neutron\nnumbers ranging from 60 to 160 by employing the KIDS functionals constructed to\nsatisfy both neutron-matter equation of state or neutron star observation and\nselected nuclear data. Results: Considering the nuclear deformation improves\nthe description of the binding energies and radii. We find that the discrepancy\nfrom the experimental data is more significant for neutron-rich/deficient\nisotopes and this can be made isotope independent by changing the slope\nparameter of the symmetry energy. Conclusions: The KIDS functional is applied\nto the mid-shell nuclei for the first time. The onset and evolution of\ndeformation are nicely described for the Nd isotopes. The KIDS functional is\ncompetent to a global fitting for a better description of nuclear properties in\nthe nuclear chart.\n"}
+{"abstract": "  Weak supervision has shown promising results in many natural language\nprocessing tasks, such as Named Entity Recognition (NER). Existing work mainly\nfocuses on learning deep NER models only with weak supervision, i.e., without\nany human annotation, and shows that by merely using weakly labeled data, one\ncan achieve good performance, though still underperforms fully supervised NER\nwith manually/strongly labeled data. In this paper, we consider a more\npractical scenario, where we have both a small amount of strongly labeled data\nand a large amount of weakly labeled data. Unfortunately, we observe that\nweakly labeled data does not necessarily improve, or even deteriorate the model\nperformance (due to the extensive noise in the weak labels) when we train deep\nNER models over a simple or weighted combination of the strongly labeled and\nweakly labeled data. To address this issue, we propose a new multi-stage\ncomputational framework -- NEEDLE with three essential ingredients: (1) weak\nlabel completion, (2) noise-aware loss function, and (3) final fine-tuning over\nthe strongly labeled data. Through experiments on E-commerce query NER and\nBiomedical NER, we demonstrate that NEEDLE can effectively suppress the noise\nof the weak labels and outperforms existing methods. In particular, we achieve\nnew SOTA F1-scores on 3 Biomedical NER datasets: BC5CDR-chem 93.74,\nBC5CDR-disease 90.69, NCBI-disease 92.28.\n"}
+{"abstract": "  Using a combinatorial description of Stiefel-Whitney classes of closed flat\nmanifolds with diagonal holonomy representation, we show that no\nHantzsche-Wendt manifold of dimension greater than three does not admit a\nspin$^c$ structure.\n"}
+{"abstract": "  Learning high-dimensional distributions is an important yet challenging\nproblem in machine learning with applications in various domains. In this\npaper, we introduce new techniques to formulate the problem as solving\nFokker-Planck equation in a lower-dimensional latent space, aiming to mitigate\nchallenges in high-dimensional data space. Our proposed model consists of\nlatent-distribution morphing, a generator and a parameterized Fokker-Planck\nkernel function. One fascinating property of our model is that it can be\ntrained with arbitrary steps of latent distribution morphing or even without\nmorphing, which makes it flexible and as efficient as Generative Adversarial\nNetworks (GANs). Furthermore, this property also makes our latent-distribution\nmorphing an efficient plug-and-play scheme, thus can be used to improve\narbitrary GANs, and more interestingly, can effectively correct failure cases\nof the GAN models. Extensive experiments illustrate the advantages of our\nproposed method over existing models.\n"}
+{"abstract": "  In this work, we provide an analytical proof of the robustness of topological\nentanglement under a model of random local perturbations. We define a notion of\naverage topological subsystem purity and show that, in the context of quantum\ndouble models, this quantity does detect topological order and is robust under\nthe action of a random quantum circuit of shallow depth.\n"}
+{"abstract": "  We propose a minimal generalization of the celebrated Markov-Chain Monte\nCarlo algorithm which allows for an arbitrary number of configurations to be\nvisited at every Monte Carlo step. This is advantageous when a parallel\ncomputing machine is available, or when many biased configurations can be\nevaluated at little additional computational cost. As an example of the former\ncase, we report a significant reduction of the thermalization time for the\nparadigmatic Sherrington-Kirkpatrick spin-glass model. For the latter case, we\nshow that, by leveraging on the exponential number of biased configurations\nautomatically computed by Diagrammatic Monte Carlo, we can speed up\ncomputations in the Fermi-Hubbard model by two orders of magnitude.\n"}
+{"abstract": "  It is increasingly important to understand the spatial dynamics of epidemics.\nWhile there are numerous mathematical models of epidemics, there is a scarcity\nof physical systems with sufficiently well-controlled parameters to allow\nquantitative model testing. It is also challenging to replicate the macro\nnon-equilibrium effects of complex models in microscopic systems. In this work,\nwe demonstrate experimentally a physics analog of epidemic spreading using\noptically driven non-equilibrium phase transitions in strongly interacting\nRydberg atoms. Using multiple laser beams we can impose any desired spatial\nstructure. We observe spatially localized phase transitions and their interplay\nin different parts of the sample. These phase transitions simulate the outbreak\nof an infectious disease in multiple locations, as well as the dynamics towards\nherd immunity and endemic state in different regimes. The reported results\nindicate that Rydberg systems are versatile enough to model complex\nspatial-temporal dynamics.\n"}
+{"abstract": "  This article describes an algorithm that provides visual odometry estimates\nfrom sequential pairs of RGBD images. The key contribution of this article on\nRGBD odometry is that it provides both an odometry estimate and a covariance\nfor the odometry parameters in real-time via a representative covariance\nmatrix. Accurate, real-time parameter covariance is essential to effectively\nfuse odometry measurements into most navigation systems. To date, this topic\nhas seen little treatment in research which limits the impact existing RGBD\nodometry approaches have for localization in these systems. Covariance\nestimates are obtained via a statistical perturbation approach motivated by\nreal-world models of RGBD sensor measurement noise. Results discuss the\naccuracy of our RGBD odometry approach with respect to ground truth obtained\nfrom a motion capture system and characterizes the suitability of this approach\nfor estimating the true RGBD odometry parameter uncertainty.\n"}
+{"abstract": "  We treat a setting in which two priority wireless service classes are offered\nin a given area by a drone small cell (DSC). Specifically, we consider\nbroadband (BB) user with high priority and reliability requirements that\ncoexists with random access machine-type-communications (MTC) devices. The\ndrone serves both connectivity types with a combination of orthogonal slicing\nof the wireless resources and dynamic horizontal opportunistic positioning\n(D-HOP). We treat the D-HOP as a computational geometry function over\nstochastic BB user locations which requires careful adjustment in the\ndeployment parameters to ensure MTC service at all times. Using an information\ntheoretic approach, we optimize DSC deployment properties and radio resource\nallocation for the purpose of maximizing the average rate of BB users. While\nrespecting the strict dual service requirements we analyze how system\nperformance is affected by stochastic user positioning and density, topology,\nand reliability constraints combinations. The numerical results show that this\napproach outperforms static DSCs that fit the same coverage constraints, with\noutstanding performance in the urban setting.\n"}
+{"abstract": "  In this paper we first investigate the equatorial circular orbit structure of\nKerr black holes with scalar hair (KBHsSH) and highlight their most prominent\nfeatures which are quite distinct from the exterior region of ordinary bald\nKerr black holes, i.e. peculiarities that arise from the combined bound system\nof a hole with an off-center, self-gravitating distribution of scalar matter.\nSome of these traits are incompatible with the thin disk approach, thus we\nidentify and map out various regions in the parameter space respectively. All\nthe solutions for which the stable circular orbital velocity (and angular\nmomentum) curve is continuous are used for building thin and optically thick\ndisks around them, from which we extract the radiant energy fluxes,\nluminosities and efficiencies. We compare the results in batches with the same\nspin parameter $j$ but different normalized charges, and the profiles are\nrichly diverse. Because of the existence of a conserved scalar charge, $Q$,\nthese solutions are non-unique in the $(M, J)$ parameter space. Furthermore,\n$Q$ cannot be extracted asymptotically from the metric functions. Nevertheless,\nby constraining the parameters through different observations, the luminosity\nprofile could in turn be used to constrain the Noether charge and characterize\nthe spacetime, should KBHsSH exist.\n"}
+{"abstract": "  In this work, we propose a novel missile guidance algorithm that combines\ndeep learning based trajectory prediction with nonlinear model predictive\ncontrol. Although missile guidance and threat interception is a well-studied\nproblem, existing algorithms' performance degrades significantly when the\ntarget is pulling high acceleration attack maneuvers while rapidly changing its\ndirection. We argue that since most threats execute similar attack maneuvers,\nthese nonlinear trajectory patterns can be processed with modern machine\nlearning methods to build high accuracy trajectory prediction algorithms. We\ntrain a long short-term memory network (LSTM) based on a class of simulated\nstructured agile attack patterns, then combine this predictor with quadratic\nprogramming based nonlinear model predictive control (NMPC). Our method, named\nnonlinear model based predictive control with target acceleration predictions\n(NMPC-TAP), significantly outperforms compared approaches in terms of miss\ndistance, for the scenarios where the target/threat is executing agile\nmaneuvers.\n"}
+{"abstract": "  The sub-Saturn ($\\sim$4--8$R_{\\oplus}$) occurrence rate rises with orbital\nperiod out to at least $\\sim$300 days. In this work we adopt and test the\nhypothesis that the decrease in their occurrence towards the star is a result\nof atmospheric mass loss, which can transform sub-Saturns into sub-Neptunes\n($\\lesssim$4$R_{\\oplus}$) more efficiently at shorter periods. We show that\nunder the mass loss hypothesis, the sub-Saturn occurrence rate can be leveraged\nto infer their underlying core mass function, and by extension that of gas\ngiants. We determine that lognormal core mass functions peaked near\n$\\sim$10--20$M_{\\oplus}$ are compatible with the sub-Saturn period\ndistribution, the distribution of observationally-inferred sub-Saturn cores,\nand gas accretion theories. Our theory predicts that close-in sub-Saturns\nshould be $\\sim$50\\% less common and $\\sim$30\\% more massive around rapidly\nrotating stars; this should be directly testable for stars younger than\n$\\lesssim$500 Myr. We also predict that the sub-Jovian desert becomes less\npronounced and opens up at smaller orbital periods around M stars compared to\nsolar-type stars ($\\sim$0.7 days vs.~$\\sim$3 days). We demonstrate that\nexceptionally low-density sub-Saturns, \"Super-Puffs\", can survive intense\nhydrodynamic escape to the present day if they are born with even larger\natmospheres than they currently harbor; in this picture, Kepler 223 d began\nwith an envelope $\\sim$1.5$\\times$ the mass of its core and is currently losing\nits envelope at a rate $\\sim$2$\\times 10^{-3}M_{\\oplus}~\\mathrm{Myr}^{-1}$. If\nthe predictions from our theory are confirmed by observations, the core mass\nfunction we predict can also serve to constrain core formation theories of\ngas-rich planets.\n"}
+{"abstract": "  Atomic Switch Networks (ASN) comprising silver iodide (AgI) junctions, a\nmaterial previously unexplored as functional memristive elements within\nhighly-interconnected nanowire networks, were employed as a neuromorphic\nsubstrate for physical Reservoir Computing (RC). This new class of ASN-based\ndevices has been physically characterized and utilized to classify spoken digit\naudio data, demonstrating the utility of substrate-based device architectures\nwhere intrinsic material properties can be exploited to perform computation\nin-materio. This work demonstrates high accuracy in the classification of\ntemporally analyzed Free-Spoken Digit Data (FSDD). These results expand upon\nthe class of viable memristive materials available for the production of\nfunctional nanowire networks and bolster the utility of ASN-based devices as\nunique hardware platforms for neuromorphic computing applications involving\nmemory, adaptation and learning.\n"}
+{"abstract": "  This paper presents a coverage-guided grammar-based fuzzing technique for\nautomatically generating a corpus of concise test inputs for programs such as\ncompilers. We walk-through a case study of a compiler designed for education\nand the corresponding problem of generating meaningful test cases to provide to\nstudents. The prior state-of-the-art solution is a combination of fuzzing and\ntest-case reduction techniques such as variants of delta-debugging. Our key\ninsight is that instead of attempting to minimize convoluted fuzzer-generated\ntest inputs, we can instead grow concise test inputs by construction using a\nform of iterative deepening. We call this approach Bonsai Fuzzing. Experimental\nresults show that Bonsai Fuzzing can generate test corpora having inputs that\nare 16--45% smaller in size on average as compared to a fuzz-then-reduce\napproach, while achieving approximately the same code coverage and\nfault-detection capability.\n"}
+{"abstract": "  Betweeness centrality is one of the most important concepts in graph\nanalysis. It was recently extended to link streams, a graph generalization\nwhere links arrive over time. However, its computation raises non-trivial\nissues, due in particular to the fact that time is considered as continuous. We\nprovide here the first algorithms to compute this generalized betweenness\ncentrality, as well as several companion algorithms that have their own\ninterest. They work in polynomial time and space, we illustrate them on typical\nexamples, and we provide an implementation.\n"}
+{"abstract": "  Deep learning approaches often require huge datasets to achieve good\ngeneralization. This complicates its use in tasks like image-based medical\ndiagnosis, where the small training datasets are usually insufficient to learn\nappropriate data representations. For such sensitive tasks it is also important\nto provide the confidence in the predictions. Here, we propose a way to learn\nand use probabilistic labels to train accurate and calibrated deep networks\nfrom relatively small datasets. We observe gains of up to 22% in the accuracy\nof models trained with these labels, as compared with traditional approaches,\nin three classification tasks: diagnosis of hip dysplasia, fatty liver, and\nglaucoma. The outputs of models trained with probabilistic labels are\ncalibrated, allowing the interpretation of its predictions as proper\nprobabilities. We anticipate this approach will apply to other tasks where few\ntraining instances are available and expert knowledge can be encoded as\nprobabilities.\n"}
+{"abstract": "  Bayesian structure learning allows inferring Bayesian network structure from\ndata while reasoning about the epistemic uncertainty -- a key element towards\nenabling active causal discovery and designing interventions in real world\nsystems. In this work, we propose a general, fully differentiable framework for\nBayesian structure learning (DiBS) that operates in the continuous space of a\nlatent probabilistic graph representation. Contrary to existing work, DiBS is\nagnostic to the form of the local conditional distributions and allows for\njoint posterior inference of both the graph structure and the conditional\ndistribution parameters. This makes our formulation directly applicable to\nposterior inference of complex Bayesian network models, e.g., with nonlinear\ndependencies encoded by neural networks. Using DiBS, we devise an efficient,\ngeneral purpose variational inference method for approximating distributions\nover structural models. In evaluations on simulated and real-world data, our\nmethod significantly outperforms related approaches to joint posterior\ninference.\n"}
+{"abstract": "  Object detection in natural scenes can be a challenging task. In many\nreal-life situations, the visible spectrum is not suitable for traditional\ncomputer vision tasks. Moving outside the visible spectrum range, such as the\nthermal spectrum or the near-infrared (NIR) images, is much more beneficial in\nlow visibility conditions, NIR images are very helpful for understanding the\nobject's material quality. In this work, we have taken images with both the\nThermal and NIR spectrum for the object detection task. As multi-spectral data\nwith both Thermal and NIR is not available for the detection task, we needed to\ncollect data ourselves. Data collection is a time-consuming process, and we\nfaced many obstacles that we had to overcome. We train the YOLO v3 network from\nscratch to detect an object from multi-spectral images. Also, to avoid\noverfitting, we have done data augmentation and tune hyperparameters.\n"}
+{"abstract": "  We construct the systems of bi-orthogonal polynomials on the unit circle\nwhere the Toeplitz structure of the moment determinants is replaced by $\n\\det(w_{2j-k})_{0\\leq j,k \\leq N-1} $ and the corresponding Vandermonde modulus\nsquared is replaced by $ \\prod_{1 \\le j < k \\le N}(\\zeta^{2}_k -\n\\zeta^{2}_j)(\\zeta^{-1}_k - \\zeta^{-1}_j) $. This is the simplest case of a\ngeneral system of $pj-qk$ with $p,q$ co-prime integers. We derive analogues of\nthe structures well known in the Toeplitz case: third order recurrence\nrelations, determinantal and multiple-integral representations, their\nreproducing kernel and Christoffel-Darboux sum, and associated\n(Carath{\\'e}odory) functions. We close by giving full explicit details for the\nsystem defined by the simple weight $ w(\\zeta)=e^{\\zeta}$, which is a\nspecialisation of a weight arising from averages of moments of derivatives of\ncharacteristic polynomials over $USp(2N)$, $SO(2N)$ and $O^-(2N)$.\n"}
+{"abstract": "  Two-dimensional (2D) van der Waals (vdWs) materials have gathered a lot of\nattention recently. However, the majority of these materials have Curie\ntemperatures that are well below room temperature, making it challenging to\nincorporate them into device applications. In this work, we synthesized a\nroom-temperature vdW magnetic crystal Fe$_5$GeTe$_2$ with a Curie temperature\nT$_c = 332$ K, and studied its magnetic properties by vibrating sample\nmagnetometry (VSM) and broadband ferromagnetic resonance (FMR) spectroscopy.\nThe experiments were performed with external magnetic fields applied along the\nc-axis (H$\\parallel$c) and the ab-plane (H$\\parallel$ab), with temperatures\nranging from 300 K to 10 K. We have found a sizable Land\\'e g-factor difference\nbetween the H$\\parallel$c and H$\\parallel$ab cases. In both cases, the Land\\'e\ng-factor values deviated from g = 2. This indicates contribution of orbital\nangular momentum to the magnetic moment. The FMR measurements reveal that\nFe$_5$GeTe$_2$ has a damping constant comparable to Permalloy. With reducing\ntemperature, the linewidth was broadened. Together with the VSM data, our\nmeasurements indicate that Fe$_5$GeTe$_2$ transitions from ferromagnetic to\nferrimagnetic at lower temperatures. Our experiments highlight key information\nregarding the magnetic state and spin scattering processes in Fe$_5$GeTe$_2$,\nwhich promote the understanding of magnetism in Fe$_5$GeTe$_2$, leading to\nimplementations of Fe$_5$GeTe$_2$ based room-temperature spintronic devices.\n"}
+{"abstract": "  We develop a hybrid model of galactic chemical evolution that combines a\nmulti-ring computation of chemical enrichment with a prescription for stellar\nmigration and the vertical distribution of stellar populations informed by a\ncosmological hydrodynamic disc galaxy simulation. Our fiducial model adopts\nempirically motivated forms of the star formation law and star formation\nhistory, with a gradient in outflow mass loading tuned to reproduce the\nobserved metallicity gradient. With this approach, the model reproduces many of\nthe striking qualitative features of the Milky Way disc's abundance structure:\n(i) the dependence of the [O/Fe]-[Fe/H] distribution on radius $R_\\text{gal}$\nand midplane distance $|z|$; (ii) the changing shapes of the [O/H] and [Fe/H]\ndistributions with $R_\\text{gal}$ and $|z|$; (iii) a broad distribution of\n[O/Fe] at sub-solar metallicity and changes in the [O/Fe] distribution with\n$R_\\text{gal}$, $|z|$, and [Fe/H]; (iv) a tight correlation between [O/Fe] and\nstellar age for [O/Fe] $>$ 0.1; (v) a population of young and intermediate-age\n$\\alpha$-enhanced stars caused by migration-induced variability in the Type Ia\nsupernova rate; (vi) non-monotonic age-[O/H] and age-[Fe/H] relations, with\nlarge scatter and a median age of $\\sim$4 Gyr near solar metallicity.\nObservationally motivated models with an enhanced star formation rate $\\sim$2\nGyr ago improve agreement with the observed age-[Fe/H] and age-[O/H] relations,\nbut worsen agreement with the observed age-[O/Fe] relation. None of our models\npredict an [O/Fe] distribution with the distinct bimodality seen in the\nobservations, suggesting that more dramatic evolutionary pathways are required.\nAll code and tables used for our models are publicly available through the\nVersatile Integrator for Chemical Evolution (VICE;\nhttps://pypi.org/project/vice).\n"}
+{"abstract": "  We study the problem of zeroth-order (black-box) optimization of a Lipschitz\nfunction $f$ defined on a compact subset $\\mathcal X$ of $\\mathbb R^d$, with\nthe additional constraint that algorithms must certify the accuracy of their\nrecommendations. We characterize the optimal number of evaluations of any\nLipschitz function $f$ to find and certify an approximate maximizer of $f$ at\naccuracy $\\varepsilon$. Under a weak assumption on $\\mathcal X$, this optimal\nsample complexity is shown to be nearly proportional to the integral\n$\\int_{\\mathcal X} \\mathrm{d}\\boldsymbol x/( \\max(f) - f(\\boldsymbol x) +\n\\varepsilon )^d$. This result, which was only (and partially) known in\ndimension $d=1$, solves an open problem dating back to 1991. In terms of\ntechniques, our upper bound relies on a slightly improved analysis of the DOO\nalgorithm that we adapt to the certified setting and then link to the above\nintegral. Our instance-dependent lower bound differs from traditional\nworst-case lower bounds in the Lipschitz setting and relies on a local\nworst-case analysis that could likely prove useful for other learning tasks.\n"}
+{"abstract": "  Hyperbolic phonon polaritons (HPhPs) sustained in van der Waals (vdW)\nmaterials exhibit extraordinary capabilities of confining long-wave\nelectromagnetic fields to the deep subwavelength scale. In stark contrast to\nthe uniaxial vdW hyperbolic materials such as hexagonal boron nitride (h-BN),\nthe recently emerging biaxial hyperbolic materials such as {\\alpha}-MoO3 and\n{\\alpha}-V2O5 further bring new degree of freedoms in controlling light at the\nflatland, due to their distinctive in-plane hyperbolic dispersion. However, the\ncontrolling and focusing of such in-plane HPhPs are to date remain elusive.\nHere, we propose a versatile technique for launching, controlling and focusing\nof in-plane HPhPs in {\\alpha}-MoO3 with geometrically designed plasmonic\nantennas. By utilizing high resolution near-field optical imaging technique, we\ndirectly excited and mapped the HPhPs wavefronts in real space. We find that\nsubwavelength manipulating and focusing behavior are strongly dependent on the\ncurvature of antenna extremity. This strategy operates effectively in a\nbroadband spectral region. These findings can not only provide fundamental\ninsights into manipulation of light by biaxial hyperbolic crystals at\nnanoscale, but also open up new opportunities for planar nanophotonic\napplications.\n"}
+{"abstract": "  Secret Unknown Ciphers (SUC) have been proposed recently as digital\nclone-resistant functions overcoming some of Physical(ly) Unclonable Functions\n(PUF) downsides, mainly their inconsistency because of PUFs analog nature. In\nthis paper, we propose a new practical mechanism for creating internally random\nciphers in modern volatile and non-volatile SoC FPGAs, coined as SRAM-SUC. Each\ncreated random cipher inside a SoC FPGA constitutes a robust digital PUF. This\nwork also presents a class of involutive SUCs, optimized for the targeted SoC\nFPGA architecture, as sample realization of the concept; it deploys a generated\nclass of involutive 8-bit S-Boxes, that are selected randomly from a defined\nlarge set through an internal process inside the SoC FPGA. Hardware and\nsoftware implementations show that the resulting SRAM-SUC has ultra-low latency\ncompared to well-known PUF-based authentication mechanisms. SRAM-SUC requires\nonly $2.88/0.72 \\mu s$ to generate a response for a challenge at 50/200 MHz\nrespectively. This makes SRAM-SUC a promising and appealing solution for\nUltra-Reliable Low Latency Communication (URLLC).\n"}
+{"abstract": "  It is known that any $m$-gonal form of $\\rank n \\ge 5$ is almost regular.\n  In this article, we study the sufficiently large integers which are\nrepresented by (almost regular) $m$-gonal forms of $\\rank n \\ge 6$.\n"}
+{"abstract": "  Two stochastic models are proposed to describe the evolution of the COVID-19\npandemic. In the first model the population is partitioned into four\ncompartments: susceptible $S$, infected $I$, removed $R$ and dead people $D$.\nIn order to have a cross validation, a deterministic version of such a model is\nalso devised which is represented by a system of ordinary differential\nequations with delays. In the second stochastic model two further compartments\nare added: the class $A$ of asymptomatic individuals and the class $L$ of\nisolated infected people. Effects such as social distancing measures are easily\nincluded and the consequences are analyzed. Numerical solutions are obtained\nwith Monte Carlo simulations. Quantitative predictions are provided which can\nbe useful for the evaluation of political measures, e.g. the obtained results\nsuggest that strategies based on herd immunity are too risky.\n"}
+{"abstract": "  We develop a simple and elegant method for lossless compression using latent\nvariable models, which we call 'bits back with asymmetric numeral systems'\n(BB-ANS). The method involves interleaving encode and decode steps, and\nachieves an optimal rate when compressing batches of data. We demonstrate it\nfirstly on the MNIST test set, showing that state-of-the-art lossless\ncompression is possible using a small variational autoencoder (VAE) model. We\nthen make use of a novel empirical insight, that fully convolutional generative\nmodels, trained on small images, are able to generalize to images of arbitrary\nsize, and extend BB-ANS to hierarchical latent variable models, enabling\nstate-of-the-art lossless compression of full-size colour images from the\nImageNet dataset. We describe 'Craystack', a modular software framework which\nwe have developed for rapid prototyping of compression using deep generative\nmodels.\n"}
+{"abstract": "  We construct a Green function, which can identify the topological nature of\ninteracting systems. It is equivalent to the single-particle Green function of\neffective non-interacting particles, the Bloch Hamiltonian of which is given by\nthe inverse of the full Green function of the original interacting particles at\nzero frequency. The topological nature of the interacting insulators is\noriginated from the coincidence of the poles and the zeros of the diagonal\nelements of the constructed Green function. The cross of the zeros in the\nmomentum space closely relates to the topological nature of insulators. As a\ndemonstration, using the zero's cross, we identify the topological phases of\nmagnetic insulators, where both the ionic potential and the spin exchange\nbetween conduction electrons and magnetic moments are present together with the\nspin-orbital coupling. The topological phase identification is consistent with\nthe topological invariant of the magnetic insulators. We also found an\nantiferromagnetic state with topologically breaking of the spin symmetry, where\nelectrons with one spin orientation are in topological insulating state, while\nelectrons with the opposite spin orientation are in topologically trivial one.\n"}
+{"abstract": "  Radical progress in the field of deep learning (DL) has led to unprecedented\naccuracy in diverse inference tasks. As such, deploying DL models across mobile\nplatforms is vital to enable the development and broad availability of the\nnext-generation intelligent apps. Nevertheless, the wide and optimised\ndeployment of DL models is currently hindered by the vast system heterogeneity\nof mobile devices, the varying computational cost of different DL models and\nthe variability of performance needs across DL applications. This paper\nproposes OODIn, a framework for the optimised deployment of DL apps across\nheterogeneous mobile devices. OODIn comprises a novel DL-specific software\narchitecture together with an analytical framework for modelling DL\napplications that: (1) counteract the variability in device resources and DL\nmodels by means of a highly parametrised multi-layer design; and (2) perform a\nprincipled optimisation of both model- and system-level parameters through a\nmulti-objective formulation, designed for DL inference apps, in order to adapt\nthe deployment to the user-specified performance requirements and device\ncapabilities. Quantitative evaluation shows that the proposed framework\nconsistently outperforms status-quo designs across heterogeneous devices and\ndelivers up to 4.3x and 3.5x performance gain over highly optimised platform-\nand model-aware designs respectively, while effectively adapting execution to\ndynamic changes in resource availability.\n"}
+{"abstract": "  Theoretically, domain adaptation is a well-researched problem. Further, this\ntheory has been well-used in practice. In particular, we note the bound on\ntarget error given by Ben-David et al. (2010) and the well-known\ndomain-aligning algorithm based on this work using Domain Adversarial Neural\nNetworks (DANN) presented by Ganin and Lempitsky (2015). Recently, multiple\nvariants of DANN have been proposed for the related problem of domain\ngeneralization, but without much discussion of the original motivating bound.\nIn this paper, we investigate the validity of DANN in domain generalization\nfrom this perspective. We investigate conditions under which application of\nDANN makes sense and further consider DANN as a dynamic process during\ntraining. Our investigation suggests that the application of DANN to domain\ngeneralization may not be as straightforward as it seems. To address this, we\ndesign an algorithmic extension to DANN in the domain generalization case. Our\nexperimentation validates both theory and algorithm.\n"}
+{"abstract": "  Symbolic music understanding, which refers to the understanding of music from\nthe symbolic data (e.g., MIDI format, but not audio), covers many music\napplications such as genre classification, emotion classification, and music\npieces matching. While good music representations are beneficial for these\napplications, the lack of training data hinders representation learning.\nInspired by the success of pre-training models in natural language processing,\nin this paper, we develop MusicBERT, a large-scale pre-trained model for music\nunderstanding. To this end, we construct a large-scale symbolic music corpus\nthat contains more than 1 million music songs. Since symbolic music contains\nmore structural (e.g., bar, position) and diverse information (e.g., tempo,\ninstrument, and pitch), simply adopting the pre-training techniques from NLP to\nsymbolic music only brings marginal gains. Therefore, we design several\nmechanisms, including OctupleMIDI encoding and bar-level masking strategy, to\nenhance pre-training with symbolic music data. Experiments demonstrate the\nadvantages of MusicBERT on four music understanding tasks, including melody\ncompletion, accompaniment suggestion, genre classification, and style\nclassification. Ablation studies also verify the effectiveness of our designs\nof OctupleMIDI encoding and bar-level masking strategy in MusicBERT.\n"}
+{"abstract": "  We present an ab initio derivation method for effective low-energy\nHamiltonians of material with strong spin-orbit interactions. The effective\nHamiltonian is described in terms of the Wannier function in the spinor form,\nand effective interactions are derived with the constrained random phase\napproximation (cRPA) method. Based on this formalism and the developed code, we\nderive an effective Hamiltonian of a strong spin-orbit interaction material\nCa5Ir3O12. This system consists of three edge-shared IrO6 octahedral chains\narranged along the c axis, and the three Ir atoms in the ab plane compose a\ntriangular lattice. For such a complicated structure, we need to set up the\nWannier spinor function under the local coordinate system. We found that a\ndensity-functional band structure near the Fermi level is formed by local dxy\nand dyz orbitals. Then, we constructed the ab initio dxy/dyz model. The\nestimated nearest neighbor transfer t is close to 0.2 eV, and the cRPA onsite U\nand neighboring V electronic interactions are found to be 2.4-2.5 eV and 1 eV,\nrespectively. The resulting characteristic correlation strength defined by\n(U-V)/t is above 7, and thus this material is classified as a strongly\ncorrelated electron system. The onsite transfer integral involved in the\nspin-orbit interaction is 0.2 eV, which is comparable to the onsite exchange\nintegrals near 0.2 eV, indicating that the spin-orbit-interaction physics would\ncompete with the Hund physics. Based on these calculated results, we discuss\npossible rich ground-state low-energy electronic structures of spin, charge and\norbitals with competing Hund, spin-orbit and strong correlation physics.\n"}
+{"abstract": "  We study the problem of {\\em crowdsourced PAC learning} of Boolean-valued\nfunctions through enriched queries, a problem that has attracted a surge of\nrecent research interests. In particular, we consider that the learner may\nquery the crowd to obtain a label of a given instance or a comparison tag of a\npair of instances. This is a challenging problem and only recently have\nbudget-efficient algorithms been established for the scenario where the\nmajority of the crowd are correct. In this work, we investigate the\nsignificantly more challenging case that the majority are incorrect which\nrenders learning impossible in general. We show that under the {semi-verified\nmodel} of Charikar~et~al.~(2017), where we have (limited) access to a trusted\noracle who always returns the correct annotation, it is possible to learn the\nunderlying function while the labeling cost is significantly mitigated by the\nenriched and more easily obtained queries.\n"}
+{"abstract": "  Ultrasound tomography (UST) scanners allow quantitative images of the human\nbreast's acoustic properties to be derived with potential applications in\nscreening, diagnosis and therapy planning. Time domain full waveform inversion\n(TD-FWI) is a promising UST image formation technique that fits the parameter\nfields of a wave physics model by gradient-based optimization. For high\nresolution 3D UST, it holds three key challenges: Firstly, its central building\nblock, the computation of the gradient for a single US measurement, has a\nrestrictively large memory footprint. Secondly, this building block needs to be\ncomputed for each of the $10^3-10^4$ measurements, resulting in a massive\nparallel computation usually performed on large computational clusters for\ndays. Lastly, the structure of the underlying optimization problem may result\nin slow progression of the solver and convergence to a local minimum. In this\nwork, we design and evaluate a comprehensive computational strategy to overcome\nthese challenges: Firstly, we exploit a gradient computation based on time\nreversal that dramatically reduces the memory footprint at the expense of one\nadditional wave simulation per source. Secondly, we break the dependence on the\nnumber of measurements by using source encoding (SE) to compute stochastic\ngradient estimates. Also we describe a more accurate, TD-specific SE technique\nwith a finer variance control and use a state-of-the-art stochastic LBFGS\nmethod. Lastly, we design an efficient TD multi-grid scheme together with\npreconditioning to speed up the convergence while avoiding local minima. All\ncomponents are evaluated in extensive numerical proof-of-concept studies\nsimulating a bowl-shaped 3D UST breast scanner prototype. Finally, we\ndemonstrate that their combination allows us to obtain an accurate 442x442x222\nvoxel image with a resolution of 0.5mm using Matlab on a single GPU within 24h.\n"}
+{"abstract": "  We study some local spectral properties of contraction operators on $\\ell_p$,\n$1<p<\\infty$ from a Baire category point of view, with respect to the\nStrong$^*$ Operator Topology. In particular, we show that a typical contraction\non $\\ell_p$ has Dunford's Property (C) but neither Bishop's Property $(\\beta)$\nnor the Decomposition Property $(\\delta)$, and is completely indecomposable. We\nalso obtain some results regarding the asymptotic behavior of orbits of typical\ncontractions on $\\ell_p$.\n"}
+{"abstract": "  The morphological classification of galaxies is a relevant probe for galaxy\nevolution and unveils its connection with cosmological structure formation. To\nthis scope, it is fundamental to recover galaxy morphologies over large areas\nof the sky. In this paper, we present a morphological catalogue for galaxies in\nthe Stripe-82 area, observed with S-PLUS, till a magnitude limit of $r\\le17$,\nusing the state-of-the-art of Convolutional Neural Networks (CNNs) for computer\nvision. This analysis will then be extended to the whole S-PLUS survey data,\ncovering $\\simeq 9300$ $deg^{2}$ of the celestial sphere in twelve optical\nbands. We find that the network's performance increases with 5 broad bands and\nadditional 3 narrow bands compared to our baseline with 3 bands. However, it\ndoes lose performance when using the full $12$ band image information.\nNevertheless, the best result is achieved with 3 bands, when using pre-trained\nnetwork weights in an ImageNet dataset. These results enhance the importance of\nprevious knowledge in the neural network weights based on training in non\nrelated extensive datasets. Thus, we release a model pre-trained in several\nbands that could be adapted to other surveys. We develop a catalogue of 3274\ngalaxies in Stripe-82 that are not presented in Galaxy Zoo 1 (GZ1). We also add\nclassification to 4686 galaxies considered ambiguous in GZ1 dataset. Finally,\nwe present a prospect of a novel way to take advantage of $12$ bands\ninformation for morphological classification using multiband morphometric\nfeatures. The morphological catalogues are publicly available.\n"}
+{"abstract": "  In this paper, we consider the conditional regularity of weak solution to the\n3D Navier--Stokes equations. More precisely, we prove that if one directional\nderivative of velocity, say $\\partial_3 u,$ satisfies $\\partial_3 u \\in\nL^{p_0,1}(0,T; L^{q_0}(\\mathbb{R}^3))$ with $\\frac{2}{p_{0}}+\\frac{3}{q_{0}}=2$\nand $\\frac{3}{2}<q_0< +\\infty,$ then the weak solution is regular on $(0,T].$\nThe proof is based on the new local energy estimates introduced by Chae-Wolf\n(arXiv:1911.02699) and Wang-Wu-Zhang (arXiv:2005.11906).\n"}
+{"abstract": "  A bridgeless cubic graph $G$ is said to have a 2-bisection if there exists a\n2-vertex-colouring of $G$ (not necessarily proper) such that: (i) the colour\nclasses have the same cardinality, and (ii) the monochromatic components are\neither an isolated vertex or an edge. In 2016, Ban and Linial conjectured that\nevery bridgeless cubic graph, apart from the well-known Petersen graph, admits\na 2-bisection. In the same paper it was shown that every Class I bridgeless\ncubic graph admits such a bisection. The Class II bridgeless cubic graphs which\nare critical to many conjectures in graph theory are snarks, in particular,\nthose with excessive index at least 5, that is, whose edge-set cannot be\ncovered by four perfect matchings. Moreover, Esperet et al. state that a\npossible counterexample to Ban--Linial's Conjecture must have circular flow\nnumber at least 5. The same authors also state that although empirical evidence\nshows that several graphs obtained from the Petersen graph admit a 2-bisection,\nthey can offer nothing in the direction of a general proof. Despite some\nsporadic computational results, until now, no general result about snarks\nhaving excessive index and circular flow number both at least 5 has been\nproven. In this work we show that treelike snarks, which are an infinite family\nof snarks heavily depending on the Petersen graph and with both their circular\nflow number and excessive index at least 5, admit a 2-bisection.\n"}
+{"abstract": "  We analyze the Nelson-Barr approach to the Strong CP Problem. We derive the\nnecessary conditions in order to simultaneously reproduce the CKM phase and the\nquark masses. Then we quantify the irreducible contributions to the QCD\ntopological angle, namely the corrections arising from loops of the colored\nfermion mediators that characterize these models. Corrections analytic in the\ncouplings first arise at 3-loop order and are safely below current bounds;\nnon-analytic effects are 2-loop order and decouple as the mediators exceed a\nfew TeV. We discuss collider, electroweak, and flavor bounds and argue that\nmost of the parameter space above the TeV scale is still allowed in models with\ndown-type mediators, whereas other scenarios are more severely constrained.\nWith two or more families of mediators the dominant experimental bound is due\nto the neutron electric dipole moment.\n"}
+{"abstract": "  Chiral Effective Field Theory ($\\chi$EFT) has been extensively used to study\nthe $NN$ interaction during the last three decades. In Effective Field Theories\n(EFTs) the renormalization is performed order by order including the necessary\ncounter terms. Due to the strong character of the $NN$ interaction a\nnon-perturbative resummation is needed. In this work we will review some of the\nmethods proposed to completely remove cutoff dependencies. The methods covered\nare renormalization with boundary conditions, renormalization with one counter\nterm in momentum space (or equivalently substractive renormalization) and the\nexact $N/D$ method. The equivalence between the methods up to one\nrenormalization condition will be checked showing results in the $NN$ system.\nThe exact $N/D$ method allows to go beyond the others, and using a toy model it\nis shown how it can renormalize singular repulsive interactions.\n"}
+{"abstract": "  The inelastic dark matter model is one kind of popular models for the light\ndark matter (DM) below $O(1)$ GeV. If the mass splitting between DM excited and\nground states is small enough, the co-annihilation becomes the dominant channel\nfor thermal relic density and the DM excited state can be long-lived at the\ncollider scale. We study scalar and fermion inelastic dark matter models for $\n{\\cal O}(1) $ GeV DM at Belle II with $ U(1)_D $ dark gauge symmetry broken\ninto its $Z_2$ subgroup. We focus on dilepton displaced vertex signatures from\ndecays of the DM excited state. With the help of precise displaced vertex\ndetection ability at Belle II, we can explore the DM spin, mass and mass\nsplitting between DM excited and ground states. Especially, we show scalar and\nfermion DM candidates can be discriminated and the mass and mass splitting of\nDM sector can be determined within the percentage of deviation for some\nbenchmark points. Furthermore, the allowed parameter space to explain the\nexcess of muon $(g-2)_\\mu$ is also studied and it can be covered in our\ndisplaced vertex analysis during the early stage of Belle II experiment.\n"}
+{"abstract": "  Hyperkalemia is a potentially life-threatening condition that can lead to\nfatal arrhythmias. Early identification of high risk patients can inform\nclinical care to mitigate the risk. While hyperkalemia is often a complication\nof acute kidney injury (AKI), it also occurs in the absence of AKI. We\ndeveloped predictive models to identify intensive care unit (ICU) patients at\nrisk of developing hyperkalemia by using the Medical Information Mart for\nIntensive Care (MIMIC) and the eICU Collaborative Research Database (eICU-CRD).\nOur methodology focused on building multiple models, optimizing for\ninterpretability through model selection, and simulating various clinical\nscenarios.\n  In order to determine if our models perform accurately on patients with and\nwithout AKI, we evaluated the following clinical cases: (i) predicting\nhyperkalemia after AKI within 14 days of ICU admission, (ii) predicting\nhyperkalemia within 14 days of ICU admission regardless of AKI status, and\ncompared different lead times for (i) and (ii). Both clinical scenarios were\nmodeled using logistic regression (LR), random forest (RF), and XGBoost.\n  Using observations from the first day in the ICU, our models were able to\npredict hyperkalemia with an AUC of (i) 0.79, 0.81, 0.81 and (ii) 0.81, 0.85,\n0.85 for LR, RF, and XGBoost respectively. We found that 4 out of the top 5\nfeatures were consistent across the models. AKI stage was significant in the\nmodels that included all patients with or without AKI, but not in the models\nwhich only included patients with AKI. This suggests that while AKI is\nimportant for hyperkalemia, the specific stage of AKI may not be as important.\nOur findings require further investigation and confirmation.\n"}
+{"abstract": "  The transmission through a magnetic layer of correlated electrons sandwiched\nbetween non-interacting normal-metal leads is studied within model\ncalculations. We consider the linear regime in the framework of the\nMeir-Wingreen formalism, according to which the transmission can be interpreted\nas the overlap of the spectral function of the surface layer of the leads with\nthat of the central region. By analyzing these spectral functions, we show that\na change of the coupling parameter between the leads and the central region\nsignificantly and non-trivially affects the conductance. The role of band\nstructure effects for the transmission is clarified. For a strong coupling\nbetween the leads and the central layer, high-intensity localized states are\nformed outside the overlapping bands, while for weaker coupling this\nhigh-intensity spectral weight is formed within the leads' continuum band\naround the Fermi energy. A local Coulomb interaction in the central region\nmodifies the high-intensity states, and hence the transmission. For the present\nsetup, the major effect of the local interaction consists in shifts of the band\nstructure, since any sharp features are weakened due to the macroscopic\nextension of the configuration in the directions perpendicular to the transport\ndirection.\n"}
+{"abstract": "  We use photometric and kinematic data from Gaia DR2 to explore the structure\nof the star forming region associated with the molecular cloud of Perseus.\nApart from the two well known clusters, IC 348 and NGC 1333, we present five\nnew clustered groups of young stars, which contain between 30 and 300 members,\nnamed Autochthe, Alcaeus, Heleus, Electryon and Mestor. We demonstrate these\nare co-moving groups of young stars, based on how the candidate members are\ndistributed in position, proper motion, parallax and colour-magnitude space. By\ncomparing their colour-magnitude diagrams to isochrones we show that they have\nages between 1 and 5 Myr. Using 2MASS and WISE colours we find that the\nfraction of stars with discs in each group ranges from 10 to 50 percent. The\nyoungest of the new groups is also associated with a reservoir of cold dust,\naccording to the Planck map at 353 GHz. We compare the ages and proper motions\nof the five new groups to those of IC 348 and NGC 1333. Autochthe is clearly\nlinked with NGC 1333 and may have formed in the same star formation event. The\nseven groups separate roughly into two sets which share proper motion, parallax\nand age: Heleus, Electryon, Mestor as the older set, and NGC 1333, Autochthe as\nthe younger set. Alcaeus is kinematically related to the younger set, but at a\nmore advanced age, while the properties of IC 348 overlap with both sets. All\nolder groups in this star forming region are located at higher galactic\nlatitude.\n"}
+{"abstract": "  In this paper, we derive a set of equations of motions for binaries on\neccentric orbits undergoing spin-induced precession that can efficiently be\nintegrated on the radiation-reaction timescale. We find a family of solutions\nwith a computation cost improved by a factor $10$ - $50$ down to $\\sim 10$ ms\nper waveform evaluation compared to waveforms obtained by directly integrating\nthe precession equations, that maintain a mismatch of the order $10^{-4}$ -\n$10^{-6}$ for waveforms lasting a million orbital cycles and a thousand\nspin-induced precession cycles. We express it in terms of parameters that make\nthe solution regular in the equal-mass limit, thus bypassing a problem of\nprevious similar solutions. We point to ways in which the solution presented in\nthis paper can be perturbed to take into account effects such as general\nquadrupole momenta and post-Newtonian corrections to the precession equations.\nThis new waveform, with its improved efficiency and its accuracy, makes\npossible Bayesian parameter estimation using the full spin and eccentricity\nparameter volume for long lasting inspiralling signals such as stellar-origin\nblack hole binaries observed by LISA.\n"}
+{"abstract": "  COVID-19 Prevention, which combines the soft approaches and best practices\nfor public health safety, is the only recommended solution from the health\nscience and management society side considering the pandemic era. In an attempt\nto evaluate the validity of such claims in a conflict and COVID-19-affected\ncountry like Afghanistan, we conducted a large-scale digital social experiment\nusing conversational AI and social platforms from an info-epidemiology and an\ninfoveillance perspective. This served as a means to uncover an underling\ntruth, give large-scale facilitation support, extend the soft impact of\ndiscussion to multiple sites, collect, diverge, converge and evaluate a large\namount of opinions and concerns from health experts, patients and local people,\ndeliberate on the data collected and explore collective prevention approaches\nof COVID-19. Finally, this paper shows that deciding a prevention measure that\nmaximizes the probability of finding the ground truth is intrinsically\ndifficult without utilizing the support of an AI-enabled discussion systems.\n"}
+{"abstract": "  We continue the investigation of systems of hereditarily rigid relations\nstarted in Couceiro, Haddad, Pouzet and Sch\\\"olzel [1]. We observe that on a\nset $V$ with $m$ elements, there is a hereditarily rigid set $\\mathcal R$ made\nof $n$ tournaments if and only if $m(m-1)\\leq 2^n$. We ask if the same\ninequality holds when the tournaments are replaced by linear orders. This\nproblem has an equivalent formulation in terms of separation of linear orders.\nLet $h_{\\rm Lin}(m)$ be the least cardinal $n$ such that there is a family\n$\\mathcal R$ of $n$ linear orders on an $m$-element set $V$ such that any two\ndistinct ordered pairs of distinct elements of $V$ are separated by some member\nof $\\mathcal R$, then $ \\lceil \\log_2 (m(m-1))\\rceil\\leq h_{\\rm Lin}(m)$ with\nequality if $m\\leq 7$. We ask whether the equality holds for every $m$. We\nprove that $h_{\\rm Lin}(m+1)\\leq h_{\\rm Lin}(m)+1$. If $V$ is infinite, we show\nthat $h_{\\rm Lin}(m)= \\aleph_0$ for $m\\leq 2^{\\aleph_0}$. More generally, we\nprove that the two equalities $h_{\\rm Lin}(m)= log_2 (m)= d({\\rm Lin}(V))$\nhold, where $\\log_2 (m)$ is the least cardinal $\\mu$ such that $m\\leq 2^\\mu$,\nand $d({\\rm Lin}(V))$ is the topological density of the set ${\\rm Lin}(V)$ of\nlinear orders on $V$ (viewed as a subset of the power set $\\mathcal{P}(V\\times\nV)$ equipped with the product topology). These equalities follow from the {\\it\nGeneralized Continuum Hypothesis}, but we do not know whether they hold without\nany set theoretical hypothesis.\n"}
+{"abstract": "  The pantograph differential equation and its solution, the deformed\nexponential function, are remarkable objects that appear in areas as diverse as\ncombinatorics, number theory, statistical mechanics, and electrical\nengineering. In this article we describe a new surprising application of these\nobjects in graph theory, by showing that the set of all cliques is not forcing\nfor quasirandomness. This provides a natural example of an infinite family of\ngraphs, which is not forcing, and answers a natural question posed by P. Horn.\n"}
+{"abstract": "  Classical, i.e. non-quantum, communications include configurations with\nmultiple-input multiple-output (MIMO) channels. Some associated signal\nprocessing tasks consider these channels in a symmetric way, i.e. by assigning\nthe same role to all channel inputs, and similarly to all channel outputs.\nThese tasks especially include channel identification/estimation and channel\nequalization, tightly connected with source separation. Their most challenging\nversion is the blind one, i.e. when the receivers have (almost) no prior\nknowledge about the emitted signals. Other signal processing tasks consider\nclassical communication channels in an asymmetric way. This especially includes\nthe situation when data are sent by Emitter 1 to Receiver 1 through a main\nchannel, and an \"intruder\" (including Receiver 2) interferes with that channel\nso as to extract information, thus performing so-called eavesdropping, while\nReceiver 1 may aim at detecting that intrusion. Part of the above processing\ntasks have been extended to quantum channels, including those that have several\nquantum bits (qubits) at their input and output. For such quantum channels,\nbeyond previously reported work for symmetric scenarios, we here address\nasymmetric (blind and non-blind) ones, with emphasis on intrusion detection and\nadditional comments about eavesdropping. To develop fundamental concepts, we\nfirst consider channels with exchange coupling as a toy model. We especially\nuse the general quantum information processing framework that we recently\ndeveloped, to derive new attractive intrusion detection methods based on a\nsingle preparation of each state. Finally, we discuss how the proposed methods\nmight be extended, beyond the specific class of channels analyzed here.\n"}
+{"abstract": "  Sensor and control data of modern mechatronic systems are often available as\nheterogeneous time series with different sampling rates and value ranges.\nSuitable classification and regression methods from the field of supervised\nmachine learning already exist for predictive tasks, for example in the context\nof condition monitoring, but their performance scales strongly with the number\nof labeled training data. Their provision is often associated with high effort\nin the form of person-hours or additional sensors. In this paper, we present a\nmethod for unsupervised feature extraction using autoencoder networks that\nspecifically addresses the heterogeneous nature of the database and reduces the\namount of labeled training data required compared to existing methods. Three\npublic datasets of mechatronic systems from different application domains are\nused to validate the results.\n"}
+{"abstract": "  This paper proposes a receding horizon active learning and control problem\nfor dynamical systems in which Gaussian Processes (GPs) are utilized to model\nthe system dynamics. The active learning objective in the optimization problem\nis presented by the exact conditional differential entropy of GP predictions at\nmultiple steps ahead, which is equivalent to the log determinant of the GP\nposterior covariance matrix. The resulting non-convex and complex optimization\nproblem is solved by the Sequential Convex Programming algorithm that exploits\nthe first-order approximations of non-convex functions. Simulation results of\nan autonomous racing car example verify that using the proposed method can\nsignificantly improve data quality for model learning while solving time is\nhighly promising for real-time applications.\n"}
+{"abstract": "  It is well known the method of determining the center of mass of a triangular\npiece in which it is hanged from each one of its vertices while drawing from\nthe vertice its verticals. The intersection of the three verticals is\nconsidered as the center of mass, verified by equilibrating the piece over a\npoint object. But not everybody knows that the method was developed by\nArquimedes 2,300 years ago, determining the geometrical elements to the medians\nof the triangle. He demonstrated theoretically its result and, inspired on his\ndemonstration we developed another one, which lends to the ideia and methods of\nthe differential and integral calculus.\n"}
+{"abstract": "  This paper proposes integrating semantics-oriented similarity representation\ninto RankingMatch, a recently proposed semi-supervised learning method. Our\nmethod, dubbed ReRankMatch, aims to deal with the case in which labeled and\nunlabeled data share non-overlapping categories. ReRankMatch encourages the\nmodel to produce the similar image representations for the samples likely\nbelonging to the same class. We evaluate our method on various datasets such as\nCIFAR-10, CIFAR-100, SVHN, STL-10, and Tiny ImageNet. We obtain promising\nresults (4.21% error rate on CIFAR-10 with 4000 labels, 22.32% error rate on\nCIFAR-100 with 10000 labels, and 2.19% error rate on SVHN with 1000 labels)\nwhen the amount of labeled data is sufficient to learn semantics-oriented\nsimilarity representation. The code is made publicly available at\nhttps://github.com/tqtrunghnvn/ReRankMatch.\n"}
+{"abstract": "  This paper studies the channel capacity of intensity-modulation\ndirect-detection (IM/DD) visible light communication (VLC) systems under both\noptical and electrical power constraints. Specifically, it derives the\nasymptotic capacities in the high and low signal-to-noise ratio (SNR) regimes\nunder peak, first-moment, and second-moment constraints. The results show that\nfirst- and second-moment constraints are never simultaneously active in the\nasymptotic low-SNR regime, and only in few cases in the asymptotic high-SNR\nregime. Moreover, the second-moment constraint is more stringent in the\nasymptotic low-SNR regime than in the high-SNR regime.\n"}
+{"abstract": "  Molecular motor gliding motility assays based on myosin/actin or\nkinesin/microtubules are of interest for nanotechnology applications ranging\nfrom cargo-trafficking in lab-on-a-chip devices to novel biocomputation\nstrategies. Prototype systems are typically monitored by expensive and bulky\nfluorescence microscopy systems and the development of integrated, direct\nelectric detection of single filaments would strongly benefit applications and\nscale-up. We present estimates for the viability of such a detector by\ncalculating the electrostatic potential change generated at a carbon nanotube\ntransistor by a motile actin filament or microtubule under realistic gliding\nassay conditions. We combine this with detection limits based on previous\nstate-of-the-art experiments using carbon nanotube transistors to detect\ncatalysis by a bound lysozyme molecule and melting of a bound short-strand DNA\nmolecule. Our results show that detection should be possible for both actin and\nmicrotubules using existing low ionic strength buffers given good device\ndesign, e.g., by raising the transistor slightly above the guiding channel\nfloor. We perform studies as a function of buffer ionic strength, height of the\ntransistor above the guiding channel floor, presence/absence of the casein\nsurface passivation layer for microtubule assays and the linear charge density\nof the actin filaments/microtubules. We show that detection of microtubules is\na more likely prospect given their smaller height of travel above the surface,\nhigher negative charge density and the casein passivation, and may possibly be\nachieved with the nanoscale transistor sitting directly on the guiding channel\nfloor.\n"}
+{"abstract": "  Entities, as the essential elements in relation extraction tasks, exhibit\ncertain structure. In this work, we formulate such structure as distinctive\ndependencies between mention pairs. We then propose SSAN, which incorporates\nthese structural dependencies within the standard self-attention mechanism and\nthroughout the overall encoding stage. Specifically, we design two alternative\ntransformation modules inside each self-attention building block to produce\nattentive biases so as to adaptively regularize its attention flow. Our\nexperiments demonstrate the usefulness of the proposed entity structure and the\neffectiveness of SSAN. It significantly outperforms competitive baselines,\nachieving new state-of-the-art results on three popular document-level relation\nextraction datasets. We further provide ablation and visualization to show how\nthe entity structure guides the model for better relation extraction. Our code\nis publicly available.\n"}
+{"abstract": "  We introduce \\emph{k-positive representations}, a large class of\n$\\{1,\\ldots,k\\}$--Anosov surface group representations into PGL(E) that share\nmany features with Hitchin representations, and we study their degenerations:\nunless they are Hitchin, they can be deformed to non-discrete representations,\nbut any limit is at least (k-3)-positive and irreducible limits are\n(k-1)-positive. A major ingredient, of independent interest, is a general limit\ntheorem for positively ratioed representations.\n"}
+{"abstract": "  The recent availability of commercial-off-the-shelf (COTS) legged robot\nplatforms have opened up new opportunities in deploying legged systems into\ndifferent scenarios. While the main advantage of legged robots is their ability\nto traverse unstructured terrain, there are still large gaps between what robot\nplatforms can achieve and their animal counterparts. Therefore, when deploying\nas part of a heterogeneous robot team of different platforms, it is beneficial\nto understand the different scenarios where a legged platform would perform\nbetter than a wheeled, tracked or aerial platform. Two COTS quadruped robots,\nGhost Robotics' Vision 60 and Boston Dynamics' Spot, were deployed into a\nheterogeneous team. A description of some of the challenges faced while\nintegrating the platforms, as well as some experiments in traversing different\nterrains are provided to give insight into the real-world deployment of legged\nrobots.\n"}
+{"abstract": "  We give a simple proof of a classical theorem by A.M\\'at\\'e, P.Nevai, and\nV.Totik on asymptotic behavior of orthogonal polynomials on the unit circle. It\nis based on a new real-variable approach involving an entropy estimate for the\northogonality measure. Our second result is an extension of a theorem by\nG.Freud on averaged convergence of Fourier series. We also discuss some related\nopen problems in the theory of orthogonal polynomials on the unit circle.\n"}
+{"abstract": "  Location-based games (LBG) impose virtual spaces on top of physical\nlocations. Studies have explored LBG from various perspectives. However, a\ncomprehensive study of who these players are, their traits, their\ngratifications, and the links between them is conspicuously absent from the\nliterature. In this paper, we aim to address this lacuna through a series of\nsurveys with 2390 active LBG players utilizing Tondello's Player Traits Model\nand Scale of Game playing Preferences, and Hamari's scale of LBG\ngratifications. Our findings (1) illustrate an association between player\nsatisfaction and social aspects of the studied games, (2) explicate how the\ncore-loops of the studied games impact the expressed gratifications and the\naffine traits of players, and (3) indicate a strong distinction between\nhardcore and casual players based on both traits and gratifications. Overall\nour findings shed light into the players of LBG, their traits, and\ngratifications they derive from playing LBGs.\n"}
+{"abstract": "  As the interaction between the black holes and highly energetic infalling\ncharged matter receives quantum corrections, the basic laws of black hole\nmechanics have to be carefully rederived. Using the covariant phase space\nformalism, we generalize the first law of black hole mechanics, both\n\"equilibrium state\" and \"physical process\" versions, in the presence of\nnonlinear electrodynamics fields, defined by Lagrangians depending on both\nquadratic electromagnetic invariants, $F_{ab}F^{ab}$ and $F_{ab}\\,{\\star\nF}^{ab}$. Derivation of this law demands a specific treatment of the Lagrangian\nparameters, similar to embedding of the cosmological constant into\nthermodynamic context. Furthermore, we discuss the validity of energy\nconditions, several complementing proofs of the zeroth law of black hole\nelectrodynamics and some aspects of the recently generalized Smarr formula, its\n(non-)linearity and relation to the first law.\n"}
+{"abstract": "  We consider zero-error communication over a two-transmitter deterministic\nadversarial multiple access channel (MAC) governed by an adversary who has\naccess to the transmissions of both senders (hence called omniscient) and aims\nto maliciously corrupt the communication. None of the encoders, jammer and\ndecoder is allowed to randomize using private or public randomness. This\nenforces a combinatorial nature of the problem. Our model covers a large family\nof channels studied in the literature, including all deterministic discrete\nmemoryless noisy or noiseless MACs. In this work, given an arbitrary\ntwo-transmitter deterministic omniscient adversarial MAC, we characterize when\nthe capacity region\n  1) has nonempty interior (in particular, is two-dimensional);\n  2) consists of two line segments (in particular, has empty interior);\n  3) consists of one line segment (in particular, is one-dimensional);\n  4) or only contains $ (0,0) $ (in particular, is zero-dimensional).\n  This extends a recent result by Wang, Budkuley, Bogdanov and Jaggi (2019)\nfrom the point-to-point setting to the multiple access setting. Indeed, our\nconverse arguments build upon their generalized Plotkin bound and involve\ndelicate case analysis. One of the technical challenges is to take care of both\n\"joint confusability\" and \"marginal confusability\". In particular, the\ntreatment of marginal confusability does not follow from the point-to-point\nresults by Wang et al. Our achievability results follow from random coding with\nexpurgation.\n"}
+{"abstract": "  Contemporary works on abstractive text summarization have focused primarily\non high-resource languages like English, mostly due to the limited availability\nof datasets for low/mid-resource ones. In this work, we present XL-Sum, a\ncomprehensive and diverse dataset comprising 1 million professionally annotated\narticle-summary pairs from BBC, extracted using a set of carefully designed\nheuristics. The dataset covers 44 languages ranging from low to high-resource,\nfor many of which no public dataset is currently available. XL-Sum is highly\nabstractive, concise, and of high quality, as indicated by human and intrinsic\nevaluation. We fine-tune mT5, a state-of-the-art pretrained multilingual model,\nwith XL-Sum and experiment on multilingual and low-resource summarization\ntasks. XL-Sum induces competitive results compared to the ones obtained using\nsimilar monolingual datasets: we show higher than 11 ROUGE-2 scores on 10\nlanguages we benchmark on, with some of them exceeding 15, as obtained by\nmultilingual training. Additionally, training on low-resource languages\nindividually also provides competitive performance. To the best of our\nknowledge, XL-Sum is the largest abstractive summarization dataset in terms of\nthe number of samples collected from a single source and the number of\nlanguages covered. We are releasing our dataset and models to encourage future\nresearch on multilingual abstractive summarization. The resources can be found\nat \\url{https://github.com/csebuetnlp/xl-sum}.\n"}
+{"abstract": "  In this work, we aim to achieve efficient end-to-end learning of driving\npolicies in dynamic multi-agent environments. Predicting and anticipating\nfuture events at the object level are critical for making informed driving\ndecisions. We propose an Instance-Aware Predictive Control (IPC) approach,\nwhich forecasts interactions between agents as well as future scene structures.\nWe adopt a novel multi-instance event prediction module to estimate the\npossible interaction among agents in the ego-centric view, conditioned on the\nselected action sequence of the ego-vehicle. To decide the action at each step,\nwe seek the action sequence that can lead to safe future states based on the\nprediction module outputs by repeatedly sampling likely action sequences. We\ndesign a sequential action sampling strategy to better leverage predicted\nstates on both scene-level and instance-level. Our method establishes a new\nstate of the art in the challenging CARLA multi-agent driving simulation\nenvironments without expert demonstration, giving better explainability and\nsample efficiency.\n"}
+{"abstract": "  Quantifying the confidence (or conversely the uncertainty) of a prediction is\na highly desirable trait of an automatic system, as it improves the robustness\nand usefulness in downstream tasks. In this paper we investigate confidence\nestimation for end-to-end automatic speech recognition (ASR). Previous work has\naddressed confidence measures for lattice-based ASR, while current machine\nlearning research mostly focuses on confidence measures for unstructured deep\nlearning. However, as the ASR systems are increasingly being built upon deep\nend-to-end methods, there is little work that tries to develop confidence\nmeasures in this context. We fill this gap by providing an extensive benchmark\nof popular confidence methods on four well-known speech datasets. There are two\nchallenges we overcome in adapting existing methods: working on structured data\n(sequences) and obtaining confidences at a coarser level than the predictions\n(words instead of tokens). Our results suggest that a strong baseline can be\nobtained by scaling the logits by a learnt temperature, followed by estimating\nthe confidence as the negative entropy of the predictive distribution and,\nfinally, sum pooling to aggregate at word level.\n"}
+{"abstract": "  Image guidance in minimally invasive interventions is usually provided using\nlive 2D X-ray imaging. To enhance the information available during the\nintervention, the preoperative volume can be overlaid over the 2D images using\n2D/3D image registration. Recently, deep learning-based 2D/3D registration\nmethods have shown promising results by improving computational efficiency and\nrobustness. However, there is still a gap in terms of registration accuracy\ncompared to traditional optimization-based methods. We aim to address this gap\nby incorporating traditional methods in deep neural networks using known\noperator learning. As an initial step in this direction, we propose to learn\nthe update step of an iterative 2D/3D registration framework based on the\nPoint-to-Plane Correspondence model. We embed the Point-to-Plane Correspondence\nmodel as a known operator in our deep neural network and learn the update step\nfor the iterative registration. We show an improvement of 1.8 times in terms of\nregistration accuracy for the update step prediction compared to learning\nwithout the known operator.\n"}
+{"abstract": "  In this paper, we focus on the nonlinear least squares:\n$\\mbox{min}_{\\mathbf{x} \\in \\mathbb{H}^d}\\| |A\\mathbf{x}|-\\mathbf{b}\\|$ where\n$A\\in \\mathbb{H}^{m\\times d}$, $\\mathbf{b} \\in \\mathbb{R}^m$ with $\\mathbb{H}\n\\in \\{\\mathbb{R},\\mathbb{C} \\}$ and consider the uniqueness and stability of\nsolutions. Such problem arises, for instance, in phase retrieval and absolute\nvalue rectification neural networks. For the case where\n$\\mathbf{b}=|A\\mathbf{x}_0|$ for some $\\mathbf{x}_0\\in \\mathbb{H}^d$, many\nresults have been developed to characterize the uniqueness and stability of\nsolutions. However, for the case where $\\mathbf{b} \\neq |A\\mathbf{x}_0| $ for\nany $\\mathbf{x}_0\\in \\mathbb{H}^d$, there is no existing result for it to the\nbest of our knowledge. In this paper, we first focus on the uniqueness of\nsolutions and show for any matrix $A\\in \\mathbb{H}^{m \\times d}$ there always\nexists a vector $\\mathbf{b} \\in \\mathbb{R}^m$ such that the solution is not\nunique. But, in real case, such ``bad'' vectors $\\mathbf{b}$ are negligible,\nnamely, if $\\mathbf{b} \\in \\mathbb{R}_{+}^m$ does not lie in some measure zero\nset, then the solution is unique. We also present some conditions under which\nthe solution is unique. For the stability of solutions, we prove that the\nsolution is never uniformly stable. But if we restrict the vectors $\\mathbf{b}$\nto any convex set then it is stable.\n"}
+{"abstract": "  We study the free energy and limit shape problem for the five-vertex model\nwith periodic \"genus zero\" weights. We derive the exact phase diagram, free\nenergy and surface tension for this model. We show that its surface tension has\ntrivial potential and use this to give explicit parameterizations of limit\nshapes.\n"}
+{"abstract": "  The goal of this paper is to establish Beilinson-Bernstein type localization\ntheorems for quantizations of some conical symplectic resolutions. We prove the\nfull localization theorems for finite and affine type A Nakajima quiver\nvarieties. The proof is based on two partial results that hold in more general\nsituations. First, we establish an exactness result for global section functor\nif there is a tilting generator that has a rank 1 summand. Second, we examine\nwhen the global section functor restricts to an equivalence between categories\n$\\mathcal{O}$.\n"}
+{"abstract": "  While the wiretap secret key capacity remains unknown for general source\nmodels even in the two-user case, we obtained a single-letter characterization\nfor a large class of multi-user source models with a linear wiretapper who can\nobserve any linear combinations of the source. We introduced the idea of\nirreducible sources to show existence of an optimal communication scheme that\nachieves perfect omniscience with minimum leakage of information to the\nwiretapper. This implies a duality between the problems of wiretap secret key\nagreement and secure omniscience, and such duality potentially holds for more\ngeneral sources.\n"}
+{"abstract": "  Functional connectomes derived from functional magnetic resonance imaging\nhave long been used to understand the functional organization of the brain.\nNevertheless, a connectome is intrinsically linked to the atlas used to create\nit. In other words, a connectome generated from one atlas is different in scale\nand resolution compared to a connectome generated from another atlas. Being\nable to map connectomes and derived results between different atlases without\nadditional pre-processing is a crucial step in improving interpretation and\ngeneralization between studies that use different atlases. Here, we use optimal\ntransport, a powerful mathematical technique, to find an optimum mapping\nbetween two atlases. This mapping is then used to transform time series from\none atlas to another in order to reconstruct a connectome. We validate our\napproach by comparing transformed connectomes against their \"gold-standard\"\ncounterparts (i.e., connectomes generated directly from an atlas) and\ndemonstrate the utility of transformed connectomes by applying these\nconnectomes to predictive models based on a different atlas. We show that these\ntransformed connectomes are significantly similar to their \"gold-standard\"\ncounterparts and maintain individual differences in brain-behavior\nassociations, demonstrating both the validity of our approach and its utility\nin downstream analyses. Overall, our approach is a promising avenue to increase\nthe generalization of connectome-based results across different atlases.\n"}
+{"abstract": "  A $d$-dimensional framework is a pair $(G,p)$, where $G=(V,E)$ is a graph and\n$p$ is a map from $V$ to ${\\mathbb{R}}^d$. The length of an edge $uv\\in E$ in\n$(G,p)$ is the distance between $p(u)$ and $p(v)$. The framework is said to be\nglobally rigid in ${\\mathbb{R}}^d$ if the graph $G$ and its edge lengths\nuniquely determine $(G,p)$, up to congruence. A graph $G$ is called globally\nrigid in ${\\mathbb{R}}^d$ if every $d$-dimensional generic framework $(G,p)$ is\nglobally rigid.\n  In this paper, we consider the problem of reconstructing a graph from the set\nof edge lengths arising from a generic framework. Roughly speaking, a graph $G$\nis strongly reconstructible in ${\\mathbb{C}}^d$ if it is uniquely determined by\nthe set of (unlabeled) edge lengths of any generic framework $(G,p)$ in\n$d$-space, along with the number of its vertices. It is known that if $G$ is\nglobally rigid in ${\\mathbb{R}}^d$ on at least $d+2$ vertices, then it is\nstrongly reconstructible in ${\\mathbb{C}}^d$. We strengthen this result and\nshow that under the same conditions, $G$ is in fact fully reconstructible in\n${\\mathbb{C}}^d$, which means that the set of edge lengths alone is sufficient\nto uniquely reconstruct $G$, without any constraint on the number of vertices.\n  We also prove that if $G$ is globally rigid in ${\\mathbb{R}}^d$ on at least\n$d+2$ vertices, then the $d$-dimensional generic rigidity matroid of $G$ is\nconnected. This result generalizes Hendrickson's necessary condition for global\nrigidity and gives rise to a new combinatorial necessary condition.\n  Finally, we provide new families of fully reconstructible graphs and use them\nto answer some questions regarding unlabeled reconstructibility posed in recent\npapers.\n"}
+{"abstract": "  Characterizing metastable neural dynamics in finite-size spiking networks\nremains a daunting challenge. We propose to address this challenge in the\nrecently introduced replica-mean-field (RMF) limit. In this limit, networks are\nmade of infinitely many replicas of the finite network of interest, but with\nrandomized interactions across replica. Such randomization renders certain\nexcitatory networks fully tractable at the cost of neglecting activity\ncorrelations, but with explicit dependence on the finite size of the neural\nconstituents. However, metastable dynamics typically unfold in networks with\nmixed inhibition and excitation. Here, we extend the RMF computational\nframework to point-process-based neural network models with exponential\nstochastic intensities, allowing for mixed excitation and inhibition. Within\nthis setting, we show that metastable finite-size networks admit multistable\nRMF limits, which are fully characterized by stationary firing rates.\nTechnically, these stationary rates are determined as solutions to a set of\ndelayed differential equations under certain regularity conditions that any\nphysical solutions shall satisfy. We solve this original problem by combining\nthe resolvent formalism and singular-perturbation theory. Importantly, we find\nthat these rates specify probabilistic pseudo-equilibria which accurately\ncapture the neural variability observed in the original finite-size network. We\nalso discuss the emergence of metastability as a stochastic bifurcation, which\ncan also be interpreted as a static phase transition in the RMF limits. In\nturn, we expect to leverage the static picture of RMF limits to infer purely\ndynamical features of metastable finite-size networks, such as the transition\nrates between pseudo-equilibria.\n"}
+{"abstract": "  In this note we introduce and study the almost commuting varieties for the\nsymplectic Lie algebras.\n"}
+{"abstract": "  Integrated semiconductor mode-locked lasers have shown promise in many\napplications and are readily fabricated using generic InP photonic integration\nplatforms. However, the passive waveguides offered in such platforms have\nrelatively high linear and nonlinear losses that limit the performance of these\nlasers. By extending such lasers with, for example, an external cavity the\nperformance can be increased considerably. In this paper, we demonstrate for\nthe first time that a high-performance mode-locked laser can be achieved with a\nbutt-coupling integration technique using chip scale silicon nitride\nwaveguides. A platform-independent SiN/SU8 coupler design is used to couple\nbetween the silicon nitride external cavity and the III/V active chip.\nMode-locked lasers at 2.18 GHz and 15.5 GHz repetition rates are demonstrated\nwith Lorentzian RF linewidths several orders of magnitude smaller than what has\nbeen demonstrated on monolithic InP platforms. The RF linewidth was 31 Hz for\nthe 2.18 GHz laser.\n"}
+{"abstract": "  We demonstrate the reliable generation of 1-mJ, 31-fs pulses with an average\npower of 1 kW by post-compression of 200-fs pulses from a coherently combined\nYb:fiber laser system in an argon-filled Herriott-type multi-pass cell with an\noverall compression efficiency of 96%. We also analyze the output beam,\nrevealing essentially no spatio-spectral couplings or beam quality loss.\n"}
+{"abstract": "  We consider the approach of replacing trees by multi-indices as an index set\nof the abstract model space $\\mathsf{T}$ introduced by Otto, Sauer, Smith and\nWeber to tackle quasi-linear singular SPDEs. We show that this approach is\nconsistent with the postulates of regularity structures when it comes to the\nstructure group $\\mathsf{G}$. In particular, $\\mathsf{G}\\subset{\\rm\nAut}(\\mathsf{T})$ arises from a Hopf algebra $\\mathsf{T}^+$ and a comodule\n$\\Delta\\colon\\mathsf{T}\\rightarrow \\mathsf{T}^+\\otimes\\mathsf{T}$. In fact,\nthis approach, where the dual $\\mathsf{T}^*$ of the abstract model space\n$\\mathsf{T}$ naturally embeds into a formal power series algebra, allows to\ninterpret $\\mathsf{G}^*\\subset{\\rm Aut}(\\mathsf{T}^*)$ as a Lie group arising\nfrom a Lie algebra $\\mathsf{L} \\subset{\\rm End}(\\mathsf{T}^*)$ consisting of\nderivations on this power series algebra. These derivations in turn are the\ninfinitesimal generators of two actions on the space of pairs (nonlinearities,\nfunctions of space-time mod constants). These actions are shift of space-time\nand tilt by space-time polynomials. The Hopf algebra $\\mathsf{T}^+$ arises from\na coordinate representation of the universal enveloping algebra ${\\rm\nU}(\\mathsf{L})$ of the Lie algebra $\\mathsf{L}$. The coordinates are determined\nby an underlying pre-Lie algebra structure of the derived algebra of\n$\\mathsf{L}$. Strong finiteness properties, which are enforced by gradedness\nand the restrictive definition of $\\mathsf{T}$, allow for this purely algebraic\nconstruction of $\\mathsf{G}$. We also argue that there exist pre-Lie algebra\nand Hopf algebra morphisms between our structure and the tree-based one in the\ncases of branched rough paths (Grossman-Larson, Connes-Kreimer) and of the\ngeneralized parabolic Anderson model.\n"}
+{"abstract": "  Previous approaches to learned cardinality estimation have focused on\nimproving average estimation error, but not all estimates matter equally. Since\nlearned models inevitably make mistakes, the goal should be to improve the\nestimates that make the biggest difference to an optimizer. We introduce a new\nloss function, Flow-Loss, that explicitly optimizes for better query plans by\napproximating the optimizer's cost model and dynamic programming search\nalgorithm with analytical functions. At the heart of Flow-Loss is a reduction\nof query optimization to a flow routing problem on a certain plan graph in\nwhich paths correspond to different query plans. To evaluate our approach, we\nintroduce the Cardinality Estimation Benchmark, which contains the ground truth\ncardinalities for sub-plans of over 16K queries from 21 templates with up to 15\njoins. We show that across different architectures and databases, a model\ntrained with Flow-Loss improves the cost of plans (using the PostgreSQL cost\nmodel) and query runtimes despite having worse estimation accuracy than a model\ntrained with Q-Error. When the test set queries closely match the training\nqueries, both models improve performance significantly over PostgreSQL and are\nclose to the optimal performance (using true cardinalities). However, the\nQ-Error trained model degrades significantly when evaluated on queries that are\nslightly different (e.g., similar but not identical query templates), while the\nFlow-Loss trained model generalizes better to such situations. For example, the\nFlow-Loss model achieves up to 1.5x better runtimes on unseen templates\ncompared to the Q-Error model, despite leveraging the same model architecture\nand training data.\n"}
+{"abstract": "  Data-driven, automatic design space exploration of neural accelerator\narchitecture is desirable for specialization and productivity. Previous\nframeworks focus on sizing the numerical architectural hyper-parameters while\nneglect searching the PE connectivities and compiler mappings. To tackle this\nchallenge, we propose Neural Accelerator Architecture Search (NAAS) which\nholistically searches the neural network architecture, accelerator\narchitecture, and compiler mapping in one optimization loop. NAAS composes\nhighly matched architectures together with efficient mapping. As a data-driven\napproach, NAAS rivals the human design Eyeriss by 4.4x EDP reduction with 2.7%\naccuracy improvement on ImageNet under the same computation resource, and\noffers 1.4x to 3.5x EDP reduction than only sizing the architectural\nhyper-parameters.\n"}
+{"abstract": "  It has previously been shown that response transformations can be very\neffective in improving dimension reduction outcomes for a continuous response.\nThe choice of transformation used can make a big difference in the\nvisualization of the response versus the dimension reduced regressors. In this\narticle, we provide an automated approach for choosing parameters of\ntransformation functions to seek optimal results. A criterion based on an\ninfluence measure between dimension reduction spaces is utilized for choosing\nthe optimal parameter value of the transformation. Since influence measures can\nbe time-consuming for large data sets, two efficient criteria are also\nprovided. Given that a different transformation may be suitable for each\ndirection required to form the subspace, we also employ an iterative approach\nto choosing optimal parameter values. Several simulation studies and a real\ndata example highlight the effectiveness of the proposed methods.\n"}
+{"abstract": "  Let $X$ be a sufficiently large positive integer. We prove that one may\nchoose a subset $S$ of primes with cardinality $O(\\log X)$, such that a\npositive proportion of integers less than $X$ can be represented by $x^2 + p\ny^2$ for at least one of $p \\in S$.\n"}
+{"abstract": "  The difference in COVID 19 death rates across political regimes has caught a\nlot of attention. The \"efficient autocracy\" view suggests that autocracies may\nbe more efficient at putting in place policies that contain COVID 19 spread. On\nthe other hand, the \"biasing autocracy\" view underlines that autocracies may be\nunder reporting their COVID 19 data. We use fixed effect panel regression\nmethods to discriminate between the two sides of the debate. Our results show\nthat a third view may in fact be prevailing: once pre-determined\ncharacteristics of countries are accounted for, COVID 19 death rates equalize\nacross political regimes. The difference in death rate across political regime\nseems therefore to be primarily due to omitted variable bias.\n"}
+{"abstract": "  In this study, we examine the superconducting instability of a\nquasi-one-dimensional lattice in the Hubbard model based on the random-phase\napproximation (RPA) and the fluctuation exchange (FLEX) approximation. We find\nthat a spin-singlet pair density wave (PDW-singlet) with a center-of-mass\nmomentum of $2k_F$ can be stabilized when the one-dimensionality becomes\nprominent toward the perfect nesting of the Fermi surface. The obtained pair is\na mixture of even-frequency and odd-frequency singlet ones. The dominant\neven-frequency component does not have nodal lines on the Fermi surface. This\nPDW-singlet state is more favorable as compared to RPA when self-energy\ncorrection is introduced in the FLEX approximation.\n"}
+{"abstract": "  To improve the performance of deep learning, mixup has been proposed to force\nthe neural networks favoring simple linear behaviors in-between training\nsamples. Performing mixup for transfer learning with pre-trained models however\nis not that simple, a high capacity pre-trained model with a large\nfully-connected (FC) layer could easily overfit to the target dataset even with\nsamples-to-labels mixed up. In this work, we propose SMILE - Self-Distilled\nMixup for EffIcient Transfer LEarning. With mixed images as inputs, SMILE\nregularizes the outputs of CNN feature extractors to learn from the mixed\nfeature vectors of inputs (sample-to-feature mixup), in addition to the mixed\nlabels. Specifically, SMILE incorporates a mean teacher, inherited from the\npre-trained model, to provide the feature vectors of input samples in a\nself-distilling fashion, and mixes up the feature vectors accordingly via a\nnovel triplet regularizer. The triple regularizer balances the mixup effects in\nboth feature and label spaces while bounding the linearity in-between samples\nfor pre-training tasks. Extensive experiments have been done to verify the\nperformance improvement made by SMILE, in comparisons with a wide spectrum of\ntransfer learning algorithms, including fine-tuning, L2-SP, DELTA, and RIFLE,\neven with mixup strategies combined. Ablation studies show that the vanilla\nsample-to-label mixup strategies could marginally increase the linearity\nin-between training samples but lack of generalizability, while SMILE\nsignificantly improve the mixup effects in both label and feature spaces with\nboth training and testing datasets. The empirical observations backup our\ndesign intuition and purposes.\n"}
+{"abstract": "  Invariant risk minimization (IRM) has recently emerged as a promising\nalternative for domain generalization. Nevertheless, the loss function is\ndifficult to optimize for nonlinear classifiers and the original optimization\nobjective could fail when pseudo-invariant features and geometric skews exist.\nInspired by IRM, in this paper we propose a novel formulation for domain\ngeneralization, dubbed invariant information bottleneck (IIB). IIB aims at\nminimizing invariant risks for nonlinear classifiers and simultaneously\nmitigating the impact of pseudo-invariant features and geometric skews.\nSpecifically, we first present a novel formulation for invariant causal\nprediction via mutual information. Then we adopt the variational formulation of\nthe mutual information to develop a tractable loss function for nonlinear\nclassifiers. To overcome the failure modes of IRM, we propose to minimize the\nmutual information between the inputs and the corresponding representations.\nIIB significantly outperforms IRM on synthetic datasets, where the\npseudo-invariant features and geometric skews occur, showing the effectiveness\nof proposed formulation in overcoming failure modes of IRM. Furthermore,\nexperiments on DomainBed show that IIB outperforms $13$ baselines by $0.9\\%$ on\naverage across $7$ real datasets.\n"}
+{"abstract": "  This paper presents a vehicle speed planning system called the energy-optimal\ndeceleration planning system (EDPS), which aims to maximize energy recuperation\nof regenerative braking of connected and autonomous electrified vehicles. A\nrecuperation energy-optimal speed profile is computed based on the impending\ndeceleration requirements for turning or stopping at an intersection. This is\ncomputed to maximize the regenerative braking energy while satisfying the\nphysical limits of an electrified powertrain. In automated driving, the\npowertrain of an electrified vehicle can be directly controlled by the vehicle\ncontrol unit such that it follows the computed optimal speed profile. To obtain\nsmooth optimal deceleration speed profiles, optimal deceleration commands are\ndetermined by a parameterized polynomial-based deceleration model that is\nobtained by regression analyses with real vehicle driving test data. The\nparameters are dependent on preview information such as residual time and\ndistance as well as target speed. The key design parameter is deceleration\ntime, which determines the deceleration speed profile to satisfy the residual\ntime and distance constraints as well as the target speed requirement. The\nbounds of deceleration commands corresponding to the physical limits of the\npowertrain are deduced from realistic deceleration test driving. The state\nconstraints are dynamically updated by considering the anticipated road load\nand the deceleration preference. For validation and comparisons of the EDPS\nwith different preview distances, driving simulation tests with a virtual road\nenvironment and vehicle-to-infrastructure connectivity are presented. It is\nshown that the longer preview distance in the EDPS, the more\nenergy-recuperation. In comparison with driver-in-the-loop simulation tests,\nEDPS-based autonomous driving shows improvements in energy recuperation and\nreduction in trip time.\n"}
+{"abstract": "  We present augmented Lagrangian Schur complement preconditioners and robust\nmultigrid methods for incompressible Stokes problems with extreme viscosity\nvariations. Such Stokes systems arise, for instance, upon linearization of\nnonlinear viscous flow problems, and they can have severely inhomogeneous and\nanisotropic coefficients. Using an augmented Lagrangian formulation for the\nincompressibility constraint makes the Schur complement easier to approximate,\nbut results in a nearly singular (1,1)-block in the Stokes system. We present\neigenvalue estimates for the quality of the Schur complement approximation. To\ncope with the near-singularity of the (1,1)-block, we extend a multigrid scheme\nwith a discretization-dependent smoother and transfer operators from\ntriangular/tetrahedral to the quadrilateral/hexahedral finite element\ndiscretizations $[\\mathbb{Q}_k]^d\\times \\mathbb{P}_{k-1}^{\\text{disc}}$, $k\\geq\n2$, $d=2,3$. Using numerical examples with scalar and with anisotropic\nfourth-order tensor viscosity arising from linearization of a viscoplastic\nconstitutive relation, we confirm the robustness of the multigrid scheme and\nthe overall efficiency of the solver. We present scalability results using up\nto 28,672 parallel tasks for problems with up to 1.6 billion unknowns and a\nviscosity contrast up to ten orders of magnitude.\n"}
+{"abstract": "  We investigate, through a data-driven contact tracing model, the transmission\nof COVID-19 inside buses during distinct phases of the pandemic in a large\nBrazilian city. From this microscopic approach, we recover the networks of\nclose contacts within consecutive time windows. A longitudinal comparison is\nthen performed by upscaling the traced contacts with the transmission computed\nfrom a mean-field compartmental model for the entire city. Our results show\nthat the effective reproduction numbers inside the buses, $Re^{bus}$, and in\nthe city, $Re^{city}$, followed a compatible behavior during the first wave of\nthe local outbreak. Moreover, by distinguishing the close contacts of\nhealthcare workers in the buses, we discovered that their transmission,\n$Re^{health}$, during the same period, was systematically higher than\n$Re^{bus}$. This result reinforces the need for special public transportation\npolicies for highly exposed groups of people.\n"}
+{"abstract": "  Recent advances in machine learning have become increasingly popular in the\napplications of phase transitions and critical phenomena. By machine learning\napproaches, we try to identify the physical characteristics in the\ntwo-dimensional percolation model. To achieve this, we adopt Monte Carlo\nsimulation to generate dataset at first, and then we employ several approaches\nto analyze the dataset. Four kinds of convolutional neural networks (CNNs), one\nvariational autoencoder (VAE), one convolutional VAE (cVAE), one principal\ncomponent analysis (PCA), and one $k$-means are used for identifying order\nparameter, the permeability, and the critical transition point. The former\nthree kinds of CNNs can simulate the two order parameters and the permeability\nwith high accuracy, and good extrapolating performance. The former two kinds of\nCNNs have high anti-noise ability. To validate the robustness of the former\nthree kinds of CNNs, we also use the VAE and the cVAE to generate new\npercolating configurations to add perturbations into the raw configurations. We\nfind that there is no difference by using the raw or the perturbed\nconfigurations to identify the physical characteristics, under the prerequisite\nof corresponding labels. In the case of lacking labels, we use unsupervised\nlearning to detect the physical characteristics. The PCA, a classical\nunsupervised learning, performs well when identifying the permeability but\nfails to deduce order parameter. Hence, we apply the fourth kinds of CNNs with\ndifferent preset thresholds, and identify a new order parameter and the\ncritical transition point. Our findings indicate that the effectiveness of\nmachine learning still needs to be evaluated in the applications of phase\ntransitions and critical phenomena.\n"}
+{"abstract": "  Uniform large transition-edge sensor (TES) arrays are fundamental for the\nnext generation of X-ray space observatories. These arrays are required to\nachieve an energy resolution $\\Delta E$ < 3 eV full-width-half-maximum (FWHM)\nin the soft X-ray energy range. We are currently developing X-ray\nmicrocalorimeter arrays for use in future laboratory and space-based X-ray\nastrophysics experiments and ground-based spectrometers. In this contribution\nwe report on the development and the characterization of a uniform 32$\\times$32\npixel array with 140$\\times$30 $\\mu$m$^2$ Ti/Au TESs with Au X-ray absorber. We\nreport upon extensive measurements on 60 pixels in order to show the uniformity\nof our large TES array. The averaged critical temperature is $T_\\mathrm{c}$ =\n89.5$\\pm$0.5 mK and the variation across the array ($\\sim$1 cm) is less than\n1.5 mK. We found a large region of detector's bias points between 20\\% and 40\\%\nof the normal-state resistance where the energy resolution is constantly lower\nthan 3 eV. In particular, results show a summed X-ray spectral resolution\n$\\Delta E_\\mathrm{FWHM}$ = 2.50$\\pm$0.04 eV at a photon energy of 5.9 keV,\nmeasured in a single-pixel mode using a frequency domain multiplexing (FDM)\nreadout system developed at SRON/VTT at bias frequencies ranging from 1 to 5\nMHz. Moreover we compare the logarithmic resistance sensitivity with respect to\ntemperature and current ($\\alpha$ and $\\beta$ respectively) and their\ncorrelation with the detector's noise parameter $M$, showing an homogeneous\nbehaviour for all the measured pixels in the array.\n"}
+{"abstract": "  We present a new algebraic method for solving the inverse problem of quantum\nscattering theory based on the Marchenko theory. We applied a triangular wave\nset for the Marchenko equation kernel expansion in a separable form. The\nseparable form allows a reduction of the Marchenko equation to a system of\nlinear equations. For the zero orbital angular momentum, a linear expression of\nthe kernel expansion coefficients is obtained in terms of the Fourier series\ncoefficients of a function depending on the momentum $q$ and determined by the\nscattering data in the finite range of $q$. It is shown that a Fourier series\non a finite momentum range ($0<q<\\pi/h$) of a $q(1-S)$ function ($S$ is the\nscattering matrix) defines the potential function of the corresponding radial\nSchr\\\"odinger equation with $h$-step accuracy. A numerical algorithm is\ndeveloped for the reconstruction of the optical potential from scattering data.\nThe developed procedure is applied to analyze the $^{1}S_{0}NN$ data up to 3\nGeV. It is shown that these data are described by optical energy-independent\npartial potential.\n"}
+{"abstract": "  We generalize the Jacobi no-core shell model (J-NCSM) to study\ndouble-strangeness hypernuclei. All particle conversions in the strangeness\n$S=-1,-2$ sectors are explicitly taken into account. In two-body space, such\ntransitions may lead to the coupling between states of identical particles and\nof non-identical ones. Therefore, a careful consideration is required when\ndetermining the combinatorial factors that connect the many-body potential\nmatrix elements and the free-space two-body potentials. Using second\nquantization, we systematically derive the combinatorial factors in question\nfor $S=0,-1,-2$ sectors. As a first application, we use the J-NCSM to\ninvestigate $\\Lambda \\Lambda$ s-shell hypernuclei based on hyperon-hyperon (YY)\npotentials derived within chiral effective field theory at leading order (LO)\nand up to next-to-leading order (NLO). We find that the LO potential overbinds\n$^{\\text{ }\\text{ }\\text{ } \\text{}6}_{\\Lambda \\Lambda}\\text{He}$ while the\nprediction of the NLO interaction is close to experiment. Both interactions\nalso yield a bound state for $^{\\text{ }\\text{ }\\text{ } \\text{}5}_{\\Lambda\n\\Lambda}\\text{He}$. The $^{\\text{}\\text{ }\\text{ }\\text{}4}_{\\Lambda\n\\Lambda}\\text{H}$ system is predicted to be unbound.\n"}
+{"abstract": "  In online advertising, recommender systems try to propose items from a list\nof products to potential customers according to their interests. Such systems\nhave been increasingly deployed in E-commerce due to the rapid growth of\ninformation technology and availability of large datasets. The ever-increasing\nprogress in the field of artificial intelligence has provided powerful tools\nfor dealing with such real-life problems. Deep reinforcement learning (RL) that\ndeploys deep neural networks as universal function approximators can be viewed\nas a valid approach for design and implementation of recommender systems. This\npaper provides a comparative study between value-based and policy-based deep RL\nalgorithms for designing recommender systems for online advertising. The\nRecoGym environment is adopted for training these RL-based recommender systems,\nwhere the long short term memory (LSTM) is deployed to build value and policy\nnetworks in these two approaches, respectively. LSTM is used to take account of\nthe key role that order plays in the sequence of item observations by users.\nThe designed recommender systems aim at maximising the click-through rate (CTR)\nfor the recommended items. Finally, guidelines are provided for choosing proper\nRL algorithms for different scenarios that the recommender system is expected\nto handle.\n"}
+{"abstract": "  During the era of the High Luminosity LHC (HL-LHC) the devices in its\nexperiments will be subjected to increased radiation levels with high fluxes of\nneutrons and charged hadrons, especially in the inner detectors. A systematic\nprogram of radiation tests with neutrons and charged hadrons is being carried\nout by the CMS and ATLAS Collaborations in view of the upgrade of the\nexperiments, in order to cope with the higher luminosity at HL-LHC and the\nassociated increase in the pile-up events and radiation fluxes. In this work,\nresults from a complementary radiation study with $^{60}$Co-$\\gamma$ photons\nare presented. The doses are equivalent to those that the outer layers of the\nsilicon tracker systems of the two big LHC experiments will be subjected to.\nThe devices in this study are float-zone oxygenated p-type MOS capacitors. The\nresults of CV measurements on these devices are presented as a function of the\ntotal absorbed radiation dose following a specific annealing protocol. The\nmeasurements are compared with the results of a TCAD simulation.\n"}
+{"abstract": "  Fast Fourier Transform based phase screen simulations give accurate results\nonly when the screen size ($G$) is much larger than the outer scale parameter\n($L_0$). Otherwise, they fall short in correctly predicting both the low and\nhigh frequency behaviours of turbulence induced phase distortions. Sub-harmonic\ncompensation is a commonly used technique that aids in low-frequency correction\nbut does not solve the problem for all values of screen size to outer scale\nparameter ratios $(G/L_0$). A subharmonics based approach will lead to unequal\nsampling or weights calculation for subharmonics addition at the low-frequency\nrange and patch normalization factor. We have modified the subharmonics based\napproach by introducing a Gaussian phase autocorrelation matrix that\ncompensates for these shortfalls. We show that the maximum relative error in\nstructure function with respect to theoretical value is as small as 0.5-3% for\n$(G/L_0$) ratio of 1/1000 even for screen sizes up to 100 m diameter.\n"}
+{"abstract": "  We summarise the results of a study performed within the GENIE global\nanalysis framework, revisiting the GENIE bare-nucleon cross-section tuning and,\nin particular, the tuning of a) the inclusive cross-section, b) the\ncross-section of low-multiplicity inelastic channels (single-pion and\ndouble-pion production), and c) the relative contributions of resonance and\nnon-resonance processes to these final states. The same analysis was performed\nwith several different comprehensive cross-section model sets available in\nGENIE Generator v3. In this work we performed a careful investigation of the\nobserved tensions between exclusive and inclusive data, and installed analysis\nimprovements to handle systematics in historic data. All tuned model\nconfigurations discussed in this paper are available through public releases of\nthe GENIE Generator. With this paper we aim to support the consumers of these\nphysics tunes by providing comprehensive summaries of our alternate model\nconstructions, of the relevant datasets and their systematics, and of our\ntuning procedure and results.\n"}
+{"abstract": "  We evaluate the usefulness of holographic stabilizer codes for practical\npurposes by studying their allowed sets of fault-tolerantly implementable\ngates. We treat them as subsystem codes and show that the set of transversally\nimplementable logical operations is contained in the Clifford group for\nsufficiently localized logical subsystems. As well as proving this concretely\nfor several specific codes, we argue that this restriction naturally arises in\nany stabilizer subsystem code that comes close to capturing certain properties\nof holography. We extend these results to approximate encodings,\nlocality-preserving gates, certain codes whose logical algebras have\nnon-trivial centers, and discuss cases where restrictions can be made to other\nlevels of the Clifford hierarchy. A few auxiliary results may also be of\ninterest, including a general definition of entanglement wedge map for any\nsubsystem code, and a thorough classification of different correctability\nproperties for regions in a subsystem code.\n"}
+{"abstract": "  The growing interest in axion-like particles (ALPs) stems from the fact that\nthey provide successful theoretical explanations of physics phenomena, from the\nanomaly of the CP-symmetry conservation in strong interactions to the\nobservation of an unexpectedly large TeV photon flux from astrophysical\nsources, at distances where the strong absorption by the intergalactic medium\nshould make the signal very dim. In this latter condition, which is the focus\nof this review, a possible explanation is that TeV photons convert to ALPs in\nthe presence of strong and/or extended magnetic fields, such as those in the\ncore of galaxy clusters or around compact objects, or even those in the\nintergalactic space. This mixing affects the observed ${\\gamma}$-ray spectrum\nof distant sources, either by signal recovery or the production of\nirregularities in the spectrum, called \"wiggles\", according to the specific\nmicroscopic realization of the ALP and the ambient magnetic field at the\nsource, and in the Milky Way, where ALPs may be converted back to ${\\gamma}$\nrays. ALPs are also proposed as candidate particles for the Dark Matter.\nImaging Atmospheric Cherenkov telescopes (IACTs) have the potential to detect\nthe imprint of ALPs in the TeV spectrum from several classes of sources. In\nthis contribution, we present the ALP case and review the past decade of\nsearches for ALPs with this class of instruments.\n"}
+{"abstract": "  We consider the nonlocal double phase equation \\begin{align*} \\mathrm{P.V.}\n&\\int_{\\mathbb{R}^n}|u(x)-u(y)|^{p-2}(u(x)-u(y))K_{sp}(x,y)\\,dy\\\\\n&+\\mathrm{P.V.} \\int_{\\mathbb{R}^n}\na(x,y)|u(x)-u(y)|^{q-2}(u(x)-u(y))K_{tq}(x,y)\\,dy=0, \\end{align*} where\n$1<p\\leq q$ and the modulating coefficient $a(\\cdot,\\cdot)\\geq0$. Under some\nsuitable hypotheses, we first use the De Giorgi-Nash-Moser methods to derive\nthe local H\\\"{o}lder continuity for bounded weak solutions, and then establish\nthe relationship between weak solutions and viscosity solutions to such\nequations.\n"}
+{"abstract": "  The goal of this paper is to provide a general purpose result for the\ncoupling of exploration processes of random graphs, both undirected and\ndirected, with their local weak limits when this limit is a marked\nGalton-Watson process. This class includes in particular the configuration\nmodel and the family of inhomogeneous random graphs with rank-1 kernel.\nVertices in the graph are allowed to have attributes on a general separable\nmetric space and can potentially influence the construction of the graph\nitself. The coupling holds for any fixed depth of a breadth-first exploration\nprocess.\n"}
+{"abstract": "  Using the novel notion of parablender, P. Berger proved that the existence of\nfinitely many attractors is not Kolmogorov typical in parametric families of\ndiffeomorphisms. Here, motivated by the concept of Newhouse domains we define\nBerger domains for families of diffeomorphisms. As an application, we show that\nthe coexistence of infinitely many attracting invariant smooth circles is\nKolmogorov typical in certain non-sectionally dissipative Berger domains of\nparametric families in dimension three or greater.\n"}
+{"abstract": "  The combination of Winograd's algorithm and systolic array architecture has\ndemonstrated the capability of improving DSP efficiency in accelerating\nconvolutional neural networks (CNNs) on FPGA platforms. However, handling\narbitrary convolution kernel sizes in FPGA-based Winograd processing elements\nand supporting efficient data access remain underexplored. In this work, we are\nthe first to propose an optimized Winograd processing element (WinoPE), which\ncan naturally support multiple convolution kernel sizes with the same amount of\ncomputing resources and maintains high runtime DSP efficiency. Using the\nproposed WinoPE, we construct a highly efficient systolic array accelerator,\ntermed WinoCNN. We also propose a dedicated memory subsystem to optimize the\ndata access. Based on the accelerator architecture, we build accurate resource\nand performance modeling to explore optimal accelerator configurations under\ndifferent resource constraints. We implement our proposed accelerator on\nmultiple FPGAs, which outperforms the state-of-the-art designs in terms of both\nthroughput and DSP efficiency. Our implementation achieves DSP efficiency up to\n1.33 GOPS/DSP and throughput up to 3.1 TOPS with the Xilinx ZCU102 FPGA. These\nare 29.1\\% and 20.0\\% better than the best solutions reported previously,\nrespectively.\n"}
+{"abstract": "  We present semantic correctness proofs of automatic differentiation (AD). We\nconsider a forward-mode AD method on a higher order language with algebraic\ndata types, and we characterise it as the unique structure preserving macro\ngiven a choice of derivatives for basic operations. We describe a rich\nsemantics for differentiable programming, based on diffeological spaces. We\nshow that it interprets our language, and we phrase what it means for the AD\nmethod to be correct with respect to this semantics. We show that our\ncharacterisation of AD gives rise to an elegant semantic proof of its\ncorrectness based on a gluing construction on diffeological spaces. We explain\nhow this is, in essence, a logical relations argument. Throughout, we show how\nthe analysis extends to AD methods for computing higher order derivatives using\na Taylor approximation.\n"}
+{"abstract": "  Dynamically probing systems of ultrastrongly coupled light and matter by\nadvanced coherent control has been recently proposed as a unique tool for\ndetecting peculiar quantum features of this regime. Coherence allows in\nprinciple on-demand conversion of virtual photons dressing the entangled\neigenstates of the system to real ones, with unitary efficiency and remarkable\nrobustness. Here we study this effect in the presence of decoherence, showing\nthat also in far from ideal regimes is it possible to probe such peculiar\nfeatures.\n"}
+{"abstract": "  For regular continued fraction, if a real number $x$ and its rational\napproximation $p/q$ satisfying $|x-p/q|<1/q^2$, then, after deleting the last\ninteger of the partial quotients of $p/q$, the sequence of the remaining\npartial quotients is a prefix of that of $x$. In this paper, we show that the\nsituation is completely different if we consider the Hurwitz continued fraction\nexpansions of a complex number and its rational approximations. More\nspecifically, we consider the set $E(\\psi)$ of complex numbers which are well\napproximated with the given bound $\\psi$ and have quite different Hurwitz\ncontinued fraction expansions from that of their rational approximations. The\nHausdorff and packing dimensions of such set are determined. It turns out that\nits packing dimension is always full for any given approximation bound $\\psi$\nand its Hausdorff dimension is equal to that of the $\\psi$-approximable set\n$W(\\psi)$ of complex numbers. As a consequence, we also obtain an analogue of\nthe classical Jarn\\'ik Theorem in real case.\n"}
+{"abstract": "  High performance but unverified controllers, e.g., artificial\nintelligence-based (a.k.a. AI-based) controllers, are widely employed in\ncyber-physical systems (CPSs) to accomplish complex control missions. However,\nguaranteeing the safety and reliability of CPSs with this kind of controllers\nis currently very challenging, which is of vital importance in many real-life\nsafety-critical applications. To cope with this difficulty, we propose in this\nwork a Safe-visor architecture for sandboxing unverified controllers in CPSs\noperating in noisy environments (a.k.a. stochastic CPSs). The proposed\narchitecture contains a history-based supervisor, which checks inputs from the\nunverified controller and makes a compromise between functionality and safety\nof the system, and a safety advisor that provides fallback when the unverified\ncontroller endangers the safety of the system. Both the history-based\nsupervisor and the safety advisor are designed based on an approximate\nprobabilistic relation between the original system and its finite abstraction.\nBy employing this architecture, we provide formal probabilistic guarantees on\npreserving the safety specifications expressed by accepting languages of\ndeterministic finite automata (DFA). Meanwhile, the unverified controllers can\nstill be employed in the control loop even though they are not reliable. We\ndemonstrate the effectiveness of our proposed results by applying them to two\n(physical) case studies.\n"}
+{"abstract": "  Owning to the sub-standards being developed by IEEE Time-Sensitive Networking\n(TSN) Task Group, the traditional IEEE 802.1 Ethernet is enhanced to support\nreal-time dependable communications for future time- and safety-critical\napplications. Several sub-standards have been recently proposed that introduce\nvarious traffic shapers (e.g., Time-Aware Shaper (TAS), Asynchronous Traffic\nShaper (ATS), Credit-Based Shaper (CBS), Strict Priority (SP)) for flow control\nmechanisms of queuing and scheduling, targeting different application\nrequirements. These shapers can be used in isolation or in combination and\nthere is limited work that analyzes, evaluates and compares their performance,\nwhich makes it challenging for end-users to choose the right combination for\ntheir applications. This paper aims at (i) quantitatively comparing various\ntraffic shapers and their combinations, (ii) summarizing, classifying and\nextending the architectures of individual and combined traffic shapers and\ntheir Network calculus (NC)-based performance analysis methods and (iii)\nfilling the gap in the timing analysis research on handling two novel hybrid\narchitectures of combined traffic shapers, i.e., TAS+ATS+SP and TAS+ATS+CBS. A\nlarge number of experiments, using both synthetic and realistic test cases, are\ncarried out for quantitative performance comparisons of various individual and\ncombined traffic shapers, from the perspective of upper bounds of delay,\nbacklog and jitter. To the best of our knowledge, we are the first to\nquantitatively compare the performance of the main traffic shapers in TSN. The\npaper aims at supporting the researchers and practitioners in the selection of\nsuitable TSN sub-protocols for their use cases.\n"}
+{"abstract": "  Grid-synchronization stability (GSS) is an emerging stability issue of\ngrid-tied voltage source converters (VSCs), which can be provoked by severe\ngrid voltage sags. Although a qualitative understanding of the mechanism behind\nthe loss of synchronization has been acquired in recent studies, an analytical\nmethod for quantitative assessment of GSS of grid-tied VSCs is still missing.\nTo bridge this gap, a dedicated Lyapunov function is analytically proposed, and\nits corresponding stability criterion for GSS analysis of grid-tied VSCs is\nrigorously constructed. Both theoretical analysis and simulation result\ndemonstrate that the proposed method can provide a credible GSS evaluation\ncompared to the previous EAC/EF-based method. Therefore, it can be applied for\nfast GSS evaluation of the grid-tied VSCs as is exemplified in this paper, as\nwell as an analytical tool for some GSS-related issues, e.g., GSS-oriented\nparameter design and stabilization control.\n"}
+{"abstract": "  The aim of this thesis project is to investigate the bit commitment protocol\nin the framework of operational probabilistic theories. In particular a careful\nstudy is carried on the feasibility of bit commitment in the non-local boxes\ntheory. New aspects of the theory are also presented.\n"}
+{"abstract": "  Does consciousness collapse the quantum wave function? This idea was taken\nseriously by John von Neumann and Eugene Wigner but is now widely dismissed. We\ndevelop the idea by combining a mathematical theory of consciousness\n(integrated information theory) with an account of quantum collapse dynamics\n(continuous spontaneous localization). Simple versions of the theory are\nfalsified by the quantum Zeno effect, but more complex versions remain\ncompatible with empirical evidence. In principle, versions of the theory can be\ntested by experiments with quantum computers. The upshot is not that\nconsciousness-collapse interpretations are clearly correct, but that there is a\nresearch program here worth exploring.\n"}
+{"abstract": "  In the budget-feasible allocation problem, a set of items with varied sizes\nand values are to be allocated to a group of agents. Each agent has a budget\nconstraint on the total size of items she can receive. The goal is to compute a\nfeasible allocation that is envy-free (EF), in which the agents do not envy\neach other for the items they receive, nor do they envy a charity, who is\nendowed with all the unallocated items. Since EF allocations barely exist even\nwithout budget constraints, we are interested in the relaxed notion of\nenvy-freeness up to one item (EF1). The computation of both exact and\napproximate EF1 allocations remains largely open, despite a recent effort by Wu\net al. (IJCAI 2021) in showing that any budget-feasible allocation that\nmaximizes the Nash Social Welfare (NSW) is 1/4-approximate EF1. In this paper,\nwe move one step forward by showing that for agents with identical additive\nvaluations, a 1/2-approximate EF1 allocation can be computed in polynomial\ntime. For the uniform-budget and two-agent cases, we propose efficient\nalgorithms for computing an exact EF1 allocation. We also consider the large\nbudget setting, i.e., when the item sizes are infinitesimal compared with the\nagents' budgets, and show that both the NSW maximizing allocation and the\nallocation our polynomial-time algorithm computes have an approximation close\nto 1 regarding EF1.\n"}
+{"abstract": "  We present the first study of baryon-baryon interactions in the continuum\nlimit of lattice QCD, finding unexpectedly large lattice artifacts.\nSpecifically, we determine the binding energy of the $H$ dibaryon at a single\nquark-mass point. The calculation is performed at six values of the lattice\nspacing $a$, using O($a$)-improved Wilson fermions at the SU(3)-symmetric point\nwith $m_\\pi=m_K\\approx 420$ MeV. Energy levels are extracted by applying a\nvariational method to correlation matrices of bilocal two-baryon interpolating\noperators computed using the distillation technique. Our analysis employs\nL\\\"uscher's finite-volume quantization condition to determine the scattering\nphase shifts from the spectrum and vice versa, both above and below the\ntwo-baryon threshold. We perform global fits to the lattice spectra using\nparametrizations of the phase shift, supplemented by terms describing\ndiscretization effects, then extrapolate the lattice spacing to zero. The phase\nshift and the binding energy determined from it are found to be strongly\naffected by lattice artifacts. Our estimate of the binding energy in the\ncontinuum limit of three-flavor QCD is $B_H^{\\text{SU(3)}_{\\rm\nf}}=4.56\\pm1.13_{\\rm stat}\\pm0.63_{\\rm syst}$ MeV.\n"}
+{"abstract": "  Building on ideas from probabilistic programming, we introduce the concept of\nan expectation programming framework (EPF) that automates the calculation of\nexpectations. Analogous to a probabilistic program, an expectation program is\ncomprised of a mix of probabilistic constructs and deterministic calculations\nthat define a conditional distribution over its variables. However, the focus\nof the inference engine in an EPF is to directly estimate the resulting\nexpectation of the program return values, rather than approximate the\nconditional distribution itself. This distinction allows us to achieve\nsubstantial performance improvements over the standard probabilistic\nprogramming pipeline by tailoring the inference to the precise expectation we\ncare about. We realize a particular instantiation of our EPF concept by\nextending the probabilistic programming language Turing to allow so-called\ntarget-aware inference to be run automatically, and show that this leads to\nsignificant empirical gains compared to conventional posterior-based inference.\n"}
+{"abstract": "  We have built SinSpell, a comprehensive spelling checker for the Sinhala\nlanguage which is spoken by over 16 million people, mainly in Sri Lanka.\nHowever, until recently, Sinhala had no spelling checker with acceptable\ncoverage. Sinspell is still the only open source Sinhala spelling checker.\nSinSpell identifies possible spelling errors and suggests corrections. It also\ncontains a module which auto-corrects evident errors. To maintain accuracy,\nSinSpell was designed as a rule-based system based on Hunspell. A set of words\nwas compiled from several sources and verified. These were divided into\nmorphological classes, and the valid roots, suffixes and prefixes for each\nclass were identified, together with lists of irregular words and exceptions.\nThe errors in a corpus of Sinhala documents were analysed and commonly\nmisspelled words and types of common errors were identified. We found that the\nmost common errors were in vowel length and similar sounding letters. Errors\ndue to incorrect typing and encoding were also found. This analysis was used to\ndevelop the suggestion generator and auto-corrector.\n"}
+{"abstract": "  Most successful computer vision models transform low-level features, such as\nGabor filter responses, into richer representations of intermediate or\nmid-level complexity for downstream visual tasks. These mid-level\nrepresentations have not been explored for event cameras, although it is\nespecially relevant to the visually sparse and often disjoint spatial\ninformation in the event stream. By making use of locally consistent\nintermediate representations, termed as superevents, numerous visual tasks\nranging from semantic segmentation, visual tracking, depth estimation shall\nbenefit. In essence, superevents are perceptually consistent local units that\ndelineate parts of an object in a scene. Inspired by recent deep learning\narchitectures, we present a novel method that employs lifetime augmentation for\nobtaining an event stream representation that is fed to a fully convolutional\nnetwork to extract superevents. Our qualitative and quantitative experimental\nresults on several sequences of a benchmark dataset highlights the significant\npotential for event-based downstream applications.\n"}
+{"abstract": "  In this paper, we introduce a general family of $q$-hypergeometric\npolynomials and investigate several $q$-series identities such as an extended\ngenerating function and a Srivastava-Agarwal type bilinear generating function\nfor this family of $q$-hypergeometric polynomials. We give a transformational\nidentity involving generating functions for the generalized $q$-hypergeometric\npolynomials which we have introduced here. We also point out relevant\nconnections of the various $q$-results, which we investigate here, with those\nin several related earlier works on this subject. We conclude this paper by\nremarking that it will be a rather trivial and inconsequential exercise to give\nthe so-called $(p,q)$-variations of the $q$-results, which we have investigated\nhere, because the additional parameter $p$ is obviously redundant.\n"}
+{"abstract": "  Erd\\H{o}s and P\\'{o}sa proved in 1965 that there is a duality between the\nmaximum size of a packing of cycles and the minimum size of a vertex set\nhitting all cycles. Such a duality does not hold if we restrict to odd cycles.\nHowever, in 1999, Reed proved an analogue for odd cycles by relaxing packing to\nhalf-integral packing. We prove a far-reaching generalisation of the theorem of\nReed; if the edges of a graph are labelled by finitely many abelian groups,\nthen there is a duality between the maximum size of a half-integral packing of\ncycles whose values avoid a fixed finite set for each abelian group and the\nminimum size of a vertex set hitting all such cycles.\n  A multitude of natural properties of cycles can be encoded in this setting,\nfor example cycles of length at least $\\ell$, cycles of length $p$ modulo $q$,\ncycles intersecting a prescribed set of vertices at least $t$ times, and cycles\ncontained in given $\\mathbb{Z}_2$-homology classes in a graph embedded on a\nfixed surface. Our main result allows us to prove a duality theorem for cycles\nsatisfying a fixed set of finitely many such properties.\n"}
+{"abstract": "  On a bounded strictly pseudoconvex domain in $\\mathbb{C}^n$, $n >1$, the\nsmoothness of the Cheng-Yau solution to Fefferman's complex Monge-Amp\\`ere\nequation up to the boundary is obstructed by a local curvature invariant of the\nboundary, the CR obstruction density $\\mathcal{O}$. While local examples of\nobstruction flat CR manifolds are plentiful, the only known compact examples\nare the spherical CR manifolds. We consider the obstruction flatness problem\nfor small deformations of the standard CR 3-sphere. That rigidity holds for the\nCR sphere was previously known (in all dimensions) for the case of embeddable\nCR structures, where it also holds at the infinitesimal level. In the\n3-dimensional case, however, a CR structure need not be embeddable. While in\nthe nonembeddable case we may no longer interpret the obstruction density\n$\\mathcal{O}$ in terms of the boundary regularity of Fefferman's equation (or\nthe logarithmic singularity of the Bergman kernel) the equation\n$\\mathcal{O}\\equiv 0$ is still of great interest, e.g., since it corresponds to\nthe Bach flat equation of conformal gravity for the Fefferman space of the CR\nstructure (a conformal Lorentzian 4-manifold). Unlike in the embeddable case,\nit turns out that in the nonembeddable case there is an infinite dimensional\nspace of solutions to the linearized obstruction flatness equation on the\nstandard CR 3-sphere and this space defines a natural complement to the tangent\nspace of the embeddable deformations. In spite of this, we show that the CR\n3-sphere does not admit nontrivial obstruction flat deformations, embeddable or\nnonembeddable.\n"}
+{"abstract": "  The off-shell anomalous chromomagnetic dipole moment of the standard model\nquarks ($u$, $d$, $s$, $c$ and $b$), at the $Z$ gauge boson mass scale, is\ncomputed by using the $\\overline{\\textrm{MS}}$ scheme. The numerical results\ndisagree with all the previous predictions reported in the literature and show\na discrepancy of up to two orders of magnitude in certain situations.\n"}
+{"abstract": "  The asymptotic expansions of the Wright functions of the second kind,\nintroduced by Mainardi [see Appendix F of his book {\\it Fractional Calculus and\nWaves in Linear Viscoelasticity}, (2010)], $$ F_\\sigma(x)=\\sum_{n=0}^\\infty\n\\frac{(-x)^n}{n! \\g(-n\\sigma)}~,\\quad M_\\sigma(x)=\\sum_{n=0}^\\infty\n\\frac{(-x)^n}{n! \\g(-n\\sigma+1-\\sigma)}\\quad(0<\\sigma<1)$$ for $x\\to\\pm\\infty$\nare presented. The situation corresponding to the limit $\\sigma\\to1^-$ is\nconsidered, where $M_\\sigma(x)$ approaches the Dirac delta function\n$\\delta(x-1)$. Numerical results are given to demonstrate the accuracy of the\nexpansions derived in the paper, together with graphical illustrations that\nreveal the transition to a Dirac delta function as $\\sigma\\to 1^-$.\n"}
+{"abstract": "  Topics concerning metric dimension related invariants in graphs are nowadays\nintensively studied. This compendium of combinatorial and computational results\non this topic is an attempt of surveying those contributions that are of the\nhighest interest for the research community dealing with several variants of\nmetric dimension in graphs.\n"}
+{"abstract": "  We describe in dialogue form a possible way of discovering and investigating\n10-adic numbers starting from the naive question about a `largest natural\nnumber'. Among the topics we pursue are possibilities of extensions to\ntransfinite 10-adic numbers, 10-adic representations of rational numbers, zero\ndivisors, square roots and 10-adic roots of higher degree of natural numbers,\nand applications of 10-adic number representation in computer arithmetic. The\nparticipants of the dialogue are idealized embodiments of different\nphilosophical attitudes towards mathematics. The article aims at illustrating\nhow these attitudes interact, in both jarring and stimulating ways, and how\nthey impact mathematical development.\n"}
+{"abstract": "  Conversion of temporal to spatial correlations in the cortex is one of the\nmost intriguing functions in the brain. The learning at synapses triggering the\ncorrelation conversion can take place in a wide integration window, whose\ninfluence on the correlation conversion remains elusive. Here, we propose a\ngeneralized associative memory model with arbitrary Hebbian length. The model\ncan be analytically solved, and predicts that a small Hebbian length can\nalready significantly enhance the correlation conversion, i.e., the\nstimulus-induced attractor can be highly correlated with a significant number\nof patterns in the stored sequence, thereby facilitating state transitions in\nthe neural representation space. Moreover, an anti-Hebbian component is able to\nreshape the energy landscape of memories, akin to the function of sleep. Our\nwork thus establishes the fundamental connection between associative memory,\nHebbian length, and correlation conversion in the brain.\n"}
+{"abstract": "  The increasing usage of machine learning (ML) coupled with the software\narchitectural challenges of the modern era has resulted in two broad research\nareas: i) software architecture for ML-based systems, which focuses on\ndeveloping architectural techniques for better developing ML-based software\nsystems, and ii) ML for software architectures, which focuses on developing ML\ntechniques to better architect traditional software systems. In this work, we\nfocus on the former side of the spectrum with a goal to highlight the different\narchitecting practices that exist in the current scenario for architecting\nML-based software systems. We identify four key areas of software architecture\nthat need the attention of both the ML and software practitioners to better\ndefine a standard set of practices for architecting ML-based software systems.\nWe base these areas in light of our experience in architecting an ML-based\nsoftware system for solving queuing challenges in one of the largest museums in\nItaly.\n"}
+{"abstract": "  In a first-of-its-kind study, this paper proposes the formulation of\nconstructing prediction intervals (PIs) in a time series as a bi-objective\noptimization problem and solves it with the help of Nondominated Sorting\nGenetic Algorithm (NSGA-II). We also proposed modeling the chaos present in the\ntime series as a preprocessor in order to model the deterministic uncertainty\npresent in the time series. Even though the proposed models are general in\npurpose, they are used here for quantifying the uncertainty in macroeconomic\ntime series forecasting. Ideal PIs should be as narrow as possible while\ncapturing most of the data points. Based on these two objectives, we formulated\na bi-objective optimization problem to generate PIs in 2-stages, wherein\nreconstructing the phase space using Chaos theory (stage-1) is followed by\ngenerating optimal point prediction using NSGA-II and these point predictions\nare in turn used to obtain PIs (stage-2). We also proposed a 3-stage hybrid,\nwherein the 3rd stage invokes NSGA-II too in order to solve the problem of\nconstructing PIs from the point prediction obtained in 2nd stage. The proposed\nmodels when applied to the macroeconomic time series, yielded better results in\nterms of both prediction interval coverage probability (PICP) and prediction\ninterval average width (PIAW) compared to the state-of-the-art Lower Upper\nBound Estimation Method (LUBE) with Gradient Descent (GD). The 3-stage model\nyielded better PICP compared to the 2-stage model but showed similar\nperformance in PIAW with added computation cost of running NSGA-II second time.\n"}
+{"abstract": "  Cobaltates have rich spin-states and diverse properties. Using spin-state\npictures and firstprinciples calculations, here we study the electronic\nstructure and magnetism of the mixed-valent double perovskite YBaCo2O6. We find\nthat YBaCo2O6 is in the formal intermediate-spin (IS) Co3+/low-spin (LS) Co4+\nground state. The hopping of eg electron from IS-Co3+ to LS-Co4+ via double\nexchange gives rise to a ferromagnetic half-metallicity, which well accounts\nfor the recent experiments. The reduction of both magnetization and Curie\ntemperature by oxygen vacancies is discussed, aided with Monte Carlo\nsimulations. We also explore several other possible spin-states and their\ninteresting electronic/magnetic properties. Moreover, we predict that a volume\nexpansion more than 3% would tune YBaCo2O6 into the high-spin (HS) Co3+/LS Co4+\nferromagnetic state and simultaneously drive a metal-insulator transition.\nTherefore, spin-states are a useful parameter for tuning the material\nproperties of cobaltates.\n"}
+{"abstract": "  This paper presents an equilibrium model of dynamic trading, learning, and\npricing by strategic investors with trading targets and price impact. Since\ntrading targets are private, rebalancers and liquidity providers filter the\nchild order flow over time to estimate the latent underlying parent trading\ndemand imbalance and its expected impact on subsequent price pressure dynamics.\nWe prove existence of the equilibrium and solve for equilibrium trading\nstrategies and prices in terms of the solution to a system of coupled ODEs. We\nshow that trading strategies are combinations of trading towards investor\ntargets, liquidity provision for other investors' demands, and front-running\nbased on learning about latent underlying trading demand imbalances and future\nprice pressure.\n"}
+{"abstract": "  Let $R$ be a B\\'ezout domain, and let $A,B,C\\in R^{n\\times n}$ with\n$ABA=ACA$. If $AB$ and $CA$ are group invertible, we prove that $AB$ is similar\nto $CA$. Moreover, we have $(AB)^{\\#}$ is similar to $(CA)^{\\#}$. This\ngeneralize the main result of Cao and Li(Group inverses for matrices over a\nB\\'ezout domain, {\\it Electronic J. Linear Algebra}, {\\bf 18}(2009), 600--612).\n"}
+{"abstract": "  We investigate periods, quasi-periods, logarithms, and quasi-logarithms of\nAnderson $t$-modules, as well as their hyperderivatives. We develop a\ncomprehensive account of how these values can be obtained through rigid\nanalytic trivializations of abelian and $\\mathbf{A}$-finite $t$-modules. To do\nthis we build on the exponentiation theorem of Anderson and investigate\nquasi-periodic extensions of $t$-modules through Anderson generating functions.\nBy applying these results to prolongation $t$-modules of Maurischat, we\nintegrate hyperderivatives of these values together with previous work of\nBrownawell and Denis in this framework.\n"}
+{"abstract": "  Calibration is a vital aspect of the performance of risk prediction models,\nbut research in the context of ordinal outcomes is scarce. This study compared\ncalibration measures for risk models predicting a discrete ordinal outcome, and\ninvestigated the impact of the proportional odds assumption on calibration and\noverfitting. We studied the multinomial, cumulative, adjacent category,\ncontinuation ratio, and stereotype logit/logistic models. To assess\ncalibration, we investigated calibration intercepts and slopes, calibration\nplots, and the estimated calibration index. Using large sample simulations, we\nstudied the performance of models for risk estimation under various conditions,\nassuming that the true model has either a multinomial logistic form or a\ncumulative logit proportional odds form. Small sample simulations were used to\ncompare the tendency for overfitting between models. As a case study, we\ndeveloped models to diagnose the degree of coronary artery disease (five\ncategories) in symptomatic patients. When the true model was multinomial\nlogistic, proportional odds models often yielded poor risk estimates, with\ncalibration slopes deviating considerably from unity even on large model\ndevelopment datasets. The stereotype logistic model improved the calibration\nslope, but still provided biased risk estimates for individual patients. When\nthe true model had a cumulative logit proportional odds form, multinomial\nlogistic regression provided biased risk estimates, although these biases were\nmodest. Non-proportional odds models require more parameters to be estimated\nfrom the data, and hence suffered more from overfitting. Despite larger sample\nsize requirements, we generally recommend multinomial logistic regression for\nrisk prediction modeling of discrete ordinal outcomes.\n"}
+{"abstract": "  The discovery of the Meissner effect was a turning point in the history of\nsuperconductivity. It demonstrated that superconductivity is an equilibrium\nstate of matter, thus allowing to use thermodynamics for its study. This\nprovided a justification for the two-fluid model of Gorter and Casimir, a\nseminal thermodynamic theory founded on a postulate of zero entropy of the\nsuperconducting (S) component of conduction electrons. It also demonstrated\nthat, apart from zero resistivity, the S phase is also characterized by zero\nmagnetic induction, used as a basic postulate in the theory of Londons\nunderlying the understanding of electromagnetic properties of superconductors.\nHere the experimental and theoretical aspects of the Meissner effect are\nreviewed. The reader will see that, in spite of almost nine decades age, the\nLondon theory still contains questions, the answers to which can lead to a\nrevision of the standard picture of the Meissner state (MS) and other\nsuperconducting states. An attempt is made to take a fresh look at\nelectrodynamics of the MS and try resolve the issues associated with this most\nimportant state of all superconductors. It is shown that the concept of Cooper\npairing along with the Bohr-Sommerfeld quantization condition allows one to\nconstruct a semi-classical theoretical model consistently addressing properties\nof the MS and beyond, including non-equilibrium properties of superconductors\ncaused by the total current. As follows from the model, the three \"big zeros\"\nof superconductivity (zero resistance, induction and entropy) have equal weight\nand grow from a single root: quantization of the angular momentum of paired\nelectrons. The model predicts some yet unknown effects. If confirmed, they can\nhelp in studies of microscopic properties of all superconductors. Preliminary\nexperimental results suggesting the need to revise the standard picture of the\nMS are presented.\n"}
+{"abstract": "  The geometry of the Ellis-Bronnikov wormhole is implemented in the Rastall\nand k-essence theories of gravity with a self-interacting scalar field. The\nform of the scalar field potential is determined in both cases. A stability\nanalysis with respect to spherically symmetric time-dependent perturbations is\ncarried out, and it shows that in k-essence theory the wormhole is unstable,\nlike the original version of this geometry supported by a massless phantom\nscalar field in general relativity. In Rastall's theory, it turns out that a\nperturbative approach reveals the same inconsistency that was found previously\nfor black hole solutions: time-dependent perturbations of the static\nconfiguration prove to be excluded by the equations of motion, and the wormhole\nis, in this sense, stable under spherical perturbations.\n"}
+{"abstract": "  Quadratic Unconstrained Binary Optimization (QUBO) is a broad class of\noptimization problems with many practical applications. To solve its hard\ninstances in an exact way, known classical algorithms require exponential time\nand several approximate methods have been devised to reduce such cost. With the\ngrowing maturity of quantum computing, quantum algorithms have been proposed to\nspeed up the solution by using either quantum annealers or universal quantum\ncomputers. Here we apply the divide-and-conquer approach to reduce the original\nproblem to a collection of smaller problems whose solutions can be assembled to\nform a single Polynomial Binary Unconstrained Optimization instance with fewer\nvariables. This technique can be applied to any QUBO instance and leads to\neither an all-classical or a hybrid quantum-classical approach. When quantum\nheuristics like the Quantum Approximate Optimization Algorithm (QAOA) are used,\nour proposal leads to a double advantage: a substantial reduction of quantum\nresources, specifically an average of ~42% fewer qubits to solve MaxCut on\nrandom 3-regular graphs, together with an improvement in the quality of the\napproximate solutions reached.\n"}
+{"abstract": "  Numerous contemporary investigations in condensed matter physics are devoted\nto high temperature (high-$T_c$ ) cuprate superconductors. Despite its unique\neffulgence among research subjects, the enigma of the high-$T_c$ mechanism\nstill persists. One way to advance its understanding is to discover and study\nnew analogous systems. Here we begin a novel exploration of the natural mineral\nmurunskite, K$_2$FeCu$_3$S$_4$, as an interpolation compound between cuprates\nand ferropnictides, the only known high-$T_c$ superconductors at ambient\npressure. Because in-depth studies can be carried out only on single crystals,\nwe have mastered the synthesis and growth of high quality specimens. Similar to\nthe cuprate parent compounds, these show semiconducting behavior in resistivity\nand optical transmittance, and an antiferromagnetic ordering at 100 K.\nSpectroscopy (XPS) and calculations (DFT) concur that the sulfur 3$p$ orbitals\nare partially open, making them accessible for charge manipulation, which is a\nprerequisite for superconductivity in analogous layered structures. DFT\nindicates that the valence band is more cuprate-like, while the conduction band\nis more pnictide-like. With appropriate doping strategies, this parent compound\npromises exciting future developments.\n"}
+{"abstract": "  A teleparallel geometry is an n-dimensional manifold equipped with a frame\nbasis and an independent spin connection. For such a geometry, the curvature\ntensor vanishes and the torsion tensor is non-zero. A straightforward approach\nto characterizing teleparallel geometries is to compute scalar polynomial\ninvariants constructed from the torsion tensor and its covariant derivatives.\nAn open question has been whether the set of all scalar polynomial torsion\ninvariants, $\\mathcal{I}_T$ uniquely characterize a given teleparallel\ngeometry. In this paper we show that the answer is no and construct the most\ngeneral class of teleparallel geometries in four dimensions which cannot be\ncharacterized by $\\mathcal{I}_T$. As a corollary we determine all teleparallel\ngeometries which have vanishing scalar polynomial torsion invariants.\n"}
+{"abstract": "  In a brief article published in 1931 and expanded in 1935, the Indian\nastrophysicist Subrahmanyan Chandrasekhar shared an important astronomical\ndiscovery where he introduced what is now known as Chandrasekhar limit. This\nlimit establishes the maximum mass that a white dwarf can reach, which is the\nstellar remnant that is generated when a low mass star has used up its nuclear\nfuel. The present work has a double purpose. The first is to present a\nheuristic derivation of the Chandrasekhar limit. The second is to clarify the\ngenesis of the discovery of Chandrasekhar, as well as the conceptual aspects of\nthe subject. The exhibition only uses high school algebra, as well as some\ngeneral notions of classical physics and quantum theory.\n"}
+{"abstract": "  In the big data and AI era, context is widely exploited as extra information\nwhich makes it easier to learn a more complex pattern in machine learning\nsystems. However, most of the existing related studies seldom take context into\naccount. The difficulty lies in the unknown generalization ability of both\ncontext and its modeling techniques across different scenarios. To fill the\nabove gaps, we conduct a large-scale analytical and empirical study on the\nspatiotemporal crowd prediction (STCFP) problem that is a widely-studied and\nhot research topic. We mainly make three efforts:(i) we develop new taxonomy\nabout both context features and context modeling techniques based on extensive\ninvestigations in prevailing STCFP research; (ii) we conduct extensive\nexperiments on seven datasets with hundreds of millions of records to\nquantitatively evaluate the generalization ability of both distinct context\nfeatures and context modeling techniques; (iii) we summarize some guidelines\nfor researchers to conveniently utilize context in diverse applications.\n"}
+{"abstract": "  Hindsight rationality is an approach to playing general-sum games that\nprescribes no-regret learning dynamics for individual agents with respect to a\nset of deviations, and further describes jointly rational behavior among\nmultiple agents with mediated equilibria. To develop hindsight rational\nlearning in sequential decision-making settings, we formalize behavioral\ndeviations as a general class of deviations that respect the structure of\nextensive-form games. Integrating the idea of time selection into\ncounterfactual regret minimization (CFR), we introduce the extensive-form\nregret minimization (EFR) algorithm that achieves hindsight rationality for any\ngiven set of behavioral deviations with computation that scales closely with\nthe complexity of the set. We identify behavioral deviation subsets, the\npartial sequence deviation types, that subsume previously studied types and\nlead to efficient EFR instances in games with moderate lengths. In addition, we\npresent a thorough empirical analysis of EFR instantiated with different\ndeviation types in benchmark games, where we find that stronger types typically\ninduce better performance.\n"}
+{"abstract": "  We study vacuum birefringence and x-ray photon scattering in the head-on\ncollision of x-ray free electron and high-intensity laser pulses. Resorting to\nanalytical approximations for the numbers of attainable signal photons, we\nanalyze the behavior of the phenomenon under the variation of various\nexperimental key-parameters and provide new analytical scalings. Our optimized\napproximations allow for quantitatively accurate results on the one-percent\nlevel. We in particular demonstrate that an appropriate choice of the x-ray\nfocus and pulse duration can significantly improve the signal for given laser\nparameters, using the experimental parameters to be available at the Helmholtz\nInternational Beamline for Extreme Fields at the European XFEL as example. Our\nresults are essential for the identification of the optimal choice of\nparameters in a discovery experiment of vacuum birefringence at the\nhigh-intensity frontier.\n"}
+{"abstract": "  In this paper, the average achievable rate and error probability of a\nreconfigurable intelligent surface (RIS) aided systems is investigated for the\nfinite blocklength (FBL) regime. The performance loss due to the presence of\nphase errors arising from limited quantization levels as well as hardware\nimpairments at the RIS elements is also discussed. First, the composite channel\ncontaining the direct path plus the product of reflected channels through the\nRIS is characterized. Then, the distribution of the received signal-to-noise\nratio (SNR) is matched to a Gamma random variable whose parameters depend on\nthe total number of RIS elements, phase errors and the channels' path loss.\nNext, by considering the FBL regime, the achievable rate expression and error\nprobability are identified and the corresponding average rate and average error\nprobability are elaborated based on the proposed SNR distribution. Furthermore,\nthe impact of the presence of phase error due to either limited quantization\nlevels or hardware impairments on the average rate and error probability is\ndiscussed. The numerical results show that Monte Carlo simulations conform to\nmatched Gamma distribution to received SNR for sufficiently large number of RIS\nelements. In addition, the system reliability indicated by the tightness of the\nSNR distribution increases when RIS is leveraged particularly when only the\nreflected channel exists. This highlights the advantages of RIS-aided\ncommunications for ultra-reliable and low-latency systems. The difference\nbetween Shannon capacity and achievable rate in FBL regime is also discussed.\nAdditionally, the required number of RIS elements to achieve a desired error\nprobability in the FBL regime will be significantly reduced when the phase\nshifts are performed without error.\n"}
+{"abstract": "  In this paper we prove that any local automorphism on the solvable Leibniz\nalgebras with null-filiform and naturally graded non-Lie filiform nilradicals,\nwhose dimension of complementary space is maximal is an automorphism.\nFurthermore, the same problem concerning 2-local automorphisms of such algebras\nis investigated and we obtain the analogously results for 2-local\nautomorphisms.\n"}
+{"abstract": "  Geometric phases are robust against certain types of local noises, and thus\nprovide a promising way towards high-fidelity quantum gates. However, comparing\nwith the dynamical ones, previous implementations of nonadiabatic geometric\nquantum gates usually require longer evolution time, due to the needed longer\nevolution path. Here, we propose a scheme to realize nonadiabatic geometric\nquantum gates with short paths based on simple pulse control techniques,\ninstead of deliberated pulse control in previous investigations, which can thus\nfurther suppress the influence from the environment induced noises.\nSpecifically, we illustrate the idea on a superconducting quantum circuit,\nwhich is one of the most promising platforms for realizing practical quantum\ncomputer. As the current scheme shortens the geometric evolution path, we can\nobtain ultra-high gate fidelity, especially for the two-qubit gate case, as\nverified by our numerical simulation. Therefore, our protocol suggests a\npromising way towards high-fidelity and roust quantum computation on a\nsolid-state quantum system.\n"}
+{"abstract": "  Heterogeneous Ultra-Dense Network (HUDN) is one of the vital networking\narchitectures due to its ability to enable higher connectivity density and\nultra-high data rates. Rational user association and power control schedule in\nHUDN can reduce wireless interference. This paper proposes a novel idea for\nresolving the joint user association and power control problem: the optimal\nuser association and Base Station transmit power can be represented by channel\ninformation. Then, we solve this problem by formulating an optimal\nrepresentation function. We model the HUDNs as a heterogeneous graph and train\na Graph Neural Network (GNN) to approach this representation function by using\nsemi-supervised learning, in which the loss function is composed of the\nunsupervised part that helps the GNN approach the optimal representation\nfunction and the supervised part that utilizes the previous experience to\nreduce useless exploration. We separate the learning process into two parts,\nthe generalization-representation learning (GRL) part and the\nspecialization-representation learning (SRL) part, which train the GNN for\nlearning representation for generalized scenario quasi-static user distribution\nscenario, respectively. Simulation results demonstrate that the proposed\nGRL-based solution has higher computational efficiency than the traditional\noptimization algorithm, and the performance of SRL outperforms the GRL.\n"}
+{"abstract": "  Ferroelectric tunnel junctions (FTJ) based on hafnium zirconium oxide\n(Hf1-xZrxO2; HZO) are a promising candidate for future applications, such as\nlow-power memories and neuromorphic computing. The tunneling electroresistance\n(TER) is tunable through the polarization state of the HZO film. To circumvent\nthe challenge of fabricating thin ferroelectric HZO layers in the tunneling\nrange of 1-3 nm range, ferroelectric/dielectric double layer sandwiched between\ntwo symmetric metal electrodes are used. Due to the decoupling of the\nferroelectric polarization storage layer and a dielectric tunneling layer with\na higher bandgap, a significant TER ratio between the two polarization states\nis obtained. By exploiting previously reported switching behaviour and the\ngradual tunability of the resistance, FTJs can be used as potential candidates\nfor the emulation of synapses for neuromorphic computing in spiking neural\nnetworks. The implementation of two major components of a synapse are shown:\nlong term depression/potentiation by varying the amplitude/width/number of\nvoltage pulses applied to the artificial FTJ synapse, and\nspike-timing-dependent-plasticity curves by applying time-delayed voltages at\neach electrode. These experimental findings show the potential of spiking\nneural networks and neuromorphic computing that can be implemented with\nhafnia-based FTJs.\n"}
+{"abstract": "  Deep learning is expected to offer new opportunities and a new paradigm for\nthe field of architecture. One such opportunity is teaching neural networks to\nvisually understand architectural elements from the built environment. However,\nthe availability of large training datasets is one of the biggest limitations\nof neural networks. Also, the vast majority of training data for visual\nrecognition tasks is annotated by humans. In order to resolve this bottleneck,\nwe present a concept of a hybrid system using both building information\nmodeling (BIM) and hyperrealistic (photorealistic) rendering to synthesize\ndatasets for training a neural network for building object recognition in\nphotos. For generating our training dataset BIMrAI, we used an existing BIM\nmodel and a corresponding photo-realistically rendered model of the same\nbuilding. We created methods for using renderings to train a deep learning\nmodel, trained a generative adversarial network (GAN) model using these\nmethods, and tested the output model on real-world photos. For the specific\ncase study presented in this paper, our results show that a neural network\ntrained with synthetic data; i.e., photorealistic renderings and BIM-based\nsemantic labels, can be used to identify building objects from photos without\nusing photos in the training data. Future work can enhance the presented\nmethods using available BIM models and renderings for more generalized mapping\nand description of photographed built environments.\n"}
+{"abstract": "  Confinement remains one the most interesting and challenging nonperturbative\nphenomenon in non-Abelian gauge theories. Recent semiclassical (for SU(2)) and\nlattice (for QCD) studies have suggested that confinement arises from\ninteractions of statistical ensembles of instanton-dyons with the Polyakov\nloop. In this work, we extend studies of semiclassical ensemble of dyons to the\n$SU(3)$ Yang-Mills theory. We find that such interactions do generate the\nexpected first-order deconfinement phase transition. The properties of the\nensemble, including correlations and topological susceptibility, are studied\nover a range of temperatures above and below $T_c$. Additionally, the dyon\nensemble is studied in the Yang-Mills theory containing an extra\ntrace-deformation term. It is shown that such a term can cause the theory to\nremain confined and even retain the same topological observables at high\ntemperatures.\n"}
+{"abstract": "  We derive the first positivity bounds for low-energy Effective Field Theories\n(EFTs) that are not invariant under Lorentz boosts. \"Positivity bounds\" are the\nlow-energy manifestation of certain fundamental properties in the UV -- to date\nthey have been used to constrain a wide variety of EFTs, however since all of\nthe existing bounds require Lorentz invariance they are not directly applicable\nwhen this symmetry is broken, such as for most cosmological and condensed\nmatter systems. From the UV axioms of unitarity, causality and locality, we\nderive an infinite family of bounds which (derivatives of) the $2\\to2$ EFT\nscattering amplitude must satisfy even when Lorentz boosts are broken (either\nspontaneously or explicitly). We apply these bounds to the leading-order EFT of\nboth a superfluid and the scalar fluctuations produced during inflation,\ncomparing in the latter case with the current observational constraints on\nprimordial non-Gaussianity.\n"}
+{"abstract": "  In this paper we consider the nature of the cosmological constant as due by\nquantum fluctuations. Quantum fluctuations are generated at Planckian scales by\nnoncommutative effects and watered down at larger scales up to a decoherence\nscale $L_D$ where classicality is reached. In particular, we formally depict\nthe presence of the scale at $L_D$ by adopting a renormalization group\napproach. As a result, an analogy arises between the expression for the\nobserved cosmological constant $\\overline{\\Lambda}$ generated by quantum\nfluctuations and the one expected by a renormalization group approach, provided\nthat the renormalization scale $\\mu$ is suitably chosen. In this framework, the\ndecoherence scale $L_D$ is naturally identified with the value ${\\mu}_D$, with\n$\\hbar{\\mu}_D$ representing the minimum allowed particle-momentum for our\nvisible universe. Finally, by mimicking renormalization group approach, we\npresent a technique to formally obtain a non-trivial infrared (IR) fixed point\nat $\\mu=\\mu_D$ in our model.\n"}
+{"abstract": "  We find that, under certain conditions, protoplanetary disks may\nspontaneously generate multiple, concentric gas rings without an embedded\nplanet through an eccentric cooling instability. Using both linear theory and\nnon-linear hydrodynamics simulations, we show that a variety of background\nstates may trap a slowly precessing, one-armed spiral mode that becomes\nunstable when a gravitationally-stable disk rapidly cools. The angular momentum\nrequired to excite this spiral comes at the expense of non-uniform mass\ntransport that generically results in multiple rings. For example, one\nlong-term hydrodynamics simulation exhibits four long-lived, axisymmetric gas\nrings. We verify the instability evolution and ring formation mechanism from\nfirst principles with our linear theory, which shows remarkable agreement with\nthe simulation results. Dust trapped in these rings may produce observable\nfeatures consistent with observed disks. Additionally, direct detection of the\neccentric gas motions may be possible when the instability saturates, and any\nresidual eccentricity leftover in the rings at later times may also provide\ndirect observational evidence of this mechanism.\n"}
+{"abstract": "  Let $(F_n)_{n\\ge 1}$ be the Fibonacci sequence. Define $P(F_n): =\n(\\sum_{i=1}^n F_i)_{n\\ge 1}$; that is, the function $P$ gives the sequence of\npartial sums of $(F_n)$. In this paper, we first give an identity involving\n$P^k(F_n)$, which is the resulting sequence from applying $P$ to $(F_n)$ $k$\ntimes. Second, we provide a combinatorial interpretation of the numbers in\n$P^k(F_n)$.\n"}
+{"abstract": "  We consider an interacting collective spin model known as coupled top (CT),\nexhibiting a rich variety of phenomena related to quantum transitions and\nergodicity, which we explore and find their connection with underlying\ndynamics. The ferromagnetic interaction between the spins leads to the quantum\nphase transition (QPT) as well as a dynamical transition at a critical coupling\nstrength, and both the transitions are accompanied by excited state quantum\nphase transitions at critical energy densities. Above QPT, the onset of chaos\nin the CT model occurs in an intermediate coupling strength, which is analyzed\nboth classically and quantum mechanically. However, a detailed analysis reveals\nthe presence of non-ergodic multifractal eigenstates in the chaotic regime. We\nquantify the degree of ergodicity of the eigenstates from the relative\nentanglement entropy and multifractal dimensions, revealing its variation with\nenergy density across the energy band. We probe such energy dependent ergodic\nbehavior of states from non-equilibrium dynamics, which is also supplemented by\nphase space mixing in classical dynamics. Moreover, we identify another source\nof deviation from ergodicity due to the formation of `quantum scars' arising\nfrom the unstable steady states and periodic orbits. Unlike the ergodic states,\nthe scarred eigenstates violate Berry's conjecture even in the chaotic regime,\nleading to the athermal non-ergodic behavior. Finally, we discuss the detection\nof non-ergodic behavior and dynamical signature of quantum scars by using\n`out-of-time-order correlator', which has relevance in the recent experiments.\n"}
+{"abstract": "  In this paper, we present the Quantum Information Software Developer Kit -\nQiskit, for teaching quantum computing to undergraduate students, with basic\nknowledge of quantum mechanics postulates. We focus on presenting the\nconstruction of the programs on any common laptop or desktop computer and their\nexecution on real quantum processors through the remote access to the quantum\nhardware available on the IBM Quantum Experience platform. The codes are made\navailable throughout the text so that readers, even with little experience in\nscientific computing, can reproduce them and adopt the methods discussed in\nthis paper to address their own quantum computing projects. The results\npresented are in agreement with theoretical predictions and show the\neffectiveness of the Qiskit package as a robust classroom working tool for the\nintroduction of applied concepts of quantum computing and quantum information\ntheory.\n"}
+{"abstract": "  Researchers and designers have incorporated social media affordances into\nlearning technologies to engage young people and support personally relevant\nlearning, but youth may reject these attempts because they do not meet user\nexpectations. Through in-depth case studies, we explore the sociotechnical\necosystems of six teens (ages 15-18) working at a science center that had\nrecently introduced a digital badge system to track and recognize their\nlearning. By analyzing interviews, observations, ecological momentary\nassessments, and system data, we examined tensions in how badges as connected\nlearning technologies operate in teens' sociotechnical ecosystems. We found\nthat, due to issues of unwanted context collapse and incongruent identity\nrepresentations, youth only used certain affordances of the system and did so\nsporadically. Additionally, we noted that some features seemed to prioritize\nvalues of adult stakeholders over youth. Using badges as a lens, we reveal\ncritical tensions and offer design recommendations for networked learning\ntechnologies.\n"}
+{"abstract": "  Overfitting is one of the critical problems in deep neural networks. Many\nregularization schemes try to prevent overfitting blindly. However, they\ndecrease the convergence speed of training algorithms. Adaptive regularization\nschemes can solve overfitting more intelligently. They usually do not affect\nthe entire network weights. This paper detects a subset of the weighting layers\nthat cause overfitting. The overfitting recognizes by matrix and tensor\ncondition numbers. An adaptive regularization scheme entitled Adaptive Low-Rank\n(ALR) is proposed that converges a subset of the weighting layers to their\nLow-Rank Factorization (LRF). It happens by minimizing a new Tikhonov-based\nloss function. ALR also encourages lazy weights to contribute to the\nregularization when epochs grow up. It uses a damping sequence to increment\nlayer selection likelihood in the last generations. Thus before falling the\ntraining accuracy, ALR reduces the lazy weights and regularizes the network\nsubstantially. The experimental results show that ALR regularizes the deep\nnetworks well with high training speed and low resource usage.\n"}
+{"abstract": "  A word equation with one variable in a free group is given as $U = V$, where\nboth $U$ and $V$ are words over the alphabet of generators of the free group\nand $X, X^{-1}$, for a fixed variable $X$. An element of the free group is a\nsolution when substituting it for $X$ yields a true equality (interpreted in\nthe free group) of left- and right-hand sides. It is known that the set of all\nsolutions of a given word equation with one variable is a finite union of sets\nof the form $\\{\\alpha w^i \\beta \\: : \\: i \\in \\mathbb Z \\}$, where $\\alpha, w,\n\\beta$ are reduced words over the alphabet of generators, and a polynomial-time\nalgorithm (of a high degree) computing this set is known. We provide a cubic\ntime algorithm for this problem, which also shows that the set of solutions\nconsists of at most a quadratic number of the above-mentioned sets. The\nalgorithm uses only simple tools of word combinatorics and group theory and is\nsimple to state. Its analysis is involved and focuses on the combinatorics of\noccurrences of powers of a word within a larger word.\n"}
+{"abstract": "  Salient human detection (SHD) in dynamic 360{\\deg} immersive videos is of\ngreat importance for various applications such as robotics, inter-human and\nhuman-object interaction in augmented reality. However, 360{\\deg} video SHD has\nbeen seldom discussed in the computer vision community due to a lack of\ndatasets with large-scale omnidirectional videos and rich annotations. To this\nend, we propose SHD360, the first 360{\\deg} video SHD dataset which contains\nvarious real-life daily scenes. Since so far there is no method proposed for\n360{\\deg} image/video SHD, we systematically benchmark 11 representative\nstate-of-the-art salient object detection (SOD) approaches on our SHD360, and\nexplore key issues derived from extensive experimenting results. We hope our\nproposed dataset and benchmark could serve as a good starting point for\nadvancing human-centric researches towards 360{\\deg} panoramic data. The\ndataset is available at https://github.com/PanoAsh/SHD360.\n"}
+{"abstract": "  Accurate and trustworthy epidemic forecasting is an important problem that\nhas impact on public health planning and disease mitigation. Most existing\nepidemic forecasting models disregard uncertainty quantification, resulting in\nmis-calibrated predictions. Recent works in deep neural models for\nuncertainty-aware time-series forecasting also have several limitations; e.g.\nit is difficult to specify meaningful priors in Bayesian NNs, while methods\nlike deep ensembling are computationally expensive in practice. In this paper,\nwe fill this important gap. We model the forecasting task as a probabilistic\ngenerative process and propose a functional neural process model called EPIFNP,\nwhich directly models the probability density of the forecast value. EPIFNP\nleverages a dynamic stochastic correlation graph to model the correlations\nbetween sequences in a non-parametric way, and designs different stochastic\nlatent variables to capture functional uncertainty from different perspectives.\nOur extensive experiments in a real-time flu forecasting setting show that\nEPIFNP significantly outperforms previous state-of-the-art models in both\naccuracy and calibration metrics, up to 2.5x in accuracy and 2.4x in\ncalibration. Additionally, due to properties of its generative process,EPIFNP\nlearns the relations between the current season and similar patterns of\nhistorical seasons,enabling interpretable forecasts. Beyond epidemic\nforecasting, the EPIFNP can be of independent interest for advancing principled\nuncertainty quantification in deep sequential models for predictive analytics\n"}
+{"abstract": "  In fingerprint-based systems, the size of databases increases considerably\nwith population growth. In developing countries, because of the difficulty in\nusing a central system when enlisting voters, it often happens that several\nregional voter databases are created and then merged to form a central\ndatabase. A process is used to remove duplicates and ensure uniqueness by\nvoter. Until now, companies specializing in biometrics use several costly\ncomputing servers with algorithms to perform large-scale deduplication based on\nfingerprints. These algorithms take a considerable time because of their\ncomplexity in O (n2), where n is the size of the database. This article\npresents an algorithm that can perform this operation in O (2n), with just a\ncomputer. It is based on the development of an index obtained using a 5 * 5\nmatrix performed on each fingerprint. This index makes it possible to build\nclusters of O (1) in size in order to compare fingerprints. This approach has\nbeen evaluated using close to 11 4000 fingerprints, and the results obtained\nshow that this approach allows a penetration rate of less than 1%, an almost O\n(1) identification, and an O (n) deduplication. A base of 10 000 000\nfingerprints can be deduplicated with a just computer in less than two hours,\ncontrary to several days and servers for the usual tools.\n  Keywords: fingerprint, cluster, index, deduplication.\n"}
+{"abstract": "  Content replication to many destinations is a common use case in the Internet\nof Things (IoT). The deployment of IP multicast has proven inefficient, though,\ndue to its lack of layer-2 support by common IoT radio technologies and its\nsynchronous end-to-end transmission, which is highly susceptible to\ninterference. Information-centric networking (ICN) introduced hop-wise\nmulti-party dissemination of cacheable content, which has proven valuable in\nparticular for low-power lossy networking regimes. Even NDN, however, the most\nprominent ICN protocol, suffers from a lack of deployment.\n  In this paper, we explore how multiparty content distribution in an\ninformation-centric Web of Things (WoT) can be built on CoAP. We augment the\nCoAP proxy by request aggregation and response replication functions, which\ntogether with proxy caches enable asynchronous group communication. In a\nfurther step, we integrate content object security with OSCORE into the CoAP\nmulticast proxy system, which enables ubiquitous caching of certified authentic\ncontent. In our evaluation, we compare NDN with different deployment models of\nCoAP, including our data-centric approach in realistic testbed experiments. Our\nfindings indicate that multiparty content distribution based on CoAP proxies\nperforms equally well as NDN, while remaining fully compatible with the\nestablished IoT protocol world of CoAP on the Internet.\n"}
+{"abstract": "  Models for strongly interacting fermions in disordered clusters forming an\narray, with electron hopping between sites, reproduce the linear dependence on\ntemperature of the resistivity, typical of the strange metal phase of High\nTemperature Superconducting materials (Extended Sachdev-Ye-Kitaev (SYK)\nmodels). Our hydrodynamical approach to the marginal Fermi liquid emerging out\nof the interaction, identifies the low energy collective excitations of the\nsystem in its coherent phase. These neutral excitations diffuse in the lattice,\nbut the diffusion is heavily hindered by coupling to the pseudo Goldstone modes\nof the conformal broken symmetry SYK phase, which are local in space. A\ncritical temperature for superconductivity arises in the electron liquid,in\ncase these excitations are assumed to mediate an attractive Cooper-pairing, in\nthe electron liquid, which is not BCS-like.\n"}
+{"abstract": "  We consider the problem of active and sequential beam tracking at mmWave\nfrequencies and above. We focus on the dynamic scenario of a UAV to UAV\ncommunications where we formulate the problem to be equivalent to tracking an\noptimal beamforming vector along the line-of-sight path. In this setting, the\nresulting beam ideally points in the direction of the angle of arrival with\nsufficiently high resolution. Existing solutions account for predictable\nmovements or small random movements using filtering strategies or by accounting\nfor predictable mobility but must resort to re-estimation protocols when\ntracking fails due to unpredictable movements. We propose an algorithm for\nactive learning of the AoA through evolving a Bayesian posterior probability\nbelief which is utilized for a sequential selection of beamforming vectors. We\npropose an adaptive pilot allocation strategy based on a trade-off of mutual\ninformation versus spectral efficiency. Numerically, we analyze the performance\nof our proposed algorithm and demonstrate significant improvements over\nexisting strategies.\n"}
+{"abstract": "  In this paper, the necessity theory for commutators of multilinear singular\nintegral operators on weighted Lebesgue spaces is investigated. The results\nrelax the restriction of the weights class to the general multiple weights,\nwhich can be regarded as an essential improvement of\n\\cite{ChafCruz2018,GLW2020}. Our approach elaborates on a commonly expanding\nthe kernel locally by Fourier series, recovering many known results but\nyielding also numerous new ones. In particular, we answer the question about\nthe necessity theory of the iterated commutators of the multilinear singular\nintegral operators.\n"}
+{"abstract": "  Wireless Sensor Networks (WSNs) are groups of spatially distributed and\ndedicated autonomous sensors for monitoring (and recording) the physical\nconditions of the environment (and organizing the collected data at a central\nlocation). They have been a topic of interest due to their versatility and\ndiverse capabilities despite having simple sensors measuring local quantities\nsuch as temperature, pH, or pressure. We delve into understanding how such\nnetworks can be utilized for localization, and propose a technique for\nimproving conditions of living for animals and humans on the IIT Bombay campus.\n"}
+{"abstract": "  Building an interactive artificial intelligence that can ask questions about\nthe real world is one of the biggest challenges for vision and language\nproblems. In particular, goal-oriented visual dialogue, where the aim of the\nagent is to seek information by asking questions during a turn-taking dialogue,\nhas been gaining scholarly attention recently. While several existing models\nbased on the GuessWhat?! dataset have been proposed, the Questioner typically\nasks simple category-based questions or absolute spatial questions. This might\nbe problematic for complex scenes where the objects share attributes or in\ncases where descriptive questions are required to distinguish objects. In this\npaper, we propose a novel Questioner architecture, called Unified Questioner\nTransformer (UniQer), for descriptive question generation with referring\nexpressions. In addition, we build a goal-oriented visual dialogue task called\nCLEVR Ask. It synthesizes complex scenes that require the Questioner to\ngenerate descriptive questions. We train our model with two variants of CLEVR\nAsk datasets. The results of the quantitative and qualitative evaluations show\nthat UniQer outperforms the baseline.\n"}
+{"abstract": "  We revisit the problem of the gauge invariance in the Coleman-Weinberg model\nin which a $U(1)$ gauge symmetry is driven spontaneously broken by radiative\ncorrections. It was noticed in previous work that masses in this model are not\ngauge invariant at one-loop order. In our analysis, we use the dressed\npropagators of scalars which include a resummation of the one-loop self-energy\ncorrection to the tree-level propagator. We calculate the one-loop self-energy\ncorrection to the vector meson using these dressed propagators. We find that\nthe pole mass of the vector meson calculated using the dressed propagator is\ngauge invariant at the vacuum determined using the effective potential\ncalculated with a resummation of daisy diagrams.\n"}
+{"abstract": "  We implement four algorithms for solving linear Diophantine equations in the\nnaturals: a lexicographic enumeration algorithm, a completion procedure, a\ngraph-based algorithm, and the Slopes algorithm. As already known, the\nlexicographic enumeration algorithm and the completion procedure are slower\nthan the other two algorithms. We compare in more detail the graph-based\nalgorithm and the Slopes algorithm. In contrast to previous comparisons, our\nwork suggests that they are equally fast on small inputs, but the graph-based\nalgorithm gets much faster as the input grows. We conclude that implementations\nof AC-unification algorithms should use the graph-based algorithm for maximum\nefficiency.\n"}
+{"abstract": "  We study orbital evolution of multi-planet systems that form a resonant\nchain, with nearest neighbours close to first order commensurabilities,\nincorporating orbital circularisation produced by tidal interaction with the\ncentral star. We develop a semi-analytic model applicable when the relative\nproximities to commensurability, though small, are large compared to\nepsilon^(2/3) , with epsilon being a measure of the characteristic planet to\ncentral star mass ratio. This enables determination of forced eccentricities as\nwell as which resonant angles enter libration. When there are no active linked\nthree body Laplace resonances, the rate of evolution of the semi-major axes may\nalso be determined. We perform numerical simulations of the HD 158259 and EPIC\n245950175 systems finding that the semi-analytic approach works well in the\nformer case but not so well in the latter case on account of the effects of\nthree active three body Laplace resonances which persist during the evolution.\nFor both systems we estimate that if the tidal parameter, Q', significantly\nexceeds 1000, tidal effects are unlikely to have influenced period ratios\nsignificantly since formation. On the other hand if Q' < ~ 100 tidal effects\nmay have produced significant changes including the formation of three body\nLaplace resonances in the case of the EPIC 245950175 system.\n"}
+{"abstract": "  Decades of research on Internet congestion control (CC) has produced a\nplethora of algorithms that optimize for different performance objectives.\nApplications face the challenge of choosing the most suitable algorithm based\non their needs, and it takes tremendous efforts and expertise to customize CC\nalgorithms when new demands emerge. In this paper, we explore a basic question:\ncan we design a single CC algorithm to satisfy different objectives? We propose\nMOCC, the first multi-objective congestion control algorithm that attempts to\naddress this challenge. The core of MOCC is a novel multi-objective\nreinforcement learning framework for CC that can automatically learn the\ncorrelations between different application requirements and the corresponding\noptimal control policies. Under this framework, MOCC further applies transfer\nlearning to transfer the knowledge from past experience to new applications,\nquickly adapting itself to a new objective even if it is unforeseen. We provide\nboth user-space and kernel-space implementation of MOCC. Real-world experiments\nand extensive simulations show that MOCC well supports multi-objective,\ncompeting or outperforming the best existing CC algorithms on individual\nobjectives, and quickly adapting to new applications (e.g., 14.2x faster than\nprior work) without compromising old ones.\n"}
+{"abstract": "  Analysis of burden of underregistration in tuberculosis data in Brazil, from\n2012 to 2014. Approches of Oliveira et al. (2020) and Stoner et al. (2019) are\napplied. The main focus is to illustrated how the approach of Oliveira et al.\n(2020) can be applied when the clustering structure is not previously\navailable.\n"}
+{"abstract": "  The HERMES-TP/SP (High Energy Rapid Modular Ensemble of Satellites --\nTechnologic and Scientific Pathfinder) is an in-orbit demonstration of the\nso-called distributed astronomy concept. Conceived as a mini-constellation of\nsix 3U nano-satellites hosting a new miniaturized detector, HERMES-TP/SP aims\nat the detection and accurate localisation of bright high-energy transients\nsuch as Gamma-Ray Bursts. The large energy band, the excellent temporal\nresolution and the wide field of view that characterize the detectors of the\nconstellation represent the key features for the next generation high-energy\nall-sky monitor with good localisation capabilities that will play a pivotal\nrole in the future of Multi-messenger Astronomy. In this work, we will describe\nin detail the temporal techniques that allow the localisation of bright\ntransient events taking advantage of their almost simultaneous observation by\nspatially spaced detectors. Moreover, we will quantitatively discuss the\nall-sky monitor capabilities of the HERMES Pathfinder as well as its achievable\naccuracies on the localisation of the detected Gamma-Ray Bursts.\n"}
+{"abstract": "  Neural personalized recommendation models are used across a wide variety of\ndatacenter applications including search, social media, and entertainment.\nState-of-the-art models comprise large embedding tables that have billions of\nparameters requiring large memory capacities. Unfortunately, large and fast\nDRAM-based memories levy high infrastructure costs. Conventional SSD-based\nstorage solutions offer an order of magnitude larger capacity, but have worse\nread latency and bandwidth, degrading inference performance. RecSSD is a near\ndata processing based SSD memory system customized for neural recommendation\ninference that reduces end-to-end model inference latency by 2X compared to\nusing COTS SSDs across eight industry-representative models.\n"}
+{"abstract": "  Despite the accomplishments of Generative Adversarial Networks (GANs) in\nmodeling data distributions, training them remains a challenging task. A\ncontributing factor to this difficulty is the non-intuitive nature of the GAN\nloss curves, which necessitates a subjective evaluation of the generated output\nto infer training progress. Recently, motivated by game theory, duality gap has\nbeen proposed as a domain agnostic measure to monitor GAN training. However, it\nis restricted to the setting when the GAN converges to a Nash equilibrium. But\nGANs need not always converge to a Nash equilibrium to model the data\ndistribution. In this work, we extend the notion of duality gap to proximal\nduality gap that is applicable to the general context of training GANs where\nNash equilibria may not exist. We show theoretically that the proximal duality\ngap is capable of monitoring the convergence of GANs to a wider spectrum of\nequilibria that subsumes Nash equilibria. We also theoretically establish the\nrelationship between the proximal duality gap and the divergence between the\nreal and generated data distributions for different GAN formulations. Our\nresults provide new insights into the nature of GAN convergence. Finally, we\nvalidate experimentally the usefulness of proximal duality gap for monitoring\nand influencing GAN training.\n"}
+{"abstract": "  In this work, the distance between a quark-antiquark pair is analyzed through\nboth the confinement potential as well as the hadronic total cross section.\nUsing the Helmholtz free energy, entropy is calculated near the minimum of the\ntotal cross section through the confinement potential. A fitting procedure for\nthe proton-proton total cross section is performed, defining the fitting\nparameters. Therefore, the only free parameter remaining in the model is the\nmass scale $\\kappa$ used to define the running coupling constant of the\nlight-front approach to QCD. The mass scale controls the distance $r$ between\nthe quark-antiquark pair and, under some conditions, it allows the occurrence\nof free quarks even in the confinement regime of QCD.\n"}
+{"abstract": "  There have been many studies of the instability of a flexible plate or flag\nto flapping motions, and of large-amplitude flapping. Here we use inviscid\nsimulations and a linearized model to study more generally how key quantities\n-- mode number (or wavenumber), frequency, and amplitude -- depend on the two\ndimensionless parameters, flag mass and bending stiffness. In the limit of\nsmall flag mass, flags perform traveling wave motions that move at nearly the\nspeed of the oncoming flow. The flag mode number scales as the -1/4 power of\nbending stiffness. The flapping frequency has the same scaling, with an\nadditional slight increase with flag mass in the small-mass regime. The\nflapping amplitude scales approximately as flag mass to the 1/2 power. For\nlarge flag mass, the dominant mode number is low (0 or 1), the flapping\nfrequency tends to zero, and the amplitude saturates in the neighborhood of its\nupper limit (the flag length). In a linearized model, the fastest growing modes\nhave somewhat different power law scalings for wavenumber and frequency. We\ndiscuss how the numerical scalings are consistent with a weakly nonlinear\nmodel.\n"}
+{"abstract": "  In this paper, we propose FedChain, a novel framework for\nfederated-blockchain systems, to enable effective transferring of tokens\nbetween different blockchain networks. Particularly, we first introduce a\nfederated-blockchain system together with a cross-chain transfer protocol to\nfacilitate the secure and decentralized transfer of tokens between chains. We\nthen develop a novel PoS-based consensus mechanism for FedChain, which can\nsatisfy strict security requirements, prevent various blockchain-specific\nattacks, and achieve a more desirable performance compared to those of other\nexisting consensus mechanisms. Moreover, a Stackelberg game model is developed\nto examine and address the problem of centralization in the FedChain system.\nFurthermore, the game model can enhance the security and performance of\nFedChain. By analyzing interactions between the stakeholders and chain\noperators, we can prove the uniqueness of the Stackelberg equilibrium and find\nthe exact formula for this equilibrium. These results are especially important\nfor the stakeholders to determine their best investment strategies and for the\nchain operators to design the optimal policy to maximize their benefits and\nsecurity protection for FedChain. Simulations results then clearly show that\nthe FedChain framework can help stakeholders to maximize their profits and the\nchain operators to design appropriate parameters to enhance FedChain's security\nand performance.\n"}
+{"abstract": "  The Hauser-Feshbach Fission Fragment Decay (HF$^3$D) model is extended to\ncalculate the prompt fission neutron spectrum (PFNS) for the thermal neutron\ninduced fission on $^{235}$U, where the evaporated neutrons from all possible\nfission fragment pairs are aggregated. By studying model parameter\nsensitivities on the calculated PFNS, as well as non-statistical behavior of\nlow-lying discrete level spin distribution, we conclude that discrepancies\nbetween the aggregation calculation and the experimental PFNS seen at higher\nneutron emission energies can be attributed to both the primary fission\nfragment yield distribution and the possible high spin states that are not\npredicted by the statistical theory of nuclear structure.\n"}
+{"abstract": "  Weight sharing has become a de facto standard in neural architecture search\nbecause it enables the search to be done on commodity hardware. However, recent\nworks have empirically shown a ranking disorder between the performance of\nstand-alone architectures and that of the corresponding shared-weight networks.\nThis violates the main assumption of weight-sharing NAS algorithms, thus\nlimiting their effectiveness. We tackle this issue by proposing a\nregularization term that aims to maximize the correlation between the\nperformance rankings of the shared-weight network and that of the standalone\narchitectures using a small set of landmark architectures. We incorporate our\nregularization term into three different NAS algorithms and show that it\nconsistently improves performance across algorithms, search-spaces, and tasks.\n"}
+{"abstract": "  There is no unique way to encode a quantum algorithm into a quantum circuit.\nWith limited qubit counts, connectivities, and coherence times, circuit\noptimization is essential to make the best use of near-term quantum devices. We\nintroduce two separate ideas for circuit optimization and combine them in a\nmulti-tiered quantum circuit optimization protocol called AQCEL. The first\ningredient is a technique to recognize repeated patterns of quantum gates,\nopening up the possibility of future hardware co-optimization. The second\ningredient is an approach to reduce circuit complexity by identifying zero- or\nlow-amplitude computational basis states and redundant gates. As a\ndemonstration, AQCEL is deployed on an iterative and efficient quantum\nalgorithm designed to model final state radiation in high energy physics. For\nthis algorithm, our optimization scheme brings a significant reduction in the\ngate count without losing any accuracy compared to the original circuit.\nAdditionally, we have investigated whether this can be demonstrated on a\nquantum computer using polynomial resources. Our technique is generic and can\nbe useful for a wide variety of quantum algorithms.\n"}
+{"abstract": "  Modern scattering-type scanning near-field optical microscopy (s-SNOM) has\nbecome an indispensable tool in material research. However, as the s-SNOM\ntechnique marches into the far-infrared (IR) and terahertz (THz) regimes,\nemerging experiments sometimes produce puzzling results. For example, anomalies\nin the near-field optical contrast have been widely reported. In this Letter,\nwe systematically investigate a series of extreme subwavelength metallic\nnanostructures via s-SNOM near-field imaging in the GHz to THz frequency range.\nWe find that the near-field material contrast is greatly impacted by the\nlateral size of the nanostructure, while the spatial resolution is practically\nindependent of it. The contrast is also strongly affected by the connectivity\nof the metallic structures to a larger metallic ground plane. The observed\neffect can be largely explained by a quasi-electrostatic analysis. We also\ncompare the THz s-SNOM results to those of the mid-IR regime, where the\nsize-dependence becomes significant only for smaller structures. Our results\nreveal that the quantitative analysis of the near-field optical material\ncontrasts in the long-wavelength regime requires a careful assessment of the\nsize and configuration of metallic (optically conductive) structures.\n"}
+{"abstract": "  Audio and vision are two main modalities in video data. Multimodal learning,\nespecially for audiovisual learning, has drawn considerable attention recently,\nwhich can boost the performance of various computer vision tasks. However, in\nvideo summarization, existing approaches just exploit the visual information\nwhile neglect the audio information. In this paper, we argue that the audio\nmodality can assist vision modality to better understand the video content and\nstructure, and further benefit the summarization process. Motivated by this, we\npropose to jointly exploit the audio and visual information for the video\nsummarization task, and develop an AudioVisual Recurrent Network (AVRN) to\nachieve this. Specifically, the proposed AVRN can be separated into three\nparts: 1) the two-stream LSTM is utilized to encode the audio and visual\nfeature sequentially by capturing their temporal dependency. 2) the audiovisual\nfusion LSTM is employed to fuse the two modalities by exploring the latent\nconsistency between them. 3) the self-attention video encoder is adopted to\ncapture the global dependency in the video. Finally, the fused audiovisual\ninformation, and the integrated temporal and global dependencies are jointly\nused to predict the video summary. Practically, the experimental results on the\ntwo benchmarks, \\emph{i.e.,} SumMe and TVsum, have demonstrated the\neffectiveness of each part, and the superiority of AVRN compared to those\napproaches just exploiting visual information for video summarization.\n"}
+{"abstract": "  We present results of three wide-band directed searches for continuous\ngravitational waves from 15 young supernova remnants in the first half of the\nthird Advanced LIGO and Virgo observing run. We use three search pipelines with\ndistinct signal models and methods of identifying noise artifacts. Without\nephemerides of these sources, the searches are conducted over a frequency band\nspanning from 10~Hz to 2~kHz. We find no evidence of continuous gravitational\nradiation from these sources. We set upper limits on the intrinsic signal\nstrain at 95\\% confidence level in sample sub-bands, estimate the sensitivity\nin the full band, and derive the corresponding constraints on the fiducial\nneutron star ellipticity and $r$-mode amplitude. The best 95\\% confidence\nconstraints placed on the signal strain are $7.7\\times 10^{-26}$ and $7.8\\times\n10^{-26}$ near 200~Hz for the supernova remnants G39.2--0.3 and G65.7+1.2,\nrespectively. The most stringent constraints on the ellipticity and $r$-mode\namplitude reach $\\lesssim 10^{-7}$ and $ \\lesssim 10^{-5}$, respectively, at\nfrequencies above $\\sim 400$~Hz for the closest supernova remnant\nG266.2--1.2/Vela Jr.\n"}
+{"abstract": "  We investigate the numerical artifact known as a carbuncle, in the solution\nof the shallow water equations. We propose a new Riemann solver that is based\non a local measure of the entropy residual and aims to avoid carbuncles while\nmaintaining high accuracy. We propose a new challenging test problem for\nshallow water codes, consisting of a steady circular hydraulic jump that can be\nphysically unstable. We show that numerical methods are prone to either\nsuppress the instability completely or form carbuncles. We test existing cures\nfor the carbuncle. In our experiments, only the proposed method is able to\navoid unphysical carbuncles without suppressing the physical instability.\n"}
+{"abstract": "  Universal gate sets for quantum computing have been known for decades, yet no\nuniversal gate set has been proposed for particle-conserving unitaries, which\nare the operations of interest in quantum chemistry. In this work, we show that\ncontrolled single-excitation gates in the form of Givens rotations are\nuniversal for particle-conserving unitaries. Single-excitation gates describe\nan arbitrary $U(2)$ rotation on the two-qubit subspace spanned by the states\n$|01\\rangle, |10\\rangle$, while leaving other states unchanged -- a\ntransformation that is analogous to a single-qubit rotation on a dual-rail\nqubit. The proof is constructive, so our result also provides an explicit\nmethod for compiling arbitrary particle-conserving unitaries. Additionally, we\ndescribe a method for using controlled single-excitation gates to prepare an\narbitrary state of a fixed number of particles. We derive analytical gradient\nformulas for Givens rotations as well as decompositions into single-qubit and\nCNOT gates. Our results offer a unifying framework for quantum computational\nchemistry where every algorithm is a unique recipe built from the same\nuniversal ingredients: Givens rotations.\n"}
+{"abstract": "  The widespread significance of Android IoT devices is due to its flexibility\nand hardware support features which revolutionized the digital world by\nintroducing exciting applications almost in all walks of daily life, such as\nhealthcare, smart cities, smart environments, safety, remote sensing, and many\nmore. Such versatile applicability gives incentive for more malware attacks. In\nthis paper, we propose a framework which continuously aggregates multiple user\ntrained models on non-overlapping data into single model. Specifically for\nmalware detection task, (i) we propose a novel user (local) neural network\n(LNN) which trains on local distribution and (ii) then to assure the model\nauthenticity and quality, we propose a novel smart contract which enable\naggregation process over blokchain platform. The LNN model analyzes various\nstatic and dynamic features of both malware and benign whereas the smart\ncontract verifies the malicious applications both for uploading and downloading\nprocesses in the network using stored aggregated features of local models. In\nthis way, the proposed model not only improves malware detection accuracy using\ndecentralized model network but also model efficacy with blockchain. We\nevaluate our approach with three state-of-the-art models and performed deep\nanalyses of extracted features of the relative model.\n"}
+{"abstract": "  Attempting to reconcile general relativity with quantum mechanics is one of\nthe great undertakings of contemporary physics. Here we present how the\nincompatibility between the two theories arises in the simple thought\nexperiment of preparing a heavy object in a quantum superposition. Following\nPenrose's analysis of the problem, we determine the requirements on physical\nparameters to perform experiments where both theories potentially interplay. We\nuse these requirements to compare different systems, focusing on mechanical\noscillators which can be coupled to superconducting circuits.\n"}
+{"abstract": "  Discriminative correlation filters (DCF) and siamese networks have achieved\npromising performance on visual tracking tasks thanks to their superior\ncomputational efficiency and reliable similarity metric learning, respectively.\nHowever, how to effectively take advantages of powerful deep networks, while\nmaintaining the real-time response of DCF, remains a challenging problem.\nEmbedding the cross-correlation operator as a separate layer into siamese\nnetworks is a popular choice to enhance the tracking accuracy. Being a key\ncomponent of such a network, the correlation layer is updated online together\nwith other parts of the network. Yet, when facing serious disturbance, fused\ntrackers may still drift away from the target completely due to accumulated\nerrors. To address these issues, we propose a coarse-to-fine tracking\nframework, which roughly infers the target state via an online-updating DCF\nmodule first and subsequently, finely locates the target through an\noffline-training asymmetric siamese network (ASN). Benefitting from the\nguidance of DCF and the learned channel weights obtained through exploiting the\ngiven ground-truth template, ASN refines feature representation and implements\nprecise target localization. Systematic experiments on five popular tracking\ndatasets demonstrate that the proposed DCF-ASN achieves the state-of-the-art\nperformance while exhibiting good tracking efficiency.\n"}
+{"abstract": "  The Fermilab Muon $g-2$ experiment recently reported its first measurement of\nthe anomalous magnetic moment $a_\\mu^{\\textrm{FNAL}}$, which is in full\nagreement with the previous BNL measurement and pushes the world average\ndeviation $\\Delta a_\\mu^{2021}$ from the Standard Model to a significance of\n$4.2\\sigma$. Here we provide an extensive survey of its impact on beyond the\nStandard Model physics. We use state-of-the-art calculations and a\nsophisticated set of tools to make predictions for $a_\\mu$, dark matter and LHC\nsearches in a wide range of simple models with up to three new fields, that\nrepresent some of the few ways that large $\\Delta a_\\mu$ can be explained. In\naddition for the particularly well motivated Minimal Supersymmetric Standard\nModel, we exhaustively cover the scenarios where large $\\Delta a_\\mu$ can be\nexplained while simultaneously satisfying all relevant data from other\nexperiments. Generally, the $\\Delta a_\\mu$ result can only be explained by\nrather small masses and/or large couplings and enhanced chirality flips, which\ncan lead to conflicts with limits from LHC and dark matter experiments. Our\nresults show that the new measurement excludes a large number of models and\nprovides crucial constraints on others. Two-Higgs doublet and leptoquark models\nprovide viable explanations of $a_\\mu$ only in specific versions and in\nspecific parameter ranges. Among all models with up to three fields, only\nmodels with chirality enhancements can accommodate $a_\\mu$ and dark matter\nsimultaneously. The MSSM can simultaneously explain $a_\\mu$ and dark matter for\nBino-like LSP in several coannihilation regions. Allowing under abundance of\nthe dark matter relic density, the Higgsino- and particularly Wino-like LSP\nscenarios become promising explanations of the $a_\\mu$ result.\n"}
+{"abstract": "  It is a classical theorem of Sarason that an analytic function of bounded\nmean oscillation ($BMOA$), is of vanishing mean oscillation if and only if its\nrotations converge in norm to the original function as the angle of the\nrotation tends to zero. In a series of two papers Blasco et al. have raised the\nproblem of characterizing all semigroups of holomorphic functions $(\\varphi_t)$\nthat can replace the semigroup of rotations in Sarason's Theorem. We give a\ncomplete answer to this question, in terms of a logarithmic vanishing\noscillation condition on the infinitesimal generator of the semigroup\n$(\\varphi_t)$. In addition we confirm the conjecture of Blasco et al. that all\nsuch semigroups are elliptic. We also investigate the analogous question for\nthe Bloch and the little Bloch space and surprisingly enough we find that the\nsemigroups for which the Bloch version of Sarason's Theorem holds are exactly\nthe same as in the $BMOA$ case.\n"}
+{"abstract": "  Nested sampling is an important tool for conducting Bayesian analysis in\nAstronomy and other fields, both for sampling complicated posterior\ndistributions for parameter inference, and for computing marginal likelihoods\nfor model comparison. One technical obstacle to using nested sampling in\npractice is the requirement (for most common implementations) that prior\ndistributions be provided in the form of transformations from the unit\nhyper-cube to the target prior density. For many applications - particularly\nwhen using the posterior from one experiment as the prior for another - such a\ntransformation is not readily available. In this letter we show that parametric\nbijectors trained on samples from a desired prior density provide a\ngeneral-purpose method for constructing transformations from the uniform base\ndensity to a target prior, enabling the practical use of nested sampling under\narbitrary priors. We demonstrate the use of trained bijectors in conjunction\nwith nested sampling on a number of examples from cosmology.\n"}
+{"abstract": "  We live in momentous times. The science community is empowered with an\narsenal of cosmic messengers to study the Universe in unprecedented detail.\nGravitational waves, electromagnetic waves, neutrinos and cosmic rays cover a\nwide range of wavelengths and time scales. Combining and processing these\ndatasets that vary in volume, speed and dimensionality requires new modes of\ninstrument coordination, funding and international collaboration with a\nspecialized human and technological infrastructure. In tandem with the advent\nof large-scale scientific facilities, the last decade has experienced an\nunprecedented transformation in computing and signal processing algorithms. The\ncombination of graphics processing units, deep learning, and the availability\nof open source, high-quality datasets, have powered the rise of artificial\nintelligence. This digital revolution now powers a multi-billion dollar\nindustry, with far-reaching implications in technology and society. In this\nchapter we describe pioneering efforts to adapt artificial intelligence\nalgorithms to address computational grand challenges in Multi-Messenger\nAstrophysics. We review the rapid evolution of these disruptive algorithms,\nfrom the first class of algorithms introduced in early 2017, to the\nsophisticated algorithms that now incorporate domain expertise in their\narchitectural design and optimization schemes. We discuss the importance of\nscientific visualization and extreme-scale computing in reducing\ntime-to-insight and obtaining new knowledge from the interplay between models\nand data.\n"}
+{"abstract": "  We study the relationship between the eluder dimension for a function class\nand a generalized notion of rank, defined for any monotone \"activation\" $\\sigma\n: \\mathbb{R} \\to \\mathbb{R}$, which corresponds to the minimal dimension\nrequired to represent the class as a generalized linear model. When $\\sigma$\nhas derivatives bounded away from $0$, it is known that $\\sigma$-rank gives\nrise to an upper bound on eluder dimension for any function class; we show\nhowever that eluder dimension can be exponentially smaller than $\\sigma$-rank.\nWe also show that the condition on the derivative is necessary; namely, when\n$\\sigma$ is the $\\mathrm{relu}$ activation, we show that eluder dimension can\nbe exponentially larger than $\\sigma$-rank.\n"}
+{"abstract": "  Software needs to be secure, in particular, when deployed to critical\ninfrastructures. Secure coding guidelines capture practices in industrial\nsoftware engineering to ensure the security of code. This study aims to assess\nthe level of awareness of secure coding in industrial software engineering, the\nskills of software developers to spot weaknesses in software code, avoid them,\nand the organizational support to adhere to coding guidelines. The approach\ndraws on well-established theories of policy compliance, neutralization theory,\nand security-related stress and the authors' many years of experience in\nindustrial software engineering and on lessons identified from training secure\ncoding in the industry. The paper presents the questionnaire design for the\nonline survey and the first analysis of data from the pilot study.\n"}
+{"abstract": "  Contextual word-representations became a standard in modern natural language\nprocessing systems. These models use subword tokenization to handle large\nvocabularies and unknown words. Word-level usage of such systems requires a way\nof pooling multiple subwords that correspond to a single word. In this paper we\ninvestigate how the choice of subword pooling affects the downstream\nperformance on three tasks: morphological probing, POS tagging and NER, in 9\ntypologically diverse languages. We compare these in two massively multilingual\nmodels, mBERT and XLM-RoBERTa. For morphological tasks, the widely used `choose\nthe first subword' is the worst strategy and the best results are obtained by\nusing attention over the subwords. For POS tagging both of these strategies\nperform poorly and the best choice is to use a small LSTM over the subwords.\nThe same strategy works best for NER and we show that mBERT is better than\nXLM-RoBERTa in all 9 languages. We publicly release all code, data and the full\nresult tables at \\url{https://github.com/juditacs/subword-choice}.\n"}
+{"abstract": "  It is essential that software systems be tolerant to degradations in\ncomponents they rely on. There are patterns and techniques which software\nengineers use to ensure their systems gracefully degrade. Despite these\ntechniques being available in practice, tuning and configuration is hard to get\nright and it is expensive to explore possible changes to components and\ntechniques in complex systems. To fill these gaps, we propose Quartermaster to\nmodel and simulate systems and fault-tolerant techniques. We anticipate that\nQuartermaster will be useful to further research on graceful degradation and\nhelp inform software engineers about techniques that are most appropriate for\ntheir use cases.\n"}
+{"abstract": "  The coherent coupling between a quartz electro-mechanical resonator at room\ntemperature and trapped ions in a 7-tesla Penning trap has been demonstrated\nfor the first time. The signals arising from the coupling remain for\nintegration times in the orders of seconds. From the measurements carried out,\nwe demonstrate that the coupling allows detecting the reduced-cyclotron\nfrequency ($\\nu_+$) within times below 10~ms and with an improved resolution\ncompared to conventional electronic detection schemes. A resolving power\n$\\nu_+/\\Delta \\nu_+=2.4\\times10^{7}$ has been reached in single measurements.\nIn this publication we present the first results, emphasizing the novel\nfeatures of the quartz resonator as fast non-destructive ion-trap detector\ntogether with different ways to analyze the data and considering aspects like\nprecision, resolution and sensitivity.\n"}
+{"abstract": "  Evidence for broken time reversal symmetry (TRS) has been found in the\nsuperconducting states of the $R_5$Rh$_6$Sn$_{18}$ (R = Sc, Y, Lu) compounds\nwith a centrosymmetric caged crystal structure, but the origin of this\nphenomenon is unresolved. Here we report neutron diffraction measurements of\nsingle crystals with $R$=Lu, as well as measurements of the temperature\ndependence of the magnetic penetration depth using a self-induced tunnel\ndiode-oscillator (TDO) based technique, together with band structure\ncalculations using density functional theory. Neutron diffraction measurements\nreveal that the system crystallizes in a tetragonal caged structure, and that\none of nominal Lu sites in the Lu$_5$Rh$_6$Sn$_{18}$ structure is occupied by\nSn, yielding a composition Lu$_{5-x}$Rh$_6$Sn$_{18+x}$ ($x=1$). The low\ntemperature penetration depth shift $\\Delta\\lambda(T)$ exhibits an exponential\ntemperature dependence below around $0.3T_c$, giving clear evidence for fully\ngapped superconductivity. The derived superfluid density is reasonably well\naccounted for by a single gap $s$-wave model, whereas agreement cannot be found\nfor models of TRS breaking states with two-component order parameters.\nMoreover, band structure calculations reveal multiple bands crossing the Fermi\nlevel, and indicate that the aforementioned TRS breaking states would be\nexpected to have nodes on the Fermi surface, in constrast to the observations.\n"}
+{"abstract": "  Interface science has become a key aspect for fundamental research questions\nand for the understanding, design and optimization of urgently needed energy\nand information technologies. As the interface properties change during\noperation, e.g. under applied electrochemical stimulus, and because multiple\nbulk and interface processes coexist and compete, detailed operando\ncharacterization is needed. In this perspective, I present an overview of the\nstate-of-the art and challenges in selected X-ray spectroscopic techniques,\nconcluding that among others, interface-sensitivity remains a major concern in\nthe available techniques. I propose and discuss a new method to extract\ninterface-information from nominally bulk sensitive techniques, and critically\nevaluate the selection of X-ray energies for the recently developed meniscus\nX-ray photoelectron spectroscopy, a promising operando tool to characterize the\nsolid-liquid interface. I expect that these advancements along with further\ndevelopments in time and spatial resolution will expand our ability to probe\nthe interface electronic and molecular structure with sub-nm depth and complete\nour understanding of charge transfer processes during operation.\n"}
+{"abstract": "  We introduce DeepCert, a tool-supported method for verifying the robustness\nof deep neural network (DNN) image classifiers to contextually relevant\nperturbations such as blur, haze, and changes in image contrast. While the\nrobustness of DNN classifiers has been the subject of intense research in\nrecent years, the solutions delivered by this research focus on verifying DNN\nrobustness to small perturbations in the images being classified, with\nperturbation magnitude measured using established Lp norms. This is useful for\nidentifying potential adversarial attacks on DNN image classifiers, but cannot\nverify DNN robustness to contextually relevant image perturbations, which are\ntypically not small when expressed with Lp norms. DeepCert addresses this\nunderexplored verification problem by supporting:(1) the encoding of real-world\nimage perturbations; (2) the systematic evaluation of contextually relevant DNN\nrobustness, using both testing and formal verification; (3) the generation of\ncontextually relevant counterexamples; and, through these, (4) the selection of\nDNN image classifiers suitable for the operational context (i)envisaged when a\npotentially safety-critical system is designed, or (ii)observed by a deployed\nsystem. We demonstrate the effectiveness of DeepCert by showing how it can be\nused to verify the robustness of DNN image classifiers build for two benchmark\ndatasets (`German Traffic Sign' and `CIFAR-10') to multiple contextually\nrelevant perturbations.\n"}
+{"abstract": "  In our earlier publication we introduced the Spectrally Integrated Voigt\nFunction (SIVF) as an alternative to the traditional Voigt function for the\nHITRAN-based applications [Quine & Abrarov, JQSRT 2013]. It was shown that\napplication of the SIVF enables us to reduce spectral resolution without loss\nof accuracy in computation of the spectral radiance. As a further development,\nin this study we present more efficient SIVF approximations derived by using a\nnew sampling method based on incomplete cosine expansion of the sinc function\n[Abrarov & Quine, Appl. Math. Comput. 2015]. Since the SIVF mathematically\nrepresents the mean value integral of the Voigt function, this method accounts\nfor area under the curve of the Voigt function. Consequently, the total band\nradiance, defined as the integrated spectral radiance within a given spectral\nregion, can also retain its accuracy even at low spectral resolution. Our\nnumerical results demonstrate that application of the SIVF may be promising for\nmore rapid line-by-line computation in atmospheric models utilizing the HITRAN\nmolecular spectroscopic database. Such an approach may be particularly\nefficient to implement a retrieval algorithm for the greenhouse gases from the\nNIR space data collected by Earth-orbiting micro-spectrometers like Argus 1000\nfor their operation in a real-time mode. The real-time mode operation of the\nmicro-spectrometers can be advantageous for instant decision making during\nflight for more efficient data collection from space.\n"}
+{"abstract": "  The unit distance graph $G_{\\mathbb{R}^d}^1$ is the infinite graph whose\nnodes are points in $\\mathbb{R}^d$, with an edge between two points if the\nEuclidean distance between these points is 1. The 2-dimensional version\n$G_{\\mathbb{R}^2}^1$ of this graph is typically studied for its chromatic\nnumber, as in the Hadwiger-Nelson problem. However, other properties of unit\ndistance graphs are rarely studied. Here, we consider the restriction of\n$G_{\\mathbb{R}^d}^1$ to closed convex subsets $X$ of $\\mathbb{R}^d$. We show\nthat the graph $G_{\\mathbb{R}^d}^1[X]$ is connected precisely when the radius\nof $r(X)$ of $X$ is equal to 0, or when $r(X)\\geq 1$ and the affine dimension\nof $X$ is at least 2. For hyperrectangles, we give bounds for the graph\ndiameter in the critical case that the radius is exactly 1.\n"}
+{"abstract": "  Humans are able to form a complex mental model of the environment they move\nin. This mental model captures geometric and semantic aspects of the scene,\ndescribes the environment at multiple levels of abstractions (e.g., objects,\nrooms, buildings), includes static and dynamic entities and their relations\n(e.g., a person is in a room at a given time). In contrast, current robots'\ninternal representations still provide a partial and fragmented understanding\nof the environment, either in the form of a sparse or dense set of geometric\nprimitives (e.g., points, lines, planes, voxels) or as a collection of objects.\nThis paper attempts to reduce the gap between robot and human perception by\nintroducing a novel representation, a 3D Dynamic Scene Graph(DSG), that\nseamlessly captures metric and semantic aspects of a dynamic environment. A DSG\nis a layered graph where nodes represent spatial concepts at different levels\nof abstraction, and edges represent spatio-temporal relations among nodes. Our\nsecond contribution is Kimera, the first fully automatic method to build a DSG\nfrom visual-inertial data. Kimera includes state-of-the-art techniques for\nvisual-inertial SLAM, metric-semantic 3D reconstruction, object localization,\nhuman pose and shape estimation, and scene parsing. Our third contribution is a\ncomprehensive evaluation of Kimera in real-life datasets and photo-realistic\nsimulations, including a newly released dataset, uHumans2, which simulates a\ncollection of crowded indoor and outdoor scenes. Our evaluation shows that\nKimera achieves state-of-the-art performance in visual-inertial SLAM, estimates\nan accurate 3D metric-semantic mesh model in real-time, and builds a DSG of a\ncomplex indoor environment with tens of objects and humans in minutes. Our\nfinal contribution shows how to use a DSG for real-time hierarchical semantic\npath-planning. The core modules in Kimera are open-source.\n"}
+{"abstract": "  Motivated by a variety of online matching platforms, we consider demand and\nsupply units which are located i.i.d. in $[0,1]^d$, and each demand unit needs\nto be matched with a supply unit. The goal is to minimize the expected average\ndistance between matched pairs (the \"cost\"). We model dynamic arrivals of one\nor both of demand and supply with uncertain locations of future arrivals, and\ncharacterize the scaling behavior of the achievable cost in terms of system\nsize (number of supply units), as a function of the dimension $d$. Our\nachievability results are backed by concrete matching algorithms. Across cases,\nwe find that the platform can achieve cost (nearly) as low as that achievable\nif the locations of future arrivals had been known beforehand. Furthermore, in\nall cases except one, cost nearly as low as the expected distance to the\nnearest neighboring supply unit is achievable, i.e., the matching constraint\ndoes not cause an increase in cost either. The aberrant case is where only\ndemand arrivals are dynamic, and $d=1$; excess supply significantly reduces\ncost in this case.\n"}
+{"abstract": "  We prove that the uniqueness results obtained in \\cite{urrea} for the\nBenjamin equation, cannot be extended for any pair of non-vanishing solutions.\nOn the other hand, we study uniqueness results of solutions of the Benjamin\nequation. With this purpose, we showed that for any solutions $u$ and $v$\ndefined in $\\R\\times [0,T]$, if there exists an open set $I\\subset \\R$ such\nthat $u(\\cdot,0)$ and $v(\\cdot,0)$ agree in $I$, $\\p_t u(\\cdot,0)$ and $\\p_t\nv(\\cdot,0)$ agree in $I$, then $u\\equiv v$. To finish, a better version of this\nuniqueness result is also established.\n"}
+{"abstract": "  Contagion processes have been proven to fundamentally depend on the\nstructural properties of the interaction networks conveying them. Many real\nnetworked systems are characterized by clustered substructures representing\neither collections of all-to-all pair-wise interactions (cliques) and/or group\ninteractions, involving many of their members at once. In this work, focusing\non interaction structures represented as simplicial complexes, we present a\ndiscrete-time microscopic model of complex contagion for a\nsusceptible-infected-susceptible dynamics. Introducing a particular edge clique\ncover and a heuristic to find it, the model accounts for the higher-order\ndynamical correlations among the members of the substructures\n(cliques/simplices). The analytical computation of the critical point reveals\nthat higher-order correlations are responsible for its dependence on the\nhigher-order couplings. While such dependence eludes any mean-field model, the\npossibility of a bi-stable region is extended to structured populations.\n"}
+{"abstract": "  The tensor product of props was defined by Hackney and Robertson as an\nextension of the Boardman-Vogt product of operads to more general monoidal\ntheories. Theories that factor as tensor products include the theory of\ncommutative monoids and the theory of bialgebras. We give a topological\ninterpretation (and vast generalisation) of this construction as a\nlow-dimensional projection of a \"smash product of pointed directed spaces\".\nHere directed spaces are embodied by combinatorial structures called\ndiagrammatic sets, while Gray products replace cartesian products. The\ncorrespondence is mediated by a web of adjunctions relating diagrammatic sets,\npros, probs, props, and Gray-categories. The smash product applies to\npresentations of higher-dimensional theories and systematically produces\nhigher-dimensional coherence cells.\n"}
+{"abstract": "  An extension of QPTL is considered where functional dependencies among the\nquantified variables can be restricted in such a way that their current values\nare independent of the future values of the other variables. This restriction\nis tightly connected to the notion of behavioral strategies in game-theory and\nallows the resulting logic to naturally express game-theoretic concepts. The\nfragment where only restricted quantifications are considered, called\nbehavioral quantifications, can be decided, for both model checking and\nsatisfiability, in 2ExpTime and is expressively equivalent to QPTL, though\nsignificantly less succinct.\n"}
+{"abstract": "  Significant memory and computational requirements of large deep neural\nnetworks restrict their application on edge devices. Knowledge distillation\n(KD) is a prominent model compression technique for deep neural networks in\nwhich the knowledge of a trained large teacher model is transferred to a\nsmaller student model. The success of knowledge distillation is mainly\nattributed to its training objective function, which exploits the soft-target\ninformation (also known as \"dark knowledge\") besides the given regular hard\nlabels in a training set. However, it is shown in the literature that the\nlarger the gap between the teacher and the student networks, the more difficult\nis their training using knowledge distillation. To address this shortcoming, we\npropose an improved knowledge distillation method (called Annealing-KD) by\nfeeding the rich information provided by the teacher's soft-targets\nincrementally and more efficiently. Our Annealing-KD technique is based on a\ngradual transition over annealed soft-targets generated by the teacher at\ndifferent temperatures in an iterative process, and therefore, the student is\ntrained to follow the annealed teacher output in a step-by-step manner. This\npaper includes theoretical and empirical evidence as well as practical\nexperiments to support the effectiveness of our Annealing-KD method. We did a\ncomprehensive set of experiments on different tasks such as image\nclassification (CIFAR-10 and 100) and NLP language inference with BERT-based\nmodels on the GLUE benchmark and consistently got superior results.\n"}
+{"abstract": "  We rectify an incorrect citation of the reference in obtaining the Gaussian\nupper bound for heat kernels of the Schr\\\"odinger type operators\n$(-\\Delta)^2+V^2$.\n"}
+{"abstract": "  Fundamental to the theory of continued fractions is the fact that every\ninfinite continued fraction with positive integer coefficients converges;\nhowever, it is unknown precisely which continued fractions with integer\ncoefficients (not necessarily positive) converge. Here we present a simple test\nthat determines whether an integer continued fraction converges or diverges. In\naddition, for convergent continued fractions the test specifies whether the\nlimit is rational or irrational.\n  An attractive way to visualise integer continued fractions is to model them\nas paths on the Farey graph, which is a graph embedded in the hyperbolic plane\nthat induces a tessellation of the hyperbolic plane by ideal triangles. With\nthis geometric representation of continued fractions our test for convergence\ncan be interpreted in a particularly elegant manner, giving deeper insight into\nthe nature of continued fraction convergence.\n"}
+{"abstract": "  Several deep learning methods for phase retrieval exist, but most of them\nfail on realistic data without precise support information. We propose a novel\nmethod based on single-instance deep generative prior that works well on\ncomplex-valued crystal data.\n"}
+{"abstract": "  Three-body calculations of $K\\bar{K}N$ system with quantum numbers $I=1/2$,\n$J^{\\pi}=(\\frac{1}{2})^{+}$ were performed. Using separable potentials for\ntwo-body interactions, we calculated the $\\pi\\Sigma$ mass spectra for the\n$(\\bar{K}N)_{I=0}+K^{+}\\rightarrow(\\pi\\Sigma)^{0}K^{+}$ reaction on the basis\nof three-body Alt-Grassberger-Sandhas equations in the momentum representation.\nIn this regard, different types of $\\bar{K}N-\\pi\\Sigma$ potentials based on\nphenomenological and chiral SU(3) approach are used. The possibility to observe\nthe trace of $\\Lambda(1405)$ resonance in $(\\pi\\Sigma)^{0}$ mass spectra was\nstudied. Using the $\\chi^{2}$ fitting, it was shown that the mass of\n$\\Lambda$(1405) resonance is about 1417 $\\mathrm{MeV}/c^{2}$.\n"}
+{"abstract": "  Multi-Agent Path Finding (MAPF) is a challenging combinatorial problem that\nasks us to plan collision-free paths for a team of cooperative agents. In this\nwork, we show that one of the reasons why MAPF is so hard to solve is due to a\nphenomenon called pairwise symmetry, which occurs when two agents have many\ndifferent paths to their target locations, all of which appear promising, but\nevery combination of them results in a collision. We identify several classes\nof pairwise symmetries and show that each one arises commonly in practice and\ncan produce an exponential explosion in the space of possible collision\nresolutions, leading to unacceptable runtimes for current state-of-the-art\n(bounded-sub)optimal MAPF algorithms. We propose a variety of reasoning\ntechniques that detect the symmetries efficiently as they arise and resolve\nthem by using specialized constraints to eliminate all permutations of pairwise\ncolliding paths in a single branching step. We implement these ideas in the\ncontext of the leading optimal MAPF algorithm CBS and show that the addition of\nthe symmetry reasoning techniques can have a dramatic positive effect on its\nperformance - we report a reduction in the number of node expansions by up to\nfour orders of magnitude and an increase in scalability by up to thirty times.\nThese gains allow us to solve to optimality a variety of challenging MAPF\ninstances previously considered out of reach for CBS.\n"}
+{"abstract": "  Multiple instance learning (MIL) is a powerful tool to solve the weakly\nsupervised classification in whole slide image (WSI) based pathology diagnosis.\nHowever, the current MIL methods are usually based on independent and identical\ndistribution hypothesis, thus neglect the correlation among different\ninstances. To address this problem, we proposed a new framework, called\ncorrelated MIL, and provided a proof for convergence. Based on this framework,\nwe devised a Transformer based MIL (TransMIL), which explored both\nmorphological and spatial information. The proposed TransMIL can effectively\ndeal with unbalanced/balanced and binary/multiple classification with great\nvisualization and interpretability. We conducted various experiments for three\ndifferent computational pathology problems and achieved better performance and\nfaster convergence compared with state-of-the-art methods. The test AUC for the\nbinary tumor classification can be up to 93.09% over CAMELYON16 dataset. And\nthe AUC over the cancer subtypes classification can be up to 96.03% and 98.82%\nover TCGA-NSCLC dataset and TCGA-RCC dataset, respectively. Implementation is\navailable at: https://github.com/szc19990412/TransMIL.\n"}
+{"abstract": "  Temporal language grounding (TLG) is a fundamental and challenging problem\nfor vision and language understanding. Existing methods mainly focus on fully\nsupervised setting with temporal boundary labels for training, which, however,\nsuffers expensive cost of annotation. In this work, we are dedicated to weakly\nsupervised TLG, where multiple description sentences are given to an untrimmed\nvideo without temporal boundary labels. In this task, it is critical to learn a\nstrong cross-modal semantic alignment between sentence semantics and visual\ncontent. To this end, we introduce a novel weakly supervised temporal adjacent\nnetwork (WSTAN) for temporal language grounding. Specifically, WSTAN learns\ncross-modal semantic alignment by exploiting temporal adjacent network in a\nmultiple instance learning (MIL) paradigm, with a whole description paragraph\nas input. Moreover, we integrate a complementary branch into the framework,\nwhich explicitly refines the predictions with pseudo supervision from the MIL\nstage. An additional self-discriminating loss is devised on both the MIL branch\nand the complementary branch, aiming to enhance semantic discrimination by\nself-supervising. Extensive experiments are conducted on three widely used\nbenchmark datasets, \\emph{i.e.}, ActivityNet-Captions, Charades-STA, and\nDiDeMo, and the results demonstrate the effectiveness of our approach.\n"}
+{"abstract": "  Recent advances in implicit neural representations show great promise when it\ncomes to generating numerical solutions to partial differential equations.\nCompared to conventional alternatives, such representations employ\nparameterized neural networks to define, in a mesh-free manner, signals that\nare highly-detailed, continuous, and fully differentiable. In this work, we\npresent a novel machine learning approach for topology optimization -- an\nimportant class of inverse problems with high-dimensional parameter spaces and\nhighly nonlinear objective landscapes. To effectively leverage neural\nrepresentations in the context of mesh-free topology optimization, we use\nmultilayer perceptrons to parameterize both density and displacement fields.\nOur experiments indicate that our method is highly competitive for minimizing\nstructural compliance objectives, and it enables self-supervised learning of\ncontinuous solution spaces for topology optimization problems.\n"}
+{"abstract": "  Multi-Target Multi-Camera (MTMC) vehicle tracking is an essential task of\nvisual traffic monitoring, one of the main research fields of Intelligent\nTransportation Systems. Several offline approaches have been proposed to\naddress this task; however, they are not compatible with real-world\napplications due to their high latency and post-processing requirements. In\nthis paper, we present a new low-latency online approach for MTMC tracking in\nscenarios with partially overlapping fields of view (FOVs), such as road\nintersections. Firstly, the proposed approach detects vehicles at each camera.\nThen, the detections are merged between cameras by applying cross-camera\nclustering based on appearance and location. Lastly, the clusters containing\ndifferent detections of the same vehicle are temporally associated to compute\nthe tracks on a frame-by-frame basis. The experiments show promising\nlow-latency results while addressing real-world challenges such as the a priori\nunknown and time-varying number of targets and the continuous state estimation\nof them without performing any post-processing of the trajectories.\n"}
+{"abstract": "  We provide a systematic method to compute tree-level scattering amplitudes\nwith spinning external states from amplitudes with scalar external states in\narbitrary spacetime dimensions. We write down analytic answers for various\nscattering amplitudes, including the four graviton amplitude due to the massive\nspin $J$ exchange. We verify the results by computing angular distributions in\n3 + 1 dimensions using various identities involving Jacobi polynomials.\n"}
+{"abstract": "  We discuss a set of heterotic and type II string theory compactifications to\n1+1 dimensions that are characterized by factorized internal worldsheet CFTs of\nthe form $V_1\\otimes \\bar V_2$, where $V_1, V_2$ are self-dual (super) vertex\noperator algebras. In the cases with spacetime supersymmetry, we show that the\nBPS states form a module for a Borcherds-Kac-Moody (BKM) (super)algebra, and we\nprove that for each model the BKM (super)algebra is a symmetry of genus zero\nBPS string amplitudes. We compute the supersymmetric indices of these models\nusing both Hamiltonian and path integral formalisms. The path integrals are\nmanifestly automorphic forms closely related to the Borcherds-Weyl-Kac\ndenominator. Along the way, we comment on various subtleties inherent to these\nlow-dimensional string compactifications.\n"}
+{"abstract": "  Observations restrict the parameter space of Holographic Dark Energy (HDE) so\nthat a turning point in the Hubble parameter $H(z)$ is inevitable. Concretely,\ncosmic microwave background (CMB), baryon acoustic oscillations (BAO) and Type\nIa supernovae (SNE) data put the turning point in the future, but removing SNE\nresults in an observational turning point at positive redshift. From the\nperspective of theory, not only does the turning point violate the Null Energy\nCondition (NEC), but as we argue, it may be interpreted as an evolution of the\nHubble constant $H_0$ with redshift, which is at odds with the very FLRW\nframework within which data has been analysed. Tellingly, neither of these are\nproblems for the flat $\\Lambda$CDM model, and a direct comparison of fits\nfurther disfavours HDE relative to flat $\\Lambda$CDM.\n"}
+{"abstract": "  The conditional value-at-risk (CVaR) is a useful risk measure in fields such\nas machine learning, finance, insurance, energy, etc. When measuring very\nextreme risk, the commonly used CVaR estimation method of sample averaging does\nnot work well due to limited data above the value-at-risk (VaR), the quantile\ncorresponding to the CVaR level. To mitigate this problem, the CVaR can be\nestimated by extrapolating above a lower threshold than the VaR using a\ngeneralized Pareto distribution (GPD), which is often referred to as the\npeaks-over-threshold (POT) approach. This method often requires a very high\nthreshold to fit well, leading to high variance in estimation, and can induce\nsignificant bias if the threshold is chosen too low. In this paper, we derive a\nnew expression for the GPD approximation error of the CVaR, a bias term induced\nby the choice of threshold, as well as a bias correction method for the\nestimated GPD parameters. This leads to the derivation of a new estimator for\nthe CVaR that we prove to be asymptotically unbiased. In a practical setting,\nwe show through experiments that our estimator provides a significant\nperformance improvement compared with competing CVaR estimators in finite\nsamples. As a consequence of our bias correction method, it is also shown that\na much lower threshold can be selected without introducing significant bias.\nThis allows a larger portion of data to be be used in CVaR estimation compared\nwith the typical POT approach, leading to more stable estimates. As secondary\nresults, a new estimator for a second-order parameter of heavy-tailed\ndistributions is derived, as well as a confidence interval for the CVaR which\nenables quantifying the level of variability in our estimator.\n"}
+{"abstract": "  In this paper we have studied subgrid multiscale stabilized formulation with\ndynamic subscales for non-Newtonian Casson fluid flow model tightly coupled\nwith variable coefficients ADR ($VADR$) equation. The Casson viscosity\ncoefficient is taken to be dependent upon solute mass concentration. This paper\npresents the stability and convergence analyses of the stabilized finite\nelement solution. The proposed expressions of the stabilization parameters\nhelps in obtaining optimal order of convergences. Appropriate numerical\nexperiments have been provided.\n"}
+{"abstract": "  The mushroom body of the fruit fly brain is one of the best studied systems\nin neuroscience. At its core it consists of a population of Kenyon cells, which\nreceive inputs from multiple sensory modalities. These cells are inhibited by\nthe anterior paired lateral neuron, thus creating a sparse high dimensional\nrepresentation of the inputs. In this work we study a mathematical\nformalization of this network motif and apply it to learning the correlational\nstructure between words and their context in a corpus of unstructured text, a\ncommon natural language processing (NLP) task. We show that this network can\nlearn semantic representations of words and can generate both static and\ncontext-dependent word embeddings. Unlike conventional methods (e.g., BERT,\nGloVe) that use dense representations for word embedding, our algorithm encodes\nsemantic meaning of words and their context in the form of sparse binary hash\ncodes. The quality of the learned representations is evaluated on word\nsimilarity analysis, word-sense disambiguation, and document classification. It\nis shown that not only can the fruit fly network motif achieve performance\ncomparable to existing methods in NLP, but, additionally, it uses only a\nfraction of the computational resources (shorter training time and smaller\nmemory footprint).\n"}
+{"abstract": "  Despite previous success in generating audio-driven talking heads, most of\nthe previous studies focus on the correlation between speech content and the\nmouth shape. Facial emotion, which is one of the most important features on\nnatural human faces, is always neglected in their methods. In this work, we\npresent Emotional Video Portraits (EVP), a system for synthesizing high-quality\nvideo portraits with vivid emotional dynamics driven by audios. Specifically,\nwe propose the Cross-Reconstructed Emotion Disentanglement technique to\ndecompose speech into two decoupled spaces, i.e., a duration-independent\nemotion space and a duration dependent content space. With the disentangled\nfeatures, dynamic 2D emotional facial landmarks can be deduced. Then we propose\nthe Target-Adaptive Face Synthesis technique to generate the final high-quality\nvideo portraits, by bridging the gap between the deduced landmarks and the\nnatural head poses of target videos. Extensive experiments demonstrate the\neffectiveness of our method both qualitatively and quantitatively.\n"}
+{"abstract": "  Unbiased random vectors i.e. distributed uniformly in n-dimensional space,\nare widely applied and the computational cost of generating a vector increases\nonly linearly with n. On the other hand, generating uniformly distributed\nrandom vectors in its subspaces typically involves the inefficiency of\nrejecting vectors falling outside, or re-weighting a non-uniformly distributed\nset of samples. Both approaches become severely ineffective as n increases. We\npresent an efficient algorithm to generate uniformly distributed random\ndirections in n-dimensional cones, to aid searching and sampling tasks in high\ndimensions.\n"}
+{"abstract": "  By using an equivalent form of the uniform Lopatinski condition for 1-shocks,\nwe prove that the stability condition found by the energy method in [A.\nMorando, Y. Trakhinin, P. Trebeschi, Structural stability of shock waves in 2D\ncompressible elastodynamics, Math. Ann. 378 (2020) 1471-1504] for the\nrectilinear shock waves in two-dimensional flows of compressible isentropic\ninviscid elastic materials is not only sufficient but also necessary for\nuniform stability (implying structural nonlinear stability of corresponding\ncurved shock waves). The key point of our spectral analysis is a delicate study\nof the transition between uniform and weak stability. Moreover, we prove that\nthe rectilinear shock waves are never violently unstable, i.e., they are always\neither uniformly or weakly stable.\n"}
+{"abstract": "  We consider the Grassman manifold $G(E)$ as the subset of all orthogonal\nprojections of a given Euclidean space $E$ and obtain some explicit formulas\nconcerning the differential geometry of $G(E)$ as a submanifold of $L(E,E)$\nendowed with the Hilbert-Schmidt inner product. Most of these formulas can be\nnaturally extended to the infinite dimensional Hilbert space case.\n"}
+{"abstract": "  Fair representation learning is an attractive approach that promises fairness\nof downstream predictors by encoding sensitive data. Unfortunately, recent work\nhas shown that strong adversarial predictors can still exhibit unfairness by\nrecovering sensitive attributes from these representations. In this work, we\npresent Fair Normalizing Flows (FNF), a new approach offering more rigorous\nfairness guarantees for learned representations. Specifically, we consider a\npractical setting where we can estimate the probability density for sensitive\ngroups. The key idea is to model the encoder as a normalizing flow trained to\nminimize the statistical distance between the latent representations of\ndifferent groups. The main advantage of FNF is that its exact likelihood\ncomputation allows us to obtain guarantees on the maximum unfairness of any\npotentially adversarial downstream predictor. We experimentally demonstrate the\neffectiveness of FNF in enforcing various group fairness notions, as well as\nother attractive properties such as interpretability and transfer learning, on\na variety of challenging real-world datasets.\n"}
+{"abstract": "  Broader disclosive transparency$-$truth and clarity in communication\nregarding the function of AI systems$-$is widely considered desirable.\nUnfortunately, it is a nebulous concept, difficult to both define and quantify.\nThis is problematic, as previous work has demonstrated possible trade-offs and\nnegative consequences to disclosive transparency, such as a confusion effect,\nwhere \"too much information\" clouds a reader's understanding of what a system\ndescription means. Disclosive transparency's subjective nature has rendered\ndeep study into these problems and their remedies difficult. To improve this\nstate of affairs, We introduce neural language model-based probabilistic\nmetrics to directly model disclosive transparency, and demonstrate that they\ncorrelate with user and expert opinions of system transparency, making them a\nvalid objective proxy. Finally, we demonstrate the use of these metrics in a\npilot study quantifying the relationships between transparency, confusion, and\nuser perceptions in a corpus of real NLP system descriptions.\n"}
+{"abstract": "  We introduce a novel combination of Bayesian Models (BMs) and Neural Networks\n(NNs) for making predictions with a minimum expected risk. Our approach\ncombines the best of both worlds, the data efficiency and interpretability of a\nBM with the speed of a NN. For a BM, making predictions with the lowest\nexpected loss requires integrating over the posterior distribution. When exact\ninference of the posterior predictive distribution is intractable,\napproximation methods are typically applied, e.g. Monte Carlo (MC) simulation.\nFor MC, the variance of the estimator decreases with the number of samples -\nbut at the expense of increased computational cost. Our approach removes the\nneed for iterative MC simulation on the CPU at prediction time. In brief, it\nworks by fitting a NN to synthetic data generated using the BM. In a single\nfeed-forward pass, the NN gives a set of point-wise approximations to the BM's\nposterior predictive distribution for a given observation. We achieve risk\nminimized predictions significantly faster than standard methods with a\nnegligible loss on the test dataset. We combine this approach with Active\nLearning to minimize the amount of data required for fitting the NN. This is\ndone by iteratively labeling more data in regions with high predictive\nuncertainty of the NN.\n"}
+{"abstract": "  We study the roots of a random polynomial over the field of $p$-adic numbers.\nFor a random monic polynomial with i.i.d. coefficients in $\\mathbb{Z}_p$, we\nobtain an estimate for the expected number of roots of this polynomial. In\nparticular, if the coefficients take the values $\\pm1$ with equal probability,\nthe expected number of $p$-adic roots converges to\n$\\left(p-1\\right)/\\left(p+1\\right)$ as the degree of the polynomial tends to\n$\\infty$.\n"}
+{"abstract": "  Datasets are mathematical objects (e.g., point clouds, matrices, graphs,\nimages, fields/functions) that have shape. This shape encodes important\nknowledge about the system under study. Topology is an area of mathematics that\nprovides diverse tools to characterize the shape of data objects. In this work,\nwe study a specific tool known as the Euler characteristic (EC). The EC is a\ngeneral, low-dimensional, and interpretable descriptor of topological spaces\ndefined by data objects. We revise the mathematical foundations of the EC and\nhighlight its connections with statistics, linear algebra, field theory, and\ngraph theory. We discuss advantages offered by the use of the EC in the\ncharacterization of complex datasets; to do so, we illustrate its use in\ndifferent applications of interest in chemical engineering such as process\nmonitoring, flow cytometry, and microscopy. We show that the EC provides a\ndescriptor that effectively reduces complex datasets and that this reduction\nfacilitates tasks such as visualization, regression, classification, and\nclustering.\n"}
+{"abstract": "  Purpose: To develop a deep learning method on a nonlinear manifold to explore\nthe temporal redundancy of dynamic signals to reconstruct cardiac MRI data from\nhighly undersampled measurements.\n  Methods: Cardiac MR image reconstruction is modeled as general compressed\nsensing (CS) based optimization on a low-rank tensor manifold. The nonlinear\nmanifold is designed to characterize the temporal correlation of dynamic\nsignals. Iterative procedures can be obtained by solving the optimization model\non the manifold, including gradient calculation, projection of the gradient to\ntangent space, and retraction of the tangent space to the manifold. The\niterative procedures on the manifold are unrolled to a neural network, dubbed\nas Manifold-Net. The Manifold-Net is trained using in vivo data with a\nretrospective electrocardiogram (ECG)-gated segmented bSSFP sequence.\n  Results: Experimental results at high accelerations demonstrate that the\nproposed method can obtain improved reconstruction compared with a compressed\nsensing (CS) method k-t SLR and two state-of-the-art deep learning-based\nmethods, DC-CNN and CRNN.\n  Conclusion: This work represents the first study unrolling the optimization\non manifolds into neural networks. Specifically, the designed low-rank manifold\nprovides a new technical route for applying low-rank priors in dynamic MR\nimaging.\n"}
+{"abstract": "  Surgical training in medical school residency programs has followed the\napprenticeship model. The learning and assessment process is inherently\nsubjective and time-consuming. Thus, there is a need for objective methods to\nassess surgical skills. Here, we use the Preferred Reporting Items for\nSystematic Reviews and Meta-Analyses (PRISMA) guidelines to systematically\nsurvey the literature on the use of Deep Neural Networks for automated and\nobjective surgical skill assessment, with a focus on kinematic data as putative\nmarkers of surgical competency. There is considerable recent interest in deep\nneural networks (DNN) due to the availability of powerful algorithms, multiple\ndatasets, some of which are publicly available, as well as efficient\ncomputational hardware to train and host them. We have reviewed 530 papers, of\nwhich we selected 25 for this systematic review. Based on this review, we\nconcluded that DNNs are powerful tools for automated, objective surgical skill\nassessment using both kinematic and video data. The field would benefit from\nlarge, publicly available, annotated datasets that are representative of the\nsurgical trainee and expert demographics and multimodal data beyond kinematics\nand videos.\n"}
+{"abstract": "  The constant changes in the software industry, practices, and methodologies\nimpose challenges to teaching and learning current software engineering\nconcepts and skills. DevOps is particularly challenging because it covers\ntechnical concepts, such as pipeline automation, and non-technical ones, such\nas team roles and project management. The present study investigates a course\nsetup to introduce these concepts to software engineering undergraduates. We\ndesigned the course by employing coding to associate DevOps concepts to Agile,\nLean, and Open source practices and tools. We present the main aspects of this\nproject-oriented DevOps course, with 240 students enrolled in it since its\nfirst offering in 2016. We conducted an empirical study, with both a\nquantitative and qualitative analysis, to evaluate this project-oriented course\nsetup. We collected the data from the projects repository and students\nperceptions from a questionnaire. We mined 148 repositories (corresponding to\n72 projects) and obtained 86 valid responses to the questionnaire. We also\nmapped the concepts which are more challenging to students learn from\nexperience. The results evidence that first-hand experience facilitates the\ncomprehension of DevOps concepts and enriches classes discussions. We present a\nset of lessons learned, which may help professors better design and conduct\nproject-oriented courses to cover DevOps concepts.\n"}
+{"abstract": "  We examine the problem of generating temporally and spatially dense 4D human\nbody motion. On the one hand generative modeling has been extensively studied\nas a per time-frame static fitting problem for dense 3D models such as mesh\nrepresentations, where the temporal aspect is left out of the generative model.\nOn the other hand, temporal generative models exist for sparse human models\nsuch as marker-based capture representations, but have not to our knowledge\nbeen extended to dense 3D shapes. We propose to bridge this gap with a\ngenerative auto-encoder-based framework, which encodes morphology, global\nlocomotion including translation and rotation, and multi-frame temporal motion\nas a single latent space vector. To assess its generalization and factorization\nabilities, we train our model on a cyclic locomotion subset of AMASS,\nleveraging the dense surface models it provides for an extensive set of motion\ncaptures. Our results validate the ability of the model to reconstruct 4D\nsequences of human locomotions within a low error bound, and the meaningfulness\nof latent space interpolation between latent vectors representing different\nmulti-frame sequences and locomotion types. We also illustrate the benefits of\nthe approach for 4D human motion prediction of future frames from initial human\nlocomotion frames, showing promising abilities of our model to learn realistic\nspatio-temporal features of human motion. We show that our model allows for\ndata completion of both spatially and temporally sparse data.\n"}
+{"abstract": "  The results of the analysis of the deviation of the force equilibrium for\nions from the neoclassical theory prediction, calculated using the direct\nmeasurements of the radial electric field, in the view of its possible local\nand nonlocal correlation with the profiles of electron, Te, and ion, Ti,\ntemperatures in the T-10 tokamak are presented. Local correlations are analyzed\nby means of the Pearson's correlation. Nonlocal correlations are treated with\nan inverse problem under the assumption of an integral equation relationship\nbetween the deviation and Te and Ti profiles. The discharges with zero, weak\nand strong auxiliary heating (electron cyclotron resonance heating) are\nanalyzed. It is found that the electrons substantially (not less than ions)\ncontribute to the deviation of the ion equilibrium from the neoclassical theory\nprediction both in the local and nonlocal models.\n"}
+{"abstract": "  Two-dimensional transition metal dichalcogenides (TMDs) can adopt one of the\nseveral possible structures, with most common being the trigonal prismatic and\noctahedral symmetry phases. Since the structure determines the electronic\nproperties, being able to predict phase-preferences of TMDs from just the\nknowledge of the constituent atoms is highly desired, but has remained a\nlong-standing problem. In this study, we combine high-throughput quantum\nmechanical computations with machine learning algorithms to discover novel TMDs\nand study their chemical stability, as well as their phase preferences. Our\nanalysis provides insights into determining physiochemical factors that dictate\nthe phase-preference of a TMD, identifying and even going beyond the attributes\nconsidered by earlier researchers in predicting crystal structures. We show\nthat the machine learning algorithms are powerful tools that can be used not\nonly to find new materials with targeted properties, but also to find\nconnections between elemental attributes and the target property/properties\nthat were not previously obvious.\n"}
+{"abstract": "  Incorporating external knowledge into Named Entity Recognition (NER) systems\nhas been widely studied in the generic domain. In this paper, we focus on\nclinical domain where only limited data is accessible and interpretability is\nimportant. Recent advancement in technology and the acceleration of clinical\ntrials has resulted in the discovery of new drugs, procedures as well as\nmedical conditions. These factors motivate towards building robust zero-shot\nNER systems which can quickly adapt to new medical terminology. We propose an\nauxiliary gazetteer model and fuse it with an NER system, which results in\nbetter robustness and interpretability across different clinical datasets. Our\ngazetteer based fusion model is data efficient, achieving +1.7 micro-F1 gains\non the i2b2 dataset using 20% training data, and brings + 4.7 micro-F1 gains on\nnovel entity mentions never presented during training. Moreover, our fusion\nmodel is able to quickly adapt to new mentions in gazetteers without\nre-training and the gains from the proposed fusion model are transferable to\nrelated datasets.\n"}
+{"abstract": "  Using a series of detector measurements taken at different locations to\nlocalize a source of radiation is a well-studied problem. The source of\nradiation is sometimes constrained to a single point-like source, in which case\nthe location of the point source can be found using techniques such as maximum\nlikelihood. Recent advancements have shown the ability to locate point sources\nin 2D and even 3D, but few have studied the effect of intervening material on\nthe problem. In this work we examine gamma-ray data taken from a freely moving\nsystem and develop voxelized 3-D models of the scene using data from the\nonboard LiDAR. Ray casting is used to compute the distance each gamma ray\ntravels through the scene material, which is then used to calculate attenuation\nassuming a single attenuation coefficient for solids within the geometry.\nParameter estimation using maximum likelihood is performed to simultaneously\nfind the attenuation coefficient, source activity, and source position that\nbest match the data. Using a simulation, we validate the ability of this method\nto reconstruct the true location and activity of a source, along with the true\nattenuation coefficient of the structure it is inside, and then we apply the\nmethod to measured data with sources and find good agreement.\n"}
+{"abstract": "  Quantum classification and hypothesis testing are two tightly related\nsubjects, the main difference being that the former is data driven: how to\nassign to quantum states $\\rho(x)$ the corresponding class $c$ (or hypothesis)\nis learnt from examples during training, where $x$ can be either tunable\nexperimental parameters or classical data \"embedded\" into quantum states. Does\nthe model generalize? This is the main question in any data-driven strategy,\nnamely the ability to predict the correct class even of previously unseen\nstates. Here we establish a link between quantum machine learning\nclassification and quantum hypothesis testing (state and channel\ndiscrimination) and then show that the accuracy and generalization capability\nof quantum classifiers depend on the (R\\'enyi) mutual informations $I(C{:}Q)$\nand $I_2(X{:}Q)$ between the quantum state space $Q$ and the classical\nparameter space $X$ or class space $C$. Based on the above characterization, we\nthen show how different properties of $Q$ affect classification accuracy and\ngeneralization, such as the dimension of the Hilbert space, the amount of\nnoise, and the amount of neglected information from $X$ via, e.g., pooling\nlayers. Moreover, we introduce a quantum version of the Information Bottleneck\nprinciple that allows us to explore the various tradeoffs between accuracy and\ngeneralization. Finally, in order to check our theoretical predictions, we\nstudy the classification of the quantum phases of an Ising spin chain, and we\npropose the Variational Quantum Information Bottleneck (VQIB) method to\noptimize quantum embeddings of classical data to favor generalization.\n"}
+{"abstract": "  Data augmentation has been successfully used in many areas of deep-learning\nto significantly improve model performance. Typically data augmentation\nsimulates realistic variations in data in order to increase the apparent\ndiversity of the training-set. However, for opcode-based malware analysis,\nwhere deep learning methods are already achieving state of the art performance,\nit is not immediately clear how to apply data augmentation. In this paper we\nstudy different methods of data augmentation starting with basic methods using\nfixed transformations and moving to methods that adapt to the data. We propose\na novel data augmentation method based on using an opcode embedding layer\nwithin the network and its corresponding opcode embedding matrix to perform\nadaptive data augmentation during training. To the best of our knowledge this\nis the first paper to carry out a systematic study of different augmentation\nmethods applied to opcode sequence based malware classification.\n"}
+{"abstract": "  We consider the problem of finding the matching map between two sets of $d$\ndimensional vectors from noisy observations, where the second set contains\noutliers. The matching map is then an injection, which can be consistently\nestimated only if the vectors of the second set are well separated. The main\nresult shows that, in the high-dimensional setting, a detection region of\nunknown injection can be characterized by the sets of vectors for which the\ninlier-inlier distance is of order at least $d^{1/4}$ and the inlier-outlier\ndistance is of order at least $d^{1/2}$. These rates are achieved using the\nestimated matching minimizing the sum of logarithms of distances between\nmatched pairs of points. We also prove lower bounds establishing optimality of\nthese rates. Finally, we report results of numerical experiments on both\nsynthetic and real world data that illustrate our theoretical results and\nprovide further insight into the properties of the estimators studied in this\nwork.\n"}
+{"abstract": "  Nonlinear Compton scattering is an inelastic scattering process where a\nphoton is emitted due to the interaction between an electron and an intense\nlaser field. With the development of X-ray free-electron lasers, the intensity\nof X-ray laser is greatly enhanced, and the signal from X-ray nonlinear Compton\nscattering is no longer weak. Although the nonlinear Compton scattering by an\ninitially free electron has been thoroughly investigated, the mechanis of\nnonrelativistic nonlinear Compton scattering of X-ray photons by bound\nelectrons is unclear yet. Here, we present a frequency-domain formulation based\non the nonperturbative quantum electrodynamic to study nonlinear Compton\nscattering of two photons off a bound electron inside an atom in a strong X-ray\nlaser field. In contrast to previous theoretical works, our results clearly\nreveal the existence of anomalous redshift phenomenon observed experimentally\nby Fuchs et al. (Nat. Phys. 11, 964 (2015)) and suggest its origin as the\nbinding energy of the electron as well as the momentum transfer from incident\nphotons to the electron during the scattering process. Our work builds a bridge\nbetween intense-laser atomic physics and Compton scattering process that can be\nused to study atomic structure and dynamics at high laser intensities.\n"}
+{"abstract": "  Two-dimensional (2D) ferromagnetic and ferroelectric materials attract\nunprecedented attention due to the spontaneous-symmetry-breaking induced novel\nproperties and multifarious potential applications. Here we systematically\ninvestigate a large family (148) of 2D MGeX3 (M = metal elements, X =\nO/S/Se/Te) by means of the high-throughput first-principles calculations, and\nfocus on their possible ferroic properties including ferromagnetism,\nferroelectricity, and ferroelasticity. We discover eight stable 2D ferromagnets\nincluding five semiconductors and three half-metals, 21 2D antiferromagnets,\nand 11 stable 2D ferroelectric semiconductors including two multiferroic\nmaterials. Particularly, MnGeSe3 and MnGeTe3 are predicted to be\nroom-temperature 2D ferromagnetic half metals with Tc of 490 and 308 K,\nrespectively. It is probably for the first time that ferroelectricity is\nuncovered in 2D MGeX3 family, which derives from the spontaneous symmetry\nbreaking induced by unexpected displacements of Ge-Ge atomic pairs, and we also\nreveal that the electric polarizations are in proportion to the ratio of\nelectronegativity of X and M atoms, and IVB group metal elements are highly\nfavored for 2D ferroelectricity. Magnetic tunnel junction and water-splitting\nphotocatalyst based on 2D ferroic MGeX3 are proposed as examples of wide\npotential applications. The atlas of ferroicity in 2D MGeX3 materials will spur\ngreat interest in experimental studies and would lead to diverse applications.\n"}
+{"abstract": "  Understanding user dynamics in online communities has become an active\nresearch topic and can provide valuable insights for human behavior analysis\nand community management. In this work, we investigate the \"bandwagon fan\"\nphenomenon, a special case of user dynamics, to provide a large-scale\ncharacterization of online fan loyalty in the context of professional sports\nteams. We leverage the existing structure of NBA-related discussion forums on\nReddit, investigate the general bandwagon patterns, and trace the behavior of\nbandwagon fans to capture latent behavioral characteristics. We observe that\nbetter teams attract more bandwagon fans, but they do not necessarily come from\nweak teams. Our analysis of bandwagon fan flow also shows different trends for\ndifferent teams, as the playoff season progresses. Furthermore, we compare\nbandwagon users with non-bandwagon users in terms of their activity and\nlanguage usage. We find that bandwagon users write shorter comments but receive\nbetter feedback, and use words that show less attachment to their affiliated\nteams. Our observations allow for more effective identification of bandwagon\nusers and prediction of users' future bandwagon behavior in a season, as\ndemonstrated by the significant improvement over the baseline method in our\nevaluation results.\n"}
+{"abstract": "  In this paper, blow-up solutions of autonomous ordinary differential\nequations (ODEs) which are unstable under perturbations of initial conditions\nare studied. Combining dynamical systems machinery (e.g. phase space\ncompactifications, time-scale desingularizations of vector fields) with tools\nfrom computer-assisted proofs (e.g. rigorous integrators, parameterization\nmethod for invariant manifolds), these unstable blow-up solutions are obtained\nas trajectories on stable manifolds of hyperbolic (saddle) equilibria at\ninfinity. In this process, important features are obtained: smooth dependence\nof blow-up times on initial points near blow-up, level set distribution of\nblow-up times, singular behavior of blow-up times on unstable blow-up\nsolutions, organization of the phase space via separatrices (stable manifolds).\nIn particular, we show that unstable blow-up solutions themselves, and\nsolutions defined globally in time connected by those blow-up solutions can\nseparate initial conditions into two regions where solution trajectories are\neither globally bounded or blow-up, no matter how the large initial points are.\n"}
+{"abstract": "  An optimal finite-time process drives a given initial distribution to a given\nfinal one in a given time at the lowest cost as quantified by total entropy\nproduction. We prove that for system with discrete states this optimal process\ninvolves non-conservative driving, i.e., a genuine driving affinity, in\ncontrast to the case of system with continuous states. In a multicyclic\nnetwork, the optimal driving affinity is bounded by the number of states within\neach cycle. If the driving affects forward and backwards rates\nnon-symmetrically, the bound additionally depends on a structural parameter\ncharacterizing this asymmetry.\n"}
+{"abstract": "  This paper describes the submissions by team HWR to the Dravidian Language\nIdentification (DLI) shared task organized at VarDial 2021 workshop. The DLI\ntraining set includes 16,674 YouTube comments written in Roman script\ncontaining code-mixed text with English and one of the three South Dravidian\nlanguages: Kannada, Malayalam, and Tamil. We submitted results generated using\ntwo models, a Naive Bayes classifier with adaptive language models, which has\nshown to obtain competitive performance in many language and dialect\nidentification tasks, and a transformer-based model which is widely regarded as\nthe state-of-the-art in a number of NLP tasks. Our first submission was sent in\nthe closed submission track using only the training set provided by the shared\ntask organisers, whereas the second submission is considered to be open as it\nused a pretrained model trained with external data. Our team attained shared\nsecond position in the shared task with the submission based on Naive Bayes.\nOur results reinforce the idea that deep learning methods are not as\ncompetitive in language identification related tasks as they are in many other\ntext classification tasks.\n"}
+{"abstract": "  We empirically determined the speed of light by measuring the variation in\nlongitudinal mode frequencies, or the beat frequencies, of an\nadjustable-length, open-cavity helium-neon laser as a function of its cavity\nlength. The TEM$_{00}$ mode lasing output of the laser was analyzed using a\nfast frequency photodiode detector and a radio frequency spectrum analyzer. A\nFabry-Perot interferometer was used to monitor the intensity of the\nlongitudinal modes and we found that the phenomena of frequency pushing and\npulling had little effects on the beat frequency measurements. Plotting the\nreciprocal of the beat frequency as a function of the change in cavity length,\nthe speed of light was found, by using linear weighted least squares\nregression, to be $(2.997 \\pm 0.003) \\times 10^{8}$ ms$^{-1}$. This value is\n$0.3 \\sigma$ away from the defined value of speed of light and is accurate to 1\npart in 3200.\n"}
+{"abstract": "  In this paper, we consider the estimation of a low Tucker rank tensor from a\nnumber of noisy linear measurements. The general problem covers many specific\nexamples arising from applications, including tensor regression, tensor\ncompletion, and tensor PCA/SVD. We propose a Riemannian Gauss-Newton (RGN)\nmethod with fast implementations for low Tucker rank tensor estimation.\nDifferent from the generic (super)linear convergence guarantee of RGN in the\nliterature, we prove the first quadratic convergence guarantee of RGN for\nlow-rank tensor estimation under some mild conditions. A deterministic\nestimation error lower bound, which matches the upper bound, is provided that\ndemonstrates the statistical optimality of RGN. The merit of RGN is illustrated\nthrough two machine learning applications: tensor regression and tensor SVD.\nFinally, we provide the simulation results to corroborate our theoretical\nfindings.\n"}
+{"abstract": "  The outbreak of a novel coronavirus causing severe acute respiratory syndrome\nin December 2019 has escalated into a worldwide pandemic. In this work, we\npropose a compartmental model to describe the dynamics of transmission of\ninfection and use it to obtain the optimal vaccination control. The model\naccounts for the various stages of the vaccination and the optimisation is\nfocused on minimising the infections to protect the population and relieve the\nhealthcare system. As a case study we selected the Republic of Ireland. We use\ndata provided by Ireland's COVID-19 Data-Hub and simulate the evolution of the\npandemic with and without the vaccination in place for two different scenarios,\none representative of a national lockdown situation and the other indicating\nlooser restrictions in place. One of the main findings of our work is that the\noptimal approach would involve a vaccination programme where the older\npopulation is vaccinated in larger numbers earlier while simultaneously part of\nthe younger population also gets vaccinated to lower the risk of transmission\nbetween groups.\n"}
+{"abstract": "  Deep neural network (DNN) generally takes thousands of iterations to optimize\nvia gradient descent and thus has a slow convergence. In addition, softmax, as\na decision layer, may ignore the distribution information of the data during\nclassification. Aiming to tackle the referred problems, we propose a novel\nmanifold neural network based on non-gradient optimization, i.e., the\nclosed-form solutions. Considering that the activation function is generally\ninvertible, we reconstruct the network via forward ridge regression and low\nrank backward approximation, which achieve the rapid convergence. Moreover, by\nunifying the flexible Stiefel manifold and adaptive support vector machine, we\ndevise the novel decision layer which efficiently fits the manifold structure\nof the data and label information. Consequently, a jointly non-gradient\noptimization method is designed to generate the network with closed-form\nresults. Eventually, extensive experiments validate the superior performance of\nthe model.\n"}
+{"abstract": "  In this work we introduce the graph-theoretic notion of mendability: for each\nlocally checkable graph problem we can define its mending radius, which\ncaptures the idea of how far one needs to modify a partial solution in order to\n\"patch a hole.\"\n  We explore how mendability is connected to the existence of efficient\nalgorithms, especially in distributed, parallel, and fault-tolerant settings.\nIt is easy to see that $O(1)$-mendable problems are also solvable in $O(\\log^*\nn)$ rounds in the LOCAL model of distributed computing. One of the surprises is\nthat in paths and cycles, a converse also holds in the following sense: if a\nproblem $\\Pi$ can be solved in $O(\\log^* n)$, there is always a restriction\n$\\Pi' \\subseteq \\Pi$ that is still efficiently solvable but that is also\n$O(1)$-mendable.\n  We also explore the structure of the landscape of mendability. For example,\nwe show that in trees, the mending radius of any locally checkable problem is\n$O(1)$, $\\Theta(\\log n)$, or $\\Theta(n)$, while in general graphs the structure\nis much more diverse.\n"}
+{"abstract": "  We discuss various aspects of positive kernel method of quantization of the\none-parameter groups $\\tau_t \\in \\mbox{Aut}(P,\\vartheta)$ of automorphisms of a\n$G$-principal bundle $P(G,\\pi,M)$ with a fixed connection form $\\vartheta$ on\nits total space $P$. We show that the generator $\\hat{F}$ of the unitary flow\n$U_t = e^{it \\hat{F}}$ being the quantization of $\\tau_t $ is realized by a\ngeneralized Kirillov-Kostant-Souriau operator whose domain consists of sections\nof some vector bundle over $M$, which are defined by suitable positive kernel.\n  This method of quantization applied to the case when $G=GL(N,\\mathbb{C})$ and\n$M$ is a non-compact Riemann surface leads to quantization of the arbitrary\nholomorphic flow $\\tau_t^{hol} \\in \\mbox{Aut}(P,\\vartheta)$. For the above\ncase, we present the integral decompositions of the positive kernels on\n$P\\times P$ invariant with respect to the flows $\\tau_t^{hol}$ in terms of\nspectral measure of $\\hat{F}$. These decompositions generalize the ones given\nby Bochner theorem for a positive kernels on $\\mathbb{C} \\times \\mathbb{C}$\ninvariant with respect to the one-parameter groups of translations of complex\nplane.\n"}
+{"abstract": "  Robots have to face challenging perceptual settings, including changes in\nviewpoint, lighting, and background. Current simulated reinforcement learning\n(RL) benchmarks such as DM Control provide visual input without such\ncomplexity, which limits the transfer of well-performing methods to the real\nworld. In this paper, we extend DM Control with three kinds of visual\ndistractions (variations in background, color, and camera pose) to produce a\nnew challenging benchmark for vision-based control, and we analyze state of the\nart RL algorithms in these settings. Our experiments show that current RL\nmethods for vision-based control perform poorly under distractions, and that\ntheir performance decreases with increasing distraction complexity, showing\nthat new methods are needed to cope with the visual complexities of the real\nworld. We also find that combinations of multiple distraction types are more\ndifficult than a mere combination of their individual effects.\n"}
+{"abstract": "  In this paper, we propose two novel approaches for hypergraph comparison. The\nfirst approach transforms the hypergraph into a graph representation for use of\nstandard graph dissimilarity measures. The second approach exploits the\nmathematics of tensors to intrinsically capture multi-way relations. For each\napproach, we present measures that assess hypergraph dissimilarity at a\nspecific scale or provide a more holistic multi-scale comparison. We test these\nmeasures on synthetic hypergraphs and apply them to biological datasets.\n"}
+{"abstract": "  The notion of topological equivalence plays an essential role in the study of\ndynamical systems of flows. However, it is inherently difficult to generalize\nthis concept to systems without well-posedness in the sense of Hadamard. In\nthis study, we formulate a notion of \"topological equivalence\" between such\nsystems based on the axiomatic theory of topological dynamics proposed by\nYorke, and discuss its relation with the usual definition. During this process,\nwe generalize Yorke's theory to the action of topological groups.\n"}
+{"abstract": "  In this paper, we study an infeasible interior-point method for linear\noptimization with full-Newton step. The introduced method uses an algebraic\nequivalent transformation on the centering equation of the system which defines\nthe central path. We prove that the method finds an $\\varepsilon$-optimal\nsolution of the underlying problem in polynomial time.\n"}
+{"abstract": "  We describe our work on information extraction in medical documents written\nin German, especially detecting negations using an architecture based on the\nUIMA pipeline. Based on our previous work on software modules to cover medical\nconcepts like diagnoses, examinations, etc. we employ a version of the NegEx\nregular expression algorithm with a large set of triggers as a baseline. We\nshow how a significantly smaller trigger set is sufficient to achieve similar\nresults, in order to reduce adaptation times to new text types. We elaborate on\nthe question whether dependency parsing (based on the Stanford CoreNLP model)\nis a good alternative and describe the potentials and shortcomings of both\napproaches.\n"}
+{"abstract": "  In a recent publication a procedure was developed which can be used to derive\ncompletely gauge invariant models from general Lagrangian densities with $N$\norder of derivatives and $M$ rank of tensor potential. This procedure was then\nused to show that unique models follow for each order, namely classical\nelectrodynamics for $N = M = 1$ and linearized Gauss-Bonnet gravity for $N = M\n= 2$. In this article, the nature of the connection between these two well\nexplored physical models is further investigated by means of an additional\ncommon property; a complete dual formulation. First we give a review of\nGauss-Bonnet gravity and the dual formulation of classical electrodynamics. The\ndual formulation of linearized Gauss-Bonnet gravity is then developed. It is\nshown that the dual formulation of linearized Gauss-Bonnet gravity is analogous\nto the homogenous half of Maxwell's theory; both have equations of motion\ncorresponding to the (second) Bianchi identity, built from the dual form of\ntheir respective field strength tensors. In order to have a dually symmetric\ncounterpart analogous to the non-homogenous half of Maxwell's theory, the first\ninvariant derived from the procedure in $N = M = 2$ can be introduced. The\ncomplete gauge invariance of a model with respect to Noether's first theorem,\nand not just the equation of motion, is a necessary condition for this dual\nformulation. We show that this result can be generalized to the higher spin\ngauge theories, where the spin-$n$ curvature tensors for all $N = M = n$ are\nthe field strength tensors for each $n$. These completely gauge invariant\nmodels correspond to the Maxwell-like higher spin gauge theories whose\nequations of motion have been well explored in the literature.\n"}
+{"abstract": "  \"For how many days during the past 30 days was your mental health not good?\"\nThe responses to this question measure self-reported mental health and can be\nlinked to important covariates in the National Health and Nutrition Examination\nSurvey (NHANES). However, these count variables present major distributional\nchallenges: the data are overdispersed, zero-inflated, bounded by 30, and\nheaped in five- and seven-day increments. To meet these challenges, we design a\nsemiparametric estimation and inference framework for count data regression.\nThe data-generating process is defined by simultaneously transforming and\nrounding (STAR) a latent Gaussian regression model. The transformation is\nestimated nonparametrically and the rounding operator ensures the correct\nsupport for the discrete and bounded data. Maximum likelihood estimators are\ncomputed using an EM algorithm that is compatible with any continuous data\nmodel estimable by least squares. STAR regression includes asymptotic\nhypothesis testing and confidence intervals, variable selection via information\ncriteria, and customized diagnostics. Simulation studies validate the utility\nof this framework. STAR is deployed to study the factors associated with\nself-reported mental health and demonstrates substantial improvements in\ngoodness-of-fit compared to existing count data regression models.\n"}
+{"abstract": "  For a hyperplane arrangement in a real vector space, the coefficients of its\nPoincar\\'{e} polynomial have many interpretations. An interesting one is\nprovided by the Varchenko-Gel'fand ring, which is the ring of functions from\nthe chambers of the arrangement to the integers with pointwise addition and\nmultiplication. Varchenko and Gel'fand gave a simple presentation for this\nring, along with a filtration and associated graded ring whose Hilbert series\nis the Poincar\\'{e} polynomial. We generalize these results to cones defined by\nintersections of halfspaces of some of the hyperplanes and prove a novel result\nfor the Varchenko-Gel'fand ring of an arrangement: when the arrangement is\nsupersolvable the associated graded ring of the arrangement is Koszul.\n"}
+{"abstract": "  Compared with twisted bilayer graphene, twisted double bilayer graphene\n(TDBG) provides another important platform to realize the moir\\'e flat bands.\nIn this paper, we first calculate the valley Chern number phase diagram of TDBG\nin the parameter space spanned by the twist angle and the interlayer electric\npotential. To include the effects of interactions, we then phenomenologically\nintroduce the spin-splitting and valley-splitting. We find that when the valley\nsplitting is larger than the bandwidth of the first conduction band so that a\ngap is opened and the spin splitting is relatively weak, the orbital Chern\ninsulator emerges at half-filling, associated with a large orbital\nmagnetization (OM). Further calculations suggest that there is no sign reversal\nof the OM when the Fermi energy goes from the bottom to the top of the\nhalf-filling gap, as the OM remains negative in both AB-AB stacking and AB-BA\nstacking. The implications of our results for the ongoing experiments are also\ndiscussed.\n"}
+{"abstract": "  Let $\\mathcal{H}_{d,g,r}$ be the Hilbert scheme parametrizing smooth\nirreducible and non-degenerate curves of degree $d$ and genus $g$ in\n$\\mathbb{P}^r.$ We denote by $\\mathcal{H}^\\mathcal{L}_{d,g,r}$ the union of\nthose components of $\\mathcal{H}_{d,g,r}$ whose general element is linearly\nnormal. In this article we show that $\\mathcal{H}^\\mathcal{L}_{d,g,r}$ ($d\\ge\ng+r-3$) is non-empty in a certain optimal range of triples $(d,g,r)$ and is\nempty outside the range. This settles the existence (or non-emptiness if one\nprefers) of the Hilbert scheme $\\mathcal{H}^\\mathcal{L}_{d,g,r}$ of linearly\nnormal curves of degree $d$ and genus $g$ in $\\mathbb{P}^r$ for $g+r-3\\le d\\le\ng+r$, $r\\ge 3$. We also determine all the triples $(d,g,r)$ with $g+r-3\\le d\\le\ng+r$ for which $\\mathcal{H}^\\mathcal{L}_{d,g,r}$ is reducible (or irreducible).\n"}
+{"abstract": "  Continuous-time quantum walks (CTQWs) provide a valuable model for quantum\ntransport, universal quantum computation and quantum spatial search, among\nothers. Recently, the empowering role of new degrees of freedom in the\nHamiltonian generator of CTQWs, which are the complex phases along the loops of\nthe underlying graph, was acknowledged for its interest in optimizing or\nsuppressing transport on specific topologies. We argue that the\nquantum-classical distance, a figure of merit which was introduced to capture\nthe difference in dynamics between a CTQW and its classical, stochastic\ncounterpart, guides the optimization of parameters of the Hamiltonian to\nachieve better quantum transport on cycle graphs and spatial search to the\nquantum speed limit without an oracle on complete graphs, the latter also\nimplying fast uniform mixing. We compare the variations of this quantity with\nthe 1-norm of coherence and the Inverse Participation Ratio, showing that the\nquantum-classical distance is linked to both, but in a topology-dependent\nrelation, which is key to spot the most interesting quantum evolution in each\ncase.\n"}
+{"abstract": "  Expanding visual categorization into a novel domain without the need of extra\nannotation has been a long-term interest for multimedia intelligence.\nPreviously, this challenge has been approached by unsupervised domain\nadaptation (UDA). Given labeled data from a source domain and unlabeled data\nfrom a target domain, UDA seeks for a deep representation that is both\ndiscriminative and domain-invariant. While UDA focuses on the target domain, we\nargue that the performance on both source and target domains matters, as in\npractice which domain a test example comes from is unknown. In this paper we\nextend UDA by proposing a new task called unsupervised domain expansion (UDE),\nwhich aims to adapt a deep model for the target domain with its unlabeled data,\nmeanwhile maintaining the model's performance on the source domain. We propose\nKnowledge Distillation Domain Expansion (KDDE) as a general method for the UDE\ntask. Its domain-adaptation module can be instantiated with any existing model.\nWe develop a knowledge distillation based learning mechanism, enabling KDDE to\noptimize a single objective wherein the source and target domains are equally\ntreated. Extensive experiments on two major benchmarks, i.e., Office-Home and\nDomainNet, show that KDDE compares favorably against four competitive\nbaselines, i.e., DDC, DANN, DAAN, and CDAN, for both UDA and UDE tasks. Our\nstudy also reveals that the current UDA models improve their performance on the\ntarget domain at the cost of noticeable performance loss on the source domain.\n"}
+{"abstract": "  Autonomous drone swarms are a burgeoning technology with significant\napplications in the field of mapping, inspection, transportation and\nmonitoring. To complete a task, each drone has to accomplish a sub-goal within\nthe context of the overall task at hand and navigate through the environment by\navoiding collision with obstacles and with other agents in the environment. In\nthis work, we choose the task of optimal coverage of an environment with drone\nswarms where the global knowledge of the goal states and its positions are\nknown but not of the obstacles. The drones have to choose the Points of\nInterest (PoI) present in the environment to visit, along with the order to be\nvisited to ensure fast coverage. We model this task in a simulation and use an\nagent-oriented approach to solve the problem. We evaluate different policy\nnetworks trained with reinforcement learning algorithms based on their\neffectiveness, i.e. time taken to map the area and efficiency, i.e.\ncomputational requirements. We couple the task assignment with path planning in\nan unique way for performing collision avoidance during navigation and compare\na grid-based global planning algorithm, i.e. Wavefront and a gradient-based\nlocal planning algorithm, i.e. Potential Field. We also evaluate the Potential\nField planning algorithm with different cost functions, propose a method to\nadaptively modify the velocity of the drone when using the Huber loss function\nto perform collision avoidance and observe its effect on the trajectory of the\ndrones. We demonstrate our experiments in 2D and 3D simulations.\n"}
+{"abstract": "  We present a new suite of mock galaxy catalogs mimicking the low-redshift\nUniverse, based on an updated halo occupation distribution (HOD) model and a\nscaling relation between optical properties and the neutral hydrogen (HI)\ncontent of galaxies. Our algorithm is constrained by observations of the\nluminosity function and luminosity- and colour-dependent clustering of SDSS\ngalaxies, as well as the HI mass function and HI-dependent clustering of\nmassive HI-selected galaxies in the ALFALFA survey. Mock central and satellite\ngalaxies with realistic values of $r$-band luminosity, $g-r$ and $u-r$ colour,\nstellar mass and HI mass are populated in an $N$-body simulation, inheriting a\nnumber of properties of the density and tidal environment of their host halos.\nThe host halo of each central galaxy is also `baryonified' with realistic\nspatial distributions of stars as well as hot and cold gas, along with the\ncorresponding rotation curve. Our default HOD assumes that galaxy properties\nare a function of group halo mass alone, and can optionally include effects\nsuch as galactic conformity and colour-dependent galaxy assembly bias. The\nmocks predict the relation between the stellar mass and HI mass of massive HI\ngalaxies, as well as the 2-point cross-correlation function of spatially\nco-located optical and HI-selected samples. They enable novel null tests for\ngalaxy assembly bias, provide predictions for the HI velocity width function,\nand clarify the origin and universality of the radial acceleration relation in\nthe $\\Lambda$CDM framework.\n"}
+{"abstract": "  Many quantum mechanical experiments can be viewed as multi-round interactive\nprotocols between known quantum circuits and an unknown quantum process. Fully\nquantum \"coherent\" access to the unknown process is known to provide an\nadvantage in many discrimination tasks compared to when only incoherent access\nis permitted, but it is unclear if this advantage persists when the process is\nnoisy. Here, we show that a quantum advantage can be maintained when\ndistinguishing between two noisy single qubit rotation channels. Numerical and\nanalytical calculations reveal a distinct transition between optimal\nperformance by fully coherent and fully incoherent protocols as a function of\nnoise strength. Moreover, the size of the region of coherent quantum advantage\nshrinks inverse polynomially in the number of channel uses, and in an\nintermediate regime an improved strategy is a hybrid of fully-coherent and\nfully-incoherent subroutines. The fully coherent protocol is based on quantum\nsignal processing, suggesting a generalizable algorithmic framework for the\nstudy of quantum advantage in the presence of realistic noise.\n"}
+{"abstract": "  The Humphreys-Davidson (HD) limit empirically defines a region of high\nluminosities (log L > 5.5) and low effective temperatures (T < 20kK) on the\nHertzsprung-Russell Diagram in which hardly any supergiant stars are observed.\nAttempts to explain this limit through instabilities arising in near- or\nsuper-Eddington winds have been largely unsuccessful. Using modern stellar\nevolution we aim to re-examine the HD limit, investigating the impact of\nenhanced mixing on massive stars. We construct grids of stellar evolution\nmodels appropriate for the Small and Large Magellanic Clouds (SMC, LMC), as\nwell as for the Galaxy, spanning various initial rotation rates and convective\novershooting parameters. Significantly enhanced mixing apparently steers\nstellar evolution tracks away from the region of the HD limit. To quantify the\nexcess of over-luminous stars in stellar evolution simulations we generate\nsynthetic populations of massive stars, and make detailed comparisons with\ncatalogues of cool (T < 12.5kK) and luminous (log L > 4.7) stars in the SMC and\nLMC. We find that adjustments to the mixing parameters can lead to agreement\nbetween the observed and simulated red supergiant populations, but for hotter\nsupergiants the simulations always over-predict the number of very luminous\n(log L > 5.4) stars compared to observations. The excess of luminous\nsupergiants decreases for enhanced mixing, possibly hinting at an important\nrole mixing has in explaining the HD limit. Still, the HD limit remains\nunexplained for hotter supergiants.\n"}
+{"abstract": "  We theoretically investigate the influence of a longitudinal laser\npolarization component from beam focusing on spin dynamics in Kapitza-Dirac\nscattering by solving the relativistic Dirac equation with time-dependent\nperturbation theory. The transverse spacial dependence of the longitudinal beam\npolarization component is accounted for, by approximating a Gaussian beam with\nplane-wave components. We find that corrections from a longitudinal laser beam\npolarization component approximately scale with the second power of the\ndiffraction angle $\\epsilon$, from which we conclude that a related influence\nfrom beam focusing can be made negligibly small for sufficiently low beam foci.\n"}
+{"abstract": "  CAPTCHAs are a defense mechanism to prevent malicious bot programs from\nabusing websites on the Internet. hCaptcha is a relatively new but emerging\nimage CAPTCHA service. This paper presents an automated system that can break\nhCaptcha challenges with a high success rate. We evaluate our system against\n270 hCaptcha challenges from live websites and demonstrate that it can solve\nthem with 95.93% accuracy while taking only 18.76 seconds on average to crack a\nchallenge. We run our attack from a docker instance with only 2GB memory (RAM),\n3 CPUs, and no GPU devices, demonstrating that it requires minimal resources to\nlaunch a successful large-scale attack against the hCaptcha system.\n"}
+{"abstract": "  In this paper, we make a comprehensive study of the properties of a gapped\nDirac semimetal model, which was originally proposed in the magnetoinfrared\nspectroscopy measurement of ZeTe$_5$, and includes both the linear and\nparabolic dispersions in all three directions. We find that, depending on the\nband inversion parameters, $\\zeta'$ and $\\zeta_z'$, the model can support three\ndifferent phases: the single Dirac point (DP) phase, the double DPs phase and\nthe Dirac ring phase. The three different phases can be distinguished by their\nlow-energy features in the density of states (DOS) and optical conductivity. At\nhigh energy, both the DOS and optical conductivity exhibit power-law like\nbehaviors, with the asymptotic exponents depending heavily on the signs of\n$\\zeta'$ and $\\zeta_z'$. Moreover, the thumb-of-rule formula between the DOS\nand optical conductivity is satisfied only when $(\\zeta',\\zeta_z')>0$. The\nimplications of our results for experiments are discussed.\n"}
+{"abstract": "  We determine the limiting distribution and the explicit tail behavior for the\nmaximal displacement of a branching symmetric stable process with spatially\ninhomogeneous branching structure. Here the branching rate is a Kato class\nmeasure with compact support and can be singular with respect to the Lebesgue\nmeasure.\n"}
+{"abstract": "  We consider the problem of assigning items to platforms in the presence of\ngroup fairness constraints. In the input, each item belongs to certain\ncategories, called classes in this paper. Each platform specifies the group\nfairness constraints through an upper bound on the number of items it can serve\nfrom each class. Additionally, each platform also has an upper bound on the\ntotal number of items it can serve. The goal is to assign items to platforms so\nas to maximize the number of items assigned while satisfying the upper bounds\nof each class. In some cases, there is a revenue associated with matching an\nitem to a platform, then the goal is to maximize the revenue generated.\n  This problem models several important real-world problems like ad-auctions,\nscheduling, resource allocations, school choice etc.We also show an interesting\nconnection to computing a generalized maximum independent set on hypergraphs\nand ranking items under group fairness constraints.\n  We show that if the classes are arbitrary, then the problem is NP-hard and\nhas a strong inapproximability. We consider the problem in both online and\noffline settings under natural restrictions on the classes. Under these\nrestrictions, the problem continues to remain NP-hard but admits approximation\nalgorithms with small approximation factors. We also implement some of the\nalgorithms. Our experiments show that the algorithms work well in practice both\nin terms of efficiency and the number of items that get assigned to some\nplatform.\n"}
+{"abstract": "  Classical algorithms for predicting the equilibrium geometry of strongly\ncorrelated molecules require expensive wave function methods that become\nimpractical already for few-atom systems. In this work, we introduce a\nvariational quantum algorithm for finding the most stable structure of a\nmolecule by explicitly considering the parametric dependence of the electronic\nHamiltonian on the nuclear coordinates. The equilibrium geometry of the\nmolecule is obtained by minimizing a more general cost function that depends on\nboth the quantum circuit and the Hamiltonian parameters, which are\nsimultaneously optimized at each step. The algorithm is applied to find the\nequilibrium geometries of the $\\mathrm{H}_2$, $\\mathrm{H}_3^+$,\n$\\mathrm{BeH}_2$ and $\\mathrm{H}_2\\mathrm{O}$ molecules. The quantum circuits\nused to prepare the electronic ground state for each molecule were designed\nusing an adaptive algorithm where excitation gates in the form of Givens\nrotations are selected according to the norm of their gradient. All quantum\nsimulations are performed using the PennyLane library for quantum\ndifferentiable programming. The optimized geometrical parameters for the\nsimulated molecules show an excellent agreement with their counterparts\ncomputed using classical quantum chemistry methods.\n"}
+{"abstract": "  In recent years, Deep Learning (DL) has been successfully applied to detect\nand classify Radio Frequency (RF) Signals. A DL approach is especially useful\nsince it identifies the presence of a signal without needing full protocol\ninformation, and can also detect and/or classify non-communication waveforms,\nsuch as radar signals. In this work, we focus on the different pre-processing\nsteps that can be used on the input training data, and test the results on a\nfixed DL architecture. While previous works have mostly focused exclusively on\neither time-domain or frequency domain approaches, we propose a hybrid image\nthat takes advantage of both time and frequency domain information, and tackles\nthe classification as a Computer Vision problem. Our initial results point out\nlimitations to classical pre-processing approaches while also showing that it's\npossible to build a classifier that can leverage the strengths of multiple\nsignal representations.\n"}
+{"abstract": "  Analyzing and interpreting the exact behavior of new delay-based congestion\ncontrol protocols with complex non-linear control loops is exceptionally\ndifficult in highly variable networks such as cellular networks. This paper\nproposes a Model-Driven Interpretability (MDI) congestion control framework,\nwhich derives a model version of a delay-based protocol by simplifying a\ncongestion control protocol's response into a guided random walk over a\ntwo-dimensional Markov model. We demonstrate the case for the MDI framework by\nusing MDI to analyze and interpret the behavior of two delay-based protocols\nover cellular channels: Verus and Copa. Our results show a successful\napproximation of throughput and delay characteristics of the protocols' model\nversions across variable network conditions. The learned model of a protocol\nprovides key insights into an algorithm's convergence properties.\n"}
+{"abstract": "  Multidimensional or multiplex bioanalysis represents a crucial approach to\nimprove diagnostic precision, increase assay throughput and advance fundamental\ndiscoveries in analytical industry, life science and nanomedicine. Along this\nline, bio-interfacing magnetic particles have been playing an important role.\nFully exploiting the properties of magnetic particles is the key to tailoring\nrecent technology development for better translational outcomes. In this\nmini-review, typical magnetophysical dimensions of magnetic particles are\nintroduced. Recent progress of implementing these dimensions with advanced\nsensor and actuator technologies in multiplex bioanalysis is discussed.\nOutlooks on potential biomedical applications and challenges are provided.\n"}
+{"abstract": "  We explore constraints on various new physics resonances from four top-quark\nproduction based on current experimental data. Both light and heavy resonances\nare studied in the work. A comparison of full width effect and narrow width\napproximation is also made.\n"}
+{"abstract": "  The properties of Pt-based materials can be intriguing due to the importance\nof spin-orbit coupling for Pt. Herein, we report four new phases with formulas\nM3Pt23Ge11 (M = Ca, Sr, Ba and Eu), which adopt the same structure type as\nCe3Pt23Si11. Magnetic susceptibility measurements indicate that none of the\nphases is superconducting above 1.8 K, while for Eu3Pt23Ge11 ferromagnetic\nordering is observed at ~ 3 K. The low Curie temperature for that material\ncompared to that of Eu3Pt23Si11 may be due to its larger Eu-Eu distance. One\npotential factor that destabilizes the structure of other rare-earth based\nM3Pt23Ge11 is demonstrated through COHP calculations.\n"}
+{"abstract": "  Linguistic knowledge is of great benefit to scene text recognition. However,\nhow to effectively model linguistic rules in end-to-end deep networks remains a\nresearch challenge. In this paper, we argue that the limited capacity of\nlanguage models comes from: 1) implicitly language modeling; 2) unidirectional\nfeature representation; and 3) language model with noise input.\nCorrespondingly, we propose an autonomous, bidirectional and iterative ABINet\nfor scene text recognition. Firstly, the autonomous suggests to block gradient\nflow between vision and language models to enforce explicitly language\nmodeling. Secondly, a novel bidirectional cloze network (BCN) as the language\nmodel is proposed based on bidirectional feature representation. Thirdly, we\npropose an execution manner of iterative correction for language model which\ncan effectively alleviate the impact of noise input. Additionally, based on the\nensemble of iterative predictions, we propose a self-training method which can\nlearn from unlabeled images effectively. Extensive experiments indicate that\nABINet has superiority on low-quality images and achieves state-of-the-art\nresults on several mainstream benchmarks. Besides, the ABINet trained with\nensemble self-training shows promising improvement in realizing human-level\nrecognition. Code is available at https://github.com/FangShancheng/ABINet.\n"}
+{"abstract": "  Existing approaches in video captioning concentrate on exploring global frame\nfeatures in the uncompressed videos, while the free of charge and critical\nsaliency information already encoded in the compressed videos is generally\nneglected. We propose a video captioning method which operates directly on the\nstored compressed videos. To learn a discriminative visual representation for\nvideo captioning, we design a residuals-assisted encoder (RAE), which spots\nregions of interest in I-frames under the assistance of the residuals frames.\nFirst, we obtain the spatial attention weights by extracting features of\nresiduals as the saliency value of each location in I-frame and design a\nspatial attention module to refine the attention weights. We further propose a\ntemporal gate module to determine how much the attended features contribute to\nthe caption generation, which enables the model to resist the disturbance of\nsome noisy signals in the compressed videos. Finally, Long Short-Term Memory is\nutilized to decode the visual representations into descriptions. We evaluate\nour method on two benchmark datasets and demonstrate the effectiveness of our\napproach.\n"}
+{"abstract": "  The short-term economic consequences of the critical measures employed to\ncurb the transmission of Covid-19 are all too familiar, but the consequences of\nisolation and loneliness resulting from those measures on the mental well-being\nof the population and their ensuing long-term economic effects are largely\nunknown. Here we offer a stochastic agent-based model to investigate social\nrestriction measures in a community where the feelings of loneliness of the\nagents dwindle when they are socializing and grow when they are alone. In\naddition, the intensity of those feelings, which are measured by a real\nvariable that we term degree of loneliness, determines whether the agent will\nseek social contact or not. We find that decrease of the number, quality or\nduration of social contacts lead the community to enter a regime of burnout in\nwhich the degree of loneliness diverges, although the number of lonely agents\nat a given moment amounts to only a fraction of the total population. This\nregime of mental breakdown is separated from the healthy regime, where the\ndegree of loneliness is finite, by a continuous phase transition. We show that\nthe community dynamics is described extremely well by a simple mean-field\ntheory so our conclusions can be easily verified for different scenarios and\nparameter settings. The appearance of the burnout regime illustrates neatly the\nside effects of social distancing, which give to many of us the choice between\nphysical infection and mental breakdown.\n"}
+{"abstract": "  Stability of thin lubricating fluid coated slippery surfaces depends on the\nsurface energy of the underlying solid surface. High energy solid surfaces,\ncoated with thin lubricating oil, lead to the dewetting of the oil films upon\ndepositing aqueous drops on it. The total surface energy, which is due to the\nlong range and short range interactions, also predict the instability of thin\nlubricating films under the given condition. In this article, we present\nexperimental study of dewetting of thin lubricating oil films sandwiched\nbetween hydrophilic solid surface and aqueous drops. Fluorescence imaging of\nlubricant film and wetting behavior of aqueous drops are used for the analysis.\nWe find that the dewetting dynamics and the final pattern depend strongly on\nthe thickness of the lubricating oil film.\n"}
+{"abstract": "  In this paper we analyze spectra in the phenomenological supersymmetric\nStandard Model that simultaneously result in the right dark-matter relic\ndensity $\\Omega_{\\rm DM} h^2$, offer an explanation for the $(g-2)_{\\mu}$\ndiscrepancy $\\Delta a_{\\mu}$ and are minimally fine-tuned. We discuss the LHC\nphenomenology resulting from these spectra and the sensitivity of dark-matter\ndirect detection experiments to these spectra. We find that the latter type of\nexperiments with sensitivity to the spin-dependent dark-matter-nucleon\nscattering cross section $\\sigma_{\\rm SD,p}$ will probe all of our found\nsolutions.\n"}
+{"abstract": "  We report on three pre-registered studies testing whether people in the\nposition of describing a decision problem to decision-makers exploit this\nopportunity for their benefit, by choosing descriptions that may be potentially\nbeneficial for themselves. In Study 1, recipients of an extreme dictator game\n(where dictators can either take the whole pie for themselves or give it\nentirely to the receiver) are asked to choose the instructions used to\nintroduce the game to dictators, among six different instructions that are\nknown from previous research to affect dictators' decisions. The results\ndemonstrate that some dictator game recipients tend to choose instructions that\nmake them more likely to receive a higher payoff. Study 2 shows that people who\nchoose descriptions that make them more likely to receive a higher payoff\nindeed believe that they will receive a higher payoff. Study 3 shows that\nreceivers are more likely than dictators to choose these descriptions. In sum,\nour work suggests that some people choose descriptions that are beneficial to\nthemselves; we also found some evidence that deliberative thinking and young\nage are associated with this tendency.\n"}
+{"abstract": "  Emotion dynamics is a framework for measuring how an individual's emotions\nchange over time. It is a powerful tool for understanding how we behave and\ninteract with the world. In this paper, we introduce a framework to track\nemotion dynamics through one's utterances. Specifically we introduce a number\nof utterance emotion dynamics (UED) metrics inspired by work in Psychology. We\nuse this approach to trace emotional arcs of movie characters. We analyze\nthousands of such character arcs to test hypotheses that inform our broader\nunderstanding of stories. Notably, we show that there is a tendency for\ncharacters to use increasingly more negative words and become increasingly\nemotionally discordant with each other until about 90 percent of the narrative\nlength. UED also has applications in behavior studies, social sciences, and\npublic health.\n"}
+{"abstract": "  The error-correction code based proof-of-work (ECCPoW) algorithm is based on\na low-density parity-check (LDPC) code. The ECCPoW is possible to impair ASIC\nwith its time-varying capability of the parameters of LDPC code. Previous\nresearches on the ECCPoW algorithm have presented its theory and implementation\non Bitcoin. But they do not discuss how stable the block generation time is. A\nfinite mean block generation time (BGT) and none heavy-tail BGT distribution\nare the ones of the focus in this study. In the ECCPoW algorithm, BGT may show\na long-tailed distribution due to time-varying cryptographic puzzles. Thus, it\nis of interest to see if the BGT distribution is not heavy-tailed and if it\nshows a finite mean. If the distribution is heavy-tailed, then confirmation of\na transaction cannot be guaranteed. We present implementation, simulation, and\nvalidation of ECCPoW Ethereum. In implementation, we explain how the ECCPoW\nalgorithm is integrated into Ethereum 1.0 as a new consensus algorithm. In the\nsimulation, we perform a multinode simulation to show that the ECCPoW Ethereum\nworks well with automatic difficulty change. In the validation, we present the\nstatistical results of the two-sample Anderson-Darling test to show that the\ndistribution of BGT satisfies the necessary condition of the exponential\ndistribution. Our implementation is downloadable at\nhttps://github.com/cryptoecc/ETH-ECC.\n"}
+{"abstract": "  In relativistic Quantum Field Theory (QFT) ideal measurements of certain\nobservables are physically impossible without violating causality. This prompts\ntwo questions: i) can a given observable be ideally measured in QFT, and ii) if\nnot, in what sense can it be measured? Here we formulate a necessary and\nsufficient condition that any measurement, and more generally any state update\n(quantum operation), must satisfy to respect causality. Our focus is scalar\nQFT, although our results should be applicable to observables in fermionic QFT.\nWe argue that for unitary `kicks' and operations involving 1-parameter families\nof Kraus operators, e.g. Gaussian measurements, the only causal observables are\nsmeared fields and the identity - the basic observables in QFT. We provide\nexamples with more complicated operators such as products of smeared fields,\nand show that the associated state updates are acausal, and hence impossible.\nDespite this, one can still recover expectation values of such operators, and\nwe show how to do this using only causal measurements of smeared fields.\n"}
+{"abstract": "  To date, mainstream target speech separation (TSS) approaches are formulated\nto estimate the complex ratio mask (cRM) of the target speech in time-frequency\ndomain under supervised deep learning framework. However, the existing deep\nmodels for estimating cRM are designed in the way that the real and imaginary\nparts of the cRM are separately modeled using real-valued training data pairs.\nThe research motivation of this study is to design a deep model that fully\nexploits the temporal-spectral-spatial information of multi-channel signals for\nestimating cRM directly and efficiently in complex domain. As a result, a novel\nTSS network is designed consisting of two modules, a complex neural spatial\nfilter (cNSF) and an MVDR. Essentially, cNSF is a cRM estimation model and an\nMVDR module is cascaded to the cNSF module to reduce the nonlinear speech\ndistortions introduced by neural network. Specifically, to fit the cRM target,\nall input features of cNSF are reformulated into complex-valued representations\nfollowing the supervised learning paradigm. Then, to achieve good hierarchical\nfeature abstraction, a complex deep neural network (cDNN) is delicately\ndesigned with U-Net structure. Experiments conducted on simulated multi-channel\nspeech data demonstrate the proposed cNSF outperforms the baseline NSF by 12.1%\nscale-invariant signal-to-distortion ratio and 33.1% word error rate.\n"}
+{"abstract": "  Training Graph Convolutional Networks (GCNs) is expensive as it needs to\naggregate data recursively from neighboring nodes. To reduce the computation\noverhead, previous works have proposed various neighbor sampling methods that\nestimate the aggregation result based on a small number of sampled neighbors.\nAlthough these methods have successfully accelerated the training, they mainly\nfocus on the single-machine setting. As real-world graphs are large, training\nGCNs in distributed systems is desirable. However, we found that the existing\nneighbor sampling methods do not work well in a distributed setting.\nSpecifically, a naive implementation may incur a huge amount of communication\nof feature vectors among different machines. To address this problem, we\npropose a communication-efficient neighbor sampling method in this work. Our\nmain idea is to assign higher sampling probabilities to the local nodes so that\nremote nodes are accessed less frequently. We present an algorithm that\ndetermines the local sampling probabilities and makes sure our skewed neighbor\nsampling does not affect much the convergence of the training. Our experiments\nwith node classification benchmarks show that our method significantly reduces\nthe communication overhead for distributed GCN training with little accuracy\nloss.\n"}
+{"abstract": "  Extracting relevant properties of empirical signals generated by nonlinear,\nstochastic, and high-dimensional systems is a challenge of complex systems\nresearch. Open questions are how to differentiate chaotic signals from\nstochastic ones, and how to quantify nonlinear and/or high-order temporal\ncorrelations. Here we propose a new technique to reliably address both\nproblems. Our approach follows two steps: first, we train an artificial neural\nnetwork (ANN) with flicker (colored) noise to predict the value of the\nparameter, $\\alpha$, that determines the strength of the correlation of the\nnoise. To predict $\\alpha$ the ANN input features are a set of probabilities\nthat are extracted from the time series by using symbolic ordinal analysis.\nThen, we input to the trained ANN the probabilities extracted from the time\nseries of interest, and analyze the ANN output. We find that the $\\alpha$ value\nreturned by the ANN is informative of the temporal correlations present in the\ntime series. To distinguish between stochastic and chaotic signals, we exploit\nthe fact that the difference between the permutation entropy (PE) of a given\ntime series and the PE of flicker noise with the same $\\alpha$ parameter is\nsmall when the time series is stochastic, but it is large when the time series\nis chaotic. We validate our technique by analysing synthetic and empirical time\nseries whose nature is well established. We also demonstrate the robustness of\nour approach with respect to the length of the time series and to the level of\nnoise. We expect that our algorithm, which is freely available, will be very\nuseful to the community.\n"}
+{"abstract": "  Federated learning enables multiple participants to collaboratively train a\nmodel without aggregating the training data. Although the training data are\nkept within each participant and the local gradients can be securely\nsynthesized, recent studies have shown that such privacy protection is\ninsufficient. The global model parameters that have to be shared for\noptimization are susceptible to leak information about training data. In this\nwork, we propose Confined Gradient Descent (CGD) that enhances privacy of\nfederated learning by eliminating the sharing of global model parameters. CGD\nexploits the fact that a gradient descent optimization can start with a set of\ndiscrete points and converges to another set at the neighborhood of the global\nminimum of the objective function. It lets the participants independently train\non their local data, and securely share the sum of local gradients to benefit\neach other. We formally demonstrate CGD's privacy enhancement over traditional\nFL. We prove that less information is exposed in CGD compared to that of\ntraditional FL. CGD also guarantees desired model accuracy. We theoretically\nestablish a convergence rate for CGD. We prove that the loss of the proprietary\nmodels learned for each participant against a model learned by aggregated\ntraining data is bounded. Extensive experimental results on two real-world\ndatasets demonstrate the performance of CGD is comparable with the centralized\nlearning, with marginal differences on validation loss (mostly within 0.05) and\naccuracy (mostly within 1%).\n"}
+{"abstract": "  Based on recent perturbative and non-perturbative lattice calculations with\nalmost quark flavors and the thermal contributions from photons, neutrinos,\nleptons, electroweak particles, and scalar Higgs bosons, various thermodynamic\nquantities, at vanishing net-baryon densities, such as pressure, energy\ndensity, bulk viscosity, relaxation time, and temperature have been calculated\nup to the TeV-scale, i.e. covering hadron, QGP and electroweak (EW) phases in\nthe early Universe. This remarkable progress motivated the present study to\ndetermine the possible influence of the bulk viscosity in the early Universe\nand to understand how this would vary from epoch to epoch. We have taken into\nconsideration first- (Eckart) and second-order (Israel-Stewart) theories for\nthe relativistic cosmic fluid and integrated viscous equations of state in\nFriedmann equations. Nonlinear nonhomogeneous differential equations are\nobtained as analytical solutions. For Israel-Stewart, the differential\nequations are very sophisticated to be solved. They are outlined here as\nroad-maps for future studies. For Eckart theory, the only possible solution is\nthe functionality, $H(a(t))$, where $H(t)$ is the Hubble parameter and $a(t)$\nis the scale factor, but none of them so far could to be directly expressed in\nterms of either proper or cosmic time $t$. For Eckart-type viscous background,\nespecially at finite cosmological constant, non-singular $H(t)$ and $a(t)$ are\nobtained, where $H(t)$ diverges for QCD/EW and asymptotic EoS. For non-viscous\nbackground, the dependence of $H(a(t))$ is monotonic. The same conclusion can\nbe drawn for an ideal EoS. We also conclude that the rate of decreasing\n$H(a(t))$ with increasing $a(t)$ varies from epoch to epoch, at vanishing and\nfinite cosmological constant. These results obviously help in improving our\nunderstanding of the nucleosynthesis and the cosmological large-scale\nstructure.\n"}
+{"abstract": "  We check the dynamical and observational features of four typologies of\nlogotropic dark energy models, leading to a \\emph{thermodynamic cosmic speed\nup} fueled by a single fluid that unifies dark energy and dark matter. We first\npresent two principal Anton-Schmidt fluids where the Gr\\\"uneisen parameter\n$\\gamma_{\\rm G}$ is free to vary and then fixed to the special value\n$\\gamma_{\\rm G}=\\tfrac{5}{6}$. We also investigate the pure logotropic model,\ncorresponding to $\\gamma_{\\rm G}=-\\frac{1}{6}$. Finally, we propose a new\nlogotropic paradigm that works as a generalized logotropic fluid, in which we\nsplit the role of dark matter and baryons. We demonstrate that the logotropic\nparadigms may present drawbacks in perturbations, showing a negative adiabatic\nsound speed which make perturbations unstable. The Anton-Schmidt model with\n$\\gamma_{\\rm G}=\\frac{5}{6}$ is ruled out while the generalized logotropic\nfluid seems to be the most suitable one, albeit weakly disfavored than the\n$\\Lambda$CDM model. We combine low- and higher-redshift domains through\nexperimental fits based on Monte Carlo Markov Chain procedures, taking into\naccount supernovae Ia catalogue, Hubble measurements and $\\sigma_8$ data\npoints. We consider two model selection criteria to infer the statistical\nsignificance of the four models. We conclude there is statistical advantage to\nhandle the Anton-Schmidt fluid with the Gr\\\"uneisen parameter free to vary\nand/or fixed to $\\gamma_{\\rm G}=-\\frac{1}{6}$. The generalized logotropic fluid\nindicates suitable results, statistically favored than the other models, until\nthe sound speed is positive, becoming unstable in perturbations elsewhere. We\nemphasize that the $\\Lambda$CDM paradigm works statistically better than any\nkinds of logotropic and generalized logotropic models, while the\nChevallier-Polarski-Linder parametrization is statistically comparable with\nlogotropic scenarios.\n"}
+{"abstract": "  Using \\emph{TESS} we are doing a systematic study of outbursting AM~CVn\nsystems to place some limits on the current outbursts models. We present the\n\\emph{TESS} light curve (LC) for 9 AM~CVns showing both superoutbursts (SOs)\nand normal outbursts (NOs). The continuous coverage of the outbursts with\n\\emph{TESS} allows us to place stringent limits on the duration and structures\nof the SOs and the NOs. We present evidence that in at least some of the\nsystems enhanced mass transfer (EMT) has to be taken into account to explain\nthe observed LC of the SOs and rebrighthening phase after the SOs. For others,\nthe colour evolution from simultaneous observations in $g$ and $r$ with ZTF\ndiffers from previously reported colour evolution of longer period AM~CVns\nwhere EMT is responsible for the SO. We also find that due to the lack of\nsufficiently high cadence coverage the duration of many systems might have been\noverestimated in previous ground-based surveys. We report the SO duration for 6\nAM~CVns. We also found that precursors are a common feature of SOs in AM~CVns\nand are seen in the LC of 5 of the 6 reported SOs. Finally, the 10-minute and\n2-minute cadence LCs from \\emph{TESS} also allowed us to find two new candidate\norbital periods of AM~CVns, both of which are in reasonably good agreement with\nthe predictions for their periods based on their past outburst histories.\n"}
+{"abstract": "  Shapley values provide model agnostic feature attributions for model outcome\nat a particular instance by simulating feature absence under a global\npopulation distribution. The use of a global population can lead to potentially\nmisleading results when local model behaviour is of interest. Hence we consider\nthe formulation of neighbourhood reference distributions that improve the local\ninterpretability of Shapley values. By doing so, we find that the\nNadaraya-Watson estimator, a well-studied kernel regressor, can be expressed as\na self-normalised importance sampling estimator. Empirically, we observe that\nNeighbourhood Shapley values identify meaningful sparse feature relevance\nattributions that provide insight into local model behaviour, complimenting\nconventional Shapley analysis. They also increase on-manifold explainability\nand robustness to the construction of adversarial classifiers.\n"}
+{"abstract": "  The interaction between cracks, as well as their propagation, in\nsingle-crystal aluminum is investigated at the atomic scale using the molecular\ndynamics method and the modified embedded atom method. The results demonstrated\nthat the crack propagation in aluminum is a quite complex process, which is\naccompanied by micro-crack growth, merging, stress shielding, dislocation\nemission, and phase transformation of the crystal structure. The main\ndeformation mechanisms at the front of the fatigue crack are holes, slip bands,\nand cross-slip bands. During crack propagation, there are interactions between\ncracks. Such interactions inhibit the phase transition at the crack tip, and\nalso affect the direction and speed of crack propagation. The intensity of the\ninteraction between two cracks decreases with the increase of the distance\nbetween them and increases with increasing crack size. Moreover, while this\ninteraction has no effect on the elastic modulus of the material, it affects\nits strength limit.\n"}
+{"abstract": "  Management of invasive species and pathogens requires information about the\ntraffic of potential vectors. Such information is often taken from vector\ntraffic models fitted to survey data. Here, user-specific data collected via\nmobile apps offer new opportunities to obtain more accurate estimates and to\nanalyze how vectors' individual preferences affect propagule flows. However,\ndata voluntarily reported via apps may lack some trip records, adding a\nsignificant layer of uncertainty. We show how the benefits of app-based data\ncan be exploited despite this drawback.\n  Based on data collected via an angler app, we built a stochastic model for\nangler traffic in the Canadian province Alberta. There, anglers facilitate the\nspread of whirling disease, a parasite-induced fish disease. The model is\ntemporally and spatially explicit and accounts for individual preferences and\nrepeating behaviour of anglers, helping to address the problem of missing trip\nrecords.\n  We obtained estimates of angler traffic between all subbasins in Alberta. The\nmodel's accuracy exceeds that of direct empirical estimates even when fewer\ndata were used to fit the model. The results indicate that anglers' local\npreferences and their tendency to revisit previous destinations reduce the\nnumber of long inter-waterbody trips potentially dispersing whirling disease.\nAccording to our model, anglers revisit their previous destination in 64% of\ntheir trips, making these trips irrelevant for the spread of whirling disease.\nFurthermore, 54% of fishing trips end in individual-specific spatially\ncontained areas with mean radius of 54.7km. Finally, although the fraction of\ntrips that anglers report was unknown, we were able to estimate the total\nyearly number of fishing trips in Alberta, matching an independent empirical\nestimate.\n"}
+{"abstract": "  We consider dark matter which have non-zero electromagnetic form factors like\nelectric/magnetic dipole moments and anapole moment for fermionic dark matter\nand Rayleigh form factor for scalar dark matter. We consider dark matter mass\n$m_\\chi > \\cal{ O}({\\rm MeV})$ and put constraints on their mass and\nelectromagnetic couplings from CMB and LSS observations. Fermionic dark matter\nwith non-zero electromagnetic form factors can annihilate to $e^+ e^-$ and\nscalar dark matter can annihilate to $2\\gamma$ at the time of recombination and\ndistort the CMB. We analyze dark matter with multipole moments with Planck and\nBAO observations. We find upper bounds on anapole moment $g_{A}<7.163\\times\n10^{3} \\text{GeV}^{-2}$, electric dipole moment ${\\cal D}<7.978\\times 10^{-9}\n\\text{e-cm}$, magnetic dipole moment ${\\mu}<2.959\\times 10^{-7} \\mu_B$, and the\nbound on Rayleigh form factor of dark matter is $g_4/\\Lambda_4^2<1.085\\times\n10^{-2}\\text{GeV}^{-2}$ with $95\\%$C.L.\n"}
+{"abstract": "  Observations of plasma waves by the Fields Suite and of electrons by the\nSolar Wind Electrons Alphas and Protons Investigation (SWEAP) on Parker Solar\nProbe provide strong evidence for pitch angle scattering of strahl-energy\nelectrons by narrowband whistler-mode waves at radial distances less than ~0.3\nAU. We present two example intervals of a few hours that include 8 waveform\ncaptures with whistler-mode waves and 26 representative electron distributions\nthat are examined in detail. Two were narrow; 17 were clearly broadened, and 8\nwere very broad. The two with narrow strahl occurred when there were either no\nwhistlers or very intermittent low amplitude waves. Six of the eight broadest\ndistributions were associated with intense, long duration waves. Approximately\nhalf of the observed electron distributions have features consistent with an\nenergy dependent scattering mechanism, as would be expected from interactions\nwith narrowband waves. A comparison of the wave power in the whistler-mode\nfrequency band to pitch angle width and a measure of anisotropy provides\nadditional evidence for the electron scattering by whistler-mode waves. The\npitch angle broadening occurs in over an energy range comparable to that\nobtained for the n=1 (co-streaming) resonance for the observed wave and plasma\nparameters. The additional observation that the heat flux is lower in the\ninterval with multiple switchbacks may provide clues to the nature of\nswitchbacks. These results provide strong evidence that the heat flux is\nreduced by narroweband whistler-mode waves scattering of strahl-energy\nelectrons.\n"}
+{"abstract": "  In recent years, space missions such as Kepler and TESS have discovered many\nclose-in planets with significant atmospheres consisting of hydrogen and\nhelium: mini-Neptunes. This indicates that these planets formed early in\ngas-rich disks while avoiding the runaway gas accretion that would otherwise\nhave turned them into hot-Jupiters. A solution is to invoke a long\nKelvin-Helmholtz contraction (or cooling) timescale, but it has also been\nsuggested that thermodynamical cooling can be prevented by hydrodynamical\nplanet atmosphere-disk recycling. We investigate the efficacy of the recycling\nhypothesis in preventing the collapse of the atmosphere, check for the\nexistence of a steady state configuration, and determine the final atmospheric\nmass to core mass ratio. We use three-dimensional radiation-hydrodynamic\nsimulations to model the formation of planetary proto-atmospheres. Equations\nare solved in a local frame centered on the planet. Ignoring small oscillations\nthat average to zero over time, the simulations converge to a steady state\nwhere the velocity field of the gas becomes constant in time. In a steady\nstate, the energy loss by radiative cooling is fully compensated by the\nrecycling of the low entropy gas in the planetary atmosphere with high entropy\ngas from the circumstellar disk. For close-in planets, recycling naturally\nhalts the cooling of planetary proto-atmospheres, preventing them from\ncontracting toward the runaway regime and collapsing into gas giants.\n"}
+{"abstract": "  Platforms that support online commentary, from social networks to news sites,\nare increasingly leveraging machine learning to assist their moderation\nefforts. But this process does not typically provide feedback to the author\nthat would help them contribute according to the community guidelines. This is\nprohibitively time-consuming for human moderators to do, and computational\napproaches are still nascent. This work focuses on models that can help suggest\nrephrasings of toxic comments in a more civil manner. Inspired by recent\nprogress in unpaired sequence-to-sequence tasks, a self-supervised learning\nmodel is introduced, called CAE-T5. CAE-T5 employs a pre-trained text-to-text\ntransformer, which is fine tuned with a denoising and cyclic auto-encoder loss.\nExperimenting with the largest toxicity detection dataset to date (Civil\nComments) our model generates sentences that are more fluent and better at\npreserving the initial content compared to earlier text style transfer systems\nwhich we compare with using several scoring systems and human evaluation.\n"}
+{"abstract": "  Normal type Ia supernovae (SNe) are thought to arise from the thermonuclear\nexplosion of massive ($>0.8$ M$_\\odot$) carbon-oxygen white dwarfs (WDs),\nalthough the exact mechanism is debated. In some models helium accretion onto a\ncarbon-oxygen (CO) WD from a companion was suggested to dynamically trigger a\ndetonation of the accreted helium shell. The helium detonation then produces a\nshock that after converging on itself close to the core of the CO-WD, triggers\na secondary carbon detonation and gives rise to an energetic explosion.\nHowever, most studies of such scenarios have been done in one or two\ndimensions, and/or did not consider self-consistent models for the accretion\nand the He-donor. Here we make use of detailed 3D simulation to study the\ninteraction of a He-rich hybrid $0.69\\,\\mathrm{M_\\odot}$ HeCO WD with a more\nmassive $0.8\\,\\mathrm{M_\\odot}$ CO~WD. We find that accretion from the hybrid\nWD onto the CO~WD gives rise to a helium detonation. However, the helium\ndetonation does not trigger a carbon detonation in the CO~WD. Instead, the\nhelium detonation burns through the accretion stream to also burn the helium\nshell of the donor hybrid HeCO-WD. The detonation of its massive helium shell\nthen compresses its CO core, and triggers its detonation and full destruction.\nThe explosion gives rise to a faint, likely highly reddened transient,\npotentially observable by the Vera Rubin survey, and the high-velocity ($\\sim\n1000\\,\\mathrm{km s^{-1}}$) ejection of the heated surviving CO~WD companion.\nPending on uncertainties in stellar evolution we estimate the rate of such\ntransient to be up to $\\sim10\\%$ of the rate of type Ia SNe.\n"}
+{"abstract": "  Molecular dynamics simulations are performed to provide a detailed\nunderstanding of the functional degradation of shape memory alloys at small\nscale. The origin of the experimentally reported accumulation of plastic\ndeformation and the anomalous sudden increase of the residual strain under\ncyclic mechanical loading are explained by detailed insights into the relevant\natomic scale processes. Our work reveals that the mechanical response of\nshape-memory-alloy pillars under cyclic compression is significantly influenced\nby the presence of an amorphous-like surface region as experimentally induced\nby focused ion beam milling. The main factor responsible for the observed\ndegradation of superelasticity under cyclic loading is the accumulated plastic\ndeformation and the resultant retained martensite originating from a synergetic\ncontribution of the amorphous and crystalline shape-memory-alloy regions. We\nshow that the reported sudden diminishment of the stress plateaus and\nhysteresis under cyclic loading is caused by the increased stability of the\nmartensite phase due to the presence of the amorphous phase. Based on the\nidentified mechanism responsible for the degradation, we validate reported\nmethods of recovering the superelasticity and propose a new method to prohibit\nthe synergetic contribution of the amorphous and crystalline regions, such as\nto achieve a sustainable operation of shape memory alloys at small scale.\n"}
+{"abstract": "  This is a technical report, containing all the lemma and proposition proofs\nin paper \"Topological Constraints on Identifying Additive Link Metrics via\nEnd-to-end Paths Measurements\" by Liang Ma, Ting He, Kin K. Leung, Don Towsley,\nand Ananthram Swami, published in Annual Conference of The International\nTechnology Alliance (ACITA), 2012.\n"}
+{"abstract": "  In video data, busy motion details from moving regions are conveyed within a\nspecific frequency bandwidth in the frequency domain. Meanwhile, the rest of\nthe frequencies of video data are encoded with quiet information with\nsubstantial redundancy, which causes low processing efficiency in existing\nvideo models that take as input raw RGB frames. In this paper, we consider\nallocating intenser computation for the processing of the important busy\ninformation and less computation for that of the quiet information. We design a\ntrainable Motion Band-Pass Module (MBPM) for separating busy information from\nquiet information in raw video data. By embedding the MBPM into a two-pathway\nCNN architecture, we define a Busy-Quiet Net (BQN). The efficiency of BQN is\ndetermined by avoiding redundancy in the feature space processed by the two\npathways: one operating on Quiet features of low-resolution, while the other\nprocesses Busy features. The proposed BQN outperforms many recent video\nprocessing models on Something-Something V1, Kinetics400, UCF101 and HMDB51\ndatasets.\n"}
+{"abstract": "  A key tool astronomers have to investigate the nature of extragalactic\ntransients is their position on their host galaxies. Galactocentric offsets,\nenclosed fluxes and the fraction of light statistic are widely used at\ndifferent wavelengths to help infer the nature of transient progenitors.\nMotivated by the proposed link between magnetars and fast radio bursts (FRBs),\nwe create a face-on image of the Milky Way using best estimates of its size,\nstructure and colour. We place Galactic magnetars, pulsars, low mass and high\nmass X-ray binaries on this image, using the available distance information.\nGalactocentric offsets, enclosed fluxes and fraction of light distributions for\nthese systems are compared to extragalactic transient samples. We find that\nFRBs follow the distributions for Galactic neutron stars closest, with 24 (75\nper cent) of the Anderson-Darling tests we perform having a p-value greater\nthan 0.05. This suggests that FRBs are located on their hosts in a manner\nconsistent with Galactic neutron stars on the Milky Way's light, although we\ncannot determine which specific neutron star population is the best match. The\nGalactic distributions are consistent with other extragalactic transients much\nless often across the range of comparisons made, with type Ia SNe in second\nplace, at only 33 per cent of tests exceeding 0.05. Overall, our results\nprovide further support for FRB models invoking isolated young neutron stars,\nor binaries containing a neutron star.\n"}
+{"abstract": "  We introduce the GAMBIT Universal Model Machine (GUM), a tool for\nautomatically generating code for the global fitting software framework GAMBIT,\nbased on Lagrangian-level inputs. GUM accepts models written symbolically in\nFeynRules and SARAH formats, and can use either tool along with MadGraph and\nCalcHEP to generate GAMBIT model, collider, dark matter, decay and spectrum\ncode, as well as GAMBIT interfaces to corresponding versions of SPheno,\nmicrOMEGAs, Pythia and Vevacious (C++). In this paper we describe the features,\nmethods, usage, pathways, assumptions and current limitations of GUM. We also\ngive a fully worked example, consisting of the addition of a Majorana fermion\nsimplified dark matter model with a scalar mediator to GAMBIT via GUM, and\ncarry out a corresponding fit.\n"}
+{"abstract": "  We study the relationship between the categorical entropy of the twist and\ncotwist functors along a spherical functor. In particular, we prove the\ncategorical entropy of the twist functor coincides with that of the cotwist\nfunctor if the essential image of the right adjoint functor of the spherical\nfunctor contains a split-generator. We also see our results generalize the\ncomputations of the categorical entropy of spherical twists and\n$\\mathbb{P}$-twists by Ouchi and Fan. As an application, we apply our results\nto the Gromov--Yomdin type conjecture by Kikuta--Takahashi.\n"}
+{"abstract": "  Cloud Native Application CNApp (as a distributed system) is a collection of\nindependent components (micro-services) interacting via communication\nprotocols. This gives rise to present an abstract architecture of CNApp as\ndynamically re-configurable acyclic directed multi graph where vertices are\nmicroservices, and edges are the protocols. Generic mechanisms for such\nreconfigurations evidently correspond to higher-level functions (functionals).\nThis implies also internal abstract architecture of microservice as a\ncollection of event-triggered serverless functions (including functions\nimplementing the protocols) that are dynamically composed into event-dependent\ndata-flow graphs. Again, generic mechanisms for such compositions correspond to\ncalculus of functionals and relations.\n"}
+{"abstract": "  3D ultrasound (US) has become prevalent due to its rich spatial and\ndiagnostic information not contained in 2D US. Moreover, 3D US can contain\nmultiple standard planes (SPs) in one shot. Thus, automatically localizing SPs\nin 3D US has the potential to improve user-independence and\nscanning-efficiency. However, manual SP localization in 3D US is challenging\nbecause of the low image quality, huge search space and large anatomical\nvariability. In this work, we propose a novel multi-agent reinforcement\nlearning (MARL) framework to simultaneously localize multiple SPs in 3D US. Our\ncontribution is four-fold. First, our proposed method is general and it can\naccurately localize multiple SPs in different challenging US datasets. Second,\nwe equip the MARL system with a recurrent neural network (RNN) based\ncollaborative module, which can strengthen the communication among agents and\nlearn the spatial relationship among planes effectively. Third, we explore to\nadopt the neural architecture search (NAS) to automatically design the network\narchitecture of both the agents and the collaborative module. Last, we believe\nwe are the first to realize automatic SP localization in pelvic US volumes, and\nnote that our approach can handle both normal and abnormal uterus cases.\nExtensively validated on two challenging datasets of the uterus and fetal\nbrain, our proposed method achieves the average localization accuracy of 7.03\ndegrees/1.59mm and 9.75 degrees/1.19mm. Experimental results show that our\nlight-weight MARL model has higher accuracy than state-of-the-art methods.\n"}
+{"abstract": "  This paper focuses on a question raised by Holm and J{\\o}rgensen, who asked\nif the induced cotorsion pairs $(\\Phi({\\sf X}),\\Phi({\\sf X})^{\\perp})$ and\n$(^{\\perp}\\Psi({\\sf Y}),\\Psi({\\sf Y}))$ in $\\mathrm{Rep}(Q,{\\sf{A}})$ -- the\ncategory of all $\\sf{A}$-valued representations of a quiver $Q$ -- are complete\nwhenever $(\\sf X,\\sf Y)$ is a complete cotorsion pair in an abelian category\n$\\sf{A}$ satisfying some mild conditions. Recently, Odaba\\c{s}{\\i} gave an\naffirmative answer if the quiver $Q$ is rooted and the cotorsion pair $(\\sf\nX,\\sf Y)$ is further hereditary. In this paper, we improve Odaba\\c{s}{\\i}'s\nwork by removing the hereditary assumption on the cotorsion pair. As an\napplication, we show under certain mild conditions that if a subcategory $\\sf\nL$, which is not necessarily closed under direct summands, of $\\sf A$ is\nspecial precovering (resp., preenveloping), then $\\Phi(\\sf L)$ (resp.,\n$\\Psi(\\sf L)$) is special precovering (resp., preenveloping) in\n$\\mathrm{Rep}(Q,{\\sf{A}})$.\n"}
+{"abstract": "  We present the analytic evaluation of the two-loop corrections to the\namplitude for the scattering of four fermions in Quantum Electrodynamics, $f^-\n+ f^+ + F^- + F^+ \\to 0$, with $f$ and $F$ representing a massless and a\nmassive lepton, respectively. Dimensional regularization is employed to\nevaluate the loop integrals. Ultraviolet divergences are removed by\nrenormalizing the coupling constant in the ${\\overline{\\text{MS}}}$-scheme, and\nthe lepton mass as well as the external fields in the on-shell scheme. The\nanalytic result for the renormalized amplitude is expressed as Laurent series\naround $d=4$ space-time dimensions, and contains Generalized Polylogarithms\nwith up to weight four. The structure of the residual infrared divergences of\nthe virtual amplitude is in agreement with the prediction of the Soft Collinear\nEffective Theory. Our analytic results are an essential ingredient for the\ncomputation of the scattering cross section for massive fermion-pair production\nin massless fermion-pair annihilation, i.e. $f^- f^+ \\to F^- F^+$, and crossing\nrelated processes such as the elastic scattering $f F \\to f F$, with up to\nNext-to-Next to Leading Order accuracy.\n"}
+{"abstract": "  The topological structure of vacuum is the cornerstone of non-Abelian gauge\ntheories describing strong and electroweak interactions within the standard\nmodel of particle physics. However, transitions between different topological\nsectors of the vacuum (believed to be at the origin of the baryon asymmetry of\nthe Universe) have never been observed directly. An experimental observation of\nsuch transitions in Quantum Chromodynamics (QCD) has become possible in\nheavy-ion collisions, where the chiral magnetic effect converts the chiral\nasymmetry (generated by topological transitions in hot QCD matter) into an\nelectric current, under the presence of the magnetic field produced by the\ncolliding ions. The Relativistic Heavy Ion Collider program on heavy-ion\ncollisions such as the Zr-Zr and Ru-Ru isobars, thus has the potential to\nuncover the topological structure of vacuum in a laboratory experiment. This\ndiscovery would have far-reaching implications for the understanding of QCD,\nthe origin of the baryon asymmetry in the present-day Universe, and for other\nareas, including condensed matter physics.\n"}
+{"abstract": "  We present the first observation of instability in weakly magnetized,\npressure dominated plasma Couette flow firmly in the Hall regime. Strong Hall\ncurrents couple to a low frequency electromagnetic mode that is driven by\nhigh-$\\beta$ ($>1$) pressure profiles. Spectroscopic measurements show heating\n(factor of 3) of the cold, unmagnetized ions via a resonant Landau damping\nprocess. A linear theory of this instability is derived that predicts positive\ngrowth rates at finite $\\beta$ and shows the stabilizing effect of very large\n$\\beta$, in line with observations.\n"}
+{"abstract": "  Using recent machine learning results that present an information-theoretic\nperspective on underfitting and overfitting, we prove that deciding whether an\nencodable learning algorithm will always underfit a dataset, even if given\nunlimited training time, is undecidable. We discuss the importance of this\nresult and potential topics for further research, including\ninformation-theoretic and probabilistic strategies for bounding learning\nalgorithm fit.\n"}
+{"abstract": "  In this independent report fAshIon after fashion, we examine the development\nof fAshIon (artificial intelligence (AI) in fashion) and explore its\npotentiality to become a major disruptor of the fashion industry in the near\nfuture. To do this, we investigate AI technologies used in the fashion industry\nthrough several lenses. We summarise fAshIon studies conducted over the past\ndecade and categorise them into seven groups: Overview, Evaluation, Basic Tech,\nSelling, Styling, Design, and Buying. The datasets mentioned in fAshIon\nresearch have been consolidated on one GitHub page for ease of use. We analyse\nthe authors' backgrounds and the geographic regions treated in these studies to\ndetermine the landscape of fAshIon research. The results of our analysis are\npresented with an aim to provide researchers with a holistic view of research\nin fAshIon. As part of our primary research, we also review a wide range of\ncases of applied fAshIon in the fashion industry and analyse their impact on\nthe industry, markets and individuals. We also identify the challenges\npresented by fAshIon and suggest that these may form the basis for future\nresearch. We finally exhibit that many potential opportunities exist for the\nuse of AI in fashion which can transform the fashion industry embedded with AI\ntechnologies and boost profits.\n"}
+{"abstract": "  Let $G$ be a semisimple simply connected complex algebraic group. Let $U$ be\nthe unipotent radical of a Borel subgroup in $G$. We describe the coordinate\nrings of $U$ (resp., $G/U$, $G$) in terms of two (resp., four, eight)\nbirational charts introduced in [L94, L19] in connection with the study of\ntotal positivity.\n"}
+{"abstract": "  A novel observable measuring the $C\\!P$ asymmetry in multi-body decays of\nheavy mesons, which is called the forward-backward asymmetry induced $C\\!P$\nasymmetry (FBI-$C\\!P$A), $A_{CP}^{FB}$, is introduced. This observable has the\ndual advantages that 1) it can isolate the $C\\!P$ asymmetry associated with the\ninterference of the $S$- and $P$-wave amplitude from that associated with the\n$S$- or $P$-wave amplitude alone; 2) it can effectively almost double the\nstatistics comparing to the conventionally defined regional $C\\!P$ asymmetry.\nWe also suggest to perform the measurements of FBI-$C\\!P$A in some three-body\ndecay channels of charm and beauty mesons.\n"}
+{"abstract": "  Chv\\'{a}tal conjectured in 1973 the existence of some constant $t$ such that\nall $t$-tough graphs with at least three vertices are hamiltonian. While the\nconjecture has been proven for some special classes of graphs, it remains open\nin general. We say that a graph is $(K_2 \\cup 3K_1)$-free if it contains no\ninduced subgraph isomorphic to $K_2 \\cup 3K_1$, where $K_2 \\cup 3K_1$ is the\ndisjoint union of an edge and three isolated vertices. In this paper, we show\nthat every 3-tough $(K_2 \\cup 3K_1)$-free graph with at least three vertices is\nhamiltonian.\n"}
+{"abstract": "  Decision trees provide a rich family of highly non-linear but efficient\nmodels, due to which they continue to be the go-to family of predictive models\nby practitioners across domains. But learning trees is challenging due to their\ndiscrete decision boundaries. The state-of-the-art (SOTA) techniques resort to\n(a) learning soft trees thereby losing logarithmic inference time; or (b) using\nmethods tailored to specific supervised learning settings, requiring access to\nlabeled examples and loss function. In this work, by leveraging techniques like\noverparameterization and straight-through estimators, we propose a novel method\nthat enables accurate end-to-end gradient based tree training and can be\ndeployed in a variety of settings like offline supervised learning and online\nlearning with bandit feedback. Using extensive validation on standard\nbenchmarks, we demonstrate that our method provides best of both worlds, i.e.,\nit is competitive to, and in some cases more accurate than methods designed\nspecifically for the supervised settings; and in bandit settings, where most\nexisting tree learning techniques are not applicable, our models are still\naccurate and significantly outperform the applicable SOTA methods.\n"}
+{"abstract": "  The Bender-Knuth involutions on semistandard Young tableaux are known to\ncoincide with the tableau switching on horizontal border strips of two adjacent\nletters, together with the swapping of those letters. Motivated by this\ncoincidence and using the shifted tableau switching due to Choi, Nam and Oh\n(2019), we consider a shifted version of the Bender-Knuth involutions and\ndefine a shifted version of the Berenstein-Kirillov group (1995). Similarly to\nthe classical case, the shifted version of the Berenstein-Kirillov group also\nacts on the straight-shaped shifted tableau crystals introduced by Gillespie,\nLevinson and Purbhoo (2020), via partial Sch\\\"utzenberger involutions, thus\ncoinciding with the action of the cactus group on the same crystal, due to the\nauthor. Following the works of Halacheva (2016, 2020), and Chmutov, Glick and\nPylyavskyy (2020), on the relation between the actions of the\nBerenstein-Kirillov group and the cactus group on a crystal of straight-shaped\nYoung tableaux, we also show that the shifted Berenstein-Kirillov group is\nisomorphic to a quotient of the cactus group. Not all the known relations that\nhold in the classic Berenstein-Kirillov group need to be satisfied by the\nshifted Bender-Knuth involutions, but the ones implying the relations of the\ncactus group are verified. Hence, we have an alternative presentation for the\ncactus group in terms of the shifted Bender-Knuth involutions. We also use the\nshifted growth diagrams due to Thomas and Yong (2016) to provide an alternative\nproof concerning the mentioned cactus group action.\n"}
+{"abstract": "  Process-induced defects are the leading cause of discrepancies between\nas-designed and as-manufactured additive manufacturing (AM) product behavior.\nEspecially for metal lattices, the variations in the printed geometry cannot be\nneglected. Therefore, the evaluation of the influence of microstructural\nvariability on their mechanical behavior is crucial for the quality assessment\nof the produced structures. Commonly, the as-manufactured geometry can be\nobtained by computed tomography (CT). However, to incorporate all\nprocess-induced defects into the numerical analysis is often computationally\ndemanding. Thus, commonly this task is limited to a predefined set of\nconsidered variations, such as strut size or strut diameter. In this work, a\nCT-based binary random field is proposed to generate statistically equivalent\ngeometries of periodic metal lattices. The proposed random field model in\ncombination with the Finite Cell Method (FCM), an immersed boundary method,\nallows to efficiently evaluate the influence of the underlying microstructure\non the variability of the mechanical behavior of AM products. Numerical\nanalysis of two lattices manufactured at different scales shows an excellent\nagreement with experimental data. Furthermore, it provides a unique insight\ninto the effects of the process on the occurring geometrical variations and\nfinal mechanical behavior.\n"}
+{"abstract": "  Graphene quantum dots (GQDs) represent single layers up to dozens of graphene\nlayers smaller than 30 nm. GQDs are newish molecules that have aroused great\ninterest in research because of their exceptional and manageable optical,\nelectrical, chemical, and structural properties. In this work, we report\nelectrostatic potential energy maps, or molecular electrostatic potential\nsurfaces, illustrate the charge distributions of GQDs three-dimensionally.\nKnowledge of the charge distributions can be used to determine how GQDs\ninteract with one another. To analyze the distribution of molecular charges\naccurately, a large number of electrostatic potential energy values must be\ncalculated. The best way to transmit these data is to visualize them as in the\nelectrostatic potential map. A ZINDO semi-empirical quantum chemistry method\nthen imposes the calculated data onto an electron density model of the GQDs\nderived from the Schr\\\"odinger equation. To make the electrostatic potential\nenergy data of GQDs easy to interpret, a color spectrum, with red as the lowest\nelectrostatic potential energy value and blue as the highest, is employed to\nconvey the varying intensities of the electrostatic potential energy values.\nThe results of the four GQD models suggest that the energy of the ionization\npotential lies in a range of -7.20 eV to -5.31 eV and the electron affinity is\n-2.65 to -0.24 eV.\n"}
+{"abstract": "  This paper presents a novel architecture for model predictive control (MPC)\nbased indoor climate control of multi-zone buildings to provide energy\nefficiency. Unlike prior works we do not assume the availability of a\nhigh-resolution multi-zone building model, which is challenging to obtain.\nInstead, the architecture uses a low-resolution model of the building which is\ndivided into a small number of \"meta-zones\" that can be easily identified using\nexisting data-driven modeling techniques. The proposed architecture is\nhierarchical. At the higher level, an MPC controller uses the low-resolution\nmodel to make decisions for the air handling unit (AHU) and the meta-zones.\nSince the meta-zones are fictitious, a lower level controller converts the\nhigh-level MPC decisions into commands for the individual zones by solving a\nprojection problem that strikes a trade-off between two potentially conflicting\ngoals: the AHU-level decisions made by the MPC are respected while the climate\nof the individual zones is maintained within the comfort bounds. The\nperformance of the proposed controller is assessed via simulations in a\nhigh-fidelity simulation testbed and compared to that of a rule-based\ncontroller that is used in practice. Simulations in multiple weather conditions\nshow the effectiveness of the proposed controller in terms of energy savings,\nclimate control, and computational tractability.\n"}
+{"abstract": "  Let $K$ be an imaginary quadratic field, with associated quadratic character\n$\\alpha$. We construct an analytic $p$-adic $L$-function interpolating the\ntwisted adjoint $L$-values $L(1, \\mathrm{ad}(f) \\otimes \\alpha)$ as $f$ varies\nin a Hida family; these special values are non-critical in the sense of\nDeligne. Our approach is based on Greenberg--Stevens' idea of $\\Lambda$-adic\nmodular symbols, which considers cohomology with values in a space of $p$-adic\nmeasures.\n"}
+{"abstract": "  Robot navigation in a safe way for complex and crowded situations is studied\nin this work. When facing complex environments with both static and dynamic\nobstacles, in existing works unicycle nonholonomic robots are prone to two\nextreme behaviors, one is to fall into dead ends formed by obstacles, and the\nother is to not complete the navigation task in time due to excessive collision\navoidance.As a result, we propose the R-SARL framework, which is based on a\ndeep reinforcement learning algorithm and where we augment the reward function\nto avoid collisions. In particular, we estimate unsafe interactions between the\nrobot and obstacles in a look-ahead distance and penalize accordingly, so that\nthe robot can avoid collisions in advance and reach its destination\nsafely.Furthermore, we penalize frequent excessive detours to reduce the\ntimeout and thus improve the efficiency of navigation.We test our method in\nvarious challenging and complex crowd navigation tasks. The results show that\nour method improves navigation performance and outperforms state-of-the-art\nmethods.\n"}
+{"abstract": "  Derivations of Borns Rule are of interest because they tell us what's\nconnected to what in quantum mechanics if we ever need to change or modify it.\n"}
+{"abstract": "  Terrestrial planets with large water inventories are likely ubiquitous and\nwill be among the first Earth-sized planets to be characterized with upcoming\ntelescopes. It has previously been argued that waterworlds-particularly those\npossessing more than 1% H$_2$O-experience limited melt production and\noutgassing due to the immense pressure overburden of their overlying oceans,\nunless subject to high internal heating. But an additional, underappreciated\nobstacle to outgassing on waterworlds is the high solubility of volatiles in\nhigh-pressure melts. Here, we investigate this phenomenon and show that\nvolatile solubilities in melts probably prevent almost all magmatic outgassing\nfrom waterworlds. Specifically, for Earth-like gravity and oceanic crust\ncomposition, oceans or water ice exceeding 10-100 km in depth (0.1-1 GPa)\npreclude the exsolution of volatiles from partial melt of silicates. This\nsolubility limit compounds the pressure overburden effect as large surface\noceans limit both melt production and degassing from any partial melt that is\nproduced. We apply these calculations to Trappist-1 planets to show that, given\ncurrent mass and radius constraints and implied surface water inventories,\nTrappist-1f and -1g are unlikely to experience volcanic degassing. While other\nmechanisms for interior-surface volatile exchange are not completely excluded,\nthe suppression of magmatic outgassing simplifies the range of possible\natmospheric evolution trajectories and has implications for interpretation of\nostensible biosignature gases, which we illustrate with a coupled model of\nplanetary interior-climate-atmosphere evolution.\n"}
+{"abstract": "  For $\\beta<\\frac13$, we consider $C^\\beta(\\mathbb{T}^3\\times [0,T])$ weak\nsolutions of the incompressible Euler equations that do not conserve the\nkinetic energy. We prove that for such solutions the closed and non-empty set\nof singular times $\\mathcal{B}$ satisfies $\\dim_{\\mathcal{H}}(\\mathcal{B})\\geq\n\\frac{2\\beta}{1-\\beta}$. This lower bound on the Hausdorff dimension of the\nsingular set in time is intrinsically linked to the H\\\"older regularity of the\nkinetic energy and we conjecture it to be sharp. As a first step in this\ndirection, for every $\\beta<\\beta'<\\frac{1}{3}$ we are able to construct, via a\nconvex integration scheme, non-conservative $C^\\beta(\\mathbb{T}^3\\times [0,T])$\nweak solutions of the incompressible Euler system such that\n$\\dim_{\\mathcal{H}}(\\mathcal{B})\\leq\n\\frac{1}{2}+\\frac{1}{2}\\frac{2\\beta'}{1-\\beta'}$. The structure of the wild\nsolutions that we build allows moreover to deduce non-uniqueness of\n$C^\\beta(\\mathbb{T}^3\\times [0,T])$ weak solutions of the Cauchy problem for\nEuler from every smooth initial datum.\n"}
+{"abstract": "  Road detection or traversability analysis has been a key technique for a\nmobile robot to traverse complex off-road scenes. The problem has been mainly\nformulated in early works as a binary classification one, e.g. associating\npixels with road or non-road labels. Whereas understanding scenes with\nfine-grained labels are needed for off-road robots, as scenes are very diverse,\nand the various mechanical performance of off-road robots may lead to different\ndefinitions of safe regions to traverse. How to define and annotate\nfine-grained labels to achieve meaningful scene understanding for a robot to\ntraverse off-road is still an open question. This research proposes a\ncontrastive learning based method. With a set of human-annotated anchor\npatches, a feature representation is learned to discriminate regions with\ndifferent traversability, a method of fine-grained semantic segmentation and\nmapping is subsequently developed for off-road scene understanding. Experiments\nare conducted on a dataset of three driving segments that represent very\ndiverse off-road scenes. An anchor accuracy of 89.8% is achieved by evaluating\nthe matching with human-annotated image patches in cross-scene validation.\nExamined by associated 3D LiDAR data, the fine-grained segments of visual\nimages are demonstrated to have different levels of toughness and terrain\nelevation, which represents their semantical meaningfulness. The resultant maps\ncontain both fine-grained labels and confidence values, providing rich\ninformation to support a robot traversing complex off-road scenes.\n"}
+{"abstract": "  This paper presents new machine learning approaches to approximate the\nsolution of optimal stopping problems. The key idea of these methods is to use\nneural networks, where the hidden layers are generated randomly and only the\nlast layer is trained, in order to approximate the continuation value. Our\napproaches are applicable for high dimensional problems where the existing\napproaches become increasingly impractical. In addition, since our approaches\ncan be optimized using a simple linear regression, they are very easy to\nimplement and theoretical guarantees can be provided. In Markovian examples our\nrandomized reinforcement learning approach and in non-Markovian examples our\nrandomized recurrent neural network approach outperform the state-of-the-art\nand other relevant machine learning approaches.\n"}
+{"abstract": "  Simple closed curves in the plane can be mapped to nontrivial knots under the\naction of origami foldings that allow the paper to self-intersect. We show all\ntame knot types may be produced in this manner, motivating the development of a\nnew knot invariant, the fold number, defined as the minimum number of creases\nrequired to obtain an equivalent knot. We study this invariant, presenting a\nbound on the fold number by the diagram stick number as well as a class of\ntorus knots with constant fold number. We also pursue a characterization of\nthose foldings which admit nontrivial knots, giving a proof that no \"physically\nrealizable\", or proper, foldings can admit nontrivial knots. A number of\nquestions are posed for further study.\n"}
+{"abstract": "  Terahertz-based nano-networks are emerging as a groundbreaking technology\nable to play a decisive role in future medical applications owing to their\nability to precisely quantify figures, such as the viral load in a patient or\nto predict sepsis shock or heart attacks before they occur. Due to the\nextremely limited size of the devices composing these nano-networks, the use of\nthe Terahertz (THz) band has emerged as the enabling technology for their\ncommunication. However, the characteristics of the THz band, which strictly\nreduce the communication range inside the human body, together with the energy\nlimitations of nano-nodes make the in-body deployment of nano-nodes a\nchallenging task. To overcome these problems, we propose a novel in-body\nflow-guided nano-network architecture consisting of three different devices: i)\nnano-node, ii) nano-router, and iii) bio-sensor. As the performance of this\ntype of nano-network has not been previously explored, a theoretical framework\ncapturing all its particularities is derived to properly model its behavior and\nevaluate its feasibility in real medical applications. Employing this\nanalytical model, a thorough sensitivity study of its key parameters is\naccomplished. Finally, we analyze the terahertz flow-guided nano-network design\nto satisfy the requirements of several medical applications of interest.\n"}
+{"abstract": "  In this paper, an energy-consistent finite difference scheme for the\ncompressible hydrodynamic and magnetohydrodynamic (MHD) equations is\nintroduced. For the compressible magnetohydrodynamics, an energy-consistent\nfinite difference formulation is derived using the product rule for the spatial\ndifference. The conservation properties of the internal, kinetic, and magnetic\nenergy equations can be satisfied in the discrete level without explicitly\nsolving the total energy equation. The shock waves and discontinuities in the\nnumerical solution are stabilized by nonlinear filtering schemes. An\nenergy-consistent discretization of the filtering schemes is also derived by\nintroducing the viscous and resistive heating rates. The resulting\nenergy-consistent formulation can be implemented with the various kinds of\ncentral difference, nonlinear filtering, and time integration schemes. The\nsecond- and fifth-order schemes are implemented based on the proposed\nformulation. The conservation properties and the robustness of the present\nschemes are demonstrated via one- and two-dimensional numerical tests. The\nproposed schemes successfully handle the most stringent problems in extremely\nhigh Mach number and low beta conditions.\n"}
+{"abstract": "  The high-temperature superconducting cuprates are governed by intertwined\nspin, charge, and superconducting orders. While various state-of-the-art\nnumerical methods have demonstrated that these phases also manifest themselves\nin doped Hubbard models, they differ on which is the actual ground state.\nFinite cluster methods typically indicate that stripe order dominates while\nembedded quantum cluster methods, which access the thermodynamic limit by\ntreating long-range correlations with a dynamical mean field, conclude that\nsuperconductivity does. Here, we report the observation of fluctuating spin and\ncharge stripes in the doped single-band Hubbard model using a quantum Monte\nCarlo dynamical cluster approximation (DCA) method. By resolving both the\nfluctuating spin and charge orders using DCA, we demonstrate for the first time\nthat they survive in the doped Hubbard model in the thermodynamic limit. This\ndiscovery also provides a new opportunity to study the influence of fluctuating\nstripe correlations on the model's pairing correlations within a unified\nnumerical framework.\n"}
+{"abstract": "  Mispronunciation detection and diagnosis (MDD) is designed to identify\npronunciation errors and provide instructive feedback to guide non-native\nlanguage learners, which is a core component in computer-assisted pronunciation\ntraining (CAPT) systems. However, MDD often suffers from the data-sparsity\nproblem due to that collecting non-native data and the associated annotations\nis time-consuming and labor-intensive. To address this issue, we explore a\nfully end-to-end (E2E) neural model for MDD, which processes learners' speech\ndirectly based on raw waveforms. Compared to conventional hand-crafted acoustic\nfeatures, raw waveforms retain more acoustic phenomena and potentially can help\nneural networks discover better and more customized representations. To this\nend, our MDD model adopts a co-called SincNet module to take input a raw\nwaveform and covert it to a suitable vector representation sequence. SincNet\nemploys the cardinal sine (sinc) function to implement learnable bandpass\nfilters, drawing inspiration from the convolutional neural network (CNN). By\ncomparison to CNN, SincNet has fewer parameters and is more amenable to human\ninterpretation. Extensive experiments are conducted on the L2-ARCTIC dataset,\nwhich is a publicly-available non-native English speech corpus compiled for\nresearch on CAPT. We find that the sinc filters of SincNet can be adapted\nquickly for non-native language learners of different nationalities.\nFurthermore, our model can achieve comparable mispronunciation detection\nperformance in relation to state-of-the-art E2E MDD models that take input the\nstandard handcrafted acoustic features. Besides that, our model also provides\nconsiderable improvements on phone error rate (PER) and diagnosis accuracy.\n"}
+{"abstract": "  To achieve higher coding efficiency, Versatile Video Coding (VVC) includes\nseveral novel components, but at the expense of increasing decoder\ncomputational complexity. These technologies at a low bit rate often create\ncontouring and ringing effects on the reconstructed frames and introduce\nvarious blocking artifacts at block boundaries. To suppress those visual\nartifacts, the VVC framework supports four post-processing filter operations.\nThe interoperation of these filters introduces extra signaling bits and\neventually becomes overhead at higher resolution video processing. In this\npaper, a novel deep learning-based model is proposed for sample adaptive offset\n(SAO) nonlinear filtering operation and substantiated the merits of intra-inter\nframe quality enhancement. We introduced a variable filter size multi-scale CNN\n(MSCNN) to improve the denoising operation and incorporated strided\ndeconvolution for further computation improvement. We demonstrated that our\ndeconvolution model can effectively be trained by leveraging the high-frequency\nedge features learned in a parallel fashion using feature fusion and residual\nlearning. The simulation results demonstrate that the proposed method\noutperforms the baseline VVC method in BD-BR, BD-PSNR measurements and achieves\nan average of 3.762 % bit rate saving on the standard video test sequences.\n"}
+{"abstract": "  Autism spectrum disorder is a developmental disorder characterized by\nsignificant social, communication, and behavioral challenges. Individuals\ndiagnosed with autism, intellectual, and developmental disabilities (AUIDD)\ntypically require long-term care and targeted treatment and teaching. Effective\ntreatment of AUIDD relies on efficient and careful behavioral observations done\nby trained applied behavioral analysts (ABAs). However, this process\noverburdens ABAs by requiring the clinicians to collect and analyze data,\nidentify the problem behaviors, conduct pattern analysis to categorize and\npredict categorical outcomes, hypothesize responsiveness to treatments, and\ndetect the effects of treatment plans. Successful integration of digital\ntechnologies into clinical decision-making pipelines and the advancements in\nautomated decision-making using Artificial Intelligence (AI) algorithms\nhighlights the importance of augmenting teaching and treatments using novel\nalgorithms and high-fidelity sensors. In this article, we present an\nAI-Augmented Learning and Applied Behavior Analytics (AI-ABA) platform to\nprovide personalized treatment and learning plans to AUIDD individuals. By\ndefining systematic experiments along with automated data collection and\nanalysis, AI-ABA can promote self-regulative behavior using reinforcement-based\naugmented or virtual reality and other mobile platforms. Thus, AI-ABA could\nassist clinicians to focus on making precise data-driven decisions and increase\nthe quality of individualized interventions for individuals with AUIDD.\n"}
+{"abstract": "  A graph $G=(V,E)$ with geodesic distance $d(\\cdot,\\cdot)$ is said to be\nresolved by a non-empty subset $R$ of its vertices when, for all vertices $u$\nand $v$, if $d(u,r)=d(v,r)$ for each $r\\in R$, then $u=v$. The metric dimension\nof $G$ is the cardinality of its smallest resolving set. In this manuscript, we\npresent and investigate the notions of resolvability and metric dimension when\nthe geodesic distance is truncated with a certain threshold $k$; namely, we\nmeasure distances in $G$ using the metric $d_k(u,v):=\\min\\{d(u,v),k+1\\}$. We\ndenote the metric dimension of $G$ with respect to $d_k$ as $\\beta_k(G)$. We\nstudy the behavior of this quantity with respect to $k$ as well as the diameter\nof $G$. We also characterize the truncated metric dimension of paths and cycles\nas well as graphs with extreme metric dimension, including graphs of order $n$\nsuch that $\\beta_k(G)=n-2$ and $\\beta_k(G)=n-1$. We conclude with a study of\nvarious problems related to the truncated metric dimension of trees.\n"}
+{"abstract": "  We revisit the squeezed-limit non-Gaussianity in the single-field\nnon-attractor inflation models from the viewpoint of the cosmological soft\ntheorem. In the single-field attractor models, inflaton's trajectories with\ndifferent initial conditions effectively converge into a single trajectory in\nthe phase space, and hence there is only one \\emph{clock} degree of freedom\n(DoF) in the scalar part. Its long-wavelength perturbations can be absorbed\ninto the local coordinate renormalization and lead to the so-called\n\\emph{consistency relation} between $n$- and $(n+1)$-point functions. On the\nother hand, if the inflaton dynamics deviates from the attractor behavior, its\nlong-wavelength perturbations cannot necessarily be absorbed and the\nconsistency relation is expected not to hold any longer. In this work, we\nderive a formula for the squeezed bispectrum including the explicit correction\nto the consistency relation, as a proof of its violation in the non-attractor\ncases. First one must recall that non-attractor inflation needs to be followed\nby attractor inflation in a realistic case. Then, even if a specific\nnon-attractor phase is effectively governed by a single DoF of phase space\n(represented by the exact ultra-slow-roll limit) and followed by a single-DoF\nattractor phase, its transition phase necessarily involves two DoF in dynamics\nand hence its long-wavelength perturbations cannot be absorbed into the local\ncoordinate renormalization. Thus, it can affect local physics, even taking\naccount of the so-called \\emph{local observer effect}, as shown by the fact\nthat the bispectrum in the squeezed limit can go beyond the consistency\nrelation. More concretely, the observed squeezed bispectrum does not vanish in\ngeneral for long-wavelength perturbations exiting the horizon during a\nnon-attractor phase.\n"}
+{"abstract": "  Our environment, whether at work, in public spaces, or at home, is becoming\nmore connected, and increasingly responsive. Meal preparation even when it\ninvolves simply heating ready-made food can be perceived as a complex process\nfor people with disabilities. This research aimed to prototype, using a\nco-Design approach a Community Supported Appliance (CSA) by developing a Domain\nSpecific Language (DSL), precisely created for a semi-automated cooking\nprocess. The DSL was shaped and expressed in the idiom of the users and allowed\nthe CSA to support independence for users while performing daily cooking\nactivities.\n"}
+{"abstract": "  Nowadays, more and more clinical trials choose combinational agents as the\nintervention to achieve better therapeutic responses. However, dose-finding for\ncombinational agents is much more complicated than single agent as the full\norder of combination dose toxicity is unknown. Therefore, regular phase I\ndesigns are not able to identify the maximum tolerated dose (MTD) of\ncombinational agents. Motivated by such needs, plenty of novel phase I clinical\ntrial designs for combinational agents were proposed. With so many available\ndesigns, research that compare their performances, explore parameters' impacts,\nand provide recommendations is very limited. Therefore, we conducted a\nsimulation study to evaluate multiple phase I designs that proposed to identify\nsingle MTD for combinational agents under various scenarios. We also explored\ninfluences of different design parameters. In the end, we summarized the pros\nand cons of each design, and provided a general guideline in design selection.\n"}
+{"abstract": "  Erosion, as a key control of landslide dynamics, significantly increases the\ndestructive power by rapidly amplifying its volume, mobility and impact energy.\nMobility is directly linked to the threat posed by an erosive landslide. No\nclear-cut mechanical condition has been presented so far for when, how and how\nmuch energy the erosive landslide gains or loses, resulting in enhanced or\nreduced mobility. We pioneer a mechanical model for the energy budget of an\nerosive landslide that controls its mobility. A fundamentally new understanding\nis that the increased inertia due to the increased mass is related to an\nentrainment velocity. With this, the true inertia of an erosive landslide can\nbe ascertained, making a breakthrough in correctly determining the mobility of\nthe erosive landslide. Outstandingly, erosion velocity regulates the energy\nbudget and decides whether the landslide mobility will be enhanced or reduced.\nThis provides the first-ever explicit mechanical quantification of the state of\nerosional energy and a precise description of mobility. This addresses the\nlong-standing question of why many erosive landslides generate higher mobility,\nwhile others reduce mobility. By introducing three key concepts:\nerosion-velocity, entrainment-velocity and energy-velocity, we demonstrate that\nerosion and entrainment are essentially different processes. Landslides gain\nenergy and enhance mobility if the erosion velocity is greater than the\nentrainment velocity. We introduce two dimensionless numbers, mobility scaling\nand erosion number, delivering explicit measure of mobility. We establish a\nmechanism of landslide-propulsion providing the erosion-thrust to the\nlandslide. Analytically obtained velocity indicates that erosion controls the\nlandslide dynamics. We also present a full set of dynamical equations in\nconservative form which correctly includes the erosion induced net momentum\nproduction.\n"}
+{"abstract": "  We report fabrication of EuSb$_2$ single-crystalline films and investigation\nof their quantum transport. First-principles calculations demonstrate that\nEuSb$_2$ is a magnetic topological nodal-line semimetal protected by\nnonsymmorphic symmetry. Observed Shubnikov-de Haas oscillations with multiple\nfrequency components exhibit small effective masses and two-dimensional\nfield-angle dependence even in a 250 nm thick film, further suggesting possible\ncontributions of surface states. This finding of the high-mobility magnetic\ntopological semimetal will trigger further investigation of exotic quantum\ntransport phenomena by controlling magnetic order in topological semimetal\nfilms.\n"}
+{"abstract": "  Self-organization is frequently observed in active collectives, from ant\nrafts to molecular motor assemblies. General principles describing\nself-organization away from equilibrium have been challenging to identify. We\noffer a unifying framework that models the behavior of complex systems as\nlargely random, while capturing their configuration-dependent response to\nexternal forcing. This allows derivation of a Boltzmann-like principle for\nunderstanding and manipulating driven self-organization. We validate our\npredictions experimentally in shape-changing robotic active matter, and outline\na methodology for controlling collective behavior. Our findings highlight how\nemergent order depends sensitively on the matching between external patterns of\nforcing and internal dynamical response properties, pointing towards future\napproaches for design and control of active particle mixtures and\nmetamaterials.\n"}
+{"abstract": "  This study more complex digital platforms in early stages in the two-sided\nmarket to produce powerful network effects. In this study, I use Transfer\nEntropy to look for super users who connect hominids in different networks to\nachieve higher network effects in the digital platform in the two-sided market,\nwhich has recently become more complex. And this study also aims to redefine\nthe decision criteria of product managers by helping them define users with\nstronger network effects. With the development of technology, the structure of\nthe industry is becoming more difficult to interpret and the complexity of\nbusiness logic is increasing. This phenomenon is the biggest problem that makes\nit difficult for start-ups to challenge themselves. I hope this study will help\nproduct managers create new digital economic networks, enable them to make\nprioritized, data-driven decisions, and find users who can be the hub of the\nnetwork even in small products.\n"}
+{"abstract": "  The time division multiple access (TDMA) technique has been applied in\nautomotive multiple-input multiple-output (MIMO) radar. However, it suffers\nfrom the transmit energy loss, and as a result the parameter estimation\nperformance degradation when the number of transmit elements increases. To\ntackle these problem, a transmit beamspace (TB) Doppler division multiple\naccess (DDMA) approach is proposed. First, a phase modulation matrix with empty\nDoppler spectrum is introduced. By exploiting the empty Doppler spectrum, a\ntest function based on sequential detection is developed to mitigate the\nDoppler ambiguity in DDMA waveform. Then, a discrete Fourier transform\n(DFT)-based TB in slow-time is formed.The proposed method can achieve waveform\ndiversity in Doppler domain and generate a TB in slow-time that concentrates\nthe transmitted power in a fixed spatial region to improve the transmit energy\ndistribution for automotive MIMO radar, which is favored by medium/long range\nradar (MRR/LRR) applications. As compared to the conventional TDMA technique,\nthe proposed TB DDMA approach can fully exploit the transmission capabilities\nof all transmit elements to ensure that the emitted power is efficiently used\nand inherits easy implementation. Moreover, the proposed TB DDMA method avoids\nthe trade-off between the active time for each transmit antenna and the frame\ntime. Simulation results verify the effectiveness of the proposed TB DDMA\napproach for automotive MIMO radar.\n"}
+{"abstract": "  The pore-solid interface and its characteristics play a key role in chemical\ninteractions between minerals in the solid soil matrix and the liquid in pore\nspace and, consequently, solute transport in soils. Specific surface area\n(SSA), typically measured to characterize the pore-solid interface, depends not\nonly on the particles size distribution, but also particle shapes and surface\nroughness. In this note, we investigate the effects of surface roughness and\nprobing molecule size on SSA estimation, employ concepts from fractals, and\ntheoretically estimate specific surface area from particle size distribution\nand water retention curve (WRC). The former is used to characterize the\nparticle sizes and the latter to approximately quantify the pore-solid\ninterface roughness by determining the surface fractal dimension Ds. To\nevaluate our approach, we use five Washington and twenty one Arizona soils for\nwhich both particle size distributions and water retention curves were\naccurately measured over a wide range of particle sizes and matric potentials.\nComparison with the experiments show that the proposed method estimates the SSA\nreasonably well with root mean square error RMSE = 16.8 and 30.1 m2/g for the\nWashington and Arizona datasets, respectively.\n"}
+{"abstract": "  The present work proposes a deep-learning-based approach for the\nclassification of COVID-19 coughs from non-COVID-19 coughs and that can be used\nas a low-resource-based tool for early detection of the onset of such\nrespiratory diseases. The proposed system uses the ResNet-50 architecture, a\npopularly known Convolutional Neural Network (CNN) for image recognition tasks,\nfed with the log-Mel spectrums of the audio data to discriminate between the\ntwo types of coughs. For the training and validation of the proposed deep\nlearning model, this work utilizes the Track-1 dataset provided by the DiCOVA\nChallenge 2021 organizers. Additionally, to increase the number of\nCOVID-positive samples and to enhance variability in the training data, it has\nalso utilized a large open-source database of COVID-19 coughs collected by the\nEPFL CoughVid team. Our developed model has achieved an average validation AUC\nof 98.88%. Also, applying this model on the Blind Test Set released by the\nDiCOVA Challenge, the system has achieved a Test AUC of 75.91%, Test\nSpecificity of 62.50%, and Test Sensitivity of 80.49%. Consequently, this\nsubmission has secured 16th position in the DiCOVA Challenge 2021 leader-board.\n"}
+{"abstract": "  We introduce the generalized Heisenberg algebra appropriate for realizations\nof the $\\mathfrak{gl}(n)$ algebra. Linear realizations of the\n$\\mathfrak{gl}(n)$ algebra are presented and the corresponding star product,\ncoproduct of momenta and twist are constructed. The dual realization and dual\n$\\mathfrak{gl}(n)$ algebra are considered. Finally, we present a general\nrealization of the $\\mathfrak{gl}(n)$ algebra, the corresponding coproduct of\nmomenta and two classes of twists. These results can be applied to physical\ntheories on noncommutative spaces of the $\\mathfrak{gl}(n)$ type.\n"}
+{"abstract": "  This paper investigates the transient stability of power systems co-dominated\nby different types of grid-forming (GFM) devices. Synchronous generators (SGs\nand VSGs) and droop-controlled inverters are typical GFM devices in modern\npower systems. SGs/VSGs are able to provide inertia while droop-controlled\ninverters are generally inertialess. The transient stability of power systems\ndominated by homogeneous GFM devices has been extensively studied. Regarding\nthe hybrid system jointly dominated by heterogeneous GFM devices, the transient\nstability is rarely reported. This paper aims to fill this gap. It is found\nthat the synchronization behavior of the hybrid system can be described by a\nsecond-order motion equation, resembling the swing equation of SGs. Moreover,\ntwo significant differences from conventional power systems are discovered. The\nfirst is that the droop control dramatically enhances the damping effect,\ngreatly affecting the transient stability region. The second is that the\nfrequency state variable exhibits a jump at the moment of fault disturbances,\nthus impacting the post-fault initial-state location and stability assessment.\nThe underlying mechanism behind the two new characteristics is clarified and\nthe impact on the transient stability performance is analyzed and verified. The\nfindings provide new insights into transient stability of power systems hosting\nheterogeneous devices.\n"}
+{"abstract": "  We present DeepMVI, a deep learning method for missing value imputation in\nmultidimensional time-series datasets. Missing values are commonplace in\ndecision support platforms that aggregate data over long time stretches from\ndisparate sources, and reliable data analytics calls for careful handling of\nmissing data. One strategy is imputing the missing values, and a wide variety\nof algorithms exist spanning simple interpolation, matrix factorization methods\nlike SVD, statistical models like Kalman filters, and recent deep learning\nmethods. We show that often these provide worse results on aggregate analytics\ncompared to just excluding the missing data. DeepMVI uses a neural network to\ncombine fine-grained and coarse-grained patterns along a time series, and\ntrends from related series across categorical dimensions. After failing with\noff-the-shelf neural architectures, we design our own network that includes a\ntemporal transformer with a novel convolutional window feature, and kernel\nregression with learned embeddings. The parameters and their training are\ndesigned carefully to generalize across different placements of missing blocks\nand data characteristics. Experiments across nine real datasets, four different\nmissing scenarios, comparing seven existing methods show that DeepMVI is\nsignificantly more accurate, reducing error by more than 50% in more than half\nthe cases, compared to the best existing method. Although slower than simpler\nmatrix factorization methods, we justify the increased time overheads by\nshowing that DeepMVI is the only option that provided overall more accurate\nanalytics than dropping missing values.\n"}
+{"abstract": "  The quantum relative entropy is a measure of the distinguishability of two\nquantum states, and it is a unifying concept in quantum information theory:\nmany information measures such as entropy, conditional entropy, mutual\ninformation, and entanglement measures can be realized from it. As such, there\nhas been broad interest in generalizing the notion to further understand its\nmost basic properties, one of which is the data processing inequality. The\nquantum f-divergence of Petz is one generalization of the quantum relative\nentropy, and it also leads to other relative entropies, such as the Petz--Renyi\nrelative entropies. In this contribution, I introduce the optimized quantum\nf-divergence as a related generalization of quantum relative entropy. I prove\nthat it satisfies the data processing inequality, and the method of proof\nrelies upon the operator Jensen inequality, similar to Petz's original\napproach. Interestingly, the sandwiched Renyi relative entropies are particular\nexamples of the optimized f-divergence. Thus, one benefit of this approach is\nthat there is now a single, unified approach for establishing the data\nprocessing inequality for both the Petz--Renyi and sandwiched Renyi relative\nentropies, for the full range of parameters for which it is known to hold.\n"}
+{"abstract": "  Dynamics and textures of magnetic domain walls (DWs) may largely alter the\nelectronic behaviors in a Weyl semimetal system via emergent gauge fields.\nHowever, very little is known about even the basic properties of these domain\nwalls in Weyl materials. In this work, we imaged the spontaneous magnetization\nand magnetic susceptibility of a ferromagnetic (FM) Weyl semimetal CeAlSi using\nscanning SQUID microscopy. We observed the ferromagnetic DWs lined-up with the\n[100] direction (or other degenerate directions). We also discovered the\ncoexistence of stable and metastable domain phases, which arise likely due to\nmagnetoelastic and magnetostriction effects and are expected to be highly\ntunable with small strains. We applied an in-plane external field as the CeAlSi\nsample was cooled down to below the magnetic phase transition of 8.3K, showing\nthat the pattern of FM domains is strongly correlated with both the amplitude\nand the orientation of the external field even for weak fields of a few Gauss.\nThe area of stable domains increases with field and reaches maximum when the\nfield is parallel to the main crystallographic axes of the CeAlSi crystal. Our\nresults suggest that the manipulation of these heterogeneous phases can provide\na practical way to study the interplay between magnetism and electronic\nproperties in Weyl systems, and that these systems can even serve as a new\nplatform for magnetic sensors.\n"}
+{"abstract": "  This research focuses on predicting the demand for air taxi urban air\nmobility (UAM) services during different times of the day in various geographic\nregions of New York City using machine learning algorithms (MLAs). Several\nride-related factors (such as month of the year, day of the week and time of\nthe day) and weather-related variables (such as temperature, weather conditions\nand visibility) are used as predictors for four popular MLAs, namely, logistic\nregression, artificial neural networks, random forests, and gradient boosting.\nExperimental results suggest gradient boosting to consistently provide higher\nprediction performance. Specific locations, certain time periods and weekdays\nconsistently emerged as critical predictors.\n"}
+{"abstract": "  A long-lasting belief is that the gravitational stress by the moon would be\nresponsible for earthquakes because of causing a tidal deformation of Earth's\ncrust. Even worse, earthquakes are sometimes said to be correlated with\neclipses. We review the origin of this wrong statement and show that the idea\nis owed to a fallacious perception of coincidence. In ancient times the two\ncatastrophes were linked interpreting the announcement of Doomsday, while in\nmodern times a quasi-scientific essay disseminated such an interrelation\nshortly before the theory of tectonics.\n"}
+{"abstract": "  The need for open scientific knowledge graphs is ever increasing. While there\nare large repositories of open access articles and free publication indexes,\nthere are still few free knowledge graphs exposing citation networks, and often\ntheir coverage is partial. Consequently, most evaluation processes based on\ncitation counts rely on commercial citation databases. Things are changing\nthanks to the Initiative for Open Citations (I4OC, https://i4oc.org) and the\nInitiative for Open Abstracts (I4OA, https://i4oa.org), whose goal is to\ncampaign for scholarly publishers to open the reference lists and the other\nmetadata of their articles. This paper investigates the growth of the open\nbibliographic metadata and open citations in two scientific knowledge graphs,\nOpenCitations' COCI and Crossref, with an experiment on the Italian National\nScientific Qualification (NSQ), the National process for University Professor\nqualification which uses data from commercial indexes. We simulated the\nprocedure by only using such open data and explored similarities and\ndifferences with the official results. The outcomes of the experiment show that\nthe amount of open bibliographic metadata and open citation data currently\navailable in the two scientific knowledge graphs adopted is not yet enough for\nobtaining results similar to those provided using commercial databases.\n"}
+{"abstract": "  In this work, we analyze the creation of the discharge asymmetry and the\nconcomitant formation of the DC self-bias voltage in capacitively coupled radio\nfrequency plasmas driven by multi-frequency waveforms, as a function of the\nelectrode surface characteristics. For this latter, we consider and vary the\ncoefficients that characterize the elastic reflection of the electrons from the\nsurfaces and the ion-induced secondary electron yield. Our investigations are\nbased on Particle-in-Cell/Monte Carlo Collision simulations of the plasma and\non a model that aids the understanding of the computational results. Electron\nreflection from the electrodes is found to affect slightly the discharge\nasymmetry in the presence of multi-frequency excitation, whereas secondary\nelectrons cause distinct changes to the asymmetry of the plasma as a function\nof the phase angle between the harmonics of the driving voltage waveform and as\na function the number of these harmonics.\n"}
+{"abstract": "  End-to-end (E2E) modeling is advantageous for automatic speech recognition\n(ASR) especially for Japanese since word-based tokenization of Japanese is not\ntrivial, and E2E modeling is able to model character sequences directly. This\npaper focuses on the latest E2E modeling techniques, and investigates their\nperformances on character-based Japanese ASR by conducting comparative\nexperiments. The results are analyzed and discussed in order to understand the\nrelative advantages of long short-term memory (LSTM), and Conformer models in\ncombination with connectionist temporal classification, transducer, and\nattention-based loss functions. Furthermore, the paper investigates on\neffectivity of the recent training techniques such as data augmentation\n(SpecAugment), variational noise injection, and exponential moving average. The\nbest configuration found in the paper achieved the state-of-the-art character\nerror rates of 4.1%, 3.2%, and 3.5% for Corpus of Spontaneous Japanese (CSJ)\neval1, eval2, and eval3 tasks, respectively. The system is also shown to be\ncomputationally efficient thanks to the efficiency of Conformer transducers.\n"}
+{"abstract": "  We propose a new sampling algorithm combining two quite powerful ideas in the\nMarkov chain Monte Carlo literature -- adaptive Metropolis sampler and\ntwo-stage Metropolis-Hastings sampler. The proposed sampling method will be\nparticularly very useful for high-dimensional posterior sampling in Bayesian\nmodels with expensive likelihoods. In the first stage of the proposed\nalgorithm, an adaptive proposal is used based on the previously sampled states\nand the corresponding acceptance probability is computed based on an\napproximated inexpensive target density. The true expensive target density is\nevaluated while computing the second stage acceptance probability only if the\nproposal is accepted in the first stage. The adaptive nature of the algorithm\nguarantees faster convergence of the chain and very good mixing properties. On\nthe other hand, the two-stage approach helps in rejecting the bad proposals in\nthe inexpensive first stage, making the algorithm computationally efficient. As\nthe proposals are dependent on the previous states the chain loses its Markov\nproperty, but we prove that it retains the desired ergodicity property. The\nperformance of the proposed algorithm is compared with the existing algorithms\nin two simulated and two real data examples.\n"}
+{"abstract": "  The integration of behavioral phenomena into mechanistic models of cognitive\nfunction is a fundamental staple of cognitive science. Yet, researchers are\nbeginning to accumulate increasing amounts of data without having the temporal\nor monetary resources to integrate these data into scientific theories. We seek\nto overcome these limitations by incorporating existing machine learning\ntechniques into an open-source pipeline for the automated construction of\nquantitative models. This pipeline leverages the use of neural architecture\nsearch to automate the discovery of interpretable model architectures, and\nautomatic differentiation to automate the fitting of model parameters to data.\nWe evaluate the utility of these methods based on their ability to recover\nquantitative models of human information processing from synthetic data. We\nfind that these methods are capable of recovering basic quantitative motifs\nfrom models of psychophysics, learning and decision making. We also highlight\nweaknesses of this framework and discuss future directions for their\nmitigation.\n"}
+{"abstract": "  Many advances that have improved the robustness and efficiency of deep\nreinforcement learning (RL) algorithms can, in one way or another, be\nunderstood as introducing additional objectives, or constraints, in the policy\noptimization step. This includes ideas as far ranging as exploration bonuses,\nentropy regularization, and regularization toward teachers or data priors when\nlearning from experts or in offline RL. Often, task reward and auxiliary\nobjectives are in conflict with each other and it is therefore natural to treat\nthese examples as instances of multi-objective (MO) optimization problems. We\nstudy the principles underlying MORL and introduce a new algorithm,\nDistillation of a Mixture of Experts (DiME), that is intuitive and\nscale-invariant under some conditions. We highlight its strengths on standard\nMO benchmark problems and consider case studies in which we recast offline RL\nand learning from experts as MO problems. This leads to a natural algorithmic\nformulation that sheds light on the connection between existing approaches. For\noffline RL, we use the MO perspective to derive a simple algorithm, that\noptimizes for the standard RL objective plus a behavioral cloning term. This\noutperforms state-of-the-art on two established offline RL benchmarks.\n"}
+{"abstract": "  The first attempt is made to provide a quantitative theoretical\ninterpretation of the WASA-at-COSY experimental data on the basic double-pion\nproduction reactions $pn \\to d \\pi^0\\pi^0$ and $pn \\to d \\pi^+\\pi^-$ in the\nenergy region $T_p =1$ - $1.3$ GeV [P. Adlarson et al., Phys. Lett. B 721, 229\n(2013)]. The data are analyzed within a model based on production and decay of\nan intermediate $I(J^P)=0(3^+)$ dibaryon resonance $\\mathcal{D}_{03}$ (denoted\nalso as $d^*(2380)$). The observed decrease of the near-threshold enhancement\n(the so-called ABC effect) in the reaction $pn \\to d \\pi^+\\pi^-$ in comparison\nto that in the reaction $pn \\to d \\pi^0\\pi^0$ is explained (at least partially)\nto be due to isospin symmetry violation in the two-pion decay of an\nintermediate near-threshold scalar $\\sigma$ meson emitted from the\n$\\mathcal{D}_{03}$ dibaryon resonance under conditions of the partial chiral\nsymmetry restoration.\n"}
+{"abstract": "  Visual navigation is often cast as a reinforcement learning (RL) problem.\nCurrent methods typically result in a suboptimal policy that learns general\nobstacle avoidance and search behaviours. For example, in the target-object\nnavigation setting, the policies learnt by traditional methods often fail to\ncomplete the task, even when the target is clearly within reach from a human\nperspective. In order to address this issue, we propose to learn to imagine a\nlatent representation of the successful (sub-)goal state. To do so, we have\ndeveloped a module which we call Foresight Imagination (ForeSIT). ForeSIT is\ntrained to imagine the recurrent latent representation of a future state that\nleads to success, e.g. either a sub-goal state that is important to reach\nbefore the target, or the goal state itself. By conditioning the policy on the\ngenerated imagination during training, our agent learns how to use this\nimagination to achieve its goal robustly. Our agent is able to imagine what the\n(sub-)goal state may look like (in the latent space) and can learn to navigate\ntowards that state. We develop an efficient learning algorithm to train ForeSIT\nin an on-policy manner and integrate it into our RL objective. The integration\nis not trivial due to the constantly evolving state representation shared\nbetween both the imagination and the policy. We, empirically, observe that our\nmethod outperforms the state-of-the-art methods by a large margin in the\ncommonly accepted benchmark AI2THOR environment. Our method can be readily\nintegrated or added to other model-free RL navigation frameworks.\n"}
+{"abstract": "  We present a general theory of interpolation error estimates for smooth\nfunctions and inverse inequalities on anisotropic meshes. In our theory, the\nerror of interpolation is bound in terms of the diameter of a simplex and a\ngeometric parameter. In the two-dimensional case, our geometric parameter is\nequivalent to the circumradius of a triangle. In the three-dimensional case,\nour geometric parameter also represents the flatness of a tetrahedron. This\npaper also includes corrections to an error in \"General theory of interpolation\nerror estimates on anisotropic meshes\" (Japan Journal of Industrial and Applied\nMathematics, 38 (2021) 163-191), in which Theorem 2 was incorrect.\n"}
+{"abstract": "  The nonzero bulk viscosity signals breaking of the scale invariance. We\ndemonstrate that a disorder in two-dimensional noninteracting electron gas in a\nperpendicular magnetic field results in the nonzero disorder-averaged bulk\nviscosity. We derive analytic expression for the bulk viscosity within the\nself-consistent Born approximation. This residual bulk viscosity provides the\nlower bound for the bulk viscosity of 2D interacting electrons at low enough\ntemperatures.\n"}
+{"abstract": "  Cochlear implants (CIs) are implantable medical devices that can restore the\nhearing sense of people suffering from profound hearing loss. The CI uses a set\nof electrode contacts placed inside the cochlea to stimulate the auditory nerve\nwith current pulses. The exact location of these electrodes may be an important\nparameter to improve and predict the performance with these devices. Currently\nthe methods used in clinics to characterize the geometry of the cochlea as well\nas to estimate the electrode positions are manual, error-prone and time\nconsuming. We propose a Markov random field (MRF) model for CI electrode\nlocalization for cone beam computed tomography (CBCT) data-sets. Intensity and\nshape of electrodes are included as prior knowledge as well as distance and\nangles between contacts. MRF inference is based on slice sampling particle\nbelief propagation and guided by several heuristics. A stochastic search finds\nthe best maximum a posteriori estimation among sampled MRF realizations. We\nevaluate our algorithm on synthetic and real CBCT data-sets and compare its\nperformance with two state of the art algorithms. An increase of localization\nprecision up to 31.5% (mean), or 48.6% (median) respectively, on real CBCT\ndata-sets is shown.\n"}
+{"abstract": "  We investigate the $\\Lambda$-polytopes, a convex-linear structure recently\ndefined and applied to the classical simulation of quantum computation with\nmagic states by sampling. There is one such polytope, $\\Lambda_n$, for every\nnumber $n$ of qubits. We establish two properties of the family $\\{\\Lambda_n,\nn\\in \\mathbb{N}\\}$, namely (i) Any extremal point (vertex) $A_\\alpha \\in\n\\Lambda_m$ can be used to construct vertices in $\\Lambda_n$, for all $n>m$.\n(ii) For vertices obtained through this mapping, the classical simulation of\nquantum computation with magic states can be efficiently reduced to the\nclassical simulation based on the preimage $A_\\alpha$. In addition, we describe\na new class of vertices in $\\Lambda_2$ which is outside the known\nclassification. While the hardness of classical simulation remains an open\nproblem for most extremal points of $\\Lambda_n$, the above results extend\nefficient classical simulation of quantum computations beyond the presently\nknown range.\n"}
+{"abstract": "  GAN inversion aims to invert a given image back into the latent space of a\npretrained GAN model, for the image to be faithfully reconstructed from the\ninverted code by the generator. As an emerging technique to bridge the real and\nfake image domains, GAN inversion plays an essential role in enabling the\npretrained GAN models such as StyleGAN and BigGAN to be used for real image\nediting applications. Meanwhile, GAN inversion also provides insights on the\ninterpretation of GAN's latent space and how the realistic images can be\ngenerated. In this paper, we provide an overview of GAN inversion with a focus\non its recent algorithms and applications. We cover important techniques of GAN\ninversion and their applications to image restoration and image manipulation.\nWe further elaborate on some trends and challenges for future directions.\n"}
+{"abstract": "  Non-Maximum Suppression (NMS) is essential for object detection and affects\nthe evaluation results by incorporating False Positives (FP) and False\nNegatives (FN), especially in crowd occlusion scenes. In this paper, we raise\nthe problem of weak connection between the training targets and the evaluation\nmetrics caused by NMS and propose a novel NMS-Loss making the NMS procedure can\nbe trained end-to-end without any additional network parameters. Our NMS-Loss\npunishes two cases when FP is not suppressed and FN is wrongly eliminated by\nNMS. Specifically, we propose a pull loss to pull predictions with the same\ntarget close to each other, and a push loss to push predictions with different\ntargets away from each other. Experimental results show that with the help of\nNMS-Loss, our detector, namely NMS-Ped, achieves impressive results with Miss\nRate of 5.92% on Caltech dataset and 10.08% on CityPersons dataset, which are\nboth better than state-of-the-art competitors.\n"}
+{"abstract": "  For abelian surfaces of Picard rank 1, we perform explicit computations of\nthe cohomological rank functions of the ideal sheaf of one point, and in\nparticular of the basepoint-freeness threshold. Our main tool is the relation\nbetween cohomological rank functions and Bridgeland stability. In virtue of\nrecent results of Caucci and Ito, these computations provide new information on\nthe syzygies of polarized abelian surfaces.\n"}
+{"abstract": "  The Zakharov system in dimension $d\\leqslant 3$ is shown to be locally\nwell-posed in Sobolev spaces $H^s \\times H^l$, extending the previously known\nresult. We construct new solution spaces by modifying the $X^{s,b}$ spaces,\nspecifically by introducing temporal weights. We use contraction mapping\nprinciple to prove local well-posedness in the same. The result obtained is\nsharp up to endpoints.\n"}
+{"abstract": "  A common trait involving the opinion dynamics in social networks is an anchor\non interacting network to characterize the opinion formation process among\nparticipating social actors, such as information flow, cooperative and\nantagonistic influence, etc. Nevertheless, interacting networks are generally\npublic for social groups, as well as other individuals who may be interested\nin. This blocks a more precise interpretation of the opinion formation process\nsince social actors always have complex feeling, motivation and behavior, even\nbeliefs that are personally private. In this paper, we formulate a general\nconfiguration on describing how individual's opinion evolves in a distinct\nfashion. It consists of two functional networks: interacting network and\nappraisal network. Interacting network inherits the operational properties as\nDeGroot iterative opinion pooling scheme while appraisal network, forming a\nbelief system, quantifies certain cognitive orientation to interested\nindividuals' beliefs, over which the adhered attitudes may have the potential\nto be antagonistic. We explicitly show that cooperative appraisal network\nalways leads to consensus in opinions. Antagonistic appraisal network, however,\ncauses opinion cluster. It is verified that antagonistic appraisal network\naffords to guarantee consensus by imposing some extra restrictions. They hence\nbridge a gap between the consensus and the clusters in opinion dynamics. We\nfurther attain a gauge on the appraisal network by means of the random convex\noptimization approach. Moreover, we extend our results to the case of mutually\ninterdependent issues.\n"}
+{"abstract": "  The multiple Birkhoff recurrence theorem states that for any $d\\in\\mathbb N$,\nevery system $(X,T)$ has a multiply recurrent point $x$, i.e. $(x,x,\\ldots, x)$\nis recurrent under $\\tau_d=:T\\times T^2\\times \\ldots \\times T^d$. It is natural\nto ask if there always is a multiply minimal point, i.e. a point $x$ such that\n$(x,x,\\ldots,x)$ is $\\tau_d$-minimal. A negative answer is presented in this\npaper via studying the horocycle flows.\n  However, it is shown that for any minimal system $(X,T)$ and any non-empty\nopen set $U$, there is $x\\in U$ such that $\\{n\\in{\\mathbb Z}: T^nx\\in U,\n\\ldots, T^{dn}x\\in U\\}$ is piecewise syndetic; and that for a PI minimal\nsystem, any $M$-subsystem of $(X^d, \\tau_d)$ is minimal.\n"}
+{"abstract": "  We generalize the Hasse invariant of local class field theory to the tame\nBrauer group of a higher dimensional local field, and use it to study the\narithmetic of central simple algebras over such fields, which are given {\\it a\npriori} as tensor products of standard cyclic algebras.\n  We also compute the tame Brauer dimension (or {\\it period-index bound}) and\nthe cyclic length of a general henselian-valued field of finite rank and finite\nresidue field.\n"}
+{"abstract": "  A waveform model for the eccentric binary black holes named SEOBNRE has been\nused to analyze the LIGO-Virgo's gravitational wave data by several groups. The\naccuracy of this model has been validated by comparing it with numerical\nrelativity. However, SEOBNRE is a time-domain model, and the efficiency for\ngenerating waveforms is a bottleneck in data analysis. To overcome this\ndisadvantage, we offer a reduced-order surrogate model for eccentric binary\nblack holes based on the SEOBNRE waveforms. This surrogate model (SEOBNRE\\_S)\ncan simulate the complete inspiral-merger-ringdown waves with enough accuracy,\ncovering eccentricities from 0 to 0.25 (0.1), and mass ratio from 1:1 to 5:1\n(2:1) for nonspinning (spinning) binaries. The speed of waveform generation is\naccelerated about $10^2 \\sim 10^3$ times than the original SEOBNRE model.\nTherefore SEOBNRE\\_S could be helpful in the analysis of LIGO data to find\npotential eccentricities.\n"}
+{"abstract": "  In the recent paper arXiv:1807.02721, B. Lawrence and A. Venkatesh develop a\nmethod of proving finiteness theorems in arithmetic geometry by studying the\ngeometry of families over a base variety. Their results include a new proof of\nboth the $S$-unit theorem and Faltings' theorem, obtained by constructing and\nstudying suitable abelian-by-finite families over\n$\\mathbb{P}^1\\setminus\\{0,1,\\infty\\}$ and over an arbitrary curve of genus\n$\\geq 2$ respectively. In this paper, we apply this strategy to reprove\nSiegel's theorem: we construct an abelian-by-finite family on a punctured\nelliptic curve to prove finiteness of $S$-integral points on elliptic curves.\n"}
+{"abstract": "  We have adapted the Vera C. Rubin Observatory Legacy Survey of Space and Time\n(LSST) Science Pipelines to process data from the Gravitational-Wave Optical\nTransient Observer (GOTO) prototype. In this paper, we describe how we used the\nRubin Observatory LSST Science Pipelines to conduct forced photometry\nmeasurements on nightly GOTO data. By comparing the photometry measurements of\nsources taken on multiple nights, we find that the precision of our photometry\nis typically better than 20~mmag for sources brighter than 16 mag. We also\ncompare our photometry measurements against colour-corrected PanSTARRS\nphotometry, and find that the two agree to within 10~mmag (1$\\sigma$) for\nbright (i.e., $\\sim14^{\\rm th}$~mag) sources to 200~mmag for faint (i.e.,\n$\\sim18^{\\rm th}$~mag) sources. Additionally, we compare our results to those\nobtained by GOTO's own in-house pipeline, {\\sc gotophoto}, and obtain similar\nresults. Based on repeatability measurements, we measure a $5\\sigma$ L-band\nsurvey depth of between 19 and 20 magnitudes, depending on observing\nconditions. We assess, using repeated observations of non-varying standard SDSS\nstars, the accuracy of our uncertainties, which we find are typically\noverestimated by roughly a factor of two for bright sources (i.e., $<15^{\\rm\nth}$~mag), but slightly underestimated (by roughly a factor of 1.25) for\nfainter sources ($>17^{\\rm th}$~mag). Finally, we present lightcurves for a\nselection of variable sources, and compare them to those obtained with the\nZwicky Transient Factory and GAIA. Despite the Rubin Observatory LSST Science\nPipelines still undergoing active development, our results show that they are\nalready delivering robust forced photometry measurements from GOTO data.\n"}
+{"abstract": "  We consider the problem of an inextensible but flexible fiber advected by a\nsteady chaotic flow, and ask the simple question whether the fiber can\nspontaneously knot itself. Using a 1D Cosserat model, a simple local viscous\ndrag model and discrete contact forces, we explore the probability of finding\nknots at any given time when the fiber is interacting with the ABC class of\nflows. The bending rigidity is shown to have a marginal effect compared to that\nof increasing the fiber length. Complex knots are formed up to 11 crossings,\nbut some knots are more probable than others. The finite-time Lyapunov exponent\nof the flow is shown to have a positive effect on the knot probability.\nFinally, contact forces appear to be crucial since knotted configurations can\nremain stable for times much longer than the turnover time of the flow,\nsomething that is not observed when the fiber can freely cross itself.\n"}
+{"abstract": "  We give a survey for the results in [Yeu20a, Yeu20b, Yeu20c], which attempts\nto relate the derived categories under general classes of flips and flops. We\nindicate how the approach fails because of what appears to be a formal problem.\nWe give some ideas, and record some failed attempts, to fix this problem. We\nalso present some new examples.\n"}
+{"abstract": "  Motivated by the need for decentralized learning, this paper aims at\ndesigning a distributed algorithm for solving nonconvex problems with general\nlinear constraints over a multi-agent network. In the considered problem, each\nagent owns some local information and a local variable for jointly minimizing a\ncost function, but local variables are coupled by linear constraints. Most of\nthe existing methods for such problems are only applicable for convex problems\nor problems with specific linear constraints. There still lacks a distributed\nalgorithm for such problems with general linear constraints and under nonconvex\nsetting. In this paper, to tackle this problem, we propose a new algorithm,\ncalled \"proximal dual consensus\" (PDC) algorithm, which combines a proximal\ntechnique and a dual consensus method. We build the theoretical convergence\nconditions and show that the proposed PDC algorithm can converge to an\n$\\epsilon$-Karush-Kuhn-Tucker solution within $\\mathcal{O}(1/\\epsilon)$\niterations. For computation reduction, the PDC algorithm can choose to perform\ncheap gradient descent per iteration while preserving the same order of\n$\\mathcal{O}(1/\\epsilon)$ iteration complexity. Numerical results are presented\nto demonstrate the good performance of the proposed algorithms for solving a\nregression problem and a classification problem over a network where agents\nhave only partial observations of data features.\n"}
+{"abstract": "  Fall prevalence is high among elderly people, which is challenging due to the\nsevere consequences of falling. This is why rapid assistance is a critical\ntask. Ambient assisted living (AAL) uses recent technologies such as 5G\nnetworks and the internet of medical things (IoMT) to address this research\narea. Edge computing can reduce the cost of cloud communication, including high\nlatency and bandwidth use, by moving conventional healthcare services and\napplications closer to end-users. Artificial intelligence (AI) techniques such\nas deep learning (DL) have been used recently for automatic fall detection, as\nwell as supporting healthcare services. However, DL requires a vast amount of\ndata and substantial processing power to improve its performance for the IoMT\nlinked to the traditional edge computing environment. This research proposes an\neffective fall detection framework based on DL algorithms and mobile edge\ncomputing (MEC) within 5G wireless networks, the aim being to empower\nIoMT-based healthcare applications. We also propose the use of a deep gated\nrecurrent unit (DGRU) neural network to improve the accuracy of existing\nDL-based fall detection methods. DGRU has the advantage of dealing with\ntime-series IoMT data, and it can reduce the number of parameters and avoid the\nvanishing gradient problem. The experimental results on two public datasets\nshow that the DGRU model of the proposed framework achieves higher accuracy\nrates compared to the current related works on the same datasets.\n"}
+{"abstract": "  Semantic image segmentation aims to obtain object labels with precise\nboundaries, which usually suffers from overfitting. Recently, various data\naugmentation strategies like regional dropout and mix strategies have been\nproposed to address the problem. These strategies have proved to be effective\nfor guiding the model to attend on less discriminative parts. However, current\nstrategies operate at the image level, and objects and the background are\ncoupled. Thus, the boundaries are not well augmented due to the fixed semantic\nscenario. In this paper, we propose ObjectAug to perform object-level\naugmentation for semantic image segmentation. ObjectAug first decouples the\nimage into individual objects and the background using the semantic labels.\nNext, each object is augmented individually with commonly used augmentation\nmethods (e.g., scaling, shifting, and rotation). Then, the black area brought\nby object augmentation is further restored using image inpainting. Finally, the\naugmented objects and background are assembled as an augmented image. In this\nway, the boundaries can be fully explored in the various semantic scenarios. In\naddition, ObjectAug can support category-aware augmentation that gives various\npossibilities to objects in each category, and can be easily combined with\nexisting image-level augmentation methods to further boost performance.\nComprehensive experiments are conducted on both natural image and medical image\ndatasets. Experiment results demonstrate that our ObjectAug can evidently\nimprove segmentation performance.\n"}
+{"abstract": "  An approximate but straight forward projection method to molecular many\nalpha-particle states is proposed and the overlap to the shell model space is\ndetermined. The resulting space is in accordance with the shell model, but\nstill contains states which are not completely symmetric under permutations of\nthe alpha-particles, which is one reason to call the construction\nsemi-microscopic. A new contribution is the construction of the 6- and\n7-$\\alpha$-particle spaces. The errors of the method propagate toward larger\nnumber of alpha-particles and larger shell excitations. In order to show the\neffectiveness of the construction proposed, the so obtained spaces are applied,\nwithin an algebraic cluster model, to $^{20}$Ne, $^{24}$Mg and $^{28}$Si, each\ntreated as a many-alpha-particle system. Former results on $^{12}$C and\n$^{16}$O are resumed.\n"}
+{"abstract": "  We report the discovery of a sextuply-eclipsing sextuple star system from\nTESS data, TIC 168789840, also known as TYC 7037-89-1, the first known sextuple\nsystem consisting of three eclipsing binaries. The target was observed in\nSectors 4 and 5 during Cycle 1, with lightcurves extracted from TESS Full Frame\nImage data. It was also previously observed by the WASP survey and ASAS-SN. The\nsystem consists of three gravitationally-bound eclipsing binaries in a\nhierarchical structure of an inner quadruple system with an outer binary\nsubsystem. Follow-up observations from several different observatories were\nconducted as a means of determining additional parameters. The system was\nresolved by speckle interferometry with a 0.\"42 separation between the inner\nquadruple and outer binary, inferring an estimated outer period of ~2 kyr. It\nwas determined that the fainter of the two resolved components is an 8.217 day\neclipsing binary, which orbits the inner quadruple that contains two eclipsing\nbinaries with periods of 1.570 days and 1.306 days. MCMC analysis of the\nstellar parameters has shown that the three binaries of TIC 168789840 are\n\"triplets\", as each binary is quite similar to the others in terms of mass,\nradius, and Teff. As a consequence of its rare composition, structure, and\norientation, this object can provide important new insight into the formation,\ndynamics, and evolution of multiple star systems. Future observations could\nreveal if the intermediate and outer orbital planes are all aligned with the\nplanes of the three inner eclipsing binaries.\n"}
+{"abstract": "  An $L^2$ version of the classical Denjoy-Carleman theorem regarding\nquasi-analytic functions was proved by P. Chernoff on $\\mathbb R^n$ using\niterates of the Laplacian. We give a simple proof of this theorem which\ngeneralizes the result on $\\mathbb R^n$ for any $p\\in [1, 2]$. We then extend\nthis result to Riemannian symmetric spaces of compact and noncompact type for\n$K$-biinvariant functions.\n"}
+{"abstract": "  Consider the complete graph on $n$ vertices. To each vertex assign an Ising\nspin that can take the values $-1$ or $+1$. Each spin $i \\in [n]=\\{1,2,\\dots,\nn\\}$ interacts with a magnetic field $h \\in [0,\\infty)$, while each pair of\nspins $i,j \\in [n]$ interact with each other at coupling strength $n^{-1}\nJ(i)J(j)$, where $J=(J(i))_{i \\in [n]}$ are i.i.d. non-negative random\nvariables drawn from a prescribed probability distribution $\\mathcal{P}$. Spins\nflip according to a Metropolis dynamics at inverse temperature $\\beta \\in\n(0,\\infty)$. We show that there are critical thresholds $\\beta_c$ and\n$h_c(\\beta)$ such that, in the limit as $n\\to\\infty$, the system exhibits\nmetastable behaviour if and only if $\\beta \\in (\\beta_c, \\infty)$ and $h \\in\n[0,h_c(\\beta))$. Our main result are sharp asymptotics, up to a multiplicative\nerror $1+o_n(1)$, of the average crossover time from any metastable state to\nthe set of states with lower free energy. We use standard techniques of the\npotential-theoretic approach to metastability. The leading order term in the\nasymptotics does not depend on the realisation of $J$, while the correction\nterms do. The leading order of the correction term is $\\sqrt{n}$ times a\ncentred Gaussian random variable with a complicated variance depending on\n$\\beta,h,\\mathcal{P}$ and on the metastable state. The critical thresholds\n$\\beta_c$ and $h_c(\\beta)$ depend on $\\mathcal{P}$, and so does the number of\nmetastable states. We derive an explicit formula for $\\beta_c$ and identify\nsome properties of $\\beta \\mapsto h_c(\\beta)$. Interestingly, the latter is not\nnecessarily monotone, meaning that the metastable crossover may be re-entrant.\n"}
+{"abstract": "  Many numerical schemes for hyperbolic systems require a piecewise polynomial\nreconstruction of the cell averaged values, and to simulate perturbed steady\nstates accurately we require a so called 'well balanced' reconstruction scheme.\nFor the shallow water system this involves reconstructing in surface elevation,\nto which modifications must be made as the fluid depth becomes small to ensure\npositivity.\n  We investigate the scheme proposed in Skevington (2021) though numerical\nexperiments, demonstrating its ability to resolve steady and near steady states\nat high accuracy. We also present a modification to the scheme which enables\nthe resolution of slowly moving shocks and dam break problems without\ncompromising the well balanced property.\n"}
+{"abstract": "  Adaptable, reconfigurable and programmable are key functionalities for the\nnext generation of silicon-based photonic processors, neural and quantum\nnetworks. Phase change technology offers proven non-volatile electronic\nprogrammability, however the materials used to date have shown prohibitively\nhigh optical losses which are incompatible with integrated photonic platforms.\nHere, we demonstrate the capability of the previously unexplored material\nSb$_2$Se$_3$ for ultralow-loss programmable silicon photonics. The favorable\ncombination of large refractive index contrast and ultralow losses seen in\nSb$_2$Se$_3$ facilitates an unprecedented optical phase control exceeding\n10$\\pi$ radians in a Mach-Zehnder interferometer. To demonstrate full control\nover the flow of light, we introduce nanophotonic digital patterning as a\nconceptually new approach at a footprint orders of magnitude smaller than state\nof the art interferometer meshes. Our approach enables a wealth of\npossibilities in high-density reconfiguration of optical functionalities on\nsilicon chip.\n"}
+{"abstract": "  We present a new stochastic particle system on networks which describes the\nflocking behavior and pattern formation. More precisely, we consider\nCucker-Smale particles with decentralized formation control and multiplicative\nnoises on symmetric and connected networks. Under suitable assumptions on the\ninitial configurations and the network structure, we establish time-asymptotic\nstochastic flocking behavior and pattern formation of solutions for the\nproposed stochastic particle system. Our approach is based on the Lyapunov\nfunctional energy estimates, and it does not require any spectral information\nof the graph associated with the network structure.\n"}
+{"abstract": "  This article studies descent theory in the setting of Berkovich spaces. We\ngive sufficient conditions for a given fibered category over the category of\nk-affinoid algebras to be a stack for the Berkovich analogue of the\nfaithfully-flat topology. We give some applications to the faithfully flat\ndescent of morphisms and show that some descent data are always effective. We\nalso show that the property of being algebraic for a morphism between the\nanalytification of two schemes is a local property for the faithfully-flat\ntopology.\n"}
+{"abstract": "  We prove that a compact polyhedron $P$ collapses to a subpolyhedron $Q$ if\nand only if there exists a piecewise linear free deformation retraction of $P$\nonto $Q$.\n"}
+{"abstract": "  In this paper we give an explicit expression for a star product on the super\nMinkowski space written in the supertwistor formalism. The big cell of the\nsuper Grassmannian Gr(2|0, 4|1) is identified with the chiral, super Minkowki\nspace. The super Grassmannian is an homogeneous space under the action of the\ncomplexification SL(4|1) of SU(2,2|1), the superconformal group in dimension 4,\nsignature (1,3) and supersymmetry N=1. The quantization is done by substituting\nthe groups and homogeneous spaces by their quantum deformed counterparts. The\ncalculations are done in Manin's formalism. When we restrict to the big cell we\ncan compute explicitly an expression for the super star product in the\nMinkowski superspace associated to this deformation and the choice of a certain\nbasis of monomials.\n"}
+{"abstract": "  We prove that the topological type of a normal surface singularity $(X,0)$\nprovides finite bounds for the multiplicity and polar multiplicity of $(X,0)$,\nas well as for the combinatorics of the families of generic hyperplane sections\nand of polar curves of the generic plane projections of $(X,0)$. A key\ningredient in our proof is a topological bound of the growth of the Mather\ndiscrepancies of $(X,0)$, which allows us to bound the number of point blowups\nnecessary to achieve factorization of any resolution of $(X,0)$ through its\nNash transform. This fits in the program of polar explorations, the quest to\ndetermine the generic polar variety of a singular surface germ, to which the\nfinal part of the paper is devoted.\n"}
+{"abstract": "  Fast radio bursts (FRBs) are very short and bright transients visible over\nextragalactic distances. The radio pulse undergoes dispersion caused by free\nelectrons along the line of sight, most of which are associated with the\nlarge-scale structure (LSS). The total dispersion measure therefore increases\nwith the line of sight and provides a distance estimate to the source. We\npresent the first measurement of the Hubble constant using the dispersion\nmeasure -- redshift relation of FRBs with identified host counterpart and\ncorresponding redshift information. A sample of nine currently available FRBs\nyields a constraint of $H_0 = 62.3 \\pm 9.1 \\,\\rm{km}\n\\,\\rm{s}^{-1}\\,\\rm{Mpc}^{-1}$, accounting for uncertainty stemming from the\nLSS, host halo and Milky Way contributions to the observed dispersion measure.\nThe main current limitation is statistical, and we estimate that a few hundred\nevents with corresponding redshifts are sufficient for a per cent measurement\nof $H_0$. This is a number well within reach of ongoing FRB searches. We\nperform a forecast using a realistic mock sample to demonstrate that a\nhigh-precision measurement of the expansion rate is possible without relying on\nother cosmological probes. FRBs can therefore arbitrate the current tension\nbetween early and late time measurements of $H_0$ in the near future.\n"}
+{"abstract": "  Sentence-level relation extraction (RE) aims at identifying the relationship\nbetween two entities in a sentence. Many efforts have been devoted to this\nproblem, while the best performing methods are still far from perfect. In this\npaper, we revisit two problems that affect the performance of existing RE\nmodels, namely entity representation and noisy or ill-defined labels. Our\nimproved baseline model, incorporated with entity representations with typed\nmarkers, achieves an F1 of 74.6% on TACRED, significantly outperforms previous\nSOTA methods. Furthermore, the presented new baseline achieves an F1 of 91.1%\non the refined Re-TACRED dataset, demonstrating that the pre-trained language\nmodels achieve unexpectedly high performance on this task. We release our code\nto the community for future research.\n"}
+{"abstract": "  MnBi$_2$Te$_4$ (MBT) is a promising antiferromagnetic topological insulator\nwhose films provide access to novel and technologically important topological\nphases, including quantum anomalous Hall states and axion insulators. MBT\ndevice behavior is expected to be sensitive to the various collinear and\nnon-collinear magnetic phases that are accessible in applied magnetic fields.\nHere, we use classical Monte Carlo simulations and electronic structure models\nto calculate the ground state magnetic phase diagram as well as topological and\noptical properties for few layer films with thicknesses up to six septuple\nlayers. Using magnetic interaction parameters appropriate for MBT, we find that\nit is possible to prepare a variety of different magnetic stacking sequences,\nsome of which have sufficient symmetry to disallow non-reciprocal optical\nresponse and Hall transport coefficients. Other stacking arrangements do yield\nlarge Faraday and Kerr signals, even when the ground state Chern number\nvanishes.\n"}
+{"abstract": "  In experiments that study social phenomena, such as peer influence or herd\nimmunity, the treatment of one unit may influence the outcomes of others. Such\n\"interference between units\" violates traditional approaches for causal\ninference, so that additional assumptions are required to model the underlying\nsocial mechanism. We propose an approach that requires no such assumptions,\nallowing for interference that is both unmodeled and strong, with confidence\nintervals found using only the randomization of treatment. Additionally, the\napproach allows for the usage of regression, matching, or weighting, as may\nbest fit the application at hand. Inference is done by bounding the\ndistribution of the estimation error over all possible values of the unknown\ncounterfactual, using an integer program. Examples are shown using a vaccine\ntrial and two experiments investigating social influence.\n"}
+{"abstract": "  In this Letter, we explore nonrelativistic string solutions in various\nsubsectors of the $ SU(1,2|3) $ SMT strings that correspond to different spin\ngroups and satisfy the respective BPS bounds. In particular, we carry out an\nexplicit analysis on rotating string solutions in the light of recently\nproposed SMT limits. We explore newly constructed SMT limits of type IIB\n(super) strings on $ AdS_5 \\times S^5 $ and estimate the corresponding leading\norder stringy corrections near the respective BPS bounds.\n"}
+{"abstract": "  We introduce a sequential learning algorithm to address a robust controller\ntuning problem, which in effect, finds (with high probability) a candidate\nsolution satisfying the internal performance constraint to a chance-constrained\nprogram which has black-box functions. The algorithm leverages ideas from the\nareas of randomised algorithms and ordinal optimisation, and also draws\ncomparisons with the scenario approach; these have all been previously applied\nto finding approximate solutions for difficult design problems. By exploiting\nstatistical correlations through black-box sampling, we formally prove that our\nalgorithm yields a controller meeting the prescribed probabilistic performance\nspecification. Additionally, we characterise the computational requirement of\nthe algorithm with a probabilistic lower bound on the algorithm's stopping\ntime. To validate our work, the algorithm is then demonstrated for tuning model\npredictive controllers on a diesel engine air-path across a fleet of vehicles.\nThe algorithm successfully tuned a single controller to meet a desired tracking\nerror performance, even in the presence of the plant uncertainty inherent\nacross the fleet. Moreover, the algorithm was shown to exhibit a sample\ncomplexity comparable to the scenario approach.\n"}
+{"abstract": "  We classify all fundamental electrically charged thin shells in general\nrelativity, i.e., static spherically symmetric perfect fluid thin shells with a\nMinkowski spacetime interior and a Reissner-Nordstr\\\"om spacetime exterior,\ncharacterized by the spacetime mass and electric charge. The fundamental shell\ncan exist in three states, nonextremal, extremal, and overcharged. The\nnonextremal state allows the shell to be located such that its radius can be\noutside its own gravitational radius, or can be inside its own Cauchy radius.\nThe extremal state allows the shell to be located such that its radius can be\noutside its own gravitational radius, or can be inside it. The overcharged\nstate allows the shell to be located anywhere. There is a further division, one\nhas to specify the orientation of the shell, i.e., whether the normal out of\nthe shell points toward increasing or decreasing radii. There is still a\nsubdivision in the extremal state when the shell is at the gravitational\nradius, in that the shell can approach it from above or from below. The shell\nis assumed to be composed of an electrically charged perfect fluid, and the\nenergy conditions are tested. Carter-Penrose diagrams are drawn for the shell\nspacetimes. There are fourteen cases in the classification of the fundamental\nshells, namely, nonextremal star shells, nonextremal tension shell black holes,\nnonextremal tension shell regular and nonregular black holes, nonextremal\ncompact shell naked singularities, Majumdar-Papapetrou star shells, extremal\ntension shell singularities, extremal tension shell regular and nonregular\nblack holes, Majumdar-Papapetrou compact shell naked singularities,\nMajumdar-Papapetrou shell quasiblack holes, extremal null shell quasinonblack\nholes, extremal null shell singularities, Majumdar-Papapetrou null shell\nsingularities, overcharged star shells, and overcharged compact shell naked\nsingularities.\n"}
+{"abstract": "  The Stochastic Loewner equation, introduced by Schramm, gives us a powerful\nway to study and classify critical random curves and interfaces in\ntwo-dimensional statistical mechanics. New kind of stochastic Loewner equation,\ncalled fractional stochastic Loewner evolution (FSLE), has been proposed for\nthe first time. Using the fractional time series as the driving function of the\nLoewner equation and local fractional integrodifferential operators, we\nintroduce a large class of fractal curves. We argue that the FSLE curves,\nbesides the fractal dimension calculations, have crucial differences which\ncaused by the Hurst index of the driving function. This extension opens a new\nway to classify different types of scaling curves based on the Hurst index of\nthe corresponding driving function. Such formalism appear to be suitable to\ndeal with the study of a wide range of two-dimensional curves appearing in\nstatistical mechanics or natural phenomena.\n"}
+{"abstract": "  We present the photometric and spectroscopic analysis of three Type II SNe:\n2014cx, 2014cy and 2015cz. SN 2014cx is a conventional Type IIP with a shallow\nslope (0.2 mag/50d) and an atypical short plateau ($\\sim$86 d). SNe 2014cy and\n2015cz show relatively large decline rates (0.88 and 1.64 mag/50d,\nrespectively) at early times before settling to the plateau phase, unlike the\ncanonical Type IIP/L SN light curves. All of them are normal luminosity SN II\nwith an absolute magnitude at mid-plateau of\nM$_{V,14cx}^{50}$=$-$16.6$\\pm$0.4$\\,\\rm{mag}$,\nM$_{V,14cy}^{50}$=$-$16.5$\\,\\pm\\,$0.2$\\,\\rm{mag}$ and\nM$_{V,15cz}^{50}$=$-$17.4$\\,\\pm\\,$0.3$\\,\\rm{mag}$. A relatively broad range of\n$^{56}$Ni masses is ejected in these explosions (0.027-0.070 M$_\\odot$). The\nspectra show the classical evolution of Type II SNe, dominated by a blue\ncontinuum with broad H lines at early phases and narrower metal lines with P\nCygni profiles during the plateau. High-velocity H I features are identified in\nthe plateau spectra of SN 2014cx at 11600 km s$^{-1}$, possibly a sign of\nejecta-circumstellar interaction. The spectra of SN 2014cy exhibit strong\nabsorption profile of H I similar to normal luminosity events whereas strong\nmetal lines akin to sub-luminous SNe. The analytical modelling of the\nbolometric light curve of the three events yields similar radii for the three\nobjects within errors (478, 507 and 608 R$_\\odot$ for SNe 2014cx, 2014cy and\n2015cz, respectively) and a range of ejecta masses (15.0, 22.2 and 18.7\nM$_\\odot$ for SNe 2014cx, 2014cy and 2015cz), and a modest range of explosion\nenergies (3.3 - 6.0 foe where 1 foe = 10$^{51}$ erg).\n"}
+{"abstract": "  Compton scattering imaging using high-energy synchrotron x-rays allows the\nvisualization of the spatio-temporal lithiation state in lithium-ion batteries\nprobed in-operando. Here, we apply this imaging technique to the commercial\n18650-type cylindrical lithium-ion battery. Our analysis of the lineshapes of\nthe Compton scattering spectra taken from different electrode layers reveals\nthe emergence of inhomogeneous lithiation patterns during the charge-discharge\ncycles. Moreover, these patterns exhibit oscillations in time where the\ndominant period corresponds to the time scale of the charging curve.\n"}
+{"abstract": "  Classifiers tend to propagate biases present in the data on which they are\ntrained. Hence, it is important to understand how the demographic identities of\nthe annotators of comments affect the fairness of the resulting model. In this\npaper, we focus on the differences in the ways men and women annotate comments\nfor toxicity, investigating how these differences result in models that amplify\nthe opinions of male annotators. We find that the BERT model as-sociates toxic\ncomments containing offensive words with male annotators, causing the model to\npredict 67.7% of toxic comments as having been annotated by men. We show that\nthis disparity between gender predictions can be mitigated by removing\noffensive words and highly toxic comments from the training data. We then apply\nthe learned associations between gender and language to toxic language\nclassifiers, finding that models trained exclusively on female-annotated data\nperform 1.8% better than those trained solely on male-annotated data and that\ntraining models on data after removing all offensive words reduces bias in the\nmodel by 55.5% while increasing the sensitivity by 0.4%.\n"}
+{"abstract": "  We study leptonic CP and flavor violations in supersymmetric (SUSY) grand\nunified theory (GUT) with right handed neutrinos, paying attention to the\nrenormalization group effects on the slepton mass matrices due to the neutrino\nand GUT Yukawa interactions. In particular, we study in detail the impacts of\nthe so-called Casas- Ibarra parameters on CP and flavor violating observables.\nThe renormalization group effects induce CP and flavor violating elements of\nthe SUSY breaking scalar mass squared matrices, which may result in sizable\nleptonic CP and flavor violating signals. Assuming seesaw formula for the\nactive neutrino masses, the renormalization group effects have been often\nthought to be negligible as the right-handed neutrino masses become small. With\nthe most general form of the neutrino Yukawa matrix, i.e., taking into account\nthe Casas-Ibarra parameters, however, this is not the case. We found that the\nmaximal possible sizes of signals of leptonic CP and flavor violating processes\nare found to be insensitive to the mass scale of the right-handed neutrinos and\nthat they are as large as (or larger than) the present experimental bounds\nirrespective of the right-handed neutrino masses.\n"}
+{"abstract": "  Resonant transmission of light is a surface-wave assisted phenomenon that\nenables funneling light through subwavelength apertures milled in otherwise\nopaque metallic screens. In this work, we introduce a deep learning approach to\nefficiently compute and design the optical response of a single subwavelength\nslit perforated in a metallic screen and surrounded by periodic arrangements of\nindentations. First, we show that a semi-analytical framework based on a\ncoupled-mode theory formalism is a robust and efficient method to generate the\nlarge training datasets required in the proposed approach. Second, we discuss\nhow simple, densely connected artificial neural networks can accurately learn\nthe mapping from the geometrical parameters defining the topology of the system\nto its corresponding transmission spectrum. Finally, we report on a deep\nlearning tandem architecture able to perform inverse design tasks for the\nconsidered class of systems. We expect this work to stimulate further work on\nthe application of deep learning to the analysis of light-matter interaction in\nnanostructured metallic films.\n"}
+{"abstract": "  Given an on-board diagnostics (OBD) dataset and a physics-based emissions\nprediction model, this paper aims to develop an accurate and\ncomputational-efficient AI (Artificial Intelligence) method that predicts\nvehicle emissions. The problem is of societal importance because vehicular\nemissions lead to climate change and impact human health. This problem is\nchallenging because the OBD data does not contain enough parameters needed by\nhigh-order physics models. Conversely, related work has shown that low-order\nphysics models have poor predictive accuracy when using available OBD data.\nThis paper uses a divergent window co-occurrence pattern detection method to\ndevelop a spatiotemporal variability-aware AI model for predicting emission\nvalues from the OBD datasets. We conducted a case study using real-world OBD\ndata from a local public transportation agency. Results show that the proposed\nAI method has approximately 65% improved predictive accuracy than a non-AI\nlow-order physics model and is approximately 35% more accurate than a baseline\nmodel.\n"}
+{"abstract": "  The present work demonstrates a robust protocol for probing localized\nelectronic structure in condensed-phase systems, operating in terms of a\nrecently proposed theory for decomposing the results of Kohn-Sham density\nfunctional theory in a basis of spatially localized molecular orbitals\n[Eriksen, J. Chem. Phys. 153, 214109 (2020)]. In an initial application to\nliquid, ambient water and the assessment of the solvation energy and the\nembedded dipole moment of H$_2$O in solution, we find that both properties are\namplified on average -- in accordance with expectation -- and that correlations\nare indeed observed to exist between them. However, the simulated\nsolvent-induced shift to the dipole moment of water is found to be\nsignificantly dampened with respect to typical literature values. The local\nnature of our methodology has further allowed us to evaluate the convergence of\nbulk properties with respect to the extent of the underlying one-electron basis\nset, ranging from single-$\\zeta$ to full (augmented) quadruple-$\\zeta$ quality.\nAlbeit a pilot example, our work paves the way towards future studies of local\neffects and defects in more complex phases, e.g., liquid mixtures and even\nsolid-state crystals.\n"}
+{"abstract": "  In a complex community, species continuously adapt to each other. On rare\noccasions, the adaptation of a species can lead to the extinction of others,\nand even its own. \"Adaptive dynamics\" is the standard mathematical framework to\ndescribe evolutionary changes in community interactions, and in particular,\npredict adaptation driven extinction. Unfortunately, most authors implement the\nequations of adaptive dynamics through computer simulations, that require\nassuming a large number of questionable parameters and fitness functions. In\nthis study we present analytical solutions to adaptive dynamics equations,\nthereby clarifying how outcomes depend on any computational input. We develop\ngeneral formulas that predict equilibrium abundances over evolutionary time\nscales. Additionally, we predict which species will go extinct next, and when\nthis will happen.\n"}
+{"abstract": "  We obtain an estimate for the cubic Weyl sum which improves the bound\nobtained from Weyl differencing for short ranges of summation. In particular,\nwe show that for any $\\varepsilon>0$ there exists some $\\delta>0$ such that for\nany coprime integers $a,q$ and real number $\\gamma$ we have \\begin{align*}\n\\sum_{1\\le n \\le N}e\\left(\\frac{an^3}{q}+\\gamma n\\right)\\ll (qN)^{1/4}\nq^{-\\delta}, \\end{align*} provided $q^{1/3+\\varepsilon}\\le N \\le\nq^{1/2-\\varepsilon}$. Our argument builds on some ideas of Enflo.\n"}
+{"abstract": "  We show that every irreducible integral Apollonian packing can be set in the\nEuclidean space so that all of its tangency spinors and all reduced coordinates\nand co-curvatures are integral. As a byproduct, we prove that in any integral\nDescartes configuration, the sum of the curvatures of two adjacent disks can be\nwritten as a sum of two squares. Descartes groups are defined, and an\ninteresting occurrence of the Fibonacci sequence is found.\n"}
+{"abstract": "  Neural networks are prone to learning shortcuts -- they often model simple\ncorrelations, ignoring more complex ones that potentially generalize better.\nPrior works on image classification show that instead of learning a connection\nto object shape, deep classifiers tend to exploit spurious correlations with\nlow-level texture or the background for solving the classification task. In\nthis work, we take a step towards more robust and interpretable classifiers\nthat explicitly expose the task's causal structure. Building on current\nadvances in deep generative modeling, we propose to decompose the image\ngeneration process into independent causal mechanisms that we train without\ndirect supervision. By exploiting appropriate inductive biases, these\nmechanisms disentangle object shape, object texture, and background; hence,\nthey allow for generating counterfactual images. We demonstrate the ability of\nour model to generate such images on MNIST and ImageNet. Further, we show that\nthe counterfactual images can improve out-of-distribution robustness with a\nmarginal drop in performance on the original classification task, despite being\nsynthetic. Lastly, our generative model can be trained efficiently on a single\nGPU, exploiting common pre-trained models as inductive biases.\n"}
+{"abstract": "  The transference principle of Green and Tao enabled various authors to\ntransfer Szemer\\'edi's theorem on long arithmetic progressions in dense sets to\nvarious sparse sets of integers, mostly sparse sets of primes. In this paper,\nwe provide a transference principle which applies to general affine-linear\nconfigurations of finite complexity. We illustrate the broad applicability of\nour transference principle with the case of almost twin primes, by which we\nmean either Chen primes or ''bounded gap primes'', as well as with the case of\nprimes of the form $x^2+y^2+1$. Thus, we show that in these sets of primes the\nexistence of solutions to finite complexity systems of linear equations is\ndetermined by natural local conditions. These applications rely on a recent\nwork of the last two authors on Bombieri-Vinogradov type estimates for\nnilsequences.\n"}
+{"abstract": "  In a context of global economy, addressing SMEs performance within a local\nframework appears rather a naive approach. The key drawback of such an approach\nstems from its restriction to socio-economic factors that might lead to biased\ndecisions regarding potential venues for performance improvement. In practice,\nthe key objective of performance analysis consists in identifying benchmarks\nfor best managerial practices with respect to resource allocation as well as\nproduction level setting. Conducting the analysis within a specific country,\nlet it be a developing country, may be misleading. Although, the best of the\nclass (benchmark) can be a valid reference for its peers within the same class,\nits status might not be preserved if the analysis is projected outside the\nborders of the class. Indeed, the likelihood for outperformance is high. In\norder to set targets for global competition, decision makers ought to look at\nthe concept of performance from a broader geographical perspective, instead of\nconfining it to a local scope. Here, we analyze, through a case study, SMEs\nperformance within local and global production technology frameworks and we\nhighlight the impact of the economy scope on various decisions. Data\nenvelopment analysis (DEA) is used as a mathematical tool to support such\ndecisions.\n"}
+{"abstract": "  The spread of a matrix is defined as the maximum of distances between any two\neigenvalues of that matrix. In this paper we investigate spread maximization as\na function on compact convex subset of the set of real symmetric matrices. We\nprovide some general results and further, we study spread maximizing problem on\nthe set of symmetric matrices with entries restricted to the interval. In\nparticular, we develop some results by X. Zhan, S. M. Fallat and J. J. Xing.\n"}
+{"abstract": "  We discuss a new mass matrix with specific texture zeros for the quarks. The\nthree flavor mixing angles for the quarks are functions of the quark masses and\ncan be calculated. The following ratios among CKM matrix elements are given by\nratios of quark masses: |Vtd/Vts| ' q md /ms and |Vub/Vcb| ' p mu/mc . Also we\ncan calculate two CKM matrix elements: |Vcb| ' |Vts| ' 2 (ms/mb ). This\nrelation as well as the relation |Vtd/Vts| ' q md /ms are in good agreement\nwith the experimental data. There is a problem with the relation |Vub/Vcb| ' p\nmu/mc , probably due to wrong estimates of the quark masses mu and m\n"}
+{"abstract": "  Context. Solar magnetic pores are, due to their concentrated magnetic fields,\nsuitable guides for magnetoacoustic waves. Recent observations have shown that\npropagating energy flux in pores is subject to strong damping with height;\nhowever, the reason is still unclear. Aims. We investigate possible damping\nmechanisms numerically to explain the observations. Methods. We performed 2D\nnumerical magnetohydrodynamic (MHD) simulations, starting from an equilibrium\nmodel of a single pore inspired by the observed properties. Energy was inserted\ninto the bottom of the domain via different vertical drivers with a period of\n30s. Simulations were performed with both ideal MHD and non-ideal effects.\nResults. While the analysis of the energy flux for ideal and non-ideal MHD\nsimulations with a plane driver cannot reproduce the observed damping, the\nnumerically predicted damping for a localized driver closely corresponds with\nthe observations. The strong damping in simulations with localized driver was\ncaused by two geometric effects, geometric spreading due to diverging field\nlines and lateral wave leakage.\n"}
+{"abstract": "  In the conventional robust $T$-colluding private information retrieval (PIR)\nsystem, the user needs to retrieve one of the possible messages while keeping\nthe identity of the requested message private from any $T$ colluding servers.\nMotivated by the possible heterogeneous privacy requirements for different\nmessages, we consider the $(N, T_1:K_1, T_2:K_2)$ two-level PIR system with a\ntotal of $K_2$ messages in the system, where $T_1\\geq T_2$ and $K_1\\leq K_2$.\nAny one of the $K_1$ messages needs to be retrieved privately against $T_1$\ncolluding servers, and any one of the full set of $K_2$ messages needs to be\nretrieved privately against $T_2$ colluding servers. We obtain a lower bound to\nthe capacity by proposing two novel coding schemes, namely the non-uniform\nsuccessive cancellation scheme and the non-uniform block cancellation scheme. A\ncapacity upper bound is also derived. The gap between the upper bound and the\nlower bounds is analyzed, and shown to vanish when $T_1=T_2$. Lastly, we show\nthat the upper bound is in general not tight by providing a stronger bound for\na special setting.\n"}
+{"abstract": "  By implicitly recognizing a user based on his/her speech input, speaker\nidentification enables many downstream applications, such as personalized\nsystem behavior and expedited shopping checkouts. Based on whether the speech\ncontent is constrained or not, both text-dependent (TD) and text-independent\n(TI) speaker recognition models may be used. We wish to combine the advantages\nof both types of models through an ensemble system to make more reliable\npredictions. However, any such combined approach has to be robust to incomplete\ninputs, i.e., when either TD or TI input is missing. As a solution we propose a\nfusion of embeddings network foenet architecture, combining joint learning with\nneural attention. We compare foenet with four competitive baseline methods on a\ndataset of voice assistant inputs, and show that it achieves higher accuracy\nthan the baseline and score fusion methods, especially in the presence of\nincomplete inputs.\n"}
+{"abstract": "  Explaining the decisions of models is becoming pervasive in the image\nprocessing domain, whether it is by using post-hoc methods or by creating\ninherently interpretable models. While the widespread use of surrogate\nexplainers is a welcome addition to inspect and understand black-box models,\nassessing the robustness and reliability of the explanations is key for their\nsuccess. Additionally, whilst existing work in the explainability field\nproposes various strategies to address this problem, the challenges of working\nwith data in the wild is often overlooked. For instance, in image\nclassification, distortions to images can not only affect the predictions\nassigned by the model, but also the explanation. Given a clean and a distorted\nversion of an image, even if the prediction probabilities are similar, the\nexplanation may still be different. In this paper we propose a methodology to\nevaluate the effect of distortions in explanations by embedding perceptual\ndistances that tailor the neighbourhoods used to training surrogate explainers.\nWe also show that by operating in this way, we can make the explanations more\nrobust to distortions. We generate explanations for images in the Imagenet-C\ndataset and demonstrate how using a perceptual distances in the surrogate\nexplainer creates more coherent explanations for the distorted and reference\nimages.\n"}
+{"abstract": "  We develop a microscopic and atomistic theory of electron spin-based qubits\nin gated quantum dots in a single layer of transition metal dichalcogenides.\nThe qubits are identified with two degenerate locked spin and valley states in\na gated quantum dot. The two-qubit states are accurately described using a\nmulti-million atom tight-binding model solved in wavevector space. The\nspin-valley locking and strong spin-orbit coupling result in two degenerate\nstates, one of the qubit states being spin-down located at the $+K$ valley of\nthe Brillouin zone, and the other state located at the $-K$ valley with spin\nup. We describe the qubit operations necessary to rotate the spin-valley qubit\nas a combination of the applied vertical electric field, enabling spin-orbit\ncoupling in a single valley, with a lateral strongly localized valley-mixing\ngate.\n"}
+{"abstract": "  Automated systems that negotiate with humans have broad applications in\npedagogy and conversational AI. To advance the development of practical\nnegotiation systems, we present CaSiNo: a novel corpus of over a thousand\nnegotiation dialogues in English. Participants take the role of campsite\nneighbors and negotiate for food, water, and firewood packages for their\nupcoming trip. Our design results in diverse and linguistically rich\nnegotiations while maintaining a tractable, closed-domain environment. Inspired\nby the literature in human-human negotiations, we annotate persuasion\nstrategies and perform correlation analysis to understand how the dialogue\nbehaviors are associated with the negotiation performance. We further propose\nand evaluate a multi-task framework to recognize these strategies in a given\nutterance. We find that multi-task learning substantially improves the\nperformance for all strategy labels, especially for the ones that are the most\nskewed. We release the dataset, annotations, and the code to propel future work\nin human-machine negotiations: https://github.com/kushalchawla/CaSiNo\n"}
+{"abstract": "  We introduce codimension three magnetically charged surface operators in\nfive-dimensional (5d) $\\mathcal{N}=1$ supersymmetric gauge on $T^2 \\times\n\\mathbb{R}^3$. We evaluate the vacuum expectation values (vevs) of surface\noperators by supersymmetric localization techniques. Contributions of Monopole\nbubbling effects to the path integral are given by elliptic genera of world\nvolume theories on D-branes. Our result gives an elliptic deformation of the\nSUSY localization formula \\cite{Ito:2011ea} (resp. \\cite{Okuda:2019emk,\nAssel:2019yzd}) of BPS 't Hooft loops (resp. bare monopole operators) in 4d\n$\\mathcal{N}=2$ (resp. 3d $\\mathcal{N}=4$) gauge theories. We define\ndeformation quantizations of vevs of surface operators in terms of the\nWeyl-Wigner transform, where the $\\Omega$-background parameter plays the role\nof the Planck constant. For 5d $\\mathcal{N}=1^*$ gauge theory, we find that the\ndeformation quantization of the surface operators in the anti-symmetric\nrepresentations agrees with the type A elliptic Ruijsenaars operators. The\nmutual commutativity of these difference operators is related to the\ncommutativity of products of 't Hooft surface operators.\n"}
+{"abstract": "  Squeezed, nonclassical states are an integral tool of quantum metrology due\nto their ability to push the sensitivity of a measurement apparatus beyond the\nlimits of classical states. While their creation in light has become a standard\ntechnique, the production of squeezed states of the collective excitations in\ngases of ultracold atoms, the phonons of a Bose-Einstein condensate (BEC), is a\ncomparably recent problem. This task is continuously gaining relevance with a\ngrowing number of proposals for BEC-based quantum metrological devices and the\npossibility to apply them in the detection of gravitational waves. The\nobjective of this thesis is to find whether the recently described effect of an\noscillating external potential on a uniform BEC can be exploited to generate\ntwo-mode squeezed phonon states, given present day technology. This question\nbrings together elements of a range of fields beyond cold atoms, such as\ngeneral relativity and Efimov physics. To answer it, the full transformation\ncaused by the oscillating potential on an initially thermal phononic state is\nconsidered, allowing to find an upper bound for the magnitude of this\nperturbation as well as to quantify the quality of the final state with respect\nto its use in metrology. These findings are then applied to existing\nexperiments to judge the feasibility of the squeezing scheme and while the\nresults indicate that they are not well suited for it, a setup is proposed that\nallows for its efficient implementation and seems within experimental reach. In\nview of the vast parameter space leaving room for optimization, the considered\nmechanism could find applications not only in the gravitational wave detector\nthat originally motivated this work, but more generally in the field of quantum\nmetrology based on ultracold atoms.\n"}
+{"abstract": "  Artificial intelligence (AI) is supposed to help us make better choices. Some\nof these choices are small, like what route to take to work, or what music to\nlisten to. Others are big, like what treatment to administer for a disease or\nhow long to sentence someone for a crime. If AI can assist with these big\ndecisions, we might think it can also help with hard choices, cases where\nalternatives are neither better, worse nor equal but on a par. The aim of this\npaper, however, is to show that this view is mistaken: the fact of parity shows\nthat there are hard limits on AI in decision making and choices that AI cannot,\nand should not, resolve.\n"}
+{"abstract": "  This paper shows a simple parameter substitution, which makes use of the\nreciprocal relation of typical objective functions with typical random\nparameters. Thereby, the accuracy of first-order probabilistic analysis\nimproves significantly at almost no additional computational cost. The\nparameter substitution requires a transformation of the stochastic distribution\nof the substituted parameter, which is explained for different cases.\n"}
+{"abstract": "  The Covering Salesman Problem (CSP) is a generalization of the Traveling\nSalesman Problem in which the tour is not required to visit all vertices, as\nlong as all vertices are covered by the tour. The objective of CSP is to find a\nminimum length Hamiltonian cycle over a subset of vertices that covers an\nundirected graph. In this paper, valid inequalities from the generalized\ntraveling salesman problem are applied to the CSP in addition to new valid\ninequalities that explore distinct aspects of the problem. A branch-and-cut\nframework assembles exact and heuristic separation routines for integer and\nfractional CSP solutions. Computational experiments show that the proposed\nframework outperformed methodologies from literature with respect to optimality\ngaps. Moreover, optimal solutions were proven for several previously unsolved\ninstances.\n"}
+{"abstract": "  In this paper, we investigate the complexity of the central path of\nsemidefinite optimization through the lens of real algebraic geometry. To that\nend, we propose an algorithm to compute real univariate representations\ndescribing the central path and its limit point, where the limit point is\ndescribed by taking the limit of central solutions, as bounded points in the\nfield of algebraic Puiseux series. As a result, we derive an upper bound\n$2^{O(m+n^2)}$ on the degree of the Zariski closure of the central path, when\n$\\mu$ is sufficiently small, and for the complexity of describing the limit\npoint, where $m$ and $n$ denote the number of affine constraints and size of\nthe symmetric matrix, respectively. Furthermore, by the application of the\nquantifier elimination to the real univariate representations, we provide a\nlower bound $1/\\gamma$, with $\\gamma =2^{O(m+n^2)}$, on the convergence rate of\nthe central path.\n"}
+{"abstract": "  We take a broad look at the problem of identifying the magnetic solar causes\nof space weather. With the lackluster performance of extrapolations based upon\nmagnetic field measurements in the photosphere, we identify a region in the\nnear UV part of the spectrum as optimal for studying the development of\nmagnetic free energy over active regions. Using data from SORCE, Hubble Space\nTelescope, and SKYLAB, along with 1D computations of the near-UV (NUV) spectrum\nand numerical experiments based on the MURaM radiation-MHD and HanleRT\nradiative transfer codes, we address multiple challenges. These challenges are\nbest met through a combination of near UV lines of bright \\ion{Mg}{2}, and\nlines of \\ion{Fe}{2} and \\ion{Fe}{1} (mostly within the $4s-4p$ transition\narray) which form in the chromosphere up to $2\\times10^4$ K. Both Hanle and\nZeeman effects can in principle be used to derive vector magnetic fields.\nHowever, for any given spectral line the $\\tau=1$ surfaces are generally\ngeometrically corrugated owing to fine structure such as fibrils and spicules.\nBy using multiple spectral lines spanning different optical depths, magnetic\nfields across nearly-horizontal surfaces can be inferred in regions of low\nplasma $\\beta$, from which free energies, magnetic topology and other\nquantities can be derived.\n  Based upon the recently-reported successful suborbital space measurements of\nmagnetic fields with the CLASP2 instrument,\n  we argue that a modest space-borne telescope will be able to make significant\nadvances in the attempts to predict solar eruptions. Difficulties associated\nwith blended lines are shown to be minor in an Appendix.\n"}
+{"abstract": "  To investigate the influence of the orifice geometry on near-field coherent\nstructures in a jet, Fourier-POD is applied. Velocity and vorticity snapshots\nobtained from tomographic particle image velocimetry at the downstream distance\nof two equivalent orifice diameters are analysed. Jets issuing from a circular\norifice and from a fractal orifice are examined, where the fractal geometry is\nobtained from a repeating fractal pattern applied to a base square shape. While\nin the round jet energy is mostly contained at wavenumber m=0, associated to\nthe characteristic Kelvin-Helmholtz vortex rings, in the fractal jet modal\nstructures at the fundamental azimuthal wavenumber m=4 capture the largest\namount of energy. The second part of the study focuses on the relationship\nbetween streamwise vorticity and streamwise velocity, to characterise the role\nof the orifice geometry on the lift-up mechanism recently found to be active in\nturbulent jets. The averaging of the streamwise vorticity conditioned on\nintense positive fluctuations of streamwise velocity reveals a pair of\nvorticity structures of opposite sign flanking the conditioning point, inducing\na radial flow towards the jet periphery. This pair of structures is observed in\nboth jets, even if the azimuthal extent of this pattern is 30% larger in the\njet issuing from the circular orifice. This evidences that the orifice geometry\ndirectly influences the interaction between velocity and vorticity.\n"}
+{"abstract": "  Purpose: Radiation therapy treatment planning is a trial-and-error, often\ntime-consuming process. An optimal dose distribution based on a specific\nanatomy can be predicted by pre-trained deep learning (DL) models. However,\ndose distributions are often optimized based on not only patient-specific\nanatomy but also physician preferred trade-offs between planning target volume\n(PTV) coverage and organ at risk (OAR) sparing. Therefore, it is desirable to\nallow physicians to fine-tune the dose distribution predicted based on patient\nanatomy. In this work, we developed a DL model to predict the individualized 3D\ndose distributions by using not only the anatomy but also the desired PTV/OAR\ntrade-offs, as represented by a dose volume histogram (DVH), as inputs.\nMethods: The desired DVH, fine-tuned by physicians from the initially predicted\nDVH, is first projected onto the Pareto surface, then converted into a vector,\nand then concatenated with mask feature maps. The network output for training\nis the dose distribution corresponding to the Pareto optimal DVH. The\ntraining/validation datasets contain 77 prostate cancer patients, and the\ntesting dataset has 20 patients. Results: The trained model can predict a 3D\ndose distribution that is approximately Pareto optimal. We calculated the\ndifference between the predicted and the optimized dose distribution for the\nPTV and all OARs as a quantitative evaluation. The largest average error in\nmean dose was about 1.6% of the prescription dose, and the largest average\nerror in the maximum dose was about 1.8%. Conclusions: In this feasibility\nstudy, we have developed a 3D U-Net model with the anatomy and desired DVH as\ninputs to predict an individualized 3D dose distribution. The predicted dose\ndistributions can be used as references for dosimetrists and physicians to\nrapidly develop a clinically acceptable treatment plan.\n"}
+{"abstract": "  Under the environment of big data streams, it is a common situation where the\nvariable set of a model may change according to the condition of data streams.\nIn this paper, we propose a homogenization strategy to represent the\nheterogenous models that are gradually updated in the process of data streams.\nWith the homogenized representations, we can easily construct various online\nupdating statistics such as parameter estimation, residual sum of squares and\n$F$-statistic for the heterogenous updating regression models. The main\ndifference from the classical scenarios is that the artificial covariates in\nthe homogenized models are not identically distributed as the natural\ncovariates in the original models, consequently, the related theoretical\nproperties are distinct from the classical ones. The asymptotical properties of\nthe online updating statistics are established, which show that the new method\ncan achieve estimation efficiency and oracle property, without any constraint\non the number of data batches. The behavior of the method is further\nillustrated by various numerical examples from simulation experiments.\n"}
+{"abstract": "  Infrared nanospectroscopy based on Fourier transform infrared near-field\nspectroscopy (nano-FTIR) is an emerging nanoanalytical tool with large\napplication potential for label-free mapping and identification of organic and\ninorganic materials with nanoscale spatial resolution. However, the detection\nof thin molecular layers and nanostructures on standard substrates is still\nchallenged by weak signals. Here, we demonstrate a significant enhancement of\nnano-FTIR signals of a thin organic layer by exploiting polariton-resonant\ntip-substrate coupling and surface polariton illumination of the probing tip.\nWhen the molecular vibration matches the tip-substrate resonance, we achieve up\nto nearly one order of magnitude signal enhancement on a phonon-polaritonic\nquartz (c-SiO2) substrate, as compared to nano-FTIR spectra obtained on metal\n(Au) substrates, and up to two orders of magnitude when compared to the\nstandard infrared spectroscopy substrate CaF2. Our results will be of critical\nimportance for boosting nano-FTIR spectroscopy towards the routine detection of\nmonolayers and single molecules.\n"}
+{"abstract": "  We define an attractive gravity probe surface (AGPS) as a compact 2-surface\n$S_\\alpha$ with positive mean curvature $k$ satisfying $r^a D_a k / k^2 \\ge\n\\alpha$ (for a constant $\\alpha>-1/2$) in the local inverse mean curvature\nflow, where $r^a D_a k$ is the derivative of $k$ in the outward unit normal\ndirection. For asymptotically flat spaces, any AGPS is proved to satisfy the\nareal inequality $A_\\alpha \\le 4\\pi [ ( 3+4\\alpha)/(1+2\\alpha) ]^2(Gm)^2$,\nwhere $A_{\\alpha}$ is the area of $S_\\alpha$ and $m$ is the\nArnowitt-Deser-Misner (ADM) mass. Equality is realized when the space is\nisometric to the $t=$constant hypersurface of the Schwarzschild spacetime and\n$S_\\alpha$ is an $r=\\mathrm{constant}$ surface with $r^a D_a k / k^2 = \\alpha$.\nWe adapt the two methods of the inverse mean curvature flow and the conformal\nflow. Therefore, our result is applicable to the case where $S_\\alpha$ has\nmultiple components. For anti-de Sitter (AdS) spaces, a similar inequality is\nderived, but the proof is performed only by using the inverse mean curvature\nflow. We also discuss the cases with asymptotically locally AdS spaces.\n"}
+{"abstract": "  The recent surge of complex attention-based deep learning architectures has\nled to extraordinary results in various downstream NLP tasks in the English\nlanguage. However, such research for resource-constrained and morphologically\nrich Indian vernacular languages has been relatively limited. This paper\nproffers team SPPU\\_AKAH's solution for the TechDOfication 2020 subtask-1f:\nwhich focuses on the coarse-grained technical domain identification of short\ntext documents in Marathi, a Devanagari script-based Indian language. Availing\nthe large dataset at hand, a hybrid CNN-BiLSTM attention ensemble model is\nproposed that competently combines the intermediate sentence representations\ngenerated by the convolutional neural network and the bidirectional long\nshort-term memory, leading to efficient text classification. Experimental\nresults show that the proposed model outperforms various baseline machine\nlearning and deep learning models in the given task, giving the best validation\naccuracy of 89.57\\% and f1-score of 0.8875. Furthermore, the solution resulted\nin the best system submission for this subtask, giving a test accuracy of\n64.26\\% and f1-score of 0.6157, transcending the performances of other teams as\nwell as the baseline system given by the organizers of the shared task.\n"}
+{"abstract": "  Let $P$ be a bounded convex subset of $\\mathbb R^n$ of positive volume.\nDenote the smallest degree of a polynomial $p(X_1,\\dots,X_n)$ vanishing on\n$P\\cap\\mathbb Z^n$ by $r_P$ and denote the smallest number $u\\geq0$ such that\nevery function on $P\\cap\\mathbb Z^n$ can be interpolated by a polynomial of\ndegree at most $u$ by $s_P$. We show that the values $(r_{d\\cdot P}-1)/d$ and\n$s_{d\\cdot P}/d$ for dilates $d\\cdot P$ converge from below to some numbers\n$v_P,w_P>0$ as $d$ goes to infinity. The limits satisfy $v_P^{n-1}w_P \\leq\nn!\\cdot\\operatorname{vol}(P)$. When $P$ is a triangle in the plane, we show\nequality: $v_Pw_P = 2\\operatorname{vol}(P)$. These results are obtained by\nlooking at the set of standard monomials of the vanishing ideal of $d\\cdot\nP\\cap\\mathbb Z^n$ and by applying the Bernstein--Kushnirenko theorem. Finally,\nwe study irreducible Laurent polynomials that vanish with large multiplicity at\na point. This work is inspired by questions about Seshadri constants.\n"}
+{"abstract": "  Neural cellular automata (Neural CA) are a recent framework used to model\nbiological phenomena emerging from multicellular organisms. In these systems,\nartificial neural networks are used as update rules for cellular automata.\nNeural CA are end-to-end differentiable systems where the parameters of the\nneural network can be learned to achieve a particular task. In this work, we\nused neural CA to control a cart-pole agent. The observations of the\nenvironment are transmitted in input cells, while the values of output cells\nare used as a readout of the system. We trained the model using deep-Q\nlearning, where the states of the output cells were used as the Q-value\nestimates to be optimized. We found that the computing abilities of the\ncellular automata were maintained over several hundreds of thousands of\niterations, producing an emergent stable behavior in the environment it\ncontrols for thousands of steps. Moreover, the system demonstrated life-like\nphenomena such as a developmental phase, regeneration after damage, stability\ndespite a noisy environment, and robustness to unseen disruption such as input\ndeletion.\n"}
+{"abstract": "  Linear regression is a fundamental modeling tool in statistics and related\nfields. In this paper, we study an important variant of linear regression in\nwhich the predictor-response pairs are partially mismatched. We use an\noptimization formulation to simultaneously learn the underlying regression\ncoefficients and the permutation corresponding to the mismatches. The\ncombinatorial structure of the problem leads to computational challenges. We\npropose and study a simple greedy local search algorithm for this optimization\nproblem that enjoys strong theoretical guarantees and appealing computational\nperformance. We prove that under a suitable scaling of the number of mismatched\npairs compared to the number of samples and features, and certain assumptions\non problem data; our local search algorithm converges to a nearly-optimal\nsolution at a linear rate. In particular, in the noiseless case, our algorithm\nconverges to the global optimal solution with a linear convergence rate. We\nalso propose an approximate local search step that allows us to scale our\napproach to much larger instances. We conduct numerical experiments to gather\nfurther insights into our theoretical results and show promising performance\ngains compared to existing approaches.\n"}
+{"abstract": "  This is a PhD Thesis on the connection between subfactors (more precisely,\ntheir corresponding fusion categories) and Conformal Field Theory (CFT).\nBesides being a mathematically interesting topic on its own, subfactors have\nalso attracted the attention of physicists, since there is a conjectured\ncorrespondence between these and CFTs. Although there is quite a persuasive\nbody of evidence for this conjecture, there are some gaps: there exists a set\nof exceptional subfactors with no known counterpart CFT. Hence, it is necessary\nto develop new techniques for building a CFT from a subfactor. Here, it is\nuseful to study the underlying mathematical structure in more detail: The even\nparts of every subfactor give rise to two Unitary Fusion Categories (UFCs), and\nit is a promising direction to study quantum spin systems constructed from\nthese categories to find a connection to CFTs. The simplest example that\nrequires new techniques for building a CFT is the Haagerup subfactor, since it\nis the smallest subfactor with index larger than 4. In this thesis, we\ninvestigate the question whether there is a CFT corresponding to the Haagerup\nsubfactor via lattice models in one and two dimensions. The first task here is\nto find the F-symbols of the fusion category since these are crucial\ningredients for the construction of a physical model in all of the models we\nconsider in this thesis. We then investigate microscopic models such as the\ngolden chain model and the Levin-Wen model in order to find evidence for a\ncorresponding CFT. We find that there is no evidence for a corresponding CFT\nfrom the investigation of the UFCs directly and it is necessary to expand these\nstudies to the corresponding unitary modular tensor category, which can, for\ninstance, be obtained via the excitations of the Levin-Wen model.\n"}
+{"abstract": "  We study a mathematical model capturing the support/resistance line method (a\ntechnique in technical analysis) where the underlying stock price transitions\nbetween two states of nature in a path-dependent manner. For optimal stopping\nproblems with respect to a general class of reward functions and dynamics,\nusing probabilistic methods, we show that the value function is $C^1$ and\nsolves a general free boundary problem. Moreover, for a wide range of\nutilities, we prove that the best time to buy and sell the stock is obtained by\nsolving free boundary problems corresponding to two linked optimal stopping\nproblems. We use this to numerically compute optimal trading strategies for\nseveral types of dynamics and varying degrees of relative risk aversion. We\nthen compare the strategies with the standard trading rule to investigate the\nviability of this form of technical analysis.\n"}
+{"abstract": "  We give a new criterion guaranteeing existence of model structures\nleft-induced along a functor admitting both adjoints. This works under the\nhypothesis that the functor induces idempotent adjunctions at the homotopy\ncategory level. As an application, we construct new model structures on cubical\nsets, prederivators, marked simplicial sets and simplicial spaces modeling\n$\\infty$-categories and $\\infty$-groupoids.\n"}
+{"abstract": "  Active matter comprises individually driven units that convert locally stored\nenergy into mechanical motion. Interactions between driven units lead to a\nvariety of non-equilibrium collective phenomena in active matter. One of such\nphenomena is anomalously large density fluctuations, which have been observed\nin both experiments and theories. Here we show that, on the contrary, density\nfluctuations in active matter can also be greatly suppressed. Our experiments\nare carried out with marine algae ($\\it{Effrenium\\ voratum}$) which swim in\ncircles at the air-liquid interfaces with two different eukaryotic flagella.\nCell swimming generates fluid flow which leads to effective repulsions between\ncells in the far field. Long-range nature of such repulsive interactions\nsuppresses density fluctuations and generates disordered hyperuniform states\nunder a wide range of density conditions. Emergence of hyperuniformity and\nassociated scaling exponent are quantitatively reproduced in a numerical model\nwhose main ingredients are effective hydrodynamic interactions and uncorrelated\nrandom cell motion. Our results demonstrate a new form of collective state in\nactive matter and suggest the possibility to use hydrodynamic flow for\nself-assembly in active matter.\n"}
+{"abstract": "  We consider a fully directed self-avoiding walk model on a cubic lattice to\nmimic the conformations of an infinitely long confined flexible polymer chain;\nand the confinement condition is achieved by two parallel athermal plates. The\nconfined polymer system is under good solvent condition and we revisit this\nproblem to solve the real polymer's model for any length of chain and also for\nany separation in between the plates. The equilibrium statistics of the\nconfined polymer chain is derived using an analytical calculations based on the\ngenerating function technique. The force of the confinement, the surface\ntension and the monomer density profile of confined chain is obtained. We\npropose that a method of calculations is suitable to understand thermodynamics\nof an arbitrary length confined polymer chain.\n"}
+{"abstract": "  With the recent advances of the Internet of Things, and the increasing\naccessibility of ubiquitous computing resources and mobile devices, the\nprevalence of rich media contents, and the ensuing social, economic, and\ncultural changes, computing technology and applications have evolved quickly\nover the past decade. They now go beyond personal computing, facilitating\ncollaboration and social interactions in general, causing a quick proliferation\nof social relationships among IoT entities. The increasing number of these\nrelationships and their heterogeneous social features have led to computing and\ncommunication bottlenecks that prevent the IoT network from taking advantage of\nthese relationships to improve the offered services and customize the delivered\ncontent, known as relationship explosion. On the other hand, the quick advances\nin artificial intelligence applications in social computing have led to the\nemerging of a promising research field known as Artificial Social Intelligence\n(ASI) that has the potential to tackle the social relationship explosion\nproblem. This paper discusses the role of IoT in social relationships detection\nand management, the problem of social relationships explosion in IoT and\nreviews the proposed solutions using ASI, including social-oriented\nmachine-learning and deep-learning techniques.\n"}
+{"abstract": "  The present paper is a continuation of earlier work by Gunnar Carlsson and\nthe first author on a motivic variant of the classical Becker-Gottlieb transfer\nand an additivity theorem for such a transfer by the present authors. Here, we\nestablish a motivic variant of the classical Segal-Becker theorem relating the\nclassifying space of a 1-dimensional torus with the spectrum defining\n(algebraic) K-theory.\n"}
+{"abstract": "  Graph Neural Networks have revolutionized many machine learning tasks in\nrecent years, ranging from drug discovery, recommendation systems, image\nclassification, social network analysis to natural language understanding. This\npaper shows their efficacy in modeling relationships between products and\nmaking predictions for unseen product networks. By representing products as\nnodes and their relationships as edges of a graph, we show how an inductive\ngraph neural network approach, named GraphSAGE, can efficiently learn\ncontinuous representations for nodes and edges. These representations also\ncapture product feature information such as price, brand, or engineering\nattributes. They are combined with a classification model for predicting the\nexistence of the relationship between products. Using a case study of the\nChinese car market, we find that our method yields double the prediction\nperformance compared to an Exponential Random Graph Model-based method for\npredicting the co-consideration relationship between cars. While a vanilla\nGraphSAGE requires a partial network to make predictions, we introduce an\n`adjacency prediction model' to circumvent this limitation. This enables us to\npredict product relationships when no neighborhood information is known.\nFinally, we demonstrate how a permutation-based interpretability analysis can\nprovide insights on how design attributes impact the predictions of\nrelationships between products. This work provides a systematic method to\npredict the relationships between products in many different markets.\n"}
+{"abstract": "  In Survival Analysis, the observed lifetimes often correspond to individuals\nfor which the event occurs within a specific calendar time interval. With such\ninterval sampling, the lifetimes are doubly truncated at values determined by\nthe birth dates and the sampling interval. This double truncation may induce a\nsystematic bias in estimation, so specific corrections are needed. A relevant\ntarget in Survival Analysis is the hazard rate function, which represents the\ninstantaneous probability for the event of interest. In this work we introduce\na flexible estimation approach for the hazard rate under double truncation.\nSpecifically, a kernel smoother is considered, in both a fully nonparametric\nsetting and a semiparametric setting in which the incidence process fits a\ngiven parametric model. Properties of the kernel smoothers are investigated\nboth theoretically and through simulations. In particular, an asymptotic\nexpression of the mean integrated squared error is derived, leading to a\ndata-driven bandwidth for the estimators. The relevance of the semiparametric\napproach is emphasized, in that it is generally more accurate and, importantly,\nit avoids the potential issues of nonexistence or nonuniqueness of the fully\nnonparametric estimator. Applications to the age of diagnosis of Acute Coronary\nSyndrome (ACS) and AIDS incubation times are included.\n"}
+{"abstract": "  We are interested in dendrites for which all invariant measures of\nzero-entropy mappings have discrete spectrum, and we prove that this holds when\nthe closure of the endpoint set of the dendrite is countable. This solves an\nopen question which was around for awhile, almost completing the\ncharacterization of dendrites with this property.\n"}
+{"abstract": "  The Radial Basis Function-generated finite differences became a popular\nvariant of local meshless strong form methods due to its robustness regarding\nthe position of nodes and its controllable order of accuracy. In this paper, we\npresent a GPU accelerated numerical solution of Poisson's equation on scattered\nnodes in 2D for orders from 2 up to 6. We specifically study the effect of\nusing different orders on GPU acceleration efficiency.\n"}
+{"abstract": "  This paper considers optimal control of a quadrotor unmanned aerial vehicles\n(UAV) using the discrete-time, finite-horizon, linear quadratic regulator\n(LQR). The state of a quadrotor UAV is represented as an element of the matrix\nLie group of double direct isometries, $SE_2(3)$. The nonlinear system is\nlinearized using a left-invariant error about a reference trajectory, leading\nto an optimal gain sequence that can be calculated offline. The reference\ntrajectory is calculated using the differentially flat properties of the\nquadrotor. Monte-Carlo simulations demonstrate robustness of the proposed\ncontrol scheme to parametric uncertainty, state-estimation error, and initial\nerror. Additionally, when compared to an LQR controller that uses a\nconventional error definition, the proposed controller demonstrates better\nperformance when initial errors are large.\n"}
+{"abstract": "  We propose an alternative reconstruction for weighted essentially\nnon-oscillatory schemes with adaptive order (WENO-AO) for solving hyperbolic\nconservation laws. The alternative reconstruction has a more concise form than\nthe original WENO-AO reconstruction. Moreover, it is a strictly convex\ncombination of polynomials with unequal degrees. Numerical examples show that\nthe alternative reconstruction maintains the accuracy and robustness of the\nWENO-AO schemes.\n"}
+{"abstract": "  This thesis presents three results in geometric analysis. We first analyze\nthe curve-shortening flow on figure eight curves in the plane. Afterwards, we\nexamine the point-wise curvature preserving flow on space curves. Lastly, we\npresent an abridgment of our work on a family of three-dimensional Lie groups,\nwhich, when equipped with canonical left-invariant metrics, interpolate between\nSol and hyperbolic space.\n"}
+{"abstract": "  We learn, in an unsupervised way, an embedding from sequences of radar images\nthat is suitable for solving place recognition problem using complex radar\ndata. We experiment on 280 km of data and show performance exceeding\nstate-of-the-art supervised approaches, localising correctly 98.38% of the time\nwhen using just the nearest database candidate.\n"}
+{"abstract": "  We study the Monge-Ampere equation with some power nonlinear term. A solution\nu is called to be Euclidean complete if it is an entire solution defined over\nthe whole R^n or its graph is a large hypersurface satisfying the large\ncondition on boundary \\partial\\Omega in case \\Omega\\not=R^n. In this paper, we\nwill give various sharp conditions on p and \\Omega classifying the Euclidean\ncomplete solution. Our results clarify and extend largely the existence theorem\nof Cirstea-Trombetti (Calc. Var., 31, 2008, 167-186) for bounded convex domain\nand p>n.\n"}
+{"abstract": "  Starting from the model in Koch-Vargiolu (2019), we test the real impact of\ncurrent renewable installed power in the electricity price in Italy, and assess\nhow much the renewable installation strategy which was put in place in Italy\ndeviated from the optimal one obtained from the model in the period 2012--2018.\nTo do so, we consider the Ornstein-Uhlenbeck (O-U) process, including an\nexogenous increasing process influencing the mean reverting term, which is\ninterpreted as the current renewable installed power. Using real data of\nelectricity price, photovoltaic and wind energy production from the six main\nItalian price zones, we estimate the parameters of the model and obtain\nquantitative results, such as the production of photovoltaic energy impacts the\nNorth zone, while wind is significant for Sardinia and the Central North zone\ndoes not present electricity price impact. Then we implement the solution of\nthe singular optimal control problem of installing renewable power production\ndevices, in order to maximize the profit of selling the produced energy in the\nmarket net of installation costs. We extend the results of \\cite{KV} to the\ncase when no impact on power price is presented, and to the case when $N$\nplayers can produce electricity by installing renewable power plants. We are\nthus able to describe the optimal strategy and compare it with the real\ninstallation strategy that was put in place in Italy.\n"}
+{"abstract": "  On 13 May 1787, a convict fleet of 11 ships left Portsmouth, England, on a\n24,000 km, 8-month-long voyage to New South Wales. The voyage would take the\n\"First Fleet\" under Captain Arthur Phillip via Tenerife (Canary Islands), the\nport of Rio de Janeiro (Brazil), Table Bay at the southern extremity of the\nAfrican continent and the southernmost cape of present-day Tasmania to their\ndestination of Botany Bay. Given the navigation tools available at the time and\nthe small size of the convoy's ships, their safe arrival within a few days of\neach other was a phenomenal achievement. This was particularly so, because they\nhad not lost a single ship and only a relatively small number of crew and\nconvicts. Phillip and his crew had only been able to ensure their success\nbecause of the presence of crew members who were highly proficient in practical\nastronomy, most notably Lieutenant William Dawes. We explore in detail his\neducational background and the events leading up to Dawes' appointment by the\nBoard of Longitude as the convoy's dedicated astronomer-cum-Marine. In addition\nto Dawes, John Hunter, second captain of the convoy's flagship H.M.S. Sirius,\nLieutenant William Bradley and Lieutenant Philip Gidley King were also experts\nin navigation and longitude determination, using both chronometers and \"lunar\ndistance\" measurements. The historical record of the First Fleet's voyage is\nremarkably accurate, even by today's standards.\n"}
+{"abstract": "  In this paper, we introduce a novel iterative algorithm which carries out\n$\\alpha$-divergence minimisation by ensuring a systematic decrease in the\n$\\alpha$-divergence at each step. In its most general form, our framework\nallows us to simultaneously optimise the weights and components parameters of a\ngiven mixture model. Notably, our approach permits to build on various methods\npreviously proposed for $\\alpha$-divergence minimisation such as gradient or\npower descent schemes. Furthermore, we shed a new light on an integrated\nExpectation Maximization algorithm. We provide empirical evidence that our\nmethodology yields improved results, all the while illustrating the numerical\nbenefits of having introduced some flexibility through the parameter $\\alpha$\nof the $\\alpha$-divergence.\n"}
+{"abstract": "  The scaling behavior for the rectification of bipolar nanopores is studied\nusing the Nernst-Planck equation coupled to the Local Equilibrium Monte Carlo\nmethod. The bipolar nanopore's wall carries $\\sigma$ and $-\\sigma$ surface\ncharge densities in its two half regions axially. Scaling means that the device\nfunction (rectification) depends on the system parameters (pore length, $H$,\npore radius, $R$, concentration, $c$, voltage, $U$, and surface charge density,\n$\\sigma$) via a single scaling parameter that is a smooth analytical function\nof the system parameters. Here, we suggest using a modified Dukhin number,\n$\\mathrm{mDu}=|\\sigma|l_{\\mathrm{B}}^{*}\\lambda_{\\mathrm{D}}HU/(RU_{0})$, where\n$l_{\\mathrm{B}}^{*}=8\\pi l_{\\mathrm{B}}$, $l_{\\mathrm{B}}$ is the Bjerrum\nlength, $\\lambda_{\\mathrm{D}}$ is the Debye length, and $U_{0}$ is a reference\nvoltage. We show how scaling depends on $H$, $U$, and $\\sigma$ and through what\nmechanisms these parameters influence the pore's behavior.\n"}
+{"abstract": "  We present the analytic calculation of two-loop master integrals that are\nrelevant for $tW$ production at hadron colliders. We focus on the integral\nfamilies with only one massive propagator. After choosing a canonical basis,\nthe differential equations for the master integrals can be transformed into the\n$d$ln form. The boundaries are determined by simple direct integrations or\nregularity conditions at kinematic points without physical singularities. The\nanalytical results in this work are expressed in terms of multiple\npolylogarithms, and have been checked with numerical computations.\n"}
+{"abstract": "  Deep learning models are vulnerable to adversarial examples. As a more\nthreatening type for practical deep learning systems, physical adversarial\nexamples have received extensive research attention in recent years. However,\nwithout exploiting the intrinsic characteristics such as model-agnostic and\nhuman-specific patterns, existing works generate weak adversarial perturbations\nin the physical world, which fall short of attacking across different models\nand show visually suspicious appearance. Motivated by the viewpoint that\nattention reflects the intrinsic characteristics of the recognition process,\nthis paper proposes the Dual Attention Suppression (DAS) attack to generate\nvisually-natural physical adversarial camouflages with strong transferability\nby suppressing both model and human attention. As for attacking, we generate\ntransferable adversarial camouflages by distracting the model-shared similar\nattention patterns from the target to non-target regions. Meanwhile, based on\nthe fact that human visual attention always focuses on salient items (e.g.,\nsuspicious distortions), we evade the human-specific bottom-up attention to\ngenerate visually-natural camouflages which are correlated to the scenario\ncontext. We conduct extensive experiments in both the digital and physical\nworld for classification and detection tasks on up-to-date models (e.g.,\nYolo-V5) and significantly demonstrate that our method outperforms\nstate-of-the-art methods.\n"}
+{"abstract": "  In the past few years, researches on advanced driver assistance systems\n(ADASs) have been carried out and deployed in intelligent vehicles. Systems\nthat have been developed can perform different tasks, such as lane keeping\nassistance (LKA), lane departure warning (LDW), lane change warning (LCW) and\nadaptive cruise control (ACC). Real time lane detection and tracking (LDT) is\none of the most consequential parts to performing the above tasks. Images which\nare extracted from the video, contain noise and other unwanted factors such as\nvariation in lightening, shadow from nearby objects and etc. that requires\nrobust preprocessing methods for lane marking detection and tracking.\nPreprocessing is critical for the subsequent steps and real time performance\nbecause its main function is to remove the irrelevant image parts and enhance\nthe feature of interest. In this paper, we survey preprocessing methods for\ndetecting lane marking as well as tracking lane boundaries in real time\nfocusing on vision-based system.\n"}
+{"abstract": "  Increasing volume of user-generated human-centric video content and their\napplications, such as video retrieval and browsing, require compact\nrepresentations that are addressed by the video summarization literature.\nCurrent supervised studies formulate video summarization as a\nsequence-to-sequence learning problem and the existing solutions often neglect\nthe surge of human-centric view, which inherently contains affective content.\nIn this study, we investigate the affective-information enriched supervised\nvideo summarization task for human-centric videos. First, we train a visual\ninput-driven state-of-the-art continuous emotion recognition model (CER-NET) on\nthe RECOLA dataset to estimate emotional attributes. Then, we integrate the\nestimated emotional attributes and the high-level representations from the\nCER-NET with the visual information to define the proposed affective video\nsummarization architectures (AVSUM). In addition, we investigate the use of\nattention to improve the AVSUM architectures and propose two new architectures\nbased on temporal attention (TA-AVSUM) and spatial attention (SA-AVSUM). We\nconduct video summarization experiments on the TvSum database. The proposed\nAVSUM-GRU architecture with an early fusion of high level GRU embeddings and\nthe temporal attention based TA-AVSUM architecture attain competitive video\nsummarization performances by bringing strong performance improvements for the\nhuman-centric videos compared to the state-of-the-art in terms of F-score and\nself-defined face recall metrics.\n"}
+{"abstract": "  Accurate lighting estimation is challenging yet critical to many computer\nvision and computer graphics tasks such as high-dynamic-range (HDR) relighting.\nExisting approaches model lighting in either frequency domain or spatial domain\nwhich is insufficient to represent the complex lighting conditions in scenes\nand tends to produce inaccurate estimation. This paper presents NeedleLight, a\nnew lighting estimation model that represents illumination with needlets and\nallows lighting estimation in both frequency domain and spatial domain jointly.\nAn optimal thresholding function is designed to achieve sparse needlets which\ntrims redundant lighting parameters and demonstrates superior localization\nproperties for illumination representation. In addition, a novel spherical\ntransport loss is designed based on optimal transport theory which guides to\nregress lighting representation parameters with consideration of the spatial\ninformation. Furthermore, we propose a new metric that is concise yet effective\nby directly evaluating the estimated illumination maps rather than rendered\nimages. Extensive experiments show that NeedleLight achieves superior lighting\nestimation consistently across multiple evaluation metrics as compared with\nstate-of-the-art methods.\n"}
+{"abstract": "  We evaluate the rotational velocity of stars observed by the Pristine survey\ntowards the Galactic anticentre, spanning a wide range of metallicities from\nthe extremely metal-poor regime ($\\mathrm{[Fe/H]}<-3$) to nearly solar\nmetallicity. In the Galactic anticentre direction, the rotational velocity\n($V_{\\phi}$) is similar to the tangential velocity in the galactic longitude\ndirection ($V_{\\ell}$). This allows us to estimate $V_{\\phi}$ from Gaia early\ndata-release 3 (Gaia EDR3) proper motions for stars without radial velocity\nmeasurements. This substantially increases the sample of stars in the outer\ndisc with estimated rotational velocities. Our stellar sample towards the\nanticentre is dominated by a kinematical thin disc with a mean rotation of\n$\\sim -220$ km $\\mathrm{s}^{-1}$. However, our analysis reveals the presence of\nmore stellar substructures. The most intriguing is a well populated extension\nof the kinematical thin disc down to $\\mathrm{[Fe/H]} \\sim -2$. A scarser fast\nrotating population reaching the extremely metal-poor regime, down to\n$\\mathrm{[Fe/H]} \\sim -3.5$ is also detected, but without statistical\nsignificance to unambiguously state whether this is the extremely metal-poor\nextension of the thin disc or the high rotating tail of hotter structures (like\nthe thick disc or the halo). In addition, a more slowly rotating kinematical\nthick disc component is also required to explain the observed $V_{\\ell}$\ndistribution at $\\mathrm{[Fe/H]} > -1.5$. Furthermore, we detect signatures of\na \"heated disc\", the so-called Splash, at metallicities higher than $\\sim-1.5$.\nFinally, at $\\mathrm{[Fe/H]} < -1.5$ our anticentre sample is dominated by a\nkinematical halo with a net prograde motion.\n"}
+{"abstract": "  Van Zuylen et al. introduced the notion of a popular ranking in a voting\ncontext, where each voter submits a strictly-ordered list of all candidates. A\npopular ranking $\\pi$ of the candidates is at least as good as any other\nranking $\\sigma$ in the following sense: if we compare $\\pi$ to $\\sigma$, at\nleast half of all voters will always weakly prefer~$\\pi$. Whether a voter\nprefers one ranking to another is calculated based on the Kendall distance.\n  A more traditional definition of popularity -- as applied to popular\nmatchings, a well-established topic in computational social choice -- is\nstricter, because it requires at least half of the voters \\emph{who are not\nindifferent between $\\pi$ and $\\sigma$} to prefer~$\\pi$. In this paper, we\nderive structural and algorithmic results in both settings, also improving upon\nthe results by van Zuylen et al. We also point out strong connections to the\nfamous open problem of finding a Kemeny consensus with 3 voters.\n"}
+{"abstract": "  This contribution is a review of the deep and powerful connection between the\nlarge scale properties of critical systems and their description in terms of a\nfield theory. Although largely applicable to many other models, the details of\nthis connection are illustrated in the class of two-dimensional Abelian\nsandpile models. Bulk and boundary height variables, spanning tree related\nobservables, boundary conditions and dissipation are all discussed in this\ncontext and found to have a proper match in the field theoretic description.\n"}
+{"abstract": "  Graph convolutional neural networks (GCNNs) are nonlinear processing tools to\nlearn representations from network data. A key property of GCNNs is their\nstability to graph perturbations. Current analysis considers deterministic\nperturbations but fails to provide relevant insights when topological changes\nare random. This paper investigates the stability of GCNNs to stochastic graph\nperturbations induced by link losses. In particular, it proves the expected\noutput difference between the GCNN over random perturbed graphs and the GCNN\nover the nominal graph is upper bounded by a factor that is linear in the link\nloss probability. We perform the stability analysis in the graph spectral\ndomain such that the result holds uniformly for any graph. This result also\nshows the role of the nonlinearity and the architecture width and depth, and\nallows identifying handle to improve the GCNN robustness. Numerical simulations\non source localization and robot swarm control corroborate our theoretical\nfindings.\n"}
+{"abstract": "  Potential strategies for the development and large-scale application of\nrenewable energy sources aimed at reducing the usage of carbon-based fossil\nfuels are assessed here, especially in the event of the abandonment of such\nfuels. The aim is to aid the initiative to reduce the harmful effects of\ncarbon-based fossil fuels on the environment and ensure a reduction in\ngreenhouse gases and sustainability of natural resources. Small-scale renewable\nenergy application for heating, cooling, and electricity generation in\nhouseholds and commercial buildings are already underway around the world.\nHydrogen (H2) and ammonia (NH3), which are presently produced using fossil\nfuels, already have significant applications in society and industry, and are\ntherefore good candidates for large-scale production through the use of\nrenewable energy sources. This will help to reduce the greenhouse gas emissions\nappreciably around the world. While the first-generation biofuels production\nusing food crops may not be suitable for long-range fuel production, due to\ncompetition with the food supply, the 2nd, 3rd and 4th generation biofuels have\nthe potential to produce large, worldwide supplies of fuels. Production of\nadvanced biofuels will not increase the emission of greenhouse gases, and the\nammonia produced through the use of renewable energy resources will serve as\nfertilizer for biofuels production. The perspective of renewable energy\nsources, such as technology status, economics, overall environmental benefits,\nobstacles for commercialization, relative competitiveness of various renewable\nenergy sources, etc., are also discussed whenever applicable.\n"}
+{"abstract": "  Interactions among multiple time series of positive random variables are\ncrucial in diverse financial applications, from spillover effects to volatility\ninterdependence. A popular model in this setting is the vector Multiplicative\nError Model (vMEM) which poses a linear iterative structure on the dynamics of\nthe conditional mean, perturbed by a multiplicative innovation term. A main\nlimitation of vMEM is however its restrictive assumption on the distribution of\nthe random innovation term. A Bayesian semiparametric approach that models the\ninnovation vector as an infinite location-scale mixture of multidimensional\nkernels with support on the positive orthant is used to address this major\nshortcoming of vMEM. Computational complications arising from the constraints\nto the positive orthant are avoided through the formulation of a slice sampler\non the parameter-extended unconstrained version of the model. The method is\napplied to simulated and real data and a flexible specification is obtained\nthat outperforms the classical ones in terms of fitting and predictive power.\n"}
+{"abstract": "  Kagome magnets are believed to have numerous exotic physical properties due\nto the possible interplay between lattice geometry, electron correlation and\nband topology. Here, we report the large anomalous Hall effect in the kagome\nferromagnet LiMn$_6$Sn$_6$, which has a Curie temperature of 382 K and easy\nplane along with the kagome lattice. At low temperatures, unsaturated positive\nmagnetoresistance and opposite signs of ordinary Hall coefficient for\n$\\rho_{xz}$ and $\\rho_{yx}$ indicate the coexistence of electrons and holes in\nthe system. A large intrinsic anomalous Hall conductivity of 380 $\\Omega^{-1}$\ncm$^{-1}$, or 0.44 $e^2/h$ per Mn layer, is observed in $\\sigma_{xy}^A$. This\nvalue is significantly larger than those in other $R$Mn$_6$Sn$_6$ ($R$ = rare\nearth elements) kagome compounds. Band structure calculations show several band\ncrossings, including a spin-polarized Dirac point at the K point, close to the\nFermi energy. The calculated intrinsic Hall conductivity agrees well with the\nexperimental value, and shows a maximum peak near the Fermi energy. We\nattribute the large anomalous Hall effect in LiMn$_6$Sn$_6$ to the band\ncrossings closely located near the Fermi energy.\n"}
+{"abstract": "  Prior work has proved that Translation memory (TM) can boost the performance\nof Neural Machine Translation (NMT). In contrast to existing work that uses\nbilingual corpus as TM and employs source-side similarity search for memory\nretrieval, we propose a new framework that uses monolingual memory and performs\nlearnable memory retrieval in a cross-lingual manner. Our framework has unique\nadvantages. First, the cross-lingual memory retriever allows abundant\nmonolingual data to be TM. Second, the memory retriever and NMT model can be\njointly optimized for the ultimate translation goal. Experiments show that the\nproposed method obtains substantial improvements. Remarkably, it even\noutperforms strong TM-augmented NMT baselines using bilingual TM. Owning to the\nability to leverage monolingual data, our model also demonstrates effectiveness\nin low-resource and domain adaptation scenarios.\n"}
+{"abstract": "  The use of Reinforcement Learning (RL) agents in practical applications\nrequires the consideration of suboptimal outcomes, depending on the familiarity\nof the agent with its environment. This is especially important in\nsafety-critical environments, where errors can lead to high costs or damage. In\ndistributional RL, the risk-sensitivity can be controlled via different\ndistortion measures of the estimated return distribution. However, these\ndistortion functions require an estimate of the risk level, which is difficult\nto obtain and depends on the current state. In this work, we demonstrate the\nsuboptimality of a static risk level estimation and propose a method to\ndynamically select risk levels at each environment step. Our method ARA\n(Automatic Risk Adaptation) estimates the appropriate risk level in both known\nand unknown environments using a Random Network Distillation error. We show\nreduced failure rates by up to a factor of 7 and improved generalization\nperformance by up to 14% compared to both risk-aware and risk-agnostic agents\nin several locomotion environments.\n"}
+{"abstract": "  We extend the theory of Interacting Hopf algebras with an order primitive,\nand give a sound and complete axiomatisation of the prop of polyhedral cones.\nNext, we axiomatise an affine extension and prove soundness and completeness\nfor the prop of polyhedra.\n"}
+{"abstract": "  Accurate phase diagrams of multicomponent plasmas are required for the\nmodeling of dense stellar plasmas, such as those found in the cores of white\ndwarf stars and the crusts of neutron stars. Those phase diagrams have been\ncomputed using a variety of standard techniques, which suffer from physical and\ncomputational limitations. Here, we present an efficient and accurate method\nthat overcomes the drawbacks of previously used approaches. In particular,\nfinite-size effects are avoided as each phase is calculated separately; the\nplasma electrons and volume changes are explicitly taken into account; and\narbitrary analytic fits to simulation data are avoided. Furthermore, no\nsimulations at uninteresting state conditions, i.e., away from the phase\ncoexistence curves, are required, which improves the efficiency of the\ntechnique. The method consists of an adaptation of the so-called Gibbs-Duhem\nintegration approach to electron-ion plasmas, where the coexistence curve is\ndetermined by direct numerical integration of its underlying Clapeyron\nequation. The thermodynamics properties of the coexisting phases are evaluated\nseparately using Monte Carlo simulations in the isobaric semi-grand canonical\nensemble. We describe this Monte Carlo-based Clapeyron integration method,\nincluding its basic principles, our extension to electron-ion plasmas, and our\nnumerical implementation. We illustrate its applicability and benefits with the\ncalculation of the melting curve of dense C/O plasmas under conditions relevant\nfor white dwarf cores and provide analytic fits to implement this new melting\ncurve in white dwarf models. While this work focuses on the liquid-solid phase\nboundary of dense two-component plasmas, a wider range of physical systems and\nphase boundaries are within the scope of the Clapeyron integration method,\nwhich had until now only been applied to simple model systems of neutral\nparticles.\n"}
+{"abstract": "  We prove that if there is an elementary embedding from the universe to\nitself, then there is a proper class of measurable successor cardinals.\n"}
+{"abstract": "  In the present paper, we first give a detailed study on the pQCD corrections\nto the leading-twist part of BSR. Previous pQCD corrections to the\nleading-twist part derived under conventional scale-setting approach up to\n${\\cal O}(\\alpha_s^4)$-level still show strong renormalization scale\ndependence. The principle of maximum conformality (PMC) provides a systematic\nway to eliminate conventional renormalization scale-setting ambiguity by\ndetermining the accurate $\\alpha_s$-running behavior of the process with the\nhelp of renormalization group equation. Our calculation confirms the PMC\nprediction satisfies the standard renormalization group invariance, e.g. its\nfixed-order prediction does scheme-and-scale independent. In low $Q^2$-region,\nthe effective momentum of the process is small and to have a reliable\nprediction, we adopt four low-energy $\\alpha_s$ models to do the analysis. Our\npredictions show that even though the high-twist terms are generally power\nsuppressed in high $Q^2$-region, they shall have sizable contributions in low\nand intermediate $Q^2$ domain. By using the more accurate scheme-and-scale\nindependent pQCD prediction, we present a novel fit of the non-perturbative\nhigh-twist contributions by comparing with the JLab data.\n"}
+{"abstract": "  We investigate a result on convergence of double sequences of numbers and how\nit extends to measurable functions.\n"}
+{"abstract": "  The problem of joint design of transmit waveforms and receive filters is\ndesirable in many application scenarios of multiple-input multiple-output\n(MIMO) radar systems. In this paper, the joint design problem is investigated\nunder the signal-to-interference-plus-noise ratio (SINR) performance metric, in\nwhich case the problem is formulated to maximize the SINR at the receiver side\nsubject to some practical transmit waveform constraints. A numerical algorithm\nis proposed for problem resolution based on the manifold optimization method,\nwhich has been shown to be powerful and flexible to address nonconvex\nconstrained optimization problems in many engineering applications. The\nproposed algorithm is able to efficiently solve the SINR maximization problem\nwith different waveform constraints under a unified framework. Numerical\nexperiments show that the proposed algorithm outperforms the existing\nbenchmarks in terms of computation efficiency and achieves comparable SINR\nperformance.\n"}
+{"abstract": "  Here, we report successful single crystal growth of SnSb2Te4 using the\nself-flux method. Unidirectional crystal growth is confirmed through X Ray\nDiffraction (XRD) pattern taken on mechanically cleaved crystal flake while the\nrietveld refined Powder XRD (PXRD) pattern confirms the phase purity of the\ngrown crystal. Scanning Electron Microscopy (SEM) image and Energy Dispersive\nX-Ray analysis (EDAX) confirm crystalline morphology and exact stoichiometry of\nconstituent elements. Vibrational Modes observed in Raman spectra also confirm\nthe formation of the SnSb2Te4 phase. DC resistivity measurements confirm the\nmetallic character of the grown crystal. Magneto-transport measurements up to\n5T show a nonsaturating low magneto-resistance percentage. V type cusp and\nHikami Larkin Nagaoka (HLN) fitting at lower field confirms the Weak\nAnti-localization (WAL) effect in SnSb2Te4. Density Functional Theory (DFT)\ncalculations were showing topological non-trivial electronic band structure. It\nis the first-ever report on MR study and WAL analysis of SnSb2Te4 single\ncrystal.\n"}
+{"abstract": "  We consider controlling the false discovery rate for testing many time series\nwith an unknown cross-sectional correlation structure. Given a large number of\nhypotheses, false and missing discoveries can plague an analysis. While many\nprocedures have been proposed to control false discovery, most of them either\nassume independent hypotheses or lack statistical power. A problem of\nparticular interest is in financial asset pricing, where the goal is to\ndetermine which ``factors\" lead to excess returns out of a large number of\npotential factors. Our contribution is two-fold. First, we show the consistency\nof Fama and French's prominent method under multiple testing. Second, we\npropose a novel method for false discovery control using double bootstrapping.\nWe achieve superior statistical power to existing methods and prove that the\nfalse discovery rate is controlled. Simulations and a real data application\nillustrate the efficacy of our method over existing methods.\n"}
+{"abstract": "  In this paper, we study how to efficiently and reliably detect active devices\nand estimate their channels in a multiple-input multiple-output (MIMO)\northogonal frequency-division multiplexing (OFDM) based grant-free\nnon-orthogonal multiple access (NOMA) system to enable massive machine-type\ncommunications (mMTC). First, by exploiting the correlation of the channel\nfrequency responses in narrow-band mMTC, we propose a block-wise linear channel\nmodel. Specifically, the continuous OFDM subcarriers in the narrow-band are\ndivided into several sub-blocks and a linear function with only two variables\n(mean and slope) is used to approximate the frequency-selective channel in each\nsub-block. This significantly reduces the number of variables to be determined\nin channel estimation and the sub-block number can be adjusted to reliably\ncompensate the channel frequency-selectivity. Second, we formulate the joint\nactive device detection and channel estimation in the block-wise linear system\nas a Bayesian inference problem. By exploiting the block-sparsity of the\nchannel matrix, we develop an efficient turbo message passing (Turbo-MP)\nalgorithm to resolve the Bayesian inference problem with near-linear\ncomplexity. We further incorporate machine learning approaches into Turbo-MP to\nlearn unknown prior parameters. Numerical results demonstrate the superior\nperformance of the proposed algorithm over state-of-the-art algorithms.\n"}
+{"abstract": "  The quest for nonmagnetic Weyl semimetals with high tunability of phase has\nremained a demanding challenge. As the symmetry breaking control parameter, the\nferroelectric order can be steered to turn on/off the Weyl semimetals phase,\nadjust the band structures around the Fermi level, and enlarge/shrink the\nmomentum separation of Weyl nodes which generate the Berry curvature as the\nemergent magnetic field. Here, we report the realization of a ferroelectric\nnonmagnetic Weyl semimetal based on indium doped Pb1 xSnxTe alloy where the\nunderlying inversion symmetry as well as mirror symmetry is broken with the\nstrength of ferroelectricity adjustable via tuning indium doping level and\nSn/Pb ratio. The transverse thermoelectric effect, i.e., Nernst effect both for\nout of plane and in plane magnetic field geometry, is exploited as a Berry\ncurvature sensitive experimental probe to manifest the generation of Berry\ncurvature via the redistribution of Weyl nodes under magnetic fields. The\nresults demonstrate a clean non-magnetic Weyl semimetal coupled with highly\ntunable ferroelectric order, providing an ideal platform for manipulating the\nWeyl fermions in nonmagnetic system.\n"}
+{"abstract": "  The processes of the coronal plasma heating and cooling were previously shown\nto significantly affect the dynamics of slow magnetoacoustic (MA) waves,\ncausing amplification or attenuation, and also dispersion. However, the entropy\nmode is also excited in such a thermodynamically active plasma and is affected\nby the heating/cooling misbalance too. This mode is usually associated with the\nphenomenon of coronal rain and formation of prominences. Unlike the adiabatic\nplasmas, the properties and evolution of slow MA and entropy waves in\ncontinuously heated and cooling plasmas get mixed. Different regimes of the\nmisbalance lead to a variety of scenarios for the initial perturbation to\nevolve. In order to describe properties and evolution of slow MA and entropy\nwaves in various regimes of the misbalance, we obtained an exact analytical\nsolution of the linear evolutionary equation. Using the characteristic\ntimescales and the obtained exact solution, we identified regimes with\nqualitatively different behaviour of slow MA and entropy modes. For some of\nthose regimes, the spatio-temporal evolution of the initial Gaussian pulse is\nshown. In particular, it is shown that slow MA modes may have a range of\nnon-propagating harmonics. In this regime, perturbations caused by slow MA and\nentropy modes in a low-$\\beta$ plasma would look identically in observations,\nas non-propagating disturbances of the plasma density (and temperature) either\ngrowing or decaying with time. We also showed that the partition of the initial\nenergy between slow MA and entropy modes depends on the properties of the\nheating and cooling processes involved. The obtained exact analytical solution\ncould be further applied to the interpretation of observations and results of\nnumerical modelling of slow MA waves in the corona and the formation and\nevolution of coronal rain.\n"}
+{"abstract": "  We discuss how colour flows can be used to simplify the computation of matrix\nelements, and in the context of parton shower Monte Carlos with accuracy beyond\nleading-colour. We show that, by systematically employing them, the results for\ntree-level matrix elements and their soft limits can be given in a closed form\nthat does not require any colour algebra. The colour flows that we define are a\nnatural generalization of those exploited by existing Monte Carlos; we\nconstruct their representations in terms of different but conceptually\nequivalent quantities, namely colour loops and dipole graphs, and examine how\nthese objects may help to extend the accuracy of Monte Carlos through the\ninclusion of subleading-colour effects. We show how the results that we obtain\ncan be used, with trivial modifications, in the context of QCD+QED simulations,\nsince we are able to put the gluon and photon soft-radiation patterns on the\nsame footing. We also comment on some peculiar properties of gluon-only colour\nflows, and their relationships with established results in the mathematics of\npermutations.\n"}
+{"abstract": "  State-of-the-art music recommender systems are based on collaborative\nfiltering, which builds upon learning similarities between users and songs from\nthe available listening data. These approaches inherently face the cold-start\nproblem, as they cannot recommend novel songs with no listening history.\nContent-aware recommendation addresses this issue by incorporating content\ninformation about the songs on top of collaborative filtering. However, methods\nfalling in this category rely on a shallow user/item interaction that\noriginates from a matrix factorization framework. In this work, we introduce\nneural content-aware collaborative filtering, a unified framework which\nalleviates these limits, and extends the recently introduced neural\ncollaborative filtering to its content-aware counterpart. We propose a\ngenerative model which leverages deep learning for both extracting content\ninformation from low-level acoustic features and for modeling the interaction\nbetween users and songs embeddings. The deep content feature extractor can\neither directly predict the item embedding, or serve as a regularization prior,\nyielding two variants (strict} and relaxed) of our model. Experimental results\nshow that the proposed method reaches state-of-the-art results for a cold-start\nmusic recommendation task. We notably observe that exploiting deep neural\nnetworks for learning refined user/item interactions outperforms approaches\nusing a more simple interaction model in a content-aware framework.\n"}
+{"abstract": "  An automated treatment of iterated integrals based on letters induced by\nreal-valued quadratic forms and Kummer--Poincar\\'e letters is presented. These\nquantities emerge in analytic single and multi--scale Feynman diagram\ncalculations. To compactify representations, one wishes to apply general\nproperties of these quantities in computer-algebraic implementations. We\nprovide the reduction to basis representations, expansions, analytic\ncontinuation and numerical evaluation of these quantities.\n"}
+{"abstract": "  As shown in earlier work, skew-adjoint linear differential operators, mapping\nefforts into flows, give rise to Dirac structures on a bounded spatial domain\nby a proper definition of boundary variables. In the present paper this is\nextended to pairs of linear differential operators defining a formally\nskew-adjoint relation between flows and efforts. Furthermore it is shown how\nthe underlying repeated integration by parts operation can be streamlined by\nthe use of two-variable polynomial calculus. Dirac structures defined by\nformally skew adjoint operators and differential operator effort constraints\nare treated within the same framework. Finally it is sketched how the approach\ncan be also used for Lagrangian subspaces on bounded domains.\n"}
+{"abstract": "  Recent observations made with Advanced LIGO and Advanced Virgo have initiated\nthe era of gravitational-wave astronomy. The number of events detected by these\n\"2nd Generation\" (2G) ground-based observatories is partially limited by noise\narising from temperature-induced position fluctuations of the test mass mirror\nsurfaces used for probing spacetime dynamics. The design of next-generation\ngravitational-wave observatories addresses this limitation by using\ncryogenically cooled test masses; current approaches for continuously removing\nheat (resulting from absorbed laser light) rely on heat extraction via\nblack-body radiation or conduction through suspension fibres. As a\ncomplementing approach for extracting heat during observational runs, we\ninvestigate cooling via helium gas impinging on the test mass in free molecular\nflow. We establish a relation between cooling power and corresponding\ndisplacement noise, based on analytical models, which we compare to numerical\nsimulations. Applying this theoretical framework with regard to the conceptual\ndesign of the Einstein Telescope (ET), we find a cooling power of 10 mW at 18 K\nfor a gas pressure that exceeds the ET design strain noise goal by at most a\nfactor of $\\sim 3$ in the signal frequency band from 3 to 11 Hz. A cooling\npower of 100 mW at 18 K corresponds to a gas pressure that exceeds the ET\ndesign strain noise goal by at most a factor of $\\sim 11$ in the band from 1 to\n28 Hz.\n"}
+{"abstract": "  In 1955, Lehto showed that, for every measurable function $\\psi$ on the unit\ncircle $\\mathbb T,$ there is function $f$ holomorphic in the unit disc $\\mathbb\nD,$ having $\\psi$ as radial limit a.e. on $\\mathbb T.$ We consider an analogous\nboundary value problem, where the unit disc is replaced by a Stein domain on a\ncomplex manifold and radial approach to a boundary point $p$ is replaced by\n(asymptotically) total approach to $p.$\n"}
+{"abstract": "  How difficult are interactive theorem provers to use? We respond by reviewing\nthe formalization of Hilbert's tenth problem in Isabelle/HOL carried out by an\nundergraduate research group at Jacobs University Bremen. We argue that, as\ndemonstrated by our example, proof assistants are feasible for beginners to\nformalize mathematics. With the aim to make the field more accessible, we also\nsurvey hurdles that arise when learning an interactive theorem prover. Broadly,\nwe advocate for an increased adoption of interactive theorem provers in\nmathematical research and curricula.\n"}
+{"abstract": "  The ALS-U light source will implement on-axis single-train swap-out injection\nemploying an accumulator between the booster and storage rings. The accumulator\nring design is a twelve period triple-bend achromat that will be installed\nalong the inner circumference of the storage-ring tunnel. A non-conventional\ninjection scheme will be utilized for top-off off-axis injection from the\nbooster into the accumulator ring meant to accommodate a large $\\sim 300$~nm\nemittance beam into a vacuum-chamber with a limiting horizontal aperture radius\nas small as $8$ mm. The scheme incorporates three dipole kickers distributed\nover three sectors, with two kickers perturbing the stored beam and the third\naffecting both the stored and the injected beam trajectories. This paper\ndescribes this ``3DK'' injection scheme and how it fits the accumulator ring's\nparticular requirements. We describe the design and optimization process, and\nhow we evaluated its fitness as a solution for booster-to-accumulator ring\ninjection.\n"}
+{"abstract": "  Quantum channels, which break entanglement, incompatibility, or nonlocality,\nare not useful for entanglement-based, one-sided device-independent, or\ndevice-independent quantum information processing, respectively. Here, we show\nthat such breaking channels are related to certain temporal quantum\ncorrelations, i.e., temporal separability, channel unsteerability, temporal\nunsteerability, and macrorealism. More specifically, we first define the\nsteerability-breaking channel, which is conceptually similar to the\nentanglement and nonlocality-breaking channels and prove that it is identical\nto the incompatibility-breaking channel. Similar to the hierarchy relations of\nthe temporal and spatial quantum correlations, the hierarchy of non-breaking\nchannels is discussed. We then introduce the concept of the channels which\nbreak temporal correlations, explain how they are related to the standard\nbreaking channels, and prove the following results: (1) A certain measure of\ntemporal nonseparability can be used to quantify a non-entanglement-breaking\nchannel in the sense that the measure is a memory monotone under the framework\nof the resource theory of the quantum memory. (2) A non-steerability-breaking\nchannel can be certified with channel steering because the\nsteerability-breaking channel is equivalent to the incompatibility-breaking\nchannel. (3) The temporal steerability and non-macrorealism can, respectively,\ndistinguish the steerability-breaking and the nonlocality-breaking unital\nchannel from their corresponding non-breaking channels. Finally, a\ntwo-dimensional depolarizing channel is experimentally implemented as a\nproof-of-principle example to compare the temporal quantum correlations with\nnon-breaking channels.\n"}
+{"abstract": "  We obtain exact densities of contractible and non-contractible loops in the\nO(1) model on a strip of the square lattice rolled into an infinite cylinder of\nfinite even circumference $L$. They are also equal to the densities of critical\npercolation clusters on forty five degree rotated square lattice rolled into a\ncylinder, which do not or do wrap around the cylinder respectively. The results\nare presented as explicit rational functions of $L$ taking rational values for\nany even $L$. Their asymptotic expansions in the large $L$ limit have\nirrational coefficients reproducing the earlier results in the leading orders.\nThe solution is based on a mapping to the six-vertex model and the use of\ntechnique of Baxter's T-Q equation.\n"}
+{"abstract": "  To date, close to fifty presumed black hole binary mergers were observed by\nthe LIGO and Virgo detectors. The analyses have been done with an assumption\nthat these objects are black holes by limiting the spin prior to the Kerr\nbound. However, the above assumption is not valid for superspinars, which have\nthe Kerr geometry but rotate beyond the Kerr bound. In this study, we\ninvestigate whether and how the limited spin prior range causes a bias in\nparameter estimation for superspinars if they are detected. To this end, we\nestimate binary parameters of the simulated inspiral signals of the\ngravitational waves of compact binaries by assuming that at least one component\nof them is a superspinar. We have found that when the primary is a superspinar,\nboth mass and spin parameters are biased in parameter estimation due to the\nlimited spin prior range. In this case, the extended prior range is strongly\nfavored compared to the limited one. On the other hand, when the primary is a\nblack hole, we do not see much bias in parameter estimation due to the limited\nspin prior range, even though the secondary is a superspinar. We also apply the\nanalysis to black hole binary merger events GW170608 and GW190814, which have a\nlong and loud inspiral signal. We do not see any preference of superspinars\nfrom the model selection for both events. We conclude that the extension of the\nspin prior range is necessary for accurate parameter estimation if highly\nspinning primary objects are found, while it is difficult to identify\nsuperspinars if they are only the secondary objects. Nevertheless, the bias in\nparameter estimation of spin for the limited spin prior range can be a clue of\nthe existence of superspinars.\n"}
+{"abstract": "  Image classification models deployed in the real world may receive inputs\noutside the intended data distribution. For critical applications such as\nclinical decision making, it is important that a model can detect such\nout-of-distribution (OOD) inputs and express its uncertainty. In this work, we\nassess the capability of various state-of-the-art approaches for\nconfidence-based OOD detection through a comparative study and in-depth\nanalysis. First, we leverage a computer vision benchmark to reproduce and\ncompare multiple OOD detection methods. We then evaluate their capabilities on\nthe challenging task of disease classification using chest X-rays. Our study\nshows that high performance in a computer vision task does not directly\ntranslate to accuracy in a medical imaging task. We analyse factors that affect\nperformance of the methods between the two tasks. Our results provide useful\ninsights for developing the next generation of OOD detection methods.\n"}
+{"abstract": "  Referring to a recent experiment, we theoretically study the process of a\ntwo-channel decay of the diatomic silver anion (Ag$_2^-$), namely the\nspontaneous electron ejection giving Ag$_2$ + e$^-$ and the dissociation\nleading to Ag$^-$ + Ag. The ground state potential energy curves of the silver\nmolecules of diatomic neutral and negative ion were calculated using proper\npseudo-potentials and atomic basis sets. We also estimated the non-adiabatic\nelectronic coupling between the ground state of Ag$_2^-$ and the ground state\nof Ag$_2$ + e$^-$, which in turn allowed us to estimate the minimal and mean\nvalues of the electron autodetachment lifetimes. The relative energies of the\nrovibrational levels allow the description of the spontaneous electron emission\nprocess, while the description of the rotational dissociation is treated with\nthe quantum dynamics method as well as time-independent methods. The results of\nour calculations are verified by comparison with experimental data.\n"}
+{"abstract": "  To manage the COVID-19 epidemic effectively, decision-makers in public health\nneed accurate forecasts of case numbers. A potential near real-time predictor\nof future case numbers is human mobility; however, research on the predictive\npower of mobility is lacking. To fill this gap, we introduce a novel model for\nepidemic forecasting based on mobility data, called mobility marked Hawkes\nmodel. The proposed model consists of three components: (1) A Hawkes process\ncaptures the transmission dynamics of infectious diseases. (2) A mark modulates\nthe rate of infections, thus accounting for how the reproduction number R\nvaries across space and time. The mark is modeled using a regularized Poisson\nregression based on mobility covariates. (3) A correction procedure\nincorporates new cases seeded by people traveling between regions. Our model\nwas evaluated on the COVID-19 epidemic in Switzerland. Specifically, we used\nmobility data from February through April 2020, amounting to approximately 1.5\nbillion trips. Trip counts were derived from large-scale telecommunication\ndata, i.e., cell phone pings from the Swisscom network, the largest\ntelecommunication provider in Switzerland. We compared our model against\nvarious state-of-the-art baselines in terms of out-of-sample root mean squared\nerror. We found that our model outperformed the baselines by 15.52%. The\nimprovement was consistently achieved across different forecast horizons\nbetween 5 and 21 days. In addition, we assessed the predictive power of\nconventional point of interest data, confirming that telecommunication data is\nsuperior. To the best of our knowledge, our work is the first to predict the\nspread of COVID-19 from telecommunication data. Altogether, our work\ncontributes to previous research by developing a scalable early warning system\nfor decision-makers in public health tasked with controlling the spread of\ninfectious diseases.\n"}
+{"abstract": "  Dropout has been demonstrated as a simple and effective module to not only\nregularize the training process of deep neural networks, but also provide the\nuncertainty estimation for prediction. However, the quality of uncertainty\nestimation is highly dependent on the dropout probabilities. Most current\nmodels use the same dropout distributions across all data samples due to its\nsimplicity. Despite the potential gains in the flexibility of modeling\nuncertainty, sample-dependent dropout, on the other hand, is less explored as\nit often encounters scalability issues or involves non-trivial model changes.\nIn this paper, we propose contextual dropout with an efficient structural\ndesign as a simple and scalable sample-dependent dropout module, which can be\napplied to a wide range of models at the expense of only slightly increased\nmemory and computational cost. We learn the dropout probabilities with a\nvariational objective, compatible with both Bernoulli dropout and Gaussian\ndropout. We apply the contextual dropout module to various models with\napplications to image classification and visual question answering and\ndemonstrate the scalability of the method with large-scale datasets, such as\nImageNet and VQA 2.0. Our experimental results show that the proposed method\noutperforms baseline methods in terms of both accuracy and quality of\nuncertainty estimation.\n"}
+{"abstract": "  The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with\ncurrently no effective antiviral drug except treatments for symptomatic\ntherapy. Flux balance analysis is an efficient method to analyze metabolic\nnetworks. It allows optimizing for a metabolic function and thus e.g.,\npredicting the growth rate of a specific cell or the production rate of a\nmetabolite of interest. Here flux balance analysis was applied on human lung\ncells infected with severe acute respiratory syndrome coronavirus 2\n(SARS-CoV-2) to reposition metabolic drugs and drug combinations against the\nreplication of the SARS-CoV-2 virus within the host tissue. Making use of\nexpression data sets of infected lung tissue, genome-scale COVID-19-specific\nmetabolic models were reconstructed. Then host-specific essential genes and\ngene-pairs were determined through in-silico knockouts that permit reducing the\nviral biomass production without affecting the host biomass. Key pathways that\nare associated with COVID-19 severity in lung tissue are related to oxidative\nstress, as well as ferroptosis, sphingolipid metabolism, cysteine metabolism,\nand fat digestion. By in-silico screening of FDA approved drugs on the putative\ndisease-specific essential genes and gene-pairs, 45 drugs and 99 drug\ncombinations were predicted as promising candidates for COVID-19 focused drug\nrepositioning (https://github.com/sysbiolux/DCcov). Among the 45 drug\ncandidates, six antiviral drugs were found and seven drugs that are being\ntested in clinical trials against COVID-19. Other drugs like gemcitabine,\nrosuvastatin and acetylcysteine, and drug combinations like\nazathioprine-pemetrexed might offer new chances for treating COVID-19.\n"}
+{"abstract": "  The ensemble covariance matrix of a wide sense stationary signal spatially\nsampled by a full linear array is positive semi-definite and Toeplitz. However,\nthe direct augmented covariance matrix of an augmentable sparse array is\nToeplitz but not positive semi-definite, resulting in negative eigenvalues that\npose inherent challenges in its applications, including model order estimation\nand source localization. The positive eigenvalues-based covariance matrix for\naugmentable sparse arrays is robust but the matrix is unobtainable when all\nnoise eigenvalues of the direct augmented matrix are negative, which is a\npossible case. To address this problem, we propose a robust covariance matrix\nfor augmentable sparse arrays that leverages both positive and negative noise\neigenvalues. The proposed covariance matrix estimate can be used in conjunction\nwith subspace based algorithms and adaptive beamformers to yield accurate\nsignal direction estimates.\n"}
+{"abstract": "  Application of Machine Learning algorithms to the medical domain is an\nemerging trend that helps to advance medical knowledge. At the same time, there\nis a significant a lack of explainable studies that promote informed,\ntransparent, and interpretable use of Machine Learning algorithms. In this\npaper, we present explainable multi-class classification of the Covid-19 mental\nhealth data. In Machine Learning study, we aim to find the potential factors to\ninfluence a personal mental health during the Covid-19 pandemic. We found that\nRandom Forest (RF) and Gradient Boosting (GB) have scored the highest accuracy\nof 68.08% and 68.19% respectively, with LIME prediction accuracy 65.5% for RF\nand 61.8% for GB. We then compare a Post-hoc system (Local Interpretable\nModel-Agnostic Explanations, or LIME) and an Ante-hoc system (Gini Importance)\nin their ability to explain the obtained Machine Learning results. To the best\nof these authors knowledge, our study is the first explainable Machine Learning\nstudy of the mental health data collected during Covid-19 pandemics.\n"}
+{"abstract": "  We consider the problem of mapping a logical quantum circuit onto a given\nhardware with limited two-qubit connectivity. We model this problem as an\ninteger linear program, using a network flow formulation with binary variables\nthat includes the initial allocation of qubits and their routing. We consider\nseveral cost functions: an approximation of the fidelity of the circuit, its\ntotal depth, and a measure of cross-talk, all of which can be incorporated in\nthe model. Numerical experiments on synthetic data and different hardware\ntopologies indicate that the error rate and depth can be optimized\nsimultaneously without significant loss. We test our algorithm on a large\nnumber of quantum volume circuits, optimizing for error rate and depth; our\nalgorithm significantly reduces the number of CNOTs compared to Qiskit's\ndefault transpiler SABRE, and produces circuits that, when executed on\nhardware, exhibit higher fidelity.\n"}
+{"abstract": "  Solar active region 12673 produced two successive X-class flares (X2.2 and\nX9.3) approximately 3 hours apart in September 2017. The X9.3 was the recorded\nlargest solar flare in Solar Cycle 24. In this study we perform a\ndata-constrained magnetohydrodynamic simulation taking into account the\nobserved photospheric magnetic field to reveal the initiation and dynamics of\nthe X2.2 and X9.3 flares. According to our simulation, the X2.2 flare is first\ntriggered by magnetic reconnection at a local site where at the photosphere the\nnegative polarity intrudes into the opposite-polarity region. This magnetic\nreconnection expels the innermost field lines upward beneath which the magnetic\nflux rope is formed through continuous reconnection with external twisted field\nlines. Continuous magnetic reconnection after the X2.2 flare enhances the\nmagnetic flux rope, which is lifted up and eventually erupts via the torus\ninstability. This gives rise to the X9.3 flare.\n"}
+{"abstract": "  Interconnects are a major discriminator for superconducting digital\ntechnology, enabling energy efficient data transfer and high-bandwidth\nheterogeneous integration. We report a method to simulate propagation of\npicosecond pulses in superconducting passive transmission lines (PTLs). A\nfrequency-domain propagator model obtained from the Ansys High Frequency\nStructure Simulator (HFSS) field solver is incorporated in a Cadence Spectre\ncircuit model, so that the particular PTL geometry can be simulated in the\ntime-domain. The Mattis-Bardeen complex conductivity of the superconductor is\nencoded in the HFSS field solver as a complex-conductivity insulator.\nExperimental and simulation results show that Nb 20 Ohm microstrip PTLs with\n1um width can support propagation of a single-flux-quantum pulse up to 7mm and\na double-flux-quantum pulse up to 28mm.\n"}
+{"abstract": "  We present and analyze optical photometry and high resolution SALT spectra of\nthe symbiotic recurrent nova V3890 Sgr at quiescence. The orbital period,\nP=747.6 days has been derived from both photometric and spectroscopic data. Our\ndouble-line spectroscopic orbits indicate that the mass ratio is\nq=M_g/M_WD=0.78+/-0.05, and that the component masses are M_WD=1.35+/-0.13\nMsun, and M_g=1.05+/-0.11 Msun. The orbit inclination is approximately 67-69\ndegr. The red giant is filling (or nearly filling) its Roche lobe, and the\ndistance set by its Roche lobe radius, d=9 kpc, is consistent with that\nresulting from the giant pulsation period. The outburst magnitude of V3890 Sgr\nis then very similar to those of RNe in the Large Magellanic Cloud. V3890 Sgr\nshows remarkable photometric and spectroscopic activity between the nova\neruptions with timescales similar to those observed in the symbiotic recurrent\nnovae T CrB and RS Oph and Z And-type symbiotic systems. The active source has\na double-temperature structure which we have associated with the presence of an\naccretion disc. The activity would be then caused by changes in the accretion\nrate. We also provide evidence that V3890 Sgr contains a CO WD accreting at a\nhigh, a few 1e-8 - 1e-7 Msun/yr, rate. The WD is growing in mass, and should\ngive rise to a Type Ia supernova within about 1,000,000 yrs - the expected\nlifetime of the red giant.\n"}
+{"abstract": "  Real-world graphs are massive in size and we need a huge amount of space to\nstore them. Graph compression allows us to compress a graph so that we need a\nlesser number of bits per link to store it. Of many techniques to compress a\ngraph, a typical approach is to find clique-like caveman or traditional\ncommunities in a graph and encode those cliques to compress the graph. On the\nother side, an alternative approach is to consider graphs as a collection of\nhubs connecting spokes and exploit it to arrange the nodes such that the\nresulting adjacency matrix of the graph can be compressed more efficiently. We\nperform an empirical comparison of these two approaches and show that both\nmethods can yield good results under favorable conditions. We perform our\nexperiments on ten real-world graphs and define two cost functions to present\nour findings.\n"}
+{"abstract": "  The lifetimes of localized nonlinear modes in both the\n$\\beta$-Fermi-Pasta-Ulam-Tsingou ($\\beta$-FPUT) chain and a cubic $\\beta$-FPUT\nlattice are studied as functions of perturbation amplitude, and by extension,\nthe relative strength of the nonlinear interactions compared to the linear\npart. We first recover the well known result that localized nonlinear\nexcitations (LNEs) produced by a bond squeeze can be reduced to an approximate\ntwo-frequency solution and then show that the nonlinear term in the potential\ncan lead to the production of secondary frequencies within the phonon band.\nThis can affect the stability and lifetime of the LNE by facilitating\ninteractions between the LNE and a low energy acoustic background which can be\nregarded as \"noise\" in the system. In the one dimensional FPUT chain, the LNE\nis stabilized by low energy acoustic emissions at early times; in some cases\nallowing for lifetimes several orders of magnitude larger than the oscillation\nperiod. The longest lived LNEs are found to satisfy the parameter dependence\n$\\mathcal{A}\\sqrt{\\beta}\\approx1.1$ where $\\beta$ is the relative nonlinear\nstrength and $\\mathcal{A}$ is the displacement amplitude of the center\nparticles in the LNE. In the cubic FPUT lattice, the LNE lifetime $T$ decreases\nrapidly with increasing amplitude $\\mathcal{A}$ and is well described by the\ndouble log relationship $\\log_{10}\\log_{10}(T)\\approx\n-(0.15\\pm0.01)\\mathcal{A}\\sqrt{\\beta}+(0.62\\pm0.02)$.\n"}
+{"abstract": "  Link prediction is a fundamental challenge in network science. Among various\nmethods, similarity-based algorithms are popular for their simplicity,\ninterpretability, high efficiency and good performance. In this paper, we show\nthat the most elementary local similarity index Common Neighbor (CN) can be\nlinearly decomposed by eigenvectors of the adjacency matrix of the target\nnetwork, with each eigenvector's contribution being proportional to the square\nof the corresponding eigenvalue. As in many real networks, there is a huge gap\nbetween the largest eigenvalue and the second largest eigenvalue, the CN index\nis thus dominated by the leading eigenvector and much useful information\ncontained in other eigenvectors may be overlooked. Accordingly, we propose a\nparameter-free algorithm that ensures the contributions of the leading\neigenvector and the secondary eigenvector the same. Extensive experiments on\nreal networks demonstrate that the prediction performance of the proposed\nalgorithm is remarkably better than well-performed local similarity indices in\nthe literature. A further proposed algorithm that can adjust the contribution\nof leading eigenvector shows the superiority over state-of-the-art algorithms\nwith tunable parameters for its competitive accuracy and lower computational\ncomplexity.\n"}
+{"abstract": "  The great influence of Bitcoin has promoted the rapid development of\nblockchain-based digital currencies, especially the altcoins, since 2013.\nHowever, most altcoins share similar source codes, resulting in concerns about\ncode innovations. In this paper, an empirical study on existing altcoins is\ncarried out to offer a thorough understanding of various aspects associated\nwith altcoin innovations. Firstly, we construct the dataset of altcoins,\nincluding source code repositories, GitHub fork relations, and market\ncapitalizations (cap). Then, we analyze the altcoin innovations from the\nperspective of source code similarities. The results demonstrate that more than\n85% of altcoin repositories present high code similarities. Next, a temporal\nclustering algorithm is proposed to mine the inheritance relationship among\nvarious altcoins. The family pedigrees of altcoin are constructed, in which the\naltcoin presents similar evolution features as biology, such as power-law in\nfamily size, variety in family evolution, etc. Finally, we investigate the\ncorrelation between code innovations and market capitalization. Although we\nfail to predict the price of altcoins based on their code similarities, the\nresults show that altcoins with higher innovations reflect better market\nprospects.\n"}
+{"abstract": "  Reinforcement Learning (RL) is a semi-supervised learning paradigm which an\nagent learns by interacting with an environment. Deep learning in combination\nwith RL provides an efficient method to learn how to interact with the\nenvironment is called Deep Reinforcement Learning (deep RL). Deep RL has gained\ntremendous success in gaming - such as AlphaGo, but its potential have rarely\nbeing explored for challenging tasks like Speech Emotion Recognition (SER). The\ndeep RL being used for SER can potentially improve the performance of an\nautomated call centre agent by dynamically learning emotional-aware response to\ncustomer queries. While the policy employed by the RL agent plays a major role\nin action selection, there is no current RL policy tailored for SER. In\naddition, extended learning period is a general challenge for deep RL which can\nimpact the speed of learning for SER. Therefore, in this paper, we introduce a\nnovel policy - \"Zeta policy\" which is tailored for SER and apply Pre-training\nin deep RL to achieve faster learning rate. Pre-training with cross dataset was\nalso studied to discover the feasibility of pre-training the RL Agent with a\nsimilar dataset in a scenario of where no real environmental data is not\navailable. IEMOCAP and SAVEE datasets were used for the evaluation with the\nproblem being to recognize four emotions happy, sad, angry and neutral in the\nutterances provided. Experimental results show that the proposed \"Zeta policy\"\nperforms better than existing policies. The results also support that\npre-training can reduce the training time upon reducing the warm-up period and\nis robust to cross-corpus scenario.\n"}
+{"abstract": "  Decentralized cryptocurrency exchanges offer compelling security benefits\nover centralized exchanges: users control their funds and avoid the risk of an\nexchange hack or malicious operator. However, because user assets are fully\naccessible by a secret key, decentralized exchanges pose significant internal\nsecurity risks for trading firms and automated trading systems, where a\ncompromised system can result in total loss of funds. Centralized exchanges\nmitigate this risk through API key based security policies that allow\nprofessional users to give individual traders or automated systems specific and\ncustomizable access rights such as trading or withdrawal limits. Such policies,\nhowever, are not compatible with decentralized exchanges, where all exchange\noperations require a signature generated by the owner's secret key. This paper\nintroduces a protocol based upon multiparty computation that allows for the\ncreation of API keys and security policies that can be applied to any existing\ndecentralized exchange. Our protocol works with both ECDSA and EdDSA signature\nschemes and prioritizes efficient computation and communication. We have\ndeployed this protocol on Nash exchange, as well as around several\nEthereum-based automated market maker smart contracts, where it secures the\ntrading accounts and wallets of thousands of users.\n"}
+{"abstract": "  In-phase synchronization is a stable state of identical Kuramoto oscillators\ncoupled on a network with identical positive connections, regardless of network\ntopology. However, this fact does not mean that the networks always synchronize\nin-phase because other attractors besides the stable state may exist. The\ncritical connectivity $\\mu_{\\mathrm{c}}$ is defined as the network connectivity\nabove which only the in-phase state is stable for all the networks. In other\nwords, below $\\mu_{\\mathrm{c}}$, one can find at least one network which has a\nstable state besides the in-phase sync. The best known evaluation of the value\nso far is $0.6828\\cdots\\leq\\mu_{\\mathrm{c}}\\leq0.75$. In this paper, focusing\non the twisted states of the circulant networks, we provide a method to\nsystematically analyze the linear stability of all possible twisted states on\nall possible circulant networks. This method using integer programming enables\nus to find the densest circulant network having a stable twisted state besides\nthe in-phase sync, which breaks a record of the lower bound of the\n$\\mu_{\\mathrm{c}}$ from $0.6828\\cdots$ to $0.6838\\cdots$. We confirm the\nvalidity of the theory by numerical simulations of the networks not converging\nto the in-phase state.\n"}
+{"abstract": "  We study two-photon scattering in a mixed cavity optomechanical system, which\nis composed of a single-mode cavity field coupled to a single-mode mechanical\noscillation via both the first-order and quadratic optomechanical interactions.\nBy solving the scattering problem within the Wigner-Weisskopf framework, we\nobtain the analytical scattering state and find four physical processes\nassociated with the two-photon scattering in this system. We calculate the\ntwo-photon scattering spectrum and find that two-photon frequency\nanticorrelation can be induced in the scattering process. We also establish the\nrelationship between the parameters of the mixed cavity optomechanical system\nand the characteristics of the two-photon scattering spectrum. This work not\nonly provides a scattering means to create correlated photon pairs, but also\npresents a spectrometric method to characterize the optomechanical systems.\n"}
+{"abstract": "  Survey scientists increasingly face the problem of high-dimensionality in\ntheir research as digitization makes it much easier to construct\nhigh-dimensional (or \"big\") data sets through tools such as online surveys and\nmobile applications. Machine learning methods are able to handle such data, and\nthey have been successfully applied to solve \\emph{predictive} problems.\nHowever, in many situations, survey statisticians want to learn about\n\\emph{causal} relationships to draw conclusions and be able to transfer the\nfindings of one survey to another. Standard machine learning methods provide\nbiased estimates of such relationships. We introduce into survey statistics the\ndouble machine learning approach, which gives approximately unbiased estimators\nof causal parameters, and show how it can be used to analyze survey nonresponse\nin a high-dimensional panel setting.\n"}
+{"abstract": "  Scanning tunneling microscope lithography can be used to create\nnanoelectronic devices in which dopant atoms are precisely positioned in a Si\nlattice within $\\sim$1 nm of a target position. This exquisite precision is\npromising for realizing various quantum technologies. However, a potentially\nimpactful form of disorder is due to incorporation kinetics, in which the\nnumber of P atoms that incorporate into a single lithographic window is\nmanifestly uncertain. We present experimental results indicating that the\nlikelihood of incorporating into an ideally written three-dimer single-donor\nwindow is $63 \\pm 10\\%$ for room-temperature dosing, and corroborate these\nresults with a model for the incorporation kinetics. Nevertheless, further\nanalysis of this model suggests conditions that might raise the incorporation\nrate to near-deterministic levels. We simulate bias spectroscopy on a chain of\ncomparable dimensions to the array in our yield study, indicating that such an\nexperiment may help confirm the inferred incorporation rate.\n"}
+{"abstract": "  We compute the phase diagram of the simplest holographic bottom-up model of\nconformal interfaces. The model consists of a thin domain wall between\nthree-dimensional Anti-de Sitter (AdS) vacua, anchored on a boundary circle. We\ndistinguish five phases depending on the existence of a black hole, the\nintersection of its horizon with the wall, and the fate of inertial observers.\nWe show that, like the Hawking-Page phase transition, the capture of the wall\nby the horizon is also a first order transition and comment on its field-theory\ninterpretation. The static solutions of the domain-wall equations include\ngravitational avatars of the Faraday cage, black holes with negative specific\nheat, and an intriguing phenomenon of suspended vacuum bubbles corresponding to\nan exotic interface/anti-interface fusion. Part of our analysis overlaps with\nrecent work by Simidzija and Van Raamsdonk but the interpretation is different.\n"}
+{"abstract": "  We study the extent to which it is possible to approximate the optimal value\nof a Unique Games instance in Fixed-Point Logic with Counting (FPC). We prove\ntwo new FPC-inexpressibility results for Unique Games: the existence of a (1/2,\n1/3 + $\\delta$)-inapproximability gap, and inapproximability to within any\nconstant factor. Previous recent work has established similar\nFPC-inapproximability results for a small handful of other problems. Our\nconstruction builds upon some of these ideas, but contains a novel technique.\nWhile most FPC-inexpressibility results are based on variants of the\nCFI-construction, ours is significantly different.\n"}
+{"abstract": "  We report some recent results on analytic pseudodifferential operators, also\nknown as Wick operators. An important tool in our study is the Bargmann\ntransform which provides a coupling between the classical (real) and analytic\npseudodifferential calculus. Since the Bargmann transform of Hermite functions\ngives rise to formal power series in the complex domain, the results are\nformulated in terms of the Bargmann images of Pilipovi\\'c spaces.\n"}
+{"abstract": "  We study the shape of the normalized stable L\\'{e}vy tree $\\mathcal{T}$ near\nits root. We show that, when zooming in at the root at the proper speed with a\nscaling depending on the index of stability, we get the unnormalized Kesten\ntree. In particular the limit is described by a tree-valued Poisson point\nprocess which does not depend on the initial normalization. We apply this to\nstudy the asymptotic behavior of additive functionals of the form\n\\[\\mathbf{Z}_{\\alpha,\\beta}=\\int_{\\mathcal{T}} \\mu(\\mathrm{d} x) \\int_0^{H(x)}\n\\sigma_{r,x}^\\alpha \\mathfrak{h}_{r,x}^\\beta\\,\\mathrm{d} r\\]as\n$\\max(\\alpha,\\beta) \\to \\infty$, where $\\mu$ is the mass measure on\n$\\mathcal{T}$, $H(x)$ is the height of $x$ and $\\sigma_{r,x}$ (resp.\n$\\mathfrak{h}_{r,x}$) is the mass (resp. height) of the subtree of\n$\\mathcal{T}$ above level $r$ containing $x$. Such functionals arise as scaling\nlimits of additive functionals of the size and height on conditioned\nBienaym{\\'e}-Galton-Watson trees.\n"}
+{"abstract": "  Order-agnostic autoregressive distribution (density) estimation (OADE), i.e.,\nautoregressive distribution estimation where the features can occur in an\narbitrary order, is a challenging problem in generative machine learning. Prior\nwork on OADE has encoded feature identity by assigning each feature to a\ndistinct fixed position in an input vector. As a result, architectures built\nfor these inputs must strategically mask either the input or model weights to\nlearn the various conditional distributions necessary for inferring the full\njoint distribution of the dataset in an order-agnostic way. In this paper, we\npropose an alternative approach for encoding feature identities, where each\nfeature's identity is included alongside its value in the input. This feature\nidentity encoding strategy allows neural architectures designed for sequential\ndata to be applied to the OADE task without modification. As a proof of\nconcept, we show that a Transformer trained on this input (which we refer to as\n\"the DEformer\", i.e., the distribution estimating Transformer) can effectively\nmodel binarized-MNIST, approaching the performance of fixed-order\nautoregressive distribution estimating algorithms while still being entirely\norder-agnostic. Additionally, we find that the DEformer surpasses the\nperformance of recent flow-based architectures when modeling a tabular dataset.\n"}
+{"abstract": "  The long fascination antiferromagnetic materials have exerted on the\nscientific community over about a century has been entirely renewed recently\nwith the discovery of several unexpected phenomena including various classes of\nanomalous spin and charge Hall effects and unconventional magnonic transport,\nbut also homochiral magnetic entities such as skyrmions. With these\nbreakthroughs, antiferromagnets standout as a rich playground for the\ninvestigation of novel topological behaviors, and as promising candidate\nmaterials for disruptive low-power microelectronic applications. Remarkably,\nthe newly discovered phenomena are all related to the topology of the magnetic,\nelectronic or magnonic ground state of the antiferromagnets. This review\nexposes how non-trivial topology emerges at different levels in\nantiferromagnets and explores the novel mechanisms that have been discovered\nrecently. We also discuss how novel classes of quantum magnets could enrich the\ncurrently expanding field of antiferromagnetic spintronics and how spin\ntransport can in turn favor a better understanding of exotic quantum\nexcitations.\n"}
+{"abstract": "  This study presents a physically consistent displacement-driven reformulation\nof the concept of action-at-a-distance, which is at the foundation of nonlocal\nelasticity. In contrast to existing approaches that adopts an integral\nstress-strain constitutive relation, the displacement-driven approach is\npredicated on an integral strain-displacement relation. The most remarkable\nconsequence of this reformulation is that the (total) strain energy is\nguaranteed to be convex and positive-definite without imposing any constraint\non the symmetry of the kernels. This feature is critical to enable the\napplication of nonlocal formulations to general continua exhibiting asymmetric\ninteractions; ultimately a manifestation of material heterogeneity. Remarkably,\nthe proposed approach also enables a strong satisfaction of the locality\nrecovery condition and of the laws of thermodynamics, which are not foregone\nconclusions in most classical nonlocal elasticity theories. Additionally, the\nformulation is frame-invariant and the nonlocal operator remains physically\nconsistent at boundaries. The study is complemented by a detailed analysis of\nthe dynamic response of the nonlocal continuum and of its intrinsic dispersion\nleading to the consideration that the choice of nonlocal kernels should depend\non the specific material. Examples of exponential or power-law kernels are\npresented in order to demonstrate the applicability of the method to different\nclasses of nonlocal media. The ability to admit generalized kernels reinforces\nthe generalized nature of the displacement-driven approach over existing\nintegral methodologies, which typically lead to simplified differential models\nbased on exponential kernels. The theoretical formulation is also leveraged to\nsimulate the static response of nonlocal beams and plates illustrating the\nintrinsic consistency of the approach, which is free from unwanted boundary\neffects.\n"}
+{"abstract": "  Recent studies on semantic frame induction show that relatively high\nperformance has been achieved by using clustering-based methods with\ncontextualized word embeddings. However, there are two potential drawbacks to\nthese methods: one is that they focus too much on the superficial information\nof the frame-evoking verb and the other is that they tend to divide the\ninstances of the same verb into too many different frame clusters. To overcome\nthese drawbacks, we propose a semantic frame induction method using masked word\nembeddings and two-step clustering. Through experiments on the English FrameNet\ndata, we demonstrate that using the masked word embeddings is effective for\navoiding too much reliance on the surface information of frame-evoking verbs\nand that two-step clustering can improve the number of resulting frame clusters\nfor the instances of the same verb.\n"}
+{"abstract": "  A recent strand of research in structural proof theory aims at exploring the\nnotion of analytic calculi (i.e. those calculi that support general and modular\nproof-strategies for cut elimination), and at identifying classes of logics\nthat can be captured in terms of these calculi. In this context, Wansing\nintroduced the notion of proper display calculi as one possible design\nframework for proof calculi in which the analiticity desiderata are realized in\na particularly transparent way. Recently, the theory of properly displayable\nlogics (i.e. those logics that can be equivalently presented with some proper\ndisplay calculus) has been developed in connection with generalized Sahlqvist\ntheory (aka unified correspondence). Specifically, properly displayable logics\nhave been syntactically characterized as those axiomatized by analytic\ninductive axioms, which can be equivalently and algorithmically transformed\ninto analytic structural rules so that the resulting proper display calculi\nenjoy a set of basic properties: soundness, completeness, conservativity, cut\nelimination and subformula property. In this context, the proof that the given\ncalculus is complete w.r.t. the original logic is usually carried out\nsyntactically, i.e. by showing that a (cut free) derivation exists of each\ngiven axiom of the logic in the basic system to which the analytic structural\nrules algorithmically generated from the given axiom have been added. However,\nso far this proof strategy for syntactic completeness has been implemented on a\ncase-by-case base, and not in general. In this paper, we address this gap by\nproving syntactic completeness for properly displayable logics in any normal\n(distributive) lattice expansion signature. Specifically, we show that for\nevery analytic inductive axiom a cut free derivation can be effectively\ngenerated which has a specific shape, referred to as pre-normal form.\n"}
+{"abstract": "  We initiate the study of fine-grained completeness theorems for exact and\napproximate optimization in the polynomial-time regime. Inspired by the first\ncompleteness results for decision problems in P (Gao, Impagliazzo, Kolokolova,\nWilliams, TALG 2019) as well as the classic class MaxSNP and\nMaxSNP-completeness for NP optimization problems (Papadimitriou, Yannakakis,\nJCSS 1991), we define polynomial-time analogues MaxSP and MinSP, which contain\na number of natural optimization problems in P, including Maximum Inner\nProduct, general forms of nearest neighbor search and optimization variants of\nthe $k$-XOR problem. Specifically, we define MaxSP as the class of problems\ndefinable as $\\max_{x_1,\\dots,x_k} \\#\\{ (y_1,\\dots,y_\\ell) :\n\\phi(x_1,\\dots,x_k, y_1,\\dots,y_\\ell) \\}$, where $\\phi$ is a quantifier-free\nfirst-order property over a given relational structure (with MinSP defined\nanalogously). On $m$-sized structures, we can solve each such problem in time\n$O(m^{k+\\ell-1})$. Our results are:\n  - We determine (a sparse variant of) the Maximum/Minimum Inner Product\nproblem as complete under *deterministic* fine-grained reductions: A strongly\nsubquadratic algorithm for Maximum/Minimum Inner Product would beat the\nbaseline running time of $O(m^{k+\\ell-1})$ for *all* problems in MaxSP/MinSP by\na polynomial factor.\n  - This completeness transfers to approximation: Maximum/Minimum Inner Product\nis also complete in the sense that a strongly subquadratic $c$-approximation\nwould give a $(c+\\varepsilon)$-approximation for all MaxSP/MinSP problems in\ntime $O(m^{k+\\ell-1-\\delta})$, where $\\varepsilon > 0$ can be chosen\narbitrarily small. Combining our completeness with~(Chen, Williams, SODA 2019),\nwe obtain the perhaps surprising consequence that refuting the OV Hypothesis is\n*equivalent* to giving a $O(1)$-approximation for all MinSP problems in\nfaster-than-$O(m^{k+\\ell-1})$ time.\n"}
+{"abstract": "  We study baryonic matter with isospin asymmetry, including fully dynamically\nits interplay with pion condensation. To this end, we employ the holographic\nWitten-Sakai-Sugimoto model and the so-called homogeneous ansatz for the gauge\nfields in the bulk to describe baryonic matter. Within the confined geometry\nand restricting ourselves to the chiral limit, we map out the phase structure\nin the presence of baryon and isospin chemical potentials, showing that for\nsufficiently large chemical potentials condensed pions and isospin-asymmetric\nbaryonic matter coexist. We also present first results of the same approach in\nthe deconfined geometry and demonstrate that this case, albeit technically more\ninvolved, is better suited for comparisons with and predictions for real-world\nQCD. Our study lays the ground for future improved holographic studies aiming\ntowards a realistic description of charge neutral, beta-equilibrated matter in\ncompact stars, and also for more refined comparisons with lattice studies at\nnonzero isospin chemical potential.\n"}
+{"abstract": "  This paper studies recurrence phenomena in iterative holomorphic dynamics of\ncertain multi-valued maps. In particular, we prove an analogue of the\nPoincar\\'e recurrence theorem for meromorphic correspondences with respect to\ncertain dynamically interesting measures associated with them. Meromorphic\ncorrespondences present a significant measure-theoretic obstacle: the image of\na Borel set under a meromorphic correspondence need not be Borel. We manage\nthis issue using the Measurable Projection Theorem, which is an aspect of\ndescriptive set theory. We also prove a result concerning invariance properties\nof the supports of the measures mentioned.\n"}
+{"abstract": "  Polynomial chaos expansions (PCEs) have been used in many real-world\nengineering applications to quantify how the uncertainty of an output is\npropagated from inputs. PCEs for models with independent inputs have been\nextensively explored in the literature. Recently, different approaches have\nbeen proposed for models with dependent inputs to expand the use of PCEs to\nmore real-world applications. Typical approaches include building PCEs based on\nthe Gram-Schmidt algorithm or transforming the dependent inputs into\nindependent inputs. However, the two approaches have their limitations\nregarding computational efficiency and additional assumptions about the input\ndistributions, respectively. In this paper, we propose a data-driven approach\nto build sparse PCEs for models with dependent inputs. The proposed algorithm\nrecursively constructs orthonormal polynomials using a set of monomials based\non their correlations with the output. The proposed algorithm on building\nsparse PCEs not only reduces the number of minimally required observations but\nalso improves the numerical stability and computational efficiency. Four\nnumerical examples are implemented to validate the proposed algorithm.\n"}
+{"abstract": "  Gaia provided the largest-ever catalogue of white dwarf stars. We use this\ncatalogue, along with the third public data release of the Zwicky Transient\nFacility (ZTF), to identify new eclipsing white dwarf binaries. Our method\nexploits light curve statistics and the Box Least Squares algorithm to detect\nperiodic light curve variability. The search revealed 18 new binaries, of which\n17 are eclipsing. We use the position in the Gaia H-R diagram to classify these\nbinaries and find that the majority of these white dwarfs have main sequence\ncompanions. We identify one system as a candidate eclipsing white dwarf--brown\ndwarf binary and a further two as extremely low mass (ELM) white dwarf\nbinaries. We also provide identification spectroscopy for 17 of our 18\nbinaries. Running our search method on mock light curves with real ZTF\nsampling, we estimate our efficiency of detecting objects with light curves\nsimilar to the ones of the newly discovered binaries. Many more binaries are to\nbe found in the ZTF footprint as the data releases grow, so our survey is\nongoing.\n"}
+{"abstract": "  Proton radiography is a widely-fielded diagnostic used to measure magnetic\nstructures in plasma. The deflection of protons with multi-MeV kinetic energy\nby the magnetic fields is used to infer their path-integrated field strength.\nHere, the use of tomographic methods is proposed for the first time to lift the\ndegeneracy inherent in these path-integrated measurements, allowing full\nreconstruction of spatially resolved magnetic field structures in three\ndimensions. Two techniques are proposed which improve the performance of\ntomographic reconstruction algorithms in cases with severely limited numbers of\navailable probe beams, as is the case in laser-plasma interaction experiments\nwhere the probes are created by short, high-power laser pulse irradiation of\nsecondary foil targets. The methods are equally applicable to optical probes\nsuch as shadowgraphy and interferometry [M. Kasim et al. Phys. Rev. E 95,\n023306 (2017)], thereby providing a disruptive new approach to three\ndimensional imaging across the physical sciences and engineering disciplines.\n"}
+{"abstract": "  This paper proposes a method to probabilistically quantify the moments (mean\nand variance) of excavated material during excavation by aggregating the prior\nmoments of the grade blocks around the given bucket dig location. By modelling\nthe moments as random probability density functions (pdf) at sampled locations,\na formulation of the sums of Gaussian based uncertainty estimation is presented\nthat jointly estimates the location pdfs, as well as the prior values for\nuncertainty coming from ore body knowledge (obk) sub block models. The moments\ncalculated at each random location is a single Gaussian and they are the\ncomponents of Gaussian mixture distribution. The overall uncertainty of the\nexcavated material at the given bucket location is represented by the Gaussian\nMixture Model (GMM) and therefore moment matching method is proposed to\nestimate the moments of the reduced GMM. The method was tested in a region at a\nPilbara iron ore deposit situated in the Brockman Iron Formation of the\nHamersley Province, Western Australia, and suggests a frame work to quantify\nthe uncertainty in the excavated material that hasn't been studied anywhere in\nthe literature yet.\n"}
+{"abstract": "  We describe a physics program at the Relativistic Heavy Ion Collider (RHIC)\nwith tagged forward protons. The program started with the proton-proton elastic\nscattering experiment (PP2PP), for which a set of Roman Pot stations was build.\nThe PP2PP experiment took data at RHIC as a dedicated experiment at the\nbeginning of RHIC operations. To expand the physics program to include\nnon-elastic channels with forward protons, like Central Exclusive Production\n(CEP), Central Production (CP) and Single Diffraction Dissociation (SD), the\nexperiment with its equipment was merged with the STAR experiment at RHIC.\nConsequently the expanded program, which included both elastic and inelastic\nchannels became part of the physics program and operations of the STAR\nexperiment. In this paper we shall describe the physics results obtained by the\nPP2PP and STAR experiments to date.\n"}
+{"abstract": "  In the present paper, we study the existence of best proximity pair in\nultrametric spaces. We show, under suitable assumptions, that the proximinal\npair $(A,B)$ has a best proximity pair. As a consequence we generalize a well\nknown best approximation result and we derive some fixed point theorems.\nMoreover, we provide examples to illustrate the obtained results.\n"}
+{"abstract": "  By using the quantum extremal island formula, we perform a simple calculation\nof the generalized entanglement entropy of Hawking radiation from the two\ndimensional Liouville black hole. No reasonable island was found when\nextremizing the generalized entropy. We explain qualitatively the reason why\nthe page curve cannot be reproduced in the present model. This suggests that\nthe islands may not necessarily save the information paradox for the Liouville\nblack holes.\n"}
+{"abstract": "  We substantially extend our relaxation theory for perturbed many-body quantum\nsystems from [Phys. Rev. Lett. 124, 120602 (2020)] by establishing an\nanalytical prediction for the time-dependent observable expectation values\nwhich depends on only two characteristic parameters of the perturbation\noperator: its overall strength and its range or band width. Compared to the\nprevious theory, a significantly larger range of perturbation strengths is\ncovered. The results are obtained within a typicality framework by solving the\npertinent random matrix problem exactly for a certain class of banded\nperturbations and by demonstrating the (approximative) universality of these\nsolutions, which allows us to adopt them to considerably more general classes\nof perturbations. We also verify the prediction by comparison with several\nnumerical examples.\n"}
+{"abstract": "  With an increase in low-cost machine learning APIs, advanced machine learning\nmodels may be trained on private datasets and monetized by providing them as a\nservice. However, privacy researchers have demonstrated that these models may\nleak information about records in the training dataset via membership inference\nattacks. In this paper, we take a closer look at another inference attack\nreported in literature, called attribute inference, whereby an attacker tries\nto infer missing attributes of a partially known record used in the training\ndataset by accessing the machine learning model as an API. We show that even if\na classification model succumbs to membership inference attacks, it is unlikely\nto be susceptible to attribute inference attacks. We demonstrate that this is\nbecause membership inference attacks fail to distinguish a member from a nearby\nnon-member. We call the ability of an attacker to distinguish the two (similar)\nvectors as strong membership inference. We show that membership inference\nattacks cannot infer membership in this strong setting, and hence inferring\nattributes is infeasible. However, under a relaxed notion of attribute\ninference, called approximate attribute inference, we show that it is possible\nto infer attributes close to the true attributes. We verify our results on\nthree publicly available datasets, five membership, and three attribute\ninference attacks reported in literature.\n"}
+{"abstract": "  We have investigated the magneto-transport properties of beta-Bi4I4 bulk\ncrystal, which was recently theoretically proposed and experimentally\ndemonstrated to be a topological insulator. At low temperature T and magnetic\nfield B, a series of Shubnikov-De Haas(SdH) oscillations are observed on the\nmagnetoresistivity (MR). The detailed analysis reveals a light cyclotron mass\nof 0.1 me, and the field angle dependence of MR reveals that the SdH\noscillations originate from a convex Fermi surface. In the extreme quantum\nlimit (EQL) region, there is a metal-insulator transition occurring soon after\nthe EQL. We perform the scaling analysis, and all the isotherms fall onto a\nuniversal scaling with a fitted critical exponent of 6.5. The enormous value of\ncritical exponent implies this insulating quantum phase originated from strong\nelectron-electron interactions in high fields. However, in the far end of EQL,\nboth the longitudinal and Hall resistivity increase exponentially with B, and\nthe temperature dependence of the MR reveals an energy gap induced by the high\nmagnetic field, signifying a magnetic freeze-out effect. Our findings indicate\nthat bulk beta-Bi4I4 is an excellent candidate for a 3D topological system for\nexploring EQL physics and relevant exotic quantum phases.\n"}
+{"abstract": "  In an active power distribution system, Volt-VAR optimization (VVO) methods\nare employed to achieve network-level objectives such as minimization of\nnetwork power losses. The commonly used model-based centralized and distributed\nVVO algorithms perform poorly in the absence of a communication system and with\nmodel and measurement uncertainties. In this paper, we proposed a model-free\nlocal Volt-VAR control approach for network-level optimization that does not\nrequire communication with other decision-making agents. The proposed algorithm\nis based on extremum-seeking approach that uses only local measurements to\nminimize the network power losses. To prove that the proposed extremum-seeking\ncontroller converges to the optimum solution, we also derive mathematical\nconditions for which the loss minimization problem is convex with respect to\nthe control variables. Local controllers pose stability concerns during highly\nvariable scenarios. Thus, the proposed extremum-seeking controller is\nintegrated with an adaptive-droop control module to provide a stable local\ncontrol response. The proposed approach is validated using IEEE 4-bus and IEEE\n123-bus systems and achieves the loss minimization objective while maintaining\nthe voltage within the pre-specific limits even during highly variable DER\ngeneration scenarios.\n"}
+{"abstract": "  Technology has changed both our way of life and the way in which we learn.\nStudents now attend lectures with laptops and mobile phones, and this situation\nis accentuated in the case of students on Computer Science degrees, since they\nrequire their computers in order to participate in both theoretical and\npractical lessons. Problems, however, arise when the students' social networks\nare opened on their computers and they receive notifications that interrupt\ntheir work. We set up a workshop regarding time, thoughts and attention\nmanagement with the objective of teaching our students techniques that would\nallow them to manage interruptions, concentrate better and definitively make\nbetter use of their time. Those who took part in the workshop were then\nevaluated to discover its effects. The results obtained are quite optimistic\nand are described in this paper with the objective of encouraging other\nuniversities to perform similar initiatives.\n"}
+{"abstract": "  Multi-scale 3D characterization is widely used by materials scientists to\nfurther their understanding of the relationships between microscopic structure\nand macroscopic function. Scientific computed tomography (CT) instruments are\none of the most popular choices for 3D non-destructive characterization of\nmaterials at length scales ranging from the angstrom-scale to the micron-scale.\nThese instruments typically have a source of radiation that interacts with the\nsample to be studied and a detector assembly to capture the result of this\ninteraction. A collection of such high-resolution measurements are made by\nre-orienting the sample which is mounted on a specially designed stage/holder\nafter which reconstruction algorithms are used to produce the final 3D volume\nof interest. The end goal of scientific CT scans include determining the\nmorphology,chemical composition or dynamic behavior of materials when subjected\nto external stimuli. In this article, we will present an overview of recent\nadvances in reconstruction algorithms that have enabled significant\nimprovements in the performance of scientific CT instruments - enabling faster,\nmore accurate and novel imaging capabilities. In the first part, we will focus\non model-based image reconstruction algorithms that formulate the inversion as\nsolving a high-dimensional optimization problem involving a data-fidelity term\nand a regularization term. In the last part of the article, we will present an\noverview of recent approaches using deep-learning based algorithms for\nimproving scientific CT instruments.\n"}
+{"abstract": "  Random K-out graphs, denoted $\\mathbb{H}(n;K)$, are generated by each of the\n$n$ nodes drawing $K$ out-edges towards $K$ distinct nodes selected uniformly\nat random, and then ignoring the orientation of the arcs. Recently, random\nK-out graphs have been used in applications as diverse as random (pairwise) key\npredistribution in ad-hoc networks, anonymous message routing in\ncrypto-currency networks, and differentially-private federated averaging. In\nmany applications, connectivity of the random K-out graph when some of its\nnodes are dishonest, have failed, or have been captured is of practical\ninterest. We provide a comprehensive set of results on the connectivity and\ngiant component size of $\\mathbb{H}(n;K_n,\\gamma_n)$, i.e., random K-out graph\nwhen $\\gamma_n$ of its nodes, selected uniformly at random, are deleted. First,\nwe derive conditions for $K_n$ and $n$ that ensure, with high probability\n(whp), the connectivity of the remaining graph when the number of deleted nodes\nis $\\gamma_n=\\Omega(n)$ and $\\gamma_n=o(n)$, respectively. Next, we derive\nconditions for $\\mathbb{H}(n;K_n,\\gamma_n)$ to have a giant component, i.e., a\nconnected subgraph with $\\Omega(n)$ nodes, whp. This is also done for different\nscalings of $\\gamma_n$ and upper bounds are provided for the number of nodes\noutside the giant component. Simulation results are presented to validate the\nusefulness of the results in the finite node regime.\n"}
+{"abstract": "  The increasing complexity of modern robotic systems and the environments they\noperate in necessitates the formal consideration of safety in the presence of\nimperfect measurements. In this paper we propose a rigorous framework for\nsafety-critical control of systems with erroneous state estimates. We develop\nthis framework by leveraging Control Barrier Functions (CBFs) and unifying the\nmethod of Backup Sets for synthesizing control invariant sets with robustness\nrequirements -- the end result is the synthesis of Measurement-Robust Control\nBarrier Functions (MR-CBFs). This provides theoretical guarantees on safe\nbehavior in the presence of imperfect measurements and improved robustness over\nstandard CBF approaches. We demonstrate the efficacy of this framework both in\nsimulation and experimentally on a Segway platform using an onboard\nstereo-vision camera for state estimation.\n"}
+{"abstract": "  We collect data at all frequencies for the new sources classified as unknown\nactive galactic nuclei (AGNs) in the latest Burst Alert Telescope (BAT) all-sky\nhard X-ray catalog. Focusing on the 36 sources with measured redshift, we\ncompute their spectral energy distribution (SED) from radio to $\\gamma$-rays\nwith the aim to classify these objects. We apply emission models that attempt\nto reproduce the obtained SEDs, including: i) a standard thin accretion disk\ntogether with an obscuring torus and a X-ray corona; ii) a two temperature\nthick advection-dominated flow; iii) an obscured AGN model, accounting for\nabsorption along the line of sight at kiloelectronvolt energies and in the\noptical band; and iv) a phenomenological model to describe the jet emission in\nblazar-like objects. We integrate the models with the SWIRE template libraries\nto account for the emission of the host galaxy. For every source we found a\ngood agreement between data and our model. Considering that the sources were\nselected in the hard X-ray band, which is rather unaffected by absorption, we\nexpected and found a large fraction of absorbed radio-quiet AGNs (31 out of 36)\nand some additional rare radio-loud sources (5 out of 36), since the jet\nemission in hard X-rays is important for aligned jets owing to the boost\nproduced by the beaming effect. With our work we can confirm the hypothesis\nthat a number of galaxies, whose optical spectra lack AGN emission features,\nhost an obscured active nucleus. The approach we used proved to be efficient in\nrapidly identifying objects, which commonly used methods were not able to\nclassify.\n"}
+{"abstract": "  Most asteroids are somewhat elongated and have non-zero lightcurve\namplitudes. Such asteroids can be detected in large-scale sky surveys even if\ntheir mean magnitudes are fainter than the stated sensitivity limits. We\nexplore the detection of elongated asteroids under a set of idealized but\nuseful approximations. We find that objects up to 1 magnitude fainter than a\nsurvey's sensitivity limit are likely to be detected, and that the effect is\nmost pronounced for asteroids with lightcurve amplitudes 0.1-0.4 mag.This\nimposes a bias on the derived size and shape distributions of the population\nthat must be properly accounted for.\n"}
+{"abstract": "  This paper investigates the problem of online statistical inference of model\nparameters in stochastic optimization problems via the Kiefer-Wolfowitz\nalgorithm with random search directions. We first present the asymptotic\ndistribution for the Polyak-Ruppert-averaging type Kiefer-Wolfowitz (AKW)\nestimators, whose asymptotic covariance matrices depend on the function-value\nquery complexity and the distribution of search directions. The distributional\nresult reflects the trade-off between statistical efficiency and function query\ncomplexity. We further analyze the choices of random search directions to\nminimize the asymptotic covariance matrix, and conclude that the optimal search\ndirection depends on the optimality criteria with respect to different summary\nstatistics of the Fisher information matrix. Based on the asymptotic\ndistribution result, we conduct online statistical inference by providing two\nconstruction procedures of valid confidence intervals. We provide numerical\nexperiments verifying our theoretical results with the practical effectiveness\nof the procedures.\n"}
+{"abstract": "  Adversarial training is actively studied for learning robust models against\nadversarial examples. A recent study finds that adversarially trained models\ndegenerate generalization performance on adversarial examples when their weight\nloss landscape, which is loss changes with respect to weights, is sharp.\nUnfortunately, it has been experimentally shown that adversarial training\nsharpens the weight loss landscape, but this phenomenon has not been\ntheoretically clarified. Therefore, we theoretically analyze this phenomenon in\nthis paper. As a first step, this paper proves that adversarial training with\nthe L2 norm constraints sharpens the weight loss landscape in the linear\nlogistic regression model. Our analysis reveals that the sharpness of the\nweight loss landscape is caused by the noise aligned in the direction of\nincreasing the loss, which is used in adversarial training. We theoretically\nand experimentally confirm that the weight loss landscape becomes sharper as\nthe magnitude of the noise of adversarial training increases in the linear\nlogistic regression model. Moreover, we experimentally confirm the same\nphenomena in ResNet18 with softmax as a more general case.\n"}
+{"abstract": "  Cross-lingual text classification aims at training a classifier on the source\nlanguage and transferring the knowledge to target languages, which is very\nuseful for low-resource languages. Recent multilingual pretrained language\nmodels (mPLM) achieve impressive results in cross-lingual classification tasks,\nbut rarely consider factors beyond semantic similarity, causing performance\ndegradation between some language pairs. In this paper we propose a simple yet\neffective method to incorporate heterogeneous information within and across\nlanguages for cross-lingual text classification using graph convolutional\nnetworks (GCN). In particular, we construct a heterogeneous graph by treating\ndocuments and words as nodes, and linking nodes with different relations, which\ninclude part-of-speech roles, semantic similarity, and document translations.\nExtensive experiments show that our graph-based method significantly\noutperforms state-of-the-art models on all tasks, and also achieves consistent\nperformance gain over baselines in low-resource settings where external tools\nlike translators are unavailable.\n"}
+{"abstract": "  We consider the problem of computing the Fr\\'echet distance between two\ncurves for which the exact locations of the vertices are unknown. Each vertex\nmay be placed in a given uncertainty region for that vertex, and the objective\nis to place vertices so as to minimise the Fr\\'echet distance. This problem was\nrecently shown to be NP-hard in 2D, and it is unclear how to compute an optimal\nvertex placement at all.\n  We present the first general algorithmic framework for this problem. We prove\nthat it results in a polynomial-time algorithm for curves in 1D with intervals\nas uncertainty regions. In contrast, we show that the problem is NP-hard in 1D\nin the case that vertices are placed to maximise the Fr\\'echet distance.\n  We also study the weak Fr\\'echet distance between uncertain curves. While\nfinding the optimal placement of vertices seems more difficult than the regular\nFr\\'echet distance -- and indeed we can easily prove that the problem is\nNP-hard in 2D -- the optimal placement of vertices in 1D can be computed in\npolynomial time. Finally, we investigate the discrete weak Fr\\'echet distance,\nfor which, somewhat surprisingly, the problem is NP-hard already in 1D.\n"}
+{"abstract": "  While reinforcement learning (RL) is gaining popularity in energy systems\ncontrol, its real-world applications are limited due to the fact that the\nactions from learned policies may not satisfy functional requirements or be\nfeasible for the underlying physical system. In this work, we propose PROjected\nFeasibility (PROF), a method to enforce convex operational constraints within\nneural policies. Specifically, we incorporate a differentiable projection layer\nwithin a neural network-based policy to enforce that all learned actions are\nfeasible. We then update the policy end-to-end by propagating gradients through\nthis differentiable projection layer, making the policy cognizant of the\noperational constraints. We demonstrate our method on two applications:\nenergy-efficient building operation and inverter control. In the building\noperation setting, we show that PROF maintains thermal comfort requirements\nwhile improving energy efficiency by 4% over state-of-the-art methods. In the\ninverter control setting, PROF perfectly satisfies voltage constraints on the\nIEEE 37-bus feeder system, as it learns to curtail as little renewable energy\nas possible within its safety set.\n"}
+{"abstract": "  The chiral magnetic effect (CME) is a novel transport phenomenon, arising\nfrom the interplay between quantum anomalies and strong magnetic fields in\nchiral systems. In high-energy nuclear collisions, the CME may survive the\nexpansion of the quark-gluon plasma fireball and be detected in experiments.\nOver the past decade, the experimental searches for the CME have aroused\nextensive interest at the Relativistic Heavy Ion Collider (RHIC) and the Large\nHadron Collider (LHC). The main goal of this article is to investigate three\npertinent experimental approaches: the $\\gamma$ correlator, the $R$ correlator\nand the signed balance functions. We will exploit both simple Monte Carlo\nsimulations and a realistic event generator (EBE-AVFD) to verify the\nequivalence in the kernel-component observables among these methods and to\nascertain their sensitivities to the CME signal for the isobaric collisions at\nRHIC.\n"}
+{"abstract": "  AN Cam is a little-studied eclipsing binary containing somewhat evolved\ncomponents in an orbit with a period of 21.0 d and an eccentricity of 0.47. A\nspectroscopic orbit based on photoelectric radial velocities was published in\n1977. AN Cam has been observed using the TESS satellite in three sectors: the\ndata were obtained in long-cadence mode and cover nine eclipses. By modelling\nthese data and published radial velocities we obtain masses of 1.380 +/- 0.021\nMsun and 1.402 +/- 0.025 Msun, and radii of 2.159 +/- 0.012 Rsun and 2.646 +/-\n0.014 Rsun. We also derive a precise orbital ephemeris from these data and\nrecent times of minimum light, but find that the older times of minimum light\ncannot be fitted assuming a constant orbital period. This could be caused by\nastrophysical or instrumental effects; forthcoming TESS observations will help\nthe investigation of this issue. We use the Gaia EDR3 parallax and\noptical/infrared apparent magnitudes to measure effective temperatures of 6050\n+/- 150 K and 5750 +/- 150 K: the primary star is hotter but smaller and less\nmassive than its companion. A comparison with theoretical models indicates that\nthe system has an approximately solar chemical composition and an age of 3.3\nGyr. Despite the similarity of their masses the two stars are in different\nevolutionary states: the primary is near the end of its main-sequence lifetime\nand the secondary is now a subgiant. AN Cam is a promising candidate for\nconstraining the strength of convective core overshooting in 1.4 Msun stars.\n"}
+{"abstract": "  We develop a framework to study posterior contraction rates in sparse high\ndimensional generalized linear models (GLM). We introduce a new family of GLMs,\ndenoted by clipped GLM, which subsumes many standard GLMs and makes minor\nmodification of the rest. With a sparsity inducing prior on the regression\ncoefficients, we delineate sufficient conditions on true data generating\ndensity that leads to minimax optimal rates of posterior contraction of the\ncoefficients in $\\ell_1$ norm. Our key contribution is to develop sufficient\nconditions commensurate with the geometry of the clipped GLM family, propose\nprior distributions which do not require any knowledge of the true parameters\nand avoid any assumption on the growth rate of the true coefficient vector.\n"}
+{"abstract": "  Although abbreviations are fairly common in handwritten sources, particularly\nin medieval and modern Western manuscripts, previous research dealing with\ncomputational approaches to their expansion is scarce. Yet abbreviations\npresent particular challenges to computational approaches such as handwritten\ntext recognition and natural language processing tasks. Often, pre-processing\nultimately aims to lead from a digitised image of the source to a normalised\ntext, which includes expansion of the abbreviations. We explore different\nsetups to obtain such a normalised text, either directly, by training HTR\nengines on normalised (i.e., expanded, disabbreviated) text, or by decomposing\nthe process into discrete steps, each making use of specialist models for\nrecognition, word segmentation and normalisation. The case studies considered\nhere are drawn from the medieval Latin tradition.\n"}
+{"abstract": "  A 1d random geometric graph (1d RGG) is built by joining a random sample of\n$n$ points from an interval of the real line with probability $p$. We count the\nnumber of $k$-hop paths between two vertices of the graph in the case where the\nspace is the 1d interval $[0,1]$. We show how the $k$-hop path count between\ntwo vertices at Euclidean distance $|x-y|$ is in bijection with the volume\nenclosed by a uniformly random $d$-dimensional lattice path joining the corners\nof a $(k-1)$-dimensional hyperrectangular lattice. We are able to provide the\nprobability generating function and distribution of this $k$-hop path count as\na sum over lattice paths, incorporating the idea of restricted integer\npartitions with limited number of parts. We therefore demonstrate and describe\nan important link between spatial random graphs, and lattice path\ncombinatorics, where the $d$-dimensional lattice paths correspond to spatial\npermutations of the geometric points on the line.\n"}
+{"abstract": "  We experimentally demonstrate the steady-state generation of propagating\nWigner-negative states from a continuously driven superconducting qubit. We\nreconstruct the Wigner function of the radiation emitted into propagating modes\ndefined by their temporal envelopes, using digital filtering. For an optimized\ntemporal filter, we observe a large Wigner logarithmic negativity, in excess of\n0.08, in agreement with theory. The fidelity between the theoretical\npredictions and the states generated experimentally is up to 99%, reaching\nstate-of-the-art realizations in the microwave frequency domain. Our results\nprovide a new way to generate and control nonclassical states, and may enable\npromising applications such as quantum networks and quantum computation based\non waveguide quantum electrodynamics.\n"}
+{"abstract": "  We use numerical bootstrap techniques to study correlation functions of\nscalars transforming in the adjoint representation of $SU(N)$ in three\ndimensions. We obtain upper bounds on operator dimensions for various\nrepresentations and study their dependence on $N$. We discover new families of\nkinks, one of which could be related to bosonic QED${}_3$. We then specialize\nto the cases $N=3,4$, which have been conjectured to describe a phase\ntransition respectively in the ferromagnetic complex projective model $CP^2$\nand the antiferromagnetic complex projective model $ACP^{3}$. Lattice\nsimulations provide strong evidence for the existence of a second order phase\ntransition, while an effective field theory approach does not predict any fixed\npoint. We identify a set of assumptions that constrain operator dimensions to\nsmall regions overlapping with the lattice predictions.\n"}
+{"abstract": "  Electronic health records (EHRs), digital collections of patient healthcare\nevents and observations, are ubiquitous in medicine and critical to healthcare\ndelivery, operations, and research. Despite this central role, EHRs are\nnotoriously difficult to process automatically. Well over half of the\ninformation stored within EHRs is in the form of unstructured text (e.g.\nprovider notes, operation reports) and remains largely untapped for secondary\nuse. Recently, however, newer neural network and deep learning approaches to\nNatural Language Processing (NLP) have made considerable advances,\noutperforming traditional statistical and rule-based systems on a variety of\ntasks. In this survey paper, we summarize current neural NLP methods for EHR\napplications. We focus on a broad scope of tasks, namely, classification and\nprediction, word embeddings, extraction, generation, and other topics such as\nquestion answering, phenotyping, knowledge graphs, medical dialogue,\nmultilinguality, interpretability, etc.\n"}
+{"abstract": "  This paper is a primer on cryptographic accumulators and how to apply them\npractically. A cryptographic accumulator is a space- and time-efficient data\nstructure used for set-membership tests. Since it is possible to represent any\ncomputational problem where the answer is yes or no as a set-membership\nproblem, cryptographic accumulators are invaluable data structures in computer\nscience and engineering. But, to the best of our knowledge, there is neither a\nconcise survey comparing and contrasting various types of accumulators nor a\nguide for how to apply the most appropriate one for a given application.\nTherefore, we address that gap by describing cryptographic accumulators while\npresenting their fundamental and so-called optional properties. We discuss the\neffects of each property on the given accumulator's performance in terms of\nspace and time complexity, as well as communication overhead.\n"}
+{"abstract": "  The combination of topology and quantum criticality can give rise to an\nexotic mix of counterintuitive effects. Here, we show that unexpected\ntopological properties take place in a paradigmatic strongly-correlated\nHamiltonian: the 1D extended Bose-Hubbard model. In particular, we reveal the\npresence of two distinct topological quantum critical points with localized\nedge states and gapless bulk excitations. Our results show that the topological\ncritical points separate two phases, one topologically protected and the other\ntopologically trivial, both characterized by a long-range ordered string\ncorrelation function. The long-range order persists also at the topological\ncritical points and it reflects the presence of localized edge states protected\nby a finite charge gap. Finally, we introduce a super-resolution quantum gas\nmicroscopy scheme for dipolar dysprosium atoms, which provides a reliable route\ntowards the experimental study of topological quantum critical points.\n"}
+{"abstract": "  Accurately segmenting a variety of clinically significant lesions from whole\nbody computed tomography (CT) scans is a critical task on precision oncology\nimaging, denoted as universal lesion segmentation (ULS). Manual annotation is\nthe current clinical practice, being highly time-consuming and inconsistent on\ntumor's longitudinal assessment. Effectively training an automatic segmentation\nmodel is desirable but relies heavily on a large number of pixel-wise labelled\ndata. Existing weakly-supervised segmentation approaches often struggle with\nregions nearby the lesion boundaries. In this paper, we present a novel\nweakly-supervised universal lesion segmentation method by building an attention\nenhanced model based on the High-Resolution Network (HRNet), named AHRNet, and\npropose a regional level set (RLS) loss for optimizing lesion boundary\ndelineation. AHRNet provides advanced high-resolution deep image features by\ninvolving a decoder, dual-attention and scale attention mechanisms, which are\ncrucial to performing accurate lesion segmentation. RLS can optimize the model\nreliably and effectively in a weakly-supervised fashion, forcing the\nsegmentation close to lesion boundary. Extensive experimental results\ndemonstrate that our method achieves the best performance on the publicly\nlarge-scale DeepLesion dataset and a hold-out test set.\n"}
+{"abstract": "  Limited by the small keyboard, most mobile apps support the automatic login\nfeature for better user experience. Therefore, users avoid the inconvenience of\nretyping their ID and password when an app runs in the foreground again.\nHowever, this auto-login function can be exploited to launch the so-called\n\"data-clone attack\": once the locally-stored, auto-login depended data are\ncloned by attackers and placed into their own smartphones, attackers can break\nthrough the login-device number limit and log in to the victim's account\nstealthily. A natural countermeasure is to check the consistency of\ndevicespecific attributes. As long as the new device shows different device\nfingerprints with the previous one, the app will disable the auto-login\nfunction and thus prevent data-clone attacks. In this paper, we develop\nVPDroid, a transparent Android OS-level virtualization platform tailored for\nsecurity testing. With VPDroid, security analysts can customize different\ndevice artifacts, such as CPU model, Android ID, and phone number, in a virtual\nphone without user-level API hooking. VPDroid's isolation mechanism ensures\nthat user-mode apps in the virtual phone cannot detect device-specific\ndiscrepancies. To assess Android apps' susceptibility to the data-clone attack,\nwe use VPDroid to simulate data-clone attacks with 234 most-downloaded apps.\nOur experiments on five different virtual phone environments show that\nVPDroid's device attribute customization can deceive all tested apps that\nperform device-consistency checks, such as Twitter, WeChat, and PayPal. 19\nvendors have confirmed our report as a zero-day vulnerability. Our findings\npaint a cautionary tale: only enforcing a device-consistency check at client\nside is still vulnerable to an advanced data-clone attack.\n"}
+{"abstract": "  Experimental results of the $p(^{13}{\\rm B},d)^{12}{\\rm B}$ transfer reaction\nto the low-lying states in $^{12}$B are reported. The optical potential\nparameters for the entrance channel are extracted from the elastic scattering\n$p$($^{13}{\\rm B}$, $p$) measured in the same experiment, while those for the\nexit channel are global ones. Spectroscopic factors associated with the $p$-,\n$s$-, and $d$-wave neutron transfer to the known $^{12}$B states, are extracted\nby comparing the deuteron angular distributions with the calculation results.\nThe separated $s$- and $d$-wave intruder strengths in $^{13}{\\rm B}_{\\rm g.s.}$\nwere determined to be $10(2)\\%$ and $6(1)\\%$, respectively, which follow\nroughly the systematics for the $N$ = 8 neutron-rich isotones. The measured\ntotal intruder strength is in good agreement with the shell model calculation,\nwhile the individual ones evolve quite differently. Particularly, the sudden\nchange of the $d$-wave intensity between $^{13}$B and $^{12}$Be needs further\ntheoretical interpretation.\n"}
+{"abstract": "  Traffic anomaly detection has played a crucial role in Intelligent\nTransportation System (ITS). The main challenges of this task lie in the highly\ndiversified anomaly scenes and variational lighting conditions. Although much\nwork has managed to identify the anomaly in homogenous weather and scene, few\nresolved to cope with complex ones. In this paper, we proposed a dual-modality\nmodularized methodology for the robust detection of abnormal vehicles. We\nintroduced an integrated anomaly detection framework comprising the following\nmodules: background modeling, vehicle tracking with detection, mask\nconstruction, Region of Interest (ROI) backtracking, and dual-modality tracing.\nConcretely, we employed background modeling to filter the motion information\nand left the static information for later vehicle detection. For the vehicle\ndetection and tracking module, we adopted YOLOv5 and multi-scale tracking to\nlocalize the anomalies. Besides, we utilized the frame difference and tracking\nresults to identify the road and obtain the mask. In addition, we introduced\nmultiple similarity estimation metrics to refine the anomaly period via\nbacktracking. Finally, we proposed a dual-modality bilateral tracing module to\nrefine the time further. The experiments conducted on the Track 4 testset of\nthe NVIDIA 2021 AI City Challenge yielded a result of 0.9302 F1-Score and\n3.4039 root mean square error (RMSE), indicating the effectiveness of our\nframework.\n"}
+{"abstract": "  In comparison to conventional traffic designs, shared spaces promote a more\npleasant urban environment with slower motorized movement, smoother traffic,\nand less congestion. In the foreseeable future, shared spaces will be populated\nwith a mixture of autonomous vehicles (AVs) and vulnerable road users (VRUs)\nlike pedestrians and cyclists. However, a driver-less AV lacks a way to\ncommunicate with the VRUs when they have to reach an agreement of a\nnegotiation, which brings new challenges to the safety and smoothness of the\ntraffic. To find a feasible solution to integrating AVs seamlessly into\nshared-space traffic, we first identified the possible issues that the\nshared-space designs have not considered for the role of AVs. Then an online\nquestionnaire was used to ask participants about how they would like a driver\nof the manually driving vehicle to communicate with VRUs in a shared space. We\nfound that when the driver wanted to give some suggestions to the VRUs in a\nnegotiation, participants thought that the communications via the driver's body\nbehaviors were necessary. Besides, when the driver conveyed information about\nher/his intentions and cautions to the VRUs, participants selected different\ncommunication methods with respect to their transport modes (as a driver,\npedestrian, or cyclist). These results suggest that novel eHMIs might be useful\nfor AV-VRU communication when the original drivers are not present. Hence, a\npotential eHMI design concept was proposed for different VRUs to meet their\nvarious expectations. In the end, we further discussed the effects of the eHMIs\non improving the sociality in shared spaces and the autonomous driving systems.\n"}
+{"abstract": "  Lie algebroids provide a natural medium to discuss classical systems,\nhowever, quantum systems have not been considered. In aim of this paper is to\nattempt to rectify this situation. Lie algebroids are reviewed and their use in\nclassical systems is described. The geometric structure of the Schr\\\"{o}dinger\nand Heisenberg representations of quantum systems is examined and their\nrelationship to Lie algebroids is explored. Geometrically, a quantum system is\nseen to be a collection of bounded, linear, self-adjoint operators on a\nHilbert, or more precisely, a K\\\"{a}hler manifold. The geometry of the\nSchr\\\"{o}dinger representation is given by the Poisson structure of the\nco-adjoint orbits on the dual of the Lie algebra. Finally, it is shown that the\nSchr\\\"{o}dinger and Heisenberg representations are equivalent.\n"}
+{"abstract": "  Security related questions for Cyber Physical Systems (CPS) have attracted\nmuch research attention in searching for novel methods for attack-resilient\ncontrol and/or estimation. Specifically, false data injection attacks (FDIAs)\nhave been shown to be capable of bypassing bad data detection (BDD), while\narbitrarily compromising the integrity of state estimators and robust\ncontroller even with very sparse measurements corruption. Moreover, based on\nthe inherent sparsity of pragmatic attack signals, $\\ell_1$-minimization scheme\nhas been used extensively to improve the design of attack-resilient estimators.\nFor this, the theoretical maximum for the percentage of compromised nodes that\ncan be accommodated has been shown to be $50\\%$. In order to guarantee correct\nstate recoveries for larger percentage of attacked nodes, researchers have\nbegun to incorporate prior information into the underlying resilient observer\ndesign framework. For the most pragmatic cases, this prior information is often\nobtained through some data-driven machine learning process. Existing results\nhave shown strong positive correlation between the tolerated attack percentages\nand the precision of the prior information. In this paper, we present a pruning\nmethod to improve the precision of the prior information, given corresponding\nstochastic uncertainty characteristics of the underlying machine learning\nmodel. Then a weighted $\\ell_1$-minimization is proposed based on the pruned\nprior. The theoretical and simulation results show that the pruning method\nsignificantly improves the observer performance for much larger attack\npercentages, even when moderately accurate machine learning model used.\n"}
+{"abstract": "  Many modern imaging applications can be modeled as compressed sensing linear\ninverse problems. When the measurement operator involved in the inverse problem\nis sufficiently random, denoising Scalable Message Passing (SMP) algorithms\nhave a potential to demonstrate high efficiency in recovering compressed data.\nOne of the key components enabling SMP to achieve fast convergence, stability\nand predictable dynamics is the Onsager correction that must be updated at each\niteration of the algorithm. This correction involves the denoiser's divergence\nthat is traditionally estimated via the Black-Box Monte Carlo (BB-MC) method\n\\cite{MC-divergence}. While the BB-MC method demonstrates satisfying accuracy\nof estimation, it requires executing the denoiser additional times at each\niteration and might lead to a substantial increase in computational cost of the\nSMP algorithms. In this work we develop two Large System Limit models of the\nOnsager correction for denoisers operating within SMP algorithms and use these\nmodels to propose two practical classes of divergence estimators that require\nno additional executions of the denoiser and demonstrate similar or superior\ncorrection compared to the BB-MC method.\n"}
+{"abstract": "  Object detection is widely studied in computer vision filed. In recent years,\ncertain representative deep learning based detection methods along with solid\nbenchmarks are proposed, which boosts the development of related researchs.\nHowever, existing detection methods still suffer from undesirable performance\nunder challenges such as camouflage, blur, inter-class similarity, intra-class\nvariance and complex environment. To address this issue, we propose LGA-RCNN\nwhich utilizes a loss-guided attention (LGA) module to highlight representative\nregion of objects. Then, those highlighted local information are fused with\nglobal information for precise classification and localization.\n"}
+{"abstract": "  We theoretically investigate the formation of $W$ states in a tripartite\nsystem composed of three charge qubits coupled to vibrational modes. The\nelectromechanical coupling is responsable for second order virtual processes\nthat result in an effective electron-electron interaction between neighbor\nqubits, which yields to the formation of $W$ states. Based on the Lang-Firsov\ntransformation and perturbation theory, we analytically solve the quantum\ndynamics, providing a mathematical expression for the maximally entangled $W$\nstate. Dephasing is also taken into accout, paying particular attention on the\nrobustness of bipartite entanglement against local dephasing processes.\n"}
+{"abstract": "  We study estimating inherent human disagreement (annotation label\ndistribution) in natural language inference task. Post-hoc smoothing of the\npredicted label distribution to match the expected label entropy is very\neffective. Such simple manipulation can reduce KL divergence by almost half,\nyet will not improve majority label prediction accuracy or learn label\ndistributions. To this end, we introduce a small amount of examples with\nmultiple references into training. We depart from the standard practice of\ncollecting a single reference per each training example, and find that\ncollecting multiple references can achieve better accuracy under the fixed\nannotation budget. Lastly, we provide rich analyses comparing these two methods\nfor improving label distribution estimation.\n"}
+{"abstract": "  In this paper, we develop and study approximately smooth basis constructions\nfor isogeometric analysis over two-patch domains. One key element of\nisogeometric analysis is that it allows high order smoothness within one patch.\nHowever, for representing complex geometries, a multi-patch construction is\nneeded. In this case, a $C^0$-smooth basis is easy to obtain, whereas\n$C^1$-smooth isogeometric functions require a special construction. Such spaces\nare of interest when solving numerically fourth-order PDE problems, such as the\nbiharmonic equation and the Kirchhoff-Love plate or shell formulation, using an\nisogeometric Galerkin method.\n  With the construction of so-called analysis-suitable $G^1$ (in short,\nAS-$G^1$) parametrizations, as introduced in (Collin, Sangalli, Takacs; CAGD,\n2016), it is possible to construct $C^1$ isogeometric spaces which possess\noptimal approximation properties. These geometries need to satisfy certain\nconstraints along the interfaces and additionally require that the regularity\n$r$ and degree $p$ of the underlying spline space satisfy $1 \\leq r \\leq p-2$.\nThe problem is that most complex geometries are not AS-$G^1$ geometries.\nTherefore, we define basis functions for isogeometric spaces by enforcing\napproximate $C^1$ conditions following the basis construction from (Kapl,\nSangalli, Takacs; CAGD, 2017). For this reason, the defined function spaces are\nnot exactly $C^1$ but only approximately.\n  We study the convergence behavior and define function spaces that converge\noptimally under $h$-refinement, by locally introducing functions of higher\npolynomial degree and lower regularity. The convergence rate is optimal in\nseveral numerical tests performed on domains with non-trivial interfaces. While\nan extension to more general multi-patch domains is possible, we restrict\nourselves to the two-patch case and focus on the construction over a single\ninterface.\n"}
+{"abstract": "  We show that a closed orientable 3--dimensional manifold admits a round fold\nmap into the plane, i.e. a fold map whose critical value set consists of\ndisjoint simple closed curves isotopic to concentric circles, if and only if it\nis a graph manifold, generalizing the characterization for simple stable maps\ninto the plane. Furthermore, we also give a characterization of closed\norientable graph manifolds that admit directed round fold maps into the plane,\ni.e.\\ round fold maps such that the number of regular fiber components of a\nregular value increases toward the central region in the plane.\n"}
+{"abstract": "  Inflexible combined heat and power (CHP) plants and uncertain wind power\nproduction result in excess power in distribution networks, which leads to\ninverse power flow challenging grid operations. Power-to-X facilities such as\nelectrolysers and electric boilers can offer extra flexibility to the\nintegrated energy system. In this regard, we aim to jointly determine the\noptimal Power-to-X facility sizing and integrated energy system operations in\nthis study. To account for wind power uncertainties, a distributionally robust\nchance-constrained model is developed to characterize wind power uncertainties\nusing ambiguity sets. Linear decision rules are applied to analytically express\nreal-time recourse actions when uncertainties are exposed, which allows the\npropagation of wind power uncertainties to gas and heat systems. Accordingly,\nthe developed three-stage distributionally robust chance-constrained model is\nconverted into a computationally tractable single-stage mixed-integer conic\nmodel. A case study validates the effectiveness of introducing the electrolyser\nand electric boiler into the integrated energy system, with respect to the\ndecreased system cost, expanded CHP plant flexibility and reduced inverse power\nflow. The developed distributionally robust optimization model exhibits better\neffectiveness and robustness compared to a chance-constrained optimization\nmodel assuming wind forecast errors follow Gaussian distribution. Detailed\nprofit analysis reveals that although the overall system cost is minimized, the\nprofit is distributed unevenly across various stakeholders in the system.\n"}
+{"abstract": "  Among exoplanets, the small-size population constitutes the dominant one,\nwith a diversity of properties and compositions ranging from rocky to gas\ndominated envelope. While a large fraction of them have masses and radii\nsimilar to or smaller than Neptune, yet none share common properties in term of\norbital period and insulation with our ice giants. These exoplanets belong to\nmulti-planet systems where planets are closely packed within the first tenth of\nAU and often exposed to strong irradiation from their host star. Their\nformation process, subsequent evolution, and fate are still debated and trigger\nnew developments of planet formation models. This paper reviews the\ncharacteristics and properties of this extended sample of planets with radii\nbetween $\\sim$ 1.6 and 4.0$_\\oplus$. Even though we still lack real\nNeptune/Uranus analogues, these exoplanets provide us with key observational\nconstraints that allow the formation of our ice giants to be placed in a more\ngeneral framework than the sole example of our solar system.\n"}
+{"abstract": "  A new robust stochastic volatility (SV) model having Student-t marginals is\nproposed. Our process is defined through a linear normal regression model\ndriven by a latent gamma process that controls temporal dependence. This gamma\nprocess is strategically chosen to enable us to find an explicit expression for\nthe pairwise joint density function of the Student-t response process. With\nthis at hand, we propose a composite likelihood (CL) based inference for our\nmodel, which can be straightforwardly implemented with a low computational\ncost. This is a remarkable feature of our Student-t SV process over existing SV\nmodels in the literature that involve computationally heavy algorithms for\nestimating parameters. Aiming at a precise estimation of the parameters related\nto the latent process, we propose a CL Expectation-Maximization algorithm and\ndiscuss a bootstrap approach to obtain standard errors. The finite-sample\nperformance of our composite likelihood methods is assessed through Monte Carlo\nsimulations. The methodology is motivated by an empirical application in the\nfinancial market. We analyze the relationship, across multiple time periods,\nbetween various US sector Exchange-Traded Funds returns and individual\ncompanies' stock price returns based on our novel Student-t model. This\nrelationship is further utilized in selecting optimal financial portfolios.\n"}
+{"abstract": "  Efficient generation of spin polarization is very important for spintronics\nand quantum computation. We propose a new mechanism of Chiral Phonon activated\nSpin Seebeck (CPASS) effect in nonmagnetic materials in the absence of magnetic\nfield and spin-orbital coupling. Owing to nonequilibrium distribution of chiral\nphonons under temperature gradient, we investigate the resulted chiral phonon\nactivated spin polarization by solving the Boltzmann transport equation. The\nCPASS coefficients, with both band and phonon-drag contributions, exhibit\nlinear dependence on the temperature gradient. The above two contributions are\nopposite for negative charge carriers and their relative magnitude is tunable\nby the chemical potential modulation. The CPASS effect where the spin\naccumulation is induced by chiral phonons, provides opportunities for the\nexploration of advanced spintronic devices based on chiral materials even in\nthe absence of magnetic order and spin-orbit coupling.\n"}
+{"abstract": "  Real-world networks and knowledge graphs are usually heterogeneous networks.\nRepresentation learning on heterogeneous networks is not only a popular but a\npragmatic research field. The main challenge comes from the heterogeneity --\nthe diverse types of nodes and edges. Besides, for a given node in a HIN, the\nsignificance of a neighborhood node depends not only on the structural distance\nbut semantics. How to effectively capture both structural and semantic\nrelations is another challenge. The current state-of-the-art methods are based\non the algorithm of meta-path and therefore have a serious disadvantage -- the\nperformance depends on the arbitrary choosing of meta-path(s). However, the\nselection of meta-path(s) is experience-based and time-consuming. In this work,\nwe propose a novel meta-path-free representation learning on heterogeneous\nnetworks, namely Heterogeneous graph Convolutional Networks (HCN). The proposed\nmethod fuses the heterogeneity and develops a $k$-strata algorithm ($k$ is an\ninteger) to capture the $k$-hop structural and semantic information in\nheterogeneous networks. To the best of our knowledge, this is the first attempt\nto break out of the confinement of meta-paths for representation learning on\nheterogeneous networks. We carry out extensive experiments on three real-world\nheterogeneous networks. The experimental results demonstrate that the proposed\nmethod significantly outperforms the current state-of-the-art methods in a\nvariety of analytic tasks.\n"}
+{"abstract": "  Discrete max-linear Bayesian networks are directed graphical models specified\nby the same recursive structural equations as max-linear models but with\ndiscrete innovations. When all of the random variables in the model are binary,\nthese models are isomorphic to the conjunctive Bayesian network (CBN) models of\nBeerenwinkel, Eriksson, and Sturmfels. Many of the techniques used to study CBN\nmodels can be extended to discrete max-linear models and similar results can be\nobtained. In particular, we extend the fact that CBN models are toric varieties\nafter linear change of coordinates to all discrete max-linear models.\n"}
+{"abstract": "  Mars northern polar latitudes are known to harbor an enhanced 3 ${\\mu}$m\nspectral signature when observed from orbit. This may indicate a greater amount\nof surface adsorbed or bound water, although it has not yet been possible to\neasily reconcile orbital observations with ground measurements by Phoenix. Here\nwe re-analyzed OMEGA/Mars Express observations acquired during the Northern\nsummer to further characterize this 3 ${\\mu}$m absorption band increase. We\nidentify the presence of a new specific spectral signature composed of an\nadditional narrow absorption feature centered at 3.03 ${\\mu}$m coupled with an\nabsorption at ${\\lambda}$ ${\\geq}$ 3.8 ${\\mu}$m. This signature is\nhomogeneously distributed over a high-albedo open ring surrounding the\ncircumpolar low-albedo terrains between ~ 68{\\deg}N and 76{\\deg}N and ~\n0{\\deg}E and 270{\\deg}E. This location includes the Phoenix landing site. This\nfeature shows no time variability and can be confidently attributed to a\nseasonally stable surface component. All together, the stability, spectral\nshape and absence of significant correlation with other signatures in the 1 $-$\n2.5 ${\\mu}$m range discard interpretations relying on water ice or easily\nexchangeable adsorbed water. Sulfates, notably anhydrite, provide interesting\ncomparisons to several sections of the spectrum. Analogies with Earth samples\nalso show that the spectral signature could result from a latitudinal\nmodification of the hydration state and/or grains size of salts contaminants.\nWhile the exact full spectral shape cannot be easily reproduced, plausible\nexplanations to this observation seem to involve geologically recent water\nalteration at high northern latitudes.\n"}
+{"abstract": "  This paper discusses the current critique against neural network-based\nNatural Language Understanding (NLU) solutions known as language models. We\nargue that much of the current debate rests on an argumentation error that we\nwill refer to as the singleton fallacy: the assumption that language, meaning,\nand understanding are single and uniform phenomena that are unobtainable by\n(current) language models. By contrast, we will argue that there are many\ndifferent types of language use, meaning and understanding, and that (current)\nlanguage models are build with the explicit purpose of acquiring and\nrepresenting one type of structural understanding of language. We will argue\nthat such structural understanding may cover several different modalities, and\nas such can handle several different types of meaning. Our position is that we\ncurrently see no theoretical reason why such structural knowledge would be\ninsufficient to count as \"real\" understanding.\n"}
+{"abstract": "  We classify 2+1 dimensional integrable systems with nonlocality of the\nintermediate long wave type. Links to the 2+1 dimensional waterbag system\n  are established. Dimensional reductions of integrable systems constructed in\nthis paper provide dispersive regularisations of hydrodynamic equations\ngoverning propagation of long nonlinear waves in a shear flow with piecewise\nlinear velocity profile (for special values of vorticities).\n"}
+{"abstract": "  Influenza, an infectious disease, causes many deaths worldwide. Predicting\ninfluenza victims during epidemics is an important task for clinical, hospital,\nand community outbreak preparation. On-line user-generated contents (UGC),\nprimarily in the form of social media posts or search query logs, are generally\nused for prediction for reaction to sudden and unusual outbreaks. However, most\nstudies rely only on the UGC as their resource and do not use various UGCs. Our\nstudy aims to solve these questions about Influenza prediction: Which model is\nthe best? What combination of multiple UGCs works well? What is the nature of\neach UGC? We adapt some models, LASSO Regression, Huber Regression, Support\nVector Machine regression with Linear kernel (SVR) and Random Forest, to test\nthe influenza volume prediction in Japan during 2015 - 2018. For that, we use\non-line five data resources: (1) past flu patients, (2) SNS (Twitter), (3)\nsearch engines (Yahoo! Japan), (4) shopping services (Yahoo! Shopping), and (5)\nQ&A services (Yahoo! Chiebukuro) as resources of each model. We then validate\nrespective resources contributions using the best model, Huber Regression, with\nall resources except one resource. Finally, we use Bayesian change point method\nfor ascertaining whether the trend of time series on any resources is reflected\nin the trend of flu patient count or not. Our experiments show Huber Regression\nmodel based on various data resources produces the most accurate results. Then,\nfrom the change point analysis, we get the result that search query logs and\nsocial media posts for three years represents these resources as a good\npredictor. Conclusions: We show that Huber Regression based on various data\nresources is strong for outliers and is suitable for the flu prediction.\nAdditionally, we indicate the characteristics of each resource for the flu\nprediction.\n"}
+{"abstract": "  Social media marketing is an emerging marketing technique worldwide. This\nresearch concentrates on how effectively social media can be used to promote a\nproduct in tourism industry. The efficient use of social media develops a\ntourism company in terms of sales, branding, reach and relationship management.\nThe study aims to find the best social media platform to promote and develop a\ntourism company and the customer opinion towards planning a trip through\nonline. It also concentrates on customer response for online offers and\ndiscounts in those social media platforms. The study attempts to understand and\ncreate suitable model for social media marketing for tourism companies with a\nsample size of 400. The sampling technique used in this study is purposive\nsampling method. The purposive sample can also be called as judgemental sample.\nNormally the sample will be selected based on the knowledge possessed by the\nrespondents on a particular phenomenon. Here, the study is been conducted among\nthe people who use social media. The sampling technique helped the researcher\nto identify the target sample i.e., the social media users.\n"}
+{"abstract": "  In recent years, physiological signal based authentication has shown great\npromises,for its inherent robustness against forgery. Electrocardiogram (ECG)\nsignal, being the most widely studied biosignal, has also received the highest\nlevel of attention in this regard. It has been proven with numerous studies\nthat by analyzing ECG signals from different persons, it is possible to\nidentify them, with acceptable accuracy. In this work, we present, EDITH, a\ndeep learning-based framework for ECG biometrics authentication system.\nMoreover, we hypothesize and demonstrate that Siamese architectures can be used\nover typical distance metrics for improved performance. We have evaluated EDITH\nusing 4 commonly used datasets and outperformed the prior works using less\nnumber of beats. EDITH performs competitively using just a single heartbeat\n(96-99.75% accuracy) and can be further enhanced by fusing multiple beats (100%\naccuracy from 3 to 6 beats). Furthermore, the proposed Siamese architecture\nmanages to reduce the identity verification Equal Error Rate (EER) to 1.29%. A\nlimited case study of EDITH with real-world experimental data also suggests its\npotential as a practical authentication system.\n"}
+{"abstract": "  We prove that the 2D finite depth capillary water wave equations admit no\nsolitary wave solutions. This closes the existence/non-existence problem for\nsolitary water waves in 2D, under the classical assumptions of\nincompressibility and irrotationality, and with the physical parameters being\ngravity, surface tension and the fluid depth.\n"}
+{"abstract": "  We consider the punctured plane with volume density $|x|^\\alpha$ and\nperimeter density $|x|^\\beta$. We show that centred balls are uniquely\nisoperimetric for indices $(\\alpha,\\beta)$ which satisfy the conditions\n$\\alpha-\\beta+1>0$, $\\alpha\\leq 2\\beta$ and $\\alpha(\\beta+1)\\leq\\beta^2$ except\nin the case $\\alpha=\\beta=0$ which corresponds to the classical isoperimetric\ninequality.\n"}
+{"abstract": "  Dynamic languages, such as Python and Javascript, trade static typing for\ndeveloper flexibility and productivity. Lack of static typing can cause\nrun-time exceptions and is a major factor for weak IDE support. To alleviate\nthese issues, PEP 484 introduced optional type annotations for Python. As\nretrofitting types to existing codebases is error-prone and laborious,\nlearning-based approaches have been proposed to enable automatic type\nannotations based on existing, partially annotated codebases. However, it is\nstill quite challenging for learning-based approaches to give a relevant\nprediction in the first suggestion or the first few ones. In this paper, we\npresent Type4Py, a deep similarity learning-based hierarchical neural network\nmodel that learns to discriminate between types of the same kind and dissimilar\ntypes in a high-dimensional space, which results in clusters of types. Nearest\nneighbor search suggests a list of likely types for arguments, variables, and\nfunctions' return. The results of the quantitative and qualitative evaluation\nindicate that Type4Py significantly outperforms state-of-the-art approaches at\nthe type prediction task. Considering the Top-1 prediction, Type4Py obtains a\nMean Reciprocal Rank of 72.5%, which is 10.87% and 16.45% higher than that of\nTypilus and TypeWriter, respectively.\n"}
+{"abstract": "  Quantifying entanglement properties of mixed states in quantum field theory\nvia entanglement of purification and reflected entropy is a new and challenging\nsubject. In this work, we study both quantities for two spherical subregions\nfar away from each other in the vacuum of a conformal field theory in any\nnumber of dimensions. Using lattice techniques, we find an elementary proof\nthat the decay of both, the entanglement of purification and reflected entropy,\nis enhanced with respect to the mutual information behaviour by a logarithm of\nthe distance between the subregions. In the case of the Ising spin chain at\ncriticality and the related free fermion conformal field theory, we compute\nalso the overall coefficients numerically for the both quantities of interest.\n"}
+{"abstract": "  Spin-dependent transport phenomena due to relativistic spin-orbit coupling\nand broken space-inversion symmetry are often difficult to interpret\nmicroscopically, in particular when occurring at surfaces or interfaces. Here\nwe present a theoretical and experimental study of spin-orbit torque and\nunidirectional magnetoresistance in a model room-temperature ferromagnet NiMnSb\nwith inversion asymmetry in the bulk of this half-heusler crystal. Besides the\nangular dependence on magnetization, the competition of Rashba and\nDresselhaus-like spin-orbit couplings results in the dependence of these\neffects on the crystal direction of the applied electric field. The\nphenomenology that we observe highlights potential inapplicability of commonly\nconsidered approaches for interpreting experiments. We point out that, in\ngeneral, there is no direct link between the current-induced non-equilibrium\nspin polarization inferred from the measured spin-orbit torque and the\nunidirectional magnetiresistance. We also emphasize that the unidirectional\nmagnetoresistance has not only longitudinal but also transverse components in\nthe electric field -- current indices which complicates its separation from the\nthermoelectric contributions to the detected signals in common experimental\ntechniques. We use the theoretical results to analyze our measurements of the\non-resonance and off-resonance mixing signals in microbar devices fabricated\nfrom an epitaxial NiMnSb film along different crystal directions. Based on the\nanalysis we extract an experimental estimate of the unidirectional\nmagnetoresistance in NiMnSb.\n"}
+{"abstract": "  Medical visual question answering (Med-VQA) has tremendous potential in\nhealthcare. However, the development of this technology is hindered by the\nlacking of publicly-available and high-quality labeled datasets for training\nand evaluation. In this paper, we present a large bilingual dataset, SLAKE,\nwith comprehensive semantic labels annotated by experienced physicians and a\nnew structural medical knowledge base for Med-VQA. Besides, SLAKE includes\nricher modalities and covers more human body parts than the currently available\ndataset. We show that SLAKE can be used to facilitate the development and\nevaluation of Med-VQA systems. The dataset can be downloaded from\nhttp://www.med-vqa.com/slake.\n"}
+{"abstract": "  Recently, Space-Time Memory Network (STM) based methods have achieved\nstate-of-the-art performance in semi-supervised video object segmentation\n(VOS). A crucial problem in this task is how to model the dependency both among\ndifferent frames and inside every frame. However, most of these methods neglect\nthe spatial relationships (inside each frame) and do not make full use of the\ntemporal relationships (among different frames). In this paper, we propose a\nnew transformer-based framework, termed TransVOS, introducing a vision\ntransformer to fully exploit and model both the temporal and spatial\nrelationships. Moreover, most STM-based approaches employ two separate encoders\nto extract features of two significant inputs, i.e., reference sets (history\nframes with predicted masks) and query frame (current frame), respectively,\nincreasing the models' parameters and complexity. To slim the popular\ntwo-encoder pipeline while keeping the effectiveness, we design a single\ntwo-path feature extractor to encode the above two inputs in a unified way.\nExtensive experiments demonstrate the superiority of our TransVOS over\nstate-of-the-art methods on both DAVIS and YouTube-VOS datasets.\n"}
+{"abstract": "  In the present work, single- and segregated-network PINN architectures are\napplied to predict momentum, species and temperature distributions of a dry air\nhumidification problem in a simple 2D rectangular domain. The created PINN\nmodels account for variable fluid properties, species- and heat-diffusion and\nconvection. Both the mentioned PINN architectures were trained using different\nhyperparameter settings, such as network width and depth to find the\nbest-performing configuration. It is shown that the segregated-network PINN\napproach results in on-average 62% lower losses when compared to the\nsingle-network PINN architecture for the given problem. Furthermore, the\nsingle-network variant struggled to ensure species mass conservation in\ndifferent areas of the computational domain, whereas, the segregated approach\nsuccessfully maintained species conservation. The PINN predicted velocity,\ntemperature and species profiles for a given set of boundary conditions were\ncompared to results generated using OpenFOAM software. Both the single- and\nsegregated-network PINN models produced accurate results for temperature and\nvelocity profiles, with average percentage difference relative to the CFD\nresults of approximately 7.5% for velocity and 8% for temperature. The mean\nerror percentages for the species mass fractions are 9\\% for the single-network\nmodel and 1.5% for the segregated-network approach. To showcase the\napplicability of PINNs for surrogate modelling of multi-species problems, a\nparameterised version of the segregated-network PINN is trained which could\nproduce results for different water vapour inlet velocities. The normalised\nmean absolute percentage errors, relative to the OpenFOAM results, across three\npredicted cases for velocity and temperature are approximately 7.5% and 2.4%\nfor water vapour mass fraction.\n"}
+{"abstract": "  We introduce the problem of constructing explicit variety evasive subspace\nfamilies. Given a family $\\mathcal{F}$ of subvarieties of a projective or\naffine space, a collection $\\mathcal{H}$ of projective or affine $k$-subspaces\nis $(\\mathcal{F},\\epsilon)$-evasive if for every $\\mathcal{V}\\in\\mathcal{F}$,\nall but at most $\\epsilon$-fraction of $W\\in\\mathcal{H}$ intersect every\nirreducible component of $\\mathcal{V}$ with (at most) the expected dimension.\nThe problem of constructing such an explicit subspace family generalizes both\ndeterministic black-box polynomial identity testing (PIT) and the problem of\nconstructing explicit (weak) lossless rank condensers.\n  Using Chow forms, we construct explicit $k$-subspace families of polynomial\nsize that are evasive for all varieties of bounded degree in a projective or\naffine $n$-space. As one application, we obtain a complete derandomization of\nNoether's normalization lemma for varieties of low degree in a projective or\naffine $n$-space. In another application, we obtain a simple polynomial-time\nblack-box PIT algorithm for depth-4 arithmetic circuits with bounded top fan-in\nand bottom fan-in that are not in the Sylvester-Gallai configuration, improving\nand simplifying a result of Gupta (ECCC TR 14-130).\n  As a complement of our explicit construction, we prove a tight lower bound\nfor the size of $k$-subspace families that are evasive for degree-$d$ varieties\nin a projective $n$-space. When $n-k=n^{\\Omega(1)}$, the lower bound is\nsuperpolynomial unless $d$ is bounded. The proof uses a dimension-counting\nargument on Chow varieties that parametrize projective subvarieties.\n"}
+{"abstract": "  We present the results of a 3D global magnetohydrodynamic (MHD) simulation of\nan AM CVn system that was aimed at exploring eccentricity growth in the\naccretion disc self-consistently from a first principles treatment of the MHD\nturbulence. No significant eccentricity growth occurs in the simulation. In\norder to investigate the reasons why, we ran 2D alpha disc simulations with\nalpha values of 0.01, 0.1, and 0.2, and found that only the latter two exhibit\nsignificant eccentricity growth. We present an equation expressing global\neccentricity evolution in terms of contributing forces and use it to analyze\nthe simulations. As expected, we find that the dominant term contributing to\nthe growth of eccentricity is the tidal gravity of the companion star. In the\n2D simulations, the alpha viscosity directly contributes to eccentricity\ngrowth. In contrast, the overall magnetic forces in the 3D simulation damp\neccentricity. We also analyzed the mode-coupling mechanism of Lubow, and\nconfirmed that the spiral wave excited by the 3:1 resonance was the dominant\ncontributor to eccentricity growth in the 2D $\\alpha=0.1$ simulations, but\nother waves also contribute significantly. We found that the $\\alpha=0.1$ and\n0.2 simulations had more relative mass at larger radii compared to the\n$\\alpha=0.01$ and 3D MHD simulation, which also had an effective $\\alpha$ of\n0.01. This suggests that in 3D MHD simulations without sufficient poloidal\nmagnetic flux, MRI turbulence does not saturate at a high enough $\\alpha$ to\nspread the disc to large enough radii to reproduce the superhumps observed in\nreal systems.\n"}
+{"abstract": "  The problem of open-set noisy labels denotes that part of training data have\na different label space that does not contain the true class. Lots of\napproaches, e.g., loss correction and label correction, cannot handle such\nopen-set noisy labels well, since they need training data and test data to\nshare the same label space, which does not hold for learning with open-set\nnoisy labels. The state-of-the-art methods thus employ the sample selection\napproach to handle open-set noisy labels, which tries to select clean data from\nnoisy data for network parameters updates. The discarded data are seen to be\nmislabeled and do not participate in training. Such an approach is intuitive\nand reasonable at first glance. However, a natural question could be raised\n\"can such data only be discarded during training?\". In this paper, we show that\nthe answer is no. Specifically, we discuss that the instances of discarded data\ncould consist of some meaningful information for generalization. For this\nreason, we do not abandon such data, but use instance correction to modify the\ninstances of the discarded data, which makes the predictions for the discarded\ndata consistent with given labels. Instance correction are performed by\ntargeted adversarial attacks. The corrected data are then exploited for\ntraining to help generalization. In addition to the analytical results, a\nseries of empirical evidences are provided to justify our claims.\n"}
+{"abstract": "  We propose a new hash function QHFM based on controlled alternate quantum\nwalks with memory on cycles, where the jth message bit decides whether to run\nquantum walk with one-step memory or to run quantum walk with two-step memory\nat the jth time step, and the hash value is calculated from the resulting\nprobability distribution of the walker. Numerical simulation shows that the\nproposed hash function has near-ideal statistical performance and is at least\non a par with the state-of-the-art hash functions based on quantum walks in\nterms of sensitivity of hash value to message, diffusion and confusion\nproperties, uniform distribution property, and collision resistance property;\nand theoretical analysis indicates that the time and space complexity of the\nnew scheme are not greater than those of its peers. The good performance of\nQHFM suggests that quantum walks that differ not only in coin operators but\nalso in memory lengths can be combined to build good hash functions, which, in\nturn, enriches the construction of controlled alternate quantum walks.\n"}
+{"abstract": "  Recent progress in self-supervised learning has resulted in models that are\ncapable of extracting rich representations from image collections without\nrequiring any explicit label supervision. However, to date the vast majority of\nthese approaches have restricted themselves to training on standard benchmark\ndatasets such as ImageNet. We argue that fine-grained visual categorization\nproblems, such as plant and animal species classification, provide an\ninformative testbed for self-supervised learning. In order to facilitate\nprogress in this area we present two new natural world visual classification\ndatasets, iNat2021 and NeWT. The former consists of 2.7M images from 10k\ndifferent species uploaded by users of the citizen science application\niNaturalist. We designed the latter, NeWT, in collaboration with domain experts\nwith the aim of benchmarking the performance of representation learning\nalgorithms on a suite of challenging natural world binary classification tasks\nthat go beyond standard species classification. These two new datasets allow us\nto explore questions related to large-scale representation and transfer\nlearning in the context of fine-grained categories. We provide a comprehensive\nanalysis of feature extractors trained with and without supervision on ImageNet\nand iNat2021, shedding light on the strengths and weaknesses of different\nlearned features across a diverse set of tasks. We find that features produced\nby standard supervised methods still outperform those produced by\nself-supervised approaches such as SimCLR. However, improved self-supervised\nlearning methods are constantly being released and the iNat2021 and NeWT\ndatasets are a valuable resource for tracking their progress.\n"}
+{"abstract": "  Smartphone apps for exposure notification and contact tracing have been shown\nto be effective in controlling the COVID-19 pandemic. However, Bluetooth Low\nEnergy tokens similar to those broadcast by existing apps can still be picked\nup far away from the transmitting device. In this paper, we present a new class\nof methods for detecting whether or not two Wi-Fi-enabled devices are in\nimmediate physical proximity, i.e. 2 or fewer meters apart, as established by\nthe U.S. Centers for Disease Control and Prevention (CDC). Our goal is to\nenhance the accuracy of smartphone-based exposure notification and contact\ntracing systems. We present a set of binary machine learning classifiers that\ntake as input pairs of Wi-Fi RSSI fingerprints. We empirically verify that a\nsingle classifier cannot generalize well to a range of different environments\nwith vastly different numbers of detectable Wi-Fi Access Points (APs). However,\nspecialized classifiers, tailored to situations where the number of detectable\nAPs falls within a certain range, are able to detect immediate physical\nproximity significantly more accurately. As such, we design three classifiers\nfor situations with low, medium, and high numbers of detectable APs. These\nclassifiers distinguish between pairs of RSSI fingerprints recorded 2 or fewer\nmeters apart and pairs recorded further apart but still in Bluetooth range. We\ncharacterize their balanced accuracy for this task to be between 66.8% and\n77.8%.\n"}
+{"abstract": "  We give a complete classification of left invariant para-K\\\"ahler structures\non four-dimensional simply connected Lie groups up to an automorphism. As an\napplication we discuss some curvatures properties of the canonical connection\nassociated to these structures as flat, Ricci flat and existence of Ricci\nsolitons.\n"}
+{"abstract": "  This paper studies the convergence of a spatial semi-discretization for a\nbackward semilinear stochastic parabolic equation. The filtration is general,\nand the spatial semi-discretization uses the standard continuous piecewise\nlinear element method. Firstly, higher regularity of the solution to the\ncontinuous equation is derived. Secondly, the first-order spatial accuracy is\nderived for the spatial semi-discretization. Thirdly, an application of the\ntheoretical result to a stochastic linear quadratic control problem is\npresented.\n"}
+{"abstract": "  Quantum mechanics dictates the band-structure of materials that is essential\nfor functional electronic components. With increased miniaturization of devices\nit becomes possible to exploit the full potential of quantum mechanics through\nthe principles of superpositions and entanglement. We propose a new class of\nquantum rectifiers that can leverage entanglement to dramatically increase\nperformance by coupling two small spin chains through an effective double-slit\ninterface. Simulations show that rectification is enhanced by several orders of\nmagnitude even in small systems and should be realizable using several of the\nquantum technology platforms currently available.\n"}
+{"abstract": "  Light bridges (LBs) are bright lanes that divide an umbra into multiple parts\nin some sunspots. Persistent oscillatory bright fronts at a temperature of\n$\\sim$$10^5$ K are commonly observed above LBs in the 1400/1330 \\AA~passbands\nof the Interface Region Imaging Spectrograph (IRIS). Based on IRIS\nobservations, we report small-scale bright blobs from the oscillating bright\nfront above a light bridge. Some of these blobs reveal a clear acceleration,\nwhereas the others do not. The average speed of these blobs projected onto the\nplane of sky is $71.7\\pm14.7$ km s$^{-1}$, with an initial acceleration of\n$1.9\\pm1.3$ km s$^{-2}$. These blobs normally reach a projected distance of\n3--7 Mm from their origin sites. From the transition region images we find an\naverage projected area of $0.57\\pm0.37$ Mm$^{2}$ for the blobs. The blobs were\nalso detected in multi-passbands of the Solar Dynamics Observatory, but not in\nthe H$\\alpha$ images. These blobs are likely to be plasma ejections, and we\ninvestigate their kinematics and energetics. Through emission measure analyses,\nthe typical temperature and electron density of these blobs are found to be\naround $10^{5.47}$ K and $10^{9.7}$ cm$^{-3}$, respectively. The estimated\nkinetic and thermal energies are on the order of $10^{22.8}$ erg and\n$10^{23.3}$ erg, respectively. These small-scale blobs appear to show three\ndifferent types of formation process. They are possibly triggered by induced\nreconnection or release of enhanced magnetic tension due to interaction of\nadjacent shocks, local magnetic reconnection between emerging magnetic bipoles\non the light bridge and surrounding unipolar umbral fields, and plasma\nacceleration or instability caused by upward shocks, respectively.\n"}
+{"abstract": "  To realize spin wave logic gates programmable phase inverters are essential.\nWe image with phase-resolved Brillouin light scattering microscopy propagating\nspin waves in a one-dimensional magnonic crystal consisting of dipolarly\ncoupled magnetic nanostripes. We demonstrate phase shifts upon a single\nnanostripe of opposed magnetization. Using micromagnetic simulations we model\nour experimental finding in a wide parameter space of bias fields and wave\nvectors. We find that low-loss phase inversion is achieved, when the internal\nfield of the oppositely magnetized nanostripe is tuned such that the latter\nsupports a resonant standing spin wave mode with odd quantization number at the\ngiven frequency. Our results are key for the realization of phase inverters\nwith optimized signal transmission.\n"}
+{"abstract": "  This work is the first to employ and adapt the image-to-image translation\nconcept based on conditional generative adversarial networks (cGAN) towards\nlearning a forward and an inverse solution operator of partial differential\nequations (PDEs). Even though the proposed framework could be applied as a\nsurrogate model for the solution of any PDEs, here we focus on steady-state\nsolutions of coupled hydro-mechanical processes in heterogeneous porous media.\nStrongly heterogeneous material properties, which translate to the\nheterogeneity of coefficients of the PDEs and discontinuous features in the\nsolutions, require specialized techniques for the forward and inverse solution\nof these problems. Additionally, parametrization of the spatially heterogeneous\ncoefficients is excessively difficult by using standard reduced order modeling\ntechniques. In this work, we overcome these challenges by employing the\nimage-to-image translation concept to learn the forward and inverse solution\noperators and utilize a U-Net generator and a patch-based discriminator. Our\nresults show that the proposed data-driven reduced order model has competitive\npredictive performance capabilities in accuracy and computational efficiency as\nwell as training time requirements compared to state-of-the-art data-driven\nmethods for both forward and inverse problems.\n"}
+{"abstract": "  Identifying the underlying mechanisms behind the excitation of transverse\noscillations in coronal loops is essential for their role as diagnostic tools\nin coronal seismology and their potential use as wave heating mechanisms of the\nsolar corona. In this paper, we explore the concept of these transverse\noscillations being excited through a self-sustaining process, caused by\nAlfv\\'{e}nic vortex shedding from strong background flows interacting with\ncoronal loops. We show for the first time in 3D simulations that vortex\nshedding can generate transverse oscillations in coronal loops, in the\ndirection perpendicular to the flow due to periodic \"pushing\" by the vortices.\nBy plotting the power spectral density we identify the excited frequencies of\nthese oscillations. We see that these frequencies are dependent both on the\nspeed of the flow, as well as the characteristics of the oscillating loop.\nThis, in addition to the fact that the background flow is constant and not\nperiodic, makes us treat this as a self-oscillating process. Finally, the\namplitudes of the excited oscillations are near constant in amplitude, and are\ncomparable with the observations of decay-less oscillations. This makes the\nmechanism under consideration a possible interpretation of these undamped waves\nin coronal loops.\n"}
+{"abstract": "  One-loop $W$ boson contributions to the decay $H\\rightarrow Z\\gamma$ in the\ngeneral $R_\\xi$ gauge are presented. The analytical results are expressed in\nterms of well-known Passarino-Veltman functions which their numerical\nevaluations can be generated using {\\tt LoopTools}. In the limit $d\\rightarrow\n4$, we have shown that these analytical results are independent of the\nunphysical parameter $\\xi$ and consistent with previous results. The gauge\nparameter independence are also checked numerically for consistence. Our\nresults are also well stable with different values of $\\xi =0, 1, 100,$ and\n$\\xi \\rightarrow \\infty$.\n"}
+{"abstract": "  The performance and cost of Bi-2212/Ag wire is limited by the large fraction\nof silver matrix (~3:1) that is required in the oxide-powder-in-tube\nfabrication process. An alternative fabrication process is being developed in\nwhich fine-powder Bi-2212 is uni-axially compressed to form bars with a thin Ag\nfoil sheath. The fine powder naturally textures (aligns the a-b planes\nperpendicular to the direction of compaction) with texture >80% using 200 MPa\ncompression. A billet is formed by stacking trapezoidal-cross-section bars in a\nsymmetric 8-12-16 pattern around a Ag rod and enclosing in a Ag-wall extrusion\ncan. The billet is extruded and drawn to fine wire. Results are presented on\npresent status of the development and testing.\n"}
+{"abstract": "  Identifying the roles of individual units is critical for understanding the\nmechanism of convolutional neural networks (CNNs). However, it is challenging\nto give the fully automatic and quantitative measures for effectiveness\nassessment of individual units in CNN. To this end, we propose a novel method\nfor quantitatively clarifying the status and usefulness of single unit of CNN\nin image classification tasks. The technical substance of our method is ranking\nthe importance of unit for each class in classification based on calculation of\nspecifically defined entropy using algebraic topological tools. It could be\nimplemented totally by machine without any human intervention. Some interesting\nphenomena including certain kind of phase transition are observed via the\nevolution of accuracy and loss of network in the successive ablation process of\nunits. All of the network units are divided into four categories according to\ntheir performance on training and testing data. The role categorization is\nexcellent startpoint for network construction and simplification. The diverse\nutility and contribution to the network generalization of units in\nclassification tasks are thoroughly illustrated by extensive experiments on\nnetwork (VGG) and dataset (ImageNet) with considerable scale. It is easy for\nour method to have extensional applications on other network models and tasks\nwithout essential difficulties.\n"}
+{"abstract": "  This paper develops new analytical process noise covariance models for both\nabsolute and relative spacecraft states. Process noise is always present when\npropagating a spacecraft state due to dynamics modeling deficiencies.\nAccurately modeling this noise is essential for sequential orbit determination\nand improves satellite conjunction analysis. A common approach called state\nnoise compensation models process noise as zero-mean Gaussian white noise\naccelerations. The resulting process noise covariance can be evaluated\nnumerically, which is computationally intensive, or through a widely used\nanalytical model that is restricted to an absolute Cartesian state and small\npropagation intervals. Moreover, mathematically rigorous, analytical process\nnoise covariance models for relative spacecraft states are not currently\navailable. To address these limitations of the state of the art, new analytical\nprocess noise covariance models are developed for state noise compensation for\nboth Cartesian and orbital element absolute state representations by leveraging\nspacecraft relative dynamics models. Two frameworks are then presented for\nmodeling the process noise covariance of relative spacecraft states by assuming\neither small or large interspacecraft separation. The presented techniques are\nvalidated through numerical simulations.\n"}
+{"abstract": "  While game theory has been transformative for decision-making, the\nassumptions made can be overly restrictive in certain instances. In this work,\nwe focus on some of the assumptions underlying rationality such as mutual\nconsistency and best response, and consider ways to relax these assumptions\nusing concepts from level-$k$ reasoning and quantal response equilibrium (QRE)\nrespectively. Specifically, we provide an information-theoretic two-parameter\nmodel that can relax both mutual consistency and best response, but can recover\napproximations of level-$k$, QRE, or typical Nash equilibrium behaviour in the\nlimiting cases. The proposed Quantal Hierarchy model is based on a recursive\nform of the variational free energy principle, representing self-referential\ngames as (pseudo) sequential decisions. Bounds in player processing abilities\nare captured as information costs, where future chains of reasoning are\ndiscounted, implying a hierarchy of players where lower-level players have\nfewer processing resources. We demonstrate the applicability of the proposed\nmodel to several canonical economic games.\n"}
+{"abstract": "  Recently a conformally invariant action describing the Wilson-Fischer fixed\npoint in $D=4-\\epsilon$ dimensions in the presence of a {\\em finite} UV cutoff\nwas constructed \\cite{Dutta}. In the present paper we construct two composite\noperator perturbations of this action with definite scaling dimension also in\nthe presence of a finite cutoff. Thus the operator (as well as the fixed point\naction) is well defined at all momenta $0\\leq p\\leq \\infty$ and at low energies\nthey reduce to $\\int_x \\phi^2$ and $\\int _x \\phi^4$ respectively. The\nconstruction includes terms up to $O(\\lamda^2)$. In the presence of a finite\ncutoff they mix with higher order irrelevant operators. The dimensions are also\ncalculated to this order and agree with known results.\n"}
+{"abstract": "  We consider the practicalities of defining, simulating, and characterizing\n\"Liquids\" from a pedagogical standpoint based on atomistic computer\nsimulations. For simplicity and clarity we study two-dimensional systems\nthroughout. In addition to the infinite-ranged Lennard-Jones 12/6 potential we\nconsider two shorter-ranged families of pair potentials. At zero pressure one\nof them includes just nearest neighbors. The other longer-ranged family\nincludes twelve additional neighbors. We find that these further neighbors can\nhelp stabilize the liquid phase.\n  What about liquids? To implement Wikipedia's definition of liquids as\nconforming to their container we begin by formulating and imposing\nsmooth-container boundary conditions. To encourage conformation further we add\na vertical gravitational field. Gravity helps stabilize the relatively vague\nliquid-gas interface. Gravity reduces the messiness associated with the\ncuriously-named \"spinodal\" (tensile) portion of the phase diagram. Our\nsimulations are mainly isothermal. We control the kinetic temperature with\nNos\\'e-Hoover thermostating, extracting or injecting heat so as to impose a\nmean kinetic temperature over time. Our simulations stabilizing density\ngradients and the temperature provide critical-point estimates fully consistent\nwith previous efforts from free energy and Gibbs' ensemble simulations. This\nagreement validates our approach.\n"}
+{"abstract": "  Recent work has highlighted the role of initialization scale in determining\nthe structure of the solutions that gradient methods converge to. In\nparticular, it was shown that large initialization leads to the neural tangent\nkernel regime solution, whereas small initialization leads to so called \"rich\nregimes\". However, the initialization structure is richer than the overall\nscale alone and involves relative magnitudes of different weights and layers in\nthe network. Here we show that these relative scales, which we refer to as\ninitialization shape, play an important role in determining the learned model.\nWe develop a novel technique for deriving the inductive bias of gradient-flow\nand use it to obtain closed-form implicit regularizers for multiple cases of\ninterest.\n"}
+{"abstract": "  The success of the current generation of Noisy Intermediate-Scale Quantum\n(NISQ) hardware shows that quantum hardware may be able to tackle complex\nproblems even without error correction. One outstanding issue is that of\ncoherent errors arising from the increased complexity of these devices. These\nerrors can accumulate through a circuit, making their impact on algorithms hard\nto predict and mitigate. Iterative algorithms like Quantum Imaginary Time\nEvolution are susceptible to these errors. This article presents the\ncombination of both noise tailoring using Randomized Compiling and error\nmitigation with a purification. We also show that Cycle Benchmarking gives an\nestimate of the reliability of the purification. We apply this method to the\nQuantum Imaginary Time Evolution of a Transverse Field Ising Model and report\nan energy estimation and a ground state infidelity both below 1\\%. Our\nmethodology is general and can be used for other algorithms and platforms. We\nshow how combining noise tailoring and error mitigation will push forward the\nperformance of NISQ devices.\n"}
+{"abstract": "  For a compact set of actions, an invariant of Kushnirenko's entropy type is\nchosen in such a way that on this set it is equal to zero, but will be infinity\nfor typical actions. As a consequence, we show that typical measure-preserving\ntransformations are not isomorphic to geometric shape exchange transformations.\nThis problem arose in connection with the result of Chaika and Davis about the\natypical nature of IETs.\n"}
+{"abstract": "  Neural language models exhibit impressive performance on a variety of tasks,\nbut their internal reasoning may be difficult to understand. Prior art aims to\nuncover meaningful properties within model representations via probes, but it\nis unclear how faithfully such probes portray information that the models\nactually use. To overcome such limitations, we propose a technique, inspired by\ncausal analysis, for generating counterfactual embeddings within models. In\nexperiments testing our technique, we produce evidence that suggests some\nBERT-based models use a tree-distance-like representation of syntax in\ndownstream prediction tasks.\n"}
+{"abstract": "  By ungauging a recently discovered lattice rotor model for Chern-Simons\ntheory, we create an exactly soluble path integral on spacetime lattice for\n$U^\\kappa(1)$ Symmetry Protected Topological (SPT) phases in $2+1$ dimensions\nwith a non-zero Hall conductance. We then convert the path integral on a $2+1$d\nspacetime lattice into a $2$d Hamiltonian lattice model, and show that the\nHamiltonian consists of mutually commuting local projectors. We confirm the\nnon-zero Hall conductance by calculating the Chern number of the exact ground\nstate. It has recently been suggested that no commuting projector model can\nhost a nonzero Hall conductance. We evade this no-go theorem by considering a\nrotor model, with a countably infinite number of states per site.\n"}
+{"abstract": "  With the need of fast retrieval speed and small memory footprint, document\nhashing has been playing a crucial role in large-scale information retrieval.\nTo generate high-quality hashing code, both semantics and neighborhood\ninformation are crucial. However, most existing methods leverage only one of\nthem or simply combine them via some intuitive criteria, lacking a theoretical\nprinciple to guide the integration process. In this paper, we encode the\nneighborhood information with a graph-induced Gaussian distribution, and\npropose to integrate the two types of information with a graph-driven\ngenerative model. To deal with the complicated correlations among documents, we\nfurther propose a tree-structured approximation method for learning. Under the\napproximation, we prove that the training objective can be decomposed into\nterms involving only singleton or pairwise documents, enabling the model to be\ntrained as efficiently as uncorrelated ones. Extensive experimental results on\nthree benchmark datasets show that our method achieves superior performance\nover state-of-the-art methods, demonstrating the effectiveness of the proposed\nmodel for simultaneously preserving semantic and neighborhood information.\\\n"}
+{"abstract": "  Toroidal rotation is well known to play significant roles in the edge\ntransport and L-H transition dynamics of tokamaks. Our recent calculation finds\nthat a sufficiently strong localized toroidal rotation can directly bring out\nthe formation of edge pressure pedestal with reversed magnetic shear that is\nreminiscent of an H-mode plasma, purely through the effects of toroidal\nrotation on the tokamak MHD equilibrium itself. In particular, the enhanced\nedge toroidal rotation enables a substantial peaking of the parallel current\nprofile near edge in higher $\\beta$ regimes, which leads to the flattening or\nreversal of the local $q$ (safety factor) profile. Here the formation of\npressure pedestal along with the reversed magnetic shear region is shown to be\nthe natural outcome of the MHD tokamak equilibrium in a self-consistent\nresponse to the presence of a localized toroidal rotation typically observed in\nH-mode or QH-mode.\n"}
+{"abstract": "  The exchange of information between an open quantum system and its\nenvironment allows us to discriminate among different kinds of dynamics, in\nparticular detecting memory effects to characterize non-Markovianity. Here, we\ninvestigate the role played by the system-environment correlations and the\nenvironmental evolution in the flow of information. First, we derive general\nconditions ensuring that two generalized dephasing microscopic models of the\nglobal system-environment evolution result exactly in the same open-system\ndynamics, for any initial state of the system. Then, we use the trace distance\nto quantify the distinct contributions to the information inside and outside\nthe open system in the two models. Our analysis clarifies how the interplay\nbetween system-environment correlations and environmental-state\ndistinguishability can lead to the same information flow from and toward the\nopen system, despite significant qualitative and quantitative differences at\nthe level of the global evolution.\n"}
+{"abstract": "  Recently a non-supersymmetric conformal field theory with an exactly marginal\ndeformation in the large $N$ limit was constructed by\nChaudhuri-Choi-Rabinovici. On a non-supersymmetric conformal manifold, $c$\ncoefficient of the trace anomaly in four dimensions would generically change.\nIn this model, we, however, find that it does not change at the first\nnon-trivial order given by three-loop diagrams.\n"}
+{"abstract": "  We propose a logic of knowledge for impure simplicial complexes. Impure\nsimplicial complexes represent distributed systems under uncertainty over which\nprocesses are still active (are alive) and which processes have failed or\ncrashed (are dead). Our work generalizes the logic of knowledge for pure\nsimplicial complexes, where all processes are alive, by Goubault et al. Our\nlogical semantics has a satisfaction relation defined simultaneously with a\ndefinability relation. The latter restricts which formulas are allowed to have\na truth value: dead processes cannot know or be ignorant of any proposition,\nand live processes cannot know or be ignorant of propositions involving\nprocesses they know to be dead. The logic satisfies some but not all axioms and\nrules of the modal logic S5. Impure simplicial complexes correspond to Kripke\nmodels where each agent's accessibility relation is an equivalence relation on\na subset of the domain only, and otherwise empty, and where each propositional\nvariable is known by an agent. We also propose a notion of bisimulation for\nimpure simplexes and show bisimulation correspondence on certain finitary\nsimplexes. % Dynamic aspects of our semantics, such as how to formalize\npossibly incomplete tasks and algorithms in distributed computing, is left for\nfuture research.\n"}
+{"abstract": "  Missing node attributes is a common problem in real-world graphs. Graph\nneural networks have been demonstrated powerful in graph representation\nlearning, however, they rely heavily on the completeness of graph information.\nFew of them consider the incomplete node attributes, which can bring great\ndamage to the performance in practice. In this paper, we propose an innovative\nnode representation learning framework, Wasserstein graph diffusion (WGD), to\nmitigate the problem. Instead of feature imputation, our method directly learns\nnode representations from the missing-attribute graphs. Specifically, we extend\nthe message passing schema in general graph neural networks to a Wasserstein\nspace derived from the decomposition of attribute matrices. We test WGD in node\nclassification tasks under two settings: missing whole attributes on some nodes\nand missing only partial attributes on all nodes. In addition, we find WGD is\nsuitable to recover missing values and adapt it to tackle matrix completion\nproblems with graphs of users and items. Experimental results on both tasks\ndemonstrate the superiority of our method.\n"}
+{"abstract": "  Non-potential magnetic energy promptly released in solar flares is converted\nto other forms of energy. This may include nonthermal energy of\nflare-accelerated particles, thermal energy of heated flaring plasma, and\nkinetic energy of eruptions, jets, up/down flows, and stochastic (turbulent)\nplasma motions. The processes or parameters governing partitioning of the\nreleased energy between these components is an open question. How these\ncomponents are distributed between distinct flaring loops and what controls\nthese spatial distributions is also unclear. Here, based on multi-wavelength\ndata and 3D modeling, we quantify the energy partitioning and spatial\ndistribution in the well observed SOL2014-02-16T064620 solar flare of class\nC1.5. Nonthermal emissions of this flare displayed a simple impulsive\nsingle-spike light curves lasting about 20\\,s. In contrast, the thermal\nemission demonstrated at least three distinct heating episodes, only one of\nwhich was associated with the nonthermal component. The flare was accompanied\nby up and down flows and substantial turbulent velocities. The results of our\nanalysis suggest that (i) the flare occurs in a multi-loop system that included\nat least three distinct flux tubes; (ii) the released magnetic energy is\ndivided unevenly between the thermal and nonthermal components in these loops;\n(iii) only one of these three flaring loops contains an energetically important\namount of nonthermal electrons, while two other loops remain thermal; (iv) the\namounts of direct plasma heating and that due to nonthermal electron loss are\ncomparable; (v) the kinetic energy in the flare footpoints constitute only a\nminor fraction compared with the thermal and nonthermal energies.\n"}
+{"abstract": "  This article develops for the first time a rigorous analysis of Hibler's\nmodel of sea ice dynamics. Identifying Hibler's ice stress as a quasilinear\nsecond order operator and regarding Hibler's model as a quasilinear evolution\nequation, it is shown that Hibler's coupled sea ice model, i.e., the model\ncoupling velocity, thickness and compactness of sea ice, is locally strongly\nwell-posed within the $L_q$-setting and also globally strongly well-posed for\ninitial data close to constant equilibria.\n"}
+{"abstract": "  Ensemble data from Earth system models has to be calibrated and\npost-processed. I propose a novel member-by-member post-processing approach\nwith neural networks. I bridge ideas from ensemble data assimilation with\nself-attention, resulting into the self-attentive ensemble transformer. Here,\ninteractions between ensemble members are represented as additive and dynamic\nself-attentive part. As proof-of-concept, I regress global ECMWF ensemble\nforecasts to 2-metre-temperature fields from the ERA5 reanalysis. I demonstrate\nthat the ensemble transformer can calibrate the ensemble spread and extract\nadditional information from the ensemble. As it is a member-by-member approach,\nthe ensemble transformer directly outputs multivariate and spatially-coherent\nensemble members. Therefore, self-attention and the transformer technique can\nbe a missing piece for a non-parametric post-processing of ensemble data with\nneural networks.\n"}
+{"abstract": "  Cold spots are sub-wavelength regions which might emerge near a plasmonic\nnanoantenna, should one or more components of some far-field illumination\ncancel out with scattered light. With a simplest-case demonstration using two\ndipolar scatterers, we show that by changing only the polarisation and\namplitude of two plane waves, a unique, zero-magnitude and super sub-wavelength\ncold spot can be created anywhere in the space around a nanoantenna. This\ntechnique is a means for ultra-fast, remote, and non-mechanical sub-wavelength\nelectric field manipulation.\n"}
+{"abstract": "  Bayesian inference allows to obtain useful information on the parameters of\nmodels, either in computational statistics or more recently in the context of\nBayesian Neural Networks. The computational cost of usual Monte Carlo methods\nfor sampling a posteriori laws in Bayesian inference scales linearly with the\nnumber of data points. One option to reduce it to a fraction of this cost is to\nresort to mini-batching in conjunction with unadjusted discretizations of\nLangevin dynamics, in which case only a random fraction of the data is used to\nestimate the gradient. However, this leads to an additional noise in the\ndynamics and hence a bias on the invariant measure which is sampled by the\nMarkov chain. We advocate using the so-called Adaptive Langevin dynamics, which\nis a modification of standard inertial Langevin dynamics with a dynamical\nfriction which automatically corrects for the increased noise arising from\nmini-batching. We investigate the practical relevance of the assumptions\nunderpinning Adaptive Langevin (constant covariance for the estimation of the\ngradient), which are not satisfied in typical models of Bayesian inference, and\nquantify the bias induced by minibatching in this case. We also show how to\nextend AdL in order to systematically reduce the bias on the posterior\ndistribution by considering a dynamical friction depending on the current value\nof the parameter to sample.\n"}
+{"abstract": "  In localization microscopy, subnanometer precision is possible but supporting\naccuracy is challenging, and no study has demonstrated reliable traceability to\nthe International System of Units (SI). To do so, we measure the positions of\nnanoscale apertures in a reference array by traceable atomic-force microscopy,\ncreating a master standard. We perform correlative measurements of this\nstandard by optical microscopy, correcting position errors from optical\naberrations by a Zernike calibration. We establish an uncertainty field due to\nlocalization errors and scale uncertainty, with regions of position\ntraceability to within a 68 % coverage interval of +/- 1.0 nm. These results\nenable localization metrology with high throughput, which we apply to measure\nworking standards, validating the subnanometer accuracy of lithographic pitch.\n"}
+{"abstract": "  Motivated by entropic optimal transport, time reversal of diffusion processes\nis revisited. An integration by parts formula is derived for the carr\\'e du\nchamp of a Markov process in an abstract space. It leads to a time reversal\nformula for a wide class of diffusion processes in $ \\mathbb{R}^n$ possibly\nwith singular drifts, extending the already known results in this domain.\n  The proof of the integration by parts formula relies on stochastic\nderivatives. Then, this formula is applied to compute the semimartingale\ncharacteristics of the time-reversed $P^*$ of a diffusion measure $P$ provided\nthat the relative entropy of $P$ with respect to another diffusion measure $R$\nis finite, and the semimartingale characteristics of the time-reversed $R^*$\nare known (for instance when the reference path measure $R$ is reversible).\n  As an illustration of the robustness of this method, the integration by parts\nformula is also employed to derive a time-reversal formula for a random walk on\na graph.\n"}
+{"abstract": "  Collecting and aggregating information from several probability measures or\nhistograms is a fundamental task in machine learning. One of the popular\nsolution methods for this task is to compute the barycenter of the probability\nmeasures under the Wasserstein metric. However, approximating the Wasserstein\nbarycenter is numerically challenging because of the curse of dimensionality.\nThis paper proposes the projection robust Wasserstein barycenter (PRWB) that\nhas the potential to mitigate the curse of dimensionality. Since PRWB is\nnumerically very challenging to solve, we further propose a relaxed PRWB\n(RPRWB) model, which is more tractable. The RPRWB projects the probability\nmeasures onto a lower-dimensional subspace that maximizes the Wasserstein\nbarycenter objective. The resulting problem is a max-min problem over the\nStiefel manifold. By combining the iterative Bregman projection algorithm and\nRiemannian optimization, we propose two new algorithms for computing the RPRWB.\nThe complexity of arithmetic operations of the proposed algorithms for\nobtaining an $\\epsilon$-stationary solution is analyzed. We incorporate the\nRPRWB into a discrete distribution clustering algorithm, and the numerical\nresults on real text datasets confirm that our RPRWB model helps improve the\nclustering performance significantly.\n"}
+{"abstract": "  Context. We report the discovery of VVV-CL160, a new nearby globular cluster\n(GC) with extreme kinematics, located in the Galactic plane at $l = 10.1477$\ndeg, $b = 0.2999$ deg. Aims. We aim to characterize the physical properties of\nthis new GC and place it in the context of the Milky Way, exploring its\npossible connection with the known GC NGC 6544 and with the Hrid halo stream.\nMethods. VVV-CL160 was originally detected in the VISTA Variables in the V\\'ia\nL\\'actea (VVV) survey. We use the proper motions (PMs) from the updated VVV\nInfrared Astrometric Catalog (VIRAC2) to select GC members and make deep\nnear-infrared color-magnitude diagrams (CMDs) to study the cluster properties.\nWe also fit King models to the decontaminated sample to determine the GC\nstructural parameters. Results. VVV-CL160 has an unusually large PM for a\nGalactic GC as measured with VIRAC2 and Gaia EDR3: $\\mu_{\\alpha}\\cos(\\delta)$ =\n$-2.3 \\pm 0.1 $ mas yr$^{-1}$ and $\\mu_{\\delta}$ = $-16.8 \\pm 0.1 $ mas\nyr$^{-1}$. The kinematics are similar to those of the known GC NGC 6544 and the\nHrid halo stream. We estimate a reddening of $E(J-K) = 1.95$ mag and an\nextinction of $A_{k}= 1.40$ mag for VVV-CL160. We also measure a distance\nmodulus of $(m-M) = 13.01$ mag and a distance of $D_{\\odot} = 4.0 \\pm 0.5$ kpc.\nThis places the GC at $z=29$ pc above the Galactic plane and at a\ngalactocentric distance of $R_G=4.2$ kpc. We also measure a metallicity of\n$[Fe/H] = -1.4 \\pm 0.2$ dex for an adopted age of $t=12$ Gyr; King model fits\nof the PM-decontaminated sample reveal a concentrated GC, with core radius\n$r_{c}= 22.8\"$ and tidal radius $r_{t}= 50'$. .... We also explore the possible\nassociation of this new GC with other GCs and halo streams. Conclusions. Based\non the locations and kinematics, we suggest that VVV-CL160, along with NGC\n6544, may be associated with the extension of the Hrid halo stream.\n"}
+{"abstract": "  A rank-$r$ integer matrix $A$ is $\\Delta$-modular if the determinant of each\n$r \\times r$ submatrix has absolute value at most $\\Delta$. The class of\n$1$-modular, or unimodular, matrices is of fundamental significance in both\ninteger programming theory and matroid theory. A 1957 result of Heller shows\nthat the maximum number of nonzero, pairwise non-parallel rows of a rank-$r$\nunimodular matrix is ${r + 1 \\choose 2}$. We prove that, for each sufficiently\nlarge integer $r$, the maximum number of nonzero, pairwise non-parallel rows of\na rank-$r$ $2$-modular matrix is ${r + 2 \\choose 2} - 2$.\n"}
+{"abstract": "  Remote sensing images and techniques are powerful tools to investigate earth\nsurface. Data quality is the key to enhance remote sensing applications and\nobtaining a clear and noise-free set of data is very difficult in most\nsituations due to the varying acquisition (e.g., atmosphere and season),\nsensor, and platform (e.g., satellite angles and sensor characteristics)\nconditions. With the increasing development of satellites, nowadays Terabytes\nof remote sensing images can be acquired every day. Therefore, information and\ndata fusion can be particularly important in the remote sensing community. The\nfusion integrates data from various sources acquired asynchronously for\ninformation extraction, analysis, and quality improvement. In this chapter, we\naim to discuss the theory of spatiotemporal fusion by investigating previous\nworks, in addition to describing the basic concepts and some of its\napplications by summarizing our prior and ongoing works.\n"}
+{"abstract": "  Deep neural networks give state-of-the-art accuracy for reconstructing images\nfrom few and noisy measurements, a problem arising for example in accelerated\nmagnetic resonance imaging (MRI). However, recent works have raised concerns\nthat deep-learning-based image reconstruction methods are sensitive to\nperturbations and are less robust than traditional methods: Neural networks (i)\nmay be sensitive to small, yet adversarially-selected perturbations, (ii) may\nperform poorly under distribution shifts, and (iii) may fail to recover small\nbut important features in an image. In order to understand the sensitivity to\nsuch perturbations, in this work, we measure the robustness of different\napproaches for image reconstruction including trained and un-trained neural\nnetworks as well as traditional sparsity-based methods. We find, contrary to\nprior works, that both trained and un-trained methods are vulnerable to\nadversarial perturbations. Moreover, both trained and un-trained methods tuned\nfor a particular dataset suffer very similarly from distribution shifts.\nFinally, we demonstrate that an image reconstruction method that achieves\nhigher reconstruction quality, also performs better in terms of accurately\nrecovering fine details. Our results indicate that the state-of-the-art\ndeep-learning-based image reconstruction methods provide improved performance\nthan traditional methods without compromising robustness.\n"}
+{"abstract": "  Efficiently approximating local curvature information of the loss function is\na key tool for optimization and compression of deep neural networks. Yet, most\nexisting methods to approximate second-order information have high\ncomputational or storage costs, which can limit their practicality. In this\nwork, we investigate matrix-free, linear-time approaches for estimating\nInverse-Hessian Vector Products (IHVPs) for the case when the Hessian can be\napproximated as a sum of rank-one matrices, as in the classic approximation of\nthe Hessian by the empirical Fisher matrix. We propose two new algorithms as\npart of a framework called M-FAC: the first algorithm is tailored towards\nnetwork compression and can compute the IHVP for dimension $d$, if the Hessian\nis given as a sum of $m$ rank-one matrices, using $O(dm^2)$ precomputation,\n$O(dm)$ cost for computing the IHVP, and query cost $O(m)$ for any single\nelement of the inverse Hessian. The second algorithm targets an optimization\nsetting, where we wish to compute the product between the inverse Hessian,\nestimated over a sliding window of optimization steps, and a given gradient\ndirection, as required for preconditioned SGD. We give an algorithm with cost\n$O(dm + m^2)$ for computing the IHVP and $O(dm + m^3)$ for adding or removing\nany gradient from the sliding window. These two algorithms yield\nstate-of-the-art results for network pruning and optimization with lower\ncomputational overhead relative to existing second-order methods.\nImplementations are available at [9] and [17].\n"}
+{"abstract": "  With a growing number of cores per socket in modern data-centers where\nmulti-tenancy of a diverse set of applications must be efficiently supported,\neffective sharing of the last level cache is a very important problem. This is\nchallenging because modern workloads exhibit dynamic phase behavior - their\ncache requirements & sensitivity vary across different execution points. To\ntackle this problem, we propose Com-CAS, a compiler-guided cache apportioning\nsystem that provides smart cache allocation to co-executing applications in a\nsystem. The front-end of Com-CAS is primarily a compiler-framework equipped\nwith learning mechanisms to predict cache requirements, while the backend\nconsists of an allocation framework with a pro-active scheduler that apportions\ncache dynamically to co-executing applications. Our system improved average\nthroughput by 21%, with a maximum of 54% while maintaining the worst individual\napplication execution time degradation within 15% to meet SLA requirements.\n"}
+{"abstract": "  The performance of algorithms for neural architecture search strongly depends\non the parametrization of the search space. We use contrastive learning to\nidentify networks across different initializations based on their data\nJacobians, and automatically produce the first architecture embeddings\nindependent from the parametrization of the search space. Using our contrastive\nembeddings, we show that traditional black-box optimization algorithms, without\nmodification, can reach state-of-the-art performance in Neural Architecture\nSearch. As our method provides a unified embedding space, we perform for the\nfirst time transfer learning between search spaces. Finally, we show the\nevolution of embeddings during training, motivating future studies into using\nembeddings at different training stages to gain a deeper understanding of the\nnetworks in a search space.\n"}
+{"abstract": "  The field of natural language understanding has experienced exponential\nprogress in the last few years, with impressive results in several tasks. This\nsuccess has motivated researchers to study the underlying knowledge encoded by\nthese models. Despite this, attempts to understand their semantic capabilities\nhave not been successful, often leading to non-conclusive, or contradictory\nconclusions among different works. Via a probing classifier, we extract the\nunderlying knowledge graph of nine of the most influential language models of\nthe last years, including word embeddings, text generators, and context\nencoders. This probe is based on concept relatedness, grounded on WordNet. Our\nresults reveal that all the models encode this knowledge, but suffer from\nseveral inaccuracies. Furthermore, we show that the different architectures and\ntraining strategies lead to different model biases. We conduct a systematic\nevaluation to discover specific factors that explain why some concepts are\nchallenging. We hope our insights will motivate the development of models that\ncapture concepts more precisely.\n"}
+{"abstract": "  Convolutional Neural Networks (CNNs) are successful deep learning models in\nthe field of computer vision. To get the maximum advantage of CNN model for\nHuman Action Recognition (HAR) using inertial sensor data, in this paper, we\nuse 4 types of spatial domain methods for transforming inertial sensor data to\nactivity images, which are then utilized in a novel fusion framework. These\nfour types of activity images are Signal Images (SI), Gramian Angular Field\n(GAF) Images, Markov Transition Field (MTF) Images and Recurrence Plot (RP)\nImages. Furthermore, for creating a multimodal fusion framework and to exploit\nactivity image, we made each type of activity images multimodal by convolving\nwith two spatial domain filters : Prewitt filter and High-boost filter.\nResnet-18, a CNN model, is used to learn deep features from multi-modalities.\nLearned features are extracted from the last pooling layer of each ReNet and\nthen fused by canonical correlation based fusion (CCF) for improving the\naccuracy of human action recognition. These highly informative features are\nserved as input to a multiclass Support Vector Machine (SVM). Experimental\nresults on three publicly available inertial datasets show the superiority of\nthe proposed method over the current state-of-the-art.\n"}
+{"abstract": "  The paper presents a new approach to multiview video coding using Screen\nContent Coding. It is assumed that for a time instant the frames corresponding\nto all views are packed into a single frame, i.e. the frame-compatible approach\nto multiview coding is applied. For such coding scenario, the paper\ndemonstrates that Screen Content Coding can be efficiently used for multiview\nvideo coding. Two approaches are considered: the first using standard HEVC\nScreen Content Coding, and the second using Advanced Screen Content Coding. The\nlatter is the original proposal of the authors that exploits quarter-pel motion\nvectors and other nonstandard extensions of HEVC Screen Content Coding. The\nexperimental results demonstrate that multiview video coding even using\nstandard HEVC Screen Content Coding is much more efficient than simulcast HEVC\ncoding. The proposed Advanced Screen Content Coding provides virtually the same\ncoding efficiency as MV-HEVC, which is the state-of-the-art multiview video\ncompression technique. The authors suggest that Advanced Screen Content Coding\ncan be efficiently used within the new Versatile Video Coding (VVC) technology.\nNevertheless a reference multiview extension of VVC does not exist yet,\ntherefore, for VVC-based coding, the experimental comparisons are left for\nfuture work.\n"}
+{"abstract": "  Motivated by the observation of the first hidden charm pentaquarks by the\nLHCb collaboration in 2015 and the updated analysis with an order-of-magnitude\nlarger data set in 2019, we estimate their cross sections for the prompt\nproduction as well as their heavy quark spin partners, in the\n$\\Sigma_c^{(*)}\\bar{D}^{(*)}$ hadronic molecular picture, at the center-of-mass\nenergy $7~\\mathrm{TeV}$ in the $pp$ collision. Their cross sections are several\n$\\mathrm{nb}$ and we would expect several tens hidden charm pentaquark events\nin the LHC based on its current integrated luminosity. The cross sections show\na sizable deviation of the cross sections for hidden charm pentaquarks with the\nthird isospin component $I_z=+\\frac{1}{2}$ ($P_c^+$) from those with\n$I_z=-\\frac{1}{2}$ ($P_c^0$). The cross sections decrease dramatically with the\nincreasing transverse momentum. Our study can also tell where to search for the\nmissing hidden charm pentaquarks. The confirmation of the complete hidden charm\npentaquarks in the heavy quark symmetry would further verify their\n$\\Sigma_c^{(*)}\\bar{D}^{(*)}$ molecular interpretation. In addition, the\nrelative strength among these cross sections for pentaquarks can help us to\nidentify the quantum numbers of the $P_c(4440)$ and $P_c(4457)$.\n"}
+{"abstract": "  This work studies the quantitative stability of the quadratic optimal\ntransport map between a fixed probability density $\\rho$ and a probability\nmeasure $\\mu$ on R^d , which we denote T$\\mu$. Assuming that the source density\n$\\rho$ is bounded from above and below on a compact convex set, we prove that\nthe map $\\mu$ $\\rightarrow$ T$\\mu$ is bi-H{\\\"o}lder continuous on large\nfamilies of probability measures, such as the set of probability measures whose\nmoment of order p > d is bounded by some constant. These stability estimates\nshow that the linearized optimal transport metric W2,$\\rho$($\\mu$, $\\nu$) =\nT$\\mu$ -- T$\\nu$ L 2 ($\\rho$,R d) is bi-H{\\\"o}lder equivalent to the\n2-Wasserstein distance on such sets, justifiying its use in applications.\n"}
+{"abstract": "  Speedrunning in general means to play a video game fast, i.e. using all means\nat one's disposal to achieve a given goal in the least amount of time possible.\nTo do so, a speedrun must be planned in advance, or routed, as it is referred\nto by the community. This paper focuses on discovering challenges and defining\nmodels needed when trying to approach the problem of routing algorithmically.\nIt provides an overview of relevant speedrunning literature, extracting vital\ninformation and formulating criticism. Important categorizations are pointed\nout and a nomenclature is build to support professional discussion. Different\nconcepts of graph representations are presented and their potential is\ndiscussed with regard to solving the speedrun routing optimization problem.\nVisions both for problem modeling as well as solving are presented and assessed\nregarding suitability and expected challenges. This results in a vision of\npotential solutions and what will be addressed in the future.\n"}
+{"abstract": "  Many engineering problems involve the optimization of computationally\nexpensive models for which derivative information is not readily available. The\nBayesian optimization (BO) framework is a particularly promising approach for\nsolving these problems, which uses Gaussian process (GP) models and an expected\nutility function to systematically tradeoff between exploitation and\nexploration of the design space. BO, however, is fundamentally limited by the\nblack-box model assumption that does not take into account any underlying\nproblem structure. In this paper, we propose a new algorithm, COBALT, for\nconstrained grey-box optimization problems that combines multivariate GP models\nwith a novel constrained expected utility function whose structure can be\nexploited by state-of-the-art nonlinear programming solvers. COBALT is compared\nto traditional BO on seven test problems including the calibration of a\ngenome-scale bioreactor model to experimental data. Overall, COBALT shows very\npromising performance on both unconstrained and constrained test problems.\n"}
+{"abstract": "  Neural lexicalized PCFGs (L-PCFGs) have been shown effective in grammar\ninduction. However, to reduce computational complexity, they make a strong\nindependence assumption on the generation of the child word and thus bilexical\ndependencies are ignored. In this paper, we propose an approach to parameterize\nL-PCFGs without making implausible independence assumptions. Our approach\ndirectly models bilexical dependencies and meanwhile reduces both learning and\nrepresentation complexities of L-PCFGs. Experimental results on the English WSJ\ndataset confirm the effectiveness of our approach in improving both running\nspeed and unsupervised parsing performance.\n"}
+{"abstract": "  A common practice in many auctions is to offer bidders an opportunity to\nimprove their bids, known as a Best and Final Offer (BAFO) stage. This final\nbid can depend on new information provided about either the asset or the\ncompetitors. This paper examines the effects of new information regarding\ncompetitors, seeking to determine what information the auctioneer should\nprovide assuming the set of allowable bids is discrete. The rational strategy\nprofile that maximizes the revenue of the auctioneer is the one where each\nbidder makes the highest possible bid that is lower than his valuation of the\nitem. This strategy profile is an equilibrium for a large enough number of\nbidders, regardless of the information released. We compare the number of\nbidders needed for this profile to be an equilibrium under different\ninformation settings. We find that it becomes an equilibrium with fewer bidders\nwhen less additional information is made available to the bidders regarding the\ncompetition. It follows that when the number of bidders is a priori unknown,\nthere are some advantages to the auctioneer to not reveal information.\n"}
+{"abstract": "  Using isobaric Monte Carlo simulations, we map out the entire phase diagram\nof a system of hard cylindrical particles of length $L$ and diameter $D$, using\nan improved algorithm to identify the overlap condition between two cylinders.\nBoth the prolate $L/D>1$ and the oblate $L/D<1$ phase diagrams are reported\nwith no solution of continuity. In the prolate $L/D>1$ case, we find\nintermediate nematic \\textrm{N} and smectic \\textrm{SmA} phases in addition to\na low density isotropic \\textrm{I} and a high density crystal \\textrm{X} phase,\nwith \\textrm{I-N-SmA} and \\textrm{I-SmA-X} triple points. An apparent columnar\nphase \\textrm{C} is shown to be metastable as in the case of spherocylinders.\nIn the oblate $L/D<1$ case, we find stable intermediate cubatic \\textrm{Cub},\nnematic \\textrm{N}, and columnar \\textrm{C} phases with \\textrm{I-N-Cub},\n\\textrm{N-Cub-C}, and \\textrm{I-Cub-C} triple points. Comparison with previous\nnumerical and analytical studies is discussed. The present study, accounting\nfor the explicit cylindrical shape, paves the way to more sophisticated models\nwith important biological applications, such as viruses and nucleosomes.\n"}
+{"abstract": "  Person re-identification (re-ID) in the scenario with large spatial and\ntemporal spans has not been fully explored. This is partially because that,\nexisting benchmark datasets were mainly collected with limited spatial and\ntemporal ranges, e.g., using videos recorded in a few days by cameras in a\nspecific region of the campus. Such limited spatial and temporal ranges make it\nhard to simulate the difficulties of person re-ID in real scenarios. In this\nwork, we contribute a novel Large-scale Spatio-Temporal LaST person re-ID\ndataset, including 10,862 identities with more than 228k images. Compared with\nexisting datasets, LaST presents more challenging and high-diversity re-ID\nsettings, and significantly larger spatial and temporal ranges. For instance,\neach person can appear in different cities or countries, and in various time\nslots from daytime to night, and in different seasons from spring to winter. To\nour best knowledge, LaST is a novel person re-ID dataset with the largest\nspatio-temporal ranges. Based on LaST, we verified its challenge by conducting\na comprehensive performance evaluation of 14 re-ID algorithms. We further\npropose an easy-to-implement baseline that works well on such challenging re-ID\nsetting. We also verified that models pre-trained on LaST can generalize well\non existing datasets with short-term and cloth-changing scenarios. We expect\nLaST to inspire future works toward more realistic and challenging re-ID tasks.\nMore information about the dataset is available at\nhttps://github.com/shuxjweb/last.git.\n"}
+{"abstract": "  We construct a symmetric invertible binary pairing function $F(m,n)$ on the\nset of positive integers with a property of $F(m,n)=F(n,m)$. Then we provide a\ncomplete proof of its symmetry and bijectivity, from which the construction of\nsymmetric invertible binary pairing functions on any custom set of integers\ncould be seen.\n"}
+{"abstract": "  This paper presents OpenCV2X, the first publicly available, open-source\nsimulation model of the Third Generation Partnership Project (3GPP) Release 14\nCellular Vehicle to Everything (C-V2X) sidelink, which forms the basis for 5G\nNR Mode 2 under later releases. This model is fully compliant with the existing\nvehicular service and application layers, including messaging sets as defined\nby the automotive and standards communities providing a fully standardised,\ncross-layer communication model. Using this model, we show how the current\nsidelink scheduling mechanism performs poorly when scheduling applications with\nhighly aperiodic communication characteristics, such as ETSI Cooperative\nAwareness Messages (CAMs). We then provide the first indepth evaluation of\ndedicated per-packet aperiodic scheduling mechanisms, in contrast to schemes\nthat parameterise the existing algorithm. This paper highlights that the level\nof aperiodicity exhibited by the application model greatly impacts scheduling\nperformance. Finally, we analyse how such scheduling mechanisms might co-exist.\n"}
+{"abstract": "  The variational quantum eigensolver (VQE) is one of the most representative\nquantum algorithms in the noisy intermediate-size quantum (NISQ) era, and is\ngenerally speculated to deliver one of the first quantum advantages for the\nground-state simulations of some non-trivial Hamiltonians. However, short\nquantum coherence time and limited availability of quantum hardware resources\nin the NISQ hardware strongly restrain the capacity and expressiveness of VQEs.\nIn this Letter, we introduce the variational quantum-neural hybrid eigensolver\n(VQNHE) in which the shallow-circuit quantum ansatz can be further enhanced by\nclassical post-processing with neural networks. We show that VQNHE consistently\nand significantly outperforms VQE in simulating ground-state energies of\nquantum spins and molecules given the same amount of quantum resources. More\nimportantly, we demonstrate that for arbitrary post-processing neural\nfunctions, VQNHE only incurs an polynomial overhead of processing time and\nrepresents the first scalable method to exponentially accelerate VQE with\nnon-unitary post-processing that can be efficiently implemented in the NISQ\nera.\n"}
+{"abstract": "  It is good practice to name test methods such that they are comprehensible to\ndevelopers; they must be written in such a way that their purpose and\nfunctionality are clear to those who will maintain them. Unfortunately, there\nis little automated support for writing or maintaining the names of test\nmethods. This can lead to inconsistent and low-quality test names and increase\nthe maintenance cost of supporting these methods. Due to this risk, it is\nessential to help developers in maintaining their test method names over time.\nIn this paper, we use grammar patterns, and how they relate to test method\nbehavior, to understand test naming practices. This data will be used to\nsupport an automated tool for maintaining test names.\n"}
+{"abstract": "  This paper proposes a general destriping framework using flatness\nconstraints, where we can handle various regularization functions in a unified\nmanner. Removing stripe noise, i.e., destriping, from remote sensing images is\nan essential task in terms of visual quality and subsequent processing. Most of\nthe existing methods are designed by combining a particular image\nregularization with a stripe noise characterization that cooperates with the\nregularization, which precludes us to examine different regularizations to\nadapt to various target images. To resolve this, we formulate the destriping\nproblem as a convex optimization problem involving a general form of image\nregularization and the flatness constraints, a newly introduced stripe noise\ncharacterization. This strong characterization enables us to consistently\ncapture the nature of stripe noise, regardless of the choice of image\nregularization. For solving the optimization problem, we also develop an\nefficient algorithm based on a diagonally preconditioned primal-dual splitting\nalgorithm (DP-PDS), which can automatically adjust the stepsizes. The\neffectiveness of our framework is demonstrated through destriping experiments,\nwhere we comprehensively compare combinations of image regularizations and\nstripe noise characterizations using hyperspectral images (HSI) and infrared\n(IR) videos.\n"}
+{"abstract": "  Biology offers many examples of large-scale, complex, concurrent systems:\nmany processes take place in parallel, compete on resources and influence each\nother's behavior. The scalable modeling of biological systems continues to be a\nvery active field of research. In this paper we introduce a new approach based\non Event-B, a state-based formal method with refinement as its central\ningredient, allowing us to check for model consistency step-by-step in an\nautomated way. Our approach based on functions leads to an elegant and concise\nmodeling method. We demonstrate this approach by constructing what is, to our\nknowledge, the largest ever built Event-B model, describing the ErbB signaling\npathway, a key evolutionary pathway with a significant role in development and\nin many types of cancer. The Event-B model for the ErbB pathway describes 1320\nmolecular reactions through 242 events.\n"}
+{"abstract": "  Computer vision and image processing address many challenging applications.\nWhile the last decade has seen deep neural network architectures\nrevolutionizing those fields, early methods relied on 'classic', i.e.,\nnon-learned approaches. In this study, we explore the differences between\nclassic and deep learning (DL) algorithms to gain new insight regarding which\nis more suitable for a given application. The focus is on two challenging\nill-posed problems, namely faint edge detection and multispectral image\nregistration, studying recent state-of-the-art DL and classic solutions. While\nthose DL algorithms outperform classic methods in terms of accuracy and\ndevelopment time, they tend to have higher resource requirements and are unable\nto perform outside their training space. Moreover, classic algorithms are more\ntransparent, which facilitates their adoption for real-life applications. As\nboth classes of approaches have unique strengths and limitations, the choice of\na solution is clearly application dependent.\n"}
+{"abstract": "  Recently, flow-based methods have achieved promising success in video frame\ninterpolation. However, electron microscopic (EM) images suffer from unstable\nimage quality, low PSNR, and disorderly deformation. Existing flow-based\ninterpolation methods cannot precisely compute optical flow for EM images since\nonly predicting each position's unique offset. To overcome these problems, we\npropose a novel interpolation framework for EM images that progressively\nsynthesizes interpolated features in a coarse-to-fine manner. First, we extract\nmissing intermediate features by the proposed temporal spatial-adaptive (TSA)\ninterpolation module. The TSA interpolation module aggregates temporal contexts\nand then adaptively samples the spatial-related features with the proposed\nresidual spatial adaptive block. Second, we introduce a stacked deformable\nrefinement block (SDRB) further enhance the reconstruction quality, which is\naware of the matching positions and relevant features from input frames with\nthe feedback mechanism. Experimental results demonstrate the superior\nperformance of our approach compared to previous works, both quantitatively and\nqualitatively.\n"}
+{"abstract": "  The underlying theme of this paper is to explore the various facets of power\nsystems data through the lens of graph signal processing (GSP), laying down the\nfoundations of the Grid-GSP framework. Grid-GSP provides an interpretation for\nthe spatio-temporal properties of voltage phasor measurements, by showing how\nthe well-known power systems modeling supports a generative low-pass graph\nfilter model for the state variables, namely the voltage phasors. Using the\nmodel we formalize the empirical observation that voltage phasor measurement\ndata lie in a low-dimensional subspace and tie their spatio-temporal structure\nto generator voltage dynamics. The Grid-GSP generative model is then\nsuccessfully employed to investigate the problems pertaining to the grid of\ndata sampling and interpolation, network inference, detection of anomalies and\ndata compression. Numerical results on a large synthetic grid that mimics the\nreal-grid of the state of Texas, ACTIVSg2000, and on real-world measurements\nfrom ISO-New England verify the efficacy of applying Grid-GSP methods to\nelectric grid data.\n"}
+{"abstract": "  In this paper, we study both convergence and bounded variation properties of\na new fully discrete conservative Lagrangian--Eulerian scheme to the entropy\nsolution in the sense of Kruzhkov (scalar case) by using a weak asymptotic\nanalysis. We discuss theoretical developments on the conception of no-flow\ncurves for hyperbolic problems within scientific computing. The resulting\nalgorithms have been proven to be effective to study nonlinear wave formations\nand rarefaction interactions. We present experiments to a study based on the\nuse of the Wasserstein distance to show the effectiveness of the no-flow curves\napproach in the cases of shock interaction with an entropy wave related to the\ninviscid Burgers' model problem and to a 2x2 nonlocal traffic flow symmetric\nsystem of type Keyfitz--Kranzer.\n"}
+{"abstract": "  Recent developments in graph theoretic analysis of complex networks have led\nto deeper understanding of brain networks. Many complex networks show similar\nmacroscopic behaviors despite differences in the microscopic details. Probably\ntwo most often observed characteristics of complex networks are scale-free and\nsmall-world properties. In this paper, we will explore whether brain networks\nfollow scale-free and small-worldness among other graph theory properties.\n"}
+{"abstract": "  Motivated by previous works on a Floquet version of the PXP model [Mukherjee\n{\\it et al.} Phys. Rev. B 102, 075123 (2020), Mukherjee {\\it et al.} Phys. Rev.\nB 101, 245107 (2020)], we study a one-dimensional spin-$1/2$ lattice model with\nthree-spin interactions in the same constrained Hilbert space (where all\nconfigurations with two adjacent $S^z=\\uparrow$ spins are excluded). We show\nthat this model possesses an extensive fragmentation of the Hilbert space which\nleads to a breakdown of thermalization upon unitary evolution starting from a\nlarge class of simple initial states. Despite the non-integrable nature of the\nHamiltonian, many of its high-energy eigenstates admit a quasiparticle\ndescription. A class of these, which we dub as \"bubble eigenstates\", have\ninteger eigenvalues (including mid-spectrum zero modes) and strictly localized\nquasiparticles while another class contains mobile quasiparticles leading to a\ndispersion in momentum space. Other anomalous eigenstates that arise due to a\n{\\it secondary} fragmentation mechanism, including those that lead to flat\nbands in momentum space due to destructive quantum interference, are also\ndiscussed. The consequences of adding a (non-commuting) staggered magnetic\nfield and a PXP term respectively to this model, where the former preserves the\nHilbert space fragmentation while the latter destroys it, are discussed. A\nFloquet version with time-dependent staggered field also evades thermalization\nwith additional features like freezing of exponentially many states at special\ndrive frequencies. Finally, we map the model to a $U(1)$ lattice gauge theory\ncoupled to dynamical fermions and discuss the interpretation of some of these\nanomalous states in this language. A class of gauge-invariant states show\nreduced mobility of the elementary charged excitations with only certain\ncharge-neutral objects being mobile suggesting a connection to fractons.\n"}
+{"abstract": "  The recently discovered monad, Tx = Selection (x -> r) -> r, provides an\nelegant way to finnd optimal strategies in sequential games. During this\nthesis, a library was developed which provides a set of useful functions using\nthe selection monad to compute optimal games and AIs for sequential games. In\norder to explore the selection monads ability to support these AI\nimplementations, three example case studies were developed using Haskell: The\ntwo-player game Connect Four, a Sudoku solver and a simplified version of\nChess. These case studies show how to elegantly implement a game AI.\nFurthermore, a performance analysis of these case studies was done, identifying\nthe major points where performance can be increased.\n"}
+{"abstract": "  Amenability is a notion of facial exposedness for convex cones that is\nstronger than being facially dual complete (or \"nice\") which is, in turn,\nstronger than merely being facially exposed. Hyperbolicity cones are a family\nof algebraically structured closed convex cones that contain all spectrahedra\n(linear sections of positive semidefinite cones) as special cases. It is known\nthat all spectrahedra are amenable. We establish that all hyperbolicity cones\nare amenable. As part of the argument, we show that any face of a hyperbolicity\ncone is a hyperbolicity cone. As a corollary, we show that the intersection of\ntwo hyperbolicity cones, not necessarily sharing a common relative interior\npoint, is a hyperbolicity cone.\n"}
+{"abstract": "  A two-component-two-dimensional coupled with one-component-three-dimensional\n(2C2Dcw1C3D) flow may also be called a real Schur flow (RSF), as its velocity\ngradient is uniformly of real Schur form, the latter being the intrinsic local\nproperty of any general flows. The thermodynamic and `vortic' fine structures\nof RSF are exposed and, in particular, the complete set of equations governing\na (viscous and/or driven) 2C2Dcw1C3D flow are derived. The Lie invariances of\nthe decomposed vorticity 2-forms of RSFs in $d$-dimensional Euclidean space\n$\\mathbb{E}^d$ for any interger $d\\ge 3$ are also proven, and many\nLie-invariant fine results, such as those of the combinations of the entropic\nand vortic quantities, including the invariances of the decomposed Ertel\npotential vorticity (and their multiplications by any interger powers of\nentropy) 3-forms, then follow.\n"}
+{"abstract": "  This article addresses extraction of physically meaningful information from\nSTEM EELS and EDX spectrum-images using methods of Multivariate Statistical\nAnalysis. The problem is interpreted in terms of data distribution in a\nmulti-dimensional factor space, which allows for a straightforward and\nintuitively clear comparison of various approaches. A new computationally\nefficient and robust method for finding physically meaningful endmembers in\nspectrum-image datasets is presented. The method combines the geometrical\napproach of Vertex Component Analysis with the statistical approach of Bayesian\ninference. The algorithm is described in detail at an example of EELS\nspectrum-imaging of a multi-compound CMOS transistor.\n"}
+{"abstract": "  Protoplanetary disks are thought to evolve viscously, where the disk mass -\nthe reservoir available for planet formation - decreases over time as material\nis accreted onto the central star. Observations show a correlation between dust\nmass and the stellar accretion rate, as expected from viscous theory. However,\nthe gas mass inferred from 13CO and C18O line fluxes, which should be a more\ndirect measure, shows no such correlation. Using thermochemical DALI models, we\ninvestigate how 13CO and C18O J=3-2 line fluxes change over time in a viscously\nevolving disk. We also investigate if the chemical conversion of CO through\ngrain-surface chemistry combined with viscous evolution can explain the\nobservations of disks in Lupus. The 13CO and C18O 3-2 line fluxes increase over\ntime due to their optically thick emitting regions growing in size as the disk\nexpands viscously. The C18O 3-2 emission is optically thin throughout the disk\nfor only a subset of our models (Mdisk (t = 1 Myr) < 1e-3 Msun). For these\ndisks the integrated C18O flux decreases with time, similar to the disk mass.\nThe C18O 3-2 fluxes for the bulk of the disks in Lupus (with Mdust < 5e-5 Msun)\ncan be reproduced to within a factor of ~2 with viscously evolving disks in\nwhich CO is converted into other species through grain-surface chemistry driven\nby a cosmic-ray ionization rate zeta_cr ~ 5e-17 - 1e-16 s^-1. However,\nexplaining the stacked C18O upper limits requires a lower average abundance\nthan our models can produce and they cannot explain the observed 13CO fluxes,\nwhich, for most disks, are more than an order of magnitude fainter than what\nour models predict. Reconciling the 13CO fluxes of viscously evolving disks\nwith the observations requires either a combination of efficient vertical\nmixing and a high zeta_cr or low mass disks (Mdust < 3e-5 Msun) being much\nthinner and/or smaller than their more massive counterparts.\n"}
+{"abstract": "  Electrospinning has exhibited excellent benefits to treat the trauma for\ntissue engineering due to its produced micro/nano fibrous structure. It can\neffectively adhere to the tissue surface for long-term continuous therapy. This\npaper develops a robotic electrospinning platform for endoluminal therapy. The\nplatform consists of a continuum manipulator, the electrospinning device, and\nthe actuation unit. The continuum manipulator has two bending sections to\nfacilitate the steering of the tip needle for a controllable spinning\ndirection. Non-circular joint profile is carefully designed to enable a\nconstant length of the centreline of a continuum manipulator for stable fluid\ntransmission inside it. Experiments are performed on a bronchus phantom, and\nthe steering ability and bending limitation in each direction are also\ninvestigated. The endoluminal electrospinning is also fulfilled by a trajectory\nfollowing and points targeting experiments. The effective adhesive area of the\nproduced fibre is also illustrated. The proposed robotic electrospinning shows\nits feasibility to precisely spread more therapeutic drug to construct fibrous\nstructure for potential endoluminal treatment.\n"}
+{"abstract": "  Extended Affine (EA) equivalence is the equivalence relation between two\nvectorial Boolean functions $F$ and $G$ such that there exist two affine\npermutations $A$, $B$, and an affine function $C$ satisfying $G = A \\circ F\n\\circ B + C$. While a priori simple, it is very difficult in practice to test\nwhether two functions are EA-equivalent. This problem has two variants:\nEA-testing deals with figuring out whether the two functions can be\nEA-equivalent, and EA-recovery is about recovering the tuple $(A,B,C)$ if it\nexists.\n  In this paper, we present a new efficient algorithm that efficiently solves\nthe EA-recovery problem for quadratic functions. Though its worst-case\ncomplexity is obtained when dealing with APN functions, it supersedes all\npreviously known algorithms in terms of performance, even in this case. This\napproach is based on the Jacobian matrix of the functions, a tool whose study\nin this context can be of independent interest.\n  In order to tackle EA-testing efficiently, the best approach in practice\nrelies on class invariants. We provide an overview of the literature on said\ninvariants along with a new one based on the \\emph{ortho-derivative} which is\napplicable to quadratic APN functions, a specific type of functions that is of\ngreat interest, and of which tens of thousands need to be sorted into distinct\nEA-classes. Our ortho-derivative-based invariant is both very fast to compute,\nand highly discriminating.\n"}
+{"abstract": "  Cosmic shear estimation is an essential scientific goal for large galaxy\nsurveys. It refers to the coherent distortion of distant galaxy images due to\nweak gravitational lensing along the line of sight. It can be used as a tracer\nof the matter distribution in the Universe. The unbiased estimation of the\nlocal value of the cosmic shear can be obtained via Bayesian analysis which\nrelies on robust estimation of the galaxies ellipticity (shape) posterior\ndistribution. This is not a simple problem as, among other things, the images\nmay be corrupted with strong background noise. For current and coming surveys,\nanother central issue in galaxy shape determination is the treatment of\nstatistically dominant overlapping (blended) objects. We propose a Bayesian\nConvolutional Neural Network based on Monte-Carlo Dropout to reliably estimate\nthe ellipticity of galaxies and the corresponding measurement uncertainties. We\nshow that while a convolutional network can be trained to correctly estimate\nwell calibrated aleatoric uncertainty, -- the uncertainty due to the presence\nof noise in the images -- it is unable to generate a trustworthy ellipticity\ndistribution when exposed to previously unseen data (i.e. here, blended\nscenes). By introducing a Bayesian Neural Network, we show how to reliably\nestimate the posterior predictive distribution of ellipticities along with\nrobust estimation of epistemic uncertainties. Experiments also show that\nepistemic uncertainty can detect inconsistent predictions due to unknown\nblended scenes.\n"}
+{"abstract": "  Binary stars are abundant in nearby galaxies, but are typically unaccounted\nfor in simulations of the high redshift Universe. Stellar population synthesis\nmodels that include the effects of binary evolution result in greater relative\nabundances of ionizing photons that could significantly affect the ambient\nionizing background during the epoch of hydrogen reionization, additionally\nleading to differences in galaxy gas content and star formation. We use\nhydrodynamic cosmological simulations including in situ multifrequency\nradiative transfer to evaluate the effects of a high binary fraction in\nreionization-era galaxies on traits of the early intergalactic medium and the\nabundance of H I and He II ionizing photons. We further extend this to analyze\nthe traits of enriched gas. In comparing metrics generated using a fiducial\nsimulation assuming single stars with one incorporating a high binary fraction,\nwe find that binary stars cause H I reionization to complete earlier and at an\naccelerated pace, while also increasing the abundances of high-ionization\nmetals (C IV and Si IV) in simulated absorption spectra while reducing the\nabundance of low-ionization states (O I, Si II, and C II). However, through\nincreased photoheating of galactic and circumgalactic gas, they simultaneously\nreduce the rate of star formation in low-mass galaxies, slowing the ongoing\nprocess of enrichment and suppressing their own ionizing background. This\npotentially contributes to a slower He II reionization process at $z\\geq5$, and\nfurther indicates that self-regulation of galaxies could be underestimated when\nneglecting binary stellar evolution.\n"}
+{"abstract": "  We present a concept for a machine-learning classification of hard X-ray\n(HXR) emissions from solar flares observed by the Reuven Ramaty High Energy\nSolar Spectroscopic Imager (RHESSI), identifying flares that are either\nocculted by the solar limb or located on the solar disk. Although HXR\nobservations of occulted flares are important for particle-acceleration\nstudies, HXR data analyses for past observations were time consuming and\nrequired specialized expertise. Machine-learning techniques are promising for\nthis situation, and we constructed a sample model to demonstrate the concept\nusing a deep-learning technique. Input data to the model are HXR spectrograms\nthat are easily produced from RHESSI data. The model can detect occulted flares\nwithout the need for image reconstruction nor for visual inspection by experts.\nA technique of convolutional neural networks was used in this model by\nregarding the input data as images. Our model achieved a classification\naccuracy better than 90 %, and the ability for the application of the method to\neither event screening or for an event alert for occulted flares was\nsuccessfully demonstrated.\n"}
+{"abstract": "  The mathematical similarities between non-relativistic wavefunction\npropagation in quantum mechanics and image propagation in scalar diffraction\ntheory are used to develop a novel understanding of time and paths through\nspacetime as a whole. It is well known that Feynman's original derivation of\nthe path integral formulation of non-relativistic quantum mechanics uses\ntime-slicing to calculate amplitudes as sums over all possible paths through\nspace, but along a definite curve through time. Here, a 3+1D spacetime wave\ndistribution and its 4-momentum dual are formally developed which have no\nexternal time parameter and therefore cannot change or evolve in the usual\nsense. Time is thus seen \"from the outside\". A given 3+1D momentum\nrepresentation of a system encodes complete dynamical information, describing\nthe system's spacetime behavior as a whole. A comparison is made to the\nmathematics of holograms, and properties of motion for simple systems are\nderived.\n"}
+{"abstract": "  Existing open-source modeling frameworks dedicated to energy systems\noptimization typically utilize (mixed-integer) linear programming ((MI)LP)\nformulations, which lack modeling freedom for technical system design and\noperation. We present COMANDO, an open-source Python package for\ncomponent-oriented modeling and optimization for nonlinear design and operation\nof integrated energy systems. COMANDO allows to assemble system models from\ncomponent models including nonlinear, dynamic and discrete characteristics.\nBased on a single system model, different deterministic and stochastic problem\nformulations can be obtained by varying objective function and underlying data,\nand by applying automatic or manual reformulations. The flexible open-source\nimplementation allows for the integration of customized routines required to\nsolve challenging problems, e.g., initialization, problem decomposition, or\nsequential solution strategies. We demonstrate features of COMANDO via case\nstudies, including automated linearization, dynamic optimization, stochastic\nprogramming, and the use of nonlinear artificial neural networks as surrogate\nmodels in a reduced-space formulation for deterministic global optimization.\n"}
+{"abstract": "  The emerging large-scale and data-hungry algorithms require the computations\nto be delegated from a central server to several worker nodes. One major\nchallenge in the distributed computations is to tackle delays and failures\ncaused by the stragglers. To address this challenge, introducing efficient\namount of redundant computations via distributed coded computation has received\nsignificant attention. Recent approaches in this area have mainly focused on\nintroducing minimum computational redundancies to tolerate certain number of\nstragglers. To the best of our knowledge, the current literature lacks a\nunified end-to-end design in a heterogeneous setting where the workers can vary\nin their computation and communication capabilities. The contribution of this\npaper is to devise a novel framework for joint scheduling-coding, in a setting\nwhere the workers and the arrival of stream computational jobs are based on\nstochastic models. In our initial joint scheme, we propose a systematic\nframework that illustrates how to select a set of workers and how to split the\ncomputational load among the selected workers based on their differences in\norder to minimize the average in-order job execution delay. Through\nsimulations, we demonstrate that the performance of our framework is\ndramatically better than the performance of naive method that splits the\ncomputational load uniformly among the workers, and it is close to the ideal\nperformance.\n"}
+{"abstract": "  We study the realisation of higher-form symmetries in the holographic dual of\ngauge theories coupled to probe matter in the fundamental. We particularly\nfocus on the dual of U(N) gauge theory coupled to fundamental matter. We\ndemonstrate the existence of a continuous 1-form symmetry associated with the\nconservation of magnetic flux and show that this symmetry is spontaneously\nbroken in the IR when the flavour degrees of freedom are gapped. We numerically\ncompute the spectral function of the 2-form current and demonstrate the\nexistence of the associated Goldstone mode. We compare to expectations at\nweak-coupling.\n"}
+{"abstract": "  We consider the motion of an electron in an atom subjected to a strong\nlinearly polarized laser field. We identify the invariant structures organizing\na very specific subset of trajectories, namely recollisions. Recollisions are\ntrajectories which first escape the ionic core (i.e., ionize) and later return\nto this ionic core, for instance, to transfer the energy gained during the\nlarge excursion away from the core to bound electrons. We consider the role\nplayed by the directions transverse to the polarization direction in the\nrecollision process. We compute the family of two-dimensional invariant tori\nassociated with a specific hyperbolic-elliptic periodic orbit and their stable\nand unstable manifolds. We show that these manifolds organize recollisions in\nphase space.\n"}
+{"abstract": "  The Operating Room Scheduling (ORS) problem is the task of assigning patients\nto operating rooms, taking into account different specialties, lengths and\npriority scores of each planned surgery, operating room session durations, and\nthe availability of beds for the entire length of stay both in the Intensive\nCare Unit and in the wards. A proper solution to the ORS problem is of primary\nimportance for the healthcare service quality and the satisfaction of patients\nin hospital environments. In this paper we first present a solution to the\nproblem based on Answer Set Programming (ASP). The solution is tested on\nbenchmarks with realistic sizes and parameters, on three scenarios for the\ntarget length on 5-day scheduling, common in small-medium sized hospitals, and\nresults show that ASP is a suitable solving methodology for the ORS problem in\nsuch setting. Then, we also performed a scalability analysis on the schedule\nlength up to 15 days, which still shows the suitability of our solution also on\nlonger plan horizons. Moreover, we also present an ASP solution for the\nrescheduling problem, i.e. when the off-line schedule cannot be completed for\nsome reason. Finally, we introduce a web framework for managing ORS problems\nvia ASP that allows a user to insert the main parameters of the problem, solve\na specific instance, and show results graphically in real-time. Under\nconsideration in Theory and Practice of Logic Programming (TPLP).\n"}
+{"abstract": "  We report on the first results of the Noble and Alkali Spin Detectors for\nUltralight Coherent darK matter (NASDUCK) collaboration. We search for the\ninteractions of Axion-Like Particles (ALPs) with atomic spins using an\nearth-based precision quantum detector as it traverses through the galactic\ndark matter halo. The detector is composed of spin-polarized xenon gas which\ncan coherently interact with a background ALP dark matter field and an in-situ\nrubidium Floquet optical-magnetometer. Conducting a five months-long search, we\nderive new constraints on ALP-proton and ALP-neutron interactions in the\n$4\\times 10^{-15}-4\\times 10^{-12}{~\\rm eV/c^2}$ mass range. Our limits on the\nALP-proton (ALP-neutron) couplings improve upon previous terrestrial bounds by\nup to 3 orders of magnitude for masses above $4\\times 10^{-14}{~\\rm eV/c^2}$\n($4\\times 10^{-13}{~\\rm eV/c^2}$). Moreover, barring the uncertain supernova\nconstraints, the ALP-proton bound improves on all existing terrestrial and\nastrophysical limits, partially closing the unexplored region for couplings in\nthe range $10^{-6}~{\\rm GeV^{-1}}$ to $2\\times 10^{-5}~{\\rm GeV^{-1}}$.\nFinally, we also cast bounds on pseudo-scalar dark matter models in which dark\nmatter is quadratically-coupled to the nucleons.\n"}
+{"abstract": "  In this paper, we investigate properties of orbits of Hermann actions as\nsubmanifolds without assuming the commutability of involutions which define\nHermann actions. In particular, we compute the second fundamental form of\norbits of Hermann action, and give a sufficient condition for orbits of Hermann\naction to be weakly reflective (resp. arid) submanifolds.\n"}
+{"abstract": "  In this paper, we explore the impact of a galactic bar on the inspiral\ntime-scale of a massive perturber (MP) within a Milky Way-like galaxy. We\nintegrate the orbit of MPs in a multi-component galaxy model via a\nsemi-analytical approach including an accurate treatment for dynamical friction\ngeneralized to rotationally supported backgrounds. We compare the MP evolution\nin a galaxy featuring a Milky Way-like rotating bar to the evolution within an\nanalogous axisymmetric galaxy without the bar. We find that the bar presence\nmay significantly affect the inspiral, sometimes making it shorter by a factor\nof a few, sometimes hindering it for a Hubble time, implying that dynamical\nfriction alone is greatly insufficient to fully characterize the orbital decay.\nThe effect of the bar is more prominent for initially in-plane, prograde MPs,\nespecially those crossing the bar co-rotation radius or outer Lindblad\nresonance. In the barred galaxy, we find the sinking of the most massive MPs\n(>~10^7.5 Msun) approaching the galaxy from large separations (>~8 kpc) to be\nmost efficiently hampered. Neglecting the effect of global torques associated\nto the non-symmetric mass distribution is thus not advisable even within our\nidealized, smooth Milky Way model, and it should be avoided when dealing with\nmore complex and realistic galaxy systems. This has important implications for\nthe orbital decay of massive black holes in late-type spirals, the natural\ncandidate sources to be detected with the Laser Interferometer Space Antenna\n(LISA).\n"}
+{"abstract": "  We present a combined theoretical and experimental study of X-ray optical\nwave mixing. This class of nonlinear phenomena combines the strengths of\nspectroscopic techniques from the optical domain, with the high-resolution\ncapabilities of X-rays. In particular, the spectroscopic sensitivity of these\nphenomena can be exploited to selectively probe valence dynamics. Specifically,\nwe focus on the effect of X-ray parametric down-conversion. We present a\ntheoretical description of the process, from which we deduce the observable\nnonlinear response of valence charges. Subsequently, we simulate scattering\npatterns for realistic conditions and identify characteristic signatures of the\nnonlinear conversion. For the observation of this signature, we present a\ndedicated experimental setup and results of a detailed investigation. However,\nwe do not find evidence of the nonlinear effect. This finding stands in strong\ncontradiction to previous claims of proof-of-principle demonstrations.\nNevertheless, we are optimistic to employ related X-ray optical wave mixing\nprocesses on the basis of the methods presented here for probing valence\ndynamics in the future.\n"}
+{"abstract": "  Estimation of prevalence of undocumented SARS-CoV-2 infections is critical\nfor understanding the overall impact of the Covid-19 disease. In fact,\nunveiling uncounted cases has fundamental implications for public policy\ninterventions strategies. In the present work, we show a basic yet effective\napproach to estimate the actual number of people infected by Sars-Cov-2, by\nusing epidemiological raw data reported by official health institutions in the\nlargest EU countries and USA.\n"}
+{"abstract": "  We discuss the rejection-free event-chain Monte-Carlo algorithm and several\napplications to dense soft matter systems. Event-chain Monte-Carlo is an\nalternative to standard local Markov-chain Monte-Carlo schemes, which are based\non detailed balance, for example the well-known Metropolis-Hastings algorithm.\nEvent-chain Monte-Carlo is a Markov chain Monte-Carlo scheme that uses\nso-called lifting moves to achieve global balance without rejections (maximal\nglobal balance). It has been originally developed for hard sphere systems but\nis applicable to many soft matter systems and particularly suited for dense\nsoft matter systems with hard core interactions, where it gives significant\nperformance gains compared to a local Monte-Carlo simulation. The algorithm can\nbe generalized to deal with soft interactions and with three-particle\ninteractions, as they naturally arise, for example, in bead-spring models of\npolymers with bending rigidity. We present results for polymer melts, where the\nevent-chain algorithm can be used for an efficient initialization. We then move\non to large systems of semiflexible polymers that form bundles by attractive\ninteractions and can serve as model systems for actin filaments in the\ncytoskeleton. The event chain algorithm shows that these systems form networks\nof bundles which coarsen similar to a foam. Finally, we present results on\nliquid crystal systems, where the event-chain algorithm can equilibrate large\nsystems containing additional colloidal disks very efficiently, which reveals\nthe parallel chaining of disks.\n"}
+{"abstract": "  Advanced building control methods such as model predictive control (MPC)\noffer significant potential benefits to both consumers and grid operators, but\nthe high computational requirements have acted as barriers to more widespread\nadoption. Local control computation requires installation of expensive\ncomputational hardware, while cloud computing introduces data security and\nprivacy concerns. In this paper, we drastically reduce the local computational\nrequirements of advanced building control through a reinforcement learning\n(RL)-based approach called Behavioral Cloning, which represents the MPC policy\nas a neural network that can be locally implemented and quickly computed on a\nlow-cost programmable logic controller. While previous RL and approximate MPC\nmethods must be specifically trained for each building, our key improvement is\nthat our controller can generalize to many buildings, electricity rates, and\nthermostat setpoint schedules without additional, effort-intensive retraining.\nTo provide this versatility, we have adapted the traditional Behavioral Cloning\napproach through (1) a constraint-informed parameter grouping (CIPG) method\nthat provides a more efficient representation of the training data; (2) an\nMPC-Guided training data generation method using the DAgger algorithm that\nimproves stability and constraint satisfaction; and (3) a new deep learning\nmodel-structure called reverse-time recurrent neural networks (RT-RNN) that\nallows future information to flow backward in time to more effectively\ninterpret the temporal information in disturbance predictions. The result is an\neasy-to-deploy, generalized behavioral clone of MPC that can be implemented on\na programmable logic controller and requires little building-specific\ncontroller tuning, reducing the effort and costs associated with implementing\nsmart residential heat pump control.\n"}
+{"abstract": "  Let $\\mathcal{G}^{(\\lambda)}$ be a group scheme which deforms $\\mathbb{G}_a$\nto $\\mathbb{G}_m$. We explicitly describe the Cartier dual of the $l$-th\nFrobenius type kernel $N_l$ of the group scheme $\\mathcal{E}^{(\\lambda,\\mu;D)}$\nwhich is an extension of $\\mathcal{G}^{(\\lambda)}$ by $\\mathcal{G}^{(\\mu)}$.\nHere we assume that the base ring $A$ is a $\\mathbb{Z}_{(p)}$-algebra\ncontaining some nilpotent elements. The obtained result generalizes a previous\nresult by N. Aki and M. Amano (Tsukuba J.Math. $\\textbf{34}$ (2010)) which\nassumes that $A$ is an $\\mathbb{F}_p$-algebra.\n"}
+{"abstract": "  The competition of depletion attractions and longer-ranged repulsions between\ncolloidal particles in colloid-polymer mixtures leads to the formation of\nheterogeneous gel-like structures. For instance, gel networks, i.e., states\nwhere the colloids arrange in thin strands that span the whole system occur at\nlow packing fractions for attractions that are stronger than those at the\nbinodal line of equilibrium liquid-fluid phase separation. By using Brownian\ndynamics simulations we explore the formation, structure, and ageing dynamics\nof gel networks. We determine reduced network that focus on the essential\nconnections in a gel network. We compare the observed properties to those of\nbulky gels or cluster fluids. Our results demonstrate that both the structure\nas well as the (often slow) dynamics of the stable or meta-stable heterogenous\nstates in colloid-polymer mixtures possess distinct features on various length\nand time scales and thus are richly divers.\n"}
+{"abstract": "  Recent studies revealed that the electric multipole moments of insulators\nresult in fractional electric charges localized to the hinges and corners of\nthe sample. We here explore the magnetic analog of this relation. We show that\na collinear antiferromagnet with spin $S$ defined on a $d$-dimensional cubic\nlattice features fractionally quantized magnetization $M_{\\text{c}}^z=S/2^d$ at\nthe corners. We find that the quantization is robust even in the presence of\ngapless excitations originating from the spontaneous formation of the N\\'eel\norder, although the localization length diverges, suggesting a power-law\nlocalization of the corner magnetization. When the spin rotational symmetry\nabout the $z$ axis is explicitly broken, the corner magnetization is no longer\nsharply quantized. Even in this case, we numerically find that the deviation\nfrom the quantized value is negligibly small based on quantum Monte Carlo\nsimulations.\n"}
+{"abstract": "  Video streaming platforms such as Youtube, Twitch, and DLive allow users to\nlive-stream video content for viewers who can optionally express their\nappreciation through monetary donations. DLive is one of the smaller and\nlesser-known streaming platforms, and historically has had fewer content\nmoderation practices. It has thus become a popular place for violent extremists\nand other clandestine groups to earn money and propagandize. What is the\nfinancial structure of the DLive streaming ecosystem and how much money is\nchanging hands? In the past it has been difficult to understand how far-right\nextremists fundraise via podcasts and video streams because of the secretive\nnature of the activity and because of the difficulty of getting data from\nsocial media platforms. This paper describes a novel experiment to collect and\nanalyze data from DLive's publicly available ledgers of transactions in order\nto understand the financial structure of the clandestine, extreme far-right\nvideo streaming community. The main findings of this paper are, first, that the\nmajority of donors are using micropayments in varying frequencies, but a small\nhandful of donors spend large amounts of money to finance their favorite\nstreamers. Next, the timing of donations to high-profile far-right streamers\nfollows a fairly predictable pattern that is closely tied to a broadcast\nschedule. Finally, the far-right video streaming financial landscape is divided\ninto separate cliques which exhibit very little crossover in terms of sizable\ndonations. This work will be important to technology companies, policymakers,\nand researchers who are trying to understand how niche social media services,\nincluding video platforms, are being exploited by extremists to propagandize\nand fundraise.\n"}
+{"abstract": "  Charts often contain visually prominent features that draw attention to\naspects of the data and include text captions that emphasize aspects of the\ndata. Through a crowdsourced study, we explore how readers gather takeaways\nwhen considering charts and captions together. We first ask participants to\nmark visually prominent regions in a set of line charts. We then generate text\ncaptions based on the prominent features and ask participants to report their\ntakeaways after observing chart-caption pairs. We find that when both the chart\nand caption describe a high-prominence feature, readers treat the doubly\nemphasized high-prominence feature as the takeaway; when the caption describes\na low-prominence chart feature, readers rely on the chart and report a\nhigher-prominence feature as the takeaway. We also find that external\ninformation that provides context, helps further convey the caption's message\nto the reader. We use these findings to provide guidelines for authoring\neffective chart-caption pairs.\n"}
+{"abstract": "  In sorted range selection problem, the aim is to preprocess a given array\nA[1: n] so as to answers queries of type: given two indices i,j ($1 \\le i\\le j\n\\le n$) and an integer k, report k smallest elements in sorted order present in\nthe sub-array A[i: j] Brodal et.al.[2] have shown that the problem can be\nsolved in O(k) time after O(n log n) preprocessing in linear space. In this\npaper we discuss another tradeoff. We reduce preprocessing time to O(n), but\nquery time is O(k log k), again using linear space. Our method is very simple.\n"}
+{"abstract": "  Let n respondents rank order d items, and suppose that d << n. Our main task\nis to uncover and display the structure of the observed rank data by an\nexploratory riffle shuffling procedure which sequentially decomposes the n\nvoters into a finite number of coherent groups plus a noisy group : where the\nnoisy group represents the outlier voters and each coherent group is composed\nof a finite number of coherent clusters. We consider exploratory riffle\nshuffling of a set of items to be equivalent to optimal two blocks seriation of\nthe items with crossing of some scores between the two blocks. A riffle\nshuffled coherent cluster of voters within its coherent group is essentially\ncharacterized by the following facts : a) Voters have identical first TCA\nfactor score, where TCA designates taxicab correspondence analysis, an L1\nvariant of correspondence analysis ; b) Any preference is easily interpreted as\nriffle shuffling of its items ; c) The nature of different riffle shuffling of\nitems can be seen in the structure of the contingency table of the first-order\nmarginals constructed from the Borda scorings of the voters ; d) The first TCA\nfactor scores of the items of a coherent cluster are interpreted as Borda scale\nof the items. We also introduce a crossing index, which measures the extent of\ncrossing of scores of voters between the two blocks seriation of the items. The\nnovel approach is explained on the benchmarking SUSHI data set, where we show\nthat this data set has a very simple structure, which can also be communicated\nin a tabular form.\n"}
+{"abstract": "  This paper proposes a novel machine-learning approach for predicting AC-OPF\nsolutions that features a fast and scalable training. It is motivated by the\ntwo critical considerations: (1) the fact that topology optimization and the\nstochasticity induced by renewable energy sources may lead to fundamentally\ndifferent AC-OPF instances; and (2) the significant training time needed by\nexisting machine-learning approaches for predicting AC-OPF. The proposed\napproach is a 2-stage methodology that exploits a spatial decomposition of the\npower network that is viewed as a set of regions. The first stage learns to\npredict the flows and voltages on the buses and lines coupling the regions, and\nthe second stage trains, in parallel, the machine-learning models for each\nregion. Experimental results on the French transmission system (up to 6,700\nbuses and 9,000 lines) demonstrate the potential of the approach. Within a\nshort training time, the approach predicts AC-OPF solutions with very high\nfidelity and minor constraint violations, producing significant improvements\nover the state-of-the-art. The results also show that the predictions can seed\na load flow optimization to return a feasible solution within 0.03% of the\nAC-OPF objective, while reducing running times significantly.\n"}
+{"abstract": "  With increasing automation in passenger vehicles, the study of safe and\nsmooth occupant-vehicle interaction and control transitions is key. In this\nstudy, we focus on the development of contextual, semantically meaningful\nrepresentations of the driver state, which can then be used to determine the\nappropriate timing and conditions for transfer of control between driver and\nvehicle. To this end, we conduct a large-scale real-world controlled data study\nwhere participants are instructed to take-over control from an autonomous agent\nunder different driving conditions while engaged in a variety of distracting\nactivities. These take-over events are captured using multiple driver-facing\ncameras, which when labelled result in a dataset of control transitions and\ntheir corresponding take-over times (TOTs). We then develop and train TOT\nmodels that operate sequentially on mid to high-level features produced by\ncomputer vision algorithms operating on different driver-facing camera views.\nThe proposed TOT model produces continuous predictions of take-over times\nwithout delay, and shows promising qualitative and quantitative results in\ncomplex real-world scenarios.\n"}
+{"abstract": "  In this paper, we improve speech translation (ST) through effectively\nleveraging large quantities of unlabeled speech and text data in different and\ncomplementary ways. We explore both pretraining and self-training by using the\nlarge Libri-Light speech audio corpus and language modeling with CommonCrawl.\nOur experiments improve over the previous state of the art by 2.6 BLEU on\naverage on all four considered CoVoST 2 language pairs via a simple recipe of\ncombining wav2vec 2.0 pretraining, a single iteration of self-training and\ndecoding with a language model. Different to existing work, our approach does\nnot leverage any other supervision than ST data. Code and models will be\npublicly released.\n"}
+{"abstract": "  Machine learning systems are often trained using data collected from\nhistorical decisions. If past decisions were biased, then automated systems\nthat learn from historical data will also be biased. We propose a black-box\napproach to identify and remove biased training data. Machine learning models\ntrained on such debiased data (a subset of the original training data) have low\nindividual discrimination, often 0%. These models also have greater accuracy\nand lower statistical disparity than models trained on the full historical\ndata. We evaluated our methodology in experiments using 6 real-world datasets.\nOur approach outperformed seven previous approaches in terms of individual\ndiscrimination and accuracy.\n"}
+{"abstract": "  Convolutional neural networks (CNNs) learn to extract representations of\ncomplex features, such as object shapes and textures to solve image recognition\ntasks. Recent work indicates that CNNs trained on ImageNet are biased towards\nfeatures that encode textures and that these alone are sufficient to generalize\nto unseen test data from the same distribution as the training data but often\nfail to generalize to out-of-distribution data. It has been shown that\naugmenting the training data with different image styles decreases this texture\nbias in favor of increased shape bias while at the same time improving\nrobustness to common corruptions, such as noise and blur. Commonly, this is\ninterpreted as shape bias increasing corruption robustness. However, this\nrelationship is only hypothesized. We perform a systematic study of different\nways of composing inputs based on natural images, explicit edge information,\nand stylization. While stylization is essential for achieving high corruption\nrobustness, we do not find a clear correlation between shape bias and\nrobustness. We conclude that the data augmentation caused by style-variation\naccounts for the improved corruption robustness and increased shape bias is\nonly a byproduct.\n"}
+{"abstract": "  We investigate the photon pumping effect in a topological model consisting of\na periodically driven spin-1/2 coupled to a quantum cavity mode out of the\nadiabatic limit. In the strong-drive adiabatic limit, a quantized frequency\nconversion of photons is expected as the temporal analog of the Hall current.\nWe numerically establish a novel photon pumping phenomenon in the\nexperimentally accessible nonadiabatic driving regime for a broad region of the\nparameter space. The photon frequency conversion efficiency exhibits strong\nfluctuations and high efficiency that can reach up 80% of the quantized value\nfor commensurate frequency combinations. We link the pumping properties to the\ndelocalization of the corresponding Floquet states which display multifractal\nbehavior as the result of hybridization between localized and delocalized\nsectors. Finally we demonstrate that the quantum coherence properties of the\ninitial state are preserved during the frequency conversion process in both the\nstrong and ultra-weak-drive limit.\n"}
+{"abstract": "  We study the optimal investment policy of a firm facing both technological\nand cash-flow uncertainty. At any point in time, the firm can decide to invest\nin a standalone technology or to wait for a technological breakthrough.\nBreakthroughs occur when market conditions become favorable enough, exceeding a\ncertain threshold value that is ex-ante unknown to the firm. A microfoundation\nfor this assumption is that a breakthrough occurs when the share of the surplus\nfrom the new technology accruing to its developer is high enough to cover her\nprivately observed cost. We show that the relevant Markov state variables for\nthe firm's optimal investment policy are the current market conditions and\ntheir current historic maximum, and that the firm optimally invests in the\nstand-alone technology only when market conditions deteriorate enough after\nreaching a maximum. Empirically, investments in new technologies requiring the\nactive cooperation of developers should thus take place in booms, whereas\ninvestments in state-of-the-art technologies should take place in busts.\nMoreover, the required return for investing in the stand-alone technology is\nalways higher than if this were the only available technology and can take\narbitrarily large values following certain histories. Finally, a decrease in\ndevelopment costs, or an increase in the value of the new technology, makes the\nfirm more prone to bear downside risk and to delay investment in the\nstand-alone technology.\n"}
+{"abstract": "  In this paper, we present the XMUSPEECH system for Task 1 of 2020\nPersonalized Voice Trigger Challenge (PVTC2020). Task 1 is a joint wake-up word\ndetection with speaker verification on close talking data. The whole system\nconsists of a keyword spotting (KWS) sub-system and a speaker verification (SV)\nsub-system. For the KWS system, we applied a Temporal Depthwise Separable\nConvolution Residual Network (TDSC-ResNet) to improve the system's performance.\nFor the SV system, we proposed a multi-task learning network, where phonetic\nbranch is trained with the character label of the utterance, and speaker branch\nis trained with the label of the speaker. Phonetic branch is optimized with\nconnectionist temporal classification (CTC) loss, which is treated as an\nauxiliary module for speaker branch. Experiments show that our system gets\nsignificant improvements compared with baseline system.\n"}
+{"abstract": "  In order to transmit electrical energy in a continuous and quality manner, it\nis necessary to control it from the point of production to the point of\nconsumption. Therefore, protection of transmission and distribution lines is\nessential at every stage from production to consumption. The main function of\nthe protection relays in electrical installations should be deactivated as soon\nas possible in the event of short circuits in the system. The most important\npart of the system is energy transmission lines and distance protection relays\nthat protect these lines. An accurate error location technique is required to\nmake fast and efficient work. Transformer neutral point grounding in\ntransmission lines affects the operation of the zero component current during\nthe single phase to ground short circuit failure of a power system. Considering\nthe relationship between the grounding system and protection systems, an\nappropriate grounding choice should be made. Artificial neural network (ANN)\nhas been used in order to accurately locate short circuit faults in different\ngrounding systems in transmission lines. Compared with support vector machines\n(SVM) for testing inside ANN The transmission line model is made in the\nPSCAD-EMTDC simulation program. Data sets were created by recording the image\nof the impedance change of the R-X impedance diagram of the distance protection\nrelay in short circuit faults created in different grounding systems. The\nrelated focal points in the images are given as an introduction to different\nANN models using feature extraction and image processing techniques and the ANN\nmodel with the highest fault location estimation accuracy was chosen.\n"}
+{"abstract": "  Two intermetallic FeAl compounds with Al content of 70.68 and 72.17 at.pct\nwere studied using M\\\"ossbauer spectroscopy (5 to 296 K) and X-ray diffraction\n(15 to 300 K). The compounds were found to crystallize in the orthorhombic Cmcm\nspace group (eta-phase). The collected data revealed that dynamics of the Fe\natoms (harmonic in entire temperature range) is significantly different that Al\natoms. For the latter strong anharmonicity was evidenced. Moreover, it was\nfound that partial filling of the different Al sites leads to occurrence of low\nand high symmetry coordination of Fe atoms, which was reflected in occurrence\nof two distinct doublets in M\\\"ossbauer spectra. All spectral parameters of the\ndoublets as well as the Debye temperature, force constant, kinetic and\npotential energies of vibrations were determined. Those results revealed\nsignificant differences between both alloys, likely originating from\napproaching the stability boundary of the eta-phase for Fe-Al 72.17 at.pct\nalloy.\n"}
+{"abstract": "  In clinical practice, medical image interpretation often involves\nmulti-labeled classification, since the affected parts of a patient tend to\npresent multiple symptoms or comorbidities. Recently, deep learning based\nframeworks have attained expert-level performance on medical image\ninterpretation, which can be attributed partially to large amounts of accurate\nannotations. However, manually annotating massive amounts of medical images is\nimpractical, while automatic annotation is fast but imprecise (possibly\nintroducing corrupted labels). In this work, we propose a new regularization\napproach, called Flow-Mixup, for multi-labeled medical image classification\nwith corrupted labels. Flow-Mixup guides the models to capture robust features\nfor each abnormality, thus helping handle corrupted labels effectively and\nmaking it possible to apply automatic annotation. Specifically, Flow-Mixup\ndecouples the extracted features by adding constraints to the hidden states of\nthe models. Also, Flow-Mixup is more stable and effective comparing to other\nknown regularization methods, as shown by theoretical and empirical analyses.\nExperiments on two electrocardiogram datasets and a chest X-ray dataset\ncontaining corrupted labels verify that Flow-Mixup is effective and insensitive\nto corrupted labels.\n"}
+{"abstract": "  Multi-scale biomedical knowledge networks are expanding with emerging\nexperimental technologies that generates multi-scale biomedical big data. Link\nprediction is increasingly used especially in bipartite biomedical networks to\nidentify hidden biological interactions and relationshipts between key entities\nsuch as compounds, targets, gene and diseases. We propose a Graph Neural\nNetworks (GNN) method, namely Graph Pair based Link Prediction model (GPLP),\nfor predicting biomedical network links simply based on their topological\ninteraction information. In GPLP, 1-hop subgraphs extracted from known network\ninteraction matrix is learnt to predict missing links. To evaluate our method,\nthree heterogeneous biomedical networks were used, i.e. Drug-Target Interaction\nnetwork (DTI), Compound-Protein Interaction network (CPI) from NIH Tox21, and\nCompound-Virus Inhibition network (CVI). Our proposed GPLP method significantly\noutperforms over the state-of-the-art baselines. In addition, different network\nincompleteness is analysed with our devised protocol, and we also design an\neffective approach to improve the model robustness towards incomplete networks.\nOur method demonstrates the potential applications in other biomedical\nnetworks.\n"}
+{"abstract": "  Assuming Lehmer's conjecture, we estimate the degree of the trace field\n  $K(M_{p/q})$ of a hyperbolic Dehn-filling $M_{p/q}$ of a 1-cusped\n  hyperbolic 3-manifold $M$ by \\begin{equation*}\n  \\dfrac{1}{C}(\\max\\;\\{|p|,|q|\\})\\leq \\text{deg }K(M_{p/q})\n  \\leq C(\\max\\;\\{|p|,|q|\\}) \\end{equation*} where $C=C_M$ is a constant that\ndepends on $M$.\n"}
+{"abstract": "  The calculation of the MP2 correlation energy for extended systems can be\nviewed as a multi-dimensional integral in the thermodynamic limit, and the\nstandard method for evaluating the MP2 energy can be viewed as a trapezoidal\nquadrature scheme. We demonstrate that existing analysis neglects certain\ncontributions due to the non-smoothness of the integrand, and may significantly\nunderestimate finite-size errors. We propose a new staggered mesh method, which\nuses two staggered Monkhorst-Pack meshes for occupied and virtual orbitals,\nrespectively, to compute the MP2 energy. The staggered mesh method circumvents\na significant error source in the standard method, in which certain quadrature\nnodes are always placed on points where the integrand is discontinuous. One\nsignificant advantage of the proposed method is that there are no tunable\nparameters, and the additional numerical effort needed can be negligible\ncompared to the standard MP2 calculation. Numerical results indicate that the\nstaggered mesh method can be particularly advantageous for quasi-1D systems, as\nwell as quasi-2D and 3D systems with certain symmetries.\n"}
+{"abstract": "  Unsupervised concept identification through clustering, i.e., identification\nof semantically related words and phrases, is a common approach to identify\ncontextual primitives employed in various use cases, e.g., text dimension\nreduction, i.e., replace words with the concepts to reduce the vocabulary size,\nsummarization, and named entity resolution. We demonstrate the first results of\nan unsupervised approach for the identification of groups of persons as actors\nextracted from a set of related articles. Specifically, the approach clusters\nmentions of groups of persons that act as non-named entity actors in the texts,\ne.g., \"migrant families\" = \"asylum-seekers.\" Compared to our baseline, the\napproach keeps the mentions of the geopolitical entities separated, e.g., \"Iran\nleaders\" != \"European leaders,\" and clusters (in)directly related mentions with\ndiverse wording, e.g., \"American officials\" = \"Trump Administration.\"\n"}
+{"abstract": "  The electroweak symmetry breaking (EWSB) mechanism is still an undecided\nquestion in particle physics. We propose to utilize the single top quark and\nHiggs associated production ($th$), $Zh$ production via gluon fusion at the LHC\nto probe the couplings between the Higgs and the gauge bosons and further to\ntest the EWSB. We demonstrate that the $th$ and $gg\\to Zh$ productions are\nsensitive to the relative sign of couplings ($ht\\bar{t}$, $hWW$) and\n($ht\\bar{t}$, $hZZ$), respectively. We find that the relative sign between\n$hWW$ and $hZZ$ couplings could be fully determined after combining the present\nmeasurements from $gg\\to h$, $t\\bar{t}h$ and the $th$, $Zh$ channels, as well\nas $tZj$ and $Zt\\bar{t}$ production at the 13 TeV LHC, and this conclusion is\nnot sensitive to the possible new physics contribution induced by $Zt\\bar{t}$\ncouplings in the $gg\\to Zh$ production.\n"}
+{"abstract": "  A single transverse mode high pulse-energy VECSEL was developed. The GaSb\nbased VECSEL emits at a wavelength of 2.04 microns with a peak power exceeding\n500W while maintaining good beam quality. The cavity employs a Pockels cell\ncombined with a low-loss thin film polarizer to selectively dump the\nintracavity energy into a 10ns pulse. The laser has promise for incoherent\nLiDAR, materials processing, gas sensing, and nonlinear optics.\n"}
+{"abstract": "  Deep learning-based video manipulation methods have become widely accessible\nto the masses. With little to no effort, people can quickly learn how to\ngenerate deepfake (DF) videos. While deep learning-based detection methods have\nbeen proposed to identify specific types of DFs, their performance suffers for\nother types of deepfake methods, including real-world deepfakes, on which they\nare not sufficiently trained. In other words, most of the proposed deep\nlearning-based detection methods lack transferability and generalizability.\nBeyond detecting a single type of DF from benchmark deepfake datasets, we focus\non developing a generalized approach to detect multiple types of DFs, including\ndeepfakes from unknown generation methods such as DeepFake-in-the-Wild (DFW)\nvideos. To better cope with unknown and unseen deepfakes, we introduce a\nConvolutional LSTM-based Residual Network (CLRNet), which adopts a unique model\ntraining strategy and explores spatial as well as the temporal information in\ndeepfakes. Through extensive experiments, we show that existing defense methods\nare not ready for real-world deployment. Whereas our defense method (CLRNet)\nachieves far better generalization when detecting various benchmark deepfake\nmethods (97.57% on average). Furthermore, we evaluate our approach with a\nhigh-quality DeepFake-in-the-Wild dataset, collected from the Internet\ncontaining numerous videos and having more than 150,000 frames. Our CLRNet\nmodel demonstrated that it generalizes well against high-quality DFW videos by\nachieving 93.86% detection accuracy, outperforming existing state-of-the-art\ndefense methods by a considerable margin.\n"}
+{"abstract": "  Colonies of bacterial cells endowed with a pili-based self-propulsion\nmachinery represent an ideal system for studying how active adhesion forces,\nmediated by dynamic, bond-like interactions, affect structure and dynamics of\nmany-particle systems. We introduce a molecular-dynamics-simulation-based\napproach to study Neisseria gonorrhoeae colonies. A generic, adaptable\nsimulation method for particle systems with fluctuating bond-like interactions\nis devised. The simulations are employed to investigate growth of bacterial\ncolonies and the dependence of the colony structure on cell-cell interactions.\nIn colonies consisting only of wild-type cells, active pilus retraction is\nfound to enhance local ordering. For mixed colonies consisting of different\ntypes of cells, the simulations show a segregation depending on the\npili-mediated interactions among different cells. Both results are in good\nqualitative agreement with experimental observations. By representing an\nexperimental setup in silico, we study the power-spectral density of\ncolony-shape fluctuations and the fluctuation-response relation. Simulations\npredict a strong violation of the equilibrium fluctuation-response relation\nacross the measurable frequency range. Furthermore, we show that active force\ngeneration enables colonies to spread on adhesive surfaces and to invade narrow\nchannels. Our work presents a foundation for quantitative studies of the\nphysics of many-particle systems with active adhesion forces in complex\ngeometries.\n"}
+{"abstract": "  We investigate in detail the spectrum of gravitational waves induced by a\npeaked primordial curvature power spectrum generated in single field\ninflationary models. We argue that the $f_{\\rm NL}$ parameter can be inferred\nby measuring the high frequency spectral tilt of the induced gravitational\nwaves. We also show that the intrinsically non-Gaussian impact of $f_{\\rm NL}$\nin $\\Omega_{\\rm GW}$ is to broaden its peak, although at a negligible level in\norder not to overproduce primordial black holes. We discuss possible\ndegeneracies in the high frequency spectral tilt between $f_{\\rm NL}$ and a\ngeneral equation of state of the universe $w$. Finally, we discuss the\nconstraints on the amplitude, peak and slope (or equivalently, $f_{\\rm NL}$) of\nthe primordial power spectrum by combining current and future gravitational\nwave experiments with limits on $\\mu$ distortions from the cosmic microwave\nbackground.\n"}
+{"abstract": "  Using the Markovian master equation for quantum quasiparticles, we show that\nconvection in the stellar photosphere generates plasma waves by an irreversible\nprocess akin to Zeldovich superradiance and sonic booms. In the Sun, this\nmechanism is most efficient in quiet regions with magnetic fields of order one\ngauss. Most energy is carried by Alfven waves with megahertz frequencies, which\ntravel upwards until they reach a height at which they dissipate via mode\nconversion. This gives the right power flux for the observed energy transport\nfrom the colder photosphere to the hotter corona.\n"}
+{"abstract": "  We study high-dimensional Bayesian linear regression with product priors.\nUsing the nascent theory of non-linear large deviations (Chatterjee and\nDembo,2016), we derive sufficient conditions for the leading-order correctness\nof the naive mean-field approximation to the log-normalizing constant of the\nposterior distribution. Subsequently, assuming a true linear model for the\nobserved data, we derive a limiting infinite dimensional variational formula\nfor the log normalizing constant of the posterior. Furthermore, we establish\nthat under an additional \"separation\" condition, the variational problem has a\nunique optimizer, and this optimizer governs the probabilistic properties of\nthe posterior distribution. We provide intuitive sufficient conditions for the\nvalidity of this \"separation\" condition. Finally, we illustrate our results on\nconcrete examples with specific design matrices.\n"}
+{"abstract": "  Objective: Deep learning-based neural decoders have emerged as the prominent\napproach to enable dexterous and intuitive control of neuroprosthetic hands.\nYet few studies have materialized the use of deep learning in clinical settings\ndue to its high computational requirements. Methods: Recent advancements of\nedge computing devices bring the potential to alleviate this problem. Here we\npresent the implementation of a neuroprosthetic hand with embedded deep\nlearning-based control. The neural decoder is designed based on the recurrent\nneural network (RNN) architecture and deployed on the NVIDIA Jetson Nano - a\ncompacted yet powerful edge computing platform for deep learning inference.\nThis enables the implementation of the neuroprosthetic hand as a portable and\nself-contained unit with real-time control of individual finger movements.\nResults: The proposed system is evaluated on a transradial amputee using\nperipheral nerve signals (ENG) with implanted intrafascicular microelectrodes.\nThe experiment results demonstrate the system's capabilities of providing\nrobust, high-accuracy (95-99%) and low-latency (50-120 msec) control of\nindividual finger movements in various laboratory and real-world environments.\nConclusion: Modern edge computing platforms enable the effective use of deep\nlearning-based neural decoders for neuroprosthesis control as an autonomous\nsystem. Significance: This work helps pioneer the deployment of deep neural\nnetworks in clinical applications underlying a new class of wearable biomedical\ndevices with embedded artificial intelligence.\n"}
+{"abstract": "  For $G = \\mathrm{GL}_2, \\mathrm{SL}_2, \\mathrm{PGL}_2$ we compute the\nintersection E-polynomials and the intersection Poincar\\'e polynomials of the\n$G$-character variety of a compact Riemann surface $C$ and of the moduli space\nof $G$-Higgs bundles on $C$ of degree zero. We derive several results\nconcerning the P=W conjectures for these singular moduli spaces.\n"}
+{"abstract": "  Getting a robust time-series clustering with best choice of distance measure\nand appropriate representation is always a challenge. We propose a novel\nmechanism to identify the clusters combining learned compact representation of\ntime-series, Auto Encoded Compact Sequence (AECS) and hierarchical clustering\napproach. Proposed algorithm aims to address the large computing time issue of\nhierarchical clustering as learned latent representation AECS has a length much\nless than the original length of time-series and at the same time want to\nenhance its performance.Our algorithm exploits Recurrent Neural Network (RNN)\nbased under complete Sequence to Sequence(seq2seq) autoencoder and\nagglomerative hierarchical clustering with a choice of best distance measure to\nrecommend the best clustering. Our scheme selects the best distance measure and\ncorresponding clustering for both univariate and multivariate time-series. We\nhave experimented with real-world time-series from UCR and UCI archive taken\nfrom diverse application domains like health, smart-city, manufacturing etc.\nExperimental results show that proposed method not only produce close to\nbenchmark results but also in some cases outperform the benchmark.\n"}
+{"abstract": "  The all-optical synchronization systems used in various X-ray free-electron\nlasers (XFEL) such as the European XFEL observe the transient fields of passing\nelectron bunches coupled into one or more pickups in the Bunch Arrival Time\nMonitors (BAM). The extracted signal is then amplitude modulated on reference\nlaser pulses in a Mach-Zehnder type electro-optical modulator. With the\nemerging demand for future experiments with ultra-short FEL shots, fs precision\nis required for the synchronization systems even with 1 pC bunches. Since the\nsensitivity of the BAM depends in particular on the slope of the bipolar signal\nat the zero-crossing and thus, also on the bunch charge, a redesign with the\naim of a significant increase by optimized geometry and bandwidth is\ninevitable. In this contribution the theoretical foundations of the pickup\nsignal are aggregated and treated with a focus on ultra-short bunches as well\nas a general formulation. A possible new pickup concept is simulated and its\nperformance is compared to the previous concept. A significant improvement of\nslope and voltage is found. The improvement is mainly achieved by the reduced\ndistance to the beam and a higher bandwidth.\n"}
+{"abstract": "  Reconstructing under-sampled k-space measurements in Compressed Sensing MRI\n(CS-MRI) is classically solved with regularized least-squares. Recently, deep\nlearning has been used to amortize this optimization by training reconstruction\nnetworks on a dataset of under-sampled measurements. Here, a crucial design\nchoice is the regularization function(s) and corresponding weight(s). In this\npaper, we explore a novel strategy of using a hypernetwork to generate the\nparameters of a separate reconstruction network as a function of the\nregularization weight(s), resulting in a regularization-agnostic reconstruction\nmodel. At test time, for a given under-sampled image, our model can rapidly\ncompute reconstructions with different amounts of regularization. We analyze\nthe variability of these reconstructions, especially in situations when the\noverall quality is similar. Finally, we propose and empirically demonstrate an\nefficient and data-driven way of maximizing reconstruction performance given\nlimited hypernetwork capacity. Our code is publicly available at\nhttps://github.com/alanqrwang/RegAgnosticCSMRI.\n"}
+{"abstract": "  One of the challenges in a task oriented natural language application like\nthe Google Assistant, Siri, or Alexa is to localize the output to many\nlanguages. This paper explores doing this by applying machine translation to\nthe English output. Using machine translation is very scalable, as it can work\nwith any English output and can handle dynamic text, but otherwise the problem\nis a poor fit. The required quality bar is close to perfection, the range of\nsentences is extremely narrow, and the sentences are often very different than\nthe ones in the machine translation training data. This combination of\nrequirements is novel in the field of domain adaptation for machine\ntranslation. We are able to reach the required quality bar by building on\nexisting ideas and adding new ones: finetuning on in-domain translations,\nadding sentences from the Web, adding semantic annotations, and using automatic\nerror detection. The paper shares our approach and results, together with a\ndistillation model to serve the translation models at scale.\n"}
+{"abstract": "  For an ordinal $\\alpha$, an $\\alpha$-ITRM is a machine model of transfinite\ncomputability that operates on finitely many registers, each of which can\ncontain an ordinal $\\rho<\\alpha$; they were introduced by Koepke in \\cite{KM}.\n  In \\cite{alpha itrms}, it was shown that the $\\alpha$-ITRM-computable subsets\nof $\\alpha$ are exactly those in a level $L_{\\beta(\\alpha)}$ of the\nconstructible hierarchy. It was conjectured in \\cite{alpha itrms} that\n$\\beta(\\alpha)$ is the first limit of admissible ordinals above $\\alpha$. Here,\nwe show that this is false; in particular, even the computational strength of\n$\\omega^{\\omega}$-ITRMs goes far beyond $\\omega_{\\omega}^{\\text{CK}}$.\n"}
+{"abstract": "  Flow over a surface can be stratified by imposing a fixed mean vertical\ntemperature (density) gradient profile throughout or via cooling at the\nsurface. These distinct mechanisms can act simultaneously to establish a stable\nstratification in a flow. Here, we perform a series of direct numerical\nsimulations of open-channel flows to study adaptation of a neutrally stratified\nturbulent flow under the combined or independent action of the aforementioned\nmechanisms. We force the fully developed flow with a constant mass flow rate.\nThis flow forcing technique enables us to keep the bulk Reynolds number\nconstant throughout our investigation and avoid complications arising from the\nacceleration of the bulk flow when a constant pressure gradient approach were\nto be adopted to force the flow instead. When both stratification mechanisms\nare active, the dimensionless stratification perturbation number emerges as an\nexternal flow control parameter, in addition to the Reynolds, Froude, and\nPrandtl numbers. We demonstrate that significant deviations from the\nMonin-Obukhov similarity formulation are possible when both types of\nstratification mechanisms are active within an otherwise weakly stable flow,\neven when the flux Richardson number is well below 0.2. An extended version of\nthe similarity theory due to Zilitinkevich and Calanca shows promise in\npredicting the dimensionless shear for cases where both types of stratification\nmechanisms are active, but the extended theory is less accurate for gradients\nof scalar. The degree of deviation from neutral dimensionless shear as a\nfunction of the vertical coordinate emerges as a qualitative measure of the\nstrength of stable stratification for all the cases investigated in this study.\n"}
+{"abstract": "  Articulatory-to-acoustic (forward) mapping is a technique to predict speech\nusing various articulatory acquisition techniques as input (e.g. ultrasound\ntongue imaging, MRI, lip video). The advantage of lip video is that it is\neasily available and affordable: most modern smartphones have a front camera.\nThere are already a few solutions for lip-to-speech synthesis, but they mostly\nconcentrate on offline training and inference. In this paper, we propose a\nsystem built from a backend for deep neural network training and inference and\na fronted as a form of a mobile application. Our initial evaluation shows that\nthe scenario is feasible: a top-5 classification accuracy of 74% is combined\nwith feedback from the mobile application user, making sure that the speaking\nimpaired might be able to communicate with this solution.\n"}
+{"abstract": "  We investigate the hypothesis that within a combination of a 'number concept'\nplus a 'substantive concept', such as 'eleven animals,' the identity and\nindistinguishability present on the level of the concepts, i.e., all eleven\nanimals are identical and indistinguishable, gives rise to a statistical\nstructure of the Bose-Einstein type similar to how Bose-Einstein statistics is\npresent for identical and indistinguishable quantum particles. We proceed by\nidentifying evidence for this hypothesis by extracting the statistical data\nfrom the World-Wide-Web utilizing the Google Search tool. By using the\nKullback-Leibler divergence method, we then compare the obtained distribution\nwith the Maxwell-Boltzmann as well as with the Bose-Einstein distributions and\nshow that the Bose-Einstein's provides a better fit as compared to the\nMaxwell-Boltzmanns.\n"}
+{"abstract": "  We present an application of invariant polynomials in machine learning. Using\nthe methods developed in previous work, we obtain two types of generators of\nthe Lorentz- and permutation-invariant polynomials in particle momenta; minimal\nalgebra generators and Hironaka decompositions. We discuss and prove some\napproximation theorems to make use of these invariant generators in machine\nlearning algorithms in general and in neural networks specifically. By\nimplementing these generators in neural networks applied to regression tasks,\nwe test the improvements in performance under a wide range of hyperparameter\nchoices and find a reduction of the loss on training data and a significant\nreduction of the loss on validation data. For a different approach on\nquantifying the performance of these neural networks, we treat the problem from\na Bayesian inference perspective and employ nested sampling techniques to\nperform model comparison. Beyond a certain network size, we find that networks\nutilising Hironaka decompositions perform the best.\n"}
+{"abstract": "  Developing state-machine replication protocols for practical use is a complex\nand labor-intensive process because of the myriad of essential tasks (e.g.,\ndeployment, communication, recovery) that need to be taken into account in an\nimplementation. In this paper, we show how this problem can be addressed with\nstream-based replication, a novel approach that implements a replication\nprotocol as application on top of a data-stream processing framework. With such\nframework already handling most essential tasks and furthermore providing means\nfor debugging and monitoring, this technique has the key benefit of\nsignificantly minimizing overhead for both programmers as well as system\noperators. Our first stream-based protocol Tara tolerates crashes and comprises\nfull-fledged mechanisms for request handling, checkpointing, and view changes.\nStill, Tara's prototype implementation, which is based on Twitter's Heron\nframework, consists of fewer than 1,500 lines of application-level code.\n"}
+{"abstract": "  Evaluating Software testability can assist software managers in optimizing\ntesting budgets and identifying opportunities for refactoring. In this paper,\nwe abandon the traditional approach of pursuing testability measurements based\non the correlation between software metrics and test characteristics observed\non past projects, e.g., the size, the organization or the code coverage of the\ntest cases. We propose a radically new approach that exploits automatic test\ngeneration and mutation analysis to quantify the amount of evidence about the\nrelative hardness of identifying effective test cases. We introduce two novel\nevidence-based testability metrics, describe a prototype to compute them, and\ndiscuss initial findings on whether our measurements can reflect actual\ntestability issues.\n"}
+{"abstract": "  The high reflect beamforming gain of the intelligent reflecting surface (IRS)\nmakes it appealing not only for wireless information transmission but also for\nwireless power transfer. In this letter, we consider an IRS-assisted wireless\npowered communication network, where a base station (BS) transmits energy to\nmultiple users grouped into multiple clusters in the downlink, and the\nclustered users transmit information to the BS in the manner of hybrid\nnon-orthogonal multiple access and time division multiple access in the uplink.\nWe investigate optimizing the reflect beamforming of the IRS and the time\nallocation among the BS's power transfer and different user clusters'\ninformation transmission to maximize the throughput of the network, and we\npropose an efficient algorithm based on the block coordinate ascent,\nsemidefinite relaxation, and sequential rank-one constraint relaxation\ntechniques to solve the resultant problem. Simulation results have verified the\neffectiveness of the proposed algorithm and have shown the impact of user\nclustering setup on the throughput performance of the network.\n"}
+{"abstract": "  We prove normalization for (univalent, Cartesian) cubical type theory,\nclosing the last major open problem in the syntactic metatheory of cubical type\ntheory. Our normalization result is reduction-free, in the sense of yielding a\nbijection between equivalence classes of terms in context and a tractable\nlanguage of $\\beta/\\eta$-normal forms. As corollaries we obtain both\ndecidability of judgmental equality and the injectivity of type constructors.\n"}
+{"abstract": "  This paper presents a simple unsupervised visual representation learning\nmethod with a pretext task of discriminating all images in a dataset using a\nparametric, instance-level classifier. The overall framework is a replica of a\nsupervised classification model, where semantic classes (e.g., dog, bird, and\nship) are replaced by instance IDs. However, scaling up the classification task\nfrom thousands of semantic labels to millions of instance labels brings\nspecific challenges including 1) the large-scale softmax computation; 2) the\nslow convergence due to the infrequent visiting of instance samples; and 3) the\nmassive number of negative classes that can be noisy. This work presents\nseveral novel techniques to handle these difficulties. First, we introduce a\nhybrid parallel training framework to make large-scale training feasible.\nSecond, we present a raw-feature initialization mechanism for classification\nweights, which we assume offers a contrastive prior for instance discrimination\nand can clearly speed up converge in our experiments. Finally, we propose to\nsmooth the labels of a few hardest classes to avoid optimizing over very\nsimilar negative pairs. While being conceptually simple, our framework achieves\ncompetitive or superior performance compared to state-of-the-art unsupervised\napproaches, i.e., SimCLR, MoCoV2, and PIC under ImageNet linear evaluation\nprotocol and on several downstream visual tasks, verifying that full instance\nclassification is a strong pretraining technique for many semantic visual\ntasks.\n"}
+{"abstract": "  Oxygen defective cerium oxides exhibits a non classical giant\nelectromechanical response that is superior to lead based electrostrictors. In\nthis work, we report the key role of acceptor dopants, with different size and\nvalence Mg2+, Sc3+, Gd3+, and La3+, on polycrystalline bulk ceria. Different\ndopants tune the electrostrictive properties by changing the electrosteric\ndopant defect interactions. We find two distinct electromechanical behaviors\nwhen the interaction is weak dopant vacancy binding energy 0.3 eV,\nelectrostriction displays high coefficient, up to 10-17 m2V-2, with strongly\ntime dependent effects. In contrast, we observe no time dependent effects when\nthe interaction becomes strong 0.6 eV.\n"}
+{"abstract": "  We performed photometric and spectroscopic investigations of NSVS 5029961 for\nthe first time. The new BV(RI)$_c$-band light curves were obtained with the\n1.0-m telescope at Weihai Observatory of Shandong University. Applying the\nWilson-Devinney program, we found that NSVS 5029961 is an A-subtype shallow\ncontact binary with extremely low mass ratio (q = 0.1515, f = 19.1\\%). Six\nspectra have been obtained by LAMOST, and many chromospheric activity emission\nline indicators were detected in the spectra, revealing that the target\nexhibits strong chromospheric activity. We calculated the absolute parameters\nwith the photometric solutions and Gaia distance, and estimated the initial\nmasses of the two components and the age of the binary. The evolutionary status\nwas discussed by using the mass-radius and mass-luminosity diagrams. The result\nshows the primary component is a little evolved star and the secondary\ncomponent has evolved away from the main sequence. The formation and evolution\ninvestigations of NSVS 5029661 indicate that it may have evolved from a\ndetached binary with short period and low mass ratio by angular momentum loss\nvia magnetic braking and case A mass transfer, and is in a stable contact stage\nat present.\n"}
+{"abstract": "  Federated learning is an emerging research paradigm for enabling\ncollaboratively training deep learning models without sharing patient data.\nHowever, the data from different institutions are usually heterogeneous across\ninstitutions, which may reduce the performance of models trained using\nfederated learning. In this study, we propose a novel heterogeneity-aware\nfederated learning method, SplitAVG, to overcome the performance drops from\ndata heterogeneity in federated learning. Unlike previous federated methods\nthat require complex heuristic training or hyper parameter tuning, our SplitAVG\nleverages the simple network split and feature map concatenation strategies to\nencourage the federated model training an unbiased estimator of the target data\ndistribution. We compare SplitAVG with seven state-of-the-art federated\nlearning methods, using centrally hosted training data as the baseline on a\nsuite of both synthetic and real-world federated datasets. We find that the\nperformance of models trained using all the comparison federated learning\nmethods degraded significantly with the increasing degrees of data\nheterogeneity. In contrast, SplitAVG method achieves comparable results to the\nbaseline method under all heterogeneous settings, that it achieves 96.2% of the\naccuracy and 110.4% of the mean absolute error obtained by the baseline in a\ndiabetic retinopathy binary classification dataset and a bone age prediction\ndataset, respectively, on highly heterogeneous data partitions. We conclude\nthat SplitAVG method can effectively overcome the performance drops from\nvariability in data distributions across institutions. Experimental results\nalso show that SplitAVG can be adapted to different base networks and\ngeneralized to various types of medical imaging tasks.\n"}
+{"abstract": "  We investigate the use of programmable optical lattices for quantum\nsimulation of Hubbard models, determining analytic expressions for the hopping\nand Hubbard U, finding that they are suitable for emulating strongly correlated\nsystems with arbitrary structures, including those with multiple site basis and\nimpurities. Programmable potentials are highly flexible, with the ability to\ncontrol the depth and shape of individual sites in the optical lattice\ndynamically. Quantum simulators of Hubbard models with (1) arbitrary basis are\nrequired to represent many real materials of contemporary interest, (2) broken\ntranslational symmetry are needed to study impurity physics, and (3) dynamical\nlattices are needed to investigate strong correlation out of equilibrium. We\nderive analytic expressions for Hubbard Hamiltonians in programmable potential\nsystems. We find experimental parameters for quantum simulation of Hubbard\nmodels with arbitrary basis, concluding that programmable optical lattices are\nsuitable for this purpose. We discuss how programmable optical lattices can be\nused for quantum simulation of dynamical multi-band Hubbard models that\nrepresent complicated compounds, impurities, and non-equilibrium physics.\n"}
+{"abstract": "  The superconducting TMD 4Hb-TaS$_2$ consists of alternating layers of H and T\nstructures, which in their bulk form are metallic and Mott-insulating,\nrespectively. Recently, this compound has been proposed as a candidate chiral\nsuperconductor, due to an observed enhancement of the muon spin relaxation at\n$T_c$. 4Hb-TaS$_2$ also exhibits a puzzling $T$-linear specific heat at low\ntemperatures, which is unlikely to be caused by disorder. Elucidating the\norigin of this behavior is an essential step in discerning the true nature of\nthe superconducting ground state. Here, we propose a simple model that\nattributes the $T$-linear specific heat to the emergence of a robust multi-band\ngapless superconducting state. We show that an extended regime of gapless\nsuperconductivity naturally appears when the pair-breaking scattering rate on\ndistinct Fermi-surface pockets differs significantly, and the pairing\ninteraction is predominantly intra-pocket. Using a tight-binding model derived\nfrom first-principle calculations, we show that the pair-breaking scattering\nrate promoted by slow magnetic fluctuations on the T layers, which arise from\nproximity to a Mott transition, can be significantly different in the various\nH-layer dominated Fermi pockets depending on their hybridization with T-layer\nstates. Thus, our results suggest that the ground state of 4Hb-TaS$_2$ consists\nof Fermi pockets displaying gapless superconductivity, which are shunted by\nsuperconducting Fermi pockets that are nearly decoupled from the T-layers.\n"}
+{"abstract": "  Boosting Trees are one of the most successful statistical learning approaches\nthat involve sequentially growing an ensemble of simple regression trees (i.e.,\n\"weak learners\"). However, gradient boosted trees are not yet available for\nspatially correlated data. This paper proposes a new gradient Boosted Trees\nalgorithm for Spatial Data (Boost-S) with covariate information. Boost-S\nintegrates the spatial correlation structure into the classical framework of\ngradient boosted trees. Each tree is grown by solving a regularized\noptimization problem, where the objective function involves two penalty terms\non tree complexity and takes into account the underlying spatial correlation. A\ncomputationally-efficient algorithm is proposed to obtain the ensemble trees.\nThe proposed Boost-S is applied to the spatially-correlated FDG-PET\n(fluorodeoxyglucose-positron emission tomography) imaging data collected during\ncancer chemoradiotherapy. Our numerical investigations successfully demonstrate\nthe advantages of the proposed Boost-S over existing approaches for this\nparticular application.\n"}
+{"abstract": "  We prove that strength and slice rank of homogeneous polynomials of degree $d\n\\geq 5$ over an algebraically closed field of characteristic zero coincide\ngenerically. To show this, we establish a conjecture of Catalisano, Geramita,\nGimigliano, Harbourne, Migliore, Nagel and Shin concerning dimensions of secant\nvarieties of the varieties of reducible homogeneous polynomials. These\nstatements were already known in degrees $2\\leq d\\leq 7$ and $d=9$.\n"}
+{"abstract": "  Burr and Erd\\H{o}s in 1975 conjectured, and Chv\\'atal, R\\\"odl, Szemer\\'edi\nand Trotter later proved, that the Ramsey number of any bounded degree graph is\nlinear in the number of vertices. In this paper, we disprove the natural\ndirected analogue of the Burr--Erd\\H{o}s conjecture, answering a question of\nBuci\\'c, Letzter, and Sudakov. If $H$ is an acyclic digraph, the oriented\nRamsey number of $H$, denoted $\\overrightarrow{r_{1}}(H)$, is the least $N$\nsuch that every tournament on $N$ vertices contains a copy of $H$. We show that\nfor any $\\Delta \\geq 2$ and any sufficiently large $n$, there exists an acyclic\ndigraph $H$ with $n$ vertices and maximum degree $\\Delta$ such that \\[\n\\overrightarrow{r_{1}}(H)\\ge n^{\\Omega(\\Delta^{2/3}/ \\log^{5/3} \\Delta)}. \\]\nThis proves that $\\overrightarrow{r_{1}}(H)$ is not always linear in the number\nof vertices for bounded-degree $H$. On the other hand, we show that\n$\\overrightarrow{r_{1}}(H)$ is nearly linear in the number of vertices for\ntypical bounded-degree acyclic digraphs $H$, and obtain linear or nearly linear\nbounds for several natural families of bounded-degree acyclic digraphs.\n  For multiple colors, we prove a quasipolynomial upper bound\n$\\overrightarrow{r_{k}}(H)=2^{(\\log n)^{O_{k}(1)}}$ for all bounded-degree\nacyclic digraphs $H$ on $n$ vertices, where $\\overrightarrow{r_k}(H)$ is the\nleast $N$ such that every $k$-edge-colored tournament on $N$ vertices contains\na monochromatic copy of $H$. For $k\\geq 2$ and $n\\geq 4$, we exhibit an acyclic\ndigraph $H$ with $n$ vertices and maximum degree $3$ such that\n$\\overrightarrow{r_{k}}(H)\\ge n^{\\Omega(\\log n/\\log\\log n)}$, showing that\nthese Ramsey numbers can grow faster than any polynomial in the number of\nvertices.\n"}
+{"abstract": "  We explore data reduction and correction steps and processed data\nreproducibility in the emerging single crystal total scattering based technique\nof three-dimensional differential atomic pair distribution function\n(3D-$\\Delta$PDF) analysis. All steps from sample measurement to data-processing\nare outlined in detail using a CuIr$_2$S$_4$ example crystal studied in a setup\nequipped with a high-energy x-ray beam and a flat panel area detector.\nComputational overhead as it pertains to data-sampling and the associated data\nprocessing steps is also discussed. Various aspects of the final 3D-$\\Delta$PDF\nreproducibility are explicitly tested by varying data-processing order and\nincluded steps, and by carrying out a crystal-to-crystal data comparison. We\nidentify situations in which the 3D-$\\Delta$PDF is robust, and caution against\na few particular cases which can lead to inconsistent 3D-$\\Delta$PDFs. Although\nnot all the approaches applied here-in will be valid across all systems, and a\nmore in-depth analysis of some of the effects of the data processing steps may\nstill needed, the methods collected here-in represent the start of a more\nsystematic discussion about data processing and corrections in this field.\n"}
+{"abstract": "  Machine learning models that offer excellent predictive performance often\nlack the interpretability necessary to support integrated human machine\ndecision-making. In clinical medicine and other high-risk settings, domain\nexperts may be unwilling to trust model predictions without explanations. Work\nin explainable AI must balance competing objectives along two different axes:\n1) Explanations must balance faithfulness to the model's decision-making with\ntheir plausibility to a domain expert. 2) Domain experts desire local\nexplanations of individual predictions and global explanations of behavior in\naggregate. We propose to train a proxy model that mimics the behavior of the\ntrained model and provides fine-grained control over these trade-offs. We\nevaluate our approach on the task of assigning ICD codes to clinical notes to\ndemonstrate that explanations from the proxy model are faithful and replicate\nthe trained model behavior.\n"}
+{"abstract": "  We obtain a Fourier dimension estimate for sets of exact approximation order\nintroduced by Bugeaud for certain approximation functions $\\psi$. This Fourier\ndimension estimate implies that these sets of exact approximation order contain\nnormal numbers.\n"}
+{"abstract": "  There is growing interest in ASR systems that can recognize phones in a\nlanguage-independent fashion. There is additionally interest in building\nlanguage technologies for low-resource and endangered languages. However, there\nis a paucity of realistic data that can be used to test such systems and\ntechnologies. This paper presents a publicly available, phonetically\ntranscribed corpus of 2255 utterances (words and short phrases) in the\nendangered Tangkhulic language East Tusom (no ISO 639-3 code), a Tibeto-Burman\nlanguage variety spoken mostly in India. Because the dataset is transcribed in\nterms of phones, rather than phonemes, it is a better match for universal phone\nrecognition systems than many larger (phonemically transcribed) datasets. This\npaper describes the dataset and the methodology used to produce it. It further\npresents basic benchmarks of state-of-the-art universal phone recognition\nsystems on the dataset as baselines for future experiments.\n"}
+{"abstract": "  We proposed a simple and efficient modular single-source surface integral\nequation (SS-SIE) formulation for electromagnetic analysis of arbitrarily\nconnected penetrable and perfectly electrical conductor (PEC) objects in\ntwo-dimensional space. In this formulation, a modular equivalent model for each\npenetrable object consisting of the composite structure is first independently\nconstructed through replacing it by the background medium, no matter whether it\nis surrounded by the background medium, other media, or partially connected\nobjects, and enforcing an equivalent electric current density on the boundary\nto remain fields in the exterior region unchanged. Then, by combining all the\nmodular models and any possible PEC objects together, an equivalent model for\nthe composite structure can be derived. The troublesome junction handling\ntechniques are not needed and non-conformal meshes are intrinsically supported.\nThe proposed SS-SIE formulation is simple to implement, efficient, and\nflexible, which shows significant performance improvement in terms of CPU time\ncompared with the original SS-SIE formulation and the\nPoggio-Miller-Chang-Harrington-Wu-Tsai (PMCHWT) formulation. Several numerical\nexamples including the coated dielectric cuboid, the large lossy objects, the\nplanar layered dielectric structure, and the partially connected dielectric and\nPEC structure are carried out to validate its accuracy, efficiency and\nrobustness.\n"}
+{"abstract": "  This research quantitatively and qualitatively analyzes the factors\nresponsible for the water level variations in Lake Toba, North Sumatra\nProvince, Indonesia. According to several studies carried out from 1993 to\n2020, changes in the water level were associated with climate variability,\nclimate change, and human activities. Furthermore, these studies stated that\nreduced rainfall during the rainy season due to the El Nino Southern\nOscillation (ENSO) and the continuous increase in the maximum and average\ntemperatures were some of the effects of climate change in the Lake Toba\ncatchment area. Additionally, human interventions such as industrial\nactivities, population growth, and damage to the surrounding environment of the\nLake Toba watershed had significant impacts in terms of decreasing the water\nlevel. However, these studies were unable to determine the factor that had the\nmost significant effect, although studies on other lakes worldwide have shown\nthese factors are the main causes of fluctuations or decreases in water levels.\nA simulation study of Lake Toba's water balance showed the possibility of\nhaving a water surplus until the mid-twenty-first century. The input discharge\nwas predicted to be greater than the output; therefore, Lake Toba could be\noptimized without affecting the future water level. However, the climate\nprojections depicted a different situation, with scenarios predicting the\npossibility of extreme climate anomalies, demonstrating drier climatic\nconditions in the future. This review concludes that it is necessary to conduct\nan in-depth, comprehensive, and systematic study to identify the most dominant\nfactor among the three that is causing the decrease in the Lake Toba water\nlevel and to describe the future projected water level.\n"}
+{"abstract": "  This paper presents the CUHK-EE voice cloning system for ICASSP 2021 M2VoC\nchallenge. The challenge provides two Mandarin speech corpora: the AIShell-3\ncorpus of 218 speakers with noise and reverberation and the MST corpus\nincluding high-quality speech of one male and one female speakers. 100 and 5\nutterances of 3 target speakers in different voice and style are provided in\ntrack 1 and 2 respectively, and the participants are required to synthesize\nspeech in target speaker's voice and style. We take part in the track 1 and\ncarry out voice cloning based on 100 utterances of target speakers. An\nend-to-end voicing cloning system is developed to accomplish the task, which\nincludes: 1. a text and speech front-end module with the help of forced\nalignment, 2. an acoustic model combining Tacotron2 and DurIAN to predict\nmelspectrogram, 3. a Hifigan vocoder for waveform generation. Our system\ncomprises three stages: multi-speaker training stage, target speaker adaption\nstage and target speaker synthesis stage. Our team is identified as T17. The\nsubjective evaluation results provided by the challenge organizer demonstrate\nthe effectiveness of our system. Audio samples are available at our demo page:\nhttps://daxintan-cuhk.github.io/CUHK-EE-system-M2VoC-challenge/ .\n"}
+{"abstract": "  The use of cumulative incidence functions for characterizing the risk of one\ntype of event in the presence of others has become increasingly popular over\nthe past decade. The problems of modeling, estimation and inference have been\ntreated using parametric, nonparametric and semi-parametric methods. Efforts to\ndevelop suitable extensions of machine learning methods, such as regression\ntrees and related ensemble methods, have begun comparatively recently. In this\npaper, we propose a novel approach to estimating cumulative incidence curves in\na competing risks setting using regression trees and associated ensemble\nestimators. The proposed methods employ augmented estimators of the Brier score\nrisk as the primary basis for building and pruning trees, and lead to methods\nthat are easily implemented using existing R packages. Data from the Radiation\nTherapy Oncology Group (trial 9410) is used to illustrate these new methods.\n"}
+{"abstract": "  The two years experience with Active Metal Casting flat bonds shows that this\ntechnology is suitable for the heat fluxes expected in Tore Supra (10\nMW/m${}^2$). Tests were pursued up to 3330 cycles, with elements still\nfunctional. At higher heat fluxes, fatigue damage is observed, but the bond\nresists remarkably well with no tile detachment. Examination of such\ndeliberately damaged bonds showed distributed cracking, proving the absence of\nany weak link. The limitations to those higher heat fluxes are more related to\nthe design and the base materials than to the bond itself.\n"}
+{"abstract": "  The class of gross substitutes (GS) set functions plays a central role in\nEconomics and Computer Science. GS belongs to the hierarchy of {\\em complement\nfree} valuations introduced by Lehmann, Lehmann and Nisan, along with other\nprominent classes: $GS \\subsetneq Submodular \\subsetneq XOS \\subsetneq\nSubadditive$. The GS class has always been more enigmatic than its counterpart\nclasses, both in its definition and in its relation to the other classes. For\nexample, while it is well understood how closely the Submodular, XOS and\nSubadditive classes (point-wise) approximate one another, approximability of\nthese classes by GS remained wide open.\n  Our main result is the existence of a submodular valuation (one that is also\nbudget additive) that cannot be approximated by GS within a ratio better than\n$\\Omega(\\frac{\\log m}{\\log\\log m})$, where $m$ is the number of items. En\nroute, we uncover a new symmetrization operation that preserves GS, which may\nbe of independent interest.\n  We show that our main result is tight with respect to budget additive\nvaluations. Additionally, for a class of submodular functions that we refer to\nas {\\em concave of Rado} valuations (this class greatly extends budget additive\nvaluations), we show approximability by GS within an $O(\\log^2m)$ factor.\n"}
+{"abstract": "  Two-dimensional infrared spectroscopy experiments have presented new results\nregarding the dynamics of the hydrated excess proton (aka <q>hydronium</q>\ncation solvated in water). It has been suggested by these experiments that the\nhydrated excess proton has an anisotropy reorientation timescale of 2.5 ps,\nwhich can be viewed as being somewhat long lived. Through the use of both the\nreactive molecular dynamics Multistate-Empirical Valence Bond method and\nExperiment Directed Simulation Ab Initio Molecular Dynamics we show that\ntimescales of the same magnitude are obtained that correspond to proton\ntransport, while also involving structural reorientations of the hydrated\nproton structure that correspond to the so-called <q>special pair dance</q>.\nThe latter is a process predicted by prior computational studies in which the\ncentral hydrated hydronium in a distorted Eigen cation\n(H<sub>9</sub>O<sub>4</sub><sup>+</sup>) structure continually switches special\npair partners with its strongly hydrogen-bonded neighboring water molecules.\nThese dynamics are further characterized through the time-evolution of\ninstantaneous normal modes. It is concluded that the hydrated excess proton has\na spectral signature unique from the other protons in the hydrated proton\ncomplex. However, the results call into question the use of a static picture\nbased on a simple effective one dimensional potential well to describe the\nhydrated excess proton in water. Instead, they more conclusively point to a\ndistorted and dynamic Eigen cation as the most prevalent hydrated proton\nspecies in acid solutions of dilute to moderate concentrations.\n"}
+{"abstract": "  We study solutions to Nahm's equations with continuous symmetries and, under\ncertain (mild) hypotheses, we classify the corresponding Ans\\\"atze. Using our\nclassification, we construct novel Nahm data, and prescribe methods for\ngenerating further solutions. Finally, we use these results to construct new\nBPS monopoles with spherical symmetry.\n"}
+{"abstract": "  This paper proposes a computational technique based on \"deep unfolding\" to\nsolving the finite-time maximum hands-off control problem for discrete-time\nnonlinear stochastic systems. In particular, we seek a sparse control input\nsequence that stabilizes the system such that the expected value of the square\nof the final states is small by training a deep neural network. The proposed\ntechnique is demonstrated by a numerical experiment.\n"}
+{"abstract": "  The data paper is an emerging academic genre that focuses on the description\nof research data objects. However, there is a lack of empirical knowledge about\nthis rising genre in quantitative science studies, particularly from the\nperspective of its linguistic features. To fill this gap, this research aims to\noffer a first quantitative examination of which rhetorical moves-rhetorical\nunits performing a coherent narrative function-are used in data paper\nabstracts, as well as how these moves are used. To this end, we developed a new\nclassification scheme for rhetorical moves in data paper abstracts by expanding\na well-received system that focuses on English-language research article\nabstracts. We used this expanded scheme to classify and analyze rhetorical\nmoves used in two flagship data journals, Scientific Data and Data in Brief. We\nfound that data papers exhibit a combination of IMRaD- and data-oriented moves\nand that the usage differences between the journals can be largely explained by\njournal policies concerning abstract and paper structure. This research offers\na novel examination of how the data paper, a novel data-oriented knowledge\nrepresentation, is composed, which greatly contributes to a deeper\nunderstanding of data and data publication in the scholarly communication\nsystem.\n"}
+{"abstract": "  We discuss tensor categories motivated by CFT, their unitarizability and\napplications to various models including the affine VOAs. We discuss\nclassification of type A Verlinde fusion categories.\n  We propose an approach to Kazhdan-Lusztig-Finkelberg theorem. This theorem\ngives a ribbon equivalence between the fusion category associated to a quantum\ngroup at a certain root of unity and that associated to a corresponding affine\nvertex operator algebra at a suitable positive integer level. We develop ideas\nby Wenzl.\n  Our results rely on the notion of weak-quasi-Hopf algebra of\n  Drinfeld-Mack-Schomerus. We were also guided by Drinfeld original proof, by\n  Bakalov and Kirillov and Neshveyev and Tuset work for a generic parameter.\n  Wenzl described a fusion tensor product in quantum group fusion categories,\nand related it to the unitary structure. Given two irreducible objects, the\ninner product of the fusion tensor product is induced by the braiding of\nU_q(g), with q a suitable root of 1. Moreover, the paper suggests a suitable\nuntwisting procedure to make the unitary structure trivial. Then it also\ndescribes a continuous path that intuitively connects objects of the quantum\ngroup fusion category to representations of the simple Lie group defining the\naffine Lie algebra. We study this procedure.\n  One of our main results is the construction of a Hopf algebra in a weak sense\nassociated to quantum group fusion category and of a twist of it giving a wqh\nstructure on the Zhu algebra and a unitary modular fusion category structure on\nthe representation category of the affine Lie algebra, confirming an early view\nby Frenkel and Zhu.\n  We show that this modular fusion category structure is equivalent to that\nobtained via the tensor product theory of VOAs by Huang and Lepowsky. This\ngives a direct proof of FKL theorem.\n"}
+{"abstract": "  We present spatially resolved Hubble Space Telescope grism spectroscopy of 15\ngalaxies at $z\\sim0.8$ drawn from the DEEP2 survey. We analyze H$\\alpha$+[N\nII], [S II] and [S III] emission on kpc scales to explore which mechanisms are\npowering emission lines at high redshifts, testing which processes may be\nresponsible for the well-known offset of high redshift galaxies from the\n$z\\sim0$ locus in the [O III]/H$\\beta$ versus [N II]/H$\\alpha$ BPT\n(Baldwin-Phillips-Terlevich) excitation diagram. We study spatially resolved\nemission line maps to examine evidence for active galactic nuclei (AGN),\nshocks, diffuse ionized gas (DIG), or escaping ionizing radiation, all of which\nmay contribute to the BPT offsets observed in our sample. We do not find\nsignificant evidence of AGN in our sample and quantify that, on average, AGN\nwould need to contribute $\\sim$25% of the H$\\alpha$ flux in the central\nresolution element in order to cause the observed BPT offsets. We find weak\n($2\\sigma$) evidence of DIG emission at low surface brightnesses, yielding an\nimplied total DIG emission fraction of $\\sim$20%, which is not significant\nenough to be the dominant emission line driver in our sample. In general we\nfind that the observed emission is dominated by star forming H II regions. We\ndiscuss trends with demographic properties and the possible role of\n$\\alpha$-enhanced abundance patterns in the emission spectra of high redshift\ngalaxies. Our results indicate that photo-ionization modeling with stellar\npopulation synthesis inputs is a valid tool to explore the specific star\nformation properties which may cause BPT offsets, to be explored in future\nwork.\n"}
+{"abstract": "  We present properties and invariants of Hamiltonian circuits in rectangular\ngrids. It is proved that all circuits on a $2n \\times 2n$ chessboard have at\nleast $4n$ turns and at least $2n$ straights if $n$ is even and $2n+2$\nstraights if $n$ is odd. The minimum number of turns and straights are\npresented and proved for circuits on an $n \\times (n+1)$ chessboard. For the\ngeneral case of an $n \\times m$ chessboard similar results are stated but not\nall proofs are given.\n"}
+{"abstract": "  In this paper, we propose a new approach to train Generative Adversarial\nNetworks (GANs) where we deploy a double-oracle framework using the generator\nand discriminator oracles. GAN is essentially a two-player zero-sum game\nbetween the generator and the discriminator. Training GANs is challenging as a\npure Nash equilibrium may not exist and even finding the mixed Nash equilibrium\nis difficult as GANs have a large-scale strategy space. In DO-GAN, we extend\nthe double oracle framework to GANs. We first generalize the players'\nstrategies as the trained models of generator and discriminator from the best\nresponse oracles. We then compute the meta-strategies using a linear program.\nFor scalability of the framework where multiple generators and discriminator\nbest responses are stored in the memory, we propose two solutions: 1) pruning\nthe weakly-dominated players' strategies to keep the oracles from becoming\nintractable; 2) applying continual learning to retain the previous knowledge of\nthe networks. We apply our framework to established GAN architectures such as\nvanilla GAN, Deep Convolutional GAN, Spectral Normalization GAN and Stacked\nGAN. Finally, we conduct experiments on MNIST, CIFAR-10 and CelebA datasets and\nshow that DO-GAN variants have significant improvements in both subjective\nqualitative evaluation and quantitative metrics, compared with their respective\nGAN architectures.\n"}
+{"abstract": "  In-band full-duplex systems promise to further increase the throughput of\nwireless systems, by simultaneously transmitting and receiving on the same\nfrequency band. However, concurrent transmission generates a strong\nself-interference signal at the receiver, which requires the use of\ncancellation techniques. A wide range of techniques for analog and digital\nself-interference cancellation have already been presented in the literature.\nHowever, their evaluation focuses on cases where the underlying physical\nparameters of the full-duplex system do not vary significantly. In this paper,\nwe focus on adaptive digital cancellation, motivated by the fact that physical\nsystems change over time. We examine some of the different cancellation methods\nin terms of their performance and implementation complexity, considering the\ncost of both cancellation and training. We then present a comparative analysis\nof all these methods to determine which perform better under different system\nperformance requirements. We demonstrate that with a neural network approach,\nthe reduction in arithmetic complexity for the same cancellation performance\nrelative to a state-of-the-art polynomial model is several orders of magnitude.\n"}
+{"abstract": "  The subject of this paper is to study the decay of solutions for two systems\nof laminated Timoshenko beams with interfacial slip in the whole space R\nsubject to a thermal effect of type III acting only on one component. When the\nthermal effect is acting via the second or third component of the laminated\nTimoshenko beam (rotation angle displacement or dynamic of the slip), we prove\nthat both systems are polynomially stable and obtain stability estimates in the\nL2 -norm of solutions and their higher order derivatives with respect of the\nspace variable. The decay rates, as well as the absence and presence of the\nregularity-loss type property, depend on the regularity of the initial data and\nthe speeds of wave propagations. However, when the thermal effect is acting via\nthe first comoponent (transversal displacement), we introduce a new stability\nnumber \\c{hi} and prove that the stability of systems is equivalent to \\c{hi}\n6= 0. An application to a case of lower order coupling terms will be also\ngiven. To prove our results, we use the energy method in Fourier space combined\nwith well chosen weight functions to build appropriate Lyapunov functionals.\n"}
+{"abstract": "  Evolution of 3D graphics and graphical worlds has brought issues like content\noptimization, real-time processing, rendering, and shared storage limitation\nunder consideration. Generally, different simplification approaches are used to\nmake 3D meshes viable for rendering. However, many of these approaches ignore\nvertex attributes for instanced 3D meshes. In this paper, we implement and\nevaluate a simple and improved version to simplify instanced 3D textured\nmodels. The approach uses different vertex attributes in addition to geometry\nto simplify mesh instances. The resulting simplified models demonstrate\nefficient time-space requirements and better visual quality.\n"}
+{"abstract": "  Composite fermions (CFs) are the particles underlying the novel phenomena\nobserved in partially filled Landau levels. Both microscopic wave functions and\nsemi-classical dynamics suggest that a CF is a dipole consisting of an electron\nand a double $2h/e$ quantum vortex, and its motion is subject to a Berry\ncurvature that is uniformly distributed in the momentum space. Based on the\npicture, we study the electromagnetic response of composite fermions. We find\nthat the response in the long-wavelength limit has a form identical to that of\nthe Dirac CF theory. To obtain the result, we show that the Berry curvature\ncontributes a half-quantized Hall conductance which, notably, is independent of\nthe filling factor of a Landau level and not altered by the presence of\nimpurities. The latter is because CFs undergo no side-jumps when scattered by\nquenched impurities in a Landau-level with the particle-hole symmetry. The\nremainder of the response is from an effective system that has the same Fermi\nwavevector, effective density, Berry phase, and therefore long-wavelength\nresponse to electromagnetic fields as a Dirac CF system. By interpreting the\nhalf-quantized Hall conductance as a contribution from a redefined vacuum, we\ncan explicitly show the emergence of a Dirac CF effective description from the\ndipole picture. We further determine corrections due to electric quadrupoles\nand magnetic moments of CFs and show deviations from the Dirac CF theory when\nmoving away from the long wavelength limit.\n"}
+{"abstract": "  Temporal action localization is an important yet challenging task in video\nunderstanding. Typically, such a task aims at inferring both the action\ncategory and localization of the start and end frame for each action instance\nin a long, untrimmed video.While most current models achieve good results by\nusing pre-defined anchors and numerous actionness, such methods could be\nbothered with both large number of outputs and heavy tuning of locations and\nsizes corresponding to different anchors. Instead, anchor-free methods is\nlighter, getting rid of redundant hyper-parameters, but gains few attention. In\nthis paper, we propose the first purely anchor-free temporal localization\nmethod, which is both efficient and effective. Our model includes (i) an\nend-to-end trainable basic predictor, (ii) a saliency-based refinement module\nto gather more valuable boundary features for each proposal with a novel\nboundary pooling, and (iii) several consistency constraints to make sure our\nmodel can find the accurate boundary given arbitrary proposals. Extensive\nexperiments show that our method beats all anchor-based and actionness-guided\nmethods with a remarkable margin on THUMOS14, achieving state-of-the-art\nresults, and comparable ones on ActivityNet v1.3. Code is available at\nhttps://github.com/TencentYoutuResearch/ActionDetection-AFSD.\n"}
+{"abstract": "  We continue the study of rateless codes for transmission of information\nacross channels whose rate of erasure is unknown. In such a code, an infinite\nstream of encoding symbols can be generated from the message and sent across\nthe erasure channel, and the decoder can decode the message after it has\nsuccessfully collected a certain number of encoding symbols. A rateless erasure\ncode is real-time oblivious if rather than collecting encoding symbols as they\nare received, the receiver either immediately decodes or discards each symbol\nit receives.\n  Efficient real-time oblivious erasure correction uses a feedback channel in\norder to maximize the probability that a received encoding symbol is decoded\nrather than discarded. We construct codes which are real-time oblivious, but\nrequire fewer feedback messages and have faster decoding compared to previous\nwork. Specifically, for a message of length $k'$, we improve the expected\ncomplexity of the feedback channel from $O(\\sqrt{k'})$ to $O(1)$, and the\nexpected decoding complexity from $O(k'\\log(k'))$ to $O(k')$. Our method\ninvolves using an appropriate block erasure code to first encode the $k'$\nmessage symbols, and then using a truncated version of the real-time oblivious\nerasure correction of Beimel et al (2007) to transmit the encoded message to\nthe receiver, which then uses the decoding algorithm for the outer code to\nrecover the message.\n"}
+{"abstract": "  Multimodal Sentiment Analysis (MuSe) 2021 is a challenge focusing on the\ntasks of sentiment and emotion, as well as physiological-emotion and\nemotion-based stress recognition through more comprehensively integrating the\naudio-visual, language, and biological signal modalities. The purpose of MuSe\n2021 is to bring together communities from different disciplines; mainly, the\naudio-visual emotion recognition community (signal-based), the sentiment\nanalysis community (symbol-based), and the health informatics community. We\npresent four distinct sub-challenges: MuSe-Wilder and MuSe-Stress which focus\non continuous emotion (valence and arousal) prediction; MuSe-Sent, in which\nparticipants recognise five classes each for valence and arousal; and\nMuSe-Physio, in which the novel aspect of `physiological-emotion' is to be\npredicted. For this years' challenge, we utilise the MuSe-CaR dataset focusing\non user-generated reviews and introduce the Ulm-TSST dataset, which displays\npeople in stressful depositions. This paper also provides detail on the\nstate-of-the-art feature sets extracted from these datasets for utilisation by\nour baseline model, a Long Short-Term Memory-Recurrent Neural Network. For each\nsub-challenge, a competitive baseline for participants is set; namely, on test,\nwe report a Concordance Correlation Coefficient (CCC) of .4616 CCC for\nMuSe-Wilder; .4717 CCC for MuSe-Stress, and .4606 CCC for MuSe-Physio. For\nMuSe-Sent an F1 score of 32.82 % is obtained.\n"}
+{"abstract": "  GRB 190114C was a bright burst that occurred in the local Universe (z=0.425).\nIt was the first gamma-ray burst (GRB) ever detected at TeV energies, thanks to\nMAGIC. We characterize the ambient medium properties of the host galaxy through\nthe study of the absorbing X-ray column density. Joining Swift, XMM-Newton, and\nNuSTAR observations, we find that the GRB X-ray spectrum is characterized by a\nhigh column density that is well in excess of the expected Milky Way value and\ndecreases, by a factor of ~2, around ~$10^5$ s. Such a variability is not\ncommon in GRBs. The most straightforward interpretation of the variability in\nterms of photoionization of the ambient medium is not able to account for the\ndecrease at such late times, when the source flux is less intense. Instead, we\ninterpret the decrease as due to a clumped absorber, denser along the line of\nsight and surrounded by lower-density gas. After the detection at TeV energies\nof GRB 190114C, two other GRBs were promptly detected. They share a high value\nof the intrinsic column density and there are hints for a decrease of the\ncolumn density, too. We speculate that a high local column density might be a\ncommon ingredient for TeV-detected GRBs.\n"}
+{"abstract": "  Many commonsense reasoning NLP tasks involve choosing between one or more\npossible answers to a question or prompt based on knowledge that is often\nimplicit. Large pretrained language models (PLMs) can achieve near-human\nperformance on such tasks, while providing little human-interpretable evidence\nof the underlying reasoning they use. In this work, we show how to use these\nsame models to generate such evidence: inspired by the contrastive nature of\nhuman explanations, we use PLMs to complete explanation prompts which contrast\nalternatives according to the key attribute(s) required to justify the correct\nanswer (for example, peanuts are usually salty while raisins are sweet).\nConditioning model decisions on these explanations improves performance on two\ncommonsense reasoning benchmarks, as compared to previous non-contrastive\nalternatives. These explanations are also judged by humans to be more relevant\nfor solving the task, and facilitate a novel method to evaluate explanation\nfaithfulfness.\n"}
+{"abstract": "  Motivated by dark coronal lanes in SOHO / EIT 284 {\\AA} EUV observations we\nconstruct and optimize an atmosphere model of the AR 8535 sunspot by adding a\ncool and dense component in the volume of plasma along open field lines\ndetermined using the Potential Field Source Surface (PFSS) extrapolation. Our\nmodel qualitatively reproduces the observed reduced microwave brightness\ntemperature in the northern part of the sunspot in the VLA observations from 13\nMay 1999 and provides a physical explanation for the coronal dark lanes. We\npropose application of this method to other sunspots with such observed dark\nregions in EUV or soft X-rays and with concurrent microwave observations to\ndetermine the significance of open field regions. The connection between open\nfields and the resulting plasma temperature and density change is of relevance\nfor slow solar wind source investigations.\n"}
+{"abstract": "  Auxiliary information attracts more and more attention in the area of machine\nlearning. Attempts so far to include such auxiliary information in\nstate-of-the-art learning process have often been based on simply appending\nthese auxiliary features to the data level or feature level. In this paper, we\nintend to propose a novel training method with new options and architectures.\nSiamese labels, which were used in the training phase as auxiliary modules.\nWhile in the testing phase, the auxiliary module should be removed. Siamese\nlabel module makes it easier to train and improves the performance in testing\nprocess. In general, the main contributions can be summarized as, 1) Siamese\nLabels are firstly proposed as auxiliary information to improve the learning\nefficiency; 2) We establish a new architecture, Siamese Labels Auxiliary\nNetwork (SilaNet), which is to assist the training of the model; 3) Siamese\nLabels Auxiliary Network is applied to compress the model parameters by 50% and\nensure the high accuracy at the same time. For the purpose of comparison, we\ntested the network on CIFAR-10 and CIFAR100 using some common models. The\nproposed SilaNet performs excellent efficiency both on the accuracy and\nrobustness.\n"}
+{"abstract": "  In classical newsvendor model, piece-wise linear shortage and excess costs\nare balanced out to determine the optimal order quantity. However, for critical\nperishable commodities, severity of the costs may be much more than linear. In\nthis paper we discuss a generalisation of the newsvendor model with piece-wise\npolynomial cost functions to accommodate their severity. In addition, the\nstochastic demand has been assumed to follow a completely unknown probability\ndistribution. Subsequently, non-parametric estimator of the optimal order\nquantity has been developed from a random polynomial type estimating equation\nusing a random sample on demand. Strong consistency of the estimator has been\nproven when the true optimal order quantity is unique. The result has been\nextended to the case where multiple solutions for optimal order quantity are\navailable. Probability of existence of the estimated optimal order quantity has\nbeen studied through extensive simulation experiments. Simulation results\nindicate that the non-parametric method provides robust yet efficient estimator\nof the optimal order quantity in terms of mean square error.\n"}
+{"abstract": "  Backdoor attacks have been considered a severe security threat to deep\nlearning. Such attacks can make models perform abnormally on inputs with\npredefined triggers and still retain state-of-the-art performance on clean\ndata. While backdoor attacks have been thoroughly investigated in the image\ndomain from both attackers' and defenders' sides, an analysis in the frequency\ndomain has been missing thus far.\n  This paper first revisits existing backdoor triggers from a frequency\nperspective and performs a comprehensive analysis. Our results show that many\ncurrent backdoor attacks exhibit severe high-frequency artifacts, which persist\nacross different datasets and resolutions. We further demonstrate these\nhigh-frequency artifacts enable a simple way to detect existing backdoor\ntriggers at a detection rate of 98.50% without prior knowledge of the attack\ndetails and the target model. Acknowledging previous attacks' weaknesses, we\npropose a practical way to create smooth backdoor triggers without\nhigh-frequency artifacts and study their detectability. We show that existing\ndefense works can benefit by incorporating these smooth triggers into their\ndesign consideration. Moreover, we show that the detector tuned over stronger\nsmooth triggers can generalize well to unseen weak smooth triggers. In short,\nour work emphasizes the importance of considering frequency analysis when\ndesigning both backdoor attacks and defenses in deep learning.\n"}
+{"abstract": "  Machine learning models for the ad-hoc retrieval of documents and passages\nhave recently shown impressive improvements due to better language\nunderstanding using large pre-trained language models. However, these\nover-parameterized models are inherently non-interpretable and do not provide\nany information on the parts of the documents that were used to arrive at a\ncertain prediction.\n  In this paper we introduce the select and rank paradigm for document ranking,\nwhere interpretability is explicitly ensured when scoring longer documents.\nSpecifically, we first select sentences in a document based on the input query\nand then predict the query-document score based only on the selected sentences,\nacting as an explanation. We treat sentence selection as a latent variable\ntrained jointly with the ranker from the final output. We conduct extensive\nexperiments to demonstrate that our inherently interpretable select-and-rank\napproach is competitive in comparison to other state-of-the-art methods and\nsometimes even outperforms them. This is due to our novel end-to-end training\napproach based on weighted reservoir sampling that manages to train the\nselector despite the stochastic sentence selection. We also show that our\nsentence selection approach can be used to provide explanations for models that\noperate on only parts of the document, such as BERT.\n"}
+{"abstract": "  In this paper, we present a thorough study of maximizing a regularized\nnon-monotone submodular function subject to various constraints, i.e., $\\max \\{\ng(A) - \\ell(A) : A \\in \\mathcal{F} \\}$, where $g \\colon 2^\\Omega \\to\n\\mathbb{R}_+$ is a non-monotone submodular function, $\\ell \\colon 2^\\Omega \\to\n\\mathbb{R}_+$ is a normalized modular function and $\\mathcal{F}$ is the\nconstraint set. Though the objective function $f := g - \\ell$ is still\nsubmodular, the fact that $f$ could potentially take on negative values\nprevents the existing methods for submodular maximization from providing a\nconstant approximation ratio for the regularized submodular maximization\nproblem. To overcome the obstacle, we propose several algorithms which can\nprovide a relatively weak approximation guarantee for maximizing regularized\nnon-monotone submodular functions. More specifically, we propose a continuous\ngreedy algorithm for the relaxation of maximizing $g - \\ell$ subject to a\nmatroid constraint. Then, the pipage rounding procedure can produce an integral\nsolution $S$ such that $\\mathbb{E} [g(S) - \\ell(S)] \\geq e^{-1}g(OPT) -\n\\ell(OPT) - O(\\epsilon)$. Moreover, we present a much faster algorithm for\nmaximizing $g - \\ell$ subject to a cardinality constraint, which can output a\nsolution $S$ with $\\mathbb{E} [g(S) - \\ell(S)] \\geq (e^{-1} - \\epsilon) g(OPT)\n- \\ell(OPT)$ using $O(\\frac{n}{\\epsilon^2} \\ln \\frac 1\\epsilon)$ value oracle\nqueries. We also consider the unconstrained maximization problem and give an\nalgorithm which can return a solution $S$ with $\\mathbb{E} [g(S) - \\ell(S)]\n\\geq e^{-1} g(OPT) - \\ell(OPT)$ using $O(n)$ value oracle queries.\n"}
+{"abstract": "  In this paper, we present an uncertainty-aware INVASE to quantify predictive\nconfidence of healthcare problem. By introducing learnable Gaussian\ndistributions, we lever-age their variances to measure the degree of\nuncertainty. Based on the vanilla INVASE, two additional modules are proposed,\ni.e., an uncertainty quantification module in the predictor, and a reward\nshaping module in the selector. We conduct extensive experiments on UCI-WDBC\ndataset. Notably, our method eliminates almost all predictive bias with only\nabout 20% queries, while the uncertainty-agnostic counterpart requires nearly\n100% queries. The open-source implementation with a detailed tutorial is\navailable at\nhttps://github.com/jx-zhong-for-academic-purpose/Uncertainty-aware-INVASE/blob/main/tutorialinvase%2B.ipynb.\n"}
+{"abstract": "  The estimation of the intrinsic dimension of a dataset is a fundamental step\nin most dimensionality reduction techniques. This article illustrates\nintRinsic, an R package that implements novel state-of-the-art likelihood-based\nestimators of the intrinsic dimension of a dataset. In detail, the methods\nincluded in this package are the TWO-NN, Gride, and Hidalgo models. To allow\nthese novel estimators to be easily accessible, the package contains a few\nhigh-level, intuitive functions that rely on a broader set of efficient,\nlow-level routines. intRinsic encompasses models that fall into two categories:\nhomogeneous and heterogeneous intrinsic dimension estimators. The first\ncategory contains the TWO-NN and Gride models. The functions dedicated to these\ntwo methods carry out inference under both the frequentist and Bayesian\nframeworks. In the second category we find Hidalgo, a Bayesian mixture model,\nfor which an efficient Gibbs sampler is implemented. After discussing the\ntheoretical background, we demonstrate the performance of the models on\nsimulated datasets. This way, we can assess the results by comparing them with\nthe ground truth. Then, we employ the package to study the intrinsic dimension\nof the Alon dataset, obtained from a famous microarray experiment. We show how\nthe estimation of homogeneous and heterogeneous intrinsic dimensions allows us\nto gain valuable insights about the topological structure of a dataset.\n"}
+{"abstract": "  The rate and pathways of relaxation of a magnetic medium to its equilibrium\nfollowing excitation with intense and short laser pulses are the key\ningredients of ultrafast optical control of spins. Here we study experimentally\nthe evolution of the magnetization and magnetic anisotropy of thin films of a\nferromagnetic metal galfenol (Fe$_{0.81}$Ga$_{0.19}$) resulting from excitation\nwith a femtosecond laser pulse. From the temporal evolution of the hysteresis\nloops we deduce that the magnetization $M_S$ and magnetic anisotropy parameters\n$K$ recover within a nanosecond, and the ratio between $K$ and $M_S$ satisfies\nthe thermal equilibrium's power law in the whole time range spanning from a few\npicoseconds to 3 nanoseconds. We further use the experimentally obtained\nrelaxation times of $M_S$ and $K$ to analyze the laser-induced precession and\ndemonstrate how they contribute to its frequency evolution at the nanosecond\ntimescale.\n"}
+{"abstract": "  Magnons and phonons are two fundamental neutral excitations of magnetically\nordered materials which can significantly dominate the low-energy thermal\nproperties. In this work we study the interplay of magnons and phonons in\nhoneycomb and Kagome lattices. When the mirror reflection with respect to the\nmagnetic ordering direction is broken, the symmetry-allowed in-plane\nDzyaloshinskii-Moriya (DM) interaction will couple the magnons to the phonons\nand the magnon-polaron states are formed. Besides, both lattice structures also\nallow for an out-of-plane DM interaction rendering the uncoupled magnons to be\ntopological. Our aim is to study the interplay of such topological magnons with\nphonons. We show that the hybridization between magnons and phonons can\nsignificantly redistribute the Berry curvature among the bands. Especially, we\nfound that the topological magnon band becomes trivial while the hybridized\nstates at lower energy acquire Berry curvature strongly peaked near the avoided\ncrossings. As such the thermal Hall conductivity of topological magnons shows\nsignificant changes due to coupling to the phonons.\n"}
+{"abstract": "  Strapdown inertial navigation research involves the parameterization and\ncomputation of the attitude, velocity and position of a rigid body in a chosen\nreference frame. The community has long devoted to finding the most concise and\nefficient representation for the strapdown inertial navigation system (INS).\nThe current work is motivated by simplifying the existing dual quaternion\nrepresentation of the kinematic model. This paper proposes a compact and\nelegant representation of the body's attitude, velocity and position, with the\naid of a devised trident quaternion tool in which the position is accounted for\nby adding a second imaginary part to the dual quaternion. Eventually, the\nkinematics of strapdown INS are cohesively unified in one concise differential\nequation, which bears the same form as the classical attitude quaternion\nequation. In addition, the computation of this trident quaternion-based\nkinematic equation is implemented with the recently proposed functional\niterative integration approach. Numerical results verify the analysis and show\nthat incorporating the new representation into the functional iterative\nintegration scheme achieves high inertial navigation computation accuracy as\nwell.\n"}
+{"abstract": "  Comoving pairs, even at the separations of $\\mathcal{O}(10^6)\\,$AU, are a\npredicted reservoir of conatal stars. We present detailed chemical abundances\nof 62 stars in 31 comoving pairs with separations of $10^2 - 10^7\\,$AU and 3D\nvelocity differences $< 2 \\mathrm{\\ km \\ s^{-1}}$. This sample includes both\nbound comoving pairs/wide binaries and unbound comoving pairs. Observations\nwere taken using the MIKE spectrograph on the Magellan/Clay Telescope at high\nresolution ($\\mathrm{R} \\sim 45,000$) with a typical signal-to-noise ratio of\n150 per pixel. With these spectra, we measure surface abundances for 24\nelements, including Li, C, Na, Mg, Al, Si, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni,\nCu, Zn, Sr, Y, Zr, Ba, La, Nd, Eu. Taking iron as the representative element,\nour sample of wide binaries is chemically homogeneous at the level of $0.05$\ndex, which agrees with prior studies on wide binaries. Importantly, even\nsystems at separations $2\\times10^5-10^7\\,$AU are homogeneous to $0.09$ dex, as\nopposed to the random pairs which have a dispersion of $0.23\\,$dex. Assuming a\nmixture model of the wide binaries and random pairs, we find that $73 \\pm 22\\%$\nof the comoving pairs at separations $2\\times10^5-10^7\\,$AU are conatal. Our\nresults imply that a much larger parameter space of phase space may be used to\nfind conatal stars, to study M-dwarfs, star cluster evolution, exoplanets,\nchemical tagging, and beyond.\n"}
+{"abstract": "  Flux-integrated semi-exclusive differential and integral cross sections for\nquasi-elastic neutrino charged-current scattering on argon are analyzed. We\ncalculate these cross sections using the relativistic distorted-wave impulse\napproximation and compare with recent MicroBooNE data. We found that the\nmeasured cross sections can be described well within the experimental\nuncertainties with value of the nucleon axial mass $1 < M_A < 1.2$ GeV. The\ncontribution of the exclusive channel $\\boldmath{(\\nu_{\\mu}, \\mu p)}$ to the\nflux-integrated inclusive cross sections is about 50\\%.\n"}
+{"abstract": "  Chromospheric umbral oscillations produce periodic brightenings in the core\nof some spectral lines, known as umbral flashes. They are also accompanied by\nfluctuations in velocity, temperature, and, according to several recent works,\nmagnetic field. In this study, we aim to ascertain the accuracy of the magnetic\nfield determined from inversions of the Ca II 8542 \\AA\\ line. We have developed\nnumerical simulations of wave propagation in a sunspot umbra. Synthetic Stokes\nprofiles emerging from the simulated atmosphere were computed and then inverted\nusing the NICOLE code. The atmospheres inferred from the inversions have been\ncompared with the original parameters from the simulations. Our results show\nthat the inferred chromospheric fluctuations in velocity and temperature match\nthe known oscillations from the numerical simulation. In contrast, the vertical\nmagnetic field obtained from the inversions exhibits an oscillatory pattern\nwith a $\\sim$300 G peak-to-peak amplitude which is absent in the simulation. We\nhave assessed the error in the inferred parameters by performing numerous\ninversions with slightly different configurations of the same Stokes profiles.\nWe find that when the atmosphere is approximately at rest, the inversion tends\nto favor solutions that underestimate the vertical magnetic field strength. On\nthe contrary, during umbral flashes, the values inferred from most of the\ninversions are concentrated at stronger fields than those from the simulation.\nOur analysis provides a quantification of the errors associated with the\ninversions of the Ca II 8542 \\AA\\ line and suggests caution with the\ninterpretation of the inferred magnetic field fluctuations.\n"}
+{"abstract": "  Unrestricted mutation of shared state is a source of many well-known\nproblems. The predominant safe solutions are pure functional programming, which\nbans mutation outright, and flow sensitive type systems, which depend on\nsophisticated typing rules. Mutable value semantics is a third approach that\nbans sharing instead of mutation, thereby supporting part-wise in-place\nmutation and local reasoning, while maintaining a simple type system. In the\npurest form of mutable value semantics, references are second-class: they are\nonly created implicitly, at function boundaries, and cannot be stored in\nvariables or object fields. Hence, variables can never share mutable state.\n  Because references are often regarded as an indispensable tool to write\nefficient programs, it is legitimate to wonder whether such a discipline can\ncompete other approaches. As a basis for answering that question, we\ndemonstrate how a language featuring mutable value semantics can be compiled to\nefficient native code. This approach relies on stack allocation for static\ngarbage collection and leverages runtime knowledge to sidestep unnecessary\ncopies.\n"}
+{"abstract": "  We prove the existence of multiple noise-induced transitions in the\nLasota-Mackey map, which is a class of one dimensional random dynamical system\nwith additive noise. The result is achieved by the help of rigorous computer\nassisted estimates. We first approximate the stationary distribution of the\nrandom dynamical system and then compute certified error intervals for the\nLyapunov exponent. We find that the sign of the Lyapunov exponent changes at\nleast three times when increasing the noise amplitude. We also show numerical\nevidence that the standard non-rigorous numerical approximation by finite-time\nLyapunov exponent is valid with our model for a sufficiently large number of\niterations. Our method is expected to work for a broad class of nonlinear\nstochastic phenomena.\n"}
+{"abstract": "  The results of a long-term multiwavelength study of the powerful flat\nspectrum radio quasar 3C 454.3 using {\\it Fermi}-LAT and Swift XRT/UVOT data\nare reported. In the $\\gamma$-ray band, {\\it Fermi}-LAT observations show\nseveral major flares when the source flux was $>10^{-5}\\:{\\rm\nphoton\\:cm^{-2}\\:s^{-1}}$; the peak $\\gamma$-ray flux above $141.6$ MeV,\n$(9.22\\pm1.96)\\times10^{-5}\\:{\\rm photon\\:cm^{-2}\\:s^{-1}}$ observed on MJD\n55519.33, corresponds to $2.15\\times10^{50}\\:{\\rm erg\\:s^{-1}}$ isotropic\n$\\gamma$-ray luminosity. The analysis of Swift XRT and UVOT data revealed a\nflux increase, although with smaller amplitudes, also in the X-ray and\noptical/UV bands. The X-ray emission of 3C 454.3 is with a hard spectral index\nof $\\Gamma_{\\rm X}=1.16-1.75$, and the flux in the flaring states increased up\nto $(1.80\\pm0.18)\\times10^{-10}{\\rm erg\\:cm^{-2}\\: s^{-1}}$. Through combining\nthe analyzed data, it was possible to assemble 362 high-quality and\nquasi-simultaneous spectral energy distributions of 3C 454.3 in 2008-2018 which\nall were modeled within a one-zone leptonic scenario assuming the emission\nregion is within the broad line region, involving synchrotron, synchrotron\nself-Compton and external Compton mechanisms. Such an extensive modeling is the\nkey for constraining the underlying emission mechanisms in the 3C 454.3 jet and\nallows to derive the physical parameters of the jet and investigate their\nevolution in time. The modeling suggests that during the flares, along with the\nvariation of emitting electron parameters, the Doppler boosting factor\nincreased substantially implying that the emission in these periods has most\nlikely originated in a faster moving region.\n"}
+{"abstract": "  This article argues that low latency, high bandwidth, device proliferation,\nsustainable digital infrastructure, and data privacy and sovereignty continue\nto motivate the need for edge computing research even though its initial\nconcepts were formulated more than a decade ago.\n"}
+{"abstract": "  With the promulgation of data protection laws (e.g., GDPR in 2018), privacy\npreservation has become a general agreement in applications where cross-domain\nsensitive data are utilized. Out of many privacy-preserving techniques,\nfederated learning (FL) has received much attention as a bridge for secure data\nconnection and cooperation. Although FL's research works have surged, some\nclassical data modeling methods are not well accommodated in FL. In this paper,\nwe propose the first masking-based federated singular vector decomposition\nmethod, called FedSVD. FedSVD protects the raw data through a singular value\ninvariance mask, which can be further removed from the SVD results. Compared\nwith prior privacy-preserving SVD solutions, FedSVD has lossless results, high\nconfidentiality, and excellent scalability. We provide privacy proof showing\nthat FedSVD has guaranteed data confidentiality. Empirical experiments on\nreal-life datasets and synthetic data have verified the effectiveness of our\nmethod. The reconstruction error of FedSVD is around 0.000001% of the raw data,\nvalidating the lossless property of FedSVD. The scalability of FedSVD is nearly\nthe same as the standalone SVD algorithm. Hence, FedSVD can bring privacy\nprotection almost without sacrificing any computation time or communication\noverhead.\n"}
+{"abstract": "  Unmanned Aerial Vehicles (UAVs), as a recently emerging technology, enabled a\nnew breed of unprecedented applications in different domains. This technology's\nongoing trend is departing from large remotely-controlled drones to networks of\nsmall autonomous drones to collectively complete intricate tasks time and\ncost-effectively. An important challenge is developing efficient sensing,\ncommunication, and control algorithms that can accommodate the requirements of\nhighly dynamic UAV networks with heterogeneous mobility levels. Recently, the\nuse of Artificial Intelligence (AI) in learning-based networking has gained\nmomentum to harness the learning power of cognizant nodes to make more\nintelligent networking decisions by integrating computational intelligence into\nUAV networks. An important example of this trend is developing learning-powered\nrouting protocols, where machine learning methods are used to model and predict\ntopology evolution, channel status, traffic mobility, and environmental factors\nfor enhanced routing.\n  This paper reviews AI-enabled routing protocols designed primarily for aerial\nnetworks, including topology-predictive and self-adaptive learning-based\nrouting algorithms, with an emphasis on accommodating highly-dynamic network\ntopology. To this end, we justify the importance and adaptation of AI into UAV\nnetwork communications. We also address, with an AI emphasis, the closely\nrelated topics of mobility and networking models for UAV networks, simulation\ntools and public datasets, and relations to UAV swarming, which serve to choose\nthe right algorithm for each scenario. We conclude by presenting future trends,\nand the remaining challenges in AI-based UAV networking, for different aspects\nof routing, connectivity, topology control, security and privacy, energy\nefficiency, and spectrum sharing.\n"}
+{"abstract": "  Superconducting radio-frequency (SRF) niobium cavities are the modern means\nof particle acceleration and an enabling technology for record coherence\nsuperconducting quantum systems and ultra-sensitive searches for new physics.\nHere, we report a systematic effect in Nb cavities indicative of improved\nsuperconducting properties - an anomalous decrease (dip) in the resonant\nfrequency at temperatures just below the critical temperature $T_\\mathrm{c}$.\nThe frequency dip magnitude correlates with cavity quality factor, near-surface\nimpurity distribution, and $T_\\mathrm{c}$. It is also a precursor of the\npeculiar decrease in the BCS surface impedance with increasing RF current. A\nfirst demonstration of the coherence peak in the AC conductivity in Nb SRF\ncavities is also presented and found to correlate with a large frequency dip.\n"}
+{"abstract": "  Recent discovery of spin-orbit torques (SOTs) within magnetic single-layers\nhas attracted attention in the field of spintronics. However, it has remained\nelusive as to how to understand and how to tune the SOTs. Here, utilizing the\nsingle layers of chemically disordered Fe$_x$Pt$_{1-x}$, we unveil the\nmechanism of the \"unexpected\" bulk SOTs by studying their dependence on the\nintroduction of a controlled vertical composition gradient and on temperature.\nWe find that the bulk damping like SOT arises from an imbalanced internal spin\ncurrent that is transversely polarized and independent of the magnetization\norientation. The torque can be strong only in the presence of a vertical\ncomposition gradient and the SOT efficiency per electric field is insensitive\nto temperature but changes sign upon reversal of the orientation of the\ncomposition gradient, which are in analogue to behaviors of the strain. From\nthese characteristics we conclude that the imbalanced internal spin current\noriginates from a bulk spin Hall effect and that the associated inversion\nasymmetry that allows for a non-zero net torque is most likely a strain\nnon-uniformity induced by the composition gradient. The fieldlike SOT is a\nrelatively small bulk effect compared to the dampinglike SOT. This work points\nto the possibility of developing low-power single-layer SOT devices by strain\nengineering.\n"}
+{"abstract": "  In this paper we consider distributed adaptive stabilization for uncertain\nmultivariable linear systems with a time-varying diagonal matrix gain. We show\nthat uncertain multivariable linear systems are stabilizable by diagonal matrix\nhigh gains if the system matrix is an H-matrix with positive diagonal entries.\nBased on matrix measure and stability theory for diagonally dominant systems,\nwe consider two classes of uncertain linear systems, and derive a threshold\ncondition to ensure their exponential stability by a monotonically increasing\ndiagonal gain matrix. When each individual gain function in the matrix gain is\nupdated by state-dependent functions using only local state information, the\nboundedness and convergence of both system states and adaptive matrix gains are\nguaranteed. We apply the adaptive distributed stabilization approach to\nadaptive synchronization control for large-scale complex networks consisting of\nnonlinear node dynamics and time-varying coupling weights. A unified framework\nfor adaptive synchronization is proposed that includes several general design\napproaches for adaptive coupling weights to guarantee network synchronization.\n"}
+{"abstract": "  Buildings rarely perform as designed/simulated and and there are numerous\ntangible benefits if this gap is reconciled. A new scientific yet pragmatic\nmethodology - called Enhanced Parameter Estimation (EPE) - is proposed that\nallows physically relevant parameter estimation rather than a blind force-fit\nto energy use data. It calibrates a rapidly and inexpensively created\nsimulation model of the building in two stages: (a) building shell calibration\nwith the HVAC system is replaced by an by an ideal system that meets the loads.\nEPE identifies a small number of high-level heat flows in the energy balance,\ncalculates them with specifically tailored individual driving functions,\nintroduces physically significant parameters to best accomplish energy balance,\nand, estimates the parameters and their uncertainty bounds. Calibration is thus\ndone with corrective heat flows without any arbitrary tuning of input\nparameters, (b) HVAC system calibration with the building shell replaced by a\nbox with only process loads; as many parameters as the data allows are\nestimated. Calibration accuracy is enhanced by machine learning of the residual\nerrors. The EPE methodology is demonstrated through: a synthetic building and\none an actual 75,000 Sq.Ft. building in Pennsylvania. A subsequent paper will\nprovide further details and applications.\n"}
+{"abstract": "  In this article, we improve our main results from \\emph{Chow groups and\n$L$-derivatives of automorphic motives for unitary groups} in two direction:\nFirst, we allow ramified places in the CM extension $E/F$ at which we consider\nrepresentations that are spherical with respect to a certain special maximal\ncompact subgroup, by formulating and proving an analogue of the Kudla--Rapoport\nconjecture for exotic smooth Rapoport--Zink spaces. Second, we lift the\nrestriction on the components at split places of the automorphic\nrepresentation, by proving a more general vanishing result on certain\ncohomology of integral models of unitary Shimura varieties with Drinfeld level\nstructures.\n"}
+{"abstract": "  Cache side-channel attacks lead to severe security threats to the settings\nthat a CPU is shared across users, e.g., in the cloud. The existing attacks\nrely on sensing the micro-architectural state changes made by victims, and this\nassumption can be invalidated by combining spatial (\\eg, Intel CAT) and\ntemporal isolation (\\eg, time protection). In this work, we advance the state\nof cache side-channel attacks by showing stateless cache side-channel attacks\nthat cannot be defeated by both spatial and temporal isolation.\n  This side-channel exploits the timing difference resulted from interconnect\ncongestion. Specifically, to complete cache transactions, for Intel CPUs, cache\nlines would travel across cores via the CPU mesh interconnect. Nonetheless, the\nmesh links are shared by all cores, and cache isolation does not segregate the\ntraffic. An attacker can generate interconnect traffic to contend with the\nvictim's on a mesh link, hoping that extra delay will be measured. With the\nvariant delays, the attacker can deduce the memory access pattern of a victim\nprogram, and infer its sensitive data. Based on this idea, we implement Volcano\nand test it against the existing RSA implementations of JDK. We found the RSA\nprivate key used by a victim process can be partially recovered. In the end, we\npropose a few directions for defense and call for the attention of the security\ncommunity.\n"}
+{"abstract": "  Transport is called nonreciprocal when not only the sign, but also the\nabsolute value of the current, depends on the polarity of the applied voltage.\nIt requires simultaneously broken inversion and time-reversal symmetries, e.g.,\nby the interplay of spin-orbit coupling and magnetic field. So far, observation\nof nonreciprocity was always tied to resistivity, and dissipationless\nnonreciprocal circuit elements were elusive. Here, we engineer fully\nsuperconducting nonreciprocal devices based on highly-transparent Josephson\njunctions fabricated on InAs quantum wells. We demonstrate supercurrent\nrectification far below the transition temperature. By measuring Josephson\ninductance, we can link nonreciprocal supercurrent to the asymmetry of the\ncurrent-phase relation, and directly derive the supercurrent magnetochiral\nanisotropy coefficient for the first time. A semi-quantitative model well\nexplains the main features of our experimental data. Nonreciprocal Josephson\njunctions have the potential to become for superconducting circuits what\n$pn$-junctions are for traditional electronics, opening the way to novel\nnondissipative circuit elements.\n"}
+{"abstract": "  We propose a framework for reinforcement learning (RL) in fine time\ndiscretization and a learning algorithm in this framework. One of the main\ngoals of RL is to provide a way for physical machines to learn optimal behavior\ninstead of being programmed. However, the machines are usually controlled in\nfine time discretization. The most common RL methods apply independent random\nelements to each action, which is not suitable in that setting. It is not\nfeasible because it causes the controlled system to jerk, and does not ensure\nsufficient exploration since a single action is not long enough to create a\nsignificant experience that could be translated into policy improvement. In the\nRL framework introduced in this paper, policies are considered that produce\nactions based on states and random elements autocorrelated in subsequent time\ninstants. The RL algorithm introduced here approximately optimizes such a\npolicy. The efficiency of this algorithm is verified against three other RL\nmethods (PPO, SAC, ACER) in four simulated learning control problems (Ant,\nHalfCheetah, Hopper, and Walker2D) in diverse time discretization. The\nalgorithm introduced here outperforms the competitors in most cases considered.\n"}
+{"abstract": "  Kiukas, Lahti and Ylinen asked the following general question. When is a\npositive operator measure projection valued? A version of this question\nformulated in terms of operator moments was posed in a recent paper of the\npresent authors. Let $T$ be a selfadjoint operator and $F$ be a Borel\nsemispectral measure on the real line with compact support. For which positive\nintegers $p< q$ do the equalities $T^k =\\int_{\\mathbb{R}} x^k F(dx)$, $k=p, q$,\nimply that $F$ is a spectral measure? In the present paper, we completely solve\nthe second problem. The answer is affirmative if $p$ is odd and $q$ is even,\nand negative otherwise. The case $(p,q)=(1,2)$ closely related to intrinsic\nnoise operator was solved by several authors including Kruszy\\'{n}ski and de\nMuynck as well as Kiukas, Lahti and Ylinen. The counterpart of the second\nproblem concerning the multiplicativity of unital positive linear maps on\n$C^*$-algebras is also solved.\n"}
+{"abstract": "  Thompson sampling is a popular algorithm for solving multi-armed bandit\nproblems, and has been applied in a wide range of applications, from website\ndesign to portfolio optimization. In such applications, however, the number of\nchoices (or arms) $N$ can be large, and the data needed to make adaptive\ndecisions require expensive experimentation. One is then faced with the\nconstraint of experimenting on only a small subset of $K \\ll N$ arms within\neach time period, which poses a problem for traditional Thompson sampling. We\npropose a new Thompson Sampling under Experimental Constraints (TSEC) method,\nwhich addresses this so-called \"arm budget constraint\". TSEC makes use of a\nBayesian interaction model with effect hierarchy priors, to model correlations\nbetween rewards on different arms. This fitted model is then integrated within\nThompson sampling, to jointly identify a good subset of arms for\nexperimentation and to allocate resources over these arms. We demonstrate the\neffectiveness of TSEC in two problems with arm budget constraints. The first is\na simulated website optimization study, where TSEC shows noticeable\nimprovements over industry benchmarks. The second is a portfolio optimization\napplication on industry-based exchange-traded funds, where TSEC provides more\nconsistent and greater wealth accumulation over standard investment strategies.\n"}
+{"abstract": "  We report on a theoretical model for image formation in full-field optical\ncoherence tomography (FFOCT). Because the spatial incoherence of the\nillumination acts as a virtual confocal pinhole in FFOCT, its imaging\nperformance is equivalent to a scanning time-gated coherent confocal\nmicroscope. In agreement with optical experiments enabling a precise control of\naberrations, FFOCT is shown to have nearly twice the resolution of standard\nimaging at moderate aberration level. Beyond a rigorous study on the\nsensitivity of FFOCT with respect to aberrations, this theoretical model paves\nthe way towards an optimized design of adaptive optics and \\rev{computational\ntools} for high-resolution and deep imaging of biological tissues.\n"}
+{"abstract": "  It is a long-lasting task to understand heat conduction phenomena beyond\nFourier. Besides the low-temperature experiments on extremely pure crystals, it\nhas turned out recently that heterogeneous materials with macro-scale size can\nalso show thermal effects that cannot be modelled by the Fourier equation. This\nis called over-diffusive propagation, different from low-temperature\nobservations, and is found in numerous samples made from metal foam, rocks, and\ncomposites. The measured temperature history is indeed similar to what\nFourier's law predicts but the usual evaluation cannot provide reliable thermal\nparameters. This paper is a report on our experiments on several rock types,\neach type having multiple samples with different thicknesses. We show that\nsize-dependent thermal behaviour can occur for both Fourier and non-Fourier\nsituations. Moreover, based on the present experimental data, we find an\nempirical relation between the Fourier and non-Fourier parameters, which may be\nhelpful in later experiments to develop a more robust and reliable evaluation\nprocedure.\n"}
+{"abstract": "  We consider a two-player zero-sum deterministic differential game where each\nplayer uses both continuous and impulse controls in infinite-time horizon. We\nassume that the impulses supposed to be of general term and the costs depend on\nthe state of the system. We use the dynamic programming principle and viscosity\nsolutions approach to show existence and uniqueness of a solution for the\nHamilton-Jacobi-Bellman-Isaacs (HJBI) partial differential equations (PDEs) of\nthe game. We prove under Isaacs condition that the upper and lower value\nfunctions coincide.\n"}
+{"abstract": "  This paper addresses the problem of combining Byzantine resilience with\nprivacy in machine learning (ML). Specifically, we study if a distributed\nimplementation of the renowned Stochastic Gradient Descent (SGD) learning\nalgorithm is feasible with both differential privacy (DP) and\n$(\\alpha,f)$-Byzantine resilience. To the best of our knowledge, this is the\nfirst work to tackle this problem from a theoretical point of view. A key\nfinding of our analyses is that the classical approaches to these two\n(seemingly) orthogonal issues are incompatible. More precisely, we show that a\ndirect composition of these techniques makes the guarantees of the resulting\nSGD algorithm depend unfavourably upon the number of parameters of the ML\nmodel, making the training of large models practically infeasible. We validate\nour theoretical results through numerical experiments on publicly-available\ndatasets; showing that it is impractical to ensure DP and Byzantine resilience\nsimultaneously.\n"}
+{"abstract": "  Image captioning has conventionally relied on reference-based automatic\nevaluations, where machine captions are compared against captions written by\nhumans. This is in contrast to the reference-free manner in which humans assess\ncaption quality.\n  In this paper, we report the surprising empirical finding that CLIP (Radford\net al., 2021), a cross-modal model pretrained on 400M image+caption pairs from\nthe web, can be used for robust automatic evaluation of image captioning\nwithout the need for references. Experiments spanning several corpora\ndemonstrate that our new reference-free metric, CLIPScore, achieves the highest\ncorrelation with human judgements, outperforming existing reference-based\nmetrics like CIDEr and SPICE. Information gain experiments demonstrate that\nCLIPScore, with its tight focus on image-text compatibility, is complementary\nto existing reference-based metrics that emphasize text-text similarities.\nThus, we also present a reference-augmented version, RefCLIPScore, which\nachieves even higher correlation. Beyond literal description tasks, several\ncase studies reveal domains where CLIPScore performs well (clip-art images,\nalt-text rating), but also where it is relatively weaker in comparison to\nreference-based metrics, e.g., news captions that require richer contextual\nknowledge.\n"}
+{"abstract": "  We establish a sharp bilinear estimate for the Klein--Gordon propagator in\nthe spirit of recent work of Beltran--Vega. Our approach is inspired by work in\nthe setting of the wave equation due to Bez, Jeavons and Ozawa. As a\nconsequence of our main bilinear estimate, we deduce several sharp estimates of\nnull-form type and recover some sharp Strichartz estimates found by Quilodr\\'an\nand Jeavons.\n"}
+{"abstract": "  Modelling the dynamics of urban venues is a challenging task as it is\nmultifaceted in nature. Demand is a function of many complex and nonlinear\nfeatures such as neighborhood composition, real-time events, and seasonality.\nRecent advances in Graph Convolutional Networks (GCNs) have had promising\nresults as they build a graphical representation of a system and harness the\npotential of deep learning architectures. However, there has been limited work\nusing GCNs in a temporal setting to model dynamic dependencies of the network.\nFurther, within the context of urban environments, there has been no prior work\nusing dynamic GCNs to support venue demand analysis and prediction. In this\npaper, we propose a novel deep learning framework which aims to better model\nthe popularity and growth of urban venues. Using a longitudinal dataset from\nlocation technology platform Foursquare, we model individual venues and venue\ntypes across London and Paris. First, representing cities as connected networks\nof venues, we quantify their structure and note a strong community structure in\nthese retail networks, an observation that highlights the interplay of\ncooperative and competitive forces that emerge in local ecosystems of retail\nbusinesses. Next, we present our deep learning architecture which integrates\nboth spatial and topological features into a temporal model which predicts the\ndemand of a venue at the subsequent time-step. Our experiments demonstrate that\nour model can learn spatio-temporal trends of venue demand and consistently\noutperform baseline models. Relative to state-of-the-art deep learning models,\nour model reduces the RSME by ~ 28% in London and ~ 13% in Paris. Our approach\nhighlights the power of complex network measures and GCNs in building\nprediction models for urban environments. The model could have numerous\napplications within the retail sector to better model venue demand and growth.\n"}
+{"abstract": "  Non-uniform message quantization techniques such as\nreconstruction-computation-quantization (RCQ) improve error-correction\nperformance and decrease hardware complexity of low-density parity-check (LDPC)\ndecoders that use a flooding schedule. Layered MinSum RCQ (L-msRCQ) enables\nmessage quantization to be utilized for layered decoders and irregular LDPC\ncodes. We investigate field-programmable gate array (FPGA) implementations of\nL-msRCQ decoders. Three design methods for message quantization are presented,\nwhich we name the Lookup, Broadcast, and Dribble methods. The decoding\nperformance and hardware complexity of these schemes are compared to a layered\noffset MinSum (OMS) decoder. Simulation results on a (16384, 8192)\nprotograph-based raptor-like (PBRL) LDPC code show that a 4-bit L-msRCQ decoder\nusing the Broadcast method can achieve a 0.03 dB improvement in\nerror-correction performance while using 12% fewer registers than the OMS\ndecoder. A Broadcast-based 3-bit L-msRCQ decoder uses 15% fewer lookup tables,\n18% fewer registers, and 13% fewer routed nets than the OMS decoder, but\nresults in a 0.09 dB loss in performance.\n"}
+{"abstract": "  We examine a description of available cross section data for symmetric space\nstar (SST) configurations in the neutron-deuteron (nd) and proton-deuteron (pd)\nbreakup reaction using numerically exact solutions of the three-nucleon (3N)\nFaddeev equation based on two- and three-nucleon (semi)phenomenological and\nchiral forces. The predicted SST cross sections are very stable with respect to\nthe underlying dynamics for incoming nucleon laboratory energies below $\\approx\n25$ MeV. We discuss possible origins of the surprising discrepancies between\ntheory and data found in low-energy nd and pd SST breakup measurements.\n"}
+{"abstract": "  SrTiO$_3$ exhibits superconductivity for carrier densities $10^{19}-10^{21}$\ncm$^{-3}$. Across this range, the Fermi level traverses a number of vibrational\nmodes in the system, making it ideal for studying dilute superconductivity. We\nuse high-resolution planar-tunneling spectroscopy to probe chemically-doped\nSrTiO$_3$ across the superconducting dome. The over-doped superconducting\nboundary aligns, with surprising precision, to the Fermi energy crossing the\nDebye energy. Superconductivity emerges with decreasing density, maintaining\nthroughout the Bardeen-Cooper-Schrieffer (BCS) gap to transition-temperature\nratio, despite being in the anti-adiabatic regime. At lowest superconducting\ndensities, the lone remaining adiabatic phonon van Hove singularity is the soft\ntransverse-optic mode, associated with the ferroelectric instability. We\nsuggest a scenario for pairing mediated by this mode in the presence of\nspin-orbit coupling, which naturally accounts for the superconducting dome and\nBCS ratio.\n"}
+{"abstract": "  In today's context, deploying data-driven services like recommendation on\nedge devices instead of cloud servers becomes increasingly attractive due to\nprivacy and network latency concerns. A common practice in building compact\non-device recommender systems is to compress their embeddings which are\nnormally the cause of excessive parameterization. However, despite the vast\nvariety of devices and their associated memory constraints, existing\nmemory-efficient recommender systems are only specialized for a fixed memory\nbudget in every design and training life cycle, where a new model has to be\nretrained to obtain the optimal performance while adapting to a smaller/larger\nmemory budget. In this paper, we present a novel lightweight recommendation\nparadigm that allows a well-trained recommender to be customized for arbitrary\ndevice-specific memory constraints without retraining. The core idea is to\ncompose elastic embeddings for each item, where an elastic embedding is the\nconcatenation of a set of embedding blocks that are carefully chosen by an\nautomated search function. Correspondingly, we propose an innovative approach,\nnamely recommendation with universally learned elastic embeddings (RULE). To\nensure the expressiveness of all candidate embedding blocks, RULE enforces a\ndiversity-driven regularization when learning different embedding blocks. Then,\na performance estimator-based evolutionary search function is designed,\nallowing for efficient specialization of elastic embeddings under any memory\nconstraint for on-device recommendation. Extensive experiments on real-world\ndatasets reveal the superior performance of RULE under tight memory budgets.\n"}
+{"abstract": "  Emerging interests have been brought to recognize previously unseen objects\ngiven very few training examples, known as few-shot object detection (FSOD).\nRecent researches demonstrate that good feature embedding is the key to reach\nfavorable few-shot learning performance. We observe object proposals with\ndifferent Intersection-of-Union (IoU) scores are analogous to the intra-image\naugmentation used in contrastive approaches. And we exploit this analogy and\nincorporate supervised contrastive learning to achieve more robust objects\nrepresentations in FSOD. We present Few-Shot object detection via Contrastive\nproposals Encoding (FSCE), a simple yet effective approach to learning\ncontrastive-aware object proposal encodings that facilitate the classification\nof detected objects. We notice the degradation of average precision (AP) for\nrare objects mainly comes from misclassifying novel instances as confusable\nclasses. And we ease the misclassification issues by promoting instance level\nintra-class compactness and inter-class variance via our contrastive proposal\nencoding loss (CPE loss). Our design outperforms current state-of-the-art works\nin any shot and all data splits, with up to +8.8% on standard benchmark PASCAL\nVOC and +2.7% on challenging COCO benchmark. Code is available at: https:\n//github.com/MegviiDetection/FSCE\n"}
+{"abstract": "  We propose a simple approach to implement a tunable, high power and narrow\nlinewidth laser source based on a series of highly coherent tones from an\nelectro-optic frequency comb and a set of 3 DFB slave lasers. We experimentally\ndemonstrate approximately 1.25 THz (10 nm) of tuning within the C-Band centered\nat 192.9 THz (1555 nm). The output power is approximately 100 mW (20 dBm), with\na side band suppression ratio greater than 55 dB, and a linewidth below 400 Hz\nacross the full range of tunability. This approach is scalable and may be\nextended to cover a significantly broader optical spectral range.\n"}
+{"abstract": "  Time-reparametrization invariance in general relativistic space-time does not\nallow us to single out a time in quantum mechanics in a mechanical way of\nmeasurement. Motivated by this problem, we examine this gauge invariance in the\nground state of the quasi-stationary coarse-grained state of a long-range\ninteracting closed system of identical or identified, macroscopic, and\nspatiotemporally inhomogeneous Bose-Einstein condensates in the thermodynamic\nand Newtonian limits. As a result, we find that it is a theoretical\ncounterexample of this gauge invariance, except for proper-time translational\ninvariance, at a coarse-grained level.\n"}
+{"abstract": "  The H^2-regularity of variational solutions to a two-dimensional transmission\nproblem with geometric constraint is investigated, in particular when part of\nthe interface becomes part of the outer boundary of the domain due to the\nsaturation of the geometric constraint. In such a situation, the domain\nincludes some non-Lipschitz subdomains with cusp points, but it is shown that\nthis feature does not lead to a regularity breakdown. Moreover, continuous\ndependence of the solutions with respect to the domain is established.\n"}
+{"abstract": "  We study particle production and unitarity violation caused by a curved\ntarget space right after inflation. We use the inflaton field value instead of\ncosmic time as the time variable, and derive a semiclassical formula for the\nspectrum of produced particles. We then derive a simple condition for unitarity\nviolation during preheating, which we confirm by our semiclassical method and\nnumerical solution. This condition depends not only on the target space\ncurvature but also on the height of the inflaton potential at the end of\ninflation. This condition tells us, for instance, that unitarity is violated in\nrunning kinetic inflation and Higgs inflation, while unitarity is conserved in\n$\\alpha$-attractor inflation and Higgs-Palatini inflation.\n"}
+{"abstract": "  A minimal absent word of a sequence x, is a sequence yt hat is not a factorof\nx, but all of its proper factors are factors of x as well. The set of minimal\nabsent words uniquely defines the sequence itself. In recent times minimal\nabsent words have been used in order to compare sequences. In fact, to do this,\none can compare the sets of their minimal absent words. Chairungasee and\nCrochemorein [2] define a distance between pairs of sequences x and y, where\nthe symmetric difference of the sets of minimal absent words of x and y is\ninvolved. Here, weconsider a different distance, introduced in [1], based on a\nspecific subset of such symmetric difference that, in our opinion, better\ncapture the different features ofthe considered sequences. We show the result\nof some experiments where the distance is tested on a dataset of genetic\nsequences by 11 living species, in order to compare the new distance with the\nones existing in literature.\n"}
+{"abstract": "  The Semi-Digital Hadronic Calorimeter (SDHCAL) is proposed to equip the\nfuture ILC detector. A technological prototype of the SDHCAL developed within\nthe CALICE collaboration has been extensively tested in test beams. We review\nhere the prototype performances in terms of hadronic shower reconstruction from\nthe most recent analyses test beam data.\n"}
+{"abstract": "  We propose a worldsheet formula for tree-level correlation functions\ndescribing a scalar field with arbitrary mass and quartic self-interaction in\nde Sitter space, which is a simple model for inflationary cosmology. The\ncorrelation functions are located on the future boundary of the spacetime and\nare Fourier-transformed to momentum space. Our formula is supported on\nmass-deformed scattering equations involving conformal generators in momentum\nspace and reduces to the CHY formula for $\\phi^4$ amplitudes in the flat space\nlimit. Using the global residue theorem, we verify that it reproduces the\nWitten diagram expansion at four and six points, and sketch the extension to\n$n$ points.\n"}
+{"abstract": "  Generative adversarial networks (GANs) are one of the greatest advances in AI\nin recent years. With their ability to directly learn the probability\ndistribution of data, and then sample synthetic realistic data. Many\napplications have emerged, using GANs to solve classical problems in machine\nlearning, such as data augmentation, class unbalance problems, and fair\nrepresentation learning. In this paper, we analyze and highlight fairness\nconcerns of GANs model. In this regard, we show empirically that GANs models\nmay inherently prefer certain groups during the training process and therefore\nthey're not able to homogeneously generate data from different groups during\nthe testing phase. Furthermore, we propose solutions to solve this issue by\nconditioning the GAN model towards samples' group or using ensemble method\n(boosting) to allow the GAN model to leverage distributed structure of data\nduring the training phase and generate groups at equal rate during the testing\nphase.\n"}
+{"abstract": "  The classification of time-series data is pivotal for streaming data and\ncomes with many challenges. Although the amount of publicly available datasets\nincreases rapidly, deep neural models are only exploited in a few areas.\nTraditional methods are still used very often compared to deep neural models.\nThese methods get preferred in safety-critical, financial, or medical fields\nbecause of their interpretable results. However, their performance and\nscale-ability are limited, and finding suitable explanations for time-series\nclassification tasks is challenging due to the concepts hidden in the numerical\ntime-series data. Visualizing complete time-series results in a cognitive\noverload concerning our perception and leads to confusion. Therefore, we\nbelieve that patch-wise processing of the data results in a more interpretable\nrepresentation. We propose a novel hybrid approach that utilizes deep neural\nnetworks and traditional machine learning algorithms to introduce an\ninterpretable and scale-able time-series classification approach. Our method\nfirst performs a fine-grained classification for the patches followed by sample\nlevel classification.\n"}
+{"abstract": "  In growing active matter systems, a large collection of engineered or living\nautonomous units metabolize free energy and create order at different length\nscales as they proliferate and migrate collectively. One such example is\nbacterial biofilms, which are surface-attached aggregates of bacterial cells\nembedded in an extracellular matrix. However, how bacterial growth coordinates\nwith cell-surface interactions to create distinctive, long-range order in\nbiofilms remains elusive. Here we report a collective cell reorientation\ncascade in growing Vibrio cholerae biofilms, leading to a differentially\nordered, spatiotemporally coupled core-rim structure reminiscent of a blooming\naster. Cell verticalization in the core generates differential growth that\ndrives radial alignment of the cells in the rim, while the radially aligned rim\nin turn generates compressive stresses that expand the verticalized core. Such\nself-patterning disappears in adhesion-less mutants but can be restored through\nopto-manipulation of growth. Agent-based simulations and two-phase active\nnematic modeling reveal the strong interdependence of the driving forces for\nthe differential ordering. Our findings provide insight into the collective\ncell patterning in bacterial communities and engineering of phenotypes and\nfunctions of living active matter.\n"}
+{"abstract": "  In vehicle-to-everything (V2X) communications, reliability is one of the most\nimportant performance metrics in safety-critical applications such as advanced\ndriving, remote driving, and vehicle platooning. In this paper, the link\nreliability of unicast concurrent transmission in mode 1 (centralized mode) of\n5G New Radio based V2X (NR-V2X) is analyzed. The closed-form expression of link\nreliability for concurrent unicast transmission is firstly derived for a\nhighway scenario under a given interference distance distribution. On this\nbasis, according to the macroscopic configuration of the system, a method to\ncontrol the number of concurrent transmission nodes is proposed, including the\ncommunication range, message packet size, and the number of lanes, etc. The\nresults indicate that the proposed method can maximize the system load on the\npremise of satisfying the link reliability requirements.\n"}
+{"abstract": "  We present optical VLT/MUSE integral field spectroscopy data of the merging\ngalaxy NGC 1487. We use fitting techniques to study the ionized gas emission of\nthis merger and its main morphological and kinematical properties. We measured\nflat and sometimes inverted oxygen abundance gradients in the subsystems\ncomposing NGC 1487, explained by metal mixing processes common in merging\ngalaxies. We also measured widespread star-forming bursts, indicating that\nphotoionisation by stars is the primary ionization source of the galaxy. The\nkinematic map revealed a rotating pattern in the gas in the northern tail of\nthe system, suggesting that the galaxy may be in the process of rebuilding a\ndisc. The gas located in the central region has larger velocity dispersion\n($\\sigma\\approx 50$ km s$^{-1}$) than the remaining regions, indicating\nkinematic heating, possibly owing to the ongoing interaction. Similar trends\nwere, however, not observed in the stellar velocity-dispersion map, indicating\nthat the galaxy has not yet achieved equilibrium, and the nebular and stellar\ncomponents are still kinematically decoupled. Based on all our measurements and\nfindings, and specially on the mass estimates, metallicity gradients and\nvelocity fields of the system, we propose that NGC 1487 is the result of an\nongoing merger event involving smallish dwarf galaxies within a group, in a\npre-merger phase, resulting in a relic with mass and physical parameters\nsimilar to a dwarf galaxy. Thus, we may be witnessing the formation of a dwarf\ngalaxy by merging of smaller clumps at z=0.\n"}
+{"abstract": "  Current dense symmetric eigenvalue (EIG) and singular value decomposition\n(SVD) implementations may suffer from the lack of concurrency during the\ntridiagonal and bidiagonal reductions, respectively. This performance\nbottleneck is typical for the two-sided transformations due to the Level-2 BLAS\nmemory-bound calls. Therefore, the current state-of-the-art EIG and SVD\nimplementations may achieve only a small fraction of the system's sustained\npeak performance. The QR-based Dynamically Weighted Halley (QDWH) algorithm may\nbe used as a pre-processing step toward the EIG and SVD solvers, while\nmitigating the aforementioned bottleneck. QDWH-EIG and QDWH-SVD expose more\nparallelism, while relying on compute-bound matrix operations. Both run closer\nto the sustained peak performance of the system, but at the expense of\nperforming more FLOPS than the standard EIG and SVD algorithms. In this paper,\nwe introduce a new QDWH-based solver for computing the partial spectrum for EIG\n(QDWHpartial-EIG) and SVD (QDWHpartial-SVD) problems. By optimizing the\nrational function underlying the algorithms only in the desired part of the\nspectrum, QDWHpartial-EIG and QDWHpartial-SVD algorithms efficiently compute a\nfraction (say 1-20%) of the corresponding spectrum. We develop high-performance\nimplementations of QDWHpartial-EIG and QDWHpartial-SVD on distributed-memory\nanymore systems and demonstrate their numerical robustness. Experimental\nresults using up to 36K MPI processes show performance speedups for\nQDWHpartial-SVD up to 6X and 2X against PDGESVD from ScaLAPACK and KSVD,\nrespectively. QDWHpartial-EIG outperforms PDSYEVD from ScaLAPACK up to 3.5X but\nremains slower compared to ELPA. QDWHpartial-EIG achieves, however, a better\noccupancy of the underlying hardware by extracting higher sustained peak\nperformance than ELPA, which is critical moving forward with accelerator-based\nsupercomputers.\n"}
+{"abstract": "  We investigate attosecond time delays in the emission of photoelectrons using\na hierarchy of models of the $CO_2$ molecule including the strong field\napproximation, Coulomb-scattering, short-range parts of the molecular\npotential, Hartree and Hartree-Fock descriptions. In addition, we present an\n{\\it ab initio} calculation based on quantum-chemical structure in combination\nwith strong-field techniques, which fully includes multi-electron exchange and\ncorrelation. Every single of these model constituents is found to modify delays\non the scale of 10 as or more, with exchange and correlation having the most\npronounced effect.\n"}
+{"abstract": "  We study the notion of indistinguishability obfuscation for null quantum\ncircuits (quantum null-iO). We present a construction assuming: - The quantum\nhardness of learning with errors (LWE). - Post-quantum indistinguishability\nobfuscation for classical circuits. - A notion of ''dual-mode'' classical\nverification of quantum computation (CVQC).\n  We give evidence that our notion of dual-mode CVQC exists by proposing a\nscheme that is secure assuming LWE in the quantum random oracle model (QROM).\n  Then we show how quantum null-iO enables a series of new cryptographic\nprimitives that, prior to our work, were unknown to exist even making heuristic\nassumptions. Among others, we obtain the first witness encryption scheme for\nQMA, the first publicly verifiable non-interactive zero-knowledge (NIZK) scheme\nfor QMA, and the first attribute-based encryption (ABE) scheme for BQP.\n"}
+{"abstract": "  One salient feature of cooperative formation tracking is its distributed\nnature that relies on localized control and information sharing over a sparse\ncommunication network. That is, a distributed control manner could be prone to\nmalicious attacks and unreliable communication that deteriorate the formation\ntracking performance or even destabilize the whole multi-agent system. This\npaper studies a safe and reliable time-varying output formation tracking\nproblem of linear multi-agent systems, where an attacker adversely injects any\nunbounded time-varying signals (false data injection (FDI) attacks), while an\ninterruption of communication channels between the agents is caused by an\nunreliable network. Both characteristics improve the practical relevance of the\nproblem to be addressed, which poses some technical challenges to the\ndistributed algorithm design and stability analysis. To mitigate the adverse\neffect, a novel resilient distributed control architecture is established to\nguarantee time-varying output formation tracking exponentially. The key\nfeatures of the proposed framework are threefold: 1) an observer-based\nidentifier is integrated to compensate for adverse effects; 2) a reliable\ndistributed algorithm is proposed to deal with time-varying topologies caused\nby unreliable communication; and 3) in contrast to the existing remedies that\ndeal with attacks as bounded disturbances/faults with known knowledge, we\npropose resilience strategies to handle unknown and unbounded attacks for\nexponential convergence of dynamic formation tracking errors, whereas most of\nexisting results achieve uniformly ultimately boundedness (UUB) results.\nNumerical simulations are given to show the effectiveness of the proposed\ndesign.\n"}
+{"abstract": "  Linear embedding transformation has been shown to be effective for zero-shot\ncross-lingual transfer tasks and achieve surprisingly promising results.\nHowever, cross-lingual embedding space mapping is usually studied in static\nword-level embeddings, where a space transformation is derived by aligning\nrepresentations of translation pairs that are referred from dictionaries. We\nmove further from this line and investigate a contextual embedding alignment\napproach which is sense-level and dictionary-free. To enhance the quality of\nthe mapping, we also provide a deep view of properties of contextual\nembeddings, i.e., anisotropy problem and its solution. Experiments on zero-shot\ndependency parsing through the concept-shared space built by our embedding\ntransformation substantially outperform state-of-the-art methods using\nmultilingual embeddings.\n"}
+{"abstract": "  This paper provides an $H_2$ optimal scheme for reducing diffusively coupled\nsecond-order systems evolving over undirected networks. The aim is to find a\nreduced-order model that not only approximates the input-output mapping of the\noriginal system but also preserves crucial structures, such as the second-order\nform, asymptotically stability, and diffusive couplings. To this end, an $H_2$\noptimal approach based on a convex relaxation is implemented to reduce the\ndimension, yielding a lower order asymptotically stable approximation of the\noriginal second-order network system. Then, a novel graph reconstruction\napproach is employed to convert the obtained model to a reduced system that is\ninterpretable as an undirected diffusively coupled network. Finally, the\neffectiveness of the proposed method is illustrated via a large-scale networked\nmass-spring-damper system.\n"}
+{"abstract": "  The radio, optical, and $\\gamma$-ray light curves of the blazar S5 1803+784,\nfrom the beginning of the {\\it Fermi} Large Area Telescope (LAT) mission in\nAugust 2008 until December 2018, are presented. The aim of this work is to look\nfor correlations among different wavelengths useful for further theoretical\nstudies. We analyzed all the data collected by {\\it Fermi} LAT for this source,\ntaking into account the presence of nearby sources, and we collected optical\ndata from our own observations and public archive data to build the most\ncomplete optical and $\\gamma$-ray light curve possible. Several $\\gamma$-ray\nflares ($\\mathrm{F>2.3~10^{-7} ph(E>0.1 GeV)~cm^{-2}~s^{-1}}$) with optical\ncoverage were detected, all but one with corresponding optical enhancement; we\nalso found two optical flares without a $\\gamma$-ray counterpart. We obtained\ntwo {\\it Swift} Target of Opportunity observations during the strong flare of\n2015. Radio observations performed with VLBA and EVN through our proposals in\nthe years 2016-2020 were analyzed to search for morphological changes after the\nmajor flares. The optical/$\\gamma$-ray flux ratio at the flare peak varied for\neach flare. Very minor optical V-I color changes were detected during the\nflares. The X-ray spectrum was well fitted by a power law with photon spectral\nindex $\\alpha$=1.5, nearly independent of the flux level: no clear correlation\nwith the optical or the $\\gamma$-ray emission was found. The $\\gamma$-ray\nspectral shape was well fitted by a power law with average photon index\n$\\alpha$= 2.2. These findings support an Inverse Compton origin for the\nhigh-energy emission of the source, nearly co-spatial with the optically\nemitting region. The radio maps showed two new components originating from the\ncore and moving outwards, with ejection epochs compatible with the dates of the\ntwo largest $\\gamma$-ray flares.\n"}
+{"abstract": "  Deep convolutional networks have attracted great attention in image\nrestoration and enhancement. Generally, restoration quality has been improved\nby building more and more convolutional block. However, these methods mostly\nlearn a specific model to handle all images and ignore difficulty diversity. In\nother words, an area in the image with high frequency tend to lose more\ninformation during compressing while an area with low frequency tends to lose\nless. In this article, we adrress the efficiency issue in image SR by\nincorporating a patch-wise rolling network(PRN) to content-adaptively recover\nimages according to difficulty levels. In contrast to existing studies that\nignore difficulty diversity, we adopt different stage of a neural network to\nperform image restoration. In addition, we propose a rolling strategy that\nutilizes the parameters of each stage more flexible. Extensive experiments\ndemonstrate that our model not only shows a significant acceleration but also\nmaintain state-of-the-art performance.\n"}
+{"abstract": "  We give an efficient algorithm for learning a binary function in a given\nclass C of bounded VC dimension, with training data distributed according to P\nand test data according to Q, where P and Q may be arbitrary distributions over\nX. This is the generic form of what is called covariate shift, which is\nimpossible in general as arbitrary P and Q may not even overlap. However,\nrecently guarantees were given in a model called PQ-learning (Goldwasser et\nal., 2020) where the learner has: (a) access to unlabeled test examples from Q\n(in addition to labeled samples from P, i.e., semi-supervised learning); and\n(b) the option to reject any example and abstain from classifying it (i.e.,\nselective classification). The algorithm of Goldwasser et al. (2020) requires\nan (agnostic) noise tolerant learner for C. The present work gives a\npolynomial-time PQ-learning algorithm that uses an oracle to a \"reliable\"\nlearner for C, where reliable learning (Kalai et al., 2012) is a model of\nlearning with one-sided noise. Furthermore, our reduction is optimal in the\nsense that we show the equivalence of reliable and PQ learning.\n"}
+{"abstract": "  Drilling and Extraction Automated System (DREAMS) is a fully automated\nprototype-drilling rig that can drill, extract water and assess subsurface\ndensity profiles from simulated lunar and Martian subsurface ice. DREAMS system\nis developed by the Texas A&M drilling automation team and composed of four\nmain components: 1- tensegrity rig structure, 2- drilling system, 3- water\nextracting and heating system, and 4- electronic hardware, controls, and\nmachine algorithm. The vertical and rotational movements are controlled by\nusing an Acme rod, stepper, and rotary motor. DREAMS is a unique system and\ndifferent from other systems presented before in the NASA Rascal-Al competition\nbecause 1- It uses the tensegrity structure concept to decrease the system\nweight, improve mobility, and easier installation in space. 2- It cuts rock\nlayers by using a short bit length connected to drill pipes. This drilling\nmethodology is expected to drill hundreds and thousands of meters below the\nmoon and Martian surfaces without any anticipated problems (not only 1 m.). 3-\nDrilling, heating, and extraction systems are integrated into one system that\ncan work simultaneously or individually to save time and cost.\n"}
+{"abstract": "  We consider the problem of forecasting multiple values of the future of a\nvector time series, using some past values. This problem, and related ones such\nas one-step-ahead prediction, have a very long history, and there are a number\nof well-known methods for it, including vector auto-regressive models,\nstate-space methods, multi-task regression, and others. Our focus is on low\nrank forecasters, which break forecasting up into two steps: estimating a\nvector that can be interpreted as a latent state, given the past, and then\nestimating the future values of the time series, given the latent state\nestimate. We introduce the concept of forecast consistency, which means that\nthe estimates of the same value made at different times are consistent. We\nformulate the forecasting problem in general form, and focus on linear\nforecasters, for which we propose a formulation that can be solved via convex\noptimization. We describe a number of extensions and variations, including\nnonlinear forecasters, data weighting, the inclusion of auxiliary data, and\nadditional objective terms. We illustrate our methods with several examples.\n"}
+{"abstract": "  Deep image inpainting aims to restore damaged or missing regions in an image\nwith realistic contents. While having a wide range of applications such as\nobject removal and image recovery, deep inpainting techniques also have the\nrisk of being manipulated for image forgery. A promising countermeasure against\nsuch forgeries is deep inpainting detection, which aims to locate the inpainted\nregions in an image. In this paper, we make the first attempt towards universal\ndetection of deep inpainting, where the detection network can generalize well\nwhen detecting different deep inpainting methods. To this end, we first propose\na novel data generation approach to generate a universal training dataset,\nwhich imitates the noise discrepancies exist in real versus inpainted image\ncontents to train universal detectors. We then design a Noise-Image\nCross-fusion Network (NIX-Net) to effectively exploit the discriminative\ninformation contained in both the images and their noise patterns. We\nempirically show, on multiple benchmark datasets, that our approach outperforms\nexisting detection methods by a large margin and generalize well to unseen deep\ninpainting techniques. Our universal training dataset can also significantly\nboost the generalizability of existing detection methods.\n"}
+{"abstract": "  The recently established formalism of a worldline quantum field theory, which\ndescribes the classical scattering of massive bodies in Einstein gravity, is\ngeneralized up to quadratic order in spin -- for a pair of Kerr black holes\nrevealing a hidden ${\\mathcal N}=2$ supersymmetry. The far-field time-domain\nwaveform of the gravitational waves produced in such a spinning encounter is\ncomputed at leading order in the post-Minkowskian (weak field, but generic\nvelocity) expansion, and exhibits this supersymmetry. From the waveform we\nextract the leading-order total radiated angular momentum in a generic\nreference frame, and the total radiated energy in the center-of-mass frame to\nleading order in a low-velocity approximation.\n"}
+{"abstract": "  We develop a uniform coalgebraic approach to Thomason and J\\'{o}nsson-Tarski\ntype dualities for various classes of neighborhood frames and neighborhood\nalgebras. In the first part of the paper we construct an endofunctor on the\ncategory of complete and atomic Boolean algebras that is dual to the double\npowerset functor on $\\mathsf{Set}$. This allows us to show that Thomason\nduality for neighborhood frames can be viewed as an algebra-coalgebra duality.\nWe generalize this approach to any class of algebras for an endofunctor\npresented by one-step axioms in the language of infinitary modal logic. As a\nconsequence, we obtain a uniform approach to dualities for various classes of\nneighborhood frames, including monotone neighborhood frames, pretopological\nspaces, and topological spaces.\n  In the second part of the paper we develop a coalgebraic approach to\nJ\\'{o}nsson-Tarski duality for neighborhood algebras and descriptive\nneighborhood frames. We introduce an analogue of the Vietoris endofunctor on\nthe category of Stone spaces and show that descriptive neighborhood frames are\nisomorphic to coalgebras for this endofunctor. This allows us to obtain a\ncoalgebraic proof of the duality between descriptive neighborhood frames and\nneighborhood algebras. Using one-step axioms in the language of finitary modal\nlogic, we restrict this duality to other classes of neighborhood algebras\nstudied in the literature, including monotone modal algebras and contingency\nalgebras.\n  We conclude the paper by connecting the two types of dualities via canonical\nextensions, and discuss when these extensions are functorial.\n"}
+{"abstract": "  The main topic of this paper is a brief overview of the field of Artificial\nIntelligence. The core of this paper is a practical implementation of an\nalgorithm for object detection and tracking. The ability to detect and track\nfast-moving objects is crucial for various applications of Artificial\nIntelligence like autonomous driving, ball tracking in sports, robotics or\nobject counting. As part of this paper the Fully Convolutional Neural Network\n\"CueNet\" was developed. It detects and tracks the cueball on a labyrinth game\nrobustly and reliably. While CueNet V1 has a single input image, the approach\nwith CueNet V2 was to take three consecutive 240 x 180-pixel images as an input\nand transform them into a probability heatmap for the cueball's location. The\nnetwork was tested with a separate video that contained all sorts of\ndistractions to test its robustness. When confronted with our testing data,\nCueNet V1 predicted the correct cueball location in 99.6% of all frames, while\nCueNet V2 had 99.8% accuracy.\n"}
+{"abstract": "  Novelty detection is the task of recognizing samples that do not belong to\nthe distribution of the target class. During training, the novelty class is\nabsent, preventing the use of traditional classification approaches. Deep\nautoencoders have been widely used as a base of many unsupervised novelty\ndetection methods. In particular, context autoencoders have been successful in\nthe novelty detection task because of the more effective representations they\nlearn by reconstructing original images from randomly masked images. However, a\nsignificant drawback of context autoencoders is that random masking fails to\nconsistently cover important structures of the input image, leading to\nsuboptimal representations - especially for the novelty detection task. In this\npaper, to optimize input masking, we have designed a framework consisting of\ntwo competing networks, a Mask Module and a Reconstructor. The Mask Module is a\nconvolutional autoencoder that learns to generate optimal masks that cover the\nmost important parts of images. Alternatively, the Reconstructor is a\nconvolutional encoder-decoder that aims to reconstruct unperturbed images from\nmasked images. The networks are trained in an adversarial manner in which the\nMask Module generates masks that are applied to images given to the\nReconstructor. In this way, the Mask Module seeks to maximize the\nreconstruction error that the Reconstructor is minimizing. When applied to\nnovelty detection, the proposed approach learns semantically richer\nrepresentations compared to context autoencoders and enhances novelty detection\nat test time through more optimal masking. Novelty detection experiments on the\nMNIST and CIFAR-10 image datasets demonstrate the proposed approach's\nsuperiority over cutting-edge methods. In a further experiment on the UCSD\nvideo dataset for novelty detection, the proposed approach achieves\nstate-of-the-art results.\n"}
+{"abstract": "  We consider a meal delivery service fulfilling dynamic customer requests\ngiven a set of couriers over the course of a day. A courier's duty is to\npick-up an order from a restaurant and deliver it to a customer. We model this\nservice as a Markov decision process and use deep reinforcement learning as the\nsolution approach. We experiment with the resulting policies on synthetic and\nreal-world datasets and compare those with the baseline policies. We also\nexamine the courier utilization for different numbers of couriers. In our\nanalysis, we specifically focus on the impact of the limited available\nresources in the meal delivery problem. Furthermore, we investigate the effect\nof intelligent order rejection and re-positioning of the couriers. Our\nnumerical experiments show that, by incorporating the geographical locations of\nthe restaurants, customers, and the depot, our model significantly improves the\noverall service quality as characterized by the expected total reward and the\ndelivery times. Our results present valuable insights on both the courier\nassignment process and the optimal number of couriers for different order\nfrequencies on a given day. The proposed model also shows a robust performance\nunder a variety of scenarios for real-world implementation.\n"}
+{"abstract": "  Let $(\\pi_{\\mathbf{z}},V_{\\mathbf{z}})$ be an unramified principal series\nrepresentation of a reductive group over a nonarchimedean local field,\nparametrized by an element $\\mathbf{z}$ of the maximal torus in the Langlands\ndual group. If $v$ is an element of the Weyl group $W$, then the standard\nintertwining integral $\\mathcal{A}_v$ maps $V_{\\mathbf{z}}$ to\n$V_{v\\mathbf{z}}$. Letting $\\psi^{\\mathbf{z}}_w$ with $w\\in W$ be a suitable\nbasis of the Iwahori fixed vectors in $V_{\\mathbf{z}}$, and\n$\\widehat\\psi^{\\mathbf{z}}_w$ a basis of the contragredient representation, we\ndefine $\\sigma(u,v,w)$ (for $u,v,w\\in W$) to be $\\langle\n\\mathcal{A}_v\\psi_u^{\\mathbf{z}},\\widehat\\psi^{v\\mathbf{z}}_w\\rangle$. This is\nan interesting function and we initiate its study. We show that given $u$ and\n$w$, there is a minimal $v$ such that $\\sigma(u,v,w)\\neq 0$. Denoting this $v$\nas $v_\\hbox{min}=v_\\hbox{min}(u,w)$, we will prove that\n$\\sigma(u,v_\\hbox{min},w)$ is a polynomial of the cardinality $q$ of the\nresidue field. Indeed if $v>v_\\hbox{min}$, then $\\sigma(u,v,w)$ is a rational\nfunction of $\\mathbf{z}$ and $q$, whose denominator we describe. But if\n$v=v_\\hbox{min}$, the dependence on $\\mathbf{z}$ disappears. We will express\n$\\sigma(u,v_\\hbox{min},w)$ as the Poincar\\'e polynomial of a Bruhat interval.\nThe proof leads to fairly intricate considerations of the Bruhat order.\n  Thus our results require us to prove some facts that may be of independent\ninterest, relating the Bruhat order $\\leqslant$ and the weak Bruhat order\n$\\leqslant_R$. For example we will prove (for finite Coxeter groups) the\nfollowing \"mixed meet\" property. If $u, w$ are elements of $W$, then there\nexists a unique element $m \\in W$ that is maximal with respect to the condition\nthat $m \\leqslant_R u$ and $m \\leqslant w$. Thus if $z \\leqslant_R u$ and $z\n\\leqslant w$, then $x \\leqslant m$. The value $v_\\hbox{min}$ is $m^{-1}u$.\n"}
+{"abstract": "  Performative distribution shift captures the setting where the choice of\nwhich ML model is deployed changes the data distribution. For example, a bank\nwhich uses the number of open credit lines to determine a customer's risk of\ndefault on a loan may induce customers to open more credit lines in order to\nimprove their chances of being approved. Because of the interactions between\nthe model and data distribution, finding the optimal model parameters is\nchallenging. Works in this area have focused on finding stable points, which\ncan be far from optimal. Here we introduce performative gradient descent\n(PerfGD), which is the first algorithm which provably converges to the\nperformatively optimal point. PerfGD explicitly captures how changes in the\nmodel affects the data distribution and is simple to use. We support our\nfindings with theory and experiments.\n"}
+{"abstract": "  We investigate robust linear consensus over networks under\ncapacity-constrained communication. The capacity of each edge is encoded as an\nupper bound on the number of state variables that can be communicated\ninstantaneously. When the edge capacities are small compared to the\ndimensionality of the state vectors, it is not possible to instantaneously\ncommunicate full state information over every edge. We investigate how robust\nconsensus (small steady state variance of the states) can be achieved within a\nlinear time-invariant setting by optimally assigning edges to state-dimensions.\nWe show that if a finite steady state variance of the states can be achieved,\nthen both the minimum cut capacity and the total capacity of the network should\nbe sufficiently large. Optimal and approximate solutions are provided for some\nspecial classes of graphs. We also consider the related problem of optimally\nallocating additional capacity on a feasible initial solution. We show that\nthis problem corresponds to the maximization of a submodular function subject\nto a matroid constraint, which can be approximated via a greedy algorithm.\n"}
+{"abstract": "  We propose an exact model of anyon ground states including higher Landau\nlevels, and use it to obtain fractionally quantized Hall states at filling\nfractions $\\nu=p/(p(m-1)+1)$ with $m$ odd, from integer Hall states at $\\nu=p$\nthrough adiabatic localization of magnetic flux. For appropriately chosen\ntwo-body potential interactions, the energy gap remains intact during the\nprocess. The construction hence establishes the existence of incompressible\nstates at these fillings.\n"}
+{"abstract": "  The velocity of dislocations is derived analytically to incorporate and\npredict the intriguing effects induced by the preferential solute segregation\nand Cottrell atmospheres in both two-dimensional and three-dimensional binary\nsystems of various crystalline symmetries. The corresponding mesoscopic\ndescription of defect dynamics is constructed through the amplitude formulation\nof the phase-field crystal model which has been shown to accurately capture\nelasticity and plasticity in a wide variety of systems. Modifications of the\nPeach-Koehler force as a result of solute concentration variations and\ncompositional stresses are presented, leading to interesting new predictions of\ndefect motion due to effects of Cottrell atmospheres. These include the\ndeflection of dislocation glide paths, the variation of climb speed and\ndirection, and the change or prevention of defect annihilation, all of which\nplay an important role in determining the fundamental behaviors of complex\ndefect network and dynamics. The analytic results are verified by numerical\nsimulations.\n"}
+{"abstract": "  We investigate the tail asymptotics of the response time distribution for the\ncancel-on-start (c.o.s.) and cancel-on-completion (c.o.c.) variants of\nredundancy-$d$ scheduling and the fork-join model with heavy-tailed job sizes.\nWe present bounds, which only differ in the pre-factor, for the tail\nprobability of the response time in the case of the first-come first-served\n(FCFS) discipline. For the c.o.s. variant we restrict ourselves to\nredundancy-$d$ scheduling, which is a special case of the fork-join model. In\nparticular, for regularly varying job sizes with tail index $-\\nu$ the tail\nindex of the response time for the c.o.s. variant of redundancy-$d$ equals\n$-\\min\\{d_{\\mathrm{cap}}(\\nu-1),\\nu\\}$, where $d_{\\mathrm{cap}} =\n\\min\\{d,N-k\\}$, $N$ is the number of servers and $k$ is the integer part of the\nload. This result indicates that for $d_{\\mathrm{cap}} < \\frac{\\nu}{\\nu-1}$ the\nwaiting time component is dominant, whereas for $d_{\\mathrm{cap}} >\n\\frac{\\nu}{\\nu-1}$ the job size component is dominant. Thus, having $d = \\lceil\n\\min\\{\\frac{\\nu}{\\nu-1},N-k\\} \\rceil$ replicas is sufficient to achieve the\noptimal asymptotic tail behavior of the response time. For the c.o.c. variant\nof the fork-join($n_{\\mathrm{F}},n_{\\mathrm{J}}$) model the tail index of the\nresponse time, under some assumptions on the load, equals $1-\\nu$ and\n$1-(n_{\\mathrm{F}}+1-n_{\\mathrm{J}})\\nu$, for identical and i.i.d. replicas,\nrespectively; here the waiting time component is always dominant.\n"}
+{"abstract": "  We study cosmic evolution based on the fixed points in the dynamical analysis\nof the Degenerate Higher-Order Scalar-Tensor (DHOST) theories. We consider the\nDHOST theories in which the propagation speed of gravitational waves is equal\nto the speed of light, the tensor perturbations do not decay to dark energy\nperturbations, and the scaling solutions exist. The scaling fixed point\nassociated with late time acceleration of universe can be either stable or\nsaddle depending on the parameters of the theory. For some ranges of the\nparameters, the accelerated scaling point and the field dominated point can be\nsimultaneously stable. Cosmic evolution will reach the accelerated scaling\npoint if the time derivative of the scalar field in the theory is positive\nduring the matter domination. If the time derivative of the scalar field is\nnegative during the matter domination, the background universe will evolve\ntowards the field dominated point. The density parameter of the matter can be\nlarger than unity before reaching the scaling fixed point if the deviation from\nthe Einstein theory of gravity is too large and the initial conditions for the\ndynamical variables during the matter domination are significantly different\nfrom the accelerated scaling point. The stabilities of $\\phi$MDE fixed point\nare similar to the coupled dark energy models. In our consideration, the\nuniverse can only evolve from the $\\phi$MDE to the field dominated point.\n"}
+{"abstract": "  We study a bottom-up holographic description of the QCD colour\nsuperconducting phase in the presence of higher derivative corrections. We\nexpand this holographic model in the context of Gauss-Bonnet (GB) gravity. The\nCooper pair condensate has been investigated in the deconfinement phase for\ndifferent values of the GB coupling parameter $\\lambda_{G B}$, we observe a\nchange in the value of the critical chemical potential $\\mu_c$ in comparison to\nEinstein gravity. We find that $\\mu_c$ grows as $\\lambda_{G B}$ increases. We\nadd four fermion interactions and show that in the presence of these\ncorrections the main interesting features of the model are still present and\nthat the intrinsic attractive interaction can not be switched off. This study\nsuggests to find GB corrections to equation of state of holographic QCD matter.\n"}
+{"abstract": "  This paper proposes a new concept in which a digital twin derived from a\ndigital product description will automatically perform assembly planning and\norchestrate the production resources in a manufacturing cell. Thus the\nmanufacturing cell has generic services with minimal assumptions about what\nkind of product will be assembled, while the digital product description is\ndesigned collaboratively between the designer at an OEM and automated services\nat potential manufacturers. This has several advantages. Firstly, the resulting\nversatile manufacturing facility can handle a broad variety of products with\nminimal or no reconfiguration effort, so it can cost-effectively offer its\nservices to a large number of OEMs. Secondly, a solution is presented to the\nproblem of performing concurrent product design and assembly planning over the\norganizational boundary. Thirdly, the product design at the OEM is not\nconstrained to the capabilities of specific manufacturing facilities. The\nconcept is presented in general terms in UML and an implementation is provided\nin a 3D simulation environment using Automation Markup Language for digital\nproduct descriptions. Finally, two case studies are presented and applications\nin a real industrial context are discussed.\n"}
+{"abstract": "  We present a practical analysis of the fermion sign problem in fermionic path\nintegral Monte Carlo (PIMC) simulations in the grand-canonical ensemble (GCE).\nAs a representative model system, we consider electrons in a $2D$ harmonic\ntrap. We find that the sign problem in the GCE is even more severe than in the\ncanonical ensemble at the same conditions, which, in general, makes the latter\nthe preferred option. Despite these difficulties, we show that fermionic PIMC\nsimulations in the GCE are still feasible in many cases, which potentially\ngives access to important quantities like the compressiblity or the Matsubara\nGreens function. This has important implications for contemporary fields of\nresearch such as warm dense matter, ultracold atoms, and electrons in quantum\ndots.\n"}
+{"abstract": "  We present a general approach to obtain effective field theories for\ntopological crystalline insulators whose low-energy theories are described by\nmassive Dirac fermions. We show that these phases are characterized by the\nresponses to spatially dependent mass parameters with interfaces. These mass\ninterfaces implement the dimensional reduction procedure such that the state of\ninterest is smoothly deformed into a topological crystal, which serves as a\nrepresentative state of a phase in the general classification. Effective field\ntheories are obtained by integrating out the massive Dirac fermions, and\nvarious quantized topological terms are uncovered. Our approach can be\ngeneralized to other crystalline symmetry protected topological phases and\nprovides a general strategy to derive effective field theories for such\ncrystalline topological phases.\n"}
+{"abstract": "  We investigate the creation of scalar particles inside a region delimited by\na bubble which is expanding with non-zero acceleration. The bubble is modelled\nas a thin shell and plays the role of a moving boundary, thus influencing the\nfluctuations of the test scalar field inside it. Bubbles expanding in Minkowski\nspacetime as well as those dividing two de Sitter spacetimes are explored in a\nunified way. Our results for the Bogoliubov coefficient $\\beta_k$ in the\nadiabatic approximation show that in all cases the creation of scalar particles\ndecreases with the mass, and is much more significant in the case of nonzero\ncurvature. They also show that the dynamics of the bubble and its size are\nrelevant for particle creation, but in the dS-dS case the combination of both\neffects leads to a behaviour different from that of Minkowski space-time, due\nto the presence of a length scale (the Hubble radius of the internal geometry).\n"}
+{"abstract": "  Deep neural networks have emerged as very successful tools for image\nrestoration and reconstruction tasks. These networks are often trained\nend-to-end to directly reconstruct an image from a noisy or corrupted\nmeasurement of that image. To achieve state-of-the-art performance, training on\nlarge and diverse sets of images is considered critical. However, it is often\ndifficult and/or expensive to collect large amounts of training images.\nInspired by the success of Data Augmentation (DA) for classification problems,\nin this paper, we propose a pipeline for data augmentation for accelerated MRI\nreconstruction and study its effectiveness at reducing the required training\ndata in a variety of settings. Our DA pipeline, MRAugment, is specifically\ndesigned to utilize the invariances present in medical imaging measurements as\nnaive DA strategies that neglect the physics of the problem fail. Through\nextensive studies on multiple datasets we demonstrate that in the low-data\nregime DA prevents overfitting and can match or even surpass the state of the\nart while using significantly fewer training data, whereas in the high-data\nregime it has diminishing returns. Furthermore, our findings show that DA can\nimprove the robustness of the model against various shifts in the test\ndistribution.\n"}
+{"abstract": "  Quantum circuits that are classically simulatable tell us when quantum\ncomputation becomes less powerful than or equivalent to classical computation.\nSuch classically simulatable circuits are of importance because they illustrate\nwhat makes universal quantum computation different from classical computers. In\nthis work, we propose a novel family of classically simulatable circuits by\nmaking use of dual-unitary quantum circuits (DUQCs), which have been recently\ninvestigated as exactly solvable models of non-equilibrium physics, and we\ncharacterize their computational power. Specifically, we investigate the\ncomputational complexity of the problem of calculating local expectation values\nand the sampling problem of one-dimensional DUQCs, and we generalize them to\ntwo spatial dimensions. We reveal that a local expectation value of a DUQC is\nclassically simulatable at an early time, which is linear in a system length.\nIn contrast, in a late time, they can perform universal quantum computation,\nand the problem becomes a BQP-complete problem. Moreover, classical simulation\nof sampling from a DUQC turns out to be hard.\n"}
+{"abstract": "  In this work, we consider a binary classification problem and cast it into a\nbinary hypothesis testing framework, where the observations can be perturbed by\nan adversary. To improve the adversarial robustness of a classifier, we include\nan abstain option, where the classifier abstains from making a decision when it\nhas low confidence about the prediction. We propose metrics to quantify the\nnominal performance of a classifier with an abstain option and its robustness\nagainst adversarial perturbations. We show that there exist a tradeoff between\nthe two metrics regardless of what method is used to choose the abstain region.\nOur results imply that the robustness of a classifier with an abstain option\ncan only be improved at the expense of its nominal performance. Further, we\nprovide necessary conditions to design the abstain region for a 1- dimensional\nbinary classification problem. We validate our theoretical results on the MNIST\ndataset, where we numerically show that the tradeoff between performance and\nrobustness also exist for the general multi-class classification problems.\n"}
+{"abstract": "  Off-policy evaluation learns a target policy's value with a historical\ndataset generated by a different behavior policy. In addition to a point\nestimate, many applications would benefit significantly from having a\nconfidence interval (CI) that quantifies the uncertainty of the point estimate.\nIn this paper, we propose a novel deeply-debiasing procedure to construct an\nefficient, robust, and flexible CI on a target policy's value. Our method is\njustified by theoretical results and numerical experiments. A Python\nimplementation of the proposed procedure is available at\nhttps://github.com/RunzheStat/D2OPE.\n"}
+{"abstract": "  The successful amalgamation of cryptocurrency and consumer Internet of Things\n(IoT) devices can pave the way for novel applications in machine-to-machine\neconomy. However, the lack of scalability and heavy resource requirements of\ninitial blockchain designs hinders the integration as they prioritized\ndecentralization and security. Numerous solutions have been proposed since the\nemergence of Bitcoin to achieve this goal. However, none of them seem to\ndominate and thus it is unclear how consumer devices will be adopting these\napproaches. Therefore, in this paper, we critically review the existing\nintegration approaches and cryptocurrency designs that strive to enable\nmicro-payments among consumer devices. We identify and discuss solutions under\nthree main categories; direct integration, payment channel network and new\ncryptocurrency design. The first approach utilizes a full node to interact with\nthe payment system. Offline channel payment is suggested as a second layer\nsolution to solve the scalability issue and enable instant payment with low\nfee. New designs converge to semi-centralized scheme and focuson lightweight\nconsensus protocol that does not require highcomputation power which might mean\nloosening the initial designchoices in favor of scalability. We evaluate the\npros and cons ofeach of these approaches and then point out future\nresearchchallenges. Our goal is to help researchers and practitioners tobetter\nfocus their efforts to facilitate micro-payment adoptions.\n"}
+{"abstract": "  We show that it is possible to have arbitrarily long sequences of Alices and\nBobs so every (Alice, Bob) pair violates a Bell inequality. We propose an\nexperiment to observe this effect with two Alices and two Bobs.\n"}
+{"abstract": "  One of the main goal of large-scale structure surveys is to test the\nconsistency of General Relativity at cosmological scales. In the $\\Lambda$CDM\nmodel of cosmology, the relations between the fields describing the geometry\nand the content of our Universe are uniquely determined. In particular, the two\ngravitational potentials -- that describe the spatial and temporal fluctuations\nin the geometry -- are equal. Whereas large classes of dark energy models\npreserve this equality, theories of modified gravity generally create a\ndifference between the potentials, known as anisotropic stress. Even though\nmeasuring this anisotropic stress is one of the key goals of large-scale\nstructure surveys, there are currently no methods able to measure it directly.\nCurrent methods all rely on measurements of galaxy peculiar velocities (through\nredshift-space distortions), from which the time component of the metric is\ninferred, assuming that dark matter follows geodesics. If this is not the case,\nall the proposed tests fail to measure the anisotropic stress. In this letter,\nwe propose a novel test which directly measures anisotropic stress, without\nrelying on any assumption about the unknown dark matter. Our method uses\nrelativistic effects in the galaxy number counts to provide a direct\nmeasurement of the time component of the metric. By comparing this with lensing\nobservations our test provides a direct measurement of the anisotropic stress.\n"}
+{"abstract": "  Intensification and poleward expansion of upwelling favourable winds have\nbeen predicted as a response to anthropogenic global climate change and have\nrecently been documented in most Eastern Boundary Upwelling Ecosystems of the\nworld. To identify how these processes are impacting nearshore oceanographic\nhabitats and, especially, long term trends of primary productivity in the\nHumboldt Upwelling Ecosystem (HUE), we analysed time series of sea level\npressure, wind stress, sea surface and atmospheric surface temperatures, and\nChlorophyll-a, as a proxy for primary productivity, along 26{\\deg} - 36{\\deg}\nS. We show that climate induced trends in primary productivity are highly\nheterogeneous across the region. On the one hand, the well documented poleward\nmigration of the South Pacific Anticyclone (SPA) has led to decreased spring\nupwelling winds in the region between ca. 30{\\deg} and 34{\\deg} S, and to their\nintensification to the south. Decreased winds have produced slight increases in\nsea surface temperature and a pronounced and meridionally extensive decrease in\nsurface Chlorophyll-a in this region of central Chile. To the north of 30{\\deg}\nS, significant increases in upwelling winds, decreased SST, and enhanced\nChlorophyll-a concentration are observed in the nearshore. We show that this\nincreased in upwelling driven coastal productivity is probably produced by the\nincreased land-sea pressure gradients (Bakun's effect) that have occurred over\nthe past two decades north of 30{\\deg} S. Thus, climate drivers along the HUE\nare inducing contrasting trends in oceanographic conditions and primary\nproductivity, which can have far-reaching consequences for coastal pelagic and\nbenthic ecosystems and lead to geographic displacements of the major fisheries.\n"}
+{"abstract": "  Extensions of a set partition obtained by imposing bounds on the size of the\nparts and the coloring of some of the elements are examined. Combinatorial\nproperties and the generating functions of some counting sequences associated\nwith these partitions are established. Connections with Riordan arrays are\npresented.\n"}
+{"abstract": "  Finding the largest cardinality feasible subset of an infeasible set of\nlinear constraints is the Maximum Feasible Subsystem problem (MAX FS). Solving\nthis problem is crucial in a wide range of applications such as machine\nlearning and compressive sensing. Although MAX FS is NP-hard, useful heuristic\nalgorithms exist, but these can be slow for large problems. We extend the\nexisting heuristics for the case of dense constraint matrices to greatly\nincrease their speed while preserving or improving solution quality. We test\nthe extended algorithms on two applications that have dense constraint\nmatrices: binary classification, and sparse recovery in compressive sensing. In\nboth cases, speed is greatly increased with no loss of accuracy.\n"}
+{"abstract": "  Simulations of high energy density physics are expensive in terms of\ncomputational resources. In particular, the computation of opacities of plasmas\nin the non-local thermal equilibrium (NLTE) regime can consume as much as 90\\%\nof the total computational time of radiation hydrodynamics simulations for high\nenergy density physics applications. Previous work has demonstrated that a\ncombination of fully-connected autoencoders and a deep jointly-informed neural\nnetwork (DJINN) can successfully replace the standard NLTE calculations for the\nopacity of krypton. This work expands this idea to combining multiple elements\ninto a single surrogate model with the focus here being on the autoencoder.\n"}
+{"abstract": "  We consider the minimal seesaw model, the Standard Model extended by two\nright-handed neutrinos, for explaining the neutrino masses and mixing angles\nmeasured in oscillation experiments. When one of right-handed neutrinos is\nlighter than the electroweak scale, it can give a sizable contribution to\nneutrinoless double beta ($0\\nu \\beta \\beta$) decay. We show that the detection\nof the $0 \\nu \\beta \\beta$ decay by future experiments gives a significant\nimplication to the search for such light right-handed neutrino.\n"}
+{"abstract": "  In the Stochastic Thermodynamics theory, heat is a random variable with a\nprobability distribution associated. Studies in the distribution of heat are\nmostly in the overdamped regime. Here we solve the heat distribution in the\nunderdamped regime for three different cases: the free particle, the linear\npotential, and the harmonic potential. The results are exact and generalize\nknown results in the literature.\n"}
+{"abstract": "  For safety of autonomous driving, vehicles need to be able to drive under\nvarious lighting, weather, and visibility conditions in different environments.\nThese external and environmental factors, along with internal factors\nassociated with sensors, can pose significant challenges to perceptual data\nprocessing, hence affecting the decision-making and control of the vehicle. In\nthis work, we address this critical issue by introducing a framework for\nanalyzing robustness of the learning algorithm w.r.t varying quality in the\nimage input for autonomous driving. Using the results of sensitivity analysis,\nwe further propose an algorithm to improve the overall performance of the task\nof \"learning to steer\". The results show that our approach is able to enhance\nthe learning outcomes up to 48%. A comparative study drawn between our approach\nand other related techniques, such as data augmentation and adversarial\ntraining, confirms the effectiveness of our algorithm as a way to improve the\nrobustness and generalization of neural network training for autonomous\ndriving.\n"}
+{"abstract": "  Semi-supervised domain adaptation (SSDA) aims to solve tasks in target domain\nby utilizing transferable information learned from the available source domain\nand a few labeled target data. However, source data is not always accessible in\npractical scenarios, which restricts the application of SSDA in real world\ncircumstances. In this paper, we propose a novel task named Semi-supervised\nSource Hypothesis Transfer (SSHT), which performs domain adaptation based on\nsource trained model, to generalize well in target domain with a few\nsupervisions. In SSHT, we are facing two challenges: (1) The insufficient\nlabeled target data may result in target features near the decision boundary,\nwith the increased risk of mis-classification; (2) The data are usually\nimbalanced in source domain, so the model trained with these data is biased.\nThe biased model is prone to categorize samples of minority categories into\nmajority ones, resulting in low prediction diversity. To tackle the above\nissues, we propose Consistency and Diversity Learning (CDL), a simple but\neffective framework for SSHT by facilitating prediction consistency between two\nrandomly augmented unlabeled data and maintaining the prediction diversity when\nadapting model to target domain. Encouraging consistency regularization brings\ndifficulty to memorize the few labeled target data and thus enhances the\ngeneralization ability of the learned model. We further integrate Batch\nNuclear-norm Maximization into our method to enhance the discriminability and\ndiversity. Experimental results show that our method outperforms existing SSDA\nmethods and unsupervised model adaptation methods on DomainNet, Office-Home and\nOffice-31 datasets. The code is available at\nhttps://github.com/Wang-xd1899/SSHT.\n"}
+{"abstract": "  Dual function radar communications (DFRC) systems are attractive technologies\nfor autonomous vehicles, which utilize electromagnetic waves to constantly\nsense the environment while simultaneously communicating with neighbouring\ndevices. An emerging approach to implement DFRC systems is to embed information\nin radar waveforms via index modulation (IM). Implementation of DFRC schemes in\nvehicular systems gives rise to strict constraints in terms of cost, power\nefficiency, and hardware complexity. In this paper, we extend IM-based DFRC\nsystems to utilize sparse arrays and frequency modulated continuous waveforms\n(FMCWs), which are popular in automotive radar for their simplicity and low\nhardware complexity. The proposed FMCW-based radar-communications system (FRaC)\noperates at reduced cost and complexity by transmitting with a reduced number\nof radio frequency modules, combined with narrowband FMCW signalling. This is\nachieved via array sparsification in transmission, formulating a virtual\nmultiple-input multiple-output array by combining the signals in one coherent\nprocessing interval, in which the narrowband waveforms are transmitted in a\nrandomized manner. Performance analysis and numerical results show that the\nproposed radar scheme achieves similar resolution performance compared with a\nwideband radar system operating with a large receive aperture, while requiring\nless hardware overhead. For the communications subsystem, FRaC achieves higher\nrates and improved error rates compared to dual-function signalling based on\nconventional phase modulation.\n"}
+{"abstract": "  We deploy and demonstrate the capabilities of the magnetic field model\ndeveloped by Ewertowski & Basu (2013) by fitting observed polarimetry data of\nthe prestellar core FeSt 1-457. The analytic hourglass magnetic field function\nderived directly from Maxwell's equations yields a central-to-surface magnetic\nfield strength ratio in the equatorial plane, as well as magnetic field\ndirections with relative magnitudes throughout the core. This fit emerges from\na comparison of a single plane of the model with the polarization map that\nresults from the integrated properties of the magnetic field and dust\nthroughout the core. Importantly, our fit is independent of any assumed density\nprofile of the core. We check the robustness of the fit by using the POLARIS\ncode to create synthetic polarization maps that result from the integrated\nscattering and emission properties of the dust grains and their radiative\ntransfer, employing an observationally-motivated density profile. We find that\nthe synthetic polarization maps obtained from the model also provides a good\nfit to the observed polarimetry. Our model fits the striking feature of\nsignificant curvature of magnetic field lines in the outer part of FeSt 1-457.\nCombined with independent column density estimates, we infer that the core of\nsize $R_{\\rm gas}$ has a mildly supercritical mass-to-flux ratio and may have\nformed through dynamical motions starting from a significantly larger radius\n$R$. A breakdown of flux-freezing through neutral-ion slip (ambipolar\ndiffusion) could be responsible for effecting such a transition from a\nlarge-scale magnetic field structure to a more compact gas structure.\n"}
+{"abstract": "  Uncertainty relations play a crucial role in quantum mechanics. Well-defined\nmethods exist for the derivation of such uncertainties for pairs of\nobservables. Specific methods also allow to obtain time-energy uncertainty\nrelations. However, in these cases, different approaches are associated with\ndifferent meanings and interpretations. The one of interest here revolves\naround the idea of whether quantum mechanics inherently imposes a fundamental\nminimum duration for energy measurements with a certain precision. In our\nstudy, we investigate within the Page and Wootters timeless framework how\nenergy measurements modify the relative \"flow of time\" between internal and\nexternal clocks. This provides a unified framework for discussing the subject,\nallowing us to recover previous results and derive new ones. In particular, we\nshow that the duration of an energy measurement carried out by an external\nsystem cannot be performed arbitrarily fast from the perspective of the\ninternal clock. Moreover, we show that during any energy measurement the\nevolution given by the internal clock is non-unitary.\n"}
+{"abstract": "  Recent success of deep neural networks (DNNs) hinges on the availability of\nlarge-scale dataset; however, training on such dataset often poses privacy\nrisks for sensitive training information. In this paper, we aim to explore the\npower of generative models and gradient sparsity, and propose a scalable\nprivacy-preserving generative model DATALENS. Comparing with the standard PATE\nprivacy-preserving framework which allows teachers to vote on one-dimensional\npredictions, voting on the high dimensional gradient vectors is challenging in\nterms of privacy preservation. As dimension reduction techniques are required,\nwe need to navigate a delicate tradeoff space between (1) the improvement of\nprivacy preservation and (2) the slowdown of SGD convergence. To tackle this,\nwe take advantage of communication efficient learning and propose a novel noise\ncompression and aggregation approach TOPAGG by combining top-k compression for\ndimension reduction with a corresponding noise injection mechanism. We\ntheoretically prove that the DATALENS framework guarantees differential privacy\nfor its generated data, and provide analysis on its convergence. To demonstrate\nthe practical usage of DATALENS, we conduct extensive experiments on diverse\ndatasets including MNIST, Fashion-MNIST, and high dimensional CelebA, and we\nshow that, DATALENS significantly outperforms other baseline DP generative\nmodels. In addition, we adapt the proposed TOPAGG approach, which is one of the\nkey building blocks in DATALENS, to DP SGD training, and show that it is able\nto achieve higher utility than the state-of-the-art DP SGD approach in most\ncases. Our code is publicly available at https://github.com/AI-secure/DataLens.\n"}
+{"abstract": "  We discuss a mechanism where charged lepton masses are derived from one-loop\ndiagrams mediated by particles in a dark sector including a dark matter\ncandidate. We focus on a scenario where the muon and electron masses are\ngenerated at one loop with new ${\\cal O}(1)$ Yukawa couplings. The measured\nmuon anomalous magnetic dipole moment, $(g-2)_\\mu$, can be explained in this\nframework. As an important prediction, the muon and electron Yukawa couplings\ncan largely deviate from their standard model predictions, and such deviations\ncan be tested at High-Luminosity LHC and future $e^+e^-$ colliders.\n"}
+{"abstract": "  We introduce a new combinatorial principle which we call $\\clubsuit_{AD}$.\nThis principle asserts the existence of a certain multi-ladder system with\nguessing and almost-disjointness features, and is shown to be sufficient for\ncarrying out de Caux type constructions of topological spaces.\n  Our main result states that strong instances of $\\clubsuit_{AD}$ follow from\nthe existence of a Souslin tree. It is also shown that the weakest instance of\n$\\clubsuit_{AD}$ does not follow from the existence of an almost Souslin tree.\n  As an application, we obtain a simple, de Caux type proof of Rudin's result\nthat if there is a Souslin tree, then there is an $S$-space which is Dowker.\n"}
+{"abstract": "  In this paper, we study the pattern occurrence in $k$-ary words. We prove an\nexplicit upper bound on the number of $k$-ary words avoiding any given pattern\nusing a random walk argument. Additionally, we reproduce several already known\nresults and establish a simple connection among pattern occurrences in\npermutations and $k$-ary words. A simple consequence of this connection is that\nWilf-equivalence of two patterns in words implies their Wilf-equivalence in\npermutations.\n"}
+{"abstract": "  We propose a framework to model an operational conversational negation by\napplying worldly context (prior knowledge) to logical negation in compositional\ndistributional semantics. Given a word, our framework can create its negation\nthat is similar to how humans perceive negation. The framework corrects logical\nnegation to weight meanings closer in the entailment hierarchy more than\nmeanings further apart. The proposed framework is flexible to accommodate\ndifferent choices of logical negations, compositions, and worldly context\ngeneration. In particular, we propose and motivate a new logical negation using\nmatrix inverse.\n  We validate the sensibility of our conversational negation framework by\nperforming experiments, leveraging density matrices to encode graded entailment\ninformation. We conclude that the combination of subtraction negation and\nphaser in the basis of the negated word yields the highest Pearson correlation\nof 0.635 with human ratings.\n"}
+{"abstract": "  In recent years, an intensive study of strong approximation of stochastic\ndifferential equations (SDEs) with a drift coefficient that may have\ndiscontinuities in space has begun. In many of these results it is assumed that\nthe drift coefficient satisfies piecewise regularity conditions and the\ndiffusion coefficient is Lipschitz continuous and non-degenerate at the\ndiscontinuity points of the drift coefficient. For scalar SDEs of that type the\nbest $L_p$-error rate known so far for approximation of the solution at the\nfinal time point is $3/4$ in terms of the number of evaluations of the driving\nBrownian motion and it is achieved by the transformed equidistant\nquasi-Milstein scheme, see [M\\\"uller-Gronbach, T., and Yaroslavtseva, L., A\nstrong order 3/4 method for SDEs with discontinuous drift coefficient, to\nappear in IMA Journal of Numerical Analysis]. Recently in [M\\\"uller-Gronbach,\nT., and Yaroslavtseva, L., Sharp lower error bounds for strong approximation of\nSDEs with discontinuous drift coefficient by coupling of noise,\narXiv:2010.00915 (2020)] it has been shown that for such SDEs the $L_p$-error\nrate $3/4$ can not be improved in general by no numerical method based on\nevaluations of the driving Brownian motion at fixed time points. In the present\narticle we construct for the first time in the literature a method based on\nsequential evaluations of the driving Brownian motion, which achieves an\n$L_p$-error rate of at least $1$ in terms of the average number of evaluations\nof the driving Brownian motion for such SDEs.\n"}
+{"abstract": "  Due to the strong coupling between magnetism and ferroelectricity,\n$(\\mathrm{ND}_4)_2\\mathrm{FeCl}_5\\cdot\\mathrm{D}_2\\mathrm{O}$ exhibits several\nintriguing magnetic and electric phases. In this letter, we include high-order\nonsite spin anisotropic interactions in a spin model that successfully captures\nthe ferroelectric phase transitions of\n$(\\mathrm{ND}_4)_2\\mathrm{FeCl}_5\\cdot\\mathrm{D}_2\\mathrm{O}$ under a magnetic\nfield and produces the large weights of high-order harmonic components in the\ncycloid structure that are observed from neutron diffraction experiments.\nMoreover, we predict a new ferroelectric phase sandwiched between the FE II and\nFE III phases in a magnetic field. Our results emphasize the importance of the\nhigh-order spin anisotropic interactions and provide a guideline to understand\nmultiferroic materials with rich phase diagrams.\n"}
+{"abstract": "  Continued population growth and urbanization is shifting research to consider\nthe quality of urban green space over the quantity of these parks, woods, and\nwetlands. The quality of urban green space has been hitherto measured by expert\nassessments, including in-situ observations, surveys, and remote sensing\nanalyses. Location data platforms, such as TripAdvisor, can provide people's\nopinion on many destinations and experiences, including UGS. This paper\nleverages Artificial Intelligence techniques for opinion mining and text\nclassification using such platform's reviews as a novel approach to urban green\nspace quality assessments. Natural Language Processing is used to analyze\ncontextual information given supervised scores of words by implementing\ncomputational analysis. Such an application can support local authorities and\nstakeholders in their understanding of and justification for future investments\nin urban green space.\n"}
+{"abstract": "  As a highly data-driven application, recommender systems could be affected by\ndata bias, resulting in unfair results for different data groups, which could\nbe a reason that affects the system performance. Therefore, it is important to\nidentify and solve the unfairness issues in recommendation scenarios. In this\npaper, we address the unfairness problem in recommender systems from the user\nperspective. We group users into advantaged and disadvantaged groups according\nto their level of activity, and conduct experiments to show that current\nrecommender systems will behave unfairly between two groups of users.\nSpecifically, the advantaged users (active) who only account for a small\nproportion in data enjoy much higher recommendation quality than those\ndisadvantaged users (inactive). Such bias can also affect the overall\nperformance since the disadvantaged users are the majority. To solve this\nproblem, we provide a re-ranking approach to mitigate this unfairness problem\nby adding constraints over evaluation metrics. The experiments we conducted on\nseveral real-world datasets with various recommendation algorithms show that\nour approach can not only improve group fairness of users in recommender\nsystems, but also achieve better overall recommendation performance.\n"}
+{"abstract": "  Humans race drones faster than algorithms, despite being limited to a fixed\ncamera angle, body rate control, and response latencies in the order of\nhundreds of milliseconds. A better understanding of the ability of human pilots\nof selecting appropriate motor commands from highly dynamic visual information\nmay provide key insights for solving current challenges in vision-based\nautonomous navigation. This paper investigates the relationship between human\neye movements, control behavior, and flight performance in a drone racing task.\nWe collected a multimodal dataset from 21 experienced drone pilots using a\nhighly realistic drone racing simulator, also used to recruit professional\npilots. Our results show task-specific improvements in drone racing performance\nover time. In particular, we found that eye gaze tracks future waypoints (i.e.,\ngates), with first fixations occurring on average 1.5 seconds and 16 meters\nbefore reaching the gate. Moreover, human pilots consistently looked at the\ninside of the future flight path for lateral (i.e., left and right turns) and\nvertical maneuvers (i.e., ascending and descending). Finally, we found a strong\ncorrelation between pilots eye movements and the commanded direction of\nquadrotor flight, with an average visual-motor response latency of 220 ms.\nThese results highlight the importance of coordinated eye movements in\nhuman-piloted drone racing. We make our dataset publicly available.\n"}
+{"abstract": "  We investigate formation of Bose-Einstein condensates under non-equilibrium\nconditions using numerical simulations of the three-dimensional\nGross-Pitaevskii equation. For this, we set initial random weakly nonlinear\nexcitations and the forcing at high wave numbers, and study propagation of the\nturbulent spectrum toward the low wave numbers. Our primary goal is to compare\nthe results for the evolving spectrum with the previous results obtained for\nthe kinetic equation of weak wave turbulence. We demonstrate existence of a\nregime for which good agreement with the wave turbulence results is found in\nterms of the main features of the previously discussed self-similar solution.\nIn particular, we find a reasonable agreement with the low-frequency and the\nhigh-frequency power-law asymptotics of the evolving solution, including the\nanomalous power-law exponent $x^* \\approx 1.24$ for the three-dimensional\nwaveaction spectrum. We also study the regimes of very weak turbulence, when\nthe evolution is affected by the discreteness of the Fourier space, and the\nstrong turbulence regime when emerging condensate modifies the wave dynamics\nand leads to formation of strongly nonlinear filamentary vortices.\n"}
+{"abstract": "  Development of memory devices with ultimate performance has played a key role\nin innovation of modern electronics. As a mainstream technology nonvolatile\nmemory devices have manifested high capacity and mechanical reliability,\nhowever current major bottlenecks include low extinction ratio and slow\noperational speed. Although substantial effort has been employed to improve\ntheir performance, a typical hundreds of micro- or even milli- second write\ntime remains a few orders of magnitude longer than their volatile counterparts.\nWe have demonstrated nonvolatile, floating-gate memory devices based on van der\nWaals heterostructures with atomically sharp interfaces between different\nfunctional elements, and achieved ultrahigh-speed programming/erasing\noperations verging on an ultimate theoretical limit of nanoseconds with\nextinction ratio up to 10^10. This extraordinary performance has allowed new\ndevice capabilities such as multi-bit storage, thus opening up unforeseen\napplications in the realm of modern nanoelectronics and offering future\nfabrication guidelines for device scale-up.\n"}
+{"abstract": "  The optical bistability have been studied theoretically in a multi-mode\noptomechanical system with two mechanical oscillators independently coupled to\ntwo cavities in addition to direct tunnel coupling between cavities. It is\nproved that the bistable behavior of mean intracavity photon number in the\nright cavity can be tuned by adjusting the strength of the pump laser beam\ndriving the left cavity. And the mean intracavity photon number is relatively\nlarger in the red sideband regime than that in the blue sideband regime.\nMoreover, we have shown that the double optical bistability of intracavity\nphoton in the right cavity and the two steady-state positions of mechanical\nresonators can be observed when the control field power is increased to a\ncritical value. Besides, the critical values for observing bistability and\ndouble bistability can be tuned by adjusting the coupling coefficient between\ntwo cavities and the coupling rates between cavities mode and mechanical mode.\n"}
+{"abstract": "  Let $1<g_1<\\ldots<g_{\\varphi(p-1)}<p-1$ be the ordered primitive roots\nmodulo~$p$. We study the pseudorandomness of the binary sequence $(s_n)$\ndefined by $s_n\\equiv g_{n+1}+g_{n+2}\\bmod 2$, $n=0,1,\\ldots$. In particular,\nwe study the balance, linear complexity and $2$-adic complexity of $(s_n)$. We\nshow that for a typical $p$ the sequence $(s_n)$ is quite unbalanced. However,\nthere are still infinitely many $p$ such that $(s_n)$ is very balanced. We also\nprove similar results for the distribution of longer patterns. Moreover, we\ngive general lower bounds on the linear complexity and $2$-adic complexity\nof~$(s_n)$ and state sufficient conditions for attaining their maximums. Hence,\nfor carefully chosen $p$, these sequences are attractive candidates for\ncryptographic applications.\n"}
+{"abstract": "  We study a frequency-modulated quantum harmonic oscillator as a thermodynamic\nsystem. For this purpose, we introduce an `invariant' thermal state by using\nErmakov-Lewis-Riesenfeld invariant in place of an initial state. This\nprescription enables us to analyze the thermodynamics of the oscillator system\nregardless of whether the process is slowly varying (adiabatic) or not\n(nonadiabatic). We introduce a quantity $\\mathscr{S}$ that describes the\n`nonadiabaticity' contribution satisfactorily. We write down the thermodynamics\nof the oscillator system by using this quantity in addition to the ordinary\nthermodynamical ones. As a result, we extend the first law of thermodynamics to\nnonadiabatic processes. We discuss universality for the method and some\npossible applications. In short, we suggest a schematic procedure for obtaining\na measure of the `degree of nonadiabaticity' and present an application to the\nthermodynamics of the squeezed quantum oscillators.\n"}
+{"abstract": "  Representation learning has been widely studied in the context of\nmeta-learning, enabling rapid learning of new tasks through shared\nrepresentations. Recent works such as MAML have explored using\nfine-tuning-based metrics, which measure the ease by which fine-tuning can\nachieve good performance, as proxies for obtaining representations. We present\na theoretical framework for analyzing representations derived from a MAML-like\nalgorithm, assuming the available tasks use approximately the same underlying\nrepresentation. We then provide risk bounds on the best predictor found by\nfine-tuning via gradient descent, demonstrating that the algorithm can provably\nleverage the shared structure. The upper bound applies to general function\nclasses, which we demonstrate by instantiating the guarantees of our framework\nin the logistic regression and neural network settings. In contrast, we\nestablish the existence of settings where any algorithm, using a representation\ntrained with no consideration for task-specific fine-tuning, performs as well\nas a learner with no access to source tasks in the worst case. This separation\nresult underscores the benefit of fine-tuning-based methods, such as MAML, over\nmethods with \"frozen representation\" objectives in few-shot learning.\n"}
+{"abstract": "  Network pruning is an effective method to reduce the computational expense of\nover-parameterized neural networks for deployment on low-resource systems.\nRecent state-of-the-art techniques for retraining pruned networks such as\nweight rewinding and learning rate rewinding have been shown to outperform the\ntraditional fine-tuning technique in recovering the lost accuracy (Renda et\nal., 2020), but so far it is unclear what accounts for such performance. In\nthis work, we conduct extensive experiments to verify and analyze the uncanny\neffectiveness of learning rate rewinding. We find that the reason behind the\nsuccess of learning rate rewinding is the usage of a large learning rate.\nSimilar phenomenon can be observed in other learning rate schedules that\ninvolve large learning rates, e.g., the 1-cycle learning rate schedule (Smith\net al., 2019). By leveraging the right learning rate schedule in retraining, we\ndemonstrate a counter-intuitive phenomenon in that randomly pruned networks\ncould even achieve better performance than methodically pruned networks\n(fine-tuned with the conventional approach). Our results emphasize the\ncruciality of the learning rate schedule in pruned network retraining - a\ndetail often overlooked by practitioners during the implementation of network\npruning. One-sentence Summary: We study the effective of different retraining\nmechanisms while doing pruning\n"}
+{"abstract": "  For broad nanoscale applications, it is crucial to implement more functional\nproperties, especially those ferroic orders, into two-dimensional materials.\nHere GdI$_3$ is theoretically identified as a honeycomb antiferromagnet with\nlarge $4f$ magnetic moment. The intercalation of metal atoms can dope electrons\ninto Gd's $5d$-orbitals, which alters its magnetic state and lead to Peierls\ntransition. Due to the strong electron-phonon coupling, the Peierls transition\ninduces prominent ferroelasticity, making it a multiferroic system. The strain\nfrom undirectional stretching can be self-relaxed via resizing of triple\nferroelastic domains, which can protect the magnet aganist mechnical breaking\nin flexible applications.\n"}
+{"abstract": "  The temperature scales of screening of local magnetic and orbital moments are\nimportant characteristics of strongly correlated substances. In a recent paper\nX. Deng et al. using dynamic mean-field theory (DMFT) have identified\ntemperature scales of the onset of screening in orbital and spin channels in\nsome correlated metals from the deviation of temperature dependence of local\nsusceptibility from the Curie law. We argue that the scales obtained this way\nare in fact much larger, than the corresponding Kondo temperatures, and,\ntherefore, do not characterize the screening process. By reanalyzing the\nresults of this paper we find the characteristic (Kondo) temperatures for\nscreening in the spin channel $T_K\\approx 100$ K for V$_2$O$_3$ and $T_K\\approx\n350$ K for Sr$_2$RuO$_4$, which are almost an order of magnitude smaller than\nthose for the onset of the screening estimated in the paper ($1000$ K and\n$2300$ K, respectively); for V$_2$O$_3$ the obtained temperature scale $T_K$ is\ntherefore comparable to the temperature of completion of the screening, $T^{\\rm\ncomp}\\sim 25$ K, which shows that the screening in this material can be\ndescribed in terms of a single temperature scale.\n"}
+{"abstract": "  We present a multi-line survey of the interstellar medium (ISM) in two $z>6$\nquasar (QSO) host galaxies, PJ231-20 ($z=6.59$) and PJ308-21 ($z=6.23$), and\ntheir two companion galaxies. Observations were carried out using the Atacama\nLarge (sub-)Millimeter Array (ALMA). We targeted eleven transitions including\natomic fine structure lines (FSLs) and molecular lines: [NII]$_{\\rm 205\\mu m}$,\n[CI]$_{\\rm 369\\mu m}$, CO ($J_{\\rm up} = 7, 10, 15, 16$), H$_2$O\n$3_{12}-2_{21}$, $3_{21}-3_{12}$, $3_{03}-2_{12}$, and the OH$_{\\rm 163\\mu m}$\ndoublet. The underlying far-infrared (FIR) continuum samples the Rayleigh-Jeans\ntail of the respective dust emission. By combining this information with our\nearlier ALMA [CII]$_{\\rm 158\\mu m}$ observations, we explore the effects of\nstar formation and black hole feedback on the galaxies' ISM using the CLOUDY\nradiative transfer models. We estimate dust masses, spectral indexes, IR\nluminosities, and star-formation rates from the FIR continuum. The analysis of\nthe FSLs indicates that the [CII]$_{\\rm 158\\mu m}$ and [CI]$_{\\rm 369\\mu m}$\nemission arises predominantly from the neutral medium in photodissociation\nregions (PDRs). We find that line deficits are in agreement with those of local\nluminous infrared galaxies. The CO spectral line energy distributions (SLEDs),\nreveal significant high-$J$ CO excitation in both quasar hosts. Our CO SLED\nmodeling of the quasar PJ231-20 shows that PDRs dominate the molecular mass and\nCO luminosities for $J_{\\rm up}\\le 7$, while the $J_{\\rm up}\\ge10$ CO emission\nis likely driven by X-ray dissociation regions produced by the active galactic\nnucleus (AGN) at the very center of the quasar host [abridged].\n"}
+{"abstract": "  The central idea of this review is to consider quantum field theory models\nrelevant for particle physics and replace the fermionic matter in these models\nby a bosonic one. This is mostly motivated by the fact that bosons are more\n``accessible'' and easier to manipulate for experimentalists, but this\n``substitution'' also leads to new physics and novel phenomena. It allows us to\ngain new information about among other things confinement and the dynamics of\nthe deconfinement transition. We will thus consider bosons in dynamical\nlattices corresponding to the bosonic Schwinger or Z$_2$ Bose-Hubbard models.\nAnother central idea of this review concerns atomic simulators of paradigmatic\nmodels of particle physics theory such as the Creutz-Hubbard ladder, or\nGross-Neveu-Wilson and Wilson-Hubbard models. Finally, we will briefly describe\nour efforts to design experimentally friendly simulators of these and other\nmodels relevant for particle physics.\n"}
+{"abstract": "  How can neural networks trained by contrastive learning extract features from\nthe unlabeled data? Why does contrastive learning usually need much stronger\ndata augmentations than supervised learning to ensure good representations?\nThese questions involve both the optimization and statistical aspects of deep\nlearning, but can hardly be answered by analyzing supervised learning, where\nthe target functions are the highest pursuit. Indeed, in self-supervised\nlearning, it is inevitable to relate to the optimization/generalization of\nneural networks to how they can encode the latent structures in the data, which\nwe refer to as the feature learning process.\n  In this work, we formally study how contrastive learning learns the feature\nrepresentations for neural networks by analyzing its feature learning process.\nWe consider the case where our data are comprised of two types of features: the\nmore semantically aligned sparse features which we want to learn from, and the\nother dense features we want to avoid. Theoretically, we prove that contrastive\nlearning using $\\mathbf{ReLU}$ networks provably learns the desired sparse\nfeatures if proper augmentations are adopted. We present an underlying\nprinciple called $\\textbf{feature decoupling}$ to explain the effects of\naugmentations, where we theoretically characterize how augmentations can reduce\nthe correlations of dense features between positive samples while keeping the\ncorrelations of sparse features intact, thereby forcing the neural networks to\nlearn from the self-supervision of sparse features. Empirically, we verified\nthat the feature decoupling principle matches the underlying mechanism of\ncontrastive learning in practice.\n"}
+{"abstract": "  In this paper, we propose a transformer based approach for visual grounding.\nUnlike previous proposal-and-rank frameworks that rely heavily on pretrained\nobject detectors or proposal-free frameworks that upgrade an off-the-shelf\none-stage detector by fusing textual embeddings, our approach is built on top\nof a transformer encoder-decoder and is independent of any pretrained detectors\nor word embedding models. Termed VGTR -- Visual Grounding with TRansformers,\nour approach is designed to learn semantic-discriminative visual features under\nthe guidance of the textual description without harming their location ability.\nThis information flow enables our VGTR to have a strong capability in capturing\ncontext-level semantics of both vision and language modalities, rendering us to\naggregate accurate visual clues implied by the description to locate the\ninterested object instance. Experiments show that our method outperforms\nstate-of-the-art proposal-free approaches by a considerable margin on five\nbenchmarks while maintaining fast inference speed.\n"}
+{"abstract": "  Microservices have become popular in the past few years, attracting the\ninterest of both academia and industry. Despite of its benefits, this new\narchitectural style still poses important challenges, such as resilience,\nperformance and evolution. Self-adaptation techniques have been applied\nrecently as an alternative to solve or mitigate those problems. However, due to\nthe range of quality attributes that affect microservice architectures, many\ndifferent self-adaptation strategies can be used. Thus, to understand the\nstate-of-the-art of the use of self-adaptation techniques and mechanisms in\nmicroservice-based systems, this work conducted a systematic mapping, in which\n21 primary studies were analyzed considering qualitative and quantitative\nresearch questions. The results show that most studies focus on the Monitor\nphase (28.57%) of the adaptation control loop, address the self-healing\nproperty (23.81%), apply a reactive adaptation strategy (80.95%) in the system\ninfrastructure level (47.62%) and use a centralized approach (38.10%). From\nthose, it was possible to propose some research directions to fill existing\ngaps.\n"}
+{"abstract": "  We recently found the globular cluster (GC) EXT8 in M31 to have an extremely\nlow metallicity of [Fe/H]=-2.91+/-0.04 using high-resolution spectroscopy. Here\nwe present a colour-magnitude diagram (CMD) for EXT8, obtained with the Wide\nField Camera 3 on board the Hubble Space Telescope. Compared with the CMDs of\nmetal-poor Galactic GCs, we find that the upper red giant branch (RGB) of EXT8\nis about 0.03 mag bluer in F606W-F814W and slightly steeper, as expected from\nthe low spectroscopic metallicity. The observed colour spread on the upper RGB\nis consistent with being caused entirely by the measurement uncertainties, and\nwe place an upper limit of sigma(F606W-F814W)=0.015 mag on any intrinsic colour\nspread. The corresponding metallicity spread can be up to sigma([Fe/H])=0.2 dex\nor >0.7 dex, depending on the isochrone library adopted. The horizontal branch\n(HB) is located mostly on the blue side of the instability strip and has a tail\nextending to at least M(F606W)=+3, as in the Galactic GC M15. We identify two\ncandidate RR Lyrae variables and several UV-luminous post-HB/post AGB star\ncandidates, including one very bright (M(F300X)=-3.2) source near the centre of\nEXT8. The surface brightness of EXT8 out to a radius of 25 arcsec is well\nfitted by a Wilson-type profile with an ellipticity of epsilon=0.20, a\nsemi-major axis core radius of 0.25\", and a central surface brightness of 15.2\nmag per square arcsec in the F606W band, with no evidence of extra-tidal\nstructure. Overall, EXT8 has properties consistent with it being a \"normal\",\nbut very metal-poor GC, and its combination of relatively high mass and very\nlow metallicity thus remains challenging to explain in the context of GC\nformation theories operating within the hierarchical galaxy assembly paradigm.\n"}
+{"abstract": "  The next-generation non-volatile memory (NVM) is striding into computer\nsystems as a new tier as it incorporates both DRAM's byte-addressability and\ndisk's persistency. Researchers and practitioners have considered building\npersistent memory by placing NVM on the memory bus for CPU to directly load and\nstore data. As a result, cache-friendly data structures have been developed for\nNVM. One of them is the prevalent B+-tree. State-of-the-art in-NVM B+-trees\nmainly focus on the optimization of write operations (insertion and deletion).\nHowever, search is of vital importance for B+-tree. Not only search-intensive\nworkloads benefit from an optimized search, but insertion and deletion also\nrely on a preceding search operation to proceed. In this paper, we attentively\nstudy a sorted B+-tree node that spans over contiguous cache lines. Such cache\nlines exhibit a monotonically increasing trend and searching a target key\nacross them can be accelerated by estimating a range the key falls into. To do\nso, we construct a probing Sentinel Array in which a sentinel stands for each\ncache line of B+-tree node. Checking the Sentinel Array avoids scanning\nunnecessary cache lines and hence significantly reduces cache misses for a\nsearch. A quantitative evaluation shows that using Sentinel Arrays boosts the\nsearch performance of state-of-the-art in-NVM B+-trees by up to 48.4% while the\ncost of maintaining of Sentinel Array is low.\n"}
+{"abstract": "  Kontsevich introduced certain ribbon graphs as cell decompositions for\ncombinatorial models of moduli spaces of complex curves with boundaries in his\nproof of Witten's conjecture. In this work, we define four types of generalised\nKontsevich graphs and find combinatorial relations among them. We call the main\ntype ciliated maps and use the auxiliary ones to show they satisfy a Tutte\nrecursion that we turn into a combinatorial interpretation of the loop\nequations of topological recursion for a large class of spectral curves. It\nfollows that ciliated maps, which are Feynman graphs for the Generalised\nKontsevich matrix Model (GKM), are computed by topological recursion. Our\nparticular instance of the GKM relates to the r-KdV integrable hierarchy and\nsince the string solution of the latter encodes intersection numbers with\nWitten's $r$-spin class, we find an identity between ciliated maps and $r$-spin\nintersection numbers, implying that they are also governed by topological\nrecursion. In turn, this paves the way towards a combinatorial understanding of\nWitten's class. This new topological recursion perspective on the GKM provides\nconcrete tools to explore the conjectural symplectic invariance property of\ntopological recursion for large classes of spectral curves.\n"}
+{"abstract": "  In recent years, the Internet of Things (IoT) technology has led to the\nemergence of multiple smart applications in different vital sectors including\nhealthcare, education, agriculture, energy management, etc. IoT aims to\ninterconnect several intelligent devices over the Internet such as sensors,\nmonitoring systems, and smart appliances to control, store, exchange, and\nanalyze collected data. The main issue in IoT environments is that they can\npresent potential vulnerabilities to be illegally accessed by malicious users,\nwhich threatens the safety and privacy of gathered data. To face this problem,\nseveral recent works have been conducted using microservices-based architecture\nto minimize the security threats and attacks related to IoT data. By employing\nmicroservices, these works offer extensible, reusable, and reconfigurable\nsecurity features. In this paper, we aim to provide a survey about\nmicroservices-based approaches for securing IoT applications. This survey will\nhelp practitioners understand ongoing challenges and explore new and promising\nresearch opportunities in the IoT security field. To the best of our knowledge,\nthis paper constitutes the first survey that investigates the use of\nmicroservices technology for securing IoT applications.\n"}
+{"abstract": "  The past decades have witnessed the prosperity of graph mining, with a\nmultitude of sophisticated models and algorithms designed for various mining\ntasks, such as ranking, classification, clustering and anomaly detection.\nGenerally speaking, the vast majority of the existing works aim to answer the\nfollowing question, that is, given a graph, what is the best way to mine it? In\nthis paper, we introduce the graph sanitation problem, to answer an orthogonal\nquestion. That is, given a mining task and an initial graph, what is the best\nway to improve the initially provided graph? By learning a better graph as part\nof the input of the mining model, it is expected to benefit graph mining in a\nvariety of settings, ranging from denoising, imputation to defense. We\nformulate the graph sanitation problem as a bilevel optimization problem, and\nfurther instantiate it by semi-supervised node classification, together with an\neffective solver named GaSoliNe. Extensive experimental results demonstrate\nthat the proposed method is (1) broadly applicable with respect to different\ngraph neural network models and flexible graph modification strategies, (2)\neffective in improving the node classification accuracy on both the original\nand contaminated graphs in various perturbation scenarios. In particular, it\nbrings up to 25% performance improvement over the existing robust graph neural\nnetwork methods.\n"}
+{"abstract": "  In the context of DP-SGD each round communicates a local SGD update which\nleaks some new information about the underlying local data set to the outside\nworld. In order to provide privacy, Gaussian noise is added to local SGD\nupdates. However, privacy leakage still aggregates over multiple training\nrounds. Therefore, in order to control privacy leakage over an increasing\nnumber of training rounds, we need to increase the added Gaussian noise per\nlocal SGD update. This dependence of the amount of Gaussian noise $\\sigma$ on\nthe number of training rounds $T$ may impose an impractical upper bound on $T$\n(because $\\sigma$ cannot be too large) leading to a low accuracy global model\n(because the global model receives too few local SGD updates). This makes\nDP-SGD much less competitive compared to other existing privacy techniques.\n  We show for the first time that for $(\\epsilon,\\delta)$-differential privacy\n$\\sigma$ can be chosen equal to $\\sqrt{2(\\epsilon +\\ln(1/\\delta))/\\epsilon}$\nfor $\\epsilon=\\Omega(T/N^2)$. In many existing machine learning problems, $N$\nis always large and $T=O(N)$. Hence, $\\sigma$ becomes ``independent'' of any\n$T=O(N)$ choice with $\\epsilon=\\Omega(1/N)$ (aggregation of privacy leakage\nincreases to a limit). This means that our $\\sigma$ only depends on $N$ rather\nthan $T$. This important discovery brings DP-SGD to practice -- as also\ndemonstrated by experiments -- because $\\sigma$ can remain small to make the\ntrained model have high accuracy even for large $T$ as usually happens in\npractice.\n"}
+{"abstract": "  We discuss functoriality properties of the Ozsvath-Szabo contact invariant,\nand expose a number of results which seemed destined for folklore. We clarify\nthe (in)dependence of the invariant on the basepoint, prove that it is\nfunctorial with respect to contactomorphisms, and show that it is strongly\nfunctorial under Stein cobordisms.\n"}
+{"abstract": "  The Standing Wave (SW) TESLA niobium-based superconducting radio frequency\nstructure is limited to an accelerating gradient of about 50 MV/m by the\ncritical RF magnetic field. To break through this barrier, we explore the\noption of niobium-based traveling wave (TW) structures. Optimization of TW\nstructures was done considering experimentally known limiting electric and\nmagnetic fields. It is shown that a TW structure can have an accelerating\ngradient above 70 MeV/m that is about 1.5 times higher than contemporary\nstanding wave structures with the same critical magnetic field. The other\nbenefit of TW structures shown is R/Q about 2 times higher than TESLA structure\nthat reduces the dynamic heat load by a factor of 2. A method is proposed how\nto make TW structures multipactor-free. Some design proposals are offered to\nfacilitate fabrication. Further increase of the real-estate gradient\n(equivalent to 80 MV/m active gradient) is also possible by increasing the\nlength of the accelerating structure because of higher group velocity and\ncell-to-cell coupling. Realization of this work opens paths to ILC energy\nupgrades beyond 1 TeV to 3 TeV in competition with CLIC. The paper will discuss\ncorresponding opportunities and challenges.\n"}
+{"abstract": "  We present here a detailed calculation of opacities for Fe~XVII at the\nphysical conditions corresponding to the base of the Solar convection zone.\nMany ingredients are involved in the calculation of opacities. We review the\nimpact of each ingredient on the final monochromatic and mean opacities\n(Rosseland and Planck). The necessary atomic data were calculated with the\n$R$-matrix and the distorted-wave (DW) methods. We study the effect of\nbroadening, of resolution, of the extent of configuration sets and of\nconfiguration interaction to understand the differences between several\ntheoretical predictions as well as the existing large disagreement with\nmeasurements. New Dirac $R$-matrix calculations including all configurations up\nto the $n=$ 4, 5 and $6$ complexes have been performed as well as corresponding\nBreit--Pauli DW calculations. The DW calculations have been extended to include\nautoionizing initial levels. A quantitative contrast is made between comparable\nDW and $R$-matrix models. We have reached self-convergence with $n=6$\n$R$-matrix and DW calculations. Populations in autoionizing initial levels\ncontribute significantly to the opacities and should not be neglected. The\n$R$-matrix and DW results are consistent under the similar treatment of\nresonance broadening. The comparison with the experiment shows a persistent\ndifference in the continuum while the filling of the windows shows some\nimprovement. The present study defines our path to the next generation of\nopacities and opacity tables for stellar modeling.\n"}
+{"abstract": "  The aim of this article is to provide characterizations for\nsubadditivity-like growth conditions for the so-called associated weight\nfunctions in terms of the defning weight sequence. Such growth requirements\narise frequently in the literature and are standard when dealing with\nultradifferentiable function classes defned by Braun-Meise-Taylor weight\nfunctions since they imply or even characterize important and desired\nconsequences for the underlying function spaces, e.g. closedness under\ncomposition.\n"}
+{"abstract": "  In a pervious paper Weidmann shows that there a bound on the number of orbits\nof edges in a tree on which a finitely generated group acts\n$(k,C)$-acylindrically. In this paper we extend this result to actions which\nare $k$-acylindrical except on a family of groups with \"finite height\". We also\ngive an example which gives a negative result to a conjecture of Weidmann from\nthe same paper and produce a sharp bound for groups acting $k$--acylindrically.\n"}
+{"abstract": "  The growing demand for connected devices and the increase in investments in\nthe Internet of Things (IoT) sector induce the growth of the market for this\ntechnology. IoT permeates all areas of life of an individual, from smartwatches\nto entire home assistants and solutions in different areas. The IoT concept is\ngradually increasing all over the globe. IoT projects induce an articulation of\nstudies in software engineering to prepare the development and operation of\nsoftware systems materialized in physical objects and structures interconnected\nwith embedded software and hosted in clouds. IoT projects have boundaries\nbetween development and operation stages. This study search for evidence in\nscientific literature to support these boundaries through Development and\nOperations (DevOps) principles. We rely on a Systematic Literature Review to\ninvestigate the relations of DevOps in IoT software systems. As a result, we\nidentify concepts, characterize the benefits and challenges in the context of\nknowledge previously reported in primary studies in the literature. The main\ncontributions of this paper are: (i) discussion of benefits and challenges for\nDevOps in IoT software systems, (ii) identification of tools, concepts, and\nprogramming languages used, and, (iii) perceived pipeline for this kind of\nsoftware development.\n"}
+{"abstract": "  A music mashup combines audio elements from two or more songs to create a new\nwork. To reduce the time and effort required to make them, researchers have\ndeveloped algorithms that predict the compatibility of audio elements. Prior\nwork has focused on mixing unaltered excerpts, but advances in source\nseparation enable the creation of mashups from isolated stems (e.g., vocals,\ndrums, bass, etc.). In this work, we take advantage of separated stems not just\nfor creating mashups, but for training a model that predicts the mutual\ncompatibility of groups of excerpts, using self-supervised and semi-supervised\nmethods. Specifically, we first produce a random mashup creation pipeline that\ncombines stem tracks obtained via source separation, with key and tempo\nautomatically adjusted to match, since these are prerequisites for high-quality\nmashups. To train a model to predict compatibility, we use stem tracks obtained\nfrom the same song as positive examples, and random combinations of stems with\nkey and/or tempo unadjusted as negative examples. To improve the model and use\nmore data, we also train on \"average\" examples: random combinations with\nmatching key and tempo, where we treat them as unlabeled data as their true\ncompatibility is unknown. To determine whether the combined signal or the set\nof stem signals is more indicative of the quality of the result, we experiment\non two model architectures and train them using semi-supervised learning\ntechnique. Finally, we conduct objective and subjective evaluations of the\nsystem, comparing them to a standard rule-based system.\n"}
+{"abstract": "  The success of Convolutional Neural Networks (CNNs) in computer vision is\nmainly driven by their strong inductive bias, which is strong enough to allow\nCNNs to solve vision-related tasks with random weights, meaning without\nlearning. Similarly, Long Short-Term Memory (LSTM) has a strong inductive bias\ntowards storing information over time. However, many real-world systems are\ngoverned by conservation laws, which lead to the redistribution of particular\nquantities -- e.g. in physical and economical systems. Our novel\nMass-Conserving LSTM (MC-LSTM) adheres to these conservation laws by extending\nthe inductive bias of LSTM to model the redistribution of those stored\nquantities. MC-LSTMs set a new state-of-the-art for neural arithmetic units at\nlearning arithmetic operations, such as addition tasks, which have a strong\nconservation law, as the sum is constant over time. Further, MC-LSTM is applied\nto traffic forecasting, modelling a pendulum, and a large benchmark dataset in\nhydrology, where it sets a new state-of-the-art for predicting peak flows. In\nthe hydrology example, we show that MC-LSTM states correlate with real-world\nprocesses and are therefore interpretable.\n"}
+{"abstract": "  In this work, we address the problem of formal safety verification for\nstochastic cyber-physical systems (CPS) equipped with ReLU neural network (NN)\ncontrollers. Our goal is to find the set of initial states from where, with a\npredetermined confidence, the system will not reach an unsafe configuration\nwithin a specified time horizon. Specifically, we consider discrete-time LTI\nsystems with Gaussian noise, which we abstract by a suitable graph. Then, we\nformulate a Satisfiability Modulo Convex (SMC) problem to estimate upper bounds\non the transition probabilities between nodes in the graph. Using this\nabstraction, we propose a method to compute tight bounds on the safety\nprobabilities of nodes in this graph, despite possible over-approximations of\nthe transition probabilities between these nodes. Additionally, using the\nproposed SMC formula, we devise a heuristic method to refine the abstraction of\nthe system in order to further improve the estimated safety bounds. Finally, we\ncorroborate the efficacy of the proposed method with simulation results\nconsidering a robot navigation example and comparison against a\nstate-of-the-art verification scheme.\n"}
+{"abstract": "  Galactic charged cosmic rays (notably electrons, positrons, antiprotons and\nlight antinuclei) are powerful probes of dark matter annihilation or decay, in\nparticular for candidates heavier than a few MeV or tiny evaporating primordial\nblack holes. Recent measurements by PAMELA, AMS-02, or VOYAGER on positrons and\nantiprotons already translate into constraints on several models over a large\nmass range. However, these constraints depend on Galactic transport models, in\nparticular the diffusive halo size, subject to theoretical and statistical\nuncertainties. We update the so-called MIN-MED-MAX benchmark transport\nparameters that yield generic minimal, median and maximal dark-matter induced\nfluxes; this reduces the uncertainties on fluxes by a factor of about 2 for\npositrons and 6 for antiprotons, with respect to their former version. We also\nprovide handy fitting formulae for the associated predicted secondary\nantiproton and positron background fluxes. Finally, for more refined analyses,\nwe provide the full details of the model parameters and covariance matrices of\nuncertainties.\n"}
+{"abstract": "  It has been shown beyond reasonable doubt that the majority (about 95%) of\nthe total energy budget of the universe is given by the dark components, namely\nDark Matter and Dark Energy. What constitutes these components remains to be\nsatisfactorily understood however, despite a number of promising candidates. An\nassociated conundrum is that of the coincidence, i.e. the question as to why\nthe Dark Matter and Dark Energy densities are of the same order of magnitude at\nthe present epoch, after evolving over the entire expansion history of the\nuniverse. In an attempt to address these, we consider a quantum potential\nresulting from a quantum corrected Raychaudhuri/Friedmann equation in presence\nof a cosmic fluid, which is presumed to be a Bose-Einstein condensate (BEC) of\nultralight bosons. For a suitable and physically motivated macroscopic ground\nstate wavefunction of the BEC, we show that a unified picture of the cosmic\ndark sector can indeed emerge, thus resolving the issue of the coincidence. The\neffective Dark energy component turns out to be a cosmological constant, by\nvirtue of a residual homogeneous term in the quantum potential. Furthermore,\ncomparison with the observational data gives an estimate of the mass of the\nconstituent bosons in the BEC, which is well within the bounds predicted from\nother considerations.\n"}
+{"abstract": "  We present a new form of intermittency, L\\'evy on-off intermittency, which\narises from multiplicative $\\alpha$-stable white noise close to an instability\nthreshold. We study this problem in the linear and nonlinear regimes, both\ntheoretically and numerically, for the case of a pitchfork bifurcation with\nfluctuating growth rate. We compute the stationary distribution analytically\nand numerically from the associated fractional Fokker-Planck equation in the\nStratonovich interpretation. We characterize the system in the parameter space\n$(\\alpha,\\beta)$ of the noise, with stability parameter $\\alpha\\in (0,2)$ and\nskewness parameter $\\beta\\in[-1,1]$. Five regimes are identified in this\nparameter space, in addition to the well-studied Gaussian case $\\alpha=2$.\nThree regimes are located at $1<\\alpha<2$, where the noise has finite mean but\ninfinite variance. They are differentiated by $\\beta$ and all display a\ncritical transition at the deterministic instability threshold, with on-off\nintermittency close to onset. Critical exponents are computed from the\nstationary distribution. Each regime is characterized by a specific form of the\ndensity and specific critical exponents, which differ starkly from the Gaussian\ncase. A finite or infinite number of integer-order moments may converge,\ndepending on parameters. Two more regimes are found at $0<\\alpha\\leq 1$. There,\nthe mean of the noise diverges, and no critical transition occurs. In one case\nthe origin is always unstable, independently of the distance $\\mu$ from the\ndeterministic threshold. In the other case, the origin is conversely always\nstable, independently of $\\mu$. We thus demonstrate that an instability subject\nto non-equilibrium, power-law-distributed fluctuations can display\nsubstantially different properties than for Gaussian thermal fluctuations, in\nterms of statistics and critical behavior.\n"}
+{"abstract": "  Object pose estimation from a single RGB image is a challenging problem due\nto variable lighting conditions and viewpoint changes. The most accurate pose\nestimation networks implement pose refinement via reprojection of a known,\ntextured 3D model, however, such methods cannot be applied without high quality\n3D models of the observed objects. In this work we propose an approach, namely\nan Innovation CNN, to object pose estimation refinement that overcomes the\nrequirement for reprojecting a textured 3D model. Our approach improves initial\npose estimation progressively by applying the Innovation CNN iteratively in a\nstochastic gradient descent (SGD) framework. We evaluate our method on the\npopular LINEMOD and Occlusion LINEMOD datasets and obtain state-of-the-art\nperformance on both datasets.\n"}
+{"abstract": "  In order to connect galaxy clusters to their progenitor protoclusters, we\nmust constrain the star formation histories within their member galaxies and\nthe timescale of virial collapse. In this paper we characterize the complex\nstar-forming properties of a $z=2.5$ protocluster in the COSMOS field using\nALMA dust continuum and new VLA CO(1-0) observations of two filaments\nassociated with the structure, sometimes referred to as the \"Hyperion\"\nprotocluster. We focus in particular on the protocluster \"core\" which has\npreviously been suggested as the highest redshift bona fide galaxy cluster\ntraced by extended X-ray emission in a stacked Chandra/XMM image. We re-analyze\nthis data and refute these claims, finding that at least 40 $\\pm$ 17% of\nextended X-ray sources of similar luminosity and size at this redshift arise\ninstead from Inverse Compton scattering off recently extinguished radio\ngalaxies rather than intracluster medium. Using ancillary COSMOS data, we also\nconstrain the SEDs of the two filaments' eight constituent galaxies from the\nrest-frame UV to radio. We do not find evidence for enhanced star formation\nefficiency in the core and conclude that the constituent galaxies are already\nmassive (M$_{\\star} \\approx 10^{11} M_{\\odot}$), with molecular gas reservoirs\n$>10^{10} M_{\\odot}$ that will be depleted within 200-400 Myr. Finally, we\ncalculate the halo mass of the nested core at $z=2.5$ and conclude that it will\ncollapse into a cluster of 2-9 $\\times 10^{14} M_{\\odot}$, comparable to the\nsize of the Coma cluster at $z=0$ and accounting for at least 50% of the total\nestimated halo mass of the extended \"Hyperion\" structure.\n"}
+{"abstract": "  Quantum-mechanical correlations of interacting fermions result in the\nemergence of exotic phases. Magnetic phases naturally arise in the\nMott-insulator regime of the Fermi-Hubbard model, where charges are localized\nand the spin degree of freedom remains. In this regime, the occurrence of\nphenomena such as resonating valence bonds, frustrated magnetism, and spin\nliquids is predicted. Quantum systems with engineered Hamiltonians can be used\nas simulators of such spin physics to provide insights beyond the capabilities\nof analytical methods and classical computers. To be useful, methods for the\npreparation of intricate many-body spin states and access to relevant\nobservables are required. Here, we show the quantum simulation of magnetism in\nthe Mott-insulator regime with a linear quantum-dot array. We characterize the\nenergy spectrum for a Heisenberg spin chain, from which we can identify when\nthe conditions for homogeneous exchange couplings are met. Next, we study the\nmultispin coherence with global exchange oscillations in both the singlet and\ntriplet subspace of the Heisenberg Hamiltonian. Last, we adiabatically prepare\nthe low-energy global singlet of the homogeneous spin chain and probe it with\ntwo-spin singlettriplet measurements on each nearest-neighbor pair and the\ncorrelations therein. The methods and control presented here open new\nopportunities for the simulation of quantum magnetism benefiting from the\nflexibility in tuning and layout of gate-defined quantum-dot arrays.\n"}
+{"abstract": "  The exotic range of known planetary systems has provoked an equally exotic\nrange of physical explanations for their diverse architectures. However,\nconstraining formation processes requires mapping the observed exoplanet\npopulation to that which initially formed in the protoplanetary disc. Numerous\nresults suggest that (internal or external) dynamical perturbation alters the\narchitectures of some exoplanetary systems. Isolating planets that have evolved\nwithout any perturbation can help constrain formation processes. We consider\nthe Kepler multiples, which have low mutual inclinations and are unlikely to\nhave been dynamically perturbed. We apply a modelling approach similar to that\nof Mulders et al. (2018), additionally accounting for the two-dimensionality of\nthe radius ($R =0.3-20\\,R_\\oplus$) and period ($P= 0.5-730$ days) distribution.\nWe find that an upper limit in planet mass of the form $M_{\\rm{lim}} \\propto\na^\\beta \\exp(-a_{\\rm{in}}/a)$, for semi-major axis $a$ and a broad range of\n$a_{\\rm{in}}$ and $\\beta$, can reproduce a distribution of $P$, $R$ that is\nindistinguishable from the observed distribution by our comparison metric. The\nindex is consistent with $\\beta= 1.5$, expected if growth is limited by\naccretion within the Hill radius. This model is favoured over models assuming a\nseparable PDF in $P$, $R$. The limit, extrapolated to longer periods, is\ncoincident with the orbits of RV-discovered planets ($a>0.2$ au,\n$M>1\\,M_{\\rm{J}}$) around recently identified low density host stars, hinting\nat isolation mass limited growth. We discuss the necessary circumstances for a\ncoincidental age-related bias as the origin of this result, concluding that\nsuch a bias is possible but unlikely. We conclude that, in light of the\nevidence that some planetary systems have been dynamically perturbed, simple\nmodels for planet growth during the formation stage are worth revisiting.\n"}
+{"abstract": "  Data is the key factor to drive the development of machine learning (ML)\nduring the past decade. However, high-quality data, in particular labeled data,\nis often hard and expensive to collect. To leverage large-scale unlabeled data,\nself-supervised learning, represented by contrastive learning, is introduced.\nThe objective of contrastive learning is to map different views derived from a\ntraining sample (e.g., through data augmentation) closer in their\nrepresentation space, while different views derived from different samples more\ndistant. In this way, a contrastive model learns to generate informative\nrepresentations for data samples, which are then used to perform downstream ML\ntasks. Recent research has shown that machine learning models are vulnerable to\nvarious privacy attacks. However, most of the current efforts concentrate on\nmodels trained with supervised learning. Meanwhile, data samples' informative\nrepresentations learned with contrastive learning may cause severe privacy\nrisks as well.\n  In this paper, we perform the first privacy analysis of contrastive learning\nthrough the lens of membership inference and attribute inference. Our\nexperimental results show that contrastive models trained on image datasets are\nless vulnerable to membership inference attacks but more vulnerable to\nattribute inference attacks compared to supervised models. The former is due to\nthe fact that contrastive models are less prone to overfitting, while the\nlatter is caused by contrastive models' capability of representing data samples\nexpressively. To remedy this situation, we propose the first privacy-preserving\ncontrastive learning mechanism, Talos, relying on adversarial training.\nEmpirical results show that Talos can successfully mitigate attribute inference\nrisks for contrastive models while maintaining their membership privacy and\nmodel utility.\n"}
+{"abstract": "  Many ecological and spatial processes are complex in nature and are not\naccurately modeled by linear models. Regression trees promise to handle the\nhigh-order interactions that are present in ecological and spatial datasets,\nbut fail to produce physically realistic characterizations of the underlying\nlandscape. The \"autocart\" (autocorrelated regression trees) R package extends\nthe functionality of previously proposed spatial regression tree methods\nthrough a spatially aware splitting function and novel adaptive inverse\ndistance weighting method in each terminal node. The efficacy of these autocart\nmodels, including an autocart extension of random forest, is demonstrated on\nmultiple datasets. This highlights the ability of autocart to model complex\ninteractions between spatial variables while still providing physically\nrealistic representations of the landscape.\n"}
+{"abstract": "  The lightest neutralino, assumed to be the lightest supersymmetric particle,\nis proposed to be a dark matter (DM) candidate for the mass $\\cal{O}$(100) GeV.\nConstraints from various direct dark matter detection experiments and Planck\nmeasurements exclude a substantial region of parameter space of the minimal\nsupersymmetric standard model (MSSM). However, a \"mild-tempered\" neutralino\nwith dominant bino composition and a little admixture of Higgsino is found to\nbe a viable candidate for DM. Within the MSSM framework, we revisit the allowed\nregion of parameter space that is consistent with all existing constraints.\nRegions of parameters that are not sensitive to direct detection experiments,\nknown as \"blind spots,\" are also revisited. Complimentary to the direct\ndetection of DM particles, a mild-tempered neutralino scenario is explored at\nthe LHC with the center of mass energy $\\rm \\sqrt{s}$=13 TeV through the\ntop-squark pair production, and its subsequent decays with the\nstandard-model-like Higgs boson in the final state. Our considered channel is\nfound to be very sensitive also to the blind spot scenario. Detectable signal\nsensitivities are achieved using the cut-based method for the high luminosity\noptions $\\rm 300$ and $\\rm 3000 ~fb^{-1}$, which are further improved by\napplying the multi-variate analysis technique.\n"}
+{"abstract": "  Various autonomous or assisted driving strategies have been facilitated\nthrough the accurate and reliable perception of the environment around a\nvehicle. Among the commonly used sensors, radar has usually been considered as\na robust and cost-effective solution even in adverse driving scenarios, e.g.,\nweak/strong lighting or bad weather. Instead of considering to fuse the\nunreliable information from all available sensors, perception from pure radar\ndata becomes a valuable alternative that is worth exploring. In this paper, we\npropose a deep radar object detection network, named RODNet, which is\ncross-supervised by a camera-radar fused algorithm without laborious annotation\nefforts, to effectively detect objects from the radio frequency (RF) images in\nreal-time. First, the raw signals captured by millimeter-wave radars are\ntransformed to RF images in range-azimuth coordinates. Second, our proposed\nRODNet takes a sequence of RF images as the input to predict the likelihood of\nobjects in the radar field of view (FoV). Two customized modules are also added\nto handle multi-chirp information and object relative motion. Instead of using\nhuman-labeled ground truth for training, the proposed RODNet is\ncross-supervised by a novel 3D localization of detected objects using a\ncamera-radar fusion (CRF) strategy in the training stage. Finally, we propose a\nmethod to evaluate the object detection performance of the RODNet. Due to no\nexisting public dataset available for our task, we create a new dataset, named\nCRUW, which contains synchronized RGB and RF image sequences in various driving\nscenarios. With intensive experiments, our proposed cross-supervised RODNet\nachieves 86% average precision and 88% average recall of object detection\nperformance, which shows the robustness to noisy scenarios in various driving\nconditions.\n"}
+{"abstract": "  Stochastic gradient algorithms are often unstable when applied to functions\nthat do not have Lipschitz-continuous and/or bounded gradients. Gradient\nclipping is a simple and effective technique to stabilize the training process\nfor problems that are prone to the exploding gradient problem. Despite its\nwidespread popularity, the convergence properties of the gradient clipping\nheuristic are poorly understood, especially for stochastic problems. This paper\nestablishes both qualitative and quantitative convergence results of the\nclipped stochastic (sub)gradient method (SGD) for non-smooth convex functions\nwith rapidly growing subgradients. Our analyses show that clipping enhances the\nstability of SGD and that the clipped SGD algorithm enjoys finite convergence\nrates in many cases. We also study the convergence of a clipped method with\nmomentum, which includes clipped SGD as a special case, for weakly convex\nproblems under standard assumptions. With a novel Lyapunov analysis, we show\nthat the proposed method achieves the best-known rate for the considered class\nof problems, demonstrating the effectiveness of clipped methods also in this\nregime. Numerical results confirm our theoretical developments.\n"}
+{"abstract": "  We present the first results from the Quasar Feedback Survey, a sample of 42\nz<0.2, [O III] luminous AGN (L[O III]>10^42.1 ergs/s) with moderate radio\nluminosities (i.e. L(1.4GHz)>10^23.4 W/Hz; median L(1.4GHz)=5.9x10^23 W/Hz).\nUsing high spatial resolution (~0.3-1 arcsec), 1.5-6 GHz radio images from the\nVery Large Array, we find that 67 percent of the sample have spatially extended\nradio features, on ~1-60 kpc scales. The radio sizes and morphologies suggest\nthat these may be lower radio luminosity versions of compact, radio-loud AGN.\nBy combining the radio-to-infrared excess parameter, spectral index, radio\nmorphology and brightness temperature, we find radio emission in at least 57\npercent of the sample that is associated with AGN-related processes (e.g. jets,\nquasar-driven winds or coronal emission). This is despite only 9.5-21 percent\nbeing classified as radio-loud using traditional criteria. The origin of the\nradio emission in the remainder of the sample is unclear. We find that both the\nestablished anti-correlation between radio size and the width of the [O III]\nline, and the known trend for the most [O III] luminous AGN to be associated\nwith spatially-extended radio emission, also hold for our sample of moderate\nradio luminosity quasars. These observations add to the growing evidence of a\nconnection between the radio emission and ionised gas in quasar host galaxies.\nThis work lays the foundation for deeper investigations into the drivers and\nimpact of feedback in this unique sample.\n"}
+{"abstract": "  The European Spallation Source (ESS), currently finishing its construction,\nwill soon provide the most intense neutron beams for multi-disciplinary\nscience. At the same time, it will also produce a high-intensity neutrino flux\nwith an energy suitable for precision measurements of Coherent Elastic\nNeutrino-Nucleus Scattering. We describe some physics prospects, within and\nbeyond the Standard Model, of employing innovative detector technologies to\ntake the most out of this large flux. We show that, compared to current\nmeasurements, the ESS will provide a much more precise understanding of\nneutrino and nuclear properties.\n"}
+{"abstract": "  With approximately 50 binary black hole events detected by LIGO/Virgo to date\nand many more expected in the next few years, gravitational-wave astronomy is\nshifting from individual-event analyses to population studies. We perform a\nhierarchical Bayesian analysis on the GWTC-2 catalog by combining several\nastrophysical formation models with a population of primordial black holes. We\ncompute the Bayesian evidence for a primordial population compared to the null\nhypothesis, and the inferred fraction of primordial black holes in the data. We\nfind that these quantities depend on the set of assumed astrophysical models:\nthe evidence for primordial black holes against an astrophysical-only\nmultichannel model is decisively favored in some scenarios, but it is\nsignificantly reduced in the presence of a dominant stable-mass-transfer\nisolated formation channel. The primordial channel can explain mergers in the\nupper mass gap such as GW190521, but (depending on the astrophysical channels\nwe consider) a significant fraction of the events could be of primordial origin\neven if we neglected GW190521. The tantalizing possibility that LIGO/Virgo may\nhave already detected black holes formed after inflation should be verified by\nreducing uncertainties in astrophysical and primordial formation models, and it\nmay ultimately be confirmed by third-generation interferometers.\n"}
+{"abstract": "  The paper is devoted to the participation of the TUDublin team in\nConstraint@AAAI2021 - COVID19 Fake News Detection Challenge. Today, the problem\nof fake news detection is more acute than ever in connection with the pandemic.\nThe number of fake news is increasing rapidly and it is necessary to create AI\ntools that allow us to identify and prevent the spread of false information\nabout COVID-19 urgently. The main goal of the work was to create a model that\nwould carry out a binary classification of messages from social media as real\nor fake news in the context of COVID-19. Our team constructed the ensemble\nconsisting of Bidirectional Long Short Term Memory, Support Vector Machine,\nLogistic Regression, Naive Bayes and a combination of Logistic Regression and\nNaive Bayes. The model allowed us to achieve 0.94 F1-score, which is within 5\\%\nof the best result.\n"}
+{"abstract": "  We consider the moduli space $\\mathcal{M}_{\\nu}$ of torsion-free,\nasymptotically conical (AC) Spin(7)-structures which are defined on the same\nmanifold and asymptotic to the same Spin(7)-cone with decay rate $\\nu<0$. We\nshow that $\\mathcal{M}_{\\nu}$ is an orbifold if $\\nu$ is a generic rate in the\nnon-$L^2$ regime $(-4,0)$. Infinitesimal deformations are given by topological\ndata and solutions to a non-elliptic first-order PDE system on the compact link\nof the asymptotic cone. As an application, we show that the classical\nBryant-Salamon metric on the bundle of positive spinors on $S^4$ has no\ncontinuous deformations as an AC Spin(7)-metric.\n"}
+{"abstract": "  The advent of large pre-trained language models has given rise to rapid\nprogress in the field of Natural Language Processing (NLP). While the\nperformance of these models on standard benchmarks has scaled with size,\ncompression techniques such as knowledge distillation have been key in making\nthem practical. We present, MATE-KD, a novel text-based adversarial training\nalgorithm which improves the performance of knowledge distillation. MATE-KD\nfirst trains a masked language model based generator to perturb text by\nmaximizing the divergence between teacher and student logits. Then using\nknowledge distillation a student is trained on both the original and the\nperturbed training samples. We evaluate our algorithm, using BERT-based models,\non the GLUE benchmark and demonstrate that MATE-KD outperforms competitive\nadversarial learning and data augmentation baselines. On the GLUE test set our\n6 layer RoBERTa based model outperforms BERT-Large.\n"}
+{"abstract": "  Simulated images of a black hole surrounded by optically thin emission\ntypically display two main features: a central brightness depression and a\nnarrow, bright \"photon ring\" consisting of strongly lensed images superposed on\ntop of the direct emission. The photon ring closely tracks a theoretical curve\non the image plane corresponding to light rays that asymptote to unstably bound\nphoton orbits around the black hole. This critical curve has a size and shape\nthat are purely governed by the Kerr geometry; in contrast, the size, shape,\nand depth of the observed brightness depression all depend on the details of\nthe emission region. For instance, images of spherical accretion models display\na distinctive dark region -- the \"black hole shadow\" -- that completely fills\nthe photon ring. By contrast, in models of equatorial disks extending to the\nblack hole's event horizon, the darkest region in the image is restricted to a\nmuch smaller area -- an inner shadow -- whose edge lies near the direct lensed\nimage of the equatorial horizon. Using both semi-analytic models and general\nrelativistic magnetohydrodynamic (GRMHD) simulations, we demonstrate that the\nphoton ring and inner shadow may be simultaneously visible in submillimeter\nimages of M87*, where magnetically arrested disk (MAD) simulations predict that\nthe emission arises in a thin region near the equatorial plane. We show that\nthe relative size, shape, and centroid of the photon ring and inner shadow can\nbe used to estimate the black hole mass and spin, breaking degeneracies in\nmeasurements of these quantities that rely on the photon ring alone. Both\nfeatures may be accessible to direct observation via high-dynamic-range images\nwith a next-generation Event Horizon Telescope.\n"}
+{"abstract": "  In this paper we investigate Erd\\H{o}s-Ko-Rado theorems in ovoidal circle\ngeometries. We prove that in M\\\"obius planes of even order greater than 2, and\novoidal Laguerre planes of odd order, the largest families of circles which\npairwise intersect in at least one point, consist of all circles through a\nfixed point. In ovoidal Laguerre planes of even order, a similar result holds,\nbut there is one other type of largest family of pairwise intersecting circles.\nAs a corollary, we prove that the largest families of polynomials over $\\mathbb\nF_q$ of degree at most $k$, with $2 \\leq k < q$, which pairwise take the same\nvalue on at least one point, consist of all polynomials $f$ of degree at most\n$k$ such that $f(x) = y$ for some fixed $x$ and $y$ in $\\mathbb F_q$. We also\ndiscuss this problem for ovoidal Minkowski planes, and we investigate the\nlargest families of circles pairwise intersecting in two points in circle\ngeometries.\n"}
+{"abstract": "  We investigated laser-induced periodic surface structures (LIPSS) generated\non indium-tin-oxide (ITO) thin films with femtosecond laser pulses in the\ninfrared region. Using pulses between 1.6 and 2.4 ${\\mu}$m central wavelength,\nwe observed robust LIPSS morphologies with a periodicity close to\n${\\lambda}$/10. Supporting finite-difference time-domain calculations suggest\nthat the surface forms are rooted in the field localization in the surface pits\nleading to a periodically increased absorption of the laser pulse energy that\ncreates the observed periodic structures.\n"}
+{"abstract": "  We study the 2+1 dimensional continuum model for the evolution of stepped\nepitaxial surface under long-range elastic interaction proposed by Xu and Xiang\n(SIAM J. Appl. Math. 69, 1393-1414, 2009). The long-range interaction term and\nthe two length scales in this model makes PDE analysis challenging. Moreover,\nunlike in the 1+1 dimensional case, there is a nonconvexity contribution (of\nthe gradient norm of the surface height) in the total energy in the 2+1\ndimensional case, and it is not easy to prove that the solution is always in\nthe well-posed regime during the evolution. In this paper, we propose a\nmodified 2+1 dimensional continuum model and prove the existence and uniqueness\nof both the static and dynamic solutions and derive a minimum energy scaling\nlaw for it. We show that the minimum energy surface profile is mainly attained\nby surfaces with step meandering instability. This is essentially different\nfrom the energy scaling law for the 1+1 dimensional epitaxial surfaces under\nelastic effects attained by step bunching surface profiles. We also discuss the\ntransition from the step bunching instability to the step meandering\ninstability in 2+1 dimensions.\n"}
+{"abstract": "  Full connectivity of qubits is necessary for most quantum algorithms, which\nis difficult to directly implement on Noisy Intermediate-Scale Quantum\nprocessors. However, inserting swap gate to enable the two-qubit gates between\nuncoupled qubits significantly decreases the computation result fidelity. To\nthis end, we propose a Special-Purpose Quantum Processor Design method that can\ndesign suitable structures for different quantum algorithms. Our method extends\nthe processor structure from two-dimensional lattice graph to general planar\ngraph and arranges the physical couplers according to the two-qubit gate\ndistribution between the logical qubits of the quantum algorithm and the\nphysical constraints. Experimental results show that our design methodology,\ncompared with other methods, could reduce the number of extra swap gates per\ntwo-qubit gate by at least 104.2% on average. Also, our method's advantage over\nother methods becomes more obvious as the depth and qubit number increase. The\nresult reveals that our method is competitive in improving computation result\nfidelity and it has the potential to demonstrate quantum advantage under the\ntechnical conditions.\n"}
+{"abstract": "  In this paper, we consider the Potts-SOS model where the spin takes values in\nthe set $\\{0, 1, 2\\}$ on the Cayley tree of order two. We describe all the\ntranslation-invariant splitting Gibbs measures for this model in some\nconditions. Moreover, we investigate whether these Gibbs measures are extremal\nor non-extremal in the set of all Gibbs measures.\n"}
+{"abstract": "  Machine learning models often use spurious patterns such as \"relying on the\npresence of a person to detect a tennis racket,\" which do not generalize. In\nthis work, we present an end-to-end pipeline for identifying and mitigating\nspurious patterns for image classifiers. We start by finding patterns such as\n\"the model's prediction for tennis racket changes 63% of the time if we hide\nthe people.\" Then, if a pattern is spurious, we mitigate it via a novel form of\ndata augmentation. We demonstrate that this approach identifies a diverse set\nof spurious patterns and that it mitigates them by producing a model that is\nboth more accurate on a distribution where the spurious pattern is not helpful\nand more robust to distribution shift.\n"}
+{"abstract": "  Tensegrity structures are lightweight, can undergo large deformations, and\nhave outstanding robustness capabilities. These unique properties inspired\nroboticists to investigate their use. However, the morphological design,\ncontrol, assembly, and actuation of tensegrity robots are still difficult\ntasks. Moreover, the stiffness of tensegrity robots is still an underestimated\ndesign parameter. In this article, we propose to use easy to assemble, actuated\ntensegrity modules and body-brain co-evolution to design soft tensegrity\nmodular robots. Moreover, we prove the importance of tensegrity robots\nstiffness showing how the evolution suggests a different morphology, control,\nand locomotion strategy according to the modules stiffness.\n"}
+{"abstract": "  We derive the conjugate prior of the Dirichlet and beta distributions and\nexplore it with numerical examples to gain an intuitive understanding of the\ndistribution itself, its hyperparameters, and conditions concerning its\nconvergence. Due to the prior's intractability, we proceed to define and\nanalyze a closed-form approximation. Finally, we provide an algorithm\nimplementing this approximation that enables fully tractable Bayesian conjugate\ntreatment of Dirichlet and beta likelihoods without the need for Monte Carlo\nsimulations.\n"}
+{"abstract": "  We consider non-convex stochastic optimization using first-order algorithms\nfor which the gradient estimates may have heavy tails. We show that a\ncombination of gradient clipping, momentum, and normalized gradient descent\nyields convergence to critical points in high-probability with best-known rates\nfor smooth losses when the gradients only have bounded $\\mathfrak{p}$th moments\nfor some $\\mathfrak{p}\\in(1,2]$. We then consider the case of second-order\nsmooth losses, which to our knowledge have not been studied in this setting,\nand again obtain high-probability bounds for any $\\mathfrak{p}$. Moreover, our\nresults hold for arbitrary smooth norms, in contrast to the typical SGD\nanalysis which requires a Hilbert space norm. Further, we show that after a\nsuitable \"burn-in\" period, the objective value will monotonically decrease for\nevery iteration until a critical point is identified, which provides intuition\nbehind the popular practice of learning rate \"warm-up\" and also yields a\nlast-iterate guarantee.\n"}
+{"abstract": "  We give a local characterization for the Cuntz semigroup of AI-algebras\nbuilding upon Shen's characterization of dimension groups. Using this result,\nwe provide an abstract characterization for the Cuntz semigroup of AI-algebras.\n"}
+{"abstract": "  Much recent interest has focused on the design of optimization algorithms\nfrom the discretization of an associated optimization flow, i.e., a system of\ndifferential equations (ODEs) whose trajectories solve an associated\noptimization problem. Such a design approach poses an important problem: how to\nfind a principled methodology to design and discretize appropriate ODEs. This\npaper aims to provide a solution to this problem through the use of contraction\ntheory. We first introduce general mathematical results that explain how\ncontraction theory guarantees the stability of the implicit and explicit Euler\nintegration methods. Then, we propose a novel system of ODEs, namely the\nAccelerated-Contracting-Nesterov flow, and use contraction theory to establish\nit is an optimization flow with exponential convergence rate, from which the\nlinear convergence rate of its associated optimization algorithm is immediately\nestablished. Remarkably, a simple explicit Euler discretization of this flow\ncorresponds to the Nesterov acceleration method. Finally, we present how our\napproach leads to performance guarantees in the design of optimization\nalgorithms for time-varying optimization problems.\n"}
+{"abstract": "  Depth maps captured with commodity sensors are often of low quality and\nresolution; these maps need to be enhanced to be used in many applications.\nState-of-the-art data-driven methods of depth map super-resolution rely on\nregistered pairs of low- and high-resolution depth maps of the same scenes.\nAcquisition of real-world paired data requires specialized setups. Another\nalternative, generating low-resolution maps from high-resolution maps by\nsubsampling, adding noise and other artificial degradation methods, does not\nfully capture the characteristics of real-world low-resolution images. As a\nconsequence, supervised learning methods trained on such artificial paired data\nmay not perform well on real-world low-resolution inputs. We consider an\napproach to depth super-resolution based on learning from unpaired data. While\nmany techniques for unpaired image-to-image translation have been proposed,\nmost fail to deliver effective hole-filling or reconstruct accurate surfaces\nusing depth maps. We propose an unpaired learning method for depth\nsuper-resolution, which is based on a learnable degradation model, enhancement\ncomponent and surface normal estimates as features to produce more accurate\ndepth maps. We propose a benchmark for unpaired depth SR and demonstrate that\nour method outperforms existing unpaired methods and performs on par with\npaired.\n"}
+{"abstract": "  We consider the Schr\\\"odinger equation with nonlinear derivative term on\n$[0,+\\infty)$ under Robin boundary condition at $0$. Using a virial argument,\nwe obtain the existence of blowing up solutions and using variational\ntechniques, we obtain stability and instability by blow up results for standing\nwaves.\n"}
+{"abstract": "  A key question concerning collective decisions is whether a social system can\nsettle on the best available option when some members learn from others instead\nof evaluating the options on their own. This question is challenging to study,\nand previous research has reached mixed conclusions, because collective\ndecision outcomes depend on the insufficiently understood complex system of\ncognitive strategies, task properties, and social influence processes. This\nstudy integrates these complex interactions together in one general yet\npartially analytically tractable mathematical framework using a dynamical\nsystem model. In particular, it investigates how the interplay of the\nproportion of social learners, the relative merit of options, and the type of\nconformity response affect collective decision outcomes in a binary choice. The\nmodel predicts that when the proportion of social learners exceeds a critical\nthreshold, a bi-stable state appears in which the majority can end up favoring\neither the higher- or lower-merit option, depending on fluctuations and initial\nconditions. Below this threshold, the high-merit option is chosen by the\nmajority. The critical threshold is determined by the conformity response\nfunction and the relative merits of the two options. The study helps reconcile\ndisagreements about the effect of social learners on collective performance and\nproposes a mathematical framework that can be readily adapted to extensions\ninvestigating a wider variety of dynamics.\n"}
+{"abstract": "  Dirbusting is a technique used to brute force directories and file names on\nweb servers while monitoring HTTP responses, in order to enumerate server\ncontents. Such a technique uses lists of common words to discover the hidden\nstructure of the target website. Dirbusting typically relies on response codes\nas discovery conditions to find new pages. It is widely used in web application\npenetration testing, an activity that allows companies to detect websites\nvulnerabilities. Dirbusting techniques are both time and resource consuming and\ninnovative approaches have never been explored in this field. We hence propose\nan advanced technique to optimize the dirbusting process by leveraging\nArtificial Intelligence. More specifically, we use semantic clustering\ntechniques in order to organize wordlist items in different groups according to\ntheir semantic meaning. The created clusters are used in an ad-hoc implemented\nnext-word intelligent strategy. This paper demonstrates that the usage of\nclustering techniques outperforms the commonly used brute force methods.\nPerformance is evaluated by testing eight different web applications. Results\nshow a performance increase that is up to 50% for each of the conducted\nexperiments.\n"}
+{"abstract": "  Decision-making policies for agents are often synthesized with the constraint\nthat a formal specification of behaviour is satisfied. Here we focus on\ninfinite-horizon properties. On the one hand, Linear Temporal Logic (LTL) is a\npopular example of a formalism for qualitative specifications. On the other\nhand, Steady-State Policy Synthesis (SSPS) has recently received considerable\nattention as it provides a more quantitative and more behavioural perspective\non specifications, in terms of the frequency with which states are visited.\nFinally, rewards provide a classic framework for quantitative properties. In\nthis paper, we study Markov decision processes (MDP) with the specification\ncombining all these three types. The derived policy maximizes the reward among\nall policies ensuring the LTL specification with the given probability and\nadhering to the steady-state constraints. To this end, we provide a unified\nsolution reducing the multi-type specification to a multi-dimensional long-run\naverage reward. This is enabled by Limit-Deterministic B\\\"uchi Automata (LDBA),\nrecently studied in the context of LTL model checking on MDP, and allows for an\nelegant solution through a simple linear programme. The algorithm also extends\nto the general $\\omega$-regular properties and runs in time polynomial in the\nsizes of the MDP as well as the LDBA.\n"}
+{"abstract": "  We study the design of revenue-maximizing bilateral trade mechanisms in the\ncorrelated private value environment. We assume the designer only knows the\nexpectations of the agents' values, but knows neither the marginal distribution\nnor the correlation structure. The performance of a mechanism is evaluated in\nthe worst-case over the uncertainty of joint distributions that are consistent\nwith the known expectations. Among all dominant-strategy incentive compatible\nand ex-post individually rational mechanisms, we provide a complete\ncharacterization of the maxmin trade mechanisms and the worst-case joint\ndistributions.\n"}
+{"abstract": "  The design of moderators and cold sources of neutrons is a key point in\nresearch-reactor physics, requiring extensive knowledge of the scattering\nproperties of very important light molecular liquids such as methane, hydrogen\nand their deuterated counterparts. Inelastic scattering measurements constitute\nthe basic source of such information but are difficult to perform, the more so\nwhen high accuracy is required, and additional experimental information is\nscarce. The need of data covering as large as possible portions of the\nkinematic Q-E plane thus pushes towards the use of computable models, validated\nby testing them, mainly, against integral quantities (either known from theory\nor measured) such as spectral moments and total cross section data. A few\nrecent experiments demonstrated that, at least for the self contribution, which\ndominates in the incoherent scattering case of hydrogen, accurate calculations\ncan be performed by means of quantum simulations of the velocity\nautocorrelation function. This method is shown here to be by far superior to\nthe use of standard analytical models devised, although rather cleverly, for\ngeneric classical samples. The neutron dynamic structure factor (and\nconsequently the well-known S({\\alpha},{\\beta}) of parahydrogen and deuterium,\nsuitable for use in packages like NJOY, are given and shown to agree very well\nwith total cross section measurements and expected quantum properties.\n"}
+{"abstract": "  Observations of the synchrotron and inverse Compton emissions from\nultrarelativistic electrons in astrophysical sources can reveal a great deal\nabout the energy-momentum relations of those electrons. They can thus be used\nto place bounds on the possibility of Lorentz violation in the electron sector.\nRecent $\\gamma$-ray telescope data allow the Lorentz-violating electron\n$c^{\\nu\\mu}$ parameters to be constrained extremely well, so that all bounds\nare at the level of $7\\times 10^{-16}$ or better.\n"}
+{"abstract": "  We define a renormalized volume for a region in an asymptotically hyperbolic\nEinstein manifold that is bounded by a Graham-Witten minimal surface and the\nconformal infinity. We prove a Gauss-Bonnet theorem for the renormalized\nvolume, and compute its derivative under variations of the minimal\nhypersurface.\n"}
+{"abstract": "  We present a symmetry-based scheme to create 0D second-order topological\nmodes in continuous 2D systems. We show that a metamaterial with a\n\\textit{p6m}-symmetric pattern exhibits two Dirac cones, which can be gapped in\ntwo distinct ways by deforming the pattern. Combining the deformations in a\nsingle system then emulates the 2D Jackiw-Rossi model of a topological vortex,\nwhere 0D in-gap bound modes are guaranteed to exist. We exemplify our approach\nwith simple hexagonal, Kagome and honeycomb lattices. We furthermore formulate\na quantitative method to extract the topological properties from finite-element\nsimulations, which facilitates further optimization of the bound mode\ncharacteristics. Our scheme enables the realization of second-order topology in\na wide range of experimental systems.\n"}
+{"abstract": "  A superdiagonal composition is one in which the $i$-th part or summand is of\nsize greater than or equal to $i$. In this paper, we study the number of\npalindromic superdiagonal compositions and colored superdiagonal compositions.\nIn particular, we give generating functions and explicit combinatorial formulas\ninvolving binomial coefficients and Stirling numbers of the first kind.\n"}
+{"abstract": "  Relativistic AGN jets exhibit multi-timescale variability and a broadband\nnon-thermal spectrum extending from radio to gamma-rays. These highly\nmagnetized jets are prone to undergo several Magneto-hydrodynamic (MHD)\ninstabilities during their propagation in space and could trigger jet radiation\nand particle acceleration. This work aims to study the implications of\nrelativistic kink mode instability on the observed long-term variability in the\ncontext of the twisting in-homogeneous jet model. To achieve this, we\ninvestigate the physical configurations preferable for forming kink mode\ninstability by performing high-resolution 3D relativistic MHD simulations of a\nportion of highly magnetized jets. In particular, we perform simulations of\ncylindrical plasma column with Lorentz factor $\\geq 5$ and study the effects of\nmagnetization values and axial wave-numbers with decreasing pitch on the onset\nand growth of kink instability. We have confirmed the impact of axial\nwave-number on the dynamics of the plasma column including the growth of the\ninstability. In this work, we have further investigated the connection between\nthe dynamics of the plasma column with its time-varying emission features. From\nour analysis, we find a correlated trend between the growth rate of kink mode\ninstability and the flux variability obtained from the simulated light curve.\n"}
+{"abstract": "  Virtually anything can be and is ranked; people and animals, universities and\ncountries, words and genes. Rankings reduce the components of highly complex\nsystems into ordered lists, aiming to capture the fitness or ability of each\nelement to perform relevant functions, and are being used from socioeconomic\npolicy to knowledge extraction. A century of research has found regularities in\nranking lists across nature and society when data is aggregated over time. Far\nless is known, however, about ranking dynamics, when the elements change their\nrank in time. To bridge this gap, here we explore the dynamics of 30 ranking\nlists in natural, social, economic, and infrastructural systems, comprising\nmillions of elements, whose temporal scales span from minutes to centuries. We\nfind that the flux governing the arrival of new elements into a ranking list\nreveals systems with identifiable patterns of stability: in high-flux systems\nonly the top of the list is stable, while in low-flux systems the top and\nbottom are equally stable. We show that two basic mechanisms - displacement and\nreplacement of elements - are sufficient to understand and quantify ranking\ndynamics. The model uncovers two regimes in the dynamics of ranking lists: a\nfast regime dominated by long-range rank changes, and a slow regime driven by\ndiffusion. Our results indicate that the balance between robustness and\nadaptability characterizing the dynamics of complex systems might be governed\nby random processes irrespective of the details of each system.\n"}
+{"abstract": "  Recent years have seen considerable research activities devoted to video\nenhancement that simultaneously increases temporal frame rate and spatial\nresolution. However, the existing methods either fail to explore the intrinsic\nrelationship between temporal and spatial information or lack flexibility in\nthe choice of final temporal/spatial resolution. In this work, we propose an\nunconstrained space-time video super-resolution network, which can effectively\nexploit space-time correlation to boost performance. Moreover, it has complete\nfreedom in adjusting the temporal frame rate and spatial resolution through the\nuse of the optical flow technique and a generalized pixelshuffle operation. Our\nextensive experiments demonstrate that the proposed method not only outperforms\nthe state-of-the-art, but also requires far fewer parameters and less running\ntime.\n"}
+{"abstract": "  In Zero-shot learning (ZSL), we classify unseen categories using textual\ndescriptions about their expected appearance when observed (class embeddings)\nand a disjoint pool of seen classes, for which annotated visual data are\naccessible. We tackle ZSL by casting a \"vanilla\" convolutional neural network\n(e.g. AlexNet, ResNet-101, DenseNet-201 or DarkNet-53) into a zero-shot\nlearner. We do so by crafting the softmax classifier: we freeze its weights\nusing fixed seen classification rules, either semantic (seen class embeddings)\nor visual (seen class prototypes). Then, we learn a data-driven and\nZSL-tailored feature representation on seen classes only to match these fixed\nclassification rules. Given that the latter seamlessly generalize towards\nunseen classes, while requiring not actual unseen data to be computed, we can\nperform ZSL inference by augmenting the pool of classification rules at test\ntime while keeping the very same representation we learnt: nowhere re-training\nor fine-tuning on unseen data is performed. The combination of semantic and\nvisual crafting (by simply averaging softmax scores) improves prior\nstate-of-the-art methods in benchmark datasets for standard, inductive ZSL.\nAfter rebalancing predictions to better handle the joint inference over seen\nand unseen classes, we outperform prior generalized, inductive ZSL methods as\nwell. Also, we gain interpretability at no additional cost, by using neural\nattention methods (e.g., grad-CAM) as they are. Code will be made publicly\navailable.\n"}
+{"abstract": "  Learning from implicit feedback is one of the most common cases in the\napplication of recommender systems. Generally speaking, interacted examples are\nconsidered as positive while negative examples are sampled from uninteracted\nones. However, noisy examples are prevalent in real-world implicit feedback. A\nnoisy positive example could be interacted but it actually leads to negative\nuser preference. A noisy negative example which is uninteracted because of\nunawareness of the user could also denote potential positive user preference.\nConventional training methods overlook these noisy examples, leading to\nsub-optimal recommendations. In this work, we propose a novel framework to\nlearn robust recommenders from implicit feedback. Through an empirical study,\nwe find that different models make relatively similar predictions on clean\nexamples which denote the real user preference, while the predictions on noisy\nexamples vary much more across different models. Motivated by this observation,\nwe propose denoising with cross-model agreement(DeCA) which aims to minimize\nthe KL-divergence between the real user preference distributions parameterized\nby two recommendation models while maximizing the likelihood of data\nobservation. We employ the proposed DeCA on four state-of-the-art\nrecommendation models and conduct experiments on four datasets. Experimental\nresults demonstrate that DeCA significantly improves recommendation performance\ncompared with normal training and other denoising methods. Codes will be\nopen-sourced.\n"}
+{"abstract": "  Meta-learning, or learning to learn, is a technique that can help to overcome\nresource scarcity in cross-lingual NLP problems, by enabling fast adaptation to\nnew tasks. We apply model-agnostic meta-learning (MAML) to the task of\ncross-lingual dependency parsing. We train our model on a diverse set of\nlanguages to learn a parameter initialization that can adapt quickly to new\nlanguages. We find that meta-learning with pre-training can significantly\nimprove upon the performance of language transfer and standard supervised\nlearning baselines for a variety of unseen, typologically diverse, and\nlow-resource languages, in a few-shot learning setup.\n"}
+{"abstract": "  This manuscript presents an algorithm for obtaining an approximation of\nnonlinear high order control affine dynamical systems, that leverages the\ncontrolled trajectories as the central unit of information. As the fundamental\nbasis elements leveraged in approximation, higher order control occupation\nkernels represent iterated integration after multiplication by a given\ncontroller in a vector valued reproducing kernel Hilbert space. In a\nregularized regression setting, the unique optimizer for a particular\noptimization problem is expressed as a linear combination of these occupation\nkernels, which converts an infinite dimensional optimization problem to a\nfinite dimensional optimization problem through the representer theorem.\nInterestingly, the vector valued structure of the Hilbert space allows for\nsimultaneous approximation of the drift and control effectiveness components of\nthe control affine system. Several experiments are performed to demonstrate the\neffectiveness of the approach.\n"}
+{"abstract": "  In 1930, Wilhelm Magnus introduced the so-called Freiheitssatz: Let $F$ be a\nfree group with basis $\\mathcal{X}$ and let $r$ be a cyclically reduced element\nof $F$ which contains a basis element $x \\in \\mathcal{X}$, then every\nnon-trivial element of the normal closure of $r$ in $F$ contains the basis\nelement $x$. Equivalently, the subgroup freely generated by $\\mathcal{X}\n\\backslash \\{x\\}$ embeds canonically into the quotient group $F / \\langle \\!\n\\langle r \\rangle \\! \\rangle_{F}$. In this article, we want to introduce a\nFreiheitssatz for amalgamated products $G=A \\ast_{U} B$ of free groups $A$ and\n$B$, where $U$ is a maximal cyclic subgroup in $A$ and $B$: If an element $r$\nof $G$ is neither conjugate to an element of $A$ nor $B$, then the factors $A$,\n$B$ embed canonically into $G / \\langle \\! \\langle r \\rangle \\! \\rangle_{G}$.\n"}
+{"abstract": "  In this article, we prove a $p$-adic analogue of the local invariant cycle\ntheorem for $H^2$ in mixed characteristics. As a result, for a smooth\nprojective variety $X$ over a $p$-adic local field $K$ with a proper flat\nregular model $\\mathcal{X}$ over $O_K$, we show that the natural map\n$Br(\\mathcal{X})\\rightarrow Br(X_{\\bar{K}})^{G_K}$ has a finite kernel and a\nfinite cokernel. And we prove that the natural map\n$Hom(Br(X)/Br(K)+Br(\\mathcal{X}), \\mathbb{Q}/\\mathbb{Z}) \\rightarrow Alb_X(K)$\nhas a finite kernel and a finite cokernel, generalizing Lichtenbaum's duality\nbetween Brauer groups and Jacobians for curves to arbitrary dimensions.\n"}
+{"abstract": "  Quasiparticles and analog models are ubiquitous in the study of physical\nsystems. Little has been written about quasiparticles on manifolds with\nanticommuting co-ordinates, yet they are capable of emulating a surprising\nrange of physical phenomena. This paper introduces a classical model of free\nfields on a manifold with anticommuting co-ordinates, identifies the region of\nsuperspace which the model inhabits, and shows that the model emulates the\nbehaviour of a five-species interacting quantum field theory on\n$\\mathbb{R}^{1,3}$. The Lagrangian of this model arises entirely from the\nanticommutation property of the manifold co-ordinates.\n"}
+{"abstract": "  We study the problem of controlling oscillations in closed loop by combining\npositive and negative feedback in a mixed configuration. We develop a complete\ndesign procedure to set the relative strength of the two feedback loops to\nachieve steady oscillations. The proposed design takes advantage of dominance\ntheory and adopts classical harmonic balance and fast/slow analysis to regulate\nthe frequency of oscillations. The design is illustrated on a simple two-mass\nsystem, a setting that reveals the potential of the approach for locomotion,\nmimicking approaches based on central pattern generators.\n"}
+{"abstract": "  On-device training for personalized learning is a challenging research\nproblem. Being able to quickly adapt deep prediction models at the edge is\nnecessary to better suit personal user needs. However, adaptation on the edge\nposes some questions on both the efficiency and sustainability of the learning\nprocess and on the ability to work under shifting data distributions. Indeed,\nnaively fine-tuning a prediction model only on the newly available data results\nin catastrophic forgetting, a sudden erasure of previously acquired knowledge.\nIn this paper, we detail the implementation and deployment of a hybrid\ncontinual learning strategy (AR1*) on a native Android application for\nreal-time on-device personalization without forgetting. Our benchmark, based on\nan extension of the CORe50 dataset, shows the efficiency and effectiveness of\nour solution.\n"}
+{"abstract": "  Deep learning based methods hold state-of-the-art results in image denoising,\nbut remain difficult to interpret due to their construction from poorly\nunderstood building blocks such as batch-normalization, residual learning, and\nfeature domain processing. Unrolled optimization networks propose an\ninterpretable alternative to constructing deep neural networks by deriving\ntheir architecture from classical iterative optimization methods, without use\nof tricks from the standard deep learning tool-box. So far, such methods have\ndemonstrated performance close to that of state-of-the-art models while using\ntheir interpretable construction to achieve a comparably low learned parameter\ncount. In this work, we propose an unrolled convolutional dictionary learning\nnetwork (CDLNet) and demonstrate its competitive denoising performance in both\nlow and high parameter count regimes. Specifically, we show that the proposed\nmodel outperforms the state-of-the-art denoising models when scaled to similar\nparameter count. In addition, we leverage the model's interpretable\nconstruction to propose an augmentation of the network's thresholds that\nenables state-of-the-art blind denoising performance and near-perfect\ngeneralization on noise-levels unseen during training.\n"}
+{"abstract": "  Differentially private (DP) stochastic convex optimization (SCO) is a\nfundamental problem, where the goal is to approximately minimize the population\nrisk with respect to a convex loss function, given a dataset of i.i.d. samples\nfrom a distribution, while satisfying differential privacy with respect to the\ndataset. Most of the existing works in the literature of private convex\noptimization focus on the Euclidean (i.e., $\\ell_2$) setting, where the loss is\nassumed to be Lipschitz (and possibly smooth) w.r.t. the $\\ell_2$ norm over a\nconstraint set with bounded $\\ell_2$ diameter. Algorithms based on noisy\nstochastic gradient descent (SGD) are known to attain the optimal excess risk\nin this setting.\n  In this work, we conduct a systematic study of DP-SCO for $\\ell_p$-setups.\nFor $p=1$, under a standard smoothness assumption, we give a new algorithm with\nnearly optimal excess risk. This result also extends to general polyhedral\nnorms and feasible sets. For $p\\in(1, 2)$, we give two new algorithms, whose\ncentral building block is a novel privacy mechanism, which generalizes the\nGaussian mechanism. Moreover, we establish a lower bound on the excess risk for\nthis range of $p$, showing a necessary dependence on $\\sqrt{d}$, where $d$ is\nthe dimension of the space. Our lower bound implies a sudden transition of the\nexcess risk at $p=1$, where the dependence on $d$ changes from logarithmic to\npolynomial, resolving an open question in prior work [TTZ15] . For $p\\in (2,\n\\infty)$, noisy SGD attains optimal excess risk in the low-dimensional regime;\nin particular, this proves the optimality of noisy SGD for $p=\\infty$. Our work\ndraws upon concepts from the geometry of normed spaces, such as the notions of\nregularity, uniform convexity, and uniform smoothness.\n"}
+{"abstract": "  Biological muscles have always attracted robotics researchers due to their\nefficient capabilities in compliance, force generation, and mechanical work.\nMany groups are working on the development of artificial muscles, however,\nstate-of-the-art methods still fall short in performance when compared with\ntheir biological counterpart. Muscles with high force output are mostly rigid,\nwhereas traditional soft actuators take much space and are limited in strength\nand producing displacement. In this work, we aim to find a reasonable trade-off\nbetween these features by mimicking the striated structure of skeletal muscles.\nFor that, we designed an artificial pneumatic myofibril composed of multiple\ncontraction units that combine stretchable and inextensible materials. Varying\nthe geometric parameters and the number of units in series provides flexible\nadjustment of the desired muscle operation. We derived a mathematical model\nthat predicts the relationship between the input pneumatic pressure and the\ngenerated output force. A detailed experimental study is conducted to validate\nthe performance of the proposed bio-inspired muscle.\n"}
+{"abstract": "  Finding information about tourist places to visit is a challenging problem\nthat people face while visiting different countries. This problem is\naccentuated when people are coming from different countries, speak different\nlanguages, and are from all segments of society. In this context, visitors and\npilgrims face important problems to find the appropriate doaas when visiting\nholy places. In this paper, we propose a mobile application that helps the user\nfind the appropriate doaas for a given holy place in an easy and intuitive\nmanner. Three different options are developed to achieve this goal: 1) manual\nsearch, 2) GPS location to identify the holy places and therefore their\ncorresponding doaas, and 3) deep learning (DL) based method to determine the\nholy place by analyzing an image taken by the visitor. Experiments show good\nperformance of the proposed mobile application in providing the appropriate\ndoaas for visited holy places.\n"}
+{"abstract": "  So far, topological band theory is discussed mainly for systems described by\neigenvalue problems. Here, we develop a topological band theory described by a\ngeneralized eigenvalue problem (GEVP). Our analysis elucidates that\nnon-Hermitian topological band structures may emerge for systems described by a\nGEVP with Hermitian matrices. The above result is verified by analyzing a\ntwo-dimensional toy model where symmetry-protected exceptional rings (SPERs)\nemerge although the matrices involved are Hermitian. Remarkably, these SPERs\nare protected by emergent symmetry, which is unique to the systems described by\nthe GEVP. Furthermore, these SPERs elucidate the origin of the characteristic\ndispersion of hyperbolic metamaterials which is observed in experiments.\n"}
+{"abstract": "  We provide a framework for proving convergence to the directed landscape, the\ncentral object in the Kardar-Parisi-Zhang universality class. For last passage\nmodels, we show that compact convergence to the Airy line ensemble implies\nconvergence to the Airy sheet. In i.i.d. environments, we show that Airy sheet\nconvergence implies convergence of distances and geodesics to their\ncounterparts in the directed landscape. Our results imply convergence of\nclassical last passage models and interacting particle systems. Our framework\nis built on the notion of a directed metric, a generalization of metrics which\nbehaves better under limits. As a consequence of our results, we present a\nsolution to an old problem: the scaled longest increasing subsequence in a\nuniform permutation converges to the directed geodesic.\n"}
+{"abstract": "  It is known that an ideal triangulation of a compact $3$-manifold with\nnonempty boundary is minimal if and only if it contains the minimum number of\nedges among all ideal triangulations of the manifold. Therefore, any ideal\none-edge triangulation (i.e., an ideal singular triangulation with exactly one\nedge) is minimal. Vesnin, Turaev, and the first author showed that an ideal\ntwo-edge triangulation is minimal if no $3$-$2$ Pachner move can be applied. In\nthis paper we show that any of the so-called poor ideal three-edge\ntriangulations is minimal. We exploit this property to construct minimal ideal\ntriangulations for an infinite family of hyperbolic $3$-manifolds with totally\ngeodesic boundary.\n"}
+{"abstract": "  Virtual reality (VR) head-mounted displays (HMD) appear to be effective\nresearch tools, which may address the problem of ecological validity in\nneuropsychological testing. However, their widespread implementation is\nhindered by VR induced symptoms and effects (VRISE) and the lack of skills in\nVR software development. This study offers guidelines for the development of VR\nsoftware in cognitive neuroscience and neuropsychology, by describing and\ndiscussing the stages of the development of Virtual Reality Everyday Assessment\nLab (VR-EAL), the first neuropsychological battery in immersive VR. Techniques\nfor evaluating cognitive functions within a realistic storyline are discussed.\nThe utility of various assets in Unity, software development kits, and other\nsoftware are described so that cognitive scientists can overcome challenges\npertinent to VRISE and the quality of the VR software. In addition, this pilot\nstudy attempts to evaluate VR-EAL in accordance with the necessary criteria for\nVR software for research purposes. The VR neuroscience questionnaire (VRNQ;\nKourtesis et al., 2019b) was implemented to appraise the quality of the three\nversions of VR-EAL in terms of user experience, game mechanics, in-game\nassistance, and VRISE. Twenty-five participants aged between 20 and 45 years\nwith 12-16 years of full-time education evaluated various versions of VR-EAL.\nThe final version of VR-EAL achieved high scores in every sub-score of the VRNQ\nand exceeded its parsimonious cut-offs. It also appeared to have better in-game\nassistance and game mechanics, while its improved graphics substantially\nincreased the quality of the user experience and almost eradicated VRISE. The\nresults substantially support the feasibility of the development of effective\nVR research and clinical software without the presence of VRISE during a\n60-minute VR session.\n"}
+{"abstract": "  We consider collisions between stars moving near the speed of light around\nsupermassive black holes (SMBHs), with mass\n$M_{\\bullet}\\gtrsim10^8\\,M_{\\odot}$, without being tidally disrupted. The\noverall rate for collisions taking place in the inner $\\sim1$ pc of galaxies\nwith $M_{\\bullet}=10^8,10^9,10^{10}\\,M_{\\odot}$ are $\\Gamma\\sim5,0.07,0.02$\nyr$^{-1}$, respectively. We further calculate the differential collision rate\nas a function of total energy released, energy released per unit mass lost, and\ngalactocentric radius. The most common collisions will release energies on the\norder of $\\sim10^{49}-10^{51}$ erg, with the energy distribution peaking at\nhigher energies in galaxies with more massive SMBHs. Depending on the host\ngalaxy mass and the depletion timescale, the overall rate of collisions in a\ngalaxy ranges from a small percentage to several times larger than that of\ncore-collapse supernovae (CCSNe) for the same host galaxy. In addition, we show\nexample light curves for collisions with varying parameters, and find that the\npeak luminosity could reach or even exceed that of superluminous supernovae\n(SLSNe), although with light curves with much shorter duration. Weaker events\ncould initially be mistaken for low-luminosity supernovae. In addition, we note\nthat these events will likely create streams of debris that will accrete onto\nthe SMBH and create accretion flares that may resemble tidal disruption events\n(TDEs).\n"}
+{"abstract": "  Custom currencies (ERC-20) on Ethereum are wildly popular, but they are\nsecond class to the primary currency Ether. Custom currencies are more complex\nand more expensive to handle than the primary currency as their accounting is\nnot natively performed by the underlying ledger, but instead in user-defined\ncontract code. Furthermore, and quite importantly, transaction fees can only be\npaid in Ether.\n  In this paper, we focus on being able to pay transaction fees in custom\ncurrencies. We achieve this by way of a mechanism permitting short term\nliabilities to pay transaction fees in conjunction with offers of custom\ncurrencies to compensate for those liabilities. This enables block producers to\naccept custom currencies in exchange for settling liabilities of transactions\nthat they process.\n  We present formal ledger rules to handle liabilities together with the\nconcept of babel fees to pay transaction fees in custom currencies. We also\ndiscuss how clients can determine what fees they have to pay, and we present a\nsolution to the knapsack problem variant that block producers have to solve in\nthe presence of babel fees to optimise their profits.\n"}
+{"abstract": "  Control systems of interest are often invariant under Lie groups of\ntransformations. Given such a control system, assumed to not be static feedback\nlinearizable, a verifiable geometric condition is described and proven to\nguarantee its dynamic feedback linearizability. Additionally, a systematic\nprocedure for obtaining all the system trajectories is shown to follow from\nthis condition. Besides smoothness and the existence of symmetry, no further\nassumption is made on the local form of a control system, which is therefore\npermitted to be fully nonlinear and time varying. Likewise, no constraints are\nimposed on the local form of the dynamic compensator. Particular attention is\ngiven to those systems requiring non-trivial dynamic extensions; that is,\nbeyond augmentation by chains of integrators. Nevertheless, the results are\nillustrated by an example of each type. Firstly, a control system that can be\ndynamically linearized by a chain of integrators, and secondly, one which does\nnot possess any linearizing chains of integrators and for which a dynamic\nfeedback linearization is nevertheless derived. These systems are discussed in\nsome detail. The constructions have been automated in the Maple package\nDifferentialGeometry.\n"}
+{"abstract": "  Learning embedding spaces of suitable geometry is critical for representation\nlearning. In order for learned representations to be effective and efficient,\nit is ideal that the geometric inductive bias aligns well with the underlying\nstructure of the data. In this paper, we propose Switch Spaces, a data-driven\napproach for learning representations in product space. Specifically, product\nspaces (or manifolds) are spaces of mixed curvature, i.e., a combination of\nmultiple euclidean and non-euclidean (hyperbolic, spherical) manifolds. To this\nend, we introduce sparse gating mechanisms that learn to choose, combine and\nswitch spaces, allowing them to be switchable depending on the input data with\nspecialization. Additionally, the proposed method is also efficient and has a\nconstant computational complexity regardless of the model size. Experiments on\nknowledge graph completion and item recommendations show that the proposed\nswitch space achieves new state-of-the-art performances, outperforming pure\nproduct spaces and recently proposed task-specific models.\n"}
+{"abstract": "  Stellar parameters of 25 planet-hosting stars and abundances of Li, C, O, Na,\nMg, Al, S, Si, Ca, Sc, Ti, V, Cr, Mn, Fe, Ni, Zn, Y, Zr, Ba, Ce, Pr, Nd, Sm and\nEu, were studied based on homogeneous high resolution spectra and uniform\ntechniques. The iron abundance [Fe/H] and key elements (Li, C, O, Mg, Si)\nindicative of the planet formation, as well as the dependencies of [El/Fe] on\n$T_{cond}$, were analyzed. The iron abundances determined in our sample stars\nwith detected massive planets range within -0.3<[Fe/H]<0.4. The behaviour of\n[C/Fe], [O/Fe], [Mg/Fe] and [Si/Fe] relative to [Fe/H] is consistent with the\nGalactic Chemical Evolution trends. The mean values of C/O and [C/O] are <C/O>=\n0.48 +/-0.07 and <[C/O]>=-0.07 +/-0.07, which are slightly lower than solar\nones. The Mg/Si ratios range from 0.83 to 0.95 for four stars in our sample and\nfrom 1.0 to 1.86 for the remaining 21 stars. Various slopes of [El/Fe] vs.\nTcond were found. The dependencies of the planetary mass on metallicity, the\nlithium abundance, the C/O and Mg/Si ratios, and also on the [El/Fe]-Tcond\nslopes were considered.\n"}
+{"abstract": "  A digital quantum simulation of the Agassi model from nuclear physics with a\ntrapped-ion quantum platform is proposed and analyzed. The proposal is worked\nout for the case with four different sites, to be implemented in a four-ion\nsystem. Numerical simulations and analytical estimations are presented to\nillustrate the feasibility of this proposal with current technology. The\nproposed approach is fully scalable to a larger number of sites. The use of a\nquantum correlation function as a probe to explore the quantum phases by\nquantum simulating the time dynamics, with no need of computing the ground\nstate, is also studied. Evidence that the amplitude of the quantum Rabi\noscillations in this quantum simulation is correlated with the different\nquantum phases of the system is given. This approach establishes an avenue for\nthe digital quantum simulation of useful models in nuclear physics with\ntrapped-ion systems.\n"}
+{"abstract": "  Coordination and cooperation between humans and autonomous agents in\ncooperative games raises interesting questions of human decision making and\nbehaviour changes. Here we report our findings from a group formation game in a\nsmall-world network of different mixes of human and agent players, aiming to\nachieve connected clusters of the same colour by swapping places with\nneighbouring players using non-overlapping information. In the experiments the\nhuman players are incentivized by rewarding to prioritize their own cluster\nwhile the model of agents' decision making is derived from our previous\nexperiment of purely cooperative game between human players. The experiments\nwere performed by grouping the players in three different setups to investigate\nthe overall effect of having cooperative autonomous agents within teams. We\nobserve that the change in the behavior of human subjects adjusts to playing\nwith autonomous agents by being less risk averse, while keeping the overall\nperformance efficient by splitting the behaviour into selfish and cooperative\nin the two actions performed during the rounds of the game. Moreover, results\nfrom two hybrid human-agent setups suggest that the group composition affects\nthe evolution of clusters. Our findings indicate that in purely or lesser\ncooperative settings, providing more control to humans could help in maximizing\nthe overall performance of hybrid systems.\n"}
+{"abstract": "  In this paper, we construct high order energy dissipative and conservative\nlocal discontinuous Galerkin methods for the Fornberg-Whitham type equations.\nWe give the proofs for the dissipation and conservation for related\nconservative quantities. The corresponding error estimates are proved for the\nproposed schemes. The capability of our schemes for different types of\nsolutions is shown via several numerical experiments. The dissipative schemes\nhave good behavior for shock solutions, while for a long time approximation,\nthe conservative schemes can reduce the shape error and the decay of amplitude\nsignificantly\n"}
+{"abstract": "  Electromagnetic observations have provided strong evidence for the existence\nof massive black holes in the center of galaxies, but their origin is still\npoorly known. Different scenarios for the formation and evolution of massive\nblack holes lead to different predictions for their properties and merger\nrates. LISA observations of coalescing massive black hole binaries could be\nused to reverse engineer the problem and shed light on these mechanisms. In\nthis paper, we introduce a pipeline based on hierarchical Bayesian inference to\ninfer the mixing fraction between different theoretical models by comparing\nthem to LISA observations of massive black hole mergers. By testing this\npipeline against simulated LISA data, we show that it allows us to accurately\ninfer the properties of the massive black hole population as long as our\ntheoretical models provide a reliable description of the Universe. We also show\nthat measurement errors, including both instrumental noise and weak lensing\nerrors, have little impact on the inference.\n"}
+{"abstract": "  Active solids consume energy to allow for actuation, shape change, and wave\npropagation not possible in equilibrium. Whereas active interfaces have been\nrealized across many experimental systems, control of three-dimensional (3D)\nbulk materials remains a challenge. Here, we develop continuum theory and\nmicroscopic simulations that describe a 3D soft solid whose boundary\nexperiences active surface stresses. The competition between active boundary\nand elastic bulk yields a broad range of previously unexplored phenomena, which\nare demonstrations of so-called active elastocapillarity. In contrast to thin\nshells and vesicles, we discover that bulk 3D elasticity controls snap-through\ntransitions between different anisotropic shapes. These transitions meet at a\ncritical point, allowing a universal classification via Landau theory. The\nactive surface modifies elastic wave propagation to allow zero, or even\nnegative, group velocities. These phenomena offer robust principles for\nprogramming shape change and functionality into active solids, from robotic\nmetamaterials down to shape-shifting nanoparticles.\n"}
+{"abstract": "  The validity of the Riemann Hypothesis (RH) on the location of the\nnon-trivial zeros of the Riemann $\\zeta$-function is directly related to the\ngrowth of the Mertens function $M(x) \\,=\\,\\sum_{k=1}^x \\mu(k)$, where $\\mu(k)$\nis the M\\\"{o}bius coefficient of the integer $k$: the RH is indeed true if the\nMertens function goes asymptotically as $M(x) \\sim x^{1/2 + \\epsilon}$, where\n$\\epsilon$ is an arbitrary strictly positive quantity. This behavior can be\nestablished on the basis of a new probabilistic approach based on the global\nproperties of Mertens function. To this aim we derive a series of probabilistic\nresults concerning the prime number distribution along the series of\nsquare-free numbers which shows that the Mertens function is subject to a\nnormal distribution. We also show that the validity of the RH also implies the\nvalidity of the Generalized Riemann Hypothesis for the Dirichlet $L$-functions.\nNext we study the local properties of the Mertens function, i.e. its variation\ninduced by each M\\\"{o}bius coefficient restricted to the square-free numbers.\nWe perform a massive statistical analysis on these coefficients, applying to\nthem a series of randomness tests of increasing precision and complexity, for a\ntotal number of eighteen different tests. The successful outputs of all these\ntests (each of them with a level of confidence of $99\\%$ that all the\nsub-sequences analyzed are indeed random) can be seen as impressive\n\"experimental\" confirmations of the brownian nature of the restricted\nM\\\"{o}bius coefficients and the probabilistic normal law distribution of the\nMertens function analytically established earlier. In view of the theoretical\nprobabilistic argument and the large battery of statistical tests, we can\nconclude that while a violation of the RH is strictly speaking not impossible,\nit is however extremely improbable.\n"}
+{"abstract": "  Tracking humans in crowded video sequences is an important constituent of\nvisual scene understanding. Increasing crowd density challenges visibility of\nhumans, limiting the scalability of existing pedestrian trackers to higher\ncrowd densities. For that reason, we propose to revitalize head tracking with\nCrowd of Heads Dataset (CroHD), consisting of 9 sequences of 11,463 frames with\nover 2,276,838 heads and 5,230 tracks annotated in diverse scenes. For\nevaluation, we proposed a new metric, IDEucl, to measure an algorithm's\nefficacy in preserving a unique identity for the longest stretch in image\ncoordinate space, thus building a correspondence between pedestrian crowd\nmotion and the performance of a tracking algorithm. Moreover, we also propose a\nnew head detector, HeadHunter, which is designed for small head detection in\ncrowded scenes. We extend HeadHunter with a Particle Filter and a color\nhistogram based re-identification module for head tracking. To establish this\nas a strong baseline, we compare our tracker with existing state-of-the-art\npedestrian trackers on CroHD and demonstrate superiority, especially in\nidentity preserving tracking metrics. With a light-weight head detector and a\ntracker which is efficient at identity preservation, we believe our\ncontributions will serve useful in advancement of pedestrian tracking in dense\ncrowds.\n"}
+{"abstract": "  Physical isolation, so called air-gapping, is an effective method for\nprotecting security-critical computers and networks. While it might be possible\nto introduce malicious code through the supply chain, insider attacks, or\nsocial engineering, communicating with the outside world is prevented.\nDifferent approaches to breach this essential line of defense have been\ndeveloped based on electromagnetic, acoustic, and optical communication\nchannels. However, all of these approaches are limited in either data rate or\ndistance, and frequently offer only exfiltration of data. We present a novel\napproach to infiltrate data to and exfiltrate data from air-gapped systems\nwithout any additional hardware on-site. By aiming lasers at already built-in\nLEDs and recording their response, we are the first to enable a long-distance\n(25m), bidirectional, and fast (18.2kbps in & 100kbps out) covert communication\nchannel. The approach can be used against any office device that operates LEDs\nat the CPU's GPIO interface.\n"}
+{"abstract": "  Neural architecture search (NAS) is a hot topic in the field of automated\nmachine learning and outperforms humans in designing neural architectures on\nquite a few machine learning tasks. Motivated by the natural representation\nform of neural networks by the Cartesian genetic programming (CGP), we propose\nan evolutionary approach of NAS based on CGP, called CGPNAS, to solve sentence\nclassification task. To evolve the architectures under the framework of CGP,\nthe operations such as convolution are identified as the types of function\nnodes of CGP, and the evolutionary operations are designed based on\nEvolutionary Strategy. The experimental results show that the searched\narchitectures are comparable with the performance of human-designed\narchitectures. We verify the ability of domain transfer of our evolved\narchitectures. The transfer experimental results show that the accuracy\ndeterioration is lower than 2-5%. Finally, the ablation study identifies the\nAttention function as the single key function node and the linear\ntransformations along could keep the accuracy similar with the full evolved\narchitectures, which is worthy of investigation in the future.\n"}
+{"abstract": "  We formulate a theory of shape valid for objects of arbitrary dimension whose\ncontours are path connected. We apply this theory to the design and modeling of\nviable trajectories of complex dynamical systems. Infinite families of\nqualitatively similar shapes are constructed giving as input a finite ordered\nset of characteristic points (landmarks) and the value of a continuous\nparameter $\\kappa \\in (0,\\infty)$. We prove that all shapes belonging to the\nsame family are located within the convex hull of the landmarks. The theory is\nconstructive in the sense that it provides a systematic means to build a\nmathematical model for any shape taken from the physical world. We illustrate\nthis with a variety of examples: (chaotic) time series, plane curves, space\nfilling curves, knots and strange attractors.\n"}
+{"abstract": "  A longstanding issue in the study of quantum chromodynamics (QCD) is its\nbehavior at nonzero baryon density, which has implications for many areas of\nphysics. The path integral has a complex integrand when the quark chemical\npotential is nonzero and therefore has a sign problem, but it also has a\ngeneralized $\\mathcal PT$ symmetry. We review some new approaches to $\\mathcal\nPT$-symmetric field theories, including both analytical techniques and methods\nfor lattice simulation. We show that $\\mathcal PT$-symmetric field theories\nwith more than one field generally have a much richer phase structure than\ntheir Hermitian counterparts, including stable phases with patterning behavior.\nThe case of a $\\mathcal PT$-symmetric extension of a $\\phi^4$ model is\nexplained in detail. The relevance of these results to finite density QCD is\nexplained, and we show that a simple model of finite density QCD exhibits a\npatterned phase in its critical region.\n"}
+{"abstract": "  In the mean field regime, neural networks are appropriately scaled so that as\nthe width tends to infinity, the learning dynamics tends to a nonlinear and\nnontrivial dynamical limit, known as the mean field limit. This lends a way to\nstudy large-width neural networks via analyzing the mean field limit. Recent\nworks have successfully applied such analysis to two-layer networks and\nprovided global convergence guarantees. The extension to multilayer ones\nhowever has been a highly challenging puzzle, and little is known about the\noptimization efficiency in the mean field regime when there are more than two\nlayers.\n  In this work, we prove a global convergence result for unregularized\nfeedforward three-layer networks in the mean field regime. We first develop a\nrigorous framework to establish the mean field limit of three-layer networks\nunder stochastic gradient descent training. To that end, we propose the idea of\na \\textit{neuronal embedding}, which comprises of a fixed probability space\nthat encapsulates neural networks of arbitrary sizes. The identified mean field\nlimit is then used to prove a global convergence guarantee under suitable\nregularity and convergence mode assumptions, which -- unlike previous works on\ntwo-layer networks -- does not rely critically on convexity. Underlying the\nresult is a universal approximation property, natural of neural networks, which\nimportantly is shown to hold at \\textit{any} finite training time (not\nnecessarily at convergence) via an algebraic topology argument.\n"}
+{"abstract": "  The topic of my research is \"Learning and Upgrading in Global Value Chains:\nAn Analysis of India's Manufacturing Sector\". To analyse India's learning and\nupgrading through position, functions, specialisation & value addition of\nmanufacturing GVCs, it is required to quantify the extent, drivers, and impacts\nof India's Manufacturing links in GVCs. I have transformed this overall broad\nobjective into three fundamental questions: (1) What is the extent of India's\nManufacturing Links in GVCs? (2) What are the determinants of India's\nManufacturing Links in GVCs? (3) What are the impacts of India's Manufacturing\nLinks in GVCs? These three objectives represent my three chapters in my PhD\nthesis.\n"}
+{"abstract": "  We calculate single-logarithmic corrections to the small-$x$ flavor-singlet\nhelicity evolution equations derived recently in the double-logarithmic\napproximation. The new single-logarithmic part of the evolution kernel sums up\npowers of $\\alpha_s \\, \\ln (1/x)$, which are an important correction to the\ndominant powers of $\\alpha_s \\, \\ln^2 (1/x)$ summed up by the\ndouble-logarithmic kernel at small values of Bjorken $x$ and with $\\alpha_s$\nthe strong coupling constant. The single-logarithmic terms arise separately\nfrom either the longitudinal or transverse momentum integrals. Consequently,\nthe evolution equations we derive simultaneously include the small-$x$\nevolution kernel and the leading-order polarized DGLAP splitting functions. We\nfurther enhance the equations by calculating the running coupling corrections\nto the kernel.\n"}
+{"abstract": "  In this note we prove that almost cap sets $A \\subset \\mathbb{F}_q^n$, i.e.,\nthe subsets of $\\mathbb{F}_q^n$ that do not contain too many arithmetic\nprogressions of length three, satisfy that $|A| < c_q^n$ for some $c_q < q$. As\na corollary we prove a multivariable analogue of Ellenberg-Gijswijt theorem.\n"}
+{"abstract": "  Given a graph $G$, a dominating set of $G$ is a set $S$ of vertices such that\neach vertex not in $S$ has a neighbor in $S$. The domination number of $G$,\ndenoted $\\gamma(G)$, is the minimum size of a dominating set of $G$. The\nindependent domination number of $G$, denoted $i(G)$, is the minimum size of a\ndominating set of $G$ that is also independent. Note that every graph has an\nindependent dominating set, as a maximal independent set is equivalent to an\nindependent dominating set.\n  Let $G$ be a connected $k$-regular graph that is not $K_{k, k}$ where $k\\geq\n4$. Generalizing a result by Lam, Shiu, and Sun, we prove that $i(G)\\le\n\\frac{k-1}{2k-1}|V(G)|$, which is tight for $k = 4$. This answers a question by\nGoddard et al. in the affirmative. We also show that $\\frac{i(G)}{\\gamma(G)}\n\\le \\frac{k^3-3k^2+2}{2k^2-6k+2}$, strengthening upon a result of Knor,\n\\v{S}krekovski, and Tepeh. In addition, we prove that a graph $G'$ with maximum\ndegree at most $4$ satisfies $i(G') \\le \\frac{5}{9}|V(G')|$, which is also\ntight.\n"}
+{"abstract": "  Sunquakes are helioseismic power enhancements initiated by solar flares, but\nnot all flares generate sunquakes. It is curious why some flares cause\nsunquakes while others do not. Here we propose a hypothesis to explain the\ndisproportionate occurrence of sunquakes: during a flare's impulsive phase when\nthe flare's impulse acts upon the photosphere, delivered by shock waves,\nenergetic particles from higher atmosphere, or by downward Lorentz Force, a\nsunquake tends to occur if the background oscillation at the flare footpoint\nhappens to oscillate downward in the same direction with the impulse from\nabove. To verify this hypothesis, we select 60 strong flares in Solar Cycle 24,\nand examine the background oscillatory velocity at the sunquake sources during\nthe flares' impulsive phases. Since the Doppler velocity observations at\nsunquake sources are usually corrupted during the flares, we reconstruct the\noscillatory velocity in the flare sites using helioseismic holography method\nwith an observation-based Green's function. A total of 24 flares are found to\nbe sunquake active, giving a total of 41 sunquakes. It is also found that in\n3-5 mHz frequency band, 25 out of 31 sunquakes show net downward oscillatory\nvelocities during the flares' impulsive phases, and in 5-7 mHz frequency band,\n33 out of 38 sunquakes show net downward velocities. These results support the\nhypothesis that a sunquake more likely occurs when a flare impacts a\nphotospheric area with a downward background oscillation.\n"}
+{"abstract": "  Multivariate max-stable processes are important for both theoretical\ninvestigations and various statistical applications motivated by the fact that\nthese are limiting processes, for instance of stationary multivariate regularly\nvarying time series, [1]. In this contribution we explore the relation between\nhomogeneous functionals and multivariate max-stable processes and discuss the\nconnections between multivariate max-stable process and zonoid / max-zonoid\nequivalence. We illustrate our results considering Brown-Resnick and Smith\nprocesses.\n"}
+{"abstract": "  Radiation therapy treatment planning is a complex process, as the target dose\nprescription and normal tissue sparing are conflicting objectives. Automated\nand accurate dose prediction for radiation therapy planning is in high demand.\nIn this study, we propose a novel learning-based ensemble approach, named\nLE-NAS, which integrates neural architecture search (NAS) with knowledge\ndistillation for 3D radiotherapy dose prediction. Specifically, the prediction\nnetwork first exhaustively searches each block from enormous architecture\nspace. Then, multiple architectures are selected with promising performance and\ndiversity. To reduce the inference time, we adopt the teacher-student paradigm\nby treating the combination of diverse outputs from multiple searched networks\nas supervisions to guide the student network training. In addition, we apply\nadversarial learning to optimize the student network to recover the knowledge\nin teacher networks. To the best of our knowledge, we are the first to\ninvestigate the combination of NAS and knowledge distillation. The proposed\nmethod has been evaluated on the public OpenKBP dataset, and experimental\nresults demonstrate the effectiveness of our method and its superior\nperformance to the state-of-the-art method.\n"}
+{"abstract": "  We analyze the dynamics of a single spiral galaxy from a general relativistic\nviewpoint. We employ the known family of stationary axially-symmetric solutions\nto Einstein gravity coupled with dust in order to model the halo external to\nthe bulge. In particular, we generalize the known results of Balasin and\nGrumiller, relaxing the condition of co-rotation, thus including non\nco-rotating dust. This further highlights the discrepancy between Newtonian\ntheory of gravity and general relativity at low velocities and energy\ndensities. We investigate the role of dragging in simulating dark matter\neffects. In particular, we show that non co-rotance further reduce the amount\nof energy density required to explain the rotation curves for spiral galaxies.\n"}
+{"abstract": "  A biopsy is the only diagnostic procedure for accurate histological\nconfirmation of breast cancer. When sonographic placement is not feasible, a\nMagnetic Resonance Imaging(MRI)-guided biopsy is often preferred. The lack of\nreal-time imaging information and the deformations of the breast make it\nchallenging to bring the needle precisely towards the tumour detected in\npre-interventional Magnetic Resonance (MR) images. The current manual\nMRI-guided biopsy workflow is inaccurate and would benefit from a technique\nthat allows real-time tracking and localisation of the tumour lesion during\nneedle insertion. This paper proposes a robotic setup and software architecture\nto assist the radiologist in targeting MR-detected suspicious tumours. The\napproach benefits from image fusion of preoperative images with intraoperative\noptical tracking of markers attached to the patient's skin. A hand-mounted\nbiopsy device has been constructed with an actuated needle base to drive the\ntip toward the desired direction. The steering commands may be provided both by\nuser input and by computer guidance. The workflow is validated through phantom\nexperiments. On average, the suspicious breast lesion is targeted with a radius\ndown to 2.3 mm. The results suggest that robotic systems taking into account\nbreast deformations have the potentials to tackle this clinical challenge.\n"}
+{"abstract": "  This paper develops \\emph{iterative Covariance Regulation} (iCR), a novel\nmethod for active exploration and mapping for a mobile robot equipped with\non-board sensors. The problem is posed as optimal control over the $SE(3)$ pose\nkinematics of the robot to minimize the differential entropy of the map\nconditioned the potential sensor observations. We introduce a differentiable\nfield of view formulation, and derive iCR via the gradient descent method to\niteratively update an open-loop control sequence in continuous space so that\nthe covariance of the map estimate is minimized. We demonstrate autonomous\nexploration and uncertainty reduction in simulated occupancy grid environments.\n"}
+{"abstract": "  Blockchain was mainly introduced for secure transactions in connection with\nthe mining of cryptocurrency Bitcoin. This article discusses the fundamental\nconcepts of blockchain technology and its components, such as block header,\ntransaction, smart contracts, etc. Blockchain uses the distributed databases,\nso this article also explains the advantages of distributed Blockchain over a\ncentrally located database. Depending on the application, Blockchain is broadly\ncategorized into two categories; Permissionless and Permissioned. This article\nelaborates on these two categories as well. Further, it covers the consensus\nmechanism and its working along with an overview of the Ethereum platform.\nBlockchain technology has been proved to be one of the remarkable techniques to\nprovide security to IoT devices. An illustration of how Blockchain will be\nuseful for IoT devices has been given. A few applications are also illustrated\nto explain the working of Blockchain with IoT.\n"}
+{"abstract": "  In this paper, we propose a novel learning-based polygonal point set tracking\nmethod. Compared to existing video object segmentation~(VOS) methods that\npropagate pixel-wise object mask information, we propagate a polygonal point\nset over frames.\n  Specifically, the set is defined as a subset of points in the target contour,\nand our goal is to track corresponding points on the target contour. Those\noutputs enable us to apply various visual effects such as motion tracking, part\ndeformation, and texture mapping. To this end, we propose a new method to track\nthe corresponding points between frames by the global-local alignment with\ndelicately designed losses and regularization terms. We also introduce a novel\nlearning strategy using synthetic and VOS datasets that makes it possible to\ntackle the problem without developing the point correspondence dataset. Since\nthe existing datasets are not suitable to validate our method, we build a new\npolygonal point set tracking dataset and demonstrate the superior performance\nof our method over the baselines and existing contour-based VOS methods. In\naddition, we present visual-effects applications of our method on part\ndistortion and text mapping.\n"}
+{"abstract": "  We investigate the predictive performance of two novel CNN-DNN machine\nlearning ensemble models in predicting county-level corn yields across the US\nCorn Belt (12 states). The developed data set is a combination of management,\nenvironment, and historical corn yields from 1980-2019. Two scenarios for\nensemble creation are considered: homogenous and heterogeneous ensembles. In\nhomogenous ensembles, the base CNN-DNN models are all the same, but they are\ngenerated with a bagging procedure to ensure they exhibit a certain level of\ndiversity. Heterogenous ensembles are created from different base CNN-DNN\nmodels which share the same architecture but have different levels of depth.\nThree types of ensemble creation methods were used to create several ensembles\nfor either of the scenarios: Basic Ensemble Method (BEM), Generalized Ensemble\nMethod (GEM), and stacked generalized ensembles. Results indicated that both\ndesigned ensemble types (heterogenous and homogenous) outperform the ensembles\ncreated from five individual ML models (linear regression, LASSO, random\nforest, XGBoost, and LightGBM). Furthermore, by introducing improvements over\nthe heterogeneous ensembles, the homogenous ensembles provide the most accurate\nyield predictions across US Corn Belt states. This model could make 2019 yield\npredictions with a root mean square error of 866 kg/ha, equivalent to 8.5%\nrelative root mean square, and could successfully explain about 77% of the\nspatio-temporal variation in the corn grain yields. The significant predictive\npower of this model can be leveraged for designing a reliable tool for corn\nyield prediction which will, in turn, assist agronomic decision-makers.\n"}
+{"abstract": "  Annotating the right set of data amongst all available data points is a key\nchallenge in many machine learning applications. Batch active learning is a\npopular approach to address this, in which batches of unlabeled data points are\nselected for annotation, while an underlying learning algorithm gets\nsubsequently updated. Increasingly larger batches are particularly appealing in\nsettings where data can be annotated in parallel, and model training is\ncomputationally expensive. A key challenge here is scale - typical active\nlearning methods rely on diversity techniques, which select a diverse set of\ndata points to annotate, from an unlabeled pool. In this work, we introduce\nActive Data Shapley (ADS) -- a filtering layer for batch active learning that\nsignificantly increases the efficiency of active learning by pre-selecting,\nusing a linear time computation, the highest-value points from an unlabeled\ndataset. Using the notion of the Shapley value of data, our method estimates\nthe value of unlabeled data points with regards to the prediction task at hand.\nWe show that ADS is particularly effective when the pool of unlabeled data\nexhibits real-world caveats: noise, heterogeneity, and domain shift. We run\nexperiments demonstrating that when ADS is used to pre-select the\nhighest-ranking portion of an unlabeled dataset, the efficiency of\nstate-of-the-art batch active learning methods increases by an average factor\nof 6x, while preserving performance effectiveness.\n"}
+{"abstract": "  In this work we address the problem of solving ill-posed inverse problems in\nimaging where the prior is a variational autoencoder (VAE). Specifically we\nconsider the decoupled case where the prior is trained once and can be reused\nfor many different log-concave degradation models without retraining. Whereas\nprevious MAP-based approaches to this problem lead to highly non-convex\noptimization algorithms, our approach computes the joint (space-latent) MAP\nthat naturally leads to alternate optimization algorithms and to the use of a\nstochastic encoder to accelerate computations. The resulting technique (JPMAP)\nperforms Joint Posterior Maximization using an Autoencoding Prior. We show\ntheoretical and experimental evidence that the proposed objective function is\nquite close to bi-convex. Indeed it satisfies a weak bi-convexity property\nwhich is sufficient to guarantee that our optimization scheme converges to a\nstationary point. We also highlight the importance of correctly training the\nVAE using a denoising criterion, in order to ensure that the encoder\ngeneralizes well to out-of-distribution images, without affecting the quality\nof the generative model. This simple modification is key to providing\nrobustness to the whole procedure. Finally we show how our joint MAP\nmethodology relates to more common MAP approaches, and we propose a\ncontinuation scheme that makes use of our JPMAP algorithm to provide more\nrobust MAP estimates. Experimental results also show the higher quality of the\nsolutions obtained by our JPMAP approach with respect to other non-convex MAP\napproaches which more often get stuck in spurious local optima.\n"}
+{"abstract": "  In this paper, we introduce and test our algorithm to create a road network\nrepresentation for city-scale active transportation simulation models. The\nalgorithm relies on open and universal data to ensure applicability for\ndifferent cities around the world. In addition to the major roads, their\ngeometries and the road attributes typically used in transport modelling (e.g.,\nspeed limit, number of lanes, permitted travel modes), the algorithm also\ncaptures minor roads usually favoured by pedestrians and cyclists, along with\nroad attributes such as bicycle-specific infrastructure, traffic signals, and\nroad gradient. Furthermore, it simplifies the network's complex geometries and\nmerges parallel roads if applicable to make it suitable for large-scale\nsimulations. To examine the utility and performance of the algorithm, we used\nit to create a network representation for Greater Melbourne, Australia and\ncompared the output with a network created using an existing transport\nsimulation toolkit along with another network from an existing city-scale\ntransport model from the Victorian government. Through simulation experiments\nwith these networks, we illustrated that our algorithm achieves a very good\nbalance between simulation accuracy and run-time. For routed trips on our\nnetwork for walking and cycling it is of comparable accuracy to the common\nnetwork conversion tools in terms of travel distances of the shortest paths\nwhile being more than two times faster when used for simulating different\nsample sizes. Therefore, our algorithm offers a flexible solution for building\naccurate and efficient road networks for city-scale active transport models for\ndifferent cities around the world.\n"}
+{"abstract": "  We show that the space of anti-symplectic involutions of a monotone\n$S^2\\times S^2$ whose fixed points set is a Lagrangian sphere is connected.\nThis follows from a stronger result, namely that any two anti-symplectic\ninvolutions in that space are Hamiltonian isotopic.\n"}
+{"abstract": "  High purity iron is obtained from vanadium-titanium magnetite (VTM) by\none-step coal-based direct reduction-smelting process with coal as reductant\nand sodium carbonate (Na2CO3) as additives. Industrial experiments show that\nthe process of treating molten iron with a large amount of Na2CO3 is effective\nin removing titanium from molten iron. However, the studies are rarely\nconducted in thermodynamic relationship between titanium and other components\nof molten iron, under the condition of a large amount of Na2CO3 additives. In\nthis study, through the thermodynamic database software Factsage8.0, the\neffects of melting temperature, sodium content and oxygen content on the\nremoval of titanium from molten iron are studied. The results of thermodynamic\ncalculation show that the removal of titanium from molten iron needs to be\nunder the condition of oxidation, and the temperature should be below the\ncritical temperature of titanium removal (the highest temperature at which\ntitanium can be removed). Relatively low temperature and high oxygen content\ncontribute to the removal of titanium from molten iron. The high oxygen content\nis conducive to the simultaneous removal of titanium and phosphorus from molten\niron. In addition, from a thermodynamic point of view, excessive sodium\naddition inhibits the removal of titanium from molten iron.\n"}
+{"abstract": "  We present the first formal verification of approximation algorithms for\nNP-complete optimization problems: vertex cover, independent set, set cover,\ncenter selection, load balancing, and bin packing. We uncover incompletenesses\nin existing proofs and improve the approximation ratio in one case. All proofs\nare uniformly invariant based.\n"}
+{"abstract": "  We generalize the classical shadow tomography scheme to a broad class of\nfinite-depth or finite-time local unitary ensembles, known as locally scrambled\nquantum dynamics, where the unitary ensemble is invariant under local basis\ntransformations. In this case, the reconstruction map for the classical shadow\ntomography depends only on the average entanglement feature of classical\nsnapshots. We provide an unbiased estimator of the quantum state as a linear\ncombination of reduced classical snapshots in all subsystems, where the\ncombination coefficients are solely determined by the entanglement feature. We\nalso bound the number of experimental measurements required for the tomography\nscheme, so-called sample complexity, by formulating the operator shadow norm in\nthe entanglement feature formalism. We numerically demonstrate our approach for\nfinite-depth local unitary circuits and finite-time local-Hamiltonian generated\nevolutions. The shallow-circuit measurement can achieve a lower tomography\ncomplexity compared to the existing method based on Pauli or Clifford\nmeasurements. Our approach is also applicable to approximately locally\nscrambled unitary ensembles with a controllable bias that vanishes quickly.\nSurprisingly, we find a single instance of time-dependent local Hamiltonian\nevolution is sufficient to perform an approximate tomography as we numerically\ndemonstrate it using a paradigmatic spin chain Hamiltonian modeled after\ntrapped ion or Rydberg atom quantum simulators. Our approach significantly\nbroadens the application of classical shadow tomography on near-term quantum\ndevices.\n"}
+{"abstract": "  We consider the inverse scattering on the quantum graph associated with the\nhexagonal lattice. Assuming that the potentials on the edges are compactly\nsupported and symmetric, we show that the S-matrix for all energies in any\ngiven open set in the continuous spectrum determines the potentials.\n"}
+{"abstract": "  We introduce a framework that abstracts Reinforcement Learning (RL) as a\nsequence modeling problem. This allows us to draw upon the simplicity and\nscalability of the Transformer architecture, and associated advances in\nlanguage modeling such as GPT-x and BERT. In particular, we present Decision\nTransformer, an architecture that casts the problem of RL as conditional\nsequence modeling. Unlike prior approaches to RL that fit value functions or\ncompute policy gradients, Decision Transformer simply outputs the optimal\nactions by leveraging a causally masked Transformer. By conditioning an\nautoregressive model on the desired return (reward), past states, and actions,\nour Decision Transformer model can generate future actions that achieve the\ndesired return. Despite its simplicity, Decision Transformer matches or exceeds\nthe performance of state-of-the-art model-free offline RL baselines on Atari,\nOpenAI Gym, and Key-to-Door tasks.\n"}
+{"abstract": "  Recently, moir\\'{e} superlattices have attracted considerable attentions\nbecause they are found to exhibit intriguing electronic phenomena of tunable\nMott insulators and unconventional superconductivity. These phenomena are\nhighly related to the physical mechanism of the interlayer coupling. However,\nup to now, there has not existed any theory that can completely interpret the\nexperimental results of the interlayer conductance of moir\\'{e} superlattice.\nIn order to solve this problem, the superposition of periods and the\ncorresponding coherence, which are the essential characteristics of moir\\'{e}\nsuperlattice, should be considered more sufficiently. Therefore, it is quite\nnecessary to introduce optical methods to study moir\\'{e} superlattices. Here,\nwe develop a theory for moir\\'{e} superlattices which are founded on\ntraditional optical scattering theory. The theory can interpret both the\ncontinuously decreasing background and the peak of the interlayer conductance\nobserved in the experiments by a unified mechanism. We show that, the\ndecreasing background of the interlayer conductance arises from the increasing\nstrength of the interface potential, and the peak roots from the scattering\nresonance of the interface potential. The present work is crucial for\nunderstanding the interlayer coupling of the moir\\'{e} superlattice, and\nprovide a solid theoretical foundation for the application of moir\\'{e}\nsuperlattice.\n"}
+{"abstract": "  While deep learning has enabled great advances in many areas of music,\nlabeled music datasets remain especially hard, expensive, and time-consuming to\ncreate. In this work, we introduce SimCLR to the music domain and contribute a\nlarge chain of audio data augmentations to form a simple framework for\nself-supervised, contrastive learning of musical representations: CLMR. This\napproach works on raw time-domain music data and requires no labels to learn\nuseful representations. We evaluate CLMR in the downstream task of music\nclassification on the MagnaTagATune and Million Song datasets and present an\nablation study to test which of our music-related innovations over SimCLR are\nmost effective. A linear classifier trained on the proposed representations\nachieves a higher average precision than supervised models on the MagnaTagATune\ndataset, and performs comparably on the Million Song dataset. Moreover, we show\nthat CLMR's representations are transferable using out-of-domain datasets,\nindicating that our method has strong generalisability in music classification.\nLastly, we show that the proposed method allows data-efficient learning on\nsmaller labeled datasets: we achieve an average precision of 33.1% despite\nusing only 259 labeled songs in the MagnaTagATune dataset (1% of the full\ndataset) during linear evaluation. To foster reproducibility and future\nresearch on self-supervised learning in music, we publicly release the\npre-trained models and the source code of all experiments of this paper.\n"}
+{"abstract": "  Current observations present unprecedented opportunities to probe the true\nnature of black holes, which must harbor new physics beyond General Relativity\nto provide singularity-free descriptions. To test paradigms for this new\nphysics, it is necessary to bridge the gap all the way from theoretical\ndevelopments of new-physics models to phenomenological developments such as\nsimulated images of black holes embedded in astrophysical disk environments. In\nthis paper, we construct several steps along this bridge. We construct a novel\nfamily of regular black-hole spacetimes based on a locality principle which\nties new physics to local curvature scales. We then characterize these\nspacetimes in terms of a complete set of curvature invariants and analyze the\nergosphere and both the outer event as well as distinct Killing horizon. Our\ncomprehensive study of the shadow shape at various spins and inclinations\nreveals characteristic image features linked to the locality principle. We also\nexplore the photon rings as an additional probe of the new-physics effects. A\nsimple analytical disk model enables us to generate simulated images of the\nregular spinning black hole and test whether the characteristic image-features\nare visible in the intensity map.\n"}
+{"abstract": "  It is well known that the K\\\"ahler-Ricci flow on a K\\\"ahler manifold $X$\nadmits a long-time solution if and only if $X$ is a minimal model, i.e., the\ncanonical line bundle $K_X$ is nef. The abundance conjecture in algebraic\ngeometry predicts that $K_X$ must be semi-ample when $X$ is a projective\nminimal model. We prove that if $K_X$ is semi-ample, then the diameter is\nuniformly bounded for long-time solutions of the normalized K\\\"ahler-Ricci\nflow. Our diameter estimate combined with the scalar curvature estimate in [34]\nfor long-time solutions of the K\\\"ahler-Ricci flow are natural extensions of\nPerelman's diameter and scalar curvature estimates for short-time solutions on\nFano manifolds. We further prove that along the normalized K\\\"ahler-Ricci flow,\nthe Ricci curvature is uniformly bounded away from singular fibres of $X$ over\nits unique algebraic canonical model $X_{can}$ if the Kodaira dimension of $X$\nis one. As an application, the normalized K\\\"ahler-Ricci flow on a minimal\nthreefold $X$ always converges sequentially in Gromov-Hausdorff topology to a\ncompact metric space homeomorphic to its canonical model $X_{can}$, with\nuniformly bounded Ricci curvature away from the critical set of the\npluricanonical map from $X$ to $X_{can}$.\n"}
+{"abstract": "  A general overview of the existing difference ring theory for symbolic\nsummation is given. Special emphasis is put on the user interface: the\ntranslation and back translation of the corresponding representations within\nthe term algebra and the formal difference ring setting. In particular,\ncanonical (unique) representations and their refinements in the introduced term\nalgebra are explored by utilizing the available difference ring theory. Based\non that, precise input-output specifications of the available tools of the\nsummation package Sigma are provided.\n"}
+{"abstract": "  It is believed that the $\\pm J$ Ising spin-glass does not order at finite\ntemperatures in dimension $d=2$. However, using a graphical representation and\na contour argument, we prove rigorously the existence of a finite-temperature\nphase transition in $d\\geq 2$ with $T_c \\geq 0.4$. In the graphical\nrepresentation, the low-temperature phase allows for the coexistence of\nmultiple infinite clusters each with a rigidly aligned spin-overlap state.\nThese clusters correlate negatively with each other, and are entropically\nstable without breaking any global symmetry. They can emerge in most graph\nstructures and disorder measures.\n"}
+{"abstract": "  We survey a number of different methods for computing $L(\\chi,1-k)$ for a\nDirichlet character $\\chi$, with particular emphasis on quadratic characters.\nThe main conclusion is that when $k$ is not too large (for instance $k\\le100$)\nthe best method comes from the use of Eisenstein series of half-integral\nweight, while when $k$ is large the best method is the use of the complete\nfunctional equation, unless the conductor of $\\chi$ is really large, in which\ncase the previous method again prevails.\n"}
+{"abstract": "  Hydrogen bonding liquids, typically water and alcohols, are known to form\nlabile structures (network, chains, etc...), hence the lifetime of such\nstructures is an important microscopic parameter, which can be calculated in\ncomputer simulations. Since these cluster entities are mostly statistical in\nnature, one would expect that, in the short time regime, their lifetime\ndistribution would be a broad Gaussian-like function of time, with a single\nmaximum representing their mean lifetime, and weakly dependent on criteria such\nas the bonding distance and angle, much similarly to non-hydrogen bonding\nsimple liquids, while the long time part is known to have some power law\ndependence. Unexpectedly, all the hydrogen bonding liquids studied herein,\nnamely water and alcohols, display highly hierarchic three types of specific\nlifetimes, in the sub-picosecond range 0-0.5ps The dominant lifetime very\nstrongly depends on the bonding distance criterion and is related to hydrogen\nbonded pairs. This mode is absent in non-H-bonding simple liquids. The\nsecondary and tertiary mean lifetimes are related to clusters, and are nearly\nindependent on the bonding criterion. Of these two lifetimes, only the first\none can be related to that of simple liquids, which poses the question of the\nnature of the third life time. The study of acohols reveals that this 3rd\nlifetime is related to the topology of H-bonded clusters, and that its\ndistribution may be also affected by the alkyl tail surrounding \"bath\". This\nstudy reveals that hydrogen bonding liquids have a universal hierarchy of\nhydrogen bonding lifetimes with a timescale regularity across very different\ntypes, and which depend on the topology of the cluster structures\n"}
+{"abstract": "  Scattering by an isolated defect embedded in a dielectric medium of two\ndimensional periodicity is of interest in many sub-fields of electrodynamics.\nPresent approaches to compute this scattering rely either on the Born\napproximation and its quasi-analytic extensions, or on \\emph{ab-initio}\ncomputation that requires large domain sizes to reduce the effects of boundary\nconditions. The Born approximation and its extensions are limited in scope,\nwhile the ab-initio approach suffers from its high numerical cost. In this\npaper, I introduce a hybrid scheme in which an effective local electric\nsusceptibility tensor of a defect is estimated by solving an inverse problem\nefficiently. The estimated tensor is embedded into an S-matrix formula based on\nthe reciprocity theorem. With this embedding, the computation of the S-matrix\nof the defect requires field solutions only in the unit cell of the background.\nIn practice, this scheme reduces the computational cost by almost two orders of\nmagnitude, while sacrificing little in accuracy. The scheme demonstrates that\nstatistical estimation can capture sufficient information from cheap\ncalculations to compute quantities in the far field. I outline the fundamental\ntheory and algorithms to carry out the computations in high dielectric contrast\nmaterials, including metals. I demonstrate the capabilities of this approach\nwith examples from optical inspection of nano-electronic circuitry where the\nBorn approximation fails and the existing methods for its extension are also\ninapplicable.\n"}
+{"abstract": "  A nonlinear Markov chain is a discrete time stochastic process whose\ntransitions depend on both the current state and the current distribution of\nthe process. The nonlinear Markov chain over a infinite state space can be\nidentified by a continuous mapping (the so-called nonlinear Markov operator)\ndefined on a set of all probability distributions (which is a simplex). In the\npresent paper, we consider a continuous analogue of the mentioned mapping\nacting on $L^1$-spaces. Main aim of the current paper is to investigate\nprojective surjectivity of quadratic stochastic operators (QSO) acting on the\nset of all probability measures. To prove the main result, we study the\nsurjectivity of infinite dimensional nonlinear Markov operators and apply them\nto the projective surjectivity of a QSO. Furthermore, the obtained result has\nbeen applied for the existence of positive solution of some Hammerstein\nintegral equations.\n"}
+{"abstract": "  Coherent gate errors are a concern in many proposed quantum computing\narchitectures. These errors can be effectively handled through composite pulse\nsequences for single-qubit gates, however, such techniques are less feasible\nfor entangling operations. In this work, we benchmark our coherent errors by\ncomparing the actual performance of composite single-qubit gates to the\npredicted performance based on characterization of individual single-qubit\nrotations. We then propose a compilation technique, which we refer to as hidden\ninverses, that creates circuits robust to these coherent errors. We present\nexperimental data showing that these circuits suppress both overrotation and\nphase misalignment errors in our trapped ion system.\n"}
+{"abstract": "  We reformulate Euclidean general relativity without cosmological constant as\nan action governing the complex structure of twistor space. Extending Penrose's\nnon-linear graviton construction, we find a correspondence between twistor\nspaces with partially integrable almost complex structures and four-dimensional\nspace-times with off-shell metrics. Using this, we prove that our twistor\naction reduces to Plebanski's action for general relativity via the Penrose\ntransform. This should lead to new insights into the geometry of graviton\nscattering as well as to the derivation of computational tools like\ngravitational MHV rules.\n"}
+{"abstract": "  Real-time PCR, or Real-time Quantitative PCR (qPCR) is an effective approach\nto quantify nucleic acid samples. Given the complicated reaction system along\nwith thermal cycles, there has been long-term confusion on accurately\ncalculating the initial nucleic acid amounts from the fluorescence signals.\nAlthough many improved algorithms had been proposed, the classical threshold\nmethod is still the primary choice in the routine application. In this study,\nwe will first illustrate the origin of the linear relationship between the\nthreshold value and logarithm of the initial nucleic acid amount by\nreconstructing the PCR reaction process with stochastic simulations. We then\ndevelop a new method for the absolute quantification of nucleic acid samples\nwith qPCR. By monitoring the fluorescence signal changes in every stage of the\nthermal cycle, we are able to calculate a representation of the step-wise\nefficiency change. This is the first work calculated PCR efficiency change\ndirectly from the fluorescence signal, without fitting or sophisticated\nanalysis. Our results revealed that the efficiency change during the PCR\nprocess is complicated and can not be modeled simply by monotone function\nmodel. Based on the calculated efficiency, we illustrate a new absolute qPCR\nanalysis method for accurately determining nucleic acid amount. The efficiency\nproblem is completely avoided in this new method.\n"}
+{"abstract": "  The prior independent framework for algorithm design considers how well an\nalgorithm that does not know the distribution of its inputs approximates the\nexpected performance of the optimal algorithm for this distribution. This paper\ngives a method that is agnostic to problem setting for proving lower bounds on\nthe prior independent approximation factor of any algorithm. The method\nconstructs a correlated distribution over inputs that can be generated both as\na distribution over i.i.d. good-for-algorithms distributions and as a\ndistribution over i.i.d. bad-for-algorithms distributions. Prior independent\nalgorithms are upper-bounded by the optimal algorithm for the latter\ndistribution even when the true distribution is the former. Thus, the ratio of\nthe expected performances of the Bayesian optimal algorithms for these two\ndecompositions is a lower bound on the prior independent approximation ratio.\nThe techniques of the paper connect prior independent algorithm design, Yao's\nMinimax Principle, and information design. We apply this framework to give new\nlower bounds on several canonical prior independent mechanism design problems.\n"}
+{"abstract": "  In the t-U-V Hubbard model on the square lattice we found self-consistent\nanalytic solution for the ground state with coexisting d-wave symmetric bond\nordered pair density wave (PDW) and spin (SDW) or charge (CDW) density waves,\nas observed in some high-temperature superconductors. In particular, the\nsolution gives the same periodicity for CDW and PDW, and a pseudogap in the\nfermi-excitation spectrum.\n"}
+{"abstract": "  The paper deals with cubic 1-variable polynomials whose Julia sets are\nconnected. Fixing a bounded type rotation number, we obtain a slice of such\npolynomials with the origin being a fixed Siegel point of the specified\nrotation number. Such slices as parameter spaces were studied by S. Zakeri, so\nwe call them Zakeri slices. We give a model of the central part of a slice (the\nsubset of the slice that can be approximated by hyperbolic polynomials with\nJordan curve Julia sets), and a continuous projection from the central part to\nthe model. The projection is defined dynamically and agrees with the\ndynamical-analytic parameterization of the Principal Hyperbolic Domain by\nPetersen and Tan Lei.\n"}
+{"abstract": "  The partial Latin square extension problem is to fill as many as possible\nempty cells of a partially filled Latin square. This problem is a useful model\nfor a wide range of relevant applications in diverse domains. This paper\npresents the first massively parallel hybrid search algorithm for this\ncomputationally challenging problem based on a transformation of the problem to\npartial graph coloring. The algorithm features the following original elements.\nBased on a very large population (with more than $10^4$ individuals) and modern\ngraphical processing units, the algorithm performs many local searches in\nparallel to ensure an intensified exploitation of the search space. It employs\na dedicated crossover with a specific parent matching strategy to create a\nlarge number of diversified and information-preserving offspring at each\ngeneration. Extensive experiments on 1800 benchmark instances show a high\ncompetitiveness of the algorithm compared with the current best performing\nmethods. Competitive results are also reported on the related Latin square\ncompletion problem. Analyses are performed to shed lights on the understanding\nof the main algorithmic components. The code of the algorithm will be made\npublicly available.\n"}
+{"abstract": "  In this study, we formulate a mathematical model incorporating age specific\ntransmission dynamics of COVID-19 to evaluate the role of vaccination and\ntreatment strategies in reducing the size of COVID-19 burden. Initially, we\nestablish the positivity and boundedness of the solutions of the model and\ncalculate the basic reproduction number. We then formulate an optimal control\nproblem with vaccination and treatment as control variables. Optimal\nvaccination and treatment policies are analysed for different values of the\nweight constant associated with the cost of vaccination and different\ntransmissibility levels. Findings from these suggested that the combined\nstrategies(vaccination and treatment) worked best in minimizing the infection\nand disease induced mortality. In order to reduce COVID-19 infection and\nCOVID-19 induced deaths to maximum, it was observed that optimal control\nstrategy should be prioritized to population with age greater than 40 years.\nNot much difference was found between individual strategies and combined\nstrategies in case of mild epidemic ($R_0 \\in (0, 2)$). For higher values of\n$R_0 (R_0 \\in (2, 10))$ the combined strategies was found to be best in terms\nof minimizing the overall infection. The infection curves varying the\nefficacies of the vaccines were also analysed and it was found that higher\nefficacy of the vaccine resulted in lesser number of infection and COVID\ninduced death.\n"}
+{"abstract": "  The chiral hinge modes are the key feature of a second order topological\ninsulator in three dimensions. Here we propose a quadrupole index in\ncombination of a slab Chern number in the bulk to characterize the flowing\npattern of chiral hinge modes along the hinges at the intersection of the\nsurfaces of a sample. We further utilize the topological field theory to\ndemonstrate the correspondent connection of the chiral hinge modes to the\nquadrupole index and the slab Chern number, and present a picture of\nthree-dimensional quantum anomalous Hall effect as a consequence of chiral\nhinge modes. The two bulk topological invariants can be measured in electric\ntransport and magneto-optical experiments. In this way we establish the\nbulk-hinge correspondence in a three-dimensional second order topological\ninsulator.\n"}
+{"abstract": "  In the near future, the Deep Underground Neutrino Experiment and the European\nSpallation Source aim to reach unprecedented sensitivity in the search for\nneutron-antineutron ($n\\text{-}\\bar{n}$) oscillations, whose observation would\ndirectly imply $|\\Delta B| = 2$ violation and hence might hint towards a close\nlink to the mechanism behind the observed baryon asymmetry of the Universe. In\nthis work, we explore the consequences of such a discovery for baryogenesis\nfirst within a model-independent effective field theory approach. We then\nrefine our analysis by including a source of CP violation and different\nhierarchies between the scales of new physics using a simplified model. We\nanalyse the implication for baryogenesis in different scenarios and confront\nour results with complementary experimental constraints from dinucleon decay,\nLHC, and meson oscillations. We find that for a small mass hierarchy between\nthe new degrees of freedom, an observable rate for $n\\text{-}\\bar{n}$\noscillation would imply that the washout processes are too strong to generate\nany sizeable baryon asymmetry, even if the CP violation is maximal. On the\nother hand, for a large hierarchy between the new degrees of freedom, our\nanalysis shows that successful baryogenesis can occur over a large part of the\nparameter space, opening the window to be probed by current and future\ncolliders and upcoming $n\\text{-}\\bar{n}$ oscillation searches.\n"}
+{"abstract": "  We study a precise and computationally tractable notion of operator\ncomplexity in holographic quantum theories, including the ensemble dual of\nJackiw-Teitelboim gravity and two-dimensional holographic conformal field\ntheories. This is a refined, \"microcanonical\" version of K-complexity that\napplies to theories with infinite or continuous spectra (including quantum\nfield theories), and in the holographic theories we study exhibits exponential\ngrowth for a scrambling time, followed by linear growth until saturation at a\ntime exponential in the entropy $\\unicode{x2014}$a behavior that is\ncharacteristic of chaos. We show that the linear growth regime implies a\nuniversal random matrix description of the operator dynamics after scrambling.\nOur main tool for establishing this connection is a \"complexity renormalization\ngroup\" framework we develop that allows us to study the effective operator\ndynamics for different timescales by \"integrating out\" large K-complexities. In\nthe dual gravity setting, we comment on the empirical match between our version\nof K-complexity and the maximal volume proposal, and speculate on a connection\nbetween the universal random matrix theory dynamics of operator growth after\nscrambling and the spatial translation symmetry of smooth black hole interiors.\n"}
+{"abstract": "  In reliability analysis, methods used to estimate failure probability are\noften limited by the costs associated with model evaluations. Many of these\nmethods, such as multifidelity importance sampling (MFIS), rely upon a\ncomputationally efficient, surrogate model like a Gaussian process (GP) to\nquickly generate predictions. The quality of the GP fit, particularly in the\nvicinity of the failure region(s), is instrumental in supplying accurately\npredicted failures for such strategies. We introduce an entropy-based GP\nadaptive design that, when paired with MFIS, provides more accurate failure\nprobability estimates and with higher confidence. We show that our greedy data\nacquisition strategy better identifies multiple failure regions compared to\nexisting contour-finding schemes. We then extend the method to batch selection,\nwithout sacrificing accuracy. Illustrative examples are provided on benchmark\ndata as well as an application to an impact damage simulator for National\nAeronautics and Space Administration (NASA) spacesuits.\n"}
+{"abstract": "  Recent literature has underscored the importance of dataset documentation\nwork for machine learning, and part of this work involves addressing\n\"documentation debt\" for datasets that have been used widely but documented\nsparsely. This paper aims to help address documentation debt for BookCorpus, a\npopular text dataset for training large language models. Notably, researchers\nhave used BookCorpus to train OpenAI's GPT-N models and Google's BERT models,\neven though little to no documentation exists about the dataset's motivation,\ncomposition, collection process, etc. We offer a preliminary datasheet that\nprovides key context and information about BookCorpus, highlighting several\nnotable deficiencies. In particular, we find evidence that (1) BookCorpus\nlikely violates copyright restrictions for many books, (2) BookCorpus contains\nthousands of duplicated books, and (3) BookCorpus exhibits significant skews in\ngenre representation. We also find hints of other potential deficiencies that\ncall for future research, including problematic content, potential skews in\nreligious representation, and lopsided author contributions. While more work\nremains, this initial effort to provide a datasheet for BookCorpus adds to\ngrowing literature that urges more careful and systematic documentation for\nmachine learning datasets.\n"}
+{"abstract": "  Session-based recommendation (SBR) learns users' preferences by capturing the\nshort-term and sequential patterns from the evolution of user behaviors. Among\nthe studies in the SBR field, graph-based approaches are a relatively powerful\nkind of way, which generally extract item information by message aggregation\nunder Euclidean space. However, such methods can't effectively extract the\nhierarchical information contained among consecutive items in a session, which\nis critical to represent users' preferences. In this paper, we present a\nhyperbolic contrastive graph recommender (HCGR), a principled session-based\nrecommendation framework involving Lorentz hyperbolic space to adequately\ncapture the coherence and hierarchical representations of the items. Within\nthis framework, we design a novel adaptive hyperbolic attention computation to\naggregate the graph message of each user's preference in a session-based\nbehavior sequence. In addition, contrastive learning is leveraged to optimize\nthe item representation by considering the geodesic distance between positive\nand negative samples in hyperbolic space. Extensive experiments on four\nreal-world datasets demonstrate that HCGR consistently outperforms\nstate-of-the-art baselines by 0.43$\\%$-28.84$\\%$ in terms of $HitRate$, $NDCG$\nand $MRR$.\n"}
+{"abstract": "  In this note we show a simple formula for the coefficients of the polynomial\nassociated with the sums of powers of the terms of an arbitrary arithmetic\nprogression. This formula consists of a double sum involving only ordinary\nbinomial coefficients and binomial powers. Arguably, this is the simplest\nformula that can probably be found for the said coefficients. Furthermore, as a\nby-product, we give an explicit formula for the Bernoulli polynomials involving\nthe Stirling numbers of the first and second kind.\n"}
+{"abstract": "  As Stefan Kopp and Nicole Kramer say in their recent paper[Frontiers in\nPsychology 12 (2021) 597], despite some very impressive demonstrations over the\nlast decade or so, we still don't know how how to make a computer have a half\ndecent conversation with a human. They argue that the capabilities required to\ndo this include incremental joint co-construction and mentalizing. Although\nagreeing whole heartedly with their statement of the problem, this paper argues\nfor a different approach to the solution based on the \"new\" AI of situated\naction.\n"}
+{"abstract": "  Cell proliferation, apoptosis, and myosin-dependent contraction can generate\nelastic stress and strain in living tissues, which may be dissipated by\ninternal rearrangement through cell topological transition and cytoskeletal\nreorganization. Moreover, cells and tissues can change their sizes in response\nto mechanical cues. The present work demonstrates the role of tissue\ncompressibility and internal rearranging activities on its size and mechanics\nregulation in the context of differential growth induced by a field of\ngrowth-promoting chemical factors. We develop a mathematical model based on\nfinite elasticity and growth theory and the reference map techniques to\ndescribe the coupled tissue growth and mechanics in the Eulerian frame. We\nincorporate the tissue rearrangement by introducing a rearranging rate to the\nreference map evolution, leading to elastic-energy dissipation when tissue\ngrowth and deformation are in radial symmetry. By linearizing the model, we\nshow that the stress follows the Maxwell-type viscoelastic relaxation. The\nrearrangement rate, which we call tissue fluidity, sets the stress relaxation\ntime, and the ratio between the shear modulus and the fluidity sets the tissue\nviscosity. By nonlinear simulation of growing tissue spheroids and discs with\ngraded growth rates along the radius, we find that the tissue compressibility\nand fluidity influence their equilibrium size. By comparing the nonlinear\nsimulations with the linear analytical solutions, we show the size change as a\nnonlinear effect due to the advection of the tissue density flow, which only\noccurs when both tissue compressibility and fluidity are small. We apply the\nmodel to study tumor spheroid growth and epithelial disc growth when a\nreaction-diffusion process determines the growth-promoting factor field.\n"}
+{"abstract": "  It has been recently claimed that primordial magnetic fields could relieve\nthe cosmological Hubble tension. We consider the impact of such fields on the\nformation of the first cosmological objects, mini-halos forming stars, for\npresent-day field strengths in the range of $2\\times 10^{-12}$ - $2\\times\n10^{-10}$ G. These values correspond to initial ratios of Alv\\'en velocity to\nthe speed of sound of $v_a/c_s\\approx 0.03 - 3$. We find that when $v_a/c_s\\ll\n1$, the effects are modest. However, when $v_a\\sim c_s$, the starting time of\nthe gravitational collapse is delayed and the duration extended as much as by\n$\\Delta$z = 2.5 in redshift. When $v_a > c_s$, the collapse is completely\nsuppressed and the mini-halos continue to grow and are unlikely to collapse\nuntil reaching the atomic cooling limit. Employing current observational limits\non primordial magnetic fields we conclude that inflationary produced primordial\nmagnetic fields could have a significant impact on first star formation,\nwhereas post-inflationary produced fields do not.\n"}
+{"abstract": "  This is the second part of a paper describing a new concept of separation of\nvariables applied to the classical Clebsch integrable case. The quadratures\nobtained in Part I (also uploaded in arXiv.org) lead to a new type of the Abel\nmap which contains Abelian integrals on two different algebraic curves.\n  Here we interprete it as from the product of the two curves to the Prym\nvariety of one of them, show that the map is well defined although not a\nbijection. We analyse its properties and formulate a new extention of the\nRiemann vanishing theorem, which allows to invert the map in terms of\ntheta-functions of higher order.\n  Lastly, we describe how to express the original variables of the Clebsch\nsystem in terms of the preimages of the map. This enables one to obtain\ntheta-function solution whose structure is different from that found long time\nago by F. K\\\"otter.\n"}
+{"abstract": "  To improve the security and robustness of autonomous driving models, this\npaper presents SMET, a scenariobased metamorphic testing tool for autonomous\ndriving models. The metamorphic relationship is divided into three dimensions\n(time, space, and event) and demonstrates its effectiveness through case\nstudies in two types of autonomous driving models with different\noutputs.Experimental results show that this tool can well detect potential\ndefects of the autonomous driving model, and complex scenes are more effective\nthan simple scenes.\n"}
+{"abstract": "  B-mode ultrasound imaging is a popular medical imaging technique. Like other\nimage processing tasks, deep learning has been used for analysis of B-mode\nultrasound images in the last few years. However, training deep learning models\nrequires large labeled datasets, which is often unavailable for ultrasound\nimages. The lack of large labeled data is a bottleneck for the use of deep\nlearning in ultrasound image analysis. To overcome this challenge, in this work\nwe exploit Auxiliary Classifier Generative Adversarial Network (ACGAN) that\ncombines the benefits of data augmentation and transfer learning in the same\nframework. We conduct experiment on a dataset of breast ultrasound images that\nshows the effectiveness of the proposed approach.\n"}
+{"abstract": "  In the last three decades, several constructions of quantum error-correcting\ncodes were presented in the literature. Among these codes, there are the\nasymmetric ones, i.e., quantum codes whose $Z$-distance $d_z$ is different from\nits $X$-distance $d_x$. The topological quantum codes form an important class\nof quantum codes, where the toric code, introduced by Kitaev, was the first\nfamily of this type. After Kitaev's toric code, several authors focused\nattention on investigating its structure and the constructions of new families\nof topological quantum codes over Euclidean and hyperbolic surfaces. As a\nconsequence of establishing the existence and the construction of asymmetric\ntopological quantum codes in Theorem \\ref{main}, the main result of this paper,\nwe introduce the class of hyperbolic asymmetric codes. Hence, families of\nEuclidean and hyperbolic asymmetric topological quantum codes are presented. An\nanalysis regarding the asymptotic behavior of their distances $d_x$ and $d_z$\nand encoding rates $k/n$ versus the compact orientable surface's genus is\nprovided due to the significant difference between the asymmetric distances\n$d_x$ and $d_z$ when compared with the corresponding parameters of topological\ncodes generated by other tessellations. This inherent unequal error-protection\nis associated with the nontrivial homological cycle of the $\\{p,q\\}$\ntessellation and its dual, which may be appropriately explored depending on the\napplication, where $p\\neq q$ and $(p-2)(q-2)\\ge 4$. Three families of codes\nderived from the $\\{7,3\\}$, $\\{5,4\\}$, and $\\{10,5\\}$ tessellations are\nhighlighted.\n"}
+{"abstract": "  Since the 1970s, most airlines have incorporated computerized support for\nmanaging disruptions during flight schedule execution. However, existing\nplatforms for airline disruption management (ADM) employ monolithic system\ndesign methods that rely on the creation of specific rules and requirements\nthrough explicit optimization routines, before a system that meets the\nspecifications is designed. Thus, current platforms for ADM are unable to\nreadily accommodate additional system complexities resulting from the\nintroduction of new capabilities, such as the introduction of unmanned aerial\nsystems (UAS), operations and infrastructure, to the system. To this end, we\nuse historical data on airline scheduling and operations recovery to develop a\nsystem of artificial neural networks (ANNs), which describe a predictive\ntransfer function model (PTFM) for promptly estimating the recovery impact of\ndisruption resolutions at separate phases of flight schedule execution during\nADM. Furthermore, we provide a modular approach for assessing and executing the\nPTFM by employing a parallel ensemble method to develop generative routines\nthat amalgamate the system of ANNs. Our modular approach ensures that current\nindustry standards for tardiness in flight schedule execution during ADM are\nsatisfied, while accurately estimating appropriate time-based performance\nmetrics for the separate phases of flight schedule execution.\n"}
+{"abstract": "  Multi-step effects between bound, resonant, and non-resonant states have been\ninvestigated by the continuum-discretized coupled-channels method (CDCC). In\nthe CDCC, a resonant state is treated as multiple states fragmented in a\nresonance energy region, although it is described as a single state in usual\ncoupled-channel calculations. For such the fragmented resonant states, one-step\nand multi-step contributions to the cross sections should be carefully\ndiscussed because the cross sections obtained by the one-step calculation\ndepend on the number of those states, which corresponds to the size of the\nmodel space. To clarify the role of the multi-step effects, we propose the\none-step calculation without model-space dependence for the fragmented resonant\nstates. Furthermore, we also discuss the multi-step effects between the ground,\n$2^{+}_{1}$ resonant, and non-resonant states in $^6$He for proton inelastic\nscattering.\n"}
+{"abstract": "  Coherent control of interfering one- and two-photon processes has for decades\nbeen the subject of research to achieve the redirection of photocurrent. The\npresent study develops two-pathway coherent control of ground state helium atom\nabove-threshold photoionization for energies up to the $N=2$ threshold, based\non a multichannel quantum defect and R-matrix calculation. Three parameters are\ncontrolled in our treatment: the optical interference phase $\\Delta\\Phi$, the\nreduced electric field strength\n$\\chi=\\mathcal{E}_{\\omega}^2/{\\mathcal{E}_{2\\omega}}$, and the final state\nenergy $\\epsilon$. A small energy change near a resonance is shown to flip the\nemission direction of photoelectrons with high efficiency, through an example\nwhere $90\\%$ of photoelectrons whose energy is near the $2p^2\\ ^1S^e$ resonance\nflip their emission direction. However, the large fraction of photoelectrons\nionized at the intermediate state energy, which are not influenced by the\noptical control, make this control scheme challenging to realize\nexperimentally.\n"}
+{"abstract": "  This paper describes the most efficient way to manage operations on ranges of\nelements within an ordered set. The goal is to improve existing solutions, by\noptimizing the average-case time complexity and getting rid of heavy\nmultiplicative constants in the worst-case, without sacrificing space\ncomplexity. This is a high-impact operation in practical applications,\nperformed by introducing a new data structure called Wise Red-Black Tree, an\naugmented version of the Red-Black Tree.\n"}
+{"abstract": "  For any integer $m\\ge 2$ and a set $V\\subset \\{1,\\dots,m\\}$, let $(m,V)$\ndenote the union of congruence classes of the elements in $V$ modulo $m$. We\nstudy the Hankel determinants for the number of Dyck paths with peaks avoiding\nthe heights in the set $(m,V)$. For any set $V$ of even elements of an even\nmodulo $m$, we give an explicit description of the sequence of Hankel\ndeterminants in terms of subsequences of arithmetic progression of integers.\nThere are numerous instances for varied $(m,V)$ with periodic sequences of\nHankel determinants. We present a sufficient condition for the set $(m,V)$ such\nthat the sequence of Hankel determinants is periodic, including even and odd\nmodulus $m$.\n"}
+{"abstract": "  We prove Conjecture 4.16 of the paper [EL21] of Elagin and Lunts; namely,\nthat a smooth projective curve of genus at least 1 over a field has diagonal\ndimension 2.\n"}
+{"abstract": "  We characterize the performance of a system based on a magnetoresistor array.\nThis instrument is developed to map the magnetic field, and to track a dipolar\nmagnetic source in the presence of a static homogeneous field. The position and\norientation of the magnetic source with respect to the sensor frame is\nretrieved together with the orientation of the frame with respect to the\nenvironmental field. A nonlinear best-fit procedure is used, and its precision,\ntime performance, and reliability are analyzed. This analysis is performed in\nview of the practical application for which the system is designed that is an\neye-tracking diagnostics and rehabilitative tool for medical purposes, which\nrequire high speed ($\\ge 100$~Sa/s) and sub-millimetric spatial resolution. A\nthroughout investigation on the results makes it possible to list several\nobservations, suggestions, and hints, which will be useful in the design of\nsimilar setups.\n"}
+{"abstract": "  The introduction of pretrained language models has reduced many complex\ntask-specific NLP models to simple lightweight layers. An exception to this\ntrend is coreference resolution, where a sophisticated task-specific model is\nappended to a pretrained transformer encoder. While highly effective, the model\nhas a very large memory footprint -- primarily due to dynamically-constructed\nspan and span-pair representations -- which hinders the processing of complete\ndocuments and the ability to train on multiple instances in a single batch. We\nintroduce a lightweight end-to-end coreference model that removes the\ndependency on span representations, handcrafted features, and heuristics. Our\nmodel performs competitively with the current standard model, while being\nsimpler and more efficient.\n"}
+{"abstract": "  Scheduling a sports tournament is a complex optimization problem, which\nrequires a large number of hard constraints to satisfy. Despite the\navailability of several such constraints in the literature, there remains a gap\nsince most of the new sports events pose their own unique set of requirements,\nand demand novel constraints. Specifically talking of the strictly time bound\nevents, ensuring fairness between the different teams in terms of their rest\ndays, traveling, and the number of successive games they play, becomes a\ndifficult task to resolve, and demands attention. In this work, we present a\nsimilar situation with a recently played sports event, where a suboptimal\nschedule favored some of the sides more than the others. We introduce various\ncompetitive parameters to draw a fairness comparison between the sides and\npropose a weighting criterion to point out the sides that enjoyed this schedule\nmore than the others. Furthermore, we use root mean squared error between an\nideal schedule and the actual ones for each side to determine unfairness in the\ndistribution of rest days across their entire schedules. The latter is crucial,\nsince successively playing a large number of games may lead to sportsmen\nburnout, which must be prevented.\n"}
+{"abstract": "  Stimulated by the exciting progress in experiments, we carry out a combined\nanalysis of the masses, and strong and radiative decay properties of the $B$\nand $B_s$-meson states up to the second orbital excitations. Based on our good\ndescriptions of the mass and decay properties for the low-lying\nwell-established states $B_1(5721)$, $B_2^*(5747)$, $B_{s1}(5830)$ and\n$B_{s2}^*(5840)$, we give a quark model classification for the high mass\nresonances observed in recent years. It is found that (i) the $B_{J}(5840)$\nresonance may be explained as the low mass mixed state $B(|SD\\rangle_L)$ via\n$2^3S_1$-$1^3D_1$ mixing, or the pure $B(2^3S_1)$ state, or $B(2^1S_0)$. (ii)\nThe $B_J(5970)$ resonance may be assigned as the $1^3D_3$ state in the $B$\nmeson family, although it as a pure $2^3S_1$ state cannot be excluded. (iii)\nThe narrow structure around 6064 MeV observed in the $B^+K^-$ mass spectrum at\nLHCb may be mainly caused by the $B_{sJ}(6109)$ resonance decaying into\n$B^{*+}K^-$, and favors the assignment of the high mass $1D$-wave mixed state\n$B_s(1D'_2)$ with $J^P=2^-$, although it as the $1^3D_3$ state cannot be\nexcluded. (iv) The relatively broader $B_{sJ}(6114)$ structure observed at LHCb\nmay be explained with the mixed state $B_s(|SD\\rangle_H)$ via $2^3S_1$-$1^3D_1$\nmixing, or a pure $1^3D_1$ state. Most of the missing $1P$-, $1D$-, and\n$2S$-wave $B$- and $B_s$-meson states have a relatively narrow width, they are\nmost likely to be observed in their dominant decay channels with a larger data\nsample at LHCb.\n"}
+{"abstract": "  We study the magnetization process of the $S=1$ Heisenberg model on a two-leg\nladder with farther neighbor spin-exchange interaction. We consider the\ninteraction that couples up to the next-nearest neighbor rungs and find an\nexactly solvable regime where the ground states become product states. The\nnext-nearest neighbor interaction tends to stabilize magnetization plateaus at\nmultiples of 1/6. In most of the exactly solvable regime, a single\nmagnetization curve shows two series of plateaus with different periodicities.\n"}
+{"abstract": "  In conversational analyses, humans manually weave multimodal information into\nthe transcripts, which is significantly time-consuming. We introduce a system\nthat automatically expands the verbatim transcripts of video-recorded\nconversations using multimodal data streams. This system uses a set of\npreprocessing rules to weave multimodal annotations into the verbatim\ntranscripts and promote interpretability. Our feature engineering contributions\nare two-fold: firstly, we identify the range of multimodal features relevant to\ndetect rapport-building; secondly, we expand the range of multimodal\nannotations and show that the expansion leads to statistically significant\nimprovements in detecting rapport-building.\n"}
+{"abstract": "  Bayesian neural networks that incorporate data augmentation implicitly use a\n``randomly perturbed log-likelihood [which] does not have a clean\ninterpretation as a valid likelihood function'' (Izmailov et al. 2021). Here,\nwe provide several approaches to developing principled Bayesian neural networks\nincorporating data augmentation. We introduce a ``finite orbit'' setting which\nallows likelihoods to be computed exactly, and give tight multi-sample bounds\nin the more usual ``full orbit'' setting. These models cast light on the origin\nof the cold posterior effect. In particular, we find that the cold posterior\neffect persists even in these principled models incorporating data\naugmentation. This suggests that the cold posterior effect cannot be dismissed\nas an artifact of data augmentation using incorrect likelihoods.\n"}
+{"abstract": "  We use VANDELS spectroscopic data overlapping with the $\\simeq$7 Ms Chandra\nDeep Field South survey to extend studies of high-mass X-ray binary systems\n(XRBs) in 301 normal star-forming galaxies in the redshift range $3 < z < 5.5$.\nOur analysis evaluates correlations between X-ray luminosities ($L_X$), star\nformation rates (SFR) and stellar metallicities ($Z_\\star$) to higher redshifts\nand over a wider range in galaxy properties than hitherto. Using a stacking\nanalysis performed in bins of both redshift and SFR for sources with robust\nspectroscopic redshifts without AGN signatures, we find convincing evolutionary\ntrends in the ratio $L_X$/SFR to the highest redshifts probed, with a stronger\ntrend for galaxies with lower SFRs. Combining our data with published samples\nat lower redshift, the evolution of $L_X$/SFR to $z\\simeq5$ proceeds as $(1 +\nz)^{1.03 \\pm 0.02}$. Using stellar metallicities derived from photospheric\nabsorption features in our spectroscopic data, we confirm indications at lower\nredshifts that $L_X$/SFR is stronger for metal-poor galaxies. We use\nsemi-analytic models to show that metallicity dependence of $L_X$/SFR alone may\nnot be sufficient to fully explain the observed redshift evolution of X-ray\nemission from high-mass XRBs, particularly for galaxies with SFR $<30$\n$M_\\odot$ yr$^{-1}$. We speculate that the discrepancy may arise due to reduced\noverall stellar ages in the early Universe leading to higher $L_X$/SFR for the\nsame metallicity. We use our data to define the redshift-dependent contribution\nof XRBs to the integrated X-ray luminosity density and, in comparison with\nmodels, find that the contribution of high-mass XRBs to the cosmic X-ray\nbackground at $z>6$ may be $\\gtrsim 0.25$ dex higher than previously estimated.\n"}
+{"abstract": "  The renormalized contribution of fermions to the curvature masses of vector\nand axial-vector mesons is derived with two different methods at leading order\nin the loop expansion applied to the (2+1)-flavor constituent quark-meson\nmodel. The corresponding contribution to the curvature masses of the scalar and\npseudoscalar mesons, already known in the literature, is rederived in a\ntransparent way. The temperature dependence of the curvature mass of various\n(axial-)vector modes obtained by decomposing the curvature mass tensor is\ninvestigated along with the (axial-)vector--(pseudo)scalar mixing. All\nfermionic corrections are expressed as simple integrals that involve at finite\ntemperature only the Fermi-Dirac distribution function modified by the\nPolyakov-loop degrees of freedom. The renormalization of the (axial-)vector\ncurvature mass allows us to lift a redundancy in the original Lagrangian of the\nglobally symmetric extended linear sigma model, in which terms already\ngenerated by the covariant derivative were reincluded with different coupling\nconstants.\n"}
+{"abstract": "  This paper introduces on-the-way choice of retail outlet as a form of\nconvenience shopping. It presents a model of on-the-way choice of retail outlet\nand applies the model in the context of fuel retailing to explore its\nimplications for segmentation and spatial competition. The model is a latent\nclass random utility choice model. An application to gas station choices\nobserved in a medium-sized Asian city show the model to fit substantially\nbetter than existing models. The empirical results indicate consumers may adopt\none of two decision strategies. When adopting an immediacy-oriented strategy\nthey behave in accordance with the traditional gravity-based retail models and\ntend to choose the most spatially convenient outlet. When following a\ndestination-oriented strategy they focus more on maintaining their overall trip\nefficiency and so will tend to visit outlets located closer to their main\ndestination and are more susceptible to retail agglomeration effects. The paper\ndemonstrates how the model can be used to inform segmentation and local\ncompetition analyses that account for variations in these strategies as well as\nvariations in consumer type, origin and time of travel. Simulations of a\nduopoly setting further demonstrate the implications.\n"}
+{"abstract": "  For a commutative, unital and integral quantale V, we generalize to V-groups\nthe results developed by Gran and Michel for preordered groups. We first of all\nshow that, in the category V-Grp of V-groups, there exists a torsion theory\nwhose torsion and torsion-free subcategories are given by those of indiscrete\nand separated V-groups, respectively. It turns out that this torsion theory\ninduces a monotone-light factorization system that we characterize, and it is\nthen possible to describe the coverings in V-Grp. We next classify these\ncoverings as internal actions of a Galois groupoid. Finally, we observe that\nthe subcategory of separated V-groups is also a torsion-free subcategory for a\npretorsion theory whose torsion subcategory is the one of symmetric V-groups.\nAs recently proved by Clementino and Montoli, this latter category is actually\nnot only coreflective, as it is the case for any torsion subcategory, but also\nreflective.\n"}
+{"abstract": "  The paper focuses on the a posteriori tuning of a generative model in order\nto favor the generation of good instances in the sense of some external\ndifferentiable criterion. The proposed approach, called Boltzmann Tuning of\nGenerative Models (BTGM), applies to a wide range of applications. It covers\nconditional generative modelling as a particular case, and offers an affordable\nalternative to rejection sampling. The contribution of the paper is twofold.\nFirstly, the objective is formalized and tackled as a well-posed optimization\nproblem; a practical methodology is proposed to choose among the candidate\ncriteria representing the same goal, the one best suited to efficiently learn a\ntuned generative model. Secondly, the merits of the approach are demonstrated\non a real-world application, in the context of robust design for energy\npolicies, showing the ability of BTGM to sample the extreme regions of the\nconsidered criteria.\n"}
+{"abstract": "  We solve the decidability problem for Boolean Set Theory with unordered\ncartesian product.\n"}
+{"abstract": "  A key component of the baryon cycle in galaxies is the depletion of metals\nfrom the gas to the dust phase in the neutral ISM. The METAL (Metal Evolution,\nTransport and Abundance in the Large Magellanic Cloud) program on the Hubble\nSpace Telescope acquired UV spectra toward 32 sightlines in the half-solar\nmetallicity LMC, from which we derive interstellar depletions (gas-phase\nfractions) of Mg, Si, Fe, Ni, S, Zn, Cr, and Cu. The depletions of different\nelements are tightly correlated, indicating a common origin. Hydrogen column\ndensity is the main driver for depletion variations. Correlations are weaker\nwith volume density, probed by CI fine structure lines, and distance to the LMC\ncenter. The latter correlation results from an East-West variation of the\ngas-phase metallicity. Gas in the East, compressed side of the LMC encompassing\n30 Doradus and the Southeast HI over-density is enriched by up to +0.3dex,\nwhile gas in the West side is metal-deficient by up to -0.5dex. Within the\nparameter space probed by METAL, no correlation with molecular fraction or\nradiation field intensity are found. We confirm the factor 3-4 increase in\ndust-to-metal and dust-to-gas ratios between the diffuse (logN(H)~20 cm-2) and\nmolecular (logN(H)~22 cm-2) ISM observed from far-infrared, 21 cm, and CO\nobservations. The variations of dust-to-metal and dust-to-gas ratios with\ncolumn density have important implications for the sub-grid physics of chemical\nevolution, gas and dust mass estimates throughout cosmic times, and for the\nchemical enrichment of the Universe measured via spectroscopy of damped\nLyman-alpha systems.\n"}
+{"abstract": "  Concave Utility Reinforcement Learning (CURL) extends RL from linear to\nconcave utilities in the occupancy measure induced by the agent's policy. This\nencompasses not only RL but also imitation learning and exploration, among\nothers. Yet, this more general paradigm invalidates the classical Bellman\nequations, and calls for new algorithms. Mean-field Games (MFGs) are a\ncontinuous approximation of many-agent RL. They consider the limit case of a\ncontinuous distribution of identical agents, anonymous with symmetric\ninterests, and reduce the problem to the study of a single representative agent\nin interaction with the full population. Our core contribution consists in\nshowing that CURL is a subclass of MFGs. We think this important to bridge\ntogether both communities. It also allows to shed light on aspects of both\nfields: we show the equivalence between concavity in CURL and monotonicity in\nthe associated MFG, between optimality conditions in CURL and Nash equilibrium\nin MFG, or that Fictitious Play (FP) for this class of MFGs is simply\nFrank-Wolfe, bringing the first convergence rate for discrete-time FP for MFGs.\nWe also experimentally demonstrate that, using algorithms recently introduced\nfor solving MFGs, we can address the CURL problem more efficiently.\n"}
+{"abstract": "  In the near future, the redshift drift observations in optical and radio\nbands will provide precise measurements on $H(z)$ covering the redshift ranges\nof $2<z<5$ and $0<z<0.3$. In addition, gravitational wave (GW) standard siren\nobservations could make measurements on the dipole anisotropy of luminosity\ndistance, which will also provide the $H(z)$ measurements in the redshift range\nof $0<z<3$. In this work, we propose a multi-messenger and multi-wavelength\nobservational strategy to measure $H(z)$ based on the three next-generation\nprojects, E-ELT, SKA, and DECIGO, and we wish to see whether the future $H(z)$\nmeasurements could provide tight constraints on dark-energy parameters. The\ndark energy models we consider include $\\Lambda$CDM, $w$CDM, CPL, HDE, and\nI$\\Lambda$CDM models. It is found that E-ELT, SKA1, and DECIGO are highly\ncomplementary in constraining dark energy models. Although any one of these\nthree data sets can only give rather weak constraints on each model we\nconsider, the combination of them could significantly break the parameter\ndegeneracies and give much tighter constraints on almost all the cosmological\nparameters. Moreover, we find that the combination of E-ELT, SKA1, DECIGO, and\nCMB could further improve the constraints on dark energy parameters, e.g.,\n$\\sigma(w_0)=0.024$ and $\\sigma(w_a)=0.17$ in the CPL model, which means that\nthese three promising probes will play a key role in helping reveal the nature\nof dark energy.\n"}
+{"abstract": "  Hardware specialization is becoming a key enabler of energyefficient\nperformance. Future systems will be increasingly heterogeneous, integrating\nmultiple specialized and programmable accelerators, each with different memory\ndemands. Traditionally, communication between accelerators has been\ninefficient, typically orchestrated through explicit DMA transfers between\ndifferent address spaces. More recently, industry has proposed unified coherent\nmemory which enables implicit data movement and more data reuse, but often\nthese interfaces limit the coherence flexibility available to heterogeneous\nsystems. This paper demonstrates the benefits of fine-grained coherence\nspecialization for heterogeneous systems. We propose an architecture that\nenables low-complexity independent specialization of each individual coherence\nrequest in heterogeneous workloads by building upon a simple and flexible\nbaseline coherence interface, Spandex. We then describe how to optimize\nindividual memory requests to improve cache reuse and performance-critical\nmemory latency in emerging heterogeneous workloads. Collectively, our\ntechniques enable significant gains, reducing execution time by up to 61% or\nnetwork traffic by up to 99% while adding minimal complexity to the Spandex\nprotocol.\n"}
+{"abstract": "  Automatic image and digit recognition is a computationally challenging task\nfor image processing and pattern recognition, requiring an adequate\nappreciation of the syntactic and semantic importance of the image for the\nidentification ofthe handwritten digits. Image and Pattern Recognition has been\nidentified as one of the driving forces in the research areas because of its\nshifting of different types of applications, such as safety frameworks,\nclinical frameworks, diversion, and so on.In this study, for recognition, we\nimplemented a hybrid neural network model that is capable of recognizing the\ndigit of MNISTdataset and achieved a remarkable result. The proposed neural\nmodel network can extract features from the image and recognize the features in\nthe layer by layer. To expand, it is so important for the neural network to\nrecognize how the proposed modelcan work in each layer, how it can generate\noutput, and so on. Besides, it also can recognize the auto-encoding system and\nthe variational auto-encoding system of the MNIST dataset. This study will\nexplore those issues that are discussed above, and the explanation for them,\nand how this phenomenon can be overcome.\n"}
+{"abstract": "  In the current work we discuss the notion of gateways as a means for\ninteroperability across different blockchain systems. We discuss two key\nprinciples for the design of gateway nodes and scalable gateway protocols,\nnamely (i) the opaque ledgers principle as the analogue of the autonomous\nsystems principle in IP datagram routing, and (ii) the externalization of value\nprinciple as the analogue of the end-to-end principle in the Internet\narchitecture. We illustrate the need for a standard gateway protocol by\ndescribing a unidirectional asset movement protocol between two peer gateways,\nunder the strict condition of both blockchains being private/permissioned with\ntheir ledgers inaccessible to external entities. Several aspects of gateways\nand the gateway protocol is discussed, including gateway identities, gateway\ncertificates and certificate hierarchies, passive locking transactions by\ngateways, and the potential use of delegated hash-locks to expand the\nfunctionality of gateways.\n"}
+{"abstract": "  The Wilson action for Euclidean lattice gauge theory defines a\npositive-definite transfer matrix that corresponds to a unitary lattice gauge\ntheory time-evolution operator if analytically continued to real time. Hoshina,\nFujii, and Kikukawa (HFK) recently pointed out that applying the Wilson action\ndiscretization to continuum real-time gauge theory does not lead to this, or\nany other, unitary theory and proposed an alternate real-time lattice gauge\ntheory action that does result in a unitary real-time transfer matrix. The\ncharacter expansion defining the HFK action is divergent, and in this work we\napply a path integral contour deformation to obtain a convergent representation\nfor U(1) HFK path integrals suitable for numerical Monte Carlo calculations. We\nalso introduce a class of real-time lattice gauge theory actions based on\nanalytic continuation of the Euclidean heat-kernel action. Similar divergent\nsums are involved in defining these actions, but for one action in this class\nthis divergence takes a particularly simple form, allowing construction of a\npath integral contour deformation that provides absolutely convergent\nrepresentations for U(1) and SU(N) real-time lattice gauge theory path\nintegrals. We perform proof-of-principle Monte Carlo calculations of real-time\nU(1) and SU(3) lattice gauge theory and verify that exact results for unitary\ntime evolution of static quark-antiquark pairs in (1 + 1)D are reproduced.\n"}
+{"abstract": "  Recently, huge attention has been drawn to improve optical sensing devices\nbased on photonic resonators in the presence of graphene. In this paper, based\non the transfer matrix approach and TE polarization for the incident\nelectromagnetic waves, we numerically evaluate the transmission and reflection\nspectra for one-dimensional photonic resonators and surface plasmon resonances\nwith strained graphene, respectively. We proved that a relatively small strain\nfield in graphene can modulate linearly polarized resonant modes within the\nphotonic bandgap of the defective crystal. Moreover, we study the strain\neffects on the surface plasmon resonances created by the evanescent wave\ntechnique at the interference between a monolayer graphene and prism.\n"}
+{"abstract": "  We reveal that phononic thermal transport in graphene is not immune to grain\nboundaries (GBs) aligned along the direction of the temperature gradient.\nNon-equilibrium molecular dynamics simulations uncover a large reduction in the\nphononic thermal conductivity ($\\kappa_p$) along linear ultra-narrow GBs\ncomprising periodically-repeating pentagon-heptagon dislocations. Green's\nfunction calculations and spectral energy density analysis indicate that\n$\\kappa_p$ is the complex manifestation of the periodic strain field, which\nbehaves as a reflective diffraction grating with both diffuse and specular\nphonon reflections, and represents a source of anharmonic phonon-phonon\nscattering. Our findings provide new insights into the integrity of the\nphononic thermal transport in GB graphene.\n"}
+{"abstract": "  This work studies the inverse boundary problem for the two photon absorption\nradiative transport equation. We show that the absorption coefficients and\nscattering coefficients can be uniquely determined from the \\emph{albedo}\noperator. If scattering is absent, we do not require smallness of the incoming\nsource and the reconstructions of the absorption coefficients are explicit.\n"}
+{"abstract": "  This paper introduces a large-scale multimodal and multilingual dataset that\naims to facilitate research on grounding words to images in their contextual\nusage in language. The dataset consists of images selected to unambiguously\nillustrate concepts expressed in sentences from movie subtitles. The dataset is\na valuable resource as (i) the images are aligned to text fragments rather than\nwhole sentences; (ii) multiple images are possible for a text fragment and a\nsentence; (iii) the sentences are free-form and real-world like; (iv) the\nparallel texts are multilingual. We set up a fill-in-the-blank game for humans\nto evaluate the quality of the automatic image selection process of our\ndataset. We show the utility of the dataset on two automatic tasks: (i)\nfill-in-the blank; (ii) lexical translation. Results of the human evaluation\nand automatic models demonstrate that images can be a useful complement to the\ntextual context. The dataset will benefit research on visual grounding of words\nespecially in the context of free-form sentences, and can be obtained from\nhttps://doi.org/10.5281/zenodo.5034604 under a Creative Commons licence.\n"}
+{"abstract": "  Existing dialog state tracking (DST) models are trained with dialog data in a\nrandom order, neglecting rich structural information in a dataset. In this\npaper, we propose to use curriculum learning (CL) to better leverage both the\ncurriculum structure and schema structure for task-oriented dialogs.\nSpecifically, we propose a model-agnostic framework called Schema-aware\nCurriculum Learning for Dialog State Tracking (SaCLog), which consists of a\npreview module that pre-trains a DST model with schema information, a\ncurriculum module that optimizes the model with CL, and a review module that\naugments mispredicted data to reinforce the CL training. We show that our\nproposed approach improves DST performance over both a transformer-based and\nRNN-based DST model (TripPy and TRADE) and achieves new state-of-the-art\nresults on WOZ2.0 and MultiWOZ2.1.\n"}
+{"abstract": "  Modelling of stellar radiative intensities in various spectral pass-bands\nplays an important role in stellar physics. At the same time the direct\ncalculations of the high-resolution spectrum and then integrating it over the\ngiven spectral pass-band is computationally demanding due to the vast number of\natomic and molecular lines. This is particularly so when employing\nthree-dimensional (3D) models of stellar atmospheres. To accelerate the\ncalculations, one can employ approximate methods, e.g., the use of Opacity\nDistribution Functions (ODFs). Generally, ODFs provide a good approximation of\ntraditional spectral synthesis i.e., computation of intensities through filters\nwith strictly rectangular transmission function. However, their performance\nstrongly deteriorates when the filter transmission noticeably changes within\nits pass-band, which is the case for almost all filters routinely used in\nstellar physics. In this context, the aims of this paper are a) to generalize\nthe ODFs method for calculating intensities through filters with arbitrary\ntransmission functions; b) to study the performance of the standard and\ngeneralized ODFs methods for calculating intensities emergent from 3D models of\nstellar atmosphere. For this purpose we use the newly-developed MPS-ATLAS\nradiative transfer code to compute intensities emergent 3D cubes simulated with\nthe radiative magnetohydrodynamics code MURaM. The calculations are performed\nin the 1.5D regime, i.e., along many parallel rays passing through the\nsimulated cube. We demonstrate that generalized ODFs method allows accurate and\nfast syntheses of spectral intensities and their centre-to-limb variations.\n"}
+{"abstract": "  In this paper, we first consider null geodesics of a class of charged,\nspherical and asymptotically flat hairy black holes in an\nEinstein-Maxwell-scalar theory with a non-minimal coupling for the scalar and\nelectromagnetic fields. Remarkably, we show that there are two unstable\ncircular orbits for a photon in a certain parameter regime, corresponding to\ntwo unstable photon spheres of different sizes outside the event horizon. To\nillustrate the optical appearance of photon spheres, we then consider a simple\nspherical model of optically thin accretion on the hairy black hole, and obtain\nthe accretion image seen by a distant observer. In the single photon sphere\ncase, only one bright ring appears in the image, and is identified as the edge\nof the black hole shadow. Whereas in the case with two photon spheres, there\ncan be two concentric bright rings of different radii in the image, and the\nsmaller one serves as the boundary of the shadow, whose radius goes to zero at\nthe critical charge.\n"}
+{"abstract": "  Blockchain-based cryptocurrencies, facilitating the convenience of payment by\nproviding a decentralized online solution, have not been widely adopted so far\ndue to slow confirmation of transactions. Offline delegation offers an\nefficient way to exchange coins. However, in such an approach, the coins that\nhave been delegated confront the risk of being spent twice since the\ndelegator's behaviour cannot be restricted easily on account of the absence of\neffective supervision. Even if a third party can be regarded as a judge between\nthe delegator and delegatee to secure transactions, she still faces the threat\nof being compromised or providing misleading assure. Moreover, the approach\nequipped with a third party contradicts the real intention of decentralized\ncryptocurrency systems. In this paper, we propose \\textit{DelegaCoin}, an\noffline delegatable cryptocurrency system to mitigate such an issue. We exploit\ntrusted execution environments (TEEs) as decentralized \"virtual agents\" to\nprevent malicious delegation. In DelegaCoin, an owner can delegate his coins\nthrough offline-transactions without interacting with the blockchain network. A\nformal model and analysis, prototype implementation, and further evaluation\ndemonstrate that our scheme is provably secure and practically feasible.\n"}
+{"abstract": "  Coronavirus disease 2019 (COVID-19) is one of the most infectious diseases\nand one of the greatest challenge due to global health crisis. The virus has\nbeen transmitted globally and spreading so fast with high incidence. While, the\nvirus still pandemic, the government scramble to seek antiviral treatment and\nvaccines to combat the diseases. This study was conducted to investigate the\ninfluence of air pressure, air temperature, and relative humidity on the number\nof confirmed cases in COVID-19. Based on the result, the calculation of\nreproduced correlation through path decompositions and subsequent comparison to\nthe empirical correlation indicated that the path model fits the empirical\ndata. The identified factor significantly influenced the number of confirmed\ncases of COVID-19. Therefore, the number of daily confirmed cases of COVID-19\nmay reduce as the amount of relative humidity increases; relative humidity will\nincrease as the amount of air temperature decreases; and the amount of air\ntemperature will decrease as the amount of air pressure decreases. Thus, it is\nrecommended that policy-making bodies consider the result of this study when\nimplementing programs for COVID-19 and increase public awareness on the effects\nof weather condition, as it is one of the factors to control the number of\nCOVID-19 cases.\n"}
+{"abstract": "  We initiate the study of the rational SFT capacities of Siegel using tools in\ntoric algebraic geometry. In particular, we derive new (often sharp) bounds for\nthe RSFT capacities of a strongly convex toric domain in dimension $4$. These\nbounds admit descriptions in terms of both lattice optimization and (toric)\nalgebraic geometry. Applications include (a) an extremely simple lattice\nformula for for many RSFT capacities of a large class of convex toric domains,\n(b) new computations of the Gromov width of a class of product symplectic\nmanifolds and (c) an asymptotics law for the RSFT capacities of all strongly\nconvex toric domains.\n"}
+{"abstract": "  Using polarization-resolved Raman spectroscopy, we investigate layer number,\ntemperature, and magnetic field dependence of Raman spectra in one- to\nfour-layer $\\mathrm{CrI_{3}}$. Layer-number-dependent Raman spectra show that\nin the paramagnetic phase a doubly degenerated $E_{g}$ mode of monolayer\n$\\mathrm{CrI_{3}}$ splits into one $A_{g}$ and one $B_{g}$ mode in N-layer (N >\n1) $\\mathrm{CrI_{3}}$ due to the monoclinic stacking. Their energy separation\nincreases in thicker samples until an eventual saturation.\nTemperature-dependent measurements further show that the split modes tend to\nmerge upon cooling but remain separated until 10 K, indicating a failed attempt\nof the monoclinic-to-rhombohedral structural phase transition that is present\nin the bulk crystal. Magnetic-field-dependent measurements reveal an additional\nmonoclinic distortion across the magnetic-field-induced layered\nantiferromagnetism-to-ferromagnetism phase transition. We propose a structural\nchange that consists of both a lateral sliding toward the rhombohedral stacking\nand a decrease in the interlayer distance to explain our experimental\nobservations.\n"}
+{"abstract": "  Extended Berkeley Packet Filter (BPF) has emerged as a powerful method to\nextend packet-processing functionality in the Linux operating system. BPF\nallows users to write code in high-level languages (like C or Rust) and execute\nthem at specific hooks in the kernel, such as the network device driver. To\nensure safe execution of a user-developed BPF program in kernel context, Linux\nuses an in-kernel static checker. The checker allows a program to execute only\nif it can prove that the program is crash-free, always accesses memory within\nsafe bounds, and avoids leaking kernel data.\n  BPF programming is not easy. One, even modest-sized BPF programs are deemed\ntoo large to analyze and rejected by the kernel checker. Two, the kernel\nchecker may incorrectly determine that a BPF program exhibits unsafe behaviors.\nThree, even small performance optimizations to BPF code (e.g., 5% gains) must\nbe meticulously hand-crafted by expert developers. Traditional optimizing\ncompilers for BPF are often inadequate since the kernel checker's safety\nconstraints are incompatible with rule-based optimizations.\n  We present K2, a program-synthesis-based compiler that automatically\noptimizes BPF bytecode with formal correctness and safety guarantees. K2\nproduces code with 6--26% reduced size, 1.36%--55.03% lower average\npacket-processing latency, and 0--4.75% higher throughput (packets per second\nper core) relative to the best clang-compiled program, across benchmarks drawn\nfrom Cilium, Facebook, and the Linux kernel. K2 incorporates several\ndomain-specific techniques to make synthesis practical by accelerating\nequivalence-checking of BPF programs by 6 orders of magnitude.\n"}
+{"abstract": "  We present a novel watermarking scheme to verify the ownership of DNN models.\nExisting solutions embedded watermarks into the model parameters, which were\nproven to be removable and detectable by an adversary to invalidate the\nprotection. In contrast, we propose to implant watermarks into the model\narchitectures. We design new algorithms based on Neural Architecture Search\n(NAS) to generate watermarked architectures, which are unique enough to\nrepresent the ownership, while maintaining high model usability. We further\nleverage cache side channels to extract and verify watermarks from the\nblack-box models at inference. Theoretical analysis and extensive evaluations\nshow our scheme has negligible impact on the model performance, and exhibits\nstrong robustness against various model transformations.\n"}
+{"abstract": "  We estimate a general mixture of Markov jump processes. The key novel feature\nof the proposed mixture is that the transition intensity matrices of the Markov\nprocesses comprising the mixture are entirely unconstrained. The Markov\nprocesses are mixed with distributions that depend on the initial state of the\nmixture process. The new mixture is estimated from its continuously observed\nrealizations using the EM algorithm, which provides the maximum likelihood (ML)\nestimates of the mixture's parameters. To obtain the standard errors of the\nestimates of the mixture's parameters, we employ Louis' (1982) general formula\nfor the observed Fisher information matrix. We also derive the asymptotic\nproperties of the ML estimators. Simulation study verifies the estimates'\naccuracy and confirms the consistency and asymptotic normality of the\nestimators. The developed methods are applied to a medical dataset, for which\nthe likelihood ratio test rejects the constrained mixture in favor of the\nproposed unconstrained one. This application exemplifies the usefulness of a\nnew unconstrained mixture for identification and characterization of\nhomogeneous subpopulations in a heterogeneous population.\n"}
+{"abstract": "  We discuss the prospects of diffractive dijet photoproduction at the EIC to\ndistinguish different fits of diffractive proton PDFs, different schemes of\nfactorization breaking, to determine diffractive nuclear PDFs and pion PDFs\nfrom leading neutron production.\n"}
+{"abstract": "  This note introduces a simple metric for benchmarking shock-capturing\nschemes. This metric is especially focused on the shock-capturing overshoots,\nwhich may undermine the robustness of numerical simulations, as well as the\nreliability of numerical results. The idea is to numerically solve the model\nlinear advection equation with an initial condition of a square wave\ncharacterized with different wavenumbers. After one step of temporal evolution,\nthe exact numerical overshoot error can be easily determined and shown as a\nfunction of the CFL number and the reduced wavenumber. With the overshoot error\nquantified by the present metric, a number of representative shock-capturing\nschemes are analyzed accordingly, and several findings including the amplitude\nof overshoots non-monotonously varying with the CFL number, and the amplitude\nof overshoots significantly depending on the reduced wavenumber of the square\nwaves (discontinuities), are newly discovered, which are not before.\n"}
+{"abstract": "  The auto feature extraction capability of deep neural networks (DNN) endows\nthem the potentiality for analysing complicated electroencephalogram (EEG) data\ncaptured from brain functionality research. This work investigates the\npotential coherent correspondence between the region-of-interest (ROI) for DNN\nto explore, and ROI for conventional neurophysiological oriented methods to\nwork with, exemplified in the case of working memory study. The attention\nmechanism induced by global average pooling (GAP) is applied to a public EEG\ndataset of working memory, to unveil these coherent ROIs via a classification\nproblem. The result shows the alignment of ROIs from different research\ndisciplines. This work asserts the confidence and promise of utilizing DNN for\nEEG data analysis, albeit in lack of the interpretation to network operations.\n"}
+{"abstract": "  Capacitance measurements as a function of voltage, frequency and temperature\nare useful tools to identify fundamental parameters that affect solar cell\noperation. Techniques such as capacitance-voltage (CV), Mott-Schottky analysis\nand thermal admittance spectroscopy (TAS) measurements are therefore frequently\nemployed to obtain relevant parameters of the perovskite absorber layer in\nperovskite solar cells. However, state-of-the-art perovskite solar cells employ\nthin electron and hole transport layers that improve contact selectivity. These\nselective contacts are often quite resistive in nature, which implies that\ntheir capacitances will contribute to the total capacitance and thereby affect\nthe extraction of the capacitance of the perovskite layer. Based on this\npremise, we develop a simple multilayer model that considers the perovskite\nsolar cell as a series connection of the geometric capacitance of each layer in\nparallel with their voltage-dependent resistances. Analysis of this model\nyields fundamental limits to the resolution of spatial doping profiles and\nminimum values of doping/trap densities, built-in voltages and activation\nenergies. We observe that most of the experimental\ncapacitance-voltage-frequency-temperature data, calculated doping/trap\ndensities and activation energies reported in literature are within these\ncut-off values derived, indicating that the capacitance response of the\nperovskite solar cell is indeed strongly affected by the capacitance of its\nselective contacts.\n"}
+{"abstract": "  The photoionization of the CO molecule from the C$-1s$ orbital does not obey\nthe Franck-Condon approximation, as a consequence of the nuclear recoil that\naccompanies the direct emission and intra-molecular scattering of the\nphotoelectron. We use an analytical model to investigate the temporal signature\nof the entangled nuclear and electronic motion in this process. We show that\nthe photoelectron emission delay can be decomposed into its localization and\nresonant-confinement components. Finally, photoionization by a broadband\nsoft-x-ray pulse results in a coherent vibrational ionic state with a tunable\ndelay with respect to the classical sudden-photoemission limit.\n"}
+{"abstract": "  Reconfigurable intelligent surfaces (RIS) are a key enabler of various new\napplications in 6G smart radio environments. By utilizing an RIS prototype\nsystem, this paper aims to enhance self-interference (SI) cancellation for\nin-band full-duplex(FD) communication systems. In FD communication, SI of a\nnode severely limits the performance of the node by shadowing the received\nsignal from a distant node with its own transmit signal. To this end, we\npropose to assist SI cancellation by exploiting a RIS to form a suitable\ncancellation signal, thus canceling the leaked SI in the analog domain.\nBuilding upon a 64 element RIS prototype we present results of RIS-assisted SI\ncancellation from a real testbed. Given a suitable amount of initial analog\nisolation, we are able to cancel the leaked signal by as much as -85 dB. The\npresented case study shows promising performance to build an FD communication\nsystem on this foundation.\n"}
+{"abstract": "  Graph Neural Networks (GNNs) have recently shown to be powerful tools for\nrepresenting and analyzing graph data. So far GNNs is becoming an increasingly\ncritical role in software engineering including program analysis, type\ninference, and code representation. In this paper, we introduce GraphGallery, a\nplatform for fast benchmarking and easy development of GNNs based software.\nGraphGallery is an easy-to-use platform that allows developers to automatically\ndeploy GNNs even with less domain-specific knowledge. It offers a set of\nimplementations of common GNN models based on mainstream deep learning\nframeworks. In addition, existing GNNs toolboxes such as PyG and DGL can be\neasily incorporated into the platform. Experiments demonstrate the reliability\nof implementations and superiority in fast coding. The official source code of\nGraphGallery is available at https://github.com/EdisonLeeeee/GraphGallery and a\ndemo video can be found at https://youtu.be/mv7Zs1YeaYo.\n"}
+{"abstract": "  The Granular Integration Through Transients (GITT) formalism gives a\ntheoretical description of the rheology of moderately dense granular flows and\nsuspensions. In this work, we extend the GITT equations beyond the case of\nsimple shear flows studied before. Applying this to the particular example of\nextensional flows, we show that the predicted behavior is somewhat different\nfrom that of the more frequently studied simple shear case, as illustrated by\nthe possibility of non monotonous evolution of the effective friction\ncoefficient $\\mu$ with the inertial number $\\mathcal{I}$. By the reduction of\nthe GITT equations to simple toy-models, we provide a generalization of the\n$\\mu(\\mathcal{I})$-law true for any type of flow deformation. Our analysis also\nincludes a study of the Trouton ratio, which is shown to behave quite similarly\nto that of dense colloidal suspensions.\n"}
+{"abstract": "  We present observations of 86 meteor radio afterglows (MRAs) using the new\nbroadband imager at the Long Wavelength Array Sevilleta (LWA-SV) station. The\nMRAs were detected using the all-sky images with a bandwidth up to 20 MHz. We\nfit the spectra with both a power law and a log-normal function. When fit with\na power law, the spectra varied from flat to steep and the derived spectral\nindex distribution from the fit peaked at -1.73. When fit with a log-normal\nfunction, the spectra exhibits turnovers at frequencies between 30-40 MHz, and\nappear to be a better functional fit to the spectra. We compared the spectral\nparameters from the two fitting methods with the physical properties of MRAs.\nWe observe a weak correlation between the log-normal turnover frequency and the\naltitude of MRAs. The spectral indices from the power law fit do not show any\nstrong correlations with the physical properties of MRAs. However, the full\nwidth half maximum (FWHM) duration of MRAs is correlated with the local time,\nincidence angle, luminosity and optically derived kinetic energy of parent\nmeteoroid. Also, the average luminosity of MRAs seems to be correlated with the\nkinetic energy of parent meteoroid and the altitude at which they occur.\n"}
+{"abstract": "  We continue the study of Harish-Chandra bimodules in the setting of the\nDeligne categories $\\mathrm{Rep}(G_t)$, that was started in the previous work\nof the first author (arXiv:2002.01555). In this work we construct a family of\nHarish-Chandra bimodules that generalize simple finite dimensional bimodules in\nthe classical case. It turns out that they have finite $K$-type, which is a\nnon-vacuous condition for the Harish-Chandra bimodules in $\\mathrm{Rep}(G_t)$.\nThe full classification of (simple) finite $K$-type bimodules is yet unknown.\n  This construction also yields some examples of central characters $\\chi$ of\nthe universal enveloping algebra $U(\\mathfrak{g}_t)$ for which the quotient\n$U_\\chi$ is not simple, and, thereby, it allows us to partially solve a\nquestion posed by Pavel Etingof in one of his works.\n"}
+{"abstract": "  Progressive Quenching (PQ) is a stochastic process during which one fixes one\nafter another the degrees of freedom of a globally coupled Ising spin system\nwhile letting it thermalize through a heat bath. It has previously been shown\nthat during PQ, the mean equilibrium spin value follows a martingale process\nand that this process can characterize the memory of the system. In the present\nstudy, we find that the aforementioned martingale implies a local invariance of\nthe path-weight for the total quenched magnetization, the Markovian process\nwhose increment is the lastly fixed spin. Consequently, PQ lets the probability\ndistribution for the total quenched magnetization evolve while keeping the\nBoltzmann-like factor, or a canonical structure under constraint, which\nconsists of a path-independent potential and a path-counting entropy. Moreover,\nwhen the PQ starts from full equilibrium, the probability distribution at each\nstage of PQ is found to be the limit distribution of what we call Recycled\nQuenching (RQ), the process in which a randomly chosen quenched spin is\nunquenched after a single step of PQ. The local invariance is a new consequence\nof the martingale property, and not an application of known theorems for\nmartingale process.\n"}
+{"abstract": "  Existing unsupervised visual odometry (VO) methods either match pairwise\nimages or integrate the temporal information using recurrent neural networks\nover a long sequence of images. They are either not accurate, time-consuming in\ntraining or error accumulative. In this paper, we propose a method consisting\nof two camera pose estimators that deal with the information from pairwise\nimages and a short sequence of images respectively. For image sequences, a\nTransformer-like structure is adopted to build a geometry model over a local\ntemporal window, referred to as Transformer-based Auxiliary Pose Estimator\n(TAPE). Meanwhile, a Flow-to-Flow Pose Estimator (F2FPE) is proposed to exploit\nthe relationship between pairwise images. The two estimators are constrained\nthrough a simple yet effective consistency loss in training. Empirical\nevaluation has shown that the proposed method outperforms the state-of-the-art\nunsupervised learning-based methods by a large margin and performs comparably\nto supervised and traditional ones on the KITTI and Malaga dataset.\n"}
+{"abstract": "  Emerging ultra-low-power tiny scale computing devices in Cyber-Physical\nSystems %and Internet of Things (IoT) run on harvested energy, are\nintermittently powered, have limited computational capability, and perform\nsensing and actuation functions under the control of a dedicated firmware\noperating without the supervisory control of an operating system. Wirelessly\nupdating or patching the firmware of such devices is inevitable. We consider\nthe challenging problem of simultaneous and secure firmware updates or patching\nfor a typical class of such devices -- Computational Radio Frequency\nIdentification (CRFID) devices. We propose Wisecr, the first secure and\nsimultaneous wireless code dissemination mechanism to multiple devices that\nprevent malicious code injection attacks and intellectual property (IP) theft,\nwhilst enabling remote attestation of code installation. Importantly, Wisecr is\nengineered to comply with existing ISO compliant communication protocol\nstandards employed by CRFID devices and systems. We comprehensively evaluate\nWisecr's overhead, demonstrate its implementation over standards-compliant\nprotocols, analyze its security and implement an end-to-end realization with\npopular CFRID devices -- the open-source code is released on GitHub.\n"}
+{"abstract": "  Twin support vector machine (TWSVM) and twin support vector regression (TSVR)\nare newly emerging efficient machine learning techniques which offer promising\nsolutions for classification and regression challenges respectively. TWSVM is\nbased upon the idea to identify two nonparallel hyperplanes which classify the\ndata points to their respective classes. It requires to solve two small sized\nquadratic programming problems (QPPs) in lieu of solving single large size QPP\nin support vector machine (SVM) while TSVR is formulated on the lines of TWSVM\nand requires to solve two SVM kind problems. Although there has been good\nresearch progress on these techniques; there is limited literature on the\ncomparison of different variants of TSVR. Thus, this review presents a rigorous\nanalysis of recent research in TWSVM and TSVR simultaneously mentioning their\nlimitations and advantages. To begin with we first introduce the basic theory\nof support vector machine, TWSVM and then focus on the various improvements and\napplications of TWSVM, and then we introduce TSVR and its various enhancements.\nFinally, we suggest future research and development prospects.\n"}
+{"abstract": "  In this paper, the hybrid metric-Palatini gravity is an approach to modified\ngravity in which is added to the usual Einstein-Hilbert action a supplementary\nterm containing a Palatini-type correction of the form $f({\\cal R},T)$. Here,\n${\\cal R}$ is the Palatini curvature scalar, which is constructed from an\nindependent connection and $T$ is the trace of the energy-momentum tensor. This\ntheory describes a non-minimal coupling between matter and geometry. The\nmodified Einstein field equations in this hybrid metric-Palatini approach are\nobtained. Then, it is investigated whether this modified theory of gravity and\nits field equations allow G\\\"{o}del-type solutions, which essentially lead to\nviolation of causality. Considering physically well-motivated matter sources,\ncausal and non-causal solutions are explored.\n"}
+{"abstract": "  An unresolved problem in Deep Learning is the ability of neural networks to\ncope with domain shifts during test-time, imposed by commonly fixing network\nparameters after training. Our proposed method Meta Test-Time Training (MT3),\nhowever, breaks this paradigm and enables adaption at test-time. We combine\nmeta-learning, self-supervision and test-time training to learn to adapt to\nunseen test distributions. By minimizing the self-supervised loss, we learn\ntask-specific model parameters for different tasks. A meta-model is optimized\nsuch that its adaption to the different task-specific models leads to higher\nperformance on those tasks. During test-time a single unlabeled image is\nsufficient to adapt the meta-model parameters. This is achieved by minimizing\nonly the self-supervised loss component resulting in a better prediction for\nthat image. Our approach significantly improves the state-of-the-art results on\nthe CIFAR-10-Corrupted image classification benchmark. Our implementation is\navailable on GitHub.\n"}
+{"abstract": "  We propose a novel image based localization system using graph neural\nnetworks (GNN). The pretrained ResNet50 convolutional neural network (CNN)\narchitecture is used to extract the important features for each image.\nFollowing, the extracted features are input to GNN to find the pose of each\nimage by either using the image features as a node in a graph and formulate the\npose estimation problem as node pose regression or modelling the image features\nthemselves as a graph and the problem becomes graph pose regression. We do an\nextensive comparison between the proposed two approaches and the state of the\nart single image localization methods and show that using GNN leads to enhanced\nperformance for both indoor and outdoor environments.\n"}
+{"abstract": "  This article studies Paley's theory for lacunary Fourier series on\n(nonabelian) discrete groups.\n  The results unify and generalize the work of Rudin for abelian discrete\ngroups and the work of Lust-Piquard and Pisier for operator valued functions,\nand provide new examples of Paley sequences and $\\Lambda(p)$ sets on free\ngroups.\n"}
+{"abstract": "  In this research article, we have reported periodic cellular automata rules\nfor different gait state prediction and classification of the gait data using\nextreme machine Leaning (ELM). This research is the first attempt to use\ncellular automaton to understand the complexity of bipedal walk. Due to\nnonlinearity, varying configurations throughout the gait cycle and the passive\njoint located at the unilateral foot-ground contact in bipedal walk resulting\nvariation of dynamic descriptions and control laws from phase to phase for\nhuman gait is making difficult to predict the bipedal walk states. We have\ndesigned the cellular automata rules which will predict the next gait state of\nbipedal steps based on the previous two neighbour states. We have designed\ncellular automata rules for normal walk. The state prediction will help to\ncorrectly design the bipedal walk. The normal walk depends on next two states\nand has total 8 states. We have considered the current and previous states to\npredict next state. So we have formulated 16 rules using cellular automata, 8\nrules for each leg. The priority order maintained using the fact that if right\nleg in swing phase then left leg will be in stance phase. To validate the model\nwe have classified the gait Data using ELM [1] and achieved accuracy 60%. We\nhave explored the trajectories and compares with another gait trajectories.\nFinally we have presented the error analysis for different joints.\n"}
+{"abstract": "  We study the problem of estimating the total number of searches (volume) of\nqueries in a specific domain, which were submitted to a search engine in a\ngiven time period. Our statistical model assumes that the distribution of\nsearches follows a Zipf's law, and that the observed sample volumes are biased\naccordingly to three possible scenarios. These assumptions are consistent with\nempirical data, with keyword research practices, and with approximate\nalgorithms used to take counts of query frequencies. A few estimators of the\nparameters of the distribution are devised and experimented, based on the\nnature of the empirical/simulated data. For continuous data, we recommend using\nnonlinear least square regression (NLS) on the top-volume queries, where the\nbound on the volume is obtained from the well-known Clauset, Shalizi and Newman\n(CSN) estimation of power-law parameters. For binned data, we propose using a\nChi-square minimization approach restricted to the top-volume queries, where\nthe bound is obtained by the binned version of the CSN method. Estimations are\nthen derived for the total number of queries and for the total volume of the\npopulation, including statistical error bounds. We apply the methods on the\ndomain of recipes and cooking queries searched in Italian in 2017. The observed\nvolumes of sample queries are collected from Google Trends (continuous data)\nand SearchVolume (binned data). The estimated total number of queries and total\nvolume are computed for the two cases, and the results are compared and\ndiscussed.\n"}
+{"abstract": "  We discuss the associated $c\\bar{c}$ and $l^+l^-$ pairs production in\nultraperipheral heavy-ion collisions at high energies. Such a channel provides\na novel probe for double-parton scattering (DPS) at small $x$ enabling one to\nprobe the photon density inside the nucleus. We have derived an analog of the\nstandard central $pp$ pocket formula and studied the kinematical dependence of\nthe effective cross section. Taking into account both elastic and non-elastic\ncontributions, we have shown predictions for the DPS $c\\bar c l^+l^-$\nproduction cross section differential in charm quark rapidity and dilepton\ninvariant mass and rapidity for LHC and a future collider.\n"}
+{"abstract": "  It is well-known that fractal signals appear in many fields of science. LAN\nand WWW traces, wireless traffic, VBR resources, etc. are among the ones with\nthis behavior in computer networks traffic flows. An important question in\nthese applications is how long a measured trace should be to obtain reliable\nestimates of de Hurst index (H). This paper addresses this question by first\nproviding a thorough study of estimator for short series based on the behavior\nof bias, standard deviation (s), Root-Mean-Square Error (RMSE), and convergence\nwhen using Gaussian H-Self-Similar with Stationary Increments signals (H-sssi\nsignals). Results show that Whittle-type estimators behave the best when\nestimating H for short signals. Based on the results, empirically derived the\nminimum trace length for the estimators is proposed. Finally for testing the\nresults, the application of estimators to real traces is accomplished.\nImmediate applications from this can be found in the real-time estimation of H\nwhich is useful in agent-based control of Quality of Service (QoS) parameters\nin the high-speed computer network traffic flows.\n"}
+{"abstract": "  A graph $G=(V,E)$ is word-representable if and only if there exists a word\n$w$ over the alphabet $V$ such that letters $x$ and $y$, $x\\neq y$, alternate\nin $w$ if and only if $xy\\in E$. A split graph is a graph in which the vertices\ncan be partitioned into a clique and an independent set. There is a long line\nof research on word-representable graphs in the literature, and recently,\nword-representability of split graphs has attracted interest.\n  In this paper, we first give a characterization of word-representable split\ngraphs in terms of permutations of columns of the adjacency matrices. Then, we\nfocus on the study of word-representability of split graphs obtained by\niterations of a morphism, the notion coming from combinatorics on words. We\nprove a number of general theorems and provide a complete classification in the\ncase of morphisms defined by $2\\times 2$ matrices.\n"}
+{"abstract": "  Colon and Lung cancer is one of the most perilous and dangerous ailments that\nindividuals are enduring worldwide and has become a general medical problem. To\nlessen the risk of death, a legitimate and early finding is particularly\nrequired. In any case, it is a truly troublesome task that depends on the\nexperience of histopathologists. If a histologist is under-prepared it may even\nhazard the life of a patient. As of late, deep learning has picked up energy,\nand it is being valued in the analysis of Medical Imaging. This paper intends\nto utilize and alter the current pre-trained CNN-based model to identify lung\nand colon cancer utilizing histopathological images with better augmentation\ntechniques. In this paper, eight distinctive Pre-trained CNN models, VGG16,\nNASNetMobile, InceptionV3, InceptionResNetV2, ResNet50, Xception, MobileNet,\nand DenseNet169 are trained on LC25000 dataset. The model performances are\nassessed on precision, recall, f1score, accuracy, and auroc score. The results\nexhibit that all eight models accomplished noteworthy results ranging from 96%\nto 100% accuracy. Subsequently, GradCAM and SmoothGrad are also used to picture\nthe attention images of Pre-trained CNN models classifying malignant and benign\nimages.\n"}
+{"abstract": "  The focus of this paper is to address the knowledge acquisition bottleneck\nfor Named Entity Recognition (NER) of mutations, by analysing different\napproaches to build manually-annotated data. We address first the impact of\nusing a single annotator vs two annotators, in order to measure whether\nmultiple annotators are required. Once we evaluate the performance loss when\nusing a single annotator, we apply different methods to sample the training\ndata for second annotation, aiming at improving the quality of the dataset\nwithout requiring a full pass. We use held-out double-annotated data to build\ntwo scenarios with different types of rankings: similarity-based and confidence\nbased. We evaluate both approaches on: (i) their ability to identify training\ninstances that are erroneous (cases where single-annotator labels differ from\ndouble-annotation after discussion), and (ii) on Mutation NER performance for\nstate-of-the-art classifiers after integrating the fixes at different\nthresholds.\n"}
+{"abstract": "  Integrated circuit (IC) piracy and overproduction are serious issues that\nthreaten the security and integrity of a system. Logic locking is a type of\nhardware obfuscation technique where additional key gates are inserted into the\ncircuit. Only the correct key can unlock the functionality of that circuit\notherwise the system produces the wrong output. In an effort to hinder these\nthreats on ICs, we have developed a probability-based logic locking technique\nto protect the design of a circuit. Our proposed technique called \"ProbLock\"\ncan be applied to combinational and sequential circuits through a critical\nselection process. We used a filtering process to select the best location of\nkey gates based on various constraints. Each step in the filtering process\ngenerates a subset of nodes for each constraint. We also analyzed the\ncorrelation between each constraint and adjusted the strength of the\nconstraints before inserting key gates. We have tested our algorithm on 40\nbenchmarks from the ISCAS '85 and ISCAS '89 suite.\n"}
+{"abstract": "  As part of a chemo-dynamical survey of five nearby globular clusters with\n2dF/AAOmega on the AAT, we have obtained kinematic information for the globular\ncluster NGC3201. Our new observations confirm the presence of a significant\nvelocity gradient across the cluster which can almost entirely be explained by\nthe high proper motion of the cluster. After subtracting the contribution of\nthis perspective rotation, we found a remaining rotation signal with an\namplitude of $\\sim1\\ km/s$ around a different axis to what we expect from the\ntidal tails and the potential escapers, suggesting that this rotation is\ninternal and can be a remnant of its formation process. At the outer part, we\nfound a rotational signal that is likely a result from potential escapers. The\nproper motion dispersion at large radii reported by Bianchini et al. has\npreviously been attributed to dark matter. Here we show that the LOS dispersion\nbetween 0.5-1 Jacobi radius is lower, yet above the predictions from an N-body\nmodel of NGC3201 that we ran for this study. Based on the simulation, we find\nthat potential escapers cannot fully explain the observed velocity dispersion.\nWe also estimate the effect on the velocity dispersion of different amounts of\nstellar-mass black holes and unbound stars from the tidal tails with varying\nescape rates and find that these effects cannot explain the difference between\nthe LOS dispersion and the N-body model. Given the recent discovery of tidal\ntail stars at large distances from the cluster, a dark matter halo is an\nunlikely explanation. We show that the effect of binary stars, which is not\nincluded in the N-body model, is important and can explain part of the\ndifference in dispersion. We speculate that the remaining difference must be\nthe result of effects not included in the N-body model, such as initial cluster\nrotation, velocity anisotropy and Galactic substructure.\n"}
+{"abstract": "  We study the possibilities of building a non-autoregressive speech-to-text\ntranslation model using connectionist temporal classification (CTC), and use\nCTC-based automatic speech recognition as an auxiliary task to improve the\nperformance. CTC's success on translation is counter-intuitive due to its\nmonotonicity assumption, so we analyze its reordering capability. Kendall's tau\ndistance is introduced as the quantitative metric, and gradient-based\nvisualization provides an intuitive way to take a closer look into the model.\nOur analysis shows that transformer encoders have the ability to change the\nword order and points out the future research direction that worth being\nexplored more on non-autoregressive speech translation.\n"}
+{"abstract": "  The Cholesky factorization of the moment matrix is applied to discrete\northogonal polynomials on the homogeneous lattice. In particular, semiclassical\ndiscrete orthogonal polynomials, which are built in terms of a discrete Pearson\nequation, are studied. The Laguerre-Freud structure semi-infinite matrix that\nmodels the shifts by $\\pm 1$ in the independent variable of the set of\northogonal polynomials is introduced. In the semiclassical case it is proven\nthat this Laguerre-Freud matrix is banded. From the well known fact that\nmoments of the semiclassical weights are logarithmic derivatives of generalized\nhypergeometric functions, it is shown how the contiguous relations for these\nhypergeometric functions translate as symmetries for the corresponding moment\nmatrix. It is found that the 3D Nijhoff-Capel discrete Toda lattice describes\nthe corresponding contiguous shifts for the squared norms of the orthogonal\npolynomials. The continuous Toda for these semiclassical discrete orthogonal\npolynomials is discussed and the compatibility equations are derived. It also\nshown that the Kadomtesev-Petvishvilii equation is connected to an adequate\ndeformed semiclassical discrete weight, but in this case the deformation do not\nsatisfy a Pearson equation.\n"}
+{"abstract": "  This paper is the first of a pair that aims to classify a large number of the\ntype $II$ quantum subgroups of the categories\n$\\mathcal{C}(\\mathfrak{sl}_{r+1},k)$. In this work we classify the braided\nauto-equivalences of the categories of local modules for all known type $I$\nquantum subgroups of $\\mathcal{C}(\\mathfrak{sl}_{r+1},k)$. We find that the\nsymmetries are all non-exceptional except for four cases (up to level-rank\nduality). These exceptional cases are the orbifolds $\\mathcal{C}(\n\\mathfrak{sl}_{2},16)_{\\operatorname{Rep}(\\mathbb{Z}_2)}$, $\\mathcal{C}(\n\\mathfrak{sl}_{3},9)_{\\operatorname{Rep}(\\mathbb{Z}_3)}$, $\\mathcal{C}(\n\\mathfrak{sl}_{4},8)_{\\operatorname{Rep}(\\mathbb{Z}_4)}$, and $\\mathcal{C}(\n\\mathfrak{sl}_{5},5)_{\\operatorname{Rep}(\\mathbb{Z}_5)}$.\n  We develop several technical tools in this work. We give a skein theoretic\ndescription of the orbifold quantum subgroups of\n$\\mathcal{C}(\\mathfrak{sl}_{r+1},k)$. Our methods here are general, and the\ntechniques developed will generalise to give skein theory for any orbifold of a\nbraided tensor category. We also give a formulation of orthogonal level-rank\nduality in the type $D$-$D$ case, which is used to construct one of the\nexceptionals. Finally we uncover an unexpected connection between quadratic\ncategories and exceptional braided auto-equivalences of the orbifolds. We use\nthis connection to construct two of the four exceptionals.\n  In the sequel to this paper we will use the classified braided\nauto-equivalences to construct the corresponding type $II$ quantum subgroups of\nthe categories $\\mathcal{C}(\\mathfrak{sl}_{r+1},k)$. When paired with Gannon's\ntype $I$ classification for $r\\leq 6$, this will complete the type $II$\nclassification for these same ranks.\n  This paper includes an appendix by Terry Gannon, which provides useful\nresults on the dimensions of objects in the categories\n$\\mathcal{C}(\\mathfrak{sl}_{r+1},k)$.\n"}
+{"abstract": "  How cooperation emerges in human societies is both an evolutionary enigma,\nand a practical problem with tangible implications for societal health.\nPopulation structure has long been recognized as a catalyst for cooperation\nbecause local interactions enable reciprocity. Analysis of this phenomenon\ntypically assumes bi-directional social interactions, even though real-world\ninteractions are often uni-directional. Uni-directional interactions -- where\none individual has the opportunity to contribute altruistically to another, but\nnot conversely -- arise in real-world populations as the result of\norganizational hierarchies, social stratification, popularity effects, and\nendogenous mechanisms of network growth. Here we expand the theory of\ncooperation in structured populations to account for both uni- and\nbi-directional social interactions. Even though directed interactions remove\nthe opportunity for reciprocity, we find that cooperation can nonetheless be\nfavored in directed social networks and that cooperation is provably maximized\nfor networks with an intermediate proportion of directed interactions, as\nobserved in many empirical settings. We also identify two simple structural\nmotifs that allow efficient modification of interaction directionality to\npromote cooperation by orders of magnitude. We discuss how our results relate\nto the concepts of generalized and indirect reciprocity.\n"}
+{"abstract": "  The purpose of this paper is to discuss the categorical structure for a\nmethod of defining quantum invariants of knots, links and three-manifolds.\nThese invariants can be defined in terms of right integrals on certain Hopf\nalgebras. We call such an invariant of 3-manifolds a Hennings invariant. The\nwork reported in this paper has its background in previous work of the authors.\nThe present paper gives an abstract description of these structures and shows\nhow the Hopf algebraic image of a knot lies in the center of the corresponding\nHopf algebra. The paper also shows how all the axiomatic properties of a\nquasi-triangular Hopf algebra are involved in the topology via a functor from\nthe Tangle Category to the Diagrammatic Category of a Hopf Algebra. The\ninvariants described in this paper generalize to invariants of rotational\nvirtual knots. The contents of this paper are an update of the original 1998\nversion published in JKTR.\n"}
+{"abstract": "  We study a stochastic program where the probability distribution of the\nuncertain problem parameters is unknown and only indirectly observed via\nfinitely many correlated samples generated by an unknown Markov chain with $d$\nstates. We propose a data-driven distributionally robust optimization model to\nestimate the problem's objective function and optimal solution. By leveraging\nresults from large deviations theory, we derive statistical guarantees on the\nquality of these estimators. The underlying worst-case expectation problem is\nnonconvex and involves $\\mathcal O(d^2)$ decision variables. Thus, it cannot be\nsolved efficiently for large $d$. By exploiting the structure of this problem,\nwe devise a customized Frank-Wolfe algorithm with convex direction-finding\nsubproblems of size $\\mathcal O(d)$. We prove that this algorithm finds a\nstationary point efficiently under mild conditions. The efficiency of the\nmethod is predicated on a dimensionality reduction enabled by a dual\nreformulation. Numerical experiments indicate that our approach has better\ncomputational and statistical properties than the state-of-the-art methods.\n"}
+{"abstract": "  Poset games are a class of combinatorial game that remain unsolved. Soltys\nand Wilson proved that computing wining strategies is in \\textbf{PSPACE} and\naside from special cases such as Nim and N-Free games, \\textbf{P} time\nalgorithms for finding ideal play are unknown. This paper presents methods\ncalculate the nimber of posets games allowing for the classification of winning\nor losing positions. The results present an equivalence of ideal strategies on\nposets that are seemingly unrelated.\n"}
+{"abstract": "  An accurate and precise measurement of the spins of individual merging black\nholes is required to understand their origin. While previous studies have\nindicated that most of the spin information comes from the inspiral part of the\nsignal, the informative spin measurement of the heavy binary black hole system\nGW190521 suggests that the merger and ringdown can contribute significantly to\nthe spin constraints for such massive systems. We perform a systematic study\ninto the measurability of the spin parameters of individual heavy binary black\nhole mergers using a numerical relativity surrogate waveform model including\nthe effects of both spin-induced precession and higher-order modes. We find\nthat the spin measurements are driven by the merger and ringdown parts of the\nsignal for GW190521-like systems, but the uncertainty in the measurement\nincreases with the total mass of the system. We are able to place meaningful\nconstraints on the spin parameters even for systems observed at moderate\nsignal-to-noise ratios, but the measurability depends on the exact\nsix-dimensional spin configuration of the system. Finally, we find that the\nazimuthal angle between the in-plane projections of the component spin vectors\nat a given reference frequency cannot be well-measured for most of our\nsimulated configurations even for signals observed with high signal-to-noise\nratios.\n"}
+{"abstract": "  Taking pictures through glass windows almost always produces undesired\nreflections that degrade the quality of the photo. The ill-posed nature of the\nreflection removal problem reached the attention of many researchers for more\nthan decades. The main challenge of this problem is the lack of real training\ndata and the necessity of generating realistic synthetic data. In this paper,\nwe proposed a single image reflection removal method based on context\nunderstanding modules and adversarial training to efficiently restore the\ntransmission layer without reflection. We also propose a complex data\ngeneration model in order to create a large training set with various type of\nreflections. Our proposed reflection removal method outperforms\nstate-of-the-art methods in terms of PSNR and SSIM on the SIR benchmark\ndataset.\n"}
+{"abstract": "  A common view on the brain learning processes proposes that the three classic\nlearning paradigms -- unsupervised, reinforcement, and supervised -- take place\nin respectively the cortex, the basal-ganglia, and the cerebellum. However,\ndopamine outbursts, usually assumed to encode reward, are not limited to the\nbasal ganglia but also reach prefrontal, motor, and higher sensory cortices. We\npropose that in the cortex the same reward-based trial-and-error processes\nmight support not only the acquisition of motor representations but also of\nsensory representations. In particular, reward signals might guide\ntrial-and-error processes that mix with associative learning processes to\nsupport the acquisition of representations better serving downstream action\nselection. We tested the soundness of this hypothesis with a computational\nmodel that integrates unsupervised learning (Contrastive Divergence) and\nreinforcement learning (REINFORCE). The model was tested with a task requiring\ndifferent responses to different visual images grouped in categories involving\neither colour, shape, or size. Results show that a balanced mix of unsupervised\nand reinforcement learning processes leads to the best performance. Indeed,\nexcessive unsupervised learning tends to under-represent task-relevant features\nwhile excessive reinforcement learning tends to initially learn slowly and then\nto incur in local minima. These results stimulate future empirical studies on\ncategory learning directed to investigate similar effects in the extrastriate\nvisual cortices. Moreover, they prompt further computational investigations\ndirected to study the possible advantages of integrating unsupervised and\nreinforcement learning processes.\n"}
+{"abstract": "  We provide the first inner bounds for sending private classical information\nover a quantum multiple access channel. We do so by using three powerful\ninformation theoretic techniques: rate splitting, quantum simultaneous decoding\nfor multiple access channels, and a novel smoothed distributed covering lemma\nfor classical quantum channels. Our inner bounds are given in the one shot\nsetting and accordingly the three techniques used are all very recent ones\nspecifically designed to work in this setting. The last technique is new to\nthis work and is our main technical advancement. For the asymptotic iid\nsetting, our one shot inner bounds lead to the natural quantum analogue of the\nbest classical inner bounds for this problem.\n"}
+{"abstract": "  This paper defines a security injection region (SIR) to guarantee reliable\noperation of water distribution systems (WDS) under extreme conditions. The\nmodel of WDSs is highly nonlinear and nonconvex. Understanding the accurate\nSIRs of WDSs involves the analysis of nonlinear constraints, which is\ncomputationally expensive. To reduce the computational burden, this paper first\ninvestigates the convexity of the SIR of WDSs under certain conditions. Then,\nan algorithm based on a monotone inner polytope sequence is proposed to\neffectively and accurately determine these SIRs. The proposed algorithm\nestimates a sequence of inner polytopes that converge to the whole convex\nregion. Each polytope adds a new area to the SIR. The algorithm is validated on\ntwo different WDSs, and the conclusion is drawn. The computational study shows\nthis method is applicable and fast for both systems.\n"}
+{"abstract": "  We present a new empirical model to predict solar energetic particle (SEP)\nevent-integrated and peak intensity spectra between 10 and 130 MeV at 1 AU,\nbased on multi-point spacecraft measurements from the Solar TErrestrial\nRElations Observatory (STEREO), the Geostationary Operational Environmental\nSatellites (GOES) and the Payload for Antimatter Matter Exploration and\nLight-nuclei Astrophysics (PAMELA) satellite experiment. The analyzed data\nsample includes 32 SEP events occurring between 2010 and 2014, with a\nstatistically significant proton signal at energies in excess of a few tens of\nMeV, unambiguously recorded at three spacecraft locations. The spatial\ndistributions of SEP intensities are reconstructed by assuming an\nenergy-dependent 2D Gaussian functional form, and accounting for the\ncorrelation between the intensity and the speed of the parent coronal mass\nejection (CME), and the magnetic field line connection angle. The CME\nmeasurements used are from the Space Weather Database Of Notifications,\nKnowledge, Information (DONKI). The model performance, including its\nextrapolations to lower/higher energies, is tested by comparing with the\nspectra of 20 SEP events not used to derive the model parameters. Despite the\nsimplicity of the model, the observed and predicted event-integrated and peak\nintensities at Earth and at the STEREO spacecraft for these events show\nremarkable agreement, both in the spectral shapes and their absolute values.\n"}
+{"abstract": "  We study some of the main properties (masses and open-flavor strong decay\nwidths) of $4P$ and $5P$ charmonia. While there are two candidates for the\n$\\chi_{\\rm c0}(4P,5P)$ states, the $X(4500)$ and $X(4700)$, the properties of\nthe other members of the $\\chi_{\\rm c}(4P,5P)$ multiplets are still completely\nunknown. With this in mind, we start to explore the charmonium interpretation\nfor these mesons. Our second goal is to investigate if the apparent mismatch\nbetween the Quark Model (QM) predictions for $\\chi_{\\rm c0}(4P,5P)$ states and\nthe properties of the $X(4500)$ and $X(4700)$ mesons can be overcome by\nintroducing threshold corrections in the QM formalism. According to our\ncoupled-channel model results for the threshold mass shifts, the $\\chi_{\\rm\nc0}(5P) \\rightarrow X(4700)$ assignment is unacceptable, while the $\\chi_{\\rm\nc0}(4P) \\rightarrow X(4500)$ or $X(4700)$ assignments cannot be completely\nruled out.\n"}
+{"abstract": "  The single-top production is an important process at the LHC to test the\nStandard Model (SM) and search for the new physics beyond the SM. Although the\ncomplete next-to-next-to-leading order (NNLO) QCD correction to the single-top\nproduction is crucial, this calculation is still challenging at present. In\norder to efficiently reduce the NNLO single-top amplitude, we improve the\nauxiliary mass flow (AMF) method by introducing the $\\epsilon$ truncation. For\ndemonstration we choose one typical planar double-box diagram for the $tW$\nproduction. It is shown that one coefficient of the form factors on its\namplitude can be systematically reduced into the linear combination of 198\nscalar integrals.\n"}
+{"abstract": "  In this work, we address the task of referring image segmentation (RIS),\nwhich aims at predicting a segmentation mask for the object described by a\nnatural language expression. Most existing methods focus on establishing\nunidirectional or directional relationships between visual and linguistic\nfeatures to associate two modalities together, while the multi-scale context is\nignored or insufficiently modeled. Multi-scale context is crucial to localize\nand segment those objects that have large scale variations during the\nmulti-modal fusion process. To solve this problem, we propose a simple yet\neffective Cascaded Multi-modal Fusion (CMF) module, which stacks multiple\natrous convolutional layers in parallel and further introduces a cascaded\nbranch to fuse visual and linguistic features. The cascaded branch can\nprogressively integrate multi-scale contextual information and facilitate the\nalignment of two modalities during the multi-modal fusion process. Experimental\nresults on four benchmark datasets demonstrate that our method outperforms most\nstate-of-the-art methods. Code is available at\nhttps://github.com/jianhua2022/CMF-Refseg.\n"}
+{"abstract": "  This paper investigates moving networks of Unmanned Aerial Vehicles (UAVs),\nsuch as drones, as one of the innovative opportunities brought by the 5G. With\na main purpose to extend connectivity and guarantee data rates, the drones\nrequire hovering locations due to limitations such as flight time and coverage\nsurface. We provide analytic bounds on the requirements in terms of\nconnectivity extension for vehicular networks served by fixed Enhanced Mobile\nBroadBand (eMBB) infrastructure, where both vehicular networks and\ninfrastructures are modeled using stochastic and fractal geometry as a model\nfor urban environment. We prove that assuming $n$ mobile nodes (distributed\naccording to a hyperfractal distribution of dimension $d_F$) and an average of\n$\\rho$ Next Generation NodeB (gNBs), distributed like an hyperfractal of\ndimension $d_r$ if $\\rho=n^\\theta$ with $\\theta>d_r/4$ and letting $n$ tending\nto infinity (to reflect megalopolis cities), then the average fraction of\nmobile nodes not covered by a gNB tends to zero like\n$O\\left(n^{-\\frac{(d_F-2)}{d_r}(2\\theta-\\frac{d_r}{2})}\\right)$. Interestingly,\nwe then prove that the average number of drones, needed to connect each mobile\nnode not covered by gNBs is comparable to the number of isolated mobile nodes.\nWe complete the characterisation by proving that when $\\theta<d_r/4$ the\nproportion of covered mobile nodes tends to zero. We provide insights on the\nintelligent placement of the \"garage of drones\", the home location of these\nnomadic infrastructure nodes, such as to minimize what we call the\n\"flight-to-coverage time\". We provide a fast procedure to select the relays\nthat will be garages (and store drones) in order to minimize the number of\ngarages and minimize the delay. Finally we confirm our analytical results using\nsimulations carried out in Matlab.\n"}
+{"abstract": "  In many machine learning problems, large-scale datasets have become the\nde-facto standard to train state-of-the-art deep networks at the price of heavy\ncomputation load. In this paper, we focus on condensing large training sets\ninto significantly smaller synthetic sets which can be used to train deep\nneural networks from scratch with minimum drop in performance. Inspired from\nthe recent training set synthesis methods, we propose Differentiable Siamese\nAugmentation that enables effective use of data augmentation to synthesize more\ninformative synthetic images and thus achieves better performance when training\nnetworks with augmentations. Experiments on multiple image classification\nbenchmarks demonstrate that the proposed method obtains substantial gains over\nthe state-of-the-art, 7% improvements on CIFAR10 and CIFAR100 datasets. We show\nwith only less than 1% data that our method achieves 99.6%, 94.9%, 88.5%, 71.5%\nrelative performance on MNIST, FashionMNIST, SVHN, CIFAR10 respectively. We\nalso explore the use of our method in continual learning and neural\narchitecture search, and show promising results.\n"}
+{"abstract": "  Common grounding is the process of creating and maintaining mutual\nunderstandings, which is a critical aspect of sophisticated human\ncommunication. While various task settings have been proposed in existing\nliterature, they mostly focus on creating common ground under static context\nand ignore the aspect of maintaining them overtime under dynamic context. In\nthis work, we propose a novel task setting to study the ability of both\ncreating and maintaining common ground in dynamic environments. Based on our\nminimal task formulation, we collected a large-scale dataset of 5,617 dialogues\nto enable fine-grained evaluation and analysis of various dialogue systems.\nThrough our dataset analyses, we highlight novel challenges introduced in our\nsetting, such as the usage of complex spatio-temporal expressions to create and\nmaintain common ground. Finally, we conduct extensive experiments to assess the\ncapabilities of our baseline dialogue system and discuss future prospects of\nour research.\n"}
+{"abstract": "  Numerical solutions to the Eikonal equation are computed using variants of\nthe fast marching method, the fast sweeping method, and the fast iterative\nmethod. In this paper, we provide a unified view of these algorithms that\nhighlights their similarities and suggests a wider class of Eikonal solvers. We\nthen use this framework to justify applying concurrent priority scheduling\ntechniques to Eikonal solvers. We demonstrate that doing so results in good\nparallel performance for a problem from seismology. We explain why existing\nEikonal solvers may produce different results despite using the same\ndifferencing scheme and demonstrate techniques to address these discrepancies.\nThese techniques allow us to obtain deterministic output from our asynchronous\nfine-grained parallel Eikonal solver.\n"}
+{"abstract": "  The $\\beta$-decay of neutron-rich $^{129}$In into $^{129}$Sn was studied\nusing the GRIFFIN spectrometer at the ISAC facility at TRIUMF. The study\nobserved the half-lives of the ground state and each of the $\\beta$-decaying\nisomers. The level scheme of $^{129}$Sn has been expanded with thirty-one new\n$\\gamma$-ray transitions and nine new excited levels, leading to a\nre-evaluation of the $\\beta$-branching ratios and level spin assignments. The\nobservation of the $\\beta$-decay of the (29/2$^{+}$) 1911-keV isomeric state in\n$^{129}$In is reported for the first time, with a branching ratio of\n2.0(5)$\\%$.\n"}
+{"abstract": "  Given a set $B$ of operators between subspaces of $L_p$ spaces, we\ncharacterize the operators between subspaces of $L_p$ spaces that remain\nbounded on the $X$-valued $L_p$ space for every Banach space on which elements\nof the original class $B$ are bounded.\n  This is a form of the bipolar theorem for a duality between the class of\nBanach spaces and the class of operators between subspaces of $L_p$ spaces,\nessentially introduced by Pisier. The methods we introduce allow us to recover\nalso the other direction --characterizing the bipolar of a set of Banach\nspaces--, which had been obtained by Hernandez in 1983.\n"}
+{"abstract": "  A Banach space $X$ has \\textit{property $(K)$}, whenever every weak* null\nsequence in the dual space admits a convex block subsequence\n$(f_{n})_{n=1}^\\infty$ so that $\\langle f_{n},x_{n}\\rangle\\to 0$ as $n\\to\n\\infty$ for every weakly null sequence $(x_{n})_{n=1}^\\infty$ in $X$; $X$ has\n\\textit{property $(\\mu^{s})$} if every weak$^{*}$ null sequence in $X^{*}$\nadmits a subsequence so that all of its subsequences are Ces\\`{a}ro convergent\nto $0$ with respect to the Mackey topology. Both property $(\\mu^{s})$ and\nreflexivity (or even the Grothendieck property) imply property $(K)$. In the\npresent paper we propose natural ways for quantifying the aforementioned\nproperties in the spirit of recent results concerning other familiar properties\nof Banach spaces.\n"}
+{"abstract": "  Video interpolation aims to generate a non-existent intermediate frame given\nthe past and future frames. Many state-of-the-art methods achieve promising\nresults by estimating the optical flow between the known frames and then\ngenerating the backward flows between the middle frame and the known frames.\nHowever, these methods usually suffer from the inaccuracy of estimated optical\nflows and require additional models or information to compensate for flow\nestimation errors. Following the recent development in using deformable\nconvolution (DConv) for video interpolation, we propose a light but effective\nmodel, called Pyramid Deformable Warping Network (PDWN). PDWN uses a pyramid\nstructure to generate DConv offsets of the unknown middle frame with respect to\nthe known frames through coarse-to-fine successive refinements. Cost volumes\nbetween warped features are calculated at every pyramid level to help the\noffset inference. At the finest scale, the two warped frames are adaptively\nblended to generate the middle frame. Lastly, a context enhancement network\nfurther enhances the contextual detail of the final output. Ablation studies\ndemonstrate the effectiveness of the coarse-to-fine offset refinement, cost\nvolumes, and DConv. Our method achieves better or on-par accuracy compared to\nstate-of-the-art models on multiple datasets while the number of model\nparameters and the inference time are substantially less than previous models.\nMoreover, we present an extension of the proposed framework to use four input\nframes, which can achieve significant improvement over using only two input\nframes, with only a slight increase in the model size and inference time.\n"}
+{"abstract": "  The Planck or the quantum gravity scale, being $16$ orders of magnitude\ngreater than the electroweak scale, is often considered inaccessible by current\nexperimental techniques. However, it was shown recently by one of the current\nauthors that quantum gravity effects via the Generalized Uncertainty Principle\naffects the time required for free wavepackets to double their size, and this\ndifference in time is at or near current experimental accuracies [1, 2]. In\nthis work, we make an important improvement over the earlier study, by taking\ninto account the leading order relativistic correction, which naturally appears\nin the systems under consideration, due to the significant mean velocity of the\ntravelling wavepackets. Our analysis shows that although the relativistic\ncorrection adds nontrivial modifications to the results of [1, 2], the earlier\nclaims remain intact and are in fact strengthened. We explore the potential for\nthese results being tested in the laboratory.\n"}
+{"abstract": "  We calculate exact analytic expressions for the average cluster numbers\n$\\langle k \\rangle_{\\Lambda_s}$ on infinite-length strips $\\Lambda_s$, with\nvarious widths, of several different lattices, as functions of the bond\noccupation probability, $p$. It is proved that these expressions are rational\nfunctions of $p$. As special cases of our results, we obtain exact values of\n$\\langle k \\rangle_{\\Lambda_s}$ and derivatives of $\\langle k\n\\rangle_{\\Lambda_s}$ with respect to $p$, evaluated at the critical percolation\nprobabilities $p_{c,\\Lambda}$ for the corresponding infinite two-dimensional\nlattices $\\Lambda$. We compare these exact results with an analytic finite-size\ncorrection formula and find excellent agreement. We also analyze how unphysical\npoles in $\\langle k \\rangle_{\\Lambda_s}$ determine the radii of convergence of\nseries expansions for small $p$ and for $p$ near to unity. Our calculations are\nperformed for infinite-length strips of the square, triangular, and honeycomb\nlattices with several types of transverse boundary conditions.\n"}
+{"abstract": "  This article discusses a high-dimensional visualization technique called the\ntour, which can be used to view data in more than three dimensions. We review\nthe theory and history behind the technique, as well as modern software\ndevelopments and applications of the tour that are being found across the\nsciences and machine learning.\n"}
+{"abstract": "  We design a multiferroic metal that combines seemingly incompatible\nferromagnetism, ferroelectricity, and metallicity by hole doping a\ntwo-dimensional (2D) ferroelectric with high density of states near the Fermi\nlevel. The strong magnetoelectric effect is demonstrated in hole-doped and\narsenic-doped monolayer {\\alpha}-In2Se3 using first-principles calculations.\nTaking advantage of the oppositely charged surfaces created by an out-of-plane\npolarization, the 2D magnetization and metallicity can be electrically switched\non and off in an asymmetrically doped monolayer. The substitutional arsenic\ndefect pair exhibits an intriguing electric field-tunable charge\ndisproportionation process accompanied with an on-off switch of local magnetic\nmoments. The charge ordering process can be controlled by tuning the relative\nstrength of on-site Coulomb repulsion and defect dipole-polarization coupling\nvia strain engineering. Our design principle relying on no transition metal\nbroadens the materials design space for 2D multiferroic metals.\n"}
+{"abstract": "  We study the synthesis of a policy in a Markov decision process (MDP)\nfollowing which an agent reaches a target state in the MDP while minimizing its\ntotal discounted cost. The problem combines a reachability criterion with a\ndiscounted cost criterion and naturally expresses the completion of a task with\nprobabilistic guarantees and optimal transient performance. We first establish\nthat an optimal policy for the considered formulation may not exist but that\nthere always exists a near-optimal stationary policy. We additionally provide a\nnecessary and sufficient condition for the existence of an optimal policy. We\nthen restrict our attention to stationary deterministic policies and show that\nthe decision problem associated with the synthesis of an optimal stationary\ndeterministic policy is NP-complete. Finally, we provide an exact algorithm\nbased on mixed-integer linear programming and propose an efficient\napproximation algorithm based on linear programming for the synthesis of an\noptimal stationary deterministic policy.\n"}
+{"abstract": "  Pseudo-code written by natural language is helpful for novice developers'\nprogram comprehension. However, writing such pseudo-code is time-consuming and\nlaborious. Motivated by the research advancements of sequence-to-sequence\nlearning and code semantic learning, we propose a novel deep pseudo-code\ngeneration method DeepPseudo via code feature extraction and Transformer. In\nparticular, DeepPseudo utilizes a Transformer encoder to perform encoding for\nsource code and then use a code feature extractor to learn the knowledge of\nlocal features. Finally, it uses a pseudo-code generator to perform decoding,\nwhich can generate the corresponding pseudo-code. We choose two corpora (i.e.,\nDjango and SPoC) from real-world large-scale projects as our empirical\nsubjects. We first compare DeepPseudo with seven state-of-the-art baselines\nfrom pseudo-code generation and neural machine translation domains in terms of\nfour performance measures. Results show the competitiveness of DeepPseudo.\nMoreover, we also analyze the rationality of the component settings in\nDeepPseudo.\n"}
+{"abstract": "  A novel approach to efficiently treat pure-state equality constraints in\noptimal control problems (OCPs) using a Riccati recursion algorithm is\nproposed. The proposed method transforms a pure-state equality constraint into\na mixed state-control constraint such that the constraint is expressed by\nvariables at a certain previous time stage. It is showed that if the solution\nsatisfies the second-order sufficient conditions of the OCP with the\ntransformed mixed state-control constraints, it is a local minimum of the OCP\nwith the original pure-state constraints. A Riccati recursion algorithm is\nderived to solve the OCP using the transformed constraints with linear time\ncomplexity in the grid number of the horizon, in contrast to a previous\napproach that scales cubically with respect to the total dimension of the\npure-state equality constraints. Numerical experiments on the whole-body\noptimal control of quadrupedal gaits that involve pure-state equality\nconstraints owing to contact switches demonstrate the effectiveness of the\nproposed method over existing approaches.\n"}
+{"abstract": "  Using large-scale fully-kinetic two-dimensional particle-in-cell simulations,\nwe investigate the effects of shock rippling on electron acceleration at\nlow-Mach-number shocks propagating in high-$\\beta$ plasmas, in application to\nmerger shocks in galaxy clusters. We find that the electron acceleration rate\nincreases considerably when the rippling modes appear. The main acceleration\nmechanism is stochastic shock-drift acceleration, in which electrons are\nconfined at the shock by pitch-angle scattering off turbulence and gain energy\nfrom the motional electric field. The presence of multi-scale magnetic\nturbulence at the shock transition and the region immediately behind the main\nshock overshoot is essential for electron energization. Wide-energy non-thermal\nelectron distributions are formed both upstream and downstream of the shock.\nThe maximum energy of the electrons is sufficient for their injection into\ndiffusive shock acceleration. We show for the first time that the downstream\nelectron spectrum has a~power-law form with index $p\\approx 2.5$, in agreement\nwith observations.\n"}
+{"abstract": "  The Capsule Network is widely believed to be more robust than Convolutional\nNetworks. However, there are no comprehensive comparisons between these two\nnetworks, and it is also unknown which components in the CapsNet affect its\nrobustness. In this paper, we first carefully examine the special designs in\nCapsNet that differ from that of a ConvNet commonly used for image\nclassification. The examination reveals five major new/different components in\nCapsNet: a transformation process, a dynamic routing layer, a squashing\nfunction, a marginal loss other than cross-entropy loss, and an additional\nclass-conditional reconstruction loss for regularization. Along with these\nmajor differences, we conduct comprehensive ablation studies on three kinds of\nrobustness, including affine transformation, overlapping digits, and semantic\nrepresentation. The study reveals that some designs, which are thought critical\nto CapsNet, actually can harm its robustness, i.e., the dynamic routing layer\nand the transformation process, while others are beneficial for the robustness.\nBased on these findings, we propose enhanced ConvNets simply by introducing the\nessential components behind the CapsNet's success. The proposed simple ConvNets\ncan achieve better robustness than the CapsNet.\n"}
+{"abstract": "  We study the 2018 Martian Global DustStorm (GDS 2018) over the Southern Polar\nRegion using images obtained by the Visual Monitoring Camera (VMC) on board\nMars Express during June and July 2018. Dust penetrated into the polar cap\nregion but never covered the cap completely, and its spatial distribution was\nnonhomogeneous and rapidly changing. However, we detected long but narrow\naerosol curved arcs with a length of 2,000-3,000 km traversing part of the cap\nand crossing the terminator into the night side. Tracking discrete dust clouds\nallowed measurements of their motions that were towards the terminator with\nvelocities up to 100 m/s. The images of the dust projected into the Martian\nlimb show maximum altitudes of around 70 km but with large spatial and temporal\nvariations. We discuss these results in the context of the predictions of a\nnumerical model for dust storm scenario.\n"}
+{"abstract": "  Recent observations have shown that circumbinary discs can be misaligned with\nrespect to the binary orbital plane.The lack of spherical symmetry, together\nwith the non-planar geometry of these systems, causes differential precession\nwhich might induce the propagation of warps. While gas dynamics in such\nenvironments is well understood, little is known about dusty discs. In this\nwork, we analytically study the problem of dust traps formation in misaligned\ncircumbinary discs. We find that pile-ups may be induced not by pressure\nmaxima, as the usual dust traps, but by a difference in precession rates\nbetween the gas and dust. Indeed, this difference makes the radial drift\ninefficient in two locations, leading to the formation of two dust rings whose\nposition depends on the system parameters. This phenomenon is likely to occur\nto marginally coupled dust particles $(\\text{St}\\gtrsim1)$ as both the effect\nof gravitational and drag force are considerable. We then perform a suite of\nthree-dimensional SPH numerical simulations to compare the results with our\ntheoretical predictions. We explore the parameter space, varying stellar mass\nratio, disc thickness, radial extension, and we find a general agreement with\nthe analytical expectations. Such dust pile-up prevents radial drift, fosters\ndust growth and may thus promote the planet formation in circumbinary discs.\n"}
+{"abstract": "  Quantum computing holds a great promise and this work proposes to use new\nquantum data networks (QDNs) to connect multiple small quantum computers to\nform a cluster. Such a QDN differs from existing QKD networks in that the\nformer must deliver data qubits reliably within itself. Two types of QDNs are\nstudied, one using teleportation and the other using tell-and-go (TAG) to\nexchange quantum data. Two corresponding quantum transport protocols (QTPs),\nnamed Tele-QTP and TAG-QTP, are proposed to address many unique design\nchallenges involved in reliable delivery of data qubits, and constraints\nimposed by quantum physics laws such as the no-cloning theorem, and limited\navailability of quantum memory.\n  The proposed Tele-QTP and TAG-QTP are the first transport layer protocols for\nQDNs, complementing other works on the network protocol stack. Tele-QTP and\nTAG-QTP have novel mechanisms to support congestion-free and reliable delivery\nof streams of data qubits by managing the limited quantum memory at end hosts\nas well as intermediate nodes. Both analysis and extensive simulations show\nthat the proposed QTPs can achieve a high throughput and fairness. This study\nalso offers new insights into potential tradeoffs involved in using the two\nmethods, teleportation and TAG, in two types of QDNs.\n"}
+{"abstract": "  Many complex processes can be viewed as dynamical systems of interacting\nagents. In many cases, only the state sequences of individual agents are\nobserved, while the interacting relations and the dynamical rules are unknown.\nThe neural relational inference (NRI) model adopts graph neural networks that\npass messages over a latent graph to jointly learn the relations and the\ndynamics based on the observed data. However, NRI infers the relations\nindependently and suffers from error accumulation in multi-step prediction at\ndynamics learning procedure. Besides, relation reconstruction without prior\nknowledge becomes more difficult in more complex systems. This paper introduces\nefficient message passing mechanisms to the graph neural networks with\nstructural prior knowledge to address these problems. A relation interaction\nmechanism is proposed to capture the coexistence of all relations, and a\nspatio-temporal message passing mechanism is proposed to use historical\ninformation to alleviate error accumulation. Additionally, the structural prior\nknowledge, symmetry as a special case, is introduced for better relation\nprediction in more complex systems. The experimental results on simulated\nphysics systems show that the proposed method outperforms existing\nstate-of-the-art methods.\n"}
+{"abstract": "  We present new 3 mm observations of the ionized gas toward the nuclear\nstarburst in the nearby (D ~ 3.5 Mpc) galaxy NGC 253. With ALMA, we detect\nemission from the H40-alpha and He40-alpha lines in the central 200 pc of this\ngalaxy on spatial scales of ~4 pc. The recombination line emission primarily\noriginates from a population of approximately a dozen embedded super star\nclusters in the early stages of formation. We find that emission from these\nclusters is characterized by electron temperatures ranging from 7000-10000 K\nand measure an average singly-ionized helium abundance <Y+> = 0.25 +/- 0.06,\nboth of which are consistent with values measured for HII regions in the center\nof the Milky Way. We also report the discovery of unusually broad-linewidth\nrecombination line emission originating from seven of the embedded clusters. We\nsuggest that these clusters contribute to the launching of the large-scale hot\nwind observed to emanate from the central starburst. Finally, we use the\nmeasured recombination line fluxes to improve the characterization of overall\nembedded cluster properties, including the distribution of cluster masses and\nthe fractional contribution of the clustered star formation to the total\nstarburst, which we estimate is at least 50%.\n"}
+{"abstract": "  Numerical solutions of the Enskog-Vlasov (EV) equation are used to determine\nthe velocity distribution function of atoms spontaneously evaporating into\nnear-vacuum conditions. It is found that an accurate approximation is provided\nby a half-Maxwellian including a drift velocity combined with different\ncharacteristic temperatures for the velocity components normal and parallel to\nthe liquid-vapor interface. The drift velocity and the temperature anisotropy\nreduce as the liquid bulk temperature decreases but persist for relatively low\ntemperatures corresponding to a vapor behaviour which is only slightly\nnon-ideal. Deviations from the undrifted isotropic half-Maxwellian are shown to\nbe consequences of collisions in the liquid-vapor interface which\npreferentially backscatter atoms with lower normal-velocity component.\n"}
+{"abstract": "  Brain tissue is a heterogeneous material, constituted by a soft matrix filled\nwith cerebrospinal fluid. The interactions between, and the complexity of each\nof these components are responsible for the non-linear rate-dependent behaviour\nthat characterizes what is one of the most complex tissue in nature. Here, we\ninvestigate the influence of the cutting rate on the fracture properties of\nbrain, through wire cutting experiments. We also present a model for the\nrate-dependent behaviour of fracture propagation in soft materials, which\ncomprises the effects of fluid interaction through a poro-hyperelastic\nformulation. The method is developed in the framework of finite strain\ncontinuum mechanics, implemented in a commercial finite element code, and\napplied to the case of an edge-crack remotely loaded by a controlled\ndisplacement. Experimental and numerical results both show a toughening effect\nwith increasing rates, which is linked to the energy dissipated by the\nfluid-solid interactions in the process zone ahead of the crack.\n"}
+{"abstract": "  In this paper, we design decentralized control strategies for the\ntwo-dimensional movement of autonomous vehicles on lane-free roads. The bicycle\nkinematic model is used to model the dynamics of the vehicles, and each vehicle\ndetermines its control input based only on its own speed and on the distance\nfrom other (adjacent) vehicles and the boundary of the road. Potential\nfunctions and Barbalat's lemma are employed to prove the following properties,\nwhich are ensured by the proposed controller: (i) the vehicles do not collide\nwith each other or with the boundary of the road; (ii) the speeds of all\nvehicles are always positive, i.e., no vehicle moves backwards at any time;\n(iii) the speed of all vehicles remain below a given speed limit; (iv) all\nvehicle speeds converge to a given longitudinal speed set-point; and (v) the\naccelerations, lateral speeds, and orientations of all vehicles tend to zero.\nThe efficiency of the proposed 2-D cruise controllers is illustrated by means\nof numerical examples.\n"}
+{"abstract": "  A multi-modal framework to generated user intention distributions when\noperating a mobile vehicle is proposed in this work. The model learns from past\nobserved trajectories and leverages traversability information derived from the\nvisual surroundings to produce a set of future trajectories, suitable to be\ndirectly embedded into a perception-action shared control strategy on a mobile\nagent, or as a safety layer to supervise the prudent operation of the vehicle.\nWe base our solution on a conditional Generative Adversarial Network with\nLong-Short Term Memory cells to capture trajectory distributions conditioned on\npast trajectories, further fused with traversability probabilities derived from\nvisual segmentation with a Convolutional Neural Network. The proposed\ndata-driven framework results in a significant reduction in error of the\npredicted trajectories (versus the ground truth) from comparable strategies in\nthe literature (e.g. Social-GAN) that fail to account for information other\nthan the agent's past history. Experiments were conducted on a dataset\ncollected with a custom wheelchair model built onto the open-source urban\ndriving simulator CARLA, proving also that the proposed framework can be used\nwith a small, un-annotated dataset.\n"}
+{"abstract": "  Several physical mechanisms are involved in excavating granular materials\nbeneath a vertical jet of gas. These occur, for example, beneath the exhaust\nplume of a rocket landing on the soil of the Moon or Mars. A series of\nexperiments and simulations have been performed to provide a detailed view of\nthe complex gas/soil interactions. Measurements have also been taken from the\nApollo lunar landing videos and from photographs of the resulting terrain, and\nthese help to demonstrate how the interactions extrapolate into the lunar\nenvironment. It is important to understand these processes at a fundamental\nlevel to support the on-going design of higher-fidelity numerical simulations\nand larger-scale experiments. These are needed to enable future lunar\nexploration wherein multiple hardware assets will be placed on the Moon within\nshort distances of one another. The high-velocity spray of soil from landing\nspacecraft must be accurately predicted and controlled lest it erosively damage\nthe surrounding hardware.\n"}
+{"abstract": "  Terahertz (THz) emission spectroscopy is a powerful method that allows one to\nmeasure the ultrafast dynamics of polarization, current, or magnetization in a\nmaterial based on THz emission from the material. However, the practical\nimplementation of this method can be challenging, and can result in significant\nerrors in the reconstruction of the quantity of interest. Here, we\nexperimentally and theoretically demonstrate a rigorous method of signal\nreconstruction in THz emission spectroscopy, and describe the main experimental\nand theoretical sources of reconstruction error. We identify the linear\nline-of-sight geometry of the THz emission spectrometer as the optimal\nconfiguration for accurate, fully calibrated THz signal reconstruction. As an\nexample, we apply our reconstruction method to ultrafast THz magnetometry\nexperiment, where we recover the ultrafast magnetization dynamics in a\nphotoexcited iron film, including both its temporal shape and absolute\nmagnitude.\n"}
+{"abstract": "  We demonstrate an external cavity laser formed by combining a silicon nitride\nphotonic integrated circuit with a reflective semiconductor optical amplifier.\nThe laser uses an alignment tolerant edge coupler formed by a multi-mode\nwaveguide splitter right at the edge of the silicon nitride chip that relaxes\nthe required alignment to the III-V gain chip and equally splits the power\namong its two output waveguides. Both the ground and first order mode are\nexcited in the coupler and reach the quadrature condition at the waveguide\njunction, ensuring equal power to be coupled to both. Two high-quality-factor\nring resonators arranged in Vernier configuration close a Sagnac loop between\nthe two waveguides. In addition to wideband frequency tuning, they result in a\nlonger effective cavity length. The alignment tolerant coupler increases the\nalignment tolerance in the two directions parallel to the chip surface by a\nfactor 3 relative to conventional edge couplers, making it ideal for gain chip\nintegration via pick-and-place technology. Lasing is maintained in a\nmisalignment range of $\\pm$6 $\\mu$m in the direction along the edge of the\nchip. A Lorentzian laser linewidth of 42 kHz is achieved.\n"}
+{"abstract": "  Engineering Thermodynamics has been the core course of many science and\nengineering majors around the world, including energy and power, mechanical\nengineering, civil engineering, aerospace, cryogenic refrigeration, food\nengineering, chemical engineering, and environmental engineering, among which\ngas power cycle is one of the important contents. However, many Engineering\nThermodynamics textbooks focus only on evaluating the thermal efficiency of gas\npower cycle, while the important concept of specific cycle work is ignored.\nBased on the generalized temperature-entropy diagram for the gas power cycles\nproposed by the authors, an ideal Otto cycle and an ideal Miller-Diesel cycle\nare taking as examples for the thermodynamic analyses of gas power cycles. The\noptimum compression ratio (or the pressure ratio) for the maximum specific\ncycle work or the maximum mean effective pressure is analyzed and determined.\nThe ideal Otto and the ideal Miller-Diesel cycles, and also other gas power\ncycles for movable applications, are concluded that the operation under the\nmaximum specific cycle work or the maximum mean effective pressure, instead of\nunder the higher efficiency, is more economic and more reasonable. We concluded\nthat the very important concept, i.e., the optimum compression (or pressure)\nratio for the gas power cycles, should be emphasized in the Engineering\nThermodynamics teaching process and in the latter revised or the newly edited\ntextbooks, in order to better guide the engineering applications.\n"}
+{"abstract": "  We investigate the potential of a recently proposed model for 3D compressible\nMHD turbulence (Chevillard et al. 2010; Durrive et al. 2021) to be used as a\ntool to characterize statistically 2D and 3D turbulent data. This model is\nparametrized by a dozen of free (intuitive, physically motivated) parameters,\nwhich control the statistics of the fields (density, velocity and magnetic\nfields). The present study is a proof of concept study: (i) we restrict\nourselves to the incompressible hydrodynamical part of the model, (ii) we\nconsider as data centroid velocity maps, and (iii) we let only three of the\nfree parameters vary (namely the correlation length, the Hurst parameter and\nthe intermittency parameter). Within this framework, we demonstrate that, given\na centroid velocity map, we can find in an automated manner (i.e. by a Markov\nChain Monte Carlo analysis) values of the parameters such that the model\nresembles the given map, i.e. which reproduces its statistics fairly well.\nHence, thanks to this procedure, one may characterize statistically, and thus\ncompare, various turbulent data. In other words, we show how this model may be\nused as a metric to compare observational or simulated data sets. In addition,\nbecause this model is numerically particularly fast (nearly 500 times faster\nthan the numerical simulation we use to generate our reference data) it may be\nused as a surrogate model. Finally, by this process we also initiate the first\nsystematic exploration of the parameter space of this model. Doing so, we show\nhow the parameters impact the visual and the statistical properties of centroid\nvelocity maps, and exhibit the correlations between the various parameters,\nproviding new insight into the model.\n"}
+{"abstract": "  An essential problem of swarm robotics is how members of the swarm knows the\npositions of other robots. The main aim of this research is to develop a\ncost-effective and simple vision-based system to detect the range, bearing, and\nheading of the robots inside a swarm using a multi-purpose passive landmark. A\nsmall Zumo robot equipped with Raspberry Pi, PiCamera is utilized for the\nimplementation of the algorithm, and different kinds of multipurpose passive\nlandmarks with nonsymmetrical patterns, which give reliable information about\nthe range, bearing and heading in a single unit, are designed. By comparing the\nrecorded features obtained from image analysis of the landmark through\nsystematical experimentation and the actual measurements, correlations are\nobtained, and algorithms converting those features into range, bearing and\nheading are designed. The reliability and accuracy of algorithms are tested and\nerrors are found within an acceptable range.\n"}
+{"abstract": "  We investigate a well defined heterostructure constituted by magnetic Fe\nlayers sandwiched between graphene (Gr) and Ir(111). The challenging task to\navoid Fe-C solubility and Fe-Ir intermixing has been achieved with atomic\ncontrolled Fe intercalation at moderate temperature below 500 K. Upon\nintercalation of a single ordered Fe layer in registry with the Ir substrate,\nan intermixing of the Gr bands and Fe d states breaks the symmetry of the Dirac\ncone, with a downshift in energy of the apex by about 3 eV, and well-localized\nFe intermixed states induced in the energy region just below the Fermi level.\nFirst principles electronic structure calculations show a large spin splitting\nof the Fe states, resulting in a majority spin channel almost fully occupied\nand strongly hybridized with Gr {\\pi} states. X-ray magnetic circular dichroism\non the Gr/Fe/Ir heterostructure reveals an ordered spin configuration with a\nferromagnetic response of Fe layer(s), with enhanced spin and orbital\nconfigurations with respect to the bcc-Fe bulk values. The magnetization\nswitches from a perpendicular easy magnetization axis when the Fe single layer\nis lattice matched with the Ir(111) surface to a parallel one when the Fe thin\nfilm is almost commensurate with graphene.\n"}
+{"abstract": "  The dynamics of the coupled electron-nuclear spin system is studied in an\nensemble of singly-charged (In,Ga)As/GaAs quantum dots (QDs) using periodic\noptical excitation at 1 GHz repetition rate. In combination with the\nelectron-nuclei interaction, the highly repetitive excitation allows us to lock\nthe electron spins into magnetic resonance in a transverse external magnetic\nfield. Sweeping the field to higher values, the locking leads to an effective\n\"diamagnetic\" response of significant strength due to dynamic nuclear\npolarization, which shields the QD electrons at least partly from the external\nfield and can even keep the internal magnetic field constant up to 1.3 T field\nvariation. We model the effect through a magnetic field-dependent polarization\nrate of the nuclei, from which we suggest a strategy for adjusting the nuclear\npolarization through the detuning between optical excitation and electronic\ntransition, in addition to tuning the magnetic field.\n"}
+{"abstract": "  We develop a novel approach to non-relativistic closed bosonic string theory\nthat is based on a string $1/c^2$ expansion of the relativistic string, where\n$c$ is the speed of light. This approach has the benefit that one does not need\nto take a limit of a string in a near-critical Kalb-Ramond background. The\n$1/c^2$-expanded Polyakov action at next-to-leading order reproduces the known\naction of non-relativistic string theory provided that the target space obeys\nan appropriate foliation constraint. We compute the spectrum in a flat target\nspace, with one circle direction that is wound by the string, up to\nnext-to-leading order and show that it reproduces the spectrum of the\nGomis-Ooguri string.\n"}
+{"abstract": "  In this note we continue giving the characterisation of weights for\ntwo-weight Hardy inequalities to hold on general metric measure spaces\npossessing polar decompositions. Since there may be no differentiable structure\non such spaces, the inequalities are given in the integral form in the spirit\nof Hardy's original inequality. This is a continuation of our paper [M.\nRuzhansky and D. Verma. Hardy inequalities on metric measure spaces, Proc. R.\nSoc. A., 475(2223):20180310, 2018] where we treated the case $p\\leq q$. Here\nthe remaining range $p>q$ is considered, namely, $0<q<p$, $1<p<\\infty.$ We give\nexamples obtaining new weighted Hardy inequalities on $\\mathbb R^n$, on\nhomogeneous groups, on hyperbolic spaces, and on Cartan-Hadamard manifolds. We\nnote that doubling conditions are not required for our analysis.\n"}
+{"abstract": "  In continual learning, a system learns from non-stationary data streams or\nbatches without catastrophic forgetting. While this problem has been heavily\nstudied in supervised image classification and reinforcement learning,\ncontinual learning in neural networks designed for abstract reasoning has not\nyet been studied. Here, we study continual learning of analogical reasoning.\nAnalogical reasoning tests such as Raven's Progressive Matrices (RPMs) are\ncommonly used to measure non-verbal abstract reasoning in humans, and recently\noffline neural networks for the RPM problem have been proposed. In this paper,\nwe establish experimental baselines, protocols, and forward and backward\ntransfer metrics to evaluate continual learners on RPMs. We employ experience\nreplay to mitigate catastrophic forgetting. Prior work using replay for image\nclassification tasks has found that selectively choosing the samples to replay\noffers little, if any, benefit over random selection. In contrast, we find that\nselective replay can significantly outperform random selection for the RPM\ntask.\n"}
+{"abstract": "  A set $D$ of vertices in an isolate-free graph $G$ is a semitotal dominating\nset of $G$ if $D$ is a dominating set of $G$ and every vertex in $D$ is within\ndistance $2$ from another vertex of $D$.The semitotal domination number of $G$\nis the minimum cardinality of a semitotal dominating set of $G$ and is denoted\nby $\\gamma_{t2}(G)$. In this paper after computation of semitotal domination\nnumber of specific graphs, we count the number of this kind of dominating sets\nof arbitrary size in some graphs.\n"}
+{"abstract": "  Many techniques have been proposed for image reconstruction in medical\nimaging that aim to recover high-quality images especially from limited or\ncorrupted measurements. Model-based reconstruction methods have been\nparticularly popular (e.g., in magnetic resonance imaging and tomographic\nmodalities) and exploit models of the imaging system's physics together with\nstatistical models of measurements, noise and often relatively simple object\npriors or regularizers. For example, sparsity or low-rankness based\nregularizers have been widely used for image reconstruction from limited data\nsuch as in compressed sensing. Learning-based approaches for image\nreconstruction have garnered much attention in recent years and have shown\npromise across biomedical imaging applications. These methods include synthesis\ndictionary learning, sparsifying transform learning, and different forms of\ndeep learning involving complex neural networks. We briefly discuss classical\nmodel-based reconstruction methods and then review reconstruction methods at\nthe intersection of model-based and learning-based paradigms in detail. This\nreview includes many recent methods based on unsupervised learning, and\nsupervised learning, as well as a framework to combine multiple types of\nlearned models together.\n"}
+{"abstract": "  Common statistical measures of uncertainty such as $p$-values and confidence\nintervals quantify the uncertainty due to sampling, that is, the uncertainty\ndue to not observing the full population. However, sampling is not the only\nsource of uncertainty. In practice, distributions change between locations and\nacross time. This makes it difficult to gather knowledge that transfers across\ndata sets. We propose a measure of uncertainty or instability that quantifies\nthe distributional instability of a statistical parameter with respect to\nKullback-Leibler divergence, that is, the sensitivity of the parameter under\ngeneral distributional perturbations within a Kullback-Leibler divergence ball.\nIn addition, we propose measures to elucidate the instability of parameters\nwith respect to directional or variable-specific shifts. Measuring instability\nwith respect to directional shifts can be used to detect the type of shifts a\nparameter is sensitive to. We discuss how such knowledge can inform data\ncollection for improved estimation of statistical parameters under shifted\ndistributions. We evaluate the performance of the proposed measure on real data\nand show that it can elucidate the distributional (in-)stability of a parameter\nwith respect to certain shifts and can be used to improve the accuracy of\nestimation under shifted distributions.\n"}
+{"abstract": "  We propose the gentle measurement principle (GMP) as one of the principles at\nthe foundation of quantum mechanics. It asserts that if a set of states can be\ndistinguished with high probability, they can be distinguished by a measurement\nthat leaves the states almost invariant, including correlation with a reference\nsystem. While GMP is satisfied in both classical and quantum theories, we show,\nwithin the framework of general probabilistic theories, that it imposes strong\nrestrictions on the law of physics. First, the measurement uncertainty of a\npair of observables cannot be significantly larger than the preparation\nuncertainty. Consequently, the strength of the CHSH nonlocality cannot be\nmaximal. The parameter in the stretched quantum theory, a family of general\nprobabilistic theories that includes the quantum theory, is also limited.\nSecond, the conditional entropy defined in terms of a data compression theorem\nsatisfies the chain inequality. Not only does it imply information causality\nand Tsirelson's bound, but it singles out the quantum theory from the stretched\none. All these results show that GMP would be one of the principles at the\nheart of quantum mechanics.\n"}
+{"abstract": "  We give some formulas of poly-Cauchy numbers by the $r$-Stirling transform.\nIn the case of the classical or poly-Bernoulli numbers, the formulas are with\nStirling numbers of the first kind. In our case of the classical or poly-Cauchy\nnumbers, the formulas are with Stirling numbers of the second kind. We also\ndiscuss annihilation formulas for poly-Cauchy number with negative indices.\n"}
+{"abstract": "  In this paper, we introduce a complete system for autonomous flight of\nquadrotors in dynamic environments with onboard sensing. Extended from existing\nwork, we develop an occlusion-aware dynamic perception method based on depth\nimages, which classifies obstacles as dynamic and static. For representing\ngeneric dynamic environment, we model dynamic objects with moving ellipsoids\nand fuse static ones into an occupancy grid map. To achieve dynamic avoidance,\nwe design a planning method composed of modified kinodynamic path searching and\ngradient-based optimization. The method leverages manually constructed\ngradients without maintaining a signed distance field (SDF), making the\nplanning procedure finished in milliseconds. We integrate the above methods\ninto a customized quadrotor system and thoroughly test it in realworld\nexperiments, verifying its effective collision avoidance in dynamic\nenvironments.\n"}
+{"abstract": "  The Banach-Picard iteration is widely used to find fixed points of locally\ncontractive (LC) maps. This paper extends the Banach-Picard iteration to\ndistributed settings; specifically, we assume the map of which the fixed point\nis sought to be the average of individual (not necessarily LC) maps held by a\nset of agents linked by a communication network. An additional difficulty is\nthat the LC map is not assumed to come from an underlying optimization problem,\nwhich prevents exploiting strong global properties such as convexity or\nLipschitzianity. Yet, we propose a distributed algorithm and prove its\nconvergence, in fact showing that it maintains the linear rate of the standard\nBanach-Picard iteration for the average LC map. As another contribution, our\nproof imports tools from perturbation theory of linear operators, which, to the\nbest of our knowledge, had not been used before in the theory of distributed\ncomputation.\n"}
+{"abstract": "  Cosmos has always sparked human curiosity to unveil and speculate its\nfascinating secrets. This curiosity has ultimately opened a window to other\nworlds. After years of observation, computation, and data analysis, scientists\nhave revealed the diversity in exoplanets that have been helpful in the\ncharacterization and further investigation of biosignatures and\ntechnosignatures. This article presents some of the scientific advances made in\nextraterrestrial planetary science that guarantees to search through the thick\nclouds of the mysterious planets in the near future.\n"}
+{"abstract": "  Let $G$ be a group with identity $e$. Let $R$ be a $G$-graded commutative\nring with identity and $M$ a graded $R$-module. In this paper, we introduce the\nconcept of graded $I_{e}$-prime submodule as a generalization of a graded prime\nsubmodule for $I=\\oplus_{g\\in G}I_{g}$ a fixed graded ideal of $R$. We give a\nnumber of results concerning of these classes of graded submodules and their\nhomogeneous components. A proper graded submodule $N$ of $M$ is said to be a\ngraded $I_{e}$-prime submodule of $M$ if whenever $% r_{g}\\in h(R)$ and\n$m_{h}\\in h(M)$ with $r_{g}m_{h}\\in N-I_{e}N,$ then either $r_{g}\\in (N:_{R}M)$\nor $m_{h}\\in N.$\n"}
+{"abstract": "  Acquisition of training data for the standard semantic segmentation is\nexpensive if requiring that each pixel is labeled. Yet, current methods\nsignificantly deteriorate in weakly supervised settings, e.g. where a fraction\nof pixels is labeled or when only image-level tags are available. It has been\nshown that regularized losses - originally developed for unsupervised low-level\nsegmentation and representing geometric priors on pixel labels - can\nconsiderably improve the quality of weakly supervised training. However, many\ncommon priors require optimization stronger than gradient descent. Thus, such\nregularizers have limited applicability in deep learning. We propose a new\nrobust trust region approach for regularized losses improving the\nstate-of-the-art results. Our approach can be seen as a higher-order\ngeneralization of the classic chain rule. It allows neural network optimization\nto use strong low-level solvers for the corresponding regularizers, including\ndiscrete ones.\n"}
+{"abstract": "  We present the confirmation of a new sub-Neptune close to the transition\nbetween super-Earths and sub-Neptunes transiting the M2 dwarf TOI- 269 (TIC\n220479565, V = 14.4 mag, J = 10.9 mag, Rstar = 0.40 Rsun, Mstar = 0.39 Msun, d\n= 57 pc). The exoplanet candidate has been identified in multiple TESS sectors,\nand validated with high-precision spectroscopy from HARPS and ground-based\nphotometric follow-up from ExTrA and LCO-CTIO. We determined mass, radius, and\nbulk density of the exoplanet by jointly modeling both photometry and radial\nvelocities with juliet. The transiting exoplanet has an orbital period of P =\n3.6977104 +- 0.0000037 days, a radius of 2.77 +- 0.12 Rearth, and a mass of 8.8\n+- 1.4 Mearth. Since TOI-269 b lies among the best targets of its category for\natmospheric characterization, it would be interesting to probe the atmosphere\nof this exoplanet with transmission spectroscopy in order to compare it to\nother sub-Neptunes. With an eccentricity e = 0.425+0.082-0.086, TOI-269 b has\none of the highest eccentricities of the exoplanets with periods less than 10\ndays. The star being likely a few Gyr old, this system does not appear to be\ndynamically young. We surmise TOI-269 b may have acquired its high eccentricity\nas it migrated inward through planet-planet interactions.\n"}
+{"abstract": "  As Artificial Intelligence as a Service gains popularity, protecting\nwell-trained models as intellectual property is becoming increasingly\nimportant. Generally speaking, there are two common protection methods:\nownership verification and usage authorization. In this paper, we propose\nNon-Transferable Learning (NTL), a novel approach that captures the exclusive\ndata representation in the learned model and restricts the model generalization\nability to certain domains. This approach provides effective solutions to both\nmodel verification and authorization. For ownership verification, watermarking\ntechniques are commonly used but are often vulnerable to sophisticated\nwatermark removal methods. Our NTL-based model verification approach instead\nprovides robust resistance to state-of-the-art watermark removal methods, as\nshown in extensive experiments for four of such methods over the digits,\nCIFAR10 & STL10, and VisDA datasets. For usage authorization, prior solutions\nfocus on authorizing specific users to use the model, but authorized users can\nstill apply the model to any data without restriction. Our NTL-based\nauthorization approach instead provides data-centric usage protection by\nsignificantly degrading the performance of usage on unauthorized data. Its\neffectiveness is also shown through experiments on a variety of datasets.\n"}
+{"abstract": "  Neural networks have emerged as a powerful way to approach many practical\nproblems in quantum physics. In this work, we illustrate the power of deep\nlearning to predict the dynamics of a quantum many-body system, where the\ntraining is \\textit{based purely on monitoring expectation values of\nobservables under random driving}. The trained recurrent network is able to\nproduce accurate predictions for driving trajectories entirely different than\nthose observed during training. As a proof of principle, here we train the\nnetwork on numerical data generated from spin models, showing that it can learn\nthe dynamics of observables of interest without needing information about the\nfull quantum state. This allows our approach to be applied eventually to actual\nexperimental data generated from a quantum many-body system that might be open,\nnoisy, or disordered, without any need for a detailed understanding of the\nsystem. This scheme provides considerable speedup for rapid explorations and\npulse optimization. Remarkably, we show the network is able to extrapolate the\ndynamics to times longer than those it has been trained on, as well as to the\ninfinite-system-size limit.\n"}
+{"abstract": "  We study the dynamics of the quasi-one-dimensional Ising-Heisenberg\nantiferromagnet BaCo2V2O8 under a transverse magnetic field. Combining\ninelastic neutron scattering experiments and theoretical analyses by field\ntheories and numerical simulations, we mainly elucidate the structure of the\nspin excitation spectrum in the high field phase, appearing above the quantum\nphase transition point mu0Hc ~ 10 T. We find that it is characterized by\ncollective solitonic excitations superimposed on a continuum. These solitons\nare strongly bound in pairs due to the effective staggered field induced by the\nnondiagonal g tensor of the compound, and are topologically different from the\nfractionalized spinons in the weak field region. The dynamical susceptibility\nnumerically calculated with the infinite time-evolving block decimation method\nshows an excellent agreement with the measured spectra, which enables us to\nidentify the dispersion branches with elementary excitations. The lowest energy\ndispersion has an incommensurate nature and has a local minimum at an\nirrational wave number due to the applied transverse field.\n"}
+{"abstract": "  In this paper, we explore generalizable, perception-to-action robotic\nmanipulation for precise, contact-rich tasks. In particular, we contribute a\nframework for closed-loop robotic manipulation that automatically handles a\ncategory of objects, despite potentially unseen object instances and\nsignificant intra-category variations in shape, size and appearance. Previous\napproaches typically build a feedback loop on top of a real-time 6-DOF pose\nestimator. However, representing an object with a parameterized transformation\nfrom a fixed geometric template does not capture large intra-category shape\nvariation. Hence we adopt the keypoint-based object representation proposed in\nkPAM for category-level pick-and-place, and extend it to closed-loop\nmanipulation policies with contact-rich tasks. We first augment keypoints with\nlocal orientation information. Using the oriented keypoints, we propose a novel\nobject-centric action representation in terms of regulating the linear/angular\nvelocity or force/torque of these oriented keypoints. This formulation is\nsurprisingly versatile -- we demonstrate that it can accomplish contact-rich\nmanipulation tasks that require precision and dexterity for a category of\nobjects with different shapes, sizes and appearances, such as peg-hole\ninsertion for pegs and holes with significant shape variation and tight\nclearance. With the proposed object and action representation, our framework is\nalso agnostic to the robot grasp pose and initial object configuration, making\nit flexible for integration and deployment.\n"}
+{"abstract": "  Utilizing intelligent reflecting surface (IRS) was proven to be efficient in\nimproving the energy efficiency for wireless networks. In this paper, we\ninvestigate the passive beamforming and channel estimation for IRS assisted\nwireless communications with low-resolution analog-to-digital converters (ADCs)\nat the receiver. We derive the approximate achievable rate by using the\nBussgang theorem. Based on the derived analytical achievable rate expression,\nwe maximize the achievable rate by using semidefinite programming (SDP),\nbranch-and-bound (BB), and gradient-based approaches. A maximum likelihood (ML)\nestimator is then proposed for channel estimation by considering the\n$\\mathrm{1}$-bit quantization ADC. Numerical result shows that the proposed\nbeamforming design and channel estimation method significantly outperforms the\nexisting methods.\n"}
+{"abstract": "  The impact of ram pressure stripping on galaxy evolution is well known.\nRecent multi-wavelength data have revealed many examples of galaxies undergoing\nstripping, often accompanied with multi-phase tails. As energy transfer in the\nmulti-phase medium is an outstanding question in astrophysics, galaxies in\nstripping are great objects to study. Despite the recent burst of observational\nevidence, the relationship between gas in different phases in the tails is\npoorly known. Here we report a strong linear correlation between the X-ray\nsurface brightness and the H$\\alpha$ surface brightness of the diffuse gas in\nthe stripped tails at $\\sim$ 10 - 40 kpc scales, with a slope of $\\sim$ 3.5.\nThis discovery provides evidence for the mixing of the stripped interstellar\nmedium with the hot intracluster medium as the origin of the multi-phase tails.\nThe established relation in stripped tails, also in comparison with the likely\nrelated correlations in similar environments like galactic winds and X-ray cool\ncores, provides an important test for models of energy transfer in the\nmulti-phase gas. It also indicates the importance of the H$\\alpha$ data to\nstudy clumping and turbulence in the intracluster medium.\n"}
+{"abstract": "  Automated metaphor detection is a challenging task to identify metaphorical\nexpressions of words in a sentence. To tackle this problem, we adopt\npre-trained contextualized models, e.g., BERT and RoBERTa. To this end, we\npropose a novel metaphor detection model, namely metaphor-aware late\ninteraction over BERT (MelBERT). Our model not only leverages contextualized\nword representation but also benefits from linguistic metaphor identification\ntheories to distinguish between the contextual and literal meaning of words.\nOur empirical results demonstrate that MelBERT outperforms several strong\nbaselines on four benchmark datasets, i.e., VUA-18, VUA-20, MOH-X, and TroFi.\n"}
+{"abstract": "  Lending decisions are usually made with proprietary models that provide\nminimally acceptable explanations to users. In a future world without such\nsecrecy, what decision support tools would one want to use for justified\nlending decisions? This question is timely, since the economy has dramatically\nshifted due to a pandemic, and a massive number of new loans will be necessary\nin the short term. We propose a framework for such decisions, including a\nglobally interpretable machine learning model, an interactive visualization of\nit, and several types of summaries and explanations for any given decision. The\nmachine learning model is a two-layer additive risk model, which resembles a\ntwo-layer neural network, but is decomposable into subscales. In this model,\neach node in the first (hidden) layer represents a meaningful subscale model,\nand all of the nonlinearities are transparent. Our online visualization tool\nallows exploration of this model, showing precisely how it came to its\nconclusion. We provide three types of explanations that are simpler than, but\nconsistent with, the global model: case-based reasoning explanations that use\nneighboring past cases, a set of features that were the most important for the\nmodel's prediction, and summary-explanations that provide a customized sparse\nexplanation for any particular lending decision made by the model. Our\nframework earned the FICO recognition award for the Explainable Machine\nLearning Challenge, which was the first public challenge in the domain of\nexplainable machine learning.\n"}
+{"abstract": "  The complex nature of lithium-ion battery degradation has led to many machine\nlearning based approaches to health forecasting being proposed in literature.\nHowever, machine learning can be computationally intensive. Linear approaches\nare faster but have previously been too inflexible for successful prognosis.\nFor both techniques, the choice and quality of the inputs is a limiting factor\nof performance. Piecewise-linear models, combined with automated feature\nselection, offer a fast and flexible alternative without being as\ncomputationally intensive as machine learning. Here, a piecewise-linear\napproach to battery health forecasting was compared to a Gaussian process\nregression tool and found to perform equally well. The input feature selection\nprocess demonstrated the benefit of limiting the correlation between inputs.\nFurther trials found that the piecewise-linear approach was robust to changing\ninput size and availability of training data.\n"}
+{"abstract": "  Spatial structuring of an optical pulse can lead in some cases upon free\npropagation to changes in its temporal profile. For example, introducing\nconventional angular dispersion into the field results in the pulse\nencountering group-velocity dispersion in free space. However, only limited\ncontrol is accessible via this strategy. Here we show that precise and\nversatile control can be exercised in free space over the dispersion profile of\nso-called `space-time' wave packets: a class of pulsed beams undergirded by\nnon-differentiable angular dispersion. This abstract mathematical feature\nallows us to tune the magnitude and sign of the different dispersion orders\nwithout introducing losses, thereby realizing arbitrary dispersion profiles,\nand achieving dispersion values unattainable in optical materials away from\nresonance. Unlike optical materials and photonic structures in which the values\nof the different dispersion orders are not independent of each other, these\norders are addressable separately using our strategy. These results demonstrate\nthe versatility of space-time wave packets as a platform for structured light\nand point towards their utility in nonlinear and quantum optics.\n"}
+{"abstract": "  Collaborative filtering (CF) has achieved great success in the field of\nrecommender systems. In recent years, many novel CF models, particularly those\nbased on deep learning or graph techniques, have been proposed for a variety of\nrecommendation tasks, such as rating prediction and item ranking. These newly\npublished models usually demonstrate their performance in comparison to\nbaselines or existing models in terms of accuracy improvements. However, others\nhave pointed out that many newly proposed models are not as strong as expected\nand are outperformed by very simple baselines.\n  This paper proposes a simple linear model based on Matrix Factorization (MF),\ncalled UserReg, which regularizes users' latent representations with explicit\nfeedback information for rating prediction. We compare the effectiveness of\nUserReg with three linear CF models that are widely-used as baselines, and with\na set of recently proposed complex models that are based on deep learning or\ngraph techniques. Experimental results show that UserReg achieves overall\nbetter performance than the fine-tuned baselines considered and is highly\ncompetitive when compared with other recently proposed models. We conclude that\nUserReg can be used as a strong baseline for future CF research.\n"}
+{"abstract": "  We establish Arazy-Cwikel type properties for the family of couples\n$(\\ell^{p},\\ell^{q})$, $0\\le p<q\\le\\infty$, and show that $(\\ell^{p},\\ell^{q})\n$ is a Calder\\'on-Mityagin couple if and only if $q\\ge1$. Moreover, we identify\ninterpolation orbits of elements with respect to this couple for all $p$ and\n$q$ such that $0\\le p<q\\le\\infty$ and obtain a simple positive solution of a\nLevitina-Sukochev-Zanin problem, clarifying its connections with whether\n$(\\ell^{p},\\ell^{q})$ has the Calder\\'on-Mityagin property or not.\n"}
+{"abstract": "  The online reconstruction of muon tracks in High Energy Physics experiments\nis a highly demanding task, typically performed with programmable logic boards,\nsuch as FPGAs. Complex analytical algorithms are executed in a quasi-real-time\nenvironment to identify, select and reconstruct local tracks in often\nnoise-rich environments. A novel approach to the generation of local triggers\nbased on an hybrid combination of Artificial Neural Networks and analytical\nmethods is proposed, targeting the muon reconstruction for drift tube\ndetectors. The proposed algorithm exploits Neural Networks to solve otherwise\ncomputationally expensive analytical tasks for the unique identification of\ncoherent signals and the removal of the geometrical ambiguities. The proposed\napproach is deployed on state-of-the-art FPGA and its performances are\nevaluated on simulation and on data collected from cosmic rays.\n"}
+{"abstract": "  Numerical simulations of monolayer dust crystals in an RF complex plasma were\nperformed to examine the crystal structure and quantify the effects of\nincluding the collision enhanced ion current in the charging model. A GEC cell\nsimilar to a previous experimental work was modeled for a range of RF voltages,\nusing a continuum description for the plasma and a particle description for\ndust grains. The time history of each dust grain was monitored. The dust charge\nwas computed using both the OML and the collision enhanced charging (CEC) model\napplicable to the sheath region. The dust model accounted for the electric\nforce, ion drag force, neutral drag force, gravity, and the ion wake. The CEC\nmodel produced a lower charge and lower electric force which agreed better with\nthe experimental data. Then dust crystals composed of 40 to 100 grains were\nmodeled and the levitation height and inter-particle spacing of the resulting\ncrystals was examined. Including the collision enhanced current reduced the\ninter-particle spacing but only had a minor effect on the levitation height.\n"}
+{"abstract": "  We introduce an algebraic model based on the expansion of the determinant of\ntwo matrices, one of which is generic, to check the additivity of Z^d-valued\nset functions. Each individual term of the expansion is deformed through a\nmonomial factor in d indeterminates with exponents defined by the set function.\nA family of sparse polynomials is derived from Grassmann-Plucker relations, and\ntheir compatibility is linked to the factorisation of hyperdeterminants in the\nring of Laurent polynomials. It is proved that, in broad generality, this\ndeformation returns a determinantal expansion if and only if it is induced by a\ndiagonal matrix of monomials acting as a kernel into the initial determinant\nexpansion, which guarantees the additivity of the set function. The hypotheses\nunderlying this result are tested through the construction of counterexamples,\nand their implications are explored in terms of complexity reduction, with\nspecial attention to permutations of families of subsets.\n"}
+{"abstract": "  The Kahn--Saks inequality is a classical result on the number of linear\nextensions of finite posets. We give a new proof of this inequality for posets\nof width two using explicit injections of lattice paths. As a consequence we\nobtain a $q$-analogue, a multivariate generalization and an equality condition\nin this case. We also discuss the equality conditions of the Kahn--Saks\ninequality for general posets and prove several implications between conditions\nconjectured to be equivalent.\n"}
+{"abstract": "  In quantum metrology, nonlinear many-body interactions can enhance the\nprecision of Hamiltonian parameter estimation to surpass the Heisenberg\nscaling. Here, we consider the estimation of the interaction strength in linear\nsystems with long-range interactions and using the Kitaev chains as a case\nstudy, we establish a transition from the Heisenberg to super-Heisenberg\nscaling in the quantum Fisher information by varying the interaction range. We\nfurther show that quantum control can improve the prefactor of the quantum\nFisher information. Our results explore the advantage of optimal quantum\ncontrol and long-range interactions in many-body quantum metrology.\n"}
+{"abstract": "  The optical domain is a promising field for physical implementation of neural\nnetworks, due to the speed and parallelism of optics. Extreme Learning Machines\n(ELMs) are feed-forward neural networks in which only output weights are\ntrained, while internal connections are randomly selected and left untrained.\nHere we report on a photonic ELM based on a frequency-multiplexed fiber setup.\nMultiplication by output weights can be performed either offline on a computer,\nor optically by a programmable spectral filter. We present both numerical\nsimulations and experimental results on classification tasks and a nonlinear\nchannel equalization task.\n"}
+{"abstract": "  We study the practical consequences of dataset sampling strategies on the\nperformance of recommendation algorithms. Recommender systems are generally\ntrained and evaluated on samples of larger datasets. Samples are often taken in\na naive or ad-hoc fashion: e.g. by sampling a dataset randomly or by selecting\nusers or items with many interactions. As we demonstrate, commonly-used data\nsampling schemes can have significant consequences on algorithm performance --\nmasking performance deficiencies in algorithms or altering the relative\nperformance of algorithms, as compared to models trained on the complete\ndataset. Following this observation, this paper makes the following main\ncontributions: (1) characterizing the effect of sampling on algorithm\nperformance, in terms of algorithm and dataset characteristics (e.g. sparsity\ncharacteristics, sequential dynamics, etc.); and (2) designing SVP-CF, which is\na data-specific sampling strategy, that aims to preserve the relative\nperformance of models after sampling, and is especially suited to long-tail\ninteraction data. Detailed experiments show that SVP-CF is more accurate than\ncommonly used sampling schemes in retaining the relative ranking of different\nrecommendation algorithms.\n"}
+{"abstract": "  In this work we leverage commonsense knowledge in form of knowledge paths to\nestablish connections between sentences, as a form of explicitation of implicit\nknowledge. Such connections can be direct (singlehop paths) or require\nintermediate concepts (multihop paths). To construct such paths we combine two\nmodel types in a joint framework we call Co-nnect: a relation classifier that\npredicts direct connections between concepts; and a target prediction model\nthat generates target or intermediate concepts given a source concept and a\nrelation, which we use to construct multihop paths. Unlike prior work that\nrelies exclusively on static knowledge sources, we leverage language models\nfinetuned on knowledge stored in ConceptNet, to dynamically generate knowledge\npaths, as explanations of implicit knowledge that connects sentences in texts.\nAs a central contribution we design manual and automatic evaluation settings\nfor assessing the quality of the generated paths. We conduct evaluations on two\nargumentative datasets and show that a combination of the two model types\ngenerates meaningful, high-quality knowledge paths between sentences that\nreveal implicit knowledge conveyed in text.\n"}
+{"abstract": "  Continuous practices that rely on automation in the software development\nworkflow have been widely adopted by industry for over a decade. Despite this\nwidespread use, software development remains a primarily human-driven activity\nthat is highly creative and collaborative. There has been extensive research on\nhow continuous practices rely on automation and its impact on software quality\nand development velocity, but relatively little has been done to understand how\nautomation impacts developer behavior and collaboration. In this paper, we\nintroduce a socio-technical theory about continuous practices. The ADEPT theory\ncombines constructs that include humans, processes, documentation, automation\nand the project environment, and describes propositions that relate these\nconstructs. The theory was derived from phenomena observed in previous\nempirical studies. We show how the ADEPT theory can explain and describe\nexisting continuous practices in software development, and how it can be used\nto generate new propositions for future studies to understand continuous\npractices and their impact on the social and technical aspects of software\ndevelopment.\n"}
+{"abstract": "  In this paper, the relativistic quantum dynamics of a scalar particle under\nthe effect of Lorentz symmetry violation determined by a tensor\n$(K_{F})_{\\mu\\,\\nu\\,\\alpha\\,\\beta}$ out of the Standard Model Extension is\ninvestigated. We see that the bound-state solution of the modified Klein-Gordon\nequation can be obtained, and the spectrum of energy and the wave function\ndepends on the Lorentz symmetry breaking parameters\n"}
+{"abstract": "  We study the fundamental problem of frequency estimation under both privacy\nand communication constraints, where the data is distributed among $k$ parties.\nWe consider two application scenarios: (1) one-shot, where the data is static\nand the aggregator conducts a one-time computation; and (2) streaming, where\neach party receives a stream of items over time and the aggregator continuously\nmonitors the frequencies. We adopt the model of multiparty differential privacy\n(MDP), which is more general than local differential privacy (LDP) and\n(centralized) differential privacy. Our protocols achieve optimality (up to\nlogarithmic factors) permissible by the more stringent of the two constraints.\nIn particular, when specialized to the $\\varepsilon$-LDP model, our protocol\nachieves an error of $\\sqrt{k}/(e^{\\Theta(\\varepsilon)}-1)$ using $O(k\\max\\{\n\\varepsilon, \\frac{1}{\\varepsilon} \\})$ bits of communication and $O(k \\log u)$\nbits of public randomness, where $u$ is the size of the domain.\n"}
+{"abstract": "  Focal plane wavefront sensing (FPWFS) is appealing for several reasons.\nNotably, it offers high sensitivity and does not suffer from non-common path\naberrations (NCPA). The price to pay is a high computational burden and the\nneed for diversity to lift any phase ambiguity. If those limitations can be\novercome, FPWFS is a great solution for NCPA measurement, a key limitation for\nhigh-contrast imaging, and could be used as adaptive optics wavefront sensor.\nHere, we propose to use deep convolutional neural networks (CNNs) to measure\nNCPA based on focal plane images. Two CNN architectures are considered:\nResNet-50 and U-Net which are used respectively to estimate Zernike\ncoefficients or directly the phase. The models are trained on labelled datasets\nand evaluated at various flux levels and for two spatial frequency contents (20\nand 100 Zernike modes). In these idealized simulations we demonstrate that the\nCNN-based models reach the photon noise limit in a large range of conditions.\nWe show, for example, that the root mean squared (rms) wavefront error (WFE)\ncan be reduced to < $\\lambda$/1500 for $2 \\times 10^6$ photons in one iteration\nwhen estimating 20 Zernike modes. We also show that CNN-based models are\nsufficiently robust to varying signal-to-noise ratio, under the presence of\nhigher-order aberrations, and under different amplitudes of aberrations.\nAdditionally, they display similar to superior performance compared to\niterative phase retrieval algorithms. CNNs therefore represent a compelling way\nto implement FPWFS, which can leverage the high sensitivity of FPWFS over a\nbroad range of conditions.\n"}
+{"abstract": "  We analyze the behavior of third-order in time linear and nonlinear sound\nwaves in thermally relaxing fluids and gases as the sound diffusivity vanishes.\nThe nonlinear acoustic propagation is modeled by the\nJordan--Moore--Gibson--Thompson equation both in its Westervelt and in its\nKuznetsov-type forms, that is, including quadratic nonlinearities of the type\n$(u^2)_{tt}$ and $ (u_t^2 + |\\nabla u|^2)_t$. As it turns out, sufficiently\nsmooth solutions of these equations converge in the energy norm to the\nsolutions of the corresponding inviscid models at a linear rate. Numerical\nexperiments illustrate our theoretical findings.\n"}
+{"abstract": "  A growing number of people engage in online health forums, making it\nimportant to understand the quality of the advice they receive. In this paper,\nwe explore the role of expertise in responses provided to help-seeking posts\nregarding mental health. We study the differences between (1) interactions with\npeers; and (2) interactions with self-identified mental health professionals.\nFirst, we show that a classifier can distinguish between these two groups,\nindicating that their language use does in fact differ. To understand this\ndifference, we perform several analyses addressing engagement aspects,\nincluding whether their comments engage the support-seeker further as well as\nlinguistic aspects, such as dominant language and linguistic style matching.\nOur work contributes toward the developing efforts of understanding how health\nexperts engage with health information- and support-seekers in social networks.\nMore broadly, it is a step toward a deeper understanding of the styles of\ninteractions that cultivate supportive engagement in online communities.\n"}
+{"abstract": "  The NA62 experiment at CERN reports searches for $K^+\\to\\mu^+N$ and\n$K^+\\to\\mu^+\\nu X$ decays, where $N$ and $X$ are massive invisible particles,\nusing the 2016-2018 data set. The $N$ particle is assumed to be a heavy neutral\nlepton, and the results are expressed as upper limits of ${\\cal O}(10^{-8})$ of\nthe neutrino mixing parameter $|U_{\\mu4}|^2$ for $N$ masses in the range\n200-384 MeV/$c^2$ and lifetime exceeding 50 ns. The $X$ particle is considered\na scalar or vector hidden sector mediator decaying to an invisible final state,\nand upper limits of the decay branching fraction for $X$ masses in the range\n10-370 MeV/$c^2$ are reported for the first time, ranging from ${\\cal\nO}(10^{-5})$ to ${\\cal O}(10^{-7})$. An improved upper limit of $1.0\\times\n10^{-6}$ is established at 90% CL on the $K^+\\to\\mu^+\\nu\\nu\\bar\\nu$ branching\nfraction.\n"}
+{"abstract": "  Spoken Language Understanding (SLU) aims to extract the semantics frame of\nuser queries, which is a core component in a task-oriented dialog system. With\nthe burst of deep neural networks and the evolution of pre-trained language\nmodels, the research of SLU has obtained significant breakthroughs. However,\nthere remains a lack of a comprehensive survey summarizing existing approaches\nand recent trends, which motivated the work presented in this article. In this\npaper, we survey recent advances and new frontiers in SLU. Specifically, we\ngive a thorough review of this research field, covering different aspects\nincluding (1) new taxonomy: we provide a new perspective for SLU filed,\nincluding single model vs. joint model, implicit joint modeling vs. explicit\njoint modeling in joint model, non pre-trained paradigm vs. pre-trained\nparadigm;(2) new frontiers: some emerging areas in complex SLU as well as the\ncorresponding challenges; (3) abundant open-source resources: to help the\ncommunity, we have collected, organized the related papers, baseline projects\nand leaderboard on a public website where SLU researchers could directly access\nto the recent progress. We hope that this survey can shed a light on future\nresearch in SLU field.\n"}
+{"abstract": "  When detecting anomalies in audio, it can often be necessary to consider\nconcept drift: the distribution of the data may drift over time because of\ndynamically changing environments, and anomalies may become normal as time\nelapses. We propose to use adaptive Huffman coding for anomaly detection in\naudio with concept drift. Compared with the existing method of adaptive\nGaussian mixture modeling (AGMM), adaptive Huffman coding does not require a\npriori information about the clusters and can adjust the number of clusters\ndynamically depending on the amount of variation in the audio. To control the\nsize of the Huffman tree, we propose to merge clusters that are close to each\nother instead of replacing rare clusters with new data. This reduces redundancy\nin the Huffman tree while ensuring that it never forgets past information. On a\ndataset of audio with concept drift which we have curated ourselves, our\nproposed method achieves higher area under the curve (AUC) compared with AGMM\nand fixed-length Huffman trees. The proposed approach is also time-efficient\nand can be easily extended to other types of time series data (e.g., video).\n"}
+{"abstract": "  The Supergiant X-ray binary Vela X-1 represents one of the best astrophysical\nsources to investigate the wind environment of a O/B star irradiated by an\naccreting neutron star. Previous studies and hydrodynamic simulations of the\nsystem revealed a clumpy environment and the presence of two wakes: an\naccretion wake surrounding the compact object and a photoionisation wake\ntrailing it along the orbit. Our goal is to conduct, for the first time,\nhigh-resolution spectroscopy on Chandra/HETG data at the orbital phase\n$\\varphi_\\mathrm{orb} \\approx 0.75$, when the line of sight is crossing the\nphotoionisation wake. We aim to conduct plasma diagnostics, inferring the\nstructure and the geometry of the wind. We perform a blind search employing a\nBayesian Block algorithm to find discrete spectral features and identify them\nthanks to the most recent laboratory results or through atomic databases.\nPlasma properties are inferred both with empirical techniques and with\nphotoionisation models within CLOUDY and SPEX. We detect and identify five\nnarrow radiative recombination continua (Mg XI-XII, Ne IX-X, O VIII) and\nseveral emission lines from Fe, S, Si, Mg, Ne, Al, and Na, including four\nHe-like triplets (S XV, Si XIII, Mg XI, and Ne IX). Photoionisation models well\nreproduce the overall spectrum, except for the near-neutral fluorescence lines\nof Fe, S, and Si. We conclude that the plasma is mainly photoionised, but more\nthan one component is most likely present, consistent with a multi-phase plasma\nscenario, where denser and colder clumps of matter are embedded in the hot,\nphotoionised wind of the companion star. Simulations with the future X-ray\nsatellites Athena and XRISM show that a few hundred seconds of exposure will be\nsufficient to disentangle the lines of the Fe K$\\alpha$ doublet and the He-like\nFe XXV, improving, in general, the determination of the plasma parameters.\n"}
+{"abstract": "  We determine the asymptotic behaviour of certain incomplete Betafunctions.\n"}
+{"abstract": "  We generalise a result of Kazarian regarding Kadomtsev-Petviashvili\nintegrability for single Hodge integrals to general cohomological field\ntheories related to Hurwitz-type counting problems or hypergeometric\ntau-functions. The proof uses recent results on the relations between\nhypergeometric tau-functions and topological recursion, as well as the\nEynard-DOSS correspondence between topological recursion and cohomological\nfield theories. In particular, we recover the result of Alexandrov of KP\nintegrability for triple Hodge integrals with a Calabi-Yau condition.\n"}
+{"abstract": "  We compute successfully the launching of two magnetic winds from two\ncircumbinary disks formed after a common envelope event. The launching is\nproduced by the increase of magnetic pressure due to the collapse of the disks.\nThe collapse is due to internal torques produced by a weak poloidal magnetic\nfield. The first wind can be described as a wide jet, with an average mass-loss\nrate of $\\sim 1.3 \\times 10^{-7}$ \\Moy\\ and a maximum radial velocity of $\\sim\n230$ \\kms. The outflow has a half-opening angle of $\\sim 20^{\\circ}$. Narrow\njets are also formed intermittently with velocities up to 3,000 \\kms, with\nmass-loss rates of $\\sim 6 \\times 10^{-12} $ \\Moy\\ during short periods of\ntime. The second wind can be described as a wide X-wind, with an average\nmass-loss rate of $\\sim 1.68 \\times 10^{-7}$ \\Moy\\ and a velocity of $\\sim 30$\n\\kms. A narrow jet is also formed with a velocity of 250 \\kms, and a mass-loss\nrates of $\\sim 10^{-12} $ \\Moy.\n  The computed jets are used to provide inflow boundary conditions for\nsimulations of proto-planetary nebulae. The wide jet evolves into a molecular\ncollimated outflow within a few astronomical units, producing proto-planetary\nnebulae with bipolar, elongated shapes, whose kinetic energies reach $\\sim 4\n\\times 10^{45}$ erg at 1,000 years. Similarities with observed features in\nW43A, OH231.8+4.2, and Hen 3-1475 are discussed.\n  The computed wide X-wind produces proto-planetary nebulae with slower\nexpansion velocities, with bipolar and elliptical shapes, and possible starfish\ntype and quadrupolar morphology.\n"}
+{"abstract": "  Data from Direct Numerical Simulations of disperse bubbly flows in a vertical\nchannel are used to study the effect of the bubbles on the carrier-phase\nturbulence. A new method is developed, based on the barycentric map approach,\nthat allows to quantify the anisotropy and componentiality of the flow at any\nscale. Using this the bubbles are found to significantly enhance flow\nanisotropy at all scales compared with the unladen case, and for some bubble\ncases, very strong anisotropy persists down to the smallest flow scales. The\nstrongest anisotropy observed was for the cases involving small bubbles.\nConcerning the inter-scale energy transfer, our results indicate that for the\nbubble-laden cases, the energy transfer is from large to small scales, just as\nfor the unladen case. However, there is evidence of an upscale transfer when\nconsidering the transfer of energy associated with particular components of the\nvelocity field. Although the direction of the energy transfer is the same with\nand without the bubbles, the transfer is much stronger for the bubble-laden\ncases, suggesting that the bubbles play a strong role in enhancing the activity\nof the nonlinear term in the flow. The normalized forms of the fourth and\nsixth-order structure functions are also considered, and reveal that the\nintroduction of bubbles into the flow strongly enhances intermittency in the\ndissipation range, but suppresses it at larger scales. This strong enhancement\nof the dissipation scale intermittency has significant implications for\nunderstanding how the bubbles might modify the mixing properties of turbulent\nflows.\n"}
+{"abstract": "  Preference judgments have been demonstrated as a better alternative to graded\njudgments to assess the relevance of documents relative to queries. Existing\nwork has verified transitivity among preference judgments when collected from\ntrained judges, which reduced the number of judgments dramatically. Moreover,\nstrict preference judgments and weak preference judgments, where the latter\nadditionally allow judges to state that two documents are equally relevant for\na given query, are both widely used in literature. However, whether\ntransitivity still holds when collected from crowdsourcing, i.e., whether the\ntwo kinds of preference judgments behave similarly remains unclear. In this\nwork, we collect judgments from multiple judges using a crowdsourcing platform\nand aggregate them to compare the two kinds of preference judgments in terms of\ntransitivity, time consumption, and quality. That is, we look into whether\naggregated judgments are transitive, how long it takes judges to make them, and\nwhether judges agree with each other and with judgments from TREC. Our key\nfindings are that only strict preference judgments are transitive. Meanwhile,\nweak preference judgments behave differently in terms of transitivity, time\nconsumption, as well as of quality of judgment.\n"}
+{"abstract": "  In the past decades, DNA has been intensely studied and exploited in\ndifferent research areas of nanoscience and nanotechnology. At first glance,\nDNA-based nanophotonics seems to deviate quite far from the original goal of\nNadrian Seeman, the founder of DNA nanotechnology, who hoped to organize\nbiological entities using DNA in high-resolution crystals. As a matter of fact,\nDNA-based nanophotonics does closely follow his central spirit. That is, apart\nfrom being a genetic material for inheritance, DNA is also an ideal material\nfor building molecular devices.\n"}
+{"abstract": "  In this paper, we present an autonomous navigation system for goal-driven\nexploration of unknown environments through deep reinforcement learning (DRL).\nPoints of interest (POI) for possible navigation directions are obtained from\nthe environment and an optimal waypoint is selected, based on the available\ndata. Following the waypoints, the robot is guided towards the global goal and\nthe local optimum problem of reactive navigation is mitigated. Then, a motion\npolicy for local navigation is learned through a DRL framework in a simulation.\nWe develop a navigation system where this learned policy is integrated into a\nmotion planning stack as the local navigation layer to move the robot between\nwaypoints towards a global goal. The fully autonomous navigation is performed\nwithout any prior knowledge while a map is recorded as the robot moves through\nthe environment. Experiments show that the proposed method has an advantage\nover similar exploration methods, without reliance on a map or prior\ninformation in complex static as well as dynamic environments.\n"}
+{"abstract": "  In nanopore sequencing, electrical signal is measured as DNA molecules pass\nthrough the sequencing pores. Translating these signals into DNA bases (base\ncalling) is a highly non-trivial task, and its quality has a large impact on\nthe sequencing accuracy. The most successful nanopore base callers to date use\nconvolutional neural networks (CNN) to accomplish the task.\n  Convolutional layers in CNNs are typically composed of filters with constant\nwindow size, performing best in analysis of signals with uniform speed.\nHowever, the speed of nanopore sequencing varies greatly both within reads and\nbetween sequencing runs. Here, we present dynamic pooling, a novel neural\nnetwork component, which addresses this problem by adaptively adjusting the\npooling ratio. To demonstrate the usefulness of dynamic pooling, we developed\ntwo base callers: Heron and Osprey. Heron improves the accuracy beyond the\nexperimental high-accuracy base caller Bonito developed by Oxford Nanopore.\nOsprey is a fast base caller that can compete in accuracy with Guppy\nhigh-accuracy mode, but does not require GPU acceleration and achieves a near\nreal-time speed on common desktop CPUs.\n  Availability: https://github.com/fmfi-compbio/osprey,\nhttps://github.com/fmfi-compbio/heron\n  Keywords: nanopore sequencing, base calling, convolutional neural networks,\npooling\n"}
+{"abstract": "  Deep neural network (DNN)-based speech enhancement ordinarily requires clean\nspeech signals as the training target. However, collecting clean signals is\nvery costly because they must be recorded in a studio. This requirement\ncurrently restricts the amount of training data for speech enhancement to less\nthan 1/1000 of that of speech recognition which does not need clean signals.\nIncreasing the amount of training data is important for improving the\nperformance, and hence the requirement of clean signals should be relaxed. In\nthis paper, we propose a training strategy that does not require clean signals.\nThe proposed method only utilizes noisy signals for training, which enables us\nto use a variety of speech signals in the wild. Our experimental results showed\nthat the proposed method can achieve the performance similar to that of a DNN\ntrained with clean signals.\n"}
+{"abstract": "  Deep Metric Learning (DML) is helpful in computer vision tasks. In this\npaper, we firstly introduce DML into image co-segmentation. We propose a novel\nTriplet loss for Image Segmentation, called IS-Triplet loss for short, and\ncombine it with traditional image segmentation loss. Different from the general\nDML task which learns the metric between pictures, we treat each pixel as a\nsample, and use their embedded features in high-dimensional space to form\ntriples, then we tend to force the distance between pixels of different\ncategories greater than of the same category by optimizing IS-Triplet loss so\nthat the pixels from different categories are easier to be distinguished in the\nhigh-dimensional feature space. We further present an efficient triple sampling\nstrategy to make a feasible computation of IS-Triplet loss. Finally, the\nIS-Triplet loss is combined with 3 traditional image segmentation losses to\nperform image segmentation. We apply the proposed approach to image\nco-segmentation and test it on the SBCoseg dataset and the Internet dataset.\nThe experimental result shows that our approach can effectively improve the\ndiscrimination of pixels' categories in high-dimensional space and thus help\ntraditional loss achieve better performance of image segmentation with fewer\ntraining epochs.\n"}
+{"abstract": "  The domain of Embodied AI has recently witnessed substantial progress,\nparticularly in navigating agents within their environments. These early\nsuccesses have laid the building blocks for the community to tackle tasks that\nrequire agents to actively interact with objects in their environment. Object\nmanipulation is an established research domain within the robotics community\nand poses several challenges including manipulator motion, grasping and\nlong-horizon planning, particularly when dealing with oft-overlooked practical\nsetups involving visually rich and complex scenes, manipulation using mobile\nagents (as opposed to tabletop manipulation), and generalization to unseen\nenvironments and objects. We propose a framework for object manipulation built\nupon the physics-enabled, visually rich AI2-THOR framework and present a new\nchallenge to the Embodied AI community known as ArmPointNav. This task extends\nthe popular point navigation task to object manipulation and offers new\nchallenges including 3D obstacle avoidance, manipulating objects in the\npresence of occlusion, and multi-object manipulation that necessitates long\nterm planning. Popular learning paradigms that are successful on PointNav\nchallenges show promise, but leave a large room for improvement.\n"}
+{"abstract": "  Houghton's groups $H_2, H_3, \\ldots$ are certain infinite permutation groups\nacting on a countably infinite set; they have been studied, among other things,\nfor their finiteness properties. In this note we describe all of the finite\nindex subgroups of each Houghton group, and their isomorphism types. Using the\nstandard notation that $d(G)$ denotes the minimal size of generating set for\n$G$ we then show, for each $n\\in \\{2, 3,\\ldots\\}$ and $U$ of finite index in\n$H_n$, that $d(U)\\in\\{d(H_n), d(H_n)+1\\}$ and characterise when each of these\ncases occurs.\n"}
+{"abstract": "  This article aims at developing a model based optimization for reduction of\ntemporal unwrapping and field estimation errors in multi-echo acquisition of\nGradient Echo sequence. Using the assumption that the phase is linear along the\ntemporal dimension, the field estimation is performed by application of unity\nrank approximation to the Hankel matrix formed using the complex exponential of\nthe channel combined phase at each echo time. For the purpose of maintaining\nconsistency with the observed complex data, the linear phase evolution model is\nformulated as an optimization problem with a cost function that involves a\nfidelity term and a unity rank prior, implemented using alternating\nminimization. Itoh s algorithm applied to the multi-echo phase estimated from\nthis linear phase evolution model is able to reduce the unwrapping errors as\ncompared to the unwrapping when directly applied to the measured phase.\nSecondly, the improved accuracy of the frequency fit in comparison to\nestimation using weighted least-square regression and penalized maximum\nlikelihood is demonstrated using numerical simulation of field perturbation due\nto magnetic susceptibility effect. It is shown that the field can be estimated\nwith 80 percent reduction in mean absolute error in comparison to wLSR and 66\npercent reduction with respect to penalized maximum likelihood. The improvement\nin performance becomes more pronounced with increasing strengths of field\ngradient magnitudes and echo spacing.\n"}
+{"abstract": "  Willems' fundamental lemma and system level synthesis both characterize a\nlinear dynamic system by its input/output sequences. In this work, we extend\nthe application of the fundamental lemma from deterministic to uncertain LTI\nsystems and then further prove this extension to be equivalent to system level\nsynthesis. Based on this uncertain extension, a robust closed-loop data-enabled\npredictive control scheme is proposed, where a causal feedback control law is\nfurther derived. Two numerical experiments, including the temperature control\nof a single-zone building, are carried out to validate the effectiveness of the\nproposed data-driven controller.\n"}
+{"abstract": "  Pretrained language models have shown success in many natural language\nprocessing tasks. Many works explore incorporating knowledge into language\nmodels. In the biomedical domain, experts have taken decades of effort on\nbuilding large-scale knowledge bases. For example, the Unified Medical Language\nSystem (UMLS) contains millions of entities with their synonyms and defines\nhundreds of relations among entities. Leveraging this knowledge can benefit a\nvariety of downstream tasks such as named entity recognition and relation\nextraction. To this end, we propose KeBioLM, a biomedical pretrained language\nmodel that explicitly leverages knowledge from the UMLS knowledge bases.\nSpecifically, we extract entities from PubMed abstracts and link them to UMLS.\nWe then train a knowledge-aware language model that firstly applies a text-only\nencoding layer to learn entity representation and applies a text-entity fusion\nencoding to aggregate entity representation. Besides, we add two training\nobjectives as entity detection and entity linking. Experiments on the named\nentity recognition and relation extraction from the BLURB benchmark demonstrate\nthe effectiveness of our approach. Further analysis on a collected probing\ndataset shows that our model has better ability to model medical knowledge.\n"}
+{"abstract": "  Cloud computing has the capacity to transform many parts of the research\necosystem, from particular research areas to overall strategic decision making\nand policy. Scientometrics sits at the boundary between research and the\ndecision making and evaluation processes of research. One of the biggest\nchallenges in research policy and strategy is having access to data that allows\niterative analysis to inform decisions. Many of these decisions are based on\n\"global\" measures such as benchmark metrics that are hard to source. In this\narticle, Cloud technologies are explored in this context. A novel visualisation\ntechnique is presented and used as a means to explore the potential for scaling\nscientometrics by democratising both access to data and compute capacity using\nthe Cloud.\n"}
+{"abstract": "  We present photometric and spectroscopic observations of Supernova 2020oi (SN\n2020oi), a nearby ($\\sim$17 Mpc) type-Ic supernova (SN Ic) within the\ngrand-design spiral M100. We undertake a comprehensive analysis to characterize\nthe evolution of SN 2020oi and constrain its progenitor system. We detect flux\nin excess of the fireball rise model $\\delta t \\approx 2.5$ days from the date\nof explosion in multi-band optical and UV photometry from the Las Cumbres\nObservatory and the Neil Gehrels Swift Observatory, respectively. The derived\nSN bolometric luminosity is consistent with an explosion with $M_{\\rm ej} =\n0.81 \\pm 0.03 M_{\\odot}$, $E_{k}= 0.79 \\pm 0.09 \\times 10^{51} \\rm{erg}\n\\rm{s}^{-1}$, and $M_{\\rm Ni56} = 0.08 \\pm 0.02 M_{\\odot}$. Inspection of the\nevent's decline reveals the highest $\\Delta m_{15,\\rm{bol}}$ reported for a\nstripped-envelope event to date. Modeling of optical spectra near event peak\nindicates a partially mixed ejecta comparable in composition to the ejecta\nobserved in SN 1994I, while the earliest spectrum shows signatures of a\npossible interaction with material of a distinct composition surrounding the SN\nprogenitor. Further, Hubble Space Telescope (HST) pre-explosion imaging reveals\na stellar cluster coincident with the event. From the cluster photometry, we\nderive the mass and age of the SN progenitor using stellar evolution models\nimplemented in the BPASS library. Our results indicate that SN 2020oi occurred\nin a binary system from a progenitor of mass $M_{\\rm ZAMS} \\approx 9.5 \\pm 1.0\nM_{\\odot}$, corresponding to an age of $27 \\pm 7$ Myr. SN 2020oi is the dimmest\nSN Ic event to date for which an early-time flux excess has been observed, and\nthe first in which an early excess is unlikely to be associated with\nshock-cooling.\n"}
+{"abstract": "  Following the increasing interest and adoption of FaaS systems, benchmarking\nframeworks for determining non-functional properties have also emerged. While\nexisting (microbenchmark) frameworks only evaluate single aspects of FaaS\nplatforms, a more holistic, application-driven approach is still missing. In\nthis paper, we design and present BeFaaS, an extensible application-centric\nbenchmarking framework for FaaS environments that focuses on the evaluation of\nFaaS platforms through realistic and typical examples of FaaS applications.\nBeFaaS includes a built-in e-commerce benchmark, is extensible for new workload\nprofiles and new platforms, supports federated benchmark runs in which the\nbenchmark application is distributed over multiple providers, and supports a\nfine-grained result analysis. Our evaluation compares three major FaaS\nproviders in single cloud provider setups and shows that BeFaaS is capable of\nrunning each benchmark automatically with minimal configuration effort and\nproviding detailed insights for each interaction.\n"}
+{"abstract": "  Confluent cell monolayers and epithelia tissues show remarkable patterns and\ncorrelations in structural arrangements and actively-driven collective flows.\nWe simulate these properties using multiphase field models. The models are\nbased on cell deformations and cell-cell interactions and we investigate the\ninfluence of microscopic details to incorporate active forces on emerging\nphenomena. We compare four different approaches, one in which the activity is\ndetermined by a random orientation, one where the activity is related to the\ndeformation of the cells and two models with subcellular details to resolve the\nmechanochemical interactions underlying cell migration. The models are compared\nwith respect to generic features, such as solid-to-liquid phase transitions,\ncell shape variability, emerging nematic properties, as well as vorticity\ncorrelations and flow patterns in large confluent monolayers and confinements.\nAll results are compared with experimental data for a large variety of cell\ncultures. The appearing qualitative differences of the models show the\nimportance of microscopic details and provide a route towards predictive\nsimulations of patterns and correlations in cell colonies.\n"}
+{"abstract": "  In this paper we generalize the definition of rationalizability for square\nroots of polynomials introduced by M. Besier and the first author to field\nextensions. We then show that the rationalizability of a set of field\nextensions is equivalent to the rationalizability of the compositum of the\nfield extensions, providing a new strategy to prove rationalizability of sets\nof square roots of polynomials.\n"}
+{"abstract": "  We show how to infer sharp partial regularity results for relaxed minimizers\nof degenerate, nonuniformly elliptic quasiconvex functionals, using tools from\nNonlinear Potential Theory. In particular, in the setting of functionals with\n$(p,q)$-growth - according to the terminology of Marcellini [52] - we derive\noptimal local regularity criteria under minimal assumptions on the data.\n"}
+{"abstract": "  Let $G$ be a simple graph with $2n$ vertices and a perfect matching. We\ndenote by $f(G)$ and $F(G)$ the minimum and maximum forcing number of $G$,\nrespectively.\n  Hetyei obtained that the maximum number of edges of graphs $G$ with a unique\nperfect matching is $n^2$. We know that $G$ has a unique perfect matching if\nand only if $f(G)=0$. Along this line, we generalize the classical result to\nall graphs $G$ with $f(G)=k$ for $0\\leq k\\leq n-1$, and characterize\ncorresponding extremal graphs as well. Hence we get a non-trivial lower bound\nof $f(G)$ in terms of the order and size. For bipartite graphs, we gain\ncorresponding stronger results. Further, we obtain a new upper bound of $F(G)$.\nFor bipartite graphs $G$, Che and Chen (2013) obtained that $f(G)=n-1$ if and\nonly if $G$ is complete bipartite graph $K_{n,n}$. We completely characterize\nall bipartite graphs $G$ with $f(G)= n-2$.\n"}
+{"abstract": "  We investigate ion-scale kinetic plasma instabilities at the collisionless\nshock using linear theory and nonlinear Particle-in-Cell (PIC) simulations. We\nfocus on the Alfv\\'en-ion-cyclotron (AIC), mirror, and Weibel instabilities,\nwhich are all driven unstable by the effective temperature anisotropy induced\nby the shock-reflected ions within the transition layer of a strictly\nperpendicular shock. We conduct linear dispersion analysis with a homogeneous\nplasma model to mimic the shock transition layer by adopting a ring\ndistribution with finite thermal spread to represent the velocity distribution\nof the reflected ions. We find that, for wave propagation parallel to the\nambient magnetic field, the AIC instability at lower Alfv\\'en Mach numbers\ntends to transition to the Weibel instability at higher Alfv\\'en Mach numbers.\nThe instability property is, however, also strongly affected by the sound Mach\nnumber. We conclude that the instability at a strong shock with Alfv\\'en and\nsound Mach numbers both in excess of $\\sim 20{\\rm -}40$ may be considered as\nWeibel-like in the sense that the reflected ions behave essentially\nunmagnetized. Two-dimensional PIC simulations confirm the linear theory and\nfind that, with typical parameters of young supernova remnant shocks, the ring\ndistribution model produces magnetic fluctuations of the order of the\nbackground magnetic field, which is smaller than those observed in previous PIC\nsimulations for Weibel-dominated shocks. This indicates that the assumption of\nthe gyrotropic reflected ion distribution may not be adequate to quantitatively\npredict nonlinear behaviors of the dynamics in high Mach number shocks.\n"}
+{"abstract": "  The objective of this paper is to construct the accurate (say, to 11 decimal\nplaces) frequencies of the quasinormal modes of the 5-dimensional\nSchwarzschild-Tangherlini black hole using three major techniques: the Hill\ndeterminant method, the continued fractions method and the WKB-Pad\\'e method\nand to discuss the limitations of each. It is shown that for the massless\nscalar, gravitational tensor, gravitational vector and electromagnetic vector\nperturbations considered in this paper, the Hill determinant method and the\nmethod of continued fractions (both with the convergence acceleration) always\ngive identical results, whereas the WKB-Pad\\'e method gives the results that\nare amazingly accurate in most cases. Notable exception are the gravitational\nvector perturbations ($j =2$ and $\\ell = 2 $), for which the WKB-Pad\\'e\napproach apparently does not work. Here we have interesting situation in which\nthe WKB-based methods (WKB-Pad\\'e and WKB-Borel-Le Roy) give the complex\nfrequency that differs from the from the result obtained within the framework\nof the continued fraction method and the Hill determinant method. For the\nfundamental mode, deviation of the real part of frequency from the exact value\nis $0.5\\%$ whereas the deviation of the imaginary part is $2.7\\%.$ For $\\ell\n\\geq 3$ the accuracy of the WKB results is similar again to the accuracy\nobtained for other perturbations. The case of the gravitational scalar\nperturbations is briefly discussed.\n"}
+{"abstract": "  In order to plan a safe maneuver, self-driving vehicles need to understand\nthe intent of other traffic participants. We define intent as a combination of\ndiscrete high-level behaviors as well as continuous trajectories describing\nfuture motion. In this paper, we develop a one-stage detector and forecaster\nthat exploits both 3D point clouds produced by a LiDAR sensor as well as\ndynamic maps of the environment. Our multi-task model achieves better accuracy\nthan the respective separate modules while saving computation, which is\ncritical to reducing reaction time in self-driving applications.\n"}
+{"abstract": "  We study the the impact of Run2 LHC data on general Composite Higgs\nscenarios, where non-linear effects, mixing with additional scalars and new\nfermionic degrees of freedom could simultaneously contribute to the\nmodification of Higgs properties. We obtain new experimental limits on the\nscale of compositeness, the mixing with singlets and doublets with the Higgs,\nand the mass and mixing angle of top-partners. We also show that for scenarios\nwhere new fermionic degrees of freedom are involved in electroweak symmetry\nbreaking, there is an interesting interplay among Higgs coupling measurements,\nboosted Higgs properties, SMEFT global analyses, and direct searches for\nsingle- and double-production of vector-like quarks.\n"}
+{"abstract": "  Compressive sensing (CS) is a mathematically elegant tool for reducing the\nsampling rate, potentially bringing context-awareness to a wider range of\ndevices. Nevertheless, practical issues with the sampling and reconstruction\nalgorithms prevent further proliferation of CS in real world domains,\nespecially among heterogeneous ubiquitous devices. Deep learning (DL) naturally\ncomplements CS for adapting the sampling matrix, reconstructing the signal, and\nlearning form the compressed samples. While the CS-DL integration has received\nsubstantial research interest recently, it has not yet been thoroughly\nsurveyed, nor has the light been shed on practical issues towards bringing the\nCS-DL to real world implementations in the ubicomp domain. In this paper we\nidentify main possible ways in which CS and DL can interplay, extract key ideas\nfor making CS-DL efficient, identify major trends in CS-DL research space, and\nderive guidelines for future evolution of CS-DL within the ubicomp domain.\n"}
+{"abstract": "  To respond to the increasing need for bone repair strategies, various types\nof biomaterials have been developed. Among those, calcium phosphate ceramics\n(CPCs) are promising since they possess a chemical composition similar to that\nof bones. To be suitable for implants, CPCs need to fulfill a number of\nbiological and mechanical requirements. Fatigue resistance and toughness are\ntwo key mechanical properties that are still challenging to obtain in CPCs.\nThis paper thus reviews and discusses current progress in the processing of\nCPCs with bioinspired microstructures for load-bearing applications. First,\nmethods to obtain CPCs with bioinspired structure at individual lengthscales,\nnamely nano-, micro-, and macroscale are discussed. Then, approaches to attain\nsynergetic contribution of all lengthscales through a complex and biomimetic\nhierarchical structure are reviewed. The processing methods and their design\ncapabilities are presented and the mechanical properties of the materials they\ncan produce are analysed. Their limitations and challenges are finally\ndiscussed to suggest new directions for the fabrication of biomimetic bone\nimplants with satisfactory properties. The paper could help biomedical\nresearchers, materials scientists and engineers to join forces to create the\nnext generation of bone implants.\n"}
+{"abstract": "  The supernova neutrino flavor evolution in the presence of the non-trivial\nneutrino magnetic moment and strong magnetic field is numerically derived using\nthe two-flavor and single-angle approximation. The novel properties of\ncollective neutrino oscillations are studied and distinct patterns of flavor\nand spin-flavor spectral splits are presented. Finally we also discuss how the\nneutrino magnetic moment affects the observable supernova neutrino energy\nspectra.\n"}
+{"abstract": "  Under the federated learning paradigm, a set of nodes can cooperatively train\na machine learning model with the help of a centralized server. Such a server\nis also tasked with assigning a weight to the information received from each\nnode, and often also to drop too-slow nodes from the learning process. Both\ndecisions have major impact on the resulting learning performance, and can\ninterfere with each other in counterintuitive ways. In this paper, we focus on\nedge networking scenarios and investigate existing and novel approaches to such\nmodel-weighting and node-dropping decisions. Leveraging a set of real-world\nexperiments, we find that popular, straightforward decision-making approaches\nmay yield poor performance, and that considering the quality of data in\naddition to its quantity can substantially improve learning.\n"}
+{"abstract": "  We prove a myriad of results related to the stabilizer in an algebraic group\n$G$ of a generic vector in a representation $V$ of $G$ over an algebraically\nclosed field $k$. Our results are on the level of group schemes, which carries\nmore information than considering both the Lie algebra of $G$ and the group\n$G(k)$ of $k$-points. For $G$ simple and $V$ faithful and irreducible, we prove\nthe existence of a stabilizer in general position, sometimes called a principal\norbit type. We determine those $G$ and $V$ for which the stabilizer in general\nposition is smooth, or $\\dim V/G < \\dim G$, or there is a $v \\in V$ whose\nstabilizer in $G$ is trivial.\n"}
+{"abstract": "  Recent development of lensless imagers has enabled three-dimensional (3D)\nimaging through a thin piece of optics in close proximity to a camera sensor. A\ngeneral challenge of wide-field lensless imaging is the high computational\ncomplexity and slow speed to reconstruct 3D objects through iterative\noptimization process. Here, we demonstrated GEOMScope, a lensless 3D microscope\nthat forms image through a single layer of microlens array and reconstructs\nobjects through a geometrical-optics-based pixel back projection algorithm and\nbackground suppressions. Compared to others, our method allows local\nreconstruction, which significantly reduces the required computation resource\nand increases the reconstruction speed by orders of magnitude. This enables\nnear real-time object reconstructions across a large volume of 23x23x5 mm^3,\nwith a lateral resolution of 40 um and axial resolution of 300 um. Our system\nopens new avenues for broad biomedical applications such as endoscopy, which\nrequires both miniaturized device footprint and real-time high resolution\nvisualization.\n"}
+{"abstract": "  We study the additive functional $X_n(\\alpha)$ on conditioned Galton-Watson\ntrees given, for arbitrary complex $\\alpha$, by summing the $\\alpha$th power of\nall subtree sizes. Allowing complex $\\alpha$ is advantageous, even for the\nstudy of real $\\alpha$, since it allows us to use powerful results from the\ntheory of analytic functions in the proofs.\n  For $\\Re\\alpha < 0$, we prove that $X_n(\\alpha)$, suitably normalized, has a\ncomplex normal limiting distribution; moreover, as processes in $\\alpha$, the\nweak convergence holds in the space of analytic functions in the left\nhalf-plane. We establish, and prove similar process-convergence extensions of,\nlimiting distribution results for $\\alpha$ in various regions of the complex\nplane. We focus mainly on the case where $\\Re\\alpha > 0$, for which\n$X_n(\\alpha)$, suitably normalized, has a limiting distribution that is not\nnormal but does not depend on the offspring distribution $\\xi$ of the\nconditioned Galton-Watson tree, assuming only that $E[\\xi] = 1$ and $0 <\n\\mathrm{Var} [\\xi] < \\infty$. Under a weak extra moment assumption on $\\xi$, we\nprove that the convergence extends to moments, ordinary and absolute and mixed,\nof all orders.\n  At least when $\\Re\\alpha > \\frac12$, the limit random variable $Y(\\alpha)$\ncan be expressed as a function of a normalized Brownian excursion.\n"}
+{"abstract": "  MINERvA presents a new analysis of inclusive charged-current neutrino\ninteractions on a hydrocarbon target. We report single and double-differential\ncross sections in muon transverse and longitudinal momentum. These measurements\nare compared to neutrino interaction generator predictions from GENIE, NuWro,\nGiBUU, and NEUT. In addition, comparisons against models with different\ntreatments of multi-nucleon correlations, nuclear effects, resonant pion\nproduction, and deep inelastic scattering are presented. The data recorded\ncorresponds to $10.61\\times10^{20}$ protons on target with a peak neutrino\nenergy of approximately 6 GeV. The higher energy and larger statistics of these\ndata extend the kinematic range for model testing beyond previous MINERvA\ninclusive charged-current measurements. The results are not well modeled by\nseveral generator predictions using a variety of input models.\n"}
+{"abstract": "  Square-root topology is a recently emerged subfield describing a class of\ninsulators and superconductors whose topological nature is only revealed upon\nsquaring their Hamiltonians, i.e., the finite energy edge states of the\nstarting square-root model inherit their topological features from the\nzero-energy edge states of a known topological insulator/superconductor present\nin the squared model. Focusing on one-dimensional models, we show how this\nconcept can be generalized to $2^n$-root topological insulators and\nsuperconductors, with $n$ any positive integer, whose rules of construction are\nsystematized here. Borrowing from graph theory, we introduce the concept of\narborescence of $2^n$-root topological insulators/superconductors which\nconnects the Hamiltonian of the starting model for any $n$, through a series of\nsquaring operations followed by constant energy shifts, to the Hamiltonian of\nthe known topological insulator/superconductor, identified as the source of its\ntopological features. Our work paves the way for an extension of $2^n$-root\ntopology to higher-dimensional systems.\n"}
+{"abstract": "  A subset of QuantISED Sensor PIs met virtually on May 26, 2020 to discuss a\nresponse to a charge by the DOE Office of High Energy Physics. In this\ndocument, we summarize the QuantISED sensor community discussion, including a\nconsideration of HEP science enabled by quantum sensors, describing the\ndistinction between Quantum 1.0 and Quantum 2.0, and discussing\nsynergies/complementarity with the new DOE NQI centers and with research\nsupported by other SC offices.\n  Quantum 2.0 advances in sensor technology offer many opportunities and new\napproaches for HEP experiments. The DOE HEP QuantISED program could support a\nportfolio of small experiments based on these advances. QuantISED experiments\ncould use sensor technologies that exemplify Quantum 2.0 breakthroughs. They\nwould strive to achieve new HEP science results, while possibly spinning off\nother domain science applications or serving as pathfinders for future HEP\nscience targets. QuantISED experiments should be led by a DOE laboratory, to\ntake advantage of laboratory technical resources, infrastructure, and expertise\nin the safe and efficient construction, operation, and review of experiments.\n  The QuantISED PIs emphasized that the quest for HEP science results under the\nQuantISED program is distinct from the ongoing DOE HEP programs on the energy,\nintensity, and cosmic frontiers. There is robust evidence for the existence of\nparticles and phenomena beyond the Standard Model, including dark matter, dark\nenergy, quantum gravity, and new physics responsible for neutrino masses,\ncosmic inflation, and the cosmic preference for matter over antimatter. Where\nis this physics and how do we find it? The QuantISED program can exploit new\ncapabilities provided by quantum technology to probe these kinds of science\nquestions in new ways and over a broader range of science parameters than can\nbe achieved with conventional techniques.\n"}
+{"abstract": "  This article concerns with the global H\\\"older regularity of weak solutions\nto a class of problems involving the fractional $(p,q)$-Laplacian, denoted by\n$(-\\Delta)^{s_1}_{p}+(-\\Delta)^{s_2}_{q}$, for $1<p,q<\\infty$ and $s_1,s_2\\in\n(0,1)$. We use a suitable Caccioppoli inequality and local boundedness result\nin order to prove the weak Harnack type inequality. Consequently, by employing\na suitable iteration process, we establish the interior H\\\"older regularity for\nlocal weak solutions, which need not be assumed bounded. The global H\\\"older\nregularity result we prove expands and improves the regularity results of\nGiacomoni, Kumar and Sreenadh (arXiv: 2102.06080) to the subquadratic case\n(that is, $q<2$) and more general right hand side, which requires a different\nand new approach. Moreover, we establish a nonlocal Harnack type inequality for\nweak solutions, which is of independent interest.\n"}
+{"abstract": "  This work investigates the use of interactively updated label suggestions to\nimprove upon the efficiency of gathering annotations on the task of opinion\nmining in German Covid-19 social media data. We develop guidelines to conduct a\ncontrolled annotation study with social science students and find that\nsuggestions from a model trained on a small, expert-annotated dataset already\nlead to a substantial improvement - in terms of inter-annotator agreement(+.14\nFleiss' $\\kappa$) and annotation quality - compared to students that do not\nreceive any label suggestions. We further find that label suggestions from\ninteractively trained models do not lead to an improvement over suggestions\nfrom a static model. Nonetheless, our analysis of suggestion bias shows that\nannotators remain capable of reflecting upon the suggested label in general.\nFinally, we confirm the quality of the annotated data in transfer learning\nexperiments between different annotator groups. To facilitate further research\nin opinion mining on social media data, we release our collected data\nconsisting of 200 expert and 2,785 student annotations.\n"}
+{"abstract": "  It is well-known that the training of Deep Neural Networks (DNN) can be\nformalized in the language of optimal control. In this context, this paper\nleverages classical turnpike properties of optimal control problems to attempt\na quantifiable answer to the question of how many layers should be considered\nin a DNN. The underlying assumption is that the number of neurons per layer --\ni.e., the width of the DNN -- is kept constant. Pursuing a different route than\nthe classical analysis of approximation properties of sigmoidal functions, we\nprove explicit bounds on the required depths of DNNs based on asymptotic\nreachability assumptions and a dissipativity-inducing choice of the\nregularization terms in the training problem. Numerical results obtained for\nthe two spiral task data set for classification indicate that the proposed\nestimates can provide non-conservative depth bounds.\n"}
+{"abstract": "  The bulk properties of nodal line materials have been an important research\ntopic in recent years. In this paper, we study the orbital magnetic\nsusceptibility and the Hall conductivity of nodal line materials using the\nformalism with thermal Green's functions and find characteristic singular\nbehaviors of them. It is shown that, in the vicinity of the gapless nodal line,\nthe orbital magnetic susceptibility shows a $\\delta$-function singularity and\nthe Hall conductivity shows a step function behavior in their chemical\npotential dependences. Furthermore, these singular behaviors are found to show\nstrong field angle dependences corresponding to the orientation of the nodal\nline in the momentum space. These singular behaviors and strong angle\ndependences will give clear evidence for the presence of the nodal line and its\norientation and can be used to experimentally detect nodal line materials.\n"}
+{"abstract": "  The past 20 years have witnessed a renewal of interest in the subject of\ndouble-diffusive processes in astrophysics, and their impact on stellar\nevolution. This lecture aims to summarize the state of the field as of early\n2019, although the reader should bear in mind that it is rapidly evolving. An\nAnnual Review of Fluid Mechanics article entitled \"Double-diffusive convection\nat low Prandtl number\" (Garaud, 2018) contains a reasonably comprehensive\nreview of the topic, up to the summer of 2017. I focus here on presenting what\nI hope are clear derivations of some of the most important results with an\nastrophysical audience in mind, and discuss their implications for stellar\nevolution, both in an observational context, and in relation to previous work\non the subject.\n"}
+{"abstract": "  Variation of positional information, measured by the two-body excess entropy\n$\\mathsf{S}_\\mathrm{2}$, is studied across the liquid-solid equilibrium\ntransition in a simple two-dimensional system. Analysis reveals a master\nrelation between $\\mathsf{S}_\\mathrm{2}$ and the freezing temperature\n$T_{\\mathrm{f}}$, from which a scaling law is extracted:\n$-\\mathsf{S}_\\mathrm{2}\\sim |T_{\\mathrm{f}} - T| ^{-1/3}$. Theoretical and\npractical implications of the observed universality are discussed.\n"}
+{"abstract": "  Large pre-trained multilingual models like mBERT, XLM-R achieve state of the\nart results on language understanding tasks. However, they are not well suited\nfor latency critical applications on both servers and edge devices. It's\nimportant to reduce the memory and compute resources required by these models.\nTo this end, we propose pQRNN, a projection-based embedding-free neural encoder\nthat is tiny and effective for natural language processing tasks. Without\npre-training, pQRNNs significantly outperform LSTM models with pre-trained\nembeddings despite being 140x smaller. With the same number of parameters, they\noutperform transformer baselines thereby showcasing their parameter efficiency.\nAdditionally, we show that pQRNNs are effective student architectures for\ndistilling large pre-trained language models. We perform careful ablations\nwhich study the effect of pQRNN parameters, data augmentation, and distillation\nsettings. On MTOP, a challenging multilingual semantic parsing dataset, pQRNN\nstudents achieve 95.9\\% of the performance of an mBERT teacher while being 350x\nsmaller. On mATIS, a popular parsing task, pQRNN students on average are able\nto get to 97.1\\% of the teacher while again being 350x smaller. Our strong\nresults suggest that our approach is great for latency-sensitive applications\nwhile being able to leverage large mBERT-like models.\n"}
+{"abstract": "  In this paper, we present general one-loop form factors for $H\\rightarrow\n\\gamma^* \\gamma^*$ in $R_{\\xi}$ gauge, considering all cases of two on-shell,\none on-shell and two off-shell for final photons. The calculations are\nperformed in standard model and in arbitrary beyond the standard models which\ncharged scalar particles may be exchanged in one-loop diagrams. Analytic\nresults for the form factors are shown in general forms which are expressed in\nterms of the Passarino-Veltman functions. We also confirm the results in\nprevious computations which are available for the case of two on-shell photons.\nThe $\\xi$-independent of the result is also discussed. We find that numerical\nresults are good stability with varying $\\xi=0,1$ and $\\xi\\rightarrow \\infty$.\n"}
+{"abstract": "  We present a gauge theory of the conformal group in four spacetime dimensions\nwith a non-vanishing torsion. In particular, we allow for a completely\nantisymmetric torsion, equivalent by Hodge duality to an axial vector whose\npresence does not spoil the conformal invariance of the theory, in contrast\nwith claims of antecedent literature. The requirement of conformal invariance\nimplies a differential condition (in particular, a Killing equation) on the\naforementioned axial vector which leads to a Maxwell-like equation in a\nfour-dimensional curved background. We also give some preliminary results in\nthe context of $\\mathcal{N}=1$ four-dimensional conformal supergravity in the\ngeometric approach, showing that if we only allow for the constraint of\nvanishing supertorsion all the other constraints imposed in the spacetime\napproach are a consequence of the closure of the Bianchi identities in\nsuperspace. This paves the way towards a future complete investigation of the\nconformal supergravity using the Bianchi identities in the presence a\nnon-vanishing (super)torsion.\n"}
+{"abstract": "  The Transformer architecture has been successful across many domains,\nincluding natural language processing, computer vision and speech recognition.\nIn keyword spotting, self-attention has primarily been used on top of\nconvolutional or recurrent encoders. We investigate a range of ways to adapt\nthe Transformer architecture to keyword spotting and introduce the Keyword\nTransformer (KWT), a fully self-attentional architecture that exceeds\nstate-of-the-art performance across multiple tasks without any pre-training or\nadditional data. Surprisingly, this simple architecture outperforms more\ncomplex models that mix convolutional, recurrent and attentive layers. KWT can\nbe used as a drop-in replacement for these models, setting two new benchmark\nrecords on the Google Speech Commands dataset with 98.6% and 97.7% accuracy on\nthe 12 and 35-command tasks respectively.\n"}
+{"abstract": "  Controlled breakdown has recently emerged as a highly appealing technique to\nfabricate solid-state nanopores for a wide range of biosensing applications.\nThis technique relies on applying an electric field of approximately 0.6-1 V/nm\nacross the membrane to induce a current, and eventually, breakdown of the\ndielectric. However, a detailed description of how electrical conduction\nthrough the dielectric occurs during controlled breakdown has not yet been\nreported. Here, we study electrical conduction and nanopore formation in\nSiN$_x$ membranes during controlled breakdown. We show that depending on the\nmembrane stoichiometry, electrical conduction is limited by either oxidation\nreactions that must occur at the membrane-electrolyte interface (Si-rich\nSiN$_x$), or electron transport across the dielectric (stoichiometric\nSi$_3$N$_4$). We provide several important implications resulting from\nunderstanding this process which will aid in further developing controlled\nbreakdown in the coming years, particularly for extending this technique to\nintegrate nanopores with on-chip nanostructures.\n"}
+{"abstract": "  Let A be an idempotent algebra on a finite domain. By mediating between\nresults of Chen and Zhuk, we argue that if A satisfies the polynomially\ngenerated powers property (PGP) and B is a constraint language invariant under\nA (that is, in Inv(A)), then QCSP(B) is in NP. In doing this we study the\nspecial forms of PGP, switchability and collapsibility, in detail, both\nalgebraically and logically, addressing various questions such as decidability\non the way.\n  We then prove a complexity-theoretic converse in the case of infinite\nconstraint languages encoded in propositional logic, that if Inv(A) satisfies\nthe exponentially generated powers property (EGP), then QCSP(Inv(A)) is\nco-NP-hard. Since Zhuk proved that only PGP and EGP are possible, we derive a\nfull dichotomy for the QCSP, justifying what we term the Revised Chen\nConjecture. This result becomes more significant now the original Chen\nConjecture is known to be false.\n  Switchability was introduced by Chen as a generalisation of the already-known\ncollapsibility. For three-element domain algebras A that are switchable and\nomit a G-set, we prove that, for every finite subset D of Inv(A), Pol(D) is\ncollapsible. The significance of this is that, for QCSP on finite structures\n(over a three-element domain), all QCSP tractability (in P) explained by\nswitchability is already explained by collapsibility.\n"}
+{"abstract": "  We find a novel one-parameter family of integrable quadratic Cremona maps of\nthe plane preserving a pencil of curves of degree 6 and of genus 1. They turn\nout to serve as Kahan-type discretizations of a novel family of quadratic\nvector fields possessing a polynomial integral of degree 6 whose level curves\nare of genus 1, as well. These vector fields are non-homogeneous\ngeneralizations of reduced Nahm systems for magnetic monopoles with icosahedral\nsymmetry, introduced by Hitchin, Manton and Murray. The straightforward Kahan\ndiscretization of these novel non-homogeneous systems is non-integrable.\nHowever, this drawback is repaired by introducing adjustments of order\n$O(\\epsilon^2)$ in the coefficients of the discretization, where $\\epsilon$ is\nthe stepsize.\n"}
+{"abstract": "  We investigate here the final state of gravitational collapse of a\nnon-spherical and non-marginally bound dust cloud as modeled by the Szekeres\nspacetime. We show that a directionally globally naked singularity can be\nformed in this case near the collapsing cloud boundary, and not at its\ngeometric center as is typically the case for a spherical gravitational\ncollapse. This is a strong curvature naked singularity in the sense of Tipler\ncriterion on gravitational strength. The null geodesics escaping from the\nsingularity would be less scattered in this case in certain directions since\nthe singularity is close to the boundary of the cloud as is the case in the\ncurrent scenario. The physical implications are pointed out.\n"}
+{"abstract": "  In this paper we demonstrate the capability of the method of Lagrangian\ndescriptors to unveil the phase space structures that characterize transport in\nhigh-dimensional symplectic maps. In order to illustrate its use, we apply it\nto a four-dimensional symplectic map model that is used in chemistry to explore\nthe nonlinear dynamics of van der Waals complexes. The advantage of this\ntechnique is that it allows us to easily and effectively extract the invariant\nmanifolds that determine the dynamics of the system under study by means of\nexamining the intersections of the underlying phase space structures with\nlow-dimensional slices. With this approach, one can perform a full\ncomputational phase space tomography from which three-dimensional\nrepresentations of the higher-dimensional phase space can be systematically\nreconstructed. This analysis may be of much help for the visualization and\nunderstanding of the nonlinear dynamical mechanisms that take place in\nhigh-dimensional systems. In this context, we demonstrate how this tool can be\nused to detect whether the stable and unstable manifolds of the system\nintersect forming turnstile lobes that enclose a certain phase space volume,\nand the nature of their intersection.\n"}
+{"abstract": "  Dialog systems enriched with external knowledge can handle user queries that\nare outside the scope of the supporting databases/APIs. In this paper, we\nfollow the baseline provided in DSTC9 Track 1 and propose three subsystems,\nKDEAK, KnowleDgEFactor, and Ens-GPT, which form the pipeline for a\ntask-oriented dialog system capable of accessing unstructured knowledge.\nSpecifically, KDEAK performs knowledge-seeking turn detection by formulating\nthe problem as natural language inference using knowledge from dialogs,\ndatabases and FAQs. KnowleDgEFactor accomplishes the knowledge selection task\nby formulating a factorized knowledge/document retrieval problem with three\nmodules performing domain, entity and knowledge level analyses. Ens-GPT\ngenerates a response by first processing multiple knowledge snippets, followed\nby an ensemble algorithm that decides if the response should be solely derived\nfrom a GPT2-XL model, or regenerated in combination with the top-ranking\nknowledge snippet. Experimental results demonstrate that the proposed pipeline\nsystem outperforms the baseline and generates high-quality responses, achieving\nat least 58.77% improvement on BLEU-4 score.\n"}
+{"abstract": "  We develop efficient randomized algorithms to solve the black-box\nreconstruction problem for polynomials over finite fields, computable by depth\nthree arithmetic circuits with alternating addition/multiplication gates, such\nthat output gate is an addition gate with in-degree two. These circuits compute\npolynomials of form $G\\times(T_1 + T_2)$, where $G,T_1,T_2$ are product of\naffine forms, and polynomials $T_1,T_2$ have no common factors. Rank of such a\ncircuit is defined as dimension of vector space spanned by all affine factors\nof $T_1$ and $T_2$. For any polynomial $f$ computable by such a circuit,\n$rank(f)$ is defined to be the minimum rank of any such circuit computing it.\nOur work develops randomized reconstruction algorithms which take as input\nblack-box access to a polynomial $f$ (over finite field $\\mathbb{F}$),\ncomputable by such a circuit. Here are the results.\n  1 [Low rank]: When $5\\leq rank(f) = O(\\log^3 d)$, it runs in time\n$(nd^{\\log^3d}\\log |\\mathbb{F}|)^{O(1)}$, and, with high probability, outputs a\ndepth three circuit computing $f$, with top addition gate having in-degree\n$\\leq d^{rank(f)}$.\n  2 [High rank]: When $rank(f) = \\Omega(\\log^3 d)$, it runs in time $(nd\\log\n|\\mathbb{F}|)^{O(1)}$, and, with high probability, outputs a depth three\ncircuit computing $f$, with top addition gate having in-degree two.\n  Ours is the first blackbox reconstruction algorithm for this circuit class,\nthat runs in time polynomial in $\\log |\\mathbb{F}|$. This problem has been\nmentioned as an open problem in [GKL12] (STOC 2012)\n"}
+{"abstract": "  We exploit a two-dimensional model [7], [6] and [1] describing the elastic\nbehavior of the wall of a flexible blood vessel which takes interaction with\nsurrounding muscle tissue and the 3D fluid flow into account. We study time\nperiodic flows in a cylinder with such compound boundary conditions. The main\nresult is that solutions of this problem do not depend on the period and they\nare nothing else but the time independent Poiseuille flow. Similar solutions of\nthe Stokes equations for the rigid wall (the no-slip boundary condition) depend\non the period and their profile depends on time.\n"}
+{"abstract": "  Diffractive zone plate optics uses a thin micro-structure pattern to alter\nthe propagation direction of the incoming light wave. It has found important\napplications in extreme-wavelength imaging where conventional refractive lenses\ndo not exist. The resolution limit of zone plate optics is determined by the\nsmallest width of the outermost zone. In order to improve the achievable\nresolution, significant efforts have been devoted to the fabrication of very\nsmall zone width with ultrahigh placement accuracy. Here, we report the use of\na diffractometer setup for bypassing the resolution limit of zone plate optics.\nIn our prototype, we mounted the sample on two rotation stages and used a\nlow-resolution binary zone plate to relay the sample plane to the detector. We\nthen performed both in-plane and out-of-plane sample rotations and captured the\ncorresponding raw images. The captured images were processed using a Fourier\nptychographic procedure for resolution improvement. The final achievable\nresolution of the reported setup is not determined by the smallest width\nstructures of the employed binary zone plate; instead, it is determined by the\nmaximum angle of the out-of-plane rotation. In our experiment, we demonstrated\n8-fold resolution improvement using both a resolution target and a titanium\ndioxide sample. The reported approach may be able to bypass the fabrication\nchallenge of diffractive elements and open up new avenues for microscopy with\nextreme wavelengths.\n"}
+{"abstract": "  Grasping unseen objects in unconstrained, cluttered environments is an\nessential skill for autonomous robotic manipulation. Despite recent progress in\nfull 6-DoF grasp learning, existing approaches often consist of complex\nsequential pipelines that possess several potential failure points and\nrun-times unsuitable for closed-loop grasping. Therefore, we propose an\nend-to-end network that efficiently generates a distribution of 6-DoF\nparallel-jaw grasps directly from a depth recording of a scene. Our novel grasp\nrepresentation treats 3D points of the recorded point cloud as potential grasp\ncontacts. By rooting the full 6-DoF grasp pose and width in the observed point\ncloud, we can reduce the dimensionality of our grasp representation to 4-DoF\nwhich greatly facilitates the learning process. Our class-agnostic approach is\ntrained on 17 million simulated grasps and generalizes well to real world\nsensor data. In a robotic grasping study of unseen objects in structured\nclutter we achieve over 90% success rate, cutting the failure rate in half\ncompared to a recent state-of-the-art method.\n"}
+{"abstract": "  Chimeric Antigen Receptor (CAR) T-cell therapy is an immunotherapy that has\nrecently become highly instrumental in the fight against life-threatening\ndiseases. A variety of modeling and computational simulation efforts have\naddressed different aspects of CAR T therapy, including T-cell activation, T-\nand malignant cell population dynamics, therapeutic cost-effectiveness\nstrategies, and patient survival analyses. In this article, we present a\nsystematic review of those efforts, including mathematical, statistical, and\nstochastic models employing a wide range of algorithms, from differential\nequations to machine learning. To the best of our knowledge, this is the first\nreview of all such models studying CAR T therapy. In this review, we provide a\ndetailed summary of the strengths, limitations, methodology, data used, and\ndata lacking in current published models. This information may help in\ndesigning and building better models for enhanced prediction and assessment of\nthe benefit-risk balance associated with novel CAR T therapies, as well as with\nthe data collection essential for building such models.\n"}
+{"abstract": "  The nature of dark matter (DM) is one of the most fascinating unresolved\nchallenges of modern physics. One of the perspective hypotheses suggests that\nDM consists of ultralight bosonic particles in the state of Bose-Einstein\ncondensate (BEC). The superfluid nature of BEC must dramatically affect the\nproperties of DM matter including quantization of the angular momentum. Angular\nmomentum quantum in the form of a vortex line is expected to produce a\nconsiderable impact on the luminous matter in galaxies including density\ndistribution and rotation curves. We investigate the evolution of spinning DM\ncloud with typical galactic halo mass and radius. Analytically and numerically\nstationary vortex soliton states with different topological charges have been\nanalyzed. It has been shown that while all multi-charged vortex states are\nunstable, a single-charged vortex soliton is extremely robust and survives\nduring the lifetime of the Universe.\n"}
+{"abstract": "  Real-world time series data often present recurrent or repetitive patterns\nand it is often generated in real time, such as transportation passenger\nvolume, network traffic, system resource consumption, energy usage, and human\ngait. Detecting anomalous events based on machine learning approaches in such\ntime series data has been an active research topic in many different areas.\nHowever, most machine learning approaches require labeled datasets, offline\ntraining, and may suffer from high computation complexity, consequently\nhindering their applicability. Providing a lightweight self-adaptive approach\nthat does not need offline training in advance and meanwhile is able to detect\nanomalies in real time could be highly beneficial. Such an approach could be\nimmediately applied and deployed on any commodity machine to provide timely\nanomaly alerts. To facilitate such an approach, this paper introduces SALAD,\nwhich is a Self-Adaptive Lightweight Anomaly Detection approach based on a\nspecial type of recurrent neural networks called Long Short-Term Memory (LSTM).\nInstead of using offline training, SALAD converts a target time series into a\nseries of average absolute relative error (AARE) values on the fly and predicts\nan AARE value for every upcoming data point based on short-term historical AARE\nvalues. If the difference between a calculated AARE value and its corresponding\nforecast AARE value is higher than a self-adaptive detection threshold, the\ncorresponding data point is considered anomalous. Otherwise, the data point is\nconsidered normal. Experiments based on two real-world open-source time series\ndatasets demonstrate that SALAD outperforms five other state-of-the-art anomaly\ndetection approaches in terms of detection accuracy. In addition, the results\nalso show that SALAD is lightweight and can be deployed on a commodity machine.\n"}
+{"abstract": "  Uncertainty is the only certainty there is. Modeling data uncertainty is\nessential for regression, especially in unconstrained settings. Traditionally\nthe direct regression formulation is considered and the uncertainty is modeled\nby modifying the output space to a certain family of probabilistic\ndistributions. On the other hand, classification based regression and ranking\nbased solutions are more popular in practice while the direct regression\nmethods suffer from the limited performance. How to model the uncertainty\nwithin the present-day technologies for regression remains an open issue. In\nthis paper, we propose to learn probabilistic ordinal embeddings which\nrepresent each data as a multivariate Gaussian distribution rather than a\ndeterministic point in the latent space. An ordinal distribution constraint is\nproposed to exploit the ordinal nature of regression. Our probabilistic ordinal\nembeddings can be integrated into popular regression approaches and empower\nthem with the ability of uncertainty estimation. Experimental results show that\nour approach achieves competitive performance. Code is available at\nhttps://github.com/Li-Wanhua/POEs.\n"}
+{"abstract": "  BiVO4, a visible-light response photocatalyst, has shown tremendous potential\nbecause of abundant raw material sources, good stability and low cost. There\nexist some limitations for further applicaitions due to poor capability to\nseparate electron-hole pairs. In fact, a single-component modification strategy\nis barely adequate to obtain highy efficient photocatalytic performance. In\nthis work, P substituted some of the V atoms from VO4 oxoanions, namely P was\ndoped into the V sites in the host lattice of BiVO4 by a hydrothermal route.\nMeanwhile, Ag as an attractive and efficient electron-cocatalyst was\nselectively modified on the (010) facet of BiVO4 nanosheets via facile\nphoto-deposition. As a result, the obtained dually modified BiVO4 sheets\nexhibited enhanced photocatalytic degradation property of methylene blue (MB).\nIn detail, photocatalytic rate constant (k) was 2.285 min-1g-1, which was 2.78\ntimes higher than pristine BiVO4 nanosheets. Actually, P-doping favored the\nformation of O vacancies, led to more charge carriers, and facilitated\nphotocatalytic reaction. On the other hand, metallic Ag loaded on (010) facet\neffectively transferred photogenerated electrons, which consequently helped\nelectron-hole pairs separation. The present work may enlighten new thoughts for\nsmart design and controllable synthesis of highly efficient photocatalytic\nmaterials.\n"}
+{"abstract": "  The initial value problem for Hookean incompressible viscoelastictic motion\nin three space dimensions has global strong solutions with small displacements.\n"}
+{"abstract": "  Medical imaging datasets usually exhibit domain shift due to the variations\nof scanner vendors, imaging protocols, etc. This raises the concern about the\ngeneralization capacity of machine learning models. Domain generalization (DG),\nwhich aims to learn a model from multiple source domains such that it can be\ndirectly generalized to unseen test domains, seems particularly promising to\nmedical imaging community. To address DG, recent model-agnostic meta-learning\n(MAML) has been introduced, which transfers the knowledge from previous\ntraining tasks to facilitate the learning of novel testing tasks. However, in\nclinical practice, there are usually only a few annotated source domains\navailable, which decreases the capacity of training task generation and thus\nincreases the risk of overfitting to training tasks in the paradigm. In this\npaper, we propose a novel DG scheme of episodic training with task augmentation\non medical imaging classification. Based on meta-learning, we develop the\nparadigm of episodic training to construct the knowledge transfer from episodic\ntraining-task simulation to the real testing task of DG. Motivated by the\nlimited number of source domains in real-world medical deployment, we consider\nthe unique task-level overfitting and we propose task augmentation to enhance\nthe variety during training task generation to alleviate it. With the\nestablished learning framework, we further exploit a novel meta-objective to\nregularize the deep embedding of training domains. To validate the\neffectiveness of the proposed method, we perform experiments on\nhistopathological images and abdominal CT images.\n"}
+{"abstract": "  In this paper we consider the inhomogeneous nonlinear Schr\\\"odinger (INLS)\nequation \\begin{align}\\label{inls} i \\partial_t u +\\Delta u +|x|^{-b}\n|u|^{2\\sigma}u = 0, \\,\\,\\, x \\in \\mathbb{R}^N \\end{align} with $N\\geq 3$. We\nfocus on the intercritical case, where the scaling invariant Sobolev index\n$s_c=\\frac{N}{2}-\\frac{2-b}{2\\sigma}$ satisfies $0<s_c<1$. In a previous work,\nfor radial initial data in $\\dot H^{s_c}\\cap \\dot H^1$, we prove the existence\nof blow-up solutions and also a lower bound for the blow-up rate. Here we\nextend these results to the non-radial case. We also prove an upper bound for\nthe blow-up rate and a concentration result for general finite time blow-up\nsolutions in $H^1$.\n"}
+{"abstract": "  The COVID-19 pandemic has forced changes in production and especially in\nhuman interaction, with \"social distancing\" a standard prescription for slowing\ntransmission of the disease. This paper examines the economic effects of social\ndistancing at the aggregate level, weighing both the benefits and costs to\nprolonged distancing. Specifically we fashion a model of economic recovery when\nthe productive capacity of factors of production is restricted by social\ndistancing, building a system of equations where output growth and social\ndistance changes are interdependent. The model attempts to show the complex\ninteractions between output levels and social distancing, developing cycle\npaths for both variables. Ultimately, however, defying gravity via prolonged\nsocial distancing shows that a lower growth path is inevitable as a result.\n"}
+{"abstract": "  Variations in the solar wind (SW) parameters with scales of several years are\nan important characteristic of solar activity and the basis for a long-term\nspace weather forecast. We examine the behavior of interplanetary parameters\nover 21-24 solar cycles (SCs) on the basis of OMNI database\n(https://spdf.gsfc.nasa.gov/pub/data/omni). Since changes in parameters can be\nassociated both with changes in the number of different large-scale types of\nSW, and with variations in the values of these parameters at different phases\nof the solar cycle and during the transition from one cycle to another, we\nselect the entire study period in accordance with the Catalog of large-scale SW\ntypes for 1976-2019 (See the site http://www.iki.rssi.ru/pub/omni, [Yermolaev\net al., 2009]), which covers the period from 21 to 24 SCs, and in accordance\nwith the phases of the cycles, and averaging the parameters at selected\nintervals. In addition to a sharp drop in the number of ICMEs (and associated\nSheath types), there is a noticeable drop in the value (by 20-40%) of plasma\nparameters and magnetic field in different types of solar wind at the end of\nthe 20th century and a continuation of the fall or persistence at a low level\nin the 23-24 cycles. Such a drop in the solar wind is apparently associated\nwith a decrease in solar activity and manifests itself in a noticeable decrease\nin space weather factors.\n"}
+{"abstract": "  A new control approach is proposed for the grid insertion of Power Park\nModules (PPMs). It allows full participation of these modules to ancillary\nservices. This means that, not only their control have some positive impact on\nthe grid frequency and voltage dynamics, but they can effectively participate\nto existing primary and secondary control loops together with the classic\nthermal/inertia synchronous generators and fulfill the same specifications both\nfrom the control and contractual points of view. To achieve such level of\nperformances, a system approach based on an innovatory control model is\nproposed. The latter control model drops classic hypothesis for separation of\nvoltage and frequency dynamics used till now in order to gather these dynamics\ninto a small size model. From the system point of view, dynamics are grouped by\ntime-scales of phenomena in the proposed control model. This results in more\nperformant controls in comparison to classic approaches which orient controls\nto physical actuators (control of grid side converter and of generator side\nconverter). Also, this allows coordination between control of converters and\ngenerator or, in case of multimachines specifications, among several PPMs. From\nthe control synthesis point of view, classic robust approaches are used (like,\ne.g., H-infinity synthesis). Implementation and validation tests are presented\nfor wind PPMs but the approach holds for any other type of PPM. These results\nwill be further used to control the units of the new concept of Dynamic Virtual\nPower Plant introduced in the H2020 POSYTYF project.\n"}
+{"abstract": "  We introduce circular evolutes and involutes of framed curves in the\nEuclidean space. Circular evolutes of framed curves stem from the curvature\ncircles of Bishop directions and singular value sets of normal surfaces of\nBishop directions. On the other hand, involutes of framed curves are direct\ngeneralizations of involutes of regular space curves and frontals in the\nEuclidean plane. We investigate properties of normal surfaces, circular\nevolutes, and involutes of framed curves. We can observe that taking circular\nevolutes and involutes of framed curves are opposite operations under suitable\nassumptions, similarly to evolutes and involutes of fronts in the Euclidean\nplane. Furthermore, we investigate the relations among singularities of normal\nsurfaces, circular evolutes, and involutes of framed curves.\n"}
+{"abstract": "  In this paper, a real-world transportation problem is addressed, concerning\nthe collection and the transportation of biological sample tubes from sampling\npoints to a main hospital. Blood and other biological samples are collected in\ndifferent centers during morning hours. Then, the samples are transported to\nthe main hospital, for their analysis, by a fleet of vehicles located in\ngeographically distributed depots. Each sample has a limited lifetime and must\narrive to the main hospital within that time. If a sample cannot arrive to the\nhospital within the lifetime, either is discarded or must be processed in\ndedicated facilities called Spoke Centers.Two Mixed Integer Linear Programming\nformulations and an Adaptive Large Neighborhood Search (ALNS) metaheuristic\nalgorithm have been developed for the problem. Computational experiments on\ndifferent sets of instances based on real-life data provided by the Local\nHealthcare Authority of Bologna, Italy, are presented. A comparison on small\ninstances with the optimal solutions obtained by the formulations shows the\neffectiveness of the proposed ALNS algorithm. On real-life instances, different\nbatching policies of the samples are evaluated. The results show that the ALNS\nalgorithm is able to find solutions in which all the samples are delivered on\ntime, while in the real case about the 40% [5] of the samples is delivered\nlate.\n"}
+{"abstract": "  In this work, we study the transfer learning problem under high-dimensional\ngeneralized linear models (GLMs), which aim to improve the fit on target data\nby borrowing information from useful source data. Given which sources to\ntransfer, we propose an oracle algorithm and derive its $\\ell_2$-estimation\nerror bounds. The theoretical analysis shows that under certain conditions,\nwhen the target and source are sufficiently close to each other, the estimation\nerror bound could be improved over that of the classical penalized estimator\nusing only target data. When we don't know which sources to transfer, an\nalgorithm-free transferable source detection approach is introduced to detect\ninformative sources. The detection consistency is proved under the\nhigh-dimensional GLM transfer learning setting. Extensive simulations and a\nreal-data experiment verify the effectiveness of our algorithms.\n"}
+{"abstract": "  We extend results of parametric geometry of numbers to a general diagonal\nflow on the space of lattices. Moreover, we compute the Hausdorff dimension of\nthe set of trajectories with every given behavior, with respect to a\nnonstandard metric and thereby attain bounds on the standard ones.\n"}
+{"abstract": "  In the rectangle stabbing problem, we are given a set $\\cR$ of axis-aligned\nrectangles in $\\RR^2$, and the objective is to find a minimum-cardinality set\nof horizontal and/or vertical lines such that each rectangle is intersected by\none of these lines. The standard LP relaxation for this problem is known to\nhave an integrality gap of 2, while a better intergality gap of 1.58.. is known\nfor the special case when $\\cR$ is a set of horizontal segments. In this paper,\nwe consider two more special cases: when $\\cR$ is a set of horizontal and\nvertical segments, and when $\\cR$ is a set of unit squares. We show that the\nintegrality gap of the standard LP relaxation in both cases is stricly less\nthan $2$. Our rounding technique is based on a generalization of the {\\it\nthreshold rounding} idea used by Kovaleva and Spieksma (SIAM J. Disc. Math\n2006), which may prove useful for rounding the LP relaxations of other\ngeometric covering problems.\n"}
+{"abstract": "  We study a quasi-two-dimensional macroscopic system of magnetic spherical\nparticles settled on a shallow concave dish under a temporally oscillating\nmagnetic field. The system reaches a stationary state where the energy losses\nfrom collisions and friction with the concave dish surface are compensated by\nthe continuous energy input coming from the oscillating magnetic field. Random\nparticle motions show some similarities with the motions of atoms and molecules\nin a glass or a crystal-forming fluid. Because of the curvature of the surface,\nparticles experience an additional force toward the center of the concave dish.\nWhen decreasing the magnetic field, the effective temperature is decreased and\ndiffusive particle motion slows. For slow cooling rates we observe\ncrystallization, where the particles organize into a hexagonal lattice. We\nstudy the birth of the crystalline nucleus and the subsequent growth of the\ncrystal. Our observations support non-classical theories of crystal formation.\nInitially a dense amorphous aggregate of particles forms, and then in a second\nstage this aggregate rearranges internally to form the crystalline nucleus. As\nthe aggregate grows, the crystal grows in its interior. After a certain size,\nall the aggregated particles are part of the crystal and after that, crystal\ngrowth follows the classical theory for crystal growth.\n"}
+{"abstract": "  The most important direction in the development of fundamental and applied\nphysics is the study of the properties of optical systems at the nanoscale in\norder to create optical and quantum computers, biosensors, single-photon\nsources for quantum informatics, devices for DNA sequencing, sensors of various\nfields, etc. In all these cases, nanoscale light sources - dye molecules,\nquantum dots (epitaxial or colloidal), color centers in crystals, and\nnanocontacts in metals - are of key importance. In the nanoenvironment, the\ncharacteristics of these elementary quantum systems - pumping rates, radiative\nand non-radiative decay rates, the local density of states, lifetimes, level\nshifts - experience changes that can be used intentionally to create nanoscale\nlight sources with desired properties. This review presents an analysis of\nactual theoretical and experimental works in the field of elementary quantum\nsystems radiation control using plasmonic and dielectric nanostructures,\nmetamaterials, and nanoparticles made from metamaterials.\n"}
+{"abstract": "  A fusion boundary-plasma domain is defined by axisymmetric magnetic surfaces\nwhere the geometry is often complicated by the presence of one or more\nX-points; and modeling boundary plasmas usually relies on computational grids\nthat account for the magnetic field geometry. The new grid generator INGRID\n(Interactive Grid Generator) presented here is a Python-based code for\ncalculating grids for fusion boundary plasma modeling, for a variety of\nconfigurations with one or two X-points in the domain. Based on a given\ngeometry of the magnetic field, INGRID first calculates a skeleton grid which\nconsists of a small number of quadrilateral patches; then it puts a subgrid on\neach of the patches, and joins them in a global grid. This domain partitioning\nstrategy makes possible a uniform treatment of various configurations with one\nor two X-points in the domain. This includes single-null, double-null, and\nother configurations with two X-points in the domain. The INGRID design allows\ngenerating grids either interactively, via a parameter-file driven GUI, or\nusing a non-interactive script-controlled workflow. Results of testing\ndemonstrate that INGRID is a flexible, robust, and user-friendly\ngrid-generation tool for fusion boundary-plasma modeling.\n"}
+{"abstract": "  The general Next-to-Minimal Supersymmetric Standard Model (NMSSM) describes\nthe singlino-dominated dark-matter (DM) property by four independent\nparameters: singlet-doublet Higgs coupling coefficient $\\lambda$, Higgsino mass\n$\\mu_{tot}$, DM mass $m_{\\tilde{\\chi}_1^0}$, and singlet Higgs self-coupling\ncoefficient $\\kappa$. The first three parameters strongly influence the\nDM-nucleon scattering rate, while $\\kappa$ usually affects the scattering only\nslightly. This characteristic implies that singlet-dominated particles may form\na secluded DM sector. Under such a theoretical structure, the DM achieves the\ncorrect abundance by annihilating into a pair of singlet-dominated Higgs bosons\nby adjusting $\\kappa$'s value. Its scattering with nucleons is suppressed when\n$\\lambda v/\\mu_{tot}$ is small. This speculation is verified by sophisticated\nscanning of the theory's parameter space with various experiment constraints\nconsidered. In addition, the Bayesian evidence of the general NMSSM and that of\n$Z_3$-NMSSM is computed. It is found that, at the cost of introducing one\nadditional parameter, the former is approximately $3.3 \\times 10^3$ times the\nlatter. This result corresponds to Jeffrey's scale of 8.05 and implies that the\nconsidered experiments strongly prefer the general NMSSM to the $Z_3$-NMSSM.\n"}
+{"abstract": "  First principles approaches have been successful in solving many-body\nHamiltonians for real materials to an extent when correlations are weak or\nmoderate. As the electronic correlations become stronger often embedding\nmethods based on first principles approaches are used to better treat the\ncorrelations by solving a suitably chosen many-body Hamiltonian with a higher\nlevel theory. Such combined methods are often referred to as second principles\napproaches. At such level of the theory the self energy, i.e. the functional\nthat embodies the stronger electronic correlations, is either a function of\nenergy or momentum or both. The success of such theories is commonly measured\nby the quality of the self energy functional. However, self-consistency in the\nself-energy should, in principle, also change the real space charge\ndistribution in a correlated material and be able to modify the electronic\neigenfunctions, which is often undermined in second principles approaches. Here\nwe study the impact of charge self-consistency within two example cases:\nTiSe$_{2}$, a three-dimensional charge-density-wave candidate material, and\nCrBr$_{3}$, a two-dimensional ferromagnet, and show how real space charge\nre-distribution due to correlation effects taken into account within a first\nprinciples Green's function based many-body perturbative approach is key in\ndriving qualitative changes to the final electronic structure of these\nmaterials.\n"}
+{"abstract": "  The Lightning Network (LN) is a prominent payment channel network aimed at\naddressing Bitcoin's scalability issues. Due to the privacy of channel\nbalances, senders cannot reliably choose sufficiently liquid payment paths and\nresort to a trial-and-error approach, trying multiple paths until one succeeds.\nThis leaks private information and decreases payment reliability, which harms\nthe user experience. This work focuses on the reliability and privacy of LN\npayments. We create a probabilistic model of the payment process in the LN,\naccounting for the uncertainty of the channel balances. This enables us to\nexpress payment success probabilities for a given payment amount and a path.\nApplying negative Bernoulli trials for single- and multi-part payments allows\nus to compute the expected number of payment attempts for a given amount,\nsender, and receiver. As a consequence, we analytically derive the optimal\nnumber of parts into which one should split a payment to minimize the expected\nnumber of attempts. This methodology allows us to define service level\nobjectives and quantify how much private information leaks to the sender as a\nside effect of payment attempts. We propose an optimized path selection\nalgorithm that does not require a protocol upgrade. Namely, we suggest that\nnodes prioritize paths that are most likely to succeed while making payment\nattempts. A simulation based on the real-world LN topology shows that this\nmethod reduces the average number of payment attempts by 20% compared to a\nbaseline algorithm similar to the ones used in practice. This improvement will\nincrease to 48% if the LN protocol is upgraded to implement the channel\nrebalancing proposal described in BOLT14.\n"}
+{"abstract": "  The structural evolution of laser-excited systems of gold has previously been\nmeasured through ultrafast MeV electron diffraction. However, there has been a\nlong-standing inability of atomistic simulations to provide a consistent\npicture of the melt process, concluding in large discrepancies between the\npredicted threshold energy density for complete melt, as well as the transition\nbetween heterogeneous and homogeneous melting. We make use of two-temperature\nclassical molecular dynamics simulations utilizing three highly successful\ninteratomic potentials and reproduce electron diffraction data presented by Mo\net al. We recreate the experimental electron diffraction data employing both a\nconstant and temperature-dependent electron-ion equilibration rate. In all\ncases we are able to match time-resolved electron diffraction data, and find\nconsistency between atomistic simulations and experiments, only by allowing\nlaser energy to be transported away from the interaction region. This\nadditional energy-loss pathway, which scales strongly with laser fluence, we\nattribute to hot electrons leaving the target on a timescale commensurate with\nmelting.\n"}
+{"abstract": "  With the growing size of data sets, feature selection becomes increasingly\nimportant. Taking interactions of original features into consideration will\nlead to extremely high dimension, especially when the features are categorical\nand one-hot encoding is applied. This makes it more worthwhile mining useful\nfeatures as well as their interactions. Association rule mining aims to extract\ninteresting correlations between items, but it is difficult to use rules as a\nqualified classifier themselves. Drawing inspiration from association rule\nmining, we come up with a method that uses association rules to select features\nand their interactions, then modify the algorithm for several practical\nconcerns. We analyze the computational complexity of the proposed algorithm to\nshow its efficiency. And the results of a series of experiments verify the\neffectiveness of the algorithm.\n"}
+{"abstract": "  Satellite communication is experiencing a new dawn thanks to low earth orbit\nmega constellations being deployed at an unprecedented speed. Fueled by the\nrenewed interest in non-terrestrial networks (NTN), the Third Generation\nPartnership Project (3GPP) is preparing 5G NR, NB-IoT and LTE-M for NTN\noperation. This article is focused on LTE-M and the essential adaptations\nneeded for supporting satellite communication. Specifically, the major\nchallenges facing LTE-M NTN at the physical and higher layers are discussed and\npotential solutions are outlined.\n"}
+{"abstract": "  Mainstream compilers perform a multitude of analyses and optimizations on the\ngiven input program. Each analysis pass may generate a program-abstraction.\nEach optimization pass is typically composed of multiple alternating phases of\ninspection of program-abstractions and transformations of the program. Upon\ntransformation of a program, the program-abstractions generated by various\nanalysis passes may become inconsistent with the program's modified state.\nConsequently, the downstream transformations may be considered unsafe until the\nrelevant program-abstractions are stabilized, i.e., the program-abstractions\nare made consistent with the modified program. In general, the existing\ncompiler frameworks do not perform automated stabilization of the\nprogram-abstractions and instead leave it to the optimization writer to deal\nwith the complex task of identifying the relevant program-abstractions to\nstabilize, the points where the stabilization is to be performed, and the exact\nprocedure of stabilization. Similarly, adding new analyses becomes a challenge\nas one has to understand which all existing optimizations may impact the newly\nadded program-abstractions. In this paper, we address these challenges by\nproviding the design and implementation of a novel generalized compiler-design\nframework called Homeostasis.\n  Homeostasis can be used to guarantee the trigger of automated stabilization\nof relevant program-abstractions under every possible transformation of the\nprogram. Interestingly, Homeostasis provides such guarantees not only for the\nexisting optimization passes but also for any future optimizations that may be\nadded to the framework. We have implemented our proposed ideas in the IMOP\ncompiler framework, for OpenMP C programs. We present an evaluation which shows\nthat Homeostasis is efficient and easy to use.\n"}
+{"abstract": "  To properly contrast the Deepfake phenomenon the need to design new Deepfake\ndetection algorithms arises; the misuse of this formidable A.I. technology\nbrings serious consequences in the private life of every involved person.\nState-of-the-art proliferates with solutions using deep neural networks to\ndetect a fake multimedia content but unfortunately these algorithms appear to\nbe neither generalizable nor explainable. However, traces left by Generative\nAdversarial Network (GAN) engines during the creation of the Deepfakes can be\ndetected by analyzing ad-hoc frequencies. For this reason, in this paper we\npropose a new pipeline able to detect the so-called GAN Specific Frequencies\n(GSF) representing a unique fingerprint of the different generative\narchitectures. By employing Discrete Cosine Transform (DCT), anomalous\nfrequencies were detected. The \\BETA statistics inferred by the AC coefficients\ndistribution have been the key to recognize GAN-engine generated data.\nRobustness tests were also carried out in order to demonstrate the\neffectiveness of the technique using different attacks on images such as JPEG\nCompression, mirroring, rotation, scaling, addition of random sized rectangles.\nExperiments demonstrated that the method is innovative, exceeds the state of\nthe art and also give many insights in terms of explainability.\n"}
+{"abstract": "  We propose a self-supervised framework to learn scene representations from\nvideo that are automatically delineated into background, characters, and their\nanimations. Our method capitalizes on moving characters being equivariant with\nrespect to their transformation across frames and the background being constant\nwith respect to that same transformation. After training, we can manipulate\nimage encodings in real time to create unseen combinations of the delineated\ncomponents. As far as we know, we are the first method to perform unsupervised\nextraction and synthesis of interpretable background, character, and animation.\nWe demonstrate results on three datasets: Moving MNIST with backgrounds, 2D\nvideo game sprites, and Fashion Modeling.\n"}
+{"abstract": "  In a recent study [Phys. Rev. X 10, 021042 (2020)], we showed using\nlarge-scale density matrix renormalization group (DMRG) simulations on infinite\ncylinders that the triangular lattice Hubbard model has a chiral spin liquid\nphase. In this work, we introduce hopping anisotropy in the model, making one\nof the three distinct bonds on the lattice stronger or weaker compared with the\nother two. We implement the anisotropy in two inequivalent ways, one which\nrespects the mirror symmetry of the cylinder and one which breaks this\nsymmetry. In the full range of anisotropy, from the square lattice to weakly\ncoupled one-dimensional chains, we find a variety of phases. Near the isotropic\nlimit we find the three phases identified in our previous work: metal, chiral\nspin liquid, and 120$^\\circ$ spiral order; we note that a recent paper suggests\nthe apparently metallic phase may actually be a Luther-Emery liquid, which\nwould also be in agreement with our results. When one bond is weakened by a\nrelatively small amount, the ground state quickly becomes the square lattice\nN\\'{e}el order. When one bond is strengthened, the story is much less clear,\nwith the phases that we find depending on the orientation of the anisotropy and\non the cylinder circumference. While our work is to our knowledge the first\nDMRG study of the anisotropic triangular lattice Hubbard model, the overall\nphase diagram we find is broadly consistent with that found previously using\nother methods, such as variational Monte Carlo and dynamical mean field theory.\n"}
+{"abstract": "  Efficient and low-complexity beamforming design is an important element of\nsatellite communication systems with mobile receivers equipped with phased\narrays. In this work, we apply the simultaneous perturbation stochastic\napproximation (SPSA) method with successive sub-array selection for finding the\noptimal antenna weights that maximize the received signal power at a uniform\nplane array (UPA). The proposed algorithms are based on iterative gradient\napproximation by injecting some carefully designed perturbations on the\nparameters to be estimated. Additionally, the successive sub-array selection\ntechnique enhances the performance of SPSA-based algorithms and makes them less\nsensitive to the initial beam direction. Simulation results show that our\nproposed algorithms can achieve efficient and reliable performance even when\nthe initial beam direction is not well aligned with the satellite direction.\n"}
+{"abstract": "  We present cosmological parameter measurements from the effective field\ntheory-based full-shape analysis of the power spectrum of emission line\ngalaxies (ELGs). First, we perform extensive tests on simulations and determine\nappropriate scale cuts for the perturbative description of the ELG power\nspectrum. We study in detail non-linear redshift-space distortions\n(``fingers-of-God'') for this sample and show that they are somewhat weaker\nthan those of luminous red galaxies. This difference is not significant for\ncurrent data, but may become important for future surveys like Euclid/DESI.\nThen we analyze recent measurements of the ELG power spectrum from the extended\nBaryon acoustic Oscillation Spectroscopic Survey (eBOSS) within the\n$\\nu\\Lambda$CDM model. Combined with the BBN baryon density prior, the ELG pre-\nand post-reconstructed power spectra alone constrain the matter density\n$\\Omega_m=0.257_{-0.045}^{+0.031}$, the current mass fluctuation amplitude\n$\\sigma_8=0.571_{-0.076}^{+0.052}$, and the Hubble constant\n$H_0=84.5_{-7}^{+5.8}$ km/s/Mpc (all at 68\\% CL). Combining with other\nfull-shape and BAO data we measure $\\Omega_m=0.327_{-0.016}^{+0.014}$,\n$\\sigma_8=0.69_{-0.045}^{+0.038}$, and $H_0=68.6_{-1.1}^{+1}$ km/s/Mpc. The\ntotal neutrino mass is constrained to be $M_{\\rm tot}<0.63$ eV (95\\% CL) from\nthe BBN, full-shape and BAO data only. Finally, we discuss the apparent $\\sim\n3\\sigma$ discrepancy in the inferred clustering amplitude between our full\nshape analysis and the cosmic microwave background data.\n"}
+{"abstract": "  As the killer application of blockchain technology, blockchain-based payments\nhave attracted extensive attention ranging from hobbyists to corporates to\nregulatory bodies. Blockchain facilitates fast, secure, and cross-border\npayments without the need for intermediaries such as banks. Because blockchain\ntechnology is still emerging, systematically organised knowledge providing a\nholistic and comprehensive view on designing payment applications that use\nblockchain is yet to be established. If such knowledge could be established in\nthe form of a set of blockchain-specific patterns, architects could use those\npatterns in designing a payment application that leverages blockchain.\nTherefore, in this paper, we first identify a token's lifecycle and then\npresent 12 patterns that cover critical aspects in enabling the state\ntransitions of a token in blockchain-based payment applications. The lifecycle\nand the annotated patterns provide a payment-focused systematic view of system\ninteractions and a guide to effective use of the patterns.\n"}
+{"abstract": "  In the star formation process, the vital impact of environmental factors such\nas feedback from massive stars and stellar density on the form of the initial\nmass function (IMF) at low-mass end is yet to be understood. Hence a\nsystematic, highly sensitive observational analysis of a sample of regions\nunder diverse environmental conditions is essential. We analyse the IMF of\neight young clusters ($<$5 Myr), namely IC1848-West, IC1848-East, NGC 1893, NGC\n2244, NGC 2362, NGC 6611, Stock 8 and Cygnus OB2, which are located at the\nGalactocentric distance ($R_g$) range $\\sim$6-12 kpc along with nearby cluster\nIC348 using deep near-IR photometry and Gaia DR2. These clusters are embedded\nin massive stellar environments of radiation strength $log(L_{FUV}/L_{\\odot})$\n$\\sim$2.6 to 6.8, $log(L_{EUV})$ $\\sim$42.2 to 50.85 photons/s, with stellar\ndensity in the range of $\\sim$170 - 1220 stars/pc$^2$. After structural\nanalysis and field decontamination we obtain an unbiased, uniformly sensitive\nsample of pre-main-sequence members of the clusters down to brown-dwarf regime.\nThe lognormal fit to the IMF of nine clusters gives the mean characteristic\nmass ($m_c$) and $\\sigma$ of 0.32$\\pm$0.02 $M_\\odot$ and 0.47$\\pm$0.02,\nrespectively. We compare the IMF with that of low- and high-mass clusters\nacross the Milky Way. We also check for any systematic variation with respect\nto the radiation field strength, stellar density as well with $R_g$. We\nconclude that there is no strong evidence for environmental effect in the\nunderlying form of the IMF of these clusters.\n"}
+{"abstract": "  A hypothetical pseudo-scalar particle axion, which is an immediate result of\nthe Peccei-Quinn solution to the strong CP problem, may couple to gluons and\nlead to an oscillating electric dipole moment (EDM) of fundamental particles.\nThis paper proposes a novel method of probing the axion-induced oscillating EDM\nin storage rings, using a radiofrequency (RF) Wien Filter. The Wien Filter at\nthe frequency of the sidebands of the axion and $g-2$ frequency,\n$f_\\text{axion} \\pm f_{g-2}$, generates a spin resonance in the presence of an\noscillating EDM, as confirmed both by an analytical estimation of the spin\nequations and independently by simulation. A brief systematic study also shows\nthat this method is unlikely to be limited by Wien Filter misalignment issues.\n"}
+{"abstract": "  Pre-trained representations are becoming crucial for many NLP and perception\ntasks. While representation learning in NLP has transitioned to training on raw\ntext without human annotations, visual and vision-language representations\nstill rely heavily on curated training datasets that are expensive or require\nexpert knowledge. For vision applications, representations are mostly learned\nusing datasets with explicit class labels such as ImageNet or OpenImages. For\nvision-language, popular datasets like Conceptual Captions, MSCOCO, or CLIP all\ninvolve a non-trivial data collection (and cleaning) process. This costly\ncuration process limits the size of datasets and hence hinders the scaling of\ntrained models. In this paper, we leverage a noisy dataset of over one billion\nimage alt-text pairs, obtained without expensive filtering or post-processing\nsteps in the Conceptual Captions dataset. A simple dual-encoder architecture\nlearns to align visual and language representations of the image and text pairs\nusing a contrastive loss. We show that the scale of our corpus can make up for\nits noise and leads to state-of-the-art representations even with such a simple\nlearning scheme. Our visual representation achieves strong performance when\ntransferred to classification tasks such as ImageNet and VTAB. The aligned\nvisual and language representations enables zero-shot image classification and\nalso set new state-of-the-art results on Flickr30K and MSCOCO image-text\nretrieval benchmarks, even when compared with more sophisticated\ncross-attention models. The representations also enable cross-modality search\nwith complex text and text + image queries.\n"}
+{"abstract": "  We compute the differential yield for quark anti-quark dijet production in\nhigh-energy electron-proton and electron-nucleus collisions at small $x$ as a\nfunction of the relative momentum $\\boldsymbol{P}_\\perp$ and momentum imbalance\n$\\boldsymbol{k}_\\perp$ of the dijet system for different photon virtualities\n$Q^2$, and study the elliptic and quadrangular anisotropies in the relative\nangle between $\\boldsymbol{P}_\\perp$ and $\\boldsymbol{k}_\\perp$. We review and\nextend the analysis in [1], which compared the results of the Color Glass\nCondensate (CGC) with those obtained using the transverse momentum dependent\n(TMD) framework. In particular, we include in our comparison the improved TMD\n(ITMD) framework, which resums kinematic power corrections of the ratio\n$k_\\perp$ over the hard scale $Q_\\perp$. By comparing ITMD and CGC results we\nare able to isolate genuine higher saturation contributions in the ratio\n$Q_s/Q_\\perp$ which are resummed only in the CGC. These saturation\ncontributions are in addition to those in the Weizs\\\"ackerWilliams gluon TMD\nthat appear in powers of $Q_s/k_\\perp$. We provide numerical estimates of these\ncontributions for inclusive dijet production at the future Electron-Ion\nCollider, and identify kinematic windows where they can become relevant in the\nmeasurement of dijet and dihadron azimuthal correlations. We argue that such\nmeasurements will allow the detailed experimental study of both kinematic power\ncorrections and genuine gluon saturation effects.\n"}
+{"abstract": "  With the increasing popularity of calcium imaging data in neuroscience\nresearch, methods for analyzing calcium trace data are critical to address\nvarious questions. The observed calcium traces are either analyzed directly or\ndeconvolved to spike trains to infer neuronal activities. When both approaches\nare applicable, it is unclear whether deconvolving calcium traces is a\nnecessary step. In this article, we compare the performance of using calcium\ntraces or their deconvolved spike trains for three common analyses: clustering,\nprincipal component analysis (PCA), and population decoding. Our simulations\nand applications to real data suggest that the estimated spike data outperform\ncalcium trace data for both clustering and PCA. Although calcium trace data\nshow higher predictability than spike data at each time point, spike history or\ncumulative spike counts is comparable to or better than calcium traces in\npopulation decoding.\n"}
+{"abstract": "  A cosmological model with an energy transfer between dark matter (DM) and\ndark energy (DE) can give rise to comparable energy densities at the present\nepoch. The present work deals with the perturbation analysis, parameter\nestimation and Bayesian evidence calculation of interacting models with\ndynamical coupling parameter that determines the strength of the interaction.\nWe have considered two cases, where the interaction is a more recent phenomenon\nand where the interaction is a phenomenon in the distant past. Moreover, we\nhave considered the quintessence DE equation of state with\nChevallier-Polarski-Linder (CPL) parametrisation and energy flow from DM to DE.\nUsing the current observational datasets like the cosmic microwave background\n(CMB), baryon acoustic oscillation (BAO), Type Ia Supernovae (SNe Ia) and\nredshift-space distortions (RSD), we have estimated the mean values of the\nparameters. Using the perturbation analysis and Bayesian evidence calculation,\nwe have shown that interaction present as a brief early phenomenon is preferred\nover a recent interaction.\n"}
+{"abstract": "  Besides the spirals induced by the Lindblad resonances, planets can generate\na family of tightly wound spirals through buoyancy resonances. The excitation\nof buoyancy resonances depends on the thermal relaxation timescale of the gas.\nBy computing timescales of various processes associated with thermal\nrelaxation, namely, radiation, diffusion, and gas-dust collision, we show that\nthe thermal relaxation in protoplanetary disks' surface layers\n($Z/R\\gtrsim0.1$) and outer disks ($R\\gtrsim100$ au) is limited by infrequent\ngas-dust collisions. The use of isothermal equation of state or rapid cooling,\ncommon in protoplanetary disk simulations, is therefore not justified. Using\nthree-dimensional hydrodynamic simulations, we show that the collision-limited\nslow thermal relaxation provides favorable conditions for buoyancy resonances\nto develop. Buoyancy resonances produce predominantly vertical motions, whose\nmagnitude at the $^{12}$CO emission surface is of order of $100~{\\rm m~s}^{-1}$\nfor Jovian-mass planets, sufficiently large to detect using molecular line\nobservations with ALMA. We generate synthetic observations and describe\ncharacteristic features of buoyancy resonances in Keplerian-subtracted moment\nmaps and velocity channel maps. Based on the morphology and magnitude of the\nperturbation, we propose that the tightly wound spirals observed in TW Hya\ncould be driven by a (sub-)Jovian-mass planet at 90 au. We discuss how\nnon-Keplerian motions driven by buoyancy resonances can be distinguished from\nthose driven by other origins. We argue that observations of multiple lines\ntracing different heights, with sufficiently high spatial/spectral resolution\nand sensitivity to separate the emission arising from the near and far sides of\nthe disk, will help constrain the origin of non-Keplerian motions.\n"}
+{"abstract": "  Mt. Abu Faint Object Spectrograph and Camera - Pathfinder (MFOSC-P) is an\nimager-spectrograph developed for the Physical Research Laboratory (PRL) 1.2m\ntelescope at Gurushikhar, Mt. Abu, India. MFOSC-P is based on a focal reducer\nconcept and provides seeing limited imaging (with a sampling of 3.3 pixels per\narc-second) in Bessell's B, V, R, I and narrow-band H-$\\alpha$ filters. The\ninstrument uses three plane reflection gratings, covering the spectral range of\n4500-8500$\\AA$, with three different resolutions of 500, 1000, and 2000 around\ntheir central wavelengths. MFOSC-P was conceived as a pathfinder instrument for\na next-generation instrument on the PRL's 2.5m telescope which is coming up at\nMt. Abu. The instrument was developed during 2015-2019 and successfully\ncommissioned on the PRL 1.2m telescope in February 2019. The designed\nperformance has been verified with laboratory characterization tests and on-sky\ncommissioning observations. Different science programs covering a range of\nobjects are being executed with MFOSC-P since then, e.g., spectroscopy of\nM-dwarfs, novae $\\&$ symbiotic systems, and detection of H-$\\alpha$ emission in\nstar-forming regions. MFOSC-P presents a novel design and cost-effective way to\ndevelop a FOSC (Faint Object Spectrograph and Camera) type of instrument on a\nshorter time-scale of development. The design and development methodology\npresented here is most suitable in helping the small aperture telescope\ncommunity develop such a versatile instrument, thereby diversifying the science\nprograms of such observatories.\n"}
+{"abstract": "  The size of drops generated by the capillary-driven disintegration of liquid\nligaments plays a fundamental role in several important natural phenomena,\nranging from heat and mass transfer at the ocean-atmosphere interface to\npathogen transmission. The inherent non-linearity of the equations governing\nthe ligament destabilization lead to significant differences in the resulting\ndrop sizes, owing to small fluctuations in the myriad initial conditions.\nPrevious experiments and simulations reveal a variety of drop size\ndistributions, corresponding to competing underlying physical interpretations.\nHere, we perform numerical simulations of individual ligaments, the\ndeterministic breakup of which is triggered by random initial surface\ncorrugations. Stochasticity is incorporated by simulating a large ensemble of\nsuch ligaments, each realization corresponding to a random but unique initial\nconfiguration. The resulting probability distributions reveal three stable drop\nsizes, generated via a sequence of two distinct stages of breakup. The\nprobability of the large sizes is described by volume-weighted Poisson and\nLog-Normal distributions for the first and second breakup stages, respectively.\nThe study demonstrates a precisely controllable and reproducible framework,\nwhich can be employed to investigate the mechanisms responsible for the\npolydispersity in drop sizes found in complex fluid fragmentation scenarios.\n"}
+{"abstract": "  Single image super-resolution (SISR) deals with a fundamental problem of\nupsampling a low-resolution (LR) image to its high-resolution (HR) version.\nLast few years have witnessed impressive progress propelled by deep learning\nmethods. However, one critical challenge faced by existing methods is to strike\na sweet spot of deep model complexity and resulting SISR quality. This paper\naddresses this pain point by proposing a linearly-assembled pixel-adaptive\nregression network (LAPAR), which casts the direct LR to HR mapping learning\ninto a linear coefficient regression task over a dictionary of multiple\npredefined filter bases. Such a parametric representation renders our model\nhighly lightweight and easy to optimize while achieving state-of-the-art\nresults on SISR benchmarks. Moreover, based on the same idea, LAPAR is extended\nto tackle other restoration tasks, e.g., image denoising and JPEG image\ndeblocking, and again, yields strong performance. The code is available at\nhttps://github.com/dvlab-research/Simple-SR.\n"}
+{"abstract": "  In this paper, we examine the state art of quantum computing and analyze its\npotential effects in scientific computing and cybersecurity. Additionally, a\nnon-technical description of the mechanics of the listed form of computing is\nprovided to educate the reader for better understanding of the arguments\nprovided. The purpose of this study is not only to increase awareness in this\nnescient technology, but also serve as a general reference guide for any\nindividual wishing to study other applications of quantum computing in areas\nthat include finance, chemistry, and data science. Lastly, an educated argument\nis provided in the discussion section that addresses the implications this form\nof computing will have in the main areas examined.\n"}
+{"abstract": "  Although pain is frequent in old age, older adults are often undertreated for\npain. This is especially the case for long-term care residents with moderate to\nsevere dementia who cannot report their pain because of cognitive impairments\nthat accompany dementia. Nursing staff acknowledge the challenges of\neffectively recognizing and managing pain in long-term care facilities due to\nlack of human resources and, sometimes, expertise to use validated pain\nassessment approaches on a regular basis. Vision-based ambient monitoring will\nallow for frequent automated assessments so care staff could be automatically\nnotified when signs of pain are displayed. However, existing computer vision\ntechniques for pain detection are not validated on faces of older adults or\npeople with dementia, and this population is not represented in existing facial\nexpression datasets of pain. We present the first fully automated vision-based\ntechnique validated on a dementia cohort. Our contributions are threefold.\nFirst, we develop a deep learning-based computer vision system for detecting\npainful facial expressions on a video dataset that is collected unobtrusively\nfrom older adult participants with and without dementia. Second, we introduce a\npairwise comparative inference method that calibrates to each person and is\nsensitive to changes in facial expression while using training data more\nefficiently than sequence models. Third, we introduce a fast contrastive\ntraining method that improves cross-dataset performance. Our pain estimation\nmodel outperforms baselines by a wide margin, especially when evaluated on\nfaces of people with dementia. Pre-trained model and demo code available at\nhttps://github.com/TaatiTeam/pain_detection_demo\n"}
+{"abstract": "  Commercial electricity production from marine renewable sources is becoming a\nnecessity at a global scale. Offshore wind and solar resources can be combined\nto reduce construction and maintenance costs. In this respect, the aim of this\nstudy is two-fold: i) analyse offshore wind and solar resource and their\nvariability in the Mediterranean Sea at the annual and seasonal scales based on\nthe recently published ERA5 reanalysis dataset, and; ii) perform a preliminary\nassessment of some important features of complementarity, synergy, and\navailability of the examined resources using an event-based probabilistic\napproach. A robust coefficient of variation is introduced to examine the\nvariability of each resource and a joint coefficient of variation is\nimplemented for the first time to evaluate the joint variability of offshore\nwind and solar potential. The association between the resources is examined by\nintroducing a robust measure of correlation, along with the Pearson's r and\nKendall's tau correlation coefficient and the corresponding results are\ncompared. Several metrics are used to examine the degree of complementarity\naffected by variability and intermittency issues. Areas with high potential and\nlow variability for both resources include the Aegean and Alboran seas, while\nsignificant synergy (over 52%) is identified in the gulfs of Lion, Gabes and\nSidra, Aegean Sea and northern Cyprus Isl. The advantage of combining these two\nresources is highlighted at selected locations in terms of the monthly energy\nproduction.\n"}
+{"abstract": "  In the classroom environment, search tools are the means for students to\naccess Web resources. The perspectives of students, researchers, and industry\npractitioners lead the ongoing research debate in this area. In this article,\nwe argue in favor of incorporating a new voice into this debate: teachers. We\nshowcase the value of involving teachers in all aspects related to the design\nof search tools for the classroom; from the beginning till the end. Driven by\nour research experience designing, developing, and evaluating new tools to\nsupport children's information discovery in the classroom, we share insights on\nthe role of the experts-in-the-loop, i.e., teachers who provide the connection\nbetween search tools and students. And yes, in our case, always involving a\nteacher as a research partner.\n"}
+{"abstract": "  We present a model that jointly learns the denotations of words together with\ntheir groundings using a truth-conditional semantics. Our model builds on the\nneurosymbolic approach of Mao et al. (2019), learning to ground objects in the\nCLEVR dataset (Johnson et al., 2017) using a novel parallel attention\nmechanism. The model achieves state of the art performance on visual question\nanswering, learning to detect and ground objects with question performance as\nthe only training signal. We also show that the model is able to learn flexible\nnon-canonical groundings just by adjusting answers to questions in the training\nset.\n"}
+{"abstract": "  In this paper, we deal with random attractors for dynamical systems forced by\na deterministic noise. These kind of systems are modeled as skew products where\nthe dynamics of the forcing process are described by the base transformation.\nHere, we consider skew products over the Bernoulli shift with the unit interval\nfiber. We study the geometric structure of maximal attractors, the orbit\nstability and stability of mixing of these skew products under random\nperturbations of the fiber maps. We show that there exists an open set\n$\\mathcal{U}$ in the space of such skew products so that any skew product\nbelonging to this set admits an attractor which is either a continuous\ninvariant graph or a bony graph attractor. These skew products have negative\nfiber Lyapunov exponents and their fiber maps are non-uniformly contracting,\nhence the non-uniform contraction rates are measured by Lyapnnov exponents.\nFurthermore, each skew product of $\\mathcal{U}$ admits an invariant ergodic\nmeasure whose support is contained in that attractor. Additionally, we show\nthat the invariant measure for the perturbed system is continuous in the\nHutchinson metric.\n"}
+{"abstract": "  In the three-dimensional anti-de Sitter spacetime/two-dimensional conformal\nfield theory correspondence, we derive the imaginary-time path-integral of a\nnon-relativistic particle in the anti-de Sitter bulk space, which is dual to\nthe ground state, from the holographic principle. This derivation is based on\n(i) the author's previous argument that the holographic principle asserts that\nthe anti-de Sitter bulk space as a holographic tensor network after\nclassicalization has as many stochastic classicalized spin degrees of freedom\nas there are sites and (ii) the reinterpretation of the Euclidean action of a\nfree particle as the action of classicalized spins.\n"}
+{"abstract": "  Explainable deep learning models are advantageous in many situations. Prior\nwork mostly provide unimodal explanations through post-hoc approaches not part\nof the original system design. Explanation mechanisms also ignore useful\ntextual information present in images. In this paper, we propose MTXNet, an\nend-to-end trainable multimodal architecture to generate multimodal\nexplanations, which focuses on the text in the image. We curate a novel dataset\nTextVQA-X, containing ground truth visual and multi-reference textual\nexplanations that can be leveraged during both training and evaluation. We then\nquantitatively show that training with multimodal explanations complements\nmodel performance and surpasses unimodal baselines by up to 7% in CIDEr scores\nand 2% in IoU. More importantly, we demonstrate that the multimodal\nexplanations are consistent with human interpretations, help justify the\nmodels' decision, and provide useful insights to help diagnose an incorrect\nprediction. Finally, we describe a real-world e-commerce application for using\nthe generated multimodal explanations.\n"}
+{"abstract": "  Fractional Dzherbashian-Nersesian operator is considered and three famous\nfractional order derivatives namely Riemann-Liouville, Caputo and Hilfer\nderivatives are shown to be special cases of the earlier one. The expression\nfor Laplace transform of fractional Dzherbashian-Nersesian operator is\nconstructed. Inverse problems of recovering space dependent and time dependent\nsource terms of a time fractional diffusion equation with involution and\ninvolving fractional Dzherbashian-Nersesian operator are considered. The\nresults on existence and uniqueness for the solutions of inverse problems are\nestablished. The results obtained here generalize several known results.\n"}
+{"abstract": "  Two-dimensional multilinked structures can benefit aerial robots in both\nmaneuvering and manipulation because of their deformation ability. However,\ncertain types of singular forms must be avoided during deformation. Hence, an\nadditional 1 Degrees-of-Freedom (DoF) vectorable propeller is employed in this\nwork to overcome singular forms by properly changing the thrust direction. In\nthis paper, we first extend modeling and control methods from our previous\nworks for an under-actuated model whose thrust forces are not unidirectional.\nWe then propose a planning method for the vectoring angles to solve the\nsingularity by maximizing the controllability under arbitrary robot forms.\nFinally, we demonstrate the feasibility of the proposed methods by experiments\nwhere a quad-type model is used to perform trajectory tracking under\nchallenging forms, such as a line-shape form, and the deformation passing these\nchallenging forms.\n"}
+{"abstract": "  This paper demonstrates the applicability of the combination of concurrent\nlearning as a tool for parameter estimation and non-parametric Gaussian Process\nfor online disturbance learning. A control law is developed by using both\ntechniques sequentially in the context of feedback linearization. The\nconcurrent learning algorithm estimates the system parameters of structured\nuncertainty without requiring persistent excitation, which are used in the\ndesign of the feedback linearization law. Then, a non-parametric Gaussian\nProcess learns unstructured uncertainty. The closed-loop system stability for\nthe nth-order system is proven using the Lyapunov stability theorem. The\nsimulation results show that the tracking error is minimized (i) when true\nvalues of model parameters have not been provided, (ii) in the presence of\ndisturbances introduced once the parameters have converged to their true values\nand (iii) when system parameters have not converged to their true values in the\npresence of disturbances.\n"}
+{"abstract": "  Five-dimensional $\\mathcal{N}=1$ theories with gauge group $U(N)$, $SU(N)$,\n$USp(2N)$ and $SO(N)$ are studied at large rank through localization on a large\nsphere. The phase diagram of theories with fundamental hypermultiplets is\nuniversal and characterized by third order phase transitions, with the\nexception of $U(N)$, that shows both second and third order transitions. The\nphase diagram of theories with adjoint or (anti-)symmetric hypermultiplets is\nalso determined and found to be universal. Moreover, Wilson loops in\nfundamental and antisymmetric representations of any rank are analyzed in this\nlimit. Quiver theories are discussed as well. All the results substantiate the\n$\\mathcal{F}$-theorem.\n"}
+{"abstract": "  We compute the bigraded homotopy ring of the Borel $C_2$-equivariant\n$K(1)$-local sphere. This captures many of the patterns seen among\n$\\text{Im}~J$-type elements in $\\mathbb{R}$-motivic and $C_2$-equivariant\nstable stems. In addition, it provides a streamlined approach to understanding\nthe $K(1)$-localizations of stunted projective spaces.\n"}
+{"abstract": "  Software debugging, and program repair are among the most time-consuming and\nlabor-intensive tasks in software engineering that would benefit a lot from\nautomation. In this paper, we propose a novel automated program repair approach\nbased on CodeBERT, which is a transformer-based neural architecture pre-trained\non large corpus of source code. We fine-tune our model on the ManySStuBs4J\nsmall and large datasets to automatically generate the fix codes. The results\nshow that our technique accurately predicts the fixed codes implemented by the\ndevelopers in 19-72% of the cases, depending on the type of datasets, in less\nthan a second per bug. We also observe that our method can generate\nvaried-length fixes (short and long) and can fix different types of bugs, even\nif only a few instances of those types of bugs exist in the training dataset.\n"}
+{"abstract": "  We propose a novel text-analytic approach for incorporating textual\ninformation into structural economic models and apply this to study the effects\nof tax news. We first develop a novel semi-supervised two-step topic model that\nautomatically extracts specific information regarding future tax policy changes\nfrom text. We also propose an approach for transforming such textual\ninformation into an economically meaningful time series to be included in a\nstructural econometric model as variable of interest or instrument. We apply\nour method to study the effects of fiscal foresight, in particular the\ninformational content in speeches of the U.S. president about future tax\nreforms, and find that our semi-supervised topic model can successfully extract\ninformation about the direction of tax changes. The extracted information\npredicts (exogenous) future tax changes and contains signals that are not\npresent in previously considered (narrative) measures of (exogenous) tax\nchanges. We find that tax news triggers a significant yet delayed response in\noutput.\n"}
+{"abstract": "  Volkov states are exact solutions of the Dirac equation in the presence of an\narbitrary plane wave. Volkov states, as well as free photon states, are not\nstable in the presence of the background plane-wave field but \"decay\" as\nelectrons/positrons can emit photons and photons can transform into\nelectron-positron pairs. By using the solutions of the corresponding\nSchwinger-Dyson equations within the locally-constant field approximation, we\ncompute the probabilities of nonlinear single Compton scattering and nonlinear\nBreit-Wheeler pair production by including the effects of the decay of\nelectron, positron, and photon states. As a result, we find that the\nprobabilities of these processes can be expressed as the integral over the\nlight-cone time of the known probabilities valid for stable states per unit of\nlight-cone time times a light-cone time-dependent exponential damping function\nfor each interacting particle. The exponential function for an incoming\n(outgoing) either electron/positron or photon at each light-cone time\ncorresponds to the total probability that either the electron/positron emits a\nphoton via nonlinear Compton scattering or the photon transforms into an\nelectron-positron pair via nonlinear Breit-Wheeler pair production until that\nlight-cone time (from that light-cone time on). It is interesting that the\nexponential damping terms depend not only on the particles momentum but also on\ntheir spin (for electrons/positrons) and polarization (for photons). This\nadditional dependence on the discrete quantum numbers prevents the application\nof the electron/positron spin and photon polarization sum-rules, which\nsignificantly simplify the computations in the perturbative regime.\n"}
+{"abstract": "  General Relativity is an extremely successful theory, at least for weak\ngravitational fields, however, it breaks down at very high energies, such as in\ncorrespondence of the initial singularity. Quantum Gravity is expected to\nprovide more physical insights concerning this open question. Indeed, one\nalternative scenario to the Big Bang, that manages to completely avoid the\nsingularity, is offered by Loop Quantum Cosmology (LQC), which predicts that\nthe Universe undergoes a collapse to an expansion through a bounce. In this\nwork, we use metric $f(R)$ gravity to reproduce the modified Friedmann\nequations which have been obtained in the context of modified loop quantum\ncosmologies. To achieve this, we apply an order reduction method to the $f(R)$\nfield equations, and obtain covariant effective actions that lead to a bounce,\nfor specific models of modified LQC, considering matter as a scalar field.\n"}
+{"abstract": "  Operations typically used in machine learning al-gorithms (e.g. adds and soft\nmax) can be implemented bycompact analog circuits. Analog Application-Specific\nIntegrated Circuit (ASIC) designs that implement these algorithms using\ntechniques such as charge sharing circuits and subthreshold transistors,\nachieve very high power efficiencies. With the recent advances in deep learning\nalgorithms, focus has shifted to hardware digital accelerator designs that\nimplement the prevalent matrix-vector multiplication operations. Power in these\ndesigns is usually dominated by the memory access power of off-chip DRAM needed\nfor storing the network weights and activations. Emerging dense non-volatile\nmemory technologies can help to provide on-chip memory and analog circuits can\nbe well suited to implement the needed multiplication-vector operations coupled\nwith in-computing memory approaches. This paper presents abrief review of\nanalog designs that implement various machine learning algorithms. It then\npresents an outlook for the use ofanalog circuits in low-power deep network\naccelerators suitable for edge or tiny machine learning applications.\n"}
+{"abstract": "  We address the problem of tensor decomposition in application to\ndirection-of-arrival (DOA) estimation for transmit beamspace (TB)\nmultiple-input multiple-output (MIMO) radar. A general 4-order tensor model\nthat enables computationally efficient DOA estimation is designed. Whereas\nother tensor decomposition-based methods treat all factor matrices as\narbitrary, the essence of the proposed DOA estimation method is to fully\nexploit the Vandermonde structure of the factor matrices to take advantage of\nthe shift-invariance between and within different subarrays. Specifically, the\nreceived signal of TB MIMO radar is expressed as a 4-order tensor. Depending on\nthe target Doppler shifts, the constructed tensor is reshaped into two distinct\n3-order tensors. A computationally efficient tensor decomposition method is\nproposed to decompose the Vandermonde factor matrices. The generators of the\nVandermonde factor matrices are computed to estimate the phase rotations\nbetween subarrays, which can be utilized as a look-up table for finding target\nDOA. It is further shown that our proposed method can be used in a more general\nscenario where the subarray structures can be arbitrary but identical. The\nproposed DOA estimation method requires no prior information about the tensor\nrank and is guaranteed to achieve precise decomposition result. Simulation\nresults illustrate the performance improvement of the proposed DOA estimation\nmethod as compared to conventional DOA estimation techniques for TB MIMO Radar.\n"}
+{"abstract": "  In this article, we prove a series of integral formulae for a codimension-one\nfoliated sub-Riemannian manifold, i.e., a Riemannian manifold $(M,g)$ equipped\nwith a distribution ${\\mathcal D}=T{\\mathcal F}\\oplus\\,{\\rm span}(N)$, where\n${\\mathcal F}$ is a foliation of $M$ and $N$ a unit vector field $g$-orthogonal\nto ${\\mathcal F}$. Our integral formulas involve $r$th mean curvatures of\n${\\mathcal F}$, Newton transformations of the shape operator of ${\\mathcal F}$\nwith respect to $N$ and the curvature tensor of induced connection on\n${\\mathcal D}$ and generalize some known integral formulas (due to\nBrito-Langevin-Rosenberg, Andrzejewski-Walczak and the author) for\ncodimension-one foliations. We apply our formulas to sub-Riemannian manifolds\nwith restrictions on the curvature and extrinsic geometry of a foliation.\n"}
+{"abstract": "  In this paper, we will prove a finite dimensional approximation scheme for\nthe Wiener measure on closed Riemannian manifolds, establishing a\ngeneralization for $L_{1}$-functionals, of the approach followed by Andersson\nand Driver on [2]. This scheme is motived by the measure theoretic techniques\nof [15]. Moreover, we will embed the concept of stochastic line integral in\nthis scheme. This concept will propitiate some applications of path integration\nin Riemannian manifolds that provides with an alternative formulation of\nclassical geometric concepts bringing to them an original point of view.\n"}
+{"abstract": "  Transparency - the provision of information about what personal data is\ncollected for which purposes, how long it is stored, or to which parties it is\ntransferred - is one of the core privacy principles underlying regulations such\nas the GDPR. Technical approaches for implementing transparency in practice\nare, however, only rarely considered. In this paper, we present a novel\napproach for doing so in current, RESTful application architectures and in line\nwith prevailing agile and DevOps-driven practices. For this purpose, we\nintroduce 1) a transparency-focused extension of OpenAPI specifications that\nallows individual service descriptions to be enriched with transparency-related\nannotations in a bottom-up fashion and 2) a set of higher-order tools for\naggregating respective information across multiple, interdependent services and\nfor coherently integrating our approach into automated CI/CD-pipelines.\nTogether, these building blocks pave the way for providing transparency\ninformation that is more specific and at the same time better reflects the\nactual implementation givens within complex service architectures than current,\noverly broad privacy statements.\n"}
+{"abstract": "  We present the open-source pyratbay framework for exoplanet atmospheric\nmodeling, spectral synthesis, and Bayesian retrieval. The modular design of the\ncode allows the users to generate atmospheric 1D parametric models of the\ntemperature, abundances (in thermochemical equilibrium or\nconstant-with-altitude), and altitude profiles in hydrostatic equilibrium;\nsample ExoMol and HITRAN line-by-line cross sections with custom resolving\npower and line-wing cutoff values; compute emission or transmission spectra\nconsidering cross sections from molecular line transitions, collision-induced\nabsorption, Rayleigh scattering, gray clouds, and alkali resonance lines; and\nperform Markov chain Monte Carlo atmospheric retrievals for a given transit or\neclipse dataset. We benchmarked the pyratbay framework by reproducing\nline-by-line cross-section sampling of ExoMol cross sections, producing\ntransmission and emission spectra consistent with petitRADTRANS models,\naccurately retrieving the atmospheric properties of simulated transmission and\nemission observations generated with TauREx models, and closely reproducing\nAura retrieval analyses of the space-based transmission spectrum of HD 209458b.\nFinally, we present a retrieval analysis of a population of transiting\nexoplanets, focusing on those observed in transmission with the HST WFC3/G141\ngrism. We found that this instrument alone can confidently identify when a\ndataset shows H2O-absorption features; however, it cannot distinguish whether a\nmuted H2O feature is caused by clouds, high atmospheric metallicity, or low H2O\nabundance. Our results are consistent with previous retrieval analyses. The\npyratbay code is available at PyPI (pip install pyratbay) and conda. The code\nis heavily documented (https://pyratbay.readthedocs.io) and tested to provide\nmaximum accessibility to the community and long-term development stability.\n"}
+{"abstract": "  Volumetric imaging by fluorescence microscopy is often limited by anisotropic\nspatial resolution from inferior axial resolution compared to the lateral\nresolution. To address this problem, here we present a deep-learning-enabled\nunsupervised super-resolution technique that enhances anisotropic images in\nvolumetric fluorescence microscopy. In contrast to the existing deep learning\napproaches that require matched high-resolution target volume images, our\nmethod greatly reduces the effort to put into practice as the training of a\nnetwork requires as little as a single 3D image stack, without a priori\nknowledge of the image formation process, registration of training data, or\nseparate acquisition of target data. This is achieved based on the optimal\ntransport driven cycle-consistent generative adversarial network that learns\nfrom an unpaired matching between high-resolution 2D images in lateral image\nplane and low-resolution 2D images in the other planes. Using fluorescence\nconfocal microscopy and light-sheet microscopy, we demonstrate that the trained\nnetwork not only enhances axial resolution, but also restores suppressed visual\ndetails between the imaging planes and removes imaging artifacts.\n"}
+{"abstract": "  A crucial question in galaxy formation is what role new accretion has in star\nformation. Theoretical models have predicted a wide range of correlation\nstrengths between halo accretion and galaxy star formation. Previously, we\npresented a technique to observationally constrain this correlation strength\nfor isolated Milky Way-mass galaxies at $z\\sim 0.12$, based on the correlation\nbetween halo accretion and the density profile of neighbouring galaxies. By\napplying this technique to both observational data from the Sloan Digital Sky\nSurvey and simulation data from the UniverseMachine, where we can test\ndifferent correlation strengths, we ruled out positive correlations between\ndark matter accretion and recent star formation activity. In this work, we\nexpand our analysis by (1) applying our technique separately to red and blue\nneighbouring galaxies, which trace different infall populations, (2)\ncorrelating dark matter accretion rates with $D_{n}4000$ measurements as a\nlonger-term quiescence indicator than instantaneous star-formation rates, and\n(3) analyzing higher-mass isolated central galaxies with $10^{11.0} <\nM_*/M_\\odot < 10^{11.5}$ out to $z\\sim 0.18$. In all cases, our results are\nconsistent with non-positive correlation strengths with $\\gtrsim 85$ per cent\nconfidence, suggesting that processes such as gas recycling dominate star\nformation in massive $z=0$ galaxies.\n"}
+{"abstract": "  Discretization of the uniform norm of functions from a given finite\ndimensional subspace of continuous functions is studied. We pay special\nattention to the case of trigonometric polynomials with frequencies from an\narbitrary finite set with fixed cardinality. We give two different proofs of\nthe fact that for any $N$-dimensional subspace of the space of continuous\nfunctions it is sufficient to use $e^{CN}$ sample points for an accurate upper\nbound for the uniform norm. Previous known results show that one cannot improve\non the exponential growth of the number of sampling points for a good\ndiscretization theorem in the uniform norm.\n  Also, we prove a general result, which connects the upper bound on the number\nof sampling points in the discretization theorem for the uniform norm with the\nbest $m$-term bilinear approximation of the Dirichlet kernel associated with\nthe given subspace. We illustrate application of our technique on the example\nof trigonometric polynomials.\n"}
+{"abstract": "  This paper presents a multi-lead fusion method for the accurate and automated\ndetection of the QRS complex location in 12 lead ECG (Electrocardiogram)\nsignals. The proposed multi-lead fusion method accurately delineates the QRS\ncomplex by the fusion of detected QRS complexes of the individual 12 leads. The\nproposed algorithm consists of two major stages. Firstly, the QRS complex\nlocation of each lead is detected by the single lead QRS detection algorithm.\nSecondly, the multi-lead fusion algorithm combines the information of the QRS\ncomplex locations obtained in each of the 12 leads. The performance of the\nproposed algorithm is improved in terms of Sensitivity and Positive\nPredictivity by discarding the false positives. The proposed method is\nvalidated on the ECG signals with various artifacts, inter and intra subject\nvariations. The performance of the proposed method is validated on the long\nduration recorded ECG signals of St. Petersburg INCART database with\nSensitivity of 99.87% and Positive Predictivity of 99.96% and on the short\nduration recorded ECG signals of CSE (Common Standards for Electrocardiography)\nmulti-lead database with 100% Sensitivity and 99.13% Positive Predictivity.\n"}
+{"abstract": "  This work presents a hybrid modeling approach to data-driven learning and\nrepresentation of unknown physical processes and closure parameterizations.\nThese hybrid models are suitable for situations where the mechanistic\ndescription of dynamics of some variables is unknown, but reasonably accurate\nobservational data can be obtained for the evolution of the state of the\nsystem. In this work, we propose machine learning to account for missing\nphysics and then data assimilation to correct the prediction. In particular, we\ndevise an effective methodology based on a recurrent neural network to model\nthe unknown dynamics. A long short-term memory (LSTM) based correction term is\nadded to the predictive model in order to take into account hidden physics.\nSince LSTM introduces a black-box approach for the unknown part of the model,\nwe investigate whether the proposed hybrid neural-physical model can be further\ncorrected through a sequential data assimilation step. We apply this framework\nto the weakly nonlinear Lorenz model that displays quasiperiodic oscillations,\nthe highly nonlinear Lorenz model, and two-scale Lorenz model. The hybrid\nneural-physics model yields accurate results for the weakly nonlinear Lorenz\nmodel with the predicted state close to the true Lorenz model trajectory. For\nthe highly nonlinear Lorenz model and the two-scale Lorenz model, the hybrid\nneural-physics model deviates from the true state due to the accumulation of\nprediction error from one time step to the next time step. The ensemble Kalman\nfilter approach takes into account the prediction error and updates the\ndiverged prediction using available observations in order to provide a more\naccurate state estimate. The successful synergistic integration of neural\nnetwork and data assimilation for low-dimensional system shows the potential\nbenefits of the proposed hybrid-neural physics model for complex systems.\n"}
+{"abstract": "  The unique electronic and magnetic properties of Lanthanides molecular\ncomplexes place them at the forefront of the race towards high-temperature\nsingle-ion magnets and magnetic quantum bits. The design of compounds of this\nclass has so far been almost exclusively driven by static crystal field\nconsiderations, with emphasis on increasing the magnetic anisotropy barrier.\nThis guideline has now reached its maximum potential and new progress can only\ncome from a deeper understanding of spin-phonon relaxation mechanisms. In this\nwork we compute relaxation times fully ab initio and unveil the nature of all\nspin-phonon relaxation mechanisms, namely Orbach and Raman pathways, in a\nprototypical Dy single-ion magnet. Computational predictions are in agreement\nwith the experimental determination of spin relaxation time and crystal field\nanisotropy, and show that Raman relaxation, dominating at low temperature, is\ntriggered by low-energy phonons and little affected by further engineering of\ncrystal field axiality. A comprehensive analysis of spin-phonon coupling\nmechanism reveals that molecular vibrations beyond the ion's first coordination\nshell can also assume a prominent role in spin relaxation through an\nelectrostatic polarization effect. Therefore, this work shows the way forward\nin the field by delivering a novel and complete set of chemically-sound design\nrules tackling every aspect of spin relaxation at any temperature\n"}
+{"abstract": "  The evaluation of nucleation rates from molecular dynamics trajectories is\nhampered by the slow nucleation time scale and impact of finite size effects.\nHere, we show that accurate nucleation rates can be obtained in a very general\nfashion relying only on the free energy barrier, transition state theory (TST),\nand a simple dynamical correction for diffusive recrossing. In this setup, the\ntime scale problem is overcome by using enhanced sampling methods, in casu\nmetadynamics, whereas the impact of finite size effects can be naturally\ncircumvented by reconstructing the free energy surface from an appropriate\nensemble. Approximations from classical nucleation theory are avoided. We\ndemonstrate the accuracy of the approach by calculating macroscopic rates of\ndroplet nucleation from argon vapor, spanning sixteen orders of magnitude and\nin excellent agreement with literature results, all from simulations of very\nsmall (512 atom) systems.\n"}
+{"abstract": "  We report the largest broadband terahertz (THz) polarizer based on a flexible\nultra-transparent cyclic olefin copolymer (COC). The COC polarizers were\nfabricated by nanoimprint soft lithography with the lowest reported pitch of 2\nor 3 micrometers and depth of 3 micrometers and sub-wavelength Au bilayer wire\ngrid. Fourier Transform Infrared spectroscopy in a large range of 0.9 -20 THz\nshows transmittance of bulk materials such as doped and undoped Si and\npolymers. COC polarizers present more than doubled transmission intensity and\nlarger transmitting band when compared to Si. COC polarizers present superior\nperformance when compared to Si polarizers, with extinctions ratios of at least\n4.4 dB higher and registered performance supported by numerical simulations.\nFabricated Si and COC polarizers' show larger operation gap when compared to a\ncommercial polarizer. Fabrication of these polarizers can be easily up-scaled\nwhich certainly meets functional requirements for many THz devices and\napplications, such as high transparency, lower cost fabrication and flexible\nmaterial.\n"}
+{"abstract": "  The Agda Universal Algebra Library (UALib) is a library of types and programs\n(theorems and proofs) we developed to formalize the foundations of universal\nalgebra in dependent type theory using the Agda programming language and proof\nassistant. The UALib includes a substantial collection of definitions,\ntheorems, and proofs from general algebra and equational logic, including many\nexamples that exhibit the power of inductive and dependent types for\nrepresenting and reasoning about relations, algebraic structures, and\nequational theories. In this paper we discuss the logical foundations on which\nthe library is built, and describe the types defined in the first 13 modules of\nthe library. Special attention is given to aspects of the library that seem\nmost interesting or challenging from a type theory or mathematical foundations\nperspective.\n"}
+{"abstract": "  Accurate and explainable health event predictions are becoming crucial for\nhealthcare providers to develop care plans for patients. The availability of\nelectronic health records (EHR) has enabled machine learning advances in\nproviding these predictions. However, many deep learning based methods are not\nsatisfactory in solving several key challenges: 1) effectively utilizing\ndisease domain knowledge; 2) collaboratively learning representations of\npatients and diseases; and 3) incorporating unstructured text. To address these\nissues, we propose a collaborative graph learning model to explore\npatient-disease interactions and medical domain knowledge. Our solution is able\nto capture structural features of both patients and diseases. The proposed\nmodel also utilizes unstructured text data by employing an attention regulation\nstrategy and then integrates attentive text features into a sequential learning\nprocess. We conduct extensive experiments on two important healthcare problems\nto show the competitive prediction performance of the proposed method compared\nwith various state-of-the-art models. We also confirm the effectiveness of\nlearned representations and model interpretability by a set of ablation and\ncase studies.\n"}
+{"abstract": "  Trajectory planning is commonly used as part of a local planner in autonomous\ndriving. This paper considers the problem of planning a\ncontinuous-curvature-rate trajectory between fixed start and goal states that\nminimizes a tunable trade-off between passenger comfort and travel time. The\nproblem is an instance of infinite dimensional optimization over two continuous\nfunctions: a path, and a velocity profile. We propose a simplification of this\nproblem that facilitates the discretization of both functions. This paper also\nproposes a method to quickly generate minimal-length paths between start and\ngoal states based on a single tuning parameter: the second derivative of\ncurvature. Furthermore, we discretize the set of velocity profiles along a\ngiven path into a selection of acceleration way-points along the path.\nGradient-descent is then employed to minimize cost over feasible choices of the\nsecond derivative of curvature, and acceleration way-points, resulting in a\nmethod that repeatedly solves the path and velocity profiles in an iterative\nfashion. Numerical examples are provided to illustrate the benefits of the\nproposed methods.\n"}
+{"abstract": "  Data sharing by researchers is a centerpiece of Open Science principles and\nscientific progress. For a sample of 6019 researchers, we analyze the\nextent/frequency of their data sharing. Specifically, the relationship with the\nfollowing four variables: how much they value data citations, the extent to\nwhich their data-sharing activities are formally recognized, their perceptions\nof whether sufficient credit is awarded for data sharing, and the reported\nextent to which data citations motivate their data sharing. In addition, we\nanalyze the extent to which researchers have reused openly accessible data, as\nwell as how data sharing varies by professional age-cohort, and its\nrelationship to the value they place on data citations. Furthermore, we\nconsider most of the explanatory variables simultaneously by estimating a\nmultiple linear regression that predicts the extent/frequency of their data\nsharing. We use the dataset of the State of Open Data Survey 2019 by Springer\nNature and Digital Science. Results do allow us to conclude that a desire for\nrecognition/credit is a major incentive for data sharing. Thus, the possibility\nof receiving data citations is highly valued when sharing data, especially\namong younger researchers, irrespective of the frequency with which it is\npracticed. Finally, the practice of data sharing was found to be more prevalent\nat late research career stages, despite this being when citations are less\nvalued and have a lower motivational impact. This could be due to the fact that\nlater-career researchers may benefit less from keeping their data private.\n"}
+{"abstract": "  Visual object tracking, which is representing a major interest in image\nprocessing field, has facilitated numerous real world applications. Among them,\nequipping unmanned aerial vehicle (UAV) with real time robust visual trackers\nfor all day aerial maneuver, is currently attracting incremental attention and\nhas remarkably broadened the scope of applications of object tracking. However,\nprior tracking methods have merely focused on robust tracking in the\nwell-illuminated scenes, while ignoring trackers' capabilities to be deployed\nin the dark. In darkness, the conditions can be more complex and harsh, easily\nposing inferior robust tracking or even tracking failure. To this end, this\nwork proposed a novel discriminative correlation filter based tracker with\nillumination adaptive and anti dark capability, namely ADTrack. ADTrack firstly\nexploits image illuminance information to enable adaptability of the model to\nthe given light condition. Then, by virtue of an efficient and effective image\nenhancer, ADTrack carries out image pretreatment, where a target aware mask is\ngenerated. Benefiting from the mask, ADTrack aims to solve a dual regression\nproblem where dual filters, i.e., the context filter and target focused filter,\nare trained with mutual constraint. Thus ADTrack is able to maintain\ncontinuously favorable performance in all-day conditions. Besides, this work\nalso constructed one UAV nighttime tracking benchmark UAVDark135, comprising of\nmore than 125k manually annotated frames, which is also very first UAV\nnighttime tracking benchmark. Exhaustive experiments are extended on\nauthoritative daytime benchmarks, i.e., UAV123 10fps, DTB70, and the newly\nbuilt dark benchmark UAVDark135, which have validated the superiority of\nADTrack in both bright and dark conditions on a single CPU.\n"}
+{"abstract": "  In this paper, we propose a novel framework to translate a portrait\nphoto-face into an anime appearance. Our aim is to synthesize anime-faces which\nare style-consistent with a given reference anime-face. However, unlike typical\ntranslation tasks, such anime-face translation is challenging due to complex\nvariations of appearances among anime-faces. Existing methods often fail to\ntransfer the styles of reference anime-faces, or introduce noticeable\nartifacts/distortions in the local shapes of their generated faces. We propose\nAniGAN, a novel GAN-based translator that synthesizes high-quality anime-faces.\nSpecifically, a new generator architecture is proposed to simultaneously\ntransfer color/texture styles and transform local facial shapes into anime-like\ncounterparts based on the style of a reference anime-face, while preserving the\nglobal structure of the source photo-face. We propose a double-branch\ndiscriminator to learn both domain-specific distributions and domain-shared\ndistributions, helping generate visually pleasing anime-faces and effectively\nmitigate artifacts. Extensive experiments on selfie2anime and a new face2anime\ndataset qualitatively and quantitatively demonstrate the superiority of our\nmethod over state-of-the-art methods. The new dataset is available at\nhttps://github.com/bing-li-ai/AniGAN .\n"}
+{"abstract": "  Double-parton scattering is investigated using events with a Z boson and\njets. The Z boson is reconstructed using only the dimuon channel. The\nmeasurements are performed with proton-proton collision data recorded by the\nCMS experiment at the LHC at $\\sqrt{s} =$ 13 TeV, corresponding to an\nintegrated luminosity of 35.9 fb$^{-1}$ collected in the year 2016.\nDifferential cross sections of Z + $\\geq$ 1 jet and Z + $\\geq$ 2 jets are\nmeasured with transverse momentum of the jets above 20 GeV and pseudorapidity\n$|\\eta|$ $\\lt$ 2.4. Several distributions with sensitivity to double-parton\nscattering effects are measured as functions of the angle and the transverse\nmomentum imbalance between the Z boson and the jets. The measured distributions\nare compared with predictions from several event generators with different\nhadronization models and different parameter settings for multiparton\ninteractions. The measured distributions show a dependence on the hadronization\nand multiparton interaction simulation parameters, and are important input for\nfuture improvements of the simulations.\n"}
+{"abstract": "  In this study, we investigate the metastable behavior of Metropolis-type\nGlauber dynamics associated with the Blume-Capel model with zero chemical\npotential and zero external field at very low temperatures. The corresponding\nanalyses for the same model with zero chemical potential and positive small\nexternal field were performed in [Cirillo and Nardi, Journal of Statistical\nPhysics, 150: 1080-1114, 2013] and [Landim and Lemire, Journal of Statistical\nPhysics, 164: 346-376, 2016]. We obtain both large deviation-type and\npotential-theoretic results on the metastable behavior in our setting. To this\nend, we perform highly thorough investigation on the energy landscape, where it\nis revealed that no critical configurations exist and alternatively a massive\nflat plateau of saddle configurations resides therein.\n"}
+{"abstract": "  The Multi-Phase Transport model (AMPT) is used to investigate the\nlongitudinal broadening of the transverse momentum two-particle correlator\n$C_{2}\\left(\\Delta\\eta,\\Delta\\varphi\\right)$, and its utility to extract the\nspecific shear viscosity, $\\eta/s$, of the quark-gluon plasma formed in\nultra-relativistic heavy ion collisions. The results from these model studies\nindicate that the longitudinal broadening of\n$C_{2}\\left(\\Delta\\eta,\\Delta\\varphi\\right)$ is sensitive to the magnitude of\n$\\eta/s$. However, reliable extraction of the longitudinal broadening of the\ncorrelator requires the suppression of possible self-correlations associated\nwith the definition of the collision centrality.\n"}
+{"abstract": "  We show that for $\\Pi_2$-properties of second or third order arithmetic as\nformalized in appropriate natural signatures the apparently weaker notion of\nforcibility overlaps with the standard notion of consistency (assuming large\ncardinal axioms).\n  Among such $\\Pi_2$-properties we mention: the negation of the Continuum\nhypothesis, Souslin Hypothesis, the negation of Whitehead's conjecture on free\ngroups, the non-existence of outer automorphisms for the Calkin algebra, etc...\nIn particular this gives an a posteriori explanation of the success forcing\n(and forcing axioms) met in producing models of such properties.\n  Our main results relate generic absoluteness theorems for second order\narithmetic, Woodin's axiom $(*)$ and forcing axioms to Robinson's notion of\nmodel companionship (as applied to set theory). We also briefly outline in\nwhich ways these results provide an argument to refute the Continuum\nhypothesis.\n"}
+{"abstract": "  In Part I of this study, we obtained the ray (group) velocity gradients and\nHessians with respect to the ray locations, directions and the anisotropic\nmodel parameters, at nodal points along ray trajectories, considering general\nanisotropic (triclinic) media and both, quasi-compressional and quasi-shear\nwaves. Ray velocity derivatives for anisotropic media with higher symmetries\nwere considered particular cases of general anisotropy. In this part, Part II,\nwe follow the computational workflow presented in Part I, formulating the ray\nvelocity derivatives directly for polar anisotropic (transverse isotropy with\ntilted axis of symmetry, TTI) media for the coupled qP and qSV waves and for SH\nwaves. The acoustic approximation for qP waves is considered a special case.\nThe medium properties, normally specified at regular three-dimensional fine\ngrid points, are the five material parameters: the axial compressional and\nshear velocities and the three Thomsen parameters, and two geometric\nparameters: the polar angles defining the local direction of the medium\nsymmetry axis. All the parameters are assumed spatially (smoothly) varying,\nwhere their gradients and Hessians can be reliably computed. Two case examples\nare considered; the first represents compacted shale/sand rocks (with positive\nanellipticity) and the second, unconsolidated sand rocks with strong negative\nanellipticity (manifesting a qSV triplication). The ray velocity derivatives\nobtained in this part are first tested by comparing them with the corresponding\nnumerical (finite difference) derivatives. Additionally, we show that exactly\nthe same results (ray velocity derivatives) can be obtained if we transform the\ngiven polar anisotropic model parameters (five material and two geometric) into\nthe twenty-one stiffness tensor components of a general anisotropic (triclinic)\nmedium, and apply the theory derived in Part I.\n"}
+{"abstract": "  We provide a simple analysis of the big-bang nucleosynthesis (BBN)\nsensitivity to the light dark matter (DM) generated by the thermal freeze-in\nmechanism. It is shown that the ratio of the effective neutrino number shift\n$\\Delta N_{\\nu}$ over the DM relic density $\\omega\\equiv \\Omega h^2$, denoted\nby $R_\\chi\\equiv\\Delta N_\\nu/\\omega$, cancels the decaying particle mass and\nthe feeble coupling, rendering therefore a simple visualization of $\\Delta\nN_{\\nu}$ at the BBN epoch in terms of the DM mass. This property drives one to\nconclude that the shift with a sensitivity of $\\Delta N_{\\nu}\\simeq\n\\mathcal{O}(0.1)$ cannot originate from a single warm DM under the\nLyman-$\\alpha$ forest constraints. For the cold-plus-warm DM scenarios where\nthe Lyman-$\\alpha$ constraints are diluted, the ratio $R_\\chi$ can be\npotentially used to test the thermal freeze-in mechanism in generating a small\nwarm component of DM and a possible excess at the level of $\\Delta\nN_{\\nu}\\simeq \\mathcal{O}(0.01)$.\n"}
+{"abstract": "  We present a scheme for ground-state cooling of a mechanical resonator by\nsimultaneously coupling it to a superconducting qubit and a cavity field. The\nHamiltonian describing the hybrid system dynamics is systematically derived.\nThe cooling process is driven by a red-detuned ac drive on the qubit and a\nlaser drive on the optomechanical cavity. We have investigated cooling in the\nweak and the strong coupling regimes for both the individual system, i.e.,\nqubit assisted cooling and optomechanical cooling, and compared them with the\neffective hybrid cooling. It is shown that hybrid cooling is more effective\ncompared to the individual cooling mechanisms, and could be applied in both the\nresolved and the unresolved sideband regimes.\n"}
+{"abstract": "  Filtering packet traffic and rules of permit/denial of data packets into\nnetwork nodes are granted by facilitating Access Control Lists (ACL). This\npaper proposes a procedure of adding a link load threshold value to the access\ncontrol list rules option, which acts on the basis of threshold value. The\nultimate goal of this enhanced ACL is to avoid congestion in targeted\nsubnetworks. The link load threshold value allows to decide that packet traffic\nis rerouted by the router to avoid congestion, or packet drop happens on the\nbasis of packet priorities. The packet rerouting in case of high traffic loads,\nbased on new packet filtering procedure for congestion avoidance, will result\nin the reduction of the overall packet drop ratio, and of over-subscription in\ncongested subnetworks.\n"}
+{"abstract": "  The prompt emission of GRBs has been investigated for more than 50 years but\nremains poorly understood. Commonly, spectral and temporal profiles of\n{\\gamma}-ray emission are analysed. However, they are insufficient for a\ncomplete picture on GRB-related physics. The addition of polarization\nmeasurements provides invaluable information towards the understanding of these\nastrophysical sources. In recent years, dedicated polarimeters, such as POLAR\nand GAP, were built. The former of which observed low levels of polarization as\nwell as a temporal evolution of the polarization angle. It was understood that\na larger sample of GRB polarization measurements and time resolved studies are\nnecessary to constrain theoretical models. The POLAR-2 mission aims to address\nthis by increasing the effective area by an order of magnitude compared to\nPOLAR. POLAR-2 is manifested for launch on board the China Space Station in\n2024 and will operate for at least 2 years. Insight from POLAR will aid in the\nimprovement of the overall POLAR-2 design. Major improvements (compared to\nPOLAR) will include the replacement of multi-anode PMTs (MAPMTs) with SiPMs,\nincrease in sensitive volume and further technological upgrades. POLAR-2 is\nprojected to measure about 50 GRBs per year with equal or better quality\ncompared to the best seen by POLAR. The instrument design, preliminary results\nand anticipated scientific potential of this mission will be discussed.\n"}
+{"abstract": "  Dynamical magnets can pump spin currents into superconductors. To understand\nsuch a phenomenon, we develop a method utilizing the generalized Usadel\nequation to describe time-dependent situations in superconductors in contact\nwith dynamical ferromagnets. Our proof-of-concept theory is valid when there is\nsufficient dephasing at finite temperatures, and when the ferromagnetic\ninsulators are weakly polarized. We derive the effective equation of motion for\nthe Keldysh Green's function focusing on a thin film superconductor sandwiched\nbetween two noncollinear ferromagnetic insulators of which one is dynamical. In\nturn, we compute the spin currents in the system as a function of the\ntemperature and the magnetizations' relative orientations. When the induced\nZeeman splitting is weak, we find that the spin accumulation in the\nsuperconducting state is smaller than in the normal states due to the lack of\nquasiparticle states inside the gap. This feature gives a lower backflow spin\ncurrent from the superconductor as compared to a normal metal. Furthermore, in\nsuperconductors, we find that the ratio between the backflow spin current in\nthe parallel and anti-parallel magnetization configuration depends strongly on\ntemperature, in contrast to the constant ratio in normal metals.\n"}
+{"abstract": "  Data augmentation is an inexpensive way to increase training data diversity\nand is commonly achieved via transformations of existing data. For tasks such\nas classification, there is a good case for learning representations of the\ndata that are invariant to such transformations, yet this is not explicitly\nenforced by classification losses such as the cross-entropy loss. This paper\ninvestigates the use of training objectives that explicitly impose this\nconsistency constraint and how it can impact downstream audio classification\ntasks. In the context of deep convolutional neural networks in the supervised\nsetting, we show empirically that certain measures of consistency are not\nimplicitly captured by the cross-entropy loss and that incorporating such\nmeasures into the loss function can improve the performance of audio\nclassification systems. Put another way, we demonstrate how existing\naugmentation methods can further improve learning by enforcing consistency.\n"}
+{"abstract": "  We use numerical simulations to demonstrate third-harmonic generation with\nnear-unity nonlinear circular dichroism (CD) and high conversion efficiency ($\n10^{-2}\\ \\text{W}^{-2}$) in asymmetric Si-on-SiO$_2$ metasurfaces. The working\nprinciple relies on the selective excitation of a quasi-bound state in the\ncontinuum, characterized by a very high ($>10^5$) quality-factor. By tuning\nmulti-mode interference with the variation of the metasurface geometrical\nparameters, we show the possibility of independent control of linear CD and\nnonlinear CD. Our results pave the way for the development of all-dielectric\nmetasurfaces for nonlinear chiro-optical devices with high conversion\nefficiency.\n"}
+{"abstract": "  Recent advances in representation learning have demonstrated an ability to\nrepresent information from different modalities such as video, text, and audio\nin a single high-level embedding vector. In this work we present a\nself-supervised learning framework that is able to learn a representation that\ncaptures finer levels of granularity across different modalities such as\nconcepts or events represented by visual objects or spoken words. Our framework\nrelies on a discretized embedding space created via vector quantization that is\nshared across different modalities. Beyond the shared embedding space, we\npropose a Cross-Modal Code Matching objective that forces the representations\nfrom different views (modalities) to have a similar distribution over the\ndiscrete embedding space such that cross-modal objects/actions localization can\nbe performed without direct supervision. In our experiments we show that the\nproposed discretized multi-modal fine-grained representation (e.g.,\npixel/word/frame) can complement high-level summary representations (e.g.,\nvideo/sentence/waveform) for improved performance on cross-modal retrieval\ntasks. We also observe that the discretized representation uses individual\nclusters to represent the same semantic concept across modalities.\n"}
+{"abstract": "  We obtained high-resolution spectra of the ultra-cool M-dwarf TRAPPIST-1\nduring the transit of its planet `b' using two high dispersion near-infrared\nspectrographs, IRD instrument on the Subaru 8.2m telescope and HPF instrument\non the 10m Hobby-Eberly Telescope. These spectroscopic observations are\ncomplemented by a photometric transit observation for planet `b' using the\nAPO/ARCTIC, which assisted us to capture the correct transit times for our\ntransit spectroscopy. Using the data obtained by the new IRD and HPF\nobservations, as well as the prior transit observations of planets `b', `e' and\n`f' from IRD, we attempt to constrain the atmospheric escape of the planet\nusing the He I triplet 10830 {\\AA} absorption line. We do not detect evidence\nfor any primordial extended H-He atmospheres in all three planets. To limit any\nplanet related absorption, we place an upper limit on the equivalent widths of\n<7.754 m{\\AA} for planet `b', <10.458 m{\\AA} for planet `e', and <4.143 m{\\AA}\nfor planet `f' at 95% confidence from the IRD data, and <3.467 m{\\AA} for\nplanet `b' at 95% confidence from HPF data. Using these limits along with a\nsolar-like composition isothermal Parker wind model, we attempt to constrain\nthe mass-loss rates for the three planets. For TRAPPIST-1b, our models exclude\nthe highest possible energy-limited rate for a wind temperature <5000 K. This\nnon-detection of extended atmospheres having low mean-molecular weight in all\nthree planets aids in further constraining their atmospheric composition by\nsteering the focus towards the search of high molecular weight species in their\natmospheres.\n"}
+{"abstract": "  With the development of deep networks on various large-scale datasets, a\nlarge zoo of pretrained models are available. When transferring from a model\nzoo, applying classic single-model based transfer learning methods to each\nsource model suffers from high computational burden and cannot fully utilize\nthe rich knowledge in the zoo. We propose \\emph{Zoo-Tuning} to address these\nchallenges, which learns to adaptively transfer the parameters of pretrained\nmodels to the target task. With the learnable channel alignment layer and\nadaptive aggregation layer, Zoo-Tuning \\emph{adaptively aggregates channel\naligned pretrained parameters} to derive the target model, which promotes\nknowledge transfer by simultaneously adapting multiple source models to\ndownstream tasks. The adaptive aggregation substantially reduces the\ncomputation cost at both training and inference. We further propose lite\nZoo-Tuning with the temporal ensemble of batch average gating values to reduce\nthe storage cost at the inference time. We evaluate our approach on a variety\nof tasks, including reinforcement learning, image classification, and facial\nlandmark detection. Experiment results demonstrate that the proposed adaptive\ntransfer learning approach can transfer knowledge from a zoo of models more\neffectively and efficiently.\n"}
+{"abstract": "  In recent cross-disciplinary studies involving both optics and computing,\nsingle-photon-based decision-making has been demonstrated by utilizing the\nwave-particle duality of light to solve multi-armed bandit problems.\nFurthermore, entangled-photon-based decision-making has managed to solve a\ncompetitive multi-armed bandit problem in such a way that conflicts of\ndecisions among players are avoided while ensuring equality. However, as these\nstudies are based on the polarization of light, the number of available choices\nis limited to two, corresponding to two orthogonal polarization states. Here we\npropose a scalable principle to solve competitive decision-making situations by\nusing the orbital angular momentum of photons based on its high dimensionality,\nwhich theoretically allows an unlimited number of arms. Moreover, by extending\nthe Hong-Ou-Mandel effect to more than two states, we theoretically establish\nan experimental configuration able to generate multi-photon states with orbital\nangular momentum and conditions that provide conflict-free selections at every\nturn. We numerically examine total rewards regarding three-armed bandit\nproblems, for which the proposed strategy accomplishes almost the theoretical\nmaximum, which is greater than a conventional mixed strategy intending to\nrealize Nash equilibrium. This is thanks to the quantum interference effect\nthat achieves no-conflict selections, even in the exploring phase to find the\nbest arms.\n"}
+{"abstract": "  We show that the moduli stack of elliptic surfaces of Kodaira dimension one\nand without multiple fiber satisfies a weak form of the topological\nhyperbolicity.\n"}
+{"abstract": "  We present analysis of the spatial density structure for the outer disk from\n8$-$14 \\,kpc with the LAMOST DR5 13534 OB-type stars and observe similar\nflaring on north and south sides of the disk implying that the flaring\nstructure is symmetrical about the Galactic plane, for which the scale height\nat different Galactocentric distance is from 0.14 to 0.5 \\,kpc. By using the\naverage slope to characterize the flaring strength we find that the thickness\nof the OB stellar disk is similar but flaring is slightly stronger compared to\nthe thin disk as traced by red giant branch stars, possibly implying that\nsecular evolution is not the main contributor to the flaring but perturbation\nscenarios such as interactions with passing dwarf galaxies should be more\npossible. When comparing the scale height of OB stellar disk of the north and\nsouth sides with the gas disk, the former one is slightly thicker than the\nlater one by $\\approx$ 33 and 9 \\,pc, meaning that one could tentatively use\nyoung OB-type stars to trace the gas properties. Meanwhile, we unravel that the\nradial scale length of the young OB stellar disk is 1.17 $\\pm$ 0.05 \\,kpc,\nwhich is shorter than that of the gas disk, confirming that the gas disk is\nmore extended than stellar disk. What is more, by considering the mid-plane\ndisplacements ($Z_{0}$) in our density model we find that almost all of $Z_{0}$\nare within 100 \\,pc with the increasing trend as Galactocentric distance\nincreases.\n"}
+{"abstract": "  Velocity-space anisotropy can significantly modify fusion reactivity. The\nnature and magnitude of this modification depends on the plasma temperature, as\nwell as the details of how the anisotropy is introduced. For plasmas that are\nsufficiently cold compared to the peak of the fusion cross-section, anisotropic\ndistributions tend to have higher yields than isotropic distributions with the\nsame thermal energy. At higher temperatures, it is instead isotropic\ndistributions that have the highest yields. However, the details of this\nbehavior depend on exactly how the distribution differs from an isotropic\nMaxwellian. This paper describes the effects of anisotropy on fusion yield for\nthe class of anisotropic distribution functions with the same energy\ndistribution as a 3D isotropic Maxwellian, and compares those results with the\nyields from bi-Maxwellian distributions. In many cases, especially for plasmas\nsomewhat below reactor-regime temperatures, the effects of anisotropy can be\nsubstantial.\n"}
+{"abstract": "  The first generation of blockchain focused on digital currencies and secure\nstorage, management and transfer of tokenized values. Thereafter, the focus has\nbeen shifting from currencies to a broader application space. In this paper, we\nsystematically explore marketplace types and properties, and consider the\nmechanisms required to support those properties through blockchain. We propose\na generic and configurable framework for blockchain-based marketplaces, and\ndescribe how popular marketplace types, price discovery policies, and other\nconfiguration parameters are implemented within the framework by presenting\nconcrete event-based algorithms. Finally, we consider three use cases with\nwidely diverging properties and show how the proposed framework supports them.\n"}
+{"abstract": "  A tangle is a connected topological space constructed by gluing several\ncopies of the unit interval $[0, 1]$. We explore which tangles guarantee\nenvy-free allocations of connected shares for n agents, meaning that such\nallocations exist no matter which monotonic and continuous functions represent\nagents' valuations. Each single tangle $\\mathcal{T}$ corresponds in a natural\nway to an infinite topological class $\\mathcal{G}(\\mathcal{T})$ of multigraphs,\nmany of which are graphs. This correspondence links EF fair division of tangles\nto EFk$_{outer}$ fair division of graphs. We know from Bil\\`o et al that all\nHamiltonian graphs guarantee EF1$_{outer}$ allocations when the number of\nagents is 2, 3, 4 and guarantee EF2$_{outer}$ allocations for arbitrarily many\nagents. We show that exactly six tangles are stringable; these guarantee EF\nconnected allocations for any number of agents, and their associated\ntopological classes contain only Hamiltonian graphs. Any non-stringable tangle\nhas a finite upper bound r on the number of agents for which EF allocations of\nconnected shares are guaranteed. Most graphs in the associated non-stringable\ntopological class are not Hamiltonian, and a negative transfer theorem shows\nthat for each $k \\geq 1$ most of these graphs fail to guarantee EFk$_{outer}$\nallocations of vertices for r + 1 or more agents. This answers a question posed\nin Bil\\`o et al, and explains why a focus on Hamiltonian graphs was necessary.\nWith bounds on the number of agents, however, we obtain positive results for\nsome non-stringable classes. An elaboration of Stromquist's moving knife\nprocedure shows that the non-stringable lips tangle guarantees envy-free\nallocations of connected shares for three agents. We then modify the discrete\nversion of Stromquist's procedure in Bil\\`o et al to show that all graphs in\nthe topological class guarantee EF1$_{outer}$ allocations for three agents.\n"}
+{"abstract": "  This paper studies properties of binary runlength-limited sequences with\nadditional constraints on their Hamming weight and/or their number of runs of\nidentical symbols. An algebraic and a probabilistic (entropic) characterization\nof the exponential growth rate of the number of such sequences, i.e., their\ninformation capacity, are obtained by using the methods of multivariate\nanalytic combinatorics, and properties of the capacity as a function of its\nparameters are stated. The second-order term in the asymptotic expansion of the\nrate of these sequences is also given, and the typical values of the relevant\nquantities are derived. Several applications of the results are illustrated,\nincluding bounds on codes for weight-preserving and run-preserving channels\n(e.g., the run-preserving insertion-deletion channel), a sphere-packing bound\nfor channels with sparse error patterns, and the asymptotics of constant-weight\nsub-block constrained sequences. In addition, the asymptotics of a closely\nrelated notion -- $ q $-ary sequences with fixed Manhattan weight -- is briefly\ndiscussed, and an application in coding for molecular timing channels is\nillustrated.\n"}
+{"abstract": "  We propose novel triple-${\\bm q}$ multipole orders as possible candidates for\nthe two distinct low-temperature symmetry broken phases in quadrupolar system\nPrV$_2$Al$_{20}$. An analysis of the experiment under [111] magnetic fields\nindicates that the {\\it ferro} octupole moments in the lower temperature phase\narise from the {\\it antiferro} octupole interactions. We demonstrate that the\ntriple-${\\bm q}$ multipole orders can solve this seemingly inconsistent issue.\nAnisotropies of quadrupole moments stabilize a triple-${\\bm q}$ order, which\nfurther leads to the second transition to a coexisting phase with triple-${\\bm\nq}$ octupole moments. The cubic invariant of quadrupole moments formed by the\ntriple-${\\bm q}$ components and the characteristic couplings with the octupole\nmoments in their free energy play important roles. We analyze a multipolar\nexchange model by mean-field approximation and discuss the temperature and\nmagnetic field phase diagrams. Many of the microscopic results, such as the\nnumber of phases and the magnitudes of critical fields in the phase diagrams,\nare qualitatively consistent with the experiments in PrV$_2$Al$_{20}$.\n"}
+{"abstract": "  Rotational-vibrational transitions of the fundamental vibrational modes of\nthe $^{12}$C$^{14}$N$^+$ and $^{12}$C$^{15}$N$^+$ cations have been observed\nfor the first time using a cryogenic ion trap apparatus with an action\nspectroscopy scheme. The lines P(3) to R(3) of $^{12}$C$^{14}$N$^+$ and R(1) to\nR(3) of $^{12}$C$^{15}$N$^+$ have been measured, limited by the trap\ntemperature of approximately 4 K and the restricted tuning range of the\ninfrared laser. Spectroscopic parameters are presented for both isotopologues,\nwith band origins at 2000.7587(1) and 1970.321(1) cm$^{-1}$, respectively, as\nwell as an isotope independent fit combining the new and the literature data.\n"}
+{"abstract": "  WD 0145+234 is a white dwarf that is accreting metals from a circumstellar\ndisc of planetary material. It has exhibited a substantial and sustained\nincrease in 3-5 micron flux since 2018. Follow-up Spitzer photometry reveals\nthat emission from the disc had begun to decrease by late 2019. Stochastic\nbrightening events superimposed on the decline in brightness suggest the\nliberation of dust during collisional evolution of the circumstellar solids. A\nsimple model is used to show that the observations are indeed consistent with\nongoing collisions. Rare emission lines from circumstellar gas have been\ndetected at this system, supporting the emerging picture of white dwarf debris\ndiscs as sites of collisional gas and dust production.\n"}
+{"abstract": "  Magnetic fields lines are trapped in black hole event horizons by accreting\nplasma. If the trapped field lines are lightly loaded with plasma, then their\nmotion is controlled by their footpoints on the horizon and thus by the spin of\nthe black hole. In this paper, we investigate the boundary layer between\nlightly loaded polar field lines and a dense, equatorial accretion flow. We\npresent an analytic model for aligned prograde and retrograde accretion systems\nand argue that there is significant shear across this \"jet-disk boundary\" at\nmost radii for all black hole spins. Specializing to retrograde aligned\naccretion, where the model predicts the strongest shear, we show numerically\nthat the jet-disk boundary is unstable. The resulting mixing layer episodically\nloads plasma onto trapped field lines where it is heated, forced to rotate with\nthe hole, and permitted to escape outward into the jet. In one case we follow\nthe mass loading in detail using Lagrangian tracer particles and find a\ntime-averaged mass-loading rate ~ 0.01 Mdot.\n"}
+{"abstract": "  Game publishers and anti-cheat companies have been unsuccessful in blocking\ncheating in online gaming. We propose a novel, vision-based approach that\ncaptures the final state of the frame buffer and detects illicit overlays. To\nthis aim, we train and evaluate a DNN detector on a new dataset, collected\nusing two first-person shooter games and three cheating software. We study the\nadvantages and disadvantages of different DNN architectures operating on a\nlocal or global scale. We use output confidence analysis to avoid unreliable\ndetections and inform when network retraining is required. In an ablation\nstudy, we show how to use Interval Bound Propagation to build a detector that\nis also resistant to potential adversarial attacks and study its interaction\nwith confidence analysis. Our results show that robust and effective\nanti-cheating through machine learning is practically feasible and can be used\nto guarantee fair play in online gaming.\n"}
+{"abstract": "  In this paper, we prove that for the doubly symmetric binary distribution,\nthe lower increasing envelope and the upper envelope of the\nminimum-relative-entropy region are respectively convex and concave. We also\nprove that another function induced the minimum-relative-entropy region is\nconcave. These two envelopes and this function were previously used to\ncharacterize the optimal exponents in strong small-set expansion problems and\nstrong Brascamp--Lieb inequalities. The results in this paper, combined with\nthe strong small-set expansion theorem derived by Yu, Anantharam, and Chen\n(2021), and the strong Brascamp--Lieb inequality derived by Yu (2021), confirm\npositively Ordentlich--Polyanskiy--Shayevitz's conjecture on the strong\nsmall-set expansion (2019) and Polyanskiy's conjecture on the strong\nBrascamp--Lieb inequality (2016). The proofs in this paper are based on the\nequivalence between the convexity of a function and the convexity of the set of\nminimizers of its Lagrangian dual.\n"}
+{"abstract": "  Deep learning techniques have the power to identify the degree of\nmodification of high energy jets traversing deconfined QCD matter on a\njet-by-jet basis. Such knowledge allows us to study jets based on their\ninitial, rather than final energy. We show how this new technique provides\nunique access to the genuine configuration profile of jets over the transverse\nplane of the nuclear collision, both with respect to their production point and\ntheir orientation. Effectively removing the selection biases induced by\nfinal-state interactions, one can in this way analyse the potential azimuthal\nanisotropies of jet production associated to initial-state effects.\nAdditionally, we demonstrate the capability of our new method to locate with\nremarkable precision the production point of a dijet pair in the nuclear\noverlap region, in what constitutes an important step forward towards the long\nterm quest of using jets as tomographic probes of the quark-gluon plasma.\n"}
+{"abstract": "  In \\cite{CMW19}, the authors introduced $k$-entanglement breaking linear maps\nto understand the entanglement breaking property of completely positive maps on\ntaking composition. In this article, we do a systematic study of\n$k$-entanglement breaking maps. We prove many equivalent conditions for a\n$k$-positive linear map to be $k$-entanglement breaking, thereby study the\nmapping cone structure of $k$-entanglement breaking maps. We discuss examples\nof $k$-entanglement breaking maps and some of their significance. As an\napplication of our study, we characterize completely positive maps that reduce\nSchmidt number on taking composition with another completely positive map.\n"}
+{"abstract": "  We consider a locally uniformly strictly elliptic second order partial\ndifferential operator in $\\mathbb{R}^d$, $d\\ge 2$, with low regularity\nassumptions on its coefficients, as well as an associated Hunt process and\nsemigroup. The Hunt process is known to solve a corresponding stochastic\ndifferential equation that is pathwise unique. In this situation, we study the\nrelation of invariance, infinitesimal invariance, recurrence, transience,\nconservativeness and $L^r$-uniqueness, and present sufficient conditions for\nnon-existence of finite infinitesimally invariant measures as well as finite\ninvariant measures. Our main result is that recurrence implies uniqueness of\ninfinitesimally invariant measures, as well as existence and uniqueness of\ninvariant measures, both in subclasses of locally finite measures. We can hence\nmake in particular use of various explicit analytic criteria for recurrence\nthat have been previously developed in the context of (generalized) Dirichlet\nforms and present diverse examples and counterexamples for uniqueness of\ninfinitesimally invariant, as well as invariant measures and an example where\n$L^1$-uniqueness fails for one infinitesimally invariant measure but holds for\nanother and pathwise uniqueness holds. Furthermore, we illustrate how our\nresults can be applied to related work and vice versa.\n"}
+{"abstract": "  Superconductors with kagome lattices have been identified for over 40 years,\nwith a superconducting transition temperature TC up to 7K. Recently, certain\nkagome superconductors have been found to exhibit an exotic charge order, which\nintertwines with superconductivity and persists to a temperature being one\norder of magnitude higher than TC. In this work, we use scanning tunneling\nmicroscopy (STM) to study the charge order in kagome superconductor RbV3Sb5. We\nobserve both a 2x2 chiral charge order and nematic surface superlattices\n(predominantly 1x4). We find that the 2x2 charge order exhibits intrinsic\nchirality with magnetic field tunability. Defects can scatter electrons to\nintroduce standing waves, which couple with the charge order to cause extrinsic\neffects. While the chiral charge order resembles that discovered in KV3Sb5, it\nfurther interacts with the nematic surface superlattices that are absent in\nKV3Sb5 but exist in CsV3Sb5.\n"}
+{"abstract": "  Stochastic processes offer a fundamentally different paradigm of dynamics\nthan deterministic processes that students are most familiar with, the most\nprominent example of the latter being Newtons laws of motion. Here, we discuss\nin a pedagogical manner a simple and illustrative example of stochastic\nprocesses in the form of a particle undergoing standard Brownian diffusion,\nwith the additional feature of the particle resetting repeatedly and at random\ntimes to its initial condition. Over the years, many different variants of this\nsimple setting have been studied, including extensions to many-body interacting\nsystems, all of which serve as illustrations of peculiar static and dynamic\nfeatures that characterize stochastic dynamics at long times. We will provide\nin this work a brief overview of this active and rapidly evolving field by\nconsidering the arguably simplest example of Brownian diffusion in one\ndimension. Along the way, we will learn about some of the general techniques\nthat a physicist employs to study stochastic processes.\n"}
+{"abstract": "  In this paper, we prove various radius results and obtain sufficient\nconditions using the convolution for the Ma-Minda classes $\\mathcal{S}^*(\\psi)$\nand $\\mathcal{C}(\\psi)$ of starlike and convex analytic functions. We also\nobtain the Bohr radius for the class $ S_{f}(\\psi):=\n\\{g(z)=\\sum_{k=1}^{\\infty}b_k z^k : g \\prec f \\}$ of subordinants, where $f\\in\n\\mathcal{S}^*(\\psi).$ The results are improvements and generalizations of\nseveral well known results.\n"}
+{"abstract": "  Checkpointing large amounts of related data concurrently to stable storage is\na common I/O pattern of many HPC applications. However, such a pattern\nfrequently leads to I/O bottlenecks that lead to poor scalability and\nperformance. As modern HPC infrastructures continue to evolve, there is a\ngrowing gap between compute capacity vs. I/O capabilities. Furthermore, the\nstorage hierarchy is becoming increasingly heterogeneous: in addition to\nparallel file systems, it comprises burst buffers, key-value stores, deep\nmemory hierarchies at node level, etc. In this context, state of art is\ninsufficient to deal with the diversity of vendor APIs, performance and\npersistency characteristics. This extended abstract presents an overview of\nVeloC (Very Low Overhead Checkpointing System), a checkpointing runtime\nspecifically design to address these challenges for the next generation\nExascale HPC applications and systems. VeloC offers a simple API at user level,\nwhile employing an advanced multi-level resilience strategy that transparently\noptimizes the performance and scalability of checkpointing by leveraging\nheterogeneous storage.\n"}
+{"abstract": "  We calculate the energy levels of a system of neutrinos undergoing collective\noscillations as functions of an effective coupling strength and radial distance\nfrom the neutrino source using the quantum Lanczos (QLanczos) algorithm\nimplemented on IBM Q quantum computer hardware. Our calculations are based on\nthe many-body neutrino interaction Hamiltonian introduced in Ref.\\\n\\cite{Patwardhan2019}. We show that the system Hamiltonian can be separated\ninto smaller blocks, which can be represented using fewer qubits than those\nneeded to represent the entire system as one unit, thus reducing the noise in\nthe implementation on quantum hardware. We also calculate transition\nprobabilities of collective neutrino oscillations using a Trotterization method\nwhich is simplified before subsequent implementation on hardware. These\ncalculations demonstrate that energy eigenvalues of a collective neutrino\nsystem and collective neutrino oscillations can both be computed on quantum\nhardware with certain simplification to within good agreement with exact\nresults.\n"}
+{"abstract": "  We study the algorithmic content of Pontryagin - van Kampen duality. We prove\nthat the dualization is computable in the important cases of compact and\nlocally compact totally disconnected Polish abelian groups. The applications of\nour main results include solutions to questions of Kihara and Ng about\npresentations of connected Polish spaces, and an unexpected arithmetical\ncharacterisation of direct products of solenoid groups among all Polish groups.\n"}
+{"abstract": "  Recent studies have demonstrated a perceivable improvement on the performance\nof neural machine translation by applying cross-lingual language model\npretraining (Lample and Conneau, 2019), especially the Translation Language\nModeling (TLM). To alleviate the need for expensive parallel corpora by TLM, in\nthis work, we incorporate the translation information from dictionaries into\nthe pretraining process and propose a novel Bilingual Dictionary-based Language\nModel (BDLM). We evaluate our BDLM in Chinese, English, and Romanian. For\nChinese-English, we obtained a 55.0 BLEU on WMT-News19 (Tiedemann, 2012) and a\n24.3 BLEU on WMT20 news-commentary, outperforming the Vanilla Transformer\n(Vaswani et al., 2017) by more than 8.4 BLEU and 2.3 BLEU, respectively.\nAccording to our results, the BDLM also has advantages on convergence speed and\npredicting rare words. The increase in BLEU for WMT16 Romanian-English also\nshows its effectiveness in low-resources language translation.\n"}
+{"abstract": "  Near the surface of any neutron star there is a thin heat blanketing envelope\nthat produces substantial thermal insulation of warm neutron star interiors and\nthat relates the internal temperature of the star to its effective surface\ntemperature. Physical processes in the blanketing envelopes are reasonably\nclear but the chemical composition is not. The latter circumstance complicates\ninferring physical parameters of matter in the stellar interiors from\nobservations of the thermal surface radiation of the stars and urges one to\nelaborate the models of blanketing envelopes. We outline physical properties of\nthese envelopes, particularly, the equation of state, thermal conduction, ion\ndiffusion and others. Various models of heat blankets are reviewed, such as\ncomposed of separate layers of different elements, or containing diffusive\nbinary ion mixtures in or out of diffusion equilibrium. The effects of strong\nmagnetic fields in the envelopes are outlined as well as the effects of high\ntemperatures which induce strong neutrino emission in the envelopes themselves.\nFinally, we discuss how the properties of the heat blankets affect thermal\nevolution of neutron stars and the ability to infer important information on\ninternal structure of neutron stars from observations.\n"}
+{"abstract": "  We study the optical-pump induced ultrafast transient change of the X-ray\nabsorption at the $L_3$ absorption resonances of the transition metals Ni and\nFe in Fe$_{0.5}$Ni$_{0.5}$ alloy. We find the effect for both elements to occur\nsimultaneously on a femtosecond timescale. This effect may hence be used as a\nhandy cross-correlation scheme providing a time-zero reference for ultrafast\noptical-pump soft X-ray-probe measurement. The method benefits from a\nrelatively simple experimental setup as the sample itself acts as\ntime-reference tool. In particular, this technique works with low flux\nultrafast soft X-ray sources. The measurements are compared to the\ncross-correlation method introduced in an earlier publication.\n"}
+{"abstract": "  We propose a system of conservation laws with relaxation source terms (i.e.\nbalance laws) for non-isothermal viscoelastic flows of Maxwell fluids. The\nsystem is an extension of the polyconvex elastodynamics of hyperelastic bodies\nusing additional structure variables. It is obtained by writing the Helmholtz\nfree energy as the sum of a volumetric energy density (function of the\ndeterminant of the deformation gradient det F and the temperature $\\theta$ like\nthe standard perfect-gas law or Noble-Abel stiffened-gas law) plus a polyconvex\nstrain energy density function of F, $\\theta$ and of symmetric\npositive-definite structure tensors that relax at a characteristic time scale.\nOne feature of our model is that it unifies various ideal materials ranging\nfrom hyperelastic solids to perfect fluids, encompassing fluids with memory\nlike Maxwell fluids. We establish a strictly convex mathematical entropy to\nshow that the system is symmetric-hyperbolic. Another feature of the proposed\nmodel is therefore the short-time existence and uniqueness of smooth solutions,\nwhich define genuinely causal viscoelastic flows with waves propagating at\nfinite speed. In heat-conductors, we complement the system by a\nMaxwell-Cattaneo equation for an energy-flux variable. The system is still\nsymmetric-hyperbolic, and smooth evolutions with finite-speed waves remain\nwell-defined.\n"}
+{"abstract": "  We introduce an optimisation method for variational quantum algorithms and\nexperimentally demonstrate a 100-fold improvement in efficiency compared to\nnaive implementations. The effectiveness of our approach is shown by obtaining\nmulti-dimensional energy surfaces for small molecules and a spin model. Our\nmethod solves related variational problems in parallel by exploiting the global\nnature of Bayesian optimisation and sharing information between different\noptimisers. Parallelisation makes our method ideally suited to next generation\nof variational problems with many physical degrees of freedom. This addresses a\nkey challenge in scaling-up quantum algorithms towards demonstrating quantum\nadvantage for problems of real-world interest.\n"}
+{"abstract": "  This work aims to interpolate parametrized Reduced Order Model (ROM) basis\nconstructed via the Proper Orthogonal Decomposition (POD) to derive a robust\nROM of the system's dynamics for an unseen target parameter value. A novel\nnon-intrusive Space-Time (ST) POD basis interpolation scheme is proposed, for\nwhich we define ROM spatial and temporal basis \\emph{curves on compact Stiefel\nmanifolds}. An interpolation is finally defined on a \\emph{mixed part} encoded\nin a square matrix directly deduced using the space part, the singular values\nand the temporal part, to obtain an interpolated snapshot matrix, keeping track\nof accurate space and temporal eigenvectors. Moreover, in order to establish a\nwell-defined curve on the compact Stiefel manifold, we introduce a new\nprocedure, the so-called oriented SVD. Such an oriented SVD produces unique\nright and left eigenvectors for generic matrices, for which all singular values\nare distinct. It is important to notice that the ST POD basis interpolation\ndoes not require the construction and the subsequent solution of a\nreduced-order FEM model as classically is done. Hence it is avoiding the\nbottleneck of standard POD interpolation which is associated with the\nevaluation of the nonlinear terms of the Galerkin projection on the governing\nequations. As a proof of concept, the proposed method is demonstrated with the\nadaptation of rigid-thermoviscoplastic finite element ROMs applied to a typical\nnonlinear open forging metal forming process. Strong correlations of the ST POD\nmodels with respect to their associated high-fidelity FEM counterpart\nsimulations are reported, highlighting its potential use for near real-time\nparametric simulations using off-line computed ROM POD databases.\n"}
+{"abstract": "  Forecasting demand is one of the fundamental components of a successful\nrevenue management system in hospitality. The industry requires understandable\nmodels that contribute to adaptability by a revenue management department to\nmake data-driven decisions. Data analysis and forecasts prove an essential role\nfor the time until the check-in date, which differs per day of week. This paper\naims to provide a new model, which is inspired by cubic smoothing splines,\nresulting in smooth demand curves per rate class over time until the check-in\ndate. This model regulates the error between data points and a smooth curve,\nand is therefore able to capture natural guest behavior. The forecast is\nobtained by solving a linear programming model, which enables the incorporation\nof industry knowledge in the form of constraints. Using data from a major hotel\nchain, a lower error and 13.3% more revenue is obtained.\n"}
+{"abstract": "  We consider a standard federated learning (FL) architecture where a group of\nclients periodically coordinate with a central server to train a statistical\nmodel. We develop a general algorithmic framework called FedLin to tackle some\nof the key challenges intrinsic to FL, namely objective heterogeneity, systems\nheterogeneity, and infrequent and imprecise communication. Our framework is\nmotivated by the observation that under these challenges, various existing FL\nalgorithms suffer from a fundamental speed-accuracy conflict: they either\nguarantee linear convergence but to an incorrect point, or convergence to the\nglobal minimum but at a sub-linear rate, i.e., fast convergence comes at the\nexpense of accuracy. In contrast, when the clients' local loss functions are\nsmooth and strongly convex, we show that FedLin guarantees linear convergence\nto the global minimum, despite arbitrary objective and systems heterogeneity.\nWe then establish matching upper and lower bounds on the convergence rate of\nFedLin that highlight the effects of intermittent communication. Finally, we\nshow that FedLin preserves linear convergence rates under aggressive gradient\nsparsification, and quantify the effect of the compression level on the\nconvergence rate. Our work is the first to provide tight linear convergence\nrate guarantees, and constitutes the first comprehensive analysis of gradient\nsparsification in FL.\n"}
+{"abstract": "  Catacondensed benzenoids (those benzenoids having no carbon atom belonging to\nthree hexagonal rings) form the simplest class of polycyclic aromatic\nhydrocarbons (PAH). They have a long history of study and are of wide chemical\nimportance. In this paper, mathematical possibilities for natural extension of\nthe notion of a catacondensed benzenoid are discussed, leading under plausible\nchemically and physically motivated restrictions to the notion of a\ncatacondensed chemical hexagonal complex (CCHC). A general polygonal complex is\na topological structure composed of polygons that are glued together along\ncertain edges. A polygonal complex is flat if none of its edges belong to more\nthan two polygons. A connected flat polygonal complex determines an orientable\nor nonorientable surface, possibly with boundary. A CCHC is then a connected\nflat polygonal complex all of whose polygons are hexagons and each of whose\nvertices belongs to at most two hexagonal faces. We prove that all CCHC are\nKekulean and give formulas for counting the perfect matchings in a series of\nexamples based on expansion of cubic graphs in which the edges are replaced by\nlinear polyacenes of equal length. As a preliminary assessment of the likely\nstability of molecules with CCHC structure, all-electron quantum chemical\ncalculations are applied to molecular structures based on several CCHC, using\neither linear or kinked unbranched catafused polyacenes as the expansion motif.\nThe systems examined all have closed shells according to H\\\"uckel theory and\nall correspond to minima on the potential surface, thus passing the most basic\ntest for plausibility as a chemical species.\n"}
+{"abstract": "  In this work GEM and single-hole Thick GEM structures, composed of different\ncoating materials, are studied. The used foils incorporate conductive layers\nmade of copper, aluminium, molybdenum, stainless steel, tungsten and tantalum.\nThe main focus of the study is the determination of the material dependence of\nthe formation of electrical discharges in GEM-based detectors. For this task,\ndischarge probability measurements are conducted with several Thick GEM samples\nusing a basic electronics readout chain. In addition to that, optical\nspectroscopy methods are employed to study the light emitted during discharges\nfrom the different foils. It is observed that the light spectra of GEMs include\nemission lines from the conductive layer material. This indicates the presence\nof the foil material in the discharge plasma after the initial spark. However,\nno lines associated with the coating material are observed while studying spark\ndischarges induced in Thick GEMs. It is concluded that the conductive layer\nmaterial does not play a substantial role in terms of stability against primary\ndischarges. However, a strong material dependence is observed in the case of\nsecondary discharge formation, pointing to molybdenum coating as the one\nproviding increased stability.\n"}
+{"abstract": "  Is there a constant $r_0$ such that, in any invariant tree network linking\nrate-$1$ Poisson points in the plane, the mean within-network distance between\npoints at Euclidean distance $r$ is infinite for $r > r_0$? We prove a slightly\nweaker result. This is a continuum analog of a result of Benjamini et al (2001)\non invariant spanning trees of the integer lattice.\n"}
+{"abstract": "  Bose polarons, quasi-particles composed of mobile impurities surrounded by\ncold Bose gas, can experience strong interactions mediated by the many-body\nenvironment and form bipolaron bound states. Here we present a detailed study\nof heavy polarons in a one-dimensional Bose gas by formulating a\nnon-perturbative theory and complementing it with exact numerical simulations.\nWe develop an analytic approach for weak boson-boson interactions and\narbitrarily strong impurity-boson couplings. Our approach is based on a\nmean-field theory that accounts for deformations of the superfluid by the\nimpurities and in this way minimizes quantum fluctuations. The mean-field\nequations are solved exactly in Born-Oppenheimer (BO) approximation leading to\nan analytic expression for the interaction potential of heavy polarons which is\nfound to be in excellent agreement with quantum Monte-Carlo (QMC) results. In\nthe strong-coupling limit the potential substantially deviates from the\nexponential form valid for weak coupling and has a linear shape at short\ndistances. Taking into account the leading-order Born-Huang corrections we\ncalculate bipolaron binding energies for impurity-boson mass ratios as low as 3\nand find excellent agreement with QMC results.\n"}
+{"abstract": "  This work concerns video-language pre-training and representation learning.\nIn this now ubiquitous training scheme, a model first performs pre-training on\npaired videos and text (e.g., video clips and accompanied subtitles) from a\nlarge uncurated source corpus, before transferring to specific downstream\ntasks. This two-stage training process inevitably raises questions about the\ngeneralization ability of the pre-trained model, which is particularly\npronounced when a salient domain gap exists between source and target data\n(e.g., instructional cooking videos vs. movies). In this paper, we first bring\nto light the sensitivity of pre-training objectives (contrastive vs.\nreconstructive) to domain discrepancy. Then, we propose a simple yet effective\nframework, CUPID, to bridge this domain gap by filtering and adapting source\ndata to the target data, followed by domain-focused pre-training. Comprehensive\nexperiments demonstrate that pre-training on a considerably small subset of\ndomain-focused data can effectively close the source-target domain gap and\nachieve significant performance gain, compared to random sampling or even\nexploiting the full pre-training dataset. CUPID yields new state-of-the-art\nperformance across multiple video-language and video tasks, including\ntext-to-video retrieval [72, 37], video question answering [36], and video\ncaptioning [72], with consistent performance lift over different pre-training\nmethods.\n"}
+{"abstract": "  Automatic image aesthetics assessment is a computer vision problem that deals\nwith the categorization of images into different aesthetic levels. The\ncategorization is usually done by analyzing an input image and computing some\nmeasure of the degree to which the image adhere to the key principles of\nphotography (balance, rhythm, harmony, contrast, unity, look, feel, tone and\ntexture). Owing to its diverse applications in many areas, automatic image\naesthetic assessment has gained significant research attention in recent years.\nThis paper presents a literature review of the recent techniques of automatic\nimage aesthetics assessment. A large number of traditional hand crafted and\ndeep learning based approaches are reviewed. Key problem aspects are discussed\nsuch as why some features or models perform better than others and what are the\nlimitations. A comparison of the quantitative results of different methods is\nalso provided at the end.\n"}
+{"abstract": "  We assume a generic real singlet scalar extension of the Standard Model\nliving in the vacuum $(v,w)$ at the electroweak scale with $v=246$ GeV and $w$\nbeing respectively the Higgs and the singlet scalar vacuum expectation values.\nBy requiring {\\it absolute} vacuum stability for the vacuum $(v,w)$, the\npositivity condition and the perturbativity up to the Planck scale, we show\nthat the viable space of parameters in the model is strongly constrained for\nvarious singlet scalar vacuum expectation values $w=0.1, 1, 10, 100$ TeV. Also,\nit turns out that the singlet scalar mass can be from a few GeV up to less than\nTeV.\n"}
+{"abstract": "  The constant increase in the complexity of data networks motivates the search\nfor strategies that make it possible to reduce current monitoring times. This\npaper shows the way in which multilayer network representation and the\napplication of multiscale analysis techniques, as applied to software-defined\nnetworks, allows for the visualization of anomalies from \"coarse views of the\nnetwork topology\". This implies the analysis of fewer data, and consequently\nthe reduction of the time that a process takes to monitor the network. The fact\nthat software-defined networks allow for the obtention of a global view of\nnetwork behavior facilitates detail recovery from affected zones detected in\nmonitoring processes. The method is evaluated by calculating the reduction\nfactor of nodes, checked during anomaly detection, with respect to the total\nnumber of nodes in the network.\n"}
+{"abstract": "  This paper explores an efficient solution for Space-time Super-Resolution,\naiming to generate High-resolution Slow-motion videos from Low Resolution and\nLow Frame rate videos. A simplistic solution is the sequential running of Video\nSuper Resolution and Video Frame interpolation models. However, this type of\nsolutions are memory inefficient, have high inference time, and could not make\nthe proper use of space-time relation property. To this extent, we first\ninterpolate in LR space using quadratic modeling. Input LR frames are\nsuper-resolved using a state-of-the-art Video Super-Resolution method. Flowmaps\nand blending mask which are used to synthesize LR interpolated frame is reused\nin HR space using bilinear upsampling. This leads to a coarse estimate of HR\nintermediate frame which often contains artifacts along motion boundaries. We\nuse a refinement network to improve the quality of HR intermediate frame via\nresidual learning. Our model is lightweight and performs better than current\nstate-of-the-art models in REDS STSR Validation set.\n"}
+{"abstract": "  How has the solar wind evolved to reach what it is today? In this review, I\ndiscuss the long-term evolution of the solar wind, including the evolution of\nobserved properties that are intimately linked to the solar wind: rotation,\nmagnetism and activity. Given that we cannot access data from the solar wind 4\nbillion years ago, this review relies on stellar data, in an effort to better\nplace the Sun and the solar wind in a stellar context. I overview some clever\ndetection methods of winds of solar-like stars, and derive from these an\nobserved evolutionary sequence of solar wind mass-loss rates. I then link these\nobservational properties (including, rotation, magnetism and activity) with\nstellar wind models. I conclude this review then by discussing implications of\nthe evolution of the solar wind on the evolving Earth and other solar system\nplanets. I argue that studying exoplanetary systems could open up new avenues\nfor progress to be made in our understanding of the evolution of the solar\nwind.\n"}
+{"abstract": "  Despite continuous increasing of the number of ICRF sources, their sky\ncoverage is still not satisfactory. The goal of this study is to discuss some\nnew considerations for extending the ICRF source list. Statistical analysis of\nthe ICRF catalog allows us to identify less populated sky regions where new\nICRF sources or additional observations of the current ICRF sources are most\ndesirable to improve both the uniformity of the source distribution and the\nuniformity of the distribution of the position errors. It is also desirable to\ninclude more sources with high redshift in the ICRF list. These sources may be\nof interest for astrophysics. To select prospective new ICRF sources, the OCARS\ncatalog is used. The number of sources in OCARS is about three times greater\nthan in the ICRF3, which gives us an opportunity to select new ICRF sources\nthat have already be tested and detected in astrometric and geodetic VLBI\nexperiments.\n"}
+{"abstract": "  We give a short and unified proof of the Brundan-Kleshchev isomorphism\nbetween blocks of cyclotomic Hecke algebras and cyclotomic\nKhovanovLauda-Rouquier algebras of type A.\n"}
+{"abstract": "  While averages and typical fluctuations often play a major role to understand\nthe behavior of a non-equilibrium system, this nonetheless is not always true.\nRare events and large fluctuations are also pivotal when a thorough analysis of\nthe system is being done. In this context, the statistics of extreme\nfluctuations in contrast to the average plays an important role, as has been\ndiscussed in fields ranging from statistical and mathematical physics to\nclimate, finance and ecology. Herein, we study Extreme Value Statistics (EVS)\nof stochastic resetting systems which have recently gained lot of interests due\nto its ubiquitous and enriching applications in physics, chemistry, queuing\ntheory, search processes and computer science. We present a detailed analysis\nfor the finite and large time statistics of extremals (maximum and arg-maximum\ni.e., the time when the maximum is reached) of the spatial displacement in such\nsystem. In particular, we derive an exact renewal formula that relates the\njoint distribution of maximum and arg-maximum of the reset process to the\nstatistical measures of the underlying process. Benchmarking our results for\nthe maximum of a reset-trajectory that pertain to the Gumbel class for large\nsample size, we show that the arg-maximum density attains to a uniform\ndistribution regardless of the underlying process at a large observation time.\nThis emerges as a manifestation of the renewal property of the resetting\nmechanism. The results are augmented with a wide spectrum of Markov and\nnon-Markov stochastic processes under resetting namely simple diffusion,\ndiffusion with drift, Ornstein-Uhlenbeck process and random acceleration\nprocess in one dimension. Rigorous results are presented for the first two\nset-ups while the latter two are supported with heuristic and numerical\nanalysis.\n"}
+{"abstract": "  We develop the geometrical, analytical, and computational framework for\nreactive island theory for three degrees-of-freedom time-independent\nHamiltonian systems. In this setting, the dynamics occurs in a 5-dimensional\nenergy surface in phase space and is governed by four-dimensional stable and\nunstable manifolds of a three-dimensional normally hyperbolic invariant sphere.\nThe stable and unstable manifolds have the geometrical structure of spherinders\nand we provide the means to investigate the ways in which these spherinders and\ntheir intersections determine the dynamical evolution of trajectories. This\ngeometrical picture is realized through the computational technique of\nLagrangian descriptors. In a set of trajectories, Lagrangian descriptors allow\nus to identify the ones closest to a stable or unstable manifold. Using an\napproximation of the manifold on a surface of section we are able to calculate\nthe flux between two regions of the energy surface.\n"}
+{"abstract": "  Some data analysis applications comprise datasets, where explanatory\nvariables are expensive or tedious to acquire, but auxiliary data are readily\navailable and might help to construct an insightful training set. An example is\nneuroimaging research on mental disorders, specifically learning a\ndiagnosis/prognosis model based on variables derived from expensive Magnetic\nResonance Imaging (MRI) scans, which often requires large sample sizes.\nAuxiliary data, such as demographics, might help in selecting a smaller sample\nthat comprises the individuals with the most informative MRI scans. In active\nlearning literature, this problem has not yet been studied, despite promising\nresults in related problem settings that concern the selection of instances or\ninstance-feature pairs.\n  Therefore, we formulate this complementary problem of Active Selection of\nClassification Features (ASCF): Given a primary task, which requires to learn a\nmodel f: x-> y to explain/predict the relationship between an\nexpensive-to-acquire set of variables x and a class label y. Then, the\nASCF-task is to use a set of readily available selection variables z to select\nthese instances, that will improve the primary task's performance most when\nacquiring their expensive features z and including them to the primary training\nset.\n  We propose two utility-based approaches for this problem, and evaluate their\nperformance on three public real-world benchmark datasets. In addition, we\nillustrate the use of these approaches to efficiently acquire MRI scans in the\ncontext of neuroimaging research on mental disorders, based on a simulated\nstudy design with real MRI data.\n"}
+{"abstract": "  Concept drift detection is a crucial task in data stream evolving\nenvironments. Most of state of the art approaches designed to tackle this\nproblem monitor the loss of predictive models. However, this approach falls\nshort in many real-world scenarios, where the true labels are not readily\navailable to compute the loss. In this context, there is increasing attention\nto approaches that perform concept drift detection in an unsupervised manner,\ni.e., without access to the true labels. We propose a novel approach to\nunsupervised concept drift detection based on a student-teacher learning\nparadigm. Essentially, we create an auxiliary model (student) to mimic the\nbehaviour of the primary model (teacher). At run-time, our approach is to use\nthe teacher for predicting new instances and monitoring the mimicking loss of\nthe student for concept drift detection. In a set of experiments using 19 data\nstreams, we show that the proposed approach can detect concept drift and\npresent a competitive behaviour relative to the state of the art approaches.\n"}
+{"abstract": "  We describe in this paper an optimal control strategy for shaping a\nlarge-scale swarm of particles using boundary global actuation. This problem\narises as a key challenge in many swarm robotics applications, especially when\nthe robots are passive particles that need to be guided by external control\nfields. The system is large-scale and underactuated, making the control\nstrategy at the microscopic particle level infeasible. We consider the\nKolmogorov forward equation associated to the stochastic process of the single\nparticle to encode the macroscopic behaviour of the particles swarm. The\ncontrol inputs shape the velocity field of the density dynamics according to\nthe physical model of the actuators. We find the optimal actuation considering\nan optimal control problem whose state dynamics is governed by a linear\nparabolic advection-diffusion equation where the control induces a transport\nfield. From a theoretical standpoint, we show the existence of a solution to\nthe resulting nonlinear optimal control problem. From a numerical standpoint,\nwe employ the discrete adjoint method to accurately compute the reduced\ngradient and we show how it commutes with the optimize-then-discretize\napproach. Finally, numerical simulations show the effectiveness of the control\nstrategy in driving the density sufficiently close to the target.\n"}
+{"abstract": "  The aim of this work is to present an overview about the combination of the\nReduced Basis Method (RBM) with two different approaches for Fluid-Structure\nInteraction (FSI) problems, namely a monolithic and a partitioned approach. We\nprovide the details of implementation of two reduction procedures, and we then\napply them to the same test case of interest. We first implement a reduction\ntechnique that is based on a monolithic procedure where we solve the fluid and\nthe solid problems all at once. We then present another reduction technique\nthat is based on a partitioned (or segregated) procedure: the fluid and the\nsolid problems are solved separately and then coupled using a fixed point\nstrategy. The toy problem that we consider is based on the Turek-Hron benchmark\ntest case, with a fluid Reynolds number Re = 100.\n"}
+{"abstract": "  We establish that for any proper action of a Lie group on a manifold the\nassociated equivariant differentiable cohomology groups with coefficients in\nmodules of $\\mathcal{C}^\\infty$-functions vanish in all degrees except than\nzero. Furthermore let $G$ be a Lie group of $CR$-automorphisms of a strictly\npseudo-convex $CR$-manifold $M$. We associate to $G$ a canonical class in the\nfirst differential cohomology of $G$ with coefficients in the\n$\\mathcal{C}^\\infty$-functions on $M$. This class is non-zero if and only if\n$G$ is essential in the sense that there does not exist a $CR$-compatible\nstrictly pseudo-convex pseudo-Hermitian structure on $M$ which is preserved by\n$G$. We prove that a closed Lie subgroup $G$ of $CR$-automorphisms acts\nproperly on $M$ if and only if its canonical class vanishes. As a consequence\nof Schoen's theorem, it follows that for any strictly pseudo-convex\n$CR$-manifold $M$, there exists a compatible strictly pseudo-convex\npseudo-Hermitian structure such that the CR-automorphism group for $M$ and the\ngroup of pseudo-Hermitian transformations coincide, except for two kinds of\nspherical $CR$-manifolds. Similar results hold for conformal Riemannian and\nK\\\"ahler manifolds.\n"}
+{"abstract": "  Recent work has made significant progress in helping users to automate single\ndata preparation steps, such as string-transformations and table-manipulation\noperators (e.g., Join, GroupBy, Pivot, etc.). We in this work propose to\nautomate multiple such steps end-to-end, by synthesizing complex data pipelines\nwith both string transformations and table-manipulation operators. We propose a\nnovel \"by-target\" paradigm that allows users to easily specify the desired\npipeline, which is a significant departure from the traditional by-example\nparadigm. Using by-target, users would provide input tables (e.g., csv or json\nfiles), and point us to a \"target table\" (e.g., an existing database table or\nBI dashboard) to demonstrate how the output from the desired pipeline would\nschematically \"look like\". While the problem is seemingly underspecified, our\nunique insight is that implicit table constraints such as FDs and keys can be\nexploited to significantly constrain the space to make the problem tractable.\nWe develop an Auto-Pipeline system that learns to synthesize pipelines using\nreinforcement learning and search. Experiments on large numbers of real\npipelines crawled from GitHub suggest that Auto-Pipeline can successfully\nsynthesize 60-70% of these complex pipelines with up to 10 steps.\n"}
+{"abstract": "  We show that certain global anomalies can be detected in an elementary\nfashion by analyzing the way the symmetry algebra is realized on the torus\nHilbert space of the anomalous theory. Distinct anomalous behaviours imprinted\nin the Hilbert space are identified with the distinct cohomology \"layers\" that\nappear in the classification of anomalies in terms of cobordism groups. We\nillustrate the manifestation of the layers in the Hilbert for a variety of\nanomalous symmetries and spacetime dimensions, including time-reversal\nsymmetry, and both in systems of fermions and in anomalous topological quantum\nfield theories (TQFTs) in 2+1d. We argue that anomalies can imply an exact\nbose-fermi degeneracy in the Hilbert space, thus revealing a supersymmetric\nspectrum of states; we provide a sharp characterization of when this phenomenon\noccurs and give nontrivial examples in various dimensions, including in\nstrongly coupled QFTs. Unraveling the anomalies of TQFTs leads us to develop\nthe construction of the Hilbert spaces, the action of operators and the modular\ndata in spin TQFTs, material that can be read on its own.\n"}
+{"abstract": "  Detecting human-object interactions (HOI) is an important step toward a\ncomprehensive visual understanding of machines. While detecting non-temporal\nHOIs (e.g., sitting on a chair) from static images is feasible, it is unlikely\neven for humans to guess temporal-related HOIs (e.g., opening/closing a door)\nfrom a single video frame, where the neighboring frames play an essential role.\nHowever, conventional HOI methods operating on only static images have been\nused to predict temporal-related interactions, which is essentially guessing\nwithout temporal contexts and may lead to sub-optimal performance. In this\npaper, we bridge this gap by detecting video-based HOIs with explicit temporal\ninformation. We first show that a naive temporal-aware variant of a common\naction detection baseline does not work on video-based HOIs due to a\nfeature-inconsistency issue. We then propose a simple yet effective\narchitecture named Spatial-Temporal HOI Detection (ST-HOI) utilizing temporal\ninformation such as human and object trajectories, correctly-localized visual\nfeatures, and spatial-temporal masking pose features. We construct a new video\nHOI benchmark dubbed VidHOI where our proposed approach serves as a solid\nbaseline.\n"}
+{"abstract": "  With the rise of digital currency systems that rely on blockchain to ensure\nledger security, the ability to perform cross-chain transactions is becoming a\ncrucial interoperability requirement. Such transactions allow not only funds to\nbe transferred from one blockchain to another (as done in atomic swaps), but\nalso a blockchain to verify the inclusion of any event on another blockchain.\nCross-chain bridges are protocols that allow on-chain exchange of\ncryptocurrencies, on-chain transfer of assets to sidechains, and cross-shard\nverification of events in sharded blockchains, many of which rely on Byzantine\nfault tolerance (BFT) for scalability. Unfortunately, existing bridge protocols\nthat can transfer funds from a BFT blockchain incur significant computation\noverhead on the destination blockchain, resulting in a high gas cost for smart\ncontract verification of events. In this paper, we propose Horizon, a\ngas-efficient, cross-chain bridge protocol to transfer assets from a BFT\nblockchain to another blockchain (e.g., Ethereum) that supports basic smart\ncontract execution.\n"}
+{"abstract": "  Teacher-student models provide a framework in which the typical-case\nperformance of high-dimensional supervised learning can be described in closed\nform. The assumptions of Gaussian i.i.d. input data underlying the canonical\nteacher-student model may, however, be perceived as too restrictive to capture\nthe behaviour of realistic data sets. In this paper, we introduce a Gaussian\ncovariate generalisation of the model where the teacher and student can act on\ndifferent spaces, generated with fixed, but generic feature maps. While still\nsolvable in a closed form, this generalization is able to capture the learning\ncurves for a broad range of realistic data sets, thus redeeming the potential\nof the teacher-student framework. Our contribution is then two-fold: First, we\nprove a rigorous formula for the asymptotic training loss and generalisation\nerror. Second, we present a number of situations where the learning curve of\nthe model captures the one of a realistic data set learned with kernel\nregression and classification, with out-of-the-box feature maps such as random\nprojections or scattering transforms, or with pre-learned ones - such as the\nfeatures learned by training multi-layer neural networks. We discuss both the\npower and the limitations of the framework.\n"}
+{"abstract": "  In this paper, we propose a formalization of the process of exploitation of\nSQL injection vulnerabilities. We consider a simplification of the dynamics of\nSQL injection attacks by casting this problem as a security capture-the-flag\nchallenge. We model it as a Markov decision process, and we implement it as a\nreinforcement learning problem. We then deploy reinforcement learning agents\ntasked with learning an effective policy to perform SQL injection; we design\nour training in such a way that the agent learns not just a specific strategy\nto solve an individual challenge but a more generic policy that may be applied\nto perform SQL injection attacks against any system instantiated randomly by\nour problem generator. We analyze the results in terms of the quality of the\nlearned policy and in terms of convergence time as a function of the complexity\nof the challenge and the learning agent's complexity. Our work fits in the\nwider research on the development of intelligent agents for autonomous\npenetration testing and white-hat hacking, and our results aim to contribute to\nunderstanding the potential and the limits of reinforcement learning in a\nsecurity environment.\n"}
+{"abstract": "  Most quantum information tasks based on Bell tests relie on the assumption of\nmeasurement independence. However, it is difficult to ensure that the\nassumption of measurement independence is always met in experimental\noperations, so it is crucial to explore the effects of relaxing this assumption\non Bell tests. In this paper, we discuss the effects of relaxing the assumption\nof measurement independence on 1-parameter family of Bell (1-PFB) tests. For\nboth general and factorizable input distributions, we establish the\nrelationship among measurement dependence, guessing probability, and the\nmaximum value of 1-PFB correlation function that Eve can fake. The\ndeterministic strategy when Eve fakes the maximum value is also given. We\ncompare the unknown information rate of Chain inequality and 1-PFB inequality,\nand find the range of the parameter in which it is more difficult for Eve to\nfake the maximum quantum violation in 1-PFB inequality than in Chain\ninequality.\n"}
+{"abstract": "  For task-oriented dialog systems, training a Reinforcement Learning (RL)\nbased Dialog Management module suffers from low sample efficiency and slow\nconvergence speed due to the sparse rewards in RL.To solve this problem, many\nstrategies have been proposed to give proper rewards when training RL, but\ntheir rewards lack interpretability and cannot accurately estimate the\ndistribution of state-action pairs in real dialogs. In this paper, we propose a\nmulti-level reward modeling approach that factorizes a reward into a\nthree-level hierarchy: domain, act, and slot. Based on inverse adversarial\nreinforcement learning, our designed reward model can provide more accurate and\nexplainable reward signals for state-action pairs.Extensive evaluations show\nthat our approach can be applied to a wide range of reinforcement\nlearning-based dialog systems and significantly improves both the performance\nand the speed of convergence.\n"}
+{"abstract": "  In this paper, we study the generalized gapped k-mer filters and derive a\nclosed form solution for their coefficients. We consider nonnegative integers\n$\\ell$ and $k$, with $k\\leq \\ell$, and an $\\ell$-tuple\n$B=(b_1,\\ldots,b_{\\ell})$ of integers $b_i\\geq 2$, $i=1,\\ldots,\\ell$. We\nintroduce and study an incidence matrix $A=A_{\\ell,k;B}$. We develop a\nM\\\"obius-like function $\\nu_B$ which helps us to obtain closed forms for a\ncomplete set of mutually orthogonal eigenvectors of $A^{\\top} A$ as well as a\ncomplete set of mutually orthogonal eigenvectors of $AA^{\\top}$ corresponding\nto nonzero eigenvalues.\n  The reduced singular value decomposition of $A$ and combinatorial\ninterpretations for the nullity and rank of $A$, are among the consequences of\nthis approach.\n  We then combine the obtained formulas, some results from linear algebra, and\ncombinatorial identities of elementary symmetric functions and $\\nu_B$, to\nprovide the entries of the Moore-Penrose pseudo-inverse matrix $A^{+}$ and the\nGapped k-mer filter matrix $A^{+} A$.\n"}
+{"abstract": "  Total charge and energy evaluations for the electron beams generated in the\nlaser wakefield acceleration (LWFA) is the primary step in the determination of\nthe required target and laser parameters. Particle-in-cell (PIC) simulations is\nan efficient numerical tool that can provide such evaluations unless the effect\nof numerical dispersion is not diminished. The numerical dispersion, which is\nspecific for the PIC modeling, affects not only the dephasing lengths in LWFA\nbut also the total amount of the self-injected electrons. A numerical error of\nthe order of $10^{-4}-10^{-3}$ in the calculation of the speed of light results\nin a significant error in the total injected charge and energy gain of the\naccelerated electron bunches. In the standard numerical approach, the numerical\ncorrection of the speed of light either requires infinitely small spatial grid\nresolution (which needs large computation platform) or force to compromise with\nthe numerical accuracy. A simple and easy to implement numerical scheme is\nshown to suppress the numerical dispersion of the electromagnetic pulse in PIC\nsimulations even with a modest spatial resolution, and without any special\ntreatments to the core structure of the numerical algorithm. Evaluated charges\nof the self-injected electron bunches become essentially lower owing to the\nbetter calculations of the wake phase velocity.\n"}
+{"abstract": "  We study the scheduling of jobs on a single parallel-batching machine with\nnon-identical job sizes and incompatible job families. Jobs from the same\nfamily have the same processing time and can be loaded into a batch, as long as\nthe batch size respects the machine capacity. The objective is to minimize the\ntotal weighted completion time. The problem combines two classic combinatorial\nproblems, namely bin packing and single machine scheduling. We develop three\nnew mixed-integer linear-programming formulations, namely an assignment-based\nformulation, a time-indexed formulation (TIF), and a set-partitioning\nformulation (SPF). We also propose a column generation (CG) algorithm for the\nSPF, which is the basis for a branch-and-price (B&P) algorithm and a CG-based\nheuristic. We develop a preprocessing method to reduce the formulation size. A\nheuristic framework based on proximity search is also developed using the TIF.\nThe SPF and B&P can solve instances with non-unit and unit job durations to\noptimality with up to 80 and 150 jobs within reasonable runtime limits,\nrespectively. The proposed heuristics perform better than the methods from the\nliterature.\n"}
+{"abstract": "  Implementation attacks like side-channel and fault attacks pose a\nconsiderable threat to cryptographic devices that are physically accessible by\nan attacker. As a consequence, devices like smart cards implement corresponding\ncountermeasures like redundant computation and masking. Recently, statistically\nineffective fault attacks (SIFA) were shown to be able to circumvent these\nclassical countermeasure techniques. We present a new approach for verifying\nthe SIFA protection of arbitrary masked implementations in both hardware and\nsoftware. The proposed method uses Boolean dependency analysis, factorization,\nand known properties of masked computations to show whether the fault detection\nmechanism of redundant masked circuits can leak information about the processed\nsecret values. We implemented this new method in a tool called Danira, which\ncan show the SIFA resistance of cryptographic implementations like AES S-Boxes\nwithin minutes.\n"}
+{"abstract": "  We demonstrate theoretically and experimentally that a specifically designed\nmicrocavity driven in the optical parametric oscillation regime exhibits\nlighthouse-like emission, i.e., an emission focused around a single direction.\nRemarkably, the emission direction of this micro-lighthouse is continuously\ncontrolled by the linear polarization of the incident laser, and angular beam\nsteering over \\unit{360}{\\degree} is demonstrated. Theoretically, this\nunprecedented effect arises from the interplay between the nonlinear optical\nresponse of microcavity exciton-polaritons, the difference in the subcavities\nforming the microcavity, and the rotational invariance of the device.\n"}
+{"abstract": "  Simulation-based Dynamic Traffic Assignment models have important\napplications in real-time traffic management and control. The efficacy of these\nsystems rests on the ability to generate accurate estimates and predictions of\ntraffic states, which necessitates online calibration. A widely used solution\napproach for online calibration is the Extended Kalman Filter (EKF), which --\nalthough appealing in its flexibility to incorporate any class of parameters\nand measurements -- poses several challenges with regard to calibration\naccuracy and scalability, especially in congested situations for large-scale\nnetworks. This paper addresses these issues in turn so as to improve the\naccuracy and efficiency of EKF-based online calibration approaches for large\nand congested networks. First, the concept of state augmentation is revisited\nto handle violations of the Markovian assumption typically implicit in online\napplications of the EKF. Second, a method based on graph-coloring is proposed\nto operationalize the partitioned finite-difference approach that enhances\nscalability of the gradient computations.\n  Several synthetic experiments and a real world case study demonstrate that\napplication of the proposed approaches yields improvements in terms of both\nprediction accuracy and computational performance. The work has applications in\nreal-world deployments of simulation-based dynamic traffic assignment systems.\n"}
+{"abstract": "  The ground state of an antiferromagnetic Heisenberg model on L X L clusters\njoined by a single bond and balanced Bethe clusters are investigated with\nquantum Monte Carlo and mean field theory. The improved Monte Carlo method of\nSandvik and Evertz is used and the observables include valence bond and loop\nvalence bond observables introduced by Lin and Sandvik as well as the valence\nbond entropy and the second Renyi entropy. For the bisecting of the Bethe\ncluster, in disagreement with our previous results and in agreement with mean\nfield theory, the valence loop entropy and the second Renyi entropy scale as\nthe logarithm of the number of sites in the cluster. For bisecting the L X L -\nL X L clusters, the valence bond entropy scales as L, however, the loop entropy\nand the entanglement entropy scale as the ln(L). The calculations suggest that\nthe area law is essentially correct and linking high entanglement objects will\nnot generate much more entanglement.\n"}
+{"abstract": "  The formation and presence of clathrate hydrates could influence the\ncomposition and stability of planetary ices and comets; they are at the heart\nof the development of numerous complex planetary models, all of which include\nthe necessary condition imposed by their stability curves, some of which\ninclude the cage occupancy or host-guest content and the hydration number, but\nfewer take into account the kinetics aspects. We measure the\ntemperature-dependent-diffusion-controlled formation of the carbon dioxide\nclathrate hydrate in the 155-210~K range in order to establish the clathrate\nformation kinetics at low temperature. We exposed thin water ice films of a few\nmicrons in thickness deposited in a dedicated infrared transmitting closed cell\nto gaseous carbon dioxide maintained at a pressure of a few times the pressure\nat which carbon dioxide clathrate hydrate is thermodynamically stable. The time\ndependence of the clathrate formation was monitored with the recording of\nspecific infrared vibrational modes of CO2 with a Fourier Transform InfraRed\n(FTIR) spectrometer. These experiments clearly show a two-step clathrate\nformation, particularly at low temperature, within a relatively simple\ngeometric configuration. We satisfactorily applied a model combining surface\nclathration followed by a bulk diffusion-relaxation growth process to the\nexperiments and derived the temperature-dependent-diffusion coefficient for the\nbulk spreading of clathrate. The derived apparent activation energy\ncorresponding to this temperature-dependent-diffusion coefficient in the\nconsidered temperature range is E_a = 24.7 +/- 9.7 kJ/mol. The kinetics\nparameters favour a possible carbon dioxide clathrate hydrate nucleation mainly\nin planets or satellites.\n"}
+{"abstract": "  The quantum Cram\\'er-Rao bound is a cornerstone of modern quantum metrology,\nas it provides the ultimate precision in parameter estimation. In the\nmultiparameter scenario, this bound becomes a matrix inequality, which can be\ncast to a scalar form with a properly chosen weight matrix. Multiparameter\nestimation thus elicits tradeoffs in the precision with which each parameter\ncan be estimated. We show that, if the information is encoded in a unitary\ntransformation, we can naturally choose the weight matrix as the metric tensor\nlinked to the geometry of the underlying algebra $\\mathfrak{su}(n)$, with\napplications in numerous fields. This ensures an intrinsic bound that is\nindependent of the choice of parametrization.\n"}
+{"abstract": "  The constant growth in the number of malware - software or code fragment\npotentially harmful for computers and information networks - and the use of\nsophisticated evasion and obfuscation techniques have seriously hindered\nclassic signature-based approaches. On the other hand, malware detection\nsystems based on machine learning techniques started offering a promising\nalternative to standard approaches, drastically reducing analysis time and\nturning out to be more robust against evasion and obfuscation techniques. In\nthis paper, we propose a malware taxonomic classification pipeline able to\nclassify Windows Portable Executable files (PEs). Given an input PE sample, it\nis first classified as either malicious or benign. If malicious, the pipeline\nfurther analyzes it in order to establish its threat type, family, and\nbehavior(s). We tested the proposed pipeline on the open source dataset EMBER,\ncontaining approximately 1 million PE samples, analyzed through static\nanalysis. Obtained malware detection results are comparable to other academic\nworks in the current state of art and, in addition, we provide an in-depth\nclassification of malicious samples. Models used in the pipeline provides\ninterpretable results which can help security analysts in better understanding\ndecisions taken by the automated pipeline.\n"}
+{"abstract": "  As an analogy of superalgebra of multivector fields with the Schounte\nbracket, we introduce a non-trivial superbracket on differential forms of\nmanifold. We show properties of this new superalgebra. We extend this\nsuperalgebra by adding one factor. The new extended superalgebra should be\nstudied more widely and in deep. We study Betti numbers of double weighted\nhomology groups by the Euler vector field. In appendix, we explain our bracket\nis produced like as the Schouten bracket.\n"}
+{"abstract": "  The charge, spin, and composition degrees of freedom in high-entropy alloy\nendow it with tunable valence and spin states, infinite combinations and\nexcellent mechanical performance. Meanwhile, the stacking, interlayer, and\nangle degrees of freedom in van der Waals material bring it with exceptional\nfeatures and technological applications. Integration of these two distinct\nmaterial categories while keeping their merits would be tempting. Based on this\nheuristic thinking, we design and explore a new range of materials (i.e.,\ndichalcogenides, halides and phosphorus trisulfides) with multiple metallic\nconstitutions and intrinsic layered structure, which are coined as high-entropy\nvan der Waals materials. Millimeter-scale single crystals with homogeneous\nelement distribution can be efficiently acquired and easily exfoliated or\nintercalated in this materials category. Multifarious physical properties like\nsuperconductivity, magnetic ordering, metal-insulator transition and corrosion\nresistance have been exploited. Further research based on the concept of\nhigh-entropy van der Waals materials will enrich the high-throughput design of\nnew systems with intriguing properties and practical applications.\n"}
+{"abstract": "  In this paper, we study the Sobolev extension property of Lp-quasidisks which\nare the generalizations of the classical quasidisks. After that, we also find\nsome applications of their Sobolev extension property.\n"}
+{"abstract": "  Leakage outside of the qubit computational subspace poses a threatening\nchallenge to quantum error correction (QEC). We propose a scheme using two\nleakage-reduction units (LRUs) that mitigate these issues for a transmon-based\nsurface code, without requiring an overhead in terms of hardware or QEC-cycle\ntime as in previous proposals. For data qubits we consider a microwave drive to\ntransfer leakage to the readout resonator, where it quickly decays, ensuring\nthat this negligibly affects the coherence within the computational subspace\nfor realistic system parameters. For ancilla qubits we apply a\n$|1\\rangle\\leftrightarrow|2\\rangle$ $\\pi$ pulse conditioned on the measurement\noutcome. Using density-matrix simulations of the distance-3 surface code we\nshow that the average leakage lifetime is reduced to almost 1 QEC cycle, even\nwhen the LRUs are implemented with limited fidelity. Furthermore, we show that\nthis leads to a significant reduction of the logical error rate. This LRU\nscheme opens the prospect for near-term scalable QEC demonstrations.\n"}
+{"abstract": "  For the exactly solvable model of exponential last passage percolation on\n$\\mathbb{Z}^2$, consider the geodesic $\\Gamma_n$ joining $(0,0)$ and $(n,n)$\nfor large $n$. It is well known that the transversal fluctuation of $\\Gamma_n$\naround the line $x=y$ is $n^{2/3+o(1)}$ with high probability. We obtain the\nexponent governing the decay of the small ball probability for $\\Gamma_{n}$ and\nestablish that for small $\\delta$, the probability that $\\Gamma_{n}$ is\ncontained in a strip of width $\\delta n^{2/3}$ around the diagonal is $\\exp\n(-\\Theta(\\delta^{-3/2}))$ uniformly in high $n$. We also obtain optimal small\ndeviation estimates for the one point distribution of the geodesic showing that\nfor $\\frac{t}{2n}$ bounded away from $0$ and $1$, we have\n$\\mathbb{P}(|x(t)-y(t)|\\leq \\delta n^{2/3})=\\Theta(\\delta)$ uniformly in high\n$n$, where $(x(t),y(t))$ is the unique point where $\\Gamma_{n}$ intersects the\nline $x+y=t$. Our methods are expected to go through for other exactly solvable\nmodels of planar last passage percolation and, upon taking the $n\\to \\infty$\nlimit, provide analogous estimates for geodesics in the directed landscape.\n"}
+{"abstract": "  We propose a modified quantum teleportation scheme to increase the\nteleportation accuracy by applying a cubic phase gate to the displaced squeezed\nstate. We have described the proposed scheme in Heisenberg's language,\nevaluating it from the point of view of adding an error in teleportation, and\nhave shown that it allows achieving less error than the original scheme.\nRepeating the description in the language of wave functions, we have found the\nrange of the displacement values, at which our conclusions will be valid. Using\nthe example of teleportation of the vacuum state, we have shown that the scheme\nallows one to achieve high fidelity values.\n"}
+{"abstract": "  Non-Hermitian skin effects and exceptional points are topological phenomena\ncharacterized by integer winding numbers. In this study, we give methods to\ntheoretically detect skin effects and exceptional points by generalizing\ninversion symmetry. The generalization of inversion symmetry is unique to\nnon-Hermitian systems. We show that parities of the winding numbers can be\ndetermined from energy eigenvalues on the inversion-invariant momenta when\ngeneralized inversion symmetry is present. The simple expressions for the\nwinding numbers allow us to easily analyze skin effects and exceptional points\nin non-Hermitian bands. We also demonstrate the methods for (second-order) skin\neffects and exceptional points by using lattice models.\n"}
+{"abstract": "  Neurofibromatosis type 1 (NF1) is an autosomal dominant tumor predisposition\nsyndrome that involves the central and peripheral nervous systems. Accurate\ndetection and segmentation of neurofibromas are essential for assessing tumor\nburden and longitudinal tumor size changes. Automatic convolutional neural\nnetworks (CNNs) are sensitive and vulnerable as tumors' variable anatomical\nlocation and heterogeneous appearance on MRI. In this study, we propose deep\ninteractive networks (DINs) to address the above limitations. User interactions\nguide the model to recognize complicated tumors and quickly adapt to\nheterogeneous tumors. We introduce a simple but effective Exponential Distance\nTransform (ExpDT) that converts user interactions into guide maps regarded as\nthe spatial and appearance prior. Comparing with popular Euclidean and geodesic\ndistances, ExpDT is more robust to various image sizes, which reserves the\ndistribution of interactive inputs. Furthermore, to enhance the tumor-related\nfeatures, we design a deep interactive module to propagate the guides into\ndeeper layers. We train and evaluate DINs on three MRI data sets from NF1\npatients. The experiment results yield significant improvements of 44% and 14%\nin DSC comparing with automated and other interactive methods, respectively. We\nalso experimentally demonstrate the efficiency of DINs in reducing user burden\nwhen comparing with conventional interactive methods. The source code of our\nmethod is available at \\url{https://github.com/Jarvis73/DINs}.\n"}
+{"abstract": "  Medical terminology normalization aims to map the clinical mention to\nterminologies come from a knowledge base, which plays an important role in\nanalyzing Electronic Health Record(EHR) and many downstream tasks. In this\npaper, we focus on Chinese procedure terminology normalization. The expression\nof terminologies are various and one medical mention may be linked to multiple\nterminologies. Previous study explores some methods such as multi-class\nclassification or learning to rank(LTR) to sort the terminologies by literature\nand semantic information. However, these information is inadequate to find the\nright terminologies, particularly in multi-implication cases. In this work, we\npropose a combined recall and rank framework to solve the above problems. This\nframework is composed of a multi-task candidate generator(MTCG), a keywords\nattentive ranker(KAR) and a fusion block(FB). MTCG is utilized to predict the\nmention implication number and recall candidates with semantic similarity. KAR\nis based on Bert with a keywords attentive mechanism which focuses on keywords\nsuch as procedure sites and procedure types. FB merges the similarity come from\nMTCG and KAR to sort the terminologies from different perspectives. Detailed\nexperimental analysis shows our proposed framework has a remarkable improvement\non both performance and efficiency.\n"}
+{"abstract": "  Manipulating valley-dependent Berry phase effects provides remarkable\nopportunities for both fundamental research and practical applications. Here,\nby referring to effective model analysis, we propose a general scheme for\nrealizing topological magneto-valley phase transitions. More importantly, by\nusing valley-half-semiconducting VSi2N4 as an outstanding example, we\ninvestigate sign change of valley-dependent Berry phase effects which drive the\nchange-in-sign valley anomalous transport characteristics via external means\nsuch as biaxial strain, electric field, and correlation effects. As a result,\nthis gives rise to quantized versions of valley anomalous transport phenomena.\nOur findings not only uncover a general framework to control valley degree of\nfreedom, but also motivate further research in the direction of multifunctional\nquantum devices in valleytronics and spintronics.\n"}
+{"abstract": "  The rise of algorithmic decision-making has spawned much research on fair\nmachine learning (ML). Financial institutions use ML for building risk\nscorecards that support a range of credit-related decisions. Yet, the\nliterature on fair ML in credit scoring is scarce. The paper makes three\ncontributions. First, we revisit statistical fairness criteria and examine\ntheir adequacy for credit scoring. Second, we catalog algorithmic options for\nincorporating fairness goals in the ML model development pipeline. Last, we\nempirically compare different fairness processors in a profit-oriented credit\nscoring context using real-world data. The empirical results substantiate the\nevaluation of fairness measures, identify suitable options to implement fair\ncredit scoring, and clarify the profit-fairness trade-off in lending decisions.\nWe find that multiple fairness criteria can be approximately satisfied at once\nand recommend separation as a proper criterion for measuring the fairness of a\nscorecard. We also find fair in-processors to deliver a good balance between\nprofit and fairness and show that algorithmic discrimination can be reduced to\na reasonable level at a relatively low cost. The codes corresponding to the\npaper are available on GitHub.\n"}
+{"abstract": "  Complex singularities have been suggested in propagators of confined\nparticles, e.g., the Landau-gauge gluon propagator. We rigorously reconstruct\nMinkowski propagators from Euclidean propagators with complex singularities. As\na result, the analytically continued Wightman function is holomorphic in the\ntube, and the Lorentz symmetry and locality are kept intact, whereas the\nreconstructed Wightman function violates the temperedness and the positivity\ncondition. Moreover, we argue that complex singularities correspond to confined\nzero-norm states in an indefinite metric state space.\n"}
+{"abstract": "  For a fixed positive $\\epsilon$, we show the existence of a constant\n$C_\\epsilon$ with the following property: Given a $\\pm1$-edge-labeling\n$c:E(K_n)\\to \\{ -1,1\\}$ of the complete graph $K_n$ with $c(E(K_n))=0$, and a\nspanning forest $F$ of $K_n$ of maximum degree $\\Delta$, one can determine in\npolynomial time an isomorphic copy $F'$ of $F$ in $K_n$ with $|c(E(F'))|\\leq\n\\left(\\frac{3}{4}+\\epsilon\\right)\\Delta+C_\\epsilon.$ Our approach is based on\nthe method of conditional expectation.\n"}
+{"abstract": "  Current perception systems often carry multimodal imagers and sensors such as\n2D cameras and 3D LiDAR sensors. To fuse and utilize the data for downstream\nperception tasks, robust and accurate calibration of the multimodal sensor data\nis essential. We propose a novel deep learning-driven technique (CalibDNN) for\naccurate calibration among multimodal sensor, specifically LiDAR-Camera pairs.\nThe key innovation of the proposed work is that it does not require any\nspecific calibration targets or hardware assistants, and the entire processing\nis fully automatic with a single model and single iteration. Results comparison\namong different methods and extensive experiments on different datasets\ndemonstrates the state-of-the-art performance.\n"}
+{"abstract": "  In the present study, we propose to implement a new framework for estimating\ngenerative models via an adversarial process to extend an existing GAN\nframework and develop a white-box controllable image cartoonization, which can\ngenerate high-quality cartooned images/videos from real-world photos and\nvideos. The learning purposes of our system are based on three distinct\nrepresentations: surface representation, structure representation, and texture\nrepresentation. The surface representation refers to the smooth surface of the\nimages. The structure representation relates to the sparse colour blocks and\ncompresses generic content. The texture representation shows the texture,\ncurves, and features in cartoon images. Generative Adversarial Network (GAN)\nframework decomposes the images into different representations and learns from\nthem to generate cartoon images. This decomposition makes the framework more\ncontrollable and flexible which allows users to make changes based on the\nrequired output. This approach overcomes any previous system in terms of\nmaintaining clarity, colours, textures, shapes of images yet showing the\ncharacteristics of cartoon images.\n"}
+{"abstract": "  The time average expected age of information (AoI) is studied for status\nupdates sent over an error-prone channel from an energy-harvesting transmitter\nwith a finite-capacity battery. Energy cost of sensing new status updates is\ntaken into account as well as the transmission energy cost better capturing\npractical systems. The optimal scheduling policy is first studied under the\nhybrid automatic repeat request (HARQ) protocol when the channel and energy\nharvesting statistics are known, and the existence of a threshold-based optimal\npolicy is shown. For the case of unknown environments, average-cost\nreinforcement-learning algorithms are proposed that learn the system parameters\nand the status update policy in real-time. The effectiveness of the proposed\nmethods is demonstrated through numerical results.\n"}
+{"abstract": "  We identify potential early markets for fusion energy and their projected\ncost targets, based on analysis and synthesis of many relevant, recent studies\nand reports. Because private fusion companies aspire to start commercial\ndeployment before 2040, we examine cost requirements for fusion-generated\nelectricity, process heat, and hydrogen production based on today's market\nprices but with various adjustments relating to possible scenarios in 2035,\nsuch as \"business-as-usual,\" high renewables penetration, and carbon pricing up\nto 100 \\$/tCO$_2$. Key findings are that fusion developers should consider\nfocusing initially on high-priced global electricity markets and including\nintegrated thermal storage in order to maximize revenue and compete in markets\nwith high renewables penetration. Process heat and hydrogen production will be\ntough early markets for fusion, but may open up to fusion as markets evolve and\nif fusion's levelized cost of electricity falls below 50 \\$/MWh$_\\mathrm{e}$.\nFinally, we discuss potential ways for a fusion plant to increase revenue via\ncogeneration (e.g., desalination, direct air capture, or district heating) and\nto lower capital costs (e.g., by minimizing construction times and interest or\nby retrofitting coal plants).\n"}
+{"abstract": "  We study synchronizing partial DFAs, which extend the classical concept of\nsynchronizing complete DFAs and are a special case of synchronizing unambiguous\nNFAs. A partial DFA is called synchronizing if it has a word (called a reset\nword) whose action brings a non-empty subset of states to a unique state and is\nundefined for all other states. While in the general case the problem of\nchecking whether a partial DFA is synchronizing is PSPACE-complete, we show\nthat in the strongly connected case this problem can be efficiently reduced to\nthe same problem for a complete DFA. Using combinatorial, algebraic, and formal\nlanguages methods, we develop techniques that relate main synchronization\nproblems for strongly connected partial DFAs with the same problems for\ncomplete DFAs. In particular, this includes the \\v{C}ern\\'{y} and the rank\nconjectures, the problem of finding a reset word, and upper bounds on the\nlength of the shortest reset words of literal automata of finite prefix codes.\nWe conclude that solving fundamental synchronization problems is equally hard\nin both models, as an essential improvement of the results for one model\nimplies an improvement for the other.\n"}
+{"abstract": "  In this work, the antibacterial activity of the polymeric precursor\ndicarbonyldichlororuthenium has been studied against Escherichia coli and\nStaphylococcus aureus. This Ru carbonyl precursor shows minimum inhibitory\nconcentration at nanogram per millilitre, which renders it a novel\nantimicrobial polymer without any organic ligands. Besides,\ndicarbonyldichlororuthenium antimicrobial activity is markedly boosted under\nphotoirradiation, which can be ascribed to the enhanced generation of reactive\noxygen species under UV irradiation. This compound has been able to inhibit\nbacterial growth via the disruption of bacterial membranes and triggering\nupregulation of stress responses as shown in microscopic measurements. The\nactivity of polymeric ruthenium as an antibacterial material is significant\neven at very low concentrations while remaining biocompatible to the mammalian\ncells at much higher concentrations. This study proves that this simple Ru\ncarbonyl precursor can be used as an antimicrobial compound with high activity\nand a low toxicity profile in the context of need for new antimicrobial agents\nto fight bacterial infections.\n"}
+{"abstract": "  The radio galaxy 1321+045 is a rare example of a young, compact steep\nspectrum source located in the center of a z=0.263 galaxy cluster. Using a\ncombination of Chandra, VLBA, VLA, MERLIN and IRAM 30m observations, we\ninvestigate the conditions which have triggered this outburst. We find that the\npreviously identified 5 kpc scale radio lobes are probably no longer powered by\nthe AGN, which seems to have launched a new ~20 pc jet on a different axis,\nlikely within the last few hundred years. We estimate the enthalpy of the lobes\nto be 8.48 [+6.04,-3.56] x10^57 erg, only sufficient to balance cooling in the\nsurrounding 16 kpc for ~9 Myr. The cluster ICM properties are similar to those\nof rapidly cooling nearby clusters, with a low central entropy (8.6 [+2.2,-1.4]\nkev cm^2 within 8 kpc), short central cooling time (390 [+170,-150] Myr), and\nt_cool/t_ff and t_cool/t_eddy ratios indicative of thermal instability out to\n~45 kpc. Despite previous detection of Halpha emission from the BCG, our IRAM\n30m observations do not detect CO emission in either the (1-0) or (3-2)\ntransitions. We place 3sigma limits on the molecular gas mass of M_mol\n<=7.7x10^9 Msol and <=5.6x10^9 Msol from the two lines respectively. We find\nindications of a recent minor cluster merger which has left a ~200 kpc tail of\nstripped gas in the ICM, and probably induced sloshing motions.\n"}
+{"abstract": "  Recent spectroscopic observations by sensitive radio telescopes require\naccurate molecular spectral line frequencies to identify molecular species in a\nforest of lines detected. To measure rest frequencies of molecular spectral\nlines in the laboratory, an emission-type millimeter and submillimeter-wave\nspectrometer utilizing state-of-the-art radio-astronomical technologies is\ndeveloped. The spectrometer is equipped with a 200 cm glass cylinder cell, a\ntwo sideband (2SB) Superconductor-Insulator-Superconductor (SIS) receiver in\nthe 230 GHz band, and wide-band auto-correlation digital spectrometers. By\nusing the four 2.5 GHz digital spectrometers, a total instantaneous bandwidth\nof the 2SB SIS receiver of 8 GHz can be covered with a frequency resolution of\n88.5 kHz. Spectroscopic measurements of CH$_3$CN and HDO are carried out in the\n230 GHz band so as to examine frequency accuracy, stability, sensitivity, as\nwell as intensity calibration accuracy of our system. As for the result of\nCH$_3$CN, we confirm that the frequency accuracy for lines detected with\nsufficient signal to noise ratio is better than 1 kHz, when the high resolution\nspectrometer having a channel resolution of 17.7 kHz is used. In addition, we\ndemonstrate the capability of this system by spectral scan measurement of\nCH$_3$OH from 216 GHz to 264 GHz. We assign 242 transitions of CH$_3$OH, 51\ntransitions of $^{13}$CH$_3$OH, and 21 unidentified emission lines for 295\ndetected lines. Consequently, our spectrometer demonstrates sufficient\nsensitivity, spectral resolution, and frequency accuracy for in-situ\nexperimental-based rest frequency measurements of spectral lines on various\nmolecular species.\n"}
+{"abstract": "  Achieving human-level performance on some of Machine Reading Comprehension\n(MRC) datasets is no longer challenging with the help of powerful Pre-trained\nLanguage Models (PLMs). However, it is necessary to provide both answer\nprediction and its explanation to further improve the MRC system's reliability,\nespecially for real-life applications. In this paper, we propose a new\nbenchmark called ExpMRC for evaluating the explainability of the MRC systems.\nExpMRC contains four subsets, including SQuAD, CMRC 2018, RACE$^+$, and C$^3$\nwith additional annotations of the answer's evidence. The MRC systems are\nrequired to give not only the correct answer but also its explanation. We use\nstate-of-the-art pre-trained language models to build baseline systems and\nadopt various unsupervised approaches to extract evidence without a\nhuman-annotated training set. The experimental results show that these models\nare still far from human performance, suggesting that the ExpMRC is\nchallenging. Resources will be available through\nhttps://github.com/ymcui/expmrc\n"}
+{"abstract": "  We consider thermal effects in the propagation of gravitational waves on a\ncosmological background. In particular, we consider scalar field cosmologies\nand study gravitational modes near cosmological singularities. We point out\nthat the contribution of thermal radiation can heavily affect the dynamics of\ngravitational waves giving enhancement or dissipation effects both at quantum\nand classical level.These effects are considered both in General Relativity and\nin modified theories like $F(R)$ gravity which can be easily reduced to\nscalar-tensor cosmology. The possible detection and disentanglement of standard\nand scalar gravitational modes on the stochastic background are also discussed.\n"}
+{"abstract": "  Exceptional points (EPs), i.e., non-Hermitian degeneracies at which\neigenvalues and eigenvectors coalesce, can be realized by tuning the gain/loss\ncontrast of different modes in non-Hermitian systems or by engineering the\nasymmetric coupling of modes. Here we demonstrate a mechanism that can achieve\nEPs of arbitrary order by employing the non-reciprocal coupling of spinning\ncylinders sitting on a dielectric waveguide. The spinning motion breaks the\ntime-reversal symmetry and removes the degeneracy of opposite chiral modes of\nthe cylinders. Under the excitation of a linearly polarized plane wave, the\nchiral mode of one cylinder can unidirectionally couple to the same mode of the\nother cylinder via the spin-orbit interaction associated with the evanescent\nwave of the waveguide. The structure can give rise to arbitrary-order EPs that\nare robust against spin-flipping perturbations, in contrast to conventional\nsystems relying on spin-selective excitations. In addition, we show that\nhigher-order EPs in the proposed system are accompanied by enhanced optical\nisolation, which may find applications in designing novel optical isolators,\nnonreciprocal optical devices, and topological photonics.\n"}
+{"abstract": "  The design of the cross-section of an FRP-reinforced concrete beam is an\niterative process of estimating both its dimensions and the reinforcement\nratio, followed by the check of the compliance of a number of strength and\nserviceability constraints. The process continues until a suitable solution is\nfound. Since there are infinite solutions to the problem, it appears convenient\nto define some optimality criteria so as to measure the relative goodness of\nthe different solutions. This paper intends to develop a preliminary least-cost\nsection design model that follows the recommendations in the ACI 440.1 R-06,\nand uses a relatively new artificial intelligence technique called particle\nswarm optimization (PSO) to handle the optimization tasks. The latter is based\non the intelligence that emerges from the low-level interactions among a number\nof relatively non-intelligent individuals within a population.\n"}
+{"abstract": "  We study the problem of testing the null hypothesis that X and Y are\nconditionally independent given Z, where each of X, Y and Z may be functional\nrandom variables. This generalises testing the significance of X in a\nregression model of scalar response Y on functional regressors X and Z. We show\nhowever that even in the idealised setting where additionally (X, Y, Z) has a\nGaussian distribution, the power of any test cannot exceed its size. Further\nmodelling assumptions are needed and we argue that a convenient way of\nspecifying these is based on choosing methods for regressing each of X and Y on\nZ. We propose a test statistic involving inner products of the resulting\nresiduals that is simple to compute and calibrate: type I error is controlled\nuniformly when the in-sample prediction errors are sufficiently small. We show\nthis requirement is met by ridge regression in functional linear model settings\nwithout requiring any eigen-spacing conditions or lower bounds on the\neigenvalues of the covariance of the functional regressor. We apply our test in\nconstructing confidence intervals for truncation points in truncated functional\nlinear models and testing for edges in a functional graphical model for EEG\ndata.\n"}
+{"abstract": "  Multifidelity simulation methodologies are often used in an attempt to\njudiciously combine low-fidelity and high-fidelity simulation results in an\naccuracy-increasing, cost-saving way. Candidates for this approach are\nsimulation methodologies for which there are fidelity differences connected\nwith significant computational cost differences. Physics-informed Neural\nNetworks (PINNs) are candidates for these types of approaches due to the\nsignificant difference in training times required when different fidelities\n(expressed in terms of architecture width and depth as well as optimization\ncriteria) are employed. In this paper, we propose a particular multifidelity\napproach applied to PINNs that exploits low-rank structure. We demonstrate that\nwidth, depth, and optimization criteria can be used as parameters related to\nmodel fidelity, and show numerical justification of cost differences in\ntraining due to fidelity parameter choices. We test our multifidelity scheme on\nvarious canonical forward PDE models that have been presented in the emerging\nPINNs literature.\n"}
+{"abstract": "  Recent work has shown that many problems of satisfiability and resiliency in\nworkflows may be viewed as special cases of the authorization policy existence\nproblem (APEP), which returns an authorization policy if one exists and 'No'\notherwise. However, in many practical settings it would be more useful to\nobtain a 'least bad' policy than just a 'No', where 'least bad' is\ncharacterized by some numerical value indicating the extent to which the policy\nviolates the base authorization relation and constraints. Accordingly, we\nintroduce the Valued APEP, which returns an authorization policy of minimum\nweight, where the (non-negative) weight is determined by the constraints\nviolated by the returned solution. We then establish a number of results\nconcerning the parameterized complexity of Valued APEP. We prove that the\nproblem is fixed-parameter tractable (FPT) if the set of constraints satisfies\ntwo restrictions, but is intractable if only one of these restrictions holds.\n(Most constraints known to be of practical use satisfy both restrictions.) We\nalso introduce a new type of resiliency for workflow satisfiability problem,\nshow how it can be addressed using Valued APEP and use this to build a set of\nbenchmark instances for Valued APEP. Following a set of computational\nexperiments with two mixed integer programming (MIP) formulations, we\ndemonstrate that the Valued APEP formulation based on the user profile concept\nhas FPT-like running time and usually significantly outperforms a naive\nformulation.\n"}
+{"abstract": "  Sample efficiency and risk-awareness are central to the development of\npractical reinforcement learning (RL) for complex decision-making. The former\ncan be addressed by transfer learning and the latter by optimizing some utility\nfunction of the return. However, the problem of transferring skills in a\nrisk-aware manner is not well-understood. In this paper, we address the problem\nof risk-aware policy transfer between tasks in a common domain that differ only\nin their reward functions, in which risk is measured by the variance of reward\nstreams. Our approach begins by extending the idea of generalized policy\nimprovement to maximize entropic utilities, thus extending policy improvement\nvia dynamic programming to sets of policies and levels of risk-aversion. Next,\nwe extend the idea of successor features (SF), a value function representation\nthat decouples the environment dynamics from the rewards, to capture the\nvariance of returns. Our resulting risk-aware successor features (RaSF)\nintegrate seamlessly within the RL framework, inherit the superior task\ngeneralization ability of SFs, and incorporate risk-awareness into the\ndecision-making. Experiments on a discrete navigation domain and control of a\nsimulated robotic arm demonstrate the ability of RaSFs to outperform\nalternative methods including SFs, when taking the risk of the learned policies\ninto account.\n"}
+{"abstract": "  Using Langevin dynamics simulations, we study the hysteresis in unzipping of\nlonger double stranded DNA chains whose ends are subjected to a time dependent\nperiodic force with frequency $\\omega$ and amplitude $G$ keeping the other end\nfixed. We find that the area of the hysteresis loop, $A_{loop}$, scales as\n$1/\\omega$ at higher frequencies, whereas it scales as\n$(G-G_c)^{\\alpha}\\omega^{\\beta}$ with exponents $\\alpha=1$ and $\\beta=1.25$ in\nthe low frequency regime. These values are same as the exponents obtained in\nMonte Carlo simulation studies of a directed self avoiding walk model of a\nhomopolymer DNA [R. Kapri, Phys. Rev. E 90, 062719 (2014)], and the block\ncopolymer DNA [R. K. Yadav and R. Kapri, Phys. Rev. E 103, 012413 (2021)] on a\nsquare lattice, and differs from the values reported earlier using Langevin\ndynamics simulation studies on a much shorter DNA hairpins.\n"}
+{"abstract": "  In spoken conversational question answering (SCQA), the answer to the\ncorresponding question is generated by retrieving and then analyzing a fixed\nspoken document, including multi-part conversations. Most SCQA systems have\nconsidered only retrieving information from ordered utterances. However, the\nsequential order of dialogue is important to build a robust spoken\nconversational question answering system, and the changes of utterances order\nmay severely result in low-quality and incoherent corpora. To this end, we\nintroduce a self-supervised learning approach, including incoherence\ndiscrimination, insertion detection, and question prediction, to explicitly\ncapture the coreference resolution and dialogue coherence among spoken\ndocuments. Specifically, we design a joint learning framework where the\nauxiliary self-supervised tasks can enable the pre-trained SCQA systems towards\nmore coherent and meaningful spoken dialogue learning. We also utilize the\nproposed self-supervised learning tasks to capture intra-sentence coherence.\nExperimental results demonstrate that our proposed method provides more\ncoherent, meaningful, and appropriate responses, yielding superior performance\ngains compared to the original pre-trained language models. Our method achieves\nstate-of-the-art results on the Spoken-CoQA dataset.\n"}
+{"abstract": "  Chalcogenide phase change materials (PCMs) have been extensively applied in\ndata storage, and they are now being proposed for high resolution displays,\nholographic displays, reprogrammable photonics, and all-optical neural\nnetworks. These wide-ranging applications all exploit the radical property\ncontrast between the PCMs different structural phases, extremely fast switching\nspeed, long-term stability, and low energy consumption. Designing PCM photonic\ndevices requires an accurate model to predict the response of the device during\nphase transitions. Here, we describe an approach that accurately predicts the\nmicrostructure and optical response of phase change materials during laser\ninduced heating. The framework couples the Gillespie Cellular Automata approach\nfor modelling phase transitions with effective medium theory and Fresnel\nequations. The accuracy of the approach is verified by comparing the PCM\noptical response and microstructure evolution with the results of nanosecond\nlaser switching experiments. We anticipate that this approach to simulating the\nswitching response of PCMs will become an important component for designing and\nsimulating programmable photonics devices. The method is particularly important\nfor predicting the multi-level optical response of PCMs, which is important for\nall-optical neural networks and PCM-programmable perceptrons.\n"}
+{"abstract": "  We present an automatic COVID1-19 diagnosis framework from lung CT-scan slice\nimages. In this framework, the slice images of a CT-scan volume are first\nproprocessed using segmentation techniques to filter out images of closed lung,\nand to remove the useless background. Then a resampling method is used to\nselect one or multiple sets of a fixed number of slice images for training and\nvalidation. A 3D CNN network with BERT is used to classify this set of selected\nslice images. In this network, an embedding feature is also extracted. In cases\nwhere there are more than one set of slice images in a volume, the features of\nall sets are extracted and pooled into a global feature vector for the whole\nCT-scan volume. A simple multiple-layer perceptron (MLP) network is used to\nfurther classify the aggregated feature vector. The models are trained and\nevaluated on the provided training and validation datasets. On the validation\ndataset, the accuracy is 0.9278 and the F1 score is 0.9261.\n"}
+{"abstract": "  We review the status of the anomalies in $b\\to s \\ell\\ell$ transitions, and\ncomment on the impact of the most recent measurements in 2019 and 2020 on the\nglobal fits. We also discuss a few developments in the theory calculation of\nlocal and non-local form factors.\n"}
+{"abstract": "  In this paper, we introduce the new task of controllable text edition, in\nwhich we take as input a long text, a question, and a target answer, and the\noutput is a minimally modified text, so that it fits the target answer. This\ntask is very important in many situations, such as changing some conditions,\nconsequences, or properties in a legal document, or changing some key\ninformation of an event in a news text. This is very challenging, as it is hard\nto obtain a parallel corpus for training, and we need to first find all text\npositions that should be changed and then decide how to change them. We\nconstructed the new dataset WikiBioCTE for this task based on the existing\ndataset WikiBio (originally created for table-to-text generation). We use\nWikiBioCTE for training, and manually labeled a test set for testing. We also\npropose novel evaluation metrics and a novel method for solving the new task.\nExperimental results on the test set show that our proposed method is a good\nfit for this novel NLP task.\n"}
+{"abstract": "  A mixture preorder is a preorder on a mixture space (such as a convex set)\nthat is compatible with the mixing operation. In decision theoretic terms, it\nsatisfies the central expected utility axiom of strong independence. We\nconsider when a mixture preorder has a multi-representation that consists of\nreal-valued, mixture-preserving functions. If it does, it must satisfy the\nmixture continuity axiom of Herstein and Milnor (1953). Mixture continuity is\nsufficient for a mixture-preserving multi-representation when the dimension of\nthe mixture space is countable, but not when it is uncountable. Our strongest\npositive result is that mixture continuity is sufficient in conjunction with a\nnovel axiom we call countable domination, which constrains the order complexity\nof the mixture preorder in terms of its Archimedean structure. We also consider\nwhat happens when the mixture space is given its natural weak topology.\nContinuity (having closed upper and lower sets) and closedness (having a closed\ngraph) are stronger than mixture continuity. We show that continuity is\nnecessary but not sufficient for a mixture preorder to have a\nmixture-preserving multi-representation. Closedness is also necessary; we leave\nit as an open question whether it is sufficient. We end with results concerning\nthe existence of mixture-preserving multi-representations that consist entirely\nof strictly increasing functions, and a uniqueness result.\n"}
+{"abstract": "  Widespread processes in nature and technology are governed by the dynamical\ntransition whereby a material in an initially solid-like state then yields\nplastically. Major unresolved questions concern whether any material will yield\nsmoothly and gradually (ductile behaviour) or fail abruptly and\ncatastrophically (brittle behaviour); the roles of sample annealing, disorder\nand shear band formation in the onset of yielding and failure; and, most\nimportantly from a practical viewpoint, whether any impending catastrophic\nfailure can be anticipated before it happens. We address these questions by\nstudying the yielding of slowly sheared athermal amorphous materials, within a\nminimal mesoscopic lattice elastoplastic model. Our contributions are fourfold.\nFirst, we elucidate whether yielding will be ductile or brittle, for any given\nlevel of sample annealing. Second, we show that yielding comprises two distinct\nstages: a pre-failure stage, in which small levels of strain heterogeneity\nslowly accumulate, followed by a catastrophic brittle failure event, in which a\ncrack quickly propagates across the sample via a cooperating line of plastic\nevents. Third, we provide an expression for the slowly growing level of strain\nheterogeneity in the pre-failure stage, expressed in terms of the macroscopic\nstress-strain curve and the sample size, and in excellent agreement with our\nsimulation results. Fourth, we elucidate the basic mechanism via which a crack\nthen nucleates and provide an approximate expression for the probability\ndistribution of shear strains at which failure occurs, as determined by the\ndisorder inherent in the sample, expressed in terms of a single annealing\nparameter, and the system size. Importantly, this indicates a possible route to\npredicting sudden material failure, before it occurs.\n"}
+{"abstract": "  When a three-dimensional material is constructed by stacking different\ntwo-dimensional layers into an ordered structure, new and unique physical\nproperties can emerge. An example is the delafossite PdCoO2, which consists of\nalternating layers of metallic Pd and Mott-insulating CoO2 sheets. To\nunderstand the nature of the electronic coupling between the layers that gives\nrise to the unique properties of PdCoO2, we revealed its layer-resolved\nelectronic structure combining standing-wave X-ray photoemission spectroscopy\nand ab initio many-body calculations. Experimentally, we have decomposed the\nmeasured valence band spectrum into contributions from Pd and CoO2 layers.\nComputationally, we find that many-body interactions in Pd and CoO2 layers are\nhighly different. Holes in the CoO2 layer interact strongly with\ncharge-transfer excitons in the same layer, whereas holes in the Pd layer\ncouple to plasmons in the Pd layer. Interestingly, we find that holes in states\nhybridized across both layers couple to both types of excitations\n(charge-transfer excitons or plasmons), with the intensity of photoemission\nsatellites being proportional to the projection of the state onto a given\nlayer. This establishes satellites as a sensitive probe for inter-layer\nhybridization. These findings pave the way towards a better understanding of\ncomplex many-electron interactions in layered quantum materials.\n"}
+{"abstract": "  Recent query explanation systems help users understand anomalies in\naggregation results by proposing predicates that describe input records that,\nif deleted, would resolve the anomalies. However, it can be difficult for users\nto understand how a predicate was chosen, and these approaches are limited to\nerrors that can be resolved through deletion. In contrast, data errors may be\ndue to group-wise errors, such as missing records or systematic value errors.\nThis paper presents Reptile, an explanation system for hierarchical data. Given\nan anomalous aggregate query result, Reptile recommends the next drill-down\nattribute,and ranks the drill-down groups based on the extent repairing the\ngroup's statistics to its expected values resolves the anomaly. Reptile\nefficiently trains a multi-level model that leverages the data's hierarchy to\nestimate the expected values, and uses a factorised representation of the\nfeature matrix to remove redundancies due to the data's hierarchical structure.\nWe further extend model training to support factorised data, and develop a\nsuite of optimizations that leverage the data's hierarchical structure. Reptile\nreduces end-to-end runtimes by more than 6 times compared to a Matlab-based\nimplementation, correctly identifies 21/30 data errors in John Hopkin's\nCOVID-19 data, and correctly resolves 20/22 complaints in a user study using\ndata and researchers from Columbia University's Financial Instruments Sector\nTeam.\n"}
+{"abstract": "  We designed Si-based all-dielectric 1 $\\times$ 2 TE and TM power splitters\nwith various splitting ratios and simulated them using the inverse design of\nadjoint and numerical 3D finite-difference time-domain methods. The proposed\ndevices exhibit ultra-high transmission efficiency above 98 and 99\\%, and\nexcess losses below 0.1 and 0.035 dB, for TE and TM splitters, respectively.\nThe merits of these devices include a minor footprint of 2.2 $\\times$ 2.2\n$\\mu$m $\\times$ $\\mu$m and a broad operating bandwidth of 200 nm with a center\nwavelength of $\\lambda$ = 1.55 $\\mu$ m.\n"}
+{"abstract": "  Numerical continuation in the context of optimization can be used to mitigate\nconvergence issues due to a poor initial guess. In this work, we extend this\nidea to Riemannian optimization problems, that is, the minimization of a target\nfunction on a Riemannian manifold. For this purpose, a suitable homotopy is\nconstructed between the original problem and a problem that admits an easy\nsolution. We develop and analyze a path-following numerical continuation\nalgorithm on manifolds for solving the resulting parameter-dependent equation.\nTo illustrate our developments, we consider two typical classical applications\nof Riemannian optimization: the computation of the Karcher mean and low-rank\nmatrix completion. We demonstrate that numerical continuation can yield\nimprovements for challenging instances of both problems.\n"}
+{"abstract": "  Iso-edge domains are a variant of the iso-Delaunay decomposition introduced\nby Voronoi. They were introduced by Baranovskii & Ryshkov in order to solve the\ncovering problem in dimension $5$.\n  In this work we revisit this decomposition and prove the following new\nresults:\n  $\\bullet$ We review the existing theory and give a general mass-formula for\nthe iso-edge domains.\n  $\\bullet$ We prove that the associated toroidal compactification of the\nmoduli space of principally polarized abelian varieties is projective.\n  $\\bullet$ We prove the Conway--Sloane conjecture in dimension $5$.\n  $\\bullet$ We prove that the quadratic forms for which the conorms are\nnon-negative are exactly the matroidal ones in dimension $5$.\n"}
+{"abstract": "  In this paper we study exact boundary controllability for a linear wave\nequation with strong and weak interior degeneration of the coefficient in the\nprinciple part of the elliptic operator. The objective is to provide a\nwell-posedness analysis of the corresponding system and derive conditions for\nits controllability through boundary actions. Passing to a relaxed version of\nthe original problem, we discuss existence and uniqueness of solutions, and\nusing the HUM method we derive conditions on the rate of degeneracy for both\nexact boundary controllability and the lack thereof.\n"}
+{"abstract": "  Modularity is a quantity which has been introduced in the context of complex\nnetworks in order to quantify how close a network is to an ideal modular\nnetwork in which the nodes form small interconnected communities that are\njoined together with relatively few edges. In this paper, we consider this\nquantity on a recent probabilistic model of complex networks introduced by\nKrioukov et al. (Phys. Rev. E 2010).\n  This model views a complex network as an expression of hidden hierarchies,\nencapsulated by an underlying hyperbolic space. For certain parameters, this\nmodel was proved to have typical features that are observed in complex networks\nsuch as power law degree distribution, bounded average degree, clustering\ncoefficient that is asymptotically bounded away from zero, and ultra-small\ntypical distances. In the present work, we investigate its modularity and we\nshow that, in this regime, it converges to 1 in probability.\n"}
+{"abstract": "  Setting an effective reserve price for strategic bidders in repeated auctions\nis a central question in online advertising. In this paper, we investigate how\nto set an anonymous reserve price in repeated auctions based on historical bids\nin a way that balances revenue and incentives to misreport. We propose two\nsimple and computationally efficient methods to set reserve prices based on the\nnotion of a clearing price and make them robust to bidder misreports. The first\napproach adds random noise to the reserve price, drawing on techniques from\ndifferential privacy. The second method applies a smoothing technique by adding\nnoise to the training bids used to compute the reserve price. We provide\ntheoretical guarantees on the trade-offs between the revenue performance and\nbid-shading incentives of these two mechanisms. Finally, we empirically\nevaluate our mechanisms on synthetic data to validate our theoretical findings.\n"}
+{"abstract": "  This paper explores the genotype-phenotype relationship. It outlines\nconditions under which the dependence of quantitative trait on the genome might\nbe predictable, based on measurement of a limited subset of genotypes. It uses\nthe theory of real-valued Boolean functions in a systematic way to translate\ntrait data into the Fourier domain. Important trait features, such as the\nroughness of the trait landscape or the modularity of a trait have a simple\nFourier interpretation. Roughness at a gene location corresponds to high\nsensitivity to mutation, while a modular organization of gene activity reduces\nsuch sensitivity.\n  Traits where rugged loci are rare will naturally compress gene data in the\nFourier domain, leading to a sparse representation of trait data, concentrated\nin identifiable, low-level coefficients. This Fourier representation of a trait\norganizes epistasis in a form which is isometric to the trait data. As Fourier\nmatrices are known to be maximally incoherent with the standard basis, this\npermits employing compressive sensing techniques to work from data sets that\nare relatively small -- sometimes even polynomial -- compared to the\nexponentially large sets of possible genomes.\n  This theory provides a theoretical underpinning for systematic use of Boolean\nfunction machinery to dissect the dependency of a trait on the genome and\nenvironment.\n"}
+{"abstract": "  One of the open questions in the field of interaction design is \"what inputs\nor interaction techniques should be used with augmented reality devices?\" The\ntransition from a touchpad and a keyboard to a multi-touch device was\nrelatively small. The transition from a multi-touch device to an HMD with no\ncontrollers or clear surface to interact with is more complicated. This book is\na guide for how to figure out what interaction techniques and modalities people\nprefer when interacting with those devices. The name of the technique covered\nhere is Elicitation. Elicitation is a form of participatory design, meaning\ndesign with direct end-user involvement. By running an elicitation study\nresearchers can observe unconstrained human interactions with emerging\ntechnologies to help guide input design.\n"}
+{"abstract": "  We use a sample of 14 massive, dynamically relaxed galaxy clusters to\nconstrain the Hubble Constant, $H_0$, by combining X-ray and Sunyaev-Zel'dovich\n(SZ) effect signals measured with Chandra, Planck and Bolocam. This is the\nfirst such analysis to marginalize over an empirical, data-driven prior on the\noverall accuracy of X-ray temperature measurements, while our restriction to\nthe most relaxed, massive clusters also minimizes astrophysical systematics.\nFor a cosmological-constant model with $\\Omega_m = 0.3$ and $\\Omega_{\\Lambda} =\n0.7$, we find $H_0 = 67.3^{+21.3}_{-13.3}$ km/s/Mpc, limited by the temperature\ncalibration uncertainty (compared to the statistically limited constraint of\n$H_0 = 72.3^{+7.6}_{-7.6}$ km/s/Mpc). The intrinsic scatter in the X-ray/SZ\npressure ratio is found to be $13 \\pm 4$ per cent ($10 \\pm 3$ per cent when two\nclusters with significant galactic dust emission are removed from the sample),\nconsistent with being primarily due to triaxiality and projection. We discuss\nthe prospects for reducing the dominant systematic limitation to this analysis,\nwith improved X-ray calibration and/or precise measurements of the relativistic\nSZ effect providing a plausible route to per cent level constraints on $H_0$.\n"}
+{"abstract": "  Understanding the fracture toughness of glasses is of prime importance for\nscience and technology. We study it here using extensive atomistic simulations\nin which the interaction potential, glass transition cooling rate and loading\ngeometry are systematically varied, mimicking a broad range of experimentally\naccessible properties. Glasses' nonequilibrium mechanical disorder is\nquantified through $A_{\\rm g}$, the dimensionless prefactor of the universal\nspectrum of nonphononic excitations, which measures the abundance of soft\nglassy defects that affect plastic deformability. We show that while a\nbrittle-to-ductile transition might be induced by reducing the cooling rate,\nleading to a reduction in $A_{\\rm g}$, iso-$\\!A_{\\rm g}$ glasses are either\nbrittle or ductile depending on the degree of Poisson contraction under\nunconstrained uniaxial tension. Eliminating Poisson contraction using\nconstrained tension reveals that iso-$\\!A_{\\rm g}$ glasses feature similar\ntoughness, and that varying $A_{\\rm g}$ under these conditions results in\nsignificant toughness variation. Our results highlight the roles played by both\nsoft defects and loading geometry (which affects the activation of defects) in\nthe toughness of glasses.\n"}
+{"abstract": "  We consider a multiuser diffusion-based molecular communication (MC) system\nwhere multiple spatially distributed transmitter (TX)-receiver (RX) pairs\nestablish point-to-point communication links employing the same type of\nsignaling molecules. To realize the full potential of such a system, an\nin-depth understanding of the interplay between the spatial user density and\ninter-user interference (IUI) and its impact on system performance in an\nasymptotic regime with large numbers of users is needed. In this paper, we\nadopt a three-dimensional (3-D) system model with multiple independent and\nspatially distributed point-to-point transmission links, where both the TXs and\nRXs are positioned according to a regular hexagonal grid, respectively. Based\non this model, we first derive an expression for the channel impulse responses\n(CIRs) of all TX-RX links in the system. Then, we provide the maximum\nlikelihood (ML) decision rule for the RXs and show that it reduces to a\nthreshold-based detector. We derive an analytical expression for the\ncorresponding detection threshold which depends on the statistics of the MC\nchannel and the statistics of the IUI. Furthermore, we derive an analytical\nexpression for the bit error rate (BER) and the achievable rate of a single\ntransmission link. Finally, we propose a new performance metric, which we refer\nto as area rate efficiency (ARE), that captures the tradeoff between the user\ndensity and IUI. The ARE characterizes how efficiently given TX and RX areas\nare used for information transmission and is given in terms of bits per area\nunit. We show that there exists an optimal user density for maximization of the\nARE. Results from particle-based and Monte Carlo simulations validate the\naccuracy of the expressions derived for the CIR, optimal detection threshold,\nBER, and ARE.\n"}
+{"abstract": "  Intermediate mass ratio inspiral (IMRI) binaries -- containing stellar-mass\nblack holes coalescing into intermediate-mass black holes ($M>100M_{\\odot}$) --\nare a highly anticipated source of gravitational waves (GWs) for Advanced\nLIGO/Virgo. Their detection and source characterization would provide a unique\nprobe of strong-field gravity and stellar evolution. Due to the asymmetric\ncomponent masses and the large primary, these systems generically excite\nsubdominant modes while reducing the importance of the dominant quadrupole\nmode. Including higher order harmonics can also result in a $10\\%-25\\%$\nincrease in signal-to-noise ratio for IMRIs, which may help to detect these\nsystems. We show that by including subdominant GW modes into the analysis we\ncan achieve a precise characterization of IMRI source properties. For example,\nwe find that the source properties for IMRIs can be measured to within\n$2\\%-15\\%$ accuracy at a fiducial signal-to-noise ratio of 25 if subdominant\nmodes are included. When subdominant modes are neglected, the accuracy degrades\nto $9\\%-44\\%$ and significant biases are seen in chirp mass, mass ratio,\nprimary spin and luminosity distances. We further demonstrate that including\nsubdominant modes in the waveform model can enable an informative measurement\nof both individual spin components and improve the source localization by a\nfactor of $\\sim$10. We discuss some important astrophysical implications of\nhigh-precision source characterization enabled by subdominant modes such as\nconstraining the mass gap and probing formation channels.\n"}
+{"abstract": "  In this paper, we report about a large-scale online discussion with 1099\ncitizens on the Afghanistan Sustainable Development Goals.\n"}
+{"abstract": "  We make a spectral analysis of the massive Dirac operator in a tubular\nneighborhood of an unbounded planar curve,subject to infinite mass boundary\nconditions. Under general assumptions on the curvature, we locate the essential\nspectrum and derive an effective Hamiltonian on the base curve which\napproximates the original operator in the thin-strip limit. We also investigate\nthe existence of bound states in the non-relativistic limit and give a\ngeometric quantitative condition for the bound states to exist.\n"}
+{"abstract": "  Current neuroimaging techniques provide paths to investigate the structure\nand function of the brain in vivo and have made great advances in understanding\nAlzheimer's disease (AD). However, the group-level analyses prevalently used\nfor investigation and understanding of the disease are not applicable for\ndiagnosis of individuals. More recently, deep learning, which can efficiently\nanalyze large-scale complex patterns in 3D brain images, has helped pave the\nway for computer-aided individual diagnosis by providing accurate and automated\ndisease classification. Great progress has been made in classifying AD with\ndeep learning models developed upon increasingly available structural MRI data.\nThe lack of scale-matched functional neuroimaging data prevents such models\nfrom being further improved by observing functional changes in pathophysiology.\nHere we propose a potential solution by first learning a\nstructural-to-functional transformation in brain MRI, and further synthesizing\nspatially matched functional images from large-scale structural scans. We\nevaluated our approach by building computational models to discriminate\npatients with AD from healthy normal subjects and demonstrated a performance\nboost after combining the structural and synthesized functional brain images\ninto the same model. Furthermore, our regional analyses identified the temporal\nlobe to be the most predictive structural-region and the parieto-occipital lobe\nto be the most predictive functional-region of our model, which are both in\nconcordance with previous group-level neuroimaging findings. Together, we\ndemonstrate the potential of deep learning with large-scale structural and\nsynthesized functional MRI to impact AD classification and to identify AD's\nneuroimaging signatures.\n"}
+{"abstract": "  Since most industrial control applications use PID controllers, PID tuning\nand anti-windup measures are significant problems. This paper investigates\ntuning the feedback gains of a PID controller via back-calculation and\nautomatic differentiation tools. In particular, we episodically use a cost\nfunction to generate gradients and perform gradient descent to improve\ncontroller performance. We provide a theoretical framework for analyzing this\nnon-convex optimization and establish a relationship between back-calculation\nand disturbance feedback policies. We include numerical experiments on linear\nsystems with actuator saturation to show the efficacy of this approach.\n"}
+{"abstract": "  Risk is traditionally described as the expected likelihood of an undesirable\noutcome, such as collisions for autonomous vehicles. Accurately predicting risk\nor potentially risky situations is critical for the safe operation of\nautonomous vehicles. In our previous work, we showed that risk could be\ncharacterized by two components: 1) the probability of an undesirable outcome\nand 2) an estimate of how undesirable the outcome is (loss). This paper is an\nextension to our previous work. In this paper, using our trained deep\nreinforcement learning model for navigating around crowds, we developed a\nrisk-based decision-making framework for the autonomous vehicle that integrates\nthe high-level risk-based path planning with the reinforcement learning-based\nlow-level control. We evaluated our method in a high-fidelity simulation such\nas CARLA. This work can improve safety by allowing an autonomous vehicle to one\nday avoid and react to risky situations.\n"}
+{"abstract": "  Inelastic Cooper pair tunneling across a voltage-biased Josephson junction in\nseries with one or more microwave cavities can generate photons via resonant\nprocesses in which the energy lost by the Cooper pair matches that of the\nphoton(s) produced. We generalise previous theoretical treatments of such\nsystems to analyse cases where two or more different photon generation\nprocesses are resonant simultaneously. We also explore in detail a specific\ncase where generation of a single photon in one cavity mode is simultaneously\nresonant with the generation of two photons in a second mode. We find that the\ncoexistence of the two resonances leads to effective couplings between the\nmodes which in turn generate entanglement.\n"}
+{"abstract": "  Quantile regression is a field with steadily growing importance in\nstatistical modeling. It is a complementary method to linear regression, since\ncomputing a range of conditional quantile functions provides a more accurate\nmodelling of the stochastic relationship among variables, especially in the\ntails. We introduce a non-restrictive and highly flexible nonparametric\nquantile regression approach based on C- and D-vine copulas. Vine copulas allow\nfor separate modeling of marginal distributions and the dependence structure in\nthe data, and can be expressed through a graph theoretical model given by a\nsequence of trees. This way we obtain a quantile regression model, that\novercomes typical issues of quantile regression such as quantile crossings or\ncollinearity, the need for transformations and interactions of variables. Our\napproach incorporates a two-step ahead ordering of variables, by maximizing the\nconditional log-likelihood of the tree sequence, while taking into account the\nnext two tree levels. Further, we show that the nonparametric conditional\nquantile estimator is consistent. The performance of the proposed methods is\nevaluated in both low- and high-dimensional settings using simulated and real\nworld data. The results support the superior prediction ability of the proposed\nmodels.\n"}
+{"abstract": "  We revisit the problem of local normality of Kraus-Polley-Reents infravacuum\nrepresentations and provide a straightforward proof based on the Araki-Yamagami\ncriterion. We apply this result to the theory of superselection sectors.\nNamely, we extend the novel formalism of second conjugate classes and relative\nnormalizers to the local relativistic setting.\n"}
+{"abstract": "  In this essay we give a general picture about the evolution of Grohendieck's\nideas regarding the notion of space. Starting with his fundamental work in\nalgebraic geometry, where he introduces schemes and toposes as generalizations\nof classical notions of spaces, passing through tame topology and ending with\nthe formulation of a geometry of forms, we show how the ideas of Grothendieck\nevolved from pure mathematical considerations to physical and philosophical\nquestions about the nature and structure of space and its mathematical models.\n"}
+{"abstract": "  Manipulating and cooling small particles with light are long-standing\nchallenges in many areas of science, from the foundations of physics to\napplications in biology and nano-technology. Light fields can, in particular,\nbe used to isolate mesoscopic particles from their environment by levitating\nthem optically. These levitated particles of micron size and smaller exhibit\npristine mechanical resonances and can be cooled down to their motional quantum\nground state. Significant roadblocks on the way to scale up levitation from a\nsingle to multiple particles in close proximity are the requirements to\nconstantly monitor the particles' positions as well as to engineer light fields\nthat react fast and appropriately to their displacements. Given the complexity\nof light scattering between particles, each of these two challenges currently\nseems insurmountable already in itself. Here, we present an approach that\nsolves both problems at once by forgoing any local information on the\nparticles. Instead, our procedure is based on the far-field information stored\nin the scattering matrix and its changes with time. We demonstrate how to\ncompose from these ingredients a linear energy-shift operator, whose maximal or\nminimal eigenstates are identified as the incoming wavefronts that implement\nthe most efficient heating or cooling of a moving ensemble of\narbitrarily-shaped levitated particles, respectively. We expect this optimal\napproach to be a game-changer for the collective manipulation of multiple\nparticles on-the-fly, i.e., without the necessity to track them. An\nexperimental implementation is suggested based on stroboscopic scattering\nmatrix measurements and a time-adaptive injection of the optimal light fields.\n"}
+{"abstract": "  We study the utility of the lexical translation model (IBM Model 1) for\nEnglish text retrieval, in particular, its neural variants that are trained\nend-to-end. We use the neural Model1 as an aggregator layer applied to\ncontext-free or contextualized query/document embeddings. This new approach to\ndesign a neural ranking system has benefits for effectiveness, efficiency, and\ninterpretability. Specifically, we show that adding an interpretable neural\nModel 1 layer on top of BERT-based contextualized embeddings (1) does not\ndecrease accuracy and/or efficiency; and (2) may overcome the limitation on the\nmaximum sequence length of existing BERT models. The context-free neural Model\n1 is less effective than a BERT-based ranking model, but it can run efficiently\non a CPU (without expensive index-time precomputation or query-time operations\non large tensors). Using Model 1 we produced best neural and non-neural runs on\nthe MS MARCO document ranking leaderboard in late 2020.\n"}
+{"abstract": "  Teaching cases based on stories about real organizations are a powerful means\nof storytelling. These cases closely parallel real-world situations and can\ndeliver on pedagogical objectives as writers can use their creative license to\ncraft a storyline that better focuses on the specific principles, concepts, and\nchallenges they want to address in their teaching. The method instigates\ncritical discussion, draws out relevant experiences from students, encourages\nquestioning of accepted practices, and creates dialogue between theory and\npractice. We present Horizon, a case study of a firm that suffers a\ncatastrophic incident of Intellectual Property (IP) theft. The case study was\ndeveloped to teach information security management (ISM) principles in key\nareas such as strategy, risk, policy and training to postgraduate Information\nSystems and Information Technology students at the University of Melbourne,\nAustralia.\n"}
+{"abstract": "  This article constructs the Shiromizu-Maeda-Sasaki 3-brane in the context of\nfive-dimensional Einstein-Chern-Simons gravity. We started by considering\nIsrael's junction condition for Lovelock's theory to read the junctions\nconditions for AdS-Chern-Simons gravity. Using the S-expansion procedure, we\nmapped the AdS-Chern-Simons junction conditions to Einstein-Chern-Simons\ngravity, allowing us to derive effective four-dimensional Einstein-Chern-Simons\nfield equations.\n"}
+{"abstract": "  In this paper we consider the existence and stability of multi-spike\nsolutions to the fractional Gierer-Meinhardt model with periodic boundary\nconditions. In particular we rigorously prove the existence of symmetric and\nasymmetric two-spike solutions using a Lyapunov-Schmidt reduction. The linear\nstability of these two-spike solutions is then rigorously analyzed and found to\nbe determined by the eigenvalues of a certain $2\\times 2$ matrix. Our rigorous\nresults are complemented by formal calculations of $N$-spike solutions using\nthe method of matched asymptotic expansions. In addition, we explicitly\nconsider examples of one- and two-spike solutions for which we numerically\ncalculate their relevant existence and stability thresholds. By considering a\none-spike solution we determine that the introduction of fractional diffusion\nfor the activator or inhibitor will respectively destabilize or stabilize a\nsingle spike solution with respect to oscillatory instabilities. Furthermore,\nwhen considering two-spike solutions we find that the range of parameter values\nfor which asymmetric two-spike solutions exist and for which symmetric\ntwo-spike solutions are stable with respect to competition instabilities is\nexpanded with the introduction of fractional inhibitor diffusivity. However our\ncalculations indicate that asymmetric two-spike solutions are always linearly\nunstable.\n"}
+{"abstract": "  We present a language model that combines a large parametric neural network\n(i.e., a transformer) with a non-parametric episodic memory component in an\nintegrated architecture. Our model uses extended short-term context by caching\nlocal hidden states -- similar to transformer-XL -- and global long-term memory\nby retrieving a set of nearest neighbor tokens at each timestep. We design a\ngating function to adaptively combine multiple information sources to make a\nprediction. This mechanism allows the model to use either local context,\nshort-term memory, or long-term memory (or any combination of them) on an ad\nhoc basis depending on the context. Experiments on word-based and\ncharacter-based language modeling datasets demonstrate the efficacy of our\nproposed method compared to strong baselines.\n"}
+{"abstract": "  This paper considers mean field games in a multi-agent Markov decision\nprocess (MDP) framework. Each player has a continuum state and binary action,\nand benefits from the improvement of the condition of the overall population.\nBased on an infinite horizon discounted individual cost, we show existence of a\nstationary equilibrium, and prove its uniqueness under a positive externality\ncondition. We further analyze comparative statics of the stationary equilibrium\nby quantitatively determining the impact of the effort cost.\n"}
+{"abstract": "  We present a new method, exact in $\\alpha'$, to explicitly compute string\ntree-level amplitudes involving one massive state and any number of massless\nones. This construction relies on the so-called twisted heterotic string, which\nadmits only gauge multiplets, a gravitational multiplet, and a single massive\nsupermultiplet in its spectrum. In this simplified model, we determine the\nmoduli-space integrand of all amplitudes with one massive state using\nBerends-Giele currents of the gauge multiplet. These integrands are then\nstraightforwardly mapped to gravitational amplitudes in the twisted heterotic\nstring and to the corresponding massive amplitudes of the conventional type-I\nand type-II superstrings.\n"}
+{"abstract": "  Context: It is not uncommon for a new team member to join an existing Agile\nsoftware development team, even after development has started. This new team\nmember faces a number of challenges before they are integrated into the team\nand can contribute productively to team progress. Ideally, each newcomer should\nbe supported in this transition through an effective team onboarding program,\nalthough prior evidence suggests that this is challenging for many\norganisations. Objective: We seek to understand how Agile teams address the\nchallenge of team onboarding in order to inform future onboarding design.\nMethod: We conducted an interview survey of eleven participants from eight\norganisations to investigate what onboarding activities are common across Agile\nsoftware development teams. We also identify common goals of onboarding from a\nsynthesis of literature. A repertory grid instrument is used to map the\ncontributions of onboarding techniques to onboarding goals. Results: Our study\nreveals that a broad range of team onboarding techniques, both formal and\ninformal, are used in practice. It also shows that particular techniques that\nhave high contributions to a given goal or set of goals. Conclusions: In\npresenting a set of onboarding goals to consider and an evidence-based\nmechanism for selecting techniques to achieve the desired goals it is expected\nthat this study will contribute to better-informed onboarding design and\nplanning. An increase in practitioner awareness of the options for supporting\nnew team members is also an expected outcome.\n"}
+{"abstract": "  Immersive viewing is emerging as the next interface evolution for\nhuman-computer interaction. A truly wireless immersive application necessitates\nimmense data delivery with ultra-low latency, raising stringent requirements\nfor next-generation wireless networks. A potential solution for addressing\nthese requirements is through the efficient usage of in-device storage and\ncomputation capabilities. This paper proposes a novel location-based coded\ncache placement and delivery scheme, which leverages the nested code modulation\n(NCM) to enable multi-rate multicasting transmission. To provide a uniform\nquality of experience in different network locations, we formulate a linear\nprogramming cache allocation problem. Next, based on the users' spatial\nrealizations, we adopt an NCM based coded delivery algorithm to efficiently\nserve a distinct group of users during each transmission. Numerical results\ndemonstrate that the proposed location-based delivery method significantly\nincreases transmission efficiency compared to state of the art.\n"}
+{"abstract": "  The discovery of the disentanglement properties of the latent space in GANs\nmotivated a lot of research to find the semantically meaningful directions on\nit. In this paper, we suggest that the disentanglement property is closely\nrelated to the geometry of the latent space. In this regard, we propose an\nunsupervised method for finding the semantic-factorizing directions on the\nintermediate latent space of GANs based on the local geometry. Intuitively, our\nproposed method, called Local Basis, finds the principal variation of the\nlatent space in the neighborhood of the base latent variable. Experimental\nresults show that the local principal variation corresponds to the semantic\nfactorization and traversing along it provides strong robustness to image\ntraversal. Moreover, we suggest an explanation for the limited success in\nfinding the global traversal directions in the latent space, especially W-space\nof StyleGAN2. We show that W-space is warped globally by comparing the local\ngeometry, discovered from Local Basis, through the metric on Grassmannian\nManifold. The global warpage implies that the latent space is not well-aligned\nglobally and therefore the global traversal directions are bound to show\nlimited success on it.\n"}
+{"abstract": "  Developers often encounter unfamiliar code during software maintenance which\nconsumes a significant amount of time for comprehension, especially for novice\nprogrammers. Automated techniques that analyze a source code and present key\ninformation to the developers can lead to an effective comprehension of the\ncode. Researchers have come up with automated code summarization techniques\nthat focus on code summarization by generating brief summaries rather than\naiding its comprehension. Existing debuggers represent the execution states of\nthe program but they do not show the complete execution at a single point.\nStudies have revealed that the effort required for program comprehension can be\nreduced if novice programmers are provided with worked examples. Hence, we\npropose COSPEX (Comprehension using Summarization via Program Execution) - an\nAtom plugin that dynamically extracts key information for every line of code\nexecuted and presents it to the developers in the form of an interactive\nexample-like dynamic information instance. As a preliminary evaluation, we\npresented 14 undergraduates having Python programming experience up to 1 year\nwith a code comprehension task in a user survey. We observed that COSPEX helped\nnovice programmers in program comprehension and improved their understanding of\nthe code execution. The source code and tool are available at:\nhttps://bit.ly/3utHOBM, and the demo on Youtube is available at:\nhttps://bit.ly/2Sp08xQ.\n"}
+{"abstract": "  To enable large-scale Internet of Things (IoT) deployment, Low-power\nwide-area networking (LPWAN) has attracted a lot of research attention with the\ndesign objectives of low-power consumption, wide-area coverage, and low cost.\nIn particular, long battery lifetime is central to these technologies since\nmany of the IoT devices will be deployed in hard-toaccess locations. Prediction\nof the battery lifetime depends on the accurate modelling of power consumption.\nThis paper presents detailed power consumption models for two cellular IoT\ntechnologies: Narrowband Internet of Things (NB-IoT) and Long Term Evolution\nfor Machines (LTE-M). A comprehensive power consumption model based on User\nEquipment (UE) states and procedures for device battery lifetime estimation is\npresented. An IoT device power measurement testbed has been setup and the\nproposed model has been validated via measurements with different coverage\nscenarios and traffic configurations, achieving the modelling inaccuracy within\n5%. The resulting estimated battery lifetime is promising, showing that the\n10-year battery lifetime requirement specified by 3GPP can be met with proper\nconfiguration of traffic profile, transmission, and network parameters.\n"}
+{"abstract": "  Most of the existing video self-supervised methods mainly leverage temporal\nsignals of videos, ignoring that the semantics of moving objects and\nenvironmental information are all critical for video-related tasks. In this\npaper, we propose a novel self-supervised method for video representation\nlearning, referred to as Video 3D Sampling (V3S). In order to sufficiently\nutilize the information (spatial and temporal) provided in videos, we\npre-process a video from three dimensions (width, height, time). As a result,\nwe can leverage the spatial information (the size of objects), temporal\ninformation (the direction and magnitude of motions) as our learning target. In\nour implementation, we combine the sampling of the three dimensions and propose\nthe scale and projection transformations in space and time respectively. The\nexperimental results show that, when applied to action recognition, video\nretrieval and action similarity labeling, our approach improves the\nstate-of-the-arts with significant margins.\n"}
+{"abstract": "  Quantum secure data transfer is an important topic for quantum cyber\nsecurity. We propose a scheme to realize quantum secure data transfer in the\nbasis of quantum secure direct communication (QSDC). In this proposal, the\ntransmitted data is encoded in the pulse shape of a single optical qubit, which\nis emitted from a trapped atom owned by the sender and received by the receiver\nwith another trapped atom. The encoding process can be implemented with high\nfidelity by controlling the time-dependent driving pulse on the trapped atom to\nmanipulate the Rabi frequency in accordance with the target pulse shape of the\nemitted photons. In the receiving process, we prove that, the single photon can\nbe absorbed with arbitrary probability by selecting appropriate driving pulse.\nWe also show that, based on the QSDC protocol, the data transfer process is\nimmune to the individual attacks.\n"}
+{"abstract": "  Rate-splitting multiple access (RSMA) has been recognized as a promising\nphysical layer strategy for 6G. Motivated by ever increasing popularity of\ncache-enabled content delivery in wireless communications, this paper proposes\nan innovative multigroup multicast transmission scheme based on RSMA for\ncache-aided cloud-radio access networks (C-RAN). Our proposed scheme not only\nexploits the properties of content-centric communications and local caching at\nthe base stations (BSs), but also incorporates RSMA to better manage\ninterference in multigroup multicast transmission with statistical channel\nstate information (CSI) known at the central processor (CP) and the BSs. At the\nRSMA-enabled cloud CP, the message of each multicast group is split into a\nprivate and a common part with the former private part being decoded by all\nusers in the respective group and the latter common part being decoded by\nmultiple users from other multicast groups. Common message decoding is done for\nthe purpose of mitigating the interference. In this work, we jointly optimize\nthe clustering of BSs and the precoding with the aim of maximizing the minimum\nrate among all multicast groups to guarantee fairness serving all groups. The\nproblem is a mixed-integer non-linear stochastic program (MINLSP), which is\nsolved by a practical algorithm we proposed including a heuristic clustering\nalgorithm for assigning a set of BSs to serve each user followed by an\nefficient iterative algorithm that combines the sample average approximation\n(SAA) and weighted minimum mean square error (WMMSE) to solve the stochastic\nnon-convex sub-problem of precoder design. Numerical results show the explicit\nmax-min rate gain of our proposed transmission scheme compared to the\nstate-of-the-art trivial interference processing methods. Therefore, we\nconclude that RSMA is a promising technique for cache-aided C-RAN.\n"}
+{"abstract": "  In this paper, we investigate statistics on alternating words under\ncorrespondence between ``possible reflection paths within several layers of\nglass'' and ``alternating words''. For\n$v=(v_1,v_2,\\cdots,v_n)\\in\\mathbb{Z}^{n}$, we say $P$ is a path within $n$\nglass plates corresponding to $v$, if $P$ has exactly $v_i$ reflections\noccurring at the $i^{\\rm{th}}$ plate for all $i\\in\\{1,2,\\cdots,n\\}$. We give a\nrecursion for the number of paths corresponding to $v$ satisfying $v \\in\n\\mathbb{Z}^n$ and $\\sum_{i\\geq 1} v_i=m$. Also, we establish recursions for\nstatistics around the number of paths corresponding to a given vector\n$v\\in\\mathbb{Z}^n$ and a closed form for $n=3$. Finally, we give a equivalent\ncondition for the existence of path corresponding to a given vector $v$.\n"}
+{"abstract": "  Many models such as Long Short Term Memory (LSTMs), Gated Recurrent Units\n(GRUs) and transformers have been developed to classify time series data with\nthe assumption that events in a sequence are ordered. On the other hand, fewer\nmodels have been developed for set based inputs, where order does not matter.\nThere are several use cases where data is given as partially-ordered sequences\nbecause of the granularity or uncertainty of time stamps. We introduce a novel\ntransformer based model for such prediction tasks, and benchmark against\nextensions of existing order invariant models. We also discuss how transition\nprobabilities between events in a sequence can be used to improve model\nperformance. We show that the transformer-based equal-time model outperforms\nextensions of existing set models on three data sets.\n"}
+{"abstract": "  In this paper, we show that a simple generalization of the holographic axion\nmodel can realize spontaneous breaking of translational symmetry by considering\na special gauge-axion higher derivative term. The finite real part and\nimaginary part of the stress tensor imply that the dual boundary system is a\nviscoelastic solid. By calculating quasi-normal modes and making a comparison\nwith predictions from the elasticity theory, we verify the existence of phonons\nand pseudo-phonons, where the latter is realized by introducing a weak explicit\nbreaking of translational symmetry, in the transverse channel. Finally, we\ndiscuss how the phonon dynamics affects the charge transport.\n"}
+{"abstract": "  In the past decades, many graph drawing techniques have been proposed for\ngenerating aesthetically pleasing graph layouts. However, it remains a\nchallenging task since different layout methods tend to highlight different\ncharacteristics of the graphs. Recently, studies on deep learning based graph\ndrawing algorithm have emerged but they are often not generalizable to\narbitrary graphs without re-training. In this paper, we propose a Convolutional\nGraph Neural Network based deep learning framework, DeepGD, which can draw\narbitrary graphs once trained. It attempts to generate layouts by compromising\namong multiple pre-specified aesthetics considering a good graph layout usually\ncomplies with multiple aesthetics simultaneously. In order to balance the\ntrade-off, we propose two adaptive training strategies which adjust the weight\nfactor of each aesthetic dynamically during training. The quantitative and\nqualitative assessment of DeepGD demonstrates that it is capable of drawing\narbitrary graphs effectively, while being flexible at accommodating different\naesthetic criteria.\n"}
+{"abstract": "  One-shot anonymous unselfishness in economic games is commonly explained by\nsocial preferences, which assume that people care about the monetary payoffs of\nothers. However, during the last ten years, research has shown that different\ntypes of unselfish behaviour, including cooperation, altruism, truth-telling,\naltruistic punishment, and trustworthiness are in fact better explained by\npreferences for following one's own personal norms - internal standards about\nwhat is right or wrong in a given situation. Beyond better organising various\nforms of unselfish behaviour, this moral preference hypothesis has recently\nalso been used to increase charitable donations, simply by means of\ninterventions that make the morality of an action salient. Here we review\nexperimental and theoretical work dedicated to this rapidly growing field of\nresearch, and in doing so we outline mathematical foundations for moral\npreferences that can be used in future models to better understand selfless\nhuman actions and to adjust policies accordingly. These foundations can also be\nused by artificial intelligence to better navigate the complex landscape of\nhuman morality.\n"}
+{"abstract": "  Safety is a critical concern when deploying reinforcement learning agents for\nrealistic tasks. Recently, safe reinforcement learning algorithms have been\ndeveloped to optimize the agent's performance while avoiding violations of\nsafety constraints. However, few studies have addressed the non-stationary\ndisturbances in the environments, which may cause catastrophic outcomes. In\nthis paper, we propose the context-aware safe reinforcement learning (CASRL)\nmethod, a meta-learning framework to realize safe adaptation in non-stationary\nenvironments. We use a probabilistic latent variable model to achieve fast\ninference of the posterior environment transition distribution given the\ncontext data. Safety constraints are then evaluated with uncertainty-aware\ntrajectory sampling. The high cost of safety violations leads to the rareness\nof unsafe records in the dataset. We address this issue by enabling prioritized\nsampling during model training and formulating prior safety constraints with\ndomain knowledge during constrained planning. The algorithm is evaluated in\nrealistic safety-critical environments with non-stationary disturbances.\nResults show that the proposed algorithm significantly outperforms existing\nbaselines in terms of safety and robustness.\n"}
+{"abstract": "  In 2019 P. Patak and M. Tancer obtained the following higher-dimensional\ngeneralization of the Heawood inequality on embeddings of graphs into surfaces.\nWe expose this result in a short well-structured way accessible to\nnon-specialists in the field.\n  Let $\\Delta_n^k$ be the union of $k$-dimensional faces of the $n$-dimensional\nsimplex.\n  Theorem. (a) If $\\Delta_n^k$ PL embeds into the connected sum of $g$ copies\nof the Cartesian product $S^k\\times S^k$ of two $k$-dimensional spheres, then\n$g\\ge\\dfrac{n-2k}{k+2}$.\n  (b) If $\\Delta_n^k$ PL embeds into a closed $(k-1)$-connected PL\n$2k$-manifold $M$, then $(-1)^k(\\chi(M)-2)\\ge\\dfrac{n-2k}{k+1}$.\n"}
+{"abstract": "  We theoretically study the correlated insulator states, quantum anomalous\nHall (QAH) states, and field-induced topological transitions between different\ncorrelated states in twisted multilayer graphene systems. Taking twisted\nbilayer-monolayer graphene and twisted double-bilayer graphene as examples, we\nshow that both systems stay in spin polarized, $C_{3z}$-broken insulator states\nwith zero Chern number at 1/2 filling of the flat bands under finite\ndisplacement fields. In some cases these spin polarized, nematic insulator\nstates are in the quantum valley Hall phase by virtue of the nontrivial band\ntopology of the systems. The spin polarized insulator state is quasi-degenerate\nwith the valley polarized state if only the dominant intra-valley Coulomb\ninteraction is included. Such quasi-degeneracy can be split by atomic on-site\ninteractions such that the spin polarized, nematic state become the unique\nground state. Such a scenario applies to various twisted multilayer graphene\nsystems at 1/2 filling, thus can be considered as a universal mechanism.\nMoreover, under vertical magnetic fields, the orbital Zeeman splittings and the\nfield-induced change of charge density in twisted multilayer graphene systems\nwould compete with the atomic Hubbard interactions, which can drive transitions\nfrom spin polarized zero-Chern-number states to valley-polarized QAH states\nwith small onset magnetic fields.\n"}
+{"abstract": "  The problem of estimating the size of a population based on a subset of\nindividuals observed across multiple data sources is often referred to as\ncapture-recapture or multiple-systems estimation. This is fundamentally a\nmissing data problem, where the number of unobserved individuals represents the\nmissing data. As with any missing data problem, multiple-systems estimation\nrequires users to make an untestable identifying assumption in order to\nestimate the population size from the observed data. Approaches to\nmultiple-systems estimation often do not emphasize the role of the identifying\nassumption during model specification, which makes it difficult to decouple the\nspecification of the model for the observed data from the identifying\nassumption. We present a re-framing of the multiple-systems estimation problem\nthat decouples the specification of the observed-data model from the\nidentifying assumptions, and discuss how log-linear models and the associated\nno-highest-order interaction assumption fit into this framing. We present an\napproach to computation in the Bayesian setting which takes advantage of\nexisting software and facilitates various sensitivity analyses. We demonstrate\nour approach in a case study of estimating the number of civilian casualties in\nthe Kosovo war. Code used to produce this manuscript is available at\nhttps://github.com/aleshing/revisiting-identifying-assumptions.\n"}
+{"abstract": "  To solve the hierarchy problem, the relaxion must remain trapped in the\ncorrect minimum, even if the electroweak symmetry is restored after reheating.\nIn this scenario, the relaxion starts rolling again until the backreaction\npotential, with its set of local minima, reappears. Depending on the time of\nbarrier reappearance, Hubble friction alone may be insufficient to retrap the\nrelaxion in a large portion of the parameter space. Thus, an additional source\nof friction is required, which might be provided by coupling to a dark\nphoton.The dark photon experiences a tachyonic instability as the relaxion\nrolls, which slows down the relaxion by backreacting to its motion, and\nefficiently creates anisotropies in the dark photon energy-momentum tensor,\nsourcing gravitational waves. We calculate the spectrum of the resulting\ngravitational wave background from this new mechanism, and evaluate its\nobservability by current and future experiments. We further investigate the\npossibility that the coherently oscillating relaxion constitutes dark matter\nand present the corresponding constraints from gravitational waves.\n"}
+{"abstract": "  A regular left-order on finitely generated group $G$ is a total,\nleft-multiplication invariant order on $G$ whose corresponding positive cone is\nthe image of a regular language over the generating set of the group under the\nevaluation map. We show that admitting regular left-orders is stable under\nextensions and wreath products and give a classification of the groups all\nwhose left-orders are regular left-orders. In addition, we prove that solvable\nBaumslag-Solitar groups $B(1,n)$ admits a regular left-order if and only if\n$n\\geq -1$. Finally, Hermiller and Sunic showed that no free product admits a\nregular left-order, however we show that if $A$ and $B$ are groups with regular\nleft-orders, then $(A*B)\\times \\mathbb{Z}$ admits a regular left-order.\n"}
+{"abstract": "  Let $(X, D_{X})$ be an arbitrary pointed stable curve of topological type\n$(g_{X}, n_{X})$ over an algebraically closed field of characteristic $p>0$. We\nprove that the generalized Hasse-Witt invariants of prime-to-$p$ cyclic\nadmissible coverings of $(X, D_{X})$ attain maximum. As applications, we obtain\nan anabelian formula for $(g_{X}, n_{X})$, and prove that the field structures\nassociated to inertia subgroups of marked points can be reconstructed\ngroup-theoretically from open continuous homomorphisms of admissible\nfundamental groups. Moreover, the formula for maximum generalized Hasse-Witt\ninvariants and the result concerning reconstructions of field structures play\nimportant roles in the theory of moduli spaces of fundamental groups developed\nby the author of the present paper.\n"}
+{"abstract": "  Deep learning (DL) based language models achieve high performance on various\nbenchmarks for Natural Language Inference (NLI). And at this time, symbolic\napproaches to NLI are receiving less attention. Both approaches (symbolic and\nDL) have their advantages and weaknesses. However, currently, no method\ncombines them in a system to solve the task of NLI. To merge symbolic and deep\nlearning methods, we propose an inference framework called NeuralLog, which\nutilizes both a monotonicity-based logical inference engine and a neural\nnetwork language model for phrase alignment. Our framework models the NLI task\nas a classic search problem and uses the beam search algorithm to search for\noptimal inference paths. Experiments show that our joint logic and neural\ninference system improves accuracy on the NLI task and can achieve state-of-art\naccuracy on the SICK and MED datasets.\n"}
+{"abstract": "  We continue the study of AdS loop amplitudes in the spectral representation\nand in position space. We compute the finite coupling 4-point function in\nposition space for the large-$N$ conformal Gross Neveu model on $AdS_3$. The\nresummation of loop bubble diagrams gives a result proportional to a tree-level\ncontact diagram. We show that certain families of fermionic Witten diagrams can\nbe easily computed from their companion scalar diagrams. Thus, many of the\nresults and identities of [1] are extended to the case of external fermions. We\nderive a spectral representation for ladder diagrams in AdS. Finally, we\ncompute various bulk 2-point correlators, extending the results of [1].\n"}
+{"abstract": "  Transparent Conductive Oxides (TCOs) are a class of materials that combine\nhigh optical transparency with high electrical conductivity. This property\nmakes them uniquely appealing as transparent-conductive electrodes in solar\ncells and interesting for optoelectronics and infrared-plasmonics applications.\nOne of the new challenges that researchers and engineers are facing is merging\noptical and electrical control in a single device for developing\nnext-generation photovoltaic, opto-electronic devices and energyefficient\nsolid-state lighting. In this work, we investigated the possible variations in\nthe dielectric properties of aluminum-doped ZnO (AZO) upon gating, by means of\nSpectroscopic Ellipsometry (SE). We investigated the electrical-bias-dependent\noptical response of thin AZO films fabricated by magnetron sputtering, within a\nparallel-plane capacitor configuration. We address the possibility to control\ntheir optical and electric performances by applying bias, monitoring the effect\nof charge injection/depletion in the AZO layer by means of in-operando SE vs\napplied gate voltage.\n"}
+{"abstract": "  We describe a framework to assemble permanent-magnet cubes in 3D-printed\nframes to construct dipole, quadrupole, and solenoid magnets, whose field, in\nthe absence of iron, can be calculated analytically in three spatial\ndimensions. Rotating closely spaced dipoles and quadrupoles in opposite\ndirections allows us to adjust the integrated strength of a multipole.\nContributions of unwanted harmonics are calculated and found to be moderate. We\nthen combine multiple magnets to construct beam-line modules: chicane, triplet\ncell, and solenoid focusing system.\n"}
+{"abstract": "  We study the evolution of qubits amplitudes in a one-dimensional chain\nconsisting of three equidistantly spaced noninteracting qubits embedded in an\nopen waveguide. The study is performed in the frame of single-excitation\nsubspace, where the only qubit in the chain is initially excited. We show that\nthe dynamics of qubits amplitudes crucially depend on the value of $kd$, where\n$k$ is the wave vector, $d$ is a distance between neighbor qubits. If $kd$ is\nequal to an integer multiple of $\\pi$, then the qubits are excited to a\nstationary level. In this case, it is the dark states which prevent qubits from\ndecaying to zero even though they do not contribute to the output spectrum of\nphoton emission. For other values of $kd$ the excitations of qubits exhibit the\ndamping oscillations which represent the vacuum Rabi oscillations in a\nthree-qubit system. In this case, the output spectrum of photon radiation is\ndetermined by a subradiant state which has the lowest decay rate. We also\ninvestigated the case with the frequency of a central qubit being different\nfrom that of the edge qubits. In this case, the qibits decay rates can be\ncontrolled by the frequency detuning between the central and the edge qubits.\n"}
+{"abstract": "  We demonstrate that the recent measurement of the anomalous magnetic moment\nof the muon and dark matter can be simultaneously explained within the Minimal\nSupersymmetric Standard Model. Dark matter is a mostly-bino state, with the\nrelic abundance obtained via co-annihilations with either the sleptons or wino.\nThe most interesting regions of parameter space will be tested by the next\ngeneration of dark matter direct detection experiments.\n"}
+{"abstract": "  In [arxiv:2106.02560] we proposed a reduced density matrix functional theory\n(RDMFT) for calculating energies of selected eigenstates of interacting\nmany-fermion systems. Here, we develop a solid foundation for this so-called\n$\\boldsymbol{w}$-RDMFT and present the details of various derivations. First,\nwe explain how a generalization of the Ritz variational principle to ensemble\nstates with fixed weights $\\boldsymbol{w}$ in combination with the constrained\nsearch would lead to a universal functional of the one-particle reduced density\nmatrix. To turn this into a viable functional theory, however, we also need to\nimplement an exact convex relaxation. This general procedure includes Valone's\npioneering work on ground state RDMFT as the special case\n$\\boldsymbol{w}=(1,0,\\ldots)$. Then, we work out in a comprehensive manner a\nmethodology for deriving a compact description of the functional's domain. This\nleads to a hierarchy of generalized exclusion principle constraints which we\nillustrate in great detail. By anticipating their future pivotal role in\nfunctional theories and to keep our work self-contained, several required\nconcepts from convex analysis are introduced and discussed.\n"}
+{"abstract": "  Fuzzing is a technique widely used in vulnerability detection. The process\nusually involves writing effective fuzz driver programs, which, when done\nmanually, can be extremely labor intensive. Previous attempts at automation\nleave much to be desired, in either degree of automation or quality of output.\nIn this paper, we propose IntelliGen, a framework that constructs valid fuzz\ndrivers automatically. First, IntelliGen determines a set of entry functions\nand evaluates their respective chance of exhibiting a vulnerability. Then,\nIntelliGen generates fuzz drivers for the entry functions through hierarchical\nparameter replacement and type inference. We implemented IntelliGen and\nevaluated its effectiveness on real-world programs selected from the Android\nOpen-Source Project, Google's fuzzer-test-suite and industrial collaborators.\nIntelliGen covered on average 1.08X-2.03X more basic blocks and 1.36X-2.06X\nmore paths over state-of-the-art fuzz driver synthesizers FUDGE and FuzzGen.\nIntelliGen performed on par with manually written drivers and found 10 more\nbugs.\n"}
+{"abstract": "  A $k$-regular graph is called a divisible design graph (DDG for short) if its\nvertex set can be partitioned into $m$ classes of size $n$, such that two\ndistinct vertices from the same class have exactly $\\lambda_1$ common\nneighbors, and two vertices from different classes have exactly $\\lambda_2$\ncommon neighbors. $4\\times n$-lattice graph is the line graph of $K_{4,n}$.\nThis graph is a DDG with parameters $(4n,n+2,n-2,2,4,n)$. In the paper we\nconsider DDGs with these parameters. We prove that if $n$ is odd then such\ngraph can only be a $4\\times n$-lattice graph. If $n$ is even we characterise\nall DDGs with such parameters. Moreover, we characterise all DDGs with\nparameters $(4n,3n-2,3n-6,2n-2,4,n)$ which are related to $4\\times n$-lattice\ngraphs.\n"}
+{"abstract": "  This paper presents a novel and flexible multi-task multi-layer Bayesian\nmapping framework with readily extendable attribute layers. The proposed\nframework goes beyond modern metric-semantic maps to provide even richer\nenvironmental information for robots in a single mapping formalism while\nexploiting existing inter-layer correlations. It removes the need for a robot\nto access and process information from many separate maps when performing a\ncomplex task and benefits from the correlation between map layers, advancing\nthe way robots interact with their environments. To this end, we design a\nmulti-task deep neural network with attention mechanisms as our front-end to\nprovide multiple observations for multiple map layers simultaneously. Our\nback-end runs a scalable closed-form Bayesian inference with only logarithmic\ntime complexity. We apply the framework to build a dense robotic map including\nmetric-semantic occupancy and traversability layers. Traversability ground\ntruth labels are automatically generated from exteroceptive sensory data in a\nself-supervised manner. We present extensive experimental results on publicly\navailable data sets and data collected by a 3D bipedal robot platform on the\nUniversity of Michigan North Campus and show reliable mapping performance in\ndifferent environments. Finally, we also discuss how the current framework can\nbe extended to incorporate more information such as friction, signal strength,\ntemperature, and physical quantity concentration using Gaussian map layers. The\nsoftware for reproducing the presented results or running on customized data is\nmade publicly available.\n"}
+{"abstract": "  The level set method is a widely used tool for solving reachability and\ninvariance problems. However, some shortcomings, such as the difficulties of\nhandling dissipation function and constructing terminal conditions for solving\nthe Hamilton-Jacobi partial differential equation, limit the application of the\nlevel set method in some problems with non-affine nonlinear systems and\nirregular target sets. This paper proposes a method that can effectively avoid\nthe above tricky issues and thus has better generality. In the proposed method,\nthe reachable or invariant sets with different time horizons are characterized\nby some non-zero sublevel sets of a value function. This value function is not\nobtained by solving a viscosity solution of the partial differential equation\nbut by recursion and interpolation approximation. At the end of this paper,\nsome examples are taken to illustrate the accuracy and generality of the\nproposed method.\n"}
+{"abstract": "  We prove that a special variety of quadratically constrained quadratic\nprograms, occurring frequently in conjunction with the design of wave systems\nobeying causality and passivity (i.e. systems with bounded response),\nuniversally exhibit strong duality. Directly, the problem of continuum\n(\"grayscale\" or \"effective medium\") device design for any (complex) quadratic\nwave objective governed by independent quadratic constraints can be solved as a\nconvex program. The result guarantees that performance limits for many common\nphysical objectives can be made nearly \"tight\", and suggests far-reaching\nimplications for problems in optics, acoustics, and quantum mechanics.\n"}
+{"abstract": "  PQ-type adjacency polytopes $\\nabla^{\\rm PQ}_G$ are lattice polytopes arising\nfrom finite graphs $G$. There is a connection between $\\nabla^{\\rm PQ}_G$ and\nthe engineering problem known as power-flow study, which models the balance of\nelectric power on a network of power generation. In particular, the normalized\nvolume of $\\nabla^{\\rm PQ}_G$ plays a central role. In the present paper, we\nfocus the case where $G$ is a join graph. In fact, formulas of the\n$h^*$-polynomial and the normalized volume of $\\nabla^{\\rm PQ}_G$ of a join\ngraph $G$ are presented. Moreover, we give explicit formulas of the\n$h^*$-polynomial and the normalized volume of $\\nabla^{\\rm PQ}_G$ when $G$ is a\ncomplete multipartite graph or a wheel graph.\n"}
+{"abstract": "  In this paper, given a linear system of equations A x = b, we are finding\nlocations in the plane to place objects such that sending waves from the source\npoints and gathering them at the receiving points solves that linear system of\nequations. The ultimate goal is to have a fast physical method for solving\nlinear systems. The issue discussed in this paper is to apply a fast and\naccurate algorithm to find the optimal locations of the scattering objects. We\ntackle this issue by using asymptotic expansions for the solution of the\nunderlyingpartial differential equation. This also yields a potentially faster\nalgorithm than the classical BEM for finding solutions to the Helmholtz\nequation.\n"}
+{"abstract": "  In this paper, we provide a precise description of the compatibility\nconditions for the initial data so that one can show the existence and\nuniqueness of regular short-time solution to the Neumann initial-boundary\nproblem of a class of Landau-Lifshitz-Gilbert system with spin-polarized\ntransport, which is a strong nonlinear coupled parabolic system with non-local\nenergy.\n"}
+{"abstract": "  We prove F\\\"{o}llmer's pathwise It\\^{o} formula for a Banach space-valued\nc\\`{a}dl\\`{a}g path. We also relax the assumption on the sequence of partitions\nalong which we treat the quadratic variation of a path.\n"}
+{"abstract": "  We show that the global assumptions on the H-flux in the definition of\nT-duality for principal torus bundles by Bunke, Rumpf, and Schick are not\nrequired. That is, these global conditions are implied by the Poincar\\'e bundle\ncondition. This is proved using a new and equivalent \"Thom class\" formulation\nof T-duality for principal torus bundles. We then generalise the local\nformulation of T-duality by Bunke, Schick, and Spitzweck to the torus case.\n"}
+{"abstract": "  let $\\widetilde{\\bf U}^\\imath$ be a quasi-split universal $\\imath$quantum\ngroup associated to a quantum symmetric pair $(\\widetilde{\\bf U},\n\\widetilde{\\bf U}^\\imath)$ of Kac-Moody type with a diagram involution $\\tau$.\nWe establish the Serre-Lusztig relations for $\\widetilde{\\bf U}^\\imath$\nassociated to a simple root $i$ such that $i \\neq \\tau i$, complementary to the\nSerre-Lusztig relations associated to $i=\\tau i$ which we obtained earlier. A\nconjecture on braid group symmetries on $\\widetilde{\\bf U}^\\imath$ associated\nto $i$ disjoint from $\\tau i$ is formulated.\n"}
+{"abstract": "  We propose a novel distributed monetary system called Hearsay that tolerates\nboth Byzantine and rational behavior without the need for agents to reach\nconsensus on executed transactions. Recent work [5, 10, 15] has shown that\ndistributed monetary systems do not require consensus and can operate using a\nbroadcast primitive with weaker guarantees, such as reliable broadcast.\nHowever, these protocols assume that some number of agents may be Byzantine and\nthe remaining agents are perfectly correct. For the application of a monetary\nsystem in which the agents are real people with economic interests, the\nassumption that agents are perfectly correct may be too strong. We expand upon\nthis line of thought by weakening the assumption of correctness and instead\nadopting a fault tolerance model which allows up to $t < \\frac{N}{3}$ agents to\nbe Byzantine and the remaining agents to be rational. A rational agent is one\nwhich will deviate from the protocol if it is in their own best interest. Under\nthis fault tolerance model, Hearsay implements a monetary system in which all\nrational agents achieve agreement on executed transactions. Moreover, Hearsay\nrequires only a single broadcast per transaction. In order to incentivize\nrational agents to behave correctly in Hearsay, agents are rewarded with\ntransaction fees for participation in the protocol and punished for noticeable\ndeviations from the protocol. Additionally, Hearsay uses a novel broadcast\nprimitive called Rational Reliable Broadcast to ensure that agents can\nbroadcast messages under Hearsay's fault tolerance model. Rational Reliable\nBroadcast achieves equivalent guarantees to Byzantine Reliable Broadcast [7]\nbut can tolerate the presence of rational agents. To show this, we prove that\nfollowing the Rational Reliable Broadcast protocol constitutes a Nash\nequilibrium between rational agents and may therefore be of independent\ninterest.\n"}
+{"abstract": "  In this work we consider the online control of a known linear dynamic system\nwith adversarial disturbance and adversarial controller cost. The goal in\nonline control is to minimize the regret, defined as the difference between\ncumulative cost over a period $T$ and the cumulative cost for the best policy\nfrom a comparator class. For the setting we consider, we generalize the\npreviously proposed online Disturbance Response Controller (DRC) to the\nadaptive gradient online Disturbance Response Controller. Using the modified\ncontroller, we present novel regret guarantees that improves the established\nregret guarantees for the same setting. We show that the proposed online\nlearning controller is able to achieve intermediate intermediate regret rates\nbetween $\\sqrt{T}$ and $\\log{T}$ for intermediate convex conditions, while it\nrecovers the previously established regret results for general convex\ncontroller cost and strongly convex controller cost.\n"}
+{"abstract": "  Quantum measurement is ultimately a physical process, resulting from an\ninteraction between the measured system and a measuring apparatus. Considering\nthe physical process of measurement within a thermodynamic context naturally\nraises the following question: How can the work and heat be interpreted? In the\npresent paper we model the measurement process for an arbitrary discrete\nobservable as a measurement scheme. Here the system to be measured is first\nunitarily coupled with an apparatus and subsequently the compound system is\nobjectified with respect to a pointer observable, thus producing definite\nmeasurement outcomes. The work can therefore be interpreted as the change in\ninternal energy of the compound system due to the unitary coupling. By the\nfirst law of thermodynamics, the heat is the subsequent change in internal\nenergy of this compound due to pointer objectification. We argue that the\napparatus serves as a stable record for the measurement outcomes only if the\npointer observable commutes with the Hamiltonian and show that such\ncommutativity implies that the uncertainty of heat will necessarily be\nclassical.\n"}
+{"abstract": "  Crack microgeometries pose a paramount influence on effective elastic\ncharacteristics and sonic responses. Geophysical exploration based on seismic\nmethods are widely used to assess and understand the presence of fractures.\nNumerical simulation as a promising way for this issue, still faces some\nchallenges. With the rapid development of computers and computational\ntechniques, discrete-based numerical approaches with desirable properties have\nbeen increasingly developed, but have not yet extensively applied to seismic\nresponse simulation for complex fractured media. For this purpose, we apply the\ncoupled LSM-DFN model (Liu and Fu, 2020b) to examining the validity in\nemulating elastic wave propagation and scattering in naturally-fractured media.\nBy comparing to the theoretical values, the implement of the schema is\nvalidated with input parameters optimization. Moreover, dynamic elastic moduli\nfrom seismic responses are calculated and compared with static ones from\nquasi-static loading of uniaxial compression tests. Numerical results are\nconsistent with the tendency of theoretical predictions and available\nexperimental data. It shows the potential for reproducing the seismic responses\nin complex fractured media and quantitatively investigating the correlations\nand differences between static and dynamic elastic moduli.\n"}
+{"abstract": "  Three-point correlators of spinning operators admit multiple tensor\nstructures compatible with conformal symmetry. For conserved currents in three\ndimensions, we point out that helicity commutes with conformal transformations\nand we use this to construct three-point structures which diagonalize helicity.\nIn this helicity basis, OPE data is found to be diagonal for mean-field\ncorrelators of conserved currents and stress tensor. Furthermore, we use\nLorentzian inversion formula to obtain anomalous dimensions for conserved\ncurrents at bulk tree-level order in holographic theories, which we compare\nwith corresponding flat-space gluon scattering amplitudes.\n"}
+{"abstract": "  The essence of the microgrid cyber-physical system (CPS) lies in the cyclical\nconversion of information flow and energy flow. Most of the existing coupling\nmodels are modeled with static networks and interface structures, in which the\nclosed-loop data flow characteristic is not fully considered. It is difficult\nfor these models to accurately describe spatiotemporal deduction processes,\nsuch as microgrid CPS attack identification, risk propagation, safety\nassessment, defense control, and cascading failure. To address this problem, a\nmodeling method for the coupling relations of microgrid CPS driven by hybrid\nspatiotemporal events is proposed in the present work. First, according to the\ntopological correlation and coupling logic of the microgrid CPS, the cyclical\nconversion mechanism of information flow and energy flow is analyzed, and a\nmicrogrid CPS architecture with multi-agents as the core is constructed. Next,\nthe spatiotemporal evolution characteristic of the CPS is described by hybrid\nautomata, and the task coordination mechanism of the multi-agent CPS terminal\nis designed. On this basis, a discrete-continuous correlation and terminal\nstructure characteristic representation method of the CPS based on\nheterogeneous multi-groups are then proposed. Finally, four spatiotemporal\nevents, namely state perception, network communication, intelligent\ndecision-making, and action control, are defined. Considering the constraints\nof the temporal conversion of information flow and energy flow, a microgrid CPS\ncoupling model is established, the effectiveness of which is verified by\nsimulating false data injection attack (FDIA) scenarios.\n"}
+{"abstract": "  Assessment and reporting of skills is a central feature of many digital\nlearning platforms. With students often using multiple platforms,\ncross-platform assessment has emerged as a new challenge. While technologies\nsuch as Learning Tools Interoperability (LTI) have enabled communication\nbetween platforms, reconciling the different skill taxonomies they employ has\nnot been solved at scale. In this paper, we introduce and evaluate a\nmethodology for finding and linking equivalent skills between platforms by\nutilizing problem content as well as the platform's clickstream data. We\npropose six models to represent skills as continuous real-valued vectors and\nleverage machine translation to map between skill spaces. The methods are\ntested on three digital learning platforms: ASSISTments, Khan Academy, and\nCognitive Tutor. Our results demonstrate reasonable accuracy in skill\nequivalency prediction from a fine-grained taxonomy to a coarse-grained one,\nachieving an average recall@5 of 0.8 between the three platforms. Our skill\ntranslation approach has implications for aiding in the tedious, manual process\nof taxonomy to taxonomy mapping work, also called crosswalks, within the\ntutoring as well as standardized testing worlds.\n"}
+{"abstract": "  The ability to transfer coherent quantum information between systems is a\nfundamental component of quantum technologies and leads to coherent\ncorrelations within the global quantum process. However correlation structures\nin quantum channels are less studied than those in quantum states. Motivated by\nrecent techniques in randomized benchmarking, we develop a range of results for\nefficient estimation of correlations within a bipartite quantum channel. We\nintroduce sub-unitarity measures that are invariant under local changes of\nbasis, generalize the unitarity of a channel, and allow for the analysis of\ncoherent information exchange within channels. Using these, we show that\nunitarity is monogamous, and provide a novel information-disturbance relation.\nWe then define a notion of correlated unitarity that quantifies the\ncorrelations within a given channel. Crucially, we show that this measure is\nstrictly bounded on the set of separable channels and therefore provides a\nwitness of non-separability. Finally, we describe how such measures for\neffective noise channels can be efficiently estimated within different\nrandomized benchmarking protocols. We find that the correlated unitarity can be\nestimated in a SPAM-robust manner for any separable quantum channel, and show\nthat a benchmarking/tomography protocol with mid-circuit resets can reliably\nwitness non-separability for sufficiently small reset errors. The tools we\ndevelop provide information beyond that obtained via simultaneous randomized\nbenchmarking and so could find application in the analysis of cross-talk and\ncoherent errors in quantum devices.\n"}
+{"abstract": "  In this work, we want to learn to model the dynamics of similar yet distinct\ngroups of interacting objects. These groups follow some common physical laws\nthat exhibit specificities that are captured through some vectorial\ndescription. We develop a model that allows us to do conditional generation\nfrom any such group given its vectorial description. Unlike previous work on\nlearning dynamical systems that can only do trajectory completion and require a\npart of the trajectory dynamics to be provided as input in generation time, we\ndo generation using only the conditioning vector with no access to generation\ntime's trajectories. We evaluate our model in the setting of modeling human\ngait and, in particular pathological human gait.\n"}
+{"abstract": "  Although monitoring and covering are fundamental goals of a wireless sensor\nnetwork (WSN), the accidental death of sensors or the running out of their\nenergy would result in holes in the WSN. Such holes have the potential to\ndisrupt the primary functions of WSNs. This paper investigates the hole\ndetection and healing problems in hybrid WSNs with non-identical sensor sensing\nranges. In particular, we aim to propose centralized algorithms for detecting\nholes in a given region and maximizing the area covered by a WSN in the\npresence of environmental obstacles. To precisely identify the boundary of the\nholes, we use an additively weighted Voronoi diagram and a polynomial-time\nalgorithm.Furthermore, since this problem is known to be computationally\ndifficult, we propose a centralized greedy 1/2-approximation algorithm to\nmaximize the area covered by sensors. Finally, we implement the algorithms and\nrun simulations to show that our approximation algorithm efficiently covers the\nholes by moving the mobile sensors.\n"}
+{"abstract": "  Toward achieving robust and defensive neural networks, the robustness against\nthe weight parameters perturbations, i.e., sharpness, attracts attention in\nrecent years (Sun et al., 2020). However, sharpness is known to remain a\ncritical issue, \"scale-sensitivity.\" In this paper, we propose a novel\nsharpness measure, Minimum Sharpness. It is known that NNs have a specific\nscale transformation that constitutes equivalent classes where functional\nproperties are completely identical, and at the same time, their sharpness\ncould change unlimitedly. We define our sharpness through a minimization\nproblem over the equivalent NNs being invariant to the scale transformation. We\nalso develop an efficient and exact technique to make the sharpness tractable,\nwhich reduces the heavy computational costs involved with Hessian. In the\nexperiment, we observed that our sharpness has a valid correlation with the\ngeneralization of NNs and runs with less computational cost than existing\nsharpness measures.\n"}
+{"abstract": "  We ask the question: to what extent can recent large-scale language and image\ngeneration models blend visual concepts? Given an arbitrary object, we identify\na relevant object and generate a single-sentence description of the blend of\nthe two using a language model. We then generate a visual depiction of the\nblend using a text-based image generation model. Quantitative and qualitative\nevaluations demonstrate the superiority of language models over classical\nmethods for conceptual blending, and of recent large-scale image generation\nmodels over prior models for the visual depiction.\n"}
+{"abstract": "  This technical report presents a Systematic Literature Review (SLR) study\nthat focuses on identifying and classifying the recent research practices\npertaining to CPS development through MDE approaches. The study evaluates 140\nresearch papers published during 2010-2018. Accordingly, a comprehensive\nanalysis of various MDE approaches used in the development life-cycle of CPS is\npresented. Furthermore, the study identifies the research gaps and areas that\nneed more investigation. The contribution helps researchers and practitioners\nto get an overall understanding of the research trends and existing challenges\nfor further research/development.\n"}
+{"abstract": "  Dark energy stars research is an issue of great interest since recent\nastronomical observations with respect to measurements in distant supernovas,\ncosmic microwave background and weak gravitational lensing confirm that the\nuniverse is undergoing a phase of accelerated expansion and this cosmological\nbehavior is caused by the presence of a cosmic fluid which has a strong\nnegative pressure that allows to explain the expanding universe. In this paper,\nwe obtained new relativistic stellar configurations within the framework of\nEinstein-Gauss-Bonnet (EGB) gravity considering negative anisotropic pressures\nand the equation of state pr={\\omega}\\r{ho} where pr is the radial pressure,\n{\\omega} is the dark energy parameter, and \\r{ho} is the dark energy density.\nWe have chosen a modified version of metric potential proposed by\nKorkina-Orlyanskii (1991). For the new solutions we checked that the radial\npressure, metric coefficients, energy density and anisotropy are well defined\nand are regular in the interior of the star and are dependent of the values of\nthe Gauss-Bonnet coupling constant. The solutions found can be used in the\ndevelopment of dark energy stars models satisfying all physical acceptability\nconditions, but the causality condition and strong energy condition cannot be\nsatisfied.\n"}
+{"abstract": "  Establishing and approaching the fundamental limit of orbital angular\nmomentum (OAM) multiplexing are necessary and increasingly urgent for current\nmultiple-input multiple-output research. In this work, we elaborate the\nfundamental limit in terms of independent scattering channels (or degrees of\nfreedom of scattered fields) through angular-spectral analysis, in conjunction\nwith a rigorous Green function method. The scattering channel limit is\nuniversal for arbitrary spatial mode multiplexing, which is launched by a\nplanar electromagnetic device, such as antenna, metasurface, etc, with a\npredefined physical size. As a proof of concept, we demonstrate both\ntheoretically and experimentally the limit by a metasurface hologram that\ntransforms orthogonal OAM modes to plane-wave modes scattered at critically\nseparated angular-spectral regions. Particularly, a minimax optimization\nalgorithm is applied to suppress angular spectrum aliasing, achieving good\nperformances in both full-wave simulation and experimental measurement at\nmicrowave frequencies. This work offers a theoretical upper bound and\ncorresponding approach route for engineering designs of OAM multiplexing.\n"}
+{"abstract": "  This paper considers the problem of the valuation for integer numbers of the\nzeta function and of five other functions which are naturally associated to it.\nA relatively elementary approach is exposed, which closely connects this still\npartially open problem to five themes of parity: the notions of parity of a\nfunction and of parity of the degree of a polynomial are here related to the\ndistinctions of parity concerning the natural argument of the six considered\nfunctions as well as the integer numbers of which some inverse powers are\nsummed. The adopted method essentially aims at enabling the students in\nmathematics to have an entry into this problem.\n"}
+{"abstract": "  Neural Ordinary Differential Equations (NODEs), a framework of\ncontinuous-depth neural networks, have been widely applied, showing exceptional\nefficacy in coping with some representative datasets. Recently, an augmented\nframework has been successfully developed for conquering some limitations\nemergent in application of the original framework. Here we propose a new class\nof continuous-depth neural networks with delay, named as Neural Delay\nDifferential Equations (NDDEs), and, for computing the corresponding gradients,\nwe use the adjoint sensitivity method to obtain the delayed dynamics of the\nadjoint. Since the differential equations with delays are usually seen as\ndynamical systems of infinite dimension possessing more fruitful dynamics, the\nNDDEs, compared to the NODEs, own a stronger capacity of nonlinear\nrepresentations. Indeed, we analytically validate that the NDDEs are of\nuniversal approximators, and further articulate an extension of the NDDEs,\nwhere the initial function of the NDDEs is supposed to satisfy ODEs. More\nimportantly, we use several illustrative examples to demonstrate the\noutstanding capacities of the NDDEs and the NDDEs with ODEs' initial value.\nSpecifically, (1) we successfully model the delayed dynamics where the\ntrajectories in the lower-dimensional phase space could be mutually\nintersected, while the traditional NODEs without any argumentation are not\ndirectly applicable for such modeling, and (2) we achieve lower loss and higher\naccuracy not only for the data produced synthetically by complex models but\nalso for the real-world image datasets, i.e., CIFAR10, MNIST, and SVHN. Our\nresults on the NDDEs reveal that appropriately articulating the elements of\ndynamical systems into the network design is truly beneficial to promoting the\nnetwork performance.\n"}
+{"abstract": "  The difference in the density of states for up- and down-spin electrons in a\nferromagnet (F) results in spin-dependent scattering of electrons at a\nferromagnet / nonmagnetic (F/N) interface. In a F/N/F spin-valve, this causes a\ncurrent-independent difference in resistance ($\\Delta R$) between antiparallel\n(AP) and parallel (P) magnetization states. Giant magnetoresistance (GMR),\n$\\Delta R = R(AP) - R(P)$, is positive due to increased scattering of majority\nand minority spin-electrons in the AP-state. If N is substituted for a\nsuperconductor (S), there exists a competition between GMR and the\nsuperconducting spin-valve effect: in the AP-state the net magnetic exchange\nfield acting on S is lowered and the superconductivity is reinforced meaning\n$R(AP)$ decreases. For current-perpendicular-to-plane (CPP) spin-valves,\nexisting experimental studies show that GMR dominates ($\\Delta R>0$) over the\nsuperconducting spin valve effect ($\\Delta R<0$) [J. Y. Gu et al., Phys. Rev. B\n66, 140507(R) (2002)]. Here, however, we report a crossover from GMR ($\\Delta R\n> 0$) to the superconducting spin valve effect ($\\Delta R < 0$) in CPP F/S/F\nspin-valves as the superconductor thickness decreases below a critical value.\n"}
+{"abstract": "  We consider Markov Decision Processes (MDPs) in which every stationary policy\ninduces the same graph structure for the underlying Markov chain and further,\nthe graph has the following property: if we replace each recurrent class by a\nnode, then the resulting graph is acyclic. For such MDPs, we prove the\nconvergence of the stochastic dynamics associated with a version of optimistic\npolicy iteration (OPI), suggested in Tsitsiklis (2002), in which the values\nassociated with all the nodes visited during each iteration of the OPI are\nupdated.\n"}
+{"abstract": "  Forecast of football outcomes in terms of Home Win, Draw and Away Win relies\nlargely on ex ante probability elicitation of these events and ex post\nverification of them via computation of probability scoring rules (Brier,\nRanked Probability, Logarithmic, Zero-One scores). Usually, appraisal of the\nquality of forecasting procedures is restricted to reporting mean score values.\nThe purpose of this article is to propose additional tools of verification,\nsuch as score decompositions into several components of special interest.\nGraphical and numerical diagnoses of reliability and discrimination and kindred\nstatistical methods are presented using different techniques of binning (fixed\nthresholds, quantiles, logistic and iso regression). These procedures are\nillustrated on probability forecasts for the outcomes of the UEFA Champions\nLeague (C1) at the end of the group stage based on typical Poisson regression\nmodels with reasonably good results in terms of reliability as compared to\nthose obtained from bookmaker odds and whatever the technique used. Links with\nresearch in machine learning and different areas of application (meteorology,\nmedicine) are discussed.\n"}
+{"abstract": "  In this work, we present an analysis tool to help golf beginners compare\ntheir swing motion with experts' swing motion. The proposed application\nsynchronizes videos with different swing phase timings using the latent\nfeatures extracted by a neural network-based encoder and detects key frames\nwhere discrepant motions occur. We visualize synchronized image frames and 3D\nposes that help users recognize the difference and the key factors that can be\nimportant for their swing skill improvement.\n"}
+{"abstract": "  Translation between natural language and source code can help software\ndevelopment by enabling developers to comprehend, ideate, search, and write\ncomputer programs in natural language. Despite growing interest from the\nindustry and the research community, this task is often difficult due to the\nlack of large standard datasets suitable for training deep neural models,\nstandard noise removal methods, and evaluation benchmarks. This leaves\nresearchers to collect new small-scale datasets, resulting in inconsistencies\nacross published works. In this study, we present CoDesc -- a large parallel\ndataset composed of 4.2 million Java methods and natural language descriptions.\nWith extensive analysis, we identify and remove prevailing noise patterns from\nthe dataset. We demonstrate the proficiency of CoDesc in two complementary\ntasks for code-description pairs: code summarization and code search. We show\nthat the dataset helps improve code search by up to 22\\% and achieves the new\nstate-of-the-art in code summarization. Furthermore, we show CoDesc's\neffectiveness in pre-training--fine-tuning setup, opening possibilities in\nbuilding pretrained language models for Java. To facilitate future research, we\nrelease the dataset, a data processing tool, and a benchmark at\n\\url{https://github.com/csebuetnlp/CoDesc}.\n"}
+{"abstract": "  The double copy is a well-established relationship between gravity and gauge\ntheories. It relates perturbative scattering amplitudes as well as classical\nsolutions, and recently there has been mounting evidence that it also applies\nto non-perturbative information. In this paper, we consider the holonomy\nproperties of manifolds in gravity and prescribe a single copy of gravitational\nholonomy that differs from the holonomy in gauge theory. We discuss specific\ncases and give examples where the single copy holonomy group is reduced. Our\nresults may prove useful in extending the classical double copy. We also\nclarify previous misconceptions in the literature regarding gravitational\nWilson lines and holonomy.\n"}
+{"abstract": "  The mass spectra of isovector $\\Upsilon$, $\\psi$, $\\phi$, and $\\omega$ meson\nresonances are investigated, in the AdS/QCD and information entropy setups. The\ndifferential configurational entropy is employed to obtain the mass spectra of\nradial $S$-wave resonances, with higher excitation levels, in each one of these\nmeson families, whose respective first undisclosed states are discussed and\nmatched up to candidates in the Particle Data Group.\n"}
+{"abstract": "  Here we consider a one-dimensional $q$-state Potts model with an external\nmagnetic field and an anisotropic interaction that selects neighboring sites\nthat are in the spin state 1. The present model exhibits an unusual behavior in\nthe low-temperature region, where we observe an anomalous vigorous change in\nthe entropy for a given temperature. There is a steep behavior at a given\ntemperature in entropy as a function of temperature, quite similar to\nfirst-order discontinuity, but there is no jump in the entropy. Similarly,\nsecond derivative quantities like specific heat and magnetic susceptibility\nalso exhibit a strong acute peak rather similar to second-order phase\ntransition divergence, but once again there is no singularity at this point.\nCorrelation length also confirms this anomalous behavior at the same given\ntemperature, showing a strong and sharp peak which easily one may confuse with\na divergence. The temperature where occurs this anomalous feature we call\npseudo-critical temperature. We have analyzed physical quantities, like\ncorrelation length, entropy, magnetization, specific heat, magnetic\nsusceptibility, and distant pair correlation functions. Furthermore, we analyze\nthe pseudo-critical exponent that satisfy a class of universality previously\nidentified in the literature for other one-dimensional models, these\npseudo-critical exponents are: for correlation length $\\nu=1$, specific heat\n$\\alpha=3$ and magnetic susceptibility $\\mu=3$.\n"}
+{"abstract": "  We show that the connectedness of the set of parameters for which the\nover-rotation interval of a bimodal interval map is constant. In other words,\nthe over-rotation interval is a monotone function of a bimodal interval map.\n"}
+{"abstract": "  Motivated by applications in cognitive radio networks, we consider the\ndecentralized multi-player multi-armed bandit problem, without collision nor\nsensing information. We propose Randomized Selfish KL-UCB, an algorithm with\nvery low computational complexity, inspired by the Selfish KL-UCB algorithm,\nwhich has been abandoned as it provably performs sub-optimally in some cases.\nWe subject Randomized Selfish KL-UCB to extensive numerical experiments showing\nthat it far outperforms state-of-the-art algorithms in almost all environments,\nsometimes by several orders of magnitude, and without the additional knowledge\nrequired by state-of-the-art algorithms. We also emphasize the potential of\nthis algorithm for the more realistic dynamic setting, and support our claims\nwith further experiments. We believe that the low complexity and high\nperformance of Randomized Selfish KL-UCB makes it the most suitable for\nimplementation in practical systems amongst known algorithms.\n"}
+{"abstract": "  Chronometric dating is becoming increasingly important in areas such as the\nOrigin and evolution of Life on Earth and other planets, Origin and evolution\nof the Earth and the Solar System... Electron Spin Resonance (ESR) dating is\nbased on exploiting effects of contamination by chemicals or ionizing\nradiation, on ancient matter through its absorption spectrum and lineshape.\nInterpreting absorption spectra as probability density functions (pdf), we use\nthe notion of Information Theory (IT) distance allowing us to position the\nmeasured lineshape with respect to standard limiting pdf's (Lorentzian and\nGaussian). This paves the way to perform dating when several interaction\npatterns between unpaired spins are present in geologic, planetary, meteorite\nor asteroid matter namely classical-dipolar (for ancient times) and\nquantum-exchange-coupled (for recent times). In addition, accurate bounds to\nage are provided by IT from the evaluation of distances with respect to the\nLorentz and Gauss distributions. Dating arbitrary periods of\ntimes~\\cite{Anderson} and exploiting IT to introduce rigorous and accurate date\nvalues might have interesting far reaching implications not only in Geophysics,\nGeochronology~\\cite{Bahain}, Planetary Science but also in Mineralogy,\nArchaeology, Biology, Anthropology~\\cite{Aitken},\nPaleoanthropology~\\cite{Taylor,Richter}...\n"}
+{"abstract": "  Let $p$ be a fixed odd prime. Let $E$ be an elliptic curve defined over a\nnumber field $F$ with good supersingular reduction at all primes above $p$. We\nstudy both the classical and plus/minus Selmer groups over the cyclotomic\n$\\mathbb{Z}_p$-extension of $F$. In particular, we give sufficient conditions\nfor these Selmer groups to not contain a non-trivial sub-module of finite\nindex. Furthermore, when $p$ splits completely in $F$, we calculate the Euler\ncharacteristics of the plus/minus Selmer groups over the compositum of all\n$\\mathbb{Z}_p$-extensions of $F$ when they are defined.\n"}
+{"abstract": "  For a pair of bounded linear Hilbert space operators $A$ and $B$ one\nconsiders the Lebesgue type decompositions of $B$ with respect to $A$ into an\nalmost dominated part and a singular part, analogous to the Lebesgue\ndecomposition for a pair of measures (in which case one speaks of an absolutely\ncontinuous and a singular part). A complete parametrization of all Lebesgue\ntype decompositions will be given, and the uniqueness of such decompositions\nwill be characterized. In addition, it will be shown that the almost dominated\npart of $B$ in a Lebesgue type decomposition has an abstract Radon-Nikodym\nderivative with respect to the operator $A$.\n"}
+{"abstract": "  This paper presents a novel microwave photonic (MWP) radar scheme that is\ncapable of optically generating and processing broadband linear\nfrequency-modulated (LFM) microwave signals without using any radio-frequency\n(RF) sources. In the transmitter, a broadband LFM microwave signal is generated\nby controlling the period-one (P1) oscillation of an optically injected\nsemiconductor laser. After targets reflection, photonic de-chirping is\nimplemented based on a dual-drive Mach-Zehnder modulator (DMZM), which is\nfollowed by a low-speed analog-to-digital converter (ADC) and digital signal\nprocesser (DSP) to reconstruct target information. Without the limitations of\nexternal RF sources, the proposed radar has an ultra-flexible tunability, and\nthe main operating parameters are adjustable, including central frequency,\nbandwidth, frequency band, and temporal period. In the experiment, a fully\nphotonics-based Ku-band radar with a bandwidth of 4 GHz is established for\nhigh-resolution detection and inverse synthetic aperture radar (ISAR) imaging.\nResults show that a high range resolution reaching ~1.88 cm, and a\ntwo-dimensional (2D) imaging resolution as high as ~1.88 cm x ~2.00 cm are\nachieved with a sampling rate of 100 MSa/s in the receiver. The flexible\ntunability of the radar is also experimentally investigated. The proposed radar\nscheme features low cost, simple structure, and high reconfigurability, which,\nhopefully, is to be used in future multifunction adaptive and miniaturized\nradars.\n"}
+{"abstract": "  Comprehensive control of the domain wall nucleation process is crucial for\nspin-based emerging technologies ranging from random-access and storage-class\nmemories over domain-wall logic concepts to nanomagnetic logic. In this work,\nfocused Ga+ ion-irradiation is investigated as an effective means to control\ndomain-wall nucleation in Ta/CoFeB/MgO nanostructures. We show that analogously\nto He+ irradiation, it is not only possible to reduce the perpendicular\nmagnetic anisotropy but also to increase it significantly, enabling new,\nbidirectional manipulation schemes. First, the irradiation effects are assessed\non film level, sketching an overview of the dose-dependent changes in the\nmagnetic energy landscape. Subsequent time-domain nucleation characteristics of\nirradiated nanostructures reveal substantial increases in the anisotropy fields\nbut surprisingly small effects on the measured energy barriers, indicating\nshrinking nucleation volumes. Spatial control of the domain wall nucleation\npoint is achieved by employing focused irradiation of pre-irradiated magnets,\nwith the diameter of the introduced circular defect controlling the coercivity.\nSpecial attention is given to the nucleation mechanisms, changing from a\nStoner-Wohlfarth particle's coherent rotation to depinning from an anisotropy\ngradient. Dynamic micromagnetic simulations and related measurements are used\nin addition to model and analyze this depinning-dominated magnetization\nreversal.\n"}
+{"abstract": "  The bulk-boundary correspondence in one dimension asserts that the physical\nquantities defined in the bulk and at the edge are connected, as well\nestablished in the argument for electric polarization. Recently, a spectral\nbulk-boundary correspondence (SBBC), an extended version of the conventional\nbulk-boundary correspondence to energy-dependent spectral functions, such as\nGreen's functions, has been proposed in chiral symmetric systems, in which the\nchiral operator anticommutes with the Hamiltonian. In this study, we extend the\nSBBC to a system with impurity scattering and dynamical self-energies,\nregardless of the presence or absence of a gap in the energy spectrum.\nMoreover, the SBBC is observed to hold even in a system without chiral\nsymmetry, which substantially generalizes its concept. The SBBC is demonstrated\nwith concrete models, such as superconducting nanowires and a\nSu-Schrieffer-Heeger model. Its potential applications and certain remaining\nissues are also discussed.\n"}
+{"abstract": "  6-14 micron Spitzer spectra obtained at 6 epochs between April 2005 and\nOctober 2008 are used to determine temporal changes in dust features associated\nwith Sakurai's Object (V4334 Sgr), a low mass post-AGB star that has been\nforming dust in an eruptive event since 1996. The obscured carbon-rich\nphotosphere is surrounded by a 40-milliarcsec torus and 32 arcsec PN. An\ninitially rapid mid-infrared flux decrease stalled after 21 April 2008.\nOptically-thin emission due to nanometre-sized SiC grains reached a minimum in\nOctober 2007, increased rapidly between 21-30 April 2008 and more slowly to\nOctober 2008. 6.3-micron absorption due to PAHs increased throughout. 20\nmicron-sized SiC grains might have contributed to the 6-7 micron absorption\nafter May 2007. Mass estimates based on the optically-thick emission agree with\nthose in the absorption features if the large SiC grains formed before May 1999\nand PAHs formed in April-June 1999. Estimated masses of PAH and large-SiC\ngrains in October 2008, were 3 x 10 -9 Msun and 10 -8 Msun, respectively. Some\nof the submicron-sized silicates responsible for a weak 10 micron absorption\nfeature are probably located within the PN because the optical depth decreased\nbetween October 2007 and October 2008. 6.9 micron absorption assigned to ~10\nmicron-sized crystalline melilite silicates increased between April 2005 and\nOctober 2008. Abundance and spectroscopic constraints are satisfied if about\n2.8 per cent cent of the submicron-sized silicates coagulated to form\nmelilites. This figure is similar to the abundance of melilite-bearing\ncalcium-aluminium-rich inclusions in chondritic meteorites.\n"}
+{"abstract": "  Lithium-ion battery manufacturing is a highly complicated process with\nstrongly coupled feature interdependencies, a feasible solution that can\nanalyse feature variables within manufacturing chain and achieve reliable\nclassification is thus urgently needed. This article proposes a random forest\n(RF)-based classification framework, through using the out of bag (OOB)\npredictions, Gini changes as well as predictive measure of association (PMOA),\nfor effectively quantifying the importance and correlations of battery\nmanufacturing features and their effects on the classification of electrode\nproperties. Battery manufacturing data containing three intermediate product\nfeatures from the mixing stage and one product parameter from the coating stage\nare analysed by the designed RF framework to investigate their effects on both\nthe battery electrode active material mass load and porosity. Illustrative\nresults demonstrate that the proposed RF framework not only achieves the\nreliable classification of electrode properties but also leads to the effective\nquantification of both manufacturing feature importance and correlations. This\nis the first time to design a systematic RF framework for simultaneously\nquantifying battery production feature importance and correlations by three\nvarious quantitative indicators including the unbiased feature importance (FI),\ngain improvement FI and PMOA, paving a promising solution to reduce model\ndimension and conduct efficient sensitivity analysis of battery manufacturing.\n"}
+{"abstract": "  Deep neural networks (DNNs) in the infinite width/channel limit have received\nmuch attention recently, as they provide a clear analytical window to deep\nlearning via mappings to Gaussian Processes (GPs). Despite its theoretical\nappeal, this viewpoint lacks a crucial ingredient of deep learning in finite\nDNNs, laying at the heart of their success -- feature learning. Here we\nconsider DNNs trained with noisy gradient descent on a large training set and\nderive a self consistent Gaussian Process theory accounting for strong\nfinite-DNN and feature learning effects. Applying this to a toy model of a\ntwo-layer linear convolutional neural network (CNN) shows good agreement with\nexperiments. We further identify, both analytical and numerically, a sharp\ntransition between a feature learning regime and a lazy learning regime in this\nmodel. Strong finite-DNN effects are also derived for a non-linear two-layer\nfully connected network. Our self consistent theory provides a rich and\nversatile analytical framework for studying feature learning and other non-lazy\neffects in finite DNNs.\n"}
+{"abstract": "  This research seeks to measure the impact of people with technological\nknowledge on regional digital economic activity and the implications of\nprosperous cities' contagion effect on neighbouring ones. The focus of this\nstudy is quantitative, cross-sectional, and its design is correlational-causal.\nThis study covers seven micro-regions of Minas Gerais in Brazil, organized in\n89 municipalities, with 69% urban population and 31% rural. The data used\nconsisted of 4,361 observations obtained in the Brazilian government's public\nrepositories, organized into panel data, and analysed using partial least\nsquares, micro-regional spatial regression, and identification patterns with\nmachine learning. The confirmatory analysis of the regression test establishes\na significant impact between the CE's technological knowledge and the digital\neconomic activity AED through a predictive value of R2 = .749, \\b{eta} = .867,\np = .000 (value t = 18,298). With high notoriety among the variables, public\nand private university institutions (IUPP), professors with doctorates and\nmasters (DCNT), and information technology occupations (CBO). A geographic\nconcentration of companies that demand technology-based skills had effects by\nslowing down the development of small municipalities, suggesting the\ndevelopment of new government technology initiatives that support new business\nmodels based on technological knowledge.\n"}
+{"abstract": "  This paper addresses the approximation of fractional harmonic maps. Besides a\nunit-length constraint, one has to tackle the difficulty of nonlocality. We\nestablish weak compactness results for critical points of the fractional\nDirichlet energy on unit-length vector fields. We devise and analyze numerical\nmethods for the approximation of various partial differential equations related\nto fractional harmonic maps. The compactness results imply the convergence of\nnumerical approximations. Numerical examples on spin chain dynamics and point\ndefects are presented to demonstrate the effectiveness of the proposed methods.\n"}
+{"abstract": "  We show the relation between three non trivial sectors of M2-brane theory\nformulated in the LCG connected among them by canonical transformations. These\nsectors correspond to the supermembrane theory formulated on a $M_9\\times T^2$\non three different constant three-form backgrounds: M2-brane with constant\n$C_{-}$, M2-brane with constant $C_{\\pm}$ and M2-brane with a generic constant\n$C_3$ denoted as CM2-brane. The first two exhibit a purely discrete\nsupersymmetric spectrum once the central charge condition, or equivalently, the\ncorresponding flux condition has been turned on. The CM2-brane is conjectured\nto share this spectral property once that fluxes $C_{\\pm}$ are turned on. As\nshown in [1] they are duals to three inequivalent sectors of the D2-branes with\nspecific worldvolume and background RR and NSNS quantization conditions on each\ncase.\n"}
+{"abstract": "  With the rapid growth of blockchain, an increasing number of users have been\nattracted and many implementations have been refreshed in different fields.\nEspecially in the cryptocurrency investment field, blockchain technology has\nshown vigorous vitality. However, along with the rise of online business,\nnumerous fraudulent activities, e.g., money laundering, bribery, phishing, and\nothers, emerge as the main threat to trading security. Due to the openness of\nEthereum, researchers can easily access Ethereum transaction records and smart\ncontracts, which brings unprecedented opportunities for Ethereum scams\ndetection and analysis. This paper mainly focuses on the Ponzi scheme, a\ntypical fraud, which has caused large property damage to the users in Ethereum.\nBy verifying Ponzi contracts to maintain Ethereum's sustainable development, we\nmodel Ponzi scheme identification and detection as a node classification task.\nIn this paper, we first collect target contracts' transactions to establish\ntransaction networks and propose a detecting model based on graph convolutional\nnetwork (GCN) to precisely distinguishPonzi contracts. Experiments on different\nreal-world Ethereum datasets demonstrate that our proposed model has promising\nresults compared with general machine learning methods to detect Ponzi schemes.\n"}
+{"abstract": "  Strain engineering of perovskite quantum dots (pQDs) enables widely-tunable\nphotonic device applications. However, manipulation at the single-emitter level\nhas never been attempted. Here, we present a tip-induced control approach\ncombined with tip-enhanced photoluminescence (TEPL) spectroscopy to engineer\nstrain, bandgap, and emission quantum yield of a single pQD. Single\nCsPbBr$_{x}$I$_{3-x}$ pQDs are clearly resolved through hyperspectral TEPL\nimaging with $\\sim$10 nm spatial resolution. The plasmonic tip then directly\napplies pressure to a single pQD to facilitate a bandgap shift up to $\\sim$62\nmeV with Purcell-enhanced PL quantum yield as high as $\\sim$10$^5$ for the\nstrain-induced pQD. Furthermore, by systematically modulating the tip-induced\ncompressive strain of a single pQD, we achieve dynamical bandgap engineering in\na reversible manner. In addition, we facilitate the quantum dot coupling for a\npQD ensemble with $\\sim$0.8 GPa tip pressure at the nanoscale. Our approach\npresents a new strategy to tune the nano-opto-electro-mechanical properties of\npQDs at the single-crystal level.\n"}
+{"abstract": "  Deep neural networks with batch normalization (BN-DNNs) are invariant to\nweight rescaling due to their normalization operations. However, using weight\ndecay (WD) benefits these weight-scale-invariant networks, which is often\nattributed to an increase of the effective learning rate when the weight norms\nare decreased. In this paper, we demonstrate the insufficiency of the previous\nexplanation and investigate the implicit biases of stochastic gradient descent\n(SGD) on BN-DNNs to provide a theoretical explanation for the efficacy of\nweight decay. We identity two implicit biases of SGD on BN-DNNs: 1) the weight\nnorms in SGD training remain constant in the continuous-time domain and keep\nincreasing in the discrete-time domain; 2) SGD optimizes weight vectors in\nfully-connected networks or convolution kernels in convolution neural networks\nby updating components lying in the input feature span, while leaving those\ncomponents orthogonal to the input feature span unchanged. Thus, SGD without WD\naccumulates weight noise orthogonal to the input feature span, and cannot\neliminate such noise. Our empirical studies corroborate the hypothesis that\nweight decay suppresses weight noise that is left untouched by SGD.\nFurthermore, we propose to use weight rescaling (WRS) instead of weight decay\nto achieve the same regularization effect, while avoiding performance\ndegradation of WD on some momentum-based optimizers. Our empirical results on\nimage recognition show that regardless of optimization methods and network\narchitectures, training BN-DNNs using WRS achieves similar or better\nperformance compared with using WD. We also show that training with WRS\ngeneralizes better compared to WD, on other computer vision tasks.\n"}
+{"abstract": "  We study stationary black holes in the presence of an external strong\nmagnetic field. In the case where the gravitational backreaction of the\nmagnetic field is taken into account, such an scenario is well described by the\nErnst-Wild solution to Einstein-Maxwell field equations, representing a\ncharged, stationary black hole immersed in a Melvin magnetic universe. This\nsolution, however, describes a physical situation only in the region close to\nthe black hole. This is due to the following two reasons: Firstly, Melvin\nspacetime is not asymptotically locally flat; secondly, the non-static\nErnst-Wild solution is not even asymptotically Melvin due to the infinite\nextension of its ergoregion. All this might seem to be an obstruction to\naddress an scenario like this; for instance, it seems to be an obstruction to\ncompute conserved charges as this usually requires a clear notion of\nasymptotia. Here, we circumvent this obstruction by providing a method to\ncompute the conserved charges of such a black hole by restricting the analysis\nto the near horizon region. We compute the Wald entropy, the mass, the electric\ncharge, and the angular momentum of stationary black holes in highly magnetized\nenvironments from the horizon perspective, finding results in complete\nagreement with other formalisms.\n"}
+{"abstract": "  In this thesis, we try to resolve the alleged problem of non-unitarity for\nvarious anisotropic cosmological models. Using Wheeler-DeWitt formulation, we\nquantized the anisotropic models with variable spatial curvature, namely\nBianchi II and Bianchi VI. We showed that Hamiltonian of respective models\nadmits self-adjoint extension, thus unitary evolution. We further extended the\nunitary evolution for higher dimensional anisotropic cosmological models. We\nalso showed that unitarity of the model preserves the Noether symmetry but\nloses the scale invariance. In later part of this thesis, we showed the\nequivalence of Jordan and Einstein frames at the quantum level for the flat FRW\nmodel. Obtained expressions for wave packet matched exactly in both the frames\nindicating the equivalence of frames. We also showed that equivalence holds\ntrue for various anisotropic quantum cosmological models, i.e., Bianchi I, V,\nX, LRS Bianchi-I and Kantowski-Sachs models.\n"}
+{"abstract": "  In this work, we study music/video cross-modal recommendation, i.e.\nrecommending a music track for a video or vice versa. We rely on a\nself-supervised learning paradigm to learn from a large amount of unlabelled\ndata. We rely on a self-supervised learning paradigm to learn from a large\namount of unlabelled data. More precisely, we jointly learn audio and video\nembeddings by using their co-occurrence in music-video clips. In this work, we\nbuild upon a recent video-music retrieval system (the VM-NET), which originally\nrelies on an audio representation obtained by a set of statistics computed over\nhandcrafted features. We demonstrate here that using audio representation\nlearning such as the audio embeddings provided by the pre-trained MuSimNet,\nOpenL3, MusicCNN or by AudioSet, largely improves recommendations. We also\nvalidate the use of the cross-modal triplet loss originally proposed in the\nVM-NET compared to the binary cross-entropy loss commonly used in\nself-supervised learning. We perform all our experiments using the Music Video\nDataset (MVD).\n"}
+{"abstract": "  We prove a conjecture of Zagier about the inverse of a $(K-1)\\times (K-1)$\nmatrix $A=A_{K}$ using elementary methods. This formula allows one to express\nthe the product of single zeta values $\\zeta(2r)\\zeta(2K+1-2r)$, $1\\leq r\\leq\nK-1$, in terms of the double zeta values $\\zeta(2r,2K+1-2r)$, $1\\leq r\\leq K-1$\nand $\\zeta(2K+1)$.\n"}
+{"abstract": "  We propose a new Lagrange multiplier approach to construct positivity\npreserving schemes for parabolic type equations. The new approach introduces a\nspace-time Lagrange multiplier to enforce the positivity with the\nKarush-Kuhn-Tucker (KKT) conditions. We then use a predictor-corrector approach\nto construct a class of positivity schemes: with a generic semi-implicit or\nimplicit scheme as the prediction step, and the correction step, which enforces\nthe positivity, can be implemented with negligible cost. We also present a\nmodification which allows us to construct schemes which, in addition to\npositivity preserving, is also mass conserving. This new approach is not\nrestricted to any particular spatial discretization and can be combined with\nvarious time discretization schemes. We establish stability results for our\nfirst- and second-order schemes under a general setting, and present ample\nnumerical results to validate the new approach.\n"}
+{"abstract": "  We present a simple regulator-type framework designed specifically for\nmodelling formation of dwarf galaxies. We explore sensitivity of model\npredictions for the stellar mass--halo mass and stellar mass--metallicity\nrelations to different modelling choices and parameter values. Despite its\nsimplicity, when coupled with realistic mass accretion histories of haloes from\nsimulations and reasonable choices for model parameter values, the framework\ncan reproduce a remarkably broad range of observed properties of dwarf galaxies\nover seven orders of magnitude in stellar mass. In particular, we show that the\nmodel can simultaneously match observational constraints on the stellar\nmass-halo mass relation, as well as observed relations between stellar mass and\ngas phase and stellar metallicities, gas mass, size, and star formation rate,\nas well as general form and diversity of star formation histories (SFHs) of\nobserved dwarf galaxies. The model can thus be used to predict photometric\nproperties of dwarf galaxies hosted by dark matter haloes in $N$-body\nsimulations, such as colors, surface brightnesses, and mass-to-light ratios and\nto forward model observations of dwarf galaxies. We present examples of such\nmodelling and show that colors and surface brightness distributions of model\ngalaxies are in good agreement with observed distributions for dwarfs in recent\nobservational surveys. We also show that in contrast with the common\nassumption, the absolute magnitude-halo mass relation is generally predicted to\nhave a non-power law form in the dwarf regime, and that the fraction of haloes\nthat host detectable ultrafaint galaxies is sensitive to reionization redshift\n(zrei) and is predicted to be consistent with observations for zrei<~9.\n"}
+{"abstract": "  This paper is devoted to a fractional generalization of the Dirichlet\ndistribution. The form of the multivariate distribution is derived assuming\nthat the $n$ partitions of the interval $[0,W_n]$ are independent and\nidentically distributed random variables following the generalized\nMittag-Leffler distribution. The expected value and variance of the\none-dimensional marginal are derived as well as the form of its probability\ndensity function. A related generalized Dirichlet distribution is studied that\nprovides a reasonable approximation for some values of the parameters. The\nrelation between this distribution and other generalizations of the Dirichlet\ndistribution is discussed. Monte Carlo simulations of the one-dimensional\nmarginals for both distributions are presented.\n"}
+{"abstract": "  Can machine learning help us make better decisions about a changing planet?\nIn this paper, we illustrate and discuss the potential of a promising corner of\nmachine learning known as _reinforcement learning_ (RL) to help tackle the most\nchallenging conservation decision problems. RL is uniquely well suited to\nconservation and global change challenges for three reasons: (1) RL explicitly\nfocuses on designing an agent who _interacts_ with an environment which is\ndynamic and uncertain, (2) RL approaches do not require massive amounts of\ndata, (3) RL approaches would utilize rather than replace existing models,\nsimulations, and the knowledge they contain. We provide a conceptual and\ntechnical introduction to RL and its relevance to ecological and conservation\nchallenges, including examples of a problem in setting fisheries quotas and in\nmanaging ecological tipping points. Four appendices with annotated code provide\na tangible introduction to researchers looking to adopt, evaluate, or extend\nthese approaches.\n"}
+{"abstract": "  In a news recommender system, a reader's preferences change over time. Some\npreferences drift quite abruptly (short-term preferences), while others change\nover a longer period of time (long-term preferences). Although the existing\nnews recommender systems consider the reader's full history, they often ignore\nthe dynamics in the reader's behavior. Thus, they cannot meet the demand of the\nnews readers for their time-varying preferences. In addition, the\nstate-of-the-art news recommendation models are often focused on providing\naccurate predictions, which can work well in traditional recommendation\nscenarios. However, in a news recommender system, diversity is essential, not\nonly to keep news readers engaged, but also to play a key role in a democratic\nsociety. In this PhD dissertation, our goal is to build a news recommender\nsystem to address these two challenges. Our system should be able to: (i)\naccommodate the dynamics in reader behavior; and (ii) consider both accuracy\nand diversity in the design of the recommendation model. Our news recommender\nsystem can also work for unprofiled, anonymous and short-term readers, by\nleveraging the rich side information of the news items and by including the\nimplicit feedback in our model. We evaluate our model with multiple evaluation\nmeasures (both accuracy and diversity-oriented metrics) to demonstrate the\neffectiveness of our methods.\n"}
+{"abstract": "  The search of new superhard materials has received a strong impulse by\nindustrial demands for low-cost alternatives to diamond and $c$-BN, such as\nmetal borides. In this Letter we introduce a new family of superhard materials,\n\"fused borophenes\", containing 2D boron layers which are interlinked to form a\n3D network. These materials, identified through a high-throughput scan of\nBxC1-x structures, exhibit Vicker's hardnesses comparable to those of the best\ncommercial metal borides. Due to their low formation enthalpies, fused\nborophenes could be synthesized by high-temperature methods, starting from\nappropriate precursors, or through quenching of high-pressure phases.\n"}
+{"abstract": "  Network slicing is emerging as a promising method to provide sought-after\nversatility and flexibility to cope with ever-increasing demands. To realize\nsuch potential advantages and to meet the challenging requirements of various\nnetwork slices in an on-demand fashion, we need to develop an agile and\ndistributed mechanism for resource provisioning to different network slices in\na heterogeneous multi-resource multi-domain mobile network environment. We\nformulate inter-domain resource provisioning to network slices in such an\nenvironment as an optimization problem which maximizes social welfare among\nnetwork slice tenants (so that maximizing tenants' satisfaction), while\nminimizing operational expenditures for infrastructure service providers at the\nsame time. To solve the envisioned problem, we implement an iterative auction\ngame among network slice tenants, on one hand, and a plurality of price-taking\nsubnet service providers, on the other hand. We show that the proposed solution\nmethod results in a distributed privacy-saving mechanism which converges to the\noptimal solution of the described optimization problem. In addition to\nproviding analytical results to characterize the performance of the proposed\nmechanism, we also employ numerical evaluations to validate the results,\ndemonstrate convergence of the presented algorithm, and show the enhanced\nperformance of the proposed approach (in terms of resource utilization,\nfairness and operational costs) against the existing solutions.\n"}
+{"abstract": "  We investigate the effect of thermal fluctuations on the two-particle\nspectral function for a disordered $s$-wave superconductor in two dimensions,\nfocusing on the evolution of the collective amplitude and phase modes. We find\nthree main effects of thermal fluctuations: (a) the phase mode is softened with\nincreasing temperature reflecting the decrease of superfluid stiffness; (b)\nremarkably, the non-dispersive collective amplitude modes at finite energy near\n${\\bf q}=[0,0]$ and ${\\bf q}=[\\pi,\\pi]$ survive even in presence of thermal\nfluctuations in the disordered superconductor; and (c) the scattering of the\nthermally excited fermionic quasiparticles leads to low energy incoherent\nspectral weight that forms a strongly momentum-dependent background halo around\nthe phase and amplitude collective modes and broadens them. Due to momentum and\nenergy conservation constraints, this halo has a boundary which disperses\nlinearly at low momenta and shows a strong dip near the $[\\pi,\\pi]$ point in\nthe Brillouin zone.\n"}
+{"abstract": "  We analyze the gravitational-wave signal GW190521 under the hypothesis that\nit was generated by the merger of two nonspinning black holes on hyperbolic\norbits. The best configuration matching the data corresponds to two black holes\nof source frame masses of $81^{+62}_{-25}M_\\odot$ and $52^{+32}_{-32}M_\\odot$\nundergoing two encounters and then merging into an intermediate-mass black\nhole. Under the hyperbolic merger hypothesis, we find an increase of one unit\nin the recovered signal-to-noise ratio and a 14 e-fold increase in the maximum\nlikelihood value compared to a quasi-circular merger with precessing spins. We\nconclude that our results support the first gravitational-wave detection from\nthe dynamical capture of two stellar-mass black holes.\n"}
+{"abstract": "  We investigate the problem of when big mapping class groups are generated by\ninvolutions. Restricting our attention to the class of self-similar surfaces,\nwhich are surfaces with self-similar ends space, as defined by Mann and Rafi,\nand with 0 or infinite genus, we show that, when the set of maximal ends is\ninfinite, then the mapping class groups of these surfaces are generated by\ninvolutions, normally generated by a single involution, and uniformly perfect.\nIn fact, we derive this statement as a corollary of the corresponding statement\nfor the homeomorphism groups of these surfaces. On the other hand, among\nself-similar surfaces with one maximal end, we produce infinitely many examples\nin which their big mapping class groups are neither perfect nor generated by\ntorsion elements. These groups also do not have the automatic continuity\nproperty.\n"}
+{"abstract": "  We have undertaken a systematic study of FRI and FRII radio galaxies with the\nupgraded Giant Metrewave Radio Telescope (uGMRT) and MeerKAT. The main goal is\nto explore whether the unprecedented few $\\mu$Jy sensitivity reached in the\nrange 550-1712 MHz at the resolution of $\\sim4^{\\prime\\prime}-7^{\\prime\\prime}$\nreveals new features in the radio emission which might need us to revise our\ncurrent classification scheme for classical radio galaxies. In this paper we\npresent the results for the first set of four radio galaxies, i.e. 4C 12.02, 4C\n12.03, CGCG 044-046 and CGCG 021-063. The sources have been selected from the\n4C sample with well-defined criteria, and have been imaged with the uGMRT in\nthe range 550-850 MHz (band 4) and with the MeerKAT in the range 856-1712 MHz\n(L-band). Full resolution images are presented for all sources in the sample,\ntogether with MeerKAT in-band spectral images. Additionally, the uGMRT-MeerKAT\nspectral image and MeerKAT L-band polarisation structure are provided for CGCG\n044-046. Our images contain a wealth of morphological details, such as\nfilamentary structure in the emission from the lobes, radio emission beyond the\nhot-spots in three sources, and misalignments. We briefly discuss the overall\nproperties of CGCG 044-046 in the light of the local environment as well, and\nshow possible restarted activity in 4C 12.03 which needs to be confirmed. We\nconclude that at least for the sources presented here, the classical FRI/FRII\nmorphological classification still holds with the current improved imaging\ncapabilities, but the richness in details also suggests caution in the\nsystematic morphological classification carried out with automatic procedures\nin surveys with poorer sensitivity and angular resolution.\n"}
+{"abstract": "  Despite many proposed algorithms to provide robustness to deep learning (DL)\nmodels, DL models remain susceptible to adversarial attacks. We hypothesize\nthat the adversarial vulnerability of DL models stems from two factors. The\nfirst factor is data sparsity which is that in the high dimensional data space,\nthere are large regions outside the support of the data distribution. The\nsecond factor is the existence of many redundant parameters in the DL models.\nOwing to these factors, different models are able to come up with different\ndecision boundaries with comparably high prediction accuracy. The appearance of\nthe decision boundaries in the space outside the support of the data\ndistribution does not affect the prediction accuracy of the model. However,\nthey make an important difference in the adversarial robustness of the model.\nWe propose that the ideal decision boundary should be as far as possible from\nthe support of the data distribution.\\par In this paper, we develop a training\nframework for DL models to learn such decision boundaries spanning the space\naround the class distributions further from the data points themselves.\nSemi-supervised learning was deployed to achieve this objective by leveraging\nunlabeled data generated in the space outside the support of the data\ndistribution. We measure adversarial robustness of the models trained using\nthis training framework against well-known adversarial attacks We found that\nour results, other regularization methods and adversarial training also support\nour hypothesis of data sparcity. We show that the unlabeled data generated by\nnoise using our framework is almost as effective as unlabeled data, sourced\nfrom existing data sets or generated by synthesis algorithms, on adversarial\nrobustness. Our code is available at\nhttps://github.com/MahsaPaknezhad/AdversariallyRobustTraining.\n"}
+{"abstract": "  The environmental performance of shared micromobility services compared to\nprivate alternatives has never been assessed using an integrated modal Life\nCycle Assessment (LCA) relying on field data. Such an LCA is conducted on three\nshared micromobility services in Paris - bikes, second-generation e-scooters,\nand e-mopeds - and their private alternatives. Global warming potential,\nprimary energy consumption, and the three endpoint damages are calculated.\nSensitivity analyses on vehicle lifespan, shipping, servicing distance, and\nelectricity mix are conducted. Electric micromobility ranks between active\nmodes and personal ICE modes. Its impacts are globally driven by vehicle\nmanufacturing. Ownership does not affect directly the environmental\nperformance: the vehicle lifetime mileage does. Assessing the sole carbon\nfootprint leads to biased environmental decision-making, as it is not\ncorrelated to the three damages: multicriteria LCA is mandatory to preserve the\nplanet. Finally, a major change of paradigm is needed to eco-design modern\ntransportation policies.\n"}
+{"abstract": "  The missing mass refers to the probability of elements not observed in a\nsample, and since the work of Good and Turing during WWII, has been studied\nextensively in many areas including ecology, linguistic, networks and\ninformation theory.\n  This work determines what is the \\emph{maximal variance of the missing mass},\nfor any sample and alphabet sizes. The result helps in understanding the\nmissing mass concentration properties.\n"}
+{"abstract": "  A linear argument must be consumed exactly once in the body of its function.\nA linear type system can verify the correct usage of resources such as file\nhandles and manually managed memory. But this verification requires\nbureaucracy. This paper presents linear constraints, a front-end feature for\nlinear typing that decreases the bureaucracy of working with linear types.\nLinear constraints are implicit linear arguments that are to be filled in\nautomatically by the compiler. Linear constraints are presented as a qualified\ntype system,together with an inference algorithm which extends GHC's existing\nconstraint solver algorithm. Soundness of linear constraints is ensured by the\nfact that they desugar into Linear Haskell.\n"}
+{"abstract": "  One remarkable feature of Weyl semimetals is the manifestation of their\ntopological nature in the form of the Fermi-arc surface states. In a recent\ncalculation by \\cite{Johansson2018}, the current-induced spin polarization or\nEdelstein effect has been predicted, within the semiclassical Boltzmann theory,\nto be strongly amplified in a Weyl semimetal TaAs due to the existence of the\nFermi arcs. Motivated by this result, we calculate the Edelstein response of an\neffective model for an inversion-symmetry-breaking Weyl semimetal in the\npresence of an interface using linear response theory. The scatterings from\nscalar impurities are included and the vertex corrections are computed within\nthe self-consistent ladder approximation. At chemical potentials close to the\nWeyl points, we find the surface states have a much stronger response near the\ninterface than the bulk states by about one to two orders of magnitude. At\nhigher chemical potentials, the surface states' response near the interface\ndecreases to be about the same order of magnitude as the bulk states' response.\nWe attribute this phenomenon to the decoupling between the Fermi arc states and\nbulk states at energies close to the Weyl points. The surface states which are\neffectively dispersing like a one-dimensional chiral fermion become nearly\nnondissipative. This leads to a large surface vertex correction and, hence, a\nstrong enhancement of the surface states' Edelstein response.\n"}
+{"abstract": "  Unsupervised domain adaptation (UDA) becomes more and more popular in\ntackling real-world problems without ground truth of the target domain. Though\na mass of tedious annotation work is not needed, UDA unavoidably faces the\nproblem how to narrow the domain discrepancy to boost the transferring\nperformance. In this paper, we focus on UDA for semantic segmentation task.\nFirstly, we propose a style-independent content feature extraction mechanism to\nkeep the style information of extracted features in the similar space, since\nthe style information plays a extremely slight role for semantic segmentation\ncompared with the content part. Secondly, to keep the balance of pseudo labels\non each category, we propose a category-guided threshold mechanism to choose\ncategory-wise pseudo labels for self-supervised learning. The experiments are\nconducted using GTA5 as the source domain, Cityscapes as the target domain. The\nresults show that our model outperforms the state-of-the-arts with a noticeable\ngain on cross-domain adaptation tasks.\n"}
+{"abstract": "  Explicating implicit reasoning (i.e. warrants) in arguments is a\nlong-standing challenge for natural language understanding systems. While\nrecent approaches have focused on explicating warrants via crowdsourcing or\nexpert annotations, the quality of warrants has been questionable due to the\nextreme complexity and subjectivity of the task. In this paper, we tackle the\ncomplex task of warrant explication and devise various methodologies for\ncollecting warrants. We conduct an extensive study with trained experts to\nevaluate the resulting warrants of each methodology and find that our\nmethodologies allow for high-quality warrants to be collected. We construct a\npreliminary dataset of 6,000 warrants annotated over 600 arguments for 3\ndebatable topics. To facilitate research in related downstream tasks, we\nrelease our guidelines and preliminary dataset.\n"}
+{"abstract": "  Based on a description of an amorphous solid as a collection of coupled\nnanosize molecular clusters referred as basic blocks, we analyse the\nstatistical properties of its Hamiltonian. The information is then used to\nderive the ensemble averaged density of the vibrational states (non-phonon)\nwhich turns out to be a Gaussian in the bulk of the spectrum and an Airy\nfunction in the low frequency regime. A comparison with experimental data for\nfive glasses confirms validity of our theoretical predictions.\n"}
+{"abstract": "  Schramm-Loewner evolution arises from driving the Loewner differential\nequation with $\\sqrt{\\kappa}B$ where $\\kappa > 0$ is a fixed parameter. In this\npaper, we drive the Loewner differential equation with non-constant random\nparameter, i.e. $d\\xi(t) = \\sqrt{\\kappa_t}dB_t$. We show that in case\n$\\kappa_t$ is bounded below or above $8$, the construction still yields a\ncontinuous trace. This is true in both cases either when driving the forward\nequation or the backward equation by $\\sqrt{\\kappa_t}dB_t$. In the case of the\nforward equation, we develop a new argument to show the result, without the\nneed of analysing the time-reversed equation.\n"}
+{"abstract": "  We consider the problem of learning latent features (aka embedding) for users\nand items in a recommendation setting. Given only a user-item interaction\ngraph, the goal is to recommend items for each user. Traditional approaches\nemploy matrix factorization-based collaborative filtering methods. Recent\nmethods using graph convolutional networks (e.g., LightGCN) achieve\nstate-of-the-art performance. They learn both user and item embedding. One\nmajor drawback of most existing methods is that they are not inductive; they do\nnot generalize for users and items unseen during training. Besides, existing\nnetwork models are quite complex, difficult to train and scale. Motivated by\nLightGCN, we propose a graph convolutional network modeling approach for\ncollaborative filtering CF-GCN. We solely learn user embedding and derive item\nembedding using light variant CF-LGCN-U performing neighborhood aggregation,\nmaking it scalable due to reduced model complexity. CF-LGCN-U models naturally\npossess the inductive capability for new items, and we propose a simple\nsolution to generalize for new users. We show how the proposed models are\nrelated to LightGCN. As a by-product, we suggest a simple solution to make\nLightGCN inductive. We perform comprehensive experiments on several benchmark\ndatasets and demonstrate the capabilities of the proposed approach.\nExperimental results show that similar or better generalization performance is\nachievable than the state of the art methods in both transductive and inductive\nsettings.\n"}
+{"abstract": "  Based on Lorentz invariance and Born reciprocity invariance, the canonical\nquantization of Special Relativity (SR) has been shown to provide a unified\norigin for the existence of Dirac's Hamiltonian and a self adjoint time\noperator that circumvents Pauli's objection. As such, this approach restores to\nQuantum Mechanics (QM) the treatment of space and time on an equivalent footing\nas that of momentum and energy. Second quantization of the time operator field\nfollows step by step that of the Dirac Hamiltonian field. It introduces the\nconcept of time quanta, in a similar way to the energy quanta in Quantum Field\nTheory (QFT). An early connection is found allready in Feshbach's unified\ntheory of nuclear reactions. Its possible relevance in current developments\nsuch as Feshbach resonances in the fields of cold atom systems, of\nBose-Einstein condensates and in the problem of time in Quantum Gravity is\nnoted. .\n"}
+{"abstract": "  We reframe common tasks in jet physics in probabilistic terms, including jet\nreconstruction, Monte Carlo tuning, matrix element - parton shower matching for\nlarge jet multiplicity, and efficient event generation of jets in complex,\nsignal-like regions of phase space. We also introduce Ginkgo, a simplified,\ngenerative model for jets, that facilitates research into these tasks with\ntechniques from statistics, machine learning, and combinatorial optimization.\nWe review some of the recent research in this direction that has been enabled\nwith Ginkgo. We show how probabilistic programming can be used to efficiently\nsample the showering process, how a novel trellis algorithm can be used to\nefficiently marginalize over the enormous number of clustering histories for\nthe same observed particles, and how dynamic programming, A* search, and\nreinforcement learning can be used to find the maximum likelihood clustering in\nthis enormous search space. This work builds bridges with work in hierarchical\nclustering, statistics, combinatorial optmization, and reinforcement learning.\n"}
+{"abstract": "  Voice Activity Detection (VAD) is not easy task when the input audio signal\nis noisy, and it is even more complicated when the input is not even an audio\nrecording. This is the case with Silent Speech Interfaces (SSI) where we record\nthe movement of the articulatory organs during speech, and we aim to\nreconstruct the speech signal from this recording. Our SSI system synthesizes\nspeech from ultrasonic videos of the tongue movement, and the quality of the\nresulting speech signals are evaluated by metrics such as the mean squared\nerror loss function of the underlying neural network and the Mel-Cepstral\nDistortion (MCD) of the reconstructed speech compared to the original. Here, we\nfirst demonstrate that the amount of silence in the training data can have an\ninfluence both on the MCD evaluation metric and on the performance of the\nneural network model. Then, we train a convolutional neural network classifier\nto separate silent and speech-containing ultrasound tongue images, using a\nconventional VAD algorithm to create the training labels from the corresponding\nspeech signal. In the experiments our ultrasound-based speech/silence separator\nachieved a classification accuracy of about 85\\% and an AUC score around 86\\%.\n"}
+{"abstract": "  We propose a classical emulation methodology to emulate quantum phenomena\narising from any non-classical quantum state using only a finite set of\ncoherent states or their statistical mixtures. This allows us to successfully\nreproduce well-known quantum effects using resources that can be much more\nfeasibly generated in the laboratory. We present a simple procedure to\nexperimentally carry out quantum-state emulation with coherent states that also\napplies to any general set of classical states that are easier to generate, and\ndemonstrate its capabilities in observing the Hong-Ou-Mandel effect, violating\nBell inequalities and witnessing quantum non-classicality.\n"}
+{"abstract": "  Robots may soon play a role in higher education by augmenting learning\nenvironments and managing interactions between instructors and learners.\nLittle, however, is known about how the presence of robots in the learning\nenvironment will influence academic integrity. This study therefore\ninvestigates if and how college students cheat while engaged in a collaborative\nsorting task with a robot. We employed a 2x2 factorial design to examine the\neffects of cheating exposure (exposure to cheating or no exposure) and task\nclarity (clear or vague rules) on college student cheating behaviors while\ninteracting with a robot. Our study finds that prior exposure to cheating on\nthe task significantly increases the likelihood of cheating. Yet, the tendency\nto cheat was not impacted by the clarity of the task rules. These results\nsuggest that normative behavior by classmates may strongly influence the\ndecision to cheat while engaged in an instructional experience with a robot.\n"}
+{"abstract": "  Multiple small- to middle-scale cities, mostly located in northern China,\nbecame epidemic hotspots during the second wave of the spread of COVID-19 in\nearly 2021. Despite qualitative discussions of potential social-economic\ncauses, it remains unclear how this pattern could be accounted for from a\nquantitative approach. Through the development of an urban epidemic hazard\nindex (EpiRank), we came up with a mathematical explanation for this\nphenomenon. The index is constructed from epidemic simulations on a multi-layer\ntransportation network model on top of local SEIR transmission dynamics, which\ncharacterizes intra- and inter-city compartment population flow with a detailed\nmathematical description. Essentially, we argue that these highlighted cities\npossess greater epidemic hazards due to the combined effect of large regional\npopulation and small inter-city transportation. The proposed index, dynamic and\napplicable to different epidemic settings, could be a useful indicator for the\nrisk assessment and response planning of urban epidemic hazards in China; the\nmodel framework is modularized and can be adapted for other nations without\nmuch difficulty.\n"}
+{"abstract": "  Simulations, along with other similar applications like virtual worlds and\nvideo games, require computational models of intelligence that generate\nrealistic and credible behavior for the participating synthetic characters.\nCognitive architectures, which are models of the fixed structure underlying\nintelligent behavior in both natural and artificial systems, provide a\nconceptually valid common basis, as evidenced by the current efforts towards a\nstandard model of the mind, to generate human-like intelligent behavior for\nthese synthetic characters. Sigma is a cognitive architecture and system that\nstrives to combine what has been learned from four decades of independent work\non symbolic cognitive architectures, probabilistic graphical models, and more\nrecently neural models, under its graphical architecture hypothesis. Sigma\nleverages an extended form of factor graphs towards a uniform grand unification\nof not only traditional cognitive capabilities but also key non-cognitive\naspects, creating unique opportunities for the construction of new kinds of\ncognitive models that possess a Theory-of-Mind and that are perceptual,\nautonomous, interactive, affective, and adaptive. In this paper, we will\nintroduce Sigma along with its diverse capabilities and then use three distinct\nproof-of-concept Sigma models to highlight combinations of these capabilities:\n(1) Distributional reinforcement learning models in; (2) A pair of adaptive and\ninteractive agent models that demonstrate rule-based, probabilistic, and social\nreasoning; and (3) A knowledge-free exploration model in which an agent\nleverages only architectural appraisal variables, namely attention and\ncuriosity, to locate an item while building up a map in a Unity environment.\n"}
+{"abstract": "  We present an approach for implementing a formally certified loop-invariant\ncode motion optimization by composing an unrolling pass and a formally\ncertified yet efficient global subexpression elimination.This approach is\nlightweight: each pass comes with a simple and independent proof of\ncorrectness.Experiments show the approach significantly narrows the performance\ngap between the CompCert certified compiler and state-of-the-art optimizing\ncompilers.Our static analysis employs an efficient yet verified hashed set\nstructure, resulting in fast compilation.\n"}
+{"abstract": "  The radiation magnetohydrodynamics (RMHD) system couples the ideal\nmagnetohydrodynamics equations with a gray radiation transfer equation. The\nmain challenge is that the radiation travels at the speed of light while the\nmagnetohydrodynamics changes with the time scale of the fluid. The time scales\nof these two processes can vary dramatically. In order to use mesh sizes and\ntime steps that are independent of the speed of light, asymptotic preserving\n(AP) schemes in both space and time are desired. In this paper, we develop an\nAP scheme in both space and time for the RMHD system. Two different scalings\nare considered. One results in an equilibrium diffusion limit system, while the\nother results in a non-equilibrium system. The main idea is to decompose the\nradiative intensity into three parts, each part is treated differently with\nsuitable combinations of explicit and implicit discretizations guaranteeing the\nfavorable stability conditionand computational efficiency. The performance of\nthe AP method is presented, for both optically thin and thick regions, as well\nas for the radiative shock problem.\n"}
+{"abstract": "  The fine-tuning of the universe for life, the idea that the constants of\nnature (or ratios between them) must belong to very small intervals in order\nfor life to exist, has been debated by scientists for several decades. Several\ncriticisms have emerged concerning probabilistic measurement of life-permitting\nintervals. Herein, a Bayesian statistical approach is used to assign an upper\nbound for the probability of tuning, which is invariant with respect to change\nof physical units, and under certain assumptions it is small whenever the\nlife-permitting interval is small on a relative scale. The computation of the\nupper bound of the tuning probability is achieved by first assuming that the\nprior is chosen by the principle of maximum entropy (MaxEnt). The unknown\nparameters of this MaxEnt distribution are then handled in such a way that the\nweak anthropic principle is not violated. The MaxEnt assumption is \"maximally\nnoncommittal with regard to missing information.\" This approach is sufficiently\ngeneral to be applied to constants of current cosmological models, or to other\nconstants possibly under different models. Application of the MaxEnt model\nreveals, for example, that the ratio of the universal gravitational constant to\nthe square of the Hubble constant is finely tuned in some cases, whereas the\namplitude of primordial fluctuations is not.\n"}
+{"abstract": "  It will be presented in this chapter a historical account of the consistent\nhistories interpretation of quantum mechanics based on primary and secondary\nliterature. Firstly, the formalism of the consistent histories approach will be\noutlined.\n  Secondly, the works by Robert Griffiths and Roland Omn\\`es will be discussed.\nGriffiths' seminal 1984 paper, the first physicist to have proposed a\nconsistent-histories interpretation of quantum mechanics, followed by Omn\\`es'\n1990 paper, were instrumental to the consistent-histories model based on\nBoolean logic.\n  Thirdly, Murray Gell-Mann and James Hartle's steps to their own version of\nconsistent-histories approach, motivated by a cosmological perspective, will\nthen be described and evaluated. Gell-Mann and Hartle understood that\nspontaneous decoherence could path the way to a concrete physical model to\nGriffiths' consistent histories.\n  Moreover, the collective biography of these figures will be put in the\ncontext of the role played by the Santa Fe Institute, co-founded by Gell-Mann\nin 1984 in Santa Fe, New Mexico, where Hartle is also a member of the external\nfaculty.\n"}
+{"abstract": "  In this paper, we prove a limiting absorption principle for high-order\nSchr\\\"odinger operators with a large class of potentials which generalize some\nresults by A. Ionescu and W. Schlag. Our main idea is to handle the boundary\noperators by the restriction theorem of Fourier transform. Two key tools we use\nin this paper are the Stein--Tomas theorem in Lorentz spaces and a sharp trace\nlemma given by S. Agmon and L. H\\\"ormander\n"}
+{"abstract": "  Kernel herding is a method used to construct quadrature formulas in a\nreproducing kernel Hilbert space. Although there are some advantages of kernel\nherding, such as numerical stability of quadrature and effective outputs of\nnodes and weights, the convergence speed of worst-case integration error is\nslow in comparison to other quadrature methods. To address this problem, we\npropose two improved versions of the kernel herding algorithm. The fundamental\nconcept of both algorithms involves approximating negative gradients with a\npositive linear combination of vertex directions. We analyzed the convergence\nand validity of both algorithms theoretically; in particular, we showed that\nthe approximation of negative gradients directly influences the convergence\nspeed. In addition, we confirmed the accelerated convergence of the worst-case\nintegration error with respect to the number of nodes and computational time\nthrough numerical experiments.\n"}
+{"abstract": "  Nucleus segmentation is a challenging task due to the crowded distribution\nand blurry boundaries of nuclei. Recent approaches represent nuclei by means of\npolygons to differentiate between touching and overlapping nuclei and have\naccordingly achieved promising performance. Each polygon is represented by a\nset of centroid-to-boundary distances, which are in turn predicted by features\nof the centroid pixel for a single nucleus. However, using the centroid pixel\nalone does not provide sufficient contextual information for robust prediction.\nTo handle this problem, we propose a Context-aware Polygon Proposal Network\n(CPP-Net) for nucleus segmentation. First, we sample a point set rather than\none single pixel within each cell for distance prediction. This strategy\nsubstantially enhances contextual information and thereby improves the\nrobustness of the prediction. Second, we propose a Confidence-based Weighting\nModule, which adaptively fuses the predictions from the sampled point set.\nThird, we introduce a novel Shape-Aware Perceptual (SAP) loss that constrains\nthe shape of the predicted polygons. Here, the SAP loss is based on an\nadditional network that is pre-trained by means of mapping the centroid\nprobability map and the pixel-to-boundary distance maps to a different nucleus\nrepresentation. Extensive experiments justify the effectiveness of each\ncomponent in the proposed CPP-Net. Finally, CPP-Net is found to achieve\nstate-of-the-art performance on three publicly available databases, namely\nDSB2018, BBBC06, and PanNuke. Code of this paper will be released.\n"}
+{"abstract": "  Signs of new physics are probed in the context of an Effective Field Theory\nusing events containing one or more top quarks in association with additional\nleptons. Data consisting of proton-proton collisions at a center-of-mass energy\nof $\\sqrt{s}=$13 TeV was collected at the LHC by the CMS experiment in 2017. We\napply a novel technique to parameterize 16 dimension-six EFT operators in terms\nof the respective Wilson coefficients (WCs). A simultaneous fit is performed to\nthe data in order to extract the two standard deviation confidence intervals\n(CIs) of the 16 WCs. The Standard Model value of zero is completely contained\nin most CIs, and is not excluded by a statistically significant amount in any\ninterval.\n"}
+{"abstract": "  In the novel superfluid polar phase realized in liquid 3He in highly\nanisotropic aerogels, a quantum transition to the polar-distorted A (PdA) phase\nmay occur at a low but finite pressure Pc(0). It is shown that a nontrivial\nquantum dynamics of the critical fluctuation of the PdA order is induced by the\npresence of both the columnar-like impurity scattering leading to the\nAnderson's Theorem for the polar phase and the line node of the quasiparticle\ngap in the state, and that, in contrast to the situation of the normal to the B\nphase transition in isotropic aerogels, a weakly divergent behavior of the\ncompressibility appears in the quantum critical region close to Pc(0).\n"}
+{"abstract": "  Sanitizers are a relatively recent trend in software engineering. They aim at\nautomatically finding bugs in programs, and they are now commonly available to\nprogrammers as part of compiler toolchains. For example, the LLVM project\nincludes out-of-the-box sanitizers to detect thread safety (tsan), memory\n(asan,msan,lsan), or undefined behaviour (ubsan) bugs.\n  In this article, we present nsan, a new sanitizer for locating and debugging\nfloating-point numerical issues, implemented inside the LLVM sanitizer\nframework. nsan puts emphasis on practicality. It aims at providing precise,\nand actionable feedback, in a timely manner.\n  nsan uses compile-time instrumentation to augment each floating-point\ncomputation in the program with a higher-precision shadow which is checked for\nconsistency during program execution. This makes nsan between 1 and 4 orders of\nmagnitude faster than existing approaches, which allows running it routinely as\npart of unit tests, or detecting issues in large production applications.\n"}
+{"abstract": "  Rutherford scattering formula plays an important role in plasma classical\ntransport. It is urgent to need a magnetized Rutherford scattering formula\nsince the magnetic field increases significantly in different fusion areas\n(e.g. tokamak magnetic field, self-generated magnetic field, and compressed\nmagnetic field). The electron-ion Coulomb collisions perpendicular to the\nexternal magnetic field are studied in this paper. The scattering angle is\ndefined according to the electron trajectory and asymptotic line (without\nmagnetic field). A magnetized Rutherford scattering formula is obtained\nanalytically under the weak magnetic field approximation. It is found that the\nscattering angle decreases as external magnetic field increases. It is easy to\nfind the scattering angle decreasing significantly as incident distance, and\nincident velocity increasing. It is shown that the theoretical results agree\nwell with numerical calculation by checking the dependence of scattering angle\non external magnetic field.\n"}
+{"abstract": "  We adapt the arguments in the recent work of Duyckaerts, Landoulsi, and\nRoudenko to establish a scattering result at the sharp threshold for the $3d$\nfocusing cubic NLS with a repulsive potential. We treat both the case of\nshort-range potentials as previously considered in the work of Hong, as well as\nthe inverse-square potential, previously considered in the work of the authors.\n"}
+{"abstract": "  Password managers help users more effectively manage their passwords,\nencouraging them to adopt stronger passwords across their many accounts. In\ncontrast to desktop systems where password managers receive no system-level\nsupport, mobile operating systems provide autofill frameworks designed to\nintegrate with password managers to provide secure and usable autofill for\nbrowsers and other apps installed on mobile devices. In this paper, we evaluate\nmobile autofill frameworks on iOS and Android, examining whether they achieve\nsubstantive benefits over the ad-hoc desktop environment or become a\nproblematic single point of failure. Our results find that while the frameworks\naddress several common issues, they also enforce insecure behavior and fail to\nprovide password managers sufficient information to override the frameworks'\ninsecure behavior, resulting in mobile managers being less secure than their\ndesktop counterparts overall. We also demonstrate how these frameworks act as a\nconfused deputy in manager-assisted credential phishing attacks. Our results\ndemonstrate the need for significant improvements to mobile autofill\nframeworks. We conclude the paper with recommendations for the design and\nimplementation of secure autofill frameworks.\n"}
+{"abstract": "  Purity and coherence of a quantum state are recognized as useful resources\nfor various information processing tasks. In this article, we propose a\nfidelity based valid measure of purity and coherence monotone and establish a\nrelationship between them. This formulation of coherence is extended to quantum\ncorrelation relative to measurement. We have also studied the role of weak\nmeasurement on purity.\n"}
+{"abstract": "  The main objective of this paper is to outline a theoretical framework to\nanalyse how humans' decision-making strategies under uncertainty manage the\ntrade-off between information gathering (exploration) and reward seeking\n(exploitation). A key observation, motivating this line of research, is the\nawareness that human learners are amazingly fast and effective at adapting to\nunfamiliar environments and incorporating upcoming knowledge: this is an\nintriguing behaviour for cognitive sciences as well as an important challenge\nfor Machine Learning. The target problem considered is active learning in a\nblack-box optimization task and more specifically how the\nexploration/exploitation dilemma can be modelled within Gaussian Process based\nBayesian Optimization framework, which is in turn based on uncertainty\nquantification. The main contribution is to analyse humans' decisions with\nrespect to Pareto rationality where the two objectives are improvement expected\nand uncertainty quantification. According to this Pareto rationality model, if\na decision set contains a Pareto efficient (dominant) strategy, a rational\ndecision maker should always select the dominant strategy over its dominated\nalternatives. The distance from the Pareto frontier determines whether a choice\nis (Pareto) rational (i.e., lays on the frontier) or is associated to\n\"exasperate\" exploration. However, since the uncertainty is one of the two\nobjectives defining the Pareto frontier, we have investigated three different\nuncertainty quantification measures and selected the one resulting more\ncompliant with the Pareto rationality model proposed. The key result is an\nanalytical framework to characterize how deviations from \"rationality\" depend\non uncertainty quantifications and the evolution of the reward seeking process.\n"}
+{"abstract": "  The finite temperature phase diagram of QCD with two massless quark flavors\nis not yet understood because of the subtle effects of anomalous $U_A(1)$\nsymmetry. In this work we address this issue by studying the fate of the\nanomalous $U_A(1)$ symmetry in $2+1$ flavor QCD just above the chiral crossover\ntransition temperature $T_c$, lowering the light quark mass towards the chiral\nlimit along line of constant physical strange quark mass. We use the gauge\nconfigurations generated using the Highly Improved Staggered Quark (HISQ)\ndiscretization on lattice volumes $32^3\\times8$ and $56^3\\times 8$ to study the\nrenormalized eigenvalue spectrum of QCD with valence overlap Dirac operator. We\nhave implemented new numerical techniques that have allowed us to measure about\n$100$-$200$ eigenvalues of the gauge ensembles with light quark masses $\\gtrsim\n0.6$ MeV. From a detailed analysis of the dependence of the renormalized\neigenvalue spectrum and $U_A(1)$ breaking observables on the light quark mass,\nour study suggests $U_A(1)$ is broken at $T\\gtrsim T_c$ even when the chiral\nlimit is approached.\n"}
+{"abstract": "  We consider the 3D damped driven Maxwell--Schr\\\"odinger equations in a\nbounded region under suitable boundary conditions. We establish new a priori\nestimates, which provide the existence of global finite energy weak solutions\nand bounded absorbing set. The proofs rely on the Sobolev type estimates for\nmagnetic Schr\\\"odinger operator.\n"}
+{"abstract": "  We study the minimal number of existential quantifiers needed to define a\ndiophantine set over a field and relate this number to the essential dimension\nof the functor of points associated to such a definition.\n"}
+{"abstract": "  We propose a new method to estimate causal effects from nonexperimental data.\nEach pair of sample units is first associated with a stochastic 'treatment' -\ndifferences in factors between units - and an effect - a resultant outcome\ndifference. It is then proposed that all such pairs can be combined to provide\nmore accurate estimates of causal effects in observational data, provided a\nstatistical model connecting combinatorial properties of treatments to the\naccuracy and unbiasedness of their effects. The article introduces one such\nmodel and a Bayesian approach to combine the $O(n^2)$ pairwise observations\ntypically available in nonexperimnetal data. This also leads to an\ninterpretation of nonexperimental datasets as incomplete, or noisy, versions of\nideal factorial experimental designs.\n  This approach to causal effect estimation has several advantages: (1) it\nexpands the number of observations, converting thousands of individuals into\nmillions of observational treatments; (2) starting with treatments closest to\nthe experimental ideal, it identifies noncausal variables that can be ignored\nin the future, making estimation easier in each subsequent iteration while\ndeparting minimally from experiment-like conditions; (3) it recovers individual\ncausal effects in heterogeneous populations. We evaluate the method in\nsimulations and the National Supported Work (NSW) program, an intensively\nstudied program whose effects are known from randomized field experiments. We\ndemonstrate that the proposed approach recovers causal effects in common NSW\nsamples, as well as in arbitrary subpopulations and an order-of-magnitude\nlarger supersample with the entire national program data, outperforming\nStatistical, Econometrics and Machine Learning estimators in all cases...\n"}
+{"abstract": "  A general method is proposed for identifying the gauge-invariant part of the\nmetric perturbation within linearized gravity, and the six independent gauge\ninvariants per se, for an arbitrary background metric. For the Minkowski\nbackground, the operator that projects the metric perturbation on the invariant\nsubspace is proportional to the well-known dispersion operator of linear\ngravitational waves in vacuum.\n"}
+{"abstract": "  This ongoing work attempts to understand and address the requirements of\nUNICEF, a leading organization working in children's welfare, where they aim to\ntackle the problem of air quality for children at a global level. We are\nmotivated by the lack of a proper model to account for heavily fluctuating air\nquality levels across the world in the wake of the COVID-19 pandemic, leading\nto uncertainty among public health professionals on the exact levels of\nchildren's exposure to air pollutants. We create an initial model as per the\nagency's requirement to generate insights through a combination of virtual\nmeetups and online presentations. Our research team comprised of UNICEF's\nresearchers and a group of volunteer data scientists. The presentations were\ndelivered to a number of scientists and domain experts from UNICEF and\ncommunity champions working with open data. We highlight their feedback and\npossible avenues to develop this research further.\n"}
+{"abstract": "  The number of reviews on Amazon has grown significantly over the years.\nCustomers who made purchases on Amazon provide reviews by rating the product\nfrom 1 to 5 stars and sharing a text summary of their experience and opinion of\nthe product. The ratings of a product are averaged to provide an overall\nproduct rating. We analyzed what ratings score customers give to a specific\nproduct (a music track) in order to build a recommender model for digital music\ntracks on Amazon. We test various traditional models along with our proposed\ndeep neural network (DNN) architecture to predict the reviews rating score. The\nAmazon review dataset contains 200,000 data samples; we train the models on 70%\nof the dataset and test the performance of the models on the remaining 30% of\nthe dataset.\n"}
+{"abstract": "  Dynamical properties of ultradiscrete Hopf bifurcation, similar to those of\nthe standard Hopf bifurcation, are discussed by proposing a simple model of\nultradiscrete equations with max-plus algebra. In ultradiscrete Hopf\nbifurcation, limit cycles emerge depending on the value of a bifurcation\nparameter in the model. The limit cycles are composed of a finite number of\ndiscrete states. Furthermore, the model exhibits excitability. The model is\nderived from two different dynamical models with Hopf bifurcation by means of\nultradiscretization; it is a candidate for a normal form for ultradiscrete Hopf\nbifurcation.\n"}
+{"abstract": "  If A is a finite-dimensional symmetric algebra, then it is well-known that\nthe only silting complexes in $\\mathrm{K^b}(\\mathrm{proj}A)$ are the tilting\ncomplexes. In this note we investigate to what extent the same can be said for\nweakly symmetric algebras. On one hand, we show that this holds for all\ntilting-discrete weakly symmetric algebras. In particular, a tilting-discrete\nweakly symmetric algebra is also silting-discrete. On the other hand, we also\nconstruct an example of a weakly symmetric algebra with silting complexes that\nare not tilting.\n"}
+{"abstract": "  Are critical points important in the Solar Probe Mission? This is a brief\ndiscussion of the nature of critical points in solar wind models, what this\nmeans physically in the 'real' solar wind, and what can be expected along a\nnominal Solar Probe Orbit. The conclusion is that the regions where the wind\nbecomes transonic and trans-Alfvenic, which may be irregular and varying, may\nreveal interesting physics, but the mathematically defined critical points\nthemselves are of less importance.\n"}
+{"abstract": "  Ferromagnet/heavy metal (FM/HM) multilayer thin films with $C_{2v}$ symmetry\nhave the potential to host antiskyrmions and other chiral spin textures via an\nanisotropic Dzyaloshinkii-Moriya interaction (DMI). Here, we present a\ncandidate material system that also has a strong uniaxial magnetocrystalline\nanisotropy aligned in the plane of the film. This system is based on a new\nCo/Pt epitaxial relationship, which is the central focus of this work:\nhexagonal closed-packed Co$(10\\bar{1}0)[0001]$ $\\parallel$ face-centered cubic\nPt$(110)[001]$. We characterized the crystal structure and magnetic properties\nof our films using X-ray diffraction techniques and magnetometry respectively,\nincluding q-scans to determine stacking fault densities and their correlation\nwith the measured magnetocrystalline anisotropy constant and thickness of Co.\nIn future ultrathin multilayer films, we expect this epitaxial relationship to\nfurther enable an anisotropic DMI while supporting interfacial perpendicular\nmagnetic anisotropy. The anticipated confluence of these properties, along with\nthe tunability of multilayer films, make this material system a promising\ntestbed for unveiling new spin configurations in FM/HM films.\n"}
+{"abstract": "  We present the envelope of holomorphy of a classical truncated tube domain.\n"}
+{"abstract": "  Recent years have witnessed an upsurge of interest in employing flexible\nmachine learning models for instrumental variable (IV) regression, but the\ndevelopment of uncertainty quantification methodology is still lacking. In this\nwork we present a novel quasi-Bayesian procedure for IV regression, building\nupon the recently developed kernelized IV models and the dual/minimax\nformulation of IV regression. We analyze the frequentist behavior of the\nproposed method, by establishing minimax optimal contraction rates in $L_2$ and\nSobolev norms, and discussing the frequentist validity of credible balls. We\nfurther derive a scalable inference algorithm which can be extended to work\nwith wide neural network models. Empirical evaluation shows that our method\nproduces informative uncertainty estimates on complex high-dimensional\nproblems.\n"}
+{"abstract": "  Thermotropic biaxial nematic phases seem to be rare, but biaxial smectic A\nphases less so. Here we use molecular field theory to study a simple\ntwo-parameter model, with one parameter promoting a biaxial phase and the\nsecond promoting smecticity. The theory combines the biaxial Maier-Saupe and\nMcMillan models. We use alternatively the Sonnet-Virga-Durand (SVD) and\ngeometric mean approximations (GMA) to characterize molecular biaxiality by a\nsingle parameter. For non-zero smecticity and biaxiality, the model always\npredicts a ground state biaxial smectic A phase. For a low degree of smectic\norder, the phase diagram is very rich, predicting uniaxial and biaxial nematic\nand smectic phases, with in addition a variety of tricritical and tetracritical\npoints. For higher degrees of smecticity, the region of stability of the\nbiaxial nematic phase is restricted and eventually disappears, yielding to the\nbiaxial smectic phase. Phase diagrams from the two alternative approximations\nfor molecular biaxiality are similar, except inasmuch that SVD allows for a\nfirst order isotropic-nematic biaxial transition, whereas GMA predicts a Landau\npoint separating isotropic and biaxial nematic phases. We speculate that the\nrarity of thermotropic biaxial nematic phases is partly a consequence of the\npresence of stabler analogous smectic phases.\n"}
+{"abstract": "  Recently, FGSM adversarial training is found to be able to train a robust\nmodel which is comparable to the one trained by PGD but an order of magnitude\nfaster. However, there is a failure mode called catastrophic overfitting (CO)\nthat the classifier loses its robustness suddenly during the training and\nhardly recovers by itself. In this paper, we find CO is not only limited to\nFGSM, but also happens in $\\mbox{DF}^{\\infty}$-1 adversarial training. Then, we\nanalyze the geometric properties for both FGSM and $\\mbox{DF}^{\\infty}$-1 and\nfind they have totally different decision boundaries after CO. For FGSM, a new\ndecision boundary is generated along the direction of perturbation and makes\nthe small perturbation more effective than the large one. While for\n$\\mbox{DF}^{\\infty}$-1, there is no new decision boundary generated along the\ndirection of perturbation, instead the perturbation generated by\n$\\mbox{DF}^{\\infty}$-1 becomes smaller after CO and thus loses its\neffectiveness. We also experimentally analyze three hypotheses on potential\nfactors causing CO. And then based on the empirical analysis, we modify the\nRS-FGSM by not projecting perturbation back to the $l_\\infty$ ball. By this\nsmall modification, we could achieve $47.56 \\pm 0.37\\% $ PGD-50-10 accuracy on\nCIFAR10 with $\\epsilon=8/255$ in contrast to $43.57 \\pm 0.30\\% $ by RS-FGSM and\nalso further extend the working range of $\\epsilon$ from 8/255 to 11/255 on\nCIFAR10 without CO occurring.\n"}
+{"abstract": "  Traditional automated theorem provers have relied on manually tuned\nheuristics to guide how they perform proof search. Recently, however, there has\nbeen a surge of interest in the design of learning mechanisms that can be\nintegrated into theorem provers to improve their performance automatically. In\nthis work, we introduce TRAIL, a deep learning-based approach to theorem\nproving that characterizes core elements of saturation-based theorem proving\nwithin a neural framework. TRAIL leverages (a) an effective graph neural\nnetwork for representing logical formulas, (b) a novel neural representation of\nthe state of a saturation-based theorem prover in terms of processed clauses\nand available actions, and (c) a novel representation of the inference\nselection process as an attention-based action policy. We show through a\nsystematic analysis that these components allow TRAIL to significantly\noutperform previous reinforcement learning-based theorem provers on two\nstandard benchmark datasets (up to 36% more theorems proved). In addition, to\nthe best of our knowledge, TRAIL is the first reinforcement learning-based\napproach to exceed the performance of a state-of-the-art traditional theorem\nprover on a standard theorem proving benchmark (solving up to 17% more\nproblems).\n"}
+{"abstract": "  Families of coupled solitons of $\\mathcal{PT}$-symmetric physical models with\ngain and loss in fractional dimension and in settings with and without\ncross-interactions modulation (CIM), are reported. Profiles, powers, stability\nareas, and propagation dynamics of the obtained $\\mathcal{PT}$-symmetric\ncoupled solitons are investigated. By comparing the results of the models with\nand without CIM, we find that the stability area of the model with CIM is much\nbroader than the one without CIM. Remarkably, oscillating\n$\\mathcal{PT}$-symmetric coupled solitons can also exist in the model of CIM\nwith the same coefficients of the self- and cross-interactions modulations. In\naddition, the period of these oscillating coupled solitons can be controlled by\nthe linear coupling coefficient.\n"}
+{"abstract": "  An additional scalar degree of freedom for a gravitational wave is often\npredicted in theories of gravity beyond general relativity and can be used for\na model-agnostic test of gravity. In this letter, we report the direct search\nfor the scalar-tensor mixed polarization modes of gravitational waves from\ncompact binaries in a strong regime of gravity by analyzing the data of\nGW170814 and GW170817, which are the merger events of binary black holes and\nbinary neutron stars, respectively. Consequently, we obtain the constraints on\nthe ratio of scalar-mode amplitude to tensor-mode amplitude: $\\lesssim 0.20$\nfor GW170814 and $\\lesssim 0.068$ for GW170817, which are the tightest\nconstraints on the scalar amplitude in a strong regime of gravity before\nmerger.\n"}
+{"abstract": "  Cherenkov radiation generated by a charge moving along one of the faces of a\ndielectric prism is analyzed. Unlike our previous works, here we suppose that\nthe charge moves from the top of the prism to its base. We use the technique\nwhich was called by us \"the aperture method\". However here we develop the new\nversion of this technique which is suitable for objects with plane faces: we\nuse inside the object only the expansion over plane waves. This approach is\nconvenient for objects having plane borders especially in the case of two or\nmore borders on which the waves are reflected and/or refracted. Using this\ntechnique we obtain the field on the aperture and then apply Stretton-Chu\nformulas (aperture integrals). Further, the main attention is paid to the\ncalculation of the radiation field in the Fraunhofer (far-field) area. We\nobtain the expressions for the Fourier transforms of the field components in\nform of the single integrals. Using them, the series of typical angular\ndiagrams are computed and physical conclusions are made.\n"}
+{"abstract": "  In this paper, we examine the potential for a reconfigurable intelligent\nsurface (RIS) to be powered by energy harvested from information signals. This\nfeature might be key to reap the benefits of RIS technology's lower power\nconsumption compared to active relays. We first identify the main RIS\npower-consuming components and then propose an energy harvesting and power\nconsumption model. Furthermore, we formulate and solve the problem of the\noptimal RIS placement together with the amplitude and phase response adjustment\nof its elements in order to maximize the signal-to-noise ratio (SNR) while\nharvesting sufficient energy for its operation. Finally, numerical results\nvalidate the autonomous operation potential and reveal the range of power\nconsumption values that enables it.\n"}
+{"abstract": "  Hierarchical and k-medoids clustering are deterministic clustering algorithms\nbased on pairwise distances. Using these same pairwise distances, we propose a\nnovel stochastic clustering method based on random partition distributions. We\ncall our method CaviarPD, for cluster analysis via random partition\ndistributions. CaviarPD first samples clusterings from a random partition\ndistribution and then finds the best cluster estimate based on these samples\nusing algorithms to minimize an expected loss. We compare CaviarPD with\nhierarchical and k-medoids clustering through eight case studies. Cluster\nestimates based on our method are competitive with those of hierarchical and\nk-medoids clustering. They also do not require the subjective choice of the\nlinkage method necessary for hierarchical clustering. Furthermore, our\ndistribution-based procedure provides an intuitive graphical representation to\nassess clustering uncertainty.\n"}
+{"abstract": "  We present the full magnetic g tensors of the $^{6}$H$_{5/2}$Z$_{1}$ and\n$^{4}$G$_{5/2}$A$_{1}$ electronic states for both crystallographic sites in\nSm$^{3+}$:Y$_{2}$SiO$_{5}$, deduced through the use of Raman heterodyne\nspectroscopy performed along 9 different crystallographic directions. The\nmaximum principle g values were determined to be 0.447 (site 1) and 0.523 (site\n2) for the ground state and 2.490 (site 1) and 3.319 (site 2) for the excited\nstate. The determination of these g tensors provide essential spin Hamiltonian\nparameters that can be utilized in future magnetic and hyperfine studies of\nSm$^{3+}$:Y$_{2}$SiO$_{5}$, with applications in quantum information storage\nand communication devices.\n"}
+{"abstract": "  In this paper, we derive second order hydrodynamic traffic models from\nkinetic-controlled equations for driver-assist vehicles. At the vehicle level\nwe take into account two main control strategies synthesising the action of\nadaptive cruise controls and cooperative adaptive cruise controls. The\nresulting macroscopic dynamics fulfil the anisotropy condition introduced in\nthe celebrated Aw-Rascle-Zhang model. Unlike other models based on heuristic\narguments, our approach unveils the main physical aspects behind frequently\nused hydrodynamic traffic models and justifies the structure of the resulting\nmacroscopic equations incorporating driver-assist vehicles. Numerical insights\nshow that the presence of driver-assist vehicles produces an aggregate\nhomogenisation of the mean flow speed, which may also be steered towards a\nsuitable desired speed in such a way that optimal flows and traffic\nstabilisation are reached.\n"}
+{"abstract": "  We study the quantum quench in two coupled Tomonaga-Luttinger Liquids (TLLs),\nfrom the off-critical to the critical regime, relying on the conformal field\ntheory approach and the known solutions for single TLLs. We consider a squeezed\nform of the initial state, whose low energy limit is fixed in a way to describe\na massive and a massless mode, and we encode the non-equilibrium dynamics in a\nproper rescaling of the time. In this way, we compute several correlation\nfunctions, which at leading order factorize into multipoint functions evaluated\nat different times for the two modes. Depending on the observable, the\ncontribution from the massive or from the massless mode can be the dominant\none, giving rise to exponential or power-law decay in time, respectively. Our\nresults find a direct application in all the quench problems where, in the\nscaling limit, there are two independent massless fields: these include the\nHubbard model, the Gaudin-Yang gas, and tunnel-coupled tubes in cold atoms\nexperiments.\n"}
+{"abstract": "  We developed a noncontact measurement system for monitoring the respiration\nof multiple people using millimeter-wave array radar. To separate the radar\nechoes of multiple people, conventional techniques cluster the radar echoes in\nthe time, frequency, or spatial domain. Focusing on the measurement of the\nrespiratory signals of multiple people, we propose a method called\nrespiratory-space clustering, in which individual differences in the\nrespiratory rate are effectively exploited to accurately resolve the echoes\nfrom human bodies. The proposed respiratory-space clustering can separate\nechoes, even when people are located close to each other. In addition, the\nproposed method can be applied when the number of targets is unknown and can\naccurately estimate the number and positions of people. We perform multiple\nexperiments involving five or seven participants to verify the performance of\nthe proposed method, and quantitatively evaluate the estimation accuracy for\nthe number of people and the respiratory intervals. The experimental results\nshow that the average root-mean-square error in estimating the respiratory\ninterval is 196 ms using the proposed method. The use of the proposed method,\nrather the conventional method, improves the accuracy of the estimation of the\nnumber of people by 85.0%, which indicates the effectiveness of the proposed\nmethod for the measurement of the respiration of multiple people.\n"}
+{"abstract": "  The band structure, density of states, and the Fermi surface of a tungsten\noxide WO$_{2.9}$ with idealized crystal structure (ideal octahedra WO$_6$\ncreating a \"square lattice\") is obtained within the density functional theory\nin the generalized gradient approximation. Because of the oxygen vacancies\nordering this system is equivalent to the compound W$_{20}$O$_{58}$\n(Magn\\'{e}li phase), which has 78 atoms in unit cell. We show that\n5$d$-orbitals of tungsten atoms located immediately around the voids in the\nzigzag chains of edge-sharing octahedra give the dominant contribution near the\nFermi level. These particular tungsten atoms are responsible of a low-energy\nproperties of the system.\n"}
+{"abstract": "  Process mining studies ways to derive value from process executions recorded\nin event logs of IT-systems, with process discovery the task of inferring a\nprocess model for an event log emitted by some unknown system. One quality\ncriterion for discovered process models is generalization. Generalization seeks\nto quantify how well the discovered model describes future executions of the\nsystem, and is perhaps the least understood quality criterion in process\nmining. The lack of understanding is primarily a consequence of generalization\nseeking to measure properties over the entire future behavior of the system,\nwhen the only available sample of behavior is that provided by the event log\nitself. In this paper, we draw inspiration from computational statistics, and\nemploy a bootstrap approach to estimate properties of a population based on a\nsample. Specifically, we define an estimator of the model's generalization\nbased on the event log it was discovered from, and then use bootstrapping to\nmeasure the generalization of the model with respect to the system, and its\nstatistical significance. Experiments demonstrate the feasibility of the\napproach in industrial settings.\n"}
+{"abstract": "  Multiple-Intent Inverse Reinforcement Learning (MI-IRL) seeks to find a\nreward function ensemble to rationalize demonstrations of different but\nunlabelled intents. Within the popular expectation maximization (EM) framework\nfor learning probabilistic MI-IRL models, we present a warm-start strategy\nbased on up-front clustering of the demonstrations in feature space. Our\ntheoretical analysis shows that this warm-start solution produces a\nnear-optimal reward ensemble, provided the behavior modes satisfy mild\nseparation conditions. We also propose a MI-IRL performance metric that\ngeneralizes the popular Expected Value Difference measure to directly assesses\nlearned rewards against the ground-truth reward ensemble. Our metric elegantly\naddresses the difficulty of pairing up learned and ground truth rewards via a\nmin-cost flow formulation, and is efficiently computable. We also develop a\nMI-IRL benchmark problem that allows for more comprehensive algorithmic\nevaluations. On this problem, we find our MI-IRL warm-start strategy helps\navoid poor quality local minima reward ensembles, resulting in a significant\nimprovement in behavior clustering. Our extensive sensitivity analysis\ndemonstrates that the quality of the learned reward ensembles is improved under\nvarious settings, including cases where our theoretical assumptions do not\nnecessarily hold. Finally, we demonstrate the effectiveness of our methods by\ndiscovering distinct driving styles in a large real-world dataset of driver GPS\ntrajectories.\n"}
+{"abstract": "  We give here a proof of the convergence of the Stochastic Gradient Descent\n(SGD) in a self-contained manner.\n"}
+{"abstract": "  Our aim was to determine the initial Li content of two clusters of similar\nmetallicity but very different ages, the old open cluster NGC 2243 and the\nmetal-rich globular cluster NGC 104. We compared the lithium abundances derived\nfor a large sample of stars (from the turn-off to the red giant branch) in each\ncluster. For NGC 2243 the Li abundances are from the catalogues released by the\nGaia-ESO Public Spectroscopic Survey, while for NGC 104 we measured the Li\nabundance using FLAMES/GIRAFFE spectra, which include archival data and new\nobservations. We took the initial Li of NGC 2243 to be the lithium measured in\nstars on the hot side of the Li dip. We used the difference between the initial\nabundances and the post first dredge-up Li values of NGC 2243, and by adding\nthis amount to the post first dredge-up stars of NGC~104 we were able to infer\nthe initial Li of this cluster. Moreover, we compared our observational results\nto the predictions of theoretical stellar models for the difference between the\ninitial Li abundance and that after the first dredge-up. The initial lithium\ncontent of NGC 2243 was found to be A(Li)_i = 2.85dex by taking the average Li\nabundance measured from the five hottest stars with the highest lithium\nabundance. This value is 1.69 dex higher than the lithium abundance derived in\npost first dredge-up stars. By adding this number to the lithium abundance\nderived in the post first dredge-up stars in NGC~104, we infer a lower limit of\nits initial lithium content of A(Li)_i= 2.30dex. Stellar models predict similar\nvalues. Therefore, our result offers important insights for further theoretical\ndevelopments.\n"}
+{"abstract": "  In this paper, we study the Orienteering Aisle-graphs Single-access Problem\n(OASP), a variant of the orienteering problem for a robot moving in a so-called\nsingle-access aisle-graph, i.e., a graph consisting of a set of rows that can\nbe accessed from one side only. Aisle-graphs model, among others, vineyards or\nwarehouses. Each aisle-graph vertex is associated with a reward that a robot\nobtains when visits the vertex itself. As the robot's energy is limited, only a\nsubset of vertices can be visited with a fully charged battery. The objective\nis to maximize the total reward collected by the robot with a battery charge.\nWe first propose an optimal algorithm that solves OASP in O(m^2 n^2) time for\naisle-graphs with a single access consisting of m rows, each with n vertices.\nWith the goal of designing faster solutions, we propose four greedy sub-optimal\nalgorithms that run in at most O(mn (m+n)) time. For two of them, we guarantee\nan approximation ratio of 1/2(1-1/e), where e is the base of the natural\nlogarithm, on the total reward by exploiting the well-known submodularity\nproperty. Experimentally, we show that these algorithms collect more than 80%\nof the optimal reward.\n"}
+{"abstract": "  In this paper, for a locally compact commutative hypergroup $K$ and for a\npair $(\\Phi_1, \\Phi_2)$ of Young functions satisfying sequence condition, we\ngive a necessary condition in terms of aperiodic elements of the center of $K,$\nfor the convolution $f\\ast g$ to exist a.e., where $f$ and $g$ are arbitrary\nelements of Orlicz spaces $L^{\\Phi_1}(K)$ and $L^{\\Phi_2}(K)$, respectively. As\nan application, we present some equivalent conditions for compactness of a\ncompactly generated locally compact abelian group. Moreover, we also\ncharacterize compact convolution operators from $L^1_w(K)$ into $L^\\Phi_w(K)$\nfor a weight $w$ on a locally compact hypergroup $K$.\n"}
+{"abstract": "  Elasticities in depth, width, kernel size and resolution have been explored\nin compressing deep neural networks (DNNs). Recognizing that the kernels in a\nconvolutional neural network (CNN) are 4-way tensors, we further exploit a new\nelasticity dimension along the input-output channels. Specifically, a novel\nnuclear-norm rank minimization factorization (NRMF) approach is proposed to\ndynamically and globally search for the reduced tensor ranks during training.\nCorrelation between tensor ranks across multiple layers is revealed, and a\ngraceful tradeoff between model size and accuracy is obtained. Experiments then\nshow the superiority of NRMF over the previous non-elastic variational Bayesian\nmatrix factorization (VBMF) scheme.\n"}
+{"abstract": "  Electronic topology in metallic kagome compounds is under intense scrutiny.\nWe present transport experiments in Na2/3CoO2 in which the Na order\ndifferentiates a Co kagome sub-lattice in the triangular CoO2 layers. Hall and\nmagnetoresistance (MR) data under high fields give evidence for the coexistence\nof light and heavy carriers. At low temperatures, the dominant light carrier\nconductivity at zero field is suppressed by a B-linear MR suggesting Dirac like\nquasiparticles. Lifshitz transitions induced at large B and T unveil the lower\nmobility carriers. They display a negative B^2 MR due to scattering from\nmagnetic moments likely pertaining to a flat band. We underline an analogy with\nheavy Fermion physics.\n"}
+{"abstract": "  The work attempts to unify the conceptual model of the user's virtual\ncomputer environment, with the aim of combining the local environments of\noperating systems and the global Internet environment into a single virtual\nenvironment built on general principles. To solve this problem, it is proposed\nto unify the conceptual basis of these environments. The existing conceptual\nbasis of operating systems, built on the \"desktop\" metaphor, contains redundant\nconcepts associated with computer architecture. The use of the spatial\nconceptual basis \"object - place\" with the concepts of \"domain\", \"site\", and\n\"data object\" allows to completely virtualize the user environment, separating\nit from the hardware concepts. The virtual concept \"domain\" is becoming a\nuniversal way of structuring the user's space. The introduction of this concept\nto describe the environments of operating systems provides at the mental level\nthe integration of the structures of the local and global space. The use of the\nconcept of \"personal domain\" will allow replacing the concept of \"personal\ncomputer\" in the mind of the user. The virtual concept of \"site\" as an\nenvironment for activities and data storage will allow abandoning such concepts\nas \"application\" (program), or \"memory device\". The site in the mind of the\nuser is a virtual environment that includes both places for storing data\nobjects and places for working with them. The introduction of the concept\n\"site\" into the structure of operating systems environments and the concept of\n\"data site\" into the structure of the global network integrates the structure\nof the global and local space in the user's mind. The introduction of the\nconcept of \"portal\" as a means of integrating information necessary for\ninteraction, allows ensuring the methodological homogeneity of the user's work\nin a single virtual environment.\n"}
+{"abstract": "  Existing gradient-based meta-learning approaches to few-shot learning assume\nthat all tasks have the same input feature space. However, in the real world\nscenarios, there are many cases that the input structures of tasks can be\ndifferent, that is, different tasks may vary in the number of input modalities\nor data types. Existing meta-learners cannot handle the heterogeneous task\ndistribution (HTD) as there is not only global meta-knowledge shared across\ntasks but also type-specific knowledge that distinguishes each type of tasks.\nTo deal with task heterogeneity and promote fast within-task adaptions for each\ntype of tasks, in this paper, we propose HetMAML, a task-heterogeneous\nmodel-agnostic meta-learning framework, which can capture both the\ntype-specific and globally shared knowledge and can achieve the balance between\nknowledge customization and generalization. Specifically, we design a\nmulti-channel backbone module that encodes the input of each type of tasks into\nthe same length sequence of modality-specific embeddings. Then, we propose a\ntask-aware iterative feature aggregation network which can automatically take\ninto account the context of task-specific input structures and adaptively\nproject the heterogeneous input spaces to the same lower-dimensional embedding\nspace of concepts. Our experiments on six task-heterogeneous datasets\ndemonstrate that HetMAML successfully leverages type-specific and globally\nshared meta-parameters for heterogeneous tasks and achieves fast within-task\nadaptions for each type of tasks.\n"}
+{"abstract": "  Video anomaly detection is a challenging task because of diverse abnormal\nevents. To this task, methods based on reconstruction and prediction are wildly\nused in recent works, which are built on the assumption that learning on normal\ndata, anomalies cannot be reconstructed or predicated as good as normal\npatterns, namely the anomaly result with more errors. In this paper, we propose\nto discriminate anomalies from normal ones by the duality of normality-granted\noptical flow, which is conducive to predict normal frames but adverse to\nabnormal frames. The normality-granted optical flow is predicted from a single\nframe, to keep the motion knowledge focused on normal patterns. Meanwhile, We\nextend the appearance-motion correspondence scheme from frame reconstruction to\nprediction, which not only helps to learn the knowledge about object\nappearances and correlated motion, but also meets the fact that motion is the\ntransformation between appearances. We also introduce a margin loss to enhance\nthe learning of frame prediction. Experiments on standard benchmark datasets\ndemonstrate the impressive performance of our approach.\n"}
+{"abstract": "  Latest study reports that plasma emission can be generated by energetic\nelectrons of DGH distribution via the electron cyclotron maser instability\n(ECMI) in plasmas characterized by a large ratio of plasma oscillation\nfrequency to electron gyro-frequency ($\\omega_{pe}/\\Omega_{ce}$). In this\nstudy, on the basis of the ECMI-plasma emission mechanism, we examine the\ndouble plasma resonance (DPR) effect and the corresponding plasma emission at\nboth harmonic (H) and fundamental (F) bands using PIC simulations with various\n$\\omega_{pe}/\\Omega_{ce}$. This allows us to directly simulate the feature of\nzebra pattern (ZP) observed in solar radio bursts for the first time. We find\nthat (1) the simulations reproduce the DPR effect nicely for the upper hybrid\n(UH) and Z modes, as seen from their variation of intensity and linear growth\nrate with $\\omega_{pe}/\\Omega_{ce}$, (2) the intensity of the H emission is\nstronger than that of the F emission by $\\sim$ 2 orders of magnitude and vary\nperiodically with increasing $\\omega_{pe}/\\Omega_{ce}$, while the F emission is\ntoo weak to be significant, therefore we suggest that it is the H emission\naccounting for solar ZPs, (3) the peak-valley contrast of the total intensity\nof H is $\\sim 4$, and the peak lies around integer values of\n$\\omega_{pe}/\\Omega_{ce}$ (= 10 and 11) for the present parameter setup. We\nalso evaluate the effect of energy of energetic electrons on the\ncharacteristics of ECMI-excited waves and plasma radiation. The study provides\nnovel insight on the physical origin of ZPs of solar radio bursts.\n"}
+{"abstract": "  In order to solve the critical issues in Wireless Sensor Networks (WSNs),\nwith concern for limited sensor lifetime, nature-inspired algorithms are\nemerging as a suitable method. Getting optimal network coverage is one of those\nchallenging issues that need to be examined critically before any network\nsetup. Optimal network coverage not only minimizes the consumption of limited\nenergy of battery-driven sensors but also reduce the sensing of redundant\ninformation. In this paper, we focus on nature-inspired optimization algorithms\nconcerning the optimal coverage in WSNs. In the first half of the paper, we\nhave briefly discussed the taxonomy of the optimization algorithms along with\nthe problem domains in WSNs. In the second half of the paper, we have compared\nthe performance of two nature-inspired algorithms for getting optimal coverage\nin WSNs. The first one is a combined Improved Genetic Algorithm and Binary Ant\nColony Algorithm (IGABACA), and the second one is Lion Optimization (LO). The\nsimulation results confirm that LO gives better network coverage, and the\nconvergence rate of LO is faster than that of IGA-BACA. Further, we observed\nthat the optimal coverage is achieved at a lesser number of generations in LO\nas compared to IGA-BACA. This review will help researchers to explore the\napplications in this field as well as beyond this area. Keywords: Optimal\nCoverage, Bio-inspired Algorithm, Lion Optimization, WSNs.\n"}
+{"abstract": "  This paper proposes a novel scheme for mitigating strong interferences, which\nis applicable to various wireless scenarios, including full-duplex wireless\ncommunications and uncoordinated heterogenous networks. As strong interferences\ncan saturate the receiver's analog-to-digital converters (ADC), they need to be\nmitigated both before and after the ADCs, i.e., via hybrid processing. The key\nidea of the proposed scheme, namely the Hybrid Interference Mitigation using\nAnalog Prewhitening (HIMAP), is to insert an M-input M-output analog phase\nshifter network (PSN) between the receive antennas and the ADCs to spatially\nprewhiten the interferences, which requires no signal information but only an\nestimate of the covariance matrix. After interference mitigation by the PSN\nprewhitener, the preamble can be synchronized, the signal channel response can\nbe estimated, and thus a minimum mean squared error (MMSE) beamformer can be\napplied in the digital domain to further mitigate the residual interferences.\nThe simulation results verify that the HIMAP scheme can suppress interferences\n80dB stronger than the signal by using off-the-shelf phase shifters (PS) of\n6-bit resolution.\n"}
+{"abstract": "  In the standard (classic) approach, galaxy clustering measurements from\nspectroscopic surveys are compressed into baryon acoustic oscillations and\nredshift space distortions measurements, which in turn can be compared to\ncosmological models. Recent works have shown that avoiding this intermediate\nstep and fitting directly the full power spectrum signal (full modelling) leads\nto much tighter constraints on cosmological parameters. Here we show where this\nextra information is coming from and extend the classic approach with one\nadditional effective parameter, such that it captures, effectively, the same\namount of information as the full modelling approach, but in a\nmodel-independent way. We validate this new method (ShapeFit) on mock catalogs,\nand compare its performance to the full modelling approach finding both to\ndeliver equivalent results. The ShapeFit extension of the classic approach\npromotes the standard analyses at the level of full modelling ones in terms of\ninformation content, with the advantages of i) being more model independent;\nii) offering an understanding of the origin of the extra cosmological\ninformation; iii) allowing a robust control on the impact of observational\nsystematics.\n"}
+{"abstract": "  INTRODUCTION: Wald's, the likelihood ratio (LR) and Rao's score tests and\ntheir corresponding confidence intervals (CIs), are the three most common\nestimators of parameters of Generalized Linear Models. On finite samples, these\nestimators are biased. The objective of this work is to analyze the coverage\nerrors of the CI estimators in small samples for the log-Poisson model (i.e.\nestimation of incidence rate ratio) with innovative evaluation criteria, taking\nin account the overestimation/underestimation unbalance of coverage errors and\nthe variable inclusion rate and follow-up in epidemiological studies.\n  METHODS: Exact calculations equivalent to Monte Carlo simulations with an\ninfinite number of simulations have been used. Underestimation errors (due to\nthe upper bound of the CI) and overestimation coverage errors (due to the lower\nbound of the CI) have been split. The level of confidence has been analyzed\nfrom $0.95$ to $1-10^{-6}$, allowing the interpretation of P-values below\n$10^{-6}$ for hypothesis tests.\n  RESULTS: The LR bias was small (actual coverage errors less than 1.5 times\nthe nominal errors) when the expected number of events in both groups was above\n1, even when unbalanced (e.g. 10 events in one group vs 1 in the other). For\n95% CI, Wald's and the Score estimators showed high bias even when the number\nof events was large ($\\geq 20$ in both groups) when groups were unbalanced. For\nsmall P-values ($<10^{-6}$), the LR kept acceptable bias while Wald's and the\nscore P-values had severely inflated errors ($\\times 100$).\n  CONCLUSION: The LR test and LR CI should be used.\n"}
+{"abstract": "  Recovering the wavelength from disordered speckle patterns has become an\nexciting prospect as a wavelength measurement method due to its high resolution\nand simple design. In previous studies, panel cameras have been used to detect\nthe subtle differences between speckle patterns. However, the volume,\nbandwidth, sensitivity, and cost (in non-visible bands) associated with panel\ncameras have hindered their utility in broader applications, especially in high\nspeed and low-cost measurements. In this work, we broke the limitations imposed\nby panel cameras by using a quadrant detector (QD) to capture the speckle\nimages. In the scheme of QD detection, speckle images are directly filtered by\nconvolution, where the kernel is equal to one quarter of a speckle pattern.\nFirst, we proposed an up-sampling algorithm to pre-process the QD data. Then a\nnew convolution neural network (CNN) based algorithm, shallow residual network\n(SRN), was proposed to train the up-sampled images. The experimental results\nshow that a resolution of 4 fm (~ 0.5 MHz) was achieved at 1550nm with an\nupdating speed of ~ 1 kHz. More importantly, the SRN shows excellent\nrobustness. The wavelength can be precisely reconstructed from raw QD data\nwithout any averaging, even where there exists apparent noise. The low-cost,\nsimple structure, high speed and robustness of this design promote the\nspeckle-based wavemeter to the industrial grade. In addition, without the\nrestriction of panel cameras, it is believed that this wavemeter opens new\nroutes in many other fields, such as distributed optical fiber sensors, optical\ncommunications, and laser frequency stabilization.\n"}
+{"abstract": "  The hidden ancestor graph is a new stochastic model for a vertex-labelled\nmultigraph $G$ in which the observable vertices are the leaves $L$ of a random\nrooted tree $T$, whose edges and non-leaf nodes are hidden. The likelihood of\nan edge in $G$ between two vertices in $L$ depends on the height of their\nlowest common ancestor in $T$. The label of a vertex $v \\in L$ depends on a\nrandomized label inheritance mechanism within $T$ such that vertices with the\nsame parent often have the same label. High label assortativity, high average\nlocal clustering, heavy tailed vertex degree distribution, and sparsity, can\nall coexist in this model. The agreement edges (end point labels agree) and the\nconflict edges (end point labels differ) constitute complementary subgraphs,\nuseful for testing anomaly correction algorithms. Instances with a hundred\nmillion edges can easily be built on a workstation in minutes.\n"}
+{"abstract": "  We give a simple proof for the global convergence of gradient descent in\ntraining deep ReLU networks with the standard square loss, and show some of its\nimprovements over the state-of-the-art. In particular, while prior works\nrequire all the hidden layers to be wide with width at least $\\Omega(N^8)$ ($N$\nbeing the number of training samples), we require a single wide layer of\nlinear, quadratic or cubic width depending on the type of initialization.\nUnlike many recent proofs based on the Neural Tangent Kernel (NTK), our proof\nneed not track the evolution of the entire NTK matrix, or more generally, any\nquantities related to the changes of activation patterns during training.\nInstead, we only need to track the evolution of the output at the last hidden\nlayer, which can be done much more easily thanks to the Lipschitz property of\nReLU. Some highlights of our setting: (i) all the layers are trained with\nstandard gradient descent, (ii) the network has standard parameterization as\nopposed to the NTK one, and (iii) the network has a single wide layer as\nopposed to having all wide hidden layers as in most of NTK-related results.\n"}
+{"abstract": "  Vision models trained on multimodal datasets can benefit from the wide\navailability of large image-caption datasets. A recent model (CLIP) was found\nto generalize well in zero-shot and transfer learning settings. This could\nimply that linguistic or \"semantic grounding\" confers additional generalization\nabilities to the visual feature space. Here, we systematically evaluate various\nmultimodal architectures and vision-only models in terms of unsupervised\nclustering, few-shot learning, transfer learning and adversarial robustness. In\neach setting, multimodal training produced no additional generalization\ncapability compared to standard supervised visual training. We conclude that\nwork is still required for semantic grounding to help improve vision models.\n"}
+{"abstract": "  Detecting cyber-anomalies and attacks are becoming a rising concern these\ndays in the domain of cybersecurity. The knowledge of artificial intelligence,\nparticularly, the machine learning techniques can be used to tackle these\nissues. However, the effectiveness of a learning-based security model may vary\ndepending on the security features and the data characteristics. In this paper,\nwe present \"CyberLearning\", a machine learning-based cybersecurity modeling\nwith correlated-feature selection, and a comprehensive empirical analysis on\nthe effectiveness of various machine learning based security models. In our\nCyberLearning modeling, we take into account a binary classification model for\ndetecting anomalies, and multi-class classification model for various types of\ncyber-attacks. To build the security model, we first employ the popular ten\nmachine learning classification techniques, such as naive Bayes, Logistic\nregression, Stochastic gradient descent, K-nearest neighbors, Support vector\nmachine, Decision Tree, Random Forest, Adaptive Boosting, eXtreme Gradient\nBoosting, as well as Linear discriminant analysis. We then present the\nartificial neural network-based security model considering multiple hidden\nlayers. The effectiveness of these learning-based security models is examined\nby conducting a range of experiments utilizing the two most popular security\ndatasets, UNSW-NB15 and NSL-KDD. Overall, this paper aims to serve as a\nreference point for data-driven security modeling through our experimental\nanalysis and findings in the context of cybersecurity.\n"}
+{"abstract": "  Concept of p-frame with the help of b-linear functional in the case of\nn-Banach space is being presented and its few properties, one of them,\nCartesian product of two p-frames again becomes a p-frame, have been discussed.\nFinally, the perturbation results and the stability of p-frame in n-Banach\nspace with respect to b-linear functional are being studied.\n"}
+{"abstract": "  Cannabis legalization has been welcomed by many U.S. states but its role in\nescalation from tobacco e-cigarette use to cannabis vaping is unclear.\nMeanwhile, cannabis vaping has been associated with new lung diseases and\nrising adolescent use. To understand the impact of cannabis legalization on\nescalation, we design an observational study to estimate the causal effect of\nrecreational cannabis legalization on the development of pro-cannabis attitude\nfor e-cigarette users. We collect and analyze Twitter data which contains\nopinions about cannabis and JUUL, a very popular e-cigarette brand. We use\nweakly supervised learning for personal tweet filtering and classification for\nstance detection. We discover that recreational cannabis legalization policy\nhas an effect on increased development of pro-cannabis attitudes for users\nalready in favor of e-cigarettes.\n"}
+{"abstract": "  In finite element calculations, the integral forms are usually evaluated\nusing nested loops over elements, and over quadrature points. Many such forms\n(e.g. linear or multi-linear) can be expressed in a compact way, without the\nexplicit loops, using a single tensor contraction expression by employing the\nEinstein summation convention. To automate this process and leverage existing\nhigh performance codes, we first introduce a notation allowing trivial\ndifferentiation of multi-linear finite element forms. Based on that we propose\nand describe a new transpiler from Einstein summation based expressions,\naugmented to allow defining multi-linear finite element weak forms, to regular\ntensor contraction expressions. The resulting expressions are compatible with a\nnumber of Python scientific computing packages, that implement, optimize and in\nsome cases parallelize the general tensor contractions. We assess the\nperformance of those packages, as well as the influence of operand memory\nlayouts and tensor contraction paths optimizations on the elapsed time and\nmemory requirements of the finite element form evaluations. We also compare the\nefficiency of the transpiled weak form implementations to the C-based functions\navailable in the finite element package SfePy.\n"}
+{"abstract": "  Efficient methods for loading given classical data into quantum circuits are\nessential for various quantum algorithms. In this paper, we propose an\nalgorithm called that can effectively load all the components of a given\nreal-valued data vector into the amplitude of quantum state, while the previous\nproposal can only load the absolute values of those components. The key of our\nalgorithm is to variationally train a shallow parameterized quantum circuit,\nusing the results of two types of measurement; the standard computational-basis\nmeasurement plus the measurement in the Hadamard-transformed basis, introduced\nin order to handle the sign of the data components. The variational algorithm\nchanges the circuit parameters so as to minimize the sum of two costs\ncorresponding to those two measurement basis, both of which are given by the\nefficiently-computable maximum mean discrepancy. We also consider the problem\nof constructing the singular value decomposition entropy via the stock market\ndataset to give a financial market indicator; a quantum algorithm (the\nvariational singular value decomposition algorithm) is known to produce a\nsolution faster than classical, which yet requires the sign-dependent amplitude\nencoding. We demonstrate, with an in-depth numerical analysis, that our\nalgorithm realizes loading of time-series of real stock prices on quantum state\nwith small approximation error, and thereby it enables constructing an\nindicator of the financial market based on the stock prices.\n"}
+{"abstract": "  Helical edge states of two-dimensional topological insulators show a gap in\nthe density of states (DOS) and suppressed conductance in the presence of\nordered magnetic impurities. Here we will consider the dynamical effects on the\nDOS and transmission when the magnetic impurities are driven periodically.\nUsing the Floquet formalism and Green's functions, the system properties are\nstudied as a function of the driving frequency and the potential energy\ncontribution of the impurities. We see that increasing the potential part\ncloses the DOS gap for all driving regimes. The transmission gap is also\nclosed, showing a pronounced asymmetry as a function of energy. These features\nindicate that the dynamical transport properties could yield valuable\ninformation about the magnetic impurities.\n"}
+{"abstract": "  Subglacial lakes are isolated, cold-temperature and high-pressure water\nenvironments hidden under ice sheets, which might host extreme microorganisms.\nHere, we use two-dimensional direct numerical simulations in order to\ninvestigate the characteristic temperature fluctuations and velocities in\nfreshwater subglacial lakes as functions of the ice overburden pressure, $p_i$,\nthe water depth, $h$, and the geothermal flux, $F$. Geothermal heating is the\nunique forcing mechanism as we consider a flat ice-water interface. Subglacial\nlakes are fully convective when $p_i$ is larger than the critical pressure\n$p_*\\approx 2848$ dbar, but self organize into a lower convective bulk and an\nupper stably-stratified layer when $p_i < p_*$, because of the existence at low\npressure of a density maximum at temperature $T_d$ greater than the freezing\ntemperature $T_f$. For both high and low $p_i$, we demonstrate that the Nusselt\nnumber $Nu$ and Reynolds number $Re$ satisfy classical scaling laws provided\nthat an effective Rayleigh number $Ra_{eff}$ is considered. We show that the\nconvective and stably-stratified layers at low pressure are dynamically\ndecoupled at leading order because plume penetration is weak and induces\nlimited entrainment of the stable fluid. From the empirical power law equation\nfor $Nu$ with $Ra_{eff}$, we derive two sets of closed-form expressions for the\nvariables of interest, including the unknown bottom temperature, in terms of\nthe problem parameters $p_i$, $h$ and $F$. The two predictions correspond to\ntwo limiting regimes obtained when the effective thermal expansion coefficient\nis either approximately constant or linearly proportional to the temperature\ndifference driving the convection.\n"}
+{"abstract": "  We consider the canonical periodic review lost sales inventory system with\npositive lead-times and stochastic i.i.d. demand under the average cost\ncriterion. We introduce a new policy that places orders such that the expected\ninventory level at the time of arrival of an order is at a fixed level and call\nit the Projected Inventory Level (PIL) policy. We prove that this policy has a\ncost-rate superior to the equivalent system where excess demand is back-ordered\ninstead of lost and is therefore asymptotically optimal as the cost of losing a\nsale approaches infinity under mild distributional assumptions. We further show\nthat this policy dominates the constant order policy for any finite lead-time\nand is therefore asymptotically optimal as the lead-time approaches infinity\nfor the case of exponentially distributed demand per period. Numerical results\nshow this policy also performs superior relative to other policies.\n"}
+{"abstract": "  This paper describes a novel approach to emulate a universal quantum computer\nwith a wholly classical system, one that uses a signal of bounded duration and\namplitude to represent an arbitrary quantum state. The signal may be of any\nmodality (e.g. acoustic, electromagnetic, etc.) but this paper will focus on\nelectronic signals. Individual qubits are represented by in-phase and\nquadrature sinusoidal signals, while unitary gate operations are performed\nusing simple analog electronic circuit devices. In this manner, the Hilbert\nspace structure of a multi-qubit quantum state, as well as a universal set of\ngate operations, may be fully emulated classically. Results from a programmable\nprototype system are presented and discussed.\n"}
+{"abstract": "  Generative adversarial networks (GANs), e.g., StyleGAN2, play a vital role in\nvarious image generation and synthesis tasks, yet their notoriously high\ncomputational cost hinders their efficient deployment on edge devices. Directly\napplying generic compression approaches yields poor results on GANs, which\nmotivates a number of recent GAN compression works. While prior works mainly\naccelerate conditional GANs, e.g., pix2pix and CycleGAN, compressing\nstate-of-the-art unconditional GANs has rarely been explored and is more\nchallenging. In this paper, we propose novel approaches for unconditional GAN\ncompression. We first introduce effective channel pruning and knowledge\ndistillation schemes specialized for unconditional GANs. We then propose a\nnovel content-aware method to guide the processes of both pruning and\ndistillation. With content-awareness, we can effectively prune channels that\nare unimportant to the contents of interest, e.g., human faces, and focus our\ndistillation on these regions, which significantly enhances the distillation\nquality. On StyleGAN2 and SN-GAN, we achieve a substantial improvement over the\nstate-of-the-art compression method. Notably, we reduce the FLOPs of StyleGAN2\nby 11x with visually negligible image quality loss compared to the full-size\nmodel. More interestingly, when applied to various image manipulation tasks,\nour compressed model forms a smoother and better disentangled latent manifold,\nmaking it more effective for image editing.\n"}
+{"abstract": "  Property-preserving hash functions allow for compressing long inputs $x_0$\nand $x_1$ into short hashes $h(x_0)$ and $h(x_1)$ in a manner that allows for\ncomputing a predicate $P(x_0, x_1)$ given only the two hash values without\nhaving access to the original data. Such hash functions are said to be\nadversarially robust if an adversary that gets to pick $x_0$ and $x_1$ after\nthe hash function has been sampled, cannot find inputs for which the predicate\nevaluated on the hash values outputs the incorrect result.\n  In this work we construct robust property-preserving hash functions for the\nhamming-distance predicate which distinguishes inputs with a hamming distance\nat least some threshold $t$ from those with distance less than $t$. The\nsecurity of the construction is based on standard lattice hardness assumptions.\n  Our construction has several advantages over the best known previous\nconstruction by Fleischhacker and Simkin. Our construction relies on a single\nwell-studied hardness assumption from lattice cryptography whereas the previous\nwork relied on a newly introduced family of computational hardness assumptions.\nIn terms of computational effort, our construction only requires a small number\nof modular additions per input bit, whereas previously several exponentiations\nper bit as well as the interpolation and evaluation of high-degree polynomials\nover large fields were required. An additional benefit of our construction is\nthat the description of the hash function can be compressed to $\\lambda$ bits\nassuming a random oracle. Previous work has descriptions of length\n$\\mathcal{O}(\\ell \\lambda)$ bits for input bit-length $\\ell$, which has a\nsecret structure and thus cannot be compressed.\n  We prove a lower bound on the output size of any property-preserving hash\nfunction for the hamming distance predicate. The bound shows that the size of\nour hash value is not far from optimal.\n"}
+{"abstract": "  Quantum State Sharing (QSS) is a protocol by which a (secret) quantum state\nmay be securely split, shared between multiple potentially dishonest players,\nand reconstructed. Crucially the players are each assumed to be dishonest, and\nso QSS requires that only a collaborating authorised subset of players can\naccess the original secret state; any dishonest unauthorised conspiracy cannot\nreconstruct it. We analyse a QSS protocol involving three untrusted players and\ndemonstrate that quantum steering is the required resource which enables the\nprotocol to proceed securely. We analyse the level of steering required to\nshare any single-mode Gaussian secret which enables the states to be shared\nwith the optimal use of resources.\n"}
+{"abstract": "  Consider the universal gate set for quantum computing consisting of the gates\nX, CX, CCX, omega^dagger H, and S. All of these gates have matrix entries in\nthe ring Z[1/2,i], the smallest subring of the complex numbers containing 1/2\nand i. Amy, Glaudell, and Ross proved the converse, i.e., any unitary matrix\nwith entries in Z[1/2,i] can be realized by a quantum circuit over the above\ngate set using at most one ancilla. In this paper, we give a finite\npresentation by generators and relations of U_n(Z[1/2,i]), the group of unitary\nnxn-matrices with entries in Z[1/2,i].\n"}
+{"abstract": "  In this work, we explore the recently proposed new Tsallis agegraphic dark\nenergy model in a flat FLRW Universe by taking the conformal time as IR cutoff\nwith interaction. The deceleration parameter of the interacting new Tsallis\nagegraphic dark energy model provides the phase transition of the Universe from\ndecelerated to accelerated phase. The EoS parameter of the model shows a rich\nbehaviour as it can be quintessence-like or phantom-like depending on the\ninteraction ($b^2$) and parameter $B$. The evolutionary trajectories of the\nstatefinder parameters and $(\\omega_D, \\omega_D^{'})$ planes are plotted by\nconsidering the initial condition $\\Omega_{D}^{0} =0.73$, $H_{0}= 67$ according\nto $\\Lambda$CDM observational Planck 2018 data for different $b^2$ and $B$. The\nmodel shows both quintessence and Chaplygin gas behaviour in the statefinder\n$(r, s)$ and $(r, q)$ pair planes for different $b^2$ and $B$.\n"}
+{"abstract": "  These lectures present some basic ideas and techniques in the spectral\nanalysis of lattice Schrodinger operators with disordered potentials. In\ncontrast to the classical Anderson tight binding model, the randomness is also\nallowed to possess only finitely many degrees of freedom. This refers to\ndynamically defined potentials, i.e., those given by evaluating a function\nalong an orbit of some ergodic transformation (or of several commuting such\ntransformations on higher-dimensional lattices). Classical localization\ntheorems by Frohlich--Spencer for large disorders are presented, both for\nrandom potentials in all dimensions, as well as even quasi-periodic ones on the\nline. After providing the needed background on subharmonic functions, we then\ndiscuss the Bourgain-Goldstein theorem on localization for quasiperiodic\nSchrodinger cocycles assuming positive Lyapunov exponents.\n"}
+{"abstract": "  We study at the single-photon level the nonreciprocal excitation transfer\nbetween emitters coupled with a common waveguide. Non-Markovian retarded\neffects are taken into account due to the large separation distance between\ndifferent emitter-waveguide coupling ports. It is shown that the excitation\ntransfer between the emitters of a small-atom dimer can be obviously\nnonreciprocal by introducing between them a coherent coupling channel with\nnontrivial coupling phase. We prove that for dimer models the nonreciprocity\ncannot coexist with the decoherence-free giant-atom structure although the\nlatter markedly lengthens the lifetime of the emitters. In view of this, we\nfurther propose a giant-atom trimer which supports both nonreciprocal transfer\n(directional circulation) of the excitation and greatly lengthened lifetime.\nSuch a trimer model also exhibits incommensurate emitter-waveguide entanglement\nfor different initial states in which case the excitation transfer is however\nreciprocal. We believe that the proposals in this paper are of potential\napplications in large-scale quantum networks and quantum information\nprocessing.\n"}
+{"abstract": "  We prove the non-linear asymptotic stability of the Schwarzschild family as\nsolutions to the Einstein vacuum equations in the exterior of the black hole\nregion: general vacuum initial data, with no symmetry assumed, sufficiently\nclose to Schwarzschild data evolve to a vacuum spacetime which (i) possesses a\ncomplete future null infinity $\\mathcal{I}^+$ (whose past $J^-(\\mathcal{I}^+)$\nis moreover bounded by a regular future complete event horizon\n$\\mathcal{H}^+$), (ii) remains close to Schwarzschild in its exterior, and\n(iii) asymptotes back to a member of the Schwarzschild family as an appropriate\nnotion of time goes to infinity, provided that the data are themselves\nconstrained to lie on a teleologically constructed codimension-$3$\n\"submanifold\" of moduli space. This is the full nonlinear asymptotic stability\nof Schwarzschild since solutions not arising from data lying on this\nsubmanifold should by dimensional considerations approach a Kerr spacetime with\nrotation parameter $a\\neq 0$, i.e. such solutions cannot satisfy (iii). The\nproof employs teleologically normalised double null gauges, is expressed\nentirely in physical space and makes essential use of the analysis in our\nprevious study of the linear stability of the Kerr family around Schwarzschild\n[DHR], as well as techniques developed over the years to control the\nnon-linearities of the Einstein equations. The present work, however, is\nentirely self-contained. In view of the recent [DHR19, TdCSR20] our approach\ncan be applied to the full non-linear asymptotic stability of the subextremal\nKerr family.\n"}
+{"abstract": "  Automatic extraction of forum posts and metadata is a crucial but challenging\ntask since forums do not expose their content in a standardized structure.\nContent extraction methods, therefore, often need customizations such as\nadaptations to page templates and improvements of their extraction code before\nthey can be deployed to new forums. Most of the current solutions are also\nbuilt for the more general case of content extraction from web pages and lack\nkey features important for understanding forum content such as the\nidentification of author metadata and information on the thread structure.\n  This paper, therefore, presents a method that determines the XPath of forum\nposts, eliminating incorrect mergers and splits of the extracted posts that\nwere common in systems from the previous generation. Based on the individual\nposts further metadata such as authors, forum URL and structure are extracted.\nWe also introduce Harvest, a new open source toolkit that implements the\npresented methods and create a gold standard extracted from 52 different Web\nforums for evaluating our approach. A comprehensive evaluation reveals that\nHarvest clearly outperforms competing systems.\n"}
+{"abstract": "  We provide a number of new conjectures and questions concerning the syzygies\nof $\\mathbb{P}^1\\times \\mathbb{P}^1$. The conjectures are based on computing\nthe graded Betti tables and related data for large number of different\nembeddings of $\\mathbb{P}^1\\times \\mathbb{P}^1$. These computations utilize\nlinear algebra over finite fields and high-performance computing.\n"}
+{"abstract": "  This study examines the influence of learning in a female teacher homeroom\nclass in elementary school on pupils' voting behavior later in life, using\nindependently collected individual-level data. Further, we evaluate its effect\non preference for women's participation in the workplace in adulthood. Our\nstudy found that having a female teacher in the first year of school makes\nindividuals more likely to vote for female candidates, and to prefer policy for\nfemale labor participation in adulthood. However, the effect is only observed\namong males, and not female pupils. These findings offer new evidence for the\nfemale socialization hypothesis.\n"}
+{"abstract": "  Main results are, firstly, a generalization of the Conley-Zehnder index from\nODEs to the delay equation at hand and, secondly, the equality of the Morse\nindex and the clockwise normalized Conley-Zehnder index.\n"}
+{"abstract": "  We revisit to investigate shadows cast by Kerr-like wormholes. The boundary\nof the shadow is determined by unstable circular photon orbits. We find that,\nin certain parameter regions, the orbit is located at the throat of the\nKerr-like wormhole, which was not considered in the literature. In these cases,\nthe existence of the throat alters the shape of the shadow significantly, and\nmakes it possible for us to differentiate it from that of a Kerr black hole.\n"}
+{"abstract": "  Low-electron-dose observation is indispensable for observing various samples\nusing a transmission electron microscope; consequently, image processing has\nbeen used to improve transmission electron microscopy (TEM) images. To apply\nsuch image processing to in situ observations, we here apply a convolutional\nneural network to TEM imaging. Using a dataset that includes short-exposure\nimages and long-exposure images, we develop a pipeline for processed\nshort-exposure images, based on end-to-end training. The quality of images\nacquired with a total dose of approximately 5 e- per pixel becomes comparable\nto that of images acquired with a total dose of approximately 1000 e- per\npixel. Because the conversion time is approximately 8 ms, in situ observation\nat 125 fps is possible. This imaging technique enables in situ observation of\nelectron-beam-sensitive specimens.\n"}
+{"abstract": "  A strong connection between cluster algebras and representation theory was\nestablished by the cluster category. Cluster characters, like the original\nCaldero-Chapoton (CC) map, are maps from certain triangulated categories to\ncluster algebras and they have generated much interest. Holm and J{\\o}rgensen\nconstructed a modified CC map from a sufficiently nice triangulated category to\na commutative ring, which is a generalised frieze under some conditions. In\ntheir construction, a quotient $K_{0}^{sp}(\\mathcal{T})/M$ of a Grothendieck\ngroup of a cluster tilting subcategory $\\mathcal{T}$ is used. In this article,\nwe show that this quotient is the Grothendieck group of a certain\nextriangulated category, thereby exposing the significance of it and the\nrelevance of extriangulated structures. We use this to define another modified\nCC map that recovers the one of Holm--J{\\o}rgensen.\n  We prove our results in a higher homological context. Suppose $\\mathcal{S}$\nis a $(d+2)$-angulated category with subcategories\n$\\mathcal{X}\\subseteq\\mathcal{T}\\subseteq\\mathcal{S}$, where $\\mathcal{X}$ is\nfunctorially finite and $\\mathcal{T}$ is $2d$-cluster tilting, satisfying some\nmild conditions. We show there is an isomorphism between the Grothendieck group\n$K_{0}(\\mathcal{S},\\mathbb{E}_{\\mathcal{X}},\\mathfrak{s}_{\\mathcal{X}})$ of the\ncategory $\\mathcal{S}$, equipped with the $d$-exangulated structure induced by\n$\\mathcal{X}$, and the quotient $K_{0}^{sp}(\\mathcal{T})/N$, where $N$ is the\nhigher analogue of $M$ above. When $\\mathcal{X}=\\mathcal{T}$ the isomorphism is\ninduced by the higher index with respect to $\\mathcal{T}$ introduced recently\nby J{\\o}rgensen. Thus, in the general case, we can understand the map taking an\nobject in $\\mathcal{S}$ to its $K_{0}$-class in\n$K_{0}(\\mathcal{S},\\mathbb{E}_{\\mathcal{X}},\\mathfrak{s}_{\\mathcal{X}})$ as a\nhigher index with respect to the rigid subcategory $\\mathcal{X}$.\n"}
+{"abstract": "  In this paper, a three-dimensional light detection and ranging simultaneous\nlocalization and mapping (SLAM) method is proposed that is available for\ntracking and mapping with 500--1000 Hz processing. The proposed method\nsignificantly reduces the number of points used for point cloud registration\nusing a novel ICP metric to speed up the registration process while maintaining\naccuracy. Point cloud registration with ICP is less accurate when the number of\npoints is reduced because ICP basically minimizes the distance between points.\nTo avoid this problem, symmetric KL-divergence is introduced to the ICP cost\nthat reflects the difference between two probabilistic distributions. The cost\nincludes not only the distance between points but also differences between\ndistribution shapes. The experimental results on the KITTI dataset indicate\nthat the proposed method has high computational efficiency, strongly\noutperforms other methods, and has similar accuracy to the state-of-the-art\nSLAM method.\n"}
+{"abstract": "  A new metric for quantifying pairwise vertex connectivity in graphs is\ndefined and an implementation presented. While general in nature, it features a\ncombination of input features well-suited for social networks, including\napplicability to directed or undirected graphs, weighted edges, and computes\nusing the impact from all-paths between the vertices. Moreover, the $O(V+E)$\nmethod is applicable to large graphs. Comparisons with other techniques are\nincluded.\n"}
+{"abstract": "  Topological orders are a prominent paradigm for describing quantum many-body\nsystems without symmetry-breaking orders. We present a topological quantum\nfield theoretical (TQFT) study on topological orders in five-dimensional\nspacetime ($5$D) in which \\textit{topological excitations} include not only\npoint-like \\textit{particles}, but also two types of spatially extended\nobjects: closed string-like \\textit{loops} and two-dimensional closed\n\\textit{membranes}. Especially, membranes have been rarely explored in the\nliterature of topological orders. By introducing higher-form gauge fields, we\nconstruct exotic TQFT actions that include mixture of two distinct types of\n$BF$ topological terms and many twisted topological terms. The gauge\ntransformations are properly defined and utilized to compute level quantization\nand classification of TQFTs. Among all TQFTs, some are not in Dijkgraaf-Witten\ncohomological classification. To characterize topological orders, we concretely\nconstruct all braiding processes among topological excitations, which leads to\nvery exotic links formed by closed spacetime trajectories of particles, loops,\nand membranes. For each braiding process, we construct gauge-invariant Wilson\noperators and calculate the associated braiding statistical phases. As a\nresult, we obtain expressions of link invariants all of which have manifest\ngeometric interpretation. Following Wen's definition, the boundary theory of a\ntopological order exhibits gravitational anomaly. We expect that the\ncharacterization and classification of 5D topological orders in this paper\nencode information of 4D gravitational anomaly. Further consideration, e.g.,\nputting TQFTs on 5D manifolds with boundaries, is left to future work.\n"}
+{"abstract": "  This article is the first of two in which we develop a geometric framework\nfor analysing silent and anisotropic big bang singularities. The results of the\npresent article concern the asymptotic behaviour of solutions to linear systems\nof wave equations on the corresponding backgrounds. The main features are the\nfollowing: The assumptions do not involve any symmetry requirements and are\nweak enough to be consistent with most big bang singularities for which the\nasymptotic geometry is understood. The asymptotic rate of growth/decay of\nsolutions to linear systems of wave equations along causal curves going into\nthe singularity is determined by model systems of ODE's (depending on the\ncausal curve). Moreover, the model systems are essentially obtained by dropping\nspatial derivatives and localising the coefficients along the causal curve.\nThis is in accordance with the BKL proposal. Note, however, that we here prove\nthis statement, we do not assume it. If the coefficients of the unknown and its\nexpansion normalised normal derivatives converge sufficiently quickly along a\ncausal curve, we obtain leading order asymptotics (along the causal curve) and\nprove that the localised energy estimate (along the causal curve) is optimal.\nIn this setting, it is also possible to specify the leading order asymptotics\nof solutions along the causal curve. On the other hand, the localised energy\nestimate typically entails a substantial loss of derivatives.\n  In the companion article, we deduce geometric conclusions by combining the\nframework with Einstein's equations. In particular, the combination reproduces\nthe Kasner map and yields partial bootstrap arguments.\n"}
+{"abstract": "  Synthetic data generation has become essential in last years for feeding\ndata-driven algorithms, which surpassed traditional techniques performance in\nalmost every computer vision problem. Gathering and labelling the amount of\ndata needed for these data-hungry models in the real world may become\nunfeasible and error-prone, while synthetic data give us the possibility of\ngenerating huge amounts of data with pixel-perfect annotations. However, most\nsynthetic datasets lack from enough realism in their rendered images. In that\ncontext UnrealROX generation tool was presented in 2019, allowing to generate\nhighly realistic data, at high resolutions and framerates, with an efficient\npipeline based on Unreal Engine, a cutting-edge videogame engine. UnrealROX\nenabled robotic vision researchers to generate realistic and visually plausible\ndata with full ground truth for a wide variety of problems such as class and\ninstance semantic segmentation, object detection, depth estimation, visual\ngrasping, and navigation. Nevertheless, its workflow was very tied to generate\nimage sequences from a robotic on-board camera, making hard to generate data\nfor other purposes. In this work, we present UnrealROX+, an improved version of\nUnrealROX where its decoupled and easy-to-use data acquisition system allows to\nquickly design and generate data in a much more flexible and customizable way.\nMoreover, it is packaged as an Unreal plug-in, which makes it more comfortable\nto use with already existing Unreal projects, and it also includes new features\nsuch as generating albedo or a Python API for interacting with the virtual\nenvironment from Deep Learning frameworks.\n"}
+{"abstract": "  In the context of tomographic cosmic shear surveys, there exists a nulling\ntransformation of weak lensing observations (also called BNT transform) that\nallows us to simplify the correlation structure of tomographic cosmic shear\nobservations, as well as to build observables that depend only on a localised\nrange of redshifts and thus independent from the low-redshift/small-scale\nmodes. This procedure renders possible accurate, and from-first-principles,\npredictions of the convergence and aperture mass one-point distributions (PDF).\nWe here explore other consequences of this transformation on the (reduced)\nnumerical complexity of the estimation of the joint PDF between nulled bins and\ndemonstrate how to use these results to make theoretical predictions.\n"}
+{"abstract": "  We investigate the spin Seebeck effect and spin pumping in a junction between\na ferromagnetic insulator and a magnetic impurity deposited on a normal metal.\nBy the numerical renormalization group calculation, we show that spin current\nis enhanced by the Kondo effect. This spin current is suppressed by increase of\nthe temperature or the magnetic field which is comparable with the Kondo\ntemperature. Our results indicate that spin transport can be a direct probe of\nspin excitation in strongly correlated systems.\n"}
+{"abstract": "  In this review, we summarize recent progress on the possible phases of\nquantum chromodynamics (QCD) in the presence of a strong magnetic field, mainly\nfrom the views of the chiral effective Nambu--Jona-Lasinio model. Four kinds of\nphase transitions are explored in detail: chiral symmetry breaking and\nrestoration, neutral pseudoscalar superfluidity, charged pion superfluidity and\ncharged rho superconductivity. In particular, we revisit the unsolved problems\nof inverse magnetic catalysis effect and competition between the chiral density\nwave and solitonic modulation phases. It is shown that useful results can be\nobtained by adopting self-consistent schemes.\n"}
+{"abstract": "  Quantum algorithms offer significant speedups over their classical\ncounterparts for a variety of problems. The strongest arguments for this\nadvantage are borne by algorithms for quantum search, quantum phase estimation,\nand Hamiltonian simulation, which appear as subroutines for large families of\ncomposite quantum algorithms. A number of these quantum algorithms were\nrecently tied together by a novel technique known as the quantum singular value\ntransformation (QSVT), which enables one to perform a polynomial transformation\nof the singular values of a linear operator embedded in a unitary matrix. In\nthe seminal GSLW'19 paper on QSVT [Gily\\'en, Su, Low, and Wiebe, ACM STOC\n2019], many algorithms are encompassed, including amplitude amplification,\nmethods for the quantum linear systems problem, and quantum simulation. Here,\nwe provide a pedagogical tutorial through these developments, first\nillustrating how quantum signal processing may be generalized to the quantum\neigenvalue transform, from which QSVT naturally emerges. Paralleling GSLW'19,\nwe then employ QSVT to construct intuitive quantum algorithms for search, phase\nestimation, and Hamiltonian simulation, and also showcase algorithms for the\neigenvalue threshold problem and matrix inversion. This overview illustrates\nhow QSVT is a single framework comprising the three major quantum algorithms,\nthus suggesting a grand unification of quantum algorithms.\n"}
+{"abstract": "  With music becoming an essential part of daily life, there is an urgent need\nto develop recommendation systems to assist people targeting better songs with\nfewer efforts. As the interactions between users and songs naturally construct\na complex network, community detection approaches can be applied to reveal\nusers' potential interests on songs by grouping relevant users & songs to the\nsame community. However, as the types of interaction could be heterogeneous, it\nchallenges conventional community detection methods designed originally for\nhomogeneous networks. Although there are existing works on heterogeneous\ncommunity detection, they are mostly task-driven approaches and not feasible\nfor specific music recommendation. In this paper, we propose a genetic based\napproach to learn an edge-type usefulness distribution (ETUD) for all\nedge-types in heterogeneous music networks. ETUD can be regarded as a linear\nfunction to project all edges to the same latent space and make them\ncomparable. Therefore a heterogeneous network can be converted to a homogeneous\none where those conventional methods are eligible to use. We validate the\nproposed model on a heterogeneous music network constructed from an online\nmusic streaming service. Results show that for conventional methods, ETUD can\nhelp to detect communities significantly improving music recommendation\naccuracy while simultaneously reducing user searching cost.\n"}
+{"abstract": "  It is standard to assume that the Wigner distribution of a mixed quantum\nstate consisting of square-integrable functions is a quasi-probability\ndistribution, that is that its integral is one and that the marginal properties\nare satisfied. However this is in general not true. We introduce a class of\nquantum states for which this property is satisfied, these states are dubbed\n\"Feichtinger states\" because they are defined in terms of a class of functional\nspaces (modulation spaces) introduced in the 1980's by H. Feichtinger. The\nproperties of these states are studied, which gives us the opportunity to prove\nan extension to the general case of a result of Jaynes on the non-uniqueness of\nthe statistical ensemble generating a density operator. As a bonus we obtain a\nresult for convex sums of Wigner transforms.\n"}
+{"abstract": "  Most recent approaches for online action detection tend to apply Recurrent\nNeural Network (RNN) to capture long-range temporal structure. However, RNN\nsuffers from non-parallelism and gradient vanishing, hence it is hard to be\noptimized. In this paper, we propose a new encoder-decoder framework based on\nTransformers, named OadTR, to tackle these problems. The encoder attached with\na task token aims to capture the relationships and global interactions between\nhistorical observations. The decoder extracts auxiliary information by\naggregating anticipated future clip representations. Therefore, OadTR can\nrecognize current actions by encoding historical information and predicting\nfuture context simultaneously. We extensively evaluate the proposed OadTR on\nthree challenging datasets: HDD, TVSeries, and THUMOS14. The experimental\nresults show that OadTR achieves higher training and inference speeds than\ncurrent RNN based approaches, and significantly outperforms the\nstate-of-the-art methods in terms of both mAP and mcAP. Code is available at\nhttps://github.com/wangxiang1230/OadTR.\n"}
+{"abstract": "  In this review, we describe the application of one of the most popular deep\nlearning-based language models - BERT. The paper describes the mechanism of\noperation of this model, the main areas of its application to the tasks of text\nanalytics, comparisons with similar models in each task, as well as a\ndescription of some proprietary models. In preparing this review, the data of\nseveral dozen original scientific articles published over the past few years,\nwhich attracted the most attention in the scientific community, were\nsystematized. This survey will be useful to all students and researchers who\nwant to get acquainted with the latest advances in the field of natural\nlanguage text analysis.\n"}
+{"abstract": "  We revisit the possibilities of accommodating the experimental indications of\nthe lepton flavor universality violation in $b$-hadron decays in the minimal\nscenarios in which the Standard Model is extended by the presence of a single\n$\\mathcal{O}(1\\,\\mathrm{TeV})$ leptoquark state. To do so we combine the most\nrecent low energy flavor physics constraints, including\n$R_{K^{(\\ast)}}^\\mathrm{exp}$ and $R_{D^{(\\ast)}}^\\mathrm{exp}$, and combine\nthem with the bounds on the leptoquark masses and their couplings to quarks and\nleptons as inferred from the direct searches at the LHC and the studies of the\nlarge $p_T$ tails of the $pp\\to \\ell\\ell$ differential cross section. We find\nthat none of the scalar leptoquarks of $m_\\mathrm{LQ} \\simeq 1\\div 2$ TeV can\naccommodate the $B$-anomalies alone. Only the vector leptoquark, known as\n$U_1$, can provide a viable solution which, in the minimal setup, provides an\ninteresting prediction, i.e. a lower bound to the lepton flavor violating $b\\to\ns\\mu^\\pm\\tau^\\mp$ decay modes, such as $\\mathcal{B}(B\\to K\\mu\\tau) \\gtrsim\n0.7\\times 10^{-7}$.\n"}
+{"abstract": "  This work aims to tackle the challenging heterogeneous graph encoding problem\nin the text-to-SQL task. Previous methods are typically node-centric and merely\nutilize different weight matrices to parameterize edge types, which 1) ignore\nthe rich semantics embedded in the topological structure of edges, and 2) fail\nto distinguish local and non-local relations for each node. To this end, we\npropose a Line Graph Enhanced Text-to-SQL (LGESQL) model to mine the underlying\nrelational features without constructing meta-paths. By virtue of the line\ngraph, messages propagate more efficiently through not only connections between\nnodes, but also the topology of directed edges. Furthermore, both local and\nnon-local relations are integrated distinctively during the graph iteration. We\nalso design an auxiliary task called graph pruning to improve the\ndiscriminative capability of the encoder. Our framework achieves\nstate-of-the-art results (62.8% with Glove, 72.0% with Electra) on the\ncross-domain text-to-SQL benchmark Spider at the time of writing.\n"}
+{"abstract": "  In this note, we give an elementary proof of the result given by Schenzel\nthat there are functorial isomorphisms between local cohomology groups and\n\\v{C}ech cohomology groups, by using weakly proregular sequences. In [Sch03],\nhe used notions of derived category theory in his proof, but we do not use them\nin this paper.\n"}
+{"abstract": "  We consider the 1d CFT defined by the half-BPS Wilson line in planar\n$\\mathcal{N}=4$ super Yang-Mills. Using analytic bootstrap methods we derive\nthe four-point function of the super-displacement operator at fourth order in a\nstrong coupling expansion. Via AdS/CFT, this corresponds to the first\nthree-loop correlator in AdS ever computed. To do so we address the operator\nmixing problem by considering a family of auxiliary correlators. We further\nextract the anomalous dimension of the lightest non-protected operator and find\nagreement with the integrability-based numerical result of Grabner, Gromov and\nJulius.\n"}
+{"abstract": "  Open source development, to a great extent, is a type of social movement in\nwhich shared ideologies play critical roles. For participants of open source\ndevelopment, ideology determines how they make sense of things, shapes their\nthoughts, actions, and interactions, enables rich social dynamics in their\nprojects and communities, and hereby realizes profound impacts at both\nindividual and organizational levels. While software engineering researchers\nhave been increasingly recognizing ideology's importance in open source\ndevelopment, the notion of \"ideology\" has shown significant ambiguity and\nvagueness, and resulted in theoretical and empirical confusion. In this\narticle, we first examine the historical development of ideology's\nconceptualization, and its theories in multiple disciplines. Then, we review\nthe extant software engineering literature related to ideology. We further\nargue the imperatives of developing an empirical theory of ideology in open\nsource development, and propose a research agenda for developing such a theory.\nHow such a theory could be applied is also discussed.\n"}
+{"abstract": "  We study the possibilities of factorizations of products of nuclear operators\nof different types through the Schatten-von Neumann operators in Hilbert spaces\nwith giving some applications to eigenvalues problems.\n"}
+{"abstract": "  Understanding multivariate extreme events play a crucial role in managing the\nrisks of complex systems since extremes are governed by their own mechanisms.\nConditional on a given variable exceeding a high threshold (e.g.\\ traffic\nintensity), knowing which high-impact quantities (e.g\\ air pollutant levels)\nare the most likely to be extreme in the future is key. This article\ninvestigates the contribution of marginal extreme events on future extreme\nevents of related quantities. We propose an Extreme Event Propagation framework\nto maximise counterfactual causation probabilities between a known cause and\nfuture high-impact quantities. Extreme value theory provides a tool for\nmodelling upper tails whilst vine copulas are a flexible device for capturing a\nlarge variety of joint extremal behaviours. We optimise for the probabilities\nof causation and apply our framework to a London road traffic and air\npollutants dataset. We replicate documented atmospheric mechanisms beyond\nlinear relationships. This provides a new tool for quantifying the propagation\nof extremes in a large variety of applications.\n"}
+{"abstract": "  This paper establishes an extended representation theorem for unit-root VARs.\nA specific algebraic technique is devised to recover stationarity from the\nsolution of the model in the form of a cointegrating transformation. Closed\nforms of the results of interest are derived for integrated processes up to the\n4-th order. An extension to higher-order processes turns out to be within the\nreach on an induction argument.\n"}
+{"abstract": "  Acoustic Echo Cancellation (AEC) whose aim is to suppress the echo originated\nfrom acoustic coupling between loudspeakers and microphones, plays a key role\nin voice interaction. Linear adaptive filter (AF) is always used for handling\nthis problem. However, since there would be some severe effects in real\nscenarios, such nonlinear distortions, background noises, and microphone\nclipping, it would lead to considerable residual echo, giving poor performance\nin practice. In this paper, we propose an end-to-end network structure for echo\ncancellation, which is directly done on time-domain audio waveform. It is\ntransformed to deep representation by temporal convolution, and modelled by\nLong Short-Term Memory (LSTM) for considering temporal property. Since time\ndelay and severe reverberation may exist at the near-end with respect to the\nfar-end, a local attention is employed for alignment. The network is trained\nusing multitask learning by employing an auxiliary classification network for\ndouble-talk detection. Experiments show the superiority of our proposed method\nin terms of the echo return loss enhancement (ERLE) for single-talk periods and\nthe perceptual evaluation of speech quality (PESQ) score for double-talk\nperiods in background noise and nonlinear distortion scenarios.\n"}
+{"abstract": "  We study the Lie point symmetries and the similarity transformations for the\npartial differential equations of the nonlinear one-dimensional\nmagnetohydrodynamic system with the Hall term known as HMHD system. For this\n1+1 system of partial differential equations we find that is invariant under\nthe action of a seventh dimensional Lie algebra. Furthermore, the\none-dimensional optimal system is derived while the Lie invariants are applied\nfor the derivation of similarity transformations. We present different kinds of\noscillating solutions.\n"}
+{"abstract": "  Mobile robots have disrupted the material handling industry which is\nwitnessing radical changes. The requirement for enhanced automation across\nvarious industry segments often entails mobile robotic systems operating in\nlogistics facilities with little/no infrastructure. In such environments,\nout-of-box low-cost robotic solutions are desirable. Wireless connectivity\nplays a crucial role in successful operation of such mobile robotic systems. A\nwireless mesh network of mobile robots is an attractive solution; however, a\nnumber of system-level challenges create unique and stringent service\nrequirements. The focus of this paper is the role of Bluetooth mesh technology,\nwhich is the latest addition to the Internet-of-Things (IoT) connectivity\nlandscape, in addressing the challenges of infrastructure-less connectivity for\nmobile robotic systems. It articulates the key system-level design challenges\nfrom communication, control, cooperation, coverage, security, and\nnavigation/localization perspectives, and explores different capabilities of\nBluetooth mesh technology for such challenges. It also provides performance\ninsights through real-world experimental evaluation of Bluetooth mesh while\ninvestigating its differentiating features against competing solutions.\n"}
+{"abstract": "  Numerical models of weather and climate critically depend on long-term\nstability of integrators for systems of hyperbolic conservation laws. While\nsuch stability is often obtained from (physical or numerical) dissipation\nterms, physical fidelity of such simulations also depends on properly\npreserving conserved quantities, such as energy, of the system. To address this\napparent paradox, we develop a variational integrator for the shallow water\nequations that conserves energy, but dissipates potential enstrophy. Our\napproach follows the continuous selective decay framework [F. Gay-Balmaz and D.\nHolm. Selective decay by Casimir dissipation in inviscid fluids. Nonlinearity,\n26(2):495, 2013], which enables dissipating an otherwise conserved quantity\nwhile conserving the total energy. We use this in combination with the\nvariational discretization method [D. Pavlov, P. Mullen, Y. Tong, E. Kanso, J.\nMarsden and M. Desbrun. Structure-preserving discretization of incompressible\nfluids. Physica D: Nonlinear Phenomena, 240(6):443-458, 2011] to obtain a\ndiscrete selective decay framework. This is applied to the shallow water\nequations, both in the plane and on the sphere, to dissipate the potential\nenstrophy. The resulting scheme significantly improves the quality of the\napproximate solutions, enabling long-term integrations to be carried out.\n"}
+{"abstract": "  Conceptual abstraction and analogy-making are key abilities underlying\nhumans' abilities to learn, reason, and robustly adapt their knowledge to new\ndomains. Despite of a long history of research on constructing AI systems with\nthese abilities, no current AI system is anywhere close to a capability of\nforming humanlike abstractions or analogies. This paper reviews the advantages\nand limitations of several approaches toward this goal, including symbolic\nmethods, deep learning, and probabilistic program induction. The paper\nconcludes with several proposals for designing challenge tasks and evaluation\nmeasures in order to make quantifiable and generalizable progress in this area.\n"}
+{"abstract": "  We consider the problem of offline reinforcement learning (RL) -- a\nwell-motivated setting of RL that aims at policy optimization using only\nhistorical data. Despite its wide applicability, theoretical understandings of\noffline RL, such as its optimal sample complexity, remain largely open even in\nbasic settings such as \\emph{tabular} Markov Decision Processes (MDPs).\n  In this paper, we propose Off-Policy Double Variance Reduction (OPDVR), a new\nvariance reduction based algorithm for offline RL. Our main result shows that\nOPDVR provably identifies an $\\epsilon$-optimal policy with\n$\\widetilde{O}(H^2/d_m\\epsilon^2)$ episodes of offline data in the\nfinite-horizon stationary transition setting, where $H$ is the horizon length\nand $d_m$ is the minimal marginal state-action distribution induced by the\nbehavior policy. This improves over the best known upper bound by a factor of\n$H$. Moreover, we establish an information-theoretic lower bound of\n$\\Omega(H^2/d_m\\epsilon^2)$ which certifies that OPDVR is optimal up to\nlogarithmic factors. Lastly, we show that OPDVR also achieves rate-optimal\nsample complexity under alternative settings such as the finite-horizon MDPs\nwith non-stationary transitions and the infinite horizon MDPs with discounted\nrewards.\n"}
+{"abstract": "  The loss and gain of volatile elements during planet formation is key for\nsetting their subsequent climate, geodynamics, and habitability. Two broad\nregimes of volatile element transport in and out of planetary building blocks\nhave been identified: that occurring when the nebula is still present, and that\noccurring after it has dissipated. Evidence for volatile element loss in\nplanetary bodies after the dissipation of the solar nebula is found in the high\nMn to Na abundance ratio of Mars, the Moon, and many of the solar system's\nminor bodies. This volatile loss is expected to occur when the bodies are\nheated by planetary collisions and short-lived radionuclides, and enter a\nglobal magma ocean stage early in their history. The bulk composition of\nexo-planetary bodies can be determined by observing white dwarfs which have\naccreted planetary material. The abundances of Na, Mn, and Mg have been\nmeasured for the accreting material in four polluted white dwarf systems.\nWhilst the Mn/Na abundances of three white dwarf systems are consistent with\nthe fractionations expected during nebula condensation, the high Mn/Na\nabundance ratio of GD362 means that it is not (>3 sigma). We find that heating\nof the planetary system orbiting GD362 during the star's giant branch evolution\nis insufficient to produce such a high Mn/Na. We, therefore, propose that\nvolatile loss occurred in a manner analogous to that of the solar system\nbodies, either due to impacts shortly after their formation or from heating by\nshort-lived radionuclides. We present potential evidence for a magma ocean\nstage on the exo-planetary body which currently pollutes the atmosphere of\nGD362.\n"}
+{"abstract": "  Patch lattices, introduced by G. Cz\\'edli and E.T. Schmidt in 2013, are the\nbuilding stones for slim (and so necessarily finite and planar) semimodular\nlattices with respect to gluing. Slim semimodular lattices were introduced by\nG. Gr\\\"atzer and E. Knapp in 2007, and they have been intensively studied since\nthen. Outside lattice theory, these lattices played the main role in adding a\nuniqueness part to the classical Jordan--H\\\"older theorem for groups by G.\nCz\\'edli and E.T. Schmidt in 2011, and they also led to results in\ncombinatorial geometry. In this paper, we prove that slim patch lattices are\nexactly the absolute retracts with more than two elements for the category of\nslim semimodular lattices with length-preserving lattice embeddings as\nmorphisms. Also, slim patch lattices are the same as the maximal objects $L$ in\nthis category such that $|L|>2$. Furthermore, slim patch lattices are\ncharacterized as the algebraically closed lattices $L$ in this category such\nthat $|L|>2$. Finally, we prove that if we consider $\\{0,1\\}$-preserving\nlattice homomorphisms rather than length-preserving ones, then the absolute\nretracts for the class of slim semimodular lattices are the at most 4-element\nboolean lattices.\n"}
+{"abstract": "  We study first order phase transitions in Randall-Sundrum models in the early\nuniverse dual to confinement in large-$N$ gauge theories. The transition rate\nto the confined phase is suppressed by a factor $\\exp(-N^2)$, and may not\ncomplete for $N \\gg 1$, instead leading to an eternally inflating phase. To\navoid this fate, the resulting constraint on $N$ makes the RS effective field\ntheory only marginally under control. We present a mechanism where the IR brane\nremains stabilized at very high temperature, so that the theory stays in the\nconfined phase at all times after inflation and reheating. We call this\nmechanism avoided deconfinement. The mechanism involves adding new scalar\nfields on the IR brane which provide a stablilizing contribution to the radion\npotential at finite temperature, in a spirit similar to Weinberg's symmetry\nnon-restoration mechanism. Avoided deconfinement allows for a viable cosmology\nfor theories with parametrically large $N$. Early universe cosmological\nphenomena such as WIMP freeze-out, axion abundance, baryogenesis, phase\ntransitions, and gravitational wave signatures are qualitatively modified.\n"}
+{"abstract": "  This paper presents TS2Vec, a universal framework for learning\nrepresentations of time series in an arbitrary semantic level. Unlike existing\nmethods, TS2Vec performs contrastive learning in a hierarchical way over\naugmented context views, which enables a robust contextual representation for\neach timestamp. Furthermore, to obtain the representation of an arbitrary\nsub-sequence in the time series, we can apply a simple aggregation over the\nrepresentations of corresponding timestamps. We conduct extensive experiments\non time series classification tasks to evaluate the quality of time series\nrepresentations. As a result, TS2Vec achieves significant improvement over\nexisting SOTAs of unsupervised time series representation on 125 UCR datasets\nand 29 UEA datasets. The learned timestamp-level representations also achieve\nsuperior results in time series forecasting and anomaly detection tasks. A\nlinear regression trained on top of the learned representations outperforms\nprevious SOTAs of time series forecasting. Furthermore, we present a simple way\nto apply the learned representations for unsupervised anomaly detection, which\nestablishes SOTA results in the literature. The source code is publicly\navailable at https://github.com/yuezhihan/ts2vec.\n"}
+{"abstract": "  In this paper we consider $ X(\\bar\\varphi)$ anisotropic symmetric space $\n2\\pi$ of periodic functions of $m$ variables, in particular, the generalized\nLorentz space $L_{\\bar{\\psi},\\bar{\\tau}}^{*}(\\mathbb{T}^{m})$ and\nNikol'skii--Besov's class $S_{X(\\bar{\\varphi}),\\bar{\\theta}}^{\\bar r}B$. The\narticle proves an embedding theorem for the Nikol'skii - Besov class in the\ngeneralized Lorentz space and establishes an upper bound for the best\napproximations by trigonometric polynomials with harmonic numbers from the\nhyperbolic cross of functions from the class\n$S_{X(\\bar{\\varphi}),\\bar{\\theta}}^{\\bar r}B$.\n"}
+{"abstract": "  Early detection and quantification of tumour growth would help clinicians to\nprescribe more accurate treatments and provide better surgical planning.\nHowever, the multifactorial and heterogeneous nature of lung tumour progression\nhampers identification of growth patterns. In this study, we present a novel\nmethod based on a deep hierarchical generative and probabilistic framework\nthat, according to radiological guidelines, predicts tumour growth, quantifies\nits size and provides a semantic appearance of the future nodule. Unlike\nprevious deterministic solutions, the generative characteristic of our approach\nalso allows us to estimate the uncertainty in the predictions, especially\nimportant for complex and doubtful cases. Results of evaluating this method on\nan independent test set reported a tumour growth balanced accuracy of 74%, a\ntumour growth size MAE of 1.77 mm and a tumour segmentation Dice score of 78%.\nThese surpassed the performances of equivalent deterministic and alternative\ngenerative solutions (i.e. probabilistic U-Net, Bayesian test dropout and\nPix2Pix GAN) confirming the suitability of our approach.\n"}
+{"abstract": "  Most studies for postselected weak measurement in optomechanical system focus\non using a single photon as a measured system. However, we find that using weak\ncoherent light instead of a single photon can also amplify the mirror's\nposition displacement of one photon. In the WVA regime (Weak Value\nAmplification regime), the weak value of one photon can lie outside the\neigenvalue spectrum, proportional to the difference of the mirror's position\ndisplacement between the successful and failed postselection and its successful\npostselection probability is dependent of the mean photon number of the\ncoherent light and improved by adjusting it accordingly. Outside the WVA\nregime, the amplification limit can reach the level of the vacuum fluctuations,\nand when the mean photon number and the optomechanical coupling parameter are\nselected appropriately, its successful postselection probability becomes\nhigher, which is beneficial to observe the maximum amplification value under\nthe current experimental conditions. This result breaks the constraint that it\nis difficult to detect outside the WVA regime. This opens up a new regime for\nthe study of a single photon nonlinearity in optomechanical system.\n"}
+{"abstract": "  We present a novel approach to the formation controlling of aerial robot\nswarms that demonstrates the flocking behavior. The proposed method stems from\nthe Unmanned Aerial Vehicle (UAV) dynamics; thus, it prevents any unattainable\ncontrol inputs from being produced and subsequently leads to feasible\ntrajectories. By modeling the inter-agent relationships using a pairwise energy\nfunction, we show that interacting robot swarms constitute a Markov Random\nField. Our algorithm builds on the Mean-Field Approximation and incorporates\nthe collective behavioral rules: cohesion, separation, and velocity alignment.\nWe follow a distributed control scheme and show that our method can control a\nswarm of UAVs to a formation and velocity consensus with real-time collision\navoidance. We validate the proposed method with physical and high-fidelity\nsimulation experiments.\n"}
+{"abstract": "  Determining which image regions to concentrate on is critical for\nHuman-Object Interaction (HOI) detection. Conventional HOI detectors focus on\neither detected human and object pairs or pre-defined interaction locations,\nwhich limits learning of the effective features. In this paper, we reformulate\nHOI detection as an adaptive set prediction problem, with this novel\nformulation, we propose an Adaptive Set-based one-stage framework (AS-Net) with\nparallel instances and interaction branches. To attain this, we map a trainable\ninteraction query set to an interaction prediction set with a transformer. Each\nquery adaptively aggregates the interaction-relevant features from global\ncontexts through multi-head co-attention. Besides, the training process is\nsupervised adaptively by matching each ground truth with the interaction\nprediction. Furthermore, we design an effective instance-aware attention module\nto introduce instructive features from the instance branch into the interaction\nbranch. Our method outperforms previous state-of-the-art methods without any\nextra human pose and language features on three challenging HOI detection\ndatasets. Especially, we achieve over $31\\%$ relative improvement on a\nlarge-scale HICO-DET dataset. Code is available at\nhttps://github.com/yoyomimi/AS-Net.\n"}
+{"abstract": "  Thermal evolution models suggest that the luminosities of both Uranus and\nNeptune are inconsistent with the classical assumption of an adiabatic\ninterior. Such models commonly predict Uranus to be brighter and, recently,\nNeptune to be fainter than observed. In this work, we investigate the influence\nof a thermally conductive boundary layer on the evolution of Uranus- and\nNeptune-like planets. This thermal boundary layer (TBL) is assumed to be\nlocated deep in the planet, and be caused by a steep compositional gradient\nbetween a H-He-dominated outer envelope and an ice-rich inner envelope. We\ninvestigate the effect of TBL thickness, thermal conductivity, and the time of\nTBL formation on the planet's cooling behaviour. The calculations were\nperformed with our recently developed tool based on the Henyey method for\nstellar evolution. We make use of state-of-the-art equations of state for\nhydrogen, helium, and water, as well as of thermal conductivity data for water\ncalculated via ab initio methods. We find that even a thin conductive layer of\na few kilometres has a significant influence on the planetary cooling. In our\nmodels, Uranus' measured luminosity can only be reproduced if the planet has\nbeen near equilibrium with the solar incident flux for an extended time. For\nNeptune, we find a range of solutions with a near constant effective\ntemperature at layer thicknesses of 15 km or larger, similar to Uranus. In\naddition, we find solutions for thin TBLs of few km and strongly enhanced\nthermal conductivity. A $\\sim$ 1$~$Gyr later onset of the TBL reduces the\npresent $\\Delta T$ by an order of magnitude to only several 100 K. Our models\nsuggest that a TBL can significantly influence the present planetary luminosity\nin both directions, making it appear either brighter or fainter than the\nadiabatic case.\n"}
+{"abstract": "  In this paper, we want to prove positive mass theorems for ALF and ALG\nmanifolds with model spaces $\\mathbb R^{n-1}\\times \\mathbb S^1$ and $\\mathbb\nR^{n-2}\\times \\mathbb T^2$ respectively in dimensions no greater than $7$\n(Theorem \\ref{ALFPMT0}). { Different from the compatibility condition for spin\nstructure in \\cite[Theorem 2]{minerbe2008a}, we show that some type of\nincompressible condition for $\\mathbb S^1$ and $\\mathbb T^2$ is enough to\nguarantee the nonnegativity of the mass.} As in the asymptotically flat case,\nwe reduce the desired positive mass theorems to those ones concerning\nnon-existence of positive scalar curvature metrics on closed manifolds coming\nfrom generalize surgery to $n$-torus. { Finally, we investigate certain fill-in\nproblems and obtain an optimal bound for total mean curvature of admissible\nfill-ins for flat product $2$-torus $\\mathbb S^1(l_1)\\times \\mathbb S^1(l_2)$.}\n"}
+{"abstract": "  Transient stability assessment (TSA) is a cornerstone for resilient\noperations of today's interconnected power grids. This paper is a confluence of\nquantum computing, data science and machine learning to potentially address the\npower system TSA challenge. We devise a quantum TSA (qTSA) method to enable\nscalable and efficient data-driven transient stability prediction for bulk\npower systems, which is the first attempt to tackle the TSA issue with quantum\ncomputing. Our contributions are three-fold: 1) A low-depth, high\nexpressibility quantum neural network for accurate and noise-resilient TSA; 2)\nA quantum natural gradient descent algorithm for efficient qTSA training; 3) A\nsystematical analysis on qTSA's performance under various quantum factors. qTSA\nunderpins a foundation of quantum-enabled and data-driven power grid stability\nanalytics. It renders the intractable TSA straightforward and effortless in the\nHilbert space, and therefore provides stability information for power system\noperations. Extensive experiments on quantum simulators and real quantum\ncomputers verify the accuracy, noise-resilience, scalability and universality\nof qTSA.\n"}
+{"abstract": "  Fake news on social media has become a hot topic of research as it negatively\nimpacts the discourse of real news in the public. Specifically, the ongoing\nCOVID-19 pandemic has seen a rise of inaccurate and misleading information due\nto the surrounding controversies and unknown details at the beginning of the\npandemic. The FakeNews task at MediaEval 2020 tackles this problem by creating\na challenge to automatically detect tweets containing misinformation based on\ntext and structure from Twitter follower network. In this paper, we present a\nsimple approach that uses BERT embeddings and a shallow neural network for\nclassifying tweets using only text, and discuss our findings and limitations of\nthe approach in text-based misinformation detection.\n"}
+{"abstract": "  We measured $^{35}$Cl abundances in 52 M giants with metallicities between\n-0.5 $<$ [Fe/H] $<$ 0.12. Abundances and atmospheric parameters were derived\nusing infrared spectra from CSHELL on the IRTF and from optical echelle\nspectra. We measured Cl abundances by fitting a H$^{35}$Cl molecular feature at\n3.6985 $\\mu$m with synthetic spectra. We also measured the abundances of O, Ca,\nTi, and Fe using atomic absorption lines. We find that the [Cl/Fe] ratio for\nour stars agrees with chemical evolution models of Cl and the [Cl/Ca] ratio is\nbroadly consistent with the solar ratio over our metallicity range. Both\nindicate that Cl is primarily made in core-collapse supernovae with some\ncontributions from Type Ia SN. We suggest other potential nucleosynthesis\nprocesses, such as the $\\nu$-process, are not significant producers of Cl.\nFinally, we also find our Cl abundances are consistent with H II and planetary\nnebular abundances at a given oxygen abundance, although there is scatter in\nthe data.\n"}
+{"abstract": "  H\\\"ormander's propagation of singularities theorem does not fully describe\nthe propagation of singularities in subelliptic wave equations, due to the\nexistence of doubly characteristic points. In the present paper, building upon\na visionary conference paper by R. Melrose \\cite{Mel86}, we prove that\nsingularities of subelliptic wave equations only propagate along\nnull-bicharacteristics and abnormal extremal lifts of singular curves, which\nare well-known curves in optimal control theory. We first revisit in depth the\nideas sketched by R. Melrose in \\cite{Mel86}, notably providing a full proof of\nits main statement. Making more explicit computations, we then explain how\nsub-Riemannian geometry and abnormal extremals come into play. This result\nshows that abnormal extremals have an important role in the classical-quantum\ncorrespondence between sub-Riemannian geometry and subelliptic operators. As a\nconsequence, for $x\\neq y$ and denoting by $K_G$ the wave kernel, we obtain\nthat the singular support of the distribution $t\\mapsto K_G(t,x,y)$ is included\nin the set of lengths of the normal geodesics joining $x$ and $y$, at least up\nto the time equal to the minimal length of a singular curve joining $x$ and\n$y$.\n"}
+{"abstract": "  The world needs around 150 Pg of negative carbon emissions to mitigate\nclimate change. Global soils may provide a stable, sizeable reservoir to help\nachieve this goal by sequestering atmospheric carbon dioxide as soil organic\ncarbon (SOC). In turn, SOC can support healthy soils and provide a multitude of\necosystem benefits. To support SOC sequestration, researchers and policy makers\nmust be able to precisely measure the amount of SOC in a given plot of land.\nSOC measurement is typically accomplished by taking soil cores selected at\nrandom from the plot under study, mixing (compositing) some of them together,\nand analyzing (assaying) the composited samples in a laboratory. Compositing\nreduces assay costs, which can be substantial. Taking samples is also costly.\nGiven uncertainties and costs in both sampling and assay along with a desired\nestimation precision, there is an optimal composite size that will minimize the\nbudget required to achieve that precision. Conversely, given a fixed budget,\nthere is a composite size that minimizes uncertainty. In this paper, we\ndescribe and formalize sampling and assay for SOC and derive the optima for\nthree commonly used assay methods: dry combustion in an elemental analyzer,\nloss-on-ignition, and mid-infrared spectroscopy. We demonstrate the utility of\nthis approach using data from a soil survey conducted in California. We give\nrecommendations for practice and provide software to implement our framework.\n"}
+{"abstract": "  The search of close (a<=5 au) giant planet(GP) companions with radial\nvelocity(RV) around young stars and the estimate of their occurrence rates is\nimportant to constrain the migration timescales. Furthermore, this search will\nallow the giant planet occurrence rates to be computed at all separations via\nthe combination with direct imaging techniques. The RV search around young\nstars is a challenge as they are generally faster rotators than older stars of\nsimilar spectral types and they exhibit signatures of spots or pulsation in\ntheir RV time series. Specific analyses are necessary to characterize, and\npossibly correct for, this activity. Our aim is to search for planets around\nyoung nearby stars and to estimate the GP occurrence rates for periods up to\n1000 days. We used the SOPHIE spectrograph to observe 63 A-M young (<400 Myr)\nstars. We used our SAFIR software to compute the RVs and other spectroscopic\nobservables. We then combined this survey with the HARPS YNS survey to compute\nthe companion occurrence rates on a total of 120 young A-M stars. We report one\nnew trend compatible with a planetary companion on HD109647. We also report\nHD105693 and HD112097 as binaries, and we confirm the binarity of HD2454,\nHD13531, HD17250A, HD28945, HD39587, HD131156, HD 142229, HD186704A, and HD\n195943. We constrained for the first time the orbital parameters of HD195943B.\nWe refute the HD13507 single brown dwarf (BD) companion solution and propose a\ndouble BD companion solution. Based on our sample of 120 young stars, we obtain\na GP occurrence rate of 1_{-0.3}^{+2.2}% for periods lower than 1000 days, and\nwe obtain an upper limit on BD occurrence rateof 0.9_{-0.9}^{+2}% in the same\nperiod range. We report a possible lack of close (1<P<1000 days) GPs around\nyoung FK stars compared to their older counterparts, with a confidence level of\n90%.\n"}
+{"abstract": "  The current status and future prospects of searches for axion-like particles\n(ALPs) at colliders, mostly focused on the CERN LHC, are summarized.\nConstraints on ALPs with masses above a few GeV that couple to photons, as well\nas to Z or Higgs bosons, have been set at the LHC through searches for new\n$a\\to\\gamma\\gamma$ resonances in di-, tri-, and four-photon final states.\nInclusive and exclusive diphotons in proton-proton and lead-lead collisions,\npp, PbPb $\\to a \\to \\gamma\\gamma (+X)$, as well as exotic Z and Higgs boson\ndecays, pp $\\to \\mathrm{Z},\\mathrm{H}\\to a\\gamma \\to 3\\gamma$ and pp $\\to\n\\mathrm{H}\\to aa \\to 4\\gamma$, have been analyzed. Exclusive searches in PbPb\ncollisions provide the best exclusion limits for ALP masses $m_a\\approx 5-$100\nGeV, whereas the other channels are the most competitive ones over $m_a\\approx\n100$ GeV$-$2.6 TeV. Integrated ALP production cross sections up to $\\sim$100 nb\nare excluded at 95% confidence level, corresponding to constraints on\naxion-photon couplings down to $g_{a\\gamma}\\approx$ 0.05 TeV$^{-1}$, over broad\nmass ranges. Factors of 10$-$100 improvements in these limits are expected at\nthe LHC approaching $g_{a\\gamma}\\approx 10^{-3}$ TeV$^{-1}$ over $m_a\\approx 1$\nGeV$-$5 TeV in the next decade.\n"}
+{"abstract": "  Using only linear optical elements, the creation of dual-rail photonic\nentangled states is inherently probabilistic. Known entanglement generation\nschemes have low success probabilities, requiring large-scale multiplexing to\nachieve near-deterministic operation of quantum information processing\nprotocols. In this paper, we introduce multiple techniques and methods to\ngenerate photonic entangled states with high probability, which have the\npotential to reduce the footprint of Linear Optical Quantum Computing (LOQC)\narchitectures drastically. Most notably, we are showing how to improve Bell\nstate preparation from four single photons to up to p=2/3, boost Type-I fusion\nto 75% with a dual-rail Bell state ancilla and improve Type-II fusion beyond\nthe limits of Bell state discrimination.\n"}
+{"abstract": "  Computational meshes arising from shape optimization routines commonly suffer\nfrom decrease of mesh quality or even destruction of the mesh. In this work, we\nprovide an approach to regularize general shape optimization problems to\nincrease both shape and volume mesh quality. For this, we employ pre-shape\ncalculus (cf. arXiv:2012.09124). Existence of regularized solutions is\nguaranteed. Further, consistency of modified pre-shape gradient systems is\nestablished. We present pre-shape gradient system modifications, which permit\nsimultaneous shape optimization with mesh quality improvement. Optimal shapes\nto the original problem are left invariant under regularization. The\ncomputational burden of our approach is limited, since additional solution of\npossibly larger (non-)linear systems for regularized shape gradients is not\nnecessary. We implement and compare pre-shape gradient regularization\napproaches for a hard to solve 2D problem. As our approach does not depend on\nthe choice of metrics representing shape gradients, we employ and compare\nseveral different metrics.\n"}
+{"abstract": "  The Tibet AS$\\gamma$ experiment has measured $\\gamma$-ray flux of supernova\nremnant G106.3+2.7 up to 100 TeV, suggesting it {being} potentially a\n\"PeVatron\". Challenge arises when the hadronic scenario requires a hard proton\nspectrum (with spectral index $\\approx 1.8$), while {usual observations and\nnumerical simulations prefer} a soft proton spectrum {(with spectral index\n$\\geq 2$)}. In this paper, we explore an alternative scenario to explain the\n$\\gamma$-ray spectrum of G106.3+2.7 within the current understanding of\nacceleration and escape processes. We consider that the cosmic ray {particles}\nare scattered by the turbulence driven via Bell instability. The resulting\nhadronic $\\gamma$-ray spectrum is novel, dominating the contribution to the\nemission above 10\\,TeV, and can explain the bizarre broadband spectrum of\nG106.3+2.7 in combination with leptonic emission from the remnant.\n"}
+{"abstract": "  Online learning with expert advice is widely used in various machine learning\ntasks. It considers the problem where a learner chooses one from a set of\nexperts to take advice and make a decision. In many learning problems, experts\nmay be related, henceforth the learner can observe the losses associated with a\nsubset of experts that are related to the chosen one. In this context, the\nrelationship among experts can be captured by a feedback graph, which can be\nused to assist the learner's decision making. However, in practice, the nominal\nfeedback graph often entails uncertainties, which renders it impossible to\nreveal the actual relationship among experts. To cope with this challenge, the\npresent work studies various cases of potential uncertainties, and develops\nnovel online learning algorithms to deal with uncertainties while making use of\nthe uncertain feedback graph. The proposed algorithms are proved to enjoy\nsublinear regret under mild conditions. Experiments on real datasets are\npresented to demonstrate the effectiveness of the novel algorithms.\n"}
+{"abstract": "  The recursive expansion of tree level multitrace Einstein-Yang-Mills (EYM)\namplitudes induces a refined graphic expansion, by which any tree-level EYM\namplitude can be expressed as a summation over all possible refined graphs.\nEach graph contributes a unique coefficient as well as a proper combination of\ncolor-ordered Yang-Mills (YM) amplitudes. This expansion allows one to evaluate\nEYM amplitudes through YM amplitudes, the latter have much simpler structures\nin four dimensions than the former. In this paper, we classify the refined\ngraphs for the expansion of EYM amplitudes into $\\text{N}^{\\,k}$MHV sectors.\nAmplitudes in four dimensions, which involve $k+2$ negative-helicity particles,\nat most get non-vanishing contribution from graphs in $\\text{N}^{\\,k'(k'\\leq\nk)}$MHV sectors. By the help of this classification, we evaluate the\nnon-vanishing amplitudes with two negative-helicity particles in four\ndimensions. We establish a correspondence between the refined graphs for\nsingle-trace amplitudes with $(g^-_i,g^-_j)$ or $(h^-_i,g^-_j)$ configuration\nand the spanning forests of the known Hodges determinant form. Inspired by this\ncorrespondence, we further propose a symmetric formula of double-trace\namplitudes with $(g^-_i,g^-_j)$ configuration. By analyzing the cancellation\nbetween refined graphs in four dimensions, we prove that any other tree\namplitude with two negative-helicity particles has to vanish.\n"}
+{"abstract": "  Control barrier functions (CBFs) have recently become a powerful method for\nrendering desired safe sets forward invariant in single- and multi-agent\nsystems. In the multi-agent case, prior literature has considered scenarios\nwhere all agents cooperate to ensure that the corresponding set remains\ninvariant. However, these works do not consider scenarios where a subset of the\nagents are behaving adversarially with the intent to violate safety bounds. In\naddition, prior results on multi-agent CBFs typically assume that control\ninputs are continuous and do not consider sampled-data dynamics. This paper\npresents a framework for normally-behaving agents in a multi-agent system with\nheterogeneous control-affine, sampled-data dynamics to render a safe set\nforward invariant in the presence of adversarial agents. The proposed approach\nconsiders several aspects of practical control systems including input\nconstraints, clock asynchrony and disturbances, and distributed calculation of\ncontrol inputs. Our approach also considers functions describing safe sets\nhaving high relative degree with respect to system dynamics. The efficacy of\nthese results are demonstrated through simulations.\n"}
+{"abstract": "  In this paper, we investigate over-the-air model aggregation in a federated\nedge learning (FEEL) system. We introduce a Markovian probability model to\ncharacterize the intrinsic temporal structure of the model aggregation series.\nWith this temporal probability model, we formulate the model aggregation\nproblem as to infer the desired aggregated update given all the past\nobservations from a Bayesian perspective. We develop a message passing based\nalgorithm, termed temporal-structure-assisted gradient aggregation (TSA-GA), to\nfulfil this estimation task with low complexity and near-optimal performance.\nWe further establish the state evolution (SE) analysis to characterize the\nbehaviour of the proposed TSA-GA algorithm, and derive an explicit bound of the\nexpected loss reduction of the FEEL system under certain standard regularity\nconditions. In addition, we develop an expectation maximization (EM) strategy\nto learn the unknown parameters in the Markovian model. We show that the\nproposed TSAGA algorithm significantly outperforms the state-of-the-art, and is\nable to achieve comparable learning performance as the error-free benchmark in\nterms of both convergence rate and final test accuracy.\n"}
+{"abstract": "  In 2019, Reyes & Wright used the NASA Astrophysics Data System (ADS) to\ninitiate a comprehensive bibliography for SETI accessible to the public. Since\nthen, updates to the library have been incomplete, partly due to the difficulty\nin managing the large number of false positive publications generated by\nsearching ADS using simple search terms. In preparation for a recent update,\nthe scope of the library was revised and reexamined. The scope now includes\nsocial sciences and commensal SETI. Results were curated based on five SETI\nkeyword searches: \"SETI\", \"technosignature\", \"Fermi Paradox,\" \"Drake Equation\",\nand \"extraterrestrial intelligence.\" These keywords returned 553 publications\nthat merited inclusion in the bibliography that were not previously present. A\ncurated library of false positive results is now concurrently maintained to\nfacilitate their exclusion from future searches. A search query and workflow\nwas developed to capture nearly all SETI-related papers indexed by ADS while\nminimizing false positives. These tools will enable efficient, consistent\nupdates of the SETI library by future curators, and could be adopted for other\nbibliography projects as well.\n"}
+{"abstract": "  The article is devoted to the development of numerical methods for solving\nsaddle point problems and variational inequalities with simplified requirements\nfor the smoothness conditions of functionals. Recently there were proposed some\nnotable methods for optimization problems with strongly monotone operators. Our\nfocus here is on newly proposed techniques for solving strongly convex-concave\nsaddle point problems. One of the goals of the article is to improve the\nobtained estimates of the complexity of introduced algorithms by using\naccelerated methods for solving auxiliary problems. The second focus of the\narticle is introducing an analogue of the boundedness condition for the\noperator in the case of arbitrary (not necessarily Euclidean) prox structure.\nWe propose an analogue of the mirror descent method for solving variational\ninequalities with such operators, which is optimal in the considered class of\nproblems.\n"}
+{"abstract": "  We consider a neutrinophilic $U(1)$ extension of the standard model (SM)\nwhich couples only to SM isosinglet neutral fermions, charged under the new\ngroup. The neutral fermions couple to the SM matter fields through Yukawa\ninteractions. The neutrinos in the model get their masses from a standard\ninverse-seesaw mechanism while an added scalar sector is responsible for the\nbreaking of the gauged $U(1)$ leading to a light neutral gauge boson ($Z'$),\nwhich has minimal interaction with the SM sector. We study the phenomenology of\nhaving such a light $Z'$ in the context of neutrinophilic interactions as well\nas the role of allowing kinetic mixing between the new $U(1)$ group with the SM\nhypercharge group. We show that current experimental searches allow for a very\nlight $Z'$ if it does not couple to SM fields directly and highlight the search\nstrategies at the LHC. We observe that multilepton final states in the form of\n$(4\\ell + \\slashed{E}_T)$ and $(3\\ell + 2j + \\slashed{E}_T)$ could be crucial\nin discovering such a neutrinophilic gauge boson lying in a mass range of\n$200$--$500$ GeV.\n"}
+{"abstract": "  We show that the naive mean-field approximation correctly predicts the\nleading term of the logarithmic lower tail probabilities for the number of\ncopies of a given subgraph in $G(n,p)$ and of arithmetic progressions of a\ngiven length in random subsets of the integers in the entire range of densities\nwhere the mean-field approximation is viable.\n  Our main technical result provides sufficient conditions on the maximum\ndegrees of a uniform hypergraph $\\mathcal{H}$ that guarantee that the\nlogarithmic lower tail probabilities for the number of edges induced by a\nbinomial random subset of the vertices of $\\mathcal{H}$ can be\nwell-approximated by considering only product distributions. This may be\ninterpreted as a weak, probabilistic version of the hypergraph container lemma\nthat is applicable to all sparser-than-average (and not only independent) sets.\n"}
+{"abstract": "  Many studies are devoted to the design of radiomic models for a prediction\ntask. When no effective model is found, it is often difficult to know whether\nthe radiomic features do not include information relevant to the task or\nbecause of insufficient data. We propose a downsampling method to answer that\nquestion when considering a classification task into two groups. Using two\nlarge patient cohorts, several experimental configurations involving different\nnumbers of patients were created. Univariate or multivariate radiomic models\nwere designed from each configuration. Their performance as reflected by the\nYouden index (YI) and Area Under the receiver operating characteristic Curve\n(AUC) was compared to the stable performance obtained with the highest number\nof patients. A downsampling method is described to predict the YI and AUC\nachievable with a large number of patients. Using the multivariate models\ninvolving machine learning, YI and AUC increased with the number of patients\nwhile they decreased for univariate models. The downsampling method better\nestimated YI and AUC obtained with the largest number of patients than the YI\nand AUC obtained using the number of available patients and identifies the lack\nof information relevant to the classification task when no such information\nexists.\n"}
+{"abstract": "  The current role of data-driven science is constantly increasing its\nimportance within Astrophysics, due to the huge amount of multi-wavelength data\ncollected every day, characterized by complex and high-volume information\nrequiring efficient and as much as possible automated exploration tools.\nFurthermore, to accomplish main and legacy science objectives of future or\nincoming large and deep survey projects, such as JWST, LSST and Euclid, a\ncrucial role is played by an accurate estimation of photometric redshifts,\nwhose knowledge would permit the detection and analysis of extended and\npeculiar sources by disentangling low-z from high-z sources and would\ncontribute to solve the modern cosmological discrepancies. The recent\nphotometric redshift data challenges, organized within several survey projects,\nlike LSST and Euclid, pushed the exploitation of multi-wavelength and\nmulti-dimensional data observed or ad hoc simulated to improve and optimize the\nphotometric redshifts prediction and statistical characterization based on both\nSED template fitting and machine learning methodologies. But they also provided\na new impetus in the investigation on hybrid and deep learning techniques,\naimed at conjugating the positive peculiarities of different methodologies,\nthus optimizing the estimation accuracy and maximizing the photometric range\ncoverage, particularly important in the high-z regime, where the spectroscopic\nground truth is poorly available. In such a context we summarize what learned\nand proposed in more than a decade of research.\n"}
+{"abstract": "  Using a neural network potential (ANI-1ccx) generated from quantum data on a\nlarge data set of molecules and pairs of molecules, isothermal, constant volume\nsimulations demonstrate that the model can be as accurate as ab initio\nmolecular dynamics for simulations of pure liquid water and the aqueous\nsolvation of a methane molecule. No theoretical or experimental data for the\nliquid phase is used to train the model, suggesting that the ANI-1ccx approach\nis an effective method to link high level ab initio methods to potentials for\nlarge scale simulations.\n"}
+{"abstract": "  IEEE 802.11p standard defines wireless technology protocols that enable\nvehicular transportation and manage traffic efficiency. A major challenge in\nthe development of this technology is ensuring communication reliability in\nhighly dynamic vehicular environments, where the wireless communication\nchannels are doubly selective, thus making channel estimation and tracking a\nrelevant problem to investigate. In this paper, a novel deep learning\n(DL)-based weighted interpolation estimator is proposed to accurately estimate\nvehicular channels especially in high mobility scenarios. The proposed\nestimator is based on modifying the pilot allocation of the IEEE 802.11p\nstandard so that more transmission data rates are achieved. Extensive numerical\nexperiments demonstrate that the developed estimator significantly outperforms\nthe recently proposed DL-based frame-by-frame estimators in different vehicular\nscenarios, while substantially reducing the overall computational complexity.\n"}
+{"abstract": "  This work deals with a new generalization of $r$-Stirling numbers using\n$l$-tuple of permutations and partitions called $(l,r)$-Stirling numbers of\nboth kinds. We study various properties of these numbers using combinatorial\ninterpretations and symmetric functions. Also, we give a limit representation\nof the multiple zeta function using $(l,r)$-Stirling of the first kind.\n"}
+{"abstract": "  Low-mass ($M_{\\rm{500}}<5\\times10^{14}{\\rm{M_\\odot}}$) galaxy clusters have\nbeen largely unexplored in radio observations, due to the inadequate\nsensitivity of existing telescopes. However, the upgraded GMRT (uGMRT) and the\nLow Frequency ARray (LoFAR), with unprecedented sensitivity at low frequencies,\nhave paved the way to closely study less massive clusters than before. We have\nstarted the first large-scale programme to systematically search for diffuse\nradio emission from low-mass galaxy clusters, chosen from the Planck\nSunyaev-Zel'dovich cluster catalogue. We report here the detection of diffuse\nradio emission from four of the 12 objects in our sample, shortlisted from the\ninspection of the LoFAR Two Meter Sky Survey (LoTSS-I), followed up by uGMRT\nBand 3 deep observations. The clusters PSZ2~G089 (Abell~1904) and PSZ2~G111\n(Abell~1697) are detected with relic-like emission, while PSZ2~G106 is found to\nhave an intermediate radio halo and PSZ2~G080 (Abell~2018) seems to be a\nhalo-relic system. PSZ2~G089 and PSZ2~G080 are among the lowest-mass clusters\ndiscovered with a radio relic and a halo-relic system, respectively. A high\n($\\sim30\\%$) detection rate, with powerful radio emission ($P_{1.4\\ {\\rm\nGHz}}\\sim10^{23}~{\\rm{W~Hz^{-1}}}$) found in most of these objects, opens up\nprospects of studying radio emission in galaxy clusters over a wider mass\nrange, to much lower-mass systems.\n"}
+{"abstract": "  We study the strong magnetic field limit for a nonlinear Iwatsuka-type model,\ni.e. a nonlinear Schr\\\"odinger equation in two spatial dimensions with a\nmagnetic vector potential that only depends on the $x$-coordinate. Using a\nhigh-frequency averaging technique, we show that this equation can be\neffectively described by a nonlocal nonlinear model, which is no longer\ndispersive. We also prove that, in this asymptotic regime, inhomogeneous\nnonlinearities are confined along the $y$-axis.\n"}
+{"abstract": "  Answering in negative a question of M. Hru\\v{s}\\'ak, we construct a Borel\nideal not extendable to any $F_\\sigma$ ideal and such that it is not\nKat\\v{e}tov above the ideal $\\mathrm{conv}$.\n"}
+{"abstract": "  Mechanical disorder in solids, which is generated by a broad range of\nphysical processes and controls various material properties, appears in a wide\nvariety of forms. Defining unified and measurable dimensionless quantifiers,\nallowing quantitative comparison of mechanical disorder across widely different\nphysical systems, is therefore an important goal. Two such coarse-grained\ndimensionless quantifiers (among others) appear in the literature, one is\nrelated to the spectral broadening of discrete phononic bands in finite-size\nsystems (accessible through computer simulations) and the other is related the\nspatial fluctuations of the shear modulus in macroscopically large systems. The\nlatter has been recently shown to determine the amplitude of wave attenuation\nrates in the low-frequency limit (accessible through laboratory experiments).\nHere, using two alternative and complementary theoretical approaches linked to\nthe vibrational spectra of solids, we derive a basic scaling relation between\nthe two dimensionless quantifiers. This scaling relation, which is supported by\nsimulational data, shows that the two apparently distinct quantifiers are in\nfact intrinsically related, giving rise to a unified quantifier of mechanical\ndisorder in solids. We further discuss the obtained results in the context of\nthe unjamming transition taking place in soft sphere packings at low confining\npressures, in addition to their implications for our understanding of the\nlow-frequency vibrational spectra of disordered solids in general, and in\nparticular those of glassy systems.\n"}
+{"abstract": "  The problem of Bayesian reduced rank regression is considered in this paper.\nWe propose, for the first time, to use Langevin Monte Carlo method in this\nproblem. A spectral scaled Student prior distrbution is used to exploit the\nunderlying low-rank structure of the coefficient matrix. We show that our\nalgorithms are significantly faster than the Gibbs sampler in high-dimensional\nsetting. Simulation results show that our proposed algorithms for Bayesian\nreduced rank regression are comparable to the state-of-the-art method where the\nrank is chosen by cross validation.\n"}
+{"abstract": "  Multi-regional interaction among neuronal populations underlies the brain's\nprocessing of rich sensory information in our daily lives. Recent neuroscience\nand neuroimaging studies have increasingly used naturalistic stimuli and\nexperimental design to identify such realistic sensory computation in the\nbrain. However, existing methods for cross-areal interaction analysis with\ndimensionality reduction, such as reduced-rank regression and canonical\ncorrelation analysis, have limited applicability and interpretability in\nnaturalistic settings because they usually do not appropriately 'demix' neural\ninteractions into those associated with different types of task parameters or\nstimulus features (e.g., visual or audio). In this paper, we develop a new\nmethod for cross-areal interaction analysis that uses the rich task or stimulus\nparameters to reveal how and what types of information are shared by different\nneural populations. The proposed neural demixed shared component analysis\ncombines existing dimensionality reduction methods with a practical neural\nnetwork implementation of functional analysis of variance with latent\nvariables, thereby efficiently demixing nonlinear effects of continuous and\nmultimodal stimuli. We also propose a simplifying alternative under the\nassumptions of linear effects and unimodal stimuli. To demonstrate our methods,\nwe analyzed two human neuroimaging datasets of participants watching\nnaturalistic videos of movies and dance movements. The results demonstrate that\nour methods provide new insights into multi-regional interaction in the brain\nduring naturalistic sensory inputs, which cannot be captured by conventional\ntechniques.\n"}
+{"abstract": "  Speaker embeddings extracted with deep 2D convolutional neural networks are\ntypically modeled as projections of first and second order statistics of\nchannel-frequency pairs onto a linear layer, using either average or attentive\npooling along the time axis. In this paper we examine an alternative pooling\nmethod, where pairwise correlations between channels for given frequencies are\nused as statistics. The method is inspired by style-transfer methods in\ncomputer vision, where the style of an image, modeled by the matrix of\nchannel-wise correlations, is transferred to another image, in order to produce\na new image having the style of the first and the content of the second. By\ndrawing analogies between image style and speaker characteristics, and between\nimage content and phonetic sequence, we explore the use of such channel-wise\ncorrelations features to train a ResNet architecture in an end-to-end fashion.\nOur experiments on VoxCeleb demonstrate the effectiveness of the proposed\npooling method in speaker recognition.\n"}
+{"abstract": "  For a team of robots to work collaboratively, it is crucial that each robot\nhave the ability to determine the position of their neighbors, relative to\nthemselves, in order to execute tasks autonomously. This letter presents an\nalgorithm for determining the three-dimensional relative position between two\nmobile robots, each using nothing more than a single ultra-wideband\ntransceiver, an accelerometer, a rate gyro, and a magnetometer. A sliding\nwindow filter estimates the relative position at selected keypoints by\ncombining the distance measurements with acceleration estimates, which each\nagent computes using an on-board attitude estimator. The algorithm is\nappropriate for real-time implementation, and has been tested in simulation and\nexperiment, where it comfortably outperforms standard estimators. A positioning\naccuracy of less than 1 meter is achieved with inexpensive sensors.\n"}
+{"abstract": "  Room Temperature Ionic Liquids (RTILs) are molten salts which exhibit uniques\nphysical and chemical properties, commonly harnessed for lubrication and energy\napplications. The pure ionic nature of RTIL leads to strong electrostatic\ninteractions among the liquid, furthermore exalted in the presence of\ninterfaces and confinement. In this work, we use a tuning-fork based dynamic\nSurface Force Tribometer (TF-SFT), which allows probing both the rheological\nand the tribological properties of RTILs films confined between a millimetric\nsphere and a surface, over a wide range of confinements. When the RTIL is\nconfined between metallic surfaces, we evidence an abrupt change of its\nrheological properties below a threshold confinement. This is reminiscent of a\nrecently reported confinement induced capillary freezing, here observed with a\nwide contact area. In parallel, we probe the tribological response of the film\nunder imposed nanometric shear deformation and unveil a yielding behaviour of\nthe interfacial solid phase below this threshold confinement. This is\ncharacterized by a transition from an elastic to a plastic regime, exhibiting\nstriking similarities with the response of glassy materials. This transition to\nyielding of the RTIL in metallic confinement leads overall to a reduction in\nfriction and offers a self-healing protection of the surfaces avoiding direct\ncontact, with obvious applications in tribology.\n"}
+{"abstract": "  Recently, it has been shown theoretically that fluorescence microscopy using\nrandom illuminations (RIM) yields a doubled lateral resolution and an improved\noptical sectioning. Moreover, an algorithm called algoRIM, based on variance\nmatching, has been successfully validated on numerous biological applications.\nHere, we propose a proof of uniqueness of the RIM variance equation, which\ncorresponds to a first theoretical validation of algoRIM.\n"}
+{"abstract": "  Reliable evaluation of adversarial defenses is a challenging task, currently\nlimited to an expert who manually crafts attacks that exploit the defense's\ninner workings or approaches based on an ensemble of fixed attacks, none of\nwhich may be effective for the specific defense at hand. Our key observation is\nthat adaptive attacks are composed of reusable building blocks that can be\nformalized in a search space and used to automatically discover attacks for\nunknown defenses. We evaluated our approach on 24 adversarial defenses and show\nthat it outperforms AutoAttack, the current state-of-the-art tool for reliable\nevaluation of adversarial defenses: our tool discovered significantly stronger\nattacks by producing 3.0\\%-50.8\\% additional adversarial examples for 10\nmodels, while obtaining attacks with slightly stronger or similar strength for\nthe remaining models.\n"}
+{"abstract": "  In this paper we compare the experimental HERA data with the next-to-leading\norder approach (NLO) of Ref.[C.~Contreras, E.~Levin, R.~Meneses and\nM.~Sanhueza,Eur. Phys. J. C 80 (2020) no.11, 1029). This approach includes the\nre-summed NLO corrections to the kernel of the evolution equation, the correct\nasymptotic behaviour in the NLO at $\\tau = r^2 Q^2_s \\,\\gg\\,1$; the impact\nparameter dependence of the saturation scale in accord with the Froissarrt\ntheorem as well as the non-linear corrections. In this paper, we successfully\ndescribe the experimental data with the quality, which is not worse, than in\nthe leading order fits with larger number of the phenomenological parameters.\nIt is demonstrated, that the data could be described, taking into account both\nthe diffusion on $\\ln(k_T)$, which stems from perturbative QCD, and the\nGribov's diffusion in impact parameters. It is shown an ability to describe the\ndata at rather large values of $\\alpha_S$.\n"}
+{"abstract": "  Soft glassy materials such as mayonnaise, wet clays, or dense microgels\ndisplay under external shear a solid-to-liquid transition. Such a shear-induced\ntransition is often associated with a non-monotonic stress response, in the\nform of a stress maximum referred to as \"stress overshoot\". This ubiquitous\nphenomenon is characterized by the coordinates of the maximum in terms of\nstress $\\sigma_\\text{M}$ and strain $\\gamma_\\text{M}$ that both increase as\nweak power laws of the applied shear rate. Here we rationalize such power-law\nscalings using a continuum model that predicts two different regimes in the\nlimit of low and high applied shear rates. The corresponding exponents are\ndirectly linked to the steady-state rheology and are both associated with the\nnucleation and growth dynamics of a fluidized region. Our work offers a\nconsistent framework for predicting the transient response of soft glassy\nmaterials upon start-up of shear from the local flow behavior to the global\nrheological observables.\n"}
+{"abstract": "  Temporal correspondence - linking pixels or objects across frames - is a\nfundamental supervisory signal for the video models. For the panoptic\nunderstanding of dynamic scenes, we further extend this concept to every\nsegment. Specifically, we aim to learn coarse segment-level matching and fine\npixel-level matching together. We implement this idea by designing two novel\nlearning objectives. To validate our proposals, we adopt a deep siamese model\nand train the model to learn the temporal correspondence on two different\nlevels (i.e., segment and pixel) along with the target task. At inference time,\nthe model processes each frame independently without any extra computation and\npost-processing. We show that our per-frame inference model can achieve new\nstate-of-the-art results on Cityscapes-VPS and VIPER datasets. Moreover, due to\nits high efficiency, the model runs in a fraction of time (3x) compared to the\nprevious state-of-the-art approach.\n"}
+{"abstract": "  Lines and circles pose significant scalability challenges in synthetic\ngeometry. A line with $n$ points implies ${n \\choose 3}$ collinearity atoms, or\nalternatively, when lines are represented as functions, equality among ${n\n\\choose 2}$ different lines. Similarly, a circle with $n$ points implies ${n\n\\choose 4}$ cocyclicity atoms or equality among ${n \\choose 3}$ circumcircles.\nWe introduce a new mathematical concept of $k$-equivalence relations, which\ngeneralizes equality ($k=1$) and includes both lines ($k=2$) and circles\n($k=3$), and present an efficient proof-producing procedure to compute the\nclosure of a $k$-equivalence relation.\n"}
+{"abstract": "  We present a study of the incidence of active galactic nucleus (AGN) in a\nsample of major merging systems at 0.3<z<2.5. Galaxies in this merger sample\nhave projected separations between 3 to 15 kpc and are selected from the\nCANDELS/3D-HST catalogs using a peak-finding algorithm. AGNs in mergers and\nnon-mergers are identified on the basis of their X-ray emission, optical lines,\nmid-infrared colors, and radio emission. Among galaxies with adequate\nmeasurements to find potential AGNs, we find a similar fraction of AGNs in\nmergers (16.4%) compared to the fraction found in non-merging galaxies (15.4%).\nIn mergers, this fraction is obtained by assuming that, in unresolved\nobservations, only one of the merging galaxies is the AGN source. The\nsimilarity between the fractions is possibly due to the higher availability of\ncold gas at high redshifts, where the excess of nuclear activity as a result of\nmerging is less important than at lower redshifts. Star-forming galaxies have a\nhigher incidence of AGNs than quiescent galaxies. In particular, starbursts in\nmergers are the most common sites of AGN activity since they present higher AGN\nfractions and black hole accretion rates. We find no clear correlation between\nthe black hole accretion rate and the galaxy properties (i.e., star-formation\nrate, stellar mass) in mergers and non-mergers. However, mergers seem to have a\nhigher correlation with star formation than non-mergers, which possibly\nindicates that the merging process is starting to influence the star formation\nand AGN activity even at this pre-coalescence stage.\n"}
+{"abstract": "  We consider the weighted Sobolev spaces associated with non-isotropic\ndilations of Calder\\'on-Torchinsky and characterize the spaces by the square\nfunctions of Marcinkiewicz type including those defined with repeated uses of\naveraging operation.\n"}
+{"abstract": "  We forecast the reionization history constraints, inferred from Lyman-alpha\ndamping wing absorption features, for a future sample of $\\sim 20$ $z \\geq 6$\ngamma-ray burst (GRB) afterglows. We describe each afterglow spectrum by a\nthree-parameter model. First, L characterizes the size of the ionized region\n(the \"bubble size\") around a GRB host halo. Second, $\\langle{x_{\\rm\nHI}\\rangle}$ is the volume-averaged neutral fraction outside of the ionized\nbubble around the GRB, which is approximated as spatially uniform. Finally,\n$N_{\\mathrm{HI}}$ denotes the column-density of a local damped Lyman-alpha\nabsorber (DLA) associated with the GRB host galaxy. The size distribution of\nionized regions is extracted from a numerical simulation of reionization, and\nevolves strongly across the Epoch of Reionization (EoR). The model DLA column\ndensities follow the empirical distribution determined from current GRB\nafterglow spectra. We use a Fisher matrix formalism to forecast the\n$\\langle{x_{\\rm HI}(z)\\rangle}$ constraints that can be obtained from follow-up\nspectroscopy of afterglows with SNR = 20 per R=3,000 resolution element at the\ncontinuum. We find that the neutral fraction may be determined to better than\n10-15\\% (1-$\\sigma$) accuracy from this data across multiple independent\nredshift bins at $z \\sim 6-10$, spanning much of the EoR, although the\nprecision degrades somewhat near the end of reionization. A more futuristic\nsurvey with $80$ GRB afterglows at $z \\geq 6$ can improve the precision here by\na factor of $2$ and extend measurements out to $z \\sim 14$. We further discuss\nhow these constraints may be combined with estimates of the escape fraction of\nionizing photons, derived from the DLA column density distribution towards GRBs\nextracted at slightly lower redshift. This combination will help in testing\nwhether we have an accurate census of the sources that reionized the universe.\n"}
+{"abstract": "  The first chapter is a critical review and a case study in eBusiness, with\nspecial attention to the digital currencies resource and its possibilities. 2.\nchapter attempts to incorporate the UTAUT model with perceived risk theory to\nexplore its impact on the intention to use m-government services. 3. chapter\naims to assess the level of gender inclusivity in the municipal e-procurement\nprocesses in the City of Johannesburg as a case study. It uses a GAD approach.\n4. chapter examines the impediments that derail the intensive uptake of\neLearning programmes in a particular higher education institution. The study\nadopted an inductive research paradigm that followed a qualitative research\nstrategy. Data were collected by means of one-on-one in-depth interviews from\nselected faculty members at a nominated institution of higher learning. 5.\nchapter investigated the role of KMS in enhancing the export performance of\nfirms operating within the manufacturing sector in Zimbabwe. The study used a\nquantitative approach in which a survey questionnaire was distributed to 555\nmanagers drawn from 185 manufacturing firms based in Harare. Data analyses\ninvolved the use of descriptive statistics, Spearman correlations and\nregression analysis. In the sixth chapter, a survey was undertaken on 131 SMEs\nfrom the Pelagonija region in order to determine the current level of SME\ndigitalization within the region. It is aimed to compare with the EU average\nand to make conclusions on the impact of the SME digitalization on region GDP\ngrowth as well as revenues collection. The last chapter s purpose was to\ndevelop a measuring and modelling framework, an instrument of IBSQ for the\nSouth African banking sector. Snowball and convenience sampling, both\nnon-probability techniques were used to recruit participants for the study. A\ntotal of 310 Internet banking customer responses were utilised in the analysis.\n"}
+{"abstract": "  An almost Fuchsian manifold is a hyperbolic 3-manifold of the type $S\\times\n\\mathbb{R}$ which admits a closed minimal surface (homeomorphic to $S$) with\nthe maximum principal curvature $\\lambda_0 <1$, while a weakly almost Fuchsian\nmanifold is of the same type but it admits a closed minimal surface with\n$\\lambda_0 <= 1$. We first prove that any weakly almost Fuchsian manifold is in\nfact quasi-Fuchsian, and we construct a Canary-Storm type compactification for\nthe weakly almost Fuchsian space. We apply these results to prove uniform upper\nbounds on the volume of the convex core and Hausdorff dimension for the limit\nset of weakly almost Fuchsian manifolds and to give examples of quasi-Fuchsian\nmanifolds which admit unique minimal surfaces (resp. stable minimal surfaces)\nwithout being almost Fuchsian (resp. weakly almost Fuchsian). We also show that\nfor every $g$ there is an $\\epsilon$ depending only on $g$ such that if a\nclosed hyperbolic 3-manifold fibers over the circle with fiber a surface of\ngenus $g$, then any embedded minimal surface isotopic to the fiber has the\nmaximum principal curvature $\\lambda_0$ larger than $1+\\epsilon$.\n"}
+{"abstract": "  We solve time-harmonic Maxwell's equations in anisotropic, spatially\nhomogeneous media in intersections of $L^p$-spaces. The material laws are\ntime-independent. The analysis requires Fourier restriction-extension estimates\nfor perturbations of Fresnel's wave surface. This surface can be decomposed\ninto finitely many components of the following three types: smooth surfaces\nwith non-vanishing Gaussian curvature, smooth surfaces with Gaussian curvature\nvanishing along one-dimensional submanifolds, but without flat points, and\nsurfaces with conical singularities. Our estimates are based on new\nBochner-Riesz estimates with negative index for non-elliptic surfaces.\n"}
+{"abstract": "  Previously we constructed Calabi-Yau threefolds by a differential-geometric\ngluing method using Fano threefolds with their smooth anticanonical $K3$\ndivisors in arXiv:1305.0074. In this article we further consider the\ndiffeomorphic types of the resulting Calabi-Yau threefolds starting from\ndifferent pairs of Fano threefolds of Picard number one.\n"}
+{"abstract": "  Electric field measurements of the Time Domain Sampler (TDS) receiver, part\nof the Radio and Plasma Waves (RPW) instrument on board Solar Orbiter, often\nexhibit very intense broadband wave emissions at frequencies below 20~kHz in\nthe spacecraft frame. In this paper, we present a year-long study of\nelectrostatic fluctuations observed in the solar wind at an interval of\nheliocentric distances from 0.5 to 1~AU. The RPW/TDS observations provide a\nnearly continuous data set for a statistical study of intense waves below the\nlocal plasma frequency. The on-board and continuously collected and processed\nproperties of waveform snapshots allow for the mapping plasma waves at\nfrequencies between 200~Hz and 20~kHz. We used the triggered waveform snapshots\nand a Doppler-shifted solution of the dispersion relation for wave mode\nidentification in order to carry out a detailed spectral and polarization\nanalysis. Electrostatic ion-acoustic waves are the common wave emissions\nobserved between the local electron and proton plasma frequency in the soler\nwind. The occurrence rate of ion-acoustic waves peaks around perihelion at\ndistances of 0.5~AU and decreases with increasing distances, with only a few\nwaves detected per day at 0.9~AU. Waves are more likely to be observed when the\nlocal proton moments and magnetic field are highly variable. A more detailed\nanalysis of more than 10000 triggered waveform snapshots shows the mean wave\nfrequency at about 3 kHz and wave amplitude about 2.5 mV/m. The wave amplitude\nvaries as 1/R^(1.38) with the heliocentric distance. The relative phase\ndistribution between two components of the E-field shows a mostly linear wave\npolarization. Electric field fluctuations are closely aligned with the\ndirections of the ambient field lines. Only a small number (3%) of ion-acoustic\nwaves are observed at larger magnetic discontinuities.\n"}
+{"abstract": "  Human mobility is crucial to understand the transmission pattern of COVID-19\non spatially embedded geographic networks. This pattern seems unpredictable,\nand the propagation appears unstoppable, resulting in over 350,000 death tolls\nin the U.S. by the end of 2020. Here, we create the spatiotemporal inter-county\nmobility network using 10 TB (Terabytes) trajectory data of 30 million smart\ndevices in the U.S. in the first six months of 2020. We investigate its bound\npercolation by removing the weakly connected edges. The mobility network\nbecomes vulnerable and prone to reach its criticality and thus experience\nsurprisingly abrupt phase transitions. Despite the complex behaviors of the\nmobility network, we devised a novel approach to identify a small, manageable\nset of recurrent critical bridges, connecting the giant component and the\nsecond-largest component. These adaptive links, located across the United\nStates, played a key role as valves connecting components in divisions and\nregions during the pandemic. Beyond, our numerical results unveil that network\ncharacteristics determine the critical thresholds and the bridge locations. The\nfindings provide new insights into managing and controlling the connectivity of\nmobility networks during unprecedented disruptions. The work can also\npotentially offer practical future infectious diseases both globally and\nlocally.\n"}
+{"abstract": "  In the perovskite oxide SrCrO$_{3}$ the interplay between crystal structure,\nstrain and orbital ordering enables a transition from a metallic to an\ninsulating electronic structure under certain conditions. We identified a\nnarrow window of oxygen partial pressure in which highly strained SrCrO$_{3}$\nthin films can be grown using radio-frequency (RF) off-axis magnetron\nsputtering on three different substrates,\n(LaAlO$_{3}$)$_{0.3}$-(Sr$_{2}$TaAlO$_{6}$)$_{0.7}$ (LSAT), SrTiO$_{3}$ (STO)\nand DyScO$_{3}$ (DSO). X-ray diffraction and atomic force microscopy confirmed\nthe quality of the films and a metal-insulator transition driven by the\nsubstrate induced strain was demonstrated.\n"}
+{"abstract": "  In this survey we provide an overview of our recent results concerning Ricci\nde Turck flow on spaces with isolated conical singularities. The crucial\ncharacteristic of the flow is that it preserves the conical singularity. Under\ncertain conditions, Ricci flat metrics with isolated conical singularities are\nstable and positive scalar curvature is preserved under the flow. We also\ndiscuss the relation to Perelman's entropies in the singular setting, and\noutline open questions and future reseach directions.\n"}
+{"abstract": "  For given quantum (non-commutative) spaces $\\mathbb{P}$ and $\\mathbb{O}$ we\nstudy the quantum space of maps $\\mathbb{M}_{\\mathbb{P},\\mathbb{O}}$ from\n$\\mathbb{P}$ to $\\mathbb{O}$. In case of finite quantum spaces these objects\nturn out to be behind a large class of maps which generalize the classical\n$\\mathrm{qc}$-correlations known from quantum information theory to the setting\nof quantum input and output sets. We prove a number of important functorial\nproperties of the mapping\n$(\\mathbb{P},\\mathbb{O})\\mapsto\\mathbb{M}_{\\mathbb{P},\\mathbb{O}}$ and use them\nto study various operator algebraic properties of the $\\mathrm{C}^*$-algebras\n$\\operatorname{C}(\\mathbb{M}_{\\mathbb{P},\\mathbb{O}})$ such as the lifting\nproperty and residual finite dimensionality. Inside\n$\\operatorname{C}(\\mathbb{M}_{\\mathbb{P},\\mathbb{O}})$ we construct a universal\noperator system $\\mathbb{S}_{\\mathbb{P},\\mathbb{O}}$ related to $\\mathbb{P}$\nand $\\mathbb{O}$ and show, among other things, that the embedding\n$\\mathbb{S}_{\\mathbb{P},\\mathbb{O}}\\subset\\operatorname{C}(\\mathbb{M}_{\\mathbb{P},\\mathbb{O}})$\nis hyperrigid, $\\operatorname{C}(\\mathbb{M}_{\\mathbb{P},\\mathbb{O}})$ is the\n$\\mathrm{C}^*$-envelope of $\\mathbb{S}_{\\mathbb{P},\\mathbb{O}}$ and that a\nlarge class of non-signalling correlations on the quantum sets $\\mathbb{P}$ and\n$\\mathbb{O}$ arise from states on\n$\\operatorname{C}(\\mathbb{M}_{\\mathbb{P},\\mathbb{O}})\\otimes_{\\rm{max}}\\operatorname{C}(\\mathbb{M}_{\\mathbb{P},\\mathbb{O}})$\nas well as states on the commuting tensor product\n$\\mathbb{S}_{\\mathbb{P},\\mathbb{O}}\\otimes_{\\rm{c}}\\mathbb{S}_{\\mathbb{P},\\mathbb{O}}$.\nFinally we introduce and study the notion of a synchronous correlation with\nquantum input and output sets, prove several characterizations of such\ncorrelations and their relation to traces on\n$\\operatorname{C}(\\mathbb{M}_{\\mathbb{P},\\mathbb{O}})$.\n"}
+{"abstract": "  Along with the increasing popularity of agile software development, software\nwork has become much more social than ever. Contemporary software teams rely on\na variety of collaborative practices, such as pair programming, the topic of\nour study. Many agilists advocated the importance of collocation, face-to-face\ninteraction, and physical artefacts incorporated in the shared workspace, which\nthe COVID-19 pandemic made unavailable; most software companies around the\nworld were forced to send their engineers to work from home. As software\nprojects and teams overnight turned into dis-tributed collaborations, we\nquestion what happened to the pair programming practice in the work-from-home\nmode. This paper reports on a longitudinal study of remote pair programming in\ntwo companies. We conducted 38 interviews with 30 engineers from Norway,\nSweden, and the USA, and used the results of a survey in one of the case\ncompanies. Our study is unique as we collected the data longitudinally in\nApril/May 2020, Sep/Oct 2020, and Jan/Feb 2021. We found that pair programming\nhas decreased and some interviewees report not pairing at all for almost a full\nyear. The experiences of those who paired vary from actively co-editing the\ncode by using special tools to more passively co-reading and discussing the\ncode and solutions by sharing the screen. Finally, we found that the interest\nin and the use of PP over time, since the first months of forced work from home\nto early 2021, has admittedly increased, also as a social practice.\n"}
+{"abstract": "  We study Naruse-Newton coefficients, which are obtained from expanding\ndescent polynomials in a Newton basis introduced by Jiradilok and McConville.\nThese coefficients $C_0, C_1, \\ldots$ form an integer sequence associated to\neach finite set of positive integers. For fixed nonnegative integers $a<b$, we\nexamine the set $R_{a, b}$ of all ratios $\\frac{C_a}{C_b}$ over finite sets of\npositive integers. We characterize finite sets for which $\\frac{C_a}{C_b}$ is\nminimized and provide a construction to prove $R_{a, b}$ is unbounded above. We\nuse this construction to obtain results on the closure of $R_{a, b}$. We also\nexamine properties of Naruse-Newton coefficients associated with doubleton\nsets, such as unimodality and log-concavity. Finally, we find an explicit\nformula for all ratios $\\frac{C_a}{C_b}$ of Naruse-Newton coefficients\nassociated with ribbons of staircase shape.\n"}
+{"abstract": "  The tilted balance among competing interactions can yield a rich variety of\nground states of quantum matter. In most Ce-based heavy fermion systems, this\ncan often be qualitatively described by the famous Doniach phase diagram, owing\nto the competition between the Kondo screening and the\nRuderman-Kittel-Kasuya-Yoshida exchange interaction. Here, we report an unusual\npressure-temperature phase diagram beyond the Doniach one in CeCuP2. At ambient\npressure, CeCuP2 displays typical heavy-fermion behavior, albeit with a very\nlow carrier density. With lowering temperature, it shows a crossover from a non\nFermi liquid to a Fermi liquid at around 2.4 K. But surprisingly, the Kondo\ncoherence temperature decreases with increasing pressure, opposite to that in\nmost Ce-based heavy fermion compounds. Upon further compression, two\nsuperconducting phases are revealed. At 48.0 GPa, the transition temperature\nreaches 6.1 K, the highest among all Ce-based heavy fermion superconductors. We\nargue for possible roles of valence tuning and fluctuations associated with its\nspecial crystal structure in addition to the hybridization effect. These\nunusual phase diagrams suggest that CeCuP2 is a novel platform for studying the\nrich heavy fermions physics beyond the conventional Doniach paradigm.\n"}
+{"abstract": "  We present a compression algorithm for parton densities using synthetic\nreplicas generated from the training of a Generative Adversarial Network (GAN).\nThe generated replicas are used to further enhance the statistics of a given\nMonte Carlo PDF set prior to compression. This results in a compression\nmethodology that is able to provide a compressed set with smaller number of\nreplicas and a more adequate representation of the original probability\ndistribution. We also address the question of whether the GAN could be used as\nan alternative mechanism to avoid the fitting of large number of replicas.\n"}
+{"abstract": "  Travellers in autonomous vehicles (AVs) need not to walk to the destination\nany more after parking like those in conventional human-driven vehicles (HVs).\nInstead, they can drop off directly at the destination and AVs can cruise for\nparking autonomously. It is a revolutionary change that such parking autonomy\nof AVs may increase the potential parking span substantially and affect the\nspatial parking equilibrium. Given this, from urban planners' perspective, it\nis of great necessity to reconsider the planning of parking supply along the\ncity. To this end, this paper is the first to examine the spatial parking\nequilibrium considering the mix of AVs and HVs with parking cruising effect. It\nis found that the equilibrium solution of travellers' parking location choices\ncan be biased due to the ignorance of cruising effects. On top of that, the\noptimal parking span of AVs at given parking supply should be no less than that\nat equilibrium. Besides, the optimal parking planning to minimize the total\nparking cost is also explored in a bi-level parking planning design problem\n(PPDP). While the optimal differentiated pricing allows the system to achieve\noptimal parking distribution, this study suggests that it is beneficial to\nencourage AVs to cruise further to park by reserving less than enough parking\nareas for AVs.\n"}
+{"abstract": "  We propose a nanoscale device consisting of a double quantum dot with strong\nintra- and inter- dot Coulomb repulsions. In this design, the current can only\nflow through the lower dot, but is triggered by the gate-controlled occupancy\nof the upper dot. At low temperatures, our calculations predict the double dot\nto pass through a narrow Kondo regime, resulting in highly sensitive switching\ncharacteristics between three well-defined states : insulating, normal\nconduction and resonant conduction.\n"}
+{"abstract": "  In this paper we explore a special class of metric spaces called smocked\nmetric spaces and study their tangent cones at infinity. We prove that under\nthe right hypotheses, the rescaled limits of balls converge in both the\nGromov-Hausdorff and Intrinsic Flat sense to normed spaces. This paper will be\napplied in upcoming work by Kazaras and Sormani concerning Gromov's conjectures\non the properties of GH and SWIF limits of Riemannian manifolds with positive\nscalar curvature.\n"}
+{"abstract": "  We consider training models on private data that are distributed across user\ndevices. To ensure privacy, we add on-device noise and use secure aggregation\nso that only the noisy sum is revealed to the server. We present a\ncomprehensive end-to-end system, which appropriately discretizes the data and\nadds discrete Gaussian noise before performing secure aggregation. We provide a\nnovel privacy analysis for sums of discrete Gaussians and carefully analyze the\neffects of data quantization and modular summation arithmetic. Our theoretical\nguarantees highlight the complex tension between communication, privacy, and\naccuracy. Our extensive experimental results demonstrate that our solution is\nessentially able to match the accuracy to central differential privacy with\nless than 16 bits of precision per value.\n"}
+{"abstract": "  Among spin-crossover complexes, Fe-porphyrin (FeP) stands out for molecular\nspintronic applications: An intricate, yet favourable balance between ligand\nfields, charge transfer, and the Coulomb interaction makes FeP highly\nmanipulable, while its planar structure facilitates device integration. Here,\nwe theoretically design a mechanical spin-switch device in which external\nstrain triggers the intrinsic magneto-structural coupling of FeP through a\npurely organic embedding. Exploiting the chemical compatibility and\nstretchability of graphene nanoribbon electrodes, we overcome common\nreliability and reproducibility issues of conventional inorganic setups. The\ncompetition between the Coulomb interaction and distortion-induced changes in\nligand fields requires methodologies beyond the state-of-the-art: Combining\ndensity functional theory with many-body techniques, we demonstrate\nexperimentally feasible tensile strain to trigger a low-spin ($S=1$) to\nhigh-spin ($S=2$) crossover. Concomitantly, the current through the device\ntoggles by over an order of magnitude, adding a fully planar mechanical\ncurrent-switch unit to the panoply of molecular spintronics.\n"}
+{"abstract": "  In this paper, we study the topological properties of complex polynomial\nHamiltonian differential systems of degree $n$ having an isochronous center of\nMorse type. Firstly, we prove that if the critical level curve possessing an\nisochronous center contains only a single singular point, then the vanishing\ncycle associated to this center represents a zero homology cycle on the compact\nRiemann surface of a generic level curve. Our result provides a positive answer\nto a question asked by L. Gavrilov under a quite simple condition and can be\napplied to achieve an equivalent description of the Jacobian conjecture on\n$\\mathbb{C}^2$. Secondly, we obtain a very simple but useful\n  necessary condition for isochronicity of Hamiltonian systems, which is that\nthe $(n+1)$-degree part of the Hamiltonian function must have a factor with\nmultipicity no less than $(n+1)/2$. Thirdly, we show a relation between\nGavrilov's question and the conjecture proposed by X. Jarque and J.\nVilladelprat on the non-isochronicity of real Hamiltonian systems of even\ndegree $n$.\n"}
+{"abstract": "  This paper analyzes the performance of maximum-ratio transmission\n(MRT)/maximum-ratio combining (MRC) scheme in a dual-hop non-orthogonal\nmultiple access (NOMA) full-duplex (FD) relay networks in the presence of\nresidual hardware impairments (RHIs). The effects of channel estimation errors\n(CEEs) and imperfect successive interference cancellation are also considered\nfor a realistic performance analysis. In the network, the base station and\nmultiple users utilize MRT and MRC, respectively, while a dedicated relay\nconsisting of two antennas, one for receiving and the other for broadcasting,\noperates in amplify-and-forward mode. For performance criterion, exact outage\nprobability (OP) expression is derived for Nakagami-m fading channels.\nFurthermore, a tight lower bound and asymptotic expressions are also derived to\nprovide more insights into the obtained OP in terms of diversity order and\narray gain. The obtained numerical results demonstrate the importance of\nloop-interference cancellation process at FD relay in order for the\ninvestigated system to perform better than half-duplex-NOMA counterpart. Also,\na performance trade-off between the MRT and MRC schemes is observed in the\npresence of CEEs among users. Furthermore, it is shown that RHIs have a\nsignificant effect on the performance of users with lower power coefficients,\nhowever it does not change the diversity order. RHIs and CEEs have the most and\nleast deterioration effects on the system performance, respectively.\n"}
+{"abstract": "  This paper reviews the first NTIRE challenge on quality enhancement of\ncompressed video, with a focus on the proposed methods and results. In this\nchallenge, the new Large-scale Diverse Video (LDV) dataset is employed. The\nchallenge has three tracks. Tracks 1 and 2 aim at enhancing the videos\ncompressed by HEVC at a fixed QP, while Track 3 is designed for enhancing the\nvideos compressed by x265 at a fixed bit-rate. Besides, the quality enhancement\nof Tracks 1 and 3 targets at improving the fidelity (PSNR), and Track 2 targets\nat enhancing the perceptual quality. The three tracks totally attract 482\nregistrations. In the test phase, 12 teams, 8 teams and 11 teams submitted the\nfinal results of Tracks 1, 2 and 3, respectively. The proposed methods and\nsolutions gauge the state-of-the-art of video quality enhancement. The homepage\nof the challenge: https://github.com/RenYang-home/NTIRE21_VEnh\n"}
+{"abstract": "  Deep neural networks have achieved promising performance in supervised point\ncloud applications, but manual annotation is extremely expensive and\ntime-consuming in supervised learning schemes. Unsupervised domain adaptation\n(UDA) addresses this problem by training a model with only labeled data in the\nsource domain but making the model generalize well in the target domain.\nExisting studies show that self-supervised learning using both source and\ntarget domain data can help improve the adaptability of trained models, but\nthey all rely on hand-crafted designs of the self-supervised tasks. In this\npaper, we propose a learnable self-supervised task and integrate it into a\nself-supervision-based point cloud UDA architecture. Specifically, we propose a\nlearnable nonlinear transformation that transforms a part of a point cloud to\ngenerate abundant and complicated point clouds while retaining the original\nsemantic information, and the proposed self-supervised task is to reconstruct\nthe original point cloud from the transformed ones. In the UDA architecture, an\nencoder is shared between the networks for the self-supervised task and the\nmain task of point cloud classification or segmentation, so that the encoder\ncan be trained to extract features suitable for both the source and the target\ndomain data. Experiments on PointDA-10 and PointSegDA datasets show that the\nproposed method achieves new state-of-the-art performance on both\nclassification and segmentation tasks of point cloud UDA. Code will be made\npublicly available.\n"}
+{"abstract": "  Distant supervision (DS) is a well established technique for creating\nlarge-scale datasets for relation extraction (RE) without using human\nannotations. However, research in DS-RE has been mostly limited to the English\nlanguage. Constraining RE to a single language inhibits utilization of large\namounts of data in other languages which could allow extraction of more diverse\nfacts. Very recently, a dataset for multilingual DS-RE has been released.\nHowever, our analysis reveals that the proposed dataset exhibits unrealistic\ncharacteristics such as 1) lack of sentences that do not express any relation,\nand 2) all sentences for a given entity pair expressing exactly one relation.\nWe show that these characteristics lead to a gross overestimation of the model\nperformance. In response, we propose a new dataset, DiS-ReX, which alleviates\nthese issues. Our dataset has more than 1.5 million sentences, spanning across\n4 languages with 36 relation classes + 1 no relation (NA) class. We also modify\nthe widely used bag attention models by encoding sentences using mBERT and\nprovide the first benchmark results on multilingual DS-RE. Unlike the competing\ndataset, we show that our dataset is challenging and leaves enough room for\nfuture research to take place in this field.\n"}
+{"abstract": "  Coronal upflows at the edges of active regions (AR), which are a possible\nsource of slow solar wind, have been found to connect with dynamics in the\ntransition region. To infer at what scale transition region dynamics connect to\nAR upflows, we investigate the statistical properties of the small-scale\ndynamics in the transition region underneath the upflows at the edge of AR NOAA\n11934. With observations from the Interface Region Imaging Spectragraph (IRIS),\nwe found that the Si IV 1403\\,\\AA\\ Doppler map consists of numerous\nblue-shifted and red-shifted patches mostly with sizes less than 1\\,$Mm^2$. The\nblue-shifted structures in the transition region tend to be brighter than the\nred-shifted ones, but their nonthermal velocities have no significant\ndifference. With the SWAMIS feature tracking procedure, in IRIS slit-jaw\n1400\\,\\AA\\ images we found that dynamic bright dots with an average size of\nabout 0.3\\,$Mm^2$ and lifetimes mostly less than 200\\,s spread all over the\nregion. Most of the bright dots appear to be localised, without clear signature\nof propagation of plasma to a long distance on the projection plane. Surge-like\nmotions with speeds about 15 km/s could be seen in some events at the\nboundaries of the upflow region, where the magnetic field appear to be\ninclined. We conclude that the transition region dynamics connecting to coronal\nupflows should occur in very fine scale, suggesting that the corresponding\ncoronal upflows should also be highly-structured. It is also plausible that the\ntransition region dynamics might just act as stimulation at the coronal base\nthat then drives the upflows in the corona.\n"}
+{"abstract": "  The probabilistic learning on manifolds (PLoM) introduced in 2016 has solved\ndifficult supervised problems for the ``small data'' limit where the number N\nof points in the training set is small. Many extensions have since been\nproposed, making it possible to deal with increasingly complex cases. However,\nthe performance limit has been observed and explained for applications for\nwhich $N$ is very small (50 for example) and for which the dimension of the\ndiffusion-map basis is close to $N$. For these cases, we propose a novel\nextension based on the introduction of a partition in independent random\nvectors. We take advantage of this novel development to present improvements of\nthe PLoM such as a simplified algorithm for constructing the diffusion-map\nbasis and a new mathematical result for quantifying the concentration of the\nprobability measure in terms of a probability upper bound. The analysis of the\nefficiency of this novel extension is presented through two applications.\n"}
+{"abstract": "  Confidentiality hinders the publication of authentic, labeled datasets of\npersonal and enterprise data, although they could be useful for evaluating\nknowledge graph construction approaches in industrial scenarios. Therefore, our\nplan is to synthetically generate such data in a way that it appears as\nauthentic as possible. Based on our assumption that knowledge workers have\ncertain habits when they produce or manage data, generation patterns could be\ndiscovered which can be utilized by data generators to imitate real datasets.\nIn this paper, we initially derived 11 distinct patterns found in real\nspreadsheets from industry and demonstrate a suitable generator called Data\nSprout that is able to reproduce them. We describe how the generator produces\nspreadsheets in general and what altering effects the implemented patterns\nhave.\n"}
+{"abstract": "  The use of classical computers to simulate quantum computing has been\nsuccessful in aiding the study of quantum algorithms and circuits that are too\ncomplex to examine analytically. Current implementations of quantum computing\nsimulators are limited to two-level quantum systems. Recent advances in\nhigh-dimensional quantum computing systems have demonstrated the viability of\nworking with multi-level superposition and entanglement. These advances allow\nan agile increase in the number of dimensions of the system while maintaining\nquantum entanglement, achieving higher encoding of information and making\nquantum algorithms less vulnerable to decoherence and computational errors. In\nthis paper, we introduce QuantumSkynet, a novel high-dimensional cloud-based\nquantum computing simulator. This platform allows simulations of qudit-based\nquantum algorithms. We also propose a unified generalization of\nhigh-dimensional quantum gates, which are available for simulations in\nQuantumSkynet. Finally, we report simulations and their results for qudit-based\nversions of the Deutsch--Jozsa and quantum phase estimation algorithms using\nQuantumSkynet.\n"}
+{"abstract": "  The two-field equations governing fully nonlinear dynamics of the drift wave\n(DW) and geodesic acoustic mode (GAM) in the toroidal geometry are derived in\nnonlinear gyrokinetic framework. Two stages with distinctive features are\nidentified and analyzed. In the linear growth stage, the set of nonlinear\nequations can be reduced to the intensively studied parametric decay\ninstability (PDI), accounting for the spontaneous resonant excitation of GAM by\nDW. The main results of previous works on spontaneous GAM excitation, e.g., the\nmuch enhanced GAM group velocity and the nonlinear growth rate of GAM, are\nreproduced from numerical solution of the two-field equations. In the fully\nnonlinear stage, soliton structures are observed to form due to the balancing\nof the self-trapping effect by the spontaneously excited GAM and kinetic\ndispersiveness of DW. The soliton structures enhance turbulence spreading from\nDW linearly unstable to stable region, exhibiting convective propagation\ninstead of typical linear dispersive process, and is thus, expected to induce\ncore-edge interaction and nonlocal transport.\n"}
+{"abstract": "  Due to the mobility and frequent disconnections, the correctness of mobile\ninteraction systems, such as mobile robot systems and mobile payment systems,\nare often difficult to analyze. This paper introduces three critical properties\nof systems, called system connectivity, interaction soundness and data\nvalidity, and presents a related modeling and analysis method, based on a kind\nof Petri nets called VPN. For a given system, a model including component nets\nand interaction structure nets is constructed by using VPNs. The component net\ndescribes the internal process of each component, while the interaction\nstructure net reflects the dynamic interaction between components. Based on\nthis model, three properties are defined and analyzed. The case study of a\npractical mobile payment system shows the effectiveness of the proposed method.\n"}
+{"abstract": "  Point defects in insulators are considered promising candidates for quantum\ntechnologies. In keeping with this, we present an extensive optically-detected\nmagnetic resonance (ODMR) study at room-temperature on individual TR12 centers\n(ZPL at 471nm), which are known in the literature since 1956. In this work we\nfound TR12 centers to show a strong ODMR signal under optical saturation. These\nobserved defects were created in high-purity epitaxial layers of diamond by\nstandard irradiation and annealing processes. From the analysis of the ODMR\nspectra along with antibunching measurements and coherent population trapping,\nwe proposed the energy level structure of TR12 center, consisting of ground\nstate and excited state singlets complemented by a metastable triplet in\nbetween. Mapping the fluorescence dependence of the center on an external\nmagnetic field and on the polarization of laser excitation, allows us to\nidentify twelve inequivalent orientations for TR12 centers. This includes the\nexact orientations of the dipole transition and the triplet axes in the diamond\nlattice in full agreement with the results of modeling based on the proposed\nlevel structure. Furthermore, a static Jahn-Teller effect was detected through\nfluorescence switching between two levels at low optical excitation power,\ndirectly observable in the real-time fluorescence signal for various\npolarization of laser excitation. Based on these results we discuss the\nprospects of the TR12 center in diamond for quantum sensing and quantum\ninformation processing.\n"}
+{"abstract": "  The aims of this article is to generalize some useful Besicovitch-Morse type\ncovering lemmas in complete Riemannian manifolds and try to find more spaces\nsuch that the so-called BCP and WBCP are equivalent while these two properties\nare weaker and still useful. We also get interest in the best constants of\nBesicovitch-type covering properties in Euclidean spaces and sorted out the\nbest results of related problems before giving a new proof of Besicovitch\ncovering theorem in the one-dimensional case.\n"}
+{"abstract": "  Since a rigorous microscopic treatment of a nematic fluid system based on a\npairwise interaction potential is immensely complex we had introduced a simple\nmean field potential which was a modification of the Maier-Saupe potential in a\nprevious paper. Building up on that here we have modified that potential to\ntake into account the various aspects of a smectic A-nematic phase transition.\nIn particular we have studied the dependence of the phase transition on the\ncoupling coefficient between the nematic and smectic order parameters which in\nturn depends on the length of alkyl chain, existence of tricritical point,\nvariation of entropy and specific heat as well as the dependence of the phase\ntransition on pressure.\n"}
+{"abstract": "  Learned networks in the domain of visual recognition and cognition impress in\npart because even though they are trained with datasets many orders of\nmagnitude smaller than the full population of possible images, they exhibit\nsufficient generalization to be applicable to new and previously unseen data.\nAlthough many have examined issues regarding generalization from several\nperspectives, we wondered If a network is trained with a biased dataset that\nmisses particular samples corresponding to some defining domain attribute, can\nit generalize to the full domain from which that training dataset was\nextracted? It is certainly true that in vision, no current training set fully\ncaptures all visual information and this may lead to Selection Bias. Here, we\ntry a novel approach in the tradition of the Thought Experiment. We run this\nthought experiment on a real domain of visual objects that we can fully\ncharacterize and look at specific gaps in training data and their impact on\nperformance requirements. Our thought experiment points to three conclusions:\nfirst, that generalization behavior is dependent on how sufficiently the\nparticular dimensions of the domain are represented during training; second,\nthat the utility of any generalization is completely dependent on the\nacceptable system error; and third, that specific visual features of objects,\nsuch as pose orientations out of the imaging plane or colours, may not be\nrecoverable if not represented sufficiently in a training set. Any currently\nobserved generalization in modern deep learning networks may be more the result\nof coincidental alignments and whose utility needs to be confirmed with respect\nto a system's performance specification. Our Thought Experiment Probe approach,\ncoupled with the resulting Bias Breakdown can be very informative towards\nunderstanding the impact of biases.\n"}
+{"abstract": "  The expanding application in Micro-Air Vehicles has encouraged many\nresearchers to understand the unsteady flow around a flapping foil at a low\nReynolds number. We numerically investigate an incompressible unsteady flow\naround a two-dimensional pitching airfoil (SD7003) at high reduced frequency in\nthe laminar regime. This study interrogates the effect of different unsteady\nparameters, namely amplitude (A), reduced frequency (k), Reynolds number (Re),\nand asymmetry parameter (S) for pitching motion on the force coefficients. The\ninviscid theoretical model is utilized to calculate the lift coefficient for\nsinusoidal motion in the viscous regime, and a comparison is made with the\nnumerical results. The theoretical analysis identifies the influence of the\nnon-circulatory lift over circulatory lift at a high reduced frequency.\nFurther, the results indicate that the reduced frequency (k) and asymmetry\nparameter (S) have a significant impact on the instantaneous and time-averaged\nforce coefficients as well as on the vortex structure in the wake. Finally, the\nFast Fourier Transformation (FFT) analysis is carried out over a simulated case\nwith fixed amplitude and Reynolds number for distinct k and S values. The\nfindings confirm that the dominant frequency in the flow (k*) has a direct\ncorrelation to the airfoil pitching frequency (k).\n"}
+{"abstract": "  Numerous efforts have been invested in improving the effectiveness of bug\nlocalization techniques, whereas little attention is paid to making these tools\nrun more efficiently in continuously evolving software repositories. This paper\nfirst analyzes the information retrieval model behind a classic bug\nlocalization tool, BugLocator, and builds a mathematical foundation\nillustrating that the model can be updated incrementally when codebase or bug\nreports evolve. Then, we present IncBL, a tool for Incremental Bug Localization\nin evolving software repositories. IncBL is evaluated on the Bugzbook dataset,\nand the results show that IncBL can significantly reduce the running time by\n77.79% on average compared with the re-computing the model, while maintaining\nthe same level of accuracy. We also implement IncBL as a Github App that can be\neasily integrated into open-source projects on GitHub. Users can deploy and use\nIncBL locally as well. The demo video for IncBL can be viewed at\nhttps://youtu.be/G4gMuvlJSb0, and the source code can be found at\nhttps://github.com/soarsmu/IncBL.\n"}
+{"abstract": "  Recently, DETR and Deformable DETR have been proposed to eliminate the need\nfor many hand-designed components in object detection while demonstrating good\nperformance as previous complex hand-crafted detectors. However, their\nperformance on Video Object Detection (VOD) has not been well explored. In this\npaper, we present TransVOD, an end-to-end video object detection model based on\na spatial-temporal Transformer architecture. The goal of this paper is to\nstreamline the pipeline of VOD, effectively removing the need for many\nhand-crafted components for feature aggregation, e.g., optical flow, recurrent\nneural networks, relation networks. Besides, benefited from the object query\ndesign in DETR, our method does not need complicated post-processing methods\nsuch as Seq-NMS or Tubelet rescoring, which keeps the pipeline simple and\nclean. In particular, we present temporal Transformer to aggregate both the\nspatial object queries and the feature memories of each frame. Our temporal\nTransformer consists of three components: Temporal Deformable Transformer\nEncoder (TDTE) to encode the multiple frame spatial details, Temporal Query\nEncoder (TQE) to fuse object queries, and Temporal Deformable Transformer\nDecoder to obtain current frame detection results. These designs boost the\nstrong baseline deformable DETR by a significant margin (3%-4% mAP) on the\nImageNet VID dataset. TransVOD yields comparable results performance on the\nbenchmark of ImageNet VID. We hope our TransVOD can provide a new perspective\nfor video object detection. Code will be made publicly available at\nhttps://github.com/SJTU-LuHe/TransVOD.\n"}
+{"abstract": "  Video question answering (Video QA) presents a powerful testbed for\nhuman-like intelligent behaviors. The task demands new capabilities to\nintegrate video processing, language understanding, binding abstract linguistic\nconcepts to concrete visual artifacts, and deliberative reasoning over\nspacetime. Neural networks offer a promising approach to reach this potential\nthrough learning from examples rather than handcrafting features and rules.\nHowever, neural networks are predominantly feature-based - they map data to\nunstructured vectorial representation and thus can fall into the trap of\nexploiting shortcuts through surface statistics instead of true systematic\nreasoning seen in symbolic systems. To tackle this issue, we advocate for\nobject-centric representation as a basis for constructing spatio-temporal\nstructures from videos, essentially bridging the semantic gap between low-level\npattern recognition and high-level symbolic algebra. To this end, we propose a\nnew query-guided representation framework to turn a video into an evolving\nrelational graph of objects, whose features and interactions are dynamically\nand conditionally inferred. The object lives are then summarized into resumes,\nlending naturally for deliberative relational reasoning that produces an answer\nto the query. The framework is evaluated on major Video QA datasets,\ndemonstrating clear benefits of the object-centric approach to video reasoning.\n"}
+{"abstract": "  With the recent developments in neural networks, there has been a resurgence\nin algorithms for the automatic generation of simulation ready electronic\ncircuits from hand-drawn circuits. However, most of the approaches in\nliterature were confined to classify different types of electrical components\nand only a few of those methods have shown a way to rebuild the circuit\nschematic from the scanned image, which is extremely important for further\nautomation of netlist generation. This paper proposes a real-time algorithm for\nthe automatic recognition of hand-drawn electrical circuits based on object\ndetection and circuit node recognition. The proposed approach employs You Only\nLook Once version 5 (YOLOv5) for detection of circuit components and a novel\nHough transform based approach for node recognition. Using YOLOv5 object\ndetection algorithm, a mean average precision (mAP0.5) of 98.2% is achieved in\ndetecting the components. The proposed method is also able to rebuild the\ncircuit schematic with 80% accuracy with a near-real time performance of 0.33s\nper schematic generation.\n"}
+{"abstract": "  We present a novel deep neural network (DNN) architecture for compressing an\nimage when a correlated image is available as side information only at the\ndecoder. This problem is known as distributed source coding (DSC) in\ninformation theory. In particular, we consider a pair of stereo images, which\ngenerally have high correlation with each other due to overlapping fields of\nview, and assume that one image of the pair is to be compressed and\ntransmitted, while the other image is available only at the decoder. In the\nproposed architecture, the encoder maps the input image to a latent space,\nquantizes the latent representation, and compresses it using entropy coding.\nThe decoder is trained to extract the common information between the input\nimage and the correlated image, using only the latter. The received latent\nrepresentation and the locally generated common information are passed through\na decoder network to obtain an enhanced reconstruction of the input image. The\ncommon information provides a succinct representation of the relevant\ninformation at the receiver. We train and demonstrate the effectiveness of the\nproposed approach on the KITTI and Cityscape datasets of stereo image pairs.\nOur results show that the proposed architecture is capable of exploiting the\ndecoder-only side information, and outperforms previous work on stereo image\ncompression with decoder side information.\n"}
+{"abstract": "  Ly-alpha emitting galaxies and giant Ly-alpha blobs (LABs) have been\nextensively observed to study the formation history of galaxies. However, the\norigin of their extended Ly-alpha emission, especially of LABs, remains\ncontroversial. Polarization signals from some LABs have been discovered, and\nthis is commonly interpreted as strong evidence supporting that the extended\nLy-alpha emission originates from the resonance scattering. The Monte Carlo\nLy-alpha radiative transfer code LaRT is updated to investigate the\npolarization of Ly-alpha using the Stokes vector formalism. We apply LaRT to a\nfew models to explore the fundamental polarization properties of Ly-alpha.\nInterestingly, individual Ly-alpha photon packets are found to be almost\ncompletely polarized by a sufficient number of scatterings (N_scatt > 10^4-10^5\nin a static medium) or Doppler shifts induced by gas motion, even starting from\nunpolarized light. It is also found that the polarization pattern can exhibit a\nnon-monotonically increasing pattern in some cases, besides the commonly-known\ntrend that the polarization monotonically increases with radius. The\npolarization properties are primarily determined by the degree of polarization\nof individual photon packets and the anisotropy of the Ly-alpha radiation\nfield, which are eventually controlled by the medium's optical depth and\nvelocity field. If once Ly-alpha photon packets achieve ~100% polarization, the\nradial profile of polarization appears to correlate with the surface brightness\nprofile. A steep surface brightness profile tends to yield a rapid increase of\nthe linear polarization near the Ly-alpha source location. In contrast, a\nshallow surface brightness profile gives rise to a slowly increasing\npolarization pattern.\n"}
+{"abstract": "  In this work we elaborate on holographic description of the path-integral\noptimization in conformal field theories (CFT) using Hartle-Hawking wave\nfunctions in Anti-de Sitter spacetimes. We argue that the maximization of the\nHartle-Hawking wave function is equivalent to the path-integral optimization\nprocedure in CFT. In particular, we show that metrics that maximize gravity\nwave functions computed in particular holographic geometries, precisely match\nthose derived in the path-integral optimization procedure for their dual CFT\nstates. The present work is a detailed version of \\cite{Boruch:2020wax} and\ncontains many new results such as analysis of excited states in various\ndimensions including JT gravity, and a new way of estimating holographic\npath-integral complexity from Hartle-Hawking wave functions. Finally, we\ngeneralize the analysis to Lorentzian Anti-de Sitter and de Sitter geometries\nand use it to shed light on path-integral optimization in Lorentzian CFTs.\n"}
+{"abstract": "  The possibility of long-baseline quantum experiments in space makes it\nnecessary to better understand the time evolution of relativistic quantum\nparticles in a weakly varying gravitational field. We explain why conventional\ntreatments by traditional quantum optics and atomic physics based on quantum\nmechanics may become inadequate when faced with issues related to locality,\nsimultaneity, signaling, causality, etc. Quantum field theory is needed. Adding\nthe effects of gravitation, we are led to Quantum Field Theory in Curved\nSpacetime (QFTCST). This well-established theory should serve as the canonical\nreference theory to a large class of proposed space experiments testing the\nfoundations of gravitation and quantum theory, and the basic notions of quantum\ninformation theory in relativistic settings.\n  This is the first in a series of papers treating near-term quantum optics and\nmatter waves experiments in space from the perspective of QFTCST. We analyze\nthe quantum motion of photons and of scalar massive particles using QFTCST with\napplication to interferometer experiments. Our main result is that, for\nphotons, the weak gravitational field is to leading order completely equivalent\nto an inhomogeneous dielectric, thus allowing for a description of quantum\noptics experiments in curved space using familiar notions from the theory of\noptical media. We also discuss interference experiments that probe first-order\nquantum coherence, the importance of a covariant particle detection theory, and\nthe relevance of time of arrival measurements. For massive particles with\ninternal structure, we identify a novel gravity-induced phase shift that\noriginates from the different gravitational masses attributed to the excited\ninternal states. This phase shift can in principle be measured in space\nexperiments.\n"}
+{"abstract": "  Different AdS-Rindler wedges can be mapped to each other using bulk\nisometries. In this paper we address how the boundary representations\ncorresponding to the AdS-Rindler wedges transform under such isometries. We\nshow that when a bulk wedge is mapped to another using a bulk isometry, their\nboundary representations are mapped by the conformal transformation\ncorresponding to the isometry. We comment on the import of this result on the\nrelation between AdS/CFT and quantum error correction.\n"}
+{"abstract": "  Markov Population Models are a widespread formalism used to model the\ndynamics of complex systems, with applications in Systems Biology and many\nother fields. The associated Markov stochastic process in continuous time is\noften analyzed by simulation, which can be costly for large or stiff systems,\nparticularly when a massive number of simulations has to be performed (e.g. in\na multi-scale model). A strategy to reduce computational load is to abstract\nthe population model, replacing it with a simpler stochastic model, faster to\nsimulate. Here we pursue this idea, building on previous works and constructing\na generator capable of producing stochastic trajectories in continuous space\nand discrete time. This generator is learned automatically from simulations of\nthe original model in a Generative Adversarial setting. Compared to previous\nworks, which rely on deep neural networks and Dirichlet processes, we explore\nthe use of state of the art generative models, which are flexible enough to\nlearn a full trajectory rather than a single transition kernel.\n"}
+{"abstract": "  In this paper, we exploit the capability of multi-agent deep reinforcement\nlearning (MA-DRL) technique to generate a transmit power pool (PP) for Internet\nof things (IoT) networks with semi-grant-free non-orthogonal multiple access\n(SGF-NOMA). The PP is mapped with each resource block (RB) to achieve\ndistributed transmit power control (DPC). We first formulate the resource\n(sub-channel and transmit power) selection problem as stochastic Markov game,\nand then solve it using two competitive MA-DRL algorithms, namely double deep Q\nnetwork (DDQN) and Dueling DDQN. Each GF user as an agent tries to find out the\noptimal transmit power level and RB to form the desired PP. With the aid of\ndueling processes, the learning process can be enhanced by evaluating the\nvaluable state without considering the effect of each action at each state.\nTherefore, DDQN is designed for communication scenarios with a small-size\naction-state space, while Dueling DDQN is for a large-size case. Our results\nshow that the proposed MA-Dueling DDQN based SGF-NOMA with DPC outperforms the\nSGF-NOMA system with the fixed-power-control mechanism and networks with pure\nGF protocols with 17.5% and 22.2% gain in terms of the system throughput,\nrespectively. Moreover, to decrease the training time, we eliminate invalid\nactions (high transmit power levels) to reduce the action space. We show that\nour proposed algorithm is computationally scalable to massive IoT networks.\nFinally, to control the interference and guarantee the quality-of-service\nrequirements of grant-based users, we find the optimal number of GF users for\neach sub-channel.\n"}
+{"abstract": "  We propose in this paper a new nonlinear mathematical model of an oscillating\nwater column. The one-dimensional shallow water equations in the presence of\nthis device are essentially reformulated as two transmission problems: the\nfirst one is associated with a step in front of the device and the second one\nis related to the interaction between waves and a fixed partially-immersed\nstructure. By taking advantage of free surface Bernoulli's equation, we close\nthe system by deriving a transmission condition that involves a time-dependent\nair pressure inside the chamber of the device, instead of a constant\natmospheric pressure as in the previous work \\cite{bocchihevergara2021}. We\nthen show that the second transmission problem can be reduced to a quasilinear\nhyperbolic initial boundary value problem with a semilinear boundary condition\ndetermined by an ODE depending on the trace of the solution to the PDE at the\nboundary. Local well-posedness for general problems of this type is established\nvia an iterative scheme by using linear estimates for the PDE and nonlinear\nestimates for the ODE. Finally, the well-posedness of the transmission problem\nrelated to the wave-structure interaction in the oscillating water column is\nobtained as an application of the general theory.\n"}
+{"abstract": "  Scene text image super-resolution (STISR) aims to improve the resolution and\nvisual quality of low-resolution (LR) scene text images, and consequently boost\nthe performance of text recognition. However, most of existing STISR methods\nregard text images as natural scene images, ignoring the categorical\ninformation of text. In this paper, we make an inspiring attempt to embed\ncategorical text prior into STISR model training. Specifically, we adopt the\ncharacter probability sequence as the text prior, which can be obtained\nconveniently from a text recognition model. The text prior provides categorical\nguidance to recover high-resolution (HR) text images. On the other hand, the\nreconstructed HR image can refine the text prior in return. Finally, we present\na multi-stage text prior guided super-resolution (TPGSR) framework for STISR.\nOur experiments on the benchmark TextZoom dataset show that TPGSR can not only\neffectively improve the visual quality of scene text images, but also\nsignificantly improve the text recognition accuracy over existing STISR\nmethods. Our model trained on TextZoom also demonstrates certain generalization\ncapability to the LR images in other datasets.\n"}
+{"abstract": "  The main goal of this paper is to discuss how to integrate the possibilities\nof crowdsourcing platforms with systems supporting workflow to enable the\nengagement and interaction with business tasks of a wider group of people.\nThus, this work is an attempt to expand the functional capabilities of typical\nbusiness systems by allowing selected process tasks to be performed by\nunlimited human resources. Opening business tasks to crowdsourcing, within\nestablished Business Process Management Systems (BPMS) will improve the\nflexibility of company processes and allow for lower work-load and greater\nspecialization among the staff employed on-site. The presented conceptual work\nis based on the current international standards in this field, promoted by\nWorkflows Management Coalition. To this end, the functioning of business\nplatforms was analysed and their functionality was presented visually, followed\nby a proposal and a discussion of how to implement crowdsourcing into workflow\nsystems.\n"}
+{"abstract": "  The beautiful structures of single and multi-domain proteins are clearly\nordered in some fashion but cannot be readily classified using group theory\nmethods that are successfully used to describe periodic crystals. For this\nreason, protein structures are considered to be aperiodic, and may have evolved\nthis way for functional purposes, especially in instances that require a\ncombination of softness and rigidity within the same molecule. By analyzing the\nsolved protein structures, we show that orientational symmetry is broken in the\naperiodic arrangement of the secondary structural elements (SSEs), which we\ndeduce by calculating the nematic order parameter, $P_{2}$. We find that the\nfolded structures are nematic droplets with a broad distribution of $P_{2}$. We\nargue that non-zero values of $P_{2}$, leads to an arrangement of the SSEs that\ncan resist mechanical forces, which is a requirement for allosteric proteins.\nSuch proteins, which resist mechanical forces in some regions while being\nflexible in others, transmit signals from one region of the protein to another\n(action at a distance) in response to binding of ligands (oxygen, ATP or other\nsmall molecules).\n"}
+{"abstract": "  Both single-laser and two-laser experiments were conducted to look into the\nion-imaging of Br*(2P1/2) and Br(2P3/2) photo-fragmented from\n1-bromo-2-methylbutane in the range 232-240 nm via a detection scheme of (2+1)\nresonance-enhanced multiphoton ionization. The angular analysis of these\nphotofragment distributions yields the anisotropy parameter beta = 1.88 +/-\n0.06 for the Br* excited state which arises from a parallel transition, while\nbeta = 0.63 +/- 0.09 for the Br ground state indicates the contribution from\nboth a perpendicular transition and a non-adiabatic transition. When a hexapole\ncoupled with an orienting field was implemented, the parent molecules are\nspatially oriented to yield an orientation efficiency |<cos theta>| of 0.15.\nBesides the chi angle between the recoil velocity v and the transition dipole\nmoment mu, orienting molecules allows for the evaluation of the angle alpha\nbetween v and the permanent molecular dipole moment d. The angular analysis of\nBr* photofragment distribution yields chi to be 11.5 degrees and alpha in the\nrange from 160 degrees to 180 degrees with weak dependency. In the two-laser\nexperiments, the angular anisotropy of Br photofragment distribution was found\nto be smaller (0.38 +/- 0.10) when the photolysis wavelength was red-shifted to\n240 nm, suggesting the increasing contributions from perpendicular transitions.\n"}
+{"abstract": "  Particularly important to hurricane risk assessment for coastal regions is\nfinding accurate approximations of return probabilities of maximum windspeeds.\nSince extremes in maximum windspeed have a direct relationship to minimums in\nthe central pressure, accurate windspeed return estimates rely heavily on\nproper modeling of the central pressure minima. Using the HURDAT2 database, we\nshow that the central pressure minima of hurricane events can be appropriately\nmodeled by a nonstationary extreme value distribution. We also provide and\nvalidate a Poisson distribution with a nonstationary rate parameter to model\nreturns of hurricane events. Using our nonstationary models and numerical\nsimulation techniques from established literature, we perform a simulation\nstudy to model returns of maximum windspeeds of hurricane events along the\nNorth Atlantic Coast. We show that our revised model agrees with current data\nand results in an expectation of higher maximum windspeeds for all regions\nalong the coast with the highest maximum windspeeds occurring in the northern\npart of the coast.\n"}
+{"abstract": "  In this paper, I generalize the Naszodi-Mendonca method in order to identify\nchanges in marital preferences over multiple dimensions, such as the partners'\nrace and education level. Similar to the original Naszodi-Mendonca method,\npreferences are identified by the generalized method through estimating their\neffects on marriage patterns, in particular, on the share of inter-racial\ncouples, and the share of educationally homogamous couples. This is not a\nsimple task because marriage patterns are shaped not only by marital\npreferences, but also by the distribution of marriageable males and females by\ntraits. The generalized Naszodi-Mendonca method is designed for constructing\ncounterfactuals to perform the decomposition. I illustrate the application of\nthe generalized Naszodi-Mendonca method by decomposing changes in the\nprevalence of racial and educational homogamy in the 1980s using US data from\nIPUMS.\n"}
+{"abstract": "  To enable multiple missiles to attack a maneuvering target simultaneously,\nfixed-time distributed cooperative guidance laws are proposed in this paper.\nHere, we present a novel fixed-time fast nonsingular terminal sliding mode\nsurface (FNTSMS). In particular, the sliding mode surface not only avoids\nsingularities but also has the characteristic of a settling time boundary\nregardless of the initial conditions. Based on the FNTSMS, we have developed a\ndistributed guidance law, which has the characteristic of fixed-time\nconvergence. The guidance law achieves the consensus of range-to-go, relative\nvelocity along and perpendicular to the line of sight (LOS) direction to\nrealize the simultaneous attack. In addition, a saturation function is\nintroduced to avoid the chattering problem caused by the commonly used sign\nfunction. Furthermore, the distributed cooperative guidance law with\ncommunication failure is considered and proved theoretically, which shows that\nthe proposed guidance law has excellent performance. Finally, the simulation\nresults verify the performance of the distributed guidance law and its\nrobustness against communication topology mutations, and explain the phenomenon\nin detail.\n"}
+{"abstract": "  For better clustering performance, appropriate representations are critical.\nAlthough many neural network-based metric learning methods have been proposed,\nthey do not directly train neural networks to improve clustering performance.\nWe propose a meta-learning method that train neural networks for obtaining\nrepresentations such that clustering performance improves when the\nrepresentations are clustered by the variational Bayesian (VB) inference with\nan infinite Gaussian mixture model. The proposed method can cluster unseen\nunlabeled data using knowledge meta-learned with labeled data that are\ndifferent from the unlabeled data. For the objective function, we propose a\ncontinuous approximation of the adjusted Rand index (ARI), by which we can\nevaluate the clustering performance from soft clustering assignments. Since the\napproximated ARI and the VB inference procedure are differentiable, we can\nbackpropagate the objective function through the VB inference procedure to\ntrain the neural networks. With experiments using text and image data sets, we\ndemonstrate that our proposed method has a higher adjusted Rand index than\nexisting methods do.\n"}
+{"abstract": "  Deployed machine learning models are confronted with the problem of changing\ndata over time, a phenomenon also called concept drift. While existing\napproaches of concept drift detection already show convincing results, they\nrequire true labels as a prerequisite for successful drift detection.\nEspecially in many real-world application scenarios-like the ones covered in\nthis work-true labels are scarce, and their acquisition is expensive.\nTherefore, we introduce a new algorithm for drift detection, Uncertainty Drift\nDetection (UDD), which is able to detect drifts without access to true labels.\nOur approach is based on the uncertainty estimates provided by a deep neural\nnetwork in combination with Monte Carlo Dropout. Structural changes over time\nare detected by applying the ADWIN technique on the uncertainty estimates, and\ndetected drifts trigger a retraining of the prediction model. In contrast to\ninput data-based drift detection, our approach considers the effects of the\ncurrent input data on the properties of the prediction model rather than\ndetecting change on the input data only (which can lead to unnecessary\nretrainings). We show that UDD outperforms other state-of-the-art strategies on\ntwo synthetic as well as ten real-world data sets for both regression and\nclassification tasks.\n"}
+{"abstract": "  The present study examined 4698 Indian Coronary Artery Disease research\npublications, as indexed in Web of Science database during 1990-2019, with a\nview to understand their growth rate, global share, citation impact,\ninternational collaborative papers, distribution of publications by broad\nsubjects, productivity and citation profile of top organizations and authors,\nand preferred media of communication. The Indian publications registered an\nannual average growth rate of 11.47%, global share of 1.14%, international\ncollaborative publications share of 38.89% and its citation impact averaged to\n25.58 citations per paper. Among broad subjects, Cardiovascular System &\nCardiology contributed the largest publications share of 19.14% in Indian\ncoronary artery disease output, followed by Neurosciences & Neurology (14.94%),\nPharmacology & Pharmacy (8.51%), etc. during 1990-2019. Among various\norganizations and authors contributing to Indian coronary artery disease\nresearch, the top 20 organizations and top 30 authors together contributed\n40.70% and 37.29% respectively as their share of Indian publication output and\n38.36% and 33.13% respectively as their share of Indian citation output during\n1990-2019. Among 1222 contributing journals in Indian coronary artery disease\nresearch, the top 30 journals registered 30.80% share during 1990-2019. There\nis an urgent need to increase the publication output, improve research quality\nand improve international collaboration. Indian government also needs to come\nup with a policy for identification, screening, diagnosis and treatment of\ncoronary artery disease patients, besides curriculum reform in teaching,\ncapacity building, patient education and political support are badly needed.\n"}
+{"abstract": "  Fix $R>1$ and let $A_R=\\{1/R\\le |z|\\le R \\}$ be an annulus. Also, let $K(R)$\ndenote the smallest constant such that $A_R$ is a $K(R)$-spectral set for the\nbounded linear operator $T\\in \\mathcal{B}(H)$ whenever $||T||\\le R$ and\n$||T^{-1}||\\le R.$ We show that $K(R)\\ge 2, \\text{ for all } R>1. $ This\nimproves on previous results by Badea, Beckermann and Crouzeix.\n"}
+{"abstract": "  We describe an improvement on the magnetic scalar potential approach to the\ndesign of an electromagnet, which incorporates the need to wind the coil as a\nhelix. Any magnetic field that can be described by a magnetic scalar potential\nis produced with high fidelity within a Target region; all fields are confined\nwithin a larger Return. The helical winding only affects the field in the\nReturn.\n"}
+{"abstract": "  We introduce the use of conditional generative adversarial networks\nforgeneralised gravitational wave burst generation in the time\ndomain.Generativeadversarial networks are generative machine learning models\nthat produce new databased on the features of the training data set. We\ncondition the network on fiveclasses of time-series signals that are often used\nto characterise gravitational waveburst searches: sine-Gaussian, ringdown,\nwhite noise burst, Gaussian pulse and binaryblack hole merger. We show that the\nmodel can replicate the features of these standardsignal classes and, in\naddition, produce generalised burst signals through interpolationand class\nmixing. We also present an example application where a convolutional\nneuralnetwork classifier is trained on burst signals generated by our\nconditional generativeadversarial network. We show that a convolutional neural\nnetwork classifier trainedonly on the standard five signal classes has a poorer\ndetection efficiency than aconvolutional neural network classifier trained on a\npopulation of generalised burstsignals drawn from the combined signal class\nspace.\n"}
+{"abstract": "  The analogy between self-similar time series with given Hurst exponent H and\nMarkovian, Gaussian stochastic processes with multiplicative noise and entropic\nindex q (Borland, PRE 57, 6, 6634-6642, 1998) allows us to explain the\nempirical results reported in (Pavithran et al., EPL, 129 2020 24004) and\n(Pavithran et al. Sci. Reports 10.1 (2020) 1-8) with the help of the properties\nof the nonextensive entropy Sq of index q: a dominant oscillating mode arises\nas H goes to zero in many different systems and its amplitude is proportional\nto 1/ H^2 . Thus, a decrease of H acts as precursor of large oscillations of\nthe state variable, which corresponds to catastrophic events in many problems\nof practical interest. In contrast, if H goes to 1 then the time series is\nstrongly intermittent, fluctuations of the state variable follow a power law\nwhose exponent depends on H, and exceedingly large event are basically\nunpredictable. These predictions agree with observations in problems of\naeroacoustics, aeroelasticity, electric engineering, hydrology, laser physics,\nmeteorology, plasma physics, plasticity, polemology, seismology and\nthermoacoustics.\n"}
+{"abstract": "  We discuss relations between the initial boundary value problem (IBVP) and\nquasi-local Hamiltonians in GR. The latter have traditionally been based on\nDirichlet boundary conditions, which however are shown here to be ill-posed for\nthe IBVP. We present and analyse several other choices of boundary conditions\nwhich are better behaved with respect to the IBVP and carry out a corresponding\nHamiltonian analysis, using the framework of the covariant phase space method.\n"}
+{"abstract": "  Providing personalized explanations for recommendations can help users to\nunderstand the underlying insight of the recommendation results, which is\nhelpful to the effectiveness, transparency, persuasiveness and trustworthiness\nof recommender systems. Current explainable recommendation models mostly\ngenerate textual explanations based on pre-defined sentence templates. However,\nthe expressiveness power of template-based explanation sentences are limited to\nthe pre-defined expressions, and manually defining the expressions require\nsignificant human efforts. Motivated by this problem, we propose to generate\nfree-text natural language explanations for personalized recommendation. In\nparticular, we propose a hierarchical sequence-to-sequence model (HSS) for\npersonalized explanation generation. Different from conventional sentence\ngeneration in NLP research, a great challenge of explanation generation in\ne-commerce recommendation is that not all sentences in user reviews are of\nexplanation purpose. To solve the problem, we further propose an auto-denoising\nmechanism based on topical item feature words for sentence generation.\nExperiments on various e-commerce product domains show that our approach can\nnot only improve the recommendation accuracy, but also the explanation quality\nin terms of the offline measures and feature words coverage. This research is\none of the initial steps to grant intelligent agents with the ability to\nexplain itself based on natural language sentences.\n"}
+{"abstract": "  We consider the single image super-resolution (SISR) problem, where a\nhigh-resolution (HR) image is generated based on a low-resolution (LR) input.\nRecently, generative adversarial networks (GANs) become popular to hallucinate\ndetails. Most methods along this line rely on a predefined single-LR-single-HR\nmapping, which is not flexible enough for the SISR task. Also, GAN-generated\nfake details may often undermine the realism of the whole image. We address\nthese issues by proposing best-buddy GANs (Beby-GAN) for rich-detail SISR.\nRelaxing the immutable one-to-one constraint, we allow the estimated patches to\ndynamically seek the best supervision during training, which is beneficial to\nproducing more reasonable details. Besides, we propose a region-aware\nadversarial learning strategy that directs our model to focus on generating\ndetails for textured areas adaptively. Extensive experiments justify the\neffectiveness of our method. An ultra-high-resolution 4K dataset is also\nconstructed to facilitate future super-resolution research.\n"}
+{"abstract": "  A long-standing challenge in artificial intelligence is lifelong learning. In\nlifelong learning, many tasks are presented in sequence and learners must\nefficiently transfer knowledge between tasks while avoiding catastrophic\nforgetting over long lifetimes. On these problems, policy reuse and other\nmulti-policy reinforcement learning techniques can learn many tasks. However,\nthey can generate many temporary or permanent policies, resulting in memory\nissues. Consequently, there is a need for lifetime-scalable methods that\ncontinually refine a policy library of a pre-defined size. This paper presents\na first approach to lifetime-scalable policy reuse. To pre-select the number of\npolicies, a notion of task capacity, the maximal number of tasks that a policy\ncan accurately solve, is proposed. To evaluate lifetime policy reuse using this\nmethod, two state-of-the-art single-actor base-learners are compared: 1) a\nvalue-based reinforcement learner, Deep Q-Network (DQN) or Deep Recurrent\nQ-Network (DRQN); and 2) an actor-critic reinforcement learner, Proximal Policy\nOptimisation (PPO) with or without Long Short-Term Memory layer. By selecting\nthe number of policies based on task capacity, D(R)QN achieves near-optimal\nperformance with 6 policies in a 27-task MDP domain and 9 policies in an\n18-task POMDP domain; with fewer policies, catastrophic forgetting and negative\ntransfer are observed. Due to slow, monotonic improvement, PPO requires fewer\npolicies, 1 policy for the 27-task domain and 4 policies for the 18-task\ndomain, but it learns the tasks with lower accuracy than D(R)QN. These findings\nvalidate lifetime-scalable policy reuse and suggest using D(R)QN for larger and\nPPO for smaller library sizes.\n"}
+{"abstract": "  We prove that the general linear groups of the integers, Gaussian integers,\nand Eisenstein integers satisfy homological stability of slope 1 when using\n$\\mathbb{Z}[1/2]$-coefficients and of slope $2/3$ when using\n$\\mathbb{Z}$-coefficients.\n"}
+{"abstract": "  The sources of reliable, code-level information about vulnerabilities that\naffect open-source software (OSS) are scarce, which hinders a broad adoption of\nadvanced tools that provide code-level detection and assessment of vulnerable\nOSS dependencies.\n  In this paper, we study the extent to which the output of off-the-shelf\nstatic code analyzers can be used as a source of features to represent commits\nin Machine Learning (ML) applications. In particular, we investigate how such\nfeatures can be used to construct embeddings and train ML models to\nautomatically identify source code commits that contain vulnerability fixes.\n  We analyze such embeddings for security-relevant and non-security-relevant\ncommits, and we show that, although in isolation they are not different in a\nstatistically significant manner, it is possible to use them to construct a ML\npipeline that achieves results comparable with the state of the art.\n  We also found that the combination of our method with commit2vec represents a\ntangible improvement over the state of the art in the automatic identification\nof commits that fix vulnerabilities: the ML models we construct and commit2vec\nare complementary, the former being more generally applicable, albeit not as\naccurate.\n"}
+{"abstract": "  We review a combinatoric approach to the Hodge Conjecture for Fermat\nVarieties and announce new cases where the conjecture is true.\n"}
+{"abstract": "  This article studies the impact of online news on social and economic\nconsumer perceptions through the application of semantic network analysis.\nUsing almost 1.3 million online articles on Italian media covering a period of\nfour years, we assessed the incremental predictive power of economic-related\nkeywords on the Consumer Confidence Index. We transformed news into networks of\nco-occurring words and calculated the semantic importance of specific keywords,\nto see if words appearing in the articles could anticipate consumers'\njudgements about the economic situation. Results show that economic-related\nkeywords have a stronger predictive power if we consider the current households\nand national situation, while their predictive power is less significant with\nregards to expectations about the future. Our indicator of semantic importance\noffers a complementary approach to estimate consumer confidence, lessening the\nlimitations of traditional survey-based methods.\n"}
+{"abstract": "  We establish a direct connection between general tensor networks and deep\nfeed-forward artificial neural networks. The core of our results is the\nconstruction of neural-network layers that efficiently perform tensor\ncontractions, and that use commonly adopted non-linear activation functions.\nThe resulting deep networks feature a number of edges that closely matches the\ncontraction complexity of the tensor networks to be approximated. In the\ncontext of many-body quantum states, this result establishes that\nneural-network states have strictly the same or higher expressive power than\npractically usable variational tensor networks. As an example, we show that all\nmatrix product states can be efficiently written as neural-network states with\na number of edges polynomial in the bond dimension and depth logarithmic in the\nsystem size. The opposite instead does not hold true, and our results imply\nthat there exist quantum states that are not efficiently expressible in terms\nof matrix product states or practically usable PEPS, but that are instead\nefficiently expressible with neural network states.\n"}
+{"abstract": "  Moir\\'e super-potentials in two-dimensional materials allow unprecedented\ncontrol of the ratio between kinetic and interaction energy. By this, they pave\nthe way to study a wide variety of strongly correlated physics under a new\nlight. In particular, the transition metal dichalcogenides (TMDs) are promising\ncandidate \"quantum simulators\" of the Hubbard model on a triangular lattice.\nIndeed, Mott and generalized Wigner crystals have been observed in such\ndevices. Here we theoretically propose to extend this model into the\nmulti-orbital regime by focusing on electron doped systems at filling higher\nthan 2. As opposed to hole bands, the electronic bands in TMD materials include\ntwo, nearly degenerate species, which can be viewed as two orbitals with\ndifferent effective mass and binding energy. Using realistic band-structure\nparameters and a slave-rotor mean-field theory, we find that an orbitally\nselective Mott (OSM) phase can be stabilized over a wide range of fillings,\nwhere one band is locked in a commensurate Mott state, while the other remains\nitinerant with variable density. This scenario thus, realizes the basic\ningredients in the Kondo lattice model: A periodic lattice of localized\nmagnetic moments interacting with metallic states. We also discuss possible\nexperimental signatures of the OSM state.\n"}
+{"abstract": "  Mitchell Feigenbaum discovered an intriguing property of viewing images\nthrough cylindrical mirrors or looking into water. Because the eye is a lens\nwith an opening of about 5mm, many different rays of reflected images reach the\neye, and need to be interpreted by the visual system. This has the surprising\neffect that what one perceives depends on the orientation of the head, whether\nit is tilted or not. I explain and illustrate this phenomenon on the example of\na human eye looking at a ruler immersed in water.\n"}
+{"abstract": "  The restoration lemma by Afek, Bremler-Barr, Kaplan, Cohen, and Merritt\n[Dist. Comp. '02] proves that, in an undirected unweighted graph, any\nreplacement shortest path avoiding a failing edge can be expressed as the\nconcatenation of two original shortest paths. However, the lemma is\ntiebreaking-sensitive: if one selects a particular canonical shortest path for\neach node pair, it is no longer guaranteed that one can build replacement paths\nby concatenating two selected shortest paths. They left as an open problem\nwhether a method of shortest path tiebreaking with this desirable property is\ngenerally possible.\n  We settle this question affirmatively with the first general construction of\nrestorable tiebreaking schemes. We then show applications to various problems\nin fault-tolerant network design. These include a faster algorithm for subset\nreplacement paths, more efficient fault-tolerant (exact) distance labeling\nschemes, fault-tolerant subset distance preservers and $+4$ additive spanners\nwith improved sparsity, and fast distributed algorithms that construct these\nobjects. For example, an almost immediate corollary of our restorable\ntiebreaking scheme is the first nontrivial distributed construction of sparse\nfault-tolerant distance preservers resilient to three faults.\n"}
+{"abstract": "  We give a new proof for the central limit theorem in probability for the\ndirected polymer model in a bounded environment with bond disorder in the\ninterior of the weak disorder phase. In the same setup, we also show that the\nlarge deviation rate function agrees with that of the underlying random walk.\nIn addition, for the Brownian polymer model, we show that the central limit\ntheorem holds almost surely in the whole weak disorder phase.\n  The results are proved using the moment bound from [20] and a new tool\nintroduced in this paper, which allows a quantitative comparison between the\nassociated martingales at different inverse temperatures. This comparison is\nmade precise using the noise operator $T_\\rho$ acting on the environment by\nindependent resampling.\n"}
+{"abstract": "  A splinter is a notion of singularity that has seen numerous recent\napplications, especially in connection with the direct summand theorem, the\nmixed characteristic minimal model program, Cohen-Macaulayness of absolute\nintegral closures and cohomology vanishing theorems. Nevertheless, many basic\nquestions about these singularities remain elusive. One outstanding problem is\nwhether the splinter property spreads from a point to an open neighborhood of a\nnoetherian scheme. Our paper addresses this problem in prime characteristic,\nwhere we show that a locally noetherian scheme that has finite Frobenius or\nthat is locally essentially of finite type over a quasi-excellent local ring\nhas an open splinter locus. In particular, all varieties over fields of\npositive characteristic have open splinter loci. Intimate connections are\nestablished between the openness of splinter loci and $F$-compatible ideals,\nwhich are prime characteristic analogues of log canonical centers. We prove the\nsurprising fact that for a large class of noetherian rings with pure (aka\nuniversally injective) Frobenius, the splinter condition is detected by the\nsplitting of a single generically \\'etale finite extension. We also show that\nfor a noetherian $\\textbf{N}$-graded ring over a field, the homogeneous maximal\nideal detects the splinter property.\n"}
+{"abstract": "  We consider accelerated black hole horizons with and without defects. These\nhorizons appear in the $C$-metric solution to Einstein equations and in its\ngeneralization to the case where external fields are present. These solutions\nrealize a variety of physical processes, from the decay of a cosmic string by a\nblack hole pair nucleation to the creation of a black hole pair by an external\nelectromagnetic field. Here, we show that such geometries exhibit an infinite\nset of symmetries in their near horizon region, generalizing in this way\nprevious results for smooth isolated horizons. By considering the limit close\nto both the black hole and the acceleration horizons, we show that a sensible\nset of asymptotic boundary conditions gets preserved by supertranslation and\nsuperrotation transformations. By acting on the geometry with such\ntransformations, we derive the superrotated, supertranslated version of the\n$C$-metric and compute the associated conserved charges.\n"}
+{"abstract": "  Multi-access coded caching schemes from cross resolvable designs (CRD) have\nbeen reported recently \\cite{KNRarXiv}. To be able to compare coded caching\nschemes with different number of users and possibly with different number of\ncaches a new metric called rate-per-user was introduced and it was shown that\nunder this new metric the schemes from CRDs perform better than the\nMaddah-Ali-Niesen scheme in the large memory regime. In this paper a new class\nof CRDs is presented and it is shown that the multi-access coded caching\nschemes derived from these CRDs perform better than the Maddah-Ali-Niesen\nscheme in the entire memory regime. Comparison with other known multi-access\ncoding schemes is also presented.\n"}
+{"abstract": "  The security of the Internet rests on a small number of open-source\ncryptographic libraries: a vulnerability in any one of them threatens to\ncompromise a significant percentage of web traffic. Despite this potential for\nsecurity impact, the characteristics and causes of vulnerabilities in\ncryptographic software are not well understood. In this work, we conduct the\nfirst comprehensive analysis of cryptographic libraries and the vulnerabilities\naffecting them. We collect data from the National Vulnerability Database,\nindividual project repositories and mailing lists, and other relevant sources\nfor eight widely used cryptographic libraries.\n  Among our most interesting findings is that only 27.2% of vulnerabilities in\ncryptographic libraries are cryptographic issues while 37.2% of vulnerabilities\nare memory safety issues, indicating that systems-level bugs are a greater\nsecurity concern than the actual cryptographic procedures. In our investigation\nof the causes of these vulnerabilities, we find evidence of a strong\ncorrelation between the complexity of these libraries and their (in)security,\nempirically demonstrating the potential risks of bloated cryptographic\ncodebases. We further compare our findings with non-cryptographic systems,\nobserving that these systems are, indeed, more complex than similar\ncounterparts, and that this excess complexity appears to produce significantly\nmore vulnerabilities in cryptographic libraries than in non-cryptographic\nsoftware.\n"}
+{"abstract": "  Recent advances in quantum engineering have given us the ability to design\nhybrid systems with novel properties normally not present in the regime they\noperate in. The coupling of spin ensembles and magnons to microwave resonators\nhas for instance lead to a much richer understanding of collective effects in\nthese systems and their potential quantum applications. We can also hybridize\nelectron and nuclear spin ensembles together in the solid-state regime to\ninvestigate collective effects normally only observed in the atomic, molecular\nand optical world. Here we explore in the solid state regime the dynamics of a\ndouble domain nuclear spin ensemble coupled to the Nambu-Goldstone boson in\nGaAs semiconductors and show it exhibits both collective and individual\nrelaxation (thermalization) on very different time scales. Further the\ncollective relaxation of the nuclear spin ensemble is what one would expect\nfrom superradiant decay. This opens up the possibility for the exploration of\nnovel collective behaviour in solid state systems where the natural energies\nassociated with those spins are much less than the thermal energy.\n"}
+{"abstract": "  In this paper, we develop a computational multiscale to solve the parabolic\nwave approximation with heterogeneous and variable media. Parabolic wave\napproximation is a technique to approximate the full wave equation. One benefit\nof the method is that: one wave propagation direction can be taken as an\nevolution direction, and we then can discretize it using a classical scheme\nlike Backward Euler. Consequently, we obtain a set of quasi-gas-dynamic (QGD)\nmodels with different heterogeneous permeability fields. Then, we employ\nconstraint energy minimization generalized multiscale finite element method\n(CEM-GMsFEM) to perform spatial discretization for the problem. The resulting\nsystem can be solved by combining the central difference in time evolution. Due\nto the variable media, we apply the technique of proper orthogonal\ndecomposition (POD) to further the dimension of the problem and solve the\ncorresponding model problem in the POD space instead of in the whole multiscale\nspace spanned by all possible multiscale basis functions. We prove the\nstability of the full discretization scheme and give the convergence analysis\nof the proposed approximation scheme. Numerical results verify the\neffectiveness of the proposed method.\n"}
+{"abstract": "  Nickel-based complex oxides have served as a playground for decades in the\nquest for a copper-oxide analog of the high-temperature (high-Tc)\nsuperconductivity. They may provide key points towards understanding the\nmechanism of the high-Tc and an alternative route for a room-temperature\nsuperconductor. The recent discovery of superconductivity in the infinite-layer\nnickelate thin films has put this pursuit to an end. Having complete control in\nmaterial preparation and a full understanding of the properties and electronic\nstructures becomes the center of gravity of current research in nickelates.\nThus far, material synthesis remains challenging. The demonstration of perfect\ndiamagnetism is still missing, and understanding the role of the interface and\nbulk to the superconducting properties is still lacking. Here, we synthesized\nhigh-quality Nd0.8Sr0.2NiO2 thin films with different thicknesses and\ninvestigated the interface and strain effects on the electrical, magnetic and\noptical properties. The perfect diamagnetism is demonstrated, confirming the\noccurrence of superconductivity in the thin films. Unlike the thick films in\nwhich the normal state Hall coefficient (RH) changes signs from negative to\npositive as the temperature decreases, the RH of the films thinner than 6.1-nm\nremains negative at the whole temperature range below 300 K, suggesting a\nthickness-driven band structure modification. The X-ray spectroscopy reveals\nthe Ni-O hybridization nature in doped finite-layer nickelates, and the\nhybridization is enhanced as the thickness decreases. Consistent with band\nstructure calculations on nickelate/SrTiO3 interfaces, the interface and strain\neffect induce the dominating electron-like band in the ultrathin film, thus\ncausing the sign-change of the RH.\n"}
+{"abstract": "  Recommending medications for patients using electronic health records (EHRs)\nis a crucial data mining task for an intelligent healthcare system. It can\nassist doctors in making clinical decisions more efficiently. However, the\ninherent complexity of the EHR data renders it as a challenging task: (1)\nMultilevel structures: the EHR data typically contains multilevel structures\nwhich are closely related with the decision-making pathways, e.g., laboratory\nresults lead to disease diagnoses, and then contribute to the prescribed\nmedications; (2) Multiple sequences interactions: multiple sequences in EHR\ndata are usually closely correlated with each other; (3) Abundant noise: lots\nof task-unrelated features or noise information within EHR data generally\nresult in suboptimal performance. To tackle the above challenges, we propose a\nmultilevel selective and interactive network (MeSIN) for medication\nrecommendation. Specifically, MeSIN is designed with three components. First,\nan attentional selective module (ASM) is applied to assign flexible attention\nscores to different medical codes embeddings by their relevance to the\nrecommended medications in every admission. Second, we incorporate a novel\ninteractive long-short term memory network (InLSTM) to reinforce the\ninteractions of multilevel medical sequences in EHR data with the help of the\ncalibrated memory-augmented cell and an enhanced input gate. Finally, we employ\na global selective fusion module (GSFM) to infuse the multi-sourced information\nembeddings into final patient representations for medications recommendation.\nTo validate our method, extensive experiments have been conducted on a\nreal-world clinical dataset. The results demonstrate a consistent superiority\nof our framework over several baselines and testify the effectiveness of our\nproposed approach.\n"}
+{"abstract": "  Quantum information is typically encoded in the state of a qubit that is\ndecoupled from the environment. In contrast, waveguide quantum electrodynamics\nstudies qubits coupled to a mode continuum, exposing them to a loss channel and\ncausing quantum information to be lost before coherent operations can be\nperformed. Here we restore coherence by realizing a dark state that exploits\nsymmetry properties and interactions between four qubits. Dark states decouple\nfrom the waveguide and are thus a valuable resource for quantum information but\nalso come with a challenge: they cannot be controlled by the waveguide drive.\nWe overcome this problem by designing a drive that utilizes the symmetry\nproperties of the collective state manifold allowing us to selectively drive\nboth bright and dark states. The decay time of the dark state exceeds that of\nthe waveguide-limited single qubit by more than two orders of magnitude.\nSpectroscopy on the second excitation manifold provides further insight into\nthe level structure of the hybridized system. Our experiment paves the way for\nimplementations of quantum many-body physics in waveguides and the realization\nof quantum information protocols using decoherence-free subspaces.\n"}
+{"abstract": "  Sparse neural networks have been widely applied to reduce the computational\ndemands of training and deploying over-parameterized deep neural networks. For\ninference acceleration, methods that discover a sparse network from a\npre-trained dense network (dense-to-sparse training) work effectively.\nRecently, dynamic sparse training (DST) has been proposed to train sparse\nneural networks without pre-training a dense model (sparse-to-sparse training),\nso that the training process can also be accelerated. However, previous\nsparse-to-sparse methods mainly focus on Multilayer Perceptron Networks (MLPs)\nand Convolutional Neural Networks (CNNs), failing to match the performance of\ndense-to-sparse methods in the Recurrent Neural Networks (RNNs) setting. In\nthis paper, we propose an approach to train intrinsically sparse RNNs with a\nfixed parameter count in one single run, without compromising performance.\nDuring training, we allow RNN layers to have a non-uniform redistribution\nacross cell gates for better regularization. Further, we propose SNT-ASGD, a\nnovel variant of the averaged stochastic gradient optimizer, which\nsignificantly improves the performance of all sparse training methods for RNNs.\nUsing these strategies, we achieve state-of-the-art sparse training results,\nbetter than the dense-to-sparse methods, with various types of RNNs on Penn\nTreeBank and Wikitext-2 datasets. Our codes are available at\nhttps://github.com/Shiweiliuiiiiiii/Selfish-RNN.\n"}
+{"abstract": "  Attention-based pre-trained language models such as GPT-2 brought\nconsiderable progress to end-to-end dialogue modelling. However, they also\npresent considerable risks for task-oriented dialogue, such as lack of\nknowledge grounding or diversity. To address these issues, we introduce\nmodified training objectives for language model finetuning, and we employ\nmassive data augmentation via back-translation to increase the diversity of the\ntraining data. We further examine the possibilities of combining data from\nmultiples sources to improve performance on the target dataset. We carefully\nevaluate our contributions with both human and automatic methods. Our model\nsubstantially outperforms the baseline on the MultiWOZ data and shows\ncompetitive performance with state of the art in both automatic and human\nevaluation.\n"}
+{"abstract": "  The incremental poses computed through odometry can be integrated over time\nto calculate the pose of a device with respect to an initial location. The\nresulting global pose may be used to formulate a second, consistency based,\nloss term in a deep odometry setting. In such cases where multiple losses are\nimposed on a network, the uncertainty over each output can be derived to weigh\nthe different loss terms in a maximum likelihood setting. However, when\nimposing a constraint on the integrated transformation, due to how only\nodometry is estimated at each iteration of the algorithm, there is no\ninformation about the uncertainty associated with the global pose to weigh the\nglobal loss term. In this paper, we associate uncertainties with the output\nposes of a deep odometry network and propagate the uncertainties through each\niteration. Our goal is to use the estimated covariance matrix at each\nincremental step to weigh the loss at the corresponding step while weighting\nthe global loss term using the compounded uncertainty. This formulation\nprovides an adaptive method to weigh the incremental and integrated loss terms\nagainst each other, noting the increase in uncertainty as new estimates arrive.\nWe provide quantitative and qualitative analysis of pose estimates and show\nthat our method surpasses the accuracy of the state-of-the-art Visual Odometry\napproaches. Then, uncertainty estimates are evaluated and comparisons against\nfixed baselines are provided. Finally, the uncertainty values are used in a\nrealistic example to show the effectiveness of uncertainty quantification for\nlocalization.\n"}
+{"abstract": "  Learning to model and reconstruct humans in clothing is challenging due to\narticulation, non-rigid deformation, and varying clothing types and topologies.\nTo enable learning, the choice of representation is the key. Recent work uses\nneural networks to parameterize local surface elements. This approach captures\nlocally coherent geometry and non-planar details, can deal with varying\ntopology, and does not require registered training data. However, naively using\nsuch methods to model 3D clothed humans fails to capture fine-grained local\ndeformations and generalizes poorly. To address this, we present three key\ninnovations: First, we deform surface elements based on a human body model such\nthat large-scale deformations caused by articulation are explicitly separated\nfrom topological changes and local clothing deformations. Second, we address\nthe limitations of existing neural surface elements by regressing local\ngeometry from local features, significantly improving the expressiveness.\nThird, we learn a pose embedding on a 2D parameterization space that encodes\nposed body geometry, improving generalization to unseen poses by reducing\nnon-local spurious correlations. We demonstrate the efficacy of our surface\nrepresentation by learning models of complex clothing from point clouds. The\nclothing can change topology and deviate from the topology of the body. Once\nlearned, we can animate previously unseen motions, producing high-quality point\nclouds, from which we generate realistic images with neural rendering. We\nassess the importance of each technical contribution and show that our approach\noutperforms the state-of-the-art methods in terms of reconstruction accuracy\nand inference time. The code is available for research purposes at\nhttps://qianlim.github.io/SCALE .\n"}
+{"abstract": "  The X and Gamma Imaging Spectrometer instrument on-board the THESEUS mission\n(selected by ESA in the framework of the Cosmic Vision M5 launch opportunity,\ncurrently in phase A) is based on a detection plane composed of several\nthousands of single active elements. Each element comprises a 4.5x4.5x30 mm 3\nCsI(Tl) scintillator bar, optically coupled at both ends to Silicon Drift\nDetectors (SDDs). The SDDs acts both as photodetectors for the scintillation\nlight and as direct X-ray sensors. In this paper the design of the XGIS\ndetection plane is reviewed, outlining the strategic choices in terms of\nmodularity and redundancy of the system. Results on detector-electronics\nprototypes are also described. Moreover, the design and development of the\nlow-noise front-end electronics is presented, emphasizing the innovative\narchitectural design based on custom-designed Application-Specific Integrated\nCircuits (ASICs).\n"}
+{"abstract": "  In the last few years, significant advances have been made in understanding\nthe distributions of exoplanet populations and the architecture of planetary\nsystems. We review the recent progress of planet statistics, with a focus on\nthe inner <~ 1 AU region of the planetary system that has been fairly\nthoroughly surveyed by the Kepler mission. We also discuss the theoretical\nimplications of these statistical results for planet formation and dynamical\nevolution.\n"}
+{"abstract": "  Let $G=(V(G),E(G))$ be a simple graph with vertex set $V(G)$ and edge set\n$E(G)$. Let $S$ be a subset of $V(G)$, and let $B(S)$ be the set of neighbours\nof $S$ in $V(G) \\setminus S$. The differential $\\partial(S)$ of $S$ is defined\nas $|B(S)|-|S|$. The maximum value of $\\partial(S)$ taken over all subsets\n$S\\subseteq V$ is the differential $\\partial(G)$ of $G$. A graph operator is a\nmapping $F: G\\rightarrow G'$, where $G$ and $G'$ are families of graphs.The\ngraph $\\S{G}$ is defined as the graph obtained from $G$ con bipartici\\'on de\nv\\'ertices $V(G)\\cup E(G)$, donde hay tantas aristas entre $v \\in V(G)$ y $e\n\\in E(G)$, como veces $e$ sea incidente con $v$ en $G$. In this paper we study\nthe relationship between $\\partial(G)$ and $\\partial(\\S{G})$. Besides, we\nrelate the differential of a graph with known parameters of a graph, namely,\nits domination and independence number.\n"}
+{"abstract": "  Recently, heatmap regression models have become the mainstream in locating\nfacial landmarks. To keep computation affordable and reduce memory usage, the\nwhole procedure involves downsampling from the raw image to the output heatmap.\nHowever, how much impact will the quantization error introduced by downsampling\nbring? The problem is hardly systematically investigated among previous works.\nThis work fills the blank and we are the first to quantitatively analyze the\nnegative gain. The statistical results show the NME generated by quantization\nerror is even larger than 1/3 of the SOTA item, which is a serious obstacle for\nmaking a new breakthrough in face alignment. To compensate the impact of\nquantization effect, we propose a novel method, called Heatmap In Heatmap(HIH),\nwhich leverages two categories of heatmaps as label representation to encode\ncoordinate. And in HIH, the range of one heatmap represents a pixel of the\nother category of heatmap. Also, we even combine the face alignment with\nsolutions of other fields to make a comparison. Extensive experiments on\nvarious benchmarks show the feasibility of HIH and the superior performance\nthan other solutions. Moreover, the mean error reaches to 4.18 on WFLW, which\nexceeds SOTA a lot. Our source code are made publicly available at\nsupplementary material.\n"}
+{"abstract": "  Increased levels of digitalization in society expose companies to new\nsecurity threats, requiring them to establish adequate security and privacy\nmeasures. Additionally, the presence of exogenous forces like new regulations,\ne.g., GDPR and the global COVID-19 pandemic, pose new challenges for companies\nthat should preserve an adequate level of security while having to adapt to\nchange. In this paper, we investigate such challenges through a two-phase study\nin companies located in Denmark -- a country characterized by a high level of\ndigitalization and trust -- focusing on software development and tech-related\ncompanies. Our results show a number of issues, most notably i) a misalignment\nbetween software developers and management when it comes to the implementation\nof security and privacy measures, ii) difficulties in adapting company\npractices in light of implementing GDPR compliance, and iii) different views on\nthe need to adapt security measures to cope with the COVID-19 pandemic.\n"}
+{"abstract": "  In our paper, we present Deep Learning models with a layer differentiated\ntraining method which were used for the SHARED TASK@ CONSTRAINT 2021 sub-tasks\nCOVID19 Fake News Detection in English and Hostile Post Detection in Hindi. We\npropose a Layer Differentiated training procedure for training a pre-trained\nULMFiT arXiv:1801.06146 model. We used special tokens to annotate specific\nparts of the tweets to improve language understanding and gain insights on the\nmodel making the tweets more interpretable. The other two submissions included\na modified RoBERTa model and a simple Random Forest Classifier. The proposed\napproach scored a precision and f1 score of 0.96728972 and 0.967324832\nrespectively for sub-task \"COVID19 Fake News Detection in English\". Also,\nCoarse-Grained Hostility f1 Score and Weighted FineGrained f1 score of 0.908648\nand 0.533907 respectively for sub-task Hostile Post Detection in Hindi. The\nproposed approach ranked 61st out of 164 in the sub-task \"COVID19 Fake News\nDetection in English and 18th out of 45 in the sub-task Hostile Post Detection\nin Hindi\".\n"}
+{"abstract": "  The angular-averaged differential cross section (dcs) of the elastic electron\nproton (ep) scattering, covering Q^2 < 1.0GeV^2, was fitted via a combined\nmodified eq-scatterings where q is a point particle. The modifications\nrepresent the cloud-covering effects to q. An energy-decaying ratio (edr) was\nderived by inspecting the generated dcs ep from the form factor data gathered\nat Mainz Microtron (A1-Collaboration) and Continuous Electron Beam Accelerator\nFacility (Jefferson Laboratory) when compared to the dcs eq with modified\nrelativistic recoil factor. The diminishing cloud layer, edr, has a decay rate\nof -2.8 for the data sets under investigation. The formulated SBM and SEM\nfitting models use the bare and effective u and d-quark masses, respectively,\nwhile SCBM and SCEM integrate other considerations. Three comparison methods\nwere used and all of them favor the models with other additional\nconsiderations. SCEM was the most favored model in general.\n"}
+{"abstract": "  The periodic microphases that self-assemble in systems with competing\nshort-range attractive and long-range repulsive interactions are structurally\nboth rich and elegant. Significant theoretical and computational efforts have\nthus been dedicated to untangling their properties. By contrast, disordered\nmicrophases, which are structurally just as rich but nowhere near as elegant,\nhave not been as carefully considered. Part of the difficulty is that simple\nmean-field descriptions make a homogeneity assumption that washes away all of\ntheir structural features. Here, we study disordered microphases by exactly\nsolving a SALR model on the Bethe lattice. By sidestepping the homogenization\nassumption, this treatment recapitulates many of the key structural regimes of\ndisordered microphases, including particle and void cluster fluids as well as\ngelation. This analysis also provides physical insight into the relationship\nbetween various structural and thermal observables, between criticality and\nphysical percolation, as well as between glassiness and microphase ordering.\n"}
+{"abstract": "  In order to formalize Distributed Ledger Technologies and their\ninterconnections, a recent line of research work has formulated the notion of\nDistributed Ledger Object (DLO), which is a concurrent object that maintains a\ntotally ordered sequence of records, abstracting blockchains and distributed\nledgers. Through DLO, the Atomic Appends problem, intended as the need of a\nprimitive able to append multiple records to distinct ledgers in an atomic way,\nis studied as a basic interconnection problem among ledgers.\n  In this work, we propose the Distributed Grow-only Set object (DSO), which\ninstead of maintaining a sequence of records, as in a DLO, maintains a set of\nrecords in an immutable way: only Add and Get operations are provided. This\nobject is inspired by the Grow-only Set (G-Set) data type which is part of the\nConflict-free Replicated Data Types. We formally specify the object and we\nprovide a consensus-free Byzantine-tolerant implementation that guarantees\neventual consistency. We then use our Byzantine-tolerant DSO (BDSO)\nimplementation to provide consensus-free algorithmic solutions to the Atomic\nAppends and Atomic Adds (the analogous problem of atomic appends applied on\nG-Sets) problems, as well as to construct consensus-free Single-Writer BDLOs.\nWe believe that the BDSO has applications beyond the above-mentioned problems.\n"}
+{"abstract": "  We study the network pricing problem where the leader maximizes their revenue\nby determining the optimal amounts of tolls to charge on a set of arcs, under\nthe assumption that the followers will react rationally and choose the shortest\npaths to travel. Many distinct single-level reformulations to this bilevel\noptimization program have been proposed, however, their relationship has not\nbeen established. In this paper, we aim to build a connection between those\nreformulations and explore the combination of the path representation with\nvarious modeling options, allowing us to generate 12 different reformulations\nof the problem. Moreover, we propose a new path enumeration scheme, path-based\npreprocessing, and hybrid framework to further improve performance and\nrobustness when solving the final model. We provide numerical results,\ncomparing all the derived reformulations and confirming the efficiency of the\nnovel dimensionality reduction procedures.\n"}
+{"abstract": "  The rapidly evolving field of Artificial Intelligence necessitates automated\napproaches to co-design neural network architecture and neural accelerators to\nmaximize system efficiency and address productivity challenges. To enable joint\noptimization of this vast space, there has been growing interest in\ndifferentiable NN-HW co-design. Fully differentiable co-design has reduced the\nresource requirements for discovering optimized NN-HW configurations, but fail\nto adapt to general hardware accelerator search spaces. This is due to the\nexistence of non-synthesizable (invalid) designs in the search space of many\nhardware accelerators. To enable efficient and realizable co-design of\nconfigurable hardware accelerators with arbitrary neural network search spaces,\nwe introduce RHNAS. RHNAS is a method that combines reinforcement learning for\nhardware optimization with differentiable neural architecture search. RHNAS\ndiscovers realizable NN-HW designs with 1.84x lower latency and 1.86x lower\nenergy-delay product (EDP) on ImageNet and 2.81x lower latency and 3.30x lower\nEDP on CIFAR-10 over the default hardware accelerator design.\n"}
+{"abstract": "  We review Hodge structures, relating filtrations, Galois Theory and\nJordan-Holder structures. The prototypical case of periods of Riemann surfaces\nis compared with the Galois-Artin framework of algebraic numbers.\n"}
+{"abstract": "  We consider the $L^\\infty$-optimal mass transportation problem \\[\n\\min_{\\Pi(\\mu, \\nu)} \\gamma-\\mathrm{ess\\,sup\\,} c(x,y), \\] for a new class of\ncosts $c(x,y)$ for which we introduce a tentative notion of twist condition. In\nparticular we study the conditions under which the infinitely-motonone\nminimizers are induced by a transportation map. We also state a uniqueness\nresult for infinitely cyclically monotone Monge minimizers that corresponds to\nthis class of cost functions. We compare the results to previous works.\n"}
+{"abstract": "  Aims: Our Gulf War Illness (GWI) study conducts combinatorial screening of\nmany interactive neural and humoral biomarkers in order to establish\npredictive, diagnostic, and therapeutic targets. We encounter obstacles at\nevery stage of the biomarker discovery process, from sample acquisition,\nbio-marker extraction to multi-aspect, multi-way interaction analysis, due to\nthe study complexity and lack of support for complex data problem solutions. We\nintroduce a novel data platform, named ROSALIND, to overcome the challenges,\nfoster healthy and vital collaborations and advance scientific inquiries.\n  Main methods: ROSALIND is a researcher-centered, study-specific data\nplatform. It provides vital support of individual creativity and effort in\ncollaborative research. We follow the principles etched in the platform name -\nROSALIND stands for resource organisms with self-governed accessibility,\nlinkability, integrability, neutrality, and dependability. We translate, encode\nand implement the principles in the platform with novel use of advanced\nconcepts and techniques to ensure and protect data integrity and research\nintegrity. From a researcher's vantage point, ROSALIND embodies nuance\nutilities and advanced functionalities in one system, beyond conventional\nstorage, archive and data management.\n  Key findings: The deployment of ROSALIND in our GWI study in recent 12 months\nhas accelerated the pace of data experiment and analysis, removed numerous\nerror sources, and increased research quality and productivity.\n  Significance: ROSALIND seems the first to address data integrity and research\nintegrity in tandem with digital measures and means. It also promises a new\ntype of distributed research networks with individualized data platforms\nconnected in various self-organized collaboration configurations.\n"}
+{"abstract": "  We analyse the behaviour of the exponential sampling series $S_{w}^{\\chi}f$\nat jump discontinuity of the bounded signal $f.$ We obtain a representation\nlemma that is used for analysing the series $S_{w}^{\\chi}f$ and we establish\napproximation of jump discontinuity functions by the series $S_{w}^{\\chi}f.$\nThe rate of approximation of the exponential sampling series $S_{w}^{\\chi}f$ is\nobtained in terms of logarithmic modulus of continuity of functions and the\nround-off and time-jitter errors are also studied. Finally we give some\ngraphical representation of approximation of discontinuous functions by\n$S_{w}^{\\chi}f$ using suitable kernels.\n"}
+{"abstract": "  The Minho Quotation Resource was originally released in 2012. It provided\napproximately 500,000 quotes from business leaders, analysts and politicians\nthat spanned the period from 2008 to 2012. The original resource had several\nfailings which include a large number of missing job titles and affiliations as\nwell as unnormalised job titles which produced a large variation in spellings\nand formats of the same employment position. Also, there were numerous\nduplicate posts. This update has standardised the job title text as well as the\nimputation of missing job titles and affiliations. Duplicate quotes have been\ndeleted. This update also provides some metaphor and simile extraction as well\nas an emotion distribution of the quotes. This update has also replaced an\nantiquated version of Lucene index with a JSONL format as well as a rudimentary\ninterface that can query the data supplied with the resource. It is hoped that\nthis update will encourage the study of business communication in a time of a\nfinancial crisis.\n"}
+{"abstract": "  We present the development of a machine learning based pipeline to fully\nautomate the calibration of the frequency comb used to read out optical/IR\nMicrowave Kinetic Inductance Detector (MKID) arrays. This process involves\ndetermining the resonant frequency and optimal drive power of every pixel (i.e.\nresonator) in the array, which is typically done manually. Modern optical/IR\nMKID arrays, such as DARKNESS (DARK-speckle Near-infrared Energy-resolving\nSuperconducting Spectrophotometer) and MEC (MKID Exoplanet Camera), contain\n10-20,000 pixels, making the calibration process extremely time consuming; each\n2000 pixel feedline requires 4-6 hours of manual tuning. Here we present a\npipeline which uses a single convolutional neural network (CNN) to perform both\nresonator identification and tuning simultaneously. We find that our pipeline\nhas performance equal to that of the manual tuning process, and requires just\ntwelve minutes of computational time per feedline.\n"}
+{"abstract": "  Artificial spin ice systems have seen burgeoning interest due to their\nintriguing physics and potential applications in reprogrammable memory, logic\nand magnonics. Integration of artificial spin ice with functional magnonics is\na relatively recent research direction, with a host of promising results. As\nthe field progresses, direct in-depth comparisons of distinct artificial spin\nsystems are crucial to advancing the field. While studies have investigated the\neffects of different lattice geometries, little comparison exists between\nsystems comprising continuously connected nanostructures, where spin-waves\npropagate via dipole-exchange interaction, and systems with nanobars\ndisconnected at vertices where spin-wave propagation occurs via stray\ndipolar-field. Gaining understanding of how these very different coupling\nmethods affect both spin-wave dynamics and magnetic reversal is key for the\nfield to progress and provides crucial system-design information including for\nfuture systems containing combinations of connected and disconnected elements.\nHere, we study the magnonic response of two kagome spin ices via Brillouin\nlight scattering, a continuously connected system and a disconnected system\nwith vertex gaps. We observe distinct high-frequency dynamics and magnetization\nreversal regimes between the systems, with key distinctions in spin-wave\nlocalization and mode quantization, microstate-trajectory during reversal and\ninternal field-profiles. These observations are pertinent for the fundamental\nunderstanding of artificial spin systems and broader design and engineering of\nreconfigurable functional magnonic crystals.\n"}
+{"abstract": "  Studies using asteroseismic ages and rotation rates from star-spot rotation\nhave indicated that standard age-rotation relations may break down roughly\nhalf-way through the main sequence lifetime, a phenomenon referred to as\nweakened magnetic braking. While rotation rates from spots can be difficult to\ndetermine for older, less active stars, rotational splitting of asteroseismic\noscillation frequencies can provide rotation rates for both active and\nquiescent stars, and so can confirm whether this effect really takes place on\nthe main sequence.\n  We obtained asteroseismic rotation rates of 91 main sequence stars showing\nhigh signal-to-noise modes of oscillation. Using these new rotation rates,\nalong with effective temperatures, metallicities and seismic masses and ages,\nwe built a hierarchical Bayesian mixture model to determine whether the\nensemble more closely agreed with a standard rotational evolution scenario, or\none where weakened magnetic braking takes place. The weakened magnetic braking\nscenario was found to be 98.4% more likely for our stellar ensemble, adding to\nthe growing body of evidence for this stage of stellar rotational evolution.\nThis work represents the largest catalogue of seismic rotation on the main\nsequence to date, opening up possibilities for more detailed ensemble analysis\nof rotational evolution with Kepler.\n"}
+{"abstract": "  We establish the existence and uniqueness of solutions to stochastic 2D\nNavier-Stokes equations in a time-dependent domain driven by Brownian motion. A\nmartingale solution is constructed through domain transformation and\nappropriate Galerkin approximations on time-dependent spaces. The probabilistic\nstrong solution follows from the pathwise uniqueness and the Yamada-Watanable\ntheorem.\n"}
+{"abstract": "  Visible-infrared cross-modality person re-identification (VI-ReID), whose aim\nis to match person images between visible and infrared modality, is a\nchallenging cross-modality image retrieval task. Most existing works integrate\nbatch normalization layers into their neural network, but we found out that\nbatch normalization layers would lead to two types of distribution gap: 1)\ninter-mini-batch distribution gap -- the distribution gap of the same modality\nbetween each mini-batch; 2) intra-mini-batch modality distribution gap -- the\ndistribution gap of different modality within the same mini-batch. To address\nthese problems, we propose a new batch normalization layer called Modality\nBatch Normalization (MBN), which normalizes each modality sub-mini-batch\nrespectively instead of the whole mini-batch, and can reduce these distribution\ngap significantly. Extensive experiments show that our MBN is able to boost the\nperformance of VI-ReID models, even with different datasets, backbones and\nlosses.\n"}
+{"abstract": "  Defect detection in the manufacturing industry is of utmost importance for\nproduct quality inspection. Recently, optical defect detection has been\ninvestigated as an anomaly detection using different deep learning methods.\nHowever, the recent works do not explore the use of point pattern features,\nsuch as SIFT for anomaly detection using the recently developed set-based\nmethods. In this paper, we present an evaluation of different point pattern\nfeature detectors and descriptors for defect detection application. The\nevaluation is performed within the random finite set framework. Handcrafted\npoint pattern features, such as SIFT as well as deep features are used in this\nevaluation. Random finite set-based defect detection is compared with\nstate-of-the-arts anomaly detection methods. The results show that using point\npattern features, such as SIFT as data points for random finite set-based\nanomaly detection achieves the most consistent defect detection accuracy on the\nMVTec-AD dataset.\n"}
+{"abstract": "  We compute the sphere and disk partition functions in semiclassical Liouville\nand analogous quantities in double-scaled matrix integrals. The quantity\nsphere/disk^2 is unambiguous and we find a precise numerical match between the\nLiouville answer and the matrix integral answer. An application is to show that\nthe sphere partition function in JT gravity is infinite.\n"}
+{"abstract": "  2019 is the bicentenary of George Gabriel Stokes, who in 1851 described the\ndrag - Stokes drag - on a body moving immersed in a fluid, and 2020 is the\ncentenary of Christopher Robin Milne, for whom the game of poohsticks was\ninvented; his father A. A. Milne's \"The House at Pooh Corner\", in which it was\nfirst described in print, appeared in 1928. So this is an apt moment to review\nthe state of the art of the fluid mechanics of a solid body in a complex fluid\nflow, and one floating at the interface between two fluids in motion.\nPoohsticks pertains to the latter category, when the two fluids are water and\nair.\n"}
+{"abstract": "  We prove that all Gibbs measures of the $q$-state Potts model on\n$\\mathbb{Z}^2$ are linear combinations of the extremal measures obtained as\nthermodynamic limits under free or monochromatic boundary conditions. In\nparticular all Gibbs measures are invariant under translations. This statement\nis new at points of first-order phase transition, that is at $T=T_{c}(q)$ when\n$q>4$. In this case the structure of Gibbs measures is the most complex in the\nsense that there exist $q+1$ distinct extremal measures.\n  Most of the work is devoted to the FK-percolation model on $\\mathbb{Z}^{2}$\nwith $q\\geq 1$, where we prove that every Gibbs measure is a linear combination\nof the free and wired ones. The arguments are non-quantitative and follow the\nspirit of the seminal works of Aizenman and Higuchi, which established the\nGibbs structure for the two-dimensional Ising model. Infinite-range\ndependencies in FK-percolation (i.e., a weaker spatial Markov property) pose\nserious additional difficulties compared to the case of the Ising model. For\nexample, it is not automatic, albeit true, that thermodynamic limits are Gibbs.\nThe result for the Potts model is then derived using the Edwards-Sokal coupling\nand auto-duality. The latter ingredient is necessary since applying the\nEdwards-Sokal procedure to a Gibbs measure for the Potts model does not\nautomatically produce a Gibbs measure for FK-percolation.\n  Finally, the proof is generic enough to adapt to the FK-percolation and Potts\nmodels on the triangular and hexagonal lattices and to the loop $O(n)$ model in\nthe range of parameters for which its spin representation is positively\nassociated.\n"}
+{"abstract": "  Label Smoothing (LS) improves model generalization through penalizing models\nfrom generating overconfident output distributions. For each training sample\nthe LS strategy smooths the one-hot encoded training signal by distributing its\ndistribution mass over the non-ground truth classes. We extend this technique\nby considering example pairs, coined PLS. PLS first creates midpoint samples by\naveraging random sample pairs and then learns a smoothing distribution during\ntraining for each of these midpoint samples, resulting in midpoints with high\nuncertainty labels for training. We empirically show that PLS significantly\noutperforms LS, achieving up to 30% of relative classification error reduction.\nWe also visualize that PLS produces very low winning softmax scores for both in\nand out of distribution samples.\n"}
+{"abstract": "  Mobile phones enable the collection of a wealth of private information, from\nunique identifiers (e.g., email addresses), to a user's location, to their text\nmessages. This information can be harvested by apps and sent to third parties,\nwhich can use it for a variety of purposes. In this paper we perform the\nlargest study of private information collection (PIC) on Android to date.\nLeveraging an anonymized dataset collected from the customers of a popular\nmobile security product, we analyze the flows of sensitive information\ngenerated by 2.1M unique apps installed by 17.3M users over a period of 21\nmonths between 2018 and 2019. We find that 87.2% of all devices send private\ninformation to at least five different domains, and that actors active in\ndifferent regions (e.g., Asia compared to Europe) are interested in collecting\ndifferent types of information. The United States (62% of the total) and China\n(7% of total flows) are the countries that collect most private information.\nOur findings raise issues regarding data regulation, and would encourage\npolicymakers to further regulate how private information is used by and shared\namong the companies and how accountability can be truly guaranteed.\n"}
+{"abstract": "  We consider the problem of absence of backscattering in the transport of\nManakov solitons on a line. The concept of transparent boundary conditions is\nused for modeling the reflectionless propagation of Manakov vector solitons in\na one-dimensional domain. Artificial boundary conditions that ensure the\nabsence of backscattering are derived and their numerical implementation is\ndemonstrated.\n"}
+{"abstract": "  The concept of social trust has attracted an attention of information\nprocessors/data scientists and information consumers / business firms. One of\nthe main reasons for acquiring the value of SBD is to provide frameworks and\nmethodologies using which the credibility of online social services users can\nbe evaluated. These approaches should be scalable to accommodate large-scale\nsocial data. Hence, there is a need for well comprehending of social trust to\nimprove and expand the analysis process and inferring credibility of social big\ndata. Given the exposed environment's settings and fewer limitations related to\nonline social services, the medium allows legitimate and genuine users as well\nas spammers and other low trustworthy users to publish and spread their\ncontent. This chapter presents an overview of the notion of credibility in the\ncontext of SBD. It also list an array of approaches to measure and evaluate the\ntrustworthiness of users and their contents. Finally, a case study is presented\nthat incorporates semantic analysis and machine learning modules to measure and\npredict users' trustworthiness in numerous domains in different time periods.\nThe evaluation of the conducted experiment validates the applicability of the\nincorporated machine learning techniques to predict highly trustworthy\ndomain-based users.\n"}
+{"abstract": "  This paper studies a single-machine scheduling problem with a non-renewable\nresource (NR-SSP) and total weighted completion time criterion. The\nnon-renewable resource is consumed when the machine starts processing a job. We\nconsider the case where each job's weight in the objective function is\nproportional to its resource consumption amount. The problem is known to be\nNP-hard in this case. We propose a 3-approximation list scheduling algorithm\nfor this problem. Besides, we show that the approximation ratio 3 is tight for\nthe algorithm.\n"}
+{"abstract": "  We show that the infimum of the dual volume of the convex core of a convex\nco-compact hyperbolic $3$-manifold with incompressible boundary coincides with\nthe infimum of the Riemannian volume of its convex core, as we vary the\ngeometry by quasi-isometric deformations. We deduce a linear lower bound of the\nvolume of the convex core of a quasi-Fuchsian manifold in terms of the length\nof its bending measured lamination, with optimal multiplicative constant.\n"}
+{"abstract": "  Before the recent publication of the correspondence between Gauss and Encke,\nnothing was known about the role that John Taylor, a cotton merchant from\nLiverpool, had played in the life of Gotthold Eisenstein. In this article, we\nwill bring together what we have discovered about John Taylor's life.\n"}
+{"abstract": "  We introduce a theoretical framework for understanding and predicting the\ncomplexity of sequence classification tasks, using a novel extension of the\ntheory of Boolean function sensitivity. The sensitivity of a function, given a\ndistribution over input sequences, quantifies the number of disjoint subsets of\nthe input sequence that can each be individually changed to change the output.\nWe argue that standard sequence classification methods are biased towards\nlearning low-sensitivity functions, so that tasks requiring high sensitivity\nare more difficult. To that end, we show analytically that simple lexical\nclassifiers can only express functions of bounded sensitivity, and we show\nempirically that low-sensitivity functions are easier to learn for LSTMs. We\nthen estimate sensitivity on 15 NLP tasks, finding that sensitivity is higher\non challenging tasks collected in GLUE than on simple text classification\ntasks, and that sensitivity predicts the performance both of simple lexical\nclassifiers and of vanilla BiLSTMs without pretrained contextualized\nembeddings. Within a task, sensitivity predicts which inputs are hard for such\nsimple models. Our results suggest that the success of massively pretrained\ncontextual representations stems in part because they provide representations\nfrom which information can be extracted by low-sensitivity decoders.\n"}
+{"abstract": "  Heart Sound (also known as phonocardiogram (PCG)) analysis is a popular way\nthat detects cardiovascular diseases (CVDs). Most PCG analysis uses supervised\nway, which demands both normal and abnormal samples. This paper proposes a\nmethod of unsupervised PCG analysis that uses beta variational auto-encoder\n($\\beta-\\text{VAE}$) to model the normal PCG signals. The best performed model\nreaches an AUC (Area Under Curve) value of 0.91 in ROC (Receiver Operating\nCharacteristic) test for PCG signals collected from the same source. Unlike\nmajority of $\\beta-\\text{VAE}$s that are used as generative models, the\nbest-performed $\\beta-\\text{VAE}$ has a $\\beta$ value smaller than 1. Further\nexperiments then find that the introduction of a light weighted KL divergence\nbetween distribution of latent space and normal distribution improves the\nperformance of anomaly PCG detection based on anomaly scores resulted by\nreconstruction loss. The fact suggests that anomaly score based on\nreconstruction loss may be better than anomaly scores based on latent vectors\nof samples\n"}
+{"abstract": "  We introduce a new family of particle evolution samplers suitable for\nconstrained domains and non-Euclidean geometries. Stein Variational Mirror\nDescent and Mirrored Stein Variational Gradient Descent minimize the\nKullback-Leibler (KL) divergence to constrained target distributions by\nevolving particles in a dual space defined by a mirror map. Stein Variational\nNatural Gradient exploits non-Euclidean geometry to more efficiently minimize\nthe KL divergence to unconstrained targets. We derive these samplers from a new\nclass of mirrored Stein operators and adaptive kernels developed in this work.\nWe demonstrate that these new samplers yield accurate approximations to\ndistributions on the simplex, deliver valid confidence intervals in\npost-selection inference, and converge more rapidly than prior methods in\nlarge-scale unconstrained posterior inference. Finally, we establish the\nconvergence of our new procedures under verifiable conditions on the target\ndistribution.\n"}
+{"abstract": "  In this paper we consider anisotropic Lorentz-Karamata space $2\\pi$ of\nperiodic functions of $m$ variables and Nikol'skii--Besov's class . In this\npaper, we establish order-sharp estimates of the best approximation by\ntrigonometric polynomials with harmonic numbers from the step hyperbolic cross\nof functions from the Nikol'skii - Besov class in the norm of the anisotropic\nLorentz-Karamata space.\n"}
+{"abstract": "  Multicore CPU architectures have been established as a structure for\ngeneral-purpose systems for high-performance processing of applications. Recent\nmulticore CPU has evolved as a system architecture based on non-uniform memory\narchitecture. For the technique of using the kernel space that shifts the tasks\nto the ideal memory node, the characteristics of the applications of the\nuser-space cannot be considered. Therefore, kernel level approaches cannot\nexecute memory scheduling to recognize the importance of user applications.\nMoreover, users need to run applications after sufficiently understanding the\nmulticore CPU based on non-uniform memory architecture to ensure the high\nperformance of the user's applications. This paper presents a user-space memory\nscheduler that allocates the ideal memory node for tasks by monitoring the\ncharacteristics of non-uniform memory architecture. From our experiment, the\nproposed system improved the performance of the application by up to 25%\ncompared to the existing system.\n"}
+{"abstract": "  Using the Floquet Hamiltonian derived based on the time-dependent\nperturbation theory, we investigated the quasienergy bands of a one-dimensional\ntime-Floquet photonic crystal. The time-Floquet photonic crystal contains two\nalternating layers labeled as A and B, and the permittivity of A layer is\nmodulated periodically in time. We showed that the quasienergy bands are\nreciprocal when the modulation function is a function of time only, while the\nquasienergy bands could be nonreciprocal when the permittivity is modulated in\nboth time and space through an unique combination. In the former case, the\ncoupling between the positive (negative) and positive (negative) bands results\nin quasienergy gaps, while the coupling between the positive and negative bands\nleads to pairs of exception points, when the modulation is on the real part of\nthe permittivity. In the latter case, the coupling between the positive\n(negative) and positive (negative) bands still results in quasienergy gaps.\nHowever, the coupling between the positive and negative bands leads to\nquasienergy gaps at a small modulation speed and pairs of exceptional points at\na high modulation speed.\n"}
+{"abstract": "  The many unusual properties of the enigmatic AT2018cow suggested that at\nleast some subset of the empirical class of fast blue optical transients\n(FBOTs) represents a genuinely new astrophysical phenomenon. Unfortunately, the\nintrinsic rarity and fleeting nature of these events have made it difficult to\nidentify additional examples early enough to acquire the observations necessary\nto constrain theoretical models. We present here the Zwicky Transient Facility\ndiscovery of AT2020xnd (ZTF20acigmel, the \"Camel\") at z=0.243, the first\nunambiguous AT2018cow analog to be found and confirmed in real time. AT2018cow\nand AT2020xnd share all key observational properties: a fast optical rise,\nsustained high photospheric temperature, absence of a second peak attributable\nto ejection of a radioactively-heated stellar envelope, extremely luminous\nradio, millimetre, and X-ray emission, and a dwarf-galaxy host. This supports\nthe argument that AT2018cow-like events represent a distinct phenomenon from\nslower-evolving radio-quiet supernovae, likely requiring a different progenitor\nor a different central engine. The sample properties of the four known members\nof this class to date disfavour tidal disruption models but are consistent with\nthe alternative model of an accretion powered jet following the direct collapse\nof a massive star to a black hole. Contextual filtering of alert streams\ncombined with rapid photometric verification using multi-band imaging provides\nan efficient way to identify future members of this class, even at high\nredshift.\n"}
+{"abstract": "  This paper is concerned with the parabolic-elliptic Keller-Segel system with\nnonlinear diffusion and signal-dependent sensitivity\n\\begin{align}\\tag{KS}\\label{system} \\begin{cases}\nu_t=\\Delta(u+1)^m-\\nabla\\cdot(u\\chi(v)\\nabla v),\\quad &x\\in\\Omega, t>0,\\\\\n0=\\Delta v-v+u, &x\\in\\Omega, t>0 \\end{cases} \\end{align} under homogeneous\nNewmann boundary conditions and initial conditions, where\n$\\Omega=B_R(0)\\subset\\mathbb{R}^N$ ($N\\geq3,\\ R>0$) is a ball, $m\\geq 1$,\n$\\chi$ is a function satisfying that $\\chi(s)\\geq\\chi_0(a+s)^{-k}$ ($k>0$,\n$\\chi_0>0$, $a\\geq 0$) for all $s>0$ and some conditions. If the case that\n$m=1$ and $\\chi(s)=\\chi_0s^{-k}$, Nagai-Senba established finite-time blow-up\nof solutions under the smallness conditions on a moment of initial data $u(x,\n0)$ and some condition for $k\\in(0,1)$. Moreover, if the case that\n$\\chi(s)\\equiv(\\mbox{const.})$, Sugiyama showed finite-time blow-up of\nsolutions under the condition $m\\in[1,2-\\frac{2}{N})$. According to two\nprevious works, it seems that the smallness conditions of $m$ and $k$ leads to\nfinite-time blow-up of solutions. The purpose of this paper is to give the\nrelationship which depends only on $m$, $k$ and $N$ such that there exists\ninitial data which corresponds finite-time blow-up solutions.\n"}
+{"abstract": "  The LIGO-Virgo gravitational-wave (GW) observation unveiled the new\npopulation of black holes (BHs) that appears to have an extended mass spectrum\nup to around $70M_\\odot$, much heavier than the previously-believed mass range\n($\\sim 8M_\\odot$). In this paper, we study the capability of a microlensing\nobservation of stars in the Milky Way (MW) bulge region to identify BHs of GW\nmass scales, taking into account the microlensing parallax characterized by the\nparameter $\\pi_{\\rm E}\\propto M^{-1/2}$ ($M$ is the mass of a lens), which is a\ndimension-less quantity defined by the ratio of the astronomical unit to the\nprojected Einstein radius. First, assuming that BHs follow the same spatial and\nvelocity distributions of stars as predicted by the standard MW model, we show\nthat microlensing events with long light curve timescales, $t_{\\rm E}\\gtrsim\n100~{\\rm days}$, and small parallax effects, $\\pi_{\\rm E}\\sim 10^{-2}$, are\ndominated by BH lenses compared to stellar-mass lenses. Second, using a Markov\nchain Monte Carlo analysis of the simulated light curve, we show that BH lens\ncandidates are securely identified on individual basis, if the parallax effect\nis detected or well constrained to the precision of a percent level in\n$\\pi_{\\rm E}$. We also discuss that a microlensing event of an\nintermediate-mass BH of $\\sim 1000M_\\odot$, if it occurs, can be identified in\na distinguishable way from stellar-mass BHs.\n"}
+{"abstract": "  In this work we propose a novel and fully automated method for extracting the\nyarn geometrical features in woven composites so that a direct parametrization\nof the textile reinforcement is achieved (e.g., FE mesh). Thus, our aim is not\nonly to perform yarn segmentation from tomographic images but rather to provide\na complete descriptive modeling of the fabric. As such, this direct approach\nimproves on previous methods that use voxel-wise masks as intermediate\nrepresentations followed by re-meshing operations (yarn envelope estimation).\nThe proposed approach employs two deep neural network architectures (U-Net and\nMask RCNN). First, we train the U-Net to generate synthetic CT images from the\ncorresponding FE simulations. This allows to generate large quantities of\nannotated data without requiring costly manual annotations. This data is then\nused to train the Mask R-CNN, which is focused on predicting contour points\naround each of the yarns in the image. Experimental results show that our\nmethod is accurate and robust for performing yarn instance segmentation on CT\nimages, this is further validated by quantitative and qualitative analyses.\n"}
+{"abstract": "  In this paper, we explore the use of pre-trained language models to learn\nsentiment information of written texts for speech sentiment analysis. First, we\ninvestigate how useful a pre-trained language model would be in a 2-step\npipeline approach employing Automatic Speech Recognition (ASR) and\ntranscripts-based sentiment analysis separately. Second, we propose a pseudo\nlabel-based semi-supervised training strategy using a language model on an\nend-to-end speech sentiment approach to take advantage of a large, but\nunlabeled speech dataset for training. Although spoken and written texts have\ndifferent linguistic characteristics, they can complement each other in\nunderstanding sentiment. Therefore, the proposed system can not only model\nacoustic characteristics to bear sentiment-specific information in speech\nsignals, but learn latent information to carry sentiments in the text\nrepresentation. In these experiments, we demonstrate the proposed approaches\nimprove F1 scores consistently compared to systems without a language model.\nMoreover, we also show that the proposed framework can reduce 65% of human\nsupervision by leveraging a large amount of data without human sentiment\nannotation and boost performance in a low-resource condition where the human\nsentiment annotation is not available enough.\n"}
+{"abstract": "  We analyze the properties of the scattering solutions obtained as the pole of\nthe S- and K-matrix with the help of the Jost function framework and the\nStrum-Liouville theory within the Hartree-Fock-Bogoliubov(HFB) framework, and\nclarify the scattering solutions which can be defined as the physical state. We\nfound that there are three types of the resonances; \"{\\it shape resonance}\",\n\"{\\it particle-type}\" and \"{\\it hole-type quasiparticle resonances}\", and\nanother two types of solutions are given as the independent S-matrix and\nK-matrix poles. The shape resonance is formed by the Hartree-Fock(HF) mean\nfield potential, is not affected by the pairing correlation so much. The\nparticle-type and hole-type quasiparticle resonances originate from the\nparticle and hole states by the configuration mixing effect by pairing. All of\nresonance are represented by the S-matrix pole which has the corresponding\nK-matrix pole. Two other types of solutions are given by the independent\nS-matrix and K-matrix poles. These poles are formed by the HF mean field\npotential. The effect of pairing for the independent S-matrix pole is small,\nbut the one for the independent K-matrix pole has the remarkable effect. The\nindependent K-matrix pole destroys the quasiparticle resonance as it approaches\nto the resonance by the pairing effect. The wave function of all resonances\nhave the characteristic structure of the metastable property. However, the\nmetastable structure of the wave function of the quasiparticle resonance can be\nbroken by the independent standing wave solution or the Fano effect.\n"}
+{"abstract": "  The multifractal formalism for measures in its original formulation is\nchecked for special classes of measures such as doubling, self-similar, and\nGibbs-like ones. Out of these classes, suitable conditions should be taken into\naccount to prove the validity of the multifractal formalism. In the present\nwork, a large class of measures satisfying a weak condition known as quasi\nAhlfors is considered in the framework of mixed multifractal analysis. A joint\nmultifractal analysis of finitely many quasi Ahlfors probability measures is\ndeveloped. Mixed variants of multifractal generalizations of Hausdorff and\npacking measures, and corresponding dimensions are introduced. By applying\nconvexity arguments, some properties of these measures and dimensions are\nestablished. Finally, an associated multifractal formalism is introduced and\nproved to hold for the class of quasi Ahlfors measures.\n"}
+{"abstract": "  In recent work, G. E. Andrews and G. Simay prove a surprising relation\ninvolving parity palindromic compositions, and ask whether a combinatorial\nproof can be found. We extend their results to a more general class of\ncompositions that are palindromic modulo $m$, that includes the parity\npalindromic case when $m=2$. We then provide combinatorial proofs for the cases\n$m=2$ and $m=3$.\n"}
+{"abstract": "  With rapidly evolving internet technologies and emerging tools, sports\nrelated videos generated online are increasing at an unprecedentedly fast pace.\nTo automate sports video editing/highlight generation process, a key task is to\nprecisely recognize and locate the events in the long untrimmed videos. In this\ntech report, we present a two-stage paradigm to detect what and when events\nhappen in soccer broadcast videos. Specifically, we fine-tune multiple action\nrecognition models on soccer data to extract high-level semantic features, and\ndesign a transformer based temporal detection module to locate the target\nevents. This approach achieved the state-of-the-art performance in both two\ntasks, i.e., action spotting and replay grounding, in the SoccerNet-v2\nChallenge, under CVPR 2021 ActivityNet workshop. Our soccer embedding features\nare released at https://github.com/baidu-research/vidpress-sports. By sharing\nthese features with the broader community, we hope to accelerate the research\ninto soccer video understanding.\n"}
+{"abstract": "  The concept of $\\check{H}^n-$bubles was defined and investigated. In this\npaper we generalize this conception for some other functors $F$. Open questions\nare formulated.\n"}
+{"abstract": "  Accelerators magnets must have minimal magnetic field imperfections for\nreducing particle-beam instabilities. In the case of coils made of\nhigh-temperature superconducting (HTS) tapes, the field imperfections from\npersistent currents need to be carefully evaluated. In this paper we study the\nuse of superconducting screens based on HTS tapes for reducing the magnetic\nfield imperfections in accelerator magnets. The screens exploit the\nmagnetization by persistent currents to cancel out the magnetic field error.\nThe screens are aligned with the main field components, such that only the\nundesired field components are compensated. The screens are passive,\nself-regulating, and do not require any external source of energy. Measurements\nin liquid nitrogen at 77 Kelvin show for dipole-field configurations a\nsignificant reduction of the magnetic-field error up to a factor of four. The\nresidual error is explained via numerical simulations, accounting for the\ngeometrical imperfections in the HTS screens, thus achieving satisfactory\nagreement with experimental results. Simulations show that if screens are\nincreased in width and thickness, and operated at 4.5 Kelvin, field errors may\nbe eliminated almost entirely for the typical excitation cycles of accelerator\nmagnets.\n"}
+{"abstract": "  Object Detection (OD) is an important computer vision problem for industry,\nwhich can be used for quality control in the production lines, among other\napplications. Recently, Deep Learning (DL) methods have enabled practitioners\nto train OD models performing well on complex real world images. However, the\nadoption of these models in industry is still limited by the difficulty and the\nsignificant cost of collecting high quality training datasets. On the other\nhand, when applying OD to the context of production lines, CAD models of the\nobjects to be detected are often available. In this paper, we introduce a fully\nautomated method that uses a CAD model of an object and returns a fully trained\nOD model for detecting this object. To do this, we created a Blender script\nthat generates realistic labeled datasets of images containing the object,\nwhich are then used for training the OD model. The method is validated\nexperimentally on two practical examples, showing that this approach can\ngenerate OD models performing well on real images, while being trained only on\nsynthetic images. The proposed method has potential to facilitate the adoption\nof object detection models in industry as it is easy to adapt for new objects\nand highly flexible. Hence, it can result in significant costs reduction, gains\nin productivity and improved products quality.\n"}
+{"abstract": "  The extension of the Standard Model with two gauge-singlet Majorana fermions\ncan simultaneously explain two beyond-the-Standard-model phenomena: neutrino\nmasses and oscillations, as well as the origin of the matter-antimatter\nasymmetry in the Universe. The parameters of such a model are constrained by\nthe neutrino oscillation data, direct accelerator searches, big bang\nnucleosynthesis, and requirement of successful baryogenesis. We show that the\ncombination of all these constraints still leaves an allowed region in the\nparameter space below the kaon mass. This region can be probed by the further\nsearches of NA62, DUNE, or SHiP experiments.\n"}
+{"abstract": "  Dynamical models of Solar System evolution have suggested that P-/D-type\nvolatile-rich asteroids formed in the outer Solar System and may be genetically\nrelated to the Jupiter Trojans, the comets and small KBOs. Indeed, their\nspectral properties resemble that of anhydrous cometary dust.\nHigh-angular-resolution images of P-type asteroid (87) Sylvia with VLT/SPHERE\nwere used to reconstruct its 3D shape, and to study the dynamics of its two\nsatellites. We also model Sylvia's thermal evolution. The shape of Sylvia\nappears flattened and elongated. We derive a volume-equivalent diameter of 271\n+/- 5 km, and a low density of 1378 +/- 45 kg.m-3. The two satellites orbit\nSylvia on circular, equatorial orbits. The oblateness of Sylvia should imply a\ndetectable nodal precession which contrasts with the fully-Keplerian dynamics\nof the satellites. This reveals an inhomogeneous internal structure, suggesting\nthat Sylvia is differentiated. Sylvia's low density and differentiated interior\ncan be explained by partial melting and mass redistribution through water\npercolation. The outer shell would be composed of material similar to\ninterplanetary dust particles (IDPs) and the core similar to aqueously altered\nIDPs or carbonaceous chondrite meteorites such as the Tagish Lake meteorite.\nNumerical simulations of the thermal evolution of Sylvia show that for a body\nof such size, partial melting was unavoidable due to the decay of long-lived\nradionuclides. In addition, we show that bodies as small as 130-150 km in\ndiameter should have followed a similar thermal evolution, while smaller\nobjects, such as comets and the KBO Arrokoth, must have remained pristine, in\nagreement with in situ observations of these bodies. NASA Lucy mission target\n(617) Patroclus (diameter~140 km) may, however, be differentiated.\n"}
+{"abstract": "  The ACM A.M. Turing Award is commonly acknowledged as the highest distinction\nin the realm of computer science. Since 1960s, it has been awarded to computer\nscientists who made outstanding contributions. The significance of this award\nis far-reaching to the laureates as well as their research teams. However,\nunlike the Nobel Prize that has been extensively investigated, little research\nhas been done to explore this most important award. To this end, we propose the\nTuring Number (TN) index to measure how far a specific scholar is to this\naward. Inspired by previous works on Erdos Number and Bacon Number, this index\nis defined as the shortest path between a given scholar to any Turing Award\nLaureate. Experimental results suggest that TN can reflect the closeness of\ncollaboration between scholars and Turing Award Laureates. With the correlation\nanalysis between TN and metrics from the bibliometric-level and network-level,\nwe demonstrate that TN has the potential of reflecting a scholar's academic\ninfluence and reputation.\n"}
+{"abstract": "  We define the concept of energy-variational solutions for the Navier--Stokes\nand Euler equations. The underlying relative energy inequality holds as an\nequality for classical solutions and if the additional variable vanishes, these\nsolutions are equivalent to the weak formulation with the strong energy\ninequality. By introducing an additional defect variable in time, all\nrestrictions and all concatenations of energy-variational solutions are again\nenergy-variational solutions. Via the criterion of maximal dissipation, a\nunique solution is selected that is not only continuously depending on the data\nbut also turns out to be a unique weak solution.\n"}
+{"abstract": "  The first measurement of the production of pions, kaons, (anti-)protons and\n$\\phi$ mesons at midrapidity in Xe-Xe collisions at $\\sqrt{s_{\\rm NN}} = 5.44$\nTeV is presented. Transverse momentum ($p_{\\rm T}$) spectra and $p_{\\rm\nT}$-integrated yields are extracted in several centrality intervals bridging\nfrom p-Pb to mid-central Pb-Pb collisions in terms of final-state multiplicity.\nThe study of Xe-Xe and Pb-Pb collisions allows systems at similar\ncharged-particle multiplicities but with different initial geometrical\neccentricities to be investigated. A detailed comparison of the spectral shapes\nin the two systems reveals an opposite behaviour for radial and elliptic flow.\nIn particular, this study shows that the radial flow does not depend on the\ncolliding system when compared at similar charged-particle multiplicity. In\nterms of hadron chemistry, the previously observed smooth evolution of particle\nratios with multiplicity from small to large collision systems is also found to\nhold in Xe-Xe. In addition, our results confirm that two remarkable features of\nparticle production at LHC energies are also valid in the collision of\nmedium-sized nuclei: the lower proton-to-pion ratio with respect to the thermal\nmodel expectations and the increase of the $\\phi$-to-pion ratio with increasing\nfinal-state multiplicity.\n"}
+{"abstract": "  Highlights are presented about the science to be done with SKA. as well as\nstate of the art science already done today with its precursors (MeerKAT,\nASKAP) and pathfinders (LOFAR, NenuFAR), with accent on the expected\nbreakthroughs.\n"}
+{"abstract": "  Federated Learning (FL) has received a significant amount of attention in the\nindustry and research community due to its capability of keeping data on local\ndevices. To aggregate the gradients of local models to train the global model,\nexisting works require that the global model and the local models are the same.\nHowever, Internet of Things (IoT) devices are inherently diverse regarding\ncomputation speed and onboard memory. In this paper, we propose an FL framework\ntargeting the heterogeneity of IoT devices. Specifically, local models are\ncompressed from the global model, and the gradients of the compressed local\nmodels are used to update the global model. We conduct preliminary experiments\nto illustrate that our framework can facilitate the design of IoT-aware FL.\n"}
+{"abstract": "  Microgrid (MG) energy management is an important part of MG operation.\nVarious entities are generally involved in the energy management of an MG,\ne.g., energy storage system (ESS), renewable energy resources (RER) and the\nload of users, and it is crucial to coordinate these entities. Considering the\nsignificant potential of machine learning techniques, this paper proposes a\ncorrelated deep Q-learning (CDQN) based technique for the MG energy management.\nEach electrical entity is modeled as an agent which has a neural network to\npredict its own Q-values, after which the correlated Q-equilibrium is used to\ncoordinate the operation among agents. In this paper, the Long Short Term\nMemory networks (LSTM) based deep Q-learning algorithm is introduced and the\ncorrelated equilibrium is proposed to coordinate agents. The simulation result\nshows 40.9% and 9.62% higher profit for ESS agent and photovoltaic (PV) agent,\nrespectively.\n"}
+{"abstract": "  The phenomenon of quantum entanglement marks one of the furthest departures\nfrom classical physics and is indispensable for quantum information processing.\nDespite its fundamental importance, the distribution of entanglement over long\ndistances trough photons is unfortunately hindered by unavoidable decoherence\neffects. Entanglement distillation is a means of restoring the quality of such\ndiluted entanglement by concentrating it into a pair of qubits. Conventionally,\nthis would be done by distributing multiple photon pairs and distilling the\nentanglement into a single pair. Here, we turn around this paradigm by\nutilising pairs of single photons entangled in multiple degrees of freedom.\nSpecifically, we make use of the polarisation and the energy-time domain of\nphotons, both of which are extensively field-tested. We experimentally chart\nthe domain of distillable states and achieve relative fidelity gains up to 13.8\n%. Compared to the two-copy scheme, the distillation rate of our single-copy\nscheme is several orders of magnitude higher, paving the way towards\nhigh-capacity and noise-resilient quantum networks.\n"}
+{"abstract": "  We theoretically study the superconducting properties of multi-band\ntwo-dimensional transition metal oxide superconductors by analyzing not only\nthe role played by conventional singlet pairings, but also by the triplet order\nparameters, favored by the spin-orbit couplings present in these ma- terials.\nIn particular, we focus on the two-dimensional electron gas at the (001)\ninterface between LaAlO3 and SrTiO3 band insulators where the low electron\ndensities and the sizeable spin-orbit couplings affect the superconducting\nfeatures. Our theoretical study is based on an extended su- perconducting\nmean-field analysis of the typical multi-band tight-binding Hamiltonian, as\nwell as on a parallel analysis of the effective electronic bands in the\nlow-momentum limit, including static on-site and inter-site intra-band\nattractive potentials under applied magnetic fields. The presence of triplet\npairings is able to strongly reduce the singlet order parameters which, as a\nresult, are no longer a monotonic function of the charge density. The interplay\nbetween the singlet and the triplet pairings affects the dispersion of\nquasi-particle excitations in the Brillouin zone and also induces anisotropy in\nthe superconducting behavior under the action of an in-plane and of an out-\nof-plane magnetic fields. Finally, non-trivial topological superconducting\nstates become stable as a function of the charge density, as well as of the\nmagnitude and of the orientation of the magnetic field. In addition to the\nchiral, time-reversal breaking, topological superconducting phase, favored by\nthe linear Rashba couplings and by the on-site attractive potentials in the\npresence of an out- of-plane magnetic field, we find that a time-reversal\ninvariant topological helical superconducting phase is promoted by not-linear\nspin-orbit couplings and by the inter-site attractive interactions in the\nabsence of magnetic field.\n"}
+{"abstract": "  In classical Iwasawa theory, we mainly study codimension one behavior of\narithmetic modules. Relatively recently, F. M. Bleher, T. Chinburg, R.\nGreenberg, M. Kakde, G. Pappas, R. Sharifi, and M. J. Taylor started studying\nhigher codimension behavior of unramified Iwasawa modules which are conjectured\nto be pseudo-null. In this paper, by developing a general algebraic theory on\nperfect complexes, we obtain a new perspective of their works. That allows us\nto extend the results to equivariant settings and, even in non-equivariant\nsettings, to obtain more refined results concerning the higher codimension\nbehavior.\n"}
+{"abstract": "  We compare and contrast the stellar structures of isolated Local Group dwarf\ngalaxies, as traced by their oldest stellar populations, with the satellite\ndwarf galaxies of the Milky Way and M31. All Local Group dwarfs with Mv < -6\nand surface brightness < 26.5 mags. per square arcsec. are considered, taking\nadvantage of measurements from surveys that use similar observations and\nanalysis techniques. For the isolated dwarfs, we use the results from Solitary\nLocal (Solo) Dwarf Galaxy Survey. We begin by confirming that the structural\nand dynamical properties of the two satellite populations are not obviously\nstatistically different from each other, but we note that there many more\nsatellites around M31 than around the Milky Way down to equivalent magnitude\nand surface brightness limits. We find that dwarfs in close proximity to a\nmassive galaxy generally show more scatter in their Kormendy relations than\nthose in isolation. Specifically, isolated Local Group dwarf galaxies show a\ntighter trend of half-light radius versus magnitude than the satellite\npopulations, and similar effects are also seen for related parameters. There\nappears to be a transition in the structural and dynamical properties of the\ndwarf galaxy population around ~400 kpc from the Milky Way and M31, such that\nthe smallest, faintest, most circular dwarf galaxies are found closer than this\nseparation. We discuss the impact of selection effects on our analysis, and we\nargue that our results point to the significance of tidal interactions on the\npopulation of systems within approximately 400 kpc from the MW and M31.\n"}
+{"abstract": "  Quantifying the amount of polarization is crucial for understanding and\nstudying political polarization in political and social systems. Several\nmethods are used commonly to measure polarization in social networks by purely\ninspecting their structure. We analyse eight of such methods and show that all\nof them yield high polarization scores even for random networks with similar\ndensity and degree distributions to typical real-world networks. Further, some\nof the methods are sensitive to degree distributions and relative sizes of the\npolarized groups. We propose normalization to the existing scores and a minimal\nset of tests that a score should pass in order for it to be suitable for\nseparating polarized networks from random noise. The performance of the scores\nincreased by 38%-220% after normalization in a classification task of 203\nnetworks. Further, we find that the choice of method is not as important as\nnormalization, after which most of the methods have better performance than the\nbest-performing method before normalization. This work opens up the possibility\nto critically assess and compare the features and performance of different\nmethods for measuring structural polarization.\n"}
+{"abstract": "  Equilibrium statistical mechanics rests on the assumption of ergodic dynamics\nof a system modulo the conservation laws of local observables: extremization of\nentropy immediately gives Gibbs' ensemble (GE) for energy conserving systems\nand a generalized version of it (GGE) when the number of local conserved\nquantities (LCQ) is more than one. Through the last decade, statistical\nmechanics has been extended to describe the late-time behaviour of periodically\ndriven (Floquet) quantum matter starting from a generic state. The structure\nbuilt on the fundamental assumptions of ergodicity and identification of the\nrelevant \"conservation laws\" in this inherently non-equilibrium setting. More\nrecently, it has been shown that the statistical mechanics has a much richer\nstructure due to the existence of {\\it emergent} conservation laws: these are\napproximate but stable conservation laws arising {\\it due to the drive}, and\nare not present in the undriven system. Extensive numerical and analytical\nresults support perpetual stability of these emergent (though approximate)\nconservation laws, probably even in the thermodynamic limit. This banks on the\nrecent finding of a sharp ergodicity threshold for Floquet thermalization in\nclean, interacting non-integrable Floquet systems. This opens up a new\npossibility of stable Floquet engineering in such systems. This review intends\nto give a theoretical overview of these developments. We conclude by briefly\nsurveying the experimental scenario.\n"}
+{"abstract": "  In this paper we will solve an open problem raised by Man\\'asevich and Mawhin\ntwenty years ago on the structure of the periodic eigenvalues of the vectorial\n$p$-Laplacian. This is an Euler-Lagrangian equation on the plane or in higher\ndimensional Euclidean spaces. The main result obtained is that for any exponent\n$p$ other than $2$, the vectorial $p$-Laplacian on the plane will admit\ninfinitely many different sequences of periodic eigenvalues with a given\nperiod. These sequences of eigenvalues are constructed using the notion of\nscaling momenta we will introduce. The whole proof is based on the complete\nintegrability of the equivalent Hamiltonian system, the tricky reduction to\n$2$-dimensional dynamical systems, and a number-theoretical distinguishing\nbetween different sequences of eigenvalues. Some numerical simulations to the\nnew sequences of eigenvalues and eigenfunctions will be given. Several further\nconjectures towards to the panorama of the spectral sets will be imposed.\n"}
+{"abstract": "  We give asymptotic upper and lower bounds for the real and imaginary parts of\ncycle integrals of the classical modular j-function along geodesics that\ncorrespond to Markov irrationalities.\n"}
+{"abstract": "  We report sub-arcsecond ALMA observations between 272 - 375 GHz towards Sgr\nA*'s Circumnuclear disk (CND). Our data comprises 8 individual pointings, with\nsignificant SiO (8(7) - 7(6)) and SO (7 - 6) emission detected towards 98\npositions within these pointings. Additionally, we identify H2CS (9(1,9) -\n8(1,8)), OCS (25 - 24) and CH3OH (2(1,1) - 2(0,2)) towards a smaller subset of\npositions. By using the observed peak line flux density together with a\nBayesian Inference technique informed by radiative transfer models, we\nsystematically recover the physical gas conditions towards each of these\npositions. We estimate that the bulk of the surveyed gas has temperature T <\n500 K and density n $\\lessapprox 10^{6}$ cm$^{-3}$, consistent with previous\nstudies of similar positions as traced by HCN clumps. However, we identify an\nuncharacteristically hot (T $\\approx 600$ K) and dense (n $\\approx 10^{6}$\ncm$^{-3}$) source in the Northeastern Arm. This position is found to be\napproximately consistent with a gravitationally bound region dominated by\nturbulence. We also identify a nearby cold (T $\\approx 60$ K) and extremely\ndense (n $\\approx 10^{7}$ cm$^{-3}$) position that is again potentially bound\nand dominated by turbulence. We also determine that the total gas mass\ncontained within the CND is M $\\approx 4 \\times 10^{4}$ $M_{\\odot}$.\nFurthermore, we qualitatively note that the observed chemical enrichment across\nlarge scales within the CND is consistent with bulk grain processing, though\nmultiple desorption mechanisms are plausibly responsible. Further chemical\nmodelling is required to identify the physical origin of the grain-processing,\nas well as the localised H2CS and OCS emission.\n"}
+{"abstract": "  The rampant phenomenon of overpopulation and the remarkable increase of human\nmovements over the last decade have caused an aggressive re-emergence of dengue\nfever, which made it the subject of several research fields. In this regard,\nmathematical modeling, and notably through compartmental systems, is considered\nas an eminent tool to obtain a clear overview of this disease's prevalence\nbehavior. In reality, and like all epidemics, the dengue spread phenomenon is\noften subject to some randomness due to the different natural environment\nfluctuations. For this reason, a mathematical formulation that considers\nsuitably as much as possible the external stochasticity is indeed required. By\nthis token, we strive in this work to present and analyze a generalized\nstochastic dengue model that incorporates both slight and huge environmental\nperturbations. More precisely, our proposed model is represented under the form\nof an It\\^o-L\\'evy stochastic differential equations system that we demonstrate\nits mathematical well-posedness and biological significance. Based on some\nnovel analytical techniques, we prove, and under appropriate hypothetical\nframeworks, of course, two important asymptotic properties, namely: extinction\nand persistence in the mean. The theoretical findings show that the dynamics of\nour disturbed dengue model are mainly determined by the parameters that are\nnarrowly related to the small perturbations' intensities and the jumps\nmagnitudes. In the end, we give certain numerical illustrative examples to\nsupport our theoretical findings and to highlight the effect of the adopted\nmathematical techniques on the results.\n"}
+{"abstract": "  The advent of Stage IV weak lensing surveys will open up a new era in\nprecision cosmology. These experiments will offer more than an\norder-of-magnitude leap in precision over existing surveys, and we must ensure\nthat the accuracy of our theory matches this. Accordingly, it is necessary to\nexplicitly evaluate the impact of the theoretical assumptions made in current\nanalyses on upcoming surveys. One effect typically neglected in present\nanalyses is the Doppler-shift of the measured source comoving distances. Using\nFisher matrices, we calculate the biases on the cosmological parameter values\ninferred from a Euclid-like survey, if the correction for this Doppler-shift is\nomitted. We find that this Doppler-shift can be safely neglected for Stage IV\nsurveys. The code used in this investigation is made publicly available.\n"}
+{"abstract": "  Recent simulations have shown that asymmetries in the ejecta distribution of\nsupernova remnants (SNR) can still reflect asymmetries from the initial\nsupernova explosion. Thus, their study provides a great means to test and\nconstrain model predictions in relation to the distributions of heavy elements\nor the neutron star kicks, both of which are key to better understanding the\nexplosion mechanisms in core-collapse supernovae. The use of a novel blind\nsource separation method applied to the megasecond X-ray observations of the\nwell-known Cassiopeia A SNR has revealed maps of the distribution of the ejecta\nendowed with an unprecedented level of detail and clearly separated from\ncontinuum emission. Our method also provides a three-dimensional view of the\nejecta by disentangling the red- and blue-shifted spectral components and\nassociated images of the Si, S, Ar, Ca and Fe, providing insights into the\nmorphology of the ejecta distribution in Cassiopeia A. These mappings allow us\nto thoroughly investigate the asymmetries in the heavy elements distribution\nand probe simulation predictions about the neutron star kicks and the relative\nasymmetries between the different elements. We find in our study that most of\nthe ejecta X-ray flux stems from the red-shifted component, suggesting an\nasymmetry in the explosion. In addition, the red-shifted ejecta can physically\nbe described as a broad, relatively symmetric plume, whereas the blue-shifted\nejecta is more similar to a dense knot. The neutron star also moves directly\nopposite to the red-shifted parts of the ejecta similar to what is seen with\n44Ti. Regarding the morphological asymmetries, it appears that heavier elements\nhave more asymmetrical distributions, which confirms predictions made by\nsimulations. This study is a showcase of the capacities of new analysis methods\nto revisit archival observations to fully exploit their scientific content.\n"}
+{"abstract": "  Pixel-wise regression is probably the most common problem in fine-grained\ncomputer vision tasks, such as estimating keypoint heatmaps and segmentation\nmasks. These regression problems are very challenging particularly because they\nrequire, at low computation overheads, modeling long-range dependencies on\nhigh-resolution inputs/outputs to estimate the highly nonlinear pixel-wise\nsemantics. While attention mechanisms in Deep Convolutional Neural\nNetworks(DCNNs) has become popular for boosting long-range dependencies,\nelement-specific attention, such as Nonlocal blocks, is highly complex and\nnoise-sensitive to learn, and most of simplified attention hybrids try to reach\nthe best compromise among multiple types of tasks. In this paper, we present\nthe Polarized Self-Attention(PSA) block that incorporates two critical designs\ntowards high-quality pixel-wise regression: (1) Polarized filtering: keeping\nhigh internal resolution in both channel and spatial attention computation\nwhile completely collapsing input tensors along their counterpart dimensions.\n(2) Enhancement: composing non-linearity that directly fits the output\ndistribution of typical fine-grained regression, such as the 2D Gaussian\ndistribution (keypoint heatmaps), or the 2D Binormial distribution (binary\nsegmentation masks). PSA appears to have exhausted the representation capacity\nwithin its channel-only and spatial-only branches, such that there is only\nmarginal metric differences between its sequential and parallel layouts.\nExperimental results show that PSA boosts standard baselines by $2-4$ points,\nand boosts state-of-the-arts by $1-2$ points on 2D pose estimation and semantic\nsegmentation benchmarks.\n"}
+{"abstract": "  The cosmological term, $\\Lambda$, was introduced $104$ years ago by Einstein\nin his gravitational field equations. Whether $\\Lambda$ is a rigid quantity or\na dynamical variable in cosmology has been a matter of debate for many years,\nespecially after the introduction of the general notion of dark energy (DE).\n$\\Lambda$ is associated to the vacuum energy density, $\\rho_{\\rm vac}$, and one\nmay expect that it evolves slowly with the cosmological expansion. Herein we\npresent a devoted study testing this possibility using the promising class of\nrunning vacuum models (RVM's). We use a large string $SNIa+BAO+H(z)+LSS+CMB$ of\nmodern cosmological data, in which for the first time the CMB part involves the\nfull Planck 2018 likelihood for these models. We test the dependence of the\nresults on the threshold redshift $z_*$ at which the vacuum dynamics is\nactivated in the recent past and find positive signals up to $\\sim4.0\\sigma$\nfor $z_*\\simeq 1$. The RVM's prove very competitive against the standard\n$\\Lambda$CDM model and give a handle for solving the $\\sigma_8$ tension and\nalleviating the $H_0$ one.\n"}
+{"abstract": "  Task-parallel programs often enjoy deadlock freedom under certain\nrestrictions, such as the use of structured join operations, as in Cilk and\nX10, or the use of asynchronous task futures together with deadlock-avoiding\npolicies such as Known Joins or Transitive Joins. However, the promise, a\npopular synchronization primitive for parallel tasks, does not enjoy\ndeadlock-freedom guarantees. Promises can exhibit deadlock-like bugs; however,\nthe concept of a deadlock is not currently well-defined for promises.\n  To address these challenges, we propose an ownership semantics in which each\npromise is associated to the task which currently intends to fulfill it.\nOwnership immediately enables the identification of bugs in which a task fails\nto fulfill a promise for which it is responsible. Ownership further enables the\ndiscussion of deadlock cycles among tasks and promises and allows us to\nintroduce a robust definition of deadlock-like bugs for promises.\n  Cycle detection in this context is non-trivial because it is concurrent with\nchanges in promise ownership. We provide a lock-free algorithm for precise\nruntime deadlock detection. We show how to obtain the memory consistency\ncriteria required for the correctness of our algorithm under TSO and the Java\nand C++ memory models. An evaluation compares the execution time and memory\nusage overheads of our detection algorithm on benchmark programs relative to an\nunverified baseline. Our detector exhibits a 12% (1.12$\\times$) geometric mean\ntime overhead and a 6% (1.06$\\times$) geometric mean memory overhead, which are\nsmaller overheads than in past approaches to deadlock cycle detection.\n"}
+{"abstract": "  Program verification is to develop the program's proof system, and to prove\nthe proof system soundness with respect to a trusted operational semantics of\nthe program. However, many practical program verifiers are not based on\noperational semantics and can't seriously validate the program. Matching logic\nis proposed to make program verification based on operational semantics. In\nthis paper, following Grigore Ro{\\c{s}}u 's work, we consider matching logic\nfor parallel imperative language(PIMP). According to our investigation, this\npaper is the first study on matching logic for PIMP. In our matching logic, we\nredefine \"interference-free\" to character parallel rule and prove the soundness\nof matching logic to the operational semantics of PIMP. We also link PIMP's\noperational semantics and PIMP's verification formally by constructing a\nmatching logic verifier for PIMP which executes rewriting logic semantics\nsymbolically on configuration patterns and is sound and complete to matching\nlogic for PIMP. That is our matching logic verifier for PIMP is sound to the\noperational semantics of PIMP. Finally, we also verify the matching logic\nverifier through an example which is a standard problem in parallel\nprogramming.\n"}
+{"abstract": "  Ordinary differential equations (ODEs) are widely used to model complex\ndynamics that arises in biology, chemistry, engineering, finance, physics, etc.\nCalibration of a complicated ODE system using noisy data is generally very\ndifficult. In this work, we propose a two-stage nonparametric approach to\naddress this problem. We first extract the de-noised data and their higher\norder derivatives using boundary kernel method, and then feed them into a\nsparsely connected deep neural network with ReLU activation function. Our\nmethod is able to recover the ODE system without being subject to the curse of\ndimensionality and complicated ODE structure. When the ODE possesses a general\nmodular structure, with each modular component involving only a few input\nvariables, and the network architecture is properly chosen, our method is\nproven to be consistent. Theoretical properties are corroborated by an\nextensive simulation study that demonstrates the validity and effectiveness of\nthe proposed method. Finally, we use our method to simultaneously characterize\nthe growth rate of Covid-19 infection cases from 50 states of the USA.\n"}
+{"abstract": "  We calculate gluon self-energy using quark energy projectors in a general\nquark-gluon plasma. By separating the quark field into a positive- and a\nnegative-energy mode, the quark loop constructed with the same mode is always\nconvergent, and the divergence appears only in the mixed loop with different\nmodes and is medium independent. After removing the divergence in vacuum, we\nobtain the one-loop gluon self-energy at finite temperature, chemical potential\nand quarks mass without approximation. With the method of quark loop\nresummation, we calculate non-perturbatively the gluon Debye mass and\nthermodynamic potential. In the limit of small gluon momentum in comparison\nwith temperature, chemical potential and quark mass, our calculation comes back\nto the known HTL/HDL results in literature.\n"}
+{"abstract": "  Braiding operations are challenging to create topological quantum computers.\nIt is unclear whether braiding operations can be executed with any materials.\nAlthough various calculations based on Majorana fermions show braiding\npossibilities, a braiding operation with a Majorana fermion has not yet been\nexperimentally proven. Herein, braiding operations are demonstrated using a\nmolecular topological superconductor (MTSC) that utilizes the topological\nproperties intrinsic in molecules. The braiding operations were implemented by\ncontrolling two MTSC modules made by pelletizing crystals of\n4,5,9,10-tetrakis(dodecyloxy)pyrene, which is proposed as the first MTSC\nmaterial through n-MOSFETs. It shows the elements of topological quantum\ncomputers that can be demonstrated without an external magnetic field at room\ntemperature.\n"}
+{"abstract": "  Most water in the universe may be superionic, and its thermodynamic and\ntransport properties are crucial for planetary science but difficult to probe\nexperimentally or theoretically. We use machine learning and free energy\nmethods to overcome the limitations of quantum mechanical simulations, and\ncharacterize hydrogen diffusion, superionic transitions, and phase behaviors of\nwater at extreme conditions. We predict that a close-packed superionic phase\nwith mixed stacking is stable over a wide temperature and pressure range, while\na body-centered cubic phase is only thermodynamically stable in a small window\nbut is kinetically favored. Our phase boundaries, which are consistent with the\nexisting-albeit scarce-experimental observations, help resolve the fractions of\ninsulating ice, different superionic phases, and liquid water inside of ice\ngiants.\n"}
+{"abstract": "  Real-time rendering and animation of humans is a core function in games,\nmovies, and telepresence applications. Existing methods have a number of\ndrawbacks we aim to address with our work. Triangle meshes have difficulty\nmodeling thin structures like hair, volumetric representations like Neural\nVolumes are too low-resolution given a reasonable memory budget, and\nhigh-resolution implicit representations like Neural Radiance Fields are too\nslow for use in real-time applications. We present Mixture of Volumetric\nPrimitives (MVP), a representation for rendering dynamic 3D content that\ncombines the completeness of volumetric representations with the efficiency of\nprimitive-based rendering, e.g., point-based or mesh-based methods. Our\napproach achieves this by leveraging spatially shared computation with a\ndeconvolutional architecture and by minimizing computation in empty regions of\nspace with volumetric primitives that can move to cover only occupied regions.\nOur parameterization supports the integration of correspondence and tracking\nconstraints, while being robust to areas where classical tracking fails, such\nas around thin or translucent structures and areas with large topological\nvariability. MVP is a hybrid that generalizes both volumetric and\nprimitive-based representations. Through a series of extensive experiments we\ndemonstrate that it inherits the strengths of each, while avoiding many of\ntheir limitations. We also compare our approach to several state-of-the-art\nmethods and demonstrate that MVP produces superior results in terms of quality\nand runtime performance.\n"}
+{"abstract": "  We make quantitative improvements to recently obtained results on the\nstructure of the image of a large difference set under certain quadratic forms\nand other homogeneous polynomials. Previous proofs used deep results of\nBenoist-Quint on random walks in certain subgroups of\n$\\operatorname{SL}_r(\\mathbb{Z})$ (the symmetry groups of these quadratic\nforms) that were not of a quantitative nature. Our new observation relies on\nnoticing that rather than studying random walks, one can obtain more\nquantitative results by considering polynomial orbits of these group actions\nthat are not contained in cosets of submodules of $\\mathbb{Z}^r$ of small\nindex. Our main new technical tool is a uniform Furstenberg-S\\'{a}rk\\\"{o}zy\ntheorem that holds for a large class of polynomials not necessarily vanishing\nat zero, which may be of independent interest and is derived from a density\nincrement argument and Hua's bound on polynomial exponential sums.\n"}
+{"abstract": "  Person image synthesis, e.g., pose transfer, is a challenging problem due to\nlarge variation and occlusion. Existing methods have difficulties predicting\nreasonable invisible regions and fail to decouple the shape and style of\nclothing, which limits their applications on person image editing. In this\npaper, we propose PISE, a novel two-stage generative model for Person Image\nSynthesis and Editing, which is able to generate realistic person images with\ndesired poses, textures, or semantic layouts. For human pose transfer, we first\nsynthesize a human parsing map aligned with the target pose to represent the\nshape of clothing by a parsing generator, and then generate the final image by\nan image generator. To decouple the shape and style of clothing, we propose\njoint global and local per-region encoding and normalization to predict the\nreasonable style of clothing for invisible regions. We also propose\nspatial-aware normalization to retain the spatial context relationship in the\nsource image. The results of qualitative and quantitative experiments\ndemonstrate the superiority of our model on human pose transfer. Besides, the\nresults of texture transfer and region editing show that our model can be\napplied to person image editing.\n"}
+{"abstract": "  Given a closed manifold of positive Yamabe invariant and for instance\npositive Morse functions upon it, the conformally prescribed scalar curvature\nproblem raises the question, whether or not such functions can by conformally\nchanging the metric be realised as the scalar curvature of this manifold. As we\nshall quantify depending on the shape and structure of such functions, every\nlack of a solution for some candidate function leads to existence of\nenergetically uniformly bounded solutions for entire classes of related\ncandidate functions.\n"}
+{"abstract": "  In the animation industry, cartoon videos are usually produced at low frame\nrate since hand drawing of such frames is costly and time-consuming. Therefore,\nit is desirable to develop computational models that can automatically\ninterpolate the in-between animation frames. However, existing video\ninterpolation methods fail to produce satisfying results on animation data.\nCompared to natural videos, animation videos possess two unique characteristics\nthat make frame interpolation difficult: 1) cartoons comprise lines and smooth\ncolor pieces. The smooth areas lack textures and make it difficult to estimate\naccurate motions on animation videos. 2) cartoons express stories via\nexaggeration. Some of the motions are non-linear and extremely large. In this\nwork, we formally define and study the animation video interpolation problem\nfor the first time. To address the aforementioned challenges, we propose an\neffective framework, AnimeInterp, with two dedicated modules in a\ncoarse-to-fine manner. Specifically, 1) Segment-Guided Matching resolves the\n\"lack of textures\" challenge by exploiting global matching among color pieces\nthat are piece-wise coherent. 2) Recurrent Flow Refinement resolves the\n\"non-linear and extremely large motion\" challenge by recurrent predictions\nusing a transformer-like architecture. To facilitate comprehensive training and\nevaluations, we build a large-scale animation triplet dataset, ATD-12K, which\ncomprises 12,000 triplets with rich annotations. Extensive experiments\ndemonstrate that our approach outperforms existing state-of-the-art\ninterpolation methods for animation videos. Notably, AnimeInterp shows\nfavorable perceptual quality and robustness for animation scenarios in the\nwild. The proposed dataset and code are available at\nhttps://github.com/lisiyao21/AnimeInterp/.\n"}
+{"abstract": "  Tensor network states have been a very prominent tool for the study of\nquantum many-body physics, thanks to their physically relevant entanglement\nproperties and their ability to encode symmetries. In the last few years, the\nformalism has been extended and applied to theories with local symmetries to -\nlattice gauge theories. In the contraction of tensor network states as well as\ncorrelation functions of physical observables with respect to them, one uses\nthe so-called transfer operator, whose local properties dictate the long-range\nbehaviour of the state. In this work we study transfer operators of tensor\nnetwork states (in particular, PEPS - projected entangled pair states) in the\ncontext of lattice gauge theories, and consider the implications of the local\nsymmetry on their structure and properties. We focus on the Wilson loop - a\nnonlocal, gauge-invariant observable which is central to pure gauge theories,\nwhose long range decay behaviour probes the confinement or deconfinement of\nstatic charges. Using the symmetry, we show how to handle its contraction, and\nformulate conditions relating local properties to its decay fashion.\n"}
+{"abstract": "  We optimised the magnetic field homogeneity of two canonical designs for\nmobile microfluidic NMR applications: two parallel magnets with an air gap and\na modified Halbach array. Along with the influence of the sample length,\ngeneral design guidelines will be presented. For a fair comparison the\nsensitive length of the sample has been chosen to be the same as the gap size\nbetween the magnets to ensure enough space for the transmitting and receiving\nunit, as well as basic electric shimming components. Keeping the compactness of\nthe final device in mind, a box with an edge length 5 times the gap size has\nbeen defined, in which the complete magnet configuration should fit. With the\nchosen boundary conditions, the simple parallel cuboid configuration reaches\nthe best homogeneity without active shimming (0.5$\\mathrm{B_{s}}$, 41 ppm),\nwhile the Pseudo-Halbach configuration has the highest field strength\n(0.9$\\mathrm{B_{s}}$, 994 ppm), assuming perfect magnets. However, permanent\nmagnet configurations suffer from imperfections, such as magnetisation,\nfabrication and positioning errors, which results in worse magnetic field\nhomogeneities than expected from simulations using a fixed optimised parameter\nset. We present a sensitivity analysis for a magnetic cube and the results of\nstudies of the variations in the magnetisation and angle of magnetisation of\nmagnets purchased from different suppliers, composed of different materials and\ncoatings, and of different sizes. We performed a detailed Monte Carlo\nsimulation on the effect of the measured distribution of magnetic properties on\nthe mentioned configurations. The cuboid design shows a mean homogeneity of 430\nppm (std dev. 350 ppm), the Pseudo-Halbach has a mean homogeneity of 1086 ppm\n(std dev. 8 ppm).\n"}
+{"abstract": "  Over the last decade, Programmable Logic Controllers (PLCs) have been\nincreasingly targeted by attackers to obtain control over industrial processes\nthat support critical services. Such targeted attacks typically require\ndetailed knowledge of system-specific attributes, including hardware\nconfigurations, adopted protocols, and PLC control-logic, i.e. process\ncomprehension. The consensus from both academics and practitioners suggests\nstealthy process comprehension obtained from a PLC alone, to conduct targeted\nattacks, is impractical. In contrast, we assert that current PLC programming\npractices open the door to a new vulnerability class based on control-logic\nconstructs. To support this, we propose the concept of Process Comprehension at\na Distance (PCaaD), as a novel methodological and automatable approach for\nsystem-agnostic exploitation of PLC library functions, leading to the targeted\nexfiltration of operational data, manipulation of control-logic behavior, and\nestablishment of covert command and control channels through unused memory. We\nvalidate PCaaD on widely used PLCs, by identification of practical attacks.\n"}
+{"abstract": "  Emil Post's tag system problem is the question of whether or not a tag system\n$\\{N=3, P(0)=00, P(1)=1101\\}$ has a configuration, simulation of which will\nnever halt or end up in a loop. For the past decades, there were several\nattempts to find an answer to this question, including a recent study by\nWolfram (2021), during which the first $2^{84}$ initial configurations were\nchecked. This paper presents a family of configurations of this type in a form\nof strings $a^{n} b c^{m}$, that evolve to $a^{n+1} b c^{m+1}$ after a finite\namount of steps. The proof of this behavior for all non-negative $n$ and $m$ is\ndescribed further in a paper as a finite verification procedure, which is\ncomputationally bounded by 20000 iterations of tag. All corresponding code can\nbe found at\nhttps://github.com/nikitakurilenko/post-tag-infinitely-growing-configurations.\n"}
+{"abstract": "  3D reconstruction has lately attracted increasing attention due to its wide\napplication in many areas, such as autonomous driving, robotics and virtual\nreality. As a dominant technique in artificial intelligence, deep learning has\nbeen successfully adopted to solve various computer vision problems. However,\ndeep learning for 3D reconstruction is still at its infancy due to its unique\nchallenges and varying pipelines. To stimulate future research, this paper\npresents a review of recent progress in deep learning methods for Multi-view\nStereo (MVS), which is considered as a crucial task of image-based 3D\nreconstruction. It also presents comparative results on several publicly\navailable datasets, with insightful observations and inspiring future research\ndirections.\n"}
+{"abstract": "  Traditional approaches to activity recognition involve the use of wearable\nsensors or cameras in order to recognise human activities. In this work, we\nextract fine-grained physical layer information from WiFi devices for the\npurpose of passive activity recognition in indoor environments. While such data\nis ubiquitous, few approaches are designed to utilise large amounts of\nunlabelled WiFi data. We propose the use of self-supervised contrastive\nlearning to improve activity recognition performance when using multiple views\nof the transmitted WiFi signal captured by different synchronised receivers. We\nconduct experiments where the transmitters and receivers are arranged in\ndifferent physical layouts so as to cover both Line-of-Sight (LoS) and non LoS\n(NLoS) conditions. We compare the proposed contrastive learning system with\nnon-contrastive systems and observe a 17.7% increase in macro averaged F1 score\non the task of WiFi based activity recognition, as well as significant\nimprovements in one- and few-shot learning scenarios.\n"}
+{"abstract": "  3D printing has revolutionized the manufacturing of volumetric components and\nstructures for use in various fields. Owing to the advent of photo-curable\nresins, several fully volumetric light-based techniques have been recently\ndeveloped to push further the resolution and speed limitations of 3D printing.\nHowever, these new approaches only work with homogeneous and relatively\ntransparent resins so that the incident light patterns used for\nphoto-polymerization are not impacted along their propagation through the\nmaterial. Herein, we describe a strategy to print in scattering materials. It\nconsists of characterizing how light is distorted by the curable resin and then\napplying a digital correction to the light patterns to counteract the effect of\nscattering. Using a tomographic volumetric printer, we experimentally\ndemonstrate the importance of taking light scattering into account when\ncomputing the projected patterns and show that our applied correction\nsignificantly improves printability, even when the object size exceeds the\nscattering mean free path of light.\n"}
+{"abstract": "  Brain function relies on a precisely coordinated and dynamic balance between\nthe functional integration and segregation of distinct neural systems.\nCharacterizing the way in which neural systems reconfigure their interactions\nto give rise to distinct but hidden brain states remains an open challenge. In\nthis paper, we propose a Bayesian model-based characterization of latent brain\nstates and showcase a novel method based on posterior predictive discrepancy\nusing the latent block model to detect transitions between latent brain states\nin blood oxygen level-dependent (BOLD) time series. The set of estimated\nparameters in the model includes a latent label vector that assigns network\nnodes to communities, and also block model parameters that reflect the weighted\nconnectivity within and between communities. Besides extensive in-silico model\nevaluation, we also provide empirical validation (and replication) using the\nHuman Connectome Project (HCP) dataset of 100 healthy adults. Our results\nobtained through an analysis of task-fMRI data during working memory\nperformance show appropriate lags between external task demands and\nchange-points between brain states, with distinctive community patterns\ndistinguishing fixation, low-demand and high-demand task conditions.\n"}
+{"abstract": "  For globally connected devices like smart phones, personal computers and\nInternet-of-things devices, the ability to generate random numbers is essential\nfor execution of cryptographic protocols responsible for information security.\nGenerally, a random number generator should be small, robust, utilize as few\nhardware and energy resources as possible, yet provide excellent randomness at\na high enough speed (bitrate) for a given purpose. In this work we present a\nquantum random number generator (QRNG) which makes use of a photoelectric\neffect in single-photon avalanche diodes (SPADs) as a source of randomness and\nis scalable to any desired bitrate. We use the random flip-flop method in which\nrandom bits are obtained by periodic sampling of a randomly toggling flip-flop.\nFor the first time we investigate this method in detail and find that, out of\ntwo main imperfections, bias is due only to hardware imperfections while\nautocorrelation predominantly resides with the method itself. SPADs are\nintegrated on a silicon chip together with passive quenching and digital\npulse-shaping circuitry, using a standard 180 nm CMOS process. A separate FPGA\nchip derives random numbers from the detection signals. The basic QRNG cell,\nmade of only two SPADs and a few logic circuits, can generate up to 20 Mbit/s\nthat pass NIST statistical tests without any further postprocessing. This\ntechnology allows integration of a QRNG on a single silicon chip using readily\navailable industrial processes.\n"}
+{"abstract": "  The rise of personal assistants has made conversational question answering\n(ConvQA) a very popular mechanism for user-system interaction. State-of-the-art\nmethods for ConvQA over knowledge graphs (KGs) can only learn from crisp\nquestion-answer pairs found in popular benchmarks. In reality, however, such\ntraining data is hard to come by: users would rarely mark answers explicitly as\ncorrect or wrong. In this work, we take a step towards a more natural learning\nparadigm - from noisy and implicit feedback via question reformulations. A\nreformulation is likely to be triggered by an incorrect system response,\nwhereas a new follow-up question could be a positive signal on the previous\nturn's answer. We present a reinforcement learning model, termed CONQUER, that\ncan learn from a conversational stream of questions and reformulations. CONQUER\nmodels the answering process as multiple agents walking in parallel on the KG,\nwhere the walks are determined by actions sampled using a policy network. This\npolicy network takes the question along with the conversational context as\ninputs and is trained via noisy rewards obtained from the reformulation\nlikelihood. To evaluate CONQUER, we create and release ConvRef, a benchmark\nwith about 11k natural conversations containing around 205k reformulations.\nExperiments show that CONQUER successfully learns to answer conversational\nquestions from noisy reward signals, significantly improving over a\nstate-of-the-art baseline.\n"}
+{"abstract": "  Let $K$ be a field and $X$, $Y$ denote matrices such that, the entries of $X$\nare either indeterminates over $K$ or $0$ and the entries of $Y$ are\nindeterminates over $K$ which are different from those appearing in $X$. We\nconsider ideals of the form $I_{1}(XY)$, which is the ideal generated by the\n$1\\times 1$ minors of the matrix $XY$. We prove that the quotient ring $K[X,\nY]/I_{1}(XY)$ admits an ASL structure for certain $X$ and $Y$.\n"}
+{"abstract": "  We apply the diabatic approach, specially suited for a QCD based study of\nconventional (quark-antiquark) and unconventional (quark-antiquark +\nmeson-meson) meson states, to the description of hidden-bottom mesons. A\nspectral analysis of the $I=0$, $J^{++}$ and $1^{--}$ resonances with masses up\nto about $10.8$ GeV is carried out. Masses and widths of all the experimentally\nknown resonances, including conventional and unconventional states, can be well\nreproduced. In particular, we predict a significant $B\\bar{B}^{\\ast}$ component\nin $\\Upsilon(10580)$. We also predict the existence of a not yet discovered\nunconventional $1^{++}$ narrow state, with a significant\n$B_{s}\\bar{B}_{s}^{\\ast}$ content making it to decay into $\\Upsilon(1S)\\phi$,\nwhose experimental discovery would provide definite support to our theoretical\nanalysis.\n"}
+{"abstract": "  In a previous paper, we computed the energy density and the non-linear energy\ncascade rate for transverse kink waves using Elsasser variables. In this paper,\nwe focus on the standing kink waves, which are impulsively excited in coronal\nloops by external perturbations. We present an analytical calculation to\ncompute the damping time due to the non-linear development of the\nKelvin-Helmholtz instability. The main result is that the damping time is\ninversely proportional to the oscillation amplitude. We compare the damping\ntimes from our formula with the results of numerical simulations and\nobservations. In both cases we find a reasonably good match. The comparison\nwith the simulations show that the non-linear damping dominates in the high\namplitude regime, while the low amplitude regime shows damping by resonant\nabsorption. In the comparison with the observations, we find a power law\ninversely proportional to the amplitude $\\eta^{-1}$ as an outer envelope for\nour Monte Carlo data points.\n"}
+{"abstract": "  Multilingual Neural Machine Translation (MNMT) has aroused widespread\ninterest due to its efficiency. An exciting advantage of MNMT models is that\nthey could also translate between unsupervised (zero-shot) language directions.\nLanguage tag (LT) strategies are often adopted to indicate the translation\ndirections in MNMT. In this paper, we demonstrate that the LTs are not only\nindicators for translation directions but also crucial to zero-shot translation\nqualities. Unfortunately, previous work tends to ignore the importance of LT\nstrategies. We demonstrate that a proper LT strategy could enhance the\nconsistency of semantic representations and alleviate the off-target issue in\nzero-shot directions. Experimental results show that by ignoring the source\nlanguage tag (SLT) and adding the target language tag (TLT) to the encoder, the\nzero-shot translations could achieve a +8 BLEU score difference over other LT\nstrategies in IWSLT17, Europarl, TED talks translation tasks.\n"}
+{"abstract": "  Cohort analysis is a pervasive activity in web analytics. One divides users\ninto groups according to specific criteria and tracks their behavior over time.\nDespite its extensive use, academic circles do not discuss cohort analysis to\nevaluate user behavior online. This work introduces an unsupervised\nnon-parametric approach to group Internet users based on their activities. In\ncomparison, canonical methods in marketing and engineering-based techniques\nunderperform. COHORTNEY is the first machine learning-based cohort analysis\nalgorithm with a robust theoretical explanation.\n"}
+{"abstract": "  We study ancient Ricci flows which admit asymptotic solitons in the sense of\nPerelman. We prove that the asymptotic solitons must coincide with Bamler's\ntangent flows at infinity. Furthermore, we show that Perelman's\n$\\nu$-functional is uniformly bounded on such ancient solutions; this fact\nleads to logarithmic Sobolev inequalities and Sobolev inequalities. Lastly, as\nan important tool for the proofs of the above results, we also show that, for a\ncomplete Ricci flow with bounded curvature, the bound of the Nash entropy\ndepends only on the local geometry around an $H_n$-center of its base point.\n"}
+{"abstract": "  A deep equilibrium model uses implicit layers, which are implicitly defined\nthrough an equilibrium point of an infinite sequence of computation. It avoids\nany explicit computation of the infinite sequence by finding an equilibrium\npoint directly via root-finding and by computing gradients via implicit\ndifferentiation. In this paper, we analyze the gradient dynamics of deep\nequilibrium models with nonlinearity only on weight matrices and non-convex\nobjective functions of weights for regression and classification. Despite\nnon-convexity, convergence to global optimum at a linear rate is guaranteed\nwithout any assumption on the width of the models, allowing the width to be\nsmaller than the output dimension and the number of data points. Moreover, we\nprove a relation between the gradient dynamics of the deep implicit layer and\nthe dynamics of trust region Newton method of a shallow explicit layer. This\nmathematically proven relation along with our numerical observation suggests\nthe importance of understanding implicit bias of implicit layers and an open\nproblem on the topic. Our proofs deal with implicit layers, weight tying and\nnonlinearity on weights, and differ from those in the related literature.\n"}
+{"abstract": "  Owing to its low vapor pressure, low toxicity, high thermal and electrical\nconductivities, eutectic Ga-In (EGaIn) has shown a great potential for smart\nmaterial applications in flexible devices, cooling in micro-devices,\nself-healing reconfigurable materials, and actuators. For such applications,\nEGaIn is maintained above its melting point, below which it undergoes\nsolidification and complex phase separation. A scientific understanding of the\nstructural and compositional evolution during thermal cycling could help\nfurther assess the application range of Ga and other low-melting-point fusible\nalloys. Here, we use an integrated suite of cryogenically-enabled advanced\nmicroscopy & microanalysis to better understand phase separation and (re)mixing\nprocesses in EGaIn. We reveal an overlooked thermal-stimulus-response behavior\nfor frozen mesoscale EGaIn at cryogenic temperatures, with a sudden volume\nexpansion observed during in-situ heat-cycling, associated with the\nimmiscibility between Ga and In during cooling and the formation of metastable\nGa phases. These results emphasize the importance of the kinetics of\nrejuvenation, and open new paths for EGaIn in sensor applications.\n"}
+{"abstract": "  Deep learning has achieved impressive performance on many tasks in recent\nyears. However, it has been found that it is still not enough for deep neural\nnetworks to provide only point estimates. For high-risk tasks, we need to\nassess the reliability of the model predictions. This requires us to quantify\nthe uncertainty of model prediction and construct prediction intervals. In this\npaper, We explore the uncertainty in deep learning to construct the prediction\nintervals. In general, We comprehensively consider two categories of\nuncertainties: aleatory uncertainty and epistemic uncertainty. We design a\nspecial loss function, which enables us to learn uncertainty without\nuncertainty label. We only need to supervise the learning of regression task.\nWe learn the aleatory uncertainty implicitly from the loss function. And that\nepistemic uncertainty is accounted for in ensembled form. Our method correlates\nthe construction of prediction intervals with the uncertainty estimation.\nImpressive results on some publicly available datasets show that the\nperformance of our method is competitive with other state-of-the-art methods.\n"}
+{"abstract": "  We improve on a construction of Mestre--Shioda to produce some families of\ncurves $X/\\mathbb{Q}$ of record rank relative to the genus $g$ of $X$. Our\nfirst main result is that for any integer $g \\geqslant 8$ with $g \\equiv 2\n\\pmod 3$, there exist infinitely many genus $g$ hyperelliptic curves over\n$\\mathbb{Q}$ with at least $8g+32$ $\\mathbb{Q}$-points and Mordell--Weil rank\n$\\geqslant 4g + 15$ over $\\mathbb{Q}$. Our second main theorem is that if $g+1$\nis an odd prime and $K$ contains the $g+1$-th roots of unity, then there exist\ninfinitely many genus $g$ hyperelliptic curves over $K$ with Mordell--Weil rank\nat least $6g$ over $K$.\n"}
+{"abstract": "  Simultaneous localization and mapping (SLAM) is a fundamental capability\nrequired by most autonomous systems. In this paper, we address the problem of\nloop closing for SLAM based on 3D laser scans recorded by autonomous cars. Our\napproach utilizes a deep neural network exploiting different cues generated\nfrom LiDAR data for finding loop closures. It estimates an image overlap\ngeneralized to range images and provides a relative yaw angle estimate between\npairs of scans. Based on such predictions, we tackle loop closure detection and\nintegrate our approach into an existing SLAM system to improve its mapping\nresults. We evaluate our approach on sequences of the KITTI odometry benchmark\nand the Ford campus dataset. We show that our method can effectively detect\nloop closures surpassing the detection performance of state-of-the-art methods.\nTo highlight the generalization capabilities of our approach, we evaluate our\nmodel on the Ford campus dataset while using only KITTI for training. The\nexperiments show that the learned representation is able to provide reliable\nloop closure candidates, also in unseen environments.\n"}
+{"abstract": "  Due to its high lethality amongst the elderly, the safety of nursing homes\nhas been of central importance during the COVID-19 pandemic. With test\nprocedures becoming available at scale, such as antigen or RT-LAMP tests, and\nincreasing availability of vaccinations, nursing homes might be able to safely\nrelax prohibitory measures while controlling the spread of infections (meaning\nan average of one or less secondary infections per index case). Here, we\ndevelop a detailed agent-based epidemiological model for the spread of\nSARS-CoV-2 in nursing homes to identify optimal prevention strategies. The\nmodel is microscopically calibrated to high-resolution data from nursing homes\nin Austria, including detailed social contact networks and information on past\noutbreaks. We find that the effectiveness of mitigation testing depends\ncritically on the timespan between test and test result, the detection\nthreshold of the viral load for the test to give a positive result, and the\nscreening frequencies of residents and employees. Under realistic conditions\nand in absence of an effective vaccine, we find that preventive screening of\nemployees only might be sufficient to control outbreaks in nursing homes,\nprovided that turnover times and detection thresholds of the tests are low\nenough. If vaccines that are moderately effective against infection and\ntransmission are available, control is achieved if 80% or more of the\ninhabitants are vaccinated, even if no preventive testing is in place and\nresidents are allowed to have visitors. Since these results strongly depend on\nvaccine efficacy against infection, retention of testing infrastructures,\nregular voluntary screening and sequencing of virus genomes is advised to\nenable early identification of new variants of concern.\n"}
+{"abstract": "  Membership inference (MI) attacks affect user privacy by inferring whether\ngiven data samples have been used to train a target learning model, e.g., a\ndeep neural network. There are two types of MI attacks in the literature, i.e.,\nthese with and without shadow models. The success of the former heavily depends\non the quality of the shadow model, i.e., the transferability between the\nshadow and the target; the latter, given only blackbox probing access to the\ntarget model, cannot make an effective inference of unknowns, compared with MI\nattacks using shadow models, due to the insufficient number of qualified\nsamples labeled with ground truth membership information.\n  In this paper, we propose an MI attack, called BlindMI, which probes the\ntarget model and extracts membership semantics via a novel approach, called\ndifferential comparison. The high-level idea is that BlindMI first generates a\ndataset with nonmembers via transforming existing samples into new samples, and\nthen differentially moves samples from a target dataset to the generated,\nnon-member set in an iterative manner. If the differential move of a sample\nincreases the set distance, BlindMI considers the sample as non-member and vice\nversa.\n  BlindMI was evaluated by comparing it with state-of-the-art MI attack\nalgorithms. Our evaluation shows that BlindMI improves F1-score by nearly 20%\nwhen compared to state-of-the-art on some datasets, such as Purchase-50 and\nBirds-200, in the blind setting where the adversary does not know the target\nmodel's architecture and the target dataset's ground truth labels. We also show\nthat BlindMI can defeat state-of-the-art defenses.\n"}
+{"abstract": "  We propose a novel high dynamic range (HDR) video reconstruction method with\nnew tri-exposure quad-bayer sensors. Thanks to the larger number of exposure\nsets and their spatially uniform deployment over a frame, they are more robust\nto noise and spatial artifacts than previous spatially varying exposure (SVE)\nHDR video methods. Nonetheless, the motion blur from longer exposures, the\nnoise from short exposures, and inherent spatial artifacts of the SVE methods\nremain huge obstacles. Additionally, temporal coherence must be taken into\naccount for the stability of video reconstruction. To tackle these challenges,\nwe introduce a novel network architecture that divides-and-conquers these\nproblems. In order to better adapt the network to the large dynamic range, we\nalso propose LDR-reconstruction loss that takes equal contributions from both\nthe highlighted and the shaded pixels of HDR frames. Through a series of\ncomparisons and ablation studies, we show that the tri-exposure quad-bayer with\nour solution is more optimal to capture than previous reconstruction methods,\nparticularly for the scenes with larger dynamic range and objects with motion.\n"}
+{"abstract": "  We derive a limiting absorption principle on any compact interval in\n$\\mathbb{R} \\backslash \\{0\\}$ for the free massless Dirac operator, $H_0 =\n\\alpha \\cdot (-i \\nabla)$ in $[L^2(\\mathbb{R}^n)]^N$, $n \\geq 2$,\n$N=2^{\\lfloor(n+1)/2\\rfloor}$, and then prove the absence of singular\ncontinuous spectrum of interacting massless Dirac operators $H = H_0 +V$, where\n$V$ decays like $O(|x|^{-1 - \\varepsilon})$.\n  Expressing the spectral shift function $\\xi(\\,\\cdot\\,; H,H_0)$ as normal\nboundary values of regularized Fredholm determinants, we prove that for\nsufficiently decaying $V$, $\\xi(\\,\\cdot\\,;H,H_0) \\in C((-\\infty,0) \\cup\n(0,\\infty))$, and that the left and right limits at zero, $\\xi(0_{\\pm};\nH,H_0)$, exist.\n  Introducing the non-Fredholm operator $\\boldsymbol{D}_{\\boldsymbol{A}} =\n\\frac{d}{dt} + \\boldsymbol{A}$ in\n$L^2\\big(\\mathbb{R};[L^2(\\mathbb{R}^n)]^N\\big)$, where $\\boldsymbol{A} =\n\\boldsymbol{A_-} + \\boldsymbol{B}$, $\\boldsymbol{A_-}$, and $\\boldsymbol{B}$\nare generated in terms of $H, H_0$ and $V$, via $A(t) = A_- + B(t)$, $A_- =\nH_0$, $B(t)=b(t) V$, $t \\in \\mathbb{R}$, assuming $b$ is smooth, $b(-\\infty) =\n0$, $b(+\\infty) = 1$, and introducing $\\boldsymbol{H_1} =\n\\boldsymbol{D}_{\\boldsymbol{A}}^{*} \\boldsymbol{D}_{\\boldsymbol{A}}$,\n$\\boldsymbol{H_2} = \\boldsymbol{D}_{\\boldsymbol{A}}\n\\boldsymbol{D}_{\\boldsymbol{A}}^{*}$, one of the principal results in this\nmanuscript expresses the $k$th resolvent regularized Witten index\n$W_{k,r}(\\boldsymbol{D}_{\\boldsymbol{A}})$ ($k \\in \\mathbb{N}$, $k \\geq \\lceil\nn/2 \\rceil$) in terms of spectral shift functions as \\[\nW_{k,r}(\\boldsymbol{D}_{\\boldsymbol{A}}) = \\xi(0_+; \\boldsymbol{H_2},\n\\boldsymbol{H_1}) = [\\xi(0_+;H,H_0) + \\xi(0_-;H,H_0)]/2. \\] Here\n$L^2(\\mathbb{R};\\mathcal{H}) = \\int_{\\mathbb{R}}^{\\oplus} dt \\, \\mathcal{H}$\nand $\\boldsymbol{T} = \\int_{\\mathbb{R}}^{\\oplus} dt \\, T(t)$ abbreviate direct\nintegrals.\n"}
+{"abstract": "  The one-loop contributions to the trilinear neutral gauge boson couplings\n$ZZV^\\ast$ ($V=\\gamma,Z,Z'$), parametrized in terms of one $CP$-conserving\n$f_5^{V}$ and one $CP$-violating $f_4^{V}$ form factors, are calculated in\nmodels with $CP$-violating flavor changing neutral current couplings mediated\nby the $Z$ gauge boson and an extra neutral gauge boson $Z'$. Analytical\nresults are presented in terms of both Passarino-Veltman scalar functions and\nclosed form functions. Constraints on the vector and axial couplings of the $Z$\ngauge boson $\\left|g_{{VZ}}^{tu}\\right|< 0.0096$ and\n$\\left|g_{{VZ}}^{tc}\\right|<0.011$ are obtained from the current experimental\ndata on the $t\\rightarrow Z q$ decays. It is found that in the case of the\n$ZZ\\gamma^\\ast$ vertex the only non-vanishing form factor is $f_5^{\\gamma}$,\nwhich can be of the order of $10^{-3}$, whereas for the $ZZZ^\\ast$ vertex both\nform factors $f_5^{Z}$ and $f_4^{Z}$ are non-vanishing and can be of the order\nof $10^{-6}$ and $10^{-5}$, respectively. Our estimates for $f_5^{\\gamma}$ and\n$f_5^{Z}$ are smaller than those predicted by the standard model, where\n$f_4^{Z}$ is absent up to the one loop level. We also estimate the $ZZ{Z'}^{*}$\nform factors arising from both diagonal and non-diagonal $Z'$ couplings within\na few extension models. It is found that in the diagonal case $f_{5}^{Z'}$ is\nthe only non-vanishing form factor and its real and imaginary parts can be of\nthe order of $10^{-1}-10^{-2}$ and $ 10^{-2}-10^{-3}$, respectively, with the\ndominant contributions arising from the light quarks and leptons. In the\nnon-diagonal case $f_{5}^{Z^\\prime}$ can be of the order of $10^{-4}$, whereas\n$f_4^{Z'}$ can reach values as large as $10^{-7}-10^{-8}$, with the largest\ncontributions arising from the $Z'tq$ couplings.\n"}
+{"abstract": "  Large-scale sky surveys have played a transformative role in our\nunderstanding of astrophysical transients, only made possible by increasingly\npowerful machine learning-based filtering to accurately sift through the vast\nquantities of incoming data generated. In this paper, we present a new\nreal-bogus classifier based on a Bayesian convolutional neural network that\nprovides nuanced, uncertainty-aware classification of transient candidates in\ndifference imaging, and demonstrate its application to the datastream from the\nGOTO wide-field optical survey. Not only are candidates assigned a\nwell-calibrated probability of being real, but also an associated confidence\nthat can be used to prioritise human vetting efforts and inform future model\noptimisation via active learning. To fully realise the potential of this\narchitecture, we present a fully-automated training set generation method which\nrequires no human labelling, incorporating a novel data-driven augmentation\nmethod to significantly improve the recovery of faint and nuclear transient\nsources. We achieve competitive classification accuracy (FPR and FNR both below\n1%) compared against classifiers trained with fully human-labelled datasets,\nwhilst being significantly quicker and less labour-intensive to build. This\ndata-driven approach is uniquely scalable to the upcoming challenges and data\nneeds of next-generation transient surveys. We make our data generation and\nmodel training codes available to the community.\n"}
+{"abstract": "  The weighted $k$-server problem is a natural generalization of the $k$-server\nproblem in which the cost incurred in moving a server is the distance traveled\ntimes the weight of the server. Even after almost three decades since the\nseminal work of Fiat and Ricklin (1994), the competitive ratio of this problem\nremains poorly understood, even on the simplest class of metric spaces -- the\nuniform metric spaces. In particular, in the case of randomized algorithms\nagainst the oblivious adversary, neither a better upper bound that the doubly\nexponential deterministic upper bound, nor a better lower bound than the\nlogarithmic lower bound of unweighted $k$-server, is known. In this article, we\nmake significant progress towards understanding the randomized competitive\nratio of weighted $k$-server on uniform metrics. We cut down the triply\nexponential gap between the upper and lower bound to a singly exponential gap\nby proving that the competitive ratio is at least exponential in $k$,\nsubstantially improving on the previously known lower bound of about $\\ln k$.\n"}
+{"abstract": "  Recently, many authors have embraced the study of certain properties of\nmodules such as projectivity, injectivity and flatness from an alternative\npoint of view. Rather than saying a module has a certain property or not, each\nmodule is assigned a relative domain which, somehow, measures to which extent\nit has this particular property. In this work, we introduce a new and fresh\nperspective on flatness of modules. However, we will first investigate a more\ngeneral context by introducing domains relative to a precovering class $\\x$. We\ncall these domains $\\x$-precover completing domains. In particular, when $\\x$\nis the class of flat modules, we call them flat-precover completing domains.\nThis approach allows us to provide a common frame for a number of classical\nnotions. Moreover, some known results are generalized and some classical rings\nare characterized in terms of these domains.\n"}
+{"abstract": "  Cometary outbursts offer a valuable window into the composition of comet\nnuclei with their forceful ejection of dust and volatiles in explosive events,\nrevealing the interior components of the comet. Understanding how different\ntypes of outbursts influence the dust properties and volatile abundances to\nbetter interpret what signatures can be attributed to primordial composition\nand what features are the result of processing is an important task best\nundertaken with a multi-instrument approach. The European Space Agency\n\\textit{Rosetta} mission to 67P/Churyumov-Gerasimenko carried a suite of\ninstruments capable of carrying out this task in the near-nucleus coma with\nunprecedented spatial and spectral resolution. In this work we discuss two\noutbursts that occurred November 7 2015 and were observed by three instruments\non board: the Alice ultraviolet spectrograph, the Visual Infrared and Thermal\nImaging Spectrometer (VIRTIS), and the Optical, Spectroscopic, and Infrared\nRemote Imaging System (OSIRIS). Together the observations show that mixed gas\nand dust outbursts can have different spectral signatures representative of\ntheir initiating mechanisms, with the first outburst showing indicators of a\ncliff collapse origin and the second more representative of fresh volatiles\nbeing exposed via a deepening fracture. This analysis opens up the possibility\nof remote spectral classification of cometary outbursts with future work.\n"}
+{"abstract": "  The search for suitable materials for solid-state stationary storage of green\nhydrogen is pushing the implementation of efficient renewable energy systems.\nThis involves rational design and modification of cheap alloys for effective\nstorage in mild conditions of temperature and pressure. Among many\nintermetallic compounds described in the literature, TiFe-based systems have\nrecently regained vivid interest as materials for practical applications since\nthey are low-cost and they can be tuned to match required pressure and\noperation conditions. This work aims to provide a comprehensive review of\npublications involving chemical substitution in TiFe-based compounds for\nguiding compound design and materials selection in current and future hydrogen\nstorage applications. Mono- and multi-substituted compounds modify TiFe\nthermodynamics and are beneficial for many hydrogenation properties. They will\nbe reviewed and deeply discussed, with a focus on manganese substitution.\n"}
+{"abstract": "  We report a series of measurements of the effect of an electric field on the\nfrequency of the ultranarrow linewidth $^7F_0 \\rightarrow$ $^5D_0$ optical\ntransition of $\\rm Eu^{3+}$ ions in an $\\rm Y_2SiO_5$ matrix at cryogenic\ntemperatures. We provide linear Stark coefficients along two dielectric axes\nand for the two different substitution sites of the $\\rm Eu^{3+}$ ions, with an\nunprecedented accuracy, and an upper limit for the quadratic Stark shift. The\nmeasurements, which indicate that the electric field sensitivity is a factor of\nseven larger for site 1 relative to site 2 for a particular direction of the\nelectric field are of direct interest both in the context of quantum\ninformation processing and laser frequency stabilization with rare-earth doped\ncrystals, in which electric fields can be used to engineer experimental\nprotocols by tuning transition frequencies.\n"}
+{"abstract": "  We investigate the lattice regularization of $\\mathcal{N} = 4$ supersymmetric\nYang-Mills theory, by stochastically computing the eigenvalue mode number of\nthe fermion operator. This provides important insight into the non-perturbative\nrenormalization group flow of the lattice theory, through the definition of a\nscale-dependent effective mass anomalous dimension. While this anomalous\ndimension is expected to vanish in the conformal continuum theory, the finite\nlattice volume and lattice spacing generically lead to non-zero values, which\nwe use to study the approach to the continuum limit. Our numerical results,\ncomparing multiple lattice volumes, 't Hooft couplings, and numbers of colors,\nconfirm convergence towards the expected continuum result, while quantifying\nthe increasing significance of lattice artifacts at larger couplings.\n"}
+{"abstract": "  We use the data from Gaia Early Data Release 3 (EDR3) to study the kinematic\nproperties of Milky Way globular clusters. We measure the mean parallaxes and\nproper motions (PM) for 170 clusters, determine the PM dispersion profiles for\nmore than 100 clusters, uncover rotation signatures in more than 20 objects,\nand find evidence for radial or tangential PM anisotropy in a dozen richest\nclusters. At the same time, we use the selection of cluster members to explore\nthe reliability and limitations of the Gaia catalogue itself. We find that the\nformal uncertainties on parallax and PM are underestimated by 10-20% in dense\ncentral regions even for stars that pass numerous quality filters. We explore\nthe the spatial covariance function of systematic errors, and determine a lower\nlimit on the uncertainty of average parallaxes and PM at the level 0.01 mas and\n0.025 mas/yr, respectively. Finally, a comparison of mean parallaxes of\nclusters with distances from various literature sources suggests that the\nparallaxes (after applying the zero-point correction suggested by Lindegren et\nal. 2021) are overestimated by 0.01+-0.003 mas. Despite these caveats, the\nquality of Gaia astrometry has been significantly improved in EDR3 and provides\nvaluable insights into the properties of star clusters.\n"}
+{"abstract": "  We consider the task of minimizing the sum of smooth and strongly convex\nfunctions stored in a decentralized manner across the nodes of a communication\nnetwork whose links are allowed to change in time. We solve two fundamental\nproblems for this task. First, we establish the first lower bounds on the\nnumber of decentralized communication rounds and the number of local\ncomputations required to find an $\\epsilon$-accurate solution. Second, we\ndesign two optimal algorithms that attain these lower bounds: (i) a variant of\nthe recently proposed algorithm ADOM (Kovalev et al., 2021) enhanced via a\nmulti-consensus subroutine, which is optimal in the case when access to the\ndual gradients is assumed, and (ii) a novel algorithm, called ADOM+, which is\noptimal in the case when access to the primal gradients is assumed. We\ncorroborate the theoretical efficiency of these algorithms by performing an\nexperimental comparison with existing state-of-the-art methods.\n"}
+{"abstract": "  A model based on the classic non-interacting Ehrenfest urn model with\ntwo-urns is generalized to $M$ urns with the introduction of interactions for\nparticles within the same urn. As the inter-particle interaction strength is\nvaried, phases of different levels of non-uniformity emerge and their\nstabilities are calculated analytically. In particular, coexistence of locally\nstable uniform and non-uniform phases connected by first-order transition\noccurs. The phase transition threshold and energy barrier can be derived\nexactly together with the phase diagram obtained analytically. These analytic\nresults are further confirmed by Monte Carlo simulations.\n"}
+{"abstract": "  Recently, Balliu, Brandt, and Olivetti [FOCS '20] showed the first\n$\\omega(\\log^* n)$ lower bound for the maximal independent set (MIS) problem in\ntrees. In this work we prove lower bounds for a much more relaxed family of\ndistributed symmetry breaking problems. As a by-product, we obtain improved\nlower bounds for the distributed MIS problem in trees.\n  For a parameter $k$ and an orientation of the edges of a graph $G$, we say\nthat a subset $S$ of the nodes of $G$ is a $k$-outdegree dominating set if $S$\nis a dominating set of $G$ and if in the induced subgraph $G[S]$, every node in\n$S$ has outdegree at most $k$. Note that for $k=0$, this definition coincides\nwith the definition of an MIS. For a given $k$, we consider the problem of\ncomputing a $k$-outdegree dominating set. We show that, even in regular trees\nof degree at most $\\Delta$, in the standard \\LOCAL model, there exists a\nconstant $\\epsilon>0$ such that for $k\\leq \\Delta^\\epsilon$, for the problem of\ncomputing a $k$-outdegree dominating set, any randomized algorithm requires at\nleast $\\Omega(\\min\\{\\log\\Delta,\\sqrt{\\log\\log n}\\})$ rounds and any\ndeterministic algorithm requires at least $\\Omega(\\min\\{\\log\\Delta,\\sqrt{\\log\nn}\\})$ rounds.\n  The proof of our lower bounds is based on the recently highly successful\nround elimination technique. We provide a novel way to do simplifications for\nround elimination, which we expect to be of independent interest. Our new proof\nis considerably simpler than the lower bound proof in [FOCS '20]. In\nparticular, our round elimination proof uses a family of problems that can be\ndescribed by only a constant number of labels. The existence of such a proof\nfor the MIS problem was believed impossible by the authors of [FOCS '20].\n"}
+{"abstract": "  In the present paper, we will study geometric properties of harmonic mappings\nwhose analytic and co-analytic parts are (shifted) generated functions of\ncompletely monotone sequences.\n"}
+{"abstract": "  The number of cyber attacks has increased tremendously in the last few years.\nThis resulted into both human and financial losses at the individual and\norganization levels. Recently, cyber-criminals are leveraging new skills and\ncapabilities by employing anti-forensics activities, techniques and tools to\ncover their tracks and evade any possible detection. Consequently,\ncyber-attacks are becoming more efficient and more sophisticated. Therefore,\ntraditional cryptographic and non-cryptographic solutions and access control\nsystems are no longer enough to prevent such cyber attacks, especially in terms\nof acquiring evidence for attack investigation. Hence, the need for\nwell-defined, sophisticated, and advanced forensics investigation tools are\nhighly required to track down cyber criminals and to reduce the number of cyber\ncrimes. This paper reviews the different forensics and anti-forensics methods,\ntools, techniques, types, and challenges, while also discussing the rise of the\nanti-anti-forensics as a new forensics protection mechanism against\nanti-forensics activities. This would help forensics investigators to better\nunderstand the different anti-forensics tools, methods and techniques that\ncyber criminals employ while launching their attacks. Moreover, the limitations\nof the current forensics techniques are discussed, especially in terms of\nissues and challenges. Finally, this paper presents a holistic view from a\nliterature point of view over the forensics domain and also helps other fellow\ncolleagues in their quest to further understand the digital forensics domain.\n"}
+{"abstract": "  We discuss the characteristics of the patterns of the vascular networks in a\nmathematical model for angiogenesis. Based on recent in vitro experiments, this\nmathematical model assumes that the elongation and bifurcation of blood vessels\nduring angiogenesis are determined by the density of endothelial cells at the\ntip of the vascular network, and describes the dynamical changes in vascular\nnetwork formation using a system of simultaneous ordinary differential\nequations. The pattern of formation strongly depends on the supply rate of\nendothelial cells by cell division, the branching angle, and also on the\nconnectivity of vessels. By introducing reconnection of blood vessels, the\nstatistical distribution of the size of islands in the network is discussed\nwith respect to bifurcation angles and elongation factor distributions. The\ncharacteristics of the obtained patterns are analysed using multifractal\ndimension and other techniques.\n"}
+{"abstract": "  Famous double-slit or double-path experiments, implemented in a Young's or\nMach-Zehnder interferometer, have confirmed the dual nature of quantum matter,\nWhen a stream of photons, neutrons, atoms, or molecules, passes through two\nslits, either wave-like interference fringes build up on a screen, or\nparticle-like which-path distribution can be ascertained. These quantum objects\nexhibit both wave and particle properties but exclusively, depending on the way\nthey are measured. In an equivalent Mach-Zehnder configuration, the object\ndisplays either wave or particle nature in the presence or absence of a\nbeamsplitter, respectively, that represents the choice of which-measurement.\nWheeler further proposed a gedanken experiment, in which the choice of\nwhich-measurement is delayed, i.e. determined after the object has already\nentered the interferometer, so as to exclude the possibility of predicting\nwhich-measurement it will confront. The delayed-choice experiments have enabled\nsignificant demonstrations of genuine two-path duality of different quantum\nobjects. Recently, a quantum controlled version of delayed-choice was proposed\nby Ionicioiu and Terno, by introducing a quantum-controlled beamsplitter that\nis in a coherent superposition of presence and absence. It represents a\ncontrollable experiment platform that can not only reveal wave and particle\ncharacters, but also their superposition. Moreover, a quantitative description\nof two-slit duality relation was initialized in Wootters and Zurek's seminal\nwork and formalized by Greenberger,et. al. as D2+V2<=1, where D is the\ndistinguishability of whichpath information, and V is the contrast visibility\nof interference. In this regard, getting which-path information exclusively\nreduces the interference visibility, and vice versa. This double-path duality\nrelation has been tested in pioneer experiments and recently in delayed-choice\nmeasurements.\n"}
+{"abstract": "  We will introduce BumbleBee, a transformer model that will generate MIDI\nmusic data . We will tackle the issue of transformers applied to long sequences\nby implementing a longformer generative model that uses dilating sliding\nwindows to compute the attention layers. We will compare our results to that of\nthe music transformer and Long-Short term memory (LSTM) to benchmark our\nresults. This analysis will be performed using piano MIDI files, in particular\n, the JSB Chorales dataset that has already been used for other research works\n(Huang et al., 2018)\n"}
+{"abstract": "  It is well known that the Newton method may not converge when the initial\nguess does not belong to a specific quadratic convergence region. We propose a\nfamily of new variants of the Newton method with the potential advantage of\nhaving a larger convergence region as well as more desirable properties near a\nsolution. We prove quadratic convergence of the new family, and provide\nspecific bounds for the asymptotic error constant. We illustrate the advantages\nof the new methods by means of test problems, including two and six variable\npolynomial systems, as well as a challenging signal processing example. We\npresent a numerical experimental methodology which uses a large number of\nrandomized initial guesses for a number of methods from the new family, in turn\nproviding advice as to which of the methods employed is preferable to use in a\nparticular search domain.\n"}
+{"abstract": "  We study the log-rank conjecture from the perspective of point-hyperplane\nincidence geometry. We formulate the following conjecture: Given a point set in\n$\\mathbb{R}^d$ that is covered by constant-sized sets of parallel hyperplanes,\nthere exists an affine subspace that accounts for a large (i.e.,\n$2^{-{\\operatorname{polylog}(d)}}$) fraction of the incidences. Alternatively,\nour conjecture may be interpreted linear-algebraically as follows: Any rank-$d$\nmatrix containing at most $O(1)$ distinct entries in each column contains a\nsubmatrix of fractional size $2^{-{\\operatorname{polylog}(d)}}$, in which each\ncolumn contains one distinct entry. We prove that our conjecture is equivalent\nto the log-rank conjecture.\n  Motivated by the connections above, we revisit well-studied questions in\npoint-hyperplane incidence geometry without structural assumptions (i.e., the\nexistence of partitions). We give an elementary argument for the existence of\ncomplete bipartite subgraphs of density $\\Omega(\\epsilon^{2d}/d)$ in any\n$d$-dimensional configuration with incidence density $\\epsilon$. We also\nimprove an upper-bound construction of Apfelbaum and Sharir (SIAM J. Discrete\nMath. '07), yielding a configuration whose complete bipartite subgraphs are\nexponentially small and whose incidence density is $\\Omega(1/\\sqrt d)$.\nFinally, we discuss various constructions (due to others) which yield\nconfigurations with incidence density $\\Omega(1)$ and bipartite subgraph\ndensity $2^{-\\Omega(\\sqrt d)}$.\n  Our framework and results may help shed light on the difficulty of improving\nLovett's $\\tilde{O}(\\sqrt{\\operatorname{rank}(f)})$ bound (J. ACM '16) for the\nlog-rank conjecture; in particular, any improvement on this bound would imply\nthe first bipartite subgraph size bounds for parallel $3$-partitioned\nconfigurations which beat our generic bounds for unstructured configurations.\n"}
+{"abstract": "  In-pipe robots are promising solutions for condition assessment, leak\ndetection, water quality monitoring in a variety of other tasks in pipeline\nnetworks. Smart navigation is an extremely challenging task for these robots as\na result of highly uncertain and disturbing environment for operation. Wireless\ncommunication to control these robots during operation is not feasible if the\npipe material is metal since the radio signals are destroyed in the pipe\nenvironment, and hence, this challenge is still unsolved. In this paper, we\nintroduce a method for smart navigation for our previously designed in-pipe\nrobot [1] based on particle filtering and a two-phase motion controller. The\nrobot is given the map of the operation path with a novel approach and the\nparticle filtering determines the straight and non-straight configurations of\nthe pipeline. In the straight paths, the robot follows a linear quadratic\nregulator (LQR) and proportional-integral-derivative (PID) based controller\nthat stabilizes the robot and tracks a desired velocity. In non-straight paths,\nthe robot follows the trajectory that a motion trajectory generator block plans\nfor the robot. The proposed method is a promising solution for smart navigation\nwithout the need for wireless communication and capable of inspecting long\ndistances in water distribution systems.\n"}
+{"abstract": "  We consider topologically twisted $\\mathcal{N}=2$, $SU(2)$ gauge theory with\na massive adjoint hypermultiplet on a smooth, compact four-manifold $X$. A\nconsistent formulation requires coupling the theory to a ${\\rm Spin}^c$\nstructure, which is necessarily non-trivial if $X$ is non-spin. We derive\nexplicit formulae for the topological correlation functions when $b_2^+\\geq 1$.\nWe demonstrate that, when the ${\\rm Spin}^c$ structure is canonically\ndetermined by an almost complex structure and the mass is taken to zero, the\npath integral reproduces known results for the path integral of the\n$\\mathcal{N}=4$ gauge theory with Vafa-Witten twist. On the other hand, we\nreproduce results from Donaldson-Witten theory after taking a suitable infinite\nmass limit. The topological correlators are functions of the UV coupling\nconstant $\\tau_{\\rm uv}$ and we confirm that they obey the expected $S$-duality\ntransformation laws. The holomorphic part of the partition function is a\ngenerating function for the Euler numbers of the matter (or obstruction) bundle\nover the instanton moduli space. For $b_2^+=1$, we derive a non-holomorphic\ncontribution to the path integral, such that the partition function and\ncorrelation functions are mock modular forms rather than modular forms. We\ncomment on the generalization of this work to the large class of\n$\\mathcal{N}=2$ theories of class $S$.\n"}
+{"abstract": "  This paper studies constrained text generation, which is to generate\nsentences under certain pre-conditions. We focus on CommonGen, the task of\ngenerating text based on a set of concepts, as a representative task of\nconstrained text generation. Traditional methods mainly rely on supervised\ntraining to maximize the likelihood of target sentences.However, global\nconstraints such as common sense and coverage cannot be incorporated into the\nlikelihood objective of the autoregressive decoding process. In this paper, we\nconsider using reinforcement learning to address the limitation, measuring\nglobal constraints including fluency, common sense and concept coverage with a\ncomprehensive score, which serves as the reward for reinforcement learning.\nBesides, we design a guided decoding method at the word, fragment and sentence\nlevels. Experiments demonstrate that our method significantly increases the\nconcept coverage and outperforms existing models in various automatic\nevaluations.\n"}
+{"abstract": "  This study focuses on the synthesis of FeRh nanoparticles via pulsed laser\nablation in liquid and on controlling the oxidation of the synthesized\nnanoparticles. Formation of monomodal {\\gamma}-FeRh nanoparticles was confirmed\nby transmission electron microscopy (TEM) and their composition confirmed by\natom probe tomography (APT). On these particles, three major contributors to\noxidation were analysed: 1) dissolved oxygen in the organic solvents, 2) the\nbound oxygen in the solvent and 3) oxygen in the atmosphere above the solvent.\nThe decrease of oxidation for optimized ablation conditions was confirmed\nthrough energy-dispersive X-ray (EDX) and M\\\"ossbauer spectroscopy.\nFurthermore, the time dependence of oxidation was monitored for dried FeRh\nnanoparticles powders using ferromagnetic resonance spectroscopy (FMR). By\nmagnetophoretic separation, B2-FeRh nanoparticles could be extracted from the\nsolution and characteristic differences of nanostrand formation between\n{\\gamma}-FeRh and B2-FeRh nanoparticles were observed.\n"}
+{"abstract": "  A number of industrial applications, such as smart grids, power plant\noperation, hybrid system management or energy trading, could benefit from\nimproved short-term solar forecasting, addressing the intermittent energy\nproduction from solar panels. However, current approaches to modelling the\ncloud cover dynamics from sky images still lack precision regarding the spatial\nconfiguration of clouds, their temporal dynamics and physical interactions with\nsolar radiation. Benefiting from a growing number of large datasets, data\ndriven methods are being developed to address these limitations with promising\nresults. In this study, we compare four commonly used Deep Learning\narchitectures trained to forecast solar irradiance from sequences of\nhemispherical sky images and exogenous variables. To assess the relative\nperformance of each model, we used the Forecast Skill metric based on the smart\npersistence model, as well as ramp and time distortion metrics. The results\nshow that encoding spatiotemporal aspects of the sequence of sky images greatly\nimproved the predictions with 10 min ahead Forecast Skill reaching 20.4% on the\ntest year. However, based on the experimental data, we conclude that, with a\ncommon setup, Deep Learning models tend to behave just as a 'very smart\npersistence model', temporally aligned with the persistence model while\nmitigating its most penalising errors. Thus, despite being captured by the sky\ncameras, models often miss fundamental events causing large irradiance changes\nsuch as clouds obscuring the sun. We hope that our work will contribute to a\nshift of this approach to irradiance forecasting, from reactive to\nanticipatory.\n"}
+{"abstract": "  Recent technological advances allowed the coherent optical manipulation of\nhigh-energy electron wavepackets with attosecond precision. Here we\ntheoretically investigate the collision of optically-modulated pulsed electron\nbeams with atomic targets and reveal a quantum interference associated with\ndifferent momentum components of the incident broadband electron pulse, which\ncoherently modulates both the elastic and inelastic scattering cross sections.\nWe show that the quantum interference has a high spatial sensitivity at the\nlevel of Angstroms, offering potential applications in high-resolution\nultrafast electron microscopy. Our findings are rationalized by a simple model.\n"}
+{"abstract": "  Autonomous vehicles have a great potential in the application of both civil\nand military fields, and have become the focus of research with the rapid\ndevelopment of science and economy. This article proposes a brief review on\nlearning-based decision-making technology for autonomous vehicles since it is\nsignificant for safer and efficient performance of autonomous vehicles.\nFirstly, the basic outline of decision-making technology is provided. Secondly,\nrelated works about learning-based decision-making methods for autonomous\nvehicles are mainly reviewed with the comparison to classical decision-making\nmethods. In addition, applications of decision-making methods in existing\nautonomous vehicles are summarized. Finally, promising research topics in the\nfuture study of decision-making technology for autonomous vehicles are\nprospected.\n"}
+{"abstract": "  This paper investigates the cooperative planning and control problem for\nmultiple connected autonomous vehicles (CAVs) in different scenarios. In the\nexisting literature, most of the methods suffer from significant problems in\ncomputational efficiency. Besides, as the optimization problem is nonlinear and\nnonconvex, it typically poses great difficultly in determining the optimal\nsolution. To address this issue, this work proposes a novel and completely\nparallel computation framework by leveraging the alternating direction method\nof multipliers (ADMM). The nonlinear and nonconvex optimization problem in the\nautonomous driving problem can be divided into two manageable subproblems; and\nthe resulting subproblems can be solved by using effective optimization methods\nin a parallel framework. Here, the differential dynamic programming (DDP)\nalgorithm is capable of addressing the nonlinearity of the system dynamics\nrather effectively; and the nonconvex coupling constraints with small\ndimensions can be approximated by invoking the notion of semi-definite\nrelaxation (SDR), which can also be solved in a very short time. Due to the\nparallel computation and efficient relaxation of nonconvex constraints, our\nproposed approach effectively realizes real-time implementation and thus also\nextra assurance of driving safety is provided. In addition, two transportation\nscenarios for multiple CAVs are used to illustrate the effectiveness and\nefficiency of the proposed method.\n"}
+{"abstract": "  A black-box optimization algorithm such as Bayesian optimization finds\nextremum of an unknown function by alternating inference of the underlying\nfunction and optimization of an acquisition function. In a high-dimensional\nspace, such algorithms perform poorly due to the difficulty of acquisition\nfunction optimization. Herein, we apply quantum annealing (QA) to overcome the\ndifficulty in the continuous black-box optimization. As QA specializes in\noptimization of binary problems, a continuous vector has to be encoded to\nbinary, and the solution of QA has to be translated back. Our method has the\nfollowing three parts: 1) Random subspace coding based on axis-parallel\nhyperrectangles from continuous vector to binary vector. 2) A quadratic\nunconstrained binary optimization (QUBO) defined by acquisition function based\non nonnegative-weighted linear regression model which is solved by QA. 3) A\npenalization scheme to ensure that the QA solution can be translated back. It\nis shown in benchmark tests that its performance using D-Wave Advantage$^{\\rm\nTM}$ quantum annealer is competitive with a state-of-the-art method based on\nthe Gaussian process in high-dimensional problems. Our method may open up a new\npossibility of quantum annealing and other QUBO solvers including quantum\napproximate optimization algorithm (QAOA) using a gated-quantum computers, and\nexpand its range of application to continuous-valued problems.\n"}
+{"abstract": "  Internet of quantum blockchains (IoB) will be the future Internet. In this\npaper, we make two new contributions to IoB: developing a block based quantum\nchannel networking technology to handle its security modeling in face of the\nquantum supremacy and establishing IoB based FinTech platform model with\ndynamic pricing for stable digital currency. The interaction between our new\ncontributions is also addressed. In doing so, we establish a generalized IoB\nsecurity model by quantum channel networking in terms of both time and space\nquantum entanglements with quantum key distribution (QKD). Our IoB can interact\nwith general structured things (e.g., supply chain systems) having online\ntrading and payment capability via stable digital currency and can handle\nvector-valued data streams requiring synchronized services. Thus, within our\ndesigned QKD, a generalized random number generator for private and public keys\nis proposed by a mixed zero-sum and non-zero-sum resource-competition pricing\npolicy. The effectiveness of this policy is justified by diffusion modeling\nwith approximation theory and numerical implementations.\n"}
+{"abstract": "  Several efficient distributed algorithms have been developed for\nmatrix-matrix multiplication: the 3D algorithm, the 2D SUMMA algorithm, and the\n2.5D algorithm. Each of these algorithms was independently conceived and they\ntrade-off memory needed per node and the inter-node data communication volume.\n  The convolutional neural network (CNN) computation may be viewed as a\ngeneralization of matrix-multiplication combined with neighborhood stencil\ncomputations. We develop communication-efficient distributed-memory algorithms\nfor CNNs that are analogous to the 2D/2.5D/3D algorithms for matrix-matrix\nmultiplication.\n"}
+{"abstract": "  In subsurface multiphase flow simulations, poor nonlinear solver performance\nis a significant runtime sink. The system of fully implicit mass balance\nequations is highly nonlinear and often difficult to solve for the nonlinear\nsolver, generally Newton(-Raphson). Strong nonlinearities can cause Newton\niterations to converge very slowly. This frequently results in time step cuts,\nleading to computationally expensive simulations. Much literature has looked\ninto how to improve the nonlinear solver through enhancements or safeguarding\nupdates. In this work, we take a different approach; we aim to improve\nconvergence with a smoother finite volume discretization scheme which is more\nsuitable for the Newton solver.\n  Building on recent work, we propose a novel total velocity hybrid upwinding\nscheme with weighted average flow mobilities (WA-HU TV) that is unconditionally\nmonotone and extends to compositional multiphase simulations. Analyzing the\nsolution space of a one-cell problem, we demonstrate the improved properties of\nthe scheme and explain how it leverages the advantages of both phase potential\nupwinding and arithmetic averaging. This results in a flow subproblem that is\nsmooth with respect to changes in the sign of phase fluxes, and is well-behaved\nwhen phase velocities are large or when co-current viscous forces dominate.\nAdditionally, we propose a WA-HU scheme with a total mass (WA-HU TM)\nformulation that includes phase densities in the weighted averaging.\n  The proposed WA-HU TV consistently outperforms existing schemes, yielding\nbenefits from 5\\% to over 50\\% reduction in nonlinear iterations. The WA-HU TM\nscheme also shows promising results; in some cases leading to even more\nefficiency. However, WA-HU TM can occasionally also lead to convergence issues.\nOverall, based on the current results, we recommend the adoption of the WA-HU\nTV scheme as it is highly efficient and robust.\n"}
+{"abstract": "  We provide a sufficient condition for the monogamy inequality of multi-party\nquantum entanglement of arbitrary dimensions in terms of entanglement of\nformation. Based on the classical-classical-quantum(ccq) states whose quantum\nparts are obtained from the two-party reduced density matrices of a three-party\nquantum state, we show the additivity of the mutual information of the ccq\nstates guarantees the monogamy inequality of the three-party pure state in\nterms of EoF. After illustrating the result with some examples, we generalize\nour result of three-party systems into any multi-party systems of arbitrary\ndimensions.\n"}
+{"abstract": "  Four dimensional scanning transmission electron microscopy (4D STEM) records\nthe scattering of electrons in a material in great detail. The benefits offered\nby 4D STEM are substantial, with the wealth of data it provides facilitating\nfor instance high precision, high electron dose efficiency phase imaging via\ncenter of mass or ptychography based analysis. However the requirement for a 2D\nimage of the scattering to be recorded at each probe position has long placed a\nsevere bottleneck on the speed at which 4D STEM can be performed. Recent\nadvances in camera technology have greatly reduced this bottleneck, with the\ndetection efficiency of direct electron detectors being especially well suited\nto the technique. However even the fastest frame driven pixelated detectors\nstill significantly limit the scan speed which can be used in 4D STEM, making\nthe resulting data susceptible to drift and hampering its use for low dose beam\nsensitive applications. Here we report the development of the use of an event\ndriven Timepix3 direct electron camera that allows us to overcome this\nbottleneck and achieve 4D STEM dwell times down to 100~ns; orders of magnitude\nfaster than what has been possible with frame based readout. We characterise\nthe detector for different acceleration voltages and show that the method is\nespecially well suited for low dose imaging and promises rich datasets without\ncompromising dwell time when compared to conventional STEM imaging.\n"}
+{"abstract": "  The ejecta velocities of type-Ia supernovae (SNe Ia), as measured by the Si\nII $\\lambda 6355$ line, have been shown to correlate with other supernova\nproperties, including color and standardized luminosity. We investigate these\nresults using the Foundation Supernova Survey, with a spectroscopic data\nrelease presented here, and photometry analyzed with the SALT2 light-curve\nfitter. We find that the Foundation data do not show significant evidence for\nan offset in color between SNe Ia with high and normal photospheric velocities,\nwith $\\Delta c = 0.005 \\pm 0.014$. Our SALT2 analysis does show evidence for\nredder high-velocity SN Ia in other samples, including objects from the\nCarnegie Supernova Project, with a combined sample yielding $\\Delta c = 0.017\n\\pm 0.007$. When split on velocity, the Foundation SN Ia also do not show a\nsignificant difference in Hubble diagram residual, $\\Delta HR = 0.015 \\pm\n0.049$ mag. Intriguingly, we find that SN Ia ejecta velocity information may be\ngleaned from photometry, particularly in redder optical bands. For\nhigh-redshift SN Ia, these rest-frame red wavelengths will be observed by the\nNancy Grace Roman Space Telescope. Our results also confirm previous work that\nSN Ia host-galaxy stellar mass is strongly correlated with ejecta velocity:\nhigh-velocity SN Ia are found nearly exclusively in high-stellar-mass hosts.\nHowever, host-galaxy properties alone do not explain velocity-dependent\ndifferences in supernova colors and luminosities across samples. Measuring and\nunderstanding the connection between intrinsic explosion properties and\nsupernova environments, across cosmic time, will be important for precision\ncosmology with SNe Ia.\n"}
+{"abstract": "  This paper presents a formally verified quantifier elimination (QE) algorithm\nfor first-order real arithmetic by linear and quadratic virtual substitution\n(VS) in Isabelle/HOL. The Tarski-Seidenberg theorem established that the\nfirst-order logic of real arithmetic is decidable by QE. However, in practice,\nQE algorithms are highly complicated and often combine multiple methods for\nperformance. VS is a practically successful method for QE that targets formulas\nwith low-degree polynomials. To our knowledge, this is the first work to\nformalize VS for quadratic real arithmetic including inequalities. The proofs\nnecessitate various contributions to the existing multivariate polynomial\nlibraries in Isabelle/HOL. Our framework is modularized and easily expandable\n(to facilitate integrating future optimizations), and could serve as a basis\nfor developing practical general-purpose QE algorithms. Further, as our\nformalization is designed with practicality in mind, we export our development\nto SML and test the resulting code on 378 benchmarks from the literature,\ncomparing to Redlog, Z3, Wolfram Engine, and SMT-RAT. This identified\ninconsistencies in some tools, underscoring the significance of a verified\napproach for the intricacies of real arithmetic.\n"}
+{"abstract": "  Benefiting from large-scale pre-training, we have witnessed significant\nperformance boost on the popular Visual Question Answering (VQA) task. Despite\nrapid progress, it remains unclear whether these state-of-the-art (SOTA) models\nare robust when encountering examples in the wild. To study this, we introduce\nAdversarial VQA, a new large-scale VQA benchmark, collected iteratively via an\nadversarial human-and-model-in-the-loop procedure. Through this new benchmark,\nwe discover several interesting findings. (i) Surprisingly, we find that during\ndataset collection, non-expert annotators can easily attack SOTA VQA models\nsuccessfully. (ii) Both large-scale pre-trained models and adversarial training\nmethods achieve far worse performance on the new benchmark than over standard\nVQA v2 dataset, revealing the fragility of these models while demonstrating the\neffectiveness of our adversarial dataset. (iii) When used for data\naugmentation, our dataset can effectively boost model performance on other\nrobust VQA benchmarks. We hope our Adversarial VQA dataset can shed new light\non robustness study in the community and serve as a valuable benchmark for\nfuture work.\n"}
+{"abstract": "  We report on the discovery and validation of a two-planet system around a\nbright (V = 8.85 mag) early G dwarf (1.43 $R_{\\odot}$, 1.15 $M_{\\odot}$, TOI\n2319) using data from NASA's Transiting Exoplanet Survey Satellite (TESS).\nThree transit events from two planets were detected by citizen scientists in\nthe month-long TESS light curve (sector 25), as part of the Planet Hunters TESS\nproject. Modelling of the transits yields an orbital period of \\Pb\\ and radius\nof $3.41 _{ - 0.12 } ^ { + 0.14 }$ $R_{\\oplus}$ for the inner planet, and a\nperiod in the range 19.26-35 days and a radius of $5.83 _{ - 0.14 } ^ { + 0.14\n}$ $R_{\\oplus}$ for the outer planet, which was only seen to transit once. Each\nsignal was independently statistically validated, taking into consideration the\nTESS light curve as well as the ground-based spectroscopic follow-up\nobservations. Radial velocities from HARPS-N and EXPRES yield a tentative\ndetection of planet b, whose mass we estimate to be $11.56 _{ - 6.14 } ^ { +\n6.58 }$ $M_{\\oplus}$, and allow us to place an upper limit of $27.5$\n$M_{\\oplus}$ (99 per cent confidence) on the mass of planet c. Due to the\nbrightness of the host star and the strong likelihood of an extended H/He\natmosphere on both planets, this system offers excellent prospects for\natmospheric characterisation and comparative planetology.\n"}
+{"abstract": "  Cell polarization underlies many cellular processes, such as differentiation,\nmigration, and budding. Many living cells, such as budding yeast and fission\nyeast, use cytoskeletal structures to actively transport proteins to one\nlocation on the membrane and create a high density spot of membrane-bound\nproteins. Yet, the thermodynamic constraints on filament-based cell\npolarization remain unknown. We show by mathematical modeling that cell\npolarization requires detailed balance to be broken, and we quantify the\nfree-energy cost of maintaining a polarized state of the cell. Our study\nreveals that detailed balance cannot only be broken via the active transport of\nproteins along filaments, but also via a chemical modification cycle, allowing\ndetailed balance to be broken by the shuttling of proteins between the\nfilament, membrane, and cytosol. Our model thus shows that cell polarization\ncan be established via two distinct driving mechanisms, one based on active\ntransport and one based on non-equilibrium binding. Furthermore, the model\npredicts that the driven binding process dissipates orders of magnitude less\nfree-energy than the transport-based process to create the same membrane spot.\nActive transport along filaments may be sufficient to create a polarized\ndistribution of membrane-bound proteins, but an additional chemical\nmodification cycle of the proteins themselves is more efficient and less\nsensitive to the physical exclusion of proteins on the transporting filaments,\nproviding insight in the design principles of the Pom1/Tea1/Tea4 system in\nfission yeast and the Cdc42 system in budding yeast.\n"}
+{"abstract": "  The sequential quantum random access code (QRAC) allows two or more decoders\nto obtain a desired message with higher success probability than the best\nclassical bounds by appropriately modulating the measurement sharpness. Here,\nwe propose an entanglement-assisted sequential QRAC protocol which can enable\ndevice-independent tasks. By relaxing the equal sharpness and mutually unbiased\nmeasurement limits, we widen the sharpness modulation region from a\none-dimensional interval to a two-dimensional triangle. Then, we demonstrate\nour scheme experimentally and get more than 27 standard deviations above the\nclassical bound even when both decoders perform approximately projective\nmeasurements. We use the observed success probability to quantify the\nconnection among sequential QRAC, measurement sharpness, measurement\nbiasedness, and measurement incompatibility. Finally, we show that our protocol\ncan be applied to sequential device-independent randomness expansion and our\nmeasurement strategy can enhance the success probability of decoding the entire\ninput string. Our results may promote a deeper understanding of the\nrelationship among quantum correlation, quantum measurement, and quantum\ninformation processing.\n"}
+{"abstract": "  We introduce a new method for Estimation of Signal Parameters based on\nIterative Rational Approximation (ESPIRA) for sparse exponential sums. Our\nalgorithm uses the AAA algorithm for rational approximation of the discrete\nFourier transform of the given equidistant signal values. We show that ESPIRA\ncan be interpreted as a matrix pencil method applied to Loewner matrices. These\nLoewner matrices are closely connected with the Hankel matrices which are\nusually employed for signal recovery. Due to the construction of the Loewner\nmatrices via an adaptive selection of index sets, the matrix pencil method is\nstabilized. ESPIRA achieves similar recovery results for exact data as ESPRIT\nand the matrix pencil method but with less computational effort. Moreover,\nESPIRA strongly outperforms ESPRIT and the matrix pencil method for noisy data\nand for signal approximation by short exponential sums.\n"}
+{"abstract": "  For a certain function $J(s)$ we prove that the identity\n$$\\frac{\\zeta(2s)}{\\zeta(s)}-\\left(s-\\frac{1}{2}\\right)J(s)=\\frac{\\zeta(2s+1)}{\\zeta(s+1/2)},\n$$ holds in the half-plane Re$(s)>1/2$ and both sides of the equality are\nanalytic in this half-plane.\n"}
+{"abstract": "  To survive a learning management system (LMS) implementation an understanding\nof the needs of the various stakeholders is necessary. The goal of every LMS\nimplementation is to ensure the use of the system by instructors and students\nto enhance teaching and communication thereby enhancing learning outcomes of\nthe students. If the teachers and students do not use the system, the system is\nuseless. This research is motivated by the importance of identifying and\nunderstanding various stakeholders involved in the LMS implementation process\nin order to anticipate possible challenges and identify critical success\nfactors essential for the effective implementation and adoption of a new LMS\nsystem. To this end, we define the term stakeholder. We conducted a stakeholder\nanalysis to identify the key stakeholders in an LMS implementation process. We\nthen analyze their goals and needs, and how they collaborate in the\nimplementation process. The findings of this work will provide institutions of\nhigher learning an overview of the implementation process and useful insights\ninto the needs of the stakeholders, which will in turn ensure an increase in\nthe level of success achieved when implementing a LMS.\n"}
+{"abstract": "  Let $(\\Omega, \\mathcal{M}, \\mu)$ be a measure space. In this paper we\nestablish the set of G\\^ateaux differentiability for the usual norm of\n$L^{1}(\\Omega, \\mu)$ and the corresponding derivative formulae at each point in\nthis set.\n"}
+{"abstract": "  Haptic feedback is critical in a broad range of\nhuman-machine/computer-interaction applications. However, the high cost and low\nportability/wearability of haptic devices remains an unresolved issue, severely\nlimiting the adoption of this otherwise promising technology. Electrotactile\ninterfaces have the advantage of being more portable and wearable due to its\nreduced actuators' size, as well as benefiting from lower power consumption and\nmanufacturing cost. The usages of electrotactile feedback have been explored in\nhuman-computer interaction and human-machine-interaction for facilitating\nhand-based interactions in applications such as prosthetics, virtual reality,\nrobotic teleoperation, surface haptics, portable devices, and rehabilitation.\nThis paper presents a systematic review and meta-analysis of electrotactile\nfeedback systems for hand-based interactions in the last decade. We categorize\nthe different electrotactile systems according to their type of stimulation and\nimplementation/application. We also present and discuss a quantitative\ncongregation of the findings, so as to offer a high-level overview into the\nstate-of-art and suggest future directions. Electrotactile feedback was\nsuccessful in rendering and/or augmenting most tactile sensations, eliciting\nperceptual processes, and improving performance in many scenarios, especially\nin those where the wearability/portability of the system is important. However,\nknowledge gaps, technical drawbacks, and methodological limitations were\ndetected, which should be addressed in future studies.\n"}
+{"abstract": "  The linear contextual bandit literature is mostly focused on the design of\nefficient learning algorithms for a given representation. However, a contextual\nbandit problem may admit multiple linear representations, each one with\ndifferent characteristics that directly impact the regret of the learning\nalgorithm. In particular, recent works showed that there exist \"good\"\nrepresentations for which constant problem-dependent regret can be achieved. In\nthis paper, we first provide a systematic analysis of the different definitions\nof \"good\" representations proposed in the literature. We then propose a novel\nselection algorithm able to adapt to the best representation in a set of $M$\ncandidates. We show that the regret is indeed never worse than the regret\nobtained by running LinUCB on the best representation (up to a $\\ln M$ factor).\nAs a result, our algorithm achieves constant regret whenever a \"good\"\nrepresentation is available in the set. Furthermore, we show that the algorithm\nmay still achieve constant regret by implicitly constructing a \"good\"\nrepresentation, even when none of the initial representations is \"good\".\nFinally, we empirically validate our theoretical findings in a number of\nstandard contextual bandit problems.\n"}
+{"abstract": "  This paper develops a novel approach to necessary optimality conditions for\nconstrained variational problems defined in generally incomplete subspaces of\nabsolutely continuous functions. Our approach involves reducing a variational\nproblem to a (nondynamic) problem of constrained optimization in a normed space\nand then applying the results recently obtained for the latter class using\ngeneralized differentiation. In this way, we derive necessary optimality\nconditions for nonconvex problems of the calculus of variations with velocity\nconstraints under the weakest metric subregularity-type constraint\nqualification. The developed approach leads us to a short and simple proof of\nthe First-order necessary optimality conditions for such and related problems\nin broad spaces of functions, including those of class C^k.\n"}
+{"abstract": "  Satellite imagery analytics have numerous human development and disaster\nresponse applications, particularly when time series methods are involved. For\nexample, quantifying population statistics is fundamental to 67 of the 231\nUnited Nations Sustainable Development Goals Indicators, but the World Bank\nestimates that over 100 countries currently lack effective Civil Registration\nsystems. To help address this deficit and develop novel computer vision methods\nfor time series data, we present the Multi-Temporal Urban Development SpaceNet\n(MUDS, also known as SpaceNet 7) dataset. This open source dataset consists of\nmedium resolution (4.0m) satellite imagery mosaics, which includes 24 images\n(one per month) covering >100 unique geographies, and comprises >40,000 km2 of\nimagery and exhaustive polygon labels of building footprints therein, totaling\nover 11M individual annotations. Each building is assigned a unique identifier\n(i.e. address), which permits tracking of individual objects over time. Label\nfidelity exceeds image resolution; this \"omniscient labeling\" is a unique\nfeature of the dataset, and enables surprisingly precise algorithmic models to\nbe crafted. We demonstrate methods to track building footprint construction (or\ndemolition) over time, thereby directly assessing urbanization. Performance is\nmeasured with the newly developed SpaceNet Change and Object Tracking (SCOT)\nmetric, which quantifies both object tracking as well as change detection. We\ndemonstrate that despite the moderate resolution of the data, we are able to\ntrack individual building identifiers over time. This task has broad\nimplications for disaster preparedness, the environment, infrastructure\ndevelopment, and epidemic prevention.\n"}
+{"abstract": "  This paper studies fixed step-size stochastic approximation (SA) schemes,\nincluding stochastic gradient schemes, in a Riemannian framework. It is\nmotivated by several applications, where geodesics can be computed explicitly,\nand their use accelerates crude Euclidean methods. A fixed step-size scheme\ndefines a family of time-homogeneous Markov chains, parametrized by the\nstep-size. Here, using this formulation, non-asymptotic performance bounds are\nderived, under Lyapunov conditions. Then, for any step-size, the corresponding\nMarkov chain is proved to admit a unique stationary distribution, and to be\ngeometrically ergodic. This result gives rise to a family of stationary\ndistributions indexed by the step-size, which is further shown to converge to a\nDirac measure, concentrated at the solution of the problem at hand, as the\nstep-size goes to 0. Finally, the asymptotic rate of this convergence is\nestablished, through an asymptotic expansion of the bias, and a central limit\ntheorem.\n"}
+{"abstract": "  We consider the lattice analog of a recently proposed continuum model for the\npropagation of one- and two-photon states in a random medium. We find that\nthere is localization of single photons in an energy band centered at the\nresonant energy of the atoms. Moreover, there is also localization of photons\nat arbitrarily large energies. For the case of two photons, there is\nlocalization in an energy band centered at twice the resonant frequency.\n"}
+{"abstract": "  Many-electron problems pose some of the greatest challenges in computational\nscience, with important applications across many fields of modern science.\nFermionic quantum Monte Carlo (QMC) methods are among the most powerful\napproaches to these problems. However, they can be severely biased when\ncontrolling the fermionic sign problem using constraints, as is necessary for\nscalability. Here we propose an approach that combines constrained QMC with\nquantum computing tools to reduce such biases. We experimentally implement our\nscheme using up to 16 qubits in order to unbias constrained QMC calculations\nperformed on chemical systems with as many as 120 orbitals. These experiments\nrepresent the largest chemistry simulations performed on quantum computers\n(more than doubling the size of prior electron correlation calculations), while\nobtaining accuracy competitive with state-of-the-art classical methods. Our\nresults demonstrate a new paradigm of hybrid quantum-classical algorithm,\nsurpassing the popular variational quantum eigensolver in terms of potential\ntowards the first practical quantum advantage in ground state many-electron\ncalculations.\n"}
+{"abstract": "  In recent years, graph theoretic considerations have become increasingly\nimportant in the design of HPC interconnection topologies. One approach is to\nseek optimal or near-optimal families of graphs with respect to a particular\ngraph theoretic property, such as diameter. In this work, we consider\ntopologies which optimize the spectral gap. In particular, we study a novel HPC\ntopology, SpectralFly, designed around the Ramanujan graph construction of\nLubotzky, Phillips, and Sarnak (LPS). We show combinatorial properties, such as\ndiameter, bisection bandwidth, average path length, and resilience to link\nfailure, of SpectralFly topologies are better than, or comparable to, similarly\nconstrained DragonFly, SlimFly, and BundleFly topologies. Additionally, we\nsimulate the performance of SpectralFly topologies on a representative sample\nof physics-inspired HPC workloads using the Structure Simulation Toolkit\nMacroscale Element Library simulator and demonstrate considerable benefit to\nusing the LPS construction as the basis of the SpectralFly topology.\n"}
+{"abstract": "  We have developed a simulation tool to model self-limited processes such as\natomic layer deposition and atomic layer etching inside reactors of arbitrary\ngeometry. In this work, we have applied this model to two standard types of\ncross-flow reactors: a cylindrical reactor and a model 300 mm wafer reactor,\nand explored both ideal and non-ideal self-limited kinetics. For the\ncylindrical tube reactor the full simulation results agree well with analytic\nexpressions obtained using a simple plug flow model, though the presence of\naxial diffusion tends to soften growth profiles with respect to the plug flow\ncase. Our simulations also allowed us to model the output of in-situ techniques\nsuch as quartz crystal microbalance and mass spectrometry, providing a way of\ndiscriminating between ideal and non-ideal surface kinetics using in-situ\nmeasurements. We extended the simulations to consider two non-ideal\nself-limited processes: soft-saturating processes characterized by a slow\nreaction pathway, and processes where surface byproducts can compete with the\nprecursor for the same pool of adsorption sites, allowing us to quantify their\nimpact in the thickness variability across 300 mm wafer substrates.\n"}
+{"abstract": "  Single image super-resolution (SISR) algorithms reconstruct high-resolution\n(HR) images with their low-resolution (LR) counterparts. It is desirable to\ndevelop image quality assessment (IQA) methods that can not only evaluate and\ncompare SISR algorithms, but also guide their future development. In this\npaper, we assess the quality of SISR generated images in a two-dimensional (2D)\nspace of structural fidelity versus statistical naturalness. This allows us to\nobserve the behaviors of different SISR algorithms as a tradeoff in the 2D\nspace. Specifically, SISR methods are traditionally designed to achieve high\nstructural fidelity but often sacrifice statistical naturalness, while recent\ngenerative adversarial network (GAN) based algorithms tend to create more\nnatural-looking results but lose significantly on structural fidelity.\nFurthermore, such a 2D evaluation can be easily fused to a scalar quality\nprediction. Interestingly, we find that a simple linear combination of a\nstraightforward local structural fidelity and a global statistical naturalness\nmeasures produce surprisingly accurate predictions of SISR image quality when\ntested using public subject-rated SISR image datasets. Code of the proposed\nSFSN model is publicly available at \\url{https://github.com/weizhou-geek/SFSN}.\n"}
+{"abstract": "  Strongly confined NaVO$^+$ segregation and its thermo-responsive\nfunctionality at the interface between simple sputter-deposited amorphous\nvanadium oxide thin films and soda-lime glass was substantiated in the present\nstudy by in-situ temperature-controlled Time of Flight Secondary Ion Mass\nSpectrometry (ToF-SIMS). The obtained ToF-SIMS depth profiles provided\nunambiguous evidence for a reversible transformation that caused systematic\nswitching of the NaVO$^+$/ Na$^+$ and Na$^+$/ VO$^+$ intensities upon cycling\nthe temperature between 25 $^\\circ$C and 340 $^\\circ$C. Subsequently, NaVO\ncomplexes were found to be reversibly formed (at 300 $^\\circ$C) in vanadium\noxide diffused glass, leading to thermo-responsive electrical behaviour of the\nthin film glass system. This new segregation -- and diffusion-dependent\nmultifunctionality of NaVO$^+$ -- points towards applications as an advanced\nmaterial for thermo-optical switches, in smart windows or in thermal sensors.\n"}
+{"abstract": "  Going beyond the simplified gluonic cascades, we introduce both gluon and\nquark degrees of freedom for partonic cascades inside the medium. We then solve\nthe set of coupled evolution equations numerically with splitting kernels\ncalculated for static, exponential, and Bjorken expanding media to arrive at\nmedium-modified parton spectra for quark and gluon initiated jets. Using these,\nwe calculate the inclusive jet $R_{AA}$ where the phenomenologically driven\ncombinations of quark and gluon jet fractions are included. Then, the rapidity\ndependence of the jet $R_{AA}$ is examined. We also study the path-length\ndependence of jet quenching for different types of expanding media by\ncalculating the jet $v_{2}$. Additionally, we qualitatively study the\nsensitivity of observables on the time of the onset of the quenching for the\ncase of Bjorken media. All calculations are compared with recently measured\ndata.\n"}
+{"abstract": "  We present a novel decision tree-based synthesis algorithm of ranking\nfunctions for verifying program termination. Our algorithm is integrated into\nthe workflow of CounterExample Guided Inductive Synthesis (CEGIS). CEGIS is an\niterative learning model where, at each iteration, (1) a synthesizer\nsynthesizes a candidate solution from the current examples, and (2) a validator\naccepts the candidate solution if it is correct, or rejects it providing\ncounterexamples as part of the next examples. Our main novelty is in the design\nof a synthesizer: building on top of a usual decision tree learning algorithm,\nour algorithm detects cycles in a set of example transitions and uses them for\nrefining decision trees. We have implemented the proposed method and obtained\npromising experimental results on existing benchmark sets of (non-)termination\nverification problems that require synthesis of piecewise-defined lexicographic\naffine ranking functions.\n"}
+{"abstract": "  In the past few years we have seen great advances in object perception\n(particularly in 4D space-time dimensions) thanks to deep learning methods.\nHowever, they typically rely on large amounts of high-quality labels to achieve\ngood performance, which often require time-consuming and expensive work by\nhuman annotators. To address this we propose an automatic annotation pipeline\nthat generates accurate object trajectories in 3D space (i.e., 4D labels) from\nLiDAR point clouds. The key idea is to decompose the 4D object label into two\nparts: the object size in 3D that's fixed through time for rigid objects, and\nthe motion path describing the evolution of the object's pose through time.\nInstead of generating a series of labels in one shot, we adopt an iterative\nrefinement process where online generated object detections are tracked through\ntime as the initialization. Given the cheap but noisy input, our model produces\nhigher quality 4D labels by re-estimating the object size and smoothing the\nmotion path, where the improvement is achieved by exploiting aggregated\nobservations and motion cues over the entire trajectory. We validate the\nproposed method on a large-scale driving dataset and show a 25% reduction of\nhuman annotation efforts. We also showcase the benefits of our approach in the\nannotator-in-the-loop setting.\n"}
+{"abstract": "  Gastric diffuse-type adenocarcinoma represents a disproportionately high\npercentage of cases of gastric cancers occurring in the young, and its relative\nincidence seems to be on the rise. Usually it affects the body of the stomach,\nand presents shorter duration and worse prognosis compared with the\ndifferentiated (intestinal) type adenocarcinoma. The main difficulty\nencountered in the differential diagnosis of gastric adenocarcinomas occurs\nwith the diffuse-type. As the cancer cells of diffuse-type adenocarcinoma are\noften single and inconspicuous in a background desmoplaia and inflammation, it\ncan often be mistaken for a wide variety of non-neoplastic lesions including\ngastritis or reactive endothelial cells seen in granulation tissue. In this\nstudy we trained deep learning models to classify gastric diffuse-type\nadenocarcinoma from WSIs. We evaluated the models on five test sets obtained\nfrom distinct sources, achieving receiver operator curve (ROC) area under the\ncurves (AUCs) in the range of 0.95-0.99. The highly promising results\ndemonstrate the potential of AI-based computational pathology for aiding\npathologists in their diagnostic workflow system.\n"}
+{"abstract": "  The mechanism of bacterial cell size control has been a mystery for decades,\nwhich involves the well-coordinated growth and division in the cell cycle. The\nrevolutionary modern techniques of microfluidics and the advanced live imaging\nanalysis techniques allow long term observations and high-throughput analysis\nof bacterial growth on single cell level, promoting a new wave of quantitative\ninvestigations on this puzzle. Taking the opportunity, this theoretical study\naims to clarify the stochastic nature of bacterial cell size control under the\nassumption of the accumulation mechanism, which is favoured by recent\nexperiments on species of bacteria. Via the master equation approach with\nproperly chosen boundary conditions, the distributions concerned in cell size\ncontrol are estimated and are confirmed by experiments. In this analysis, the\ninter-generation Green's function is analytically evaluated as the key to\nbridge two kinds of statistics used in batch-culture and mother machine\nexperiments. This framework allows us to quantify the noise level in growth and\naccumulation according to experimental data. As a consequence of non-Gaussian\nnoises of the added sizes, the non-equilibrium nature of bacterial cell size\nhomeostasis is predicted, of which the biological meaning requires further\ninvestigation.\n"}
+{"abstract": "  We investigate the modification of the Higgs signals from vector boson fusion\nat the LHC arising from higher-dimensional effective operators involving\nquarks, electroweak gauge bosons and the 125-GeV scalar discovered in 2012.\nTaking a few of the admissible dimension-6 operators as illustration, we work\nwithin the framework of the Standard Model Effective Field Theory (SMEFT) and\nidentify kinematic variables that can reflect the presence of such effective\noperators. The useful variables turn out to be the geometric mean of the\ntransverse momenta of the two forward jets produced in VBF and the rapidity\ndifference between the two forward jets. We identify the shift in event\npopulation caused by the effective operators in the same, spanned by the above\nkinematic variables. Minimum values of the Wilson coefficients of the chosen\ndimension-6 operators are identified, for which they can be probed at the\n$3\\sigma$ level in the high luminosity run of the LHC at 14 TeV. Projected\nexclusion limits on some of the couplings, obtained from our analysis, can\nsignificantly improve the limits on such couplings derived from electroweak\nprecision data.\n"}
+{"abstract": "  Which classes can be learned properly in the online model? -- that is, by an\nalgorithm that at each round uses a predictor from the concept class. While\nthere are simple and natural cases where improper learning is necessary, it is\nnatural to ask how complex must the improper predictors be in such cases. Can\none always achieve nearly optimal mistake/regret bounds using \"simple\"\npredictors?\n  In this work, we give a complete characterization of when this is possible,\nthus settling an open problem which has been studied since the pioneering works\nof Angluin (1987) and Littlestone (1988). More precisely, given any concept\nclass C and any hypothesis class H, we provide nearly tight bounds (up to a log\nfactor) on the optimal mistake bounds for online learning C using predictors\nfrom H. Our bound yields an exponential improvement over the previously best\nknown bound by Chase and Freitag (2020).\n  As applications, we give constructive proofs showing that (i) in the\nrealizable setting, a near-optimal mistake bound (up to a constant factor) can\nbe attained by a sparse majority-vote of proper predictors, and (ii) in the\nagnostic setting, a near-optimal regret bound (up to a log factor) can be\nattained by a randomized proper algorithm.\n  A technical ingredient of our proof which may be of independent interest is a\ngeneralization of the celebrated Minimax Theorem (von Neumann, 1928) for binary\nzero-sum games. A simple game which fails to satisfy Minimax is \"Guess the\nLarger Number\", where each player picks a number and the larger number wins.\nThe payoff matrix is infinite triangular. We show this is the only obstruction:\nif a game does not contain triangular submatrices of unbounded sizes then the\nMinimax Theorem holds. This generalizes von Neumann's Minimax Theorem by\nremoving requirements of finiteness (or compactness), and captures precisely\nthe games of interest in online learning.\n"}
+{"abstract": "  Frequently, population studies feature pyramidally-organized data represented\nusing Hierarchical Bayesian Models (HBM) enriched with plates.These models can\nbecome prohibitively large in settings such as neuroimaging, where a sample is\ncomposed of a functional MRI signal measured on 64 thousand brain locations,\nacross 4 measurement sessions, and at least tens of subjects. Even a reduced\nexample on a specific cortical region of 300 brain locations features around 1\nmillion parameters, hampering the usage of modern density estimation techniques\nsuch as Simulation-Based Inference (SBI) or structured Variational Inference\n(VI).To infer parameter posterior distributions in this challenging class of\nproblems, we designed a novel methodology that automatically produces a\nvariational family dual to a target HBM. This variational family, represented\nas a neural network, consists in the combination of an attention-based\nhierarchical encoder feeding summary statistics to a set of normalizing flows.\nOur automatically-derived neural network exploits exchangeability in the\nplate-enriched HBM and factorizes its parameter space. The resulting\narchitecture reduces by orders of magnitude its parameterization with respect\nto that of a typical SBI or structured VI representation, while maintaining\nexpressivity.Our method performs inference on the specified HBM in an amortized\nsetup: once trained, it can readily be applied to a new data sample to compute\nthe parameters' full posterior.We demonstrate the capability and scalability of\nour method on simulated data, as well as a challenging high-dimensional brain\nparcellation experiment. We also open up several questions that lie at the\nintersection between SBI techniques, structured Variational Inference, and\ninference amortization.\n"}
+{"abstract": "  We introduce a convergent finite difference method for solving the optimal\ntransportation problem on the sphere. The method applies to both the\ntraditional squared geodesic cost (arising in mesh generation) and a\nlogarithmic cost (arising in the reflector antenna design problem). At each\npoint on the sphere, we replace the surface PDE with a Generated Jacobian\nequation posed on the local tangent plane using geodesic normal coordinates.\nThe discretization is inspired by recent monotone methods for the\nMonge-Amp\\`ere equation, but requires significant adaptations in order to\ncorrectly handle the mix of gradient and Hessian terms appearing inside the\nnonlinear determinant operator, as well as the singular logarithmic cost\nfunction. Numerical results demonstrate the success of this method on a wide\nrange of challenging problems involving both the squared geodesic and the\nlogarithmic cost functions.\n"}
+{"abstract": "  As the use of artificial intelligence (AI) in high-stakes decision-making\nincreases, the ability to contest such decisions is being recognised in AI\nethics guidelines as an important safeguard for individuals. Yet, there is\nlittle guidance on how AI systems can be designed to support contestation. In\nthis paper we explain that the design of a contestation process is important\ndue to its impact on perceptions of fairness and satisfaction. We also consider\ndesign challenges, including a lack of transparency as well as the numerous\ndesign options that decision-making entities will be faced with. We argue for a\nhuman-centred approach to designing for contestability to ensure that the needs\nof decision subjects, and the community, are met.\n"}
+{"abstract": "  The authors reply to the Comment arXiv:2104.03770 by P. Canfield et. al.\n"}
+{"abstract": "  Machine learning (ML) has been widely used for efficient resource allocation\n(RA) in wireless networks. Although superb performance is achieved on small and\nsimple networks, most existing ML-based approaches are confronted with\ndifficulties when heterogeneity occurs and network size expands. In this paper,\nspecifically focusing on power control/beamforming (PC/BF) in heterogeneous\ndevice-to-device (D2D) networks, we propose a novel unsupervised learning-based\nframework named heterogeneous interference graph neural network (HIGNN) to\nhandle these challenges. First, we characterize diversified link features and\ninterference relations with heterogeneous graphs. Then, HIGNN is proposed to\nempower each link to obtain its individual transmission scheme after limited\ninformation exchange with neighboring links. It is noteworthy that HIGNN is\nscalable to wireless networks of growing sizes with robust performance after\ntrained on small-sized networks. Numerical results show that compared with\nstate-of-the-art benchmarks, HIGNN achieves much higher execution efficiency\nwhile providing strong performance.\n"}
+{"abstract": "  Hierarchical clustering is a stronger extension of one of today's most\ninfluential unsupervised learning methods: clustering. The goal of this method\nis to create a hierarchy of clusters, thus constructing cluster evolutionary\nhistory and simultaneously finding clusterings at all resolutions. We propose\nfour traits of interest for hierarchical clustering algorithms: (1) empirical\nperformance, (2) theoretical guarantees, (3) cluster balance, and (4)\nscalability. While a number of algorithms are designed to achieve one to two of\nthese traits at a time, there exist none that achieve all four.\n  Inspired by Bateni et al.'s scalable and empirically successful Affinity\nClustering [NeurIPs 2017], we introduce Affinity Clustering's successor,\nMatching Affinity Clustering. Like its predecessor, Matching Affinity\nClustering maintains strong empirical performance and uses Massively Parallel\nCommunication as its distributed model. Designed to maintain provably balanced\nclusters, we show that our algorithm achieves good, constant factor\napproximations for Moseley and Wang's revenue and Cohen-Addad et al.'s value.\nWe show Affinity Clustering cannot approximate either function. Along the way,\nwe also introduce an efficient $k$-sized maximum matching algorithm in the MPC\nmodel.\n"}
+{"abstract": "  The learning of a new language remains to this date a cognitive task that\nrequires considerable diligence and willpower, recent advances and tools\nnotwithstanding. In this paper, we propose Broccoli, a new paradigm aimed at\nreducing the required effort by seamlessly embedding vocabulary learning into\nusers' everyday information diets. This is achieved by inconspicuously\nswitching chosen words encountered by the user for their translation in the\ntarget language. Thus, by seeing words in context, the user can assimilate new\nvocabulary without much conscious effort. We validate our approach in a careful\nuser study, finding that the efficacy of the lightweight Broccoli approach is\ncompetitive with traditional, memorization-based vocabulary learning. The low\ncognitive overhead is manifested in a pronounced decrease in learners' usage of\nmnemonic learning strategies, as compared to traditional learning. Finally, we\nestablish that language patterns in typical information diets are compatible\nwith spaced-repetition strategies, thus enabling an efficient use of the\nBroccoli paradigm. Overall, our work establishes the feasibility of a novel and\npowerful \"install-and-forget\" approach for embedded language acquisition.\n"}
+{"abstract": "  Recently, graph neural networks (GNNs) have achieved remarkable performances\nfor quantum mechanical problems. However, a graph convolution can only cover a\nlocalized region, and cannot capture long-range interactions of atoms. This\nbehavior is contrary to theoretical interatomic potentials, which is a\nfundamental limitation of the spatial based GNNs. In this work, we propose a\nnovel attention-based framework for molecular property prediction tasks. We\nrepresent a molecular conformation as a discrete atomic sequence combined by\natom-atom distance attributes, named Geometry-aware Transformer (GeoT). In\nparticular, we adopt a Transformer architecture, which has been widely used for\nsequential data. Our proposed model trains sequential representations of\nmolecular graphs based on globally constructed attentions, maintaining all\nspatial arrangements of atom pairs. Our method does not suffer from cost\nintensive computations, such as angle calculations. The experimental results on\nseveral public benchmarks and visualization maps verified that keeping the\nlong-range interatomic attributes can significantly improve the model\npredictability.\n"}
+{"abstract": "  Many machine learning problems can be formulated as minimax problems such as\nGenerative Adversarial Networks (GANs), AUC maximization and robust estimation,\nto mention but a few. A substantial amount of studies are devoted to studying\nthe convergence behavior of their stochastic gradient-type algorithms. In\ncontrast, there is relatively little work on their generalization, i.e., how\nthe learning models built from training examples would behave on test examples.\nIn this paper, we provide a comprehensive generalization analysis of stochastic\ngradient methods for minimax problems under both convex-concave and\nnonconvex-nonconcave cases through the lens of algorithmic stability. We\nestablish a quantitative connection between stability and several\ngeneralization measures both in expectation and with high probability. For the\nconvex-concave setting, our stability analysis shows that stochastic gradient\ndescent ascent attains optimal generalization bounds for both smooth and\nnonsmooth minimax problems. We also establish generalization bounds for both\nweakly-convex-weakly-concave and gradient-dominated problems.\n"}
+{"abstract": "  Actively tunable optical filters based on chalcogenide phase-change materials\n(PCMs) are an emerging technology with applications across chemical\nspectroscopy and thermal imaging. The refractive index of an embedded PCM thin\nfilm is modulated through an amorphous-to-crystalline phase transition induced\nthrough thermal stimulus. Performance metrics include transmittance, passband\ncenter wavelength (CWL), and bandwidth; ideally monitored during operation (in\nsitu) or after a set number of tuning cycles to validate real-time operation.\nMeasuring these aforementioned metrics in real-time is challenging.\nFourier-transform infrared spectroscopy (FTIR) provides the gold-standard for\nperformance characterization, yet is expensive and inflexible -- incorporating\nthe PCM tuning mechanism is not straightforward, hence in situ electro-optical\nmeasurements are challenging. In this work, we implement an open-source\nMATLAB-controlled real-time performance characterization system consisting of\nan inexpensive linear variable filter (LVF) and mid-wave infrared camera,\ncapable of switching the PCM-based filters while simultaneously recording in\nsitu filter performance metrics and spectral filtering profile. These metrics\nare calculated through pixel intensity measurements and displayed on a\ncustom-developed graphical user interface in real-time. The CWL is determined\nthrough spatial position of intensity maxima along the LVF's longitudinal axis.\nFurthermore, plans are detailed for a future experimental system that further\nreduces cost, is compact, and utilizes a near-infrared camera.\n"}
+{"abstract": "  We study an optimization problem related to the approximation of given data\nby a linear combination of transformed modes. In the simplest case, the\noptimization problem reduces to a minimization problem well-studied in the\ncontext of proper orthogonal decomposition. Allowing transformed modes in the\napproximation renders this approach particularly useful to compress data with\ntransported quantities, which are prevalent in many flow applications. We prove\nthe existence of a solution to the infinite-dimensional optimization problem.\nTowards a numerical implementation, we compute the gradient of the cost\nfunctional and derive a suitable discretization in time and space. We\ndemonstrate the theoretical findings with three challenging numerical examples.\n"}
+{"abstract": "  With time, machine learning models have increased in their scope,\nfunctionality and size. Consequently, the increased functionality and size of\nsuch models requires high-end hardware to both train and provide inference\nafter the fact. This paper aims to explore the possibilities within the domain\nof model compression, discuss the efficiency of combining various levels of\npruning and quantization, while proposing a quality measurement metric to\nobjectively decide which combination is best in terms of minimizing the\naccuracy delta and maximizing the size reduction factor.\n"}
+{"abstract": "  We consider the classic problem of computing the Longest Common Subsequence\n(LCS) of two strings of length $n$. While a simple quadratic algorithm has been\nknown for the problem for more than 40 years, no faster algorithm has been\nfound despite an extensive effort. The lack of progress on the problem has\nrecently been explained by Abboud, Backurs, and Vassilevska Williams [FOCS'15]\nand Bringmann and K\\\"unnemann [FOCS'15] who proved that there is no\nsubquadratic algorithm unless the Strong Exponential Time Hypothesis fails.\nThis has led the community to look for subquadratic approximation algorithms\nfor the problem.\n  Yet, unlike the edit distance problem for which a constant-factor\napproximation in almost-linear time is known, very little progress has been\nmade on LCS, making it a notoriously difficult problem also in the realm of\napproximation. For the general setting, only a naive\n$O(n^{\\varepsilon/2})$-approximation algorithm with running time\n$\\tilde{O}(n^{2-\\varepsilon})$ has been known, for any constant $0 <\n\\varepsilon \\le 1$. Recently, a breakthrough result by Hajiaghayi, Seddighin,\nSeddighin, and Sun [SODA'19] provided a linear-time algorithm that yields a\n$O(n^{0.497956})$-approximation in expectation; improving upon the naive\n$O(\\sqrt{n})$-approximation for the first time.\n  In this paper, we provide an algorithm that in time $O(n^{2-\\varepsilon})$\ncomputes an $\\tilde{O}(n^{2\\varepsilon/5})$-approximation with high\nprobability, for any $0 < \\varepsilon \\le 1$. Our result (1) gives an\n$\\tilde{O}(n^{0.4})$-approximation in linear time, improving upon the bound of\nHajiaghayi, Seddighin, Seddighin, and Sun, (2) provides an algorithm whose\napproximation scales with any subquadratic running time $O(n^{2-\\varepsilon})$,\nimproving upon the naive bound of $O(n^{\\varepsilon/2})$ for any $\\varepsilon$,\nand (3) instead of only in expectation, succeeds with high probability.\n"}
+{"abstract": "  Game semantics is a denotational semantics presenting compositionally the\ncomputational behaviour of various kinds of effectful programs. One of its\ncelebrated achievement is to have obtained full abstraction results for\nprogramming languages with a variety of computational effects, in a single\nframework. This is known as the semantic cube or Abramsky's cube, which for\nsequential deterministic programs establishes a correspondence between certain\nconditions on strategies (''innocence'', ''well-bracketing'', ''visibility'')\nand the absence of matching computational effects. Outside of the sequential\ndeterministic realm, there are still a wealth of game semantics-based full\nabstraction results; but they no longer fit in a unified canvas. In particular,\nGhica and Murawski's fully abstract model for shared state concurrency (IA)\ndoes not have a matching notion of pure parallel program-we say that\nparallelism and interference (i.e. state plus semaphores) are entangled. In\nthis paper we construct a causal version of Ghica and Murawski's model, also\nfully abstract for IA. We provide compositional conditions parallel innocence\nand sequentiality, respectively banning interference and parallelism, and\nleading to four full abstraction results. To our knowledge, this is the first\nextension of Abramsky's semantic cube programme beyond the sequential\ndeterministic world.\n"}
+{"abstract": "  Microfabricated ion-trap devices offer a promising pathway towards scalable\nquantum computing. Research efforts have begun to focus on the engineering\nchallenges associated with developing large-scale ion-trap arrays and networks.\nHowever, increasing the size of the array and integrating on-chip electronics\ncan drastically increase the power dissipation within the ion-trap chips. This\nleads to an increase in the operating temperature of the ion-trap and limits\nthe device performance. Therefore, effective thermal management is an essential\nconsideration for any large-scale architecture. Presented here is the\ndevelopment of a modular cooling system designed for use with multiple\nion-trapping experiments simultaneously. The system includes an extensible\ncryostat that permits scaling of the cooling power to meet the demands of a\nlarge network. Following experimental testing on two independent ion-trap\nexperiments, the cooling system is expected to deliver a net cooling power of\n111 W at ~70 K to up to four experiments. The cooling system is a step towards\nmeeting the practical challenges of operating large-scale quantum computers\nwith many qubits.\n"}
+{"abstract": "  In this paper, we mainly show that generalized hyperharmonic number sums with\nreciprocal binomial coefficients can be expressed in terms of classical\n(alternating) Euler sums, zeta values and generalized (alternating) harmonic\nnumbers.\n"}
+{"abstract": "  We demonstrate a method for the simultaneous determination of the\nthermoelectric figure of merit of multiple martials by means of the lock-in\nthermography (LIT) technique. This method is based on the thermal analyses of\nthe transient temperature distribution induced by the Peltier effect and Joule\nheating, which enables high-throughput estimation of the thermal diffusivity,\nthermal conductivity, volumetric heat capacity, Seebeck or Peltier coefficient\nof the materials. The LIT-based approach has high reproducibility and\nreliability because it offers sensitive noncontact temperature measurements and\ndoes not require the installation of an external heater. By performing the same\nmeasurements and analyses with applying an external magnetic field, the\nmagnetic field and/or magnetization dependences of the Seebeck or Peltier\ncoefficient and thermal conductivity can be determined simultaneously. We\ndemonstrate the validity of this method by using several ferromagnetic metals\n(Ni, Ni$_{95}$Pt$_{5}$, and Fe) and a nonmagnetic metal (Ti). The proposed\nmethod will be useful for materials research in thermoelectrics and spin\ncaloritronics and for investigation of magneto-thermal and\nmagneto-thermoelectric transport properties.\n"}
+{"abstract": "  We use holographic methods to show that photons emitted by a strongly coupled\nplasma subject to a magnetic field are linearly polarized regardless of their\nfour-momentum, except when they propagate along the field direction. The\ngravitational dual is constructed using a 5D truncation of 10-dimensional type\nIIB supergravity, and includes a scalar field in addition to the constant\nmagnetic one. In terms of the geometry of the collision experiment that we\nmodel, our statement is that any photon produced there has to be in its only\npolarization state parallel to the reaction plane.\n"}
+{"abstract": "  Label the vertices of the complete graph $K_v$ with the integers $\\{ 0, 1,\n\\ldots, v-1 \\}$ and define the length of the edge between $x$ and $y$ to be\n$\\min( |x-y| , v - |x-y| )$. Let $L$ be a multiset of size $v-1$ with\nunderlying set contained in $\\{ 1, \\ldots, \\lfloor v/2 \\rfloor \\}$. The\nBuratti-Horak-Rosa Conjecture is that there is a Hamiltonian path in $K_v$\nwhose edge lengths are exactly $L$ if and only if for any divisor $d$ of $v$\nthe number of multiples of $d$ appearing in $L$ is at most $v-d$.\n  We introduce \"growable realizations,\" which enable us to prove many new\ninstances of the conjecture and to reprove known results in a simpler way. As\nexamples of the new method, we give a complete solution when the underlying set\nis contained in $\\{ 1,4,5 \\}$ or in $\\{ 1,2,3,4 \\}$ and a partial result when\nthe underlying set has the form $\\{ 1, x, 2x \\}$. We believe that for any set\n$U$ of positive integers there is a finite set of growable realizations that\nimplies the truth of the Buratti-Horak-Rosa Conjecture for all but finitely\nmany multisets with underlying set $U$.\n"}
+{"abstract": "  While most simulations of the epoch of reionization have focused on\nsingle-stellar populations in star-forming dwarf galaxies, products of binary\nevolution are expected to significantly contribute to emissions of\nhydrogen-ionizing photons. Among these products are stripped stars (or helium\nstars), which have their envelopes stripped from interactions with binary\ncompanions, leaving an exposed helium core. Previous work has suggested these\nstripped stars can dominate the LyC photon output of high-redshift low\nluminosity galaxies. Other sources of hard radiation in the early universe\ninclude zero-metallicity Population III stars, which may have similar SED\nproperties to galaxies with radiation dominated by stripped star emissions.\nHere, we use two metrics (the power-law exponent over wavelength intervals\n240-500 \\r{A}, 600-900 \\r{A}, and 1200-2000 \\r{A}, and the ratio of total\nluminosity in FUV wavelengths to LyC wavelengths) to compare the SEDs of\nsimulated galaxies with only single-stellar evolution, galaxies containing\nstripped stars, and galaxies containing Population III stars, with four\ndifferent IMFs. We find that stripped stars significantly alter the SEDs in the\nLyC range of galaxies at the epoch of reionization. SEDs in galaxies with\nstripped stars present have lower power-law indices in the LyC range and lower\nFUV to LyC luminosity ratios. These differences in SEDs are present at all\nconsidered luminosities ($M_{UV} > -15$, AB system), and are most pronounced\nfor lower luminosity galaxies. We also find that SEDs of galaxies with stripped\nstars and Pop III stars are distinct from each other for all tested IMFs.\n"}
+{"abstract": "  In canonical quantum gravity, the presence of spatial boundaries naturally\nleads to a boundary quantum states, representing quantum boundary conditions\nfor the bulk fields. As a consequence, quantum states of the bulk geometry\nneeds to be upgraded to wave-functions valued in the boundary Hilbert space:\nthe bulk become quantum operator acting on boundary states. We apply this to\nloop quantum gravity and describe spin networks with 2d boundary as\nwave-functions mapping bulk holonomies to spin states on the boundary. This\nsets the bulk-boundary relation in a clear mathematical framework, which allows\nto define the boundary density matrix induced by a bulk spin network states\nafter tracing out the bulk degrees of freedom. We ask the question of the bulk\nreconstruction and prove a boundary-to-bulk universal reconstruction procedure,\nto be understood as a purification of the mixed boundary state into a pure bulk\nstate. We further perform a first investigation in the algebraic structure of\ninduced boundary density matrices and show how correlations between bulk\nexcitations, i.e. quanta of 3d geometry, get reflected into the boundary\ndensity matrix.\n"}
+{"abstract": "  In this article we provide a framework for the study of Hecke operators\nacting on the Bredon (co)homology of an arithmetic discrete group. Our main\ninterest lies in the study of Hecke operators for Bianchi groups. Using the\nBaum-Connes conjecture, we can transfer computations in Bredon homology to\nobtain a Hecke action on the $K$-theory of the reduced $C^{*}$-algebra of the\ngroup. We show the power of this method giving explicit computations for the\ngroup $SL_2(\\mathbb{Z}[i])$. In order to carry out these computations we use an\nAtiyah-Segal type spectral sequence together with the Bredon homology of the\nclassifying space for proper actions.\n"}
+{"abstract": "  The enhanced star forming activity, typical of starburst galaxies, powers\nstrong galactic winds expanding on kpc scales and characterized by bubble\nstructures. Here we discuss the possibility that particle acceleration may take\nplace at the termination shock of such winds. We calculate the spectrum of such\nparticles and their maximum energy, that turns out to be in the range $\\sim\n10-10^2$ PeV for typical values of the parameters. Cosmic rays accelerated at\nthe termination shock can be advected towards the edge of the wind bubble and\neventually escape into extragalactic space. We also calculate the flux of gamma\nrays and neutrinos produced by hadronic interactions in the bubble as well as\nthe diffuse flux resulting from the superposition of the contribution of\nstarburst galaxies on cosmological scales. Finally we compute the diffuse flux\nof cosmic rays from starburst bubbles and compare it with existing data.\n"}
+{"abstract": "  Nearly three decades ago, Bar-Noy, Motwani and Naor showed that no online\nedge-coloring algorithm can edge color a graph optimally. Indeed, their work,\ntitled \"the greedy algorithm is optimal for on-line edge coloring\", shows that\nthe competitive ratio of $2$ of the na\\\"ive greedy algorithm is best possible\nonline. However, their lower bound required bounded-degree graphs, of maximum\ndegree $\\Delta = O(\\log n)$, which prompted them to conjecture that better\nbounds are possible for higher-degree graphs. While progress has been made\ntowards resolving this conjecture for restricted inputs and arrivals or for\nrandom arrival orders, an answer for fully general \\emph{adversarial} arrivals\nremained elusive.\n  We resolve this thirty-year-old conjecture in the affirmative, presenting a\n$(1.9+o(1))$-competitive online edge coloring algorithm for general graphs of\ndegree $\\Delta = \\omega(\\log n)$ under vertex arrivals. At the core of our\nresults, and of possible independent interest, is a new online algorithm which\nrounds a fractional bipartite matching $x$ online under vertex arrivals,\nguaranteeing that each edge $e$ is matched with probability $(1/2+c)\\cdot x_e$,\nfor a constant $c>0.027$.\n"}
+{"abstract": "  The vacancy concentration at finite temperatures is studied for a series of\n(CoCrFeMn)$_{1-x_\\mathrm{Ni}}$Ni$_{x_\\mathrm{Ni}}$ alloys by grand-canonical\nMonte-Carlo (MC) simulations. The vacancy formation energies are calculated\nfrom a classical interatomic potential and exhibit a distribution due to the\ndifferent chemical environments of the vacated sites. In dilute alloys, this\ndistribution features multiple discrete peaks, while concentrated alloys\nexhibit an unimodal distribution as there are many different chemical\nenvironments of similar vacancy formation energy. MC simulations using a\nnumerically efficient bond-counting model confirm that the vacancy\nconcentration even in concentrated alloys may be calculated by the established\nMaxwell-Boltzmann equation weighted by the given distribution of formation\nenergies. We calculate the variation of vacancy concentration as function of Ni\ncontent in the (CoCrFeMn)$_{x_\\mathrm{Ni}}$Ni$_{1-x_\\mathrm{Ni}}$ and prove the\nexcellent agreement of the thermodynamic model and the results from the\ngrand-canonical Monte-Carlo simulations.\n"}
+{"abstract": "  Remote photoplethysmography (rPPG), a family of techniques for monitoring\nblood volume changes, may be especially useful for widespread contactless\nhealth monitoring using face video from consumer-grade visible-light cameras.\nThe COVID-19 pandemic has caused the widespread use of protective face masks.\nWe found that occlusions from cloth face masks increased the mean absolute\nerror of heart rate estimation by more than 80\\% when deploying methods\ndesigned on unmasked faces. We show that augmenting unmasked face videos by\nadding patterned synthetic face masks forces the model to attend to the\nperiocular and forehead regions, improving performance and closing the gap\nbetween masked and unmasked pulse estimation. To our knowledge, this paper is\nthe first to analyse the impact of face masks on the accuracy of pulse\nestimation and offers several novel contributions: (a) 3D CNN-based method\ndesigned for remote photoplethysmography in a presence of face masks, (b) two\npublicly available pulse estimation datasets acquired from 86 unmasked and 61\nmasked subjects, (c) evaluations of handcrafted algorithms and a 3D CNN trained\non videos of unmasked faces and with masks synthetically added, and (d) data\naugmentation method to add a synthetic mask to a face video.\n"}
+{"abstract": "  This work mainly addresses continuous-time multi-agent consensus networks\nwhere an adverse attacker affects the convergence performances of said\nprotocol. In particular, we develop a novel secure-by-design approach in which\nthe presence of a network manager monitors the system and broadcasts encrypted\ntasks (i.e. hidden edge weight assignments) to the agents involved. Each agent\nis then expected to decode the received codeword containing data on the task\nthrough appropriate decoding functions by leveraging advanced security\nprinciples, such as objective coding and information localization. Within this\nframework, a stability analysis is conducted for showing the robustness to\nchannel tampering in the scenario where part of the codeword corresponding to a\nsingle link in the system is corrupted. A trade-off between objective coding\ncapabilityand network robustness is also pointed out. To support these\nnovelties, an application example on decentralized estimation is provided.\nMoreover, such an investigation on robust stability is as well extended in the\ndiscrete-time domain. Further numerical simulations are given to validate the\ntheoretical results in both the time domains.\n"}
+{"abstract": "  Imputation is a popular technique for handling missing data. We consider a\nnonparametric approach to imputation using the kernel ridge regression\ntechnique and propose consistent variance estimation. The proposed variance\nestimator is based on a linearization approach which employs the entropy method\nto estimate the density ratio. The root-n consistency of the imputation\nestimator is established when a Sobolev space is utilized in the kernel ridge\nregression imputation, which enables us to develop the proposed variance\nestimator. Synthetic data experiments are presented to confirm our theory.\n"}
+{"abstract": "  Remote sensing scene classification deals with the problem of classifying\nland use/cover of a region from images. To predict the development and\nsocioeconomic structures of cities, the status of land use in regions are\ntracked by the national mapping agencies of countries. Many of these agencies\nuse land use types that are arranged in multiple levels. In this paper, we\nexamined the efficiency of a hierarchically designed CNN based framework that\nis suitable for such arrangements. We use NWPU-RESISC45 dataset for our\nexperiments and arranged this data set in a two level nested hierarchy. We have\ntwo cascaded deep CNN models initiated using DenseNet-121 architectures. We\nprovide detailed empirical analysis to compare the performances of this\nhierarchical scheme and its non hierarchical counterpart, together with the\nindividual model performances. We also evaluated the performance of the\nhierarchical structure statistically to validate the presented empirical\nresults. The results of our experiments show that although individual\nclassifiers for different sub-categories in the hierarchical scheme perform\nwell, the accumulation of classification errors in the cascaded structure\nprevents its classification performance from exceeding that of the non\nhierarchical deep model.\n"}
+{"abstract": "  The purpose of this paper is to define statistically convergent sequences\nwith respect to the metrics on generalized metric spaces (g-metric spaces) and\ninvestigate basic properties of this statistical form of convergence.\n"}
+{"abstract": "  Ethereum smart contracts are automated decentralized applications on the\nblockchain that describe the terms of the agreement between buyers and sellers,\nreducing the need for trusted intermediaries and arbitration. However, the\ndeployment of smart contracts introduces new attack vectors into the\ncryptocurrency systems. In particular, programming flaws in smart contracts can\nbe and have already been exploited to gain enormous financial profits. It is\nthus an emerging yet crucial issue to detect vulnerabilities of different\nclasses in contracts in an efficient manner. Existing machine learning-based\nvulnerability detection methods are limited and only inspect whether the smart\ncontract is vulnerable, or train individual classifiers for each specific\nvulnerability, or demonstrate multi-class vulnerability detection without\nextensibility consideration. To overcome the scalability and generalization\nlimitations of existing works, we propose ESCORT, the first Deep Neural Network\n(DNN)-based vulnerability detection framework for Ethereum smart contracts that\nsupport lightweight transfer learning on unseen security vulnerabilities, thus\nis extensible and generalizable. ESCORT leverages a multi-output NN\narchitecture that consists of two parts: (i) A common feature extractor that\nlearns the semantics of the input contract; (ii) Multiple branch structures\nwhere each branch learns a specific vulnerability type based on features\nobtained from the feature extractor. Experimental results show that ESCORT\nachieves an average F1-score of 95% on six vulnerability types and the\ndetection time is 0.02 seconds per contract. When extended to new vulnerability\ntypes, ESCORT yields an average F1-score of 93%. To the best of our knowledge,\nESCORT is the first framework that enables transfer learning on new\nvulnerability types with minimal modification of the DNN model architecture and\nre-training overhead.\n"}
+{"abstract": "  Kinetic simulations and theory demonstrate that whistler waves can excite\noblique, short-wavelength fluctuations through secondary drift instabilities if\na population of sufficiently cold plasma is present. The excited modes lead to\nheating of the cold populations and damping of the primary whistler waves. The\ninstability threshold depends on the density and temperature of the cold\npopulation and can be relatively small if the temperature of the cold\npopulation is sufficiently low. This mechanism may thus play a significant role\nin controlling amplitude of whistlers in the regions of the Earth's\nmagnetosphere where cold background plasma of sufficient density is present.\n"}
+{"abstract": "  Knowledge graph embedding has been an active research topic for knowledge\nbase completion (KGC), with progressive improvement from the initial TransE,\nTransH, RotatE et al to the current state-of-the-art QuatE. However, QuatE\nignores the multi-faceted nature of the entity and the complexity of the\nrelation, only using rigorous operation on quaternion space to capture the\ninteraction between entitiy pair and relation, leaving opportunities for better\nknowledge representation which will finally help KGC. In this paper, we propose\na novel model, QuatDE, with a dynamic mapping strategy to explicitly capture\nthe variety of relational patterns and separate different semantic information\nof the entity, using transition vectors to adjust the point position of the\nentity embedding vectors in the quaternion space via Hamilton product,\nenhancing the feature interaction capability between elements of the triplet.\nExperiment results show QuatDE achieves state-of-the-art performance on three\nwell-established knowledge graph completion benchmarks. In particular, the MR\nevaluation has relatively increased by 26% on WN18 and 15% on WN18RR, which\nproves the generalization of QuatDE.\n"}
+{"abstract": "  One of the features of the unconventional $s_\\pm$ state in iron-based\nsuperconductors is possibility to transform to the $s_{++}$ state with the\nincrease of the nonmagnetic disorder. Detection of such a transition would\nprove the existence of the $s_\\pm$ state. Here we study the temperature\ndependence of the London magnetic penetration depth within the two-band model\nfor the $s_\\pm$ and $s_{++}$ superconductors. By solving Eliashberg equations\naccounting for the spin-fluctuation mediated pairing and nonmagnetic impurities\nin the $T$-matrix approximation, we have derived a set of specific signatures\nof the $s_\\pm \\to s_{++}$ transition: (1) sharp change in the behavior of the\npenetration depth $\\lambda_{L}$ as a function of the impurity scattering rate\nat low temperatures; (2) before the transition, the slope of $\\Delta\n\\lambda_{L}(T) = \\lambda_{L}(T)-\\lambda_{L}(0)$ increases as a function of\ntemperature, and after the transition this value decreases; (3) the sharp jump\nin the inverse square of the penetration depth as a function of the impurity\nscattering rate, $\\lambda_{L}^{-2}(\\Gamma_a)$, at the transition; (4) change\nfrom the single-gap behavior in the vicinity of the transition to the two-gap\nbehavior upon increase of the impurity scattering rate in the superfluid\ndensity $\\rho_{s}(T)$.\n"}
+{"abstract": "  We extend applications of Furstenberg boundary theory to the study of\n$C^*$-algebras associated to minimal actions $\\Gamma\\!\\curvearrowright\\! X$ of\ndiscrete groups $\\Gamma$ on locally compact spaces $X$. We introduce boundary\nmaps on $(\\Gamma,X)$-$C^*$-algebras and investigate their applications in this\ncontext. Among other results, we completely determine when $C^*$-algebras\ngenerated by covariant representations arising from stabilizer subgroups are\nsimple. We also characterize the intersection property of locally compact\n$\\Gamma$-spaces and simplicity of their associated crossed products.\n"}
+{"abstract": "  Deep Reinforcement Learning (RL) techniques can benefit greatly from\nleveraging prior experience, which can be either self-generated or acquired\nfrom other entities. Action advising is a framework that provides a flexible\nway to transfer such knowledge in the form of actions between teacher-student\npeers. However, due to the realistic concerns, the number of these interactions\nis limited with a budget; therefore, it is crucial to perform these in the most\nappropriate moments. There have been several promising studies recently that\naddress this problem setting especially from the student's perspective. Despite\ntheir success, they have some shortcomings when it comes to the practical\napplicability and integrity as an overall solution to the learning from advice\nchallenge. In this paper, we extend the idea of advice reusing via teacher\nimitation to construct a unified approach that addresses both advice collection\nand advice utilisation problems. We also propose a method to automatically tune\nthe relevant hyperparameters of these components on-the-fly to make it able to\nadapt to any task with minimal human intervention. The experiments we performed\nin 5 different Atari games verify that our algorithm either surpasses or\nperforms on-par with its top competitors while being far simpler to be\nemployed. Furthermore, its individual components are also found to be providing\nsignificant advantages alone.\n"}
+{"abstract": "  A new approach to deal with the scattering amplitudes in Glauber theory is\nproposed. It relies on the use of generating function, that has been explicitly\nfound. The method is applied to the analytical calculation of the\nnucleus-nucleus elastic scattering amplitudes in the all interaction orders of\nthe Glauber theory.\n"}
+{"abstract": "  Neutral hydrogen (HI) intensity mapping is a promising technique to probe the\nlarge-scale structure of the Universe, improving our understanding on the\nlate-time accelerated expansion. In this work, we first scrutinize how an\nalternative cosmology, interacting Dark Energy, can affect the 21-cm angular\npower spectrum relative to the concordance $\\Lambda$CDM model. We re-derive the\n21-cm brightness temperature fluctuation in the context of such interaction and\nuncover an extra new contribution. Then we estimate the noise level of three\nupcoming HI intensity mapping surveys, BINGO, SKA1-MID Band$\\,$1 and Band$\\,$2,\nrespectively, and employ a Fisher matrix approach to forecast their constraints\non the interacting Dark Energy model. We find that while $\\textit{Planck}\\,$\n2018 maintains its dominion over early-Universe parameter constraints, BINGO\nand SKA1-MID Band$\\,$2 put complementary bounding to the latest CMB\nmeasurements on dark energy equation of state $w$, the interacting strength\n$\\lambda_i$ and the reduced Hubble constant $h$, and SKA1-MID Band$\\,$1 even\noutperforms $\\textit{Planck}\\,$ 2018 in these late-Universe parameter\nconstraints. The expected minimum uncertainties are given by SKA1-MID\nBand$\\,$1+$\\textit{Planck}\\,$: $\\sim 0.35\\%$ on $w$, $\\sim 0.27\\%$ on $h$,\n$\\sim 0.61\\%$ on HI bias $b_{\\rm HI}$, and an absolute uncertainty of about\n$3\\times10^{-4}$ ($7\\times10^{-4}$) on $\\lambda_{1}$ ($\\lambda_{2}$). Moreover,\nwe quantify the effect of increasing redshift bins and inclusion of\nredshift-space distortions in updating the constraints. Our results indicate a\nbright prospect for HI intensity mapping surveys in constraining interacting\nDark Energy, whether on their own or further by a joint analysis with other\nmeasurements.\n"}
+{"abstract": "  It is still an open and challenging problem for mobile robots navigating\nalong time-efficient and collision-free paths in a crowd. The main challenge\ncomes from the complex and sophisticated interaction mechanism, which requires\nthe robot to understand the crowd and perform proactive and foresighted\nbehaviors. Deep reinforcement learning is a promising solution to this problem.\nHowever, most previous learning methods incur a tremendous computational\nburden. To address these problems, we propose a graph-based deep reinforcement\nlearning method, SG-DQN, that (i) introduces a social attention mechanism to\nextract an efficient graph representation for the crowd-robot state; (ii)\ndirectly evaluates the coarse q-values of the raw state with a learned dueling\ndeep Q network(DQN); and then (iii) refines the coarse q-values via online\nplanning on possible future trajectories. The experimental results indicate\nthat our model can help the robot better understand the crowd and achieve a\nhigh success rate of more than 0.99 in the crowd navigation task. Compared\nagainst previous state-of-the-art algorithms, our algorithm achieves an\nequivalent, if not better, performance while requiring less than half of the\ncomputational cost.\n"}
+{"abstract": "  Epidemic processes on random graphs or networks are marked by localization of\nactivity that can trap the dynamics into a metastable state, confined to a\nsubextensive part of the network, before visiting an absorbing configuration.\nQuasistationary (QS) method is a technique to deal with absorbing states for\nfinite sizes and has played a central role in the investigation of epidemic\nprocesses on heterogeneous networks where localization is a hallmark. The\nstandard QS method possesses high computer and algorithmic complexity for large\nsystems besides parameters whose choice are not systematic. However, simpler\napproaches, such as a reflecting boundary condition (RBC), are not able to\ncapture the localization effects as the standard QS method does. In the present\nwork, we propose a QS method that consists of reactivating nodes proportionally\nto the time they were active along the preceding simulation. The method is\ncompared with the standard QS and RBC methods for the\nsusceptible-infected-susceptible model on complex networks, which is a\nprototype of a dynamic process with strong and localization effects. We\nverified that the method performs as well the as standard QS in all\ninvestigated simulations, providing the same scaling exponents, epidemic\nthresholds, and localized phases, thus overcoming the limitations of other\nsimpler approaches. We also report that the present method has significant\nreduction of the computer and algorithmic complexity than the standard QS\nmethods. So, this method arises as a simpler and efficient tool to analyze\nlocalization on heterogeneous structures through QS simulations.\n"}
+{"abstract": "  As 5th Generation research reaches the twilight, the research community must\ngo beyond 5G and look towards the 2030 connectivity landscape, namely 6G. In\nthis context, this work takes a step towards the 6G vision by proposing a next\ngeneration communication platform, which aims to extend the rigid coverage area\nof fixed deployment networks by considering virtual mobile small cells (MSC)\nthat are created on demand. Relying on emerging computing paradigms such as NFV\n(Network Function Virtualization) and SDN (Software Defined Networking), these\ncells can harness radio and networking capability locally reducing protocol\nsignaling latency and overhead. These MSCs constitute an intelligent pool of\nnetworking resources that can collaborate to form a wireless network of MSCs\nproviding a communication platform for localized, ubiquitous and reliable\nconnectivity. The technology enablers for implementing the MSC concept are also\naddressed in terms of virtualization, lightweight wireless security, and energy\nefficient RF. The benefits of the MSC architecture towards reliable and\nefficient cell offloading are demonstrated as a use-case.\n"}
+{"abstract": "  In this study, 18F-FDG PET/CT brain scans of 50 patients with head and neck\nmalignant lesions were employed to systematically assess the relationship\nbetween the amount of injected dose (10%, 8%, 6%, 5%, 4%, 3%, 2%, and 1% of\nstandard dose) and the image quality through measuring standard image quality\nmetrics (peak-signal-to-noise-ration (PSNR), structural similarity index\n(SSIM), root mean square error (RMSE), and standard uptake value (SUV) bias)\nfor the whole head region as well as within the malignant lesions, considering\nthe standard-dose PET images as reference. Furthermore, we evaluated the impact\nof post-reconstruction Gaussian filtering on the PET images in order to reduce\nnoise and improve the signal-to-noise ratio at different low-dose levels.\nSignificant degradation of PET image quality and tumor detectability was\nobserved with a decrease in the injected dose by more than 5%, leading to a\nremarkable increase in RMSE from 0.173 SUV (at 5%) to 1.454 SUV (at 1%). The\nquantitative investigation of the malignant lesions demonstrated that SUVmax\nbias greatly increased in low-dose PET images (in particular at 1%, 2%, 3%\nlevels) before applying the post-reconstruction filter, while applying the\nGaussian filter on low-dose PET images led to a significant reduction in SUVmax\nbias. The SUVmean bias within the malignant lesions was negligible (less than\n1%) in low-dose PET images; however, this bias increased significantly after\napplying the post-reconstruction filter. In conclusion, it is strongly\nrecommended that the SUVmax bias and SUVmean bias in low-dose PET images should\nbe considered prior to and following the application of the post-reconstruction\nGuassain filter, respectively.\n"}
+{"abstract": "  As an emerging and challenging problem in the computer vision community,\nweakly supervised object localization and detection plays an important role for\ndeveloping new generation computer vision systems and has received significant\nattention in the past decade. As methods have been proposed, a comprehensive\nsurvey of these topics is of great importance. In this work, we review (1)\nclassic models, (2) approaches with feature representations from off-the-shelf\ndeep networks, (3) approaches solely based on deep learning, and (4) publicly\navailable datasets and standard evaluation metrics that are widely used in this\nfield. We also discuss the key challenges in this field, development history of\nthis field, advantages/disadvantages of the methods in each category, the\nrelationships between methods in different categories, applications of the\nweakly supervised object localization and detection methods, and potential\nfuture directions to further promote the development of this research field.\n"}
+{"abstract": "  We prove an inequality bounding the renormalized area of a complete minimal\nsurface in hyperbolic space in terms of the conformal length of its ideal\nboundary.\n"}
+{"abstract": "  Automatic evaluation metrics are a crucial component of dialog systems\nresearch. Standard language evaluation metrics are known to be ineffective for\nevaluating dialog. As such, recent research has proposed a number of novel,\ndialog-specific metrics that correlate better with human judgements. Due to the\nfast pace of research, many of these metrics have been assessed on different\ndatasets and there has as yet been no time for a systematic comparison between\nthem. To this end, this paper provides a comprehensive assessment of recently\nproposed dialog evaluation metrics on a number of datasets. In this paper, 23\ndifferent automatic evaluation metrics are evaluated on 10 different datasets.\nFurthermore, the metrics are assessed in different settings, to better qualify\ntheir respective strengths and weaknesses. Metrics are assessed (1) on both the\nturn level and the dialog level, (2) for different dialog lengths, (3) for\ndifferent dialog qualities (e.g., coherence, engaging), (4) for different types\nof response generation models (i.e., generative, retrieval, simple models and\nstate-of-the-art models), (5) taking into account the similarity of different\nmetrics and (6) exploring combinations of different metrics. This comprehensive\nassessment offers several takeaways pertaining to dialog evaluation metrics in\ngeneral. It also suggests how to best assess evaluation metrics and indicates\npromising directions for future work.\n"}
+{"abstract": "  We present best practices and tools for professionals who support\ncomputational and data intensive (CDI) research projects. The practices\nresulted from an initiative that brings together national projects and\nuniversity teams that include individual or groups of such professionals. We\nfocus particularly on practices that differ from those in a general software\nengineering context. The paper also describes the initiative , the Xpert\nNetwork , where participants exchange successes, challenges, and general\ninformation about their activities, leading to increased productivity,\nefficiency, and coordination in the ever growing community of scientists that\nuse computational and data-intensive research methods.\n"}
+{"abstract": "  Neural architecture search (NAS) has been successfully applied to tasks like\nimage classification and language modeling for finding efficient\nhigh-performance network architectures. In ASR field especially end-to-end ASR,\nthe related research is still in its infancy. In this work, we focus on\napplying NAS on the most popular manually designed model: Conformer, and then\npropose an efficient ASR model searching method that benefits from the natural\nadvantage of differentiable architecture search (Darts) in reducing\ncomputational overheads. We fuse Darts mutator and Conformer blocks to form a\ncomplete search space, within which a modified architecture called\nDarts-Conformer cell is found automatically. The entire searching process on\nAISHELL-1 dataset costs only 0.7 GPU days. Replacing the Conformer encoder by\nstacking searched cell, we get an end-to-end ASR model (named as\nDarts-Conformner) that outperforms the Conformer baseline by 4.7\\% on the\nopen-source AISHELL-1 dataset. Besides, we verify the transferability of the\narchitecture searched on a small dataset to a larger 2k-hour dataset. To the\nbest of our knowledge, this is the first successful attempt to apply\ngradient-based architecture search in the attention-based encoder-decoder ASR\nmodel.\n"}
+{"abstract": "  We review state-of-the-art formal methods applied to the emerging field of\nthe verification of machine learning systems. Formal methods can provide\nrigorous correctness guarantees on hardware and software systems. Thanks to the\navailability of mature tools, their use is well established in the industry,\nand in particular to check safety-critical applications as they undergo a\nstringent certification process. As machine learning is becoming more popular,\nmachine-learned components are now considered for inclusion in critical\nsystems. This raises the question of their safety and their verification. Yet,\nestablished formal methods are limited to classic, i.e. non machine-learned\nsoftware. Applying formal methods to verify systems that include machine\nlearning has only been considered recently and poses novel challenges in\nsoundness, precision, and scalability.\n  We first recall established formal methods and their current use in an\nexemplar safety-critical field, avionic software, with a focus on abstract\ninterpretation based techniques as they provide a high level of scalability.\nThis provides a golden standard and sets high expectations for machine learning\nverification. We then provide a comprehensive and detailed review of the formal\nmethods developed so far for machine learning, highlighting their strengths and\nlimitations. The large majority of them verify trained neural networks and\nemploy either SMT, optimization, or abstract interpretation techniques. We also\ndiscuss methods for support vector machines and decision tree ensembles, as\nwell as methods targeting training and data preparation, which are critical but\noften neglected aspects of machine learning. Finally, we offer perspectives for\nfuture research directions towards the formal verification of machine learning\nsystems.\n"}
+{"abstract": "  Zeroth-order (ZO, also known as derivative-free) methods, which estimate the\ngradient only by two function evaluations, have attracted much attention\nrecently because of its broad applications in machine learning community. The\ntwo function evaluations are normally generated with random perturbations from\nstandard Gaussian distribution. To speed up ZO methods, many methods, such as\nvariance reduced stochastic ZO gradients and learning an adaptive Gaussian\ndistribution, have recently been proposed to reduce the variances of ZO\ngradients. However, it is still an open problem whether there is a space to\nfurther improve the convergence of ZO methods. To explore this problem, in this\npaper, we propose a new reinforcement learning based ZO algorithm (ZO-RL) with\nlearning the sampling policy for generating the perturbations in ZO\noptimization instead of using random sampling. To find the optimal policy, an\nactor-critic RL algorithm called deep deterministic policy gradient (DDPG) with\ntwo neural network function approximators is adopted. The learned sampling\npolicy guides the perturbed points in the parameter space to estimate a more\naccurate ZO gradient. To the best of our knowledge, our ZO-RL is the first\nalgorithm to learn the sampling policy using reinforcement learning for ZO\noptimization which is parallel to the existing methods. Especially, our ZO-RL\ncan be combined with existing ZO algorithms that could further accelerate the\nalgorithms. Experimental results for different ZO optimization problems show\nthat our ZO-RL algorithm can effectively reduce the variances of ZO gradient by\nlearning a sampling policy, and converge faster than existing ZO algorithms in\ndifferent scenarios.\n"}
+{"abstract": "  In this work, we investigate the role of multi-nucleon (MN) effects (mainly\n2p-2h and RPA) on the sensitivity measurement of various neutrino oscillation\nparameters, in the disappearance channel of NO$\\nu$A (USA) experiment.\nShort-range correlations and detector effects have also been included in the\nanalysis. We use the kinematical method of reconstruction of the incoming\nneutrino energy, both at the near and far detectors. The extrapolation\ntechnique has been used to estimate oscillated events at the far detector. The\nlatest global best fit values of various light neutrino oscillation parameters\nhave been used in the analysis. We find that MN effects increase uncertainty in\nthe measurement of neutrino oscillation parameters, while lower detector\nefficiency is reflected in more uncertainty. This study can give useful insight\ninto precision studies at long-baseline neutrino experiments in future\nmeasurements.\n"}
+{"abstract": "  Predicting future frames of video sequences is challenging due to the complex\nand stochastic nature of the problem. Video prediction methods based on\nvariational auto-encoders (VAEs) have been a great success, but they require\nthe training data to contain multiple possible futures for an observed video\nsequence. This is hard to be fulfilled when videos are captured in the wild\nwhere any given observation only has a determinate future. As a result,\ntraining a vanilla VAE model with these videos inevitably causes posterior\ncollapse. To alleviate this problem, we propose a novel VAE structure, dabbed\nVAE-in-VAE or VAE$^2$. The key idea is to explicitly introduce stochasticity\ninto the VAE. We treat part of the observed video sequence as a random\ntransition state that bridges its past and future, and maximize the likelihood\nof a Markov Chain over the video sequence under all possible transition states.\nA tractable lower bound is proposed for this intractable objective function and\nan end-to-end optimization algorithm is designed accordingly. VAE$^2$ can\nmitigate the posterior collapse problem to a large extent, as it breaks the\ndirect dependence between future and observation and does not directly regress\nthe determinate future provided by the training data. We carry out experiments\non a large-scale dataset called Cityscapes, which contains videos collected\nfrom a number of urban cities. Results show that VAE$^2$ is capable of\npredicting diverse futures and is more resistant to posterior collapse than the\nother state-of-the-art VAE-based approaches. We believe that VAE$^2$ is also\napplicable to other stochastic sequence prediction problems where training data\nare lack of stochasticity.\n"}
+{"abstract": "  Bone is mineralized tissue constituting the skeletal system, supporting and\nprotecting body organs and tissues. At the molecular level, mineralized\ncollagen fibril is the basic building block of bone tissue, and hence,\nunderstanding bone properties down to fundamental tissue structures enables to\nbetter identify the mechanisms of structural failures and damages. While\nefforts have focused on the study of the micro- and macro-scale viscoelasticity\nrelated to bone damage and healing based on creep, mineralized collagen has not\nbeen explored on a molecular level. We report a study that aims at\nsystematically exploring the viscoelasticity of collagenous fibrils with\ndifferent mineralization levels. We investigate the dynamic mechanical response\nupon cyclic and impulsive loads to observe the viscoelastic phenomena from\neither shear or extensional strains via molecular dynamics. We perform a\nsensitivity analysis with several key benchmarks: intrafibrillar mineralization\npercentage, hydration state, and external load amplitude. Our results show a\ngrowth of the dynamic moduli with an increase of mineral percentage, pronounced\nat low strains. When intrafibrillar water is present, the material softens the\nelastic component but considerably increases its viscosity, especially at high\nfrequencies. This behaviour is confirmed from the material response upon\nimpulsive loads, in which water drastically reduces the relaxation times\nthroughout the input velocity range by one order of magnitude, with respect to\nthe dehydrated counterparts. We find that upon transient loads, water has a\nmajor impact on the mechanics of mineralized fibrillar collagen, being able to\nimprove the capability of the tissue to passively and effectively dissipate\nenergy, especially after fast and high-amplitude external loads.\n"}
+{"abstract": "  Determining whether two particle systems are similar is a common problem in\nparticle simulations. When the comparison should be invariant under\npermutations, orthogonal transformations, and translations of the systems,\nspecial techniques are needed. We present an algorithm that can test particle\nsystems of finite size for similarity and, if they are similar, can find the\noptimal alignment between them. Our approach is based on an invariant version\nof the root mean square deviation (RMSD) measure and is capable of finding the\nglobally optimal solution in $O(n^3)$ operations where $n$ is the number of\nthree-dimensional particles.\n"}
+{"abstract": "  As observations of the Hubble parameter from both early and late sources have\nimproved, the tension between these has increased to be well above the\n5$\\sigma$ threshold. Given this, the need for an explanation of such a tension\nhas grown. In this paper, we explore a set of 7 assumptions, and show that, in\norder to alleviate the Hubble tension, a model needs to break at least one of\nthese 7, providing a quick and easy to apply check for new model proposals. We\nalso use this framework to make a rough categorisation of current proposed\nmodels, and show the existence of at least one under-explored avenue of\nalleviating the Hubble tension.\n"}
+{"abstract": "  Humans express feelings or emotions via different channels. Take language as\nan example, it entails different sentiments under different visual-acoustic\ncontexts. To precisely understand human intentions as well as reduce the\nmisunderstandings caused by ambiguity and sarcasm, we should consider\nmultimodal signals including textual, visual and acoustic signals. The crucial\nchallenge is to fuse different modalities of features for sentiment analysis.\nTo effectively fuse the information carried by different modalities and better\npredict the sentiments, we design a novel multi-head attention based fusion\nnetwork, which is inspired by the observations that the interactions between\nany two pair-wise modalities are different and they do not equally contribute\nto the final sentiment prediction. By assigning the acoustic-visual,\nacoustic-textual and visual-textual features with reasonable attention and\nexploiting a residual structure, we attend to attain the significant features.\nWe conduct extensive experiments on four public multimodal datasets including\none in Chinese and three in English. The results show that our approach\noutperforms the existing methods and can explain the contributions of bimodal\ninteraction in multiple modalities.\n"}
+{"abstract": "  The article considers a two-level open quantum system, whose evolution is\ngoverned by the Gorini--Kossakowski--Lindblad--Sudarshan master equation with\nHamiltonian and dissipation superoperator depending, correspondingly, on\npiecewise constant coherent and incoherent controls with constrained\nmagnitudes. Additional constraints on controls' variations are also considered.\nThe system is analyzed using Bloch parametrization of the system's density\nmatrix. We adapt the section method for obtaining outer parallelepipedal and\npointwise estimations of reachable and controllability sets in the Bloch ball\nvia solving a number of problems for optimizing coherent and incoherent\ncontrols with respect to some objective criteria. The differential evolution\nand dual annealing optimization methods are used. The numerical results show\nhow the reachable sets' estimations depend on distances between the system's\ninitial states and the Bloch ball's center point, final times, constraints on\ncontrols' magnitudes and variations.\n"}
+{"abstract": "  The COVID-19 pandemic due to the novel coronavirus SARS CoV-2 has inspired\nremarkable breakthroughs in development of vaccines against the virus and the\nlaunch of several phase 3 vaccine trials in Summer 2020 to evaluate vaccine\nefficacy (VE). Trials of vaccine candidates using mRNA delivery systems\ndeveloped by Pfizer-BioNTech and Moderna have shown substantial VEs of 94-95%,\nleading the US Food and Drug Administration to issue Emergency Use\nAuthorizations and subsequent widespread administration of the vaccines. As the\ntrials continue, a key issue is the possibility that VE may wane over time.\nEthical considerations dictate that all trial participants be unblinded and\nthose randomized to placebo be offered vaccine, leading to trial protocol\namendments specifying unblinding strategies. Crossover of placebo subjects to\nvaccine complicates inference on waning of VE. We focus on the particular\nfeatures of the Moderna trial and propose a statistical framework based on a\npotential outcomes formulation within which we develop methods for inference on\nwhether or not VE wanes over time and estimation of VE at any post-vaccination\ntime. The framework clarifies assumptions made regarding individual- and\npopulation-level phenomena and acknowledges the possibility that subjects who\nare more or less likely to become infected may be crossed over to vaccine\ndifferentially over time. The principles of the framework can be adapted\nstraightforwardly to other trials.\n"}
+{"abstract": "  This Report provides an extensive review of the experimental programme of\ndirect detection searches of particle dark matter. It focuses mostly on\nEuropean efforts, both current and planned, but does it within a broader\ncontext of a worldwide activity in the field. It aims at identifying the\nvirtues, opportunities and challenges associated with the different\nexperimental approaches and search techniques. It presents scientific and\ntechnological synergies, both existing and emerging, with some other areas of\nparticle physics, notably collider and neutrino programmes, and beyond. It\naddresses the issue of infrastructure in light of the growing needs and\nchallenges of the different experimental searches. Finally, the Report makes a\nnumber of recommendations from the perspective of a long-term future of the\nfield. They are introduced, along with some justification, in the opening\nOverview and Recommendations section and are next summarised at the end of the\nReport. Overall, we recommend that the direct search for dark matter particle\ninteractions with a detector target should be given top priority in\nastroparticle physics, and in all particle physics, and beyond, as a positive\nmeasurement will provide the most unambiguous confirmation of the particle\nnature of dark matter in the Universe.\n"}
+{"abstract": "  We present an analysis of the spatial clustering of 695 Ly$\\alpha$-emitting\ngalaxies (LAE) in the MUSE-Wide survey. All objects have spectroscopically\nconfirmed redshifts in the range $3.3<z<6$. We employ the K-estimator of\nAdelberger et al. (2005), adapted and optimized for our sample. We also explore\nthe standard two-point correlation function approach, which is however less\nsuited for a pencil-beam survey such as ours. The results from both approaches\nare consistent. We parametrize the clustering properties by, (i) modelling the\nclustering signal with a power law (PL), and (ii) adopting a Halo Occupation\nDistribution (HOD) model. Applying HOD modeling, we infer a large-scale bias of\n$b_{\\rm{HOD}}=2.80^{+0.38}_{-0.38}$ at a median redshift of the number of\ngalaxy pairs $\\langle z_{\\rm pair}\\rangle\\simeq3.82$, while the PL analysis\nresults in $b_{\\rm{PL}}=3.03^{+1.51}_{-0.52}$\n($r_0=3.60^{+3.10}_{-0.90}\\;h^{-1}$Mpc and $\\gamma=1.30^{+0.36}_{-0.45}$). The\nimplied typical dark matter halo (DMH) mass is\n$\\log(M_{\\rm{DMH}}/[h^{-1}\\rm{M}_\\odot])=11.34^{+0.23}_{-0.27}$. We study\npossible dependencies of the clustering signal on object properties by\nbisecting the sample into disjoint subsets, considering Ly$\\alpha$ luminosity,\nUV absolute magnitude, Ly$\\alpha$ equivalent width, and redshift as variables.\nWe find a suggestive trend of more luminous Ly$\\alpha$ emitters residing in\nmore massive DMHs than their lower Ly$\\alpha$ luminosity counterparts. We also\ncompare our results to mock LAE catalogs based on a semi-analytic model of\ngalaxy formation and find a stronger clustering signal than in our observed\nsample. By adopting a galaxy-conserving model we estimate that the LAEs in the\nMUSE-Wide survey will typically evolve into galaxies hosted by halos of\n$\\log(M_{\\rm{DMH}}/[h^{-1}\\rm{M}_\\odot])\\approx13.5$ at redshift zero,\nsuggesting that we observe the ancestors of present-day galaxy groups.\n"}
+{"abstract": "  We consider the reflection seismology problem of recovering the locations of\ninterfaces and the amplitudes of reflection coefficients from seismic data,\nwhich are vital for estimating the subsurface structure. The reflectivity\ninversion problem is typically solved using greedy algorithms and iterative\ntechniques. Sparse Bayesian learning framework, and more recently, deep\nlearning techniques have shown the potential of data-driven approaches to solve\nthe problem. In this paper, we propose a weighted minimax-concave\npenalty-regularized reflectivity inversion formulation and solve it through a\nmodel-based neural network. The network is referred to as deep-unfolded\nreflectivity inversion network (DuRIN). We demonstrate the efficacy of the\nproposed approach over the benchmark techniques by testing on synthetic 1-D\nseismic traces and 2-D wedge models and validation with the simulated 2-D\nMarmousi2 model and real data from the Penobscot 3D survey off the coast of\nNova Scotia, Canada.\n"}
+{"abstract": "  We show that all the semi-smooth stable complex Godeaux surfaces, classified\nin [FPR18a], are smoothable,\n  and that the moduli stack is smooth of the expected dimension 8 at the\ncorresponding points.\n"}
+{"abstract": "  Machine learning on encrypted data can address the concerns related to\nprivacy and legality of sharing sensitive data with untrustworthy service\nproviders. Fully Homomorphic Encryption (FHE) is a promising technique to\nenable machine learning and inferencing while providing strict guarantees\nagainst information leakage. Since deep convolutional neural networks (CNNs)\nhave become the machine learning tool of choice in several applications,\nseveral attempts have been made to harness CNNs to extract insights from\nencrypted data. However, existing works focus only on ensuring data security\nand ignore security of model parameters. They also report high level\nimplementations without providing rigorous analysis of the accuracy, security,\nand speed trade-offs involved in the FHE implementation of generic primitive\noperators of a CNN such as convolution, non-linear activation, and pooling. In\nthis work, we consider a Machine Learning as a Service (MLaaS) scenario where\nboth input data and model parameters are secured using FHE. Using the CKKS\nscheme available in the open-source HElib library, we show that operational\nparameters of the chosen FHE scheme such as the degree of the cyclotomic\npolynomial, depth limitations of the underlying leveled HE scheme, and the\ncomputational precision parameters have a major impact on the design of the\nmachine learning model (especially, the choice of the activation function and\npooling method). Our empirical study shows that choice of aforementioned design\nparameters result in significant trade-offs between accuracy, security level,\nand computational time. Encrypted inference experiments on the MNIST dataset\nindicate that other design choices such as ciphertext packing strategy and\nparallelization using multithreading are also critical in determining the\nthroughput and latency of the inference process.\n"}
+{"abstract": "  The Spin Physics Detector, a universal facility for studying the nucleon spin\nstructure and other spin-related phenomena with polarized proton and deuteron\nbeams, is proposed to be placed in one of the two interaction points of the\nNICA collider that is under construction at the Joint Institute for Nuclear\nResearch (Dubna, Russia). At the heart of the project there is huge experience\nwith polarized beams at JINR.\n  The main objective of the proposed experiment is the comprehensive study of\nthe unpolarized and polarized gluon content of the nucleon. Spin measurements\nat the Spin Physics Detector at the NICA collider have bright perspectives to\nmake a unique contribution and challenge our understanding of the spin\nstructure of the nucleon. In this document the Conceptual Design of the Spin\nPhysics Detector is presented.\n"}
+{"abstract": "  We study the measurement of Higgs boson self-couplings through $2\\rightarrow\n3$ vector boson scattering (VBS) processes in the framework of Standard Model\neffective field theory (SMEFT) at both proton and lepton colliders. The SMEFT\ncontribution to the amplitude of the $2\\to3$ VBS processes, taking $W_L\nW_L\\rightarrow W_L W_L h$ and $W_L W_L\\rightarrow h h h$ as examples, exhibits\nenhancement with the energy\n$\\frac{\\mathcal{A}^{\\text{BSM}}}{\\mathcal{A}^{\\text{SM}}} \\sim\n\\frac{E^2}{\\Lambda^2}$, which indicates the sensitivity of these processes to\nthe related dimension-six operators in SMEFT. Simulation of the full processes\nat both hadron and lepton colliders with a variety of collision energies are\nperformed to estimate the allowed region on $c_6$ and $c_{\\Phi_1}$. Especially\nwe find that, with the help of exclusively choosing longitudinal polarizations\nin the final states and suitable $p_T$ cuts, $WWh$ process is as important as\nthe more widely studied triple Higgs production ($hhh$) in the measurement of\nHiggs self-couplings. Our analysis indicates that these processes can play\nimportant roles in the measurement of Higgs self-couplings at future 100 TeV pp\ncolliders and muon colliders. However, their cross sections are generally tiny\nat low energy machines, which makes them much more challenging to explore.\n"}
+{"abstract": "  The proposition that life can spread from one planetary system to another\n(interstellar panspermia) has a long history, but this hypothesis is difficult\nto test through observations. We develop a mathematical model that takes\nparameters such as the microbial survival lifetime, the stellar velocity\ndispersion, and the dispersion of ejecta into account in order to assess the\nprospects for detecting interstellar panspermia. We show that the correlations\nbetween pairs of life-bearing planetary systems (embodied in the\npair-distribution function from statistics) may serve as an effective\ndiagnostic of interstellar panspermia, provided that the velocity dispersion of\nejecta is greater than the stellar dispersion. We provide heuristic estimates\nof the model parameters for various astrophysical environments, and conclude\nthat open clusters and globular clusters appear to represent the best targets\nfor assessing the viability of interstellar panspermia.\n"}
+{"abstract": "  In this work, we present a number of generator matrices of the form $[I_{2n}\n\\ | \\ \\tau_k(v)],$ where $I_{kn}$ is the $kn \\times kn$ identity matrix, $v$ is\nan element in the group matrix ring $M_2(R)G$ and where $R$ is a finite\ncommutative Frobenius ring and $G$ is a finite group of order 18. We employ\nthese generator matrices and search for binary $[72,36,12]$ self-dual codes\ndirectly over the finite field $\\mathbb{F}_2.$ As a result, we find 134 Type I\nand 1 Type II codes of this length, with parameters in their weight enumerators\nthat were not known in the literature before. We tabulate all of our findings.\n"}
+{"abstract": "  Multiexcitons in monolayer WSe2 exhibit a suite of optoelectronic phenomena\nthat are unique to those of their single exciton constituents. Here,\nphotoluminescence action spectroscopy shows that multiexciton formation is\nenhanced with increasing optical excitation energy. This enhancement is\nattributed to the multiexciton formation processes from an electron-hole plasma\nand results in over 300% more multiexciton emission than at lower excitation\nenergies at 4 K. The energetic onset of the enhancement coincides with the\nquasiparticle bandgap, corroborating the role of the electron-hole plasma, and\nthe enhancement diminishes with increasing temperature. The results reveal that\nthe strong interactions responsible for ultrafast exciton formation also affect\nmultiexciton phenomena, and both multiexciton and single exciton states play\nsignificant roles in plasma thermalization in 2D semiconductors.\n"}
+{"abstract": "  The recently discovered non-Hermitian skin effect (NHSE) manifests the\nbreakdown of current classification of topological phases in\nenergy-nonconservative systems, and necessitates the introduction of\nnon-Hermitian band topology. So far, all NHSE observations are based on one\ntype of non-Hermitian band topology, in which the complex energy spectrum winds\nalong a closed loop. As recently characterized along a synthetic dimension on a\nphotonic platform, non-Hermitian band topology can exhibit almost arbitrary\nwindings in momentum space, but their actual phenomena in real physical systems\nremain unclear. Here, we report the experimental realization of NHSE in a\none-dimensional (1D) non-reciprocal acoustic crystal. With direct acoustic\nmeasurement, we demonstrate that a twisted winding, whose topology consists of\ntwo oppositely oriented loops in contact rather than a single loop, will\ndramatically change the NHSE, following previous predictions of unique features\nsuch as the bipolar localization and the Bloch point for a Bloch-wave-like\nextended state. This work reveals previously unnoticed features of NHSE, and\nprovides the observation of physical phenomena originating from complex\nnon-Hermitian winding topology.\n"}
+{"abstract": "  Neuroevolutionary algorithms, automatic searches of neural network structures\nby means of evolutionary techniques, are computationally costly procedures. In\nspite of this, due to the great performance provided by the architectures which\nare found, these methods are widely applied. The final outcome of\nneuroevolutionary processes is the best structure found during the search, and\nthe rest of the procedure is commonly omitted in the literature. However, a\ngood amount of residual information consisting of valuable knowledge that can\nbe extracted is also produced during these searches. In this paper, we propose\nan approach that extracts this information from neuroevolutionary runs, and use\nit to build a metamodel that could positively impact future neural architecture\nsearches. More specifically, by inspecting the best structures found during\nneuroevolutionary searches of generative adversarial networks with varying\ncharacteristics (e.g., based on dense or convolutional layers), we propose a\nBayesian network-based model which can be used to either find strong neural\nstructures right away, conveniently initialize different structural searches\nfor different problems, or help future optimization of structures of any type\nto keep finding increasingly better structures where uninformed methods get\nstuck into local optima.\n"}
+{"abstract": "  This study analyzes the case of Romanian births, jointly distributed by\nage-groups of mother and father covering 1958-2019 under the potential\ninfluence of significant disruptors. Significant events such as anti-abortion\nlaws application or abrogation, communism fall, and migration and their impact\nare analyzed. While in practice we may find pro and contra examples, a general\ncontroversy arises regarding whether births should or should not obey the\nBenford Law (BL). Moreover, the significant disruptors' impacts are not\ndetailed discussed in such analysis. I find the distribution of births is First\nDigit Benford Law (BL1) conformant on the entire sample, but mixed results\nregarding the BL obedience in the dynamic analysis and by main sub-periods.\nEven though many disruptors are analyzed, only the 1967 Anti-abortion Decree\nhas a significant impact. I capture an average lag of 15 years between the\nevent, the Anti-abortion Decree, and the start of distortion of the births\ndistribution. The distortion persists around 25 years, almost the entire\nfertility life (15 to 39) for the majority of the people from the cohorts born\nin 1967-1968.\n"}
+{"abstract": "  We implement an algorithm for the computation of Schouten bracket of weakly\nnonlocal Hamiltonian operators in three different computer algebra systems:\nMaple, Reduce and Mathematica. This class of Hamiltonian operators encompass\nalmost all the examples coming from the theory of (1+1)-integrable evolutionary\nPDEs\n"}
+{"abstract": "  We provide a rigorous analysis of the quantum optimal control problem in the\nsetting of a linear combination $s(t)B+(1-s(t))C$ of two noncommuting\nHamiltonians $B$ and $C$. This includes both quantum annealing (QA) and the\nquantum approximate optimization algorithm (QAOA). The target is to minimize\nthe energy of the final ``problem'' Hamiltonian $C$, for a time-dependent and\nbounded control schedule $s(t)\\in [0,1]$ and $t\\in \\mc{I}:= [0,t_f]$. It was\nrecently shown, in a purely closed system setting, that the optimal solution to\nthis problem is a ``bang-anneal-bang'' schedule, with the bangs characterized\nby $s(t)= 0$ and $s(t)= 1$ in finite subintervals of $\\mc{I}$, in particular\n$s(0)=0$ and $s(t_f)=1$, in contrast to the standard prescription $s(0)=1$ and\n$s(t_f)=0$ of quantum annealing. Here we extend this result to the open system\nsetting, where the system is described by a density matrix rather than a pure\nstate. This is the natural setting for experimental realizations of QA and\nQAOA. For finite-dimensional environments and without any approximations we\nidentify sufficient conditions ensuring that either the bang-anneal,\nanneal-bang, or bang-anneal-bang schedules are optimal, and recover the\noptimality of $s(0)=0$ and $s(t_f)=1$. However, for infinite-dimensional\nenvironments and a system described by an adiabatic Redfield master equation we\ndo not recover the bang-type optimal solution. In fact we can only identify\nconditions under which $s(t_f)=1$, and even this result is not recovered in the\nfully Markovian limit. The analysis, which we carry out entirely within the\ngeometric framework of Pontryagin Maximum Principle, simplifies using the\ndensity matrix formulation compared to the state vector formulation.\n"}
+{"abstract": "  We identify the exact microscopic structure of the G photoluminescence center\nin silicon by first principles calculations with including a self-consistent\nmany-body perturbation method, which is a telecommunication wavelength single\nphoton source. The defect constitutes of $\\text{C}_\\text{s}\\text{C}_\\text{i}$\ncarbon impurities in its\n$\\text{C}_\\text{s}-\\text{Si}_\\text{i}-\\text{C}_\\text{s}$ configuration in the\nneutral charge state, where $s$ and $i$ stand for the respective substitutional\nand interstitial positions in the Si lattice. We reveal that the observed fine\nstructure of its optical signals originates from the athermal rotational\nreorientation of the defect. We attribute the monoclinic symmetry reported in\noptically detected magnetic resonance measurements to the reduced tunneling\nrate at very low temperatures. We discuss the thermally activated motional\naveraging of the defect properties and the nature of the qubit state.\n"}
+{"abstract": "  Transition metal dichalcogenides (TMDs) have been a core constituent of 2D\nmaterial research throughout the last decade. Over this time, research focus\nhas progressively shifted from synthesis and fundamental investigations, to\nexploring their properties for applied research such as electrochemical\napplications and integration in electrical devices. Due to the rapid pace of\ndevelopment, priority is often given to application-oriented aspects while\ncareful characterisation and analysis of the TMD materials themselves is\noccasionally neglected. This can be particularly evident for characterisations\ninvolving X-ray photoelectron spectroscopy (XPS), where measurement,\npeak-fitting, and analysis can be complex and nuanced endeavours requiring\nspecific expertise. To improve the availability and accessibility of reference\ninformation, here we present a detailed peak-fitted XPS analysis of ten\ntransition metal chalcogenides. The materials were synthesised as large-area\nthin-films on SiO2 using direct chalcogenisation of pre-deposited metal films.\nAlongside XPS, the Raman spectra with several excitation wavelengths for each\nmaterial are also provided. These complementary characterisation methods can\nprovide a more complete understanding of the composition and quality of the\nmaterial. As material stability is a crucial factor when considering\napplications, the in-air thermal stability of the TMDs was investigated after\nseveral annealing points up to 400 {\\deg}C. This delivers a trend of evolving\nXPS and Raman spectra for each material which improves interpretation of their\nspectra while also indicating their ambient thermal limits. This provides an\naccessible library and set of guidelines to characterise, compare, and discuss\nTMD XPS and Raman spectra.\n"}
+{"abstract": "  The properties of ideal tri-functional dendrimers with forty-five,\nninety-three and one hundred and eighty-nine branches are investigated. Three\nmethods are employed to calculate the mean-square radius of gyration,\n$g$-ratios, asphericity, shape parameters and form factor. These methods\ninclude a Kirchhoff matrix eigenvalue technique, the graph theory approach of\nBenhamou et al. (2004), and Monte Carlo simulations using a growth algorithm. A\nnovel technique for counting paths in the graph representation of the\ndendrimers is presented. All the methods are in excellent agreement with each\nother and with available theoretical predictions. Dendrimers become more\nsymmetrical as the generation and the number of branches increase.\n"}
+{"abstract": "  The colonisation of a soft passive material by motile cells such as bacteria\nis common in biology. The resulting colonies of the invading cells are often\nobserved to exhibit intricate patterns whose morphology and dynamics can depend\non a number of factors, particularly the mechanical properties of the substrate\nand the motility of the individual cells. We use simulations of a minimal 2D\nmodel of self-propelled rods moving through with a passive compliant medium\nconsisting of particles that offer elastic resistance before being plastically\ndisplaced from their equilibrium positions. It is observed that the\nmotility-induced clustering of active (self-propelled) particles is crucial for\nunderstanding the morphodynamics of colonisation. Clustering enables motile\ncolonies to spread faster than they would have as isolated particles. The\ncolonisation rate depends non-monotonically on substrate stiffness with a\ndistinct maximum at a non-zero value of substrate stiffness. This is observed\nto be due to a change in the morphology of clusters. Furrow networks created by\nthe active particles have a fractal-like structure whose dimension varies\nsystematically with substrate stiffness but is less sensitive to particle\nactivity. The power-law growth exponent of the furrowed area is smaller than\nunity, suggesting that, to sustain such extensive furrow networks, colonies\nmust regulate their overall growth rate.\n"}
+{"abstract": "  We use exponent pairs to establish the existence of many $x^a$-smooth numbers\nin short intervals $[x-x^b,x]$, when $a>1/2$. In particular, $b=1-a-a(1-a)^3$\nis admissible. Assuming the exponent-pairs conjecture, one can take\n$b=(1-a)/2+\\epsilon$. As an application, we show that $[x-x^{0.4872},x]$\ncontains many practical numbers when $x$ is large.\n"}
+{"abstract": "  Dust attenuation of an inclined galaxy can cause additional asymmetries in\nobservations, even if the galaxy has a perfectly symmetric structure. {Taking\nadvantage of the integral field spectroscopic data observed by the SDSS-IV\nMaNGA survey, we investigate the asymmetries of the emission-line and continuum\nmaps of star-forming disk galaxies.} We define new parameters, $A_a$ and $A_b$,\nto estimate the asymmetries of a galaxy about its major and minor axes,\nrespectively. Comparing $A_a$ and $A_b$ in different inclination bins, we\nattempt to detect the asymmetries caused by dust. For the continuum images, we\nfind that $A_a$ increases with the inclination, while the $A_b$ is a constant\nas inclination changes. Similar trends are found for $g-r$, $g-i$ and $r-i$\ncolor images. The dependence of the asymmetry on inclination suggests a thin\ndust layer with a scale height smaller than the stellar populations. For the\nH$\\alpha$ and H$\\beta$ images, neither $A_a$ nor $A_b$ shows a significant\ncorrelation with inclination. Also, we do not find any significant dependence\nof the asymmetry of $E(B-V)_g$ on inclination, implying that the dust in the\nthick disk component is not significant. Compared to the SKIRT simulation, the\nresults suggest that the thin dust disk has an optical depth $\\tau_V\\sim0.2$.\nThis is the first time that the asymmetries caused by the dust attenuation and\nthe inclination are probed statistically with a large sample. Our results\nindicate that the combination of the dust attenuation and the inclination\neffects is a potential indicator of the 3D disk orientation.\n"}
+{"abstract": "  We present a novel method for sampling iso-likelihood contours in nested\nsampling using a type of machine learning algorithm known as normalising flows\nand incorporate it into our sampler nessai. Nessai is designed for problems\nwhere computing the likelihood is computationally expensive and therefore the\ncost of training a normalising flow is offset by the overall reduction in the\nnumber of likelihood evaluations. We validate our sampler on 128 simulated\ngravitational wave signals from compact binary coalescence and show that it\nproduces unbiased estimates of the system parameters. Subsequently, we compare\nour results to those obtained with dynesty and find good agreement between the\ncomputed log-evidences whilst requiring 2.07 times fewer likelihood\nevaluations. We also highlight how the likelihood evaluation can be\nparallelised in nessai without any modifications to the algorithm. Finally, we\noutline diagnostics included in nessai and how these can be used to tune the\nsampler's settings.\n"}
+{"abstract": "  Recently, the LHCb Collaboration reported on the evidence for a hidden charm\npentaquark state with strangeness, i.e., $P_{cs}(4459)$, in the $J/\\psi\\Lambda$\ninvariant mass distribution of the $\\Xi_b^-\\to J/\\psi \\Lambda K^-$ decay. In\nthis work, assuming that $P_{cs}(4459)$ is a $\\bar{D}^*\\Xi_c$ molecular state,\nwe study this decay via triangle diagrams $\\Xi_b\\rightarrow\n\\bar{D}_s^{(*)}\\Xi_c\\to (\\bar{D}^{(*)}\\bar{K})\\Xi_c\\to P_{cs} \\bar{K}\\to\n(J/\\psi\\Lambda) \\bar{K}$. Our study shows that the production yield of a spin\n3/2 $\\bar{D}^*\\Xi_c$ state is approximately one order of magnitude larger than\nthat of a spin $1/2$ state due to the interference of $\\bar{D}_s\\Xi_c$ and\n$\\bar{D}_s^*\\Xi_c$ intermediate states. We obtain a model independent\nconstraint on the product of couplings $g_{P_{cs}\\bar{D}^*\\Xi_c}$ and\n$g_{P_{cs}J/\\psi\\Lambda}$. With the predictions of two particular molecular\nmodels as inputs, we calculate the branching ratio of $\\Xi_b^-\\to\n(P_{cs}\\to)J/\\psi\\Lambda K^- $ and compare it with the experimental\nmeasurement. We further predict the lineshape of this decay which could be\nuseful to future experimental studies.\n"}
+{"abstract": "  A graph $G$ is called a $2K_2$-free graph if it does not contain $2K_2$ as an\ninduced subgraph. In 2014, Broersma, Patel and Pyatkin showed that every\n25-tough $2K_2$-free graph on at least three vertices is Hamiltonian. Recently,\nShan improved this result by showing that 3-tough is sufficient instead of\n25-tough. In this paper, we show that every 2-tough $2K_2$-free graph on at\nleast three vertices is Hamiltonian, which was conjectured by Gao and\nPasechnik.\n"}
+{"abstract": "  Deformable image registration is fundamental for many medical image analyses.\nA key obstacle for accurate image registration is the variations in image\nappearance. Recently, deep learning-based registration methods (DLRs), using\ndeep neural networks, have computational efficiency that is several orders of\nmagnitude greater than traditional optimization-based registration methods\n(ORs). A major drawback, however, of DLRs is a disregard for the\ntarget-pair-specific optimization that is inherent in ORs and instead they rely\non a globally optimized network that is trained with a set of training samples\nto achieve faster registration. Thus, DLRs inherently have degraded ability to\nadapt to appearance variations and perform poorly, compared to ORs, when image\npairs (fixed/moving images) have large differences in appearance. Hence, we\npropose an Appearance Adjustment Network (AAN) where we leverage anatomy edges,\nthrough an anatomy-constrained loss function, to generate an anatomy-preserving\nappearance transformation. We designed the AAN so that it can be readily\ninserted into a wide range of DLRs, to reduce the appearance differences\nbetween the fixed and moving images. Our AAN and DLR's network can be trained\ncooperatively in an unsupervised and end-to-end manner. We evaluated our AAN\nwith two widely used DLRs - Voxelmorph (VM) and FAst IMage registration (FAIM)\n- on three public 3D brain magnetic resonance (MR) image datasets - IBSR18,\nMindboggle101, and LPBA40. The results show that DLRs, using the AAN, improved\nperformance and achieved higher results than state-of-the-art ORs.\n"}
+{"abstract": "  Angle-resolved photoemission spectroscopy (ARPES) is one of the most powerful\nexperimental techniques in condensed matter physics. Synchrotron ARPES, which\nuses photons with high flux and continuously tunable energy, has become\nparticularly important. However, an excellent synchrotron ARPES system must\nhave features such as a small beam spot, super-high energy resolution, and a\nuser-friendly operation interface. A synchrotron beamline and an endstation\n(BL03U) were designed and constructed at the Shanghai Synchrotron Radiation\nFacility. The beam spot size at the sample position is 7.5 (V) $\\mu$m $\\times$\n67 (H) $\\mu$m, and the fundamental photon range is 7-165 eV; the ARPES system\nenables photoemission with an energy resolution of 2.67 meV@21.2 eV. In\naddition, the ARPES system of this endstation is equipped with a six-axis\ncryogenic sample manipulator (the lowest temperature is 7 K) and is integrated\nwith an oxide molecular beam epitaxy system and a scanning tunneling\nmicroscope, which can provide an advanced platform for in-situ characterization\nof the fine electronic structure of condensed matter.\n"}
+{"abstract": "  Run-and-tumble particles, frequently considered today for modeling bacterial\nlocomotion, naturally appear outside a biological context as well, e.g. for\nproducing waves in the telegraph process. Here, we use a wave function to drive\ntheir propulsion and tumbling. Such quantum-active motion realizes a jittery\nmotion of Dirac electrons (as in the famous Zitterbewegung): the Dirac electron\nis a run-and-tumble particle, where the tumbling is between chiralities. We\nvisualize the trajectories in diffraction and double slit experiments for\nelectrons. In particular, that yields the time-of-arrival statistics of the\nelectrons at the screen. Finally, we observe that away from pure quantum\nguidance, run-and-tumble particles with suitable spacetime-dependent parameters\nproduce an interference pattern as well.\n"}
+{"abstract": "  We introduce a basis set consisting of three-dimensional Deslauriers--Dubuc\nwavelets and numerically solve the Schr\\\"odinger equations of hydrogen atom,\nhelium atom, hydrogen molecule ion, hydrogen molecule, and lithium hydride\nmolecule with Hartree-Fock and DFT methods. We also compute the 2s and 2p\nexcited states of hydrogen. The Coulomb singularity at the nucleus is handled\nby using a pseudopotential. Results are compared with those of CCCBDB and\nBigDFT. The eigenvalue problem is solved with Arnoldi and Lanczos methods, and\nthe Poisson equation with GMRES and CGNR methods. The various matrix elements\nare computed using the biorthogonality relations of the interpolating wavelets.\n"}
+{"abstract": "  A ubiquitous challenge in design space exploration or uncertainty\nquantification of complex engineering problems is the minimization of\ncomputational cost. A useful tool to ease the burden of solving such systems is\nmodel reduction. This work considers a stochastic model reduction method (SMR),\nin the context of polynomial chaos (PC) expansions, where low-fidelity (LF)\nsamples are leveraged to form a stochastic reduced basis. The reduced basis\nenables the construction of a bi-fidelity (BF) estimate of a quantity of\ninterest from a small number of high-fidelity (HF) samples. A successful BF\nestimate approximates the quantity of interest with accuracy comparable to the\nHF model and computational expense close to the LF model. We develop new error\nbounds for the SMR approach and present a procedure to practically utilize\nthese bounds in order to assess the appropriateness of a given pair of LF and\nHF models for BF estimation. The effectiveness of the SMR approach, and the\nutility of the error bound are presented in three numerical examples.\n"}
+{"abstract": "  Membranes derived from ultrathin polymeric films are promising to meet fast\nseparations, but currently available approaches to produce polymer films with\ngreatly reduced thicknesses on porous supports still faces challenges. Here,\ndefect-free ultrathin polymer covering films (UPCFs) are realized by a facile\ngeneral approach of rapid solvent evaporation. By fast evaporating dilute\npolymer solutions, we realize ultrathin coating (~30 nm) of porous substrates\nexclusively on the top surface, forming UPCFs with a block copolymer of\npolystyrene-block-poly(2-vinyl pyridine) at room temperature or a homopolymer\nof poly(vinyl alcohol) (PVA) at elevated temperatures. With subsequent\nselective swelling to the block copolymer and crosslinking to PVA, the\nresulting bi-layered composite structures serve as highly permeable membranes\ndelivering ~2-10 times higher permeability in ultrafiltration and pervaporation\napplications than state-of-the-art separation membranes with similar rejections\nand selectivities. This work opens up a new, facile avenue for the controllable\nfabrication of ultrathin coatings on porous substrates, which shows great\npotentials in membrane-based separations and other areas.\n"}
+{"abstract": "  Semantic Scene Completion aims at reconstructing a complete 3D scene with\nprecise voxel-wise semantics from a single-view depth or RGBD image. It is a\ncrucial but challenging problem for indoor scene understanding. In this work,\nwe present a novel framework named Scene-Instance-Scene Network\n(\\textit{SISNet}), which takes advantages of both instance and scene level\nsemantic information. Our method is capable of inferring fine-grained shape\ndetails as well as nearby objects whose semantic categories are easily\nmixed-up. The key insight is that we decouple the instances from a coarsely\ncompleted semantic scene instead of a raw input image to guide the\nreconstruction of instances and the overall scene. SISNet conducts iterative\nscene-to-instance (SI) and instance-to-scene (IS) semantic completion.\nSpecifically, the SI is able to encode objects' surrounding context for\neffectively decoupling instances from the scene and each instance could be\nvoxelized into higher resolution to capture finer details. With IS,\nfine-grained instance information can be integrated back into the 3D scene and\nthus leads to more accurate semantic scene completion. Utilizing such an\niterative mechanism, the scene and instance completion benefits each other to\nachieve higher completion accuracy. Extensively experiments show that our\nproposed method consistently outperforms state-of-the-art methods on both real\nNYU, NYUCAD and synthetic SUNCG-RGBD datasets. The code and the supplementary\nmaterial will be available at \\url{https://github.com/yjcaimeow/SISNet}.\n"}
+{"abstract": "  We prove that a system of equations introduced by Demailly (to attack a\nconjecture of Griffiths) has a smooth solution for a direct sum of ample line\nbundles on a Riemann surface. We also reduce the problem for general vector\nbundles to an a priori estimate using Leray-Schauder degree theory.\n"}
+{"abstract": "  We use $Gaia$ eDR3 data and legacy spectroscopic surveys to map the Milky Way\ndisc substructure towards the Galactic Anticenter at heliocentric distances\n$d\\geq10\\,\\rm{kpc}$. We report the discovery of multiple previously undetected\nnew filaments embedded in the outer disc in highly extincted regions. Stars in\nthese over-densities have distance gradients expected for disc material and\nmove on disc-like orbits with $v_{\\phi}\\sim170-230\\,\\rm{km\\,s^{-1}}$, showing\nsmall spreads in energy. Such a morphology argues against a quiescently growing\nGalactic thin disc. Some of these structures are interpreted as excited outer\ndisc material, kicked up by satellite impacts and currently undergoing\nphase-mixing (\"feathers\"). Due to the long timescale in the outer disc regions,\nthese structures can stay coherent in configuration space over several Gyrs. We\nnevertheless note that some of these structures could also be folds in the\nperturbed disc seen in projection from the Sun's location. A full 6D\nphase-space characterization and age dating of these structure should help\ndistinguish between the two possible morphologies.\n"}
+{"abstract": "  Scoring rules aggregate individual rankings by assigning some points to each\nposition in each ranking such that the total sum of points provides the overall\nranking of the alternatives. They are widely used in sports competitions\nconsisting of multiple contests. We study the tradeoff between two risks in\nthis setting: (1) the threat of early clinch when the title has been clinched\nbefore the last contest(s) of the competition take place; (2) the danger of\nwinning the competition without finishing first in any contest. In particular,\nfour historical points scoring systems of the Formula One World Championship\nare compared with the family of geometric scoring rules, recently proposed by\nan axiomatic approach. The schemes used in practice are found to be competitive\nwith respect to these goals, and the current rule seems to be a reasonable\ncompromise close to the Pareto frontier. Our results shed more light on the\nevolution of the Formula One points scoring systems and contribute to the issue\nof choosing the set of point values.\n"}
+{"abstract": "  We consider a stochastic model describing the spiking activity of a countable\nset of neurons spatially organized into a homogeneous tree of degree $d$, $d\n\\geq 2$; the degree of a neuron is just the number of connections it has.\nRoughly, the model is as follows. Each neuron is represented by its membrane\npotential, which takes non-negative integer values. Neurons spike at Poisson\nrate 1, provided they have strictly positive membrane potential. When a spike\noccurs, the potential of the spiking neuron changes to 0, and all neurons\nconnected to it receive a positive amount of potential. Moreover, between\nsuccessive spikes and without receiving any spiking inputs from other neurons,\neach neuron's potential behaves independently as a pure death process with\ndeath rate $\\gamma \\geq 0$. In this article, we show that if the number $d$ of\nconnections is large enough, then the process exhibits at least two phase\ntransitions depending on the choice of rate $\\gamma$: For large values of\n$\\gamma$, the neural spiking activity almost surely goes extinct; For small\nvalues of $\\gamma$, a fixed neuron spikes infinitely many times with a positive\nprobability, and for \"intermediate\" values of $\\gamma$, the system has a\npositive probability of always presenting spiking activity, but, individually,\neach neuron eventually stops spiking and remains at rest forever.\n"}
+{"abstract": "  For a complex projective manifold, Walker has defined a regular homomorphism\nlifting Griffiths' Abel-Jacobi map on algebraically trivial cycle classes to a\ncomplex abelian variety, which admits a finite homomorphism to the Griffiths\nintermediate Jacobian. Recently Suzuki gave an alternate, Hodge-theoretic,\nconstruction of this Walker Abel-Jacobi map. We provide a third construction\nbased on a general lifting property for surjective regular homomorphisms, and\nprove that the Walker Abel-Jacobi map descends canonically to any field of\ndefinition of the complex projective manifold. In addition, we determine the\nimage of the l-adic Bloch map restricted to algebraically trivial cycle classes\nin terms of the coniveau filtration.\n"}
+{"abstract": "  For every $p\\in(0,\\infty)$, a new metric invariant called umbel $p$-convexity\nis introduced. The asymptotic notion of umbel convexity captures the geometry\nof countably branching trees, much in the same way as Markov convexity, the\nlocal invariant which inspired it, captures the geometry of bounded degree\ntrees. Umbel convexity is used to provide a \"Poincar\\'e-type\" metric\ncharacterization of the class of Banach spaces that admit an equivalent norm\nwith Rolewicz's property $(\\beta)$. We explain how a relaxation of umbel\n$p$-convexity, called umbel cotype $p$, plays a role in obtaining compression\nrate bounds for coarse embeddings of countably branching trees. Local analogs\nof these invariants, fork $q$-convexity and fork cotype $q$, are introduced and\ntheir relationship to Markov $q$-convexity and relaxations of the $q$-tripod\ninequality is discussed. The metric invariants are estimated for a large class\nof Heisenberg groups. Finally, a new characterization of non-negative curvature\nis given.\n"}
+{"abstract": "  Distance metrics and their nonlinear variant play a crucial role in machine\nlearning based real-world problem solving. We demonstrated how Euclidean and\ncosine distance measures differ not only theoretically but also in real-world\nmedical application, namely, outcome prediction of drug prescription. Euclidean\ndistance exhibits favorable properties in the local geometry problem. To this\nregard, Euclidean distance can be applied under short-term disease with\nlow-variation outcome observation. Moreover, when presenting to highly variant\nchronic disease, it is preferable to use cosine distance. These different\ngeometric properties lead to different submanifolds in the original embedded\nspace, and hence, to different optimizing nonlinear kernel embedding\nframeworks. We first established the geometric properties that we needed in\nthese frameworks. From these properties interpreted their differences in\ncertain perspectives. Our evaluation on real-world, large-scale electronic\nhealth records and embedding space visualization empirically validated our\napproach.\n"}
+{"abstract": "  Federated edge learning (FEEL) is a promising distributed learning technique\nfor next-generation wireless networks. FEEL preserves the user's privacy,\nreduces the communication costs, and exploits the unprecedented capabilities of\nedge devices to train a shared global model by leveraging a massive amount of\ndata generated at the network edge. However, FEEL might significantly shorten\nenergy-constrained participating devices' lifetime due to the power consumed\nduring the model training round. This paper proposes a novel approach that\nendeavors to minimize computation and communication energy consumption during\nFEEL rounds to address this issue. First, we introduce a modified local\ntraining algorithm that intelligently selects only the samples that enhance the\nmodel's quality based on a predetermined threshold probability. Then, the\nproblem is formulated as joint energy minimization and resource allocation\noptimization problem to obtain the optimal local computation time and the\noptimal transmission time that minimize the total energy consumption\nconsidering the worker's energy budget, available bandwidth, channel states,\nbeamforming, and local CPU speed. After that, we introduce a tractable solution\nto the formulated problem that ensures the robustness of FEEL. Our simulation\nresults show that our solution substantially outperforms the baseline FEEL\nalgorithm as it reduces the local consumed energy by up to 79%.\n"}
+{"abstract": "  A class of bivariate infinite series solutions of the elliptic and hyperbolic\nKepler equations is described, adding to the handful of 1-D series that have\nbeen found throughout the centuries. This result is based on an iterative\nprocedure for the analytical computation of all the higher-order partial\nderivatives of the eccentric anomaly with respect to the eccentricity $e$ and\nmean anomaly $M$ in a given base point $(e_c,M_c)$ of the $(e,M)$ plane.\nExplicit examples of such bivariate infinite series are provided, corresponding\nto different choices of $(e_c,M_c)$, and their convergence is studied\nnumerically. In particular, the polynomials that are obtained by truncating the\ninfinite series up to the fifth degree reach high levels of accuracy in\nsignificantly large regions of the parameter space $(e,M)$. Besides their\ntheoretical interest, these series can be used for designing 2-D spline\nnumerical algorithms for efficiently solving Kepler's equations for all values\nof the eccentricity and mean anomaly.\n"}
+{"abstract": "  Galaxy internal structure growth has long been accused of inhibiting star\nformation in disc galaxies. We investigate the potential physical connection\nbetween the growth of dispersion-supported stellar structures (e.g. classical\nbulges) and the position of galaxies on the star-forming main sequence at\n$z\\sim0$. Combining the might of the SAMI and MaNGA galaxy surveys, we measure\nthe $\\lambda_{Re}$ spin parameter for 3781 galaxies over $9.5 < \\log M_{\\star}\n[\\rm{M}_{\\odot}] < 12$. At all stellar masses, galaxies at the locus of the\nmain sequence possess $\\lambda_{Re}$ values indicative of intrinsically\nflattened discs. However, above $\\log M_{\\star}[\\rm{M}_{\\odot}]\\sim10.5$ where\nthe main sequence starts bending, we find tantalising evidence for an increase\nin the number of galaxies with dispersion-supported structures, perhaps\nsuggesting a connection between bulges and the bending of the main sequence.\nMoving above the main sequence, we see no evidence of any change in the typical\nspin parameter in galaxies once gravitationally-interacting systems are\nexcluded from the sample. Similarly, up to 1 dex below the main sequence,\n$\\lambda_{Re}$ remains roughly constant and only at very high stellar masses\n($\\log M_{\\star}[\\rm{M}_{\\odot}]>11$), do we see a rapid decrease in\n$\\lambda_{Re}$ once galaxies decline in star formation activity. If this trend\nis confirmed, it would be indicative of different quenching mechanisms acting\non high- and low-mass galaxies. The results suggest that while a population of\ngalaxies possessing some dispersion-supported structure is already present on\nthe star-forming main sequence, further growth would be required after the\ngalaxy has quenched to match the kinematic properties observed in passive\ngalaxies at $z\\sim0$.\n"}
+{"abstract": "  We propose two deep neural network-based methods for solving semi-martingale\noptimal transport problems. The first method is based on a\nrelaxation/penalization of the terminal constraint, and is solved using deep\nneural networks. The second method is based on the dual formulation of the\nproblem, which we express as a saddle point problem, and is solved using\nadversarial networks. Both methods are mesh-free and therefore mitigate the\ncurse of dimensionality. We test the performance and accuracy of our methods on\nseveral examples up to dimension 10. We also apply the first algorithm to a\nportfolio optimization problem where the goal is, given an initial wealth\ndistribution, to find an investment strategy leading to a prescribed terminal\nwealth distribution.\n"}
+{"abstract": "  We present Meta Learning for Knowledge Distillation (MetaDistil), a simple\nyet effective alternative to traditional knowledge distillation (KD) methods\nwhere the teacher model is fixed during training. We show the teacher network\ncan learn to better transfer knowledge to the student network (i.e., learning\nto teach) with the feedback from the performance of the distilled student\nnetwork in a meta learning framework. Moreover, we introduce a pilot update\nmechanism to improve the alignment between the inner-learner and meta-learner\nin meta learning algorithms that focus on an improved inner-learner.\nExperiments on various benchmarks show that MetaDistil can yield significant\nimprovements compared with traditional KD algorithms and is less sensitive to\nthe choice of different student capacity and hyperparameters, facilitating the\nuse of KD on different tasks and models. The code is available at\nhttps://github.com/JetRunner/MetaDistil\n"}
+{"abstract": "  The origin and composition of ultra-high energy cosmic rays (UHECRs) remain a\nmystery. The common lore is that UHECRs are deflected from their primary\ndirections by the Galactic and extragalactic magnetic fields. Here we describe\nan extragalactic contribution to the deflection of UHECRs that does not depend\non the strength and orientation of the initial seed field. Using the\nIllustrisTNG simulations, we show that outflow-driven magnetic bubbles created\nby feedback processes during galaxy formation deflect approximately half of all\n$10^{20}$ eV protons by $1^{\\circ}$ or more, and up to $20$-$30^{\\circ}$. This\nimplies that the deflection in the intergalactic medium must be taken into\naccount in order to identify the sources of UHECRs.\n"}
+{"abstract": "  To understand the true nature of black holes, fundamental theoretical\ndevelopments should be linked all the way to observational features of black\nholes in their natural astrophysical environments. Here, we take several steps\nto establish such a link. We construct a family of spinning, regular black-hole\nspacetimes based on a locality principle for new physics and analyze their\nshadow images. We identify characteristic image features associated to\nregularity (increased compactness and relative stretching) and to the locality\nprinciple (cusps and asymmetry) that persist in the presence of a simple\nanalytical disk model. We conjecture that these occur as universal features of\ndistinct classes of regular black holes based on different sets of construction\nprinciples for the corresponding spacetimes.\n"}
+{"abstract": "  We develop a convergence-rate analysis of momentum with cyclical step-sizes.\nWe show that under some assumption on the spectral gap of Hessians in machine\nlearning, cyclical step-sizes are provably faster than constant step-sizes.\nMore precisely, we develop a convergence rate analysis for quadratic objectives\nthat provides optimal parameters and shows that cyclical learning rates can\nimprove upon traditional lower complexity bounds. We further propose a\nsystematic approach to design optimal first order methods for quadratic\nminimization with a given spectral structure. Finally, we provide a local\nconvergence rate analysis beyond quadratic minimization for the proposed\nmethods and illustrate our findings through benchmarks on least squares and\nlogistic regression problems.\n"}
+{"abstract": "  We study the constraints imposed by perturbative unitarity on the new physics\ninterpretation of the muon $g-2$ anomaly. Within a Standard Model Effective\nField Theory (SMEFT) approach, we find that scattering amplitudes sourced by\neffective operators saturate perturbative unitarity at about 1 PeV. This\ncorresponds to the highest energy scale that needs to be probed in order to\nresolve the new physics origin of the muon $g-2$ anomaly. On the other hand,\nsimplified models (e.g.~scalar-fermion Yukawa theories) in which renormalizable\ncouplings are pushed to the boundary of perturbativity still imply new on-shell\nstates below 200 TeV. We finally suggest that the highest new physics scale\nresponsible for the anomalous effect can be reached in non-renormalizable\nmodels at the PeV scale.\n"}
+{"abstract": "  Self-Admitted Technical Debt (SATD) is a metaphorical concept to describe the\nself-documented addition of technical debt to a software project in the form of\nsource code comments. SATD can linger in projects and degrade source-code\nquality, but it can also be more visible than unintentionally added or\nundocumented technical debt. Understanding the implications of adding SATD to a\nsoftware project is important because developers can benefit from a better\nunderstanding of the quality trade-offs they are making. However, empirical\nstudies, analyzing the survivability and removal of SATD comments, are\nchallenged by potential code changes or SATD comment updates that may interfere\nwith properly tracking their appearance, existence, and removal. In this paper,\nwe propose SATDBailiff, a tool that uses an existing state-of-the-art SATD\ndetection tool, to identify SATD in method comments, then properly track their\nlifespan. SATDBailiff is given as input links to open source projects, and its\noutput is a list of all identified SATDs, and for each detected SATD,\nSATDBailiff reports all its associated changes, including any updates to its\ntext, all the way to reporting its removal. The goal of SATDBailiff is to aid\nresearchers and practitioners in better tracking SATDs instances and providing\nthem with a reliable tool that can be easily extended. SATDBailiff was\nvalidated using a dataset of previously detected and manually validated SATD\ninstances.\n  SATDBailiff is publicly available as an open-source, along with the manual\nanalysis of SATD instances associated with its validation, on the project\nwebsite\n"}
+{"abstract": "  Requirements engineering (RE) is a key area to address sustainability\nconcerns in system development. Approaches have been proposed to elicit\nsustainability requirements from interested stakeholders before system design.\nHowever, existing strategies lack the proper high-level view to deal with the\nsocietal and long-term impacts of the transformation entailed by the\nintroduction of a new technological solution. This paper proposes to go beyond\nthe concept of system requirements and stakeholders' goals, and raise the\ndegree of abstraction by focusing on the notions of drivers, barriers and\nimpacts that a system can have on the environment in which it is deployed.\nFurthermore, we suggest to narrow the perspective to a single domain, as the\neffect of a technology is context-dependent. To put this vision into practice,\nwe interview 30 cross-disciplinary experts in the representative domain of\nrural areas, and we analyse the transcripts to identify common themes. As a\nresult, we provide drivers, barriers and positive or negative impacts\nassociated to the introduction of novel technical solutions in rural areas.\nThis RE-relevant information could hardly be identified if interested\nstakeholders were interviewed before the development of a single specific\nsystem. This paper contributes to the literature with a fresh perspective on\nsustainability requirements, and with a domain-specific framework grounded on\nexperts' opinions. The conceptual framework resulting from our analysis can be\nused as a reference baseline for requirements elicitation endeavours in rural\nareas that need to account for sustainability concerns.\n"}
+{"abstract": "  In the Standard Model of particle physics, charged leptons of different\nflavour couple to the electroweak force carriers with the same interaction\nstrength. This property, known as lepton flavour universality, was found to be\nconsistent with experimental evidence in a wide range of particle decays.\nLepton flavour universality can be tested by comparing branching fractions in\nratios such as $R_K = \\mathcal{B}(B^+ \\rightarrow K^+ \\mu^+\n\\mu^-)/\\mathcal{B}(B^+ \\rightarrow K^+ e^+ e^-)$. This observable is measured\nusing proton-proton collision data recorded with the LHCb detector at CERN's\nLarge Hadron Collider corresponding to an integrated luminosity of $9\n\\rm{~fb}^{-1}$. For a dilepton invariant mass range of $q^{2} \\in\n[1.1,6.0]~\\rm{Ge}\\kern -0.1em \\rm{V}^{2}$, the measured value of\n$R_{K}=0.846\\,^{+\\,0.042}_{-\\,0.039}\\,^{+\\,0.013}_{-\\,0.012}$, where the first\nuncertainty is statistic and the second systematic, is in tension with the\nStandard Model predicted value at the $3.1\\sigma$ level raising evidence for\nlepton flavour universality violation in $B^+ \\rightarrow K^+ \\ell^+ \\ell^-$\ndecays.\n"}
+{"abstract": "  In a modern distributed storage system, storage nodes are organized in racks,\nand the cross-rack communication dominates the system bandwidth. In this paper,\nwe focus on the rack-aware storage system. The initial setting was immediately\nrepairing every single node failure. However, multiple node failures are\nfrequent, and some systems may even wait for multiple nodes failures to occur\nbefore repairing them in order to keep costs down. For the purpose of still\nbeing able to repair them properly when multiple failures occur, we relax the\nrepair model of the rack-aware storage system. In the repair process, the\ncross-rack connections (i.e., the number of helper racks connected for repair\nwhich is called repair degree) and the intra-rack connections (i.e., the number\nof helper nodes in the rack contains the failed node) are all reduced. We focus\non minimizing the cross-rack bandwidth in the rack-aware storage system with\nmultiple erasure tolerances. First, the fundamental tradeoff between the repair\nbandwidth and the storage size for functional repair is established. Then, the\ntwo extreme points corresponding to the minimum storage and minimum cross-rack\nrepair bandwidth are obtained. Second, the explicitly construct corresponding\nto the two points are given. Both of them have minimum sub-packetization level\n(i.e., the number of symbols stored in each node) and small repair degree.\nBesides, the size of underlying finite field is approximately the block length\nof the code. Finally, for the convenience of practical use, we also establish a\ntransformation to convert our codes into systematic codes.\n"}
+{"abstract": "  The evolution of electronic media is a mixed blessing. Due to the easy\naccess, low cost, and faster reach of the information, people search out and\ndevour news from online social networks. In contrast, the increasing acceptance\nof social media reporting leads to the spread of fake news. This is a minacious\nproblem that causes disputes and endangers societal stability and harmony. Fake\nnews spread has gained attention from researchers due to its vicious nature.\nproliferation of misinformation in all media, from the internet to cable news,\npaid advertising and local news outlets, has made it essential for people to\nidentify the misinformation and sort through the facts. Researchers are trying\nto analyze the credibility of information and curtail false information on such\nplatforms. Credibility is the believability of the piece of information at\nhand. Analyzing the credibility of fake news is challenging due to the intent\nof its creation and the polychromatic nature of the news. In this work, we\npropose a model for detecting fake news. Our method investigates the content of\nthe news at the early stage i.e. when the news is published but is yet to be\ndisseminated through social media. Our work interprets the content with\nautomatic feature extraction and the relevance of the text pieces. In summary,\nwe introduce stance as one of the features along with the content of the\narticle and employ the pre-trained contextualized word embeddings BERT to\nobtain the state-of-art results for fake news detection. The experiment\nconducted on the real-world dataset indicates that our model outperforms the\nprevious work and enables fake news detection with an accuracy of 95.32%.\n"}
+{"abstract": "  Machine-learning force fields (MLFF) should be accurate, computationally and\ndata efficient, and applicable to molecules, materials, and interfaces thereof.\nCurrently, MLFFs often introduce tradeoffs that restrict their practical\napplicability to small subsets of chemical space or require exhaustive datasets\nfor training. Here, we introduce the Bravais-Inspired Gradient-Domain Machine\nLearning (BIGDML) approach and demonstrate its ability to construct reliable\nforce fields using a training set with just 10-200 geometries for materials\nincluding pristine and defect-containing 2D and 3D semiconductors and metals,\nas well as chemisorbed and physisorbed atomic and molecular adsorbates on\nsurfaces. The BIGDML model employs the full relevant symmetry group for a given\nmaterial, does not assume artificial atom types or localization of atomic\ninteractions and exhibits high data efficiency and state-of-the-art energy\naccuracies (errors substantially below 1 meV per atom) for an extended set of\nmaterials. Extensive path-integral molecular dynamics carried out with BIGDML\nmodels demonstrate the counterintuitive localization of benzene--graphene\ndynamics induced by nuclear quantum effects and allow to rationalize the\nArrhenius behavior of hydrogen diffusion coefficient in a Pd crystal for a wide\nrange of temperatures.\n"}
+{"abstract": "  We study the effects of elastic anisotropy on the Landau-de Gennes critical\npoints for nematic liquid crystals, in a square domain. The elastic anisotropy\nis captured by a parameter, $L_2$, and the critical points are described by\nthree degrees of freedom. We analytically construct a symmetric critical point\nfor all admissible values of $L_2$, which is necessarily globally stable for\nsmall domains i.e., when the square edge length, $\\lambda$, is small enough. We\nperform asymptotic analyses and numerical studies to discover at least $5$\nclasses of these symmetric critical points - the $WORS$, $Ring^{\\pm}$,\n$Constant$ and $pWORS$ solutions, of which the $WORS$, $Ring^+$ and $Constant$\nsolutions can be stable. Furthermore, we demonstrate that the novel $Constant$\nsolution is energetically preferable for large $\\lambda$ and large $L_2$, and\nprove associated stability results that corroborate the stabilising effects of\n$L_2$ for reduced Landau-de Gennes critical points. We complement our analysis\nwith numerically computed bifurcation diagrams for different values of $L_2$,\nwhich illustrate the interplay of elastic anisotropy and geometry for nematic\nsolution landscapes, at low temperatures.\n"}
+{"abstract": "  Conversational agents (CAs) represent an emerging research field in health\ninformation systems, where there are great potentials in empowering patients\nwith timely information and natural language interfaces. Nevertheless, there\nhave been limited attempts in establishing prescriptive knowledge on designing\nCAs in the healthcare domain in general, and diabetes care specifically. In\nthis paper, we conducted a Design Science Research project and proposed three\ndesign principles for designing health-related CAs that embark on artificial\nintelligence (AI) to address the limitations of existing solutions. Further, we\ninstantiated the proposed design and developed AMANDA - an AI-based\nmultilingual CA in diabetes care with state-of-the-art technologies for\nnatural-sounding localised accent. We employed mean opinion scores and system\nusability scale to evaluate AMANDA's speech quality and usability,\nrespectively. This paper provides practitioners with a blueprint for designing\nCAs in diabetes care with concrete design guidelines that can be extended into\nother healthcare domains.\n"}
+{"abstract": "  In this work, we propose three pilot assignment schemes to reduce the effect\nof pilot contamination in cell-free massive multiple-input-multiple-output\n(MIMO) systems. Our first algorithm, which is based on the idea of random\nsequential adsorption (RSA) process from the statistical physics literature,\ncan be implemented in a distributed and scalable manner while ensuring a\nminimum distance among the co-pilot users. Further, leveraging the rich\nliterature of the RSA process, we present an approximate analytical approach to\naccurately determine the density of the co-pilot users as well as the pilot\nassignment probability for the typical user in this network. We also develop\ntwo optimization-based centralized pilot allocation schemes with the primary\ngoal of benchmarking the RSA-based scheme. The first centralized scheme is\nbased only on the user locations (just like the RSA-based scheme) and\npartitions the users into sets of co-pilot users such that the minimum distance\nbetween two users in a partition is maximized. The second centralized scheme\ntakes both user and remote radio head (RRH) locations into account and provides\na near-optimal solution in terms of sum-user spectral efficiency (SE). The\ngeneral idea is to first cluster the users with similar propagation conditions\nwith respect to the RRHs using spectral graph theory and then ensure that the\nusers in each cluster are assigned different pilots using the branch and price\n(BnP) algorithm. Our simulation results demonstrate that despite admitting\ndistributed implementation, the RSA-based scheme has a competitive performance\nwith respect to the first centralized scheme in all regimes as well as to the\nnear-optimal second scheme when the density of RRHs is high.\n"}
+{"abstract": "  For those deformations that satisfy a certain non-degeneracy condition, we\ndescribe the structure of certain simple modules of the deformations of the\nsubcharacter algebra of a finite group. For finite abelian groups, we prove\nthat the deformation given by the inclusion of the natural numbers, which\ncorresponds to the algebra generated by the fibred bisets over a field of\ncharacteristic zero, is not semisimple. In the cyclic group of prime order\ncase, we provide a complete description of the semisimple deformations.\n"}
+{"abstract": "  We present a quantitative, near-term experimental blueprint for the quantum\nsimulation of topological insulators using lattice-trapped ultracold polar\nmolecules. In particular, we focus on the so-called Hopf insulator, which\nrepresents a three-dimensional topological state of matter existing outside the\nconventional tenfold way and crystalline-symmetry-based classifications of\ntopological insulators. Its topology is protected by a \\emph{linking number}\ninvariant, which necessitates long-range spin-orbit coupled hoppings for its\nrealization. While these ingredients have so far precluded its realization in\nsolid state systems and other quantum simulation architectures, in a companion\nmanuscript [1901.08597] we predict that Hopf insulators can in fact arise\nnaturally in dipolar interacting systems. Here, we investigate a specific such\narchitecture in lattices of polar molecules, where the effective `spin' is\nformed from sublattice degrees of freedom. We introduce two techniques that\nallow one to optimize dipolar Hopf insulators with large band gaps, and which\nshould also be readily applicable to the simulation of other exotic\nbandstructures. First, we describe the use of Floquet engineering to control\nthe range and functional form of dipolar hoppings and second, we demonstrate\nthat molecular AC polarizabilities (under circularly polarized light) can be\nused to precisely tune the resonance condition between different rotational\nstates. To verify that this latter technique is amenable to current generation\nexperiments, we calculate from first principles the AC polarizability for\n$\\sigma^+$ light for ${}^{40}$K$^{87}$Rb. Finally, we show that experiments are\ncapable of detecting the unconventional topology of the Hopf insulator by\nvarying the termination of the lattice at its edges, which gives rise to three\ndistinct classes of edge mode spectra.\n"}
+{"abstract": "  This paper investigates the relationship between the spread of the COVID-19\npandemic, the state of community activity, and the financial index performance\nacross 20 countries. First, we analyze which countries behaved similarly in\n2020 with respect to one of three multivariate time series: daily COVID-19\ncases, Apple mobility data and national equity index price. Next, we study the\ntrajectories of all three of these attributes in conjunction to determine which\nexhibited greater similarity. Finally, we investigate whether country financial\nindices or mobility data responded quicker to surges in COVID-19 cases. Our\nresults indicate that mobility data and national financial indices exhibited\nthe most similarity in their trajectories, with financial indices responding\nquicker. This suggests that financial market participants may have interpreted\nand responded to COVID-19 data more efficiently than governments. Further,\nresults imply that efforts to study community mobility data as a leading\nindicator for financial market performance during the pandemic were misguided.\n"}
+{"abstract": "  We introduce two methods for improving the performance of agents meeting for\nthe first time to accomplish a communicative task. The methods are: (1)\n`message mutation' during the generation of the communication protocol; and (2)\nrandom permutations of the communication channel. These proposals are tested\nusing a simple two-player game involving a `teacher' who generates a\ncommunication protocol and sends a message, and a `student' who interprets the\nmessage. After training multiple agents via self-play we analyse the\nperformance of these agents when they are matched with a stranger, i.e. their\nzero-shot communication performance. We find that both message mutation and\nchannel permutation positively influence performance, and we discuss their\neffects.\n"}
+{"abstract": "  This position paper draws from the complexity of dark patterns to develop\narguments for differentiated interventions. We propose a matrix of\ninterventions with a \\textit{measure axis} (from user-directed to\nenvironment-directed) and a \\textit{scope axis} (from general to specific). We\nfurthermore discuss a set of interventions situated in different fields of the\nintervention spaces. The discussions at the 2021 CHI workshop \"What can CHI do\nabout dark patterns?\" should help hone the matrix structure and fill its fields\nwith specific intervention proposals.\n"}
+{"abstract": "  Cyclic codes are among the most important families of codes in coding theory\nfor both theoretical and practical reasons. Despite their prominence and\nintensive research on cyclic codes for over a half century, there are still\nopen problems related to cyclic codes. In this work, we use recent results on\nthe equivalence of cyclic codes to create a more efficient algorithm to\npartition cyclic codes by equivalence based on cyclotomic cosets. This\nalgorithm is then implemented to carry out computer searches for both cyclic\ncodes and quasi-cyclic (QC) codes with good parameters. We also generalize\nthese results to repeated-root cases. We have found several new linear codes\nthat are cyclic or QC as an application of the new approach, as well as more\ndesirable constructions for linear codes with best known parameters. With the\nadditional new codes obtained through standard constructions, we have found a\ntotal of 14 new linear codes.\n"}
+{"abstract": "  Nonlocal quantum correlations among the quantum subsystems play essential\nroles in quantum science. The violation of the Svetlichny inequality provides\nsufficient conditions of genuine tripartite nonlocality. We provide tight upper\nbounds on the maximal quantum value of the Svetlichny operators under local\nfiltering operations, and present a qualitative analytical analysis on the\nhidden genuine nonlocality for three-qubit systems. We investigate in detail\ntwo classes of three-qubit states whose hidden genuine nonlocalities can be\nrevealed by local filtering.\n"}
+{"abstract": "  We present a novel method named truncated hierarchical unstructured splines\n(THU-splines) that supports both local $h$-refinement and unstructured\nquadrilateral meshes. In a THU-spline construction, an unstructured\nquadrilateral mesh is taken as the input control mesh, where the\ndegenerated-patch method [18] is adopted in irregular regions to define\n$C^1$-continuous bicubic splines, whereas regular regions only involve $C^2$\nB-splines. Irregular regions are then smoothly joined with regular regions\nthrough the truncation mechanism [29], leading to a globally smooth spline\nconstruction. Subsequently, local refinement is performed following the\ntruncated hierarchical B-spline construction [10] to achieve a flexible\nrefinement without propagating to unanticipated regions. Challenges lie in\nrefining transition regions where a mixed types of splines play a role.\nTHU-spline basis functions are globally $C^1$-continuous and are non-negative\neverywhere except near extraordinary vertices, where slight negativity is\ninevitable to retain refinability of the spline functions defined using the\ndegenerated-patch method. Such functions also have a finite representation that\ncan be easily integrated with existing finite element or isogeometric codes\nthrough B\\'{e}zier extraction.\n"}
+{"abstract": "  A new technical method is developed for soft x-ray spectroscopy of near-edge\nx-ray absorption fine structure (NEXAFS). The measurement is performed with\ncontinuously rotating linearly polarized light over 2$\\pi$, generated by a\nsegmented undulator. A demonstration of the rotational NEXAFS experiment was\nsuccessfully made with a 2D film, showing detailed polarization-dependence in\nintensity of the molecular orbitals. The present approach provides varieties of\ntechnical opportunities that are compatible with the state-of-the-art\nexperiments in nano-space and under the $operando$ condition.\n"}
+{"abstract": "  This article concerns the predictive modeling for spatio-temporal data as\nwell as model interpretation using data information in space and time. We\ndevelop a novel approach based on supervised dimension reduction for such data\nin order to capture nonlinear mean structures without requiring a prespecified\nparametric model. In addition to prediction as a common interest, this approach\nemphasizes the exploration of geometric information from the data. The method\nof Pairwise Directions Estimation (PDE; Lue, 2019) is implemented in our\napproach as a data-driven function searching for spatial patterns and temporal\ntrends. The benefit of using geometric information from the method of PDE is\nhighlighted, which aids effectively in exploring data structures. We further\nenhance PDE, referring to it as PDE+, by incorporating kriging to estimate the\nrandom effects not explained in the mean functions. Our proposal can not only\nincrease prediction accuracy, but also improve the interpretation for modeling.\nTwo simulation examples are conducted and comparisons are made with four\nexisting methods. The results demonstrate that the proposed PDE+ method is very\nuseful for exploring and interpreting the patterns and trends for\nspatio-temporal data. Illustrative applications to two real datasets are also\npresented.\n"}
+{"abstract": "  This paper describes a recently initiated research project aiming at\nsupporting development of computerised dialogue systems that handle breaches of\nconversational norms such as the Gricean maxims, which describe how dialogue\nparticipants ideally form their utterances in order to be informative,\nrelevant, brief, etc. Our approach is to model dialogue and norms with\nco-operating distributed grammar systems (CDGSs), and to develop methods to\ndetect breaches and to handle them in dialogue systems for verbal human-robot\ninteraction.\n"}
+{"abstract": "  As more people flock to social media to connect with others and form virtual\ncommunities, it is important to research how members of these groups interact\nto understand human behavior on the Web. In response to an increase in hate\nspeech, harassment and other antisocial behaviors, many social media companies\nhave implemented different content and user moderation policies. On Reddit, for\nexample, communities, i.e, subreddits, are occasionally banned for violating\nthese policies. We study the effect of these regulatory actions as well as when\na community experiences a significant external event like a political election\nor a market crash. Overall, we find that most subreddit bans prompt a small,\nbut statistically significant, number of active users to leave the platform;\nthe effect of external events varies with the type of event. We conclude with a\ndiscussion on the effectiveness of the bans and wider implications for the\nonline content moderation.\n"}
+{"abstract": "  We isolate a new preservation class of Suslin forcings and prove several\nassociated consistency results in the choiceless theory ZF+DC regarding\ncountable chromatic numbers of various Borel hypergraphs.\n"}
+{"abstract": "  Extremely compact objects trap gravitational waves or neutrinos, assumed to\nmove along null geodesics in the trapping regions. The trapping of neutrinos\nwas extensively studied for spherically symmetric extremely compact objects\nconstructed under the simplest approximation of the uniform energy density\ndistribution, with radius located under the photosphere of the external\nspacetime; in addition, uniform emissivity distribution of neutrinos was\nassumed in these studies. Here we extend the studies of the neutrino trapping\nfor the case of the extremely compact Tolman VII objects representing the\nsimplest generalization of the internal Schwarzschild solution with uniform\ndistribution of the energy density, and the correspondingly related\ndistribution of the neutrino emissivity that is thus again proportional to the\nenergy density; radius of such extremely compact objects can overcome the\nphotosphere of the external Schwarzschild spacetime. In dependence on the\nparameters of the Tolman VII spacetimes, we determine the \"local\" and \"global\"\ncoefficients of efficiency of the trapping and demonstrate that the role of the\ntrapping is significantly stronger than in the internal Schwarzschild\nspacetimes. Our results indicate possible influence of the neutrino trapping in\ncooling of neutron stars.\n"}
+{"abstract": "  Quantum computing provides a new way for approaching problem solving,\nenabling efficient solutions for problems that are hard on classical computers.\nIt is based on leveraging how quantum particles behave. With researchers around\nthe world showing quantum supremacy and the availability of cloud-based quantum\ncomputers with free accounts for researchers, quantum computing is becoming a\nreality. In this paper, we explore both the opportunities and challenges that\nquantum computing has for location determination research. Specifically, we\nintroduce an example for the expected gain of using quantum algorithms by\nproviding an efficient quantum implementation of the well-known RF\nfingerprinting algorithm and run it on an instance of the IBM Quantum\nExperience computer. The proposed quantum algorithm has a complexity that is\nexponentially better than its classical algorithm version, both in space and\nrunning time. We further discuss both software and hardware research challenges\nand opportunities that researchers can build on to explore this exciting new\ndomain.\n"}